bayesm/0000755000176000001440000000000013134414120011553 5ustar ripleyusersbayesm/inst/0000755000176000001440000000000013123305446012540 5ustar ripleyusersbayesm/inst/doc/0000755000176000001440000000000013123305446013305 5ustar ripleyusersbayesm/inst/doc/bayesm_Overview_Vignette.html0000644000176000001440000654717713123305445021240 0ustar ripleyusers bayesm Overview

1 Introduction

bayesm is an R package that facilitates statistical analysis using Bayesian methods. The package provides a set of functions for commonly used models in applied microeconomics and quantitative marketing.

The goal of this vignette is to make it easier for users to adopt bayesm by providing a comprehensive overview of the package’s contents and detailed examples of certain functions. We begin with the overview, followed by a discussion of how to work with bayesm. The discussion covers the structure of function arguments, the required input data formats, and the various output formats. The final section provides detailed examples to demonstrate Bayesian inference with the linear normal, multinomial logit, and hierarchical multinomial logit regression models.

2 Package Contents

For ease of exposition, we have grouped the package contents into:

  • Posterior sampling functions
  • Utility functions
  • S3 methods1
  • Datasets

Because the first two groups contain many functions, we organize them into subgroups by purpose. Below, we display each group of functions in a table with one column per subgroup.

Posterior Sampling Functions
Linear Models Limited Dependent Variable Models Hierarchical Models Density Estimation
runireg* rbprobitGibbs** rhierLinearModel rnmixGibbs*
runiregGibbs rmnpGibbs rhierLinearMixture rDPGibbs
rsurGibbs* rmvpGibbs rhierMnlRwMixture*
rivGibbs rmnlIndepMetrop rhierMnlDP
rivDP rscaleUsage rbayesBLP

rnegbinRw rhierNegbinRw

rordprobitGibbs

*bayesm offers the utility function breg with a related but limited set of capabilities as runireg — similarly with rmultireg for rsurGibbs, rmixGibbs for rnmixGibbs, and rhierBinLogit for rhierMnlRwMixture.

**rbiNormGibbs provides a tutorial-like example of Gibbs Sampling from a bivariate normal distribution.

Utility Functions
Log-Likelihood 
(data vector)		 Log Density (univariate) Random Draws Mixture-of-Normals Miscellaneous
llmnl lndIChisq rdirichlet clusterMix cgetC
llmnp lndIWishart rmixture eMixMargDen condMom
llnhlogit lndMvn rmvst mixDen createX

lndMvst rtrun mixDenBi ghkvec


rwishart momMix logMargDenNR




mnlHess




mnpProb




nmat




numEff




simnhlogit
S3 Methods
Plot Methods Summary Methods
plot.bayesm.mat summary.bayesm.mat
plot.bayesm.nmix summary.bayesm.nmix
plot.bayesm.hcoef summary.bayesm.var
Datasets
bank customerSat orangeJuice
camera detailing Scotch
cheese margarine tuna

Some discussion of the naming conventions may be warranted. All functions use CamelCase but begin lowercase. Posterior sampling functions begin with r to match R’s style of naming random number generation functions since these functions all draw from (posterior) distributions. Common abbreviations include DP for Dirichlet Process, IV for instrumental variables, MNL and MNP for multinomial logit and probit, SUR for seemingly unrelated regression, and hier for hierarchical. Utility functions that begin with ll calculate the log-likelihood of a data vector, while those that begin with lnd provide the log-density. Other abbreviations should be straighforward; please see the help file for a specific function if its name is unclear.

3 Working with bayesm

We expect most users of the package to interact primarily with the posterior sampling functions. These functions take a consistent set of arguments as inputs (Data, Prior, and Mcmc — each is a list) and they return output in a consistent format (always a list). summary and plot generic functions can then be used to facilitate analysis of the output because of the classes and methods defined in bayesm. The following subsections describe the format of the standardized function arguments as well as the required format of the data inputs and the format of the output from bayesm’s posterior sampling functions.

3.1 Input: Function Arguments

The posterior sampling functions take three arguments: Data, Prior, and Mcmc. Each argument is a list.

As a minimal example, assume you’d like to perform a linear regression and that you have in your work space y (a vector of length \(n\)) and X (a matrix of dimension \(n \times p\)). For this example, we utilize the default values for Prior and so we do not specify the Prior argument. These components (Data, Prior, and Mcmc as well as their arguments including R and nprint) are discussed in the subsections that follow. Then the bayesm syntax is simply:

mydata <- list(y = y, X = X)
mymcmc <- list(R = 1e6, nprint = 0)

out <- runireg(Data = mydata, Mcmc = mymcmc)

The list elements of Data, Prior, and Mcmc must be named. For example, you could not use the following code because the Data argument mydata2 has unnamed elements.

mydata2 <- list(y, X)
out <- runireg(Data = mydata2, Mcmc = mymcmc)

3.1.1 Data Argument

bayesm’s posterior sampling functions do not accept data stored in dataframes; data must be stored as vectors or matrices.

For non-hierarchical models, the list of input data simply contains the approprate data vectors or matrices from the set {y, X, w, z}2 and possibly a scalar (length one vector) from the set {k, p}.

  • For functions that require only a single data argument (such as the two density estimation functions, rnmixGibbs and rDPGibbs), y is that argument. More typically, y is used for LHS3 variable(s) and X for RHS variables. For estimation using instrumental variables, variables in the structural equation are separated into “exogenous†variables w and an “edogenous†variable x; z is a matrix of instruments.

  • For the scalars, p indicates the number of choice alternatives in discrete choice models and k is used as the maximum ordinate in models for ordinal data (e.g., rordprobitGibbs).

For hierarchical models, the input data has up to 3 components. We’ll discuss these components using the mixed logit model (rhierMnlRwMixture) as an example. For rhierMnlRwMixture, the Data argument is list(lgtdata, Z, p).

  1. The first component for all hierarchical models is required. It is a list of lists named either regdata or lgtdata, depending on the function. In rhierMnlRwMixture, lgtdata is a list of lists, with each interior list containing a vector or one-column matrix of multinomial outcomes y and a design matrix of covariates X. In Example 3 below, we show how to convert data from a data frame to this required list-of-lists format.

  2. The second component, Z, is present but optional for all hierarchical models. Z is a matrix of cross-sectional unit characteristics that drive the mean responses; that is, a matrix of covariates for the individual parameters (e.g. \(\beta_i\)’s). For example, the model (omitting the priors) for rhierMnlRwMixture is:

    \[ y_i \sim \text{MNL}(X_i'\beta_i) \hspace{1em} \text{with} \hspace{1em} \beta_i = Z \Delta_i + u_i \hspace{1em} \text{and} \hspace{1em} u_i \sim N(\mu_j, \Sigma_j) \]

    where \(i\) indexes individuals and \(j\) indexes cross-sectional unit characteristics.

  3. The third component (if accepted) is a scalar, such as p or k, and like the arguments by the same names in the non-hierarchical models, is used to indicate the size of the choice set or the maximum value of a scale or count variable. In rhierMnlRwMixture, the argument is p, which is used to indicate the number of choice alternatives.

Note that rbayesBLP (the hierarchical logit model with aggregate data as in Berry, Levinsohn, and Pakes (1995) and Jiang, Manchanda, and Rossi (2009)) deviates slightly from the standard data input. rbayesBLP uses j instead of p to be consistent with the literature and calls the LHS variable share rather than y to emphasize that aggregate (market share instead of individual choice) data are required.

3.1.2 Prior Argument

Specification of prior distributions is model-specific, so our discussion here is brief.

All posterior sampling functions offer default values for parameters of prior distributions. These defaults were selected to yield proper yet reasonably-diffuse prior distributions (assuming the data are in approximately unit scale). In addition, these defaults are consistent across functions. For example, normal priors have default values of mean 0 with value 0.01 for the scaling factor of the prior precision matrix.

Specification of the prior is important. Significantly more detail can be found in chapters 2–5 of BSM4 and the help files for the posterior sampling functions. We strongly recommend you consult them before accepting the default values.

3.1.3 Mcmc Argument

The Mcmc argument controls parameters of the sampling algorithm. The most common components of this list include:

  • R: the number of MCMC draws
  • keep: a thinning parameter indicating that every keep\(^\text{th}\) draw should be retained
  • nprint: an option to print the estimated time remaining to the console after each nprint\(^\text{th}\) draw

MCMC methods are non-iid. As a result, a large simulation size may be required to get reliable results. We recommend setting R large enough to yield an adequate effective sample size and letting keep default to the value 1 unless doing so imposes memory constraints. A careful reader of the bayesm help files will notice that many of the examples set R equal to 1000 or less. This low number of draws was chosen for speed, as the examples are meant to demonstrate how to run the code and do not necessarily suggest best practices for a proper statistical analysis.

nprint defaults to 100, but for large R, you may want to increase the nprint option. Alternatively, you can set nprint=0, in which case the priors will still be printed to the console, but the estimated time remaining will not.

Additional components of the Mcmc argument are function-specific, but typically include starting values for the algorithm. For example, the Mcmc argument for runiregGibbs takes sigmasq as a scalar element of the list. The Gibbs Sampler for runiregGibbs first draws \(\beta | \sigma^2\), then draws \(\sigma^2 | \beta\), and then repeats. For the first draw of \(\beta\) in the MCMC chain, a value of \(\sigma^2\) is required. The user can specify a value using Mcmc$sigmasq, or the user can omit the argument and the function will use its default (sigmasq = var(Data$y)).

3.2 Output: Returned Results

bayesm posterior sampling functions return a consistent set of results and output to the user. At a minimum, this output includes draws from the posterior distribution. bayesm provides a set of summary and plot methods to facilitate analysis of this output, but the user is free to analyze the results as he or she sees fit.

3.2.1 Output Formats

All bayesm posterior sampling functions return a list. The elements of that list include a set of vectors, matrices, and/or arrays (and possibly a list), the exact set of which depend on the function.

  • Vectors are returned for draws of parameters with no natural grouping. For example, runireg implements and iid sampler to draw from the posterior of a homoskedastic univariate regression with a conjugate prior (i.e., a Bayesian analog to OLS regression). One output list element is sigmasqdraw, a length R/keep vector for the scalar parameter \(\sigma^2\).

  • Matrices are returned for draws of parameters with a natural grouping. Again using runireg as the example, the output list includes betadraw, an R/keep \(\times\) ncol(X) matrix for the vector of \(\beta\) parameters.

    Contrary to the next bullet, draws for the parameters in a variance-covariance matrix are returned in matrix form. For example, rmnpGibbs implements a Gibbs Sampler for a multinomial probit model where one set of parameters is the \((p-1) \times (p-1)\) matrix \(\Sigma\). The output list for rmnpGibbs includes the list element sigmadraw, which is a matrix of dimension R/keep\(\times (p-1)*(p-1)\) with each row containing a draw (in vector form) for all the elements of the matrix \(\Sigma\). bayesm’s summary and plot methods (see below) are designed to handle this format.

  • Arrays are used when parameters have a natural matrix-grouping, such that the MCMC algorithm returns R/keep draws of the matrix. For example, rsurGibbs returns a list that includes Sigmadraw, an \(m \times m \times\)R/keep array, where \(m\) is the number of regression equations. As a second example, rhierLinearModel estimates a hierarchical linear regression model with a normal prior, and returns a list that includes betadraw, an \(n \times k \times\)R/keep array, where \(n\) signifies the number of individuals (each with their own \(\beta_i\)) and \(k\) signifies the number of covariates (ncol(X) = \(k\)).

  • For functions that use a mixture-of-normals or Dirichlet Process prior, the output list includes a list (nmix) pertaining to that prior. nmix is itself a list with 3 components: probdraw, zdraw, and compdraw. probdraw reports the probability that each draw came from a particular normal component; zdraw indicates which mixture-of-normals component each draw is assigned to; and compdraw provides draws for the mixture-of-normals components (i.e., mean vectors and Cholesky roots of covariance matrices). Note that if you specify a “mixture†with only one normal component, there will be no useful information in probdraw. Also note that zdraw is not relevant for density estimation and will be null except in rnmixGibbs and rDPGibbs.

3.2.2 Classes and Methods

In R generally, objects can be assigned a class and then a generic function can be used to run a method on an object with that class. The list elements in the output from bayesm posterior sampling functions are assigned special bayesm classes. The bayesm package includes summary and plot methods for use with these classes (see the table in Section 2 above). This means you can call the generic function (e.g., summary) on individual list elements of bayesm output and it will return specially-formatted summary results, including the effective sample size.

To see this, the code below provides an example using runireg. Here the generic function summary dispatches the method summary.bayesm.mat because the betadraw element of runireg’s output has class bayesm.mat. This example also shows the information about the prior that is printed to the console during the call to a posterior sampling function. Notice, however, that no remaining time is printed because nprint is set to zero.

set.seed(66)
R <- 2000
n <- 200
X <- cbind(rep(1,n), runif(n))
beta <- c(1,2)
sigsq <- 0.25
y <- X %*% beta + rnorm(n, sd = sqrt(sigsq))
out <- runireg(Data = list(y = y, X = X), Mcmc = list(R = R, nprint = 0))
##  
## Starting IID Sampler for Univariate Regression Model
##   with  200  observations
##  
## Prior Parms: 
## betabar
## [1] 0 0
## A
##      [,1] [,2]
## [1,] 0.01 0.00
## [2,] 0.00 0.01
## nu =  3  ssq=  0.5721252
##  
## MCMC parms: 
## R=  2000  keep=  1  nprint=  0
## 
summary(out$betadraw, tvalues = beta)
## Summary of Posterior Marginal Distributions 
## Moments 
##   tvalues mean std dev num se rel eff sam size
## 1       1  1.0    0.07 0.0015    0.85     1800
## 2       2  2.1    0.12 0.0029    1.01      900
## 
## Quantiles 
##   tvalues 2.5%  5% 50% 95% 97.5%
## 1       1 0.88 0.9 1.0 1.1   1.2
## 2       2 1.83 1.9 2.1 2.3   2.3
##    based on 1800 valid draws (burn-in=200)

3.3 Access to Code

bayesm was originally created as a companion to BSM, at which time most functions were written in R. The package has since been expanded to include additional functionality and most code has been converted to C++ via Rcpp for faster performance. However, for users interested in obtaining the original implementation of a posterior sampling function (in R instead of C++), you may still access the last version (2.2-5) of bayesm prior to the C++/Rcpp conversion from the package archive on CRAN.

To access the R code in the current version of bayesm, the user can simply call a function without parenthesis. For example, bayesm::runireg. However, most posterior sampling functions only perform basic checks in R and then call an unexported C++ function to do the heavy lifting (i.e., the MCMC draws). This C++ source code is not available to the user via the installed bayesm package because C++ code is compiled upon package installation on Linux machines and pre-compiled by CRAN for Mac and Windows. To access this source code, the user must download the “package source†from CRAN. This can be accomplished by clicking on the appropriate link at the bayesm package archive or by executing the R command download.packages(pkgs="bayesm", destdir=".", type="source"). Either of these methods will provide you with a compressed file “bayesm_version.tar.gz†that can be uncompressed. The C++ code can then be found in the “src†subdirectory.

4 Examples

We begin with a brief introduction to regression and Bayesian estimation. This will help set the notation and provide background for the examples that follow. We do not claim that this will be a sufficient introduction to the reader for which these ideas are new. We refer that reader to excellent texts on regression analysis by Cameron & Trivedi, Davidson & MacKinnon, Goldberger, Greene, Wasserman, and Wooldridge.5 For Bayesian methods, we recommend Gelman et al., Jackman, Marin & Robert, Rossi et al., and Zellner.6

4.1 What is Regression

Suppose you believe a variable \(y\) varies with (or is caused by) a set of variables \(x_1, x_2, \ldots, x_k\). For notational convenience, we’ll collect the set of \(x\) variables into \(X\). These variables \(y\) and \(X\) have a joint distribution \(f(y, X)\). Typically, interest will not fall on this joint distribution, but rather on the conditional distribution of the “outcome†variable \(y\) given the “explanatory†variables (or “covariatesâ€) \(x_1, x_2, \ldots, x_k\); this conditional distribution being \(f(y|X)\).

To carry out inference on the relationship between \(y\) and \(X\), the researcher then often focuses attention on one aspect of the conditional distribution, most commonly its expected value. This conditional mean is assumed to be a function \(g\) of the covariates such that \(\mathbb{E}[y|X] = g(X, \beta)\) where \(\beta\) is a vector of parameters. A function for the conditional mean is known as a “regression†function.

The canonical introductory regression model is the normal linear regression model, which assumes that \(y \sim N(X\beta, \sigma^2)\). Most students of regression will have first encountered this model as a combination of deterministic and stochastic components. There, the stochastic component is defined as deviations from the conditional mean, \(\varepsilon = y - \mathbb{E}[y|X]\), such that \(y = \mathbb{E}[y|X] + \varepsilon\) or that \(y = g(X, \beta) + \varepsilon\). The model is then augmented with the assumptions that \(g(X, \beta) = X \beta\) and \(\varepsilon \sim N(0,\sigma^2)\) so that the normal linear regression model is:

\[ y = X \beta + \varepsilon \text{ with } \varepsilon \sim N(0,\sigma^2) \hspace{1em} \text{or} \hspace{1em} y \sim N(X\beta, \sigma^2) \]

When taken to data, additional assumptions are made which include a full-rank condition on \(X\) and often that \(\varepsilon_i\) for \(i=1,\ldots,n\) are independent and identically distributed.

Our first example will demonstrate how to estimate the parameters of the normal linear regression model using Bayesian methods made available by the posterior sampling function runireg. We then provide an example to estimate the parameters of a model when \(y\) is a categorical variable. This second example is called a multinomial logit model and uses the logistic “link†function \(g(X, \beta) = [1 + exp(-X\beta)]^{-1}\). Our third and final example will extend the multinomial logit model to permit individual-level parameters. This is known as a hierarchical model and requires panel data to perform the estimation.

Before launching into the examples, we briefly introduce Bayesian methodology and contrast it with classical methods.

4.2 What is Bayesian Inference

Under classical econometric methods, \(\beta\) is most commonly estimated by minimizing the sum of squared residuals, maximizing the likelihood, or matching sample moments to population moments. The distribution of the estimators (e.g., \(\hat{\beta}\)) and test statistics derived from these methods rely on asymptotic concepts and are based on imaginary samples not observed.

In contrast, Bayesian inference provides the benefits of (a) exact sample results, (b) integration of descision-making, estimation, testing, and model selection, and (c) a full accounting of uncertainty. These benefits from Bayesian inference rely heavily on probability theory and, in particular, distributional theory, some elements of which we now briefly review.

Recall the relationship between the joint and conditional densities for random variables \(W\) and \(Z\):

\[ P_{A|B}(A=a|B=b) = \frac{P_{A,B}(A=a, B=b)}{P_B(B=b)} \]

This relationship can be used to derive Bayes’ Theorem, which we write with \(D\) for “data†and \(\theta\) as the parameters (and with implied subscripts):

\[ P(\theta|D) = \frac{P(D|\theta)P(\theta)}{P(D)} \]

Noticing that \(P(D)\) does not contain the parameters of interest (\(\theta\)) and is therefore simply a normalizing constant, we can instead write:

\[ P(\theta|D) \propto P(D|\theta)P(\theta) \]

Introducing Bayesian terminology, we have that the “Posterior†is proportional to the Likelihood times the Prior.

Thus, given (1) a dataset (\(D\)), (2) an assumption on the data generating process (the likelihood, \(P(D|\theta)\)), and (3) a specification of the prior distribution of the parameters (\(P(\theta)\)), we can find the exact (posterior) distribution of the parameters given the observed data. This is in stark contrast to classical econometric methods, which typically only provide the asymptotic distributions of estimators.

However, for any problem of practical interest, the posterior distribution is a high-dimensional object. Additionally, it may not be possible to analytically calculate the posterior or its features (e.g., marginal distributions or moments such as the mean). To handle these issues, the modern approach to Bayesian inference relies on simulation methods to sample from the (high-dimensional) posterior distribution and then construct marginal distributions (or their features) from the sampled draws of the posterior. As a result, simulation and summaries of the posterior play important roles in modern Bayesian statistics.

bayesm’s posterior sampling functions (as their name suggests) sample from posterior distributions. bayesm’s summary and plot methods can be used to analyze those draws. Unlike most classical econometric methods, the MCMC methods implemented in bayesm’s posterior sampling functions provide an estimate of the entire posterior distribution, not just a few moments. Given this “rich†result from Bayesian methods, it is best to summarize posterior distributions using histograms or quantiles. We advise that you resist the temptation to simply report the posterior mean and standard deviation; for non-normal distributions, those moments may have little meaning.

In the examples that follow, we will describe the data we use, present the model, demonstrate how to estimate it using the appropriate posterior sampling function, and provide various ways to summarize the output.

4.3 Example 1: Linear Normal Regression

4.3.1 Data

For our first example, we will use the cheese dataset, which provides 5,555 observations of weekly sales volume for a package of Borden sliced cheese, as well as a measure of promotional display activity and price. The data are aggregated to the “key†account (i.e., retailer-market) level.

data(cheese)
names(cheese) <- tolower(names(cheese))
str(cheese)
## 'data.frame':    5555 obs. of  4 variables:
##  $ retailer: Factor w/ 88 levels "ALBANY,NY - PRICE CHOPPER",..: 42 43 44 19 20 21 35 36 64 31 ...
##  $ volume  : int  21374 6427 17302 13561 42774 4498 6834 3764 5112 6676 ...
##  $ disp    : num  0.162 0.1241 0.102 0.0276 0.0906 ...
##  $ price   : num  2.58 3.73 2.71 2.65 1.99 ...

Suppose we want to assess the relationship between sales volume and price and promotional display activity. For this example, we will abstract from whether these relationships vary by retailer or whether prices are set endogenously. Simple statistics show a negative correlation between volume and price, and a positive correlation between volume and promotional activity, as we would expect.

options(digits=3)
cor(cheese$volume, cheese$price)
## [1] -0.227
cor(cheese$volume, cheese$disp)
## [1] 0.173

4.3.2 Model

We model the expected log sales volume as a linear function of log(price) and promotional activity. Specifically, we assume \(y_i\) to be iid with \(p(y_i|x_i,\beta)\) normally distributed with a mean linear in \(x\) and a variance of \(\sigma^2\). We will denote observations with the index \(i = 1, \ldots, n\) and covariates with the index \(j = 1, \ldots, k\). The model can be written as:

\[ y_i = \sum_{j=1}^k \beta_j x_{ij} + \varepsilon_i = x_i'\beta + \varepsilon_i \hspace{1em} \text{with} \hspace{1em} \varepsilon_i \sim iid\ N(0,\sigma^2) \]

or equivalently but more compactly as:

\[ y \sim MVN(X\beta,\ \sigma^2I_n) \]

Here, the notation \(N(0, \sigma^2)\) indicates a univariate normal distribution with mean \(0\) and variance \(\sigma^2\), while \(MVN(X\beta,\ \sigma^2I_n)\) indicates a multivariate normal distribution with mean vector \(X\beta\) and variance-covariance matrix \(\sigma^2I_n\). In addition, \(y_i\), \(x_{ij}\), \(\varepsilon_i\), and \(\sigma^2\) are scalars while \(x_i\) and \(\beta\) are \(k \times 1\) dimensional vectors. In the more compact notation, \(y\) is an \(n \times 1\) dimensional vector, \(X\) is an \(n \times k\) dimensional matrix with row \(x_i\), and \(I_n\) is an \(n \times n\) dimensional identity matrix. With regard to the cheese dataset, \(k = 2\) and \(n = 5,555\).

When employing Bayesian methods, the model is incomplete until the prior is specified. For our example, we elect to use natural conjugate priors, meaning the family of distributions for the prior is chosen such that, when combined with the likelihood, the posterior will be of the same distributional family. Specifically, we first factor the joint prior into marginal and conditional prior distributions:

\[ p(\beta,\sigma^2) = p(\beta|\sigma^2)p(\sigma^2) \]

We then specify the prior for \(\sigma^2\) as inverse-gamma (written in terms of a chi-squared random variable) and the prior for \(\beta|\sigma^2\) as multivariate normal:

\[ \sigma^2 \sim \frac{\nu s^2}{\chi^2_{\nu}} \hspace{1em} \text{and} \hspace{1em} \beta|\sigma^2 \sim MVN(\bar{\beta},\sigma^2A^{-1}) \]

Other than convenience, we have little reason to specify priors from these distributional families; however, we will select diffuse priors so as not to impose restrictions on the model. To do so, we must pick values for \(\nu\) and \(s^2\) (the degrees of freedom and scale parameters for the inverted chi-squared prior on \(\sigma^2\)) as well as \(\bar{\beta}\) and \(A^{-1}\) (the mean vector and variance-covariance matrix for the multivariate normal prior on the \(\beta\) vector). The bayesm posterior sampling function for this model, runireg, defaults to the following values:

  • \(\nu = 3\)
  • \(s^2 =\) var(y)
  • \(\bar{\beta} = 0\)
  • \(A = 0.01*I\)

We will use these defaults, as they are chosen to be diffuse for data with a unit scale. Thus, for each \(\beta_j | \sigma^2\) we have specified a normal prior with mean 0 and variance \(100\sigma^2\), and for \(\sigma^2\) we have specified an inverse-gamma prior with \(\nu = 3\) and \(s^2 = \text{var}(y)\). We graph these prior distributions below.

par(mfrow = c(1,2))

curve(dnorm(x,0,10), xlab = "", ylab = "", xlim = c(-30,30),
      main = expression(paste("Prior for ", beta[j])),
      col = "dodgerblue4")

nu  <- 3
ssq <- var(log(cheese$volume))
curve(nu*ssq/dchisq(x,nu), xlab = "", ylab = "", xlim = c(0,1),
      main = expression(paste("Prior for ", sigma^2)), 
      col = "darkred")

par(mfrow = c(1,1))

4.3.3 Bayesian Estimation

Although this model involves nontrivial natural conjugate priors, the posterior is available in closed form:

\[ p(\beta, \sigma^2 | y, X) \propto (\sigma^2)^{-k/2} \exp \left\{ -\frac{1}{2\sigma^2}(\beta - \bar{\beta})'(X'X+A)(\beta - \bar{\beta}) \right\} \times (\sigma^2)^{-((n+\nu_0)/2+1)} \exp \left\{ -\frac{\nu_0s_0^2 + ns^2}{2\sigma^2} \right\} \]

or

\[\begin{align*} \beta | \sigma^2, y, X &\sim N(\bar{\beta}, \sigma^2(X'X+A)^{-1}) \\ \\ \sigma^2 | y, X &\sim \frac{\nu_1s_1^2}{\chi^2_{\nu_1}}, \hspace{3em} \text{with} \> \nu_1 = \nu_0+n \hspace{1em} \text{and} \hspace{1em} s_1^2 = \frac{\nu_0s_0^2 + ns^2}{\nu_0+n} \end{align*}\]

The latter representation suggests a simulation strategy for making draws from the posterior. We draw a value of \(\sigma^2\) from its marginal posterior distribution, insert this value into the expression for the covariance matrix of the conditional normal distribution of \(\beta|\{\sigma^2,y\}\) and draw from this multivariate normal. This simulation strategy is implemented by runireg, using the defaults for Prior specified above. The code is quite simple.

dat <- list(y = log(cheese$volume), X = model.matrix( ~ price + disp, data = cheese))
out <- runireg(Data = dat, Mcmc = list(R=1e4, nprint=1e3))

Note that bayesm posterior sampling functions print out information about the prior and about MCMC progress during the function call (unless nprint is set to 0), but for presentation purposes we suppressed that output here.

runireg returns a list that we have saved in out. The list contains two elements, betadraw and sigmasqdraw, which you can verify by running str(out). betadraw is an R/keep \(\times\) ncol(X) (\(10,000 \times 3\) with a column for each of the intercept, price, and display) dimension matrix with class bayesm.mat. We can analyze or summarize the marginal posterior distributions for any \(\beta\) parameter or the \(\sigma^2\) parameter. For example, we can plot histograms of the price coefficient (even though it is known to follow a t-distrbution, see BSM Ch. 2.8) and for \(\sigma^2\). Notice how concentrated the posterior distributions are compared to their priors above.

B <- 1000+1 #burn in draws to discard 
R <- 10000

par(mfrow = c(1,2))
hist(out$betadraw[B:R,2], breaks = 30, 
     main = "Posterior Dist. of Price Coef.", 
     yaxt = "n", yaxs="i",
     xlab = "", ylab = "", 
     col = "dodgerblue4", border = "gray")
hist(out$sigmasqdraw[B:R], breaks = 30, 
     main = "Posterior Dist. of Sigma2", 
     yaxt = "n", yaxs="i",
     xlab = "", ylab = "", 
     col = "darkred", border = "gray")
par(mfrow = c(1,1))

Additionally, we can compute features of these posterior distributions. For example, the posterior means price and display are:

apply(out$betadraw[B:R,2:3], 2, mean)
## [1] -0.393  0.542

Conveniently, bayesm offers this functionality (and more) with its summary and plot methods. Notice that bayesm’s methods use a default burn-in length, calculated as trunc(0.1*nrow(X)). You can override the default by specifying the burnin argument. We see that the means for the first few retail fixed effects in the summary information below match those calculated “by hand†above.

summary(out$betadraw)
## Summary of Posterior Marginal Distributions 
## Moments 
##    mean std dev  num se rel eff sam size
## 1  9.19   0.059 0.00057    0.85     9000
## 2 -0.39   0.020 0.00019    0.87     9000
## 3  0.54   0.063 0.00072    1.18     4500
## 
## Quantiles 
##    2.5%    5%   50%   95% 97.5%
## 1  9.08  9.09  9.19  9.29  9.31
## 2 -0.43 -0.43 -0.39 -0.36 -0.35
## 3  0.42  0.44  0.54  0.64  0.66
##    based on 9000 valid draws (burn-in=1000)

The same can be done with the plot generic function. However, we reference the method directly (plot.bayesm.mat) to plot a subset of the bayesm output – specifically we plot the price coefficient. In the histogram, note that the green bars delimit a 95% Bayesian credibility interval, yellow bars shows +/- 2 numerical standard errors for the posterior mean, and the red bar indicates the posterior mean. Also notice that this pink histogram of the posterior distribution on price, which was created by calling the plot generic, matches the blue one we created “by hand†above.

The plot generic function also provides a trace plot and and ACF plot. In many applications (although not in this simple model), we cannot be certain that our draws from the posterior distribution adequately represent all areas of the posterior with nontrivial mass. This may occur, for instance, when using a “slow mixing†Markov Chain Monte Carlo (MCMC) algorithm to draw from the posterior. In such a case, we might see patterns in the trace plot and non-zero autocorrelations in the ACF plot; these will coincide with values for the Effective Sample Size less than R. (Effective Sample Size prints out with the summary generic function, as above.) Here, however, we are able to sample from the posterior distribution by taking iid draws, and so we see large Effective Sample Sizes in the summary output above, good mixing the trace plot below, and virtually no autocorrelation between draws in the ACF plot below.

plot.bayesm.mat(out$betadraw[,2])

4.4 Example 2: Multinomial Logistic Regression

4.4.1 Data

Linear regression models like the one in the previous example are best suited for continuous outcome variables. Different models (known as limited dependent variable models) have been developed for binary or multinomial outcome variables, the most popular of which — the multinomial logit — will be the subject of this section.

For this example, we analyze the margarine dataset, which provides panel data on purchases of margarine. The data are stored in two dataframes. The first, choicePrice, lists the outcome of 4,470 choice occasions as well as the choosing household and the prices of the 10 choice alternatives. The second, demos, provides demographic information about the choosing households, such as their income and family size. We begin by merging the information from these two dataframes:

data(margarine)
str(margarine)
marg <- merge(margarine$choicePrice, margarine$demos, by = "hhid")
## List of 2
##  $ choicePrice:'data.frame': 4470 obs. of  12 variables:
##   ..$ hhid    : int [1:4470] 2100016 2100016 2100016 2100016 2100016 2100016 2100016 2100024 2100024 2100024 ...
##   ..$ choice  : num [1:4470] 1 1 1 1 1 4 1 1 4 1 ...
##   ..$ PPk_Stk : num [1:4470] 0.66 0.63 0.29 0.62 0.5 0.58 0.29 0.66 0.66 0.66 ...
##   ..$ PBB_Stk : num [1:4470] 0.67 0.67 0.5 0.61 0.58 0.45 0.51 0.45 0.59 0.67 ...
##   ..$ PFl_Stk : num [1:4470] 1.09 0.99 0.99 0.99 0.99 0.99 0.99 1.08 1.08 1.09 ...
##   ..$ PHse_Stk: num [1:4470] 0.57 0.57 0.57 0.57 0.45 0.45 0.29 0.57 0.57 0.57 ...
##   ..$ PGen_Stk: num [1:4470] 0.36 0.36 0.36 0.36 0.33 0.33 0.33 0.36 0.36 0.36 ...
##   ..$ PImp_Stk: num [1:4470] 0.93 1.03 0.69 0.75 0.72 0.72 0.72 0.93 0.93 0.93 ...
##   ..$ PSS_Tub : num [1:4470] 0.85 0.85 0.79 0.85 0.85 0.85 0.85 0.85 0.85 0.85 ...
##   ..$ PPk_Tub : num [1:4470] 1.09 1.09 1.09 1.09 1.07 1.07 1.07 1.09 1.09 1.09 ...
##   ..$ PFl_Tub : num [1:4470] 1.19 1.19 1.19 1.19 1.19 1.19 1.19 1.19 1.34 1.19 ...
##   ..$ PHse_Tub: num [1:4470] 0.33 0.37 0.59 0.59 0.59 0.59 0.59 0.33 0.33 0.33 ...
##  $ demos      :'data.frame': 516 obs. of  8 variables:
##   ..$ hhid     : num [1:516] 2100016 2100024 2100495 2100560 2100610 ...
##   ..$ Income   : num [1:516] 32.5 17.5 37.5 17.5 87.5 12.5 17.5 17.5 27.5 67.5 ...
##   ..$ Fs3_4    : int [1:516] 0 1 0 0 0 0 0 0 0 0 ...
##   ..$ Fs5.     : int [1:516] 0 0 0 0 0 0 0 0 1 0 ...
##   ..$ Fam_Size : int [1:516] 2 3 2 1 1 2 2 2 5 2 ...
##   ..$ college  : int [1:516] 1 1 0 0 1 0 1 0 1 1 ...
##   ..$ whtcollar: int [1:516] 0 1 0 1 1 0 0 0 1 1 ...
##   ..$ retired  : int [1:516] 1 1 1 0 0 1 0 1 0 0 ...

Compared to the standard \(n \times k\) rectangular format for data to be used in a linear regression model, choice data may be stored in various formats, including a rectangular format where the \(p\) choice alternatives are allocated across columns or rows, or a list-of-lists format as used in bayesm’s hierarchical models, which we demonstrate in Example 3 below. For all functions — and notably those that implement multinomial logit and probit models — the data must be in the format expected by the function, and bayesm’s posterior sampling funtions are no exception.

In this example, we will implement a multinomial logit model using rmnlIndepMetrop. This posterior sampling function requires y to be a length-\(n\) vector (or an \(n \times 1\) matrix) of multinomial outcomes (\(1, \dots, p\)). That is, each element of y corresponds to a choice occasion \(i\) with the value of the element \(y_i\) indicating the choice that was made. So if the fourth alternative was chosen on the seventh choice occasion, then \(y_7 = 4\). The margarine data are stored in that format, and so we easily specify y with the following code:

y <- marg[,2]

rmnlIndepMetrop requires X to be an \(np \times k\) matrix. That is, each alternative is listed on its own row, with a group of \(p\) rows together corresponding to the alternatives available on one choice occasion. However, the margarine data are stored with the various choice alternatives in columns rather than rows, so reformatting is necessary. bayesm provides the utility function createX to assist with the conversion. createX requires the user to specify the number of choice alternatives p as well as the number of alternative-specific variables na and an \(n \times\) na matrix of alternative-specific data Xa (“a†for alternative-specific). Here, we have \(p=10\) choice alternatives with na \(=1\) alternative-specific variable (price). If we were only interested in using price as a covariate, we would code:

X1 <- createX(p=10, na=1, Xa=marg[,3:12], nd=NULL, Xd=NULL, base=1)
colnames(X1) <- c(names(marg[,3:11]), "price")
head(X1, n=10)
##       PPk_Stk PBB_Stk PFl_Stk PHse_Stk PGen_Stk PImp_Stk PSS_Tub PPk_Tub
##  [1,]       0       0       0        0        0        0       0       0
##  [2,]       1       0       0        0        0        0       0       0
##  [3,]       0       1       0        0        0        0       0       0
##  [4,]       0       0       1        0        0        0       0       0
##  [5,]       0       0       0        1        0        0       0       0
##  [6,]       0       0       0        0        1        0       0       0
##  [7,]       0       0       0        0        0        1       0       0
##  [8,]       0       0       0        0        0        0       1       0
##  [9,]       0       0       0        0        0        0       0       1
## [10,]       0       0       0        0        0        0       0       0
##       PFl_Tub price
##  [1,]       0  0.66
##  [2,]       0  0.67
##  [3,]       0  1.09
##  [4,]       0  0.57
##  [5,]       0  0.36
##  [6,]       0  0.93
##  [7,]       0  0.85
##  [8,]       0  1.09
##  [9,]       0  1.19
## [10,]       1  0.33

Notice that createX uses \(p-1\) dummy variables to distinguish the \(p\) choice alternatives. As with factor variables in linear regression, one factor must be the base; the coefficients on the other factors report deviations from the base. The user may specify the base alternative using the base argument (as we have done above), or let it default to the alternative with the highest index.

For our example, we might also like to include some “demographic†variables. These are variables that do not vary with the choice alternatives. For example, with the margarine data we might want to include family size. Here again we turn to createX, this time specifying the nd and Xd arguments (“d†for demographic):

X2 <- createX(p=10, na=NULL, Xa=NULL, nd=2, Xd=as.matrix(marg[,c(13,16)]), base=1)
print(X2[1:10,1:9]); cat("\n")
print(X2[1:10,10:18])
##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
##  [1,]    0    0    0    0    0    0    0    0    0
##  [2,]    1    0    0    0    0    0    0    0    0
##  [3,]    0    1    0    0    0    0    0    0    0
##  [4,]    0    0    1    0    0    0    0    0    0
##  [5,]    0    0    0    1    0    0    0    0    0
##  [6,]    0    0    0    0    1    0    0    0    0
##  [7,]    0    0    0    0    0    1    0    0    0
##  [8,]    0    0    0    0    0    0    1    0    0
##  [9,]    0    0    0    0    0    0    0    1    0
## [10,]    0    0    0    0    0    0    0    0    1
## 
##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
##  [1,]  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
##  [2,] 32.5  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
##  [3,]  0.0 32.5  0.0  0.0  0.0  0.0  0.0  0.0  0.0
##  [4,]  0.0  0.0 32.5  0.0  0.0  0.0  0.0  0.0  0.0
##  [5,]  0.0  0.0  0.0 32.5  0.0  0.0  0.0  0.0  0.0
##  [6,]  0.0  0.0  0.0  0.0 32.5  0.0  0.0  0.0  0.0
##  [7,]  0.0  0.0  0.0  0.0  0.0 32.5  0.0  0.0  0.0
##  [8,]  0.0  0.0  0.0  0.0  0.0  0.0 32.5  0.0  0.0
##  [9,]  0.0  0.0  0.0  0.0  0.0  0.0  0.0 32.5  0.0
## [10,]  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 32.5

Notice that createX again uses \(p-1\) dummy variables to distinguish the \(p\) choice alternatives. However, for demographic variables, the value of the demographic variable is spread across \(p-1\) columns and \(p-1\) rows.

4.4.2 Model

The logit specification was originally derived by Luce (1959) from assumptions on characteristics of choice probabilities. McFadden (1974) tied the model to rational economic theory by showing how the multinomial logit specification models choices made by a utility-maximizing consumer, assuming that the unobserved utility component is distributed Type I Extreme Value. We motivate use of this model following McFadden by assuming the decision maker chooses the alternative providing him with the highest utility, where the utility \(U_{ij}\) from the choice \(y_{ij}\) made by a decision maker in choice situation \(i\) for product \(j = 1, \ldots, p\) is modeled as the sum of a deterministic and a stochastic component:

\[ U_{ij} = V_{ij} + \varepsilon_{ij} \hspace{2em} \text{with } \varepsilon_{ij}\ \sim \text{ iid T1EV} \]

Regressors (both demographic and alternative-specific) are included in the model by assuming \(V_{ij} = x_{ij}'\beta\). These assumptions result in choice probabilities of:

\[ \text{Pr}(y_i=j) = \frac{\exp \{x_{ij}'\beta\}}{\sum_{k=1}^p\exp\{x_{ik}'\beta\}} \]

Independent priors for the components of the beta vector are specified as normal distributions. Using notation for the multivariate normal distribution, we have:

\[ \beta \sim MVN(\bar{\beta},\ A^{-1}) \]

We use bayesm’s default values for the parameters of the priors: \(\bar{\beta} = 0\) and \(A = 0.01I\).

4.4.3 Bayesian Estimation

Experience with the MNL likelihood is that the asymptotic normal approximation is excellent. rmnlIndepMetrop implements an independent Metropolis algorithm to sample from the normal approximation to the posterior distribution of \(\beta\):

\[ p(\beta | X, y) \overset{\cdot}{\propto} |H|^{1/2} \exp \left\{ \frac{1}{2}(\beta - \hat{\beta})'H(\beta - \hat{\beta}) \right\} \]

where \(\hat{\beta}\) is the MLE, \(H = \sum_i x_i A_i x_i'\), and the candidate distribution used in the Metropolis algorithm is the multivariate student t. For more detail, see Section 11 of BSM Chapter 3.

We sample from the normal approximation to the posterior as follows:

X <- cbind(X1, X2[,10:ncol(X2)])
out <- rmnlIndepMetrop(Data = list(y=y, X=X, p=10), 
                       Mcmc = list(R=1e4, nprint=1e3))

rmnlIndepMetrop returns a list that we have saved in out. The list contains 3 elements, betadraw, loglike, and acceptr, which you can verify by running str(out). betadraw is a \(10,000 \times 28\) dimension matrix with class bayesm.mat. As with the linear regression of Example 1 above, we can plot or summarize features of the the posterior distribution in many ways. For information on each marginal posterior distribution, call summary(out) or plot(out). Because we have 28 covariates (intercepts and demographic variables make up 9 columns each and there is one column for the price variable) we omit the full set of results to save space and instead, we only present summary statistics for the marginal posterior distribution for \(\beta_\text{price}\):

summary.bayesm.mat(out$betadraw[,10], names = "Price")
## Summary of Posterior Marginal Distributions 
## Moments 
##       mean std dev num se rel eff sam size
## Price -6.7    0.18 0.0039     4.5     1800
## 
## Quantiles 
##       2.5% 5%  50%  95% 97.5%
## Price   -7 -7 -6.7 -6.4  -6.3
##    based on 9000 valid draws (burn-in=1000)

In addition to summary information for a marginal posterior distribution, we can plot it. We use bayesm’s plot generic function (calling plot(out$betadraw) would provide the same plots for all 28 \(X\) variables). In the histogram, the green bars delimit a 95% Bayesian credibility interval, yellow bars shows +/- 2 numerical standard errors for the posterior mean, and the red bar indicates the posterior mean. The subsequent two plots are a trace plot and and ACF plot.

plot.bayesm.mat(out$betadraw[,10], names = "Price")

We see that the posterior is approximately normally distributed with a mean of -6.7 and a standard deviation of 0.17. The trace plot shows good mixing. The ACF plot shows a fair amount of correlation such that, even though the algorithm took R = 10,000 draws, the Effective Sample Size (as reported in the summary stats above) is only 1,800.

Because of the nonlinearity in this model, interpreting the results is more difficult than with linear regression models. We do not elaborate further on the interpretation of coefficients and methods of displaying results from multinomial logit models, but for the uninitiated reader, we refer you to excellent sources for this information authored by Kenneth Train and Gary King.7

4.5 Example 3: Hierarchical Logit

4.5.1 Data

While we could add individual-specific parameters to the previous model and use the same dataset, we elect to provide the reader with greater variety. For this example, we use the camera dataset in bayesm, which contains conjoint choice data for 332 respondents who evaluated digital cameras. These data have already been processed to exclude respondents that always answered “noneâ€, always picked the same brand, always selected the highest priced offering, or who appeared to be answering randomly.

data(camera)
length(camera)
## [1] 332
str(camera[[1]])
## List of 2
##  $ y: int [1:16] 1 2 2 4 2 2 1 1 1 2 ...
##  $ X: num [1:80, 1:10] 0 1 0 0 0 0 1 0 0 0 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : chr [1:80] "1" "2" "3" "4" ...
##   .. ..$ : chr [1:10] "canon" "sony" "nikon" "panasonic" ...
colnames(camera[[1]]$X)
##  [1] "canon"     "sony"      "nikon"     "panasonic" "pixels"   
##  [6] "zoom"      "video"     "swivel"    "wifi"      "price"

The camera data is stored in a list-of-lists format, which is the format required by bayesm’s posterior sampling functions for hierarchical models. This format has one list per individual with each list containing a vector y of choice outcomes and a matrix X of covariates. As with the multinomial logit model of the last example, y is a length-\(n_i\) vector (or one-column matrix) and X has dimensions \(n_ij \times k\) where \(n_i\) is the number of choice occasions faced by individual \(i\), \(j\) is the number of choice alternatives, and \(k\) is the number of covariates. For the camera data, \(N=332\), \(n_i=16\) for all \(i\), \(j=5\), and \(k=10\).

If your data were not in this format, it could be easily converted with a for loop and createX. For example, we can format margarine data from Example 2 above into a list-of-lists format with the following code, which simply loops over individuals, extracting and storing their y and X data one individual at a time:

data(margarine)
chpr <- margarine$choicePrice
chpr$hhid <- as.factor(chpr$hhid)
N <- nlevels(chpr$hhid)
dat <- vector(mode = "list", length = N)
for (i in 1:N) {
  dat[[i]]$y <- chpr[chpr$hhid==levels(chpr$hhid)[i], "choice"]
  dat[[i]]$X <- createX(p=10, na=1, Xa=chpr[chpr$hhid==levels(chpr$hhid)[i],3:12], nd=NULL, Xd=NULL)
}

Returning to the camera data, the first 4 covariates are binary indicators for the brands Canon, Sony, Nikon, and Panasonic. These correspond to choice (y) values of 1, 2, 3, and 4. y can also take the value 5, indicating that the respondent chose “noneâ€. The data include binary indicators for two levels of pixel count, zoom strength, swivel video display capability, and wifi connectivity. The last covaritate is price, recorded in hundreds of U.S. dollars (we leave price in these units so that we do not need to adjust the default prior settings).

When we look at overall choice outcomes, we see that the brands and the outside alternative (“noneâ€) are chosen roughly in fifths, with specific implied market shares ranging from 17%–25%:

N <- length(camera)
dat <- matrix(NA, N*16, 2)
for (i in 1:length(camera)) {
  Ni <- length(camera[[i]]$y)
  dat[((i-1)*Ni+1):(i*Ni),1] <- i
  dat[((i-1)*Ni+1):(i*Ni),2] <- camera[[i]]$y
}
round(prop.table(table(dat[,2])), 3)
## 
##     1     2     3     4     5 
## 0.207 0.176 0.195 0.169 0.253

However, when we look at a few individuals’ choices, we see much greater variability:

round(prop.table(table(dat[,1], dat[,2])[41:50,], 1), 3)
##     
##          1     2     3     4     5
##   41 0.188 0.000 0.312 0.500 0.000
##   42 0.250 0.125 0.312 0.312 0.000
##   43 0.312 0.188 0.125 0.125 0.250
##   44 0.000 0.000 0.188 0.188 0.625
##   45 0.312 0.250 0.125 0.125 0.188
##   46 0.375 0.250 0.062 0.062 0.250
##   47 0.188 0.062 0.062 0.250 0.438
##   48 0.125 0.500 0.188 0.125 0.062
##   49 0.062 0.125 0.500 0.188 0.125
##   50 0.000 0.125 0.375 0.250 0.250

It is this heterogeneity in individual choice that motivates us to employ a hierarchical model.

4.5.2 Model

Hierarchical (also known as multi-level, random-coefficient, or mixed) models allow each respondent to have his or her own coefficients. Different people have different preferences, and models that estimate individual-level coefficients can fit data better and make more accurate predictions than single-level models. These models are quite popular in marketing, as they allow, for example, promotions to be targeted to individuals with high promotional part worths — meaning those inviduals who are most likely to respond to the promotion. For more information, see Rossi et al. (1996).8

The model follows the multinomial logit specification given in Example 2 above where individuals are assumed to be rational economic agents that make utility-maximizing choices. Now, however, the model includes individual-level parameters (\(\beta_i\)) assumed to be drawn from a normal distribution and with mean values driven by cross-sectional unit characteristics \(Z\):

\[ y_i \sim \text{MNL}(x_i'\beta_i) \hspace{1em} \text{with} \hspace{1em} \beta_i = z_i' \Delta + u_i \hspace{1em} \text{and} \hspace{1em} u_i \sim MVN(\mu, \Sigma) \]

\(x_i\) is \(n_i \times k\) and \(i = 1, \ldots, N\).

We can alternatively write the middle equation as \(B=Z\Delta + U\) where \(\beta_i\), \(z_i\), and \(u_i\) are the \(i^\text{th}\) rows of \(B\), \(Z\), and \(U\). \(B\) is \(N \times k\), \(Z\) is \(N \times m\), \(\Delta\) is \(m \times k\), and \(U\) is \(N \times k\).

Note that we do not have any cross-sectional unit characteristics in the camera dataset and thus \(Z\) will be omitted.

The priors are:

\[ \text{vec}(\Delta) = \delta \sim MVN(\bar{\delta}, A_\delta^{-1}) \hspace{2em} \mu \sim MVN(\bar{\mu}, \Sigma \otimes a^{-1}) \hspace{2em} \Sigma \sim IW(\nu, V) \]

This specification of priors assumes that, conditional on the hyperparameters (that is, the parameters of the prior distribution), the \(\beta\)’s are a priori independent. This means that inference for each unit can be conducted independently of all other units, conditional on the hyperparameters, which is the Bayesian analogue of the fixed effects approach in classical statistics.

Note also that we have assumed a normal “first-stage†prior distribution over the \(\beta\)’s. rhierMnlRwMixture permits a more-flexible mixture-of-normals first-stage prior (hence the “mixture†in the function name). However, for our example, we will not include this added flexibility (Prior$ncomp = 1 below).

4.5.3 Bayesian Estimation

Although the model is more complex than the models used in the two previous examples, the increased programming difficulty for the researcher is minimal. As before, we specify Data, Prior, and Mcmc arguments, and call the posterior sampling function:

data  <- list(lgtdata = camera, p = 5)
prior <- list(ncomp = 1)
mcmc  <- list(R = 1e4, nprint = 0)

out <- rhierMnlRwMixture(Data = data, Prior = prior, Mcmc = mcmc)
## Z not specified
## Table of Y values pooled over all units
## ypooled
##    1    2    3    4    5 
## 1100  936 1035  898 1343 
##  
## Starting MCMC Inference for Hierarchical Logit:
##    Normal Mixture with 1 components for first stage prior
##    5  alternatives;  10  variables in X
##    for  332  cross-sectional units
##  
## Prior Parms: 
## nu = 13
## V 
##       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
##  [1,]   13    0    0    0    0    0    0    0    0     0
##  [2,]    0   13    0    0    0    0    0    0    0     0
##  [3,]    0    0   13    0    0    0    0    0    0     0
##  [4,]    0    0    0   13    0    0    0    0    0     0
##  [5,]    0    0    0    0   13    0    0    0    0     0
##  [6,]    0    0    0    0    0   13    0    0    0     0
##  [7,]    0    0    0    0    0    0   13    0    0     0
##  [8,]    0    0    0    0    0    0    0   13    0     0
##  [9,]    0    0    0    0    0    0    0    0   13     0
## [10,]    0    0    0    0    0    0    0    0    0    13
## mubar 
##      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,]    0    0    0    0    0    0    0    0    0     0
## Amu 
##      [,1]
## [1,] 0.01
## a 
## [1] 5
##  
## MCMC Parms: 
## s= 0.753  w=  0.1  R=  10000  keep=  1  nprint=  0
## 
## initializing Metropolis candidate densities for  332  units ...
##   completed unit # 50
##   completed unit # 100
##   completed unit # 150
##   completed unit # 200
##   completed unit # 250
##   completed unit # 300

We store the results in out, which is a list of length 4. The list elements are betadraw, nmix, loglike, and SignRes.

  • betadraw is a \(332 \times 10 \times 10,000\) array. These dimensions correspond to the number of individuals, the number of covariates, and the number of MCMC draws.

  • nmix is a list with elements probdraw, zdraw, and compdraw.

    • probdraw tells us the probability that each draw came from a particular normal component. This is relevant when there is a mixture-of-normals first-stage prior. However, since our specified prior over the \(\beta\) vector is one normal distribution, probdraw is a \(10,000 \times 1\) vector of all 1’s.

    • zdraw is NULL as it is not relevant for this function.

    • compdraw provides draws for the mixture-of-normals components. Here, compdraw is a list of 10,000 lists. The \(r^\text{th}\) of the 10,000 lists contains the \(r^\text{th}\) draw of the \(\mu\) vector (dim \(1 \times 10\)) and the Cholesky root of the \(r^\text{th}\) draw for the \(10 \times 10\) covariance matrix.

  • loglike is a \(10,000 \times 1\) vector that provides the log-likelihood of each draw.

  • SignRes relates to whether any sign restrictions were placed on the model. This is discussed in detail in a separate vignette detailing a contrainsted hierarchical multinomial logit model; it is not relevant here.

We can summarize results as before. plot(out$betadraw) provides plots for each variable that summarize the distributions of the individual parameters. For brevity, we provide just a histogram of posterior means for the 332 individual coefficients on wifi capability.

hist(apply(out$betadraw, 1:2, mean)[,9], col = "dodgerblue4", 
     xlab = "", ylab = "", yaxt="n", xlim = c(-4,6), breaks = 20,
     main = "Histogram of Posterior Means For Individual Wifi Coefs")

We see that the distribution of individual posterior means is skewed, suggesting that our assumption of a normal first-stage prior may be incorrect. We could improve this model by using the more flexible mixture-of-normals prior or, if we believe all consumers value wifi connectivity positively, we could impose a sign constraint on that set of parameters — both of which are demonstrated in the vignette for sign-constrained hierarchical multinomial logit.

5 Conclusion

We hope that this vignette has provided the reader with a thorough introduction to the bayesm package and is sufficient to enable immediate use of its posterior sampling functions for bayesian estimation. Comments or edits can be sent to the vignette authors at the email addresses below.

Authored by Dan Yavorsky and Peter Rossi. Last updated June 2017.

  1. For example, calling the generic function summary(x) on an object x with class bayesm.mat actually dispatches the method summary.bayesm.mat on x, which is equivalent to calling summary.bayes.mat(x). For a nice introduction, see Advanced R by Hadley Wickham, available online.↩

  2. Functions such as rivGibbs only permit one endogenous variable and so the function argument is lowercase: x.↩

  3. LHS and RHS stand for left-hand side and right-hand side.↩

  4. Rossi, Peter, Greg Allenby and Robert McCulloch, Bayesian Statistics and Marketing, John Wiley & Sons, 2005. Website.↩

  5. Cameron, Colin and Pravin Trivedi, Microeconometrics: Methods and Applications, Cambridge University Press, 2005.

    Davidson, Russell and James MacKinnon, Estimation and Inference in Econometrics, Oxford University Press, 1993.

    Goldberger, Arthur, A Course in Econometrics, Harvard University Press, 1991.

    Greene, William, Econometric Analysis, Prentice Hall, 2012 (7th edition).

    Wasserman, Larry, All of Statistics: A Concise Course in Statistical Inferece, Springer, 2004.

    Wooldridge, Jeffrey, Econometric Analysis of Cross Section and Panel Data, MIT Press, 2010 (2nd edition).↩

  6. Gelman, Andrew, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin, Bayesian Data Analysis, CRC Press, 2013 (3rd edition).

    Jackman, Simon, Bayesian Analysis for the Social Sciences, Wiley, 2009.

    Marin, Jean-Michel and Christian Robert, Bayesian Essentials with R, Springer, 2014 (2nd edition).

    Rossi, Peter, Greg Allenby, and Robert McCulloch, Bayesian Statistics and Marketing, Wiley, 2005.

    Zellner, Arnold, An Introduction to Bayesian Inference in Economics, Wiley, 1971.↩

  7. Train, Kenneth, Discrete Choice Models with Simulation Cambridge University Press, 2009.

    King, Gary Unifying Political Methodlogy: The Likelihood Theory of Statistical Inference, University of Michigan Press (1998) p. 108.

    King, Gary, Michael Tomz, and Jason Wittenberg, “Making the Most of Statistical Analyses: Improving Interpretation and Presentation†American Journal of Political Science, Vol. 44 (April 2000) pp. 347–361 at p. 355.↩

  8. Rossi, McCulloch, and Allenby “The Value of Purchase History Data in Target Marketing†Marketing Science (1996).↩

bayesm/inst/doc/Constrained_MNL_Vignette.R0000644000176000001440000001353313123305371020256 0ustar ripleyusers## ---- echo = FALSE------------------------------------------------------- library(bayesm) knitr::opts_chunk$set(fig.align = "center", fig.height = 3.5, warning = FALSE, error = FALSE, message = FALSE) ## ------------------------------------------------------------------------ # define function drawprior <- function (mubar_betak, nvar, ncomp, a, nu, Amu, V, ndraw) { betakstar <- double(ndraw) betak <- double(ndraw) otherbeta <- double(ndraw) mubar <- c(rep(0, nvar-1), mubar_betak) for(i in 1:ndraw) { comps=list() for(k in 1:ncomp) { Sigma <- rwishart(nu,chol2inv(chol(V)))$IW comps[[k]] <- list(mubar + t(chol(Sigma/Amu)) %*% rnorm(nvar), backsolve(chol(Sigma), diag(1,nvar)) ) } pvec <- rdirichlet(a) beta <- rmixture(1,pvec,comps)$x betakstar[i] <- beta[nvar] betak[i] <- -exp(beta[nvar]) otherbeta[i] <- beta[1] } return(list(betakstar=betakstar, betak=betak, otherbeta=otherbeta)) } set.seed(1234) ## ------------------------------------------------------------------------ # specify rhierMnlRwMixture defaults mubar_betak <- 0 nvar <- 10 ncomp <- 3 a <- rep(5, ncomp) nu <- nvar + 3 Amu <- 0.01 V <- nu*diag(c(rep(1,nvar-1),1)) ndraw <- 10000 defaultprior <- drawprior(mubar_betak, nvar, ncomp, a, nu, Amu, V, ndraw) ## ---- fig.align='center', fig.height=3.5, results='hold'----------------- # plot priors under defaults par(mfrow=c(1,3)) trimhist <- -20 hist(defaultprior$betakstar, breaks=40, col="magenta", main="Beta_k_star", xlab="", ylab="", yaxt="n") hist(defaultprior$betak[defaultprior$betak>trimhist], breaks=40, col="magenta", main="Beta_k", xlab="", ylab="", yaxt="n", xlim=c(trimhist,0)) hist(defaultprior$otherbeta, breaks=40, col="magenta", main="Other Beta", xlab="", ylab="", yaxt="n") ## ------------------------------------------------------------------------ # adjust priors for constraints mubar_betak <- 2 nvar <- 10 ncomp <- 3 a <- rep(5, ncomp) nu <- nvar + 15 Amu <- 0.1 V <- nu*diag(c(rep(4,nvar-1),0.1)) ndraw <- 10000 tightprior <- drawprior(mubar_betak, nvar, ncomp, a, nu, Amu, V, ndraw) ## ---- fig.align='center', fig.height=3.5, results='hold'----------------- # plot priors under adjusted values par(mfrow=c(1,3)) trimhist <- -20 hist(tightprior$betakstar, breaks=40, col="magenta", main="Beta_k_star", xlab="", ylab="", yaxt="n") hist(tightprior$betak[tightprior$betak>trimhist], breaks=40, col="magenta", main="Beta_k", xlab="", ylab="", yaxt="n", xlim=c(trimhist,0)) hist(tightprior$otherbeta, breaks=40, col="magenta", main="Other Beta", xlab="", ylab="", yaxt="n") ## ------------------------------------------------------------------------ library(bayesm) data(camera) length(camera) str(camera[[1]]) ## ------------------------------------------------------------------------ str(camera[[1]]$y) str(as.data.frame(camera[[1]]$X)) ## ---- echo = FALSE------------------------------------------------------- nvar <- 10 mubar <- c(rep(0,nvar-1),2) Amu <- 0.1 ncomp <- 5 a <- rep(5, ncomp) nu <- 25 V <- nu*diag(c(rep(4,nvar-1),0.1)) ## ---- eval = FALSE------------------------------------------------------- # SignRes <- c(rep(0,nvar-1),-1) # # data <- list(lgtdata=camera, p=5) # prior <- list(mubar=mubar, Amu=Amu, ncomp=ncomp, a=a, nu=nu, V=V, SignRes=SignRes) # mcmc <- list(R=1e4, nprint=0) # # out <- rhierMnlRwMixture(Data=data, Prior=prior, Mcmc=mcmc) ## ---- echo = FALSE------------------------------------------------------- temp <- capture.output( {SignRes <- c(rep(0,nvar-1),-1); data <- list(lgtdata = camera, p = 5); prior <- list(mubar=mubar, Amu=Amu, ncomp=ncomp, a=a, nu=nu, V=V, SignRes=SignRes); mcmc <- list(R = 1e4, nprint = 0); out <- rhierMnlRwMixture(Data = data, Prior = prior, Mcmc = mcmc)}, file = NULL) ## ------------------------------------------------------------------------ par(mfrow=c(1,3)) ind_hist <- function(mod, i) { hist(mod$betadraw[i , 10, ], breaks = seq(-14,0,0.5), col = "dodgerblue3", border = "grey", yaxt = "n", xlim = c(-14,0), xlab = "", ylab = "", main = paste("Ind.",i)) } ind_hist(out,1) ind_hist(out,2) ind_hist(out,3) ## ------------------------------------------------------------------------ par(mfrow=c(1,1)) hist(apply(out$betadraw[ , 10, ], 1, mean), xlim = c(-20,0), breaks = 20, col = "firebrick2", border = "gray", xlab = "", ylab = "", main = "Posterior Means for Ind. Price Params, With Sign Constraint") ## ------------------------------------------------------------------------ data0 <- list(lgtdata = camera, p = 5) prior0 <- list(ncomp = 5) mcmc0 <- list(R = 1e4, nprint = 0) ## ---- eval = FALSE------------------------------------------------------- # out0 <- rhierMnlRwMixture(Data = data0, Prior = prior0, Mcmc = mcmc0) ## ---- echo = FALSE------------------------------------------------------- temp <- capture.output( {out0 <- rhierMnlRwMixture(Data = data0, Prior = prior0, Mcmc = mcmc0)}, file = NULL) ## ------------------------------------------------------------------------ par(mfrow=c(1,3)) ind_hist <- function(mod, i) { hist(mod$betadraw[i , 10, ], breaks = seq(-12,2,0.5), col = "dodgerblue4", border = "grey", yaxt = "n", xlim = c(-12,2), xlab = "", ylab = "", main = paste("Ind.",i)) } ind_hist(out0,1) ind_hist(out0,2) ind_hist(out0,3) ## ------------------------------------------------------------------------ par(mfrow=c(1,1)) hist(apply(out0$betadraw[ , 10, ], 1, mean), xlim = c(-15,5), breaks = 20, col = "firebrick3", border = "gray", xlab = "", ylab = "", main = "Posterior Means for Ind. Price Params, No Sign Constraint") bayesm/inst/doc/Constrained_MNL_Vignette.html0000644000176000001440000566557213123305372021047 0ustar ripleyusers Hierarchical Multinomial Logit with Sign Constraints

Introduction

bayesm’s posterior sampling function rhierMnlRwMixture permits the imposition of sign constraints on the individual-specific parameters of a hierarchical multinomial logit model. This may be desired if, for example, the researcher believes there are heterogenous effects from, say, price, but that all responses should be negative (i.e., sign-constrained). This vignette provides exposition of the model, discussion of prior specification, and an example.

Model

The model follows the hierarchical multinomial logit specification given in Example 3 of the “bayesm Overview†Vignette, but will be repeated here succinctly. Individuals are assumed to be rational economic agents that make utility-maximizing choices. Utility is modeled as the sum of deterministic and stochastic components, where the inverse-logit of the probability of chosing an alternative is linear in the parameters and the error is assumed to follow a Type I Extreme Value distribution:

\[ U_{ij} = X_{ij}\beta_i + \varepsilon_{ij} \hspace{0.8em} \text{with} \hspace{0.8em} \varepsilon_{ij}\ \sim \text{ iid Type I EV} \]

These assumptions yield choice probabilities of:

\[ \text{Pr}(y_i=j) = \frac{\exp \{x_{ij}'\beta_i\}}{\sum_{k=1}^p\exp\{x_{ik}'\beta_i\}} \]

\(x_i\) is \(n_i \times k\) and \(i = 1, \ldots, N\). There are \(p\) alternatives, \(j = 1, \ldots, p\). An outside option, often denoted \(j=0\) can be introduced by assigning \(0\)’s to that option’s covariate (\(x\)) values.

We impose sign constraints by defining a \(k\)-length constraint vector SignRes that takes values from the set \(\{-1, 0, 1\}\) to define \(\beta_{ik} = f(\beta_{ik}^*)\) where \(f(\cdot)\) is as follows:

\[ \beta_{ik} = f(\beta_{ik}^*) = \left\{ \begin{array}{lcl} \exp(\beta_{ik}^*) & \text{if} & \texttt{SignRes[k]} = 1 \\ \beta_{ik}^* & \text{if} & \texttt{SignRes[k]} = 0 \\ -\exp(\beta_{ik}^*) & \text{if} & \texttt{SignRes[k]} = -1 \\ \end{array} \right. \]

The “deep†individual-specific parameters (\(\beta_i^*\)) are assumed to be drawn from a mixture of \(M\) normal distributions with mean values driven by cross-sectional unit characteristics \(Z\). That is, \(\beta_i^* = z_i' \Delta + u_i\) where \(u_i\) has a mixture-of-normals distribution.1

Considering \(\beta_i^*\) a length-\(k\) row vector, we will stack the \(N\) \(\beta_i^*\)’s vertically and write:

\[ B=Z\Delta + U \] Thus we have \(\beta_i\), \(z_i\), and \(u_i\) as the \(i^\text{th}\) rows of \(B\), \(Z\), and \(U\). \(B\) is \(N \times k\), \(Z\) is \(N \times M\), \(\Delta\) is \(M \times k\), and \(U\) is \(N \times k\) where the distribution on \(U\) is such that:

\[ \Pr(\beta_{ik}^*) = \sum_{m=1}^M \pi_m \phi(z_i' \Delta \vert \mu_j, \Sigma_j) \]

\(\phi\) is the normal pdf.

Priors

Natural conjugate priors are specified:

\[ \pi \sim \text{Dirichlet}(a) \] \[ \text{vec}(\Delta) = \delta \sim MVN(\bar{\delta}, A_\delta^{-1}) \] \[ \mu_m \sim MVN(\bar{\mu}, \Sigma_m \otimes a_\mu^{-1}) \] \[ \Sigma_m \sim IW(\nu, V) \]

This specification of priors assumes that \(\mu_m\) is independent of \(\Sigma_m\) and that, conditional on the hyperparameters, the \(\beta_i\)’s are a priori independent.

\(a\) implements prior beliefs on the number of normal components in the mixture with a default of 5. \(\nu\) is a “tightness†parameter of the inverted-Wishart distribution and \(V\) is its location matrix. Without sign constraints, they default to \(\nu=k+3\) and \(V=\nu I\), which has the effect of centering the prior on \(I\) and making it “barely properâ€. \(a_\mu\) is a tightness parameter for the priors on \(\mu\), and when no sign constraints are imposed it defaults to an extremely diffuse prior of 0.01.

These defaults assume the logit coefficients (\(\beta_{ik}\)’s) are on the order of approximately 1 and, if so, are typically reasonable hyperprior values. However, when sign constraints are imposed, say, SignRes[k]=-1 such that \(\beta_{ik} = -\exp\{\beta_{ik}^*\}\), then these hyperprior defults pile up mass near zero — a result that follows from the nature of the exponential function and the fact that the \(\beta_{ik}^*\)’s are on the log scale. Let’s show this graphically.

# define function
drawprior <- function (mubar_betak, nvar, ncomp, a, nu, Amu, V, ndraw) {
  betakstar <- double(ndraw)
  betak     <- double(ndraw)
  otherbeta <- double(ndraw)
  mubar     <- c(rep(0, nvar-1), mubar_betak)
  
  for(i in 1:ndraw) {
    comps=list()
    for(k in 1:ncomp) {
      Sigma <- rwishart(nu,chol2inv(chol(V)))$IW
      comps[[k]] <- list(mubar + t(chol(Sigma/Amu)) %*% rnorm(nvar), 
                         backsolve(chol(Sigma), diag(1,nvar)) )
    }
    pvec         <- rdirichlet(a)
    beta         <- rmixture(1,pvec,comps)$x
    betakstar[i] <- beta[nvar]
    betak[i]     <- -exp(beta[nvar])
    otherbeta[i] <- beta[1]
  }
  
  return(list(betakstar=betakstar, betak=betak, otherbeta=otherbeta))
}
set.seed(1234)
# specify rhierMnlRwMixture defaults
mubar_betak <- 0
nvar  <- 10
ncomp <- 3
a     <- rep(5, ncomp)
nu    <- nvar + 3
Amu   <- 0.01
V     <- nu*diag(c(rep(1,nvar-1),1))
ndraw <- 10000
defaultprior <- drawprior(mubar_betak, nvar, ncomp, a, nu, Amu, V, ndraw)
# plot priors under defaults
par(mfrow=c(1,3))
trimhist <- -20
hist(defaultprior$betakstar, breaks=40, col="magenta", 
     main="Beta_k_star", xlab="", ylab="", yaxt="n")
hist(defaultprior$betak[defaultprior$betak>trimhist],
     breaks=40, col="magenta", main="Beta_k",
     xlab="", ylab="", yaxt="n", xlim=c(trimhist,0))
hist(defaultprior$otherbeta, breaks=40, col="magenta",
     main="Other Beta", xlab="", ylab="", yaxt="n")

We see that the hyperprior values for constrained logit parameters are far from uninformative. As a result, rhierMnlRwMixture implements different default priors for parameters when sign constraints are imposed. In particular, \(a_\mu=0.1\), \(\nu = k + 15\), and \(V = \nu*\text{diag}(d)\) where \(d_i=4\) if \(\beta_{ik}\) is unconstrained and \(d_i=0.1\) if \(\beta_{ik}\) is constrained. Additionally, \(\bar{\mu}_m = 0\) if unconstrained and \(\bar{\mu}_m = 2\) otherwise. As the following plots show, this yields substantially less informative hyperpriors on \(\beta_{ik}^*\) without significantly affecting the hyperpriors on \(\beta_{ik}\) or \(\beta_{ij}\) (\(j \ne k\)).

# adjust priors for constraints
mubar_betak <- 2
nvar  <- 10
ncomp <- 3 
a     <- rep(5, ncomp)
nu    <- nvar + 15
Amu   <- 0.1 
V     <- nu*diag(c(rep(4,nvar-1),0.1))
ndraw <- 10000
tightprior <- drawprior(mubar_betak, nvar, ncomp, a, nu, Amu, V, ndraw)
# plot priors under adjusted values
par(mfrow=c(1,3))
trimhist <- -20
hist(tightprior$betakstar, breaks=40, col="magenta", 
     main="Beta_k_star", xlab="", ylab="", yaxt="n")
hist(tightprior$betak[tightprior$betak>trimhist],
     breaks=40, col="magenta", main="Beta_k",
     xlab="", ylab="", yaxt="n", xlim=c(trimhist,0))
hist(tightprior$otherbeta, breaks=40, col="magenta", 
     main="Other Beta", xlab="", ylab="", yaxt="n")

Example

Here we demonstrate the implementation of the hierarchical multinomial logit model with sign-constrained parameters. We return to the camera data used in Example 3 of the “bayesm Overview†Vignette. This dataset contains conjoint choice data for 332 respondents who evaluated digital cameras. The data are stored in a lists-of-lists format with one list per respondent, and each respondent’s list having two elements: a vector of choices (y) and a matrix of covariates (X). Notice the dimensions: there is one value for each choice occasion in each individual’s y vector but one row per alternative in each individual’s X matrix, making nrow(x) = 5 \(\times\) length(y) because there are 5 alternatives per choice occasion.

library(bayesm)
data(camera)
length(camera)
## [1] 332
str(camera[[1]])
## List of 2
##  $ y: int [1:16] 1 2 2 4 2 2 1 1 1 2 ...
##  $ X: num [1:80, 1:10] 0 1 0 0 0 0 1 0 0 0 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : chr [1:80] "1" "2" "3" "4" ...
##   .. ..$ : chr [1:10] "canon" "sony" "nikon" "panasonic" ...

As shown next, the first 4 covariates are binary indicators for the brands Canon, Sony, Nikon, and Panasonic. These correspond to choice (y) values of 1, 2, 3, and 4. y can also take the value 5, indicating that the respondent chose “noneâ€. The data include binary indicators for two levels of pixel count, zoom strength, swivel video display capability, and wifi connectivity. The last covaritate is price, recorded in hundreds of U.S. dollars so that the magnitude of the expected price coefficient is such that the default prior settings in rhierMnlRwMixture do not need to be adjusted.

str(camera[[1]]$y)
##  int [1:16] 1 2 2 4 2 2 1 1 1 2 ...
str(as.data.frame(camera[[1]]$X))
## 'data.frame':    80 obs. of  10 variables:
##  $ canon    : num  0 1 0 0 0 0 1 0 0 0 ...
##  $ sony     : num  0 0 0 1 0 0 0 1 0 0 ...
##  $ nikon    : num  1 0 0 0 0 0 0 0 1 0 ...
##  $ panasonic: num  0 0 1 0 0 1 0 0 0 0 ...
##  $ pixels   : num  0 1 0 0 0 1 1 1 1 0 ...
##  $ zoom     : num  1 1 0 1 0 0 0 0 0 0 ...
##  $ video    : num  0 0 0 0 0 0 1 1 1 0 ...
##  $ swivel   : num  1 1 0 1 0 1 0 0 1 0 ...
##  $ wifi     : num  0 1 1 0 0 0 1 1 1 0 ...
##  $ price    : num  0.79 2.29 1.29 2.79 0 2.79 0.79 1.79 1.29 0 ...

Let’s say we would like to estimate the part-worths of the various attributes of these digital cameras using a multinomial logit model. To incorporate individual-level heterogeneous effects, we elect to use a hierarchical (i.e., random coefficient) specification. Further, we believe that despite the heterogeneity, each consumer’s estimate price response (\(\beta_{i,\text{price}}\)) should be negative, which we will impose with a sign constraint. Following the above discussion, we use the default priors, which “adjust†automatically when sign constraints are imposed.

SignRes <- c(rep(0,nvar-1),-1)

data  <- list(lgtdata=camera, p=5)
prior <- list(mubar=mubar, Amu=Amu, ncomp=ncomp, a=a, nu=nu, V=V, SignRes=SignRes)
mcmc  <- list(R=1e4, nprint=0)

out <- rhierMnlRwMixture(Data=data, Prior=prior, Mcmc=mcmc)

While much can be done to analyze the output, we will focus here on the constrained parameters on price. We first plot the posterior distributions for the price parameter for individuals \(i=1,2,3\). Notice that the posterior distributions for the selected individual’s price parameters lie entirely below zero.

par(mfrow=c(1,3))
ind_hist <- function(mod, i) {
  hist(mod$betadraw[i , 10, ], breaks = seq(-14,0,0.5), 
     col = "dodgerblue3", border = "grey", yaxt = "n",
     xlim = c(-14,0), xlab = "", ylab = "", main = paste("Ind.",i))
}
ind_hist(out,1)
ind_hist(out,2)
ind_hist(out,3)

Next we plot a histogram of the posterior means for the 332 individual price paramters (\(\beta_{i,\text{price}}\)):

par(mfrow=c(1,1))
hist(apply(out$betadraw[ , 10, ], 1, mean), 
     xlim = c(-20,0), breaks = 20, 
     col = "firebrick2", border = "gray", xlab = "", ylab = "",
     main = "Posterior Means for Ind. Price Params, 
             With Sign Constraint")

As a point of comparison, we re-run the model without the sign constraint using the default priors (output omitted) and provide the same set of plots. Note now that the right tail of the posterior distribution of \(\beta_2^\text{price}\) extends to the right of zero.

data0  <- list(lgtdata = camera, p = 5)
prior0 <- list(ncomp = 5)
mcmc0  <- list(R = 1e4, nprint = 0)
out0 <- rhierMnlRwMixture(Data = data0, Prior = prior0, Mcmc = mcmc0)
par(mfrow=c(1,3))
ind_hist <- function(mod, i) {
  hist(mod$betadraw[i , 10, ], breaks = seq(-12,2,0.5), 
     col = "dodgerblue4", border = "grey", yaxt = "n",
     xlim = c(-12,2), xlab = "", ylab = "", main = paste("Ind.",i))
}
ind_hist(out0,1)
ind_hist(out0,2)
ind_hist(out0,3)

par(mfrow=c(1,1))
hist(apply(out0$betadraw[ , 10, ], 1, mean), 
     xlim = c(-15,5), breaks = 20, 
     col = "firebrick3", border = "gray", 
     xlab = "", ylab = "",
     main = "Posterior Means for Ind. Price Params, 
             No Sign Constraint")

Authored by Dan Yavorsky and Peter Rossi. Last updated June 2017.

  1. As documented in the helpfile for this function (accessible by ?bayesm::rhierMnlRwMixture), draws from the posterior of the constrained parameters (\(\beta\)) can be found in the output $betadraw while draws from the posterior of the unconstrained parameters (\(\beta^*\)) are available in $nmix$compdraw.↩

bayesm/inst/doc/Constrained_MNL_Vignette.Rmd0000644000176000001440000003260313117570164020605 0ustar ripleyusers--- title: "Hierarchical Multinomial Logit with Sign Constraints" output: rmarkdown::html_document: theme: spacelab highlight: pygments toc: true toc_float: true toc_depth: 3 number_sections: no fig_caption: yes vignette: > %\VignetteIndexEntry{Hierarchical Multinomial Logit with Sign Constraints} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, echo = FALSE} library(bayesm) knitr::opts_chunk$set(fig.align = "center", fig.height = 3.5, warning = FALSE, error = FALSE, message = FALSE) ``` ***** # Introduction `bayesm`'s posterior sampling function `rhierMnlRwMixture` permits the imposition of sign constraints on the individual-specific parameters of a hierarchical multinomial logit model. This may be desired if, for example, the researcher believes there are heterogenous effects from, say, price, but that all responses should be negative (i.e., sign-constrained). This vignette provides exposition of the model, discussion of prior specification, and an example. # Model The model follows the hierarchical multinomial logit specification given in Example 3 of the "`bayesm` Overview" Vignette, but will be repeated here succinctly. Individuals are assumed to be rational economic agents that make utility-maximizing choices. Utility is modeled as the sum of deterministic and stochastic components, where the inverse-logit of the probability of chosing an alternative is linear in the parameters and the error is assumed to follow a Type I Extreme Value distribution: $$ U_{ij} = X_{ij}\beta_i + \varepsilon_{ij} \hspace{0.8em} \text{with} \hspace{0.8em} \varepsilon_{ij}\ \sim \text{ iid Type I EV} $$ These assumptions yield choice probabilities of: $$ \text{Pr}(y_i=j) = \frac{\exp \{x_{ij}'\beta_i\}}{\sum_{k=1}^p\exp\{x_{ik}'\beta_i\}} $$ $x_i$ is $n_i \times k$ and $i = 1, \ldots, N$. There are $p$ alternatives, $j = 1, \ldots, p$. An outside option, often denoted $j=0$ can be introduced by assigning $0$'s to that option's covariate ($x$) values. We impose sign constraints by defining a $k$-length constraint vector `SignRes` that takes values from the set $\{-1, 0, 1\}$ to define $\beta_{ik} = f(\beta_{ik}^*)$ where $f(\cdot)$ is as follows: $$ \beta_{ik} = f(\beta_{ik}^*) = \left\{ \begin{array}{lcl} \exp(\beta_{ik}^*) & \text{if} & \texttt{SignRes[k]} = 1 \\ \beta_{ik}^* & \text{if} & \texttt{SignRes[k]} = 0 \\ -\exp(\beta_{ik}^*) & \text{if} & \texttt{SignRes[k]} = -1 \\ \end{array} \right. $$ The "deep" individual-specific parameters ($\beta_i^*$) are assumed to be drawn from a mixture of $M$ normal distributions with mean values driven by cross-sectional unit characteristics $Z$. That is, $\beta_i^* = z_i' \Delta + u_i$ where $u_i$ has a mixture-of-normals distribution.^[As documented in the helpfile for this function (accessible by `?bayesm::rhierMnlRwMixture`), draws from the posterior of the constrained parameters ($\beta$) can be found in the output `$betadraw` while draws from the posterior of the unconstrained parameters ($\beta^*$) are available in `$nmix$compdraw`.] Considering $\beta_i^*$ a length-$k$ row vector, we will stack the $N$ $\beta_i^*$'s vertically and write: $$ B=Z\Delta + U $$ Thus we have $\beta_i$, $z_i$, and $u_i$ as the $i^\text{th}$ rows of $B$, $Z$, and $U$. $B$ is $N \times k$, $Z$ is $N \times M$, $\Delta$ is $M \times k$, and $U$ is $N \times k$ where the distribution on $U$ is such that: $$ \Pr(\beta_{ik}^*) = \sum_{m=1}^M \pi_m \phi(z_i' \Delta \vert \mu_j, \Sigma_j) $$ $\phi$ is the normal pdf. # Priors Natural conjugate priors are specified: $$ \pi \sim \text{Dirichlet}(a) $$ $$ \text{vec}(\Delta) = \delta \sim MVN(\bar{\delta}, A_\delta^{-1}) $$ $$ \mu_m \sim MVN(\bar{\mu}, \Sigma_m \otimes a_\mu^{-1}) $$ $$ \Sigma_m \sim IW(\nu, V) $$ This specification of priors assumes that $\mu_m$ is independent of $\Sigma_m$ and that, conditional on the hyperparameters, the $\beta_i$'s are _a priori_ independent. $a$ implements prior beliefs on the number of normal components in the mixture with a default of 5. $\nu$ is a "tightness" parameter of the inverted-Wishart distribution and $V$ is its location matrix. Without sign constraints, they default to $\nu=k+3$ and $V=\nu I$, which has the effect of centering the prior on $I$ and making it "barely proper". $a_\mu$ is a tightness parameter for the priors on $\mu$, and when no sign constraints are imposed it defaults to an extremely diffuse prior of 0.01. These defaults assume the logit coefficients ($\beta_{ik}$'s) are on the order of approximately 1 and, if so, are typically reasonable hyperprior values. However, when sign constraints are imposed, say, `SignRes[k]=-1` such that $\beta_{ik} = -\exp\{\beta_{ik}^*\}$, then these hyperprior defults pile up mass near zero --- a result that follows from the nature of the exponential function and the fact that the $\beta_{ik}^*$'s are on the log scale. Let's show this graphically. ```{r} # define function drawprior <- function (mubar_betak, nvar, ncomp, a, nu, Amu, V, ndraw) { betakstar <- double(ndraw) betak <- double(ndraw) otherbeta <- double(ndraw) mubar <- c(rep(0, nvar-1), mubar_betak) for(i in 1:ndraw) { comps=list() for(k in 1:ncomp) { Sigma <- rwishart(nu,chol2inv(chol(V)))$IW comps[[k]] <- list(mubar + t(chol(Sigma/Amu)) %*% rnorm(nvar), backsolve(chol(Sigma), diag(1,nvar)) ) } pvec <- rdirichlet(a) beta <- rmixture(1,pvec,comps)$x betakstar[i] <- beta[nvar] betak[i] <- -exp(beta[nvar]) otherbeta[i] <- beta[1] } return(list(betakstar=betakstar, betak=betak, otherbeta=otherbeta)) } set.seed(1234) ``` ```{r} # specify rhierMnlRwMixture defaults mubar_betak <- 0 nvar <- 10 ncomp <- 3 a <- rep(5, ncomp) nu <- nvar + 3 Amu <- 0.01 V <- nu*diag(c(rep(1,nvar-1),1)) ndraw <- 10000 defaultprior <- drawprior(mubar_betak, nvar, ncomp, a, nu, Amu, V, ndraw) ``` ```{r, fig.align='center', fig.height=3.5, results='hold'} # plot priors under defaults par(mfrow=c(1,3)) trimhist <- -20 hist(defaultprior$betakstar, breaks=40, col="magenta", main="Beta_k_star", xlab="", ylab="", yaxt="n") hist(defaultprior$betak[defaultprior$betak>trimhist], breaks=40, col="magenta", main="Beta_k", xlab="", ylab="", yaxt="n", xlim=c(trimhist,0)) hist(defaultprior$otherbeta, breaks=40, col="magenta", main="Other Beta", xlab="", ylab="", yaxt="n") ``` We see that the hyperprior values for constrained logit parameters are far from uninformative. As a result, `rhierMnlRwMixture` implements different default priors for parameters when sign constraints are imposed. In particular, $a_\mu=0.1$, $\nu = k + 15$, and $V = \nu*\text{diag}(d)$ where $d_i=4$ if $\beta_{ik}$ is unconstrained and $d_i=0.1$ if $\beta_{ik}$ is constrained. Additionally, $\bar{\mu}_m = 0$ if unconstrained and $\bar{\mu}_m = 2$ otherwise. As the following plots show, this yields substantially less informative hyperpriors on $\beta_{ik}^*$ without significantly affecting the hyperpriors on $\beta_{ik}$ or $\beta_{ij}$ ($j \ne k$). ```{r} # adjust priors for constraints mubar_betak <- 2 nvar <- 10 ncomp <- 3 a <- rep(5, ncomp) nu <- nvar + 15 Amu <- 0.1 V <- nu*diag(c(rep(4,nvar-1),0.1)) ndraw <- 10000 tightprior <- drawprior(mubar_betak, nvar, ncomp, a, nu, Amu, V, ndraw) ``` ```{r, fig.align='center', fig.height=3.5, results='hold'} # plot priors under adjusted values par(mfrow=c(1,3)) trimhist <- -20 hist(tightprior$betakstar, breaks=40, col="magenta", main="Beta_k_star", xlab="", ylab="", yaxt="n") hist(tightprior$betak[tightprior$betak>trimhist], breaks=40, col="magenta", main="Beta_k", xlab="", ylab="", yaxt="n", xlim=c(trimhist,0)) hist(tightprior$otherbeta, breaks=40, col="magenta", main="Other Beta", xlab="", ylab="", yaxt="n") ``` # Example Here we demonstrate the implementation of the hierarchical multinomial logit model with sign-constrained parameters. We return to the `camera` data used in Example 3 of the "`bayesm` Overview" Vignette. This dataset contains conjoint choice data for 332 respondents who evaluated digital cameras. The data are stored in a lists-of-lists format with one list per respondent, and each respondent's list having two elements: a vector of choices (`y`) and a matrix of covariates (`X`). Notice the dimensions: there is one value for each choice occasion in each individual's `y` vector but one row per alternative in each individual's `X` matrix, making `nrow(x)` = 5 $\times$ `length(y)` because there are 5 alternatives per choice occasion. ```{r} library(bayesm) data(camera) length(camera) str(camera[[1]]) ``` As shown next, the first 4 covariates are binary indicators for the brands Canon, Sony, Nikon, and Panasonic. These correspond to choice (`y`) values of 1, 2, 3, and 4. `y` can also take the value 5, indicating that the respondent chose "none". The data include binary indicators for two levels of pixel count, zoom strength, swivel video display capability, and wifi connectivity. The last covaritate is price, recorded in hundreds of U.S. dollars so that the magnitude of the expected price coefficient is such that the default prior settings in `rhierMnlRwMixture` do not need to be adjusted. ```{r} str(camera[[1]]$y) str(as.data.frame(camera[[1]]$X)) ``` Let's say we would like to estimate the part-worths of the various attributes of these digital cameras using a multinomial logit model. To incorporate individual-level heterogeneous effects, we elect to use a hierarchical (i.e., random coefficient) specification. Further, we believe that despite the heterogeneity, each consumer's estimate price response ($\beta_{i,\text{price}}$) should be negative, which we will impose with a sign constraint. Following the above discussion, we use the default priors, which "adjust" automatically when sign constraints are imposed. ```{r, echo = FALSE} nvar <- 10 mubar <- c(rep(0,nvar-1),2) Amu <- 0.1 ncomp <- 5 a <- rep(5, ncomp) nu <- 25 V <- nu*diag(c(rep(4,nvar-1),0.1)) ``` ```{r, eval = FALSE} SignRes <- c(rep(0,nvar-1),-1) data <- list(lgtdata=camera, p=5) prior <- list(mubar=mubar, Amu=Amu, ncomp=ncomp, a=a, nu=nu, V=V, SignRes=SignRes) mcmc <- list(R=1e4, nprint=0) out <- rhierMnlRwMixture(Data=data, Prior=prior, Mcmc=mcmc) ``` ```{r, echo = FALSE} temp <- capture.output( {SignRes <- c(rep(0,nvar-1),-1); data <- list(lgtdata = camera, p = 5); prior <- list(mubar=mubar, Amu=Amu, ncomp=ncomp, a=a, nu=nu, V=V, SignRes=SignRes); mcmc <- list(R = 1e4, nprint = 0); out <- rhierMnlRwMixture(Data = data, Prior = prior, Mcmc = mcmc)}, file = NULL) ``` While much can be done to analyze the output, we will focus here on the constrained parameters on price. We first plot the posterior distributions for the price parameter for individuals $i=1,2,3$. Notice that the posterior distributions for the selected individual's price parameters lie entirely below zero. \begin{center} \textbf{Posterior Distributions for beta\_price} \end{center} ```{r} par(mfrow=c(1,3)) ind_hist <- function(mod, i) { hist(mod$betadraw[i , 10, ], breaks = seq(-14,0,0.5), col = "dodgerblue3", border = "grey", yaxt = "n", xlim = c(-14,0), xlab = "", ylab = "", main = paste("Ind.",i)) } ind_hist(out,1) ind_hist(out,2) ind_hist(out,3) ``` Next we plot a histogram of the posterior means for the 332 individual price paramters ($\beta_{i,\text{price}}$): ```{r} par(mfrow=c(1,1)) hist(apply(out$betadraw[ , 10, ], 1, mean), xlim = c(-20,0), breaks = 20, col = "firebrick2", border = "gray", xlab = "", ylab = "", main = "Posterior Means for Ind. Price Params, With Sign Constraint") ``` As a point of comparison, we re-run the model without the sign constraint using the default priors (output omitted) and provide the same set of plots. Note now that the right tail of the posterior distribution of $\beta_2^\text{price}$ extends to the right of zero. ```{r} data0 <- list(lgtdata = camera, p = 5) prior0 <- list(ncomp = 5) mcmc0 <- list(R = 1e4, nprint = 0) ``` ```{r, eval = FALSE} out0 <- rhierMnlRwMixture(Data = data0, Prior = prior0, Mcmc = mcmc0) ``` ```{r, echo = FALSE} temp <- capture.output( {out0 <- rhierMnlRwMixture(Data = data0, Prior = prior0, Mcmc = mcmc0)}, file = NULL) ``` ```{r} par(mfrow=c(1,3)) ind_hist <- function(mod, i) { hist(mod$betadraw[i , 10, ], breaks = seq(-12,2,0.5), col = "dodgerblue4", border = "grey", yaxt = "n", xlim = c(-12,2), xlab = "", ylab = "", main = paste("Ind.",i)) } ind_hist(out0,1) ind_hist(out0,2) ind_hist(out0,3) ``` ```{r} par(mfrow=c(1,1)) hist(apply(out0$betadraw[ , 10, ], 1, mean), xlim = c(-15,5), breaks = 20, col = "firebrick3", border = "gray", xlab = "", ylab = "", main = "Posterior Means for Ind. Price Params, No Sign Constraint") ``` ***** _Authored by [Dan Yavorsky](dyavorsky@gmail.com) and [Peter Rossi](perossichi@gmail.com). Last updated June 2017._ bayesm/inst/doc/bayesm_Overview_Vignette.R0000644000176000001440000001401413123305445020442 0ustar ripleyusers## ---- echo = FALSE------------------------------------------------------- library(bayesm) knitr::opts_chunk$set(fig.align = "center", fig.height = 3.5, warning = FALSE, error = FALSE, message = FALSE) ## ---- eval = FALSE------------------------------------------------------- # mydata <- list(y = y, X = X) # mymcmc <- list(R = 1e6, nprint = 0) # # out <- runireg(Data = mydata, Mcmc = mymcmc) ## ---- eval=FALSE--------------------------------------------------------- # mydata2 <- list(y, X) # out <- runireg(Data = mydata2, Mcmc = mymcmc) ## ------------------------------------------------------------------------ set.seed(66) R <- 2000 n <- 200 X <- cbind(rep(1,n), runif(n)) beta <- c(1,2) sigsq <- 0.25 y <- X %*% beta + rnorm(n, sd = sqrt(sigsq)) out <- runireg(Data = list(y = y, X = X), Mcmc = list(R = R, nprint = 0)) summary(out$betadraw, tvalues = beta) ## ------------------------------------------------------------------------ data(cheese) names(cheese) <- tolower(names(cheese)) str(cheese) ## ------------------------------------------------------------------------ options(digits=3) cor(cheese$volume, cheese$price) cor(cheese$volume, cheese$disp) ## ----fig.show = "hold"--------------------------------------------------- par(mfrow = c(1,2)) curve(dnorm(x,0,10), xlab = "", ylab = "", xlim = c(-30,30), main = expression(paste("Prior for ", beta[j])), col = "dodgerblue4") nu <- 3 ssq <- var(log(cheese$volume)) curve(nu*ssq/dchisq(x,nu), xlab = "", ylab = "", xlim = c(0,1), main = expression(paste("Prior for ", sigma^2)), col = "darkred") par(mfrow = c(1,1)) ## ---- eval = FALSE------------------------------------------------------- # dat <- list(y = log(cheese$volume), X = model.matrix( ~ price + disp, data = cheese)) # out <- runireg(Data = dat, Mcmc = list(R=1e4, nprint=1e3)) ## ---- echo = FALSE------------------------------------------------------- temp <- capture.output( {set.seed(1234); dat <- list(y = log(cheese$volume), X = model.matrix( ~ price + disp, data = cheese)); out <- runireg(Data = dat, Mcmc = list(R=1e4, nprint=1e3))}, file = NULL) ## ---- fig.show = "hold"-------------------------------------------------- B <- 1000+1 #burn in draws to discard R <- 10000 par(mfrow = c(1,2)) hist(out$betadraw[B:R,2], breaks = 30, main = "Posterior Dist. of Price Coef.", yaxt = "n", yaxs="i", xlab = "", ylab = "", col = "dodgerblue4", border = "gray") hist(out$sigmasqdraw[B:R], breaks = 30, main = "Posterior Dist. of Sigma2", yaxt = "n", yaxs="i", xlab = "", ylab = "", col = "darkred", border = "gray") par(mfrow = c(1,1)) ## ------------------------------------------------------------------------ apply(out$betadraw[B:R,2:3], 2, mean) ## ------------------------------------------------------------------------ summary(out$betadraw) ## ------------------------------------------------------------------------ plot.bayesm.mat(out$betadraw[,2]) ## ---- results = "hold"--------------------------------------------------- data(margarine) str(margarine) marg <- merge(margarine$choicePrice, margarine$demos, by = "hhid") ## ------------------------------------------------------------------------ y <- marg[,2] ## ------------------------------------------------------------------------ X1 <- createX(p=10, na=1, Xa=marg[,3:12], nd=NULL, Xd=NULL, base=1) colnames(X1) <- c(names(marg[,3:11]), "price") head(X1, n=10) ## ---- results = "hold"--------------------------------------------------- X2 <- createX(p=10, na=NULL, Xa=NULL, nd=2, Xd=as.matrix(marg[,c(13,16)]), base=1) print(X2[1:10,1:9]); cat("\n") print(X2[1:10,10:18]) ## ---- eval = FALSE------------------------------------------------------- # X <- cbind(X1, X2[,10:ncol(X2)]) # out <- rmnlIndepMetrop(Data = list(y=y, X=X, p=10), # Mcmc = list(R=1e4, nprint=1e3)) ## ---- echo = FALSE------------------------------------------------------- temp <- capture.output( {set.seed(1234); X <- cbind(X1, X2[,10:ncol(X2)]); out <- rmnlIndepMetrop(Data = list(y=y, X=X, p=10), Mcmc = list(R=1e4, nprint=1e3))}, file = NULL) ## ------------------------------------------------------------------------ summary.bayesm.mat(out$betadraw[,10], names = "Price") ## ------------------------------------------------------------------------ plot.bayesm.mat(out$betadraw[,10], names = "Price") ## ------------------------------------------------------------------------ data(camera) length(camera) str(camera[[1]]) colnames(camera[[1]]$X) ## ---- eval = FALSE------------------------------------------------------- # data(margarine) # chpr <- margarine$choicePrice # chpr$hhid <- as.factor(chpr$hhid) # N <- nlevels(chpr$hhid) # dat <- vector(mode = "list", length = N) # for (i in 1:N) { # dat[[i]]$y <- chpr[chpr$hhid==levels(chpr$hhid)[i], "choice"] # dat[[i]]$X <- createX(p=10, na=1, Xa=chpr[chpr$hhid==levels(chpr$hhid)[i],3:12], nd=NULL, Xd=NULL) # } ## ------------------------------------------------------------------------ N <- length(camera) dat <- matrix(NA, N*16, 2) for (i in 1:length(camera)) { Ni <- length(camera[[i]]$y) dat[((i-1)*Ni+1):(i*Ni),1] <- i dat[((i-1)*Ni+1):(i*Ni),2] <- camera[[i]]$y } round(prop.table(table(dat[,2])), 3) ## ------------------------------------------------------------------------ round(prop.table(table(dat[,1], dat[,2])[41:50,], 1), 3) ## ------------------------------------------------------------------------ data <- list(lgtdata = camera, p = 5) prior <- list(ncomp = 1) mcmc <- list(R = 1e4, nprint = 0) out <- rhierMnlRwMixture(Data = data, Prior = prior, Mcmc = mcmc) ## ------------------------------------------------------------------------ hist(apply(out$betadraw, 1:2, mean)[,9], col = "dodgerblue4", xlab = "", ylab = "", yaxt="n", xlim = c(-4,6), breaks = 20, main = "Histogram of Posterior Means For Individual Wifi Coefs") bayesm/inst/doc/bayesm_Overview_Vignette.Rmd0000644000176000001440000015530213117606056020776 0ustar ripleyusers--- title: "bayesm Overview" output: rmarkdown::html_document : theme: spacelab highlight: pygments toc: true toc_float: true toc_depth: 3 number_sections: yes fig_caption: yes vignette: > %\VignetteIndexEntry{bayesm Overview} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, echo = FALSE} library(bayesm) knitr::opts_chunk$set(fig.align = "center", fig.height = 3.5, warning = FALSE, error = FALSE, message = FALSE) ``` ***** # Introduction `bayesm` is an R package that facilitates statistical analysis using Bayesian methods. The package provides a set of functions for commonly used models in applied microeconomics and quantitative marketing. The goal of this vignette is to make it easier for users to adopt `bayesm` by providing a comprehensive overview of the package's contents and detailed examples of certain functions. We begin with the overview, followed by a discussion of how to work with `bayesm`. The discussion covers the structure of function arguments, the required input data formats, and the various output formats. The final section provides detailed examples to demonstrate Bayesian inference with the linear normal, multinomial logit, and hierarchical multinomial logit regression models. # Package Contents For ease of exposition, we have grouped the package contents into: - Posterior sampling functions - Utility functions - S3 methods^[For example, calling the generic function `summary(x)` on an object `x` with class `bayesm.mat` actually dispatches the method `summary.bayesm.mat` on `x`, which is equivalent to calling `summary.bayes.mat(x)`. For a nice introduction, see _Advanced R_ by Hadley Wickham, available [online](http://adv-r.had.co.nz/OO-essentials.html).] - Datasets Because the first two groups contain many functions, we organize them into subgroups by purpose. Below, we display each group of functions in a table with one column per subgroup. ----------------------------------------------------------------------------- Linear Models Limited Dependent Hierarchical Models Density Estimation \ Variable Models \ \ --------------- -------------------- -------------------- ------------------- `runireg`* `rbprobitGibbs`** `rhierLinearModel` `rnmixGibbs`* `runiregGibbs` `rmnpGibbs` `rhierLinearMixture` `rDPGibbs` `rsurGibbs`* `rmvpGibbs` `rhierMnlRwMixture`* \ `rivGibbs` `rmnlIndepMetrop` `rhierMnlDP` \ `rivDP` `rscaleUsage` `rbayesBLP` \ \ `rnegbinRw` `rhierNegbinRw` \ \ `rordprobitGibbs` ----------------------------------------------------------------------------- Table: Posterior Sampling Functions *`bayesm` offers the utility function `breg` with a related but limited set of capabilities as `runireg` --- similarly with `rmultireg` for `rsurGibbs`, `rmixGibbs` for `rnmixGibbs`, and `rhierBinLogit` for `rhierMnlRwMixture`. **`rbiNormGibbs` provides a tutorial-like example of Gibbs Sampling from a bivariate normal distribution. ------------------------------------------------------------------------------------ Log-Likelihood\ \ Log Density Random Draws Mixture-of-Normals Miscellaneous (data vector) (univariate) \ \ \ ------------------ --------------- ------------- ------------------- --------------- `llmnl` `lndIChisq` `rdirichlet` `clusterMix` `cgetC` `llmnp` `lndIWishart` `rmixture` `eMixMargDen` `condMom` `llnhlogit` `lndMvn` `rmvst` `mixDen` `createX` \ `lndMvst` `rtrun` `mixDenBi` `ghkvec` \ \ `rwishart` `momMix` `logMargDenNR` \ \ \ \ `mnlHess` \ \ \ \ `mnpProb` \ \ \ \ `nmat` \ \ \ \ `numEff` \ \ \ \ `simnhlogit` ------------------------------------------------------------------------------------ Table: Utility Functions ------------------------------------------- Plot Methods Summary Methods -------------------- ---------------------- `plot.bayesm.mat` `summary.bayesm.mat` `plot.bayesm.nmix` `summary.bayesm.nmix` `plot.bayesm.hcoef` `summary.bayesm.var` ------------------------------------------- Table: S3 Methods --------------------------------------- \ \ \ --------- -------------- -------------- `bank` `customerSat` `orangeJuice` `camera` `detailing` `Scotch` `cheese` `margarine` `tuna` --------------------------------------- Table: Datasets Some discussion of the naming conventions may be warranted. All functions use CamelCase but begin lowercase. Posterior sampling functions begin with `r` to match R's style of naming random number generation functions since these functions all draw from (posterior) distributions. Common abbreviations include _DP_ for Dirichlet Process, _IV_ for instrumental variables, _MNL_ and _MNP_ for multinomial logit and probit, _SUR_ for seemingly unrelated regression, and _hier_ for hierarchical. Utility functions that begin with `ll` calculate the log-likelihood of a data vector, while those that begin with `lnd` provide the log-density. Other abbreviations should be straighforward; please see the help file for a specific function if its name is unclear. # Working with `bayesm` We expect most users of the package to interact primarily with the posterior sampling functions. These functions take a consistent set of arguments as inputs (`Data`, `Prior`, and `Mcmc` --- each is a list) and they return output in a consistent format (always a list). `summary` and `plot` generic functions can then be used to facilitate analysis of the output because of the classes and methods defined in `bayesm`. The following subsections describe the format of the standardized function arguments as well as the required format of the data inputs and the format of the output from `bayesm`'s posterior sampling functions. ## Input: Function Arguments The posterior sampling functions take three arguments: `Data`, `Prior`, and `Mcmc`. Each argument is a **list**. As a minimal example, assume you'd like to perform a linear regression and that you have in your work space `y` (a vector of length $n$) and `X` (a matrix of dimension $n \times p$). For this example, we utilize the default values for `Prior` and so we do not specify the `Prior` argument. These components (`Data`, `Prior`, and `Mcmc` as well as their arguments including `R` and `nprint`) are discussed in the subsections that follow. Then the `bayesm` syntax is simply: ```{r, eval = FALSE} mydata <- list(y = y, X = X) mymcmc <- list(R = 1e6, nprint = 0) out <- runireg(Data = mydata, Mcmc = mymcmc) ``` The list elements of `Data`, `Prior`, and `Mcmc` must be named. For example, you could not use the following code because the `Data` argument `mydata2` has unnamed elements. ```{r, eval=FALSE} mydata2 <- list(y, X) out <- runireg(Data = mydata2, Mcmc = mymcmc) ``` ### `Data` Argument `bayesm`'s posterior sampling functions do not accept data stored in dataframes; data must be stored as vectors or matrices. For **non-hierarchical** models, the list of input data simply contains the approprate data vectors or matrices from the set \{`y`, `X`, `w`, `z`\}^[Functions such as `rivGibbs` only permit one endogenous variable and so the function argument is lowercase: `x`.] and possibly a scalar (length one vector) from the set \{`k`, `p`\}. - For functions that require only a single data argument (such as the two density estimation functions, `rnmixGibbs` and `rDPGibbs`), `y` is that argument. More typically, `y` is used for LHS^[LHS and RHS stand for left-hand side and right-hand side.] variable(s) and `X` for RHS variables. For estimation using instrumental variables, variables in the structural equation are separated into "exogenous" variables `w` and an "edogenous" variable `x`; `z` is a matrix of instruments. - For the scalars, `p` indicates the number of choice alternatives in discrete choice models and `k` is used as the maximum ordinate in models for ordinal data (e.g., `rordprobitGibbs`). For **hierarchical** models, the input data has up to 3 components. We'll discuss these components using the mixed logit model (`rhierMnlRwMixture`) as an example. For `rhierMnlRwMixture`, the `Data` argument is `list(lgtdata, Z, p)`. 1. The first component for all hierarchical models is required. It is a list of lists named either `regdata` or `lgtdata`, depending on the function. In `rhierMnlRwMixture`, `lgtdata` is a list of lists, with each interior list containing a vector or one-column matrix of multinomial outcomes `y` and a design matrix of covariates `X`. In Example 3 below, we show how to convert data from a data frame to this required list-of-lists format. 1. The second component, `Z`, is present but optional for all hierarchical models. `Z` is a matrix of cross-sectional unit characteristics that drive the mean responses; that is, a matrix of covariates for the individual parameters (e.g. $\beta_i$'s). For example, the model (omitting the priors) for `rhierMnlRwMixture` is: $$ y_i \sim \text{MNL}(X_i'\beta_i) \hspace{1em} \text{with} \hspace{1em} \beta_i = Z \Delta_i + u_i \hspace{1em} \text{and} \hspace{1em} u_i \sim N(\mu_j, \Sigma_j) $$ where $i$ indexes individuals and $j$ indexes cross-sectional unit characteristics. 1. The third component (if accepted) is a scalar, such as `p` or `k`, and like the arguments by the same names in the non-hierarchical models, is used to indicate the size of the choice set or the maximum value of a scale or count variable. In `rhierMnlRwMixture`, the argument is `p`, which is used to indicate the number of choice alternatives. Note that `rbayesBLP` (the hierarchical logit model with _aggregate_ data as in Berry, Levinsohn, and Pakes (1995) and Jiang, Manchanda, and Rossi (2009)) deviates slightly from the standard data input. `rbayesBLP` uses `j` instead of `p` to be consistent with the literature and calls the LHS variable `share` rather than `y` to emphasize that aggregate (market share instead of individual choice) data are required. ### `Prior` Argument Specification of prior distributions is model-specific, so our discussion here is brief. All posterior sampling functions offer default values for parameters of prior distributions. These defaults were selected to yield proper yet reasonably-diffuse prior distributions (assuming the data are in approximately unit scale). In addition, these defaults are consistent across functions. For example, normal priors have default values of mean 0 with value 0.01 for the scaling factor of the prior precision matrix. Specification of the prior is important. Significantly more detail can be found in chapters 2--5 of BSM[^BSM] and the help files for the posterior sampling functions. We strongly recommend you consult them before accepting the default values. [^BSM]: Rossi, Peter, Greg Allenby and Robert McCulloch, _Bayesian Statistics and Marketing_, John Wiley \& Sons, 2005. [Website](http://www.perossi.org/home/bsm-1). ### `Mcmc` Argument {#mcmcarg} The `Mcmc` argument controls parameters of the sampling algorithm. The most common components of this list include: - `R`: the number of MCMC draws - `keep`: a thinning parameter indicating that every `keep`$^\text{th}$ draw should be retained - `nprint`: an option to print the estimated time remaining to the console after each `nprint`$^\text{th}$ draw MCMC methods are non-iid. As a result, a large simulation size may be required to get reliable results. We recommend setting `R` large enough to yield an adequate effective sample size and letting `keep` default to the value 1 unless doing so imposes memory constraints. A careful reader of the `bayesm` help files will notice that many of the examples set `R` equal to 1000 or less. This low number of draws was chosen for speed, as the examples are meant to demonstrate _how_ to run the code and do not necessarily suggest best practices for a proper statistical analysis. `nprint` defaults to 100, but for large `R`, you may want to increase the `nprint` option. Alternatively, you can set `nprint=0`, in which case the priors will still be printed to the console, but the estimated time remaining will not. Additional components of the `Mcmc` argument are function-specific, but typically include starting values for the algorithm. For example, the `Mcmc` argument for `runiregGibbs` takes `sigmasq` as a scalar element of the list. The Gibbs Sampler for `runiregGibbs` first draws $\beta | \sigma^2$, then draws $\sigma^2 | \beta$, and then repeats. For the first draw of $\beta$ in the MCMC chain, a value of $\sigma^2$ is required. The user can specify a value using `Mcmc$sigmasq`, or the user can omit the argument and the function will use its default (`sigmasq = var(Data$y)`). ## Output: Returned Results `bayesm` posterior sampling functions return a consistent set of results and output to the user. At a minimum, this output includes draws from the posterior distribution. `bayesm` provides a set of `summary` and `plot` methods to facilitate analysis of this output, but the user is free to analyze the results as he or she sees fit. ### Output Formats All `bayesm` posterior sampling functions return a list. The elements of that list include a set of vectors, matrices, and/or arrays (and possibly a list), the exact set of which depend on the function. - Vectors are returned for draws of parameters with no natural grouping. For example, `runireg` implements and iid sampler to draw from the posterior of a homoskedastic univariate regression with a conjugate prior (i.e., a Bayesian analog to OLS regression). One output list element is `sigmasqdraw`, a length `R/keep` vector for the scalar parameter $\sigma^2$. - Matrices are returned for draws of parameters with a natural grouping. Again using `runireg` as the example, the output list includes `betadraw`, an `R/keep` $\times$ `ncol(X)` matrix for the vector of $\beta$ parameters. Contrary to the next bullet, draws for the parameters in a variance-covariance matrix are returned in matrix form. For example, `rmnpGibbs` implements a Gibbs Sampler for a multinomial probit model where one set of parameters is the $(p-1) \times (p-1)$ matrix $\Sigma$. The output list for `rmnpGibbs` includes the list element `sigmadraw`, which is a matrix of dimension `R/keep`$\times (p-1)*(p-1)$ with each row containing a draw (in vector form) for all the elements of the matrix $\Sigma$. `bayesm`'s `summary` and `plot` methods (see below) are designed to handle this format. - Arrays are used when parameters have a natural matrix-grouping, such that the MCMC algorithm returns `R/keep` draws of the matrix. For example, `rsurGibbs` returns a list that includes `Sigmadraw`, an $m \times m \times$`R/keep` array, where $m$ is the number of regression equations. As a second example, `rhierLinearModel` estimates a hierarchical linear regression model with a normal prior, and returns a list that includes `betadraw`, an $n \times k \times$`R/keep` array, where $n$ signifies the number of individuals (each with their own $\beta_i$) and $k$ signifies the number of covariates (`ncol(X)` = $k$). - For functions that use a mixture-of-normals or Dirichlet Process prior, the output list includes a list (`nmix`) pertaining to that prior. `nmix` is itself a list with 3 components: `probdraw`, `zdraw`, and `compdraw`. `probdraw` reports the probability that each draw came from a particular normal component; `zdraw` indicates which mixture-of-normals component each draw is assigned to; and `compdraw` provides draws for the mixture-of-normals components (i.e., mean vectors and Cholesky roots of covariance matrices). Note that if you specify a "mixture" with only one normal component, there will be no useful information in `probdraw`. Also note that `zdraw` is not relevant for density estimation and will be null except in `rnmixGibbs` and `rDPGibbs`. ### Classes and Methods In R generally, objects can be assigned a _class_ and then a _generic_ function can be used to run a _method_ on an object with that class. The list elements in the output from `bayesm` posterior sampling functions are assigned special `bayesm` classes. The `bayesm` package includes `summary` and `plot` methods for use with these classes (see the table in Section 2 above). This means you can call the generic function (e.g., `summary`) on individual list elements of `bayesm` output and it will return specially-formatted summary results, including the effective sample size. To see this, the code below provides an example using `runireg`. Here the generic function `summary` dispatches the method `summary.bayesm.mat` because the `betadraw` element of `runireg`'s output has class `bayesm.mat`. This example also shows the information about the prior that is printed to the console during the call to a posterior sampling function. Notice, however, that no remaining time is printed because `nprint` is set to zero. ```{r} set.seed(66) R <- 2000 n <- 200 X <- cbind(rep(1,n), runif(n)) beta <- c(1,2) sigsq <- 0.25 y <- X %*% beta + rnorm(n, sd = sqrt(sigsq)) out <- runireg(Data = list(y = y, X = X), Mcmc = list(R = R, nprint = 0)) summary(out$betadraw, tvalues = beta) ``` ## Access to Code `bayesm` was originally created as a companion to BSM, at which time most functions were written in R. The package has since been expanded to include additional functionality and most code has been converted to C++ via `Rcpp` for faster performance. However, for users interested in obtaining the original implementation of a posterior sampling function (in R instead of C++), you may still access the last version (2.2-5) of `bayesm` prior to the C++/`Rcpp` conversion from the [package archive](https://cran.r-project.org/src/contrib/Archive/bayesm/) on CRAN. To access the `R` code in the current version of `bayesm`, the user can simply call a function without parenthesis. For example, `bayesm::runireg`. However, most posterior sampling functions only perform basic checks in `R` and then call an unexported C++ function to do the heavy lifting (i.e., the MCMC draws). This C++ source code is not available to the user via the installed `bayesm` package because C++ code is compiled upon package installation on Linux machines and pre-compiled by CRAN for Mac and Windows. To access this source code, the user must download the "package source" from CRAN. This can be accomplished by clicking on the appropriate link at the `bayesm` [package archive](https://cran.r-project.org/src/contrib/Archive/bayesm/) or by executing the `R` command `download.packages(pkgs="bayesm", destdir=".", type="source")`. Either of these methods will provide you with a compressed file "bayesm_version.tar.gz" that can be uncompressed. The C++ code can then be found in the "src" subdirectory. # Examples We begin with a brief introduction to regression and Bayesian estimation. This will help set the notation and provide background for the examples that follow. We do not claim that this will be a sufficient introduction to the reader for which these ideas are new. We refer that reader to excellent texts on regression analysis by Cameron \& Trivedi, Davidson \& MacKinnon, Goldberger, Greene, Wasserman, and Wooldridge.[^1] For Bayesian methods, we recommend Gelman et al., Jackman, Marin \& Robert, Rossi et al., and Zellner.[^2] [^1]: Cameron, Colin and Pravin Trivedi, _Microeconometrics: Methods and Applications_, Cambridge University Press, 2005. Davidson, Russell and James MacKinnon, _Estimation and Inference in Econometrics_, Oxford University Press, 1993. Goldberger, Arthur, _A Course in Econometrics_, Harvard University Press, 1991. Greene, William, _Econometric Analysis_, Prentice Hall, 2012 (7th edition). Wasserman, Larry, _All of Statistics: A Concise Course in Statistical Inferece_, Springer, 2004. Wooldridge, Jeffrey, _Econometric Analysis of Cross Section and Panel Data_, MIT Press, 2010 (2nd edition). [^2]: Gelman, Andrew, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin, _Bayesian Data Analysis_, CRC Press, 2013 (3rd edition). Jackman, Simon, _Bayesian Analysis for the Social Sciences_, Wiley, 2009. Marin, Jean-Michel and Christian Robert, _Bayesian Essentials with R_, Springer, 2014 (2nd edition). Rossi, Peter, Greg Allenby, and Robert McCulloch, _Bayesian Statistics and Marketing_, Wiley, 2005. Zellner, Arnold, _An Introduction to Bayesian Inference in Economics_, Wiley, 1971. ## What is Regression Suppose you believe a variable $y$ varies with (or is caused by) a set of variables $x_1, x_2, \ldots, x_k$. For notational convenience, we'll collect the set of $x$ variables into $X$. These variables $y$ and $X$ have a joint distribution $f(y, X)$. Typically, interest will not fall on this joint distribution, but rather on the conditional distribution of the "outcome" variable $y$ given the "explanatory" variables (or "covariates") $x_1, x_2, \ldots, x_k$; this conditional distribution being $f(y|X)$. To carry out inference on the relationship between $y$ and $X$, the researcher then often focuses attention on one aspect of the conditional distribution, most commonly its expected value. This conditional mean is assumed to be a function $g$ of the covariates such that $\mathbb{E}[y|X] = g(X, \beta)$ where $\beta$ is a vector of parameters. A function for the conditional mean is known as a "regression" function. The canonical introductory regression model is the normal linear regression model, which assumes that $y \sim N(X\beta, \sigma^2)$. Most students of regression will have first encountered this model as a combination of deterministic and stochastic components. There, the stochastic component is defined as deviations from the conditional mean, $\varepsilon = y - \mathbb{E}[y|X]$, such that $y = \mathbb{E}[y|X] + \varepsilon$ or that $y = g(X, \beta) + \varepsilon$. The model is then augmented with the assumptions that $g(X, \beta) = X \beta$ and $\varepsilon \sim N(0,\sigma^2)$ so that the normal linear regression model is: $$ y = X \beta + \varepsilon \text{ with } \varepsilon \sim N(0,\sigma^2) \hspace{1em} \text{or} \hspace{1em} y \sim N(X\beta, \sigma^2) $$ When taken to data, additional assumptions are made which include a full-rank condition on $X$ and often that $\varepsilon_i$ for $i=1,\ldots,n$ are independent and identically distributed. Our first example will demonstrate how to estimate the parameters of the normal linear regression model using Bayesian methods made available by the posterior sampling function `runireg`. We then provide an example to estimate the parameters of a model when $y$ is a categorical variable. This second example is called a multinomial logit model and uses the logistic "link" function $g(X, \beta) = [1 + exp(-X\beta)]^{-1}$. Our third and final example will extend the multinomial logit model to permit individual-level parameters. This is known as a hierarchical model and requires panel data to perform the estimation. Before launching into the examples, we briefly introduce Bayesian methodology and contrast it with classical methods. ## What is Bayesian Inference Under classical econometric methods, $\beta$ is most commonly estimated by minimizing the sum of squared residuals, maximizing the likelihood, or matching sample moments to population moments. The distribution of the estimators (e.g., $\hat{\beta}$) and test statistics derived from these methods rely on asymptotic concepts and are based on imaginary samples not observed. In contrast, Bayesian inference provides the benefits of (a) exact sample results, (b) integration of descision-making, estimation, testing, and model selection, and (c) a full accounting of uncertainty. These benefits from Bayesian inference rely heavily on probability theory and, in particular, distributional theory, some elements of which we now briefly review. Recall the relationship between the joint and conditional densities for random variables $W$ and $Z$: $$ P_{A|B}(A=a|B=b) = \frac{P_{A,B}(A=a, B=b)}{P_B(B=b)} $$ This relationship can be used to derive Bayes' Theorem, which we write with $D$ for "data" and $\theta$ as the parameters (and with implied subscripts): $$ P(\theta|D) = \frac{P(D|\theta)P(\theta)}{P(D)} $$ Noticing that $P(D)$ does not contain the parameters of interest ($\theta$) and is therefore simply a normalizing constant, we can instead write: $$ P(\theta|D) \propto P(D|\theta)P(\theta) $$ Introducing Bayesian terminology, we have that the "Posterior" is proportional to the Likelihood times the Prior. Thus, given (1) a dataset ($D$), (2) an assumption on the data generating process (the likelihood, $P(D|\theta)$), and (3) a specification of the prior distribution of the parameters ($P(\theta)$), we can find the _exact_ (posterior) distribution of the parameters given the observed data. This is in stark contrast to classical econometric methods, which typically only provide the _asymptotic_ distributions of estimators. However, for any problem of practical interest, the posterior distribution is a high-dimensional object. Additionally, it may not be possible to analytically calculate the posterior or its features (e.g., marginal distributions or moments such as the mean). To handle these issues, the modern approach to Bayesian inference relies on simulation methods to sample from the (high-dimensional) posterior distribution and then construct marginal distributions (or their features) from the sampled draws of the posterior. As a result, simulation and summaries of the posterior play important roles in modern Bayesian statistics. `bayesm`'s posterior sampling functions (as their name suggests) sample from posterior distributions. `bayesm`'s `summary` and `plot` methods can be used to analyze those draws. Unlike most classical econometric methods, the MCMC methods implemented in `bayesm`'s posterior sampling functions provide an estimate of the entire posterior distribution, not just a few moments. Given this "rich" result from Bayesian methods, it is best to summarize posterior distributions using histograms or quantiles. We advise that you resist the temptation to simply report the posterior mean and standard deviation; for non-normal distributions, those moments may have little meaning. In the examples that follow, we will describe the data we use, present the model, demonstrate how to estimate it using the appropriate posterior sampling function, and provide various ways to summarize the output. ## Example 1: Linear Normal Regression ### Data For our first example, we will use the `cheese` dataset, which provides `r data(cheese); format(nrow(cheese), big.mark=",")` observations of weekly sales volume for a package of Borden sliced cheese, as well as a measure of promotional display activity and price. The data are aggregated to the "key" account (i.e., retailer-market) level. ```{r} data(cheese) names(cheese) <- tolower(names(cheese)) str(cheese) ``` Suppose we want to assess the relationship between sales volume and price and promotional display activity. For this example, we will abstract from whether these relationships vary by retailer or whether prices are set endogenously. Simple statistics show a negative correlation between volume and price, and a positive correlation between volume and promotional activity, as we would expect. ```{r} options(digits=3) cor(cheese$volume, cheese$price) cor(cheese$volume, cheese$disp) ``` ### Model We model the expected log sales volume as a linear function of log(price) and promotional activity. Specifically, we assume $y_i$ to be iid with $p(y_i|x_i,\beta)$ normally distributed with a mean linear in $x$ and a variance of $\sigma^2$. We will denote observations with the index $i = 1, \ldots, n$ and covariates with the index $j = 1, \ldots, k$. The model can be written as: $$ y_i = \sum_{j=1}^k \beta_j x_{ij} + \varepsilon_i = x_i'\beta + \varepsilon_i \hspace{1em} \text{with} \hspace{1em} \varepsilon_i \sim iid\ N(0,\sigma^2) $$ or equivalently but more compactly as: $$ y \sim MVN(X\beta,\ \sigma^2I_n) $$ Here, the notation $N(0, \sigma^2)$ indicates a univariate normal distribution with mean $0$ and variance $\sigma^2$, while $MVN(X\beta,\ \sigma^2I_n)$ indicates a multivariate normal distribution with mean vector $X\beta$ and variance-covariance matrix $\sigma^2I_n$. In addition, $y_i$, $x_{ij}$, $\varepsilon_i$, and $\sigma^2$ are scalars while $x_i$ and $\beta$ are $k \times 1$ dimensional vectors. In the more compact notation, $y$ is an $n \times 1$ dimensional vector, $X$ is an $n \times k$ dimensional matrix with row $x_i$, and $I_n$ is an $n \times n$ dimensional identity matrix. With regard to the `cheese` dataset, $k = 2$ and $n = 5,555$. When employing Bayesian methods, the model is incomplete until the prior is specified. For our example, we elect to use natural conjugate priors, meaning the family of distributions for the prior is chosen such that, when combined with the likelihood, the posterior will be of the same distributional family. Specifically, we first factor the joint prior into marginal and conditional prior distributions: $$ p(\beta,\sigma^2) = p(\beta|\sigma^2)p(\sigma^2) $$ We then specify the prior for $\sigma^2$ as inverse-gamma (written in terms of a chi-squared random variable) and the prior for $\beta|\sigma^2$ as multivariate normal: $$ \sigma^2 \sim \frac{\nu s^2}{\chi^2_{\nu}} \hspace{1em} \text{and} \hspace{1em} \beta|\sigma^2 \sim MVN(\bar{\beta},\sigma^2A^{-1}) $$ Other than convenience, we have little reason to specify priors from these distributional families; however, we will select diffuse priors so as not to impose restrictions on the model. To do so, we must pick values for $\nu$ and $s^2$ (the degrees of freedom and scale parameters for the inverted chi-squared prior on $\sigma^2$) as well as $\bar{\beta}$ and $A^{-1}$ (the mean vector and variance-covariance matrix for the multivariate normal prior on the $\beta$ vector). The `bayesm` posterior sampling function for this model, `runireg`, defaults to the following values: - $\nu = 3$ - $s^2 =$ `var(y)` - $\bar{\beta} = 0$ - $A = 0.01*I$ We will use these defaults, as they are chosen to be diffuse for data with a unit scale. Thus, for each $\beta_j | \sigma^2$ we have specified a normal prior with mean 0 and variance $100\sigma^2$, and for $\sigma^2$ we have specified an inverse-gamma prior with $\nu = 3$ and $s^2 = \text{var}(y)$. We graph these prior distributions below. ```{r fig.show = "hold"} par(mfrow = c(1,2)) curve(dnorm(x,0,10), xlab = "", ylab = "", xlim = c(-30,30), main = expression(paste("Prior for ", beta[j])), col = "dodgerblue4") nu <- 3 ssq <- var(log(cheese$volume)) curve(nu*ssq/dchisq(x,nu), xlab = "", ylab = "", xlim = c(0,1), main = expression(paste("Prior for ", sigma^2)), col = "darkred") par(mfrow = c(1,1)) ``` ### Bayesian Estimation Although this model involves nontrivial natural conjugate priors, the posterior is available in closed form: $$ p(\beta, \sigma^2 | y, X) \propto (\sigma^2)^{-k/2} \exp \left\{ -\frac{1}{2\sigma^2}(\beta - \bar{\beta})'(X'X+A)(\beta - \bar{\beta}) \right\} \times (\sigma^2)^{-((n+\nu_0)/2+1)} \exp \left\{ -\frac{\nu_0s_0^2 + ns^2}{2\sigma^2} \right\} $$ or \begin{align*} \beta | \sigma^2, y, X &\sim N(\bar{\beta}, \sigma^2(X'X+A)^{-1}) \\ \\ \sigma^2 | y, X &\sim \frac{\nu_1s_1^2}{\chi^2_{\nu_1}}, \hspace{3em} \text{with} \> \nu_1 = \nu_0+n \hspace{1em} \text{and} \hspace{1em} s_1^2 = \frac{\nu_0s_0^2 + ns^2}{\nu_0+n} \end{align*} The latter representation suggests a simulation strategy for making draws from the posterior. We draw a value of $\sigma^2$ from its marginal posterior distribution, insert this value into the expression for the covariance matrix of the conditional normal distribution of $\beta|\{\sigma^2,y\}$ and draw from this multivariate normal. This simulation strategy is implemented by `runireg`, using the defaults for `Prior` specified above. The code is quite simple. ```{r, eval = FALSE} dat <- list(y = log(cheese$volume), X = model.matrix( ~ price + disp, data = cheese)) out <- runireg(Data = dat, Mcmc = list(R=1e4, nprint=1e3)) ``` ```{r, echo = FALSE} temp <- capture.output( {set.seed(1234); dat <- list(y = log(cheese$volume), X = model.matrix( ~ price + disp, data = cheese)); out <- runireg(Data = dat, Mcmc = list(R=1e4, nprint=1e3))}, file = NULL) ``` Note that `bayesm` posterior sampling functions print out information about the prior and about MCMC progress during the function call (unless `nprint` is set to 0), but for presentation purposes we suppressed that output here. `runireg` returns a list that we have saved in `out`. The list contains two elements, `betadraw` and `sigmasqdraw`, which you can verify by running `str(out)`. `betadraw` is an `R/keep` $\times$ `ncol(X)` ($10,000 \times 3$ with a column for each of the intercept, price, and display) dimension matrix with class `bayesm.mat`. We can analyze or summarize the marginal posterior distributions for any $\beta$ parameter or the $\sigma^2$ parameter. For example, we can plot histograms of the price coefficient (even though it is known to follow a t-distrbution, see BSM Ch. 2.8) and for $\sigma^2$. Notice how concentrated the posterior distributions are compared to their priors above. ```{r, fig.show = "hold"} B <- 1000+1 #burn in draws to discard R <- 10000 par(mfrow = c(1,2)) hist(out$betadraw[B:R,2], breaks = 30, main = "Posterior Dist. of Price Coef.", yaxt = "n", yaxs="i", xlab = "", ylab = "", col = "dodgerblue4", border = "gray") hist(out$sigmasqdraw[B:R], breaks = 30, main = "Posterior Dist. of Sigma2", yaxt = "n", yaxs="i", xlab = "", ylab = "", col = "darkred", border = "gray") par(mfrow = c(1,1)) ``` Additionally, we can compute features of these posterior distributions. For example, the posterior means price and display are: ```{r} apply(out$betadraw[B:R,2:3], 2, mean) ``` Conveniently, `bayesm` offers this functionality (and more) with its `summary` and `plot` methods. Notice that `bayesm`'s methods use a default burn-in length, calculated as `trunc(0.1*nrow(X))`. You can override the default by specifying the `burnin` argument. We see that the means for the first few retail fixed effects in the summary information below match those calculated "by hand" above. ```{r} summary(out$betadraw) ``` The same can be done with the `plot` generic function. However, we reference the method directly (`plot.bayesm.mat`) to plot a subset of the `bayesm` output -- specifically we plot the price coefficient. In the histogram, note that the green bars delimit a 95\% Bayesian credibility interval, yellow bars shows +/- 2 numerical standard errors for the posterior mean, and the red bar indicates the posterior mean. Also notice that this pink histogram of the posterior distribution on price, which was created by calling the `plot` generic, matches the blue one we created "by hand" above. The `plot` generic function also provides a trace plot and and ACF plot. In many applications (although not in this simple model), we cannot be certain that our draws from the posterior distribution adequately represent all areas of the posterior with nontrivial mass. This may occur, for instance, when using a "slow mixing" Markov Chain Monte Carlo (MCMC) algorithm to draw from the posterior. In such a case, we might see patterns in the trace plot and non-zero autocorrelations in the ACF plot; these will coincide with values for the Effective Sample Size less than `R`. (Effective Sample Size prints out with the `summary` generic function, as above.) Here, however, we are able to sample from the posterior distribution by taking iid draws, and so we see large Effective Sample Sizes in the `summary` output above, good mixing the trace plot below, and virtually no autocorrelation between draws in the ACF plot below. ```{r} plot.bayesm.mat(out$betadraw[,2]) ``` ## Example 2: Multinomial Logistic Regression ### Data Linear regression models like the one in the previous example are best suited for continuous outcome variables. Different models (known as limited dependent variable models) have been developed for binary or multinomial outcome variables, the most popular of which --- the multinomial logit --- will be the subject of this section. For this example, we analyze the `margarine` dataset, which provides panel data on purchases of margarine. The data are stored in two dataframes. The first, `choicePrice`, lists the outcome of `r data(margarine); format(nrow(margarine$choicePrice), big.mark=",")` choice occasions as well as the choosing household and the prices of the `r max(margarine$choicePrice[,2])` choice alternatives. The second, `demos`, provides demographic information about the choosing households, such as their income and family size. We begin by merging the information from these two dataframes: ```{r, results = "hold"} data(margarine) str(margarine) marg <- merge(margarine$choicePrice, margarine$demos, by = "hhid") ``` Compared to the standard $n \times k$ rectangular format for data to be used in a linear regression model, choice data may be stored in various formats, including a rectangular format where the $p$ choice alternatives are allocated across columns or rows, or a list-of-lists format as used in `bayesm`'s hierarchical models, which we demonstrate in Example 3 below. For all functions --- and notably those that implement multinomial logit and probit models --- the data must be in the format expected by the function, and `bayesm`'s posterior sampling funtions are no exception. In this example, we will implement a multinomial logit model using `rmnlIndepMetrop`. This posterior sampling function requires `y` to be a length-$n$ vector (or an $n \times 1$ matrix) of multinomial outcomes ($1, \dots, p$). That is, each element of `y` corresponds to a choice occasion $i$ with the value of the element $y_i$ indicating the choice that was made. So if the fourth alternative was chosen on the seventh choice occasion, then $y_7 = 4$. The `margarine` data are stored in that format, and so we easily specify `y` with the following code: ```{r} y <- marg[,2] ``` `rmnlIndepMetrop` requires `X` to be an $np \times k$ matrix. That is, each alternative is listed on its own row, with a group of $p$ rows together corresponding to the alternatives available on one choice occasion. However, the `margarine` data are stored with the various choice alternatives in columns rather than rows, so reformatting is necessary. `bayesm` provides the utility function `createX` to assist with the conversion. `createX` requires the user to specify the number of choice alternatives `p` as well as the number of alternative-specific variables `na` and an $n \times$ `na` matrix of alternative-specific data `Xa` ("a" for alternative-specific). Here, we have $p=10$ choice alternatives with `na` $=1$ alternative-specific variable (price). If we were only interested in using price as a covariate, we would code: ```{r} X1 <- createX(p=10, na=1, Xa=marg[,3:12], nd=NULL, Xd=NULL, base=1) colnames(X1) <- c(names(marg[,3:11]), "price") head(X1, n=10) ``` Notice that `createX` uses $p-1$ dummy variables to distinguish the $p$ choice alternatives. As with factor variables in linear regression, one factor must be the base; the coefficients on the other factors report deviations from the base. The user may specify the base alternative using the `base` argument (as we have done above), or let it default to the alternative with the highest index. For our example, we might also like to include some "demographic" variables. These are variables that do not vary with the choice alternatives. For example, with the `margarine` data we might want to include family size. Here again we turn to `createX`, this time specifying the `nd` and `Xd` arguments ("d" for demographic): ```{r, results = "hold"} X2 <- createX(p=10, na=NULL, Xa=NULL, nd=2, Xd=as.matrix(marg[,c(13,16)]), base=1) print(X2[1:10,1:9]); cat("\n") print(X2[1:10,10:18]) ``` Notice that `createX` again uses $p-1$ dummy variables to distinguish the $p$ choice alternatives. However, for demographic variables, the value of the demographic variable is spread across $p-1$ columns and $p-1$ rows. ### Model The logit specification was originally derived by Luce (1959) from assumptions on characteristics of choice probabilities. McFadden (1974) tied the model to rational economic theory by showing how the multinomial logit specification models choices made by a utility-maximizing consumer, assuming that the unobserved utility component is distributed Type I Extreme Value. We motivate use of this model following McFadden by assuming the decision maker chooses the alternative providing him with the highest utility, where the utility $U_{ij}$ from the choice $y_{ij}$ made by a decision maker in choice situation $i$ for product $j = 1, \ldots, p$ is modeled as the sum of a deterministic and a stochastic component: \[ U_{ij} = V_{ij} + \varepsilon_{ij} \hspace{2em} \text{with } \varepsilon_{ij}\ \sim \text{ iid T1EV} \] Regressors (both demographic and alternative-specific) are included in the model by assuming $V_{ij} = x_{ij}'\beta$. These assumptions result in choice probabilities of: $$ \text{Pr}(y_i=j) = \frac{\exp \{x_{ij}'\beta\}}{\sum_{k=1}^p\exp\{x_{ik}'\beta\}} $$ Independent priors for the components of the `beta` vector are specified as normal distributions. Using notation for the multivariate normal distribution, we have: $$ \beta \sim MVN(\bar{\beta},\ A^{-1}) $$ We use `bayesm`'s default values for the parameters of the priors: $\bar{\beta} = 0$ and $A = 0.01I$. ### Bayesian Estimation Experience with the MNL likelihood is that the asymptotic normal approximation is excellent. `rmnlIndepMetrop` implements an independent Metropolis algorithm to sample from the normal approximation to the posterior distribution of $\beta$: $$ p(\beta | X, y) \overset{\cdot}{\propto} |H|^{1/2} \exp \left\{ \frac{1}{2}(\beta - \hat{\beta})'H(\beta - \hat{\beta}) \right\} $$ where $\hat{\beta}$ is the MLE, $H = \sum_i x_i A_i x_i'$, and the candidate distribution used in the Metropolis algorithm is the multivariate student t. For more detail, see Section 11 of BSM Chapter 3. We sample from the normal approximation to the posterior as follows: ```{r, eval = FALSE} X <- cbind(X1, X2[,10:ncol(X2)]) out <- rmnlIndepMetrop(Data = list(y=y, X=X, p=10), Mcmc = list(R=1e4, nprint=1e3)) ``` ```{r, echo = FALSE} temp <- capture.output( {set.seed(1234); X <- cbind(X1, X2[,10:ncol(X2)]); out <- rmnlIndepMetrop(Data = list(y=y, X=X, p=10), Mcmc = list(R=1e4, nprint=1e3))}, file = NULL) ``` `rmnlIndepMetrop` returns a list that we have saved in `out`. The list contains 3 elements, `betadraw`, `loglike`, and `acceptr`, which you can verify by running `str(out)`. `betadraw` is a $10,000 \times 28$ dimension matrix with class `bayesm.mat`. As with the linear regression of Example 1 above, we can plot or summarize features of the the posterior distribution in many ways. For information on each marginal posterior distribution, call `summary(out)` or `plot(out)`. Because we have 28 covariates (intercepts and demographic variables make up 9 columns each and there is one column for the price variable) we omit the full set of results to save space and instead, we only present summary statistics for the marginal posterior distribution for $\beta_\text{price}$: ```{r} summary.bayesm.mat(out$betadraw[,10], names = "Price") ``` In addition to summary information for a marginal posterior distribution, we can plot it. We use `bayesm`'s `plot` generic function (calling `plot(out$betadraw)` would provide the same plots for all 28 $X$ variables). In the histogram, the green bars delimit a 95\% Bayesian credibility interval, yellow bars shows +/- 2 numerical standard errors for the posterior mean, and the red bar indicates the posterior mean. The subsequent two plots are a trace plot and and ACF plot. ```{r} plot.bayesm.mat(out$betadraw[,10], names = "Price") ``` We see that the posterior is approximately normally distributed with a mean of `r format(round(mean(out$betadraw[,10]),1), big.mark=",")` and a standard deviation of `r round(sd(out$betadraw[,10]), 2)`. The trace plot shows good mixing. The ACF plot shows a fair amount of correlation such that, even though the algorithm took `R = 10,000` draws, the Effective Sample Size (as reported in the summary stats above) is only `r format(round(summary.bayesm.mat(out$betadraw[,10])[1,5],0), big.mark = ",")`. Because of the nonlinearity in this model, interpreting the results is more difficult than with linear regression models. We do not elaborate further on the interpretation of coefficients and methods of displaying results from multinomial logit models, but for the uninitiated reader, we refer you to excellent sources for this information authored by Kenneth Train and Gary King.[^3] [^3]: Train, Kenneth, _Discrete Choice Models with Simulation_ Cambridge University Press, 2009. King, Gary _Unifying Political Methodlogy: The Likelihood Theory of Statistical Inference_, University of Michigan Press (1998) p. 108. King, Gary, Michael Tomz, and Jason Wittenberg, "Making the Most of Statistical Analyses: Improving Interpretation and Presentation" American Journal of Political Science, Vol. 44 (April 2000) pp. 347--361 at p. 355. ## Example 3: Hierarchical Logit ### Data While we could add individual-specific parameters to the previous model and use the same dataset, we elect to provide the reader with greater variety. For this example, we use the `camera` dataset in `bayesm`, which contains conjoint choice data for 332 respondents who evaluated digital cameras. These data have already been processed to exclude respondents that always answered "none", always picked the same brand, always selected the highest priced offering, or who appeared to be answering randomly. ```{r} data(camera) length(camera) str(camera[[1]]) colnames(camera[[1]]$X) ``` The `camera` data is stored in a list-of-lists format, which is the format required by `bayesm`'s posterior sampling functions for hierarchical models. This format has one list per individual with each list containing a vector `y` of choice outcomes and a matrix `X` of covariates. As with the multinomial logit model of the last example, `y` is a length-$n_i$ vector (or one-column matrix) and `X` has dimensions $n_ij \times k$ where $n_i$ is the number of choice occasions faced by individual $i$, $j$ is the number of choice alternatives, and $k$ is the number of covariates. For the `camera` data, $N=332$, $n_i=16$ for all $i$, $j=5$, and $k=10$. If your data were not in this format, it could be easily converted with a `for` loop and `createX`. For example, we can format `margarine` data from Example 2 above into a list-of-lists format with the following code, which simply loops over individuals, extracting and storing their `y` and `X` data one individual at a time: ```{r, eval = FALSE} data(margarine) chpr <- margarine$choicePrice chpr$hhid <- as.factor(chpr$hhid) N <- nlevels(chpr$hhid) dat <- vector(mode = "list", length = N) for (i in 1:N) { dat[[i]]$y <- chpr[chpr$hhid==levels(chpr$hhid)[i], "choice"] dat[[i]]$X <- createX(p=10, na=1, Xa=chpr[chpr$hhid==levels(chpr$hhid)[i],3:12], nd=NULL, Xd=NULL) } ``` Returning to the `camera` data, the first 4 covariates are binary indicators for the brands Canon, Sony, Nikon, and Panasonic. These correspond to choice (`y`) values of 1, 2, 3, and 4. `y` can also take the value 5, indicating that the respondent chose "none". The data include binary indicators for two levels of pixel count, zoom strength, swivel video display capability, and wifi connectivity. The last covaritate is price, recorded in hundreds of U.S. dollars (we leave price in these units so that we do not need to adjust the default prior settings). When we look at overall choice outcomes, we see that the brands and the outside alternative ("none") are chosen roughly in fifths, with specific implied market shares ranging from 17\%--25\%: ```{r} N <- length(camera) dat <- matrix(NA, N*16, 2) for (i in 1:length(camera)) { Ni <- length(camera[[i]]$y) dat[((i-1)*Ni+1):(i*Ni),1] <- i dat[((i-1)*Ni+1):(i*Ni),2] <- camera[[i]]$y } round(prop.table(table(dat[,2])), 3) ``` However, when we look at a few individuals' choices, we see much greater variability: ```{r} round(prop.table(table(dat[,1], dat[,2])[41:50,], 1), 3) ``` It is this heterogeneity in individual choice that motivates us to employ a hierarchical model. ### Model Hierarchical (also known as multi-level, random-coefficient, or mixed) models allow each respondent to have his or her own coefficients. Different people have different preferences, and models that estimate individual-level coefficients can fit data better and make more accurate predictions than single-level models. These models are quite popular in marketing, as they allow, for example, promotions to be targeted to individuals with high promotional part worths --- meaning those inviduals who are most likely to respond to the promotion. For more information, see Rossi et al. (1996).^[Rossi, McCulloch, and Allenby "The Value of Purchase History Data in Target Marketing" Marketing Science (1996).] The model follows the multinomial logit specification given in Example 2 above where individuals are assumed to be rational economic agents that make utility-maximizing choices. Now, however, the model includes individual-level parameters ($\beta_i$) assumed to be drawn from a normal distribution and with mean values driven by cross-sectional unit characteristics $Z$: $$ y_i \sim \text{MNL}(x_i'\beta_i) \hspace{1em} \text{with} \hspace{1em} \beta_i = z_i' \Delta + u_i \hspace{1em} \text{and} \hspace{1em} u_i \sim MVN(\mu, \Sigma) $$ $x_i$ is $n_i \times k$ and $i = 1, \ldots, N$. We can alternatively write the middle equation as $B=Z\Delta + U$ where $\beta_i$, $z_i$, and $u_i$ are the $i^\text{th}$ rows of $B$, $Z$, and $U$. $B$ is $N \times k$, $Z$ is $N \times m$, $\Delta$ is $m \times k$, and $U$ is $N \times k$. Note that we do not have any cross-sectional unit characteristics in the `camera` dataset and thus $Z$ will be omitted. The priors are: $$ \text{vec}(\Delta) = \delta \sim MVN(\bar{\delta}, A_\delta^{-1}) \hspace{2em} \mu \sim MVN(\bar{\mu}, \Sigma \otimes a^{-1}) \hspace{2em} \Sigma \sim IW(\nu, V) $$ This specification of priors assumes that, conditional on the hyperparameters (that is, the parameters of the prior distribution), the $\beta$'s are _a priori_ independent. This means that inference for each unit can be conducted independently of all other units, conditional on the hyperparameters, which is the Bayesian analogue of the fixed effects approach in classical statistics. Note also that we have assumed a normal "first-stage" prior distribution over the $\beta$'s. `rhierMnlRwMixture` permits a more-flexible mixture-of-normals first-stage prior (hence the "mixture" in the function name). However, for our example, we will not include this added flexibility (`Prior$ncomp = 1` below). ### Bayesian Estimation Although the model is more complex than the models used in the two previous examples, the increased programming difficulty for the researcher is minimal. As before, we specify `Data`, `Prior`, and `Mcmc` arguments, and call the posterior sampling function: ```{r} data <- list(lgtdata = camera, p = 5) prior <- list(ncomp = 1) mcmc <- list(R = 1e4, nprint = 0) out <- rhierMnlRwMixture(Data = data, Prior = prior, Mcmc = mcmc) ``` We store the results in `out`, which is a list of length 4. The list elements are `betadraw`, `nmix`, `loglike`, and `SignRes`. - `betadraw` is a $332 \times 10 \times 10,000$ array. These dimensions correspond to the number of individuals, the number of covariates, and the number of MCMC draws. - `nmix` is a list with elements `probdraw`, `zdraw`, and `compdraw`. - `probdraw` tells us the probability that each draw came from a particular normal component. This is relevant when there is a mixture-of-normals first-stage prior. However, since our specified prior over the $\beta$ vector is one normal distribution, `probdraw` is a $10,000 \times 1$ vector of all 1's. - `zdraw` is `NULL` as it is not relevant for this function. - `compdraw` provides draws for the mixture-of-normals components. Here, `compdraw` is a list of 10,000 lists. The $r^\text{th}$ of the 10,000 lists contains the $r^\text{th}$ draw of the $\mu$ vector (dim $1 \times 10$) and the Cholesky root of the $r^\text{th}$ draw for the $10 \times 10$ covariance matrix. - `loglike` is a $10,000 \times 1$ vector that provides the log-likelihood of each draw. - `SignRes` relates to whether any sign restrictions were placed on the model. This is discussed in detail in a separate vignette detailing a contrainsted hierarchical multinomial logit model; it is not relevant here. We can summarize results as before. `plot(out$betadraw)` provides plots for each variable that summarize the distributions of the individual parameters. For brevity, we provide just a histogram of posterior means for the 332 individual coefficients on wifi capability. ```{r} hist(apply(out$betadraw, 1:2, mean)[,9], col = "dodgerblue4", xlab = "", ylab = "", yaxt="n", xlim = c(-4,6), breaks = 20, main = "Histogram of Posterior Means For Individual Wifi Coefs") ``` We see that the distribution of individual posterior means is skewed, suggesting that our assumption of a normal first-stage prior may be incorrect. We could improve this model by using the more flexible mixture-of-normals prior or, if we believe all consumers value wifi connectivity positively, we could impose a sign constraint on that set of parameters --- both of which are demonstrated in the vignette for sign-constrained hierarchical multinomial logit. # Conclusion We hope that this vignette has provided the reader with a thorough introduction to the `bayesm` package and is sufficient to enable immediate use of its posterior sampling functions for bayesian estimation. Comments or edits can be sent to the vignette authors at the email addresses below. ***** _Authored by [Dan Yavorsky](dyavorsky@gmail.com) and [Peter Rossi](perossichi@gmail.com). Last updated June 2017._ bayesm/inst/include/0000755000176000001440000000000013123305446014163 5ustar ripleyusersbayesm/inst/include/bayesm.h0000644000176000001440000001236413114117322015614 0ustar ripleyusers#ifndef __BAYESM_H__ #define __BAYESM_H__ #include #include #include #include using namespace arma; using namespace Rcpp; //CUSTOM STRUCTS-------------------------------------------------------------------------------------------------- //Used in rhierLinearMixture, rhierLinearModel, rhierMnlDP, rhierMnlRwMixture, rhierNegbinRw, and rsurGibbs struct moments{ vec y; mat X; mat XpX; vec Xpy; mat hess; }; //Used in rhierLinearMixture, rhierLinearModel, rhierMnlRWMixture, and utilityFunctions.cpp struct unireg{ vec beta; double sigmasq; }; //Used in rhierMnlDP, rhierMnlRwMixture, and utilityFunctions.cpp struct mnlMetropOnceOut{ vec betadraw; int stay; double oldll; }; //Used in rDPGibbs, rhierMnlDP, rivDP, and utilityFunctions.cpp struct lambda{ vec mubar; double Amu; double nu; mat V; }; //Used in rDPGibbs, rhierMnlDP, rivDP, and utilityFunctions.cpp struct priorAlpha{ double power; double alphamin; double alphamax; int n; }; //Used in rDPGibbs, rhierMnlDP, rivDP, and utilityFunctions.cpp struct murooti{ vec mu; mat rooti; }; //Used in rDPGibbs, rhierMnlDP, rivDP, and utilityFunctions.cpp struct thetaStarIndex{ ivec indic; std::vector thetaStar_vector; }; //Used in rhierMnlDP, rivDP struct DPOut{ ivec indic; std::vector thetaStar_vector; std::vector thetaNp1_vector; double alpha; int Istar; lambda lambda_struct; }; //EXPOSED FUNCTIONS----------------------------------------------------------------------------------------------- List rwishart(double nu, mat const& V); List rmultireg(mat const& Y, mat const& X, mat const& Bbar, mat const& A, double nu, mat const& V); vec rdirichlet(vec const& alpha); double llmnl(vec const& beta, vec const& y, mat const& X); mat lndIChisq(double nu, double ssq, mat const& X); double lndMvst(vec const& x, double nu, vec const& mu, mat const& rooti, bool NORMC); double lndMvn(vec const& x, vec const& mu, mat const& rooti); double lndIWishart(double nu, mat const& V, mat const& IW); vec rmvst(double nu, vec const& mu, mat const& root); vec breg(vec const& y, mat const& X, vec const& betabar, mat const& A); vec cgetC(double e, int k); List rmixGibbs( mat const& y, mat const& Bbar, mat const& A, double nu, mat const& V, vec const& a, vec const& p, vec const& z); //rmixGibbs contains the following support functions, which are called ONLY THROUGH rmixGibbs: drawCompsFromLabels, drawLabelsFromComps, and drawPFromLabels //SUPPORT FUNCTIONS (contained in utilityFunctions.cpp and trunNorm.cpp)----------------------------------------------------------- //Used in rmvpGibbs and rmnpGibbs vec condmom(vec const& x, vec const& mu, mat const& sigmai, int p, int j); //double rtrun1(double mu, double sigma,double trunpt, int above); <--NO LONGER USED double trunNorm(double mu,double sig, double trunpt, int above); //Used in rhierLinearModel, rhierLinearMixture and rhierMnlRWMixture mat drawDelta(mat const& x,mat const& y,vec const& z,List const& comps,vec const& deltabar,mat const& Ad); unireg runiregG(vec const& y, mat const& X, mat const& XpX, vec const& Xpy, double sigmasq, mat const& A, vec const& Abetabar, double nu, double ssq); //Used in rnegbinRW and rhierNegbinRw double llnegbin(vec const& y, vec const& lambda, double alpha, bool constant); double lpostbeta(double alpha, vec const& beta, mat const& X, vec const& y, vec const& betabar, mat const& rootA); double lpostalpha(double alpha, vec const& beta, mat const& X, vec const& y, double a, double b); //Used in rbprobitGibbs (uses breg1 and trunNorm_vec) and rordprobitGibbs (uses breg1 and rtrunVec) vec breg1(mat const& root, mat const& X, vec const& y, vec const& Abetabar); vec rtrunVec(vec const& mu,vec const& sigma, vec const& a, vec const& b); vec trunNorm_vec(vec const& mu, vec const& sig, vec const& trunpt, vec const& above); //Used in rhierMnlDP and rhierMnlRwMixture mnlMetropOnceOut mnlMetropOnce(vec const& y, mat const& X, vec const& oldbeta, double oldll,double s, mat const& incroot, vec const& betabar, mat const& rootpi); //Used in rDPGibbs, rhierMnlDP, rivDP int rmultinomF(vec const& p); mat yden(std::vector const& thetaStar, mat const& y); ivec numcomp(ivec const& indic, int k); murooti thetaD(mat const& y, lambda const& lambda_struct); thetaStarIndex thetaStarDraw(ivec indic, std::vector thetaStar_vector, mat const& y, mat ydenmat, vec const& q0v, double alpha, lambda const& lambda_struct, int maxuniq); vec q0(mat const& y, lambda const& lambda_struct); vec seq_rcpp(double from, double to, int len); //kept _rcpp due to conflict with base seq function double alphaD(priorAlpha const& priorAlpha_struct, int Istar, int gridsize); murooti GD(lambda const& lambda_struct); lambda lambdaD(lambda const& lambda_struct, std::vector const& thetaStar_vector, vec const& alim, vec const& nulim, vec const& vlim, int gridsize); //FUNCTION TIMING (contained in functionTiming.cpp)--------------------------------------------------------------- void startMcmcTimer(); void infoMcmcTimer(int rep, int R); void endMcmcTimer(); #endif bayesm/src/0000755000176000001440000000000013134410677012357 5ustar ripleyusersbayesm/src/rmixGibbs_rcpp.cpp0000644000176000001440000001303213134410700016017 0ustar ripleyusers#include "bayesm.h" //W. Taylor: we considered moving the output to struct formats but the efficiency // gains were limited and the conversions back and forth between Lists and struct were cumbersome List drawCompsFromLabels(mat const& y, mat const& Bbar, mat const& A, double nu, mat const& V, int ncomp, vec const& z){ // Wayne Taylor 3/18/2015 // Function to draw the components based on the z labels vec b, r, mu; mat yk, Xk, Ck, sigma, rooti, S, IW, CI; List temp, rw, comps(ncomp); int n = z.n_rows; vec nobincomp = zeros(ncomp); //Determine the number of observations in each component for(int i = 0; i 0) { // If there are observations in this component, draw from the posterior yk = y.rows(find(z==(k+1))); //Note k starts at 0 and z starts at 1 Xk = ones(nobincomp[k], 1); temp = rmultireg(yk, Xk, Bbar, A, nu, V); sigma = as(temp["Sigma"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> rooti = solve(trimatu(chol(sigma)),eye(sigma.n_rows,sigma.n_cols)); //trimatu interprets the matrix as upper triangular and makes solve more efficient mu = as(temp["B"]); comps(k) = List::create( Named("mu") = NumericVector(mu.begin(),mu.end()), //converts to a NumericVector, otherwise it will be interpretted as a matrix Named("rooti") = rooti ); } else { // If there are no obervations in this component, draw from the prior S = solve(trimatu(chol(V)),eye(V.n_rows,V.n_cols)); S = S * trans(S); rw = rwishart(nu, S); IW = as(rw["IW"]); CI = as(rw["CI"]); rooti = solve(trimatu(chol(IW)),eye(IW.n_rows,IW.n_cols)); b = vectorise(Bbar); r = rnorm(b.n_rows,0,1); mu = b + (CI * r) / sqrt(A(0,0)); comps(k) = List::create( Named("mu") = NumericVector(mu.begin(),mu.end()), //converts to a NumericVector, otherwise it will be interpretted as a matrix Named("rooti") = rooti); } } return(comps); } vec drawLabelsFromComps(mat const& y, vec const& p, List comps) { // Wayne Taylor 3/18/2015 // Function to determine which label is associated with each y value double logprod; vec mu, u; mat rooti; List compsk; int n = y.n_rows; vec res = zeros(n); int ncomp = comps.size(); mat prob(n,ncomp); for(int k = 0; k(compsk["mu"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> rooti = as(compsk["rooti"]); //Find log of MVN density using matrices logprod = log(prod(diagvec(rooti))); mat z(y); z.each_row() -= trans(mu); //subtracts mu from each row in z z = trans(rooti) * trans(z); z = -(y.n_cols/2.0) * log(2*M_PI) + logprod - .5 * sum(z % z, 0); // operator % performs element-wise multiplication prob.col(k) = trans(z); } prob = exp(prob); prob.each_row() %= trans(p); //element-wise multiplication // Cumulatively add each row and take a uniform draw between 0 and the cumulative sum prob = cumsum(prob, 1); u = as(runif(n)) % prob.col(ncomp-1); // Evaluative each column of "prob" until the uniform draw is less than the cumulative value for(int i = 0; i prob(i, res[i]++)); } return(res); } vec drawPFromLabels(vec const& a, vec const& z) { // Wayne Taylor 9/10/2014 // Function to draw the probabilities based on the label proportions vec a2 = a; int n = z.n_rows; //Count number of observations in each component for(int i = 0; i(n); std::vector thetaStar_vector(1); murooti thetaStar0_struct; thetaStar0_struct.mu = zeros(dimy); thetaStar0_struct.rooti = eye(dimy,dimy); thetaStar_vector[0] = thetaStar0_struct; //convert Prioralpha from List to struct priorAlpha priorAlpha_struct; priorAlpha_struct.power = PrioralphaList["power"]; priorAlpha_struct.alphamin = PrioralphaList["alphamin"]; priorAlpha_struct.alphamax = PrioralphaList["alphamax"]; priorAlpha_struct.n = PrioralphaList["n"]; //initialize lambda lambda lambda_struct; lambda_struct.mubar = zeros(dimy); lambda_struct.Amu = BayesmConstantA; lambda_struct.nu = dimy+BayesmConstantnuInc; lambda_struct.V = lambda_struct.nu*eye(dimy,dimy); //initialize alpha double alpha = BayesmConstantDPalpha; //intialize remaining variables thetaStarIndex thetaStarDrawOut_struct; std::vector new_utheta_vector(1), thetaNp1_vector(1); murooti thetaNp10_struct, out_struct; mat ydenmat; vec q0v, probs; uvec ind; int nunique, indsize; uvec spanall(dimy); for(int i = 0; i(R/keep); vec Istardraw = zeros(R/keep); vec adraw = zeros(R/keep); vec nudraw = zeros(R/keep); vec vdraw = zeros(R/keep); List thetaNp1draw(R/keep); imat inddraw = zeros(R/keep,n); //do scaling rowvec dvec, ybar; if(SCALE){ dvec = 1/sqrt(var(y,0,0)); //norm_type=0 performs normalisation using N-1, dim=0 is by column ybar = mean(y,0); y.each_row() -= ybar; //subtract ybar from each row y.each_row() %= dvec; //divide each row by dvec } //note on scaling //we model scaled y, z_i=D(y_i-ybar) D=diag(1/sigma1, ..., 1/sigma_dimy) //if p_z= 1/R sum(phi(z|mu,Sigma)) // p_y=1/R sum(phi(y|D^-1mu+ybar,D^-1SigmaD^-1) // rooti_y=Drooti_z //you might want to use quantiles instead, like median and (10,90) // start main iteration loop int mkeep = 0; if(nprint>0) startMcmcTimer(); for(int rep = 0; rep maxuniq) stop("maximum number of unique thetas exceeded"); //ydenmat is a length(thetaStar) x n array of density values given f(y[j,] | thetaStar[[i]] // note: due to remix step (below) we must recompute ydenmat each time! ydenmat = zeros(maxuniq,n); ydenmat(span(0,nunique-1),span::all) = yden(thetaStar_vector,y); thetaStarDrawOut_struct = thetaStarDraw(indic, thetaStar_vector, y, ydenmat, q0v, alpha, lambda_struct, maxuniq); thetaStar_vector = thetaStarDrawOut_struct.thetaStar_vector; indic = thetaStarDrawOut_struct.indic; nunique = thetaStar_vector.size(); //thetaNp1 and remix probs = zeros(nunique+1); for(int j = 0; j < nunique; j++){ ind = find(indic == (j+1)); indsize = ind.size(); probs[j] = indsize/(alpha+n+0.0); new_utheta_vector[0] = thetaD(y(ind,spanall),lambda_struct); thetaStar_vector[j] = new_utheta_vector[0]; } probs[nunique] = alpha/(alpha+n+0.0); int ind = rmultinomF(probs); int probssize = probs.size(); if(ind == probssize) { out_struct = GD(lambda_struct); thetaNp10_struct.mu = out_struct.mu; thetaNp10_struct.rooti = out_struct.rooti; thetaNp1_vector[0] = thetaNp10_struct; } else { out_struct = thetaStar_vector[ind-1]; thetaNp10_struct.mu = out_struct.mu; thetaNp10_struct.rooti = out_struct.rooti; thetaNp1_vector[0] = thetaNp10_struct; } //draw alpha alpha = alphaD(priorAlpha_struct,nunique,gridsize); //draw lambda lambda_struct = lambdaD(lambda_struct,thetaStar_vector,lambda_hyper["alim"],lambda_hyper["nulim"],lambda_hyper["vlim"],gridsize); //print time to completion if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); //save every keepth draw if((rep+1)%keep==0){ mkeep = (rep+1)/keep; alphadraw[mkeep-1] = alpha; Istardraw[mkeep-1] = nunique; adraw[mkeep-1] = lambda_struct.Amu; nu = lambda_struct.nu; nudraw[mkeep-1] = nu; mat V = lambda_struct.V; vdraw[mkeep-1] = V(0,0)/nu; inddraw(mkeep-1,span::all) = trans(indic); thetaNp10_struct = thetaNp1_vector[0]; if(SCALE){ thetaNp10_struct.mu = thetaNp10_struct.mu/trans(dvec)+trans(ybar); thetaNp10_struct.rooti = diagmat(dvec)*thetaNp10_struct.rooti; } //here we put the draws into the list of lists of list format useful for finite mixture of normals utilities //we have to convetr to a NumericVector for the plotting functions to work thetaNp1draw[mkeep-1] = List::create(List::create(Named("mu") = NumericVector(thetaNp10_struct.mu.begin(),thetaNp10_struct.mu.end()),Named("rooti") = thetaNp10_struct.rooti)); } } if(nprint>0) endMcmcTimer(); return List::create( Named("inddraw") = inddraw, Named("thetaNp1draw") = thetaNp1draw, Named("alphadraw") = alphadraw, Named("Istardraw") = Istardraw, Named("adraw") = adraw, Named("nudraw") = nudraw, Named("vdraw") = vdraw); } bayesm/src/Makevars0000644000176000001440000000012213134410700014031 0ustar ripleyusersPKG_LIBS = $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) PKG_CPPFLAGS = -I../inst/include/ bayesm/src/rmvst_rcpp.cpp0000644000176000001440000000070113134410700015243 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] vec rmvst(double nu, vec const& mu, mat const& root){ // Wayne Taylor 9/7/2014 // function to draw from MV s-t with nu df, mean mu, Sigma=t(root)%*%root // root is upper triangular cholesky root vec rnormd = rnorm(mu.size()); vec nvec = trans(root)*rnormd; return(nvec/sqrt(rchisq(1,nu)[0]/nu) + mu); //rchisq returns a vectorized object, so using [0] allows for the conversion to double } bayesm/src/rmnpGibbs_rcpp_loop.cpp0000644000176000001440000000774213134410700017060 0ustar ripleyusers#include "bayesm.h" //EXTRA FUNCTIONS SPECIFIC TO THE MAIN FUNCTION-------------------------------------------- vec drawwi(vec const& w, vec const& mu, mat const& sigmai, int p, int y){ // Wayne Taylor 9/8/2014 //function to draw w_i by Gibbing thru p vector int above; double bound; vec outwi = w; vec maxInd(2); for(int i = 0; i(w.n_rows); for(int i = 0; i(R/keep, pm1*pm1); mat betadraw = zeros(R/keep,k); vec wnew = zeros(Xrows); //set initial values of w,beta, sigma (or root of inv) vec wold = wnew; vec betaold = beta0; mat C = chol(solve(trimatu(sigma0),eye(sigma0.n_cols,sigma0.n_cols))); //trimatu interprets the matrix as upper triangular and makes solve more efficient //C is upper triangular root of sigma^-1 (G) = C'C mat sigmai, zmat, epsilon, S, IW, ucholinv, VSinv; vec betanew; List W; // start main iteration loop int mkeep = 0; if(nprint>0) startMcmcTimer(); for(int rep = 0; rep(W["C"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> //print time to completion if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); //save every keepth draw if((rep+1)%keep==0){ mkeep = (rep+1)/keep; betadraw(mkeep-1,span::all) = trans(betanew); IW = as(W["IW"]); sigmadraw(mkeep-1,span::all) = trans(vectorise(IW)); } wold = wnew; betaold = betanew; } if(nprint>0) endMcmcTimer(); return List::create( Named("betadraw") = betadraw, Named("sigmadraw") = sigmadraw); } bayesm/src/runireg_rcpp_loop.cpp0000644000176000001440000000414413134410700016601 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] List runireg_rcpp_loop(vec const& y, mat const& X, vec const& betabar, mat const& A, double nu, double ssq, int R, int keep, int nprint) { // Keunwoo Kim 09/09/2014 // Purpose: perform iid draws from posterior of regression model using conjugate prior // Arguments: // y,X // betabar,A prior mean, prior precision // nu, ssq prior on sigmasq // R number of draws // keep thinning parameter // Output: list of beta, sigmasq // Model: // y = Xbeta + e e ~N(0,sigmasq) // y is n x 1 // X is n x k // beta is k x 1 vector of coefficients // Prior: // beta ~ N(betabar,sigmasq*A^-1) // sigmasq ~ (nu*ssq)/chisq_nu int mkeep; double s, sigmasq; mat RA, W, IR; vec z, btilde, res, beta; int nvar = X.n_cols; int nobs = y.size(); vec sigmasqdraw(R/keep); mat betadraw(R/keep, nvar); if (nprint>0) startMcmcTimer(); for (int rep=0; rep0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if ((rep+1)%keep==0){ mkeep = (rep+1)/keep; betadraw(mkeep-1, span::all) = trans(beta); sigmasqdraw[mkeep-1] = sigmasq; } } if (nprint>0) endMcmcTimer(); return List::create( Named("betadraw") = betadraw, Named("sigmasqdraw") = NumericVector(sigmasqdraw.begin(),sigmasqdraw.end())); } bayesm/src/rhierLinearMixture_rcpp_loop.cpp0000644000176000001440000001023013134410700020741 0ustar ripleyusers#include "bayesm.h" //[[Rcpp::export]] List rhierLinearMixture_rcpp_loop(List const& regdata, mat const& Z, vec const& deltabar, mat const& Ad, mat const& mubar, mat const& Amu, double nu, mat const& V, double nu_e, vec const& ssq, int R, int keep, int nprint, bool drawdelta, mat olddelta, vec const& a, vec oldprob, vec ind, vec tau){ // Wayne Taylor 10/02/2014 int nreg = regdata.size(); int nvar = V.n_cols; int nz = Z.n_cols; mat rootpi, betabar, Abeta, Abetabar; int mkeep; unireg runiregout_struct; List regdatai, nmix; // convert List to std::vector of type "moments" std::vector regdata_vector; moments regdatai_struct; // store vector with struct for (int reg = 0; reg(regdatai["y"]); regdatai_struct.X = as(regdatai["X"]); regdatai_struct.XpX = as(regdatai["XpX"]); regdatai_struct.Xpy = as(regdatai["Xpy"]); regdata_vector.push_back(regdatai_struct); } // allocate space for draws mat oldbetas = zeros(nreg,nvar); mat taudraw(R/keep, nreg); cube betadraw(nreg, nvar, R/keep); mat probdraw(R/keep, oldprob.size()); mat Deltadraw(1,1); if(drawdelta) Deltadraw.zeros(R/keep, nz*nvar);//enlarge Deltadraw only if the space is required List compdraw(R/keep); if (nprint>0) startMcmcTimer(); for (int rep = 0; rep(mgout["p"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> ind = as(mgout["z"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> //now draw delta | {beta_i}, ind, comps if(drawdelta) olddelta = drawDelta(Z,oldbetas,ind,oldcomp,deltabar,Ad); //loop over all regression equations drawing beta_i | ind[i],z[i,],mu[ind[i]],rooti[ind[i]] for(int reg = 0; reg(oldcompreg[1]); //note: beta_i = Delta*z_i + u_i Delta is nvar x nz if(drawdelta){ olddelta.reshape(nvar,nz); betabar = as(oldcompreg[0])+olddelta*vectorise(Z(reg,span::all)); } else { betabar = as(oldcompreg[0]); } Abeta = trans(rootpi)*rootpi; Abetabar = Abeta*betabar; runiregout_struct = runiregG(regdata_vector[reg].y, regdata_vector[reg].X, regdata_vector[reg].XpX, regdata_vector[reg].Xpy, tau[reg], Abeta, Abetabar, nu_e, ssq[reg]); oldbetas(reg,span::all) = trans(runiregout_struct.beta); tau[reg] = runiregout_struct.sigmasq; } //print time to completion and draw # every nprint'th draw if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; taudraw(mkeep-1, span::all) = trans(tau); betadraw.slice(mkeep-1) = oldbetas; probdraw(mkeep-1, span::all) = trans(oldprob); if(drawdelta) Deltadraw(mkeep-1, span::all) = trans(vectorise(olddelta)); compdraw[mkeep-1] = oldcomp; } } if (nprint>0) endMcmcTimer(); nmix = List::create(Named("probdraw") = probdraw, Named("zdraw") = R_NilValue, //sets the value to NULL in R Named("compdraw") = compdraw); if(drawdelta){ return(List::create( Named("taudraw") = taudraw, Named("Deltadraw") = Deltadraw, Named("betadraw") = betadraw, Named("nmix") = nmix)); } else { return(List::create( Named("taudraw") = taudraw, Named("betadraw") = betadraw, Named("nmix") = nmix)); } } bayesm/src/lndMvn_rcpp.cpp0000644000176000001440000000114013134410700015324 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] double lndMvn(vec const& x, vec const& mu, mat const& rooti){ //Wayne Taylor 9/7/2014 // function to evaluate log of MV Normal density with mean mu, var Sigma // Sigma=t(root)%*%root (root is upper tri cholesky root) // Sigma^-1=rooti%*%t(rooti) // rooti is in the inverse of upper triangular chol root of sigma // note: this is the UL decomp of sigmai not LU! // Sigma=root'root root=inv(rooti) vec z = vectorise(trans(rooti)*(x-mu)); return((-(x.size()/2.0)*log(2*M_PI) -.5*(trans(z)*z) + sum(log(diagvec(rooti))))[0]); } bayesm/src/llmnl_rcpp.cpp0000644000176000001440000000075113134410700015213 0ustar ripleyusers#include "bayesm.h" //[[Rcpp::export]] double llmnl(vec const& beta, vec const& y, mat const& X){ // Wayne Taylor 9/7/2014 // Evaluates log-likelihood for the multinomial logit model int n = y.size(); int j = X.n_rows/n; mat Xbeta = X*beta; vec xby = zeros(n); vec denom = zeros(n); for(int i = 0; i const& thetaStar_vector){ // Wayne Taylor 3/14/2015 int dimz = z.n_cols; int dimx = x.n_cols; //variable type initializaion double sig; mat wk, zk, xk, rooti, Sigma, xt; vec yk, mu, e1, ee2, yt; uvec ind, colAllw, colAllz(dimz), colAllx(dimx); //Create the index vectors, the colAll vectors are equal to span::all but with uvecs (as required by .submat) for(int i = 0; i0){ if(isw) wk = w.submat(ind,colAllw); zk = z.submat(ind,colAllz); yk = y(ind); xk = x.submat(ind,colAllx); murooti thetaStark_struct = thetaStar_vector[k]; mu = thetaStark_struct.mu; rooti = thetaStark_struct.rooti; Sigma = solve(rooti,eye(2,2)); Sigma = trans(Sigma)*Sigma; e1 = xk-zk*delta; ee2 = mu[1] + (Sigma(0,1)/Sigma(0,0))*(e1-mu[0]); sig = sqrt(Sigma(1,1)-pow(Sigma(0,1),2.0)/Sigma(0,0)); yt = join_cols(yt,(yk-ee2)/sig); //analogous to rbind() if(isw) { xt = join_cols(xt,join_rows(xk,wk)/sig); } else { xt = join_cols(xt,xk/sig); } } } ytxtxtd out_struct; out_struct.yt = yt; out_struct.xt = xt; return(out_struct); } ytxtxtd get_ytxtd(vec const& y, mat const& z, double beta, vec const& gamma, mat const& x, mat const& w, int ncomp, ivec const& indic,std::vector const& thetaStar_vector, int dimd){ // Wayne Taylor 3/14/2015 int dimx = x.n_cols; //variable type initializaion int indsize, indicsize; vec zveck, yk, mu, ytk, u, yt; mat C, wk, zk, xk, rooti, Sigma, B, L, Li, z2, zt1, zt2, xtd; uvec colAllw, colAllz(dimd), colAllx(dimx), ind, seqindk, negseqindk; //Create index vectors (uvec) for submatrix views indicsize = indic.size(); //here the uvecs are declared once, and within each loop the correctly sized vector is extracted as needed uvec seqind(indicsize);for(int i = 0;i0){ mat xtdk(2*indsize,dimd); //extract the properly sized vector section seqindk = seqind.subvec(0,indsize-1); negseqindk = negseqind.subvec(0,indsize-1); if(isw) wk = w.submat(ind,colAllw); zk = z.submat(ind,colAllz); zveck = vectorise(trans(zk)); yk = y(ind); xk = x.submat(ind,colAllx); murooti thetaStark_struct = thetaStar_vector[k]; mu = thetaStark_struct.mu; rooti = thetaStark_struct.rooti; Sigma = solve(rooti,eye(2,2)); Sigma = trans(Sigma)*Sigma; B = C*Sigma*trans(C); L = trans(chol(B)); Li = solve(trimatl(L),eye(2,2)); // L is lower triangular, trimatl interprets the matrix as lower triangular and makes solve more efficient if(isw) { u = vectorise(yk-wk*gamma-mu[1]-beta*mu[0]); } else { u = vectorise(yk-mu[1]-beta*mu[0]); } ytk = vectorise(Li * join_cols(trans(xk-mu[0]),trans(u))); z2 = trans(join_rows(zveck,beta*zveck)); //join_rows is analogous to cbind() z2 = Li*z2; zt1 = z2(0,span::all); zt2 = z2(1,span::all); zt1.reshape(dimd,indsize); zt1 = trans(zt1); zt2.reshape(dimd,indsize); zt2=trans(zt2); xtdk(seqindk,colAllz) = zt1; xtdk(negseqindk,colAllz) = zt2; yt = join_cols(yt,ytk); xtd = join_cols(xtd,xtdk); } } ytxtxtd out_struct; out_struct.yt = yt; out_struct.xtd = xtd; return(out_struct); } DPOut rthetaDP(int maxuniq, double alpha, lambda lambda_struct, priorAlpha const& priorAlpha_struct, std::vector thetaStar_vector, ivec indic, vec const& q0v, mat const& y, int gridsize, List lambda_hyper){ // Wayne Taylor 3/14/2015 // function to make one draw from DP process // P. Rossi 1/06 // added draw of alpha 2/06 // removed lambdaD,etaD and function arguments 5/06 // removed thetaStar argument to .Call and creation of newthetaStar 7/06 // removed q0 computations as eta is not drawn 7/06 // changed for new version of thetadraw and removed calculation of thetaStar before // .Call 7/07 // y(i) ~ f(y|theta[[i]],eta) // theta ~ DP(alpha,G(lambda)) //output: // list with components: // thetaDraws: list, [[i]] is a list of the ith draw of the n theta's // where n is the length of the input theta and nrow(y) // thetaNp1Draws: list, [[i]] is ith draw of theta_{n+1} //args: // maxuniq: the maximum number of unique thetaStar values -- an error will be raised // if this is exceeded // alpha,lambda: starting values (or fixed DP prior values if not drawn). // Prioralpha: list of hyperparms of alpha prior // theta: list of starting value for theta's // thetaStar: list of unique values of theta, thetaStar[[i]] // indic: n vector of indicator for which unique theta (in thetaStar) // y: is a matrix nxk // thetaStar: list of unique values of theta, thetaStar[[i]] // q0v:a double vector with the same number of rows as y, giving \Int f(y(i)|theta,eta) dG_{lambda}(theta). int n = y.n_rows; int dimy = y.n_cols; //variable type initializaion int nunique, indsize, indp, probssize; vec probs; uvec ind; mat ydenmat; uvec spanall(dimy); for(int i = 0; i new_utheta(1), thetaNp1_vector(1); murooti thetaNp10_struct, outGD; vec p(n); p[n-1] = alpha/(alpha+(n-1)); for(int i = 0; i<(n-1); i++){ p[i] = 1/(alpha+(n-1)); } nunique = thetaStar_vector.size(); if(nunique > maxuniq) stop("maximum number of unique thetas exceeded"); //ydenmat is a length(thetaStar) x n array of density values given f(y[j,] | thetaStar[[i]] // note: due to remix step (below) we must recompute ydenmat each time! ydenmat = zeros(maxuniq,n); ydenmat(span(0,nunique-1),span::all) = yden(thetaStar_vector,y); thetaStarDrawOut_struct = thetaStarDraw(indic, thetaStar_vector, y, ydenmat, q0v, alpha, lambda_struct, maxuniq); thetaStar_vector = thetaStarDrawOut_struct.thetaStar_vector; indic = thetaStarDrawOut_struct.indic; nunique = thetaStar_vector.size(); //thetaNp1 and remix probs = zeros(nunique+1); for(int j = 0; j < nunique; j++){ ind = find(indic == (j+1)); indsize = ind.size(); probs[j] = indsize/(alpha+n+0.0); new_utheta[0] = thetaD(y(ind,spanall),lambda_struct); thetaStar_vector[j] = new_utheta[0]; } probs[nunique] = alpha/(alpha+n+0.0); indp = rmultinomF(probs); probssize = probs.size(); if(indp == probssize) { outGD = GD(lambda_struct); thetaNp10_struct.mu = outGD.mu; thetaNp10_struct.rooti = outGD.rooti; thetaNp1_vector[0] = thetaNp10_struct; } else { outGD = thetaStar_vector[indp-1]; thetaNp10_struct.mu = outGD.mu; thetaNp10_struct.rooti = outGD.rooti; thetaNp1_vector[0] = thetaNp10_struct; } //draw alpha alpha = alphaD(priorAlpha_struct,nunique,gridsize); //draw lambda lambda_struct = lambdaD(lambda_struct,thetaStar_vector,lambda_hyper["alim"],lambda_hyper["nulim"],lambda_hyper["vlim"],gridsize); DPOut out_struct; out_struct.indic = indic; out_struct.thetaStar_vector = thetaStar_vector; out_struct.thetaNp1_vector = thetaNp1_vector; out_struct.alpha = alpha; out_struct.Istar = nunique; out_struct.lambda_struct = lambda_struct; return(out_struct); } //RCPP SECTION---- //[[Rcpp::export]] List rivDP_rcpp_loop(int R, int keep, int nprint, int dimd, vec const& mbg, mat const& Abg, vec const& md, mat const& Ad, vec const& y, bool isgamma, mat const& z, vec const& x, mat const& w, vec delta, List const& PrioralphaList, int gridsize, bool SCALE, int maxuniq, double scalex, double scaley, List const& lambda_hyper,double BayesmConstantA, int BayesmConstantnu){ // Wayne Taylor 3/14/2015 int n = y.size(); int dimg = 1; if(isgamma) dimg = w.n_cols; //variable type initializaion int Istar, bgsize, mkeep; double beta; vec gammaVec, q0v, bg; mat errMat, wEmpty, V; wEmpty.reset(); //enforce 0 elements ytxtxtd out_struct; //initialize indicator vector, thetaStar, ncomp, alpha ivec indic = ones(n); std::vector thetaStar_vector(1), thetaNp1_vector(1); murooti thetaNp10_struct, thetaStar0_struct; thetaStar0_struct.mu = zeros(2); thetaStar0_struct.rooti = eye(2,2); thetaStar_vector[0] = thetaStar0_struct; //Initialize lambda lambda lambda_struct; lambda_struct.mubar = zeros(2); lambda_struct.Amu = BayesmConstantA; lambda_struct.nu = BayesmConstantnu; lambda_struct.V = lambda_struct.nu*eye(2,2); //convert Prioralpha from List to struct priorAlpha priorAlpha_struct; priorAlpha_struct.power = PrioralphaList["power"]; priorAlpha_struct.alphamin = PrioralphaList["alphamin"]; priorAlpha_struct.alphamax = PrioralphaList["alphamax"]; priorAlpha_struct.n = PrioralphaList["n"]; int ncomp = 1; double alpha = 1.0; //allocate space for draws mat deltadraw = zeros(R/keep,dimd); vec betadraw = zeros(R/keep); vec alphadraw = zeros(R/keep); vec Istardraw = zeros(R/keep); mat gammadraw = zeros(R/keep,dimg); List thetaNp1draw(R/keep); vec nudraw = zeros(R/keep); vec vdraw = zeros(R/keep); vec adraw = zeros(R/keep); if(nprint>0) startMcmcTimer(); for(int rep = 0; rep < R; rep++) { //draw beta and gamma if(isgamma){ out_struct = get_ytxt(y,z,delta,x,w,ncomp,indic,thetaStar_vector); } else { out_struct = get_ytxt(y,z,delta,x,wEmpty,ncomp,indic,thetaStar_vector); } bg = breg(out_struct.yt,out_struct.xt,mbg,Abg); beta = bg[0]; bgsize = bg.size()-1; if(isgamma) gammaVec = bg.subvec(1,bgsize); //draw delta if(isgamma){ out_struct=get_ytxtd(y,z,beta,gammaVec,x,w,ncomp,indic,thetaStar_vector,dimd); } else { out_struct=get_ytxtd(y,z,beta,gammaVec,x,wEmpty,ncomp,indic,thetaStar_vector,dimd); } delta = breg(out_struct.yt,out_struct.xtd,md,Ad); //DP process stuff- theta | lambda if(isgamma) { errMat = join_rows(x-z*delta,y-beta*x-w*gammaVec); } else { errMat = join_rows(x-z*delta,y-beta*x); } q0v = q0(errMat,lambda_struct); DPOut DPout_struct = rthetaDP(maxuniq,alpha,lambda_struct,priorAlpha_struct,thetaStar_vector,indic,q0v,errMat,gridsize,lambda_hyper); indic = DPout_struct.indic; thetaStar_vector = DPout_struct.thetaStar_vector; alpha = DPout_struct.alpha; Istar = DPout_struct.Istar; thetaNp1_vector = DPout_struct.thetaNp1_vector; thetaNp10_struct = thetaNp1_vector[0]; ncomp=thetaStar_vector.size(); lambda_struct = DPout_struct.lambda_struct; if (nprint>0) if((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; deltadraw(mkeep-1,span::all) = trans(delta); betadraw[mkeep-1] = beta; alphadraw[mkeep-1] = alpha; Istardraw[mkeep-1] = Istar; if(isgamma) gammadraw(mkeep-1,span::all) = trans(gammaVec); //We need to convert from to NumericVector so that the nmix plotting works properly (it does not work for an nx1 matrix) thetaNp1draw[mkeep-1] = List::create(List::create(Named("mu") = NumericVector(thetaNp10_struct.mu.begin(),thetaNp10_struct.mu.end()),Named("rooti") = thetaNp10_struct.rooti)); adraw[mkeep-1] = lambda_struct.Amu; nudraw[mkeep-1] = lambda_struct.nu; V = lambda_struct.V; vdraw[mkeep-1] = V(0,0)/lambda_struct.nu; } } //rescale if(SCALE){ deltadraw=deltadraw*scalex; betadraw=betadraw*scaley/scalex; if(isgamma) gammadraw=gammadraw*scaley; } if (nprint>0) endMcmcTimer(); return List::create( Named("deltadraw") = deltadraw, Named("betadraw") = betadraw, Named("alphadraw") = alphadraw, Named("Istardraw") = Istardraw, Named("gammadraw") = gammadraw, Named("thetaNp1draw") = thetaNp1draw, Named("adraw") = adraw, Named("nudraw") = nudraw, Named("vdraw") = vdraw); } bayesm/src/rwishart_rcpp.cpp0000644000176000001440000000221113134410700015731 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] List rwishart(double nu, mat const& V){ // Wayne Taylor 4/7/2015 // Function to draw from Wishart (nu,V) and IW // W ~ W(nu,V) // E[W]=nuV // WI=W^-1 // E[WI]=V^-1/(nu-m-1) // T has sqrt chisqs on diagonal and normals below diagonal int m = V.n_rows; mat T = zeros(m,m); for(int i = 0; i < m; i++) { T(i,i) = sqrt(rchisq(1,nu-i)[0]); //rchisq returns a vectorized object, so using [0] allows for the conversion to double } for(int j = 0; j < m; j++) { for(int i = j+1; i < m; i++) { T(i,j) = rnorm(1)[0]; //rnorm returns a NumericVector, so using [0] allows for conversion to double }} mat C = trans(T)*chol(V); mat CI = solve(trimatu(C),eye(m,m)); //trimatu interprets the matrix as upper triangular and makes solve more efficient // C is the upper triangular root of Wishart therefore, W=C'C // this is the LU decomposition Inv(W) = CICI' Note: this is // the UL decomp not LU! // W is Wishart draw, IW is W^-1 return List::create( Named("W") = trans(C) * C, Named("IW") = CI * trans(CI), Named("C") = C, Named("CI") = CI); } bayesm/src/lndIChisq_rcpp.cpp0000644000176000001440000000047313134410700015754 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] mat lndIChisq(double nu, double ssq, mat const& X) { // Keunwoo Kim 07/24/2014 // Purpose: evaluate log-density of scaled Inverse Chi-sq density of random variable Z=nu*ssq/chisq(nu) return(-lgamma(nu/2)+(nu/2)*log((nu*ssq)/2)-((nu/2)+1)*log(X)-(nu*ssq)/(2*X)); } bayesm/src/rnmixGibbs_rcpp_loop.cpp0000644000176000001440000000241313134410700017227 0ustar ripleyusers#include "bayesm.h" //[[Rcpp::export]] List rnmixGibbs_rcpp_loop(mat const& y, mat const& Mubar, mat const& A, double nu, mat const& V, vec const& a, vec p, vec z, int const& R, int const& keep, int const& nprint) { // Wayne Taylor 9/10/2014 int mkeep = 0; mat pdraw(R/keep,p.size()); mat zdraw(R/keep,z.size()); List compdraw(R/keep); if(nprint>0) startMcmcTimer(); // start main iteration loop for(int rep = 0; rep(out["p"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> z = as(out["z"]); // print time to completion and draw # every nprint'th draw if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; pdraw(mkeep-1,span::all) = trans(p); zdraw(mkeep-1,span::all) = trans(z); compdraw[mkeep-1] = compsd; } } if(nprint>0) endMcmcTimer(); return List::create( Named("probdraw") = pdraw, Named("zdraw") = zdraw, Named("compdraw") = compdraw); } bayesm/src/lndMvst_rcpp.cpp0000644000176000001440000000133413134410700015522 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] double lndMvst(vec const& x, double nu, vec const& mu, mat const& rooti, bool NORMC = false){ // Wayne Taylor 9/7/2014 // function to evaluate log of MVstudent t density with nu df, mean mu, // and with sigmai=rooti%*%t(rooti) note: this is the UL decomp of sigmai not LU! // rooti is in the inverse of upper triangular chol root of sigma // or Sigma=root'root root=inv(rooti) int dim = x.size(); double constant; if(NORMC){ constant = (nu/2)*log(nu)+lgamma((nu+dim)/2)-(dim/2.0)*log(M_PI)-lgamma(nu/2); } else { constant = 0.0; } vec z = vectorise(trans(rooti)*(x-mu)); return((constant-((dim+nu)/2)*log(nu+trans(z)*z)+sum(log(diagvec(rooti))))[0]); } bayesm/src/rdirichlet_rcpp.cpp0000644000176000001440000000063513134410700016227 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] vec rdirichlet(vec const& alpha){ // Wayne Taylor 4/7/2015 // Purpose: // draw from Dirichlet(alpha) int dim = alpha.size(); vec y = zeros(dim); for(int i = 0; i regdata_vector; moments regdatai_struct; // store vector with struct for (reg=0; reg(regdatai["y"]); regdatai_struct.X = as(regdatai["X"]); regdatai_struct.XpX = as(regdatai["XpX"]); regdatai_struct.Xpy = as(regdatai["Xpy"]); regdata_vector.push_back(regdatai_struct); } mat betas(nreg, nvar); mat Vbetadraw(R/keep, nvar*nvar); mat Deltadraw(R/keep, nz*nvar); mat taudraw(R/keep, nreg); cube betadraw(nreg, nvar, R/keep); if (nprint>0) startMcmcTimer(); //start main iteration loop for (int rep=0; rep(rmregout["Sigma"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> Delta = as(rmregout["B"]); //print time to completion and draw # every nprint'th draw if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; Vbetadraw(mkeep-1, span::all) = trans(vectorise(Vbeta)); Deltadraw(mkeep-1, span::all) = trans(vectorise(Delta)); taudraw(mkeep-1, span::all) = trans(tau); betadraw.slice(mkeep-1) = betas; } } if (nprint>0) endMcmcTimer(); return List::create( Named("Vbetadraw") = Vbetadraw, Named("Deltadraw") = Deltadraw, Named("betadraw") = betadraw, Named("taudraw") = taudraw); } bayesm/src/utilityFunctions.cpp0000644000176000001440000006076713134410700016462 0ustar ripleyusers#include "bayesm.h" //Used in rmvpGibbs and rmnpGibbs--------------------------------------------------------------------------------- vec condmom(vec const& x, vec const& mu, mat const& sigmai, int p, int j){ // Wayne Taylor 9/24/2014 //function to compute moments of x[j] | x[-j] //output is a vec: the first element is the conditional mean // the second element is the conditional sd vec out(2); int jm1 = j-1; int ind = p*jm1; double csigsq = 1/sigmai(ind+jm1); double m = 0.0; for(int i = 0; i .999999999) arg = .999999999; if(arg < .0000000001) arg = .0000000001; result = mu + sigma*R::qnorm(arg,0.0,1.0,1,0); return (result); } //Used in rhierLinearModel, rhierLinearMixture and rhierMnlRWMixture------------------------------------------------------ mat drawDelta(mat const& x,mat const& y,vec const& z,List const& comps,vec const& deltabar,mat const& Ad){ // Wayne Taylor 10/01/2014 // delta = vec(D) // given z and comps (z[i] gives component indicator for the ith observation, // comps is a list of mu and rooti) // y is n x p // x is n x k // y = xD' + U , rows of U are indep with covs Sigma_i given by z and comps int p = y.n_cols; int k = x.n_cols; int ncomp = comps.length(); mat xtx = zeros(k*p,k*p); mat xty = zeros(p,k); //this is the unvecced version, reshaped after the sum //Create the index vectors, the colAll vectors are equal to span::all but with uvecs (as required by .submat) uvec colAlly(p), colAllx(k); for(int i = 0; i0){ mat yi = y.submat(ind,colAlly); mat xi = x.submat(ind,colAllx); List compsi = comps[compi]; rowvec mui = as(compsi[0]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> mat rootii = trimatu(as(compsi[1])); //trimatu interprets the matrix as upper triangular yi.each_row() -= mui; //subtracts mui from each row of yi mat sigi = rootii*trans(rootii); xtx = xtx + kron(trans(xi)*xi,sigi); xty = xty + (sigi * (trans(yi)*xi)); } } xty.reshape(xty.n_rows*xty.n_cols,1); //vec(t(D)) ~ N(V^{-1}(xty + Ad*deltabar),V^{-1}) where V = (xtx+Ad) // compute the inverse of xtx+Ad mat ucholinv = solve(trimatu(chol(xtx+Ad)), eye(k*p,k*p)); //trimatu interprets the matrix as upper triangular and makes solve more efficient mat Vinv = ucholinv*trans(ucholinv); return(Vinv*(xty+Ad*deltabar) + trans(chol(Vinv))*as(rnorm(deltabar.size()))); } unireg runiregG(vec const& y, mat const& X, mat const& XpX, vec const& Xpy, double sigmasq, mat const& A, vec const& Abetabar, double nu, double ssq) { // Keunwoo Kim 09/16/2014 // Purpose: // perform one Gibbs iteration for Univ Regression Model // only does one iteration so can be used in rhierLinearModel // Model: // y = Xbeta + e e ~N(0,sigmasq) // y is n x 1 // X is n x k // beta is k x 1 vector of coefficients // Prior: // beta ~ N(betabar,A^-1) // sigmasq ~ (nu*ssq)/chisq_nu unireg out_struct; int n = y.size(); int k = XpX.n_cols; //first draw beta | sigmasq mat IR = solve(trimatu(chol(XpX/sigmasq+A)), eye(k,k)); //trimatu interprets the matrix as upper triangular and makes solve more efficient vec btilde = (IR*trans(IR)) * (Xpy/sigmasq + Abetabar); vec beta = btilde + IR*vec(rnorm(k)); //now draw sigmasq | beta double s = sum(square(y-X*beta)); sigmasq = (s + nu*ssq)/rchisq(1,nu+n)[0]; //rchisq returns a vectorized object, so using [0] allows for the conversion to double out_struct.beta = beta; out_struct.sigmasq = sigmasq; return (out_struct); } //Used in rnegbinRW and rhierNegbinRw------------------------------------------------------------------------------------- double llnegbin(vec const& y, vec const& lambda, double alpha, bool constant){ // Keunwoo Kim 11/02/2014 // Computes the log-likelihood // Arguments // y - a vector of observation // lambda - a vector of mean parameter (=exp(X*beta)) // alpha - dispersion parameter // constant - TRUE(FALSE) if it computes (un)normalized log-likeihood // PMF // pmf(y) = (y+alpha-1)Choose(y) * p^alpha * (1-p)^y // (y+alpha-1)Choose(y) = (alpha)*(alpha+1)*...*(alpha+y-1) / y! when y>=1 (0 when y=0) int i; int nobs = y.size(); vec prob = alpha/(alpha+lambda); vec logp(nobs); if (constant){ // normalized log-likelihood for (i=0; i(rnorm(X.n_cols)); double cll = llmnl(betac,y,X); double clpost = cll+lndMvn(betac,betabar,rootpi); double ldiff = clpost-oldll-lndMvn(oldbeta,betabar,rootpi); alphaminv << 1 << exp(ldiff); double alpha = min(alphaminv); if(alpha < 1) { unif = runif(1)[0]; //runif returns a NumericVector, so using [0] allows for conversion to double } else { unif=0;} if (unif <= alpha) { betadraw = betac; oldll = cll; } else { betadraw = oldbeta; stay = 1; } metropout_struct.betadraw = betadraw; metropout_struct.stay = stay; metropout_struct.oldll = oldll; return (metropout_struct); } //Used in rDPGibbs, rhierMnlDP, rivDP----------------------------------------------------------------------------- int rmultinomF(vec const& p){ // Wayne Taylor 1/28/2015 vec csp = cumsum(p); double rnd = runif(1)[0]; //runif returns a NumericVector, so using [0] allows for conversion to double int res = 0; int psize = p.size(); for(int i = 0; i < psize; i++){ if(rnd > csp[i]) res = res+1; } return(res+1); } mat yden(std::vector const& thetaStar_vector, mat const& y){ // Wayne Taylor 2/4/2015 // function to compute f(y | theta) // computes f for all values of theta in theta list of lists // arguments: // thetaStar is a list of lists. thetaStar[[i]] is a list with components, mu, rooti // y |theta[[i]] ~ N(mu,(rooti %*% t(rooti))^-1) rooti is inverse of Chol root of Sigma // output: // length(thetaStar) x n array of values of f(y[j,]|thetaStar[[i]] int nunique = thetaStar_vector.size(); int n = y.n_rows; int k = y.n_cols; mat ydenmat = zeros(nunique,n); vec mu; mat rooti, transy, quads; for(int i = 0; i < nunique; i++){ //now compute vectorized version of lndMvn //compute y_i'RIRI'y_i for all i mu = thetaStar_vector[i].mu; rooti = thetaStar_vector[i].rooti; transy = trans(y); transy.each_col() -= mu; //column-wise subtraction quads = sum(square(trans(rooti) * transy),0); //same as colSums ydenmat(i,span::all) = exp(-(k/2.0)*log(2*M_PI) + sum(log(rooti.diag())) - .5*quads); } return(ydenmat); } ivec numcomp(ivec const& indic, int k){ // Wayne Taylor 1/28/2015 //find the number of times each of k integers is in the vector indic ivec ncomp(k); for(int comp = 0; comp < k; comp++){ ncomp[comp]=sum(indic == (comp+1)); } return(ncomp); } murooti thetaD(mat const& y, lambda const& lambda_struct){ // Wayne Taylor 2/4/2015 // function to draw from posterior of theta given data y and base prior G0(lambda) // here y ~ N(mu,Sigma) // theta = list(mu=mu,rooti=chol(Sigma)^-1) // mu|Sigma ~ N(mubar,Sigma (x) Amu-1) // Sigma ~ IW(nu,V) // arguments: // y is n x k matrix of obs // lambda is list(mubar,Amu,nu,V) // output: // one draw of theta, list(mu,rooti) // Sigma=inv(rooti)%*%t(inv(rooti)) // note: we assume that y is a matrix. if there is only one obs, y is a 1 x k matrix mat X = ones(y.n_rows,1); mat A(1,1); A.fill(lambda_struct.Amu); List rout = rmultireg(y,X,trans(lambda_struct.mubar),A,lambda_struct.nu,lambda_struct.V); murooti out_struct; out_struct.mu = as(rout["B"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> out_struct.rooti = solve(chol(trimatu(as(rout["Sigma"]))),eye(y.n_cols,y.n_cols)); //trimatu interprets the matrix as upper triangular and makes solve more efficient return(out_struct); } thetaStarIndex thetaStarDraw(ivec indic, std::vector thetaStar_vector, mat const& y, mat ydenmat, vec const& q0v, double alpha, lambda const& lambda_struct, int maxuniq) { // Wayne Taylor 2/4/2015 // indic is n x 1 vector of indicator of which of thetaStar is assigned to each observation // thetaStar is list of the current components (some of which may never be used) // y is n x d matrix of observations // ydenmat is maxuniq x n matrix to store density evaluations - we assume first // length(Thetastar) rows are filled in with density evals // q0v is vector of bayes factors for new component and each observation // alpha is DP process tightness prior // lambda is list of priors for the base DP process measure // maxuniq maximum number of mixture components // yden is function to fill out an array // thetaD is function to draw theta from posterior of theta given y and G0 int n = indic.size(); ivec ncomp, indicC; int k, inc, cntNonzero; std::vector listofone_vector(1); std::vector thetaStarC_vector; //draw theta_i given theta_-i for(int i = 0; i(n-1); inc = 0; for(int j = 0; j<(n-1); j++){ if(j == i) {inc = inc + 1;} indicmi[j] = indic[inc]; inc = inc+1; } ncomp = numcomp(indicmi,k); for(int comp = 0; comp maxuniq) { stop("max number of comps exceeded"); } else { listofone_vector[0] = thetaD(y(i,span::all),lambda_struct); thetaStar_vector.push_back(listofone_vector[0]); ydenmat(k,span::all) = yden(listofone_vector,y); }} } //clean out thetaStar of any components which have zero observations associated with them //and re-write indic vector k = thetaStar_vector.size(); indicC = zeros(n); ncomp = numcomp(indic,k); cntNonzero = 0; for(int comp = 0; comp 1) { vec km1(k-1); for(int i = 0; i < (k-1); i++) km1[i] = i+1; //vector of 1:k SEE SEQ_ALONG lnk1k2 = (k/2.0)*log(2.0)+log((lambda_struct.nu-k)/2)+lgamma((lambda_struct.nu-k)/2)-lgamma(lambda_struct.nu/2)+sum(log(lambda_struct.nu/2-km1/2)); } else { lnk1k2 = (k/2.0)*log(2.0)+log((lambda_struct.nu-k)/2)+lgamma((lambda_struct.nu-k)/2)-lgamma(lambda_struct.nu/2); } constant = -(k/2.0)*log(2*M_PI)+(k/2.0)*log(lambda_struct.Amu/(1+lambda_struct.Amu)) + lnk1k2 + lambda_struct.nu*logdetR; // note: here we are using the fact that |V + S_i | = |R|^2 (1 + v_i'v_i) // where v_i = sqrt(Amu/(1+Amu))*t(R^-1)*(y_i-mubar), R is chol(V) // and S_i = Amu/(1+Amu) * (y_i-mubar)(y_i-mubar)' transy = trans(y); transy.each_col() -= lambda_struct.mubar; m = sqrt(lambda_struct.Amu/(1+lambda_struct.Amu))*trans(solve(trimatu(R),eye(y.n_cols,y.n_cols)))*transy; //trimatu interprets the matrix as upper triangular and makes solve more efficient vivi = sum(square(m),0); lnq0v = constant - ((lambda_struct.nu+1)/2)*(2*logdetR+log(1+vivi)); return(trans(exp(lnq0v))); } vec seq_rcpp(double from, double to, int len){ // Wayne Taylor 1/28/2015 // Same as R::seq() vec res(len); res[len-1] = to; res[0] = from; //note the order of these two statements is important, when gridsize = 1 res[0] will be rewritten to the correct number double increment = (res[len-1]-res[0])/(len-1); for(int i = 1; i<(len-1); i++) res[i] = res[i-1] + increment; return(res); } double alphaD(priorAlpha const& priorAlpha_struct, int Istar, int gridsize){ // Wayne Taylor 2/4/2015 // function to draw alpha using prior, p(alpha)= (1-(alpha-alphamin)/(alphamax-alphamin))**power //same as seq vec alpha = seq_rcpp(priorAlpha_struct.alphamin,priorAlpha_struct.alphamax-.000001,gridsize); vec lnprob(gridsize); for(int i = 0; i(Rout["IW"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> mat root = chol(Sigma); mat draws = rnorm(k); mat mu = lambda_struct.mubar + (1/sqrt(lambda_struct.Amu))*trans(root)*draws; murooti out_struct; out_struct.mu = mu; out_struct.rooti = solve(trimatu(root),eye(k,k)); //trimatu interprets the matrix as upper triangular and makes solve more efficient return(out_struct); } lambda lambdaD(lambda const& lambda_struct, std::vector const& thetaStar_vector, vec const& alim, vec const& nulim, vec const& vlim, int gridsize){ // Wayne Taylor 2/4/2015 // revision history // p. rossi 7/06 // vectorized 1/07 // changed 2/08 to paramaterize V matrix of IW prior to nu*v*I; then mode of Sigma=nu/(nu+2)vI // this means that we have a reparameterization to v* = nu*v // function to draw (nu, v, a) using uniform priors // theta_j=(mu_j,Sigma_j) mu_j~N(0,Sigma_j/a) Sigma_j~IW(nu,vI) // recall E[Sigma]= vI/(nu-dim-1) vec lnprob, probs, rowSumslgammaarg; int ind; //placeholder for matrix indexing murooti thetaStari_struct; mat rootii; vec mui; mat mout, rimu, arg, lgammaarg; double sumdiagriri, sumlogdiag, sumquads, adraw, nudraw, vdraw; murooti thetaStar0_struct = thetaStar_vector[0]; int d = thetaStar0_struct.mu.size(); int Istar = thetaStar_vector.size(); vec aseq = seq_rcpp(alim[0],alim[1],gridsize); vec nuseq = d-1+exp(seq_rcpp(nulim[0],nulim[1],gridsize)); //log uniform grid vec vseq = seq_rcpp(vlim[0],vlim[1],gridsize); // "brute" force approach would simply loop over the // "observations" (theta_j) and use log of the appropriate densities. To vectorize, we // notice that the "data" comes via various statistics: // 1. sum of log(diag(rooti_j) // 2. sum of tr(V%*%rooti_j%*%t(rooti_j)) where V=vI_d // 3. quadratic form t(mu_j-0)%*%rooti%*%t(rooti)%*%(mu_j-0) // thus, we will compute these first. // for documentation purposes, we leave brute force code in comment fields // extract needed info from thetastar list //mout has the rootis in form: [t(rooti_1), t(rooti_2), ...,t(rooti_Istar)] mout = zeros(d,Istar*d); ind = 0; for(int i = 0; i < Istar; i++){ thetaStari_struct = thetaStar_vector[i]; rootii = thetaStari_struct.rooti; ind = i*d; mout.submat(0, ind,d-1,ind+d-1) = trans(rootii); } sumdiagriri = sum(sum(square(mout),0)); //sum_i trace(rooti_i*trans(rooti_i)) // now get diagonals of rooti sumlogdiag = 0.0; for(int i = 0; i < Istar; i++){ ind = i*d; for(int j = 0; j < d; j++){ sumlogdiag = sumlogdiag+log(mout(j,ind+j)); } } //columns of rimu contain trans(rooti_i)*mu_i rimu = zeros(d,Istar); for(int i = 0; i < Istar; i++){ thetaStari_struct = thetaStar_vector[i]; mui = thetaStari_struct.mu; rootii = thetaStari_struct.rooti; rimu(span::all,i) = trans(rootii) * mui; } sumquads = sum(sum(square(rimu),0)); // draw a (conditionally indep of nu,v given theta_j) lnprob = zeros(aseq.size()); // for(i in seq(along=aseq)){ // for(j in seq(along=thetastar)){ // lnprob[i]=lnprob[i]+lndMvn(thetastar[[j]]$mu,c(rep(0,d)),thetastar[[j]]$rooti*sqrt(aseq[i]))} lnprob = Istar*(-(d/2.0)*log(2*M_PI))-.5*aseq*sumquads+Istar*d*log(sqrt(aseq))+sumlogdiag; lnprob = lnprob-max(lnprob) + 200; probs = exp(lnprob); probs = probs/sum(probs); adraw = aseq[rmultinomF(probs)-1]; // draw nu given v lnprob = zeros(nuseq.size()); // for(i in seq(along=nuseq)){ // for(j in seq(along=thetastar)){ // Sigma_j=crossprod(backsolve(thetastar[[j]]$rooti,diag(d))) // lnprob[i]=lnprob[i]+lndIWishart(nuseq[i],V,Sigma_j)} //same as arg = (nuseq+1-arg)/2.0; arg = zeros(gridsize,d); for(int i = 0; i < d; i++) { vec indvec(gridsize); indvec.fill(-(i+1)+1); arg(span::all,i) = indvec; } arg.each_col() += nuseq; arg = arg/2.0; lgammaarg = zeros(gridsize,d); for(int i = 0; i < gridsize; i++){ for(int j = 0; j < d; j++){ lgammaarg(i,j) = lgamma(arg(i,j)); }} rowSumslgammaarg = sum(lgammaarg,1); lnprob = zeros(gridsize); for(int i = 0; i(vseq.size()); // for(i in seq(along=vseq)){ // V=vseq[i]*diag(d) // for(j in seq(along=thetastar)){ // Sigma_j=crossprod(backsolve(thetastar[[j]]$rooti,diag(d))) // lnprob[i]=lnprob[i]+lndIWishart(nudraw,V,Sigma_j)} // lnprob=Istar*nudraw*d*log(sqrt(vseq))-.5*sumdiagriri*vseq lnprob = Istar*nudraw*d*log(sqrt(vseq*nudraw))-.5*sumdiagriri*vseq*nudraw; lnprob = lnprob-max(lnprob)+200; probs = exp(lnprob); probs = probs/sum(probs); vdraw = vseq[rmultinomF(probs)-1]; // put back into lambda lambda out_struct; out_struct.mubar = zeros(d); out_struct.Amu = adraw; out_struct.nu = nudraw; out_struct.V = nudraw*vdraw*eye(d,d); return(out_struct); } //Used in llnhlogit and simnhlogit--------------------------------------------------------------------------------- double root(double c1, double c2, double tol, int iterlim){ //function to find root of c1 - c2u = lnu int iter = 0; double uold = .1; double unew = .00001; while (iter <= iterlim && fabs(uold-unew) > tol){ uold = unew; unew=uold + (uold*(c1 -c2*uold - log(uold)))/(1. + c2*uold); if(unew < 1.0e-50) unew=1.0e-50; iter=iter+1; } return(unew); } //[[Rcpp::export]] vec callroot(vec const& c1, vec const& c2, double tol, int iterlim){ int n = c1.size(); vec u = zeros(n); for(int i = 0; i(wrap(mu)))-pnorm(gamma2-as(wrap(mu))); //pnorm takes Rcpp type NumericVector, NOT arma objects of type vec vec arg = as(temp); double epsilon = 1.0/(10^-50); for (int j=0; j1){ alpha = 1.0; } if (alpha<1){ unif = runif(1)[0]; //runif returns a NumericVector, so using [0] allows for conversion to double by extracting the first element } else{ unif = 0; } if (unif<=alpha){ dstardraw = dstarc; oldll = cll; } else{ dstardraw = olddstar; stay = 1; } return List::create( Named("dstardraw") = dstardraw, Named("oldll") = oldll, Named("stay") = stay ); } //MAIN FUNCTION--------------------------------------------------------------------------------------- // [[Rcpp::export]] List rordprobitGibbs_rcpp_loop(vec const& y, mat const& X, int k, mat const& A, vec const& betabar, mat const& Ad, double s, mat const& inc_root, vec const& dstarbar, vec const& betahat, int R, int keep, int nprint){ // Keunwoo Kim 09/09/2014 // Purpose: draw from posterior for ordered probit using Gibbs Sampler and metropolis RW // Arguments: // Data // X is nobs x nvar, y is nobs vector of 1,2,.,k (ordinal variable) // Prior // A is nvar x nvar prior preci matrix // betabar is nvar x 1 prior mean // Ad is ndstar x ndstar prior preci matrix of dstar (ncut is number of cut-offs being estimated) // dstarbar is ndstar x 1 prior mean of dstar // Mcmc // R is number of draws // keep is thinning parameter // nprint - prints the estimated time remaining for every nprint'th draw // s is scale parameter of random work Metropolis // Output: list of betadraws and cutdraws // Model: // z=Xbeta + e < 0 e ~N(0,1) // y=1,..,k, if z~c(c[k], c[k+1]) // cutoffs = c[1],..,c[k+1] // dstar = dstar[1],dstar[k-2] // set c[1]=-100, c[2]=0, ...,c[k+1]=100 // c[3]=exp(dstar[1]),c[4]=c[3]+exp(dstar[2]),..., // c[k]=c[k-1]+exp(datsr[k-2]) // Note: 1. length of dstar = length of cutoffs - 3 // 2. Be careful in assessing prior parameter, Ad. .1 is too small for many applications. // Prior: // beta ~ N(betabar,A^-1) // dstar ~ N(dstarbar, Ad^-1) int stay, i, mkeep; vec z; List metropout; int nvar = X.n_cols; int ncuts = k+1; int ncut = ncuts-3; int ndstar = k-2; int ny = y.size(); mat betadraw(R/keep, nvar); mat cutdraw(R/keep, ncuts); mat dstardraw(R/keep, ndstar); vec staydraw(R/keep); vec cutoff1(ny); vec cutoff2(ny); vec sigma(X.n_rows); sigma.ones(); // compute the inverse of trans(X)*X+A mat ucholinv = solve(trimatu(chol(trans(X)*X+A)), eye(nvar,nvar)); //trimatu interprets the matrix as upper triangular and makes solve more efficient mat XXAinv = ucholinv*trans(ucholinv); mat root = chol(XXAinv); vec Abetabar = trans(A)*betabar; // compute the inverse of Ad ucholinv = solve(trimatu(chol(Ad)), eye(ndstar,ndstar)); mat Adinv = ucholinv*trans(ucholinv); mat rootdi = chol(Adinv); // set initial values for MCMC vec olddstar(ndstar); olddstar.zeros(); vec beta = betahat; vec cutoffs = dstartoc(olddstar); double oldll = lldstar(olddstar, y, X*betahat); if (nprint>0) startMcmcTimer(); //start main iteration loop for (int rep=0; rep(metropout["dstardraw"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> oldll = as(metropout["oldll"]); cutoffs = dstartoc(olddstar); stay = as(metropout["stay"]); //print time to completion and draw # every nprint'th draw if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; cutdraw(mkeep-1,span::all) = trans(cutoffs); dstardraw(mkeep-1,span::all) = trans(olddstar); betadraw(mkeep-1,span::all) = trans(beta); staydraw[mkeep-1] = stay; } } double accept = 1-sum(staydraw)/(R/keep); if (nprint>0) endMcmcTimer(); return List::create( Named("cutdraw") = cutdraw, Named("dstardraw") = dstardraw, Named("betadraw") = betadraw, Named("accept") = accept ); } bayesm/src/rhierMnlRwMixture_rcpp_loop.cpp0000644000176000001440000002136713134410700020603 0ustar ripleyusers#include "bayesm.h" //FUNCTION SPECIFIC TO MAIN FUNCTION------------------------------------------------------ //[[Rcpp::export]] double llmnl_con(vec const& betastar, vec const& y, mat const& X, vec const& SignRes = NumericVector::create(0)){ // Wayne Taylor 7/8/2016 // Evaluates log-likelihood for the multinomial logit model WITH SIGN CONSTRAINTS // NOTE: this is exported only because it is used in the shell .R function, it will not be available to users //Reparameterize betastar to beta to allow for sign restrictions vec beta = betastar; //The default SignRes vector is a single element vector containing a zero //any() returns true if any elements of SignRes are non-zero if(any(SignRes)){ uvec signInd = find(SignRes != 0); beta.elem(signInd) = SignRes.elem(signInd) % exp(beta.elem(signInd)); //% performs element-wise multiplication } int n = y.size(); int j = X.n_rows/n; mat Xbeta = X*beta; vec xby = zeros(n); vec denom = zeros(n); for(int i = 0; i(rnorm(X.n_cols)); double cll = llmnl_con(betac,y,X,SignRes); double clpost = cll+lndMvn(betac,betabar,rootpi); double ldiff = clpost-oldll-lndMvn(oldbeta,betabar,rootpi); alphaminv << 1 << exp(ldiff); double alpha = min(alphaminv); if(alpha < 1) { unif = as_scalar(vec(runif(1))); } else { unif=0;} if (unif <= alpha) { betadraw = betac; oldll = cll; } else { betadraw = oldbeta; stay = 1; } out_struct.betadraw = betadraw; out_struct.stay = stay; out_struct.oldll = oldll; return (out_struct); } //MAIN FUNCTION------------------------------------------------------------------------------------- //[[Rcpp::export]] List rhierMnlRwMixture_rcpp_loop(List const& lgtdata, mat const& Z, vec const& deltabar, mat const& Ad, mat const& mubar, mat const& Amu, double nu, mat const& V, double s, int R, int keep, int nprint, bool drawdelta, mat olddelta, vec const& a, vec oldprob, mat oldbetas, vec ind, vec const& SignRes){ // Wayne Taylor 10/01/2014 int nlgt = lgtdata.size(); int nvar = V.n_cols; int nz = Z.n_cols; mat rootpi, betabar, ucholinv, incroot; int mkeep; mnlMetropOnceOut metropout_struct; List lgtdatai, nmix; // convert List to std::vector of struct std::vector lgtdata_vector; moments lgtdatai_struct; for (int lgt = 0; lgt(lgtdatai["y"]); lgtdatai_struct.X = as(lgtdatai["X"]); lgtdatai_struct.hess = as(lgtdatai["hess"]); lgtdata_vector.push_back(lgtdatai_struct); } // allocate space for draws vec oldll = zeros(nlgt); cube betadraw(nlgt, nvar, R/keep); mat probdraw(R/keep, oldprob.size()); vec loglike(R/keep); mat Deltadraw(1,1); if(drawdelta) Deltadraw.zeros(R/keep, nz*nvar);//enlarge Deltadraw only if the space is required List compdraw(R/keep); if (nprint>0) startMcmcTimer(); for (int rep = 0; rep(mgout["p"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> ind = as(mgout["z"]); //now draw delta | {beta_i}, ind, comps if(drawdelta) olddelta = drawDelta(Z,oldbetas,ind,oldcomp,deltabar,Ad); //loop over all LGT equations drawing beta_i | ind[i],z[i,],mu[ind[i]],rooti[ind[i]] for(int lgt = 0; lgt(oldcomplgt[1]); //note: beta_i = Delta*z_i + u_i Delta is nvar x nz if(drawdelta){ olddelta.reshape(nvar,nz); betabar = as(oldcomplgt[0])+olddelta*vectorise(Z(lgt,span::all)); } else { betabar = as(oldcomplgt[0]); } if (rep == 0) oldll[lgt] = llmnl_con(vectorise(oldbetas(lgt,span::all)),lgtdata_vector[lgt].y,lgtdata_vector[lgt].X,SignRes); //compute inc.root ucholinv = solve(trimatu(chol(lgtdata_vector[lgt].hess+rootpi*trans(rootpi))), eye(nvar,nvar)); //trimatu interprets the matrix as upper triangular and makes solve more efficient incroot = chol(ucholinv*trans(ucholinv)); metropout_struct = mnlMetropOnce_con(lgtdata_vector[lgt].y,lgtdata_vector[lgt].X,vectorise(oldbetas(lgt,span::all)), oldll[lgt],s,incroot,betabar,rootpi,SignRes); oldbetas(lgt,span::all) = trans(metropout_struct.betadraw); oldll[lgt] = metropout_struct.oldll; } //print time to completion and draw # every nprint'th draw if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; betadraw.slice(mkeep-1) = oldbetas; probdraw(mkeep-1, span::all) = trans(oldprob); loglike[mkeep-1] = sum(oldll); if(drawdelta) Deltadraw(mkeep-1, span::all) = trans(vectorise(olddelta)); compdraw[mkeep-1] = oldcomp; } } if (nprint>0) endMcmcTimer(); nmix = List::create(Named("probdraw") = probdraw, Named("zdraw") = R_NilValue, //sets the value to NULL in R Named("compdraw") = compdraw); //ADDED FOR CONSTRAINTS //If there are sign constraints, return f(betadraws) as "betadraws" //conStatus will be set to true if SignRes has any non-zero elements bool conStatus = any(SignRes); if(conStatus){ int SignResSize = SignRes.size(); //loop through each sign constraint for(int i = 0;i < SignResSize; i++){ //if there is a constraint loop through each slice of betadraw if(SignRes[i] != 0){ for(int s = 0;s < (R/keep); s++){ betadraw(span(),span(i),span(s)) = SignRes[i] * exp(betadraw(span(),span(i),span(s))); } } }//end loop through SignRes } if(drawdelta){ return(List::create( Named("Deltadraw") = Deltadraw, Named("betadraw") = betadraw, Named("nmix") = nmix, Named("loglike") = loglike, Named("SignRes") = SignRes)); } else { return(List::create( Named("betadraw") = betadraw, Named("nmix") = nmix, Named("loglike") = loglike, Named("SignRes") = SignRes)); } } bayesm/src/bayesBLP_rcpp_loop.cpp0000644000176000001440000003624313134410700016574 0ustar ripleyusers#include "bayesm.h" //SUPPORT FUNCTIONS SPECIFIC TO MAIN FUNCTION-------------------------------------------------------------------------------------- mat r2Sigma(vec const& r, int K){ // // Keunwoo Kim 10/28/2014 // // Purpose: // convert r (vector) into Sigma (matrix) // // Arguments: // r : K*(K+1)/2 length vector // K : number of parameters (=nrow(Sigma)) // // Output: // Sigma (K by K matrix) // int k, i, j; mat L = zeros(K, K); L.diag() = exp(r(span(0,K-1))); k = 0; for (i=0; i(J, J); // equivalent to struc = kron(eye(T, T), onesJJ) mat struc = zeros(J*T,J*T); for (t=0; t(T*J, T*J); mat offDiag = -choiceProb*trans(choiceProb)/H; mat Jac = struc%offDiag; Jac.diag() = sum(choiceProb%(1-choiceProb), 1)/H; double sumlogJacob = 0; for (t=0; t(J*T); vec mu1 = mu0/2; //relative increasement vec rel = (mu1 - mu0)/mu0; double max_rel = max(abs(rel)); while (max_rel > tol){ mu0 = mu1; expU = exp(u + mu0*ones(1,H)); temp1 = reshape(expU, J, T*H); expSum = 1 + sum(temp1, 0); expSum = reshape(expSum, T, H); // equivalent to expSum = kron(expSum, ones(J)); for (t=0; t(J)*expSum(t, span::all); } expSum = temp2; choiceProb = expU/expSum; share_hat = sum(choiceProb, 1)/H; mu1 = mu0 + log(share/share_hat); iter = iter + 1; rel = (mu0 - mu1)/mu0; max_rel = max(abs(rel)); } mat rtn = zeros(J*T, H+1); rtn(span::all,0) = mu1; rtn(span::all,span(1,H)) = choiceProb; return (rtn); } List rivDraw(vec const& mu, vec const& Xend, mat const& z, mat const& Xexo, vec const& theta_hat, mat const& A, vec const& deltabar, mat const& Ad, mat const& V, double nu, vec const& delta_old, mat const& Omega_old){ // // Keunwoo Kim 05/21/2015 // // Purpose: draw from posterior for linear I.V. model // // Arguments: // mu is vector of obs on lhs var in structural equation // Xend is "endogenous" var in structural eqn // Xexo is matrix of obs on "exogenous" vars in the structural eqn // z is matrix of obs on instruments // // deltabar is prior mean of delta // Ad is prior prec // theta_hat is prior mean vector for theta2,theta1 // A is prior prec of same // nu,V parms for IW on Omega // // delta_old is the starting value from the previous chain // Omega_old is the starting value from the previous chain // // Output: list of draws of delta,thetabar,Omega // // Model: // Xend=z'delta + e1 // mu=thetabar1*Xend + Xexo'thetabar2 + e2 // e1,e2 ~ N(0,Omega) // // Prior: // delta ~ N(deltabar,Ad^-1) // thetabar = vec(theta2,theta1) ~ N(theta_hat,A^-1) // Omega ~ IW(nu,V) // vec e1, ee2, bg, u, theta2; mat xt, Res, S, B, L, Li, z2, zt1, zt2, ucholinv, VSinv, mut; double sig,theta1; List out; int i; int n = mu.size(); int dimd = z.n_cols; int dimg = Xexo.n_cols; vec thetabar(dimg+1); mat C = eye(2,2); // set initial values mat Omega = Omega_old; vec delta = delta_old; mat xtd(2*n, dimd); vec zvec = vectorise(trans(z)); // // draw beta,gamma // e1 = Xend - z*delta; ee2 = (Omega(0,1)/Omega(0,0)) * e1; sig = sqrt(Omega(1,1)-((Omega(0,1)*Omega(0,1))/Omega(0,0))); mut = (mu-ee2)/sig; xt = join_rows(Xend,Xexo)/sig; bg = breg(mut,xt,theta_hat,A); theta1 = bg[0]; theta2 = bg(span(1,bg.size()-1)); // // draw delta // C(1,0) = theta1; B = C*Omega*trans(C); L = trans(chol(B)); Li = solve(trimatl(L),eye(2,2)); u = mu - Xexo*theta2; mut = vectorise(Li * trans(join_rows(Xend,u))); z2 = trans(join_rows(zvec, theta1*zvec)); z2 = Li*z2; zt1 = z2(0,span::all); zt2 = z2(1,span::all); zt1.reshape(dimd,n); zt1 = trans(zt1); zt2.reshape(dimd,n); zt2 = trans(zt2); for (i=0; i(out["IW"]); thetabar(span(0,dimg-1)) = theta2; thetabar[dimg] = theta1; return List::create( Named("deltadraw") = delta, Named("thetabardraw") = thetabar, Named("Omegadraw") = Omega ); } //MAIN FUNCTION--------------------------------------------------------------------------------------- // [[Rcpp::export]] List bayesBLP_rcpp_loop(bool IV, mat const& X, mat const& Z, vec const& share, int J, int T, mat const& v, int R, vec const& sigmasqR, mat const& A, vec const& theta_hat, vec const& deltabar, mat const& Ad, double nu0, double s0_sq, mat const& VOmega, double ssq, mat const& cand_cov, vec const& theta_bar_initial, vec const& r_initial, double tau_sq_initial, mat const& Omega_initial, vec const& delta_initial, double tol, int keep, int nprint){ // // Keunwoo Kim 05/21/2015 // // Purpose: // draw theta_bar and Sigma via hybrid Gibbs sampler (Jiang, Manchanda, and Rossi, 2009) // // Arguments: // Observation // IV: whether to use instrumental variable (TRUE or FALSE) // X: J*T by H (If IV is TRUE, the last column is endogenous variable.) // z: instrumental variables (If IV is FALSE, it is not used.) // share: vector of length J*T // // Dimension // J: number of alternatives // T: number of time // R: number of Gibbs sampling // // Prior // sigmasqR // theta_hat // A // deltabar (used when IV is TRUE) // Ad (used when IV is TRUE) // nu0 // s0_sq (used when IV is FALSE) // VOmega (used when IV is TRUE) // // Metropolis-Hastings // ssq: scaling parameter // cand_cov: var-cov matrix of random walk // // Initial values // theta_bar_initial // r_initial // tau_sq_initial (used when IV is FALSE) // Omega_initial (used when IV is TRUE) // delta_initial (used when IV is TRUE) // // Contraction mapping // tol: convergence tolerance for the contraction mapping // v: draws used for Monte-Carlo integration // // Output: // a List of theta_bar, r (Sigma), tau_sq, Omega, and delta draws // number of acceptance and loglikelihood // // Model & Prior: // shown in the below comments. int mkeep, I, jt; mat prob_t, Sigma_new, b, S, Sigma, Sigma_inv, rel, expU, share_hat, choiceProb, expSum, L, ucholinv, XXAinv, out_cont, Xexo, Xend, Omega_all, delta_all, zetaeta_old, zetaeta_new, rootiOmega; vec r_new, mu_new, theta_tilde, z, mu, err, mu0, mu1, eta_new, eta_old, tau_sq_all, zeta; double nu1, alpha, ll_new, ll_old, sumLogJaco_new, prior_new, prior_old, s1_sq, acceptrate; List ivout; double pi = M_PI; int K = theta_hat.size(); if (IV==TRUE){ Xexo = X(span::all, span(0,K-2)); Xend = X(span::all, K-1); I = Z.n_cols; } // number of MC integration draws int H = v.n_cols; // Allocate matrix for draws to be stored during MCMC if (IV==TRUE){ Omega_all = zeros(4,R/keep); delta_all = zeros(I,R/keep); }else{ tau_sq_all = zeros(R/keep); } mat theta_bar_all = zeros(K,R/keep); mat r_all = zeros(K*(K+1)/2,R/keep); mat Sigma_all = zeros(K*K,R/keep); vec ll_all = zeros(R/keep); // list to be returned to R List rtn; // initial values vec theta_bar = theta_bar_initial; mat Omega = Omega_initial; vec delta = delta_initial; vec r_old = r_initial; double tau_sq = tau_sq_initial; mat Sigma_old = r2Sigma(r_old, K); //=================================================================== // get initial mu and sumLogJaco: Contraction Mapping //=================================================================== // convert shares into mu out_cont = share2mu(Sigma_old, X, v, share, J, tol); mu = out_cont(span::all,0); choiceProb = out_cont(span::all,span(1,H)); // Jacobian double sumLogJaco_old = logJacob(choiceProb, J); vec mu_old = mu; //=================================================================== // Start MCMC //=================================================================== if (nprint>0) startMcmcTimer(); double n_accept = 0.0; for (int rep=0; rep(K*(K+1)/2); Sigma_new = r2Sigma(r_new, K); // convert share into mu_new out_cont = share2mu(Sigma_new, X, v, share, J, tol); mu_new = out_cont(span::all,0); choiceProb = out_cont(span::all,span(1,H)); // get eta_new eta_new = mu_new - X*theta_bar; // get eta_old eta_old = mu_old - X*theta_bar; if (IV==TRUE){ // get zeta zeta = Xend - Z*delta; // get ll_old zetaeta_old = join_rows(zeta, eta_old); rootiOmega = solve(trimatu(chol(Omega)), eye(2,2)); ll_old = 0; for (jt=0; jt(2), rootiOmega); } ll_old = ll_old + sumLogJaco_old; // get ll_new zetaeta_new = join_rows(zeta, eta_new); sumLogJaco_new = logJacob(choiceProb, J); ll_new = 0; for (jt=0; jt(2), rootiOmega); } ll_new = ll_new + sumLogJaco_new; }else{ // get ll_old ll_old = sum(log((1/sqrt(2*pi*tau_sq)) * exp(-(eta_old%eta_old)/(2*tau_sq)))) + sumLogJaco_old; // get ll_new sumLogJaco_new = logJacob(choiceProb, J); ll_new = sum(log((1/sqrt(2*pi*tau_sq)) * exp(-(eta_new%eta_new)/(2*tau_sq)))) + sumLogJaco_new; } // priors prior_new = sum(log((1/sqrt(2*pi*sigmasqR)) % exp(-(r_new%r_new)/(2*sigmasqR)))); prior_old = sum(log((1/sqrt(2*pi*sigmasqR)) % exp(-(r_old%r_old)/(2*sigmasqR)))); alpha = exp(ll_new + prior_new - ll_old - prior_old); if (alpha>1) {alpha = 1;} if (runif(1)[0]<=alpha) { r_old = r_new; Sigma_old = Sigma_new; mu_old = mu_new; sumLogJaco_old = sumLogJaco_new; n_accept = n_accept + 1; } //======================================================================== // STEP 2 // Draw theta_bar & tau^2 (or Omega & delta): Gibbs Sampler // mu = X*theta_bar + eta, eta~N(0,tau_sq) // (For IV case, see the comments in rivDraw above.) // Prior: // 1. theta_bar ~ N(theta_hat, A^-1) // 2. tau_sq ~ nu0*s0_sq/chisq(nu0) // Posterior: // 1. theta_bar | tau_sq ~ N(theta_tilde, (X^t X/tau_sq + A)^-1) // theta_tilde = (X^t X/tau_sq + A)^-1 * (tau_sq^-1*X^t mu + A*theta_hat) // 2. tau_sq | theta_bar ~ nu1*s1_sq/chisq(nu1) // nu1 = nu0 + n (n=J*T) // s1_sq = [nu0*s0_sq + (mu-X^t theta_bar)^t (mu-X^t theta_bar)]/[nu0 + n] //======================================================================== if (IV==TRUE){ ivout = rivDraw(mu_old, Xend, Z, Xexo, theta_hat, A, deltabar, Ad, VOmega, nu0, delta, Omega); delta = as(ivout["deltadraw"]); theta_bar = as(ivout["thetabardraw"]); Omega = as(ivout["Omegadraw"]); }else{ // compute the inverse of (trans(X)*X)/tau_sq + A ucholinv = solve(trimatu(chol((trans(X)*X)/tau_sq + A)), eye(K,K)); XXAinv = ucholinv*trans(ucholinv); theta_tilde = XXAinv * (trans(X)*mu_old/tau_sq + A*theta_hat); theta_bar = theta_tilde + ucholinv*vec(rnorm(K)); nu1 = nu0 + J*T; err = mu_old - X*theta_bar; s1_sq = (nu0*s0_sq + sum(err%err))/nu1; z = vec(rnorm(nu1)); tau_sq = nu1*s1_sq/sum(z%z); } // // print time to completion and draw # every nprint'th draw // if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); //======================================================================== // STEP 3 // Store Draws //======================================================================== if((rep+1)%keep==0){ mkeep = (rep+1)/keep; if (IV==TRUE){ Omega_all(span::all,mkeep-1) = vectorise(Omega); delta_all(span::all,mkeep-1) = delta; }else{ tau_sq_all[mkeep-1] = tau_sq; } theta_bar_all(span::all,mkeep-1) = theta_bar; r_all(span::all,mkeep-1) = r_old; Sigma_all(span::all,mkeep-1) = vectorise(r2Sigma(r_old, K)); ll_all[mkeep-1] = ll_old; } } acceptrate = n_accept/R; rtn["tausqdraw"] = tau_sq_all; rtn["Omegadraw"] = Omega_all; rtn["deltadraw"] = delta_all; rtn["thetabardraw"] = theta_bar_all; rtn["rdraw"] = r_all; rtn["Sigmadraw"] = Sigma_all; rtn["ll"] = ll_all; rtn["acceptrate"] = acceptrate; if (nprint>0) endMcmcTimer(); return (rtn); } bayesm/src/ghkvec_rcpp.cpp0000644000176000001440000001175613134410700015353 0ustar ripleyusers#include "bayesm.h" //SUPPORT FUNCTIONS SPECIFIC TO MAIN FUNCTION-------------------------------------------------------------------------------------- vec HaltonSeq(int pn, int r, int burnin, bool rand){ // Keunwoo Kim 10/28/2014 // Purpose: // create a random Halton sequence // Arguments: // pn: prime number // r: number of draws // burnin: number of initial burn // rand: if TRUE, add a random scalor to sequence // Output: // a vector of Halton sequence, size r int t; vec add; // start at 0 vec seq = zeros(r+burnin+1); // how many numbers I have drawn so far. // I have 1 draw (0) now. int index = 1; // if done==1, it is done. int done = 0; int factor = pn; do{ for (t=0; t(index)*(t+1)/factor; if ((t+2)*index-1>r+burnin){ seq(span((t+1)*index, r+burnin)) = add(span(0, r+burnin-(t+1)*index)); done = 1; }else{ seq(span((t+1)*index, (t+2)*index-1)) = add; if ((t+2)*index==r+burnin+1){ done = 1; } } } } factor = factor*pn; index = index*pn; }while (done==0); // exclude the first 0 and some initial draws seq = seq(span(burnin+1,burnin+r)); if (rand==TRUE){ // make it random seq = seq + runif(1)[0]; for (int i=0; i=1) seq[i] = seq[i]-1; } } return (seq); } bool IsPrime(int number){ // Keunwoo Kim 5/14/2015 // This function is to check whether a number is prime or not. // This is used for setting default prime numbers. for (int f=2; f(dim); double res = 0; // choose R::runif vs. Halton draws vec udraw(r*dim); mat udrawHalton(dim, r); if (HALTON){ for (j=0; j0){ pa = 0.0; pb = R::pnorm(tpz,0,1,1,0); }else{ pb = 1.0; pa = R::pnorm(tpz,0,1,1,0); } prod = prod * (pb-pa); u = udraw[i*dim+j]; arg = u*pb + (1.0-u)*pa; if (arg > .999999999) arg=.999999999; if (arg < .0000000001) arg=.0000000001; z[j] = R::qnorm(arg,0,1,1,0); } res = res + prod; } res = res / r; return (res); } //MAIN FUNCTION--------------------------------------------------------------------------------------- // [[Rcpp::export]] vec ghkvec(mat const& L, vec const& trunpt, vec const& above, int r, bool HALTON=true, vec pn=IntegerVector::create(0)){ // Keunwoo Kim 5/14/2015 // Purpose: // routine to call ghk_oneside for n different truncation points stacked in to the // vector trunpt -- puts n results in vector res // Arguments: // L: lower Cholesky root of cov. matrix // trunpt: vector of truncation points // above: truncation above(1) or below(0) // r: number of draws // HALTON: TRUE or FALSE. If FALSE, use R::runif random number generator. // pn: prime number used in Halton sequence. // burnin: number of initial burnin of draws. Only applied when HALTON is TRUE. (not used any more) // Output: // a vector of integration values int dim = above.size(); int n = trunpt.size()/dim; // // handling default arguments // // generate prime numbers if (HALTON==true && pn[0]==0){ Rcout << "Halton sequence is generated by the smallest prime numbers: \n"; Rcout << " "; pn = zeros(dim); int cand = 2; int which = 0; while (pn[dim-1]==0){ if (IsPrime(cand)){ pn[which] = cand; which = which + 1; Rprintf("%d ", cand); } cand = cand + 1; } Rcout << "\n"; } // burn-in //if (HALTON==true && burnin==NA_INTEGER){ // burnin = max(pn); // Rprintf("Initial %d (= max of prime numbers) draws are burned. \n", burnin); //} int burnin = 0; vec res(n); for (int i=0; i(dimd); if (nprint>0) startMcmcTimer(); mat xtd(2*n, dimd); vec zvec = vectorise(trans(z)); // start main iteration loop for (int rep=0; rep(out["IW"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> // print time to completion and draw # every nprint'th draw if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; deltadraw(mkeep-1, span::all) = trans(delta); betadraw[mkeep-1] = beta; gammadraw(mkeep-1, span::all) = trans(gamma); Sigmadraw(mkeep-1, span::all) = trans(vectorise(Sigma)); } } if (nprint>0) endMcmcTimer(); return List::create( Named("deltadraw") = deltadraw, Named("betadraw") = NumericVector(betadraw.begin(),betadraw.end()), Named("gammadraw") = gammadraw, Named("Sigmadraw") = Sigmadraw); } bayesm/src/rmultireg_rcpp.cpp0000644000176000001440000000427613134410700016115 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] List rmultireg(mat const& Y, mat const& X, mat const& Bbar, mat const& A, double nu, mat const& V) { // Keunwoo Kim 09/09/2014 // Purpose: draw from posterior for Multivariate Regression Model with natural conjugate prior // Arguments: // Y is n x m matrix // X is n x k // Bbar is the prior mean of regression coefficients (k x m) // A is prior precision matrix // nu, V are parameters for prior on Sigma // Output: list of B, Sigma draws of matrix of coefficients and Sigma matrix // Model: // Y=XB+U cov(u_i) = Sigma // B is k x m matrix of coefficients // Prior: // beta|Sigma ~ N(betabar,Sigma (x) A^-1) // betabar=vec(Bbar) // beta = vec(B) // Sigma ~ IW(nu,V) or Sigma^-1 ~ W(nu, V^-1) int n = Y.n_rows; int m = Y.n_cols; int k = X.n_cols; //first draw Sigma mat RA = chol(A); mat W = join_cols(X, RA); //analogous to rbind() in R mat Z = join_cols(Y, RA*Bbar); // note: Y,X,A,Bbar must be matrices! mat IR = solve(trimatu(chol(trans(W)*W)), eye(k,k)); //trimatu interprets the matrix as upper triangular and makes solve more efficient // W'W = R'R & (W'W)^-1 = IRIR' -- this is the UL decomp! mat Btilde = (IR*trans(IR)) * (trans(W)*Z); // IRIR'(W'Z) = (X'X+A)^-1(X'Y + ABbar) mat E = Z-W*Btilde; mat S = trans(E)*E; // E'E // compute the inverse of V+S mat ucholinv = solve(trimatu(chol(V+S)), eye(m,m)); mat VSinv = ucholinv*trans(ucholinv); List rwout = rwishart(nu+n, VSinv); // now draw B given Sigma // note beta ~ N(vec(Btilde),Sigma (x) Covxxa) // Cov=(X'X + A)^-1 = IR t(IR) // Sigma=CICI' // therefore, cov(beta)= Omega = CICI' (x) IR IR' = (CI (x) IR) (CI (x) IR)' // so to draw beta we do beta= vec(Btilde) +(CI (x) IR)vec(Z_mk) // Z_mk is m x k matrix of N(0,1) // since vec(ABC) = (C' (x) A)vec(B), we have // B = Btilde + IR Z_mk CI' mat CI = rwout["CI"]; //there is no need to use as(rwout["CI"]) since CI is being initiated as a mat in the same line mat draw = mat(rnorm(k*m)); draw.reshape(k,m); mat B = Btilde + IR*draw*trans(CI); return List::create( Named("B") = B, Named("Sigma") = rwout["IW"]); } bayesm/src/rsurGibbs_rcpp_loop.cpp0000644000176000001440000001031713134410700017067 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] List rsurGibbs_rcpp_loop(List const& regdata, vec const& indreg, vec const& cumnk, vec const& nk, mat const& XspXs, mat Sigmainv, mat const& A, vec const& Abetabar, double nu, mat const& V, int nvar, mat E, mat const& Y, int R, int keep, int nprint){ // Keunwoo Kim 09/19/2014 // Purpose: implement Gibbs Sampler for SUR // Arguments: // Data -- regdata // regdata is a list of lists of data for each regression // regdata[[i]] contains data for regression equation i // regdata[[i]]$y is y, regdata[[i]]$X is X // note: each regression can have differing numbers of X vars // but you must have same no of obs in each equation. // Prior -- list of prior hyperparameters // betabar,A prior mean, prior precision // nu, V prior on Sigma // Mcmc -- list of MCMC parms // R number of draws // keep -- thinning parameter // nprint - print estimated time remaining on every nprint'th draw // Output: list of betadraw,Sigmadraw // Model: // y_i = X_ibeta + e_i // y is nobs x 1 // X is nobs x k_i // beta is k_i x 1 vector of coefficients // i=1,nreg total regressions // (e_1,k,...,e_nreg,k) ~ N(0,Sigma) k=1,...,nobs // we can also write as stacked regression // y = Xbeta+e // y is nobs*nreg x 1,X is nobs*nreg x (sum(k_i)) // routine draws beta -- the stacked vector of all coefficients // Prior: // beta ~ N(betabar,A^-1) // Sigma ~ IW(nu,V) int reg, mkeep, i, j; vec beta, btilde, yti; mat IR, ucholinv, EEVinv, Sigma, Xtipyti, Ydti; List regdatai, rwout; int nreg = regdata.size(); // convert List to std::vector of struct std::vector regdata_vector; moments regdatai_struct; // store vector with struct for (reg=0; reg(regdatai["y"]); regdatai_struct.X = as(regdatai["X"]); regdata_vector.push_back(regdatai_struct); } int nobs = (regdatai_struct.y).size(); mat XtipXti = zeros(sum(nk), sum(nk)); mat Sigmadraw(R/keep, nreg*nreg); mat betadraw(R/keep, nvar); if (nprint>0) startMcmcTimer(); for (int rep=0; rep(rwout["IW"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> Sigmainv = as(rwout["W"]); //print time to completion and draw # every nprint'th draw if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; betadraw(mkeep-1, span::all) = trans(beta); Sigmadraw(mkeep-1, span::all) = trans(vectorise(Sigma)); } } if (nprint>0) endMcmcTimer(); return List::create( Named("betadraw") = betadraw, Named("Sigmadraw") = Sigmadraw); } bayesm/src/rnegbinRw_rcpp_loop.cpp0000644000176000001440000000650113134410700017062 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] List rnegbinRw_rcpp_loop(vec const& y, mat const& X, vec const& betabar, mat const& rootA, double a, double b, vec beta, double alpha, bool fixalpha, mat const& betaroot, double alphacroot, int R, int keep, int nprint){ // Keunwoo Kim 11/02/2014 // Arguments: // Data // X is nobs X nvar matrix // y is nobs vector // Prior - list containing the prior parameters // betabar, rootA - mean of beta prior, chol-root of inverse of variance covariance of beta prior // a, b - parameters of alpha prior // Mcmc - list containing // R is number of draws // keep is thinning parameter (def = 1) // nprint - print estimated time remaining on every nprint'th draw (def = 100) // betaroot - step size for beta RW // alphacroot - step size for alpha RW // beta - initial guesses for beta // alpha - initial guess for alpha // fixalpha - if TRUE, fix alpha and draw only beta // // Output: // // Model: // (y|lambda,alpha) ~ Negative Binomial(Mean = lambda, Overdispersion par = alpha) // ln(lambda) = X * beta // // Prior: // beta ~ N(betabar, A^-1) // alpha ~ Gamma(a,b) where mean = a/b and variance = a/(b^2) // vec betac; double ldiff, acc, unif, logalphac, oldlpostalpha, oldlpostbeta, clpostbeta, clpostalpha; int mkeep, rep; int nvar = X.n_cols; int nacceptbeta = 0; int nacceptalpha = 0; vec alphadraw(R/keep); mat betadraw(R/keep, nvar); if (nprint>0) startMcmcTimer(); //start main iteration loop for (rep=0; rep 1) acc = 1; if(acc < 1) {unif=runif(1)[0];} else {unif=0;} //runif returns a NumericVector, so using [0] allows for conversion to double by extracting the first element if (unif <= acc){ beta = betac; nacceptbeta = nacceptbeta + 1; } // Draw alpha if (!fixalpha){ logalphac = log(alpha) + alphacroot*rnorm(1)[0]; //rnorm returns a NumericVector, so using [0] allows for conversion to double oldlpostalpha = lpostalpha(alpha, beta, X, y, a, b); clpostalpha = lpostalpha(exp(logalphac), beta, X, y, a, b); ldiff = clpostalpha - oldlpostalpha; acc = exp(ldiff); if (acc > 1) acc = 1; if(acc < 1) {unif=runif(1)[0];} else {unif=0;} //runif returns a NumericVector, so using [0] allows for conversion to double by extracting the first element if (unif <= acc){ alpha = exp(logalphac); nacceptalpha = nacceptalpha + 1; } } if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; betadraw(mkeep-1, span::all) = trans(beta); alphadraw[mkeep-1] = alpha; } } if (nprint>0) endMcmcTimer(); return List::create( Named("betadraw") = betadraw, Named("alphadraw") = alphadraw, Named("nacceptbeta") = nacceptbeta, Named("nacceptalpha") = nacceptalpha); } bayesm/src/rscaleUsage_rcpp_loop.cpp0000644000176000001440000003060713134410700017367 0ustar ripleyusers#include "bayesm.h" #include //used for "sample" function //SUPPORT FUNCTIONS SPECIFIC TO MAIN FUNCTION-------------------------------------------------------------------------------------- double ghk(mat const& L, vec const& a, vec const& b, int const& n, int const& dim){ // Wayne Taylor 4/29/15 //routine to implement ghk with a region : a[i-1] <= x_i <= b[i-1] //r mcculloch 8/04 //L is lower triangular root of Sigma random vector is assumed to have zero mean //n is number of draws to use in GHK //dim is the dimension of L //modified 6/05 by rossi to check arg into qnorm //converted to rcpp 5/15 int i,j; NumericVector aa(1),bb(1),pa(1),pb(1),arg(1); double u,prod,mu; vec z(dim); double res=0.0; for(i=0;i0) mu = as_scalar(L(j,span(0,j-1))*z(span(0,j-1))); //previously done via a loop for(k=0;k .999999999) arg[0]=.999999999; if(arg[0] < .0000000001) arg[0]=.0000000001; z[j] = qnorm(arg,0.0,1.0)[0]; } res += prod; } res /= n; return (res); } mat dy(mat y, mat const& x, vec const& c, vec const& mu, mat const& beta, vec const& s, vec const& tau, vec const& sigma){ // Wayne Taylor 4/29/15 //Variable declaration double sigman,taun; rowvec yn; vec xn; int p = y.n_cols; int nobs = y.n_rows; //cm = conditional mean, cs = condtional standard deviation //u =uniform for truncated normal draw double cm,cs,u; // standardized truncation points (a,b) // cdf at truncation points (pa,pb) NumericVector a(1),b(1),pa(1),pb(1); double qout; //loop over coordinates of y - first by rows, then by columns for(int n = 0; n(n); double offset = p*log((double)k); vec a,b; double ghkres,lghkres; uvec xia(p),xib(p); mat Li; for(int i = 0; i(cbetarows,p); uvec ui(1),ind(p-1); int counter; uvec cbetaAllRow(cbetarows); for(int i = 0; i(2,2); S(0,0) = (n-1)*moms[2] + n*pow(moms[0],2.0); S(0,1) = (n-1)*moms[3] + n*moms[0]*(moms[1]-Lam(1,1)); S(1,0) = S(0,1); S(1,1) = (n-1)*moms[4] + n*pow(moms[1]-Lam(1,1),2.0); return(S); } double llL(mat const& Lam, int n, mat const& S, mat const& V,double nu){ //Wayne Taylor 4/29/15 int d = Lam.n_cols; double dlam = Lam(0,0)*Lam(1,1)-pow(Lam(0,1),2.0); mat M = (S+V) * solve(Lam,eye(d,d)); double ll = -.5*(n+nu+3)*log(dlam) -.5*sum(M.diag()); return(ll); } //MAIN FUNCTION------------------------------------------------------------------------------------ //[[Rcpp::export]] List rscaleUsage_rcpp_loop(int k, mat const& x, int p, int n, int R, int keep, int ndghk, int nprint, mat y, vec mu, mat Sigma, vec tau, vec sigma, mat Lambda, double e, bool domu, bool doSigma, bool dosigma, bool dotau, bool doLambda, bool doe, double nu, mat const& V, mat const& mubar, mat const& Am, vec const& gsigma, vec const& gl11,vec const& gl22, vec const& gl12, int nuL, mat const& VL, vec const& ge){ // R.McCulloch, 12/04 code for scale usage R function (rScaleUsage) // changed to R error function, P. Rossi 05/12 // converted to rcpp W. Taylor 04/15 //variable declaration int mkeep, ng, ei, pi; double eprop, eold; double Ai, A, xtx, beta, s2, m, a, b, s, qr, llold, llprop, lrat, paccept; vec cc, xty, ete, pv, h, moms(5),rgl11, rgl12a, rgl12, rgl22, absege, minvec; uvec eiu; mat Res, S, yd, Si, Vmi, Rm, Ri, Vm, mm, onev, xx, ytemp, yy, eps, dat, temp, SS; List bs, rwout; rowvec onesp = ones(p); int nk = R/keep; int ndpost = nk*keep; mat drSigma = zeros(nk,pow(p,2.0)); mat drmu = zeros(nk,p); mat drtau = zeros(nk,n); mat drsigma = zeros(nk,n); mat drLambda = zeros(nk,4); vec dre = zeros(nk); if(nprint>0) startMcmcTimer(); for(int rep = 0; rep < ndpost; rep++) { cc = cgetC(e,k); bs = condd(Sigma); y = dy(y,x,cc,mu,as(bs["beta"]),as(bs["s"]),tau,sigma); //draw Sigma if(doSigma) { Res = y; Res.each_row() -= trans(mu); Res.each_col() -= tau; Res.each_col() /= sigma; S = trans(Res)*Res; rwout = rwishart(nu+n,solve(V+S,eye(p,p))); Sigma = as(rwout["IW"]); } //draw mu if(domu) { yd = y; yd.each_col() -= tau; Si = solve(Sigma,eye(p,p)); Vmi = as_scalar(sum(1/pow(sigma,2.0)))*Si + Am; Rm = chol(Vmi); Ri = solve(trimatu(Rm),eye(p,p)); Vm = solve(Vmi,eye(p,p)); mm = Vm * (Si * (trans(yd) * (1/pow(sigma,2.0))) + Am * mubar); mu = vectorise(mm + Ri * as(rnorm(p))); } //draw tau if(dotau) { Ai = Lambda(0,0) - pow(Lambda(0,1),2.0)/Lambda(1,1); A = 1.0/Ai; onev = ones(p,1); Rm = chol(Sigma); xx = trans(solve(trans(Rm),onev)); ytemp = trans(y); ytemp.each_col() -= mu; yy = trans(solve(trans(Rm),ytemp)); xtx = accu(pow(xx,2.0)); //To get a sum of all the elements regardless of the argument type (ie. matrix or vector), use accu() xty = vectorise(xx*trans(yy)); beta = A*Lambda(0,1)/Lambda(1,1); for(int j = 0; j(p); ete = vectorise(onesp * pow(eps,2.0)); a = Lambda(1,1); b = Lambda(0,1)/Lambda(0,0); s = sqrt(Lambda(1,1)-pow(Lambda(0,1),2.0)/Lambda(0,0)); for(int j = 0; j pow(Lambda(0,1),2.0)/Lambda(1,1))); ng = rgl11.size(); pv = zeros(ng); for(int j = 0; j -sqrt(Lambda(0,0)*Lambda(1,1)))); ng = rgl12.size(); pv = zeros(ng); for(int j = 0; j pow(Lambda(0,1),2.0)/Lambda(0,0))); ng = rgl22.size(); pv = zeros(ng); for(int j = 0;j0) { e = eprop; } else { minvec << 1 << exp(lrat); paccept = min(minvec); if(rbinom(1,1,paccept)[0]==1){ e = eprop; } else { e = eold; } } } if (nprint>0) if((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; drSigma(mkeep-1,span::all) = trans(vectorise(Sigma)); drmu(mkeep-1,span::all) = trans(mu); drtau(mkeep-1,span::all) = trans(tau); drsigma(mkeep-1,span::all) = trans(sigma); drLambda(mkeep-1,span::all) = trans(vectorise(Lambda)); dre[mkeep-1] = e; } } if (nprint>0) endMcmcTimer(); return List::create( Named("ndpost") = ndpost, Named("drmu") = drmu, Named("drtau") = drtau, Named("drsigma") = drsigma, Named("drLambda") = drLambda, Named("dre") = dre, Named("drSigma") = drSigma); } bayesm/src/trunNorm.cpp0000644000176000001440000000566013134410700014701 0ustar ripleyusers// [[Rcpp::depends("RcppArmadillo")]] #include #include #include #include #include "bayesm.h" using namespace Rcpp; using namespace arma; double dexpr(double const& a) { // rossi 10/2016 // routine to draw use rejection sampling to draw from a truncated normal // truncated to the tail. Use exponential distribution as the acceptance function // a is the rejection point (usually, (a-mu)/sigma)) double x,e,e1; int success; success=0; while(success == 0){ e = -log(runif(1)[0]); e1 = -log(runif(1)[0]); if(pow(e,2) <= 2.0*e1*pow(a,2)){ x = a + e/a; success = 1; } } return(x); } double invCdfNorm(double const& a) { // rossi 10/2016 // draw from truncated normal truncated from below by a using inverse CDF method double Phia,unif,arg,z; Phia = R::pnorm(a,0.0,1.0,1,0); unif = runif(1)[0]; arg = unif*(1.0-Phia)+Phia; z = R::qnorm(arg,0.0,1.0,1,0); return(z); } double dnr(double const& a) { // rossi 10/2016 // draw from standard normal truncated below by a using rejection sampling double candz,z; int success; success=0; while(success == 0){ candz=rnorm(1)[0]; if(candz >= a){ z=candz; success =1; } } return(z); } double trunNormBelow(double const& a){ // rossi 10/2016 // draw from standard normal truncated below by a // we divide into three regions (regions given for trun below) // let a = (trunpt-mu)/sig // 1: a > 4, use exponential rejection sampling // 2: -4 < a <=4, use inverse CDF // 3: a <= -4, normal rejection sampling double z; if (a > 4){ // do tail sampling using exponential rejection z=dexpr(a); } else { if (a <= -4){ // normal rejection sampling z=dnr(a); } // -4 < a <=4 z=invCdfNorm(a); } return(z); } double trunNorm(double mu,double sig, double trunpt, int above){ // rossi 10/2016 // function to draw from N(mu,sig) truncated by trunpt // if above=0, truncate from below at trunpt // if above=1, truncate from above at trunpt double a,z,draw; if(above == 0){ a=(trunpt-mu)/sig; z= trunNormBelow(a); draw = sig*z + mu; } else { a=(mu-trunpt)/sig; z= trunNormBelow(a); draw = -sig*z + mu; } return(draw); } // vec trunNorm_multi(double mu, double sig, double trunpt, int above, int nd){ // // Dan Yavorsky 10/16 // // Just loops over trunNorm, multiple draws from single truncated distribution // vec rtn_vec(nd); // for (int i = 0; i0) startMcmcTimer(); for (int rep=0; rep0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; betadraw(mkeep-1, span::all) = trans(beta); sigmasqdraw[mkeep-1] = sigmasq; } } if (nprint>0) endMcmcTimer(); return List::create( Named("betadraw") = betadraw, Named("sigmasqdraw") = NumericVector(sigmasqdraw.begin(),sigmasqdraw.end())); } bayesm/src/clusterMix_rcpp_loop.cpp0000644000176000001440000001014213134410700017260 0ustar ripleyusers#include "bayesm.h" //EXTRA FUNCTIONS SPECIFIC TO THE MAIN FUNCTION-------------------------------------------- mat ztoSim(vec const& z){ // function to convert indicator vector to Similarity matrix // Sim is n x n matrix, Sim[i,j]=1 if pair(i,j) are in same group // z is n x 1 vector of indicators (1,...,p) int n = z.size(); vec onevec = ones(n); // equivalent to zvec=c(rep(z,n)) in R vec zvec = kron(onevec, z); vec zcomp = kron(z, onevec);// equivalent to as.numeric((zvec==zcomp)) in R mat Sim = zeros(n*n,1); for (int i=0; i(n); int groupn = 1; for (i=0; i0){ groupn = groupn + 1; } } return (z); } //MAIN FUNCTION--------------------------------------------------------------------------------------- // [[Rcpp::export]] List clusterMix_rcpp_loop(mat const& zdraw, double cutoff, bool SILENT, int nprint){ // Keunwoo Kim 10/06/2014 // Purpose: // cluster observations based on draws of indicators of // normal mixture components // Arguments: // zdraw is a R x nobs matrix of draws of indicators (typically output from rnmixGibbs) // the rth row of zdraw contains rth draw of indicators for each observations // each element of zdraw takes on up to p values for up to p groups. The maximum // number of groups is nobs. Typically, however, the number of groups will be small // and equal to the number of components used in the normal mixture fit. // cutoff is a cutoff used in determining one clustering scheme it must be // a number between .5 and 1. // nprint - print every nprint'th draw // Output: // two clustering schemes each with a vector of length nobs which gives the assignment // of each observation to a cluster // clustera (finds zdraw with similarity matrix closest to posterior mean of similarity) // clusterb (finds clustering scheme by assigning ones if posterior mean of similarity matrix cutoff and computing associated z ) int rep, i; uword index; // type uword means unsigned integer. Necessary for finding the index of min. int nobs = zdraw.n_cols; char buf[32]; // compute posterior mean of Similarity matrix if (!SILENT){ Rcout << "Computing Posterior Expectation of Similarity Matrix\n"; Rcout << "processing draws ...\n"; } mat Pmean = zeros(nobs, nobs); int R = zdraw.n_rows; for (rep=0; rep(R); for (rep=0; rep cutoff vec Pmeanvec = vectorise(Pmean); mat Sim = zeros(nobs*nobs,1); for (i=0; i=cutoff) Sim(i,0) = 1; } Sim.reshape(nobs,nobs); vec clusterb = Simtoz(Sim); return List::create( Named("clustera") = clustera, Named("clusterb") = clusterb); } bayesm/src/rtrun_rcpp.cpp0000644000176000001440000000102413134410700015241 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] NumericVector rtrun(NumericVector const& mu, NumericVector const& sigma, NumericVector const& a, NumericVector const& b){ // Wayne Taylor 9/7/2014 // function to draw from univariate truncated norm // a is vector of lower bounds for truncation // b is vector of upper bounds for truncation NumericVector FA = pnorm((a-mu)/sigma); NumericVector FB = pnorm((b-mu)/sigma); return(mu+sigma*qnorm(runif(mu.size())*(FB-FA)+FA)); } bayesm/src/Makevars.win0000644000176000001440000000012213134410700014625 0ustar ripleyusersPKG_LIBS = $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) PKG_CPPFLAGS = -I../inst/include/ bayesm/src/rhierNegbinRw_rcpp_loop.cpp0000644000176000001440000001514713134410700017700 0ustar ripleyusers#include "bayesm.h" //EXTRA FUNCTIONS SPECIFIC TO THE MAIN FUNCTION-------------------------------------------- double llnegbinpooled(std::vector regdata_vector, mat Beta, double alpha){ // Wayne Taylor 12/01/2014 // "Unlists" the regdata and calculates the negative binomial loglikelihood using individual-level betas int nreg = regdata_vector.size(); double ll = 0.0; for(int reg = 0; reg(nreg); vec clpostbeta = zeros(nreg); cube Betadraw = zeros(nreg, nvar, R/keep); vec alphadraw = zeros(R/keep); vec llike = zeros(R/keep); mat Vbetadraw = zeros(R/keep,nvar*nvar); mat Deltadraw = zeros(R/keep,nvar*nz); // convert regdata and hessdata Lists to std::vector of struct std::vector regdata_vector; moments regdatai_struct; List regdatai,hessi; // store vector with struct for (int reg = 0; reg(regdatai["y"]); regdatai_struct.X = as(regdatai["X"]); regdatai_struct.hess = as(hessi["hess"]); regdata_vector.push_back(regdatai_struct); } if (nprint>0) startMcmcTimer(); // start main iteration loop for (rep = 0; rep < R; rep++){ mat betabar = Z*Delta; // Draw betai for(int reg = 0; reg 1) acc = 1; if(acc < 1) {unif=runif(1)[0];} else {unif=0;} //runif returns a NumericVector, so using [0] allows for conversion to double by extracting the first element if (unif <= acc){ Beta(reg,span::all) = trans(betac); nacceptbeta = nacceptbeta + 1; } } // Draw alpha if (!fixalpha){ logalphac = log(alpha) + alphacroot*rnorm(1)[0]; //rnorm returns a NumericVector, so using [0] allows for conversion to double oldlpostalpha = llnegbinpooled(regdata_vector,Beta,alpha)+(a-1)*log(alpha) - b*alpha; clpostalpha = llnegbinpooled(regdata_vector,Beta,exp(logalphac))+(a-1)*logalphac - b*exp(logalphac); ldiff = clpostalpha - oldlpostalpha; acc = exp(ldiff); if (acc > 1) acc = 1; if(acc < 1) {unif=runif(1)[0];} else {unif=0;} //runif returns a NumericVector, so using [0] allows for conversion to double by extracting the first element if (unif <= acc){ alpha = exp(logalphac); nacceptalpha = nacceptalpha + 1; } } // Draw Vbeta and Delta using rmultireg List temp = rmultireg(Beta,Z,Deltabar,Adelta,nu,V); mat Vbeta = as(temp["Sigma"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> Vbetainv = solve(Vbeta,eye(nvar,nvar)); rootA = chol(Vbetainv); Delta = as(temp["B"]); if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; Betadraw.slice(mkeep-1) = Beta; alphadraw[mkeep-1] = alpha; Vbetadraw(mkeep-1,span::all) = trans(vectorise(Vbeta)); Deltadraw(mkeep-1,span::all) = trans(vectorise(Delta)); llike[mkeep-1] = llnegbinpooled(regdata_vector,Beta,alpha); } } if (nprint>0) endMcmcTimer(); return List::create( Named("llike") = llike, Named("Betadraw") = Betadraw, Named("alphadraw") = alphadraw, Named("Vbetadraw") = Vbetadraw, Named("Deltadraw") = Deltadraw, Named("acceptrbeta") = nacceptbeta/(R*nreg*1.0)*100, Named("acceptralpha") = nacceptalpha/(R*1.0)*100); } bayesm/src/rmixture_rcpp.cpp0000644000176000001440000000373413134410700015760 0ustar ripleyusers#include "bayesm.h" //FUNCTION SPECIFIC TO MAIN FUNCTION-------------------------------- vec rcomp(List comp) { // Wayne Taylor 9/10/14 //purpose: draw multivariate normal with mean and variance given by comp // arguments: // comp is a list of length 2, // comp[[1]] is the mean and comp[[2]] is R^{-1} = comp[[2]], Sigma = t(R)%*%R vec mu = comp[0]; mat rooti = comp[1]; int dim = rooti.n_cols; mat root = solve(trimatu(rooti),eye(dim,dim)); //trimatu interprets the matrix as upper triangular and makes solve more efficient return(vectorise(mu+trans(root)*as(rnorm(mu.size())))); } //[[Rcpp::export]] List rmixture(int n, vec pvec, List comps) { // Wayne Taylor 9/10/2014 // revision history: // commented by rossi 3/05 // // purpose: iid draws from mixture of multivariate normals // arguments: // n: number of draws // pvec: prior probabilities of normal components // comps: list, each member is a list comp with ith normal component // ~N(comp[[1]],Sigma), Sigma = t(R)%*%R, R^{-1} = comp[[2]] // output: // list of x (n by length(comp[[1]]) matrix of draws) and z latent indicators of // component //Draw vector of indices using base R 'sample' function mat prob(n,pvec.size()); for(int i = 0; i(runif(n)) % prob.col(pvec.size()-1); // Evaluative each column of "prob" until the uniform draw is less than the cumulative value vec z = zeros(n); for(int i = 0; i prob(i, z[i]++)); List comp0 = comps[0]; vec mu0 = comp0[0]; mat x(n,mu0.size()); //Draw from MVN from comp determined from z index //Note z starts at 1, not 0 for(int i = 0; i do not edit by hand // Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 #include "../inst/include/bayesm.h" #include #include using namespace Rcpp; // bayesBLP_rcpp_loop List bayesBLP_rcpp_loop(bool IV, mat const& X, mat const& Z, vec const& share, int J, int T, mat const& v, int R, vec const& sigmasqR, mat const& A, vec const& theta_hat, vec const& deltabar, mat const& Ad, double nu0, double s0_sq, mat const& VOmega, double ssq, mat const& cand_cov, vec const& theta_bar_initial, vec const& r_initial, double tau_sq_initial, mat const& Omega_initial, vec const& delta_initial, double tol, int keep, int nprint); RcppExport SEXP bayesm_bayesBLP_rcpp_loop(SEXP IVSEXP, SEXP XSEXP, SEXP ZSEXP, SEXP shareSEXP, SEXP JSEXP, SEXP TSEXP, SEXP vSEXP, SEXP RSEXP, SEXP sigmasqRSEXP, SEXP ASEXP, SEXP theta_hatSEXP, SEXP deltabarSEXP, SEXP AdSEXP, SEXP nu0SEXP, SEXP s0_sqSEXP, SEXP VOmegaSEXP, SEXP ssqSEXP, SEXP cand_covSEXP, SEXP theta_bar_initialSEXP, SEXP r_initialSEXP, SEXP tau_sq_initialSEXP, SEXP Omega_initialSEXP, SEXP delta_initialSEXP, SEXP tolSEXP, SEXP keepSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< bool >::type IV(IVSEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< mat const& >::type Z(ZSEXP); Rcpp::traits::input_parameter< vec const& >::type share(shareSEXP); Rcpp::traits::input_parameter< int >::type J(JSEXP); Rcpp::traits::input_parameter< int >::type T(TSEXP); Rcpp::traits::input_parameter< mat const& >::type v(vSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< vec const& >::type sigmasqR(sigmasqRSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); Rcpp::traits::input_parameter< vec const& >::type theta_hat(theta_hatSEXP); Rcpp::traits::input_parameter< vec const& >::type deltabar(deltabarSEXP); Rcpp::traits::input_parameter< mat const& >::type Ad(AdSEXP); Rcpp::traits::input_parameter< double >::type nu0(nu0SEXP); Rcpp::traits::input_parameter< double >::type s0_sq(s0_sqSEXP); Rcpp::traits::input_parameter< mat const& >::type VOmega(VOmegaSEXP); Rcpp::traits::input_parameter< double >::type ssq(ssqSEXP); Rcpp::traits::input_parameter< mat const& >::type cand_cov(cand_covSEXP); Rcpp::traits::input_parameter< vec const& >::type theta_bar_initial(theta_bar_initialSEXP); Rcpp::traits::input_parameter< vec const& >::type r_initial(r_initialSEXP); Rcpp::traits::input_parameter< double >::type tau_sq_initial(tau_sq_initialSEXP); Rcpp::traits::input_parameter< mat const& >::type Omega_initial(Omega_initialSEXP); Rcpp::traits::input_parameter< vec const& >::type delta_initial(delta_initialSEXP); Rcpp::traits::input_parameter< double >::type tol(tolSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); rcpp_result_gen = Rcpp::wrap(bayesBLP_rcpp_loop(IV, X, Z, share, J, T, v, R, sigmasqR, A, theta_hat, deltabar, Ad, nu0, s0_sq, VOmega, ssq, cand_cov, theta_bar_initial, r_initial, tau_sq_initial, Omega_initial, delta_initial, tol, keep, nprint)); return rcpp_result_gen; END_RCPP } // breg vec breg(vec const& y, mat const& X, vec const& betabar, mat const& A); RcppExport SEXP bayesm_breg(SEXP ySEXP, SEXP XSEXP, SEXP betabarSEXP, SEXP ASEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< vec const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< vec const& >::type betabar(betabarSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); rcpp_result_gen = Rcpp::wrap(breg(y, X, betabar, A)); return rcpp_result_gen; END_RCPP } // cgetC vec cgetC(double e, int k); RcppExport SEXP bayesm_cgetC(SEXP eSEXP, SEXP kSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type e(eSEXP); Rcpp::traits::input_parameter< int >::type k(kSEXP); rcpp_result_gen = Rcpp::wrap(cgetC(e, k)); return rcpp_result_gen; END_RCPP } // clusterMix_rcpp_loop List clusterMix_rcpp_loop(mat const& zdraw, double cutoff, bool SILENT, int nprint); RcppExport SEXP bayesm_clusterMix_rcpp_loop(SEXP zdrawSEXP, SEXP cutoffSEXP, SEXP SILENTSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< mat const& >::type zdraw(zdrawSEXP); Rcpp::traits::input_parameter< double >::type cutoff(cutoffSEXP); Rcpp::traits::input_parameter< bool >::type SILENT(SILENTSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); rcpp_result_gen = Rcpp::wrap(clusterMix_rcpp_loop(zdraw, cutoff, SILENT, nprint)); return rcpp_result_gen; END_RCPP } // ghkvec vec ghkvec(mat const& L, vec const& trunpt, vec const& above, int r, bool HALTON, vec pn); RcppExport SEXP bayesm_ghkvec(SEXP LSEXP, SEXP trunptSEXP, SEXP aboveSEXP, SEXP rSEXP, SEXP HALTONSEXP, SEXP pnSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< mat const& >::type L(LSEXP); Rcpp::traits::input_parameter< vec const& >::type trunpt(trunptSEXP); Rcpp::traits::input_parameter< vec const& >::type above(aboveSEXP); Rcpp::traits::input_parameter< int >::type r(rSEXP); Rcpp::traits::input_parameter< bool >::type HALTON(HALTONSEXP); Rcpp::traits::input_parameter< vec >::type pn(pnSEXP); rcpp_result_gen = Rcpp::wrap(ghkvec(L, trunpt, above, r, HALTON, pn)); return rcpp_result_gen; END_RCPP } // llmnl double llmnl(vec const& beta, vec const& y, mat const& X); RcppExport SEXP bayesm_llmnl(SEXP betaSEXP, SEXP ySEXP, SEXP XSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< vec const& >::type beta(betaSEXP); Rcpp::traits::input_parameter< vec const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); rcpp_result_gen = Rcpp::wrap(llmnl(beta, y, X)); return rcpp_result_gen; END_RCPP } // lndIChisq mat lndIChisq(double nu, double ssq, mat const& X); RcppExport SEXP bayesm_lndIChisq(SEXP nuSEXP, SEXP ssqSEXP, SEXP XSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< double >::type ssq(ssqSEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); rcpp_result_gen = Rcpp::wrap(lndIChisq(nu, ssq, X)); return rcpp_result_gen; END_RCPP } // lndIWishart double lndIWishart(double nu, mat const& V, mat const& IW); RcppExport SEXP bayesm_lndIWishart(SEXP nuSEXP, SEXP VSEXP, SEXP IWSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< mat const& >::type IW(IWSEXP); rcpp_result_gen = Rcpp::wrap(lndIWishart(nu, V, IW)); return rcpp_result_gen; END_RCPP } // lndMvn double lndMvn(vec const& x, vec const& mu, mat const& rooti); RcppExport SEXP bayesm_lndMvn(SEXP xSEXP, SEXP muSEXP, SEXP rootiSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< vec const& >::type x(xSEXP); Rcpp::traits::input_parameter< vec const& >::type mu(muSEXP); Rcpp::traits::input_parameter< mat const& >::type rooti(rootiSEXP); rcpp_result_gen = Rcpp::wrap(lndMvn(x, mu, rooti)); return rcpp_result_gen; END_RCPP } // lndMvst double lndMvst(vec const& x, double nu, vec const& mu, mat const& rooti, bool NORMC); RcppExport SEXP bayesm_lndMvst(SEXP xSEXP, SEXP nuSEXP, SEXP muSEXP, SEXP rootiSEXP, SEXP NORMCSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< vec const& >::type x(xSEXP); Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< vec const& >::type mu(muSEXP); Rcpp::traits::input_parameter< mat const& >::type rooti(rootiSEXP); Rcpp::traits::input_parameter< bool >::type NORMC(NORMCSEXP); rcpp_result_gen = Rcpp::wrap(lndMvst(x, nu, mu, rooti, NORMC)); return rcpp_result_gen; END_RCPP } // rbprobitGibbs_rcpp_loop List rbprobitGibbs_rcpp_loop(vec const& y, mat const& X, vec const& Abetabar, mat const& root, vec beta, vec const& sigma, vec const& trunpt, vec const& above, int R, int keep, int nprint); RcppExport SEXP bayesm_rbprobitGibbs_rcpp_loop(SEXP ySEXP, SEXP XSEXP, SEXP AbetabarSEXP, SEXP rootSEXP, SEXP betaSEXP, SEXP sigmaSEXP, SEXP trunptSEXP, SEXP aboveSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< vec const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< vec const& >::type Abetabar(AbetabarSEXP); Rcpp::traits::input_parameter< mat const& >::type root(rootSEXP); Rcpp::traits::input_parameter< vec >::type beta(betaSEXP); Rcpp::traits::input_parameter< vec const& >::type sigma(sigmaSEXP); Rcpp::traits::input_parameter< vec const& >::type trunpt(trunptSEXP); Rcpp::traits::input_parameter< vec const& >::type above(aboveSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); rcpp_result_gen = Rcpp::wrap(rbprobitGibbs_rcpp_loop(y, X, Abetabar, root, beta, sigma, trunpt, above, R, keep, nprint)); return rcpp_result_gen; END_RCPP } // rdirichlet vec rdirichlet(vec const& alpha); RcppExport SEXP bayesm_rdirichlet(SEXP alphaSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< vec const& >::type alpha(alphaSEXP); rcpp_result_gen = Rcpp::wrap(rdirichlet(alpha)); return rcpp_result_gen; END_RCPP } // rDPGibbs_rcpp_loop List rDPGibbs_rcpp_loop(int R, int keep, int nprint, mat y, List const& lambda_hyper, bool SCALE, int maxuniq, List const& PrioralphaList, int gridsize, double BayesmConstantA, int BayesmConstantnuInc, double BayesmConstantDPalpha); RcppExport SEXP bayesm_rDPGibbs_rcpp_loop(SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP, SEXP ySEXP, SEXP lambda_hyperSEXP, SEXP SCALESEXP, SEXP maxuniqSEXP, SEXP PrioralphaListSEXP, SEXP gridsizeSEXP, SEXP BayesmConstantASEXP, SEXP BayesmConstantnuIncSEXP, SEXP BayesmConstantDPalphaSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); Rcpp::traits::input_parameter< mat >::type y(ySEXP); Rcpp::traits::input_parameter< List const& >::type lambda_hyper(lambda_hyperSEXP); Rcpp::traits::input_parameter< bool >::type SCALE(SCALESEXP); Rcpp::traits::input_parameter< int >::type maxuniq(maxuniqSEXP); Rcpp::traits::input_parameter< List const& >::type PrioralphaList(PrioralphaListSEXP); Rcpp::traits::input_parameter< int >::type gridsize(gridsizeSEXP); Rcpp::traits::input_parameter< double >::type BayesmConstantA(BayesmConstantASEXP); Rcpp::traits::input_parameter< int >::type BayesmConstantnuInc(BayesmConstantnuIncSEXP); Rcpp::traits::input_parameter< double >::type BayesmConstantDPalpha(BayesmConstantDPalphaSEXP); rcpp_result_gen = Rcpp::wrap(rDPGibbs_rcpp_loop(R, keep, nprint, y, lambda_hyper, SCALE, maxuniq, PrioralphaList, gridsize, BayesmConstantA, BayesmConstantnuInc, BayesmConstantDPalpha)); return rcpp_result_gen; END_RCPP } // rhierLinearMixture_rcpp_loop List rhierLinearMixture_rcpp_loop(List const& regdata, mat const& Z, vec const& deltabar, mat const& Ad, mat const& mubar, mat const& Amu, double nu, mat const& V, double nu_e, vec const& ssq, int R, int keep, int nprint, bool drawdelta, mat olddelta, vec const& a, vec oldprob, vec ind, vec tau); RcppExport SEXP bayesm_rhierLinearMixture_rcpp_loop(SEXP regdataSEXP, SEXP ZSEXP, SEXP deltabarSEXP, SEXP AdSEXP, SEXP mubarSEXP, SEXP AmuSEXP, SEXP nuSEXP, SEXP VSEXP, SEXP nu_eSEXP, SEXP ssqSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP, SEXP drawdeltaSEXP, SEXP olddeltaSEXP, SEXP aSEXP, SEXP oldprobSEXP, SEXP indSEXP, SEXP tauSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< List const& >::type regdata(regdataSEXP); Rcpp::traits::input_parameter< mat const& >::type Z(ZSEXP); Rcpp::traits::input_parameter< vec const& >::type deltabar(deltabarSEXP); Rcpp::traits::input_parameter< mat const& >::type Ad(AdSEXP); Rcpp::traits::input_parameter< mat const& >::type mubar(mubarSEXP); Rcpp::traits::input_parameter< mat const& >::type Amu(AmuSEXP); Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< double >::type nu_e(nu_eSEXP); Rcpp::traits::input_parameter< vec const& >::type ssq(ssqSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); Rcpp::traits::input_parameter< bool >::type drawdelta(drawdeltaSEXP); Rcpp::traits::input_parameter< mat >::type olddelta(olddeltaSEXP); Rcpp::traits::input_parameter< vec const& >::type a(aSEXP); Rcpp::traits::input_parameter< vec >::type oldprob(oldprobSEXP); Rcpp::traits::input_parameter< vec >::type ind(indSEXP); Rcpp::traits::input_parameter< vec >::type tau(tauSEXP); rcpp_result_gen = Rcpp::wrap(rhierLinearMixture_rcpp_loop(regdata, Z, deltabar, Ad, mubar, Amu, nu, V, nu_e, ssq, R, keep, nprint, drawdelta, olddelta, a, oldprob, ind, tau)); return rcpp_result_gen; END_RCPP } // rhierLinearModel_rcpp_loop List rhierLinearModel_rcpp_loop(List const& regdata, mat const& Z, mat const& Deltabar, mat const& A, double nu, mat const& V, double nu_e, vec const& ssq, vec tau, mat Delta, mat Vbeta, int R, int keep, int nprint); RcppExport SEXP bayesm_rhierLinearModel_rcpp_loop(SEXP regdataSEXP, SEXP ZSEXP, SEXP DeltabarSEXP, SEXP ASEXP, SEXP nuSEXP, SEXP VSEXP, SEXP nu_eSEXP, SEXP ssqSEXP, SEXP tauSEXP, SEXP DeltaSEXP, SEXP VbetaSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< List const& >::type regdata(regdataSEXP); Rcpp::traits::input_parameter< mat const& >::type Z(ZSEXP); Rcpp::traits::input_parameter< mat const& >::type Deltabar(DeltabarSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< double >::type nu_e(nu_eSEXP); Rcpp::traits::input_parameter< vec const& >::type ssq(ssqSEXP); Rcpp::traits::input_parameter< vec >::type tau(tauSEXP); Rcpp::traits::input_parameter< mat >::type Delta(DeltaSEXP); Rcpp::traits::input_parameter< mat >::type Vbeta(VbetaSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); rcpp_result_gen = Rcpp::wrap(rhierLinearModel_rcpp_loop(regdata, Z, Deltabar, A, nu, V, nu_e, ssq, tau, Delta, Vbeta, R, keep, nprint)); return rcpp_result_gen; END_RCPP } // rhierMnlDP_rcpp_loop List rhierMnlDP_rcpp_loop(int R, int keep, int nprint, List const& lgtdata, mat const& Z, vec const& deltabar, mat const& Ad, List const& PrioralphaList, List const& lambda_hyper, bool drawdelta, int nvar, mat oldbetas, double s, int maxuniq, int gridsize, double BayesmConstantA, int BayesmConstantnuInc, double BayesmConstantDPalpha); RcppExport SEXP bayesm_rhierMnlDP_rcpp_loop(SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP, SEXP lgtdataSEXP, SEXP ZSEXP, SEXP deltabarSEXP, SEXP AdSEXP, SEXP PrioralphaListSEXP, SEXP lambda_hyperSEXP, SEXP drawdeltaSEXP, SEXP nvarSEXP, SEXP oldbetasSEXP, SEXP sSEXP, SEXP maxuniqSEXP, SEXP gridsizeSEXP, SEXP BayesmConstantASEXP, SEXP BayesmConstantnuIncSEXP, SEXP BayesmConstantDPalphaSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); Rcpp::traits::input_parameter< List const& >::type lgtdata(lgtdataSEXP); Rcpp::traits::input_parameter< mat const& >::type Z(ZSEXP); Rcpp::traits::input_parameter< vec const& >::type deltabar(deltabarSEXP); Rcpp::traits::input_parameter< mat const& >::type Ad(AdSEXP); Rcpp::traits::input_parameter< List const& >::type PrioralphaList(PrioralphaListSEXP); Rcpp::traits::input_parameter< List const& >::type lambda_hyper(lambda_hyperSEXP); Rcpp::traits::input_parameter< bool >::type drawdelta(drawdeltaSEXP); Rcpp::traits::input_parameter< int >::type nvar(nvarSEXP); Rcpp::traits::input_parameter< mat >::type oldbetas(oldbetasSEXP); Rcpp::traits::input_parameter< double >::type s(sSEXP); Rcpp::traits::input_parameter< int >::type maxuniq(maxuniqSEXP); Rcpp::traits::input_parameter< int >::type gridsize(gridsizeSEXP); Rcpp::traits::input_parameter< double >::type BayesmConstantA(BayesmConstantASEXP); Rcpp::traits::input_parameter< int >::type BayesmConstantnuInc(BayesmConstantnuIncSEXP); Rcpp::traits::input_parameter< double >::type BayesmConstantDPalpha(BayesmConstantDPalphaSEXP); rcpp_result_gen = Rcpp::wrap(rhierMnlDP_rcpp_loop(R, keep, nprint, lgtdata, Z, deltabar, Ad, PrioralphaList, lambda_hyper, drawdelta, nvar, oldbetas, s, maxuniq, gridsize, BayesmConstantA, BayesmConstantnuInc, BayesmConstantDPalpha)); return rcpp_result_gen; END_RCPP } // llmnl_con double llmnl_con(vec const& betastar, vec const& y, mat const& X, vec const& SignRes); RcppExport SEXP bayesm_llmnl_con(SEXP betastarSEXP, SEXP ySEXP, SEXP XSEXP, SEXP SignResSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< vec const& >::type betastar(betastarSEXP); Rcpp::traits::input_parameter< vec const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< vec const& >::type SignRes(SignResSEXP); rcpp_result_gen = Rcpp::wrap(llmnl_con(betastar, y, X, SignRes)); return rcpp_result_gen; END_RCPP } // rhierMnlRwMixture_rcpp_loop List rhierMnlRwMixture_rcpp_loop(List const& lgtdata, mat const& Z, vec const& deltabar, mat const& Ad, mat const& mubar, mat const& Amu, double nu, mat const& V, double s, int R, int keep, int nprint, bool drawdelta, mat olddelta, vec const& a, vec oldprob, mat oldbetas, vec ind, vec const& SignRes); RcppExport SEXP bayesm_rhierMnlRwMixture_rcpp_loop(SEXP lgtdataSEXP, SEXP ZSEXP, SEXP deltabarSEXP, SEXP AdSEXP, SEXP mubarSEXP, SEXP AmuSEXP, SEXP nuSEXP, SEXP VSEXP, SEXP sSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP, SEXP drawdeltaSEXP, SEXP olddeltaSEXP, SEXP aSEXP, SEXP oldprobSEXP, SEXP oldbetasSEXP, SEXP indSEXP, SEXP SignResSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< List const& >::type lgtdata(lgtdataSEXP); Rcpp::traits::input_parameter< mat const& >::type Z(ZSEXP); Rcpp::traits::input_parameter< vec const& >::type deltabar(deltabarSEXP); Rcpp::traits::input_parameter< mat const& >::type Ad(AdSEXP); Rcpp::traits::input_parameter< mat const& >::type mubar(mubarSEXP); Rcpp::traits::input_parameter< mat const& >::type Amu(AmuSEXP); Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< double >::type s(sSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); Rcpp::traits::input_parameter< bool >::type drawdelta(drawdeltaSEXP); Rcpp::traits::input_parameter< mat >::type olddelta(olddeltaSEXP); Rcpp::traits::input_parameter< vec const& >::type a(aSEXP); Rcpp::traits::input_parameter< vec >::type oldprob(oldprobSEXP); Rcpp::traits::input_parameter< mat >::type oldbetas(oldbetasSEXP); Rcpp::traits::input_parameter< vec >::type ind(indSEXP); Rcpp::traits::input_parameter< vec const& >::type SignRes(SignResSEXP); rcpp_result_gen = Rcpp::wrap(rhierMnlRwMixture_rcpp_loop(lgtdata, Z, deltabar, Ad, mubar, Amu, nu, V, s, R, keep, nprint, drawdelta, olddelta, a, oldprob, oldbetas, ind, SignRes)); return rcpp_result_gen; END_RCPP } // rhierNegbinRw_rcpp_loop List rhierNegbinRw_rcpp_loop(List const& regdata, List const& hessdata, mat const& Z, mat Beta, mat Delta, mat const& Deltabar, mat const& Adelta, double nu, mat const& V, double a, double b, int R, int keep, double sbeta, double alphacroot, int nprint, mat rootA, double alpha, bool fixalpha); RcppExport SEXP bayesm_rhierNegbinRw_rcpp_loop(SEXP regdataSEXP, SEXP hessdataSEXP, SEXP ZSEXP, SEXP BetaSEXP, SEXP DeltaSEXP, SEXP DeltabarSEXP, SEXP AdeltaSEXP, SEXP nuSEXP, SEXP VSEXP, SEXP aSEXP, SEXP bSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP sbetaSEXP, SEXP alphacrootSEXP, SEXP nprintSEXP, SEXP rootASEXP, SEXP alphaSEXP, SEXP fixalphaSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< List const& >::type regdata(regdataSEXP); Rcpp::traits::input_parameter< List const& >::type hessdata(hessdataSEXP); Rcpp::traits::input_parameter< mat const& >::type Z(ZSEXP); Rcpp::traits::input_parameter< mat >::type Beta(BetaSEXP); Rcpp::traits::input_parameter< mat >::type Delta(DeltaSEXP); Rcpp::traits::input_parameter< mat const& >::type Deltabar(DeltabarSEXP); Rcpp::traits::input_parameter< mat const& >::type Adelta(AdeltaSEXP); Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< double >::type a(aSEXP); Rcpp::traits::input_parameter< double >::type b(bSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< double >::type sbeta(sbetaSEXP); Rcpp::traits::input_parameter< double >::type alphacroot(alphacrootSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); Rcpp::traits::input_parameter< mat >::type rootA(rootASEXP); Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); Rcpp::traits::input_parameter< bool >::type fixalpha(fixalphaSEXP); rcpp_result_gen = Rcpp::wrap(rhierNegbinRw_rcpp_loop(regdata, hessdata, Z, Beta, Delta, Deltabar, Adelta, nu, V, a, b, R, keep, sbeta, alphacroot, nprint, rootA, alpha, fixalpha)); return rcpp_result_gen; END_RCPP } // rivDP_rcpp_loop List rivDP_rcpp_loop(int R, int keep, int nprint, int dimd, vec const& mbg, mat const& Abg, vec const& md, mat const& Ad, vec const& y, bool isgamma, mat const& z, vec const& x, mat const& w, vec delta, List const& PrioralphaList, int gridsize, bool SCALE, int maxuniq, double scalex, double scaley, List const& lambda_hyper, double BayesmConstantA, int BayesmConstantnu); RcppExport SEXP bayesm_rivDP_rcpp_loop(SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP, SEXP dimdSEXP, SEXP mbgSEXP, SEXP AbgSEXP, SEXP mdSEXP, SEXP AdSEXP, SEXP ySEXP, SEXP isgammaSEXP, SEXP zSEXP, SEXP xSEXP, SEXP wSEXP, SEXP deltaSEXP, SEXP PrioralphaListSEXP, SEXP gridsizeSEXP, SEXP SCALESEXP, SEXP maxuniqSEXP, SEXP scalexSEXP, SEXP scaleySEXP, SEXP lambda_hyperSEXP, SEXP BayesmConstantASEXP, SEXP BayesmConstantnuSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); Rcpp::traits::input_parameter< int >::type dimd(dimdSEXP); Rcpp::traits::input_parameter< vec const& >::type mbg(mbgSEXP); Rcpp::traits::input_parameter< mat const& >::type Abg(AbgSEXP); Rcpp::traits::input_parameter< vec const& >::type md(mdSEXP); Rcpp::traits::input_parameter< mat const& >::type Ad(AdSEXP); Rcpp::traits::input_parameter< vec const& >::type y(ySEXP); Rcpp::traits::input_parameter< bool >::type isgamma(isgammaSEXP); Rcpp::traits::input_parameter< mat const& >::type z(zSEXP); Rcpp::traits::input_parameter< vec const& >::type x(xSEXP); Rcpp::traits::input_parameter< mat const& >::type w(wSEXP); Rcpp::traits::input_parameter< vec >::type delta(deltaSEXP); Rcpp::traits::input_parameter< List const& >::type PrioralphaList(PrioralphaListSEXP); Rcpp::traits::input_parameter< int >::type gridsize(gridsizeSEXP); Rcpp::traits::input_parameter< bool >::type SCALE(SCALESEXP); Rcpp::traits::input_parameter< int >::type maxuniq(maxuniqSEXP); Rcpp::traits::input_parameter< double >::type scalex(scalexSEXP); Rcpp::traits::input_parameter< double >::type scaley(scaleySEXP); Rcpp::traits::input_parameter< List const& >::type lambda_hyper(lambda_hyperSEXP); Rcpp::traits::input_parameter< double >::type BayesmConstantA(BayesmConstantASEXP); Rcpp::traits::input_parameter< int >::type BayesmConstantnu(BayesmConstantnuSEXP); rcpp_result_gen = Rcpp::wrap(rivDP_rcpp_loop(R, keep, nprint, dimd, mbg, Abg, md, Ad, y, isgamma, z, x, w, delta, PrioralphaList, gridsize, SCALE, maxuniq, scalex, scaley, lambda_hyper, BayesmConstantA, BayesmConstantnu)); return rcpp_result_gen; END_RCPP } // rivGibbs_rcpp_loop List rivGibbs_rcpp_loop(vec const& y, vec const& x, mat const& z, mat const& w, vec const& mbg, mat const& Abg, vec const& md, mat const& Ad, mat const& V, double nu, int R, int keep, int nprint); RcppExport SEXP bayesm_rivGibbs_rcpp_loop(SEXP ySEXP, SEXP xSEXP, SEXP zSEXP, SEXP wSEXP, SEXP mbgSEXP, SEXP AbgSEXP, SEXP mdSEXP, SEXP AdSEXP, SEXP VSEXP, SEXP nuSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< vec const& >::type y(ySEXP); Rcpp::traits::input_parameter< vec const& >::type x(xSEXP); Rcpp::traits::input_parameter< mat const& >::type z(zSEXP); Rcpp::traits::input_parameter< mat const& >::type w(wSEXP); Rcpp::traits::input_parameter< vec const& >::type mbg(mbgSEXP); Rcpp::traits::input_parameter< mat const& >::type Abg(AbgSEXP); Rcpp::traits::input_parameter< vec const& >::type md(mdSEXP); Rcpp::traits::input_parameter< mat const& >::type Ad(AdSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); rcpp_result_gen = Rcpp::wrap(rivGibbs_rcpp_loop(y, x, z, w, mbg, Abg, md, Ad, V, nu, R, keep, nprint)); return rcpp_result_gen; END_RCPP } // rmixGibbs List rmixGibbs(mat const& y, mat const& Bbar, mat const& A, double nu, mat const& V, vec const& a, vec const& p, vec const& z); RcppExport SEXP bayesm_rmixGibbs(SEXP ySEXP, SEXP BbarSEXP, SEXP ASEXP, SEXP nuSEXP, SEXP VSEXP, SEXP aSEXP, SEXP pSEXP, SEXP zSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< mat const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type Bbar(BbarSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< vec const& >::type a(aSEXP); Rcpp::traits::input_parameter< vec const& >::type p(pSEXP); Rcpp::traits::input_parameter< vec const& >::type z(zSEXP); rcpp_result_gen = Rcpp::wrap(rmixGibbs(y, Bbar, A, nu, V, a, p, z)); return rcpp_result_gen; END_RCPP } // rmixture List rmixture(int n, vec pvec, List comps); RcppExport SEXP bayesm_rmixture(SEXP nSEXP, SEXP pvecSEXP, SEXP compsSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< int >::type n(nSEXP); Rcpp::traits::input_parameter< vec >::type pvec(pvecSEXP); Rcpp::traits::input_parameter< List >::type comps(compsSEXP); rcpp_result_gen = Rcpp::wrap(rmixture(n, pvec, comps)); return rcpp_result_gen; END_RCPP } // rmnlIndepMetrop_rcpp_loop List rmnlIndepMetrop_rcpp_loop(int R, int keep, double nu, vec const& betastar, mat const& root, vec const& y, mat const& X, vec const& betabar, mat const& rootpi, mat const& rooti, double oldlimp, double oldlpost, int nprint); RcppExport SEXP bayesm_rmnlIndepMetrop_rcpp_loop(SEXP RSEXP, SEXP keepSEXP, SEXP nuSEXP, SEXP betastarSEXP, SEXP rootSEXP, SEXP ySEXP, SEXP XSEXP, SEXP betabarSEXP, SEXP rootpiSEXP, SEXP rootiSEXP, SEXP oldlimpSEXP, SEXP oldlpostSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< vec const& >::type betastar(betastarSEXP); Rcpp::traits::input_parameter< mat const& >::type root(rootSEXP); Rcpp::traits::input_parameter< vec const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< vec const& >::type betabar(betabarSEXP); Rcpp::traits::input_parameter< mat const& >::type rootpi(rootpiSEXP); Rcpp::traits::input_parameter< mat const& >::type rooti(rootiSEXP); Rcpp::traits::input_parameter< double >::type oldlimp(oldlimpSEXP); Rcpp::traits::input_parameter< double >::type oldlpost(oldlpostSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); rcpp_result_gen = Rcpp::wrap(rmnlIndepMetrop_rcpp_loop(R, keep, nu, betastar, root, y, X, betabar, rootpi, rooti, oldlimp, oldlpost, nprint)); return rcpp_result_gen; END_RCPP } // rmnpGibbs_rcpp_loop List rmnpGibbs_rcpp_loop(int R, int keep, int nprint, int pm1, ivec const& y, mat const& X, vec const& beta0, mat const& sigma0, mat const& V, double nu, vec const& betabar, mat const& A); RcppExport SEXP bayesm_rmnpGibbs_rcpp_loop(SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP, SEXP pm1SEXP, SEXP ySEXP, SEXP XSEXP, SEXP beta0SEXP, SEXP sigma0SEXP, SEXP VSEXP, SEXP nuSEXP, SEXP betabarSEXP, SEXP ASEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); Rcpp::traits::input_parameter< int >::type pm1(pm1SEXP); Rcpp::traits::input_parameter< ivec const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< vec const& >::type beta0(beta0SEXP); Rcpp::traits::input_parameter< mat const& >::type sigma0(sigma0SEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< vec const& >::type betabar(betabarSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); rcpp_result_gen = Rcpp::wrap(rmnpGibbs_rcpp_loop(R, keep, nprint, pm1, y, X, beta0, sigma0, V, nu, betabar, A)); return rcpp_result_gen; END_RCPP } // rmultireg List rmultireg(mat const& Y, mat const& X, mat const& Bbar, mat const& A, double nu, mat const& V); RcppExport SEXP bayesm_rmultireg(SEXP YSEXP, SEXP XSEXP, SEXP BbarSEXP, SEXP ASEXP, SEXP nuSEXP, SEXP VSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< mat const& >::type Y(YSEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< mat const& >::type Bbar(BbarSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); rcpp_result_gen = Rcpp::wrap(rmultireg(Y, X, Bbar, A, nu, V)); return rcpp_result_gen; END_RCPP } // rmvpGibbs_rcpp_loop List rmvpGibbs_rcpp_loop(int R, int keep, int nprint, int p, ivec const& y, mat const& X, vec const& beta0, mat const& sigma0, mat const& V, double nu, vec const& betabar, mat const& A); RcppExport SEXP bayesm_rmvpGibbs_rcpp_loop(SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP, SEXP pSEXP, SEXP ySEXP, SEXP XSEXP, SEXP beta0SEXP, SEXP sigma0SEXP, SEXP VSEXP, SEXP nuSEXP, SEXP betabarSEXP, SEXP ASEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); Rcpp::traits::input_parameter< int >::type p(pSEXP); Rcpp::traits::input_parameter< ivec const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< vec const& >::type beta0(beta0SEXP); Rcpp::traits::input_parameter< mat const& >::type sigma0(sigma0SEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< vec const& >::type betabar(betabarSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); rcpp_result_gen = Rcpp::wrap(rmvpGibbs_rcpp_loop(R, keep, nprint, p, y, X, beta0, sigma0, V, nu, betabar, A)); return rcpp_result_gen; END_RCPP } // rmvst vec rmvst(double nu, vec const& mu, mat const& root); RcppExport SEXP bayesm_rmvst(SEXP nuSEXP, SEXP muSEXP, SEXP rootSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< vec const& >::type mu(muSEXP); Rcpp::traits::input_parameter< mat const& >::type root(rootSEXP); rcpp_result_gen = Rcpp::wrap(rmvst(nu, mu, root)); return rcpp_result_gen; END_RCPP } // rnegbinRw_rcpp_loop List rnegbinRw_rcpp_loop(vec const& y, mat const& X, vec const& betabar, mat const& rootA, double a, double b, vec beta, double alpha, bool fixalpha, mat const& betaroot, double alphacroot, int R, int keep, int nprint); RcppExport SEXP bayesm_rnegbinRw_rcpp_loop(SEXP ySEXP, SEXP XSEXP, SEXP betabarSEXP, SEXP rootASEXP, SEXP aSEXP, SEXP bSEXP, SEXP betaSEXP, SEXP alphaSEXP, SEXP fixalphaSEXP, SEXP betarootSEXP, SEXP alphacrootSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< vec const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< vec const& >::type betabar(betabarSEXP); Rcpp::traits::input_parameter< mat const& >::type rootA(rootASEXP); Rcpp::traits::input_parameter< double >::type a(aSEXP); Rcpp::traits::input_parameter< double >::type b(bSEXP); Rcpp::traits::input_parameter< vec >::type beta(betaSEXP); Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); Rcpp::traits::input_parameter< bool >::type fixalpha(fixalphaSEXP); Rcpp::traits::input_parameter< mat const& >::type betaroot(betarootSEXP); Rcpp::traits::input_parameter< double >::type alphacroot(alphacrootSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); rcpp_result_gen = Rcpp::wrap(rnegbinRw_rcpp_loop(y, X, betabar, rootA, a, b, beta, alpha, fixalpha, betaroot, alphacroot, R, keep, nprint)); return rcpp_result_gen; END_RCPP } // rnmixGibbs_rcpp_loop List rnmixGibbs_rcpp_loop(mat const& y, mat const& Mubar, mat const& A, double nu, mat const& V, vec const& a, vec p, vec z, int const& R, int const& keep, int const& nprint); RcppExport SEXP bayesm_rnmixGibbs_rcpp_loop(SEXP ySEXP, SEXP MubarSEXP, SEXP ASEXP, SEXP nuSEXP, SEXP VSEXP, SEXP aSEXP, SEXP pSEXP, SEXP zSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< mat const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type Mubar(MubarSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< vec const& >::type a(aSEXP); Rcpp::traits::input_parameter< vec >::type p(pSEXP); Rcpp::traits::input_parameter< vec >::type z(zSEXP); Rcpp::traits::input_parameter< int const& >::type R(RSEXP); Rcpp::traits::input_parameter< int const& >::type keep(keepSEXP); Rcpp::traits::input_parameter< int const& >::type nprint(nprintSEXP); rcpp_result_gen = Rcpp::wrap(rnmixGibbs_rcpp_loop(y, Mubar, A, nu, V, a, p, z, R, keep, nprint)); return rcpp_result_gen; END_RCPP } // rordprobitGibbs_rcpp_loop List rordprobitGibbs_rcpp_loop(vec const& y, mat const& X, int k, mat const& A, vec const& betabar, mat const& Ad, double s, mat const& inc_root, vec const& dstarbar, vec const& betahat, int R, int keep, int nprint); RcppExport SEXP bayesm_rordprobitGibbs_rcpp_loop(SEXP ySEXP, SEXP XSEXP, SEXP kSEXP, SEXP ASEXP, SEXP betabarSEXP, SEXP AdSEXP, SEXP sSEXP, SEXP inc_rootSEXP, SEXP dstarbarSEXP, SEXP betahatSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< vec const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< int >::type k(kSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); Rcpp::traits::input_parameter< vec const& >::type betabar(betabarSEXP); Rcpp::traits::input_parameter< mat const& >::type Ad(AdSEXP); Rcpp::traits::input_parameter< double >::type s(sSEXP); Rcpp::traits::input_parameter< mat const& >::type inc_root(inc_rootSEXP); Rcpp::traits::input_parameter< vec const& >::type dstarbar(dstarbarSEXP); Rcpp::traits::input_parameter< vec const& >::type betahat(betahatSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); rcpp_result_gen = Rcpp::wrap(rordprobitGibbs_rcpp_loop(y, X, k, A, betabar, Ad, s, inc_root, dstarbar, betahat, R, keep, nprint)); return rcpp_result_gen; END_RCPP } // rscaleUsage_rcpp_loop List rscaleUsage_rcpp_loop(int k, mat const& x, int p, int n, int R, int keep, int ndghk, int nprint, mat y, vec mu, mat Sigma, vec tau, vec sigma, mat Lambda, double e, bool domu, bool doSigma, bool dosigma, bool dotau, bool doLambda, bool doe, double nu, mat const& V, mat const& mubar, mat const& Am, vec const& gsigma, vec const& gl11, vec const& gl22, vec const& gl12, int nuL, mat const& VL, vec const& ge); RcppExport SEXP bayesm_rscaleUsage_rcpp_loop(SEXP kSEXP, SEXP xSEXP, SEXP pSEXP, SEXP nSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP ndghkSEXP, SEXP nprintSEXP, SEXP ySEXP, SEXP muSEXP, SEXP SigmaSEXP, SEXP tauSEXP, SEXP sigmaSEXP, SEXP LambdaSEXP, SEXP eSEXP, SEXP domuSEXP, SEXP doSigmaSEXP, SEXP dosigmaSEXP, SEXP dotauSEXP, SEXP doLambdaSEXP, SEXP doeSEXP, SEXP nuSEXP, SEXP VSEXP, SEXP mubarSEXP, SEXP AmSEXP, SEXP gsigmaSEXP, SEXP gl11SEXP, SEXP gl22SEXP, SEXP gl12SEXP, SEXP nuLSEXP, SEXP VLSEXP, SEXP geSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< int >::type k(kSEXP); Rcpp::traits::input_parameter< mat const& >::type x(xSEXP); Rcpp::traits::input_parameter< int >::type p(pSEXP); Rcpp::traits::input_parameter< int >::type n(nSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type ndghk(ndghkSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); Rcpp::traits::input_parameter< mat >::type y(ySEXP); Rcpp::traits::input_parameter< vec >::type mu(muSEXP); Rcpp::traits::input_parameter< mat >::type Sigma(SigmaSEXP); Rcpp::traits::input_parameter< vec >::type tau(tauSEXP); Rcpp::traits::input_parameter< vec >::type sigma(sigmaSEXP); Rcpp::traits::input_parameter< mat >::type Lambda(LambdaSEXP); Rcpp::traits::input_parameter< double >::type e(eSEXP); Rcpp::traits::input_parameter< bool >::type domu(domuSEXP); Rcpp::traits::input_parameter< bool >::type doSigma(doSigmaSEXP); Rcpp::traits::input_parameter< bool >::type dosigma(dosigmaSEXP); Rcpp::traits::input_parameter< bool >::type dotau(dotauSEXP); Rcpp::traits::input_parameter< bool >::type doLambda(doLambdaSEXP); Rcpp::traits::input_parameter< bool >::type doe(doeSEXP); Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< mat const& >::type mubar(mubarSEXP); Rcpp::traits::input_parameter< mat const& >::type Am(AmSEXP); Rcpp::traits::input_parameter< vec const& >::type gsigma(gsigmaSEXP); Rcpp::traits::input_parameter< vec const& >::type gl11(gl11SEXP); Rcpp::traits::input_parameter< vec const& >::type gl22(gl22SEXP); Rcpp::traits::input_parameter< vec const& >::type gl12(gl12SEXP); Rcpp::traits::input_parameter< int >::type nuL(nuLSEXP); Rcpp::traits::input_parameter< mat const& >::type VL(VLSEXP); Rcpp::traits::input_parameter< vec const& >::type ge(geSEXP); rcpp_result_gen = Rcpp::wrap(rscaleUsage_rcpp_loop(k, x, p, n, R, keep, ndghk, nprint, y, mu, Sigma, tau, sigma, Lambda, e, domu, doSigma, dosigma, dotau, doLambda, doe, nu, V, mubar, Am, gsigma, gl11, gl22, gl12, nuL, VL, ge)); return rcpp_result_gen; END_RCPP } // rsurGibbs_rcpp_loop List rsurGibbs_rcpp_loop(List const& regdata, vec const& indreg, vec const& cumnk, vec const& nk, mat const& XspXs, mat Sigmainv, mat const& A, vec const& Abetabar, double nu, mat const& V, int nvar, mat E, mat const& Y, int R, int keep, int nprint); RcppExport SEXP bayesm_rsurGibbs_rcpp_loop(SEXP regdataSEXP, SEXP indregSEXP, SEXP cumnkSEXP, SEXP nkSEXP, SEXP XspXsSEXP, SEXP SigmainvSEXP, SEXP ASEXP, SEXP AbetabarSEXP, SEXP nuSEXP, SEXP VSEXP, SEXP nvarSEXP, SEXP ESEXP, SEXP YSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< List const& >::type regdata(regdataSEXP); Rcpp::traits::input_parameter< vec const& >::type indreg(indregSEXP); Rcpp::traits::input_parameter< vec const& >::type cumnk(cumnkSEXP); Rcpp::traits::input_parameter< vec const& >::type nk(nkSEXP); Rcpp::traits::input_parameter< mat const& >::type XspXs(XspXsSEXP); Rcpp::traits::input_parameter< mat >::type Sigmainv(SigmainvSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); Rcpp::traits::input_parameter< vec const& >::type Abetabar(AbetabarSEXP); Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); Rcpp::traits::input_parameter< int >::type nvar(nvarSEXP); Rcpp::traits::input_parameter< mat >::type E(ESEXP); Rcpp::traits::input_parameter< mat const& >::type Y(YSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); rcpp_result_gen = Rcpp::wrap(rsurGibbs_rcpp_loop(regdata, indreg, cumnk, nk, XspXs, Sigmainv, A, Abetabar, nu, V, nvar, E, Y, R, keep, nprint)); return rcpp_result_gen; END_RCPP } // rtrun NumericVector rtrun(NumericVector const& mu, NumericVector const& sigma, NumericVector const& a, NumericVector const& b); RcppExport SEXP bayesm_rtrun(SEXP muSEXP, SEXP sigmaSEXP, SEXP aSEXP, SEXP bSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< NumericVector const& >::type mu(muSEXP); Rcpp::traits::input_parameter< NumericVector const& >::type sigma(sigmaSEXP); Rcpp::traits::input_parameter< NumericVector const& >::type a(aSEXP); Rcpp::traits::input_parameter< NumericVector const& >::type b(bSEXP); rcpp_result_gen = Rcpp::wrap(rtrun(mu, sigma, a, b)); return rcpp_result_gen; END_RCPP } // runireg_rcpp_loop List runireg_rcpp_loop(vec const& y, mat const& X, vec const& betabar, mat const& A, double nu, double ssq, int R, int keep, int nprint); RcppExport SEXP bayesm_runireg_rcpp_loop(SEXP ySEXP, SEXP XSEXP, SEXP betabarSEXP, SEXP ASEXP, SEXP nuSEXP, SEXP ssqSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< vec const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< vec const& >::type betabar(betabarSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< double >::type ssq(ssqSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); rcpp_result_gen = Rcpp::wrap(runireg_rcpp_loop(y, X, betabar, A, nu, ssq, R, keep, nprint)); return rcpp_result_gen; END_RCPP } // runiregGibbs_rcpp_loop List runiregGibbs_rcpp_loop(vec const& y, mat const& X, vec const& betabar, mat const& A, double nu, double ssq, double sigmasq, int R, int keep, int nprint); RcppExport SEXP bayesm_runiregGibbs_rcpp_loop(SEXP ySEXP, SEXP XSEXP, SEXP betabarSEXP, SEXP ASEXP, SEXP nuSEXP, SEXP ssqSEXP, SEXP sigmasqSEXP, SEXP RSEXP, SEXP keepSEXP, SEXP nprintSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< vec const& >::type y(ySEXP); Rcpp::traits::input_parameter< mat const& >::type X(XSEXP); Rcpp::traits::input_parameter< vec const& >::type betabar(betabarSEXP); Rcpp::traits::input_parameter< mat const& >::type A(ASEXP); Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< double >::type ssq(ssqSEXP); Rcpp::traits::input_parameter< double >::type sigmasq(sigmasqSEXP); Rcpp::traits::input_parameter< int >::type R(RSEXP); Rcpp::traits::input_parameter< int >::type keep(keepSEXP); Rcpp::traits::input_parameter< int >::type nprint(nprintSEXP); rcpp_result_gen = Rcpp::wrap(runiregGibbs_rcpp_loop(y, X, betabar, A, nu, ssq, sigmasq, R, keep, nprint)); return rcpp_result_gen; END_RCPP } // rwishart List rwishart(double nu, mat const& V); RcppExport SEXP bayesm_rwishart(SEXP nuSEXP, SEXP VSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< double >::type nu(nuSEXP); Rcpp::traits::input_parameter< mat const& >::type V(VSEXP); rcpp_result_gen = Rcpp::wrap(rwishart(nu, V)); return rcpp_result_gen; END_RCPP } // callroot vec callroot(vec const& c1, vec const& c2, double tol, int iterlim); RcppExport SEXP bayesm_callroot(SEXP c1SEXP, SEXP c2SEXP, SEXP tolSEXP, SEXP iterlimSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< vec const& >::type c1(c1SEXP); Rcpp::traits::input_parameter< vec const& >::type c2(c2SEXP); Rcpp::traits::input_parameter< double >::type tol(tolSEXP); Rcpp::traits::input_parameter< int >::type iterlim(iterlimSEXP); rcpp_result_gen = Rcpp::wrap(callroot(c1, c2, tol, iterlim)); return rcpp_result_gen; END_RCPP } static const R_CallMethodDef CallEntries[] = { {"bayesm_bayesBLP_rcpp_loop", (DL_FUNC) &bayesm_bayesBLP_rcpp_loop, 26}, {"bayesm_breg", (DL_FUNC) &bayesm_breg, 4}, {"bayesm_cgetC", (DL_FUNC) &bayesm_cgetC, 2}, {"bayesm_clusterMix_rcpp_loop", (DL_FUNC) &bayesm_clusterMix_rcpp_loop, 4}, {"bayesm_ghkvec", (DL_FUNC) &bayesm_ghkvec, 6}, {"bayesm_llmnl", (DL_FUNC) &bayesm_llmnl, 3}, {"bayesm_lndIChisq", (DL_FUNC) &bayesm_lndIChisq, 3}, {"bayesm_lndIWishart", (DL_FUNC) &bayesm_lndIWishart, 3}, {"bayesm_lndMvn", (DL_FUNC) &bayesm_lndMvn, 3}, {"bayesm_lndMvst", (DL_FUNC) &bayesm_lndMvst, 5}, {"bayesm_rbprobitGibbs_rcpp_loop", (DL_FUNC) &bayesm_rbprobitGibbs_rcpp_loop, 11}, {"bayesm_rdirichlet", (DL_FUNC) &bayesm_rdirichlet, 1}, {"bayesm_rDPGibbs_rcpp_loop", (DL_FUNC) &bayesm_rDPGibbs_rcpp_loop, 12}, {"bayesm_rhierLinearMixture_rcpp_loop", (DL_FUNC) &bayesm_rhierLinearMixture_rcpp_loop, 19}, {"bayesm_rhierLinearModel_rcpp_loop", (DL_FUNC) &bayesm_rhierLinearModel_rcpp_loop, 14}, {"bayesm_rhierMnlDP_rcpp_loop", (DL_FUNC) &bayesm_rhierMnlDP_rcpp_loop, 18}, {"bayesm_llmnl_con", (DL_FUNC) &bayesm_llmnl_con, 4}, {"bayesm_rhierMnlRwMixture_rcpp_loop", (DL_FUNC) &bayesm_rhierMnlRwMixture_rcpp_loop, 19}, {"bayesm_rhierNegbinRw_rcpp_loop", (DL_FUNC) &bayesm_rhierNegbinRw_rcpp_loop, 19}, {"bayesm_rivDP_rcpp_loop", (DL_FUNC) &bayesm_rivDP_rcpp_loop, 23}, {"bayesm_rivGibbs_rcpp_loop", (DL_FUNC) &bayesm_rivGibbs_rcpp_loop, 13}, {"bayesm_rmixGibbs", (DL_FUNC) &bayesm_rmixGibbs, 8}, {"bayesm_rmixture", (DL_FUNC) &bayesm_rmixture, 3}, {"bayesm_rmnlIndepMetrop_rcpp_loop", (DL_FUNC) &bayesm_rmnlIndepMetrop_rcpp_loop, 13}, {"bayesm_rmnpGibbs_rcpp_loop", (DL_FUNC) &bayesm_rmnpGibbs_rcpp_loop, 12}, {"bayesm_rmultireg", (DL_FUNC) &bayesm_rmultireg, 6}, {"bayesm_rmvpGibbs_rcpp_loop", (DL_FUNC) &bayesm_rmvpGibbs_rcpp_loop, 12}, {"bayesm_rmvst", (DL_FUNC) &bayesm_rmvst, 3}, {"bayesm_rnegbinRw_rcpp_loop", (DL_FUNC) &bayesm_rnegbinRw_rcpp_loop, 14}, {"bayesm_rnmixGibbs_rcpp_loop", (DL_FUNC) &bayesm_rnmixGibbs_rcpp_loop, 11}, {"bayesm_rordprobitGibbs_rcpp_loop", (DL_FUNC) &bayesm_rordprobitGibbs_rcpp_loop, 13}, {"bayesm_rscaleUsage_rcpp_loop", (DL_FUNC) &bayesm_rscaleUsage_rcpp_loop, 32}, {"bayesm_rsurGibbs_rcpp_loop", (DL_FUNC) &bayesm_rsurGibbs_rcpp_loop, 16}, {"bayesm_rtrun", (DL_FUNC) &bayesm_rtrun, 4}, {"bayesm_runireg_rcpp_loop", (DL_FUNC) &bayesm_runireg_rcpp_loop, 9}, {"bayesm_runiregGibbs_rcpp_loop", (DL_FUNC) &bayesm_runiregGibbs_rcpp_loop, 10}, {"bayesm_rwishart", (DL_FUNC) &bayesm_rwishart, 2}, {"bayesm_callroot", (DL_FUNC) &bayesm_callroot, 4}, {NULL, NULL, 0} }; RcppExport void R_init_bayesm(DllInfo *dll) { R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); R_useDynamicSymbols(dll, FALSE); } bayesm/src/rmvpGibbs_rcpp_loop.cpp0000644000176000001440000000764713134410700017074 0ustar ripleyusers#include "bayesm.h" //EXTRA FUNCTIONS SPECIFIC TO THE MAIN FUNCTION-------------------------------------------- vec drawwi_mvp(vec const& w, vec const& mu, mat const& sigmai, int p, ivec y){ //Wayne Taylor 9/8/2014 //function to draw w_i by Gibbing thru p vector int above; vec outwi = w; for(int i = 0; i(w.size()); for(int i = 0; i(R/keep, p*p); mat betadraw = zeros(R/keep,k); vec wnew = zeros(X.n_rows); //set initial values of w,beta, sigma (or root of inv) vec wold = wnew; vec betaold = beta0; mat C = chol(solve(trimatu(sigma0),eye(sigma0.n_cols,sigma0.n_cols))); //C is upper triangular root of sigma^-1 (G) = C'C //trimatu interprets the matrix as upper triangular and makes solve more efficient mat sigmai, zmat, epsilon, S, IW, ucholinv, VSinv; vec betanew; List W; // start main iteration loop int mkeep = 0; if(nprint>0) startMcmcTimer(); for(int rep = 0; rep(W["C"]); //conversion from Rcpp to Armadillo requires explict declaration of variable type using as<> //print time to completion if (nprint>0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); //save every keepth draw if((rep+1)%keep==0){ mkeep = (rep+1)/keep; betadraw(mkeep-1,span::all) = trans(betanew); IW = as(W["IW"]); sigmadraw(mkeep-1,span::all) = trans(vectorise(IW)); } wold = wnew; betaold = betanew; } if(nprint>0) endMcmcTimer(); return List::create( Named("betadraw") = betadraw, Named("sigmadraw") = sigmadraw); } bayesm/src/cgetC_rcpp.cpp0000644000176000001440000000233313134410700015120 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] vec cgetC(double e, int k){ //Wayne Taylor 4/29/15 //purpose: get a list of cutoffs for use with scale usage problems //arguments: // e: the "e" parameter from the paper // k: the point scale, eg. items are rated from 1,2,...k // output: // vector of grid points vec temp = zeros(k-1); for(int i = 0; i<(k-1); i++) temp[i] = i + 1.5; double m1 = sum(temp); temp = pow(temp,2.0); double m2 = sum(temp); vec c = zeros(k+1); //first sum to get s's, this is a waste since it should be done //once but I don't want to see this things anywhere else and it should take no time double s0 = k-1; double s1=0.0,s2=0.0,s3=0.0,s4=0.0; for(int i=1;i(ncolX); rowvec beta = zeros(ncolX); double cloglike, clpost, climp, ldiff, alpha, unif, oldloglike; vec alphaminv; if(nprint>0) startMcmcTimer(); // start main iteration loop for(int rep = 0; rep0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; betadraw(mkeep-1,span::all) = beta; loglike[mkeep-1] = oldloglike; } } if(nprint>0) endMcmcTimer(); return List::create( Named("betadraw") = betadraw, Named("loglike") = loglike, Named("naccept") = naccept); } bayesm/src/lndIWishart_rcpp.cpp0000644000176000001440000000260713134410700016327 0ustar ripleyusers#include "bayesm.h" // [[Rcpp::export]] double lndIWishart(double nu, mat const& V, mat const& IW){ // Keunwoo Kim 07/24/2014 // Wayne Taylor 7/19/2016 corrected bug: replaced: // return (...*sum((V*IWi.diag()))); // with // mat VIWi = V*IWi; // return (...*sum((VIWi.diag()))); // Purpose: evaluate log-density of inverted Wishart with normalizing constant // Arguments: // nu is d. f. parm // V is location matrix // IW is the value at which the density should be evaluated // Note: in this parameterization, E[IW]=V/(nu-k-1) int k = V.n_cols; mat Uiw = chol(IW); mat Uiwi = solve(trimatu(Uiw), eye(k,k)); //trimatu interprets the matrix as upper triangular and makes solve more efficient mat IWi = Uiwi*trans(Uiwi); mat cholV = chol(V); double lndetVd2 = sum(log(cholV.diag())); double lndetIWd2 = sum(log(Uiw.diag())); // first evaluate constant double cnst = ((nu*k)/2)*log(2.0)+((k*(k-1))/4.0)*log(M_PI); // (k*(k-1))/4 is recognized as integer. "4.0" allows it to be recognized as a double. vec seq_1_k = cumsum(ones(k)); // build c(1:k) through cumsum function vec arg = (nu+1-seq_1_k)/2.0; // lgamma cannot receive arma::vec input. Compute cnst+sum(lgamma(arg)). for (int i=0; i0 e~N(0,1) // Prior: beta ~ N(betabar,A^-1) int mkeep; vec mu; vec z; int nvar = X.n_cols; mat betadraw(R/keep, nvar); if (nprint>0) startMcmcTimer(); //start main iteration loop for (int rep=0; rep0) if ((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; betadraw(mkeep-1, span::all) = trans(beta); } } if (nprint>0) endMcmcTimer(); return List::create(Named("betadraw") = betadraw); } bayesm/src/rhierMnlDP_rcpp_loop.cpp0000644000176000001440000003071313134410700017133 0ustar ripleyusers#include "bayesm.h" //FUNCTIONS SPECIFIC TO MAIN FUNCTION------------------------------------------------------ mat drawDelta(mat const& x,mat const& y,ivec const& z,std::vector const& comps_vector,vec const& deltabar,mat const& Ad){ // Wayne Taylor 2/21/2015 // delta = vec(D) // given z and comps (z[i] gives component indicator for the ith observation, // comps is a list of mu and rooti) // y is n x p // x is n x k // y = xD' + U , rows of U are indep with covs Sigma_i given by z and comps int p = y.n_cols; int k = x.n_cols; int ncomp = comps_vector.size(); mat xtx = zeros(k*p,k*p); mat xty = zeros(p,k); //this is the unvecced version, reshaped after the sum //Create the index vectors, the colAll vectors are equal to span::all but with uvecs (as required by .submat) uvec colAlly(p), colAllx(k); for(int i = 0; i0){ mat yi = y.submat(ind,colAlly); mat xi = x.submat(ind,colAllx); murooti compsi_struct = comps_vector[compi]; yi.each_row() -= trans(compsi_struct.mu); //the subtraction operation is repeated on each row of yi mat sigi = compsi_struct.rooti*trans(compsi_struct.rooti); xtx = xtx + kron(trans(xi)*xi,sigi); xty = xty + (sigi * (trans(yi)*xi)); } } xty.reshape(xty.n_rows*xty.n_cols,1); //vec(t(D)) ~ N(V^{-1}(xty + Ad*deltabar),V^{-1}) where V = (xtx+Ad) // compute the inverse of xtx+Ad mat ucholinv = solve(trimatu(chol(xtx+Ad)), eye(k*p,k*p)); //trimatu interprets the matrix as upper triangular and makes solve more efficient mat Vinv = ucholinv*trans(ucholinv); return(Vinv*(xty+Ad*deltabar) + trans(chol(Vinv))*as(rnorm(deltabar.size()))); } DPOut rDPGibbs1(mat y, lambda lambda_struct, std::vector thetaStar_vector, int maxuniq, ivec indic, vec q0v, double alpha, priorAlpha const& priorAlpha_struct, int gridsize, List const& lambda_hyper){ // Wayne Taylor 2/21/2015 //revision history: //created from rDPGibbs by Rossi 3/08 //do one draw of DP Gibbs sampler with normal base //Model: // y_i ~ N(y|thetai) // thetai|G ~ G // G|lambda,alpha ~ DP(G|G0(lambda),alpha) //Priors: // alpha: starting value // lambda: // G0 ~ N(mubar,Sigma (x) Amu^-1) // mubar=vec(mubar) // Sigma ~ IW(nu,nu*V) V=v*I note: mode(Sigma)=nu/(nu+2)*v*I // mubar=0 // amu is uniform on grid specified by alim // nu is log uniform, nu=d-1+exp(Z) z is uniform on seq defined bvy nulim // v is uniform on sequence specificd by vlim // priorAlpha_struct: // alpha ~ (1-(alpha-alphamin)/(alphamax-alphamin))^power // alphamin=exp(digamma(Istarmin)-log(gamma+log(N))) // alphamax=exp(digamma(Istarmax)-log(gamma+log(N))) // gamma= .5772156649015328606 //output: // ind - vector of indicators for which observations are associated with which comp in thetaStar // thetaStar - list of unique normal component parms // lambda - list of of (a,nu,V) // alpha // thetaNp1 - one draw from predictive given thetaStar, lambda,alphama int n = y.n_rows; int dimy = y.n_cols; int nunique, indsize, indp, probssize; vec probs; uvec ind; mat ydenmat; uvec spanall(dimy); for(int i = 0; i new_utheta_vector(1), thetaNp1_vector(1); murooti thetaNp10_struct, outGD_struct; for(int rep = 0; rep<1; rep++) { //note we only do one loop! q0v = q0(y,lambda_struct); nunique = thetaStar_vector.size(); if(nunique > maxuniq) stop("maximum number of unique thetas exceeded"); //ydenmat is a length(thetaStar) x n array of density values given f(y[j,] | thetaStar[[i]] // note: due to remix step (below) we must recompute ydenmat each time! ydenmat = zeros(maxuniq,n); ydenmat(span(0,nunique-1),span::all) = yden(thetaStar_vector,y); thetaStarDrawOut_struct = thetaStarDraw(indic, thetaStar_vector, y, ydenmat, q0v, alpha, lambda_struct, maxuniq); thetaStar_vector = thetaStarDrawOut_struct.thetaStar_vector; indic = thetaStarDrawOut_struct.indic; nunique = thetaStar_vector.size(); //thetaNp1 and remix probs = zeros(nunique+1); for(int j = 0; j < nunique; j++){ ind = find(indic == (j+1)); indsize = ind.size(); probs[j] = indsize/(alpha+n+0.0); new_utheta_vector[0] = thetaD(y(ind,spanall),lambda_struct); thetaStar_vector[j] = new_utheta_vector[0]; } probs[nunique] = alpha/(alpha+n+0.0); indp = rmultinomF(probs); probssize = probs.size(); if(indp == probssize) { outGD_struct = GD(lambda_struct); thetaNp10_struct.mu = outGD_struct.mu; thetaNp10_struct.rooti = outGD_struct.rooti; thetaNp1_vector[0] = thetaNp10_struct; } else { outGD_struct = thetaStar_vector[indp-1]; thetaNp10_struct.mu = outGD_struct.mu; thetaNp10_struct.rooti = outGD_struct.rooti; thetaNp1_vector[0] = thetaNp10_struct; } //draw alpha alpha = alphaD(priorAlpha_struct,nunique,gridsize); //draw lambda lambda_struct = lambdaD(lambda_struct,thetaStar_vector,lambda_hyper["alim"],lambda_hyper["nulim"],lambda_hyper["vlim"],gridsize); } //note indic is the vector of indicators for each obs correspond to which thetaStar DPOut out_struct; out_struct.thetaStar_vector = thetaStar_vector; out_struct.thetaNp1_vector = thetaNp1_vector; out_struct.alpha = alpha; out_struct.lambda_struct = lambda_struct; out_struct.indic = indic; return(out_struct); } //MAIN FUNCTION------------------------------------------------------------------------------------- //[[Rcpp::export]] List rhierMnlDP_rcpp_loop(int R, int keep, int nprint, List const& lgtdata, mat const& Z, vec const& deltabar, mat const& Ad, List const& PrioralphaList, List const& lambda_hyper, bool drawdelta, int nvar, mat oldbetas, double s, int maxuniq, int gridsize, double BayesmConstantA, int BayesmConstantnuInc, double BayesmConstantDPalpha){ // Wayne Taylor 2/21/2015 //Initialize variable placeholders int mkeep, Istar; vec betabar, q0v; mat rootpi, ucholinv, incroot, V; List compdraw(R/keep), nmix; DPOut mgout_struct; mnlMetropOnceOut metropout_struct; murooti thetaStarLgt_struct; int nz = Z.n_cols; int nlgt = lgtdata.size(); // convert List to std::vector of struct List lgtdatai; std::vector lgtdata_vector; moments lgtdatai_struct; for (int lgt = 0; lgt(lgtdatai["y"]); lgtdatai_struct.X = as(lgtdatai["X"]); lgtdatai_struct.hess = as(lgtdatai["hess"]); lgtdata_vector.push_back(lgtdatai_struct); } //initialize indicator vector, delta, thetaStar, thetaNp10, alpha, oldprob ivec indic = ones(nlgt); mat olddelta; if (drawdelta) olddelta = zeros(nz*nvar); std::vector thetaStar_vector(1); murooti thetaNp10_struct, thetaStar0_struct; thetaStar0_struct.mu = zeros(nvar); thetaStar0_struct.rooti = eye(nvar,nvar); thetaStar_vector[0] = thetaStar0_struct; double alpha = BayesmConstantDPalpha; //fix oldprob (only one comp) double oldprob = 1.0; //convert Prioralpha from List to struct priorAlpha priorAlpha_struct; priorAlpha_struct.power = PrioralphaList["power"]; priorAlpha_struct.alphamin = PrioralphaList["alphamin"]; priorAlpha_struct.alphamax = PrioralphaList["alphamax"]; priorAlpha_struct.n = PrioralphaList["n"]; //initialize lambda lambda lambda_struct; lambda_struct.mubar = zeros(nvar); lambda_struct.Amu = BayesmConstantA; lambda_struct.nu = nvar+BayesmConstantnuInc; lambda_struct.V = lambda_struct.nu*eye(nvar,nvar); //allocate space for draws mat Deltadraw(1,1); if(drawdelta) Deltadraw.zeros(R/keep, nz*nvar);//enlarge Deltadraw only if the space is required cube betadraw(nlgt, nvar, R/keep); vec probdraw = zeros(R/keep); vec oldll = zeros(nlgt); vec loglike = zeros(R/keep); vec Istardraw = zeros(R/keep); vec alphadraw = zeros(R/keep); vec nudraw = zeros(R/keep); vec vdraw = zeros(R/keep); vec adraw = zeros(R/keep); if (nprint>0) startMcmcTimer(); //start main iteration loop for(int rep = 0; rep0) if((rep+1)%nprint==0) infoMcmcTimer(rep, R); if((rep+1)%keep==0){ mkeep = (rep+1)/keep; betadraw.slice(mkeep-1) = oldbetas; probdraw[mkeep-1] = oldprob; alphadraw[mkeep-1] = alpha; Istardraw[mkeep-1] = Istar; adraw[mkeep-1] = lambda_struct.Amu; nudraw[mkeep-1] = lambda_struct.nu; V = lambda_struct.V; vdraw[mkeep-1] = V(0,0)/lambda_struct.nu; loglike[mkeep-1] = sum(oldll); if(drawdelta) Deltadraw(mkeep-1, span::all) = trans(vectorise(olddelta)); thetaNp10_struct = mgout_struct.thetaNp1_vector[0]; //we have to convert to a NumericVector for the plotting functions to work compdraw[mkeep-1] = List::create(List::create(Named("mu") = NumericVector(thetaNp10_struct.mu.begin(),thetaNp10_struct.mu.end()),Named("rooti") = thetaNp10_struct.rooti)); } } if (nprint>0) endMcmcTimer(); nmix = List::create(Named("probdraw") = probdraw, Named("zdraw") = R_NilValue, //sets the value to NULL in R Named("compdraw") = compdraw); if(drawdelta){ return(List::create( Named("Deltadraw") = Deltadraw, Named("betadraw") = betadraw, Named("nmix") = nmix, Named("alphadraw") = alphadraw, Named("Istardraw") = Istardraw, Named("adraw") = adraw, Named("nudraw") = nudraw, Named("vdraw") = vdraw, Named("loglike") = loglike)); } else { return(List::create( Named("betadraw") = betadraw, Named("nmix") = nmix, Named("alphadraw") = alphadraw, Named("Istardraw") = Istardraw, Named("adraw") = adraw, Named("nudraw") = nudraw, Named("vdraw") = vdraw, Named("loglike") = loglike)); } } bayesm/NAMESPACE0000644000176000001440000000273413114117322013002 0ustar ripleyusersuseDynLib(bayesm) importFrom(Rcpp, evalCpp) importFrom(utils, flush.console) importFrom(stats, acf, cov, dnbinom, dnorm, lm, median, optim, pnorm, qchisq, quantile, rmultinom, rnorm, runif, var) importFrom(graphics, abline, boxplot, bxp, contour, hist, image, lines, par, plot, plot.default, points, polygon, text, title) importFrom(grDevices, dev.interactive, devAskNewPage, terrain.colors) ## exportPattern("^[[:alpha:]]+") this line is automatically created when using package.skeleton but should be removed to prevent the _loop functions from exporting. Instead use the export() function (as is done here) export(breg,createX,eMixMargDen,mixDen,llmnl,llmnp,llnhlogit, lndIChisq,lndIWishart,lndMvn,lndMvst,mnlHess,momMix,nmat,numEff,rdirichlet, rmixture,rmultireg, rwishart,rmvst,rtrun,rbprobitGibbs,runireg, runiregGibbs,simnhlogit,rmnpGibbs,rmixGibbs,rnmixGibbs, rmvpGibbs,rhierLinearModel,rhierMnlRwMixture,rivGibbs, rmnlIndepMetrop,rscaleUsage,ghkvec,condMom,logMargDenNR, rhierBinLogit,rnegbinRw,rhierNegbinRw,rbiNormGibbs,clusterMix,rsurGibbs, mixDenBi,mnpProb,rhierLinearMixture, summary.bayesm.mat,summary.bayesm.nmix,summary.bayesm.var, plot.bayesm.mat,plot.bayesm.hcoef,plot.bayesm.nmix, rordprobitGibbs,rivGibbs,rivDP,rDPGibbs, rhierMnlDP,cgetC,rbayesBLP) ## register S3 methods S3method(plot, bayesm.mat) S3method(plot, bayesm.nmix) S3method(plot, bayesm.hcoef) S3method(summary, bayesm.mat) S3method(summary, bayesm.var) S3method(summary, bayesm.nmix) bayesm/data/0000755000176000001440000000000013114117322012466 5ustar ripleyusersbayesm/data/detailing.rda0000644000176000001440000010076412516003354015132 0ustar ripleyusersý7zXZi"Þ6!ÏXÌê|²¶])TW"änRÊŸ’Øáà[áß^ ·ÖnŒÍV6$iú«eË'ª‰d8Xïš«/8U£BS!Ì¥ ‚ªÞ› {/„/Þã‚VÄ^¶²¾@æ< ö¼CkpšJÚ.˜/RÀÃ̆„Ñ@s,«4<úwŸˆ èŽf:8ÜÇôˆºY‹‡•{'ó˜Åà¨î‹ **ä£2Ïq%_¯¨—¤!„3Êa5E5·Ö´ß×D7žnužïÿsÁ{uYØ kAÒ»åNœ©5E‚áÅ[Å[ꜹƒߢÜp/5&¬UPD•ã¯S·õæ„Ò|&,ê±+x÷ˆ°r‘ÓÀqª±û‚_ºSná->%YX[¢ ‚]»’Òž²™!uÓŠÑ‹,áp_•V ´ŸÔ§À!ò#¡áØ£œˆƒ@rÏÉ—a.f>c›Éߟ ÖýÊÍË ]¡º¹_ ñöA™{ørnE®}šÂŒÞ˜Ga7;ŽÿMY—C w]Eæø×)ÿo¹¶ûE¿éYžòÔÐ:DPb¦.¸ Y¤@›‰‚r¯A˜L½v‹FØvn ½ï™éƸá4×%Æ~Íž´ó ÿ_Û0˜f’#-¶‚rf¢ŽWô´3àÙõÖNG dü.î ûËCJ*M€¦ÀÎX½õä±R«Hv=OÑw’”@oK=í$2ð8¢¬zÃ[„Ý&R=,­8uáœo×0Òý&‰ÿ» ;ú®á£–Ó!†³G8¥öë–-8´Û%H ¨òˆ>„µ+ }–m'6—›>â!¸Ö¡£ÁûGv8E©ÿª‘Uð¨ÿÎè‚nÔÈvcC%¥þתœñ‰Â+ìiulìÀy€ú=ì!Eä” î¾(ðeßp~_âÐæÒ —âx²ÙjrËúÁ_›øw¨ûÑ×”‡ׇь=|X/À‘vp:˜,ª¿êï–;Œ^–k¥± ñŸ‹ÑSgX¯,¡Úí¸¡7‡ÝZ›_Ò½žÏv|RâQ+°ÃÄ#ØÌT¶?ékçAå­Ö3c×g²¬ÝFõ åkq{ë …6ehó’Ò1’Ù<Ý…?Zs;Çè“ÔC^Ö–$¬Ј@Åýn´¬N Ÿ̘«õÖ WÏÙ‰ßÅ&¨7ðч!œ~. <¬ï :‹B²/߶~Ç Š¢Àt ßéGÍÌñˆ¤ÕA<‚3HVI¬Õ•öKKÚÎ׉f’š½_!~cMRÍPðÆ•Ýí÷ôÕŒj] •pGöjK\Ž…‰z¬Ž“±ºÜÂJCºeƒ,|_ô='”YáoN2&ÆÀ± ñЦÅï&’xß$ù€o¹FË_[ðþä‰Ó,áxh½–és°”Scqת¶Ž [âmF pÆD½Îú1.!jã+œQ+Ü°Á­üNðUã‹ôíly`zΕ8æ;cZÒíK2®4»afô5¢Ü7Ü)cþT`!©I^@òŒÓbŸOÓ}êÚõ' úN@7<™7áRÔ¼:¯pÚ²Ø*´¯˜EýÎ?Q'•Ä^žxFÓ RcA:ÄóÐñQ¡‰¹c%¨f¨cÈ1¨KϸÏó†$…ÔÄtpU””°Ì^O]¼ƒ«m‚4ù@2ªCÍùoßÓb$™»²N›P"Ë´†“æÊÍEA*o œÛá¥Å®´ù€™’oó¦„êÐå¥Ë­›1¶t~DRRìüèFœSîyU—{Á«TtC5aP†¯®sÉri¦¸Ì]ZL(©þÇ[ÊQ)é¬1Pp êÞjÂÑÐ2ž«mæ` L\ñX´Gj#pyŠToòsKÇ:¨k¯!9Œ¡YUR®{ÁªÕ4ýííÔóñ«G{™a¹Ô¨ÖüÍNŸò5@™c“Y9·|JIv²7ÓX)£fÇ)dNN@Íy„ÛüM¼ ƒ1²{>sǤ!œûv;TŒ:¼²éÂp»w€ÿ•Ä‹ž5–@hU¾1~kqaÞºÀ{˜¬¿ïsÌ?x=vA½žj»Wtª\%üðu˜7¼ðòxÝ¿Žö?€îók:¾öWŒ¸Ã'0ïÕdžÜ…¤î÷q9t›yÐäüj9­2P!{Ùz¤þEa¥‹Emí ¸&õ«±ô+õãZ±:†™à[œ|¹æLlP1»ÿB¯â(Éc³évL_ì·Œ_tR¾ŸuyHŠ°Æ(„µ}ìASÐñvXvl<ÙÜ$¤'ŒÓp¢;mMüwí;åxIòÏM-kçn/ãÙâ]úÑ'ƒÏ§â­É`þÅJ³˜íJè¡ÞÉØwx®ˆžMÆòùD¸G¹ ”oòн•…µÁײàÏ¡ñ¹ÀÇ À¨ƒ/óØ Æ–v·*68.D‚aà¶ß<6òIî_~ŸÙÙAh]´œ—K* ‘æz4ˆ¡ÝÚûNÇQ5¥”¸HA·“„ƒÃy4Ï9jðè¼iƒ)!N|öðÉ.¼fÁ>O`ñU­¹…D´#𢣫© Ùòîž°L.¶Ì›¡ jŒ£:UÌ6âŒáYôÁ<ÒÊ?Z))K q|(ãÒúD÷š"ÔHÆ € Êi78ßÚUkGõ$ÿ}}A£zo NZIé„šˆ# W ôÞù ”KeR•3oOY•ûÌ”µ·Ïn ¼Ù'=§ÌnE~7®wÇ…p´ùûœðA3ã…èþÿ-C;`TÊù IÓ5E^¢— f}.‰Î…¤4æ œE´}Ò@ñϛߡ„Q?³uÕå{—Ã’õñégÌL·²|¿ûrcøçHƒÊ›ÊªðÏë|dKrϨ3G”ƒD„ڕꔫ:4*@ºfœÇob<°7ëRŽÈâ—flFÌé-7–¿q¶*)V£ù8ÁttÞ¦âY4ŸÅô8ü­'/¥…ÀŸrvycAV¿¼²o—¹Zÿ ÄmŸ¦ÏOÏŽmˆUíÊe"P›1mUžL*Vá\GwžYû,á„. tX¶9Pbì@Ë’€wžpk”m×çhi3ÑspüHŠÅLô^:{µÃÆŸâøfu•ÓcšU|aId A…Üç±¥iin6¨…Ÿ :yöaË]ȳ§‰681H»¬ÛØ«J} DCˆ.FëË/¸$ðéðtÞ>›ˆ€¨Ñ JëªÿԀηÙwøÄäÕç5·oÅó"žGÚ³[˜b€§¼|÷{î 8n¿¤è‚«bl…ø YBÖ4GÓÁ1Hpmr:2£€æ+ ÞûÉ}¾A[àå@°I«½]Ø\Ü0 £1®™åG}3=Xƻɭ™0zl‰½øvÍèò¼¼©÷¤Õô§+Ì¢`Þ^ 7?ò}• ×±H12Öñ<×>e­IéŽùqǶýýÒ/ÆgeäN*›7Žkp/Ý ñ‹D æGý‰ëÓ…Ñ Òç„L¨úÓ¸`g]‚81¤‘G[﹌ýŽLænÿâ°Õª Ù)FY¸‚ì 1,·Ô€ÙzVV¾MÛ€““£B˸³ÇVÄÈJ÷…a4K¶L”2³d§&¾§û[k BI¼L²í¸3#í“’@ÀEŒÉÒÅ]j¶ß=/£ÞœÓÏñ§”»§¹Êã±Ýµœ|5 ê p=¼¸¡Äæd«çý»Ý@G™DÀ‹k´/<ă"øÞ dÚõëp*P¡GÀ&ÅZ†ðãOñZ ÿ ®tâ ¢ f,ÝwG(Tä›ÆeÝAlÁ%¬Ø¾#œÄ½IßB»>xAOjØ'ç)Ì>9¿€Ž÷ÁnèL:©Ì®²½z‹Û£‰ïDv®æY•*Lò|7ú;àl>ç»Õ³’Ø-k°}#£a$û½G&¯ÃovÈ2xû9=&zJãî|Ü>C¸7¾¨÷¬?,ã—ZÝÜ»EšÕµØg>|b ¾éášÒ»—z À½'S@wœ¹=Žî ªÓ§®wyjÜI\-„µB¢á±öc YɸuTFç7–ÊDqðÉ…ã3èÆÍíú*ATC•IÖ6~#õ¦\O ¸Çì‚ëÛˆï—Ög°^Ÿ©uŽhÐ÷zÑ„=I±ê߆ÙTv¸BÈXû­-!}ˆJTÙí[»ûk_ÅÒB¡F½¥‡¸qQ\»F_<—¸&å¾´‚ö:ü¤y1Ozr†Ëx¶Åø2ß®÷LÝB¸_—-òµ„8 *l¡ 01¹‚€¨ùÛDu·]Åé=ÍŽPmÍeñ%Î ri@Ÿt×ßßílJ P;}0uQ ¼¢•Ìãæ øJ>JsSðsiÞvO,x• @Èœ+t ¡7hnª 4ŸŠûýH)¿ù‰™¿{Þ@C/”ùDìó öÔ°9EwÎt¼rÏŘíÈ.èÍ_ÊÒ ½9Oàö?6B†žeÝÕ', U‚¤z`€o>Z¬•–€\,4U? X†ä~;*n$‰Új@^ ‘ L;]þ{BL4ñu®:á*¸ð±é›[ª³5Ñ2qéaÏ*V\J[t2Ü"D’Ú“¼‘¨—^ HíÄÙí:“ýÀV?þWr«u_V&—b|ƒ©?î¿Ø¶ZX¾¾”£}@|TíäEkŒz|×"ܵcpÅiï—že·Ç¶böPX¡Ã6 ÿ;òqOÖÊpbƒ[tˆ<¿ (ËiÓvηÎ:+ DžðiæíÀCs½½’8Œ†×¸†þ®Å9³¡éCÉö½[;ÂVe•ÅÚ2{,ûÚL4‘Pj5ÐTÌgÏÆæjíj(òíÌ.¡‚Šå`eP*4¹»ÇÏ­(¢){oëj¹ÅsTŠÆ kÒi#`ôeßé¦ÃhXÄ2®]yrjËíE¦ &Ø|»3‹fË‚ácW½vVc?S û{Cï%fù6.øW`:_˳ @ÃÐI}¶ÎC‡ÌPOyÀ ä̈ï#S„ðryŸÞRxâÝýÙݧ¨ýþ],šd-ȸÿZ;,O‚pâ+¿’¸G€”éUð6ʦ§¯¡ð#Pæ5©F“h4ÿSºf¾IÂÖ†û“ƒåð]î—ÿ²ò%:“45ˆb®Z x_¯4(j#VßîlI¯‹hî GST…Œ6FÛY~JàÀ¯ÌJLjË@‡ÛXšËB¡l÷˜1଺y+Õ•Í|B’« ³Ú(E¡/à¢b½¸l»|V*£xæJ9HJsŠ­µ½ËiÚ[^܈˜>Òr)þ¾¥Èï» H˜F¯´@†X¢ôZÑ?¼qß5¦9ZËî èê´‘È\ÙênØ1â~‰ú0—¶‘aQˆ  î·5žÞøåè*ýõ–ÿ†8«° †S€úo£} x°9+€x¿Núy5u…6„äK}™…ÊÇ ü¬S[↖²8%­WkJq‘T¾œÌ,È"GfŸØ£=>÷&~Eö#O›ÌÛO÷ü1Zôe7õ"‡†Æõóü-4ä ® ¶Ã1SVw ìxWÿ'Ó묌La±Q$>æoH¼Å÷#hú'ip˳Ðß;÷¿)Î>bAB§é©/œß¨xž^. ·ÕeÇw©&Ä Âã‚錑í˃-º†¬|‘6eE=Žr‹„0(p˜ª˜ /ša’C®4¾¢0UÓÆ°Kcº¤¨~'°+uwÅO¹b­lbñíd ‚ä~ŠG®¹*ÊA¾—ïþ«TÁ¾Ëbh+Wظ¿ãüWuï—)ðU %Z®mçh^‰ð)}¾uø¶ðö„Ÿº¼ŒIÔêÓ.=âE2Íí…½!ì!ìW°.@8åRM¿ñ¹T–»ØVå¾Àòva`w˜ÓÊ\ÄÐ8q´‰r`Æ>OŽè5e^ F¬¹I0¸Y]Ác™ÌŽÇ N²­ €’>E«2/b¡„ûÅï[Ù“ßXž*Cî °îzêsèQ'ð›sx;˜Ÿî~Ls´ÌaœD×ü = 5nLZÒe×Aä#ð«™ „ëÈ:Ý eEe’{̾ôå–ÑÅÅÓ—\d|/ÜÓwè #µpØ Cîu§Åíç¤"kKg©¦V4¤ú+¶*7ãˆÝöúl€À{™'ç)Äé{M¡¼lxI©„„½­¨¬¤Ö H—ÓŒ’¸ç{mƒsÉPë5óc®‹ê¢ÉÏTŒízΆ¥í1Y¡ÿõ¬ª˜ÿ€¨B¡HË3Üñ¿Ûü?Ú9þtŽ~Ïôf ”¨ÑÂS³÷Ú¼fùÖÚõse×ëÅSäžò»>³#®iØ%ÑÈe*%\L¢w¦3ê‚à–§%/ú ~†H-z"™ì.{ÉØœg§tLeŠv·¿5ö3x86nIéHp–Gî”gîõrŽ”±æï ø²®ö"+Ï!ÙH2ÿ¬Ï2DzÅYЈ©’…lfÄ@¿õ7çwwìeËOªNŸl >Þ²lV›¸!5üð((o\§&Ïÿ‹‡ÉÁŽ—›ÞrhÐOªqwBæ)‡Æ£¡mÏ6óõŠÈÉÝ|ü óÒ8íÄ U¬H6ªFƒä‚%A4|iëö`Š!Zo¤ë7Ò{Q’Žƒ“¯ÿs0Û'ƒäV¢AèΗ8Ø.BÝëÓ8nÙÛµê¤÷Û·G=¾¹ðóÐѽ{>±g‹Í©ˆÖ»T<•Ñ­ŒÝyßïGæM ÕãÕ_JŸhúWY@jDU ½ßÜq´LÀÞª†éœ 5ËgëÖÐHŽ†Ä„Álq ©øŒ£Á^¾ ˆ“^àÖäA>¹ú<_:_>»'^ŸÿjP`§A¿fMþ0Ù»T5»üs*¹I¼Ý^^ñd;ä'’1G@Æ‘´ 4Iä.)E„d G?gG-Ú#t3y§®D‰=äyPoǺ†`+;–ÖÜëw뉣¢9¢S4¸â€ß2T=# Óa£åE¥àø5@t·TT<Ä©"=5™çöÉVpÓ(?_ p̘Ÿ¯šÂÐÿåú߇XC$¹Ì\¨­‚ÞÏò¤ÓÌÿñmÓ«´V÷`œ–«Ú“îöT@W%¸È͸üûÉžÛªò6±%i‰áÊ6~×C W0uŸ3Îë@Áú¡b djÔ£ø"ä˜ÿ9mõ¼¯<"HÜæòý¿Ésö#LÏŒà¯À).‚¼ËFê‹©( Óü*z Æœ~iEû«ÈâT-Nã¡„¢f$áùZ8þí%;jª3YL# ý5?ýyŽežiäüW½¯DÉùwþ¨Ÿô$žÌp‰›ôQϽ· —iéÀÉÏÆØÑUéß?tÁ4™kf—·JmÿòÔù]`~mŽ¥¿HÌM“?GmQÙѼzW,È‹r `|ÏãVC“©ƒG‡Ön„+^Èv€¢õE`͆¿ O•ˆñB NŽJHï^6+©¡¬q¯EqÛQ:µt¥Öþ¸C]±t¡”[>?° €¹»ùw1áô¥mþ¯c1·•Ž±f¼oÑa SY/ˆÿîSöþe!nEWÍ¿P·±åkDƒ §'…'œ!0&JnŒ ª ,—6ÊõJ—ôMÊq„¦y+ϼ«Ê´G÷‚–µyIóWV¾czŠâH&“äÝŸþ4Óż}µ*£:%2] Û—²˜ŠáiÕúÙ1gˆa“ÈœV¥òȯàbè¨î—õ¬}Ùv;2ɺÜE8•2æd«Ð霭w­­ßí¾4c8 ërýºR÷¯|O4²ì-µÚWÙ3yïk_³ÇÈÂ5b…Oî<ÏîŸúHŸ–À?TëÏ>±qµ· ! Ì5ÅTò;ÕkÞ/nCISÔ.›4“Êœ;#Û/\”¬LyÜv‘ =CœMÀ쥈yË°ÇôtTŽ±PÇ°£x:üI”¡×Ý"rsŸ\çÒv#K5¸ãxY;$1:~e6z Õ¬¯I/!³ôzù œàݦ5|aù_u ÝßË—–nìf‡ØU»Úµ.FÐU핲¹zó£7gâ Ø'¶ULC¹ß5$/u®qS䑈 ©öó+g¯IK¦ ·_ʱݪ¥s„² 6ç¾`R쿆ÚY6¶¥šSžlb’Žmüje1œˆÓñ–Ã@zD§„‹†8ãY3"¢ä%±H—1$ýo3Ä#â".Òjf›ÚQ»Ê}/Ó¢S'ºÓ!ÕäÕQ=ÛêÙÆc(YDúàƒê~þdµ* 1¶ÕèbÒV]^:²™u¹Hi ,Ê ó! %£ù‰+…æõÅPáÓ*@úrÀqÔ»[Al«z«ñ<ÐÈÞ&NMðoºäJ ‡öÜ;²Ò,*üsz}¦Ÿ²ˆ,Æ4NšÈ~&G>ÓJvÆÖÉ0bnrèE^ ð ¥)®-ËI§áE“ ÆZT¿²ñß^ _z]2hà·>g³ƒPɸHòÈÆ )nÛÁ³Ãÿ4#ØéùËR½gŸÝ‡[3ÎÝhnzl“iŸ0ú—½œh‡ ÇQ‰ÅÊÿîYáq6%¨ßnJó»’ƒìä<½ÓšXnÖí㼡Dë¦ö²ÅÜ$ÝàÒ÷XªÞ£Ú­E±,9Û<èÛ}(Õ'ôŽÍd;˜íÎ,97kfAT+IüeºGðµÞÙƒ>ñÉ/¸E‘tMwÍ£æD»RÚ™‡p±‰*U‘U™Ç½™|‰“1ó‚¿~˜°¬wÇÈÛ¬äú3“8ä<š¥ Ys&y÷‡4’/¸mKwà[ÁÆ¢LÔŒhÎfK=¼žA½–?4úÏAûèûÄã[YǃTÿ>vÑ T7Q¨œÙ,\¼E¦ñ¢oe®À‡ÑïÃÃ@¡è$R‡9Y;]P’zäåŒvCÕf€È™µqj²¢$ áaÎ}Áw•n`}ƒxEå«û·<Ð1’ïX°5¦ýÊz—Z_?G^ºÜ1'J•Go+é)`¸–3ªÒ«<66©x…å÷Öd*;zÅ·’òKÛ ¯8øœûTÉç•2‚… æVã$ȽI¼A΃£niᥕŒ‹È«ŸžÒÉPªn¼ßEêœL4Sþ»®"ü£ „M‘7–“_OƒYÆݨ¿ IŠÃìËÓ+j/¨3—9ÞùÚﱘe$Žïcb2[w2h—m—,ßz¢9à†Œ¹ÞìÑÎs;ÀrêÞqv8„ÊgÆ÷ÓÏ^H§ ÷GÒô(§tø¢U„”òàïkÖ iÃ]eO9à%=•ëL†£ÖW<¼ü@Ô&ÛgdöÙΖ¢U+-{z»ça`TpÔÒ›ù&ÜÁç|3‚‹_űð}+”8PhDºÉ€ ds(ÚÅÔÕ™ßn‰ÿP§…Çþü÷CòJBF$0Ùèf ú‡}ˆ‘~?ðØd¨É¿rý]4XF¼Ý¹@¤÷“V2‚ášÚø „­a’s¨ê¤ µâg˜`!(X}W…}’]Ø’² óÒÞ‹ÅôXy°+P«ÞÔ ß;¯à}í(%=H "R§ådÑÒb1óJöÞÕå)uãUªÿrPY q)yÿ#Ì´Ž"˜@þ¢Ð›ì£dê;N:EÚÅW3?¿dlW=zÝy€KÀ$÷.4SÂI{A)§ÍÓÃvά)i¦îˆk’õ„¢!;å‡êë'!6­XCØ„OiÔí3ÄÁv,êðÙÉBP§+›@Š©ž=-@½ÂJÑTò~<±÷ŒF O<ç8²a°•ÌTº±BÕÄ©s㔞úic\zkÓ,Ô»L!Ÿº+¤e—pp$!,_–Ñrf/ aD~†Šôè9 ªC-u®j:uU.sÝõ´íä0xã¶uÖ5ØT”ÚÊJ£ŽÝt}§[hÁ•+i8æ+,ZzÅÒ°¨- ç‘JìŒì´F_™ þ¦Øø«Wó”UÊßs÷ƒ\«€IQð¨ôcýpéþ¿Gêú.E&ÍoÓ½4²²ÿtabO‹¯ŒúËF•­ÔІP3Ø´–£²~C·S ÛVð«z¬çRhucDIGp†<„õ%A‹pµN‡åy¼H®®eØrbÍô­ù’ff »:º‹|æ¤Ù˜ùŠP—¡(BdŽh>Ћvô&:¡öíyG<y_r;Ÿ^ `âJ”’]Án²€Uk‘ ó“ýméɱP$ôÊ÷ºëÖ\ŒË ’&‹Ç]£ WRäÚ2µ‚KÞÞÒퟜßô-óeMâÄÓ¦F‡½íi@>)gp›=–e =ø¾ÖþH»Õ Š°J(¨(ZŸ¬òØ…ãL³È·ÄÕßþXÊ–Ž³`g¦V¶èxaýfg®%“˜§¯WÓošÆ}ò¤¿$ˆ[øþEæÛàÛT ÷$˜ ‹éªœn³”´hTK1O™·\4x¡ôR•Ù»¬ŸsŸæ+Å›1–PÂݯ<@7 åo!Ë ÄëÑÃý»ú™›Õ^L®CâPeQŽ„cg9§‰%ÃÁ=4°ûjžé^6WGäIƒ‹è낼–_>dxëþHiÞ¯ðá“òˆC^{¿zúŠS¾_G¬¶öZ.Uëû&3ã›3×ÃYj48ùPûÜÔ8U:™Sž÷‰o³X¡7ØèÃkí¨êÅ!Ž˜ÕØ;À*L³Ì÷7×­$+>+ðT%¹"¡…žØ[@ ”lØí[´k7ÃÌQ” ÐBÝN…³/´lÙiÓÓKukøéÏH²ÆùÓP6:뎀­lBMŒoò«ÁF#ÂĨ tÙq†®yÊ9<œò©L¨| ùó«Aô¨šÌ'B‹÷â&¼Á¦©®8Ÿ ñuiÅÓg7ÚlI\ $'‘á†S”8èwZK®ucž¼…Hsei¦ÅGóãñþ¿ÝNH¿dñÅ )wÛöû-}StZý„û]{r;ä;yõwÍ“‡¯ÿ¯ ‡Éq¾Á¨¡tÊÏïÏv?a_S\AŠöý-m¶OWÊ Íl>Ôðžq@dÍ×€íáÃ8'Ì&$kk„FÜrn6È+D!Öê³bž‹Ð©­@·zîæ‘õ€Jï÷@mäÄ%ö90ø“žÊÄ襔º’Ó“—ütÕȇkçULÁi©»—|îEòO¿Vus7J±’¡Û2Ï–®£W©tÕ.vo3œ\rX$P¾}pœ`åÜ©Û3L¨¨5(]ß ~«€}Õ¼K!F Ý´ñ së  ßpó3¹# ²9ãià¸3ªéGq "7;§> NÙµñ7Zï¨ÂENœ˜ûo”élä†F˜5;HU)“Í‘Óväl»!\{†óý°UÅÔWàm] °àLWV³myNî¬búIÍã.¤3ä>D°úî‘P>ê[¿„‘Vi’u^)êüyÔìÙ³±þFô–6(^u ·E²#n ,þù‘8Þ÷!¹œðËo¨Z¥tÙM‡ñÔÝ;|w¤ýÕír¬_¢2ÞÔDòÙþRM4¯ö ¡ð¦\DÏ4%š›¥¬'4_kÌÔ€DÀËò7 öxô|¬&‰ØªÈÆÃgÌ:\%à=Oý¸SB¾•l΋xnÊjG!YP¯á#Óë+%0Öôi,÷~ÀF’ÄÅF¹-á¤~x¯ ˜¦ìg¸êpN :'GŠ÷/!–©,ò ŠT3þ4î;­OŒÿ ýÅ×èÕd¹›äáæ™'eÕþ„|ĽzÑìíñµÁe£Í£1-bW¬Ë=ì Ø–ˆ6*ÜTãÚÛÂaî„|TÞ®“2P# ˆÅw°:¾#ýŠlïåmÉ?u‰ÅD7f¨{`¶E›NE±„™b,jÊâÛ ‰WÇø´¯jžl¤Ob«P>)[¶öüßdy?þCIj„ã^ùÐæ‘dHzˆâXVêåo=WÑ$¼! l`¤\ù…MÆ ¸6sƒº¾ôiQعÌjï¥w?B]+»>3ê"30’b:%Én ±ÝÑ:§ÞÀªò¶·=ùYDì‚ɇ‡¥WäÏ1‚Æ Nʃvg†H‚WžP·4·k ¥¨<³ÇÚ“íµ$7}\gF¡‰¸Ød7oÒGÉþU.rèç[·P¾ûQ©{£%8Ðc¬;Jïl'¸A&2¦Ú:½)¤+¤-¶gÖÊ6ˆV²• þp{±“sœòúß3¯—JÆí§`f‰GÄ»èåMµ¶¹O¥£¢Ûa?v[wªû™5Ôäîÿ?&çοúnñ¯.Û›%$Ä]”}yÙÁco8ò]\ž}¯>ø½ad(~Q¡&+ã Þ.qz3þ3Ž°/æv3q¨’ì—2eÝ»8Kkj+OßeEHzákÍGggîlp–n“ hÄ»&ñØ€m„Ž¹^öõ:Q¥xŒN<î#é9mèÇÐRÖöý\8÷~géŒ.tÉ´´7ÒÆ‹ÇŸrÉÑ*(§Y?VäÑš(“‚ß­Yz¨rF î½-åéÌ4Ôž4V46.ã%ð`¼qâüs|@…C? jZR¥”ö™”áË骰è¾<ÛF@ „2|šT›åÒS1¦”Tµz³‰Ž„J÷¤×í ×3Y֤율+ ögµ>©®¾ašú0:tš'+¦ª j¢½Àœ;bT„˜Q2õ¿eˆxˈþQ^xÔhÿÍžÌ× ø»Ðø5¼ }ÓÿÓÄÊ+•ƒ±ÝF"™Ü}‘šZ¼}Wcf)Ú<¤ðEõ4|^>m„Ž½Ï½²³uüpòÓ;yõì˯öÓ/RÛ õ¼‚°¶K3‚W˜“l±¸H%°9@zååyé†CS¹k,‹ÞŠgéuJúó ´¸Šô@˜6åjTñUòl‹N'_ÀˆÐʇIç¿Í陚Éóò•Ÿƒ&g‘¢!õ½m­.<ÔÎö[Ž¯û'©€uï—ø …n@æ ²¯¯¨Ë5%ƒ)7fc¬Æ ¤ì\±‰Ó²UPùÊt2¹¨C‹^þýtSôB« Do¬`ý³Hê“ð½ˆyW´BscÇKB˜ÎaÒ¯X5K¶„O^´vÊA®‹o•í™"áà2Ô-4;W<õòœ"y›*ܾlRw;”$›7Áõ*íÏËZdÝ6ÿíǘ¨¾=Ê€Ä%Ös²"™K>Ž¢³ º$îÜu jˆÜß„DÙÂ¥±ÖÛ…ÖVÛ™6Ð:ùB†Ñ5G ÔlG8­ˆa¥†mEuø$×>©žþ°bÐUËÓ(÷{0 ú+ÊÒ_½  aäÔ o§j”ÕZ„ìúPf—ÐÍRøÚ)«4CÈZRêô£¶ø+Õ4@¨)ç/¬×'cÝ[i, ‹ÜˆËÁ9øóêo›GëŒf‡ÎPå‡Æµ€Áê.Fºd^Åsé4iƒ€­]~*åíÓë\ÅÆ+%¤Gu™Ù\"Ô_IE¢-$R¯¥tU»Ç„H<óÊtB×ñlè2χ'£’èì7Cä"[Ý0³5‚B[ìHÝܱ׋™"Z®e«0౦ê;å(ÿ䃓ãý±öý=å^ÉàBn&ï¹øûiÃÀ€þ8v¤?ÌŸÑzÚ >¼üR¶¾,1M!×ýÐð:¨Êžg«Z™1`±’ýýtÀä…´+Z¨†¢ _TíïæÀmyûôö-±0*ö9-Û¹LU𸠼t­åËkõaÝû.¹”§Ba ݾ„j4®œ³å‹Lm–ºHÈ´Wj®|WîV>l;p­<“ àŽ»q°|ßAóý‚•ë´i|jȃ󭚦ÐþÃË­s³Ë:Ïïì@’Ù¿l&ÔÄìüB *¯Þø7Ó±B“TáRÔáÊýZkËVy^ÌÈܧPØJÒ~Ö5h˜ùS{²‚@­X¹&ŒöR®8c-gã*ŽÁÈ’&' œæØ@ÅÑÊî=ƒ0.þÔ‹>’å¯ãôã*B«"ŒÏ¸’ =õÆšÆ_ÉEC:v´‡µ‹åþ>1¬„Õ—M¯¸¢cÊ_ewŒ‰ *žÄ³ÄÝA;2Ü×åFrzŸWŸ+ÝiGð3qt_ê!L:4àû’ôv/ÚqïË>‰#)wÙÕ-%lÙYâ&ÞÆxJ€Æk±CdþHþ€yês?q”c‹ú+bvk¦«‰£ƒs¼Î÷õvNXWøö5U ‹X,ñÚ®üÛ8â î3§- `^)ÙQü‚!™5‹J¾ÒƒÄ ÈÀÑ…:´ZÂgÇcä=ûÚ¿œø5K~L|ÔHÿ Ná”$Üj8ß䯚ÿŠö ´KkdáŽeˆ=_…+4‘…Ã@dÆ)U¶‘ÔÇa0ˆÒ?Ճą›>Ò@S@ Ó¤ŠöT~<çŸ}P. 3ïY‡U’ î©…Äoöœ#ã>vÀwÚ»,C8©&)8ÀÉ»€|ÀÞìJ}žwJžyÉ›vî²jØn¶~}߉PãÆÏ='8h@ Û8ÓÿîιŸƒU>yeCæŸÅlŸá‚”X=¼·Ž:”>qv8y°´LKŽ_ög ìgŽ‘OMÉh™É¢¤µT²öSЄ¡ûyŽ„‚}èýúG¾ŠVùÞ ¡­uJv-Û+”Ê"-ŒæÔl%K¥øSžÝÍóK|žÀf‚ó)îD¬±ˆ©úT÷½rti!Õ[·¥Yô%¼ˆ\žî|¿,ìŠåð…ºú%Äh¶åÆ'7Ekµa ´:ŠŽç>ç+¹í¾3¥J˜œ°Öˆ’¸'R5¹8' šáâV ]*¸v†Øw矠½^…iqH½²äB!ÄNxˆ±ƒxx„ÑIÄ*´å©(‚[¾®5´Ÿ¸€ŸpnYsÐÍIM,̘œ¬Û¾ L*žBߨ¹˜Î*ü·2çé–/&ÉIn  Í…›}>GhÀ‹ÆÿavX¡¥€®šbîe†Š'˜v…ÁˆÚß{ï¿bq*bòºV‚èð>Ø$Ç“ìeÍ&úÆK˜Y‘È:¶¸zv¿‹ÌÓ_Dm ˆó`VÚ½WåÃí1H·ßuhØsª,Ks¦ŽýA×*ÖèÔ†òçøËFp“T˜ÎªÃ÷¶fñ†™»ª{³tö±8 W)®› sqËí^Ÿ^AM^þmé$Ýä뱆' "o|ü@'îUjIÚ‡3R¾bûÕ {½ ʬ /Ý!žÐëÞÊ'¶VÈ25›ùìG]@œâ¢EÿÃÞ:ýÀlõ?¯¡á ‡ÏË#Ãæ>_?âRŽý´×3¬ìƒEs«U^ś龜Ei¤Ý«'ŒfÖFCŽ8AGLý›#'„†³ãŸ™LéÍS€8¡òtûÔóuyº –kŸIœIåBa´Ï•·V^*Ùˆáþ3¢ñf‘Y›_â1RI_¹É"ö÷tþ‹½)«[;¿tä˜ö“ÎÂï´f ĉ™¯X¥ððá„=¦;±&©Žàe_®ªu?í§ÝöÝ\5û‚ZÏS—k¡ÂUõû8ñˆ¤ˆêÖí 5aÞø›ÄùfS»‹ÊUL¿µûbúXN s„³[ŠS·0'ʹá‹œB‚ÝP>2'“”i©¹°\:ðwK*Ý5x$Æ¡Öz'Åþ®9•ZäÖ}¨ºÃv€Aí&fÁBüÏäú+l±¯:¬g‡Ó«XDéÜ?¢GØí N‚€.N6èlé¤ZѤmG (çLZ\Ð:øì.™$#1Þl´Dq<Îü3ùMÔ2Ÿ¸Ã€@k¥ 6%iâ øUÊûÎ! /´óâ )(²òµy½îP˱éä%G6gƒaTFïñ5Uü¤X…±çv†û±äñ¥é' Üá[ÄBxþ¶o†¥\'´pÔ«‚QÀc¤ªÎ‚œ ½G ›¢ðTð$žA†Ù¤hn²y0¿0ØQ¨ïv$<>•—Ï™ugÜ—K!·*Øä21gDRÒÍ Ñ-°ƒÚ®öª² £H;´Æl5` +2¸²•Ç‹È›dÅØ©y¢ @›šfVÌõÆx'}—õ„ßæÝ¥£µ»‹±PºË½F§4;7Åöª¦°ÄÙ02ŒÜÔg™ ,‡Ô¶.^&_÷óqo™¦³—F5Si#GÜ–ÓrÐ8‹f…A²Ø¨Ó58ÊÖ¼Ëhv!¶±N/Jú€¦ÈÞ°ª/»ÝGX¦ML0æà˜?ô±HØÔù¨˜¯¿‰bƒñ†Øa÷ç¿@e7–õœêùyÑ£ºZŒëOÏ9ã¥r~ŸÃeÝ•Ë䞨ªBÔë¿¿ro÷UãÍ08œ^4îLé—'qÌm•àºmî#$^_©Ò¯n+–1·÷Èv·Ö£õIùµ²jEÑTÓÅdÖÇ÷: ½¥1þd°YL°2áÏbí}Õ¼% ;iØ>o‹‹¸îçß¾xž˜4;™Šs-¬—J±֊¦(€)„àêò‘·&äň/ wmÁeÓ¹ñ *ž­rµ´/b›š>|5¾ô¼Éx7ŠPP‘8Á¬aŒW¸¸O0W!C$*²±¾çâ'ˆeÎCb~ZÄé~ ÀAA;‡H£Â‰À×2mÇ_ùÓÓ­ lIßEöÍ]åÑ’ú+ CT,`©X„$P1UßÈA>w¿*[*6ø‚ZaÒGò8û£„ùI¦Býž|ÕOÝ_”±C29•ff&ª ø¹’è,T¥Pñ'NúéÖ±Ž`–Q;ÐÒþÆO·Åê“@…Pö-;Ýk[)r¿V¯PFÆ›'¨¤ôzKP‰Á;jÄG•ÉebE —ÏÏd©+^Ú6û& •€• ™Áoõ=ôãoû×øÀ9šÕÌúPÚÖ4Οygu“G_8þÓî^uà ¼+/lG-|h C£ÊY® §KŸêOÇßÈ—°²np÷(-öbÍÎPDrÊ%¢I¨Æcæ4õGû)¾Éâùò¥‰°‚yfî`'ã=ÌnUÏtöÏW£väklxp>ÇOŠVÜŒHŸ?ô®`W§Öõ‡²KeS‰1T:첡m þ®lÄEü¹Eb÷|âÆ$R)æköïNyòö@ßðMšh!¤wIG?\”=i×aÊVΑְT¾`L‡/Ò©üV¬Slí¯“Q—ûCìÀóÃæ^óOì2 •JKJ7º¥Ì»íîb½tì¢aÃ\äZôP÷9¥˜©`ZËOcc±:W|Ÿ ~¼ìr3f®ÃÆF² yªeˆ&§Q <͸ìc ª`¨¢8ç"CœÓ±¶’s+&îÒ¡Í‹ÒûH´K†ÕšÅµ¶~Ž@ŠíFÃÈ0jü+X³*¸À®ÕG""Z"ÃjÄ¡F_cµ¹+=õSD”kæ×/ Ì÷ý„Ö³_䬷À±áÀ|sÅ¥ !Àì/‹ÆØì¾­j`]ËŒ·×‰Ú½‰“ö]i'Ò¼ÉÞ£ÄÒ`Ç㊮«½Š-ñ}FÖ×b}·D§Ëî~ ¬VÍ@¿¤i=ŸES$ë+YÁïØIÃq›í˜M•Ç2HÜÍ8Ìr% Ê ¯§wö%Ö|S±C/½‡øë#FŸˆÌ„jñqY~VmËÓ}{Dáèr& ¤±X¶\ù«^ÙÄi{„<2?ß}¦<ÉHtÌyÍ—"j3I²Él¬|úô”ÀÇp4­OTcò݉°.…$oªvS £A*Är6Äî&ð2*Ϭñ½J&ÛûG~IKÆ”Ú cåÜoÀ‡A@@ó>Ø¡›-:Íc!Ã]ù¯6N„)ÿ`vo‚ªn…B/Ö£$°3Ï;cJdÌ| }I˜t‚³ÑƬ ± äãd²tÙ¿3`¯UPá hï› ´(ÂÅ_îªe/€ÍáqÕdÝÎy×÷±œ= N´I²-x^èáç=£Wm½÷w(qU„S…;D,&å$œ›Ã°G>±Œ¬a}ì9A€Å;hÌb\])S×ÝÛ‡–nÂzåBFrÒaÆ?‡C½ôz±á§Uô"4& ‡Ç ¦T°äÀ’ÐË‹¤È86‚GxR|¸¯Käž÷låp:¦íñ°½qÏö%'&ð{L„w‘‚1% è›{:”2~É$)¤P\^*ù2U‰`aÊæz‚Õ‡8¯Q·uó3E†sÛ~DûIV#0®Ñœñ¾¢ùšºon¦nNZÐÆ]=‚{ø®!ìÈ“kÛר£…Y‹éÇQ«ÖüƒŒÉF%j3†ÑV¬¬¯‘ù¥±ÚŒÏõ•Žè7jnó@ÃÆ,N¤’¹MdÀR†äY-,hWªjpC]o„º;ˆ‹®¦ ¾PdÕ–â!t!“Â+¶LÜ‘Ë WýŽí_ŠZ!›3ŽÅcÎ`Ý+«µL–‹O“±ÿ²üY4Ž|hy×A²þl»-¢¨4[ÉháÖž(0ù¯:²DêÐ ëé"¥©”û2v\¿XŽM[h£9L`@p:IÇsFd?ÍÄŒÅŲUÀ@_Éb—Ó߶ÆYxûì„šÆ^ðDªBl~Åÿ&²[ˆ"k;ò´ºùéñ@2]ƒÒ@$(“J®u_Ñ Û Wk¤y¹ÇŸA³~ÂËìdQê0šÖEñ-Ìüür•!“L'uKKw÷ê¦pø¢dÌ I Ö(5j±òé¸æøÖŽ·?À5›Ìuõ ÂÝm©‡(٥ᙦ€ôa}PÌr©a›«ôÎTâ±,<Ì…7êÿÎè.’ûçŸË8ÞG°Òˆ½ë¡$ ¿m£?š*¥°½(’ù\ÏÚáÒdb y¯u›Ø ‚C|²Ò¹n×1œd+jC]·"€v‰OGEßI¼)à•¶øH#0*XÆŽ7ȵW¹O{ÀµðÖpå)x|Q`F ¤·¾Ìx[!Ô3< /Ë°6A²oð¶/´Õ 2Ñ.P¤ZòŸV:rõD*)[o´œÈQü†„?yI¿)5Ì;)¦´»¹þ Ây(€ÏßòÉÁbÖjrê Âѯæ°ÿcÍÞi:F‘½¼ÍÉOŠö‘­gÁt„ïZEâld|%lM3ðOpŠ=p£­šxÌ°¾o]o`í4”u/‚Xû²«mÍ›ži_Éþl,o.ãîVmEßÎö"Ú±̸¹HÍ bN-+»‰o»MES‹îš°ÅæüR0&ï½›€!)LG+-cÉGÄûÜ †×.þÜ\9¯ß„šú+°ãChKšŸM¶ø§ÿ¨ûc„‹!°€ìñÖ<Ê Ø–¤+”•þ ­ü¹ïÒÔò«¨'è‡ÙØY0ÝY¼pGûÙ=L0€£Œ×iö¾>ÿÿŽ3ÐØóùÎTz–Ó¦«dÚ±ä$¥H1ܱLáïqJø¢ë|ù¸aÈÝÂZJ¶ »GÎ[ÜÝǵ ]A‚ÌÛ'*-9nÎN„*j~†çÈŒ»µÍê+«p™3õœÜ—ï '|­‘gÂÃëש—x}$¹®±€%'AHyË5ÊÄÇÁ觧êwµ4ˆe,Ø[lØd4\­JcQù¦»ÿ¬ *Z/èOª™— »ûD1¥1 åjÆ`!*3Ç&#uN‚‚IP­N¿úŸÜꂦ\cãB·ªOlM –Ž"¯˜èȸ°ö7yyÂýª°¨?dj°‡ÜD“—"ÿ…«_IWlÐa”—Iæa¿˜¿@@^(ˆ2ƒÈÃßL ï6£éä'É»]3ïrø ¯WJÖàJþá—ò¢¸öÿê\ŒßñdK½õŇ°@‡=&ÔŠy7î<Ñ]]¸³¯€@I~*ÒKpˆŒĪ4NU'£‹ŽA0”=/ÌOµÞð#ì´¸÷ (®hlcbpZ÷´3ž-W4,"6™Ø³¯¯|ÌPÞÓë¦ØvCt( Ñq“_‰JÛ˜{äýââ×·Jäj|Èn¦J‡8kJ»ûÄGõîƒ&~-æðó²Å5úìŠÅ]8Š€ÞBÇÉB= 'Jœ¥åŠƒga:WºÔ0çÔ7Xx*¡ËIò ƒo´tLª¨I+]";åÔ¢V˜ÕTÐ÷üäH-wÚžþßÚg&•‚O+!zm9þ"6;Ðc˜&øḺÚú„öòÉ ”Cb¦{£Ž¸){Åt£ÀùWŒÊº»‡$&͆B¥¨ç§Bš¢Šª‚ƒÒ‘l€ãE£?9)aqó“¤'¿ ‰ó©‘ÙŽ‰ÃèðñrCgHªþHC˜hJ²Ó’§÷‹'ÁŠN~ñÔ;lh±&œ_Žwa®Eà³Þ5 Jª"ßÕÙâÙÅö¢KN‚ó¦58B)´% ²ê@Y4P@æ›y64Š‰'^<׈I˦dÒõÖ×|7×÷Iù2[¹ Õ8nS?sô!ìò%¹ëÚràJ Év5Ë–™tÛº~d˜óµ(;°{ »êqëçpTªCÒ&ýÂ÷›ÜåØZ5L#5úÍ‚Ž°2_$(_8 õ¾± ×seå Ñ4ÔWñ)_“¸¬ÉU•Ù·­\óy5Ÿz‰qȇw¢›/¢àEÑÔ‰vŸŸÄ\~™#1<ÉÊ蛪±yƒ ? ÌÅ0ç`KHKBŸàÝ®é]Åß­(4^o2›AÑsjŶñ¢éff‹íúaòöÀâKæ;FŒøþ9Ûç0š~ò±ÁàLÕAã”j©8ŸÚ{¡N¯í²xâtÉÕ¨,Kö¯òoöá¹z÷å²ñÈÄþ¡~TÚ{k(GÆ¥'ö¦gYñnÊ‘hº?Da!=h¼ƒÕ¶r½%½Ò!ƒ’Ü/A:¯¨qMè y_÷ÆFoÔ€@9ÓÎqbÈñjh,ýHvfáyFp[B¶ú2áw i*›#š:œ‡UãImkÛ ±¿ÙmmG›ùÿMjŠÿCT}£ªÊ6õúcÚ›ŠøMÔu¯»2`è=`]~ܼZö£ßm89¡€x꺂s3ÊSOŸžuçƨuæ•>·‹ðaØ°»¾ú$>éñ9ˆÜø‹€øzˆUOË!Iî^—{åPšë7¡œqׂ¢dW0³28†¬³ïå7••Ã˜@Wƒ: ¸õ.Äý´‡…«È^UÜ͚˲^é™.§¯4 2=‚§k 7Löð‡¼Ý#ÄeÁ×½-ƒ^èhŒÐÓ†³†„+v̇Æ«§V“Šð”÷•Äp@”IÚNÂXàß(dJA5QÚèñЙ¬e²`šãp«æþÌ™X^œ¤[>L>§B­&¯Ì­º¡]ʸ7”‚R*¾èS4@xþDRÚj;L^î «)ÛÙà*Ù± Sè5’Þ2ö—4ª¦„.„¯Ý (ÏE.Î|&Xõdúw<¦Gô 3¶í!ǪÃ-nÏšŸ[ë³Ñ47F¡•ÍÅò>÷çè³½ËÓg‰*üAV”PŒºùæ„Á¨ü™PœˆÄW} ÂüATéaðˆ…úíò+^ »¨òUóÈ?c8Ä×rcg°ÖÅUq3tÚÓÁZ{.”aw,ã¯Ð±(Õ>O¼dÚž}bÊ@»×sÂOl ˜ÑPÎ#Ã}ç"ÙV@ªëÚÔãô½–¯Ô¤ûÝÞÔ°æ ±g¸y®$83ì×%}µCÕ9F×Uh½ìÔº¹âä’ C;é¯E¯k§{ »Té±mGÀ Í $bw—l¤¹–ýeÂCnìñøˆ ˜ó:P|ä2ÝèÜ…oöË….[€ÙmÔ|Æ•Ð6b«ø_-•'5LCÃî¿ØÓ7góíÏ‹sÂFF  ‘%&*~Ò:£ŒiŒˆÚ€\£)_êpIáevW@¼<Ì.ûžoßÐLê¶r¾Ô‡ƒ˜Ku¬75•Ìx´zÍéøïô–½0Ogì”пKò}ƒ¥¥Œ[À^OÙ4fzKn§ª'ýƒÎ]hÕ J¾M‡ia/à%0!D»®»Tx£àü¦Mq®×ŸüøZ1Îñ¿„Ýñ„Œ›4é™q]XljÁ †»jGpc™ÿµd}E6‘^ ɤÜÏ΋š ž×XÃÈÍ­(2­Ïri M‹XQ|1?ç‚ÅÓ¨ÁÙ!R€w…Ûíçõf LÙÍÅ°ATÿ5òw°*¦õQÇñë„‘áXòµÎG”{Üê`ôƒöŽ\ äô$ÖtØÂ٦ϾÃ-òz¡’†!•~Ô(k¶‡>±iæ€}XçE£!¢¨^”8éÁ1ÛšdA;ÔõMă3òà2Ÿ ê…JX¬³-°¾Â ¨dÇi¶ñè×x*‰Ô}ÈÚ¡ºúÏÔ…í WþRÛaû;\œh<ãG” DwÛÙEGu³æ@( `û(Øí÷¦E€RLYwwû…?‚`“)óP3PêÓÆuGJç6¶}àYú 8“ÛNRYÆK¼¨6ÜßœŸí0Í6ïÄ\ÈèÛàÒȵÁ¦ÊÐET3ÑÔ3«Í]——' ËUCç£ä0[3y‡ÄZnÃû Ž™&Rü(®«ZnDè1ÝëÅÓÑE‹ù Øté wô¥õ+‹×(âÇIú¦ïÒ}͉þöî(’ßðG¯Cg‰Â¡"—™DÆÈg[Ù;,£p% §Þ—‹"\Ϭ<³í˜ûVØã²U†‘•ÆâžþUP:Ç@½Ú"Nž%ÔÆÖÅ"?wé‘}*Pfq%ô¤¿#b€ˆ3ˆv5µ³†,“ŽýpK2•‚~¢¾|ÁÔ¦Aþ鯴ŸÃÊ.šûwû6ê®zµp‹R^•¯ ú]B,ú«¤‰ÒÄQ‘Ƀ fÊ=¨é/È4  ù°[ù5çUì*òu·Tm`—øvež•m "tÇ,Ö3 ¹!”iâÁ·p¶¡ißö#m¡¯b£°4QÛÑÚ^ÇNcÉÀ êq*Q£Ä™8IõZI9ð¯©ä6k<§ôȧ8Ͼëˆ}ÖB„ݽÉeM³%EXÀØkU·QÔ†‰P.Öf¾ÓÐ!fyQ”1UæÁ|ï&afXÊ"G0¢†Ð?^”€x7\-@Fš&<äÌå^âÝ_ xšËÆHÔ™cûÌ0c:$Î%QQÅÃ{A©â.xüŸC‹‹­†D]Õ§0ÀÈ ñ˜ýYD í¹0½ÙâQ½Õˆ¿Ï~VºT¬úÝôöƒâú«/Ĉ·/fJ&üÔ˜á.Eب¢Ó™JÁô>TÜ&hšâ§JEª›'Ï|eÌöÀìÎ`µ=ò{ÊœŒl[<íó¹ühMéE$¢ töÓ8$|˜XeÏbá;¹ “œÅûjô¦®Ž›×.nb å9½!é²^¶OT=NËX%j~·ÄÝ ®{éGªæ2×rlub;s“¼ú]·ûèE‡é Ë[l;åìG” [¹0y{®7šoì=Æõ$N˜‡$C|ó1»9û9eÓ‘ùƒÖ–ƒïÚ¡ÂÏcgŸQQÀ„S‡Ùp×WÆË•ùäDf,Ü`×&6Ϻo,ý Õ¨2Ó¨,åÝ?ïì£728æâÐ}\BôùÆ‚ÙQB‹ÜÚº¢´¬Wô ³>EfÀ8Q÷Øû±1¼)ÞÇ%ÒsPø4×;Ùgó|¥¹ž¢¶ê,v ˜­Äcv$Ü<_Pv)ià g67jÇ×),„˜ñÃ%iÔwÅÕ+tÀfìå £áhÐÁñO,ëUˆ=¿bçZ0ò63¬p@!è~)&ö·E{P3hmÞˆúz&«ÕFÛÿ.së—\C6¥£ FÅKvlxðàȽZw!_âTLD©_fNꌲ[ý›T)óeÜLôlÙ'×ø'_:¥ÄÏü,ËŸþ^d­üæ-mÀ¨NdcŸ|c2לVmÈ¥r\`§IWï㾉xnâ¿C[›- > xÌLvw¹Þae$·>}{Šûvnå³Oèee¢Úv–9Ö‚ÿ/1&¥³â;– ²ê`Ï]0^¨ªB_3¥Ä{¢M†9ò×çxÊëo©§{í²%©tgÆéô¶&ç¿Î;?~/bö5¸™eQÂ¥1ƒÐSv`°Ë‹ãæËnå9àGZIC–gŸM¹ZÖ™Y0A+L²{º,°@åìµâ krÿê#Øv…bÊahˆI\R9O'ö¶V‰ ›µŽ ¥¦ÔQ¼âÉøg°w1üç©oSg,Ò>Ÿ?/ŒÈ°•PVÓ'A$)×iáI!¨O7ú  ?:Ó¡Á¢˜FCb÷ƒ:ø{uûR†©¦¥È­n•q¾6î_/uÊõ«:_?ƒ&û†ÙtwõÎGk¬KÈ`݇0×ɇ‡ùb·ÐÔüM‡-¥ž¶-WA/Á{nWüº¿wPø&2üo•èPFÔ!:Y—V±$¡R¶3•Pqqѳ6 ‚ Ã"¬ Q_-8b!và‡gù¢Ùâê¥?£;ÁêËrW½uÊz—[Ÿ\ÐÌ»Ô`•Œd§óZ„NÓO°yà«êÞÄ쀔 ´ ±t¬|¥ÛcvŒø^I‚”vÔý7ŒÇB{!ŠMcTY6 E%ÔóŒ« Bkp.bMµ5â —há«I­Cê“uφÆW3Å« "œiÜÙ°ê/Tân¡m~ç8Cu×éÌthóXú*•´ M´•½ðΦ* üxÄâÌ[È‹½R v:Ú\á%Æ£˜²è'¦—ƒt]”£èâø[Î+$PEè©z £†AÖº ÆC^Û‘f1´ùÙF¬+ÿzü>c+¹*R<[Õ¶£r8¨nrK#ÞéP±5¬ál"Q¨¾Š–$èì@(Âì»–¡ETfÙãš²ÿ6ÖÉü¨|f²õ> epc;zÒÒë‰Å-V“`ÁŸ(<¢qJ“»vÚÂS½c‹q¶Œqÿ²dVÚ'‰—^ÿæ©¿uN@h¦RnÄÿUNî{/zE¾P§>Žk˜tö~Ûˆ<‹ÀáÃã³³ÑÍÇöDõÜó´t›UEO$Ž¨ùµVJ¨q™ëÏ |¬}xÎѤv ¶Ý˜®çé`Ò=é@®¨¨}< qEü?|Lˆ-n,öoÆýÚ<`Ø|C^»=¹É\ÚpIÅÈ}Ì1Z¿ËXfÈ:“x=æ_¼p§Û-ܤ`I£ l<¶Ž‹¶Lä`”xåˆ\K&`À‘ʲþË&'F £YÑÙ¡Sgn¡n‰uA.£ G=Y~FÍ5'¢ÐCœöR÷`Ù?\Çg}c9­-Cš‰é€¹ù,µ¬^¦Û'—q(Áèÿ[@VeW©×ܳîº Öœä3ì§\РÙW^v§‰¼=àö\œ³«*“Ÿع¸Ãå Ïn—ŠBiûw ü¾æa×[€Ã:ºÓOÜ‘4ËOÞTŸ™Ò©{MY÷ȹ(í?ÜId víkSY(¿s(ÑŠ-ƒ¡}‰s‘džbôÊ>e‡#ÍÓ·c}í¡õT,8±[Ém´ZVåÜãV-“'Yæ“:­9ê*_ʸEÞg¿rkX5¬]­ÏiyÉàB÷_ó½Ë/˜mBƒ,£ÃÒŠjö@ý‰„ry'•„FLj†ø¿+(5E( <˜‹–D|ïÜ+š’$òmBÖ¬ˆàƒ"½¤û±º‚xh€Ûî5@Þ›¢'©^ÿ½ïÂÕðR°‚gµgc-ŒLY,$ú/a5ù0œœ†jpudgþ^ïønR* 0œ.ätZ!ç)+hÄþ@{¢ØYêâ%ö#àWŒŽ¤y~iPl{£e<àGáldËæ̉CàÙiâ”Çó·üñH“‡*;+é ›‹ ?ÓCž €òÉ<6–¥„Ù¾âXé [iÒ†1 HIÏ/bHÇ=O » ª£‡7Å5å=—ÖÓ]0«Ü3ƒæ+³EõE‡%:±Æ ÌËûÝ: ²Ê™,×+ï1CR7‚ô!yþe_ÂãÂëï0Ü@’.uÅ9w:®Bcsocq±]Ò´ꨜÌë$ŠO“å¸ 09F¢c¬F7OÃÙ,éç$-åC5’[ ˜SG?…žÄÌâ DiúEtXFÄ`«nŸ—»®U¶Ž1š¡Ž&|U'jÄx,W ÚõçÓŒöÅ‘*À's@;dÓ3 ë©M˜šuöl–žT\ ;¹0°¼ùË­€z –úÖ]âH¢ƒh~øg‡J„B¼}ûRY—”Å]ªÕRšµÁ<·î=?é"BÛÅŒæ”U³I‘[£ëq(+Zê9˜«( m±ð¬vËñ ”ï©Ûߺ`ÊSæDPj…–´ÌÎoH·õÎrlîP=UØgͯûc;%UsºÓ­Éaåj㉡¤óÎù±…%sML%¾ÄþŸÔf™³¼å•š3‘'Úo—k×í$é¡tòC÷Vi/´ZtØ(°ÔðÒZM‘_ÍŸC“SÇQv`VÏÔî”Ç´F64€˜šëߣ«ÔwÝËbyW.¡Âa‹;Ñ•# A¯üÒg˜ ÑÓá{µÞáÃFŽÙ¬;˜¬kud[:Óþ3²÷eW±˜LÿJJ|¾”¢»÷@[|‘ú Ï:†s¿¼ç@ÒRÏàçÌ,#|„h‹öà!+!-ÃWеX—½fH¿GÞPb œ} ðy)ŒÖLñƒHò/ØÞjÆ—aön5zA©ÚqàrÜx†;_!wþæ…IÒ¯(å'ï­ÔçñØTi¼̃ïG 8ÑH<òê˧ „^h÷í´‹2€=¾-v@š[öH7öU£øC)å#0Ôsg]Ëó«*_ŒkÝÿÛi"`«ñZÞhYÝøu½6ûumðB3äã€axGnWߧ}t|¤×M[l"{ˆß=ÝÌO‚X‡uþ³Yj:rÔœxZÄuªFêlM ¦RƒQc_ýõi3[‡÷™À¨žêW*–ÕŠå3lÆðtD Ó {Um·Ý0£èM%Ñ3%äǽõ¤°Y)xŸXGàu·…ºµ¾.×Á|µQ×1¹¶¶Û÷µZÁ‡fóFÊ“¶y±sUæà†Œ” 7’¼ EÒý›¸F¦v> ŽàZHÜë9)AsGX´UÖûéË|öÚC ŸÐ±/¯†yCìh‡Ô‘g°êuMþÄô„¹wð µmsbÞÔa*þoŸeŠÓ†š ªfÅ/Ûu[ú½”vlxüUD$%ÏN5CëÄm,Œ>n˜ùh,G¦k`àîЅU>ø±sÉ:Ȳãª#¸ªõ›¡Œ°|êv uþÓñã±8•ÆôR.•”餙Ì„³cÜ»çj²=ëumˆ¡–|„Nßh•ŒL¢ž‡iÞ5¥Ÿ |êî­¯Ft…„™‘IMg‰Zr÷#[haé$å÷·þ2ùG ¶åÙär‚­jƒv†[]ªí(é$–aD:!«ª9Ž˜WŸö†žíͶâv®&h19Ì<šHŒ@ÐŽÙÓH ¤Å†‚;'šäØùÀ³]P§ ø•tˆÚ]Y„Ú3¾i)Ðô5×¢ÃG¯©Væž&q(€\W7ÇDXkÿ 5È¥a¤ýâðgF…ÐÆ3Ë•%ãQGá_ø’=Ô£Ena¬™obŠ ˜á€­Aze¾h½Z’z—ûL(W·TSZ%îYÈÒè&)ó’_·®}¿lï†qênRô ôXl¡º‰àZlc…-Ö,œ¿†xtbHQ;Œ<±eú›æ‹”Þ\¬É3ÀI ‰¥Ž·Æ§x€L)ªõ¯T†É™^ÎTÝž¦FŸcÔÍC<£0ªQ8A¦f3°{–~Zún“óÃ,†$¡ÃÝÄ —…ДôyLª‰Çe5Bƒ,Ÿé—à;$‚B›BWÎë¼Ð²²˜©$(¼ŠšÏ/æŸVk".”MI‰…͓̔N¯Ô‡­dëÀ®b¿•:{ñ’*R\îìÁFm†>güý^JvÈEî¼j>ÿ(<®ˆ*ÙdExÒ )ÊŠ«}…£LÿÌ¢Bx=UÍã½Ð„¼^¶ õ2—ú<€§aÒ†/¯¢2‚!A_ó°£% "jÆ@W„§$d ‰Ú…2OïýWˆ+7q± ¤5fQƒlç¼;°‹p°¥nkp˜®JÙv äÎaË.^‚»¹ë5ÿI5×6ßR’’ÔFl³ëVÛa‹µ¼%ZÆ G\\ºÖtIÖä½ÈóëfeE|I“fñèN£Ô¢´eD±rÀØÇ·w”*]Þåç@ÃJ¦µr`|“СVð°0¨˜ÿ—LKÌóGZäÿŸ—Z17öÖÚäï½²nÐû¼¯ßß ˆ5|‡³)_»“¬y%uo’å£#&SˆÀ?#ïÚvöf—2Aë9²”p¶¯då¤~ÛøûGQ¢nG=ß53„¹u#âýý!D?ix(‘vY¢üYóLi’FÔ•E·Þ/BŠÚ˃_ë༧GIc²yö scM–‡6ÎMˆ&þ±'˪Ï »¹–|lÁ©¸l–i   À4’¿%."}”üÔI¾BÔ°úÉC]âÉ!3Т€ÜÙĆÀIAçŸÝÿYŸ ÕpÓÊ/$Ü-$xWBÁóX¬L'h:ßËX¥iHÙ¤ZuÓ Þiµå*Kà[b~¼ØF¨d^zç95:aÕ@¶yƒU)8ï¾Ìb×&°OÍß%Î'ïO¸€Gé&é#þ†õ- qÎÉ|bÀ®sLt…ê%*<0xWÿ8AKÿÙr5;Õq× CÆÛ)Ê›Ñ7‰"Øîl¨‡ÊÉ— ÌW’o@éì˜â÷2ª“ý± mâ‰>$›y«»E@Ž%î¼RQ1] 8UÅZñÞ@3mØ¢” óÇãÙ?{‰AlÎ"ýš?oiS¯¤ü¡ÎŽg9óÍÒm0»ñÏܳï³(“šäm®ºðsŠ+R¤Ÿç“,Vh·DQÒrY÷ô30Ç°£”//œ‹.1ÓÉ2'9×!{‹W<=ÿ?{÷ÚŽbiÿ¸ 4—rŽ}ůCRVnÝ'UÓÇÑÆÚB2ºCŽþ‘›ü–X©iØD;uE8ˆGŒÔÕ Ìy…ãËRqT”Ý6ãDî Þù>"›×6cli£9tÑå ú¸ÚJd!Ò™ÅDS‡}²ìŽ±¶pŠ®² ê1ȧžB§‡{:©GØF´ùBrrF{¡ÒoÜ×s7&§’Ì•„â¢Îy§ÓNŸÞ»6 +0àà“Y Ç]cªtë#ã?¡ä 7YfßKÿó¬¾îÓÒðž…äüS¯×8uˆ,/‰êÿæmN«Ÿ ·J U›—|I× Ü±0; Y!OZÁ¨ÜÕ2xí¦g<¸îrûÜ:$€Çÿç²áT‡šg¥¤,þ«ðkےʧÏÐlLg`äÌø¿G‘ð%<ÕvÊÉ“Í¡nFW éÑ|®:òƒßÅ2ÖÊx¿W¶¿4WÆ¥ ž>¸ÁþbO5|áŒÞ.´\âWArઅ T#çžµ¥‡¾Îû4 }~õ@ëbì"Žï¥¹»M% ½ŸLÚþÕòñ˜ i‡ÌÀ¹); ÀQ2pŠ_¾¡þkßJf;&IHë<ÌÊ÷ò!¬òº{öD7oqcîȲ]RÀj"V£Ûs ÌD”"G²˜°Ží¡¢õáX¸gˆa˻肔à\ð«”|«ß¸I`»ZÐ#¢äëì‰N¿Ö…¼Lâ¡ C^y{ ‚ Ü™¢)ä M;AËá‹Ášl»ý¨u´AÈ…_ ÁÞ¤‡yƒ²ü6f7t­ÅqûNŸmƒ¬º5¢ão¡¡Ý¤âºyè2XYanP è3ç ˆ¸£²œ'¯:¦˜Kí©B¬ºÍn6Oz­tCGïÁ? rˆkOTÖP}ïÛ dB[O‡¦<s—lÈ0*IÀ°|Då4Ÿ›4Ü%!ýÿÞmi/fcÝM^bãY ”œΠ“p·´sýþZv†Wªêßo`©Ò­ïØ«ßP&’õJDBõ  i+IÈf¶ˆ+~úÂH[#½Â¯êuðþ-¶ „”˜ˆXTÌ9Ê¥òÇÞYæ¢z$›/¥0Í°ó¿_¢¬2o-Dvfq‹Å6à *Ãç>~°ÙLÕMèýµœgÑY-«mC×îjw,I†0îÕ^Sož¢£Þ®ï¾*¥†•›¬X»éz³`O}_½A3°8ÍDŠàžqÙ?›«Ý ´2Êû\¥/"%v•$RNÀMKñPb5ò!\EdÑD`ðJìrèñšÊÔÔö_Í}º ]ÙvÐQ q,÷ÒnÒ%”sç%*ü£S…Þnð8Þ £ëš,‘’!%½nFo®†_!¤‚ýsS¾À'‘$ÉÍ á HXÈ;+Âaú‚ Žã§0©JŒÀÖQén :ûýqQóQMÏñ²³jˆQfþ’×s"kVìÔJø3FøÙªà þé®*¤¢R__Ö•ÛçÇQT¦»ö$ìîI›”$Íd€[ªÏŧŽñgŸŽvã+ø°@º„ƒ.é·wsH¸æïÂh”ñpÿ/Öë±j¥k¾’Ë7"{QHŸ ›†æcÅœ›ñÐbøðÿ5Ð-S]¿?S»d 4yûrúc—DœÁƒì¾ ÍÚAuGc¸K±ï´IVÀÝ+§ot ĵ“pL'Ó‹  8 Ø&Œ;¥+,Œ¶ÏX†JßÕÛ߸`#„`,r§MËË$_ÊA4@Ž•Òð]ç } ZI]ˆ©ß"Zž ÃÇ))ŽáØÓÉ0kOUIòjöõjª9.wž8*âÙÁ9ùÜÈ(Òê¢bå)ŠüCæ£„Ö —Ä’ÏZt²:Éâ‡Ðº˜7äa9ŒrŠÉ¡IÛ< <ɇs²¤jÓME&þ-dº¥.Mþy}ârHÛ8ҥă¢üÔ,Ö9€„ýkmuÑ‘g7ôh:v,ñX,™Æ:FcSxe×t‰lî7˜ø‚Ü;Õ¶Ö×RÜC´ß Âú»û«¢šŽ²<›ÄøŠP]œ[c-ûQlÒW•k#6”±^™LÈfЕk ,^CU´×`Øp­çjsëGyÛ1påÄM(žõ´jæº}Ýå1øØ}lðOüëä^“:Eú»Â¢‰½£­L©7 úOÅšTí¨žálŽ«¤~ û›\››àÙK?ƒÂ”ð„Þ¬tjé[uѦœ_-v^ù ì‡S%SâPEÐ$Ïb7*‹¢ÁÑI 3"IàÝa _üØ+HÑw#y ×YË?\^îô­rt› Ð^8}%¬‹ar†­•ðB…«ýîDŠü$(„~g>™Ø[­â†?„˜f+ŒI4e ¡É—Á\iD÷§®Ì4u(#_ÿR»œ0]f(˜ŽìúáN7]Kóü¾Èìk“>Ë•>*×éš\ÔdŸØóîõÆç%)lŸ&Ú¼³+Ýå0­08çéÂV=IL´©4.@…R‰XkC,¥¥¹SdÂÜlmUµ•o«·é„p[¥Zóå3¾"qqÎ>ÙïAÍ6aµN}ïzˆÊ‚Kv™õS¶”×l$qלŽç55³ô Ð**‚†Ëæ¾õÃMßuã8‹væSL̦†Þ²òÄ×Ì’¸³<©}£ž‰ò&˜ ®ôT¶ì]ÀÀò,/F»÷†åëé!ɪ¹~=œÌ0¡~‘/æ :O–®bÛ<ýÛö¦£Óøø¨щÖ{•%Ü^¨ÌƳÓkj™- Œu^ÇGHâå-ÚEóì)U3e{Õ‘Ë÷qRÁ«k¯æ…ÝéGÌ–é:f£´rtúîbdÛ‹¼Ÿ{°Î[€Î?éÏh0!øDîj\ o’aìüЧ"/„ â‹,ê‹q骼zn%M>Üêåh<8=K^õ_oí^—oŽ6@Ï*{󣊷²{°ÌÒw¯½jTVã~ 1YÚò?ЄiyðT"ÃN³xŽ¿~ªMKhYÃÿ†ÄRÉSWéÃo^S4éÅñ"]*½¿ÜíX™X]‘¡eab_X4#rÇó5 ó×ʽ vŸæŒÏV¶I ˆÙRÔSmþ‹=©gÊàuˆƒî2¢²‘t?H%U›r"ð&Eí`¾…&TTíNm ´¤_#ËâjL7ßxŒ:«%úû/T1EÈÑwu0ÕW¯ƒÜ–Šw±v¬†¥$’xðŒ7õص)ž…léfxÓ&R©¾õZ@zœ—n¶‡¾pù毒w’ ðØnò/~¿¢kÑÿÇ/'_¯E°Û|ItZt —ó)&7ž5¾?K‘Ï»lÔ‚c{Sº=f¡ò¥'ÐF¤¥Ò<×÷…%ÓZšvp]§¹SOž xw¶#³q Ñ®ZHUWï°9i"ê »êÎŒué‘‘ú[ÉÙ¸ËÂHµ áh¤+ïp9%•HK:¹$Ö8a¬G­ÙÐÛC,G€ñü׾沠“åÍ]NçV>3:ýù‘ íÁ‚q×°’ï¸-e²¹ØuK<3I—.‚oÄUàõž<øzhÌÏ2—¹BTºJÿ§[¢'ìăÐ`‹NjllúVóÖËƲÌ/ÞÊO´n…)ì‹Æ]˜¯A*;KlˆÄÊÍLj;š¸³yþúó΃³ù)+,ô>0 ‹YZbayesm/data/bank.rda0000644000176000001440000004674412516003352014112 0ustar ripleyusersý7zXZi"Þ6!ÏXÌñž4M¨])TW"änRÊŸ’Øáà[áß^ ·ÖnŒÊ£òO¤Þ_à¿1Œ–“ jF¤{>gB{Á4e5°Zsî¤Êj€Uñ÷½ÅÍ‚\KBn¢ÍWr“à#MäüE_NOáâ]ù³VŽ†gfhù—xöHG¥ö#Š™@° rˆ?öÚóºiÆð觮ª\NÓNÏt‘R'´ÃòT‘A’^¢À||É÷ÊÎ;Ò™xãwZ¼È‘Ñú•ufS%„¤@ÊP·ãÏHìišéþù‚•cLÖþ=…V]ŠíÖ´¤âÛC™(² 2ÔxÅ$E‘$$zÜU‘Ìô]!o4dy³ß/âÎCP|˷ɱÒúšÝˆ´àƒì>ÎåNÏÌy3-œ'šuôRöa¹pMÝ„‡uÔü÷Û2âw»wŸ®µ˜S7×ÿ#ÝS§M3 ÷½? µÃjdÄtCz‹b"Ä^ª*«v_\ïã(]Wã¯v”rš+avz9gM©4öþUô½VÎZªÍVAÚ#ù†^ixpÓ“ÍbªCNѱüî¤)ÌÃBÒ.^I G¨(Ê®ÎQ);ø9Ž¨Žùà·rKëSs÷rweêÛ;,c_SÊ5:€ÍU1ÖW¼Û›ï'}B~Iî+z”Yî(µcã'Öß"nðóól¼¤Ë®w}ÆzÉf30-ú¥ãùk½†Ü»òVŸÑ•v7ßš&µ?Á*áçâ¯ÞÁÚé¨+SxÁ0ÒÄ)œoÍ"í‚.*)=ü±Ç´.þPÁm‡b¦SCí¤=¤ªð ÷pjkŽÖ9AQazqãítL$€ŽPo þQtÇHì¯H'àD¢•I qrr×¥*’ÅdŠ¼uÏ•P9‰›³ŸdûYT'ÇŸ^ômÀš¹¦ä’ç]Q*Ë–EåÁ1Ä ÁöÕŠ•š¿òFw¢³‚@ÉGI" Ör̘»a€BÏíNIÏa>[¸»`~3ÛM¼”÷­Ÿ‡çõ„ÔZu(ÑN'"8›"ŸUšR§;ƒ¾ýçL<Ðä>€A² éx‘­ [ÞÁ±vž°ÝA°¬á4Ñ>Ø<æß…°j™ŸMñ½½`ý¥8¨w1$O¤„S›l¥]©5¢r CÆ=…‘=ÿc%gÕï/št9~ê•ÓªÊÐ $\*>>TI–€¥4ÒÖ:r_|Ó’>¯Ø1üÈãç>}wÔUkŽèO¨?â#±;0í?Â/ú[-dð[Ñi÷^Ê, ; OЦA=×ÄY˜'%„SgötU%›ç…‚H_5gdî©{övJ¬¤ª¯gÞäFEDQl ‘M {|i0¥`˜ípÑ@{ ©«—nÁ¥teòÞÎî;óúÞû-y"tRø sv 0‘òF–[š†ÕÖ—mºí¬Yx‡RâÛÃH@a ¾ !GѯÓK*jKÚ6CÒ]pÂ{‚~a©Zîòƒy¿êÛA{B€É‹òtG/öµë)0{DJvþœW¨æÖ¥p-öË$2iN”C-àiɈɜ\šÛ>t5˜8ÍüWc{ß-ÏV+ŸÆ}D“&u ¼ÖÇG‚æí8V°ÊÏ0¯|ÓU7ìl€VÈ=!§i¨´ÖºëPýãÔVkkR‘,OéÞK‡ù‹ŽÞH´y§?SÉÝ ¹%‰Î”€ž–œù¥òoÖÉ8öcŸú ˆKæ‰ÁŽì„!jÊDqº´¦ N;>Òo dP®â²ïKŹ,j¥ÕŘBº™‚ÈýjÈ€¼c’¾÷JÈh6ŽÚe`@ý! ƒFÕÛß…õüá¹âò¡´~2ÄTnR7ÃÏ}.ÚÝ*-úq1⟠¤‘ÁMúúœzV¼Êstºfú8˜m?‘±9¨äA¢±”Ê{ê"¼"BÆ Ë<{}ÖI‹/¡Òwœ³øßb™Ó{p7<3\wÉV *Úp_EC|v*  ÍEï$Ú,>éô+Ê‘u N›ÄàÞ™Ü?YŽC`P¨9³'éàzvÖÚßRŠÃvÂHá¤Æ¼ˆÊ…²î~ê(¢etºEOCú%Ÿ/LÙrkÛuº‡¯0‰ÔÐ5 Üb ?¾Û5`J»yo©$›2a)zR—ÿDñkSÒ‘ãæê(RV_ò ÷’¡³ëêÒˆ!R¡ÛCî÷Ã_ïgŸ>6÷æ Np|µîõÿØZø«O g'…‚x’µŒÑŠ$ARØ%6d³ æÁõrPoÈÁ’ÏüFûÅTsÝQ͉ˆÒ£Äáêó’xJ¨[TcD^s¾!¾ÚnM쨚ë \¥ÏCox„žãx6,JÆ‚u›ö¹½ç4© £àù+XPß—³g6O˜ÈJå\©åߧ™¥ÄÍšÿ…÷ÑŸù—…I—¤¼}µq Ï=)Æç¤ÿ9 ê«²…>Ú”èºCšŒÂcjõëéÊIØVÓÝ[®µáWïÑZx¾)ÓÙÙ¡ >*ÿ Ž(™¥ŒÃùcÙ¿ƒãl–5 0V,çï9kñ” ryUÀ†wÞý ÄÇÃíaŽöTýðt`Óšêõ=öúý€96oop"’Ïùתç,?Œj‘Ì–3Í„¬ýý°¡©Ï]*p±ñÇà“<顤ÒÖªxüþO0·d!Ê7£0'¡ðÔòõ$•¥W?[oƒ#ÅI$böšrÌ âTXœsî-©/ò¦¿€bù°ÌEt´˜ÙÆ‘Rw™yqŠð¿^¢½«t ý„{…; “û{€Ô-%L‹íN¤­?¾Ý@rÂœ#!Ñpq˜‡^ &w' ŽŒl± W(gba·_—tyH'GÐfÓLü è !ã’’KO,ÜO®ï=Ïežx‹Ÿéë12{©˜Gšj)rò‰dt,BùÖ‹aâú$ï.Lû9WÞàâ‚¢¢Ÿbf½ZWkd¬?¸QÛKDªÅ2»Ìàî~¶gHºj~ÅBkù À·šåO EàÞ÷[ ýóÕÍħm¬+—Æ`eIäÐz o~ø¨‰$ŒuúSh…tŽÛi (BCD3õ›ømàúVÔiŽ“õ@“¼Tãë»ùcd^ìëù·yô©0?ŽñoHá‡Mƒ;D ¢"†:bx“Y––†èœ§ îˆÒæçÜ}ºO&û?“—iCX"e±_oLQÏ=-)#}žüFÂzbFkF¯¬ZÏùüpñô´¿N,»Œ¥Ž¯`ˆ8Ï&vÀ¢”M§8ó8ÇÈ×C¡+’µ *ù›¡·uöŠu J"qLþ29à•D‚îÍ#ó:§$ν¶®ÐØ6Ñúüebr˜„R´ïJ˜‹<+·êR¼t›Æ‹‡ 5|1¶qV …QR!ªàzrâöbaÆZà !m£Â»òlµ1Y¼Y Û]ðÑoRÍ›< GsçÔ4l×)þ|¬#Ÿ” ò©xäßA‰¿rDß0¾Pþoz^’Kã_¼F.¿Ê†²ðÌå ˆO‚à$ÆzS€¿/Ã8¡Þf þ²£|ìô],L~Dg²Ô!¶úemå0¨äxoÄõïjê`QëŽjó’ÄnZ¾?’âSc<Í^cNöˆ‡‹ø5Y†’n(Û’,_† ­doÐá/Æ–ÎxÜí@Qƒëu‰cæ‰ÂŠ§6˜ô¥#K<õF™%½sæ®F¬µéÁH¼ýU©Q,&xÆõ=!n¤áõåmY–8¹P47§6ÿ©uC—7¨½;;˜=!(ÈÒ[}PP^Gh¢= þyMÜß`¤ÌѬáQéÖ;†ú7êYqù«?ÐQ¡Å£…E¬×˜+‡˜#É¡yÉ7?Þ);Yˆðå}²§‘G¥«%Iê³4÷jð_6ÉåNÈÒÂÛš›õO c.ð±àÝ Õ: Qpn§}…¦,ˆËph”}a)jª{Âa Æ>Z½eñpD}Þ¯L­¸²?‰‹;,:«s.þ2ù­í؉gã™è'ÔÒÅ[åq62:(¥PíM‰Ý<­=Ov1‡¾Ê#km÷»rùÏD ½}±{gˆ_Ëóg|°½É¢;—“˜ÿÁüpË> h{ÊMuã¿Ä B£Ôwýä8`":Â! &t£–ÿyÁ(!÷D^4#I'Á$åö¤[eÛó±Lõw]¾:PV‹²¡¡¿«@ÉRªk² >jÑVGôDT0FÌû¶*d%éV¨Ø}³á¬Ü]©‹¯o;BslkB¶+–7 ¡r¬8']U&KÙT4Y[c0¥wÏ ZÝ=¸æÒk"ï”í‘<æŒw” fé<õçF ’„d6`/'¢g»'2—…—ŠdP#l˜âšg°½¾§®Ôg–Ü 2q—Ïnt6`|×p‰ãKíAÇ$¢7ÿŒ¢Š:¾°(‹Î:KšRj³uíŸàÙ #¶G·ðä:‰zfÞÓ6ÓÆ”¯Ž¼ªN<›Ø©Jkàk Sã­ôÆšUÿ–ž»êÏÄ©=ûÙp×b¡2 Ðz»Àæcè”ADò-ŸÀ·¯ÂV¨rVqtÿ*»àTb¡?·6âDK‰c>vuà£Å [ÂÑÝh²Ï/èR‚¢`+Ø¥ôõ¯5•ÙñŸTŶxíï0UžùFeëŒF—o—õzw»¬Å~*S6_Á–Gh½f{uä¾FHÚºÉâÓuQÁ¶Áиhž™p.*¹EÎ;á’Èó¨þ>ÖH`Ãæ¥Eã$­ új†fî4Ÿþ"0™Ñ¯Ó·ÇC±Š­ì‹Ù”ÙÌÄ»¢t…hL%/ø0*uJÕIPY²v ˜ «BÖLZÒÖÓVßïuœßòNä?~àÍ\’ˆ9b•±Ý&ØföÁQÂ0âgK_!(…JsωÆë"2˜ÿªé¯s'@^ïÙ9!)×uM$´žK+IhR_P*ä(AÒUæ•B©=îD.­*×±ùg…1 »h`|Õ8§>×Ä>Ë;ëC2H`u [³ÏS…E|†3Ò”lÙ†€+½ ÐÓÕÍ£†/êØñ"ìa–S /©§ìYk‚Ó¡p•n$üïáŠß>ºÐiRÙ_~Ó¿ÈnM:"üñr­üšÙª <"ÎY*º­«Õ½¥¦æ}{[3©0§O×äé•Îà¿]ßÏq±$}¡ ‡ Z|i$uÄ<î]0]ÈêG`ÍDiÆ¢–* â§'æqÎ5BÏ{¤}Þx¼C¾]¤„·1™Äű* ¹%ò6×éÕÈ’ßF¤³ûê’FÚúÉúˆ¨}6a¢ ;˜¿ùï;.WÔ~žC˜Ç5ÛܘŸ±ãg°,'h½(ôæ’à8øhî†æp¹‹f¶‰›ÅlaÁr“;kVM7»'™ðQóÜÂÒ5Íë’½k*òSاGàn“œJ}B"Ûý>LIá»$K¿ÈÆMòúö÷rûäŠs̳Y¼âaB ù|àSpÏV[¨Lá¦æôçwliºZD`µIV»䚢@ÃQ™-‹9=¨4žN°è; Ë[£®1è¤ÇâÆokU¸J²QöZËÓHÚ:e,û]'÷ ‘OIBÛ¤[ÂÌßúÍäòTÔnñÙ{AàšØåÄŽ¿žú€à€¨¸cDñ&Íà©¡µ”N§‘¨®é”èO°/ÒV溞@´ÉÉÖ‰´C¨{|Òõß¼ó8TÏGT¤ŽoðÏÊã¸h6NìÍ< ‡Ôõäfðüo/R.ŠZ¤Y$·ðKV@d9*^ÓªÅgoõ7¬³Y)otêgoi¥¾{™¡DQBx¤;A.$¡¹ú½šm¹\w¿Ðƒç…~i.á'Ž ì†e— ž–‚ÌÆëNCÕN!t3é‹ÅR¦ÖߌDz~]b (n…Õ abﮢ (Âxh~æFñòØ—¦}4˜³u¸5i™ g úìßy¸™¦)ß`ñà¡ïiQ¶E‚IEÀ¿pˆF°t3™T.ïÊçÕçy:QDÍég´' %@Ïa”®I!þfþ_&üä¦étnű‚.€Û®.î h«»GÔ=<˜q¯à%‰óŸÿ‘ì1óñ©Üý‚ù%ãêg¨¦,Ö>ÿÈ…±;“’x½"…ñzQÛl§¸F.Æ\˜Kê „8Å4olƒÞ R£ «û¹ ´’-á_1{Mx>KwfC ‰O‡N1lÂoÔ:í‰Ì™“Xˆö¶Å‡8ˆkk~áw'=i(X7Õ:ºy^™êy›2»p¥&À}c]‹Ù™ØÜGYöž>V ²Ü¾¹/·“§Ë+ýæ^i«JiB €äÍ7,Ð ×CwmìKo½ÃBýC_,K?.×î‡X3g;ÎÈW)?@1qW£ÎŽ²«­£">SFʈÇJm^Äه뱗€h`Ð’nÄϦÁ1r›þ8Þ2[ä£]F@îá–T²³7ª‚]/fŠ3˜ýK,Lùlœ¿Pu]Š´—:hÁÄÏ%ÛÜ™É, ÅQ)xC4 €‰#ñíyÒ&âAÁ¬ÐìD+W,ýÞ¬ÛûLÀE>û‡cÅ ïà–ľXêíéµ' º÷š,ÍZ¼¹ Gz51fõÃê£~Ã×ÍZ´²¹^[ЋÖ"[4ªE„_ƒ8 mN¯Öé6'S.{¤S+Y‡î1 Q5·ŠÒÆ¥7—n• e+PõÜ¢äH ^‘X‰?Fò½S‘¦õ ¿mŸIoº–Èiô¸IK3A|n¬‰ðèš56”üë»ê@¢Drµmq%UF#z…9p'kë ±S… ô uòDÁ††J cÔãƒC×½& °PVc'@!ΈHó¦ÐV†2{‹dkÓ¥èö!Ù—ÎUÊ Øꎋº’Š#Vo– ž¥ë—Yd7ôŠÃ~gÄ'‹{Ãá%úBË«€K~â‘Œ§Ú?uOáºc95qŒÃ€ìºBmþÌ—sÄ0¸n<=5ì”üÛŽ®B]†¡c"bV´Þڢࣂʰ–ž~ó„«Ê4$€Á[^Þ^zòªÜØþQ,nç00i{0†¨9K¤Á_•Ý„ŸËAQõÿ ĸ©»Ž€¨uFRú1f}ÕýøMí»I”(LlÆvÞ˜‰¾k¦FÊO ÅNE7ð‹iÕ¥Õö¥)lƒ<ï¢A…Ï¢H^@2õÝ:&?¢©¿©ïÌŒ¹OÈŽƒ«ì×XÞ%Ë‹Ç‚£|ØŠ0É?[©hf›éá©’3Ð\*i^©þÝVèÍEÊ~µ®:'üò’þŸ½O«L-`vkÞõ) “–¢–Š­(ep•÷$–[Ÿâê-A hþQ¢ßƒh*¾þ-˜%{VV©¦áæ—xõÍ,Y Â,—¡ŸÿHµgÀaVUôâ<¨|«ü´lhŒÐq|‘Ý%L­²“Ì.9¡g ª;^Z’WnTýŽhÌ¡¤eD"É)RÛìÈÉÖ’Šñ)R±~\ŽîL¸Ü•Î"W?%>I¿àCù¨(À&¸À&öÇê9[wà1åø˜Î¨ÐKùH­î0¿äa6êð5]m¢Ò35HžÆƒ“öcbóòÿVÿp¶&ª‡3…4Ge'@Ô› .Åf£•oœûðz‡PdùP\#Ã%Þº^;ä/7›£D íËŽœs‚æL0Øl¡ŒÝMç©{lÕü{ZïøøèÆyO-Vߎ:#'d¥náöü¤G£98c~6^ rQÿ7+l– au 9¸h´wOT€°„Ú6J|­„¦o!9¿”ö7(àédãͶΓT˜o­itØÚ”-x®þu籫ÿÁåsMN˜KñSnê_ºä;bOa²9˜_£Y^Y8(“sxór™ƒ‰ƒúð7û?KnÞA|äy´L<š7„õ[>ܹûűì·¿Œ 9a¾‘Åx˜n‚…òMÀÐ(6=+[£ùÆn9ÔŠÝv^`¼oib‡ïÍCµ¯ý'zuFãÈÝ'6,Ò žeHlçW.içrgBež8Õ&nÒÎÏùå=O»Ã<*èt.¸EU´¦Ð,Â7Ç~±9(ekjä ˜øÊ Íÿ³?Lsî½jõW·l/„KݵË.êÓÞΚ‚Cñ~ŒhŸaaÐ@.kÀ "žeõu¹š½tZÃc—“æŒ?9ÇOwö¬”†Iç,¬j᧫–ÿlæ*%®¸¸ÌÛ(uk s‘zÜ_É´<ŠÝÊjùY®»¤SSƒfm#‰§…2 2©}Ÿ…F‡÷`añÔ ˆR5VmRqÝ»‘¬¹Î 1jHÄß8 ŸJ—‚¦iû9 Òh “Ö§z´^›…`ÖPKáµé™Áu«»‰ ¶&üÜŽR=–OMjCDTcf墶JB,TyÈÀ¤Ce&¸¦G»=ímBÏm<6d¹cS¬´¸SÉJé6“[žŽ!XEßÛriň×pj¶5_Äu#UÖ(æZ=Ňœ6›h`Ã-ÈÐ$†fÙœÎê.† r•ŒŠGqÚ(Ù*€5éáj ”ø¾ÔFl1K°B·Ñú[מ´Ö—ö±(¨|ˆ—a¿¥Ì”"aj­ª)arAÙ¶2à£*ïÙØ"HÓç,3žZrMl‹Eg {¨¹#—Â!²e{+í´÷úLŽ}£Î'²6+–0Î{ŲÙ*aT[ÜÀ§Ì"²Íä íOšÄ›W­­ÜIØUK×»K×Ù;cžœÖ"7°HCfJZ©ÁÉñˆº¶æ“µâ¿2RÂàhªèƒçd"\¤ä,Gï0§@mJÉ\ת gƒ¡ ¢ÚÍËá—U}'Ïú¹ÎÙñŸ©)¿•€>CÏ‹8˜…®ÛXßm}çUc—»/<Ĺ®©‚¥}ƒÒówúñzrúÜrLŽ3ð Àwöz,ü¥E|—ÑwéB5¢‚zuUP…Ô–¹ÛÃó'{tÁšE…IE]µÔžÔkaÞ9‘«¤GÎø<|.è0AlÍíÆ1é±ëëZ»ý—?Q?dE¿‹Àå(ªñ¶VXËê.? nODöZ%9³„ÕpºÃ¦¢ù¶'K}šÃ™8c‰•µç5¬[í®šÍQ˜oË;H>†Nr¡mêD@ß+ìÙÉf`†äò"A„¹Ìûw²éíÒÊù˜#Ÿµ|·g}Øè a˜;€î¯á»\ìtÂq”|J9.¥2D¿†Ã!L ¾FÊ{/;uÕIýS¨Ue« yðÄ'˜Px1›éGfA'UMæÝj,«–¼ ‘Zf,'ÑG!ÊƬÙl\KÁ‘•– ?œöMºªÝ¹“îíR¤ùJ^ÔtÁy”½ëƒ·—9åÕOÁ à˜–H¦Kúû‡/‹9ýýŠ @l5U,Yë ƒ\>²â^·b׬!ôÓ˜í·€!ùWÛ>.%±–Æ Ikgkc̬¥ð"B 8_0 Ž§ @Þq4S¾9g¡&ÜÊ7væ¤.ø“´í"sÆ7Ÿöûˆø £gjÜà0 Òê½²øçr–çCÕ鶽ßQWˈ Út /£ZÚ*(gýº ktÁxÂöeé,å½c˱å§Á«]”vï"²F'w £ê8¦dó§mä® ·ŠíE.Öõ‚êHóžùk£/æA¥SêÉü´[œD9+¤tÌp¡]趭Mœ5.¸6iŸyC+6/k!ó& ±.wymQMÛŸlA{ÿSð)´c0¦¤.÷úI8Xb¾Au†5”œ¡u–ª¾ ¶«u¬bI©IK[9I9«\†ó+|aÝUM~s:`8}b´Eƹ 3è—E¶ÒVoÊ)rÙ&I6Œ­,¨"…«L7mP,¯•ñ*[‚Ø1×’ëÜ3ÙÌy—¿•6ûuúºØjß\öò!…p.ð5ˆ_:£wñZëHùørvÿ`ŠZ@8ÜæÜäîä6»Å › í¢9F¯«.랇Žl9".9K’~oúuù'ö6ê[ÔB|Ù™àáZ…v ï–ùì)ªÉ8ÿ%ïŠáÓªâí’Æ®£QÅ hT̻޸§œý=ÆØq‰Ñ““ÿ ÷O|Š¿ ÉDwwýlI‡i:àaŸHÌŸöÔ`Íše.f$P´‰¯·<é¤fty 4± –äy_4]7·øEÅ 9‰¥ª¸£‰Æ=èé1lr‹øX{Ð"Âí_ËŸ¬v%ºpºO''%Öý­À˜ZëI£P"~š m1ÎâÚ{;¸’GΪwÕ¡ØîGðÌžMhÍT fvã~Û›ꚀGúÝ!Òæ¯åsD9yË¢†c;—,-D¢qà*ˆ9™bŸoÌ]P7U ˆAÄ+¦z¿¡ ÝqÿÃðÏiúƒÀó¼ô_ Ž>Õ†íîYÀUš¤£Ë'KQG?_¡0¶ø]Ùßí´oœV´³ÉàvÃ÷ª|¯‰ôbšÇFòõ ,jà€€;3áéZ°|@.°x”ê×q%ŠÅ ÇDöúÏ91…U^]rî³ì‚ÓºMþHôkf.<ÂVh7Š©"’¦è÷êØô»õ½~•„™—ˆ’eç„pôÑÔë|ûµ‹—ßø~L2}øÕ¼Zu¯wÎÇÄÂd¼ò™”žâWB´¤Æy“Ëa¿ßÉ_ú!ž.~kT¼;Ú°U šÝ€Êc_¿m·ÓÔs =ÑgSe0U´=±¬i&ª–ý›Ú³pš@³Lö:'¤º«‘Š·çø-¼Ë±l²@S±j&v(ölÜýÅe¾Ûó{1`ô£·Ç½ó5„EEѼãÛá¥\6n7Ce¤ÿzÚÀIÌ’b\®]6,ù̓•ÓU%.i ÍG¿ÐG"(¾sãWµÎ›¯WFcHر¦)imWܯìlÑí[/eåo¢à‚MZB *Â×6àjë óÑ‹Þ«e3±6þã8-¬dP®®V’ÏɵPDsñ4héâÖRk<Çe>Œ¸StöWb0þ”ÎÇŠ[µET?Ö}Ã9¨)Yµ†Îá¼¥ÉÓºˆc:¬6Âó£7L«y eý)#~¡16ÕO½?B>‚¹^L|7•*}) [,YêrƒêX\ˆå›Éä¸þÔ´ÈÀ²!`¯Ä_òLþŽ—žõ¡5n¯/¦ Ðcv»u&3k ‰¥C¿«(}"T1Ÿ…4Æ Ì€cp{²v€{–Þx´þ8žÊZ)öbâN{(îw+¬žÚ9‰Ü;ë¡[žSÉVô]K鹂žâx¬&?Ë­ W üFEÇ7.ÜÃI¤T6-˜—¾g ûßM¯bñBUµß]à IöÉUoù×îÞÙÕO¦ï íkëi—Ÿò™>ô?Bƒìì&´Ù N¬¡{KêF*Ïc'eëKu±Êq!qøUþÀÔø!{ÀèC ¦ÅBÛXñ®â¨X¦¸!õš„¬ìë Ø~̈e¡€=Lõëi­TšÄ\°ì¾¿)á¶>EÍ1¨ë­zòíœÑÁ³Õo“ÜTôR\ï60‹ëÁa‰lã{C`9¾ßuté¸ì HC˜hþv"10 šðS^ªÝj·ÎM]þ:L² Ø]Ù;ÎÛ5“K)ÁnàžÑÆ\x2‡•/½™YÈtnª¿¯g»Rù‡ÄâÅ­U6åðUuΟøĹÀ—Td¦ ¦§µöÍ°É¥Æ*;ëçHg Ì%¿ÈðTa¾—q9^’¸ÚºžÿL„BaUÑZ‹ÔéUÕ8½ä·i>ð‰Üc»„1Mq|.ÜrN]´ `ø½~dçp}6jýûö^z/?”`qñš½^ƒIùÁ*¦Ï-¥ý~Æe²/R+2@|†ø¦TH2?ŒcZ¦”+e¸IÃõ€€ù–${´ÒhðæÌJÚX÷Ý!J1¥§œ=På¬Z^ó̸¬•ë¿‘=¶€¸ôëúL\­9¿EÎAój§OДú«*_í’(C™ëpB¯zž¬-÷(·Œ?¥9{O…nfšíúÌT”ÔŒhÑõ°öé/ °’üÇøÜK™).!½â²ŽiqÃËg¨’QÉò¡+\hG–aд@qîb¶•Šÿ/j*¬jq}×®Ê{Ù¡Åçž`.µc¸¢ãèuÜÑßW–ëh…g1×%æp©¨"(H*è•l3}ÏbÎÆp¸h¹GŸñÚŽ½çŸÓçŒO®.í@ <òŸ¹/ŽjW-¥æ¶âeÀ¡"uË%'†òâjK¯‚Ї›ñL‹tžŒ©’ÔÑïøßÖ²ísÎ÷g—“²ªÒ_ŒÁdx¾!ì°z±ñd£³|§q›‰ ’„C?u´ÖÀšÿ>äÓžmdRÎó^ ­gŒ$ äù¬(ÛúaË`ù“OÅØL½ 矽=}pu4"RÌ5|•?ã€6·ql¡Z\쎎֓ÍV4ˆ>½`ÛU=;r“ÀpŽ5ŸaÝS‡]rM´¿%öø²ÝÓAÑû_û>ËÞ|¤–†“„s±7­ÚÎZúô÷4,í﫤ê£U&ÇJèÑéõ$¦`ÈE©]_Éèû³pð-K2ÄÆÆi“¢öjR$ôq^ìN‡ŠY÷¦G<žåƵfÚ¨«ž‘ SÛHDÊóûÂAÛŽøýOé Þ¨»ÛöSîô­š×Öº¸rÄûsÜ‹rRCl–Ír-’ió‘v<Êà ‡ï”®U‘Û%’0 î⇸œIÁáúí5Ù=¸Ûg„ÑTÀ½¯Ê Š{U‰ñ«3Á(Ħ¾oÿ{Tÿ™­:ÍäOÆq_VEJÍësáÍ°RŒ9ãUñX[Óy¹úM¸<ŠRümnƒèØ'¤pa¼* ¨kÈ7[·pl~‰: ±÷HlÜ6ß¼¶­Ódƒª;ÍÕ4„[éÄLÏ92{è°,t¹T«êKDQè)tºyݬ2cZ¿z.‹,Îår“ø<÷1EßÏÿq‡oìn²¨@ÅÎS·Ž ðÑ(#ý ›Ûh¢é±7|þ—$OS¬¸-ú&6¼—P£’mÝ0O!°mAN*`‘:Gǘð¶cö›˜†F—÷ÏòáVä³Ûä»îПƒÀàv7m°Äé¤if÷óšK«àZÅ'4x Úiï îÅ‚´`å—êØÿú§ 6É/Å)°i­9+ÓÓ=†8ž5É*<­ÔërÊø`Áu+,ÏãZ¾aÚO—÷ÿ$Õw9Ó“} ÷·œ‚œ|…™ÃÚ³!uôh5 ²Ѧ±•^>´ð襭vv0"à‹¨¥~NŽ¸8kdŠñŠ Ö|('"7àƒL¿æ2âŽýbÏfê#¯•\í§Oç+V!bÜ<ÓühxÆíŒqD]ÂZB€®W¿Ò0ƒÝ?rð6ñá·°™™Æ8ÿ®:` ¿Ã•Ã’çø±N5˜‡Ô·Œ’bOÑú3‰ &æýöËV"ud3ŠvZ5Në6TdIXF{”0›˜ËêX\"–LPåù€P5ÞÝ,ÁoM q#è„.¤ÀÊñíýR. Â-*:¢apêF³=70jg,ˆ6 ç ¿Èó,ã€<åÅé®”ì%gÛ|©«æ@fÎW{]nGE$0p¸– þþFÂ|o°òWæ W‹Üt“6‚Õða‡LT®Ó<€î‰÷8ƒî~r.š#¾ðª¤ˆ7}ŠáÓRZ%WHçÖÞƒ½'òPpÁHû‰˜hÕQÚ¯NÕ|ûW‹ÿI}iâ Ãi ˆˆtÑ¡îFSR,Þ[½©æwaAÂv¨&dmyÙÑU¹—JO²tøô^ €;ÑÙyÆ2[ÆkâVÊïé¨,@³²øDìñÌOp°h>+özÞodÚP“ ™e‰¢pv_Ñ,l߯(•73Žœm7Ϙ»$˜4;?¯-çΗÞoöðÙIxLÂã›\DÖÑWG‡0¢[EËõ–A¹Ð9é0™CÂè*›Üª¥V¼Q±Ü¹áÖ‘K‚Í£0ŒpüTs¨ÿ>ÖÄ5adnË:(SÜÊ“À<âÆîÙ­2­±Õƒ{(¨BM$É忨Pè2oDÞÀ7†^ðHFg>äå}x›î÷Òf]´2½a»0»]¿y¨+R¯ä'°up¹ùòó[XšdôÀB¯íHh0\/ötõa3§Ys¨Ö©lHÃðœC¹rËpÁ-s_–³Ï€œ;*çE#úìOùTLL‚£õ¯O½[êOÞj¦4nçýÊ9ìÃ@äôQÞÄ<>3¢©æv í8šáLŸèu¸  Ò5¯»m¸ˆ¨ï j÷xa`uþ“fÿ0À:ÖM>=È`íÆÅ u²ä§xt òh–ñdñŸ£ˆ(I% qŠVQ™{0»Ã*²A*½©"J²´N,müe DíÃà! g²IÒÄ=?Úëóš)í8*‹"ÜgŽrÔ8§/¤Çø]³ó±+pà ¸‚c1º¦ü|%® 2 Ñ^¶ñxé2&z¯¨XŽlÈu}Ã,y.5”Þb·)üõÀ““ÃLZkët™G€ð°õ¢ÍN¦¢,ÏünáÁµ©E}GÝš»&wâ/ñ0³\è¶ÄÈGr’…±T!¿ï (T"X Ò×RH" ›þÛ¹Ó9)ÃÇe P^_78R@%REÆýQëdÛ…ÅIB8€néý½ ±ûD>¦Éy7Ø·b+k”›«wŸâãÀ,KÝß‹2‘n4ý<àb«ÕÂ(£ýHï­ø} PEÑÅM‘­IĶQÑ€ŸA€ýª}<Ù[€ó_‰mÖϾ´e7IŸô¢«d Wk“Ú?ù½ü_’ÒÇ#¥z†C¦Šõä4óÞQZïÝq¼gm|ƒq-¯AYæŠÙÔ|ŸävÀc% ‘˸÷ØdÀƒNØ–ÎBÕ"YºFiv[ó½rÄh µáàÀT'䵡 ŸöÇ„ÿ­áèŠß´†ÝY,’ÊÆfŠ‰n N7øg(æئˆ±[ÞM GžÇä_€ÞýÔ7G‡÷¨¾:‘Z&¨…ý¥h7Q®áj}PŸH²HQS|q‰±X4‘‘úøJý©†vÔÇá%\¥>àŒ]U/]p3«Ö€¶ß’ø®ûiÑo X*‰ö|®Žì^_Í“0²A)&v¦…uX€ïÉÆ5…ä$Äø†ý¡”YÙÀÊÌ_²ïþ» MŒ$“Â@©K²ÚãGÝÍðîìÆZU(£;×/.SNÓòX"Tì˜EÿÕ™] dþùâÂ8ªO¿¥ãæ ìÍÈTyßÍòT7ÿòVãDG³¦‹/Á‰„f*M¥[§ÿ3PHïÛQÓvÇÄulY}TÊüã••]ô”×Ü_\Ðn/ Úˆì^˜öâ g ƒ‡*¤ƒ”wÄþ4b+V‹€MtC ® óQdüZP·J-V­æÞ´ ™ë…p ì> .$sؾúÖ\¢¼fíÏZHdÑlî:.Ü£Q¢öK.h—°?Ì+Š11âF)>NàŸÝ¹Ã}xÈDÌÛ–|hwVJvè"ñ÷ÖF^y*öûô±µåc¿ü¾¨¿ôùTôÛmìu Hã2 ±ïˆ¬Ÿp+ÿ(¼kòÀÄTݶuµÖzŠŒJ<Ú_ÂO4$…áÖÕÉËB­è˜‹P "Ö`Ϧ~–»<Hè}æ}K+“”ÀI¡U™«ÿ±ŠhI#ï²ãcÏnÌ uŠ² éˆcOÖÀcùVÉ›.j˜¿=•þ)þÖiÜñbPNÏ=pî–û§Í ˆd8F‰d¦ƒ[Ëh”×Ħgýµ’ÕsãŠZE˜ë¼ÓîZSÒ ý°•i·²Í“q€ñ@™T¬\Üφ4®E)å×rWTƒ®y¾®±Mf2ÏÅ Ý$!N¾jŒfÑËó‹9/’À°ànK0îÖñ±\2:ZÆL±µ˜A¨¿‰š•«ípW¨‹ŒY{ÄäáO•´¯¢Æ2ïñtþ§Ò”Ïo&üÌw:©þ²‰Z‚#W·ÈBÇ›$UÖ1ðssÂ/ýèIÂQ?:¸F=´‰âŸ²jÍ·/ŽFh6‘ãĘ0ß… gý'úKËÀ'<‡wñ•¦j‚ãuû]ôÒíÏ1¸› §hìdœ´MÔ?P ¾R 23¬l¦£@ð¥5fšFrg¸A|å.U±LLO`æmY!"Lõ/Â[ëZÿwéZ>Ìo¾°RfÇÍ1§JÍGJ°ÉÖ’s&U2´ Ŧ\@IÙv¢ºT8W}f…ª¦ì<íÿóBωÞ6ÈÊÈή«½¾h³!€rÿ¶¿FêGÙˆµ¯ )B®j¶ÄßÓÊÅüË&G”)p¢’…•] L»yìÿÚ‡-*ŽeäL–ª¤üXÓSJ“›‚TÏ‘Ö=ì^¥ÒOð†Râä5æŨª‹ –ç¶V‰o7JƒR-ÕÊu/Ì-Üí¶ Í¥dEË·µq8ȤdSpú¥Ôê}ÅiJS-‰‡˜ìþAµx9ûÝ´Î6ÊV±èÐP fxCÌÞÿ>ç‚BûÚ%Ô± ¨ÈÛâýi÷—WVÒ»—0^ÿš•<ö¬H´]·”WäXïùüèrÍJ g{ºù +ô²õŸ’¬Ä-™%¨C$;ž%$FÕåK´¦ÙñJ¶îd×€ü¢öNÞëwS5ß‚¹F‘Žµ„½ZóVòæÐŒo¥l´´d m®¬X–È¥ë$¿ÔøkÔ¶ãf^ :½õ" ˜]¤¿Š¸…Û oÞ?Ÿá›ü‘²¬·˜3 â$™¿S‹öK`7ê*LzÆÎŽò£¼»ÃqäN;›´ç–Ow)°O¥x®àe'EiõœÛ±ÊÚ}P±ÙÌØFHǼ˜Óÿ@õµ¼b륳ϑŒ_÷Ê+£ÀØ*ÉÚ÷¶Ó_ciô¡xLä ¯½cŒîUÊ!Z˜üãÙQËÏáÏMxÛ yˆY^@1Ó:±¿—J8è.Üm?lêóÀgŒ´8-ëWÞøt;?°[[’úã)"Ãx“o·ìb|oÍn¼… #÷žJkÞœd•.ú†4-è®ÙðSë÷Àb‘–,ŠÚìѦԉÝò™al.=š$·Ðr´MNç‡×SžÿÔ iìph•L0© –rÉQû¹O‚Ðdá{â „Ï9IyrS˜|ÀM+tV{‹L@¹=LŽ}Çv➸¹½f}pý¢ËBšñoì–½®³Ä›Õ§bô`á<˜¢–þ+AãW”äCùÖKIÃH| q¿Ž³=Å÷Œ W¸´íKУAítÀ×M¢CMl­™ÌÓLËä~Á/iô£w›pËrEU‘„ÌÅüUh:ÕÏ/ÌËÛº˜]tJF&æ²»"±*ß—wLZÅZ°>E¬¢ÂF‚yb”*o¡+…u XQ– ––óküŠæ-yk4?uŸ%êGühžyC¤Zßà½ÛlÞ~óûÝ¬È N½”kÐx Éiô”x˜Åž‰Í›vVK©›{w¡püp.¾®T»µÍçÌ*õ˜ZT¤ŒÒ¢'¢N™@åq§)¯‰Ð¢»[hbjj"}#ñ?ÖžšöÐùæ~ö’S›ÀZ«h¥šp¸5hT9”¤†×Õ¹,eõªá/QÚ…Sb_¹?69Bh.>§Ÿ:ïÞÉÀØB)ÕÊåŽBU·ði¦Ž­”ážÌËí½~†©ÃϘK–>aÊZRÞ¾÷ d‡íïe¥&Tï’vpV¶s˜ ÁxÍ´'Å­(šýcÃFà P‡øYå˜j]'þ# tÍ:AvôEß^£ÍBq„Z: ƒ“cr°ÔFó{‹ðyíúg¿Z]aI½à sÙb'>~ÖÌÀÎTÜßÔú"…TgÙÍÚ. ÁG¾nËHzÄ´'EÇ9ÂãàÇg“Ý,'Ó¿EÿÚ'q¸o­£^0Âø-¹šU —x²Ö¬n4Š.#¢™J€…°/ò/¦ñøƒøG"ŒáB{ÔÚgë°4Èþž¬½á7`ì³ø¹-ˆèlóÀûìÆ{oŠÉ‡<§'.ûahBõ§y+×cÐô¸{èàqpçd4 ør‡[\K¦ YÑcoc x˜¨=®qGQ^ 3zFH*AÂ’¿Õ¡oXZ7 C¹#Õ™¾æ?”äSX±ìO:UõÖ¤¼x¿8‡RnY€ÞcwAΨP­Dt%Üöm°¢½½wy M)œëŲ.qJ®ÎÝt"ÃÏñ*ÃÎrŽq,Ú9´Z;`Vüi Õ/íµ?ëãmÀ*·…¸Ä ‚nq°vK«"óæ(öõ¡÷ ÿV€ú§Þû}'„£S|ŸÿÚK=®‚è©iˆŠí=nÉ6ö{ojÖ$³”µOHú~‘tab#ts¾¯ã6Œ YJð®tg1ÅV•?Á¥à§]OŠÃÆ}ô“†V’¬‚h§É5×oá} þbFUâ¶Vl=S¨AµMeg¶çœ>"Š$È8Œj•SlŽ}+ I°ƒÛùl\ìÕ¼\ZY>®IëÂyâœý_ÎCLÉù‹ÜL­" oÚ[¾®D,tÓ7£(•)”ë椕kÚÁ å—æËÎe>Fç7¾Ê4”pž@ÈÞ„ßfŽ©>º=¨±Ü³MðI‚÷Ywà+Š˜¼ÿ8œ8º–:ý4a[˜—y4§å‡f¿(äÆýg(Bt0ÍéÌÏ@|fˆ:k^¬u„´+™ÆŠV(~ˆ|Ð<:B§Èq›Ân.¤ð…òîÉq<’ÿ§ð—zµ{ÂÙÀ~,‚É®$ÀÆwh$ˆ"ƒPŠªÊ/¥'FÅd»ª¼#@¾—]ùóÆó¶X¯pÛY!ÂÙkqªpOë”Ôub¢2âŒA9«×%l75éÅ­º/þ&4w·è¶[žu-+ïÓoÉóÓXŽàò¤S…u>­ªN±oÑŠ/ƒ¼;Áb!oâXò} ÅÉÒÁÀê°ûÄ=lµêù–F UwHÀ¨[3ק&ÃÝÍ>´;\ð ÕoŸû^úèu÷°ÑF™½¡¯½eâ´»—ÎÛ/ ÌgÕ Ùrv@ßíµ6¾ó`rvÙ³IÕ!ÃMT5«-%÷LÖºsw@‚—vªä±Y”,n;§Á¸Ì¥+#’s{ñ$uÁ•¯œ “é—Ý]sVw~†ÊÞ†ú?ž®ÏÊ"$˜eÐòñ¬m=—>÷³¾¼è—’¶O…áH¨üQN‘¥…¥vyÞù÷‚ê÷džhÖ5ëW^’—)ëõ÷ÈxŒžHjåuÅUß<ÄÄYïó+Á1^çîwr©¶ Y-s:z±ÅKÝÚË•ge&x'™íÌ¥Î*¨ˆr&j)vÝ…ëâõç .'8™g³BMq¢j…Ê %49E O£ñ8þ[7|y† 7DoŠèÏ ÏûÎËÃkBìNÆ–†ƒJ|×YmLÚFf2Æ6¬zÈ5ÚôýQ0 ‹YZbayesm/data/orangeJuice.rda0000644000176000001440000220354012516003371015422 0ustar ripleyusersý7zXZi"Þ6!ÏXÌøVbïþ])TW"änRÊŸ’Øáà[áß^ ·ÖnŒÊt±"±ä¾ËHrï[¯¸sÆð" P]ŽToÊJä7€ª¨Ÿá ª„}ËAC.Ô>ÛfÓвŸS>ÇŸKrðc¶òT¡Ë\Ÿ&Ñz‚åâËoí¾ §)ƒ¼ò'^Ru6í_i&æz?FeÅ:hÚÙï)TÞ:—Rš9¶7zœÐˆ9yY&šögB¬Fɱð,öD!OC‚ªÎ^!Ø5ÚÊØB O¥°4½µ± WC²°¥jÓìÕÒÙ=">æÿçÃÒñ²^+F„f=‰s Þ v|anfÊ­H–Mn’¼)ñ~[åx+$–%sßµ‹lí²N7,¤¼UµZÃ¥šwðõ[Ÿ³1è6IB,•Ja"’«5æu¤ä@"Ü—é^.ã[Ä£!µssJRjJùHH>®‚y¿´’p±ºé•_ü ¨ÉH¦Ëˆ$»ue-©^¿PöÌ0Œ;eÔìŒVQ7fïÈÁŠ>U©1í,ûŽÄf‹<‘,•ŽåÆ3ìÜi³/ã IY"±Ç£fÈ["‘s&}:!Œ·¦ëGÒEt›òE;-–‰FØöÿÈ [ÇŸ·3™µáâ+Lîa`ËþnÙw¹¬5ÐqªJÁ©})سbª ñ£]8AMbF°úA•‰J<²¬sètˆòðDǺEx2›FµFÓ´Ð5á $è±,ÏØ&ñ™ÕÇýuÄõ€ÉÆ·ªc6IhÌ9Y™1ÊÃà ºQ±ßRÌÇ#h€³£ÒJsrN¶“;ãæù<Ir|þçoJöçY­xÎæÁ´7¶à/G I€øùÓ5h—³¸sur’8ƒMÞ7œâÚGLˆL9C½É˜o½íì8zäùš¯ºU,׌Hö[³€;§r¨t#™¶†ÀgÙR…M®ß¯GxÐN79ß uJƦ" s;Ø“{¨€âYêˆ4tÿ1ãlN‹ib•‘,BBœÒ…‡|p€¼ÖÊÀR¨›!b‘¬”WƒÿO|€í$$@6‚` Ÿ˜h® œVÝçXãÓSÂ%®xÍâæ34“O@Žx¦p2QÆ1å0éˆæyÛ-«‘|‡¬pÉ‘K²Z%Τ³;HËk¯'2ìX%BTý_$Åá–Âöö•L—CöŒ€îÍ#¸\5\ÌOTØö6ûE³óì&iÄÙdÆÖ±-³$Œû~¡‰-ÑŒ½ÂÎ;Ô>PùdtJŽ(ÙYKÏW_)GÈ«îž°OjA†zÌœ#àš…h.(ŠÝÊa•K~ÉÐYÜ©ú“ILÎœj,Qã~Z7;Å{õQ­6;šGÓî©tØžÑÝùîýÔ¾}: ʹ§~‹Ò v..Ö¾÷ø­[iDªP 2žª·õmXfí„âðX]›pÚX%ÞÛœb±‚Tô@="œgYÒÉ¿N`#'@Ë4K„É4(¾pÑÈ`6ûx }>QÅwXÕØû*#PÖÝé€ðà‰Ö×<ódÛ,}Ý,ÀâgNw's;¥>úØ8i%¤tÑ@%í“îóÒw&ÚAaÀ}ëa_*’_@¦DŠ£8ÀÚœ‰ªQ`¥Gg§w]S:qÌ#„oš¨=ÆG”JÌ“cÑ€P¿y1>|Ü5 Hy_m#`VLøryf'FŒ»°ÊÖj•#ßzvÕ}hêìê aQÔɾ÷ÂÈÜ‹qœ•T sÑ4àêB:®Ð–Æ@V1yµâô2Ö´Pwד[y‚:±ƒÉ‹w´à8Ët©¬dfs®BÏEZ´<¯˜du«ÈÚÏ€‘+AÔù`Ì£9[¿N¯"ù(Ÿ ó’tLc¸Ž”U½U æÂ&¬"$4~¢…ªrþ"Ž³Ô$@œ—á„B Ó& ?Ù`:†A!ÿ„îDÑÕtî— ëÙùòׄ<~ç*°f˜6®æ$‘ÍEÜÝ“G<[¨û}lƒ?¯<ãO7¶Hó§&+ö|«´ojP.HZ @…¼ÒÞ.>å† ¶ûOêå?³âÁYËQsöšÒŽ¹º¾/]„釵Í!@ã–P,ˆÑjH+ô¿3´ø–koPüd0¤æ÷%™‘‘IHœÂ^'Gˆ{­)F˜Y\ û»O‹”»j7yâU´àgm˜‹âåçŽÊK‹HP{:<ªþF…P!ͲJÝÆO,êï÷Ÿ>5†âSÚðt+ 0vÈ‹MZv³3÷ ¼êݼ¤AéX&Ì+Òõþ\½Axq‡éØ°ZÍêý§‘ Íæyå~<ÞÓ>;̨¨%åäJо*#j¡l¡&½‘6šg_W/þ{…]kg‘¿´æ0q‰9 x“ŠÞM-k*×/¢Š#}*Po[i¿åuºHdÉhêѲ” ǽgÊÀLæ¶ÁAËC t¸¼ ÊU ^[ûÂjs}cÕMk:u°ñ2ºÎÖ©Q/ KYÈ ÎóóU ÝCé·NdÞ+·)°­]ÎG-_I!S•íïs×™‚´`›²ƒjBî0׬æX#ˆƒÊ¯ím©¤qK#XÚ?Ë À3&fŽ…êyS¡|­ÄE{:;¹ ž‘)̹'TdǾAæK(„aVZ¤98©0ÈT—0¸E‡ü•‹㱧®×¡© –>vÊÈìM¼ l—= çéfRJv;Ỵ́⌚vz#µâ>ïÆ”ç€$T‡4"ßû2ªþã'.wîê‹éŸ…Sô×x¡èÓ·¨­YÓµ®ÍLCÜÉæœ9ž¸¬ÿ„ºöU´»uAÖûÿ{ÚÜ…A^'‘Ïî$¦wN¶ÈXpc 0±Êò$¬B,.gÕ‚ê¬zSºÉב§ YèlüLCØùíÓoITJ’€[ÃlhµÅu'ÒË}á~ø˜{±'™ëJɘ_“¡Ki¹ðÓ$\[b8{Ž¬ƒù’a¾àç‘4™Ï>v\“-‡JÅ’céïÍùN_ç™ì»ý¥‰å:Tà“ÛÅÐVsÆóÍ ªû̓“õ™U|„U³¶?Ñ8aŠI>ëÀjnƒ«ì ñ ¢¿X¶»ÍÞ_üQÖÖ¢i¦Á—© ú [~»x[jyˆb‚|5gê]ô]w¢7½N(Å»vg×Ô·ûqóŒØ¶¯àE’¤òßo#9cÿß@ïe¬ÎÈ€o]äßÔ,1 ;¸É€U 'PÌÒßZ/`ñÆ5 }-“M‡µ£y€qCn-²ÜA;ýA»·hß„µú‹%åÛaí¦Û^Ë4KFcí-¨u›úûœ9qȪo9án.ãZÿH£5~òÏ$,I~«d–»:ÈR¬X¿èJhFñ–èt<:mÌ`êýÂí›P Î=bpOêç›TJr©ú³íºÞœ$aõ/2iHΈò!ù d ÒTú\(ÒB÷Lòè_-`¨#~=Ÿ†€òcj¯Æ£Ÿ;$ç3‡_p²4~nFð®µ\ è¡Ým™Ý4Z¸]E‚f•œOsP7âšZƒ±¥«E "3< á×êòO núK•mo<ßË㶷{í¡ìœÖDì\½¾{Òš‡=RùÏÞ*ª°ÀÌ„pó ¹ÞEŽÌZ­Õ•½*Vi¥‰$_š@,¨/8ì;:×Z&•að£=¥`7›†ĭiÂ,îKFA|ùâçŽ=š«„àdápç l>tSK÷ioé°ÓàPm‰ðqÊé?Õy²…-ô‰M«0žóYF*R~è`y6¦tï“bû;IjÝÑoóçÓOÍÙ1me8k{•Æÿé E–>ÛKtWƒîƒ÷?‡b“5sSühñQ >”"XæÎWöt* õ¦_Ù*8¸žgLâAÒA2ðÞ㈹½ø$I±2ÖŸ\b¡pÒ2^„Äï\÷µ¤pþòÈyGŒÌ?R0°G­Ãú‰â¢³î£¨Ö ÂQkã­Mê`-±ÏN‡:af+"ÿ+ØìÖ @©N.>¹-üˆ‹Y}º<úE:i/%ÓÎR3º нQmøßSÚúÁPD{A: õœüMØ÷gS1ª2€¾2±vÓÅ —ñ½% ÆÁü{!u=˜k-«Ðìúñ³¸ØšÈ5*¢t‹VqÛaÿÕÖ/ËÎ$ÕTI'@{ÍÖþÝ%¬äfÕÎ&Šf(~ËQLMHI…šö–ðud6“ ¥F˜)¥5||šÑòOU"&;„,˹Ï:÷¶/–~TtŠk.hSRaàùã¼írE”Ójê ÖûbÎ$šFžQÖ˜s„ ™€Mã§cMok\Óý–l"-3æ#ƒFvº©¢E£Ì} }ùÂÞÌqÍSgŸÂû«ÞÑMíú{‡‘‰O=·H£ûCÞoòâu%#ïQ¬‹ù[kR‹–*V97 Õ–·²Wý,[‡J P9:j0Â|¹±TeÝý*fX3´ •º..<Á ÛAà"Óvÿß׿·=Yýó ¦¨Ä»Ù‘eRn.zÅÆ,fk¾Knõ}]Q2R`ÑÃâÎa»§.×*¢bÂôC¾}Ñ›t®ÒBâ®ùìÊþ¶åó^¦ÒïLÛ(uwi6Bö¬08hG¬ª ÖÕ[¡Ì2Ê–œp½ÄÈ,7kxçõ¡¦'ŒçkE ¬[6­ª36¾ZÊåÔ6LÛ5sàÀ=ú`ë6: =0|Q‚ÁÂ/4B¡J' &QoÉ£FÌ÷ô@ê_(¶zç6bå_ÜFIÓö9ýÄPï76³ï ÊoöÀÂK@=Q€ô ïïû6†rm~»Œ_ƒ~€Rb=:Dûp»O/9lPZ§Dô'‹]{ã€fœ‡Gáƒ>ªfj,'bUn)mšjsÌò jâŸÙšq©"ÑÐÉß´ì†0ì cu*ã QÅ¥x Y©=;' °t¹ÚµJÕãnI˜Z.öˆù¾RTÑÑÃÆK¹ùÆÞ~öžÜ;ºfêCÀÿ¬wâ >U‹ûšHûCº5š(§>dÝN|­Ì d‘5-&Nî—‘é¬Eô ÎëùU­•y>„ôÔÄ U“åï÷ôí«ß…Ð0?Gåàn€¾!AÊíñêJ¸‰qîÙ:0‘<¿”Q9Û<Ƀ2¸æÍ4Ô?,4µÛ%º9H +uøóuo»Ø\Ì)Öçzy¶ƒŒ^,Ÿñ\°eWä’û‹L˜BjÈ‚b,ÒðÒk „0Ã& ; a¥þäb¯µ„!34´]ÆÍꧺªÚP•—îVÌctÒ:\~¿ZE©N'2¸¤v}×ä9d\v›/ræ¢È\reJÆyð@$àx—iŠyTƒT X7Äc[@¢+—½gƹ?üh`ÓÒ¹#|2ÿÅn¯Z¯Šh:™Ë¼ùY³ÞYP »$Ù{‡3HFuhC¶üÓ|²P7g„ß@íÊzÚnAîÖc/ó}·§†ËÓ ­ù×ùÏ’A«ts^hD4Ó(¿ò´¥îK 3ŸÆ1…o|CgìÕ©wr£ZSŒìKËPf^£Þ-‹nDé@óQLøNª ¢øªË¤»nÃÔãÍ©>ïo(„Y'@*Aì5®À˽!ž­7‘y²„}þ. VÁp©ÒȾ+žIõæ«`^¬FúNN–OèÅ*ñsÎlUP¦A—ü]Ó@pqÆÙ ÜfÐ8¼“<üuò€–4^pîƒÔîBùت¡ÆrŸ¸”Ü©2Ïœ8É6x3ª™ï$Øä}ÉQ¦†VÿBÞ»ÚÞ¡\/önËvƒv’p 4GB:DOxjsn_•zÎzTË1Ln7ÁÓä£jÎÛ8›¤’]?£j¼íl˜Õ— ‚aËÙZ? F¦vë:ÇžaBMhÉ0º_´Ñ|ü¨ïì°²ï*‡È%YË0äf%Û…Ÿ (Šø™7·Ü Ì`^8Z0ÿµm ³ÁtÓœâß٠挤ÅB%¡øÒYÕ™’‹°¶Pá|Žàâzýêýêë2??´¸†ã"Üùò²†¼kE‹C^éì?)¿é-¥”Æ“Fýï²™&x¼nÚP‰£ïpm̃ÂŽXv,á>8ýgy–$;zƒ €†ÈÏÜ™&Ÿú“¯wJ.·ê6ÓR#CJge‚ÎäFë¨_鬬¼ÚGù§oSà¤ë-‘¿øûáÆÖÕ’sÚ©JÚ0˜/¬ó ¹õ‚…ú•'jÚÀ¨5Gçs¯ò9~àffé(³/÷"Ü…ÝùUgĨ¬Ýž \.¸0À°Ú?°1•í‚ª’ó0¢ÏçUa1ào…mX@Ò…WÁ2[â„þ;µ›É©W6î'A)‘B…ÚÕcxßB—£´Ñ˜Ü‡*˽˜ò‰r ‡×#|*Ú¹/u^´^€ª5£ƒa‚ê•cYªÖ ¦õåÿÉY 9'9½Ö•~¦E­ïúfweј<ñi+÷+þp)J>F(û°ì@LQPjù§8zNpiÏ#a³”Û‘TœãÈEŠ9^®Qû¶ø2#€`º„Eïdgþìp‚Õ¹ÔLncnþŒm%)*ŠÊ”ÕhA,GÕ÷@b! œ/s½.-2ñs”*=Œ7©è|0°~8ÕŠŠcp“!t“ˆ£OrÿËå8‘œjš#jU@àÆ>ÛHvFÔÔ›¾-Í#n„ãí¸‡âÂlëüߡk\Ëj÷¤Å‡yH¿Åe`­í…ñuÓ~„à$UÃ÷ÜB…Šgžo:m7œç‹ŠÂ‹™Ð ó)«d º1 =OöëÊ>.™·ìtVÔÈ;¦ed¿üG\Ïbeù³ c`$µžú²B QÑÇÙù æ6;8Þ?«w½{è\„F¯¬õ~œ)uCxo1ÛêYúÛei$m^QÅ4Dm‚›ü|z1r¿f‘ø•EÉ&釠ÿζ*LÌ9z¶èíÿ’…ꬪ{ôθW»ž»“ׂÜ="Q¬†f¶ Ì41›P´oá½äíz>ãŽÍŸƒÛ, †gȽg£«S÷Z›NÅcH¯bÛäïb‡än0-BHXÌû=ºàôÛM4)TöÌäÅõ>·»R¼þÞ(îkÊfÓM'Hó\ËÐ3ÔöØ ¼ ¼“Zð †ltåSž¯Ê Fuñ¸›Æ)(¬üò”übÌÇ ›X%¯d?yt>䵫vhLÝ~‚Ö–N[lv~·xŸT|yŠv³ƒ±WǫƈU(˜·—Y„ÿÆEÓˆÛüìæŠÑr`Õ‡"¬Õ0ãr'¸(L訵Á(`°¡®Ê/1›UÏ<õ(CI2VjÆ·ù¶Ù>Â@5íX  ÓQ;ÇŠ.ÙýöЂ¥}üÓ‹8lñO™±Ê”?†vÛf)ˆlEzø¡ú¬Áéûò$ô¡T¢]çKå3 ;ñ³8‚M0Ù.P Àÿ v‰*-a9D±ì5CûÐý஀©Qõ?rº–‹{¶]ùöX‡>¹¬7ë}ˆ1FGÄs+ª·I;ËOgêØž€{"TÅ72à ÉfÝƱNsèÔ¾ ÿmÖÖŠ›VŽyµ»ÚKcáÓþôsï –vÌ^ÇñÎl©‹¼2k»„ÿXr¥u¬9¨z/׳´¤ct#DÍ¿”lÊ]:…#gXظ1µ‡£msê¥oJ¸-";MoV¹â98FÓwûý‘¤žé.-$2+ÈNÆ5~Wñ¨š¦ze”}×ÓÃÄ¿¾Öû¸ç•J½^Oö’½¾úSG šX:½Ã<ÑMÄzÒoãHSà¸oÇ$ ¶¤ÄEÆy·ªÍ9x¹\czuUÎXÍ2ô´çIÕA²š˜Ö†`ƒ*‰Ó+/wù”e”¸ŸÃ4ÿzÏ>Ø-„®ÙòŸ^êL<™ßéýx¶ó‡ ÷ j2˜o B“>aÄ¿[V¥Ø{’ê«h) @µHuGE·”å„œöسôû‰~àñ÷ù”N¿y›ÌÄgµÇçÊK¯¡ ›É$MÅ:@QX2‚ú¹›£ CŸçškÅàÀÐDß¿Ð÷C²´[Ç9âGnŸ]°‡{îF³Ó@-œ…—ƒ$Ÿ¥§z|Þ%ééFX©´µÐü‘šk2E5¶4¸kÉ‚€Ï[!dnÚ53¶wÖŸn²~C$Ï@kÖŸ—TŽœ÷J²ŸqÔÅí JBêSL4ÄÛ±’ŒŽÍÏÉç(E[[Dç¯aqH‹Œ—–×$…-žç×;ý_ž®ÈÅàzÛ3’çn÷ !àu$_ÁçŠãž¯o“¹ nÂÊ>9Wff]JüwðèPò#t¿f4ßÅ £ýàkŠ×ÇZzªãJ ·ƒaV:T[¶6 íè~yjŒT-œšÞö<3ßõˆ[L×êTyÇ©·5Ó›qƒ/5ˆþrw(©ql$$ j4 uy¿YÝT¦ó…bPEižÔýÉž‘÷Ñ$ïBkFQúè €äT§[v2%6²ª¯-ÿ-:“3¹‡ ã*^ý!4V‘ÆøÖ¤ûX_‹¬…ƒ÷×»€ÜÞšÞÛ° &švJÐ=çaµZò<tŒ‰×þÚHL†å{/®n±vææ|R\rÅÑöÃYI—³Ðý Ggä›8‡;èûyêHv×\|ŸFQnµøJîRö3só h$l¨ÿ£9:’‘ 2ZK­sïx7gC½â«6½¥ÆÒ£ò_fO–6O—ĸ#>¶ãi\زt´˜–5 ƒç-pc&&|ª²àÑ.ÐÛöqtòþÊêÕpUC8¿Ì_W˜neÈ2PrMö°CO& Ú¿WâeKLSåus2Áصi¯\2möJd‘Éž^±ˆ]^º{0½Ç¼ÌCe¯™ú²]Ì“¤Å–²ö…ý˜ÚA¨2mjÀ€z€üÿ,4Œ„éÖ^šmç¹îuÁðˆ.uæ­šìOÆ)Oþîþ-Ä=áT0s‰¨{µh˜-¬6B:»[[fa°·¯í±Àéÿï´ÓßôÇQ®2¾1Ï#ûE‰=ôÉ%frûÜŠVyOÍN=š<²ˆ Dæ« .zœCî Zp€6ˆ¢’ھƿœÞ*ùév$½}`RëË,šªÛ©æ€êã—ãeÈ#LX8³Y„§O‰fMy`$Wü¨Xãžuƒ µµÊ‹[ÓJ K¤+ÌlÅG_ŒÿiÕíÚˆs*^™¨"møc5dÚU”Øé¥J±µÃ—÷wËúÙTD„5b‘†þÑzïžèÆ0àJßmy.S€cé~w²7æg‘‘iWL‡£ñU¯ZõwŒ%*yFl²¾àXÀ1a˜dü|¹õ¶ÍëÌ6¶þ=5I篇 ÕÐ}Ö²Œ´p`{]YËš¥ †·nvMlªÒËW{9•çÉ©}õïž؆‡.ƾq5ëÝÓ[£;âYêÙ²€—‘*­lù_ÙÚDÈnqRë¬d#ð«+EJ; Ì«û½Üt [7`õi?"AŠÅ½skƒÐyét]ŠŽaë^1€ò/(¿ODÝæýÈ‘wXDËêˆ\áK6_NíÑœý*üÿ=q4aRÕíûœEÿx} öê;R^â 1ÖJ¼<âÇåsTÁà"ùjháʳh'zóîóB]Óø¦°Âh_᜘à¾Ö-Ê_×vÖ~ž®ÃYÞãaóÒœ˜¸Iõ”™ž¢}Wä»m6?Ù^"IÉú{íF诮† šw’“cÎÖmÍlœB5(¾÷US¸L|Q \L7àʹr­Æ:É«Ê\—\Ø@Ý.%—ꙌO„.úE°÷Û43C&òݽ¤v£b}b«§¿_DÁm#ÒFN‚@Oé¹€£wÎ?wœ©E-ÎqåÓrY„0 ;£@Iž)„~ú⑪Bω½9YÙ`ZÂM Ts*3Ÿ¹ò‹ÓSÛ«ÚT6¾½[vsÕýúÑÔ©_R^n^-I³ï³µUu‰°…3•ã§pmQ”6ð>’ª–×jÐÇÇ‹¶,j—T4³Åkd‰4­QQ1ã¯ö+¶wb7¢¸ãÞÄü6ˆÔœNõ¿|kòÆó>z‘$h$ÿ¶UÚ–°é°y9Ž …y€¦™ÓŸ\«`M ÙX¬Qy¡¶²ÒmŠÇê!ÚˆUÑ»b¯öÞ¥¡P…Œ7»‚låÉ r­”¤‘Ö*BoT=µH&f¿»ÆÆ,¿QØCK©©Éùf™ k=)ÓP‡“ lÕÙaܾŠÒJJeàšÜ1Jtls0³&ÎÊ oí Ê¡vØEO=˜M½î=dƒ|Wó9s_gÁÜ(†XpT Â£©r¶ùè»ÊÅ

·»°ï‘Ù[ͧ뺓$ÿzzNdºFäòœD»€C =PLcfEˆPLùZE«öÈìeyeØ86w¦j*ÅjŠ|ý1Þã?èãéa©ÌJ%þ~™rÓøh7ññ ñ6¾û__jÑó§cªìsœñhæÄng#aãû$¾Y 1¤èdÇùKQDS÷·¡`±O {X ÒàYÝPrQc³zT:Š€òW{Øäluô²œ“L¢)OÎ3¸!"®Þ²\Z›Mˆ=d¡Ç×,˜`—§ ZŽ52\xX4{²¶Šo´ÃuìUrnõÌ+Ó–g‡ø9 t²yêæ›”»ínö‰¿†zë?lŽðéÂDS„@Š*~,åE$0ãî˜ì7¯;œÀôJ”Ãýù¹¯Ó">>ç–dH2Ï ¼È¤U³4Ìjïž­dAÛÓ˜ÞË®]Xe~´Ir+eFBªUO5Ï^§ÎËb¨DÓCz3¼§:ö!­†‡!ü :,Ͷƒck¡µÈ«,;«#;IãW)«u*%îQªË»@«amrœôÅ.µo·³xTüC åÞVø"ˆ CâA`õ_Ú†Ük¯²1@˯¡äâÛz®…¾Í‰ýÜK¢‹iB«Ø é:Žíø‘;‹k}ûêÌ!ÄUï„ÎÍ×ñlÕœ)Xtw*p9eÒ$V^ð¢¿k]è¤<£‘µU %­ ¦ô=Ž(Ec\kÇ´ßÆ42šú#óå»Iæoþ$ cd=ã4¥^c1³®}ྪ:—€k8O·C–,¢$Ç“µÏƒ_A5Ó"°©wëÞ²ùÿ ÝŠÇ¡{{'?¨úed™#jV'âþÄѾ]5.ÍÆ…n`l÷gxä³YêI&ÎMT¶=90‹!$¬'Uc)Êÿ ¥¥¯jXÕûµV\çHî[s}-ÎôÒÝžÿKÚ¤7û;3©ñeeî3œå,Âv*/M¤Ÿ Ü?ÔÆ-²ŠJ°»¡@tR øS_þ]—Áؘ5{þ…ç`J^r,¨ç&DGá›F"a~ˆ7ØY@«m\‘ Àù Ô¸W6n¥3K\üR{–÷óäT#û4¢=ĢÈÞX±–(gçû±8'-÷ùÃíÓPá\ñ¾eOŠ_cH}ú‚Õ¯IªxÖ. k:D5½åòµÔsW¯<†HÑËL̉H¦+:þb¹oÎdÚ|ª ÃÖ‹*(–òÍÛcÖ8Æ« ¢§lë)Ü»½…™Ód…#½w0Íÿù žˆQ±¼åÎÛ:ìS3xGïkxœ5 šëG{ÅɺŒlUÙꦴÖø ¨ñ^ÞcÇQTNqƒ<ÎUÐÇØÁ„¼.¹jp%ôÚrL* ¥†=[Wm•¶¾Ã Óô¤Æ ?ЄŠ ڵ؊™–Ò#Ä2 ŸôaÖüÖ_›ŸúÒ>pò#uZCÝ£{UŸ¡BEwBIt©®‘Š”\’×äâ÷«y"=BÎp+v¶º’2%zç •”Œ*Èã“âÖUßÎå-‘©?>| bÑG¸'’ýl¦ÜË: +M³ÕÔÀ²ZŒ’¡À(Â…KAJ˜ŠïSù)t6™n­íëØ‹­ir@§OJï5.ñØaP÷”_¦ÿ¶ ü‰§{íƒïðÒj¤ÁI)ýœºa‰†­a+ß” cñ¿òwJÀõÃŒÒå„š]¸FïhGн¯¸ G§=î7ûuÛÈmBx¦”½Ùã!x§é/­à Æ6´ëóîÜþŠ»çM(ôõÊ×o6‰qÖ÷¡É*§HùØ}‹ŽÍšÂiàælN S";°j›Öú}¿‘7è¾RÎ/‘ŽÂÁCŠhÊßr§W»f+°Ž›ó“ê+EÙâû<ñðÍ÷­YË¡ÊM5Õí(3Ø’5X‘Ó®Ç7‹¬ãŹ3kMý*Ï%¶Ÿ¨*’õ õ^ZÿžžžïÈse#V–ÃηV‡­Ö¦· ±öÄ`œ’ðÖD{K…•ßÊè å±ùúW,ÿûºö@&– ?±AòŽ©ô*¬D”*ÓŽž€Æêáã¦+ÿÚ™½_Ÿ™¹wÏEÞT£9TÏÆæÓân¼’!°+ç2ÿe‹Ql  ',‹ØŠmà±î`’mØ%vÚl5ãÈ1ö/}½K‘l²J`?Ïÿ•nF1èŃ¢ÖRñÉw¬I`ŒJàÕ»çò轶ںu…£!Æ”c âÍ„ãx|8÷—Páÿ •Š¥‡º%HJŒ"²‰~ÉÚ•Ù1¤?{u\¡>àžÇbKPfu<ø7»³qÄw˳µ<°9®rÍŒŒˆ%¹9XGxþCÚ„çꯣg˜Nʼn ÓMü”SŒÂ\~Gh5F¹¦*pµê<Õ7©*ŒQ=f­]ÉZp\¼½&¦È'lô—Í YÅš´e<°¦›©|8Ù%ŸÜ öT†‰&F¤ù’! ÆØc&57q€®mÕ|ÿ]!½YO.rC‹Ði÷p)M;o¾p¡·Šu'+5Ö⢃ó¶^¾i¦ÙB÷à¦L0 \) '·:²ipª…`cðÜïÕ£¥n-Æðè8˜ë·|>@õwÿRÕ$_ œªJW‡²:n“>þ{­…(¤>Y.ÄÊ·¯!•-D€jI´ó-‰‰EéP1c¥Ý´Eeà¬cªQi¸\’øØ2u`ÅÅLFØV¸ìÈÛÙó@Zc8äX3ó“î|º[õ² ÿÂ/Ó×íŽu¬MŽ™±ˆ*< I“òï*ë„=záAÅåt!aX…kÙrµÐ.$wS§bô¡ßèu(Ñx¿c@ű$ñËx’>í ˆ‹Äch=,Ök•ÙÞ“qä ^wÕ—ßø¿X}„OΈó£N\åé¿kJ‘ÛžÎ|ÞäyÅ»V¡)PaGˆ{}ÇwÇ•¡¾ߧÁN̬/½}^¤ßâé‘@–Iax'©Ë½Û®±}qoºÁ€ºò‚ê£ØÑÚô€nÜöêÂrØþŸ?n° ôUí8þŽË¢\ÃjHÐ ôÒÙ˜4ö•.0:]š#!¥3DȹZ1ŠtQ£Ëª~Ÿ‡®æ<"º/ <¡òSr´Ñ'+ŽôSBúõ*´»#ÿr#B¥K_L!€ÂÃ!jéI(˜L9£9|¨ÔÎgDPOáëÈ…ø’™n(ÆÊu-› Æ@Ñ« …ÎÍVÚõ°?k†p° oÔ‡-߸!I×iv"ê]c™=±¶ÖÐä*ÿ=ô õXu”xùæÕÏzÁK(iZN’b¡ßÈPLÁâk8ÄV$I(—¬ k£Å;4ZÁ$Z¨–À±ÙzY@„§»ú/¸ºgü1»/`ØÉ@¾ÓÿÉ^i ÈåZ»-< ±:õceº€PE÷ëj ¾¿fLÖàB6L¿IAŒÕ=ß Ëo‡Œ›Niivô|4ê,°d¸Ûµ@¬V1r'Ë­¹®'†T¢Ä RÞE%xisL„ŠúéDl©øjBRÔŸør©OŽAå±nªYîÞG|¡ù|¿Dvâç·Æ˜^sµÛEaÓþ6_bÆS¢æU3Í\&uµH¹‰gö*FÏIâ¬ñþkèî0åàŠp2 à .K=bæÌ뫤æ–ø —Ź+ö4!ñªø¿Ùãê9 ºÅsÄM¢íð§½%峇´KaÁ '6pÃ=¤Ê—í}Á%Ê ˆû7¯’Í{{ÿóYç²ø] ŽÜ°íº §¼Ø»¤iJÖÞ€]¹ši;LÙ4Ì©Ò¤ÕRÎFÃ;Ö  ’ï †~N¡ãw ùQ F‚˜«"|‰Z ­(>5𰄇ºU™Ô¦=’0G U” ðzOHrå^.Ý|Æ G¸”Ú2ì öfCïnÒ–ÃÃ"¶¥³©¯’– a[ù ÍôAJÆtáþNˆ™q[g¶Þnù÷C?ŸYð“oF(<„’¯ Ûf–<“ ÐNAp¹¬4ºÙ Ÿ;„¨¨Ãýi%ï·âv@­Çò>Áb²¿'ôe8Â!YÒ…§C`xý¥µ$§…SÏöE¸Jh:¹Œ¡¿”×ÂÙA'¨_Àï0A.€•ŽÅDI?—xú¶‡°=l…m èà#¾.ñ-¬”WÔE·µ¹ÐÐ`»ÈhzËÄ"Ú_Îr)Édkí¢],b²\.éåxCfLn^Æ÷Êä{â»à(o7ƒùònOï#êÔ„, ¼öÌÝhÔí[I3nf3kM6Ò%Ñ0c%ÐvÕ„ìÎGrg G8‡Æ'n#ŸnJEM%# Ï)™pUѵGÕ~ïIÈiöé–o¯“Klë|“ø |dÎRo Mt+Pü†@µ­mO!ÐWt¿,ð¬Ö1úkÈÅÍ$ЄŠAv¹•²Ïg<Æõ1U=}›¦ª?+ ø½‘ß@¡9«’s×øÜ#>{D+ëʈõ'X±B¥¦µ7 yEô|Àq®i­qjpø[,2­ïzä]muþ=ªOAH-z±coe!«‹Û ŸÞ©$`bjo–¨¥ÒÃC®þá„KX„þ1V³ã6¶åÖÇÈ»ÎwûbI`Y×–,ÙTéoiöP)J•§ÎÛ.!U$H.¤ºšåvTt¥Èp”ÞÛÁi0 ѼULÞíÀ ©ôYù© œc"HuG¹ÀŽ ]Š'1êÕdÌî™þ²!³€ú²é ”C·êÙò6Ý=»,‡X¦é†ƒð«G­Ú•‚ e¤¢Æn“ö^u¯@LÙº;¯l­€!«Mí©Ú `vK¢V¦Ê{XÜ— œ9à‰= •ù™vE“mï\ü¤ÔBihÍìòüxˆÏÍIKÖtuë;±’‹`wÁ\ÿË4ɘ2[AÌ¿-ü•L¸(k:-œ˜›-ßp‘ÙÔí…c§.è $þçŽÌÛ<ÊaŠë³nîGºxdÞ~±’\´ÊK•K¦¬ðmÉ¡IL™¬†­ž`f;;œéfñhÓôŸà¡`À²¬\³­ìžž¥b}¼Tß&q˜v“}ˆI„&ÎéÅ`µÔ©³ ~;øý èøØzò?–3ô…º±×ÃîòÒ&¹« o”ˆB‡“B‡BÏB.†H™È¸÷uÇGk(XªÏœqÈâLM}AMjÍ' –Û¶`òAry,É[^ ,Œ75\÷"ÅÁ{&‹l Íñ±¾`áY!Për©4]®ŒPOŸ`]ip^ýÆ‘8m‘‚I¸Ù‚`^1­ÐIJ(5Fý”BÄ g¶‹/áWÞ>%íâÌöUx:ˆ pQ<²Á7DéyÿŹk|¾ŠJ¿œab郌ösã†Uß‘Y=%’øGåi¾(}¨e@gâAvó±T…¼d§Nu·Å”˜}ϲzÐ#È{¤M ­ÌÑY]‡ì·yàu#]§·Óätã{áûTësîâ®^Ý'Û¦™ô7Æ‚UÍ¿WkZxÒUk‰»îõF.$É´0XÍQy¡ïMd…™²µÞ mœùÕˆŽé(b•J™Ï³Šu@›/ü&׋}‘®ô°¯uAR SŠL™&{öûè¡k 6nPÊk‰ÍhžOeè@Ƥ\ÛUÏumÛ÷µ’~R»•&Å4û€f^Gp-ßeœ2ÁF}&žÕà† 3ÂÀ°.<´ Ó ç"3 ê…À²žÐŽ71£e‰ –#£nUgx]#c5 ¬— -.(—•'+–{΋÷Æ\8Ò«Ä΃áÊè]`®z+i9´¾¨Ê?ŽKâ&~ˆ‹[fÃwíuš½–îi¢õ.y¾Ï ‡FÚ!H«Þx‚IÀR€Þ¤ûaŠnçµÄ<wÜœ/1á3]Ƀ_sfTóFu܇óæ)X'ë÷é¶ñÉÊ^©\ø¤½×ºÞý”S;…Ì7ƒKçš+ÝF9Õ‚åÛÅ®ûàrZ üEÖ®ßæ²Ê+Æ :¿‹·öëo¢Ì¼ ík…;Uö¾‹‰–ÌÏ>ßJFÏŠ2º±•m£™Ö¦[f½˜‰²¿ ƒ`<Ø$—¨,çWAËB¹„@°cÄÏ$æÎg.»ðÞÒ…„K‘x<Ô—ä‹zX˜Z$¾jÃs?WV“'’/9³˜S/R@¼v¨ ÁnþÓÚKÇTm?5üE-¸‡œÂ3NG½ˆÙíÿõX¢ï?%>%`rnŸÇäÙ"<Û±~ãékàåØþ¨F‰h\߸€#øäÕR$.ŽäWIŒô¢q|ío+‹âñ´@ ¼ü›±Ñ_U37Ô«u&KÛ=l ¢²/!l³.A¢T]’d4:ŠÍm~SKtèÃhúššÝFoñÖ]‹ñ¶ÝƒšÁ·Íd/¿LQJtß»ôF3P¾úø}ÝØ+žÏ OÛ%ä“ÿu(-ž²E*êäû¹leA-B,zº_· xi~îd?/EX̨ûÈ!ÖiÖ°‡NǦWø¶îgâ¹,Úy¾êš@4“v@ïML ß²¾¼ßàü.Ô7¶mWê|Ä"~¬¨Þ; ”ylµ8t&´a#‡óëx°èïªñ,yÆÅçå‡ööˆ3ëuípɪdꦜ²¡e¸Këù5s}ë=KÕ£hcÐ5äG¦ŠÍ%£ñ ð8@ñÕÐG•AA$_·1âŒÚÂJ#dEðºî¯r{SŠ€H ,‚(ƒ£ân°Ü_³.#6” #{ÏjEøh^Ýáük)œ±ÇB·¿Ï¿ÝòÙ+ˆ&åAòSÖž[,Ë€Õ‰’_ë3w¥XAɯ Ó̭Ÿç$^½¯X(ó–“+eˆ òñHY}º‹öìâ •´ßÈ 1¬»ø„45'5èÕ~I ëÂØœä5ЉJ;Õç 9'ëNuU\KIkL¹:±HÀycŸÌjìÈDÃìuLX{w®p%KÁüd¹€ÕWãteéú0àÜŠ=\¹n¯Å“–Ò |RZÙ9Â¥ú-côû4$–Vjm ¸‚^#ýouÉÖ"O5óΕŒ3¨¸´tæçÔå69pJ‰t†“2oØ1m=LÕ° *ï«JrMÊ ü$<£bƒ–¯Š(ÛÈô09ÃXY_Y;Cvˆ?c:áD¥\¬-Šº&úÖ/ƇL\÷D?¹Š°qϲM•—_Š6Ÿõ”‹ÝËbîZ¤?ßô§S¸­®(VT€$ ÄíH¯›³˜/n_€zIÛx;LPæ> ñÂ\3ý›ûÿVÔøWi¡ê@¥°x*‘¥á˜Ã’*6=h¸î0V8q©“³}öžžKû¤ìÿ𹄑šw`¥²¼†ý뇹ñ<ãšÆø§°Y=’×€®xmH,°‘o´O´âÂ/˜T?u³9è¢êœ„´KBâ`ÞŸå‹.ƒŽ….ÈrÝËX@gögœ4Æ58à‹0©Q"ªa^ï¾%`¹&”žûP Ó#ñGÆqçÓn |åÅwêH_àSw°à5ÛŒ•eÓ·qJ’Ýñ>u”3ð§]ùÐÞ¼LÖÏ‘›{Ô$S»uò·>ZìjæÛ•²+‘Œ*m]¯M,Ywɬ‰tHÿSsÞp+"¡õ°îld’Î-w¦ñ‹UN?>,_·ÌŠ”¤1 ƒÉ—U&oˆ½æ¿ªÜó\óf[LzôµµD®C)Wøu~Ž³mݽÅÎ…ã®BOfi æÇ2hÉ{Èàg>ÙÄÊlºT»´e97Äts!eàÎW\)øû)Èž|¡Í@]|‹Q[†‹ÏǦ{mÍÃÃyž‰ÔQ€Òb¨Å³ Õd®OyÂ0 ûžRÚÔ K__Ž¦~]%ñϘ+]ýÒ°~Ÿ‘Û-é‘?5ù_[„›:ždÇútÈ=¡-ç7èÊeª=¯9CÌ«…G?Áò蛋y“†³/趸ý=~‘þ½©± Ð4z-†,S´Ä5ñBƒážÇ µí·•!§¦Ë›`À›iD÷åS¸\Öt"Ä+ˉLÖ@EC©H–ùó$ëœ ÅØáY'2o^rp`eD¨BÁò‡èCÒ"Äì`Ñ?Ú2£âg² ÂŽ:hàløû©øéH¸Ãñ§ItyØV¦ö… ÖQÛ¥M¢Ÿgo]£ß:!ŸßúfféÁ˜ 8 YÐx1Z® ‰Yææ·ÿ´Ôâò˜T ;ï GŠ q#ŒI"à»ýDÒ6ÁÆz¤ÚiÂͧ:åUwµvÛMùW')\Õ{qÖ9ŠÐF<o`‚)fS†/# ²þÿÂ>–]LdäbjòБHmÔ›j7vº-­¶WaQõ}3:<:NŠ²!N7ªx‰)b¼úÖõ×”Ðôi5eK ÞÝ'Í#“ÃYÀ6 8>¤úâ"Ž\ºÁZ¥ƒþ×i[G´t‡‹Ö§ÏèâQ1¨œÁ mü™ÙX_®} ¡ð,m:£ƒƒÒ±ùϺWêììÑpo,¡mMcJ¥‹Å„²is wÎ#‰¡~肽"æÃNƒÈ‹ç°UŠ#ÚTÒ¼@aš·]øÛw5zbáOf€=¹VrØmহߢ+6=ì3@‘x§Þ˜zÊ H…Rˆ¥çGáfF0ÛtûO¥°"BÜvh†‘|-DÀÜĨkìÆê_ˆ=©«ïu¬}8*4@m!±—•ûõ¿ÂýAÑ1îªm¥¾“z~¶ÚDf¡™¹C[E‡²_±2K“  ¹wÜᶂ‰Ž|¤Ì…Ç5–KZ#’%¤Í?Õ6ñÐãu%[ý–#Þk¸îUP–ÄÞÁ.§™šl?, 9DúɃ×(ÿ¯7âJ˜-ü«´FÐÅ#lÍäqXß ÷æelË{ÉÑâ‰ÐÈ[/] n¦Õ3ÂÅQ8rÖØF !ùõ)-Ò(Pvì_&µUkat-E5'y²·ÿÖ¶³d¤EÄn$F±ž}‘äóý¬I¹òÏSÀw’pœŽ:\+\qÆcƒâlL±Ýx냺LEÁuβQ{º¯¤.pòqªDPŀʲ€›·HV>9£ÐŒw ðT ø':<³(ß°ièDìú×ÝùA2BÌÍÍðÁ¤?ΆT ÂÕ©Áþâæ;y<ë]S8ùjÊHíºÿ›ÿ VM£kß\º¢¹m‰¿šWâ*È[EhÏtÂÜL–°Æ”*uã”Ú¬´ÁqdŠI¦cò­SÝãôúlÐÞ-¶€+;µ~¹„!YŒ…¦$BCS t³-Ñ©ÛYß2!»Žœ*{'‹Ae» )ì¹Ø+NK¯™Û­6`e\hÖ…Q¦&¶ðî¨øæRI*àT É5ä¹æ‘O%{(#ÇëßoØ6yX|ŽmÚ@;ë±¢™qš™¡Œéf¯+>‚39D¹gN˜XР‹©· ¯9Y|}h÷÷aÜ²Ë “¨‘õb:ɈÌjQ_O9§¬ãx)Õr/üu¬i-ö»N肈.–º˜xô¬}–Pƒ¥$ iMŠ•ôêžcSAov.—ú¸à>ƒW"Ì$»=|F–ìdà¥Þ^´^d(É”ã®?ÏïR1 ¡?ÖjÂS•¥<7äWy°W‰r{FÍ1LYõq‘•;!á´Ò³°d<Ïb‘Q±úJ¾¿«` >Ú¤oS¶}‚ÔÐ--üŸYËÞ“~#]KýÊïmGîÙþ3=›pr?Ã~¬ǹýãþVká?—;ªlÉÚeÑ66ÀÏK,J{Áš'ȇ&êÛKÁ1®éC¿Ä "؇´'@6 §*¡Ž?`ØoGw|½Kö´º*Ö\ âSúöäô[«ô¸$µ/êãñ÷x"/ DÊ´)T†X7Åf‹Š¡øKfP„gr/G{Ì3˜Œ_<®«ãâÒþ(¯pƒX÷!Q\ ÐÉn[ùwÎKñëö½ 3“ÿ±Œßœÿ­6ãúıK‘rFƒE(NO°ª)äÝTݾï·n_ˆtªrO°Å·L¤ž÷gà!JM×P¨Ó•t2øöø)4ð<Ú¸‡ÑÅœ³(@ný 3¬\ƒ:EqÌsH›wýàC9zµ]ÿjp¢o[5^ÇaZ)Ï ³¹'o”MÕÛék|9>Q‹=Æù(sVZ’(êñý0³½9qÿ…”Q˜m1‰-‰Ë Ø^õÑû[¡ƒg;£Á–zÅ&pjp¢Ý5 +(o„bR1H’Ó÷ú´µ\'¬¦«LbƒOJí#¦ÍCM#cRzmø*×ioG*tq›¬ûZ€blÔ^,Çþ{¡×ëW¨MiðÂ$Wýs¹ê"ƒ½}{töÍVä@¼¦1k.€´ÎýŠ¨0IØ,kRdõŽ‡r—mÀ¨“iQW,\‡üêÒí‚Án1ÅU†ñù¢ ›ˆä}75‡Q›»Q5¹‡Å;–çŸ^KaÃÏ!´S¦Þ¯¥â³"Þ v}fu5¥ÙC™Nt¿0t×n…Ã[U×!õªAÚ %_A@½Ö_¢kÚM8kT´ \ÞyõX¶ZZt— "»#žuƒä “F›éÖ¬@Àtk$pØ zÅîH3ÁäÒ•Ò“Ï’8L!âRZ3üê©yÔwYáè›ËjéY]¡¦ù– Ø'()¦ ¦ õô*B*+,ºÌ±—ä]÷ZØÑmT§ó®h[¼ÿà—Õf–³,;Ì ‡oo»7b–ËJŒðìƪÜ,¬cCnËÚ•‚Ôl(VqX)àZ2yP"n2åû·†Hùa¡º_ÅÒà±0ÄßnbÑ@âÉCÏÔuc½ÌÆrèïï@WcYl„×Îð}šòg²´t&”(«]Mm¾dT MUÓ^6OQ/Žƒ žGÕ9 aƒU n‚ Ôzûhd#ö^Mv~¦+'ÌB-ÂlPàý²|ÒLŠu¬¢°úSóÁ8èùnƒVƒL/,¦-˜œÑ Ú:óG˜Ûxc,žr:ÐÙž.µ×¾¤ä&5Ò|?0«Ð£Ž‰f Àà 6®?ئ®‹ƒ,H¸©™×®~k«ã•×LͪZ¦ÉÙü0 aäYNUߧôÔy{ß6þºxçj¿{çG­fÆp£@bwFºZI92‚Q €P%2{x©’¤¶ïTÚ˜Á ^€ã—$ϸÈÙª‰î)lFMe·z)ÓOð€°Vš ôÐ'ˆ’˜G†?çä&Þ’ÀM¾ÇFa¡¿A€}Ì…ŸŸòéR¶ÀŸÄ;aù>•ç:Ö_]3©Ü”6ÆÂS«ƒ`ö4c[“ª/l•YT8ÊÍßBVí*.¹7¢ÍMÔ4Ýp©‚]Oªÿü’þ­1ùŠ 7H²X·k†äR3m±¶ðleJÎݸãEÁápÍ…ˆp‡š¯‹!:+}_ÛÒ¸_.Õ‡ŸëtNÛr³áÖ@¯…w&øK“-T·­Œ2v8ô¥|"ÓæƹP;ïwßÁžvâ]Tö톸€ÞÏm$%í/ÂñEo„Š±ÍoödìÍi¨¶Æ8 È?IÌY™6„ž–_ØTÄ»3"¹–dµ,*ß$ró­*vTë^ŠQ\긘Cø¨þaéÌÅýJ­E¶“ª•ÑçÒöTÇme\¨èHýnÇÃx³ãvÓ߸¼1+¢,|µÀpP0¤Vmìx2Å‹i‡V7ÍhúÕŽÂeN%˜&c¯'Ð ä¯8 Ô]™V0-ž¸ÒÆïãõ@uæâg—*lh…¼šlêÍ6âe‹éʆO©É— /m‡T'qdðfˆ/wŠˆOÊ"Þ¾<â…øÝrV*úå¹óñãù+ù4µ!h³®L4t»úC«LqûŠÊÈmñ^b¡iI‹ˆ3îõ¨bб$zw”Fá+Jä,¦sV‡m“ Q¶1`P‰ÁÚý…‹á¯6ò·Axg4 ¨÷¨ `­«éMÝ7dNç#KÈÉ°W5;¦¯iç!Ÿó¦ùC;2Bšž!”‘Ái>`çÓ¯´éCº~á“l>AôÀŽ9Ùðfã˜/|Ä¥Kžèh¾[É€×Í÷Ã1AhOy½yûSó®fj"@ …že Ï«†³QaTö ”h%ï´TƒàñÜ·µäNbi­õ³ê_ËÏl!eƒïô|Çbç$ïL  §Xvš·>NÏÀ[¦j˜Õ“~FÞOf? Ëv®æDdü‡ØùDÿ.mòÛŠ$]GuFc«‰œ×àw¾•‰´<çÓŒº*ª”a«ß SU «ÏØÙ(\.fк;¬Kue>Â\M“€îbw‘Ÿ¶°ˆ’±oMyßÙÀÈrèîF?PÄyw0Oë RìT!øÞ”6>¶T•™cZ’ðgsLí›Òû^Å¡Q:*º~Ó- “ßìü×"­Ö²[ô'ˆÓHq]ô_À1Ý1WžyÓ'Ð E9nÞƒ‡WòÆú ð©Ø‡Ûî,Ê…°«eYÆ<Åù”ô.vuuå0Û~Æ÷†ËEó°X·•}woÕ­yzÝ¢À'æ.põI$WGܸcÞÅÏe-¸í†W'Êo—ÀÑ¥™žYtWçÂqÛšrk®ÛßçW©ÕOCU¼ OÏ_©†“;àm%¹È ˜GÖ´ZJ¨WJ…xva¬ÃŽg¿$'NÑûTù rdQŸ›æ$qÝ­ÊåHi“¢oXÖ !¨+ä@q¤K~Kwì'.÷6S5Ãm‰„S_ñ€ážNàŒ€ŸguÈJB­|>ÓB¡,¸¬©,-ç5¸jsQà)¼f_Û4ž¸¼è§ª~î÷šøŽ/ÀÓ üm;g˜Î*@þ„Œ¿ÀiÿaŸuÁ«4yÐø¿“úªàs>”'%Rab˜Ãe Sˆ¨ÀEp{¦CYýMÚî e~–;‰ZžÉXçs¾”*˜€A{¦ÉÇÒ@­CMM³d¯þÈò•þÓ¶[ê±ôTd´÷å'l$ íǦßå$ÍWõd‹Ð^¹›~ÆÚYŽ†ÂȺDü&ƒ\ÓÕÚi #ÌŒŸÊ)Ir<`ÀY–ü kÿÓÕº#iKÉJEÝzå}Õç€ã|„³üŽØpð?Òm¹ É„Û¸³žkÞäXÊ€å×tºBÄn‡2Þ öÞí"%Y#o Ƹd,ùò×ã*ÙYáãåp„úý÷þFå‚p³'n‡Ê«[ø~ £ Ú¥{Qîð¿}ÂÒƒò\†ét&Yþkr—"@vÓ7ÛK‡Vï[[ÆŒÁŸ—h¦®F*_ì }ÿ²e‚»3’M‡Þ¹»»@Î_yÓ·>¸9+ˆÕ4ÖÈîl[wh¶"‹——{«Ái¹Þ>´óÃåvßIDMFsïÄ´<" x¾ z«êàØ•D ·¥1ƒœd«® «X«e?€x/W€²{ü”Û"õT¢ñœƒPVí§†Öˆ­çÁänÕbøTm/q<Þ)É\(«‚‡Í—Z#>-L¦xxÚ[cðs“³qÙC×JA8@8Z±i‘ûX.·¢ `=ÄVwè }ü·‹0c‚M—ø"cóa÷tá0¾1¢$sà ú×ähëXü“K<‰˜XÀSJ8‘¢iYéÿŽø}È°æ% ÕØS6R)HÕ¼™XxZ¯é"SÎmø5¡Å}d<Þ}jÜ\êp‰_%€oIúš «iìä‰^Ãjµyq x©oIäSàÄðƒO²0£¬qhrªV^D‰Ud@Öjî¦Ñ¬*xXÂ\Çàú&žKÇ‘¥”ªíuÀô _\œ°ÊFT‹7NÚÈüpG`}‚’éÎ\ÙúòÄnÙ‘þM‰<¢•[‚ô4Θþô*?ìÁ%z¯WÝ*Ïåˆm=ÈYÀ’†uû§$B½®’ïM·Æm1ÛS©±%sräBQ]:[•«_¦&ÏWþÇjôùƳm„x–Sœ¬t§÷© Õ\Ë$²›JÇö ½BU:'37Û}¼×la`"·(Æyy„Õ?X¡I˜¡ž|:š(ÝÐ(ð¥”3ŸZ…¥È=)ãD›²¿ˇÀUË(êq<éû~çMêщ€éÎÚ†‚…J¥"°Y(6Ÿ"JHʼnø€MÜ…“®Â ¯ŠŸ€så)RSs,ÿšêä¿ ûºs á}¦IÍÌ0ù×m¸]=qäA[%íÁÌO`©¶—o6¡o¡Xûu¸»™µk•Ân믠øuú€ÆÉîÄÓhÉ‚`W×uÁ,Š¨2N©‘#}R¢¼ÄðÅÜ·ÅY‚›¾6C›ƒ/tu–¦„|G^`µ¦ÓËŸNRî .Îýz8m¡ìð¨„P$Øân!¤Í’)á’M®Ð%ã…m2ªÖŠ.p¦Ë’DÁ¢Ú¼u‹55eGaý”-q HÀ”ol!¦Ñ€\KV°ï@Þ¸Îî6o?\KÃΡH4v‡ åt8Ä”ȼb“ö¬?Eý:y¯Á}Mj£tÝ_Oµ °*ÄßPˆêÝÌÏZ.ƒf¯½lÿ›Rª’kïg¢Qª¥±JY3ÀŠ˜[Ëpb< êŽEŠ*êÍ8[ÂW¿”÷!–³³©ÝÙÙ?Þé,¹–fæIË3º‚'›XШòrg´+t«eɽ—ÓŠ`á0îƒÓUù¬eÏÕE…"s›˜Àc±šr¹ ™ ´,SJEà’ѧì hé›ÒÅ£‚ßC-m ©BÈ=w––,{8pÖ+y1»WýB²ZD}™ôŽRq°{”.Ž^¹Û Æƹ°?0G£‡,SÇÚ8«¾üÝ£Uñµ8’ì ÝÇ }±8Íõ*ATôÒiauáoto–oØ¥9“Z»'ÎÎû­o’Ð:«^~ÑöÜÏ bÖiq÷‡ÏÌyEA­ñ¹¨šèÁ®#ø„ÿwñ³}çyõzñ¾ÿ°÷›ŒTš!Ь¡‹ÃT„âEÎàÑÞ ¿˜–´±Ð„HÁo¡+ðc½êÖƒÄRØj$áql`Ü,ƒ€†¿SušÆ…¢Þ€NáO)5snùβïKÉ™üy樿`¾ÏîS‘”Ž¤­ÔÁYémèØR|,…oÏ&ÙÜgÂÞÇéßP°Àm£âÜ•â\>Ðôø¯?¯DŠõ!ˆ ±Ë7s[Å‹ »•}>_´‘ö›ð IN^9ûpEBÀB`šW@³9A#[žD‡;†Æƒ±C(Ÿ£õ$Ž°"'§JW·ÀTÛz7UÆ¢“K@´ãb£¤1‰—ЀÅX°^Ôbf%ÒéWÞ¤VWÚŸMè‹j::Ô åÉh`dÝ!€,hª}Ù~èÃZ¯1ßDzB°(* hSÅzáä:î»kú”5«1Æ8dJÉœ>]ÂúéK=$»§,i⼧HB1Ú—ë^l”EJClÂ7»cÖßÿúZ½ùÍàáloì^àCPáJ<šìdr1õ²/yG¶Røæ{†E‘ÎBÄi­˜³ðµ­(,Åÿd e•¿IÖ¡ù³Sþ i4¤Ö{oQ‹_—ÑÞ>jê¼E˜ß¡.DY]阈Š©ˆ[Q‡ê•Q"5f²©)=·ìÍâîryž°º¶~£+ç:µ‹ Ø}=ßÈâÄø DR‹„.Aè˜Ü÷¹ÑQJ³`©$5]+§ÑoNNøÒPë×7›/ƒØ:ýº•{õÜ,u.Ýmž+ÍšDA½c6Þ¾uµPtPʃr‚»ag9yQ/Îùmâç—•”,%l»Ò:bþBøñ1]MjOìk+q~a–ÌTî‹‹FJúªà¦)Š½ºèžõ¥!Ø*—£¬¬¥þpV0|›mÞµÛÙïRÛ¼OoèK*ç½e¯È½vï?nÇ¡A˜Âä4£dz{†6óqNô ý0Ò#*: S¸Û~g§ÛÍ[ß…¬´Psø6N IÓx©i·`æÊÒe’ô—öÿCÌ1 ÒÄ›¦…3Š7MÆX30>3$òzÕlæå4çW„Ô]!Ú³|‰zdUX7 ïõNõ¿¦IóWhÞª`„­0‡ªÑt)‹Ay¹pæŽx®Òƒ«À¯~†Áøãéù›8‹lKOu†´ö^næž  2ƒ/[Έ\‡-Í_{’Á½íˆ}½1†”ícá©–V3/ùÓ©e,íýŠt"FéÛÂàÒn¹×=„””Òê¾#â1Å~¢gê+(!Ž™«1=P…~¨Pâ;‹z{‚¢¾€5›0 [ÊÁÔDQ5»ÁäÌTÞæmˆC=âÑ#lÚTQ(Ú®\¡qSû°ûîµþÉ8;xW8F=•?B{àßÁ‹°¼XÐO°×Ìã™ô™¿®vVØ|É+¹Q²š%b`ümmŠ E.ôs#ó¢W3—Cé¹up_âYÝôÎd¦ž£®JŠ„Ýà÷ÙÅ®cq]Xaea=4?=‚×É6©6­WNpubº³èåÛ*´Ü…›¾µ÷b O^ Šâ‚*ƒ#ÍYäH¹nqÞagèvô„-êaDØÇÅM$ X8Ó#;uy¡¯†µö÷ûkýWèr»„™¶¯0£}Õ%8»¦Ù…n*F(Ü~H++§‰äT ^¾-rÉgŽþðóùå¢ûŽ¨Gt ”å{ÓnzÚNíúÑ(ÉÍ3>¬ß×ñÊéPÆsbî÷e²l×¼³Ž¿…ˆÙˆÆ7&ïÃÝ!hKóm·~úÞ|¢´2 †MèBw;;–’±õɘ·@UÍÞ,Ô%ý‘[P>†ѦNY Ñ]o=Þ„³ œ¤ûaˆÊØñÅ'aELä-(m%Ô¾JsPt!Çtä"d\l.ŸL³çÄ“ˆ=xY(ÃF‚A™/9ø;?X†Ó²¾@¿ê38Tµ‰9OÔµ&Ûgmá\5ç+:ÿ9÷ÝèÞþˆKá“å/>% +¢ïÅÆ/[ÑŠijî‚Þyh­ýiCË4H’ òºÜ÷:»¥Ûåoòø¶E3Ê'xùþ´_32üc“)ßÈBÖÄÅø3!ª£Õ3Œgß]ua5æŸ[ZcZ«aS·‡Ñ϶=K •$!Ï'Zmú#ÝÇîBš9š-ó:`p\®SèïÓÅòÎ—Ó uï¶nò}Dß5€M#SíPäøÁ°ç¸&­s\&&EÆ5õ¸vÍ Ü± ] ÑpÉ6BR­l\î+œ¼Sõ­â›ô#Oá1tÌyj;¯šR.Zï@‡-Ö|¨–þ†Ë÷kó ¾´ê»½ìz‹ˆ–ùèsáøƃåòåPÑ­¿¿ ¹îQ5Q°aÇŒÛZqœ¾«V7²RŠº5•`:è‚ž;§£J…Æ{ôeÜî`†¡ðíqË¥±ß~ÿ‰²œOM úÐgrULÜ’ŸƒóK>Í®¼#ëÛ·z9ñ².ÐK3Šo©)j¯Æ ‘{M2žtÀ Ex:ÿ•±©ã,glc/8ñ¨•xæ×8ko’C|ž`{;V?…Åæ-cûÐÃz {–Ñôá™–*Já{F£möNa9Ÿ#0®Ô¬Çšãç›ù!éã0óM,–J•˜«±ZÔ#² á{¾òCÕ9­zš8?S{Ô;hP¥“Ùï,°FZGXÛj?Ü2Í6¯™nEæX¥9µ<´ !¼oož€–΂ ¹è]wÿä·m÷º‰ã8ý'eÊœ‡‹jÎ9‹7»PãÙnGo:/² Q}¶„~\õ6ƒÔ ö ×*Òp&ƒrâL÷?Ò\óç¾V˜ó%)Måes6 ¸ÙaÚ»@E®Ÿð?ÇÉX—v£S¼ç^-^®2ùºó#…g°ê/¿¥ÿòȼùÌü¸~´Ù¦à.%À¹Õ²o¢§SïT ΔZÿ3¶m±gl5@Oì±×úËár=\Ù0ÿÒçó êÐcêÎ0‰½‹•›–’Áq—ª¼scS·%í}q‹yvX3Ë5½j®£i ”“”‹°¯,“:< £¼ÂFß\éçh€h?³¾l‹Ú‹¤íêÁr¢ÎÕ!ñ!r•$¦°¥ÍÓ`ùÜ"mëÀ¬ñ&Må†Å*[éÔ˜D9ÆÈŽ¶ °¬âÛL¨–9¿Ý$ñ¶èü´K’ßÑPan™Ñ;pìÂ@þŠñŒpJÈ.«þC/ £í‡">ø‡ª-^ë¡»? J¸$ë+y×/Ž ¯>œÕˆÛ-†‰ß§Z)࿵I«„T7(¶g Ü:u‡§zq s*ÄZž–l}y|š`Yù·ýñÃ0Êu ×vÕ°ð(¯J{W»廞Ð$B©ÄÐ/ÇUtýÀÈÖ’&’ˆ7¤wPÀTúÚÓ¬CJä‰7mkí)Y }3­AB•÷h„dÊ?ÕVß^/@s{°S줬±ÎNc€Ý(ŠÁšs§üi#]ÉÆ$à¬~Ë~ôT½5âiÕïh #>,¿!F…J¢”¾«vCf/VàŒIèäå—Ü ØäˆZæØ’—ƧúÞ&ñªš3ÄÀ?»/Z6o¥?ënþ”Yͧ#çp—3p$Ø[ˆ:7m¬|—ÙI6½#u ¢ñbs¤X*¢9#G; 9$ìJñŸÞ1µoÁÁŸ fŸ<îf1J :ìJì›(ß7ô—ŸÎ£N\/óR ÕMd7Û¾}ºãȬ¦ƒV-“ ? Y:WÀ’ODzð˜ÿÍ»Ìää\ åÌ{™¹§æà'âr~F†Âƒ/@h%TÔу [(°;ŒhâO|Zô~Æ¿’Ò8à$×çqeÞ±¨“ttú¹€.û3û.nßµ†÷Ú°8ëƒÆ¥„ÿ,T$3&æC £):{æåyX‡³áaç8w×Yˆ{'hØýÁï`’úøn%2mr—ÕŽÈ÷b² ,P…Ň–3a’Rõ,"Òý»<ƒhÊî0\<ò›4Oȳ®o¿‡±ÿïMÜb¿»„]€$ÛõT/n£p¬¯†wžÜÊ( žErÿA &É·ßzÇ4À‹·ŠÎÀžÔa›ç¦ÿÜ_ªÆ‘ºé›;¾-ÝuO¡›Í¦&¨ßcú-°ƒÓ7£ª°D‡;ÆýÀGØdIÑQ8ãRÎy=F—7ôÇ-8 Ìu—îšnÇ›Ù'hkzî3㊆Ó±SB¤q³ÃdRJÚÓÐêc%¹0ùÓ‰W@Æ¢x¨=2½”“$y$€Ã‹æöœO,øí¹à(,Å6³bûÖŠW{Z>Üsân aòá‰9pña[¨¹lˆOJp€!:ÿÚÏðÐÅèö“¦Ö©‡ñ⎭êbÕÏ/PLàà;,HíõÁ‰œEWAîÈ„äR‡ïü‘zëšE:)€¦š¥Bäºú±±qòÜÓ»[nãA×óPx+׶³MµÜH÷«ÏÂò+ ÙRU¶-Á™8㬂¬ ßÔ0µ›W•õ›MÑxS†…­<ó…yQô„› ½J4ÑÂLxm¼‹éñ:Á/ Õ+*/ 8ÒëåF™S'Ç„W£Çë¦) ¿=DA¥:€pHœ˜­<ÆÏ JïøAˆ˜M¼2”81a "šd š‚°èQðý¢×“,ãe’næÑ×»hyöÖ IUOs°’Ñz;Ÿä£1=[¹‘ÑgïD> Mˆe]F"êA»úάݦZrÂÎJ˵ÇjÈ\—K €ñÔVU«­ìµ™ÔÓàȈFÓ=Í7 mMˆfºTÎ j¤âUÁéwõ¯Ï¹9Øæ” Ç³Ë}duQso ·u-KмÈL~†k‹=¿GŠ… …<Êøh/—ky#hÞžNÌÐyb~›ÞäqÊO‚{däÊ$Š&ÝNJóz·øÎ:ãÑbïyXAg©÷j#gÍRã±û¶Ôü»CÍÔ1äuya»ô:©Æ/$Šˆ«K0”Ü쇶ÆyjqÜËЩcdìªÊò}gu¦^põ­*„”ŸAÕô£æG79dÛfª)ÿfE‡ÓÂ¤Û Ý•TXV“oiÞ„sÒTY˜7‚xµH…Agþî–§Ë.‚ R©‰øÞÛž ‰3.õ»­ôûw5ÛÚüϪ՗Hû åº–¶\(„û¸~-,¨{þå@Ùys^N®#¹6écZI¨¬Ý)ƒ0ØMI\»ß¨×>/QhÃ\Ȩ5¡Í~€‰Ó*¥×~ßæ0Ȇnè Ñ-µ;ñ­;Z„Ôó ´wÒt¡– 6@¼¼‘¼7–5BCÛ¼¡šmiX Þ§ƒÀüÊ1QÐtîØû$~ñùºw YZ»i’^4™G$íºîŒ*„áw¶Êqñ=°Î+ôÀ¯!ü¸Å´¾¹Ê/·/ Š#_-û»€9XæVÖÇs–€ÞñþÜ¥Âõ…£ºúö Y€j2…ZÜwú…ž]²4ÍBì³ÓÍ CçÙª²&G· ¿ù:bdu®Pày—ˆŠ1*™ø£6ñÈ}d ·ÖXªMº£;ǃÞá$Î[VÚ--f ã”?-É¡¶T îbFYOv/åØ«pö£uÂÓ®¡Daé$$dE[º\}‚ÔÌèÚd©Dcvƒ›o5~+gX°Š~'üÛĤJ1D´òhíÔ΋[Ó@YE]e(œžÂUz)Ot…áŽÞ ³ÆßÜ3õàÊ|ÓÑ_ Âc>ž~õº`øàG‚—£¤Y~Ê(¬WvdWl{ Äk—Œ–kxÔ³C‡ágÑ8¼nA¥§<Â󠶆òLZ è”Ýt êW1MéÉ7Ï«Ú¶]…”\9ÊÎ'zh®Ù}¾Oh `Ê©‰²©×‰y;VZ\(©AËt½Æ7Éeñ—ý¤"WŽ±¬5.d/#f»¢‹•7iȈŽÉ•iC<Ó¦FîÌÕ‚IsOw^åÖ¦õ ”²~óê3L#WX˜hÓsãÑ੸Èw­‰¬Ã Î;ŒûÛ¨|%¡Ä,ö0o§¸åòC3#\ÃwRRU»ákäƒ[1Áª¤Òž¿MUv‰xÁ­UÚ'”¦´/'$1S4wÓi§±zt‰òg††ÒîJò$ò|ŸªÒP]ŠNÖz-v!&îk#™âÎ\ƒy΄í‚äf'®å¾×JÓ »Ÿé\QÕ#ê[™8© ß·È0_1gÿ] &îp\2‘õ?]©`²9yêœBš);é!'ÙòG?4ëž¹òL@±?-ÓáÏ1ï>ß÷.maÆ.¼\¬÷JmIÂúÄ/D ìÏlLp2o¸>ÖkNÙ¦:š¼”>ܹeøÀ–â#Ë…ÎM ´’0‰µ—ýÃL‰ ¹k°¤ªÖÿ lcX/hÁ,c,äè¾fú6”QY/{C!kSPźSÌ•Šs¯=d3×åðpe³Øô†Û—÷°áÎîñS) \@!*B%þ Ò™›3¤&Ý…¸&&Åms.J>KõAטkW ðb®_Òîñ‚Ç~`ÜäôU³ÂS‘ivß·Çzcl‘°WQ'E§…FýODù‰S ÁJr:t+¸+Ä.ò(2GŽmúso¾-txúcB¾P€ð0Æe¤”¡ç6m^Éš‘˘Òá.J ¿ÈÝ„?˜$Ôuø®9oâš|Ó1V#)'¶Ööv*Zy|bé®VT5¹gLšgás£bC†/³–æ"MeãÎÍ iû~N‹„·ÌžÓ° ÒÍ"µcHa](5™¤f¦7¤ØÓ×}êþzø¢sŠ¤ŸøWkBì\Ño^T$~»?R0ôû”Ö ½OäNw‚âÐõ¦xk‘%3=«¨Â߱Ȃ˜ûöèv%‹èØÇVâ+?ûqrÄ$µÜ/OÈn,‚o–*¾œL®25Þè÷åXº¶ K(­£ì³]Ó«±Tß—+Þt“Œ6žq›+¸ `óÖd×½µçÓu~],ùÀò8‹.u°ïº0ÕÖ†¦/E?èFiÕé@MóRyñ:é#ƒ0§·¶’ú…й4Åù'ÕÞ0‹o6-½!:ÆÀ'û¼$íø:^$µCŸ!ŽJaÿÒ[¡ˆµÌm_%ݰزMUc9ñè¶ÉMˆñ Y1›,Ã*žücÅ©æôr0ñ6 È\JÈ>eD%§Uѳµÿy\ÙÑÛ•Ú Êê»ÄÂä÷¹z÷ÆëÔ®î5kè?sY”ü¤Áç"è”BuU„¥ ۳çW¯ÈA‚¥§Ü]”ý£à³SÖ÷Ý™ö@#pyÓ©ü•Z²ÂRÔ¢Ñ@\þ„Æ[GÈ'D¯ßbáR³qÂtÄ"SŽóª¶ÃÔÌ=±¼½Êvõw©=ʬ¸µB•51zÅÜ(€í¯ ÷w8¤Çp†c7­wš,Ö*D*=¹]¶]²¦‰ú˃ù=¾=GîhºI…¢º݇eÈô2f¸ñsŸ>'šfRM¡DM’_Dô`â ,·¯ÉfP­U9¥‚AN<½–k>oDEPï¥1¥¦“¥g|GcôªoÌU±ˆ;G½Û‰fª©MÀü$LÛi$^& ÔŸ…€ü©‘çþ¹yxG澇ˆö¶ÓÅ`WŒšþ#± ®š6ÊÖ ”p¸à´ œ^$».ãyÍ‚÷~À@Ã+ñýÀ¼F&¥L5Cö2êDͶ»DÆšÓó>?Ý0Ǹ&œÌŽ/ƒIç£A÷c_­< n%›•´EëõyòÉÖt‡…¿‹vTüKB«f6ÎhœêJ{ÿxÅûþ]CŽ-Á/´UFSòŒ›Ýáªîih»™¹ðÁ*%PœÕ_cíH¼A¾ÇÌÂÍB KÖéñ&µÄ„~Èœ¦œñ»ÁR4Ùi>pyUdH&ë7Ó$œ•ñ²lºp;¶A,|sÛáÞ÷l¬ÒÈ]x[ô¶ø=v•«” ?­:êA1ðÑÒŠàÃùÅgÀB?G†Ä®*él¹q )X)Rì6Ãë'XIÁÌ¢Ý]Kúª?[§9ö÷göý¡edô±q|²ÏIÞ™Óý2 ¡º(ìøƒn•´Àüwi:;ÁB­-g̈]5OxÖÀ.°îºåã¹ÇKe…Hc;vü¦°ißÔ×o‰%(#°*|¨•8+6u=:ž!3èº9šöh‚ #€í~º¹^û%ØЙã)åñp¥‰DàÇö-¶ÝãJŽÁ&þ9šZ‹ \/¾RW7³H¿ÉžÙùm Í"`О¹€apœßAQªÝ^öDµùxÄ€ ŒVšuÈsðYNÕ‚’V>,=×Ýáì&2Ú’£à¹Æ \ò჉E°¸¿<·¸{³XÙ ,ÅG4×ïU©oØß1÷Š4 h";$Éè4yvRüHPC]¹ ²3Æ~FÞÌÉô…;#^oo¸¦¤¥Ï™MW‰Ù€upØà(&|5ËPÖ§{{ÑNØ!bº·½=!´á$êôjnž†´)<”â\pë!Þ n3™¨Óõ~ÙˆxŽ‹Á^ìÏ„xx >œ¹ŽºeT`54ñ :¼n7¾ùÌ+uJ®q^ÚÎ!€sˆv°–*‰‘ÂÕ¨*×ÃÂ:Ø´†HþJm—¿A¤Öx’L«?àjª›µ¾‹B1ì¼ ˜ó0.wS¬“Ý$ïñÒà ¬*(Œ×¯Éx‰¼Ê9¹[ªD–ÀѶ"4œj¸Äí‡6ö žöv»a@šÛÚx ð7=Œf‰–}<„ð‚Ö(uðžx]†Ž¥°J цP¿¨:‡W첈³xvÇŠ¯ü¢èvð&Vœ{Mª^8£-âÞä‡S]ÆãøÈ”âû~×øøÇš†Õïe7Žn=Y9>m:,/!)ó„-õ¦›Æu)ðù0cÙó㯠EŒƒ1m…|m¸"ÿ_zðì`¿Ÿ½“-ÿèŸ6E*ì“öædª® 2#T͇ñYq›ß ? y²ú¬40§Üˆyˇ$¾ºÂÏ™çí`Ô@ÀÓÈOToŠÃJYÇØšp ý2Xߌ½ëËGŽó¨Ã[ð·ŒK“úr}®ˆËŒAù¿JáFÊ`¿•NÔ‡Dà°¾îèJïD/® &(^èR8ìÇPù½Øû†ÌWÛ5š>ÌCñÌ$ aû씸' åø̧§#ž˜°§±N¤>Š~Cä ¯Õ%«ûëbq‚1¬!R9ïÙË*[ÁŒòvpb¯JÄÉwu­qùÿŒ5ê ‹­akõGÂÍ>Å8v-ªÙm/0ØtEc™·ºf<œ{Ía˜›êËS° ¡zSÏ¢ØDÛ;©Ô^à>÷ÙK-jÔâÉ´F™¿@÷‹%ZëžR(ÀÛ×lc›¾–„¬‹^ëj?âF÷(!SV ›#¨I…Š:Ôã¬.~ºt Ä÷-¡úÉ/ㄯ"ÙIí!‘x=öYIÚ݃xr/š)(2¼K®?Ëu·žþT¾/¦Õµm<ÿg¡èœæÆ?ðßu3kùñê ƒb*éº×12¸gMRäé !v5ÝDWâÔçÚ“fqϱ¬]›8`ôFÀé­D}ÿêGsɼ+8Ë –§Ú5³³P³¶:uM€¤Øsz§QÅ4+u+°”bð£òºTâxŸÒØB‡_9N>/—ôì¬eŸü¦F„ =ŒNÔŠ~…UÌ焲/.ñ*Èâ˜E µ ˺ýèAw] •uúì[›f¶²E[Ö{E(í€<Óôlãå㻎¨´â¦°ÆÜ28­cÉépüÀrÀNØ]­­ª÷ pPõ“½ŽáW faŒÑœ³YÐW+´>NÉ—%±vÉÔÿrâ²E`êÀOÛ‚äKRV}u†W‹Ã^‹º˜°ò¯sÈ^å ësÔvÝf@S‘úGQYd›g7=özá#KÑw`Üè…Ñ>çóͼ™x} Q ™ÿQZ®ÒÃFoySóY\Sˆ6òaޱ߷È©üÝ ðïƒX̨UÚÌ\–RÖ6"6>¥Ë¤ÜÖÕ¨$=sT 'W{ãb­vm9-“¾L$ú`7ÊlÇþ<"&jÜÉ e”á—­AMénøIŽË–:¡ú̯ÁؽglØh>¼š SÞc/lnàðìÙØ÷x;xsFr·}ÖÙuÖ¾÷ÁÚÿTP?y€Ï¨OÎkÓ¿™Õrd;k™l–hº˜Aøº´diË»£1“¤¨Ò9ÜD*ó‰3> U¯ÅœÀMåhSʇX8±Æ¢Å¥Û7'B»å€rÔ09µˆ„&˜ÓÉtf4®ÝÇŒ7‰Iî´´ò<Ò>'~¢®ÈK’|–C{ ©?£Ì™Îæ˜×¼|"H~ÃdnO”4ß-H%˜ÞŽÃ=—:+<' JÈlT’ÿ‡<í5—ÕYɧb'‘ "–”(o¦Ü9*þ쌗@Aº¦%aÜê=»™‹ì„FàÞs2ýŸ/îòýþsO(­Ï ÓuNëÅ$L-F|©‘Šk)¡å˜RËGa%/Ô—?›lmÌäÆ™Œ_|ɹ”kJ‡uá$Á–]çH²f×÷#ÓnDFß0ªƒ÷I¯â…ë> …g+Y^ÚûðÛêï–’žõM½êÄq²dŽ‡w/F®j‚JTháAÝÀ0\ÛãßdBÆ~š!¿q3¿ÂKo*I$ÒÈZ¢¯ ß‚âôÏWÃÙÚJ—’e½L’¥hâ”ËÐ.É ú=uÒN/…#Oøsž&ßê‚$G¾hDÝßO~>°S?†ë:Ûá_|oYÝj§¯<çT>J¼á§Q¯ )óÔ”x sÊR·«–Žu4‡È)žVZ-¤²±ÍÐý¸ZèD X‘ÂÞQ˜7V¦AóWõ¼÷ÿÍ}²UboErœÆ(Õ@¤žg_Z'.¿ûôY«3AÅ ©x ª ÷ܼBã{°÷}Eë6b'x½"ÿ÷ï ¶“ è²G%×]˜wo×^Eòœ~y«ÃMU‘ñþù=6gO9ÇØfìî’¥Eœ\åùØÚBÉbÚZ|ÜõíM1À¡î‹ÎtŒ´¤L‘Tycâëwy¼ÀZ-Bõ(\%¸³]“kŽÔjº…óN¶åŒþdí4Œ¥ŽWÕt—‘ ÖLÒ}IɾVâvG¨}f–HpãB$ôÇ0/| ¨«oÄ!/´ÖW„(‹5÷Å•‘"ÛQR¿á‘u=ÇÂødrç×üÁ$Б¿š'•ô |çúD’c¬59š„C«ï~ñ² ëíõ}æg¬É¦ˆÓx0ÎUªØ[.T{¸ŒŠ±†5±”o­µöΚIÈQÞ„*SÝÒb’5ùãPN)wH®°”èU³¸ûÿ_£zæþä-g oÐlš…Èyãw šFßcÑì׿ê±{«BîÅxz—ïaþƒ²F6´í#ŒÅÅuƪþ^ÿ}ž!Ñ(%öY Q™L™yY×Ú•¢f”Þ;y°«?Ê=znê9ÕNt €ëÖ› MÝ&‹1¶×˜“D[Ei]£yŠÉ§ˆÆÍ6‘³à›`áƒÃ©ÁL'µBùš7Üê$?®ûT¹K€»¬*ªÙ% Em±Ä¤3ÿxKyúkú™>:A•>· {›]˜Ö­[XWʧëºÝ£@ˆUå®È³…˜çœ`|Üm×ýâ4c©%Òu{³."K5@d\”áb8ónÁ׋žñã4µÉžåVz›^P2ôˆõÚ7 lúõ º…|n®HÕ›<:ã]êh¿eÉ,›dÃÛl·ëb”¹<š€»»œ…•>кM›T¹Èý„îÍ k¨aWÈŸcgN{VÒW[Îßu€ŸèÿÄ“=‰B¥7‹Š'Œ'aóƢÂGœ,Ð¥\xŽÞ`èBÏÑ"7pi½Ç¶Só1í››!7»¶ÒÇp;GgO–H~O^ ÁMìEîjÉñØ"ã0‡"4öQZnM”ÕRÙÆB{×Eß7vÊ}úÕÓS}›Â‡GÒRÈyèO|XsÉ„¤[¨Ïí±×@Φ_!*(Ž„Aé×6Y•ÎCõýÆG0Ñ^ ñW/$ë€hÎ倱B¿7šŠD €;³Nø.Eø UË{¹X)eí¶Æh`Ü{$Y§3qÚü¸yó½øÜô[) ª¡ª^#ÿ‹'FMýå~p ~57•†|˜çŠÐ€àT‹3‘2kÿL-%ËxÐÿæzŒn­y¡’ýý`Ñ ‹-4›¼¤I±Õw£„no»Á8†Æ{΀!ÆB£Âá¤wá‘âKŠ¿”|3¬¯©,­þ9èÀÑš_§3ôËQ @:@Ïm{É~§Ðè ÇE·ð4Ý×K‰DL§/Âp ©‚g»åµÝ½ÙØ$jV tHI1·:ò{®qM6B»Aø·Öæ×꤇æQŠùp[8 tFJšßñ/hÐÇ`~:ñ= ÒØá/®~:Á*¹}–ŸŽÃ¬/ –[À_d«°mIoõŠMdès°×Wå5»[Ù9UrRaéùþ/dE]{íiG«èE—5.ˆôë—àyu7±5K¹Wí»ZäþÂÇ1gÇŸï$ãöaʼn9:efáI²Wí(’&s”¨WÒ¡Õð³l<ï¼ÑÂíI%u²+ÁºJÞwàOh%Ç%qfuÊ)Åã¨üP«¢œ “¨èF›ÓìÁqEþç*~©b³‡ Îkló?Øû/¿ÉQV8teþÞ!Á8Ç qHtr›ÎÈ’»Å'¦:¨×'GçåNÞš¢Ëˆ_Q=Ì!œ‚‡ýZÚÿ7Œ[r$QDMÒ†6ph}\-°æ¬YÇŸycغL©'(Ý£Ü|47Qú ¾šÌvEÝ¥Sß^‘p2 ñ<1 Lbo§gY¤x÷?ë°ñdÊQ'Àø[í™+“äøãfŸ&#ºÐ5ñ îÝ{~ ì±ÐÓã¼/…A¼Èç“¢Ïø}hÈÆèuú­+ÈŠ ÙÓ~81ðtÅ2FuB+ŠÂt“‰«ô¾%%‡ªUÆh¶I^ɾˆÕð}€ „û1†ÈêØ{&uH· 2üqÛ–ˆgvØœ—M‡%ý»ë’ݾ»»9姆ýôM: Œ!ÙA†L}÷×eïQßþá߈÷gÉX P)µ‚ú”³Ô)ºÅqû ìžÊîì$ÉMO¶EŠ.ß±¥Bl }`jÞ2¼F¥ßchÇäö¼ÖˆV7 ä]°]µë®B.m£ë6?HßÃKSwÃg™Ú(jZ ðŒ0C 92IbB¦’Ëòêî‰M ´ŽÞ6Ohñ£– ÕZ¤¹ýÈ1/ð«ov‡úìý —G!½ZsUW8Ï$ç&L#sãÊ+é#€îÃWq?þ±¬zÉ“ÙéÁÁ¼‘›áF°:ùÛFNÓ@%%`g…DN»K\¹uk3î`½¨ƒèòõq)cõáÕ¡fèòà9xAû3è{Økª›cÓX–bm§A%XöìÌ[‡íê³&7V¦M›—D½âÅö~T8Od WÛÎéÏ‚®8‚"" €`%´YT# 7Lb04˜A¸2eÇžÜ.e¨Ñû­0¦™Ë ¸ìkci‹ ã¿Úˆ“D˜Oº¦²ÂÍл™/2«ŠÔ ¬ø7ê;4ãÓ‘ÍØ`0• K­n®èÇŒrÞåN¼Å ±AŸ:€™ÖyÇÖ¦Çi€3“D’†·ÐD‰‰¾`xDìªÔ”»P¾ݨ"‹&ýŽwF0i’Äá‚ugùú·uXŠû@}ƒÞ,q‡(ÁºùéÜ |ƒå{S’Ÿ+¯Æ,1Àä¿ ¬±ÃŒùé øÄA2³©^Ç8CŽÎž¶RáÜÖmh”ø ö¡x£d^Hð4Ê(ËO%®°È+TCƒXl‡œÚ´ l¿¶ö\KMkayFFõ@;,B~õ¶Ÿ‹mÝ-(<ÛQþvòÏüÐ[xÞÞÝmÛŒé% wiíèy<=¨õ§2#ÈÆ’/¢ä Ž´ÝKÂ,ü·øö’ph £Ò²ôõ8±/ çžmýt4y¸šðiÜ?÷ÐïA8ÏUѵî—ð«“¿iÔqˆ“–¡Hw©C1&xÇ2JU‹c?ÕÖ¢7ÖºqVÛÎXó3¡€7Þ¤AøFGÉ:r®Âr“Ó·mÖûnÈÞ–À‘bc§cö®+ƒvÙG•|Ü„”À~”î{oÝ“Ñ1¨ Ã6ˆkúBg”É£/{Ã¥7¨´KµÄK OrÙ¦=]!vz£œ{㼘>ÌZÙÛi‚¬¼”áŽEˆœœ±Ãåy._À¹jË…Ë1ܦAÎb‘ì­ÝÔ¨™‹äðGÈÔ5Uë0[;=(Žª€©4`¹e=ƒÄÇv¶‰Âd&ú"ùl10Ž«ïyDF¯eµr^¬éå9žZ*AƤpL…\2/~ª¼ˆ%õEõô#ÙãXöyÔd Ο`Cð÷˜ÄYÅhœè_ñŸc¾5Q¹àÂC¼ÿAª¯ 5-*"*@á¤qþ¶«Èö·%˜pI^Æ <Š·{ røsOh¹ ­6nŸ²‘Ð9ðRÿÀ>œeK»†èsP­ÒRÙJ‚táö<ãš·hlÏPP‡3¬A•gõý‹”‰b-ñfé«u~ùC†"Ô"Cô6ªÔþª¸EãÇ©µI³N1d‹üïÍÌ ¡¬ë¨“J¢e :€‚×î‚‹Q¤*¦tsñÿ:5oêAÝ¢â0D|§aú<^ÁôƒÛcÄ5êž92.ÊׇUO§ºƒ+¿Ä$Ÿ+èp4Îoobˆ_·à~7ë­°2" ¬šc…S€qôl?{“,Q.cüÅP¬à«¥Î–Xñd‰ µ c˜imB(’ƒ€k–Æ´?h™ãíê =U¦ï S ƒ¹0âÜV¸0l![q„TgaŒÄÇi!C$Î… Õ]jhZ%ç¬I%@ÉÈ…ƒ|ï&v,⢆,–”dâÿîN7¤u­¶ ÏÁ”w#~üf‘·#RËïPÓQæ“íqØ斈ǔŸ†rþ\}ëÄÏ‹Kæ ÝYsálàÈôÔÚ<’Fщ¦5,&¨Á'ÐK­f¶¾ù°iuÖ*™sß¾ªŒDº¿ÇÓÅèäs,¯¡Œs•k2*¨oþ&+)¢ÎÔ`¢X„»bô‡ÝÙûzp]^¤! l~9_DWN*lÅ[ªÎ9 tüû@ϱ~: jÛâàõ1ôBJ†eè R 7&®Ñ†òLÔóypxŽ[XCåKÊ‘’R†<¿[Á7oa` ß=½÷tsd·.3ço!QpëäpÝ€fé+w’Ï™äÀƒ'†Ûëgxf#ñÉkÎ¥ÿØÏ%N –¨/*·™µ×ÝìŠH·usm2Û {-#É«)9ÙBjJ˜øq(÷ôÑÑ|Ö¢ZTÁŒêüŠ¹7èáhr–y‘tgé?Ùr04½¿sTDˆx€à]õÌc&pºÌ)b5ÒÓïB »GfûÙÃ**öÌrÏ‘ÀG÷Žô(ïØ4§þ|'è›}N'µ/t.ÂD›÷qªõ:³è“WJ6ÜõPR/åwIêÞ]­'ÙéoÝÞN§ž[Æ\¥©ub:ØYÛÆ 10=äõç×@€ü?dFÐYu&æ»$v5Ï_ö æÄ ˆÃ™{×s6êµèµL ’ñíýG\Öi+)Z”©˜‚Ÿ°Þ͸O”œÖÒ´°=†ø£[WËEK­šÊ‚)˜àk{ù™G=H•í+dUùAâ/?V˘WÊ2Ú¥iuª—çÔ®¬î —'­È”=½¢U$ÁäüÝ­Va%DûÊIøðð¼"ˆ:ùø[xþ¯u¬Iúa|ë2òÒ&ï+†-µ7©ë¦ÝeµËüÄ¿T]ÐáŒìG>óh 03ˆÀ*.Á0óÉ=\ƒœrèHðàÖ³p9‘4©÷¥ ÎyÄ`ýMjìtû[GšLg Eü€0 gÄ›嚸 !Ð[üøzåè˜ïÈFQ‹‚¿GA[‰Tg]yå‰éÆ—¬ÇÛ¸µr´¡¹‹À™Þ¼,é0¶ÀÄ”þ‡¶&«Ñ;‰×‘G°¨ «LÌ81^Ó u ›{°¸*5•ãá}µÑ;4Q¿t`HH_XAîûu€<ºŠq!¡½‰KX³¼¡@›#¾»°Ø¬ð„ZÞЭ‘ß|O¨¸!Û½A .’S)æp”Jç—Kú*ÒÓó€Û€Ye>8ÍŽRvl¬‡EÖB$8|ê“C7€!Ã}’®s,\ÞYv=`Ì×ç³Ö†4:®˜·ÖfCi^ ]Hw¬¥'ÅH2˜Ãþù˪ŽMqÖ ŸG“äá÷, >F_Œ/ÅÏ>læœLJÐAÅw¸™]Ü ŒÑ ïøF |û&˜­•kAby—ÒµÈ3[¬?ÐÚ€öáÓrä+OtTr8O‚B°&Áx{z÷@OxùÄ…~‡õ’{ðï(Ÿþ¥­ƒƒ°‚{rC¾RÁ_(ÅË6õ»pF/4fŽÏõ´X¾&»˜Ô°…í~Ž¿¥Ž™—ä[‹+— ‚ª£üîyX„³f>/õ?‚ÓEÊ.ÊØã¤L-oPº€fq”ÕƒÌ9ÖÓœöÝÜ€+ø„£’%(pŠ;ùð_,Þã¡ýs­¡]ŽDSè¤.xB†Ôb”âJÆ}^1n¬‚fásÀËyÔ"«pÂÏ—Ë{Ö*wêx`­WñÙtT8@b äbäAh²»LÑýŽ­ùíè±wç¯?á+ r‰+üž‹Ÿ¦‡Ð…‰ýÇúW¯ú*ïó¹×z_Ës òj“Ê+9q­ &Çá†M>Q–í6Zc™µiëËhž;ƒ¥ǽ ò†P*:Ú+v†x!²`åÿšÇ5~æ®L—ФtnôÖë H^ ;ºäìùž`f,ÅÆ}슊Ëñ<¾[Ì‘7/ÂÖÌSÖÞ>8Nÿ49 DòŠ¹úÖŠáJÒ¾­"jùEýO²ëÒ[Ó¹éã_ ?Z%D¸#1Y`§‰*^ªá}Å!µ>+: _Ñ™K˜µt®™°J›]r’ Š(¶R£/Ç'uÄF2ñ䩾Ü?‘¸zGÄsä!™Â¿Ö¶I·\\S€ÇâN¦j‚m4¿¥Á)a³v7Ôy.6ѧÃÐg%I%®áCf Õá8dì¨uäOßÉWÏl«Èfè;Ùµjuó©ã° õ7áÇ…ÎÃ¥IóIÒ³²FÒñ¼gž‹äE³aÄø¯FJÌ©¨à®zÆ/ŸÂà‹‰n÷Îמ¥CAÉþB‡é†K2ʃCî¹Áÿí‡ÓÔÙx#Eæ„Žxn1ž¦VIW”*¾:̈¿÷´æ ürö+œ†’0þ|CmÏgS<­6RŒ˜V,‡ÿ¦rûõ¸©GZqbIŽh&í16TÆ]-ÃWÿîÇ’o ÛH§ûî5‡b­ÅªÛ:ß…rÉÜ4˜ø9wÇòZ©ï¼ªr-Ò2®\§Ñõ6…jæ,;cT\U×î5i’™ ¢ åÇl¹çy)¤p'd©§1á]’q+hlÉæ8ÇÊcmŒÖV ©÷‚óWË[·´p­q/HùcN%<\([iñ³ÃSÉhK`´ú\¢‹'!4£°_\¨Vwåg#©¬Žh] 8ÈíÀ{cŒ4 gÓÌÎi#½ú8[ìÜ wŽÙýJsÍŸã‚ÊX ¶èî%’œgp7÷ÄÜžÇ1TÖš´jƒ¿Q…Æ!êZ?EáRº?qÐλ¶iÂh¸Å¿~~¹ .MvÅÂ.¶ê6c2fÙ*€X<¼~­}q¡žîc0Õv­åø4½i5Îâ\WáÚR"c„¸ö[~¼°ÁZ ´ü;Í?ZSnîâ Z„£:£"¯;¸sÆħ-£{å@‘/EVý˜Ôgü%ƒ•îeÏQÜ_³–*ÌÓW“[DôÍr}oÇKÿPIðvù=ù½‰î²gDþ´¢‚SãqðþÓ,ý_ù¾o§µhLH(¢ ÞKß9-zž*°¤(e¸Ôð~¾ke‘êÙþÄx ÷=µæ²ƒÓê¨r ³¯”qèd½†E¢®þv_àÀ·$òEl¾¼MY+¸ái6²#,‚–¶1CîD,+çŸÉÉdžN^ƹ@df4¬JÂeý5!€ü_ '¯­Ô––©8\|‘ì†êVÀ¥Dô+/9 4-]§u(ƒ—‰ã•))Ü(u4Ð%ñ¼‰Y¾º±ò{´¶¤©vEKcóݤ}œ€àNrö¯¢itŽî7[]xg^áìÙþ!-óµÿ3DØ’¤@y‹‰´$ ïìÙÑñ£“ò¥o† iü&ó=µ ‚ öÀÓTDË„`]ÿÏdœöÃ׈=»a¿^‹r)ÐL”JFˆu›í®]¬íq«Ê(|I¯ñ…Ðê‡w÷ÍuPƒ+ûîÒ9rø˜ÀkæuÀª·*¬½­¢®Ø1 \$žÆX¬ˆ”JÇÿÊæ}éNIŠÉÜfX£9¹ãÜ,)’öÙ Ýå–p¯¨Ï:™ªª ¥ÂçpV¡‘:­ÜÁaòßVÔ²lÚº%Èf.h;€‡¿ˆ£¸ÚÖ%çÐw b#v™ 5.º°/õ`ô‘%Q݈db7bÂӚ u”*]è MåÔ»oJòÓt@ À™ÁJ$˜¯fax!"EiP:|ƒ¤÷ZÆ•’þ3ñì,LæÓµØÊ¥üÒ=ê‰Åœ|Yf µ^Å4½Õí9Ã5e^“’hùT8UáÓ‹ÍWX§9Pª½'›õ̽¹`€+_ci#LØ5I&=µhX±ƒñ‹;‹u·ý¯aúíxnyuˆÒ2´‚ÆGAV#/æÞ±Ü%ÝÄujÞÂg<„€ÇÂ>z˃W„µrî…Ø!†c·.|°DµŸP¿AJÊðŒ‚¨dÒ©pYû“”㣗½M@ ÔšñƒzN8Ü&ŒKp‰|üèzõŠÛÛ>•ÁzÊYÛ½rM)“»ùØÀmh¹P¨0JI4ÕŠ <{Ÿ™!–ß;Çâ×}Á@âkÆjH©Obí-lš~)ð㪉¿Âã3Œ¤Ò¾ Ç‹× 3{>ôÏ­öcY¬À|š7E{!ïí-LþJyæF*RµÎÎ4]ŠóÅ$p‹Þóüò«‰4‚;Íj0œ‰æ¤‘ͯ&h²ñ k, –|ËýÞ>’̆øÝ›`Ë 0¨˜Úò¹¬½£Ê€Z#t@=µfù³dûß8v²$Ï°† ¡ŠØ·jðG8«»ÌÁ’PMƒx”òŽ>­FJéó¡†T6AòŸ¥%àWD'’ ØŠáñÿo „þ ñdvûJ)ÁÅ™µRGœ£1M~³ÍBAÌ£ÖYÐðp®¼¥„ó·Ò3g–T>0Äót…{Ÿž““åIjŒý\ !Ù,Ö}£]ß3ËÊœƒâ±³‹Ï•žñ,F8Àè×Iýô¢ËÓÙs=Óà= øY¯ýÛMX"]è˜áWÝwÛvÀeœ§• ÿº@Õ;Oýaê‹U‹:§‹57 €õwjnwH›Ö½û«À”FŠ»¡[¾ß¦22;RMª3‰gb:Ý^ña”,VÿsͨkQB’R þ¤¹A ƒºBÿ­.òu'ƒÌz}jMpæ -ÕOãÒÃÞøЯÂ_/÷¶aĉs^´È|›Ay¿iÉÔzÜ‚ /’)ǬÀÒ䔿A5,Êb£2.zf•]lã…ÜRª¡kTJr;Àƒ[싺{¤C§šZK¾ ¸0Ÿ^2„‡ò6iëk•Aˆ_*ì'¬{“û,_*o]͹xH—«‚¿Lù°Z7 2Z©Ãúî«£9ñ;øv¼|ãBãc ®4›VJÃÊŠ,Í÷>AaîÖ }+â•àS·5VÂ<|ÃPEC“ÜÂøð_íMdZJS Qc5ääÜý A)¢TàÕa|!/Ün‘¡í¦þ ’…þÜYIF÷NÊi4á´"}l³/F¿òÞvŒ„I‰Ãfè_R–6È‹c2F:ôO 'å^þ¡ª¥./u1S I†‚o|âñ€]:Œ¹ i7Òp%Ï'7"#EÅ_3—îGñg–4+š ™È)æ>ò¶©ãèæ7Zìk)zw ’<Њ õÙõžE3iLy-åãæ¨- æ€òC=Ȳä+ò{nõ¾Ù §Ê…mÂÊ6Ì¢ŠFŠí£0[¡Léùu’ˆ~ƒbž·J=³þ‡‡y%³;Û^ 8À˜³²ø#Ú5;vt¤Y!gUh[ï¿ÜYGøSOà¹/Ž ö@§P|¢; TDØÓ§›â 1´ù0äÞ-úÈŠuÈX•¯v åçzÚ÷¼,¯îÈc9õ<‰Ð ?&`LŸðÚP#0®G-貿íx!`“=žHOöâÄǺۇsjLÆOxêíŽk]0½À|=’Sò}ç'`åíK%W/‡’˺ @~g»GY¢Þ…”Å{;­%ÿHK,G»lîõ-`îàÕ¬·vr,œCƒhi³Í1Å…».÷DÑd¯HŽ( ëTí :’lšÛeéÝ%-E…²i¸ÍŒh¥™\+íìzÄ84òE½czQoØ äs2I)þâËÇí™Näbd¢8›ãQMðÑß ëšlPÁéÄF´Ø¸DåØ®éæä,xíͧ“€X&iÍŠ¶Ï­ý »J úHP{»Ò hñ— AßO>6Äxé­ea²ªÃòYhUÂë°_ ÙÌIÏE莚D î°c_ i­{X¼-›‹WßPæ‚\p·w¥0-óŸåYí|·V<³xULŒpe9ÂDÎñòL,k^°ã±üríå<% ¿Ã}JÜÛÝsì©D˺^PÕÅ4ª‘ …®!SÀÏDÉ6ãt‘Ó@ò¡†àYE"œ=sÙ’ü4ò lð³¬ã»ö<¼…~2Ï™^@Ÿž|¾ëŸC L6/* E䤤:<›SѺ]Àб ìd'šEkH_ëkLhSÆÙ82Œ`±Uþ¸ÚÍeˆà%©cf‹FIÕʱ˜‰_MÓ­œ½st:ú×w¸L\H#Ë­ëíñO¿K0BgMÙ¥ÃÙ^HXpã6@¾EË£¸âuá|ÆÚ ¤ &$rÌÚkžk.Š ‹ùŠ_…¢By­3yxõ^œG†µÁþ§ò½E0M“ªê£hîî>ì*iÛæ¢ S`Œë’¦v.õî×vH‘è¥Aé#Ëz ÷߼꯽§› žû^UjüŒÂ)&³‚Ǥƒ¶tq¿þÉØ$tá”PPÓß*©[Álêl>Šž§.SUÆs !#Í®²AÕæ¡WLŸ];Ëq\&ùl"ZJEåX9{PÜf;wAŸ!0±A1.ËŸ·„Öý鲋lõ33½8¬g-Sèšo¡'d´Bs¿»@G415˜-ÕêXöí¹~৚¼cZW1V#&ópý³(“ìR¦8 {¯S«(J&±‰a¢w® ŒR@…o´ìAÁÑôH™Ü BÄ‹ðk‚ŒI¼™-Ì@Yš´®¯!ítºÔU›½–uûÑŠÅ—d0êÛK@rµcN%2P0’Οól¾êµÅówþÕëyñ¤Ô5µq£}Ì&t`L4þéGeh眢ÒÔëBâBBó¦JòùsUàèˆ @`ú}ƒ¡{rh?]ÈxKÉøàfX?&¢öÑ¿/} fÅâokÃöUrËðêþ‘q»á{ŠýJîÀc¤XMÙmÒ"Õ ©7›ÝÇþ|§>ºDÅS†Å£0EK"sSðIÀ°8:»«m@î&“XcK"涧¿H~<ÌÌæ[9B +”ÿáá66ìîiäCËj_*f®šE°ží"?„A{Ù­3ÎÈŸÛ%L€š ñ‚¸Ê}z¯".h°¬¿>)Íø¶·7eo‰°I6·ÝÕv‡£ç ΂®ÀiŽs4ŸVÉ uá6^I»%Q¬’É®I;ªŒuVZñþ2 }ò™ýsÁêdäÓÞð§êDä‚jñL?¯°§Ù6¶r[±¥àì2_·b¨/!3ûÿ9îŨáb.“ ùø¦aA6ô¿Ópƒž…á1YU¶7ßoY­å°Ñ\àNME\œÝ@›œÃš[|HŸ„ßA~éNÒ×:N=wQÅ7™cv¾ÅM1T>À¸WæÆû)wT½7 X½aä¢;©—*ñ"hhhúóÂRp†ŠÒü›õ­ÖÊÒÊL?h=a×ÿçbú6Ë­ùi¯^¯oË« 69D'õÁ½x[Öø5ö…Ü][§ A§ã-IoéùÇæRXpFd môxpwtEN{WYÑ’ó8p´ÅjV4iÿ°‰ ^ó1{‰u“LÞ¹©]ûœ$2÷y›†ó³¬½Â÷DSÆoCJ/XÝ1Ì/AO¤ ãt&Dd:Nh‚FÄF‘*ÂéË/ás0p)Çg?uÉ°ª¹¥:3¢m‰ºi¶ôµ¿¥Sò* ^ 9kˆô½9ÈsE"ò¿ù;.£è÷] ‡ç[Œ>[Ïc%ze¬½ÅoýÐÊùPîw¡RÆXbô®åiÓ>Û75~ƒàù-ïÚOÜÐ>a’›cH Þ¨¯àðœOþý?y†«Z÷µ‘„Ú‘Pl4»T›ãÑ(ºå‰¤-ÕAdQ_rl*ÙZ»‰”@45»„zKõb¦Á*®UB#GˆÞ®s7ÿúô‘Ó"#A r8·±X,•%¯~-çK¹)\cùÝïÈN2wr/vŠl<ä—ENƒÐœtÁçžÍ«S%ãï)Ài­+¨ÈT/¸£¶r9“ó;@5Óâö¢å(ð,ɤÉÕ¦Tã ÁýQ‡H›3ëMN…vÄ 38µ$ðÛç{|æ~(€óÓSª ° ’AwZqµ f¨øÂÊÆ?ÇxóàÇT#ê”Ë¿¯»›‰dJVžšö¼ ­íü<:qÙ—$Ús!Œïãšn y^hüc›i 2uS3Æ2U‚ª†Ã;7ˆÀåÑÇò²´46ü³;PM¯›§FuWÄ^RªCq¤€kž6;%y|P”'½š ªLòÖ7÷n~÷š«à×°ÑH²DÁuoYEÚcž)´ûfó‹Ge°_aè«kØÇŠQâ:¡ØÛ¤üw#Ùaí ˜1•œXÐ~>f&*СÎß žyb4Õå\0z~uSÔô™~3¬YO§tÎm ê!ÿ̬Þ%_G›]è‰[ÇÞºZnuËñD9wTyý±*”ùMŸ–Ð ñ¬’—™9Ò6‘/G•´úY0-¹,åž!.!ª€ÐÚ#ƽGIWu\U„×[>8Äv}驵sôÍÔã¯R"äszмïTVÐ;ðdÎÃ]^Â("¦´sDMóJÛõ z˲ (-kU¹3Gý¬êtew½Ç7F¶Ý¬VµC%tèCƒõ r2&æyQW„Qê 0´o”Rf7‰KÙðRíê"­ætàô!÷4¶õ ßVE" Nh³o:Ç'œ±(òþJÌQ€ŸG Ó4ƒq+÷ô@ŽnQÈš&ïç&³5°òä4ŠaUduJÊÒgGSîúsÒ×ù¬yG´š.Ð Äü÷2¯Ï„­ñÕìŽòÚó/¥ÌóÑüÞL¥)ëÉ.Ì&ˆiìæ¨yǘIu°n9H9y0]w>ŸW£°¦AÒ•"“¬ƒ?‹ÏSg(º/)ƒÛÌ.—\R{i3HÌB#’êÝ÷šAgÌkï0DÆûSMtï‘26Vp‰m5NÃ<ÒB#k1ë[¨Ô%Þ91=8o¶X/kÀœñHÄ?.½Ö2y*EÝa_L,ižY”?p‡à@‘ƒYÜzƒÃµs}ÉÙ8n´Ë4Wãî(b€#Ûv=sý¥µG,o+U.îUO^’º®-Ž ²– ílÆ7q¥òKáC?¦ÆOxîâJn¶ƒÜ[­bKâ\Ì›S–'¼`Õ0©õ]4Ò©`‚òÝS$rŽ²91 *ŠöêëŒÚɈ«ÃF8Ž;#Hù«ÊÒεÛ.Õ¢ÜÕåå¨ÜÐçs⪌ü ³ovD—±åkx^kº&Úâ@âCRVùºªÎþæº÷<ÿ¸ÿKèl‡eØÍXÄôŸ+̺úNkFÙ.Ã{Þ"ÁòÐÝ™®;,õ#K$‘èå†C6~7Ü9º]Ô>snÔ-”ÃtDG i”ñc¹[”cz>N³Bs÷Ö=)ÿLÚ.ј‹ËšìÐù_Ytjb¯ÂÕP”®¶S¯‘ŽZÖ±9ÀUŽ¤Ò[:Ë:Ì 'Ëî‹ypÝhIæ,[œ×$¥[*XŒ‚á[€fèé1“Ø3ÎÄ+i”Ý«c¹Äþ]N-Z’ÿɯZ讽•ÏSø2«Xˆärc›xÜ»‡"CZ‘Ó•£^̵–Ï"4aòya·ç×iW8 ¨Ž¿6‡“›«n}žs&A'ŸÉG 2F:PMß+=SB@‰Ü+‡ÿEùæ„a—Ê(¨AºPï2Þ¼P SòÞæ¨'Í'ÔFmqs¤ýé¼U‰ç·’ÒìqíŒ7sÇ~ ô¶19˜ÌM¿e+{.\9ê’a(ú6ç쬄»Aì…c!yééyea ˜Âø¬¬²ŽÇ¨ÈŽé©²vø¡Ô@Ê ²÷ÉȾ»MŽÆù”£‹÷¶€89óè ;µ:À×ÃîÃ!vÚáž’d tº•<ƒF©†Ó 4FÖÚS[c(Ûƒ¬þPA¡ Q_ XG¬v÷É1¹Û#B˜®›ë]á™ÓgÀe.t÷n/jçìÛ¬—Äþ¾<°u«6[FH7ùdœ¢"ÊŸìe²yÍÅÉ]‡mS×OÅ•>½£0ÿsMtâ¾)kýNN&ûluu‰Nýõi^W¯%„èÖMï™ +<§¶Á‚å,¢_’®ø®/GÚCæRÑ~@óõ·‹.Û;æè p:Gäþ°~¦âmOäÖ8;xÚ/¼Y)€XÜ©×y…>îØ]9 á–Që׬Ý5!Yö¯~¡£Ç~H5Yœµ• w5c”J­»eO¡mázXãà'©9Ií–ÅøF÷EôV'±Á+ïÏï¢óhUrrŠ®è€ŒrÉα9:f݀䙷&ã(ÍTˆžïCJí:òÇ+&ÓH•Ÿ}(ܽ^ñE+¬‡Üè†Ç©•ŽÄéÑU Q;û=.-\?¬§È¹224nä,<±é¸Ž‹h}ž5§ûÒ‘q$æç*vbw¶–/ÔÐ8«¡¿ý(,;z®ß pq…n¼œ)]mh†ïõÉÓºIµMÚÍ1‘Æ ¤[Îáƒ9‘¬ 9Áû¼k\ší6ˆµO2”‡9;!p€ÚˆÜ~ø<æ¨hûœ’d-ð2ÏÔÿìyb6_>µ˜ùG'XÜVw̽°‡ß{à–bAܤ­ÊŽÿ; nÆæëæ-wmRžÊF›¿ö]¹š™’…koÿÅï@|–QAɿߦ67ñ24ÿ­hûä±Tb%+ŠÕ^‰bçߤ_0QÛž'ö§@ðr©(bº/ð¥òó×üvnÒlF7&”¶-oØË$ÊÁ5ŸÃ_] ÌH||²Çn4öI»çY´×f™«z#ô'ŽkÝpÌ"ýqˆÇ<çìh3À*Šël„ÌÒ«õá–6¶¯‡H5Ò›‘†³é n%?•Y½!–{AÊç“1I¨ul„ïÏçc5Ê©+©š&ëHAŸ­žkËväÉÞT5^š¿ºu+Œ)¿ÏIxk=÷£RmìŸd÷JÍi¼…­^Oñƒ‡›ÊÍŽÛúˆ­ð~™Ò}Mw­ƒžçh+ù{}¡Ö¯oTä{O,®¢˜Þ)1á„Œ¬,y1™B~RíYÔ1w]Y²r3àÖ<Ùö„ý§Â–‡FVXjàº?/®•¸ ­n“G„ V[{ÐD\¶®Òb¹pT­ÔUÌÝTôEÇ\ïVžk½ÉtY«Ký]ï\ 73*N/d’ïy¹úÀ1ÞªÐÏ.*ݵXö8î•Ù·È™™÷¥Ý¥?xÆn(‡ç=üa¾ïK<¾ʱ ¥• ¼Ñjzxn]! WúÌ´ ¶V;+˜X\HHónï7”¶Î‚Û~‡°XEdÕ±“´~Xü&¦¼œ|™ºíàð0c•”Àd˜÷kíVð&$cA¼?±JóS„_zû ¨¥’ŽQˆ[¹ýíÔYǜİ?äŠ2s§1+›¢‘|¢`Û©±Ä­rz¬ôš­ ZקŒ¡²é9®Ah}.‚Wë+ïô˜ß™zz[è{‰îž¹Ö™§èFiN«'ëγ=Cè"ˆøm¦ÊßH6у´ºý|=³ZÒRt;µIÅéHZUÌw@Úð¸ ~ÃÒãgkª'a:gvKy+"¼—œ5I5׸¸!í¢65û ))×\õ¿Ðò¨R«¦*P”¹Ækuxdɲ£¾Ó±† ”5zº@}0ê2+žøŠW¢Ô7jH–u¥ŠR,\ 2~el1¢úu_xÉÚà“uœŒÚD0EIó ÝùÍ×jŒÉn3·¾^¢WwËýúØÝE—|¢I!¥]  ¿ƒpnŸJ³xeLäÏ°kÚ‡b•YÅËKw+—é° ÿO2«–óLMÃà•ud&ɉ݅²B3Lw]z”‡Àö©kþíRàº?wüDx«à úds´c’À°Û+tÇ,ÊÊXVÂèÍ»ÞÍò#¥‰À_W¨N&"*ï/°ˆe¦Û¥ì@IÁ´“»E—E€c,ߤ†žýìG¾ù7 ŸQQ÷0S'>™V¼œ¤]„‘È¥ìû2]³A ŒeI0쀈z˜¯M²:ý“¹‘àéxEvèhˆ ”¿]`ÂZNÅ–“(­fp–}XEÁ ÇY‰y³|L^”Òh€hnŸ¯¦ü<÷[)õ´ÙåÐÊä°¸Òç' Pü4gï@Ib¸)Ñɱ\U®N£7…-HOT?J˜dÿT£sØgVõxÈæ_\…»É† …q\!:çÜá² h•ñØGÿìKCìñ˜Lh€Ü =¹ä9h@¢Kg±Ü=ÿõ*Ü󉉨Çs¯¹¶6'hGK§æ™é‹O&}²Ë‡»ú¾¤ÍÆ&˜¹Ô«–0x/ðàt§u·Æ(]¿õ^–ýò†(“°ds…N󇞳#þyæ³pª qZ“ñLÁÓŠvû*"¬à„OÆâ2ÓHß kÒ›Ab6òš¯4x€ú Ùù³¬¨¦ô ̯lPœ—‹rTn2à iõÝSÃaJ¾^¯2^æFO”,Ø-ý:8ªtò4i‡ßâ°T-ìçÜA ¬·* H/`hžIqêÕþNÁ 52tCp°vF¨,\”[ªuÏÒxH+<OÄAå**µ3¢WVU)©á‘´šª*o7¹| iôê‡}ôɞƟçÞí¾Ê^+§f#L˜Âd¨OôŽÀ*ÿ“–ÚÈ.+V艃¿9•÷8³Ekó·Ýœ´lÛ—²éF,B_ÒSCêr¹?H‘ áxfÉ\YØŒ=¾‰@=<':8æ{0ú,-P•祋æƒMu­"Eü ÷ø6™oc´€<ƒ (K¢×¯[… Ú,|Æ£èaXk;mMéù)¹ j»¥N:§‘š¤Âµ)Pœ$õvý.l=^že_g–ûIZs2l¿À*¼÷¦L)òi9<Uý?+d—Ï‚0àÑ™H0“ÌÝêíäzÜ•Tñ¡sg*'vÛ”ReVôŒrü-Gn oÏ5Ñ…½Ì4"èQºé²êòèEÅ8Ú)îcsª¦¸EU³e‘ƒ7ó’ˆ|7áÖ:”EÈbûúºÝa®Ÿ¹C®Žî3yÖ‹e÷ïïD$…h'{3lZ²ÆV¹5ck¦Y5WôrFº°èoœþœ¨œræÇôª¡i&xªfT"ÓY‹ÒàH(æ}HgU,žÆtŸ vŸÊV¶–c×q6ûiàðhÖ8[Ê;ëì ¬.¨˜îPåVEþ2çQ„ÍX·:„Ö-%ú¾ŠÃÏV¾"Cã:KÔï>4- pŽ4À¥ù¥ƒ &zPÀŒÆ´R5êÿ•ø™Åµ½•“Ns‹•§:g?ÁÏ:¹µ«oX?8äš=‡î~šø&Šëuã–ë'½‡CØ&4QEäÒî%Ë$^éÉ´—>k•ÓÈÆØ]WjeéJÜ×[aí`B̤EìÚ*ÙÁ6·#­Ò`/°ÙðCt”p FÙ? Ý !óåâ¿AÓ6€ÉgÇvA¯›CW}åéµuraÞ~þnš|Ã5þ¦¹„Žœ›«Åd ÁW!aÁÑ~5Çå• ’LØôŒÅN6¿¼¡Ä±qš”s?ö-Ó•8CvSwB²)€C¬ÕNVxÐÇÏlwæÆÃæÔZï‘ú¦04ôŠl¶.b>0¤Ñøgí~õò J£ÇþfÓE˜þS©õ¨$¯{!¾yD×`Í€ÉåɈŸ+^¯O Ïç濵çúÀå1Å|ãǘÐ9dV†5À·&H9=Ž°Š½˜-[¿açZL *`=ïÐ1zhT·Êçcñ ¯¦.9àÁÑ |%xÓÎwŽêè·ïBVˆ\ÿq|)e?ûÕ\SôV£YV‹ñ -IØ®OÜÐ|Aý|ïÈ\ÎIx ¦}¤q•^w••‡š×8d¦(þ!@ÆTÒŒ2§ˆDzjvçjú »2lÀ4¶ø~¾EUõèÜsX2õ2LaºŸSmwóX’¬ÎÆ’;Z¨µÞ0}:q@qˆÆêtßûTOümÁ=éZÑÁn/•'Wíh/L?­P£¸<Ô–7¢“ªÈGHWkQR~H*#7„ˆ†êo“güJ 6Ûo\ óŒç9â\仟ø)ô’Ø Î£/xÓ¯ÍÇðçÖÏ^8Ê„ùÿ•ùÅzÒg,°^‰l$:„¬„ÝiÉ1ûtîSRDr€Ï°üà_$zëv`´^¤Óå8LÖŽ^?9Cfz˜XZ©[°ßžGüK¡†:8YÑ ƒöññxâ·æzçúx_ßZcL7éiªi[$qx°~Ð…»>cѬC“þox„¡÷(ZÕ.Õ_Œ1,(ªb¸OA„±YŒ‡h4÷ß_Üi¿¸¢ˆ»½¾yb·+‚âÅʨû[Ôlk_o¦û¶ UI(”¥Ô:â®ì~‰UêMï|½Ë¬(­1;ówñ*P-Œ]æ’EtF…QRøàà9 mh.>3oŒäö s“%Ì9囪™´Vó æN4ñÒ†öœ6;‹êvÀGggÚOónòÏÈ·EÙŠvÖ^Pº5¨yáO;kW’yƒ6Kèø¹úãØ#$ÃÁã7o~gm¤,‚åw‡6 áªB…8¦Š]Ì…Nš­[¬t²4ï+–?œJvŠâ«7#)sýÊB1\Ö \“__Ê—AV&Þ#*¶îR> =ü€85ü…ôJáº×µ/ì_ë,© â“šÊ#Ò×®O"`yð@‚(8´ì Ân¶¦fëô:8 Ð×{Jš"#ô­u†ÔÉv(‰N”ßâ›—Mi³&ŸþÆ;ˆNÃW&[1]«ê41¢ iƒÝ “vŠçD¾BdàÑoìpÔž11¡â(J÷zé~îh¾»2_dÑ*òÀ§,ÌB†ÏVŸöioÁ7:IïïrY^ =:Áà®rø¦dhÏ<éê“ r,cùf71Âþg_õI!^‹•>E{0©ëD9ÕæF”Ù½ËdÓQBÛ: ë® {舣K:ÊÚ{(¾Õ–&6¡gøPµýM*ÙT±Ì¯=§eïwIéLÕa%/¶Ýíƒ6’¢E :¼'Ò3}ÈSGåzl™)¤ mÔ’ï݉]²R£‡FVôÊ)a¾ÌÚ ¸óZñƒôd&^ ÏÅBf| eÊœõô¬¶¼+®Šì‚ÔW+ð ø«-óbe#Ô)mø2jÿèŸÉ¾¼ù-cö–—Ê Ø”ìŸpRúXœ¦-Na´£+?™M!ÄÊ{#tÄÔ¸*Aú£0¶êL~5|f¤röb¨-!Û<Æ,RV†Æ.ô솛Ò;P`u´ãí¡¥LÅÛSw•ºMÌ|Ð]#\É)Ë'õ†é ËÊØßö…Ð~…]žsº.ÔáíÆ–í¤e<ê)ggš;Z‚ÎÊ:wãÁž*Ÿ °Ÿ6¬Ý e†¦MÐ ìd =òÔz¸KÎcTgÚ(õT[ýÆ”ÆüúÀ:Ÿ‡*’ $ïþŒ-a˜[ô1Ò­f¹ky4­G%KÓ‚ÍÄ[õþ×vŠ»”j~€ÖRÄ®²9R¶íŽ¥íPT.w9Š0\K‡†»×¿lÀ³°¿äU$ÇëDGâgMzÃs&ƒ3L2é½yeìFÕÜRNb£Š˜ êFµ£0¾ôgý¼5—Ñoðß7Qo7¿DÈö±rÛWÓÓîè…¼'=²Õ>ô5R•û½ØÜUãW{ñ´zÜmõmÔÈkÊCw‰@nýõ-v·Ÿ¯?Ð)Ukמþ‹ÊšáÔ2Åõ¨ °âzËýa†I\ÛüòÕJ™§'/c‚6€ÙߊssWþø$ãAu°¹(€GXU˜öð%B_6=)RL‡L¤ûÒ;A…Ȥ°ž¦¨Ý ‚Å ö¨¹E¦çS¡$QæZWÄdtä¨ð æTˆèKJnuŸsÐÂtòé[ Ä,ïö)Sü+iÈÊ÷p³‡#h”2£=eÓ,l'#dzk*Ü!7ýs8g-` †Õèòî—XA‘2‹ ¬øOéî$%ß)æŠùìÔiΟváDÀŒ±QP*Hvm›s{Ùìåû ¯•…¤órî©”ˆ@OÍö M’(ØÁö,üÔðKéÓHÚ •ð‚ñyŒ·"“ÕÁ›sˆx§q: ”! ìT¬=’±êK4L.Bä×(;If´-F’Ëc±A- Ñu­i¯‹ëCóËþ%“#]`B üÃܧRûkQ•:é«aÄ0–¨Å7kóõüÕõ¬VZI›÷~^å«¡|¾—.Lj*È]œ™FsI C­˜”£k607Gf&½þ ò‡õ‰+ñ„úùýø-ƒÝ\EK¨bȃ[õrX¡Ö²­nL?I€ÃèfÊ.í¥Ró^³`Œmôæê8œe¹9]3ZÉüôŸ'ëµø2À‚8=ÆìhÉ£y"D¦{îc«-ªðé˜XÕ)‡E6vÙäg¨XY6’1jìêàòc—óΓ[ˆl‚ÜÎQ„gVEáí^d秙ßßÏš\×™º÷ªw“¤Å¿²šPâìœõ =$‡qM÷3!ã®fóý,©¯8¬ˆ†”ué¸(eû{7Ï‘½ß®Š-…pSÝ ²V*ýS±GS‘ºÍ)ðÛ$`.Í~«ëÖ®à-æ³ù¼™xÏ#ÁàwÂ㯜"êåz¨4÷§„{<ù²¹&ê2²A´Ü'EƾUçP”HFÞ}záwÖMl¼nãÜ4ÈFÎY¬éBd>¿`\6Tõ´¯ŒÿF¤g!¿ùüªœ«úð5)/ëW–j£JnÁ§F7¢´b!^Ü2ºHüžQý-DA~¹r…õõëØ׈´Îˆ»—Ö$évßÉÙöáÔ0ý‚ q„'W«jwŽ!ÀúŽ$êÄ"Éê!{r¸Ûq ™WVÜŠ[ƒ(œÄø›Ìˆ|£Ã:rÇ'Dàªqð>ü\ X¶VšêdŸ>‚—X Ž,&€Á¨Uøó?é5«…†vg½Äpú2w¨EšWï«J²šWožQÁt·²?<”ù—ÐÝÁMr LAØ 6ÛŒ·>lývÜixɱdVêÉYÀl†\`Nmˆ®b%¿½)t †ÞZ¹ ³Qõž?{[¦ŽðüÝ>ÞE°ÆšÂÆÝ 3`ÍÀSwÍÐ’Ô³¥øfBuÍlÎÍX‹% @/ŸÀ„ž~p6\GĪhwJÝÌ{§ïâØ®V .„þDŠ&q¼Ü`^qFýØž{/Á¾J;1GÐjxò#Ðfc'ˆæSô€yª‘ °Æln•ìÌè–òóÐs¸ô¨ftò›ùßxú¤ªZ`ʟ祻±h3a OFù@œœÓ'ŽB¼!éž ~o/g‚å‰å>Õtiy@”1ÕFNÿˬH¼Ð‰D! ˜îéûßÔÕ#ÌÛÝÚ1µˆƒ²óã+“çÍ|²NI­«]‡¿E¤R”ô©vßQlJm7~Ýö/Âò±‘»z‰Å¢¢Î=ºWZ^ «T›}xšŸ:mûˆÿõ¯Â;ÊeLDtQ4½²ò¤å°Â»ªÕÓºgŸÙ®˜AQIþÍ' *Hk¼ÉLWŒ%…rô^"®] är¤;GÓ2‡FZæ˜k¨{äÇmcXb„ò.¡bÁóÖ úx":,D¡Í!Ý^­RÝË;uð Ç^š«ïe<—i1#Ø-óÚhöF¹Õ"£‘>{%ØYè*v”Ÿ.Í?CË;Õ‹iòØ0yèÉZÁèÖ9ž1Òˆ_A§PŠ#sÇ>¸òzÙq¯RÕ`ªßú?)Xõ ÂÈ1ÖpijC^&]sè ôпIÛ¶÷tI1/úìÜL7Ô72COXéGnÊã†gR–‚ypfñ^°ÕÍQ{྾ÂÈ/H&"–?N¯âK Åáhv èœ_âýùºø@û±VB;w6k‡öHÆî¶>-/Ÿ˜(7;5µr’ T°ÞH&ÓÅý†âÚØÓ0A»Í5’"¡›g£rCÁyM²tò|G)4i¼Jˆí.‡]#WX]¼(6C% êͧ±û9ÍòMË&5ŸnP/‹TÐ7LèpƒjãÕ ùÃú¢­2Y7ãG&*×b“JËúí.£æ›À9¡Jh¡wfrC^öŸó¯ÄÞé|ôÜÀÚ¥µ›š7nôÖñ॥¹2Z{€U#M@1pé–gŒ®û¡Ÿ Œàö$‹VQ;} |?ë˜/‘n–Á—±0gó{] Ê=Ðd„TÿÇopº5ÁÄI<ýXj*ïZ­š Adå×”zH¬í¨9PlÑ D6[/¿3XòPò(Ù3~_ä'”›ý~W•q$ë 6+5hÓ•ËùîA-k‡ýƹôëýô¥®ÿ˳„0Ó±Ÿí9á‘}½×)=¹mˆŒx*×4wÖZÔ¼ÔÈB…s€²S^m‡ï^b¦JZ œmâÔ'¸yË| ©Þl¬b“ô4¦Ï-&¤†™C ’°IçRêø%Íèþ§ ‹ç¸îUÝ8†š¼ ë!úYR(_Ð'Hõ‹ùÀ|áÒ¶7fÈ‘JGØíÿh.+D™m M{¸3å…¹Zé‡6ŽŸþ`!Wmº^Q0AÏ–bì¤Õ\ üÌç¨6ÌZ-•öμáÂ^æ–ê“~5È|x„ÒÃ#•£ãÔ‡x÷U„™´ŸaÃ+G^¥ØKaÔ·sÈÑÆYkŸorIJÔÆ©¾Xß‘Ñé}hšsSœwZv߸1b¥ëõÀ|€‚KÏmc¸rcû ’À¯äƒF_…9–X¾¿£¾!W.¥TŸâ™N@Rq¢Óx°x çe%ɬÆb–0MôÿÎuI~/Éss ð‰•ü§šÔ†©È6„ÂëŒ&ë¢ù\¹0£`= ík78A3³õf8ÎÎGæY•ålZÖA^‹}0µ»†tô©I3˜–Y ©†ÚœåS'VëëqÞÙ2íÞ‹U`¤ÆÇÒH[ˆ/bÏS˜Õ&tÒD!>™¢×k"%jLy?Ö3øƒ³"û%9 'q­A:ƒ¾ÿ&¾Ã20ÈÖ”ïûáWÚ0Ù4G&d š—'mì~Õ·ofz±ô"óó„ êM~@ÑpE;‡5œe)*¦¬$/èÁ•òúÄÁp,¢ÿ|ΧùÓîÃÚéfL—8M O<»!LÏi¯$¶s²³î3ˆsà1pQ—â*‡¾¨ ”“G’7̶à ÍÃÉ l' hTßCÎ6ÖïòÛdv¼C:z¡9"I¹™ð˜ò´ÑE˜bËÑÿ`}Ya¿=Ø5cr,‰XþÁ]þûÃS*;ù=°_O}AÊ;A×JƒüÜžl´-êoü]?˜\¤ä37»3™‡ñ²~ÆѤ·ÉÓR ,L^TŸú†â4±¿XŠãèGn?#‹ Sr Y[qf³ì€*/þ"ÑŒŠz\ÜÛþÑ–Ú‡¡÷m Ì“ë’T¿£‹<Þ4Ô¢ˆü$lyR×ï eÙ˜Õ[WϨÚA_&¬0_Ñú‡Æ\ÇZÄí;¬ˆ>`ÀÓ¼‡ôÓ_u7¤dbkÌ#º–¦1wOh¤uGȵøW:øÜÑ,ä¾­ßljiÒì^+<†Mœ@:×}§tî2Îð§Cá^^« É(ÈVÓ¹Ì$~ùZŒˆ–nÒÜ÷»r€›Ô(õ B}’e´YAˆo…—c­:‘²cäÿðÛW·í™&a¼Ñ5N‘6ã¸7PoÅ!–6§2#%NlBÆQ3¿Ì#ÿZC¯ÝÔÑHžBú¿–_Јö¼å¸W Úg`ãLE°Ñ#°‰¢|Ð4¥1.pˆÅº@Ù0VÖzôë*¦€x$’}ä܆W¾ühª×ÏÜÃ>œDæÖs•6Ãm—jê\¿!…ׇ™(q¬”œñ9´î‰Ð ƒw©ùpfÉGýéƒ"Ö€>X$“vxäâ*îïç5‡7·¢Ôk˜Bä³x@@¶ƒbM^¸Óî6'­!ä¬â#§î9Îܹ’R˜PÚrí¥ÂÕêò•É×Ó—˜qj1c÷¯0n¨ã‰»¯_9.;Ì«©å€. €sÔÙe)/÷|náÔ'¸poŸ [Tlãb¼ÇfߨGdÝÁõsá°uXôÞÚ`hI 0±G’—P«êÍ÷=}•LÝ£Ò ä¤¤à™¨~·(--Še»XJ-(B=Rú-©¼I+Ž’Ä8bC×Sû‚½˜ÿdäˆCòA"¡§%,Y•î„¨·Ÿd 8©Î–¨d¶ÍSn´Áì}Ÿ‡~Ĉ.JjMîéMãÞTp)ÙŒì%€Äw]ËÇΔùvüªš!Ölg)žÕ-ê*ÖÏRŠ%¿ñÇz‡j–è r­á‡Y¦ o½\e[^²k}Ýp:¨=çá *øÝ™üȼƒRèæ= `¼|4ùR~ßƯÞ=S}²ÏÀDÅt·ür;>¸óÐ"}I[Ñ°dK€“ÿŠ®äNí‘:D P©Ç ¨×Y/”*†"h÷Ú~p™n8“!×™íe‹Z€;YÅæ@áçõ©Ñ°{ya+à-©{«Ö‰S¤š«jÎà º1/æ,¼yáf°ÚÅÝC›’šÍcˆkîh'1Z9nïÞÚ¼ŒQ…‘ÐWiD‡bð¬Å­#鶗Bé[ÿÃ2ŸO…rÝìûŠêõÉx y<ÇþÈŽ´ˆðÔq8»–;‚nØÅy~'ÝßÏ7,ŒÜ"G@êØ RîÄz§"´VùâÐa™ E¥q̬#®-»<>aS·!]Ï|  Ã¬ÅÅ€àp¦É, PZ,ȳéO¥ÙJXAe‘3„"³íåÑÖÉ]¡AšiT†½¡ÚŸ BJðÓ!úëÈã!þëŽx³vàFµ"ìuëÈ÷î UÂ}7(m‰ç¯•:꘺ŒØ_J ŒzFf_ŠÅ¤Ø«Ö¶óÕï¨qRÉ©Á»R±`ÖrT\ºJq=ÜRéH-’’Ñ8¥R'£^xŸê¨ =§£@œv>«ŸCt÷Ï‹;•f-e4 ý6ÚrQØ—Ž\ŸóäwÄæÿ >ƒZ,¨¤t©Ô{ñÿÑá¼+H÷´WžÍ?BëÐ|:â£S¬þÃ÷tèÿxnˆ_Ê€nüP*¶4F“‹°‚(k”×iA*WÀÛ~4ÊUý.9zñOºê \øuûÊX”“EFUD:5 å¯ØǃJ½Ñt.´}z†Ä@»ÙÚ‚ÞN ’ðÎ#ªñNØëEÅx°°5˜}{pù>A)üäyõúQ_m%#ÿTËH0G5gå`üˆûÚ$‡ˆµpL3ÆØðMkZô¸a¨Ρ¯°/1¬²…#Ï|í,™w»‘¿À‹A €ÒžÀ›¶ ߤ>µ¨åâã .0g—÷z–:æ[Ù§6D²°Ö½±^7@ºÿ…¸>¡0™Ë͈Õq‡šä;ƒ`Å<Å.q²ª]Ožü!LrQýäþŒóâUlªhYÑ$Rôytä×T©Wd•©¶— ·ÀQL²clNà˜Si°#\ˆŒ 1‚f éCZêx½:âo¯‰æ—Êè ³Þ“ÁÕûc %„\†€Ào¶ÞN•­åÖúè¶Ì¦¼·,©c)ëæSç͈ýàR­•Î~Iä ÿ:NJ‚·¥;õÌîÎ=¶-:¬‹eÂà_n};C¼üJl7õmø™ýžì›à¨§jFÚô¹!†•ÖÉÚÞ{Ä“FäcÝÝIÊv *PÛ4Hv%< ëŽJ\h)Ï·¨«Ìuƒý7šKíÍâVi…Åú)÷ÅV|¯àƒ§ѽ…³Ã tIXÖ²ŸÛ+Þ7 ¼7^Ùõµ@! þûi´â­†pB—y„ºàö33rÛÃÖLS*²y¬Y>ø Ëpþ°º£oZë¾,›@‚WäÛót ³\r°â"ÕÙ:Nó¤ÜªËŒÍ…Åÿ‹´FÖjÐ~‰ÇÄ4Ë Š»‹R»Úí¯éJ/±Í*ŠQþ&CFWŽè¹þ®œ"P{­zœÔ–DŸ‰8é.o˜™®‚£jš.¯¬§+–Ž›ÙSœ j+îu¯P+·z«´ 1x¾éhüصÛ-vÕÍöM1›c^ÃÔ´Im!–\ÜÍPëóRFß»Û0[Nq)7¯[sÐom“-8˦); ºü&ðd» 3Û“/êu^¬ÜM÷ @^I²3·ë´ú{еÓ2‹ÖN+B†T ?Ïѱ }ƒ­ÇTО}ñc\¨#Üô©)šåÀXÍÑ\| Uùy¸Œþv¶‹ÝÓcÙóñÌäøу`ɹÕ`¯D맺ƒL=Ý9“Ô sT>ý#ó5åŸÏñ·Œ$–üÙ% V‚Še›úà‚txÜ¢ˆO®˜wßÙÂ.¼…Ós)íA¯ûf“E·}š;‚ºh„5C¦Ðõw85˜îüJwÍ+jM57YÁOtÏù‚¼¨Ã ‰·¸  `·ñ6ô2Ÿvm· ãJšÁæFº)ßaŸVÇÀ‚º2Í‹–QjyÖžu¿=æ\³Ò÷®=ºM;..Ö&!@ÿø½‘XaŽ‚•à¡[Íd]óh›Èû®Ò¶‡óúp;büÝPd©€§Õç'ÎjˆéôÜî/î‹IqÝ¿G;÷öh <üF Ìp Œ9 '&ý÷kÀnÐXö….Bç.ñjÐPr ©jV§yÉLj7ÂÒùCºU’(ÈpEåg™­{¢¦ûkÂ]RVq¯è÷&Vh‘–p…´¤¦ÂI®ñ—êÃ[=SªþèÛÌ BÓ6:E•´ ¡)ÐBk„: ’cfèX§Soü&w×µ|¯ì_Ž ÉзŠò¯Ó"àyÊZ¤¬øIŸ8(^±lôtKÝ8¢¯ â’1ÿÏs—·x{%<è¸ùžh^¢iïRé!´È1“»åþ=·™¯Þ,V“Äöþ eFMq!Dåñ”Yø8tŒ>”‘¥PFéMSaãSô’ï•5ÎQöøIù¶1«Ù0ŠëK¾Hp>$X"zõzú“v:–×¹ˆMÊ`ÜiJ]—^@J,­éáɲqÄô𘮗ªyÑ¢›oLþëÛ.ìƒÌÆš )+Á3K±9N¥Æ÷¨GñºoæâìQøÆĚh4„¦Öœ«*\›'MÝ#ŒÝ7±ók§Á{=ÉÆ@á„Žø"þN ]â ]ʈú}¨ªeOi“?âxˆgÝ&K”lÊX—O°¿¤ î²8„'uʾbGêNÑ©œHœÄíÇøÅó¡\+{U>Íšî¯PùV{v¨‡Hk8cm&Â~ûº Ö G`‰±Ù ׅïïþÐW†l[âÚæé>5m¹öŽ*,œ$ÕmœúzB»…ÐôKü³N4ÖÇ Ü¨›V•é±˜«wëk‹¶ÔEwjpÝðÅYþÏs&ìžAˆ;/$n%vK_¨OQØç¨ÁúÄè:¤uèÍb¢î•[òZÈôÛïR­AÆÊ*dÝ÷|¹†«Lî¼”ðO¦ • ,\ `v'pî™þ¤€*0Xó!°ŠØ=2)\ˆ¢³%?oaÉ3Ìøá¶)oG|_0¨¿ñÛ;ò‡7ú­RMþSÜ-Äú¹ÃQw`W,m±Öi"oÒSÁåôl–C§üË»þŒE°2Fă(jõûqà )Ñd77eøüMeÇÃâC„Þ …£=Wˆ®d”á¡v¬“‹`‹U ¤LYÈŸömu›.ç’tœ1þçjÀm»¦X5 c¯ÖO÷ª*!=³ Jpº[ßm4ö¯ SÍñ`{õpÁTÌ…„‡;¢ˆÓ¾ºâî·Äµ4Ì?øyEÇþ=/¨‘„Ë9œ¶ÃD¤ïEM‚´ˆ|Ú5fôŒĬ¯û:ŠA˽° £ë53û–)iKÊi_¢ûqEÊÍù°DróF×FÆMß>„š: '"®â³¾Y•±neºÇ$Âñ­i³cuì<é—¨{‘¹Æ?ƒªÌKô¥åé ÐójáÑ›¦—u›†³ñ/+ÐÆRüy‹®¬Ù‹Ø¾ bü—ª ôï1/y‹ï”†84XŒ•úí_ðPglfŒÚNmážÌ™%1•Í{³ŸÉE1>Rh&Z"MXºWÕ«eþí§£é#s_œHÛö>ùÇÑUYõj8^Ó:¶$ ˜ÎÁßûïªA\(/S_¹ÈN„*ìzXº¦ý±ƒ–7ÂÍýª™àSÇ^‹»¸×C¨uú¢Re§ï íJÏÔz#©QI€]3^ü³6€2 ü8ȵ¤i¤[ŒÆ÷(Oø>s2ÏÝ ZÛ&ÝBÉíÎn–§}¨}°açTÜ¥%µ&îÍpý÷ù•ÀäêæÑ`ÃÍ/­±óç#N ‹Yª*‘‰í÷„ã>!»àBŽŽÖ„‰Òðëi•°üõmmÂ7¹Yp* •6!xÁz ºÎàÙ ïx áü%…Ä6ðšáþ©Ù>p˜ŽýD?8éÃïo)6Dk!à#èf Qf­¯)i†èf [òhñãGø9â,|ÿŠ05øNÈ É U@¥û­1G‘xO@Æu éÂj + Ô‡ÒWsúIS, ‘ÇÊîHÖQÛ¨Þ)ê[ûÊÑ’@Ç~—å žI~RqÃbGWâ+æ‚7úrÜXËu”^£Ì@Y;Qü§È²ö¢Äö=ÚEv1 ‡[5"ÄÔ?Ñ=hTmaú>²ó?›­:—Ï¿^þ5Z.dç§j–ä 1‹ÄœZLßØ ·ÓÇa*E(.ùV± —ÂäÐÁInóy¯’½øås½Ô˜Î»ì)Æbõ«Ntxˆ’QÒ7ñî,u4Msk‘ï $£[n.Ø^Ø&Ãÿ’Zßw?q›¸f ,_Ɇ´QÆ÷£9–¿!­BwX|çh+Qý„^ôè—ã[ì‘iuC­¹VsÞíÿþA¥‘ȼkh$½³pƒù“Ô§õmЕœP®ƒ ˆNO WµŠRè䬋Ô9AhòØÅY½@£Ðå¡ÿ³Ý1ü`ñ〼gL7N–]J» \^iÌUˆP&ÙkUƒÎÔ~ ˆûezœûw†…)ö# ¦@F@Ñ-‰Òœ 3®Ùrx ¼1óÚV«¡’^?²=ØßÆ-¾ ‡ÚE]ˆð¹ý ÂCP ¼/m£[Ö4¥Kž+ÑâÖF¦¿ö™ ”7H4d6™’ù/ü9ðhÖ²ÌI nzF_ͽíX+iìq‘-­ ùàïÃ’aÀ!F©ë“õ«í[Ž\ŽÊ·Bú| ½š±šª§Z;XbÄ2Á¯÷í×ÿ|ãŽ/t3s,ÔÔfŽ‡ÄqÛZßBÞZŒ:o4¹Š»Ü*Œ‡ø Bf9åCzÔ7–î ý:åïF½ÞÏÂJŽ¯F½!Ò–oǨ½«gàk ¾ö¢Fµ´j>ix5Ž=ðþæñâ ë6Óì·ÁÉÆ;sý`‰¸¢Œ¨´a—EÒ6PϺ„k¬$ðîO(ÓnE~>cšä€5Èá¸$ûá cJa‰±ÄµÔjPÏs >úç_ Ç€Ú©ŽùóÖÛJj¹},¹Qä]>àø^ÿcÓ&åk©>’SU›ª Ñ½þnñ6ˆ€Î1U”þçM!ÕŸT~3šž«Çû”|¤ü¡¯±´5ŠÙ ÁÓU «»#„éöO†f¶HÇ"9ÊÎ?âg¾ˆ©°½5ji6DG0 àÜ~•iØ<¬š’±äÒl¨ZBø ñ;d ­ÁOÿÇe*€ùÃÞBÇ ž¾}Þö4žÀ´} ºÎ¹!’²ÛD•”ßX;†h›É øÔ7ëÄa_ÍAÃå¬E‹~ø¯»õfè”Oã6*Çùuz]LÓ©NPoÒ‘Î`׹Ň{c¥EÎ˲ôN>ÀuãÉ‹¯þô€ÙÔ|å^5©ø(mxDÅÈÙ®yƒQe.eŠŸÀÃÓóAMxZH5b id<Q1ðx÷ý‡V͘­Öƒ%> }û¨¥‘—èöèæ-4¦ÌiñƒçØiÉÈ>”)PÖ“^ms¶‘€2Ø?H‹ø$yÁ݉[Á/ûöe/PóU‚ANÞ¶JuûÇH„"æ̱jÉü:ˆ¤€†^õ¹÷K‡#O”Q{½GW\I<ôAwY{2•<² wÈzã4~ý7.ÜÅžÃQ®°ÛábW¿—Ò@Z¸Ø wN–mMQ¿›…¬bXÚO?¡‰æ¯±A— CµW&Ñò-Š>Ç#xúYºN5ÜtÑŒñOïPÐëGñƒ‚EðŠƒ›ÔåÅÃ7—ºëý—¼ä%åì¿&"a;b´;¨U‘çU­„+–0rDÁxû3•â‡xŒ7­À[}Lpóé¯Ç„¾#’¬ß»3jjB_ ì¹Ïª¡¿ÕNç‰S~ÎÚ¿ºO»¹ð™>£P|„*`* }Ú9v.'%˯É?_²Ìª±Öã?0‰ž“EÚm‹U~´Šœ:‹°LÊ'ñ5âs/ÿ·Ñë¸Ùw»Øê°ÜNøêJ¢ÊL6öT2U੘£ãæwYûVQˆOkYˆܤªh˜‰/”ª€™9„تŸ-•¡wEÁ"¶]ç½üEÿ$‹ãƒ ¾ ²ù2KÛ\žu!CB>Ô!Ÿ]$×Ø!`Òkä»OÚa»g91éé=‹ì_ qarG£Ín–@»òñKGÂÝûùYQQÊM Ãp.9‹Dæ³ÅO´DeçðˆMÖ1PØ5ÉtÍz°… qÖ–ö‘¿çb%’½'ü>çr²ø<‚¬¢fo¶PÚ,XA[§n¬›g§ÅÞ÷¹zÛv‘î´)NAo›åâ»Göjî5$ïW/™:ðÛ/€æIŽ‚ÌqÃ3Ôóè«Þp²o:X9ÞĤVFd|Ó–rÖf"G‡p(ÀîïaZÑtR­´iS»~bE€æÊÙ‰©EWN.¡¦¶×Að™ ]²+´KÚ³ÞB«I=§^q"žÞÏ(BxÌ’* ÕIÍ×F°~™¯ýýÎ+ò¬?h'¹vSà²~Í4k]lmÄß1Ò. £ NqT§ßóQ¢2Þ"è®Cˆr2HZ6˜ê™ÆS,h­cðlÖx¢35Sf‹‚KCîwClÊXµ'6 zk(í&ž…’Ô–ˆêLç©í@ØëŒaãà—ºEØ3ÎœùºMÂ]åLñs…Âq„yÐàÂTgq¿N•$Úà’×Ç•ÜBp›Mîa¿VZ’mOÀ‹Zé š å‘1 “éÀÿ{G-\z÷âÞBV2Ð2Õz—éJ€¦Ÿ…Ä‚\ÊQJA!V²Ý_©v3W™´êÀtT?]iÍ–—ßOÁQÀÏÞJ¼!v6uTzÏP݃¹,ÕGÆõ¨¾ñ‹±`GÙnh®uÝ\Ž²ý,§*ª*‡ø¿!=¨–ٌɼï EdKõdŠVÞµ©Xðlæà7p©yF€ÖîϾ€þ·°bLY?mŒÇ{ œÁGÅZÎÓK@­.xÀ{U€}ù«žCïéèN« T“±Ëÿ‚~CÁÜTçt»¼ÝWêa4«ŽÕè÷qCÚF]­(n‹æ'€Ááå †o¥8 wHƒ+‘ï휤ô±Ø½FB>½­K4P z ¸NígRé_ï‹æJRšôÞÂfX0­0À=?H¥D^èU„‡V Í®•)Ì9+«ï>þJ§$ajΆËB€|“(G¼¿)§%˜ A-o%uÎÀ½ó­j‘ôM~gä9sGfAQŠAjÏð"&ÉÈGÃòWCW"¿¬F>&°¾F}Få†Ù=ý?á*0*k2¶|›€@½‚iÃÃõX1ÌÊš¥üa(Mj€¸²ãÑ–¢bP]Ì_9'ˆéEÂh9‰°—¸Þqþ’ŒÛô#y¬Z€¾ãX]JŠš¹qvX?G‹_<¡ÿÍQq8ð8<ù:›Ò)lÊ®vÎ^2a\¿%–(d{ª¾7Mï¸õ'ël;zA/¢­Ubôœ GH3Ò¶…«P‚fNèû•)Æ_ɵP*!“œÞÐn—ˆóÖuàb§I5ïbË m¹Iý¥£qÖ ]PøÀ“ÈžŽ ¡üÂÀ¢V¢Îƒ+Î)ÚC¸0c%ÉéÄל¼b‹9“ۄЖÃ5p¾Åƒ”\%*«ß**·_7”ýÚò-•¼÷·˜èQËT·oJŸ± iSkâ¬Ler®`îj— ÿ®„´é¦àÙÈêÏüÒ÷9¡ck.¨+N½•F²³_ .£ñ¨Ÿ(¢¯F;ó,ÎE§ÐË®’’oT´Ôý7l=x¹/ ±²“Ñmw†:ïkDx9£ä× œôª±ÊÈ=ŽÔ`ÅßÇ|~ÖWñ! ATÛx–Z<ó Ýþ³Æg{Áq„v@#¤Æ°ÑrÙÂU©­ksuËjmÁ´ñôA4î¿ÜA‡”:Ròç¼·¡~Ôš§¼¦ÀÜ`Âʧ2.DgÜbßÙ»¸2¥ƒ˜1‡ÛÜ_V¶o«RGóm¬ÙÊè1| q'4¸· )jVyA­9¿nLΠdXË‚aÍ°¥ÿg“B'æ8êÌ=ßIˆƒùI1¹ÀŽÉqTdW*ÏïSÜYŃ÷ ¥ÙŠ\‹Ziu›8á~»œÿrCá1ÎMΡ|N†PìËÙ3ý«ÜÍÛâãyÄ)7¯u¢`ŠêEλ{KáHaLŽE°NRøúAp°{YŽËN.KÜAl“Q™µmcÑW¯ó9wœ2J×ÊçÙ°C¼\®Ãú}3œ®uö•ßÒÍ]}I­uzq8"þ]~ßðà9RÒðËäsªcÙÅôË!K^®ýN`ÈäJÊåó¬Oó$¸B<)ñÌk´o¶V—ôºø^·MJ –©ož<ŸÓ)åùÞl)ÇóçãšÎ b,ë'¾€Ý_~ô¼zÖh¸Î 6¾˜”×;Ë4}¨?Ö‘ôâäx„ÈÎ ƒQR1„n-€*Wrnð’=dËI¾“îY– ¿ªq <á«ý ×L5;”ˆœé]‚‚ùÉó5Nîüàïrx ™5»äVÅ£—ž,£‘!Q˜DˆÚ¤p4ˆdë›Ùª×8äT£ß®W,dÐõOš£n~otþçªCСÇ>"±wæ6ÌFõ»N“e:¨ÜåžR9¥ñá§,Ò×ä €ÖWl“¦F£ÕdÔn'›#ã¤íP§NDæÍ-ëf°g (ØÄ‚ u>×nï°Ì˜ 7³×Ö )ÈI%(Ùç,ú"U2ì Ú:o:•h~jkȃYdCûnuÀ§CúgSšþÓƒ·ädæŒÏ%¨ÂùϺ&HÜÇ@1¤°Å8á®i€«õ¤ÆXÕ9άc5(GuNÍÙ•†Kćퟩ|¬°ô!–”®¦±p`µ±Òõ"&N¶ªÿ8@è·Žf—„û}§ ªŸ)¯l‹üoÁJâ E–dHhÀX‰Ý³3Jv´Ê—Ù%äÔÌ9ºFU}‘ôâv]q*]ň£Â~ˆ`¡ï}ëRÂ)}´'–@:Wlð¸ñZ‡³ô€WXl^Òv§ñn‡]0òí«&¡j&ÎrŽïM(Ã\rVU8ƒïU7f%«ÕI¦É˜yßÀY2"É~R,ÞŽL8Y×òôîdNr|Ò–Ë”†x¾‚[óãZÞa´Åuª¬JÇ”ž‘ñ|RP¸/õÿíE[ý< ÄÌÝóžq­Þ¦ŒT¼b²Zœço“@ ;’‘ º¢ƒßì@Èãm~ÀÆÎö‰*õÍ TÒvÑMØnU­—äðJÎ îñ‹Œºœpaeê[˜èMÀÌå`©¶ú©žèž‚:»l²ã@v×Íçi0¼å†ahvf@Áaï|ñh:³‚ªWÚè…ôyý¦)Þ!Ѐ¬…pe>lêt°Ê¿sêz­+“U&¹ä ]<ös¤G εô»¥)秒¨ò'úÔº¢*‘¤Ÿþ×X¤˜SY_rˆæ˜ä\ͽ¤$d¶ëаâ!€ \ îÎ)UFï–5ŽPg¶W%³ïv´›Â^­„lˆgŽ`U¬Š¢oóBõŸ%ºØšp¤ÔµØJM^XäQÎ[þ+ÁH§pÏÁÄt¢e·sž}Ç{6M©t›¹v™w‡ÛSÛ«qêqåGö7v1GTëˆ7üLË€ùà»ý¨¿s:”·'ܪé­GÕÐd•=¦øú¿"ÅFTø>´¨=¹ e8wÁàg»¥ý™ßîÉðùP›¹ÁóÝç‚Ɉ–T¢“aÔ IÂ%hOØÔ|–8Œ¡[¿|-|@U^7“‚¼°s׫ýò’ïæGhø‘2RÚ‚9mT%¤þ!ÎÜ49XBG³¨l÷Éì·àCjÎÜc"—?6ªòs؉îjx»˜êû–?¹6J0´Ø¹9}³i"ƒ¢U2?uˆó‡@²*ŸŽÊ|(ö ‹–:²Í†«{<!ùÏÆ=ÉÙ$r 7š›—•7nÏL76Oâ‹ø·~Öœ %žS§f›¬=yˆO¢Ù¬·6WÞu‹F•ØvrPBú¥!ÁÅMn`¶ÕfÇÓ¼¸òÿbyÑp§ÓdF·¯ßo•&„ÃØOÇÞäSP¹´"tD)°õÒ›­ç,[ã|BðWÒ—DêwL°.€¿Û¶ÍÇ'80¨Þ’…Ýn;ž3.Þú2¡Œ°¦†7ì=£°ù" ´iX{Ï9X9Ÿ®®c ZÊßâ¶)鑘(%Ä]áå¼–î ®º”ûéûúoçñ%&¨õ`ÚçÄ^¹•’ N¯ Úbrùó}ýT\Ö‹Ÿ=ò±Ë•ñãÆCfìyÀÀîÛ’¯Æpêo7V¬°ÀKˆ5‹©§?Ir=Â!m‡Ì"Ãá „ê÷ŽN%+ó}dÿQÆ/b>ùVˆŸ´²*›ÐØ~.Mya̓—T’^$Bñ°›‡ÝþHzF5¿í4BÛÖ:h¦H½b]Z‹¢ÜNTØÖyÃòY±•.Nà»h>·.·£/2;äj¥*}ÁKa–\´œ®'‹¢÷Q-)'…±@á;B1-a¸' ½ ÿÈ©¦ñ ½¾RÄsq+ k®£Uýæƒs]ÖvowTµDSttÀ=ök €ž”b2ÀÄ_`(¾CÕ—QÀ·øÍ#ÀŸêÁäùàƒ>»AŠ)È |Š1°²±žÃ¥ø$}ÑýÖ ™äR3C‚èäTÛÓ×”† ·ÊÕ?z&ãWš¦‰“ýÊ$­¤Ú­e‹`àBüv‹îÚƒ^³ìE@r”¶m1ÛÌq‘¼cJ¤S‰T9¸4+³OÁ¤œU,#eF-QlÙöÕ¡·pŽ—ÿ£­±Œä5 h¸Ì!’M6x’ù¤ÀÁë_"Ã$›øk 0kO{³g¹á~Ýf~Ÿ….ц¢>tŸ€Ô%€…e×ã¡x",~ÙÑÛ½’—/Frr4€b²\>ŽU©˜%Þ<5”yÍ×ùÍ' Â^ÑѽÌeSKåC|’¢¯^u›æ«å°µ’\°ùé(5Øâ]y¸LŠo¤#ÏÛÚÑ14L£Ìü—K†¤ÿ¡Ò¡ôB E”þ±/cš'psb(/ÃöÌÚÖÓÇm5ù«|⺬eD¦Çõ”ƒ¢YtÔY²ú(Ã̓­Ã–üümmûVñ¿}9–¯Hf@zA|n²¯ΠL]üi8ˆ’‘ÁÉ[¯²˜–°ÔÅ›á~ Œ´Š|JsµÇDÕÙöÔð ÔƬ‚ØŽ^”½ê†¥•˜'z¾dB€¼&›ÙÅ*ôBkj|ln€=Ožó~ Á"xêÆl•JR¼æØv0ļþïeà‹!gï°+öª¯ºkÊí'@°%·F#ÝÚÖü‚ö›òœJ¥sÄÆ‚kwhdÅ® Ž(¹Aƒ¨‹“q ÃúƧ°]gß{„ïY4c‚ÁÕº„PÒ‚Ë58-îéÕÏG‘’f_bäÓŸqls.OÝè3çyŒè»BKÒ™§[{Ÿ¤½{RºäçÆ íÆ•ýñs«Î°Í¿ÔoX¶¬2«¥èkÆ0Û.ªˆ d¬Ö®}ËæGù±ÕÈ¥îñP'ð›?)’P¼Ú:…a×4Ivö!ïUŠ±:ZÎ@¶ŸJ*?L39t2ºûð ?}×P=DBú‡Ðœ•@…€ó/ÂÓžGi3syqH¼z©ôr§· ¿-?[ D²ºU…®NÆY€gìsœ6¦S$ñÃzå©9:dì[ˆè  ÕfÁ±Ø;ž’DºÓ•EZ ô6:P…ó|e U^ÿY|l>Ëú¾*h0bg2JW›1žK2¯ìaòª–ÅЦ’,­ x%˜‰C¸2Vè—Ö°=ðÛŒ]¹U³RÉPÂ…Ï~ k6ßÌœ¡(>ÜƦû˜™‡è±r¿ ¢®Ñ<ûå¡ÃÉ\Š¦1öÓ\Æ5‡¹»„MfÝ‘wr\*YÏrð{1 ­¿ËáwÔ‡«Ä÷ŸÀÇÂ,ËLÐS-&ý3Üù~‘ ‰ÌСÂßÂ@©ÎÛÜ\Ã+ý \™y÷¹ïç“Ô¹h{¦m¤p”§™T¸ºó Ý©Êà³#·kõ¾ácÎÊ jx¾ôÝÿÈÊéY¡?ñk0ÒÓuÿ."JõØ1\ö¹ì ø…ÍÀö¦Î9!“`muaô Òfœg“ å÷?€­-®ÿøªO «£q¦VƒrŽ>-½-¥é­pÂbìøêàY[ÂCt}T4kB‘rÊéǘW õ Âáh‹/õ5‰C ùbqG©Ð; uéÞŠ?[çR²Jë´,¸zvBÜ¿ ìhË\a©€.¦&n³m@ ‰¬6J'°J²E{jÂÖ^ʨý^ƒ¥ø±wu¶\·u˜?ÈÑeFÊG·¹.ÅÞ·ì—øý½MaßüÙÝzòÉ;Áaðë3TpáÇÖûòÖPuüz^ĸì5zG4u.´ž!•PÔêæìJIÁL[ÒÐq¾‡c†‡áõ=Ušô£8«÷fÖÊ#V_ŸÑ_-êôhû6´žöÈ"$ôLÝFûÕ~•t)Ú/sÓ%{G»15¸ Ñ8¹Ìp>ì´"ó¢Ày]/b=\xdyF_¶¿ŽQ2ÍJ”‹' “HÜq[ÄQèEr8Ìï‘IM¾cåÂÆw óÅ@ 'sÿ,©ê©rzÎÖ; ìÎýh*w¤ev¯d³ÍJÖÊGæ‰-¡ Ú¨P³A¬}x]‹eÂO V 2é_WÝ|äúÛ¹ÕÁHon¯h®|µŽÝÿRf‚HXo~#eW‰µže²;hR»¯Œ’ˆÔÃî¥N q4ƒ­Ó8m’s1 ßæBHaó÷“dÖ–8.¦"¿lL¸¡1ƒ¨h¨‡ ½ý°NòMŠ!1ô®ØJßR7>ÉI ôDñbú!ôô¡û4‚ÞÑ8p`Ñ×8¹î[¿É䳸K³ÂTš*Å©ÝHÞîçy5Œn9Vð Üv”ÿw©É`]¼„,˜™}ð‚H /HRcsS>üýª8±mÅÏ6¯.ë”R' NÞÞFÎ7]þ‹­„à´ÄOZ€ZPú¢éá£3Gf×ò9íøƒý7?^í*¼xŽAÎmÇÝÙ ˯ÚIøÚ”ÙB••µlÁüî ²£[†ø&‡.dÒç64¸·ŠiÊÎ̽™å[™†€uïaZwQ¸‚;Wß¾b[¹+ŒIh  ‰†öðM2Ö@áQhÅ÷î{¸Ý[J `ãŸ#‘^¨?>¥uI]À—#ÖÓCY„®g¹²½¨ÓÏ®6Åpq ºþª[TœÜ)ÐS ߊ–tò¿ç(þýðU¯Å%m1Þ}ÞDàPpß;|úlšòÁ~¹^p꼊7êPFHã³t,oF&ªþ`Nž@Gz+z•Åp;[$@÷¯ Ðy“PWè#X­EõØ\tN„\{oûá€ÙÜçL;8~¹µ×çtJ=ø×Ê1Q1öÐúnðj«µ ~np²AÀîm´æwþ-{ª~©a)*ÓÞÝŽX°{^.ëðòÊoæ8å³X <Õ1Æ誰ߑëÀæŠô]1fΘñ§[š;Ú}3¦øð¨â¢ÔÆ@ÚwLj(qw¿æ~«òÂ,Å¡qjœH¨¾‚€‰f\cjÓÃR•2¸HáL#~ð,oê9±¡Åd §Š„Ü醇Âm'=q&ÔÉfÔæ+R~Žj²s;«˜kgªý¯´ûQ×OrWÊí3°B;€bœ9+èj¿Ì¥ µ7ˆºšý¿Íjcj]á 8Sõ`a²Ò(“mÂjÁú|.#ø¨Ë+`­úlâš6€´]©Pé¤_/&”@*ý©¿~)djÖñïPTyf$Èå©tzØ°VªÝ;öÕ£^\=qóŽáWä°}¦hIû2š©Ö–zÛŽnžìí˜ÐÑüÜz Ç‘'Wª†Â±Láö,œOÅèÙ™3¥°‚K]ad,”y— ÷V'ôxš×ù#ÝÚ0HõkTe|ÄAh®#û te°ëQ8BØå«f9ø·@/Çô~ØTìãKŸÞ"U¢W‹É @y륗@'˜#K×Oxé £º2}{á¿¿,Úk*x-š·ƒ@o—Ý2O]›òd€¨È±ÃÈàlšUs¯ëÌöEö~îÕƒ—,;^"¾·éObCSŦ7þØ"à×ï&¡Ó®iÉãïjex†Ñù‘«Ê©‚Lp@pWº$|íëø ×öê”?Hku2â夣÷óðt˜‘•žrïH;)„10Z³TfSÆ»~͹ډÇk`ÜÜuGÄï€Pæ"FöŒ(eÑ×–‚>IÏÏ«ü w_Ï™D,X)ÂÃ[—ÎQ@³H.±èë]rìEÀæi©`¶“~¶®À>Qí­Ñ‰öÀ˜¨| îgõ¯?Ðœ„¹g…€ã`{õ?XaYPýá5=Í0i]gÂ¥¤# }KÎxk?äÏ“©@z4>)¯sÒŒ«vÖK„/pQ)I˜Sæp¸ª°Ûæ¦í›²º}¥Ä z©æŽ½^x±šs'+¿z¥›¸8“4ê܃Í. .ÌN1*|·ÝÈØ*AŽ#­–ôQ ²øÄr›“ø“‡°xW5 @ó1CÝÕoôo1ÍÍA¢Â’fo>ÌÂV@Ï“ÊT½µä;üeá`&ŠÞ†geèVͺ^¿K}3,h@|`#è‹¢¨HÂî;?ýTߢĀÞ×VX¢¦·´Po¼{Ð3Þ‰¹‹æEê@Är ?÷ñ-¸(µ~»|ÎùQøik ¼l…Í3. ·Ý7C˜¢_>ó®$%C'ìãÇó¹æ($½œØuÒ0Î'¹Öcw©̧5ù9Ú‚=z¹,›„:×ù‘è‘ÍÁÚ•©ü†€ãArmrU^[±€rËHú ¬zO ‚7önCÒíw´Å#| d‰Ê´¾ÐZ}L<íx´!$9ƒœf®AQg·¢Í+`³l)ïfX”i9Õ8Àb(DÐCB«gÎí èÃç1Ž[킯&qÝ~÷zªo!h¡N)¯åCÎe! вdŸ”&äc”IÓïñ´öÖ!³³P³þ Ø1Ù€Úòx X›š7µo"»´IÜeXóF5\¥— ÀWÈ´c hq†„¸«Bšïd¿=ÎÞ‚Ÿê¸/S f߬ï@5œóSˆzóú€éCáZºˆ7Zýë,è^ÊW`u“k%=Ô÷Ÿ'ãè5r±PQœJñðY&¶j.{™pyë>0”/’p]ýR ü""uSg“âXªÏ·®ÚÊ7É]Ø!­:Ðõ3Èwc4ØYgß(6ÆXArýdºÚ ÌB-Å ´^L©™ñã&Gi´‘»Ï]˜ò Å6“Ji§‹în‹x#’Z5n£Px)1Ö1+ôR–#^hÝ=G^Âä°ãÆX£øàEu§çiÂZŠ´dAm a4O­óp´¢”6Ó$ôO-ÊäÛcäS•!¤ÃÒtÏYïå^‘Ó±(ž;ÿ›¡¶ ÐÆ W­FŽD‚õÏx¬´Âgóò:­ð˺¶ï-€¬ h&mÄ,òÕÄ8 ÂKMú¼˜a #³äûwlhýÈCÙŠwÄÕ!%éÔâœýQIKõ@¿úzí™ç°YŒ·†Ý€µY 3 ŸK½ÓŒá¢ï`Ãû2¤'¾Ù›ðz >ÐoÛ±M 0…%Grs·³Hj5ÄØ>MÚeÞfßÚªÔ¢s9"únŸÒMR\¾Š|?ÖçØ.Nìéh*5ĹÀO¥P › ²¹bqÃú :>ÊO|7É_ÔiC£ab²0àœQëëm@ø{5Å*ð2~"ȇŠOiÄ“U$Cgúé•’¾†íµ7-a¶ƒ/ÁË/-é‚ŸæP'‡›”–ôAut/€Vr§w€[“õOÓþ'tLS¢ ¼Øvõ sOÃÓ«ÝWý¨—D$—åHøïÃØָ[ëtÏ[gGQÙˆA\ZCµ/0;¹$®•ZtIÓk”.MG7Âxõáê½ £à:¾;ú%©á›ì©7¨ÓUÐöhù‡úK½‰ˆýˆøiIgú$åÞ"sp ,â>J§†ƒ)‡Èohqg~‚éè¶î‘o‹s–Ø[P‡¼ü|ÖßjµÐ…O «7DîÌÅ"øUÈ[‘4?y‹›>U©±ô“"{Jý§ù4uÆÔ°aätÅ9—N²SáðGä¦îjš2Ã[^›D¶¡Ã|-ÃÊ4ý±Z©ÚðÛæä‰X Hf KÐþhµÿ¿oÖlí¦Ö×!þÙ¡CKÜgè"¬V…8bQ™gðIe¦KÒrü0Øúôèâò¡RjJYUðGçd÷±ª¿3týzDÓ’Yç=NÍbùlzBÊ“Ý¡Ñô>n®??€1¿«Ržúœœ+¢K>ìßP-¡óŒÝsPPåØ‚èQ.2ëœh&·2H+×Ì›ÍzŒ÷Ú+MŸ]ÉÃSÛ¢“mj)CNÚ‚»he®5i<%tuI ÿ-%Çw†°²4CÂnÅÐiVz©¾páV2Ö^owÍZ!7>Œuq4OCÀ·®¢`kêñ¸Š.°@p2÷5Iw½$žu²ƒ©Þ¾!‡¨B<Ž< [ýÒ¡JJèÂHLÜï&YÎÐãâÕ›Ÿ%š†KÜ£-EK¹Ø÷žÆ·Ô²arKʪÑÙ¥Ò 6ÌÄÙ/æg_Ö.ÔÛ÷"nª\ägÐ*î­µk½ÀÃKû±ú×»AW¶vÅh§ ¹Q´ ½8cašã„ñ±Rýµò¹¸—Ù·ïìq±¶,þS((RÚÃ;À»:C<[o§5숗KOˆM]z/ŒÂÆ!Là, öX„‚s´éŠ‚4æÌJÃ1”ÌŠ\„&zô]—Rq¦©¤,lœÌä\~Ñ3C¯‰s7üvÜ3¡I|ºä›„¥æë7Ö› ¹§öÇ.Égô@£§ÄdžZ­^c“-¾ÉóØ­w˜ÆŽÊ­>ï"b˜ùà™ABv(´FX‰”ÕÔL‘3Óòär­“µ¹Ê{BoÍ@T¢ c‰(ÞÛr$_^èMO ðêYCªÅ4ÑÐ Ö%!¾Çb#î@Dšp¹¨`aµ,Q÷0`€ÒdÆ×ëý×ÑÍÈ'Ê*Ì„s»"Fsòbñ¦ç;!¾)ãh±J{_5¹¯HƸòL ˆ9åªFpþjt9­`²Ü Ô„ÍÓ@ölÈ=œ%'_F¿=ƒ 3>ú¨ê9©c¨ËÜÚTcŸq£§&k(¬y „¤· èLú±ö.ØJ¿}!=3¿øÑ%\# Á5vÕ$j…èÖ‚c+@ÝúÝaäcŒÊÒ {ß9\®A“í2Žÿ‡: ËöÂ¥XBâ³béR;–ƒ³ŒòêÛ!.ñN­œîþ´ôyßø‰“{ïíGi½*5®qqFé<ˆ×£šQ‰©8´ç&§ÔÞ$Ëñ=n¸èKP£º±ÂšÁƒÎÊçxU'y9­w¼7µkþ©TÚ[õ…'?K¬)NvÀá#s¬ü¥ðÑit–2âšxåâ¾nÂJ÷Ϻ2ú.*+"ÙåaíëR°£µeùÒº(Üïì‘ÖgMÜ‘1‰&V̼.eì¿”A›S:ƒ„Á }û1PY¹ÿF‡èQ¢Æªß™Ù;³ŸU',-G¸øwU,ÄúTÜ®^Ãt#cŽéO/¡8Ë,#W«ÆŸûîþ2ñàyK©ßQ4È 1”Ø¿V‡ro`݃W2¦¥T Ìo5£u½¤öGÆv$öW*ˆµ.é¢ðnaÓIõzÖbM»ô~áŽuqÕV‚`fª{˜áÄ°‘X¥Õk¢y«ÆâZúãXÇ!L™¡z(Íõ÷IªˆYÝÓHñ…ͪ/=’@ Zšk9È-å¥8÷VxÖ–iO<^mº§‚Ö*ÔÄÀJâÈTÉ àÒSHH¨Ý:‚‰=˜‡h2gq÷€¬_:•;ž¢¯¥0é?e<ø"n†².š[Ÿ‡^ u3œòU®Vˆ©ÖBš2?õ2ƒnS§ú}ÇŽ6ÿäÞ¥Æ Ç&Jl"䋺/tlȈ¤%L/‘ëøUz³æ3%Ý F—4 gŽ ™¹O‚mØP‹=&¿[?´…["ÝŸa™-K TF¯ÐÇQÉÓ4¿;{f‚xè¦|‰ö•_2Žrÿq%¨šg^Zƒ ¯)¬F T?ÄíÔð?XÑŠÓ,e¥|› oDîšÀ×#m.mðz:É ·ÚǺîÿòyvcÐâ5!>kÏ!x¬á*~ã 4œe@RÕ¼‚ͳÕd8ö>z† °Qx¨ý39ë§ým ‘½Ù"0K©1—+ÀØÌ™( §õ¶Øë…dTá´¸HÙ\þ@‚ƒzR»ªyz,¤¦fÞ|û/^5éæãçdH#ŸÂøw’]¸êãf¿¦‡škû¤~Õ~^rWç(„æè—€AŒñ +ÑÑ'¼4NÇ®dõPj«Yé 2>÷þÞ¯d!Ë"[EÂh™ö{p‡°€?¡Au­oô'‚šf*Ehм¹€‹¤ûóøZX{¿«7*à't¨¡T½">Výd’Ïò ê«ö¿ð×hžã•Ôócw4èÖÈœÞÃZäi"¸bɉg\ã>¿E=@<áŽ\'PÉ<´=À´åI]jƒÑlEPèÝ1hq¢ÂÓ³w}ÖõZ1³²òùÊ}Òÿ0 m8ôÆCÝAž=}¯€E€çQh¶ÜO ¥X¡™÷%ªá9Ùj«@*–Pòæú{ÉÅÑ‹¸mŸ„UˆÛbû€ 6”Ù úzž4¿Ì_Ý5U0³7H|°Ì½ ”a¢®¼Î'KBÜ¡^qIT¿F]ο ´(L.Ô¥Oÿ&×W[£‹{·¾|©UÀá:¾ã|[ããkG—‡Ñú+ÌF­óiñ±Ä1„쌄ÁME(‹™fІӃµ‰|Ö©û¢É¸×дÔ\ë}ˆ&ÚËŠ‹Ù‚!ZηÂrS°ýÈS¬}j  ¼YËêØ<‹¶{ µ0~7ÜÈ/vv€Pé´c+(8g0ñ93}Tri¾ÖÎÎ:.©óÓ4úPfYŠÖ6CâånÐÁ<ú*›*×áÑz·âYr3CÔVÑÐÙ«þbOÁ ¤ œÄàS%&£ŸžsØQ0ÏàÔ]…î"8Šì£›ƒ oÜÚ£ ¶¨¥Óßò䥮©Þz”›÷‡|FšÀÔRHc…Zòé.ldš \OºWk±ÿ^ŸKºª„Åh#>ÊY±j`%ŸŽûwëh÷A@`]nÅ«.7%9ôúaJîÍuIÑþ¯—._ÁRÔxìñ«¿„²äð¡«‹ÎI?aﺂtå Ùr)À—…_@+*ÈÚª° ‡ÚÚg·æT'#Z'–])T—–à@a¥†ñNúD°×xr+4Ï Íˆítg#qeY“½^h‘TÐo¡™~݇ ¸¥ñ¶¹Ø€Ž-™jHm]bdº©­vNiMï¢+Ãë±›)u Òïælò¤Úüý[b•`˜ŽÛ9yœxY¢VìMò'èf[\­À¨ë«iùo b膣#÷öɇ²UœJBðë9’Aæ«ê$)§6:ts¾ÑÄb™ØÉ÷+¸W™¶›éÕ>["ÐåsÝZÉ{!\ÛÛ1 ÅùM-§µ¨¼ÓœÚ «§è3Iˆož8÷ÊKÕ±7ûË}¹SÖ‘–'Ïá¯\GtyraÝé hÞ¤þ‡"5ŸÇuWzà ¼ã}){‘µê7l*½zœD[QÄ®À^›Î îá­gAšLÛcÉБm$ˆÏ) ïwZù²Aä¾èW`kMôüß)Å[‘95Š’Æó•s!ZF ÊÁtÕÈùnïAú"`ãÍ­†Rj:N«&BÃÀ=Õ.ÃÐo¥ ®ïâyèJ†E1Ï]Äë¿ïšr—L "PžiȹÁè‹âÛ'å7TÁ‹,wÒçBó(3"¢©òÄWù~ÚI‘YVúFù…ïì&šWYsÆv¿õ“Fïcx邨ü¶ÃÅÄ9Ý'M‰ˆ1ûõ!Þ8ÔoÓÛ€Çažzãî¥ ÿ‘K^!mìö(žú a„G{ÏÒ%s)®œ^AÊã `AiîÆÌÚo ™½w)D£/^ôÎ*žW•rãÇ3¹$Ød\¼¶ìÜŽ' ”é Ñ£l)Óës/ꤣòàCƒ ðk0ë`¾Ûµ¦17O/éYôRëŠä+ )¯ƒ1.õ´S‹fù\ïUP²‰Ê€®‡×±ÏÍ4pÖöC¸KjæEÚoåþðxšÑb2ØQQéyÉÙËûJ ƒ¨w¥GF.#ÐñbÒâbˆ3ýãCVçå—Ê…­Áâãˆy—X³œÏ½fü9£ò*R¸Ì< 0¾V~ÄŒV´ÿñ>íõØîod†Â'k$mtu €¦,Þ:F—,g›‡YìV½@÷Õ&× F,òÖXÃÇ*P()ˆ!€*[þõM–~ø\Ïs–8¢´ÿ¬ô(_K £ F¿½w @c¿ƒ$ÀjqA³Ä¡AvþË ùvE0Ø2ÛÿµüI½ãqôFl‹y=ÛD«ò¨åaPD(#…m†éó¸T²rðOU•Gh÷'°²mEô<§ csèpP%ÇÑo”ê31•j±¤Ž'¦ 9Ÿd„r'‡•Ø5>¢Ÿ<ÔJ“ü§’aq`o"3© kS /ˆ•\žÒ‹Š`2ÜåõýînÐE"W3T¢ÖèïlØѼÐX#Õ›ßUêLÞÚÈpÊpV‡íKÝ_»Pe¢£åÛÚAe„ª:Ðü›…ü½ÈÜs’ݾ‹U!7Lq#À .²%sëqš-NDýÔä­Ûh}ã7°x¤?‰^rëN´òþ¤«©@‹qG£Ñ4ùW–0ª~¬«ž~˜™™Iã©â;ÌÊïÙ@•=‡Ì:Nm„Ì*VÙÚ`rbÔƒ½³l|U®O¿%È!¡6J1Ý|]ÕŠ¡‡*”°Õl<+`àp’¶º[­3cäNh÷z– î²,¦BÖ^"W‘W)tõÆÁ(µ€§/“ѯõ°j×(Æϸ‰r)sØ€¤OŸE`ÎGS®vx^ Ïô‘.öæPqïØ‚¹¡ªU(ÜäT¥œ!)™™¬¤T}Zé2-:å“'6§¸—î™éb£

þL>ºÌyO‡€’<êÕ%åõdtNl¥Ó¶&NÈ8 ·ÇêPŒæõA.qfÆ”¥‘ùqfKß`&¡].\a×\ú§ ÞÕžÎÔ1Dªînü0mdá'& Ô¢¼tý/ù>ؓ߬'éa?ÙÖ"4ïŠnl‚ö‰íQJÁ´‡QŠzaxö¯r)~Š¥¦âFø1‡;öR^>Áâ‘Õw„£§”Ã!÷`Ù—_ž q`s¾½Á)¸ºò»-î8,' ;ú“ˆ<ÿñ’NÌÑp_ܯ° õ¿[f‘¦‹•T¼@±&~µÚžM* Íx²›ÞÝ’d'¨þ¿AVŽ(DNÑ1`X^ªÝoÚ2’*j‚BK5fÖN1_Ÿ˜Ž—ÝÐXiúècZ²º˜»»¾v“\s™_Wÿäi‰ý÷Y. ª¶ÃDPá¼q¼ó_ã‡ò1ÿ©Ê&G#•×½7 é^5ÅGÇÂsé@äõ𔹲Q~ãó¨š$Mg¯ë>*~·Î@ŽïEÔ‡Òõû染4òpÅ15aUgX–xëYT'9Rê¥ÓPgz­¡HHu/„ê,c þˆ?ešÄÒû!׎é'Âl“þG­…‚ÛåQ<ÎG¥˜.'½Ÿ[˜¶Œ+¢T1Šáìhèb“9§@'&vâ·(OÏÜà:e`Wõ«{Ü—ñjVÑçm&¶¶¥@7+´zg­K¨òñÛl§ªcŸÛ@l­wôk…Ù8X.Ó«Š¼Z¿_,Zà$Ú«ˆé‹‰—¡ÓŸÅ#æR ƒ¡È=´+Òúè„%ŠsëD¥5ë°jm •K…ó£ƒû„[·nhQ» >×¾¨¬`'fò£ªÀTgeóÑg§•‘^ö§4tn»¦´ž› |!á¦-Vɨ¨K" Š´ÔŒ îºõ¾ŽB©ŒÐšÀ{ÄASþNóKçùû¬å"!~™šYSNEµ‘¹ÈæUQZî“}%ê\ ÕWfTey”žÎP×ô¹¾‚ÿ4©ÏÕòÕõdnê¹2ÒÅ «Âü¤þwÚúóX>¿*xz{$¾L–ÓH‘Lt/û<K÷àýÚgLÍ/Q©Ÿ 0ñÑÔ|¨·(ä!•Ü¶Ú^¬½J'hÉ’Ú½ÖÈyJýµx§É³ÿŠ'Dz=,­§›Ö¸d44x·ÎèY-[mƒ‹ÊØŽÓ»™¥×,ßZ™úkÑç Jšý®²Û@i¾a—CÂUúëØÎù˜¯diœÚ1›Še›{–^·EgŸš”û2R´{ÈðŸ€RTó¾†Pè&Mô1ñ÷ëZ5‚ʶê}û¤©t4Q ç>üª«uéNÐ .õÂðña†¦õ’ƒŒ°„³ÉÈÿöÒ ªyd`DÂé'ÞhÝ”˜¥fCÝ˲b(ôŽå=;#ð­ÉPV²Ïà›ÞÍÎ-J÷CóÒx f],Sâ)”ì º „™îtàž‚ˆã@Í£ÔÂ_Ö9ÝÃAw‹â›w”^öüù¤KôQ úÈgñBd bç.VI{›BpනB,_3%¢õüô°ãzÄv˜÷oÔLe&qÏuC?–Î`cñ„vîÄrðÞh˜1[ÿ’–úç¢75ö>¶ˆš LæT“—îí™[sF ´E…6«fÀˆôm²ük0G„žŠëøÇ!i?+`lðæ­ªÆ}¬Ãêw ˜½Ðl-Þ_×\úµ7r'ü„#ï7pöìÞ稅µjqÊØï oµ§A×ßpÜ I'_ö\’½ÑU<ö¥=T^n²±—ÚwT ¯PsÌD”&'Í«jîÒ_'Ó4è̺c…+²öm aŸÃ%ÒiDö"q…}‚Y¹K@§†ËãŽÌ”î7äxBÚË-áíÇÉõo—5ï—Ä>—]ðöe>˜ ’eObXÐÿVFêT¦³¬!ünúO÷ÙøÕ즆aÊ‘"8ÁÅÁ¾^F¥w‰SrGÓy|ŽÔl>„; 1œ‚;àÈjo¨— Øã|ZæÖ^ìÿªJ† Œ"ŽGrZ{D ‹0Š·w (¼O«£G ElÜòçA³‡Gûz9uûı¸Ñú>ðÔ¾]çúfÿ·ä¤N÷-ubèá€Ï}³æŠ,%Çpx…„ÙâwñC¨eç¼kÜ)I:’h{ùK`Þ{¯Â*†÷ðÕ¶`Êž©TÁš°%Î’(w£÷ 8ïaݪu‹¾/.åè7>£œg¬ÍH¦PAav“‹ÜÏà3Y´>Tu5×­p´ UìTÀzä¯bÚâè@&AU¯ù0Òò¥²ŠÌ¸Ø .ÛÏA¡V.Èb_"¶Õy¾Sˆ;âBëËÌgçN¤ÏcN—’"ç¬1W4Yº¨"¨IN{Ä"¸‚NãEŠã>Š¯p®C׳2á¦:¶šýr.®B×lo0·œÆÅ ¥§½z»‘m Œˆ4­ýxBO«í÷cv‹!BxÌ¥t%ŠÐe |T”€Üu$qdnp Ì«môˆ±U›™ý2D'GoN«;»i|WÈH€TuIn¾¸ž1º;Â䢴ðšâO²Òœ&Ø›jè÷â êL%ʨL`ÀA®Ë·êŒï…Ù½(•€œ<È ¡)¾ªa‚Ê¢ž–4¦÷IGÎõPfR¤ 3Øù²¸ä:ý‚rc³|Îã“—@׬оêHº[Ä#hÙ$·ôÍ(üp¬nÔúŸ¾R•ÛNš Æ|v¦>Úhn«¸%úauÕ¥q±HÁÞ©Š¢;û¼ùrzÚNíO&/Á¼+Θ(–‘XæçDq%+èó•Ïa2¾îqÆsSFЮÏÃ^ξ3¥FÕ*â!…Š)¦q€•…èAŽ³"í~ǘ¿M …¢Õ™E·$€xtµl„©:f}bÒp;˜¬ó œŸa')Ãw=ç ½2fô˜ÎDßc_¡¤}¡F¥þ ß±.C(Ò†ú«j€8ÄA_aSYÇ®!Þf(ÄuÙ%"9ŸæzåPÅ'N`ÿ4´ð²Ï„6!­@¼\ˆÍdïÎ ›ë%¯ë.n(”Š $Cjåç™s„w¬>Î0ÝÿÅ›S¡éF­Õj“õùæ÷Œ1³~( —.Îfâ!*=”Û>×XŒ}]-ŽCöM3¬î»¦l+èT5úȈ¬'€ÁžÝ=~Do*éú‰G4Bn5¾†ejþZ1C$ÖfÙ °¯È>/j(¿“À'hKþ;A5Ö˜æB“ŠçXX+ùûñ’^B!0"ñÖ,3@D=ÞX[{Wk×Lè¼›4ŸmÖcIîZ]K€N¡¨Ë²þv@CôG3) ¢²ËGc0X}«ËŽc ž?OeÚ-E®šÎÜö£c3Ûȃ²lûíÍÈ“­–ÁRj²¾Ð-JÒU†/a#æ5{™ˆPÜçÅdÏ õ¢cëoˆ\Ÿ–³k‚aö¼ 5õÜ_Þùò„ÍÃRWãE­*ùµ%O¦È3¤[ r=}§Ð­x†ÁÐPT.I;Llcî­°¡_ŸÈ­Œ,]ÁÞê×LäKX9[<ÕÚ{Ð? Öh‰¼¯Ëõ–èrD8=ÞY)ì´ ¢g8…s]7ª „ 6léìLå”rô…;¬šÂQó¨ÎZ?S™’6Ñ §ôwrPî~x»þAѦ˜´³° :ÅünHä‘Ù*£ƒ9;[‘[ãæi~Ñ;ÑÐËÛq‚,Õ?"Ï)&±]U8NøDH'FçQK½ z?hÄEezø~ÆYÈ2¤¯ðRUiŽKl…t0^Ç>ȾFŽœ9j˜Åc«ZM Çæ Çdˆ1î#…Õ¦bß·º¼¾ö•Õ~: ô!Susµˆ§ÐòD‡lˆ< u “W;šÏÑJÂÛb)}qÂßâáà ÜTÏê%¶%ÕϧàŸ|ÇÛ™X3À,wêÎU#¨mAÝé%Sl(vË/£îKI—¥ÆS 1…€/Æ°ÐíÐy\,7+M¼x'«¹¥Ê”I´úþPOü¥©4Ä?,¢Ýi°æÍÇhME‹]‰°5mgÖ–‡õÅ mÃ/+)¦©û&€Ø©ŠúÍþ؈S²ûÕ|KíXœ)6u]«b²®Á‚ÒÎþ‘rì.¤gå93°êej8#ûíü©„±o^úz‚|ÃJ=óŸHqtxÕœÉÁ6^R‘ðÄë:U¦]ø&i7nû(—dnþy?Ñ„a{l Ì’S¤e ”Í>0šÅiÁmtíâQÄ|#ÒßgdO8ÐÕBÌ{´úû>žK¿ü5+Öúì1'‰l(÷í×»Õ}NxÀʱ³XYÔUì¼V[:+˜œKñEgRííœ,ÙÉí`­ãzO$ÓœËÔ‘mÞ×a6DÍI:‰}á"2îà‡7d€ìžoõÞuPæÙS{b»”dî%`ºì¬f¦üÓºdØð*Í£ÄÄdð–’·|øóÆ ³A@Ê–¢Åÿˆ^ 29ƒù|Vvq̺y—HÅ+ ºz;…?ñ1ß$ÏP^ Ǥt‚‰j$ä”Qxõóx[ ãý¼]¹mØ^˜Uùµ½bŸ%÷rÌP˜ré…šŒø™yá A5î/,¹Šqö1¨R‰z¢ ÒzègP§þÀ]qظ/Xvˆv¦g!ƒHé2_‰‹7'a8}Wdb+97;/Æd»}‹›0Üœdø*ô–gAX”pó"rüÇÍâ"úÐCň[}cÕ_†™÷°g„Æ—Ùpbõ‚K%M/PÇ\Z¦s)ˆwZlš,|úÁöÚÒ_h0FýñVQ àüªò¼á3Âo’¡Ã” Í>ä—·_ß`ÜL•ãTáM¥ræB  ]»Bݶû…U(<Óœ!ëâ Ú¯•R´´!\€·ÿ±ž#²Õ.#€A=“»¥öƒâ¡5a3ÇÊGDÝvÙƒ&íDk»¼^ÆRb z¶°Ðúµ]:·ÌØ0WЙMÆбŠPîÈ'ö™LT•ºõÑ9PqÆP}79ñøÿ¨Ã®ö¼CGøgDnOÄÚ!*³»±1ÊëÒ«mƒ“eèyOÌi« Ë‘Æ*ƒOƒ]´Gï;}‹QðHHq=ç¡I¤¼¦j`o,\nøÙ¤e4kMJlgÍq”É`ŒOrZ ê¿æÖ2})ÿÚÉü±œœ—=&{÷üÏOrŸñq˳ncè¹ ¨õ*êö8ä¶XÕ82z›Cf±$þ“öwEßhØwÃfU»ŒúÚú*‘b‚CäzàÅÖó¡žùU¶*¥ðB£ôûa˜ÜØ)cšó®›!L´ùŸH-nÌ[(ÃìŠk1RÂé“Ó¼ Ò«“¿ÛóNXº˜èã5=Ìþ|–OcBLŸT‚<Ò¸N(³ tÉ''Á¦Þ=Ç 2³Äq½›G%o‘)VÁ¹ë bÛ{>¦17‡\F‹Çšñt Š´ð.»öÂÉ.“È{_ŽJP¢¢…#îoë65´ZYýmñîàaWÊfKŠI‰´1ïj€B“ÎÓˆ4TN•…¨ªôM«œD ”Ot¸5gòXÔœ€nÔ®¸T“ã²ô÷:Tó;§`;SYxÓÌ ;Ü…à8ëØ©Ò§¶³õ¹ “[ËI‰ª“3`퇴_š¾õ¼üÖ›•i—”<=ç$²ù™1¥3[Å—Oeõ•)Ið9îK²Ú`X6®¨" ɸ¯Ã ºÌ´›œäôó(±y¾ 66¡ú+j &Eì?Á€÷Wþµ{ûä8”]ð3’A5¾Ãõ¥ _äbÆÉÈö wÝ…ä® (×™ ‹/ß‚ jPoº±S>é‹9u¹ø"‘ŸŠQ½g?ÿµ4Ó°»tÉ,¸|Ãœ•ÔÍ•°¬J&[l5îŠì¥Ž‹•’å6J¹Þk—{<Ù d¶¾¾SM²·)á–ðXŒ²Í4–pï‚ä«dï†løÆ:ý1û}oþÆøÙÍæödºKÓÅ? žáe`L{Ã;„Ü,ôˆe¢3djÃÍ0Tˆ¬z™©e¨{¹iz/;ù¶ûäÑg÷åúAI…d!üçæZtéAvÃLáutX’Ö,u“à¾@¸~sÖí¹Ž'/!î-¿bÏã|ÍÈêÀäþè.¿-ßà#U‰æùO9ØÁž.T:[ËuÈOQë¦q½z²Œ{ãt‹ÓUù!DøIZ,c0÷imøàžf§/Æöp˜Y9É%Íû”½Õˆ,[â`,ó®Èɺ2…›ï#uÆù2ÕLƒßC+ã‹*¥?´™ô6”Æ7býâ†ÁxÌ”+i¿úß@a¶š§ ÝÜÎëZÅEò óº”}XXB‘ÆQ¬®u¿¢ó¤?ÛŽG7>†pˆÙŒVWø$@ß°ë:‚·4ÛÐn]:UÀÿúx$O¨Ù6« qH»1!ý/úíû¤·r` ‹ž “à#-ä–V ‘†,˜³©¬8Gç CÅ"-¬à´õƒcö¥‡Ò>JÉxgaŠÙKe:ÔÉPUlð¨à¹¼\ÆÚ<ÛÕ¬F€Ü–\Õé!ñªºlpª\ð{au~¡Öm‡/2ÚIÕ•€·é5™!Á¬Ä˜ké“ TX‚ai\©a—ãþ ¨D‰Ìž¨ÚolVÈQEƒhº{äÙ4^Â< ŸÙ^2X{ô'e‚IP/•}Ø/ɹN̪•ð%ñ0%ü•èŒ„ þ*ŸÑ`·È²ö6† €ÚOÜé}iY ßã)ºal)tÙbó+ü«‹j»¦â›gqËŠ(`s·ø\Éà#=sq{Vfòæ ÓÍ@]ñ š Üz50%&ëÿ¡cðù&ë£áTGÅzD+ܵº=|÷ÚËš™ììÇI`Ú£„%Ý0Ä’°øõpóXïBu­Ð [oÔ(føRò ¶‚P± >ÿÁ"”hÏ%ž=*kÕ¿ß0c¾Ûwm×LAŠ·"°OܦâÏ“½y(ÛŽœuI]‰Z{nÇßÏ»hÜ»‹ó4Â>Ù®)yA½3ý„œ¡sì%»Ûd‹˜æ\0ê­¦]•'”|t9¥ß°€ùŠÊòÉÍ÷âeä™e Vs;ÉРؤn*CÁŒ”–¹œ¤T1]+òEæ(ŽÈjân¸ ß ;ÀKU-´ÅoG2f"dyË× q\ù3ÈlhÍ6Ã}4¬¥€4òzK>»D[®—#)Áî GPZD‰2‚;xXÌàqŠƒ«¤5UÀÜÛPEXMIyˆ:·®JMlqù§ô‹1mUÕE£IÕlQë\Žk›ÄðmØ)ˆmÍ-W~XXÚÃîœñ±—.øÝ^ÇÅ™ý©uš¿9ŠFØ ±7_aý^ÛûY°V,|ò\ãdg/Ýre³ÁãÏ<Èç掗ÿ(”†$7øÅ}PðœFÛd8hOo@/æÄÉ@à´}µºÍÎ%#\qOé6Ü/tcS0~½t2b×\ä Z>aXÉú_}s)lä)òG‚ÇÇ•dÓè€'¡ÌÇÈ9‚4u¦¤1-A.âd­º5ƒCq×U0x_þ%¹ÚPu=ð~nìtu£Áß^Ü\-ƺ#Mh±îxŠTuYŸ´M‹9+rÔ“ß±ƒEÖÛ1Uˆüw8CŨðà¾,ÊVNÊîûej¯}iöÜû¿_Î*FLo(çHcd']›éRž¥¾ƒrËxÐþ3¼O:v„¦ëId£@üÁi®öF©7©0­¨—gCõ <^ëep¤7•™ús6@þ!~·êwúºÒù€•bÞ”Y[Òïñ^‹:±]²TÈÂ…d¨Ïʶ‚{!u©Wڎѳ¯àA¤…ó+Ì_ºv:ÄQ÷®TxÏRv#Ö¨Þ…X[x_Ÿë‹Øb'æŒ °h€»[Ëî)mx 9AXÚœ•ô®Y ˆh°kBµ”¾óõF+ùƒ9ÛÁ Š†"|'í,~¹p(NH¥H~÷Qö†–¨òî ¯&Øl×ßlxÔ4|†IwV Ž­;zs µOÐÔgh#¡ò\0(/IqŸoµ(®Xî±h…¥LÒ%ZÍèa=îé¹Él; o8»Tª¶ P»pï8gõïªáH¡Až6D– Ry• ^ÍÌ÷…,¸óTÌpÑ7 ¨®¾RôÇ/ñý´:žŸ xɤ„ 0½·+iåÍO (ÕGƨbM6•j`÷p×ÿŠÕ×ó¦Ïg×L)RâTdx˜æ;|¾Ю:5É3í2wÚ.E•™ µõ;;š>½+Òä_LVb•ðíWh{^y‰?×ÁßZÔ}š¸hýQ~'sž©êió@3%4럵°Õ¤Ó²ó׸³)EÊï›T¨’F/¢N*o·C\Ò¼'k¯&%¶Þ4:ˆ")=ß³|¦½ú’±ªcªaÂëœô"£â?™|ìfðûŸýgHT\\Ô¨Ž°¯9xd"+ªKöqrâ¦<Á#„âCí[°•.Dyýϲ£¶«yâÌå1‰_2…K\Rª²|›€9–½ˆ«Ý4Áá6ð[0gìaöb“ïõ-3ÚN^ym"¡…P-ˆÝB„¿ý\tØx’‡öÐÐD£¬+²¿˜t‰_²Ç ¯1–ûZeÝ ”òï¾àÇì$‡°w¦Rÿ+¶È¡ çå?E•šè¬êá”\sô¯ÉïP=k¹ Ý]ò#ò¶2_¿m56†´£Þ¾øW(—¾C;<‡¦àp¿ŸÁ¬1Yaþ~h"ž/wê“ n/~|¡‡«uçB’N“Ê)~Ηv›&ù<]MµÊ–Àsn[À'¿¶‡D‹ q2Yëਆu/k¨‚g¾eêl Ý=v£«(|pmJ8(ïm†ío:šÊË_Ó_n¦ÞzE§s“L,PYuƒÿ Ï³ QÈ7kS=_º¤lzøÖˆ3>W¬R$wnÀ9 œæW'æu4;j¦GBjP “Xc=´Þ©à¤çÍŸE¢†›«w‘¿jyÃŒj;Ñ™ü‹WêEáõlº?5½pBO2—ëÑ7ÜÕõ a’6‰‘2YÌž â2ØÍý4ôâ#™MÜ =—RØŠ¦køýŠT0ü-æj&m=™?â4.^eªr]=DëVcݬX™¦L¹ƒiÇ0þC[ÇŸ!"êÈÂæˆ÷M†¹J\â€|½±Ò塧v, <¼šssÖ=ú'K¥³Ê]ˆ^ºÕqu" ¾%Ô°ÆhZÏ‹¬{tpw5æ8‡:£m¹ƒ(†&‰Õ„g°HÓˆç¿OöٻIJ­)/-4‰µÛ–B1ñÀ‹G¢bd“ö Ç§uê&ÈÅXáÜõA”šÜø#P%=2ÝØwul¿€€‰&8ö?æ9•žJdÊÜ]$_ùH½ž” qÊ<-ƒÇ"'Ê…Ë¢C´ bò}«eÂö{"Ç©ÆØhŽE2(S©¸p=+쾃f˜ ÞµW£/cNÕŒ•'Ñÿó ¡÷Û=^µhïtÉò<‡·B…^Ç` Ë wŒz$"ÄoK…¶’ÏAV^«–ךʔ3Cè|î¼Ñûäk[NKó¾0^“^¼Ï¥(sLEcˆFA”½Óû™þâÈ{~:æ;Ma;4{<&Z}Ty^­ð…âØuaÇ𰞊 ¶Ç¢¿µšs—j{Öd“ìÂ@g]W„Be2ÿF Ñ·x ›“»Iï›ò`YÅ Òº²—…›‰º„€ó¿Æ K¼=~îÖWL*ågxØ0¾}ü_u¼GÍ“V£‚˲|Ìæ½~ÐzøºOeP à'ѡ转îÞ m’;)^!ÿÞaøð¨x®92‘ÓÑqÒb‰Ž o÷y kà" °8®Ç‡Ð3þ;G¢Y…½vFYåOÀ-¾XL«ãb£ÉO=Šwi#ÄÀƒ£!Žâä\cuÛ¡ÀkW¢¦ãÙ6ѽCõé¦2\‘jƒ ¤¡nŽKVFõº©¶;Ãç.­2²ˆÁÁ°-ো¢àûŸÑû<×3µù´ásŒ.ízêÆLsV:´´s¥ÕÝ/(÷ òa) }aooA:ï”æwΚe­#­=àŪ+b#Û[†ðàgXÓ.ªWßÕLÀ-<5düR Tƒ@løRox*hÌÑÌÏÐfè¿Š$0 Ÿ˜Q+JÙ¼_‘cî£zhf.‚QXmÄ·óãÑ@¼åüZ6¹¿á²¾.Ã&ÎÁ'9±æê4Zp!I.ÚbëÒ]ÚW2~ lºÁ`Ý°þÿïÞÝ ÛðŸ?›³‡ñF•)üê{íp<|‘"š¹k_ü›{•ˆYWÄÊñ3hô§7îÞ%œ• DêÖµøŽÂßë#r@KÍÔUùÎØd€™!`·{XùWŽz¦À€2qÊ”Ô4$|@?78ÒnÓª;ÊžÔµ‘Èì/qC¨™¯ ÓçÒ¹<ÙÐÌå§_T°ÕsO¡0†&yÝazbq›~—ßSêæºâÛ:< ËÖ(¦yoñcØÓsÄ1&ãc½ÏJ½¡èæÇÎjkñ¥“I%€Ô¯L ÷•¥ñÃj‰ÄŸ‹KK.Ù¤#J å`¨Šþìº/ÝmQƹBÉËJUÍ yVKŒy³PÉwå@2r>ý-ÈJö€ÍZ®ry¿:üÙI<ô‡¸«œ@Ga. /EóÍm<Æ¥ &hý!YY—ˆ P­ãÔžÁw6å3R^ß#œ7¼aåjÂß´r%þXå#² PmÆröžEC/ô½=.8ÄîBÀÜf*`¤+¨ø óµ*—u‡Ž¶µïb‡^¨(¸AÇ‹è `ÈÏ4) ™{þ¿„ÎYѯt‘Z éO)‰¥Ãàgªý›ÇƒV`îÜ(’-NVóC6La¹2®x±j­¶IºâP›ö¿ÖXPÑ …‘]J ¤thä×ÔJ aƒ“ꔺ¦q8ké )ÏB¨½½6ºúg%–IýÄ»/ÚÛ®ŸcwÉˉÅôc“ëã-Fu;Z±¢s¬ ¯ÓÒXŠu‘ëÔ?ë]ic%†a^y·–\tš’Ç_m“Ÿ†SϦ|„ûxãñÓþ³eºQEº@ŸŸûÄÞÚ4±©W¨Î/æ6XP”ewcì„ÿÏâàÅìN&Šo¡¹c=¼œíîôSZ) Gmî¢o±P;"/Ò{Û,hµýˆg\î-iÞºø+ âáȱ7D"°À+©oS ‰ç½c„Ê#QSH¦]FØJ¬TqP¥2p{`§p?u³’°‡îÚÇOe€}ŠWé$À·šwtåî ¦Uè’ŠfZ˜;~ÛpOY–HâââvªupÎØ ¡o”þƒ›0i‰¸uÕ½Ä>™¯]²]öDÛ ç#.Æ™Ck F#¹Oþ…€ãÃyN[KM`TÁœA¶¼‚n *dË•W{_pÄ‹Üç|íg~¾j ïÁ†ñêØ–c´®ñJöûÍ>ÇÓl2 ÊáÒ½“iÀV¼{Í%P‡ÉÐbÓÓXÀ¤¢³ž°2J]­æVýéî–ú/12ñ_ªàžh*ØÄ}b_H_5ª˜œýIŠCÛ3ÄÙ†IÖÁQ¤SÏø™ŽÏ~ÈÔ×¹Ý븓hª;y'‡„c-Ëa/V·ÆËÎâ6{,)ùí³Åd³ÉÎô·%@ÿDN xÕƒ?úëYÓÆÙóPÕû«èïFÏ¢]ÖÆ\ÖY7DçúÞ ÈÁ|`Qœþ¼å“'9µ%˜aШ¦(djàô/@$¢ˆ5ãW‰ÂèAIMcž¸.bh€ûóÔÁ'N,Drßj÷èã§ùEX\ZdqkdÈM=ªp彄¨S{‚ú‹"AŠC‹ÒÑa¸Ç·uŸ(IOw51ç0eöRe~Óé‰_îò÷€$z+¿ŒÜÖ¯´Æq-1·’œGyE†gV%æxöQýgÂAEÑã–æ5•ÅEéM”“#T¶vÐmnò;6¥ä‘÷î¹ÕÖ‰ç`+Èmt”q€-ÛÔk}{U‚·X “+|u<|ª©ÄògúÝB1¹¥3ÍVËW"[÷868p®m¦ã7–¹0† §â§µE¡™àžX”/Ï••« bHÚÐõÕåÇŽ‡éà£3{JÕJ·VÖÕ•ä2âÛ¯Sié½Áf×Î%ËhýZ›U‚­†ÅA¦f£¸LJ«¤ [†”^Q³RJ’/ádjJ ªô¤©Åž­ö–˜JJašµp\†Ä÷ùç!i)ëfÊŠ™X•ŽŽàû´»ŠOÅá[¿—o„[„ÚÍmßÇį]Pùò ¢x½âݵ g—ÂÂÍÒ,)0š8HÂÒ±—ÐÔFÁ†®àgÙ$b‘y‡¬G2òØþIåéžh7^/¢ù]F}¡]‡)ËwÒ °”3 ôÙWg¨ðÜfXÁOÓµcðØBÜœ7yÔ€×K! VNÞäK’µ—–"§$ë…C²¥ö£†Sóà°TÌf¾jÑ/)>8Þu6`ÍžIêoîHWc>é 㣠ާÐùã¹ý}Y†£–¸á´ä®ù–iåŸ ß ±˜ðW4⩯uBº‡á;«Tá+ D/þÔ÷>vÌJú-—ðw´“±:&ºP8†OK°k€A€Þîyõ×+ˆ~~JÛÛäï™Vö½}3*¤˜æQð*÷¯]K à@`ƒÄtÀ 6†• ¨kf'¾´ŽÝyoÛ‰žÆz]‹¹Ø6»[Ê'l,¦3] 0° +Å Å{M!¢N4£úY]8§8£Ûÿ£÷‡M‡lÞA]6Å#£<§ŽSþû F+²Þìš!óô—vßKñÁ¯?ÚíÇŒîŠ%Ó1o»MJ1ŸÁÙg©N¨˜§ý":XÄ”çH›å1°ÓßRªª•íºf*áÐwyú —2ÉÍB1ùÚgÙòñU¨£w¯jh÷æ»Ïï_1•E»#—}NÎéT í„$ÿ”‚}P¯h„\j/{ª"ÙD–çûE/þmèç‚~F ?aðoÄEI¦ëµM~è£ü2Ðl$baÍåÑ”ÆÉp_×y;h3.{¬Ãy˜ÃñÁ\Ì.MüS¾*|˜”`ÀÁÇš¨Wãõæˆ9åGjú°BSL^€”‘o¯«ÏAÅÏoÒ2òÚZÛñÏ÷‚”5A O%ǽ' }ck—W(üE÷µ‚³Br’7Ì»×€UH‚£`þãjÇÓ¥2¹I÷"¸ß% H,¡Zq€Kg¯¶oeòÈÇ'ºXåÇ'^kµ›¬\ñƒu÷­‰ºÚ€u×¼ ?žQJ$?5ÄÎåaüÌiTãEgˆ‡æ¾Ä]ë·¥·ÛÝZzoÙ¤{ãáS«7ç飃åS˽3DêFàÉë|·q­–×Ó°$§¸v8";r a£Ù¡ÿù’{yÍ)'µ8Õêè„ÕS±šHJ°(ñW3Õm$Š:Jeá˜hˆ/R¸öÕ½³wÓèü9q.Ë¢ÖûØÓ«‚^MLZììTåRDo*÷9óVÈV˜¼šgÏÒYŽKλaôûðÛNK+~óâ÷ËÁ€;×$ Ǩœ&MÅqõ‰u¶«þ¤ƒ„z‘ªã#:ãè„4F¹Ú–x˜miXD%H*Í.lɱ:‘Åó`·’E Eü%ºÇ°E­Rå#´hR/#&O¯PGÂÝÉçz¯"¨@[7ü;«¤ow®~ÓPjíUâUÍW@›AʱßÒü»'rðg2õã±ÕßNRáÒ4ôkùÖ ½%Ôbw£›Î`à®d·tlÞ'Ô*¡gðÊ‹pí¯}óêÕlL–Úõ^ÓÔpÓä&.Åz!K-ì¶EȯŠsîQéÈËJy’(9 f¤OuwÙÞP€‡Jnpˆ’·7¤~xêÚ¤N±Jãh½å±9KNÉ xfLûÛM“OÂ0îÚñ%‘bý©U½cän¾»†h!`n×2‹68â1þ8+8í]G šËæ3$¯ºGyj²ÕîÓ‚¿,+£È«µò<íŸ3ø¹› %=FŸ"²Èvƒr?]>*.• Ô–fÐ6NÞENàŽ3 g¤ÚVñ€ð7@3{fæšÊ§ÀN×€j µƒG08‘Ÿ02»‘\8Bo‰¿rªêÎl Imgƒ¬KfêÇogqÓ<±y´Ü0sHD66œ“Á±mv&ÁËä%½7çdgx¨É’¬9[ØÚ=rMÚ*4v¡'/²%N”Xõ%Û_@vÃÔyNÏ`9g¦Ò-'™®¨QßÅ3@ÚküÍUv°ˆxGRª¥ZJ÷[~SæKZ)m‚r ›Ÿ?K`Ž5K|ú'S@ ç×ü™âÊéÓ‡cB@ÜÉ4ðcΦ¢ì3ŠÉ=Å™ŸœæèUÉ­âÙ÷‚·³g¬9}ßôTÿ| ´À%[$5Oè阴Mÿùž\‹,úæD«¤h/ÞêûÛ)ÐjQŒ­Ûç-5¤ÆÊÇÖ²jˆ{˜³Jò@Hìb¬Ñºõæùuq"cÃÏ[+6=éå$:_ïöfJ¢£Ògh2ŽtB(Bú=Sÿ[J¬#©záÈœIDì[eøã_i[Û7ØÌóçFœr>y|œïrE‹Þì“@éyÙÏç}¯ Fwü½”ek§rjŒé†nIQUzŸ<ßA¶Çk…ôÁù} ™ÄQñÇ:ÜbObI­¹Šó?T+6 IômÿÌJ‹÷´„ðà ³RÁšÃcl= mÿÎFøÑ+—ÆC‚¾8å:ÅPÑ›P -þeÐ Y„sØæ§N­¶ƒ¡+iF»ƒgE8•îÐàLL¯;«–-jí3ºI»,=´Qxe ;¦nuFè±f—"Kc1Ý­ C!þº¬¼û›Å̽…‘¾u”ô)Ks9†,h‚+u7ßÿü¶³¯åùXXe'\¥™s™¦ü°N7Š©Û'Ìò}˜BKâ‡]½]%óF–Š0ÖÐêíÿG‚%XjŸü…è$È?Âø$åkÖ³¾AöŠæ|Áˆ«µ<ýb1æ6¨Øò>:!·Š’‹\ p!+_”è=Ú€¤¸=³~¤~qc4‰’Q|5¥•ÚW~›LB¹½aZ`Í4ÖïE{Ç’aêI gÈ Úô¹Ôþ[“ÂU§çQU3ðóz(4¨­WðµWú5ç¬@?ÐXF!î ÀöÆQ܈XÇ ™cTCòd£$—Í:(gÙ&H;DF=x]Ðíœ:T×5÷]‘Íæ†l/ æOø+”¯Jò¿A¾˜Ìî– ÀŠ§B!HfHìÑžˆ<Nl…IQÇ—\÷·™Ë[úWŒñÖS¦Õ3„jY‰a1›Üe[‘Zù³°^ív… ÿ^zÖÆDc­¦®aþfê?sªÕƒjä¼mÀ)«h‰5Ô`~¡v{"ºçè±Ð¬€ Ìoó²ð?2±_JÐxÅä\*àYqb(†~ÎÐÈ“iÍâL *Ò“E:LbN¡”E&GºB¨ÙÜ àeõ¡]¿®“¶—S1=WôC[(¸æY]éL9q›ž¿4ÇýX·èÐÀßÆaЄAõÉ8ªÑŒ¯›ñKù8Ôd,‘¼ê—yTË»}4„)ÛpLá¶ZÍ›r3 §– à Å$Îd8±a Î÷´núc³'0ŸH º7†øÍEË›±dÅXxMÎZ €z½ƒé°RK™¢W¸äi4ßuˆ©²‘%U8kk`L΃qƒ`’z!MaKî—85ouµÑ3°}¾ë(Ú˜T··€°«,Á>)+Ï×”öÊÎÑb£hã*Ýì¯ r†„ØÜé¦ldÛJzѭщ:_TšÔEikEá#Ü¿?ŒüÄĹ®³×ã•ü¾&ô€vöòh­¹©´3/ShG¿mÀ('²æ&UJNá,ÉÕÌ—µ̨¤“#XJU†Z®c¯ÈÌ4ôÜWÒ~ˆßŒ%Ú 5#ž!(J8ë¶^¼ˆsÚèÕ©â$½è[ «óÁë8«-‘W\Ÿ·¤Lu²ôè Õ­§méTW§™³Œ÷ùcø’°ñ_Ç/C½NêvϹRó!"»·' AE¬<çI½¿9—ÛÌp–­~Sq]D,¼’4#•ì„5÷@—}˜V¯@ѸÃ&…CM×æ†zöÔëhÁMò’:¤-¸‹K zÚœwfÿ'<,–Fj·èq¨Ûõ8¼€t†NÓB”ýö:Nr.šR‰S¸¡Vzq…á1‰ ”³^‘çÿ©€È}Ùb>@’\û‘d+¿dòNmúÿÛß[9§®ÁÜzrë‡ÝÌ„§uÊ›,#À’³´¸BÌ »a¼|M=>mÝ0 u¬á M´=#r¥Ê‘X¹¥ìùÀxÆíâCMzs5ZU¡l…)'#¿½b’ÊÅOòç}n±<²±`}øÐDÚùPB̧ƻš!ž$½`ea«´·æ=p¾æ7y-À2ú¥6¾ðÇïñãÕQÿzO¤-(/5œí²b縮ÞøÌZ·³N¹Ühô‘Œˆ'cXºƒP#Z¥ÂJ šøîp “Gê+­^Ö›ýúöªÈ6Ö^­÷ÀW»ïj>þä#èi}È u`éáÚ/‡xÐõéìÕcé‡%ûùÜü¹œÑkç‚·n,iÀÛ`IV#AðÊ“ãe}Ã#€1Lõ·ã‘znq0D1 œ]®ýd$·MýSè'¨Åª1¤ª•PЙ°ÿ¯"—Q2F®Öì¡7ð"¯è£DKš+0䨎úº~˜ÃJÎ\ÌÖœ†¼˜Ú}ö ÚÉ«&_4ú²¨?FÍH&B®DµÜ<ÚHœ÷îVSÅ2:s+Â70ëM¢  nêOMg¬ÈtW Dp…,(º =K:Ü4:gG5²éÅ@ uöPŽŒó±‚ ôù$îQÂá aÏ2bṲ́¥ïû ;†.•ó&ÕgÄ0×KËž/(§Û®±»_N–·'jš_áaõU‡BSä½»À@ÂË2û[k×=ÊØÚ" Êþi¥xU\ߎ‰Àߺ3&@ÿÿ®t÷/GyÈC´ÁÛFØi/•‰vn/…Q©¨,éÀûÍ÷¶êªl±up7xè媜Cúâ_‰F;ŒËôà(p Â¨³ŒâÝôÚð,d¯„÷µíîûpÈJÁšÞ >—¯RKºðþýZ…Ͳ#2ˆ†ŽpM›k&„`Ê%b‰7¼Õÿ“Ìœß ”¬Ãÿ^Á?u{ñ•èÖLSD¦ ó íaXò°Æ†Tå(µÕIn¸‘zw:Ø-ž‹½VÏ=°rð¸"VÃãöpcë[(áÈ“´ÿ7fÏ [ SHQ%9N9H ãïÑfj÷’Ü=Ñ2,È4·Ç¾C¶f±«°Ñ¬UJ®ï¨º [9k}]~¸€pòÏoÖ”ÛýÓgt£*ôùE?>¼þ‘>dËîzMAӯ랟ROÑ3šù<žVøË1è0YCÜzt]©ô29Ç^š~Ò\„vþ¿NÖÌD—N?*Àu=[ ¬\÷«’j-‚;š­QÝ>À½s#èw£ü¯”{i¬$•zÌ)«¼ qU©±Ú’ ¬'À±z>ñžñâà*ªóGÊ(ãtŸôÀ•Ë[e‡&R˜›FõcIe\¢Ê?¼ÌbSZ„³-ÆýCæ@Xø•ûG3 IÀŒJqãàU «¼°“ßÌýŸFnùKéÜ3«N4êß•…+[ }Ä;Ý’eö;µÚÕ¶å w7Ý{L‘ÊùxöI,!Ž®)‚¤ (¼¢Ä^&z××Ñæ×C‹.¡¬u"3]IŠêÂ3§£ã֔‧wv©mõÜ÷^ÓqåøÚ¡:ºõFæÑn tÑ6äü¤Ú‘Á²£…ÈfÉ*ÌsOïŒ8Æ\…Ïgä^¤ü1ú˜E©²i.J¨jï—1ep¹ƒÎóA9äˆkýy6ûø›^ÇÂ죡ðîÛ·e{‘¨+-¥™ºe»ÇEØ5°‹·J„ÕQË=½ÕMZ_™z N-rs™„:.0ˆQ mK%†bëŠÈñ ßßb«kž†¾Š8VÿþèÛS&Lâ¬+Q\bã.îÜÓ‡|ko˜’§ÌHõ1êÉÖ·,a°3R‚ú—Ò Ópfv© 2 ´;ÍÓ¯‘ $YwÙo‘c² Va ;/™ãø{¥7Åø|ÀÆø›7ÿxÜ*ç:„ 2Ð ˜æ¨,0ã"š™ ™‘ÈïvFÈ.W~6ú0ñ#mÛµ ®’c¸·÷–²õ¢óÝø Úí1ÉÖŒ<æ_ƒ«¬AÕ[ÏðÒ@„©/ü-§I‡œn€Ó”£»¸Œ»Èë¹zLcFe™ÒÄL€¾HN¨Œƒ¶Œ¦æDu¼» ò, öÓÏ+¡6Z³ûsñá… µêó ÙRy\èbôÈ€„ýk Û3R%jNO¨¨ï)?ârf‘ºø¿0X'ƒFËñþbì’Pø¦d¢&= 3ø—fû[`v܉ĘrRô§4o¾ OUVû Ä]S¸¿?A Oi:›‚ Jæ°2PJË.H¡;’ljIòA¡0< Bù~»HBé¥dÐ’ßoªêyFü ¡±zõù¤!.$AðØ`³R-` §ìÍ›#®ßÔð­Ä6õÄN=ë#¥ rQtîQMFÛ¶ÿpªš¶nÕ”&žH BBï¡ ä–ˆp/yeÿßë·4Z† êïn“lr|ù†÷3\ÝDâ7CÐÚ~άq}wè©ñ]ä_]ÃùÛp¶XB!*8xÜµÝ Äž˜é¥Hºý¨è« ±¸–S€RÚç•Z¨Jk-µTÜqårØ´mlëX½rû& ƒ©ãw´‘x<-µôI ëÃ|kLäLêûÿ|[·¶·hGJãK¶˜~æ~Þ6«—óˆÜ$ñæthBn7rsÇl:Sö²ÔOïárB£N“sî ‘<þùŠâkòýdYHQ…HkÿR“p€óªHúû¬1RäÂ>ËÒÖÔü/¥ ¨ÉqZû:’÷•¤ Ðh¸£Ø}4Hù+6µ–€ŽÑ"‰ ªï!\r;¤lõjk›ATKü6·Ê ›Çƒó_“V.ý·5õžs½Y`$ÛËJ~û†Æпó‘®aÊPF½C(úhø¨ÍÅ-p‰T†f^OBws0™wÀL}íF–¿'Ö8ÔóÑp;!ì2í@—\Æ|Œ,ã²cGPÊ!™yúµ•o® !¶§Þ;Åù  RæÄL&¡†³W¼q7iŸ{H‡ÿ÷mná„õÕˆ¿œ6¶Uôâ:t‰•Š1ù ÙÞMoÔÜxAŸI¡„*âõA»6‡±KýØ]ïÁ¼Õïšï×tNÇù Óìˆ-JZ4ÏGÛ»z¢ùQÆî£Cò‡BÄ wKüâ~_Œf‘s=ˆ£(•ûR²êm Ø26¬˜ªxpý 9 m£ÓµfÙ‘¢Áßø©²1)²FRλ°IŠ)Ð×/¿qßþá¥Z{ýj¡ Ö`)s´C^,Þº‡…|Kêéù´‡˜Çk®uätI6C§d ŸZhݧ¢­¶„X¢mk‡§X=R 66_ Ÿ³$Mãcå6l Ÿÿ±pnu>Š¼ù }$H~¦aë0$ïégœ¬èc*˜ÌlO ·ÊoÔØ,{§j›}Ûun‰¢r©€½(’*Ô1vé,ótÚMk?p·gMÅÀùÔ<ê‡÷Ôy]*l òQÖÍ ç}¡˜ƒdý¢_¾ÜµdY9 ¾ò‡üï^,²T«7¶ì64³¾Ïnkíê±ãRúú-¿Æ)Ëõ /fªÖIÄu&4k<Ò}΋1:,J†Ï?£ /yà•<1-ˆv˜c”â+âiøç«[—(õ.G^ L_B!;Šyêy£{ê8쎑i„|¾%q©çdUxpúl¬ÿoPØ áûUŽ†±%p¿'$ŽŸ/[“!ëBU;éIX࣬Rå+Ê»»WÞãœÜÏ;ur¸CÆ•Ìu+¥w숩h¾­Ôï$M¹¦Ÿ˜ÎÛ°æ:GDŸú¸ b²?BßBð(DAë…Ô„àxWšõ#'‚bdT2tœW·¶ 7÷à w»+· ÝQ,쬛 ĊIJÙÛß Âì—õd Þz²6=^GHH~rßUx6lû»©™|ûÂMÛõ“OO‹jó²fðª"ÊëÖHëø´y½DA`_|¹pEÚšt{7æ¸ûîw-RNþ°¨œ O[Ú ¨ÆÅËŠ·ûàõÆÙBd0VïC›Å*NËŽ”á%ü¢ÿ)|æTºþÃÌï¶@y3oUò¶Øêãxœ4ÔyÈÉvž²1¼¶O—+û dW}Þlô h ýJÊ °Ú‹´©AÓ 9²7‹½m" æäå˜õN™#œOå«M…{ \'Ž>rÌñ+íÿ^Jý XÆ¥ùŒÿ¤…_ê!ïbÇ*~ù ÕŒði0 gó³—Éw®Ö‘y0ÿ4€yËqÛ Åœ¯…² ‰•܆çÁ-*ïÛl4@ Hç%fPEÀá@‡fÇ–Ô·A-ÕòÄ.(žo&êC¢Ætª³»ÂÇ_Ö™tô8ᄘëL»`váù ߎš‰ÞätЈ“žb†j?§l ªk‹Ér ö}Û~yÄΞSŽ—XH…^£š -†¢Âq™˜Æî†ÍUYI-þÉ¢”L„Bfoª38I€J+tÿFÄè–ÄvJ!¿kLTà T†äØBmmÄy…øJdmå&ðÛvŸ_KoH©«EN¼o Õ«›’ëìš¿¬ß‹‰«|¦§=1­Iñsƒl¨À]Ÿ¯À>³œÐ­FëÛg\çJí‘PGLŸ?bDððèج¡$Ð ]Êîâ|§ ™7vjXŠ/Ñ·´ê0¶'–.ÿQ I‚ c±Ûdæ×+ì ¬ü†ÃZ›«²b³V0_PZŠ=£›m"Tf6SìÓ`™Ú8Û¡¦"»€Èï}ªaïéƒÂñk…0†è8ÿ·Éeÿß?V, ~\ïÆPM)&È}“ðVס޼R!Ý/8•ì´Du°ûK§…°âeêý¯ß›6«f]á–ø @XÍ™IeÆI‡ÔûZ®‹ö pë6•ô1AD«Õ_U”æaYÔ&ÞÂ{;ŸB3®«‘¢ûùØ0‰ oN݈½{Yí–ÁlÇ‘DëW0>Õ$ª?´3côŸ$w€©ë*G9B;(òؓѸÀ(³ú›1prº)¸’P£õÛV.`ðŠND1–Ö¾z˦…ªr­Øß,<Á6Ól”‘¢¼±hGØÒÞ^v¾Iþ+°Þªr北¿¬È4‹ïÏhO“i‹ÍÎDÆž¯lYq: [‰ Q-L‰èz …>ebžšƒ-­Ðùÿ‰ÃÀ¾›ªâ±Þó ¸lórwÚÀ¯/b"~²Ë\C¹¡óØÿµ ìÙ¡zÞ.%Õ|:þÌ°‹i_„vË5ä‰wáOn±¡Ò¼³;šÉÀ9vr+ËžŒ8¬#Àÿ÷«T-×|lE€iyý\©ÚŠ’œ?*)‰ëzA[Ôؘ»¥U°I2¶RÍ-¿´ i;RêE@ˆ!ˆË_Ð5&³>B€4ÍžO‹’ઙ¦¹š7m4Ç4ÍXs‰—Ðä­á}+B5”•ëˆ’#ày \àiXú6¥»ìŸbýü™? …—ŸM kí“[uA÷ªZ†ˆ‰œázsAº­ýíLHéPdJï‹•$©·4•€*¥¹á õ¤¨µâÐè¯ ÊßÀä0Åo,Ç%9u·¯;‚»<çß ¾Œ¼ù?øéÖB€{jÍ´VÕï”ÚºLîᛳªpØ£ô~«êí¼XWn¯œSxY¬ŽÕ¬Óræj> ;^ÒOO1–± [ŠþÇWÃ/D­£«„û†`o]Êû* õá¾$ŸÎáâ¡× %Èñ¸,õ5怮ÓÜ‚\e À±K½äÑÝÈ Œh²?†M¥.y±ËŒDq—O – NÜã0wË¡”Xi(ïRÿ—ÁG·à$• C‰†·gVJ?ë£N­ýõ¥Mi`°¥”û€]§Þ?œ;1Ú½úusªíl#’x}M?°9Šê ]ÉËiC?*Ôx'¹Ž…Pšªtwräs’Ë®„iF_:å ¿XeûÓ¦ÖH+›]ž?†e÷|…·xÞÁ‹È°¹Ï‰@n„’¼ëgßµŽn˜ÿ£)…8€cOÅá'¸lbEyN·õÅ{ÿ&Ö³_šÿP•‡œ@í¬Ä•ÀR‚:›Wg@Œjå];O}ˆRÅ۸˶}ØZUó _¶MñΤlçÆ¿ZfI÷iç ôÿ›pÚ¯Öð ÉU–Œ)' â!îuî6ê«'–n=O«õ§c4AæØ*¾-71¦ø9¤ºtÇ(ú÷¨.C^ð|&F+7 K½õœ§˜%­Ü««ÔÑÔ;|KRéìQ7- }HÉšÚ9Òû»FàeQ¾ÓÖúÉc›ÊU²„:_?pf™í(¦WÆì°9HŒÉYðžàù(~«'>ÞV—7 PT ü•‹ŠéÆC­–íoÕ¬›¹vŒ!D›JÎœa{ÞhjbúIÊ ºAoÌÙ #ŒÀè7Z$Xuï‹` &e詆´Ñ"µ}Úeb-³‹‰/]ÿÒSD O}Í %+¶dá NZr÷~ ŠÒ Î0çÈBL34i!eŽ€¸f·©ÀÔ¿ˆª-a(¯€õÿ÷ú’@©þ›=U r±¶f§Ê64´“ªø¬‚ÿvÐE€ Ïz Ò²íŒõëpòàu:n\lÀ: ]öí/soÅ¢‹Þ,O)< ”Ä ,&†u`Û×d¦£W“V‘‘(êK £‰7©´¥oàŽ‘üfYtòJ†Õàà\ç†H«%åˆ+ž|¹?tŸÀƒC/Ô&#yDPÈ{ëYþþ­ÄÚ2nwZM® EA›)¢Q›ç2“_– Xâôò_I¬œÝ2øÈc#À¼UL•—áWp´¥rOXÝ R `õØ M¤XõyV,V›óYQr л;æEhsRÈ”ˆ€ºaôµ˜©v/„¥.¤ç¶¡ìõ(–S„x)ÄQî”p$lh?¦ÉHEä#ÞùÜj‹Ü…/×ëçddþç[TÖ‰5uç!¥‹`Xkƒ5ÅJHíÂBšŠ9ç[”A¥")Yl=Ë" wœBpfù$â=}Ud« v¸¹ÚEV_­òxø±5¬ þE^S¯¤ñ™ÚVNmc<ÏTòU?œ¶z]ƪ\ýú'­Ô6¡›xåªH~ÁÙ_~’\„ÛOa/SpL!þ×ã=vN0ÝNæGê@öRúOUÄÐ#‹Ñ]B-ß«!ÖvàvÝu¼#æBc<“l1~Ñ°uÕZå*¹>Ã÷ùínçC}þ ŸÔÀ/Wæ‹iÌ„§_OW¬•D•AIþ)>]‰ÿ.¿>¹°6c]㬭mrt0Ãßóà¦e×:üšäH®_¶öÁ1O¯U2eT–¬b°ù¼rÛB°Y—H…ó—®>ó"˜®_€4ÂœÔ×Ç¿ž}1ï®™lu›`&î†>\}²‡åñþVGþÑÄôišoÞDD÷ʤæµNƒ°yÔö33}œÿÐ{H@˜Þ˜ÞuB1^Cw]9Ï'-Jl_’zè%\FX5å!Ú{±2µ]9°&‹ö¬P–%—ºEm‰pð÷õ¶ÎAòs^ÅÅGp‘8|Œ¯%Á¶’Òб-aIy7nËk·à#×Ð|Žaî)m¢­šW'góª¼Ô± uº à…*¯Pxß– ònŒøtˆÍœl‚pm/³.×Cˆeí_¦„¼E`[9 .Ò‡ß ûóÏÔ­½Y¨Ër`DÐÁŒåŽR)¢ÉgOÆŽMµ7¬‚ ì_ö}aR*Ëð‘ŽL馻B¢<#wãë$ ñCX0;=¿ªŒÍœž·w¯JÞA6àײ shÅ„ÅŠöî»0¿¶¡_¥ë%ÆJ¼÷DZËÇáq¿ˆîÐÆON'Zš¼m(/‡‘î/€ƒ„,à™bÃçiÌÁÊ1ª^à 5+°SVhª6g9½Èäô0"6PßiIÇÔyŠ–ùмO‘Óš„É„8Ú¤&^Ê®£°= ŠõûÞF¿ÅÇÐñ³b¾ÊzÄŒöÒ@ÝÈ^LZÚ/ú:úº¯Yp$ µçpÑQ¢¡s|ís7àÄ "Í­oÅ£åȵ#©QWˆrêz´ò¡ƒq9°‚RqÈ;¼¶ !åÔŠ®‘¶°}ƒ0¸LÖ1F'E×åª=¢‹ÕLÀù‹ý0_Y\Û;J«D Aø§NȽ%ËXûŸÞÖÐ×l”²·•:œtûÑÒv F:B98oÙ"xuÔï"cQ›i~|E‡¶V¸çû²v&OˆÔ¿Eæä!ÙÛ‘œ?_<„L(f'ƒÞ›1¯ ’"d½Ôַ¶v`VxVŽÁ‰"…7yxbà¡ð†B·ÊpJI­Pƒߥ›} qQº—=2Â4„xäht\ø 2¦‚=µ{àí¬b•‘pI¯Þ…ø`¤®O›%[ jkãkÊè7¥Ø[ƒ4ù †ëî p)Û[áRo©EÓ_Zs ›s¢xNxXí–SòezâhN®ÈKO OÝ9x*V™^5û…ý¢Dš¾]`é]¾Zê„„`³‘F?ÖŒô÷Æy é(ŽI¼ÖâA õeJé­©ÜÑiÎyºîH+7}öØ/Kˆ°ZeÞdž—‡ùa‹–äÒ¥Þo?¬6¹"î$„ù¯µºÙyx}äd×±,F54á¶Y?1ç$igâq.'ÞÃc@<< æ—\­M[]ñ$èO÷òôöíÌiºQM²†'þüãF•“ÄPo·ÚÄ렣ב«œ¡hÛ,ü¥Ú@7i0 \›oò‘üÿi.hÜûÂU‘I.õ¢þ¿×´Z4åòöŒja €¥?7ísáÚÒ:©;êù ‹ ØدÏÛ-¥r…ˉÁôQâjþA‡3++4pèí[éªÅǼØõ5´âà[üÖ‡ú°è?½-ij«—Ѭ- ÷÷kŠ€‚tÙfQœê&)J–þcÇïɵïy•<ìéèÙˆ²ÚïŽ/ËÉTûÈhS]ýWhxÌý–Ø”ª‘S§(¾â\:bûR/ñ3òèÿízÚcé}iƲ? ƒXÕP‰÷^¿k™DP²žšoí´ôÜ}ÜahîïŸÚßœ¯°ù}A€{ ¿¹ü¥hÈþª3Ñ!g|º”¹”®PºmûÿŒøy^á^ªÜäÕNØ#Ö2ÓŒ¼½î0ç q²ËFl—†V/!ócëƒl¶¢e œz@C\«~.Ën'ˆ¦ûžÚ­1ÔQzWá¬Ñ„{0Ե頤=Þ;˜µ Ï£gIØ)¦ëWùŽŒÍËÌÔ8^c‚_Ê~ð1ÒAµz£çtuû.Ÿ”Ç~¦Û'0r7(Dü"80ˆ‡}2d-÷¨k{!IŒð$ñWÁ·˜±ÓðcVèO7{µé¡Ÿ¡³< ‰4sE¼jõˆêHîm-¤Ã‘Í‚à+_Š\­wiªz…F¬E׳šSá­A|'b^£ô÷*©ZèKëî4éÓä³ZÝ@è˜ ¦c½Ç§×Ì!¥,}%+¾MÍõÇã»ÕñO­•2e2}AðA²c¨¸X}.G UÏ=…¹„È©m§ÇD˜-ÕêI8[탸jgêC•ÞZ_µqb9i ‡eÌœ€º5œÿ›ñÉê0ŽÀ§÷Ì¡ ÕG`+°4eaer5ª# ÕÜ%,ÒB~ØêëVxí•ÿ4ßnôŸ„m^ñ `=&È$o×*L‰"Ñ%5¬xJòúk3ßÝId_ž;F÷cÙ¾{ý”oß•&…;ßn28™—Ŧ·8¸8÷•tÜ”l|ÓW÷ztÆ%Óolï?®›jˆâiXœØÕòâ~ïÆ´‰ÇÔ¹B^—*ͦvH@•É<ÈÃJ{Änß@p! C*ŒÑì¡(³Øk¿QÓZµjp¼@ÓwváÞ f2D­&Ù g}¾ëÊ”á¼>ô>`É×j.ï&k€'$ˆ¡·ýÈF3îÄf](èÜ°Ÿ'<\_“b[Úݸ¾¢„/jëNš®9채%6ß/Y¬á±zÄ-§%3¡³k“\s\¾‰vW‚#έ¼‘‚ó›MáQ¶‹Ê[¶§¤ÍSÔŸ ³ùª­!ˆ„ÐPNFÒ!-K­s“!À}j¼µ%-2÷Æè]﬈Ã#»;MFûÓwIiçëbo”hYBu\ˆrJ®±NV´FZÆq=ëW05 ÁÜóK¼jñÒ" yù3ÕðR_|Øqô —ëWl8ÏÛ ‰£Ú¿^W ³c%ØnÄqP˱•S.=ž‹ åÀíSf±î QÜmµ!~…§X±p°ì÷W‡ŠÄïw­Ý´ P:už)¼óù§ãZŠyº$?ïQ¶QÃ9! ÅßôNìFè›S¤#:Ì4ˆ*[ÂùÙ* ÇuùüÀ½¦©³€÷ysf±»è¨¾ ‹ÛýÎUÕ ;´oÕV—°½ÉLÒí´oÇ"ßÇ'0cv{ÿ¤ßáBÚaè‡ sHÞ¾ÎôåÞÉÁ÷Ží8Icîý¡ŽCÄðÿHêí?¡Ôѹ{Q7(tÚúŸåï0¨£r´>"ž½Ñ0«1ÚÍž%)<Õwy ß`ª›NñrÐ/F‰¦dˆFÎÇì_N÷7ÛŠP±êç‘݉<¿“}F ÷¡\ðmg_[ÁV+®«oz­$l®«ýc¨±-ET‘Ùö'.©àÀH›¢›õ‘¶ñ=d'Éùßæ„5Èè+7˜Dá\©ø\Kð ˆPì)€~lÍ…É/d†ôÿ"2Š}ñnÿnBž/ÏZ."ú¡Oq_ænÏ_zîJíý áDXºÀ»È_©215ΉWK¸µñ5«,ÕiØáÍÁ>/ ã®Â—>5 ýÉv]b‚Õ©Ò#+-Ä«)CÒ̷伆îšRîÈzTÅ=ŠéfšÎHì¼0ÙÓ&tWf€*B{ëŽéVX_ØP¿„-s'¶1÷84üxßdC~ÏëM 'Æ÷’ýõã…ÄÎ KÈy¿”óȨ-K󤱯ÖT¹™H5^rµ>‡¬gÈ£K¾Ð~4<¼2Ûwì_£*ÿ™Jëœ'D¤å²„§©×Ú™™ÑM=9ƒ†˜M¨¿?TŸYwûª—î3ä¿q¶@wšPMìQ=-ñ›DåV"CøøKòy´¹±“2…sà&^¼p¶ûŽáZT5öý7î0^@÷¹ ï]pT_Ö[åçÁ ¿m •6Äôá3’º§iÀ‘<°CžSOSëPê ›ßCð©)bMËù³¦¹4JÙ̸¤²}+O™ì…ª·ÃéÜGüY˜#“·¡±¦(S<:Í,Övµ´lø0«çìôösò" 4Ë¿e½‰‰Ñ£s ¹ºÏ3ÁjDcbbÂë­£¤ôiä¹^¼\¶åax+½ýf1—”ÁÁ€nV^;|Ä BSê6™ü÷ ÕCù¿” ÀŠ–kÁ,䤞A1¬Eƒyè%ö¤3˜º¾,/c8JÂO'ÂR4y2éOÓmÚËc—iÌås<®.ä`Ý«þÁ†¨ˆèÙ¥½ÆmÔ„öÀ’%Ö‡VåõXÍŸªªC}$„nquCðÜbYÓ&Ÿï¸a+qöÂ7[8-£C±WÄyž%£Ýû¼ÍÙÿ‘éÎL0Vpa+¼¡E!~Ó)Êw€V¯w½ywBàî®i|ƒŸ…®/_(ÑJÌð[ZWCOçR ;òN–QNíAÞr]IW,Ír_èrýè|. iÁOTNØùgwÒG³=(ò ÑÄ"G4$ßi­Â¨ëtóÆÊïpÖJŠ S£:ÏŠ¥¥†þwgrúÑô_¡nPÐì™ pP§&x3Úé ~Uô!3Ò–>ƒãã™ô›ßžBâ×ß:;;¨äZo7‘“èýý”™'ß-ƒ,¿Ì4—8À7¡þèØ•¡ŒdË`X†›öh»âƒ¢z]–ŒnËÀz&žž>‚ QE«ƒ¹ ÞŸm¡ŸÍÚÉK¼m2 ¤!È€ðˆîö:NG–Û…Q.ÒvõªbUc±±±`öøV’``)®ƒ0¾Æœ4´IŸòäRÐ_Á9ÿ¹öœ`_¬YzH˜^~ BÀ·xè­éÐ$ïYÏχ“61- ê}?n®ÞÄïràR`/±Á‰ahŽÕ]~39öF|ÞZ)°$/›Ôèÿ¾G*Üuh¨é æ!Ç0\IЯu>Kb+åòád.ª”h·ÚÙ­p³¬ýhþ¶·ö’€1ß+¨/›U„ãiðE‚b„‰pº§ý /g€4ó¸â‡ˆî‚ƒ‡¸– o{w‘Ò˜¦ 0Ä`ÇèwäpjÚ3¤N kɆ-…ªTS,‰/ùH‹º„ÃÛQ»ã²Ãj¹¨^t{I :O½tdtžô6d×æÓø<¬Q<°–#üöf›DÌ ì™š|Tc«ÍùÁþŒš°t‘k˜™¬Ö8µ)·O.; ëg—Þ¨KgÎûÁR÷·û†)ô¨áýŠsLœ ׃$Ÿ7èÐΘ ]úhRÓìOÄ#¿%xAÒDü„CëÜŽïF c¾“%­–;D áÈðpG·ÐǺ•õ/¶HPt›Cñ˜zó¦nŽ#'÷.nþ{C«›ƒV$Iã•ãÖ EÒNòåŸJAhd osß–±T9¹³ºJ˜2häd蜭“¢áÖÜ?€þÕ"°êÑíáðC;ÚÞg›éxy*¥N½¯ÂôʪÒ4Ÿ“ŽÉ͵/.‚½Ê|Vʼnhœo뇫QºP8_õ|/BôX5¯6Kó& ,¯+—w>,èi çõda›Ø@S(h9OÆt‚­Rýgzî~'lümmz“Û³2õ 9A™V¬ký°òÞ}Sàtì¦ÿ® %È¢tÞ=‚©žè†4á4†xßnhÈ¢pÚÑKLÎ(n;úüf \6iÚe}î\Ü‚èõmaÞÖâI.¡ð²åeÿs¼»RÍtöØoDÖzè=¸’ëmY,ɇ¥[OãXå6d6õ|JvÝ–Å­¶ãUu'<0ˆb‡”ð àÇR‚¶œhº”¤t"PO[O»p[ ëPê¶Äº¬˜!|“­â`øé+&Ïa„éT #{yc^—©2{­ðæáY€Òò%¹.d›HéŒÏ褬BÃÅ úð#…µ¶$^d†Òï¹a Éè?áQQ cÍ™>¿³ƒ^Å×t qH+…hw®3€§jA}ú˜Å^à;\j§MQJXó óÈð#åYû•)9Qׇ½_Â祉«ÝÎÍç³X$ÜÿüX*Ë$‚Œ/Óö\7—i)¼Šê!9Ð9ý½qÿ[© °5a/§Æ—!%2´ãœ÷Ñþ<#|B2Hõbhô¤›>µ0…vl›â»¾ÒýL"¡cbóvB Ü¢!𹡨 =PB‚µ)ç:}›Y7‚‚­‡âI’_WBõß'âÚŒ(|ãÕT¥Öß•Ãù²?=4Û†Ú˜cŒ@…¥X¦¥ \?n$Ã\;¤.Mþê¹1Yä0ÑM÷²óÃÿ Òa@Þ<*}\H†i:÷ªÛ®ÌzRAƒr!‰¹³Ç6˜ƒ§f£·i¤ £ÖY;+A¤NºØdÊô©TÜOT 21H”ËJQe™š A´º¡]¤ßZ0l 5Ì(.* 'ÆuÑë¬'¶í )…Kv6¦N££Št‹ŠVm 8»¥Žr4±ö»áCT8@ãµIʇ=]fMñI}šJœïSUpð2퇪Uß W–¢ûÉ()1 pZˆ"ÊROò‰“䙯ùýøô»4×Û”AûF1{8§ …)j¯ /M- LV•46Å O†ƒ¶Uhã…mBHè “—•ZdJZd6«þ²’ñXȬ•“øÜD©[Qbþô“ÚB{ÊZF3Ó9º1aiŒÔ•NGNoFé{÷Bë¦úríØÇž(3ì™Ô¿N€ â^|J€ ¯òR(~w‰‚Ç›)ë77`ÔêæžIV'-È€Ÿ+x«¦ÛŽn4MÞpUX[Ê’Š9€j—Øh!a¬'5ž ¼7¾|XÙNQ³q/¬7 ’i¤A?fK%ʹŠ§Õ‰œU‡ôÂ6.—™¤­‚§Šúàê»å†Ð<„R8ñZ°ühPWy,¶zc*¿Ñ÷TÔ¥´+ ‡C,±»™{'­EðÑÿæ¦mex»[¥le^Þ}i¿­‚éwŸÍ…BYƒÿü¡À©bŸ$ýQª¿¦ÖSÔW4qñ­ŠÔ…BÚïãâ½ÇjN¥°0ùJÄ¥ÏÏkBp„þ"î*¦ÈY!Ü‹zÑrÈáùÓÍ<1§4^Ò߸„ßÈ×N(H E¾mU­jI©È“$fÏùË+!í†ÜŽ0¥|I|膛è#&Ç5_sQ´jXNU\+t+o¯”É;êL…¡·Ï´†Õƒ¨àÞ-®7è›Ó˘X@¾QvÙDΪl·q›nU—:¨Fª+rîíô]BÈ(€:5ðz!³|(ŽKÍj¨Al_÷ùuT}ùØäß6b=øôü(Ìrfÿ]é=›"¿T_r±.€+FÞÿ7ÓÌO²}ÔåjFyPÜÈÇÉq,MÞOò5ÎŽØu.T0\?p·çÀEÛ†ÜÝZß@±%lá}vÙ “¤¿‘tYÝÏßÅ`Ѳ¢2`'4å†lr ÎdTùŸ²µ–úÆ´ÐÉÈɽG…­þrþÌAÅGFóÂlMÍ ¤÷ì+*8¯5YåÏÑqmcœ¡YzõÅðwrnéØi…—Ä`Št9Œ‹!UÙ‹ÍV§LÊ^ÜÅ’„rxyIÔíOs‘LÈßr¾_ùltü0Ý_%ê °³U£íZæyùV*­òßÓìDûIpÖoë9X•+"9[p¸ž@‚È+&fm½È„a6uÚÌQ#”ƒè$ªÝ8½¥¥ñÄ+‡<`: u¾‹j Ú®ˆ°‰JKÊš¨ÛÝÙÿÙ0Ô£¹í?D€ ¦ê#wMÞ‰ðÓU¥cki—¹áÝ›pÔ5D©ä€MZ¼Pݬ¨ N·=8ìWÙ¿ìX‹»4ƒ™LêK@`ú,¯âÜï×;=`ªSeôýÛ¦‹Ç«„ÙPÉ %{²Ñ×rͦOìœúÀ/Ôñ pC³2®W/·pñ¸…pö%®¡Ž¿æ#¤Ê})8?²!kè^•'V•T6Èúg¾™ŠtË ï㣨…æÒúƬ gÚv§±#¡¢Åô¡‰öÐÞÀçÃIßkÜr°q~ÆÈ´‰æ=ŠÇ2ì'ìÀ»¨È@›àè#`Ȩ́>bÈô¦½d@sþk¼TtæôÈþR—v@Žú:ÏI}ý§Xn ‹j"ÁïQ·ónìÞ–´Y6ùà ±pt—–CÎT¿D¨©]Ão‹/C’t¿ ØÒŒuÀ‘ 9þ™Øø~˯ǎ_jèÌ᜕¨D“úÔõ®$ÈR‹§‹ù‰à5ûÀö lêø§5˜§À_æ_Ó„´HCNf.ƒÖ«1òÕÁ¢Ÿ~ÓFC.*xGö³…`µ,–ÓÇ«((_Ø$™“'„á%LGlP–¤í'.2'´‡Â¤0h›Ä–°6· €Þ0+~ý÷æ7(7G\ÊÃJ‹²>]kI$2ÿ¥ÚKÓã MEÁ“~ ¥”@œõøaÔ Ü·dc|7s¢y|ì”N´(>¹ézsà~a*·hYCGê£úÌšPªO .õ‘3‡pJ;Ë£l½±¬(„Ø“ïÖŒYG”£?£%<íØXB<%n½¢ü ›‘Ôû÷ %'î`9ÖÊw{ ~q×OäpÜ'ݹ Ý—±¶†íy5cOæbU?(ÃÐԞşYBÞ S´ Ïx1´+ÿT.»ÍܕÝQs$ûÉäÈBŽž`ËË‘ ™¦/|P{E^OŒáÈ2qË[ç]_R_ƒËatD‘‹7¯ÌvÍ÷+΢—’à]‡Ï›¹Æð(¶EÖÀév˜Lïð¨Áïød…¿bù=m#ŸÍúzuÕÔÐH÷Cs ÈMhŒyêh’€»|¸¸;nê™Utèn=Ò ÂŠ—ßÑ› AàP}o©pmÈôßáìW6øµ¢³ ³ÅDÊF›r=[%g²tm—CÐ`D‚Ù –š›j[²E!'€¼ü~¡É$õ)çzq5.Ïå¿¿·k^o¶¸Aäg€íÙRGwKp9áüE"ŠS´úâüñkú/‘8®w ’c-‡o+Ö2£éî'»ðïÒ3Äi2J@Ø.¼fnàu ·1¸ü!çR¸¤ Pê³tCK;pÊpSÇ­KŽ-Ýx ÄÚŸönhÔÒ~ mY"%(Ó¢Âýã<Ì¥?–ݬ¸iñN2g=71¿²Iu²ãþ“”2BZNºg%J’Îq.Ps±)ÓØëq±öD”È}Ç`Ñ­©¦¤µqA…fy+¢ÌŽ‘fQº‚ÊýGŠÞYÔ `žjžÏµì8ê´Ëß´4.¼À=²' Æï“pij|g)DXÀʬ¯™ ^>þWeëÆÿ¤}HöÓùý¾¬C֧耒» è¢øsЉ0l«›Ã±è³æ$\î.ËŽÁò‡Êjü]2bÑ]°—¯>äÞÆa—­ hçÓΑá(ø Wô|ðRÀAmƒ<ÜÈ›ÀLçZFwºÔ˜{‹ áÉœ$TŒØ<¤1¹,qïÂ^ø[/í³@#–sм#„!ð=nÍñ$HÞ £EJæ¼ÍµV×è–Ù£©ýÌÄ2] žá¿‡K€^žw„äWIžnÓƒál‘±‡hÄö+XÏ°Ÿ6´×B&¢ËÚ¿¹Ý’Ð~l’ˆã W¡|¡Ø"vñ=‡™ªx)ÓjZî ¿-?BôEyÛÅÝ ßÈLKëøvý¿³(¹SXvªARšÈ݉Ԃ b$Y$¨D˜}вägòFt“(/0áÒQì7€˜1†TRüd ݧa×Ê)H¬-VŸÄ›!Ÿ˜¼ÎÃÌöµÍ~Þ©*ýêJqÈt?pã¿ÌŸ(÷° pnû`'†Öš*Å„9œ ÔL Òù§! ^¾v8 ¯à£®eYÏ#'FX~¥Å îLðÉ9Wad4m`´ãlèõ«!{pœ·¨3Ü‘jTr‰Q R\”aw'Ýs˜² °Òs‰xK³¾I@ßßd =ãÒ§`n¦ö6.Ê·9 ô~uôŠ¦è8mß¹) ­¬ÁOx…?j,ÿ;p†}ƒºð4lÀ 5 š1ñ·"õƒ2hÐC|«nZ1ˆü¶²½Mxª_ƒÁ-ûåw™íöÏ„ D6åz‘ T=y°ÊÔ²%†%eÆ[áLsVb7ë=JIì£Ëý6ò.‹Ì„: 0.W–=‚Æ`¢h›œòw…'ãP‹E °ð:¾¤hò¦›­×N0tYp:I ¨4ö!‚ Z@L³=?ØàݾËÌWU@gçÈ9ÊìXåÅ«i Ë’I›Ä8÷3*žirŽ1è\ŽžpZ<0™×Œà Â-?…,uúËéy†÷Ž­0”ÌâçjäÓncæVªœÛ·j}vEnâØn2³çF}®%¨> ™mù8æ]4½úê®±FÝ*Ìêο~Ãi@Us¿ÞÇ Lvô×g—×ðXE¯Øwò2!¿ù¬¡8›S^ÎŽÁóÉV E(bU­¾VrPwAjƒyšBô?yÀÓ/ã"ŠH7B5³Ïi®Ã¯Ý˻߰Ÿ˜ÿ"20߃¥oõ aÞ·ÂÐ43K¼2 ×÷k$¥ÚîstÙ'ëòÀ§‹ A'Ä(ô¶â`®,åÃhüäM¦~ª^&9k¡;¢'ʃùŽ”|ÎêÀÎsê‹x ø÷!5oÓ‰qZÚšJáÿÿÉí!ØEì{ } íRÄ\Ý ±¡¸,¸w0]ûÄß½\lg¡ýë©Šõ¯ˆn^ˆhÏD·E$‹›½€—2Ï×^hcîoðN‚­ZQb £ 2œ~7~ ·üŒîëfoÒ;CŒ–ÉZËr(ÐMÖþ”KŸ{sůýgþ_¡ÿ~Šid¦¿rT;²vö\ufgùþ‡µ…â R,è h6‰W Ç õKÉù/ šc"Þ4$Ãä•´j[˜À ¶ÕŠZ;?–B…”S+BpYg‹ËCé.“‡=µ*ž´­hçƒ4Í(®AóûpÁÎÛ¹%½j¡ÐÔÏþÖlßÌmŸø…BQLÛ‚u#'É’buÙç^(… tñÿ[ßX!–#¡/ÙÔ#zwh˜‘ø<^¹,{~Þ“9¤®¨Àoû$!Ž‡ÚÅl‚YSôCšÍ üHóÂÞRVò†×:0I}¥ˆÌ.“yìôK0¦h(ËLÌb7=Þšºn“‰Þ®†Õ]t)-þÆ¡Ö˜g((vwPÿñœwðí‡í –½ž˜uáô§¹ñGðØêú픳š6&û4oôgqú#øÊÍDW¸vÕkß…“w8å‚…_œF$Ucdìá:”É1—ù”×ë¯e”£­Z#Ê-õ±F•:;ç˜ùnŠóM È^q¸ùwç¨Æì¹È©|êoäŸ(Wl7Ð;Býl®ò}¨¶W8Á탘y ÔèŨÐWWbh ØbzÃý\ƒžõIu×& êôRxm T7ŽÙèÂbTN+êV5¶/֯ʯJP¨¥õßLQ¾Ø€cGQňFÐJ ³ua^#Ÿ•·vÅùYqKÍ:,é0=‰5èßÀV!PñMJêºu Ø¢k«{.XepGŽ"O2Má!5Ý3.'Nw}vÛŽìQŠYãNœp-½m…¿²ùãËÒœIË_U-ØS.Ñ;%q_{x/¾!„•fX06œ«|6GN²’à«Ú€5SÕ¡ïa#Œ®Xž¡ê]*ªÂ ¨Í mÚ¤ûPe›ÖÇU ZÍŽ‚Xµ8a7Nh?æ±–¾² ]Yi§I[ÆÔ|ößß^ ¾Œ÷>v£]U$¶/ž€Õ…ÔcŽG ­…0ÒGX/ãy]0÷NÓÖ©uÜغœ•Á@Òå²~¸Ð&>·TŸ€wê«Ð9­‘­²\ÚUÕ¯¦·Ì| ¹Z<1>÷ŽLrß þ%Zl%3ÖÔ™U^É·‡íó¸08mS 8éVB†(~å›·¥V.ëO%ìÄÙðfÆÜÞ¿>®«½é-—¦ù½ÂG¿å2õÔú'ÉEµÌá³t<ª ö÷ É×Ù¿&É䈓5¿ž6Ní/7þr¾¸_“ÒN²{W‚5ùcô¦ÌaxqõF·Üid¾û5º4@õ Z#lø¬8`\S~I(«»o^¾€Ëzæ³5ƾ×j¸,‹@4þ¤Ò Üéš–.|H>K§REžxØ=føb6ZRÃÃ---ów«ÐH,LðÀN W¹Q²þl+ÖÍÃnÏx¡ºæ¨=¼³ «¦ž± Zsu½èE0N¦‰Í͘ZhÇãŠeš3Ÿ98RGd•½—}’úžPû:û|Z‡ :Íê02ç0¿‡Z1v<<ÜUSŸò-4µm‘{R¹FíÌôÍØIÒ@ °Æ¦ÒñÑm¼Ã¼MêM’K ´®Ëí% _UwÞþIÖÊ­9/XñìòWé€ Gµë?É9 b{s°<&°ž4· äSÿý)¥ c W2…wÿ¢8Å—Æ^%¬ªÐóãºx¦ã˜ÅeÀ•û*%h8›çú^¯pî`­-Ën)¸ß«Í@q?9³0Bfn”…óN?!÷ Çv'˜‡9wOCe?%¦¥„Û©Hý“EÝ"w 6[ÎÑËÏ2O¸yxÎ&Èt"Í%c 6do?g{ëó Ø’yºŸNG±Û4“)¯•Ñ«”¤ðZž€ß«ë·¥ÿöÀæß8‹näÜ‹cS®ájŠxà4dúúStpi è‚k»ÝÎL©ÌÕkÒF;çu ô”xPe<ãÜ×!XÍóÇÌgéBÓf·ìŸÆƒãýíAwSŒ–5<ÂGâKu÷Ù#ºÅ¿t¸F¥sáÞh eÓ’ð/\Æ6óN^Ê¿zÊ5«2ÉûžaDÞ³÷äT¤y£Ì´6~d–ýUUúâ—‚·]ÞÑ#8ÿîX&7=@‚äJ~¸ ž°¬ÌGØêÚ+ E%FfÇo½·ïÈôÛâ‰<›ŸŠ7otôs>6ùL‚œjÈåAR¾w¸¨‰_’ÂJ¬6ö fÊ´¥^ÎÒnÇIƒæŒ¨áN”ï)ye´6¶CÛ'}å}`Š^ýr¿d)£ß錮· Öă”ŒGD´´qz²3åC¥Hê ™²;YPÜEZÖw'Ç‚ñ›½WL‡r^oìj´¢O‹é©Ð7ácÁ“ŠC’sësE/¹õgÏÚ+%¬VwÂ:îÊÓP›i>¥tûÞ ÷¡†D&·ÔЉ8['súd·ÍtåÜöƒz:¬ƒwr²Œ1ñRè…a`e·÷Eµ<®¯Œîocbª<0U-áêGÍyK¤IÐ'D»NV& 9%^òi‚3"cJŽ³¨¤?v.I™r(,Ÿ€×ö¿³‚öËN9¦4cïk-ƒCæl~Q“Íã•u ’yíÉUäDÕÊ ßÙ$,l蔄T¡^¶/ë`ľ^/=<6LKáÛ¨;}›‡ {mj„ hàtoñÈÆ)Õ[ó!©ÿ'ÜùBƤƒIÕÚŽ)®àUX¢þlE³“òÓŠ$â%¶H>Ò®.š=2UP]DÇgº(n¹ôy6- ‹»¶æ†Š0=µ3ù³VáÄ%¿š”eu*àø˜MÅîjE $…¹l®5ÿA ¼n^œ¸ÿ±Ý@çêAF(Ê<¼Kf]/ñÔpºNoé”x-\¼ù®ŒƒÒú\s›ªÉYA ·õß_€&©ßÚ„i_ðöü †*¶Þ ÏtttÞŠ¹T:Œ¥ ¶¾IM¼¦+„ÆdåiíWçBÞüåìåo:«AÏHÃDJŽî±/@¿&Ìe(Z (†߯Ž:ϘÂ]EœÙ.%»Tö½0ÿbTöR-£•¤¿ö੾ƚ0–ô¤ÁšèQÐ$c\…ðÃsùEitèI°_xx,îæêb‘‹¨ÛÓöËðƹçt4Í`Í…—ÈŸÖS!ÒÓ?pÞom r‹"R$nîúú)üÖ¾Ëï¼75JA½秹 ¥[þM9]œš—)R9Á]ô’Ô¼ÞFL#àÿÒu„‰ vq>w@ô—2<–¤b)=ˆ)ú‚’ú½>&Ýv>‰(½ˆáûÇõk¹Á¬âŸ«ƒ AÆ+Ž„ž¹U•0¡‘ìÙÊÞBVØ0dÇ]óñ/Ô˜Ž›ŒÃFöàGêØ\Á^Ê2“ݶX\d5Ž­“DÐð< ¤gXEöq´…Ã{sC©£tä×… Ft¡í-œBÍuŒýÚs…¨W F¿óŒïLâW>j×öpÐuUg0¹ ɧ'žjt¡¡Ý\_¦PÊü'ÙΔ1¼=˜œŒrÙU1¼4>]ËvYÍÙ@gHžãO šJŽZà(}ný¹’ îwq¦uÜo BVuŽ‹ìŸšÑÝË]buªó7HÄ&pÁé—BA–¸ã«nOcДp&–¦Es)¢Æ{÷÷ q#ó°JJÑE<„dO &,½ãƒvª¶Ì\^¥×{$ÆÑÉä]µq›93Äze0’¬5ñÕŸnM+¡C]Ãm"Pup!L bd4m§'S0fp ÚV"!$“S ŸL©9+E·ÖK‚ËÕÀR™ã4`µRU9uí"–Öq·ìÀ³o'Ï]I_3/ ¹`M0}À³C"¬5Û8%÷Æ/fÚ£ï@ªï@çÓÞU÷TE@–´¢TCÈÚ*x­&áî(´§ì7wb¿èC/íNYÈç²wf*’§;_uºxOCÍeWM× ¢li£Í„=_Dè¥NåÂL\Øe£Ä¨+³q­=LáoáåÕ …—§L)ànÆÙëÑ sv}hø¶“÷n¥àEµŠ†õ5rPíæLã^€R‹ñIvZ$C&œÆõС{ÃYŠ¨Uáð~+©Òüú©!æ_ê^B1V§táÊ®ºM¯3®ÿh@&þY$P=‰A±CØù§¸Ü>m÷{(êOàt½aeµ…Vz¡ì+9ò¥pÄ‘tAå­¢cÙ9µ©Èð5Mòa\LàDŸ%`BM¥¨52xŸ·aì€n%éQzuv½\­! ˆuïæ¿"ìfFB¹YÂ4ÖTtX?È`†"1X9æryqWA…b¬7¦;NÔµ² ~•@pÂåï#äúó%À’Šþ<’ßP„?…ðÞ © £¶â¾“ÿ~)ô1Mn´ó€Éà´Ÿ}‘ÎË¥ayû¿Ö¦JÀ÷r^£ÊÄð.ñ¼Ð*<^¨Qœ'-õºÿ›Žç MukéÖ "p™6‹JÐØ|„—­<ž ÷•$f¶‘_°Ï’—6Är]6sb3¾`¡ÈŒiëx|7¶OK"2 ‡—öµçÚtÀ@›‹÷c<¸ëœj^I9ˆ+c߆žr[tw^`rí R¾hå4ðŠ™Å3¬hÞ¡dX¹ùb*µ±˜eŸ}MϪzèý¤­òÜ~˜”]Êÿø⇊ /9éxW¬üº/TDÕ yØ­Ë1³[9Mj ³´¡Hì܈5¼°1+1E+Bù¸Ù€Ÿ¿AWx:¦—}«ÜH•yÛIu`C}TÝ^´áá– ̹W6evõã×#ü.½ù ?­ÅŠ‘/&Û¸Þ"Ñ(-ƒ¯Ô)Fèò*/¼NêËëŽKÃ?Ûìæ‰T袔ƒ%PÅ<Lÿõœ,½eÍœ@‹Ì|th\i±ý_`yú|!Ò¬‚`jE¢ò8]yÝGð™ÖSüÅRÏt}ó_’ª©?ÆýB°àÙH3ú¿jQn ¬VSzh~ ‰ã€«ô{Ñà ±ë‚îi£qR™[e÷ˆÆðÍÛä~‘‰!ý ÕdÞ¸]ÀðUè¹½±Ú>…E†u>-,ð2’Ó'WþwóGtT¬›M@Ve°ÁÛB‘Ó»äàÉÄ)ÂqP܃‘¨™¯æGûßN!ÙDZ‰MÂqAžà$ •šÑÜ÷Dþædý—ÃHL; å,×Àeèjj}—ôt¢X.ºPàÜ¢&1|ay[±¿ü‰¡¶VUW'ðoÚ¿†ŠÍlˆkOÝnì‚à]Ò~ËKŽ³¸ÏTÐu×6½|ÎÎ:’MØY¦Óƒy¬ÛÑ4“å¹y}‰ÿrÚlï›L¦¦hC …‘ ²¸ ¯DŠ¿K`ø‘Õ±lI= öþ‘7ÓóZ²QìÅŒ§ÍÂÉHž\T]ù§×”îJçuâI*ý²Ù„ÇEåH`•y,í¿\Lè˜[ÆG*aëÑ9h3É¡ß ×ÄAÑãUEÜfñõ,<~#Yi,/¢l¯ÅÉFë¢u9 .´ðº7èn™³uƒÏÛÎ7"M\ÖÅåDBþæÁÒo¹#ôûã1ï^óK¼kº¤öömàï~:©ç~òjÒ\¢f^(Ï×áãEÝ‘|Ø^˜ë Mš<ØGlÓ£D+«gœ]3I@Oøµ/óe×Æ€x¿Îž6<¶5ãÐwæzÈø©•ÖöÛ%×LQŽ,ç㨠WÂUÆó)úôiûðAS@»Jý¸„™±êU‘ßß·#Ÿñ?]ТDñ#¥)4£«;)ºíýŠi† uËEp!-8¸Oíœ]®j\ßèÞ‚SˆéE_Í».œr(ªÔBsa™n­a<ôÔW°úó¼Í'ÌÕ™¨‹|ï“tÙüZ‚;è{ë Jc~àæ7\õ¼)ÿ—3ÚR=@ܱººÛí&G“áƒ[eʃ-¶Ð\–z˜gI¯÷™º7IÔöÍNßòEž]TÞŸ X Gª —y8{8D–q£úïÒ©â"7ñQÞħÇ~¡4Š•ä ÊH¡Š¯SJD#11¤Ñ–C£+·»Gvþ°(`g¡,yår)“¼ÅN>ÒAê:eqF÷!ÍAì¶Ü»e´X {äDÓK"/· ÈóÆg"u@ônÝ<ç¯ÛA@ÊóÐÔ(þtÉWF Ñ䟌éa±Èë4¼îý(õÞ9§ºX­(ãÀ)­ó‚öÅ@ʇžÒ_œØl…g¹ŸÐ€æõín+ÇòrI%h{îýøBÇr¬ñϲqï«éRää$/ñuŸX’Ë㣕סæò:o¨ïâÆFT‹UšÜت+Ìõ"ÁÃ&•i ™£;õÀâ¸÷».Ú*‹Û T¿Áb®n $”r|n,h^÷{j‚Êwù…^ÁSk„(_ø\ûç«évT8Iß®^Jw—lºÊÀ"Ï·‡ˆ¢'(g<"t¹íÛުɽXé>Ü1‘§¸Í˜&gSAïG=ž C€2 §‰|³.S/øôΡÈâ1 $2”†ÚÑH3­R­Ì9}Sç];_£¶W£9lDýò¶]8®"Tk `­/^[úu–ãF†Mä©rJmKÄøáRk>ö ª‡©à„³€²|nÄ(ØÛ5DCc²Q…×?$¿¿Ý(ñË3û‹<÷¢¾±Ø·Ø[‰ ©Æ½¡KxN$PGDZ·¼ŠŸš,Ÿ<_* +±Âã‡noWµ±´Ç'¡sݶ¨ÈªË»RÝ$Šjƒ±f¹nüø–°UzBx'oFöÙú0JÌ]©Ú]±ÿºJóÓåv£ªŸÊÙNÈrd¬Qo1;QVW±GñÒ§×ß|Ç+ÊJÅKï‹5ª±ƒ¸7\ºÃ·ŠÂ-a×Äs²9S©Œr.b74h6à ¶_À ºrÉ…M´Ã„Å¢žÞ/žÁçæO=F‹›™à I0ö?Ž´½[==Ú þyߌy<6{­¹8„o½kÏû[€¬µþtEzàq®65pðùø)rÿ„&óèåƒÄr€|‰cçSS*|„‚ZU3ažÿ<.ïÕ²;ZÇ&¢üi1ïgŒp€&fûóö¬FM@í —!§•“3a`ÖðšG6b\l§Íš\Ó¶e“E-\2zyú7™&cÖÙX[ÅÙÂÇU _2O¯â›1ý‚’q!2 ëU&ÜÙW—ð…OeõòPÊ©7 ¼(ËÁ±„Ëõ°¯ åê½~ _ä}[IZ¹©¤ ò·ìÁùsr}í"E‚)°í  a¯‘R™ÔÐ_A¥Žâ{9©ãl ¹æñi¾3¡úF##‹[^ã‡o"0-Ü[Ñ9a®õWdã}IGVÇß'>Ö«Ôóà©zù}¢³Úa¡êñ8&‘‡Û›4|±cl_åPæ*[ j/Ï&'Ü'ó9Œ‚çËÑÏqý|7¬cÇn¦—Îwh¯,­tÞ‹Ü­éÕ¼~¸'J+ÆTna` 6ÓÚÌï™eöp+óY\3ÉGe—g(ɼg|Ö1÷[“JyG†HnlP ZÁz#AÒeêýFx×®ögǪ?1w:R°U„–ø+ЬNwŹEIS>RêÞk ÕKJV­t“ó‰æ¾ lÚ9¢ÈèºwOZ’ãíPíËâÕ=Þñå« §„­Êu¸HÛÝç Üy™FªÝ‚¥a8 #ˆÛsÐÒŒkÈU ¥rÚ²ÕùWXà µ›FÜ• Wðgú<Ì3Íú’?Ìø8–Yø óÓ8] ã›RÓ°ÔßGúSÎ\­Ñ¢>i«RôÞèà{¯‡µŒ…N–+œH‘nøï§ñª9æŒ#î‹fû¤„ÎÿýŽ2g½ñ61I;[Ôé$±ddª©Ê#ú ù3˜bûÒ¹–[éøŽ$£½#y)Yqé¹uÛy‡Ü³xŽÆžOÐÁ[cîq³ ¥KŽ¢#”µ+Ì;¶…f¨ùnvù{l¹ý ºªV'Ù™E÷)V³ã}êË`µ5s–í®Õ¤àCØD?Œ[´èÕ¤P8®šè ØßF^"1‚uÏQ4·øˆ¹gýXä3ÑËWhasFï²p#ØàÎXƒ„µ¥Q£ï„ÁðŠtmîå;o¨R¾U¦#J„£Ì<ÄNw+4B¹dsv2Î]ýmÎ1v…K5?Îã#½¹•KåÉ4FÅŠ ¡ºîÞB~'TŒ¿Ü_Cú;)˃šgÿPçðL7b@ËÊåxLCý|¤tjð@‘öIšÃàX˜™£þÒéG7!µ¯üˆŠž’[h"ÂÍt}8žêwè \½·ãF ˆU£~@ôs$É›WïND3: k?ÿÉQ¬¬ ×ךÔÅo#ÄN>øâw‹‰ôÂ.ñ—<ìÃrÎð®’,Dy3; y´=q×.uG¡D‘ú^å¡›Iü‚?vx—¹ÿ:2…‡ÅÓ ¬ØqnÎ=4  $ÀÇ?ì6ÙøæeÛcó7Zg#kÛ?ï Gðäê -’ x<½dbYífòÇ‚A6.zörºÒ(ðIsÒýßqͼ,ÜÔ†3Öfýuú ‡·ù½‹½Ôh–C°Çº ç_& ç@÷,U]õ |²@ #â2k†g@"ÔLléyÁ©–PÑGf/vWk$’¡îKT‚FÇn#Àhpfé=LøéA–|ŽŽ×míƒL¨‹?y›¨ºß›Â˜9%†#aa(W¼ü—ØO)µã×8àb2QM×®(óZ]¿³,ç:`kRHgð*©p ¤Ô³¢ \âþ&` ¥Š½Çd?U=BºÓÆÉ´¬ò–zw’T“wQ™Þ¾,½f)£|`¶^ª¥j앶ðõôÿ艱¸†¬i U]Å(ödÈÝ,†ë §¤¦üemD€~Ä@ѧ€,›áÞ™Âvw>¸I’êéA1pQ…[–w«Ëòѯ,E_ú,;g|:)vÕ-µYtÊ%-(”²‹zåm«\aôÏ­Ë7ÂZÖšî`0 2ÐV¿{VÔEA³JÅù6IfOR¿v"õ­p¹n¡›ÿpØ(sL‘¿›È! –ìýL€É\oN ‚±Ü‘—æ¦Àä?ËÔ‚4›¶0gâìsö’;AÓoŸð·Y»¹?RøÝBµˆÝƒ_à<” x/`Óe¼ñ ¤‘wƒ;0ll-©T–7ìK|¦ÑÏÁ J~æÓØæÆÇš8/®ËŒ.äh©ñŸÿG“G¼ácÁK+—Þ¼ÁbÔaÐV´,R±ýûK Ò‰ù¿_‹5[XMƒVÑWÙI›,‰lÌIfé,±ºÁíŽDdù GrÖsðûn¤ìÀCÑ^Naq/u‹I^³QM""jäR<Þ b8gŃ,»Pc»ú;¬w-+âËHk“…MN…Õ…«†6¦òÍ–,0¯»…kr;Ã3"æPy÷n=OEçÂN|ž îæIÅgl]çWÎÜ_DÒŽ¾¿Øœj0\÷|D…=Mé&´bt’¶gê#èî/&ãáµ™{RÁdÉžWg¹¬Ñ?@Ï¢ÀScðÍ—D¤n±?N¹ù€÷”Ù™i £³Sˆ©Ôën-Å`Ö[ö˜x-E;\Ïxo1¸³]í7‡K6—ñâÙ}4î…£`ÆÓ÷¹Žjm}ZÛǯ²ô}.ý0Õ؉15IÈ6û)ѺÜÏçö0}¥’ši›€àm möÆ—Ó+>ùt<Œëv2f¶ƒÄ×wboÿäŸXÊZÍí55õÈ&Á;­ç¯dîÂZ(§ã/!÷´±’×Ù÷OÕÆŽ ±ö°0ýÅ3à*Ö½=~Ù”ðÅÃÍÈW³^ÁR¡b’xîh-(¯zCà¥Åˆ'½Ï­|Þ†`­QEõ‘øˆ6 2ö£‡1îñȹDD™QM§¡³Ïäõ×áliMôTSà H4OüþiÅ,g¡uñq¸ûïÒó^AˆD§" 5ÂZUëÝ¿›§Â Ž\l\ÆDXæ}v®É³Î10ô6&¿‹Ûÿï›?ê<½üc ŽÉhÖ Žº)Oø#¹¸ÐqnÌB͹ušNÉóášh.ÕT(dêHþŒ‰Ì×^è)ùû‡º{­„s©ÝÄ5P D¤û‚`v¢îᇘ< ÄÜhRñb:OQ#äÊÃUB9uw¹ Õ×ØFŠs+4>•d$P3Äè® ÐúaÐPT—³¯ÿxZ5˜YšLÀàVYNQç ðþƒ£êÖ´iu=KT‘í‹ßFÐrŠÛƒå \¹íÇÔ¹Ám‡#KM‘…k‚èˆÓ W­á$µ8-õ²A…)Ð6Ö±¡ž'+i“Gð 1ËYQŒ-4í ³+;ïm%á'IjD ×´#—¡ƒ1|»ÅD‡ÜôßMÜ0 ¿àÙ]³]ÀÝ¿Ÿw)ñ;Ðÿ.vé»oÁAG€ qœ9¦äè¹mbuǽm!cR'2x\Þù5üLÈ/6g«tbÞ'¥7Ú”Rp]OùøÁLfHy‘¢Îò+¶¯Ý0T9ˆ0D´hþZ3__¤|†‰å®Í{RX%q§(­p.³ãCB=/¿äÚ !„–ÅÿõÖ‹((eðg6ñ}Í¢%ðˆU§½¹ÇFebÜ(mÊ7B]ÏïÚoŒlòïMæw_½r¶vyo¬ÏÈþ-•6r4üI2䱉½ÁXt½ "A!OâSsWƒ{¤Eè¤j§våiƳtŽÙû>mêj(èÐ »;‚Z(ãÍ“ùNI%ð–&žÌø©öÙÙmDŠP[©Han80IÓ 8øå¤ÁÈH‹íŸÔDyW‘†6*ì©629T(…Ù(ª;™c´DžÅ’lX~ó°q%õkgVù¦·uUy/VÈÌ]ýÐÞq‚]YôcY=ì@7F[„:»èà×™"•‘¶ÔDÛe˜˜ÆXÎ;M|þà }© ¤NH`ÌáT¼¤ô”¥¥ÁÉâI²d‚ÂñI%™ tY¥ôÌw¥ûþqœ›S s=“Š’,©,>(5Zº™qnÜ™~ñ£‡þÅüWZù¥ìî“y@ „Àç+‰ÝœqQ#CøÈ“¡üº·Iû-ƒž Ëö‹¬0ݳŽíÊä¸he§kÁÒ-4ó®UæÿüXõn}»ø)ø©Â2k‰5 w±3o²Ê“/M?TÖTžÞw'é\S¸¿VüÎÃ)Ý7¬ y¨+1$•C•Qúì ¤Ö~d;Zy)½ŸÐž6Œ|ñ‡ŽÆ^×[àSÞ1ãìÔò„ìHÀrÍ o1¤’nSO;EAˆû“!t¨LT{쌉woL Ž¸w¸ØzT®¸`¹„»œ‘ã®^å×ʧœŸö)vA$pÝúõœ_¾*äP_é½ÎR˜¶Án2æ5êéO»ößOø\ù‘¢!ý‚ïQRî0HiåÒ¸í¨V‡Ã"Û³‘'“-eò¨^ŸGô tèpÕ/½µ^_•¾Ïi€múD³Èõ_ç©“Ðb¢½d8$#z|žÔöeÿ½äŸ´aÊe’H¸ÂÆédk…ì…´ÅUzÉZ›ÏNÖf½¹Zaq¥é™ gŒPIlMÂ…ú~SŒÆ…?!té.šÀØCÝñÑ=°™WJJ¨ÔoNJLëŽê×$‡„;.®>A{ß(úi\ˆçÀ¸iD ß±ÒS˜u`fP’^ï¨ßW{O®…x%5SÁr‹ÇžsïÍWè¿^vˆc/m ˜s|ŽH®²¼ (MÞ«ežÅ*’ˆ¸­tñdÏ6Qs–Òâ®Ã6óÉCø_¨ø<žŒ(­K‰!Û(… ˜öj¸Øx ¹ÿ¼¢%`_}„ÂÓÄhÓâ l@Ä]5èd™ë,DZ͉÷Þs‹™40¶¿~$å‚j;— FȯŒö¬È3ZÅ7ŒDýq,€€ªM3u>§I,#‚~¼¸¢ù”š~ÛºÚÃby^x¦ ÐAÆc=©l¸ÈŸ›GçëE¼ã“º×N±x/7VwÚä;ãXŽìWÖü¦n ¡b`ˈb¤×(2·{ØLF½BƼݢ  ²æÅýmÃÿÊ#æ)ïo©ƒ@-í¢ÌŒ¯œ¸eX>QSpv>F›I—ð긱lèÌÚ@ø߆E @¹±Š‹õ"z³$Á·‘õfX±ë¥…ÐeM—‡Ù–/œ`gJ\¹´¼šÆ´8€râг¹*…`–Ú#͹ą­ïQþCtU}(È£DAçgeÜ6|ƒmÖV5[²6îG‚nèµFÔf´ kºdÊ^ëµgïÍxõàDÈÔ30UÐÖÀªL ¥b‘¯Ê<÷¾÷•¥Im×… V$NÀ},ÚÉ8ü†í}M*KêÜ¦y?i7Ë„à M,NLòÔ̓±›È‹¼|2ïO«h ¦’*ßJ10>‰¬)¬%šš§,îÝý*úЇiï Œu%ûÚÓ¾®†•»rráqŒJԵܦ<ÿvÍbÇ#kSåê³Ü£˜…½R´q:èRÓö‹»\°:ÒUª%KÚŸ·áÄß"ˆ :ÉžñNeqm?‰ñ¸»Öü¯dÉ¥£¼au{é¬ß0×R›dU9’IµÅ‹N}ã”)}._6gN *€4pºQG¦«Œ|ÞAºî`ów+ˆÖ¤LƺéÞ[3ŽãŠ¥!Å€)/ÄïA<ðå×­Aæ&Ìóþ þº?S¡ºRÇG{ë0* J¥œ¶;ˆä¡Ê漋Qø)tK’Ýý€ _÷ø°,7•Žk‡ˆ<Ð[IYÜQŠï0 °K$êûˆxÃä÷3¸û}º‹ó®ÔC•5vy:=VšôÁæA„˜åHßÁaT¯À„Ž#4ù&Š±Wóµ­Ê ÊXaßA‹¾i[ÔK/±h"†±º®y ø.Ç¥Ãhê±ú= cQnÆþ’E7áÊô­÷¿)žÿ/Ú0Z¶aÔõ#SCïnð¯¥:ãD-‰B®ýìÝ‹hm ¹‡£×zž8÷>a °†A;xC®gßÈ¡j {hÝÑÈÄvúeòrü®Vg‡U݈/Â}pôõ´äCb±Sº é¹Å»eõ¿¥¾™œÐ7Í’3'Ò„mžÚÚ’AP‘$îð%žÇ7…BÛ«w5R jŽ³ÖÙIò¢Ž;ÓtDœ‹%ó¹×® <¨Ù;'¿è…|yK[ÒÎzèíj*AæüÍ,ðHîp-1>7i;“Z42 Õbs,:Qº+Uj^Ë·ê t€Y½ˆ¹ôI­ƒŸ'ÓÞʾßîN¢fâwÐh¯ÿçjŽ{§U¸GP¢XQ/µžâzùÆ“7YÑS=ÛÊž¢¨ 6p¼ëUºë vu?Íâ#ðzÇ. ¸àî!<äræO’˜^QѸýKáßJ$N•:‘3G^÷ØdÛäŠdÿ ‡@Ð Ç…€aùŽ c³ÉÄ‚ñ/ឌh X×C‘v¬>­³£°G•fá00¢L¼f\ÐH„ƒ„27wëîÀ¨óÖ%äï·óv(â/' ùo]`ú'B©@|a>ê0½™3õò}—¯V’Ë0Cðf?Õ6+–^ßo{Êy´ÚT䞈è{þ:Ú<›&OåA¨Yê05¿âµå¹aV±±'ýû—ùh&E„¡š”[üæi¤%ži‡Dƒí Vq<ÃáO…éõ‚ ¡wÝ‘ÍE1ÛhGgDÑÀ…X)ý^/ŽÒ/}òbrdW¦+©ª²ìhøœŠJ§¥¹•ŸÈaí×`úì^±) < FåÕ‰eÔ(A†ášlT8‡úDŠ8d.Q}öשnû]\ó½L*7‡ë'„š·¢!O¼w·¦‹AÆÞ1lÀ‰’U/v>%寺m ÿx«vnÙ×ò·µ f \ží•t>Õ?² Z ÓûÜ ×’öÀ¡ÜrR¥iˆ—B=Ž–ÀÖ¾”ÊP$ÑèMêŠÕþsÔm¢ÚwS° ¯%Ñ„=\’Û°Öq×mçwÒ„F³+‹o€äA'Öå50áK“çÊÜÃÿ Ó£_ï÷^<ì®|`Ë„+¾’Úþº{¾^ºòåj)Óìô3+¿Fw]¯Ù†¤;;ÍŸÅOÔă˜_ªÃÝéRûkIÃjðö=•ÛÌO ²þ¶`B –§^XAK+H”± ܈¿d¹Œ1¡$ yo7õ{íV8å+ƒÑÁÝzÅažå÷¶ºä(» î@~õØÃhW/Ä9»†¡cn(ÙV7<ÖPÝZTЩUSVãþªö]Æ7Âwo»*Ø~Ë„¨¬OÈmV¯ýU-œf·Ï?‰l²Î‘¿PejD#ê¼çæzôӂЕ {êöÝø <OóëÜ^M¹ó÷À‹oåçðÏŽoÍYcé(Ø—Šã€£6*ˆ¶ F­œ„ ‡ ©Ov[qsRiÛD^QØÇ9nëÚEÌò"Rȼ5,Ö–:ÃüCä!´¬n.é§)¡ñØy\7ûËÁ}ÌmÏY"2ÖÊSÑRi×ßû‡ýÑ7ë?ÄϽ²³AYÿ¿×©ëÀL1…%|;¸Ù¤ câ¼Ýóƒ0µÙêv•mHE~ÑQK»zñ½¹¥‚‚ÌõœÁý¸ã¶3÷·RÛ ÚÞóH@Ÿ®õ¦WºB ÖZöfk"´Ð™ÞÚGfRÅ¡çl uìsJ|¿æ63£^à:GÝX(k,¨1Ú©÷YÂÂÚe¨Ù²ÍÛoðÏE%ÞÁeZÎÊ\.ùÁøgv/’²AguãRh DÇãæboóƒÉjG؃ŒŠõœ Î[¤“$SEó`/”b­Ê§´'…EÒ¼ÑÇ@B âõÓd §÷©\Õ°%ÄM½bÍq—ÖgÂÉTx∹1©D¦¤Ô%©_†„—ß'þGoôü:UÈ!RáP.K§ê†Ð¹p7P%qþgùFøJbäQ‚¬s8—¾îK£uG ¬ Ĭ0°é~T‹ÜKŽßÒ=e}»kK3ˆfýÙÝîG˜ªp0Ó0v·ƒ{X4f<œqÐÖv¨²k¨& UeE­„rŸ ˜ô<œ^ÕuŠ8òMN‘nÉ“¢ÐV´m0–اï[(’3ŸDݹ€iHÎ1ïíg­s·µ|Ú¸òÓÊnÕ—1"]âx®8j4·¸BbJ›zõYÚRRî9(¯óú‚v‘~K?ZWQ7t”Ï׳ Œ¶“¬’^n]¢z/å¸æ@WÒâ»Õ0LÊ‚wf„Ù(–I?,(J'œY“i~YÚñcv·2Éè çÄ~Ôžôå>t ÷L~z.5ídbIÆ‚…?À€·GO#ì»çñ¥›=ßKÄ©’¢Â*,Ã(ä£à¼´DwÐ¥»ÜAÀ ’pÙ¬+#X)‡Ž¦K~'æôb7¼ØU¸L'wùì\¤fimÎ gH‡˜ãT!ÊlÕÅ+µk"¤%ùgÔlÑÕkÔÿë[ ŠŸŽ£pþ{àmUÜ X«Kƒº¡éBÛ8_C­í6Rky^2 ‘m%\` LN\›è'&Œ$r¸Ÿ˜ žÞðø±rÞµÚ囄ùºKj>xº"ò ºÂR†f™ºÍ%æÞªs0!{edëwý± ¡¸ªòé,vÓŽÃ`ùa=Rþ̘üˆô^®…¦‘~F>lc;j¹4wÝîëKì©b®ë \1"2dÄ„ß`À»Áf‘ÙK s90 …°Í†h˜ÐL!H‡÷Ñ9îcµ9!°Ì§BvWŠŒ wIðÊ}¢$вˆw ¯ŽÆð ò4‰]}!릻¸ÓÖ¼{äþ‚´btö6ׂËvý!—Ê:¤cI±l§ß~ô¶ìv>£ç—Ã:ù½PÚœf~Ó ¤q¡4ZÙ!Íò èô7²XÃpûÝCðò˜¾FCùR¢ø¨}Â&¯ ™N}¡Û¢!ÖCÛçü Y¤µ˜ä[OŒèŠþÆ/‘÷§$o¨3!ÂFaWßùˆÕ‰ëÍC«„"ß@è]³é3:8r¿z¬*d¢À=*0óVXyqR~¡ ËˈÂ惡)w*aá¥{Ä¿f‰…ÈÒB‡4PËuF9-›œ«ÏvE&'áJŽŒ ¹Å:þCs8 ‡5tÉ,]¾É0>˜í²¶ú¦‹/·x B»ä]tÕ…$À=ѹ ¹¶¬»éY ói°ŸôÅ ˜#¥…Y¨ôuçÈy ñ’Oš³˜ð.ýŸäÛ 5’­ðIuSÈÀ¢OÔú¶ônÖÄzÞ3-XùàÃnkhI(•Aw¡ó®•Õ¸™S˜«|Wò柞±¤±”íkb§>ÛD¡mñfGyW$„» Í˧|³1‚C—u {¥È×¢‘Ÿ@þ1ëšòÙ²CfY¾V'JÀ¬³¥ˆy Ý“²"¶Â†-Ø?£ØÈ3Û›H2åôÿÀ„Ž ºËhKJ€ò5Üë{¦X‘oT–)ÂbçÆ1šì¤¡‡ÝÂ=üŸ€m`ºÖÉD/Å•kgõé„aöK3®ø¸ko9Ü=ÜÒ!®FdhM“‹QÝHˆRÎ0g©a)DĪ³h†³Í€™úbùâ¢NVA¿Ã¼4±“£á=J kØž2Œ´þ ažCàìåfŸ ?œ‡ÍØíïpvýÀª ³ãœ(‚sõæÄ0²àm°ižÎE×S q*¡b4ªÚX;ïcEJõe«L# ×ãVTý@rݤí:ÊŠ…ò×»d¯ƒùµ¬+…½eæt ›ÃF>?ÒÔyoÁ¡¢1ºbÊRéF0ßõÓùÙ;õ„Þ”Ekq}Qá¥s$h|Óµ(n¡õL“d%S¿Å=Ã~¬Ý¼™ÚóUCe:–’`Fê´ìðüïâ–i£áu¦R´Kõ´3¸üqÀ8Õ>S&‡ÏÛȵ´HVq[4°éd+e¿ ñi¡2SA«7å #šÉ†÷ ߃i“)ÈxÅ•‚g±w•×ô ÊV’߯è› å®8¡7Ý…$Õø-Î/Ò_œ`^ÃþáEéóu‹iÄÓbí<‘=URÜP¹.£7¼q bÞáJLsø>É š¨e'yFŽjÎU÷5DänVl^J³±„)éÄÚ;àÄ-«Ë¿­ÙÓ9fY­†}µµQßÜ÷.•°H,þ6€¿/²^ÜþCsFŒ aUÔpq ¡Ü¶ÁN©Ö ® ÏÜ67rîõún ~ì†þ´0ïà‡þpÅ€áÈö²ØæY°*&CÃñéÊǼ_5ÇŸÑaóƶôËMLø¿‡Åqý•ÃòŸaçxsŒK߸‰¶Ì:Ç'Ëì&Áuñ u0%„kK±€À>Äùÿ,ÝLŠd6¸Æ{ý·ï-9³Æ¨ùG¤ô]Ú›ßpòýC°bÁ9¡PWrœY̳¼ë_+zcžE\ÍYŒ8&'‹]º·ßK'úì…-aW~¿°B0 ênòÝ~ ¨6°^ªÔiÓC+¡Ž/"u“i3°ðçè/Ðê€[¯0e‘á~@ICñØTF6nf[Ie}Î0ùˆSæQ üÁm¬^'t1<›faPÃbžñw|Øv8oö˜¢t%±U§ÐbZæW!UÍ*Ý]ãëÈ÷×&.‰)Gs;)èK Ž–ŒÇÛdw ¤úUìRheC_ú¥}+MàúàX† “uœ£T¯~¯ÓºJB*uß{¾™V¢Z Ãó(GW?S‚£·S¢†°U‹‹ÐÚ[Ú¦A‹³}Öx؈ŠÏµÏ·>œ-@ÎA“å…³çlù°bæ@8¯wÜ׬½1á‰ñ„í,M”ñˉÕþªÊlQK­¤EüÑt¸ÖŠ·'3 †”8vD„Xö¶ÏÏ×FNáAßðMPÍ]+£ãeqîÃa0Cd|w"ÁýR$tïÙ3Ïc!èi]¨FÉ·µRÙ« ]ê…ì-_ÖD0*Ʀk£DÒÉ" ßGú [Ú(ˆoX°P[HêW¾„_4ÆŸÎÍÓºè# ÒЀvÙ‡91¸tÅÞTï·9^2/É±7ñ·B F¬ A_Øõ¦×32É|M ±œ<Ô,/i‰³ýˆ2r£‰¤CM†X¹œ@ FvóÕ¹©o |ÛŠ(ÑzÔj-¥u& ÞÑyZ01R¯‹ ”íBý¡º¢MÊ&¾™n›FÇ;‰Æ ŨU‡×RÑ}«È)¹£hÎOX úÀ¨ß$)¬‰…SµFckë85¶p„BTyN‚~•Š;ÜaÐô F“Z.kÑálP •;7˜å»ü VNU€ZêƒCüÈ\:ÕU§\F©=Zl½Þ¼…‡Õj3Źø$ŸÒcÇó ’ iÙ•ÌË5ðUæ—J‰àôW,„ 'KìZ—\­4X[Ef“Øû¿L@ª¢VÚûækŠd ±ÀÁƒ´Ð½ªê˽óÙO1-‘µzx|vö”C–'«ö¨Ï÷t9‰„dËüÌ‹ãnµç ú½®Î ˜ ƒŠÏ}bÖ*A‰J:ùHÙ‘¼·2çÚD¶Í[‘(8=gyÖíN•xbph¤BiÈ[ýú©_×V+Ø5!é ‹‚Ù3I_!å17#±wÑM¶µ*ßH¯DpDÁVpŸŒ1^¥‹»¤°¨ûHõ`§azÛ{Ç©¯)ÁàR¿…5ß203ªæƒ°TûÛ upýq <_w³‰NXí4~71ü+·ù.Lp¶£´¸Ï®íîͧÐED~-M; yµÒF%5‡W÷Õöé}„A¯ò˸Í\"jݬÐ%1ð…³zfX>ÚÁÌ’\U–åG¯kŠírñ¤…`d×ZëÃ0Né<<€ân3ìqŒ¯FZLBØ>ñˆèŒ¾Ç¦Ô?ß—ôÕ’ù¶JÄöT–‚‚™YŠ¶Ýƒ¤B|+¸.¡"P˜:òãÀ@È+¬I²¥µ¨—ô±ÓßJPÉ­Lïºmq%31,Œ¸÷ ]|Ojîפƾó¾/˜CcÓ×%xNν‹AEÏ¡0šA6F,®ˆ T–¡ˆé†4!åuàÝÎdW®VÑ°S‚–‰ª¤=P*5ébr±FÅNþKîóäI{ÇÓ5AŒŽµòL0áÒVù1|z|Ê•GxQT¸nì3œ¡EiXgo›M–ic'éH jßcäÇý¾º½¡hDØÍÏý•]"*„n›”‘·¸t‡uæ­Ažœ '‰ÁGïκö—²Bÿè넀мºT^ nÊþ’cå¦ÈXcÛ™Ë-öB´ëàÉžÉ$s ¿gx˜ÚŽf{Ù0a­2¡äl:²ôz}„ ÌzCk‘-cµOŠíi&ðXÉÞ½ª±aàŠ—‡ôϦFz‹Í¿§¦ ‰ƒ(7¬Œœ:Š†,Âà1CÏoQmÑ˜í— X«Y-åKzâŠLËa)t³‚ý½mñ«õö¤ 9p÷Dp‘é¤ÎµQŠT0GA"ù>=»Þ’˜ÁtDkSt²e‚žb_PÃÇñ.—\Ý}(°4TpóCT)‹7üuRo $)ü<‚™d)Òø| E*¶gî*· yêƒo‚™6xq¡ãåÛäJé+Ëk¤{ÓiœÀŠWQ†þw!Ñ~»c@¹‰b°ô/9uÚõ—ç@Š'p–±x¢g t¾BŒ° t:6Àã';&íëÃ}Dyüún$½ r-áÓÊZòº| ’$ ùíW@Y{¸ŒÔéaz¸‰‘äPj`R®¦2º®ó7 ÈCü¶ ž1[þàW'ü½¾yX}Òl[Á³[{r†Þ²ÉÔÚèô1²ñiRRÒþŸìG§™õhåÑ›YhPŒ§¤jm]sXÆzô¿™Ã(–‰‡W6 Ë€úô¬ÚFùáýaÙ~ÙŸ[ï¥lÓÑôiòbRêø#!%}aÁàø5z9ìE L€Þy‚† þ‘ñ"ÍQkpnLñì0… üÙÿ¿„«ù¦O¹Ÿ#ý€52zªç Ë«yþ'ÌyjÈ[Ä›^`ºiwå ¸ÂW™žï$÷a žŒ5H„¬ÉÒaùŠ§#»y[¨ŸcÉê•ž¼j>›åD](¦UÛ’# ɃëÔþ®jéf›SvŠ¯C¼RwÎÛeUc½ý8øG0ÝÃÈ膦Ä/Ûϸàã/‚(¸>0»ÒÖÈ+ ®ù;Λ¨tÉ;^•ä:g./²ªçð]¢jI,ÿ€y7bþzItR¶ 0Ø …rúP¬KíÃlý‡ (+4-TvxIóo=è"À@­äã80Kæe×;Jx|9 »ŸýD`Ëz9k„´ä’f¸ò)'í^ó–- ‰ dj~ÿ7ò&mÊFûVØÝà‚E»gHñ]AxeºË ˆonÄ—€eA±,XxÿB©–û¯»¦fúò§Tg [$p¦-VÌö÷†3¦-ÈúñdPmNÛ-]Ì{gVGþvôÉ ¾JðD]+:Ü°Ço[š9Õµ²÷À™™™LoH•»~'Ší—-qým˜òv¨]LÞƒ_\eŽötv)rÈMxîªe©Zèg‘Õçûîõq¶FšAPÜû¨uÖlz/…¬\Æ `ÈFžãE;íDÎ‰0FL©GŽ¡·sÐ[8΀÷ ?å%Ùv]U'¦Ø mê±YÜìqÖˆ.©¦£÷úÝ_(´Ù¸hÍ*ˆˆrr€¥W1÷} ³IÆÏ©ÿa7͸ |ņòû¬©6`­±`)¤ò XØtcò–“ñò‹b[™ŠfôÖ²R!¯³Zð,Éw›ÔOI¬›ÁF‚ä°h[ÌP´ý­V8‘`ƒ£c,3áèoü†YP9ñi+Œ•Ù}¿jç½ûR†”P…læL”ÛbªœÆSgŠšôe¨ÈË)âu~ËóîeVVPÒ’¦W4LåÆŸÜbç~^Š=©Ê[˜ÖËés7|ô(œíQÖ€Úî²y½Šè1ãœ=“d4V/?zòâ–‡Ípv5“_cEgÛo¤­"ƒHFË…±²¦ûü£ÀœÛ…ü#_¹7õŸL£àÜLuÝP6ìQ S‘½\·þøùJ…½³Õ®M85\5}Á(à¤ùŸ¾•\rfûÂc˜„?¥w¹Y”/–/S¦EÓ´ß”7«V¢«™ùÄ ­Ê†ã8AuL! ˆ-çF¶N²¹7ƪ'`Y­€¡¯zÞ”¶¤ ;ݪ¦Tt¿‰r¾ªý‚貸ÇTä³×e½6µùViåúèœ0D°¯P,°Oô}2mŽŠñ+k ’—ÇÖšÓxfÐÅÐç£Õ`ñ°áñžÙrË]š2,F‰œðù¿«ÚHòBY¾Ñš"v·¦<”†© ?-"i/« h€$4ÚwKãÆòèæ­í$Î(ŸÙCNÇÛ2§{9qnO+¾Í²?8¼¤i7•OÏÁøZª5vµOŠDy¶“TýJ rÖˆÃ{ÐCP4ÞÌÖüM 7ˆŒVÞ2.|0œÃ±k >tÂMèk4¨GöžóG(ÛȼaPÐQ£Zþp¶"Šh¹ä‹ñ”¡–ZçX|¼=áb)Q¯¨¤½·×È \÷l”ž(=„¥§ï¯!£¯ÔEy»É*Nlr•¡ãÊòánQ?!0?ö£€y¥ä@ÐŒó›ô ¯íÞÛ(u¡O¶òw°ž'ÚÁÏûaÀJŠ±Åd¸¥ ™ õFlÂý†Ôm¿‡Àuª¸Â‘{Š>Ç>ê$œ‚=ºCìŽ0”:-·Ü!Èâ˜"#"Š’ü{‡(Éå®ÛÐÃËê,|GuGeÙUVU.ÃFHê——ÚEYÉ?¥{9á„ƈ–Pµùµ°˜‡µÃFnzF>{?/SAwc¬¸îÝÿàæÞÕE¤;„=£TÑm6`ÉÃW.^ö ±Æ5tÄÏó>V.ñ–Œk¿Ú£AÍ÷RݳϿ`o ¨:Ó%ŒˆÆRu¨Á[$J]Yœ/LŽøsñ·~ÛAÓ=aç O¤sµš+¼®ÄçHÊ@RAöI‡çÔ'ñ SvBlÛûY†›ü6Ûf6ÁÐÌîw9ÅÃ!ò‚òY.6ù`xˆíËN”îD)y”1~Ýz¼ !w€úÁs¤Ôì{“±Š!Ìm™QîA­Ç—ç£ÃŽeã-øM«ƒÉÅ ³<´æûRçLq2¤“ŒV:Å—e:UâиýI=úqG¨Zâ#"nm««ÀK4øT+ Bö òTÑú ÜÐÍO–…K<_4§¥)ÙrJ&EI)Ž²Ž—+²b„7îö≳ ½…¦;b:Dn>qSP¶»µ€@”–ZÒÿ®6ª»/ÿ•Ž’")_0¦'6èóѹŠ3lTá/oÍ{ ÷kËá\56Z»6àÝqØ}>{3Vè ½®aX×Ë Áa7^q€KìhCoõ}³ùm 0)Öv¿-,’f˜0#ÊoK×5²´Ã ¶¢ÔKÔ+ ¾u…†®ÔaÕ•¬1Q•QÆT.Q™ëB¹È Iak‚¹½ÉŠ·âèváHaLpåðC^è³¢“½wþ+u(õ 0Iß j[ &%}ƒ¯b«%NJ 隺b³ÔàÃq+!XÙ®H“ê­ã®bRê»ý·\£A/TÞZ²„ÁŸ§‘ßd¹T†‹snNÜ· ’®Anö_Á™¥R>_ÊÙ…ò[š˜KÀ,› “¢±2=ö8ÀÆ„׬«£ÔþïÍæ÷±®h¥ˆ— Š'CS8¿Í žz•W:ÒÂvŠê`÷&î\7z<*BfwÚe^"qb6¤!ÆÁ;'·üÒÔ5Äâÿ»3PûÙ\«±âh›KņuâTÕˆak£{›B¤?6ÎL“f·&úb¢]£r`az= ÝgH1s&\®áÿØ5üD÷iO°Hë9Ç0ð‰±®iåtèB½®Èý,¢/Óçxû\ìF³V%Q&‡-Lß4GË6VèárŸÊ¡V¦°¢æ-)Hc’²A—=ipEÖ:@‘IñÎfƒ¹BZ1æ¼,h\¥T?!Mƒ‘‘\ss¬²i&ŠøEΑ gâ# Ð¦oüœÃDQ_qRd¦A§E‘Åo8ñ†C$!W#ÉN˲Ayêm£rL€H¾²y˜%ðj EQwB|Cvu}Ð58ñÎSí‘I›=Ör‰­½É  ˆÚêfóÜ\> ³9ž`t ç¯Ù.¸û:RtžÇ¦ðo.&ÖÖÞŸžr‡¥(®’Ñ@¢lžSt²ìµ5cÙJ“9Ðñ$œ½I–G?Z–:„\¦ëžékf–Ï1F»9þ‹Z·\&#1ÿ¿I¯éíÖf^}‘ºNyô[SçN‚Ðöós—l>á¾öÒu3Z¡xg&ÿͧa›Û³”bTÀ¤î(çVÁ¦ÖZ ;æGÿt,~È’ÞÑóÎêDâ^\û)k3à{k;T}ïá}6ÿ%ªD¼6Ÿ™Ûâ`ý’æ-ÖøIá©ö•Æ]yëSßÔ)ѱþÀ•cÂWhQ1°uJ”ŽÙÇy:+Þ;Ê(¨bN§f_>P¶%Ú)#´’hÔÒCyØ7Ë¢f¬ø\›ÃE$ßÿ°ÙngB—¶û5Ä*,, ñÄ4ißZDíXv§’ ìƒ+n2^¡ëŠTèßíx.Ô $•Æ[`¼/{7DfUÇö0‰-}¶³?eÍ]ÄK•’)?ØržL°ß(‘§ %<Œîõ‚úèÔÞž9 ¦;‘”!=ÚÐ_·áSj ñ%fš&'ñÕ: ì=s¿Rü3#íO®;´4ë¬Å cU'0 0Ð;‹¢’Ž`•‚& ÝB—?›©zmåú>€ç§ÆÊq½²`g…X€0É\WŠÎ‚ŸeUMØwáå«Ø n{»!(¥Šá« ðß]Ñ|ˆ–±‹š ²fYÚÓk¸Sëtó{ %5gŒ ˆ:ª GµLIyçö™Œ ’ãúcÑòÊÔq´í.ÖIÍÅÞ>ÿ|*dxµI#¯$Ô5§îÖûÌ Å·þúºŽ š ½üÏ„+¼iã6áBQz@fŽù=´³YÝ’?ncû±Oƒ«„Žü h áñ®Œ[塦Y±’¡Œdj[eä§w‚lÐ«Ë ¦ç%0dI†T`;í\~‚Ëf¢u„J9—'– Uõ¹¨fr“™b7Ú+þª”Sª®ŒQçUÙî’”Àòy¸&ú4Oåy2AŸµçÌÝz£(c|öØÿÈJœ•<gæsàŽÝ)”À^Ì=©NÏplª°Ï9»ƒk6š*úÉp3x Éåî”ߥ@“üê²/ Ù«ÚJ~݇ÌCšS[–cåñèÛ(ørÿHŒ»LSS?M²6éþõÖ¼ÁÀm`æú˜†#÷>bî° e†{B®öQ:÷Ç 8|z£¢Û ˆ\Ë&·qÔ¾“Ú7&ãåùÒž‰~&T§×ı•óà¶üùõEä&4›‚B>eÉ4±âv3g¬¶‰Ä§…ñã Íf;>ñDNU«Úóª:]ÒÇŠä&¼Ô-ûŒ^ÔLË€êF¼"4Vbp§ÂNBñ5¯9ÖËÔ>5UØ]íßßYÎwLãèÔó{!/ôšKÒ‰‡ÖïÁŽúqå0f„³禡ˆ…)µªÕ¡l唆1F»úu»p2¼^«÷}®-¯ÿ[¤V#»ú¾€<‹joŠ¬6½ò~ôú9Ñ@uyMÛ†Z{"Ç¢ÞtAYu¤‘‰Êý¬›™ªj ÊŒø,@ear¯ÕŽ"ùnó8Ö ÿ ¥{ÂȪnØìA]yØMå´Moê€çWû2® `üëö¹1Í…wÁe*[X±Ã÷îþGó‘©Ó.içNÒ 2òH¹4(è}7Ì+ëÓaŽÅ=|—›ÔemeI÷j;®X§:ºÖ;‰Ò»Ç0ÚíË]5GÀëCߊϙn–y–ŒS/c…„þŠ6ö3` B[) ¹ßª—°ÊdŽšxìFz7Å+ZßO¯ß¯=æ¯Bè4ó Ì³‰$[Ø3œaM¦f•h¨ªÆ‘`[_ @#”CýEûiù¡IyµIº1~9{ï>³‘.–êŒPGóˆœ,@ÜUNxƪ¿wµjŽÀ+ÉÕÌ’ 1‘sìßvJ {UzÐãâ`â|Ý»¦²8·$¢Þ•¢™ »`—x›¬!ôž?@’y´öþ|XV ln™ÎÂ4Æ}½£Ù1ßP;ݱ«dãsÀÓO÷ÖæO(÷Iî! ˆÚR‰r÷Ù€†å’H™[í+Á¶G·ÔCi»@1‚ÄüÍ:o|P`2±¡1Òº†¹A÷tXŸ K-D% Æ•ÛY9›Njž/óê¡&àX÷Ÿû¤HS÷Z?ŽXñØÙÅN­¬ -C[‚5ìF–ü_R±)ÏƬk™òÛœ/žÞU´Ñï`îÄž”»v„dmàr+ÜK¯œ‚RL¾¸ºÌ°Ò~&Ì#—òhn²ƒ¿Þi2»z†ˇô„—ÕVvÔê;=Ι3`ëqKÃÉ)R¬Ê7{wúøæ`²£Ìi ##r?yÅ9!Åhêç_™lq<=çHckÞç«´qj#°+Šÿ]Iè}ô©` £ 0³¦æjçõ”„…$å¥/ÝyŽ§8-^ª“´EÇ3LJK¼©¢e‹Ÿò\Ç€ÅV Ì8ž…²³dK؆;£CøˆýÏʤûL‰¤¡È‘\RÌPû~”~_[*™BúÛP6ÜUœv³ªaØö/…™eÆš#ƒQudVаš¨Ò>“U²p}ó-4ÂH•D:(w¯ºù]Ó Gñ®~€0$š±çCpõ˜kJŽ~Û‹¦Í‘vsÁįÔUÆ¡¡¸ ,Ñbþ‘zhÇ<à<miÜy)9/Äxl<üOŽÜ)7ª¥¡˜Lü´I´$É´ÜNl5Û•–mÈϯßç’ü[€ž¯Þ(9ã`u#9mI™MpÏòR"«ð¸Ç¾Ë6T·äØ4º[^µCÞ Å´J «©‹Î)­™òɂ׳ÒU ªÑhEµ“}¤ÊyçÕN†¼“\r:+²¡ Ç?ÝñÎœm)ußëlE=׿ ÝuBp˜goraöGdÍÄðåRRiiw‚Ô‹Š0µ¸îÒuÍåž ÷6G§1GÎðÆõƒ8ãÑ·œfN%Å͆wH‹(Ý*·…™N2£÷j)Z8ñ¿mu®e}Eå6L©| 7ËÈžk˜©@¹ °™j`ð\ê1E‡£Rª´ñ-B×p¶ñhÒV-¢ƒ}i“Ú>s‚{„Û}4uÑ2hÒ å´áì]ŒT è;XÇëô!‚àp|)–:1ØlŒ»¢=S6ˆe6>·•G̵r`%>ÞK…¿Ýt÷Lçz2D~›´áÊZˆÞq´¸ûkí Ùx:‰¾Æ;¹k'ÞÝUnYðˆI/·ßÂõoÎá²¾éç°<öO¡a?6ö$KX¯6ˆÎzN+×^_ôݶv@,Åÿ"X¾šð?*moÖB0$Æ9Ì”cnñŒžº‘¼¶/GŠsm”ynQ@µ`Îrσ÷’þAoöŽshü>»˜K¬Ì^–ÝÝ‚'™¬iÈẠ\Ja:ÿÁ¡ ´ôý}Ãò¶€U®1jzHKNh®˜o¨É 2Í>«ÇÝ»£òÄ®‘.ð.»êåFU1çG¿õPk_füŽ(¥?"šòÁù ßÙÞUôÄä=§©‰VÍ2DŽ²Çô·W‰FSšÏ0F¡‰tZ*Ѐ ž ùf É{ɲF®%Ó&ôý¶cÊ­•›!“/p…¹`rÁppß­ .½_œvCÂüµôÀQÃè†2ôk&öDü_ÆÖ¶`Ήoàßj]ió¹…çøš…ÉQŽ«oGŠ2/‰ß¨³[1>G %‰ +cÊ÷+ºpöF ÀÁäâEža’¯Ï‰#º/ý-?5£ÕF1…Þ5žôé’£WÛ`ü~³‰àöÚÁ& ŽÓJý}©¸Ä–)ü>ÕŒ(»PlO_®¦´gÃd?Lä)³¶èÿÌ“/Ø–îéydµàLœ 4rÅøT¹îCNìËKå?$(älá(bNµ—{ôñj“Ã$ yÒ OŽåÇ3.€ò‚8›¾†¨ak—p„²…6ÖOZvÑjÐÔ£á Í`ÒÎw9ÝíÝHÐIë©ëÔ[ˆòÔ»™jÒ[êpæ/ì·vîf³ãlœ›ƒio]äXyÞÃÄ ïÜB,¼LÉS.ñ$§Æq[º@‚)t„o{Ó'ÆËÀ|ʉP¢96,D_CúÛô•<-õJ²×ÌDFµ/¾CÎàÿ Û_Õ®á9S"ˆàD5ßl_ÛƒLkE?¸øÿÒ‹~€ˆá6* 4Jÿ ü‘¶±AϤKvX”Éþ--T¾<}Á·³Rù~²jϳ‹ÒàµW¾2ŠL>O-I³§9“<`÷ÀT—Âè'IBIõ¥:iÜ¿4@ƒð¶Ç"xphú ¸—½ŽH_Ít› rc7ú=ü¸üœMqÇ¥e¥^žˆô+†q‹" ï _rn´$‚8Û‚ÉÏ 9`Ft2QÌåß÷Œ’N^šl$šà÷Ç2:‘Áé<½P¥ÅŠ¾éÈ*56y³åðG ×’à“5­ îdzªõ¥.io|9Í´íú úè"[mËè¹`WW€­¨K@ v¨62g}Ö…he`ÙÕüdG”SÑsÓ–ZqáJõ82¡°gñý³dï|4é™"ç­xZªÓO®þ…1— †YE¯QW¸Ù|œ[:åE÷¿¡â="Ää¶ÞIi¯¼u“Ì—z€Åd" `,‘¨Ï¢ )yÌ“| ©ƒ´Ú¦¥è=”—2NýlOÚêäçi_ [•íÜ@e9¿ ›Zé]~I'ò¼£÷E_#Õ€µüm#j),{µéU5EÇåξ¶Rõ©ÛcmÛ–>áÊJ*lÞêTóûW*6Pjšå˜N ¤wa‘$È@à;±¼_K\Ik࢔ÏÓr kNý'âÃ5ç­å¯ºc©f­oË¡ÑQ9ÖÔÉ:>6Ë÷ú)@n=ñ÷‘ÚÉ÷Ûú4ªºWȹ|Hƒ¥’*^0?Ñ¥}ãõÎßz%ë{‰¬±bÚŒ@¯šÜFœAí¯ÜÓÃg•H¶D®x9Ê!7%—'S×]a=çüˆ RáĤ-¨A¡¿¥µ­FÒn+©™ùDÂvL´p(P±OcŽÎnc1Wj öêí/õ>½¢‡žW­¤2Ð2dܵ]4‘Ùp› t™Øv¾CÞáÒ8ºtIaYÒÃ¡Δ‚é3(V²?—Œ!-`Iß±¯¡ÎY.—1©ÊgGM6•FoÉý¯­N]QÔ7³;s]a8Xý¢"—©[OåÅ,4¤ \\ŽŠÐ± u‹z:Ça´ï¼,×"uø ^`“—¥|ˆoócJ:߯jyýGú[ºü¢BMµp|/ÑqÓ£ T¥tÈsÖ5©½TÇû„µ“¹XzL“ЙŽè—oVÒÀ‰¥(Y@—NgÆ~éFºë.N¤è9¡0³@‰TÚq|f*ƒ­]¶_ÅJoÏ…WbÔÛüOç›PêÐâè±Ï¤ñLðg£YD:Id)~±ÒtŠõ|æÌÒ6ú‹oJÛ<µ‚ ßãæ%ý¥DXrtÞi6ÉâC*D†‰’,`º¹‚ß¾é-똕 Ìðô®L;½Õ3>¿«ÿŽIôÞ0­Ç!î⊶/DSžÍK”4^Ôñlv )ï]N“ˆšÒO}H™.†˜`·k&­]ˆª5ú¢üë ‰l§kóépFKZX´îäX±†ÀV,'©õ2cÀ h×gïû‡+vDÚI¹ö°¯,"Õ X0¨›køêÑ›¨ð«el`_Îx’WªÐã&”g÷dgËCSá£ÈÇ ¥zñ¶>aŒB€¾{ô¤ž08$§M@­3ù({3‡l·Îx×0’äûÃÓ3Žõ…ûpÍj»…l Ì}L-_u‰F DÂ÷²iNÈòÀìЇ¯7`vÍMopþ+Ž°ÍžÓüŽï0~zèü^èCZ4k™˜ L íKÛâ˜÷±0QXð¨p¼ªekÊ„º‹4œlm#§’ÎÏ *·aÍ×è²!3v-8¬>i-žߤÀaÿz/ÚŽ4fºœŠˆ¸M¶É\´ý'ò4æŸõÁdCgl_µ¦V;4ƒÈ½}IºCBN†HÝd‘$Æ5¿úpn Z\æoY…éøçéw•´ZTŠ-n³m¦µÙ ÊÇÿáks†Œ’œó‡>ÚN„>–Á A“(·ŸÃFÉåtÇzF {%ÈÌ…1#ñ•Ãœ?Mò½ ó»¯R…CºD· ) rbXºµëv·Š¬ÔUfêfa O+<ë6òþeVnH^ “#Ö‚¡«6ð±Â1l âüÀÎþªñ±Î}ˆ)P¹YǦ&]ÁîA—zBcÎ̵¹m³S:ÅjŒŒ•‚.u":G™(dÌC™‘ðëÉ/ëÞÄ xŽ n¡Ã#‰ÇiU„À‘WÏ@¼›äŠ÷2Ÿ#Æ~‹‚°Ñ‡ìH¾+kb <¼{Ê©ùíÿ+[ ÝĵÆ´Õ {Ž­ÄÙΉsXã%}ÔûFáñpa½žL†s¶h 1+¶ánVìIÐù´·?"¤¶œF„õZn&í²=ÏûŠý~,¸È¶D†¹‘J-›€æNµ8P̹Ø,ÌN{¾Û°×è|õæ$P\¢_ÁÑnYD5—a»¤³ î0 óY‹UÆXÓ†"Ó91ö?[5UÝã*IÔb@bÝœ30Mµ‹¦‰¬ lãÈk ëä]Bù0Á˜ÃêCÉÑm¥÷Úظó‚ojIIëh,‚ò/C‹¢Q‹À·v-pyÁ³n£SÑ þï±h¹Ó\¨üjXè¸@{F&iÖƒEµ)<|¡Keu´þ(F—ŠEÅ™BÒ¶£%„1n›kOU5R¥ì—£x’ eÂr~&¬¹çî‚NrcÄÞT½@’”+ÝB0e·Ö4m•!ö­ÅÝ…[ã°O¥{—W8uùVd±à¼y„0ZSJjÕrþjŒ»Ž6ü!¦=¼sRéŸÐ|?ytÚ% Í„ëœ4ÇŠu;Lò…éÐÈ:›Ðˆ¡¯Îˆ¼ª±ü¿š›'¾'í×°Ë3?³z-±øVQ„åwuµÑlß”™ª¤‡d dêÐg·táØ\M¢í”õOZô ŒÈl© õ+¢*¡¼ä§Áχ³‰EBNÞƒ™p– “Îx8«PF•¶Ú_ºe§He2Ó8¸è"MNd†1#Ïx'9sEw@s¾|ÏlÆ8™+žˆ"¹êœ¡ Zjn<ì-²Œ>“>fW¬Óå,üñêí`¤Ø¦ÐXÂòMsêKqÝŽÕÚ ä­’ö?ÏfQ§èMœ (…Œ\'Üóƒ.Í,ûÖ(ªr¡°±Î>lŸ€±€á€ø•®SoßÍÆë°ò´aæ€*ññ|¿$«Sø1«$(åC—)Êïë€R§À— m6\èlDAé¡ÉÓÍHÚk;ÔYQàŠôV%m`™Ê!aÁØ63A–w’{$WIµž â”!‹çê ;IÆ»fj}»Q‚q7Mžœ¢6¡}ŠÄÉò¦ñhˆi%Ö³DÍl9wV±GS¦ýÌð…~jz¾ìlèO6‘굺H¤´éµýµÀðQß±˜ðAu½ä§X!ö0éMI¬£br=Ûü£ò §9zÛG±îu%©ÀÕtl‡/á2$ÿX*u,ÆëR°‹NÿU‚ÂÁ|º²`’–qWšj­'A<}OÂ*•˜)6¥œËC/àúC~øó þ,è6"¿÷®º6ågo»´ºcÚ“ìøùÐ]¸Jcø¡r1½§~ôÛ¿q `]!`©t4/d´g±!HÒ ¹oÒT€›~›Zå1Ü 4uÙ„W·è+>G@õŸ)†úÆßÉ œœûrÍ¢ÓÄóÇW[=å„ËÇ ¸ÉªÅæ61W†ôã"ÄaEZ#y}W -ìGN|dªàÓ{?ûE{—ª Žk\OwFJ‰r„¸ò¥(4US”±nepš…~;ó—bëK ÇWÎâ…§ìc·©ŸB³åo:ËøöXvîƒúg±Õ òë@ÿ[”ˆ¥„Ȥ_¯o_ÔÈvYAfäÚ´'ž†ÃLœÝ­…FÍ'úVÃßg“©çeÏm2ÿÎ3{%4wÛ1=¥n:0\¸õ{†Ù£$ sÙ±:¶’"•jÒŽE{ñ®<€ÔJ¡^2iÑÕ¥À}#èY:y¦ö$ÿƒ$¿1DF*Û9¸ÄzEWó˜è›Ï¿ß¥Ì”vy íbGLP†äOèëßî¶Cæ×oÁÎæÞLä-lPö% ³Å÷¶w_2#nê¹c<!v$áÚÝI0-.DDÇÄ îà/”)‚eKzceä?5‹`œÉ(bœ'@HmŽÃÙÖµýi˜9h‡¢£8aÚM´ü:üù4ÞöPÁO(M…ô0í–“hð\ün÷ÍÒ°& RB/€‰òÙßܤ]ün’ðD”†„ˆé˜£É_‘TGû¾†2Ã"†9}„Q‡ÁQF¼Ç´ŒÍ¤Nä1ßþ߀âدS¨‰Z*kóªkŒ«!›]ÒW »EI¢Z³†+ê…‰]¦j(ö*Uô¥1LÐfÆ4ß|£`b‚O×X Å6­†­gôr`:Ѧ쌹²à¤ãx|`dŠÈN½“¹þ¢Ü[ê«ë¡1è¹² "æ,ݵ¬²{æcw4¥N3Ao›_ú§}Û }pŽ½&½0«—TF¿™6¯³@?GHF2  Öÿ`Íîó‡ï{(€HÛSÞ.Ãíq;¸ßÀÏÝ/uÛƧð±Z¬êÀå V×Å\Æëå ´¡žü=JHòyÒ©©ñ©s}Ùq"¦¾2b÷±¼ŒâÀ™ÖuüÍ­§«†œ)·ºÑÒ™Žý–ôü˜OzÕH”iž²æËðªg‚%1c•D[’€7œ(FŒnLß¾ˆôÐL-¾G„6קþ €R’«K èö>^ûÞÚDZs@&™ ¬™ºJèUUoˆÂþï¨û±kFf<"¿Uäc³'~{Uo›V_SmEˇ `ÒëÊÏc |_¯àô¬4$ —d(ÿò¥1¹å‚ʤ÷“úšZW_ôL¯Ê¹ß7Eaê [~TÁ‰ä¶[#Í‹Kæ),½‡¯« üCË0;vÇgXSΨÿŒz†•M0lwto—54 °É4¾¥¿Ì±6¿'PY Q²2öº§1ŠÈ¶­™ ‘EÐ?NDC.Ÿ8†˜‡‹x’® ¦q(ßñî¾&ÌJ>¼ÜPî|9ž…_€DÓÂ{üo•Ú‚Yÿ6òg–þöQ‹'î-z¢Äžg£ûÍÁ<úÊ%M¿¸Àºå¶ÑÙëzX°êKBDU¾Q¤è¼Y2‚ç){µ.õ‰-ÐT¹àN¸~©ÇxrÌ^¾ê‰wÈ„‚¢üÄ’ÞåTô ÞSBˆHù"Ôå9`vÀÓ–‡I"f¤¬±¼*¢—]ÖqþÅúä‘K…õú¹ŽALCIí$HŸ='M(‘Ï+M£yÕ¯{ùítOÍ„ –þÕ´ /-žÝµ —°¬€‡ŸµP†½À(þ˜)¦8ô‹0Áº [0é•­ô½*©DPŸ—y®êú>ôcó0ŽÌ=áXýþPWhѬ÷Æ´j¨5\Îî£w‚¡áh,H‘Îœ°øNHV-ì÷-8¨•zuÄ|Ð’ ë’äŸòøñ[tØkã,øyÂÔ-n‰ JèÛþIâ ³c¯ÙCà"Xµ irZ´bÀv6lŬPä»<1Õ|°ÏXzÁ¾¬RXä@ÀÈáQjp#ãnBùO'†Úy›?óbuàf1vÚ¥µyô¬dàOpra[„C3ê sŸ‹ðñrpÛà9ßš*]Î6~Ï R*2D©¿_ß¼hªn+ص šú«L<8Æa~¢–Ú+×vˆM»­">p–ìå·¯ÎèЧun~a÷ó·gVT™WqË©†1ûžDÎ×DB¿j0«&eBâ—`ÆØðµeŸ¨³î[X¥Ù†¨ûhêÓUgŠL•0½Ÿ™öüHH|ÑÇÏ ÉY$%¥°¸•±e¨{XƬ*O܆ԢÙæœó#͇ŸŒV)J%\‰Q{”x6ëÚ¤›ŸyÜÈÌ•›0a~aêwã:¢CÅ’fÐ{ø¤/ ã0{ñû„Œ¶¶õ/Èl[x#qåÀçÙ–xjSB±ÙÕt‡ÃZÔÒ{¥VCwßBkYÑöŽçߢ ´Ù–Š/WeÀr%9,2oTܪðP)1¾F¤Ogß²äEÚ¤˜Šü}òOó"Uö4>°v§V¢>ö‹Ó“¯­1 ï–…?NÀ+L/Z‰6¼³ ÃÁî§Ç‹ÿ êC¯XîNŠÆõ¸…Ø(¼å„JBŒ –å!»È9¨‹Yº¶™OëtZc[²/fGævÚjÕ\õ\J n“z.ª…ä¦ù'áÎË”¬º<Ü?tGŒ1˜˜õîS<¼NˆðZN„Öšª™Èö • y˯EY°XAÂ^®,ÿH#¦0€Jº ‹d3w½—Óq˜#II&10¬F qkå6á5ÊZ Q¶ZN?ЪäÒÅÜ æÜy:OtŽŽ‹H>ÊÛäÊ×ÐWº…fëAÙcë2ØeÌàæ.©Å¸":|l]ø¥|Œ:k\çõWó.;âxBq´Ý. .gq$Šü2‘f”0¶€µ àÅ"~GRÛÉ­ù#þå®æ®½…5IuŽ?ºL|sb‹¯Uäçû2{©L‚´bXœ—|FñŸÑzÃˤ;?"x’V2OH¿¦¡¤¦/ð'ßuù=öé+† ÿ©D¦Ñ™»÷äÔÐ;¹¦­Jv¿¡À<ðÈHÊ|E=™õy~¢—©.fËzíÆ©òy6ïßœKÅ&vå7§ÁÌ$oEP¾¶~¾¦위5e»…¼µµ­-Rp4åóB]Ë«˜,ñË‘QN ýüèYÛ¥lA‚˜½·ÛÙSË%(dk” 6ënÿªáíðŠÄ³]3£à/ZöEv¤Iüxç=±ã|„{ï¥ðjÞo0omሃ&!M< ff`ZÓyµ’w[K´ Ã&ÈšƒD4¸$wze¼Ò ßÓ"!i¨ý™D*&âêÖz£j™CZ«déñÞÁÙzr¬Ûkþ^GµÄùÀꎮ³gØhÛ«¾rdÀ£ *ìªÿZ&¬r ’§¾9$ápž§#_ì(Ë…Ñ•˜Š~*‘EMß$*Ê0›{ŸSç„UœÏŸÑm9ªéô<ëŠfÑ?ÂO_‰N‹çC»sÿ@ÜÑ ç|õ„C~pëÓ·Íh Ýž'–/ß’:q¿E[ŒÄ0Êac X»0oˆ ½Ø1‹±ºøK‹ÄBâ+¯Fl¹&p}Á·›‡<Ù–¯Ö/·Tì浄Æ÷öUgYg"ûôö¤‹÷ýWÎØ-×`_^ 6rUCó²yTä×u)mRžÓmSãÚ͉÷7÷}ŽEÒIŒàÅ£cñàñœçÙP¯â¾âØÚˆ#ü-+Òò‰ÅÙ<ê%òõø¨l(O¤1Û\3_Çà¼=Þf˜€ÄF;(Â’…2:ùêÞ‚í-þ?ôÈâ/í·\PYÿŠ¾Ê¾jÕ–?«nÿÉ´6÷†îÝ‹ÈÜú„ýj>ÐuY¾Á¾°} MYæp÷ üA +:oÏ“Nn2kKº¶y| ~Ÿ¯ñD D,xx¨õÛÃ*k˜OÕº#kÌoŽ¯eàñÏû&EŸFöj£;׎¯áp²3Ù<¥°®ð5»= Zfµ ð^á£åKt Î…†Õ  Ä×þA±ÖQä½ÊgsöbI‡ þP¦}(½¼»p\àIÄ6EÜðÀÅœæW½=`Ç Ç…½¨ã’À¦« NÒ¯©,&;Þã>Ê ûÑE;‰)gû£h¶ú§²õϺ ºk¢3˜Ó:^ŸÃˇ&âàóœ’ÝjK;¸æ{üÖ µO ÿJ´9%%P·Ú„v™Jb‘ƒm̦(Í$f¯$œ0}áÖÿê0ñXñ³öq†¦S»K¥PxL ÛŸe8=4€ó=ý™"ªßSOš¸F[EþÅkiyü+ûÍŒ±aM˜Ó>þ’pJ¡ÿÎÆ…¥ðѯa&ãiËN‚;G TÚÚP_Ö9°›0ÂSbÐ÷¿1¨º!… F‡òç€~¢¯i"q¬9ÞV ªä›û†‰¢ ï²¼¦‡wu]Ÿî«üƒK>‰ð†dK2¿àà&Št÷´dÉ°(rÔ'Ò[ (jû€=4ÅraQ D¹Â3-ìé-ãÁ±íö"Åq’®VÝ2¥ö É2,yùåË1FM•Sbè¹&Z›N;pJ&wió+»£ ˜ÿ‘ébîmT h¬Ñ‘i¡ÚeÁÇ@‚Œæýjåp†×§­¯¤gwy¾ýO äÉØp¥¢ÞÊPKÔÙUÁÙ¦ý‹‹ø¬2æ㵡`'%§«7ý–`BB‹<okÂ6ÊÓ£‡Ÿ}C›ã:§¬Z†süi’µ-°[ŠK¿|¦ÞŒ¼-¸›´³ë_ j|-Ÿ[˜—ràáSI°-—Sp›ïÜȬ‚yˆ.ƒÇÄò&GìòŒØ6ùR4‰!åt,‰”Ù.·¬è-ªãýXA—.ìÏÅTÀÈÏ¥gâë91ÙŽvtÿi‡>Ŷ9xD ß¾EƒÃ*®W¡,Ë$¢ûuŒ~HáÐÕµxšlDfáWYÓ·ÀB2¤&@©ì Ù<‘uâàQå‘ßØÈõX¿j^Š`ÒäýLâõh?xæ&ÆÔ„Oj;¤Ø±´ékh•V‰{MåXî /Üg¾æ¿U¾ÊDðõ“®!ûÓµ ™D<"á¤Åß<ö}ˆcd,þ–.q“Ûqá;÷| C_·o3÷7TöKíÊHÙÈ6/зV>H¶u 0@Uz¤-uWXÔ1lTC³&å»y ÂpUcÁÀÄp;,]Xg.²vö£^ÈMÌ3ÛCœÜ ƒï/®^ Û¦)à1h´OL6ûi{(¬µ.N¿È-xmÊŸaX¸Í_ ûL×ÍÆn=zÞ;ÞIûÌúÚâe¥‡“ßÇgçnÌÛÅŒ«»™öpÓp.ÁQÐ;@w9çt ¿&†I’ƒÛ½V×Qïcò†ptÖSo›Ÿ.²Ýáÿô¨nœÏ|ëÚsÊÂ@j—|ý,ä@}öêÄþÓjL/·è™Ó»Õ.œŒ~uY÷%Äf3º!ó³Êèhe+½(j¬(™ÌêTmí͸½àx͉Öj [æ§]OÑs†•.óå |zÒ×zQ¸ÝŠVZÀIÎçŠiÀ¶ëì‹åÄÏFÀ¤Š½:^W•°õ¦J¹Iðƒ‘η¸–.ÕÞEœÏ‹{@§øQÀ{AŦŸð—¶ŒÄ¿å#É°¹"šªÆP¢&Ýz™lé83Ÿž·Ò¯""hQYÆÍ ®K2Þ…¾¥Ž©6„û<‡1Ã<‰aq*F)Êß[#À*y’#ܘà²×áâ^æx²YÛs÷‚Ò3 †9!†~]©MæиÑÍøàiÉd µêŸÈ¨<Ác>¦œ oøñ4ZùßYõ2Ž¬”Ð 4>þ™“4É[aöW&«ð ^¡H6)(;@$bõ“Í4•$ÊÇ[h£p“Ã>ð¼» #Û…\âì“bÿ»¡ý; ç¡o¸® ÖgïãÚŒÛ\²B5ñQª;ÞäTßutd’·Þñ»¥Xp²Q*Žþ«…Ð2Oøã(½µn‘ïǨQ¥™rë}§ÔST›ÓôÙ9ƒkü£ŽFbèîS\êèﻩâ΢ÈVðÒy‰LÐͦ™¤öm¿5ë´,Ì%]؈cn#N|¼•ÎË=êz"q†‘™o¬ïЋ}g‹óf¹ÉèæƒöU™L—c²|e†U$"ÒôÂoa% }37öË–ÆßZ?·µãù¾«4iç¯ò°™}!TôU[u[ê…Šå5S‡iáˆb)÷LÝô0^V»¿‹Z|ÒÓÉh…ÿ€‡u‹kƒ¢¾(3,DYûî¹Ö„ó½"_û’¡ÐUàSY”{7Vìú¦P}¤øÎ;íuÅ‚­Ñ|ß–d;xTo[~¿NþÆY›†ö†ÖÙ¦S³Ô0DíÔ—;/d¢µd°išÉÞFIžYh+Äõ s«™&cN¤È롾HÔ3È_™êkXèxQúë¹cø’dÄœŸÐøÛµ(¯• Tâ~11šQý—*å:8Ä­à„vsðn.­eç‘×Â<½XDi– ö3Æ~|iD,u!L‰kÞ+&öIcÏ ìÛŽ;ÂB½#·€8‹MÇw¸b)ĪžÉÒ»’¶§²ÒÜ.g #SQŠ:¨žh‚X=Þ?ÛFöQk„bˆ…Š=X¸™°Y ƒÑnTáSú½CÍÜw#"å2ðd X½bèGÀ³ÕCƒ¾¢§“óÃz–À£[4&ý¯É˜IËbJÅa°ë<ؤj~l~¿2r!«¹T„7šz²<´wþ.\ÔJ‘¹-ˆPCúÛa²}˜_M­GÅöijr‹Q:cY¨àÐmp=<©Á<€RúXuãs}¹Ëì=Gmø&ÝâŸe" ŽkûŸÛÙÞ šž.gέû‚-f€tƒš§¤_w£"§eiþ3€€Ã0sÏøÿ2¯¡SRÊVpk,‘ v¥ôäm™GòÀÝG­}rNö}·zÿæ&Ľ#À5™ì‘FNâꛜ]"gÅø’€x'CÁ^Cû°‘ñ¾¡¤<ŸGà.é7JÔŽ æØnÈW—Sì+Ÿ þ¨®˜$wtAÄ °Éöâ,m mY mÜ;gIí P®Gì‡ËÙOÕŸy.8‹`?ÌQ‡ß`Ô¦¾¯>gäˆÐðýÀBV*+,ŽT)–ê¸jŸ\|¨ÛTdl™}è¨îø”3þ|Ù¬CÙ„7Ç÷á”ðÿ¥L±…¨ù¹Üʆz!Š¹sc†rð 3H½‡šêæ½?S~Ú˜¯Ø ]ûJ5Àl¹£¬I=ÑWkà¦Ýä²o7”ôUŠ â¹Oþz#( ¯2¨â‚Ù¶C%ÚÛp]ÝqF‚Ê°J“OnQ”áxnawðñûÕéu‚Ю7bT2<V6Ÿ`KR‰b=K“F €×,+\RתÅ1­…B#h&JåFÿ>e.èQ‡åQ_)îšú¥]œ¾Ò²ƒQžHhr‘‰I+Ð 9¹ð„r­$¥ÆÎý‘|!rX­ådè!&ääâÖÿ:š%ȶoJ¾V?’¯O”ýO7¢ocYêw¶¡‡u1ÒÜõ˜2¡¯Žå5^üejçÈÆÛ6ÊÅfè\MÒı¡Þ‹Háì­¦¬¯DŽ_v˜”âz,`~hûÇJxÛßáŠo dPÈ^2oŸö½8ߘÀñá?÷õT.ùŒðqØÝœˆŸö¢}ý·‹âðƒ7l§û­‡ûcyŽ¾ÂÌE‹°ä?ÍØ_·ÃÊ7®_+“x-r{-Õw£Ÿ}tðØ à.Vš¢& ÒÙKÁ¯Ç"¥ƒ†XlÁ‘°áþ˜H¯Aa:t?2†‰Æ[šv% ¶2Æf=õÎJòv©(…!à&u|)p]³Xªº¨v¸Ê7 ¥Šg•>ô:3ÕÓMµCB÷%g4ÙŽæ,ÉŠ¯ÝË9ÝÁióµ¾nN²Uü¾-¤èÚ`»³:»ÆÛ=H>lþ–¹Aÿö¬ æ¬húÖ€^È?øv³^ªqêÞÞb`¸ùûœ~ÛˆZRÞ›ôŸ][¥XlÒä1å"ÒñÏÒ»Zˆ¬Û²²ÐL Ix¼}”LséYñ²Ž¦O…¾ó¿¤ß}Íg‰2µdes« ‰Ê´ˆYw˜{Ûs¤YÞ¬¨ãÈ{¯råHØ— (&0ëÄõFÈÏ6½PŒó`|¬%……ªÂíüÍþSÅHXЊ”.„Ûˆ×)ât䘻&?¿9a—Ÿ‚ÔGèàð9¿Ø›ÎÊQ¾¨1¹ÿc<­·aHžÐ£½Aá„[êÇ—r'ó~ê ‚®<Õ £ÚÌøСCý糊ìÆåã¢ÕðXlIdib7ÿYšs ¥íWÒ_ àwhî·p^²\†ÍjÙV.ïø¼Í§8Ræƒ[Á˱mµžè~Hë,^» Üspª¸BÑ5Ô×L«K6²š@qè·w{ÑBñÖó4‡ZߢXÕ²ŸÅCŽ?&qû\CÄçìhY þñÎo°ÓïåC_ZzOÚÁÛì1 5£aÂ|ßSëÆRz£xÜu½XšëãIرëŠÆi–ÿ7¡lLÔ‚ŽÏSM›oz9¤tno €àgb…ãA²ÓGÏÓ=’Ž•ÏÐ,7zÆ’5E™Â+?&= P"I$m®³‡OÐãéY›NÈ;Ÿ¾x©å»€×…ÿkÂgÜz Ñ´ødQµI %uסê•%? Ÿç_ÔþôiÚë©Ø®y¯”vò-KgR-LÖ ŠûÇJÏo“-fFÝD¸÷|×dýëN·²á¾Æ èJ=ô—)õ+Gû™n|å ¿ÐzË2¾ô¯å•ëa¿RIRÒÝãˆêû¸nª‡Hïæjþ¶¢ß©\²r9È ¾ÕQ߃QÑ:ÄæThü¨—Ÿ‰Ïjiý84‰p÷§à7祗 æse`¢'x˜ÁéÚ]ˆ¯zÌÛÉYÇÑèºØ¦0„ÈR«ü@ã3Ï¶ó™“ œ«–,7¶_arÉ.Õz4a¥$fna…Ÿ؆ gå•Âê' |¨šVð•RÎYÌQ>· Ëù‡Uc£ù…C}žÄĺ ¯Uì±ìÂåg&½AŒ}^1Q¶ãºÿßèzÝñÞM;vÐCä™Ü°Vh. {~xØlÌÚØ,|~´F¼å÷V¶f`ËÈ׸MˆVÖôª®\M·e Žïù XFC(ïËôλâxÄb£Ý¦õÚûÚn¹ÛÑG£‘àâ¼çÒ¶ÏÁƒzMqZÃþŠn Å=À&ëFUBOÍ N`R/<'ô6™šX¹ÞDøR‰kà©yæMQÏWásc› 'Ê‚Ûí…Mƒtä°­Ð_61àį_‘„ØSocLŒ“vvjªÏ| j[—ƒo5oOf+SF’ŒàÝ2®WÿúÑW4ˆÒÏOCËŠ;UÆ–¯Û¢¯<ëlÃã4r Y\·äSŠØ‹‡È»ôn'÷ã§.lª® LøKIîŒ4züó²7ºÿ€<ë†|™«þMÄë–nÛ˜ gÃ6Mm´Çõo#dÅÄn¬HpÄgç±ÉŸÛï¡ú¸Dz±ê‰¤)…§J8ž™=9/Ž$%xÆᶓó}“wǘ´ÌAo÷€­6!w?ŒÖLazàZ]g«ðr¿‘~)”Á'wt†oœ.æø~¾…ú@Γ3 6¾¶õßy;PI€kEuqKVdç–¥’/¾ªºðözä•zÙx} bX p‚9rA+=r\¥µ>nù+¢ðaÇ(•Ê$²åŽ–ôÍ ¤} R¿„°ñ&sè'6”aÕQ”}4òX®¾nƒ.³D/žšÃ¹¯}XJr µûÑ«Ñ4sëÃ2š Ñ:ðØß<-]R\Þj‹Bí~=á”Õ|d§œ|™XJ0™U X³§ z9™uëŒM=ÁöhŸ3M —O œe-Ó9¯ºAýÓköth¨ŒHLvš6fn;}ºáA‡"lpW‘En†â˜‡NêV0B#6rLÛfl™B%J1%ÓÓO3€|¥.ï"$e2œíBêX™ß Š³8Í0Eìz×hÌÒá,04Óý>ðˆ/Š]&kGÇs¾ÝämÐsÔJ¹O±`±ó@ëÆå H?óó5œ‘Úp¼ío™£¹“èV‡Ž©ÇsïýQW¼­¯SÀZ*±÷ŪËVòl(Ê“Ò„ ‘œãxm[sÛÞšÌ"Mq"ñ‹V:êÞÌ[æ9G m̽×hZ­«Jbê`šäÈ?$ÓÿŽË®¶Âœ9£‘¨ÝL=Ë Ð´ï1#†FY°è;Z‰¼šn[Pâwò¿sÓ­@Ä(\œ‰¿îTðGÛG$——KþoW³ä"ßwõÊò‹—ª]“âY*¯^°&~uFœï+^£ô\—m Ën“}í÷cÿ%PJ¬æå{ÞBfž’i53¡”ÓoÛæ“,Qß³ÝĿ榇Kõˆˆå9½«ãåniÿœç6*J]š³¢ðÊö‹ËÀ:ø0"uß’fR.s`Àõq'ÕÊImˆiÜIêaƒB“—¤%ÿ¬-úµÍÈg1™Hè?ßû=8à?. ‚õSõ÷‘9Ú,$á $(Dï‹à˜eɦçSlB2\â›t}v׸.¾¤`KJ·ÁúyjKXp³ú±žGê¬'š]¤hä·ƒ)Ïñ cZŸA5)õ}0î_&›Æ%'±Ý¹§%ÄgËG;7²²€Ÿ©Ú†7~Bœ(wå³ *5.­ÙK¢íÐEí²íŒÂÁHõ|åˆÓÞ1äã od|y¥ñIôè¼ÕY𘧕;ûh*š…C5Wÿ,8ì;üq‘¸>pÏDÐ…ÒT&üüC\Ûáù'~LÈKfËV°£·zÝ]pªGFˆÖ?è»9’4Â_Ù‹ñ•‚ýؘЦñêŒ h±iÛ^_ø 5 Š­FiÖJŸ“bÕ×40 rÕ?%(ð!B&wvUšJ8Eêxa¡Ä?L×´£ÁwßÏ+×,ñ gé·Š0\í}^Õ #RÄo†£šöØ7šbZ và¯r“ïІ²°KR ô{)þуü‚p§ûI° þ€Ë§BP Ã'‹/µ§ñj5Öƒ˜½å®EƒjG)äv‹¼tá'Üävÿg?ÊYy6²#´w»Âó+ÇLNOËl‰E_2·‰S9.-âž|Í„P•óPþ™ÓjŽ”ï?—¤té"ÐäÅ÷ÉzÁiÊ ft¥?o;c ìàqW“ ¿`{“!…JqPÒa!«"Ik.·g•¼µ!1òÊ—ðÑ’Ŭ&h¦DTä›ò%SX“õyÌ؇3Ð_Š{"7ß )o‹w­Ñ!—è¶x9ßîªØ¯Î6…J'•öìÜ@íáÙÏkçáò¨‚5„ª¤ß¼Õö¬I&Ä Ñ"„“V¹rÕ·µÕÏŽgóýä”rÊɼNì·«7?ñ9ZJPc 7êÈÌ¿äR/¤ÿ6³ý\0’^VÁÎéÐÜÆœÄoœg6õŽJVèdâqÅ(±)kgû‘§— ÓKUOº…Æ~]Äú8æÑ”ÒÓcºu„A\9iò–o›\ûáJÚxæÁ5¯<âœiwÉ$k\÷oÕɶêG¶–×ï|пÂ[júÁäÞËûþð”²!+ŸkÞh¯q‘¸Ò÷¸•¨%i‘·ïÏ¡Jﻺ°Ð>ñ(5ú¢¶\ºÑìWë1ìÍ9"Æ[ú·‚‡Ûš7Íñ“hŽêàQ=[îTtzeˆÁß”ŽÉZ>FxjJcmj( ˆ H¾í©)°¸UÞýYŠe\$6!ÂÿÀ‚Â~w‰²Z]b½oI—ø±aÛYýà¤X‡å@¯ï"¯†lu?Z‚^ÊG<›µ¦†È’î¾¼ èWwH7â»8¯uH³Åøë7’W>Tüwá(@‘I `—)m˜GÃÇ 8fiqó$Ÿ€•Ò½ØQ`8Kr:zç¦ ØèP†óýÙØøJR‹¢­ù5A]BˆÇ÷!HÃáUHßô3%µ°Ñ@‰—ruvÄ,D HÕ×ûz¸îï. ÐÜGýyBTœwžÙcØoã=¶Ó~oESö‹¶a$Š…íd­6Ø›u—a-½ÐcôT›&зÁ•Ô,[á4£0kcG2X¶Ç ºj¨â\=\NÈwõpSîôéQø¸8ׇfMûÉTO}4›IeÅZ©ëÄ“f:œ^ O£†3ê%6Ëü›”P¹©õ ¨‰xxB¼â¨0…Íæ+£CÜruʳö­|CÕÂXQÃFƒÔuÝüéS¼/ cœI¥ð¸ˆ V5©±A÷ž :sZx%:tõ°LQ…÷›Hñ¨î8j»â­Qþ쇼[ƒ¥£f…AßøûØ®‹t”’`›Mó2Ç"&;ÑÙ „ Ýz•þÌÎ"»8¾âé„ßa[Ñh;bÏðDvIÙ"åD?rùŽºœ¬!Ôèõa‚³DO4I‰£±ò!(é §ƒëe_ž7ß%"d‡$„QF½3¾R`Ö„ËÀ†"õëÄ;GûÚIɲæÄλÉ×-Kn»” $à0ylƒwŠŠgHˆ½¾ˆõwߪ®ÛÓ­¿RLTïѤ eÝ™¢F\{ß7Ý61 Ú4žù2pÉ–Am6Ë»ñÛ))ˆQRºç¨tà­Û÷>LŽ`õ‰Ù¸…’PßE§ê:Ÿ•Žåã~¹ü@À`ùÓ+b#4À7  lƒQ‰fžÿèožÎë×N6)‡ è}žå.¼I«Io8ƒÛþ'ƤÜpÚ$†h1¹úجַá§g¦~„C±Í-¸^~p‡7eÎ-U®›ølYHð~ •Ðb½¬ë5P¡o>Ž…BÆ){G»ž”ù}bž†ÿ(‰¿pñÏžd žÑw¨ÁjôOHÝÍ÷{2ö?›•¥ùÚ¬°Á@tA B£®¦6“¬¨ü“•oûÙ‚oV632qÞ×5‚²qÄKÏœk°údóK¡¨Ïfï½YXFFUÏl¡µžظ³Ží·#ˆ¶SÓñ£©2)dIÂä-1´­“¶¥ÈŸˆ„³,”á%„Õ ÙBÖ™ríLx~ÁÖì…n´á‘†Kv\7 ~Å.7|@=¥ÙK' hÎÃÞ¹ùÈÁ´–‡ô‡ÉšÚ8Î(H5´ò7MêÙÙûŽQ‡ êIQÎD9Q“%°YÖ·Wü¿åïPÆ&>ŽN[0p’ËOô£Ý}{ä=TümèIøºkDloŽ<†Lú;) <¤æ¥í¶dSŠ¹!j÷xV,ÕΫ¢g[Þ*‚hßhw€´pyÙÕa]ÛRÜ(â‚Ž-UDÎ>q0èëéy™€”–›W+ªüL¥Ù•#•­Ã¥÷­ úá¾–x°¶«`¡` ŒèJ°éî“ð¹*8ánÚsòî„;Ózîƒ A£KÔ¿<¢)®`uùb%±ý±KÒ4Ð_õÜÛÔ#ót ªœÔÁA(["-Ø*®ÿ lJ)Ôå ]n&5`ÒÕßÕA)š;+:P%n ÌY™…r.^DŸRÞZl?ì&¤½J¾¾,;€9ŒææÜ'ÕË°€\k£Š<ʨ/Ph/Õ%Âó‚Á$j—Mó°ØÛ†dØ)¸@V÷bqôòÇIzäùPÊG9A%´üîYv:”çî@ ˜Ÿ'!î™Ê§­N`A|L¥ZO»¤ÏÅQör7·²ïªe[å$lÒ¹’†—6à‡é$ÿøb’ÚÓ^_Œ©cÂKMg™Vý†!pE¢pT)”¯øSKbøÛ PGZûŽ³¡V2ҞĺjfÍüÏÅPJÀ ñŽE=½pøƒoàkB7t Y1tþ‚È㯷ˆˆâ27µ÷ßÕ$yÆ~7¤%KrptÀ¢W'+p»v“€Ó8©ˆß ÞUI­ãŒ3º=þZäƒ×³”Ëÿ5„¨L&ÒRû‡iItvÅU„,Ù¡qÉ1b(Ü¢/‹CþÞŠ@A ï‚UmLxi¯•ùù'‰ ¢Bo“U‡-.WKðÛß ­ç6ù<²T†5}Ó0Ï=ª«œñq…ªóûneD¼P駆UüvúLã,ö ÀÀa ½uÎ=ð°ë0Ó½ày4-Q¯#‡^ðI+(Üžã¹ P´DIžššÆ!Vú¨…ŒL_Õ‘—`H—¬•‘ùÌÆTÅ$qµ_ÞÝ…—d`M¯{ À3íîœ*a"·#t_shÌ4Ù-¼üJE83ˈ/q<”¤j]AÐï}þƒƒ7üî†õjߤ%\<¾¡œÞ¨"WÚzf×ÛwFEG€™uÃ7ŠhÙˆù­ÌN¤wîB³ˆOkÏ|­s­ïqW3‚Ø•ÒDáìRtOp• ò™É"OúŒPNb®,v²ûDEåñr³Po„#תåEv„vêà‰™Þ~ål'Ý\$˜BYÏœPO?8&ä`È h~˜˜=¸ŸSJú§Í2r—S ´eˆã?ª« _dªt´ªË[™uE P‹"nì­á”‰ÖÜJ}åÇQê‡"±“üä²­å¦e×…Lì,8ÿ)yµÀL½ê€ÈEÖyÙùöi{šë+¤ÿÉ$YYMíö%å«fðÔÑÅÊJ ªŸÅ­ž]_<”îN)+êÔß-š¼d@lØÛ{#Há¡Eࣳ„À6ÙSËc…ó‡ ÿ?QÄNdÍ‘5ÁyÃD$ (W®oèy»iõ<-C*ÔíÛëóvè"3phpÙúèÉéM¬ ôº6\WÈH@p‰áY=ùîÂ+Ú`ýÊí³Hô§Ÿ–R±ê4AM4ÄÅ\4åÉ{^‹vE8Q`bX™_£«49‚ž.¸ý*ÅYÝc퉇Ò•†ãØ+@P èÇÒ$Ÿ dáV6ÿî1#¡N#ܽc¢QÄ‚BqÅâ`v:Tb0Ȝ˧‘}çlu¯Ì»¹l…³uÇ`§ÍÃÉ”C¹Íí0¬Þ˜4`3 Š÷«N%þ6m-‡>¨Ñd„ý*MR,-'¯2P'oi³9½®á)Ï,‚ÞHõFü ø× x»]¤ÃÇKñFÙ âï}èŒk±öªjÿoMãoiôx~äé†5‰óG{n¯+Mtà 5Ž†èŒOœq‰aEF"Ìò™€=òh3lë‘A[R9fŠBÇ&—àj¶ÁZŠÒ@& ¼Aµ++5$$¼%pj쥩‚ Ϧd`1Ì<ù¢fúmÃh:2¤àñ­ŸÿAƒ‹{} H{dnP½¥ððRF1F J¨õh Ä«»œ³}F?Šºe4:¸|Ÿ¢éò9IxNø®L—¿»¶Ø©õ»HÊgU†¹‹VÉòÃL0›ÂQÙ Rø”ºòìùçKÙ @:a_×-\6òuŠ•sµ§>ÁÒé +ÛYý¦Æâ!${§õ±A0ˆXYE].ºÅ:QD Tg[¯MMóm8 NU‰Lç×Vá|ºUá¹—¡ZQ˜?@Æë’$¾ëV“$–©%ñóGGè´=Ô4@pëΘ¼Ó½ñìÔ/ô‰7¸¢C?b:AΪ[К>f9™Õ­Öy8"ô:2ù°E‡¸™‡X7=ß•íîÝnAÊ,Ðç9X¢šd)Õéõû¡±_h÷ëMÝ:G9Ð;rML´!*©Ù"(6Æ<#hÇýY"©wæfþ£Ms&7 Ê;ÐXCËöÜÂÍŽ`Òä3ºÿ¯{ü’öçEHý8f<`{nç¨â/Ä{L«—c¨¦>%lšªœ™S¯A4%ò¹}¸u¡Ùe›ÄH”‡îÁ­våK³=êÎ6W—A=ÿ$fkï_ÑÓ¶ëg^ášQˆe]Zb“OeuNå)EïèQb)ÿêï{=ÄÊ a§Àw^~ÛË.s\ÛX*4’<Ø¿.šÎ§=“iyIÎøÞGõK`ÄæÇe3&j'Lê,´D¾dáÎpÛ׋."±Ú¹ô$ŒDïîñ6â¬.”÷0ÓjY–Ã7 qf‘Ôý™Í°7±@ÿòJ›È6êA\#?ZPºÉâ{3VgïQ¢`¨Á2“bøE9ƒv}¿Iň´Ì‰Ð°À!K~§:pWÈx®u”ŠããA°ÔßœU?V×ØÇ¥,úÁ=× Ä$ÝO"Ä> iðçíNïáÊ”4Äih òÉ7õåñ¢Œ·£#]Å¢Fü]iFé¸a/Ìü>Óö‹ªe_má8Þ‰æIéV‰ÿÀðò‚Ðô퀉FŽ—²q-Ñ–a‰¬•ÒJ]…¢2?‚yHªs…)²¼'–ÖЋòËkZ$µ.²M ÀI U¼Ý‹^4£{œˆ–ocÔ’®¶œÈgO  ÑB—ÜìJ^Þ4e‡{qWóW`”î$¦a÷"NeÙÏ)õc9Âi…ï¡¡•ú½"ˆ.;hÓ›ØMF0QUë`âPâ@ï<^¾Q¦säÕ€)F@Ž“|Ù_¯öðùÌÖç5‰Ð+†qqVœë[x¾Ý"´b HŠsBš.¦ÿ¬}à³:lÒ{l>æÊxs Ú¤×g×j!Â92Ø—ÄðÜ3ª)‘JoákÜœãéµ4wr Ã; ¿«ÛòÕO·ÿµ}Ã"Œ”%¤"ß2C"f[ë²m‰PVĸ·i@z㻨‰šÅÓeØ«9rÊú\Y]tD®–1‡B Šìë~T ¿ZÝUÚkyšƒ¿Þ(‹*®·ÎûÍ%¡BÙ`Üjcj±Ÿ´sÎï)íTÓõßQX—EÁÑ1JgÙüø£ò°°?ÑCOU«¸G:lá‘ÈþzhÊ]ÐT¸òt6 žS¶©ØsŽ°½ëëû W)$óLÝ£çUîxJ}Ù=ìguÄènáÙ¦^>‰NCêâ¡Ìˆ—&iëÀª@f÷!&ùPkŠ‡@¾(Q–ùcB[l.ýšÚ{k+NšéµùMˆË.{ŸIÑl`UrøìgþÌ}õŠô Ÿú¾©M_°ä È“s…š­¬uv¡&fç\Ûñw\È3e¬´2&9ÌãtêTyŒ:þ„VX˜5«•½}ý}*5Ú&}¬ òéÿ„—/›¸ÂìÏΫ\†J&º&—Laµ3aÅÉâÖGY})À¶aÈÅ•™£®å’!­Žvƒ…¶ :¤*I7‡U€0yÇò¹”FÉÿзJ~J•¨î¼Ò©Yži­I\¦-š¤Šn£þþ–T„ò®’p<†ÐvÔ¯âqëç³n}0¾Š¾ùûNJ.(Gº;wót¾;¾LÂsdí8†ÜfY8lW»Æ.Ž1ü“²_| !ï¶!¿à›·z)÷XÂ* QÊM•“­ÞPÌØ°–ȼn"¸Q^®3&7ùà?ýð#ùÐî@/z`âç[¡P•ŠÎÌav`†* El^`4uHr» [{mYëlìõÆMk¨ÉFÌZÐôËæ”ÒÓúœýÄàùxm%]wú8pè¥ eó?ßCmJHÙÒí&ÐÝPÌÁ*JGTÇ}+T-2öíëÐÃíÈRR½Àñìaºuj öøG.þ<‹êLi"ÀÊ­¶Æy¨Òd‘ü¼˜Œ×W2é?<Â’Ê b®á?ÿ̉tÍ M-+®ËB,</·v|˜ã«5Ê°®Ÿg„U]CÌŒ !×uêV‚b9öíÄ®«¿ß –Y×$Wc#ÊƦ]º±5l™*°z[7g¾ÔÞ -“jÄx#²–ç¡2?° LMLÈì~Eàs7")Ï÷Þ0²! };3A»,5YPTº¤îÐÑFõ\Ä‘›ù%¡:(™>•ÁZ™$‹[yÜÚ³÷_e¶¡„8¶ŠcZÏÉ}§¶O‰¢nòæXÚ½#§³­3¤âcm½xþs´¿¢ Y¬UUppG'ƒÚÍÝãq‰•ÓÔ_;®2 ¦u½1Z˜~_ ¢Ú5¨Tº/HšÈnbw¾Ä®=»RËØ·½„G•Œö€”ì{ƒðßÌZnîÛu¢SRñ ÂÆ32@6ô¨óVËÛ°†6ž•ßŠÌÏwN¨ëñ‡5³c m°ö$îd†&(Œàsat» Òqw5´Ö: Š \kÀÃ¥£Sþ‚}ý€ƒ;á8‰NÞ:SGëóþÄ8·©2m'éÅÂÂ1CÊjÚyÝ‹CV€®=f³¥wñ\ÛÔb½Ð_Gÿ Pî.Üjô;°êã×*[ÕÖ:ð¼à ³¥g¯Êp’›ñÿÿñ¯›ºÆpPGÍ–V6ÁÔº~­ÄŒ;õ-ŒÆ}RÆ%~gÒr“Ð2ÃéÈïIN$˜¦ª¦µªuèæÑ.7ÛvÊlï D6ç¬çõÇž™O}õçȘÂ*y,¥7·§ìÌQβ¬Ñƒ¹j+œ¾NŸFqœ·?ók¹Kq/ ÿ'¾Öñ>3Ù|Ÿ ;Aøì$2Tl®2kgà·ÿÀ6žš˜V3þz¤lãFlç9D€$ý=2YKì ‹¹]DAaA:·¼µ¢ã6úXˆüî†îì€zÁ×jª;è‡ÙžÖ"÷{5Lû&*rdx½©qõqkà‰»û±oôƒ}pþ½7½Î—Ž„y}˜p‰F:é¡J¡á¸ÎG%SÏq7Ʋt­Ï³ ,Ý´Þ8:©7–Dˆ1|gÿu™ÖÄE™{X©OŠ,b©2¶7R? ç$P\þ[c¡ñºv¢þÍ唀{&|ÅÒ𔶠‘Êu]žˆOdÁ\Ò$ ïK¦U¤@>y ´Î:^°:Ha‘ûÿðpòÌ ÎòâÕ~ѸeÒ–›÷óÐO߈{E?m«jå}tè,À•wí&|â»ê‰¢ý·#šQ–«™zW»*ý2+ÄQ+}&úG_ŠRÆ×êÁ"¶'rX—Ëœ‡û\c ¬âyùz·ªš/4ÆYK·€xÄ`NMg„¥;¯"†œìL§æŒX.ZÖÚ¼šrÀ¯}Žšê“#s€E¥Ñ­”Ÿÿ>ätËÙ³5©¹®EaNzŸOœ_Gñ2]Žð2ìÎ+28Hˆÿ¦ÖºV›!]‰kávŒå´Û€-d}ÇH©Ê‰Üa¬GüO{«3å¸ ëQKNZÅ‚}'fo[+ßÑ]sk¿>icc­Ìïßøü}”«^V¦iÎ ­Z9#(#no÷äã¾à§hBÜÕ—áh¶Nv°sÀ¥P¹£¬‰6”}‹GSàßù˜á½ngÖøSÁrï÷`xñù{Ç೺Šûs%…°8Ôù _ÆvN¥«*êžô¿Ößæֲl2 4ïBp`ž€ûëX¼å?A¢Hól¿¯Z˼3JÀ˜òßh=aé†]NAKÿ”_X±fº™.ö)(BcdîS‡ý«†Ëᓯ7Q(mzs›‘IßWÚ8j2×1R=IîÕ¡†¨ÕŠäb…^‘š‚ƒûB£àv»´ 0³(Dd$µŒÄàÅZ·üòé«RçUð”*](®,²e؃0ÐB‚Odv®î-@²•PZëfpO<5“C±€Ùka&¼Öx±ž'Âö;Ž‘\€Eˆ7^ $Sˆcù¬‡T2`¸ªZ\ÒÖýØÒG® §ž*¢EâŨ[Ê@{˜¥sù6í– ÙuÃçñPQ}(¼$–’z›n‚DÆÎýo/çÀ~h°Ý^OÕ¶APR½jÄ* VY òÄƨšþM‰LŠ½ø³_>©¾ 6NzmTë]RZ[ør»$m™5EuxÎÇî˯ÆÑòåàü¾'B­g9/y|­€b«NÎi¬N *xÌDC(3k‘\Ÿå *±\ß}‡u Rv[Ëó$aO,qPtCËO žglÑvÏ97—)î7y‰šJ®z¡JAvRì«Ú+vðPâ7z[×µãÀ ²È:+¯ï r…árx &Ÿ¾ÞÿËF(bpM“ëOǼëbˆ×@ƒ}vNR•p¿f´ù£ÔžƒPê+1`èF¡ÐàôG̵Ïîb )ÚZS@iÑü×™v§3fÑkTÉãZ`±+]ðm‡÷j$ÍòE÷TÑÚ»‡ù¬󻙥âP ;"ô6wÜ€â…=«Úq®°¹j;Š™¡_«ä›!yË"9ýÃs©•šûzDæ0Gç+¹þJFqW2ëA e‘¹ï[ƒòXOcc’™$³_2Å–ë5tãf/ žK&>¨ìà5 dL9-¡W¨ÚkweWëÇ¿ÒÉöܼ…å÷y ‚ ŸKÁUt71¯°Ëü§f¯ ¦ "#p£fUšˆ2¯`î'pD ‹I_NªyqpÞ؉ܛœÀ™¹µ!§ÛKV±¼j°(,wPÙ–a|2ˆ´¼_=M®ËX½2S§Õ~µЯ­â±$_úí‹“Ø€{¶ÊBæ?¤QÔ,ó+§œðlè·ü­4ƒ~RT(ùR@˜ ˆ’Ôõ(w|ê6I¦@:´u”Çl³õi@Õ«Ë„ŸJün–:¿ ù¤w»ÃqGVÉÿí$/¶k<ÁŸò[Ú³ÑÜ5ÅÙ}™MAÀ|þ°û+4aƒ“ᢽMg…¤= ïUÜšfý ç¿|…³BŸÒµß«Ÿ6Ž¦<¢£\›/Í›RÖÀ u3h̼QcÎhü¼|IŠ©À8ʇ$®©?LEa©à¿c€x/³Ä eYÀ¿¥ f¡W³áò_‰ÎŸ»o½iÖØxvq*Tc¸ mÁÖìïó‚1ÔHgcí¦c$š$´bÒò)'¯ùm±ªø'\ñÖ#öYìY}†àf˜ðž¥¦ÜæüQ²÷Jß;âŠWæ#oˆ TáëƪÀ’ìJšŒ¯b\ÕË.ΟUÎÆ_•ïn7dÅ:‡‘ÁùÐ$Hp°?À¤°Œ%f+›ÌàÑo65VŠªuTº .\þî~‡üßH7ëoÍQºuu€ÚK#ª"Ü:—·ðtÕËø˜`ð5Zºfp0o”c`%%‚кÿä±û6àrÄÈ Ó5ÿXZÆhŒ>ûéwu!ìùy‰—º]ÓÜršbv7m:Ólµö°ÊÆ}gN'GGìijÈh‰æÔ•ð/ø*$kB¸áUÿú™zpÂFDF³ð8²9uœeh¤`Ÿ>jsQY…õðä³Ô` òÞ@€‰ïu’Zœ¶¢—¦ðÒˆ¯*Qå1J ì‹%G/í·ùì±ÝvÞS€éò_&¼g±ô•'-)g{P{ì)ç 4Ϭ]¿zΨ'µ]È’í(Q°Ù†Ý·óë«ŸI!¹•^›ÇÜS[‘~ÐÙ1ápÿŠÜ8 ¹žTœ‘,m‰§¶Fc!> Ìyéìs÷·e5ÊÎß}3òSkÑ·ª)»£—üIR×RwJ2dš6ëýS ƒd´wzJ…œà`šÃJäÒ†ÀÊT†ô^ ”Ëgçù"Û²9GÔ¡ãG³ÄˆÁâvdüÿÛ‹lÀSS‚©dnOéÂ?­»+âG("¼Xj@¢”­`£Eá(¦;yOùÀaسB˜àáÿŒœ#VOøºF ÏÔl Ú‰°þý˜Û«}U-K-|ºFpµÇƒ›¥=sÑì"³á€m¬žAâŠÍ×ØUK¾%pÄ·êÉå¥ñ<¿9Ø®'ü‘"úc3c>×È÷"·â}þÙàìæþ=Óüö³1^Šóoæbqô€ÄRëÙ0³É8 »=ÿKªÛjÁvYL¾5ËÓ£€ÂF`\Ãdœœ·ùröƒ=z•Xë§YJœO¥5M˜í†í  Z£œDéýsG/«þ&Ùc8$r¹Re¡Fü'«ã#Nú±t¸Â_UèN»Ìš=‡_@ª‚ocxb×{ŽSÌè©,šåi(bIilÏ÷1œ»RmO¾i]—µ4ÿO¹bÔ™j>¯]¶'Pn¨­½úû–™aÊà=‘Ñ1Q]›õWBb¨ZÙ#´âÒz£³¾¬™\•¸ÔR-wErn¿—¤Ÿè Š`Ú<1TÚèyg™7&Ó›»ÙÄ‘ôD$ªƒƒ¤AN¼Y†/É c„/1t]V•n¢SÁÞ {ô³ø'G˜Çí`ƒüÜž?À§H;ÀãÞ%EdkWñÐÙJ© ?”YpùTöD4ÖT,ãhýc·Ý¹óU’›OÄOÐÿ~(”Ö#ád:ûâfƒ§~#òÖèf+`c00Æ2Â˺‡äžHìÀÀ“+ ÝÖ#’hèyÄåŒêîdÁBÕa¯cŒ{5‘/t¢Ç.8Tù®[´wQÎ(Q5.²z›Iªªª±ÙlÉ­ >6Éww½¡©'Hox<.Nˆ#(Xä©'d—ü6ºçÓJ#?…Ø(y…ê(RµGÕ+0x¾Þç.;‡²Ò\³Uå 2¢†®û™NAn[LÌì¾0«|v­ï.#?1uñŽ¨] EŶ²Ý|îßcÚŸh02ßpPfœõ—Áà¶ðDÃtçqÆÌ ×},O¿0GÑ{ujè1†þPæ9¿¦îÏ=*7ßé¼PoMc¨w‡•~µ}T»ÌVìå)E¢t7…«»S-t•¸Ïqky\‘™`Ñ` *6ˆÑ5®Ÿs[KUnx¥Ý.b÷ùÇ™“…³$ê«Ó;¦0/5¦97ù„ Qi¦zÕÐ'Ë/j¬x®=ЯɀŸ½éæ5BTïGøHœÜ'“„”^$z¢Ð,Ã>º¢ ŒÏÝw¤mCoåeºè Êz½gB2¬GÌk­F†Ž@œ'ᬘHØÑÁwÒÃØÇ\zÒ¬†;C9hÚ~µ5ç°å!ö¢’ÑÝE‰öxÓ(±ø±v¥lŠFl‡r 7³IeŒuB- ‚Ê@z“IÒdï,µçâCoÌJá£0qs c2ò­~ô`kÞFìõ©ô VÕ«ÜãSë·±^%’ß8"gAª”{<9:mµöØqo[B-6O¸w"ïåñ‰ZL©©¸O3Àý#PúÖÄ@£p…HhsŒŒ=>º¬±Rzå//V.ÔÁZ†‹älû¬D'ÞR˜ßå+‚XD9ý¾™Ëôƒ¥7ªgýÚ‘R"ëã Åÿ—%ö-¹mþ}’¤UÚyk@ïëcáÌ!–$fK·KfäåÓq±ùÙÔù-ëƒ-í¾~¾ÌWŽÙw±Üö³Nõ~¿„¾~ëj†ðÈ—«E©· H÷K>ܯœµ¥xp0Ñ?Ž[L7_áì—vºó<ÎPM=—Å–Ks¾ýEî)z0x¾0HØÿnLC&¹DíÑÕO#9Aé–èQ&ëÕ:·9‚)gH† Þc¾ ¼jó+(;_ò¿#ͪ€†5h £/Žñàⶒϔ}ü\%˜6vq1Ïb›Öyxà§üVN‚ëŠÞ#s 9ÓsÖ7±‹¯®í˜µOuÖMÍpY"Œ\uq̳¯æZn˜"§I¼[1,+#Ùe±ò9— ƒLáú…€ÑIGˆ>;õ¢©‚~K$'Ñ¥mßníé› XYÝ^Ú±ïV¢¦767ì¸K/½ê°ÁžF3ù¶¥ÜpÁ}±1?Ó3ÙôÕ¿#öÙ?#ZÞÁ¾È1)Q ªL@%ÿf¦EçÞ®ÅýŽCEÙ,Ékjï&áù=ÂÆÈ&“dè5ÜplWÉ|‰Šo$R¿â<1Q/õ¼R¨êÁþì ÓqD~ Ë€cÆ=X7Rq‡ô¬)â/fõeŽ±ÜÚôIƆîa¾IÂÙi>üÐo<«ú“ÑG‡qïYºC3tS9Ӈز‡8‘¾íýe=!¸Åš1†¦-ì³½¥Cj-=cô¢~KÌ–¬ôÝ,›4Ê\7± gí½ÙçØ¡Px§¦Vè¯R¼¥­œ.ºSÑáfœª4Ê…éàþ(:s©Ù-þq9·uÅ»=B“/t{†‘Ë-}ý‚ð&à†ÃŽÓP¦h< ×ûPþ‘[G—ÿ6ûûö& èê®­dýì;rå‘÷U¨µ=ŠöJb«qoü´ŒõaccC yõ.@© F6*û B“=­Õ½%^{oÄŠÊ+Œz¹³ŸÊ•…Ì%Œ)½é5‹êþšà¨ßAÄ0y³¥ÿè‚þÑ–tK{E«Í®Õ²u}à<ù»á½{¤˜ñVkƒš!'Š—ßšÞLdVþî,ª˜¤µÓ@é©&³cä£ôä•b4ãºy´9¿o‘èeY*¶ƒNŒG³PÁ“{c€Ñœ±<ƒ2 W×Nßf™¸Cª§ˆõaø([±6¹þ³fŒ¾Í%¡Æ¤žã*5cl£2‰f„jýPçðäÝeeÛ|¶Cp+F§;xÏ‘®™Ï¢Ì“lˆa Ò'²ÂWøα|Ð:+eÐøñ$† ÄãC#aÎÜ-õ±lŸ¸ôe:^¿«#¦zÀ^@ë{‚u6#¢º¶þh}¼“¼`ÀHBþÊî³#┨¼2ºuá1¼«S9ï}"†B7ð·²­ "%Þõ‰ûný«Uf¸Z³GGü±÷ Ng.ÛQýxû„üOнåçz<Åè‚1ÑowÿÞlñÐM4 =Òxº®“¶_Èwê‚M&uê ÏÔÛªlS¸BïN¶ðó4Ö%3~v8‘qÄH‡âÚ½¾”ˆ^;×Ü'­öðUÜÎ÷KÉ€Õ‘äŽe=SÖd¼ÿäŒ/J‡cp¤’ïƒLD84-ÌôÓFs’sØ·ù¾ËÜògçFh^‹X¢j '(çŒ8Qå×év‘¦ßGiJ˵Ü&­[¶r)ÍvH-Q¢x†È€—YîZ?höˆ¨†dM_tÞÈÉChë ’ŽÂûvÏ£š³ýúðE|£›´£×X2B¤6æ,mºüê¢ÊMʬ¹™Û6*Œ©nw1°Ún†‡¼¡«E¿já½ ‚Ø nÒH¨llƒ×€î´ù±ïÏúá ræspe¡o£§$_‹jJZV8õˆþK”¦Ééò3("?¸Cî9()ºù§,{Ì϶ öéö›Cõç±½D]Ázxª¹¦Ã[Úwù êkÉÝJ)­z¶ºz„²jç€"£K°c(Šéû—…–R%äÛïN$ññUØÐÜœÑÐ6ý‰e¡b…úJ‰–±¿W¢gâ;©:®L¥ØÛZÅÂÑ !òùE=XŠ{% iT†È&x>#\” q" ¬oûÖ‘|®‹ÈÛÚèQ(bº¦P°Þ£}‡'Ú9GËT‘/œƒ¶]žþI­-®‘£§æj§s£4)µ ½¶ ¿ ëŒí·"Tš¼üŸõÂ=P ÂÆë Z)WÜßÿô­®ó†Î@ãt(W̽qÍÏ¡€ƒ&ª\RØÆÿ€,мr&îMB¾õ%VïÎqˆþ+lál¼´¥ogîž»Ð.2­|ì–p4”l÷ƒÇnŒ¾O(3H±ÿ™ø®™6ŽžF<‚Nn¥:ÞŽ‹Ø  hV¼2?q%¡îxmwâN–ÎΚí›[›VR¢o†šfæAƒØzË.«²º‘”1¼;§ý=qÃBáa“y® Â2qüùðÃ-¯Èiè¥#C/E_£d¸Ù>Ïç‡{l(}‘•€®kû,i?7\¨7«L<ÇFþV÷°Š£<n%¼__úÇt×ÐjFj·†ôX·ó°ñ@ÙiÿvBÝjTùò:Iš¨¦ˆ|LpƒýÌ&'Fý U N¤Ûb(@Ô°o…Jqšê‡<þb»iº8 ‹ËŽÀÿ <‘Ë’Z ´L€•8“2ŽÛ†µËYu¬À@ 6·Òk„Ïø‹ yyËTÃLZSp $ 網»DµUÆðò@•Ë½æÿ=¹ÒBñ˜ª…ÓC²§ =E[Ã+¼ IŠЩÉåi<vÕÐ_6 h…bWæ šl èÜ9wîì[ÏaNéH <Ê^Üb. –¾—µþ- é8dÊ9ó"•Bîô%Ñ–[.eQ ø—‡RýsÄÏdºÓûR¸=Rò2Ÿcøýh¿Ô×÷h²º,µ6~0Î|±­©ÁµÖ»~“/Æiæ>b÷ù‡|‰Z˜ƒSS‡¬¡—¤x?*qÈû|Vk=²'¦Ì—oyŽ7ê­~êïʲº@²O:®‚7Ÿ–ü6­Á…âö>5â›tÊuxXë Ë{±k+r4ï>˯;=O+äz)Å )v3vs7÷Ñ˶\dÇè"椴ïØH¥7xDñ÷`_&rS“cM¤êù½¬ƒMm0ìåS°[h¬Î—êb¯v™œŸŒ‘êîfìÓœ’Î=UóÚôßòôZ“{ ‚ZsÏÆÞˆ’Åó¢€€ ²e@À‰IÕn¾ðÑ»üP¤§°ÏI~ÛYfF–ÏëeäºR˜2cÆ_ *Öïlˆ)ˆ½‘^—p¬ÒBáÇÕVE~¿Ì\b‰WšIRo@ùЦ›³Ö庉Ÿ¥Ÿþ)R?káÿ€oX«§#øFê»ÄŸ+!ÔêzêôÙà/†ÕÉÓ7»\.[[g»¾Žv|»oÌOûØU½ÏY@3FÈf°ÚÖñ€Aü)Ô‘àèÎÍ68u´qòìÍö"o)[¿ªöÓDHº1.¾?3w‹GÁýWHpnNŠ)u­BDQAa*¯Ö2ˆúòw?ã«bßÚg½«!•×ͱ&/|툦~a„ÜQ²ÉcÅwzˆÙ°´¿S£ÆÁÃÿ¶”ëODó|dj§ùÒè2¨øÈ+ó¢vè“!±)¤Ó{qÒ®ìVÇtw^uíÎ}«ôl4…tÊøa"þ±sÐ.1ýÕŸG|&³’ÔN>/×þìß'iù²V™Ë,”¾tCsœ”ÒhÇŸ±*2Q0õ°·±™z;3×.;Íë“/¤Å³@é#–ë á\Ûáý\0Õ@cŽQcHéÿTžû¿lò´¸§Žñ“×f!(6ݨRa…jòâs›Ø¯!Àï#²†VÌ°êIÁ‰á\nÙ(»¿ü#ÊneÎCŠ”`äKKdÛL}ãÌñÿʘFHÈl`‚òzc—Ôþd´Ò4$í %æàÜì£]ô]yük=¯iUyÈF 1á3F­å"'þXD¤‡}Ô|Ô¡Ró,:Aó!6›ô„DÕÙ´bë ᢞZœùÂ0Q)⎙®éB‡r¸ÿeD;ÀHöI<Åš¼ 7Š;mŸð¢%iÓL`.;B|L³›o\ì¬flS›Û¬jÑtøŽR*-ˆ¦¯ˆZ‡F0/T¡^GŒÙÿç -fYU¡3ÉD)pÿØ—|è¿°i¤M®‹2†¦Á¤¥ÀÌ]Ý^b½Ùzl6™[Ë\›j³‹¸ó ZÉ·ÀÖ–=¦­ek/¯InkÜaqËÔU“£æÓu ÊKwajoõcQÓ¾_5^ÅXvOßäüzfýGgŽŒÇvÖ”Äé°©,zì– aSTåHÆujW¦ ÜŠó˜{=Ò(.J5D”Õiå XEû¨k§C­/®:÷ü鑪')©Ü'ø­höå=ÎéhY.3¼\D¦ljO½ÄÙ^ƶ«æ “œ•õñ ¦À%¾9 ˜wBÿ%>¶”øËÓünÂüàÐÌöÉà?ÐØ6¡Œ/w *„× Z1¦œØÜD5ºqÛ]ÆÉŠÄ3ÐÓ>Íd‹;¯ª>ÐϨî?@äØ´ÈU¡Ô÷,z2m Dœ$_Òè&Y&@!×}è8à_á϶®T½¨«œ¢ù+K}ÍøùŸ"c'xª*ߥÈzWÀ:ÖØ*ãˆ8/8„¡˜N!©àCâ-ß$Pnð5 /ر^¤œ6 ÷º^ COö{ÎÇU2.ÛØ¡ ÿR EÔ*ë|tí,2"A6Ê/)¹_Tü²´üŽàHÔô®kq‹IÂîlFà£Öh ò§ƒžz™KÚ¥?ˆºD•AªéöZe(i2a»ÊÖ¶s1w^xö»äš8hnV#ç|}KÎ uó†öØ•^Ö:ÅGò7«pãáÅ,)9ofvøØÜͳ¡ÿyôø]{¢žŒ³\Iÿ}P9bˆHTý¼ÀéôUC»Vø^§£½ßYN’èOÒQÕ3•Œúö^PñÀ—ÍGe´-A€ú%’[¦[ºÖ›v+¨«RCìpLüslE¤?P„Ïåace#FâYU7.Ûü øÔl­·ÿˆK),=òÈÍJ Ã^“ð¥¬E™¨Á†IbéŸMQeh&à;}gô.uª,×*&]=óºí§7óÇA}­º{Bì“j¸”¬?„e«mì½s0­ëKÅj3ñÅ,€®ìÞ.¾ÜF5^ûzŠ°ÞKÛüúgÒ ZŸÅ©¬«øxÀ•a±Ÿ XØeüNǸº8׸ExaÔ ó%×Û£—¸´¿b;—z!²0ÂRØï:áÇç ŒûJHwanš‹çÚÔ[Qú ?nŽûª«¸ü×W'UÚ`”*‡{‚Š:^4û„ª¬+¹Äñ@]Ï~yN2l2s_Î :/yÌÛL‰š¸YÖhv‡¢~<2DBºM–‡à˜SÌ eêK^‹ ÄýaæŒvFètIkM(CX/,LãJ–ôýH9^]œà7ãQO¦pŸuý:ž…E€,~ÒSÍx¾õMúY_(ÁB½<åwb¶†¸Q`å%zåñÙWŠ°"ýpŒ)òb_­ƒÁ>óX–k›hc^[¶'U -ˆT Ht¹ó}*¥=¶ÉÕ¨‚ˆñEàÔÔ*¶ŸÌò5þž‡Ë}êt8ÉŸ” ÷«‰÷‚m*Ÿ¼gc&¶‹·Æ%5•&jõWAS,Š—Î\Î õ>_gèy-Õ4èí龊Äj›þ_ƒMëð‚ûÃ¥ë‘>·{G¤Xš“)8C_TUeIþ–œÄ(´,:Š‡(Š±p»ÎND†8¸§>†ä ^ji8ý‹Í3ºÞ]}ÉÑ:H…ñZŒŸðl1ãŽOòé••Tt>«ú¡;"ï ,„žîû×½ʼ‘Ï„ÂŽëPXA°ÙÎpß[àN½›^«%Ü¡>`b4mïw ¨ ¾íßõF¼46©O„ôøÔåÀ;;¿Ï€íÿe$¬¼<ƒP2|æPÞ2nÝ-!Šçráq¸hžÑbkVÆo~L–]âyö„0àÊ2U^϶f;È™TëZõÃ0‰5ù¢ò*×}ˆ­# dª¶%ê€m-c67°àÛ’¾K//N×{_C%‰*Íæsºƒ¸üûYé)¹É¯„œI¦tœC‘;Jå\)$r¦Í°­’ÚäiîDq9»MÓ°žì¸Ò [ Ò*¤Ž03¡ŸÙöfÇqË;ÞN?ÑOÉžü8ºÌŒ+!œÑÞáþen¿ xf©é³f$=z’¹Éé² ¾tBþàQýAvÊõ¸º‹RæüD)eÕ¬œU‡f*œÚ8ž…–h2ÄÜ‚… üÄ:8¼ùRO…}i©ù’QúG³²S;&7$Œ†CpR.OËä©À9Íi4ç=}âžðq§¶ÍÿÌg›1l-V6ëE›ÂŸøCªÄ,„TÒ²1lÏ°´¹²§®zÄü½zezkcMÕíê·ù¼‹fHP6‡«æ«{K.l¨ƒö¸³Ö:ü$³óþëB¯èŶ0ÒðWÉý•¼R£Aô$bô§Þ§ÓXœUym•ß܉ƽt­u v±ïô.ì' ¤b¸î&ÔÍ‚™?¶/þOyxÓ/µM +¯É›¯±ÞÇO©º³{i&\Ò£¿ ñÓäó“ðð•üÎálªj-ž6j¥q”%+le¸ŒÛe|öþý)A…z;ÃÌáX¯¡UÕ=À]"påÒ¬ÜÌG?üÞ0 ÇÞ„†'1ƒÈ|î=ħÑr·Ÿ0‰Ó¨˜ô£IÁoèñm¯,)ò,„gùÇ@>nk4’Œ=Äv sŠcxµ9Ð$Ú¿m„PD)¹ê¦[­¾1cÒy×Øă;ûbê_¿ã©³Ë;$¦L@ß°¥ÝáÆiY\ãÀaó2í]aœ,%W„,qBß47tJÊ9º³v¿›b€S ž:hE—bdÂð¥y,¥¦ÖÒš Dßh܉H¯ZÚ_Ì Ì û–´þ1AæàC&>9ArËO@¿ãR<·µA’€ùo×pTI€o–œªµïš=¤Ã/üÉ2UzUU•¯¶á\uÐ<®tI·ß­[dvÅu¹µ 3þŽ(ÂuÍÿûИ@³=Ó3E ù'Ö~_xPëÁj£î®[O3ÏAn(–DY0·Bð‡MŽÏÆï28‹§Ù¢€²¢ýsâiß)QñéRf÷»éO«›àÙŒ­Ëƒc€"µG3G-úõŸÇ²Àö'›6JëQ&Í~’#°P²UÖ¹¿ŸQ† bàÿÎÒ$ ÕÁf¾Ä¿F¨1xãêœE„ŸTôXv–5™sn|{yŒ¸…•=h[RJfÊ͘¢uò¼Ç„Òmñ“ ±ÉÌ» ¥÷uÚ?Yj#òÏ8 ³:1oRßöåÑSoèÏ%†ÚÆ$Œ¥* âùs&Æ7ئª†Š?ëö§oÿ3 YíÚS*ød–0àhQA­†¿Ä¯Õ'VÂáôîÂåù¤;n÷%(WÓç„ÿÿÁä#Ë“i”§jŸzÿÝÃꔀ'ìŠ1{c!0ð]&GmZvå7¶qHä?ä~“v÷Ki[­Â©¡2"Øʱ,9’åk„ÜöÚVÇØ>÷¡!Á¼ ÷NdÉúª´ôQš]±Þ_J{[yã†+¸¢|~_¦Í(/Ô®.OcN¬ÒëâMçŒeüfÓûA•¦Ñ’®R Ä›J¿ðSàV£€0Næ’r=`‘I¶–[½<Þ"ú³¤v¡®Ò)ÖWŸoÑpú‡F~ÚÏ8Uj£tÌB”OOæ=¶¢øýxœ=ª†vàÔiƒ^;£.*ú½"hÃvïÌÎ Äúï‡Ë¤Nµ~·Ç7tYð­ã<¦vãÀa:C&לIåèùØ9á% Rɹr,nþY­x½ O\§Ê³¬ùþ•ò¦/.n¨dÊPxö˜Ù ]j¼ÕŸd Ã3&B5p¦õÙ°DÎ0‚ f‹çKð«&û´Zå5ÓJ‡iŒ&Ç“¹·{5ÎIÄVÛÅ͵ ª’WqËMJ×$Œ²!Ï©· 1øãü_y´¨m×ošARMŠo¿0•ÿÏÁRD=lOÛ¯ž´9Ž”ЉÉmymP½?TŠ+ù¢t„eo+ÿôjW¹:ì=Dº‰¤«ÁÓs¾7‘}Ìújn³Çµ™J«›2X/qáK›\͇@ßÿ¥úÄ›«;¸_µ¯(4f¡/Ëû,.²ôNO†e³)`»8+e¿ˆgxôƒÈ)qÅŸG{½¾o!d¢ºâàÜw KpGßû žÁGS.¦‰÷)+­‚ï'3žgê ´¥U?Ÿ·rÉú½`^‘:Ÿ¹”ò.Ó'kðÝè-æÒ“19åû«|ـ¯['”†VÌÇÿŽ|ñ蟚<4»[Hr ¨@ÿ ‘—vçÐBašÉt ç3˜“…ǾYUi4Å08“ì:”ù?Š ±¨Ã¬pØ’x‘Ë5­5Vm¼páñ´>5¨—q?¨RØYùt]Äët±0F>Yy²T˜GõÛüQIEoæ[_…¼~s kzIPÔ}7UÀå/µj*nµß;Ú@&Ã~iµ „×H뽆ÆÅ¥vR=~7¼uÚ²ú”lPwÎicg!Êœ‡Xû!'·_ð¥B¨~Ø7BTŒÞsr|óÀü¾ZÁ?vgô# D‘ûv6Õý¼´ÓUЄãó%ivšßÕÃý°Ì¼æožF‡óÊCà `ð· ŽÆBÂᾤto6-PÊnýJ)ÚñoÈÆØËÄâà kd”eK87 a#`µ*I}c»N(­ ŽRkÕ›)º:-e ²©¶… "Ff`R‚îð^FG_ìãd×Ú»O:ðX‚IBZ'Ò{â¾$/Û]'šHÊ s}¿…Ô¸õ¬ÓÏcÑM¶ñÙÝ‘ßÓæ³8 as=’9HX£ øULhôódÑEžÂOs:M¤&ŸÂòµ¡‘çã£ö-~ÀCF 8¬š#®óÃØËúûy‚ÁäœY^{­Û3ã3ã8â€à‡SJSDÆeŠÑR¹o`*BÈá¥@ö?V/¬Îm{Sdˆ©²ÔA9È+ŸûêŽÎåAâ]yš†„ @O€}¹SÒÐwi¤È“ÃvòäSOÏ%8T>©â¤ë·$àv¦¯¬ ì®[iCú1Ø_$ù—ø‚;ͪ>|ø}Qâb‚Œ µa[$ÔÇe¬b¥&™¹$Õ9O>!¹w±¹˜c#PüÎÈäòöµÕÚW· Ó‰$”Ð!+l²`VÚÜû_WÒSHÍ€ûÑ7¤ï†þ6º8®†b®P 2#bÆëÁ(áì€ege«rƒ¨¼GÖeaàƒdÍ^€e²†(¥C¢ihk(,¶\K|‘7r#µ«â……>qVìŸ:.Øp`Š?9UÝÅ¥ŠÈÔ²áÉvb^Š‡“uN«¯ šþ:¬VYxHæOæÍÀøsrë[9¤¤íóÍ](ñ&ë„0üXXAïP˜!0ÿÿžÞ²˜ËÂ-ð‚»Èó¤^þé('.»»Æ< œ†˜¡Oõ»í`´ÛÌéZ£SVzOtŒLô“þü,8³ õÖj|po¿ñ(ý·(‘ÝÐ¥HÄÞÀµFUyPð¼^øÅOÂʉ²‚b=EÍð4g´XSsÕ…¼h`»»`™>zݨ"µÌY‚2cðºd¶=à4Ú\±êÛàäWJÍy½-FŽ§ÎŸ0v-ôϵÛ|óNø5ߟ:¯c'ÌŽ$øfº71Ѭ”iÄÕ9‡^ð XÚŸ0k9~6p÷˜ ŽOn¶À¿â$Y±™¢Eš¼E0VÍPB'ý”êžâŒ’ÆÓ}Kü©TXçéw„…O@fŸ|9?¯Ú#φH'ý³;hªûÆéöKÔACã[Õ\@¿ðáŠ×='Öé˜Äö¢W_r.AœÝJ—CO".­·ÇØ’Úkü[Ê\ˆ›!ôüô¥»šîãá(³Y¤z¸f{œíM‰£Æÿ²Ž8ÍïÉ…ïà*(Ó¸aULþ¡Lö͇w^@¹²/gi’Ž¤#H¯¬ Ot€é Õþ£˜%á]òå¥ê*›d ™’?FF£1ïðôí­Ù4ì¦s¹"\$a¿aÆ‘ø!ß,Ï „ÛU\r)¯áá+%YT» ÆØàSP`û~é[N¦v¶^çpý$†Mõðt7z 6kEñÃ4ÏG ÿú|óvÄ1P_©Â„®ë¯}ÆCÖëòüýºoHšÖjÝ•2~ÕÌÛ½ý×41Œ";Uèì$ 5¿âµ²] }nÝUʼnì”Æô„2TäÌÆü{hðåu\tS;d8œÞW–^ ):"3ÕÛ¢ã÷¸\n½)åS­?Úý¤dŒÿ¡ØWGÕÀ’"Pp> gŠg¥o‘¸ŒØ¸ÔéáðvÕõv{Ë0Gáþ´0G‰ìÙ89’†€ÛÔMù3l€? K³£Òsû©w)Åü´©€Mˆ¾NÁùžœ õK ë ›ÅWåJòT:dïàÁtW•YPd@hÜölOïiÌÏÿTâà]t5o¢` Jh8úWÙì"Õ3ËQJßD.‚ªmÐ`RtÉ$\ɽ+[(X¾ð™Z:‚ž4l¡~ÅÊœ“A0­1%·‚Ö§(&,oÊ8áe7Ö°„Çê.‰Ìí;6£Aë•‚/äC%Öš…â-ôTXw~a6²™Ç!5ˆÁŽ4m“ƒóë¡~¶ º4y%ÇÁLGà‚QâÕUß.s¡GùL¹aÈnjÊqº½ÞˆšÕÔª¯Umçºâí28îæío4êþö–5ëQö,äa:uÛŬ²f;=¶IaQ.šÇ½.—•Ù^–’f½œ‰—¯dÚÀ§=¨PœËÚ±ê­à¦Ìw“îª%g9ÇÊzqËx¹S×xî*D᯴!{ßz¬EÙd¾â•ÈEd¯,ØëDòn? Ò€?lð7/9+Õ›¸P‚㤯OÊi8B9ìQŠœmFº¹ÑbƒË8æî­Ù pëRHWŠ Í6Eˆ 8²Fíb}ý ón:Fá¹Ä’x‚I—¼”¡ªë<B“ ¯œÄÚ7°¨i|WÔSE¹_0¤i7É‚]åÔM¢½›‰\ßçz…b%ðÁÀ¹¸8 ³æÅ-yÊuªë ËæH+5q…Ýs”㮊øQŇÝý‹Ÿ)`æöØB…³>³ôÝzpÅ'U蘿ˆ)ùüÁøn<œ„a¬Ï?Cß&ÍÎ_Ú-ÏeÚ&Ž/ ÁLðWƒË‘ãBÔŸÌÓvìØY¤ØUœqNqÇæçÕ¶‡ánÊÑ/á™´ŠS‚ë#”€jåF8Tjܯñ„ìÄÊA|Åœón\pedßÏ¥ «³†/ÈKÒpÍBîrn ^ >:ý“+o»¼×ùʇÍe¢ŸHóx:]ln|–>!ÕïG/Ÿ„Úc ZŸ)àÏ®>êÁ˜ÿéË´ Öu˜ŒœàþéY´”Á!³¯á%¤•ÁpÔ)É;4NÖ9¾’q3}O·b[(˜`·;'&ªäÔòs#päz¼¶ñ—pÇš¼ÕòŸŠ-Ì ‰Æ°jÅ%ƒ²öñåö=™l{g:×Õ0ú’ôùóôY2\mû]×ÛŸ§ßü'n¸Ni­m›®G‰ñ\Xó~tÿéáýTú P9ÔÑ‹Æsôª ÔžW]{²iÇ,¹jGbï©íΕõãªWÆíŠ0&rLú Œ¥†{'Ïß;¿ÿC\zþCÇaÊ€Ù +"8Rn)b±‡–@Ž5ÜFkjÞšýÚ³Ò÷†¬dhŸ˜ÁÀìN"êX« n|³CzY½+“7ÒƒI…àxÙÍq&0ŒÎqÎƈ6$6èèÏp°e·0 Ês³xÜoÕjtMzR¸ªü&§NRE-~ÏÃXý0_Äñía’þIÔHôÍ{·Èˆ@'8¯í™†òK,¢§)3¿,^7H&ÅŽ›~3 iâisù M0™ozDêjÙ=RkV BYÏ qN}xï:zíæ[;¸Â· ˜Ø!üUµsµ¡Ø®E +¼…bnAø]Ê¡‰¥º\ vÁõ‹ jf Çö2ù,ΖAœTuˆ…¬Ûf4ˆñ ¸©d)ªõ€ˆÒÞ 4<j/`IÚ‰Ræ ¤,ÿ¾ò½+ø—}²Ô'¥“÷‡:`˜anðdFnÀøõ„‡PÉW¯zZ’åÙ?cÚÁN„N¨ i]:ܧ×.Ac šøÓ^½×žpë8x~x¹÷žðþÈÿ&Øì^ÚÐOãþA“FŸŸRÇ?Àù´¯‡«Ñ\¬çS?/Š‡¹»éúfÛºQMbøÚ ±~QXµWD&¦7R<1…ÀJºåÍ3.¶K©«9Q+Šl •››®F—÷¬ã\Œöê4´ûÓUJ6Ô«Þ ?0‰IéÛ’)9Ô¯OØ^öƒ ¥NÍ×ÇÄÚ§!šúábD¹eIù¸ü| YÞý $Li¹»}¯†r›|•¨‘FÉ(?þ÷‘±®™—BfÒYA]EP4†Sð\uLÕœ8k‰IÙ¯)²*4ÜK¦sS-ÂÐÑ”€ oDÙ?·¬ÊäPã*NjjX±Tîî:[kqÕëŠje¹ãÃGiO­sçþü0Ç>†„>³w™€6/›GâÐWG˜Òaë$™~=LÕr0VP……;Û`ø œ±ží ¸Ð¯%é+{h 9kø³ôHÂØ–˜ª{užxç(±Ž1v¡7¶}Õú­gçï]ïvH<ã¢àwZä0Ë FCë¢#Ëç© é ísPm(5`&œZÈm\©Óí?O–PÙaоÈ& ý<ˆ/–áÕQè'Ô'-â²YŒîHXŽ*6e÷‘¯üTcñD³îRÿºÛiMÒßçèbö¿„FŽd€šÇ•~*= <ácš€7e]#Mf;rã” Ã:öhqÃd%+ðeíÙ#ZslRúÈžƒhj"­ »ÿÝÞe÷°ömùJ(ƒ’¼–'n]'+|­"™“÷œîÕ÷I~oq`Ýì{5˜?+8¸=¯ \⃳ t6_d³;€J írngÖPÁÿÿ¥¼TÍž·¶p”æyÍšý΂¸`¸³ûIù"µò@>Õ{WG¤Â+ÜâþÁQœs³'"ÛÙo߸Ró'KTµ¦1¬h]°—R~âq¢õ±qÈ°‚%W) Áa»"I¾÷BàS¦ ÙuÓô[óÅ\„÷8ƒ´?<Ùì‘EºÁ¶äÿ]…w_Þ½m€Ò«°F„‘—T·W•_»7·¦\Û›×aœ,diKaŠÊq4€›´ðo4FS‚h‚o¶›:'<‹¹ÒÔAÈ ðƒnþ>.-Äîz¸oA!Ê ó«¹;Š8´X¾•æžÉÛþzwÏ®@‡|šÿ&BéæF²F(üÚý¶ëÂçeºw×CˆPwûuŸ(Oº±ŽúÑgÆø VКKå×õ÷£s®O•[eú(õ" ¤3E5N¸°9ŠbþŽã2Ž„?‹zEÉp¢N²z¬â¾fÅÍÍ:GUs7áPéQÑr¹ñ—²¦žõ’,ÆNcMÛðÕRÕòŒ#%[ÇIµÊØy +€ Ih“Ë<7æ€û=âï2›Ë-bºã‰š’r£‡ÍŸýVÚI ߺ®™bè›ùŸ·{j"—7Qö¶‹¥õ751&yVVÓ™Øñ`Ù%2¯Ÿ˜ÿ šè.ÀÈS„H¡*~W›îÊ ×‘8 ÓxÔ<¨ÈÈ&ŠðV¶u3œ_MFÄÇ`Š4—Ag–æk››l·5¤µMV2mÉó̯Ыéæ‘Û-«©½Jô%¢úU‹N`UIaÒHnçÀ¥U^"¬=ú.:>¢× þ‰Æ߀®ŒÌÄ mw:þ^ïE0…8I^ þ:ô<ñ‡øƒÿxbgè– Ê:WT1@,6H¬žBgmÌÕ€FŸÓŠFê­:8ZyPž—&aÓ—2àY™“£hh™¸àwm9?J5°gÀ 3 AF*0pvò0¼‰`×sÅ.¥]ÌæØlã àl®abiáP×µ&éýç3bÉÏB$r)ÖH‚Œƒ#Cõ35â”.š2‚¯!ÆæÞô¼®„ElçàìH÷$sƒ©¦º]Î ºÓ)›µÑóm¤0Õñ0ÉØäàÓ°ÿDh¬è/Dyªßêh‘´z4 }pÒ‚a=ãÌûø!WÂ2‡WA):¦ÓNâwÉ~ܯ¯àÆgvI¤¸¾­Ý& Éò:#´ÍQDŽéjD ä·¤Á¼<&ðê-U ÃÄöCßD»\MΙþçlË]¹etØ÷ƬcîmÓñOé’àm\‘¶v°UÔýò‰Ð|ÃÖ¼›û‡ÔiвvòÂr|)ÀU>ê”Ñw¼GÚþ̆Vƒ]”¯¡.6§·ÒIVÛ‚Ã;v°3Ö—ë§ÖÈ=À"®Ár¦1ðÏTØÀaŒÓ0Åù¼ –S‚æ¸^1X›£!©y€ð°ýÇnƇ´"„†Ò0±-ÉKÍꇿ³BPÇ"'›í§ùa ŠÊ°tš­]'ÙèäŠ8PRTE3ü¾š8%¾µ¿ð5íȬo01ü•TAe);Jãn nü·Œµ •J=@Zë?mpKòm®€.†_™Ìª¨Ëÿé\€>ƦΠw ¦q>·“‰b\Ìÿ-láQIˆ\'EâÒ»6OÖ¥dc¢REÊö,~¢ÞDm—‡vü~sßX˜¨O¡48«Š˜Ãôl=ùËìÃhŒßƒYæâÓ¿§Bu?Á¦ ó9qé½ôÒOÃÙáÜ ™’Ù­"˜0Öt}Úgh‘öÔ#¨gäk€ <-‡"å—»‚¥ÿȧ²`ÎKV«F8ÿS²A«6ƒš¢jøj„W5FÍ—&½5‘Á «ÁÊ^|2ØÁoõ ÐË ½Ê‹qŒv}_­ý½ Ã%K ‰ÓV¹ÿDUŠØ5ñ༠%\ÂÃ@ïf§ýçîÊ$ß³œʨQ‚ z`Fo—çÛ ƒNwãöNo5Á$Ü‘íÛîö¾Z¥ #FզDžDŒ.Êf[4¿K¸˜§Ù×È’;\Ùt¾ú~Q”ùØS‹º†±òÌ–@V\§£±ÄL^èÓ‹ÈÍ*ß/¦mTáo€MØå£Fàr:,¼(h‹` ÆC7dŽCÛóDŽ|µ~P ¢kæÐ72'ø#K)16Y¬À‰è­ÌØÅÙìväÎ1¢ÿ¸uö¾ç/òw Ú†ˆ»Ð÷eÀŸÿ Héï×­ G:vQFP­&œ˜fý–Ãë-˜ÊÏþá°ÀzZ»V7 =ŠRUdÐæ$4Ž«2n]F1û¹„,²D&¢ÓòI Fœ’„(g÷\0ŽñH$t5K¸ì‰->諶yë,̧œ'€¢JÔÐý%AŠÂü¼Ù.âÅTe×éÎø‡¤_1—ÈzÙëo™¨˜{¦•õˆCñæ:hËôaÌŠéN’ô©×ôÂËI‘œ¨aÊ`ÊÄtYM,[h‰}Æ¿Æ™úŸrã“Ò8Œ5GœCÕÆé…ù6ZÔCüèwöñ†IAj›0ÆÚÖ…ç-ƒ«¢iÉÓYØÑYø¥)ÃÖœ;‘EQìbM‹qlqúƒ,ùù]¶=(Ÿ™LR×æ@§ØT´Vãɳ ÍX3º_ î*@¾ó'o?d°Y'Ùª•®4 ¯ÃöÕ1Ê)Ì0mÕCòÜnt¤å1È)¦;rx¨ÀÎÎÎm-ªØ»í‰‚žçKtÀ­¿JUˆRµ@¼û‚“,é;40.pB‘*ÙyLa0OÀˆ<ÌÞ)R´F±H…Q[èÜdaÓ—$ý•äƒëšÀ—s*ÒSINöØûzøÖì͵~ñ±­jWԠͶ²Qkøxë¡ùô ¦öòÕ}ô¬Cw‰T‘ó>peUR·“ÐÒ³\j 0uä¨È6§’"m\-Ú'ªsKÒ m„Ã¶ä «)äؤ°`Õë<é`ñ4pÙX"#¥f|VÅêí‹S®OÛÉ''^ÔÇI£]gÙ·Øvhâ¥äãÁ› ­î–3¾ ²ñ-X«Ü³» °wwüPNô›ÍSɽÙfñZÓy 8幊I±¨]ì+hY´6‰¿4”p ¾•iQ®–9ÃÑóQÒe€çd¥ú6M®Ê¬­Ë¥þÀI˜uVŶ„'‰ÜO©d3ÓF!ö_×,¤W²Ú…I=×ÙäðSm5`¹A— ÈúöJ?ÓoÌXö Ζ€]ŠÔ“ÓjЂÜmS^“>®lƒ ²ÓþÄÒŒw hñÂÝJvuªy÷¶t˜Õ>—-û¬L·»v†¬…±ÕªQŸèžõ=ÑãRGxìäsÛЈôqÎ2••5"Ä3q“çv¥PGðËï'ñhšOÍ (0šf‘á ñ”ˆM\#àwÂIbå_¦âQAL˜sܸ±bk‘jwÃF™Kçhà<n­§UaðuÎd<¾ 1Ý–F~}€ä$`]Ÿ›å­ú… mqäS} °ÓjSŸ‘g–6Ûè ][ئ€ƒ®e~³?]¼&ê¡æI S™\Lv^,Íaã?jˆÿ#„û•…µðEØ‚'AWÐPm]À2cH…@p–!ž˜ìœÔ:26Ë6!åNç#¨H:¢Z^dále”Ujp²ª){d‘>J¹;ÙŒÝa·"W›eê]‡W¥&e˜ËA~5‹&n©õÌ)‹û¨¯‚ßï´â6´ÌW̾Hë¢Õ; „]¼Õi|Äa ‹ uvjùR`âD›éðÐ ]DR.MZˆÄgp³ EìQ˸‡¨3P…‹(ªÐ~ Üâ<‹ã@žîtò—y„%”w“¤­ëžëQ‹åÇkß!ßÜðÜú„Ä’àÐ5 ñQâÙĿ̼`²©Þî#:$É$"ÆD |ª^ 1ö<+A°Üè†}J꫆Ëa«@ÈA2âo£=OMm‹ ¡aïá\<´’”°\I*W4¶Pó\ŸÛ—^Ê>çyÒÀ&É +×4ŽÓ™e^ü$Í„ì(Õó«IW`¸Ûøõ¨kÝ(pµî)ä•[ÄÝ-­×_è#7Ä«wÚßRÎ?n[ÈtÁ!…x/…b,w y˜Õº¢VN­Ñ®Ío¨PºÑns©³T½ŽÎb^©‚IoÛ{î‹6Š-˜1¥=‰7@žÇ)aˆ ÓÓγÒ` åî†CÕµ þÂcŽ2Fù”)¨á¾ºÝÔ ëóÍ–+;À¶^ù0c/÷©6ËNg¡Å@ ¥lC ™e!ƒZ°0¹Ó¥ÊN 9ÁÈ‚öêYQKh¨¤GÐWø‰”wdpœä)xè›wÀÅDœ–*Êm\Hèh#0i؃hø(\Èã⦣•­ÙÆJ<胹V¸ÔVÃ]Þª x¹Å(Qì“ë]ýjêåÓÍ.çY9ý5x¾Ë“e3xgOì§jG÷_"%'RÇ9U»YPcÐ6~/JÚ7wjpÛt{ÔÀÆ_òm†ÕÆ-F…0S¢Ö>­{gŠÕhÃÿ¨þü5ïM„ŠÊvìsœ°Í*Æ †xY(ŸSRÔÜ*ÈG³†ÐÖ%Gß‹r™’Ö4*+T)FÏmÉB{Èáb¬æšfDmpÇ ^ÇÕü¬¢¡¥šå4ˈ¬ëÎÍZ9ûÂý)-ÜÓ‹™M¨ÏvÊz(׳©X™³Ösw|±²d”JÔË龸œál Ž“°ˆmÏåbóß¾ŠÉ5÷·CþúŒÆ7ŸL†€sìŒ×–WKÀ ªÜ«SƒºPç:"y©¯Ù4Ý@ ’3¬(1K»Ê¶60!é¬î¯‹TãÅc¨¼—0ø#`NíR)ëØÀGYÿåä^öìóÙÁ´Ä¡|Ž_~„²WQëu¯LÎÜ7Ìe—ˆŠXP…£{fj%zN+ÆáÀ?–0#`%®îf 3ß"éJ­«"ƒ nì2>õ`9Áƒ´w,'Í÷5BÍcèÂædÂæ\±>Q©0áœE_[9€SL7*0—s£ÜxØ6kJŒr“2’•Æ^M·šã¢bè$u¹Pa]t}O³ÿù)½–*èSš}m{C-RNSE Jˆr°æC P"˜îÄ.~pJx¶jòCÖ¸`&–î¨&1¼ü ð4«-¤5é¬#š;ŠçTÐN^‡ñß´ º¾×ô'Êf9_Û"ÐùåŠdu sÿrALyŸ¶û%g‘Ê^Sáÿ_ªÞt׬Oÿ>pÃW0Ã:ݻܰÂC=0(ºM%ÞÎJþuíäÌÍïq†/©å¯WzY¨lÀ@Ø›ZÀ¨¶ß=ÛáP&[ªZwÊ6›¥¯4)›7™“äA¦ëN;=lhê¨w–+µŽ=;$iótbèúëÙO+S(%iw~¶ ã~ˆâ·W/c}/HM ‡ËÂAYšÔRq‹t•+F• ¹‰†6p#œÝ“ i׬QúsQÒÃòqœ]؈Ù$t`–)¸ã¡Û’•‡,§´u|“Š e€Ö²:ÐËÿó‹fQ¸×ŠY$xLôI¤d$±v aˆÑ]HÏ„|)¤®”œj¸ÿê0íG(G”‰¡V²Å*?xµî›áâu*h?I~¨Êå+Ë™ˆ1Ãp3Ï£5`ÐØw÷æŒå}€/&6Þgîž×„ E‰Qͱ¢%ûž+ï³cð“¼2!¢œ¥~è'}Û—nº1N®¤LÉdØ’%'2ÄúPmO)Û®ÖHžŸÔÁ½wÎ+xS Q¡í>º]W©Ë…U°³Ö^ö¸3±ô¡æP»Î¼ÆÏÖÇø5•W„Ðà;.g îýíä ƒ;¯‘Z`Í»ÚMÔCª_t5¬óIr(‘ ‰$aŽj¬Š ¤R ÚðM'ÖÖ1 Wµ0úGó®,ßZ'ß±÷þô2OŸ°`{™«ù.s.©wzÙ3ÿùP5jÅŽ¨FZý6ñRØý¿œá=Ú\ ®Ê¹õ…Ô³#[ _Rt7¹cŸsü”ΗʛÂ82›«î¸=ÚÓ;½WçÎ#‡Næ»&îL|X8zé²5"r²–ô³xÔw”’µÒ‡6$÷qÅpµÀ.nàSnE¶èµ<׫!`V1ôÊ=~Yá§õ;=9Av†³¡“Ïü–¿Ì™nZyÇ`ï·=ÔƒžgûZ¬É Ð7°£á%r7çZÈ/˜‰Šû¤Ø?q“6Ch¹ï§,)TB:•¶Ð/öø¨{¼Uí%Õ¤x=d^V°ƒéêçÂq2f VÊ(—nîãºjðÎ8Ä/Ó„c¾¨Àçèñ€í³ÃG²ZgºÚºw>&‚ŠÉàZKÃóžB†ÀÝŸ¶ŽôÑ¡'Çi3J<ÆûVÿ~¿õFRÝÚaüeô‰–ôê—4ãœYåEûh~ž9iÈÛ,§n ·6šÐ „mÃ<4èK†îÄ={¾aWÓ›:ŸIz®=÷ðlX×>?×ë÷P±E'´nòÔÉIë4m;9ع[ùë¼;îRî|ON3 ÒÃIÆ‘¤„÷»ù:ÃÇ÷9qPŠ÷V‡d9Ç´¬€%é:ìK;«`{yzÃ!ðÙýOM—øIOöÝ®µ²( rý¢?¾ƒJþßÀû=ëCÀN–œù©AË2ÑÛšOƒ_ÙÉñd¤ïÿ¸+ñÎ:’>ã¾>ËmJsǼ¨äÍDÙÒ ™ñ Û.Ï1òæø>/Œ­F°ð‘ãr[òtúì2>]¶ÅƒMêû܇ºûß­ºÙ Ÿ.?ù°Úà‡QÍœ @ñ³#ô´Võ%àÂpÞ€z{!Ýaëͦÿ+ÈcÀnU#àáž_û¸IG!òn¶ “Ÿ*‚·¤fj¥æ´oñQs ºUÅÃ<$n(MUŠ¡Çk W$ÿÐØ’©ñC+È•,‘¶½zÝŽÝh}ÿ–ºŸœtPìÀr¬¹É.·ß•…4rX‡÷;“åëÑ0¨ó.µI‚ЇúçYU^ë}8E‹…NT¤ ÍÖ3ráWõ!QÂ@˜Ød”:x³]Å;góOF hø]:2Ô §ÊwOâr»‚÷!åƒYj¸¼dƒbÀ«hKG2q™L2ý=¸BÉëÏ£~dŒø'“[nÁIP{AîSÄu¶Ý|½-“ÛÆ\õà—æ->P7‘i¦ý!c‡˜·–“5xš2ç X#TevÀ¡qÙÂ%bWþ;ka=H;p“_Ã`'oÍ€ÙÝ̶v#ôR’µ‹*"‹/<í´v¤>q£×èdkw™¥!Än[íîÓgèŽi4 u¾{¾ìwuZ…&sFš´óBÉ™æñüfäâ=êWå,,\ÛÄÅ}Î=¹°¸ÚëcšIÀ÷çìüÊŸ»‰`)1B{3Þi蜿þœœ/ÿÛÏr ¢0>üÿRJ›2°×£(.°f¦øÜõñÛMó=ÆÏE#ž©”ah™ãÈO'k—åÔ©Œ?0¨'_æÿUå•OFñˆi©7ÎZ+ Ñ98#ÁÝÞÈ ß¶ÆÇç{kìÕÀyTß—Ÿ«írÛÝVÂnÞÙåNWBµœò·懣®ŸUÜQ™Ã]º >¼0Àn[G¬|ŽèÛ–EÄZ`jªv÷<ÔÌÛÑ9T!ìéEšå7ò sø“’‚}”TCû‡«$„©‰Ç”üpJ¦.û©Çžóã!BèCýûQê}¶AÊî¸U'C  æVA.SpLrÄ›|+ÂWØ™ŠÁøÀ5åôŒÅ=ÉñZÚÓrådågQ›$2xæFwXÕxCR0†áòOºä-ô¢¸_G'B‘òlyIþ²§R° _[n¥yÂË|9ó0j­}ICÏ ã.£pý–¿ÝÎà3Ñ9WK(=Ñb]–X÷Æ]¦‘x;c5#@§büâ,ý?÷•¾<Ïq[Æ‘æV„$¶4v@ÞnˆPõW¨ã#̯@"î.4Ï4»˜)QaƦ¾-&õ<‰Éã>Y*èœð’»²ð¥õ›Ã©ÖSÎÛ¸˜m ¯x~—YÃ5]¯‹ó`,ÿPNìÿu¥×h)Æ^BÖs„x¿«>o6ri‘ÛoÌ4ñZºÍIÕ9o)Çiáf:ÉX‰/Y@ëm/”‡¢µ<ƒ³úy[Gâšjá~¢LéïÅñÇÉdAr,À,â²¾3{A¤±ì·Š¿{O&´}É»d:¦KæÍÓ¹£Üµ§®Šù›Œ[av‹ÞSTYÙ#<öm²ñÔ÷8]i?VÓvî›ÝTÊ{Ÿ×äB6Lè ßn5;ú$ä•ìŒ:`†R¥0_)âoC£ÔŤ|qí˜7¸2@ì>dz? –×í(x\Žƒ• ÞQ8ž°ŸLé;3€ºKåJsT%K0ƒc™»:49û±ú¦UgáÜ•‘·êpŽ™ouÔ40-¿DßYR\÷Hº7UõSZ›‡¶©u‘ŸÇ;¦° ¡¯òz‘Øó¬XH3ÀN°±CvèLÿ0Mb ëáÈU”™gkéÞ2°K kŸ àóÛP\*™~{5Yß5³Ñ°g»š«m¨H`çtSÄ°7;• »û]‹ çÀ/,þT}ʬ ¸ìàÓåá@¬Å®ðBÊ„ðŽ¥]væŸqÀO"*uæ½5OOUrß=ƒu¢à°–1°mëïŸs‹f1Y1iߪWÃ`QÍa‚Ô(–À=‚Pë6LW_ƒ‹YqS›ï’¨Ö¼Åvë§ÖH±pïˆÈ¢ÃÃêÀZv¥€[¹:F=qAvrI¾.#³ÒfÏÊ®?Œó¥ÀlŠ»ù*ÜW døëîÍÙMÝŒ²'ÔÍ$‰¹$a4ý÷^tdFUÀ9xët–»JDª »N\~Œb|÷a¤B¡ìµ u1}(Çø´~· ¯ÜhV|"ŽþîbàÖ…¼è¹!:3·ÍŽö¡óÚÿñ¥e+¦4Z‚í[õŸ­AÚWÚ˜Ÿ<áýSÛ>{£ †~ÙFQl‡‚œxuÚòI&ItfÆ·³kéÛAnUUAŒCrÆõŒlRO¯š—£ðÈwÍL šl˨Ë}¸ž“Òø¡¯ç¨?$—äD‘o:íÙdb;Tn,áåQ}ì~òÒs.Š¨+Tò²ê½ÃZ™³’]-a‹\,O§ˆM\»€rÙîEë7ÔžF)uOLìó!vavÊA6{¿u”{A Lj•w½"ÂðDÞë£ ”.¶4ê…ûy§ßx±£“cÊÍT ¯C!åº)—ƒ‘/i¢x†:ý“áÏ”¡{ó\—ó|Ão±e OƒzuÌ¢š…ëƒ<Ã@ä}ôV¸Eª{K¯*ŠPBTc~BÏ~þ]#ª/¥„ThÔ*³ƒa2u;çBA¼jÎl}V•oôÌ.Dó/Ö¥¿™&®Ècéºkgp«Šçáв°YĪ5pkäc£øñÀ˜¦¿­RCÁ‰M…&ǃž»fâ=°ð’¦šlç ¯0NØ¿ÖiK‰·õ߯ԯÕU@ZgÉZP¸Sîžä¾ +4h'ü!¿h¹r”ÿ©ª¸îº'ÑI9‰£\Jeí!|6PÕDxjÅ„t)ßÆÖ(ÛÌï{z0“kuM!ŽgT[¤êßÆYoÑP¶³Ýgä_…¬»gs¹b ÞdŽ@¿ö+t‹bµE±æIž6˃çª*À±õè°…ÆÍáÑ+ârÕ<ˆGÐY*Þº$'Oˆ æ¸Ã|B§ââú ROtÑå¼=…ñáK¨EUM·¯Y\V™ì +¸i¹gs†””rÏExQ¼ý‘>_ÔK½Î~²±;âEö=|¼¼;s¹¤AmCG¯!5|a‰ß¥kc <îÎÂ3“^íx¡Œo˜ÓJã™×÷í.³§ÉÄêºðrˆJzïªÿµÕÆ¿‚´R±AO’¬ÿŒÇzÕ ZÚêÊYÿ aw~)JéÚìp€ƒ{a#i@BðɈs*L‚“â«Ø:$âBvEZ":FøXZ„›èt –š7t½­ Ö<&4ø\$;H9]â0§iŒš$Ñe†×â~ð>˜æÍãËÏ÷‘½á‚V<£*_êÛÈi=cÈ >“ýhéi¨Êwu¯F–÷¯G2%¤u·÷u]ÒuÙ7?OAXßä?  *1o²Û\¿œ~6ˆÆxQ ð+ä”—ûKE_„m Þ ô‰õľ`bx‹ 5ÊTf÷yl>G›…Gª–¹OÇWgÇ?ü¿=jk)¤Ì‚Zà£aOà¤Lö¦{UØ­›Ó:ϽÑÉð= ;…·L¢.¼æõ36•Ž- P(è\,ƒˆ¢÷5—xø°âPé˜êožBi^:¡Åf¨IôÐ:<ò<-Sÿy«‘}x¸Èd ½°AsÔ¥LaÛ—…õÆÖÙ£bàæMåó}]7Tc]]ié°îhžÄÁ­$ù~ Vïsmt¬„sÂÀD §¯ÐÈÍ÷±dkàÂÈ;: ‡€NR¥“ítoµ„TþJ¾GE^\ÜŸð<}®ìz³Š*q4h© sõI èTD<çxdìuöIr°&Ô¼°b‚ŸTVÅzÜ•µ¦Çô¶#á›ì©CBi«Wve'=€J±\5¯—b yoИôé^à„µ˜…;¤«EÌ’`¹Sô#Œ @ãŸÈÙVÏÆÿÜÛ’Eß( É –BK©' IÁý¶ÍÃB–¾é9 RÃÆñ]ó`ÅhU•)ç´Ç½Æ†œk•à\QNæ>7òtÖ—Üçm§G„œúÕë´“ `ÿí ;Ìi;‘ƒÍ5Ñ]?<ÔìŸÌ9&kézÿ€ËŽ.þùª>aL‚c}{”$#'}'\™éˆ0úû/ºFe8fÌÙyž'C°He¼m:ç-”žÅKYx9Ü!ãÎMáÞ»H;0xGµ-èò¿Ÿ®ºÑ¨OZ¸ØðÄÜN):ü>¾ ü–¬b°—*ôL¶_1øPK3è¤`âS'Y²<ŠïôíP*˜I%¬¯ÃC¡¦Îi–§E‡k —ÈÈè/BŽ*TÕËÀ WKÏl(¹êâë6×wÝb_ OÕ@êò*ë0ÒrÑÇ 6äd”0VãËÄÆ ×’ÄÓ\ÒZ¥>NÙQÇNè bÞ_íü¥…*'ÎüJƒJ¨÷`Rßtê‡V«äh€V|«Wÿ9þ]Ó¶¡Ž /°"x×ýÆ ˆï뫆y®Š{Ñt‡‹€j“Ù¤Î}ïÖ+îpdÏóèF@n¥•è@6Ì6í¨!¶•¶ud³)ËdI¢ó¨ËÀ}{OE J×;{­ºQꔃTž¥°§G T: 2NøüZDÆ©·ÊM« 6c. !B94bmÜw×ÁëâzdŸTÏ+A¸î¨® ¡O[%‚ñéeÓDº)È;Åf6™ù[Ìí²]„mŠIZ„–ÿ|+VŽrD÷Š›º¹ì|GZÖé»ÎZš)àÃdwImø¸t„¿ÿ¯ú¬/š»þžu].¸añ¡ ×§ß_Zˆ·€e5²·CTÿ”Ð5fÙ䬪Ö> Ì2Zñ@MíÞ7VÀ,JR}ÊR•—×—3º»‰ÀÿQØ \³Ž2˜šu )–[WTWË•%Î=:RÿJãpp4:mdt³Dج%]?Õ)áÁâAÐÔ@/bf-,OÇB¾pÁTIgéD”/ÇN[ðê~xƒ,Ú^’ÐMÔ7¨Í¹Õ¯“€øëŽ!®Qñºë¶Žqêí!ÁÞÞD'º3,íÉ,ï ÷Tö¯€žYMë>s8¡ã²ôØÝ°!,WÂBP]©¢3âïñ‹{´„Lú•,²ZêxŽ–‹Å¼ÜÍE!f„fØ,Éwœºb‚Âl<; Ì‘@3%®—¯G‚A£„É Íà’y: -r‘tö6­X9ÖDCÒÕÑyû(°u¦Ïï1Ã1‘zúÒÀ?Ï:›Ž½9¤½^j¤ iERÿ™¨qqKêGY²Tí2óVM¨ZL¶Ç»•Ž9ÌÄë|sÉø›å©²# vúdyž˜’F¥œÖ/¿_»Öpótu߆A»ÉÙ‘ÚÃÚ" >LõVƒhw;hÜýËÓ‚`L*äÛ¿x࿪èðAøÎå4 òF4Œð|'ô¢€³Ê¨]Ùù‰éíuŒö7Äu´4àû>G33ñ‰É‘ü9ÇM ZÑŸ”à7V—ÌëL¹6…´X‘Þ Zù+³¸Y`{×LÍêûF¶ÄÅ·ª±¬è‘ÚØ&Þ­Gˆ%öÕ‘fË –·7Ïœ©gi TË ¹c‘fŠY!1©íÄUc}K$i¦C—ó¥Q§´iÈêkQº·€cËˬsĽç—®&¸UÅ—|³zMøIŠÉ樷@qá'Z5×bØÕü¯sQ™W‡˜ÛºBlqÛ¡C¤ÌtõÅ~´=èÞÔý ¶²7m_#BV’|Dúbüy_(x|^"2ò¦4eÜäù'þ ïsC;”’¨mNa!ÙMa`¶ G Q–ä#Õ1{òyrœ\U'8‚V¤€¡ {>ó:˜mô•õ€w9»$Ì.¼å‰q6½ “Ú÷ ¨P½ei_ml{~PRdùøò4„ÃÊN¬A/œ~º¯M¥Ž?)#0šÔù(`Ù¦ñ 0ŠÏ@Q†Kp(l¾Ñr+„ Ž‰ìÍhÙm ‡Âo¾¨é¦HƒÊ±š„3ÃSHAÊñýú#àæÞ¿Íx&d3yÆ/WÙd‡¶QM’l)Šè[sœÌµÕ}$ƒžÙû׺Q=vñÑ9 dT_ {#ÁÏÁv~Y쌻ÀaÚ|ì|÷±œç–pÖž=ÒNÑ.;+¯OòFëY®ÍÌ$ˆÊË}½¸2‚À®ÞuÙ ¾ñ&DHø3I«zðŽf„zë_0•v„égUú. ª~6mðþ þ¨ŸiÃÿÁ0NQ‡'(Û4ȳi~(akÙJùsY'Mûí­††žñˆ/ Þ ¡ˆnþVóÂÌÒI…÷°õQ‚DRÌÝô‡ÇÂÆU&B¥‹ñƒðúŸÜHmvhìríìÝ #¤@kŠÉ°ñ˜¼Ô<31_Í$5¥r7 ‡L*Wä2×51”u èåšê³IÔeÀfò;Z8„y¤éSI ×0ƒÅéâ22 s~T#6mT²»Ï„ˆø`Ô¾#îA¿UG6ßò!b9(¢­ 3a¬îÍ©üDú_ñˆK‰bµuy±ëŸšÊgƒÎ¸ô ðÂÓÁ_öoˆÈûðá—ü¦„jH†ò4vúÚ’fÿý‡þ'3Êá÷ÝËb†)‚Œ#K„Y)1ÆæXnïL̆s³Å îçrDø(,õç«odÆú}ÿ>y¼n>°ŸcVtƒAݬ"ImÆ¥od)½@hϬ'ö++€¿Îô«x¡—`oïô÷t„3:J˜×¾¯6å d5Â=ª6ŸðšñvhGøb;ÈZÒqWf–ô…R?•¼²Å;u¿§*™M;ZSV}‚¶è䥢€ÞûkÃêÐ[¤˜Qr[þÔH^G@`’ùL~§¸Áwf­ib&ñN‰¯8ÿû¾={\ïñO;ð0šñ¦Q‡5wó7ŧùÞ–œ=b€ÑT:`&‡à¿¦8¹ zõàsÁüõÌ Ä/9¢ã3 ¡Ý§H÷0\ ؇Ú5Í&"ñÈ7\<‰"¡xÊÀ˜CÄBó¹žmÕ’J†%o'ä¯MSQtù Ð¶$7B[Ðe/Þ×E Wën=¶¹P‹ñ( Únb˜ÓySö‘ym},Êöebñ¿–‹¬$üBÉŸP1ö¡4Ó…Oíò zGÑ{_XüãP':”‡¸]š™¦"+rô–ì¬sVE,k„æ°cACjÍä T,@–y¹¾ÎÎ~©ˆ>ÃUâÚ£ÔÙº`…i…Ët±t÷Ê-ÀJ–‚ZY@B{'y‰]}Vñå:‡2ÞÅzša¥ 0*áêåpa:Rºã²L~ryðÝ÷XèB÷£r¶,Yy[ùÊÿ‡ÛJ²ÉégeŸ=Ê6Ú@ˆ¼Boƒî6› "–ÇÁÒ^~2ê ‰à:_:Ç£‹F8Q|ßúÝo¼å*6Î>‘y´}²•?,ä×Ç`,1 ›+}S-&3Ï÷¬ÂEÞt&÷ã*2ßáÿÝͼ²#‡ š[ý6*Åð0øªÞvƒ £éeP¥´ľŒ B˾TÔUŒ’´0û,CRéÃ8;O\ã7HnÓO~´¸‰ì7ÅÈAæ³h§<•ßG- ªey`…o7) åŸ÷„½Tñé(‘X趵áú ݸüÆ•øýDÈ;ªÛLÃu)×° ‹—†¤Ú>®Ù™pk’ìXtÀ÷Xù<âU…Û"óãcÝ›ý»5†[†C‘Ò6 ÙGîPºÑa°þ¾|îæ€Þ‚—4ÆÖ¥Š†þôI „ż‚Ø9ér» {gT嘕*x¨þE.ÄêËv¥8ÑæaŸ>[StòÍѱ´_6]zÅ•öNZ³‚–m—ã£üÀôŽàönÅ®ÏK’çT<Œ.—’å oMúBB5mp«×<Ç8þÙa}+ôݼ{Y‘™‹q”¹žÌz;*²¿y*þÛ#uuy®Øþ‹g¨òzaR»m“æŽ`Q”A:A äD ãþèm¿J(¸>z^*}ÐëOñ~*Œ·>8eFUÐÏ­®.Èn/~AtÈòrOÝ÷rQUQè÷'sñA §È`º'ʨŸŽéº}2Û2pH|ÄÝ)FÙˆ9b›E÷ @SÆ Q3Ôã:+©PáÐÈ‘T/9åT]¡\•êá±î¸‘å˜0« Úpª—7ºª8*AŠc®Ø›²–Šû¾ôðªXåS¾onOJ­´ (ÆïtöI˜L]ékæ_>Rð¹ú%¡~&æDI'¦´¾ðÛ¦rºÓ³iÇ|^o4zÑ°oàü6eÁO-iáçJj“°˜¥Méë'ñ«"†zD@.[Œ]$R«í«ÄÃaÑÙ•èñ…ÇŸÔ—w`Vß¼5¤+—?WÃo±yÿ%w…È£ý̽.¹0\'çfÛ_$ŸÉ[¢9 üÉiRd˜—3&³q\LH¾ˆŠŒ˜%CFŠëâ¶áw!ðzƒÍô@zŽ8t8ê}”žÙ¬í$QþiÉx¹ ,Ø„pæô_V'Bh‚Qý.ýO(î>õ6ݪîì†BœÝÚŠmÞÙ ˆÅ£?DÉ‘úâŒoË©ò×è*rnÍ„Df0—Êï.}§—sÒn (¸Ç]ˆVå-xXƶҦ ýP5ÊÔ4•¶!èæbÉz­QÁrmF¶ÍyÛúçWR;ÈìS‰±ìÉLz ¹9Ú»EnóCѺyöÊöwc cnŽ2P]ö†yØðáجåDÙ_õhy>½%êÕ‹`µ»ÆnΆÂ~SŽÇó¤—<ÿ\Eu)/gðŠB=Úº“7P¶‰ªúó€gÇQWÁ*¯*‰ fþÄ$¨Èt¾|ÛËtË·GØÝÀŸNðÀIТ:Ü9}çªp(ÆšÖ6YŸ$må^¨\žj¿,]Ä©Ž×ý¡³´‰ ¬¦ËÈ>y쀄iDÄ–³»@%®¨I^´’R"Âo._ËÎü¦ÖŒåOÐŽ¢¹fØp-žm÷ÏI«é`‹ƒ—¿­æ–*T( cïçõx´]ÏsBüÉ´ ƒ† Û1mkN ZCQÀª€¡Tqû[º,ÐýÛʬé{×ÿV>汨bÆ©­rçò:8 ;(…0¡·¶(觳5kU´¿_-ßÙs|+?ìÁ4ÛyõO4§–Ž§Ï?©ˆÕÂÎżÈz¿wÖCÖVt·ÑºÊ3Êž`ÿWgH§#` 0þÚ­ÂzR…Ï—4YGT+Q˜ !îÖ8sìçµ>"›”h¢Rf˜V£ #ﯳmö@í>%%Ђï`94Ì óÒëGœsËîp3‹.Z¬'„6ö˜2dðM»ž‘HoG®’(èeµÖ ¥ø ¯7iñI2G Z%Ké|5Z_È$— §ÇýÑSh”Õ$¢ ¹½ôƒ1-,–Ì4T²¥r]ÒR·ˆ>"¤:K’¾)î†ú +°Aœ£ÄÖ»8(GªK›­ˆÿá?‹¹Ø¹aH Ñ–\CLj<ÌoŠ û`׊íI% $’†0‚4’3÷W´ÞýƒÊ%Jy¶wâ*RýkÖ¤n:]‹¨Ž|Ÿut3m¢ÏêD‘n\¿)þÑ}âƼ8dÀÐBYÝü(Ú©5áíçž‹ˆýu‚9åPcŠyG&I(Š€0ˆ'±§fZå’ƒÑV¼1ß-,£¸&…/È{N6¾± ?¢»E,©£ÎÞ—ˆÓè³`(# LlMr Š*wÄÆ}Àƒél¹±í¶h\¼Z4âÁÝÿ½ÙASS«u>È$Ì… wŒãœªhÊÜ-ö J@Ù•ñ–l¸6ÞF*Ÿæ AGW’ŒÁéYÅp>’½Õgó´„Ž$ Jo/ö4Ô[†XJYóyÎîÙ“o‘Õb—V<ëN½š)`ÄsôçNE"5I%6rz&$½Î<£É~à0fÙÊÝ‘¶‹îä`n«à;Ù4ß•@¸æn|["¶§}ìvÌeÒÕCI›ÐF­BžÊ;p¬yRæ'+Ñ|óÜb)©'-“Â)ÆûCŽã)uK•n⫼Hzg¯âÁ [ÚdøðÌ @H„hëÎK–ƒ‹Øɺ&¢ä»@ ë”ýÏ ± ‰nï¦> _í¢hšýqá8Ò{‡{ÎëëràÚ@á(S^¢ZYÎ;£zªÒty œéñÖ7‡h]íQ³ñžo¨Ø&5Sd‚ÙtÈgKY‰¾ðz§LÊÓ‚\éM“Ú¤ Ϩ¥ÁŒ«eûÿþ%Eëï8ĘCQ«åã²B ›ì¬äð‡a;…²$âADI,êd.í‡í[]¤¼z²40\Ζô|@Ä’Kµ’þfVõ7_Üf«Ù*[Iâ=5}ƒy'û¢u_Kr¶íûN€ÆÌ´7ê èz´>fÏ-½R¼V¶Ì<êìÀú zz¹l›B—: WoRü:”_œ‡¡'Å2†ë9ÿp2¼&gŒ0„]?œ7+¤dÅ%]nµ<&Ì`θð¹r(N&ħ!*¦ÒTVYFBô™«»ÜÐ&±Rt»„iæÅFȈ2¹iÐÈd?Ìî‚O†Ms‡Áè[®4ÜAcñ n1e,'ŸÆ=HøxßP:žˆî÷£Àkg<ï-U¿üä: {Y‘W5J„3)¿+¦ªÌTR¶à'A´¥³>µœç‘"Rs©6»°)#<°zpûcN±i1ªg;7¥И?ì[ÃŽØ-+-#âj›ÜóޓĪ”ÕÄaõs̘:Û_Ä¿PvbÓßBcn:Ⱥ>Jƒ1d’?GÃwy’Êu@INˆ×-ÄCÜ‚1·˜a2µàÐYWɈ¥0¨’¿îÙ1xÍú¢ÄÈÞ±’%V¯+ÆS ¶ðDìÝÇ‘sΡišÆGPŽkX½I3'5èæ .›¿Ö’*ÙèûjGMßÓ6¡R‡º†¿AùäEc91™,h E’ÙßrÖQHÊ|p$ž±J„IÔ;ÛΞ-³j™öh- Ó5ü„PÂèo”8YÞq-ÏÞ»gE¨Øìâ‡T€(N¯¬áDÈ%WóÌüEr»½ð'.ji{_–Eêv”ü¿oŸ_˜QŒñrea¥úíWC2~Ëä¡2d±h¥_W_øj5 þ÷õXûeMPhØlùJQÕiM®z:G¤ìÔ!{ê_KØ+ú‰mœië¥ÈÃWNzò#0¨‹ýã*‚Åò…(EæWÏÝ3‰GßfØ„8!°6,uè˜E!ÓŒ%Q-°kgßáGÖ%›ÕŒ] üì/\J®_oŒ>Ë$¨óÏ™¡T¶w1îÍJÃò‘ýeTJ&Ö³kL«<ÇgÿÌúV %z(Æg„®¿¸¯]ß¿ÕL¨ƒýÚYØJ@¡mmÄðb]ux²ú'SB “Çb¥7í™[Õ’I`^ÅzåÇ>H¸‘›.T‹©ŸþB´#CNeB£6ÜHÄc!¤‚>çÙ(V!J R“ì.Ì Ã=tìö†·ÖX®gÜ¡:Epšh?4yr/Ó!á¤;ÀMìU÷›º–<á=á= kñ¦9ŽŠ_r?mµ‡7‰ïÓµÿ8¦¯„¤°ãQB"€[7k‘zÍ”–çpf„é²µ`ÂR>wÝÜõ«BV]„¤(õ±¹ÊÂÀÌ•zñ‰ˆh÷ª"¼.Ùˆ­ä&­ãü,½<ôßó"ôT²U‰S[sAª¤*Jáå$ßÌeÌ™ÅÞúSa×Æ;7&—ºÞž¶×L¾ñÃ(ö³Ð˜‰\YÝœâF’î-H $ÕG¡»–ÛtLí»Vô†MÏ“+ÆDtÀ²áÉŽÉ€°«}úÔC“C®*m{ ÿšŸKAöjYÌž vÏÊþU¦cÇ1k?\›á0!ïcyk'å ™ªF6uPÓ¾Ðï”TÞ](]¸ªÅ;'–¸ ªG ´ÒZ‡H»^Ë)6Ñ<ä]Ôõ'ïæØi†Y~ešïfáL¾µÚî3&ï QågÑçr¹1ÛŒôY6ïAÏqÛ˜“Ž™:ôtZ>jî—¢é‚h4ÇHuš;wÀ/WY3&"zö¹{ú#Ÿt&þTT'ˆ•žÈ/[O¤Ô秪–Ò¸8³;ÒÚÊ+λû ˜aŒÓ7èžp«ü)¯nX˜Øóà&‰Á²·¢»å(@Ÿîæ#ˆÔ|Nd&̯Aô= ¶ôP{¡² _¯óè¼â§ܳ-MžGÂÝ'ÿƒµCà‘"÷O§á(åL«Þ>e³Þ ‰òU«¾€üºó‡îd逜üÎ_š‡,æSñ ø¾ñŸ3ãÁ©®0IözP€Ái_\Fâ³'hG¼é ýÐg(<%èh³™@íŸßP_›ç™ŸŸ§ùAùO½·D{bʱìEq=UxØŸ‚¼¹©¤¸DùZ»-‰š¡ÐÌï2cvŽ¡M„Ê2çGªÓŽlbóÖY¹ylžÁ:€ÌŠoÎe!@=0ìv›gAd´Þ_„§¶É1ñNuÿ.¨8Z°À­ïÌŸ¿V¸[öº‰eJaÍf#)·¬ŠkÕGøj2Cð|'+&{’žUIq³ääY¾êÎ(ûQ5þP€:€”Pbpb’<­Ò i¥žšQÖ¬Q¸œH^(Ø»¤ÚVþCsÒ^ΟŸ¾5ys4›¡˜E ¢ëy«,—aÞ+-(&­Û‘—ÝÃÐÔ7.à¬P•&øÍධ¸Ç;eMbry†\ g©©®xáá£òt ×%!½,)Xß°,.!¬d™˜øN~ô—ÔƒG(Ìãö;Ëjø¹¿tè³)K°Àym©:î*%)SáÅÍ(LÚkºù&q¬¢L³)|/„šÚ¡ûdï=­˜} ´¹‰X¸Í_ƒ”wbº5ÿ‹7r¦Yb..Ñk~;%qÅJêA[ÈY%×™£q©Fƒ†Ø íC§Œô_Í2=Ÿ$Û! @î.øÚÖ±uoÝk6©ëðúqz.)¸¾Ye`lºn|'LÀÔó*î}¥?AºrâBYÖÝ; :ÿá$Wø Y&]‹\¹45að@ƒ-r·¥RÈp/vÃsàŸFÏÇLÚÍÒÝlãs*;iŽB¢#Òã”>Û¶ýmO²#/õúGrdJa Wðà¢%¼Þ¦¿§÷Óòým6Oîì|d~¹ô×柄B7?#½ÞçsåÓ6´Æã]~R*(ѵÚjï+`Êéãù·¢S6WZíÁ´«deïÚG©”Uìë† ù*Åw^¢AS~nC”üP7—“~'»DëÏÒA]B§ÁRy¡-Fµ` ÓtÞlõ×.æn#º%R“šù[]ü‚œ¨ß}¨ÎÈ&CÐ4òÐÊ VºŽ~ß‘3Ù “– °úÖéZö+°Ãúsõ¨)Þn‹>×CLŸÿ(Bçýb?ºV îÇÄ1æ’ ê:¡´žR)%’šÄu¥V(‰‹j†c›Dk#Þ=Ü¿Zä¬ýhÈ®ÃÓ36Ÿ8ÉrÑè4——+šˆbÕ‚ǯ/ŠmË÷ЧHþz>>Jí5Í™Ü0 Ò¨þÅ°/ò[õE”Ñ[ÏêÎW¬1š¼F‚wt™ìYîø9•;»¦òöŽår‹Ÿ³ì\.3åyiáa >¾ûhò%RVi‡(™Yð!¦ºÓÊVø„¢ùÃ!KÝÖ€»ðB4~!^ Md¢jÿ*Ÿƒ­ |W« R³Pduu#•q€®åJÒ  ŸÏ€)UƒSAZó+¢Úfð«£³T+%ùGD?|ÀÁÃYügåÍëµEV0ùç©© e|{¶ÏA™œ¯×¹Ê S™‰¯^¦…ý\hNˆæIéH@åYgN5¤ëqkþ“0Ϻ¿~„=YøªP‰¡ë æ„tm¤ìò&-ÿ úð#vCau½E°2p‹³®þTAâF‰RâÆ<ðdõá¬?>ç[¨DnkÅÚ+àôâÚƒ^dÅ…Í\§¯^P£ lüjߦUaÀújd\!Z²¸Žˆ®…bŽ- —º1é<9—éo8׶w˜)ãºë|äÎýg=væ›ý5“æá µ0l—/^hX”Þø–»2ao2#¿nº°´!Z<ˆŠ×ª4›³Ê9nb›è$?áÜ–Ìà±}{vþa½®ñÛBq¸%Xøà•48s9Ù± Ù…_òU ‘´ýXùúî)u6ò@[—vzy85£A6âÍkSj¨~M$hҰъ꟰2ØÄ)è].*{¯Ù|ÌP°Bø9¸ÎhÙ4ŽÂXö ]%mw£oy-?kº чìø0¢u¤dÁêüP~1B‘r‹íËt:•SÖ»±iƒ¯WB/àO¿ LW™ºóG8žpJ[ýT7‰›‡/Å€žIõEt ã|”7æ8&¥ˆy×G6[ky£;OSGÙ¸ÂÒÀÞ2ÑÞ­5xÑ#«WŽo*²&ÐÉj,¦/Ñûxªp†P}O {"Úpz:³kíßìNÅŒÇÒg.üÙ5& ÞÊ¿˜çä,É¡°mVžw•œ’ƒšµ}常¦Šý¤fÌŽj3L>ÿ5(,¾Ò¤CôSÉBÅHcŠzÂÔJU"ІlB…‰^>\Ø&Jj2ú^¶n胢}µ\b-"ÁD¨]ðÿà¸Ô6vG ¿ˆj ¬ â¯B«9Ü‹ª¦t îg…&¶Ø²%F˜Ä?» 5øëõ¥mû&q/hD(ÂN«C.0Àîm'•·ÝæpÆš\yoý³WäïÕñ}˺mxkŠ—í¹haYn&á~¨Ü-ˆ…éoŒxõX§y„L”h^‘†r…÷ä/aô®iêÈ%Üé'Ê°¬«>Ëú ni%œ{¿[îW/²Ãú§á4ŒåjÜÆƲU/ˆ'«] ÁÐÕ÷ƒA0†í“WÀH|Xi³*ÿwìLkŽSÉ?ÜyŽò ÊÀhŽKß ²ò,úϽ(L»ƒ§ÌŒàùŒhá“þÚÊÔC3§øÏ5Iç@\n |œÖµÝ]G˽!.K~h[+»Ìs]¦/dQ§¤Ñ¬H€ ¬¨o³9:a¡å]xY6WÒ†i¯Úú_ÀDÁ‡™ð¦C—d7‘ õ#úûŠø£›h;P;Ÿk&F )úoFn<²¨«‡Ö1õc}Ûêµq÷WÖ³¯ÈÌ— æöeôV.> Ðx£ÛL7ÝO ÊLÏ!1ÔSìï9 œË­’[ÝdC¯»Ù ¥?$E¦›Ã”1hôfµ¶Ò)>xÝ£÷é[ˆÕú½Cñö#s»2¤d½Ò6@\Q«„-§I’MÓ·cÍÝ®+Ý#1>à“j~úäªËº^[-kÚ µAwSug~Ú«)‚a姚ëÃødŽŠBs˜÷c{•fö…Y´Íá^vM­B7Z:3­ô‚ @³Àüœž\*ÿ-oÚºt,ü`&1Áò °=/“E­øV‡ | [t+xƒ¸HÉŸâ-,¤ž¡·¦á׌ËÚ$çÐ>ÚONÔÂ9fº¯–õÖñ¸-¨æÂÜÛ2dÑêûÕ!4EBJžLGI ¶o½—˜°F/BGߦƒ¼`]Љۢ*Ôk»ÍƒØ‚ þ:Ûr:ù'PAÝÙ^ƒÅ¼PÖŒ°ú6î‹Ó9:=Nf÷lÄÔ«?^dño> S/CÙi:Ù¹eÍ<åZN-ó]3 Ä´þ×/sû%±i—üòÙdÝZàHMÛMŠ5}‡>÷ÂPÞ÷™ç­±ŽòšÜ$byÛ0ßGJmQŠ|»ý²‰ÐÖ2ènë\=Yc§ùzPƒÛUÜ¥CñnÃçöì”T2ÔöqždnÞI% 4Ü‹.â…ÇU§ö©œ´²&mÌÉÇü¤‘‚ø&•Þ´›dÏB‰-W^ YŒv(üe‰œ( (Ùâqã<¨X…×0Ð7¨@Aý¼äÜíèß.õ~åslºÕ%Ä­ÉbR1T[¬}‰ ªÕ®_E »dš<ëxHÒÝ|³µyÄÎ.÷`E,¯…BmkV”(O€òŠÂôçÒe;Ö ‹k GÓ<Ù¦‘òŒE@ÁD¥I§ “¡—(À¤ÅäÂfµÓÜý}7[>[†O}4™¹©gZ®“*“;?,7tR×#vÍûŠB¨#Ì$ÿöÿ:>ýêêr¿IÒSJ—NEßå~!†Ád¾v;ÂÀCM³Ï*óßoQ†dØ&‰8ùdĵšš!» Ø7Ó &‡ÎòG žyp¯(+Î^š9Ì|jOrã<»`]SŠ³ þ'.&ß••±‘‰hÈü+ÌD­%âu»ñ*0¥˜ÜϦ?Æãî&Su‹Q;n­ ×¹KE§ôW´P¸Þìi¦<Ì©·æ[ºÔªDië am'×J¸'ª™ªº¼ÝY„èõåÇO."‹ƒ™F:å,kBÇ›ØAhD²ŽAuJÍüLåœ0n¾v{܆ßÃ4´×|½lØ´¥žøï· é·¸©gëTÛ¤—ÁÍ\A>„Àê @T‚0ã¿fÝA°Ìçžá{‹©_ÊìôgVܱ¿NR¸n=÷×Ûˆ|s™+Iå# /RÇÆ/ËøEK•»Šq¸ßÉØ?úŒyQÞšS#uˆžº‘fY)#¡8ÃûN»{!´@”®¹ë.) ŸPîÂ/MªðôÎúê5mSx#úŸDW²bøvÚ%·à–:£íõ×Fµ[XoYÍõ Õ}ü}8û]»BTáå§&óég€bïp³‰Äò–`­Ã¬‡4÷€2yG! 3"$y›Ñ–+€Y‰xÒáý=”›*3G ÉÃW\W³ (yYÐó˜+¢'^QnÞVž°õ#‰bžfAR;½ýì.†ŠšÂ{èWé4T}éz³B?r¿ÿÝNÃÃ$ZÛŸ×4 ±¬«œs»gmˆ-ˆÝŒ(b€Œ'Øe=>½ЋËel*Lð%f)C(î$H‹– 1@<ú# ­.ú€5,@ÂæšeŒC”ÎИ]48<Ô­’þ°×ѧ§*¾ó0=ÑÚ§rà•% H+¹#Æü :àu>Žm´àOÂHO¢4Ïfò ¶ɳ\hµS^yö_§›²jã”F9XB+e¨øb¤ÿðÏAMœlæ>ŠÓEu:,\fä4ekîfÍþÈVCµüG¤_EÐñld#ºaQ‚­¥z›lõfêM×…Êö£.åYª ·Ëì7§/‰¬Xd³_YáóX˜Å×ضš,«z»á†@ƒzr#ÎôÝà{SáR gwè9ÑûŠØ`¨ªJ7;•‡áˆ¨ÕDt¥;foÀƒ™Â,LaâÔ¸¡YÆ”Á„hfA¦â<¾íc$lœÅŽÑýðÀ¼™X¢ÿŸ{.Áë±®Úîfç¯2ª/êeµ*bI|ÜÒN=çUÙ( ï÷§Ñ%oIFËûœ&Õ¼„.ÆKáƘÁ~©|øJ˜—uÞÌ\R’±öðã0xÿªdBpOxÑ—á‰Îuª#d E -:%…ÏG%Ô´×X/†(rRq ôXÕÉK¹‰ækŽ’g}›„#¬¿ø[„CñS`—X 昉ç}Ô˜–"’GFü“ë±€U«›?¤ Ó_Õÿ¾û×òo`øyYÞwÏ©'K<ù•fEHã>2¹ ”`™rîA°¹ù𤻌"®ìŽ%ëÕ !äðêy§ôÔ^*Ãèó¾ƒqdñÀ‹mqƒÎ>qq7·c”Üa˜¯À6ö v­ÌaýÈ2´f¨s¡Ö[JKgPBY\eæ W¸óá`„ÒWþ]€—Œªkß•b)„Oæ¬ÕÄÕþ¦¾]ê>0Ž!aQo±i¬¹EòE0z ¾•ªáh:{ÞòíEᆴ…€W™°>¨Næè¿üC°,·Û'ÿ-/ƒçè>Èë†]-0RíGWžìeæ  ‹~Ò MØ’UCraîiq;ìǸ”GšÏ‚ÕÀg½°cá=TÆAX9|‹€/—Ö<ÄwkëÒ@&p_K‘¹”¡š‡­±N÷Õ CRäÆœd~ÒÎ비åÿ‹à…»¦ïhûÀ0sxñüm9$ñ;õs(š½«7²êåcßÿA–¾°zZxå3â]øܬ²$æËLuT„™ªàs¥änƒ‹jDÑ7Ž¦ ¥•Yxaä¤Àt,ÂÇn½_4ß”$Œ]+‘Ž &·EzÅÔº´­g¹ÔQgxtžù¡GÁò¥Q£4§Ñfá[zÍÇk-5‘ï"n² Y«*oú›FÕé§y–äv»ŠÔsàt`­`Üh&¤$}F: )P–©è_‚n¼ÈHÚkTCµvÄÝùѾ,¿¦Gî=@>MÛýCŒ¶ßvæŽð!1gµU°47äùàrxÿ¹\š$;_ V)~ˆ4¥ ß‚Õ”íóæ7„E°‡¬êD¥f–£ó§;œÿ; nØ&JÈ^ÍWŒ¸;(ÃÉåwä1!ùvS.R~Œù•ç€ öC·Á,™zžÞ•¨?á‘]t÷ï|<³uÅÅå:»C€ÖøÇÓLœFK\doJô´*e·w§úÁÿâˆ?„r¹£Ð«§©œ„,"TÍŽLÔËQéynΡúù-]–g¢‡^¿D4Äç)Þ.thÈÌDØHPÔ¶#à4x nï…kíö‰`A‡ L-‚Ôië3ÆQI«`>ÉÝõ¶v¡ËÿÄŽñ~$˜bÒ6›Ág "ì5úþõëitÖÜL!Tf©[«±AéŠàãï‘%} ‚÷09zÚy©lfÓ `wå{ì¦ ~˜to`ÛÇò¶"µ¿/¥›Z7\™Q !qÆ.cRèXê}ƒC7Öxµs;Úy “Ù/–Ô`Óš­ñŒ‡3æ?‚*¬Aú¾‡:7™žDÒ$×ËÝZµNÒ!|&ˆmö^ªÆZôs©Õ»ûË«ñS&®ÙZãîäMv"lÍqsïßád,Ú¸ ³ÍkÕÏÈÓ²9‰35TÿÞÁr”HëpúßR”xþ¹PU¾|ãÖé9cÐ.ŒoÖ‚®µÛÞøsÃkä­Zp£[›näý_É,ÊÒYãRÜ;ÂÂãe?Bµ\ÛpQ>சýG* d²äºè3J¿×8Y½C?^M£“‰}7îÀš«*°ÔZ+[±Š®ªëRäôY qrŠ€8T¼C†s+›Yò×—?âKbÑlhâ /AÝ›B·Ä ˆ¾XKÓ~GHQÌAYë7“T–]ž)þtšl×™¡¾ø¬U[û]8{*º_À™êù7KLNa b£sÍæ®ÉäüyAÔCS†x!Hïö¯Ô ·ú -öûlÝF!7U‚[;ëuwšÑGq¨¤gYalìŠìà )€ã+–Zט[²îÐwŸZŒ´±qDáÅ“ÜðxÉÅ<˜¢pY¯ñÛì´$ÁùŠ¤¿f½JÂÎï(ï0•÷=¶°!é¶÷PñãalZÑ„çè`æ ûͦ]àlZj†ÅJ–EŽ¥Ñ÷ tî˜Õô1r«1/cUóÁKÐf>Ó¸-ëÚâ#©Ò <¬U5ë2<9¶C'ßþØjIǶC@ñZÛÁôa «sï4/ÇçæüŽ‘£VÀ¼áåe@bº÷L55·Ö­È5¤]pi²Ø5 /#ÄÈ:Òì·ÍmEP]ç\'£NBüàêݪ2ô§²¸¡㘃Œ«²ù§/.í?Á@ IìLýè|*«t³ö©çÇp 3ît(>m l¨|Ϩ©‡£Vïm€‹¬3³Ö¡&À!ðçôPßù%žÐKÃƦY¼á’kÛ`‹ŸBµÙ\Jì”ê×E TC½N¸v½¯jr?œ5iï¦7mL.ÑîØe=¯íï*wRŒû±Ïb@:Îü'ñ¦>ÕùðÓvY~Æ3£5·õާʱ©#Hü2äª^9Ç‹Î4æâ¨~ù qÖ ¥E.Åß4¨èki@' dñŒ÷~5•0FýZ^_JDLƒz·"rš~½â´µiûÞ•©÷?ÉK<ÂQ¸ìÄ8–­X,zW··L¦èyheÑìŽP#GÂËzЦàôhiAº½¤ÖMƒ]?(ƒ–"™ýx à:ðü øì6ÓQ8-?Ý« ˆufß’tÁ•@Ðà½ëwHx- gÊÄŠ9D¸l¬î…ÐÆ´6U],êlûÀÆ:¡Rµ™>Óƒ„"ã¿Sk>M¾<‘õˆt½#×ñ<è3KþÚP{a½“­ï üÞ«qn:æàå•ÎVl4ÀnÄ9}ŸŒW üífŸJ¬ªi¬™Içøn¥ls¼ß'‰Ç…,}Þþûƒg›k—Mä0áºT3 Œ“Žò2*¸ÄÚ}ö¤ž­ÚX¬O÷É€ˆ“”ž)zÞýåúP÷ÂÞwO¹YR¾NT²"uÅ×0÷¥g&šªOŒ,j•ìl«<ôDw[>q:e˜¡¿âѵËäßÜÞkbrcf4/GƒJþŽð•ªÌtÚ¡ ‚®ÏzÊÃëv·¥i6–m¾ŸklºýoœvZ›€þÕBZj:£:| :•Z­;”Ë–‚£Œ=ÚX†ÛJT¦–@d`zfGtƒ: q]™‡RÕW™ì<†P æ½4E¸ÜEÚ´Öç£Tõ°*Õ{mˆØ箫.GהꚚÇÍ*(7îaº~*Š-vúV&TTdî¤òöÚ8 Ž®"’Ëé¹w_øI”Ýè.KFv'ñæ¢YsÝŠ¡ ö:Âã1Ý ýÆĤ6/R$ÄÉÍ%›a ö¼ÿ©O¦Ê?_ Ø–"š!Ä|?£`Ïv܃ž­û Û+R¢OÉ|r‚Ÿïj¦1¢£ð‡ÏoÊA®gW»}pz‘&‚ €Ït¤3' –{xžÀ\íøÅÿZN‹Œpd㵡6˜ùûV!"‹aÂ8ÁçíÖC¨W=oüj'ÛIç´±¡¢ÕGˆœHøQ.c6`K³G|ûPÁ1.¦>Åì{IâkIÿSÎ[-üi`‘È$(ÃL'7‹5nUäúœ‚ ”sÎr{ÒøÝ&6¤CS?å{ý…9ïøï_Bâ.ù(ì+—ÞÛÖÃÊÇ|°Ì4ÛP< Oò> ’^v¦ÆÏ2÷`æÿû‘âoÙªißzN_’dA[šžlŸÊ(ǹÔ_8Z!v]`—íÇà 0CZ,WÄŽãøî¶N¦ûúåïK,b¬…’}¯,$„µ‹!;æ2y Ê¿›¥èm.E»? }¯ –ò4oÍvôܦbº¬[5ÿsŠ ‡ ®G¹£”¤™µf“æ”7]S‹EϤ¸N Ûæì”O4ÂÍ45^fqߌê ÚèF_„4ÓÏcám¡žâ/÷¼¾ »¯¥ ÏìH9üsr\÷¦¡£Ãj”8 ÉéÔ À9û­OÍÃ|·aeZ7)\ËÃåìB¸Øùì9@Rdöë=@J¨ªŸª'i Åä³N\Ç¢uš#PîKnùŽõêÓñCÐAlÕk&¯kYƒM0»ŽOq|3ˆO½ãï ›/c)3e€WRûîÝ{`9Ðœ¾¸ 6£z”²¨ÑØýµÅ¬³§!=NOàBéÚíˆHØ€D'ìJßìêïwÈf Ñ[GŽèÞ¯He'ŠÂg_@-¿ŒÇÀJN4C@9ÚKçŠÈ†¦Ž 1+‰š2ó§½ëžqÄÛÅŒzÈFÙþÿ†Uø^™Y¦åg«uWŒþkÆÀºA‚&†ñs¼ŸÉ¨³Ý1mfÏä/»zs÷D¯S¶å¦ù³`ÌN¤ÛdªaåÑ"SýÝlÕòòºY•wÔ#¼ÖÂR‚Ñ£Ç+m¥Óé‰é!züǘ ?ú6 Š,‘N "Tþ¨´©C*qÙ†æ‹X2“•©8PÔSÓ!O‹Nûµù4ÀÈ)Öú"½/‰IÎüô1oОÎôµ›+•y‡¡,ãÕ@p¿Z,ò®ŽœçF>ŸKÍ{ìlµ½./³y­¶I,TÐTž/‚¢Bgz¨J{ƒŽ[_¶}ÙaÏ<žc®¥/,æÈʉ§5'ñÃÞÂÑt™'’L&)ïo³ld÷·I1R“í™\îôˆ‰I+奩lFò™ËE(3"~À(ç™(¢÷p•õ¦ðøÅr¨ñqkÿr;þ%/o•ðûÜDPë‰Ð¼ÏÑâŒ}%PœæžÈ‹–ÁpÔQ›ÁŸíW®ú(œË{¢Ceo/òâ¾cÉnÌYÛ<}  ƒÀ¿Ï¶E…6r±i0‡'-¨Ý:|ì‘¿õX,–·5ºõ‡n;ÏÖ[{wy€ï­Í85‹ŸÑ ·6¥Ö ’`bU~;c@ ”‘´ËÛEòHªWÙsÜè¹6wUèÌÜlà¥#×°ÜdI[ëd¸<žÎ;‘öŸïêSOŒC>ºÐÛœÏÐÆ2Œ§Ó!§!N²›IǾQâ@ê©­øÃ^µkô9$«“wr°†iòJ•,IÇ‹ÙYs”{ßù:)Z¥E¨ö>3É@ǵ¯QL¼"ÌÅ‹p$­^´p‚ª¸:űŒMŸ‡àí¬ƒ’ÚJçNuß/U;EÒ§ÒoœžýbÐ óó¾$Á—[°×†NFI|“øiMJ‰r= 1O€E%ƒ…i~Uèò£Jû ù>èœP‡ñ ­.•Ï'<Äi]EÄ]žËÉâ6|Jî„8ý„ñ„„táÌÁ(S¯²Ú®>SZ”@Få ~͉vjKØÃì1 ˆ™q4©žBø½AK Ž¾Ï° éØDs´æª!f¸ô(¯ØÿC½XŠBÏÂFRN*SÝ}a»×Ö½Çi•Û;³Õt¹(ˆŸx$¬öû(J;6Þ(ø“¯òÙìÐ5°dì‹·,] ?#Í$<&õGPÁÎp´pÁ*ûÁ®ßÝé-˜#câgzÞå¡èÑþÏ?šŽÓ`œ†XkZØTÿ1"Wæ3SÖ³é¶ê/H›9M» Oá“>ˆ™¶ÉÊð6ªYñ¬Õ–QÉfÊP8YÄ;ï>ŸêºoŸèÌ,­–Æö^±µûÞK6ú`JÓãù*'cby‘_ªk{nªÚ}or¢IñãµÙ9P^å*щõòB7,%ÁùfÂAÇIÒ†ÊN|sREÕ©$æZžL ü ÔÚi¾Hùj6-|h,:n©“R³úÜ)\ôBÁ‘#?#žÖÐX(^Æ]ÑnkШ¢J¼µÙUªãËàkËä•ÓlaHÐú0³ !Bq÷ÍÍ®Ñ@÷0b,N.„®š¹Ì¶‰’>„¨§eÌI½DôNm“þ %^⻦N³>¸]š´ÃkË„æìdý¡á¯›ðŠÆÇ”¬†œT³±ƒ´ìÑ?ú«GDÇ{‹8eø¤&eëuwæßžŠ Œ\ FTÇ?t¶ÆøÄóð=r9>ý¼ lLŠOÏ &E)%ÈË„|gÈÜgœ1í ¨ЉЦäj <än°qA­j]ñ™›%ïÁû ‡NÖàÚ±X‘dK•ÓZnØ#ü¡8tMœi¾$p`!Mõµ|>÷=¨:hb 1õu½—ÉP©wC/mêc·ƒbdW¨.ÚkŽ½¤/î‚ g¸Ê‘„Û[±ôâ —Jɦ:;½&,Sà‡›¼“%@Ôç è8Î[›Fà•Ž\ÌIºü|AÆ–þ^?Ög½Lž„gÇ xvÉL]§ÈÔoL~ÈN¬”ýC]U—…R]7$ÌßÔøt±Úb6‚íG³U•ø@ïœjw0Œ{H@/=Ì”ý¸ÓüQX¹ +obZ%ÒÔ2 %†_bQaˆ]›"þt{BìM½6ZºQ›§IV,Y¬œ,õĦÃSb5ö»Cy™¤ÖEA³H[ßÎ)eöÀ¹íʬ[Ú½§o”`‰ºÆ¥ƒÐg\ºªê]û²ö¾ˆ`£7E÷räÛÕS=yÚŸêEø3¦û¬G¨cÓDþ†x@­7ÜÊô€P¿ÖÆÀ»¸¯[ùÆ\æßi3©EÏ9b-µTH&&9ñô>W.Çëñ€Q,Gß«ë;P`]MöÔ½Xrö^xf°–.Œ®ç«óR–9Ä¡ÈZdµ]ü²ÿÖcãòJ#”x§íë±À0¸rE ʪ0ý)ö<,>Ž“kEÉ,!ê[Æéä…´9„?<Š+Ã%ûÓ)+Z†P•>Wôb?g”',íøb¢q!`¶¢4Eƒþ'2÷…‚‚dÖ1“ :o¨Ú×.ÂëotKr&¼I7?H¿QüJ[­f¬»³1ÉN«ÛöwW?Úâg®’ wç ‚[$R×jä 5ÿι6ˆk‘ æ½Ü¸¢>½s 4ÕEŒøïDÓîÊ©Óœ¾2ãeÖdÞS_´„2¬[¿ ÞìÏO£19ÅtYÖiÏKÑ]Ñ‚`5ŸÖ]‡ìùÊl8õŽ*^'Xž ‹¦³5Í à:Ñ¢ƒ{Rï³ßv‹ý;îÈúUX|NÖOkÑœ±¶tÖE(ƒV7 ù)qÈþîj]3•;ô€`›9åAaݻ˴V >`aãlÁuôżäFÄ·‘ŒÊw$®mgv³¢™“·1ÙÀ£«š¼K+§JH!ÄÝÄQ¥opêÐiB<,ˆxûW•ÈÆ-­¢«Î5Lrz”ùÄÓ ìa­¡e¤É_©ºUàsJìÎØ \»óRÏî“%WtB¯Eê;Àôa¬‘S!ÛNµcX®×ͽñþù­”>ÓK³®Åê'—¨rƒ–)¿Ë+ÍLöìã@Ž0P Z A9 ªQi÷P—¼§‹þ6g©\ °ßýæp¨“Å´¹ÆAQyáãW^J©³2ol}pžYB"²W7>¢k¼¥÷Ô($Ó9Ë{k“jŸìo&à‚ßÙ0iÞ®6T/TiFÞ•-W0ã¶ìÞú&‹B0tzö+çKòeR+nµ2û⛣r¦Xì©ŽG —{Ó‘kݦåǛۮëJúæœ$äHئ+§„Q¯ Àr<# v‘Ã*#Ù“Ú^ôÔJI..fïö[Jâþ¼—iž þH‚#'1 ò,®{HcÞÁÇWÔ ñØ>ö{£©‹}(±È^`ʟϤ¦ëDÉòRÕ¸ô¯ŒHÔQyh+¹ŽUvÒ/Œ_y-Õ­Bu|8ŠØ=tÓ÷g[ÏyK@Qôëùq"ìª+Wöaâ%#„]&#\B݃¶Àläu¬¹íy7Gï˜ê§yË´2ýAuôÛmÏOfŒ@`6œ¨ÿŒ5¢¨eþÄ:ÅV}ßëFps-[£È涰‡—«W½Ú+Æj¬ÑbR÷éNfb—›b0 Ù_4cv1¬Ó› {<¾H®!63ʪâÓ¤ô]ƒ rÆì©œW¹ÍîtN15šºÄBGât­'ct‡EÊñ–biGLv—Ìx9 Hƒ Yà"˜_¨ÁÝ–åÐx‡¢ž*xq§%LDïÆRt-8«ë摇‘1ò)xÝ8ˆè°Ê¸ÛÜñô1 ˽As(}n·»2žf”uBnÛ̼JÚˆÄÔL zïË-÷Z¿9¹³È9 Ω(Ãs ¥1õîy|z(vÙÙž9¹QC³£7OŸå­ 4ä“egjy!Ã̃¸N•ÁˆÝÍxØ"˜S!BO»‚&e.¤c߃éDÌd`ÏÚ’ÓÈžƒªª<èÅ4@Ûò×û¿~ÌÚœƒ`›kª€?'êÖExÝrâtº´Æ_½VŽ rÇ”d]ë›6Êjç¬ä @­À> OŠþo'ö!§®iþüò§möø$¡yÖÎVåóå$ŠRǘ c"3¯9$ÏOðÌ-ÙpÉG/R yö´ÓuÝÍ ¡Ô8ʵ™Œ¨K-¼?€…9Ú¹ù4+£Ç^ðð ]H 5à–ñ?UE€V¸Šã”ßTÑã=A2/#^M&­7[A‚\«v¶!£•á-ûU‚`Ž¡6†o\ ¿¹<¾Q£âB¤kÔ†°šGåXi¿ç£ðåZe±‰s6ô7a…º–e):Ù°r©Ž7y+à“÷ÝáÝxv²2g¸\¹ÅG±ì[ÙŠr1ŸÿLãËL•ê……šCȸÑ âc·xù!…VGBÌ÷ÇŽÿb")#²+\·àŠðH’Þ±q´Ê)ÆÝQ_=‡;-šMÆxãdÓ Að½ph³¦ …¥§8i8pOÜojÊœÅð>|C=@0ªÀ >õîCòä2Mó —ÎWh|‡y ”`ÐW½º†þ¦óøåë"©ÌVÅþ›ZH%~y¥ Ü„96"¸D ¾n6J/b€¿|caé<Ú¿”=zæ ›¤±ÝÅ-¢$<Š¡n™ÙFûž#º¿3ìûuj\õ9 w$sëÞ¥üiU†IœÏE™“0/}ll‘‘€wÿy’i»GègÈب7:wC~GÉ}ˆòÒ±ÓSè9â‚zXŒÔâ Ñe0“sýœ[—ÔƒÇ^+ò¦¥<’šz‹ò¥îœ,Ù°1JûæÊIÌš;DŒ6èX¸<^ѹ!rü‹9­ïH„Xy…ý(Ñ÷H?@0ÁAæ“1y›GÓ6¤ãûÒÂbFµóU-Á—>.—Ô”ÌÍÓÙ¦ƒÈ¨:îõE~Å}l¦°&úAK“w1ðËn$ ÊxÌ}!S‰9d° 5&žNRŠp >³bÊÉà¡fŽÁÏþ`GlsŽúú´Âm’¬a«À@ŒVÌÒmTGkíIËÐ ¤;/hÔòŸ±¯¨3dn}êjgj³-¸ÿºl§“è…þÓüîH‡²Ýçí,\7Ùe'n QESM¨‡‡b!úÍV4ß2ÈÝͤ¦ž¬Øj§Êo…kcµ.Û*ëQöÉ»ôþ ÇÁ´:Œ`]vJº?º^}œŸÀŸ#™ê–ñd˜ï¿Z¬³ ½ô»5Ga[xÐGa)ÕÍ{¼õZèv£¨öEX؃ѧµlvžq…(W ¥A‘ì-Sæ؉W8æTȼ}Z§€ÿÄfÐŽÕá1…ÏgqÙ_ù}¬dÆg„0ú@ ³Íg ‘.Œ|>WçD%'«~½=äo¬Zèæ%ºÃß)ª ǽŠw›£¹þw¬¨‘Y½BTƦõÂ*ø¬t¤ FŠ­·÷Ý—ä¨ /$z^nÅB¸-RŒè‚®ŽeQý´§R¤Æ’Û‘^ÌE,©€s´¬`ª$t.'ú ûu,Eä^Ÿ`cs86Æm’HqÀ½ë†Æ©hBa¤H-&³–%]Sr©;²ª'8P˜ ƒè^Ak¬Ô-m6vÛ³OÇ)"IoZ‹Z’•©Ô#'âÜF>7qU[IõÌ Å4LDn¬?#že-ɽÀ;:&º¼Ãj#²¯/‹µÁ®´ó²egÆúù Lò§®,§@…FwwšTŠoíá•^EI-ëá0Nã‚BonÌë§oχ©ÙIÊÓù¸-`ŒhàiÆ„Öêš«XÙü8ª!…gl$‰â£s'ü̬—ÍÙf;Aé^vü$¶#Æø}Ì—³Ò~WÆb&«]«Ù ÷öœa8…± ´\œn`>éq'†ò;tÉŒ–†Ý2â_Êt9äÔ—"©6Îöî­èªìÈvxŸòàœ˜r0z5-Ÿ•±§ëõJ ç'¿ÔNݘ8û«—…çÇ©ÚÓ­ ­Ù!ç'Áð#€Ó# ³V%0Šu/»ÒÒÏñÈa¡Ð41î§ü ÒìBlŽàÿ#麳K0_R¿.žËϺ¡hp«Ó\$»u‘f¿b±¤trÙ çFMs•×¶ß®Ð%‡@¡ïQy-EK¬DÖgj ‘‚æüE¤pˆ¾ò”‘€'Í&xœg}S^Ssd"“{ÛEIý§„¾ ìN;*Py=ê”dô&wWÙµèsue7«tÔ{]ØM«j€kgÑgÇlð½ô†áð¹h©·EÖök€½ÏE6‹LKŠ£\½çFªiÕMc Åõ‰ìÞ“ÄR?{19]“û“%Šë—ÙOŒ\ »îí1AÃKј¬7•lZV&EÈ%lÛù¨*z²4øÜ· EÒ«€1ãR†x ÕMËf¸Ó œ¹Ão §m@L„Ö—œF\3®ÉR=hJ᥇ڣGôÖ·XnâðÔqħ킾˛÷É/°ü`ýg6²==÷ªû‹Óm‹Â? 8 o:{½Æ8nUbŽùîÕþüΘ/왊ÿ+£6Þƶ2½00„÷ b.©dd.X#Ý =±‘¯¨uœ?‡¶¤ræŽ zÒDuet;Sù®åÿd}ô2,qX$'°€#Hphrh}p/íý4SAXliOP&–°ƒÜPYÍ Ÿ+ªaæȽålZ„ÅrKªÄßM}¾žF…ÔïRΫÈUö„R®Øœ&ÿˆË£ÁNµ9&ÊɣеMÁ« §ø…nè’ª]¢™³¥,žcC#%y`æ¿3}ÌÔrµìŸJÂL'V`éwï?Ð5¼"†[ZpݎݳK9 “¶Ì&MÔùÒfKk‘é«4N˜ç´À¸]‹H× ,ÿBÜ6³*ñ|0ßÙ²Q²´Ûg9®G i‡ŒE"çSÎë$·«—¦x-‰z÷è#Á5èY¯8¥uÕÛv/E~–µ hÐ;7µÛIMà¦íþÉãú<Ž§/ƒG{CèüÜ3¹žžì˜(Ïâ|0yi’Z—Ìè¾t[Ñ“hÕrsôn6ÈýóSúáMfædžSÏ;¾L| }»IfØ…F¤Ã"÷¥—d;ŠèˆlP&=dÇr.`ƒ½ö² îÎõCi‰ؤ ‹Fîåïâñ¥_uY—,?J¨Hšèl œ71· K¸ñ‹@zò0Ã4?QÉÔq·{z†3 äM‘dÃs’4:"`ACßI}–׫È.¢ÐùÙ'a¹ Š–¸G»^˜°„—zºt˜ðªù>»t• Ik_Mæà`g ØsÆ-–ÿõã_ÿOt1}E¬LìCÜ2^¸lA’Qž¶„©Á-'‘Þ@QgÐí§áXÊÊ–è‹nÜÖ˜­›§ºEj´^ø½­Œ ™©¤¨ÿÙ*…ê |äd´.+µÕ&̽‹ùŠ¡Àá_ýg7Ü7Nšád¤f‚dÒ˜nVû0D’ו[üfU‰X˜>¸£¨ãß1q TûûåÏcÌgÎ\ù²HNm®“B¸Þ'ý‚r¶áf¬ëŠ’N,F½‹å†ï÷©häì¸Ü'a]òôÔN€WÄ€8 ôÌ·eB¨ý·Ö鈚8 7¿¥Í¯ þ Ès…<Žæž¦\ÝZoÞ?‚¨jN Ö£ BwUå/|àøÂÉQ+½_ â…ÞKˆœWÆ>üÊþ5é³ôÚqEqÄßP^½ÖIµH!Røžá>Ža’wÙ’sÿ-ÅÁsãj„VD&ŽAƒ™+*éx0ÛAû^OŠ¶ߘªDš£è„n1ŒçSu×âÒ«Í64¸¥—sÂWòAÑ…×aÚëmá¢%¶mû›žˆök9kJÚ2ò 3´ŽxvQxyÉ×1§Á\µ©`Ì0Öxâ~›I×-̪x¿a=s+»–’ÜBïÆãÕï ð3s3$„ºé‡3{Ñyþª7·Š&ŸýaÞÕ_ÂþVùÏtËbÊ€©Ó¼‹ÑijœöAø~(M¯÷š in ÙßãÅŒyX÷Ám=·•›8üd b¬UÑëYU#Ù¼+àÂÛQçB€æ²_2ÒBF¾m;v°­ŒÂ:ùmGÆ·XDåîŒúÆ–}9ö7&œ«—‡Ûmz¥JÜžŽÅž5Ê­ô¦˜œŸPvX;?q15\ªãJÜó¿$N„§sˆèÏNZßßDhŽÀê€xð´Á‚Éþ¸¾ÁýÌc¢ÕmAçä7 8ähYpGŒïdOg £zÙC¡ü¤ñ/ŠÞ ‡ ¾\OJžx¹Ô¢í`Ÿ¼‘”õ=ÚOKÿê åƒLgHX>6+óo¿ØÑbx±‘÷éúÄÕLhü»Ú);Óî]|I]‚êç$úƒã<¦Ï-¤ƒD_£kvC’s¹eÛí€ê©%=»‰Áü 0ôÑw&©MYT·›/¶~$èÜ×h~±Ìh+“ÕYÊcJÒæ^"æn¢$[GÂUh$mhù†Î G®.ÊÌ Ùøt,Šn±ÚŸû¤QuÆ67të9ø9€¤6&ó}óà‰‹Êì°s¤›ºæìꮩ”x;ÅWªëéwUijƒU ¿“kq*;ÿ" )ü5F醞J¾ã‚Ñ‘‡~!¾9~zÆæ碈Xtä n¤‘Û{‚òï³D,xýàdAè™$쓇|9aK „²‚o&£JFGI OßUÒHGÏ]–e“@ñ«‰'$§^4¯ò~­î´:Nû­f¯¶’î¤Ýnç,¢ðr°fPÄdu nÍ·ûMUï@’¬-$ÿj4Þb2 «^,`ë2é|'ßÑ·g¿-š8üÊ0³aùÄ,aÝù—uåTGõœP ¿°Bƒ¥L}Š>žP¸+C4ýô´¦œœ3$ù©d%Aôù‹ZÓ1X]N¨Ó¤ž|³°Hµ3—æltÞG:€+R†µ(_ y%é2TûÔ»¹ù|*…›ó_=Cí9â!Oï“Á©È½ÅsQ»ŠMWõì0œœ©–ª'šc~ð¶1DKä% îLr)Ê‚Œ­Ú+%U\òD[B¡j‚ü j&ÿMhAì[G Úî pËOZð_:…XÀ šýä0¿ç–'ºáXJ°‚ÄÀ£OB!‰â"Œç+Ý0ÖÚâ»wÂh;É‹–Ñ•øb×™"OMéL<©@q´…šš7hä2ïˆO»PÄl84«,V!¹%ç»ÊGøÚ¹k‚»­R•C‚égÍ/Ù­²òemôm;´»7ü C9§ß6pùmb®jÑm-)n2Õ1ÿ¶ë o²ðì$˜ge×ÿò´’ØÄÈÓp›Õf¬#áe:y‹Uej‚¡Ÿç´Ç íμÌ-—4ÅK™³ «SÎb¤–\|À–ªz¶¿¦R}8YTñN/º†z-07ÄfÎ?£°G÷? A/ÉD q”¢ÏÛU¼¯pÀ:¡ 4v=mi^ >|Û#ýP¿†Cp‡çÂ/·ˆWÁÝ.Fõû&÷ZqèŒJö¥§–Æ¥÷h‚L£½)ÜÖCRÛ%ì&=1Áíbá5à‚mFvš&µyØwŠ%<4в²×Þp3TMýkíö®tb1ìj'ˆR›>_É‚*&¡ÃÓä`a&®-olÀcµ†FrÁ2¿ìÅs†8¨® ¬{UF .š·–|£vÊÕûMäD$â`\`¿—yIéê@ã÷ žê®Ъbš^6J›9ýKš­²³¾wòæÑolÝ Ú”åk‘Ã`ÝßÐ>ѵWå’xzçW°´ë¦=TJ)u"’°£M¸¨ØÚ†ÿÊ+Ányçir¿8èv~¢¨„ÉÅš`ÑŒT﫶æ^Á=œÝž\†ÞöxåùF¢æÅäÔ衼-æá© ÇÕóÉTÂZªúñ¿;Læ¶ä„ˆ÷-g1mõ'7=˜ãY.®¿°*€l,– )Ÿ¬d¬v´Åeí(À¨B¬ù¤Ë.F›Œy qL{€…³||6Zdòº~S ©ø2o´âânÈ«!¹DÉW´~/'Ú$>MÍ?2Â:ŸÄëσNa šnj[ò)ìÔ>5íq“ø)•’À¹Èý£ßsú#H;ðÆÞÿÁ/m–$†C\cìY±·®çNoÉÑV\ôÄ .º†ßð÷ëleçGµDÕ=9)U½ÀÆ®žšR0Ïlm¯7hyµæüé¥ÓTMMÄ=£æS·ìè¢jn=¤]´°Yt&òÎ{»_:*µBíW—b¾v¬êÈ`“/®gØŠÑ0ùü‰ {b"J}¨˲<ÁŒyÉ0ë6²² Û4S—¸%ãX¨ü¨Øxg»ô&n§0©‹­èÊ3@UcÿÐ-]><ï¾árJaÜ$Œï‹&äçCV.ÍÝ„ŒŠsïí{ ³¨ÈŠÒ¸ÉBé½~Bܨn Â.aW?¥,ºL‡°ç?éZ.5ì†Íg†¾ @hT²tÎÉÙMÕ³²N«7 ïÿÿe|O%Öä•í$Aû2Á“‡ÍµÌ§([š–îÈ)Á7Ó]-ÇV3ØÁVr9©WrL~aE0|þ.¸‡Ç Ÿù#|·‰EòsZgXùØYb_5a!y/Râ´âQ–8ôÔÞµ*Ê °½O‰~½Ü9ÀÌ÷rПý2©M,KxæçÓØ—I–âH7w.Öõ›¨7]·€50gü¬h&¼¹7$à{TžÄ’ù'_˜–?¨ cð¢ËDÚÑ¿}.m|ÑÅq¥ì/5Êô>ÂÚ]»‰²WXá§éª$‹ \ht=]z§±Ñלo"‰ÄQŠŽá) £Ú~.–Å%}ª=Z¥„l-À:žTöfö_‡aE†êc“’¤ê~¬i†>Lûck±·6¶dmÜD o•f_¸ñQ¢gr ã ”…•Ñ^]v%"Ãù‰`9;•6!¡È>*Òß}§ÀÿO/ýÆD£¼â;Ûžp¦’¢#¼mÕZhºjgC1Ø×AäëåÛQâ¤q­eÕ¢~DCuE¶eV¯Ùz{§º; ùhõ#ÿG=…ð$"¤ÐôlÆÓTŸË“Qdmlÿ„Dl\À>l¨?rVÓ=N ò†¤sg…#¯ašP;Þ/’Â掘d·tDŒ€wúkÒ— [ _¤øõ ¹‡ÏÄäL e×½vê9„¾{iSº¬žâ3ìž²/g…Ä®ÃjÏ´º¸ ïä¢â KÍšwGˆµ(®Orh¦5¨ýÀ6J{±æãÙCï/ãÂR÷çU%r› ;hƒX\ô>Yí½ˆ‰[‹Í„];ÍûãR­dƒ´\+*ï*N]iJ`m†Óé‡ý¥Þ¬•O±\Q¢(~mRäÑÍ¿g$ÁV?}Ýc@¯®°Èá׆åûÍþ$ЬTÍFG+üߘÞWóÑئ@Z^(»ê2¹«øpAà@—õ [ìl\†•n?òò¿2Æl,Nic×àqMìT–/ç,ã祫A£u,‚XsËÁ4:_%ZºFeì˜èB‘H;±Z2.¸!ËVá!U@Ìó^1y “Pãà æMŸH<óí¯S§â–²ŒÅ9Þ[D ‘Œèø—óöû%-ǵ€… Ï’¯7]^½¼‘4‹ ê×{û)茻±bjÁd`±W²ieͬ‡²÷«=©§Á3«Š‡¨6þW„‹†O·þÂ?VOMؽ'£Cf¢›9Fó'ŒôW†Þ]e~™ŸY¯ ¦Ë ©\·TоVMñ‘'Ýj‡ÈŠpNܪùyð¸ªÚîª f6Öe`Ñ^mG‹ç-(¥{3‹‘>Ž*´„%ä¯FîG‡~¬¨Àvg ¤E‰áï1Ò˜‰tǹÝýQ–t×ø'ä3Ú‡p8€PÉ3à¿s~û„Q6Òr=C÷Òõ:\‡ÍOýº×ÇVU-Ân‰½†i¦C÷iz‡SëiÀ§{5U8úÅ"dB¸:~Þ/X6Û¯Rû—²C+Â=¨§"ßhm“c¬Äƒ`êÆ>ÈNSZŒyuÂJèòŸd´¼ßС*¨;üÔbâ¤Çc60Ô=£«ð1Lî½.qí•Êôij rúlu­CËåZë,GÖÈ]ƒI@q’¤ŽrÅ…PŠ¶Ä¸xzpŽÏ é-Æca4pÔ³£ñ´ü‘”•otÅ ‘J€Ù>¯ N]t¾#&þ5òã÷õ>•9ͦ+y>ºŽˆ¸]§ËfG?³ß˜âM`,¯iÌ‹ Ä6dŠ®„øœmMXŸÑDÔš¨¿Ð…ìVÒ¸'òµè_:Óvïïºy^ẪãOø3[zo5púç7Réë8ÇFž:×k¿`’ëÛóp„Ÿ—Êä{G¢ ±‘Þ¡pa_*2^¬¡O«ƒRZ½‚!çÖÓÉLIq­3cš1+(ŒÜ€'ù‘ÇðD].)ÿë# %9b˜’µµdØãã‘Ý6É’Ù"’È35€ý,/÷sS bZŠ«šàZŒm§'R,-¶PÝ’®PC)œøWC4§ig¥|ó#¤íJ*WY²£ÒS7  (À‘o#šNc>äΙFü¾_û¼=ˆï+Åú2r<1‘ u¬ê,Y¦1)S”šîË)žÀkÈ…•.2 µv¥e¡ÌUWv[ä‰<¥GN Ãâ>TŸÍxø³£;ÍŒ~&;ÚŠO»ÃC‚ØwWÜzñµÏ]XµÐ®ö첌ÂñÔjw¼ãç3­¿ª~÷å2ÏEMXj1,<VÝÑïßÑåg¡`–ÃÞ¡í=£%‚&–)«¸ÓxP“šÜX‰K8ço|‡xÒÐØg1ÏÐW?Fõ]LÁ~á>í¸tiÃ!|‡ ~¡ÝÜn‰€õªÝÆ£…e_d˜ý¨"GoÀ4|’GÒ¼CrT-”`¿^ƒJˆ¤®²1ìȈ JHÈ"j¾énË6ý¤¶CÛN!-ÖÒc¼o·#`‹ü ¬‡M,ÝÁ˜ºôSùÛÅÅDú¯'·9 1¶Ëa›5óiÛѪ¡‘'ê›ËxMô«û¾*>­ß¶ò‰é¾àÆlÈʾI#ÊiKRcïå_]˜‰ à@y­·=ùY=‚¯h¦¡Sà¹ã•¥°—H“º'^’”HÄYÐL[<¾1ÔK‘1#$ìºÙÙý–í ßË™©¦§NòzËÓ%¹^AÍÁðâ`ÔÝÏR²>þV’~²¹Ø «*Ǩ|:k°Ž”Ÿ…¼ò½[™W :¡nûz¨’ê]T&†9Ž_Á´é¯ðOIX¢Ò\ò=jy"Èè_ëW.ÌR¤¥h™Ýŧ¥‡”^œžÈ@ëE†µ°3¼P£¤’7t":Iî¯3ðŽ°¾P)vbû¦šG0¢©L«=;Ù°¿ó4¾#@àUИÔšbA,¢ï(õ†1Õ¬3¾XBà|j¸¤b/Ò4—¶œ^žè2hY@upyÊSCâø.ž¥š:Ø!ɉ;Ù´RáŒcø²À^{cYx˜Ô,{RODîÝ€€— íKù1"å¦kcûWÃÑtN§û ïS*|ª©§É|:÷g‚³²P˵Ø?=záßÁ¸ZR*½È¤“tñ¼†”M8ôíuŽùñ\ /kþêLªŽº¨šøä„è™Ó½ª$Õu‹Ìï°f¯Q~ï† MÿºI”'î|XþöU,9üMy²»tÆÍÈ/_j·¡ D©y­ ÷ëFÂ|ù`×!Ïøê&Å ¼Ud×xýf˜ºžò’mzaPµÑþ àá]j« µ@ü£™âäùq¥o=–äáf÷ÓM¯býéÆ­-dï ööƬ]uèE »6M”βi× -y³‘X(XÊQ¾ÚLI‘  D•j*Ò”g9ä¿Þÿ8åʾŠgᚦ3²áYÅ…ùA8öÅ¢ÅÃPõ+ tÇðŒñõV;š^™¯P«r“9Hà‰ÃÂô“YIÛŠtTcÚÀ|ø¥Éh©¾=A 2 5V¨K†Èn-\51Ñx׉ù˜&§3;Ï wHå)¼Bd'XQðƒ7,ÏŽ‚ˆ(>}²+eã&j¹CQKÄÍø™~<ózàžC²#qô-ïà9:äo¦ ¼êU‘ÍÇÆáYÿ:Áýt;\`.³ÐÝ3­Ö„s`?Îü/ ájù:äG†Öøª"«6U×Ò ˆd*šù™ñZ¨ó'u3å±r-\~2´8¯"D1Ø©PT¿ ]ßsYÌÛZI*ÿ·þjŠzDP¯ÃéÛo*%€Ø£¡¯‰½Ñ{dj“B4͞؀§Í[“:àXÂî ¾9j9JIêo1üc)ÿJh±ñ» o·Ïú_d‡;@?.ÄWG$¢V >öEV_ͽ5•*ÿ/’!ÔŦ¢~ñâ¤k©%àùo™1bïÛÜì]…âbƒ'dëÙÖPÌe¹6ÙHu#>†*9´;lÍC;Bïà‡/z`cÍSy gq㦃£Ñ·z/$XYmÚ–™$ J¿¥ÌH#ïhnõÙïô{Š~ÒòL!ÿÁóCô)뜓Çú I ‰ÄKÚ ”LRš$ZoU}„0¦ZJ‡&uQãj Å‘cj-ÞüyÑ)õ¤8êK ÷±o‚ðâP‡uŒÑT‘Ä'ú… ÜÚpNÞ;J3ûبÅÛdÆk‘E½7Š¨®ÑÄ0âY–§þÌ W:7rv]Ø'÷Lç§Åì˜AàQîÓòDýô:l~¯­d†ÔÝôتÓ'€ËF˜(”Û‰§=×LO§ìò¦S2õ: ¤Ï©§s‰é_Üܶþ.XàCÉ1–•³Ò=V,VpÌ¥ZP¿íX÷@UzÌÅ­µuH¾‡ºâðuΛ!ÿÆÐr! ¤Q\ùk S¥l?šåÄáTƒÏ'ø´Ï)ð&y û$Ù’1<‘õÀ¸w³¾i¬ÞÊ5y.§²®RkÓ2¿£Nè.|“ ¯ï&l±"Ï°åZDU]od©ý½Šuª¸QrIðSü,ç]ĉR%FV¶Çë|¬” WgRÇßŵFá1}®Õ•ú,_©‹ÆKTòÔ€É !‹;¦Ì éÅ™Öaâ»´o4šíÍ6ÔÝOB¯”:W “ˆè6á“ô±SpãÓTWôò½Žç¾>ûd^\W]ixŸú-j&~½%(!ý0e‚Í5ó:5¼^€p6žtf_d@Rù©jæšH8 ,Z^¸À¶bF,+eàЀ¾}UÎE©}Ç#3Xù&ÁßdcIŸg¡!û…jdÖÿLI]jSQõ)™3 ¯:ÝeÚ±_ñn=Éû‚C7üdÎ4Êmrż…ù’LÜ©2ÛT@ß¿2`–v ü7 Ñ%îô‡SD¬ä’eo.Žšç^­Ô¬DÆ›G –(%ñÄèLÈÖ%€{±r$¾îsŽD9» ¤….P_‘ x^σח.¬4Ýè¶oR'úú«oèC#Ù°io/žfâó¸CæР‘ŠŒÚåÏ9ãm5[ÁõCV¢¦¥Ê›pr4¼è_ 4 +$Xæô]x*?‹aã!W’ˆ¾Ó Š=k` ¼j7!ØX»é´™Ñrk'&!ú¡Tvà"k2âŸ2þ C‰xI+Tõ £Ñòq?ò%Doå‰,š&éþÂÖ10…oE#MÄîx ë`êç¶êàÙÝùahà ö$VV(5ª™]¶DŨªäMëøsZ¨‘µý¼‡"¦Å†(Nw`Ú׳åìo4÷òç­©­M&j7m8°^bΞÂU«ÖÚ}–«Qf{ö¤Lò)þU~¼^"oI÷NîJQ t_^žn>U%ÿ~”Ò{núe•9Ðe‰ÅþBzàêÐäY¦6)Äüvµš[Œà·ÌPÄ@ŽæëÏp³|®eôÛ)J˜©Ó¸hÏÞgRµV‚´Ö`ÙcŠ „Õb»Qfsg`ߢ © ³‚Aâý0Õe¼ØÀ5lýí@}D”Ib§_½M¿Œ+L-E@2­_p4“¬hõÈ…8XqÈEËþ‹Ü7‹‹ëz9ú½šTÅ\Úv£h£$ÇÄ–ÅH®F­§ú.m!8wµ—;_¹«k¶³¤_‡®¿UŸÉë6 ºUöØ‘Ÿ˜í{TMd —ÚÒ…jè7Y“ü ÀK'e<öÇ^ÞÁ. `r·wf†UAD—¿‹1âÊ[}ÇT7‰6¥¡ªÀw œ®eåÉGn¥u¹^‘€@`å'€ÔRÁ­"¦ÃÂ謡éîî´g qHB)¢Ë4ÿõyæ}¡*ˆyéÔ«Üô¨JOÍÃ9EEý‹+qª¾*'ó•Ä4ÿi‚ÞgT²O# !({‚•q6hÌ2EMÎÚ~‡Nø †çlÚ|Ã)ÆxåÝÕˆš·Ï:Ó¡@iöµßÜÒDÙC <ÀgqíòÇlˆŸ¤Õ›*Ïòf·Mìå1›ô=µ…/g³Yy–ù#9Tq4lG›ñ x6Ç-gÿÓýð¡õA·Jˆt³¯.Z/6 ä²M}/ਥô—Õ¬:‡Oÿûõ~+•æ‰ªoî4c›Mz,.Ãga©[kN´»Ò˜®Þ ÿ=`Òº€pÙ  õâªpJ6 ™™6éÚÃó^àx¡ÕÍkFmˆGò¸PQ5ø`ÿqˆé4UÀ’þå†òý4Ñî<º[u/EõçƪYÆД‚ä0Ú15¡?[–?³Þ6ÖŽ`*ôæ}ñš.À§q5Ë«¸Öu‘ægx,‰z'¹*v™³I³£m0˜«®F“Ñ SÂÙ¥½–t&üà«Æ¢ª¿Wã kU»'ðúœ›+} p®´{õœv'‹D9›A;–…L6Ðý\Ä¡±R‘Eè4Ô£TGÔØÖJýøKhÍ«ûøùÿŒ¼(ÊQX/‹7/ø3Nè”R«RŸ½Í:•ÂútÙβ¹ÈfUÖþQî99/¿´ËHÓÊš¤¤@9–ú×_›ÒP\ý]ÍÏ-_r7 B^;¥öb möQ*j´cüJ”h¯§*xzmH3E§.ñ¤«åcCvúÏ5´ ¾ñóK:vðRsi˜ŠCíæÏ3c×aVsKÞ­’}¼>¸ÀRÞ}ø”ζï%þ¬{ª»µ¢Ú‘;GcÉ–²7­Üœ|„,d+D­ñZ~ÌngÍÉöô`³Ü'ÔÁ† l¼Îµ$”õ›ê,ÜúCòþk”î¾ê'ËÏöR wŠëšùQ™4L“EXf ƒ ësÑ‘¨Gpåào|ÐS5P…ˆ yñÿ‰Ó92 A ȱq­·#V™ür :ãT1îfüµ¹îhÂùÍÊãcÊCÙÃ'¤hÂÆоšÓöšÓ›ÌÙÅOÐ\Ü °T"j†dKQæä‚®ÈÅ„?à !8‰E«\z­ô;¼Ÿ)f¡“MúWUn’§öÜ„„NçÓÆ]dÁ)ÇÞ!& ËB°9ÆŠòè ¢¡V¯ë«‰eÙ!´ ²V¼³¡)x÷çoY9L%tÍ£ÕjèÉG2oÐS&ñFIˆÒÂ"†­ …‰óæ¯LIˆz»¾§* ^¯Z’d¾î,"Ç®éÁÓ¼#l/0…¢¾<1—?Œ·D*¥Cɤð\é0Rìq?Íj È׿( ¡'Ýûwõ“¾Ê?%F’“?ô¤Ñ8»CÙ³?MS‡BgY¸ìG÷­âæÕa$Ôtû­ò'd§Èå²'C‚‰ƒMBI.Âî3†BIðÉWsv‡=­Ó¿luÔPÖFÍ®žn˜£§Å.ô±š¥íX¸,õbF,_M\¢Å¸ñà{ÿ®rÚgõ#òð©kILÓéÞ4õŸÇ…¬‡2ΊãØo¯¡ æ¡UÐÐÊ×Ð Q8=¬fé÷IoôkâÍâîŸÇÔT|;·V›°—ÿºÝà¶ÈE[ª$†Ø mä>ÝÐñ¬’b«¹äó“}ñé/Ä• øãÊËI5Y FU8ÕÏ8íËs[f‹ª9º Š‡6¤q¤—ŠøÜÚõ\†DÛ gPZv•åÜÔØRLºØmáÒ>ùPJì櫱ýÔ5xÇ]¸=TéõX‹H?-¦³v÷þ¨616a’C#§ˆ -ÛÖȃŠ_îµTÜç7û¥=o[×PKɬ­Á4iµ.pvPú½]ýØfþP4>ç#Ì0ÂÿE/’Éà k–Ô†Cßz1·†` è‰{!¢ÄÑ™­Û]c+Ž@9¯­y ³AëBïçýðf/8ö¸Á ÑŠ¿‡¦ó×_¡D÷óVïîçþϽ/¿|2\ PÜu¡¬{7£qy7 1Û/òjà€õ³6¼í°E½ å΢%†ÀVˆñ3#è€`è¾ÞÜ!#½,Ð¥ägKéÅá5é[ìO‚ºÀn° zö€òáÂZþ]Р‹òéL9ŸžJF lA/ØË@èIž"°Iá%çÖ±Aƒí üZ²…yF„ŒªÔ½ÅªJ¦Nºu©HšoJoŽà½ H‘}0¢™ß@ZŽ(ʨÅÌ®ÍV˜Icç…ÊåHÁ@÷ŒÊj%"2„Ÿš Í·r{ uöRŒù+^ÜlN$E…z‰~þÛ ÃÛž¤FBŒd2熃Zì…£Ùˆ–éÃö6ÔG”ŸÄ(óB›Îã†#ò]þçýl“ÔûfÇ+ôéË/<ˆ®ª8!½„¤£II‚æ‚ïU뽯S«`EºèEZ #.Ó@óÛ¢M´ *_Oßh¬R¨˜Ç$»;IµPP¡ç»n ‡i¾¾çþÕtýòøÙœ=èî9ÈòóC|ûš ½1ÿ¨ÂL|¿y·òÅÊ£µñÅ7b}M)•o"›ôÁxºtø¥[õ`¾C¬3@ƈ|¢‰ýAÅ[¢KßI@T8há…‰^í4tªÂ™Ì‡E¢ü¬íXíEÀˆÿÓh¹Úº‰……/P£¶0mN«Ú÷¸5Ç~Ð)Ì’ÿˆ?=`‰@£Ù„^þýŒ¦¡i9!Ž —žK?”K©úWp' ŒÓä7FRßO_sƵ˜ fóaMVš9lº±'ÅåàU„v˜Ðæ ôæ%V+\k£½Ìˆi²"$ÔÜ–N²|ùLæMÞ°ŽI™Ôf€ŒÒýa% Îb¹S„qÔ£鯱QkiÐE½{Ø/F= ™XíœêªuK…×5¸tl]¾çðÞî7¢¦^_á"‰ÞþêCö ÀõûV7>æ>Žõï#ä1‘o²@’»?!·øJ`dç“—çCf KUÈÔ(jÖáÏA+äd.ÑÌ1ÁÎÛ‘~¸ÇžÃä8÷#HWéÈ–Žmk (ÿŸ¼Îú»Ø!³ø^5úÚüÍUàõb) 5úÄǬžPZ î]É]WjR 9bÿÉ…ŽØ!“ÖæÔó%¯n»o–!NOŸÃæB rÅàÃÕôehÞíkí1Ôç`êJ9ûäëAœ%ÏGÁØ—@LÚ/j¸àj;òýÛLB(¼;烪ø î;ºÈ\CÝ$§ÙÃÂLÓÔNÝ¥š¬ï‹Ñ7\?×W§6ÛÌ7Ñ7ƒŸi„jÞí¯MØömtª)ëö²qCr4Bˆ˜ìtÆ2q¼q ‹UùSó(iÑФ!FDrÉ3Qg˜»–ØŠ4(ñ=+ÙeÕû1ç¶.Ò¦\!¿ÙP=¿¥ÿHâ;}´±Î8ŒäÝT ”DÈÌ;h-Ÿ'o÷¥=LœQ+Q’©Sµ x«.5q“ËGùѲ#¶5æØVËh†ÈÀŽI–`±4iâ^²!Š¨Úõ¿–©©Ú{j¼^aþöcÑôì4<æ2Óë=º‰˜^ŠÅÚ>*ª=ðÁ0`^?Ôά†Ê¯2ûz#°2g.H©UOH÷ uPH]‹D‡‡l3÷&H9 bf<‘;ð(r"MýP³í™_4@“ÃñÜLt¢#XÐEÚæ8¿“ÈE^½Çÿ@0©&Ñþ› L{4µXÂ\þl2õ+˜l“¤²ÝIœà è¸Ó¬gäãúPodÁ£CX±‹ø¦c62&{MNOνž­þýÇZUÆè1XˆåØ¡Æ„Z©ko2!†w‹U ¾Ñ%Ü…¾¸ÿÔcÎò'¦^8çÃ:[Â$ì]» œÇëè?´¤QH«k9h¿åˆd^­1ÚÛ´Å&–Ò’kí. ”IÔÿV§3h½„Š£UÜT°¹Áç¸^ã¹j¶ÂV‰ÑÍÀN©Î€`„¯ûNªn’K•í§ØS.›‰±å´ûéÔPœ²Û¤"¹úòv¯¾nœ´@&áÊäçÑ=©’/¼dÊ5´Í1Gñ ™ ÄýRy/røMƒ-µc€‡õ˜ÖÊ…­"›x%_ü6MV=…ßY…ìöç£g¤Êóå{ã#§ (z€°fÓûfñÛ]¾ÚLy&˜è‰“Ûº'Åþ-cíhµ¢z˜/H x"ÑܵÕåQ ³ò$BÙ*ã,{à±Ò²ÜVÁš‡ùJTã?ò ©ô9-綣y@6ãM²äó‹Q$óÒ£©Oâ9™FúK¤›A8Õ¡ÿx¯á&¼„[PÕB¤ß¥Õhà‚W‡ŠúRŽêš‹%ªWO[k¬»'—ëìà ’l5ùï$ìßá* 'KËŠsùñ[€Эþ jKáúR©¹ôhûð)9obO@­ð%&ÀÏø ¼CtáÖskýC1þÏ€t-D•·°^šipìl É1xU½þTÞßü«ÿ˜M1'IÃÊxtÑŽïöêòD„¢u|ñgwS‰`þ¶Å‹Æoʧ|‡s„™£…¾¢ús„½S ½¶÷#£fÕUYè?CÍ5õ/{){÷ëŠ\  ,a¦ó`îÂEñ›ÈÉSŒ,ró¤ ^,óæÒ°.;ja¿qîh›i÷¹624”.ŸzV“mF¦‚ŒSd|doÈMƒg~ô.8WPÄ…+Jµ¤È‹OZ+#”ØFÓ`¿yHðÂ}Öìĺ€ì¼z¯áb1Ï’tdÌØ9œK@-èå _U¶ gN ó\Ò:2E\6ƒuÓvõ´æÊý%’™LÔŽ³0sÓ®f*³«m~>X…MsÏ7g0HJ’Êx~”¤<ÅßiUÙò«wÄ6~Ü¢s—ð|RcîeàWE&¡ÌˆYù3–/¹¾Ê~åÀñ[Öª «Îˆñ·³žmã›Ï³!X‘ò*A àg{ù(ÖY8¶vXJvÈÒcõ}Âê Õšô>“Z9÷¬¿hµi¥ðtµY¹@è"ì»À!»¹=´ycæ§@ë|cÏëG²ýÄô éÑÞ¤+ÒS³Â]÷/û£Ü··µÏÙÏîƬ^r}ö‚üL­iv^›˜þ£z.É6†Œ…ˆ…Ù›³ŸU™“C?Zé¦.9;áåÃq.¸§ù¾»”wàëŽ)yÔÊÖ.Öµœ<”0¹‰£p!‚6¬ìKaÒ}9øžØ[—õà#³A.I1KÔØ­v´ÖÀèžµB-Û!E˜”ãkL÷©ŒÌê8™C4³¬Ðê(¾n~UØxZÌŒ P:Ó´Ì0xèÝSRð;6GoÌÎìSí  ³Ÿ¼€ [&ÉOG¬ö‹7o¬\ë˜ÓžFmhy‰¶&Ák¢¸X"¶”#>8Óy‚Õm7!þ/n51©ú]A/`!:_@Ê6WKµÚ|‘•ä0FNªJß¾zéÒ=oùYC=#ʼùÛ²„¦GQ°7rÿÙUË ¢ $†…Ò]ÓÒ©‘m“6)vä­áÑ¡2ö{©^ûŠ/+ofäõMoé.®&j,‡M03(¶éE~SꀇŽ´˜f:Ø÷*l4ïrŶHÐU1 48ĉƒl9Ø´fÈ¢éóÛ²Ô˜±ŸÈ—*2—¡÷Xï&ÃÎ¥}[8˜].ªd+£6‹^wÏîôü 2;úd5ÉæN_PuñÍ<¼³]US93 Äê4”ÂôõKþÑôŒ8é™È¯éÏ£Œ:[0ÌF+xΘ¿ÌÑ ®„¢†œ¡²Bƒ¥dÃñ_2ÓHœg__z/ÅØJ#ÀŽ¦yGó_­‚hëUŒPi0¶ÞæhOüîüÇqî*CÆ´©T*ù"âVíÚ_©Ü??>‚( ¨{aÀ¹Æ±„D( Jg˜–‡gò«üˋȱ‰n̳—#F¿[°W ¤BÃeÊ<ßšWÌ µ{×b®X;|å_÷4Ç÷ïHPuŸ8Ép]š™j¼iÀ\¦}U6`á_ñð$ÿ-:†ãæçé4¸¥‹ctŸý44£%Ä®˜á?ã_GÍávDézâÄøL\ ¡ÏÍ$L×ÁÄÕSÔÈwÇ\EZ´d+k‡@¨“,Ó ÔscçLéÆïù¾½34ÒÃÞ»® _dõ Á"¥¦Xž‘â·Õh~pr¼ƒOðµJ—2ÿ<^Šê•s—Z‰³¡þÏÏ5ªp ¸­š²¹RßÔ™ú3åT1C’í!XÄÂNƒ®#Â:ÀEDãŸá5Çú†$ª¤m<!^m©b7ÆØAd0F‘‚Þ*’Yï¶Oƒs=A‰Ö‹ëFÛLϯ fd¯GRdHn)²Ÿ‹S-B•ÝøšòS­ßòA/s=á½sÏaÐ|lZ.ÕÌcü¼¶Š<ç^½ÞÐFôeÆÔyJÂÃx,9)2¹]Z; =Dg7£Æó\¡{OÑ‘²>’ŠÑClÔÌ Å觰¹×S楙†¤n! 窭¹eA½È7ØØD«×c9ÒL,‚7ÑG&]üCÉ˵5±{®åŽ 7݃„ì ¥ÇýrNí@§`š‚ xœ§¤–lû©Öcrˆ’Qbºš¡ç ØéF†eÌ.)²ï˜[¦þÙò¤2ÄfÄÅ [7›^ð‡m?y¯…‰`it¾;[G‘ ÐRú®q†<‡¶nÓ÷»¥ô—QÔÌ_eÇAŠ½²ÞÐöK¶ŽÇgsó';¢¸nÿ!¡ºäû!•Ínu.ëÁ[ŸˆüóŸ;’œªA?%áˆ4ž¨àe>»éSûÿ=é œp™\‹ëŠ¬L\Ö\*E¬aK»~ø‘墸־4³>Ñ5ÓÑÞ)6Mà¢êY(—ý܆¤Ãà@ `QRÿ¶VÕfÁƒª:“H0.jáì÷V·ãpó?ÓSMý*˺¤×1$§g;Pž}t¬DL;GÎÆÿÀ’¡CD›þh]5»ÒíÈÞ¡¢ä§ÞëÓg,ìÇÒóà 7nDXƒ3%Àäv²°BnBÓfËíßp@êÔbŽgâ4¦¿0T´Ü¾Òvãúî-âQ,ÓSTyOÒË;à¢n4¹¯ÃN€ø÷mZ9mµ/¬„À|·.<@ªx±œ=3(íEÒ(éŠÏÅom½ýB È¥Àº²ËÜøa·ƒÕ„Œ†ªª£ÂôÓ÷܈ÙSÈ’b‰ÓÎg”~*xý€Ž«ú²Ý˜ê¤•ØX¼È4â~×íP(fÌ.3¡ò`¢Ì×-&ÓM)9ˆèi—;¿‡œXÑÛ·-ä©14€Œ“käüwWòƳì ßÛñW)1s´ s@ê’?i¥¬»Û Äšafƒ7Р²:¡Ç–d‘Ä” [›wÝd¨­c›Ä|T¸~¦u~7$I)j#™ûp¨MI„ÍX3¡ìW‡oÇ(ÈÉP›¨`]Ò#TãצÿÝŒ$ð ”BaW1¤ýJC÷ŽÏ”Ò;ë厰ÝæFѸÎbuüÉ*òÁ0cÛl5‡êd¾ØòÃ…©ªç‘‚wÇÚˆp»…à<âY¬«Oæ\œ°ƒ© ì" =×]Jϵ¼C‡[ÜmíªnÅ/“¬éqƒ7pøÊÕ¦Ó¡ý”6– ©þl‘'Ž¢3ÔŒ¡Yò>dLvaöÆ¡óœ÷§ÛŠL !÷•GhÛEØ7V%ÏQvÄdå3ȱ”gè¿àÙB uuŠ2æ™Å~:9éw>³‚(kúE d£WNNݯB#“xâ7i” ÐÜ„ð(˜³ˆÅ¨Â¤ËçZj#tù&<ÁmFL7[ï€pèùÅ ^Îô=ö5ö/¨>6騆 @KÞ?ßÞ$³:«1‰í±‰_?Ác£¿ ·"åäUS]©Æø‹·×¾Ý ÐÎ(^nxGF v…d±ËÌ&‰˜ÒˆŸè·tuW*u¯L©YKÝ»Q<‚ ¶üâQÑÖl†~è/¨|úÊ’(ûõ;Cߺœ?Þ·´nõíû©ÞÛµ(’ôÏm/’9Îe€RH¦Œ1¿®]Ҟ߈YQDé½Maô Ü’»ÇÆ… ‚HUÑ9¤öß³Ež±ž±÷–±gR¸íŸw—[@Ò&4¤”\ž.9Ìlµ=(®켫Uçgöd¤x#ÊAývÅß5òÌbæ3k¥å¤Ñeó+ã)ÒRoí:"Ïp¿S/ù²ÙÄZPËè#]3±U¶¢[£éë¯6Ü,î{Ç:À"û˜/„ê9/hØ=}„ºu´¹JëwäÄ{íUxUç³…(ð æ¨u1ÄÄ ÇήßË…Œø½‚WפL¥-ýAHo-ŸoÌÀË~]ëÍjDç´EÌüº/ٹЌ¾¬¦›ÑÚavû£éZèRHσ ͹’¡Æ›¡kitRþÑÛ&ixÌ[ŒíAjº æ–z¢ö/“m‚Œ. œÀ{ƒK~×Þ¾úg@|Ž¶hdSe=¬ÙD¦×IœÈu"¸ 9Ö®ÉçHÝo™{CV-&™ùN²-þÄ¡³­]âÒÜ“#Q)¡œ-{¨Ê.rßÝBßf4¹úÆTWyl£¦ï5ó?T6/ZH,C: Ò3›Ç )J3ÄlgžÞƒÅYµ>ÇÄ0M¥)÷ɧ=¯ªÌþè<îž{¦æÄàLJä^y0S¡.u²T&h´T² ñ« ¸^ ¾XE’l‰È‘Í•Ù¡Cl•C‡PSšPžèâØt³´n¼«ðŒÕ´»·Î!OýdJ½mFaÀ¥_UÆ\&–˜ ‹È¢è,c³ R¬-u.v¢…ü6Ô%¸Üˆ/%Ë$L ¦gÆCöû¼AL<ô å`mdˆPöl‡“ÝJˆÅ î3e=Ù¿í¾£èÈg&Åü*´?†‹ü]LÊ<„‡`yÝYȧqúßòÿ‡'Ig™¹to´äêlrTª²ìðêk;ᤡ4šbGF«o–’§öI~þÚôÂ':i` 9ó jhW›Ýîm¹¿ÀaXcgEòêaÔÔï^“§ët¡ ¿Šó#±­¾²fúT¿’ÿ“PÜW©²êõmp5ö9üù$ :I»{»"lÂ~(×Aý$©é%üˆ:ƒMšŸyNà:hì€&¼ÏB£²²úxv·b• üa K #³ÕqVk?v´LÀ³*S·`x¬MÝ+߬I-Ń 7˜ŒXÄ 1ëêôµx¬ün ïÀYg·|¢ÿôš¡”Vs ™dm ‹•¯ÀZ((Œ@€Í*&äª}@IJa 'ÿ¥=¥?v½[${1ûêדàùë¦?ȽåP«kçÄÎk«nR¸žGOX¬0Í&{Q[„“M»Ýl*˜úìu#rÑ ˜¹þÞ'™ ºÏÆî É6iYK,¹CG{v6$`lv˜Û• öS 0ç!ˆgâöMX³ÞÈÑ›W~æÒŸÓöHßàJ[œêrwØ £Ý騽®…çþ}œž …Î`MuVà›Œ…5'啮™X„Œx¾ªèCÉÜNÓ‰T|K¡Z-ØvˆVõL¼ ^*¶zä9ࣗP84ŠÏh”þ{ mSÒÌI1™nL¥‰@2¢3>¸¢…Ã&c¶À9ýuWõ ›;‹U{L.Rç~@ù›wäØ_üc ì ÜæÿÒVœ'=Mœ Ü󈉢 À¡|ê¹w arW–ˆy%Aó¤>"„ ø»¬µßW$*é¼Pö½Ü“%”Žšé¨l1ng6†ýZˆî·4â‚di×*.×y& ÈÌs™,«ëI³• ]s…tX@È:þÝvzÇâ”d·ÙTþÌ`ˆÉ´v¿p\ÿ WEô ®Ï´i"îò­é|zÕÓvh•ÙÝ-L9>ÿÈÂiáÔ©öÂh0Û›¤,ý—àó¢ÚW€`µõ^—‘d+ÏÖ#{®ÉøõS>—¼)?µ(Ÿ¼Eâ ™¥g˜>SÖ£<ŽÃDÙ6llÓ°2åp;Š÷ѧ!Âxã?k§4eš`ü‡e^_%"©1hMöÀðñSçïéài("5Dð]“Ø:ÎRd+Y°ÊtLÿ \Vdã‹÷” aSˆwU tÉöòªÏ—_Ù󢨠•-L Âý…R›ƒµ{âW­&NÒPå=æÕÖXƘÜ6&P%€›!Q{¹v%§€«Á]åˆq± ÷ügÕ äu¸¼Hÿâµ m€@DºêŽÏf°ªx`³.ñ‹9ñ6¤«¨ÿ§É°ì@¯µxèZÕ«xÌ äR• žK¨^úÁþ¯³Ÿ×|ôÏhüþ8ñ} NXSr–ëô2…x´å=_ïÒ¸3Aé˜ÄwY].Û¬—È9Á;IDá`§ðX9Q(b‚@>õ8Ìñ@^ƒ·¿²ä›8ƒ ؤDÀx¤äà0¬M1l€Ð»¤¨9$ ¥K_¥dJr(-;®Á¬îÉÃF)Û1xìóºÅ›Œä…ç•uþq·]ê ²§žZé^G=±1—ž7k: þÁÜW’ØZ˜—A“N±Ù˸»ö@=`uòpGYrévèl9¼Êʼn“0.¿°â©Æ,#[$ ¡êubKµçÅzÎœ½CÞu¯öƒÉt©YyûÐýÔóÌf5ŽSÕŸsB“8mþ}ƒfÜBJÈ‹84*áVÍt.åUÏÛž¯‹˜z½ÍIèï<.À©ÿŸ¹´+âh¦œ)*guèµ²8"Ma…N]›5ʨBÖ3sŠ/Hwkðq¬X*7%É4›x¨y1:©Q¥-äøÝé{lz÷1°y’ö ù¥…‘À4ûõ!ëØæ®@«§ÖÐ:Ê‚þ;8ehÍüÜ&gV‹%8ûp7 O`ü‡¶!²VŠx yYˆsf%^/¦½GŸù¿©—èucÈA%‚©÷Ì»˜v‚ßr‡‰l ûß'pBÞ!;ŒïÅÇ×LïÕV´U¡8”UtX1c§l$qö>‚ï5ƒ £ûpîTú?¹vi³RøhŽñXþ— o‘È#öÏý¸qºç)=Q›”,§¯¢‚lg¤˜@ƒ¶§G¦öåÉJ%w HŒ} `{j­T¢Sü‘g3fLëtbªû씸#…6\J¸ÈäÕ`5/…ˆú¬ŽJákúx1q~É%žž‡€#™OÝ:ÈÈùf8 0ô+9ýT„L:àê>-^–oÜœ¤nŠ#2Ñ6´èó<ïÈÓž^áxU'P)×·÷ìòb”%öãb„¢>4Œ,³V‹ù?6XÇý’!E!¸YLjÊ˸Í„9~AŸ7iÍ›áƒø¬´·q8ÕŠ¡pÇõ°JˆeXºxRKÝ5!»ÖÀÒ7“Ï,‰È™ÔÏy^È#að¡,uVIΛ§;›{IdãŒ4›Ø¦µá@eWo±ÙŒo2*Ü ñ‡±ìꉫwa¥”R7=â1/ÒÒszbœ ?ãÚÞû¢ûð1º¶ åÔ |‰2=¹$ù–, ¤…>vb 4±ËâŸcÄ·´½È74pôL¸+çX×ë‹"JQl Â#òPÍùÎœóÿÑ…õKùL ‹Ç¦Š \„l¾¶¡ÊÀ•)’§™‘ÄÉϺ[ ÌŽÖÞaIæþ l>Õð³/C‡­Í·Ãó\24ÜGþ¼1¨ò†«Ðð“è3Z[Ð^u‡õqôõÉ¥¥Ö®§7ñ2oyK‘úE9!G¬Ÿ’ÓúÓ Ö÷qÏœG™ÎðxqåÓ38‚’¶žð)™2Ó©B$1ª‰DÀÒV~tð‰±Šè5!é‘@†1.‰&£æ”;òè{Úde*[?@…Zžh®ê&=oa&Q3GFbé:4ýz]Õà]-ÈÁIãÞYàƒ”ìVÊ Ð_~!GܵŸ×b)Œmê¸Ç›Ê½¶yªeõÙä`H*Ͷ1˜Ý̹*¢™¼ˆ]9»U=u~]šíB“Âli›â4ã¹µˆsú’‚þë‹Ùww+€þöþâÍgaŠ=1´…ˆ>ëÌϬN¡ìÀq¸á¢ŽMÏ@«Í¢´ÞXÚùðQˆw£:Çbók¦f(@Vn+ðdcsu¸<ª$ŠÛÕu™Ð#(TšHµ5@(¨-Ÿbä\ùè1oHò(ºQ›O 2’Øu†b<|+¹(þ[0kZ¢ôþs@aåõUÌtá!ó{cÜy9ELõaÀ–vsÍŠp¤|?Ä”_³±‚©WZ©1ÄÍyð 5f‘,•§KГÅuÕMºâ+Vc´øÔǦ&ƒ PnÁ.Q¹e°1g•vZï¥[Dèz;÷XwÁ|¦r–´™rs-T°†€ZÚÁ1ö7úðzé , †Þœ›£×2Þ–Ûwê‡h£É"ÔcnÝG6©˜¾‹abÕ¬…6fÖçÜqG‹Hì`Òy.í=[àš2ÌÃMκ^Bÿ¢¸g¯ží[ „ðµp¸SÅëí²=ž¬ Nã»?Ú‰ÈM ñ´À ¤ŠW-­t”cð7Óo÷ðÙ²qàʼnî iý0%’ãµe·ßCÚ;çÓq”•»»† ?¹Òo—iKƒÊ]¿Œô5#t‚ ¡AxÙ9h ¯nîª$ý3Šþ§|ä&j›w­7B¢]º„‰}§ âð›Ø^;š|„7׃ğ С ^cȯ{!dÀí©:RÔÅš@Ê#}[†ôÄyAÌ0ê';ùîw©’¹& !e6Å Ò±ŽÍ/;€yÉÕ“Ížè²ô›‚‹†Ê¬9­z÷© Š‹ÙpˆYçk”È—ñö}¢… ùQºTyÃ_ëÀg™Ùå,Vš]Ü`~¾;ûæ!çóA|Ív2b¬~ÙÞÅ µB¢ÓªaD¨´6ä ×-ud禤¼r5Ü&+›rÚš$«K˜iu$;i½¥•ÒãÐ|Q°m°, .Ó (‹ªà Ý_ƒ¥ôñ¨ääå|r­Që 9¤ãGJÈØÇOá ØÒLÍЕ†N† ¤¡M‹½»¥dÅ…'·2)'á˜|hc.﮸FÈ 02j=æÏv@v@u¾ÞÏç"µß뤉2²è¦¯ Ìq òçÏå–ãA5qfS`×λˆáÓhƒñBaÄÁö#lÑ|Ĭ ôÓå[éòâ(¾yHñ{ \¶–ØÞ*k ¨—ß»k¬ô¼g JK]ó(z»ù„R«"Ðd;uFqZ}G(~_u8µŒ’¸O#èÁ]kM'Òƒ1iT:?H’œàécj(.Ÿ×Ž®0fëPà'yšWžL´.Ç'µ§Ù]Ü™2š4pý3ëóµ¬Îž0ºË¯òW…RJ"¸@*;Þ'§gN‚Q FÒ”pÀغ…ƒBƒ}EŒ–w©/,ªsÌòšC®{BVI”éºæ;Öh|¹sy§{S›påK º¦:°ñO(쵶=ž>¦{ aäÖÕ¾¸$/À‰iÚ`Ge˜~AzÁ¾i¿ò­ïq½wG’Y¥b—%‚@¬üc•gz*Ý­î‹ÖÕkæb¦:óÜÇA/fu ä–üú°nú‚§:Ȇ»ÊF«aO]EÓRégñäf`XOíÁ|sBÅêg0sÛ8HF?y¼I»j…þΙ$õâׂ4.vÐfq’_È[¤?ˆ´$›èÜ–ŸcùÝO¤fDØbm¨wÓN½"Üé'Xy †S&p›§=å5}&ùSê2Ћ-ÄÍãœð”S+#U;ã” Š€ÕóéUùsÂÃÖ½b°l¿:sº_l«¹•ç?:wc-9FŽHžkŽÃ ÏiZ‘°ÐøiRbâ¼û„'Öu°º˜ú>Û]Ej*s¼@ä™DáÞÅÑE£øGxá0&Ò6ÚZ/‘ü®-]©õ¨$—#˜ƒÎ\)…G´š{N¹dÆ¢õc_ 6ØåqoàMGRöi?c Q¼qÍmu+ö…”}c»5U1Õ`û*ØVV¦jÝÀŽP«k3ÛÜ!d),cçÜñØøw÷DŠ­6ñFÄØ£rÄßm“k=:KU~ùѼš ýÿýi;;NÜØ¢OÓ¯ÐÑÉ9{¯'*J5–©šásÖAÀWƒÌ˜n‰×Õ’9"ÀÉBŠÛýYÄtàD ªUÏø$‚-sà!¤pû…û+l—‰/ýÑÅbܽÌD>‡¨7LHÝ×"A¡§Á…xûþ~[ðéø Ö*®¶~Û…ñ Õú-š’å0JEœ³Ä°ü½H($fÌýìÅè:械àȪ<ºÒÂ!ݱ Þýâ‰Ð\>Øs„%ô͵p·„È5ñ¼®@^fr»¹Ÿü DÄÓR„@ ¦w§¿'oT çÅ%,§¯ÜD(\~®Ã9š Öë·åVæØê»tùén’Ù0ÔlÎ^öe®ùy [U¥ôÜÁ'þf·õ§ÄLÆÅ–üóÍÇ?ù7جÖæì31ÈÎûÜ®Iô)wVj§Œ° Þ±ñ“SHvYLë~ÜžÐÑãÊ—-Ü VC6ô±„óëÅ£WüÖ§Z%7ª,$ ¹»sfžq>ý¶NõrD\§îzæÓ$G/dl ë’ÞR&ÑuFn€‰Ùc3“æJñ¬§„{³‘VLËeÑzÊÁÜ¿g ðH¬¦ÛY™›©Ðô'êçüÿ묀kN½çeiy±šh¥ÅQ>ŠòU¥W8@¼ÚŸÛÃ>4q­Xƒ\ñ¾rBp爷©WÉ9ú‚¾ çGÒxbã=£žM°aôîX³-±¸b%fŸûH»Úà…è98Ђ3nC{Ö¸;zõ¨ì²dij†a3 xìãX©»Ù*,­:®SÉÎ;Ýkdb54¿ÿ=›}N.uðD´¼†ŠCü*u§Ë;p„üFêÞ7Ô._¥ëª§Ík$<«Í¬ÙWµr±z½Ó^Ö )L– sF²4µ,,j$qx¯U"aý&£«³ªEZ*¯ê)ó¨lžŸZ,Ë,/Ø'õ]´ÛZbüÇm7|†F±"þ#ÓLï¾¾½ÇZ¹T qÂN¡h§Ôý¥–*:Äûßùi"Þ*Tgâö7«ùf¬HûŠ#áG—éü Š³á¹±È\Íš„nGÄ´ÙH'èè8AG?á7KUJ‹ŒTiƒί<®ÑÎUˆ=ÔðýÕ:ܨW3ÅV“³âï‘«ëqY*D”奥˜1Ky[¡ÃÊ)û-E±IñÇÞ¼ƒêou×ðKP¡mXËÁ‘Ủ\á”î ‚Õã—eœ)#Êô|­”q­À)ŠÈI«\Ü£7¯Õ&¥ÌUGÒIh›‹ÓP‰ÐSÅ‚áÖ?¡°WD÷¤ Ë DòWå´B¦Ì8 ©FòïPÎé;ŽÈoÀm~#뮋ÿ çÝ?Í¡åI>%ƒ›|QÀ£+LÌÔ¹²÷´]ìh,Ï9*o}Á¤c§’Í|Û¡µ8ZSþqëQÔE±;(æ¤5̲ï2Cïû,jch)ÝPG©]…L&6&\3ÎŒ#MË":^ÄGLµ&Ý;OÁ>Rc 1üôÛôµþÌÕ\šé ‘QAMW Ã=õ}…3=/óF4ÉêWMf¬(ê~ øŽW‚“i“˜ø©|'ñÏíì=­‡2þ^ wDÅüØL¢Ì02À…ÁuV2ôÞ#+.7âT!ˆˆ«rM1&•’AµäJï†þŒ¨Cã±úB,‘ŒÝ@=•¦·¬H:ï µlÔeˆŽ»z[8“HeÊQ+{ 5£d]ŽÚQù`g>Ia-Ž@É3CËF}FHèÍ(¸$¼UmmëéèÆçÞ€Ô1×ý×+1¨ˆ\0¸¾¬êµ9™%Í4+&kBŽK€²¤““Öâ?Uxo'»Ü”Ì?ýýæ'ï ¨ L—œÑ}×z·‡eÀ­ŽNìVƒ±¤T৉9³ÐCtçöãÐîx„s.ûÅœîSc¸n1ðbNîµ@”A/ByHRMºLš˜·dmqß%Ie‚n”;z•m‡‹ïŽ7;¹9Diâ6zñh)&}#Â=,1)>ïHÀ‹”hE©nÒjÀ=˜¬O@ž~f(§79«Õ àÄlâ±Ð¤ø³>‰&ŠB‰bÊׯµm;ÿÿ%âMŒ£à§|cF¤‚"6¯™L½¹4ÊéGrr“,5ö&²—C±^ËDHñ{Í¿8èÝσ¢ö3ÅñÝNÆU:XjggÈß{qHŠÃÕ l¥½‘ jd…PÖ/±lÚ[;÷‡Az'!;ò2½7¨„|]!Š”of.¬%ùÌÀ“ú~7ýº(ΖżŸ@@Ï™â`v´Ø<½?ˆ#ô¿Ø[RÙø¸R>s3ãK8s2_ Ö.5ÌVVàg|ºz†VC}“õ§eI»ÉR4®â¶­ù~--ȧˆroqâ–Q 2CYÝ ) ›gtÊXâ±0ÿŽE ‰ÑœÒ®©-T"Ы¬ –=B˜>–âôí'a,ÿ˜÷>J³ÀÁçwVõ‡b9ú6ûŒÓ&¦‘éW×ÅŸ{d`i.,áRÂÎi°&]……äjíî•ûÛ6`ÏdÄÌ@¿r¸×–Ä=¡àRƃo²Þl»ŠhPiWQpFbÀQUësŽÅöÚ1V†:¡ozÝö)W¡R=a›ùÑ„È¡c_`„qææ§-]H²—VX´\ÙÕ·›gð+ëRõÐ× à+Æ ÍnµœA%kþ¢¾F<âs…ZÍCçìøÌT0‹Ò‰Í|RW롉ŠÛˆC‰\YVÖÏy‡Ý¦Ç‚×/g‘ÁÕúÇu_»¬æJG¹i†S>“· .òîvˆíY~DÑPs ð` . à|ùk`$9ÜücæøÑ/דï:‹0wIÕ® ˆßÒoB§³‡áR`õÇœ~·yI1`ø;$RhªHÜ“äLëdÓÁ¡&Ž ºDN÷‚H¹8ùuÀÙÏyÚÆÔnœ7ÂÙ®kÇÎFC\yÅÖH)÷Gut8â ¾²³dÄ´‰Ïìu¢¨Ì›õõRFÁF`V¤E [¤ÌO¤Úµ7v§#®#«!²N–¿%Å œÒ%ÉÈKÌ_¿:ØYôlåäÊÚʈÙÆ9ÜÄz…˹aót?÷ “Y aÙ÷ kÎÞ;!ßèËqŠ ÖX”ÖP4ºÐžG#Sâ<Ìxr\À‹Ðã«0!Wš¯óáÆäa±Æ"Ý¢˜C„Bƒ¸‡áaÞ&¹‡"IÇøˆ©zD/¶ÈlÇJ²í°:‡°ç 'òé«Üè@â÷1Ìïþðý½T-ÚjrG '“Š”´Í†ÃÏD½—T0Y)·.[g½x¡OÚõŽÈñ¹TÖ… Q´0ÏÒnïÔªS¥¼N5Ÿön–Á6”Æá(‘sÿûË=/…yLÿ6Ëe§ž.ÍTO÷à¿¡ yA7£µÐµ™”$Tùò$!î1på“ÙÆüûªö‡GÝ„m^>0Mô¹-±vðòÀ”)ªK©j¡Œ<àÈ/`Éj]´’¡<Ï÷vªæt*gYh-þiíýÈÿýÇ„ºŽ k¼œjä½¾SBA®Õ0Œ¯U×K$xT£{cËí'º[у* ©7 .#9^‰ø°ÕÞ “I?h¦ô 0CÓ¬v¿£Î<~{Lý’OM9b»}E3"‡Jß«VV?ø£TÑeW{ ßz+û«eôâ°ÂJÂCm“ ¤5C‰Q‚¹½¢ Oë}ºCó¦g¥pÅL¨º0/@k!, €€íÖñ¤%ïW•ä*ÂŽèü4׬5;5©† Q.VÈŸX91ïäj¥n e( §¦³@îñWú?’T¯$òˆÚ¶–æ |F‘ÊKÕª!Áð‘3Tˆ´Ëð-|’z—BÆÛ þé'ƒGÓ’ƒ«¼%TïwŸïéCÄ|jï;è÷`¶JF<²î›ÇÎqÚ3ZpõƒàÏé˃¯ÖXÀ šbÁ¸}Î÷‘"gLjþè<¦ö6„(¸¦am‚?tþ#)O“ß6qI‘ÒDŸ”aÕ}«gM|†ë×ï¦-ÝÁßÿ&‰>f™¤Ù•&ã0h3%@c&œL¹‰Õ«Æ ³‘‹}w3G=T Þ®5úÅŠछ”‚›lÉ™¡p“¶Äš±©a›·Ï¤ºÙÂ,2Š3Þò'Q‹ŠLŶU¬Ædgîõ÷Öú1ÚX”¯Š[†¤rÊ&¯2Y9C` µ?H´<¦¹P:‡d_l›B_šœ¥ô]¶ünu±š†võ¢ rýÞ÷#Œ0s~nKº_Ì>£œG­åîj€ôË)®sž¶æ« ¿5¥"äY{m¬”ü¦GOSëõ#‰Ëí‡QÓ3û=}Ö¢¡x¤·ß âråîö0•,,3¶K‡y‚±Åga²Å#qÊh¡Wf´Z“´|îBÔXî•ŽË â ¤4æ‡Äê©E3O¡g£Ú%45žÍZií®¥ ªû­S_Ú¡_±!1·_|Á¦3–j~Æeá´ ¦_(‚Dø_¨Š!æÁh©B[,¿œ\eÍigs÷n 3üÒ^/Ù”K9î2b +ðL2ú9á@ª%/ÅËføÆ=@Ýh¢:>¡±Èõc­q‘2¿o19<ò›º-á*{®Ò ðuzàdñRç˜Xˆ**Ý6N+zÃXäúX•I—<üW’Hw¸0ÚÜQó–ÑÍo¸ái½fµ¦2#€r5ýž‘ßÜ\#’×xCôÄu(ˆÅñ]8Ú>3yü.9MøO^+g?¤º ëdƒ$ß$mºÝø¦t$ûD‹Ó¿ü<åŒÉ\oAe:3©éïAdbqQ#d¶³å©«1#â!ÔÉOÔ}¾¡/þ}–=+cs€q³»bçÜ\ß=à·pÔ27¦_^ø©ïNÞ8ù¼ /ëä©þJõŒòÚçE ê?5’Rú^ç ÏÛƒ3çŸöq׬²„Åh¿³3.Õ–ÝPrp¾Ï©)M3#Žæ0’nË Çÿ|²×–{ŒŽÎ'µ`DÏÍéÚÇýX³T6ó¯`¢J(òt "”HB! CãNÍ÷ XSq®XmŠtL Ê¢@ã¦9AîæW*+O…ö=¥™ÁCç\„'ÎÙ‹Þµ îM«{^àZ~*?qF—…øÝ Žé·‹³×´šÞ\ž¡x° OâV¶Ø0àSžêŽZøánþ¯K*ÝTùRXŒz4*Ø6Vû@±öI ‡Íâ &FHí؇ 8c9©ÑÉìÕÛ2äïÄæ¿étDt=”&Y)oªß«Žªv ,^g¶ÑØÀ½ î7ö {˜ŠàJË71¬d9qêP{‘!ºz»$Ï¢§¡VY2†kƒÙ¤±€{FºD’hÖ—ÖSÐ Û× RÂöÓ!åD{‹ñåƒÆ¢jšÜ ò;¢½½DmÓYê¦p—¥B÷ÉÜ=ç¢Ö÷ ]+„í›õôDðe!H£×˜ºB¶-= —ß5÷›þoÈdÀÜc{Vµ“¢fwÉïîèó¹ý#6_ª_P|LSYžº£¿Oúò‹ñ·ðªñ…žL ãÛqÐnÚø÷ú.MÔoX»æÅ%‘K¾þ˜=pÛðÁ‰·º˜àM_mèÓ 7*¤ù¼¢¾¡Éñ/øJ€þj½ äÀñKwQÄ, è³’íûf¹:cfúT%M©~žä}4»=°é¹ºìÇù¶È4…I ?½É;@d~Ñ»Ïy:oP>ôØQðöT'AG¡éÿ3(`60N²öà¬wAÎN›Úé\|TWÃ9“ÚÞïWxÕ®€šq—2øß=^:ðU¼aÏãéS/_æâ«u‡G8"½ "Eè %Ú†?uÈa\”YÎrì÷½ìçÇ"]Ô³•“i'ë×Ë·eMP, û)à?1L%¦ô… Ïú =×¾lr͆ %Ý}GJ&Ž¥ ®–Ì ÕçM?SnïÄôÞG–ÂèqX! ,¢/[]5So­ýúè]<·XÖY“¶€å]°Àº™*2§rêö0qÿb*lõ¶Á;°‡C`k’è<ŠÀ“Uø½ò jûã…1¿áôº)>R$ Sb„¦NºûøiNaÑwq‹·¦Õ îüHª’{áüüü©ÝR#”ëñH/ü¬}5ŒíIWÂ?µïXжrO{’B/:0zÅ©e©bᦽm%·Ežx°,¬çÉÃ< Ž©h_Å ÷ðhÆ©ýÄ£ýúÿZY4ÖBý£_QÈò°äÞûi ó2ÅÌßeÇ,ïA,µt0( y©,®˜¨Ý²B@7¸eá‡Yà)æbSÛäÉnºƒ0f R²(‘ÃC¨k×!« µñgj5­×åtÝ ©3â2mhÑ1%øÄ”êÚdˆPOoÉÓÉG=iŒÛ\èm‹¡ï—L•îÿ1”³|ºöJEÎ`á.šä÷±œu²mæÝ‹çSíLѱ4Î[qʱĉŠE'RöÓÀbMW¶M£ìSô=÷¶Ù˾;þ;°ýóqðJ7Ö]ç&ô—/š“•cï¯ trFâ’Ko;ðSŸý™ú·‹OyË÷f ö=QߢbÙ´ZA§KRÕ1}¿ÃÏ;Úbáp—×EÅø¶Ð— •ÂQ¡…i… ¡ûÏ}9£ù“„ ü«øQÚ|ú€qùßÜA:„ß;Ãø€phŽ*Ò…Y”Œ&é ÎñZ}exÒÉÉÔ1©äš´àÈáMþ ^Ø&ƒ°¤I$ÿ±øìÄåÙ&y_‹°.pñqà½úA¼<À )!…Ÿ§¢½ÀaÑ•O«¸¥ª¿>%â8qr ]'šŽò+aZ…IÆ=* ëYËGÝÑy¶… 'CüQ4ýÿ^lÒƒí쭹З¥›Úí ãÜÌ nœ»P0gÒPìÎí€%_/-õÖCôÞ—¹¹ÒIzŠ“Gû¯Í± õ°¨íî>AWżÃ@°œr<6'8¥/ÂãÕcH$”óò±†ÇõÎSí‹;燘@N~Gä¿–ùþáÆe¬Â4ýç2ö·¹®n öKà:®í5nU^å¨á@5g¿«sþ´çP9 òÔç÷:¦`m u4ù«Úw©ÆöM)­.f×-( N㛈³ÆI.zr06¥¯¥ÎJ­Ê‚4øÛŒDVêØ_‰î˜ÓîÿÜv5*Ö¯=ú2gÙGV“k.i_ó •®2héŒ6‡ “QiÞŸ%å[>nþƒÈ„Pô¥¿L’¾{U 7®LZWÄ-®”=B1q·€Œ\²y#èÂ/»4üM¯¸DÔ¦û.?ðP;^€–pÖùö(GÞܽŒD@’àq~ÿÁLeæ³â%qŒºQ‹q³7{øþÕë=T"äp¼!, -¸+*!éÅ×.$[Xð“¬Àiž°Ù„Ï€>8–‰ÈÍ€BlºyÑÂ=4©o:Ô»~ViúæjØt¹X¬¶ÈE+AòÆ-fÁ£ã“•;û3Ûî2ø[?f6ÒÂ0å&Ý€eÖ‰ ­HÛÜ©xO…ÄÊ7ãžšz¡aøÇÅH!„6‘Þ¤d‡Aئ:`¬GñuÀŽú˜¹‚£¶èœh÷‹ J3'«êÖŠk®æwê ³‚¡îB'ä»ê¤$¥Âp&~ùN­wcð. $°ïp“.Šæ;!nª¼få !Çð}pˆmÃÁixü5Í P`PêÞOD_Còš¥à+ £EŒ¿™¤z»–ÖG_”1ýòFH‰c {O<Åx³»bFh?Îq[°>–þJÚñæýÏíPj’y¬p¸yë²Â¸,ìóRÁ\0]Þ¢oV@öMªH P-l$áö)y-àœžÝgbŽÑ.r¡ºjmQ'ŒŸláĘ$Y9Î Ç TôÒm£ ] ¿{7–H°Ÿ_3M*4s$GÖâÿ]ø÷dEVGb¬™%šŽI§‘ıYòÕ;è‘tõ^úð׸1:$LlúDç8×ñ”zÆý.xðžøÙ5Ü*÷í¬>Ò±VZåaçÈN@pµ„®ªåÖ G¤¡B†º+‹?Mv=€AD¤Þi»ûðä«Á!DP•·À|û¡&¦yWºyu”æ^[ŽZ 8'&ë|üx€{ó¢i}Aõ”[«±aÎŒ{ÆÞŠ3gã¿þ¹s–@wšU¬Ó® 9ëßf8«Jjìx//îÂV‰o9ï 7ÿÂ>òYl4€ã«ˆòD8~ñäc#Íçtš–ÐÏ0j”QGäËTF•E·"½¢€XV7¿:ÿëJÁ?I&919‹¸W𸴞Í?ÃÌä+ø<­Š9°h”îcª _ ‰AÅ¢ï}tî"³ƒxPië«NÿYªýê¡J2&.‹Ÿ:gÿ)¿ÎImΈ¬ˆUnZÆŽ\Ç­¿oö×ÃåêÁIçs_£Qƒ9ÑƳA­¬Uÿ%x¬/<ùýü}¥shë¨âa<)lÂ'åH þy½à¬öß)äš¼Y0d"qd¾³uEz|äsÒ·Í¥Ê㾿~˜?ƒN-^ßç¨9F‡Ç'j¢vž¦es¹ªB”tùݼPÑ!Q36kÈ{Ó!2 2~¡ÑF6áLºÙFÛRÌíôD¬y£y./ý¢8qIŸ}ÒC‡øq6PÓ‰ð,óKH¨Zœào¥‚lÁÐï Ö&ÔémlïGèá[u Ì…ìSI/œÞm0 Kë‰Çs[O9gí‘ýš:ŸèŸ»u€¤+ËúŠR7ãdjcÈ:Ž‘¦òt2Ã/ [Uu”í¨›…;y—îìéÒä%n³¯}åÕÜžÎÂ! 63©Àu?ÙÓÅ8‡wþCÒÃ<|k$YáB”­údó‚AÕµñ°+ÇœŠ¼QÇ¡a‡ 4ÂBñ¤»5ø¶qöÒT†ï™R›‰€3i¦UŽhˆ³~¨[Ï0¥'Þ]ž—Ê”e)³:E–ÏÊuˆ@î+CaÖ7Ÿ¯8CN¡n+92àdÅ%ƒŠ–…oË­~Yᢚ¯¸hÓÕ¥TÓ²¯(r}k£J¾‘Å£Z% ™¹ð”íïÀ(íå&µäd¥š‚ñ3—‰luØ×ÌøUÕ‚¿ pOèM5²n1%LæWà\5œ’,ÃAd6l(?v-G¿ô6ý醂ØEµý§àµ»&À›H"è«…æµØ¾Û&ß †E¸‚ìV4=ù§X¥8páÑ6í˜J:º÷¸ÃÇ'—Ÿ4ÄŠvÁ>Þ\3È[-QŽ`Ÿ°ÚÞš]pKǙҦNc(H ¶·]¿EU–)¹ÎœÆH¥Û$%vδÎ7?oLÌ}? /g§Õ2&Z˜½"¾Ü¹U1‡¢–Á ¢:2û-ø MAÒÓ¹:|œý«kþ)ÚJ(²€bë–Ãÿk(É?µ'qžÀœå¯Gß¾L&ü§ø¬Az[Í""ˆÁ®6 Z%l{ _‹æï›||kGט¯Ú· Å-é©p¿ z¸[“çvÀ"uæ‡Ñ>ëZ8ÌQàÏ +è²7aY"öFÉQ׶Š)\Ÿ@¬"KňYÔ³zEž…®mQ-¡¿š0‚Ÿð5‚Ÿ‘†âEºfû΄¨8`B"‡xÎ^Öï–0œÌŠ¸i åÔ}ìO1ø~/Á860Ѐ©“~]n.Äaéå®"e;êâÇ™Ðæ"â<ëæ§!d|Má®x©P+Ÿu±X¨›3‘¢ˆj3(³Ûæ™±sókq$jWÄô†EÖ é,Ô&¾f®Ð¸€ýXZrŒÃÑ)B€“,ÜÏwüXn'ô[ýÍkÖ»­ð .T™C­]Au¯‹‚£$UÁðˆHòôÆÃ<Ž¸š)sÄ}iuX„â“É”»hü5®ì{¿ÕJýiQt`xwÕõ<%QŒmH ¿ Óà,‡Ì÷7×Ƶ.ÜÔØ'ßßÚ[3¤DQwü0ú£'ª§’ÅÐiåW{~ž†äá—í3Xªi=%TOoL^f¥f:’æP:4žõ}Bœ$ |8ù×£ìä•òÛ}!§·—Z$c‘ ºÝÈðQÃk¹³Æ U©èÕ¼éÂÖ™áôÆE\÷³úgð•s¤°$we7ÍÌÒ oí ¤ðÎÅêüÕJ¬ž†NÌncû’}mNLì§ó:£ß4þÓ¾Æk¯ažîöÜU cÄSÉLŸ]ì*‚¶34a¹ø½gë¶ìFäTƒÀÏŽ„}Ê•Bò…O°wÙ¬xžõ?›RMŠ y-¯¾[¼q†‡H „zšGûisÆœâ¤Ï~ea7² +ÂT Çç_u? Œ±ý»Ç]ª©I¦aSˆC@DÛQï³{æ\¨h¤U3Öé^RË‘„Å|á€Å%¨¤åM Š~;ëFjy{©hAå'] zR…âGgä0—(Ht`óÖ†£LA‡1#“XzÚí±u!KH–}¢`Ì„ìDAÁ «·°Ü(¼ãÂåð~5ÿ–ñò®@³ýäɸ=¬çËD¿}LTƒ/–­\]‹äFšIçò8×kŒfÈó]ò¡fM5SÙ3.[«¡9Ñ5~ZÛ_Û{2{¿-¯¦œË{ÅW¯àU’à '<6“T ¯ã+‰‡q WP­¾âaIàƒIÆÇ;#úÁ¾À•±ƒ¯…îqŠ¡,ëY×µ)ô+""·¶Íñ;« ÙI¢%œ^ãt¤Ê5ߢª8°L2"‰)-„ÕFv%œ`·¡•¶¢Â÷b¥ϲHó)`³‹ûqV*Í_L¸*ø>_ÿí…6ë3!~¨y½þ„“Ýh}oønNLl/¤JÈ–úü+Êë—ΙKÓµ¾0lÙj´Ü &#[ˆÀî®æQV·[ʤHGdzD46€åen™„††`l¨ÕFç(¼F!¾Ãy᪠°ÔC}Ð(-ik Ö4ÚÐ0úºÁÇcUŒ5µ¿=•õ´aÇ`Q’—@hú«í!= ^s/1ƒ9Õ(k{ÀùÏ"ÌĦ G,êyÒÜ+£Ô?OÂ*wܹeót§2L ZMÊÍ¿C%¿VZ¶V ê9ßp6å®J‹Š#Ö»õì´ÅiÐwª²‹5òdû/*O9#Bì*¾E;àPåDÀV¯ü“‡5ÕÒ9„g‚¿ëç”\'vz®6-­ЗþG \¯ú†»üáœæj+¡¬“<.Ì0ö–®),ÂðYáC¸Tƒ ä§ £9‹)ÿ2³ä~ÊÌÑÈ¿ó>åxÛ/{ƒ?·1ÈØ£µ S:벆MÕ'²Q‘[pUî.=…âÚTÞ`aKc:ŠP CLÎÒ\KŸYðÐϸC jr ®Å¦ò`nÝÊ=!Yð–^®¤®V:^6ˆ>þ¹¯ ÙC¯|f1ËL*µhA ¼Š_†8¾‚ëÚÜ,k-lïâ~̹Uƒ|Ü+º(8Ñe_|˜E˜Ò. ¨ös‰$«uiVÛ8rzL"&ç6¹ÀitLQu¹€¿…7O±2Š”E8&L8ï“+íXEÍ–"à­$’(»ED½z'~g¶rüw\à‹¼¶’‰t"òWc=ÚóŒ@^.¼âàrz²{*¯H¿|$€GM½µ!Ù²Ëý‡æ9«}'%KG|Âo¶_ô ó”Ü( ‘Û 82zkà6;™w‘Cœµ¥’ z‰$ؾ0|JðçW܃zòåºD¿Õ/QèÕ~6ïÄf³”º¨Õ½W.¤`·“Ev\zNˆéå E°º¶*Û8+%<.ÅšûëWoŽ3²Td–÷!ùàbO†õ¿P<‡ãéñ·b˜­ Φ½RSN 8Ñ7}ƒÄ7~ˆ¹ATR’£n/ð7$sBæ3æóÆÌUÜ ~%@!öŽÃ‡õÙE¢‰D_®+' TkUí*\“ÞF`Û&òu Jö½—Ískc«3ûÎ ¯ëy¶îø¤ ÔJ¾ J#iAÙwÎÜá¿ëGŸÑ†Êñ¦’Ú÷´€·UÐ;º”-…Ÿvž?5 f„õUÂú”¥÷o¬cVÇ:*Ÿ‹ÅRºbgÌTJ—÷úš%%úÕ"°.µG‹2‡¶[Þ°4౦,§”òE•ÝqfâI{k¼ãr;š+N+öéÙg(#>M^ÏAœC5t3 Ç6«ú8–$p€H•ìµ ’Ã7Øç£hLÊméR’.é1KÝ©øÏ\oÏj’È¥ ‡Õv¨Ý ·Ñº«ö"”ËÈ‚N6Î-ìUÑ  ÙéåÔEùyŸ…š‡½«ôº}÷>»|¹=¶¥–­wt]ùç×O´¿“·‚Ÿ=ÄGòš@Ð+ @¹B,óŽ#Mî1«ÁË=)Ö¸ììÃÜ€‡ÜÚþ:qÛÇ.i0_¤lKšõ¯³³¼tç$!¸¾Èì8k€–>² BåÝàõi."Ty@²¹êksÌd-,#©SŒJIïBEõ4ãmoSç®M¶ú»ž øåˆÄ®3©±,Ö}ÀUÈr óØ Ê0CoªsÖ(<¿5"óÍJ3HìÇÿ]•ïl´4–jDa1¢#4òEãE€2“ªèÛ³ÄSB’¾´ŽLÇêj—‰<¨I  |lr‡§g&³æ²šµK¡=/´}.€izƒÊ³dAqcª¼®º÷€á¿È•A, ýϲÊ>ÏY,×ôµ~• )¬ž]øû×ÂvúìþJÖ¦.iÿjÆ‚¦™§fXƒ«b âšE¾œÒ¢žÿ²,Êõ²­7½¦…É_á~Nà5 •UWÁ;ïÀ¶£¤g‡-›~áé¸ßAj ´Ñ6´Ý —Bú·G†²xþ3»q ?Ô›/ÆÞytÓ0ÅÓ 8`"úˆÉXj¬ˆ²-o\Ûeçÿj£†wJý0q†±^eÔ;£ )JZ<²›T;)ÒNzç‹DÐ!’0Îð3éFd LIÊà–¨ÓØ&üºY2}á.ÝÄ6®µ¤Œ‹R¦}&›<¸àäïƒ~6eg}êèTAClHÙ }Hðäé\Q)vMºþžj'¸ïÄëÀJ‘Ó3¿ß¥_3‘i€”N¹ÃŒì!ÞW(üˆ’ùoáLÿfXÀ‰Â8ÏÝÄÂ?Lüy‚{›¦‰jAÈKE³&µÀÕ¢#éÁ¢Œlpë1ƒyŒšÍYÁž—¯´ÎÕ¾\ …têFž¥x{»*¬¬´~÷`›êdÄë}úv ?ôE¼lÝ{y饗”É6ý µ„í-e˜¿`ÿ›ˆÊÍgЇ“‚Ã)‡Í´*Rh¡f†g7|ë$¥ ÓÓúxÁøWÐnh„Ÿœû¯¯A˜”Ñáñ¡”Ó(ΕB p2I.‰62Š‹¿Õ1,¯îÞö³×29‰VËtVAÒÚVbl6[[ȱnpõ;fZ[‹ œâ_RQ™5L¿gîä4dòLå?yÚ]g) y8`l“åM»MÞ1Îõ…oo€’5ÊócÎr¿Áðçﲡ|÷ Gõ[òÁ¸¬Ùdy. H¾|Û(¼¨Å¹y©#~À ^]P2Ù˜I–)õ¸¦:;½*}ÃIÀäSǃϴٽPìÍà‡Ý€"ïÈ®W·3„p?Ï6ÑxæÞ.›>E&¥è ˆÅÈ…éúXœFC]UBcT.{7ó³ôû•ÿs·WI3Ø-©ˆùV’Xþg@®{ßko»m2Ū{?l¨9T#'!Ú43¨<¡¾ÜvuþœÂÏKOoÔs[º]z´ù}xY½´ÏÃ` ÍRØöê‚k}Àºxƒx­+0 ôªëQ3ß+J^Œbš'Æz²·ˆ%ÆŸ"wù%“jÇœ¢â–Ÿ`L?ÜÐùgÓõ ÞX(G±a>èV¾FóðDÖ—Hº)#œúZÑ\•ðÏ6% ÜàËÄ1ÊåÕN‰‡G)[‡¿%ÚX Zµ3‰#í‡rÜŽq¡,h*hÁA{$ŒÒr¼ÑÊ¿˜ü4cYÁKv¶ Ûn-å©\¡ž2ñ#,A3ë—rÿjc8o’¹ecgJ)Ô)¶Jàòeº~u)Øó{/= Ñ(¦‡y^Vµl–4 s¨8 Åݸ@m$"&7̆„/‰‡ –>¤P}ª\TÜÁŒÜ›Ts’b¦ÃÖ_Sœ·¦Š)Cbdþô297:ÀÃÁ:î£ÎýÁ=ÓZ.˜Ð†“~ÅÂà„Æÿi™³š‘„ÌSÖ- è»ÉmWB²ÖºL:=æRõëšbøµ;ÿ'V‹ƒ¼¤(ùѦºjÉi›?ôéaü WC¸Ðïñ÷Ù¹ÿ×ÛíÀ)ÌÑŒP@A± ;x/@eÔ«AŽ,‘ó>røu”tùOC§çèÎd¶ãZôš, »‘oähÀ2ÍÐ…MÓh+!´f‡è…Q ?7XK²¿è'¾W:#[¯ÌRì„\Z[#:&ó™k’»1*µ´¤e'@2Õð· ‡"+÷û#Nç~ÁðõѸŽý 2ô†?¬‘áƒðòFžT?g}„ðl˜x,ïB{ïùuiR_þn xÅÛ7¿ŽÎWˆÙýn gŠT] ,è'˜¨fÚh%öÂ㳓0Ap8…èÀ¥F×Îåc è*ñ÷Æh_£ã[ðg™g×í› M¾VdF>ä‘ êRÂB=‡ÛÞƒËW¯œÓÄ“‰ôfAm@[QFÀ%Jv³«¾yÁ,шuà‡’Ñ âq¶®‰¥\tÄÌÚ+b Ru5ÓSÔ­½¡~t–9;¨+óéEKðI ¼6XDûgËž‡÷Ö/æçþ»WÌð¤CDéoJ:`âK¯Ýu:ŒC+ÝÖ;à»<Ët ßÈH—Ç7 8fG¯jƵB‡—ª%])i‘ýFåø÷=/žy¢=7±µý+™:|Kl¯ƒ•Ïàós˘ì”jˆÖßPR¿è¼k’ì\ë·è¼QŽ–¿^ç1ýi‡¨¢¹äß+‚ø1ÁuÙ§£¶A¥,q»H6Æ߃jÕ]LEG‡1“äDó^°æþÔÆ´û‚4!=‡õ²åàÜ 6Šè]Ò-† ü& ¼óŶI¤_üúPÁ q±ýŸAŸ¸½¹ºü0RǣѨ¿Õ¹Ã{bŽ«gÌ]Þ 9m3 <Œn±‡²Ä£ €Úœúj(ž³8ô’M bï5Ûx½ãüÊLÖÿóîf­˜Õ•w€dä˜ÆDA•‡êçiF³‘¾ˆ¯CîË<ÒE>£mÁ•ö¤kTÓO“Ó@Ãd÷+Oyá‹S—µ0¯™m6Œ~èVùo<ØMûý‡O¸ C¢ækfúØ«UýØ3¼]ä[C#öK!…­kÖeUÊ©H VɆøô¶ïÑZ¡àF´,ã †¡áØ•ÏÅE€€„{™öQÎ.\¹4Jdœ°õ ¯á€.è±ä_7Ji&ê”óxò ŠøŸI)W¶¼ D,hÚœ@ŒÕ…!CßûØ·:F­ÂRUy½á¦/R´öÎmð?xΫ#Ö+î)AFUò#8[…Ä·ÕÔ~øÑùš—úCÉæºÀäÖË©~ot~´À þMˆŸ9ù#1‡¯L)$Ûî¼mÌS k…6:¥1 ÚñJ"w€¢pü¼Kþ.¼v€Ú•ðvUÜ]g7'ˆ Ñ«Ùð`Âl÷å¤ÒêëÈCò1Öåü;ˆA<%¶›Ë§d4¨áçZ®[2¾ˆý¡ .H¥p¶ÛOŒ¥­>Ž2üý—Ó\¥gP’ã\”ÚnªÿfÁ¿ `aqôï!¯†â²kEd[kË @祆à-Æ/k”6Ü&„På?—Ÿ|/$½ Öœ¸Šq¯àíûˆš&ò™“âŽ/HGSÈÚpª­A7ƒæ„v¯BÓ¶èÖÉzø–R=ÛQëL¥¿ Â¥„cN-§_ÍÂP6 tNÑ% { ÈM[±›Ú`‹Uîš¿ÉsÕ‡ÚÅŒh;Ttùjÿ"‚¡*ûi<Ôšmvï—–¼Ì|a)k/ñ!A`¥wLnc÷g"Þðh¾$9ì Ä`íyO\¢ú°ûÃw"ÐÀŸÆ7"%ÂhËÕâæClïç6†³,¸®É‡ÞÄvevT^l­‘…(û-5cÔÝþÄøàÕY”`AE™Ê=K­k Í_šÚPÒq3ô­ic½ÊZÌô;ÍãšuëÞS&¿siØ|¨‘S9ª4€È!‰_€ˆl¹uóSï¾.¸â)G ¸Ã‰×‘UQPZ>½Otýi£†  QZ¯ËCj3"¾˜¯†f~›µMyâ±½Ÿ¹òªñ&Èíø <²='õ(ÌWs“®¸ýh×´ªÑæáN¬n¦ÅΪZYl â]FÍ+ö³KZ±ªO¾ˆ¹abóêÛÏ:O@$=gø.ûo.sµõV³À,`}­D¾q? Ž aÌk×Kúõ…n¸;©®Sí‚XÖgD…휵FeL|‡”ñSz9·Oˆ®/ Àšñe€×%²F*¤hë$¶àWY€"SÔyQ|§ÝßdÁ¼àxw¨Áº3Šü¬¤Å>Ûv!™5p¶—h'?l0Lê°ž¾¡BÃ= ç×ï¿Û5‚¦<àÄ·©mé×°Î…ãÛñu€Ì ÊšU @žQˆcYv„žì1à{ÄÖÉ#W¤·ÛBå¸Óéþø7¤8˜¹‡¿‹·B1fÖ3#AŒ-þù ¶¢¡ÑšSGÊ3ä7ÐÍ<÷bö7©éÜnˆ3‚ÙOŽÎƒr›Ï׌˜Ñ±En•sh’ý9âu7Ã!ìª/±ó¦Ö"áï„C-ô–IHâ¿ÖIë@!í®‡à–äõg,œö€1ÒƒL8F’ \èðœñEeê%ë7«©AGœ“ Wè}ŠÌ ùòf1_b×Mˆg4å8x´x¾ ººP 3p*Ÿñön~Nõ³8XÇAØ/7É0H¸–›R$¶0m-ShI5¦ÞafHÊ|üã଎³À±¸YçR¼ä…É Kìíå³±€ÿ¿ëðŽÎohS !d…À¿&C? Nõ™Õ*æHú×ã C¯/x1ú+µzDÔÄâÌÌSɶܟqoG]KÊüã[X[ùU´AŸwqû¿_¯h ò"¶†|å&x€ÃÒ?›jBÖÉ¢5ðÑHBwpÛL}ý`2G$q^ZÅc®âÖ.z±×P±XJ§4çŠÍŠ4&iAˆ¸w aG«ÑgùÓEœJ@2̺ú¢%*ƒÝ½ SwEuøàË4¯ã…!5ϲF"v~b\èªØAQb{Ì#ŠC^é†%¡EÈ=Îyörn‘%Êî€Ò§m6) k©kðµsNB>Cb¡Ü?êlX>>@§]—S1¦†Ó?â»»ìhz;¢:] I38Äö›ÅTË-Ý´’ˆqzE,Gbå:°cV´ç»™p€Ç1é§2\¯2o/JÇfY§iU†gÄU³eY»£•ùö´sáK´rA5 $ƵƜũŒõ8 >XµJR :oº¢³Ò,|7–B~eÖæa S³(v7€£’ ž5Ù‰æ,\´P¬k¯¹`Ï㩃ösˆö™OæJß'ß/VkÁ[±Ê¤ª_§Û, j¦ŽñÛ«ÂÖ¤2Ûåßâ²N.tâ õàûø)xSÛÌ kL«kgªú¤ßÄ?{›;>¬>Y]ì'Ǿ·ÌDõð>—÷ô3¼Ñ»lÇ™æ€ O!9³LçÂ',PÝ̲¤'Ì‘ùDž[Ñ·ªÊ}áâõ,ƒ³Ocľü“Ô3÷àêéìr1‹A!á»7Ê¥2ÜÓÿš™eØ©Æ.‹6éÄÛÀ…svÏź(’a@䤕Œ`Ê]Ðn4+÷M&5úgù Ô¸Lv¯â‡ýx²ŠnÐN”rQмP¥ÂRÍE ™.}‹£™Æÿ\1ZCá´á« Çý„ŠÖê]>äwÇT–Q,!‰S y0›qà>ÆUU®h¦|-gZç·5“}zH¤ }à @múÆ=èMѺ!sFà—A‰½OÆ{ˆêíèDq% Nƒí)&ò~ªl&øß¼˜@i;Ù’vŠq°F¸,Ä ”Ì_×d‹æ¥ 7{O±ÁÛR‰òÒø ©{5ðKÆ«1•ª@!‚~sƒ4ÊA‹\þTóÌ>Uð§-Ó}¼bÉóJ¾ÉÖöV™Hç‚Î:ÛF£)Èõ¢p݆ εêlhÚÁE°u`ƒjDz´šYYGkÕ,ñiÉ©ƒìñaD ¥.u±!O©•V&~ƒù†¶Q—p2šE(È.q|*ßØq •×²‘®S¡XÞô‡žC÷'Qÿ¹ØoßÇT«Û(‡ð ÍÖä‚íŒZǧã)´(1„;x‚5UÍnôÇ!‰BÉ7¿¬¤ý-ÝŒšÆÿS„N¼îß\Î>DAŠl÷–BSÆ”¡2O³Ã­h‡'¶² Zw´“2Sá–…c¾Xj[|"Aà®[ñ{ÅË";Œ8§•±B§ŒÆO½ôôíÒ­ÀQüaO?»³.~M²_H8*Ä÷`&\/Œ'ÀzÜ*Z_ìðÁƒ#™aÀ¹wYÓ šó×ãlïg6ÅHú0>ž ˜pE–rÚ+¤”—¶2¢KÌÉHàê×ékò±m¸-´žPS–m¡*•ý{H¯ %äÇyøËóç@=0mÈý[G3î^•ûõÜ7#T Ò= ”íÓ —܇o¯l¨IÌF»2ý@kè*‹ŸÓ¦zpâX.X%2¾ß»Yÿ–÷)Tþ\³ˆë˜t,Kcì¹ tšŽîêYÚÿ¸ä­€¬aCd¢¤ÀI>@„æ»ìîÓÛã;g±MM‡kãÇ -¬bï†õöZKîQCô|g—FSïƒ íùo(£ÏüR² I;œÕ¨]$N=2ÌFÆÈÿ´7\€r¬ y!%¦\Cae†òÝ@’ÃJZiå5•Ø¾ä¢ Ñ·^E¾S¥O Ôm’ǨÆNA]å™øù¨{AhËÁ°‰HVÿrš4ˆŒÝ&AÑ«î'X''[W|Ø©.µ,¼Ôý ½.gŠ›ø'/O. ¡®\àõuºJQaèˆÈ d(xÈÎAªüÌJ–ö^|Sê…Ëó³>)­Ûbù¿ð¬G¶¿ÄWòsþÛ0\ûXóLudOG½{Þã᪽¹—ÌÈ›-Ôf ÜÆ\¥ßdhãCc¾È#XŸfäÛñ‹¤0© r–S©¡Õ(¢ÐSŠÚ—ѽ&·8‚Ê7±ºn\öŒ“ש <­ã£‡·]çÜolj­žÞ™Âã}Sî{yfÔq*±;h—´.ø9ÿ×Zm˜¨K¼ â‰y)‰+ÉïHΙB8¯}¤o´½åT=0ø'9%@~\X0¶D½ORÔ€ÏãØÖð~G¶ð†^IN¹DkBzõåfç¶ìõ¶œª'¯“Š¿·¾§ ¾ÝZ"è)Ô[EíÎOƒ‘äP…CO0râÊsuVÝ©±IíÑÝ«÷CÄ)¯gÌk Úƒ1Š¥äÅ9ÝÏõQSŽeNáܸͨˆÄÍÒ”9Q-²=œ©-p×6ÃÎI¼¼žÕ^\Îb(Nlõ-äÚ]Š\æ·ãRõ¹0_á3Åm‰ ä/k­±úÁ5˜óùãóßÃȸÑÆYèølΩî?žÎý‚q2“«×,ßÙA-¸"ܨß1óÔð8’\‡Zß(C+Ìñ`¿+ÛÈwFY ûõ¯hÄxůýt Ö—²ˆ¦ ÉéàŠvw9Úu% 8Cv;¯²€!7pÉsõ7<°YN„A ªäîIü^|:²ÆlD”˜~wKäð\j„zž{Ü°¹Ì-È5›zCè,¼>õ ô ëÌJ²ÏÕƒ?t+mT¶ÙB Îê-SÊWBÐx„PõÓLô4eÐÖÂYýíÝø” òðã˜_½ÎL ³æ þ;Ö33_•I¸¹ƒ&åêoŒ jUò{Ÿ8Èú§±^Ù º—³¢qa(m‘…[÷…½ÐB6d§Yë´‘*É„@1ü¬ë‰×qee,×îÕ\õ`µÌêoµ’#U-OV@€ð.ZÁµõ²È~Ô|DSQ»Õëé, é„™ßþ«²r ñ¤Ž²ê‘õ© [Q”·Ó e³º$ ?p›™°Ã³ð:ÂEJ €võªÅèb.H† ‘÷´ò¾T¦9QÊ:YqKÔb§Å}I/}íû„õoÒfšœhÇ]ñ çÝpÉ:WK‰~"‹¥¢í©0§3ÿ±Ñ@T_0|EÞy;ºe?çÜôªÖ,î…LY^OunàûU9%YG˜¦e»LÅ »õ@tïqÂü/#ùXNžøóÍ721ó‚ ¡åÌ—:ËùÁ‰XmÚ½Y/ËÑ éž¿:íŸYe–£Ëµƒ¼ÔØ‹-Ø„ôÜu°M.Ù=k5þ´%Ú½×Îü5ꃠòø^~ëúÛà_¢UÜ8•øñ>.Ì[þ'•Pó[ÖïÇK¿c4iu…vA$óÑÉŠ-Ù= +QõÌj–á¤Ñ—gî¹µ—V–üiYåQàÉ@#½lüæ’“ûgy;Æy?8›²n2"ùÚ¬ÈíYErKTÓ–lb曀ŠY—9‡-ez¬8¡mznÁ6n,ÞP^i6Þ ÌÚ‹¼ÜÍMdÕ/ÑŸ˜6;ÙRAÇÍò*›Ð3ÎÜkW˜iA³‹Ñ¼Iü룔Ȳ¸ãb‡ŒKw8½YW©’z°™ô¾ú?(L/ÃË£¥}Ý÷T,ŠýR)¶]diJ,XRj /tr²˜šaD‰2´¢›Ti…ÖÕ&ì<#Ó’ì¢ö¢Ž6Ç'·Ó§œŸ*1–FAaÛÄ—®Òl ÝQbÓà×",>¢‡W2¹•")ðLu8ÎEMhLâE=HX‹É2*€¬øWŸ™?9)ÒÙw|aÁ Å¨ÇìífÖ>ôQêeWuÖ×øÎzðÊA¬*Ü’FG*«vÖ¨²*¹ƒ9$¤gR÷ê=‰¡)õä•N+½Û‘×GrÍ)¨Ól×k?ãq´A¾þ¢¡îŽ.„©†ÏÃpÓ?"uvTsY+’)…›‰Ý”%ò±ù.3ÓUû¾î â­ >Îõ¥LÌÊ!m:¼CEƒz•­¬xÖ-³]:îçÃþg~Ä8­E@Dˆ¶œ@·­ß:£Á<³ÔÈœïŒýÎqQ×cåqVß åþ@õ@ÓÖüûé^qÚ¡¢æ’ÃÇxï©ÌY#è¼JIÇB&i|Q¶ ×üÓGÄ&RQ)"£õ¦õ•™˜¨[©rzhDºÄ3o3"ÛUȹÇs¬…6+³v%if^³=ðò/5Ï`ÌMÓÚúFâØ»÷mŠXe¸B²EÖMG>3|i È£tqË5a¦vt34øEÒ,Œó“U+ûî“Fã è`¦^n/²‚eØÝR×!°1¿F½u2¥ dÖ3ºv‹Ý1Ú\9ÈKûGƯ\v>¼Ÿx–×¥OwícŠ´Öíw¬ £|©_ÉÀýÌÇ|Ú÷oôcæÐo¾U>ŸrÕž@×O |ÖÏÁ™ Ô¶¬L>.û7"^HaÁÓ¤/º½½5Ëžù ½ö&nwƒø°hüèô¶ .X®É¥'äü/§Õß ºg°ð _å¥E±iHGºÇêäK¶X¾‹*E/‘é*ï*ú_ýã.„H4íž*Ë°C·÷*òkµ$1zËêI—¨¥¸r«:åR9ðÁÕйˆh€¡R­ –®ÿ°þ&!|¼j¡|“âg»ánBÑ*Ä n® ½¦%X¾öL;.Æ¢çßÿ ΣKrÐÊõ¡9öXâp'¾ù6¨Ô¸SÂZdqì´÷í)ú¢±…Ç0”KamOã÷×nnèõ[Û‚Ñ1ðë`A¦@|zÞø¥c€íF­E ùêÆ%r¶ðŒ‚«,¥¡§MŒ¢z/Œ1¥Ëé;¦!üë$ŽÄ¼ô ·WÜY¤òW'b}Â^ïê›±­qMœôzÉ ¹åøJ‚Ý!ÝkwŒ#@°‚×:ô‰¬5ÌOjœ'Ìr‘žVl5m·¾Èjó5X„@,&,Gÿyc]Ç°0­`ù¡†ƒaUÏrjZsæ½Ù[Ib\Ü”œ?ÌWlûúÕ´”GKÔ@à×LYmNnP©­R±p§\â=uð"@YLH¿+°sJ¼m‘[Ûb¾e _K ‚7O†ºï£XgðùhÛjl¯åËE43’¥”ªK†½w¶3œ(áæ$š&&FëX¼0ÊîÙQ|¤ »jÙZDà¹ê-Žÿ/ 6 Ȥ›'1Ú Â)µ<ߣž 1å%²«]pß74¿sAz=¥ít±×–»œ´–€u{d¢MµêÓ;QëƇé|I–PbÆ3V ®Ô-›‘¶óåÁ•5/ñwË“õ ,’Iãj ×ŠªªSÍ8({D\IpŒQ"þÞH«¯êùéFÂU˜(‰òWóícÍGw$*âYs´]{Ãí}ns56HI˭袛“`^1†±¦êlYîì×}ÍZWëëîÓözzþà‰2†ŠÈŸw€AMÉ,ÖˆI`ìXÀ?f Hv¸×ÌeM±ûeæ¦"%ñÛ?%1Me4v7ƒý€‡¡›Z]QºŽP7§â/Š@^‘øl¸ÿŒç¦W&î$²H·t ?MŽ)˜}z>6?Á;•¢[Jó’RIÄ •¢å‡+÷}É)ù§´àüòø††v`@›T×±Ô5(u:w7aÚDäv|QòÄ0(ƒï&!(eÔhõœ$’"V» Åcãâbè ¸z`ÝB †»tÅ™ Ã]i“«RùFlh>wËb‹{H¨%w©·ä+ˆžukAÉÔ±.s¹9u˵NùU;pÜoR .½82ÔJòC® ¿`ªkÆÇ™À<ÓñUáêÌ HŽì!ÒÓthËÊc•W¨65 y" íøjœfŠˆ"€c7åû{[„¯]E\ýu<1_—ιP+4ß°eåíZÊ1>_†›bM£?Yt§Ê7£~ÛqaÚ9çô!ìOƒq*i]FxU17>æÏ{dzƒ_ä²€n1ÅÕrýÿ¸Fô¿Ç^ó24Ï=iü|ª+ž<^2x–2²“¹ü¶ìÕq³m0´D­‰.€ª¼aŽº9 ]:òÔG!EE®”Bh¡½ä€þгm ðOÎRkÎùÑCb±¾rv¯ÒgÈÐ_˘@}íëÔ"ºìr$_ЉæêNù„‹ˆ¡êªÒb¢3b Þjøå¡wxDÔ&ÝP“úÒ»˜Ó㺇‰ÞU,_Ÿ1üq(täR¯¸_AzãE9Ãr¨ˆYüÁQ–” Ê•·4#mMÅý¬Æ`àtRNpRx]ç#õ˧2Wáq¿þ•Î^ýN¥ ·Q9ÄOb`ßgJ%7Ç7â÷÷©9)²ÂØ‘ D£¬%Ñjò3#<,á¯_Ø7 ^‘n,>dÝtû¹[¢Ld}kèܘÄj”¯ì¹iä’]ÂI#² ºÙútU›³žÅÈs³)Þ>9fB2Tã²#AM­™ª8cî}SoYeßþ&»”Ä0Ûz¸ üÞ0f+Ôõ2t½Œ*÷ûhíô?¢’ù>íA[¸¡1$”ƒQ¬ZÌ ·ÕÆ+¼´/‡œTˆ[E/Žh3F] U&Öà_8*IZÌi9WʳêHèÃR‡Ù[b>gA6¤‰f;/è]šüëÉƦÑm¤yàRœê~‘þ[UÃþšgÐÁñ~ù(¡\êG¸±ÁJú2.4 ±ÆHàFJ|¶ ‘ AØ §`†ØPI.þ­øCÏL†‘…†Q¨ÑkÆv­k®.©B‡`ª¡$sôE6 m·)ìºK€É˜ fÎ5rƒVÓXÌvuSwƒÈ¥i,Èáy’±K)`ãî!Ò›¶hãBÿ8Sézb5æ‡Ä7ÁM¤i¥‚X‰Ùɬåˆ÷Œ1Ó7È^f¬zÁWû4P«`±×BÆL³{æ'øùåsç½×îD¦˜áÛîì€%sH}5ˆ#„•–³pöf±¾ì WÌÚ¡¦”b1FÀ%*åNzqlà‚É.˜Â;º?›6TO$nàŠx?K"E•ºBÖñ%]ÑÓ®­Ê#&&‡êôË™Ð5wlQcŸ{1ΙœóØ=¥„¹¹}.}XöEúé˜æŸ5æ‹Óz€’árÑRÖŸ”ƒX&`'.0˜#`åw°Æz1ЬLȉÜâº-ø¶s©–æ×A3Ò)çy‰ð›æ2¦M@}_£(@Ì™šÄáxÌšóîЙvÁ`É ³À—mÛ«Â]O­ ôB|÷£EاÀˆQM5ªß§l騢–~^7eLùuÄP•Éä‹äêwFùÆ4G¥wpìñ‰c1º_Š—{ç—Š:6uA$S ¨Ñã@ˆqÕná©á Äõ1JÇ#Z÷oŽÇ9ÑUP6¥ë4—£´pK2-ev\ý„,ûqWØÂ`Œ›Ô”ô¯Nú~}Þ Må>^5íò» òQ´g›¿™_ô¨’À4ö·tÚãsKMÃ:ð¾š{Kj <ØsÃÕ¬ºë/Æn»±`%ôéÁŒû%d=%(1l #%Z8xðCûãÍú‘â‹ø6Eyy‚Mòœ—üôAÉÊŒéwîs°Êà‡Üå|†‹ŽÞ-ÎBüQµ ¨¼™`T⇯qB` A öœ’.Ù*†IíµSÆÅP{i,nøuÀæ.šVW¯9 ¥~{º)=¶±8«ëùÐÈÝzôB6P™c.9O¹±`‡Ê«¾«¯BujðX¤5qÃV¡4,IOC/)‡û”œD,˜¬¿7ªo'‚Bî á@²•‰hÁƒñ³ÞÕ CEÞ" ‚@Œf÷¢+Ü›y–®,áóc:Š»¦ º¼¡“I&! ;’ H “wÐQ ¹gh¼É½³·ÅNý˜Ø|C¹¤}c7 ‚2ï%×âÊì-ËŒg,€ðÝ8œ‡yß_Vûu D¡Ãn¥)L¹­ìwçæDƒæoøÕÝ`ÃÑ‘±Ôõ‚ÅNÚ5B8lo¡ ŽšªJá^±Õ±^Î¥e„ZÑöŸJ›¯K†$ðÀ" òDruÆçV‰Þ;á‘Þ‹Ò5Ž×WÏK)›ÊŽ“óN–G(QjDhõP",R‡vŸ£d¥¢>2´œIÑ Æƒë7¡2 Ïì¬Ïu–fâ>C¨e`rwW6ÕÐY Ñm4”ÛÈq *­«5AB–? ÅC%ÆÈš¡Gçœ.V3ê6ërqßúœU&P*²¤Èó>@'‘G銀Lð<“í®f€ Ÿ›'å%íð§›!õ³»mòêWT/Ç ÓCÎ*0ÝÄMï 0ÇKäd«É(©ßŠ‘,¥g%6œ…®ìÅ h–}ã\oEpX¼|“%&²y ÀŸ€95}xF+é7 Kô%YC”a=G8Xsc Ǽ/ÿù ‡¸›2zØ“‘­¸ö¥+Õ.Y% Š5÷ ©ÿ _œŠÝ.³AŠ5,v–u ™·¡jÝ8ÏË›¢U ÖÁn—}Ðø  …|D¾eÅÿ "‡Ûº´ ¯軚‡õ©ÕMñ%饵¤9Â4ô£À0õ”¼ËÓ;vzª\FÇΪ´m÷K´_½¥ã,“±Y jáþw€ðËP‹|íµëOø5yOD‚ðÓJg¤Ÿ¥GЂ$¼‰›Œ^Èæ0Péëá©­s‹¸¿a† Lg,¥šŠ¢æ :›Ž2ÔŒe†˜úG1…qˆÆ'0ñ>·~ß^å[»¶xqmÀÖµº¨y ":¡5Ý\Sò?r³Ç;I)©Óé;8nì´—ô=|ÌÃÈ-§KË--.Û·+ݱL´öÐ)Ÿ4N×XH‡ÀPºŸÔ›ƒ*99¶Tø»†\Ž{([tÉ(’l}RQZIÊeû™)šÅŽ2%!Í'vT:ýtæ†ùåå©j³†[NßD%J·p©Gd®ú½ê [Ê3þ @íO9‘›U +ñ^\ÑüûÙ…+‚þNÊêô±ˆm¹„£¯mBÐ<œ§—‹¸¿z×Éeßf¯êýF±PÝÍÐ!†-!’ùQ*q< ¯x'³CüãÞ‚¸æìó!¾ÞgŠ½úo\¹Ä­ GkÑ?µ,*S”B0¾7= Žè¦š)ýZ–A¿ÊÖ_&§¿ igŠ/Ã7#ëÛû¢ND§ÇU÷‰/[ÌA𣻎$pYÞ`jjЫŠ?D¤¢‡/öùäÉéÓ‘gYiŒ­~žÈ£ò?eË(kt¯ÉBB†ÈíCšÊBL××͈Ù:qj^üVß &’Ji¶ÍŒÑáW#iѨïf·ë}0ÿ͹6êt\¯¤W+‘zX{ι¥»¼¸AŠM–Ìh“ù«ì]¡c •!þÉš‹+Ô[Э‘â÷ù=KV¨`€1*nõÔ=p«-‹SÂÖ‹Úñ…ym=“òßöh²IRÜ^ü©$bhf‘c˜Ë¹Ùðšã§#ãçš•¤Œ—tsÎÞÔ?Î̃qP ”{èã!ךßD¸SXà7e€2¹î?÷M»K0|3Ø=ÍìžcûLÒêi· þD×þ(§F–é R´+ÒmB6?Q#ç°±Í/µIÔ_q•öÊ·¤ ƒÙ2û±šç 5芉0ÔGY'!â6èBlªG B0ÎAóvM9PU‡pÁÉ'釈*fR&#Cþi¤>óxÔ¥Æ\ÎR阺.ÓI×Nh6à2_nl_:&~ˆl‚Yàì´%.î‡ËrÀ?u_2@ÏãˆÊûx(=ŸË½;Þ—ós'ËRN¿÷ÊVÂNÙ5X…'ög8›6!d(ñôlæ0˾Õ.y <’]èþ€'Ep«\óÛ azšåÜACvqÊ-I8ˆý6@/9áÍ‚•øÿ±Ä¯?z}epü[xãgQÍS] Ä!g#”<è´#-¿æ`n^žFT¸ôÀiúP^­7é¿ôàplËBÆL±äyŽyMbí¹V%Iîc“7 \;)Y«š>^Œ¶#@PƬøÍ™Ôe2MýÍŽX½½S FB8Ë)ÙßÆæ"ƒBl‘%á­aTDQ§–#ªÃÐÆK’gÆ@’k™ÊRóö©hÎ’·~qå=6JNeô¬z' œ_Á> iqHÅlp&“ûŒk'Ïjæh5‚þAOñ¼Pè”e=TÇfÌ(BpÈj×@p¡Ù`÷“tsëÍ[9MPg”‰òÐBœÜ­ÿgûù®ªŽvm ýü~ÑÃSŒ¥¤¥‚29:6PqÀÜÐÆÝ·vçˆÁ©Œ’ÈlžSØË”©bôžÐMêKÒùš8€xô èN£ƒãõi‹‚N"/m<~ TäBpÒúø I•+ ß7 p"»´äÏRèLç70Ì=Fòò»k.)7ƒ`ÁièÔµùÍQ±íghÀ þ».ÌYq9ìó¥Ûl:^£(j[3ß½ŠÆ„Má¿ ÷ÙÏÕ8ÊãDAÖ’ªýÓþvŽºœCÓ¡×6¯pàU'í ¶ÌŒÜ{‹.ˆ-N¿Õ G]"È%ò­MïMcsüµÜÁši͆†•ç·hXµKËú e½àx5Bð ‹nçI[ŒÔeÍX°ÍQÚÈ€6àƒß^Ž›¯cCPª˜xÞAam´P/•ù›âép⮧úÎG\ÖMäP&#œƒd†ì/ÿ¢îS{®adq$Öˆ¸XŽfÆœ“Ù¶b›ª¨[¯Uù”[Dô¼êφˆW€Å´” M Ç!Õà1ä‹ÏÔ×!c7‡[š‡Ú¦ÌØÀ0²Oþ Es´Ë†©Ñâ)å+‚€L1MD‰â¡°+tVòIQ“ g;»BAÁˆÙîYæ6 rÎt˜lÝeQ’Îãk#Øqr=´Ö¶wó’=Ý,ñ—Ü(”„[äInDóEÜ™kÈ(,š*-úŸš;«á¢s¶;ðh!Xûï|µår\÷×SG ÜrnÝád.;i¥N{†ß™¥˜Í’%ùœh2€Þ"¥g‹Ú=t:]Ëí7ø¼¿dkg(EP‘˜¾‹p”»¨Ÿ¸@ø¢½¥…ïØ9#8+[Àñ‚ð¿ Á¦L>pÕH—@‚{än/k2ïpNÙÁ23Æ ð©Sª/W©~zˆ”/3:\¹ñŒýYWåMv%N·NÝÊPøœKVä˜Éˆq¼•³{çέ#¤Í÷¹hÁä_ºbîÆûž²ÊUh6Õ¾ùªºdžÞYp«X¸3Uƒžø”]£"û¤Þ;ä'$º¶Ú꿘B4µ±¶Éب´«®0Cå»Ñ¶w©ŒñK»ÒæÑíp»U¿ï캌Oçý@Z!©2Çà£ÎÁ4-‡`&ÌØC¥K†ºbu%n8ïè”ùqŒíªSíßA$É¿‰ÁcKtµ¢NŠvänãÅ>†ÂBkGK9Ê,èô`€iò'»æódF-ʈߌ̣q÷`\ú³Ù`À&xéŽ!Ù¾|Eé~àKö ¨j¦7B¶k‚¡'æ‘úÒ—¸£°#!w~§°o®_ˆâQÌÊ©ŒaìÙÏ©®N¹n ÓIÖr‡]ˆ˜{U·Ï$¼•†r)5F‰úªífõµ–Ñ•ºÿÒ9FoÌX¦CÒ[w÷õ™ß-0!:Õ>f¶ûn­zaáµ3ÅÄ‚6-ŠÜ²ÒÑ¿ŒÓ/¯9šš)&*ÌáÆÛTÒ0êëÛܪ“vš¯©:¥>Á™KkÍ'’öù¦d×óí¶ûUr¹Ž7JÅóvjê¶î„k­«©}š"›X:R.Ú\§«"Þul¤ ¡½Ó×ä¾R¶¦ýé·±ˆ«¨(ŸÈ ÞåI§§Àæ¢R–´m¸95RÜiȚ˜ÔÆ˲©ÅXþ™V*–Ea3Ï]št‘‡¡”Œ[“ÿká$Âüâ}Û¥á´zûoÔÃÑc\õ‹Änšåt?õ’°ËÍÉc×÷äO´J÷tÆʉ•˜«¦Ñö|«M«Ÿ³¥è«R”†]3w‹²¿´oÖ+ª šcÄÄAײmvâbNæÉzëÏõ:0ZÍLhdযËß“U#½Tñdì:"¹Q`=K>X-¤µÁõúGáàЫ •´u”Ù”qSãƒrÄùÍÿpåÒ4Jãý¶Ža£Lz0¨¥…Ý|— ë9z‚qÍp&“ 9¦£±TŠt<_†#û$oøuxóo¦ GJÜ·þHÆ4sɾ\¡qŠp„%Žönݲ—ÕkeŸ¼^°Ê´açR8å®&´W#ÓIV2ç¬ ("À$ûs* 2Üt\:3Mê…Ò4LTËV}|C¤b/‚›\¢Ëãš‚66—Ëê8BÝÑû§2W–µ Þµ—Q㪠*y)Ì*¼Êd|+Â_Ú)Ü“Œú‘±ÛæÍõxuóÎGs¦}Wõ2^7¤[Ê‹oû¨4)HÍ•Ç^`=Ö6›¯Üíðå3/åcÈߴ݈Sg2M¾V1¾’lº4.y¦Þ zN=ztë¢_“Uyt†ºsüc{ì O ¬9ã‹œ³¼q1VDEzòœ´p@Sàm‡v£ÀÈ„ì%îÙº2$Ý{mÛ±…¯-y†XihÉ‹׆@JÛaxå@³<à ‚ct›2bf6ÊJ’) 4r“’ã(¦¬ó©+cü*p»_çJÓËå­9± é!vn º¬ ÎﬨÕ]cGuEôEíç‰)fy8}™måê#7j)é%%Û+ð°Æi<ÍhSèá4ê‚Êtý>@ÄÓØš$pðÑóŠÙØØ—4Q=Ü„]H=Œ`#rD ¥&tüðñaÒtz(J†Òé,Ç89<˜¦Ðœ …¿íÈK7„Ò ê°Yã0–F¾€q®•Ïp™ÝN|#žÖí¤¾=‰²³V2 Èìž0 ßÀ`xÃ-T–ðUʇœh¦ 1øßMÿâ"]éT¡ñÜF¢>سü+@ÉFC›y=tA½MöŸÌ‚Ô`[uˆkÑÝÛ -é•oÑÌ%Ô§}ùÅ ˆ%„c¬b,‘è@Œ8á¤ð­Õn~ =ê†Ê^éóÇCm‘ß#ë+õ7çÈ nz­¥Õ(°ÏsÏ+_Å^ÁÅ=²…7‚L¸†ÐdcEìMcì““GÉÊšã©ß-ªç ñ‚@ìfÑø*,íÆé/¿ £¯¸².¥+ij¯¹~T:ó.…›\kÁ[;ìè²9R˜ÆÞ'÷ÊòÁ…™ù-ý~q¢}¿š? ꨧS!4ûL\þ lpÚ—I?ß»ÿɵìdG<Ö²& -XšxÁO}ªpíâGŽñ°[‹CùØê`˜ttX~'µâyÝ®À‘\à ĉkjf_£‘%› g³í_pµýp³c!¾ßl6ƒUë³6 òèL “ãíãY&|Á8”1‡z^AñÓ†× 9¤ÛÞ"FIõ´Ê+']Ò,¶ ÂS>ÿ¥ËÆý®ηÍ}ƒ( z!ìïõlÅÓ*ÄK·³È¦£Míz|XþCfÌŒäÒäø¬ú¾&@ë“(èì[²Êk§6§ÙÌ~;ËqŸ*YCò65!9}(êàüJáC®×ã˓駟®sþ#RNrZiâáÑBÊ,¸8à6êÁ'|]¢b²X× ÷ðMƒCú 5js˜©6ǵ]Œß‡; ‰¯ªvcçPî[,Í"ÅÎò¡Ã®±¼t°¦C|íHËvÓYÚg| HåvÞµèŠ ¯›z,VXí©M겞ëo/1sœXU°ñ¨9xËÖ6л¹ìz$I"‰Ù€o_x¢Ⱥ×|·¬îÓLÓLƒ¢Ž¶ðn~m÷Y"vö£!HÝ~Îý:ÀT1 mç?ã@›iEZ=²Tõ¨ÍÑÐœ€äUüˆ„Ôø}?²”Fúœ1’£ÃÌS»;Ž.tˆízëu(»¦BeÇÛó„¬Ós¾¸¸aJ?¥œÞ@ÿbìæKƒU÷(€,÷Âx¢æÔέ᯸ĩåä4‹ž®œIÉfNúÅRßBA„r)-úžðMYŒnžHR0LGh?àîŸN!£ H¼[¦D þ) ªî2™ü‡ó$ïE/¡ Zò"GoÅK¼Hørïw#9ëi(2‘ÌÉò@u|¢ùçV’*YêÛ÷û Ý1À™XL ‹ENcÅ«Qñˆš—Á´½âr± óIó7o˜Ó$„I¶§ÊÒ]ÆÈ`Z¯´Ò×RÜŸã[JÖ“Gõi¾øwœÓràq>œÉ‰ÞéÉg|†NÅû³& ¦žHª5S…Í´»åœ‡uÇŒ»­k(…I“_Bü¢Ôœë¤†å‡Ì¿-šÞªÞ*O–A¼¤‹¦SpÀ°gVÜàÉ ¦;Ás©f…¡T7,к.BŒkœh/ž…Tf9LÄ3ÎSHsàkÊC÷qa¼õÇ"1ª·¢š‘ÕÑ›û(ìÃnFã:qo£ÂLج;pß»sÆŠí†SB%z ¶¡dd%󙮞’ü+"tøÏ|ÉXØß½»1ðè¨?ËÌÈo¯n[ÀãÏpñY:p¼Ú?24(Ý^*ïdÖ‰óþ¶äFqQçzʶ²6Èl²¡½PˆÕmBlÒ׌¾~¢ÿÕà?í^“–׋ÜÏtá´^ɹèp˜æäÇŠŸ>m'mX#Y[fwÿdiñ¢=?zâm@h‚ÈÈ÷i©FòÔån%ò¹þ‹ž¢_rБDä5Æ:.ãH„>Á"PâpïyC'{À0A2å½) šÙ¿?I‘øÿ¥'¶-ú¼ÐzäÔ~"§ëž½œÛîô9}³"WÞ¸J,Bòh(KÏ?8½d 5’ì lƒ=E[ñ#´T|ÖV¢Õ5 —±ÿ§Â'£·³³{˜ó6mÆ\ÊÂrÑ7-Š•×bŸ’ 䊽_­ÿýA mßý‹Ê3PJç¨Ë0t°/h¦éír*Ú@«ù`êM5µ¾¢Ãx_ø1‘鎡:ôÐÞúwaÕ}t *9£CGL$Bäï«!ì¡Ð%tÆT6å)lóÞ8ü½/¹ Eǘ°EÛ¢dÎ¥hüúØ÷—XÛìz«x&^G­ÒfĨJßÉ–¼ü\df–”Ÿ ®wÚê€í%s÷oVjyN–iÓ§l¼¡;>(~·’Hì±ùòxífxå·l†sªL>€ðTÓR‘”j#2¢Mùìz€Ý"ך±²epËæc¶ÞNÄä’ÃÌ…ü°aû*T@­¨!"lÃ)Xe°ª8ÑŒ&Ðì³ëÞvö‘@ûKL©‚t¼IÆôž‡£“d“"+Á1£ÐÀ`¬S?†à[g^_¥+Kû˜üð¨z„h³æù§Š2#²OJ¦’ÕÜ7j%ÂÀSöÓŽ”eÊÇ©æobD#|Sœ“•'æÎ)~Ýž2ŠoÌq8 ATI„WaË}-FjÔã™þö“´Ò3ÊqÕÜšµnè޹чeÔ<&™€’‡t¶F¡Ý²*líàÏX·æ -y#òDÎa£ÉS™§¤/ã|¥š±$åcpˆ4ýù½êã/ ­[Û\²ýX^dW¾ÂA·éÑ”žfËC²õÖ6tç&ˆmÈ™‹Ä?¬Ìî9_¦€KbÔím·U|pÚã¡Žõ¤Ï×xœëڿÙoïöÒ¹™ÔÖÜ HÎ^›$®i'dîÖMoó‡zi«kd³CZÒæ*_Ä®W¥“³‚“f/~oáî!·¿Á¢¼²“;t•gC,,,:ÖTYóá©m ¨WîQ8òNÜB×ïü­Oªðv}š}äeÅ»Ô#,ìîgƒ1„íÍÐÏû¾#rt‡ÀMJþš{s+`ÿ«>m 3M-è{šÕäV#/ÀH¶^‰­ ë4Îäµv}mì°f/£±_ŸRÐÅ(J¸Ž“7`!c§¬kŸ™Ó&À p LŸ—Jé¿sþo½8œ @ËHp}ÆSÐï­k6ëV“ÓeÜ€rÀ‰PÖ‡$ä' w_nöÛ¶ ¥}8…†íaë—Žƒ;C¸'#p"Ç =ê?\t¸£ K[é“šÕ›™)ÂÍ›ŽXDòNX«¯ï!ÇÍß G:èÔhž?K™÷Ó’î¯Õ,w¤ìªwåeG‡ó—-3DV—m×±QÞŒ$ kµ”˜/€a†&Qò1vx·Î“Gy¹Kœ”S¥hjnwK4lkÚObVÿÀѳ¦&±ÜDhõäWäÈDcQSF~ˆ±‰Hß™Û-š°8abnéûÙ£ª¬mHô²r”¤žC&ZO»Ô'7’I'oU‹"G¿¿ô"ôÄ÷ÇŽÀåoÜ[P-kÛq°°ûœ’Y‘è7Î[a:µjÁ€”j¬R©3^—I®¡P.~=,êæ›î¶öÞyùóª†ìFdćjÔ•rÎY·³Ùm¼ò'BÐÁ¶#QulUœ”ܤWËý#¹œé”{ÒZJãd<°="î,®,ñèlU‰>iòñ#Oζjƒ-¯„½ž) ³3üèŸ&ðÍÑ°ôÃT‘d<ß²ToÁ&¥¬ù§Uf 6¤XNÄ—*Q=âSø×¥|´Ñ—‰™öªì eüµêxóšø¾Y<ñvç X"Ê”1Óœ0ä²ï•œÂ~ºÒŒNÖO/‡‡ þ†”Àä¿/sÓ^¶í­¾ceÉO‡ë®V¸ØöPîƒeëªÚnŽ {ׄ 8Q…»ÙÇëÅ6ƒ²§?w¸Œ¡ôa6t™î´Gœ;n]Oh…4ÂyÛ0N\Ö»ŠÛJ.饳½0ñ›ì„õ„_t\w êŽïÿäÖƒ¤W^¿Eãssõ`0V³KÜ©ñU…Æ*¾L;ö‘ÈlgÁX)»@­¹_>•!õÖ‚>þ¼´œ 0ûIM‘œ9Id¶bÏfw0FÜ®ËFƒúN1ß?€î¯WxA\Á}iÀK¸ÏÖ1š ešé›sÄ¥ØUA­9¥­D¹ýô DIg9'#3½‚ˆb*_.3íŽ8\FŸw¤†N½?\þÄ.©x ©NÜ Ó‹[’K»ð+n´2ÏNRO›Âu‚Ú‚øˆ#M7¬žÙÜ"~IôJ>9ºB ÆTÎ~ èB³´»ÃË:SÁ¤“ˆ‚ßÇ=/lQkÛµÚ”5çÕ~”5Óߥ:­;?bçƒ~â¼ÊIŒ†ÇÍË€ÛÝŒîOöü'»IÈåQù9ˆé’õÂMj§)ÿüÏxç{\²‡žôܯéGû0<òÌ ÂÝ+·ßó³N¿´"¨hC!ÀB&Z̃ÓßZܾŽvŸ /÷¤®“¹KÉÏúÖ˜n"ŸJì•/0¯µ×ö —18Tš),(^°Oˆ]ÿ}ȈaÌoJmϽMÍ‹£¥FnÔICB\ú#@®Ò?vÿYÒéé«'ÍL‰Izêx ¬"FvŠrš§@I@Z¼Š Ž{xÇ­ý™ë*æ^ÑB"Av”›Å¤™^ÐÙç ž|E®¼¦k™<85ê^ðÙ«…WÁ”ÂÔŸ¢e>ÿC Ô5'p{¡È„'á[S98qùiÅô¡Y™Å XµSn™ÞuÐÄÆ!)\â^qK+ÈâªÃ‚€q8˜÷hf*œ$ K½ù>œ frÎðrükîÙÎB«s ¤úš^“nt¡çò-ù‹GZÜíR¡¯Uή3S—tÇu‰vðéç¶7öZCU[oTVúŸæii-9 a…“#©|LJÐ"i3Έ࣠‹¤“on «yY¹ % ê)rf:íA7C”ï6Ö€ŒÃ´‡aèX Áfú“_¯IpÞÖcÅendMÆ,[|Ãe¿ÌÆñß-‘R¥œ‘Ð õé/ᜱÃä”ÛPW䂇!¥çÞ‚žàøÜÓk:¿.¨Bíp/þØXòœp¨¯‡pî¾=ìZš‡æ5;íLÝØŠû.u Î7-HRªèù¥7ÔÙf¬¬ ;´SÅÌÍäêdâðù~;!…‹çøäkÜ—  ´ÐêÌxìêβé`ÅxP2ÓÀÚ aÔÚÁÊ…¶âÿy;–aÁþèþ‘¸û>þê¢&‰ü°‡fC× )—Ï‹ÄÖ¹BO‹wSkÚµÌÚãH³6rŽs­5?å3Gˆó Äo׌”+‚Î<¹Õ1‚u’±±›6ëÍr€G<)9¾-ÞÃ<¤å3pE+ …rÍj|‚Y[äísÜ°Y‡q€–Ož¾æxÙÄ‚‰!äÎÖ»ï…ÅÉJGøfÚÀ¡NBŽLÕíõí¼¼>5^xɂȉKã¡ %h‡tu1ú°ØÁ§u*,¦–ßU°æäŠìý¬ìDóYýãðî95僯1Û ÇEo4/‰¥‘Œ»B\ô ºpx=‘ éO3ò.€äC²ÅÄ;\ ™]&,R–rzêóßi÷»òŠ ¼0-mF¾sGК;þÜÑÚB) ~††ývqº IËØì µª¿(a‘Q,aX›Í|°µ8£­û¿RÐÒè½}Ë”"ûõÓÙE!  Iâæ߬Ó¼ó|tªˆ²^×qEIeHxöŽqìü¿Š’ù¶Zf(V·Íqœ[>VýUaß:O‚]ôÉ,hkßt - Ëດʬ,Þ–ÉÆOâÈ™è°u aó'Ê’‚ï÷ z.mMÔ%Œur)­ƒ·êÕ8Àm÷Ò™ØËkÁr1350T1]hÑÇbbhH®µã…£‰ãys5íHQ“6ÁPx¿ÞÚ¨¢Û–VÞ˜ÖÑÍã‡ËE´0ÚFìœ ´‚;YdÚ•ý¶ùN\¿Í̓訩4{ã (_Æû&’6Ô£Ÿ(›ú:óZ, *ËõIlì ³wž:ÞCDH¬* [ç44º¦'ó€ßÄ)P¥Æ§Ñ©nŸ(PwŠÆvì{þ­ÊÆîŽí¹7þ㼫K{ê<ý’D†Du`³ßU'†-¸ïeEôì¡Îޚʓšï¸rÐù~p'dúLZ?ÛŽ°€q kŹe@: ‹{DV6>¶÷È~ht]>’1‡ÍQéã×"S6±¶Q~@Ø°„ÂS<—>]×àªiÿer áÉãÑ §ÜX8+¥ÇRE\øXmÚŒ¥ m‚,%âXÜÉÖ¦Ad‘uÜ~‡ˆõÀmÚ —LˆÁ>[ãyìÿñažõ‹¶Ð7Èã#57ÞõðÑŸRK¾û“º¢sp£Ç†r‘“'à ßÀÎVwÿL3ä™?¨–Þ!s9ýG™ÈÚ@NHyñ7꧑9uÝ~÷Ì¥U{K}|‹³ ÃyÊpÕ)a…BdüÊ:rÿlÂæËRj÷%ñƒ?¤}W´(├x›˜øÝ—¦šŠµÚ9±û&^X”m`ÛY Ñʟœ®gÄogÕ öLqƒöI#9¿WâÍRÝ> ¦\ž#Ò³r[hp³f“0+Ž }ÌÞUxD.ÓAM‡ú1¦T‹Èv%ZƒH%†½qF0âi7èÊâw^ÝÎòôˆÀöŠ#Dw;¨2 ½:ÐÓØ+~v$&_VYf²"ˆÇiVV6Ôä‘"æGÛúETðÐ o0 ˜ë÷­¦Í…ÏW+BèÍ{=ÿ¹-®­ÖÆ\ò æwS]0ç²Õ+Ú.b‚›FÑm®íd–…US~I¬ÂŠºC<[õìÁvqSˆÕy¯wÕùº¢°ÉTm! çºt…¹Þ”†Ö 3ßR'~¶”sŒ»¬lÄ}´FU9ÇhE®SB'ŸÈRјôRûw« ¸¥ô:dòÚÇeï_Á=¶®IùÈêíÕ;Ž(ļՙ`ܨ…RŒ°Sù- kìS°ŒÌžêÎý§¤HUúZÁÜ÷Q3ÛDë™±ÆDßF-ÍÉX/”YïTßO]zþ{ó ¬¢ðØHºMäx\äÃ*!SM=¬n—¹öQè"«¦â³…ê.þ=Ù®’¥aùSS_Çsg„geTÿÍùäû~ì”ó |”oqHËtä0!ÚÙ&ÌütEË­Ùfœ\²¥]xT4Î|¹ÅŽÅx…Šd‚Ówã]yˆ äkõ™×–K32y?°»éBý¤gµžÞæî47ؘÙ5Hš¥dÑ>…¢Ê]€àòõ¦P°<ߣ@È¿&uòÐA„—P²œ…²æX•(‚>ee°¨¿lœøžÏæDg\ìxkZ˜d®²8Þº¨§|K@:‘„Ø kÁàáÊõ^«fÎC•\åE¾Â3·µ~¾dYŽàB›¦̱SZôdÖueñEI9[Ü"×ù@RRAœ=/X‘o!Ó(TÍ2Ûia®hǦmàb´¨ñM¿KžÂz·S^žÊšc\ÎG9"*96] ÿÿ,ú°‡½ÜÊ «µØ„2ÿ±P‚g­ÄÌÑÆrI, Ê.DÕÄkF9lg6úäíÇóò;çp%¤W槀,)gãÐDÕ›649ýË‘ØÜwÏjÃ8\þUQ&Jc!÷2s%J¹e®á9q1W5Nr56à Ýð0ÐF(½ë‰ÈA^ /Ÿ¬M¥(û;åB»E`Æÿ„jFÒç€Å-µQ–ý $ÉxA•Ù ÔøkìTŽ‰c‘™gDq)ÿ@¿Lü1•.Й(y³§÷E*¹HϦ©÷-Jzâéš%]3N˜ò´3Ô˜õ+Š·ì²Z/Dé›»•±ùøÛFücºr[`aÐd.ÒUÉtsXS4î8¼¬èB=©[•€P!~5ËmR?P˜•mù~‰“ʈÌúÐT%ѪÛH±#UD´ûHK{™µ£—JO#£“~&»”%Ÿ÷¸—²‘u*¹¸w$¸ùBŒTx±nsH LòÅï+HF—åÆûGaûa!·½‰/ò¼Ú ¡ÙJ“Ç[â‹: 1­Ý€áàb¦$Ìo&-‹Æ¨sµÂóìø¹Žª Ïi)þ.çgÊ¥Öý¶ ÑÜÖ§.ò!Ôþ8Ô⪚­¹Áä•‚ªè(;)Ýtiˆ""µoBãR–u®z+}èC. ŠZ-M®!Ù]öX¨–¹M£ÓÀ¥¾ŠèUH~N8âyÊ6]0$‘S1§š™*o¹äÌÇy­ñâô…váE#d¡k§ÛlHï+%ο÷£¡9¼ˆLÎÛÛfbíܼÈûÍN\1‡žð‹FTq=ãX[’´"šzY?ln€nœ×‹EÉèM¦KÓp‹2R ]ÏW|Ï.¡ANÔ£ô©ðÃÈAà7Cx»¯H7OðfˆC´"q‘†¸nA“Ý BÝÂPK •ô­}ù(ÐŽP4(„í¬Iv¡!f>áÅÎj^ϯpX\8^Õ¦[¯ÔÏðÇz8žpê†jŠZKÑ2ÚÚÛ±x‚‹Í±®´E*ǃWûß³·$N{BÈJRU;òjmËgʤ6×¼\ßL€™on<ƒ J”ã1oÓ †‹gyyÚRð&Öx¨ûêl·\þýÙ{I çU ñÆöP€³o‹^1\PGs˜À\EEgçhý!tÿóõyKïÐFË‘*ˆ¯½äuËOÎ=Ð"߉ê´r>]T3Ä™-esk%ÉŠ"mï}ç÷6-qÈ¡´Jh…¯ïâÿš¹,í–ˆíì(ªŽÁ,ÈȈÚ\Œ@¢Ou˜Øºóg¿Rþu¹­P‚NÃX„¸¹ÝÍF;FPÑú uuL;LÙ^ñtôàµG¬løj¾$,^ˆÿu'ÒÒ ˜^Ñøêñ¤=Lú vS%øy„#£%3›}=oF.EIÐ5ɵv:ÿ`'#L\æ`d´•ü2¬€pf (œ²¯CS¿v'üZD©¬Ç‹c®‡8mqY„iÕYÍ…Å©+úÍü2.ò¬ŠöÎè%ÿè{­ ö°ÒPn¹ÀsUj_‚J €‘ÝDBXx=Ø¡+Õvå»;ÀÄåë ¹­__Sê¬Þ5%ÈŠò›™™ä˜i˜Ü­[³OØëšZ§Œ¬LQw·jñÅÏQ¿÷^¼v½ê/븂̴eÍK é^k¯5Ú’>ËòÆöšÛfž¼È/¶×ãÖvŒ–=FFO#u»{„œ†¬xÌ\L*qªï†= Û‹ÝãzökÎ/èˆPD–@·ÿ…½bÆ)£¬ŠÏ*)'ùá¦ÛZÞ"ÃgoSüoZiǹ¶Aò2Ì™ŠÔK³0Í99ÔçoÓéÀmõëJ(}ö©U è¸÷–ªvp'± Õ*þËöôå3Î=‹!TWèmEîéƒÐÜîÌ_«­—ÆÓ×ò$#ô ÌŒî¿[ùÀ7J`þÇQP¶0á5ïâI¶a÷ª–Ÿ i¶1LH²UoŠ¡íìqJÆ _×´ŸzÔ“Òòg>²m+{ÆÍó2¼Ð~oRd•Ü*»˜.`0>ˆÊé<=§Rûi@0aU¶Ó·9ƒ“¨Z_øíCUÁÒßJ@ȧ¢çzBŠ¿47ƒFyR+G­„"ÀöôÀ?ˆ3ª”’Öª½½Ð2éwÇŒM›È$êûûyP¼_ |*:Ö,¹39)&ˆ‚"ÝÌeAñUÁ™äbÙûšj€fÅRß@ÈgºÌGw>಄¡b£UƒëHH|î à éA?ÕÈ\¾¬Ó¢,$Åwîq¥+íAZÉ+ík_‘)òšæ‰™+ñ—•5J¨¹w3ƒèÝù˜6FfmĘœ­/W­Ù7Ààäým;ý¾E­`V.í"›Ä6žïî× ÿr”­MxÖmE¯®ó†‡&A=Ë¢èï{£ôJSsø äF¿šq…Ú ¼F/5éj&ÿm+€Aüw‘¯îšòI2Lú¢…«~†˾±€½uû-íùª¼q¦/½£Õ±¯V#ññÑ?X*¢F”AH^r\{§¥6Dµƒ$Î÷‹ $&-‹ á%pñד73[@€”ç„ÈÚªPYùo‰ÆéXî-Ðöjñ(URw|sûñÛ4œ%šg6C4%|J?ΙÖ܈èt{ÁßÅƆ˹2'6 ŠzxÿX>žPDP—ýLgPjúDúO'-jYeM€µËÎ$>#\„oBFGêmÅԳ銢—Tò7‹tw™Í,÷£ƒ¹„NÌalD){À—wœátk„7®i ÷^×ÿÂËšSÂÒzÌpANätAPŽuOQˆ?¡ÞÕ†—#íEüe9å‘œ}×`_kZ¾{P™v’±ÂO£à–nHAª.ùù] cñooÅJ–‚@­þ– …$)Óp¸ìˆmè„Úc’M?`ÿQËå~¿ÃåiîÅqØõÚf` ˜à  ò²«]az„²…3HÊ.à®öÁz~ÌE,Þ^Xiò&'=>:/t[•é1é7-uæ½*2 ýƒÆÌ¥Å[ÅäÔrsÂpº©µ‚”5B¼jë~ZœñýR—V Ðö}RÉ}B­ ¾4‡âàxP¶bE=MC pÖ<Ë×Ç{'¬ðÁa´Ò¡\"ÍYêåwæáÿ$8û<×¼‰öÞ²Ì|‡·§-3ˆ#JÍÛ#£eú$ýÍ|Ä|V”ÄÆÐrù nþ¥tYA‚²CBWÈ+Ë>wz¤üGscÒÒîúÒ‰œ ÷aaZEd–8›—,f±í)"@¤ÃDŽ¿I?Éu‹sÓ~ð± mg‘>0+é¹¹R“ ¦Ý¦Üxî‹%¶_¤ñŸ‡cBòìÌÈ«fB1{mÍ{û±>?háxRØå¶':Û.Œ]go©=K&›õ'Ú h†L'QT›Ä¿¾·V,óx & ÒÍ!Ó•m–óì…© |½Å;b¬ì7í 81‰¶ùc ænÝl¬Å¾.Û>p;LJs”h†62q0în ÞÓïwåãÃ^ŸØeŽ2ÐmË+oÆéÆídUþÉ–U ô’†S? nâ ƒØfU (g 5×™ÆW®›†¿£z*ÔØÜ ?S‹Ò+1ª q&: ½9óDúvÏÌ‹0 l«›üJþxú]³<Ø×– ÊˆX+ð©‹ø®NþÒŽÙ]Ь¾Qè9¡ð» ËÏ—&ƒÔt+­p#‰§L‰b¸nq•P^Ì/Ú‚­9w7Ÿ2œ¥:c Ñ„]¬³ï·‚‹Â¥%|«,‘¾…i qC{pÌåÿÜÿXÕËoÎ_û‚Eßefؼu·yîÒÕ›¡èñü\CÑP`ŽŸ†² }Ë¿ìNZ8ìõšÇ=éšè*êßFŸ&úo$Ú.åÅøCú¯Ýߢº¸½ã\+,œU”ì®{C*qÖYF½ ~K¼-€é‚ˆa“™m\œ7&ÿåœ ÷³ànÞþJ›Ú‡„uÊ|3€Ö*ÖRÉ¡ ‹·ÈÌïG¿ä51³nÌëë;ÚT Éú^ƒs¨ý踮ˆèZ`†kômµÆV"Ai"aL[?1Dr”<‡äYÞdËF³cÜD‡r2Q0\SN¤›)Æ3'óAh‚=7Ê8§Á˜P4㥩ûÝ¿|ðpõš.Çõ%>46I [{)âSGBo(_$ªœhMtØ‚ÙM¶!Ÿõ'¡J½œØÓ]ÓRšŸÀzÇNx”\.AF/’“ú#†¦7™zÓø:Žº¿GÂÑÒ¨p›S"_˜uÞp™(U9IF€Ëäù°©~ah+ð m1C®|Õ…§òiŠr{™ûùš58(<æ´: k`ë&‡ºÿÈ€j+UÕÊ>¥rštwC‰ur¶˜IàZh;³µ /:º¬Ø+ŒtÎ9ÞɾQ ZRn}¡eñ k³ó}!>YQILOôcl‡0BºÌái ŸNê°ä°nÿgjVè&‹þÉ<ãËJ“7ŒÎ ²m6£X󈽭ÒhUŽ ´Ôq™M•¥m«s|è2èñ=‰¿­¹Ÿûà`ì–Hdæ ­11°PŒª®G}ðkwi4`îHò ] âÐ`L¾–xíeb÷ ÷Gˆ,Öçøkºóô·‹×6´CÜZ3ú2—þÀÀ.@¼ÃÌ”™—ü’ãvù>7‚tM>~Ù6ªó¿tö¾æè…ºÝ0µmô–ï÷³Ì7¡ßLv33J Úõ™RZåÇìàõ7)/žÊ>K!™ñÅx´ z†AãLº‰,=š4‚èÓXœ6¼ÿ·®õI Y¦¢:Gb­ÕPØ6Þ¢/GÀó)£½9ºj®ïREƒ’ÙîeHß–ý°@>œ—ºçp?²bÜpÌÒ©kéÝ)>Òd2^‘UžúÝbÒö†¢HmÄ2.m«CÑÑ)?¦5 ûÂÀH ‚Ç/Õ®Ú¥‰¥ý9Ù@Äòµz‰ú€h º{[0ß²W'²Ÿ-Q)<8ïRC™ú>IGXfãê(!ÿ”å ô)Aɼ¼’ËÍN Õî"ç‚©Éâú€®9 Œ ƒKØÐd–Ô_…1ã†U*Oö«òº$Ýá—Ôid›gfXÿ"ö榺4`%NæC$€‰  YRoDL$„|6+m·Jà…"ŒwŽŒžœÞŸ®;v IvÑÚ$  úŒø˜1JQžM8D!@Œ¹»pÊæ(Åz²k…Œ ÿš"¤¸zßW®âƒ½`اû ¨ ÷P@ 1ve»öiºßïrÓ¶±ñ§¯¡VÉa”KÑi*ÂO¾[ÖK@<áßvl}¤/¨"+÷=k0ÖÑàÊ#b1¨Qõ0¿gw•ða^Öº,ÐÚЖ% .–’!§z¡¹‘Úé~¡IjÄ[ƒ6+W×V½ÈŠ®í<ÒàФpø-6ž¥¼ïD›žd¤çç¡ßÕ>OMŠž C(˜»7…(³ÉkÁA¤šnx¢üB¶î=2ÙFñõˆÛ•pµyÚðǮՌ¹!­tx”?ОÃb ÞÍÞÌÅkWƒÌ4×Ãd°÷±Ç•i? K ±jTöé\õg2M¸'D/ã ömw­) êÃ:²Ãflº]ŸžW«ü¹à ?Çõ††ù[ËÑFóÆù6€c«baz¼ë˜I “Øyv0€ [þ.KG|Ÿš/›üåŒ$é ‚'¢ ´ÅFâdè@U€ ¸›ôú#\ÐÔZ‰ó÷Ä˳±9ܹò}ýšŠl4…Ôb­©¨,ãNÅ{ù— F!¹àä æâ7ÁÐèX?ê;#K›ù·Š–gŽe˜mÀe„iýp€N‰ç@ÖV·^tÊ.ìIfA.ï)INÚ;j(qz]²Óÿã1 Û‘;4˜p™‘èî àõ±ÇÊ]¤‡–‚5m*"izÐËmÏmþË xªˆó}õƒ÷ÖŽù5ÎN+iac‚dŒNYjäâЭú&]P½¯9½Ñò`ÃfsÛeùN/NdÕ¯ÈömÄPHÁ+ÉX×Ôßçüs9o;3Ú›HÈf´¼ÝóyÄNƒî¬³³Õ¶1ojÒ?Öׯ&*‘OFD‘!µíxƒ+uù‚½­§èÌÙuÜñòØÇÂûr€c+‡¿W!µ2s‘@8í'Â0mÆ8]R!1K}ÂãØ–~ÔnÅÎ*k¨ð듲Ïø´À”âÄA„¸©{|Ó€#ßt¦²#ž¥e8å ,XÝÕóxU1‘Ñ þÝ­ ÎJS{2Hg4£}I‰È"4&#ÁËÿÒä ©)˜«è„ÖHÓûà,W§n–húg3€BMª&\=/'‘€'t4×XÞÒ«i•oâú E´¬žø[jÀýc1ô½D¹[.­[ãéÀû>TQèÁÍ™úÌw]µ±k!Š@60ÄU†B·Ö,/³'|òåS^^¦vþpO"¬ßî"…Ú€m\ðïdä+Ž¨­I¼A,÷9DÆK–cRþOU9ƒ×aRv_Ö{žõî '2•ú€Û¥S2V©âF-&ÆIf6qõÿJü'Ép¤4„^œV±Âêlé?¦ª×—Ân%»Á\g+úÉÓ LëYÐä0~[ÑÌßÊTCDQà37{U%ôMæ˜J›55÷œB–»QCè9•D(Ñ{ÿà-f¬/†¹“‚VŸ2Uåè”M®J±‹ÐÓ½m™"ýð¤ooAiÕHýx¿Á¡ÚO¤lzH>£èwÏÁ_üÛ5 ïaùËqŒS¢lƒ'Ù[ŸÈ˜ÙVPÏÀß«}Fƒ2T xmOaõ6v_^…³óY#0×wœ1ÚƒmhëÎmÑ@ÌŽMñ[0IdBTõ·}È(í;þó f%ÚGók½’§)Î4¢ &:ûÞp{.™õõ÷c‰>ÓѾƒó#K>ž²ã¶vs 0-… ”ûÍéŠÕ™tR3ˆ¶:O¤6~]Û ñ‡™«A}ÕŠé*áæßxŸÂLƒ'¸#^|’Š?7gø­Ìb’[<„Ø¥R¡Mºž¦¤X\nèql"íSZÏG0 “íH¹ƒ×÷ƒ©dw²;ª‘o\(\&KtAšªô #þ ’Ëa<§°ó6t2–k–zƒM»ö †TÍ[H(àA,•åO Š‚» UºÌǶӤ y?T]˜Œ‚Ç›.Ÿ[˜ìŠ8F>´åóœ"YÓdÅô㯬R6g ¾–bö’@.ÜLäåÎÒ¯¾äá¢hmM‘«øPW/ÊXùÄ-U^ ²L¾œBÜxäé4 [29Ðÿ¢˜Nÿ{%–ð›m]#€AÔ’A9pr"Fñ†lV˜’^уÙM+š¿Uož,ÔI²3x&ê¢ýb+‚@¹÷k]tÎ3xUâælÚÄK¬çùbû¬Êî‰^EŠ·O—NÂU`gõÄdçÒÈ/3ZMQ[N+Í‘L߶Ì/VŒ)‘íÑX1ÖŒò*n%²÷‘@ÉìÃGR‚Ä­“¾‘ä>­Rð:æ 2 -"i‡¼(ûäq'³–é¡·‹Ü7ÏÛ&A¶—‰÷bSéÔ§µ‡}@t[LF ÕͦsG–•í‘oñzàpc4Ê'3ôn†VJ2SD+Æùy…°â†ŽºÛù=JOÂÊx òk‘¡œÏoe‘wkL|¬émÏäÿ¤5X•™’·¦1lëâ;ƒµ2Kåû¥§”0Qb ÔW,BÞ‘%gDW׈á Ô`ÝÙUˆŽæÊ}ì×Í‚;Bñ DÜ6¢"n‡ùw¸?éAp<دG`áeÐ)5FÀæ2¬½‡úZ5Ù³X§€â*Ì¥I%è zÊöÖRžÁ¹Ž 4½ÙYAÒÌà§ÒÒ,qüai×c)qÆl-ª‡5µÙfžL\E]»§ÛK퀢%Ý%¡6 0Ÿ )~˜zÝZ æü•G;ñ›{q "4pT¦c–FrÜ¢ NTrX¢™“濸Q[–|ù[Ó,Ò[B;‚–P”EŽÉþÈÆØmxçÛ']ÀŽ€m$7_éxš/ÊšFš$(D\EzhÂ3 _/Æ.»ä)1ªœ•šÖvº_ô‚ûŽÙ ‚R)]†C¡à ŽïÂ@æëZpöÐܯÈýý‡†—–6ýƒ­·k2¨“ÏÒÍ©4™`Tðƒp˜OÓ(šo™^L5 a"Q ÛÝjnËäÜ·gZK<=ß¡W? ¢"ÛÖÿÀ‘ûDØÒ¿ê6t¬üÃ… ×2‰r°s¬úJ€Sþ„¸gWäWK‚5ˆÎ7 ¬T•XÙ^9¯42.ÜÎì©=âOÐQvO“þ¤«Qï‹N³®•o:ÊÅ‘‹“ƒN´ž‚M‰õþ–ã¾5 >óBcúÙž«wï¼]j,Q¶ê+Žjßê@çË<2¤,IþÕ¢‘*Ûõ“ËÌÀÓ×J.åJ$âž7° LIí9nk³ê‰hçݘ$ÿ­üâ χ}í–NÒ†èk\ï¶$·«©J:"†ò qúà/ç¡%Z^MôÍIà÷8§òáC¼'‰¦; 4[Þ˜†I•)Ü.¦Ô X~ßÓ6üµ›”ÙiLHN£ÄÆ3Lv¾xÕ,ðæÜr+æ÷¢³±Æh©1"6 l+ßcKoeËD¬k/Útí!·ëq+Ó/“·40ˆÅ«#Æ¢àfÁLúïÐ7ɽϔ~œŠ¶ÍÀoK·Ê »Ëé@ŒiÑŸ¯ù,ÁQ|hXvœ² (§2Ö ™™ 9Uù„O×ô sÉ‚¿cŸz I°ä)F1}ú¶dú`ÕHŽ~FOó<½«_AÆ PC±á^ú.ºúW[è6êë3«–¹Y“¥£Œ$yÊý×]zs$ÔçÂwà.­¦=Ÿ*o[©Iâ™'>ŠÔ[JAhó¸Z[soúæ{cÉ[˜§½Ý{„0:[ëÀ”5®Dšfò¦íѧkkŠÙCI\¿`;wu³WªXkÁä¢Ê!zƒ½ ‡IGƒ‘ÊÇîŒð2ÝI‰Ò”Í4®8Uà$¾-—öÇPzÍ4=’Ô^N ?Ìwïòî,Æ%ãÉ®F%XÆÜÒ«,FzŠ!‡mùþ#]é?†{—»(‹ÈØM%Ö²éÜMé/ŽR´ˆ†d‹½ó$±ˆs 42I¤ ÏD@UÝt/¯ieÆw⩲IúÒ(át³GÉà)>kNÞ£HúšLøéæ+óçºhq œø”œ ‘ºnc NjN¤å0³üòü—FV„'ˆüQÔ™ 5eLô”Þ±±eãówŽŠ°k´åXå5‡C ,L>E‹•ä§Ì`#û©C»!†zÚG–,i;ˆÒ jk8ˆ—û³^Y“RÛ±J´{šƒ½ý´QŒ]¨G0Üì™ä>Z†þ…´¢£íªl¢ç1¼«,™ÝRŸªŸã³âÈO)ÂòßRÀ=É[u»Ó›ÎûpݸÌÖ ‚ϲˆBø”ã^–;ÏÑ{ ?¦=bõ ƒÙO>èM~ÚÓ¤ ¤ÇåR´¤*E)”^ðÐä陼Q†cTõ¶U!Ã×ÚÒ+³ÏK•£]!¶]ý6P*$¼¡WÁ¶&!Ç»_ƒ1aŠŒ ô«üC+“40/¢å’ƺ:ž…UXÏòÍÈ|Çãø˜74ôßlaTiÆæLüëÿ¯n~q.¯g°"£Ë«x( `ÒÛa*Ñêα‘ÿç´pF9O@M3—ƒdÚ¸¦¡† ?µõ¼™äµiŠäì¶('­fE¾…ðN±Ì‰<Ø^ª’®v’ö¡ð+þ:HÜ! ËÏ£Ü»í¼ˆ ãîTOÖE1sÖ%é îz¯þ&@, ~"-÷S›ÝE8#Äþ9Úð©˜*Çxån7Ðz}„«Ã1¢“ù^É_ù¹[78ÃhJVo°á?@òÍš1.HzÆÞx´í™w­åíz„à°#¡=xŠÒŒŽaò”+B<&\ž˜<•šÞFŠCókJ$´Ô¹8±ùà¢cVÔ?%£V” 3vƒ”\4î: Ÿ\\üœ=-T)á]ZçŒS¬øÄ4N—ÐùH  N=ØËßMðÞlq9{™ªsÑG ‚MJl—Åß ¤VsQeª`$ü,3¡ªåxA0yúTÆË_~øÖUŸ³ÀD¦{÷;¿N³Ky˜4U°\¾WÈã3,26¾1‘¥ýT)„©q‹ñ`_^««[ Ó@ÿu¸6©’Üß­Î^Ĉv¶‚e–Bï¢ü©ÒÒjzJC‹-çÌ÷ðFâ¬Üg¾¥‹l›˜¯’YÈg¶±8CöÃ3[Œtyÿ°XsŽ:÷ ´=ŠŸUMõ`^UƒEVdFâ)–$ïô–{$;ùÚ«ü¨6k_ˆ"Þþ)L°BÛO AdMqag ÓÉ'ÈŠ×q£v…4è¥%O‚¥A¦ed\8°_Õ^R°G´’¥aýò.ŸTL¼îÓ±ðá’E0á‚ÈT¸}?ãÏ‹\”c±@˜&@ÇmÕ†>DcõÊ^öŽr¨TƒÒü—MA ؇“<ˆ3­N©óÙ%¢gI‹øÒPV-ÿ¼|ƒ¼öô‘%[Z³¥#ø3™¨FíUcörü™*Á©è˜–·MT€÷94ĶŸKÔ0„ÛµÁúÜdOn%Î\¬‘< ªòReoKýœŠL¨=b%OX–/àòtädkûcU‡Y­Ü'MûEõ“1(dtcˆUt¼X—š³°t¢JNd.">^ØÉñ(2ZÝéö—G¦Vó[ÇȱÖK¹³AÐAK(€\díRsPAø5µaÿ1¼¦5°›üûjÚwY--M3qÂ?g {‘ÐŒ#k”³WUŠ_îê;‹…~qcëdïddƒ]¥›ÅYPã˜F9í-ET®†Þlo)<¦ñ˜)T¥Òò@B¥L ‚8´ÖÿgˆÜÍ"œj/ÎFÆŽV8ô4T˜$é¥:Kæúü¬ggB·zB]0ôƒÉ·ŠÍîÚÅ›|¬Ã"QíßÿÍ#jÂ6VYO,2Яº*i:aB! çÔœsþEÐÖÂbËU™æ}ˆôë¥z18 áu²²óÂnÁEUA)Z@CÌ!D©mJ+JD}'^T »òVÄ-­ù]™÷7ŸO€ŸéÙ’f=È à$wŸÏ^LÿNêNAÛ—¾®;í Dc´OúSÓKÀy À0”Ë`¤é-TY”ã;{QT¶.©)⃑¨ñù.?oáR;Ç@±¯žÜðÒ#>õ}Ó.¤ÀkÐÉ.5|±¦ÇÝ­†Š†ÀOŽò†î!C]QÙ3¸•)VýÌcò$†ªÛQ›;’S˜þtôd±–>ÜDñSÓ¶$ñMà øhž2Caç«KgüÑýÙ²d®ÉÓj1y­û‡R!‘“É.×½” Œ½Zç±¹¸¥åmx‚„œÜFê$!sdr Y?9üùVUC[²"~r½Ó£Ôè–zˆ€L¡(Úµ`Q¯¾õ^Q÷æv»¨{÷LTúQúd9¶Ì9g‰/y~…°Ø/(R «8ú✔×Z«A•’º·å¬= êýQV¾ü~‡^ȵ`£?ep— È3vẢ12­%˜Jå¬Õ& GS&»®%¨Ö¢–ŸàÅjÏ8Q©uB.¨ÓýJjõWŒ7ÇùùÎrµ4]SFÚ_A/Sšé›¬M·oKXܨ1¢°*îªA\±ài­ïß Â.W?/ó'¤#ýiü’½%!~fC–ð›Ô à!4Êúÿ)£eoþ@OGíç¡Á‰Ó•ËH@¥6u H¬¾è×)äóQ3O=ó2¢zwƒ¨«Ö½0ÕRS¡ñóy ˜£6p6óALBj>‹ç¾ËkçD­£$´*#ãGo‚! £t⑲෠·È½X9I]jïC¨Ú©e LmÝ‹âmkJ<$ëd‚¾Ç€‰“õŸæz³eŠ J Á Ø€Á Bì5×Eö‡3ZƒÕŠGð 'U zÿ"b"2¾r -ýéJ7ñ­àêY2‘¨¶Í{,XkuÅ ‡‹¤I用`/=HQÌܨÅðظ]»ÔJóË|× jãNÐ|Œ:±%Y1zÕURî™Û[VdâŠ(<•™S¬«éÔ)Nø±„ ˜‚¤^¯Ñ¦çåÐÖJÃyßµÂdNo=רȦ¢¥”T!عæ)ñ¦&¥EnsªF€Ë¢%§~#¯hNβÚd% F¹8MÍ?Þó‡ý ¯ôi#³üÎX¡\’zp1µX¹SŒ!†G )LÉðåy·m$:'5¿3eù­ö®'¥I;¾TŒmâð'§Ä»U¶lèKƒ*C†u¨[‹Ûº¸Pžc’Æ>Û:®+†=òCä‰ ·0Ô ‰“9ìÆÊ.ñQõqÚ§Ûœe¶}·›3A„¶ð‹ oËåù¬®w=ù|X½­CA[úCÛŒ-˜ÚÃw“a¶ʺÑ[ Þ[_`7Šˆq6à6Z Ýâа]Ù 6(§ÌIÏüØh|Û<œq²ÍE?œD4‰—¸²­¼gr[]Ûvá¨E-³añï º #¤ ªC8^¬êåý÷~œLéð¢ÙRÓä‘q¼±½-ßj¬œð‹£ ä@wÇåûMÈ8º= Oh©_Bž…M—q[§9røSnªäuYÝê|`QïªIY£sZ½Ç®<=œTäÔt˜ ”o[UGY\îw 0¤,Žo±Vݳ®ž;*Pöñ…¦Q “®ÔTVµîIpvn`8JSKðPoš®ðÔ²nnø¸ç†epV×ÆkÜ:qRúÝȬ1ôòÝÚ©_Ô@Ø } ýá„ö Øõæ‘ùî»DýSÒIÁTXŠØ¹øçÅ-»Ì}á]µ)ÔYÎ5"¦jtfÔ¼’‡ˆ-3@aÌioeëbo æß4·ô5êð;S>pAh÷aT`ù¸~àš´™äf WþÌâåÜ¢%˜zÃ>Â+D…»Ôx]4Ú BÀÂ±Ò 8â÷s”AÄ̂A_R0S´ÆÞT’!?>ÿ…×Bâˆ]oBv„›F=]T>¦ûȺ—püZÚ¨g1y±»Ý8»4óÝÀP¥µNÍ8ˆ¨u(aêol «L2X a·zc~y3¢B¥2ih)MÓÖ=ï{ÉÂFT6Ú†û¦›ðJT. «ßÆJ‰‹7T‡e*ç–!xÓî‘žD<úªá®ÂVU ¸`;îÙqÉ¢¦(uWÁx 6¡î“oSZ·ÛD0þ¥%TH܈1CÐ ŽÁÛ»)haé“°6,/ä² ‹Œ}Æx¹œÝO"Ÿ’®3‰}ñ·ÜÕ3*þ”w“û4µÊA».BO)–9ÃiœNâþ§m'Cåw;?EšREíò­Î¥$×w Èê 3õ=°Pu>ä |ÑgV,ÏøTX«{­"¯­…Vds÷¦ëef¦„zª`cГ`mo¬ª8fQ»Mþ>–Í„POöÆvBî°°ÃO®é¾¼ýÔâG.Ø09å‹»wž7ÿKçm £ÒÅ@x'”NÏg\y+‰­Ì…„;¾‘êßoÚëØSì)èGî G5·’ŶÌÙàèAtŠ:¹äý?­\qõ½aFøŸ® %Gª=®î´ o­iúk”µ%—(øòÂøƒp @ ‹¾Û_›ãIÀtoßRÌ.Êúæ],}^†¡ûòxM„;ñ²£v­†MÈ7.icn×⌠Ç_¬!ù2ú)Xø0ãÔhh®|ºïèæ´/ú£xåúá?MBÛ‚H†“Êiâ‘Ñ(_óE³4-©p"XÊkJ¢TZaÎ:>ÀdЇ´Ó=bŒì™ܬšä_õ·‰À{v D™§Aö¿xJê8¾Ð…ËàTé꘧Xý%LÓ²¿a^”J˜›—¹úŽ³ê2Œ›‹\¾'©—õc4}`RjšØ÷¤HB¿8ÏÚ7µ¼¶áp…¸%“^YýïÚ Ù«u¸(ÃÒ±DÐŒ(¹>«MfG¯­g¬ðg“¾þý™‚º/.4-Ò¢G†ˆ^‘†Ð–L´)cý{½žX‚ÉÍÏ™ÐÀ6ÇÃE´ø±±A‰›~z”»É±]õŒ¯ž™Û¬¡©ñŸ`^Õã2Q;¿ÀuÕN$œ±æ5ß)ú›²lXµ/ÆåQLÀ†¹+ìÖ¿7Tß·D¹NHB&ä!¼’e“Q³Œ\_ƪÄþ-e0k‘ø7ªþ×µ†Dˆßçe—¥¥ƒiBqŽ0ŸÐåÖ0þžFMo?f÷G°ª±µ"£äÛþ쮋“…MæþBp@¨„›x11Õ~$%|9Á€ùÿ‘TåKœ3QxHóˆÜÜZ’KãıßA¾÷ž(*yº"Çœ?œ:§ÈHáSÞ°µåNè.aÍO¥Ž;ñ,/íYª°äÅ„“1Á´§=ÅoržHܱ/2®Ö!„øÏ„Wø‘ìÐ(”ÜÙµ8Kq:›.Y1ËŒ’Kþ\§¼»%â×Å` Ÿ³ü­÷ìÜV5•DD¾áÊÉ9ëúÿBr§{‚n¥(ÖÀëç8…=`Aš?MÌYçé-ýš/~E,ChêÎbÚzãE·ÅÓ?õb*¾]eŽ¯<0-ëÕŸ¬·¢H-Ï0ÄdŒ¢i P&‘ò$OñxµÅ!\}®¡·eÆò)néCöØ>ýé¸'$‚*Š§ùG&£‡Š¬€÷Ê/ù6È!ÊSé–e¾;bçZ±q‡kïÝÕÝ? Œð:û^Y<÷‚%+»‚Òw2Å#Hó‚%eŠåõ7–âÖ &ðô­ lSÔAUØ ƒEIIzs‡ñÜY S=a\²ïÃl…·=Ö÷µO¼åWÆÁÔsúɉѤ¶Ôe…+lÞö(æ aw§“·Z{‘T9Ê×u ]`NkT™øQÍ µI\È1šßÞFø†RùyeP «,MVl«Èó>çO“eÔzõy>¤À·-œ ÓÚjÂB×DN—Ìÿ$½ä,3Z!éŠãGBâ\Å  _+ªý}­Àå¬^¾Ìä¬Ï1t.‘Ë*³ç¡¥¿p`gÁÌ[ ‹ úyKG¾ä¦9häXÆξ¬ RYƒÃS$>QaëÏOúNúžž¸ºÉr ùÃéßÃï›ù¼ªä䜊ê¾ñ9ѪÙ4ÄyDŽ £-ñ?Šò´9ŠDû½ŸïÝ=s (ÀH 8÷(Õ(à/ŒݬH4'•q6»ENõªofØ€Yõe^‰(³`û'g¼sxФ}þ;Kƒ½Ð>n´ð(¤êu‡n÷“2XÔu5 ß´pX¹”´Œ4ã•i)«ˆ8 Ü#¤þ°™ÛÇ/Ï›87#éñ yO\¿3¡¼«ð -úIÌoÝüÆåÊ`×¼—Á[©/ ptŒDÌÅBºDmR1Às‰¡‹½±ÁáøÊ‚w£•w@€†)bòíél?”´IßHÅ "Gßòf É`ÍÓ!7º*=pŠôŒEzþï1ï.näçGBš |ñvº~Œ¦ÛHÊv£—»i JgÌò×?zã#l½ªnÍ.NÙDÀ³Z‘žzÕGüõdÖlq€³Nù„?i¦ZéfZ6¿çáëšc7²øä6V/9lÛdYÈù"Kס¬Rð Øá77àûâ^òöx'ƒò¤¯GRúq¹Ïù2ëQ¼˜¹õ½\cýle=7‘K˜~Ä:ÊhêÂôcå„ÿ©s^ÍÂÐÕJ"QI¯ýïw#Hµ™—‹™€ç®V R¡uá|ŒQzXtck)Žêq a™f¦1yÍÑ™¹q¹I6¾)Ãn—Ok)ê/x:Ëgv•¤Áe:Àm&ÓŠiYä´!ˆR+¹æÅ/0€æh†Øeݺ*0¡9Ž‚3¸‡LðÌo»·¨‚“:ûÞüòW G©GíŠÆž"õ ¥NTehZ^…òüÖ  „¹_BßĬ øÇ…ð)¹é»N­“¬Þ½Ö™sú^ Õ'Bë²…㶔«høãüÕr&Òð¦†ÛJ!Â=E÷m¼®¿y*Û¤7á…ô`6vxB‡„å¶džYòâ~™ü2Àž¬ø=±Ðeí]ÆT­ÿq´×Û·-•—†peÕDV# ^¥è¬˜ ²Ù #¶”¶¡9t£Ú+æÍÉÑ…",ÝÑ¢ûU5-e½:RÂÐ,ójÊ‹¯ ÷ÐïGº‘ªÆÒ…äÍ.6¹L­dÙžF/ú&BΫ3Ft×^ž÷çÙ\Ñ;ÁËŒ½Kò–#Ú #ÖKqZ•Ð5¡ˆ Sºhpt¾bcÝ5¬9º²ÁPQ·|CÎNÉNÛöÅtæ†?íãìáE“WSå^IÇ&Ôžá.³òíßVÀíÛvT\C÷‚Tam³H±ÎÎ4Mag_8GåØÁö®ì}“«v·3žë\ËÆÒF¥yË?½ïçße¿g×í6¾9D¹Äp6Oöd’åâç§,7ÍC~ØBW^0 ¯Ã:2öx# ÞDݼ„Áè{šÒ¤(³Þ’˜:z…!ªu†ã&¹8#bƒêÉ:þfüaR–¿ˆ x™Z>ÓƒlðI‘¯-3(cÄfCÁe'ž)F*ŸT¾­¡Žô»Pò&®Òœlzcô“âN¬0˜ÿéÝÜË«nˆ‚ø •·Ç’ ‚ÿï¡ÿ“¤.ZSÜÚØ‹A‘Áóý.Gפ–·jEú\J‚½ax‘M›… D:6+v`nç…òfðoáöþG4õ:1K¦olb1é0ŽK´Ü"&CPér>{ÞØÛôfëTNòð4.S*EæSIÊ%¤4%†R Ç~Û‹ ξ‰Š\¸zÿLéT#‰•ÖSƒ­òOªÒ}6zmÙ{w³ýöùb·;½a‰¡)žH+‰½ë°4•¸6C¹K΃|Æ@]({Yéä¢Az&ƒÑ~óÓÑ@“ô ‰ïÝúò]‚pÅÅoµ«9ðãì"ëÊF>Ÿ'óAæß œzéx4!°¨ì_¦^M¯à)Ä}Êþ^ÎW\ûSHÜ£jò‘« hØ~!ìæi9Ð5w5%—TfËØ¡ØóJ’pV v§Ð;yFœ¢M숗¡Ñ4G䶻¨£w%¦¨üÔs,ƒ‹Óâ· ¿"øZËX¥Ü9¢=óDX)[éN>xÐÒE*[•è[D;Ñë(©ÏsÑg\ö®4*9EB.ç €µØ€ž%^½0“Ôí˜YŸ-1`‹º-<( ó¤ƒQ×5HŽ@ÓìýíšÝ:¥€eqZ]BA̤`Mª5MͶÀ&…€p&ćÅÏ4UŒ PV’·õ´HEiN–2‰ÁClèPøvyL†&Ï4ðŽ²uo˜ò%ª[”}´ &ÝT5hÉ@Å¿úºî‹ ý!Ñÿ ÌÚE—¦Áåd }½u¯b{ÇE}×Nµ\l†î]lmxwï†UY‹þXöÈmú_é30QÃ,°ÍŒwZÀµË^ªqrJíßÁT€ûð f´³ÂŠe¢½©ÉP2\«Ö~ŒAùù§Ï”Èüêk×»u΀‚F û9¶%¤bVð¦‰éþÙó–àùÕf( Š&rýx½&‡Cë|šü#q­@‰Š\’ÜOÃÇ›²qƒ¯UW7Ps-a ´·gTW …Sn•I—–ç%= Ãïrc_p•T¤pähnµnž<ëIÑ+¬!z~ Î{é%™k&=Ü’ôp%tû¨27–xýE ‚NªÀF ø$‡ôýª˜¿`™@#eAW˃{«ôJ_|vÙh?ÛšŠwòµÃÓˆý¸ƒj §÷ ÒÃ…XMžC–1¨,loeúÛ/ÒNwZ§dÈqã;û¹‹¼ùö!fË•Œ…¿‘±ï…W©nSA¦ÇÇfh>í%k zv=Öøft§÷Ñ—‘3NãTZ©ùÅfñß@¿$ ƒPû<'Ùkïâ‘=”=## ²é6tÁà Clnʾ5Ž¬Djj—¥ÌŇN`<ftÚÃnJ¢»è Â&Ί &|å²€¹ÞxAÕb.(%¡öð³š>"âSÍòæpÅÔ¦f¶ä¼Ð\Ù³];µJ5ÝsqH=B?PRJ£A$™J³ìÀ°ÉŒ²ÀÜ¿ÌXEnÇ¥IdGÓ¨·åàmâñ´Î2© Œa |÷ NH¯Äl™!®e’Ö¬ò_(2©së#ÁÂ)Þ< ~¬ÐHÒGãQ>J¶Ór!¹R)î —/äý®±9ð¯‚R»Íÿ|èűè åì GêªéATö_Û É›ÈH'Ý=-ÛɬEöÅ73 ãCðˆÑÙmÿh?ö3I^™Êyb4þ¦Güøį[ª•"s ;v+ùˆò$­zëS饙.ï ‡Ø´ªi–¸µÝûâ—Æò÷2Nq¶=FF)^: ÃAù±è†À›ßÊ&Md*ñ)ì³Jþ ÂçórͲ÷ÌiY~ШVX>yô?–'ÝÄ|Ù¥¼IJÎCÝìKÏÙÊfùªù5=õ㲨=I6OErƒdiìÇ8µ=n‚—(ÌNÜᬳ­˜ÞyOcBÞ¶#·î¾•ÒxW”&ãïÖéæQcÕEÅKb2‘çóGµ95‚½Nx)™¨– €ƒLå!Ûé`\©£±v§N`ëø{ŨHÈ:·¢I³íð‘$Ígô‘Š»R?ýs6& êrgÀi0$![%‰<žÚ\ᘙ™èMÝÊö !,‘rÏ'—lìã'(h±'«3·êõ¨\Å7vÇ}–ÏöMvC‡%5ˆ—ç¼âÃèqŠþ_FCÍ«aaÕé§Îq£“€(I_”Þš¿8jÄ6q& ôÒúÚç —²¹MtK¸BÊøÏÊø­<ÏÏW's+/¦ãYãpñÎr¥p)‹Joe×Ð<Ñ»öJf@uù9+úlL'[·›qªd†ø~I\âèEQXiÙoc°öù'‰aH©‰¬(–ðM²Ì殧ý>KH²hõ¬Ø Ä AçÎz¿÷a?Y ;á8ÖK “`‰jùW\Å(’þ fƒø«ýK+CfØ©à_d:Ñç%׆‰-; ®ÄH储g=6e³®n=/²:é¶iÕŽ4è— ´øÙГ—råAë÷½§‚8µêÀ 1ªå{´NÐÈ0®L‘ãŽÚ¶ýat”‹^ó`Õ¾y®`:©a¸Ñ†HRÕ½½™ïQËfVÿF¿¢"å÷äï?ˆúoŠx 4÷’eßSáÿë;¦vmÎu|â8Pû ñýmÇ7·™ ¾;@5»]zpÂÛ×k!jÇsbù‡bŽ¥ÍaŽ4SõP¢Y‚¡-Í÷¶foºonã´åm‚ýªÒ)~üQ*-/ȵb)E^Ó\$6–5Âس‚»åÄ…¬Óvr«IM¨íqÊ)~o1³€iR{ÃÃnÖ@ªÙj÷T_S„Ø oãXr$&ÅÈjXU6ñÅNlE#àôíAÈØ»á»a]<Ýyg&2÷™ŠPx”J©á{ o±ÆC`(ñ<úUÉÔ2­l¥IÕaŸ´<±fzL禦IAªÅZ’¾Dºá/8ø…س%…WÁ8ö0c SSožòä“c‚…YÖ>NáèY€‡1ª´a<Ý òÙaÃö©hI®¤¬ÅØhU¥~0¼ƒ¬úT¡È]¹ f³ÜÌ;c/«%\/ Ž4üÇ!öæD˜³–ç.làx&²Rc‘ Ø È˜VDLa ìJÕm„h >ŸßÊ£ °ªH.ž/?’£AᘋbâU7[N…&~wô’äÊÐ)¥ør6‡½·)¶ñ³@·ëÍÖ*îŽ&à­­”Ñÿ»Ç[&ÂÏ>À> ì•-(ŠÓ‹¤›Tˆ[$W;cÇ€HM¤ ‹å'"ÂŒBöG$%±€ ækâwrÄæÙe¾é»=¦u¹y÷rrã4¬&°«‡;¤ š­»~Å2œˆ¨‡Z^hÅk‚"÷˜y_'Ù?ýJ]êö#§Ê¨gß»q§zCTÓµQêÝ'—e9u2„±bÔò±h,†e¬htíÂT*­u"OTåâbÀÔ«žÿJÜRÕ‰p¿PuO@뀶ÓZÆn愈/E+¥¹[_béo÷¸³6(sç)ßÒº"Qoÿ^HUüd ¢ ™©O›Î&¤”4v‚ï}…ïq\ÀŽ ˆÈ]Ù%=~#©‚X½×G“¥.iò <_ƒ$¬RÀÄ_©ëêý½¦ÞÝbãø®÷eøDZ|+ Ú²‰"eC`Óžöx&þèOÈ1v€j\ ˆ€ùTД&1p>Žnt©Ä•®ßÄc`ÓJ„C 4õy-“<'¸2rÑoæ@56‹-1†Qam©óžèÙ±Ÿà¨ôÇÍêÕŠ’ý™Gò«OèçäÊkЇžRÜYNd%šTkCÖõþ˜kí¢gƒKÃC¿V]—÷Dd ¾c5»GW|}©©È#ģî™qÛ‚n‘âÖËÙ Ç«dÄ{r€l'®²‚G{…×Ø÷Ãý×5ĈksžÞiwV£~Ì;Ž¶³ü¾û¨Í¯ž½!ÛL©Z:¢‡òßÍ$ÙˆÄÝþžÜC?çË ì‹8B¬òk˜ÕË*"2Ç™Ò#Mà‡Õ;8É †u¬èÌAN¥ÏÔÕ;#5d30*Ð… §5Ý©(-–F}¯‡)Ž=—@ëšÀÙ7LTi íZ½øÈ« ´a²„&ê¼>Ìâ 0®+Ú@i,EžyÖãc=0ƲéÿŒ>o»L"Ùðå&E¥rÑ@€òô9gñckæÇn2ÅÓ™ó#U; L¿úŸ' >Ô 7ÖÂÑr:Û±;E1%ZåÜ‘0|"E7íÍÏO颮Äq:,Ë)Ú/®…FÐJ'L]Ê–th•vËSc«ò„L@¢òz¿°ùÞ¿Bí. !W[ô£‘{珞vIBœ Å€O@§M8FûRp¸°K¼myú³ÔÜ·P[χªO9-üƒºÖõW*!Ôy?Â^ë1Z.»…÷%à˜ÎÙ&°jjé(*J¾È¾µKEÛi“#C…³k¯Jè%®ÛYŒ™¤¸št—•æe{à&A\”è„Í…KŸF"º3Ì’%šª‘ž˜;B[ºb!¦«—/îs<ÔzaÀõát|A!<é—%È šËÛ)†|b¥pØsºå ·`ï'Äk<ƒ­‡ô³›ìÈù~ ààâÉÁ„^Ù¾Õ\á8æÕ…È2‚Íù#z ”Mý8–€ë(Œ¢šA9vÓ2Åq2[¡Zô†LÌq0;l83„ë"y>ßâßh"ÐpÁÀ4S )/WIÌŸ‰`?²¿ûü¨N<-áÞ{¨Ã¦Ê0JÃWÂŽpŽ Éz,¢P‡6/õuÉþ9*²UåG»ô_|9ðød0ÈÇjR c¤ýžÓÔ@ï²uiQó^(Îq0yIÚqiƒ­{dZg?kÌ›çËX.‘® ³°EgÜs“Œš¿sÈêA)hŽÏqßõ%¢ÆéòòÆllVŸ+­¹ïƒ—˜ ŠKîSI-;ÐD—6vL&‹)ø}^MMÏgä}1ËíA0MK(k4óì+b¡¡4|‡_í` Å…s@TµÃü?-ÂuXäò)…É °ôº3‘$cˆî‚”ïþq©:ØPʃ·¤Ò»–è0x!:õ—ßé¹p-ª·¬ûuí}aTŒžw],™œÄË%yÀiÕ~D‡‘ës4h×¹i'`Í.üºø”7$?­h4ßøž >S`PI\O¤Ô¡yí ’anöÇÀxmõ®*=39§Ò³Uâ…iÜ-ió»…pŽôˆÆÒfùö Š7Îú˜Höù9Šg½w‰ÏùÉþ«à‡¡³s˜Ñy w^L-#äœñxF¾øဠüA§ €\ükWéÓPÕÍ÷þf•1lв„^Öž4¨,Ì/Á çéu§šd<÷žJÅá.Q²naÒ+à·Ü»ø»UÑQEáZoçé ¶iÅ}ºtxШÊ:é× é ±Èaû· ÊÚ.3ÀC:Ÿ°k}NÂÕ{ Á·tÁ.r]I‹#‘ÛüÐÆóªEYEÑ®¯Þ½UÔNÏñd‹>©kì^Ìî—Lá"\¿,„TåZbõe˜:·¼Bveé«ÜZVÀûœÕ} Ïôk '.â¡ñ.÷‹7ô ›°áQÁ(AT¾óåÁÓ‡’Ý$etI«ŒàODtŽ$P†¤HÀ([›@Õ1\±¿4—ýLK[0è©iàòÕSpuÉÝÔd[Ú=œàׯ=q##B8±Ò\ìä~Ø÷ HƒB'pƒmvG^A-'j\pÇ0z- ¢H5ÅlÑ¡JtöLË!$YVMô`“á:ǃíÂ?ª†~™ÿ±{à ô¡rAX£Üá‘<Æ˪:ð=¸/IM•®_kXPVXn z¸\NÌ= Ó¡2’V\Ô­?khu3b•djw¸¦Ù]Áøß¡iâwŒC“EwÁ8:b ¾«@ÛÞm§4³ºª$ËbMó© •:…L°M}=ò]ÃèV) DÏ+;6~vˆ—ÅgÞ×­ÝR1H "£¸÷§~2¿yÔEF FX»ž´_¹Pjm=¸“W^^®ÞôI÷N¸°€0Ux¿Úþ˜Äªu¾ÛB2I©¹y2ê…SŒc öØ|àì‹Õî¡_œ[š…ðL½+6ô“÷'eMDäë‰Z‡mœ²®EiI ˆ&ÕVÖ{%‹€KC@õ«‰=â„ýÍ”Ï祔',ÍJ t0“Æ¥ s~7â*NÂ4š¡KËãÅÉwýêZ,&}kÓ#ªìΚÈ_Õן =œô_ÑU Ñà„_Úíöc#1¬ØÌ\×,÷»‚܆éý7¦dÿ¢9– ÿ„$r9ø"ˆÐÿø•%ò韢œ·ø®¢:•#ÚŽÏ5k8¼|sä^#çÄÏÞd)öóv”¼›L³G“ øf~\¦éø›4ÕÔ•»~T0’-úuCÁׇ·b9>Q7ýï;¼ù‡Óöä‚–ßꇲ™Ý¡bŒŽáÁIͯO·w_YÓRþt¬7”ýd8?{H*6%3™ZJ»L¯¯.LüUÇ*ÿ·XÊ)_“£†rÿш܊WÞÔ#ÓŸqžœ^²ÃrÂü±ÃÞxÈùѽäÏ9mFÄožp¹8î둻܉©ÿ¢Ü’ûJ9Ô |×bz•|<`Cd«Òµ‹‰DÁMG(/”N™pÕô[P!¡‡£åůSUðqÇÛÆtÕó=¿æåe¶¤j§o@íÌ%ᚉÍ÷›T38á5à× IÅw ¢,pyrçf_¹ ^¤u=jË"”^èeÂî5\ÁÏ›EòXžjM­À„8‚þRƒm ­̦˜´n!s±öè&Æ„T€ÕQ”Õ§¦‰t© 󑳸l’Äùoóïxx&dÓ.Òè(ø¡0÷ åÂT LUT¤{I{Bkl<ùb»<³Ý=V[¸U©)™*ÓÂ^vÍWå7WÞÙö­78ϹO‰FºãtšAYÆM½K”¸_°Þ‹WÝ)m`ÏbÍïñµ²éŸc~Œ€‰G(xÈ^%ã)¡Wêã‡Æ®•Žm­83~<ÃËqðPcQÈFV/½eå[%ýý\¸çYZ›Edtrz ¦$졪LþürG‹Ã.x“Š½Ð!Bî-)…ñaþ1€À¹¡}­npÅï—!–óávª‡8­~fhncèÜ¡Bç <}|!|“‰„>jðE "=@êBŒ¯t=çpÖ r¨+ÙŠfÉÞ¶93ŽÌ„:ñm“gþ¡·[ïzŽä{ò[XIp¡<‘26¹Nú—‹QýÊ’± ŠÄ…‚gŸ +Ä/T/ld…ȧE1j,·²"ù“¡ÈkË´em!‚êJ‡ ô{FèÕV|yÚ9/À—wsÅñ,5ZnŒv[CÇ ªËWGêTU53‰QÉ•üÜÆ‘ æyjó~Ùš'Øœ ³Åw6T[èš|£a{ô^ÁÕ_“ óQ1ýÖ;A"NU«h³¬ç`§cvÎR%””°‰Q{Œ[[ ŽTõf+ÑùÆÀW¦–29Ë1«CØ®{‡›k3Ïìƒñ¡¤”–}•£œ(Ê;Æ>ȱO•&0 ë$ ø7ï‚ûGR#ÏC••vÿ¼îØ};HºMd®™MÒAÞgEXqCœ…tå &bæ'n%òŠù¥8H‘˜ËAO@• Œ¢q³§¸¾ÏèÚ= r’MðŒ-GƒC/Œ{£žŠÞ÷£O€—!ú«†'ô:·}Î?íž“ôdÖa[­º(š~AÖø7Bƒ,I°h†xoëœ@2c¨ƒvW°¦†eáêúÕŠOÒYaÝiŠ-^r+éC¼±¹Ô¹/g­Ôñâï(\~DÎ˺×ÚWjG#ÉÔ79Þ¼#8õ´•/œb,©…W1GLd B,øús1æÞ-XLÓÉÍ8÷ŒwÑÊZ]¸ç<ЂԾÃgÏc*n•h©¥¤¨0É?ÙÿPa5Ä$4+ûƒfM΢¹jª Ø“@íùµ\°FLÂŽ{Ù`1Õ—ýqGÍF£’ðÆ,ƒßsñûõš]¿ûÝó]§ù‘ÍY6qr{#÷„nJjUž‰&¿ì¦Jï|ŠMsW_ˆý|ùáiäÈÎé–à$QòŸ@8Zaü*õ³bÖ“Üb<Ñ8ˆÑ`­É'ÖÒ#`‰ðcÀE ; æ+ìR"´ìqo/ÌhdH1m¾|–h¤Ë‹ÉP@ñ¬TO†gAý¶6íÞT(kÀR3>aX²2¸œx­“Óþ Ïöâœw-¥®ÒÍÍ})­Ïî:ÒYWûgèzŒcÈøž´±¾g'• ávªÑžÖ¸ª]P£hK>ÝèXòEòMKý¥ÏJ³Õ¤q}rc0t±H&\"óŒkÏY^}?{ ¥«¿C“Ûv¢w•‡v1a¬Š ÞP㘕õàÒ£Ú=œ,Ia ß3ˆáCG½;úùëd$ÆMC›ÏÌ‹’tpk3O"‹7Ì÷[´V.”I[P©ñÄ$J¤)÷9ŽÁ|5Ëéì÷÷j= ^q%€=…¸±zj›eù Ãî’ôâå‚ö$ô[ jã¦v•‹ºñØŸöß=©ü º¶d¶é_uí‰;L×ÓA]Çÿü;˜C·Á~ú6yBUWð\Êd¿:¢Z5® é¾r«ŠÖ.ÓÍéjl”ò«×+UÉOÓú–ã$sw*1ØÄ;%{äÔöäUa9N"ÜF] ÿl­óf}\¦[Ž<v98žEhÔók­¨)”x‚| ¦ïr9Ç6DÑ"ž­âU_7îÙAt¯2{UP½›];­4æfÔh#¯4ÖÀb5§Í»˜lë'èŽ)]'<@(˜ÿ¤Ë'ãa´yº NžÎeø —­±Išw|ßêYE´/ "ÒìtíÚ»~¼Qƒ;qvŽƒAÚ l"ÊP.[Û ·£‹E04¨EV¨‡°µ?s1zb¤çî„óž<‘ï=ÁÿFm¬å™krÈ¡©ÇÐkCfoýÏBâÎÀGOÔépä«CX¯ÓS “sɳÈÐd™åq0/gÄó˜øGB7&ô‹*?)ÝPÅuÞI‹XQìŽ:<¥«Î|íVÆšÈXÎ[–BŠbBš¿ Ú‘ÏØ°öÝßÝο‘×-…ö óš#Áy51ÙCUêšûÄz.e¿wäø Ù9v‘œÔOs¹²¬=¤­C4'‘V®ß°çmðè1w^§Ã¢IæîS»DŠcB©¬ŠüÐó^Q:‡Ä# 7²ƒ]‹0¼EdEŸ¿~b%£V½µŠŒú-.勵yöõ7í‹P€$ ¥à¬^ƒ(ŸU  ‹reG6?¤}þzúÍ ùÓd‘ÅØpEùÍ~û™båϪqV.¦ ¹òXÁ«3¶xÜÇpÉph´CÜGY›ö‰e%ÿ‹Î2.)nåå€Yëè4‡GòŽÁÃœH×’fRY‘ÉY1ùdÌŸ&%µ¦H­jºExg¼MœæLÎ6ùžAgKï ¹d5ÑÂvñºÐhJò4,¹B/ð\?Ûùky“êãÿ^ê®ÚŸì¡è ií?hÀÎéD×N´--ù““´jü†$Œ}}”W%”4­ (¼/UÔDÂcé· í0%nRyŠjÐK"éK2ŒÜžu+:ž½ƒÙo´¦®˜rðë–õM2 §0újÆÆ%nþ,ã OHW wq‘¬1•˜½°ëÂa8:C€Žˆñ§‚YÇÚG¡ïá˜Íïsgä „í±áõˆ*w"é´ÅLÊ1 }¦š^_‡_œ3[ö]j‰eOLPÌ^ÿÌå_û¨g¨V阫’2¹»!÷¼xýyk—”*þö+©Uh»ìà hZ.Þ®VvÕ–À@k$'ŽEøYÈ,À“Ž½®¼:Q m-Üȹ-¶+ZÄz“÷ Óµ¾…}¡oîÑÔßÚöÅÕØJ©.<“±qŒ—°-ZI•iÇfŽF×vhW*Èuq™týÚ~‡N%ÿ¶HÔ^®´§[þïÃê†|†K,~S­¦2zÏ„`-_†=p%¦GªsYåÐ¥…}’£™z0^lQé $•Ìá+&ÕOÄ ´,»à7yhD*ÿüjVŒ°èdÿ†y’O/ƬWÜ =…}S÷íW8¡ÝKþňõéÕ"Ýš qÂ+ÁŽªLéÕ·å¿‘WšóÃÕp“ëåRu@t–s›Ôþ3ü6y‰·ZÜÌð뮂+çèz}äš„Â=A¦È‡z•Óz€ýQXtoþ¤†À•òìp“®k£ä“ .ÑT Š6˜]’œ:Äs§…™ûöü±}&Z3×Oß"Ô|˜{Ž••kɺÅy-jƒ.enð™ÿŽ¹ ^+T‰[I<^É= ú,µ~ÂGXðuco¤ðF8,©½Q¶ñŒŸ=Þfâ¤iù¾aG–ŽæªBótHv#EŽ.²dE ìüT êeŸãUrÖ8õ‚|‰^Bø_QúÇq§aÕFò„ \ Ê:ZAy}6â] ¨Öј˫!ý óRмöó™“J!%l1ä¦=‹VƒQû\Q¿CáÛ áŸgµUvq8Hœ?zçÒj•ÝÊEÂyõ®:§hWJÚç43b ¢ÜÅ©b'÷r—*Jh ²9ÜÒ ä‚,φ½ÕÃ_…/(°*J?«›c zó‰×Ý3jÉ®Ãlõç µ¨Û–mOÄË1´õg»ÖÞªP„oÖð\ûá#_Mœ/? 6º]ó¸È9úå%£ËF¸Ëi­üM^[ à¥n»Å/[nQßÛ]}g„q¬:õÊA'fˆg`c>†O] )<8óɃW ט¥z‚ÎèbLD(ÔG‡y³k¼õWÒY£J>ËwXê}^£mc `}g þÕàШLñCloHuÃ;^¼YH8 †9·c¡W*‚Ö•gƒh ýý` Ú3éã"ãþÙ5£¼<ŒÙ’÷‚qÜô‡tùc•Ôÿ$ðÔ‚À…-3d!ʶ]þQåvŸÖ‹ÆWÝlqÏþ.¸rcñ†,ÑÄm§›èÏÆ¢dÀ!KxÑC'Åã0ä^ÇlØ&ËOÕƒ‚&ß sÖÕ\¹2 ýƒö)é“—ô]V¤ ÍJ ÏÕ¡T…äbãHÜKçÄeäzp€Bî_Ú¨«G€¯1ŠdAù‰ݼë .±~’ôwÎTÅA² Ò’bw`­º©´ «„![+ÐÿXkò—½×Ö`p›ð±ÌÍÚ$²×óP”ùúÀu?b=À†À¶Ù# ’Áµ‰´I2Ì¿\å†é÷ôMrÜœÞ凱Owš; ¶¾,Ãæܶ‚Ñ…8Õ™s°ßÉõc©1¯ü<© .yÏNNu”°æ J>›4½k¾tþI"“´öÿë}øÃ:åmxsÈÆ>ÌiÉÐ]Æ°óÔתéS²ÞÖŹ”3<¦¬0o²te91Èü4äWŠ A²þ¨òð³¢w#Õžc ^¡¦ú† ‚ýQ#jáÇðX‘zäãÚ¥,é/Ù…‚ú°Ý3–/¢%,báôëÃà™A̹ ›¶|êOžêjêè4KdÛ°í”sÆA¯øq¨ÕÏŠÿ&תʕ~á*ªËÆÈÀU«=›âs[àõ`gÀƒº„Y·áÏßñûI^Ç0(ÿ%*¯‘ÿ¨Õž¾š„uªJ“½Ûƒ¥àåêòö|RÃw²~‰.Ž€"]#8<AûËÕ|¦œ¶“,!äf«añÁ)¸ãf[Ì`- 僠„¹Zjôé3ñŠÄ…Ë߈.Ï—Îú÷‚„"µDéEÒkôðþ”»Û-ä™Tó\øT$Á®Î N‡NÐd‚Ü3ìƒò}¥¢¾¯Ðºk‘µ¼oÕH 7PÛŒ`3¨éºž!Y}xHdóÌgÆÎ(^fŽ8à`pÐUÔoçïtEi¬4=ϘÝàN¤/vwÉUéØ ý½H:HbÀÓªíÜ~Üñ|Þ•7ìíÔÉ“èÁèÓ¹hf½!mMu­ÛB؆êét›únéŠÒ©c¢øf¯&žúkEÖÕ/‰“ןŒ#V$ WÆu&BB:æ´å6ï y´JÆE©2`i·Þ²+ˆøÐL…kíe0!A‹a^h‰PK=÷ÿˇÌkPû¾›göôÓ®ÂlÕÂQ‚!°{]QJŸç—y?I‡¯©?‡µ)<û›cÁ^¹8œºÄ,»Ø÷§åÝ?ŠÃTå^ö£Iø« þÃÔ’Œ¾¤ÇIÂg€†BÞOºM>R–7üЖcæ5ãcMdFR•£K˜…¤­€]ZÑÕg£ø6&?ö—Ö+Ë„j\ɱæBê^EèÎj‹Ë{7´9„™Ñ•³ý¡²Ãv0j:(Ò0pJ()ž _r“¿J¨WÚ˜qé{ÑÒç+¬É»Ä¢àµÔhË]µVà³´ûÚy®ƒªm«^¢ç„ ˆDêÏ Lu L`d9a'aÛÀ—›e4Qm[bÁS>³3›Gÿ%6£o´Ü _Ÿè˯Œój &¼>¹1»Ooô„Pƒ4T0câ²#EÁ¥ÊR¶4«áÎ}Ž}âÕÈCJ]Ô›4ÍV)¦Õã–¨?Ëj®Eî-B}kIòãf†ÊjÇÍ –Œä/Ü`Ð9må‰0hƒn!súùTWÏJ!}!ìFÔRÄqRÃlwÂ+¢˜â×`9ßâÚˆÑEŠBŸzO¡&¿ý;}RWüD#ÙÏo,…k"û…èBÞŠ¶¬ŸKÝ3 Ù÷;‰i~ ¼F€‚¸LÂq­‘ZéÉÄZø°}þ"u£Èøa¼VÇNsŽ·}l§§Q¨˜ÛÒª9̹œ Åp‘™m£¸5Ÿ;…åõ#îÀ—á|tŽ€|g f­³ºòÇG÷­ðüä½”BZM“" hI.ï8-ACHŸü±ËÑLCmÁ›yâÔ§ŸÏ`náŒ#g.†<Ê#’/nù¬b¥µÀBˆç(6-¹ô<›(º.12#G$@ÈjÇ®ÄkÈÇFú‚¶@EHXå·/qàã‚EßCŽêÙ2¶cCU0ƒ*+ò$Ý•q/Ð~ñàG,Œ¶ ‡ÿv—ŠáRÿ¬Fø™áõZú’—¡×bCŸbdx¼E£i"0·òÓÙÜ/K2«ÎvOÅ\ÙÒˆZÄ^9ÒØÿõá33Æá6”º@NIÁ½‡o*ùÝáÌòX¸ÿ/ŸÌ'˜ãÁȧy Öyóâ³ižT‹qñpÿ¡…æe’8Q¦´I$• =å98 «RXód9;±›ê7ÈëœqÆüÕw®õlœdÈ:fÏ;òbàòºÙÎ7ûÁø¯F¶^ãôæp0ó{99‰Û?é²`®JWjæey.…DÚ-¢Èè§_iAì×,fö ÜÝ2£g[«ÜƒqCZáÑÂNãªî)¯jÆò×›Yü©,«> r®Hݽ¼¿ãa AåyÐZëoå |â窮בÝðsÝ@Ù‡î|(¡"ëM˜„ÔÛ’£ê¼z•ÑÍ×ÿèíü9Óƒ€ØÒˆ“°óÏ,J1ˆKï@¨˜ªéu*Gºt Ð<[«`U¡U¦§9®ôº¯˜#m\LjšE%wX9ÌÁMÆDwjV¤±û ¼;1UõÈRŸT õ ‡ßX+e+2ƒ „ç}ByÍ.•^8Ì}èQ~=rÍ+·˜[é|Ò%åaQk5C<’¼ý0?*~ÒæOt8Íž®D6Æ÷a²r 9¹ í¾ð¡¾ùu[ÄTÄeG±ö:‰ô4Zð> äÅãG×À‡Ì’²zƒjœºÈX1_=ï±ÍÐAÖ9V¸¦ä#Öeëd¡çÖ=«EÑ -¾Aä}z ±l mKªÁ´Îs4t7Qº’¯c‹¥ô=Oîóñ?fšâž°'ðߌM¦ºÌ5¾Y«€SŽh†döÕߧÆÙZ§]ýz$-Ú*ó¿î>ƒaìÖÿó›á-’Ï({,Û¹Ÿ„[H_ÿøƒnÈâZ¨æPLj’j¨—Á9§±éî• ØÜï‘´ŠÛƒº2€›ßª€ŸèóY bM?ò>‚®Ô›E*p?ÅBEP|ÄÈ‹·¼Âu“hÓ€·Kñ:Ðpk}ÄØÜ`©P?÷c ‘à¤QÄ#§áEÿ˜åžˆ“IÂîðÂØæGàÀ"æµ*dÚ-ƒ®Œ7Èñk-5 ¥Ék/â!àDœ  <8ì±ât®zÌêÆXpÐ6ñØÀ;EçìyÞUW`BëT yxä·ˆò4uÄvLŸvyoYuèˆëy²ü—Pàïø“©ÆüŸ $mÆT×Ót8"}Ü:CÇ/  r¯K¦ \Ò¦LÒ ÀÂ1,]оo—þ­î/olh]ÒBÖZ´oÚ©n†eƒfYÚ„ü ¼ŸÊEu¥.ݸb/ñ…ÔRdò½iöÂD…J8g.Jû”Ûa³uÕÕx††ïàÁ£ƒ±Xµª@©b™#M€ã4«D7SÙ¢Ët¤Ð£d­ma42±è5¢É׬<üpêOOÛbË •Q®´+…0SËÚxä›Ù™4€Mñ(I¹¯ ]ûOæ¨"a¿€]R#5Ô0*¢ö´‰¹íXÚ‰ïE¡¨…—½¸vðDÁ]Ç5Øt#uš…INØ(ˆµs‚bü ¼x©…yðsµe9ƒE’œIH¬­– ŸÀzOó~Ú(؈àP–”éïܼàŸ> ¢Ë¨é>Ùç»9–Ë*k[Žhú"'éÚ­ë䚉gr…á³5' Õݪ)56˜ºM_\ÄY0/¨éâa ¶„†„áÊŠpÛ]Ú´x zÝva%\V‘<Ö´¢šá–‘ù±(kîðp™˜ÔYÏ«.ó,~»£Eɼ*tÒ-îDõÚfLEK±ˆFz–_ÿCŸGw¤]Øû˪n^ é<«=,ÈkZ&öãD[ƒ)†ð¡œ÷?C¾d±à×­h“¤¿VàiŠðu>ÐD2k«ÜDþÙ ­è¸‹Ñ#àeÏÃ\{—a!±›¨„¯ÁÝ̵^£¢¼þbœLDq,FF-Ò @ã¢ù`ª_÷·«Ã¾)ŸÕ\²r“ÕÓùN|X¶ Ìâ,n(«¾b8äf`ÐqÍ To`¼‡ÄѾ{#œÁÜC$9‚w7¡ÿä#mò³9̸‚œf‘q°_'Mþt€‰8ù%Út€²O’î2¦¹¤_c¨ôÆì¶Á‹ÅŒNx–®ÛYf‘vìLEw+‡Ú´æ܉%•Û§É è]ÐbÕ­.¬qþŠ/iùuž=¡5Zf“‰Î.p0˜wq\ºrÖr…€0ŸÞÄvÒ îéþ×톈ºVp[C)pêÜŠ©hCR˜†Ù¹‹t;Áò£dÀØTþË1}”¥í €,/ÂÃërh <’[’{XxÊð % s„zpC{bHø—‹Ôá9‰ pI±RvΧyNï™ht/êrÕs†å’׸?4‰æKiØð@êÄèþÐ=…›™ùÖ,š“ìÏœ8‰¶ »u¶Êø*¯dÓgÈLp«×ó–Œhíç¡VÁ‰ëSgß™„úa3X©)ìÛ$t}õ¨9ª²–dgTYi“{bÖÔ}ˆ"Å ¯Å\|°pΉ½Yºo~ü¹N¼±þ’ÿØw©Š…ø Æy¸8°êБ‘¹)á_¡ÖÚÕWgäË‚ˆî£9»+$¢Á ì-Ë€‘²„»7ðd.µÑù'$è¬$Aâëݦð ,« wÔ>&6Îe˜"“ZËzë€WóqFI …ׄT•×/ÔÊeà'n°þ4ɉ¿Ì—hÖ†\KP˜{ +;WvÉå>ÌÓW7ëhd<¥)ÿA#!1Ïã0Ç4"´òpWbEÙø'‡4‚F"^ <‡TÑÀã£'!”£|ÌN-äG\}|Ð+Szæ·5oÚ#¿˜±¬¦få÷¢kd˜;9™ Öµ[LqG}¸Ísôâ ª©¬Ê»a#˜ ž]Íþ32:ˆ¢¢:äƒ Âê-U+ìì$. +‡„å“'4AZ©ÍŽ•¦5ð‹c©¶`)(A#n–}VÜÐKi‚ï’oóÖÎY¤êØz6¤öEjúÊ๾ %HÚpdŒàúORðýÜO™¨Œ$|,öBbƒ'Àò»VÕåÙVoåS¨jÓ“ÙËõ𔉿4øH¦·ˆ¨,K滄Gæ¹$§¯o­§[.nב ¨vIÃ6$ˆçbÈû}eïÚn®éÍL‰ÁoYMUIl~®Òå×ê¥ûº wžý\fª£ñ@5%Ñ»Zé”3LÍ@i§µ ÜÍ3EËi©¶•2Ƚ±õH‹Eo’YÑÓÖwÚì8øõ~¡‡q±ÿsftYL‰˜PY89Nb&XØi^ ,a,öœæ©žß‡0—onVã½Ïݨš ^X°&’©ùLJ/DË3›ÁÒ<ë¸6a[ó bâ/mX5Á^¯AËòñfÄÈçco~º ŸŒM´,œÁµ¥ïyæÑjÉ,ú=[îÀcLŽl¼1ddÓ…v¼ðeŠ9b0¿!‡ðŸ¯·ÆÁ“”Åì>J†G»£Ypò*HÜk»hBH•´”çVZHEµIOÕý@kÆO\Ú§A%Vk.YŒ lÙ/¹³O們°­ÞicC™sßTÔøÇt¢[A|?>D ‚@&z=Q;$½âP.Ü€jåöc}±¡"Ž};¢Nó1ð¯Ê?Ìá—Û­Þ…C¹¡é{àd*ú•”¼¼;«ÏPyˆGà_©j?òæ,NÙ}›Ù›jŽZï“„±ðÃÌ>Ü5%{’°-m˜?Ç8cIÄoö@ae.$W Ä"{%Qhû6"Ôü!‡ì._/ª:ÔP|®sù¿|¢= %x0Sû¢¶]¬±¢¥2ô£–÷Xã9t-8gˆË$²¦'H| ³±`ï-jÊöÏß'›U"g£˜®Àfªw²ó+1*â"AËZÚ®Ñ0T/hZå5Ý™iÂB˜£-üœµ±$³x WÑB€êÂ6¾~fE¦Ý>ÄféRM¨§Õù€5ÏÛåÄc2j%p,oРZYof.yцrFFŽJ‚⊓ÍF!}+ M‘Ñn¢ÿïYeÌ:¸‘u8E LÙ‘=ò`ŸO‚w,¿JCÛ,æ³FÄü˜­Ü§ìTjS}qäÕ ë?Ñhš3Wj „×D5ñMÆp´³ÕZ‡œ7+ôfW{žÃ½…­pÖ\°ni…™ø³¼*4`£à\ÖšV›]‚¾o›5Ζ¢±éµ øî6;#Öã´—ëafIèš0H¶x‡Dá…DîC~(8+†®¯ àÝ3¤=†BáÏÀ8¨,“œøSk^wʼï‰Éÿ/fŒ=ëÂ[Rˆ…çÛõÐW?µ´Xü¬îœ ÆÀ± Í:6°Tß$mµƒŽð­£ h(=¨¥â¼ÄnCGgSÎq4înŠb¯F&~+|íÄÙÙ)̨D¾wpdf XË/–ù¢u6Š7)¨púžº"«cˆd2^òêƒsþý€œ¬Ó1p lÀäÎÛí!ŠT^_É …³2O i=õréâQ =+óªvFƒ=ŽÖÑ# œ¤¦™ldáÃâ‰FâYt2ŠZ'1¿«QÍLvô…üW|`îV“ä±0'ÊŠô椘çQ£ÞL[¸ïu»So¶Ã1¹…28ý4a—Y ŠAY÷;lô¡ø€b_›¤”‹!žl±§^Pk;(´õÉ¡¶DˆÔ“˜ÌÓåAöY-9ïôê+_{#}Ë¢}`{ã¦4Ø”È<ã]}‡5ز¬¢g ¸‘õxëûÚ—A­¤@اêüÿу°ò’Ãhž.™ª[8»â@k¿gŠ³kvèçŧ۬ÖÕñd%NM<øÕʲ°\6³@ƒü<‡?y°¦‹Q…1°Çw{ÀAPì–FBßók±­ ïë­Dn„DZ,Ê×»'[Â,hT-ĦAÍI…V+{ÖÍA‹¢.A‘Æ¥|Àz•¼Ã…°‘ƒjHœ(ºxxzU2㊈¹Dïñ’’;$4x º.ý‚ò.ÂêfWRxÙ Yq¢S]îUa4‚ÜÕÖ§à9{‚JrkôWò9¹õÅÐÔøze•®ûz"XøÖÓ—/~ïÉ™V.P}«¢œŽí+ì êjì0¤54m­+¹¼š"X³I4džºWjÏv ±héwŽêCÓÓw@? Á úø£ø§¤¦F“D†ŒòŸL@pÛ‹’ëVBÆPU,ShüøN¸ÌßÓÊcôªç¿€úJsƦs?¨ÃJɾ"–·Ô¡6] XWÖU(…ÆÐö<-©ŒŸGÁõ^ «³Ë7€äØ(U¬õ;n`e—Y€kÅ*ÓøJ\ÉÃíQ84/ 9ƒ¶gŽòð ræ{fÄýH\/ØâÈÅ1®‹v\l×ܘ3Ær“àíY8¸pý3L’Q Þ9¡ã&îù!‘e~¯=°q†g.aNÿ›S;ßîðr88:¯¬¸î¯§™ ¨ö@‡{·¢›iî#¾à–4î]:Ùú½ÕÀ>S÷p!ïSød-T×ää6Œ Nõ†I¼û®d¦ÐíÄ>&ÊLôcõ'7”aÄ®‘¥û×®°‰ÉNEä‚Øã.´$»nȤ Û•?"V×äöƒšghF¨5ôÑÄý‰™ÉCšN?DüérôÝJÏo¬×fYpÈ w‰Ùg¢›…¤ä~ödÖ=b‚R´ñä8 ¾ÉÕË…¹™ò½’1ªÛ•Éÿ0™½¾u@Q9íA ±Ëìl!Ž¨Ñx×[!Ò^)áï]û•aÛbÿ­Kêèûs¢sû0¶©Q0C8‰rKð²¥ÊŸ%Öü˜ÒçˆñÚÊj“™£k옖³¯–áœd?ózuÝ xN‚{ùZÊÀî•RŒIù™L-dhI2ré}7TèT]¡0Ïܹwd£é'ŽôÏŽEÚúxHF¸¾Þ/PÍ&ÏR-Ûäé \£™Uä½#xŠÐÚÀ³n‘3£XBžjóPÑ`Ñ7ØeŒI%tð{5c¾÷¾'¶Wíê̾¢‚õ<œ"ûüeÙnWØA)ïul`4­…F."vÜŒîEB¬É¢Hû³X½È• õ¬…H›ðš|VLLŸ 2 [UQlȇä–nz~ ÉyæŸÏøY$4ÓVçV_‹1Ìÿ _支¶m:.„ò™gHiýs4¿†TÖ‚ýŠj Üµž9; Dý™E®æL=„„ç+rM¬U$gÝÒ8š¶Ê^‰RïÏ/È#ø,l¯k¹QýËx¶Wzh¿Kß½ÕÏŠ;s>ã¤* •€‹²@2ÄïóÕúÇ|*· ž"tñ"=¬ŒÛžõ\Èá'dgÃÏ!êWSyˆ>V‚w 9¦J´"9˼óÿ®:³ÛúOÔˆl?øU çÕ_Ay+S¸:Ä;Г±˜©öÄë>ùhkUr0!LâP”]*y1|kµ 7iz˜òôEÚ«Uµ»´£9¹‰d’Ô†¾‘lÓÄ}¤¥º­¥Èü ¼¦}qš(YÄ‚æ]`‚l®h.VÝ#ÒN+ú)Žž®ŽÌN3#‚À:ggÍz"7ð[›§ÞõÁ*CrÑš£Öí'£" ŸÜó6ߨhì^g;­…dè·?Ö ô0ÇƪL±žK›¢É‰¤Ürô±ãpwMþrªŠÒ¤õ X˜@NÀF} ÿ.pÛý‚“¼^ÃhnÞ'ûiD ‰'Ÿ9ˆX5´¦‘$£³Já·ÎáË3q¡ð[)[µ¾ ÅU{+‹‘Kc"1¸|™x|ÎáD^™Ã>Û ©Á@™1!˜eêE:à›_‡ærI ÷+°CæïT@j-i,øˆyáœÑ1ôç²ÌŒ¨Hææ5ñR6’þsVĨAp"‘ÈØhåÄwý·Îh&@óÓð 8[õ3éá-MJŽ†W»hÙcüvh|ö´!gn³æ¥øqêRŸ%Ê¿ß©¯XÚšësþ«5ìë8A²_â0”Ö}Îg ¶Ü`¶‹æM¶&k‘n‡vûÝY"Ú{f  ßø(bì1eÂ.¡×é•c¤£-^»…9®Åi¾>þœŸ‹¢“)¼%dyBVS¬2úpMY¯oÜñG±]Æb÷Cú«WG|¶>¹ar´H¥f¸aqk©Êé›ýÔ(NM¯Í’gXþâØߘ ÜfR(CåÊ–S¼¯{®íÛ+P—™äUR˜_Öó\kf˜32wƒ»p·FU/,Ú+fáøáH"زõŒYN 4ùJ¸ÌòÛ¸¼f©ÇPsÙì@Cý€Ò‰ø˜b›š#@äz‡JKzÀ²‚CŒ®üåznQº·‚®ÄH½MÐÁ*ÉÝw†IEãh†êÿL~ß| ”©Í#%$hR„fÏîäVÞFÛ{+²¯Oîu~àÀSÖ-Ä¡[ÿ/!Ãi¿èËzY7⨪F>Éñ*(òV±ë­Ìôæzé3ì¿HfƒÄ‡Å‘Sþágì+pêQšù£U•ôÚuµoJ•¤¨Ì´|‡ûw©Ÿ) W¤÷c m!¿ÿ¯Àü`â„c!xí Ñzs1[°bnÒÎŽe(´wñßeqPh¾$’“ëmT5À~g¾fÙöºí/ ¨à âò4rQõ¤¥KŸú¯àM“îà›ùØÕË°”xãg þfXFb§ÿZ+ƒu¸Dl+Ô^¥ë è³""ÇÑkò©k;*ÊBq 5ñ6?ÿn÷j­…5U¼ŽhÝñ7ççS…Eö5õã?0FoÁ‚¦€eØvWosö´$OV˜ž$±¾m,qP¥Óé9³ÐbUÓµõ滲Žþ¦ú¤•ã?¬PkÖs@“K²ŠÓñ[Ç+Öl…‹êò«WìI6%¡¹1G‘FZÿ#,IÜ imñœ/î!¹Yá h{câPÿÿË5IæO÷7ÓÊq?î_òìeOiS}¨¥úåS>0Ò4ßÇ&hAlå1çBpúÂZ°X¦uûæ&¿å>߃@«? )‰\ÁåŒë…d5¥:Þ«ÿäkvèµC¦Òò¬F´Ò|eHúsK-®×vBx~êˆÍrßx¥Z•¼Ýè>)e Z?&öh"¸‘ÔèB6F@‰¤ò¹G —K^z‹aŸ"r˜›Uif%Ë`Z‹Œ?>8”Q¯˜%VÓ÷£â¢ôõîÉŽ#«ÎË——_nÓá¡hŸ±iå²Gæó²âÏš«e¡6ʵ€kã¯ì‡Œ°’ì°Šñoxø !™äˆ%§¯Øˆ˜n©‹òÜÈë&3>XaÓv²ÕQøFÚðÝTö#Þ;sÇøà”ñµ£­)ÃV÷$lPε* ˜'Ñ:Ø+ldò:( »kØ–hš¢^¥YWŸ5 Ù™”¶Nq5NUçäwgÏ;çœíèÒé@EIYsξSŽ¸Œ‘Á¼ÈÌkd;l6”ÊÕèq,ðýººÕ럦W€ÿï¯Ò[Q$äA7Éhºà͈ÅBJãj™\y&,¤¶b"Ñ«žØÿÿµ'¦€à¿ ý½bñÇÈd^F@i—Ü ä6ƒÐ„!Û´ìÒP)ö¦ú²°U?K¶PàP¦K'ì—MÐØ Üïb4WZÀ†Iª±tÍæçYJ;G-±§YàB¸Äë꽫Éùƒd¾lºiâ üÁ=ÙØt™] YüT.†”:B­lþSòÞ)@$,—à°å•jŒÊþÚfó6±Gû@ ^ïî§Ã(”c¾²£˜Žcó/‰½q.HªÛm’ŒpØ®HçLM¥G~-{°«–,+Ýtú$¯‘Œgà;*àµÊ¹£ÚeÆyÙC¯‘BYàE,9‘€fß„ñ@—óic!lÜè—{’ ˜ŸË—ûB *•Þ0\ ‘pˆü¨¶|yY%¼As¯_Û ÚýÇ^Ž[ˆÉÆCÂåb„oÿu]Kr9{4Œ§Wê·Þ—Τoß–xq¤¡ð”;FWNw¹ÊrÛ=´*U¬Q-o#¿žqδ‘ë|>B,#Úýíí†o8ôÂ!iÒÿ<ÈÇ( ™¿šû»„ @b)zB¿e+Wá^W+£Ø*éÈ@OÕh/ÛI# &cƒú~~C”NSS Š¡Ìß ¤´p€ÚG’j'Òqÿì¢W!Oh@ýqV»ºÑŸT·¡2åÔ =ˆÖ¯Q‡ÍPxØa«]].—¯0âÔ—.¥éPl´-i‡I…#æ_¹D«˜’ ƒ\l`UÞE´‚ù÷•iãÀžn2Kú2¿Õµ[%ó“¸ðéЫŒIRÏ>r×IÞ!æ•‹ ÜH¡Ë&@…Ìa’½7sU ð-€TQ%…HW'¾ï´ßÚj‰8'";ëÙ»¹yß]1݆sƒê¦M”Ÿ%uƒcÔ¦X]Ÿª}ßbbÇ°voct³/øL]`’û zÏ`µV—1ž1‚ÝPkˆ9œ>Jÿ7úWM`  áW³¾±¦_V!\óžCKíyüocm7³÷p…!$à©ŽšS.Í® 7KØÊŠJG™5Ìæ ãS‡e3Šó/' ² -²Ð®Á•MDHùþþ!]90ëºÃÙnÚhÚL%b?| )qÁEâ}w7ÆE£ùešˆ¬h<ˆ¼À¬‚Èɪø僆ЀU®ö¶·Û T$4ßOй³jŽÒ|ôŸÛΤ޾£¶ûY RY »©M­QvÈ®~~mMâY@z-Làáž÷GÔ~ jç‘Šÿɲ9ñø  y‹<ŠzzZ­íTÌI/9w<Ðßé å~Vk}|/ïc?üB/åu¦à°AЗi£Ç[u2'Þäˆ}™åþA¥µÎ2÷Y4t@ L†3ëuž^9›ȶUA;¡AÝk8ØmÃZ ÃØÀ½fÜ´,¢µ‚} á¥$w>œþl’UË2Í éçö¢À)«:ú¤#´ƒ¯oVþvh÷HŒ®|´QdQdsss­–¢<TxØR(‰ÿúš :ŽÌ¬Dcm›A¸Ô¡¿&Ç¿qÒm$È1#½÷*gÕøí>ØÝ 6·Åjö[ÀT¡•°½å^Š ô¬WB$ר@"–"˜ ·ÒÁ\~»rt ìÐÆ8}ðX «(ºûn5…e–òrç%£#P”¿÷(ÇfбAo¤(•Ž–…%þ¦e”[åx3¾2<êìå6IðšçØäõ<ð½à»ç+ŸE©æC‡ëÙ û ’Ã’ga2-£LE뮀̾IJlÝŸeºUWuã[4@ðWÅ·z°NRžYDÕ;Ǩ÷bmŽóùýcúÊ?Í´“Z 9A¾åmÀ(w£]Ž':še4ŠLðí#ÓÍ­lgæI<ˆÔpë’Z×pÈ-Zh,h<Ù'÷U0»˜y[˽h¼xиQÃJBoÄí|¥.»öûENœ5Ûê§ô2!q…Îu tG*fá<Óº-mĪOàêWMøV—ù2ŽdCÓq²¿>ç~çÍÓ¯Xc$T ˆa˜¶@g€Æ0‡¤Sã}ÊkTRòÐ…ú«ÃÙZoÉóa·g@|`oÛ/î†íµ©óyòvÚÓ…â 8ë‡Zڳђmx ÕÜ*Ò# ɨ³(@6”ÛTÊýÀ4š…ð'ÂJÈ¢lͺ}+Ã×â‡䇷»I·:¢JÆêu(]N8<­i€ÖœŽW³¬%ä21žÑM¢’ä%—@õᬉ—sD`ˆò|&²ÜÊÂcžÎ¯°(VCR/´976ç2µÄÖa(}V¦(Ÿèvdj4+FÛ®â™2l,ðå@·ªq'B¨Cí§ãKã÷½1E2·‡×`iû˜W,&©»éwçg<ÿuÛ§þ@cùn~®à·ÖöùL­ìmu¹9 ‚{?1Ig:ŽrCΚSÐ|´c2Dm$½Jª©¶Ö HÕi?¶•´¤¶«êÞ3ýù9TºÈ1}G˜J4f¿‚nÇY8$E•2tÞÌõµx ÷fñíZµ÷&€ª)ŽLÒ/ŽÓÊ< GjK1‰)jàûf¿LÇÍããäuîb2ˆê$ÁµC}~šhÆñãH“îE»ÒûÚ9Þf#ˆîwüÂü3å¨Ð)ëûµƒÇc÷MJ®zù’QÄ*rŠ”ü­yö­ÂÐÖn[ÕCRi°r¨§ýÀ.jÔr'O4$’í'v¡%_3ï9Œ”¸H¯sÁº®…M]-¡OŵáÞAX¯BŒÔD¯|Va^È¥î[bu ³ix^$P—ÿ„Z $¸Hð‹v]‰\¨p4ñØß¹0’ÍzÀº>Âl”™þvb‹õžØ_VoÖ¥éTëÇJRh€½VÍZÆàeéÔLœ}üái~ôÞ tÙ´|{ˆgVǤVUY/YÝÌèA,ƒìÕ|Î4')ik9ýSmÛÙl¦ìàGS·½Ð<‡oæ}µÐö§Us‚2â”ÂÕD9~Æ𵈟O¿U§”)w$Xœ®›g¬~zŠZzÓ~ e¦ÙÀÒ‰»Šû$y$r¶˜àÇ/r^¶"Y¼Q"øjœ7’&ó@-ˆys¬"™ù`‰Ùø[¼ŠÛfð•çzßÅù™vÖá™ìÑg¤ÔiCÝ<ýÙNyŽïÈ_NÂiÊÝF~ç!ñïòÀh\ü—®K†´­®‚D;+¿eÊÛ’àã7P£¿éQ—­ÇùS@7-u0q1Ø–…ºWLO°þ£ÙPUÚ>€œ(ªÁ€bÃXÙBe¼ °¢jø;ÅMʺԽ†³0M0ZØ»ŒWž³ëýKµÇ.–½ß¹ñÖµvD&<”°œîêô²‘è)KI8pï”®eûˆ‰j½¶7¤(#øïU%Œ w¤¯š #ž{²­4dÜ•ŸC±4„eÆÍ øx>s†ïѾz;Ñ!D1¹橪D\ã § ¢—q4 ²$Ž;hã©Jj†0ëÎ?šHˆ“šôø4÷%'T*9ªy$"”q56Dd9x &8A ÝŒY?¡¬Ÿ -{óaV°ÿ1ŠTyÛ-ütÿâzsþ³ ù+âq ¢+\Já,ÆÍaFjâÑgïEr0-Ùó3ZÂ{¤ß¿â¼Y¬k0ª¡˜`KÕÅ[Št1n˜jh}& 1z)Ûï1$òL„4j:|+¨¯ý‘C’£ê‘&¾•­) ½å(:LmLò .;ê»ÊG(íòÎîNmTŸn;,GÕ €g§›5âßqü\ܲhË\é;‰¶5Û¨vÛ)ŽL}úF¼•»­&Ì©Þ1>Çæx ’ úI!4ÛÅs¨jÄø½ÔÑK‰ü†½g»Ò:7~ÓZ“SVË¿òÏp˜0Ì­Îu,ûRWS|ÐÀb@y™—:_ט ú‚ý«W4Åÿù¹þÌÚ×Ócw•^Ï¿eXD (¶ -Í? ˆæ¥\ “J”Ø1ÛXI塈þÌQgÖ¸\QÎðÊëx˜ˆ6ØÇÚ °Mš`÷zä‰eÚð™ÔáÃGþkÕÙ)šám¼oš37Á4’ÞÜ\ ?rÕ}ª“¹$9;$úuO–x#Ú#`¹§¶eÄ.s<>`ÔhK{sÛmbj]É é¾ŠUŒ ‹v(‹ d®*:Ù­ø ]n’Íb0般o/ü¯c3ün.ØÝw¿‘wº“YÙz_«ÿqcÈÖ5l ëdn5©§òêáÓÕÈÂ[©ÊWÜb¥‰‹›Æq"Í$_Òe!™C£‹ü¡ÃoËKÉí?”gyà–¿pÝÄ‘0$iêBMEˆ"žÍÓØ—L1Oïì–+ÞnÔN£ý‡Ò¤q ü¸(¤Ÿ#2]β« ªm”o…vSÚÞåö„‰Ê ÁF{ENÿMüåƒ{öý\nËÈ0g#T»LJÝK|ºûoqF ã<•Wrk–Í%7ºŠ©'á–³ ^|°ö_}o˜w±“¬ ÛCšG2ˆËÈ©!(u½¦Y.(æÈù§.)»¨_2SFk]nk@;YÀ¨G”…Û €\ ¹Šp§ÌG£‰÷v3,Ìù’½ÕØÑúåèž³Þø˜ØBhbÚ‘f›…Ã欌|rÖ5#K¶ùIx1†RelÜg?Ûêqej§w"—£€l=„{¯WY»Ç¿u‰‚ÉOóå{ Wºh.öÂ:ž;z;&Ñ!°º,kO=„P´­bz þ¹ÊV8ž->ƒš¶NyǺ§Ö2>W$WO-r£–aÀ\P%çÜ'¾hA9 ͧQ•“¡ú7†ò4= °Ç°4b¸@«'Û'ÙOGD‰Ò{¨²ÇÛ°3!ù½^|KyÓ{e¸î?ݤƒˆ—^Žìòƒá¹Už£×Ë —…-ÅmÆé窌ñ)'X~°µÿp ë=ÕûÐu•ÅØ‹2­‹SBBû½ðSÙ¬æÎ龬ëzäýÛ:ÒÛÈE´dk 6j†5'6a{²¸ýÔõnØ6­¸蘅-íù"¦û†ÕâKèÓÈPÅÝC¡éRéš>QÊ·šþ©M3ô09¶™|ö#…-VP)Æøò?ül 6°ü:½¬E^ÇÛ¤Û8u¹œ¶ÊËxÌ æYŸ…„¸õIP)™ Š?P»À£º™–#S„2‹2MÉ ¯MB!´{·EØŒˆ4V<7-Ø2C½~²ÿk0"óàb^Ô@ðÝìRDûü¹Ú@\Ç3¥¦&Ó¹Oµ‡Ç\é¾´-öD¸÷¨ièË‹áŽTf—z#…Æ÷ŽkÓµýÐËË%:PÒÝÖ&|õz4;{`BÔJX?©sx˜ý‰\s]•†xBØù*ý|ïÅeñ‡žã®#Ä‘PlIû›”ühKk†ïΆ]Ãé%,L¶½²!²4‘Ì“®÷ÊÒΞ›®3¨•¹Ôùr”]^÷²¹o¢ÇPb,}DÝ@¿3燤¿×‡ô1$–ß²ì%Ž*s Vd~nñ&yõ~Ô{äÔæp –¹áŠ¬&Ãģů(Z˜ÕÕzk»hù ?ß³&ÒjI¯TÍÆ…â‚åG×\Ï…{c糪Í'FF¼õŒ¶ ô1 7‹Ú8´— Î…ÐwjJW‘Dî\xŽš`Ç9%…æ²—~W§}*Où<€7<Oí·ÚŸ‡ñØ×åÂvdWå!âCÞ%U™Ý`•W6C$)[ñ‡Ÿ;RJ¡žó|ÊõVwg‹áå#Ê×é§'G#œ66°âŒ—«E„ÒŠˆÙ Æê \Ÿ "ÖÞ*™âÌÀl¡b7£­cÙ:R$ZRÕöÐpRл÷”R)Þ‹ðÄ-IÿŽÌ©í1ôC<_ø͊å ['pš÷„}æ$qSÜoL]w¥Ë«Eþ9é x›ê\?ëXF0¼ïõX“p ü­G‘ì dqm,o®kɦå@H‹§ÀèCÊ1*Ô†´Æ¤Ïž7·w_e½5’UËw÷ë¿Á® qÝQ¢l"ònkÉôŽK¾ü¤Åù1|¹ Î¬àh)tƈk£ÖmIµ€d›„i“¬M`cb'á¬–Ý oaì÷8¾k÷¾ïÎÝgGÕ»•M+8%¶?¸7ú"=fª!GcCà;ü¿KÊ׫1.@BçPLpûá&¨%WǶåA‘ѳˆŸ½²ÝßÎÜ9éj2féé¨íò L3Y¹Õ­ÃÒÀÝÂÛrœçI ï´ëò]šG #:»t±OÙwÝ.¤‚‡Ά;¸Gª_–©¬žF_XFÐùáO“SZ`~GX¦ÂvÜö2Þš2e™µ¡¨U{:³öÞ„å­˜õðß*±7e–Sw÷ŒÏJ™ÕN2ÃÙ³7¾Y€”û*àÔ;-ÏT´âyòÉ(ý¹äé_øö­-DéWÄöN|“ó±÷…žÔ‚9)8¡zÖéC”5ÞhnbÔª;Ãx>ìèI?”ˆ+æ[…Ô+° ü—åtžɦV`ú`–±‘0ù:³(!+xÕˆ–ûfŠ[Œm_/2 Á›úw̘¶7Öq’-ê®­’œrÍ~¶80çqgO »¢A”óFï*\¸•BŸºÈ¯žybìYgi¸ºZ$ñ†h¡µ²àø ûFO ”NTž ·¤²]’Çs¢ Eñû1‚¤¡ÿ/¿­Ùµ…«¿}4Œ]^©®Öâ}zv¹§`Í£nT ©“:Æx[År×>Rj Bÿ‹côøü<çŸñBRÌ?$v{ë³kîZ¹ÍÏjôô†¡Öâ—žd34Vë YÒ‹Ð È«8¾ Ö^èƘ.‘hOÑ‹²´‚¤Æú{s$ª\qœ7ÿ+݆mvŸ®lîz (I Eb€5¨wµOŠÞ”hÒìqPÇ'†UÅîé& ëD§ÁŠõ-æºÿJäû˃篔۠`wÍø·Ïq¸á±Ž ]îDÁ!ƒ:?×cbk´ðÇÞÜ‘ŽÎàînò¸c´£°p4¿ ø…šÑb6sÍd1rÙÃÄ£†‚ÊÊz=u˜*I›¦eÖd¿‡Ä,XÝŽ‰Íd-ã¡…U脬.¯s5§ª³Ÿ\¤üSè÷ÜOd±àó¶ê„ BÚ˜€Iñ¯SÝÉZPD†òöölºya|tt6“#Ü’ZO|`Xk´Ä È mˆ?Dˆl,ÒJÖ“´³¿$þÚfúœL°|Êó•F¨Ñ¢ªüÚJ·ôXæÓèîÒ¨Ûùô| ÁÎ¥’Hc k‹O<ÑvÏëdqz!ôÂ!òŒÏÊdùu2ϵ›g½wYÅç ¶â bS{G‡ðŒÄbʘ¢Ù;20?Tv~»™°úÉœÀ¬}lhø]09ÍúQûüQË[l<ãO¬@½±âüû®¥Àèýw¡]ÅNCòêŸÁªJ¬Ë$F¾fŒä‹yŒnM1 «>9 À䕃¥ÎåAÔ! ŽÚ&#^ÝFÏß F45åØÁÑ¡•;Z÷”ˆ Ivý3&iþÄ:‘#õ‘j„í¨ ç£! @¿¾ kO€ýMtn¶~Ft@j©,Óoí=ÃŽT!®ña+ÃØñ ®Íz“ô¸B©e™,þqH'+ÄŠ/f:Ä5ûfS ¿Éér=FmžÎ•Jy>X¥`!‡Œzw ÞâøoQöl@¶¤vB¦TÙ1„zTì{\»ï̧üƒ £Qöε+ž®ï_âwÀÆs±E0°àúþh#S!€¥´â¢ûY“¸W¶ÅSiX©¶¹£u½ðð€hå/ã)¿ÁŽëI¶)é”j±sÞ“2*õÓ@ÓGÓ¦ˆØ˜‡ÂTN”±ÞFc ggZG}¡k{xxè=l1ÉiB¤ý…þÃ9«ƒ+“Ì‹µaWüÁº‡g¥FÑêi†GžeƒÅ4öj)„¿VœzGÌBý+äúSy)©š£ž¾öpx·ÑÍËòö¦e\(¾™ÚUߎÄìsãÓ3½½k Œk S ¯_ªr™‹>.»a£±$Nsø/ðé„çC C¨ïßk[©Š Jjl_pòê¼\†5Æy§ phæõéêÖ@³Â?*zpòÅ|)\·ò¢@Ô€SnS¨Xu#aån¬~ $pNþÚMeЉS7ðÈðC||œ€#àãP’:éAòÁOä¼%Jx6”{Y‹Õläá-ù”Ú%I#ŸÆ)ÝÃÌ6”Š¥¸¢î E0ürŒ?®-«Ù+Ïvr’«I¦0Œ®È5ÒÇá~ýñÁHNª¹Ç$*v_ö8Uiêt?é…³À…«H"£o¦ûŠë;…³aO ©^+d¤ÃxrÏÒ â¦µK©± ~A'AØ/] @ZMŸ Å—1½ë$|‹Š{ÅH»;.’½{'µX7ÿÒÚf™-e1P£#=ãNV2? ·ÕvuÈWd!ˆ z%Ð8ç½_"„g=9ƒÿDÙ”ngó=Y÷Ðs<—[𦳪ôŽ î½(ïð•„HÔÇa l ““G- I×ùJsµpdm^h÷¸ŠG 8­ÈMlèvâxÎäíÛXT*^ RC#ÄÒÏHÂ,#G„=#,î] ‹{äÎP2ZßP€ Ä Ò)3 ·°o4†Ôž~»m:3Ê€6b-ZäejÑMÑ‘I¹ª¾Šú™VVöêÈH(Dà…,¤F¾Ïj¶?s²¤"gÅËRTk DdJunZEgÉÖª?P–m(Ù 2×ìðãpyÏì¼~KÏÒ&y†%Ÿnêwå+!˜‚B²%­û†®^‚ÆOÃíä[ñ³pŸ>®'“Æ¡'‚Ò¨’‚h“X®Q¾”o×ãôrº¶£W`”jû4&h^‡ÓÓ¥… 2cÎQ«$ȸôÙ4#i(…rèêÊwM+ÛX.^ì`÷CÝ»X<Áà´cñý9bÆÃOS*  ­£?õ*+®W÷É”½2?¸ in42çØ–cƒÿ;¹²ó%‘€ŸÈÔ ü0Ÿ=©E}Gç@š L”¨ìøyxM‚„µí¨Àð !3CÐŒ¬uJ.ÙèE XÜœ$œ*@»–ÁÊ URUE¦òk –î6¡b‘úç µl»¸²“‡a=Jh) þ’ËqöÌW”ãR1w2UöRÉ;VS}Š‡KwxØûZnö$p4±3Š³¬PdoêÏÊivGˆh¤ ¢ÉÆS”ý|F?|›|w;´šŠ”þLç€e`”ê*éækõb¨‚‘äѪ¢ƒ(`j¯½e¬­ôa…H+sQ^•5ç[˜@ôjÒß=ú˜%Óá”]0—¸J»ábH—â‚®¶Û¾Iè–šÚlÐæè·á$SŸ#¬3ÿëa5t’¿Éኽ4cÎlçÓ>0ýQºž­ËQÝ´ÙË ÕÍ–k—']Û-‹¬¡*5ñ´|ºèmÎãs`©åò2Øjœ‡œM†ðž¯‰Ô´ú§¬Ù¢‡/£×‚è¥yÊ#áhŸ@}LöEªöU1¡»Ø– ºØ(̪U첑ë{¼8Ú Êø£{´‘¾;r>ŠDÇ“Ç£¦–#PTÛ`1 P{^­§½kMØä«z=KL)ÕìÍÕÝ‚Ol¥Î‘[–yQÖ¨DñNýSå"Î3AB Ç¥½[ ÙåãŽØ0áÈõ†­[Èu„P$pH¹sðS¬ÄSèvïý`î)nÙ ‹÷ 1ƹø×eS=çcjO$¡øcåÃÁx–âSÄLUvMg\I±ÖA°†#ηŸl¤hõ¡µ[^VR•JL­À„,D ;§ !«8o·¥{Y@v)óÆëžÀÎâ︚óS'E+Ø/:ú)±éû—ýàcïSÃO$aÊ:Á,q ×]‰v”E38-i´¨0ï¬ðüt57éæЃÏ"¸öz %©rÇòî×W/Þ¸qª«ó|Ì?–ÎU˜;å¬~×[µþøO“7²°Höœap¿½‰cj¿’ÉgD46à >ÒZ JÂHY´(Á»SÐ&8¦¤Õ¾>ÔãÅ4ÁËÍÍuÅHˆ@ 4Iq ¬9ÑY_w;¢yÖ|ºãØ0iES‘` ¢Kù5*Îgý¼ç%0œÈ±G.Ê«­÷ƒ2-Ö—®„?°:•½ëxXö)WÛ_2Ýÿ™%<ñ‡ç¯`ZÀft#o>ìq}ÿhj Å8ï)¸&x”$O l6A¦\AY]þ¢¾ôÞmš˜¨ |zšÌî%Š–ƒœO¬hÛ’ÍåÒ=Öpq§&ó¤×ZšèôŸ Ïð ÕüÙOz¹+ÿؾ^0SkÞmK4`™hýÁ©Ù¦} •çŒdHÞê"MГA¯Rä_!iJׂŠv³há&ÃJ0¨#Aúg”²¾T.ÔÃX¹ý™ ¸ÁÔñh™bÕÈÐm ’ªºHyèÜ 7Â{AÖ%™g©m¿4¨@Wy~G`ƒfÓMµð‡Bp,ù³×k§FÌê/Uw+H|‘ã-2r¬Ëc1Ñuƒ[OQvn…•,]j¤[Ïž8¾ÖBª·”‚¿AÒ¥š&[æ“Ý¥Éd¼éÛœÃßÍ\%ÔAû~#Ò ˜±.f(ƒc®ò…Ô/æ‰cÔø¿=¤øSíÉ£­tŠ*vÁ™ID+ÊGøÍS¹=QÉŒ&;¹Å°ªäiŒæ;0ϸª*_Õ>©ŒW¢˜IÿæÖP<¿dCJÍV½ç óï]L‰ €X4Â\ʇ7¯ñæLãÝ€ñÀó[ÕV­ýæF ]›¢:Ãæ¢ÎNà"tv‚9‡s4Qê¢\hgâ¹[ ¶HP â7=v'“5þ#ÝÎ*>"A¡ÕܸÕÓŽrC³»Ôñ!6´1oí&ðö¦Ø–b)¢jƒ3¢ÚŒA~xìË#ˆ@…3"YÑKwwSo’p3™¹»‘Ñým®ÕòAB å ÅÕ€ ;Œ,<ŲY„š×é´l†&s8Mß&—‘(ÖßÔý…Â’…ô3u5Gà@qñ&æ‹ÿyÕKÑqŽ¥F‚æwäg›Í B0Ypˆñ†¼òí|—ëCfc½A‰}ï·@dç–~ÿd“mûøè|Xµ4˜îY…L?µïÚâ{§í 'eËLavHNhhsQ˜îYzT#ý/™Àºÿ«Á6‡´(2ÞœåHÖhäÙo]|šdŒ«% ›ý?Åλ„¸›YhÌ9¨šj+/cÑŽ_–™(…g ®‚âÒs8z~ì–ê/ÆÀ †ªcˆÕKs»Î–!‰ö OGùº7=ûž~•ê {½rÄjv°¡—wwÐõ\ C‘ýW¼ x`[*ºj@²û·ˆ$§gSpo°$&«¥ m[BnHÑCT¯hLä«Ç¾ÒgPVyƒÑgËÓˆ½V«¹Üƒ AÒÅÝ4LÎÂœÅoæŸL}õ]k¶à³¶ƒ/·öñ.©[ðqŒ³Ò­’ºcr}g8e4òK}ÿ‘w*m*y7ü»cÓ gÑ` q®èËŽîs_/öÞUv…GuqüDÿW ¿XF?­6¹mŸÀ™¯Û¡áD×ø­èì^ÝaBƒê=fA9Š Lü DÿóKƤŠÈ§3DUú9r*§«è_Ÿ\¸-ÍwYÖ€£”P8èF̨bøFœÝc"œ¡í»I&+L‚ΛVÔ…_ ª>¶Hë¨+€YgŒÞ€÷@ªœ‘ôG¨yPMheF_Ï!ɘHÐ%i5"rø \ra¬ª•2˜Ý¢!Ä”ã-ûŽ.Ù ÑÇû»´f>¨nå ckäˆUoS×e"ÄÀêk@M¼ëUU$1’›GD&Í]Qe00~k¿¬×_F£Ñ{A9å¾k—ÐEÙÖ[oÉ£ð}ú?UñNZΘ6e6.ÒÉ›æ¦@±âaŠµK‹óÒ¼j@ºQ~¹UÛꟽ iݲ}Å2œÔºiA¥‰›`¥(]2äèèé2 XÇú¸ .võÞÐü¦ŒºštwÇ2ë(ªb’2( Ýû{ÀõDˆÑÞ ý9㇆7Üiˆ£!ÍM Á_‚ÉM¢gQÍ!#q¹ùÚMª7=(óŽÕéˈ˜Ç fŒÖ8³U(3'p Wqùî™ì`”æ®8¬Yñš*Ì M»‰”I=nðï<òΣ¯P@+ ƒ€Qµ,©!z”s ¼Zã,;˜`-4r„l¼t?¼dŸéº|«+7(QÚôñ˜Ázlk¤òÖò.×ÂÐôlü¼ >Ér»Y}‘z“ÚqÂÁwhޒŨòOVÌkñ4Ë æd"‹”¿…ÉC½Òz¼ˆûâÍXO~Es}..“”z/€ —×´¨GtS×/@—ùmYÀ’Ž-„Zj¹ìŸ „UCÜ–l¥yëŒP~–x`RÎÿïSùÍ—TÆ¿§H8†EuŽºù÷ñ|÷Ø M—P×û(6‰x³Ã¾ú‰)Já8¾¨Iâ¼(<Ò&}›ù‡+g¾7Ì’e ª×®%Fk¡¦WýÉËüñZUIoåÜ6 ´ “·ÆÇ…×ȦßÞ¢Ez/á;ŒZ<¦ë^e¢ÑŽÿ /¦²†¶Ž H9€¥rs™,s;°1ÓrAµ¬iÉqèâÊj´}É@íð‡÷ƒ÷ÅÞsË|“šþ¿Xž÷i£u¥OpŽûæ¡:ÊÌ3i,« _o“GeI÷´ì¦aBcS/ˆ¡m ©µµyÕÄeM¸wÖ¥Pêb‰Žç-ˆÍY›_¬± Á7q Aw!ú´ïY{¹]ý%ø¤I§È"æGd*Ž)âNc }‘|Õïìo¸-I’VCX¥áC¡” …U¿ÚOþ8T­kvÓ4Ûuõ,E;œ•T’^0GIÏ5šå)^VÊc¶FA¶š²?WµRó{Û–I]ÌýÌ·ÔŬð ?O½¸)å~MÁ,æE‚ D‚³Ÿ£Ø>Ê é¡O2$Ï6€ÚhòÝ&¶¬´Š`Å6Ây½˜Äóò¿â†«Òáþ.‰|ºq(uHúËÄ#6ës Ñ¹^õ“·2Û¢ ÀÙÕ|ÅÙo×ÄðâP»–T}iM®…¶CºñK1'@'OHD³ýƒT¶ë1«Ñ0ÈI»³¾é¦˜Åâ]o©?ð˜[œ„}Ã:ÿ” í!F¶æ:ÿ ÈÁð¿}l›ŽQ·lî‡#¶÷·|G¢è»×¥´Ö^æßY…JsAfJèu©qÉ„fj#ijL°TH×"õóÚÿt–‰ÝÑÊ}äÍ“sÚ&kw‚™o.ÌE¬H“¨àò³qªêbê…ê±9+€ê¹9i/¨ˆÁšÓHÇ ³äJ%Ù+%x¨Cr‰Àëv "iª{åh$>®ÌŠ–ƒAüµF‡C˜Â¦û'/ƒ ìÀÊóЄ 5høÇB°»Ž×ð/Îï¬[Qìþ;€Öö0%H1µ§³3è›dÇ÷?¤{ îú³&k¸ròÓ´ƒ®Ö€×ÕÖ"Æ _?¿’À7^³©Ê+è–ïq¸Ÿ#¤Ì*‘5À\jÜB¢÷=ügô·à®&?Â=ô…%â¨MZT´fSd¿{=áp†[œìÂ:|¥ê+¦ZbÑÒZœž­D†WtíËŸ Ú=ÓƒkËݸF¨„ÜŒ†€æL £ FW÷TÂJáð™Óëg†Õ&öLîKËXSô7”yL{B]R7ŸÏ…CK\€—o–ôÿ%MŒ»Ê­ÐSÒëœæÉÉ]»W Hëe…vº¾X&‘p7]ì–Ï{ÿ¦Á0ÈÅXft/êÆ3'þâ~x£9í•ã†F@xñƒñxDêîíBV’Zm:~Sù^a–Ììã6X¿Á&Û¹rR 0¿6×[H‹¿,¸¬q‚U(½¥XAj,‡!¿„jÖ)qLyú ‘sØ_…{»ýFúÃâÅèW[mwƒMïÑtJ'óp¬Br¿Õ.];Æä:3ï3Ì|?B¥D`Ò^#ñUÇMî}Åfl@ $1“veKç›A=€º¡ ÊSj' )ÉG…¯ ëúü’gýŽŠ)°ïb†ð "ž ¬~§ ×f ìOÛ€Oƒ[ z·yk€‚ÀŸîÆ~á¬z+…;ÿaÂñ~ù¯:eM¦bRL#(×rx“L°mšoý7j–7%ƒÊÓ¨VH! ‡wu†ìÓj½º$9Ùú$…xŠ¤à_—·€ QA¯»ÅÖ50Ñ[O!’–nãÊdÕõh£ Yß Oä­Tœü©–6ÎÕÊ™#"¬”v“Ž±Z;6%Å¡u.CÏ/À†#°‘‰ÒŒ<44óÁ²^(+ÊW )ÈéÃtDò jêr_[LîºÔ…¤¸)¼‡™šh·©obÊ2rþ@Ý·)áÍp¡ó2|"¨*¼TKPzWVšs}g/¹>¬ŸÞ(S¸Oüì0Ðçät층3}“säi¾¥Õ­ðMò_)§Äm®¢{Ñå‹4Ù?3óìPy!DÑÁ‹ù“.SLÍu}0QM¤íæÔH÷?½><Çö¦˜#/ãÈ>¸…v]œt—yG6Ìœù*ÉĆ_2XÃÏÛ\ö‹HÀÛÃÃâB['ê-¤Ôió‰ÖÄíi®çza~i¥p_#×5†á;O ‚YifܬˆoV«›Ƥ¾ÒVnì5ᬃˆJr@“<ÚNãíþìp‰Âšï9z¥üþ-§n&%¿Rõ+ݸ$¸È“› £WÅúDuëw ú©`¦è·`g‡ õ}Ï„’˜ql¹?ìü ªc±ˆ\ó5v^ÁÒ ûÒÞP·¡Wñ"Kô_Î.äÑ0dŽ\.A \„ùŒo ™ x;kk±½˜Rœ¨±ÖdJק/×L†L“Ñ|²6'ˆjq:v”ñ½+×ô&˜ðï† ’òj>)/öêÔ°/Aùj6¦Ë«^¸m<©PšÈ=ö%©Q7vcûåûÜF0²t-qñÎF6òѱÙîÈlh/7ÆEØÔ©jÝÐò»¢<ÍŽÃÀh8|%%œðý_hz›Õ³ðY &IÍI—<;ïåëV˜÷_ºog!d…w¹M=}—¥åé^݇™:xð”Ûþ¦Û¶*b6…uä@ÛíÂ=×8K7shHcYl°›¨K$Qú8‘Îâ²òÁ<Û ŠJ|¥uΧ¶kŽw“¬RvîKÚ‘³­•=æļí±~,±›]'?d„Ìs‡ (_Y¬Jë{gt»ÅîÌ×ú>*æ„›ÔàÅÙ%¯]-Ÿ û ä h«Ž¹×À›t+À)Ó@Ž[aØbòôÎþ8Ú9™æ­Žå¢Üؼs „ÿ¢-úÜZcÆ¢H† |¬/U³lÎeéQZ[KD²-¶’÷ FKB:ybxÑM¡ƒø,š¶ÆA JdÑúzÈ(t;pÈG}Ä7üÑxžb¹uLjÈšX~´œ-Û3€j->hH혚IØÅBëñkÏÏ“gg]8pp étج@[Ó^«@!«CeÙÅDS±[†ŸrÍØ#ߺ*ohϾ¾Ð¢jrs×9Ã|·Ã÷ý6ÀGw¸œx™üïáãì©4ÁÃ^3tD\Y—±ž:é·°TqGM‹·có–«ÚÇ]Tìž’ê ƒ62«O탳Å?èÄ=vÒ8ÔM LYÇëtÔ#ê|÷ ݨ×Rw'ž¡^ÿXo€é¹~G&òEý3ø¬· iæÈN4È3Ê^5¯ÜP\Vó0’w±³ëûñ.Í›æÒÑg‰oýßWÓ°°²NK5LÆ ´ì`ßÅ;Œ¨ˆ°Á‡<×ÈVZlѾR«½7€àmHû`ÿ¹&×R9Þé=Ì¡ë–ñ=c$ȵOÝ“)jGÈ@ù’Å9/VÎÀÙô`<÷!ù—â¶B)¥·ùÏ4<ƒb—Ðõ³›EtÔ•M´ñºÊÆnæ£ô; š¤Ð¹$«{4ƒ|„Þ1#E]íˆû‹)d|˜[iNÍ„¯Ò3zeø^ÎzO…låàÝ÷v¿Ü›xäÅyQQ6ÈrË€Õ†Aš‰cªXë•ù[®“úà³hꌼÃ(÷¡t²UY`*•Êû+Ghà{ÿ”av¯çÝ|™Ïk ÉâÌÔ fæ†2µ]òÐÑ„W ¨ÄÌ­«ÊѲÖMÅÆ â@` ÖûÆÎ*ãU”ç¿™ƒÉ{Zé¦ÔˤE Õ5²ÕMç9yÇÇôܲç¬ù’åÛ¸ÔwJú†“çdòçQÞê5 JAãéÊÍÉ>óá9hrÖN<¸Ow|£Ç-¾àÑtCÏšxíˆÛçŽ@9k§c‘Ûz?ÚbqÖ©Ë%]™âý;D1ÿiªPŒ“ îªM›qn@, ÀÛþ,€MøõÀ¯Â'Â?wLl¸õŽÁn|K¸85IN+ç·/kûg( òÔkÑï,£ÎÂÊŽ/nÐÔï©_¦N'þešßfJóæy,â|åµ è5R?Ç,ŒÌªÛøùP0©c¿÷k ªÃC6e§+«¹ãª)—Ø5y„ütŽüËôBÀ”#ßI‚²SÒäxü7¤ÿ•OK§·§s á7tB¤á§Õ¥)°\C‘^S³K°Þ*€î«ÇFðû|ò^7tà×=¹Ý¶øc‘ކʺ¢õm Š©.Ïš‚É”¢Œ™ äœÖ(í«¶ÏΧìβK3d®ùê?:™Ì¥«­N×CÌçÆÉaUý Ú¶Ít‘ˆƒS˜ïI9Tcܺ’3?ƒ¯yñ,VG›þ ùNn.©ÆVÝ6¿nQ‹¡ßߺŽºÛRP6=j(é?¨ÖëY²Y-Tš¹r½ÈÓ8‹KMabÈË!¯Â HïèRNWáó‡iHF"Úi0Nr"ÐäëÓÝ¢Šp|VSLú‡ ÔL›V…¯Él˜N+¹n7°²5àlÕ¦ò€jöKeØú ßQ˜êX~ ãW6’ò:?±ŒH YlÎâ)´ª[yÚÁöOëBŠðõ7t4’`Úœ¾ÈÄuxbHüÅQTMVÁ©EhQß2ï‡ß3¹–/,e (>!J}Hž"£)søk¸ú2²ŸGpý®Ãì’³ÄqÈ­#}¢PT{’ë<²!Dƒ—à¡ÊIàq$ÆiŠJï¯6ð…ÍÕò·0mýÁ9Žk¬° Ü•¸˜D\»M3iÑ[~»pÕUÖ¯e‚Ö§À¨k°S”´Øo¡Ã¸€ã•”øb<ômÅJf1J€O2 ^°\d¿Äs7̈ó‡WìÿÞ<ù§ù„mñ¸³°âäÇ =}´{q7œ«I]XÉ\ó)#e…Uc¯Ö‡aYƒËá¿÷“0ÞCžqë Ý4ˆÌhù‘ã9Ü­û–Xw!A2“ªD™irᘕϯX¡¼¥i˜tï÷öQµ;!¬€©®¤óêô!ÿ¸ j c¢Ú]´»À2—;M`Ëʱ\d0}ãUtððQ•˜Ðiar¼ê¡&ß”wñ^óe$ë'¥T õA¼oZœ]R'D&ÐöëÐnàpg,AêÖAÌ7<)A,¢ñ!¹~]t‡2v}O߀ë´PêO:Ú‡N”t$P—* Mm€¥3ºë–Ž ryy¢÷59Dq¬¿7µ¶H¢7‹”J € Ú’¦ä• uV]Rx,6 áê"çÑnÂíÍ— VN5¹ê˜D¼½ð3Öë»®9‹6ÏM~_V^;‘‰Ðgúør H¬˜Ix͈èºrƒ7­èç̦:#³{./Ñù`>W›f¨O“êS!…+–Pt¦¦ÊÌÇ`ÂÂ3ËE¬ü> û‰\9æTmDý<{¿neÎ=“'S]Ov7,£Š?‚rˆ9ÀêÛðûqNùr¢‘¨ŠÕµÃÄ0CùSÞîò‹ËÂ7rÔ[ª0ÌÕqáA ¼åÌ=õ3¨‚Éhô…„Ðx'>vyMØô|a®–! ”ª¸˜ÀKË\ìwÿ M]í¡ÁŒ¯‚µá÷³£ü 2½‚†ÌÑIQ&_[•ce}¼ÜºÎëŒ`àà†]4W/Æ‹R3 A÷ÓE©M™/ëê%>â†óÙ²Pò•¨æ+&Ù&äw§-˜˜n« ¯Úв:c$á$çàÃ>kZf¯¹^°S@Å EÈ™´þKé½%{§:=dUÍ‚—Ù³¯°Œ¨8r Íf¯vVâÒ0â¾,ßNá¼=úv!Wü^,­-ž'‹OÿìêoÜÒÒ;Ü8-;@ýÓmHÐöµ´V¬áÁ¨°ëºÃ ¦°h%¼(JP[ìÎ'…+P .ÞÊÒþ¡]Cî t‡Õ8$_ÎvÓNÑu.^&Ï­BápùýÕˆQÐœþÿ÷†¹de®O£¼ÔÏGËZ‡=™½ý¯rçI}¥¿â6ÕýÕiÏòœëð£î€´e_¢A ä5YîËrØbæ‹Ò[(ç§ñO”MU:"g¡Ãéd«¦`"¦¹,u èTúlÉ 1 ®O†ÊïS«ˆJÚ¥í«UŽØ'“ qr_Oï‘œ‡ðÈ“=ã²Ó$jM*ÀäÎÌTqðü· ’%dß*|ÏÌ f¿ "ƒ`}‡ µt— “KÚÎð,.Ž»eK†y%#¶É‚?ìž•^&ù °ÞdnWj,†«Gã»·zqvL ÜQS¶LÕ“,ß3Öó44Y¯ À¿yã =çqñÕ z¯’o Æ)ŽÔl¡”j5=ôXëÇæ(8áÚöxÈ—…ì"¢@~ íX³?ë;Ÿ¹gm˜ý‘¸HßÔ­.aà!½™Ö:RjêÀ5‰=Zq¯´‡žÔù çŒãºµ†åíb©TLép4ú‡ä­.®ÛR»Spl‚©f[hjÏÎÔ¾pD$G»9] h^·ûDÓÐWD-|/"{ÑúAO^2Œæ˜äÚ£×g7g7 í3æn†]r î¹x*úÇüÍ@mê)QÄ/1ÐØÆ£øÖÃ9¼x1µl©ïÑJY®€¬åǨ3Xå©ÖJA_§$ÒöÙÌ•%Êv={‘ÿ¯‘hpw¬A¸> n èŒ)„Í:]ŽG‹¦ úÒx«À‰—ü’yJå4‹µYiÈÙ%ªÓ¨œ7`¶åÉIâ„ÍÔ‘ÜG¸í™³òèóUA¿Â¤tÔa/ñ°'YË®ì!pÔ&*S MÊfš¾¸ôÉ× ¦Á+Ø3ª~¨'-–±8$÷¨òÆIù—é1òÒ2Póé÷èé®OÒ)ªÁˆ2[J»¼YËæÙ‚>3Ö å©k½°LG«øÔrèë`'X¼öª$¢¨ a9š^Q΄¡b=äÀ}nšðU]&ßÁxJwI¬_7'›,N'ãHù‚ ,')[Ë57î¯u?€ÅxÈ‹ÓäÙtŽ~zŽù=—™”¥}Êh­‚s˜WOÿÝkÌ®ŒX‡Kº÷9€“ÆY!|ƒ’4Ë=PÚ]FöèšûÆW£Øº:9ÅŒ´‡“²]£Jxr©)0a3e— P¹=;ñö‹ù§ËUVûw ¿½äÀè µ«ÌôS¢ü,ñ$äÉK=¹+’„˜Â7øAÀ?r²&rvØšå `›>:|±z¡}A ,9ð×=mgу§¥]§ÑæBi±ÁU'Ø ßµ ‘ÇÓ$}Êø70ðu3ò²=ðxM‰=y@±¯°Pl¬ðšàlr8¬=˜ä˜pâ9_ãÙ¶p k(çÌô°Ôuê™Rö4‡:®rZ–ãT[&Å^gŽZ®Rt·(ý.šÍ“nE¦ÌHá]ý¾Ÿƒú0•z@%½³cŠ{üÁ‡™æ¹âÙÓ±,«ƒ{€Y¯þ¶ÕâÂMË)}ú¨ŒàWªã·ˆU•A‹mžÂ~œÐ°V}zC ë…ŽÆ|‹]\KcĆک¹jÙ|šÉöª ºR«¼Fú·SD“†}­|„˜Þ"qakã8l¹JM”`Fçôü–{¯IîWŸÖ®.\Ù†Æá Ô–?ÁL•Ýz}”0jZŸ ¢‘Y,§Áh[Âb›‹äÆ8ƒõò”Jšô÷t›wêÚ ÿÞhãmºAM¨O„~dƒÓ,ƒöî`Ò¾ÖÕo)Mv‡Õç4-Wc14¾fÖw|˜47 zãŸÎ¸,;ÔÎÚË ‡(}U÷òÀ€b¨ŽMþjÏýf¨†ßdë³®~ùù¥÷óªqR§òhäí½J›®‡½ùܨ Ì×GÕJ“§o… Y2ÒŽ¤õ“—õ£²=ø‘ð¥È5©ó[mª(DõJóo¸œ/”7"zLgßb~£‚oüì–j`Ó%Ë«#8²¡žÀ+.†Ø÷5RõæÐPa»°ùWñŸß¾ÂĤ=rDtö€,Š–uŠ±ul|oN-þj„Ó4»˜ae⾺z®±û“–F!—ëZN©äLSZ­j¦ØÑÛ¿Ó3Z’€>µpq]µ1µ0aKUßby䈨óÂLr»@é˜Ðk“3eÞ:{±@G’#ntîH~¯,Á¶U—°#ÿqÖtÒ†ùïa‹ºa©=“ÂçGö"#I¼Å}oS/Æf¬Ø¶´à ¬ë—˜6Ñj(!Ty-`³VX{0ÌŒúðølãz?Ág¡ø5ù”uœ(˜å^Ê îtJÒêç´Lð¨Ø ,Ó8‡ëuáPyW4íSÄâ‡P…‹6S•Ÿ©aº€ øÝ µÞ®*VõvÅzÖtë§äIq¨ÖÍ0£äÂþ¦Ü…¹ê’¡&€÷y®K4ë+@.!¯ËýÛâ™{i!|0¡£ò^ÄÙ3Í¢ƒaOàÆ7”•)§†S–`ÖmÜòù4~Ë/MWÈ6%œñŒ¬*oŸc“Ûš{5©«SˆW†BÛCo²Hѹ(A±{h-’‡G'uY¡zvù­gçÞ¥ðŠøJ"éò£\ˆw“ “ô£7}ã#…ÞTÆi …¥ºß…êJ"BÄ- œnP?´<Âá¥Çm¨ÛŽY©Gë{u´UÍؾ‹4Ô”|ංÀÜÙÒ àî)µˆ÷ DtÑf.¼ÁŒù‹XC1Wj(DÎï¹v0³Od6m09þ~÷PÒâ+q“sÞm“ï!{©‹ô¶˜_P[íŠfš¼zMëkÈOγۗ$§€ä‚Dã À!UºÇ³°7þ2ûÎG*êö¿®kH  urlo½ør|bÕ…´ý€ºgþÿð"¤ƒÍÐý]ÜŸ¡õ$ €4T¡ž_ŒžÄä¹U}Ýøš³>«uèSo2¥kX‹=Á@:ª®µ}'ÄÛ,¥%ÝqHM‚ÖèL æ4ZîP8j90o}z’ƒÅÁ`4¥žX@Òˆ±¯hã·—*#¯ßž3ÉA@$I°è¥c MGUC£— ÑruÇö°óñ˜2Ûlÿ„ï…K¢….Þ@ÐÉ’Ak°" Ô‹ÖmÞ?GzŽ{h}4†²‚Xo[`R–¿Šÿd.Ê^ÎTΟg 9+DnØÖò¸ˆ20œÅ RæV“Û˜ÖàÎßÑ–žê´ÞëÍ•c…e5ReOÖ¬«3Éø½u¯H·fqÌš1 .:Œ˜[¼ýoˆ¢®½ç.ÂkÎùó©aío®¡[‰öPÇš¾éCØ(IÁèÊÓJ ßKGÔË6°-°¸Q&1°ÌL™ËÛÐ ¶ÿŠ‰m8ø (täó¾"CfÑÁ#(mAL¶{»¨k †•Ã (,ÅfI6ì]ØÆîË”’ƒùoÚ5‰Ñ vD‡,õ,„Ã܃gNÙjwZ Ö7q/s•Ã=&„¥ê·³åÙ¿Øçè\vô¼ê\ËíMKÀ¡òÚï-%Ê`Ó›Ö5¡±^ßÿ ¦ñÆ+`Nr6•±B3x.\Y”ÊÏAÊå_§û#^ˆˆœtÚkøehÐÒÆ¥KÐÁ+0t¹pK×Rò‰i°C0ZI:ð+Ë@´>¢/"ð_@,ö•ù*œÅrß7‡¡KI_¬NãóO{,„€'çD_‰]‘^=­­d¤åÀ4ô¢ñTƒS¤291! ƒ^žÓérŒ¥»yÏÇqùäßþêwµ¼ªÔûÐ-]ÄnÀûôÆ¡^;Ù87ÜW@²…/¶@X¹•œ`ŽU»ž!+j}”ÂìRhRe©ú/£Tëéh€q CßS<,?59!*e·EŒd’óæFÇd¸ßX#T^ÚRSŒ!K–…mÑG("r“Ê6å$9ÔµIÍÝp=§¼€ÀCûÕ¦G¡- uéÁîsSÁg=ùa'ªÁËûfJ ‹á{À ˜[Ü–‚¾­‡ÅÓ½±Ê»Øƒó”]`]0­:Ñ»(×>n–ì=©¶KjÜd°Ê á®7B³ÈHˆfL?¨HˆµžmTWrÒóf‚¼æ#ìjÑ[¼ïzÍOCUFOCÿíz7a¢ŽÍä™­b¡íNõœ™-…5£YzÔwT”ÚJÇìÆ5/ëý*ü¤ÊšŸG[-šñg÷š³óÛ$>ƒ”¦ìy†é“ýnëqp"­C˜¥8Ü#d:ï X£Ú˜üTÒ3}ÍËr͇C¿ úÏZ!ì¥e7o„„N 7±ï„È×éf7ãmªÑá ƒvH•;x™V€yŠr M®[ú¡ ˆ¯_Žû§FR)‘±ãâ x¹Í0¯uvW nÉŸÈûœƒR2®Õ¯2æ«¢íuìÓA²wÆ­·ñL1Å-Ù§"Aðeø«~ ±:Î&ÿhÙx\-0ßê¡­';‘©7`ô”äd!íÎN7a`ÿ‚qKÄ,º‚Å3š4ðÛÚ=a ³ ÌĤ<¯ÿÅ~ €Ýž `óÉâfj¿ÐÎ^”YqùÐäO‰dj¿’IsSÀ¡t’/+¿\‚¹1‘EeÇ£’ÓløŸ–`ôÞÒj')Ë6'ŸzhÙÕ…z®P¬†Ïcœu$žaHúfWâ}YJ–¤ÂöÞ@šˆ²6š¢ŽW&…ò£^NÎ?Ö^aý~zm~PŸ‹MN27ú{fšÌÿAOÀßñX¸ö…Rtö aŸ 8}nm¤}ðc Áç!¥ÜŠÉ8£‘…­ÚShά Á.ÑÑÿtŠ©bgçcužS¨¾Ä©rGø»ibÙ®Ž{&Ükž¯rj—{üÞZaUƒ{T²Š:‡ÏlÉ[ À´ ©"-# psÉEåU·Ç€SKÑ6€véè¼X÷÷îI ¾Ÿy½›¦µ_~©ýPó¥,磻fÛZ˜Ží—‘«†˜åê‚$]HÖCvž×cg§÷Ó9Qç2UL½®_“©lÇ]-Lµ„ÛÆ÷ߊš'X¯Ô×X“å%$þËßêËßA˜Nõz*¡ ^°¼J¡ÂgKWl¶{ÓO µPùQ³ƒÆ*[*á7Å‘lk~·ýî¢ÇíCÂ4Þ'(6ó¼ê»øöT y›‘PbÈèï±Ñ Ì{(àžµÄ$W“ ¬Í˳|l´—çž=ß­^$y!¯‡ rñw[Ö£xñË„%m/´²Pš‘—º?À ($€g"ì‹pñ|[˜Ù~ÝßøºzƒB']ìö3ŸƒciùÍE;Û›ºd²,yDIJ5ŸUò u*´Q& ®šý¶³Á‘½E-z0”Õ¼‰n®k›ˆTO€%Ö<1èJPë H7:!ÃôóˆDäQPHêzöç¿ÕΨq±í$”ÀM)Öþ÷%Èß³›CyŒ”ÓÐdÏû°M;–ÒŽƒ[zxÄ C5-“©§íoÞØuui†qøYUés¦“ˆ,ØÅ+É8À‚Õ]r„Æ|Qn<èíäEKéq¼Ð+µCù•Ò6,ðŒ±]gh›š Ö¸G˜å1c¶øB9dÄÄ‘zÞÓÞä¡ëצDÓ~ƒÓ$¡ •òyóã«š¢· :(u?¨ÝB‰ý1Ä™9Ú…`¹÷¨å˜6™¶6B¾‡‚þØB2Ã1švxÇ´ð6Meàv›Jagù©sõ¡Ï;!Æz¢ƒ1·Rp¤åEØwì:5Ïwèyï "傽ú›#ì²pÒàJÛÛõâJ§¡Dà«é=GOYÍó/gûö7œœÀÅ´,CŠ j Ù=.7^©ÝS·Ÿ˜Q­U° ‘Ožbgµ¬¸”èARc*Àm|SÌìåú'Îl8NÛòØ”=px•Ù$Ž¦ñ” xP=%Ä— ²ƒ¯A‹ Û2hu!‹¸<êýdÝfilˆAÃæ¡¿2g÷xÀz6")zqX§2`Pt)B‰9¶>(dê™Rb·ß&™9ô¬ü²8ðÐ;‰ôݘْGëUäˆ÷é‘럸¤UÙƒKCSkœ@Qr lŒŽoÇòk÷"“³îˆûƒR˜´µ”6Ê•¹M{õéï\ÈK(M_/½¤k(ÀTÛƒ.À˜–`° Þ–õ÷~ŠùH B‘{ùÿž|høuáF?Ù8Øè®#Iñ⽕/DZ†¼ Cg¨1_`kbÇɨõ\x),ëš„È`Þ=vµw‡ì´ÞI?æ_b3ù3`¿(²E—O»Çã ëœßÙ¡Ô‚‡¡ƒW>Øa»!hmúÉÜY:ÏB1¬v¶~,~ñ]“Ñ?B?€øS8³SâòDžmhÀ˜LÈJ…-x9Z$õ‘Ï€¾ :s¹†Ê‚Ôâ‹(mlU‰'¼‹è–CùƒaŸ¾$ÞñÍÎîTåP$.({WyÖåŠVìl‡}ÈíÃNu;$Œê(ÃÇåáÍ=#Ü·,ó±MÔ˪ýÐE¥¦1wîx¼]èMò¢¬×á¶Á”ÎçÌÿbúŒ A¸{þÒbDH>ö­±à[šÓp0Ý)ädL«®÷åLs˜B¨5-*°µm8wz£ÆÞ2ËZ;˜aÌGŸÌាý©Dã~(2>Û­ ›k=™} qëEw&¢E2óͶOÞÙé߈wÕsŒ„—"êvmªæɃŒ.¦ròï}€¸”­l¹jÇQUîÅ\½ò7+–;6¬¨{xëcŒ!¤H³pý 4 c »³®ƒ¼ò««÷ö¾¢Ò;‘¯|>†Œt*äq»ñc${q1­N#Âo|BÃyÌGt`î¬LÂKsR½Ÿî˜©Ê¨~4mI!fãH¾¢^8›Hõøô°O„ÝyÌ)žhÀW\ÃÄ»¾ÆdM’v÷ãìþB°Åx71ÕV„X»Œ¯À/C-G¹h½˜:Ø™\ìj«9Ý»_k§½åpœ5Vwå\¼sc ã£)=O“ÇìV–ifÍJ^oáá/`lÒ¬~óUÅéQV­¯|‰´ó°Õ%Û´Í×€P&9J³ÒcIßá^[’Kkú¸÷c„„ým˜ äuégÄ ð6S ˆ²` 0'­RüÈ„£eÃ{: ì‘Y†«NÜ‚Ðõ§U pv0Ô›Wîûû¾È­m,s"›2«ö–ŠîyÑ)ÅÍ|?\.Áp°@×óÎlä"­øÀ1Fuxç¥é3¶2ÅVQ·’ÈY;M$vÕͬv:Y‹¼mÆ:Çü‘c«–þ¿»¯âÛF¾Ì]'Øaª|§·u¶M˜c~š²—b;kµ€­R/YzS• š(P‚³´=…Ë»Eþ JBÀ¤Ø $皌†óý!…ß×—£7¼ï¸u‡ÅbˆJW7ì»ãç3ýYï¯ÝÉžÓ¶†µiò×Æùœ¢¸nN %¾íÒ‚ÔèÁÜníÁ7YIí‰Þàý"Ðça³k$’tÆIÛ«3Ö£^°¶xÝ)õŠ(þ¸^ElFZ¢)Á©”œ3™¬òý#q矀&óŽaÔühz¿v‚­Tsʲ©EHŽs®xz• ¤åÌ%"UµC|b L[+I¨4­Ÿ³ÖÑÞ?³ 7ç`"h>qöáмëbÞÎ~qJBý¼³ª*4ÑHTí!ó"£.r$!ó¤f(:jVëŸF÷•.@ :+È•8i>¬jž#üݽvaäý,p­€á%™¬Ü ó¿)¿«ÌU¦3 }q·k x’hös ´lbN%0ð>¥¨R¥b½ã™¢»ëÔ–¡:¬}f<'‡‘ëÎMû*]Hj_º ÔÄä—Äüô~|ë»ÿÓæT#¤­ºèy™ÐõÏŒ>¼V^¥ý©ýLtåLŒàUîÓcÒ¯\¡E—¹Î<*8-`ßÊ,J¡­õ9¶¾'†ÊV6n “'ÓƒêAÍüRè-œ¥ÞÙ5ÖæÙÈQ´ý"n5)%ù…{ÁÑM_<4ÄËÈ.¶ÅœAjoT¿ãÄNÈÄ`ãYWÍâý4g÷„G~þú;oF䵺8v…vªy¹ICæ°‹?~øc_îN„·®3òî£÷Sì,ä;m,?¤nÙÃ¥Þ=ų̀3,ó¼.X·¦ÕÓ (]†*1Y.ü¤6°ï±zT¯òaW­r¸“Wö\Ð…2›`x‚1&tõßÄj%¯|5”L„;þ‰$²꺢4"D¶‰šo®Ú¾*¦Aáå£gÔ»RLÔk+«Z/Ô¼á-ªÇ9¸ªz !5qBïçèò°W8]±¬6Ú“@ÍÛ©Iì×ì³³_CA€p?Í‚(v‡ÅàŒ7*f=.Zp¿LòìÀcb'7:/4V¡i2~¶Pç2¾©¯®ü'pý…ÉéOÂi·¬‹nÝŸC_ogÅ3¹]~®¦wÍ™5.Ó$:ÿÛ9¾T:gJAí¾|Ñà ȧZ>Íôèä½ÇC ‰û>²NÔX+Í ÿÓ£:†8<^lš¤¸(‡Í#b3w-ÇÖž¼ý¿õ˜ˆžË7üçà2© †SºIU–;›š VÛ È°„JõΉ!fFÑî3-À ‚ß72ºl¸ââb®¨ÉÓ9.Òî!ƨ`oÛv>Ë¿‡Š߯¸Á á5MB•šl/Àf™ðÛ+I ›µý%;mE¸÷ÇÐÛq‡³œ‰M‘‰»«æ?o¸1*(,Ö˜¬8 3ôÌR™ZSæ,oE² 3% Æt¿¡*‹W©PŒLxgm%{ãtfáõN\¼FSYµu¼9+â—~€ÑQ'P³åÓøù¹9Å ådX±úFCåçCÀDÓ>㌠ :ðñ=ù7Ôp+é&•Ò3g¤D¥îñZ²¶„+RCÆO­ ðñ¿ÂvfºFÜø¶“ðž¨9ò£´JZß®yGt®¶>¬\L$PÂFªC Ñ?ÇüFñ¤fÌuò-xÛ©Rñq‹BØvá»O-aYO÷ÇãóHýË\gn’`z= ¾èA3Â(B‰KZë“J~º9Pyv½.™ÄóZ}ý있¿ 槮áGÕ}±ñ[ ¦S.x}sÒR)Iÿ³ÌûîåÐP`çSRŽdßžÊôÂF¿Ûè)÷=U°‰¤#àø},^5/–’Ê…ë6) Àõp,Kï-¦Rj8^x”vl¤í­ó"<øoÖ|[5@Ç£¬þÎÙìj¬xLrKs\Söþúàø³y¹JþÝño‘‘´ÞBŸ¢…_d£…ìü›Ò×ÔÝ>bŽ.90d½/Ëkdi5]$æ_|½òƒX•G€}™Š!r¦î^làȪÊþÕz'­ƒüdXñÖt’]uK><2±<§êE“ªØ§BŠþˆ­öŽàEM¡ÜLlvˆ6^ÀlEJcÓ‡uË"‚F㺨ÜGÖzýýTéÄKMù„ý몈ŠÛžÝ÷½ÂžjãÜyšT?8J<¹Ýq¦‘o/X§.P½²ÍÛBuÕîa‹7IÓüP´ƪ»‚Á{«—š€Ø"4t®ëíä²ãù ¥ x;–ÆFê!½ Ù \ÜiâdÑû¾dIŒ[PcSOkº²È4ʪڕHÇvÔcÁf“ô2u.H½‘“2ÃcÂWR$†ÜÓÅĹJH(ê¾üëdP›¼Wušh©^œ‡,šNüÝêðšW¤Q'kÜnÑ¥íuUà‰¿|ˆtW2óMÓØ=eÏ£²9~¬LÉ?½9ñÅÏí»U+ŒjE…8\¹‚œT•]iN8˜­+0ž@—˜}¹óï {g¡HÙTû¢Jm=è²ÏeÿÈ ­Vâ—ˆ‚Är¾Ë좬Vn¨4°­ŽDÍÿIý¡¡É®Œ®1U1J¼ì¤ª©‹N¹ÜuÜðkJiA“Ö…vü—Ãp€YÀ†Õz²)´Oƒˆ¿p>QÓ¾yër2œ-Ï‹‘ÍûS­z8ú»Žrœ¨•¤xG0¯FŸ†åxûµ”rxØ)!AOàö"Ø>eTˆ??ûÙ_«½ï­Œ¯¤í|Ìn秳؂êôF—#ÿx¥ë±"õ%s;ù¢ ÀIµ!ÃÖ¬þ‹nô*J¿Ü©£-~›€‚·š8¥ža8²& !+¡LWE(_oBs¾ 踅ž©ø"ŠÄ÷AÁûZ®jyªšŸØ«ù>WÈZÀIó·]x"æÌ*C†ÎÈAͲ å/®à•MÁ‘¼6$çm7ÿ´ÌßB‘ÀÎÜBÉÌãå:ÿ ªÿKÏdµò†yµÀïá8åÆa«+4š{ž>*Nž}¶g ÝqþÏJ‡ÌîJYâi™¨AäÏ3ÿŸÑ"ø«0`¬¢ Ë”Ò‹-ÒtçR‡¼F¢úk‘ –(·è(šh¼¸6Ò–È¡ÔSð L«›–•Šzq¬} í^'h¬¥AlK™ü`}‰eWS!ìùé^ úiDlŒvQLyUzá2"D“´ÑpsA¯¿ÑÀaJ” Ì}7ûѽÿJÔ% ­~næ©HÝîÌzgC?סÕâ‘ÚÏT;j®KÁ¶²‡%I’‹d³°øѦ!#ŠDL×צ7Hâ–øÊâ®*&Ù.pa0_(œ™06ùù2dõ®F 0Ÿñxex¯XÀbÅÔþþÂãæz1-ÅxgÆîÚ#r{f>UöŒ’£:>ôË!‚O|ã[ÞµÓ%æ² Ûi§ysM~Û”eàóÕÔyáiN«ó Y`GN=¯O,m,<*VCGÏH2nuùù`–ˆ`M¹Ž3+u™¡5Î$zŸ¿*‰]¸µÂÇB…#|7b›†õ7é¦þ1!ú#¢n¿YrYúɤÀ.œ ¦Bw‡ £=önÖO"11$>Ú¸@Àö]™0))™M”]®F\°hŠ¡jãÿm OÈy¥ÊU»3»À“‚™Ccýk#¯›©uS(î,bºð pSUrúxœúí–>Û/¿'~æíaOæ&¼˜la´7@œ0úÛ·}?º,Á¾7x;Ðs$Úc(Á¡öçç.®²/ÎYU݆] cïÂ@6û÷™YÛ½_ ¼U{:ȪÛß+kšºww{‚ÝAs„MØHw]p‚×ÖfáBuÙáܘ9‚0Cá3á=+ž|Rî¼7G  {à#1ú}#ïyy$ôÏ­ds'çÒÅQNVL2¿>=…Û:öId•¤5´á½ù(ŠÆOÈø­4¢t(¢:è¶Ç°- îóíåà"’Õé; Ãè½ÆëÅãüï’È~ߤT7`T–‹‡¸áU@w[Øi E²ýŽÉL½‡CDTNú—xÞTæĉÜî Íæ¶79ö”‰"é`þÑOÏbFqü¨žtƒPb¹\›ÎÍ!¿êzÄ?‚Œfúöö"]¯PRã‘béf¿Î!r´>GöÌòçÐuƃ)Ú1XAÿÃ!:ò‚°*ñÄrUKáÒÑð\žÔÐ-Z臨°UnŠÆ¾¨)5jÚ1_O^rHÈž<¸¬gm|«,IDLéTå a»‚ž½¡Ý¾S•¹}¬JLöa?"8é缘md0‡é&® oø„ë"$‰ššÿq¿ƒ\4eThZ(†ŠZ›Ej:¦-«‚D \9¤‘Ãñ×—Ù+‰`7¢mõYæë³ÀM¯ èdÁ‰ï}”øþüŽx ³qƒZ؉pá`ÆA¤$òÆŠ5Þhœdίà3¿ !ŠØW‰OU\¬Qxe”¢Y÷rtTZÝbÒšJöQuº[ñ²¯I6r°%õ¯ìV‹eX5T/„/Ʊ_J¾Bõ{ÒR! h=  YèÏëoO:zÚ}Ø{ ¢Êdø¾!pIÄobÕáÃ:´#ŽÔ~·<èPM±†1íÎnmˆ½rˆ­q>Σýp Ìç}H$Dï‹*ïÜ)],ðÁ¹[Kæ¹ÌÔÐØ=í¶/W#ùeEîAŽíÿ[ÂÇ/wÏVƒ®s9NŸñIný2ߦÌ\´‘“Ößç“iµ:kʉ⼦|?‘Áô¡T›†V`¼¬Ì_-ƒË,4Æ“¼Ü—îZ4ßFNÖ[f©>nÈEL‰‘6‡MäX™I:Ùù½óû‰!Ue‰ë*tg8ȧÀNQ&‚_©bÿ“L“›'˜GEÕ‡[‰ …YÇäšr°°˜Ü²ÚXë7L§J|2\Sº"OG8qÚc+ ìèªN,Ä CÀñ,<¿0ؼ% îÓƒÍÙêû"<ͧ³£Ÿ“7±qAL3»ßï‰üí7ê5%FBºd'°Ë0„2Êݯ•0=°ÑüN—êË#˜˜s£>É5¸a?|'…„ÀÜxºƒZMV6põÛ[’vŽQU%ÃUT×,×Ù ºÚ‡ùrÈÝ:©çîâæ‹ÀVÕsÁÀ‚êÐîH2MÇq‘. Èw­IfÑ’ôüp÷›:y{³^1ð,BQÙ¹(¬tæªÐSØGªDr~ÞÀóàAï¢]i·lH‹K_§5òCKçn s­„2QÉtÒp˜ê¦©máu XÙ…ÔàW*ÖrQµxf'³0˜…dB#žçÌYZMù§g[} /ùãª#ÄÙ¸÷;Í!NúîUgK„su ±ö¸ÿ-<Ý×°•%fA¡ˆ=ÉÐ^ô¥KFkåÓ£r~ Ï]1¿;°õµùý±˜¨´{Õqñn'Š£é¤Y-}7¬)K(Ÿ•@û”6ÅMÊ0¡XÍFâ µYO‰ÁgPåQ~òÕ>ÎÁ‹@De¯ж¾+æ¤öí|€0¡§j°˜˜ Ì›<¶Ñ9 žh bÇ`‰–Vk©H<Ýq°  °§¯,Þª9TW#…mj½ÍÂöõØØû±Æ8A €Q 4§­ÂW˜«å ÛJ»&ê4n¼¿?bÇœ樋fcº<_îÌ¥=ûC²‰Îü9× ûÎY4~#GƒC8mO©ÏÏC€¸k©û÷µ#3¤¶ï'}ðm¯Ýqö O·n¼LqÉà\Ü£¡èä‘Á8xøx¤t㫽óÈy9n ~øÏ:DFÎÖ>iÛPg˜´¹<8Ãçv —†^3qž2¨ÜÏý ­n9gTøNî. øk€ê>9\1êgËCUl¡¸Ä%nåËÂ6ÅbùŸr1û‚x.z)ä(YÁî÷_"úl‰Yß“õ5þ–¥:ÐCŠü_+ çUÌÆÀäˆøƒm:5Wõ«ÆeÕ›ç¿ßŠáÒêIJÓ ¹æ“ l£A‰Y<Æ=¸ö{Z:$B,^»ûï`¤B…™J Jœ`Á\¿­·;GôŽˆCh·† Dn>(¥k =’,vgŸ]"{ ž¦Y¤Oý¯¼MÌr Mõ†ekùTÇÇÞç7 ÑJJŃuÕÄ“ óÓÂTám$ aø-„ðÝàI2+e§§\ÁYvVµ ›éÌŠ6=§j)˜‘Ž÷8U - Ö‹V°Õ´Áœ†ó®/À–ÆátW5E–ö"AOl>`‡·ˆ18RÚtµ“fáÈ%âu®¥ÃR(5CsÖ§cPC–íûÓ”´2?ñcÓz›åSÆÈBQ¹b?Qxê_n¨gÀé‚ ä+iˆŽé7ò².o: sí„s©Î-aÎÔb&¦Eç~ä¿2'Ø(+…y¸ÛÌÎÉÊÏ$<ªàü>ô%mÓF<ó=‚@ÅÀÎýb¯4UPÀÎ9`ÓYþ^ÀÓ18º¾09…ßû‰âÛÉ°¤ b¾½ªC2³a„ä·Âª»½åU—}– ³&‚[x5÷êvUfç™grßCž»q”àÆÝF¢³°XYǤ˜õ©³ƒìD9³f­‘æ 5ƒ‡Î*Áç[—|¤Û¯ Û:“ÝlWpàˆÍÝÆÊ^ÌR ãK6ü|*R ‡v]^LÔ¬2ì€߉; awxÁH܉‡À£™'ð-~—§Ñ¡·‘ fX·ê÷Vyí’bÔ¨ƒªÄù‚ìLä2Y7šc?²¯×9ÍÎJ¨H0Exˆ Æõ¹õÙÎklÇ©åŠ Bl™gäòó€CÕ*OÙ$W‡¹ ež ãîáW‹¸›ï3dßÝþ¼Ë9K̸g,HºÂ”M-rŽ*j €m2¼¶ÈbÅžDöîÛÅ€™mVbf¶ ˜ø&C|P3‹!cõaûCㄹüuÀ×&uBUo …QÄMK„Û\ÁUi¹%"ÆÏ6åGdSC¢ao[j6^g*]™\i%¼ùQètVoÃÔkë{-VŸ”Þ…¹¬÷×)8ÏÜ#ndždlÔW‡<2øa¸Ýðˤ`ñÍkŘ榛sgXRrk («t-/S |)¾ój˜™¸†'òרV+*‹MRxiyÆ•Ð W—‘®|¸“M‚™ÎáäçÙƒç‰$åsļk¼–id÷lZW}:Xµ0êÄnèm%Ûs=bTÛAû{®O“¤•i^WRýÒunq3A4øQ´¨í³‡˜}ÖŒä§Ò¹¸Ò!Œe+Š’ëx{OàÛ‹q·ñÍSí²S¼ .Dì† ÛV²Ÿ?é•uÉ0U¡±®Ü›^ þ †Ë`Rç¾8ÿÂZßæ=Ö· ¸Î ’ǘ³hïíñl& Úª^Mµûášûú2äD¼f™…ï̺lÓLêŽTqÙ0,*‰N ûû¬áèt³rß¡ª`úT?Kïßȸã1¿W´ª™îÚ_4Š^/¶ýkHšÜ &MÚ+\Iºƒt‰gæ®t7?oM êrˆK–@@àˤ߿ïµ%j{û»¬‹o|²fª›)QúÇÝÙÜlIž³?nÞ÷6"õ¿NÞH²[¼ŒðU8ß½0OŽõ<©ê20Ö«Jt&›¢CâZbÔocÕpÃÙ %ÛqvL³éD‰´Š»>öäÉèoŸ‘5¦|–ÞS[òÓ‚…Óê›QKÓ Ú[EKŠl­[ Žž•”Ó¤'ÖíجÆ:Þù[¶ ÉŸÊoI£C5fVºÝáP!o(&0{ôiÀ˜áI’4ã1Ÿì™æ¾#ªŠãqá¦IkR|òeC‡M•‘ÖuRVW .üËoJÄÆ÷+!”žÝ•¡Æ–!¨© qQ Æÿ0ûzö±ŽþH–à÷ùÙLšëûÚål|™5‚ØZWaÝG„"Í®„ÜçߢMõƒ‰Síça£kØ¢Vâ}Þ™^ÉKo&dH©á^@ŸO°îÚ”`{…k6ÕjR†Ü$bžˆ™‰]fZ¸¢Öi0°N\*ô«^zJ&õú§¬G¹A=ªkjÕzßµX¾Æñ¨¸û¯ ¼³¤[ûbù÷ù&{w\ƒUBg %ì8¢ã™pç°·›²°Ræ‰/·&#YGØ$(sÓMÇ–{¶»£@ÏîD2\sâªÒ™åoUAÖG±ëÆ»‡,Êh÷¢ðætÞ?ü MSNM¥ø6þëǬ?ýÓÔb÷åK=«/ßXX!å ©£rj==ÜÈXpL‚d¡…@BÆÿ5{%à+ ÑÊ:­dïCð9 Ý˜_R?¼æFh3ÇXX—·šï¬ db'¦’Hry3¤ßueªýȹ) ç>{xï€4¬udš¾çVl=s–¨‚"ÙÙ·_)™ÉŒTFêôaôš ®'àšHw‘(–w½=­twb>qMI0[?ÄNºPØ\94FUe"=ÅÏÉJ”à–[“ßW¼JM©D^pUd@ªw]ó)É”)ÝûêóYeõÇÌÀô´R¦ÁoðG]`ÌöýÉñ­;ý ¾¢£XOÏQBéÉ_<£§lËÇARõñß3qm×V©¤oLjû›_b6PëÉáOÌ«oý½)Hv´ÿ*¡â4ºWQÚÕŠÒøµæuñµÀ´J“Û —2W†:ºÌ5…¦µÉ NC_\« .ªŒ£þ“`ÃvB±YóõCN«>ÿXäóß ø¨°êPàÊ@\7_9U€?P“©h1õaÓ㜠HÅóå!˜KbÿÔ4ÈSyËÏü_/±%#´K=D —:%ºØbí Q?U9ýÓ¶ëv¨i Õ™A7Ý8‘rGÏ >¿ ¶40ÍYÉ)öMÈýÉØñÛˆ±Ð¥ßí·¾(ñ°ù™!^›þß°Ê4RLÂDé ɱ”ÕÛ VUo¸8Àˆ@±ª£ÿ&Ó0eõñf€:Í%=á˜îÎ@Vƒ2ø™¾pÀ&ÑÙ ·Åãl­™*r-#æ6'Å›0 ½3Y·Wå×Ë8)ÊÍ ÿÇ`U(ÐKó‡B²&k²EíOG—Eiëζoª%¥Zÿu½>jiG·†÷ɱ µrïH*ài³æR±.îØØšÚqÅ„i^L¯éË«WX/ô@ @¼_7:É[ÄP.Š`£³ûOoÚš­¼]üçþ¶.z°CPwyA¹¡'ø„ä_ƒ{ïF‚õ'$— ƒÅåbŽ«,‹âÀþßA4×!ÛFŸ©·ì"r{ïÃØöaËG¿ó°íÇH/(o¤à¥èéúÄ”8‰-¡2U´ˆÇî§ÏNÈ\¹e/HÅnéXláÚ%QbMI×ÙŸ@g¹ÉÎ#ÂÖ{d4˜yß«s…r"÷•†e«ÀÛ̆¾,ðhªA•(0=Ö›I§,:EžºS§Ñ™Ö€’­y•Ë6VlROנɶ–Ú¼¹y½{Ì_·øÐ÷Ǧ`…ãNûÕQyMû [iik·–G×·˜óÉ#<Ì{z‰T]æÅê륩dfQds:º€;xzÝ¢8°ÈRØV•±DŽ¢~.(zJ±£l(Vø¸Xô¡JùWqº™•¤ÒÖ‰Áòiô£÷+`Ný}­kj*>õÿ\@ŸŸ\>‘Ãnƒ2&êÂu|oæô·~Ę,›Eù³Ç»›”J›‘9ã { ôïôÆ&O-_`l‘ß(Ì ‘ùLeµ7™’†þ ©pA ¼Q뙣 Ÿ÷5Ó«¦TÈpþö0‰)7£G;ÍM8Nü$ÇÔ¿·i,ÏtÿÊ´:—}SULcò ¥iË9¶«½³ù\¹Ø¿í‹ÝüØ›4c¬Ë L›Vs &s¬YéJêßzØ ·ƒðßûéiÌ9Å‹ ðt½èƒ~åW}³•SŒYCF1ƒfvTʘ?'öºr¼µU)sN®aÇÃÅ‹^—R‘¾Ðº¥ƒŠRÄ*J•[*í„j §Á{š:/Ûw#dZèñƾ}¦CÓJ<Úötà÷0L*¿2nâmAÀXg‰¥ªþšœ¹ˆ®üîn¼¬Ï² `¼oþÈä1(¡ =jÓ<,íX»Ž‰ú`ÏgãþˆéåÍ:ƒˆ ŽG|Ð[«º‘êDsÁŸ×6ØRØš…þ™ÕáÆǼf-.|MÎ#nåÈÄU܆]ýØ2ýI¡'(ãŸB!ãÚv·Ô¡Yœ@D Z¨Êñv5ÅJ6‡’¥.®áÓdõmJv¬Õf^ñ+Áz´'?€ÊÌãgléx~„Á#k)$ÙHÑÔ7œVŠ,õ4–¼wË루i²åúwÚm3`¡ã‡ÊGýo’lfN«æh¤ÔY0Çþäø“HG:ujVDfëÓ²;¹¢Ã°Ó6¸¾ßnN,mES’ϳù¬ãl°[ z!w•÷#èSæ)—âñíAɽ{ »Õ²X.ñCo£|I–Äs³'¢*„æTòyÀ—CÏv’4îNF‚)ö ¦w±“-? {IÜ«cb—ëM;ÅžBéðÀÜj$Cl.äKø»÷Õd3ÓÆöçO¥µ¨!\ÕRæßÝ^×t±ñ.·ÏEB÷€â¬Ñá>ôÃFFÝxØKÍS!4¡GŠä†SÇ#Øïèjì@tEV¢`)ܪwÈAý4ûÈI¥”;D‚ꨰV æ©w ì=I¹rU,.}‘hÈ_ùû\Ûî°éô$r±ÈoWßIÛ–Ë°ÐSôb%G߀œ›Y— ÇQ:¨E‰ åß Ï4în7<̾@‘º “ÌK–#EŽú¬Û<¾™;å©TÖ±Àˆ ûkoòhÃ)çêÃs¡ÏLHí„–k|~±N€ß¢§\fÿ0òÛ·U_ÌÔ’c÷¿‘:Rý ·ºÍÒae<ŸkˆOBqØà47û@”þ"«°Ü.5ÉMÌ7t:&lÓ2¹£îìÅxoJyè†*!XQúi ^‘uPì4¦ÊØæ ¹ˆûÝå-ÉÙ½â÷^äöˆ¾-œqH|lÈö,=m0uÀ3×fÎË™Ew¾ôÜ7Òv! *nô@`±ÆøTn”Á ÈùÚÆ6?C¡8© 8ÒTUíJî,û7[9ÜÈÝ!pÙ2v>/G;Åï6¬>#-Ž@¨1AïÕTÜñH¬ƒ£·AtBÿÄ=Aß+oöR˦»WXj¿0û9­]U£Ë‹ož–WÒá(E§pÒ€«aë‡éÁñçÊæ¿6JÄb"ëÀ׃¬OÃ÷žq'VÍm‡¹‘ƒÄ¢l$VU+ý/âXbïóÑ£¦õÝ_ù¸q¬ö:éõµTÂ{lDËI2¬Á‡p¯ÚÃ?¡×»ç'ÚWG4öq¨—¥†ð¯ÇA.^)Ý»7à·V”8fJI¹kÛsIƒÖÐí2l삵iµ} Jï&²E§‘/½,Ê -±ß ß³Xt»F?Hæ³o øMC£óålîÈf£^º¨Òª˜ÚTŠ§c à“?|íütQ;þáxŽÏ³²×¦ò¤™‹7¯É@°JæW+òKv j|~z\îU!Üôf9Ÿ^OüÇÆMšvŸE²Ém¾×ž'»e9ä¼ç¡¯ xìùá MÜH`|ÇmELK*J—íšÆ„| žìƘà 忆mà3?7Lj»aaÃ_ØÀvkF—TpÖFò@k²C]²COîói¼’2XæôcéW,J®9唹×cºQLî[Ê 7+ºúPeÕØPÁ«²‡õ%ºþÚB¿2?A¥¢T,®¯˜düÎãõXîi8!s\åвÆI„[ö’Ø>²c£ª¤©Ó×äÎgõÛi÷߶>#Ì# ÈŽ¯YÅy”¦M8•e8.ScWª a>Z Ù»ðaXûÍÒÓTÚúãxõq»]9^Çqí+v¥v-Z(ÏL–¯Ò„a3FÐ;cLÜêÚ@‹σ]jÿXn*®jG–^*eÆÁ½™fS¦”R>Ÿo :Ÿy%K˜ÿZi”k‰è“š>ßc°üJál(ìؽ·P P#ª˜åX’òxx¯•D>…t\hÄ×|+Äl½Þ>GyÙžNî+ =öRx±Ä2ù}qÈ6šÀø%ÛKÇP:µC&!³ò ª±;‚,9 N¶ý®€°JgÑƘI,Ç$" rø(»ÖÎq£·DKÑ&˜ØLT=Á‡°lÿ1¼å¢À'í«Ú‡ JG—™ANý5d' uEXá¨ïéW8¥;Žì[Í: B„’ÞÓž"Óì¡?&š!Ž-\]Å }^É–†yK__à'¯¸P¶£ÙSž´ë,6ã¯áô¤²r¦¹1ÕçÚÆק6ûƒ&»8ÿÕ'ÜcÖ=`£ ƒð£s!…ª0L­çÓ¤TÅe{sr“ÕõÎÃ6{Fë_ß…oE—ªWÊ„†šó;ýSsµ ÛÄ:6ý¯áØì9-–ía¶äÈÙëö*YX]º[?ôwÒÆÜ Qþ1Æ‹8REb¡js‚§ŒÃw¿óTmyïá%6˜_Pß(o™קÁç¹Uî|• _Š÷µoñxr»ð–\ž™¼Ÿ<øKÅošÜÔ<<]oúCˆÊ|8ر€Î&¾ÿM`¹yKïæy>x7ÅÏñËÿ|ZšN7ïy@êw¢Žlk_:‚XM£7Y¤âó£9 QOzóÆ«D­®L¨**Þ8äù„}‚õ¼v”§YßS´ ™_çäÀkÿ‹PÖùÀ¼y!¶Ô£ÂÌ×Èjǃ~ðÞ¸ü¨òœ+/«¯µªŸ>ç*v´Ã›«¡¤` 6M)">‡»8ŒÒˆŠØ±’ŠrÂæÛÇz¿UÙ2Zf7ôŠ;‰ü2u½Õ{“¢a(ç*èÿÀç%ó"zHèÀvT`p?'Òg6§O+êGîxîo{ħÉ+E ¾‡Ä©¶Ýu¿äh…¼Jê58`<Rè"ä’Óîz_yæø\9ÛnÔ…ã=H Z÷©!‹‹7Ñ«S\, )‹¨.9wËÓCC¯Ù³J²ÿt,벫'BàÏ,&Êo'¢ëÇÿ†²ãütÌG† ÑL»É$÷mA4¾9ÊËTVæwq‚–Hç'Þ;³œªu}Mí©IeI1ð‚˜óc÷ò.®ÜàõÃ@OŽŒÁ9héæRÕÒæÉ*;ê=ÃÙÉáîWªò¤SøÒ~ æÐÂ̯x[Îøt°i|’[ª¢Áið7 ïÚ6Öq!ŽiV‘¸"ºÃiçLDßB4­*ä3Tõ{r€-n§©mTÑæSÓ »ÓÄ*3ðò#Ñú©Tãfyÿk³àÖ§D(ÉnU` ‡s΂zQµ/­x“ƒ«óů†µÄðd挞0ÊÇCíÉ‚m˜Æ|Œ^,• MZ#NŽÅаŸ.ŸŸ×`ï˜Î#alGY݇ùÛ‡•WêœïÆ\ Ñ‘CEe8¼'VW‘rŸGX¹èû7U¨ôâ9Œ…À×›­Â}ðÔº¢±,Ö-g¬PÑN]}×¥Çßû¬x¨äèÙó.$ÁFß#)s*þ•¨ €7A¥ZÐ}»{jªúd>¶p¹–2\~‡6sNàþ͹íù"N8àkqÇ‚RÕ?%*™øãŒP­èZ¾i­ÙV šÖØĹÁ˜³Ý4I’]ÂDÖ°Gy©äÇx.öÖ(+€äµµ¬`mý¦Ö¡Hʵ ·?~nû—ô ×,¶Žk‡Þ. αg¿h†àžÂ"³]8l¹=Ð;ôØFÂRé·ë"eEN=£õ»éÐg,Éàš²åN–‘ñLÉŽG4?ˆ¹ ¯àm+üäâ0Öýå Ñ ¬ŠRuˆ‹¼k¸’­²”p9½ÌOª”Ίå˜;6°ø&Vbd`#$°,"µÈéCè§b†Q²´<ÏÂÆ«‚%¾©³¤ãªOÐôp0aÆ¡@ï4ˆ&-qÕå‚Ž&S8D½Õ|»4Sù+¢ï >;S¾(8l|‡Äúä÷­,_Œ)ª‚{yý‘âPÃêdžW3w““‹õI‹(‹\ºÛØæ4ÚÁg g°($˜µU_Fœ‘øóÞà7×#=¡®’» ¢{ðY‘¡;.='˜Ï$ã¢Y§©LHÈ!@,”ЬZ;j¤«‹)»mJ¤<¹'·•¸Rå&Åaû},QœõÀjmJ&›¤°•`¡hó¶\ŒÅ ‘c»ÑÇά%zÌP»vÉ U‘MPܙҌ§Ç™ªð¤ã§ýñW&ØD¨Ùì3íPé}Ô3^·cË(ÅlR‡æÑ#0 2W„bSímÔ°û–¯„’`Ô@;!PÌîr´‹ Vë#”*„ë’p9—úhóu>ü\¶¥ÁBØê«vcâoøÓµ¤;NGiÏGdÉ“x»Ž;ÑgIvDïÞ(tàȖɘQǺ/ Zp8Dø>4NÜÁÝ¿-X¤ g‰1–ùÆ5ö‚ê쉵êº-‡ƒ–Óè¹tká°Zk ß¿¢-ÐøŸpd̸xãJ6!£ŒÞ2鮂˜çbXâµé¶p€–tVïY¤¡I£zU%´±{¨ïKû(‚ûEŸ‚š>¨ÑÝ¥_ž†ãò#™w– ØpôO>÷o0)­Œ^+ÿWà@°Æ‘·­÷éŠÿvêÜí†ô§ßse·Ãè¼HKWZ¦A/KýÝcѤGsÔ[kÆŸèø2«_(-T”_uw…5û¾w”³F‹YsòŠ?¯ƒ3ç—-B©{SœNá©‚¤D˜JÛ‡¬ƒð¥:¼å¥·Ì bäÕ´v5*@"èõ=+œ… äëi ¾òÔY)ˆ"¤@V@Z=®(úØ-üôýG ÝCI×æDùª74Ð5Ž(•ý7gœ_‰bÏ3Œ{k}s†0…52”žœ5ìä]©‘Á¹â%4å»Iƒ¥tv*RÜsÆï1[’sù…åh!ëJô…/j¦óµ„áWèòCÙ]*½DL=‡ |¬Ö™ NE™2qž,┯7³NHö6aÏ Z; ¾©=œѸ8Åp%´ž{¢Q̾÷Uðî«Ïv?bÒ€ü)ÔÐzìÁk“YÙeç×Wf(ÍqŽë‹D+›• èUä›98*H«Ý®:Íê´ˆl¹í…yÛÓ8ÏžŸ®øöÚULR9!…©n´#GŽÖÔ¤‚RB¹ßÌ6CæÀŒóý·šXQÐõ‹€Ü”ãNž”¸¯÷[z”âAt,º:Áš ‹yÞhçË%ȶ,ïŒE~¤ÝvŒ¶OuÍæiõÓbn!3zà'ÛcèߊÞY`7|T˜_6œd~’tŽ.G´ì—® ø‰åý†3‘!4þɹB$ÂÔRBúÊø¿ zV’c7!*š78êFÂQ£EhßȵØÁ¼+2ÿ¿®½y|¹öÿ¦2ù™ÌG¼â‹Áh¸¤¯~±-Y?öÔ#¯¾mñƯä3m¾úÇRÈ\ÄO/>—óê9 èÍ\Æ<¹òóv#¬šBéû‘¬^1àŸtÃ63äá¸Ø¾£fE£2Ùs˜Eó‚t(wt´š·&¿‘†šg Íï«0Cáhhz[ј+¬ƒo ‘BÞ2hÉtå¡Üä~v5Ò5¨¥Åkm2{l-C&ÛR\‚9ù›üªðh”ä˜1¿Ù¡ƒj˜D…?Ø¿µÝ†_A¢é§Éö¦'*Ó[y“!fÕþ± ’N€²ØÓ¯})G¶ÏqŸEV½ó÷Ão¶í¼uyPÑ?÷C=E#¬öB`ÝÈ?ØþŸ;@ßúº4óÎõVi RóŠ’MþìXCWò“ Cä‘ ²ƒw;NO©L}3nô-0´¯ü€PÌšAî (O/‰¤±fÀ]:á0¤vrRSÅ6×fÙEÔãÑ4š•9Ÿ°ZQtðüH¼®q&Z•:”À»ÊÊKÁè˜àþdLRCLÍ=IÛØëòUÖ·¡aª^¯ÓÇB„.M²\ŧ-Ÿª¸wP/ºÐ[âó®øOJ‰¦oïqÆ=²Äyh—b  º`;÷™%‰¨°BÕOæÉÛk¯>¹s1À$xã7ýb­t {d1i%“R3bœ¸·E¿÷“g§„=¤ÙûÓÿnk†µDö£Äàj´…efŽÿ|¡ƒN (Å›py½¨# âƒyà®|±éG*Û0,Ø-„Å°ŸuëšZ¢å³4LgfÀ•ÉI¿}¿ YÅUdõ+ÚoÅì·D“ç)4›°:ÐêF¦…Ž:‰Ñ¬Hh®Ñ¿™ÑHÊàaTÞÑtÍQ·ùWŠ>ò¶À€c¥yÔû$s–TcžtJ¯æL=" }YŠ5±B’¡µã/³1è‹cF–†öDó›…J*(RÙCïÆãŒÛ–ÕW‚ 6?uJ†ÿ6»q íƒ_þP#×Ýü:Üimì,0¬ãekÎR×å´NÄ+ÕÈ1ƇL«¡pöJia]÷ªz9ÕÝé°¢×õn"Áe#¸óÂH¨‹±ÈP¸Š«©›,yN›ä ¡Ä'_°;º€{kþ¹Î9™tbyû•±If+LoÏCרüàêòëÞ‰ n R¼ÏCÖ2ëÓT­ìþQ—~!šLG”/ Ÿ †ó Kwé—'ƒÚЪŸLÜ,çrt'x Ï× ·›¨Œp]ê60fáÕp‹Ì.%OshªÅ¬fP·>íQ~Ϭ¸TÙGš<‡dEŽ{YïàðE„ Ám’¥£cúCs$×ÑÅŸ£(vøo«’%–kÌý&ŒN€Ö}pnpŠ¯Ì/>6‘@o~eñ'¥„¶ø èîøhºÈœ  ˜‹qærY®ó?u1ªj Áí€L¾eœã¼çfž.1¸ižÞøÑWж˲î˜ÿ²¥bvhyè«Ü–GrOI©»¦sÈ-ìÊôtå ˆ¹ ±,åüøì"t•þ›¶ã¦âwyP6Æž@‰‹†ÌÏD—ŽÑ ¼/K²Ç1ד¹!|æ£"â*ßœxÛÕϽï¸ñÒÔ•«0 ³Ðø ‡àÀÁÞ`†Þ¬í›»†ÝÕ!<žD­V©¸}Ü{x„VÙs¸ fkwÛ-IÁ#û+ÚFb' J¾§‡aÇÍS9Vž´‘²2s ›¯_;¨~ËRmZs·€Æ —’˜Ã²È­€¸ê†šct'pÞàiÞw/ ìZú‰ˆk§l[¯kKNaÉf–Ô=ëÖGv °.$!°—ÊJÉ&™ á3Tïø„*åÎÒO4ÐbPKÎGÈM—$õ'"±Ût®Z`dÕxo5¨=4IæZúpgj4—bÿøÂ"OÈ1½J87ÚÕ Lí-‡QN¶Fkº'Ú8¬h«"÷­ö,«Ã ²ÏÒ…ÂƉ}?©-ëØMY§@ƒBiàÆÑŸ]¹JÑÖ¦ÌM ‡§Úní8ÊÉó9×´7u±DôZ5K h…Ê\8N#ƒ?oydòE ŠËÑe×#/,2Û½pSµ½Æb¢\´–QëÔ0 ±!cZϤÓÊx›!+‡v\ïñ>ÃsÚ@q ±çÇœfðÑ€Fàë|½:›S&Œyîw  ®Å¡w ÇÃ5ß糑bÔ‘I6)‡†—~ŸY[!Á<æ^þI;" 9ÅT ¯È‚Û¶«T¸¡;<Øwöõ¿Ÿ ˜šïãMÀ"J„i7Œˆm½ÑŽ{†Ü¦8 ¶™Ç;õU"‰3*¯§ó0Çé™Çå÷NÁ¿xV5¤ˆÔ7­,DI#„편s_b‹pxŒzfEGItÒ¸qjÍHÕcú§Ë™ã¼±ÍãnÝi½y¶õ]|M,,uB:ñg1, Ö†¦Ä¿í…mSÏ]ƒ}t ð`#çÚÌŠO;Læz<ì; WTŸ 6 0,ó3Û¤=xw˜ÔÜ1³ÅrÀY6ÕI‰ó´1l²¬øJP±9VϤk¤¥`,KF£UÎra )þ¨oáɳ ÅPÄ÷ÞF®T q„’SyzÆ⪼`¯–òB/8@Q7:<€XÈ«V´ÓÅv¾wá„Â']Wk+PÑ\™£¨ÓuÃzôÓ ®¤–Ñ»¯“„:×ÕcœHô§Z»˜¸¬º]Í/ÆNÝÌš[sÀ8)>‘H2 ÐI%¨?ù͉`«YÍgô‡ª9ÇÆéN݉ýòà ujÖŽTDèÞÊ`3O¶'ÁŽaøo6^&j'Bze¨Ž~¼Þ^Ð]e¾8ÀmÇÌ•ãvÚGÙ} ˜U&Èꄬ9fˈ÷(­/Òˆ0MÈ.FªPZÄQ‹e ã@©B̫ÃFKi²KCÞÎ8i*)SÉœy\fÌ:³›Â°š´]`JçÛHS`<û ²0ó²fî›´´¬Ý¿Â·A)‚çóUq÷á‰o¥Á¦«ôY§¼ŸœÉ¦ÙÔQm<À¤÷‰ƒ#p¬ˆ1íýØ)·¶³eæêf¡ ;ÞE6è@»Dßd¦2KÝsZîÑ€ÞŠœA“.ï$0â|@á5öô…éÊM ûÛ< ŸëMr¤æ–òGcVpXÌï“&ÿHP„è;ÌæaäÑÑ×'8®Í§ÈÚè _ÿ»_qsŠ<0QñX ð Ï /Ù ŽG¾7§òµÒ˜(4Šs\Hÿ= ß߃×ʾb>ì€\êøÂ5ß©9;èã^({Ó‹C·ãÅN(Æ}ž= Qâ%¢[®Öˆ£ß¼Â<õãå𬉠r)ákKáÿ¨¢^ n®€¡¶Êgæhç yœ„g¤Eló BÏYôµiÙåôÆU]5¬ðIГýG5Nkä9J‡3Ö+ïæMŽò-­TT1fKÊ¡)ÔL(¸BF ه硗+oó\aj¢Àðt|#¸CÙLIbÝÄ1„¨ÇÌk™îÛöÞíOxdΣ§)/6ú0-~%æøn]Ølò\(gÅêßûó 2µe—€ûSARñ£µXFo|"±7‚jKbúHeÞß/žAu‹:þDWùcÝ%zGyd÷ tæÏ!efò90Þ·³ÕEûç:¼q ÜsQÄ£ „šÊäØnEG}Äx_ééÀë•‹–×ÝD/dx^£øÖŠ dÅÁ)5îÔíØ´K0ËÑüR_Ùµa>í¢jÁÉ ì¼iÈ_—ãq9Íʯ¯–3²Š!™©AoD&˜%ˆ}±ƒ‚!H9ù_ÑŸ_BÖùñE§ÓŒë±Ë…¾ˆ½l[_z[‚Èàº^²5( þuÄÙÜfVÛàc€ÅBÅÛôcÊLóÖë:¥±­ £ÓÅí bM±´­8‡Û©ã„5áÐ •çï’Sfü‹š¡œ[|lV "1DzJjb›âƒ×ƒr5SŸ+öó_UP¿™¡Æ‘[-iè|VÕ"Ùž‘J¿ê蛂äÓ!ÿìqné6“˜{äí]Q/G%[¤·ÆøDjí ÙP«ýà"u3±ë3²‡ÖhÐFCËäï%=…·³m0ˆÐäSÚ¨ƒ¦–^|m€ÒèüTea ݬGIƒ´"Ö^žßE©Hì—…¢s\{ù,““ë;µ5äüX”߀øÞH7OdöÏ­k÷À`@åÑ £7؆øÍþ}X#ˆ+þú„[/ôÿ³ÔPéÀðàÜP´ÈeB’ Ž¶½.ß‚›ìéëx#*,¦¨N•öé·Ç#fwŠ¹N„ÔÖŒ×Ø %ËŒ*;¥ÖçûiÑ8*B3Ò½»ÃÆPtOA©èRn@Œã†_Þô°t[Yø ;!˜e•Øf£q—¿D¥-×ñàû³°vQ„?Lã°Eb¤ÏÊ:ËԵʘ xpÙwÐYšOé nøfÿðm[#:þ¤M%0!.¨¸_(…øË¥¹¦°ÖŸJ ‹_Óê ½÷Ñ„œt’½ç;>ˆpð‰ïb’nÞ€“‡ÅsøEÅ<øÊ’;Aq &`"yHÒ»m:)…âBxjŽ”ñ–ø($›ZÊb'_úhÄÛ -Îæ€AŸeÐ4ôð¡íò÷ÓZÓ1"ÅÄqêÈ/³Ïäð}àd‘ãQƒs.¢qqTê0Š…üïùV¯¾@*:ïŸÚ¤‰AJiÌá™&’¾ÎJgÖ†€­½u_ñQ;:e›vÅôY~Ë´ƒc”8»e$í,=D(©éæ¥$ÉG¬rSÖêðBO']üØ…$24E©¯É ©¢ø¥n\µé.È0T N]:ó%RÁÿ…ÉLËW„±Åä^Cá¼Î:RX‘&# ©â—Ò ;ìGq4NÔÍ›!W •xÍÓ÷Iv› –ŸŠ÷ö rûHÄGîHë,`g€+¿$þ '[ ŸJ„ðçU$qžu’¢Ý 2ª£A>Sa¥Ñ·öAÌZ@BÇ÷s ªíÊiD¯—¥hÍu véœAçOL}ƒ±ÿ¥%š"<˜ó¨®5«2ZŽ2J·—ß—–œæYëÑǶèÉ`iÛùá5ÃИÇHQ]Ï©Þµ6…È×`Á8Z3Šº#¥=eY×ëÕä4²zÊû_˜/_4æD¼ž·ùÇU +‚šïˆ’‰šW8‘ØÐMž—e8R¥:«(î¥ÞG¹ûM#¤Ä•_…hÝÈ•†Â^¾Ì5½0ÂwnÛ¯[ÅÜb×?`Q $Z¬×òxe¡Ù†FÆúC”ÏÑ¿•‡¶×ß}¤Ù0öä14ÜE°[%²;ûà¾5m:x5¹{â3g%RTx@B¬2ýνCé{¥4æëÄ)/=¥ÿлON‹~WpU  ¼õ¨Y´TElHøE.äiÖ"†/Ç¢ ÞbXQCŽÿY”Lql9ûæ’§eÆŸ-„Ûlì·‰úaáª8`¿Äpt³iÇ÷õí¸Ò«áà­!dÕKîIçÔG>}GÓŠï{Zö6c,ó&P”à ï9CUÄ[‰6³û*Ú\áj"»µ¶”)J>gµ-Õ/.+u;"5ªáûoÿ³Hέ×ó_×ò¢Ý=$ýbí)ô‹ùá=Š‰ˆër¢ ^ ”s„Ô°îuè0µœ˜lðÀ2®• UÄ´D?wß© ¥=oåR«çðv·C­2öNþ%¤Æ^2ÚkÂÇGÛƒbÄÓßûdÇt½ä¡;´*ï½­ZO’ü€d¿˜ Öñ&,Á,àdŽß &i¿ÑNk´Îs“ }ÉÕ¶) üÇÄΉ”¿û 8꟮o4oiHSGŠÓ°5N$¦û>°ÛtçÆgG^kQù[DZ´ƒ…ÞÖ3ó·§ÕW33lD«©3v‹…9—¸u»¸ÊÜGîk—ïøº n–Ò¤³ßñdÊ<~gZÁV3kšy/"/ª%‘ åø¾¨ínþë Id:ÎgNL"õþžã¼èbÏ°«;oI£Þš¯—b軬¢ÖPt{ôgÀËYx³öÇ’ñö‹ø|\ϵ!2Y² *ÍêìûP'O1[2Íõ]Uh@È‘_ܲs4ĽÀ‚-«9#° oÏ748óZEùkDÛKÃÑÆ›œ­Neù `å&YÖa²ÂXî(fa™êB2Þø…?©B‚™ ÞQœ`|ÁêZ=Ä×¥© w%±Õ2 \¶Ô¼É"˜v±\2§sÖùW©wšÃ0Dßô óG`5ÙJ¸ÈKÍ‚P@"­Y”@½ûDQ—ƒû¡e¬:1vΪd/¸gþÈv¹D³ã1îQËâ·f©6’4à8Ÿó‘CªõXlÅݨ@|°úzW]¼¼/n^")ÉuZ%Pp¢€fœÄ¨ÞèE üºhxät­U’+)¾cs‰Ô©ö~ÊÆ€çå¾y¹ø}}c nÎ2à™reÔ€"i㻄@WÑׯ[¾T ŸŠ1Ê—ÏX¸y› 3@½Xp†@%j™G`Ÿ¡©„Ï_Ù%¯A%:>Ú[)ùö”k$bÿžtUÍUCtíQ3öq‰ý ßEn[ÑÔè½è½× †‘³îuœæ+k9K8B )hôæèÊÖfÍ:—ºzTœç7¬o£2êÔ oÙØõâ;>h :IÒ]1rÉ\X¬]»P3ÛÐtÄEžsv….«rzcU„Ž;Tì®ë¬_–àºù-ÏE ß•ß(¯»@56ÀÚ¹ñ}$ºÅ‡ËLÀØA]{·†R¿™ÿÔÉf@æÊE‘–'ÈPôYÝKþ-—pŠŽÚ ³ƒÃw¨ï8]oÂw 7úl­²BgRÀK˜¼éîAÄ6öU«Â÷8‹…-µÝnŸ×(ÛôU l–çMM¢]… ^>ؽèúvò’5Ea[9³ñG 8ät¬”e I€/_ß3È7[’ҙݩ½n>ö,Y×ήè4ˆ^‡§DÏ@]2ݯ‰ “õøK¿nX'lˆuf¾øãΖܹª1˜üùš·šš.Hm-­P–‘ TÞ£”îl3eÚ(6µÑåûª¸ÆÈ82ªªŽEy1ñL–OEdœSÜÊ•Ö—À ^%þ´°ÄJ%ñÅm¸“¤tay!:œ”S$ù¶ÍÔOZ H_®N‚xšÎHb´!¼¹¾‡¤Zü±ÖÝÏaå¼|5™Ø)oŽu÷z8“‡5Ò–®€iŠQð!‰Û«Ýî[†/ÅØZdd•¼ê!!Åj¢–½NÈÑ¡ŠîÈ+¬Ž_©žÙÜ–Ê«„\ ŽßJÓKkÙ¦Yà:¦^¥$¶í‹Æžªß "íh“¸ëDAþz“Å‘2d³^ô? Š¥ Kk\,÷+ø(ÁÔ%ÒüX¥€^•¶x®X%‹}Wn³¤=æ:o¦ 7øÙÒÕ-v,¾­Þ*]Òu¶¤x^‹¯î—(+z0÷o,CaØNÚdÙgïìCHn}dN¢^#®*çŸ3c[X>î%‚G4½w?µz¢ÌÚøÄãüg¼ü¿F€Æö©¥5Hd4þ>¢J]ððÉ¥]¥Ay‹Òµv?ݵ·B—ÆÚ É¡î"Š6Œ¥„Õõ¥cÅR8Ÿéø‘Ê$¨È=eÏÑÿnE™íãŠð½ù8¹›bvß6ÜßE §íJ`_Ê)0éžeÉu Ø‚ª\ïÿ%‰ °%ï"¯šõX•…#Ϙ û8qXž•ï“ç@Iµm E‘O˜AðGõþ+ÕB_V½£` ¶-Ï.Ø+ú‚¤îæØ eðj#ýg>ýûŒ`4,‡áP~êP9[äìÖnøð4"§ ÿAWæUÚÿë¡<_šw¶ìáj}u™0åºQbÆp’æå¾­&dLPÉôûG͇œaà¸?HžñUÐn œ>›È#2!¼Úõw€ŸÑñ/ðHÀ ŽC…Õ`Í»ðÜ5l$ÚÞod®BÉ%®ã1•[îüE¡ôk«å†š¼‡Ç縊mKgt ‡F{‚?Á‘…`ÿ^¿GTç ™×ò´in¹g†Z“s,Ž M[Àhr5Dº ¬-ä0;ZNFW™h}öäcØ#+GEÙðª RõTZò”NøÙ'%Ñëÿ®%¾yû@AÑsÎmÕ³[y‘…b£Î5›-Ã-‰,ÿ¥/B¸ºCqp]sS$£zðSB;ÿÕZÎO9üg*šx94÷)\{Îe£µQ61Il“ì±TD1oÆ»:ö”_»Z§ŸÀ'â‡AHº$õç39b;|)§H=ÞÅT»*×ò¦žäT™¢¯m·1>¾‚êéYWÇCòøÝ­/f¬{8nun”ÄæQ9þ/’0 ŸD®Ÿ¶ªU —ÖH¼nóƒUøcÐWÞ¯€ æK£§ÃÀìå*Kgy?¼†1ÆBÏu”?£XìFR áb7ûçŸðÀùšÕÛ§Ù‡ãàJž%µ„Ý»¾þÃÕ Sª<—°¨«Ëó©¬ÖSXiñTøåGÇı<„Sÿ5,ÈŽs8G|ꇗõxŽQ©ÚË &¨µ6Ž`»Íæ/²×äÌÄ¥ZÙ¿Y^xÖÒɉ×0büÒª‘„x~Åùîx:kŠûÚ q7o~²>K&=Úë¼óI[ÀåhZ÷¿¨·ëu›}-€Šü¬|p/A¯@HÌ'ûN“³ÅäúK¡ØÇ}ôx™×(hGÆWáž„XåZRS’yS9\ý­ í´=jþÊ4é¸ówªJ_ǪÉRñû+ðÇKtºÖߌѲY¡wzÊÏîïZ«'!Òð쨄Ľõ&ŽØÛë ÖÐG.І wrS i™žÊF?¸[d÷÷¸ÓPé(c )ÐzYg©…3œåç„TD‡.©}fägPú›f^£—ît3z àÉFž’í¹Ü³:ÚrTj±#ÒÔzºûCu/«™°ûu3ÌVd¤Ì k“q;ÿâTñ–8T* ¾R¬dÒªïG7÷nl`IÓ¨»zåØ@ßbüµ¦]‡¥¹+8ö®Œ\–$¨„ÆÊ",-5oƬ½uŸÔsph.ˆ¶É€½Ã¥ìØþ}‡V͈bmeP ,šÞ0Ïä!B°úoh^ÀvÕÚB mQ¯óÑ$6»ÿ2¼ŽÅ •dpìÒÛñhäÃD@ Áùj¿O°.\¦‹z®N ûGAB†öyºÂµÎš÷É“nzÆdE%®~^8èbæÎz9=÷¿•©[ÆŠ”›höO>ŸØ}Šîf©NžWLZ2Þ¯Ø?Ð^ÏGhÃlÔ¡2BÙd0UGNn|¾MáõN 7¤õ–’&vg¿Î<ï@:q½BØÜG€ZÛA]㲩»`mü)Öç ™{šµAFàBÊ5£ðkÄÜSÉýH0”¦Yîvø‰&±aëþ%¡Oj0¡ÅŒËb³ ĺÚÖUxn‘]6&s-É—}¿†¾zº øø(&¡ÃOúSµÑúåˆ'_þÜ‹gÙ!á/öù1slãöå P”\´:Kt+¨ò=BnÂT‹@Í ÍÊZïz6™%·Ÿ¹ày[ÿø‘êð”A/bý&„ÆòjAGÙ[ÿCsÞ7CÐ;׈(³dj8™ª V{A”q¿ÅA&)¦—±‰:ci¯ƒ½bóJšÃŒ?¶ª›Jÿ+À„%]˜–Þß³Îͧ!†Ó{× ¯ÒÁ[KFŽêˆÄé˜è¶È@Žö‡‹|ö3W®ÜL¾ûæß '€}Òê@à›¼Ài®¦ýÒæ¤n¤&¡÷gÃÜVåoA廢\£ì”;ƒŠß_»+†ÖÏ_ŠQòyæQ™´';ÚS §S¨=iaN×~0^‰<ÃiÆISH6;xâÃ$žhKÈW.õl+´Ï¬r½Ø M˜+ƒ;–ë"–”p®¡¥X»)wSæ£íý¡,ѹVŒ™¥ÍV±ï™9fIIVßrçÒÛŸðt"”ˆŠ;™X€ÌÇJü`K®JΆ ñØä%duŠ”U®Ö[ƒöŒ¢ÅOeY,Bݦ„››†ÃëoðÌ«cå@·?Ç& ì0µ(øËdøAr‡ çVà}™å¤–ï))Ý“;‡—h#<%Æ]å_ž¦ oæ=+‡4ÓúULô@¥‹uä/\§/kÓo%÷PcAgÆ ¶Žónæþ³ sÂ'mU7qý„÷«Cv¥ ¨Ë€Sé£}ÃÃ¥=¶YÕ\¹êdLI§®å¢“½¯Ï¤¥»0d½²m çk ¡¾Í:67BÏäasÓÅ{“Aøît[&0iªýIËê$Ö¡¼>½ ðì9"%“ÂTŽ‚´lîÛ¶4%Y£dƒ|èŸÀД‡®{Ü9´ù¶Q˜Í™ð–:'0ƒ¿ï~§_™ ® É?l4O)Ñÿ°¿ÜIÍgnCB´ü""A5Ž£±ø®{ÁõÆ/åø1Þ%ÎŽ ­ËY·à±…¡Xè˜þÜñã€%7Œ.äºmÊ–¦¿ûNÕÌWYº%UÉ:ïc“§Y@|ÑŒkñéùî†{Æ—:_S2š Ò{ ŽÈ< WÔM„d1Š!ô:tv~ý2ËÀÃj](TL—§‹xkAÜšëÐtD°J \Ö3VµyuäH#_xÑiâÝ_ÜŽ´°ªz'âÖ~mý>Û©ã«€o­pù”ã½õ£:·Ó/s ª†t×wopF&“ix;ôvlÉj{¼ÍXß²hè/›¥æùzaÿr“G|iQÁÜ` $®ÛÛGìRÉÕƦJÉð4Þ·Yå•e’ê nû´.§ZDÜÎrȯ¿zîÕ@QÄ[FÚ§W»÷ÂÌ¥]{èX"šâzƒiêØÔÎ_õf‰¡òèþî÷ý9"1 "tÆhØ¥Yi*ªE© ºeiª¦H¡<ÿÈÁîòììkò‡¨B²êwíy‡ÿá„fàväÜÏ+ó'‹©½V¹päËàñv¢K¦öcU[ál»ÿ±xœ6J´“iÙ‰:ÅÈ» îh눬;ÞWç£ÅˆGëò5Õ¯ûÚ?‹†iÂþ¼DÖ¥Rn+åWJÌ‘œŒ$ë]â/¯Ê§¸¬|×Wùq¢9-Ó’;v·O>–[b`Óª§ÃJ@·3òÝW@3 ò÷Îê(U7‘ó~5Â`n›NóÖdïO¼ @8ÈÏíIH?ã€+L6jØËcØ!²õ¼´kn)êšñC®¨ÒP7‡,€qíö§ïÓbÊ$ÀˆU„½B[Tæâ}ìY(g¾/É|ÉUÁ–¿“ÎÄm‚€µ®"!ãFdŠ1Ä z¸TâVR_7e!Š7"òÇõ¾Å À?¹Ï2A"mVùŠiΑÿ²Ð´á‡"y‹-tmVb=­×€œ%Á©!C±ý¢uU§X¹È‚ä¸_ü¢MZV›|4•4Hiô ÇÈ€T½’s¢¯n NZ"B "àõ¶ÄIBYEÃ9ðÑ9nt57XÈ»®AÅV;¾»ä§AsSTÈUË M•+/MH'Þš×£HH¹— “Èf’×÷Éüö÷ÝlèÖBn8ªkê×çC2íJíZŜѥx±_ ¾fÁ‰êŠ÷üâ ]וÈUù¬žQ翳ԧ>­é?2øyÌnÖDÏÑÖØÔÀ ©‹ù1wµå¿Ž0¯“nv@r÷b]£<»›f«r¢±eðñç}¯VJ`ìiYàω)uÞ°ëCaas^Úí «Û_ÊÔ‹|ì&pãêÈC£SºbãŒý„(€`¿¸ ynç‡|LOß¼w„š”Ý—FýËÙêÇt*GK„«LšÅöö½#ûã3š~54)+š(!Û'a7Ws¤ùˆ)XuàîfâOvÃsËÜhFô˜)MÁ^EÉHúKwI†G¿ò$@'Øïç·ÏÅé¹@˜Ì¤ÌE3“}+Ũ†© ÏBÇÐQ Ëîõnp~ô=$T-ö¥y %F"^ïõàƒýÎÀôµTˆ¶®ÜÄA„x+%‹ÝT±r»7¾ä{Éiú$SûÄ[àœl’¨þì."7GæDIQò¸ª/èX-`ŠwuýmV'ß’ µ™î³×Ž¯%ZØngŸü14¾.0ê8Å“­¹.‘@þ3wK9°$YοRLÖ¯€ßo1Õâ_û·øJrdäýÌ ],…ÂW,ì¨] äkëˆH¥@ö=âÏÞ8+ é³N;‹ÒÒÀ#ÒÔ¶èOb>jâ#óãÇP|¿¿>ÜB—…]Ç•Lmç/ Ák¢Ë26iX•"ð(ý®¨ó;ÿuµ0þò ¬.#M¹±_Ý¥5<1ŽÍÀ\aUÜðÄ·Œ3ã¦=¥çËí×0>Ç+ßjbìúe§&ÈÒÓ. Êê¤ycP#תq‰šêy|Å·1äÓ¶÷ÄÇN¥éoÓJ ¼ŸÁS{^YƒZ<¡y1Š/Kv\O¯fxX¦Ü;“¨š;îåDzHØ3¾²wGò8Ü”ì祚!©*òÎÞ‰¯T8îæXë5Õºqú·Oä$§ºÍ·4×ãK2+8V¡I_,ûâ\O·(ʉ'îјÍí…0yXÝ0æª`,cš~Ç;Ñ#¬ÈŒyêï+¶Ôæy !$:pµFà6 RÒÆÀ}›ø'H¯„3òqÁm¿Àî¾*b•I©‹¼åwT*J|„ YóÝ€Ã_IŸøÄv̵cÖ@ý&IÖîŽR+¾Aõ ö38ߺNâ–!tf]Ä4=¯å4OŽÞòQ„œiÓ‡´[L¼ @™ñÚãRt—½Œ)D¾Pùù—eÁ?Óùþ5ˆPu±ü š hÜ쥶]@B=àmµ{ø݆3L3nôLÒ6X÷p%çQ,3ÆÒ•²õ†':|ä#¾a­h™yµ$ã6 þY¬=i¬s±*Ý€Dù7‹ù.z¸Þ—Ûÿ½2Éúì‚Nip ë‘¥AAÑ&ï PŒøÜh±£"—&{4½ˆö‡¬$?¢¥¾öÏpÊ7}„Ag*ª›M8€F.Ï7÷ÑÔaV.Àü Vüøàô¿:³@ç,ž~z½²eYKÇ~“š|ö¯¦VU—œå]'xNK jâÉŽ—œÆÏÍ]•<j µê3¥¹ßÓ—A¸žŽ#Oã`Fö¶ÚYgsRì¶C) «ªÖϵ³Ñ ¬z/“Ì"$3üe`P¯×»\/äw´[Z‰|¯";—±‡C/膆;ð“¼3¥@½bþí)!•¸S¨“ «Æý}PÞ).ü¿wŠÆÛ{÷÷®UuÝú궾¹Ûõ;·l¯ÙÒè¹Ùk’ä®8[€íW,+I¤²+ÓW=·3» lÕ2ەߌ)àJÖˆsÌ“5ÅÀóEØ°tSX¶+£¡7Ï[ËÑóeú¯’G¦ø÷s(}-uäÌãÁñV¶ŽÚÄþ7©RSt•^,ŠøÈŽhW“?³ù&ª<®Þð­¼ ô>»á+·Þ³JêØ2ˆÝ rmE**r"†°¼'Wð‘ØwÚëiÈÿ`¿k–·‡°·bu)²E²÷=o!H£5ôž± ¸WÙã Ú¾ÆW'˜å©‘Ù›q‡hT„³Óßóµ-ÉNäšÇa=_^òX—Ù."}ùt¨LAÕKSí(Ê5[özÛ<Ës¨³R3-Qc×ø\‘Óû‚,ÁðyïôH ý¥Å8„ÓµõÙÎ3Ñ"ØtÝ Á·p¯"L*:­^ßÙÍPàm]®Ž©'—®à¦£t¶w….O€Ýí™ö•ý:€B\¥_½a3Ô†á|˜ õˆÍÊGÛ!o™óãlŠ¨ÝüúRê¼P¥fŠÈ³0–Þìzr(×¥;¢àcOhÜ“R^¤MéÃeÇÆ@ ¬>ç^O0:m¼¬?±ÖuZ°E©vs%NH“6ÛÅ!F˜ÍØÈþ«ÉÖi-Å·cÌäÉn¤jêk)nËSÆ ùæì Tu`ŽëÅg:ís(û‘†Á˜‡0ì4åcýιW¶OF[£eyMÍ= âLÝáTøˆÖ'ZŽÏ߸°tY¢0Û‚ o-‰-Ã~!î¼üóB=ý‘4Ià¿/~ßñóÃy§ÔEA_œ±»–*ç†.¼OÁ6‡De´@Ûè,ÈJ7†ö©³ÉÁƶzMS^XDqIäÐ çñÆ '’™­`:ÜLúS ø}"HéŽ|“ç\Úáé@dM¢6ÿÜG¿l ²ÎJÑpÚ”RËÿ Õíšmó^¶ÆóZr‹ãÅä°lÛa†ŠKSãÔšÞ»Öý)&xRºæךú( NÝB]»Ìû;\Ï@+Îñ,òÉlxF#ÄÆd~þ ýœetñÛº¢úT s¦*:®Ñ?°Ò¡€@ßK?ëÇ'ì°ÖɆ– ±JÈ8 êe¸"¡äÔJÿ 0ƒC•³§éÈ êªÆtlŒV_ÈçKEÖ³!Åàx÷©ÜxåÚ?£¼Î•‰â7E€t‹ Ãô,ñ¯Ú;ЬZÿ…Ðe’!IXÝwDú»Ž¢._÷Ã÷]ÜIl¬×"œº):¶Üw–wòÆX¹‚çˆqRSºo™4õl çŸpŠ',Œhs£¸.Ãâ­ |_±b.w\§\XâdQç%Öb‘STŒ»a£-_Ä:˜÷xðÍ@:?És8¨çÍ;Q¸²(¡ðO3èÆ1•ºç=úâAí•ßÆÍïp·Ö+:«l÷«-ÔoiWô .<³kÖ£„r=œ°‹„gÕÌúšÎ6a±r‘â©›ÍÅ1r.ú¬`Ø> „ð«IŠþ"Nìî±%º¾×Il1ã”wD®…’ 쥒ÿ¥&?ñܸãe-±¶I+Ë”áF{C áøЕ©èBÌ FfÂø½5”@òÊ8GHQŠÅqU¬©ÖÿÏ)àê$âtÕÍ 6¹ìÈäÂF“¤W&‘¦r$“lv_ê l‰,‹G3QýB#v0… bu¹_ªxV{9DB¯@½f{~¼5âvìtE¥tÕqBè ð×C|V$äÉkD™J.„ò㥶8G± ÿìœwíÉþ"s`ºQJá~AîXµ_ê[_Õºkm„ž¢´A™B(!Û‚Ø™Õàf){ÕR$Þ@ì"n®©aòEº$Þ£ƒlDŒwÖüŸ‰m—“ ‚JÅ:¹«»'‚0&]©dÆ´ñRïémë½ö9ŒÆªòÚ“8kY·n˼:ÖØw±¶°]Ì2\3‘\B€§ÊŸûtQÄÝfçJïP£ àç#è«fì\ñ¨â?J;òG¨‰côœ¡sFò³¦Ê«0˜Û£"¿0’ X¨#²™qy"†TÎ' ‘#kôÉÄŠ´Lz]®º]ž} 9­‹ñ§#€Gyw]ûSúÊ4HQšãÃ÷ý¬û„{/Ñ3[‘̳}X6“>|f™!HëSà“aLáR-ìçßp©aPƒ:€.ÝÔè±)øÎJÁ£ÝÁÂÌàt¾ üðË« “оŠ-‹a`£S0*a:¾þë)ªdÅ•?o¿¢ªâò<- Sá$É⤯´¦bg_‘Îr¦k± Ê‚Ü=­SU](%Ãí7«Eü_®Ë›aØÜjwOÂݦò¬Eg‚Ü Ç4u¸J|ìwmR$ñ[TÕ‡õ56xK)þÔÝäqAðU-Ÿ …t8Áu¹lù¼¢·!ãUP0ž«£´Û¿ô¨Ï üÖkG©ÍÙ8xTNÐsOH€ZàçÿC‹—ä÷õ_+wWƒ¿N·„3Ûd΃dA¶Ú,¿Û½ j§êA ŒÏãø "Ê×`%T%Ì&.Ù9¶ÉBý#èûØïhÝæ´'Y"5÷#Ÿ‘™"‡Ã@M‰Š¢Ü™¹wVT¼#¢‰@_Ôú¨ä¡ …Íç^¯B†!aH<Ä ±eÄlG1w“îïàÜfaäÞšÚÄT"´¯µ´TÈ) hA3ãoÜœ•š=¥îßL³”7òi}ȆÂ= c÷ϪÃKÅßó¹ÉH%]3žxaÕð:ÒðÏgk”(\˜ÞgYFÙQVqÐ8¡M´êwú懘«ðjE?ž ‡€È¨¹ÊEÑç-ÑO²­` ­I ¹à¼N÷¾ïùw÷ËŠûGßÊ”B&Ð&½íLAÃ¥Þnb®BÈÐã—r¢V‰¤ìÆ&ŒÍèa|P©.1Drü Ûš|ÌÎÍ€ÓMz Ã#¶ij1AËè¨*,ÿœ]bØ‘wOAkè“ãü}­’± UÐÖŒPR€Õ|ÆÕ68\ÀâMú z@Ü^Á0mŸsPL[½‹êÑÕF1,ØN-qÖ(í(nÇ'í¯\ÁxJ.Êýq ›Š«D®ÝOm”˜ÿ#'ƒRí:€/€Ã™3Ê›_ˆ)7~}HãÊî¥I5 ˜kÄžˆJ3 )cy•ô(·üZ¡·—Bg›*ú¶TYŠâÆ3NÐÔ®æ Tßî~Hc7¿mÙí?:%fxþ­‚„âé4^õˆ ÁÕìÜIíEÀ&Ä`FÛ© ³¦át8LrB‚ßNðgÈýGö¨~¯×$X¿̘ bW…µ =,ðÃ×Úµñý±Õ¤ð@}Bd G¨rߧ>݃…ø¦Å„ÅóôÈgóør_ÿ¦pb.{]óC†´†ž.¹™!ɹL)rµ²T¦9â;è´ Ôõj¡)÷áRûÙ½"ùdrØ[¹Lj‹¸¢¸«ãTðQèïË(@•È™z³OMú‹ÞQ’®ÄçÌ;0ÞC·<¨Ñ%Ã*ìù±ž55xic{½›í¾¯éa´¼ÑIϤ©ö}ïî‘ [¸eÄõN“üD|)R!,ݤ4©K¡æòµ6s2uÉ@¸`T2»C1ÅÙ£;z€ TÍ­lÿPs1‰‚xS º_Ö&¸5ò€RÔb׶wºÚÝázŒR/ßÁ¥{ãâ›ÏD:LP)ß 7;­…Ú¾¡C§ŽÃ.qÏlWqwÄ­ æJ@~VÚâe?Tá@ƒû•Tý¯â´†]»çÖÖ2 Ûs_ÿj3Ý掳z Ggž* ¹Þ´®•$AâÕ89$$ÍZ2ŒOv´äÛÛiýjó?m3ÆÜ „‚K)P=ï¬6¤Iêí¶½njQÃ7÷ 茵_¢uÍõÛC+/~½nÔf‚¬Ìöt»ÖúÖ{ä)rPfzŸZêhí^Ó±àÉBp3ÌôtŸ-IJbÎ%øLZ_ˆ§ËÜ°‘߈ÔÖá;t.µ¬9ºGNòÊyë~íêË®äR¼q~¸¶™Laò=¸zv´ÇÐå´›{Þˆ·zM·4+v]Äìg|r¬3‚D å?ü««Z?|™iÇùÌx4:ªMˆqZ¹³.C¹~dДŒ™hªõ¤}6Ánü\ìŸ*Žhc#®Ã|Sù…k¥¿„g‡"«Ÿ¶]‡i@½ÜÁ:ú,Ÿæ)ÌpñýçEè$ æ6 ¨ÑˆKC¨0[±zM`pê&GbuÖ]½I‡ÈtbHcèa#>ƒç¾kí†ÀØÂX °GOü°rÚT>ò~ÕL,&{÷šL¨ªƒê.¢¤¾|Å2dÖ¹  PÞŽŽÓdŠY)í™»Ãþ¹çaö>ìLj˜’Jz7kÃy›ÖatPVíBä{I¦^ôyF»û,L$Áàê%æüùj –šï»Q‡e>Y¸D¬"¡¹ø\ì¢&BŠjW±ŒÉÀÄó3>2oitR´yâj®7*ÒeËðçLOŸƒðµu&Có5ƒ¦7Ò[§ØÔƒ™‚‹buæa¤\ã™e¥»¦L=l½é¢³ýˆv67‹X–&À<ë@½¦§<ÚL¼kÒ ~ç”yúÓ;œ~Çc[qspæ#U‰Fó²uxNRdˆ ôRï˜É¨0è¥|ffëå‹^#v†$Ÿ™.þ-øшÈ!›>ÒJûß"…Ä!‘ÂòZìXÊdï&ñÕÚfŬÆpŽlMñI rÄ5øLZ®›k=£KŽÍ'=FÆÙ3 –¥=²Å4*:}IR R^KØçdýasðÆ¿ÛÀyòØ6´æ–øÊ 0Îif®iëaú–!ΞzU*qxÇ[v×b|%ñÙ#jê ™Â³E-žQle;ë7ª’x¾‚ºF†Ñxô´O˜p,©Wa¸þ’Ññ’OoQÏr:(Û–„¢ÓBÒ“7,H˜ +§Å.¿ 1p*TÖѬNºÃWEöÏïɆûSü"À½œÚ´w¥gWü¥³LFù£%#Ê©`™zÑsŠ|Ì ü²Ø¶X¨KòQa«¯)Ó^ä¹Í;î¬MÃ!D·#,Ã/Ó¬ÙLhux¿Dú鯧O(CwœÕ ›ÕªìF%âdvWê²¢Ÿß£'°…¾õO|QÖÙ³â|Z×HAIÓ.ÓwH´ú½}qªÌ£yWº ™'b¸?óE‰ñèêÔGd¸ˆ0Šm8ã†+÷¦N¦c±E@Úï¦8Àß\ne­¹#Ls×¢2bŒMÛ°) i$ÿ¬àR¢l#]«9H8¬üC‚ñÊÖMG-¼92%N)…$Ë…wFXúÔªƒEdÙrqÕ´Ùatnœž32ò¸ýHÂÚàA1½1PáQ^šx±­á_Ìt¾c1î®Æ÷IÝŒÿ(rÔïYt£›N £3g½4ýÔ¤é®5Û4ôá\µøìW¬^Xð±TÌnÀ?0‹©˜©co¨“ý»ö”<âröl5}à|±%ŠL°«5 0†%|÷˜?CÃÇÚv1ÔŒæªHXP¸pÆ`¶´ ™¢I´ Z— Ð`AiŸÄ}àìDAB:ÙÄÒö%±Nˆèod†I¸²ÿá¬oÞˆßë=ªUñ­mëi÷²ZÄCJ4ÈÉÀ²Ú^ÔTVø¤)¤­¼ÚàXJG/A°Œ›-¹·¥ycÞ~)}Zí¸*ÿî‘Y4‡ðu‡6‘¾á7ÑÕx8Jp{¤bnéöv³î¬‡.¶Ü5,§Ð\Ñ|«¬ü|εº;ʳGk¢ŸÝÑfþö[ÏIôù’Û4Óòþgå— Ó-ª)¢WâÈ %…ýlW؈úLô‚ù¬Õ±âÐDé#)’¥_Õà™»¢ÜnV>\“},a2ñHSq@ŠÁw!$HZÔÖbbéýë)­Ô!=‹½|+# ÄqÁç ûÞÂ:c†ö²‹\AçØÄÜ[wp4üöŽ c„'3ν®ô2(ž´5öšñQÂG¾˜ÔÔ"h(…ú gt<®7'_%“#8ÔÑAS Oý+fjxô‘Á^#8–­å,g{Á6ø'ä|Ì*÷פNNðì×f‰¾–Þ:óó$½6»Hx@KPašöªKaˆÃW=¿ÅAzÙY›‡xÑÎõÙ«IÉ{!©€ÝŽÆþO7w™ë vp7†'ƒøCã¼óàÿÜôdÏÈæ¼éâ¬îPã©‹=1©½"ªI x°-BcwgueF°¹=D0*Dì·ŸÞh‹˜¹MnÞ¥åuÁ¬ê"ÙB´ÕÉ<0»Ú’V¾*È}Qq~7BómæÖ¬Šb<¡ájž ËðCÇÚpé>€4û›ØêÅ»Æì¶\öêI'ÿLä?ýrFîÓ¥]ÂßÓ¿²SÚ,‚v¶ÇK­i›uj=e_U ÇN¢~¯~Ê¥ÄpüÇ€ˆÕÖ%¾2*(ê¿€TP˜4þ"ÑS-¬¿(eê„<“o_µÉe¢$RT|{ÆY\÷Ø™!ñÿœ0#l„Ík·f…,¨Vîš `zaFM r™~#0JÛ=‘öÈÍ;- |óÒ[¿º—¨ŽéñÕNçN(À°åJe°:Íò¥]&%æñNyÙZ@izz±_ͪ“i˜îü4œY†D°IZ®0a-ë´P˜á^BŒ¾ÆCí¬ÑŠ°c_ ‹§Îè‘mAÓòØž¢· É„¨µ®sñÅÏU¶Q+BR¦{'–`É dнÆÁk‚Žý0j Ë›*¨ÈðôMß®PæøQÙï#ï-(iŽ‡…²¥Ÿó a$ Õ“ò/kI©JoFT~à ;ÿû(.!dPT¤á”ò®ÿ|ÏX_Á¥óL‰0W>=’RaÜÔ>ï?Ó—7ÌÐhˆ4·&ÎÜ!©<‡¿ÆÚM¾•!’(£$ì*ß/"€~Õ‘Ó²ý¿ *´‚ÄÁ£(¶¤½ó¬ 6}5P%pÖçMÎ{òμ,–ÑûÑ'žQ»†]xEDþ‚Y¸&*T.}icÔËM9áe€MËqó膃S33¤êë—ãÐ*uaïïbh®\~$KHÿ÷ÀÝŒµ´Ì •Þ #Ùã§TÍehb=g?ó<C•@¿OºµµµáÇívý(mšZ %êk¨Hw0>ôY­8 9P“"ZEK¾9¿Ù0ÿÿ og·…F;¬Þ€Ðá£:Ú.ì`¾ÖÆZ@{ôòh:ŽMàƒ¶Ah²iQ³ñ}Œ ÃÜ ¦1ºÁ ×›NB^ £rAûÜBRCu9ß'×zø˟¸£ñ‹™8ÅÓk %¼Ï©¯¨coF~7ÏÉ”¢›ª'|hTžf(}hF™n;„•}ˆç¾²lc‰¯ý¶<}Ò,‚C^ §˜Ió½oðÖÓ&=p5+Û”„7DîéÛ÷äxt!â)J}x·ÝâG %B‰¤Ùë™>EБï¹ ÇN¥…¢Uì’´áo,‚÷H3â±(«gQrƒ¯Mзèâ†Ê芃­ ‘öèí‰ú(Œ'2Qƒ–{¶]ÊŠñ«ÌÐ÷~KQ¹aÉjÍX…ØÒ¹!º9èŠ(º¾Â…HÄBDÁ+Ö>è ëw5è”v° éVD‰ìœJZÓ­LÉçØFÁíOŸ…m_­NQÎœ#u>ÈG!Š1°-c”Y{…² ;™É®Mêh2”ji’ HÞ“ch-š¶·Ž¬nB¾7šlTÌâÔ(£Öû³dRîK‡££ðëûMexÀ@àfÎv/0G^ qÞ 4s!¦Ñ ŽØ¬p^UôiyôȈkÞPX¥UÃÚ[¯/ߧ™–U}išK¦{‰°"b2Ë®÷~”óÅ nø¦=O½¥WBTGÆ)ê Ô–‚Ù + !K°¯àï›­nˆdÊL“HöMµójð:QøËrÓ_&o§¬uùàì¸^&©U³›’ÜX£M ` c¤¦µò;(-p~UURÉ‹ÉÌè™äïšÐçM2œ×Â_B5&bš$s ‘ŽQÁ–-QcÛÌÓ©Sø¶æ9;uRöD k»T¯Éx’ÈfÐçï°È×d¾~ `¾èLOûôæÊmÏM#Ðê¹.1{ïò'‚Í„úÙ¨¿ôïÃ’~ã &¸ýГËrrÃuŸÛµF~$° ÊyÛyŒ0'¤RïJSí6ÕéFXŠÉ¼eÆOº¨Èd†¨»—OŸ«•æ¥æuu@cükëÜ~WA|båŠc ŸY´‘«€ßRw~¯1ˆK ³åž4…Eänñ¥1<¹˜S—εÎ ŽwsÚÎH<²—¸µî°Ôˆn¨¡ÂÞûö‹Éâî$ÝéZµÎD=ìø£!ÖöuïÚXºR€.cµ†äªul4ûAº]öáX9Ïþ ³æNý6oz¬gÜÓQŒÉÞ`e‚9ùŒ8‰ä€*—·¨ Ô|§¤ ‘)ÇzwsS$ÿyÕ–°i¨Þð´íªå*&íÞ%nìÉ+­ÍEf•ý&:ÓƒufM¿¢i¿ÛÔ©µXŸÛh;îÒJcK1ql•Ý5uÈF NaÃ|·œ^½ÓLÏ.,$ìÒÁ2ëpóᣂLóáX#‡ -Ïá=9e* ÄU²ºbÒ6&{¬m¬î¹¹°ÓA?‚|Ø{“äA¦ž¨d€uÃø™ÙBu_fA·v ·‡UÆ LþÛf“·XÎA”~W õï¡´óòV2uÌväÂ0cPû# ¶•ßzÏø­:õÁ¼úÒkŠDâxZõ†r|þ€2—ìA¹“‡PopfŸC'©Bc€GÉø¿£‰ýï$æ‰ýÇ8Õ^öèC ‡>\d¯ÜHh¸oøÁ­ÚâD^?ÎE˜Î³«³.ç–kO[šÏfÿ%J×S‚*÷bWa1µ%œxÅé"%  ðÇظÚI!ž•£å÷ÃŒ‚·>1Èá‘ø4{ì£#çħ±zª<€5KâŒ0zõC¹öœb£Ú†7Lã&iþ$D¬u܈ÎÇ®}™²&áÞÿJð'ùùçu‹#¨µÏË™›o&‡·k²õèç –RÙßá„´¥ÆY}»ÆÞwf/Êî#; ÿñr;¯0 dóq[ Üf~Š³tI?ë‰D2±Ë$×v¬>å[¯ÌÆòT¤¿„Ø6%Þ†pgKa“ÇÚ6²ãpù"'—ãRAŽ¦ 2[“mœå:lÎÄ Ò:J&ìäägÇví"ýï<7 |J’´Ûãzºk’–åøޯ̟4ð'Pîlêõô£@N³øZÿhÀtAⲸÔë©`Üyã H&1°aÛr‹Ë ¯hM­¬µ§²Ï®áßÇ-›Ôˆ›u¹N‘´?Ü ÕÃŒyg:B°eb|g®|7>V–2=3œ‰3Æ,âí[†CÅhîb.7}©S6ðy檿.¡ß‹QRÄ4¿‰ÎxZ§ŒV¿8fÄ$3öÇ„„U6Ïlu<2’T³¾žÝƒ¼±ìÁ!Íôó¡Ö‚œ75H–Ÿ\ü^R¼]B;t]F5‘®:ΨÕûŸï÷:š µ.õ–ž[\M:Å õÒÔ`¸·æŸz`2 Gʈ™ƒJR;m½ŽÞ¯îEÀã$ÅdÖz¿$äEÓi(žÂÎ~'Ó6Uzi‡‰'e c¹cTÓNx>r‡"(ïŸá‡õ\Ïïªb¶LŸwS|‡tM-==ãu^„÷š ¾¬:KuÄbÜ­¼W`Hwì;Š[ß×3è¾³yp}7çäºP] 4'}ŸAüoј½¤ê€Àx¯žœjï³ÄfÁÂZñŠ¢¸“ÍÊw7æ%¬>–ï¥3•ò¡ìd×q„“ƒ‘Ï$ý8SsË5lb³=+ô>'Ô% £ßW_ØEW7®©|΋ ¸ ^kÉkóå jÉs…ÈlñÒøf 8KZ÷_(Ù;DDœ¼§°ÅI»ßóÿûYÝá4Šçr¯Ï¶­_YfÇ°'æ4K·åe_€”('sÿë$…ç:•Xʇ{ÑN¨_Êyp„žT=®b$Ƨ«Þ:ª&&"b’WýCN˜²ª0è]6€Q£¼°ÝôÀfÚ/•æô, tXü<”:©%™…E¼cr†"0ÍMQÞÝÚa¬5Ã](ÓÓáÁ»”tŽ†úP‰o¤þÚÙߧ×éå_qµã E¬~xK÷7Íâ÷$GKLÉ°ŠÏhú85ý#FâÜ8Í©0ú†ˆ/OiPÐ8"æ^ÉÆÃÐKøšÒ‹ tÒy=b«p§Ë[aT¯¯¼ç¹;›ØŒj[®«Ø¹®+JÕ¿A ÓàE¾€îþ³~½‹Oy €û–)õVAÙƒ©¥GùDù›Tê—€ì®0‚Ô p'¨à™ÁnÁ‡T­F¨k®›‡¤tz>ÙeG?Ѐ âÉ?¯Z®Îx*ã!Wùiïñš_ÒG‰Ù–fô =eC%ã A÷X†OzDFV Ø§·˜¬ABÃÁ|uU4M@96x]¸¾`ì9Ú8ÂýÙü†ê‘ÔzÅ,–Å·’¿ýWö¢2™3Bb]ptþãeŠî"sÈUb½Ò&sÊ5[‰<ŸÙÁqT ëuaìÐwÈšž®@±Ñù° ³s3sojØp&;6vâ¾ô€#ìºï7\fÿìêÜgŽ©-Ü+Ôs3x .7hE¢;€”}¢™$OR+ÑÃà͆ý¿;—¼ÙF¦Fzß•[4÷ܪ,Et–XÍ.!cõ^’R­<áheг1lð‘Ž8°m}Ø;ÿþ®Ë¥NÑÁeÎÌ”?r]* Ju ÏjÆýH&UbXÒoÉŒ˜HM7û7ÌÅbÛZâ€ë©w ;ø;x(j)MdR/=qW¤­%ƒuŒ°Q.r[¬ô½X?á2)´†º¹ íéÞý·Úµ+fÎ š>6ÙŽÖÀÙùì²ðçFkV¿7ED<â#)Ë_œ{Øc¯#L¹<ZÕˆkØ´Vì ·<Ò1.œtÚ:r·`JòY{žq:דwVYŠDŸiY]æ!Ôßø-Ú!«"¤Pp$„Ajnî¹Wëê+åNÑNÓËU„`ºu¦ƒ÷sãbm˜æn@ž“[$q«FbË&ôCà ,÷’N¿ö{-&ÏP\¯û«Ä¦½êPƒSIZÖ‘=þq’&$8w;ÂìG»£ ̦xž,™PqPM$©Egiíñjb³ÍXû²—­óÒñn§¸¯´«øu]†ë¶È¼‰Fä†_y¸È/+Å¢´“Ǻˆ_«ÚIÜø³½|ûð‘–Ê9wCЙ¼1æyï¿•dÜ1ºEdL’[”Æiå-ʬ5ÇЪÛÿu[’ Ýˆ6}Ðcôøê€ÛàTz¹]‰{]“ê\„­ñáÖ½/b•È®óôFôÈâÿ4B£¦ê‚Xµ CW“â Ü"¦nü°Î>˜»î¹ËÔ5h‹bÖú!ýjÙ”žI«ï‹T¥Ÿª"G™nÈî°óhø×®µÈññí)xp‡´BFŽB•ŠôeEdçs‚ô·£¥&–( Ë Åžjðºlƒ^;³YÑ–a›)ÎÅéHr`Çc'3/2clåã€Ú…Ôkª‹Íùi]¹;waˆé ^_|óÿÝèe摡槺ˆ^ ¯øÅìóð¥*Ø¿‘éNÝ ‡ Š³R¯ph…¦©Rê]̨°ÿ ã`Tªº-8B¸áãqâ÷l–€çFLi¹/p#RjôÙ¼:BœævC4Ùº¹_QÊÌý¾ŠÌÏé8·¦óP2-Wo=\õŒZ%0švƒÐ'çÚ‚û “bvâ9b5ÙK'.{g^»¯lÐ_¥`¸wó³ÐU’3q¶ªí"RÜê¿Œ~$œgàf0 ƒÜY¼{Òãöy·ê–è™®,MÎQX™{Ÿ‡p"©®]\g¾Úz°=’_fÔÙ,* 3:œ•@€Ø®ë—쀪2ÃèA…©¯É®¥dö‚ À5Q)9­Ôçi/ªzêÆŠ iƒáê7ˆœÙÉÚÇO"ew"½ùbíÚº¯°ù,ÈÌ´Œ¿*¹FUÀÒéÒ-0è¶Ý§N©ElÉ!Â6:X'¯ÕÂ|¬u6G~-î¾0xö‚Š 1ä’ý+SÛ¿ÃÎñÓÒ·±S¥yx™œ•Aú ´kØåqÕü´­È¹µúïr)­í-oz°@‡€¡‘Wi4>ǘ疽£½cæ^ÏóZX«t‹)èQ. mÄŸÐx¡ÕUž$‹DÓžÂt+2ä¼áؽÉMµÔãÂÇAÐV[ò‘ChÖ~—§ØUIµ7ln^»Ü46Pó{²_ˆ6LjqÝ['iÆšŸ?:(.b\œòsfn›dë€l¼¿ÄñÜJÒµAKæÎ?½³z?È‚Ý ù¢¿àh„jùX ¶b‘rÞxÕÕ9£ªD@ÄBî;€³VÁœ*!™G˧?Èå_:œµ??µ ,ÇAJÔ‚Fæøžî|zÏÌáU’ŠòMÈ… ?‡!Mû£WWrQ¿^.»ô8Œ¿iÆšæJðµfi€*,±–8VÊŽ)©mî ¡¡+‡.|Æ'ù€È\þ±QYž|©!ä1cŠÿ]<¨õƧß©|zË ,ÊZ~¶ œ6—wîg¶Z·Ì.ö¸Ã)hm— Zýïf¶ŒßoGBp¶pœôr×úaûœ–ÕýSש.D æ«'cRP/qÓ]ÃÕâï8G1»²ŽižêL¡Â,¡EÔ¼ÀGäg/Ï–Ije$Vpc^Ó­Q0Ç´wìÔÑ]ê? hN‰ÔÛ,W6{ŒMßÛuŒ¿8(o³e^ÿï^[Ö&ÒÅܾ0QÂBq£=v2•XüçãB“µ§ÐÏ–9ßc˜´þê«áÆžÓwëõ÷?°ƒªx'ؑʌܻG¡·„ô|G‘ò®!vÐ yFÏ›ð¨æìÖ9ÈÊëÖOÄw¨Î£Õ´É­½#ÜŒ"Ѷ–M8F‡)¬%e‹²”H+·.å£=³“ÛnÄÍ¡ðÊ~X«ù×»ƒ‹ía°eÛå‘Þ¤%Hdj‰ã2ÓH XË©ƒ=|´¸ì=kÖÆN «d&·~!6îì­À~M¸ +Ëû *5*küäÍ¥~Ü0ú/`’Lð©«Ïh5qcƒÆ´u‚‘Ðêª A¤5/ûM ±ÝÄÅf?RcèGø«Ü7¢˜í)UQ9ÑŽl IÌP"ü¶ëJSÿ¼9_YÀI9„šZ¨ACwëApaæ{•à4 d=]rï…º7± .1ÝÚÂi“fþ5~\J@Ó‹¼Ü ïÆëÇÿ 3à0¬} k êEð–ñ½!F¯m·Í˜(bŒ˜Åj¤OJPux…YðÞs…³ƒNP^FçîmrÖè5Û|çh·âÏdì]bºJä/o”òʄĨì6o—·ìyxû°0A×êa†IŽÚJ}አÈ@[ö”ˆót¤GI°H5éžáÀátÖQW%|‰­qüÉ<%ÛÔ¥N vj§ûŽ±€kK÷e§œåÕ3Z2é:ÞHdΤµ3$–EÂÅ;’×믰Íj ÛC#,S僋øçtOí€ù–*kȉ…pîD€€yÒ©ÊRT Ûoûmå|ù¼æ}KÛ(ë©”?E!‘ˆ±_©ít^±3mk‚N¡•%îªß„ß`ç3*Ló÷ˆV7] ø¾ ïRÀDÌÒNdÃ5ýæï%×8\4z¾ ˆ¤-~Y‰øøÿó–w‹@àåZ(L|`˜ Ùy ³øSðlìöÐ}¾þäè"vëâ —A†ª°F!?xÄóW&hÕ+Pm”ABZ?5n7B÷Œ«µŠ™ºË¾ ;˜fD þºpð pg* ¾C‚N^à½Ý?tzîÄæÅs2û$@ %=mMÚ|Ÿ†%‘×f÷Zú÷«SK—:Üèé<Þ Böô½Ì5J}[¯—i‚‡”¹*–£†LŒ ˆGµ5n@3ú¤|1BC£QèÑdC¾V{.Y6ÿÀk—g`W>·n7}«n"¼Å~úCb¾Õe…ų¼ØbæDZ‘P ‰Üƒ9Ù%w|8V=«ÿ£=ÙlΦÌvìañ¢öu.¥šÜÆÛw÷0Õ3ш J 1;UKp\;L@,DL!jc&ÏÛäyk¢cAÅG`(²Ó&4W†¥+âAQ!dÀ>q-w.|`°A&ÁDôŠ9îË÷yÐ+_TS3oì gêvTP÷:‘'Ř(Ö‚+îÊœËùWx­—7 K±)Ð'IÀ’â' ‹Ç+ˆP&d!:î‘Íg’¶¢á<†TÔȺ¡Úݳ”ÕáÍõUøl5ZâFlO²¥¾Ìjœ^ 7Ú –¶X¶Ü~˜yÍçÜ!iH•$" ƒDf3«™®ËÙÃ$õ[p÷!bár2ÏAŠ5=¯I¼ ÂÔ¿ùe¶DøgpItßàÄíw† ¥¹­ÿÙÕ‘øà¬qC#âÿsâÁ‰¶øóMͶ¼Ö:­hÜz]~ÑÆO˜F‘ Yé^niÒàæHaú?š,´v1®îvŠè:íýs(æÄL7`  0&ZSìA£vö¡"ÖDâ5$Ú¿Ýyõ$ß2n \ôÙÔ tDÄ ¤ Ö]’§]DöÉF™BÇ Ø„·—¬vÇ«|\e¡ù>šûœ"À‡_ 8Ó¼~&èßjvŽ{}z‘èw2öô€Ð¥’Ÿ)T”!QÛÅ™¡þãÇÄw߶-Ù”)+k¹‰P-è`@ù>5AÉÔrüAÊP*V¡,øk"Þ‹Ø~YÐH¶®fg2÷ÿD–ƒ«BC¢zÚa]ó’Z>ùMÏDˆ5Í<±*eÑt± õ™i0x¯¡PÐŒJì†|.Áà•˺S{…]åÆà“O»é\2’õÅ@/<¼¶lWøF¾,ôö>„™B•¸áÔÝ©|õPÏ Â_þ/Uˆ`Ãú#µ {8Ò% ’­/àÐ9˜ª€T 9׉eµKQZæô ã"þñÕ໳c“`d*û5|ƒ»p3w6Èø–7Œ¡ð²ÓòN§Zo—äG)ïò€?#`¤á;g6bVîÚ8ë·Ù ëXaG¡JÐ&eÚ?42±^,»‰—\)¬^8y÷î;¬£v#™Ö‘æfBžI$ï¤ZŽÄ=1ƒØˆmƒ³×æd¼,hLb§Öõ‘ᤠê¶UµE àiQ ÿvŸÆ­Òd1S~jðót¤×.–ŠÃ¼¹ÑsÀ9¸6º:æ±QÃw²Ÿy'6_›~xì#<Ó×î $# ™Q€fnÍ®”ŒÉtièIÙžL©JOuIàý»þK=B=Mš¶'ÛÃî-©M<„¾Ç§Ø/½ÌÙžÜCkš})¹ÃŒ,2/qNjíšz Å„}}¾Jø VˆEŸïZ2Åó$ÈR—v7㛇4õ}n iÁ\:ñdˆùIégaU7ÔæmC»Göñ„6â×ת¤© è#3ü„üw³éÌëÏ*ôÀÉXÇÉÂíØLë'`’à] DÏD•Àò!Ym›Ô¼¬t}ij¼ J=ÌOö[9¬0Ú'ÝÇÃüÔe“˜Ç¡‘½xb‚Le;á˵$ýÖ _¥-̆Õ÷QQݵ‹œáŠã’"ÆS¢AM<·Ø/RwK ½h|›¤²³n!¸g§Aˆ&!äë*Ô߯5óøÓãðñx¹±¹ÛBpî3’–,WL—©Ãåf¯\x|¼Þs+ºku=8»äPÓ@ûåÉÐD”׸EL:÷D&w.T"•ŠOÞà¬t§æ´ò.0‹Èšnø¦Ÿ¤ð]Êù4î9جÀeËO¿|a¶<Ó†ÀKÆÍÑ<ŸÌýõNÝ “¯Åƒ»Gé¿h/üWªÑ¨Ll! ÙDFJŠYºŽ‡roµJiÉFeùKÛÕ0:ñ¡0'äž²¬†\©Î·yyÉi…üÇ#ˬRŸ&à3¯ÉƒÞ”h®î²Á.R˜LšÉl$£gÌH5‹ACöuâ`ò¥Û'ÀDœ€'Çæ#DH‰Ø78Έ“#äâ´btœ>™q„ ¾e+»ý‹ )[Ëߨ£ÌÝçõ4\Z jÁ^àAtR¯yB ºÌ¤!³DjŸ2TXún1d:*F1Å>¨Û/lX_¡¢½á}4Š`:«8êf¨Oª5Ž!5w?¾ŸÂØ7Ï ‚~ SŒÂýiQ›”à)Ðÿ­ý"N\^ ×€FÀaîvýÉÄå?#è~Úûò!PŽ¡æq¾ª)‡øB2ìQ“£ ¡k3/“Kõ¼BKºƒÎñÓè%‘|:¢]fu{%ðM·0~ ¬4>Û­®g-‘e-*ŠIó€dØñÛ<þ’Pîà fQ=ó™tmŒ_û™Ôß]çþ¾­\SǘŠø˜Fõ+HIPIÇA—HÓ™ÜÏ%‚„<"Äulqô¨¢S+âúù²‹û{wZUõŸ™ñ´¸e·ÕJt·ÆwzW;)wÙÖ¡k‹m)?}¶Ý’•rëõ‡Ö‚^ŒEÝ—a‹eÉŒBC£9Ñ\&‡ö¨Ne†ÌÏhšyB´n©¾:1nΑLÅ‚–íí>XŸ\6ÿ¼ 6Ãî]ð•ó¶h”•»º¨ ì›±‡ 鱆òHT%³­ÂQWÔhŸ•-‡ƒ¨„Û«Ås vt‘=f>te3€;áèWŒ»7XÓÄ‘þ[¡Û®Ž7ð;Ξ•žÊÀð`2gHŽvèc›ØቖŒºG5¯[ó`¡1ßÀ+n’!rfù‚ì-WmânJšœÆ“´äØA ˆ–BΪ9û £NâÔ©ªÍ ž[CÕßÞÉÑ Ë?DÛËÅa-ψKF‘iBébóvêxUÓ`uûáÝ—½nh<.]3ƒÌ’‰+ž˜£ÉÉ…}7IKÐ-»Õ¢ÆC3mçU‘Ú6Æ&òâ˜?–ZwžÄ|g;H~·Ú«zÛrq["Ÿ ùbä^‚£2üiæû ’zM N¹Ç°sãJÒàuåc £ÝѼ=¥1˜_*1"åzw1sY±è¢8w°;¯ó‹žtyÈ:I¬ïД'­âúANú•˜‹øï÷‡ql†ÝF½pE3ý©¸c¾M Û²ìPùæN`)Ã¥Wkí9$lÑê¦0V±tñ¦Ð·±³¯LB¥¶=n/IM0í+ #±LS¹rÖøtÍ5Öký ”¾wOòŽl@Z¼â²åEµä7¤«“Ø ˜`tÛ0YyC½Ò‘&à0ƒ áñˆ°ºG %`(B.@¯(“ÿz¢ÞHH° ò^áÒúÇlH"vRØ``4 %ˆQì…Z§—xªaãö¦ŽB/wÿ —Ķ€¾4iër²Q…$ Ÿmu¾JI~Ö¤ öé2øƒÎ/‰=(¦ÄU0ÇhþyÖVä,êElïÀxüF5]â—³Z“éYÒRõ>v‹ÔÊIzáš^ÈV˜ÒY×ò ù<ÓÉ=å|ÛØ%´Ës¬fsâÇš|ôJës;nõœP‰mçU°ÄRK¯õGØ–)Ï)’V{$#_ËóL'êÔ»G˼¤x©Ç§ÄwBÏh?&òŒ›Ap{óË×Qv,¾;l߸7ËËTxÀ,Œ:Øj€ºgºl2 FO`9ÅR¡ÝÔ9¿QuÑÙ™g9Œl¾‚¸IÙM_ˆi§Ø12±©ÈÀ„fÕ€ý‘¨LäÕ«–C ?¶–ÂTM#¿ô ïúÏx.æïˆgÂ×Ó×ÌÀ2*ºœ\Ö wH+~j½´‘þ)õHhF§Ÿ§$5p×ÞË "‰¿XG*°Ý1‰B‹?ÊŸ¡f{s ¬¡3RæÔžE°»yž•o©š´ÿj;®èzÁ×±áh·®ãøB—lÐX£¾ãó‘eõ„ƒÙM/ki¯#¼ƒÌ²Í8t•Ñ)–ƒßýÄM´eÌÄykõøbº@)„™õÔÆA[˜\“ŸãT3Oô{÷×°7}ç‡ó Aó ºc‹ó %ßz´âÉ]\¸öCg©* h.d¬­¦К1ÏEFpTÔ$‚¿Œ¸>çŽT劕r9D)HùîöƒkâN•¨¢u"£ç¨´µuEé:ã787«™âN%!Þ¸7…õo’%ŽÞætœG—˜Φ¦<&ê®ì´†¸Ä`q`Š2+ŠÑGÝ5˜Ò°jDɉлk9f$Eƒþ²“š²›ÂêÁEy¸`á)¸Ö/‡ï,•çÊðˆ±CÃsÿd(Ð,¤£´kàšJ‹TMSQ„ä˜H»Än9vìîþ(°ŸÙИӲUd Ü›¦ÛÅ|£,:´ˆ^ \ëÿÕorÉHÌÕütܾ˜kÉSQ‰_P_!.ͯ÷Œˆ±-ñäóI†OùÿØÎÔQC 5=–Õ¨h'#'xëèªV½(ÊÇÓp×ûM¹uù{+—ã\õrþäElö㵄_T¹Í@*[•³^Í?I´`oy‡œ¨ŒÚ{cnœ‹Ú½ê)‚8&ÖMÃ:[¨¥©îíÞ›:A߆–@Ïy¯­c Ëi³½“§÷áÇ+4,殽®Ú—zM9#羽HX{q)KïÃù(ÿœjwåªLÂ^î—Ù§Èå67¦ ±uç… #tV†±ÈŽQ‰K6ú†ÓŠ¼Z²ÿ€LôÎ,u–…ƒƒ2]:dÿÂŒVŠ*ÁÁ·«)’ÉT„‰q϶´ò]†oƒJ+Œ^VOq_ÃÿEÌë¬KXཬÉ=¶ÇùÃJª#àá1Ù}f¾¥ïd‹Â_ª[ª׌RP}莓 IA1DùÄzÛ´d‰Õ7è«:$騀°U%/¯´n#(‘&q™9V8ƒhq–ͽÈSé‡æpLº TÑÜçºWž·}‹j«Ô(_è+ˆ^yÝðƒÑè¢xOÜðr¸* éÐŒÉÞ:ö @;« !Z‹ßl¡š6|9¾¸ÿ+’Sv‰Z«Úg 7*J#Þ–\efs^W–5ã-®êJ£:®“W-®ÎOÅü8m3ç–ês;-‡µi:Ÿã@Ž¼Ùí˵ò;¤dDm ðx®ÿ%õpÈ+I²à"‡á~;ÿ2;^W1AÒÈ"ïnQ+° à&Ÿ!v÷Æ=¤)4}l&©{Váa|ÒPn´_Áè ¦fKÒ2+ÎÒbñxVíbOYæÌ{”.ÙúÍ¡#EÙ–ÒcÆ]ÓÉÓû,?‰\3Oã}wàJ3&‹äó'ЖþV$_q€ë˜šà…®Áj4Ùõ¿n*vzòbžÑñ„ê\6@b3ˆ¦®#ÅA{_lžh²‡MÄÍ¢¾fö\Š1@ý˜Ü6|BUië2K¾–\edÄñ… Á`1`]U¨=·¾ØLEí´QÛô>”„qQž6°Ò}ù-a´¦Ö ŽŒa›(ÉLEÁäE©)×Ar¦2.µÎõ° 6©œå5T^? ë°XÿÖ˜]ÛÝß³qØŠYɯ|OÅ—ÔX¦7ÔÒ”Æ2”¯B’1.º’DåȬñ9¤l—o»±øbHÉú7»Ä, ­.ˆZk¼$¤™<6Ù~ ’BÁÅmÈTë&L(,‚ CJ²Éa ô*éÉ$œÔ‹Û•¹ID'U`ӽÔùBá@Äpðß1Ux6à=¡|íHÉ<Ù/zmP06ô4ÛGasÃAãÔ ¼hscyAvÞÏëÃÿ[æ€!ÉÔ[¸¿’Áîý¤ŽâÃù:äùÁG¢·VDÂIU‘b]s^+o*æÍ(Ü-í£ù¸÷¿„½(<=œ|ƒÒ‚t·,¬ˆ'{ÿ»Šo%´\ê#¹¿g~tÍn7Çä,¦0qi£”žÛ1eË“nBá_î&ß°A»a³çØ6J€:È›¾KvŸíp!ÅÝ9ˆ«ê@ôUBÛ—‘ãF²‘ÎÂ%¾`|÷ÕlD¸ˆ‘𠉤å3¦ÍH‘²…‹à_W)wl¶ ?ZCHåã}¶A4?§Ä › Gõj·#È&ƹ0sá=f­òê3«7¹ÌG°†ÆÞ çÓŸ`NQy¬€b…VÌŸ"«ÄÈrú©–9ד“¬iáeK=D>… ÃS4ùÔ"­ð¤/(ͬœ ²Ea¼ËÚ øéåbòmü7²¨[pµ$q?Aü%:Âô±V¥WÃ)Üqׄíú·äfg®S•£Êè^®ƒÑUÆì¢Úš¤ÃöÄJº§=èyçË”…5ÃÛXÆÛ=ªfÉÁ“çÒü•c@Ùf…òÙIÂÅ2`ëZe‚Âw®ÕÖPžÑ2;ím/ âxªaµ–žú•ôÙ+TEsU;圼͙ç¿7ù¼_œ7²²Ú£6ûÃiH*ZÌ6†Ççb‘#|Œnò…“á^Ë=…Z s0æ„r"Ô2‹\Üà §¼­ª}ßqøÕ_¥ND%‘QV¯ØCm¹Ë|f(ˆñ´cËxW;Ó\‡<1g1 ¨vÕæj˜SCêEëŽÄ;Nï#àûâNñº‘¼‰û˜øï#$äЉ VCM!aF€a`î™Þ÷¿e›ð×0WüNJج¾ªo…ÎÊ"³dAP²—Ã.”¨H”‘–rØLB 2ZëÈs&ÀÚsRL&3Œõ>6ü,ÈfŒÕQ±4çz–Xó/൞åŸÎ²œY4÷Ôî¨Å •«Á¨L›^ÚWêzÁ]#DÅî¼ë\8¯ƒw÷" u/"+Ø£êf šy:,îÔSï2úÊêàf™e[†©û€pÑ”i<Ë.m "ÌF%}$d[AQ؇~ošBJ€³Vƒ†z8& ‡Ëì“–5â·‘Pòe’˜^jZ75ÌÎä›;Z¤ÛNiÛê§ gBÿüAaDî.IÆ- åk:þ0sªŸ䨓ÓP ht+†Vi·êF Q¿rnÚÉm0‡WÒ|ÀX`Ä3|Mõ¥ØÅîJØMJ<óæS10RÚ±¶ £ó§RËK‡Kb<ÑÄ4sð'$h\òïT“{rÏå ä‘ö2•œÇÏ°(Q5jÞ?‘ö ~öožVgì»o_îmhËIð|¡Z#ÝɆ» oIvD2`Þðˆ‹@Mccçå‰B¶H'ôw¸¾ óô!òœÃÃ8¢`Šó«U;ŽŸ10~QŠÓO MKb¤| Çh ¿æÙ˜(ýwO»è CžöSì(IdµneE$VÐAç¶a •à:à¼0,MD@†.G¼mµ?¢<ìõ×e£óP²ó®°krÄé6…ð5ÌöÙvŸq+‘ÐØéD)P¤¦9ŪÇ<kVv¼NðÑE"As‘n¼5Rº²±Pf•Ùe±¼¨4D bBb¥˜Æjª=Åß3}.Ëg‘ÄÁåB„Â9ôV®ž†ò]\BÍ(™l=ìUóú¥ÿ•fÈêLOuE‚=«ë|òr“I{pãoV¡CÓÙþ0_!äFZ)“k©E—UCRÌC{jaï%™+½–›]d,…•ÿ¬lÖ*5i±¼>LWhGpk‹¾%°¦}½v\ˆ>–xv…xre?gaÒ¤“v̶7—[ä™-Pÿ¬/ó+iT…ÅòÑoç ⣉y+û»Í~/ ¯ÛÒó JWYi;¦;ÇÞ°Dvf `ƒšñOâôYëWÀp‘q¶ŒÃLË“!=ÊÙ“eÀ˜b:ìXùW~3aç<ØR¹ú‰õ0ç ÒêD$Ûùû”?¢[†“{£cAìE££ð@U!IUнžÛg„~÷©tÐaL”¤@ëÆ Êí™Õ–ßø± ¼Lžîýþ8|<9æ;bÙ±aRRÓ”Y—ºŠág¼"‰T’‹Ämz¹…µÛCùk§kÑ:am/ç±<=¸ZŽª#–*nùí¨Ò×tìsYlX^d”ì(×Îu§H”9 ðBÌÓ—z—(ë„LÅ»٪%û!Í{­’Éd`À Õ¥ì ×-Xùo€-ü—)IÛç\WfÖ ¥Ûi† ìAQÉçÑî®^Žú–™öøD‡ÅIZm±ÃHÇië ÕÑUÆX#Ä,E3ŸÜçßźñ&¯­ŠP˜ïûy3Y@KÅÞýÒƒ-Øù÷h1y±þxËà©@™sÿ`?ñ@ØŸÿ.7hùèûáþ0L“ªNª6¯GIºÃ ùC•ÑX=˜Ú€æYò #Ö´4)3›'Ý< ”=4 8„Íݡǯß{Õ ŠÔfž–½¬x¾ÖfäÒÖ’cý:LÖn:½åoÏ¢jNñ’ ç¢!”ŒÅujÖõ,"e ÂþýP›k`Ä(Ò¬7f¿ Jv á܆Ÿjçï9ò|£dÔ/Ñ<ÑÌæäfQ^NVÄoV¸MwµB¬Ò%ouï±íÜ$h‡›¤OøÌ`ZÓãü áa$Q;uÑFó ,ò§Ž ½/ŽµE:”°)ni;W]iÛR¾Õç:ræQ–^žõo7wkô.Ѐž†ëÊwd•Š(ÚjS®8Š½ò,)q”trNÁ¯‰ü“Ù­€ì÷1±tA¶ |©ÓäÔrÝ^«KÂyI뱿%QíùålÛmI-¾¼_œ·ü#«óð‰¿§5\^sñú04ऩ1]ß–/4ì¤$ËýŸv0ëÎOŽª…`$™ßû(îp£KÊqÌYóÊœÃ2‹‚ OÜ•õïp¤(æÿÔoaØ\ Úše@© ^„ÎÒÙ2†u“¢®5DQQe»«I³Û#•­u•ÃCH9ƒo®Žåц¶IkiÞ¼iò0gÑȯÙi.œ³µ:ÞGäÿuq¡ _xyF–âP“­a!Y¬Þ¹±?à]ë“ÌØIÂà´ôÅìdØát«d?Ž¤½ø÷æaÖ<Ô1ä´î¼x{tàð„§Ùê!—þp˜¦y~ø .x¦žÙ,)Ýë«&"Å´}oaõu»‹ÈUø½Ø¼bWdª†ÜðŽ² F.+2•ÈÖJö׺ô¢m‰ïE…)bl´w*têjÕN©B~£ÇŠ˜Y<:æ˜`Ѧ AÄ/Y»æ Ï2p¦Gýt£-:#ˆÑ®#'¿'JA”{€+)~Žµ`Së¦ ÌZ('P¨JÂÞ×Åç:+G~Ob,]>åMìÞ;]0¯ü³#`ÔKƒN¹êC7:`1°WQ·ÁadɆ$åeãå$4_›D¤‰£%º8ÑŽh ŸKÚ y;IžtR4ÆžqJù¼ X#Uc#(d)[¥Ÿøà-¾ñÎj¾’[ä2!ÐË5¼æ u³ãÑÔ¶ˆ3ÍÈjúž—{sÍ=ŒeéÑÙ½ŒôiÒèêÌþ(ŸÞ¦¹*ªmìûù ?E ÈùD÷¶.èEÇaD-˜w­:#Q&rëtø<ÙâAs²Lˆì&CëGùeØ÷æOüØZ§µ5ÉLyn|„tê†lÝÔŒÂXszØWª•›Zºx—Í1é]:z3:Q¦Ôäç!Ut!òeYôÅ zCîcÅ÷¡éb¦£ã@V)è Ñ ªj`ÐLU¥p½;lóÇuB°î+«Ü£ ¯ÀƒŒ$\ÕÚ¼—pê0!mrúN“Ý›H}¹l¿Ã:¤‚f9»½ÿK V3*zó¦QÍÌ¢œZ´ÖÎŘŒ…,ó½èþ)õ°À-&ˆ”Únö’ Ÿ“ˆ§¡&RBH*n¡ -W±˜×Öïॕï Æa.[3 —‰¢8ÀÍp æʾyŸ~· ·¸TØÖ©C±h!qD×Ñ@J¤Q¡Ñ¦«6n_©XÜ°Jô&‚U‡%8BD$?tlžZ•ô4vf˜æjöȨ¿¸ãñ*ž0 K;ßúX\m©v¹¨×ÖZ*ôûÿÙ~åð·N5`Û.–²œá¹·mL{*eŠNŠ‡Jý§£¥ED‹òëK[ ¦“°›3þNîå« Ê—1ÓYô)r«üó’Ócv²^vœwæËæoE±wÀP8-W]>j8[r%]ˆÍfß;ÿáU§$ì TLmºa´Ú‹Õsæ‚.6jÄÝðòÊ]êU)ønDµþ¿“dÀÓ 'Ñcq„[¨œ"ÅÚ€Úi?Â5í…-¤ÄgѾûg%š¤×Š0îÛÜòå§åÍðé}€d2£ÉìVô}.RÚ1OúuÍùÐÛLÌp ê?G}óóViðÏÁcÑi7 Š…Ú4óŠäB™ØŽ†ÍG¿æ¿Ì'º}Ÿ @iOrUÚßÚ)Bo/{³“›·”4®Í•P$5KüA&ÑT*^Qñ)®Œ\BaªoI¥ ùªšßê*´¤êÜ>Ê$ì^5çÕ!Å*ºGð£C7e0,†6ú¨¼L !Ôõuó G¹s¶ãߦ’I&\»-bš&Ÿ…Ù³C?ëémé 0{•×Õ”ÌÔS˜ØÜ'׋sÊÔò1¤r¨fò—ý屯Ç(âþY:ú­­šLzô³|œÏj ÔP‡ã§©êœ÷Ï×mÿ© zÅÂ4秋ªD8¿§>Í®µB;•eÇE@FJ…é¤ù*ÊM|2:Bþ–kåÝHÌÿ}^¹ˆñ€T^)V}‚ÄíA2¢ãÖ¥tnDJ•Î2@ÇÁ©IH¨*»íQ Kରh0jþg¡°zè½ÏZx!ë°ñ,(•ö¨|;i}݇(4æ™D¦Ãn«Ýja6«"Âhœãámrà¡Ûx>õÙªƒ ØdGÈ6UÅÏÏ>¤î:dFÈ?ÕaÝtÏ7m\À yÑw.o‡®0$\Akëi@@X…1E?Ml%eT4/MPH|m;zmÎnl,’s˜’>v^—•S‰eÔo³mG ˆ°gx:šþíi‡zÂäŒGü¦šëAvÐ> x‹êè2-¶âÊ$+`ƒsl²Íb¬0 ÿsäç¶ÿ8?c™ÆÁИPñ»O³!à„ @–þ'û 7ñØ0Þ›4³dkŒÉ¢ˆ •Ñ„;K}zòåÇÚ?‰Kõ>Þ-ü¥U¸ú®lŠ¦ñî~ç’oì@Wá×ñ`ôb<Ù»îÆô5Ü"esÑ< §š£y{û‹gì;j”Ìqj• ëSJùf¨¸cCª¢‘pø; ÷<ê§$³är€«€EH–N*çÉ9™'Bj¡Ÿ7pÕ±«ÕÖ:Y»‹'Hºæ‡e‚• ½S…(»ž¶#ÓÉfE88°òüÜÃ×iIW3t÷DºzwÚ/‰]kÎN6baöu¢kÂ8áœÀú E—0âñ&~Òz!éùé~ˆ¸&ë&´Ü ¸µFG:•·fcù¦šx‹?ÍZÙ7J)±Óqꢽ«á¯r×ÓØô¿¬¼°ÖüÜ+qb»õy[6½Nh%D £N€¾J«À:Ùàí?٫­üÕØÉ;¸¢x¹‚tò4¹š$ÀÊÁ9- -@ʈgò¿µH6dƒ’9?­¯Ä݉9XÂj^2&2ru`K~·CÅ Ï& àsAs`–ôœ`ö*ÇÆß0Ñøä3%š§Ä£ç‰­F•ah/Õè®ÀLh;Á©Äöl¯øa9×Í'+0_KuÝ}óÐgŦNŒ´@ÈøßÏ\5›ºÌ9*›¤Åµ5T‚æ¸Z¦=´¸h8*Ôļ•¦µÔqFšb5,’Õ%½Ö´Ÿ³¶rŠ4’´Ã¸ÓbŽNÎüdåßfÔ ÔëÝWÍvq%wu5L9;š§˜éOà¸òøp0¥;­?¼‚^^åífè3H.*`/零WŸ`çT)&Kø 3( T{@ÎÏa·R˜1~ºBÍ«€©[‹Sù=fñÔØásV-óZ•Ÿ)F(‘†Ut½·›7#F!¿¨q›& .Û¥~òÁôg7@ŸDöë´tÁ;ž®`³éG Õð÷ÖÎ)Çþ{²†ëªgž„6±OïøÔÓ<”®»œ+÷ÁVûÝÔï\Iù¥Nƒ¥¤ffÌ=CMS°ú7,~\0²ÿ*²=ÿLJ®a~)ñí²9ô -Øúè ¨ýñä’¼JL²ƒøNÒ¥§™ Át„’4¡9-¶èU¤¦Äm0'5rùg¸–é_écB|çEkY~;E‰v%Òî÷#·þt‘ðoÌ×ÍϺWÅEÿºº•ùá•fñQÃÛ€~Ö h¥ÍJ¨`"kAao‚ôMAøª¥¶æ+þ gÕƒùȆŽ“zžíYöxûŠ—¹f9&.T´ær‘W’ìß–ÿo-è(†cyú¼ºì–<@‘ˆÖë¼BË”·OÂLç/´àzdš±xê®Ü$ÙÈfð´C¸×dÑlÝÆÈ´€®…Ys0¢q Ü›êÁÜ`a'¼ ñI(—a÷·=¼1׊ù—˜?oìȬf•ç&@uGYÝ=~•ðZ/®x˜ø!Èaæ™>±VºÙÆkçÄÃ:`[ªZFæEˆX!G'±—ê ÓÑ8 _¦”{AŒ§ÒŸmv€cŸGÈZfhÁ4ê˜F ®»8™ #÷\²€Ä+Ѩ’èµp³Ÿ£–®ŒS²•€TšsÕl_”C+.îZk Ÿð,GÁÐ/èf7A"òÀr¦s””wÉV ¼‹ûA@ŸL ža$T®Ô¬€Á¤\ò3wîoj:šÌÅ• Ü ‚â)¦”áäìžêE‡ G˜C¤œÄ§~õ]© R’¦@©/碥#f¾J7P³QO¿<ÐÍGWÛY¾¨Gl»ºÎIë’˜+‘½“Øvž{è“sî.^‡2Þ#Ãåf6Àr¾7¡š8|:£-áﵩ¢ÇÞ¾] \7ó!³Æ®8{)j•@óý£a6YçÀ”  ±Ú¦;6ò4™Î,VËœ(occƒV—Æu­öFaµ#dhle “@åŠXPP£„å©0ŸOWè|wÉâ¿ã¼bYÜ[ßíë|ú=éuq g2œjæ5†;ÜÞYZŠ×q"êžWaŽÀ]1§BɇŠ²¸<Ó¹Ÿ›è¦áÙ~sÉÑW½”€û´ &ù÷ÅÕX ]P§Ø$^kG:Ü=z¹mâ÷Š€´?ÊIÞsezÕFðj$ ×%a´<¬ 9ReÐJU-HCT; ô¿‚ÏÑ*Aÿ;æÛa¢ÞGë‹ ”ä<\Š«9œû(êàØaÅl›‹ç;>ŠÊp¶?´ÿ7=Ž7i´bDR˜¼Í{{±ZÆOüfźRh"uv¶CkÝñËñ†^jDRm°oüÔÆð«6>øÛµI7‡}¶j¬ÌAÐRßc$û$!Z£äÙžBTŒ¼ƒj4º?rV8…(E¿§Ý…Jê>•ÄÔ€úžú¬-² ^6\y ßI{ºn¡§ºq^ú?òpßK»Lyå™,!C¡Þ”+nî“@$·¼MGvÿQ ò!yö^k)|FCKÏÆÛhïä®-ÕôØÒNÕ8øi~-w0!§õ× íÐótÓÀ4“É‘}™¢:MÀ¨åÃþÔÇ\c¸>»fj]ÞR*ÜëüxœlkG„ÀVˆ†úÂ@ÒÓjNWÔÚ‹Žg½LXAÒrÖ/?ale–Îh!〢¹|*`ΞtVlý*V(øÍJ±ÁcÞ´Ñ܃¼*y1(ó]BMä’²nè™cJ8 ”9NpD Ø,]äi!Ô‚;ãˆ^|‹®Ðæ×6?[y¿XOíGjŠc'"¡JžøÍþ†Í" £ÄÚf,Ô‘Ír|ëΑlá1é †Sž!kZEž<@<&ö52EŽ¢uL†Äø®È _Vuµ{Áʳ©Iðû¡ƒ$+ ø¬&…}¤Ý~ ‰y†—ëó ¬Ÿý+ß^‚Jöµþá&¯þŽ`[’lJé)Ã(SÝzü2²UGzrëo{S­û0Jy ƒ8yàÙmô뵫∃è´œ(´JªÚÄÄ~))òŠÌ`æ+1\‡ÑoÛÏy‡-A¡ÖõLÒM,{±¨\0:°˜¡Ÿ·ó®jpµ8!ȇb‰ŸÇ£Š&¾ï<\¸dìÿÓ’ûhÒKé{ð§Â{B\ÃiR ¸^C̾Â=MÖYVø·Ï’N pSÏŒG0°šì#5†æJ–D$øIŠÜ7dÇXeºe`FˆÃb$r}åÊ´..xË\à3Ï ½fSÚØu¨‚ŽQ7ÒöÛUZç,9ËÇ?—ÛR„ü1ro(|6ª+ „~ƒ1Ÿ!Ý¿Û¥ôÓ«»½kÇŽJ­aÕôÃЩH«GüI®JÙõA_cÍ¡v™‹ºb.¨¿…Ðć–£…‹qþ ™‘¨5vÇNßU Î(0½óyÿ›¯;h¢6ä2+a01} Z"2Â*¶¶qî X£vy$§gj‚Mm{»%œ3 ‰Õ þHÄÆ¿×0Ç¥›KË‹“ùøú§Õ.ÿp¶¾`’„¾•!uÝ?tBÁÏ.¸lÈ /Lp»QõÎœðÅÙæå¹ûì—ÏUkZ²§c•AP”À0i€{EÚµ¡­ÍÞòcYf£ |áŸ$LM!ìÆGÚyàà**øäd†®¸(ƒ¹Gá~ù¥y™7¶^”Êöc³[«JÚÅ϶éèÍŠÁÿ•ô9,Œ¿ßsH di³-àûÕ¶kãÅZ0†o˜‚RŸ’¶…rá#í¶A_Dn_{m!¼£²WÅÄl€¶¿0DPf?0âÑá ¨iš12sâ5Oà[PþªPÀš0Æh½‡vì¥OtÑ›ÐAyš Y-Ûë—î+¹gÅÖËì9KŇÓa~ÞdeF2IKDTv@Ùró‰ˆAÅpwˆùó<ÿó»¹J™N¤3&Í—ý0qíØè|öÆ£÷zés µUMð¬‡j9¬6\}‘v/ö?¢ÊHIöW*Ìî:?Â7p:ExÆ›ËÉrÑð»‡t«ÉÛLÔ’ÁÇק ƒFÃ…3>@)«„u¤‡ÙDû>èZ%„d±%…âa]ÀŽˆ½6óÐC {‰Ë¦ötT¾”ºÛËßz§V1  ü¸ˆ–…Ii2hS"%ç–̆Í׆iã”ïÎ@¸[Ü!%JU~‰ÉéY;€93Áþ€Õä»Ç˜æqaÇj¡ûñD¥yI$nœf ú§­éßX€!®Ë' Åp­,拉—¯¶†’xsþè¢ØâygQ2lÃL(³®­¯xöþÆ{¶Âüs¶_WLývÝATûÄÿè!ÑUÚ˜þ_>ˆäÊÿZ㔈ŒÚHíX§.JÊo Æ (ÂÇkkRZ”§ÔksKe•q0Ô@Ë ~lœ%¸$®ø& t8ãä šûù)VÜ••ÇøÔ·Æì†?¢`rØU·ÓíìŠÙ5;eÊ‘Ì. hÏ÷Å«>igLo©ùE¾&n€Ðºè™°²¡šÛ'1B®Òl‘zĆœ"}…­}d¸>o:F<ÐQȤÐËaÀOõÞÆlYŸ›ºkõ‡dUK*6P‚´}Ø0ÚW“r©¸;Þ^§¿]&¨Kïà¨WUhç`ÎT+T?…¶6'“Mc%!aŽú’°”2!¥YÖº?‡B7£ã¦†aô{S ²·úV¹ø@¼#-Æ(¸{J Ïpε e)à®Ošïį_ÿX†ô§4­3B¦d· êF¼ù‚Z ÷Z j?M¼yû󆙅ѧiëúÙ,‘ŽºLœJ>ôäÐS4¥s•‰s’“}Vô#Ï›.5¨Vˆ²âí«[Ð*4$ÊÞð½`ZÚ¨š!{ì0Üœ9Í¿ÕFj^rê&LR¨HaìWKÉ*ƒlêÒ‚£yiB´ÿŸë—ØbY@Y2¼ÈMõý=ýÔmºE"SqV3nÇUr1}=í] ñCÆòæNôl, ZwGÔÅ {r®u©žz¹ÏTSM3+hŸkÓzÈÈ09a♵m(z$¯}Ï Y"Zi]1 Nsyž©{ð;5îÛ=²ÿ&÷ª°—šŸÂÓG\6ÅÒßó`KÝ°¯Äa ¥Þ=I]cÓâ¶0êy]ÃOxå&H«ßÌSñ½¥äÇ ]Šð™èÄHtÔœ¾Af¤)¼SÉ©xï×r°ný U ú¾.»ŸC³‹µlÍšIâÎê»Í—Z 4¸f'*ÙBé4td Y ¬,*Òæ“PZ±­ ;UÂ^•ô÷%*ŽŒ ½¬A‘CÖ6éfÖ[üÆÄ’˜èßÈùrjù'Jt-ù^]ú£»Í:Í;/ –#¬øÌô¾æcmÊø™™¾óà}Ä IDŸNcõ³òsr’j®<ò$ÊOpwÅ¿Èc?û‡$]:B±/5¡„ü$垺Hž1Â*EaبÉenÞ¥¢Uîµd}4ß‚M`Ïo2Šµ£´ Õ]×"æ@€)$ÍŸt ±_BzgC Ñy¯ùǼ)Ü(×ñ©´5e EU8|@wW¹”RÀì½b(Ë$9// ø‘éIõ´t+¸ƒgm”üå<n\ó{§‘ù,UaðÿˆKÀš¤w;«þ¿–ãb¯0›_0G|U¶ ú“Ô\ìÃ8‡K*$”£Ê`F±thÎ sÏè#!#}ÌÚra×ÛƒŒZ&¦±‚h ÇÚÍ÷sñ/?xµ¾´F‰Rw*ä› E„ÚÒoÆ«½G\þŸ¯9¦W'y¾ä{d?b çjðdIhx9D©ÉÄû¼ÔfÆMš£é(#†TË]ÆÚ,µã§ÄˇÎZCÝYb%e pæüWh“¼#ÿjjÙ*ñ p¼ÉŒÙa—23sãxc(ØQ¬ñÑä )ìLN@ÛÔÃŸðˆ ‰3#}¶é:éñÆøý¹LñoŠåœwzC$íÒï=KÂ|ÙˆÐÄ âE E™@•*>n|¦­—綣i”· ]GÊ-h\e <–…\WC: ŠôÐY¾,'8gƒ‹æÐ=øWP±ÌŒ<}éö(HëœÍV%úŒÈŸÑ†b.i¬Ï”\Ê3¼º ˆ¡XÜ[R˜@baBƒ'*ýÚ+fÞ„¡`yw<@Ĩ¢{ù“:)# ºËÉ#­íøYV‰¸a¦7gýò!§HvP<‘g,3#HOÏ뻈=¼÷8nÉÞ¤Gÿ„!l=ty!¬ûÆU6jùÒ Þïs¥K•å uLË·?RëŽóóôQ‡tüq-3áBÁ„là=®Ç«]ÖŠ˜ˆ2uê³ÂÈžËâ‚,•z®”‡]›tÞò°y~Ì«ÆL3î±|ÕbÉ)¤kÄ3‰ è@;ûR –‰Ä_øþóÁ×|¨V=R±.oH˜c£á R“RmØìÀQBŧ,ÖŸDê‡;ûÚÁWØNÄ’~i¬-©ßí|iaïh¯ gnaGç‰Â·Q]&\,ð™í¢ÿ“‚SÇŠ9¥{Rã^Üdÿ7jq¬x}})ô"žVÞKv³ß‹"®G Øiô5:× Œß³ÿíºAyóÞfŒ›ªÿ’u‡ŠûÌÝì»Á(ÒH‘Ï 7¼¾¦­4÷ŒŒ5ö’27¬/¼éì#w!whT"€Mv!ÃK3×ë:ÐÔðÝÍö0ࣩü @àjDB{S2ËbtÁu“Î0 îK¤¼ÍÓdddaP‰[_G}ë/°Uc¾f |ŽRXÝ?HÚo׬뢦o\JŸÌ™9§L™î+¾Q«ûh•q+$ãß©Œ]‡• *Á<÷Z“Ülwf4¶ô˜û™UžJHd¡T,ð×ã¥Qq'j"¾©Ì:©JO2ÚJ°«hMd11h)f0Gì(Y ;h'«†Åm°¹rœSçJW‡7Ñ·æ¾=ß»š.p]”q.È&žŸF\NÎq_ÎõXªÆ±iTàæTÀµžwQ‰ŠðcÊ]0ûuÞèkÿ„Ò%‘,È0*¨hòùÞÐðÈÕ]¯{â\þåñ*køfcÕŒW­ÂŸëÚÃEbѨäî©xÅTå„øàV‘ˆI)…¤É\0&">ü"ƒ>NkxÏH¥[U– èPEà*d¢‰žÎÌ åÒÄ“‚fhŠ¸Æ”Ê—VDÈA] Èà®9…ˆŽ\CÌ ˆÆÌqpd3S—YžØ¼?vÙ°†ôYõÉ׬k†™àØôGëŠ%Y¬äÅ>5U´"IªH#VêLø«îÕ§ Š½Œ?d=y¸ÇžôçDz%²fíé#ÿB5­Ú¥ôòÓÅ{ ×52*@‹ÚÞ‘µ @’ëç¿à¿í™yÈ“CÞ¢6¨óhºQ²×_=¾¶71PšÕÏ–kyv³ˆ“Á/#ç¬Ù]_?¥×Gâ ).sLkl ѹ ŒIT´²M$‰h 5ód9)§/¨Ø$´.牤Z™ž MÉF„x˜v@V¾1á!{-5Éël#$kù>UjUÝä”7{›'y”±2Êïon#YÈw{JR4àK;‡läi0®)<õê·çuš7:A„LþCx¦ùA/=6 ½ ut”÷±¸3œ¸ÁÝ_a7 ¦ü´Ü×nTíßÏ¥üO™_àŠ¥k–ÝÜBC§¬©ñ 9¡ÛTPšAÝ\(Ö9±÷°‡1Aj™í# Å׆“Ò¾°9$¸’ôC;^‹*¶0͵·'@ð0Ó±dÍ?M¿j}e-Ô(úpJ•¹lNTZîðÔÖ¶œôð ÚÀ…Œ:éµzjpÏá¬bU#øÎÔÌ (_¸-NEÁŒš±{ÒSï[±Ñ£¶OŽ8G÷¸!ý‡1O÷¦I"ɇz~Ž–4ÓŦ‰h¤¶éf !ÃSÍñßxEx,™ßß³€°zþç@]6õßòÒ§4y“yß”_”Âu+´•¶È…šXµK^IAã±'A;YÊ9ÆÚqsÂ!ж=–b¾BÝèœa–!I‹VÇØP*€·ÅM¯„‚ËÅí.ÿz¡’ZÃØÀÆE¥{Œ¼Ä­³Ü¹LG]— %¨6¯ÈÆsp1øIóÏÓ2fÉKpy NýNçæÅìN7gÏ…¼ô±¥: Þü%`¥*ñÃXùü³y’źœÌ¼¯ÄSt÷ýóå @Ð&¢£6œpé]P£r‚¤|û%ö KÛLb³•`vkL/©ÚX@X’Ñ奫ÅCO• GæÕ}~;¸+>>ÀÍ2s(JÈ0½˜ÛœÙ®æçO=qÎÂ(¢ð™ÑÅó}èriÆx°K`c››pv÷Êmàç¬Úq»Ð‹ð…‰F'Èã³h·+’i¸çœÞÓã ÕYÓß1¥Wí$Wö:a[dt+u7¨ ‰Ó"—¦î…B›5p±%åür ô¥ ¹Žx=XÄœo¢E¢‹8<èåå‹ ëyaÔmž)Y1c[°³7íùô?ÌÇ!ë©Æ•l—KWðVÛfWyÈã–*J ´¡NžÂãºê@|àïù´]‚b!}5™ËTÿÚŽ¾ìöK\øä{%Úß߬nŠÿ6š€{|l/~”D†|`żAmæŒX5öõ›0š ùѧ‘{Àa ¶çm¹˜B¨¡ýqZ¬¾Z¦Š›”[u-µîxÒ±å¨CÛ¯¤m?=Ák{*úÎOPqDF:ž«ûí”ñçž8¿+öñË» ûRo‹6“ì QÀÿˆÒÅ 4îb®ÌoÄ? ­µ¾ßZ[J&Å )Ѫ»º šÞKÚŒd-|Ž¢1'ÀxŒÅ0k̯0u†«,ä± vÚÐÏ à››–³q•ã=F¼8ùj‘ <"&¢;dØ“óÙ¢¬râê_‰kTÀÉ”û"³ïãpü%[¶{îaøE¸¦5]‹‚Z¾Ñ—ÍqòLV1ü‡n/hÏö”®¥m*xo…†®’“ÆA3ÐêŽO+Á#ãyjBnXQxa¸:3¤Öd(ð–?!Ô rR»Ñ‰¡ü7H~·ðiÒ£é;Ï3Ã8N0КL< ଙ÷b?ZÃËc!n®þ¢¯ápªGh´Û?V ë©Ã‚² ¯e>s˜>E¡[*4xÁ0•àa¾ÁâH6\ÕÍLÌþó¢|®ìÊKKY}oýŠ*ªÜ™nš"ÐôiïŒè WN;‡])Ãð>I=Äê ]«œÃ” è N%{VáªN'kL?ÿÅ­z7< áÎyÆOë_S:uÃlßRüýëxðN¿2æ—ÃJPÒsì‘Ü›¦£) Øå‹é“”´±K§õrNeíÙ‘ &~Ó¦ë ëPW$520èmÿ§Ž|J.B~×fsì„'ß{™›¯zÊ×øÕÛ…ûüA/z.†Þ!¥¹•a-Êš»ÔÓêX×î–l0¼øj&qÏÝÃk‹‚‹€³—ņ-œósÑÈJã2-­Ó«Ã†e¡ð«$)Þˆ#ã$\¯í/èbø—¡DhÃñ.zê±n¿¥—N0»¾¾_b o³åHÂ!ЬÒgÑ!”¨ë}Y« Ôﮟ„Â䬟èC¥ NØõnðŒ?$NðDò~¨%!´3{Ÿ—Âg±T=˜+é€d¾~áů€9ñƒúß·oµù”$ÅèƒZ¬ÅƼ¢yü#ŽÙ¹üùv“bw†Ü÷t]|iåÜ­CÕêÄ ˆ®Áû(ª;‰è_OÍõ4‚YÌáPŠâ– À;ò»'Šm´/?šÒÕtÈ]Xdƒ\°ÀŸz<:s¡äP À4ÁÙùXt^EL ¿}Wó8|;,¬Ä£/äg{¶\~ðy•n1'`ë…ceƒŽ°°]i_W5I_ˆsWQ5T&`Å+?]úŒTuYh™?ï©ryøñþµèœˆÏáëóÁñã—4Õ®IÒ+´eâoJþ|•Î÷’» FKʉW]¦P”œ"…ôßÆÈcÍÙÓìh‘¼ö"Ü õö…È’Å'Ð4ÖîàAÁ´&Ä Œv‰s±ënÌ«í Õü‹ik§oN žç}xQ8 ŠÉOøv6 ålG}Ê—uþï‹M–ÔÈÔ)Öau2Ø…Ïïæu{;JÅC;‚wõ3Y6Õ=•Þ]6-)2ax¢ÑÚs~Á4?s0‚}‰Þ›•“öXp¾«´B÷š ëIDšÐQU?q€®Ü\ÖÈžY;ºêË Éb«WÁ¼p²)Òç›ÏvjÄ*K®ÐÞä0Û¾l7˜]ßàCÜ›¸¸ê#!62™¡Û–t~-sÉW`¶Af¤çˆ3†ÜMttÛ´«‰ØW)"·è·;¢+‡I¾4o‡§(ŸÙ·‘aVê¾d&Í5гõÑbÜ©‘k.ò>ÎwØsV+«qd)¬ËαZœç‚«e;òãâüKºGXVc–VØ};pP‡í¤~F-–­Øë¼Ì'µý™;KÝÓÁ†€@óFLÃvIÊPrhwfë²-¶üÏ•åíyx„¨ÃgâžèEs*•³¥õR]E·Í¨wòoê nû|m¡ïó'j%ÿpˆÖ ®L`S5d)Tö‰;iœ 9 ˆîö*Í8ãZŒ$K}×£K§îd#ë[RDÍ4ÍleX®/|òîè ƒYhœàTõ"CÌl0ÁQ³“4 ÕkHÃ’CÙ=Þ©Ô_GF·)ø£Gt+Ä»Ü÷rà:8œ,‹Ñö/:â1âËCª<"Èw¢ŒŽ#ú³TH¦Æp„‚’ƒ Äýì†.ê1Æ°ÔQàâV"†W»_¾izWo+æ©l*ø5±,Ó%AÏhºUíÉw¿ÏÜê‹Àa~)?ÈÆ]ÎŽ`öà9@^¡Ï†é—÷!ÕªÏW4®õßÃâŒ9ïî ITñŸíÓãPÕpá|,B‹DY ¨é{AT\ät•:“ÃK/D°½±óDì} +î ìŠHgÁP¹ WškŒ‰:†S•ð)ûÂSQ€ßôN%N‘{£št?ôˆÿC'¨7î Ÿ…&r“6~餙UçÎйFÓ½œ€ E„ã’¬N·BVº†³âu¹œöY+Ññb4]èøî¿õ¸Ò WQ3Cc0‡Fë—¦«zë™PoêyÄšùoÿ•®d›¤Lób–âT¦¥Çêö=³øy”“Š‡²<Ð×ÊœâïS©îÀ«'™]Ž•äÝsÏö(÷ÖPF|I›QòQ­zç3JF€Ÿ#9’b´Çz/‰Š»ý©²!‹#ù„Æ9Î¥B œ\7µ@áµT‹‘ŠFíE\G/fQª +?Í['Ó>¬Nò}§z£8gcP àƒÔ¾ "Á¹kÌ!`" ô“!Ò7C ïPQÈøÈ1^• è¼?Þ£Ò:À.&ßV…åBsv0혴$j««Õý0dv kŒó> ¤s#ŒA>`j¿™=§Ž£¼´D’‚#Ý«ßÊbž¨éJ³¬å ß’iü0eH±IÏ‘‰ìšj7Òo‹*…®åeÙV\G±Èªò~½xâˆQç¡þÀ°@ëRØf®ÛâÝ4@q¶v ß’pzâ—ÞHã”0ˆÃϱ‡œØ¡š-10÷çÔwÀe¢”Æó+Åæ úP¶ýàDïNŸœ>C–:ö©—³Æ‰®:¦÷¢fè–°ú4VÒ·EN¦8¹™‡MÝ€n™•Ã;‰‡‚‚’¤`1™¤ú_ÌÖ#y×GÜ)˜«ã¨´s"eï‹Å°³ªNÞÑÿ§]Übn›£>-8&W‡¬æMÀÈ=ùæR©Ò…â‡Ä,}…´sc,Q¬_=J¸ Ϊ YÆ$tãIP¶öúç`ae¦K{ =ÖârÔ¡iáÖøC­ôßæZö‘0ëevL‚¨¥Ó6¡úq³Œä¥"Ó•yͺ§½øRí_±XÈÉ'*¼Ž5³’‡¶”°v8Ù¡spž11 Ô:Ø™š MµÈ%_,_á60«½ [b¡§h‰±ó ²|Õ_C ”üÉ ?pm] b½­ )rý)·,FZÁ¬ë²zªy´›²Ž/$ 矖P)ì¾F;<Ô,"Ÿ%køDÈ·ÂY÷&?•éσ®LfF£KøžþbWyhQ©Þ{W8±•œže©Qb:3É—X¹•Ÿ46k07 Aíÿ0«Þœ¢¾ŠìÂò ë~÷éh‹þð¬ó˜I¦‘ ŒòQ<®Q­Uø&&gY_â¬%“£ÜõÉóW‰Qæ7\ÛõB˜·X#ÁƒJêùã”Òd º2f¸of¤–ß‘¸27~TÀr÷¶Í—–‚G`¦DÍâ)¥e¬4ý‘aÓ£ã¤!UžÃ<Èû™èÔíòhâ¹³—L*¼1­î®pÂÙN1‘¾ùþMò’HÝÑ/Ž>8c1op¡ÇjmmžqxlD¨öèUÿ5x˜2ëáf-Íå7òNã7CåñNQßwŸè´–AúªÜU¸¶ÒgQÇÓÎ ?UËä&;ùD©j3ô®Èµb¹ãh{ˆ% „.,Ø„ôâ;Κ2ÕÜ5-ä'þ2t¨‚£ì͹4_bÖPü4ãÅGp—gÅâ³ÒK4YAOE^þµ?4âÙµ°óR‡DjÁ)\3̼®»¸o+éd±r—1ì Íe¿ 9ÑÿzÙ=lö¿øæåù£øtŸÅ¶Ž˜M¸é6ü3ìØ=¬ã3|áèÖ>Öý9L< Bl §a*¾˜›…ö­~wúCņ̃DöWcp>8ž,E%ù6j†ÑÕG¼PÔírËKš„¯)c¸Jû”Ë*ðÙœ6;‹nW:q¤ÀðÎ&KþmÐä‡C‰‚h?ê§H·+E;ìp6¼¸.ó9¬U:/M­ÊÑŠ¨&øÚ®<ˆ,ºb[yæ=Cÿ¾öl4+®u÷ñ8Ì7ÐÿA^°°B–JRß#^zž±Ý†%éŸ`ŽÞ/@W’o¦©ê åÚÇê9BFf05é¢L£PÞ”\}PPÔÏ]Þ™w½Ò¼œ%8WÙåìB]yn›A0ůéÒ›IÙ”ŽšåJ×ÄoÌÈ®YýÈèãÔP?år0×ô¥ Z× §)OgM¿ciÅ ½˜ã~PÆFì£ 7²ã+h¼×ŒÍ;B¦qŠ®s*BeISªPÍE„„µsÔ$ÇXE<\ƒEÙÓ'æÃ;Y Ú<ü˜ÅÓÉôŽnÎ*·URã¯Ðw*Ku¦eNêl)CUôS¶PPKáËP'`@–Va…6ã…òz±0Îg¼«ïÅy!_42ù¤P9 Nš]f’®ß>by…·èåî»1ŸT‡—O«G›P½øÖxˆüî>0ýò;ãæSÒõáõ‰ÈòøÞ•EŒáYö+„gœŸÒ=~&füý¯€If> ÊÈ>®,/¸ô³I’ :‚2ÕÍ×3£Ÿç¦ŒÒ~¢HÍ7¯DÔ0uü;ùÂÏTê2SJºØ1KÝVvO ]ºÊzñ±$âû4Q£þœ88wΘv¢ÈÚÇT/b÷›YÆQoqó$ØäìÀfµ]ò6Š­ò{€tFÄ–qÇø{cf¹NLYƒˆŒW‘[‘øJ[ðò!Ñ—VMãch| 8?c0Êýþìˆl„†,ã›,ñ[hª´™e_ßjÊ`*ö1/Ÿ²@¤ƒ‚Õ€Õ+7^>|¯ß:˜éJX÷ÚÔWCåÛ&` ɵ¾½‚(ìôúJ-»Dv¬qMÙ «S •…ˆ–uQoñuÅùSÊ›dS1æ²!©vv‹‹wÅêÓnØ¿½Kí uþ3§ ©IS‘æ­'i@ìyñìïLUô»|Ÿþ† ¨×ç‹Ð³Ö³³1“·tR5 CuÆ+`\Ó»û¼:‹¯‹îŸ…½2âH¿±C.cðšC´[D.IT£Å“ÛÚGF1Šo÷0ØcNu#ê“›1^ÂÜaÞŠ¹…™f7;§7È "Œ\"1Õ"F<™Ûá©lËù?ÙÍÝ°çKؔц€6ÇŸ»á¨­r6>È4ð%̉ÃÔ ³AT˜)Ì¥s²/“ƒrзÌr¢Áù¡O¯›K].=þåŒVÖWâ S&ãÀ³Â¸ÃѯÝÁšv± *åW¹`åêÒ®/ÂÛÇæ‘UÅJJ¦úox÷Õ´"ˆÖ›S/*,ù…W.LNÝ^íqtg‰víaBAçýO.„5<Õ5•Ø—¡¿ÖØÛ¬f[ –˜gPAغVió%¥GÖÑb6r¥&ª"‡n²r6NxmT ã‡Ýlhä _@¡Ã‹ªHUö¸¼Õ§ý¹½^˜s,_»ÆæÈeà:“m¹fÞ7°Ê¬qô¬8W#NñQÿ}^Wðà€’ànÜÿMÍDh|âzí^ųgŽIêÓgá\"Ö‰ȹúàÀÆŽu¶®Ê qHû©Ž†žÁó¨ÙDQ§|åVy¢Ñ>|ëA³-bÜ·+AW“ô]Sbi=²º{pCeä‹ïÃZl!`“9]NŽÔu3RîvSqŠCà {-¶± d°Np.·Ú¾Õ0–JtÞb3±lÎ@}nF4èƒZŸö ˆá…ÊÓ% ¸<¦w›áŸÝw¨•7Í_K‰ïIóæn´´Zºöì„À½N“@~^yÀwòšBÆÃI6x‡²ˆ=ŸR“œ°M±íËùé*’`ž”©ÌpoÅ› .ÓÚÈÑ–’0ÚtŸèˆâãü,L½Æ˪¨¼»²¨KÍ2Dm"áu›@¶Äù¤ä—¨£ÙÌ¡>.1÷™’ÂÛ‘%TI[Æ3 †Å‡E–Å–Ø{3;´qrϦúgæ,¥8Ã@–ñP…¡Šj&FîÿõRè3ìú‡#ªÄŽ$ýšXùɘš˜­•<-DÓ=ëN ‡{Á|Õ°õÿWb¬ðB«V9ý]J—Ô~«'ü†6‘6üqÅ›¼c‚að«nî_Ì(ìÉÕ*7 nìF’$ „ÅÈe»Äb1B¦öqãú8û:ÎwDúeá=³c«@ ÆäJHnU±.šÒP4zý÷ËgöyAYmy/ÑÜÅÿüjÑyfrÛ½%`ë(º³€Ó¥gŸôhÒÓei¨Ò‡¾?fw &5¥‰U"íãµóSps @ q¿ÀÆõ;·I‡j6í¢ëTA|%{æ ¶0Âõ›ÜäâÆ᡾'å`?vvÛs‡·™Ï¤ºz7"|oò>¸ùzî´|ðB¢.ë4«ßlö‘IãY„E0W½õ?€Ý¾«, rC¹\EòDŸÔðÊâsÉÀ@lqƒzYkä§gGö·#NñAq!_ *Ñí\Ç·N{åÔ1èCHy°)æô/(3¦D EÅÝÔ¦JL~,²ZÌ9–ç{qN–ºf4:€Š4ÈD ›ý&€äÔWVŽÉ}<·x&NƒÕ«Ð (‹÷äžÍ{»kzsÂ6¨á×jbüÔcÑï鲚k~óÐ:Üñ„*~ZìËñõ^É/¾“iV.™$*«ýÖàãßPuz7mY1÷Á3;]^0Êæ„K`ÈZq ˧ñËÖ™â~’r ¯Ä0ZØ4㱫ªü!ÑçÝÕvÍ Tµâœ–?fì¤6ê,× R5ŸÞ®0°\»Ü<–§JU+ÉÑŸ8„]Z*™ðRfœ =÷»5TI†tèÿ Ñòé€Xû‰Ï!5<àIݵ¢ bYÍØ1c˜ÝÛrÔz;äã æÕb1D– ³“öÕMY›6š\:…Xdže‹|¼b: Ìu†—ÈV‘£[ëk=¶ýNôØI÷ñö†Û¸À(q[-Zí¿fR(“2îLk—v6›gòØPe†•^úwÍKPYŠó:¶†¹ýB›TY5DöÆ o%ô¹MÔ1¸U-²Os³ 1‹€xÙñÛía Òý¯C/5©ÌnäT@˜_·®?ä1R̺³’[[eR€ÕRÅAŽÚ½"'µå›½-娠¯F*a\6†±{»U&  qµùÐzóvïYŽùKµ‘‰|ÓN~ñÈc¶†o±×׋ZG¥Ϫc,vËÃ"_0„p_N$ GT–ÈZöz++3Ä)1ê䲘•«[ ’4•¡-=Ž ÷…›E‚d'˜½ „‹z’LéÚ)1 ÝÆÛó¯©OÁ@¸Ê1¤^Š;Â:·¸wàd7Lþÿ²“ãÝ‘T{«ýÌqý†¶Ÿå¨]'¦B´›v=~ÑôhmXÿî_Pl¿O]Åá†ý§s(o€ T-åU'Å™X±ôOA>"~˜ë(Ö¬HßtÝiÏ^*dtîY¼/™žÏØ[ˆÏ{¬É-—#VØA¯(΢Ô91nï<™sEü·÷ú|ú½È‹‹Ùþmܯ—Ë¥ýÏžbljùè4Zm–еx ß ›>½/iÂÙð«ÕÇÅ~\gÐmsÞŽ‘žœòohž"Ä kÇ ©©6·ZɇLüÁ¢âž> i¶†GëÀ¾E“ô¯¶6\Ó½¾ßg¿Ê¶üA£m‡Ð5«éqÑY—úH7à,QØÎì-ÙîJÁÓ”Hdk@542z~u÷¬ÑþÉ"·™¼Wu0Ó.rbï·Æ-xBÝ9 £ÁBSªiÖ°˜{-þúõO<7s}Ë-Á<¹ü¿á0<¼ü0¯¢‚·w”lÙIƒ—†‰¢y3”$@zÈLWDu‡¸>”Ð/Ó‡Dó³Å¢û_(6%LÍŠ6/P6žû´Q©¹}“±•à¨šQg®(pbü«-\Ç0x×Üp"_¼Ý+ é«1Üh†atÈ苼±,€Â¦{Qn×õx"rUxªW¹/&;”ò½T|ðGw°²}šŒc~,’À¯aN“ž[>cÿ¼Û ¶ %üL4†݇×\ çÎzhÎÑ̘5>B6È ±±«~¤MÓ´áiUYÞ†p¼‘$BíjSwxð+ï3˜m€¢WÓ1¶BÀ¸ Shý…D—,„Gó3ØîÊÊÏ8ËñTùª"¡&T¼”ezˆ”5‚:J¤«ÎÞî=Ö$7mI “ñM¿µI·iùî SšæýDråD0Vér0HÀøÌ,Äp(qÆzqÈ#;-¶á!q›ú蟕ì±,|µµÐq¬µÁv¦­a•°H&!¼äEŽò¨MÆ~‘o¿±Å·Ã©HF`œZ¡tÞ¯ÄâûbæZåVz\ßAE(À­vÛDž+Ãö[¤l…U”øw¤Ûqíöå Þ–¨h߆S=]~°€ ’6ÙÑo^øy£ò< ó¼Ä/VŒ ‘®±¤cÙ#î!P„Ç¿¯Ïpi•@7e+Çÿ¯4@ëÞ»q×@¢Ùm}d'a2f$Bq,rFLŸÒˆ#…J‰ŽË c™ÓtÂSb¤Mqr»*ôm0y€õ ¡ŠÈ$"÷p(ie¨X.ûˆ¥)ºC¥S„"ònc›tFŠ6>dæÂuÐPC‘ãÊtù+?öÖœ«“¼ÝXR…lU !Fé æÖÜÛ¦¢(ÇÓ²õ‘ ?ø—{t#œÜÜpÐ6Öõ—ö͆rƒ“jµp¯´¤I§ªÍâ ýE”eWo§pÙpߘ¤ŸR«Ôc{µ6ì·3Ãú‘ýÇÂÙ›j>Cè J«„òƒñ™®sX ÏŒ…©NAR3b#¶ÀBÈBÉú©¿ó¯Tý/¶vM~t w#\±~-“18kìŠtdÖ‡°m%™©Ôb!’x°Ñª=cؾÈÄú"«L&R–Ó†d®{·fY·:‚âŠVq. Ý–r>)þÆÛ‰bÂJC4Më×ÅÍHcqj©£ÎWg·•Å ãPìuÎ’«ìíö·ìl27(5¶+Ÿ¡vr¶5™h?¨ò_ æû)# º`hj§Tuì}6¤í½™û”<»4ë­âÔ#]§É–4Ðó(˃•óaSüÌc°æá7Ô¥ág'¿ïµp¹ÃÕ»áÏN"=ˉ6}ú%‰ õÜnžcg¦ö挤Jý‘”òÇΰéX›kì· —…]@ÑùQ·•mÝ "tp‚œîÀR °ªÑ’óòr#cF¦Wã-Ï#¿Ü•G“Ëé¹,ÀÎ]}Œ#ñ{)[¿~˜;ß•¢ ’ÜœD秚D¤Ç×êÃÊ"¥žú>T)^˜ ié&Ý?¬ìäÒNx@npóÝcIí¦Aï¤Ö”?öù™£Y„ØÂM EQ^ñ›ÍÙ­ †å¤liDY&øºo<ñN°±:­œÀºR”é™ÓP‘‹þ0:l Ä(òÛ¯\t¯!ÎbˉAZTÝ^ž;/¹˜è›oƬD€AígI·ê‰?N¼{ÉóTëIzËø­Yù»2f_"öa•º‚óTXnÃ!.ÿˆˆ—öfn£x °Ÿñƒ:õ|ÒA+.œ©ß¹Ð?píùªË"HEß‚&›l/ÈQ]Óô•CíÕé÷HÖ<ÇXÚ*áÑ© ÆÓ° ß•¹âô;¯oï7ÓÌ/®*E³æ]Ôíþ;’¤p,‚~ß ŽD ¤É› ékôm¡F.™Â·»Úæòh—ïÈ­KÄ xQSæ0¬Òá×ﾹѣpo‡j]^—rcÁ)òÿË|ÆÎ6”³/í\)ÍÚ‘Š•'kî¼æ$N‹výã]Zd; °ƒ» €G@+³b ÄÉWPÄEµ,ùÀ\ýÚiVÖß”!op)"x—ƒ¸æÛ¬Zná\Mqo¡q7åͦÇ] b°S\¥E}ªv£¤ïãå¯ÆbDOfÛi86‹‘£Iîyâq ,®üÿ·%è—ýÙuPscC¸lf@ËJ f% §žªq¢ª³×Ô½r%û……×Î’±]lz—ósÃE6?‡R°G•!ƒT9ºšÔ’B¥r¸ÖÖŽö»åm“ï7·*8á5(•dö —8Ÿ»ÚÉóIч{§o2Ú£¥öàOŸ=Ë~ûǸÒa<åsÖø‘Í/¢ Þg•‰B}°XV\®ÒìÐè 'Ðň®íT±£ , Û§ÅánKgO1–h©Yíœå+z usk »7LëM ½Ç·Àýóƒf¯Õr+OJd=R]¡(ÐÏãùP¾E7ðcqè@«–K»:ѬÔâ¼P§ôšÜ¢ ¼ñê8¿¥W~y¾c»5úú%ŽíÉÙÚÒ‰7›êa'»2ºí»IYkVµ„v¦ÏåœÌ—bmh5g‹³$ï¤Â[/W:QИM'UòUçÆqÏO L+ ãüòb€d°'ÒÓË»ì³ v“¿È!€Ç˜óØW§ÕÎרÓïé«L 1ŒPè-r‚Áɯâ./íâ ß]cÐíàüã~²Õ™ùI:à¤5ÃAw/ƒ’Â0´òêSyÂóÄ D|’©£¿ Ë'ÛüvýÅ^ =3‘cɯoz©žr,pæôªÉßaÐÉ>€Á¡ XFìwô ò%PÌÎÅ—uŠ‚N 7 ª7\À…z›)½˜t•ˆ­Ô—Åà>Ëp—}^ qÑň´å¦tŠ3âéK}Ív;Ì¥¤vïˆL¤­±€˜ ä¢YËÕm êû ÉäÀ™÷5ª8Y¨Øˆoˆ/"Ó#3{_n«”„·fFÞ‰XT€DÛá 8çœU®s‹“ )†iÔ©0—ÅDú20aÖŸ?J+·k þŒ#@úDPac¼ •Ëéçx—EÕó²±d¾é’€"¯¼µŒ¾8un& TÂ\ l!Í0ŽÖÀ>xf+dé„r–€˜/ÝûûajYb¶ë0äHù¸w.zÃ&v|S©Á"{Îî(Ö$[!ñÍqGºMzA­Èo Ltù\4ÀvžZ¡Ç7‹l?&¸‰Z夼<”°¾ªFM8|b{ºC¯·7¾¦Š¿W8éá;à Kÿ£Å¸ºÏ3«Â ld,즭F >}ö}›Ù€®SL¦á—ž½O{àÔ€ø`/ˆçKÏyÜpñs®˜†«áõ_ÀÛ5†Ùl‡“´† … ÂÁ#(–P}_+¹gðß-i~.ˆQ– ôk.—ø¾È^›.~Jw[Éœ åœP±+\¨³äRñ5xX; +îDKW­2[קm1˜Í$¶˜¢^N¹J0vÐ+@|õ´Ñ7k· ; ÊüŒQý¼Pú¼ìø…?VŽcÌŒ®¯7w7J@#Ñ^+?÷´CB”Á Ÿ2UƒñBÓ¥±÷Ê5Êû[¿€ç[e Ý;&øÇëki;Ÿh/˜v[è@:MØþ‹kW¤>1»Ý•¤8—Œ75i™ºë±ìù¦­erøÅÔ»ùuzWݤ+c$DÑ.! ¥ÛªÑ…­üÈ°„ÀÝiÅkS„š[ƒG?ŠB3 ü»¦1&€¤í ϯ²ßGœ}ç¼ì͵HëêŠ5‰'š 5‚›Rµ“äŒÍz?O¦•%šÄέ‡G`ANä<¬¿g‡Xzç­»ëšÓQÅFžMój)ÝwàV·‘—a?U1Ž¿þý<†b¢èXü\>rþÉíÕÀ0ƒ^Ú¬9ŒÒÓÑ/|ªŽÕÞ§í%.[Î}9`ìXÒgûeªM°”‹ƒÄ³L› üº®¤›%¹:ÜDŽå†´zRFNg­õ®Èõùj†æà¦Ì†9.ÝŠkE¯.ëüÓkHwrZ?P«rŒi:¿.6ºY?È#¯½Ú¼h÷õu¥/']ÛU”Ë|³?ãu³ÀäçîXÅLº³³s£©»ÖG!F4rºßxWd4S©¨7 çGŸ’Ù"G îÌŒcIÊ'‹$Åx ³ËYä<¤!"ðpÃó‹€\Œ:@×bÃSô£ bžï43Q8£¬ôž’œÓšËUHŸ%÷J,ƒ«:¯„Ø£?*ÙÌÉàý_“ÎÍÎäŸd8ö‹7™ÑWYf‹Ô¡öu_š›¡Ìý¢ý1µxË7¿÷¥Z£C½$#íع+/Å.0ZÖ§2,5–\UwEË:DÂy#—Oi³&ÂËíQø«ŠÛ>×ñÜÒi9»TÒÐÝ•V€Ýj›O¸hÆ€'³KµA«¿Náôè;¶o€zaƒf’÷cÆèÒæô@W{Sw;`ß쬇ùDª?ù¿g§“­N8õeZÔë;ò£Ïï |0î‰@‘a¤±Æü¿ôHíØnÚ0]¿úòi‘MV«Á«ú…@[uÓ;œ°°Š³óª¹³FÀ@µ¸fl€ ¼æ‹šWEN'úÃnq¦ÍèKʲÎd£Åâ¨õ7ì…ŽÛÍÕ½íâ²Ù&¦gxhÊ<†Ýá“1ÕM¤t:Ô†*««´jÏ6Í&}›Ü·‹ÕÇM·—UêÿEgAîN[)bµ Æ×ÞÑe¹¾©–1Z¼µ=#¡ôÎ>v0|GÔJê­þ!Y0˜þ‚Ž®iXáé‹1%PƒøåÈY÷&dƒ‹Õ.ŠÔÝ È ú:÷,¼¾Ksæ {àÕǤ›wéuAìlRélû§³&¹Mí @lÅ`?ffL³¢TâRÙºWOqœq[ˆ©ü¼H8· «ž×ˆ¹½žƒ6¼¦tN+?~|%>fš+—"Ù—6M»Ôf0*·/ü’Œ¦F¬‡ÉÝZ¸“ÈúÜþc*ñs!$d\Ä"TG†[cjqgÎKî€P°'†óŽ?HµºñýìxÛl¶ñy'äÛ J:÷{k·Ø ñ ç¬wĤÛŸ ¸%ÆaoYX»5”)³ Åèf ßK6-Æì|ÀÑU¹â}×n#gvëùp-ý¨£Û:õw“ä½!göË;Vå\äÉ0™lì‰xQ- Qw£šë‰þš´?ëÎDG3dùbTŠî8Jë[ÌÝF÷‚ÃspÉû§¤'Ï_ÚXwËGCÕWÇuzcŸ|ÌÎCù¾Îr/e«ö¯yLXÇ5#'+ÿ‹ œ4šÌÄÚ—FŠˆ¾1Yj®!µv‚¦ÅlôVùCjvSñÿã‘«WY¥ÇÚõðø VU;øHrÂZ)ò+rì׎rqËïÌC/7øœ/…|3“?° 5pÜNòÂü~¢±¼f?Çc=¤‚"¨Âä.b““TÜX #z§×¨V4ì_´FU½:³®gq8®ŽnøTÖLèÙ‘¥u4WFÚ”°Û4êI7º8ˆc=ó…óRS=ÍÊí5”i [âȶÛW‚m‰\Îf—3eãäBÝÑÛ/ëñhΌՅˆêIjxvDùäø¾Iþµw Ð/Ézåb ’Œ‹>X<¸V5³¡¾,¼Ú$OÞÂØdgrŒË“À Äp¼ÌªÆ3- ßyž® üãlC8«Â†Yˆ)šöÞï+B~*z¾‚À*•yÙ U¢Ó6”ß|áP+1åu]tÑÊ­7¯Â…Û¥JÞÝïó\]j^± Tµ™›-3‹j_RˆëËx>Ýžé² bÑo’¼ÐÃþ²Kébè¬-$–)S‹äƦ¯þ r6F¯ósõz³ÚH¡%]<Íú:ˆtsæ~Bùɹ ´Ç–Þ%‹šr<Ù.å6¹:©±’Ó>E‹<Þ;g\FëÖdMÜÉÈsÖå%Ïñ@³ Íh¯6¡uþYN¨LÄݸþM3áš‹(P]1”žûSLø¤¸¶w'J³­æ;žWˆ‹ð)¯:e†‚qÓ€(Ji‘õ¡HÕlMY[ôÃázƒþb k@(Ñëßt›øìÇÏ{H‚i¦\‡–(/üA)Ãzlqì#%'YÂmé.Sdõpæ%‰ÚêÉ€0,L¸AãÕ iTõÊçeÌÀšLà˜†u’–Ñü4ûÅØqn›[F/æäÿÖ!0§À¿ˆ¼`½ìD©U\á±y|9YmÓ“BwÑM 9êy¾D­m<¾œØù ݾÄu'Î1òÚ³(ñ4V±ØÀ–dib<)íJ‹ ƒkñŒEôW zmZ:ÆÈ: S±ZÜ*p¾z¡Œ`̘ë¶' ÖXnD€ɘ¸®œâ•Ö •Á­Šð]Â|nh|:QE˜9ÔÅfzlØ¢{£õx2ØéÅeE°1P1àìt°@ó‰ÕƒÀ646ÿGå‘ÊJùn®¥ÜUm Õ²=YF?Üd¹J뼟¿èþäpiw…’;ƒùš}Ôhñ¡; 2S «’G³PäÞðþOS3–±mYör…BXL|›ôúš¶Ïÿ‚¬yÎYØÊ‘:¥§´`h\ÄS<“wnæ„ ªÃ·f|}×ùÇ´üB¶(s«ê±Z™I7ÆÒ‚»G¤ÂµËûßxLú®3Òƒ÷­ÐT£i=)œ3m¤+C©{Eå2¤u ;âøê{éà¿ZÓ®®YÍ-Šh5m7þ@xLO²9g9A¶±`(NO‚©2|wñóÈ íñmØÚ={ŒG¥ç¡]M§¼›,×6ˆa.FFœ Lòi¨’HÂ8âÀøPB’¡–?•¹™^ý‘¸žü,YÊÅŒ#’á\_â#/žN Ø…BÙƒ™ôd󊨑f­»çó™^GŒ÷ÇQ¯„œ&•Û!*˜ ‚™BÁ=ÛIÜêßo¶jQEŸ"M‰5=Ñ€‚‰É¿†±0†þ&@c)ÆÈ2Y °=ÍeŽeÓä¦ko‹ó²°ØnNF3T™˧©ö„yu ´›¸ç£¸ÃS¥€’!¥ÇY{ï;ú1”pìl¥ÌÃZ;ÐêùQ¥œw@þð?‰Õ¼øµåJK¬ŠœžÙ”nÁ{KÇÙ&šbÎÆ Dæ?«g )5ÆJŽó¨eÊÃØ-7üð‹så÷õxygMsŒ2 á!Üýw‹,åíc¥[g7†ÆÄpQ’ò{-¨çkh½Ìt“¸LW¡¡žs®pé‡Ám¬^³í®~€0uîÌ´²‚aªGTÂfnÕþˆÛ;x¶Ô3 úœBÙ-ØcVUþ„ë‹‚hÇ>êæ$OÊ$Ã:C·íÄ¿¸ªòe’¸D,ýð¹qèT¾Óa»M[®¿Æñc±·çš7”êõ`»¶"jÌ‚Ë€'ÌI`WÖkUæøÑ2~”»âIÿQ1ìüc"Š£)\ÁýyÈ:PQvÝ*ø.*O*qO¿º¾¨pO/*x6uÛ÷·¹ È`›Ym¯2Ô?)¹Ý}Œ0 ÿñ–3¹ú´AýUÃy ±î«:òx²k„þîŒNèþ!RÝÈm±éÃ@,@Ô„OGš„§¼¯VÏ* Z@ÅDÑ̘kíQ1 è‚‹ŽÞ“¨b:H]`÷ëR¯/[&@#?¤óða°Ä´×wws¾Ó!JÅÐUQuŽ´·’~eeç«Ó;RŠ’]€–Ä^½p¤È¡˯+ø¶'iËã¿#Œ¶%e…Γ°V9G«SŒ'àÝ” ¯K$qÕØbѤµ_I¹O_½Ô9SøIJMÉv-'@§d q:SåÚ,Ë%uñ—æß,Çô-< ÓÌŸÄôÿ5¼(Q‹óþüƒ pWÊn1#?À¶ßeXr‡ñ~ Ä“|”I½h£”…“R q€²Íð ÿpw„4Ÿòœ7”¡Sö EÝÎñôpl͉† ›¼ôg±'bµ¡C«³ °lÒu5iŽ#¤ë¿åùfÃ&׶Ûd{-!ô½ã¿ P¹R‡†,ÁŒrPøga±\²üïOIŠ ìÚ‚9<•ÒQ÷÷´ÈXújz–&èˆv=Ò&`ê¨@G–s1ä±—Yìåí­‰Ô]¿ö¶]TÞ)5ê㨡 wS¸Âi쎚$ÔÙ§2ØÄ|µbÆ’I¹wï\0º±3ÁŒ«ye õš,ÂâL+!1–‘²B|c‰k¼xiJ/ïPM4úì™!Ñ¡P o²£Áîî{cØM<á ÁÃÐÒ|°?wÇaa¿Ü_ÌÕ}ü­GÔjxâH>û83ÚgÃt R]ðÌÁ}!O)ï’hàÁé]|‰íšYÁ.§Ë~CÌá Ög°]Ã…·¾+¼Iâ,»I7T´C¨Ûàƒ5üáTN´B^…U8Ä !þg͆Ç/R™LÜÕŸZDS€= Ô›ÛÀ]aŒû?><#‰ #TÜäÕ¡Ê?dÆd.[˜y?wËÀkß|¼²Êšr‘sÎ{Ü~•°ø˜qÕ_šŽ¬Ï‹&¡´^^| žíM­¸æÓ¡ãkʈø­b‡2tך²õ¤ûb[Iÿµ§{nyòÀ§h.{Åc­7è¤,Ö‹7ÐXFçoNKwõvêtÞø§¢#ÙŸt¡/L"Æ ¥P´…ö¾Id¨ubX! ÑnªC8¸†>< Qì‹Mà/]­•Q”Óª…júIOÉ nÍû\gž N2ÕÛÚ_ç9À™Ì °ýoÍø”,xY˜Ô—íÞ.*º¡‹ÁõJ8a=ž–µ[Tñ ýG| aûwXoZ tâ#Ð »VÃYæ°eÝU±LeBvzôòc—¤oÆ^¯ä–Ä×I¦²SÖ0Öƒ bÀì"¨#DÌü¾³ÿ ´‹•!QÈõ¼;9Þ45¬þ6$%¹×W›üÃnJ?€LV"}|®·YYµoVl¶hà€Æ¡Dè÷wýQÕ"ét¸?k&yߊœ”Òš¼ÔÑÐõ6Ì=Ù]Ô<@Á¬mÝ£¾¿$]KŽOîS=–­æ?½?…àãš©®ûç¹­3ÒÝ’‚Ýú:%d*H¯’‹Žï©WÔ]Η ¨v^ÀŠü›nÃ/{_À•Òð¹b‰<Rl52Š”ÔSÈü&vwÂŽbïTX8õÎf›n’ p[ø­ÞCƒïºÿ¬?/USYÎã,i•“y`Ž6•[È'ÈQýÊ:ÄXgŽÝkfS²Â')ñcú"Ò߶ÊÁÄß3åÖBÜÔ(sRÏÂZi÷h|&ù.B†Lø‰å·šéóJ=©&ön_ÁüÊÒárˆÙ¥ò}©7† 'ºƒô4 ŽbøÔVe·z„F¬¸/n4‹ƒÆšç%ÇþÎ)±§É ,=»lpT|ät çbÖòÀàiç2äbúgµØà1Ê¿YùkŸey±Ê²}Š5#ɧ–u·“ ÜÖÛkôT‰§]0&HÐoVD–.r¹Ž¾)5i½½©¬E•)ÃÔ˜¹nœ—hÀŸÄÂêî3jTd©v—föË÷ü.I:…&yýK_Ù›ÑD~³a@Æ5äÄ«½®XÆÛp“ÒéžuÉŘ È9D c¡é”²g~›%7~wO°9Œ?ð¬$<¬oLé€Í2îÙ*§ÄÑÌZíÖN›x3•J7Ñ÷{ðáì^#•±ÙU™ôØrFx“Bûºú‘QPèR‚æ]¯ p=]’õ™”×y~Vb¸c)û¹ùF£¦ƒ…•³ù‡[ë(o8-ãÿwŽêÖ‹óÃWêKY0¬$p4€aRãï7; ô—†ÿÈ•ðÜ:ŽþMÿm@¶—’à-U;™Ðºzr^nL0‡¾&§x²ÇÛ×RŽWðŠö%ÃÍ4Ç´ÅÔ¡êñ¦¹Ã:4 0X›„Éø¨ ™½ðkð˜‹Ð­7ªdt ¶Š²nV¬°g ÖåEŸ[(2ÿïßp ¤ƒvÒ'c}dþU";ñÄÁ‹=ãß6^òFµà§þQQhV±$»Nª¡]e·JÁ$Á«Ö‡œPÑTèpýï÷X Jq…Z¢RõӘ壭« ¤¥½­ªZ{ª"qðàL»ÃSÆ__‡¼˜{Œ^P®ðH7»¤Ý¯¥¥‘B[¸ßÄ×›Xõëf‹HÆDj'«\N“’íœzç'é Hù`Tûß®½½ÚyùJ­¾*͉pнWÆcÀMhx›œo„ùÏ¡”_×K½MÎ H^sfAÂË”åÒZµ“UfÀ-SŸßrjßA­ñ½›u‡»Í»QÒ?É#âÚ‘çd·~qÆC ©—*ºV Ü–ÍÅËIä,)ú¶7˜†Óa`r£ Ãm·€â'½ø·¨7+dD(âḤWÉÖrª&H\ÜÓãëNÉÔ.…íè’N¥cø#dË«À¦pËÕ5^»=àk˜¦PjVóÀ'¡!ê´øã3mÕ‚ÜžbÆ+׶½/µ»ÓóÉßÜ©¼Ç0¤%…|éžò>tVë?ÐÅÔHµ…>ÍW©ÏR¬&=íHpNÕøkL31Öf¨za¡QúÆ›XÁž¨ 9ýPbÄ•Þ{õ_põúŒÉçP0Ô(LWBå$LÀv(Œr›®¸ â|òg%xXòðÎ&lX!Nº9íëpi! î•´—Ò=Fæ; ±†Œ¶‰œg±(©#e6ú&EÕþG­3ϯ‹H…„‚è¿N™8Ô„¢¡o`4?ÉÔ³u¾#¿#£ó4|¸_  éóâ«ƒí ¯~t^¨®“W:>’êdfß6=Ofn½†¾èæU6Åèï©xöÝþÌ$¡qi Wâ¢ñêm@J5uˆ!I¢Lc*óâ„îœÍëÝfÎåT[:XüÛãÐÐHÌB( ú3ƒ²,ž%Á_—CùMúv° Ò2¾Zz"È¡¥¹vÁlT n·ÂißÒ˜õ@»nÍÇ, kùi H¡é¶kYöûåëó8Š«;n«ÀÝ(&Y[FWÖoñÁ°FVh¯—ñ‘þðÁw{Ñœ·™n}ùÖÂ#´ žBD[÷+iÀLf Ž”7en}hÒÓ}ϬÅwF^!•ÃN1iUÈÊÏu$TW¯=¾ 8X`äF`÷$2ll)~ÔÀGÜ›\Ú­Kˆ.–¤rÊ_,¹åìƵ<}tËÃ<SA¶pu¸Üôâv©™¦·W&˜ØwÎÖû¢”M(®ý÷ôŒå:¼àðÀÔ‘Cf¹óuQ¢Ä  Ñ ³®zÓŠùÏ~óY']|Cƒ &2« §ä´¡ÉÛ'|éªËÍYòôGw¤×nŠëPi[Ò€vøó;®+U¹@m0Ròˆ¾½ ’g ÀîQvõ ˾…XÞÛiJE/¢!è ®æ*A^[…aÑè,H¶×”ÃúJ̃kš£oüÃSfç›9ç™ILAQV½æ½Ò$j¸Š !اcæ]a–ËfûU6M€|´]GÔCúݲxç9Pp%Ãꑘȉ‹¦™½A'Ç„B$žj(XõØš[ŽëhNH˜ñ<‹z31J$¨h”tƆÛ3Èà5A‚­¨ƒï3àâ¾wã>p¡Š4…à ô‰qu*oEŒ×.ô·}4UXS ¤}´u¥°?HjÔ­]³S×âhIk&-·ƒKE U¼ Å?ËÔKò}¹>¬F†Ð~Ê‹ú»çÏ3ê÷+v$¢!ª]ó/`U"—÷Ï­µC=4ÄI®ŸWëÜIR†r| X\¸€žýAâÑ;Ð%ÝÈ}mRÜ€7 ¤]oz:X°¬|ÃÓ'ÅÝq‡öùˆçy)Ùª¦35M¥ËœtKq9È°‘\$Š‹ËP*ãâniM Å÷‹üÌMšïhß/«²û‚D!*Äf? /G1µq)#3;¢ÀÄÅPÐ>ÆÊlZn¸Ø[ìQ×%r ’½¶§«rü÷µ xΠØaéGeΰÓE!ñ óþ9Qa%«•µÅTÕv‚‘@xdÔ°,ÈÒP$¶öA”¿ÑeSW†DümÇÿÉ×Vp êO ì²ÈÈ›™¹ÜY¢•f¹Å} JhMx áö¿¡ô¤,3µQ½¬¿íOÁüBh\¼1C÷عë‹,<hÔI h'kŒÍN|ÀÓ¼Z/Ó|ÿÑ}ò0ðí®´lRNx3cAE¬óË:DjMUÁ+uoIú†Ø ήj7ˆ³Åy” ,¡`28Œÿ’rûÇÏ}cW8'ó¨m?ýWïÅ­åœ yHÛÌÉÆOo ´$¶ù 9ÐK Ô÷Œe~áu?œ-îÔz.™ê' ßÑ8™Ý)Fòu/=èz¡cER@JIð ÞsdŽ•ê ›®3×¼BŒzSP*Óù¬½“ÁCÐÛ‚ó³.®ŠŒ›J)iè6÷FknÛ;ÔV¡’èN¸O¸©-Å´ñ¦ ·©»C“hª-Ñøf&/O)æ’½\É›šøv.ÓJ™~ïiœ‚@.„fÍTçÜ ³åÞ,„%pÖ¤o„%Xº›~{™Šµ\bŠÝ7ŸÓ‡—Ã_Ð'v•:iÔ¼Mz1)1R v¤7¨™WH Sèاɢã/›ûT49fÿüÌõ·^úM·ÎÝÖrÁÃvIØhŒé r’„ NîÙËQ­zäÏQŒ`ê ’„p1"_€/qrø(‡˜1û[ü¿l§‰KYg86-Ô¨Ÿµ°~;ŽqXºkµó7ÁE"¥(—›LN‹/|GܯåZ\àã̇öﬤ„ôßo,°ÞjFA’³»ñ`˜¢MC ŽãÂÛGáæB1¡Bs»>;nmØb;4à W«9…·Ž*®âzÄg/ÛY,kU8ô˜Y$SÑZ~X§o2‹3þ‡~¼ò-ü ýn“ŒˆriØ—x‡ã«8â•Ö/ƒ¿Þ%ÞîÞêD&ñ±B„ý…©?>XÝ¥|‡:ÓaÑÀn»†p¹Ì®äÐ&4F¶¸uu;!Pø †€ÝÔ 5êCVïÎj™Ô¸Ê€cÌÂÛ©Þ€ˆçÛ`êˆj¡ЭŠŒJ«IPå‰GÈXÖ“L Àâ‹Qª»V 0ú¶p±ÞÎàÿ°®ûV7^.ž¯/Ó*o@“üª£«O›éÇÇÒÚ<åbh% %Ý4 €"WÈKâ0li³v#"y¢âÙiúøÓØz²§‹µå˜û#×ÔɘR£>’È“œ‚Nì‹ÂÑæDÆŽÖ³§á%E¨rÎ0ö;Àv5©? ¯õ€¹òHÞ©Ï8—'IR@@zÍÚÃ|örjzÄk¬×ϤÁƒ–áI[Q‚àA½q‘%-I]#Ö5&µE2 Œ`SÎyÀ'—6ð“ìxV¿š:;Š[Ú ¬C×íj,K‹ÝÔU83>»4:ì)Ù•ÞGé„â£Û“&X‚Ú—ô&~i´ó~~âèò3™ó§ÁͻᅖáTþ\Õ/a»ò·‘~ Òkó%¯aé¯qøî=¬NÖ:y ü3`ÆÊKï"´éR¶ò7»Šm…¶eQÿ%D3‹kFk?Š[O%EV-Ž’³q¼ÿÇo‹Yâ<ð‡ê3ÿ%A³ðüÓ>N–D7$yÀœ{Ðz`2ªâûF3X lÞ®wAÏ©æ'…Ä`S=œSJKw†z÷AìLî#‹?xïêÖö{íÙ ™ÙÀñÐ$«qdÍw¥Žà ”ú]`ÝÒ…Õ£§¸!àǶ5A‰G rmƒêRש=žVø’8ÌÇØ*S‡j} |~þkc¾JŠÕ[k-s»µ¦á ÈÉßéL©lïNÊèËDøkC!CcR8Œÿ"„J³ðO9V]þÍ™Þn{±RÂÔçn53>)pXk‚8+Þ"q¯Nßû&ò×ùsj™ã{~´kAÃî 9ø†È¢.þºZàˆØÕz×^˜áaÿõÿ 㯩hC©ÞÍ‘¬›¡6M;¼åƒƒ&£ëbÐBŽëì ÃÔm¢ÕŸs({ efY¤ÿ]§ûÛ#4ñ-U™ÛOàÅ~òÝÛS¾±¶‹Ûß¼¸(wóÏêÞ‘4ˆBáRðÀ#ÉÙwØòÙà/‹Ô”‰Dc2G‹ƒe0?£ÊSŽ) mL ¿=)çãûoº_èÖN~=>r¦µÃqõ‡; m…$¦) ¾„bœEˆ·f…§$¡Œ^æð÷ ÚC°j¤ÏXÂc=zÖ ñRS0rÂÕ ùJBq »›4¨mpµ…žO…3l).^ÛE‡¿ù½H0s µ`¦ƒëÛIœÃ}ø¸"¤ÆGÉÒÇÉ°j» Ø™<¡pͦøfHÏ)G¨Á×·SÄs®Nö[œãwa<*Þ;ŒOYåF¥«ÝÀdÕ»è\2B],'3æêì#'Ä—иOÔóje‡á”·±è#.ð‹a âà pþÎ+tV‘X·”TÖ$]ÈÏ®7‰9B/ÜI{ †ŽŸ¨ ¦výasâÐúeN£(ðµþ<ñ•ç³ j9½òIÙ•æ±h©ÔUàEJeAÄ©«s O§_„‰ dáó¢Êes%`UˆÝ€t&âÉ OŠ5Í\HôB6ŸÕËã6 X4qe EÀ š‚q¶rG%Yµ—æ´ýÜNgåód¨†bÛF[›äÓ>°ä¯ ÷%…I¨\á4Oãiw÷ÎËð–\™'Ítu8zÿƒâ…Å°ðÆËÁÎâ±q¹/§ÈßÆÀDç÷i"yl5áѾûÉÇ^ùv¼»íSÁÆ °ÐŽöM½‘>lB©Ç¤¡ãÃË 1©B䥎õŠv1©tIÒâ|Å?`tƒZÐñÊÀŒx/;G@„€>­3œŒÆ)H'çÇÎ*âÃËR8 &è‚/ä×eíû‘á.÷s\ÎSïТ¤"öÑ…é„wS-m= ö‚íÉØWÐýÅb f&á© ›"_³,ée åʼn]¬/'vÕ2/ÀØ—á¦ÀÜ⦌Ç7Lk;ܘéIÕÄòYqf³œ FP½™›ÈæuÑ:~dc´Ãôsºz[M@ŸÔÖn1DŽ8µ7N>ð9ÒÒ…×®…tnŠ^mÿýh61O~xŽÂŠÅIËíLÙyYd¼lÓêo$9Ïž"iŒ—P7ä;–{Sy=ù%^2jm»jâð züÆy±G59¬¾µeêí(†$ÄNSÖ+cÛ—¼;x˜DrD'W} 3ÃÓŒv©Ó¤† ¢î¶Í—9vkVó\Ù±CÐbŸ–Y 5þ&‚­ Ü[Ð ©³ƒ·8™ ëúC/EïQ âû6\IÅ^Ðq†"[ŒJðŠºfÂH‡>°„“Ê ä[©6)S5µ¾êà!‡Uq¤ÔuƒAvp¦6ÿdžß–%êÆŽ«‹*ð_Wl•ØrH[ÛÑo•Ùî?N ÷¯Œ¿ã8Ã9%>¿»r¹SEmÉw]"1ùØ%V…óïøL®÷àÛm@s7EÉáXÆ!ƒý\5…N8e×V,X…‹ …—â·R‡b‚»57Gæ•Xÿ3­¹óäP׸¼s&Ížb²ÖR`°Þva‘î^¡2Å&ûQµèĽñ-²éÆ5~b2{Û‹Ô91j9Cû]s²a+õÁsÐsñ)³‚¾Øïþ¯s0Y.ë°WŠl©431ü¦¤>‰YÆãï¹Ö@ dzQ>>c'_ðã9~NXWàd‘ Iô ûfæö ´-©Ëùý=]ÿÆ)¶Ìâù©A"  é-Õ5ºx²ñ6V•y㦧Àhž¼µ»uÒ¶ºAXz´^»©ÜÂ#ds@Óƒës¾/‚êÛ[údØ«]oÇ®ÒIîÞC/Ò§°=[gm£4§P_šíj Î^ÚÓ¥ .sŠ;›;úœ1 €YŽ%iíe†´C˜¶0ÛhúÕMFÜ!õÌj$7™\0nÛk'¤©íý%ØgCgˆ{{¡>ƒš‰:F…™h™é´¥féý†A¡Õ~fý˜£-ðÖi¦?È35r¥ó^hÅ^šyvT#§™< ë]žµÖ¶Ñàjùënk¯”97n§ïê«W½¤Ç0}übeâ}ópM‚ü•¨ÇµNåêý„hõA5Uva¿òB•×i¦¯Sÿõ‘ÉòXµ~m&Ú—£îXìÛá‘ô½Nr¤³7P‰ŽêJÊ2³sË4=C‡*žZH%Ó/AÊ°[òDï©yº*ñ{ºqe úxÍT” ì }®hóCµj6ÁÚŸFïù§8TôžÂ¡¶¿|¤u˜óRé.PÃq¸Á$ªKaˆ´ˆô"J¤ü¥q%äØ‹³ýÉ!¶%ô÷A=µ™ê·\-YñÛ.}%SyN,FÁõ$kt³ßo‡þ†¼E Ä‡©47œ¸Á„˜‚Œ^hZÃ2‹ÃªÄâ+«Ïóù6³|ýa×ÒMÁDƪÉeâjÔœR°#Úƒ3ݳkE*ä!zí¢& S )k7]HÒMºòscîïSÂâå¡Û`Åþõ'áË‚‹PPcCÀϯú!ì9ûróJK¬j¹Nãa©ßÞt·až2ºè P—$Oa’¾sJƯÑÅöø˜›y„|ç}dÖPƒ`0lx¤'¿ÎØVtKÄö^&(˜ %DlÛúX³¥ñj÷•¥u½— p¸$r5Œ|Ìe$ƒJÕ·eW”xïü‹\âJè6éçbWë+X ss]-®yA_0Pe§’¢Æ¶õ¨r\ •l>Ðùv3(5Z÷Ÿ2UOàû.'•¸qÀôùÕÛ¶®úž“q âüJÇ÷ìÆ9ìB©Öb/ñ1-ÂÑ)ËP¸d¤oçG±†¢T£Æ´ÝÖƒÉeÅ®Ao’„˜ÞTVn,ôÕ3·þS©Œ„gë!Ÿ¶¸­?ÅW…}‚Ï䣳_´ñŠÞî¼øÒœisžË$öA[ñ³ÍqmYVä¶+çÀ„â–·Šë_lËt8<½ÍI?98?›Ù?‹ó*fC ŽíGòØ^D¼—`5wýÂ=™yh5©­\Ÿ-pÅ„dð\ˆÿu3¥SÅÂæÊdÄpá<þ­§œÞ(bÖ` Y†³ÈŽ 6yªõ6Æ©þAŠó(¸,ã ßš+Ñ<ÖYñ£[šz¡×ý£Aýðž%³Ôüõò³6eÃíÚezʯ-•maÉlÞ'#óøx1¹Må:Û6àsTqðw„YÑ€¶¡õ¶Kí«ç¨ÃgС'*~}äûÂΠZ4N…<]ﮉ~ë†v¦Nî M èU|l³4Yÿ:ÙbËŸ¹kY=÷À«)Ÿó“Ìx&Ž0;ÓK¼Îàn-=°( ïwh!…j>ažMeÒbûWã… 1@WŽùƒ©Ò2­Ï {£ÊRQ=¹0^H>qý5ú‹þ ßê·.‡ç&‘40êŸRj» ÁÍkÔ½JÒÛzòYhfî' DñDÏ̱ûïoñµ":T½\Œ÷ûÏ’Æ dØÙ µ÷¼G%:¥î„Ýà \÷×âæžäpÍbçñŽb`ÙÖ{LF2°ÈÚ‰ú¡!=Œ2úûGìú½ò3o6oX;¥Iþl†. ðþY6”ëçÀ³Á™€³èû˜ƒ9 úàƒ2{‰BìI‹’znP![å’±ùg+;Î1sÆÒ_½Ž÷¶X;à\A.Jƒ~…‘4dAd”©½¥X Ò®þ3Døó}¡—SWP&r“ˆ‘¦ ͇€mÖ…È.Â˹ìQ`gb±p…ˆ´9½RQä í†5ØeÑše{ßd$Š˜õk¢Ë•FdÊ õúiqî½ì†d0)„¬økÆn”9.³C 9k‡¶c-ZòÍ<ýRæà®Lãeý Vq&†DÀ¶u=÷€œ¿ÍÅ“(ËñŽðǨ˜“‘ã4i“ø:7¬2ãø¹Zc¢ïQÿ¤hrÎåʨú÷¢íÐ ·J<ÐâÞ¡Š:=uÏWlÊ5ˆµŸ”Fƒ•Å “S#‡#õlªÂ¿˜oE´—u±ã&À¼]49á—"¤Â`çÔ_Ù¨êÓ±—%C4Ðy«;¢F_×È‘+Àd÷ö8Ïï&Lb‘$í;œÎe³8¬k1 »4 ?*éÜrZ~ÂvÙD“ æá˜ù⢘§Ûv{>=Wgª¨ôÞj­<õ-—gã×Ybe@ÌN{îÍSä¹¹U}ùüȪ@MÂW옼ï²Ô†o¬Øˆ÷ƒoùÛM”ÅlEµðððÕ<ÉÑÀ—³™rê_Ñ.y±VÕSbŽvLt^u-4£»ýšzõgëbGt!ËO÷`BµªÂMw‹ïÿ02!ò^A~²¨S© £Ò?nù¡ñ¡‰u¯íþ»D °Ò2Ûe=d¶Ú³V6sÚ€y¸7ë„ÚY%uDjú;öø.<ÁÝ•E ðƒù" o¿i=F¹Q·ä.;·fô"h…4vèS;Þ“b…5RºÿôçÆo4½5±Ë´9ªîìûO{Â@„kG¥¥Ê‡ñ¤ÆS­CÒù·gë5Žä³™& à‡|aTyoH|/xDÖ à ûkK({Àã‡"Cž{…¡ß»æù Òºc.A p42Gî݈ŽˆŠ~  骱]á…ñà>ïʼniòâ€%ÏÀrv¯â°nÐvf¢Oç¦è«&ˆbÞé)÷œ±mhbL”ðº†&) »?Kç#ÕSùõ‡ÿÆ|ÚñNT0æHù/Ñ1d’¬Ú4©":ü¬£ýÓPH‡V{jäf§ð†þ%ÿªœzžàw'ÎOÓÈt„@æžq{ÛÆzÅÙ‰•L]ë—v’¿:|éÆmåÂ_Š’ªœÓ8×ù³Ö”)TÃD*Òljâöp¨sêðÀ?ýW í]²ŽÛÂCWVz¬”ëÍ'…v¨j‘â„? VÀlu?aubŒ­U+£ÍÙÃ\j“êò|IŠ¯s/ z©ƒåXž¶lÿ •Þéne)^ZÆÂK Ú‰¾¢'ÓÍÁÐÙz‘?°¹/m˜<íYë“´CP=öñ("ªÝ›Z>¸¬¼Ðû‚þsP'? t¸A6&>0-“>²ý/nÁ% ûs@+…ÃÖ*§¸#Œv?ÄýÑïìáò#’"X·æö4Ž¨#Ÿ½g1ÉUqç¤Wby§'ìyÑwuS&¾ÊͨÒN»ÂOK9Ž6-d~‘ŸŒÇŽMÞáÃg*táŒ-c@aO(¶ØîÖƒ! L?$æ•Ö /Ê· züÙ&«dicþƒ µÙªм4ïa¦ã*Uü䕨1¼˜ ÇMeúÛ¯E„œÜŽ,ctëùïã±W0iMÜïìå!}¶kŒ¾ä–Sh Ÿ#L® Uå;†¦-/N Ø9±‡±H[0@aÓ¬ÏÄ<´ŠáÜm””‘ñ”T[Ëá×W}à’¦2Ë`Ó‡ Î/w3„ÊMÙA¨ Ú‡hSÞ‡j\u˜ZA-¡†=ø/–»ßš…6^ºµÿ.eoºËÕ›MÅþŽºgG|Xž-\ê>aºèsÜD­PÚ½¹³Áµ÷ÈÙ;Z¾=³f˜ ólç,å™<°Œ>¸a5.`w¾šª«ù<`íø®íÃÕ¾™ªs^HÂzÜÅtŸ’Ï6p@&ˆûÖb$¨€'¾‹:ã{V´ýµŸÎ“5óKa·æFd¸$ƒXY.ŠÀIŒ@õ”b:%ÈgÄÕò‘Ûÿ8Mô…‰Õ×Y0Ü\w¯ 8ؾÒýDZú¶]Æ­·2†d}h´üêÄÖÈÈ#ž¤ÿÔJnéD´ @3r BCØÍþ —^?õ«óTBã°TyCæ¹>#²Ké2£O÷ÖáÒZü9À߉ ÐgîY?¢ës)‚^6 If|ÃÇÔ½9·µñ#/”Qa›Ü& ÞM%mÝ[ M¢XhÚåÝÕ5æ.ÊKXæÓŠþC¹^xx¢ŸxüÁ4ýS\Oçt¯dSDZÈË4tRtgJ¥Ÿ ¥j3¥Á©ãÝÿüsÞÃd—§²ë íZ𤵈W½@Žpë”ÂB¦u7ÄÁ2Kb‡ÒÇŒÁ®;›”à¬ÁÁç!ªìe: / œZ:ïé–õC,‹ŸþYMý«Â£+A4*oæ=À<á’]éß'€Ô†1¬>ä©ttÉx/ø\ ·ÿýW[LvP8DK$HIŸ³.£ÍñeOF /¯OpÍRûm›Ç:~-HW”g4ºe'=|“ 2)FŽ<¤): Ëß•ušC~Ï]˜Û0¥¦Ù_íñ¼l3ÿ.cÞ!× ÆŸã ´ Ä7¨…<<|¦0‘(ãP{T£¡2(`f¶'ø8ðùp-Ú—]Èãl/÷‰IˆbìšÃ•RCfÅU™ ÎU°4$·Áìôày µ¿.®ô‹H«M\gFŒ ä_•´ ˆoM’äã‘Ï£m“¼ô _cÛ{þ$¢®KÙO¦-y‚7¹ƒyòøϬ†ðIö¤¬”ÑÒ .zO_QióP5èÍOu‚oÆ  •¡¨Éôú²ì2\R»ÙMÛOrX$ßýjá|ʼþ9ÍrSزÒýƒüÿâ,7Õÿ”]°üßq–Œfùj‹D¾¨ɵ¹j‘$eb<ëÜúïXGÛ ÂJÿÝ8Gÿ•‡¹ëvÙÝÁ.— ¶i?²E5¶Ûèxf’K*òïáj/üo®jÌ•‚hÖB õß³Û%7»\wX{P¦ÇÅ—ÀIZ:Ýœ¼óÝõQ.vyÐè÷áINs¥47Ýs9à+7Þ’ÌÏv|#`p•[Rß`ÔÇê.,\2ùÓTBœ3ÄŒî&Ò &sZ¾/Õ¸-¼±ØT­ šR„jr1"ÂÊ_ØûXˆ#‰±%’ŒE„û…yk"Ž¯<˜HÀq@…k¦Œ’F9Fv.<ã}É»3?®pix üSt†•ƒfôùÀú1 `øOŒTAèƒ~Cý¿¨ø‘ÍcÚÇñªÄNA,” *[“|p<¤£4W'8,1GéiÏ/þaæn(~èIl¼ŒÙ"kä™®qš@ïõÚAøÁ7P[òÀ‚ iÐD%u›óÌ VŠ”8)Ml1¹–8>vñ"3I'‰ƒ$µ"߸lÍ6a©†F«,9Ÿ=n(ž{§º6”Æ®·‘R´GTù ÒuR!ßïÜ‹7ª7îšû=rXqçÅ5Áï:ô+(ò˜ ]n»arɮł1®5ÊDM “µ¾Ý %?31pøÌG°¿J"’œšj£ç”¤ÑD{BOÝh~Ýœ~> |‰Ç¯>—¯,ÙÚÔÙaÖfð3ù¥Hˆ2w’iŠ¨ßÏtÐÄŸd§Á9g vïÆ“hN¾¶qý"Û”JX¬qR‹È[;5æƒ?AʨºÎ1«ÆT9 Ѽ—×ÍraÁ(½MüàƒÃÔd%-|ƒç§õH9¹î^"ÖãÉC ´k {—µ·á'’¿Œ3œ’ÒÉv•vuj·ïùÝgiÿ‹¼[›W¹{ñ÷˜‡y `AJýí¿ƒRõ¸š·ž ©pÄMVdTJO…Ùm¯o›OâTø·0üyÅÅcO†×VìY— ±$áÈ(ÌÒCC 3¬¹Wt:ó[ýƒÇüìõ‡‚Ë(‡µçç=õðÌ*ÿ3 B¯-‚[a®a—Å“Þê [Ãé¼…]²àñb 2øU˜ˆó·Ã!’õmýN¶-ØIFD€yå!^5®ÿJ¼y¾–sÙpÁÉÇ.M pþð'y?Ñê'ò´–†¿£,CГ＼î+v;¨L$î¯a1Ê[ë²(ðWâþÚðûŸ×Ö+;ò«•¢&£v#«»C‹2QW5êuXsT­‚)…ß}6ÅNZËWìþCM!èWßë,Yïi^®' Úê—l·iE$¨È·0”Ä“%ßëA †#²WâîȵՓþ›¢]Ékéùåz“/ضÎYÝÛá´½òÛÔ…..ò ê”AÆZ)!m(Â!a I'ûyº/ш‰æbcçá\ãFMOùhR‰$›€L€8ã‰GòÊ ‰ÑñKŸÂ¾+…­ €µMI@eò>û­f[јLòûÆó·½5¸ºiâ¬DZ§Q[·®B|ðJð=ÙP¢Zr:ûÿ™KEš}Ùr²ÜÃó¾äÑòÀ2$þ¢Cù%÷ðæŒIô‹u›Ääªçuè]Ôü>Ëp1ªt¾ŠLÂñfñIÕŠ!"}jÝ¢èxTnüÇÃÕ$ÂÀͲyë6X™èÊ­b ×–~‡bÏsš*ÎG‰tWu‹aëÍ(ÓÉí3‡÷«J#PÒ+X¶ é1xÙf!Àiªå½²NïQYà96õÊÁñyùxR$ñäîE#k)ç5 }œãDe;TeÛ>¸þÎòÓ}È [ S¹„;ÅTáíô/—Hïþ(Ð8"×çlAD­õ¼yE†Æh…Q7æþ‰m² 6æ“ð1&IbñæÏL2¥øصŠ"d7y”k4¼_qqn} ºGŽ\¾Œ`i³2n(„­KóÃ¥ mlA ? ÀÇ¢¦ú-ÎmBQx£k~ï2rFˆwJlÏÒ3®§‘úH©£ûýºŸõ@>á©Þ¹“m8„Ëò,cï·0 I ƒ{`Iw7 ίåË(;n¨>=û~T_Ç¢«çËQÚÀ7V~kA †µ7ò|áï ev%ÅüX(ˆ' °÷¸|o÷ÏpÞ(¬%.©ßÞïKR]áN¿7_ºr>k<ÍÛ7€O(ŒôæˆVY‘ G¹:‰Ëûeº‚ämZõ׌÷W¡pªz?Y„E¸Äœüôò*¶“¨Pl< ¹åç,ߤùiÓ¯Œ­Ë‹š…ê˜rîž°¸Ü¤ß;{†Ážè!!Çn õÙ¾-™Î I$Áñ"¹[ Br ‘²ŸŒÈ?Õ‡ö»k9Ðía»+6èêHAi‘ú½ƒ7ùWQ¡¤©Ågë•éôáJÆÝÎ>ñvb­ÛPìå•ç~Œå'íséP,eýq,Á³ÀÐUSˆƒ7:/7Œ$£Ñ¸øXc8g•Ëf¬©JBÙ(6A»ãÛ×C[Þ÷.nuY‘{žkýJÙà}(u}McÙéOÑt-ÛCç)nšìobåh”%ý Á -ë{õW.‚7&-ÃÊçüø%ÑFšÅ"ŠAVd\¡¶s±­ù€"0‘.sqeR8ƒÄ:XäYq‡‚‚×¹ë‘I|ÍcTh9?«û¤ • ³i ®ŒF(*ο‡ €­œ–/ ¥áßäh¿SÌP‰è»6—üÂ㤠Ⅾ͎‚‘—4Ã$#'›j ÏaÓ^Èå1îcMä²=±¶üÀ† sŒkZíó«Næ…0\}hì¸ôü›gÝjCÏâñ]—ÉE==Éž',-Ër]”³jt\±¡àk¶V œ`Ïáá½-+¨½áZC‘3¯0–#WáÙ|2à¼5tè ç.G¼MpúõÞƒè ®ï˜ìƒª» $Zßm¾ZIHîïD¸\@Ö´ð«*§—ª–n”íNÞO­`µÍ9<Ìp½ö’ÎÞƒk›F4,;{gŸÞƒY<Ù¢ÆäKˆHëZ5ÍÑ"#ônàaÁæŠi•“û‹"#”Ð`ÂñÈ–ø4GTI&’¿Œ»-šüÜdeþÛz°„AàK {¢ƒŠ w°1O¸Ö/hëÔW6_ÅÒêm¹×{Ûó¯K"ÚmêͶëPFµ·i†ÞZòôlðéî4rî¯ïÎÙ”_â¢^–0+ùÓð?Àiú/ä/–©µßáݨÏE m©<[èçµö}º#tœÁR3m”ÏsDÁngþàoðÇ>ªÝÐ×ðÍmÀŒz4_›×VaÄSï?«çÒ¾"? Ã%é\J§ë/ˆr»WQû~f¸aw|+VJeÖ¯^:áy›Ù_Ej(¶$Ñì’…è§#žPB$Ž‘B×ݹÝqLÑÐ/@E{O6ÁÐúØì = $H¡z:æ_$9°§ú]Þ²–s LQ¢Bµý¡+H}šmhV-ÛÎS±RBáûùÒ¤ô¿%¸•‹úv®Îy”mšY¬Š CÕ‚bqÑ”ˆ¦ˆ»:Ù ý¶Z0TºŠy×›÷êž0—9øÁ ‹N»“TH¼Mé¶nv´e/¤ÞØŠP›u·Ü/ hØ(G`I»-ý‰®{¬7ÛåÎã‰Ó#¿ÿ½fÖ/!­¨5´G½‹LXkåõ,d Or`2È,krÇirñ7ÎÜ"À¾4þ¿&r-Ê £rùÕ’(ŸÍØýçû¯øPïµYG=ˆ2¸àô¯¹¯.š©Ÿqa:ùwYbþÁ÷Tø ×þgî ¨Ã¾kà˜@†(\ë•èéTïäeö)PŽÿ‘ô&ì«B7 `j}Û;}‰™R/ÿÈKªV' 2õµ~u™6Þ;ÀhH}ÙòÖ§Ò,o³ñÞ#¦Iþ¯@œÓLAÄê-åÖkÇ´¿ÑX~MôÛ#ˆµ#)nÇâ*çæXøClzÜ<0#N•f.51KRé'’„_£/ 7¤«mþm÷*ÜÑ5{[W‹“:› ½¹ëèÒâŠæðD%Ë ŸGKç|¸KÌ‘îúƒv„ãoz eIûpJ £9lÀ†©çºxsªéM#À€Ü‹;ÔÅ)”>IH2¸€N7J}1½‡2¤—ØÓ²<·i“Y >í.ŠGj-Téz ˜9 §$×Ls~HÊ»TáLsµù‰Õ n¢ã¹Cž\«S=‘òûäv -Ëå‘âXúÊ!7tØ ÁKl“Òn/X”ŠpP=‹ä‰ÛMQ«:OÌæÐ-7†}0ÝnÓh“à„šã¼\Ƭ€Á+2|ÊÛ«ƒûª‡MIQ,æ!õ±¾9YÛL éS‹ù£pÿf!âäiS·/)¹~Zø~ß„²”!9ß=¬|i–XÙ,Ì¡QÙ¥Ic(}×¹ì ”Çô›ºÎ{¢ú6>òók .Nmœ ôçkkE€F@Mœ+ÔésÄš¸´Ï;ò-Dè¤ÈqÑŽ½Ú‚•6§æ.%oU]~ÙèóûX§ÐÞc(KHðÚœ€”ïdsˆ~¡vÐ ëcæHù§þ¹ŒjIŽŸ@YÐèÑô ;¶îˆw3ù‡(?Ú=ˆ–¹ìÞW¾ëµéù—®_Ìø[Je~à 3¶]è0¶jÃM3nLí2K±ºIœ&S,ëFBÛi~\)¾x÷¸½ñ [*k Ì„‚¸¹•0A7Á¼º5êŠ!•Á0ý¡*ǧ÷®69 ¢š.`-­YZ:us¬j~¡{(é’™‚Â+ìÒĽÊ8ûÒÂVEË=+.õø!ÍÑäs¿sJ'QÙváßS°;”·\±34I™Ÿ T %%Ëy—1«þc›ƒÿðéÙ|%Ñü}˜Î­µX 0Ü ™¯)KtÕŠ£ °RG³Õj DØ)£i»#‘—J>ZðGˆÎîNwàΙÛâ!'2k“3Žøリ®Ç\6u9^RCå¾oS¿‡3`ëü2ÂÞÚAK„î"mRI(:õÛk]w+‹‡ ±ò¾â3Ö©‚ç–̨jŸl7‘}6 [ãσ,gsj5Yßë¡õ^`T®4É…~Lډܛjº$a¿q×2Uצ¹|ý¯»CQ’t;l[x%‘áˆU3À™–¯ƒJ ›~uãånp)‡n#AÃcð9.ïl>†íõ0þÙœµ¤wpL×窗ËÿQž d ^ëC³K)h¬’ŸiKSÞœ·x#0«!Ô¡pãwpÎÈÜ‚dQM;<¯DEò¨“Èѳ8ÈàŠÈ YqúDJÑSáŠÇ(S³îØ>PKÖ fvR¡s›@˜Ý Ó¢'ôJ^÷ƒý2ý=APÄVAP=heŸí/ XŠ”D[gÊ:!YŽ~ :“G¾Œ\æb˜÷¥°—b\È¢ež5ŽÆF¸ÓŸŸ‚&ÊåŽånÌ@y ª2OÀ~l8i49œþù>øcDªj&1Í<·”_0. *$ƒó¬Uq䳦båb›©¡Å抌?µ) $g±±%ŽS.¬^ÿ²U/©Ä…Fv„לÝTáÅÚ.í# ~ùÆ:œÕµ®dcËH_@j…c g¢‹éiô˜3çÇo^bŒ\UôD…4¢2®fæKhSb#üXÀHù5=ÍÊm¥’ƒ{„fpÓƒ@®Â¡fíH¯–êКÊEäÁ Sw¸ ÂOÊÏÑð­æÓ§kÃsˆ!fþ¦´Ý2ÚÂM£a4‹y*~~ï$Ž™J“?qfe®#5e¸°¶Ž;•&€;ÔDÄKuÅhÄ$‡7Eîع˜èý„Ú#—W¯Ä 9 ·µÑ¨pTµ ÎÓ‚G±²XÁ:9´è~ë÷ø¬®Ì¯%T¥SV½››ô÷²Ž½Îqì*èþº¿¬žÏ?8Ê섨 ‘ÇØÈlÿ0)ó †g„§M¯g.Š›QëÆ’Õ;—(ÐcqèÄ­w·ÇSúZ _’=Ö–ÄŒV4ô ;aQ“¸kÊçø ÅìvÁ¢ÁõU$§…yh°IñbjŒÛS^²5-¸ï· °ð‹®ÿŸ|{:h™¦ÍHʼn0ÝQWË~>?)5µÙ”âžFáu&‘‘ª›­ÙéΔŸÖÜený¬ãm¶B«'’»;¾DœÞ®›}äØ Ï ^Ó‡¨½ÄgãÁ(öZlwÆÏM´}›¶èZY'®$•†¹»#…¥¸Ð~!%º¥Ë“²æ׉tk¨ã« F9f‰Sqá>š .›ýâ“E'—¬5ô“õk¨¨c¡L>‹ ˆóè3É»y»ZK´YôJΈ±æíùóGö^ª4vE^Žºj--EÖB^½€H&FvË/Eó`]lÔ\‹;ÆÀ)pß(("Ðj+³¨PÙÝ¢ÀÝ“æû£iÛ§ÐCK"N …Ï°ÊmÛåHQã;¡p±38zdÄÄsz»#7 žçôéæÚ"¯/̃g—g¡³êmÉÓÑÄU¤gúÔsv¨þ@‰l²tËWþ.b…ù†nL „ÊåõŸæŽyÃÄïÒïJ¿‹aÊå@'Å`¢CfŠ¼%+‡²CÃ{)7i‹C~ɜˤ6¾Æ¿fY*G¦2MµJÑYµŠ¡9/ Dh¹È1+u[²5înà/h\–¸«_ uñ|TkˆJg÷Ûíó½+|SÒ.`I£OQ2`É?’¦+õ IaªÆ´ð²¾KFÇ×¥×,˜û‰§Ú7«eúa|CL¹Ë•Ò¡­¦´µýÈ„›ÖEÙ‹7žÉ(8] 2Dè!o ù8Gvº&øºn§èfç)pˆÓjTÛQ컡+‰…âUL&=I¸ Rå5ÛŸ,gZ<5<士 †­àQk ƘËüÝÇÃÍ]¤"u¨YgœøÒ!M^:öº:>äfÓoøÅž|©d]×Q(¥=P˜©|Uv È(í8xû*tP½‰&òìö+¨ošªà„M^Ë&É.[†S +9X¶xÛ;ÿg¾ûMHn U.ƒ'ËsbpÃYNv8PDï¨ÙÀHâfõd8K¢ôzÝ;9-Uhê|—VZÌÆCvÛäîŽ_á-߶ÈÚ.•ëQ¶&58±”!öëÃÒF¾_Ìø…í“Ì Ë6Íh Š‚'%Ý`®ÝŽÃÔÉd œÊÎJÍŠÉŒ©SÖaÊ—ã‚DŒ¬n¡MÂ¥Å:ƒüü”Â$Í1æ”gk‚˜@ƒ*©GÞ‚“†oä¡G&´ú"UösUJàÛîA)ñäßqä•Jãô+8@Ç)¹Y™*z]£ý´ zÌ<±rÙNϨ¢®ILJ,|„qæXóÐ,:͘èÉ<(‰ˆÔ#Þ=ÒŽ0E…LÉà”MзY0 ³é]ü¡“V€ËŸà„Í뙜Äÿ?Çî® S²Q.àßf‹°öq€?A^T~œY?àr™ó>`ÌôeúÖcU˜ï²ÅU‡ibf;´º{#hLàl´ Ñû]{R1Fà¿–GMð6”ÜV·Þ!=y_ì•Ô ¬÷Ñ+‡þö=ÑhúÔø–y«Ø'Z 4n/QÅ[ÙÍŸýZo°Êë=æÜ;„b2î1½þµ $V§§õ1oÞuƒ¡go%-ìÈæ /C8ÑëÊBÕæhY;{Gœšü– ]U^߬Ì8“áø⦓ë$ë_w cV·Fp@¼‚Þ(òé=Ö²£Ÿª‚ic™ƒ?0@Èü†Ãò„”¼j`aÖç~ïÀJ^IpŸ¤Ô›(—`ÀƒÒ±èIÉùjk_’­ð¶[Mº¾=LÌîÀËß3…ÙtŸ2LÉq–M5gÎÏÇ ³M­ >Cóåkï²¹¯‰o|þ±¡åú®Þl‘&¨+ìðeøÿD‚>„OÑþZÿðµ•Á—i»jYOK‚¨$\Œ>’?-wØ6jiøª˜ÎÏiÊç÷1X,ÄI=›ÿù%âžÈYÒà 7R¼«Ê`e¹S8_¥}´"àíáÓ¬âwã gŒò“ÅEͯ­+Bȉ÷Zò£[ˆ˜ÇĹBÚÅÀ¸vØãn/ü3H_h2m%)‡•wfÈ‚˜q¿á–€·uß½'ƒ#ûHMl¬§·ø ƒ¯®6nšÂ…<4=4"_™Åô¿Œ, ±YòNÂ[N¯– ­i"ïƒÅvGEdžÝ@Ü|DÈtk[B *°Ð™®õÇArÅ€š%È)·ïõüeü·;«Q×´4 ä2uÕù(*f ¨'ôKª‚þãœô[=ä«Ã³V¿ p˜z+OôΨ f&ÔÙ„ xˆG©_›7'ÊZ-.:nŽÚ^¶å¼;M¨8q%€%5z½\µ­ø+qŽ@ó+ >à¯zàFå/½½nüù7—JËŸ²}¾ÌÙØ[ˆƒc¢ÜLõ²`ÇåNü×Û¥exÜiÏ=<+¡«àXq)Æ!ÙÅiÅWãlÑŠˆî0ßÆþE&µº¿×•8)îøYôÃ.úâÕü} ‰u|ør×(ªÅœ“ïL… |2{£[d¯öê,d IÎ ÒÌ%ëè/™HNV‚š+{Eàv>Ü©tˆèÇoâäûéþš£qš´úÐ~`ENÀ£+ñ¸{Ѫ¾ñªêƉՂÓîýï å&$;ó;ir ņ ȻӴ;ucõ‘iUðnOG¥ †äŠ ¿+4Š]ŸPOC’ú¥¼¶ÖœÌPËX–]s÷rb噺œ5,ýªªãeýZ̹»g Sl…Xç>sg¡´Â^šÓo…ᆑf÷¦y7æ)[XM¢¦¯œžm_Ú{Tƒÿf.·š,%ìéïêåÍoæ­ýåzeãló­]oiçæÉ‚-L=Ÿ “#tÒ­~,Óþ{yÇ%Ï÷·õ2‘Lfð•~ì¿1üø#ÕEÉ–©©"š®}a¹ùÌ[õœÙ¦ÅƒU-Â_{Ý“ ¿z‹ ìw§ úÍ—HI“ .ì`—Ê“ù•'8¹¥ö÷{ N_':Ë_”ìp;Ècv«)œÉîÂ~¾FÙ|–zW&‰ÒÄ¡ïòàßæ ðØP¹£¶"àH qÌ€›R˜PsÅW Väü‚M É2 ^‘<¢ÏwGÅ™ V!†~›™"Õ…‚uäA:e0½kx¾R\À¨ænõ;oC_ ãß2»v“Ef6غ |QÁ†Z?¨ÆX¥+Óùx>ÖÅ£›PDæì63JÓ#E¡Ää3‡™E³W×Ù”•@å%Ê£Ò|ª¥Çg}ÎéÓç᧲Y#zf5@þtuÊ"#v’w9 ¶Œ,}œ´[ß·ò`­Ë­l’î’þIkjU{ƒdÒ—r¡Eä`18™-õ…àåIûsBT4ÊœÊìƒüð÷|Ó.µ7ÕT•A©Ì3 haZSóœJñ)¿dK±ýÿ0=,àÚ2íÏÏž„H‹þ¬¡ X#D³šŠIÑÚSTÄ8Öðª`@>2i ¡vÿ‡1–/hÈ=u 渧c¡§z­á¥w»\š=ÓN õÐ4¶¼Ã‰a‡5=]КV]ªë0=f0Ôò €Ã ac*ãèÔI8xg+Šàà$G¼º¯ncrQZ®¥nƒudVQLÈ&‹jÆ0~nx€ -jvÛg O^.:I0Ĉûø»MÑ«/lÆ•1U”°¬(*¹m…‚zAÕËHã v‘ò+Ù±ów/—îºDIÆC2Y\QÉ€Fû&m¾Ñ)›QÁ±s÷c¨,Ó¨n¢¯TQë]©I Ð6óšJƒé³|ÑMOevŠÒuK/ëŒ;$î׿(äÍÊÛò¿Áfðñ U2^ê;ëkTñ`ÀÅõ^á”!gVÒæ5ÚŒŸn|dB â“sdæÅÈcà =.Ù£–YbÚ¿JOب Š+é”ÄÓ™r °ZKz‰ÛÕ5­©¤Þ¨h%ª§´¼Hó…S õþ“ñ´7¥Eî`U¡Âõ -ŽKÓÈ€}è~ C©n–¶³Е¸ í#·ä^"Þæ{;Ì.=ë⌦àV9ß‚ÓúL’Ú™>÷A ëy[Öݎ㬑^óˆU°rWƒ²~jœcé™c@4ºéM„ôÍE/ˆ¥¬éÏeÒ¢îÏš…±'¢c#ïhâ/sµP™V¥„Ê—¸ˆ-}O'<ŸÜà~’à¡ñÖÅRz;×ëPh8@'Ôåh|®¯WŒ¶ÖÏFypAϺ¥"Ûª/b+ è|o—ó÷jHc4Y~™ô ä*\Õ6_—íÕâ¢]fÀú+Ûo:×"ˆ®97Ñìvv°Z={ùû`ãa‚@Á¶0ë|«93plˆ¼Ö±&?„¿p#reŸ)óëm3*K¤fžÆåäµvòsëzKEÿ뚊é/ OÆPy°q"VíôŽÞ bÔdÀ0ðÞóß@ò]‹9"w”ço¾t÷*‚!‡GËÎ4•¬‹e†™oàºÖ.þ1ï %×ì2 à.ª3ÞË £d¨HI»æ¡Hn)Þ4ÕñÛ.‡}ÊwÇqZÑc~kæIïà躩‡eÒzâÂáIUØEäüJÌ[jv²SPùg‰4h9õÂXpXÜÔ“oxIYè *{( vêt-d>4RQx*‚ƒæTrˆÀpQOÄa*-È/BFØSæHA“+8˜^¬ûU‰£>>®ŠÆLðNhI„wä5 ½cξSê¼åJÏs€ÓsPL*“L1=¥2K{C“‡ÛMÆË´ƒ2Q0ZÌE©¼ ø`Å(é<ÏQÇ«Uô™Þã³à$DÕÇ)¢,Hû˜¥h²uuŒK] e4ºéŽÙn‰ F¤´&Œëÿ“L+ãI„äX{R– åã⼚?Ò«ÀMÕY‚~[J,vPý'_ßÕ9²YªEt’œõœŸIXwɽiÌ‹»·’·ŒŠ9ôž¼÷l† nÆñ(ZG.ª­z«P n Û¶ËИD(즔¡·ÄA=–Û¥Á¿,†õvVHGÐÐýYKçô1ÑXÅ*¯Nn y¡Gº´p .¤¯á‚šÍfé÷?H› ¾ w¬Ø£æ}çÆ}dé³GͬbÖL-vì¹p|^7`½ SÊ,>*â×°µ«ÉÍÙáf´ùoò ó:ò'˜kA®´¯Ç5 ð)†ýnt9nê¥ `¾´pê¨s ‘p^ý©#Prþ?J <ýÍ3—q'AÚêždB×xì‰pJbÛÉÙŸcàI†ÃzÍȲ÷xä¦7C º%Ÿ`ï å‚'}Dè6ytÕŠŽp=ÞHƇí ÑùÏeR†¿qœxíK¸â7àî SÒÝya%‹×Ps…ÖpÅGèHÆ›^l°Sñ`Õ‡ t°é¿zzÕ!O_.)¿à#¼ÐZ7Ubu oæÆí…5¿ÞÑ1xƒ#´ÿ¸Á;/â¦èDY*3÷Z§f¼’E¥:¤Œ›MÈÆe7ç\`CW(¶ñMÍsÎçXPÜs‰ |ë‘,A=Kl1íÓÊšBÇmG 3{šîÙí0eú®vÖÌó=e´þ±)l—ººûîO6W8:y„ž”2ÙNðý®´ÅðìŠÂ}–¥ ¬¨þý¦ü[ŽX Ph‚hÅXudâ§:![û†`žk‰šùèbXW"à@ÑyYõtÂ[½Ë=6D}¢é?9ÝUØe[¨„[m<(Mʺç¼oW™Ø†Ž]Ué“ÓsñÔû[ûnŽ€ÍÇ˯2À¤ éSÃ`1gpȬ60NÍ®_˜U¬Zkˆæ‰,9 !Ynÿ2ËÌe»êŸÖ`±æ¦Û’¯IÔ—»Ð+×Ý¥ÀtùÝ*³Ø¡z¾µZ»ï-,縲E½Gb»Õò¡?vÞ’,Þ´¶aG„„f¦6ÛfÑó ÌŠ0`Ì ðD¤’›Ý'd}Y/:]¥•U‘¬Šu(_Ÿ¦´ÊuòAÑmìx«wÅÀ²-ŽO³#ìm¥mÇ<çydÉ,3ÿÏúžBDœŠ^mb—Ô8ÎPîê+S6Þ³òÎa™Ó£èz$έ_9‚ã#±T +h—Æ ¶ÌßÁƒ£ûcŠcß4êëßµÓ¡š4üÚ”¦É8?s¨g“ι2Ïq½[óGk’þž÷á`–Pó„˜÷] ÍË ŸL{C»`5ÁiÁÞ&—Mµ×:#±ø½Š-hï|¿âXRÓ4טí~—|%{öÂ`*$Cˆ—ýß;P¾…Š]¨µÓƒ†]]‹ “[¯Ä­´4î7ÀØ“iµ!oΰÕÜ«­9ŸcåÉQwwÍw>«w‹œÂ(múwþz·¹Å@lgÝ4uf0qÌ0°K{”æW–×Ó»òü¢/&nÅg“šÔ¬¢ÙqKVÈe?13zþÆM¶½BøŠ°­pסÜÃhä‹yaeD 6#Dk ŠÂwmàŸ†qù ¥±ø0^'Kà•o<‰Á8¹Ó'Õ­åJEO{£E¡D¸> È&f·£~wx°Æ×[->sœtZ™oœK'•äÆ ·ÈÚmM˜½~Ó»äó6iî»íÔ”,´…B{è,a¹“‡r‡±éC¹Í²ße’B×°9Y€Q‡S¡”ü ¼ó s '¿ráV¸ºUuÆ›A7BÑÓ÷ëcŒÿ°‚xobÇp?H½è»É~$‹ë CdåL¹tcL؜̽“Å '맒ÌS€ sKÙÀ;íF+ÈÊ;éæ»v®©óß´ËÔÂ,¢ú¿ÃåIÑ”Î#bÍl¼´À(Š¦+áÜšBiù¹þ$‚—WVîèy‡^ÖØJq-'â%Ö¯¤|##þ8B‡ÚÒbžër2ayÁL;É’7Âѽ3 uϼYVˆ ª½¶Öbh»ì iÃŒ…±Ê´èNÌ\Kà Ÿh\ '¤éÑ#£ Ú/¼ísh…Òàin¾™* uWmYºd§ÖeÖáBayÛ¥ž»êd$þ‡-TVÌ– ]ÅPðÇÌ™ouK§z £;bð^ í\‰[T.îŽ.Ù>'Î6>³´éqÌ£¾cÑÚ†¥¥ûOOi×ËaXúТ/ÿ%kXð r“j•ò¡4öø݇]æ•êøMÇê6TuCض¶/÷Ùe½½Âfí„ŽSûOÉÔ>úCø¡ÊG ]¡Ô4 ¤¥4ú jeó·ð|™å¸•ý7Dr†vbÜz;UˆŒ¢ˆ}ßÛ¡.˜¬Ê!6 Uô”!_ûQ±BB«¿Óõ:þ6Ó%ÅkŽ?j´kÅowеÍq$ªU?çÅßÓçe´üv Yðg!®]ÃVô!ÃÕlÀ€›y2ÿi_þÌ(à( =}6Ò#{øebܾz–Åö*ÂЩ3W3‘,§ÆÒiêbÀ‰Ižû9dÅ‹Þ²®ÿ¼Dס:_ÀŒd×p¼x~Ñ9Uóp@ó^V+"ÅTÉI/ïkEb/•Ixœ°UºÚ¼àl]v²š˜9ÇGWY#ÓXÔdAiBìB}ðr﹪£Ù{Mku¼¾j·ÉL‰íYâœFÇìvžÞøûLþ -6Þ™Û]OBéNCu Ì£lÀ¿toï5çǵ:Š°yÛââ|æ¨ ¹ñ…³dt›¨¶”¤Ö¥ˆ“ÿ¥9Ñ[ "îÙÐÚp¨IK>õlI)D(Ÿ}yIžêä_á1åuÕMìÇJ2ŸLùz.JØDy¾«AQèôÞ8v–†ô^˜päÃÿã˜S•,"«ÔVqNìPyâ(8ù®Ö®´kò~Ðt.[÷§² ­‹k´JÌbĬªç$ôÂ7áøÞ¶W ªŠük{£Ýœæ F¹XªªçŒ¯ul/Ì (Ú…Z¼¿¼Î]±qÕ`$‘á"áH£¢ó8¿æµ™Ñ÷—š$g©à=s™&´‚Ô,òët¢$ »&æüÁÌÕðÔ2;ïa{%6 ¡_ˆêoíøÏ<²=÷÷‘ÏÍTX†RE‘Ã7â$Z¯}rvL˜ˆ‚µy( º…8,·½{ë¥ÙÔd,û˜éükðt“w`bkÂJÎøé|Š?XáåÙ³ªXyÂ`´\cv`‘ÆŒ¡Ñ”p“oT¥™†sq›‰‰º”2¿F½› êJ!¯dCWoìªçùa4„|¬QwÎÍÈk+NøuÇ4a…îKåGªGÑÉKãlûµZ‚Ä­7¿Ò±¼ÀTÅ9°à¦ÐË`«\>|ýS—”P ìÚgφ\mSöú™R—Âýng¤c­K÷ ÇüÆ;‚%ÇÉÕX~}ÑĈ^+ļþg¿‰12ó'ª„‰ÓÚxöØœ}¡±Sl¤”–JAþדóMÔÒÙ¾:²o–šÑ%äµ%QhY(ø´òŠæ=‹)W¤ý¿1†¸¨boî4s;îxÒ;±’zªtÊ~º0¾e4Kj] Û—ŸAáöc®åXö çè„Z>f¶ ý *á3>| Ó+â/?N.ú«ž¤˜;í)ÒEùë§ßY̶ýV+C;øO`,¹ó¹Äí|8’¸á,RwÖšº[Ó%•£1×(,b0èû¥¹Duà½s5÷«äè0•ÀJþ@ ³Šúâ[I7uÐñ (mÁø–\uÇRþW®(5#ÓºwÑ>? 3{SxÛí‹|½½µýµ™ OƒŒùÄ«&.|mã 7ÄvèuA“á¬&:·BéØU£l6h[–Wg'ÄÖª!V Åò³K™uzƲ£Ö—+³à—ð×”¯ [oàmŠªÞ.C ‰ü]}…@©Ñv­ùŽá z‰´ŽmtŸÝ–òÆoSx‘¨<ú+þ Çý×üb5QšÆè¯Æ€=NGsWY¤ú}á¬NbI¥¸óÏ.`HËê\|’£u¾öqàˆ~å[p0/âϯlM!eÚv8ÚÁ€/?ÔÏ„gÒLyðÝîr`赯š còà]°Ä\Ú<Á 7Æç§mÈp+ê÷.tHµÆí˳s:SŽâƒ;‚ýªmé¤DÌ L»–hx½VÞ…ä¹ñi+öû¾w‰ve JÚnS}~‡ºïæùCÛEs–¤gƒG*~ ˜æpJ+’qúdxÌ×MÇúuV£•ˆyO°ÑFï0b*­ä!a UÈ'¶O’ÒòóÃq1mÌ÷ÓÃÁp³§sŽÚppå½»3ÄBJ°°yM r äcNœ–ă~,MD”˜Hc'¤ÇÊS”ºX×¼›À‚´8J<¬ólçU„Uƛ귺É{Ãq<3#'O‡…Cn·)°$­AHÜNÈ8t©$^vÑ69Ž*xìî õÝ›gí§Ü8P‡yáU9Ë ;&T”‘»¯VFdñÀøþŸvq{…ò,RJRó,°o\Y=A$¿G¡_H, ÞÕ„FC.ê+e›K(#>V}5EùÛ1èuÒìR}¯1JHaÎq˜ËÞë|†ú“Æa*‰%|ö|­€&×5ö°¿n8SV ëÊSþ•'ÿ¹¢¬Ëi­Ë¥Ù– U;ѱŒÁk+qTY×ÁIE k>©#âlŒÁ9Åa/éÆ>pËÝ&:nÁ2V"çƒ×Ìf¸GθmŒe=FiœÞ3ú‹†P²ãÚ  tÏrÂÔæm ìiEã%›\„ÉP‰b…ÿ›,ûý±NÉhl$§w™^ü˜H¶¶ý<Ƴn\ŠÿèO£®-€|CºåÞãýêò b>‚Ë*1‘á3ŽŽ~ ù-/3ZhŸX9zúSÁõ¬u¸v`V¹-AšL↦á3†Ýeà!>ÙrÂ/ Zþ Þ¤Øq¾ÜÄ&L,—çYÙq6ï4÷ä‰ËI2ïSvЛ9i‰m½DÿŸaŠ–ÑÃ'‘‡ Ô¢4k-šª3±Yf®Õ³SLµ ¼˜qt¸Ëh%?ê8·"r0Ö$|(ÇDÉï°óËy¾èêøV†öå$iž¤ž+}” ßóMß`–¨Ñ9`ž¢âfzãûÝrˆhLzôÖê§ÌyñSãà´þ+‰ÓÈÓ/¾é·ôÎ?ÂÊ;–µ‘¸ø˵x;/Fuö!N‰cqŽÞ7¥/ó _:Ža-½¯,¢¦ÜFˆú ç>—ûÅWïTRç”1Í£/$æƒÛÈ®Ï_צæŽ/A+wüÒŠÓ_†Œá¸ý|í{fÐOdÈ…ƒÑê¸;uàœˆ‘ßv øÙ¾ÅÓÑm›¡åÐøGÍô¦ ú9¨{a®~i¾‹¥z™†Ö†‹–p·û­ÒN*‡é ÊR¢­Uå  ¥œŠy¢ð4÷6¾ýz³—ßK”ÊõLFÑ÷Ȧ—’9"~öÒ‘D !â0¹ï›Ýž F¥—]°ÄÛ¨øÞoºxÖ_‘RL|ã6Lþ‡r=‚[ú×½]è;+Z™u÷3'3Þ â[Œ³¨±Ê5=ËicªÖaIŠ±^ä½ ™›~„4A&óÅÙ5†dhþSø.„k /"Q˜…£mYåÅÏÞ§À¤‹rÝéxâî/ç«¡çš4jušY#G‘ðOCÍ º±&vD#·ŸxðxöÓÁÚÊn #ÔÄK)Q‰¿æ—JqÓi9ƒQflÄ ý!S*ö„àÞ—Ûûøêh˜Dð•Êí.JJ©"ä–òí"ùŸã°´Û?®"Y¦x°-[Rzr@ž”È8úþ+J°.AÓý,Ó?Á§º-çY"´ì‚KÓ3\i“áÝ\ê<%Ê3n!ß;3`A,¶eüÁi¸×ˆ ÷/œzj}•êáv§˜avnÞÿÿU;l*æBL¯¼|›œI´;‘DÑ›æ»Ó±¾Nr¿eËÖj”«ôK¯xb¾š[@ëP³e¼6°äá:“áÝaòoš©ìÀª3¾#jt A‘Iâ;ž¸¹"ƒŽ*.ÔƦ$Â&¤Qm„ÇF/»É’:­°l¦¥É´i' JñxE!w›€wàêFn%>Ç¿ ĸfj æ­ îç}MD²«l1T©‹…`ÑéÏ¿«çw˜éó¹ª>;´†‡FŽÜ†ÚHƒ¯ž-úèô ¨¶¤fâW]1gn£|(…2µö\þ;ç×îžê†ƒæVe”þá8†îïå½H€VK›Ø‘ õþá×pÎX‰‘³_"-;ö²ÉtèŸÒÇZ\ „ôX ¦Ï÷Ì~Ú"àš–§ÚÒVª{¨–¤uë ®ßØžÅãŠUWPÛ›t”®˜ÛïkÿäGœ9ßúÀ¨œ2fÕ¼O,HBeIÔYBÕ“çV2`¿¦Ö"C‚ö\c"ÿ8Ý·wÓüÚ©ˆŒ4¡ÆHw¾d†ná$†­J=â¦÷êô¹€Uûpsù¡Ú>¤ìüwôŠçybÐI­÷/à\—ë·½Õd |°ÿú€Ìsá-µŒË4¾WeM—¦€fªQQk—|‚0Â{ ´5Ú•A‹WhÁ\bÖYEtÑbÏ7êÄ.\  ðe«—Ä3:k¢@ww]ÿÍü_’’ˆy5ܲàU«>Ip Ùê°È”5ðu¬½L™×罂š“).3ÀÂÚ[âœÎ‚dJ`’(ã.öÛp 5ÛdGÂëjàdh\y›’õ|Í'z´ö*%Çø3¬}S6:Þlu‰»ê7èØ_KrëÏêö b¥øô‰óm§d~§U™žºˆ%Áä¥ð¶"¾ˆkfZN3šPUkƒv–‘{.T‘cz¸I8A'9*öHëôǦÙñÚm“GnŒµÆsËgÉ¢aª[ä;º wû@¦™®ÇÆÏ›^<û&¹ªê8ihVCÒ“pŸ´•.z^b é²Ï#@ =˜j;¾/•Ï‚“Ñ·ús|?ªÈJ•^ ˆ#Cuè½*·z¸Ò?æ‡,ÔàëÇÎÐöo?ƒ]Ö$È G¡©îø ¦sýÇÅšAȪ%°˜äCAåÝcºÔɘ@]K1/àŸ2õº‚važ<ý5Œs²²Ú1pN­£ŠÇ!¥ô7XIòªã „ps‰V’s¨#K’ŠÌRŠƒs9P¨ä!ÕÌFk ?ã0Tþ[.XkoDè-ÃL’Ç=Qf'º†›Ëu¸±ÉrÊ= YÍxçÍ=+£¦ÇÛM»$¦tó ¨mû.ûO9Ï ßNXüå2|Èq›º €cmɾÂê}h’–g¬©C»¼ÞšÂÇl}tÆÁAãÞ`8\ÇKCD2(ûw²vÿZÔ*ËÄßñ™Zн­ÑÃ’½oœät±M/–‡"Aö.6wšð1QO¸C¹þ–P`M5@VmÒ*™ë Ýrn^y‡žî{g8a§H1ä\«Ýt*%ûwîÝúR|W/sÁ4ÿíTKÐv0dp+ÿ¨_)¡Ôº¸Øu/ÎÕù2RQ#R ÒѤ_â—€¹ÚÝÍÊéEaÃ"kÁ /ÀO«îÿ 4 çº}@‚¼×"¾…iM4gè?pYËÒ0àf B¹ñM.ÂE¡Z\´Žà±bO_ñ†|ƒfËJÕÎd‚6¨Â¯ÔGI’À%´Š°br¸ K(ø½nð_?ˆVìhdö„4#÷*‘AÊÈá×ÈÔüß1ÍÔþ»P˜ÁnT8øPï’¦ ³ÖdÐW&é`]€ ¬ÛŒmèfbÐ*ïES––Ü©p‚zV¿~ gØ@¤§[©é²@§‡§–ú ³uŒKæìYÔÕÞ&jIÐ5ÂàÞdœ'@X¤\«3ÇÉÁÓçÎ\cR TFþ¡kÕo†p ™1ùðí$k‹+ˆ¤Jsæ&ÄïY”¢ä‹Ôö”xÁîò‘f#!Y Xr_³ë/'-dìzX°û<1 ¥\}¦,ØfÈ‘œß¿MÑx¡wÝiD§\¯JFE²k™ûYdá”%]}ÎÜ•[,x^–Ô*ä–û¹&°}µFÂÊÎí “¹3oÏôz’ŽEÿ›ùQ8*ƒºqœ-:{dì¼(Ÿ‰Àªl-þœzYÙOWwSqUAŒú$\ lû6fNF­id7Šz‘F#v%f©”€²dºWÈì6+[ ^ª í™™µ" p–©XbÀ¨ÒRÉþkRÚ8è¼J)çZÚL¬ÍÓ³ö ú˜muI(í4te w5.¿ÌJÜNc@ êØ1 « ²’Lvp^{盦ÎÞI «ñkAQd´ñþ +dz‚È Fõ­LÅ¿JòÀá-â–£ˆ¶¸¹Ã\j'Wór ÕÏÈ ¬Öƒ¢­ ‘ŽŒ--¯êðÓH¤.$²¢¿ÞW‰ÉXÂÙÛÃÁ©(†è¨R‰:bà=ÆÛ"ñÅ~!¯C÷§oOÆj4ôM»Å¡.9ÄȨL¤ ÚµËØ«*¸N(6Ã*¶-§½e ì)òD| ¥ùMÏóàS+ä ’Ïb¿ÉôáoÐ ‰Ö#вCˆŸA:EÖb·4òN½#°ÂÚÄz º9ˆpXèŸ8Y·†ÛV뤖Þêæè7˜Ñ0ùÀDJqª=øï¾f3NN¶&.z}·Èü.ÑQµôOi2lÂÙ­0y¡‚5#¸k×O×ÿƒO: ßò¨¿šp0vzéϦŽ3ÝòûÅÛ§¡á³OÜ¢[Ì®µ³eÞMæhQ xêEvݵ¸n5ŠFl@vq$ZoçI:Ô‚FnQðاTu:IS‡°ìj·aú|‘PiŠr7|ÿÀT8õ}õIó«‚ÈiÑÔ#:·©Ê:Ý>Ç) ô´ [ ø΋@Žßœ¥9À. £šð¸Ç(Å+’-¼6¦µ(£¸ÚQ\!¡ë•¿ü~vò~j ’ð ¡ŸÕtÉ™C7Ý+úu*8©›þl`&›d2JÞ¥³=Ç.1†¡F[,Îã¦î“ô†B=¤¢´çÖTøÒG=v¥ù£%êQ½¹kƒ<®2¡·e{Íì=‡Ä×5ýt·As¤=²s2~î£ð¯õ© U23Ÿ_Æ懷dwîTJSUÖS6ï÷¦ ý£ˆ"ìWRΈ=—GRjc?Þß ëð†Ò¢ JIëÇ,§9•Î‡Lkeo•Ýò¾¢ñ5nÍä,½×É!1ÍNýÅZ_T[k’Êw¸±:›h <ã]ÔAG׳ #Æ@ΓX§£wÌ%-tͨÏOßbgB¼ß´ì¤È#ÊtIÔ)»ïƒD‰AêŽPw'En¿êÂ}¦„~ ¬ü‰K(ðχٞ þ¢vÿ¿.GÐ^bÜŒl—¸àû/-¾R«Å=„Ûå#1†yWÉåüŸ^ d"v¾1r¹ )ùF(#ËóT`¿Ÿ³U~-ä2Ê–>‘!ºµ,yð ö/r §hùÊ‘¤ŽãœkÆTùåÙkµsžüÙÞôÏp«Ä¾€¹ÙŸ+µ²­®±1é<`æå4ÓÕ éJIä6öÇðP»§fŠÁúâHx",s„]TR £íLÞÛnIù(¡ ¬Ò?«#œ§ëŒÊö˜É‹±[×Å…UZ‚œ³|Ò¶N3ºnܸȵ9Ã<·Z*¼ä×Ê$ Ci÷,);î"t(¿µí®+¸ƒ+ k–tMW1ê=-uëf˜f)µÓ…ò¾ ¥âPßÒ œtÓ#lÿÊñdwÐCÙìߥÒq!sÏÄéJCñÉŒ [WËYŸÞ·]mK³žLRãQ\…}Ö[d|Ñ=ÏŠ˜I̺ǶGoòlò9‘°±´9õ/®Ž•Œïª­Dnðj4F9ò¶½èОÝu©¡ç‰g/£¦úäri¨R~àíq…þRÃ{êJ²C¤˜EgVACæaLvÛdF1c&* lìÛ/Ùhit²\Fð`½â*»ê"~ü"é«0 ÿ?¹í‘v/¶y+üìWþ4¨ŸÆWØömí?ªcØ; Z·1Ê;0ä¹µ‹ß¾âZ¿ÔE*>+ÅÑ£|6uólPÔlå)J£ªfº¸î&¶Vóu@5•œê~TnH2æá/;í Ó°³/Sõÿ“Ît]Æj~mÁ…h½Z‹—Öà©>ÎyïÍN`çPP/`ÜnÛÅÿæž"tës‚û@Â`«¥‡×'E »-[61ðT_¶ˆùÛXp½T—‚ée^¬s¦¬ߢ"¤Ò´Ð#Î90x+æ@€ã()šmɯ~Ô ÝJqme¶Ç̾[zñi²mõ\ú ½œ½¬Œä›oMʘIs²01–êÒÙ;øHî+c"ý6?<Ç~ÁL­ÒùµY1(䤸‰[Àè©\a‘æg<œ6ÝŒ¬ãÊ„N º6u=<åÉìàlfÔÎ-O\õºdÝmιg1»³,ŽÁÓ¶š¹L4é¢K¸L³B}Eñ ñ2„ïqС°DSHFüÌÖ¡¶[˜³Öoµ ìÆÐmCøBžS ¬7vVî`Ö*Caê|7Lè@j,h!3þß¼¨L¾ œjâécU0ÓMž_Ä2ËÛG§_&çôýN‰¾Î®‰gTgy!žÄ©\‡œÇ‘¬Ë…&qhRËb+—HÍ”þ¡s(¹ÁÈ\b¼P-—çªXÐU Oœ_w‘Kld a{¢ÌÁ£*iN[l¨\Ý¢`é˜3Ê j¤|f°GãºÉtê¦HÂ'LD¨ë,û”µ©3²`Á±u«#'¹d–w€¹‚bS)Š@16ò!­­+ÎòÙúaˆµd.~¢8}¬@x3­øç!¤‹®I±´$Äúç_YD”¸ÅJ5h¢‰.=Ù 1ûïùlZ²+£°ÎfbxºW®‹U}lK$¥”WîUerÔÞ™úÜê«p–™ØY`×0øuœ;…«J-¬)]jÅ®ä3–?¶ûü=W¬|Ðv´þ³hE¼ê¬øuÑÕdÁï€]Í„p;€ý±ßk—z×ͽ¡Í&gÙ¾è0ÒÓ¬LÊÀlÓ+|•ip|Œ{ó*xY UPÒ:ú8‹v$ ¸ÊwÝEâ4ƒÅῈŸ°.Øæí íÐæY¾‚Ùu‚=K¯•9.´­WkÞëƒÒ·+3Q uâù?ÎÏ=s0{ëZÑ›éÀëÕ?E;ìïÁE¶S)xA ñ¥ŽÛ´ìMåÓø)ò%H‡05ã.˜Zhät©c˜1xðêÀ;@Õ¦ ¶ø–¢ fWxèøP©ÁÀ©!÷'–K7T5Å¡­„vèÿ¾üý¬‚®q¹—ìùÌÊ o9’ôƒ®UN°6ú‰d9Þ· …õ›A—s4˜{~†*l%]»< ×w0k8 Ríy·c€è‰@ƒu’•GÀ~aÕZC:Ë‹vã"»,…6%âþRÉ.ºÔúO7ët0N˜ùe­¼¢m’€Ìm¾Ñ¹™AúÚéeÔ‡,%òñƬÃn†OIqY7IÙa{h]˜ÐP—W¤Q(%d‡ÊNš§ †îAÍƱ×F#6¿‚á[@9–A (;Ö’\ú$èH¼º4®a.‚š@dÚ·T*È|)›LŒœ˜ZÇec"%ìOǽ²µBÊE  ó4Y•¦>ĆîÓ_¢*Ý Ã(Ì.DDÖ÷œ|€8q_មΤŠ„Cˆß‹÷9FlŒv*\à®:Tsý‚¯`Ywä,ë'Àr¨  <áà(8÷irEZv::ãç=6•¾‚Íû/ËÏØa¶¬ÿ]w…FˆG{'Zu–Tød NlÒ´¤\‹Ö´ßʼ“„ê¼!ðÿQ”uÑ$Rúã•ãéÍ@¸x‰²ý2·R¥1ß7‡LXNÝu‹Ù¢4¿ÆWõ‡›õ•#“HåzŸLÏkm—A?£+Ä› »Ž´^:eÈaÃèÅ]H²ƒÝk(§'9;¨Žj&ÈÄÎXhï’ðKãïñê8záEÃêlä|~¦CÏyùªí¦›5ïEzñÍŽÓuÆÕhˆÌo¯7µÑ‡6}ßmžr U)Äe%c¥™ß´^ T6Oqpƒg©r ­ )ù” Þ6™DÙ½êΊ õÂ1IÑàÀve§[Q šÃë¶4e˜b¾$§yNy:–¨™ôÀ®øZ„É3EYîÌÏÓ±@·;ñl;WŽk+û^5´ ä{V¬òI5 %zÒ»òuM,cnÂâÙ0]¾Qd¢Èœî\ᣅ•ÏJW“ ¬Œ© ¾ìÓ¯hB«šŸø6iâ>^‰³"6˜v ïœtŽñ¥„`3ë'Ôž»VgîÚŒ7ÉêTÙôÏ» ¦˜•»ÍrssmÁ ø²õFæ-Òf~äfbÙù]*¦¡?!·‰aTíåtèÕÈîK´¬Ñ¼Ú¹‹þÎpÝ™ÎuÚ±MBÐ Uv |¥Ÿª"2ê}£Ç²êý]ðüÙº¿ð}ßÀV-" ³çßßúQ³¼wØf!»@3Jéá‹`2HS!ÿ@(¯ÛôÎ4¬Bßæÿf.¹\æÚsíGGsÐ SP1Ð*‰Þâ†Q=Žkàtj ø}  Pe-ÑšŽjŽ&³×ãÞ¼j-4¯·oÑÓêÂÒåX“2óo—Y+Hôã¢Ò‘áD²)–å€b@ºG’ÖkÉ$2LùÒpéâ·##Fº-·gÀ&K-¥_™WÁÖ4…c+½’×QÁ«{*ðñoD… jA¨ â¸3b”—½ÐÅËnZè#Sô [ÆæÐÍ<–£ñX^¯ÊR®jPÌРƥ̠=¿¤¶hÊÒóמÁ¦¦8Jð²b*?Nl(ûx°±ù«ÿãžõ®ê8”|Äøײµ†ÅÞQÀ餜q'Üâ÷kz‰Ôš¯6µe—ºt¥—Ç¢›x§ z£ü ÞÖ{íh¢h~åAêH®jÃEÑSå„ÂfÂV¶=÷‡rlÕ.¤–Eà†^~l½ó·þµ˜ pÖgÆn%ŸÚá>9Eº`‰Õ×Æõ€bbZɬYBÙýžÄ`ñÂJ]’õ( rj1 ã8#q´öŒdÚ±ÌõÙ×ÉAzgû&äÕTŠP乓» CÜ…éºéTÀ!…'¦»WIêÖÁcÓÊ8ZU±‘/)ÂIæù4#áqh 0Rír¸8~H¾¤öb¹,Áé;L¹fÀoƒ=Yž«gs|Z«éH^¿KºÄ´£0ldç²Y.„>|šºãñc‡_,•é9öÍÿ EZ# -l§ÝúCOÒØš©—['°}Í÷#9Psšp ÚlÚ°!ÿ¡ôêψVÐö’î„1G1O!sy4ët¸èiÆh¹ëZ'Ó·‰@_D­Ð8Ít ®=Ö˜F'WW*¥«â«l/ÎCç}Â=./´…4¬–N#2’ L‡-™Øv|`´'?¿bûß×+«¹¶u/IûïE0w»~¡¨°zz”ç¯èx‹\àjhCPyèÐùÚT›Ö„Fëæd!`Êkß'^mÉ‚K®>̨r?Jú¦Å×Ä3šIß´9“ÀŠ,íž3Tf'!«š`¯ß;Â…­óõEéT¿Eû»e$=²»„×·x¤åÿºØ,ªÑÝ'ȢɪÁQ¬ˆÌ®Aa‹½Û¦òÐé¯):613»-{¸ÁZÇBÚ»‡¬ÖV3.ÈáYS' ryUS-ðwfý^Êö:RXV1߸}2OϘKÊLФq oG]ö2‘wц“u !»Èñ [ßG‹s¢å„Ûx0¶¸_ò—<ŒfÚá6Ò óñD)€v:¥– ÀåÑÎ,ÑAŽÒ>><’fú6AÛR±À÷ß‚é¾9“’Bñmá 2qJº'ÏzÂqÎSÕßÃ^¢È¹n#á â ûV!5šB‘+¼03¶6ã’ÄWï)äNPkÿâkãíNF P„t†õ‘kDT½Ã´¦ý€ÿTÈN'¦ˆðß7rµÖHotÍ°JržÁî‡éìdq\Çý"ÍN?>õå ˜'ùPć}¿ê½>D+׫úÏÅ\íƒP¶y²Ò§å3ÍÂ,¡ä%õ±•vè´µIí£·çå·KggR-‹±[i’RnÿÏ$ÝŸ0e ÉÅ)<·$F’—„O½ÃäZÆ_€ZóIb6ÃÜ)úõN ˆ†²éöã=ÄYìþUuK¯5–õÃg§/TŒ³Rš"žø†¬?,Iæû^óâùa÷ìe3Vø¢îHqÀ@q·ëGá#…1è;¶£6‘BBNç¾õl)=á…[•;èý‚Rvñ“¡Vp™I²Çñ)þœðûɑᤠ%3™¯ ù«¨"ßèNy’ö¥šù»¯Þ¡–©%rš¶5/½ïMþ°&"Ã[-1Ý]fؘVrsK‡ŠOéšØ×- –DÔBHô_O à¬J—Jvü¿F¦pZ< ž£d—ÌÈÓ˜Ôd)‡nv–ª¿½6ãÔBfÂí0‡ÿW"¬Ð gÁ=‹ÇW¸ƒ¯ØôÑSnÿÂ_ÌLºä1½¼àEZ>IñÃuöª%Wxë&°mþ%‡•‹ù´ÇJÂé@„*¡4ÑidÔ›€¯ŽrÑió~®\½`Û5_D8U¯¶ÍÁ€†,ѱ4:¦)ù¼”„¥—øûkŠ\B“ЖõÒ ºäg˜Ùþ‹Ñ|êšo§yã‚Ž9ók>-wµÃ/¨CžÞ¦ëT°HUËm´…r‰IþOIú¿ }d‚Ö¹QòÓ¹ÍÂcsBø^tŒÄM†I”¾ðI’órðY‰¼T%n@6á„JAé뫤/K0±Ú7I^hÿ“¼¦‡±×hK;i]´@ÎáÃïȆ¿âüðͤj+AaÕÙ_| kS’Æv,¿ŽXd·¸ö2¬ ¿w¿~•ønCûˆœª6hG”‹Ãe%JÇðµ¶áŒIáfl*ÝAYŸÔ‰7N?áÙ‚ÀÄ.UŒ:ü'—ÊÌÈÞá?œ"Ûª*T ì2!'VÊ×[Ñ3hÿϼôÆÿíâÕœ"€œã#´¥NË–áÑŽÀéÚ¡a@É·žÎXç¼ +YÁ0| G*¸â1æŒ9\ýŦðúŠ”§ þàù)Ðc5]-5MôÎë.0m’¢ºÖry­?ݪQv#,Θt,Ñ£>þÝâøxŠÉAŽŒ¾à ÀÂ5y1êÁ·+ÞŒç ýpH²IiìÝÖ¤kWš¯tÃÉ*=4™uxïž$ú½ ¢Áæ`h…£…ÝB×_ÚJI)¥A|¦¨Ød5)7І¬t9X¸†ë¶“ *2Sé¢J¿ µë;7§™°ÖöT˘óƒ2È !€Û„TDOé…R9übã î;ÅÓ˜®˜ÒYTüîõ±°˜Yß ä›¤rÂJ±6¼{%ÝD|2H„r|k ÜAÎÙº‹žC)Pá8â.^¹KâìÚ°Ñs(‡\*Òȳі—f“Î÷Ä$‚™i¢5˜s—qòÏ …Æ?Å&™ò=ÿ³À~ë§I O…yšÑÎô^Ó½Cü\×Xæ0 ÛBûôÜ|®ƒ?Èí&’QÞ9ø^Z½¡ŒCÃ5œŸYNèú:1GeÁ‘à"À±c)w 24ñ[³æ5AYj¿_'"´¹ßV\==hÏúQðâÕ$í%gº/@t½ÍQiËõ­Òž6Ùf€¯¾vÁ\…×ï‰+è4ç‹jJ°“%Ù Æ›Ï\Á5úQ‰;}'Vž'@6Ž¡0ëu“…ùÿþŒUYX¿h[ÍÍ8MÃ$´/kã{uQs` îÝ=Ä×÷€X ÓŽ¢?}®p­ŽÐÀÌ­ `;ÜÑ=ÿôP[Oï·>0¥-¨hJ(}&¶_¨MX¹7Ó‚.‘4ÃÕÊS‘e¡Vúh œÀkx¼Ðöùú~i‘z³ˆ;Îuù€t›u´”Ÿµ¥j&’!YŠ»ZÈð&U£Q“Ö ±Ëåÿ¥¬Á·¸ÙqS5²K’æPÄÛè"ó­³# Lµ“k-¡œ`b§sòAþïò‡²_:DŒo¸²âÃ)}ñªÑŠSY‹ –­uµ~ÉÝaOÐyš±²HìNTUUý 2_b¾ÁÁUõÊxnפkõqñ ‰IÀŠV‹ßMcÿ,϶yš“´­%Ñêzûc×Äö •fÉHiCTÍ[r9ãëÑò1(P#Ò¤Û*¿ù]¢ó$ð³=cG°­\/‰3†›W:q”î­›ÉV]f“±L.§FsÜd0d5¯Óù§ ¡”kì=QâG#r·€B_$J)ÔšûÕLüÚ׎ú˜#ÖÀrÖTAäz%·´^} ÙØ’µ¢põ¨ü¾y)íáj6z³D/æ„Xkù£ÿdšyºzÛþÝ¥‘hjÞ#Z–»Çˆ¿t&iö‹_•*ÿ† ÙwÖ"‚‘m—”¤\p/ª¾‚ô»ÊĦKî}VÃMè=¹½•¤ý÷A£-rÅmÅÊo9tH*ùW…‰ÝM㮕K_‘±mŸs“eœ4H×w›ÎÅ4ÞÈj”ü²Ð;/Å„W=Š• ™G‹œ%àñ=r§›‰.ÖôŽbƒ&Ó… ù°o̯Òò£Bâ}Õ²{ÏV5=ÖzûΨPÈ;™ÉSõfC¨í³Ñ-›ð` ±1u <(ý¢ŸÚQM”ño¹jáUZ-MJ=O-|`§ä‚úA.HŸ?‘Þû\Cп™kœzµ– +Öœù‘…WqÖ†(§‰ŸM<²gÔè,•ïjHS’Îœ¹•Ü48ðñv7ÿ Ý€Fe÷B1ŠM\5ã?ûýÝ3‚SÀÞc ½r4uÈ&)ïUþæì=ÑËè;))¼×¼ŠØŒ\5úAa07_AáACÜG¦-Iå:p'Ÿ_;vLóž5Ç,¾ê­a P!ÔZ†á¶oè…ÁˆÐþwaQê&1"±CÓVÉPŽªA¾°æBå²/'É#z‘yO%®q–2ÇŽ’±ký’X’C8ã*üÄflÅ6õ˜U“ôrbuÉÈô †ú;=W«s·çÖ¿æ° GI¦œQµOŽûÅ_D/Y`g0ºOžˆÎ–$zª(v·ùýÊèÞ #LÓ¿R8r@O†®l»¬ùa×)´P½‰ª‰Þ‰ñg½3þÙG£"ñïô•ÉÀÖNžì§.¹.·C¶wæùu}Yf󺣜­Ý2ý¹sZ_¤¥’XH¦"ŸXS÷?2ù"NÜ)a¡LSs×g€¨‡Û¯/ƒ¦|Šj (»œÝ{iõEyÈk J¶øå ‘S¸€M}çKÅ6n?/ûœ˜êK#H…€µú/ -Ý汎òõ;#¶kÁÈŸú?Üø[…Hµp{¯ëQóÖOïú +7d¤k§£8f ÔHH;hk°˜/ߢÐ;üþ0Ô³¶ð•ýÀ>gïãÜ›»šâ{°Qò©¯—Ênþ´çŠ°üf  0”HÖ#–Ø‚Ý R’–Å¡ç³÷îü‘V盡ëâ8˜¨ œ?ë3„ªÿ€qè!í» ’kRBAÔ­ÑŒPa¤fh'ŒA|QòÄQiOhÖãÌP‡Õ…ÛØëmdÌìU+ øyщ 2ÕjBÐ&‡ÜúÛ¶X†S[frãöñuÀŒ¤fƒ¼$*IǪJõÒ`L@Q*Úl ÀZEn^1ZòÝYêsL¬¿—óVG`{D?ªùáQÝ„· ¿çúb‚ãö¿ÆC„†µ­ sŒbN%ÍT=&¬‹yy¼»{§ë3νæ=.gí QÕHñë{GïÞtÑŸy£€‘B¨ê-½ºùC ¦‹jµŽ7Z,é<ÁdþÓÑ¡|Ÿ†Z”]/ûKT[$ÖhžIq&Á=„Fvìò¹\”¡EµÿªÄ¨nãr}±½õ_ô»àg%{òN>9á=—Ã'×£ÅêÜû:ôWþ‚f¬àjž‰G@±p—`àèO1›r 1"UÔq’OHA 7… + ô¯|c`ËÉâéÃÃFº¸Œ£‹Žî›v@hdHà««hS½+`!Ú‹¾Ø†DóµòYä•~[Óðç ±W©MÒÝ®_LBÈÁúàù3Róÿj‘õÑÙª–z«+Ô-hNï…8o†ˆ&LLO“‹žÀ¾b»©xÕy]OÆ"ËîÂñôÕÆ”ë«j}SÖÔé;1­Ý+£]âç­k”à(–;!ô¢:»4á±SxœC GyöÁ fÛ:ÔáaÛgÅ~¿Àûù$é«þó—ê>ÒŽE"z¾Û G O›ðV‘Üû¢ÂcIº—Žœì >¡¸¸ ­×'Ì+} ¤Ê h'¤UV=Ih–჌IÞíºUs•¾À”^««±hdÛSgÀõö%Bî8mtØÛ˜¥ ¨æU‡“Ä}‘Æ5{ÝA4Þ+¦ «†…ÊŒÄE#[ØXç£ê>” ®·)w»Ûþ™R¼È®2¿+ÔN6üšPNüªxƒeŸTq¸¢Glè|jšÊ¤éX£Oß%Wä!ã^‚nJ"8b!¯Ïå 7ø^ ~#æ‡?¾¬Ë³³s³íÓbK4?ˆ×C)W¦¯$uË›6ñ²hÉeFt‘5Kë®ûÕg¤ßwýÙº²H"R;Y@…âƒû¢½ ¯žCh7Y˜z–@ï¹u³Ü}’ï>› ܤ”äuˆGµšßubÃòÿ- ¡a GŽ)ÛÿøLOzÔõêæ@ªhøGd­·,S¯ŠMè°–kOP‘¤õrÒª5ð?¶j> ¨”âŒGTK.ÊûiºK»8»’oÄó‡h”àJ7ëÍ2‘Ôò⊣Ó|ÄË{¡q‹D5i:Qò¢¢w>K”Ý¿Úÿ×~S_7Š+²’?\²ûmé!/ÄnJº©Fn¾[éb†ˆÄà•h•ŸTÁBj³ÁѬŠÔ…5—ãB×ûm† ¾’à. 1ý$FÄäÖ@…?s°S1ï鱌OT®1úl¦º–‹¸Þ=¯­­âÚT.–èØè÷µ„õúáÒ3,ÏŒj™Iš›¬ô= å¹/o—/8UËßêWÛnJŽ×Ý[ Gz+õ*ð×v7Üû÷‡™:caVt¯s ŽŠéœº°¥û0y,Ò¢\”þúgø(HèÜ…Î(‘²aDºøbx»@ZÏH“Úõ?ç}ñÓù§jÓ‹3r¹iOƒíé< x¯m†D‰µÇô¼ºLýl%þ>=’-'|¶$0É{qÏWäK‹x¯uÉOY”*y!Çh¦Mõ‰ëç-ØB'f·RˆIœ±{jÇíÇÊ7S„*= n¡#ÖMœøDSŸûµcþaöØNÐ"¹ÄVè€JrÑÞÓ|šUÄÊ‘û¤ Z3Ky9èK Ÿ„¤Î .ïh3eÇ÷àÞìòA·R$"}—Šêƒ¥x&¿óÜò¢8ßHtoýÙÆäÐnÊø-…KÿëçÚ½ñ…÷WyðiãÊÔs¾scÎÝŠÖd/ ½ZŸúw¾Œ,s@²êæ ú£ÓxÏÕ¼7j‰‚Øu¾y„×lÂs©ñ¹Ã†;“ºjL)düŒ¢ïdî{³˜ªn‹ËZL«¿—O9•ŽJWíæãÞÓ”¾öp|vØ~RúIÔ ¥:4è9°Ep·›£dèL­îW̧ŽÛžáî^I@}ïd]ñÑe(†è¹˜×ÔRv J?43ºX.y„U÷­yŠá«Ð,œG­òËÌ|· cI–ÍVˆ¹·|`?Ú»—Þý\xßÐS,½¶‚ Ú #r0•üV­üV§Ôi~ì³€ÆNv…È‚‡nß©FÖ«´<¤AõA÷J°ÙyúFX*ïˆEBøh  ®±¨$ŽÓ²½ﻯõ%© R”È4à"h¦b=×Ábý·û?i…à©9šfdÊBk:·Úaqú3’ª¦'Ñø ‹ æ½<ÙN[ñMp¨Ö È^ÕL-s®¨Ü๚;|ÎÚ¯izúx3R¾¨÷øÙ|^T¼w%|£äfæ°©¦å'¥‘³E2cË÷šæÎ] Nçl”ÕF`¬)›if>£Óà›\hEiB’ª{Ô3€K›yjÿ‡m“áí´¾‡àuàȱ3tY¶[ÂÆ1*¢£\Ä…W ìôŠ¹ÇM¢Ï¸è¶×`¿…²“¾C£ø-‹¶3¶]Z$ƒÌÕpö£zùFEïðŒW.åÎ æ–%«nÌ~÷ÇVÿ4X¥Î"sföù8³8^œ]€ÁC ,:ÊWY„,Òz*ùãRÝxp5ôÚ”ÍÒƳ*£`Â8l€Ð+Ø2»˜º)›£^‡/%×x3 Þ‰ÖðçË#u8Œû,•z¨òÀùÞãcI8îSɪz&cÉ8B UèØÞá~q‘ Í÷ H~Àäï0Àǘ8¼ŽüÕjWëR”týIÐ&Z ·Aùôeâ[ªc)£:ò¨‹,·!C{×Íöäé|͘×N§N¾·=éJƒ»^ÉPA^7C¿¶8ëFC8Š}Òùø¦¯<X§0Ù—Ä ÛY6à펡†Òg3fÊ_K¦{ žN~H’\m‡a ã • ŽÈûÅÒ‡ù3=¶ã“×O^&&+¸õþCv[¹¶D_•öÞJ‹ï DAÎà>ÚRÿ©îl tkÀ”ÈM„Ønç£ÕÁ^Eý¬æz{œEP‹öÞPÕ¢¢Ð‡‘r“¿Üüýã ²>ÊÇmîííðÈ&{ –p…4 åø²5Ù&ub˜>Z6yà*GxCÇú:¡C$¹uàœv5щ1ÚwbVA<â^­öYTØD]^ßJ Wñ_WNý±˜!~üSêhCF÷¢Ã’V†[Lå­^žÑ®2]™=`Ç ŒÞÕèÝÁ‰b|'ý* ™ªö jÏ`Ö5àoz!ñEÉ=|è² «òù·•’XJ»‚¾N ·`RÄ;ÿIõÎ×¢ÑÓ½Þ þT'ÕE„‰B±LöÖkWX<$Ó'Á¥¦ÿ2Ú†©»~³2 fÜWÏ!xÿmÒìÃD€² §w0´|! >ED‚¹ÿ(O{µáT£@36àc„µM°÷âf€’ëÛÿcëûnÉC2ÌêÔc‘£¬!¡¼êØÛ#β%™ ¿Óú˽ý2â-¦u>xË0;ÚcÎMǸ¼]„ kÛ‘´8§à=[J©!eá¦Å# ,»2©¬·LkIÓHH-he®¥m¨ûTk‡%øÀ[÷l‰vøÉcähë\YZºI,Q#({þ/LÞ¿þ ÚÁ2O½íÖÇvKŸ{ªØØR f‚ѦîÐTàX’Dû?U;@y3EëiVl(ÙÄn©‰Z¬ñžT™eÌ…ï]y§Äé4SSK…¤V¬j{ØÃÃ?7æÜËA—­·LÞúŠù-q/-3Œ|‘c"æ[ˆ¾¿'‹+—'ÜÚïanÙBÎ ×Vƒy§MQ`¼|ñÞMÊTz=s7®õŽ’Ü,_A‡×IzŠ²c$µ¯¹‰Sñ-¶œõ¤ ì‰<À¶OËÆÓ%97}²Æ¹j°Ô…u¼Ú4OèbktؽÌ»!ªÁÿ9)ßfGæè¼ v°·©½› @ÜÂ<]ƒ”óãØ ‚ä°1½pt¢ãôcÏõ5ò§ñÈ8wq¶JZh/#«¾BTA¶ m=oðÚ×U©¡r‡iC¾F8_GkƒŒÅL•`¿cŒ6y•ñæ+ª¾S¶±o¨Nø¸:Ðë_Lm¨Õ¹ }¯½—€]㣰€4†÷‹Š2Š ¶ºøD雥C˜&X7ׄ*TŠ³ö+@^õÁ”j$ùÅ»KöÓ,™WYÚS1egâ³vŸ+P½o¾?„ÄiñÃp^àJ ›)§—% Ò)î\Àù¹}f$˜ýsé·±÷ e¯/Ÿ²ð™oMz#éï6´Ý>®,Þ&KÙZû„ Ýe‰SßÇ{h1Ã{-Ûܶ…犵vÂâš±†¯Þ"ÔªmÊ0täÕ§dP®p;§Û‡Õíì¥DíG„8¬Ûz@Ö7¤›ñ$™å+ÖM¸a…^ãb3—ÄâˆùzDl§ò>g 2ÁÖ¸§‹§µ AyØv’'UaÙÛ~9ñLó_yqD(®Ù«ŸŒmpÞ›'·¢j WgdFªµÜgÐG?—¥6)«4R!ßø35ÅE9AZȺ`öSac1…žƒ¨E'|ß?`Qžß&‹u}Ki{‘¹ž‘½Ãù£|£_V»umyÃÄŒHî—[ýÞs‡¤}|\‘ÚýLÀL.Òˆúpƒ™È¾òŸâYõb[G]Í#ÓŽõº÷þԶžL(´QIœ7Có`/ AÂmÙ ôþzîüÓ{ºðù5(àì?Z Nÿ~—h´ðpxz—ïÖ•½ _¾ êvyŽÑ¶ý«oŒ¹/{Å•ç€êFkëË-í(¾]:Ü8>|KU#sîL£w1§Í¥s²ÉÔ³EÎê àÎÌ6ÃTR ~ŽËa¦UÜ‘d#¯Ú'9j+™‚ŸÍÀÃcë‚},Ìžï°Â9jî(òÄл'hWÎhJ]¼wsà6½¸,¤[ò› ,:ÀGsð¦Èè ùD\; 4³/Wfþac=ࡱô;îíá Oëç##…d.«3ÁÁµ¢ö]ˆÎ‹ìúçr?Ø%kÑ)éàp¨6kuÔj¶P>`ÉIg)z&+Ö!½·5pÜ4¼Úɱp¯¡_ù¡`­Â»ieùÕ¶r«-ÕŒžÚûÓþ1>Shá†ëML'·òž*þˆ¯ß(â榓]‰J%…Âc/—á,YôkåÑîw²:$2(†ã—Z ÿù F’½a¤Uú‡]9dÿÜ ¦P’•ö¶D+»Æúœï•ïƒ>ÿ9X–¥îþœ#•Xb Ãî"rÅ_¢£b³ 5-Ž?†`}Ä—·#–>M£m|/3has/€"6›4–ë…2Jìôizîû‰clýj8KpI8^»B`bmÛª|†îsHla¦yヌɄŸµiÓŠü Îy½Ú—ä<Ô©fP dâ(S…zb,;<]:*$ÖWmiâ"˜R‡Ùçur1 «Yfùô*¤¹•¬“¥d;âûe›’§u.Ì1H\ ½£E\yA,bÀ­¬Üôq¬ÐÕìÎòÜqqúâYe¼aËšJO¿ÅÃßÙ@PEšÔ œÈ}Û<&8í°ø|À9æ¾á» ²n»qZ² Ú#*£É+LŠj!oš2x0<¦>ºŒøÀ:¡”Kò’2ÆUøè(d>6Ñì­1ƒÜùžÖéºm¤¡.l+½$‰¹Áº¹B–‰°\€{a¥Cp«€$ÏÜNiÍSˆÝîBfÞtû¬Ž c¦R¼¤‰”Ó³¥è Ã>y®×Y¢(xhô¿.‘—èôÉ{È°"A#³Þ’f&OAR­íF 2Tí«ÀzC™‘»bn|W‘PÞU,<Ä£vÎÇä ’ú9µ(r4Kú‹¯åkëÔìJHdˆô”ðÁ:ŠE‰cYè [ô?nfûðy\ 6·± z6ÇC[lý_Óü ô¿œuú—*¬+ݶš¸XRÝ+šCs5B‘ X8ßR6òNéÝëÓÍ›ŒØ€‹·¨ñ©‡f[É€»­U¡æ ÍÚϦ<Âñ–k\­„žËS±™-±œ¡4)³˜ÅÙKá äÜ•j¯—.Ó…)óÅ:Úñ²ScRõ;w¥¶;7½0Û  ºØÍNÿZúR-)#fZ²–PZ—¨þû=ªëã\çueîëÆ;LçöÔà/&ñL®˜V{ômq’£±!ÀAÉÝľ%!S|sàéM1EÿŸ…Ö–Ȩ¨Ñ­­^ßöo@±!fˆ6™ãµéê:o³‚^XÅVì>×ôÑd!¬6ÿE”9£Ëו„ÖèÍ-}ͪ;ËY²âQ"(Eg!Œ/нƒ³eœ!Ђ{+̯wâ’æn¹–Ÿb>†ãË)ê&Ò…xh£ýÐKœ#öÇ‘F²Lõà¯,,ž'1C8…@Ú-Óp<,1VÁóÖÿ*“ÍátóeÌ]‰\¼=Ë©à=N»Ž2í÷4ÑÒE%ièaèÿŸªš{T ÅoØ“¥lJô4åw»Ê°]fgµ ˜áä@^’–!¨¯‚]öñ9:½)‚ÿf•q°2Ýî䜦á79ç¯sÔ*ñ•î~¿þEt5¾e†ïw™ºkÄ$ÙükU£qãöjòxýÇK¶ +•¾Æ†\/xž×½¾|E?'ª;r{xÙMÞâ"/ø"´õ:!+ &7¨ÿêïÖ2ãI!’^Λ1ÅÅÚºUèH}òZ‰“±ðX\æwþ7cdŠ½.Þ2÷ÕMq0bÊÔ|qÇé}Ü›Qä^ÛÛºúP£O[¼MW¹÷òá·5öe: ,g«?š¼ó:TýZYlıW÷ µ p]ø½§ƒ?TVP™ê–h´áæØ“]Ð9^”Ý$i&z•ËHºú“ÛÏ4•&¿÷^<–åߨÿ%=sÊÿ?” l:2&Η%íEhÕÄëœ|\Àþôßuó6gé•>$!Szý,ö{Û<ñJõ‰ùˆº—¹ ÞËŒ' ΀7ÙQ‘Ê…?¢ØtÌ6güJŸ*hý¸èŒ‹CL<‡ü¯—e€DàÿmÊÊヅ:%áëÔôØì^—á½A¨a/Z̓wW¯V;`ív ]‰ëÿÄ› ú¼#ýºvyþÇõŒ”GF±Ö8p±úF®ÀðÈÁÁcO쩹ðôŠµùÏÇDÈ^¡Fù¶Ý4XI£1¬u-zÈÁ5(E3{at½s“¿YáÿÓù2VEŹ­¸ÈZýòÑt¾  Ü®VÃøåcªÄ`´JfÖs¯j-€â¶ê¶r¥y¿ØiÁäÆñ+n4Î-†³è<ÛØ?¹°t9II6ô¤¾aºIíÚïüqäêœNÅ™­Âüô˜ … Ôû ²ðñî/\¡>åç2„†¸‘5ç¿ŒYõ•D[Ø넘ŸE—ÀP\LÉÅN •R²¤ì1G¿ÚŽ°]. 6!šÆøÄw2a·ñßݯBô‘pZ@§~‘ˆø•’n+µñÈNP_[\ š—TL·’ÿ"+äO>uüä ¨T_Ö´=Vß0 ñ²åOTP& ÷Ê@ Á wL<+-N•™ ¢Zè.êgAo­neØ/û'¶1ÁìN;l >·v*7”¸ü VfO¢b“¶1iÌm²þ ÷ ï놱1%â³ucÓ²mžÿ>bX84#Fkê†J`qƒ’7ÈIe¥-¥¡ï9µ0òn)µ¢»TEù¸ÃÊB`{ù&Cþì×M•£!ó=‹xuo+ ºœÎGŽ®(níŸEx÷£pÍ…C·†{(BÀ~V;Pn« U ;³‘T$Ôù Ö*D.´ã¾pL±YÞ`ÔÚß6Òw]IB¬x×7œÜ yO/¦NwÑþ=)SÓeæœ?£…o^…3ýgµ¨ g¤\¬0uËÉ°Zù)Œ¶ÜŒ=z\ÁÓÒjàf+§í›¢Ĩñ‰\L&)Œ´nŽ*~^s5Q\Y"Ùí5ôk#’Ï1šæŠús¼¤?{‹h¹‡/8²Çæu¾äJkê\›ÃçZ¸VÑ­£’Õi#ø½ÔþÆ4ÿ\4»ÙÒ# 8·´4ÈÓó )ìO6Ê™™›n°¹DÏL'4¼h!Ŭ>ªŸ¾×0)jQ¬ÇCev¥ul²Ý;DQÿ.’ð7´z]ñº¡þžR•{Ä“8Ajc·&_f~–¥Æ’“7w¯pßÌÖó5Øœü‚i`#6ú8ÚÅrÊü¡ þ)‡ ¾šäd®ûpÓD>$è² kFAI1+ VwãɯÏÚûHŠ‚ã³Oú¾Âä[`¢Þ£'JË•m—ê0oÍ‘.ìy/&˜–”Üe_v °]ß^Û¬îBµv¦. ų"Ûž+Š²§ùnÌ5ªÔƒjroô§ë/q³,ñ„ ·GÕ#µ¥`”ž|_~y.Y’6ŒjA®^,°ÇÐ,ªŠA€ú ÏïäéÜV¨´blëÆìËêQ:)ØNâ_œë§‡2*sóý¢€JžCho´Ä¤·o¦‘åTÆn¬4¤~ M'âî<4ñQ%¯ã…ìÔcÓj ½Üï=8çóõ?•AˆfÛ¼à é=˜'%Ï‹L×yðuÈѳ É Tx$i\,Ít…6m7_Šçn—Y×kØÓï´h¼¡Ë¤in}†Q:!šÀ¿QPT¼Á»W#1 ¾BwLWï]‘Þ>S]Lôq Ù.×Nô#ÙUt‹ˆ¤hLýÎ,]IÆ#4ó=tEºq$þEê4¦Iê’¸'`ÂÕ¯¨lE½Xe×0E¹®OB²Ö”—Z_laB§Þ(º#÷?Κ—›È©¬HìZˆzàhy-’¡’ü X}©t‡Š¤ÁaÁÓTK3ÂÔ–O?8¸kçº#LTÔPvk‰Õs{~Gsª Ök¾–41»e7U¹yú&ù†·E)Â1<‚î=*ãÜ}@6ÝÂþ9x*n?=Jr Cþô· óOåŽEò*b 1>9lùþê/dTØN¹¼öÍyC‹ÁÞký VYGÏYvV›6 S¦fA ú¾Cªf7Ù’´Òþo4B¼T49U陦=,ã ¾†GííÕù_U,Œ…Ý ÔuB€ÿ|¹È}2þMµd†ëƃ•ÏNfôn<:Óµ=)`Å]…¤=DÏÛ¼Èd¯BT”®ý)m¤ÆA­L!>ÝÚkëÊ+1pY*î,ÊQ íëTäzzÊøs Ke²À¡ž*ÿæ ßR»¹ zIð^Rˆ¥)AQ[Öxf!¸m4Þÿ4ƒ‚‘1bÓ…b´VÑxxwýÕ'×K Ô)⠻̫+ÁsŽ ÷˜‚¯ $ÉÖ@d⾋Är(¨ñ-³÷ÈF’KÒÊÍláñÛCbíCæiþÏëø¥œ49í_µ¶õ†ª#hóëAÓEœ4†€SèÁG] ³çƒ¼AdÆܹHÎ ï:[;nÕÕ¬["‚³,=¨²,‰¦ €›çHÅ´Kp{"t¬¢DÀ|f¹Û¥´?Xéã(HÏÙÞȇ5©F•ÿäÇ ÃÉüãÕ?R;—äOñ-•›õgš Ý,’ÍF蟾a°ßÞ²\÷®³BWM?q' âÃ}úŒô‰7U'® øj™‚™„ y2,ì”jL;ŒÉ’M· X_ýì/ïîí[ÔMpŽ5oÑ^Û9Ôòe3,Nÿ d¸ ÇTü àrU\ÌÌ­°¥®Š!1Cæ¬Ï@†N+¸Ë4Ï¡ MacÌدýܼ^Æelp£|]ÈF ÀÌÝÕ!ÞhIJH‡0oÑ‘¦qçu̲Šlºps3f“^ÐÃîrZœIå}jæN†$-ÜÃÈoé.Ò¤Ÿ-Tc&ôPûä­ÇX¶› #µfe&xôPjØ'²‚'`M†Õ|=³ZÃâÔŸ«Çö'¾vð,¢ÇO» f›€ÐßÕLÏU-¡IRoçÓÌm‹RMೆʰIz‰1iÊ4l§Ñ¼r¶áø•=Ï)ú-ÁOÆð€~8·“žM‹gsôtŠ’ b…RpÈ4*Ì ôö]ègCÅ~ÜïlCn8)(èNUW8=ÄèøH‹4™·oˆ!tP„rÂZ[Â~"ËLê•äüÄ1 ɘŽ:S1|±-6)_éÎ^ütlb :Í ËDÖÍ—häüMâ¤_š ¯Oé½Ûèð·«L7Èvñfl^Fá®þ2ê„ÍÓ¯¢Š&ù%¨MLÊʯ¶FêêHŽ4-¸ŠJÒIôúpóŠoÍ N†L3Ÿ¤BÒöÚq^êßÿc¢Ê}\iYD¹tS˜£ˆœ¦ûzÍeéL,rEňç?&ôä9›¬ä U í@ &V¹yÕtå=hèÞñï&6/h/&ƒ¼ó|ùÀxHt{×éë™Üki¾5eaÒ9惔%Š“ßábOwS~jríMt9’Ö*w‚²Oæ‘ÛJt>Yø:Š„͹Ƴö$`\æ7i;˜e›;o¹È›|‰žÚ´+”®oV¡uýáG$튭 ñüÂ6žYí" WîøZ E¾·¬¤[ð'\Øúlé‘É ³QˆŠNÇ|{F\50 KZ:å»åH³WzX‘\d:GŽqÎÃ7Í §ô´Ý‚G,xcbನ¾[FÙaÈiÛÎ-•[^Pó½Vý±ËÞš½fAÀÈ÷bÍKÁOò¢þ…ªÕÒº¢¥~íù±YöqB;{×ÅK•¤l-¬_‡+Et@¹bÉ¿þËïàîÜö©t9oûf#²ÙÖãáÆYNz¬ë‡xøô݉0àB`¸½Ì†‚t%ÎzŒ" Çt¡†J{¤¶yšF‹‰Çò ‡TnëyHÓˆdüªºÁÌé; IYäEOçdpìâp7H¾£¢ `=¦©Y̵ÑhFq hyuJj­®K§ï 5£k8uµ+ú “%t T=¬ÁÑTseD>àæÎ|µÜ€•}Æá¤ðG tþöZÝþ3³yÂf²’K†C0Öàî '+‘!;‰.=fá.ìÀ!oíƒo'þ¹ZÞÏÚßØm®Àí2çq×ñ Û …¿Ab(KP#j¤¸[(ʱŽÙ÷`RÓ™UQ8‹ˆšr9…Çf[}Æïô‰R…raéç^\«¾l‘a2ŠŠ´î‹‘ã'#¼nO\7’ ç[j®dkÚ&Ö¢ëÜ.Ÿ‘†7‘=#õJ¾…MÁà&Ëž7u<ŸŽ>´LéR4\ˆ<ßòÌ™Ú ™çHPS¹G‹©¥•¸œ.£wy&Ë>©ÙfRXu´Fm(Žd`,²sp_¥%J¶s‡°0ï î¨ h`RŠÒŽE0£vfF†J` §†9R­J¬UMêŒó]ôÜZ6ÈöÕü€…F:‹J—‡Óžòѵ.A‰g°ß áÓ©;"×XêZq@ÑAg®ÍgÄeTí<Ä1bIC }1G˜²¢Á§U#\•¹ë50å•UO:ÇSMUµ CrÁ&ˆ´Óýú¢p5ŽÊ}ÁøµdË&¡ŒatžÊ]¾Úí@)þ5qÎMïY-ºìç~KjŠ{Ïc‰þr­çâ\{ <:·ô‚ÖµX%(Äè7‡«œ6~ݵ(%gnõØRlÑ<Í’tÊŠ›l ·†¦!uUŽ‰j_ÜÚBy±qÔ9ÿãm"4j/äÛY”Msüç_VöùG@°„NåïáÇê-ć²Fµ'˜£Mðv`0Û•v¢UÝÅ‘C¶š=4÷’³Ð§ç#ŸÞKŒ]&óÏ;ª®œìç†;ÁßÀ»Nîˆ;<¤Ë·%µM1?MZ¥›_É‚‰Ñ¶+ä†ÈgC&3¬ˆ»ë*²mH˜²£¦eøö{¿IcT¬vѺ¡ØÐØŠF; žj䤰•¹ÜtÂm¥14†cd­ñ94ÞÿÎ ßÅe¨[“|¥ßªÔúÍù½Gßö9öÀlÃCY%Œã  U·Q¤ˆÆ÷a×&ÒIÀ1Bm¹nNð„ÇÉ÷ÞqSe× ½áY»²gÏ~n—?€ÿt±w¼ë{·}ðK[W&vâJ׎óÍä+ý{»ll¨ÁçéOYPxƒŠùC&n aiÂ÷U*dV¨QþÞÛS¤aMÙêRG¢ô››‰yËoYÁ¶6_ ¬‘Kæîå˜&oTÝö-°ä`{úFÝ3Årù³Xââ¼úç=–7]\ŇڤŒK<~6Ÿ´råIzU 쑾àÇc6Æ°T.2< þB+Ý/æ/[1æù±;ù p¾Þ¿P³uú¬îþFÖYág똅ÆW”°ñxªJ%ÒCluµÀ(6n3žfgPÿ×ËþáÙ$ ÅF%LeÂbää;˜ršè"¦g [ýeæ¾Øè´á£]{esä1ÄcÛ˜Ëf6ÃAL+;±Æ‰!(€B8"@0M@ÍTb+WËGDÒŸÂE$ÅŽ­ÂëÖ›âöö̹Bœ{i3½²áF;D³dtùèCy7# à–ˆ‹#˜=«nâµ»x@I†ëõ‚$3H®+=õÃFÄ®g.ÌÖº‡žyì@Ù7„Êîç”KF&>Zü$R¶ œØ‹”¾ŠVB/ ‘'v36—¦vt-äæ@»¬Éë×ûW®§,ø­úYÀo¦Öþ—Ú*™S‚넆;P¯nÉàÞÀòn¨îó߉ÇiH°¬s£ä#ÁG­/Šô,$Ù–7ce[¥é`·çA7§$7cLur)ÆoiÇrOÃá³› È‚ù©g`pcÞ¹®lÔ˜5˺+è%¬àuÕæЩó(*h-…/÷öJ±þ…¥5ùüߦþ/ÔÕËâ‡êÓS;oI^^Ÿ¤ 7Yƒñi° ƒ‰ÙÞû¬?[ AYt¬Þ_Öç/$ÑMøvZöNÙRÿÿ3N3"ßð?ÙŽ®™|"ÂœþЛäÒz'+èνóäó7C°&%jŸ0²Ý0áÖEŠMù™§gå4ð»EÏ?“CAë7#\zëÿv¢ áŽüCšÒžSô¶@GPõ„i\3³¦‰µÏ…,\œŸXññ8q+µy ™`˜vÅ7¥u~ ½–ÜÖuxÞÍYEF' ·¤pf“H “f ²×²%¦!Ñ Ŧ_bT«i€o¡`!q l1ˆeCªÒÀáò ØUK“r{Ûök[Ú]9¡×ƒ?Ü~ –åEf)—7'™Ñ²nìÅ>ß¹ÊMâäõ»æôå33í$¾¨fÉ}3dÂE ¦*Dd¨Úɧ9¸(<›ñ0(ØÓ­Ré%€tnõYÝ·ï'þ/z‡nžà·?½Õ5@||Cª´ÑN`ñZl Ì· 7—[}Û}çü"NûGOûé¬à¼-¾ѨØÚkpG¼Ã7Òð¾XInÓÿÿýÌoKª%¸—€…`²íwݼ什‘ñpjë|)1€š&Þº“MjŸ)<÷BæÕØWá_¥8‚¤CÀ¨µKòùÏßÿ³6ûÍUŒ´ÏWÄWÅ°«c2 ѾI~ì½¥ ¬±0éb)Wö’©t]í`PDó¢ ˜¬úK·j”¥†æ%þÇy‘¯§ûL󖆢rA¡`ýeð'–‰‡Çn‹'–N<¾ÝI1ncáÐYƒáZGbį)¼+£ôHßµ…Ò‰ ]>ç{'Sˆp5Þjü¿×î$Ö˜ûãÎ œ t­ ƒ­ÛOG¹Aßæõ§ºY-ÛÎ.×N1¦Óœ HXé«ü[fv 20éÐ8GÆÅÑ9ëœK²zÕ)k&EeÅ~ôÕŠYrã9xÌyΟ«".j#ûÚ5kËÐe,w"tŸ €u¨¿7n3ܨüEÅÔ A—w3'Z¯—™ÏOíŽ+‰襚l6g´ÍQÔzÑJ eÅE$½\’õ«=ö<¤îû§¬Á¬’jÞ)ånïe¢ð&õ«Ý”S쉆¯£X`¬ñxüCÆì¤G.8«Ý7ØõJ/”ŠÈ¢Ê#îI]²Äuɘâ*¢ŠY†…Ôèe¨°“¶NԔȅQRc—ÎF\VŒŽš(Î{_’Ýg^ç$7,¿óää*¤‹þGqSd]Yðtôá*.‰gèO©Éü…T}…y}1&üÂÎG‘ B‰È4ØW%EKuŸÔ?ŠT&âÀ=ÈB®ñY±bO\Aq{¼:äC—Ì:¹_„-ÎP]ú°¯p†0˜Qqúê•híÚUx™}„˜æl„êÃÁ°Û=Lô‹‹_…wu¿æ•è©¤ÀÛøE:kWôSéÓ…Ë×׬i–.Sοóÿ¥ÌÕc‰ããáýËE¯‡2N•¹½ËÕø&SÀšIðq,šßIÎ&¯0Sì)t„u-³ù_¦1 aÚ£F‘S¸¨ŸÔ$à$ˆÐˆ·ÿô\9|ê~>J`žÒF)8ë¸o –PhLÑÍfÜX¹„bÑõ¬­0iCý£Jtf§¶ù‘Æœ]ž± ÜäðíZÍ‘LZ ˧kðÓïáo/®Ošôø†P†*¸b¬Â§ÖKÌ¡E­áa5Zö¶âüØÁõù`å5èiâ<ìŽnº“=Ž·«Ñ)|¾!è†k)Y=š9†{:?x²y „ÙÈL&@<Š| ÷~. {Ѷ_¹èðË‚×qÿNnU˜Ÿ‡„Æ›²yýð1€ÚMÅ)‚Êj‰¢ˆ §ü¨¨åÛûn :á#¬êy´üª†°Á9 Ö$Áœ'>ª7@ 3LP‡°l.6§?@5 øR ­5gQCQ'â˜0:ØÎa”œíúŽ‰AWÏ1GʹNŒ-ÓÇ¿üi†qŽ­ìœÏlÜR!Xd“Ô6™ H­p5k 0*ôc¦³¦vŸL¾í¸ç  #¯<ÒI6õ0DnCäõÏ|ªï­ÐpèÆ霽¶ÎÝ òZ?È“Sq“iÜyÔ¨·-dí†Y‡ØÏTþRð†ÜÍ(пDR£¬2Y&¾ûZkrõÚ¬¤Q‘ž@F—‰ß˜ Å®‘çÝŠ;‰l+àèk¢eLð†O+æjóÏS/.±FŽíŽ`™,fº êDMsXÒf ¼R=Ð9HÆÝÊm©vg}Sþ—SœB*eèÔÂ’Q®ÞQ<…/ßFª„ŒnZ% à‘§#úL`÷×qžãó=›¿/¿üóëôgNªT\åkHeƒÇˆ•SÆ—P0Ør^7 ´{f˜F p^ ±Dr|…µöâCóËV+®Nß”3RO×å~ëõó:¶¡£;B!Ñ[h¶;|SGÇ<®|d§&»QUÓù¤UÄë ŸèÅ3@màÄžƒIªJrÄï×p[óËË8-\ ýßöpRDGwྼ¦ÖÚ' í÷c( ßõÇ@Ž×DMî0 3{G?µ¹²†”sìͽn˜äæ½”N’’Ã~€§1Þ’Òï5×´VÚ{¨7׸ìúÅë#…º<ò´ƒõMj$”’n∼Y§¼„(–}c·i‡C/¬H¡>3E®ÎiMê®;jqh$cÇcck”©Òð S¸Rn3³È ”僚7öXí!žw„É:šûmᘉ1xêÐ:³wZv Á~Lå·ml|m•Õˆ¨êúÛ€–!¹çY¾ÞïÙ‘V@Fü$Ñð ÿmŸ¿K}íBã‚ßù®ÕÍÎò¸ãâôÚ9#H§¿·‹ 7ù 1!š|2Ïêµ[ Š²P_õ[óÜ”óädëŠB÷ܾغ¡F}Ö 8w´> µ|‰ªàF|¦ÇWµ‰½'ÜÕÔJM±Tj óû+Z¿d>A¾Òµ]juP„öùY××ûÛQgš£}FŒs‚àÆbcO/ênß’8c¤*90¹öþŠ*Ž£ïöœñB/TÅ"Äþn©Ä‚žøûÐ&Ha^ ŠKÒ‰´§_1·ÔüT†ÂdlòRêƒÿÇ~ŒiÁ¼xvG†-JdÍs|¾(¿6v?ÌþßUûZ²g¬®Ü•,v Ø€À­ƒMš¶~W¥šÜän¯û#ôþ†Ôè2/ŽÝÔ]ÊRŸåtg=Z—¸E|'ßá=î½Í#gH¯¡ur?» Cn¾Önó×¹xËäÔ1Û•_²Ê:,¬ué‰L(iU´J H.gèç9éû¤¬ë`€š+[Ë„6‡gìÏaˆ ±6¡†\t§ÈaÌã5A*G´Zýdð âE ð4b'–¦Å¿‡lgK>ž«*ù“k'–˜³ÊìÔ(ç‰ýs£²AY9Ñß*LÈ4†nIQ¥¦ÛÔ–…;µ<å±ÔÙýLŒ:ƒ" ˆ=Ér…ýQ-Ç šqx˜6'  ÚZàÃZ°ôäXhÒÁÓb]â¾^õ5§I U¬Úl}ÉX©­í"q?"û¶!h»ÏKiT#‘]ü©§ýó‡¥æ{¾¸*ùξª`a×{¥m<˜OuÌÅäý\LÄÿ}”Y, z+‘pHýÌ銘 mÐ^®@$s½Ê «äð:q @T¨ð½Òt4µiE¼[@uP›Ø¾ˆÆ\Ë}ùæ]’A3SwÓ‹­…*h?xwÜ›÷Ý?%´º.1fj抂•¾ðé`“|¥æ*(FëóšO¬žì=%Jü#NÃõ÷!„8)Ô{UÏ“6ê4Oñ5›Ðpá8u> <í´±÷a²4VAv/­—§SQðõUÝÜš7=Â0Èï£1ï¹s©/l›¢‡Ó¢çìÍŽL!`€¾¥Š¤GUmùLV;H‰rFº˜Ú™çd zû̇öN‘oýZ‡= Yx–pq²H_Ì¥®™AÃÌTŠ³_•qúUŽK)¤óQ<™ò¬3,Œ)EJ5Ÿbó/ê`×'ûäåO,¢˜f ”Ý›@‡S*©ÿ:g§|ÕYÙû‡ý·Š´ŽV}x…8Ñi4á3 úÍ[ ¸» Åô¹›0g*Kþ=¿¾¡°é®OWkñU²m²"œ­DIDîh¢øHJ€xåÁ¾7å!zö†lˆyMá›Æ3a¨'¤-Z«So«Þºwu0€¨žGŸhýÀÒÕ]p«,)ÆL•Gi00-b±Ç¸bÏN@(=ýÃE„6F^úxl¤Î'í7.:å/l)sèaº¡òZ£ª, }´ð .XŠÅ4½¤Ÿ"3ÂÉ2 ¬¿o«ÎÚBÀæ-[™E½¾ž¿•¡rëÜ çtè¹îé“ñº …˜å6ã{â5è)LÃàëÉH•‚7ß´Ç…ÌÜë6}é\5€ê±ãÉß §,6ºN`]lBì÷)½eáùƒðe {™¢i¿•Lvõø¹>ËHê-Ž“gˆÛ^7øS”ÂU4aÁê[e6üõ,!G>¶€Nð=pªð{~-;úVò±8®‘„3K.'X/ö™P*óèJå¸æ<ɉ¾­j½º®ð¬eyëe{78êߧs¶ýùÄ®¨qú…Ì–æ‚=:.½dhÒ5É =Òc·LV¥#Î_'O€Ê¨±?U‹dPfø6Oí;ŽK/O-’çÊ£Íæ‡ì,qÇêO©š*ë”5ýÂ<2ç½ß,t{ŽX„˜xt®Ò{47útv5 i+Î9KrÝF”á¡Z™—@ƒ¤‹Œ3}®±´ê3í@PKf¨¶Æi;÷’‘\F_j7ÍözŽ©ÓÄqyx}j”\¨„³ùḽÙ„K:­íÛÍäˆÖÖ7huj>Üš[™Ë÷®4=*ä(†©ÉÚ;7í Ÿ\oÌ‹ ö§œr꧶²^xW’™`RÊRêÜ”r¥Tá⿧¯VªïÚjrË k?á¤Dñ>KbÌsî;ç <5K"yÚ £çI~œÎcÑå¿'g1KÏëW5´h )?¾Hò÷¥.«h %N%%_ȆV.œò&ütyHÇÑ ’‘Ô~¯Uù`Fcø÷‹yvð€ÁW|È°Ž»­Í ¿D\@š‰úˆÜä /SÒsÍg[ÂÐ5Ááßåžê÷Èã¾èh~i•iT²9Ÿº?Yl<îk€9;úŸyQ©“¬S*7rSJ·Úûv˜å ýÞs-€ì1 ,–¶¶KàõðÀ°>ª‚ €U:oÌ5™Å»a§¯}~y£ZáÕÅmB‡)*çòókÁ%3K>µHP©X|¬¥s™ö|I×Þ™¶¦{öÍêœÊSgõk§8BY³#‚¡püîõZ«ÉO§X˜I¹€AC8"ëþ7JÙ—ª˜‹7¼›tx:…Ï”wéÿÀ¨çnwY õcsJ ©4¾¯<¯œ¼ìçHUfÔÞìRCÕ¶+ˆ…¢çÚ]ï7ѼãhúÍ$ë[(H…űAÕB!àÚÃVͯ)k¤ÌÆjËŠ1‚GÏñ•{¼S¹½Ø!†]ÂÅStCqõÇ4¼ɤL…‡\SåôvȶKçØw¦g:½^ô·P2’_)9LÑPÂÖÜô×B'‰`˜÷}¼‰Öá„-g·]8áWg¼1«Å²ÛWD[Å ã•Éª!„¶m‡¸¢PTìO~¸~ž£‰Ð¥áRDšHס°ÕxÓC׫ÄD8[ £Û”lßê,9¶Šüñ)–µ¹WïšÔš Á\f<›ƒx2戡ýÅÛ&{WÄBM kTðÓʧžw(Éû;–bú:Œ·ˆ¸•Ž8q¨9 =~ÑóG&ç¿Å­ò؉þ0ŸÇä[Cð/-ïî7Ñ.Ö æ°CÊ—Î{)ü]ÝiMH%ƾk×y˜õÄBN~×ÙåJ>ª>÷•`AÅ.°(Wן÷‰ÞIý!P~§I’LNo`ò_ÑékÙP©ŽV_©E‰§ø¼×4ùì×ëæͦ{Z¶œOäÖÈjr Û·Ë5ºPá>Ø+rÿeD®¥FA€è¦÷YØ4Æ””§¶Ð)SHgÀ”1YÉÉôvãi–4„ëš5”÷õªD•ŽáÐ1©ÈχÜåuÀŵþŠú¢iÏŽ¨GÊ]KÞaí.ø{œ&Õ'£µ‹_U²Õ$¼M«ôþ‹Êx¹V„Qwü´\º…Ç‘àéãc‘¦u¯ª<œbû^ݦ¼()é$Ö=ÏF0†ØÛlŒ6~4þɼ÷÷1ÍÚ؉†ªÁí®§zI?æŸÆÚðUfƒAw›[$ä5¦2•M’ÁÒ´P}Öþ¨o@,^ÇDMÜUÑ―q<êïÑ%êìvÄqV+.»µ¬Q"m` $œðÜ@z?õ-8Ž"Už¹ ¢uQ1r)lê=žʆ¨YÏËF,Æe‚ ÷Ü´1Ì¿½±#á=ÑwÆh˜ÌK Rೌ)þ¸Uí4² 1À…ÚàÉŒuì,J˜1ëG6¹ñœyX‹Ø@}W6æ`o(€Wd¢¹5[aqÊò¯GYï®5½ÉŸïÍ94ôîH%¿ÕŠŒ#"äð[PxT®N§ÎÞW'² gvÓÐØPªo‚ O}¬ÝÚuT—¢§Çæ¡¡ýA`±¢\¥ä˜É%®’?+#X°àÅò«Âxè·bó.LÖs—x)Rä3`)¡YŒ°]á*'’ræ jQ N:Ïðg®é®f RF„5þŶèÛb{ˆ$ƒŸó(s+8Ýuµ;‡"Y+ë¨ÅIl_”<â78cn×è†6ÕQšŽQ4s)V)s+®cÀ`éoŠ½¿åìbû §•¾Kæ2°~Ëö k±ý¨|™œæšÒ,uÁ›òir׺Çö|AžO‹E[çÙÜl–¼_É€³ƒˆkáߢížÓHp NÕò·%8:þÁëñ!ö’a$ýÆ Ãßb…]X•€*öTt¥FÍl7=‡ øÔd>äHÚÛ¿r«À,–ÓA Â&1²µç¹}Òt°z)5­©Ôñ£¨çn  .úÀÙ)ˆL  >Q{VT‘sú!¼þĨHÞ}NbȯÑËÉî" üßk·™&ÄKÜ$¿#óÞ/1·¦t0ôC©-]’<17‡IÏŸa_-¦«ÇN^Ȳ×Ì4ë¹×C+»¢“RþrÀ~6gÀcbµÙž¸mß’ ¨AÄtz‚ݱ-P‘ŸÏë5óˆÍÛëƒó–ˆø ¾Å—aw¿AÀJzq !îG–õ<íÕSýÖS@ùƘ_ƒÜÂB‡%TÚ¯GŒdæ±–ô!ߣÛü - ŒÒ ŠUiÑ›íÐ Þ‘P§äªq9/MÿZU;®Tþ6£pÿß(ÿ =1ÿúW´ý(1áµÏÜ:©¾½w¼Ìc£* ´’•.4Ρ!üõ£¯/Œ³ã2 q3ðuÁ’ön k+кYuÎOÁ!íH8¬ñ­‰àZ4äeÍJWÏÎå%_˜tz¾ÎàT>Í©qê•]OPy¤Ì›Ý>”>}Ô$Úzý“Ÿcó—)a£X. Pf¾Ç=cÉÛó™Áü­½9?-ÂC?lxú‡ÊŽwÀÕzUW»¼ª8"!KÐ Œ®=ê?7 d@ÝÎÛÝ¥t‚B$P#I%Ĺ Q9ºæô¦õ÷“t*´OÇÔ¼£ÌÃþ•õ•fõÙŠþ0}œCÂç‹L@‚„rÛ9n f¸õÙs ^6ãûd¦ù£ÅÖ òÂCˆt’rÑ[{Ƕ5ð Qÿ²Ù#Wn¼ö¹}s:¼;ÉîÝÕk›^)ï¸ðh”QE`=7Êß·’ϬZ*MÇ|'WjWÑhÅÍ4°ÙîòÞÒWn¨e‘±Ó(,ØJÊSXhßÜ&»ÿ·&Õöõ¯wüÒ÷>£qCÅT‚{Ö}t;:S(Q¾ ³ù@PF|:ìó°€çÚ÷9’3 [ÄÐëÌïäþgîܸLq¾DLg×Ó­®ÌÌû0\šð«â#ßÃúêûüp¶„Fjäæ¿t1k1¢*K‹'Œ»±s`GMà@¥SDAB†jÑ4ßêß”lF]H0’ §ñ“«dpD);GVIYx燉2/·ût—*¶›À:7[g0t ª¬ l°ãT *Aî`‡†Á„öâ /‰÷5–£K×à0 åF׿º¾@ªš4õBÒu¿®u#2´ð3œ R»Ì-å^ÌßX¹Ö(¡nûqx•¿Íܧ~ÎÎW" …õ£²µIB™Ý8ž¬Ê~Qcö¢+•+)Ž4¯ÍƒSéÉë ¢C”f͘Ë¡M+rî ÁlÊ"¹®I‹´‘¼í"ꇓ{¬Ú–Êmò(€›#ãšš>B¬ý°“îXSlVá®JÏK“ª¸"ªÈ“äi¸Ó`Ÿz$È Pz÷gŠr“ú²¿˜ÿi@3Àëg 6•¡ÜiŸd€Uø(z º (ÖX› á.H‘øsgtd«»”ÒÌêêÐÍ÷14Žûáô ´õ,¿©3¥++­)¦Ãô|“ëA÷²Z{¢Âf’.SnÜÍÖ›/­AÆò–p²“*t¬žžbKäWÄX‡ (<±o}ôH(ãpZ$´ÔÝŽ\’ÈdÌÔHÏ d¼tY‘Z»íª­ñ çp ‚*j81ôí¨/ƒN‡Ö.d™Ø~há:²Ëgcj±>‚Á®‚TÆþ{žL¦ß!º{D¿?:& Ÿ´¯ªVàF½Cèòs›¬ÎY‘Þ|Ÿ¨tþ…WŠ%9¹sï÷‡Ôß¾“þ*s,nÂ@ÊŽ¢»—3i ý|Ä~Ê d¶1¯žY/­[rÛ9ÊäÖi,,ºƒgÕ»e|Ií£qÏËØ4i<^¢¨$U8w1jºWß,½[̾ìƒ1å À"TdùŠ#ûš½1ïB%ˆÌÏÄ\²Qž´Cžñ7Z>tlã‰Óxê0}aT}§/:3;à;IFiµ& i6 6úÃ2¬?L ªôVæãîÆWÞÿ¦+kíž.¨Ué´ŸýªMyzëƒëewùÞ‹Ê_¡˜.8bš•‰.År$ÒÜJ“ÑXc¥? N`L9œ ýòf‹M«©üTª¦¡¤1«-Ø'QîApÒþúUDaƒ?hÊ’Ô\Ͳ%F !7 r•éÿM! >«Ã~à¶Ð<®Wv’WßõLR%/Ë…—^ÝÛ5ý«Áî¤Ôµ”ËHÜ– ¨3À{¤ ěϓ(ïÍØt-U›©âÚÏK"!~€èǹ´vW?¥Ï0­U¶ã³—NO–ßàé¿“SÝõMøu€‰RÕ€& 5‰KsÀ]å'DSñþPÅÙ$aiäê^Ó+w@|9"Î|Y5b˹'ÒÖÛÏ®n?Š|ÆɉcúT>Íçܼà4Œ€›*4¾ ºáX¸á:3€E·wøKVPngwš^Ëñì訊= s˜m§ÜÕ#:Û(T¸'y-mfÅñƒLªä/`Œv7`ÞK>¬úT͇Å„a8{€ý¯<ŽîÑfÐӔѡw&ÈÎ,äAÁÅDlî‘{q;•°VÙq¸·¡4L°kså!U¢‰-0¡ýµð àF@ôEu–tëêã3bÕžœ¨ FA£ÔÉo@—1›ì7uKtkñ苸"Ž gÙºµÍÃIå?ÙãñZ2O5êÐëO—v°Ÿü']¼ ÝB¬{.ŸA%jÁ¼‘‡Ç2t"_ÚªP•™Ù°Üt…ªÆˆÈ¿}ÜÄÑ«v‚ƒ¦ã["ÕÏcöÅ@‡ièóo~jI ôº–wý•D7‚?MȧhöOíÑl*}ë¾/`‹ñý„mL?áwŽ¿ü­?í, xÄtªÓŸ0ÕïÄ‘Ç$ UBy[R§#ól{ž ˆ«`)ï³cC#,ttù•›1o6PÚÙ)ÿfXWa*ÛÉ–Š3™ôë ƒ7=„ µ iÛw%ƒ˜»ä›?ÎU2Ý·:ò¸Óœîs°:]¸£ªŒ*V°|ˆÒÆéÇå#,t UQü~_÷æ³æûiV˜kQTá'r£Zêy¥&|äë?…ò, lç‹õ6ÑU€?Ù* v„¼ž\äíf’ó40SÅ,àÔÅ1>ƒœË-ü˜ÂŽÊmçÙ1ÕS–µìiE#Œö Ç©î…ÿ˜Û\æ¼Ê…„çf˜PPdÝhªÎÐjW´–Ãf/ϤìØ;ÉÜ÷ |çÙ+*÷m¼ûœ./,ð[ÈÓjR™Cÿ!}žKÛ{^_vFD%…7(ëí1li§Â*€cVLÈ6Nv¤6—ö¹t„»(È$ úsýŒ |Ù‹ØÙ²ÔøŽ÷ö&Ù—'Òqö0.Ê2j—ÓiíI"ÎDšÈx¬ù= Ø™Xךƒ4þ‚rÍô`;Õ°@ª—9×1ðz%ŽáºäkóôÄÍ{›‰Ã…+í8/ <“úvWßÿ$Ƚ#òÄgÔø×Ú]™ÿ<Üz2ð EJÝŽ´¿‰¤¨"DºsÂ1R4Îl¾³ËBëcqèŒCÎÆY1$%ÿ[(u˜cØ¿ÌXË3¦3~â6T€á!sócs²DÆÉ°vO’âQ…ž(Á²oäõžÆµÔíµ„ûø¶àõ¤}kŸ b°f¾žáÚð-iøÏŠñµõI¾-öZø%ššÛ’Úü1ø4*[ƒíÄ¢žžvF©¶ocz€ÊQ伺þë $ÔIç ]´<~Φ±˜ñ¿ÿ-¤ËÁµ+-Z£#úÿœ“dç§ûûÿç”»Udý<1á}›b~Nþ[¿ÝìªdyÌõ,Þ1ETôb¥Aü<Ú³7/ûBžÀ!µ…[‘çN•í~^TŽÐÃã@§¾É<ë˜>o%ÅQ°¾mhŸpŠ¤‘*röŒæ&¿Þfi“§˜L]ðg!d¦á–Q(Whèâ3Y¾OјnÍô±-tl±O€ù¬Ë–¿ üîÅtʼCd­øq|f{A—å§ bÅ0â,ÇËÑ"´µºêç½8 •®NÎîÕÉäæR†~ãý+€ËFHŠˆª³CÆÐX”^ûE 0q|˜V€” Í!EèÄ¿òŸðï¶ïlÃM*ŸfÍ~s=Š± â‰„^S/TˆÊüÁ‡®‡e¥#çã!ÕÿÊ!Ä¿æJshG Ä„:P¢ækûp­iqˆ:b :µ“hn(žcÀ´Q_WŒªÚÊIS‹ ? ¨þΊ¼TLKîwRâ[Bvx&1|Ám¥P ×Êã]ÐãÛ=xã.A iÏÔSòÀèrÔPÌ j Ë6{Š½?xy¢Ð,Iƒ×¶‡Á_$Ž€¦ú'v,îíxAomHª‹qE{i*N1Î4 õªÎ¸IF=0ç{¼Ë¢ú€Ó8¿‹Fv ”¸¡ ó£ïÔÊoö¹º?ÉÍðó`'˜M ÷|ÞˆÑzÑ¥†ÿC ÍÙqÃ’V‚ u>;쇴teLÿ“Ú–Ñ=Q\øþezˆsŸ{\·õÈžp¢Ö0” &c9Êý·Ÿ¦åVfµÝ) ëK&/1r`¤ Ž–<ø÷ÀÀtQp¥l¿ü)É«’©dx/ŽsE„6†íP)ÏßaÁNœƒ{¯Îɬׯ\^e¤Áb43|áoб´¸·QVŸ0«ÚP]Èæ‘JGæšF©à±_Ó¥";›È#‹1³chS ‹–œ yþ7r±êìXf.ØbHžZµ­¼aó˜tÜ´ó§I,œ¾BÝBU\^¦q!­Ãàó¼Lë³4‘aAu`H<íç ü“tÚ? ƒ›q„–Úíî¹ „'ÆOâ{[i{k3sиډ}g<<;{Ä„ì~pÅZig7ÒéÖÜÿ7!¥ÇŸ:ÒàçL‰eòûùiô¿ÉÃeÀÓx0 ëã˜Õ"Ú$&¬,‡6¸D7Ë‚€<»Ù¥ü-‘¿l%ôªö›ï¡#÷iJ|äõ2ál¹b)‰s$ ÷­¡?Öc®®¼IôÒ/N(æ\—sxæ^+G³aþONJL:»ùx"ÊKÓ|×lïÉ‹¶‚˜hÚ2~jPº1<Óí>Úè{Ä á ¹ÅU{ È)ÒðkW}bäéÉÛ »VòI4ß‘+ë> Ÿ`8¼¡á­ê.¡ B-%§äQu¸lÞÙ»ˆ¨Ưû&ž9åÀoçŒGúðp×åï§D+ÿ,4[û\~lyX=÷ãˆx¾Ä`.fd¤á½Ž\݈Ùâã0yñùeª?³yQsphÛÔع•ÞŒ“~7Ï&ÞL9AU$n€×$0c‚’5åNrÎ.í”2óä0.œzJ!ð&·ºû’¯>´Þ­Ç{¹OŠ]‹‹¸Xž>ŽO·W Duä¯5Ÿ1â=’{0»x÷ßÌÜyŠdYVDbÍ tátƒ¶’Æ5‘P¾Ä¼ÚÏØi•;Á-Ñx\¦#!‡Ý'wÉîCÝ­lßõd–ÐáEc›¥ERþR=8[àñËHGVú…5Fà›¸bpow¥U.AËys ¢ÁÚ+Ò+Qéo>8µç€¢žÝ£Ú§0e~\¤f  ¹JÜ¢F+$}qŸËXòÂNÿ¸Y Í¬ Û;¡ŸÝgSÆKXò‚EÙØïE†ïr«°YAÌDª:˜.IÔ3Â&*·o)¢ó‰eg~=ÕÝ®ÕXöÞ^~Æ°ãy(^¯:îˆîa»òÛìZº€e´6hŸ8“˜Cy~ƒÁo~54'mêÁÒ^]KB{ÐîµÑÕ#¤Ä`O×Ôv¬s¶MýÛ•Ó‡x ¬Ê®²Л00âóç QxÚIfV bi8Û—¬"Æ´%t^z©hE‘P–“N3ûî»LpŠÉ¿{°(Úú™þÖËþSédšP“þ)ƒ»×Òóþ ¤ÈÔFd99¹®ü#:ob»kf×Dk`·BpçÞËK –`ï-}GïÑI‡|;Š.ü÷çát€Ð^‚õ”©Â péýIKÂ¥DÞQ¡U#ù´T–Yf²XHˆW(c3jx~ï ”õ²'·d]¯H<‹–Eåk|òë¦ÍÏCŽ pR=.ðܵ×T k ZÒ`”¸'óÅI7Ámƒ”ü'“Íæh¹¥)è5JãPóp\ê?0}­BžéP …{ƒ<õ¦>$±qì|S6¸êãQyAxˆC?êHûQÖ°*¼B!¼Å»¡Ì_ð5ÿ­Ç$Hy€‘ñ†h%˜Ý’ìdµ!‡>_.Ùã͆½ çßã:>>™kòtŠ’ÝØ¡òpHüøzí´áðϨì9ìsÝ%œDz·v‚—c£n½žùÙ ê^öK:ES{¿ä„G_²‡áòP«›¬lˆV¹„DÍöCC5ºŒåÒK^1Çùe…¤>]F½¡O݈‰Pf2E$/H¨©pÃtvUæíþ¬%òîíÌ_*Ú13)e5ÒUý?NŠ`&2êVsP”:ÝòÙÐ7 9É¥ûV«‹+k'{v7>ÄχÔøoÓ\H…U)‚ÿŠ·;ö¨½J¾5wÒ7Ä‘2OF9*cL&£l¯%’Cæ¾ÀùCe3rßØ2ªžm§è÷€ÓVQ0B0fðJœ~ÕnáÞµ4 ñ4ŠðÒª¼§áÚéÒ@¾†°z¥ì1Øi$»GÉò£u2ïý¿¶„§1½ÐOºln7J\(ØŸžÐÄ·ˆÖ`ª8PZßû4E/ÔšBoѪÙ_&‹©ÔJÀ'®“Ç‘â—Á‰ŽÌ›äËr£W®Gð÷æR&µ‡´ù]ÝBìFß‘Ö¢ &s³/ñ«1±Àš:&]Ñl¸ÆRaybðñPƒY¤Ùv °áv¢p"ÔEGF¿ªÝTŠ*\4‹ðEŠéˆ×ÐiT‡¾gßïé Üþw@û—“ª1#Ç'©a“R˜šÌ›W•À±‚dÅjVžW £N¥ó…û¢Æ*ìGbb§Ì&: %ìÛw)ášg âÎǹÁ*R‹‰\H aW‚VBõ)MgKC¶ ´{‚§$ïþEBÿWßnÛVÈÆ0á„°V‹ããj—0í §`³ ¿íƒ¬vŒS5u$fuÿ s˜³n”¦R‰bŠuZöíNü¢¯.2ñ²ZÐ2p|{–aàkäçÁZejÔ¿Äê®Ú´ÊkIÂô·'1ײÌV\…“c'æÀ’ÝÞ®ŠÛ¼HùÉï®g )¨òpi»”?›Ï§f±XÎèÙoïc‘õ¤Nó?Sìann'.ÿP¥t¶@¨áÚTljŽzIÀr=U÷‰%È{ÙÕ [¯;æJ ¥ƒ†%«ÔŸf¦yÀ¼Dþ»¸ð³ýof)îR=Ä Üå)9Û2¿A>ôôÊŠûA³º·càó¡7¡ýb¶MóõµöÅà$¿ŒYÝ|o1=‰­üáIj¾,sn‚ôÅaû”ú¹Ëçc©pÙ!›¶|I¤UdÿSó铼.4×cšZ`%€yQO§ÃŸªÉ7Ͻ8öR\µ®3N†ð24®3DLÊ5xbÄ~½ƒ7eÆzQ6@rõ†‘Nø#ÙRZÜl+˜ÛK¸-%ÁmúØyå–è_6ßäå­þWÜ“tr›zúVßìÈŒÁ‰‘È®‚+ˆa1vEsš/åµð—–*|˜}yq7ë€pg”ž l/¯é»*¢AûÒïN_xîKW¤·Áh´Q2-HÍå î_@•½]`L‰kW‰A­'¹÷pÂ7ìljñˆ9O>8òô_iƒâÏö¥ðNÒ]˜:˜äáÚí=å–Ô‘ l!0‡¢ö›d\9¼ÈÏ.Þü=Å<Ò‚úm²Á“èwk¸uÒ×hwÒÀ“û[LCÝQÓOH¯£ÿøOªT殤´i‘)ÚâöÐίºõ3OJàq´Ö"/°2c*gŸ=΋Ko'±•hç,Ñç:?ÁäOIf *Ùë„|3¡¸ysŒ‰¢ôZ{(¼¤Òð•Ã›Õk)YȲeÖ$ÀŠ‹ðWÖâà©ÐÌv”¸8 ûRÓ/% È¥%F!²Ø.cŽ‘–^AÖxüM(;‰éH¿2ÐA#cr;DKúƒ­ï×ÛX~i9覦͈¤t@aô°@:ç6{Á&–iÅÈÁÇJðGeö›é€÷ò1þúºÏÐË–9"û+ûe à83],`&C¨ãï;‚îxdÐ!0§¥±uŸŠ_ Xt‹Ãú~æÍFí¾ov«P?;(‚r ú¬Î`–âÆÀT©¡[á1™hGú=´îdÌãxèZ€X$VÌzÄÈþM‹©AÞ¸tYFñâÊädå¡Å’ˆ/ÝÛÎûãX®B?qîžl™xÏ‘»Ðm O1ìK37ɽ¹rUžñ†/™˜À–è–||²Ñw=RØB´d`tî¤nå­µ)4ê"¹‹.Ÿ‹×šÛXHãÆ&ÈŽ×CÆùŽŒ¶=¾Ô7Õ¼…ÇÃ<ø™¸ä¼Pjk„‰cž§BpKãÀkš~"ÍТUXç<¦8 2Üüüñg @s+ß.A/’@[S±Æ; T˜T‡ÖÚ–©^µ„.¬2-$ÿÞ¯JáWÖr\§£x¶†$“¦ë*ÏTׄŸbÉHBþÁ>í¥€™ÇfÑÄ/¶‚VàìïÎïG2ñUîôPoFªKæg,Ê»ÐtÄ_‰ëÞ,5 Ÿ0ejìAÔ_5èÈyÀM)ò˜ÇSÝí¤ÏÔ‡à:/§õ„šZ®±~Œ3¨5QØ8¨N:”ݺÜPüæð÷ã‰ÝµÑ]YÎltnÁj ÎvÁ•(çj†""¦ŠÂИ„øIaM:Q3Õïâ]b®0œK:¶“·°m‹ïqb¼A ’A[R*üýtåt;xxÄ£oÒÈ( ÖᛡHÍä6# þDÑ•ïž1=·.œˆ(AÍŠÁòÔ‘LùC½÷¤ÆEt6Æ/_—F ÉŒ]ÅŒ©w2ÉèÔñ`É‹ôUP R(p™p>ÍùK£î™ Ñï¸B@°‘áÈZ8ˆM˜‰—.;ýÔ D*gy,]j°x$^›…$©=ÆÂáÚ2$ß© PÔhäΧ¥–E`¾³·»¬¨ûá£ûÊáô?Th†ƒMÚHæµK9âݶ^vm×—"B^ôÔ‰lo¶e ®4—ƒó<®Èç¼ ¿óÂP±» ”Ù~V+Ù’csïÆúÑŽešf­dP9v<åmÇÚ!¥`HصM?ëiT>Dé¢ò~ë¨êí3ð’i[è6^5íÍ02Rx®Ï¦¯ 1ü! Øã—4^¶Ñ3uÊ—ý,´Í¦éÂœºTû†­þÛ“—Ù¾F@H×q8o~¤âPcÿçÐ]^>Yù,*Ù›jÃÑÛ}‰éÑHÁÂCs] 2íÖù ¯ âœgÏUI*ßé1ÛÝ C ãõŽ“$'PP§G6Fxêi–ü ãÇ–67†;óCtÈt‡„åQaz;=ž8Ð$ÖÊŸ|L yäöLoÌœ¸4£ 2ŠvÓl œD~aM7íŒü5\¬Ê3®ªÖŽå[œ—¯ïëýàSXQwN’Šq¬îTÖ¹,ZÆÖÔÃvûYRÖ*ä‘h)Ò eËîY®ˆ \…ï`MrÇþø'Jyð'“–†'i,ržØöƒ‘÷ŽWký¹Ó?*† j9è(RÖ³_ìz›’ !qµîXöy1~¿G˜ ÿjÂ[h _”I=à˜™ëÇmw–{Z*%¦p’ò¢¢òh&ÛUíø‘>Ó¢*hËÙUj­)ßSÐóàPÏ |NÍe´ø_ç->­p|Ã!žÚ•³­Hë&¥g+šœ PsÇKè{\| Íÿ&Ø°Bb7¦Âb° Gµh‘Ó4âò¤Ò‡¸KæÛH¼j~;Ø 6Âx.§Sà$§UŒtC\Mº>è¸)v[eÂ’kütöàä ½vòX á¤V»7ä\E„)`Fò 0Gª<œD醃ôúUºuŽöþÎS 8ãwjüÝøy‹ŠåèÌO n—ÛÚá_è'üÃÖÅ‚d ÖÛ™8~_cù,¨öžš@ÀQéW㸭jÛ¯µè"’“ëá ð&;<«}Å”èYzóQìwkn ×.õêš—Òï÷ YlV¶¾‹tÖÈ5ʹ5Ðø§U-XØt{®‡…9ã ('t_u²eê†èkóÙ¿ á[÷âñ§–yu2ບc*œr¨~Cb­ «h];‘‹ÛçßçÖ9‚ hþ¨p>PÇ• UÈ×è”ôÚŠk½ÑÚËZµ‰›ùc $4ª(&ß?¯ŽHsZñáËEÖ=,üxˆ1Eõeš‹H@¼šké¢oLVðòuQ@X¢)@[¦5û 4…Uù)yÌÖìæà^ö%_\àp™±5‹Ð‡kkAcžÖËÙ^œù’ŸMÑ\sBEÿ-vVwŸˆÛÊã>.×YÝýIÒì™PÑ•Y9hñíôæžwòÉc¦¼VýœâDú”#öTë 3²ŒEÈë ÔNcq+ù¨`¤/Šótüuº×B÷ú¼Dæ‘BJ¨Æ_¬ðõ»L¨–) *p¬2à™èÃKByê»à¬¶æ-3´O/a¨‚8ñ®“›¿Lè‚æþN2‘!*?ÄœÜO÷8I«fXv¨úÉs¤Í|q‡c< ƒ3 Õ¯Þt/úLD \Ý=Yœ f±2@×X®0´ÆÍ=Ç__ Q½G™1lPLÿsà Ùt‚rGµLô‘ßãÊgÎvU¹jiÊâR_ÔtKÎND¸+fèå'K5uÇÓ:š,”†ï *ž€˜r5×Ay50ùÎ…y­ŠªbÔ¥ý© `°" ¦Îî@9Ká¶RwÞžôM/ÓÖY­ NÀC9ÜѦ©43&o¤6 ÚWØ·™{NTN¢‘+´%¼´!Àë·£Û% ÜÙV”`õKÿLв¿¨ZøeÉ[¤ö*Ÿî®…þ;»ho‹î‹ã»9Ƽ®:ET¥X,ÁŒ_»R7$¸™3YV7î:É'3Å…)Ulo?‘ ªºhÏ•ö¡{&l^q=™Ù»5ÅÙW"EfšüI±„Ó·õÊ…5M¹éÅájÐÏÌ÷‚Ȇ‡cøL°Á³ètBA÷tPe]Ík|%é¬úçlõÿ-°}ê–ÍäœÀΊõ ²T¹)hù×_öU‡m†ÎJY,æƒcÈs0’¹(+T&yâèQoï’Ì^ǞŹ‚é žaó6ùOaàza^¾$Tg¡îuv_­«;¨¨yÆ“z ÇKÐD¨BO{Ú9d¨Û܃:¶ ¤X²*j„…Åü«j@Þ%#Eæl…QYKNLPj¥3’¦wI÷èïŽ@Xœ÷òÉâžõ³°Éÿz×g–Uû«/õõ742N>éLj³-¼îèêX#¤óß„V·ÊHxÝ,®KÛÈ^ã¥LÛ¤©fyBôÀ@½ä „1ÁŽÎÙ” E¸—vê‘äƒz~:‘¡ˆvÑy™±>«0æª>³a ­6”=|ãsÃâKÇØ—ÿ=bÊwÍIúºv€¥.ÉÇ°.ÓkVao•{9Š¸ò')®O Á§ý׳>#ÑŒëG©m„ÈNÊ1Â)`o8 .¡¼lù\*¥¯ªz~õ1»¹âplÊ=ÿ ©ÂÅI#T1“Üq‘q‘üû~20fžD—1ŽÊ“¦ãuJ &ÖÙ+¥q ’¸7#G(—*ð‚“ÔÅs6.Y?ÊELñÐÕg3§FçE}»ÅYô ^—ó ì®;ö}U^|/¾ÊF ÿž¯éºdƒê¨ÄáCWÛ ' òUGn¡øÙGã;ÇÍÐ’]‡HRìŒ&ÂÓa£ºóCýæõ: WÑq÷šïÈÅŸÄbü׳Ë×Ëá3‘ẛ™Ò¼p9·áßÉ LÒ·lö&ó&g!P„÷^õæ£ÚùÈ·hŸŽ$X¨âM1þPÙ‡«yE¡×Áìœ>(Qõ…F…'PtxŒ ?.†|¨P±ì§Ñ{€:<êK"o}XkUaðÝMg ê;¹ûµ]}Qâð횟ûÖÐ Ðo’sÍ…nñ¦Û²³K 5U­5 (”{úO Û\-Y ¡Ÿ~žË4kùp1*·e֨˱=Lé9îýôÞ}Ë\«:ªÀ¼©¿ê'ÿ¥Z$ó&÷*íF˜K  ãß°2+Òƒ! ú¶kóZ*:ÀWU{}X§û?Œvåòã‚fÕC®Îæ‡ÇŠÑ-b‘šÝ.#r„œL°£r–ogDñÂÅø”Û!NïÞEÆjÜüR°ÊÆŽ"Eœ?Û®¦N¾…·…Db©pÒ*ŸSªŽ?‚ahÎↃ,˜ÑÒ†¼âïƒæÑÕ '½Zîü÷Þ âŽbÌ!;›?úÞÿÛnQµß"–BäK.éílìL†,úuÎΤ¨ F•G¤¤å~\F6¤xâ•Îˆ9ŽâÙ2ŸõßÙèc¼1ï¾eK6=Ïu!ûáªòSõ”%Z TÎdáñá ¼¤tu;U*Óâ7à­ñ…G¿RŒðž5N“Ds3;:C½Œ»1]Å­>©$÷uJ[õÂ1(¶?˜i/g6e…$Oö/œTy¦ ¦ì §[ÄWcÅ{Û™â¿þZo²i¥ÀÌbï#žÖkgçxÝþyî€ù–^¹t¯¬/š8ûÅÝ<¢HfÔÐ+ST|ÞW¹ô¶CöÀì¹} ŠðPÎÛû6" ÷ñl­®¿‹,^+œsb{B8sL ß·ã®@†ƒÎºtž7ßwŽ—üôìŸ3Z‚ýܵœöàôçiºð¨ÍæÐ>B|‡|Å"´§g3ú]-cN0Þþ­I†-°^²+1Š{#š°i1èd4AÔb®Ð‰J+wÇoúCõßÐùh®Û•;>þI4(‡Öu.=½ÙÛ"™–µ€öØŒ ¡¶œ°øjËè!'¨!è#?f(o§²Œ§÷ƒ!¢vX :ªë·Ãy¤sò܇-µˆ0d4ÆÍÝ—ysiíÙßì½Wfæú¥s ?ëP£l÷ý×ÎI·MW¨É«:¶D<ÏÊŸ¯’Æ° 5“¶jëEôé„E[ð Ƭ ¤-å‚›’3˜+ßùâ'”è,Ó¦c$xɧg´âý¨×'Æ6hÎãebO½ ÷“qÏÛD’À1ç¢{uãP’üJ;ÃWÐŽ2¶úéòc7ÚÖe¹#™rŽUÚV07¡5tñY—C…gœ`9SÐyûwqWlR~h99êîÜn–^ûa]`‚¾!…F cÖ!dUl`Ñò2ÖÂÄo¨¡»ÿ£ÌX¢ç 0ÉÁ>àv0sÓûOE=D nåÉ/SóW%X,üL÷©tƒr© ¡4 µM,ÓÍÕ2ÜÐÞhÈãš+*´×žáš^p¡ÒÜ=Å%´¹ËŸÑ½ƒ‰€…¨¤6!È wˆë^Šæˆ'K٢Ʀ±CGÜ#L×!õõ·á½ŸL’槌ÿ³†…QP‚êò<µ±Ü<é§r_/¥ä±¶µ†4m@¹³òU[`ÈO:q+1_͌מ÷ßB˜”#–±a >>OÒEÛöÙ)þƒ×®êrü;=üê$f­Âl­aÇЫ‚óš¹ Mæéq¦eëÝŒ( ï’tp²++úBzwÓ©Ù ²x¨ÛõÑ(òBqDÇ=Û¹N(þ‚7¥‰ÀEê•4X>ƒ=ÔÑÄò—Ç$cÇÀ[Æ1<çV„.Ÿëw¯aoÏ:ÔÀ…H®Gùøesø Ûª‰U°myag³”pÉôd(¾äôQ÷ƒÓN~gj¦Úv Ìÿz¤¨ BÒ†e™¢7#î+ ‡ä«!__Y‡O}TŽÿ¹æœj‡È¹hØÃe­ÑñÒœ{Üyc¤Í«íÊ´£úk¼Ò¬˜ÆBØ·­þN¬Á—0AÓGË™o… Òèü©‹}fºx2Þ\Þ)Q¿8õxx¶Ig¢‡ Ÿ|Aõø>íWÏóO‹êôA“×bCë0Hv¹(NàWJþeØçY ¶åŠ°k†SÞÄM–adî猒羧p=).j”©ÚvEϸÒsô»,Y¹€áø¸i G;jvÊÉ}PaCÖX•Ê_ÎHÈ]i¡¯£-Z£³`º:$è 6/°ìÛsÈÙœêr¶fY˜h ›Œ¡Ú¼‘ aâœ&Ó[í?‰!û ‰ìŠ“ü5§Œ„'kù®àA0»âÏAÄ´[l!òš`ô̈pÝoq}tà,Ò]êˆdÏÏ[µDãþ/±õ^SBõKcæÙ3í¶¡®Á\ÓHpO®ð:-ÔüCÂzRªs!Ç&¤¢ /z.±¹©µ'vm–!½ƒÍy¥r rA£;ûž¬á§ªÖÌ1—þ=uݘé SuäIOðC¡&Wq•:g«½°xô:!;Ì“;k¶hóS7ü­¯ÉU2)U-aˆ8¡ S·õo Jô’a+X0 Ù¦ÛˆpöÊ{†LúÖ¿Ú#›Uh ŠÀ++v‚gÓ Û`†µxe´6·© Ó<Á2ÅÓéã ÁLv9Oëa¨&{åh¢0UV›rÞyplWk K[h¾PÆì×ñ»;e¼^=—g;HĪ¨Ó<Áù;ÿr¬jÊœœöy¬ÔÞaK[>¥ÔàÏãJST®A%‹×~*†ÿÍÚÅïuÆ/dZ‘}<4†¾ÆÙñ꺠‡ôât篊ˆÅ2læl#EWTùz7»³(ŽÒþs@Ú¢Ì ]דH IË°0Þ0‹²6¢ãB@(ϺÿBDôØéÄž>IôT ûHÍAŽËjIÉëc! z§Ò«Î¥$l ÅRi1ºÊe|Aüô« ‹Ëœ¬©Þ¯ ¯–_ô"}øwÁŸ}zéö£’_”–ù䛺N{È2õc@¥õžE‚jWÒr±ì&ÇûñºÚÙ¬]éQÜNî8¶cv—d<¶á7 ¯tÏ‹¾Æ&Ë™°™ô TYþþrVõ°>ÚL×uI KxÜ™#Îw)”'³‹EBâŠ3ÉŽE´1 d–¾‹*mo ÛÀJ¦æ÷˜Òšó-ýdD‘Àãì2îB—ÌüÁ'|*½R'y˜ÕrHYæÌ$ÚIy kÞPQEi=d8Ž?š÷'PMïZÁE"•^â©8—Q9 ³;¸Ï¶d³ëâR’ÃÂƸˆAÂÛQH¼QþmÏ¿won³©$Ål{O’.|(üƒèÇBÏ}½À!óÌÕ}©!|°+¶XùsCÖ¿H¬gÔÅ°j$ÍÝoö¨ õŽ÷ æ/_cå˜Q(Ç,—b>Øšµò7q.êéi':Ì{©í”iLLÒWùv™ƒ³Á—&ª¦uüªKêù;ŒÅâ`Wº]‹ I^%-eaA%bBàîô>ëb…âúû+¡2õ}¥F0oœämš1R½¬ŠJ`]€ ^Å#Ú1Øšvænœ’â-‘ÂŒÜe ëHÄœ $­ÂJlp°uUVÜïÖtm7e¾r£6‡KJ=js0¢º5iOÂBaL¦øŽl¡ÕŸµµË××Ño[8Q.1£¶Î@w u Ï y u;\tAN5ö–Ânuâ\‚È9 }Üs?xM–•—›P/qó´—í!ÖÛàº(:êL¿È{ëwrrôv¢`tÏ/m¹ÎpC&q½'€@ˆ¶‚ß»/†ú“¸¦„Š3-!=úÞƒÓŠ¦Ý kùX†M(•_g 8ƒ«†ec ¬sf-bO:Æî1;IòyC}OÝ…ˆ‡Ô%Idásʃ+kð–§Â+Íü~  ›ú(“¯>î€4ôÚþogæNΨçXC“{´½:FÉ4ͺà1º¹õ̤X¨pùî p‹gMí\eí©šª¾B’F|Ç<™‰é-ÓÞ° ;t ›:áotF 5»+?LñÖñO&Ž¤vÃ"Ñ3ì”ØÎû@ö¶úûKûßHºÙCÍ¢«óü‘áá `kXU +P˜#½ ™2,[»wÙ€ªˆ 9Jb&«yü Ô5MŠyº«-ê¸ó¦mÐtï䀶%GhG¡b„¿O™‡ƒè–Ep)GEO¿°EŠl¶âCtß¼=ÙÚ‹+5ÇFïm¤²´ý Έ<´S0IûÊ¡L«tÄÉùÅ¥m3æÌ+­xß"\êU$òõçL‡$8êRym0/\zž0àA@ÛŸ’¯¸B`&.cZ†’®"ïËa$¬a ^,‹Qxš؆ŸƒÕÎÝŽI¯ƒo6L*îãÍé‰òÀ»lÀH ä$=LWÞ R‘ë#åq€ãŒp£+$dvþ߉JÁA‡Œ€ØÝéá´…ÈÁYuÜØß vEÈ;lnæ ÇÏ›~þfV]½4oüzó×rÐËŃjÿvý€ç\ˆo!ðeÊ5²)Ruó3 ">1á+¡ªƒ>¡Šs¯îñóߧu€I#‡YÚk*¼›.À¢1,Ø-ßGóÙ šZ:|ÜÖUçUñü"çqB%·µÔ.¬Ú1#ÝY Ž†^'l3Ú(TPJˆFWýë½ »oÕ¸‡rbPn¤³o;ão³C°ö[¥Éßcª#³ãPëãZœOà'P—€%‡м‘5I3þ~&RÑè¦töž×¯*Bòϯ>0—L‘“ ب#¶lõ™R©¡‡;€„qP‚øšIàŽÞEÝuÿ?⇻ïJ¼Úvñô¯Ð<¡S“ƈq{HÊ"ãœ+Ы¢k¼ã&ÓFòRjjƒ©ðÅœpS3Ïý ¯&Þ eæ ºKgLQe^ö]Y¢ifmg(k¿²`ÒÓ ²~£­žt•Ã±E…?m@Ùñ†µXöuƒáÈö±üvýSrdKKýÙ”{ nÿÚËv¤öÉÖVÇJþí¼~›þj«¨'ƒkè;ô*[#2 =ƒÆ†¿â×)¬å…•¸5aÙ#Ó-˯§î¨IÊÈ;ÈxÐàåÏÓ^b„çüœ… ÿé°™Ž®óêÔ8vÉ“&™óìˆuõ^Æ$ºM­ÁÓ[yÉÜÓŸÚTÌýHº±‹†«nj{)Aú 0Ö³&ÿƒŽÈÄΚÕŠöäoCCŠ¡‰î–˜Ðpè쨘ÔÐ…r*:‚ßó•º$#oÑ»#ÈT¸ƒ`?Ÿâ®pNÞuØ.}TJªåËÙB‚1p¯CXtî±£ž¦ˆ/0ÿ¹îã#†Ä–pçæ¬á^º›ß¿y£æ¢œ|f¹mIaÄßúM£ Å)«,^¸£z$Ø÷ÛÝK­Ž²«Æ›éXÎÏu$~¶jý§‘‘ƒäVí8q~G€J m½b«„Àò)îÑÀÖR{~CØGn¬ÿ»×Uò0JzÉ(d pžÃ"ŽÌš ØpJ^$/[Ô$6f ŒåôyLK±rÆfK¤Ê3z#zlŒO¸#·€X"oRµ¡ø;a¸;0ÌjAñ#ò,I"t";J{úÚñK~É0mo%Bˆ q‚1BZ£ûŠ­vñ»J'!†™×›ˆ€V„s~›Š0C^ÆÕÕo)p©qGÓ'°ïO1¬²¹Ý9wnº[ ²ã&OµŠ¬©Eà(Á˜¯ïƘ#÷P1½÷*¼¸cn;9ë7£œáC‹Êü½õð[ZŸs‚GÃê6S?§6žÞ’íñxcšå@HëíND=•5(E#ÉI¶\½»ÏnÛ5Šúº›‘pŒµ‘~ëâïvÇõ«Ñ°h<ÂÝ©=¨_¼P×]_[/ŽõeŒÜQ(\Kòè°)P–…㮧a‘h#©áa¸¤d_dH¢Ÿ`ãÞûžt?£Ó,ó¥þ¸ì>À„i80fTï£no³›‰´XÙ:r8÷Uâ{‡ë<ö !¤³¡Ýft.¾é,ín£,6›»äµíÑ¡1zÛ¸Á@{ûÙDáp%…Y™ˆ9.À©Bà’9á&98,—dÍåÄœxÑÿ4›Òå¦8%jÚM¤úö¹¾BT é¹,Ñ[:^ï¶7èõaŽÆ{¶‚ètUS@ÜÓ¸ÎLä—¥qC€vщj?=›½~—#\¨«b ƒ[ÇXùY\:m þ«D›’_ðK|,&sâ3fŠ[hcP`GËf¡pH`jªäká]LfiwiéMwƒëìí­ åXˆŇÕ|Ö‹m¤ºqHÓ™æ&]éL+G¥¯ïöEÁÝ·ÍS ¡‰Ju†ñ÷y7ä2=´àµ# '>~¸Pïˆ^·¤ìQ9ÃyÆü¬GÀ©A^å/i¢IÞ—üˆgK$†)4¿{5'©=¹ø°v,ɬëÊ}‚¹ýíç'~Kü‰ $å ›äe+W³{º@š2@û짴U»ÐTà˜Û>ø‹;›vIË{°ÒH/Œ›©âß¡MÌí Ĥ¡5Yì5Í°šµØÔM€„C@hS—Ørè·Ù$ï ŒcØ-Ñc…÷Á¶Ö… Öξ…à¦58ëÍÃIµ䎑`˜dïHV€Â±lVžþ~:}6] ¤0»°xtÜ“ÑÙ@Ø·J0²ù X ú¬4týÿQ,¶Qy6ˆ›wìïé(ùµ;ŸPhÈ].8“:z–œ§6S(1zð ¼âÜGbåò×IŠ lõ¤eˆkÄ®­ÑÓäGÜ‚CæûÐ(8\’=Aù!Pk6Þ¥™x̲OÕ8æî6¬²ÛÛêÁ*ÜôhE·ÄHeš®—OiËPŒò@›â’‚ŒM'™:ø“IRÅy™á¬SÎÕ“Mæ€x¶nöˆ•¿º¢]Nl¢ +Ò–Æ<½±É8GG<ˆø¢`±ñO^¦¾,‰á?Ä]Ø”{ÜŠ,„¢'5ÐY^à¶GÔ=¬ò@èmϧ€¨ L^gœ’DFÝ#å^í”hIC>áÀ†è…fÜOSAÎ _“¥ÿG²ÄáFR¦&-ÏÊÁŠ éQ¸]u%Ù§ß?à"â´kÐ},±\Ôî½z«ö ÿ&ÊbX3&öÛ†5ûá²ãhdq&扌sꙀÛ{¸ f æ²àbÖ¥ò‹—þ[Æa/¤ß‰ë~xßÝ3¨QE7’ bšÛ”º5n$…y²tž@vÝgÝÑßñ@k¨±…8(Yõ§8im‹4þähÙ’ƒhª[XKÑ°`23òe©Aø.cŒ—ã~eÚ~üÉE"ßÚÝr=]ßñËtÉwïæÁ-«h'—7b1§ü†{(iFPጞÜù¦ßgÂRàÚ½©‹GV©u&zÕ)9]Îøér³ê]([AsŠ³XCâ–I—½ea/í½šS.Û:‚Þ”)ªã“áÄl:œ(\Ä[SØ ÏЙ4äC ÕèB¬Þp¼$Èt­ñí• €¼k‡a¢[šâô7s}äã‰H¡¢îäÀ4@ç§âÑù³21šLCýÒ×wõR•Æ&×cVµW, HÅR|:ãæo‘(’†»šÒ0[÷›"¨[{ÒkŽ{wU…ú¼÷'GEÅwYêŸèH4¹Ø/ý¹|\îõ{|œþI_nH#hìN OÙ‘DsEvˆ Ëv×¾–{}çdû»é1ÿñ¢ãeèö»ân~”vÍ~ù®Õe_ Ò±ç(#+ÝÞeJ!Öëý<Ûö p\ᜰf6Ž|`•{_®­Ï¥É( ‘¯)ïŠïŠÁÚTßVë+[×3Ä÷&óV§ “[°péýM»^xG§ëéè_›x‚c×’w—×ûÝ"™ ö¾#2Ziø@eÀ1¬ÏDñ¢±M«“¤Áí:…}‹ä8ûðrk¤^óÓÒòÎ7ò:ƒP0:M¤<ï”óU£Ž':ãÞ. çó–:Ä’übkº·I´ÑI\q[ÌÑ»ë,Cý3ï-‡¾nÊô£XÐDp[Ø uÖf`ëmn € eÐ8UÚ)¨!öC{÷Ö¸dÂåô]-ÿLÞÝì~WKŸÓ̇9ð_68gŽ³ÒœVsᬊ„ÃU׉ˆ/½í˜[Ûyô´Ò^6”bšÊF·D³ ôz@w6˜ì9ñãM %Nݦ ·NÀþõÖævù~ËP·uÈÔ»¥úPÆX¾ž@ûZ˜(EœOB¨Õ÷ü˜‚‰dï’=”ÜrÛ%?0øm‡¨ì°$Nžtë©Ñ׳§V¤Uÿh,/xhNI“$1,[­* 4@«Òg~5Á‚üwÆ _>%þ\r­‘¥ÑOÙ‹ŸPš"&»rÙó-.Þð¡Mr½x0î!U×¾o³Ñ ¿¸C-!»jt½ =+ÆèﶺbÓBê‘bußÙ$¥ìœƒFžRLë øQŒ…ÂÇî–d«“Ð[¼[~HíV¤$üþ@„ü¥ÛfÝbbu#lÖ¶0½ôí¨?YnòË…7nï“­ó*ˆ>3o§\D9cBÈÁs¤=´O¿Ÿf–Â=ûLm<£r:t|Vsq¤µ€‚•):Qºý†F^d˜mkŽk·`´ÉÔtù)Ï@ B³Áxä—ŠEô4q"SŒM®f½·O¢iÕ·3ËDØ ®A£p~r6?ž/€MCÓj±r5k ¬w¯xIЂûâ–ݳ¥‚Eñ­K‹µÚPÙÓ’;ˆ§ 2ŽáˆÃ>Ò-lʵš¸|müˈq§î]L^³§h®d⣢õR˜v`^x¶à/‹”?Óƒ¢;!p#&RÊïR–qTYÑâ¹2$å<ÖE" 4iÃ<þ¡¤hÕI_ÔQ3—æùF(ÆÖ”¦¾õzƒ|h>ãà&mg‚ê«$“EÎö†(EÀt sùºÀ)8nµ®‘). š]cay¼º±Jåï&~ô¨/ÑÃάC‚ªD9-®£hG«JüѲ~þå4ÄO:äÍ2r4á 嘅&:ëL?*ó•ÌO¤V5,ý'óPhau§«ù¤–K)kÎ?ÞÈmÆ?²ËÒâ–L“H½ÿÓƒmkÍΕ©1è&|rÛ÷«únT¨{©tg¹Õñ7˜Bˆ­5 I÷Ê–DÁñ1h*p(¿&æesý)÷<ºÕyÚÌ‹Å‘]aL•ÿáÉ›Þû†•9©¡¯'÷·ÔR÷²ôlO‹=2vKdp³Å´I¸™­Øqk4Ž=x‰’ÄæiËqºê6AC苉û@¸é؈,)GsåH•a„³<5*{9¸ùtÊ&a¬›™þ fg‚€pZgS­œ·û¯)[õ 2 ²=ƒìqã®7o] Tö¿u„øû4å|èB-p¿´NC{‘ÿi¬Ñqmé8ÌŸúÜz-„Ã…ÜÅá? ¡hr?qU:µõ;«Cä ˜!nÂ#à]"ÌäÇ—`™m˜I>i6¸Éïc„ñTªlùltØ¡?qg4t£tLjßbÙÔÑOpÛsÛgÉÏ$8Ã_Pœ:q ÙIbãGäi›F5Õ3¢ÍŒ Cói« ?Û2¡U iTV`ZVŒÉ2IxBâì¢ý‡4ËÊ kŽh|U3§ôð|¤ëWjBºƒ=-£P1ˆàˆÍ;<)/-À׶Ž«Äøž™éZÎÅžËïž%õìÅ–"é_1Tc·vÎÝIÃêQq¶ˆ °\µŸ£Ì@X²ŸE"$.çð±ºño¡¬‡ZÛ^²–ÈÅ–<„¦„è7 éê­¶Ÿö;ØL³”¨šcÇÄÍ<<Ã=×ê€^7à¹Þz¬n²úZ«·Š°´¨úñãÙ†ÄZ÷FÀnêê -p%­ N#Œ^˜·”‰mcSíՇϻ¦}綸? C£* ´o3S€|ûMì¼Öœ®QmŸÖr¦ÎÌ÷3^з‚`€>‰ù¢ü²þŽWÂõùŒ%ÝîMRaêåûH“»ç "¹Mþ›!¦ånƒÎb“áDÖ d$dZ‚£JTÊû¡ÃÛh#ÇU›°cÞJ±/eGwÍc¥’ѽ:ÙEû ŠlMNÏã®I¦=y˜Š!Ñ|Äó´?Ž\€¥åÏá‚8";Ý~[¶b›ã•#üÿÓKÀ{˜s2òý•#Þî\¾•xø~–ѵE¤$Z«$–þ•å[U÷L¥ Ýøì?wDê™ 3¾ [ ÿ‡6c±ªcTú1¤ù¿&…$ÆßF  ¾éPwø¤£OjòȇUÔZ“ µk«3°¥¨hŽÝëäzÇ2Äšd<ˆ¬ëA¤óÜöÿÃM†+)â÷ä^qil"oß'YXíHzs‚Ì+[Ë™Ëvš 5õ‹Ûã³ktiz]I|'È…@T8a—»Ç/lÄ>únÛZµIÝKÞwn-Ð8í»bò™Á,>Ý{•«€†Z‡a»I(z[hû”¹cÑþÜöÆW-ÇÅ(©ÄKGm\B¿ã—}î¡[+'ØÉÐDó*ÍKn»¢2™‹hás4ˆÙcàµ7(q]pðÅ„«º˜ÌONÐçÊæ¸Ê* #oàcoë¶ÐoÄ ¤|‘§Þs½¶¥Œ.*Üû©WQÂ3sËè%ìAh¬˜‹KpjDÑmÎb›4¸5I÷AåŒù÷JaAZ~Eõq¾æ•õf¤ÞtX;SÚrœ´NÊ´úî´‹ƒ]!ÚüÙÁ¬O¥6ôè@R¼õnû$¨µÏçsM–ÖX£÷7ðÈK®.Dg~aÌ€èºËr ´®‹ÓÖq: °löQ °öÉÅ= ׬ÁÙoöëéz¬Ný0 #s˜K}=ÄÉöñÌ­™tÍÂáj‚„þD“5[””yb0Áæ³³©o ËÎ-ÿ‹›/"ªˆ»ÚÞtbb[]ËtO‚Ùwæß´f`®í!_d©¦ª¶œ/Ü€oä陘·ÂåbA7:|ù쎒^²ÛÎÀ?>À þqÁ‡¤ôvò;]['.ð<(ÃÓJÁ^áïç3Ý„s?AŠƒãäÎ6‘fÊŽx™¤ Ë¥1!ˆéMt¼zãÊ=[ä.q)Äeõblw¢y=yPã~Ê+çs‚f‘x•à]a²Ëï9eòsø%ê2Ãt†þúpŸµw¬qQÖ>ï|r:"´b,cfKžÆoï\›jrÐ0ý0àæ{ÌŽ|YØÈñ$Dg¹#_‘ÚŸ˜Êu_ÿ¿J¶÷ö•NÃätñýí'áŠå€}ó˜oŠC“w?Z‰Ô2Ñë6=ˆ`='.ÚKµ7UK¸øßöÛÜ”¼¥#&(¡‹åÜùÛ;”÷jlÞºhŠ“H´6D5+(wd“qg’$0–B¸¯.vEøõô¸É¿žŠ®Öo7ü@žñÁÙ éë¯*f¤ÞÍÐ_LøŠ‹þ€ý¼±¸Ÿ]á]QZ!ÐJ},©_$Ňˆ¹î@ݼú¸ËÍ×ùs,"c Þ£I5Еäø§–dýE`% ÿXÝaî“•±‹% V hý™—ªQE®TôT-Ì#®Æe±„¼.|$N xú¢ÑAÇ÷¢ÆZÑ=ö.kOM ššR¸Ú5âzõ{Gdßq<:uØ&€Q~ v(¹ô5lÒÒó×÷*øü€îŒú>CÛ¿Ï%þêÇ#‡êm\Ë)óýfÁWÖ¡ì}wá‚,VcK`õrņ?ª_g?O,ÿG¡Q ŒÜvuÖº—!ZÚT?^‡¯*a=²ò§uWº~żŠ ?º LáÒû·‚¼ŒþÅ vLÀ×K&[×XUŒöÕp’¿A÷3rœ»JgϪü½i¼uR%P;%¨`LÐnVÇ0ëÊ"×°Ð>ð^ÿ§Jq*?£…)û£ˆŒ—á©´+V°(qYva3p ~ZK)Â'ópˆIØ­Ì­mu´¬P³ª>Úµ‚½Â,‘$í³êBš¼$ÐY‚…kK7Ì 3®–¢ÂT­£Cÿd5•d Yö[t¸©˜–!…ª;riù„aj2÷~^3Lä©TlšJDæ¥Ò9תUíõ”e¿Ë5®4È™ ‘ÚÄÇ`!;†–4^ˆ!¿!wT-Ží08ÝâÐ] •¼•H1½lû ÀTÒÌg²‹?ÓÜ»€ËÚû8Üe’ /eíÛíôsðX\QðGÛ!/b{àïìÝ^¼kûÚ¤ƒ‚h…Êÿ«}=.{DS´½~çTœˆÀÓTm;Ït;¤jštêl/Áó“€Öl@æ¾"ê·#Öb§71†ãÐ|À׉(4]¢‘&‡iNÄŠN4+¬¥™ÑÁÿÔÕ•`è­-3—°Ÿ¾ƒ§JmG+ß)llš8« ý@>æUG€À1LV”í½"•mñÛ¶‰ÜîV#ü(òº×o†­tæãâÓÆÒu)óÛÖñ²+ý*Cúw†f´ãÑ.´ÌZ7)U.?%ÝpéÎã‰H)«r¢!–GÈB~`ÔŸû:¡ÝW¼/Å4±fê&#ÔîØÉ&ŒlÁ5¡õÓ¸õQÒ; –ªkë5Éj̲bŸãÊâì¡´'‹ {sŸ•]=zèxã¼­,S´–™ßàôzÔ‚ÔxàV!fŸea .Ã9éÛºïñÀ¾ý°è;`›ró¾Å1†Òuü|¯Kz¥4ÿÄdj±ŸÜ{36z«ßWD 8]ƒÜ\ÊfL{Õq£0)‹ÔnÓº‡_W1G¤’çÁ{­Ónc+«™-ý]zë–K0–\Q Iþŵx3ê«ÈwXÚ9"(NXÛ¨ñÿ:¬–ªCS´á‹nž/‚`‰#ÂÄÞ•ñq!æDa쎎Ôæ·Zôá\^±ÀG('”â\ñMG¯Žæ<ð%ñØ´Øšõóþ–)xôöY»;AÍÉŒÓn°áðáÔš1»§æ7DÒ¿¬%%º;˜…ú³ƒ¢ÀžûìÔJØo†Ê̽’iÔ^íCòdÞ ¦yyc%i¶Ùìl½<ð ¯UhHhw®U«TaÒ`H’ë+¾Ô)˜*ä…‡ÿ%› ºè9ë ä~0lÍÉÃãŠê Ãü Þž‚ ¤›†u²A´²Oàÿ)6_U¢RdH¬6ãÎ'wà6R¾/>-åì¢:Lr/7ƒEk>\8MJ–áIêÔ^ iÈà&C><6ØÁÍA\J>¶p'ìÞ÷€ŸÏM¢«¢5<£÷rvgºh¿â{à éÙŽT¶+ùDqÉ5HÊwJ\Fß-V¹™`ªªÆ›»­:çM?Mžæ1f›=½J @¿»*<‰3lÒŒv¥p¤˜ª9Ô`=¡ƒz¨A!Ù¶’ñ)fy µÿd×4©®Í4Rø¶¥xH‘ŽCR‘Ý[ûÛs¯Ë/3LI£Òü5©Ò}ºÇ3õ=úH\zI¡/TÞbâeaª{ÏšŒøÚÉdq󊾤Y¹ÖÞÞÏ&¢] ¥Up±zÖ,¥3÷õ)ˆë®,kL“45r‚/©RˆN•“Bž©®Ž¿2Ò:£WW3ï Ìî:‹|œè M"AÙdþH=ÒÃ;’w[hÅ“=ÿJüJð‚?ÀÛzò⃡ôšk:ù•&ˆÝ‚÷%]ÏÔÜ÷êrá³k/±|¢œ(&Œv¥c˼v%ÍŠÇ{ô¯»5™ìkÂmnG~lߥ¦¦uµ¼‰GlÔ\LM‚©|­PI¦a_ã¤@ë ÀÄ6u-ÿ¶’ñð WÞÿ®2 –Óo0ÀÝôµÎëë!AùôêÐ.@¶Ýš:ßêlp¿þ9ñ4Œ 1‘®ô§ t«{Jˆþà˜(‡ ­n¶  ±M÷QËp±›vy€e¨k 8÷aŸ¦‘ðüÐù…±cœ¿Ÿê]º·–À^u“ÍMŒ¡®“«g qcLYÕú…ª˜•cÆÎLUËÆžz*`LNÄÆ°æø ôw±z¾˜Ä$ýþߪ¶]¾É‹VR;ÈhRòJ’MÉZX w¢·›ïÍÁ.=sh“ÉצU„Õ­Lg,À7Ø8½{ˆ%Ç fíAæ¿¡¨¨V‡-;<ÖTúäU[ì‘È#Ž§òós>Œä[‹ß”žqG‚è¸V{¥…ú„Dô©zûlŒºÚÑÌI$ײÞMWBTÞîý£´ºra,œl>Èd¼9×'jäͺ”¿$|^NGÆïºA#FÖ2Ÿ™†+ò€ú‚_q>ß›3ÇÔ×Áú»zâ¦?#¦¢¡×É6Quã½`¾ËBªŠtäŸWh^Q LõAš%‡WûÔé ËWÝ'=å¼± ~26íþSõ&ƒ0Ò&™qK/Ðì Qèìø˜ýh›zÙØ`6¸à»D !Â|mƒ.¹8½—ÎglØm„øžeõäçímÑú¢4˜ƒ^ÿZœÙ\³úehoÉXRYt‡™ü°ó$,˜`zC,k*´óþŸêƒ;ºDÀ YÿŸMÀǸVáLšäî7ÞDZÎy4ÕsÚ¸âŠKÔ#z¶\÷\ ¿:ªŒÝ)>ím©5¹ÇkéÿB¢#‚ÈL¶kê¿à˜o¯}S³¬ýL¥%1êïÔçM• /Ü¿8æ-00À`…Ø°©òÌó"ÞÁƒz7ö›ÐûXkb9‚UèE0pFïUHçûì¨ò>;Ô…>±ÌR§4Tgô?—DÁŠÛvõÇÔw‹ü\TMÃ>諸%‰6I`Z1Ð< ñ¦ƒó*¡iQ,áÑ„Ø„VÏ7›‚]·Vhz5d?è¿-(d ΤÃö[žRûœE¥‰4çÛ.à©©s€ˆžÙzžoý–oµ³áÒéy†ªªzŦÿRGR gmROðœ¦7žŠ%<ùÍM¤´‰9¶É;‡þ‹«Bôt L¤^„\4Ù“ Iy{­+uB$f‚³uÃ;=MTÎ*·äeL#Ÿ ?UYÒ‹¨¸µ9r«dñ¥ø#h‘ówY4½ß#í}s†ßMðBW ‹Åu6ÀA?=ºq‚ô¾ÐéÙÒÕ4¿HÛ´ïP+hÒCMúL~·]G'äSh&¸ýÓ)n¡¬ •*âÖFìꜥf¸w_eõâ9H;-6!¹;E't¬òyu›P¼š0áÎgw¬³ñá×¼|·t.’úh…ùÞìi[¥ºö&QøgâlKA†Ú;Ê]II_µüýýÿ?~tŽ9üQ,e—p£e|7†ëIúôµ…ˆVR»fÈ”þO÷”éT^HÆó P÷k`'õ¼Qæhk¿/ä+m:…æ&mÒç)/¹òp”4s’]t”'ŸE%2/ñ $ŒÚ»’dC ˆ¦zÕéÓn~ RM¥2êèßNEΊÉÍOÚ¢G®÷F*G gÕ<&õõƒzÛ'æ ¯$¾4þ¶ÃDu[à@Öó£…òÓVï*Ýðpql¯Ã!|!¬=çþ¹VxX%¸ªç É€?<¢+üÄZŠs¼}}eb£F¯ÞÕÓœ8ð¥%S¸y8Å%ötØÞnÔ#rˆhja¦ý¢Á¥#ß\(ß‹EyãÅÒúÓ!DÞ—»½òQîI߸¾¦h¦Ä`rbD ‘¹ƒç‚Ókš(Ó:¾ÀdÈc0„ÌȉÔlзLôÒÏ–Ö±Yc6+DÆYà¿Ò”ª‚¶"©Fjñò§ˆÃªV¨)˜nsƒÀ×P¢GÜ=—="^¦Óã7.ÙaÁêÛü(ÌÛôc­Ñ­^îjþ˜@Oä`É<ùóµLÓt]›1à?‹d´—í4æú=©¾ÇF=5¡(¦jr¯ÜM^@–ˆˆ%»‘Zž¾M%2“öƒãèa¯ûh7  vhçÏY‹C©m#•©Déú"%øð\éÒnÍ/-·ŒçBIãß}p­xk@ãÙØç|‚È–æ``‚MgnؼОf¥µ(˽ (€Á†wö_`Ô·¬òuÉö^re ú !/»?÷éžrL›˜WFdk1‰èQìDšUp–t©–HðÉN¤žE\ÍS]v8dÈMDÔç;V²¼Óˆ"í! ɦÜl‡:-òÁy/Í3üC¤®!ä3}S -\»‚!ð{-nK%TCä? Mäî—t¢x #J§Pe.•vØ#V™`C®¸¶x Ñ]8ze-0þ=P µ0’GA¢Æ'¶“S|«¾=¤«f9B5Tåi€¿ø¢uvµ¨gä«\xsaEÑöÎà1°†{+0É\¨j³}îÞ£‚hsÈìºå‡éÏ›¹âùW]‡õHTiÄ}•=ꘈ‘³'!JÛ«|ðPä¥d”iÿñ @ %ú˹'nþÄßÃÞmÌaÊÍg —ûsúä­‚™ÕÂbhø8w…à̯×ü—Iºÿ©=œ±ÙA©Ÿ^!â.Fé§&ÙC1ï{Ûè!’ñÛo·ºhb'SáÈây|0z¼^c7ø`_}ÉyîœêâPk8…Y€ãàÂÀKäÿºoÊDÏr?«2‘±‹p49sÂjV„O,¼{¹Ù2H¶Œ|1¥]QTÔ—Ž–Ò¿öM;µUvAƒäû†úÚ^É%ab”ÕG†®kªv©ÿâ‚}ز¹©Å'õs¿‡œ>“Ù ~ÒAY`°$à­Û¦"oˆ ˜§‚*ûx‚@-{ñxÝg»A¿øV;€=Ʊ%Ì7‡óÝ­4ÛLuHšI¼x´#×BUŠà‚m翇®ÇHßûõüQ""/S±9OíÐüâHwÂÒˆ­¯Fã¦~çy€]Ç`ÿ˜c”¾‘_æú«“ýÀÏN0†¥}¤C®ù=;Í$5ûù¥FAë‹pýgà(c±{r¤½4Š€EŠé6û>ôäÓ¾~ëtõ:ÈMw«À©›»"Où4¦Ïð<¶ôºr‚ð±ZzôÅ-ìjK]™—%*’K’x‚¾E]¸`§ÊMuˆöžs7¹õŸ<á 4È@¹¨#"ƒ.GÐX§ÃKÆ⓸œÝ#p\÷ e./¶ =+ÊjR¶]cLâ2ßåL‡6­ÛÎYÏòØñmQÚ}ljõø™‰F R ~mzÊ–ÆêÒàÝ Nßø–G­BçAGçJ‘Ÿø`úãA?:ÈàaYÖX;ƒ×ñ¼¾?Jx* uñð¬ÁÆ:u(Få‰[Üù2ž9ñ]c{6®Êçß ¾Ñ Šì¦ì<؉ lÞlePÒ8å{4ä»Âvµ(w¾×ÛKî¶Î¶šÃœ°öjž¬ñ ç) Ý°‹©'Ê´ÃWE™0ev ÷ep‰+òx³óo3üÀ3ÁCN4}åÞM`IH˜.§AþþWHÝ=_“}ç%÷òœLìœÛ!Äu™'@¿~|æ#|Újý®?x[î>!VææMþMþKóçwð~{e¼Íßa7QžÕuô\ñž{ û‹ðÆíAõ'²¼ûíc³†Q9]´q?ûz,îˆ#ïÈÒ¤/则µ Ë\¡WWÓ&eývS¥†hÐ h­á+Iª"“dFoÁËI*Û:ÚA´Ãª?…Á™M+Ñ žSÅÉ Õ÷»LjÑGÝ‘ ËŽÐôdf‰LßÚ<Ò>Q—R7FT¢ùû4ñîJú˜Š~d.€ ;bÚù §“¹ûLøû°èÂl"¾d2-—©8„sùöؘؑ­'7¿ŠŸ»«®î‰Š¶Ñzcüè-éÅwÌ¢éd<Ýw„Þ’^ÏW”.ÊùL37—ÎSˆúÏÕoÍÎyòú¹Èt~äY!Q|ù¾±ƒg_Ñ€ +zµž$Ã̾ÂÑ~ ÖÒÒùéÒx"£’T«Â+4êͺð%\oŸÇzÓ ¶PÀú×;²@±lÏA³è0–³M)XµÆW”yQÞ¡7»ë`+åÀvv» cjÞw¿CE¶•}AK“N}:·s¥ÐC®C_^]§Ø͠ΆBE„—DQ7>¢¨æï¹Kú¨›Ñ·‡ßiIV}Œ[˦€fÌ_ˆ¨÷ƒ}F~ê†í^•‚C±k<ñ)<7ƒŒ·K¹fеUQ2£NÍ‘2¨>L ¤ÉÙS·×L7¾÷ $žŸ™«û©û߉ŠAUmÃIÝÅ÷¿pmÔß%Ô-,¦—ø|«e‰?ŽÁ ¯â Ó t"¯òÉÒ¤ƒœÛNƒßìv¨îÁî…¶èp‡• {sö4 hCê†"ò1#п{k8'ZÒXÞˆ?\‚ˆÉ}ä~Ê IÖƒ¿·~ij"jg˜iãáª×á+_Ââ­p/,¢}ré{–cᘣêšàè¹0&LÐ{·1Å­˜)¹²Ûä%•,4û Èœ½#;,÷T´¤Ûì;âÚÜýÐÝþÎbjLj°wÌ'ZE×H\à°áþ‘C>ÂÖçËêþÛZØ`Œþ|Vj÷^‰v¼CèB•Â÷Æ6@µ 1dQØøäœ-›ZÜž2Vpºìú‚#’c»ž&{"›­­Dà.¤egñä±eÏÜ/$‰i´ îSÁ*a,l#;ìä)-G~ú6$á@ PË€BJÄÞJ£y|ö.àzmÌÎ<¢ë4SÛÖ¡ˆñ*ìðxQ‘ƒ½ž¡„ˆ=k“þÝbwS5 0ú¸ÈH £;œ…F=5%F-fÅɳvºE¨””w幧=QŠ3~! —ÃÆGULàïÜà•ïaü¥kF¥û çm®»)\Ô*ƒ³bO}_@èA"!9±ÝaÈ(¾aš õõ?“Ø«X75æ³·Ï`;·XNjø”[(ê~ú•ÔkÃãuìh=Bƒ+ÿd•Sëi[¦ ˆ‡Ê!‰å‹k×­   îÓw¦x.´;2ç“Ĺ›_™ÀŒƒWÚþéHJO¸ Ä[`êÒƒØ6¹Ït¢ù‡&ÆðŽW²MH÷` °ÓþZ™,ÇÚP¦çCF°ó0d@…ƒÛ€.ܳ$V­D_Ç,àŠ èùCÁ}œ v–ø}:g…göÌC¨®*²’tD¶®ÞB¬ÅhÅpí³Yà¤-kvüÆréh“îÂ…d#Ñ!;`Š¬¢Šp‚ë 2<ôÆÊ·•¤ÌüT%eSB[û¡wGPqË6Ä4B/ü,XŒÞ±Àý{'Ú³y ”ª›^ë£P l[nd!"Ú Ô_ál¨Dl {ãgW_ò²™02îax»3e ÂÃý *Ø$÷i`‹u4V]ñ}¶Lbz@!}ª¬0…¬ïbž‘rjµÓ€óR³óqQÖ=f‹§î€;JÁœOœ‹Iš”fêþ!#˜H6½oO=~6ÑÍqîQÙ@:¼XÉé*<®ÓwE‚m˜ \ºБQlRö,—‡Ë.) Á©»¶¸®â©a6»E‚t„¡Þl›†uGDÂl^çíÇ Níúó!{ÔÖk†<·¡ôŠbež o¤%aI:¡‹Çæ<Žè餤!ÒÔ.+”áŽa¶ ×­°µÌ2m*š–x?QcEq6C7Â×ÜÚª”1éîÑE„ž4@÷Ò§c¥b,L³VßzG&B÷p­A—À}N÷¶_“óagRjÙÛɪ´ö§²r®'âʘ©íygE\g¿1•«>ØÊþ`Kh5‘ô>*ü¡…C>´ÃCs87ì_¬wÊÐÀ¨CÖ…íþ ~ƒøÓ”g÷š:g:T²1b¹Z#-FéðÑK2º)ZQ$iáÄ6±îGPÞ¨ûw›8-»çK„<®Ï‘=š—§qÍlÉxjœ‹Áßg¦åVPu,¯,/ùÅU·Ž(È20Ÿú$]ÌKáQ^Éç<½Âòüÿ9CPaÝíÒÜPaÙ ¹ÌÚë«ÐùYyø„©ƒ¿-<ÄèÇéB'·0ü©£ ]¬@ÔØ«mÒ#t—”ñ\;ÚHµÂU3‚­ËJø˜G¸Ûº0¼º­‡ÿöŒO0¿mÃGÊŒ; ¶Øë°2}&ÐãæÝR@É2wÔ²–.Ï^ui3Áù®eÜ©.–Ó†ÄÔÉ5ýÖ79Î}_*}J-)¢©Ö”—ñ82ðü°•‹åÂ(}DníÿÏ‘ÅèU&õVtßæïL¾u•#)y?RÉÑâ„\·&y%ø^ÇÑŦöŽƒ¿X&™Å°¥»½r/JMZDÝë?@„R4Lú3”@ùÄZ#:uLa›Ã‘Tº¸k »eí,N¼ôðµq jA@U«ÉÎ }…JÄÅ­Êf<\-m¡¡=Ò–ÔRK ™R%~þæ[Nõ0­8eW9CY7SŠÂ=‡¯^“Ök.éŠ`ä\Àé@Ƥ:U›°ÂHü—6ØKe¼™-aâcq«õíè‚J7¬ÒW$—é:êÿŸÞê SáÛ(ã;7l§676X’b^•üõÖiƒTκøLDDúMzH©ñÕ³å²8ÂD:Í»¾Mè ®š/;1Ž@g~Mfª§Œ,0‹OÕÌÊêf2Mö»že`ùo¢y>ò™DËJȶÿˆ½ª|±yènÕÐlÉ¥{ÚNê&³Ýÿô•QÙišáÐW(aÕùÙr¿i•Á™äc3qÒd=“]E˜| $ܦá~µƒK‚‡É=Ià÷˜æ¸HCÆ1 ±fiºí‰»Z‰G˜¤ÿG`ÌPŠý×ñõô%ïÍŽ¹”±Ëô^SÞƒ@°ârçïžÉ)A—t·df¬0mÀƒ§JæñN ÄdÓNû¾å¡\å•ÆBRçrŸÐc:Â*ÑVƾOŽ$ ÜésÜ–“,”ˆð©Ê-Âö1œ{_`µs@¹tàûµš+÷L¹Ù]{ÎœB|wŠg*z¼Îgmýç™.{Àl­4Õ’L+`²*»V.v ”®â{J@³êa´ï”·ˆ÷ж÷\Úe¡{­w’*)Û“q+2·Cmɯ²>þZ>ýظƒí¥&)­xÔ; ÙA+ÐÀ}j )øÕZÆj^ÑáUª0èãˆ|8’„HËËø’ˆøJÄdâàñu£üj‘‰Í™øÓÐ|rU[4[Ú½Ã_ØArâlÜ@.mû¯òú‰)æ(È4§È¦QáƒîlekDmüöjIÛ÷=ÜXÎE´uܳWãMñ…p`Ã@+V7ðÈjŠ!ð­6Q8¶ LjKKtȤ«ñÊŒ8ó©ý{QÖÔ¸‰§%:§ÓcDÌÌ÷äÜjú+wî‡ÜÅý©sës’!¢²ôVùæ>Î1ùo óìÒÊ~GP5ülT²9¡°bë'“[Üÿœâ=f=×–ä¸úÉX_½ùP¬¢u›³™äõT‡º½Z×L÷êPûúUÊQ0#¼ï†ÝZÄYA‡C«V9óAU°äGAÇVd‰ü yµ4l€K„}28›qDŽËd!,pfÁÿd²ÜaÌ›5s¬ÀǼ»½P=íÝ >û{!Ì‘°ÍáfL/¨C§l ùYz3Z@ͱÆË×·dBšl/¿i‰[eà˜~“ú4¨àxæɶœ¶DfÖiüc,ƒd,4+¬£\’Àù4ð"gÐÉñ7›õ&&9?7Æõ±,!™g÷ ｋÿ°-ØCi4õÊŸ!Ž©/‚¨”ÂÆà€µ^T‹MÊ`*5Å~¢ØÁXä6º3íñ½ünø ù7êðàGÖ81Šx’ü÷aŠßGw.ÐÒšþÔœ-¯L•ÆlxMÍØ[R½JyõÓ’4×sc[2x¨JŸ¤>­˜z ±Ê:8ÿ' Ñ\:kÀ½F; ”ˆN)rì93ìÜÜ"û——»Uè乫zÃpŠ©ÓãºÌ¥J¼ôgÞ’T¯KôÃ|c‚'§.è–Jûë‰wÓÀŠ£ÿ#öŽÄN¿" <¶q©HLÖ‡{ ëè©”mò 4"BÕ:Ó&þ'û-ù<¤ÞƒrÓ¼vÍB“÷YüÜE ?l'(;ÎÝ_ãXÁoOÔlôÄU9¬pÚ7{,ÍÂ@c‚ Ä r9-YÓt·×å9êÕ¬Iý×N µÈ-‡ÜðhuHîʲ¦A­5™ê2DW‘[ ×¼Ð^ä; ‡'õ¯;B<ÉaìÑ¥CQE ¤Úø'!Ÿ-3Yàè×V}DÄUßÕ8ÕJPƒVsú¶µ¿“!D¶9Þl”/à¹qô°ÍòÀ×gƒ$ ž ¦Ôã&( gzׯæLW´÷„Q3£v©V½ôcÜÎʼnh'¼õ‚BZõ”ëãnÔPÐ"ØÀ]³•:ô0%3i£M9ñžAÒÜòxÖ‘ÁQ›—@5†ßßY-Défkõ£ ä˜épæÓÇ·š†Rßäk …Ð Ù¥ãz !7±=üêv½›q:Q’rI©‘°ò™ÆU=ë‘O0ÝØ uµ¿ÄàÉà]ƒ¿zIšk p™ 9°*}ä7þè£ü¾z `¿®âŒUÏÍim>먑^„Õ¤/lÉ(­®Ð—´âs3¨ÀáÜ¢¶©nú…â²Âs,bA=±«p­G™dMbÉv “©3)ߘˆC2–c}œÕ|'ÐxÛ([mÅÐO©’Žf Ö†¾…«ñˆ{KÝAæøÁ<¸Ž÷¬yAÿ CYæ>*E Ÿó¹Üä¼3&'> hl߉h–¯= ·{ú,Jò¡Æ‡p¿ÒcÇPU|uVðBÎ=~hìoUj\O'Wÿ§l‘$d×LÕeTZûŠžÀÅ%j =ÚÍU´ý©3¿±Y£7Ð…õ¯œxó" ¤,”!T–­^•Ã"núèk8A Lô³Åñ.Rß“añâת—”ŠÍ#†a«sµ@t¸ndbgII3 Q_æªÚ²?Ò<‹P̧C~V³]Ø,ÛÍ3iW¶Š /€Àe®ß´k_O«éÉz“óÀV¹a/þTÓªF)H[ÕͬŽýß•Mc"•@]­ÊÉ–¯UY.¹—Ò÷4dÅÛ¨YxxÕ=@˜5[3$Êì á“ny­™Ü&»ytÒÌåb0”Ç6‰¹e@8r>*!1½( h,4±„¤,ýwH:E±‘V«¨Âù>œïª)nm* åg€vÔ^nê´ï¨ÞN¯Ê9¦ë-ñžZE’/J,…xŽ!餔ç ÜBÀuîÈzÂ+ÑÙ~‚rP DÚUÿÀ—7³ï8Uc ä>!hºSÚ¾„ß¼`#²pÆ#pX1ðC· f½]rbX5˜RßìròbL1„ú·§l5Û_Sy/r ƒi+öýhLAep+•n:Ñ0“•c$k‹Ü®ö6½¤6ªÎ¹ùkO¡J¸-œb¥TÞ}4“ ÚXƒ#Q±qòâ–1/%ðAüäu`»¦rN{r©äO×ÈŠÃ'±c¹OL1ê€Ã)ušÂõûé+4 ìyé05Rò\}«@’h´_æì)Ìã¸8*ô^~›-°bHÙ”ùÊåÚ-Í»ºv\«²·™ÉLê»ñóÝ„‰f#VM9Ùw ýC\¬UXµpõ™!Ì=ÅÝ«SûHNŸ‰Úx¨<ì ðb)´×@ŒñŒÐÖžKžÛjëÞ×eèyõt=B™’ÿÁ½­_¹CWêç{\ÈM/7fr«ˆy 僽ҫ¸]§ö|,HÒ$ƒ|ìÑþö)VoM\w’.§Ú]î¤ú ŸŠâ¸¬ò J÷æ0r½¶Ä3¾B¸!DÒ`0™Q€ô„Æ)ѲOçdlñßÝlàì3È4Ôo›M÷æt LH˾™š²¥ŸUæÅýIÊAŠ‘á­Š¬íŽë¥FJ MÝìrmB(‘]ǼSpFŸÜ±†p!y¹/žË}4Ô½ØrK°»8Éà F;ÀÏ/¨ô~—cYãÃ=Y-þ¡#%=Ÿ¾ÃïÊ :ú.h„¼ô|úI Nó$–pJØQ'ëG+ÙüÑ‘ oœtù¡Ru™Oº m«JiƧ†Ž¡ô»%²g§F«àÆ8þ>Ipt÷Fò"ðz{PÆ8˲Π²+§¢š›7ÒêûfnIcºÄ< þ8$%uUÑ­SÈ]§Üˆ’ô[¼±£œ#¿Øš ‰Ì+Œ¹'k|"Š i~€ßºíÒbã•Î•Þ—ãÁûnbìI=뀥â;é[@[ÓO2Á)lI­$nU:jâa[jñ§ Ë%k5OÁá±âbK»]LVM:F˜9k¥rÆ“¤qXõ8͘f8ÑÁñó{ :â TÍ»Úép¾Òæš-ûõ (õFÓsa6±TÁ֤Ѐ Â+H»-B~T(AÞúmú$•áiÉ»Äë¶Âù„—½,ò]øœ}µQJ>3”Ê5÷ê`*nƒuÐ&¬qƒºÓbtMïö‡ºº^-dƒÀ%æÙ%¬‡Üé]»ºáPù=þ& ß!GÙSGü¤Õ÷”g±³ -ËÚ¥ÒžGÇ6GAjåm„‡0PÆzÛÐóÀQ.eÎ÷¨x‰PŸ"ÌÅÓ›±ÝOÑy2M„z1Xø\šßÖÙD¾.óD…óæÆÍÍN|[Õ½›‘?Úz–qê\ÝqWÌWê¥x2ñ˜½ÖÈJ¿Qå…W2¢O~|æÆŠuÍHü‰}Õ°¨³T™nÑ3­<°”´¨v7+[hTG_âk’)+8¶ UÃCB 'Õ27#°º±N¹InÐTžÑýhê fyð#*ók/Æ·‚Š&a‹ ØŽƒùÊüѺøÑçQÓ³ð?ÔËÏ‚¾è§³„Ù#û³êÞ¼¾é›M‚ï•€N¾y‡s?åôDgkk§“®Ê µ Ê/ˆÚ#ì`öÕ—Xñ?@Ùp«óAà+{"Ë\Z!S|^¨›þfµ“ðã§J sôsçƳÁ£|Ïif89øÛÄíá 0¾qxgœ{ø¼JHÙ¸7âyÕ/ƒt¤nž…^Ì‹[/S‰Ö{áxê‡s½¡üÀñ¡ª¹}Hjl–-1+M7€öQD[:¿Ùóôd@=\ÀH,Œ»ÉX¦Õ<:¨ñj:ò¾ÐE¬ðþ‚0jFWÆ[ÖÆ\%ÁqùÉ «Ý»”WþêÝXº”êm כ͌؈FÀtgù¦3F¨„dQ¡ßˉtÁn·¬ä|}1ЕޒŒÚ0Èr÷ÜÚyw´/‾Ƌ·—ÀôÌè’=ý+…'&%%Ëꎒ1Ïá´Êº³·ä¦‡õ±KáTä×vH$µ ‹a+ÌñµÑÞò=HÏ÷#WÕ»HÒw[^¯FºðÝÛ Õ]/L?¦iÚ¿Ð)˜ ’œ¬–3íQñ\ð°£o„`™OÆ_ˆ¼¬‰Ï–¦µqZ‰ft &Ïý¿µ³>†2Cí5ã¨mûˆI2Üf´|éò‹Pö"M³wot´lŠø·8ËÉ\µœH¼ýò1J…Ô)óP‰}àxýç‹¥{ât|3ƒ\<_ýØ[’ 1öÎbtRª2vßÙÍú-1 à¿á“cfÊÔxƾ‚O .M‘jEpëÎ?5C_ÓEù A—Š ®BiLàõEOuäç¤Àš²—œƒÇÿ»Q Ý$]Žê¨=XЉé'ê-aòtJ¼3¦„+¦‡¾\½Š=EÇ&ÿ;]©8VI´Í n´ƒsuö=iÿO´ÕÕvÜ;ÑFd~ZKˆ‘¦Eó÷êVçæµD#Ž]zç’júˆ“sUB\ê?r¿ÌN*¿¨å-(¶`–Û’å_×€8šx¦J¿8x¼ QÁôÝÑ òP þnî¢*üE§£àZbˆ˜³8À,ÃE*ä:æ’L9­ëþZ.+bŒÿö|Oóƒ r¨*"ÎîØÓÂDÄ™Ý!Zƒã6Žº9ב&:X¢½‡îM¬“¦¬!«QÆx¾q5 o+iX_µÂ=¦œH»ð»õ)öŸmnCUÃ0ÐùõERõ·s¨N}ÇrJ[~•F>GOàÉÊ7ŃV-[÷‘²7ZðnÄéhz˧ÈjK†)QR„ÔÇ[7¦­© V†ÙU^:sÚânƒÔ.ʪï^ò¸“"‚Æy¯uÚ¡?²feºX Õ¼Iªâ¸ 95Wè'­çEšoÍófu<’k¡Ë¯"Àãêû"w,è.+*H.fvªç^nt†¯<³=X¥Y×O×@Wücò;f¥múJCÂ!õCŒJ®rv^qy¶„‰u{»ØHöu±ÁdΧÕo¶vx½lmžèUâYö‰$cÇ=Ìk{Ù¹BíqMü/½«ÃîïT5jAýö/e4¶è:ïO¤ Šn žQ}…™ø£°Ó)||º ¬ö«'±åTU¸qÊe™Ýi JüoÓK‘_È|±« éì¨ýǬe=¹R£VU!`9[³ö{_-Ä&6…&­ÇrMUeÄñŸöõëû\^9tÁ¸íÆ ìÎIª(±ü|/¤Õ¦['a8+T?„õ­p×QÛãïPZ«ì?vO«óÐG™+}bÄ ×¢Ko7/¬í4س %&ÃpÆIAPyz3‘å$Å×m\Ÿ1AÃÒ‚ÎÅ‘ƒh朿ò}‡‡•<‚àŸ ‡ç¬+>.Pl”|éy ÓˆÀñõcI˜Áý³ Y™qXì”KÿeÊEävIÑ]¢›Wô§ÑÞH%]¤ÛðCAC@Ò©ÎŽƒ•gÍèg W`œÜµøY®…4°Ù>ЯµÎvGò µÆ«óñÀÕ Œ5 ËŸÓÄ¿Ò»^zoÌý„°âþà·…«•dH×Ò-–œÀà¼UuR‘ß0]¥¶+!ó+2“ª¨ªú‡Ý•7•RÙ³žêsmýÆ çd+ÉVIL2ËßÃ)ø=YõãE|ÃExÍüðŠ¾9ñÜb.'/ç0¦7‰“¥a”÷ÊÁ³´¹Ä¡­²ñ£'¯F}€¯Ò¢Híñ¹j4)ÍY g3H›‹À_Ä~Q –yêŸFØuÉhJÂqÅ—ÿИæ°h>°°®Ó”ƒå;l£!9z‚™¹½d¹ÀF2ÈéTzƒk֧熭ôð|EŠÿ¨W؈ŒJ9˜.²Q=Ç‚ETÚß_Œî?rU¨lžPhê¶~?A&êÚ1™ÒaòBñF¡V˜{ûA”3‹‰^ÛðP(º~ZnÚö¸°j¢+ÞÀé½×Èä ‚¶Œ¬_²cÀõ«Œ•³0bƒyÛ°¾r,ðâƒB_=lÁŒ1@ÖTOãüjgø°`cé{¶=ƒ©êí‰RJÅ}çü,Iíϯ°ItG¼Š ¼\í,×Üðï\Dü© ¹Æjy–E»œSfße4­é ÷Q»?–r&Xvj…a5é÷ÄËŒ­PLßú˜Ý¼F¤¬?­E.©Ø\ÇèƒvÃ&ºP›ó*p‡ÉNŸgÌS=Ö¿ÆΧá6ÇÎjÀ=C.;Û^ByRË=µ}«zÏø­F‡8Ú@²·˜.‰§IaOÔ»ËiËó­ˆÙ=¢Þl'TüY„­÷Yâ£[ÎaÙÓ*í÷€/š#ÕYA¬Z„ˆÒ:ö*3½À"ÃxÕ•¾LlSïZÜ2¬.ãÎÐ_=P)ÐlLVE ‹ƒ¢½¿Â†ÀUUdqh ¸›¬¬g¬‹ùôª.(€Š¥›¾ùòf'‹¹–Ý’˜rdHñQèeZ‘Fí¦Ü߶R=Šéª GÄr½ ï2ñÀj žÚ%¸¿ó±JˆJˆ ´i'ý/K“ C¸5(Kµo{µg—m"@$c®‘æ´/ÑE=¶…Ê`7ÑÚ Y¥Ä£Íî ·3‘õùa}”ëf€j3º°@ü<jÿ”DWñ[îÆçNò.QQÍλ°uI|c¨éŸœW•ƒÒB ¤ÃÊu Ýe&3øôùÃOH ؼ(mo’×ØK„¶ Ž+ƒÝ\¶}ͦe>5Ž0"#Øææ5y–ÖDU¨é’ýܯhxœ×€ûØv’hTqà÷`ÒÆ¢§˜ñfU9…Ù![g°‰p;ÄúׇÍ#,ÓÌxQ“œ¶ÜüßÒ½÷G0K6ÀƒÎÜý„3Õ­ï§km+ÈÉBl¸¿xa®È“7¯1FÁcêø–•S7ÀxbÀÒ?~ÁdvHœ„\µ¨`PSÐfC]¯ù¹Y±ˆÊP¿yåw%Âó½!¼Æe›7*œy%=ü-æÏ¥UBÑœ¦Q•{ûD¬Xóâ·vÒ5í8Ë ¸¬iªüK:Áx‚Ël–ßâè«ç$Èl¡–_î ׯ¸·²Þ@ÞÒ æí_‚Û†3C,»Î™à;ø- ñ6ø$¬ -Xý`4ñ_L–@Z¹™á®Cu¸Ôî1 h_ÛQnyeí°á?×UÅGâ”× Géö@…¤(-K©Á³žµý·–Ø ³çÕ Š³åú=£•Ò{°¡äè^ðÓÔèm^KsFÕ[?µ<øâ¸8«uiâÀf!Åe tH.ù—4©{ˆ¶<›KVJH´žˆ]—PìÈ„œ@NÄV“íNê [#ÞŠ2ËeQX-Dx…ùÛ™¢Îº¾Çã9ÐX ƧÈ eý8­ >½F ¦7ËmFï½T'^Œ£DÒÊw±qD®4‹ülVÊ'LMwööϺËHFNÁYÙÓ4áèÄÇÕÃNüŸ›§õ ‘뫘,O­§c~«Þ\É 2ûõÍþñÜóë­~ž³²'eQîê‘­£³ª6‡d<4Í£”¼\ÎÐàkÄÛ‹B4˜i 9LF;Ó—DË'¡hæÂœ*Ò±-”8QIð¬ }úüSž¯ßx›ir$. yå€2wÛH9DÛÇÝìi«/اÓeçC3š­#¨XýÂ?+!ÿ‘c”Ê<Äê…ÌkD. àH2€v‰Ç' æ©?¡jÐIÐâ,’jrmÈõ††P³Ï7W§|Ù`€›)ßëPfp¢y1Ba²–§„¥èÀNÌËïßVZÕP>"±ÆѾŽ‹ùAˆKÏ€oùÊÓwsï=*ÁxSÝ6!œ.XüÔHá,Ó–¥âº±éUO1N62vfîÓ ý„ºýÎE/LÏ:^èý·åb´Á‘]7³î‹Ò È­dQSZOÏV­Ç(ŠÅôdÕæ(ä¨\}UûDÈã‚É2ÇNé Äè¤1¿öHÓR±5—U¿¾(»k'{,»Ò©gzovœŒµ ¼!I7¥±´ã6F”숭1…ñ±<Ñ}¡!ÙwÈšòí¶–!B ä­|8Ì1Ýhª‰È‘Þ¿«JƒVlˆnÛWLgö—H[<ý6·q9bBtj°åVüìï?MÌ¿+á‚ʦ4%[šêK&ÇÏ11PµÃšyÊaA›XP‚k]«iÛÀ‹ì{inZœ{Xž÷pÄ?][“~ì¡D0obçýóž@ÍyÄÝ à-ÒðO²)\§„,Z -o¥êýGjÍáD¢Ú*& TþrÐüü‡Ï9Xªˆü‡µÐü™pÐ{YãTI  –M«®EiQhkÁærZU¡fG‹Ô+Å>ÃÄÚ„ÁÉÝÍÝñ Ô7õªžº¦Èdצ»á±;Ñz¦HÍ@;M—™Áó¶~ÝB©NÑ´>êâ‹9»OÀ¸þÐÙD˜;ì7`Ÿ ¬‰,y•˜ÑÀ@•zM€êÊåˆÎyÆO¸1HY° ¢!ÝÆo$ÐR(d†MâÎCH& ÆSs4âÀ»f;¸›ØMÞxm5 ù"äÝżñ³ÓŒLÝÖBôVwì\ÄV<$Ó切¤'Ù55Œu…ˆc-¢7 €¢Á»oÛ!ë÷ í°íÎ- /+nÐŒåóÀT!ËV,ÉŠôHJî?Söf° hdkºÐ Ëà {H- ‹ÉT>œÊ^5ኴ‚õƒ¦ýσÔñ¨K2\›%$? ÑÜ&&t^¶ê(œwða&×OFs[Á,€ðÎû¢k@Â…F]Šðì— EÒ¾6ÌúšZ$Ô[zÙ:嘢ð5?/øQ†ÍÞüá¢À?ÜŽ3R›´nrQ dE×K”ðí sà€Ù&—Ä€À#±¡Çx~ænUF9¥®L&Xâ#Á•Là¿Bño>öO€kÎÍÁý¬B(€db!rêôÙT¢Èµ÷×Ò*ïCá6[0cÖ²Õÿe&”àÛ^­–ÔkÃÖhõùö¼Š-[OÔÖøºèµW£oAeìˆúÒÃP4±<Áyr4?=Ïû&qÏdCëIÒ NMÛ|,»÷_“%ñÃñ¢Ä luX?ÉØ à¤32Œt)yH£™PÐ+Ø l~Æ’‹VÿA§$³¸‘? «B» ÚvÀ„ù±ô¬ît,Ãð¯]¬‡`E)"m-ni§ —Mmv¼!Ìãmå³²ô7Š8ãÇJï™GÊjMßtî3u[mWNå8~ŠŸÆjKа͸©FsÇ~Ãy }Ëh–#“.aª=Yí+ôoÇ1ü<¼øø÷@ iPf( >jd÷"´Æ ?” !>ƒÍ ªóK„/*gŠü¼ðªÞèågJÈ’´Sx¹@å7@IƈÄÍÿÞ¾ŠÝ BÏû :Ú‡ bHâ9i¨4¾gi3# *¤w¥¾oGýÞ?#Æ€ÎûÞ •¨º—°›„RÍWj¦£#üôIƒÚûGO^œ’M´m±£Ä>X[ûƈBùµIzÍ?WdÉiÙ#sç´æŸžtZ½å>õW`‹¡›eÑ(bÓùtð*<}áB˧Y†ÊÛdür6!܆ ë¾ÇäER ‚ꎵ¿Qf1üǦò^ͬd(ž4KŸøN! ›4ȉH±ÞøWPUàÓYÎU³Lp<þG3p?Ø›©ûÐ}2ø‡øÅ!ÏV†¡û]«DqÑNzÇÂÖÀbÄ€¦ré–T³0‘Î륽ïuOÇð„Y]­jn¶j¶@ç:i×w#ÈÙÔF²YUc»4½ùé'¡T'jøJ„\í Oœ©$ëmÚa[Tã$$#³ÈMˆTüEëã+±„ü d¥Å$Þrµ‘g‰ŠæX-N5¤ozö¯wä¯á,Y¤¡‹@‚NâZ(cQ)Ëâò\bdN$bDh"°9$ÚªªÝhþ=Ÿä€ó™¤{ø±ýï¹v“tˆ¯ÛÅø…cbCv˜òÌ{æ±ønȈº?mÑö{ä‘´‰þ¨ :FPÄ,‘nÈUÒI¼(X²°£ïµë®å»ÎÕjú÷žbDê Eòn#éœášçŸ.êû Æ?Q±   •­…ùüF2c’‹uØ×ë1´„{ƒ¼êztyCäž›W¹)ŒâÙ¨úSvXDö„•F?²Öy¡íúV^nøEX¸HK%^vÃ.W-À.²)E ‰¤’rÔå„x6|û1QVǪ‚ÄZ¢3ãàJnÐÑÉËöéÀ:è#5O°l²~eräN£HiÄ¥¤å–³ õ«çUÓ€ì•<þO‡10+¶œ¦ÊÆÒõáåLó&~k9E÷Íbõûæ`–(³–'·pÉŠ¦Yï°}1É¢w=!ós‡®1=|{£Þz¸É8IißAŽ/ É™ ÀÃáMT¢9Zç¥K‘ $³àÀš­°8Q{ÿû3Ùò/î÷ÃèÀT0ºÆ<[± EŠñÞÂ,3x’Žf©+ëý£´Ã=ç$ÂÀжگc^K µôm†Dl¸ÛÀK+Whpáæ_€‘)g;_-ÍHWíî'¹ÔD3!!ÍùÑeýØÒŠ@ý´l¹!ñ½V| °ÊóƸÀéqÎådQ—Ѓ ;au£ NŇ%ä³ zðižà"ìýb[‘£a!vfm`[mo¯¨HY+Æg¶(¦órG)Qź6ÏN;Ž@ ù•5ë#¼Äj`D-!ÔÆ”â¯4yçéDX‘<Ý‚(êB)Jÿ++ò5öÞ,^Ÿ½æ gçí8sØ“w3b¦ç‚^%á8¹áïÉÚªQ)f{ó(’eÌV=O¨Ôî4xq üH+‡Üö¹‰Yÿía/€DSù»Â¤Ñ¬‚²`ã?ë2=‘›ú}·’xþB½RÜcŸâ¼½­©ÍÏÚ?{ÕhE×%0‘OX=%†pd–4|ÿ-+TÐ3éý€1•}üøµg±D‡ùŒÉŸ1%Eãçìcu—Kâ3`‹ÆÑí>QYP0ãH#Uèl™¦D¸Ö‚¯?Y÷_ºÂþ¾J@e%/ÙË Õr5[üÂC>µˆYµizhΚîr„¼…™ûj‹Ôª÷9ʱXa›2?^ª;m ÚÙìøÔŸX튴0 ^åø¾%7 œŽû„:1ò žúÉ\¶ªè•u«Á|:ôíŠìÞ”fÔÛ‰_iµH7-›Æöc}Ç`»½½©3¦X@^ÿ(R]Ýô*Ž%‹xWƒe xJÓ»´â2"üxé57~‘Fë=/e,1Ť ƒzZYIÉ,]‘£PÄ7˘s'F?é–ò£jÌ+`8ÂN5Oô¤í¬ÉCÙ ¸ÀsÄDU*ÞCå:âS ñÂ,c¹`úglšd èKXׯ ´:^O‰o²çäÕÊŽÔlü'‡Gxu÷|`—$1UØá¯u‹ó•”Kž&~åí0Ü ~PÑ,âù{,²|öM—BƒÂ*¿/?=žÍy0U=dN3‰k/+@Ý༠ ‡Ã³Õ )Ÿ÷·tW´JohV½^œ‡XÛ*a°¾ÄZý,‡¦ ²SÜ°Û©ôú'ûid ðŒm:U8þB ã>ß=ÒÚÊ; –?³<'Is›¢T~ËŸG᪞“ikuÔá¿ìƒÒۯȻþßtÏ7(¿zôãÝÔ†¦ÆwûÌî÷ѦašQÁÍå²F1ãf3~Me÷‚ÐF®%$žN[²¢&î‹ÿî/§™iÍÏ0¢ËO#9Ð]Ñ…ì6ÇW™“8$¢R¶ßRÿ"¸ˆk÷„ÜÔ¼(u$äV,]B¾yT>Ç6¥O¤2>ÑKÒT¿‡u^»ò€¢ÑÔ_%€ðT%¬KYýAC+'Ðk"K òˆ÷7’(ëG»”+ Ø«zòSu°MâV¸0¯‡\½­Sr¢èƒÉ­ê*#ÌplY˜éÉ£éyAïTô¤Ïà šó†µ(´`VaÜ¥¢+í~UqN˜îýçæÜô÷þ’ä©¢ äQåQSúpCKys׊‰µKÀ¨ï¿Lq姴&ÈG*šèíº¼‹‰Öùhêü0‘FZÆ•^J¨z©‘Ù£óÝuPkÈöd—6*i­÷ÿ„‘í~¬®fɘ)“¹ï˜ˆ`+]´$—PóóMÉÙ¥õ ™h4ÿ+,ð¤ ß tÜÜÇ°lrÉø>Ðt ǘftÀzN`•’ 6OpÉ߸•Ëh]npÖ…¸HEŒi7Άéĺ_ˆÎA&m£õ¹6ð¿ÒVv¦pËX»”*!r8Ôt±¶·Š­Ý^=Ã.¥[0±ÌjW©å¹aßrC!Ê…áƺÚá7jÝCû&›yÐU¥JÕÉ~§+i×Fëq–ÄòÜã¹çF­5üê>ªSàæFn Z ò×<á…ïËBF¬_£®ÌŸŽEAˆkñ°¡úØãMfe¡YïçáÎöùØ¦Û 4uØO˜À¸ºÚÞ^øiæmþ&|:“ÀËû¤G3ÕÞSåK×Ê=‚ÙW>Ú]ôA°åÍ>ÌʽÔSæë÷úø•Õ°¹¦Ð²…åÁo5à0B“x€ÿ>äÌ Õ‹±UÏ€)ø#$Ñ9›N.Ú”/þÇ+¼©f “Œ{Ns×ñXk¶¬LÙ¸½î ß™Ábq¶Öòã³ÝòûõŸ FVÁ QÖÎgø^é_9W^5C­àé²øÛûªÎÝ1Næ—¬E±ÑñÑOuØ$¿Hæ”@‚Z¢ä$¿Sé75~Ôçê٫ꆴªTCªY§†O¦ 5©ÁA%¥«†qvrgÄýî¦ù³r}¢¾R_ºøë~ò)MÏ®¯­:ûgéÜ ;ÿºÃP[¿2NÃÕ¸Q+K|õŸfìÖÆqÃåTýUqW_×éšÝæµp8Õ“¼eXg²É/Oa)5 É~]9Ú7pMÁ‹·N­?M´Ä)^›ðéŸ? \‚ά8¼—ØÌ@cÀ¢&ú²h5“,¾ï²ûœ€9‰Ž“‡Šz1÷juâ-¡à;næΞ6¿ ˼ëÑ\²dóo£ˆ³qÀjèÊ›¢áå[´AEyÑ b‹¾x…«èæÝ6ËGÏ`¶‚˺¡e¤A‰ùíy$ˆh0Xo‚˜E­<»‡7âO˜éPga—]lbÁRèjQPü HÍ †zPfk9%6ãñëý,ˆžm½·Ö@Í/RnÇÆð; –2IάIf8.¹{9ÀQ])ÿÚ舧íþÔ%Z eg‡!Œ hV!Ã7íb£éàÎÏ«öhÜ HQ9ŸžáLU|4šñ´g1ÏëHár‚c±ø*|>¹V “÷Ú°·Lÿ¯5à¾Á©®ww´‚ð¶a)œ?·Ïôñ%êKúß}Æ´ §/®az;Ó¦'Ì'Hü¢õÞhYÌÊk³ÝáUàS¬ZÙ2”;ñ"Úµ ™‘pk'Jrë¬Vò\ÙõÈß¾,í‹ÀÕ°4çC³¸º%²c¯Lgúûž8®£­ß´ÜE¬iy‘Fy Z`¾”ÀQ†¼­"3kqý&(šè £;‘Y… o%A¤è{¶,³ÉŠ|4Ež´õ!á•î°ë/¹{ +´pÑ ätE=ñÑ£9w±‹ø:ð­R\”’“wŠµÈqC=IïöëFj 1qñ+*=!£ ²ÿëå‹wªõxœ‚xs¥fë>HŽ\Ô’[sœV¬¶¤…Þ3B”a¯Ð¬Gnñc†¿²êøNÕ®iô‡»üì÷Jµó™ª2›Ž¡Τ–!¯GÕþzÖ’h»â‚5¹Š_ìfóæ¿. "hÌä”mOüØ=ÚjDâãt`_"»íš$÷˜‹zÃcU4á€ß2PR2ì È<ýüÙõíge†ncš9‡+Ç)Ì-žã<कª*[¦ðŸ©ÿ Îb0!£û 1ú:­ùå0+D˜­Þ³þjv!Ìnk¤ J¾dVáÓ,ÄdéÌÙíÕp?Ç‹EýH¡/à1Ž…ó X@$Ø"²0˜ÐiÜž-Ÿ‹c-ž½a@œCÙ @®]tèNá÷rZ¹…}ZoÏëiµÏUSƒH¨:ùk]ʈ '¡šàA`+¼ßÀ ”þ|á¥Õ‡ƒùˆ_Tÿ‰/ô{uJ|jsT«o#Œ61@ í.á œ”(x?ótÚÊcVG™rÉ3ìÓ´XÊðÓjØÙ¦¢:úÿ|><˜ ŠƒÏ3?ú­åFAé kŸEÌ&×Xã²KB2wò!ˆ§›ˆ,¿85{ñ+½Ç9)ƒ{V2º<Ûd\}Ô· eTë¶`VòHLjÍÎ ãwÍXÔÖQy¨èÐfœ—`gYë[@GÖã¬d!^D6v%œX›‚_<´¥&'Ecÿ2¬›KfRP®¾‘ã O´(ÓÜdºÅ®+HØS˜”ÑKxVh=è¬Çÿ„¤råÝoô'¯ÈÕY`9Ù\äŠÒ¨¬éf'=ªß»Šk|Ë°˜Å°¬fòö ¨¸}©%Œ‚]ï¼YN+žØ^Ö_(Ø]ed`UL§¯Æ%ÿžÄ_ßç‹ló–ŸuÄàIŠE({X{¥·É]ú0jQ+(‚¡Y`m“é;Ñ‹™ªö §ÿvÉçô5¡9pv‡3ÉxÑheM6aˆ ¸Ÿ`}Û¾ó6H¬-Òt³õ'µ%WmÇä<Ñô?|ó¾:C8‚øwØhò «KòÔæÞ¡Üd®Í(=;9€Ì P$@¸±Z LRõIê‰FÿÆ¥{™,-%u.šò¤é°´–YRù9ãÅ´—ûö*Îñ~Šjåê¦)"ì]” …±&í»yK5M´ÿ °¸…KH?B\Ë‹]”¹…ÛJ\3¿;ÑF:Ö`$áŠæh£œÇbX™ä 04«H‰c×£à˜Ðy»’ Å4^ Pø¤ßcÜK_ØÑеÏ•9r”hÆ:¥€¢îz¸ÖȨG¿ÿ4ŒMŽ2· q£m›Z:ôµLÏ&.¥é#jhÝëV6tÔµø™6(—õ´cQšNó3)Ã;èæ3h?hÈ€]âðƒ@¨¢“TñAc"‡÷ª{” ö4?½ì÷ ™¾ƒ0H·Ãl­½RJÕ®#n¢Ý·lQ‘J[ qä†_œDr¦³Ïsñæ²uAw~q2BíJ¹ä À” :†|·ƒ’Â=¹…½íô˜N`{Òe°I]À †L ´Àdÿ»;Æ¥¬‡xÞ„éjÊÌçÇ%»…‚³ef£áx '=ù—óßáY «ÙÃÈdÇ@/Ø~ #˜×ã«V+e#Ñ9VµDSM²T[×lHÆe–^­õùkÍCµ\Þ¾±…†å€`ÏzÉuˆðþЫ_'Å$(å§ÒÝ•úÃIyÎ9¡RBR‰äb$¼H#åägMné\Ëk÷Whæ‘N^ÝAe4K„¢á] …ªïØ]¾HMì×±ôý”dIuHÐ<]YFŸé‚±Ø8­©€Ø°¯©S/>{žÕUàH %Ù5¼""èLx:K£ÎTü¶"8O0³7`\ÔˆUÚ“ÿ?]¡ÿõMÑ–AY ‚^c“_¾íõpgáôæã™öPŸB–~ :·’d,‰f×^½Ÿ<»ýMóÃÄ»ç×Û$|#n.Áå/¶C;€9Tl58¿´øÄ N ëRÆÙ†G æªÑqKØߊ0íÖÝý5dÝ/1•vÇsµQÆàíz9+‘(’[AkPë©SS‰Ñ$œY0ÄÝɳb©Y¾*£GDL,G­ÿ(ÊÝbœm(òtrÜ]bTnæ—ÍÜBäë° J3JÎ>y1kHdIðe.ð„€u³I4M„)0CòL_²ðz,@€Ú2BžÁÏáäw(­qÓ™lÂoÿç{ZÁ­DôųoûÔf½YýÈr›²ñazÑŽ  íeÖ¶Dñ;:KФ)-aºúß,ÂŽùŽ¶G,Špw†ÈÞIÐÉäŽçT*Ìþ%º)’\lzLãTMèJ%FñÅJ¼(ÌÆiX7¤‰.ŸZ€’g±ÑL&H†t1ÈŠë¾¥{æD²JÞf)þ.ßg‡C²ÍÎ,™HJ†¸!t-íÖàWO›x[ !jXvúaI–o5¹u²^Ã`XÛü1pÔÒçžî_^™QNaâÃvÐÀ–$±õ¢²ã1¿ŽçSeÏ4ÌÕ:ž˜Àã 7íS‰š"\^Vô%¤É¶{öØv9£N¥‘Å!fá |Â÷çõˆ!Æ‹íõjÐs5 [_)K«b$kÉ"uƒîÁÉEQ‘Ð7°ª;ûö8”º€*póàÿ?ýEÙ–ù~íû?‘¶ûÖùW4d ü#)‹¡vÅ#ìeÃ"i4£[Ñ“Äìiä Í‹JãÆ2õ­è,î†l2§£Y(G5¬2ÞCû/~j¦[­EÓÍ¢ B¥kä{ íºêѳ—b9VÓ¨Pœ¨í€; ”UY2Ÿ´ìÄ^\Ö‡­”; âhÆ,F«ª\¼B¸Ò[ÓÚ¹š4™XŸè[ɇ¬¢’r³™›=o¤Vܾ¶dBî#tÀ€N\\×dc™¢ÙÎí³Üh.•Wf®LŒÇr¿kŸv!R±¯“½>`hCdGÖñ+bÞ¼ÙlE*CíT»Ë¼÷ý’^†îÇ¿€¶GÐa½hç³ÅY¢Võ‡ÙÄuƒ£ÇÂ^õ]ûF1ðsÛºêò´-ÆëL’³•iÂê.Û,Ù$_HÖˆûTPñpµÌætÙý²ìßÈ+8—qÊÔ ž£W‚ïÿëÉ8é-"Í+q8y’ÉB®†˜c}TL!þ׺‡„²¹5)ýt߸”y¨©KÚ(ú)ß' Æ6G Œ°yÊÝvßG³x0Så`ö:!mm=uõyW‰ÐÓ:Šê*»Ï•ð‰z“GàN¨–:’‘®Ö^ èXv'øAü*¬¥mˆ >HGò¤:®î&Ã>±6 ±÷Ã|É DÍÚLQ¡~ÒE˜ofŸ¯[R¥Ú<ý/;ÁÈ‚ MHFÉB©“eÌ7š hMòº4…d°Â¡5m_ˆèž]v±)ÌnwM‹ò« 8ú®aÂW2:%*|s´ÑZü’äwô\‰PsÙ)͈f{Ñw}ìÙTͶNIÓˆ‡øÏ̧¨‘o• w,¿ÇÒLÀÔ•aèÍ­2X±æOôȽ[ÃT¡ƒ»ÝýÄaTþ²¶‹½£r{]н±TO—j-hsCH,DJPž¡gò¹.ø‚ŠçйY‚H ¦Âƒ³ƒÏª{zDÇqùØ>ïµNÌE̸.ƒ%øÜ'Uý¼§TOkÊS[Ž•šý-ÎÒ°¸iÔ”3:Ýž^œ_ "NÃoôJ‚ìGu"«g/€Ìç>^VëÏN}ô«_4¹t”käi!€é¿ôñ1©µöÝ¢ðÙã‚_ªdŸ+î'…@ï$ܽ”þ™L@IEp6ºç„[,˜Šúêåé$]?j¤o⥾hzÝ™Y@Iµ8`½îçµ5Y²Ç«"ÍkuðþÞ¼6qý˜v®±ß‰™s2î` Z³m)¾ÖVÑ·*¦2ß Eš÷ÉÀ¯ÍJ ý(^úy+2ðh³ÏSÀl^èúJ6»RÿNƒ8Ñd1›)XžE÷Æëf8¡` $&NP­†Þ™3£ÿÕ.lQ_y{l/+ˆzæû$;ø“ñ½ÄMºCl Õ¦¨¨tL7°h§™Z-Q9¬¹B´cëÈZ‘²á ¡)ðH€•ÊÊo¶‚ö¡›ùü‰ÑŠ¸žKGt¹Ü |žoÚY ‰âH–ñæ ¨ng£I.ÿ y‡*2É Ëp¾·"¾(”ż²~Še GE.PvÓi‰£’áùñÌ][÷?ÕìlàOÂèößZ ´f¶—†Â]ÿt|9¸Uè½Ñ4O·’mp[î’¿fþ.,_Z¤øò/n3k,¿“ ,œì£2wEŒ¢5¶„âq3´AÄF_/¦£üÉ ª•{ÿº‹É!gÛ°‘Ö܉X™{ãR=åá‹Ü¾Ókgø…¶¯õq¦{Žcx{`½ó&ª“šÒ  9EEÛMZqñqõ#[,MÄÂ7› [AúíÙ¶]hˆu=Ó{VS쯪ÜÏ×4øÏŸ;á¿jÚ×¹Þ_šWÐÜ2ª½©$×H׺¥^ÿzí…sÝ9w®JÙE»k[–X»KÃÑÍb@«EÞÛA~¶€E1”HZî®÷ E@YÜ ×*YùÝÊrUÛ -1DˆYà²7ošC‡'KF~% }âÍ ;d…RO½!«;³Œý¼R ×8O!fÝâ¡k£èÉÿÜêc îÊöt·"o¿j{J)@šÛ²Kèèïàµ/êoBŠœØw:t P_AGFÎÍ/¦|ÕJÍþ?V@àðàÔL)ß$ÈExwTüÐ$‡.íí!f œS̤® ök÷€Eq¿¥Ä¶/׉Nå¹âp²ñž ö…ç ĪCän¶|­S\ÏÉ•¥Z†-³˜DËê'_'П$ÞˆÑ š[ã.ŠPzýçE©‘Ä7DÁ/úa#øÈuô¿ÌÅ@·ß‚¿Å Zµñ2PMQleÈ™¥ ó!þjlN­¬Ô›e.qÑ•]µ•y”ËÛ‰¼B+¸ktä9O¹f§6ÆÜ?vöqèú&èJR·ð:±OUZºäÜ¢^ºa]¿LèƒX ‚né#†×1+ÞœÓR.DÇÓ®”±åÏ;®/%Žˆb›?ÈH‚Bw¸Ÿ»O#Ír+À~¾Âà#¿Z²ƒågOÔJ Õï§Ûæ)³çoÖýÕh€a²ý›$;aU_ šö/Ô¸rÂñfŠj¹%wÜÌEþÍgwX£å¶+uÆ¥ ;²2CŒ>Í_΋<”÷}åƒù‰&Ö“þñ×7öNeCs¸‡™X0¾jn65©ãïŒ(ñ¶` _Ø&ï–˲VÙã›ö#qû÷Ý¿/„ˆë¢)Äî7&ÿö†Ø8=³Ä?“`ܼœîÚÁ «+¿VÈ‘¨Ù|Êqñœþ|$´ÇЋØxý·×Œ:Ã’`sq”Š93˜¡Õ~/9 ‚¸ú/®TÚHp—´ OÿŽj…ã>ÕaNí™Ý”¿äKYõœñ©K¸Wè8pW}›y„(ÇöA³úÇ«Äy¶ÿÕ.L—c‡ÛI…Z^P ÚÆ_ð«ªb8t¥ÑÁ·‰ç“ŽN[–¸À•ÂL‘RcC×JßV¸;ÆÚ[ãA!*YçËJ\½ºR]iJo¤Ó‘7À¤,Èy‚p'ê>ç~x_Hÿûeb½¼®¢ãÑê1ˆþ-Ù›&ã+ûG&jñã6¤N¬Ló➀ KH³.æm¢ûäU””¢‰J•ï4q º&Mz¹dÊáSˆêþñqã ±!FV ¥Ð);M_;Ç‚¼EQigxPNoÝ·_ ‰ žA¸w`,’쵘w\¨I%]<ÖÐ1Þ Ç§ÿs ÿÐÔì7zQÛ¡½T<ÀÙç,9G‰¿,Hìí¸‡M#ß1:6'é3À3L1Þå’GÿzÓ‡ßå#Ym+‹a €q¦àÌî^}ÇÓýǽ>!Yy¶î8Ó8äYLlÿù?Ì©±eh؈ðí™ËbTIý~'ç“°íØu¢‡’æhÎZ%»vtŸY-+Òéúë€R6 ¹F¢ÿùge®;×l(É—/®Î© ŒèJúû²S7{ÎLá²Ã´±|—±Nr;}ñJ¥Ÿ S0E0Æ/@‘^Áóþ®†Ša÷æÓCÞu»MåâÜ>ÉDz¡èœÿu8l.0°õ°IíWCÔ[.y«Ú©#ADç[÷µ~½w˜â¾¥ŠÇƒíÕn3ÂX›hÄ…ë­—õdðvsNz¶\•2CÞ]½€‹mÒÿ Ó%x \v¥©ñ„W’Û]Æ_IéÌ%$lÈ‚ °ÿœ|-Šò¬jŒâ–pS=¤ú’í­åŒ…íŸ  Âz——ÓÞõÍf+ôÈ1£²¹&ÇóaÓ"%Tã+ã%Ï1:¼%H…bè>©ÄnÍ`1„S1Ø¥¹þ‡ÍÕP:3§GDËÑÿO·YKyÃÅzDn “"ö_-ò~Ù¼é¤À?—®ï°{mæ[;euMq4èϲY -Ü«"í4¸CÆtá±ÌüØ9šÿbæ+Õ†R„=’®FÞÐ'#QÎäø(GËÇz©q½µêg&Nq4É JÏd̯láCÒ^z-iy>î“ä?ÌÂŒ'§m0´Úð“öõY‹yœ‚“ßµçÐ~}3Qïíb­èRªØ²™ö‚yÔÐôt¥ NJkÒI¿¶;–F]$wª;œ9ƒµÙâÓ ÅùÑ ÷$õ[«úÅbÃCž Ü¬x¢ÚdóÛ»)úfÞ€0·“DjGfUpïxxpcï?­ÝoÝ,K?„%ø H*_?„Q“ãÿ Ê{3qjŠd³2a£ç†<-ÜÃÀÏCü_€PñÐRU ¿ÜX:BšmÙ¯?ÑyOÁ€#™_y•xºäZQæqÃHÂ?WÜ~Hp|ˆŠ¬`üt.½'Aíã aËo:îÞ>ñ–vëb1–©¤ÏNÏPŸT1¬z’~J­RñYzâ·æKá›–%È´ê»5ûCRE®\ÕE>^g+ÀE¢N’a…©g¦ûžÊŒ®½5 ¾ÒÞ…wÄÊ2ýìˆè2UT~éÈ»Q«æT*BJñŽûU¢¹ÔÜ‘,p!C‰r=¹šcš¥ÉÝ'   ñd9’eD1CyF³–*ߙꂗ§0qÌ8úˆAá9™s ‘P.¯!(lF噬|5ö:ô¾þ8R™ RJ^Hýd”™§nøÃF”ž¿PU £h¸ö†õ&[÷Ÿ9 {“}™/ÑCÌ‚ò´ÿ>Mœ(Ñ(ÆÑäz¼@2G@T‡.a×õlgXQÚƒ®U€L•*OWS’ˆ-*Þ™Ÿyx ):NýÕ¤þ%$„û<óûâz0XfsÖ—^y%õuCõAùòF\Ìæ P‡}þuTó¸…&ºÿ?g™<Î´× ›Tuõ_€°fŠKÒ+߇Ûú>ß%²k€–d¾>†ö#ëÍKv’«–:¦R†,G¿3£Æs;;Òø*ýØeÇ.¼øwnRM!9üüísUP¼ÑY|†õhqÇL¾Êƒ(ôåQQÍ2žYð½ÌOø€œ’šŠ=?¸«^]„€ÄO X ì-í¦“XghüU´Á)ƒ¸þ…d´—¦y £QwÎËü…UH'ÎÑc-Áu é©H.™ê Фã«wNf48}7‹’!S«&_u4ÀÝï”/ž©•Þxnp(”ŠR´“ÀÅß Í9†íp&Ìk°ù°í{¹ŠmWgx ²Î…~8‡=éõÓ|ͱœˆ‰zc;„ü^DÏBMÕð¶Ùbßí”~cÁS5æüQb-lÃñ¸çúãó !ÃÌE«¥½®!n¶ûKÊïDiÈtÞRE)¦[që½rE,ÝN¶ôp…`r‹JÎ"k!OåG?âIc¤£³ŠOêÇÇî`®í³5Npâi‡ßã·Q’ÈUÑ*ø> ЦÕÏëÇg÷( Ž_®0_É)H¨˜Ó- ¢•-EùˆH´ï4Û­ôÊ½É f¿m òU«~òB11¢qjîzõ6~`-Ñe>ð™îµ![IëdæÜu…Çr³*_Ð)?†hPç6ëº3&”­ä9ó3òaù>’«ú߽ܽc .² ¤ýäüim£(é¾H†O‘‹rݸòÒ-tQ)hl“ø …ÚÄê²É§œ¬Z} è6¤çŽs° «• >e¸iRE¾ŠLn#Ì·wQý×}˜½–4JÎ>ð@"¯ÅÁjŽ7èvsL7 ÜÙKß)ö‚³(äÓÑÅj×$éÅÞXV2“<1î2l‹/>èÞ›p›"¸ÁrâY¤^šS„©Š¶6ìÚ RraÒ(5¥/î s }_èzJ:ÿ¨i,‡NûLÿ!£ÒÖ­3ÕaKi²á« ¬9â02O-ZTlÐŒÔì“q6žÃí\r9ÉúÜZrö¼ŸÆº"ô¢H—Š'¶tæ–Öb»,ÆÜ RQ°ãÍ¢J,Å~§ò! ¿EU½¸Û2Ñmªa‰À!í»ýñ'£[ú«a.ஹúç«2Š#§uæ~wÌç×uPƹFµÈm´Êlxù¨(=ÕËÞ.Ón¶LûàÌéÑ4”ùG9ØIJáôÜSK=u·×—^hÿr@î€è$ÂÛa`6ù»¨-MÅý4Ÿ[³™(lëa}Ä>ËÜêŸïUr\<°~ð½ÂÿƒTùIðR”г"aïÉ]O£÷2UùÏt`%ÞòåÅü wlÖÅ(q^8)Þ—QOC}(íF~;T‰_°ryQ.Ÿ 0:WîO_NòÐÑò¯Þ l´CªpÝä£VZË4¡ÚSd?8ÝbÙ'BíÛ2¢zÆ|¤ÖaBdi“ w)ÈèRÊMaÞÆ÷ȶ´Øñ ‡•óO’µ3Œøkã_5q–ÉËjGF.Á¸Ôu}(©YF2)-²½ï ”÷—ÜÕÂbÏ€™óÑŒ½§WiÉ„Î-Ì2Â)ãoÉ?Þ¼ZŒ²Fѯj͵ayç«uÙf0a·ºòúŽjrLl8}¥k …t]Œô#ì‹ù¨—F…áRK ‚0 *ÌÔìóFá.RÿáÌù¨7 [•#ëqî.Jš˜ÃœŸÔ!lôÂRW§ìÃO« Púa»&j jÂÚ¥£uúøª|~ËÇij[`=(JuÐ;”,A¥JVïèõQY7d*8U)H?+õ}WˆÃég˜·äw xmâBèNk’WD!ò9f)UOÝKëQù˜_ù½»„ ¦nv°G/â!.?]˜]Mr”*:à/ÍSÿ=Ée¥ëCSm¦¢‚âÐe^çñù*@GOˆ·¡«J4ÅúþøæklOR‚GEú0¯Ç&ëYÝ÷ÂQcWiâ8pÍF!K ©±ÄÑ¥¬Rƒ™ ÿW[mR–ˆ3ÇëHÒ#Jö—á”Õg4G¤wôè¹Ù×}Ûf*æù`|b?p\iCD0F…—@{£[ѵüZãÏùšáœž/1Ü°äaʼný,>{e$‹ tþij´" ü0!vâœó¯Ö!Æw³«IYºÖ8²ÅX¾G¤0ÊÑÐíQÄËBãÓÆÜÿ$}H’)«{¨Ïò®NîÜëä°ô²]x^»Ùq–Ûµ(8C2o¬$A T2‡t Á:Ó/c£" Œgl¥²ež½©Åòy4šcÛFL7”lZÿí dõòä| E˜Qý»'˜Ðrooѳ-• 'B)a´&|öŠs¾>ã6Z®f.éf Û¿¤RÚ;|ôÓ6ÓCÈ°'ø`Q/<¨W¸ ãèy+ÃW»È$ Û­ÕM_Ûщê¶V‘âè ój¬ë-5q•š(¹â䎄QÖ…-¶ ÈÁ¢z¬q-6µ=Ùt™c&¾%>ó¢_jG„n¼Õãà<¨fcSȇøÓõe¾. Ñ|ÇÍ£(%»ê}“ù´3ž—_k`jL¤àD€|xú îwvY UòQ3‹«9³‹ÑèlÝo€ Û#•˜/PÓ*þbdµ'™C]®o‹_%‘ǵ´ZÜtsíƆŽˆ%Õ•‹S‡Xm÷ÎWù™É£·ZŒ†©¸aà sn)åuñ†*ý×r¤¶fv¨Õà%ZäåômùI+«Å”_‚¥‘eû‡³±ÁУ+Ùßì_Ø9BÏ®jª/rÖËÞž‹²'b7b„/`¿*¬6€{àÉÍ-ùÉ‹v<);/j+Œvê_~Û¼ÁŸž†lm+ÁoõZC[•@Á¿ÿƒˆÜ†!Vùå@ôÎ`(ïÓZ¬ uÏ7ØÛ2@ˆÄ°¼øRi× ÈØnqSxk®¥Uâm"70ŽÛfVn,ôhk– OÙ_„0·Íh3ÿþµWêÊ4€®Öý±ôÂÀãn#¾Fm6¯U =ý¶¼È‚˜¼Î£ö*¦5Y{ •B í]!âú+ƒX”£ßÀ$¼eu¡qX1Á"ï™w2Lž)ì$?m žœGó|Ìä69Ñž°š…N©Ð€A3$ä…(4 9=÷Y«BÓc¦õ¸ºÝ–\Ÿ±à[(ågܹ§4®Fߪæe-ùSvtMôõ8‡4( /ísì°à>¹ÍMIeµÁ9³ŠoJδì_m"›ÝFê)"€å4Á8z˜·Jd;θܡ›S±êœ'kšºs’*¢(ûí%\þFb’bÔÔpìJ EöºPÙ|9ÁËþ9ŸûÒýËTÄ/ëýÁ ªJ½G4`VeäKíOœJ¥Ø‚Î,XìÒÐF<ÚÆpä5P€ Åû‡×iÊÂÐ&oWÕ«+‡ð£ø´ºØ>LŸ¡94@ØPJù{+êÀo½ª%˜¥y¢Žvð]w!*;LQ¶›»÷ 2Ú .ÁÈ.¬ÊŒs1;†sRå<;6N7nÔCÿ·: úDŸ ­±¦Ä¦kH2 ’9M¨ìxPqe*ì.ÁÌŒÏB|™,êh¬ !wC×m²pNçy¦i sªàŠûSM ì£ÔïNéêJÄÔŸª\ÖJZ©5wÉÈ<DbQ´ÐÙy¨`\pKˆ¾§M¶=ñivÍ„—•ÌܘCâ)Ж‹/̉ç¿Z+ØX0*×v—êJt&‚n|pDœ€Î*”œkwØ~ ç«eÊà õ¿Nf‡iwH?þû~„9Ü̘V·ÿÚp½ü·=óíëqP1÷phEï+G¸L¯€¼üqÚ؇¿8FÓ'§njy'ëeÍ<€Å^’UÆuëÛk„Ó|®5k7æïçÏC›¼Y'b£g‰(Eæ7H\%­él8½¼ØسÀ¯ºuF4A¤ùa£ÝïÑêk4”Ãú@<Õ(àßoÉO竤ñîÊóD&&XÉõäѤ½6JœØ©Kq ¬³Ðï#Ïí¬?ð×|¾°.Üj-Ø“®<®tà°NÛjÕ¤Crð*úyI\}1 œ¯Þ®ü¸AÛ”dÒˆ0G5© «;6ÇUSÚ¹kÆ»§å2šî(ù§šÜwÎOÒ¯–Æl³Ù*v>*?ªÎìhÆèc†d'|VИå­É{-kÇ×R`ÞM’‹µ6ï®aõKëFËHu$ÿ/ÄñØ~i¤““ÌL Ó;„:p÷ a­x¾0>ú s]Iʶ ù.í¨&ºïÓs¾Ý%•¶’€aÂÙ•&i-ßwƒU¦h2Ñ‚µN2ÈR Ü"óúì¾É×ÏŽ‘¬Ù¡ðÓ¾f7y:€Y‡ëâúÍaZ†]›íÇ;Ü 6¿ÆXýø¢ &¾½ñ¨žfç¹ø²lþ3ìç a\Ç­Þv*ªö"Ñh“º_õ!uµ] Ï7GÚ÷}صԞ‘€¯`µ˜¿ÂÞ6¸0ö¦€í+ø3._Èj¤,!–KC1Š/!èZƒÚê‹ü ;=é+yÌ”-žæˆÐ/*W¹÷{6É©ß–6¡m‚óÓ3EÜ ï„_û*Pm¯E9üyFL™IýôtƒTš€€ªC†é±1…Y[þ“Íï3ëƒ=‘W?„‘±=~êˆpü&Lcå’·4/pmGÍ©•N ‚LT˜­cÂQ>Þ?Ïy·±Œ_çxÅy`kûo⥫Ÿ€ Ú L_GV7lX6  ×¼79¢%‡nLJÆì$¹êhÛÍôA[pZãySÏÑ—ši>Yù­hÜi}âLÒ¦ˆÔœsVµ:¥³CÛÜ,ŽÌ4ØR>оrFB¨x}‹–­G9î\*ð“ŒAÒ?Óg {§,£*´é >ËÂÚ˜ J°Ô;·P.M ª(–Óûà{rb¶¯³ªc'«‹xƒ¥$c¶¶ŠW‘¢×ÍYÜ䧖cK†Ocžc¹þß"¯‡ª¶Ú Ù‚9wD}VdäÖcƒÛż›o+©ƒ³kdËŒ]ÉË×jsö[v¥Ú vê80RöŒÿßN|š…ï§DÁ†VrgL—©ó /PyÛ‚ŽœËÙÛÄÃRgIØ ¦&E@‰•½-‡Š×ÉUʬikÔ"/º:‡gª ŠäÃxˆþ,œ¦SvˆIkÒv%·™7þÑ…J~ .'A£D6£¹‡R½:­ f–#Zö"{Ojñ£sÿgèâQ›Fª_¨°,s;Ü­íòÏ8ý ½“†rØb îFcå@íÖ /Ú‡µËåvc‡•,Y’aÀáTù§wà*ç/f¥ØÝìÓÆPïûg2õ DŽ4±û%°´Ú„Irè*SúàQBøÄoˆ<É9h¶„£¥œ¸ªšú*íòXà7ÛGøÊ? ŸåžE”\ìC-õ|î#X_¾{<„•d|ã™=5ÓDâÔ‡ô1=äƒp‚U(ë£á µ®îx†°íÑ<þûA—v¯Ý貑e>´qZ/ÇåO…ûç2¿s,X„ÿÀ¤5ëÏ^3±ÐÄJØXÏ*!ψÞr¢DÏh3_NÕ†T BäĸŽÉl™¼}hè¦Iï7ÎDª'?(¹ûÈúv +g R €µ ‚®Š€3&yZe ßù7?]u§ C=>Ô#£X‡1Ž–Zظ`Á>³ÌÜ‹gÛÂi]{=h÷ß5Z?)Ü8†ù“ oÒ=1”ä~ }ÔâÏ zûs|€oòÁ¤¯\iC†è‚cmïð‡»ñ$ò“Q ®k2Ξlô ѽï˜!â9RAi*¯>&`„J!äü‚"ŒÌˆ‰ ²#ÆA¯ 1@Î"ÞXAT$Å$¶Ùß0¶™ ÕivëWÑ’M‚ ÁŠÇ¾mÎK+Zù§Áâšå¦pVp¤§%÷,HegWxõ V{öYn+ºóQJwéaôÕW7Ë¡™WdÒŠ5·NƒÙ^Ù†·h­JZ‚!`\_AÂTÇAõ¡Cè&˜À‰U3÷R×BÜZóÿfg¢êdà‘jÄÏc—\èßÄYâû$g°SêùdÏ{ú+9²Ì ùŠ‹h ¼¼¢¨Í†ÄB/ëçïØj{["pJ¹`UÏOT V·‚¥ ,§]¤ äDŠž}}Qù;^tsXaîi!ÕmEÝõÜ$G[ÉDb ^ôëÔ,·Gža§5þ$£Ÿš”+wù`øàIŒC˜˜AÒ; vÞý¯$|Ðð(mÒõß ¡:뼬‘9å4ò0Ð3'Ï_˜_˜·z«)Bóúq~§xÀUUú3Cýè·‚Pša@PuK ù¸õ\QéçrzJƒdˆü_]¬LAXS3EûÆ%¶0òv¦…¯†[ˆa<Ò˜ÊîÅñLºì¡F ŽiîÇÑ`=æÓtjŠoÿ’ò;‘Î= òRÍÆóŠ`Püª$@hjEÇî +:êv‘VÕr@~¹s¤X¾@€üçælsï“HuHø²\æ,Ø÷¸äèN2«{,³«aú5=¥r ”Ó¡NçÄÑüìaÞßÒHÞo’™ «/"\0Ï¥im'=»qêÂ@´–ÅlN´@ÙxÀù¹)¿Öl}Á9¬õÁ>.¤úæ5NÎäÎd-ºÃº4½éB $AøÙ|G©<ó#»Gí6–âà<П–²g&{¢ñËõs÷—¿Jñ9PXU@M(É5üvkjêÌ7Ÿª¤&`h]þËtÛ Ð¡% [Å"-öhë‡WQð/ ß¼ÛH-1yfÌ÷¨yÑ·©ÈE‡4VÌÛÖ· -‚Í_Ù,ÞºôÝ϶Ž¡o7NÁF{DÓÞqRô:]´ü{õ ’›жüÂæ·uïû8¥ãkay꧲Á“ë7šF2¦f´Ð Ž¶Ùʉy"ßïZ(ÎÎóÝ_Ÿ¿Ôý·tÉqÅšªë5Üݼäö´A$ˆ"wS8Vh¶iî{w;0rs¸Ëî×3<î&Ò¶‰™n` H é÷bG§Z(Ô±»µÁ«a-í¬ÄøÈøßÞØÃühB„ðRHÀ•æF¢¹Dþ9=‘ñÓþÆü“¥ªÏ ˜QØ•Ub¸vøKó#+A†ùÝ׳/ðü†@ÆgÛ;ål`]i?ì&ÉbQª|ïe‡Dqk%J‡ˆ¹|(5ÍY>4obɉ`—T„«TkÞBò%vN„ªi q-½@BÀ Ó\Óm¿²ÂÚ¤3ærôʳÑ+l™\¨‹|Ë—\æLõïlV^¾Å”Ī…øÚñ É[Šf„í÷Ía»Ïj>‚çÁ}Áý`'ú0ÂHbQváùÎ=¿ÎÓñ#&%Nµû0\‰ZÊ,­­Ü§ µ—Ó¶Ö’œ¬š£ü\·ç@gT˜šèaé„×7›hàâÇ @ÕÐlñÛrGd¶¯µwy"æÖ"[È“7[+çTÞbFKî,s #:nÛåÖz§Qa븹/XÎeN)lo)çIêc‚™«>xoÔ¢+½„ <¬sžC½øÙIHdÉ¡…!‚pí)ì?ƒ µ{ äzzj%@!IíÒ'ïàýGÊxl>ÇC[wƒW|@†zÜEQÁ³™hhU;.•Î‹õ—sþ²w`@‘'}`õÊ"=nëó‡r1hã¿ÁÎ'|ò ÿ–DÁPmd·—8Œ“Ž[KT¿Õ(?JË'¬â…êô¢¨fŽ¼,¨ÂiSHý¬ü6rÆ”NÀ€»Ô“ÀX—S¿,Œ†r\Ú‹ã|‚€úŽÛÃ@!˜b•¥ý˾ __ ]…íí”7§ KŸØ0Ð`Оvò› ·„¹X+ПX¯ÙºtU—RF܃m¥ÕÆ Èƒ÷s‚4Í£KC¸@´¸Î¡T ¤;®{­waÿú5ù9eš†-‹(±'ž¨]^r€à`ŠB¤ð,Q<õå•8nFON'®ê"ã°xãÊߤ3·Ù =Лø,JŠ¥Ë³º :ž#:½‹å;-–z±¦frGþèUmMa}-# ìÔ߸ó)«6J Ç ¤1ÉÐxúíhú¯Œµ·ÓR˜ªÓU‡r=š'ê,¢¿Qüáö­{•Š3Úƒ¾¯>Ì!÷+ ³õ_’…YD,WÆ°±,¹jñÓll¤(ø'ª 3*ó`àÖÕô+,e Áñ{µ¢ôÓ‘&§K‰¡kÓÕJËÌŠ<%…ͨÏuò‘þ}£;ø¬ê†»ÍSR0$çFÍu•A¶ˆ¸«HFEè‚%n_^¿Ë-¯Ô–ãµo7 ãÊá ë¤"pƒ»ËVg‡þз«7jM+Â){ï43öˆù¸­ˆêK×Öè“£%Ãê}ˆÆ'x\Bã”:†Ä4IÀä*#ã~c“¬•GÛ×FÂh„5œ%†iÇgõ„]%»(¡ò‰U‚,’'{f!’Âw*òæ4A:–!Oî³|18nÒv@T˜ÇEØPÔq¡ ?aãx3J9ÁÝYŸ˜Žµ†‘VÙ­E¼Oiuà’j¾‹4¯º¨ê´g8ýQ)ÅM†æ‚ߢ¹ÌVF._ë€Â¬ 5‰2Pôî߀ùÕöUÞÌ^…WŒèA‚ß“æõô“3%^Ô?>¤ÙWH­‰ší÷8ÿðNVíD݉”&y/bR5ª…ú@Ä-K)åwÙ¤týh–úŽpß?—Yn¸?d$‰Qñ÷í‰ßÖç†ÐhÁ |'¨G„XNl1£S2:ÀvXà@O[Ið¨·%×E—a’ò¥; c>–äˆBR|SlOº‡æÊ/ámúÛUu_IÀí\,êTG’Dz֣bÂÁ¬¶ö  &+Á¿à½Kü¤f}É!szç>»‰n\ùOÿ·n/ZæærÐÊ~…Oôd?_WÖ6.)”[aîEÜÍÎè2³i³œþˆ›ºÉÄ2ÌATÐ2µ3ƒ%ëH7 3Yȶ‚&æW[n©;ëaÅi?å‘J‚žÃw÷²åXÁ­Ä2È•ú‹žp™ÿ;J“Áü¢ó`Ì5¡€W‹éŸFMeºV’~RœLP ñè´ A\{-ÓïϽL=ûÛep L̬|¿ã&LyÞеú¡Þ¸v™Zß?õ¶fïoâSMüd1ƒhNïŽw—‘I ÃCÆ: >.›O¤ óŽ}ü{⻺Üß&1¦”1ftz4÷0åªJ 5bÕ˜M2íäî,©˜Èædg8óJ†¡4“À„ ¯Ul™×ø)Ñ¥Ù·ƒ3”»bÊ«¶‘+žˆuý îÑ/{ ì¿3ÑRÆîK(I󧉪,L€ü}F°-/ÓX.㘫Í"»Íõ‹YÉe3xŸRÅBúa°êÖè>f¡¬‚‡¦£)Ð׎ú*0bJ «@ïŒøø)ÅA3ÛŒv nÿl¹."Ó!bEÏÓ9âFDzZËeyå1¢°ïˆç·ò »ªþÊ$Ÿ1¥VŸ¦ç]§¨ä¶<æCCÌÌ+âÞiòl{ Ú<~ÚhÒ4Ô^ÎÏš\§5òcwwÁ¾.dÃà Ö.æœTý‚´ÀÔQ4o8#laÀÛåOM+f{0Z÷®d¼0„ñƶ±ž7‚ˆm=n× ?%oäŽìV`cØÂr|Å<—…‘¢÷‘–s£êD·M‰&×ô³js²›Õò$Èã¯íüÛ§3"ßÖûg1‘H>Bu.÷• P¹æªûììÐtÙÊT™|Ý°¯MA€~Áý£Ðs÷yæáŸcc¡™ÿ»š¬D:qAÇŸ5$4݇ó9®™¥÷o”7¼àƒ»–„­µŸ>ˆ*:½ PßRzø(^! [Hv¼ kã-DkÅ ö“\ƚ㜔ƒÑƒhpL £Ù;b6 ,‡ùX56úo*ôšqÉ©ƒÑÔÖ[DÛœ"2†®•)|£I6Ä÷kD™o™vu°E ÁÈEÀô\Æû4OÒò *¶E¥nôÕ² „•x«p #¦ò8Úpûí¸Å%¼s/ù>s^)¶ÜgùX ³¬¬Ü:#„ÐJy ÿ³.ñ¦ãÍ®ªlT׿WûM•9›@ÕczýK©ß³_y­.ùÔ5*ŒŒ#I‹z·oÓ«CYé#ßJ/à^皶¼ð¿ŸOlM z;[ˆÐgr‡˜nrŒ©Ê@\v¤J>|¥åX¹N¨ò`Ÿ85/’ÒÅzhRw-Ç&Ê‚Å%é"JùÖ FæšÙèj}'ý?‰–šéo‰Çq?:0Î-÷¼Ón½¡r@ÎìQ‚ˆôÛÍï­¨IVƒ¤™˜œCôPcPËw‰L&³¨Ag×ɹV­˜ycƒ“€eºü¬ìª3UOù{x eüæT €Oc„¸5]µ¶U˜°cà»ÇvšJ(T1HâHÁû?­|¿l>âWXU‡Î2ÑÃêˆÙcÚÁÇà[®ç„\?ºè»»ÚoÐÓ•: yüòñ*ë525©QƒÈ~”Ø…µë9ȉÝù_h°È§^rÞë™åmŠÂ$õœ©7]7Y¦7@!5³®°\Ð' n¹¸O¦àDr(Â{•áòÊÿ±$膊[pÐgX¼rEЪ˜§µ¡Ãèk²ôvrÔ­þpë[…÷¶GÓnÿƒ I˜à¥ Û•‡OÍw¯gƒ/râŽ[Í;¥_F¨‹‚ %Ð’KQjÇMØò Ê¿*ÁÜX÷Ù‡U®C´Þh@¡ë0š;:ÚØý4¡F>¿-gW®P\Q’¹ÜÉß~>j>Ë!äÊ¥Ïf7ßùrÝšâNÔHŒq¨¡[+Ó®¶ïr|¤ ¸w+¬žââ¼.l·Q”\3´OŽÊÅ=EÈU)ÏsÆy¦ \žµ±Ô†‘§¾ßøÊF;z¬ÍU­;2†\Æ y¯¿3Žmœ­˜va%ÒÀÕV"âUSŠmÛ¤Âfa ºšHj­½ét»‡ìÌ ÆnÁ²åã}“H_,FßÕjÔ,rDÍ/<«NÄSWÞsüJðvOÔ'órnŽ8¼}Vû7÷¬¨cÁñ Ž·ñCÕùì?¯g^ûFTçbøŠzI±*fR–ñl9Ö ´K¼«•›>eçá³ãö¿h¸ƒ4å ª­ äó ™î­k‹ÇMÜ_}¾‰ç«¥V§q6ÿx“b°'|B— êaûãpSÕ•j¨\eÚB=XÅAÕ‘mòÁðb l»j›¿Y¤ÇÏ2ÃG>I,  &—y橇.çíšv¼yj ¯Phr4É |ǵ­@ß-ê•õ®Ahìø&}¤*Ñ5…üað¡1dËÏ8ŒŸïN¼ú¶Î–ilá~dβ®ñZB ’¯éë+>5¿óRÇÕÈN;á˺@ÈzW+Ì»ZÂ$˜3_h±8ÊÐR´h5_q(€@¹ßg*qà%Ìáø7Žùd¹àrëQow[ƒ$‚„ÙnµºP·=uµíVð‚e%#çSÏì0O‚÷ISë› 'Žj¿q¢wn‘dý3†ÓÀjSß %PYz ÕÅ4U‘±u&ÖxVQIb‡6.zöDyé‚´¾ôQ­v°Ý‡µ…Óqÿ+Äs ò 1"ÝØM}cÉmƒï5QG'f‡/ÆPäÉ(à o8EÎÐÿ{37ÛŸŸ iÆ©|‘æhq•6x5é­Wîs‚’ç››[éÌ®ì(– hT¿#ÇS=ôì$n휒«h¦"&à“''Á\P¬o÷¤soOÙOdiHý`sªcDÀ¦hë;;0®·×&,¡øm­N³{ŒsÞ1hõëNGÄ ©Ê£=$Ú¸€dOãž½~LÞ;»«åý¡µÐ—2 ‹`u¼ôf^7F×¥ Ç&¢Ê#~뽕©êwău™@ÜÐ÷‡#[©³ß(G°r­J¹dã-½_$øyæEä)±¼Ë2ó™»ÚËc sHþ ËñW^†¿¾ „ûæHú}XZB¿Kžh C7ŒÿT<² €1ãÞLå® ófËñZÆ\sP쇞, µ%‡x£o!#ˆ¦Œä}y€ {-7õ8ã¡-XtVp)† ñ„ZE˜ÃEJTZ“ôáÄAD˜{ˆò‚Ý.´`*YˆJŠd¯r‰lø°ü&{PXß–°«^aA`ík€ ¡4ÑwvŒoËùó95¥x‰'õÚ ÎÄf¥ý¹…à‚À3² m ©k£9SªÈ:ƒë-a-£×¶ÔæÝŽ¬Ö8ù˜û^u çÉ›ªêÒZŒ éå'µ½2!o`ÝÔÙðÐà9”íhSf[‰™ÒãÅ&Ä@p `Ý7–Ù/Éb®®D§¾Š´kÁ£aÀ©uó~b %ZL÷¿m_§Ä¢<ô´z„åôñÆJ½CjáSE¯6̜ǩaKŽÄVsFØrM ÆĨ%0 Ã…|£^Ͳċï ¤‡{”¹•c¡c@þ&u’º]"9£ï“u«B€n)Iò+*Œø¿+¡´6Sü÷·üù8×–d`ú²œ"SÌòÝ,é,M«\¨”¯ˆ¢®øÜBøe@»õ”Y¸+~Nó}ì:âÁôX;7”¶êr`ÔåëU¬¼ÜKù*VjëTÀS`_¨‡30Õ¥ì5ó?ò†ÞX“þ{Š3Ñ39ZUõ*OdªÖÞU[û#i)~/¥åf2ëwí±†ÆßU4:KZóAêêþ4ÝmÎ^²Õß šò„hÄO©¢Dv²~Ž3»Ïø’™OˆÐ:M,µ§azñ:N4Ì£ëÌ=òA„mœŽ¹¢×èû¸,(A|y`SÖúiÏ™¯ àÉÁ­¸ó«â{>ÜèPvá)Oó\\"örNvÂ|î ˜¿Ž9§{#ŽþðKH/:ųÚ~ljN•'‚Y*§„Ò&™ i À÷»D"šˆâkcƒ²gNØFß•åç¦cL‡¸ ªŠ*uÈ˦Ë6Åuß—Tjžü‹–“‘C8¤LT'Y;/«)¼±ÿÇðQ|ýo'ÎSAQô¹jk÷*"t1Š‹õáuI» Qmùe°‹p$iÊІÚ+Ñ ãú>Fô  •d…nù&a\­l±àp‰µ?5»~–]­ÁîûŒÄÈ øäŽÑúaå[øùDÔËUƒÚpˆ<$V'蘗,oÿW…îœ}ý0ŒÒSQ]Ó B¤aàzUäôŒ‹Ù!¬ÕanÃcì2G…ñ…! ™+9l|´Gg\ ²6cØnB‚¸¾NX›°ðUÔtBÄzhÙÂÔ“p5U#Ög¸÷¬é?±¬1Øb=8×'Ì °ÓÆìö'Ž¸ÅkÊÞî­îFÕ«`ä¢ïJX¤—÷;ë8¼:rétÖŽŒ7ÆÔ¬UbÔÚG¢ÛêS|2ãóJkÆß1A|Õóšô—ê0OɨThTb ðÞžÙɈ2í}’La(ÒB—#,àòhm5Äa0ë"že¨Üw#! .Ø~‰R¤çAxÚ»Z´E·¯ ®aÔË Jq;ßz­>¥’q-‡×r¼*fÔ¹fv¾»·ãÆj1¤²;Û§cwÃî¨mºT»#ÉƼü>¼ÕÞ& _¯O{PïhÄ…\ˆý7á%æáÇ5ýçpŸªWÁÌ$Æ×p3p  ò|átÑàô?IqgÝæ×þªñ†Ä:ÜBz{‡Яa!ÈÕ­U€Ì"jî®r=ݙџUA‚Œ„ Šobud”eÜÿ¨ÚŒ\ý®×ˆa á#õ\u–ç”È¢¬Ðš’«÷>âLðúm+»€*ç–á5·ݹcà®°ó¿ÝRiêÍ¢(ü);Ä™‡³ïoá÷))vtZ nÜÎ40zNšI£\3Æ¢>rö$$ðÁâœÞgpI#6R…ÄÐkÛ×UÀ£B^[ÄÍç³/ôj}.Ë‘þyKP­g…Ç·Š1L\vöîJWÕ²Mqš÷·»l‰«Ò7_fMêC°>rÒ_C­UŸÍ‹yGJƳ•¯64ùpñ…?Þ†)ìmø{Uýòy9W—ˈ é ÐÙC¨Œ2¡ðå…zÙ¾×7ZÇpê„‹á·‘:\îÁÚÁØ71Tá$¡¥Ö¿j¾¬‚m•ÖÙÆbÈ4¼A[G®¹†¼ÔáñôOM´XK‹TØ*\ÒèÙ'•bËB²Š摧À}ì^ô¤h&N_ÁÄÆb‡æNf¥×Ýøf*E,XŽ›¸ó6ÿ÷ð´&ÃkX(üB´‰¯J£ÿ8ز³Ü†WDà ê(|mXr— ˆ&ïû·{fµdsðØóÕ.â^PÛÜÒ²•Ž@Ç ¡ÙA¿d†sc« ô#ä‰áýªÍè)V0µUÕÙ¨þeõÚ{iU–3go¥ŒVæ@¢lVÜ]§ë<Î ¯—A vÍ^ RÖf±ûa&9\ã(ñàsWïáù^±â?øËR2bñhá{ #Æg°~°S8¶z1Ú 0åS¤Í+ÌÆ.ÃzøøV8¾Ê‘Ïáanܤc×Î&Ws–Ï •vùõ-ÃA˜Ig:†ã›#nÄÓ^]]RäŸw“häܵ}Rt•ª¸Î%Gé™ðû…G¦ñ¾Þ¬«ÈÄBÜ8æû²Øº­ÔhÈñ«V,jÍ®e=|5’J®ü5-&¼Ù†7K2R[ÀT\“ãÔ!È`Y);Œs_Å.Y1ØZ‰r1œ"ë·Ò$Ç4=ìgN±»\‹Öà2à‘EÍû ¥@€V³§†9û6œöÜ¿}y4ùôj Š{ 5?ÛAC{íZ|ŒÃ÷Œ”QþŸNV¢ûòù¢ò9—ä¸d-sNÍ¿7o¥Z@Nô©V¥cÝÖþŽð¡-f,Ò¨ð#æøD¶ü*4˜ë¼l~BZQÖ ûB`+BšÛ¾H÷DZ~Tö¡o§mõ]ÁDqnååêsKæ—#¬^n+•ðÞÔ©’Ô×½`¤sÀXí/PzC"´›úx z— y·"¿áhÄ®¼2wÆÉêmãŒ4ÐÒ*c O¶µ?´®Èý§·S™|ä… Þ”óbµÿ_Œ(ƒþìKc¹‹ÌÄÑ0#šR½ª>Ã1óDÿí¸zrwÌkç%ªÜVrýè:$ÞŠIrK^Êk´yØó,EšÉ„²†¦ZK0ëܧ@þÛ¢ÓÛûâ—{RŒ80;‚&À”ŽB¦Yc$ÚÕG>¯‡@Êp„·Ä@ÂØVè3Ÿó‚?è¹6í/C¬Þ…Zä2.¥áý:Fã¿S“¾‡yBþêÞ….«°ãµ²®s%Þ”AZŠògØíBG0‹Ç–%¡òQ=cä¸þÓ~G’ œqeÿó3ŸkFSèããIÎ(Ê+"+ koŽÿe`>ŽÎ’¸jóøP±‡iF9R}„V:j`Ø¥=Cû2Àă™ÿ=Š4]­hÄÎeo*8ÆÅ…ÛÍAb-EE1&Æ,¸WÞ (ܼVô¼‡b­c–O51gm¶×âƆ5¡^R[gXÊ㶶D¼ªzÛ •p_F6ù”_57å¯eêäy®#fj¸û•H¦Æ—ƒïþ<™[6faYK#×W.Jdî3…/^™rÑ%¾}q{á s(ÍÞi[¿V Õû*±ôÇl`Œœ ÃøxýiÓßO)–ùŸXÿÁñæA™}q†[A®f]ùU]Y^Ö¼ N7½ã$T2 =ß«õÑåÖÔ1{¶Ë¶^lÎh»ŠeˇØZªË¤Šë Ì;3g N\…²GÔª¤oBÄ/ÃJV¤ÂOÏU¦BzøÍ.Èð”<Yév]ÄÚ6…ÿê à;Ô&AZâè좲êÍEÝ4ìü¸GÂy½Ê%‘œKú;”èédÆGê}kOÂ&«¤Ž”'*ÞX»'¾ Fç%µÛî=:„ö0•:ÅÜŒÄ}ÖbJ£m[x˯YÝ=¯ŒQZ$’K¿k§¯Rñõs°qóaòÈç¥O¹ÆÚâ°Öª2‹GñSts‘–õj‡ µ\VÚRbNo?,Îw» Ô)*,Ô~ă¶ãçþè»ñÖt@NeVl|«ªMQM*žÞ·0W€©o&uÈWöÝ@‰B/]ÎJ(Ų…µÍI}dæ•ì³˜Aï ô’Ã[:bçø.ä6?U+ °Çê!oBzÁ¶»'±.Â"|$ÌXp,6áöÅ+Õ#]Š:2äëF± 2H”îQ´ÛÌdû½ºÑ„“ëNô51ø±FA‚äÚ33J18VÂBmñ³ J.Ù×,v”ùe—Ü{Å[ý6…‘e˜©˜kÌ`÷Òœòö ¬ÚJÏqºáC#XU'‚sHîéÙÝ)Ú&K­ZÒJjÞ,ÝË\Á+¯*É£>`ƒ©¢.K™ÊÀƒU²éúòÓ0\ÄëI*‰¦ø¸ý¥¨íNóG}ª/§Î1Sµ—_ànSÙ zh§¡PkîÒ!¢’§Œ†hšŒ¹¸¿°y¸lε6Fc¦¤c7pÙ»úÊ`‰¬„½^UT’ÐœjåK-ÅÌW|ª ƒ “á(ŽF«JP+á ÎXC-}/Ífß+¸æ^¢ªÿæ+/ß#GèF•Thá 5pˆgb¬Z¢Ù¯`Õ@SåwÅJQs95dVRpÀÈH¤ò¤Ù×`ÜñÑk* $#i H¾rˆg- U/½ïÃHIw¥ ò]†¡û"ØÑYÊäšãs‹©fDé‘‚Kècµ;uõt@fäÃégÒ#lä,ùôfHå)Þ=ߌMû"ý`êÍž½4WHÅ0~÷é3®0øŠŒ»Ü`;> OÊ5íZ˜úm.Òh\Ñ¢g÷WÎÍVQþ;é8Cì´ ¦…$/˜BˆëÒë¸"7.ç>ý |Á!žâDK]¢ê¦Ç©' 8ßÁän×׬}*ÝÆÊÞ¥ olÆ1Ã9=¬W*Û¿c}Í•dakëh]蟨PÈŽZ}›L–ðþã#¸,‰‰jZµwð݇¸ªÉñ5:ÿzäèõGƒ¹±MÍåö“Ï8éU(ÍGªÍ[cÖi”´ÿ$yÀ‘Ò9kôÖÉq-Mc×dYÚ ¦¿3$ù”) ‹>„˜’U™ÙEõë×ìþ2ò«–K½ºæ*˜Áâb-„b‘ïF8:Š×¿‡ÖðˆËQð%aß¼‡ð€ÞÊ*•,ZMÄ7°UŽåhkï Ÿ ®^Ù#Vá»>‹\*ß9Ä>!úì†ðÉÇ ç¸e£ÄQ ªhKÑ–@Þax¦âÄ­ÆÛ+ÝnÔûËЃ?¥„ŠQ§A,T%!ïÝçïI¸7Œ},Šµ]p,ùN0«ý[[zZ2}>ˆwH,"£i'²Jhª¢šë¥· xU‘£°§s AW\â`s‰΃­ÖÝÁаíyW=m®ïìQ! ÷Gœ€­¸Fßí“ù›r¶ïxÌê8<æõ2yM©a}ÄöÔ ]V ÓZwâ‹B—N‡V{eørÅGÆýݶ"0óªŠz¥µ“´­Ü¨Y/ŠYÆ‚ø¶ÂòÓ­`{‚k>í®¥)…k&…ÝŽ×<ÑsÉ qLæ&´÷f/˜ÀL»í>]!ýÏÒz49€Ê|x¯ãp¹Uƒ¯‹ÐÈ[`䃻~ÀA¹c"pLÙ˜D^-Yw–—샎³ ¶¸cØFÉz=AßKešÍ©[ÀÑ45í+‡1òÈ,ãCr^ÖÑs²Ã˜¾=uµACòA‚|ÜkH¨pN£E¦­‰˜„%^Æy –¿$ÓÅòj 7ÁH€T¯=RÔ;ζµ<ɉ˜+çèƒ]~Ô¢ƒPÅÕÖû‘—¨={h _¸–J¶hùO€ŠiRdR{¬ÞáMRm}ç¯C(Låœ_cÃrœ'È%jâœl£&ž™2`dØn¸á`zâ¦hº·ÿ Œú_íö|CªÙYÌOæ Š˜ò]‡FÓ9‹9i$·‘‰DÁ+…¼É×aÃEKÉêdZñFÖ7ÅŒçs¯+"šÙ d0×ðrÀ‰0%m2•øg¢A>ÀTkòÙ"ˆšÙM¢å(x³<»fÀoy·Ù1DnM&׉r0†™*"èøÒ-B§1¿è^ ÔMÿ0Oí÷e[H×|r‹ñ-}‰&ïø Õ)[סe7æõÙ‘/IT¸äª§Ü˜øîVu±|p××ç]©À‚;Û‰œÐ¾É3q^‰Š»…rz˜ÃùSêóÞ AecÀ­ÎeÌH梨Ò:ü¶¤»©/þ9÷tߌcMüF@¾¯µAå´oµ'î­YÕ1{a¶å¥âíz4ÓÒ9ü9‰–ËXÉì¿EÑZŽ¶¯¿c`ã'°!7s `LºŠØ:D¡jÑï@„QB‘!…QrÃý‚z¨D€®XÎi­rÛÜÓÿTr”ˆÿc?G…f·Ç¦†°yABæá&ØÎ^ºfÖ†­wk½ SØp¾ù9^“kÚäY ¦C —1dåø©¥'õTsn·K#Uµ~–-¤•=>ðg—ì «dP /ŒJ8׉1“vØff"6Õ#i}Š1"òa?Ž‚LâŸ×c–’âåucW]óÃÝMò.òÕ€Ï%ƒÁS°mwŽÍXL-ïUµ¬ÎË<²"lø~ŸGŽ6ŸgÄK´0ç£r½J·±`¾¯2M®*ì󣃋v{ÿʤr5i‹Ž}‹ƒ†IŽÀZ’‚ŹöûˆY¡ÔŒø*_”r¨•BjöËg[ÿ©Å¸‚¦Ì"8#ÔŸhœÕ k>2Úˆ² tŽìC=- »,çŠ%_¼Üª’hPR»RNâ¬$KÏå™æþ³FA™ýJ#ÌF üÝÜk;:˄Ѻ…1>PøÊ·`RœæR—õÜ- ¢ÃÃrðeŽRïzSYÀ;˜Ñú?³ Ïû}¥&ñ#€¥ÝÙQÈ„•‡ ¼=€¤€³æ½¦Æ‚Ù±ÁE±KØkÍñ‚¸ûÞ¨g¢‹.º{óÀ2k$D–A¹ÞF¬{P³wÑþÇ'‚p¥š$IÓÏÎ n¦m–ñ4O‹à¹k8?ÔqE«%\5÷$f…ki¬rmüpªŒðØj2e~Åz£~ÝAfŠËºÀÊÏ¢ ¥Á\òºžÎ³ñ•ÏÔgt?^'GR·¾"(j—¹·f‘¶œO¯Z_TRPçÞœkMþ¤œæ"’ AžjÆå”:EîöÚÇ´ú¢j*”˜šq‡ž?f¨ÑïU¾âj<6ù ă掭)ëKҧܮø=ÆèY¹Nhþ°§„V.ppF©4ŽM‘¨ mËÕ¡þJïu/u:¼ÔFÝìú¹ nŽYòSï¯Qð¹´…Š‘Nj;ãlÜY—¯§‚wÀHÿyL£]ÆÏøß æÒ…Ư„Ê^—™ÕÄؼñf‰–ÛÄI¡÷ÀÚ¢ê(À£Ôí÷CÊop“íÌ:bD:¯§YŒ «ONÆ ‘ÂÃ&oeR®ö4pÊ>Ïœ"ôÞ¯`¤er!%‡ŠÄ» !0 ÀáÆ2O«dgÓŒúÔºMPA/L›4§çmË‚Ämºö\!5†y—ÆÒÂù°¾fäß·Ëß‚‚Êîx´ó4DW *]úž[Ûç-Ýk³Í.URñ9~¡—„•$øNtþÄ„OyÊéqÅÂ{V.IÕkhø-ÉÊLä–#îÅÌq#/É•mØìêÿâÀ]¡w1¶l§ P_):ƒ%Åà…y– ¨#¬uz.›ïK8 K$ʪúŒûßÿ%£¡¨ÅÍr(ãƒC‰¿ÐyˆwùÃã³4¤Í¸Ôî\ÿ8Eá/¡m©— L¨Eµ 'h·Åfd¬w cÊ*+˜U°¤Á;à{¸°ÊãZ—õoã< é%9ù$Nã&+‹¥ßɦwRÚt˜|«ùNqœ  ÒßBoFÒûöVŽb‹$¬a:\Õ2 áN­àÄ‘´‡õ.ýý¦×®Ljö¡Ê‚c;¶öJ?×Ù^*’u0¿ÅH;ƒ:¨–xÇb$p«boáPM[@ãIÔ;9ËPJ¼´e%]¥ÁzÑ_ð~`',o7μür ˆîÔ!<;¸Fì5ƒã¥6ÿ*ÙÍB2%Fstx²²m}¡êŸpšÞ“æ¸$‡8¹*gö eh‰ìÏÎÝëÏ·;‚¡ÿfvÌ’+Ópðù"sûÎîìCþ&Ò£Œƒñ:ÁÚ¸2 nB!x+»ùW+ÙÊ –\§íöhí¶’ Æ:ë=ˆ{n>0÷¡"à „³Ö£˶:ÜmùÓZDª«$ö#È‹w߯\! êãÃœ¶«ýÞ׉ý$I8‚”`Aáðû @ÌK ¾[abtµØBxBU/|­¡ß:fù§FM4Zß5mªKËY° ¦!š¹¾>®f­ªÿÓ òã9œÕE[6atî¶ì#]qŽü{wÖ&“þÝQ&Fq‡?L¯îi¸¤°·¦Ð0 Kð& GV… ·žKÛ¹è((ûÍ©Uÿ<‡ä9µÀîÔ*³m"Þ&ë@z¼a;Z+À}4I¾‚¤©d wMü"5@„j½òà…pØZ–R"!'ƒ4“©Œ6r© )/þí¤—pˆìz‹"ÿk°yÔ÷ì€DÀnçñj{£ëðšëC‡®ã n‹;ú"|y"¹lÔ,ÆZÜeÇ›·:¨kDS<}Ó“]GÈ·U³«@·¸z»WÙ±wkc…[à½7ˆŸösç™C¯{CŠ £¨CÐLø=PºËÏà³g4ý rù‘ÀA´Í…HKô<¾p„ùq¸uÑ?t•ãÚÅòAB9@[ðåíZlŽušâ`Îk£vµ•Ÿ†KêñùSŽbñhz“OŽ9Å9ÝJ’÷qÊͶd03kÅ •e×!Cà‹¶ Bê4 Ê|Æ3•®Ö5TŽ¾úBS&m€¶¥?£ò„G²qƒmâÏ©íõô/Êó¹6å,ôPì2v2¢_©e`zöÙ·äT‰{;zÅ}C`yÿC -ñ–•L”®Éè&!¸›ØÔ+©šôtŠ¡Ðüì@É×ÁEdä-Ñ&Ü@¶¡%þ®Ž¹Ó'¯ûÂÄî /‹]&n¾œë'NÛ¹ÓgEÛZ[ÐOpC€7\‡ÑÍ÷ ÞVxí‘EOžŽtoù霕&M43nG“Û*""ïµ0'!/%èn ç¬?Þù%¸?êRG«5ãúø‘¬q{«b“„Æâ›ë°¡í1wmão›W5'-tF¸¥Žƒ,ÕøO>D†DÜ*ð/(3Û¿tkÄ…"æÛ v"9ƒI[Ùû‘›\7%ôƒ Eýxœ°*ŠËâجµŒÆö6üj˜®êضò|Þ×[Ç\m¢ (&¨ œ =ý]w;PÚ3Tܽ´gžJÀÎÿb›8çƒhv‘E YD±Ë9ˆyÈê?äQWRëJP…âÕ›—J‡S»o¸ [!É/Y‡¡¶èºÝRc[Â;þd9̇Më€{eõÔ WPÂôäôÅQŒ1¨‰¾"ð5†#ZkŒ|Æ Üþdê•FB´ñ-·bðëDÜØSÜ|ÚO øÒ7.iýúq€)Øð@`Ü$šƒá–,¾/Ôtm¯×_¸º^nWÐ(£ó^WÀ­sï^Ur¼÷Á©lÑvb?î|w}êih×1–¼’ ü(’Äÿ8¯.ŵ½Õ¨#0‘Ìή¯‹‰iYÑÄ[,ù“ñ¬EšÓ¬5³>Š¾t0j‘\1“,^Íð j2ÄjË=Z ð<×=%‹÷ÞìÙW’ÂêþX«õ ~Î3Ü1Z‡"Ûd28hÓâ€ÍÅ^d>cDM°üÿ§ªrtÒnHÃbC« ¨}`vÌ¿Ý)435È’W8Wù>)¤Tø;P. ’&[Ýún]†›*qÙC.©Šp©q–Tž¡XúµÊÚ0I“úHM‰‰°qðÔ㸼 2O¬‘»õ’;ŽkZýž‰*&†_]„6íý&HIõ‡ Õ÷•iìôXåUõ*”Ëþa‡¾Ê›d¶×¾Ø!’7Ûkòy…÷¨†øÖgŠBU»Ÿ"ç“’¬4TÁ8ŸkïšOã¨23Šs¯´š¢„Üxà7€cô„_Šß ù77³ÒëM*ø—ÜèM ’S¬ÐÛutÛðr†|Ž|‰bå¤á={­h‡xý$[K´Œr7)Y þ¦s·&›Û¬ãaÜÏc+–SÓ­ ÝR+6ñ¸@³Qu8±|æLÕ”í=œ3P_¥¸•Yb7˜ÎP‚ľ1–šÄ彘c% &°¸t¨`É«o@E¢òÍﲨpQƒÓ¤äšðXPª ‹Ç®/Äägùt× -ùý˜£ÐfN~Üó°Æ(kÖ¿X©@²ï‹¦ÿ”2Nsë¯í…8?Èf`jY ½¾©|yŒ¤ãN¢ƒ6¨Ä- '‡ý“8Ú¤ÄBc “&o>®³¶ ±Õs]$L2æŠ5¬ú íÚ8™ |ŒÒû–û¹E¡ÿãòÏ3öM×Ò¦ðH6¼Ëöd3ÿSÏ[X‘A`…Y…/Ùê¦bŠ¼[—  Uô£k¤ÊVtÒß#胢š½õ}Ò©åpúèh”'¶…fÏy‚¬vuü=»9º¢´ »þm”çcâ“#`˜ìš¬LV Û#ˆ•:ÐS;pV,ÞR`!„EøûG_&ŽèI¤3 ú¶P3WËþÑú¿ð–ì—úð*,ý& ûºTá<|©»ò”˜²Ú‰[»Ÿð|oFh’R[ ÕȈÀ5Ìj ÛŸi ›¾²X[œgˆÝ[hp© h‡O{%:)Ž×`H÷ŠékcäŠÀ‡“Hû4éq†!U›~³‰'ÈWôQZ#݃­­âXT¡(HßSb=‹ˆÁQrQ‹6z°[fl­c   {ÁºµÑXR°4®‰ø™çj‰H|§îµyÙ0¥?çTE#ÍE€ø3}D[ƒÎQ qŒ©¿QÉ _³è*†§ÂôõäÝW7O†î&EüTçµD»~ 3—=EáfÀl˜Ë“~Îip%§¥%ƒ’/Ô=S•zGõ—¶f­ø=+Í£I%@k™S{AúÄ~BX³Çà¯và¡Ð’ª­’•Ý.VSWh¡û¢îã–{Pú”Ãvw ,£wÆ3Ýo3Œl†lºƒÇ(fõOäù€|»¤õ/˜ž{ 1»ò‡/›žŠF:ÞýÅ_]:7LÂC¥[Wê†ìγä¡ÈîúŸ5¤ì”K,.‰`Ø%D‹¼¦ •0¹2rF{¬à^[äR@Î{M·¨Qcéíxf] vë²YÕzû+›+5,º Eç1”lŒ2Ü ÀšM¶C”.Jy„ÐTf Âo~±ÞÊ’/%ý@©©»ô±"f°¾1þ6÷äÖa»P‚Ĩõ PÈh IHMo+OË™ýÇBå#èªç*yq%Âó)ö6£C›Î|½G®°”ŒÝHä[2¤ucï¾T É­Jƒ/I#67¥,¸'Œ€ ’å(눑bµŠû·TY²S‡´bB©ÑØK’÷·¼˜ Ñ Áç¥p#!Ü;ú¾ý¿·%¬SÀVYûè1‘wξÜú9Ľ ËÖµ ¾²ïô«$2š^øúöSÃ…õMkÑNÚ˜ýŒ\ãnþ}ìócŽygœ§¿C YÉzm„Ê]¨&³˜óø]_ªÌKsÓ°¼§`Øo®£Çí¯1©#Èöàí4@qÙÿ+†9ýN)EÞ'Kþ'Ãçšý vž.–Ô ˆ=¿S.| µ'ìeKCÍ‘g¯^eq_¯thä›í‰MÿoV…;žûÚK´/Í—\™ÞùøÉ‹ KàaþÅq;•2*n-óïI2,>~?$0ø¾YO@‹–,¿xüù’÷ÿœßŠt„3•³é< ¦ÄT!Íû‡* ¡ù½èRšÞ£$êK7cBÛ7ò;’ãÚ Ã çþ,Þ¯à‡×Õʘæj[ÔC¦ç^þMþƒïP. s4jìü;µÉxbl&KÊ!ê0kh«LÈï\UØä€2,>RáÄ)ûP†f"õØâ¸ÅDÑý@yuýч¥ßùÌQù³lÁ¦ïËjÍ}jUëøîó1Nl%IÓ/”ÐØÙÊì “Ûá É&˲äìJH^bSL•>´ê•c‰—»+9ñí­éèNn¬ÎRHV3^3È EÎ<\‰ó QBHFñ&¶-˜Ö@ÐËî•£Aý¹H€¼àÍ ¦ÎdACŠþÇJ‚Õ6LÁxL9œç…“i^͸M‘ã‘8—GŒ‚ Àî&ZÁ²Nh‘3¸µÕÐö‹ßÿnr< ËèþµÖQ“˜Î! ~ìøR.Léöà“Ü<£å#cŸ–õÏjŒ;Tl$=¨ðÖ”™ûê/ý?§œu{` @Õl(­| ëqRk9%öTwåwyµª5ÕzFÔ«þ™Î‘ÛèEÖgt µï—EXwk:xÌ}jgvZ±vîØŸò.÷mˆÿôLUbKg ¤‡n@Êîïš%åYõêÆ Û«cÁ`ª¨ó?Ó Èž¦%FŽŸ %qñØψŸ†Àwæ<Ψù¨€¤Rˆ€ƒÎ;Ë·V³D˜i§Žˆ÷IóæW©Âã.ïªõ­‡× ’KTæSP+> ¿9Õp¿(tÉ%ñë)= ÿSwY2}…G E›¡ûCÃ_á&HÂL­e®¯´ 6k–T”#lÚ,mÂ[ÒXåxªx­ ‚qHo›ì²¼9µÎ2É\ðêvµ°f…ÙHÊÜ]×Íýƒ ž³ª‰g *Ü­-¤séJ$‹C²¸*sZ ç@Φ.ÿ›/®u‚5U@ .æ ”3«Ì°pÑã̉Â_ç𹃀Àlf‹„xRˈ]¥À㙢…}0œ¥¾š~ˆº1Íë.Ç¥´–4™nÄ%í +7ÎÑùÐ]¨Jm‚ý+ˆ5#ãkÈ^Ê ~„ŒÆ{‡Ó¤¯ ŽjïR5ü¯$ñ³Œ¤,a±ì¹…Á["¢D³\¢­U¦³Ûr¯’—G©s`Q¸;<Ï¡JZ?¥bïï;Оž«Ðݬ=‘½\ U.HÍ¡v¨Pâšå'h\‹N`%F‚I~í²–<¨ö ± qõì>€s1EÔ‚ ø”©©û”¥ãJÜ"6©Ž$ج#ï‰ñõuƽpE+w.~zv'ÍåÄ$*ˇó ÄeÚn-kXÔ#oeÅ5x>y]/ªÇ\®4È fäìƒF®·ì·Z€vÜûD`ô9d8ÉçEgúƒ–ÐÏ_\ˆ…a+ ¢`>âVðD^$,Èvtlz“ÙiÕ¸Ÿ®ì`±Ô­ÚpíÅæ…ó¶SOŽù£ú+WøØ¥ý* Ky²úþÓD´6 Œ¤Ë¤¨šÞÜU”ðð%Ri¬ª‹,¡ªÍ-/?çrœÊX*¸ÿ öÉ=Ž—OMÄfÍ‘¡Î;ÔuŠ†ÿªˆ8Dj%ÙàŸ®–ÁŸE=)) ¬yý÷d¨½„Üþ(„ª,æç†tR‡¹qr}êLQÌ+R³ðfø%皮Ƙ¬hÀ½PÝlµ±‰LÃU†Õ£¬U¦zÒ…\·ÒF1Ú5Rä)¬]‡x2ZºH÷Y¿îø"专҄…¹kW‘Î{@å7ýµòï^ùÜ(¿S¬æp7¦eÑw\ãòO­-“'й§ ÂÎ3‘¾àlSîk&îŽICÂ÷h0×8ë 8Uö§»¶i=ªß»;p¾ÞwW¡Ôï[ù“CÂÃû™J µ` ¨fðLÌ Á¹GZÔÎZo¦‚äâM^ÇkP©4¿•ó¹™#‡¹žºúÐ×qY O¾Ô;¤ FŸ\Ÿœþ€™Ýš€pß¡Ô*ûeª4ÿ`LŒa€z+§ÏÃy×ø«Üâksk{—ÜlÆÞLJС”ßêHÍéµìçÁw–¡³ì3úÕ² }÷<å+¡è¡ôH4^^£»5ëÄEíÔäúÁ†”™—±:!I‰üy­È1²øvRØ|˜ÐÌáÔ‚”Žá×€ Dz-±m˜dÔÒ-Ò,á`/D©YÆ ù?åÉxkuá³g•)ºlXI P¦xKÛιîý¤²Ü›º^½pÓW- e‹ù9T«ù)¾6Ë®goE\j‚²«ÝšJWRƒ/ï¸móü¤9%Và]×ÖYX3V誵: NX¬ã»ûñÐYQ¼ÍãýNxÀnø=ENú²®[£M©³ïë|5í¡œÅÔÏi€· .º¡Y½Î¼ àøÒ9Â~ž/3Òúý_…H­ˆ~óN·î†8?m¸t6""ȵc%K“ôQ3Ûh ì̘à>ƒ>àW»þbá  ¦íÔ°zÊA úŸ_ó)_Â$2¢´å%Ìéåù~mel±ÞT…Íp`ð¼Ïb O½¦ûÎæsFÏW øžK– es´È¦w+H0VJžš/Š†¤>ÊÐÞ¢°Ð¼ß¬B^ðVÀ-˜JU»B-Ì [Ú¦à»wJ6ìmjŽéŽqDŸéëç¢ñ=áÕúhN⬠•§¶Ñ Ðï\ÌË1ò¹7`ÓÿRX¾Ü› ­uÙ.‹ `Ö&ízÖõg§œ1v#€ƒø‡>–³.díúÃÈzìLUÏ­™IÊ÷¥']ѵÖ{«S e+x=pñ4Èn¬„M8 ³THžj£@R•aÕgBŠÍ2Üa º©q™#D{oE)ºì<‰ OKS~‰2õF1}Ûvµê6ÑUJ?§ P 6¦Û KVÕ˜ig™øj­a-|©i7>ï"£=€uîÑ–ùe?D²ö×TWêOÍÚK:{ ëT&Íä•œ™”÷$–£Û=|ïb^× ;¾y “ˆ$o×åИíý:Ø>`z`¾Êï­<»O¦Ì|1l‚-aT­•æJBxvš_g€ÕM›xíý@ï݇ë8 ¦o©]Åb‚½ïg -EÒêÿ^ØÁ.514ÕØGxÄôW¦¬ ˾KHqýñ mÞÑšüѼÌÔÚà~BȨWœ£Ño9 `âÑï©ÇÀN:ôÛ`Fª€ŒöÖÓÏ^!TžÁ€#µÏˆSÍ@òp”%œ&šhœ~{ìE ©H;ÅNÝTf5ªm•úQ³yyO“¦‚2¬Õ=†’ä]gâäX‡yW9U„ÚÍ©ÐbeB‘ÈÀHD9ÚÓe<,)˜$y,H*Ep.zÅé Ó¥ñ²†uœ«¥EèHW§¿‹£Öum8ÈrTßÄæaƒÌo­UYT}×’IÐ…¿.ÃƲŽ¥¹“ªéÖâ­épÞg*æê!µØÜ]ôERÓˆië™o ew1‹l‹çê/J$¢iF¼ãUéê.wûó‡/A­ø\“øªZç…w¸¥zÒÌf› èþÂ’«õÚQt«à¼øŽ8ˆ/¤)«¼Ö¼QRƒ0£ƒnj¤ŸÆÕ5FÉÛ¸)´^Kb×`.Ÿ5žWï64]ï‘š<»«ÆøÆÁ?æÎ`íüÑ—Üý‹àk ‚ñmEäÁeŒ¯AXtU‘,k[cmÈn Æʳ ×q/žskiúƒ•¦ÿWER)êÓu(¶ò±&€R)oÚVmµÈ nK›z§m'WÏtbò}/øø¢ C™ÝGѳfRucøgdݸ™‰\Õ(»Ñ>Nëî¥6MòZ%AAHfq2Ë ¸µ&‘ñ‰lØaàëË“\QM )ðJÁ>˜$‘ིbŸAä´Ü#x aØÌžnYï:ÿt¼£Æ¬ˆU¡Ê ª}ù•ñÈviiŸË6pÑ»ŒpUºÑìXÏ!š^ÏúØTg-i@ºjÓ1÷òµµHGg{mçï³ØwG»êƒˆ¹Ë8Rîi `ã¤ð zó»§fÝ"lµHðÖœ ?O0£Ê&¿÷bÏ¥f(ͤ³È#¯-å)ô…úÔ¥º€že”"I<`/…Èÿó½ "¦¸xãÂX}rNìl?¡¬¤X|£6 Ⱥæà}Á M“üËeÍ>®“—»l]ö£æ}´ÏÈÊ ZC% CN™j±?Ê;.V Å0ì­°ó?…÷ëïª-ÿĽ$^…ÿF½`qD€XK%CJÕ” h‚Å÷·|qYnÂUÛän4VªiE’†ÒNþGÉ;ŠUÂèGÏÄh´Zò¿1ñ e™´½7®lŽ‰åJ£Ü&Žq gšœÞš n5‚y[ éVÏTËÛP··ûA”tÄ\‚'óøÿëÉzB›#lÚ9c%ê‘JÉv’ ©r(ºÂð¯·$+×Æ¢…»¡è¯QjZù˜¾ÖrN•±@)N‘¢ÜM­Á¥Z`|À¶eKöCYØ.²žÇY!Û¥g©¤\Ql‰(:¦R0<Æ‚ « ˜åPך2ÎÍ0M¨ãÇÍ;¶br@<ƒÊ8»Ös¶AI×ßám?Ô .:ýÓÚ¤z%jÁÂ)Š}uóE NþÄÏX„nŒ6)ÏÎÁ’ZÄn!–¶É,±l»®ëB)âC³„‘£FãÝ×›Ï[bÍc…aªèÑ2Ûˆ¹gÖ„‡ÓÿçfF; 3!&¢’Zª(e>Q|ž¸—YoêœxX~ënˆØ û!¶u"á5™¹OÖºn#¢¬o¼bÝÓÃdûORd Ê“KbŒ$ê¿=O°ÎêÖxºZµ³…Â[),T,­øh0CD¼Äóž#Ó‹Ý@Ã^Q J¹M%J:¦Ð]\+&ÝY7±ßå˜=ÛW äXš«Hî@=ç]I÷Û­ùüíß#Œ|HªºÃP°…Aq‹Ž¥sSèO¸Kh&ò¿QÀÒ"ê‚‚¨‹ÈYûƒÖ”K[ªöãièÀѯè1 Ò@a=ÌV"KȦiƒQ´g7çB*ÄU6©5åø[úQ:qMS;u2únåOÖP¦f¦%w‰DÞPœ¢+àÁša“, p¹!b1å¬B Ýh”«Ü¾•ƒàïBã:g¢Šã_¡ ".'Ýòu!VÙ›÷«kØE7Ó‹öf¯|†KšU•â­R+Z´ÒyòZ¸áOJ1·…}âaÝ\µ¡cåÜ%&ûÕ‡2RM8Ì÷¶¥Å [¬ŠÀ¯ ßå"á 0{6l¨ÇÍ$ãXq‚¢”]»h‚šñ;ðíÐa‰IØEŽ_˜¨³¦ë¤×%!ðÙg%–°ÇÑ`è̤À:oúìm“ݤC­BÜÈêÀ_üëg^š,us ¤€²ÎÖ^,ò» =åZ×”ˆ‹ãt†i1%¨ÚWže U~Wmt«\‘}`jÙàÒÒË/ÈT×ÿÒòTÈŸWïWÖ¤PËw|FÅf3´÷ÑØ6T™¹·†ÕOŠú²Ý±šl»?ñc;eëÌ^ÌpwˆÐâ¥Í`öÿ¸Ê†êôÛÄ+ ­HÆ­¨•ºß²æ½9r[“ÒV#üË_:À#Ä›ö[WŽltjýž&ùl 3=ØóΖ£m¬»aP†¶e÷•ÆŠÒÁ‹ÅbÐï9Ù¯­Û¦ÕJá%Oî}V…c1Tj¡%«Aˆ,Ü„¼ü¡‚F˜”Æß‘²e1/êêÝéšk&Ùtà¶?æ”\¾pG`vðRïV¿H¨Tkíz¸£V§óZž½Nr[ 4MüŒJ³¹ù+Ñ-póH»«s¹ÜD’ 0jHäüJ!¯Îšª'ôX)Û#žAÕØôêkÙŽYͬïåÈŸ»¤Wó¾#ÇxH»T¬ý…yõÈ|É@ò8˜¦¼Õ•4ãv÷Ùêö¢R3ÈÂN¸6d–FéyÇEåEáèÎ!™pQ •Àµ–ðœºérÁž<5Oö$M;T—:4ñÖ&Ü…­/ÂÆz½RùKà.@Kå|ÈZÚ²‡C\ª8úªþËŸÙ‚®wª{0Ã{€Íúá\F6pεѹnÏm†P¨çÄQ-[ï!P;¹¹sd便ߵz“ÎTR•Êf‚Çås8±(¯ý dÔ§¦Å ý×*¸ó©p8ié¼ÿYiKáÿís;LƒÊM9îèïïçÙ»%&¦äó®«T½Ø PþF,!3’kÄRMjÚV “yþÎíÒA¨äD'LØ…¨8…üôpÁ츪(†ÄXÚÔ†º~T·+î„’êÉèסϚýZ’a ô‡=AW«H74ì2pCô¹pÇ÷=` Ç_ñ‹dª*ëôCÕJ&1ÆýÔ½û;q¤ufD `ÿʘ/Å8íV `LScÅBMNûÒ7Ëg½ü(K¾ÚkÛÒAÎ^·r”@Áº[ò˜}i¼Pk“™Rv›”€³;r6¬<³C ¢É’dÔ(¾ú)Q¹Œ*IiüÃ,¼b·[ìÎVçFØ=¨@P=ëå>,nô1¥“ãþX1ubëC#|aÙF^ó€ 3·NÑ*}Aû|רÓ#0‘ñÜ©…“L@q ÐôQ‡Î§½‰`õ0ÖU~e¥¸'÷V‚áá­ËÞ{„$©\=Ôâ|¾«™ž v~eü»>Ή–PÛ¦EÛë^ÍtO×I<`æŒ^ŒÐE’+¬4¸%˜njíÔâ*3øñ²Úùxoé8kR0]@Aoqòc+ͦryÐSŸ+&¦[DE80÷S¸$—¥D2•Bœ{#x,ßêJKàÜ&Ì+éI=c.ó+Âf°ÌI$}Y×HÄ®šF¦Uõ‚IÛJí†Z~D™ÎZO/!)×ï®d† N^—FçÉ.Òs>"hÇ› nv€ÚaRºû^¡«#DD¡k¯©†÷¤IÔᯈ9~Ý5@FsYíxX÷"G)q‹ ßHº9­Kh¤YšKZWÖKÁœœžNñ^~ÀÌ †Ž.Ï ‚@¾ÆÖâ ø2[UtBô·ªŽÜN°#Ïã$`ý„Ì¡6àÓ¡Ð\Ðw¯~˜V¾°V´â³‚ÂÍæ]eT]½&þléŒû5|†Ûð^µ„WÐ(èp%ñDXÄœ—,°2\}ô Eäå-QSy@²]×—ô91K®tÓ5Cßع³El,æœEi!ÙÂÕt¦Í£Ã¥~?#ʇÊU©pAªËĦäW“6 $vÉÖ‹‰Þ0äËÜ£÷ó|á‹XR ,û~lÉ•˜5ÑðrÝPYo (21‰s:y'‹ÄB+ßÇ MànND!ë'ãIœwÆ+Ž4gVDÌØ®Þf@QÕ!SÇ”"ñV< Fc <éé4™yûûrRˆèÏìv“èH˜X‘d @ï½ô̸v×~`ÏÌÈ^­@òôcÄDD†y¿eø!v­YÉ{ìšïHn¢: Î9áCö—P-k¥RúÝ®¥ŽÑ‹ü®þTgH¶•ìy9ˆ\ö¬°|÷¥Ë‚$¯-(ÂrãÒw8öÀ´D:;í›J­7Q•ÛSp©é:•.þÒ4>¶\O)®¤ŠŒ?¸±‡Q-Àzê؆oC ±If­s¯D—ìÐAFÀTx6hžv•¢¡=0Â!¥fWˆ*ì>”™X®™Â&KéE„Ž"+åóŽkm¶VEU_„8òY•`X¨c·:Þ†ÝtÒÊZ8få(¼Q9nÁéû û>Þ3ÆŽv.…_¸åM;œÀj‰»:bb ÈÒ@) ù^q€ëoæT;¤P&ب‚¨1ðN°:ª‘<Ñã11åàï}~´®b´ /ùÑgx‰'ûÌÍN©mú›ã» 6¥Ö®8z@eZ¿+´ŒMêq£–c5Zžæ=Å´Hª ü¬q-/bçÅê+“?"Í$áËØU$•^šŸ”h'f k­‰ÁÜ._[Dh©µ‹ŒÃã~ X^Çaᯡ2s›—úEõëœæh™^½×nÙ»âknŠ?0ÛzÖ2Ç®Ô,¯ìþºkÙz¬bŒßúZˆ(‰V¼A[Âw½—QêD™×§~ªø¼^8Ž4L’ˆŠâ²O#’7V"Òp>¬éï`´ Ÿ3«)Ò=iLáê†àhuŸë™ WlŠÙ%#>gÀA<€.+@T$X®)]G«Ìùä%®˜ËžXçÎj¥`,»¢ kž„òG´0húVûŒ£§0¬ì/[Éái6Eå`±V];X*r> ¢ŸÏלº±ã=õdùsÚÞ]>ݬ½û¹¯*NÏ˺ΧÞÉ[EÿÆ¥Aü pœKdÑéÝ•¬?n L©›/Òƒõ,†–µM+&‹¬ã†Kí<ÀÆM—ÔI»|B›ÅØìòt´‚×ï†"ÍÀf–³êR${;Úa{yü6Y†{MWç%w<ðuÍ$¥€…뉎ƒ"¼ýš¬ÖkûÍC%Hê(.S‰ãë ›3s7þVÛä—¿ªo%‹LGDËÌt‘N=H©‰ôo­×dó%éHa#ïn+e˜?Ø'3‰Ï Ùíáã~œ¼/$š.Z}¬Œø9‰¥]ÂÅL-çŸ ¤—:=²WèÙp@Ö*Üî¼…ÜárÇ å¥Ë1ÑFýûbŒ¯Ì;º©ŽñyKõfL6×)Ñ‹‚v¤x€¥_ôd•ÕRnܲväæ´à3iÌ‚Ay2V€‹Ú!Yö›.Žó¡¶Öì0ë„lÝ¥º À8ZRô–è1-g]g¸l`-,Aý€à®÷‹J5Rœ‹J89x‹èEeH]íÈ稢žêR†·ÏàkXM«q~$YJø-!ÿuFxº ”x|ÆÛàÑÓ!~¢Ê&½'ªÿÕHQWXTnÕÿ‡Ä,`‡Ži©ãè×Ìú…Ê4&áfíÌõÊS URº\Ž–…¾%¬k,rEQ²’6ïAѽF‹! »bûTà6z@ˆþÁßó¤ô˜÷ý–‘Ù3"ÌÎq ¾ÎØÅ¥¦ÂC1Ââf 8Ü5P°hšÊkpXð¬•÷"wz ¼…œ’ïê[›Æ1oºg‘lPÜâæTÙa$#èOÔ÷ÒSÖ$¶kÂÅpŸeò+¤Ð}bßÉ÷”)aÛõpñ^ÅËŒ:™}ï+¨}Ñ`KmçCãxRÔ‡n6¹§¥`’Þ7‚‘‚—‹==3ÎØÇ?Žñbý$”ÉEÇeÉùÏý¸‘ìš"©¨Këž³?:òΚ$ò•¾Ò©ß(¶·eX1’H Â¡%‡yY˜úÖ%þ•ãÏçï‰URk¥IÛZ©¶¨F ã/8g-g{€c¡ÑÏ^=vBæ[Ю”êS@¢o~6-Ë×)Üèl qfÌ´ECr™n‘i'Ù•‚4^.ƒ?å ÈLÊG½Amša0þôI»¿ë 1é—)Rèl{ÍDÌ,MÒžOÓ¼s3lþŒ@‚Ö_“¾äÞò6ÒÒ…áq279¸ÌÉLõØðÍ»G[ße0¿ìPÊ¥™…c/œ„Ÿ± ‡VgÏÑ›¦˜"¡Àiã°ÔAÖ“Ÿ2ÿÔãLT´ Ï­«hÖBYyüæ7¿¤Ðõe áôFï¢a{ÌŽVÅb¢‚GÖLa]ç`=_ߤL¥\gܿæΑ ã¿›DîAÛãªÐì@iðL{.QvO“ïì×–Pr´›  Ž±¬`b‡ÌíQ—Žì¨\!Á=6­X*O¡8ªà‹’ˆDyV ß’ÛŒl­Ÿä—Ìf×düT–[ÞúÈøq³a{ ù©0­}ÓК“÷Ý[g›ÒkX5³Ð-vºhŠ£«¨¶". :ÔWOÃÕïãô¬ÌÅ AŽ 2d”ÜÊxhòónÆ*ÍK»H+4ð~H©í ÃáªpœC-’‘…Â^fÉvá94¥VvPªP?ÈȺ‹¶º}èÑš,mtà‰¥øˆHû§}ÕËÿ˜c½ÕÄ~Êé›|ÂìÔ戆ˆ°¨d-}äKó=L=ÒÆ!È0‡èÎ>ºœÏ/nÕ=IÖ/§WÀÜñøâƒöAÈ•i þµ–fþoYêœ7„øí°ËÍ*½Ï*Ëöõ¾É0ÍÎMÍÙáLäÿ[\Ÿ(4®Ÿ±ï´¼¢G áS㔲Á£;„Tä[Ly‘–馰‹¤d´Û^[G&áT•¡}”Á+_z$h’É¡FÃÌ£¥Ë¾Å—rTÖ…fýE…¬ÌSsÊŸp¼F›1d_XŒPwÂlGÀ!?`oÚZ_6®hNDŒZ¡úz¬Îiv]ÿ€ü²Š!‚Û‡…F×ÜM7­çÒ2i@ÃÎèÓ6W©í÷£¤ ãà~œà°‹™JòKô Ád-»Ç#Ç{¯žñÁÿÚ‰)Ô sǼ ¶¸Ù ËwIKb4Ó"Ó“_ ¹EKX+U§©WÚL}rb½JØÏ‘Ç%u0õvzjЈ®SùK‡ü. A ¹;¨o´tPáncÈ/VÆ‚!ÌùÁ&1Ü›ÖbÛÜEow&&?OÁ,íÓ:<ƒÂšX–õÔb·¹XàÏaý‹O¬y¢OSy=o§Ž q‘š ±‘Ñ7nÁö,fÊ•(bOžîÕ8…õR«©$Ôº5ˬ”Gßõ•aäuëÕLÆ~çö°q±yf«®,'¢³A°jG/ÛÔñeLÞ¶†ÜËlÆòUÿ"G‡­wœF Vgr¤ rúï€À3dq°_vº°ý¿¤F¡±4ÿpÛ Pâ(üúˆsz8UÃGjøÎ& ö#°,ÿ^ù øÕOa£Ei1F6@¢äw”ôøBv™½øÿVgòqvǑ؃:E«ç&èù¯–ú®y–—LØUÙcDô;LAGá8•ÊDIÈÞIØ:NïVþ¢M¦¹NõªCãýeþò§&‡…peƯџØx¨³&P. å˜ÏõvÉ[•ØQqZÙÔ¦$Ó¶Þ±?·äÓñ×áVQŽ« F#¨¹ëÏ/Mt ¶u™k„ÛF,žî, ¾o kE%‹Èê±—hùü%˜æcÿŠÅ43Þ5Ê{Ê9Ï7ÎzúžgOíZkø\‹=ãÓÝp²F/;иNƧ}é‘ï`ã°^à±ôÿE¦LÊÇœ^à!RSx!ÙïÓÅ,Ɖ^|€i¼3 ‰ÿ{;9&a=thsÃ÷«îd; ®¯Pñîq^a©.KAb¯¬­®¦ûçr1Ö!‡a ¡»V™¹¤} “ &Î?-áGf€ŒK”®y‡î¥ì qŠ£½ˆ½/ô²¤Xsg|©§—Õ0*F /Ÿ LWAìBØdÁÿXê>fŽ£òˆé¸~èW¯¼à^.ä»G ¨[öú¨ÍůDr¿C2­{Sý°œÛÄ«¾â‰nR&ÔÍ–MÆSÝ|é':]4Ð 0qÊ׌¿nUr ŽàGœ[¿³"ªåO¼Ÿ2úÊ Ý1`5k<ªÕ¦wvgxá0{ãB¬kæáú23c±á*!9 ?è'çÔõv€ÕSˆú¢ãØ2›¥¿Úï97/ÀÊCq ¡«ƒ‚$}+¬Fÿ«‡@9ŠoÕæ‹/¼ÀÞÃÏϸ>±2ëô‰;~À]ZPe“OUúÃÅïŒXp.Ô:ªFÉóú£Ò®Ô/ß Wú¥{,%_žƒ*çñ%}Ís» ñÜ’SŽÜšb¾øÛišï‘ÞS-Y%¾ß¤çÝ£{ª¢ÍE<˜ d³›¤,0Ç~/£-Î /ã‰>O•¾Œ€NXä"Üh€ÐhwëŽ"ërà1Òó%B´Œ~"ðeõ'ܘÂ)ô0eŸlrÙ_%²~—çå³ùg ÔE7›·tì|fÆbaø‹ÅMx¾oÞxŠ‚@®VÛnô­}Ò£@³=MåwfÜÙ’Sõ¦¯ÇÀ^LõÞ‰f*íXj4L”Æ íq‰{O/ìb±Þ¹S‹ÓÍ?ÑZS¹µôeµf‚½ýbV7þÉeG†Åæï»é:½C|Z¡ ´’²ûÏ¢;:‘ëè¢þcqå"±yõ&vdc3,*ï›®ì’FhÃ_þòšS˜Œ%*qb)l_pŒ&1A%V±Œ>©—jt<ó*®¦XoG ÷Y*9ãã³Ðº´3иҷ ÌœdôóZšhIñÒËÙ˜±!FÝwÏ㢉 WTKƒÔ9&Æ­3M­@:µ{uÖ h¨|çc€/ò÷]:+f§.õâjÚ‚;øà¥PÑæ–;¦¤ ÃC9¼peÙl‚ÁÕŽ7ác¥ŸŽSb«»)C…h;í'vFs°ö ä}‚: sDíéqÆ_N—K?Õ£å”^~ýújlq7Ø"È.Öa™W£¶þ»ËvUšè-®Ð;í§ÆÅ3sѽ~FòS’_}€Ô‰ôÿÜ#↪í hYP8¾4¶áÓ~®­Ì´¢`ÌCf«z#FÐÃ]Š@ngxçà5 ñDfW‡2ÔÓ¢?®YS´âÅæ àŽÒºt&óÆ·.u®iM¹ÓEe»MgÃl¦©iÚÉ?Œ'Ó¿ŠƒÏIü8¬ì9$è¨%åլ܋‘?SŸ)â*•ã“ñÁâÌ)T ¿ÝfÛhŠÈ ÀÏìü+MC†få]éîY¬Ð¸ñJ5éÛį›‹ S¿†Îâm˨‡€è‹ßó^›wÖ¥” Î Œïr@œu”Ô\šhÑì-™eä$­¼Û}ËnÌCYH’=2âv€›þú~mmg°õ:V=ñÇMŒÄ7+L+…×àÆ»åѦ4áO{¯ç·°cÖ³ˆ±×Çmâ¾$¢ ûBUvøooO-Am¹œT^+éÌâ´ï»6}öÜj‚•ydͨñfÈçª÷N &»-¤ëaز[²a8Z´šžä_q•H aFœ9[POÃVŸdÊY(,Gh/÷†[rA;•»žº•š‰:‚¿]Ç-÷Ì ”›¸²ÊD~}JctîŸ/ü¦`yM÷í=_î:Ùcz¿…‚avGœ© ²ÏW89· bÏ1¡o#‡;à*LŸ«Âë“­› ä]A˜™w1¬¥4¶·™ßô3A,2Ï«ø¢ê»Ú‘øQŒœÄÕ'lEMÁäÂ8ò$¹Ã}/UœÚ««î6‹ØóÖ‡ïÝ»NænÅ)K7 {N¡ÍýzXu{¤ù#ßöÚó–yŒCâ­=O•f·åqßaí!ï}ü‹„b±¾;@D»É‘ÑR~(ÊLÕñm‘ódJãô+Ä.¡ï{ðöûpôþúô7x`ngá(ôºä°'Ço9 )nbüÝr}A5ˆ4 ß`ØЛr¤ˆç7¢Áú´aåp—ªSÅìjTûø¸Ô1Ðè¼óñ¾`^‚—FÝ”… åZ(Š¥÷‚Þd³+ld·‡¿3Š»Wb³¶.>ÍAÍ<û…u>.Út*îÊ:úµ¹Í›g¨“{=ª °‘Ö¤*@â¿iÑ)ý4M3NŠV¡§]ç?•+† PiãmVÆeÙïAÁ^|_Æùfu`J/Í.[Ê_)£j; ñqë.;}ÞÁ²÷¯Ò`ººC{)ßOŽ‰ƒiF±¨'"u®!sŸ0³üÿøH:Œ†‚…¾<¶Ø˜awÀ*8ØŽ>}ñ’\‘“Óü\|¡ (´|ž:;àVœ§f&yþ,‚°À®öZ¦L$Ðêi#5Þæõ„ñÝ.é¨\]Í> ,*\fæ·ç¿ëùß{O{†x"s^i”‡C›±`àˆmœV+Ým¿k”-wp>ÐAÐÀÖ iFìn…ë6ƾÔ~Ï,ºE³~}3§zO"ûwfºDÜ8úá·§î¶Vq¹ B( Á,¶6E[º<ÉT2T«†"ÀFWH®ŽA¹$¬eêhæcF8e~ü›Pm|Ó¡àxI¨ä9éC %ßÒ,»|!´ Ï ´`‹èlTb´èž:ãð^É0iÚ%&eòSÎç;ï{âþœ`}Õz‚:¾‹dnæí–¨ŽtBÃ7:¾¿jž„öã7=Ã)~’e{rèô³ËØeÕ·Ñؽþ4õ[H³&÷ôªfæ]—à!!†ÞûfšûB~ÓYM{„í¹]zˆÎnÁÐAÍôCm‘9ñ»ÌK¤iTÚ0xn7æØâꮽX R M-ÑÔÏ%—ïiõU_}.®øE­å+awDy 'eðŽX | Þ{e€¯×!çV¶ÚЄh uÖ¬’w8*lCP3îÊJrz}ä…cÊÕìÂx¾íºÚfH¿¨‡Lgñ¬‚¾[–‹ë»$“—fNRØâþðN"áŒjϦ¼Oe6D²ühíXM°º,ûáòöÆEÂ4¯ ‚¶­È=èX#êÌÐkIඛ»dÊð´zh dJ-¥#FÚ‹ª{ ŸU¹½ï¬l''°¥šðh`M쟡¤þ˜ÇèÛ¥¹Þ¹ÎHÌ—íysóî²Ó&=ˆ9s!< õ7ÁI7éiÈJUÄVqj;N(­Ï¼³EXM“Ü®ý¨ññƒÜŸ·½LÜyË"i\»(iâ;G!3!®-»o@\)Dôœ@{;þûw6Ð7ãä|´eÄ5½ë[‡‹Q§Aˆ“ùKI8KîK¢ä¾°§z˜“7ì¹/GâÁê™ ˆó0q±WºDgyÉÎ?$¦N/êðHôþH±KÆ!ôYTšåöt>0ˆaX·ÙUÒˆ¤4rN—„zc4L ”äÁõRŧXƪKµô—â—QûCŸ=53Ycæ3Ž‹Yùz)So‰ÖϽèÿ);‰X§žã]Áqê[èƒó«ëwÍ14 þ&ˆ×Ú=…Ó¸¯3½ ÔH·fi¥0&—|è(ì­‹EŠ‹HâÁû†¿]½S»ÉB&¤•P¼§°_œI¡ƒÖõ׃h#õˆ’˜½…´ÿ ºÂ6zmðýþKªBÂ÷_,:G XRÀ¯^ؤ«ynôó½zr-U¼M#Å󛟞vèݨ˜@Û†N”g—›·‡”Þ=ñGR„wA'TÎGUÖ¦ ¼Yïµ í¶ )dly½ã,-”bëty[ˆdUVÀp„)ËûÒÁéna‘ÆʥѮnÕ¾¤,­[™®/a;·îðK‚(éÙZ•ÄZ¶½Y˜]ä·/Û{GZo™®Ó/÷\ý¯ÃŠ˜Þç0ôM¥aÀU×#Ü„H2dëPÛ1}’ NA«’Dº˜“® vfЂ íÀáƒ.1F]6´Ÿ25U-ýq)UmrÔX ›»sxp?àdm´-Q{‚&O>·kg@@ñCÄáÅÁÅÕ)Ó^WPÈrÊkbDÄÆ"d&°ùuù|âg´hhÊ€Ão¯±§q‹ˆæúìäw±AÒXnš¦99Ò€& ~R…†Òa¨¼{ƒŸÁ¯.^˜HîÄo“¢K¶ü_€ôLZožÓeÖI ŸlÅpÀ Ü© Q=VæœçtšCì°WúñUøTÍ.UˆÒ„¹9¿íS¶Ë«—›l•MD… ¤€ ®£¢î¿"ò¤èJ•„ÆöKþQ)Ò5Ы­tô°±%)îßT,”!òèu½Ê‚ Üß…r†Úuáã15ÚoRùö¼Y¡Nýžìª¢Ð´Îïr¬J‘±-•,Ÿx‘’tçß/1ñNžwc©×Ìp ‚ÅÁ’:hB×?‚Á¤éø匌k|lf« ­.ͬ­(‘¶áº£vq°ÿ;iö͵:ªÖÁЇߑÂòü,èÍéXpJ(qÝ‹,Ä,½Å¬ÔÆŠI)o:€ÖÃoNƒŽîã…ÈÇ‘ÐîVQEïué-TéóåœwMZ@ð(&v$¾—ÚO²Si0~żMå$8§ž9jë—ð´îX†ë®®é, 2-ùí]¥±¯†á\´Ò„ùù/ªÖpT{(ÙÄ$iÂD"­nö‰Œ‰­ÙGµ4õ{òœæStuº~r…Õ¢ØW[®Þ!Æ{¨2ð^øÿÿ±?Ñ”`ùéÕ ÉËfËoPÕf$Íð㩪©Co8•Æ¢MhŒár­dÖœ¥¶«íÐ2Žšá{jQG¼Qîh5nÍZwnÿÜtƒV©ŠþbÚ¿÷«ÌÁaÇéì(à2ËÉB‹Ò*ßü?t{1Æ»×Ä|æù[аù *¢a<éêþ÷¶aòBë)2†ómàoSåC¾³ÙÛƒÔÄù<úEc¶“½VÒÙ j”zYJO>Á´ðŠÆgT>Á‹…Jïõ°WAŽxY ãd–Ã.fîy‚]H™0ú©ÛÞªreë+ÍÞzø(é²S¥ÆH^ãðt«):]¤÷ª>“IçC§ñ#¿ó2_uçûÙƒ¸ñj é^(Õ–6¼ˆNƯ¤ ½…20mc‚ TgWË6°XX5w”X•¹æJ[{H"ÎinZ'@¼ {Mš”èNˆqf”£DDèHÄ9ª&]MŽfïT N'ÃS;2I Á½aÛP1ʲÆǽnÍ´³À5Ä`6xÅŸd¯yì\YþÉN| ¯’ÐÔÙ¢\Jñ 4¹ T[iÐÖ™5ÚeÃ@ž!Ó.éÈë„_89ßBÑåÕö?Øx±cò>Ék#@X'— fÃRÛYûâBÌ\¬P™^­cyn$áNtìòé8CþüÂÍHe ‡¬ûkYÛ°õiö¨è‡ï5Ö)ÑÓÎGц£ÈF°ï['/hÚt ¤²(6hÉZó1aSê‹^oú³£o‚i3 µç¬‚iíÂÊT’õ´^*ÑñqnW×¥8ÉÖ÷_{Q3Q¸‹»€Ô¼rœ-Ø¡jm'uçö½uC"ÝÓ,+TN͘ÀjˆÞ߇‹¬“ëÆ?æ}v¨ÏEKQ[¢´Áâs³Á*ʲúžû—€ÖÝø>Z¬ì·ØîA°ìt\ÊœÒ#\©C£¶{¥9òAîÅÔ‚ˆtš³ä9r&$VËѶ HepT…Žß+[¢š xÅ5ú®N¨é®V p·¹eL"fªï<_›ÆðAgÀ§¯-/àÌ/HqH$rq÷,qäy¢mµ(c_P$ ŠÛŒÏÄ-ñÌ›ÿa>zJ òžyŠu*àœÕ“+V=+ˆËSE´êr æq|šf)çIZ°Wµ(¹¥(“Ö¾”æ¤Q!èÜ(í§µ9$ýxŸ_Ø<ž±KÎÆÊкbT´2Í£öãÃy¸í]á×az ña—«0‘u ÀQæÅÕœ¯‡Ükÿƈĸ„¦&ê7wØqâ"”†‡©CVÇék5dù º?Á~„<ýáÒ‹¡§‰.ÞÏ’=¹)¿ §× +Šú2oÆGoÙÔ=§/ç.ÍüG¥[©xŠµÉ/rãó }qÄ†Õ i(½4%È$*Ö=R7fу߇·ê‰ÿQ›±€‚‘n(ÐÕœ$*L%Ö|êuòîÕU9Òèý:¶XS¬§?&88¸ÙÈcØLÌÁº†¾?ÍpMÀ°­ð¬n¥Cãk©ó=ÏoÊ0¥ôÑË3]2Ó^K=è£à."1ÌD¬‹¥VH5Ä÷á_ÒJ5/ÇÔ¯vðÇ"Õ{cŒ)f8Ø¡ üõGÑçÄ[Ú…¦ýW¡¹-Ê!”Zê•€#ò n¼—å~#;X_1ë"æT@'P9Hù›Êßó_âeŠ:9Fqvc¾l©† PHÇìrÕù’4MU q*ÿ>#—‹. gÅ”É7R´ÊEÈrÉÊWCwÿÑoé'žS=z² »Ç¿êWÖ*ŽHÌU}|A1Ú"Á Ðÿ —âJš>¦@\Éf:ÌEŠÄÒÎfƒWÙ¥—ÓS¡)²ÍÆdq†-Ú¿Û¢WDqïÛ8»%Ž}Ñ—ož+4dÿÈÀZŒò>æT-ÕúàŠx®›MŸPŸÐ›?ý^_.5wý€^2ÓÕ˜+ÓéÆÀ—î°OØUƒ‡8zqÞ*ÓM´9€VBøÜwD]…™€ÊŒ²ø Ž`AË»<ç@+@mˆ'nüA¡7ÔÃ÷°áb+Þ—÷[ÊŸ¥´’êƒ&‘ K$îGK†š¸çU¦ßZ_Ö‹Ü/i”ìouèÇ‘Á(èRÿ0¦üDÆ­NÔÐia®­§  gÅàfQ;ö/“ð~N«·/ 3î]|º¦A›üilÁ„eÃå2yò ÿ$ò¿ÅŒ4¾b<ÖiØGìd½j¨¦ùlí-( .vÀþ§9ü*®°ú"Î ½6Ä ™rÄÔ#–„ÝÎC"¸3¡ý{Ó…'GŽŠAkLà˜vTT.~N…ŽÝ˜È¬ÿf#‹¬pIÆ[_­³,[¦øÄ ŸIq®Ðx^Ëh·_Í·¡mÒöIgTìåsÖÒO¥Ôv¦‘› 4Ål!½sìš¹~qM7vdMlÊŒÝÀÛu¯Z&?]‡ŠM]ª7ú(q)Æ™µWÎëëÛ_… 58*¥÷Æñx~Ñ}Èä7gJTuä¡ieÏíÛ×üuâü%`Îήü½"\|•ü» íPBã,_~3ëÁ!7]7ì?áÀtü夛ÈV³)û™£øÒ9_wR0ŠáÁÿ”ÁÍšÏVFSê¨3˜–ufήæãè—F òŠ·Ké‰êÌÑs‘7mQ‡Iœvuš £pªLDf~¤‚Pµm/Ìú2Y+f‹U2g'$»¿ÂŸßßµDÎ5½‹µ„¥©\“‹y’ˆ¼.…Ðpзӊ£’²NAWe?õµQHñb‚ï˜Àéa!åÝ)í¿™ÕôŽ-ëZ(LÝJ™bà ¾d½ù¢yY£~ æMÞ¸Ç}T¶ºjþ®¨\ÉÄŸÕ-‘F¡u¿…¥ÿÏÛÇñe|á»`ö9øªÄðZ‰màÜßÍwäüŒÀø¿Ò… i!ÚK|Ú¹ÁäÂÏ{ú¸«¨‚ˆ›¸>É︀›.½Û;ÈHéþBr¡$Oªàä† î š!ãÕÚ@ÂLùy¬ÈhxÓ˜.k¨.±p¨:Wv5uÁŸÉòÌæN I°*>%½¶ék¥ªâ¯©;BTn/W¼ýF3dG"—;„ÌÖbzzìêàûö´£ E!=ˆÀpêüìWZÔ<[¿Pj­xRoö•l䥆¼»adMm`û¶Â{oÞê‚Á>™­.핽(¿‰_úV_;»(š ¦_*ªýTC‹¾9lc´‹K]‘ry®•¨M—Ï®›ë’I"¨0Œ\N†yØWÃÂòa ¾[B{¨w0ëà©àºÁôÌ¢!2ŽÝR1úÁò[öVlr©Ü§¸µX:A ¡N¤~|Œ:-Qì½cQv¿ÌNØ*—È1i|¤­Yǃ"å½úË…è}Þ­WÄé ÷ä6”ä5—£È¨ÎaEWŠ”fÝÉ`½Ðuã›ã¿ªw˜ðuiQÏÍ~fW€ÛƒU™s´LJ}‚!”Þ3 ·˜ÔûÛãJáEí±Q[Ñdj>2Hb3HØÜäÊÑ@3å™Dfl¶o|N¿S‡Œ{\¡GíTÅ3!Âm7ò„…ì|,©#‘4¤3>»Vs Ô3ú襮ê—í-5¶­ û/Uõ¹fänöÕ1÷Ƀ ›\xyú]×Á‰‚¹ó÷źÜfüĦv4ýK]F¶~&¾¼YŸôS¹õb™9–¤­Wõ£‡®ó;n“ILUå[êéOÜŽ‰÷˜àýŠ0älW?A*һ綌ÿ›e£{U-±«èæ`²šn@¡¹DbVrüœÅOMÆõäÄ.+c£]ùÔ/L¥9ø^è¶ €>ºnWéœîMuÝ6XÄ'9Ž†ÎêÝÉÉH ØzZŠ¯Û®Ú;"¤s4_xµœ¼è©<ÄñÌ*§Å®µ®jéEár6ËvÁ˜Ô% "V??^ê=2ôY)× »¼+7ýŒ_J#Z"e;•¡kiöŠ;•¬É¸ÛWH»bGŠN&èZús—.<¸~¶Á~rüâ¢þˆ¢õ}Õô€3€aSþ±ÅÊÑžéžÞwdÏ3äؤó£†m óðuâ\:ºµ¸™ƒôùS¯ÊiÉ_ PÏ5a $Øí¼ ¨»†N¼Šó|%¨è–_º~(ª‹¦4¢ÕÑ¡l ë*fìi3vÙ:KÆ­ÝL’yœ˜#ði}߯Ȩ|õ HÔ¹/( d‚'…lQ¯€²ŽJßaCÔNÜ%d˜OLâ,‹°Ovž7Wªª˜t–À÷~oåS@¥ ·•rµ?µ6*¬ìL)LÉú× Ô‡ Ú_©Ý¶2Êvx .-<æ'âà«ý_Ñœ1¦ÃXuÃ]g…å TþØ…F¦ƒã(ú08ÿpERE¦ <­C“;^Épš <Úg ,ÑŠ³l>‡µÔé¦*šÌm¦œ6Ô&z›Áë5Ð;¹Á³­xw°:9àûœ»E¬x熢ÓKAX``y1û6#QMàã|V!ë¥Mzn 6ª¬zÇ˃r"FÇÍ(Ë6ÜÎE¼>YeK¸—‚âB¬Q°ÏWóÀïj}@¼ït¯Xœe¼3Y­€:…ts³‹6Ò9 `ɦóx‚…í)‹õ¬ÆÑù²¯$Ï;®Œƒ&\W¿B¿²Á©p¼áR+7Döq¯p+û€ª[ªe±!&`=áùÑäÌ,z.È”PÈÆÅ®Ã!­›Þ9BË8¼^Ép!SÑT–ð}ñ¯†«g+yy!“,„•6&šË“d\µ|I2<©–ðÄíËÚ s‰êà›Vl62bA=º>7x®5>yù¥ñTž‘jäð.IQ÷Ð7Ož0Q?¥9cÁfõE…¬´øãaæÎíŽ¾Ú L5AÚ¢ÝËŠí¤®ÁõZâÞå#˜îÁ3ÌÏß.k˜†g(T ŸÎtð\0<¥©Ârì>và$+:p1û9¶ïñÿAˆ@ÑÑ«Þ²²S™«xì³7V¢Hniˆ“"†±¨Ãྒà ±°Öýj•êC]Mâq¡:–þž¬Lh¼aoqµ1Û(u×µP}õ‘` rŒïôk$¢Ê”?Á¼ùjU gÉÇèaLšèÄuÃxvZS‚¨méÆ+´òûÝ&Lh‘XÕÚ\ ¶9r¢NP4¸ÍÙ)Z‘9' aâg´û òmçL?ówv&Ä XÂöŒê¦òj×ûù•Ø:Tïü:  PCQp×Ú˜ø!™FŠ7Š—PÔ'ÈR݃[>Û$øS L™ÁxI½EÇÈ\šn©âÝ!â ŽrÓ .Ü”ÚÊHì )ã5&ËÔ$h¼ à娓§ %ÕxؾçF‹±·ªˆñFA\„jíJe {0¦]Q?«¬IÅ¥ùynÌ¥‡B¦ÌØb+L,@"qïXã·£ŠFs0VDUäŽuqU˲BDožË©•Ë`ÏŠû“G€Æ"AWDh ¦ZA½pßiÙZÃeÂèCábk²½ny ¾]òÄ>árð°ÿUãZã,© (t´Úf¢Ô;PbMpÓ:èЀ êÚ>Äq ‡°É·î¢Ä{.1ýhì÷쫉_—i6£ï+%«ò<¤ ÝÙÑó]ÓóJ{œ Nò¨=͈ú,bZ®¼³f^¤Ðá7«ßQ•(€9åSQ 75-„…ì–ý(·© Uñ¨ð§‘'B!93‚o·¹ÕŒÜÀ ^SɃ{• ¯<¾(‚‹ Ká%\T¤IïaßnwᕵT¤Fršòh¾Ó“lå·±¸+ÉÔíKŒ|ö+3³ñ`¶²ìÛAXDÛÝ{ï‡?Aê³ÈÎN¤Ni=fRøëB%Q£…=¼Êfá¨Ðú4¿XD·>_>hðèçJŽÔqj ODÊa÷FÈhaÆ!Äå|xŒ`Ž iE \ü¢aklV-íYĆ¾å‹j¤Êw²ò@`Ë5¯kÑ}kÞ-gÎb;ˆ¦}QØ=ÀøÇbÖ‹oz÷5$ 7ýÕýK×àØ1çË„¨Ñ8Àˆüï"ãyˆ+·Yˆþ  Ûm㼑 û¨í£cÃgòµûJ~S‘Æ´mC¤a-ývˆý)×ÊÙ§|y¨Ë'…b‡\‡+8KƉvCÙŸÃÀƒf…S c͵ðòK;'%VuŠÃiö7¿ž1Þûì´*E*wÒy0ħ-¿¸1.\Ø‹üŠa‚íà´,¯Ø‚¦)Ï;ÀHãÃó‰iJCÁ0¹Ôzß”Ä IÍøœ´PpÞ'Ó-wH·I„ħ ¿ ùåâ~å'ð/ѱ½{¹ªZ ©[ù9\7aV!1Eç,.N Œšñ24V-Èþ¥  ;Øgí:ñ<}–<ä.ÌsªIí›Ê,›Ca~8{ç›2ÍD=Fä/PÝ6ãŸp™œ0*Ä 5Kî÷nûI‚åTœ›+šXÜ3 ò ¯—¹¾:˜塊¢Ïx(¹“åÖ'¬}<‹o­žò uü$‘¸Ä㔟k—ûG}dsêÓä&E£$¬¯"¥Aº„0Þ[±?€!D¥äÔ‘rx¢Ÿ(Á)šÄôŽ«=ÚÙ¼¸Æ¶äëíjY »°˜£·[šï'#nbödÒ›k•§(¹œ³®§…xÄJí“{â(¨f”óíé§ÄržötÄH{ÒüòÌÞ‹ û`ÒÛD¦%n«CÛ„›Z¡¦ÑýÃ뻤l¦öz&ãã1cõ^€ÒÚòÄ#“9Ôô 5°:ˆ+Ïâ+Áº«ë¦–‚ýZ¤àÁÚóaز݊ad&&Ý)M|£ràç@¡ƒR_õ¸£JÈ~LK,‚´”5ÿs»…uzn¬oyr;—ö€.‚¼¾ã*҄ƨþ;Š¨Û'uÿ¹î)ÜÖS½%<’§ ’A^XÌ£cꧻáÀGˆÜ¢oåäÎNOŠÆnaÙêïuÚog*v.f‹QmöûÌŽ²îiÊ'FNÛ†m1xB®ÂyõôW­eÌp¬in=éuBTg©Ø}Ûï$º°M(çÅ ía›‹Å åŽ6ŠÓ ræoòj±ÏcÖg-wDkNN.æŸtÌó'F혾Nïj¥³ „Ÿ#{JMú'Öj}ª…9Pî¸ßªÃ³8Ñ· @¦ót,£¸©´2Û¿…Eƒ8•¤æêâÞ­|+eæ?«>m胧Ÿÿ¾“êñ­UíPñypG3ð ÔZ.7ƒ®Ã@p2dT($ú­îÚ‹¬Y¾ ) ÏÃÎ@U ÎÃÄJ´²Ð‘}nW=Ÿ¹sQóYý˜^©@5nhëm‹Ã€)§Áznlî?ª¡}~iN§Øõ8·Ðu4û|­$^º-›Ó% 6;ÍÌ8‚Ú€UqÆm½3*ß_ÞÔ;jÎÛÒO<¢åE}ÐÂEáÝØ8i@Íñø¦ !t›˜G‘×¥}‰×!¯ú#TçŠq¬×:qu&Ã7™”)áQ×{¢o—¶ï{þïŠ4Bn«ËId|O¡¾ˆ#(¬I™3&øw‘šy]Å3=²$«•ï%’"¢Ó._²•Nïdà—€#üWóRô“ýUíYÝÌïËÓâh…XT‰…oÊÿ¨z[–Ô5Q;¨¥"àóÚ¶n;ã6S}ËX^_õ«çÛÇ 8nXþ±.xò¸d;g?^CZÑêÔL4F §„L.â^p놰Ã;«d“…4NÑ7ÛQïÓ•@÷Ø¢‡‘sòi‰¿ä*^=¥ØðMòË'U³ë‘{¤ÚÎóg§›FœÎ2`m½D¨Ê™[¨Ú¢.OåÔþÇ¡eºM¸±c[Ê€L²:y­¼9T½hç?Ãáêë@7DÀRËÐÁуÄyïûv÷ç¿‹´ë¤Úé5 í ¸p"}z0“Zþ¯p/áØ-­÷çã[‡<þöœ©VîdM8ìƽ:rv÷lUkº‘ vÒ8£*ã÷ÚÌniº+KKùrp…¯ÿ'Ĭ>+yŒYÊFl¬ëä9ïÏ\¾â]ÒëbÇn•Žu°{³ºò,5yJsçˆlÁ¹¸Ö ò™à[„¤ûT‘²24zjý8VgÇNZEöºaÑÃ&)é±® SÃé¬ØSÑ.»ªïÓ¦1ÐxÐû¹Î^¦;É«Bo‡:ÙA²kÕO Ô0‡Ñ¤½~ü{? ³ÔuÖ÷¯òìqWÝí}lœñ4‰°"T6³…kV“¶Pû,¬";sÒ(Ž^ˆÓ]ônP:3×”­ísßþ&8hü­¹wÙ78¢“QÓã÷€cúÏÈËBWË]MÍzXDžò¿Óéùï£J¸|3C–N¹ c¤×S;»9 ¾l}5r >©õêµ´ TòßžzsõXeœ+rí©^3qöAFp|p_ÐtÌò¡þTmnöÔ² *gt)¢ÿ´„×{Îo#˜OBšêí•RÞ£„Xz”ßçÝa±”'vªD©‹ƒ<½;LZ–Ñ>û¿Ïƒ ¦B`NËG?úe°âLÇž½£€ÐÓ¿W„V ¹¿ÍlÞÁY®ñG:•òs¾eá¯þ× ™~§áw7óFÄÀ¢z#4ôUBÓ #ªšÃ`ˆ±J¨/%AÀ¸JþÛÈ'J(uèÆOÒ"'¾1¸.Öf~ÊRÇzT. Ãù—åêÍCҪϠé™ü‚ Ɖ`…y]Ù\X¨®¯ô Ó¥ d »iwAUâÅ$ùä>t@>ðºb+ 'a1é šþÂÖt&ëwQœxüñ'Ä+ ÄÂõUü\î¼ÃU.Ö?ëc9-ÔÔ•›Ùåm­E¤‹C·œæ%ƒ(Iiõõa·¤lù½ ¡/€V¿Âø¬¤‹S¡bÆM…ÇpDIøÒ…á’Èoÿ*®Ž’ ¯Mî·8«qbG9ç ÓvØF°s:z׫/`ÞT¬F5—Óž½¸„xT]@ª%ËÙø™B²¥F¸˜Èè|4º¾ª•¼<Š`E kã†Þqû„ÉQËíYT ð/øÕÜ‚(.|KöÇÇÙš‚:c?ìªÌÜZC³d‘Åãéµ}Ç i tÀ6jܤ5 NnÆ€ öå ›ëy¸†Í’£J9ñ°« †r¾Ðh¹I(äËÞïË?oUž®ñi(ÿ¢*,x²_ ÍXŒröü{½ô(‡Àú‘œì5Šâ¥±È¬QnM$PJɶˆ oúÚ¾(¯v…2ê¸0Š&ƽ ¿«|¨¹3m=BàÓ‹Üa†U õ·Ã/2¹¯m‚Úœõç¼ÿˆ–6<Èă0<<7Ä2ÿúLz—îyNváP9‡F öçF#íýPæ`ëvùv»0Dtéc2 Zx=GÍ:˜Á³BÍÂHd^'êÏXs¹ bÒܵÂèd"ø=¸a(Jf®‰Gf GÑkø{¦€¼#êÔ0yŒÖâYn›3¹Ñ¤®"6»˜(ã·‚EÌ:Ü)I¬iÔñÑ? §”{‹_ª¿žÛG„0Sï –R#Yl@aRØ!~)ç_r¶gpo756”$¸ËJ­´™ž¯¹•Íz‡~E‰ ˆ.Ñ[bõÇAvíÕÔRÔ)I¹]®¶£@R‘»®0"Ž >ô ¶ÛþÒlœ-q©èÇþp>òÿÊUoË‘QbÜk6ådâô|ñäÝ,ËÒCï¥%´h Hâá¥óKiõöZõu”iILϦwnfSk4ñjŸÜo’ˆ *cël¦ô?ø’"ÞN:=èä5_Eh::k£×ˆÀè6~M9ë¬Ó*pÍg¼Êñy´þwÇ@kFxÙWŸâãßâ¿Në"—ïijY­„Ü:¾ XFûa[ªˆ´È•0ŽN×ñêtÉB’‰éç×ô˜ñY„ÁQHGP˜ÐÄ'ÐÀµ[gvÕuWÑeE~Ì 5µbAò¶·©©väY÷Tì“6r«ºc2Kqºè¼pµ˜¼êg…Þô@ß°fìÎ@à•Ò¯—–´YM¬';Lk\†ô3¡|ÿ“—§Ç§5©Œçb¯ íçy£oÃ0H%þÐ/øÀBÈ#»_yòpmª2â0¹®÷ÂÚÏ6ˆUëÄqë²^àf#ø·¬õô–D¬hmq²¢Ë“#œLʇœlïf?¿ó¼ºÐcÜÞq {7•ßÔ#[òrúY4ú(Í»û&£”‘âáŸ$ ùž¶ûÄ3õÿØy€rÞYØtÿÂèÓ[”P¹h&½×ÞÀm«~‹?ŸËX^6@/O‰*:{£†£—æ·æÝŽIrb´zûá°í™ƒžÚù»Ëð¯X¯ôÕ¯Bº!¦¢FuÑ0ðä4p_ù6ð L þ£daZ¸y‡ò…7@-õç²Iyt¦~FÀÔ{ˆÑrþç²ä}Æø’këPrNÜðìZÏþÑh½¾¤x™Ÿi†_Ç7ÏYŠ»8 cŽV{ËCë((£I6oBÊ¢/qÃQÎ}ÃÄËñÌ ‚ˆÍ?Gn_64‡9˜rJhÉ£Ç#ê@Óâýxx«V8‡Û|ù”«Ì®Éî¸D^äs@q¤°¯Ë‘§ ÄK¡ ¶Üå¥[=oý VÍš<Ã.£Ž€Ž¸ºs´Âõî‰7ÇïbkÌàÍZ„ÜR„ÂJâ #(3nXU¹™Ûèåd ?\jຫv°üÉ í¢í׋#Ïï•7=©>O{1aÎ7 ôOía~ÉW¼HÚQ˳ЋZzShÎÓ©Tã–0©ø»B—Åëkäa[Î-™Æt+‘üAmRªä\Ëp“&äÑÛ–pî):‡ú† ¶°W›)²âKø§¶;x.ž$‘L½c6ë&GlþgêZ ÌMÒ'è^q©g¯vžIÿ|Wº‘@%ϧ•sMº5]áqw`ÏËC€+™–†Ët–ÓŽê·ãÞ¬ï™ÑfËÐæšm1a!ÅÞpZ§6Qœzœx0ê)˜—Õä^zb©„ѹpÈ!a•&$šÛ9$e^RkTÛˆ­îqÓ>B°;81ÙSº¼]¾'ãÎÜ¥6‰ÝU}YÓ‘Dñu¿úy²¿òCgìá[ƒÖ¯Š2\Ï£™£C¡y§ñ:Y«i|‰±=éB‘Ç3Û>°”\À‘FBpöz¦pð:ÿˆ(ˆß 2¨µ>‹<ñ£öÉb§÷ìN±$ªfìú?:Ò¿‚Wr¦ z‡âr:æ0ëÓè0d‰ñF•æñPÂ}7‚"éï£lvšRY¹—ë Äc}ÂH$Ê UëKùÎ> „—IJN>á‘皌>Šîn?УF€ÞJŸ¨«Z㵬 Í¡½®…‘ãhYL=òÙ22ë^ ­¹¢TÙ 7«“KVÝEÆÏÉtÐ0˜ô—ã´ÉWb}>±Òä–ÌøŠ\ê\å+ ?ŒN€ jRŽMMeÕqÈÏS}Œ tedÉÓ¬;Özph‹"à ýIú‹ï(lÐ`cCùô †û2–(•üiÀš0FÈ=Íƽä?œd(,£ª€œí~ÑG¤¦ý£œƒþ6Ùót“ïÔzPɽè0q{ ÕE7ŒÆtµï :(ϵE{7UKgv8× þ<¯O?[ïMÞìÄ/LȸÇ9ænÎ/uQ'Ze`cÙaƒ`/€Yn³Š®Ä±¢Ÿ &ŽGe:£úæ`?ÁŸïbH¡Tä%ŽÈoåê;÷™oœÒ$xðû§9Pû8mÌ<µ²ɾ §iÍjÅöZ°9Ð Oó:6ÆØùHî‘$í›wbU™¥!ŒFKÙVÖ|Žüfi iª#ÃÊl‘ ¦Ì:aR(0gÀ•¢Ésû€¿eè+n]é5NÚ¬ïXãµ0Ö‰ü¸¦Eƪ_sô+¶Œæ¯•6’QöToE“¥&în8v&ôH?ßGl‡êCö*ø¨ê¿(g‡8Žc¼õtž(-x’íd˜Ï(Íæ¾W…½N‚tCfAÀIˆÀñ4ŒÞúgØTD§”ýŒ'ì.„«šû„ýD…xÉ?Œ5OBU\ëÜ}gœ§ƒûlBK?DšvB!*¯öÍš,pi””÷ Å«drÎÖl†áþ·¨š±°2ÅVº®6?}d9w¼C 6i´¶&(~a%Éš‚ââ–­9 Ӡ㋲>††Ô-á^Ó”¼&>ÐPºýd­6ø˜*’,…A»|5t€ve´Š8ýðÔiþ!™%@ÿKª÷%2š1’`VÝ;Y ‘FT«ÏæyÀ(‰~\gw僳|ðçQõ¹ˆºhGôœzWÇÁ(Žª3F„ŽˆªÊ·1¶¦a"#×ÁÈ»¦–ÐP‚ Ò½6ýc÷š…&[5î$/ãñs´V~M±û_‚¸ë†PÎàÇw59¬ï,pZœÚËÐvÎXvNÖWCzO™Œ0 1â=Ù§ —< ±"uBJxwÌ-Þ¦â§$š¥á¯²ƒ;ÚK6X¨ G¶H^s H)ôÇm´oÛ®UeÄ?w£UPÂ’ºàúÃò-[ñÅRËWR?e>hUõ'M&¬-¶çe@ôÆÑÔÛKÄ4þŠZAñëàJ2C-Ù$ää üÞ/ðÐÉ™G’–¾õi] ¨JÁw¢Þ 2åM>5hÙyÅ-B‡m¨)¨ÓÅcôê¥X¯<@…Lzd¸‹¼ñcÃ!Çõ®&/À°vqíu 7°EH¿ò:­­e£×ĽëÇÎݵ½ðÂBÍxDýÁÁ(#›âQ1ÜšH"éwËw `«ì¥i]Ž‘X;ñשÙHÜUOƒÞîVºi„ñöZAº¼Q""­Áîqï]¢gÆœU‘[ö3/ž~BÂ/¨9HùÙqñ®JËÖ Ÿ­ÓÌþsTâèkÎ ˆ»Œ ´hŠ«?f(¼dÇt;È#~ª¨µó•ÄTã—¡ë©ßâ>ú×ýëû¡´Š69"—Q¹€ŽÇ˨Dnöû.Ÿ¯˜¿‰Òi¨pVˆ‚„+­±í·˜˜ŸÆ’žaÁ§ã»|:–{[— Æ·|äK±ú´zgª?ÑùéÀ¢#‹"U´i·†9¿#Õᔟu2E°J-ÈÌÓ=S ¸H›Å³VZÎh\ÚöÊÇÁ{ ªƒµ´$ymù¥Œ“þÚne‡ÐÀ`Ë!àq¸fP!iò`Ýs1ˆZa—yÈÞê­zæ²cÚí1\1ÞfÊÛ>ü3Nám4swJïæà›L&jÚ 7É“räkB‰aX,¶Ü»p5eM üÀÆÂ:\HAÑZ¯‰-¶~¡W€2Ã8oÔ-4Üc}E¦BA¿¯Ëbé²Gט¶OЈâLÚÎúZ¡ )I\÷çú„»ÌaÈ;<é}LD²¦D€Í¦|ä”lA1M6ñà1ÚyT*i)q©5êü×5JÛÜ@Ø6,öså=Ž¾¸bûB1üÉ~™©Õ¸«`P!¬yî^Ài\—s6>;ÑRSj†ÃÆé$þ¾¸ÑS˜@å*™„€šš;nd\ïhëó¯Ú(øHÒäP<“dâBÐ]÷L¡ÇN}É4äÇ P; ×8šã„±Þa½GVáEëš 2‰6håã8ÿ\Ç|×!¤h­Ñ B7h+RIª¸i4ñ„×b><…-Eòšó'‡&‰¯znÿ¶1ÙhkÝ'ê´#´ž±2R) D©Å¬7£^ç)¼˜‚¾ˆ°Õª*÷@ =´ÄÇü~ 2‡ì‚¹pÝ“Íýš‡iät™.èðu}ï%YºD{‰P9@š—ÌðÈȱ)1ìrxsÇÆPÊÉ <š.Ê$–ˆËZ–4`5þ º8 q,oD2uªhûÂåÎ#ê ›0ÚÖÜ@sfŽäÓå=³¢âb%wlÉáibFÃ6m¹ôŸÙ„!<£“©•eAÝUð’E:¹»U¿jM#LÙÉ(}J/(SáâÒìàìr²iÄŒT® lÜÑ _9ÃVþªB+„ŒL~±Z[ó”ûböÑVvœBßÙ”2@h&¢ê Êk²èÞý2dÞæ0›¬ô9‚9±÷6KÌæÃèÊì¼°¿Âˆ ý組+‹‰ÃÎülNÒx÷Xyª?çÛ;¹žŽ\J²]!1„ár,‘tä¡%m?Ã/° &Ëfz1I7 ¥ß'|"é¿E§ÔñGÞª‡5êgrBŒÐ)Ô¬.”Û1m¨ãÓ*Yyr‰ì"cÆ‚øa깚}aîŠd „·>”‰ŠûgŸÚ¤<Ëõo[ŒagzZìOA‡üðpݨŠÃï]&¨‡º<7êqòj÷â’ÓUß9ò_ø±:y3Êâ†G•dÍp8 uêNöÊè?FBDĆped¢…)vì÷A îÊÈþï-š‰ä¥oFUñ(YzY×)“«r“l¬¥Ÿ :#7Æ]Ž32çÍU%¶…jÙÛP ­X£iýr÷4fµ{Ày_•Á,=ÒfH»+w%c_aF¸}p÷uuP\öù2ìJ‘÷[Ųñ¢aö½8Wf  ˆ°”õkð¹û öblSÙnßOÀüÜ_˜PPn_\«¯ U‚þß|£çEžK3-Ìߢ¢T'y̤’]•ÿ¯ÿÉ@kç-ÿ ¾¦ ^‰ºw=p`öÔè6àK âñÕûÐîòä'Û‡¥ÖGcgÌêÑâwîUÇmÑ=ö¿z1G‘™â¸t£ªáÂ%ª/¾5mL~‹/îËc¦Ç)X&É°«²­¹½=»â‹WGb:ª}Ú?éøµ!ÈG¯9yyeIpÕŸ‡¬éŠ¨¬èœ·=‡ÃÒ8sÞ™];¥Mö9[0°„ÈÙñ/Ð8 (ª ®äòôÙKó?¼KxÂ#üDÊ:Œ‡Á I“wT"é”Àã‡2X1_¢F´ØÏƼÍÀÌà ókñâL¿u6lv”¸ŠÏ+Ÿ z–‹ô;ϸD‚`”ƒÂO¿8‹-ôk }à¶D5˜ŒªåCo±^,_ÖGö)v„Òr OceSV0±¯ÒÿS#Õ€A¬}«\ˆ?¨&ÀQ¦¬Œa£ w'zUxŠ™K ÜõÄ|–®:Ä Ÿ.€¤²ˆ…M­Ö~@Ê‘ì«á‚öáþŸä#[ºët^éå–©*b3ôSš¹Y&vön¾ë¿®ßÕ­¬ûXü ,”roU¼‹kzýá@ ؈Úcu7ÆrÚfý!º<ôÿ†¿–—v“÷Îÿ'oÄÝÏÝ©8:‰üjÛÄ ×—1V(Ð\V$¸ èM”$eŒp…y‰â™eãñ=¨—çÇÚ¿/@+4¬²¤ÜÐ] C£ÐÛ–çûä^ï èµnƒ¯7´;Ï”Gb”hŠ¥g/»8… øˆ] ø+éÃÕ™n™äQzéJJxàlN «ÔXÍSÿkå1;ÛJ´ð·]°Ëå:N†˜Ç)Éþêjcõ|QÞ¤ö^â%ܘÃf@§ Ç*‡ëjU0›ð\{Û–2Ý‘ÐC‹Ý¼7@ìDL…Ô$áá ÔjSµ±>ŸëàKK6xMÎÚ=`ÎT„¿cÈ!ym–¶ûêÿ3D‰›MÛc•ÊBEx’‘øPV¥Û&^›Óbú[Ç°9G¢OhÔ*ª9Ø€b‹nˆ_ªêçn%¬´F,˜Ç ¯¡ªš'¡œp©°ƒ ¶½<ôÆo‘/&…”ÃnT“Þèq¯õt&!6‡ŠdȪäHYån¦lÞÈaä‡e7è ½—n(ŽeÎS8+±ÞOÏg. ³!ü¨0kŠÌ9²ÇÓø0wf4àDʯº™4°,¼z@îþŽçÕS‰º ÑÔ½yåz"íƒËhíNì€q¿I-Ló…õ拾T@ BF–Ç‘6È” ˆœŒØËnùSŠ><Ú¼ÞK£…!2g¬s™>¢á³¹‹$ܪªµìà²VöÌ–»(6/.Á.qGy³}Úl‹•X¶i‹%I8ia“^»Úq1‹ïŠˆ† 3Äuvj.ù Ö;óø»eíÅõ–ïíFGú˜q(p°bðJ§ÔÓj·?ÅxÛVBè°Ç‰¡e”QÃêqQá]Ö}éèñšï¥™Q{c[ÀåÅ–?5‡Í Ñ"§ebá¼V(´ÒÚdØ™¥ 80ÖuSBfe¸c|ƒh쪳âU;ˆV…½ï¼€€zmJf¦äø—p=Ÿ(+COP»„ ™q{åvîf£ÔœÉƒG0;’Gm¥‰Äõ¦ó Ò•Û /„£€óÆß´ËWÀb.»›¨FD[§È|6´;nCÓXüÃRˆÒ.>¢¤¯áÛ‡Bþü‡5©°8OŽà“ìÖ²½U7`¹lг^7‘0fò.~ áà4ˆàÕy†tœÕ½d(X#!4׃6™z¾_IïJ·ìÇ HMQ˜¨ÃÚéÜǵi«$Ù)Ô#ˆŽªP¼Kìõ4˜YwÁO?0þ\sÖ‘š÷’öç"ÉšqäWŸ9`¡H¦}î)ÖAGË›5€uIzÏ.Í„hç@Õx¨ë¸ÐïVX=ßƆêGi1ÄS“T |ª™€ÁÚj¨‚ƒÞI™¹orM¸Zð‚©z©QH»$¤°*e2:X °6ñžÐ‹zÝO#‡ ;Ó"{›ëšiÇZò½œ³–Ól©"ÿÞ ÙRÐxû‹ˆªââºMCã4f·"=i—5Œýßîý»ÄÅ©×ÿ¯¡:KùS¹±$–4‡*yXØâ=Õ®Ÿ‹|ÖUìøÅ¿ßijª9‘V,%>L…ÄHÓah~lH’xf9[²àc6`¢…ÏR#óB†IOœ2ö‰ýæá%0¤#Ù:çÚò.‡-"Iå⥻ÊSjÈ¥Œáœ±’D²_¨ÓJ@CRµØ‹Ãxt:nžŽO‡­~&pPdØG÷tv¡òø%åç·kfvHCEb!S( tɸ¾Z $ç9.ÖEp ¬0ì %vr³&–M1GV)Š3¡èÃÜ1TÜhÊ7 È%S7{õnX°+ñ°‚8ÑÞ×Hnex+"mCCALMO¶ò„è€k@“á´sÄ–ÇùãC̳0×û :v;Ôª ¹?V&¨Û÷Àj*{*Þ' YH©rT+÷óp!˜É®¶K7vvÿ½n¼ô?Z¢ÅÑ-Å)À6*žL­ùÔ<û;EJåkìSY©B³W0h 7±£½º%ªWRJ·àð’ªåXÊRêØybσ:ÏBù×ò®r8õÈ)ô4éÅÝ"6x¢Ýbßš½ó Ü#ÐP_-©K4AR†VôpL´ôÆåy…|ŸdÚŸÞ `%ηaUˆ<6ðFÊæû:à™‹qêe³Ò‰d­‚ÖÂÍ(Ê~ê§þWXr‚¢…+½€ëÿý³Kv"¬ç¸†'`Âm Y Œ›UóŸ`ýöNÒŠ…ðÓ¸H56Ó‘¼“–0(ˆO]õ´áÓM—i-Ó ‘J\†„µþÓP‡˜Á‚ø¬Š«3É~}é¡uÐÑaf¾ßÉü=Q˜/¢ww¿vˆZ"ìãÕšSÁœXÅêCõ¹*ˆçës ýëñó3côMÂSgPÇÁ%üyo¢]Ê!"~iüIåÑÆ1AB}#‡kcú€E>—û\ãeûå’BMî[9EíÙê:±~‡ãq‹èªUxÛKP"൯k7CäPG0ùdÊ ;¹ßÖý ªV°HgOŒ¢—,Ã2Æ¢¥#DTùÏÙŽ)©¡†1%pIñÀ‹¢Âi;ßšGàÒ0˨j?Ð'¼ùÜ©OeûÅ"Wb§jfvýŠÍÆò¶5²L—džý¨Fæû,cƒ"ÉexþMè'Ávú‰  ›œM!¢¢o9+âé;F=£¨öîz,¦óúBlZ½Pû@öÿÎgã …­Ì´Ã´m¾XR`-“I<€Üûs#2.z&ìþ”úÿ*9iÖä–ë}ÅÀqÞ²Dñ–œüîã‰â°?âwNQ/øÉ Ëý÷å6mÿB¯q¦ È­'EøÍ«Iä÷½ò"‹lpš ”1öå‘ï ºÿÊÜߊ²Ÿ¼yM£»õ9¬Ý¡ 8ÿ±ÆåÝ;y—áµ8«Á'Ën “мK4ØI% ɺ—Dh9:çKQÕLCù%î×dbƒ?X×B¾gúÞ§»ZE¢ÇÐ#gÂÊ,uYôÓQà\¾õÙB‡~$VÍ'ÐEm‹fBkñ“†ƒï“~žÉFZ'7K”®¼LÄgEÄB=œ¿hô>)‹èú’Å’Óýøw~¢ò¢ÂxtØ°ÈÁm›ûtnÀ®K±]¸gä62|D.‰nX ¢¦¸G•:Ï¥ˆ9E¦™ãHFWÿG¾pˆ9]Ú¡›ô‚×€çW†к†gëþýºgŠþu äˆ!ðt”öʹ4ŸƒuÞÒªE÷|܃‚»hVCßPRz™ôé]{Âë^êM“Ì*ánÓçÂŒy–pÇŽ$>›øcŽZ fôU¥Ù÷DÌ¥v dLÇR¾÷ž ?ï â­M)ñÌR 詵8Ìgm:L_…nóé©Þè¡›Eƒ}æ²îK9¸°|ÃtÚÔÍͦôYË­‚5槖=‘»y¹DœÇH³¼G+œLr5ª.y$:ú#ÊùÖmÝ;¹'¤é]Iüц2/Cˆ¬¹œìˆãÔ †Róy¼ju.)œÃÌoÎ\¬äS_UÜ„&³J@õ$lµ¸Sƒw°+V‡E‰Sû"n,§P´ð£µßÎO¾¨b{ŽâÛE«ùk  ñ±I4 l‹Ûʤ!»¶—<Ä ®ê6Ïòhóqæ „<­ô·DÀs4µåu#åÁÔ¿‰_/Œ-Ïü§îEçQõy?¡c9ŒkÓ†Šo‰Ê˜›ÇË ix(SIåÚÃrh¶ß"Xó(¦ÑÕ«ýSXþf{(Ò·ÒLÙK;¸#™XÛÁ> ÿÙ[ÖW¥'kö üÞgA.2´£‰›í‚õBìu°ý‡ùwîñ­7×Ô9ŸÌO•Ü½Õ£Ó ÂAèøõ}»éÿˆ¶–F‰^Ìu½QBj3ƒâ¤ÔPQ¦ºýÙQ?×ý×6Ê>5ã9Á¾¨È(‚"Ì4ŒZ'¬:ÄÀŸAåì §0 @¥ «6Àf_ë6Ú§œ /r@ãÖî-Üì“Å‘ Ú8¥\!Ù¬áåiË>ðG=ox§P=o«=CÅgu~âÊbÍv?• è»B»ô`ÜkÍÎÖnÜr7Ùm%Ê£IÑùuö†5+ôJäÙŠjBIÿ'}ÛOl† ŸàfÃXþJO—ØÞÃÁ…ë•ÃFÍ.ù"eHízi(ªo­xEeã9?žWÏ 9…UÄÞ™S\‡{ÕÉ=¸,y³@ôÖ"u’WbÑ¢ÖظŸ¯âûÀ·öàÿ®åÓº$–vÈEO6²%¿FƒeåžÄ„þfýD¨Ö°1Ñ৅Z¤y¸M…ÿ‚ËWË?Q«ßé³SóØ&qL鬷3‰çÚ¿Wô«€›¹˜aUŠšUz ¼Öp–¹ ‘#ÐáÉÿ ø½àƒ<ÊŠF‰Š®ýÕøÚ/¹ä†>„j0tkïrÒÃûÑmͬ£|Nå—;qL}õ÷øïIñ¢S.›7¶`ë’ÍHQÚow«@þ‰k»±º{¶)õ‰!P:üRÒˆC ô(\—"ó÷Z¸dÓÏ{QhmÛzþÈ"à—%ùÛÁÿ!?͉LÿüãçÒoJç ¨Æ\#­ÍC‰1ÖAÜ7¨ \*©§ î ¯ ÒœŸ,Ï÷Fª\,©æ4?¸‰¤<#0láœtå$6‘ &›ßœ¨Žt‘BŠØúÇq».ÒÝ ©(ìø™*ž”&GÒÎSUˆÂt‚½ü6À }€Üh›¦P“z,u’›ñ'Ú†ÂÐëå6PP𨥂tÈ®6íyÏŸ}Á‘=AãíñÊZÉ2“ù¹Í–f*T·Ã±3¿–EØŠoFVÃf)±EÝs@Rt'×»e¸*üå’¯•oZ”.Ù’åÔ¬?æ@¹îö‰m[4±/0Ö(k÷ë'§‹6)Š½®02*Œç1[õá4ñ§æÚ*®–ï˜ãP¢ÏµD4 ó\c~¦+‚z–lÜxþm-f¼U¢‚ácûù祷áu]d|VÿKûišCø!ªXžZ›TK@[“X³&`?ňÒ½å—úU†ÀÒZ7,a¨y-öë°`#RðÛcȘÐ?×^q»ÉÖ–«é27‘hJ\îÃîe~¥bû¯ +ü†‚`¡ý•þY0¸ÇÇ+I(qÛ‰L—OÝæ’’vàÞéSÅ^NÄ7­^P«[Àdrð‘" ]-y2¼:Y~tŒ€ªg0«c¿óÒ÷7OùÔÅôKÈ æL½PtÂR^øcúÐ 0if¦.`죪6­’ny›t›1j%ö^Ï/' i6ÎŒ÷vdÏÊóXhDpŸ$oLN«ŽZ鳓-žñ†5!œ%ã½a= X~Ôáž1é7ð´N›€S®¯_-¢ÿ¥þ/ï²Ê·mC»Ï| s5‡#‰ÕH¿|‡%ŠážµlñÁlÁÀüO„ü¹¼2‰d+ÎËŠÖ~ddaÇu‹Ñ$Q‘è|~¥œVhS²ùlmí‰ Ví\GîVX©n ÚY÷A{»XõÁ×+jPé¯_}abõË H#þ¥ÅfúË“ÅžüýÞ)¶¦AH| ξd»¤BX‚# ÖÄhUêÛ¨®P¦™¸HÊa÷ÃiK¶ •åQ4P‡…îâŠr‚iÕ£7_pËÈ»×—n:4;)·³#=ÄQ¡%µã¾fûFÊïNÿÆÔ4É—q!S{ °p¦Ó™7—Þ|u×}¡Ýï mY—5‘:jú¢-–÷Z}íOö· Aé6)›7ŸæäõÌ-vrõñ>#¦!Ž²TÇ»#¹ÇÖë…Üh’° ¶p¬ž¸¢öêÂÁ¯lû©|ÛlSz/<€[K›¨Df©5=…½¸ZpòC½ULŽus=§Ñ ,¾ªòœ(ÃôãjÏó»ÌÞ9CõáÑîÌ‚‹€˜•ÇyŒÇF u¸ÔäS¢Eø°@Ë™kòëðZƒ…qÄû.Y™}û³û~†—KbLtA–&pp05±&9éN,t=Ϸ᪨Ú3–¾€o*2%&`ÛYI«Åk(r”×ø?f7o(µ7fïCñÕH‘0 ¢ƒÎº oQ² ¦9°_«µ¥ _³2¯§µI…ño8™±fœò——ܧÒq ÃPcN~Ÿ˜JjWÂ,#øÂS‘÷%ÇÔz¸Ž0Øþ=h&$‘YÐÁÄœmȲ<^1÷7Ò €ß´öæ0“B­¾ ‚Uš¥ƒ„”2LǸÀ×Ì´ÍìÃÆŠKñ¯ÉVéw)rG5/Ø-/¿eÐ"ú7„sU—ŽPûËPa,xœRRdÉkzQ„#ÑSŠÜÓrÉûn`‚shÜHj ‹gJB‚§äª¢#Ž¼ÉGí€Ú°¼2Np©¬1CÅ9½¢`·ŸIœ)®½XË6JÈ¡`¾TM³æÜ+~LmíôêÌð̱8* €>ší.ç9ªHTí­U16cB@¢s»8×éwTñž‚Q¤ 4ÒèÑÓ•f£“Ó˜ôÛíàV7?/Ú+žô‰%»g>þ´„µõÒ€±„ˆmÚWÂŽt“5.©¬ž•D­…lV²w¶l~«S´ñÏe¯|)K— âÅZ Ëï¦z+³)`ûû‘)aþ³ûèC¨–ÖsZaº0œ¹Òƒq׋êqR ŲÕRÇm–Š¡·Ã–6kÚ£NÃ-VƒrI—Xm£fÛ½Ž¡Ìã´ß#¡çUš‡ã!ª“hcd#0,容¶÷[ »'EÀ #>ÃÚWGÒ ¸4ì2'=1]8„Låó9c“}¨A~àT"½x' Õ«•Ú|Rzq Ð“ÿ›ŸðäÂX0ù©¯Ìî3¿û ›óe¡”¯r@©k;ž V¯>õ¼&dì:žú‹·ukKÅ i!Ð$NRW§·zyø’°.=N¬òÁATÔ^"™êÏÞño…ã;ž¸«‹s|Æ$Y¡Ø7•#¦pƒnĹL›_÷øŸ.í=<3 J«nãÚx›<¸3_…ÄÍ 5ÀY]AÛ\ï"å `ú¥l¨¥˜ÈÛvÝÔ&,T̈ðc™®ê¼aÕÖúPZz¥ø›íHÐù¡éók“ÅWF?t)ü¼Y•láénmå6±‡¯2C&(ÌË»m¨¸¼/ö Òa[CÒX©åc¦=.…H …–ê)8e?wÊÉÇíêñðÃá>Ëë}#žø¡Œ!9Vïà±λÉ,·þú)ŽTY¶­4,ùA- SÈêaƒ“lîûÑ.ÔJ×3ðåñz\x(²ÿ·úÏ’?Ú_å~ îáL.lví@Z‘XÔÃLÁ?“¯ã# üIc0 gîMQà­½¼Px¢•|ð댰µ·Â©ãÎI#Öˆ‘….ãzM\wúýÌ)b+—Q‘à/ïgÿ Ü s~÷ý¥£¸ ¨sê¡¢‚…´«E]SMFƼ—úõ¡˜–/I¦c·T½é"(¹a[ç+³`=ç³+ò§L¹ûÄ¡˜«*8a/à©xäÇI‘ÍN·áþËLOÝ©Fˆ<4 ½8%<5Ž¥ö8)’mIÜ Üf®êq':Ãm*ĭ<[õ= Y!yjºd¥7°Æð0 JTîñêÅÁ‡p6"M‚˜àb;Çú&†–=”L[²y†QÛ?"œ&g_–9l·‚ÎÖeœæ‘±ƒ—˜#Y78M8÷(·¿¢cÉ7]®„jå é.™ž±óHãMaÁpƒZܬg©o¸Ù÷®¥ ñãÒn¤åm³¡]kGP+w¦óµd¿ŒÇ C(ÞfGç“BfÒ¿·;ßP”¨ôRn7îMˆ¹[À;£nT§Ï¬T”…Y(h㿦q:ð*€Kð턽_nâÕ/£m6¡xí«ÿV³¥*¹²ÁI¬FÕ}£´M YIX”¡:ZnÉÝô¤sÈ…iËV„kì Iz«¥¿ˆUYÌ ö3> Á…úIþAÎö!€¿ý€¨­´}Þƒ0ñqÅßýpÄKž×µ;CO$ß+]àU³0çßTšù¹×ÛÂáq[Åbôwk‡Ífq:ßùœþ oY†ØDyL®Ú#1‘25Ï>‰¥ ÒJ3Qà…ÚæF¦Ô*YzÙÙYŸ¶Ö¢ÇgŒž*œïµ#ŽîšãÏ|y¯,DJ‰Œj*ZåT»¿t˜ÃGã^¨TsŠ6û·ÂbP—‚¦¢Ñ½Š¸L ‡Õ¶>:’dJt•Æseá"_þì ng¶ñÆH^±-~l´™õs£ÉN5¶˜úýèe¹@öd£ ò.dðþû  öåë:Ø)Dµù‹«²o'šEîÑbˆ±v«æÅ¥;^ÝO¶òæôA-©ªF»Ä*ÝíÉ&=Õ>ÙÃÌøm Xc»·égÝÁi¬¦•·Fµg.»{€WË®Jùó!·Ò4·l8Å“«©Vù›Œ³ê [÷kSKÔϽSlvÌü²/Øìô¨õiùU5qgiÉŒ´^h%9Yü†¾°vßvl#Ò¶ µô%± …Vüy#jžuA“GÜ;˜@ VŒDÓ´J;Èì+I7OŽÔŠZ(«¸°è„Ü!öѨܺz.ÝVZ„xà…="¦èÝÙH·•úd„îV⡶çÊå@öd63P‚Èç&WHoܤ)áÎXF¶¡J׎¾[Ü€4§Šßæ>hæI‹Kï `°%¬¶Û¦‚µõùŒn­hº5a×BÕºÁ]µÙóÁùÙ:x³[âÿVj™·ø.z=ü‡:27#_~§í€¸ubA4È-!o«Ò8V{çFX„YäHp|úZlÍgry¯‘¡\;¹ìË‘ ¢7[«l‰Nu@CAfb)|";`à96ž_ }‘܃*GϬöoÑ2€îS-óÕ1â!n>U^꤯W½4 1™‹^ã„h˜·¢qÖu%¤6Äg¦á~×ïÔýa-üsU‘vc9}[ºB–Ñåúùb§ÑÊP¥†éöÜké|¬„úI*ƒø•P_S®ÇŽg| nHGŠ-à³ €HIݾ¤Í“Ñ{ñH!k—½ÆÁ°_á/úÒ-»ÊT>É%Ž™Ëø„gf¥=ä¸Ë_éÏÊJµúHêëH€„9!… :ßcŒ}2. j¹˜A‚ Ñ‚qÚ÷”awMž÷£œçñ²FµÔ+ ¥È¾…™£¤ýr&ÛÎ{>.¬lºák…ˆJ^×å,5:¿š ÉsêO² ð ^¼íqØ©ÊD6ˆþ¥”h9§ô¥m¨h©Í9ŠŒ…Ð ]xü4—Rí2FY˜úR¹‰~ óäO›¬« 1û@¸czçAW ;WüàäJëÁ­0>:¼M2r U9x|JSŠfYFà Ø(ò†6îèôêðŸ1‚Îg…ªswÕH,šÅé?c<îtlXçG×Ús <Üv‡´Å_¥`Ótÿ÷ãŸ-v1´bñ Úý¡CEû曾P?²>y·\ >=4àqᾺŽ9IÇZUlÙ*´ýËê1ª j¢ÿ$D£ÓU6Õ9.jt?‚Èÿ6ÂÖÑÝ À-‡e·¯'~AzùÊ3¤;=YÉõZZ~æ‰Ò¸·zƒ(‹JhÂDh¨AQ6Ø÷ÎlÜ~HriÑzrø­ÊÙÔÄô<£´FåH¥g²—V¸B$½­šäD|†-9aïÛ–pT`Ý:Ó.Ø­Ø~…YSÎþà)ÜñŸ|ZÜ7¶Š¸Ûððìx¾ ;ôïd¶«àÂÁ²Ä9*¸o~±™ê±‹je5†ËUYÛšaðlwm-!Þ«¸ðÓ1<”ÉH®kÂ[Hv€ü–&”`¤Ùßu¦ÛÃ~DájÉêw©-W½\_CÝ\~Å€Oxæ­pÀö¨á­9–¼|pUû?x싘þåU ÑÊ‹óä_Tø$•]¨ÿ5ýbEí´PËãïQüŒ}znÀ”üÙ5á»÷­µfJj躋2úÍ–V× +ÍjH¦ezÔŠz°ÓÒkÉôÇO@í¾? ¶]øÞàt/Ý–¯¨ä(7½n¹ˆCØ?ÃÓ‘X6dLf{‰Íc‘ üc(©°¥^HûÇ õœ*Qí¤ìÜ.EöÑ/+‘ò¼ÀÐñàµ^Á—½ sÑéWqäpתJÔßÈuÁá/GÉâ0±Óô_æóØÉMäzìv¶²úàXõïwã\/ Ôªs¦‹*ØY©-Öóíö Ë=üëøõæf®{åÇ@Ò™VOn|“²ÿý÷û*wOE¬ùÁ\ÆÜ·Ëó)é<ŠhóàœdëW|…0d_Uy¹l5B1…h>aÊ\DåVìG¶3|‹¡A´´A­_FÀ—»,Í…­-ůO¿ä,·i%¢vL&à naLr©lƒO (djøœP] v:³ÇÕ Ü7»Æ…Û³€,7¼J]eu …zÿ) @Ö®úÌ )D½ÿŽð 0§ŠSÒ§8#âøQÞ)£iNšÇV¢ù-3ïÒÎæ'¦ìW¹ÅÊ-káÙ%-«É2Ùîˆ%²B6ýç9ÜÉÃ\oNùŒ€ZšíÓ:ËâzƒmÇTzËÈl:Th6÷ÁJeí:uÞŽ)ÍþÜΈ%—>Ÿ¬¥KûÄx 7:ªãÇš[ñ¤ÎGùY±;Ø8ßl¶7÷JMÑñ>†üŽJ«F&ÝòUlLø€ÿ­³€hËäàmùŸs · öv 9ñúç6ƒ_3MûÝz!¼£([q΂‡ÿYÍÆÊMuD*i–­Cìqi>û¬pô<#X³!Üê“X&Äãþ¬æá9KMðnŠ.¦¾¹}ëF :5¦)g¢ý}l-­)„öå¢-б,Ô„XVÕvú¬ûïCÍ€ ëƒÿ»Çt³Bn¨iÖøPáF0&c:LC|È44ïŽ; q$¼OŠâ|ÖVawFXú~‹–{•0¶ï“å(õÙšbï^-·ÏKyy6À.°ž¢óá0-ŠŽ~©r*¼Û(vêOS×Üõ×Ä9Ñ_LØ}.±-í`)Ö§ºõ’åJñírrª¨‹<ù4Ž*µ¦ 9å5;Z¤L³†¾à~¼'¼ö–wõˆúÞŽÿ÷ÌÊ刹3´• Ë®äáŠÌ)p–î„è¯#Úa…Ö–OÍ=ã…°5&&Z_*º±Û£ Þ£“nÁ'êÃXc§YsÁh²6`_¨´NÀ© C= T¨žú®¼|ˆ»˜ô+ò6=&·ÎÎYí2Êft)LTþ[ Bß³ªØûøÂ9«…ïhv=fg’s1J ¥:ö¿,³þãÛŒ(1¿³8÷àÄ唌1ºÂPF_šè¬ØÜ’;¸Õ€àrñÀ“?G …\0š(§Ø»/\ŵ øzýÔ§]EÒ”Lâ€#Ãq¥Ü÷Yö6ø®Û2ÿ𘙠"®5Wì/Ž6£-0ÔT­óÿË°”-âãˆY—€‰›ãÐ'ïg=>­çm8(þÐœO·ž˜aŸK'ÅÞÆýÚô¡ØQ8”( ËgÀ:HïÈUB•B™Î¶“——fü bÐw¼ñƒ©tŒ¾±»P#²³&Ìr›‰¸<Ø^ÓÔS2~ãÀô™úÅ`ëº;”+áÜöD2X{ÁFÄ£ÁˆkB W‚Å8ANÖÙ®±B©ôè S÷¼iËAŘìëp(µ8Ü‘kš)L‚vˆÂo!~+óüàònŒÁµ×Ó1L fðU+…÷¾ûG*R»^8ÇLê Ä0ÂsÏȇ%.‹åî€0Vg%ãéЛÛ{X PD#+Fc¯>í¥ß øO Æ|Ý ±¡#ÕZ²ÞÕèQ |Oë…ü›®J6¦ƒ/€ ‡&GGæ܆x$>ôªKÂý$§v©|é íZ¡ïø½1¿€CÐl¾ÝÅÇwœ¹¼DaºŠ+XNÍÔTv«óÙ¼@¾×“t'Oà©X€‘,ÕæñÜ~ß¼ÑÑ™@è©|E°3‘4T¯ o£:À+%4•½%1!VŠÛ¤#X<„}p9-l:2ø³“ònL¦Âfjœ¿ÎzxÇPXøZ ÛÞ¼ƒURøÞ*RE…ø¾.­¥˜ ¥ö7 ݘ/ÂtY˜÷ÐbÈ'‘kß´ÆŽôšy¦z6\cƒÏqNî+ɲGÝdfXÈz¿_âÊjæèȷΣâbõ€”^ù÷{'VZÛ¼¿E Ë;ßrP‹Ifœ=6nIŸ="4ÿ<çÒçà‘åˆ8éÅÞ¯GÃLà¬g±a……Ï€pÔ û~Obøy^ßø§¾³‹„¶MÿBÆnŽ¶:Û9ý³5õ]BxtsMµ… ‹Vz!gnìµy pèö+-?¡ÌÃænêŠFþ÷ÊUF³Ý Àeú” hÐäœÄO­—µµ”UþsíÕµ˜òõ2ðI0ìßûed þsíÕµ˜òõ2ðI0ìßûed þsíÕµ˜òõ2ðI0ìßûed þsíÕµ˜òõ2ðI0ìßûed þsíÕµ˜òõ2ðI0ìßûed þsíÕµ˜òõ2ðI0ìßûed þsíÕµ˜òõ2ðI0ìßûed þsíÕµ˜òõ2ðI0ìßûed þsíÕµ˜òõ2ðI0ìßûed þsíÕµ˜òõ2ðI0ìßûed þsíÕµ˜òõ2ðI0ìßûed þsíÕµ˜òõ2ðI0ìßûed þsíÕµ˜òõ2ðI0ìßûed þsíÕµ˜òõ2ðI0ìßûed þsíÕµ˜òõ2ðI0ìßûed þsíÕµ˜òõ2ðI0ìßûed þsíÕµ˜òõ2ðI0ìßûed þsíÕµ˜òõÂj_¥9µaŸ×‡¯÷&´ãÓ³T¹_·+ïE—m›6ÇÃéF4²ëAx¼­©G k“a,·CŠ¶=ç߃Ïé¡è Òùã8ÁS÷@Ü”³›(68±ÜÎZ0Âk\êТ­\KCN[¼_dmšAþ¼ à&…G·tpM VZ Þš¶­+êJ« F"×òƒ)Í{§‹ÜÆÈ(Ì»^›G”ÆÙ«gÊÙ;ÏÛJÆ{î…zÍ‘Ô•Þ"×BŒö òXzºž` K•ŠÂ0VsÔúè¾ïFak.ÌL®xœÐ㜮â¡'"š<,µ»×CÒ×Ë•yÕÈâur–¾ŸÃK·ƒJD¶¿Ì˜Q.¯­pçH1Ôò°âJo@<Ú0¸R_Šÿ€0Ë0®g´C[‘‚8G³¯ÜÁ+ª1Q\ZO~Aú¥ð¤¼r¾·´ë»Ë•c5Ó^Aöƒ;M/yTæáŽ÷&êÂ8-4U8¼ÀäØ›hØ¡lb·3+Æ×[ô«`/½‰@F©|ÓÙö² ÌO2Áê$°ÝCpÙº,›ÎÑó X7­ä[§Zœ/§ ”dGk— o͘·Üàt¢ É?ì\°©æCO:€j?þÒçˆý¹éÅOlF‹cjõþ*~c­crjôèª7訉„Hªèç£[/6¸o˨`â\!×ô"Å*»Q4 `⾃©aeq– hÀ’WðþV>§‚œsƒ,&'{b©BÁɱM§ZÆ–K-Nû°Üø>÷uÁ–þó{Ùáfáü  É[þ ëøzàŠ@ ~âÓáo„Ýk©W[mÎîÄ„ÇIê§%‘4~lìª\kùz¬Š´VNÈLY˜Ðy{ä.ÐT‡PÅpS1û¸J"g•mÁqŽ|`öé¦m2(0Æ×{¤µÛ…ν“¡ €{Ök9A¥‚…ªh£tlëýÒæ>í‘ßÑÒ<:=“8<jŽm™eL%ˆðhNêy›–]Í;ÃØDhRñãBº¤Ébr/9œ-LãBÏùBšEÔ¯öúžP/jê¦m9ÓTœX ^éäÅ%âpìVk±ÁÓwü%g¦Y!Ž®10%H. ")5í;oqR¸²BH™©©“Zêÿ®ÌËš~´ô45 ã”@k¥½haÝpHùaTÓöI1´ÃÃMú¢¨ÿSÞ&v97.PÍDD}Ù£3l^óoœ‡k UZxÙ~"B `o8 Ö°æÏ[£Ï°_UTÝ“kŒøn,I ª–‰ 8Ëåþ’~¼æï(óð´‰'ÂELÎÛnÝ!”üô©¤þoïDC¹éMk{Á-ADHÊ£Hl:bi•îøï Pòéwúûp4mÎ3ÔæÞ:á:—\5‰Ô»ŠÄ&ꊦùV(eùšM $ÉÄ=©¡Ô™™¸›åˆgçÐHaTQ¬¹Ë7ûsâ—B[¾u¸…Vë Èe¼Î‹Ò¢s\‘Vó7¡®ƒi:iR;t½Ë/©•™ýˆê»3Ê®©ƒøJX¬.Nr»Šô¸¦èã¦þy…Âå_ã‘^wú“¾—û¾Àæ|Ì"òÚEÞÎ9…þ¹Ñ:|Ø«åÜfhGžwVéþ|‘ÇrÛ¡/_.w %p²ÝvX7´dÜM|œ'qÒK¦/ã]’ûrÎPP3ë‡üûàcˆËªóǦnku«î}Y¨óFch>!ÏÛÑôwÌœ=JA‘¹o»Ýi†îzÐaHÝëÌùØmua8-E!¸˜‡UÃ/Ñe¿áÕF+?z°höàÁ6V¶FÙƒ\¼Y:åNýù§ÚffÉk¤ò™—@rr·Pœ€ó¡~7é‡ I+oJ/ó³*ú´Á¬NöÓÎ^< ÊGxV¹!’ŽÚ5 å|Ϭð(â.‘ñu˜<ùg¹Áöy™+sÙåOùÕLøHg›Ë¦vêÖrlàEbrÜwmÕÒ86V«âî¸NçÐïA†zÛ%™f‚¥-s¡Ã÷ºm{è^Ñ«Ø2”Ê ÿ:˜p Š,B*2݇`,•rŒg’•¸?{½M Rk̬iXÖîc~x„ËŸ£R M‘"±xzÌ?:ê¾*í v¡ŸÌ{@a•»,ŠiÁó•ÇHà `ÅŸµ™4Ř˖ž’#÷uäf¿im ’ÆÌcõ¿éæ<µŸ]5Œ¿K}ps‹>Áv)`¥m‚ŸöÝtŽú¯–NÖù ‘毜™Ö½+Ó`PÑ>&Fjõ$Dm·Åä™0ćùs<),µÓýôÃ^¾6žù,×ý\OLµÊèûÓi¢qb&†æI´( Ì ¬¸X5L Ê}-uôŸ…e|e,îêôx=âPfTYHë±Õ å6£™^™½†Nùj‚É´.AðÇSk‚4¢9žð©‹qÂù™‹&ŒÝ-kÁñ>•Û/¯ñ¶%üQÌ9ræÉ­o¦bŽ^¸"@i´í*ìħB2£­ƒ |ìš¹f¡€#p¿MxºŸQo…*ûµQ¾’:YŒÙÈ]ðBXÜà^à6‚Ï{5}Ö¥3¨d ¯X[ýJx“-*o”íìŒ}–]ÕpÊe”õáx±J܈ ­æå ç<ªß쫵ßûõQì­^!N„<ñ¡%1›´7Ü´  ‹à$2QÙ×¼lØ «0f {‹H·®a–°ö¿1ÔhûÂÖë]dù•&Ï ÷|Ã5—ø:ä a4¡5‰m¹£>üë™B‡:¦>é©4àjךŒî -PŽ´"©'ì?ó¢×§M¼× é x"\ó{„NÞ=E€‡O:éžóþ3¤7ß„”¢’YݬյµÈáÇñÕŸ¦¯„¦HÞ«ÃvUvþVì)WUánœH×|ŒXAA${QôÉMuxÆV J†SØ~: [©FRÓ/EÝ+I’ñW[‚†jÔ߯ÕN‘¤Ç{¿Ì¶½ÙÀ÷W†!}\°ÙhþÌñí՛őô÷Úœüüe_¨ïdp™"½V pìz®ë‹2q,‡ Ý“*ÍÙÉŒ€>Ä[í\cŒ­>>„'>%}O‘ì89kàèÅ1@¸î}$àå6V{úYe÷å;¶ò}µ2ü& Û¯é ê],V¨=ò¼Ì(iËýÆY:‰èaÓæmÓœ~4ša¹‡Ù\#o3)bLŠœ×~ò"á‹í<ûw½éè‘áË—Ì°w"lxi¥õë8T’ÏþA#´“é¡V Œ÷²Gšqç ë<#HSbÏ£ê—Q“çB3Ï?Ï»Š¿}4Ã+M¼[’»‘›çìþã<­þ-Ûô7ÉÀ äLĽü|¹"ÄÜV±I§ñ Òœ¢˜Ž&}HzÓ;Ù­ª™Š*0ÓµŸï ås›]XîDq6ó âBli8ý†d”òÃå¹L9dxÿrûÐtëp ‹)犢“¯îvó|Ú’éù§ÀhsU¶ÃÖƒ|\"Ê]³Ìì=ìÞéôäŽc‰†$¥£ñc-5\çû£øyØ;¤‹¢ý›  u»7„¹MÏáüQj¥ø@Ütâ^V¨ ¦ÁÝ¥Q5‰`¯öx"IAè1?Ï '$L:â JK–nÝ_n×+úy/ÎÛ0Û°£x]…Ź*͆ÍñuxÖ~m“ý ×<8ݨj-ï–“LJL„Vs¿{|÷`ý+5Ý…OÒTçéÄ£˜V›Ù*nZš…xmâ«Ø:ôzp÷ìþ ¿”¤Áºj ÞHÖ¯å¨}í#Y"DrHž©ž þVšF…Þ;bÔÿ<£ý?_:ˆJñµ1Š·¤EN¡~Ð! ü|U:Þì½VÌõ˜AÌ ã„UŽ¯ßäÉ%ª3”ºí5“æg0±ƒñ¤MÊèÇYX©ºõúo¼yo6ñ>j^ˆ=•”»ðï‰RÑ5\“î_Û eð¯áˆÿ±Rˆ_Ê ùñ¾9Bè­¼B$ô^‰Žï ŸüWÝš},º`™ŸcAóôB|›ªràx>Ö‹hÔ5„°¶ 2ñà0äþÈÓD_‚ÝJ³Âݧz¡’Ëò¨ ðeHÉS)’×û3Ì×Ü”ÌÐ'D†SJ¯k]ÙÉc>ï–ú4AþûsýmÓä“Ô4N|\œf­©Ø¨³Iª‚˜/hmÁñ×6ô”M2"KeöjkT÷n z+Öø±_©ý³çà»ÆÌ­Áþ믕™Yosû;ú‹=AbF¦6¦"ܤW<¶óƒ æ®Ò,6ÊVº\l³ŸóJ¡µM³Díz:MSác…0"J0ò°æîz$¿áÍIÞi ¿“Òý'C[D‰…7ïì`f‚¶Šß¹%®Ÿ“³»ÉX•‘ã5æÙwç…"YËùzë\ÍxÈQa‡ãÿx† `Fò„·|(n”²L‹iRfñxjý‰R®d,η)—±¨×È}£Dì½ÔسL£ë¡VõÊ\!½ûrþÜå‹]RN>sºJZÕ¦‹´IÙc¾Ùò9YŸ;˜œÛ Sb;ÿ´V§P`ð¤4ƒš˜¬9˜“T¨cöYÁ%HÆTFÜø s4,«@}xJ†”ÅÒö¾:¦3ù.'Æ’ž· ¨es{Ý¥h>rzd}¾D®3ç‚ÃÉ÷k÷‰²îÄ&¸öVÕ'Г? &CàbhuØ}׉Ê÷#Å•²å¯FÍ_ži>aXjäŸUótÅŸdÌ¥ЦOó;^[®‰u6>:nèWFvÇb²éÐ;,¦‰ñŽ(A4PÔöØ£,0÷cöH¨?«ÎåÌÎóÉŸYµÂMÞA¿;µWüèµ1ükÔ£+{¡S©ƒ<ðÏ;8Í™éžáçݬ®^1î;@câÅ磉rK Ç]ØO3±ëÀýr®Æ-×Û=”CS¡´Î4Ô7}Ÿn‚ÉNÇù´®»v[×p\ÑÃIyà#ʼn—Û#z"b™mD5Äzt±p”÷úÑ5³gëzÀiŽ¨úeÕCïh[vBfX0Á±Ôz¶)Ϋޯ{D¬Hà $‚£&Ù lv½Û’ZrÌ7ì²jÿ¦Ø7è~qíEcøñòsƒõ®‡^K±[ x{³Ÿ+è{EÏë |į—ð9|BiÉz3¯j°©>n*Àr?¼@Ùá®Ë»þ+W> 3-3sOÙqeþéß™!×±Øl†áUn íºÉÛ³ÎhŠÎo“pRnnê)×VTäP0rìK4{}fr°,> ×`%³mçŒ<,'CÛÎßFÛ0˜: ö„ŸÕDÉ=]_é)¬'ÌWò4ŒÕ§‘‰4Êüa”oñüçgõf g+gµT+ó+³ÚôÍ Û‚z¼§S"¿ƒ¢î<ìD¸^fF¿¢Di}zjv2táÄÏ¡äî4–¡3_ûfB‚›áµóåð:ÝOÔ附õnœHdpdþ°úÔ8¢ï øBâUDRû|½vqêc´ªZQò¤ìÉ@SFâA01·DAñq|g 'Jª³s²ÊEÃéCÊò)n­Èà3’>)Aò½¤ëàÃUº9SÊ°Q'q°Ù¬›XRÃíNUvlãÐ+þß´àÁVJªˆFÂÙ4µ9sû¸Ö®®³½áöëîÄejù`Nêº ˜)^}Äç“Â×"úËø@¡.¾™! öò{„«€‘z„k°MPåYàz È :-°eà”¡»5J[_3y+WnçI2²"ì¥ïMÕ*åŸY‹Ÿ£œêÖMMÂ&ÕØwqÀ8ä½’«Œš7jY{6³‡-ŸZ) u>ÁnÒºáèÊÅÛbãHE–‹eû‚w¢Õ¯Š¶"½ýrñóšœN#A—(úYÍ‚n Ug]k˜3ˆÓøAøÚ^7gú“ ‚–€:ì‘Sˆq¿9Çõ+Oô½ïï»mu²pOíË°Oë¯âæÞnï‚2;&µP ÷l©Iƒu%Nt¯Û¨ôFdü[Òßzkÿes”r,KŽ·MìN rÝIdå[1PÉÂ?€ÝgjMø|òÓƒÛÈZìfò­ý„™-õÐYª}åàà?“³J¿*EÃ$B”ßÖ4š«›+Æ9Çœ{ËÚJúÉ,ª!g–—LpÁLøXÐ ×3m+ƒñ~¨™¶æZìSêk@,?…jÀÖ³c`ªî€‡Ü…ŸIÓ½ObÐ?Õ0–Ím°íkDYu4MÖÞ‚CÎ9E 5f«ò¦kGâ·RN1æÚ…=g†©`¦…÷ùáŠ)½æºù¡ò›¸°Ýºš ç|1ä&™Ä•Ê{T@yßX•K?y•ÅØ è½§~ÈXLÅ%f%ö˜3b´)œ¥îÅ¡ÍÜÝú>HÖ3ù0¸à„E~Š Ç#›àR,¢>šúlh¾±mf²Y¡vy¦"ÅÏ׉~2ó³yÌ yÅŒÐë÷›¯¾çK e@™ý¤,»®ÏVȪD–>P Vž—þ'(R©D‚5V9*wƒ_û©†“oËš '#Ìü|1ÕÛ’\*3DÄö"DQþþð§’ô¯å—dïø)v±ÆøŽlŽë+–Ñä”-–<ÑYðé6&•——ÂøPPLÜYCH+½†Tv—ÔÈÀKøDÿ—Î$†Þçù9V¦{óðw6âªãµÒ6YLkŠ†îð´ê–#»~ÝÅ7«ó±Wø[êýþ“1Þ*<<ÓV»Wƒ„Ù£,CÒ3<}A–EP˜›ï«\<ËŽ"Gà2éC‰˜jõ6\?Þí·ä1PʨxüîÝÄèÀdF2ô3*þìºQ=.(§“•Üç YY‹þ¶' µ-„§³‹T'Bó†ž ½ð¼y÷åæ L­w3¿¹qÞD¡p-µQ k[mXþ3ãX{’#¥0ü<1‹¼C¦äí /"Ø”`Ø<ÝpÖnéÉ:Š!·át÷>s‘Ý"ðí 7JÎù7ëzo»]Õ~h¬9¦+™1bBÖòÏÌZ/ÊÌ°ü²*¯å³²ñ{úÈÞpÉÛ;-çGÆ´ÛçB/ÙBïý¬ïkµô8¶ÞÚüÄÁ4=ž‹çwØŸ[Ó ¹Ý¯òòzÉ¢°tŸ-iûêD¾þžŸ˜ãÁ¾&’eÉî!¼‡Q—¬¢Ÿó[¬b"’†0È»àFÈÿk1î7Vð•gÜiIµ ê )²ü¾Õ`E\jaJßêá“RRÚÕ%ÞCXX®g2:fÚÒ|WqŒÇÒf=í§rH_)+Fb’uî :÷ð xžE9}šËðш$‰šž[M”¥G÷æÎÜÊ•x¬o0‹&DY‹ø›ÃÃDü§é¡’338wš¾¹òÉsjƒÀ¶ÀW§RÌ+¸kO^Fª·‹mû"lž×±°ÞÔ‡yûéZ–ÀŒÐÎ+›/å­w7Rþî !x? ïgïɆRÜWÒFjŽqäEUd¨¹³»Àϱ½ùñxËÿ<ÚÒ4¶XSËg Á†ç°š¿3â7¿“‚#<àï‚Ì⤅¢eìÁWÓ¶[bùÞH„Ù±µÿ=*=*£€DŽ¸ÃûºíC< Yr´ãÉÀ¯õ©ê`01P[ñ´ÛséyYr9ø €éÒ׉ÈBu` g„_ (&‘7:4µÐh(5v®y{5îíYÎsz¦rw~>¶ ۗ焤/†sÅú8Sov¤\žÅQƒ†Hïl98Ô.ûž˜G@xloìËSC3#1*õ“cÞÅ‘vÓkT¾+"M›w™-ô)çÜFÀðªiQnìê›]t㦥«ÁŸ–WRY:*l"y’Hè·ƒÖ†ŽÑ?¨òMKÚ k:sÆÁ­­@ñT€Ý/§.È.^A °—ÔÖ =Ñë?dK–oV|±âo#¤]všƒÏdžÆïM£:Ð4‹ÓIÊ'§‹+Qü5¤l4; Ê¥k%¿û³nÇ>º¡ìÊÛ·í÷IíÖ€6Óñ÷/_øÆSL·Dx*é]k¦ù¡RÒ•™\o?d®³“qÙhfmþµ<¸µÅä(¨ÓÍÿû"ïÀâ©íÖ˜¨ …³)+³,} ýœLøpgŽˆ}¡ ”Ù¨-€˜ÀQÁêSïèÇGí N)­÷{{é='QbŽMEnnQû1œ?ÑÜ­« „mŠwý)Cd´ogw1óºˆu¸ã¯ÞƒLXfZ:—2"ƒÌVÚ°?8©âzº—Sð¬$:•þ-CÇä°^µTçj‡Ú>œ»Ò.m›#ðY”û­ëŽ]i„uu¿ÍVÖ§X÷g Õ_×VrŒQ©o¿”¬.k’ê";uÇ2¢ĦRK+íò_ì¡r‹û{0 K¯¹ãK«ìw-qæ0!ò–&ï2³sj8äÖŸ¢ãþ;øúŒ¦L¨Ç,³º ¯ãØèžtö;üËá %aúk×Â;ª@M“£Dç½úpB­"(h †é3¿š’``[V"MD ÈIœpŠÄ.@æhøWyGÿfY5[„*Hfì6¾;|oö_RÈžfî8Mê:#0—ì”ß[š°­È#ëÓÎ'ùLÕ¶©3óXÞ÷“sµ6ËG<øò¹§Õ+|‡Ì-?þ–†Béo3f^šÓd2íN8ÇlªÎ˜jõ¸›äËåy¹QUSöx‰è!9žCç¹Í[YP;בš¼ÄAÂ;¶à7›Ùf›9x†îîäœ_ ÐI_»íAÚ%3(vÀ£˜;Lôn[®Ðª4‚H q:«ªZÏGµàáOl |ÿ ‚3)#»&ÉÔ³µþ÷?M炳ê*8jTI)Ž¿ÇEF.§Äé¸ÍC,1‹’>s¯™³¦„Ða |B:öÛ~ NHcá~¢^Þ¿p99A‡B,ÊË2†ßçÕÙ(Æҟε#>Áí€Xt5:‚ S‹ë"Ì ÕKx™X¾y:熓\‡–ÄukI iDš½:}ÚÛÈb-qW0´S%ìîp¾)ȱ"Þ:ãiE\Ÿ.ŸðŠ½rý@P6ĸabõðþGìu^+T­zþäôˆœØ†©1³Yw­gû`y%(Í7{^¸îؼ‘›òrsJ<´þ‚Þð¨©(|n¹¤hÖ?TÞ^Žy²‚ˆÒWM8Y^Žä3}Zº(®-y׃ºµp;ˆ–xå w™u>Fb=5¸ókX ¦¸(ÃÌ+åÀŠê±P¾µöÕI…ضü"}dïQ¸‰ØðäR.Óo»ÛèN*Ð~!^õ1Œüñ”ãl ï$¯ gÜõEƒÅž5 ¿¢JüážÄVÐGË“"40ÆC/Îá^IÏâ|Ÿ&pktòðu¼ *?®’bŸÀ{S€|Ø<‚Ö™R’´ÆäTòxüRB3‚œ ÛƒÛ"sIe£l:µeß­fÆMyöýóÕÈiê¦ø³ÅÖuÚqg÷²\MÕ ï»–³ó…‡ÒîнV!ê£Ï± Õ˜NPhz?*‘—ªjlèXth„\÷þ‡bQÙQ;£ÅŒ ë?rtóoϲ7*Ù‡9ÌÌaˆIû^ÛýÇd“Þúë%Z?]—m:éÄ\dS׿ür¿VdˆP}÷Û#hó‘lS0©ÁŸä®éê[Ú¥ñ`öüµŽ$ÉΫÝhé›ãQ@YZbayesm/data/tuna.rda0000644000176000001440000011615012516003371014134 0ustar ripleyusersý7zXZi"Þ6!ÏXÌá#ÿœ*])TW"änRÊŸ’Øáà[áß^ ·ÖnŒÊ£ó¢#|nÍgHVóY1?ä,¨Æô~€â·¬Ö÷È¢h#Wœ¢YŽî€ˆ¶¸ƒÆòï ï‘¾·ëÏhtÖ+óùö—†VµýwÁ16F9õØ/û™@y©/ªä åAÍ.º¡Lì’‘úQÍ,»;¡•…íÒ‚‹P0f¢Ia£ $É2צÅB…Ê è÷WŒ˜êSúZâ^XåþWïÛ…F% 6<eúýšAusî%ÒbötÔÖv,¨&ôL}µïA‹ïá´uñBWS·’£û¥2üƽ»Môâög£ø¨5Y3¦VˆVŠ^Fì™}nÎ ¤[lzÜjÌî6µîå±ÛUX±©Ý—'ä¼pN0ÒØ:3ªpã+M,QÃý‰††ý´@BHŽKÆ£7f׃äØ\ñ ïA”m[¶58^ªVôf¢þŽp“3ZîG¶•zih(Ì(Ô yõ¬§aש$dÉ ( M˜Z¢“FÙz'z_êãíñ˜Yo˜Ñ}Aêþ»,¬‹M‚'äáÏüH,kœö-CÑHÝyZõœ/JèÚÑJD{9u’1(®öq!J ãÝ“KîóÀð²0bØñÅP7ú\‘óÚ3«-ì+†y¹ŽW³²°‚ï>’ÁQLîñ…†UÅÝühÑäIÅ9|Ù40~ôýÇ ëÖ@lÉ°°û×Âع·%7åpøæ8+—•¡k]Wz‹aŽà‚f5ý˜t}ÕýÐXF.îÓÄE²ùŠ{»Z[Ki®™Ü“þÔŒ ÍGÊÎOß[_†+õeêl}%o;ä,“ÝÀçd8ÎVÁiÐõì+‹ïS(Óð×u“»#þÝ…”z×É“œS²ÈÕSÿ2›QÇ}gú…¨¿É0•G,Ÿ«ËÔlþæ-H'^£x ”¢\Î-±^,Õš a|ÇÝVsѨؓ>ï4l¿Òá¤Ð°ÁÛG¤¹2`¹ƒ•Ý Hû·@Š(ÄcX.÷§þ¾é™ž¥Åõi˜”ý†ÚVmç#Fo&È¢)d©éaçŠ[)ve²#Ÿm™é¼àººÌ¯4yvDÌÅkD_z„óM9h{›J‚½IŽ`t}±›7D"^åŠ'™ “ YìœÂzTô—ÑB´á•jliý{ùsÂú nÕKÌE¬™Xã²FM8¥êk¨Ÿ"ú—ÔûÂáÉSCr»æ5Ãj!û••”µÉ»ãµdþè¢ì$æœ:bÊ3„§þ!\'‘i¯ž ¦wÞˆCƒ"-iBç®ä¹$ÔÁïŽd­¼í9&âý׃ìqß•˜§ÀMEQ%%E¢qú7j[mOhãÛûÆ~©T(avëÙ¿« Œ£+͸½º–(¢±R¾ƒ”ª®KrÎÇ×>¦çH(ÜîÆZ4Ê¿EÏyo4;/0;¦*€›±™éÚ-Z¯ÓÒ¾®Ü Qü¡¯ò‰­Fg~†§x‚«‚Êl2¿í„6ì{(ÍÐÜEÐWŸ£xŒ€¬ãˆõ¾Nâþ‰) …ªØ›äàÍ5œ‡t—ØŒðjºMÍÊž¦æ'gæ 6î¼q­aï(-,%uÜ_hò+ZŸ 47ï?nIM—¹«] œ%;¡H” ”¼Ò„~hwFný!¾¥Z™³[íåk²0 »XN»sŒ¼•Ëh‹ƒúµÓ7 -£{ðÓ…4ÈÐÿuŒÚë—ò«\ÄÇؾ9?Ÿ—íšõ½Çë“ÿM•0ÁÔp.€Äa~.grHJ9ö=ù©¦çÂÆ4«’õ’fY:ö»lòÔ¸Øwr{`d„)ùîöD06JÅ0|ƒ6Åid4Ö© e’™\Þž<®B†Aö"õA€¤2¦$×Ó…\ù¿#P¥Ý j¯ÏŸ$°,öÉîh=éÐÅÞè1s¡™]^WÈÄÃ~š{fõ–9œì™µIºÊüÒ ºN~—*7é%K!Øg›ÜÕpñB3i—ZR¶ÃÅ•ñ'®÷¢_攋÷Ìå±³K*Þ5÷ÂoìAjBUøC˜. ‡#©]M®ß+´é¨õös+ñ 1w+1@¯ÒZÖ–‘P!ÏŽSê1(fW=ö÷P4_íÇè…áO ;?ñ0ºsÙ“å"hM E\yRÏÈŽªçSØæKE·§ ›Š¹bý¹®2{µ\¡†.=*O³¼fv>†f›vèÿavͬ÷;/‚u¢ó†k)ÓììVMÆ·6U\ÛYÕD®»ÛâЕN« k”ÞSc3+?£ö¥Òæ› úó:»A³5‰•Ÿ`Æ>‘üäV¦Y¸ËqÈ&›òçöŠä°Ùõ€]ºŠ'‡ûI«ß×Ú­ìrþ' °ÁH ½½È`íÇjålÞr€é‚m/Í$C"å/Ç”¾äŠ¿„ㆸáÇlvì´ò2‡éЂôH„µ©³¬ÃfUŒmMFÐM€Ø&µ·/{t˜DÀãÖ˜Hw1'îúPÒHôã¿÷r塚R•ÃòŸâÛÜŒÔ8›H™(Sj¢ F0,¨®C\¶;ŒÇàØÅÓ=6<#ê-ð/oüC»ð£8p¹î§kD“­ÈãIÜ¡•ä†r}cµ…د[  ïœCOsøPá²5N)z¢-Ú´—¡òg=¹ÉŽ/a öëa÷ú3}:Ô?(oé¹»(nn-óݨ*÷Ä¢P½Lšƒ_ø…Rzµ(õG†zƒzbÿ«_tÃåJÄÀ-ŒÓ±z¦µÑñç£Î.}¾m“F1• ¥î, YK¼…3¬(ä¼@;¢úØꮸº@¢bƒèâly‘v‘£ß}Ã3@Æ¢Mí¼²¾ô³Y±áSßRñêOqaVf«$iÂfGÈQÆyOa³¥¥täq{³S+¤¾ûë¸E?ÇÅD•e•·»|Î¥ ×" rR™¢À¬ÒÈ#”‹R»7üþίèÕ~ ×ëL±Ôg›TÄÀÞö©NÊÕþëW'X ÕïÃÔñèl÷AÕ©òfrãY|p/Q!ô€pvà±÷¦mo'À…Õ}ÌV¤÷u¼49Ô‹Íœø;&γÉà„‘K8_°âÊ£èqŸD±µI\{tR,‹øŽÿnj5Éþ¸î'(Ú·¬pGò P_sbqrœ‘ª;ÓYö¾ÈS”€[³Ò¨³m o½ •ií9*‚H'­ Ew«¡ T8½d^7Àƒ†âwOìOfìï¦SÀ!„î^›zó«$”Š\0o¼8c¯˜Î³£š a•®çÆ#Cš!x…iÈ‚z-)S† ¦òÉ6–DëTú9¸7X5_|þ€ú¥ c“¶QA‰[HhÌfÖÙBïO±ì&†l}%,}ìë^YJ1‹Ÿ½Øªq·9ÀZ‘åÿW‰M™Î\"Þ³&A÷Æ—HÛ‚ *—¦E{ óÎÌÏB“GÄ0µóQ‡ß]J œ>sðS´ P’®t_«Ù³4r¤ŒŠ›Ÿ W¬›<ÿ*N:EåÞÖ5¯—Úû:x¹U+H'ºŒ5ФœLþ€ ½·(¥eÍŽüwòö÷,²ÎwÅ^äûõ÷i©¬%á?8Q7û¦–3•ÖÑ\¢o{-µ¥–éVá34Ê~Æ£ž€”1w«™Cíã[w$iGâï…Xh1;‘—ñw+›âzçC¾ò¬j¸fº2òÎOàu€Ò¶yÛ:—/ÁE Ú£õz Šx1˜F´§«l³ð(!àÞŸàBÙƒdv |º}iœ²åÛ4ÖõÞþFÑRçêÕº .6¢KB÷êñ6)Ÿ¤ò†M~S&XÀ* ¢1Ô¡ûÀ£Óò ô ]æ†:kI }²áMåÄÃ*~ÜéiÑc.ó½-h¸Ôz¹ÊÕNŠ»ºç…'• %u1òÇ3‚ŒËä†Dºæ­ÉGZ Ì>mHG¯!•©hü‹zÜ•óš £–rð—òn¨V\¨VêÇUzÚ®)IN™¦a·Øgi„Vña0Hdñ)Φª4­™’ ¸>–Ï€yˆ®<ŠŽüߺk\"ÍžX…Lˆ¾wZå»ZñjãÙ­X9°Tø+ŠNzî®· 1MÐŽ½¦:u…]c?¥“§ŸÚµáE$ f#•¹Ò™­„|‚ZÈ,#7 Ž©S5¥F7ùý&xhˆ$϶Ù醒¥ÿŒElð8/èj>áÁ;?j)ÎZ¡åÆê6, 5ÉA ‡%kˆ^qï »¨Î=É÷‘?âjpòu\Tõ";QlÏ•†0##·íƹ*J<[è:Ë·Á»Âç2ôç9†/ \kq•´ž¦á|=Ý©¼\ÍU —àOy¯2ÎÑK1d¤j¥ >9U æ[cÏ|šEAŸzÎft¶=ïem‰%4æ]QϾß¹tnWe'—ӹ§ïçÆL„°DÞâÜ5 ›úcuæ]¹ 1;ï¡ÔæÚÂ0ÓëÂ/Þ_B‚»( {p%×_wÏy<€˜„ü7ŒtAºœÅÒs'ÏT}{HMèGÁ^x ÉyîÿM¶üÇYð(@ü %RM ‰PÏ ‚íÊÀŸ¤>æmÓ”ýŪ/Íç¾m”ÝdÜùÒN•Æ:‹7h(/.¦ùt`@Fòv +«c}H{þ !ôFóªÇ%Ó—¶3bc_7ž7ƒòzï$,—~RL™Í5›äQ3ÚÚS3g@ÞS×Á{ÁIaZ!÷pý¥ÖÁ鱆äší3î¦> š Ö”¼ê¶­îø=öÛj“h_ {·j<5lú² årá(KžõaŽoi‰åH” ,¡ºƒì5ÇÐ8Õ—‚H'•÷¯ ଔ†A€JCHç/y›P‰®?™KdnH3‡õ‚ƒá19¼µW¸:Úz"$öpïÖ ±3x–w׈€[ù ÂŒPJu3¹´B¯“ªß~ôG/Zÿ/X^h|Q ¢×J<ùTÎÙ®ÐPR„žž Ä™÷^»°M0‚²—Âr…È b-‡m¼?åså±û-ˆ|¢ìœNª`´ÿkP6^yŸP,6Å÷J+VC¥îÁã8åÂ# wÅ%/–' D‡½p¹[4wÀÉçƒïçë0R{Þ´l@tÒX±Bá;®qËöq,Ê7}›·Ç° 2žn¬Bb?›…¼wÄç| W(Êýrȱ¹.(éš¿·oÒ4Y¶ ”áó»»Ì§=°e£^{¿O›[Ç ë)6ÁuÖéàÆnô"g3Ūò¥ÙAOe0_»äéÍî-,~X`"™îFj· Sr¹Ž‹49ÿç²B¨è˜ð¤ëœÎ½¨´«­©Æ†8ÕJ‚'zAù©hßS ºÃfˆËb èúž8ŽÃÕÙb§è÷µÕ>Tê3åcŸ|  ®MB³Cšÿá'9øIª ¢È,[´̇Ç"g;îQ¨¶)ѽ.¡XFɹmÐ’cœ‡„e­Vª©éÀ²Mݶ2´(c•Ý‡a¯µö”´¶B•ÆüDR„ IÑ#­f·yp~駮ýÊSÜçÍKŸ¯üûàÅ"2.XmÄÁÕ7[ßsžþ”©©Ñ§Èý±ûÉ ákžBÑ…¼*—°ìátÒÜûeJN¿Æ mY¹ÄÍ1ixŒÀúFëÝmã?QDzšÀ‚½ÎVþzó#)ð}ÄÊzçgÎ÷áwÜŒk˜œ…SË)ùÜHZæF“gon¶¸Çz¨žÖIsò—ægX,6Û“RS’Æ^6g,»ÐI}'-ïU‰Í÷ëq‡M T|û0+¯ÇÚ±ÂC£ðK/,Ô1Jå9çhVã„:¦R1מî÷«o ¿Öuš†0]Fgäž²J<~7¶¨‚CS}¦}xžd}=múå»Þ¶ÙWv|i)KëÔ¦ëçBñz‡dj¾|°\™0`Ó¯åõ~Ë÷¶êÔéK+D&»ôIC§bí®=¶ÖöuÏnÿy¸Ð‰õ‹ŸdA¦ ðô‰’{}¼:éçÞ`gZg×†Ý ×?u·¼ó¢ ò=ì?‡|Uƒ£•²Éc­û¦‹a°æðN œ–Õ#æ%Ìž8¥Æɤ‡ÅàyHVdXã{¾“xr/ûò;è®Ô.æ±ÍÔ\×[åÊ<¬ñA4‡@êVцš¶ê~Ãã\W¹2à Ô«¤u{GÚÚæýgÕ%¨ï9i`«Ñ·è”óà¡Uö!)ú>–³Ê>%<¹ô$o±Â*P‰úBið8»HJnŽR4{=š•ÓIüéÒ>,©™h +ÙÛ6$ýŽ_íÛúLêFê2Hü!¤'scf:˜spÙ Ê´ïP'+˜kG@œ¡òB<; ò·â¿’úYšÔ0 YÕüs)¤öêö`Y‘2«ù¬Ðýw!kæ}è³±—ÖÎb–”IP6íÄW©|n$¶J'\/KÍ×nÙ Í\âÿñaS¹ªÚbÂð¢è_3 ù8ä€Àò@Þf<žœWÉ øÝ‚l„ˆAtwÈÅ™^>UßtM.‡ðÉbo'é¼)lUŠGd†‰ r¯ùçøóïe/Ü­Ï£GÁî°¢°…ú½Ä>þylœ—"äÄïúÔh:4‰<Ïܾ]4…nÀ½‡'°C`ÙY÷Œ¯%í# „Óþs-~Ynó%λ ŒÙü¹àt>§ÔÕøN#S—@6‹æÓâ‘ ÁæÔ/®Ô ˆî@M™¹ñ®‘H»ŇՂ¦àKnS‘4í6õÐ) ôÓ %fÓ°ÖA¼-$ „§ˆåKÞWÏ)Ø~Vž<'w¹½ykm# µEŽÅñ ”úËN2…WŒÎÈóÈêVšÝL– ¦ûçý4u¹DÝ/U÷¹é6P¥\?ök=ÐuÄXKåI“LÜ æÏlú·1iü¶1åÌ;åÉí8î½ £¼ÛßÆ67>¦5ù\«J FûÊC@8”^g0¥øÌ1׸úu–ÞÚǵ£¹gŽyÓÄ™¯a»+‹´à]²’Ö]É…¦okE¦ùm‹á1==)8\þT|ÞO`ÏXôIÑf–[LŽ"½;Ç«±+ñK­èÄÄF!Ï„t5Õ# FT½xÛð± 18BY#”€PP°×Bø¡Á¢+íÜ\·AíÔ—Sò¥ÜgÖ [Ê Î‹­ (u2ÚO pA3'6Œ,dÈÅF´7“2XÎ`m£þ®HnâÈp‹š–~+ÙX.H›Ì°Ø8´Þ¼ªÂ˜8¤z]C ßÅ¸Ë XöVNA–¹—†7þIflíUÄïl g ±ÚvLŸ|ÖZß|JÿúŠ´†"ßDŒ%Çü³K+¦E4Gn ºd€$"åMÁ½<â mé!øÈÝ_Ua‹C5¤­Ôø<ªÎ°Ò]†Îc⪔ T2b×ÞlyÃ2hõʹÓ«ù‘Ѳˆ5íìzÂvÖêðL¯€°I°¥MïCÕ‚+2?ŽœªD`±¯À¤;ÔDmt[—³{çÎLšÙ4 èÞ xDö[1›éz”z¯\"Ž¬+ű#ý¡gzšõG zS7»5ZxíÀGC.^^¶ 3ÿÄÝôðÖñ/6¤o9ìæi!C]û£}N߉g ,+ò"ã¹Ê;|Áp7×lH«ëÛŠ: ä”'ðº;|Ãì)Ôe5·œÛcSa ohfKùÃP ÓÛT#Qó9TÒáb¹0‰—ðOdà]†ñÿèÐÂ;T-H±Á¯5÷342Æûmt-æ Ú¹øÝÔEM&’Ô˜`NaGx±å ‹Ò!hå°ÙOp?M^ÌÙÞVn¾£ó’qû¹ß••ÝmÑbï HˆRZgPÞŸG$5ºüŸÞogÖ—äÉ×i ´uÏ?*SöósâÞ}+Ö¿¨áĉ½ÛíNP:;¬Ñò ÆZ0pØk"ˆ}Z_yX!xÃ>~:xƒDì€õü¦KŒ=pÒ¤&¡û²ÜÚw-ØÕ¨c¢áÏnå›bÙhYIY ª%M¤À"ú˜—ZK3—,¤›Yó7Íúô¸=’¹çÄtÄäx_u¢ò ¢ùáÕµ‘Å€/d`&ÈÚÛd+¶£×M€Øüì¡4˜L­¢ŸÙK^Ð8—b˜[Åœ+ßCÞÙ®~v• èŒB—2™ù™øíB÷ö¬œTrP9FäÙïì`âmì³RØÑ •~\À¿Ë6¯ŒAAtçûPÒá!c@ÓÊ—)·n|Ëš6¹n¨1ùóù¼nŽëˆÇÎIÚ—Ø㎢§Þ“D² "Th GFC«öO©„ÉÅÍ A†ÓçxNüDÓºÃØÕïÍoö:œ<ó±ê9éî„ ®¤D‚JCÐéÎ×ß[ªÖ{þŽ(„ÛêKfV¥šª›-ªNöïu ™>jÂ\L§ð±¼Gè`€OÅŒâ›wcû<ε=­ƒ(êŽÃ¦=­F9)¼Óöi[2Z'ÇÎðçâÅcüd”CT¥qò[ÛÍJJ¥c4âÑSÆ€ÕpSÙŽ/ݧgfq¾B@¡cµl†Æçe.¾"nžŠªx÷áž·ò`*Zçk}¬-N¢ÔÁ¸±-yC±0gíƒaŠèèþù‹v ýÈ·M­ Ã'Áùrç7tŒ"›"ý 4l{ý9Ÿ×?uÿ)=Úšq¨&vÖ‹NÝë *ÌÔæg«Ê¶jÜà'í±óð÷Z¢ôÔvÑñðᢣäè6ñØÉïYÛÑâ„&× ¦‡x³(é™×%Y^Cj³;ý]jP>C\ƒÕ·˜ @kØtŠ™yÖï’´Ù&²ÞæF}r$[{UD÷1¨"tÓ"úÐì¢K“=+?¥<=ê Ì¥÷E·É_FKÄs&±3Dç96Èi¼®äM˜Ï)•¼I»ÖÖK\?)5™ì”næNŽŸÖú…)¹†_ÙjŠ#“.ÁñC;”!u³è¡ovoua¼v¬C"MG5y3ìYZ õcíü’ Ý^`=<ÉäƘÞ( ~ ŠÉÑêAI€H¢TŸÈ«Ç‘îÇ2Û»ò¤l¨ö‡r®×æbkP¾²3^Èé´c¶Ò?°uЖüÊl éÁJ·È7:~S«GÃv;Âà‡¯‹ÁCr×0ˆƒdð©œÐu<¨û"ÈÍÿ1ëÓuØPUÀG‘¼ÜË\±¢ªÄ0U_¾®F¯lãŠRÿþÓÀ¿X~•Ÿ+.ƒASºq²¹À¾Ô/r²GÙrÎ!á¡= ÒýÀ0þæ´Y‘/§.)ÖyΫ' 9éZrtNFt·‡*™”8sYÞD•6X;ØÕÂEÑ “ îè$¦Ghm$€ûž™ï»^@©p¾ZYO—ôÞHÔxÚŽ%OɇgcКÄ5$}çÃå—佬‚F¿[’“-í/êЧ_ÓŽkr;^-ÌIM£žÌeÞ‚ð‚“D]Šlr/蹪ßs{bÄU¹f`m™j}±3Ù¿ØJ·ÎÑ ë!þ®¬ÂƒKª‰Ãù!m­ ïÖ|Fh S¢]uHß™ ¾¡%@LÀOšÞ•Ôñݾ§ÙøÏlºï~‡¨Š•„몑Aïœé$DH¼‚4Ï°lHê×eÓe5­^«LìÎáÛ +–2f\ƒ›RDŠÏGBée7:½¨_*¸äVúù*%Á1§A^¬@¹5=uHãŒÂ“å|òŠ>äv¯Y•CIw¦ÏCFx‚ÕÚ¯â¦êܪ~–êð_K÷àÒÕŸH’§ä¦û*¬`Œ; ´¸*„ƒEI@ý‚©ÿ‡&µñ®åt{„C;gËa÷Æ-¨ø”‘–4˜ƒÃ¶ÕÙ"-Óºž'ÇIÏéH{7=aŠj›‚sZàæð(M¯&Ó!¢¾…4‡pÔ$¦ 4Þ«˜\F Ç9ßPØ¿ØðM ˆÛ@뉉‚Kì‚Òþ±(Ǫz¹Ö?ÎçuVuao¢ûE„J½ì5'ŸZB!¨‘í~šUqÖZüªÆUÒ eZp<ï}uæ·Lè§]ÃÔW,5µc˜ëªð$•‰g9H•r"" (°ìÿ«m!¤6ãýø¿¥ú½ù4øhéjJ±ôË;+¤ñ÷?}¿gê•D3œê“@rúžå­Û±ÉlVzP¹Ç#û>Ž MZ‡íH—9_›>é£Å°}˜à¬l ½—¼‹ @ÂüÍé¡ÿðï¸qH–}(袮f4_a\ðËbiã1OïÐo^±#…õ!=†á—Àç6…Eʧáþen¯~/äÀ~ãæ°œƒ<Ú=vÔM`>^ñ@fˆ)Cñ_•gk’õV!à­¯ì“2ûöÔ¸©çáVóK¤U)ÉÈþ6CÑ.É‹šîd6´¬ÃòéZmí ,@ H½›ãŽÅ¯ó] üU{„ÆÈ­P8w©W@o'«ï°yNÕ”¬T*•®•úU’wl$ÎÒÍ«³¡©ý4ó¥&M®É-Àn¿û]™ZÖQ?¦²+O´géó]`@†Ï]†%åAešÊß°€ ¶:…ù2ûWµRS¶~×Õ«Š4ÎX$Ìáæ/=±ƒÚÓû(T–¯À7šÅAÃOþÊ}Úõ"¦ílðSR8~`L’˜d¯Š¼oµ—vÔƒKRŽŠŽ]0Ï¿~‡ìþCUÞ‚' É=“ä_'u5.ßÁGH7 U–!ŠXdhÊ6€OÞ5Z 6ìW:J[î¯þlÛºˆcðƒb!)bzaäJz'¹²áN4Ç fцĢîFÄIÍø)°ÀVáyÊ3¬æ3/sŽ5À´‡*¿ñ?²Ÿ1ñäÈó2´÷<*ó€gâKü­°uÿâdÅ:ëJnî@)„V©ªD4«œòÊTôÔ@ó©•©ÂÄ5ºtõC»¼ÎóÕÖz~Wó/Öz—~s™AIÅÙ‚ «2ºakûSõjÞ†Eyç ` 3{X»þÊDÁ“R±"ßîìHXƒ²'á7?¨·årÔÅ ¬…1ä⸶ö°¹-¸rW&ò •?>/œ©'`déÆhÑÈ ¸èó¨óU5ä\£¨Ä¸"ý ׶ˆðnQD3aCkŸ\Â…]3íËEA7ø9© º"YVÄya©(yŸ~àÉŸ©U¯÷âh©P=àå#h€M ä¡N¬ûpÒ–ŸœÂµªµ.œ:åohŠžÏ¾Á쯞 1¸ûÔ>Àí¤ Û´z™óšÁÂPî#%ÿÛ"/K/È ]™tx2x”¯¯K”Žní#"±™7ኡ|8m1äHMnÉ$µOEZCù`qs6?‹Ìf6Ê"v‹6G—膟óAv›tE¦°ï_6A‘Š¤}ÍÖ-Î’öì3ÐÛ9¶S4Q‚ó×P>ýÝsDªJ°Ší¸ˆ"Å£¢œ„~ ^ŸÌ U‡×?¼}@…é뮃©¯^ˆøœúi¨^ž‰¶AŠÍ*õ;A1-´«TF!t_!…Z0r3Æ3Ôû‚˜/µŸ9=ŒšŠÅé´Ý!TdÍ©×ÖA?Sý£’òüÙvô•›A‘¡ñçÖ ž?ÛcXÅõ¼¥ª:c¥DHTßp³nAã[Y&œœÁʧÊIQèÅÚZÚã]2#˜×Ã?5)C:¿ìÛuÔèTx$ÿöÄÑâÜ¿¿‡xÇê×~Üu®&}K‰ôÈæǎ͉ÑöÉ”‘²®³Íg-Ì‚ˆR~rÍõI=ÂþlüVŠ½‰k÷š©Óé)'“ÑŸdŽh­á¡Ó]³‹S–ŠÆâIãåÛ† c¦„µ½¤ù¹.3Å/’f&ö.{€91A.Ì`Æò›$²ˆ5íN y¯_ߨyÒn[î.A´ üU‰},wÚ³?t/ñW»Çת^édô ÝTÂœÛHS ·P©tÏsÓLLdÔ*,Ñ·F^À %C…fäŒkÜ*6‹¡É} ºÇb(ü™IªèRcÆGê²›ƒf!E8”œ Påï¤ð“AGÌ3Êâj{úQw:› <ÊïN3:‚2¥ªæ¦°¢‘A8lòÙôñ5u9@u°§«“û%ЗƒÕLÍ+G[À4¢_Þ$þ@ N\‡VV½ÅýQÞðK*Ê¥Æ\:š¹ñ–L³®H´§`´#”:&ž°Ñ³-ÄY?3ZølLOyVS"êmVV&D^ÓrUâ>_Z/K4C0_9\[DDšÑíà‡0ðé»íåö%óvØ…fG‹(B ´`pêVÝ>Ù\dtRÓ"ÊéŸ òµ4„Ä^·~øÖ‹ºïd!p6F¢À†è WV¾zÝ^5kU\ÚvÊ«²ªÈ‚ä ¶-P§8gK÷îlIl9šZ2ZÇ‘zòd†‡!åÃÚ~ 4·ÎLcÞ £}¨J9·ì& GÄ•ú\´=Gqíº ž$¬Ìu<8Yäš/]æ—w(¡ê9 $ }l«@4àdš´œ,Ú¹àЖ¹ÈÇ™mÖã¾.?½÷tcЛÚÌÐÀPŒt±$‚4pÀùÙ$H«3ü\ïe ûzÇì0„üI·YçR-6é}¸n©xJ5+{xicx1è÷l*4QV¸u„@¨öGaµU"ÀOJÈî^Ôå‹fëÿ* =ÓFWçFOrv¢”{‹Y§à«dŠvSè-µ´É6à¡GáX*•Óú®{0NQ©+ÕpW·*rÝÆJ^|i§¦ˆÅÖ ¯†žâîÝh0%ƒlç¸üx°dü‚ò`~pˆ/²ðsë¬JßDJ—ÔÛ%=d s–²5ŒFHÔ§{Zk–âýôÿë_{5¨ÔÿÑÞÞ{ Ó ØnD+°›C ÉùÃÊE›û±…ZõŽüΟ›L¨JÆŠ^–;è2TÒÈçP€j)ªØF¼ÿ–éɘ1ÎóÈV¶v@B½Oç òTìWýßiÙ­“äRfÆ:ô5T ¥ÐdH`uAñê)@o ¸T5`R³l¼»£¯a_!£šdD‘qP sóÁÿÜZ6?|ØfH€TH³èlI1%úÙ—ì^IKŠùãÚYìïË?|AŽ’˜Uua.`K<ù!5GLû¹“Í3€ÿp#ã©r†Üˆ8o¹ëÎ>Õ•ÒÂGaù]y'5ήËfs@]Lšã†m³l×t þ¼|HuB2;c)pN`GC;càE)ŒxëÖAÔžcÚ…oòW$Áã…Ë4A•gÐ rv%îTËyp¯'¡Üì'Ø»ö,‡Özl€¬kî­šlçã:ç»IÁQådfD3ß$ƒKÝ"¬8âT›À¾vzЊ3±¨¸(çuJ÷P-ŠÎ [ùJ0¨º}CcŠ<¾± ¼ÞwF¢ÄõC@D*Þò*óã]”‡ÿºl…:lrU¿ª˜ùMðírÒ V,žÿtÀ Šå$1œú»–˜ÌsQ‚#Òן`¾"¯/ú èÀ, û0SÊQz÷ Žàþ3nZCƒdsçîH¾Àˆ„±¿/ÉâÀãP­wuù}+Cw€§£Ök—:,!hÁõÔR,Ä…‡Tïù‹©˜¼×ÁbŒïÞÛM °áq-ªôŽ”¾¼Œ•tDìͬ^{N@o4ö‚¥coýÍ`#Ž‡#r 9•Â EáC'y‚F@@W[+°ÞôþþAÒˆ. ¢žà™svvóp1yÞèÔ¦"Jë#É\T*P&AÑ+}ª/¬Ó#«uú"éG+½%ìäéY]^–*ÒžüŸQ©5ä?ºÙõIø.~Íõ¤ IÁƒæ £|¡Í¬û¼ó„B¶t‘ö*[P”a!ׯzËôè·ço/::âY¢f¨Ö+ç'©¢~šÊäƒj©\ÜBUh,=0†ÌÖËÑîk4ºSñå,Ÿ:@æXÅÌű Îd_JWS }†FÿÒQ:SÁ+óÕD¿ÝŒ©Cܤáñߊ{Ð| ‚rhØ«¸T’[ÕÂ2Ì)’ùY׃Ró{'øF$H°è+Ì juáÂs@Ê$+¥Ç?yÇ÷¸Ä CÛ`;¥‚7Í+žù£[.|Ã%Ð?h®ëhä ~Ž|u²¤T­RÀ v牼bAƒ%õm Ç D8([pìpkâW/ ³hØ[»^úŽªVyDúü5©ól­Yßáƒ_„! ñ&" ¶ÈíÏÆB«¡Fµ ×¦ôlAOù£Mè]Þ/…"F»· žó?©ÿﶲdfu=í~ LÜw†ò4Å¡ØS]Ì›û”à toCV#<á=QKœòhÖõ+˜ß;w|ô¾¿~Ó¸q‰;¶5Õn 4² ¯t@Û¶Öù d<bxÞøËéºé‚•»¿ªåÜš¬=èvôPx÷º(‡²[ÂÀ}‘Ö$¬þ;ýüyµȾ@ 7¡™àEjMER ħ™É›¤ÅëL!ˆã ªJ†ã1•œtΔ–² ý}ñª&¬ãCî]"!SHYшfZ*ƒûzÝW¤Ó<ÿÚ3ÊÉ~!m:¨ÿå]ýŒ£Ýk¿Ymóf†9¦äR\[¥íº`Ó:bToÚàlž²ns¨­U,¤Ö ½¤tõAQ~ÚP/‘Ó9”XõènZUœ€½A÷_Rš-ÿ ¨Ó+8cMR'²¹Þö»òÜ ^¡bÅ(Ó]& ,¤Ú×FÂe„= žmN=aøjÈåb<Ô#tÕÁ½ ?æ‰]Òq4&ˆ¯õ:a',ì“cÅâÁŃœôöôðă0¶OGVÛZ¿^U›O@Ìeë0²¯"z烌l)ü+m1^PˆÇU4¿<#6;–LÎW߉Ý"Wýmª„– 3á°NËàRå)Ô›ói»£×ÎGˆá¤ƒþ¢šøÝŽêï¤-[¸ŠiF³{f>ªè®Þ;cðF§Ø<9 Çcòû]ßÍ´p›UbY à`]-+ìÓÄF'½)xÆÇ—¦á ïЗã×'“I$eORV¦a±HI>}uª€õ‚Zñc*ùÛ¢8L‡*Yw!=-§™ps'Ò¼šæF¡ðK´[ÎF+jEqz‰.Ì»O" ’™þtðè–ãwsÖXnôHÞ§›’o¢$^FHß¿l Y·ùv[v“EQ‚q»È€]jC¹ß™b†»<>~i>“ãU­jÚ‚ØÜEÂ5LÉ’ s*TµŒI…Ý“³xYâÍÍ=xËL™¢š:BoºqH&˜ÄÍé<ì¨m„ó•¬Ídðè2fFŽyㆷۊ>Ð5ôñ%)A󃞣Ä(¿8ë[^1-IØèϤê6@ïÏV^£Îþär.‰46ìeIš4“1z²oöwZc©§E–Hà Ac¥S1‘{À Qd´0 énâ‡)«Ð~wFJpS |á'kÞÒ´AUÅm¢°.]&Ä-U~žàÅö†‹œMxéNžØŒ éFîBa³z”îó4¦Íàìê-Y ­¥ÚÿT€8C‹³EÍXŽ‡(ó;/þ<8‘“×b„Z$?peõA¢¿<:í¶|Hø4ïôT™~x6ß–ÆЛŸu.©ó¿.‡Jfd1 A qNÆå«Áî8Zë*V(Þ®WG'È›dEm wGŠ?ÿÌ%!5É;‘‚…Mf¦#`lÚ?¾°‚FÚ‹ë ©ã¹5.…A™¢x5`: ByÓñåqÍàU¤ºËÇïàòýØŠÖoB>@˜HOn,&pp÷\8ë} [°Jð–nÇ,8º(e|iÁJR´Ù´r&mÉÉ“Ë& ?vÅèÏÑb*CË¢:vä*K®Uø *PâÃF2™š=‚:¦‘(|z0B€2¸®d€Ã]LjRÈ“G ¥50K2æã¶{/ Ë®=¶q]ÄpÑÅAÒH͆³k)¡ÜlLZE8Žol3] ¾ç¢±äW ]ô /Ö3_¯Iî‹ë¨ç‰£P¦µ| ›É'C(_µ£±cò¡ßš%áÞAæmi(¾°í¥åÔ%ÖÞýè+ÿõyÇíƒï´QœLÓS1mZB~/l%|?ÜÆýýÒ=u‹JåSD÷¬ëŒ$¡¹zm¿ÍñóÑ{(‚ó?fÓYú’;® àŠ HÝÜäzéyÍâ=×бSŸðå“õlÖ:(±Ô=BÁÒ=Hã2÷<øHk+{YigùY½ŽS¶5˜ö æè*gRý¥ ûƒñcì+Ëw "zI›‰Íé´\ÖÓ,nÄ«$T¤úC?0óÚEϼ³ÒýGJnXŽ·éà]¹»Àò‡¥ ãÇ¡ñѯ4˜3µÓ®·€ÒIà<¼í¹q.ÐÖJÅö ÿ I{ºåôQÓML¹º^ÎsY¡{ÿ0EW¹?KWcÿÑÝKÙô•Ä#OõST®IØ&qGwêŸZFE{Û嘷¤F¦}_Ô¢CÂØH^ãÖ'¢9 Õ¼%Œˆw ieKu²dˆúËq¾F‚!å%Cp‹³È®¢^ÀƒKúRÍü£+g¢ìÕõÀ'®³àxHmÞÁDÅo ƒ#vî­‚ð½ÎFo¥¢­D% Úæj2ÁEVŸtQiuN©Wг÷¡7ˆ¦BÔ+G P3÷R_géñG£¼­L¶kt•ê˜3Þ®¯ê\þžú­}!&ö•ÄzbþS½k„JiÙà?‹¼ܶ‡W¾>¹L¤™ÎÇÔqä=zÂŒŠ)"´VÙÑ&¤¡sH wÓ é]5Z¤N‹¬ ¿ØsŸ“vÇkòS@K¸UbNíK­å>{·¡å0Ã|\¿Îä݈ä/3«Y>^-Ú7 É¡tÜŽñÎü™Ú´ÿqДé Qjœ"üU—ºHª¿lµl×u»F÷ýï_-LX4ˆc8×™Æb¶ §DáœÌø\r[—.«m“Õ`²GDZ± Ù´_+Ó ñ C’Íî®/‚T]$»[ÜŸ*Bb™#Ÿ¹‰.V² ñPðø›ùX{¸‚Õ®öؤ”Ñ>›<\D|ﾌ"×É]Ëujb~‘›vóC¸6ìý c;¹Ù§8ªði©ø9Ý^–]v@óõnðõå ü69ÿ‘f­G%©c£I/²`yb<šãOE@W•ì£Š¦‰ÐžÓ:ØÃŒc…˜×vÓ¶YŸ­5ÐWi´´‡õ¼V­ÁüâÖr¥Ë˜ù³ W œµØòë uÙQhÿ'î§ïèE•µÝ£B¶!ÙàK©#/²ð#m«˜+:DÈ:(Ÿ9TÃ]­(ÕZSÖÛ~€¬QVõn*ôì”Û SW ¸v—1ƒ±o@ÿQ¹‘é‚L8û‹äU²©, «+5천áü4N²“'ƒ†€Öyæ¡´E@Û5J¬q00–ÛöKà3àOÝMET<ºÍº‚®0I³œxx¯èŠ¤Ñk’!Ø…x<ë| Ið* ˆX'|5>E-9êbVmÕä}qÊ# ‡ZZP>I#9§R7d…n1bW^ ÞjÆx_(ég™?úíú…Ëø6®â—±È6Eù ÈV :¯ö•M÷2ÖAª&. R¹¯s‰²Â0S!à ɔ¬€·š«#héíW^â~ dMÖ^áú’bSGVÌCÖs°è°¬W¹ú&âù²Eru’9½¨ ÁÚÂÆS¾]_U”†ydÀå•/ÄE ¸¾~Rp­±úœ™hÿ)l¾¶€úÌx‡°=ù`lˆ¾(Ï/tgÄ| ˜ä j r»›BWMËÖÍ4EØnŠ4Œ;Ï-±Œí.ç÷w¢¸? |¹Qv§I³¨ÀôÛ¨ê%S–78¼Ü5¨[Sì–ŒšÚ?ºR VÛÞy Ê+ͨt)™\ Y 8bq fY8;¡AÞDÍY"´—„m^0±oÉéà(F¿Igm[›D ÀÞð€‡WÊbýJÔ…&ûÕV’¹•¸ðzù(t…0~<†ÙtE¹$ƒTc,·ƒŽ GKåSò*﹧6CvÄÅ)/F§Á¾ R‚Ï\n„Á€udæîX$d¼/=,0iu­#²rkÈ‘à¦_æAõ#ŸøØÂË™È1)q®lˆÕÇîÄÀ6køœÐ·Ÿé˜² ¢Öy@Òe$¶nÙ’Ÿ®èWSËÇ/…°ÉåŠ<šfB9^æÀ½c R@.º³‰P}HÉj6‰Wþµ%#´ËZWn ÙÊFÌwšC+øU¢Š8¤e !ºÙŸ·:%•Õ(¥G¡€± nA/Sû»Ž)XȺÌQHðlžm=g°R±=Q̵¹z€q¢+*–¬¦fFµN·’°Ê þœ:ØìC^;æ'þõ+>+oOÎõÒKž˜¥»ÿ˜Ýî•Žhæ?'²œjügïq„ Í`z įߕ9ˆ «`ìÎÎÑij t퇻CWË×Õ&§0?:,<˜Wr!X³à[à x»ß鳈ÞÍjŠ;wŸŠ%qYByÔÝo&#À÷…*¢1WU‚­å=Á–Œs) S>;¤Äó‰$"û¶&ɪ/ØCJâe¦åÛöŸw~{PÏ`„ÅÇÇ­ç”ÔTTÞ°#² nø-'§bE_ìÌ_Qm…Z²vŽ‡­~ÿÄ2qôLRPdo°ÿ+vùùçm©üÒ#ê‘`PäÛK£²‘¡M'rÓ2]´.ÝUt0Dw(LË×A}¬~=Î\OŸ²=Èl¤X>ËT·Úâ!¦–.m¦¡HBÕû·²ÕpPÈÓjTUg£«²’m»fS¦½ÌQi«5f¥¬5²DSl¼,#E£fÚ’{Pç@}±yæÚ)2ö é×&¤¥BÆì•×JƒsVúºªæÕ’Lã.pêlQ}c„)a¶âtø/H#¾_Ë{ãu]ßoÐ »žcÝÇd±h¼• %F¤º/ž<‰]Ê?\DbàUðà ;Ôõ:nPQ¹,u•ÉVb vð2H.?;©Ò³)Wè¹zv,Ÿ¿|O7Ê<§ bœ;àxØ×ýµk#–ÞòºHVÀ¾øàǬAŸ*ª 1ÖKþûßÓ) Tê>8õ Úi¹Æ.üZÚéwièóëÌOñáûè9ÊÚF?—™E Ÿ4ºº(¾Ù‡ÍèS%*´s˜uœJ_¶\OcÏÕxšájwfoÊÍVcPè•™jg'¹s‚3>v@×þh„3Èå^'èXK¾MÈÐq>ÝËdugE9OÏyu‰ÚÖÜÇ©âÔg{¯‘¸ ÷˜®ŠAݵsni|ßÄøĪb]V/^s·àÇzqí¢Nj(EÐ+¬;k¤d·¨ÚLÇyP^:ƒ”¬NîS@eðànïŠ<ëÁ¢ÔNÀÍŠdû»»k,h3þ,®¼`[Þ®„ûžSí ÐM'EfÂ/ÐÆ>%Ù›ÊèšõÐ3‹è˜_:Øâ-̆„v™Ñïû¢j[ Åz‰ «aeý×OÓCZ8aH\|žMœë ±€îV¿àÎöš¶E'̧w†pÐæaò—/Ì·óüjØe´57deRÜÏí–öWÁ² ÑÛýzp¥¹:^'ªÚë/ª‡8_о<­ÄŸ÷ ãWÇp©nô†.Î…?Ñ¥vŽ âa ‘»,¡í-–Ò1¼ù®RŸÎMC»æ³îbĦýzd½±¸6 Ï<¹Tw‰ÉO¯…Î|2 ·Ë›Ø¥¼Ëô6_öšÕdƒ 8âVF¯‚©Ñ]Gø­V$ÅÐ:Z¥‡¬ÍŸl’¼Ye¾sATWÌWéŸäpÙ $~?I·™RYÔfþ °³í U1S.U>4V­_xÝÒ$_YŽþ¤P(‚ÐÙh×ú´‚«¡Ínô¦Qh{TàÔh„ZúÇq,öÎ 4Y”ÕI+’匵ƒTJ$ö«a+²«@oîÇSÊ8¨ Uö ¿õu¼K6Džáа —oãà@ Eþ‹1¸óR¥‘&¦lxÓ u;gd~J„Á©yÊß5íä%GˆS¸:s;£fbxe¤c8*>éã«YÂÿ<û™Úü$n e\ûG6wÙ"1$zé(A.ïurN9¨Uäìåí2軄·pÌ¡AÅ"5&!ê‡W­Þ.Ð/yZ/™˜ä‰Ô˜Upöû¢ÿ~Á0·à- P®8 Ún‹”Ùpu)Æú¿8äuôj/dÖ>E|%µERŲ»½®8¨Ã ˆuXgC´¤òŠH#\]¤Ó>œÇõ  ŽvÉÏ0Ш‘yº³1ûg¬2yÔ÷i¦¡u䘰#N®ËpcúŽE½®ç•qß)#§F±+P.CVä÷6ì䖽ʒ¦a®ÒÆ­±¨d´Ïì `&˶fßEk©ˆŸßT׳z´$×µÂ!¯L€ Ûu@€™0MGM£>³z8à»3<‚ºÅž«ãøÖà32(P tª*¹LûÜ¥Ò‚ã riï [Üûc5ÓjF0zÝ4ž{Ùƒ!ÒR ¯R-@ÞÏ+¤F™Š£Áý,°‘¶ÙV»fu3'òò•šB€W´‡øïy'7ÐS1ö:)¢ñü‰¨" ìJ—äl¾KZU%žê¾•žõ1Í6ƒñaÎB­T}…¤·ôvšÑàÛRCxý¿U!„mq=3af­tó¤»TÍ’ Ð×X¬Qæ‡ÊyŠ¼¤2šPB‘M™`Û"÷»Ë„®ß·ÀšËS §q”…dá$ýPXHÈpp5Îsæó½a*êLÜ«2œžÇ:­_W Ý&î §‘“r㇈¦ A¡zŸ(*óüÔtµ€kSh?×'Í©äk´ëßqe¶$æõο ’ B*’â5ü; ‰¥Wžñ\%~ÒÁü}¤Iðîä( •…Tþ>ì5°eשçŒrÉ Â,¸g“zB¤Ýêslmëp8)jD)CÔ•d(ô'¤j ç[é“ŒõƒžïÝ…Žñx,ÒJ`›$¸"«]mMœŽô{¾DÔºŸÒà—6ÀÀ‡C0uJžÐøe0,röõAÚä¡y\²ÛŒ79„óK¥Ê‰¿|ãw/†õÂPŒ/Ÿfü¶{ÔK®íM¡Ð;&¡ÐªO™kmÐ#SÁ†AŽ¦¯)'Ÿs¶8Õ½ÇÄ+]9?É¢£3E÷µ„¸˜8:ÛšJAŠÚ[€—º¯ú‚e5ûÚ1Ï•öÜÿ‡ys…ˆíóx÷ð0š½z° Ð'6óÍl™Âš1Û—G¢†††Gðv¡ \kW¾—O /so×dLÄåðyÀf)vú­ì^é8¿|(¾Aå¶#;<‚:¯ŸBi…“ö*kŽ«GyB?ÈÔ£×6ÏÔ™Bü&…B;Á°-B*¨¾îP«À|ãçÆʶj²ot—»DCìÀáü-Õg–ð&ñ·u x t¬è{ØË%óÄmžDgÒŒï&ü[çgH×à)õ7øØ»·0óR ¡ùV-Â*omY@KÂÉÓ 賦ßx$¸gK:)k n?îÊÇ;èàÖwsMƒ i$“,¹š‹?rú,v÷?2„úýׇ"Mù·¿FÈ,ð³<òÅ*È5k(´À·›»×´çu],¶&ª* ÇèÓ=YàNî˃üõIÿ#®°DñÌ«gÎã²®§u?‚MÏlWl„e—8¿™PÛìJ=ƒØÕbß&¬ŒŠ2ÕaÕä…áI‹h³c-]F³X$3±ã¢4ÈF¦Ô¤§±š®Àeã„®øª«s-ìæÁ0>ÒP<+ÿÒnaˆiÙí¿*6\R/ý܇~^å}ªq«b}¦h`÷ÿ…TIüLP›t_* ¼½1û¬çÓ¬pCÄ9êáEÃtžÉXç_½†#olX;È„nàz\—EI$NÜ Öí.r|Ç@Ü+†)¼4Þð1†.¸ÏáûZð ªþz¾¬[K"zÏ »¯ÅL^‰/ÐðöÂs¼<ÖûÈœf.Ž¨i‚zͲÿO0OÉq;D<€ÀF7•=¯ßfÓ·ñ-ª‚Ý0¯â,ð/ü3´p¬A¹Š&OHÀx¼ŒS{Â2¨ÊÏu‰4'FUL" lŽÒûúì^|Ó_S¢Íj¤HÔ4qÉ ŽØ¹‡:TLåšS<‘¶ÅdýDþ¬—òlw —K‘Šœ³÷NðZ§Ì“Úò;‡?IJ†¼a2á¡ï ÿS¥2k—¶w°!>{Š­-÷Œ&ÐX̯ÜN#«yAÍ/ál¸÷QÂ"ÿ$Øwo²ÆÞ)ooè@0±ŒöJ0ÅrŸ'Ê÷žZâ¹E-céØŠsIpôÄŸ¹ŒÁÁOQ|ôaæìH»½áMüA†ñ¯Z¿%{æÔˆ„Û;¤\ÑN#v†®.·i³ŠZŒj7ûõô—|Ä° ³ Ûì.§\kñÝÓQ~žy`¾àAä™jô:Ú\ãM ×efJUnåŽcvœìílÁ UOvúæÕY¦š,ßüüÆí_ôñk§:uè€À°b¦ÀEõd;š‚ËÊ ;’y¶ÌñÕ{éu¤/Õ6*F[ “ tØb¸È(Ô?eK=s±?Öï6Œ=VÒ¯Ujâ_6jb`Á=ùF-«Bë7–Ó¾YÖáJ-cp"é ëQÕnW'°6ˆe#þ„nÖµQLñ³Ö±IT\–öŽŸL×Î0'Ò*Åþw!ï|¢n€ ðo9ìOÈ·³¯˜g{Þ„d܆©:ª,5˼ ïÃÁ8QÛLb)CyŸ ´¥£H[ÉÁ–k¢ZaDsÜñù8»u徟èscýìFîÕ€æzÆh¬ ”.Ÿ°øËt;ÊMíeeùçþËR‚oˆüSsþP&å]ÖNaÿ«G÷¨ªžH´$ÂͨšNÌ:cxŸûi 4iÙëy•¬í|ÿ±³œ®’ù®]3¢Œ9HÜ„œdð¹ÞåxcÏpÈ–BZè[»3ÈÖaÒÖÄvMSá’Å'«Éªu«âõžüµ:¤vù7“ Që_[W®Çk¥´Bc¦›:óœÆ%êæ\ÃÒ‡zgNÒÌ8hrÍEEÞßVëX‘tí¢-×M˜ ã:pç (s3Ô (8‚¢û´qüh|•r|_XÄÃÜö?Ë]ó#6#¾í\p¬ˆ‹ ê¤I’xoþäôJñ÷ÂüMOûK«m•ô±H87\α«:’¾´šm#UÍäÂŸË ·N2§æuK„JBd.WR“ñ­|\Ù6¦ ¸ö;uM9ýcHBÏp‘^æÕ&XjZƒ!zÙåû®s_%è$gÅú–ïÁeÈ`.6Fƒ× ¹},Ï«/ù3É&„\dí!ÁœÙ¸Mjr¾q{4ªB3×æ|H:ß~Ãe•Ù±¼Š+؇«økàÒø/¹Ëå¬äÔ²")½<¾ãM‹òÍ*\b‚ïµ¾•-¦Ö/©w*.èëÃve¸Fi@ °+ïWàbÖ¤¾òÂçIžÐªzÓèRg®½|à!’DÅ?ÕüU²ˆ!#ýÿô$ùŹŠ…‰¶Æ’c”\‡ó¸À-iD3xpi>Ù Ûä\¯¢Ú0j¼Œ£fœO>XÛ²m` ãN–on}[ô ˜ Åä=go/”øàW4ëâÝ|’”å’"MÂûÃphˆ>{©^C@Ó´üwRJ"‹ ŸÛóvª#\pA$8ú’ÿYs*8¸œôÚØ ÎzöÏa™!è·ÄZÞü+„(]² o!ŠŠÕ8êrˆJ0;e[¹’©«·²é€üѯ(ň<˜m¢¯ˆsKnbI'¹n–â‚áÂØzªÝN´mÝ Ð]¦£UÜO64më7’µ Ö¡Ųcn³°]FûÉÈ~*E(Ǩ®úh8¹òéãm‹ñÆI§ÇƒëúÅï $Š—¿ΖYO%­/·¼JìqíBÚÿY½–æKdù÷óîJ&x«»l€ù0Wz)ï™…ïþpÖ„Ô®þQ2¿ÈÅ _}ÿá.]“£¦ ÙA{­îïÉDÿUßF$ †ò(Y¥Sµi)€0ùXYeFïJ,8H¶Y¢|¾¸»æí.UÎôs©F£gƒr°ùöiSg±%u÷ëì¿A63Y„×@M˜×s!AJ˜ÀeçŽÛJÉJ…ÀÝ€BÛ ØQÖ}-!‚Å…šµ€4üý§ë²µÝ5³˜dØ€1á»áTåzò09L÷]B¨ ÀÂÆÐÄhBY8Ž ’AÖ‚'$¢HÙ'ýçôno¦Ü*®(_6‹2th{JM;„õ'âS<@$¾ZÿFÚXÕ[)Û‘,Ðf¥ÊÅÒX&µu¼ü1©‚¦ƒ……pëþ°‹ˆw‹ÞvS”ŠZôÓÜd,ü#ØÇ÷`\öñ³5®.f`‹)Ù¤È;È[èbé¦^0@ .%¬*5‰g_IÈ׶®sÏÄD¿[Ê%é“Ü3"lqÎLøW™)î±­ƒ±ŸŸT'm˜L/‘uF‹«¬W±¶ø±ñÍ!lÔžÇQƒ?«wDy¿ páð{„H¢ *:˜éÅD™†oÁ¹×þz5³Ðz³†!1 ¸5Q²-/ŽÌŒt<„&p‹=¤àâònaoø%÷8þYy>½k¾‚¢lQ!ì§ÆéRb•ü}˱Ϲ(Ùñ¨:êõ8€ªV»²Ea] sùX¬U’ñ–…žPƒ5L£>¾e¶¯2{@ Á :óýþu‚—¼^Ð-“~Þ­^Bø» ÈâSÌ.`ôBTk9¨yk.ÕòšZÔ»»§F9§cu‡ý¾p'ª¡@¦ W±ìÿڶ䥡í}<ò£GõÌ›€°·/m‚Êž_`Ê¿w룡†iBÔLœà9É6œÖßbÈvœ¢Ÿ5–&ñϘ÷ITó&`DŠòÑšxn؃¿ øB~Q»'[CoG•þƒ˜Q¯³îúåØa§¸c3¡OÊS ¿GcEƨݲ…5½øÈ“}æš0—¸Ož›9Èc·;½Ð ÐØÊ6]A)AØ5G¯€†<ÂŽ-=3Þº±šï…ÿœD#ý˜`Kß4c÷Ò^ØAκ¶%¼ÎÂònùµù1×°pm Q²×Ž*º{—*âËdÖ™HgŸœ¬ûŠ¹ïÃÙìËiκ„l T8øm¡xáÜÂÉèjó ¨í¶Ýþý™b_.-5jùøä·Ë]Àâ©ØÜ6.À]ìR,Õ ‘»Øòß_tkÉ‹6Ñ9PÆßï™/ FSX 0XW/ ¤ÿy§—´‘ jB´tµ÷Uéä*mˆrJGÀW˜Ý™·HR^;kP9ÚÃ?®•kFÙnE,œ‘æMb¾G›gñs”û]’LÂDZH•^ ^#š•ÌX=´ÆDÎpo4Vý³û C g_Ì¢Wlu9ðUdyá«ï6î¥ pa6Í ð!ˆ"ê&€­ŠbZ‘Štcª ‘*ÊHÕÞ*Oõ³jý´$WÍ–@øO[g#º½*5º¤µµ:¿0ÛúáÜS³mmH¿e ülÊ:ÿ,qõA ÝYÕ§ØB¦ nº{üåMD7ÊËvPW5‰!‘rò ùû½ »cîÔõ¤zÞQ!·øi¼ô¡È‘9îçnÍÕ$åuo©q‡$¼²oÔÌG„ †,÷¸åÖË\¶Ç­n‹lϹjúÿ† TÊÎK×¹Zfƒj5“u¥;…@u¤7xð2¨l5S+Ø•_,‡š(#+vÕè9¯€­ ¡·Â†„j#Y±¿þŸr Sžüó&7/ÒŠZÃæØ…çÀ^çdY@xY&u‘ªÇ]*x¥KµÅò|1fW¢=å÷rJNS™¦ó|IïùùF+¹ÀTíW‹£àEÊX¹þæ.‰˜Ïò:i²NžÞñö‘È ãs[Çg+ÊÀà3p¹¢:ëˆtŽ †óÖ¯8*Y.ðêi¿¤w,M´…åâQÙyÅ¥Æ:–£†–[`lj§ê9Ñl©Iˆ\7bÛ±ñ…-0Þ`j‰¢6-Ž¤åѲ@Š—­¸q˜7uc…Ζü9]Î8ÅW{ŸÄ¦¥'ëoÝ ¿¯Š2ŠŽÙ¬ýOÝð û¸—\vdæ´XÓ¿»å(Ñ·7©Ñœw¥v!ÍL×<Ž!h*vèj¤wº fÚ€›ìŠ –z<‹©ìôë;r¥Ñ¢B ¼ù"Øú5†’F•MGª~ˆ"Tdž¸ÒÀ¦ö™—y!Øß'\~‘Š ó†M ú¿³CúG»ûyðhlÁ<¨hÇŒŒæ×I«"’xðâLýïÚhóзB=|Ës™cÍqÎ&œ¨}2Š’Ò çàÖwÍÂá&]áY)®mË;ïò„ãLç± ×Ïø ‹°<ô‰±'/Gþoíšù‰Zø˜*|?˜€¨¶`÷¡XùÞwµ%þŒÀç 1ìt5'ÙhÚ¶º á5ŠEâðºl²dŸ}æ!•¯ÃŠ¯Gÿ©÷xèêÂœ›øíqJõ\Øàk„œQ‡ìÄ4ð¢Ñ]¨·zŽVˆ¦$Un‘ƒæŸCN´U¨~oÈ‘ô–wëAý7€¸Ï€={4ù½I¦öCN¶ÐTxY/¢vÎÇƶąkÙ7e,Ò[²VAÔeBÛ“ùqkÍ%µÒ‚v[d‰ I‘rh|­rSû«Ë;þÝ&‰w£­PðÅöPV¹D¾ü E06—v„ä.TP,¹I°öÒk6`ïi­G#e÷Å:Rõ+Lé­S~®©\ípÏä8 9ìë8O|.b35›@AX'1«¿˜Ì?‘Îçr•à^˜òM¼n¨ª…ÝÖ˜Ÿ+–O–¾ßI¿ÀI,É¡ ßö±i=vëÞH(÷€\ˆT»¢žß’)&Ï`g1)Ì@3‘ÜVîã–R|'/U*t8oU§UA—ÓÏdcÇwS鬒>°®§ovMwµ’^¬ßÉ™êÙD³¥IÒ%n¡AÁ Ñ#8Œžä9` â¢eñ¹ÊCÛk½†ß\BL< E¼!’Þ94ðå"BJ¶[å|}ØlŸÈƒl~´§Ô!å©‚` {YFõk­Ø¢^ÌÀªòŠ!”¯wÑöDèW.€÷Œê7¢´7Ÿ—xàV©XÕ4—2|Í´Žn ö¿e«™^†ªÔÇ£2=ú£æÞr{ì÷JkOò=±Ê.¥D\+Ær¤ËŸPQ`xŸ_iûê:”¤–ŸÑ$3¡~¢IMž/lCN¿§s¹Q²¡–GÄç6Þ€“IcgŠNÛ5•^kÊ:YRL`0ßÀ6xœµCá)¹é@Ô”X bTø¸¦klâÊõ2¿Ðw¡Yòh@X—ãý¹àº¬Â ÃÀþH‚,·Ürç-—ìÛä $Wö)ø¸ê¾ÅÃ@ðœcBxuÑâúw)b¥.Á¾ß.qÂVáN£*¡ºaÇhsÎÛ@vÓné > ¥QŸùÉøË XËõJŽúü•êèÍ‹Í %ÊÀ…6K1tøeFSë®~Å žìGŒjR£åҙܷ÷–­ó­æþ6ˆŽ©°y˘–YWúÃðôÒ©ª[K‹kZ©è3A¬TÍF)¢~²ôã}†d‚ÅsØ_®W'ˆ¥4ÿrypD}\ÔE0·Ñ—dàŽÞ'‹qoQ&z¨¢nû¬ÂyV/º9Ï©jõÕrà7f?¼Ç’•S–ëÓÉ(ãsšiQHº?\'¥nŠ~SMyŠš«²y8„Ùúî•Øò»Å”äWI~õ݈ß_À£ÜÛýÂ!óY=L^×\µŸŠèû|ò¹Ï Ë¯÷dŠÿ ñeÆ<·–âf(5§<òÛ^G5Îîñ "I “?ÃÖŽ@IóÄãñÂÒçWMê7õ4!dʹòéñ` Ñô‡°n݈ei$/ø\GÏÜp¶1Àô&Ë5D3¸lw;$„ÓAAUàO^ðê 9˜†?ãÁ¦ Rëñö‡ø3Ǹx_¶!!ïª%A˜•ÃsW•DbYØê–KI”ž£ÂüàSü£=Öõ"!‚X½]®Cü—¹-|”L74&wPœ&d?áAˆïCºŒ„3³+y™ûýK§§Ìk«ø‹rG6È8ÞE‡Þ!5³ì—Ðû?Ñ…ô³L8ýD¼†‰Lâ2^Ø6w”ûEèQ§F# &/Á1úf^ŸyÛ¤ÇnD†´rÃb©XÔw…uŸ^uhôCXèR«I’˜Kà/¯ 8mÉÖ·aÏä+ ž!rty`s¶¡_T¸çk0õ˜Á¥k_iáiÖÈ€ç¼ÿi^jwãK‹®AÞÖ}ó*G¼{P!^$·RªCÒí£êÖŠõeÞè±¥ìê>SÏNÏh46NÚsRi£hd 3.A÷ž•vÏ)Ä9䬀4lÔâBüÛ(Léz,=®Ìb¨ú7nçƒêÚùs(/5A£$]§„káZØåÆО·ÝNŽ³vJPÔåk¤©‰²à;Ž/6úü°ùæMˆ§ ¼}z°zúd¤¯E‹)SÆd^,ø"±Q $âìýÈ—8ѹ€¯ Äü3¬å¬©|gy72âµ€’SÖ:¢K:>±å/ÍÛ™ÅtÅ—NéÙ]z”žhúSÆn„YýøµÓ¶0Í%šåWb%”€H» píþHoàó§õày2hR¸$3EîTöJ¥¶VÍýŸˆA`S¢Á“¯˜ž[Gõð3+jQÛ·|W¯/ ü*uµ³Âž? «›|Æ°f Ádép­aEz³8­vt,CqY>ž£CðçrXç9HAKZ’¥¤¬‰ÅBMçv¾téÆžM½ñWû3‚_?…9oOÝâ.=5­ìzF‘Âò&Þ=™èžÎ»œ«ÐÔ.6š•“C­öå€ö,E§QrÐó÷ªcHh{x¦3J×uùÔÀ6Å@M!ìÉ®6ÍòÈú“+°3–•vûïªÛÙÚë»ÓÍ@ÑÉ«®á.„ùkâ•!˜ì†’BÌ^éá–»l_LŒXw~2ƒþÚë¯*û˜p¿‹¸H#š'®ßX(U(×®ÎS©µà¯jâ—Ðߜ时AQM½7%{<ƒÇ yƒt†/h!Æñ/…­¬š¶ÛT™—C©Dñoÿ{¾™ýÝ¡èkœ®~Bç}ìÏã7Á•3ßòÿ×X}¨(ŠâéÆ ™9t¬7µ—Ï)ôq \‚ Ø £¤«Fª?Ó‘Ã÷‚¡žy Æ\/ hNÛ¢ÏìDÆc† E¹M3zˆJzç]¼MTMÔ±•ÞÄ€bó*­Øš=1ñ(ð #D…‡þ{íl§gZj‚í–;Ž§¨!ú‚Q·ÌØËΕQ58[\SN¶³DÉT®Õ¾ÇN¬ƒðïg»©–Rd­bcfJµíܽØÓÌ«OUüwžî¿Õ€ë½ãU~F!6Øy,~GÅ4³™Ö*©n§(ËXA·-s‡:AtÂe/,ZÇŠ»•ëìbc³‹¿ë:;€˜Ê4Kúº裎Ï“.‹ÉÅÀq¯˜Þ`ÎK*Ò‡û©‚B¨¬^êHÔS2@üÕ3 é릳OTd"$|õj˜Pò)£'ì»Ø¶kL|{€j tDü¥ÍE„Ø©dÙvÑÜ Æ-ZÙ85SiŒH%Ú‹XijNÒEa.Û–³ R`ÌÉ-îÊGHv*ßdÁg“JŽØ›†ýïOÑ›¯ù‡€iö·ÛùéqÑ‹ïú4ž†Å›vk.}~O°ì#;ÅàH="ËÌüç’#;ž†{CC[ï¯×ãSíR‹x/Ÿ”> ÞçX‚Ó€iU*ØL’´ù¬>·›m¹öS⢠>á‡ÅÀ^ ­ÿˆüLhž†×ý>ÍõJ‰÷7U³ ð”ßÃ2Ú©§LNÍ#µ(Û U…öµŠ@OÁøH±]57ë£98¸3 °ÿü©Âª ‚ô¾t?³¼pªrjGŠožRÀýtÇuèÉ/*”\ú`´´¶È^°;ì+§"V çUMµ¢“¥kDÚÁîpÂág3{Æå =ú ÄÎœ0WªÛPÂ|6ôjEc‡ê§Õ¨X̓¼ ¨œ훽Î? š?v^§UñDªoê.§&㤘z¥ÊãOƒÂVÖñ¿ài¼+oÍ:±¸Ìj]eÇPÙÖ, j@B~CåA´ÖÁ,z+•žhŽÕ<9–>âGBÀÆ–…½¥x`¤Jm®þYuÇ^”`÷Tu‡µ—̾Mq(cQ–v,ÖXMIÌ:vŒJ4fÖ„j–RÒtÔõ`–  ³cpÞãÙ[̆‡O»^Dš²hå¦Ru¯ö´tbm©ñáìƒÖ°þ&<´Sý¤=X¢ÇÆš· &ÑÝÑGžw´t-`îA¡s÷fÄ»yvìÎ!n7í]cS‡O:TXø«’W§Øÿg˸ÎÿãLGá¿ Ö¾ž½àòhÃœq‚?ÅŒ[—þ…Õéƒ@$&Ç©‡Æ#ÊŠ´ñîÓ/rÖ~—?цzI¢ÈÙö6É·|&߆Í.µåŠ4?³Jæå°ipS·${¾ª…¾u;ä¿è.%¦^ꢑ'D±ò ¤œ¥ªH.Ÿ¸‚E·TÈ\oæ„¥ößÇ6mND«—©/*tÈöÉïøPd,6wB~£8ÚPG’"ÇÈj“}®ÝË+ µé†^!*DZ»¿ÓyHÑÏC’C0vð¨Ü:   &ÆЀ‹óS8=pW îªj*2ÏUtŽ?° þÊßkɳ}ÖP‡Ik}OŽTAãÅþ̱ѕp@1¥oµ`O†yóG¿s³§K–+;È£¨Ÿ ÒOa KH_Еüà‹. ÃÃp®,Kš © ÙA¹o“)¿#è—Õ…·6ÅV4“Z2›—µyßÍöŒòüQå ŸÆwEð«b†Võ&Gê;5Y£mZKÃô @´æ>Áç%{7'ùÁÎIqÖ8êóÒ\'oqûdwL<Çvtzy5Š»7ù¬ñCŸYHâW>ûeIΘωA'–n–µ™"©L•«¦Ð»¬ö+PXG±²D`k‚[/)ý+!ûËiMú±,‡8äFþõ±é*…ѶßÂù/¹‹Ș}å5á‚ÞŽÿQ3yœg`/$ÎÅ"X· Ç_]NG– 3&@cØZ²lGw¿ÌBA‚Ûïü4ĸñZ7äÜúµðþé?Õ÷ó‹Cã\øôá[03à%ïëíuKºÎù]P2D!èY¦<H̪ðNÃFÅhû±Ëè<œ\ɳÔìý¼ìŸ66R/–vZ‡v{ö=" 1tOI-| ,Rþ,å Î*tá% øQ ž2k?le àž¦Ð {8åÄpö6©êpÒ zÙœ`@göYÔvh°&w—lÍØsNFèÝ•kp7U˜ªǪÜçBc•awÃÎ{d7td,ŸåUÛû˜â"Ñ}ÞuO4u„w?q"£uˆI…ÎÓ4~x#ÖQ÷û,UK#!Ò«›­Ëm9Y~eûÍ]Ðn/‘À©Ïz ý…Rów\¸+}3B( ,¬šº²ÕȬ›±*Ø£S Ë|_]6šPXמsó”·”$ù·"íüGyÉܧW;ò¤ÒB\›iÂ1ä¢õ%à1Mä"¥ª@+I¿Qé„ŒYvãå¢<^Ó]tëx6É‚1œðƒRæ _µ@ýé*Þ2BÄ[9AéÚ½©loÓ nzaUÇ4"£:ëÐÆû^ö3¶\r;ÝÊôÃ(ªµ–Æ!’b§°º¬ ³“Ú+Xƒ5äñØì‹!i`dÓê“?̸&¬k`mÚºí&;º‰XÀbôdí¥ÿÁAé?°…°ó6Ç®¦Ðh÷žs‰ ‹  ÉÛ c€œàÛ_"> K8®Òex7M<Š îòFßÌ\ðO+Š¡Qø6ð Ñ lÉ=·—æ3a#nÔÖ.¾ù eÿع¬èp›­áqÙÇ¿µÛö­%05ÛK¿åoJ@öD®ç—qTOxšTovÄà>­™¾´ªûTw[¬ó?ŽÓßÀ” üߪ™‚‘HçñɵAP ‚z³”Mœ.÷ßQáe榨ôž íçÙ€8ÀéL ù 7>mbhýìCzQͺŒñ;aDÍè'LfnëîlY|ù¸°„£¨Y>8ÙsººLÍK숒ƒ½C)"öõ÷WÎé5«NR¦K3;Qrh ÷‚¿wú||üš °&÷N ràjU…õõØA¿÷ÝÊã²FW&e¿O°ã¼k|{.šÿ@Ÿ§ù£½.YñÿÂ;M "+œÉi\gÌ`ÓõÑÇÜ718ÊN¿›,Ê€ H8‹(d†XâЄo™m þ8Ž„„ÿýpiüI $Úxæ Úø=µÝ{‡?³& õÉC™:4s,®x?Öè`¨h_«Õƒ9 É æŒYÄ]S.¢C~ºÁZÌ»÷S)CR¤ ìÑ°j2XyZe†‘æ$ËU§‡ûgkÓ ŸÝ]VMU‘€”ÎwF_wÒçÈ-,¹µ^«ýûFæ ýˆ®€Ð^GH½eËÞ†i&œÄUr˸Ò5£ ï£ÝM`C=1”€u©´¥-«h\#sh$=Á–Û“®=Æò9/1´,ˆ¼0,{6|xýW"Fæß­­WÉ ¢†ÝÁiSÈ}E!À «†Þñ{'¿½ÔZ€f™Kè.¯S^D¢ƒ™å»—-bÂ Ö µÿ§ÂPwRfé°KÒn`׉þĸ¡å駜#ÓbѨ@<­Z‹¥»õÑ6ÕÜò¹Ù†mjÈfÚ»bðyº¹ø½ú,>d!PEAÀËéï~¶iî¤Í©/fä0«€Ù\š£þŽÆ¼˜õåÉ~É™ßâÒ j¬Þ¿“ÝÚµâ1@07²°@€ü˜Iü‘D ÑíG%hÿ|•É;ob"e¬ «1IpÄ~qð%¢õ¸LeWÙçkÔ›6“)Äzq¡&-ÊTîã¢òº©= ¥Àf†> €Ä®lª?ÐYE5˜÷T;´ÆXL-jk÷ïJ›É‹j¿t× µ{±‘È1÷Ž’ÊêfÔóµ\ñìˆ3ÿü(Ȯۧ59¸Eò…ëvFز“²‡´•K¯ õE«¨þSB$ Q–DÐbñ ¸.Î?vmaìÕg¢¤Brå›]{îÀšªÇµrÛxžÛ,»ý`¯É§C“-+ØÀ;mNL£ÞLczE!0wU‚9ÈjŸjœÚ(RY25Š¿÷e¤û>‘m.K8àBvä\„u»•¸mE,'"€H1RL³áÎN3[ ß͆ñ)®\d>Ï[ùxÎ$@²JëaÄ,ñr†A„_Áš}&\kKqå{ó*èˆòz—Ž‰N%îeÀIò\öõH-Èp Ød¬ÆæXê`Œç5%WÂÞ@Í«ýB Äú/¥@œO©,m"òEƒ˜RVóhA„S×.Ä)ïÅâ“yº%7OÌiJˆw…ÆbAkG•…‹#£ÑŽS.dÜ­%o¤û…Z)znš1_d¯»Í3¹íDòyÊ›?µ{ZS¥PO?oæš—m¨mÎYR¡¤Xû&o³AÂŽ™ÀŒVÛêÁd÷Ã7š}lg¡õ‹mu -|ÀT›ÛgÊVÂpnYàÀŠŸ÷_šûÊ®=çqwHx]àemØÝ¥!|÷ö¥%iÈ 4¹aÊk¥¥|k-að‹×ŠÕ5ßbºë,55ßé÷ʂň~£2O®my§Á±uÚ~UÐ]C¶»þ%§ˆº–ø³ˆ©C²‡·ÎÌDVð±—K2În¿lýÈøZ»ŸuT$Ø b7uÏ‹ö27Þ &Vý¿üíMÀº!—hÂNñr­ùw7x·®_Å–ŒúðL¨ Á kèVÉ,Âüèsn›—”X­ØDç  ?o›«µ­ùN±LU®§k-kÖ{8ò˜Eˆ¯é ‰‰ãæŸÚª~Aûç?BP_i¡ÜF|c-¿¾8—øȵ–()Ö:¹žöLSçèZ…ÈßÃæÓ‚?¼†ºXò3½e0–帛áJRW/h¡£QŠ§«OÇ㘇87—U™}Ê×´¥©ü¹é«PµÑø:0ú׆RÐ'Øû¦¶U3±Äóú&€:©á|™®[:„~mÐHH°ã “àÞ^1ÀðËÄl##‘ÿgAQÇ{•#K³áo(=R†ð KM¼"<Œ˜ï!¦‰9¸Jú$-þÝm+µ%…öwϦDħó:=†9p|?w ˜fìLÇ=ãÁ5æc ´BÃ#®o´ßrîn|N( Ç)¨‹_7þÜ$ÿ¤ôŒl9•ÀYŠq/»­BME¡59Ò cɺ5‰£Òz¥GYô#y&’¢gýi ž'¥ôÒ‰k§4á ÷ÆB¯cKŠ÷×bä«ÏÍN»u+¸ÑÃÒSJº) KÌMx|>‚V«nIlªÆ¶­SxIñ2Nø¬b\T÷þ‚•Î~5&}‡¶ÊÈXÝš±¾åLâI°Î/œl|Ò·ûš!@‚ô¥Ø±“PPÝê-Ä|]÷-Šgh);ßV]ȯCÚÇô­¹J½e,S‰“»Ñ‡]]ãÏ°”ùð“õ z ³ÏÃÉ`pjÞ¹"^{ÓùW©"ØÍ Kæýú}©‡Ì¦oéÊ…b­òªn+˶ÖÑ”wÖ©c„ì2€Ûl'ðºqLÕ%;$šnƒiV†«ªC2f™f¤B€'ϧ=\#fðgy_]Ù;1»f³-¯Ò ™Î†È#IÉžBè‘…$»J1Uã€+Ã$ GíF&n,#cUòÔÍS8nר WIóÐ4yÁn¡þÏ8ä'$4Óæ:¸œUt#ù"Áø®©j§ ݸpÌŒx·+½ãU²lù ˜ÙIíƒüG]ª¢)]¤>!p×¾ÔÏ×þ»ã~’Š Dy=qNc6ØÎøÌ"³µ¡vâof¢ÊÊžY+@G«ÝÕ6Ë_HЛ_.ìP¸ŒqÞtVžPè´ÆÔc[‰ÆÄ¡‰T6ôáÈHRMY9\pz!5…ù À…ljjç€vÿ¾þó"|yîÂðC„áŒ2Yt™@NçÊî¤&k,ë„HÈ1i2çálþÂ|m0f\…LŠéc¹·-p(]…Únx½+he³ Cê¤=òÓÀr<‰/d_S€YÎÁàÎ=!si_Ÿ@\ß¡’¼nô£c:‚²ùÕ±¿­¡¬f¯˜óÞá´÷)j§½1| Ô/a*nBÙj?Š®°¢R$)úʳSž\×ã}&t&VR$=9Ñ2ìn¾-¡2Dsƒ“r9•ö £ÚI7ºSBñŒˆµJa!0IAóúÒø0á4¹ÎOBY”YNÈ“ßgCíD @e¢›´Dâ#˜õdû°CŸY§ÄQ´JÈg}íqùÁIy9Ý”¥…~êI{‹Þ9@L¶¥ìVÇÊÇÉDÿ”°ºì㪀ئ)zSc€TùìAïi]Îb–œ“íÅÔŽ3ˆ”÷³„!þ$••8Hn$£ÐÊÖÒLT'¥©ÆdUÝA¨WcT–h °ÒÕ‡LPÉ¢ª‡Y§ïy†û3¯C«»¸(3ë'4úCÀAʘ¯km\ÂTRŒ¼RFk?ß%{âÀz™)ó'½’‚|èlÐBf~–JzóäzÆóÐ'¯#Ô0TêžÈ ¡ëµY³x ekåe\h¤.x<0Å’hëþ¤t)ì; FZ­cÁ¼Iîrèé»[´†xBo±²h ‘úSq{køüÔ¯9¥‰¯ºq¡û–Æ[ƒJ̆ÝîM´ÖB‚ MÙž>¤ª|¾ÇúýH Î7Í÷Í°‘‘¡úó:‹_ß ¥)÷ T€Ù²Ãƒ ŽŽPán -#ÆíßBymª¬ÈHᲶ_!ˆ`fºöøüѽTv /Rk‰úa‚àâ(·ܵ; d¦~™aÅ€æíè!Å”u>ì×›ývxïÒ&(û š:”©ûîÐ[zß,8±GZE¦øöË”è2ÿ›É]åO3FS® t×R¹¢`Wyc”WÔUZ˜T Ú® ]râƒúùÍ©:X¡„;±~òy¯ ®†¤Äéá<:À­¹Ê(0aŒÖ¢”<»I0RUÀfn~ÎÀƒL C®M›òŠ}Ó¼‡kG ™I†zat°˜iÈ­v2ºÖ£’ͨ„ó½œÆ#œ’œË'ÄýAëCrÁ(ïGçʾ‘±Ú )JçÖ&õI@¸ŸX¦ŠD§DÜÍבٕÐ4÷ÁŠ?~û,+ňaÜÚñ"çxu ³ž²DUÆFÀJMIÑJ¹KwP®õ7‡"(ˆ-Áí7+›:hWRT†d\´P-NéŠ!}äÚ&€úÁrŸ%` 1"W‹{vô›àŒè 84¬tÁ]½tÚWuarV_Cq¾)ôÛ4ÃÍŠ§°í ˆÿVu¶¦ç$03f©EÅÆL¦(s0²YPBõ¦dbs¨½Ô7lÞŠêí ÛÞW/2 ò »æÜÜu”vÞ·_SÁØÅÏ?¾Ý±"wo ur»é뻩7‹Ø§³fƒÎ{*ù nªÞðzÕ {`¬½C5*y$1*ßËa•¶ w<['-øXÊeM=œê)Ä“ÁÀC!ü¦¿eóÌ‚œ«#,®]”RMÓ¦'Ú V=»Ä’äÚ¬wyÍm‹ˆ†Ö¹¤•ò swþänP¬cc×zì{ær¢ó@/êim‡ðž¹j[Ot­Ú–ýÐ’Æ’(à°KtÜb¦š±aïÏb‹"ì6FõXãZ‘¾ãŒ*û¾OÒ¸7±ÑNÖƒå‘%÷ÏqÆïSÏɳ³±9>m†‚ûÑ/£¦ ~Kåpƒ?^E›¾DªƒL~åF ©ÞnfY."Ì aãW†DÊ–!*Œ1šÖç"œÖ-W¶½Ô^}K!õ«ÕSûÆÍÊÿ}ê¶È*¬AÝ0„ ð^ô¥%¼Ä³X,î‡ õqÕËŒ;”õk=‹ëö>®I¶õ¾\÷ýê·O¸ù¢¯3!°Äœb {/Q™aq³ò{ÀÀP¤vµ£ƒ3žOR/§•¡gçþ%ûøÇ@G~hÙˆC8Òì0ø :äbvþÒÜâ¹Aý5m5Ùè¡,/Š”®Ål¡òh’0JÞÀf®ÞáÒñw1ZÒDCÛ¦¼PŸáõÛu"*¦ú£6ëTç÷[Ÿ¦D­["g­ð.ö§ú‰ˆTååä]Ï egûì¡•kô•½¼&ߺÎzq×µÈ$Ò²ã7ñ–ïU\vÓ²yú漘ôõ`ñE£òBlÿJT 뼸z³OI\GÓÀ:2 ÑcÕ&e‘„‹¢ÐF¼Ú¹µöÁèŸBOŸ~ow6‚ʺÎjIÖ"±NHôªÛÞÿZ *Çn‚T3%ûmFWñ±¾7>ýåì£?Ò‘r,R±œ@XµG.À¶¶XÌ„Æm‡×(\Œ½  j7m"ñ{šÄýQc­Õ/^vl“±­2þý ñ§³ï"W`»\P߀l[5:Ðwç›1hG–®…[Ä7Ê>ìÖ¯Â&ÒÙØdÀÑBÐ&åx‡Œü—xºRKµæ¡SŽœ³Ws¤âzŽYMãÚU› lÓg„î±Ä#ˆQÔê€+“ÃX}P̳xÀVÖ»ÊìtJ—ï2R Œ9µ7ÿ”2¨f}ú·Mž³c*YßYt$¶Ù˜éaœâ'Ó´»Õ#nµù–ËK]½Bœ0ø0Hï„ÑVG0U‚:Uwq©!JðùÈh¢©üH ¹W`PÜùÀ쬽@ñö$áqJ3ë²ä#g=F-ÝLßC ñ?æ´./5i‚+#ÂgÔ dHÙœ¢‚·Ø±¬’u×^;*oíÔõ9îð¿ÉÿºÑ©#åxç%Nß¡óP Ý”ã gW¶šæ5^@Äøƒu‹ëÂd¬ÛþÂõ÷ vñ#}ÂöËÖ,“E¨8Ñ{)NªR+»B(GCHBÙ+¢3“CÃAc¦Wô`¡RùÓ´çûG–DƒU#òŸH†•Ê¹}z&¡üž½jºŠouªU?-Ÿ³`eÂa>ú´Cúm[([^@w¸Ô_ž“ÐçŠEtä'pejѪ°sÜ#Ž0`'{—×¾RžòP²%¹å¿®¹’p1«ý L‰½BùÀé(ù®Ã”¨²ïùnN”›j»žó*ÌÌ€RïŠtY*(gj³¡r¸`“~Vçb4íÆK¾°Ç©IlÝz/‚yyˆLÙÛìPÏï .Ž–ólÃiqõ±?SÅ°ø/¦>ÜOù֩鮶$WÃñŠ ,+:5Ô427¦7h•wÞ€È3x}7PP1þÐÁÐÅ™N†)§~|Û|íõKÄ= âL†uÏãO|?N©báìMÞ[ëõhV¬¥F’ƒ»á´´iÑÐ+3#ÑÈpÝ;é¹Ûå]îÙšL(19¬ "1àì8@LSÊŽ£×OjåýP'i8!,¶Õ±*^a=#zgç’Q<ÿM\÷Yoí+A—òQ òb#òh¢ŒlålÝ$-neÜ—~UŠ¨€k:_Æç%ÍÓâÚµ[f7l5«®àïú-ïÉùÜ—SB)¨‡ì¤›#}"HɆÒhÓÇÝ7ɤ1"jÙƯþ’6ÿ£‰n@‘eßGqZAÎÂõlº[TåBG°iL/4ÐXÀülvBvl¤9\6žÁäïÖ­ßO08Œ‘_pnFÜiík—k‚Åm2y¦¦» @Â+ÍOÐÞºé–$×FÍR2µ~âŠýµmôÖcßߦ·Èúù“2Áë’Fv-ã79ü®9ÚÂYÒgáÿV? Ó*jhòèY „ÅIi*2>mwLÐ’ñ²óÈžë!ì&la¢/Rÿ}–:Îè§ù"‰·•˜ÚøÅãècÈ>”ÇRu¨'o“#>’Ç›FÄû>y¹½Ì0Fuè ‰‰­¼‹ÖË♚êlÀÛgØPØŠÍy-ˆâ;-£µJ]÷MÅÂaèk€–2_z‘¬þ¬¢#õ—Q]Ò¼}¦Á[ºÑ~;†Å°PÙD°m}qA¾öê5¨ ¡¢”ˆá¨1eî¶J“¥<¶˜Õâ{xþö–%qïö ÚoíÜb´ oY9/¼”6Cx›Û ±vÚ·å ~ã9«c~xßGéA‚*¦ßþÝ®`Æ5#×ó­­/MbÎÞ]­zJ½îT£ôR½‰ßº¨nQi%ýÐã2¡a·~¬a¥uã—g#Ä)Ï{Ð$ˆ=.¸³ñw@§Q‡ºùçùC×â{KK,PÖuYhQiG‡'°š¯$ìı›ÀH:h@Fƒ‡‚3öCÀ0å)߱܌­¦gk­çÔÙ!HÎè½æ-uIãgg@À $i&Rrð( þ¹¾õS¯æÍ岎æpì%ðVå¿o’Re†I½³yÃ>±,<§Žõ²Ê`ø ­éP€×€ò¦Š ã‚«ÛPÈt$Cð[ÌÏ¢àÙ»±qM|Ø^‡.Å;oÅ \`ãÛkš¶‹:˜š$#êâjfôçʃØj—fÅ‚…h´Idæ}%[Bþ‡–Æ­ds&¢£šR¥Ì”=Y>T~“8î /}”Ju­Ë[ƒøfkA‡“ÀU)Î?Ç@‹4[v‡®ù7ö$ëê´›Õjч 2±²ø=×XÔzHQã¦>öx Ï-­t"í>`mn‘/=θ°™­Íûœìmº "0Å2Ñqd†â*ø²}ëQRŒŸA,¾^’Š(|3…N§\˹'+WÔ‡„¼YÔ„áÉKWØÔ$MŸÇ—ÀîJº3Ÿ"žºÏ‚PùULÇ€þð‚jpÐR!ÉNRï¯ÕYàA~,ù0DY­ºîˆIìEgF.–fî–>btÜ—9HÃ%`}¼¯T°×êèíº†úÂæRNPKKw““Ñáp¬Æ€h}ÌÛûÈEúï?pºÈñƒd zùv™(3îžú‡b„,Öãzbèˆ^€ #O ¯g=á¶0çÅaá4›%ß‹dñžÃÄŽz‘ºFƒAvc{ÝmÝfJ’4ä…;ÛüùQ9ZòýzÅÅóN f@Ì‹»šñq_bhÛ!ÓpV¢lËõl3žÝ lp˜†â‡þLþ™ÚFüTZ=\5|È^Ñ£ï‚p6£ä†²6ð~á/5Žíà?jg¸Ô€ÿ¶bŽÂvõÕâK»M Šlßu³½ î5ì rsd•K‹Ùïð™«Þ{QµÿóÕuu^.ãX/íRàŹAd•¿¹å„§ Aˈ-© ½Ërb@Mêƒõ´–jGK?Š‰bz—ã™EYÓ‘šOºO™3e™Ð>ãIi¨k&N[#Gd|Ú“¹EÀÝÓʾÓ8¤Ouò„SSg]hts ®nnýaíÜ^¤å>â±x£ºùRW†ˆ-0&äQNÕ´@HqœBúT¹Œ \ä û`Ñþ±‡ö¯ôË¢[»9Èqµä |óŽr Ø3š û`¶dÅ~™¯Çw¹WßcL jíœÀb†­Ð‘n•‡ÇŠÈwR^s‡Y/«8 c! ý¯«j•Ñ¤–`4‘[/*Œ5#i›8$X)~ÀQvêh$`…LÕ8í‡ò´.l«¨þF¤ 0ŒaÈŒ¾hÂM‰°e“$Pî­leÚ+=%¥ÎQš=Hr™Ê Ù§îa±eûm.àÝùô€2¨Ó›ÌgßUƒ`˜E£Pů€ÊEEíøÇ.}Kl:«+àW«Ó«Ðôˆ½Wžw×9VèþÄ®¶ÖB”XŽs[xyUäºQ6¾ÏL‘AÅÑ@(¢4ÊLÇàY””2³Ž ÉwN¼ÑÆ„\îtÞŸÀé#N–¤«[•L1…È!pÞX×Ø&䛊'$8Gr{»dúTÍÇ´7+hÈå™9£õ=ÒÄ`n`š¦Œ 1½L¥Áª¾4情&#<@¶V4¹Eÿ( ´›†ü!·¢X<©‡¨÷È7ÌŸåY-+Ø)ûÝ>S} <~2a×ÎówÁ„~OËææïÃÌ­ª°° «¸7àÌý<÷t>4} ²æ9"ë§.&ˆb¼µÖä’%/Oãùz•Rüº.loäa©AöËvs ”lì3Ñ°~ÕöÛ¥1f¶¦ü¼„AŠ¼?k˜§‹;§|5 ·ÀDÓa Æ€áf~*†Æywþ’ÔòÊo°kd ãj‡½f£]Iµó¶ wg.È÷nŠCÍ£Gr¾¡á"ïñ¤íV*8)B/ÌŸeÇfJ±ÈÇP{„ßùwV Øpº |•6bYÞ²¾|ŒYÞD@¬Ä+7iN&†ò›|±üÇxñcØ?2/§¨§!Žfø c’ŸþZev-—Ï»¶!äe/è#÷¢xV¤ý¾oPìݦyÂR×®¶ >~/Õ‹wµ@óæ{d—Ýñ·›¬·„úeÌ]Í xº§Ê"ŠF¼Ay(yÔüê'T2^nöØÕԩ¯ø嶼Dûٹل˩Iéq7U%LþAÖZþN3À¨n·Ù4ŠÉL_³1Ûsèæ}ˆŽº üÞ‚[¿’@g•¯ƒXÄaf MÉHRýVé/Ï_"Ñ£´ê/ígk¸ÎždCöæ»30Íþ³Î¥ÊgÚm:ѹmiÙlk…Ùã–JÖ$>È-‚ W}“!P‘ù=Ù¨YæiUŽõ ð×Ç;Ä̓"—ÒEøGk©“6V/UlhßE21¿î·©›CNïUƒ¢Å4%ÛïÑêUâf]Àl÷êrÉZ»x¤™¶~CÃðp5yzTÚyÔ’õNÎNB:Ê°è@Ì!|R1w±Þ-ŒNè)Š¥…ˆ$ph>#!†³B#&o2ªÂ‘3 ßH%=‡›#c]«fȘœeT!õ”’'´üÀTÍ!ò ~4Y*UÎ}…K“QÁ ?¾[PUÇ--|?+Dý˜r’™¦Ð©À~€ÖAkan·Èd´ÇGšªT¾gÛå(Á[.6ZêüáŒmw?WxéZQüœÁ‹9@¸NÃfå7½qž•+Çxab«žÞNƒ 4"ò 6 )bŠ„ìRö<‡Ž‘á·Ûc|{¿4Ý>ëmkãwc¾ê]ƒsÐx}‰ß!)AôMÇKdv6dz?ŸY²†ÇFZsI)ïòFà(â.fùwRþ ºª \b´DŸ‚µ€wÀ3a¯˜ÿ)ì xàv;žíW/À¦3ƒà³ Š“|·–íÙ? j4Ìã}aÛb# (v¶´,³a7ÓãÕ´‹ÜcÁEöä²3=FûÑÀtÌ++ÃýÆ;SaTë9HŒà/z2‰‚Â8Öœ}t»Ëó†ãÛ'ʇðÃnê LËÐ< ¹fF/¨„^ỽ“ú@8ØN,ÚBO~¹ª¥PY>JÃoüÆâBNŠ_“íRƒ–ˆŒ@ÿþM3“n "Nñ°¤¾qQ'dÂ5ûÀŒœ=&9Tªà]= ‡õóü—m]"g‚ÖG–FCÔ…ëÖoUüBçMå7ü}¬JFnO@û4okP»•M$mpP²Á‘¡€Ù5þ2؈‰²0½íû[ãþFœ _ÐÝ|<ÛçvÀ%M­cË-„ŽmÉ_>à ”s—1צ"Z¿kÀ¾æŽPß=©PQȳUò{Ùþ~k;ä7B³GÁéÒ¦ôª1,òDìxÞ‡,8z )Ï¥E<ßÕ¿*WXMÊÜØW¡Q.Gëp™·5ÒãïƵ`oÔ(Ê*ewëí Þøuaya I¶‰göâÝaò3x«!Øà9†/éÚG¼ƒ¬9”2vÜàÅ‚;ÎgêŽä¶}úw[QL¼Z7qEÙÅeP?¿Nžer1ÛǬ–e”JÑó4áÓ©Žë¡3jÅq‰*NöK‚ʹ½+rI¹F…]gV ¿e¦¹|8³à)Èçffÿ¦-vøŒôî ÆøB“oi‚u¹î^¥£Ü2ź×c—8—×fQ:Ú ÛÁëÚ©XîöÆëU¦¸R²6\ãÄv°-›ÁÇI2—¯h‡matæÉ%ÉpV*¸AøÔ=ç[µ¾Œ¹´ÉÒõÕÁb]S(4Ô—çæúñÓZ”w !õ,äÜ×V}Y–/},Y,,`Å`sºàû˜¼UÚÄšýV5m˜åO×d5>Ù1Càì`p²wìäæi¼…:ƒ.¯ÅÛM®±“¯²úx8²ßÒàDÐÿ‘ªÊôØ?ÜóEHtoP{ŸÝrS?`ãø©Ì“û˜d#êU`Iàou1/ŸŒ|y‡Ÿ„áâ„!é;Ý wä#'2×£ü&)•3Ü@sÌB€\å&ß®¡J™¶Û¼aËL ©_´sŒ)Ü«žîdšÊ$vå|˜ØºñC• DEp/&âX ¤:RN ‚hªØ6€‰7é×”m&/ØVu>5-k‹°Ò÷IíÛmȦ4ÝQE°Ìûå¹]Ž{ ;í0…;…µù×ò¢þ¬‚:iRÓ‰‰{A¤Lp@ ËÇpcÌ y—c@pû<Úw¤S&”a¼°h†”ìåÄz;ßlRfœºoŠl %fÚbl¤+K[_q´º–S;1æØÌ"|œ¿m]pñb¬_y½<æ¢è"‚ÂÇ ìHˆ’V‰nX)‡™Q–vXU“‰™šzS»îA›3—œìH¼—B…”Ð4ë=BŸ×—Óß:¬Û¼‡¯\Ùÿi¢Z˜.ON14Sˆs0õmɶGò'®QÞ”6DMH†<=£»"'åJå<ôÄ~üö‚”f´kÞr®½ G8»Tâ,\Îh<We˜ÎÏ8¸õÜéhˆ_át! ”¦:ªÎ²DD‘>Òw¬îÎNCƒbŒó˜»úý|th]“Óÿ(þñ¦6rœA¯GNÖv*·.›¹FÔGf”EPËlð1gJЪÝ%ÌÙø¸’NR†¥¢$¨>* cÕ)ÊÄÞô$ãã¸Ô¸m0a£°Òg »B^Ò Ç,¶mpÝ·+Cq©ÿóÆžÆùi‰Ýþõµ<á“ôEÀÁ‰á)OB£»ýYl:Ëçcž[ 08&tm1æÜNÊ$Bú—é?1OÁm"÷"ßå‚Sï›gŸ©è^Ãx¬Á!»qliOGHãÿø{Éxšá¤ãéã'u ýN:˜ˆXr¾ATázBúØZ£{Ü<SµP¦4*×?ÕKU±4ËÖ·²z«½þ¾Õ.#ûŒ-…ò 3!_—¤“Ûª÷;Ñ]mì\Ì{møÁðàã+v|ÜZž²÷Ëxëã Ã9aÝß 2,•À«ÙïkH‘ Å+»Ù Šýž[}Ÿ»TsKö6’Í=(‘1(—ï´…á÷·íbðÜÒOˆ9È6b¢»]ò. 1ÀðÊGdÝÝy8¾„Õ—>UÄ­üå"½#­5Uå}>|Ìœ¾¤Dêí­!ª!y®r ©vÿf5,·>DX:[•‡* ‹2hµQ¿KŠ¿mõ+ƃ·º˜ã[ï ”`{¯ÞTÛWÄ›٠á+ƒ;x¬J¼+_ê³’ ¯+$qa×<#C¥â,æ#4|I°h¹~›Y™£Cü†Ú(V qgpÌD “ðX®X6r©© e«½wÙ^É!Þïqt]°þãÏGÍ,†l`µ$Oõi¶÷»æÔ¦®˜¦ÕŽ%+CÖ"Å b{ŒŠŸ2%‰a1ƒ»i-pžw¶Î –&}Ž¿• 5O0õâuï üç@m… 8#æèxü£†ý˛ʯá9ˆ|âyJ·)+dA eV_P $Ž™ü•ñA»ª˜ð€û0£ž£ˆÏÆðÝñn…–¬Ì# O_Õ»Ð:JbùWÜ‘)ã00t\õ.XÂÅeÍPÐäO!´uÌ!8agY9²&fDU©!™vÒ¬Jäň†#ÿæ_Š„.¨LýÒÜÙ£ ®Ý¡y ˆ;êSâæ|Õ–¶Lÿñ2þž¨’QgNz¼–dgUÔ©Ùô¶„CðC9«B©—1 qøpNÔ®ò˜>‰W”ÉÎÄêþZ‚­3øÌ)}i¯àeÞïHùzˆj÷Ry„l”P¼ÿB½õkÆPfˆ™ Ï¶æšß-þ+*÷>œ¤LAjßËS®”Œ¾[›L'C†ðüñûMˆýtG…ûAˆFÁܹ§Ü…=cÏ¿È€¢åîý–™ä~ =VL½€dF¯0âÑï,äöÐzm儻x¨Wó:y$G"f⟓¿HÌ <@N†þ$bí—õug5p¡àêLùB8„É©fžä«j°Ûþ“zk'ÿ/ûfÀúðÚ.iÜä¬Ê ]6˜¥/÷Ó ÛÜÉpà‹ &4s3†¦ / ÷÷騭Å#éÂS¬è|¬gË¡:룣¹{ †´Æ¾›…ì·«UÕußcžú;ø¬á¶ÉàkJ­š‡á¦A®†ÐÖË{,Ï£îÂÛÙiMÿÛ%nrFÑË™¸€Èãb°ð>0 ‹YZbayesm/data/camera.RData0000644000176000001440000050402613114117322014642 0ustar ripleyusers‹ìý=¯$K“ ‰[UwÀm€ÄK™àO¨p7ÿˆY)P Wµ1ÛÜéì»äêü%¤L™ÿ„2E‚Ê #6¸<÷}+O<'OÙëæîæ‘yÊS•‘anfá_áOšûù¿þ·áïþíß½¼¼|yùúõ·—/__?~ûòúÏo/ß^þ³×ÿÿÕ¿ûûÿÿÍß¿~úÏ_¿üß½ þç‘~yùŸ¾þýë¿Hþõø¿oøüþþ8þŠïoǯŸ¿üÏ^?þ/_~¤ó?¼˜’·œUϨ¾Gùéeçj}Ö|«ååU/£z4ùÝå¸z½ÞíÀ;ÿ¬>¯úÐò]ÕÿewUïl;^m»Úó³´»U½ÞþÚ-Çg{~x–ûdz·«ÑóÚÿVy«=«ÚùÑcïçœ^-·ÙrîÙÓ’·=«¾ž¼5Ͷ«žÙv¥%ïrœ½^MŸõ|¯VíZÓîç«GÕoOÿ츱˯QùÙñ —oö~´{Ü]í¯Þõ»ÚFí¬Ö‡W9õÒìøîUn^r³íE“»º~­~yéë}ï=îŽÊõÒìýʪÏ*¯žÿÿüoþÿóÿþÿý¿ý/¾üþ¿ÿ×ÿÅÿó¿ü7ÿéÿöÿú»ÿÕø¿ÿÿê¯Ç7¹ÛñMþßüË_ånòor_lr·ïozïíß˽é»ù÷EñïÎ޽ܽ_÷ö?ø×)—·òûWïånùµëЮ÷CyÜùûá:îüÒäîϸÞÿôsÿ>\ï]¹Þëÿ ×±{G]¾þWÿøï_þŠXþøúÿú÷ww"ÿ“W‘úûÿÿñå/hæ/rÿú/²=ÿÛqûnâíƒÜ>¤Û‡|ûPnêíCûñáËñýíÓñö)¼}ŠoŸäíSzû”ß>•·OõíÓ›ðf#¼Ùo6›ðf#¼Ùo6›ðf#¼Ùˆo6â›øf#¾Ùˆo6â›øf#¾Ùˆo6â› y³!o6ä͆¼Ù7òfCÞlÈ› y³!o6Ò›ôf#½ÙHo6Ò›ôf#½ÙHo6Ò›ôf#¿ÙÈo6ò›üf#¿ÙÈo6ò›üf#¿ÙÈo6Ê›òf£¼Ù(o6Ê›òf£¼Ù(o6Ê›òf£~ÿÑ=þîÇ7¿ÿ»¿ÿ§þ§ßþã?ÿÓÿåvâŸþñÿôvâ?ûÿOÿzòÿÝ ‡þ‡ü?ÿÃýoùþûþçË÷ßþãõÿ|ûÿÝ?þ·ÿð_ßÄþ»ü?þãMì?ü7ÿøïþáõÃÿï®?ÿ~ëÌÿú/ùGç»yõÛ¿ýk–ôõŠúû¿Dõ/çÿ8þöãÿ{ ¹¿Ò×]o©÷Éûé÷*»«öwÛóÒ»ëíd4ín=}W_ïhþ«å®n‡Ö|WÞoMnç^òºxÍ:Xíí–Û5^^Ý~¼úçl}­úwõ}Þ;ÿ¨þÙÙ#«þÑ|^ýÜkvotöjtÖÍzŸÙýëmo¶¼fg={ò½´j×»Þv¿ïX¯Ãë}Á«¾žå:f5}«ãߨüj¿»?ž½No?zö{çw=¯Þ—VÛUOΪgtü³Ú]7wÕo/y•_/ŸfÏKnµ½ÍÊyšÞÕ4ZoV=«v½ŸãvÝ»r÷4ÒJ{TõŽÞ}3R_âªôõ^^£–ŠŸ]}÷×a¤Œ覕–ö®W¡¦£r÷4ZµûEѧQé»ör/÷Wóå|ù)rýúÇ÷?C¬_ꯪo¼ª¾ñªúƫꯪo¼ª¾ñªúÆ«ê¯joL¬½åmoyÛ[Þö–·Ýò~=¾Ëù1óù±œëùñÔð†z_?çÇp~ŒçÇÓÚqZ;NkÇií8­§µpZ §µpZ §µpj§†xjˆ§†xjˆ§†xúOãéo<ý•SƒœäÔ §954œþÊéo:ýM§¿é´–Nké´–Nké´–Nké´–Nkù´–Okù´–Okù´–Okù´–Okù´–Okå´VNkå´VNkå´VNk%¿<'¬ýö?¾ŒòÙ?ì×—32öÆ^ûñÝíûÛÿ¿½Ï3ÆgWŸ¿Wå¬É[߬ÞÝå´»|u½»ž¯½ä½æ—®îGÏÞ^þ,ãÀ¬¯y/}=¹ÕyÙó»å¥ï*¿žíþülãŽ5ߣÊÑûþè5¨Éïggç‡G祼Ê}Õ?M_ïû]ãËê|©Unt~oy^Pù^óË«üzò£ùõÜ5Zþ={³õÛK£ãÁ¬Qû»Û•µœ½ì¬Öûª]¯öÒócvÜõn÷»êÇ«öìhÇ£v½ûïêx:›oµ¿ôìÌ>ͦÑçÑç¦Ñþ´j÷þ{MOÏNïûÙzðî/Wµ-ªóÆ×>È)QžšÜ}å¨ÝüSã‹wzzÑV>ÛãÑ8ô]Ôê›Ý»ü½òû;5¬E»Z¹u/ª¹Û:|{’Ï~=Ê «Ê «Ê «ê «ê «ê «ê «ê «ê «ê‰Æêi­žÖêi­ÖÚi­ÖÚi­ÖÚi­ÖÚi­ÖN ¾??çÇp~ŒçG9?¦óc>?–óc=?žÖNdNdNdNdNdNdNdNdNdNdNdNdNdNdÂi-œÖÂi-œÖN¼N¼N¼N¼N¼N¼N¼N¼N¼N¼âi-žÖä´&§µE‡E‡E‡E‡E‡E‡E‡E‡E‡E‡E‡E‡E‡E‡ô‰píøõ+þ¾üä;†Ðþö^îç¸Öëqu×c쨜Wòž6¹*Ÿ×c÷£íx×ïèk¨×´ÊU¯ƒ«zv×»÷´ÊìùÙä]>»Ç//»ÞÓ|V{^åx•žÕûâîiÙ´«|W§)¬éQ÷ñ«Û¯5ßê´–U~µ¾GÏ_UWÙ·NcöòÊúu¾÷ýh»ÕçÝn¬þ¬^ÿj9­¾Gy¿‡yÉíjÇÖ|£ö­ÇZònÞÏÖ´ZÎVvÝï½îo³ãßèø¼Z.»î׫ý@³çUoVýZZ/¼Ëyõ~·Z£ß{·MÎ{Üœ¯WÇ«Æ?/?¬v8¬UÁ“ô)øtXŸq1^ëbÁ.íâ_eqdmÑæØT SUõ)XYÓ§…µš[6–‹uQëY\+'ö“ûɉýäÄ~rb?9±ŸœØONì''ö“ûɉýäÄ~rb?9±ŸœØONì''ö“ûɉýäÄ~rb?9±ŸœØONì''ö“ûɉýäÄ~rb?9±ŸœØONì''ö“ûɉýäÄ~rb?9±ŸœØOÎT9#PåŒ@•3UÎT9#PåŒ@•3UÎT9#PåŒ@•3UÎT9#PåŒ@•3UÎT9#PåŒ@•3UÊiíü œ?!ó'rþ„@ΟÈù9B çOäü œ?!ó'rþ„@ΟÈù9B çOäü œ?!ó'Ò>®½¬åÞ³·ão/ï.¢lßï={Ÿ¼_Ïfóy¿Æ\¥g×k³—üì´ÏÕç{i÷ëǪ^/9MÞ«½?ÊΨ~ïi‡Y½—Fõ­^¯wÿxôëå¬Þ]ù¬z¼§õv—û®üWë¿ê>é­o÷xjÕë5¶kúòQrÞÓu»ÆéÞôœw}ŒÚÝuÐô­¶‡Ùò·–ˬ_£i¶|WËg4ß®v0{<úý측%ïñª—Ôî¬>¯ñJË×ógUÏlý>ªß_ýü¸ÚþFÓè}fTΪÇû¹iö:fÇ+-Í>çXõõ伞Ã4½£~hç{ù½ô­Úµêù_‹úìá7 _*8wvUãQ×›ü3¯BlÄÉÖ(×ÖÕVþÅl•ŒvîâieoÚûë˜Æµgªœ¨rF ¦35¨éŒ@Mgj:#PÓšÎÔtF ¦35¨éŒ@Mgj:#PÓšÎÔtF ¦35(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:(:}¦Åï£fï;¾ßœöݹß4\»z»÷–÷~-˜Õ{ÕãÐl¾«^cf“w}xéõn§ÏRO»Ê{·oùGû§åÛ]¿Wëééóz-µß“{´ß^úwÙÝ5>¯ÚÝÕ~=nx·Gïü^Ó˜Ö4;~zOOöôŒæ×ôõôxO;Žê›­W«>ótߢ¾ž~íüìýÃ{x×õÎú9›v?Vý£ålM«ã_¯}[åµãQ;^ï?^÷…^ZmdzãËèuÎÚí%¯ç¯v0{ßšõc4Ÿ–VÇž>ï~ÚÓ³jתo4ÿ¬þÙü£íx´ŸŽÊYÏkrÏ6õäÍѵÖMYÌù7£\µÅ~?È7ɵ.6l^ÔY±§E¹j‹ëlòj^ ùO÷°®«–_'zº·ñ,®Mg$c:#ÓɘÎHÆtF2¦3’1‘ŒéŒdLg$c:#ÓɘÎHÆtF2¦3’1‘ŒéŒdLg$c:#ÓɘÎÅÓ‰¢Ó‰¢Ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢ó‰¢óg[ ùöÇÛ»E_n¸_ÞËáZ-yMƒÌÚ_MW?FzO¦]ÝÞþZ~»^k¬v¼’÷küªœW¾Q}»^߯ңå[¦ÐôŽêó¶k=¿Ë¿«ûÅ£Æ{¯ó»íßËín/£zwÙßÕG§[Gí_ÕžG§É¬z¬ù4=£þÍN›Ö—foô¼f¿wÞÚ^¼ÆûÙç¹G=¿yõçÑú¸ªö¾÷êÏZ~/}šþÙ4[==³~hÇ’Ûuý^÷ó]õvÕým¶ÿZõxõßQ»=ýÖtÕóƨþ«ÆÏQ}«íHË·«¼¯êš¾ÕûÖîqoºñêpthg‘^Íž¥ÙÈ tó¯ƒW{Ñ¿jùÝcN ;kxUYTúÃuô¢˜ÑÄ£ø\Ó{¶À9\[O4VO4VO4VO4VO4VO4VO4VO4VO4VO4VO4VO4VO4VO4VO4VO4VO4VO4VO4VÏ(ÍzFiÖ3J³žQšõŒÒ¬g”f=£4ë¥YÏ(ÍzFiÖ3J³žQšõŒÒ¬g”f=£4ë¥YÏ(ÍzFiÖ3J³žQšõ\0¸ž˜½ž˜½ž˜½ž˜½ž˜½ž˜½ž˜½ž˜½ž˜½ž˜½ž˜½ž˜½ž˜½ž˜½ž˜½ž˜½ž˜½ž˜½ž˜½ž˜½ž˜½ž˜½ž˜½˜½˜½˜½˜½˜½˜½˜½˜½˜½˜½˜½˜½˜½˜½˜½˜½Ÿ×Þ¢g¿áQ¶DµŒÄ5-†¬¥G½îÌ&¯ÇÐU;^¯Ç^ò«×;›¼Ëiw½íÒëý:=«÷ê´Z.»¦!zß?Ëx å¶~ï=í±jÏ{ü}ÿ¼ì[åží:®º_xOKÚëÉy—ûê¸i-çÑi¶Ñé+kþ^ºjÚÌ{Ü­‡Þ÷«~϶ƒÑ~ëuŸ³Ö¯w»º?oÍ¿Ú?¼ì¯ê³&ïv»«¯úãUλک5ïûÙë-g«^íüªŸ«r«÷KkÚ}½ï{~Ìֻ׸»êGOΫý¬ŽC£þôìZõíª_kÚegô9ÑË]÷#kº—×𫺘o'ºVÕ÷Ÿ~ŽMÝC6bI뢿*þíàÕÞ·f «DÏÞë³FE÷C6GÿZq·‚Ù½¢kÛ¥ÙÎ(ÍvFi¶3J³QšíŒÒlg”f;£4Û¥ÙÎ(ÍvFi¶3J³QšíŒÒlg”f;£4Û¥ÙÎ(ÍvFi¶3J³(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º(º}¦½k‰cox–ûÔåþv'ÿ¶ò®×¯×:ï×ìÙä=ÍpU¾Ý«WÙñ®_ïi+«~ïéƒ]zv×»Wÿ½ºÝŒê½ªÿ̦ÝÓY«v½ûéUzVï‹^ÓH£v{iWùzOsÍÊ?zºÚ¿]Ó¾½iÙé7ëù«êã*û³ÓfšÜîö0;ý7šV§½ß#FËe´x·ÇY¼ìŒÊíjÇÖ|£ö­ÇZònÞÏÖ´ZÎVvÝï½îo³ãßèø¼Z.»î׫ý@³çUoVýZZ/¼Ëyõ~·Z£ß{·MÎ{Üœ¯WÇ«Æ?/?Ì‹!ñªy/W%*tuoØ{ÿ?ìÁk\„Ù¼®u/WmáÉhÝ·ï;ØT‹î-®|ïçh¹håë]ûíøþÊøÇçŠÏííóñÍøÇçŸ>G||Nøœñ¹àsÅgØ=`÷€ÝvØ=`÷€ÝvØ=`÷€Ý»vìØ °`7Àn€Ý»v#ìFØ°a7Ân„Ý»v#ìFØØØØØØØØØØØM°›`7Án‚Ý» vì&ØM°›`7Ãn†Ý »v3ìfØÍ°›a7Ãn†Ý»v ìØ-°[`÷³í…ËE“²€òÛçß_Þ£`uqå[ò~Ý›ÍçýZt•ž]¯á^ò³ÓHWŸï¥Ý¯3«z½ä4y¯öþ(;£ú½§1fõ>j\Õ·z½Þýãѯ«³zwå³êñž&Ü]î»ò_­ÿªû¤·¾Ýã©U¯×ôÚ®éÐGÉyOÿí§{Ó}Þõ1jw×}@Ó·ÚfËßZ.³~¦Ùò]-ŸÑ|»ÚÁìñè÷³ã†–¼Ç«^þQ»³ú¼Æ+-_ÏŸU=³õû¨~õóãjûM£÷™Q9«ïç¦Ù똯´4ûœcÕדózÓôŽú¡ïå÷Ò·jתçC~«vqž†C<üAÎ;Zw“½Å†Gñ´yqê&ÖöÊíEÿªrÆ(f3íz§ñïQ€­ °U¶ªÀVت[U`« lU­*pY…Ý »vì6Øm°Û`·ÁnƒÝ»ÀÝp÷Ü€»pwîÀݸ;wàîÜ€»pwîÀݸ;wàîÜ€»pwîÀݸ;wàîÜ€»pwîÀݸ;wàîÜ€»pwîÀݸ;_àë|€¯ðuêÿÀ×ø:_àë|€¯ðu¾À×ø:_àë¿¿|üûååçø—ÑÀ÷ÇØw÷=þõ~¬~ôëÆ£üô²sµ>k>ïé“Y¹«_ãwË{O‡Y“WûÕçUZ¾«§ ®¶»ªw¶¯¶Ç]íùYÚݪ^oÿFíΖã³=?<ËýãÙÛÕèùÝÓ‰½4ZÖcïçœ^ºjÚ¶gOKÞöF§wwß—g§1G§‹½ïK^ým´¿[Ï÷ÊaÕ®5í~¾zTýöôÏŽ»ü•ŸzùfïG»ÇÝÕþê]¿«íaÔÎj}x•S/ÍŽï^åæ%7Û^4¹«ë×ê——¾Þ÷Þãî¨\/ÍÞ¯¬ú¬òêy7jQ³½hNkÔ§†59mO×þ÷íáKó"ÌÖ½‰­ÑµÆèd s÷WÖ¢¬­ÑÓöÔU~.`]Ü{ÿD?D?D?D?D?D?D?D?D?D?D?D?D?D?D?†»À×ø:_àë|€¯ðu¾À×ø:_àë|€¯ðu¾À×ø:_àë|€¯ðu¾ŽÀ×ø:_Gàë|¯#ðu¾ŽÀ×ø:_Gàë|¯#ðu¾ŽÀ×ø:_Gàë|¯#ðu¾ŽÀ×ø:_Gàë|¯#ðu¾ŽÀ×ø:_Gàëˆhíˆhíˆhí(ß_> þ½íÍ˨ÞßñÇ}zoˆøÛyþç‹?kÉû¶?*gM»_û½åW_?¼ô¯æs{üæìÏÚ•÷z¼º={{ù³Œ³vVëgv:¬—¼§K¼üÚ%ÿ(}Wùõl÷çgw¬ù=M×;¿[Ÿuúj÷8;:h~ÓòYý•›-7¯iÒÙñevÚvÔë´íè´²µ>´4ÛÎWëyÔ?kòzî-ÿž½Ùúí¥Ññ`ÖΨýÝíÊZÎ^vVë}Õ®W{éù1;îz·û]õãÕN{v´ãQ»Þýwu<Í·Ú_zvfŸ‹fÓèóÎèsÓhZµ{ÿ½¦§g§÷ýl=x÷—«Ú‹yïZã"Ñî[6½ÇÏÝ=i;ø²'§-ª¬áîx\ÃØ fUíöôu°¸†sUœ¬`ï{»½ú°þ\`ÿ p™— p™— p™— p™— p™— p™— p™— p™— p™— p™`qcAt¨ :T*ˆD‡ ¢CÑ¡‚èPAt¨ :Tí)ˆöD{ ¢=Ñžü.Àïü.Àïü.Àéœ.Àéœ.ÀéR¨þ§ pº§ pº§ pº§ pº§ pº§ pº§ pº§ pº§ pº§ pzNOÀé 8=§'àôœž€ÓpzOÀã x<§ã-þÌEž‰yohøn?à_øw§ÞÝå´»|u½«¯›£öFåW_7Õž½½üYÆY;«õ3ûÚßK£íÙû:zéªþ»[ßU~=ÛýùÙÆk¾G•£÷ýÑkzX“ß=ÎZ§ÿV§9½Ê}Õ?M_ïû]ã‹×ôçhþÙiàÑiÜ]Ï£«õ¼:;jg4–ÏÞlýöÒèx0kgÔþîve-g/;«õ¾j׫½ôü˜w½Ûý®úñj§=;Úñ¨]ïþ»:žÎæ[í/=;³ÏE³iôygô¹i´?­Ú½ÿ^ÓÓ³Óû~¶¼ûËUíåþý…)÷§Á¿‹ÓV,N[++pe®¬À•¸²boÕŠèâŠèâŠèâ ¼Y7+ðfÞ¬À›ÑÅÑÅÑÅÑÅ8´‡VàÐ ZC+ph­À¡8´‡VàÐ ZC+ph­À¡8´‡VàÐ ZC+ph­À¡8´‡VàÐÚ€CphmÀ¡ 8´‡6àІèâ†èâ†èâ†èâ|Ú€Oði>mˆ.nˆ.nˆ.nˆ.nˆ.nø¹DÃÏ%~.Ñðs‰†ŸK4ü\¢áç ?—hø¹DÃÏ%~.ÑbxùTø÷†w¿¾¼ßï—ûþý~=eÇðïjZ}L÷~œ¿ê1·=/½Þõ³ûõ˻ݎ¾æîz]z6¹«Û¡5ßÕãÆ®i³Q?V“w==êþáýZܳ·[n×xyuûñꟳõå5;›TÿìôšUÿh>¯~~õôiOŸõú¼§­òÞöfËk×ôd/­Ú½jº¸—¼¯Ãë}Á«¾žå:f5}«ãߨüj¿»?ž½No?zö{çw=¯Þ—VÛUOΪgtü³Ú]7wÕo/y•_/ŸfÏKnµ½ÍÊyšÞÕ4ZoV=«v½ŸãvÝ»rFìgÅ¡Vý g\tÚºG°Š˜sv±kë"ÇÚÞÊÝÅš{×kÄÉ=¹XW³{¿'±¶øó}ýþËÏëwÿ6DÛ6DÛ6DÛ6DÛ6DÛ6DÛ6DÛ6DÛ6DÛ6DÛ6DÛ6DÛ6DÛ6DÛ6DÛ6DÛ6DÛ6DÛ6ìÅÚ°kînÀÝ ¸»w7àîÜÝ€»pwînÀÝ ¸»w7àîÜÝ€»pwînÀÝ ¸»w7àîÜÝ€»pwînÀÝ ¸»w7àîÜÝ€»pwînÀÝ ¸»w7àîÜÝ€»pwînÀÝ ¸»w7àîÜÝNܾŸ¸ûõóÏŸ#> >'|Îø\ð¹â3ì°{ÀîñÉðï ç2ˆ÷íÿÛBÐß^îöþÝõºé5}áýø2›®ž®ñÊ·Z.Ïbg×kÛ®ë»z:Ç[Ïîz÷ê¿W·›Q½WõŸÙ´{ºkÕ®w?½JÏê}quškÖn/í*ßÝÓMVùGCWû·:i•_­ïÑóWÕÇUö{ÓiÖú•õëþ|ï{¯iUMŸw»±ú³zý«å´úõ¨éæÑrðjÇÖ|£ö­ÇZònÞÏÖ´ZÎVvÝï½îo³ãßèø¼Z.»î׫ý@³çUoVýZZ/¼Ëyõ~·Z£ß{·MÎ{Üœ¯WÇ«Æ?/?¬Q½ÃV‰vý O‰ÆÖgֺܻ·®}Û&VöÖö8þ€¬ªêS°¼¦Oð潉åbÝzÿ†ï°Õlu[ÀV°Õlu[`«l€ËìØ °`7Àn€Ý»v#ìFØ°a7Ân„Ý»v#ì ì ì ì ì ì ì ì ì ì ì&ØM°›`7Án‚Ý» vì&ØM°›a7Ãn†Ý »v3ìfØÍ°›a7ÃnÝ»v ìØ-°[`·ÀnÝ»v+ìVØ­°[a·Ân…Ý »v+ì6Øm°Û>þåÞ¿Ä¿\ð™‘Áü{ÿ/ƒiubTÎûµÓ*·ëul4yû¡É­–³5y—ÓîzÛ¥×ûufVïÕÉëuÖjÇëµîYÆ-ÿ³õ{ïé¦U{»_‹gõÌêM^ã³]ÇU÷‹]Ós«ùV§wgí{MKÍNÃY§w­ù{iÔî¬}ïqs×t¬5ÿ¨Õ똕ïé±úçÕ®îÏ[ó¯ö/û«ú¬É»ÝîjÇ«þx•ó®vjÍßû~öúFËÙªW;¿êçªÜêýÒšv_gïûž³õî5î®úÑ“ój?«ãШ?=»V}»êךvÙ}Nôòc×ýÈšîåµE»‹0k¸VÓ§Dÿöpò,ÙÁ¡f<Ý[ìZ‰JþP.JÔ´†‰µÅ¢?ØUw¾××Åö ž¶.²Ý‹NÖ°³5Jxÿàͼ€7ðfÞ À›x3oàͼ€7ðfÞ À›x3oàͼ€7ðfÞ À›x3oàͼ€7ðfÞ À›x3oàͼ€7ðfÞ À›x3oàͼ€7ðfÞ À›x3oàͼ€7ðfÞ À›x34Øm°Û`·ÁnƒÝ» vÍÍÍÍÍÍÍÍÍÍÍÍÍØÅÏ1"~ŽñsŒø™öþýãóï/÷ýý Ç¿ÿäÏ„w?6Yóy¿¾]¥gõõÜËŽ&ï=ý³ë|/yµ—«¦¥¼õzµ÷GÙÕ¿kãª~畼§zù½ú‡W»òšž°êݕϪÇëu»÷ýn}ÞþìÖÕ}Ò[ßîñÔªwuüðÒ£é{”œun´\¼ÛÅêôŸw¹Xí{?¿xµ‡Ùò·–ˬ_£i¶|WËg4ß®v0{<úý측%ïñª—Ôî¬>¯ñJË×ógUÏlý>ªß_ýü¸ÚþFÓè}fTΪÇû¹iö:fÇ+-Í>çXõõ伞Ã4½£~hç{ù½ô­Úµêù_Û“¶Í©aN¿Î.êlÆ¿›ü3/ÂlŒN¶îÁÛ‹ÖUîádUnp/æn´ó]}h×;#¢y#¢y#¢y#¢y#¢y#¢y#¢y#¢y#¢y#¢y#¢s#¢s#¢s#¢s#¢sc¤ø|¯#ðu¾ŽÀ×ø:_Gàë|¯#ðu¾ŽÀ×ø:_Gàë|¯#ðu¾ŽÀ×ø:_Gàë|¯#ðu¾ŽÀ×ø:_Gàë|£#ptŽŽÀÑ8:/Gàå¼—#ðr.ŽÀŸ8Gàbþà_þà_þà\Îà\Îà\9¨þ ? ú£¼öÇO…o¨÷†u±¸ó‡E ùÉÞ¿/wiõñÁ[Þû5cVïUW³ù®z-šMÞõá¥×»>K=í*ïÝv¼åퟖowý^­§§Ïë5wÖ~OîÑ~{éßew×ø¼jwWû}ô¸áݽó{M‹ZÓìøé=ÝÙÓ3š_Ó×Óã=9ªo¶^­úÌÓ‡‹úzúµó³÷ïiå]×;ëçlÚ5þXõ–³5­Ž½öm•×ŽGíx½ÿxÝziµÏŽ/£×9k·—¼žg¼ÚÁì}kÖÑ|ZZ?zú¼ûiOϪ]«¾Ñü³úgó¶ãÑ~:*g=¯É=Û8Ô“7/þlÝãVÁ¦ð¥qæ8W[äظç°jW‘ûÝ«àÕÞõš£k•ëì-ÖÜÅç(f-Z·[oŽ{QÑÓø7%DK&DKfDKfDKfಠ\–Ë2pY.ˈ–̈–̈–̈–ÌÀkx-¯eൠ¼–×2ðu¾ÎÀ×ø:_ç@=ð‹Qg,F±uîÎÀݸ;wgàî Ü»3pwîÎÀݸ;wgàî Ü»3pwîÎÀݸ;wgàî Ü»3pwîÎÀݸ;wgàî Ü»3pwîÎÀݸ;_gD_gD_gD_gD_çB=ð¸;wgàîŒèëŒèëŒèëŒèë\ååÓàß?°.#zDõþ4â—ïðïîׯ[ÚõX¹ÛîªýÝö¼ôz×w}ÍÊÚ½úzGó_-wu;´æ»zܘ}ݱê_•³¦]Ó»Ól›f|Ö~v•ÞU{WOyõÏ«ïóÞùGõ[§cµüÖó³õ2ÛÏW§Gõöô­N î~ŽÝ5 ¼:ÝØ“{T¹ì®·Ýï;«Óųßïzϸú:f5}«ãߨüj¿»?ž½No?zö{çw=¯Þ—VÛUOΪgtü³Ú]7wÕo/y•_/ŸfÏKnµ½ÍÊyšÞÕ4ZoV=«v½ŸãvÝ»r÷{Âj{¾*‹ý~X”؈a­xpxÏá{y '+~võ)‹0w±¸]Ý[\Ù¼ˆµ‚í­r¢ƒ5»ÆE?Ô¯‚±§ñoFteFteFteFteÆ⽋÷f,Þ›±xoF4fF4f^.ÀËx¹/àå\\€‹ pq..ÀŸ¸àâ\\YYY]€— ðr^.ÀËx¹/àå\\€‹ pq..ÀÅø·ÿàßü[€ pnÎ-À¹8·çàÙ<[€g ðlž-ˆ..ˆ..À³x¶ÏàÖÜZ€[ pkn-hÿí¿ ý´ÿ‚ö_]\]\ðóŠ‚ŸW´ÿŠö_Ñþ+ÚEû¯ß?)þ½a]FßÛøGtð¯ÅŸwê™}íÚ}½«¯iWŸï%¯öâí§÷ë’&ïý:{µQý»^¿®êw^iµ¼f_CGíôä¼î^ú½ýñÒã==¹»Üwå¿ZÿU÷Io}»ÇS«ÞÕñÃK¦ïQr«Ó®V}Z¾^²N{×ǨÝ]÷Mßj{XþµN«Žú5šV§gËg4ß®v0{<úý측%ïñª—Ôî¬>¯ñJË×ógUÏlý>ªß_ýü¸ÚþFÓè}fTΪÇû¹iö:fÇ+-Í>çXõõ伞Ã4½£~hç{ù½ô­Úµêùÿ×âÏ¿~ùó-þ¿ŸÑªñû­úúùÀç€ÏŸŸ>g|.ø\ÏÏ===== þ$èIГ 'AOÆueèÉГ¡'CO¦ø“q]åYPžz ôè)ÐS §BO…ÿþWø_á…Ý »v+ìVØmÐÓ §AOƒžoÆãÄ•¯ŸŸ>g|.øL=§?lj÷_?øðzè9 '@O€ž=þøà€ÿvì¢è_ú×þu è_ú×?ÑÞ¿?‰è}·0£‚%üãó¯è_=»òï*—ÕÇ5«^ïúš•µ{õõŽæ¿ZîêvhÍwõ¸1;mfÕ¿*gMÞõô¨û‡×k¶ÕÞn¹]ãåÕíÇ«ÎÖתWßç½óêŸ&´êÍçÕÏG§+WõöôN3®N³iiµ\¼ÇÝ]ÓÌ»Ëew½í~ß±^‡×û®ééG]Ç챦ouü•_íw÷dz×éíGÏ~ïü®çãÕûÒj»êÉYõŒŽV»«ãæ®úí%¯òëåÓìyÉ­¶·Y9¯ñ@Ó»šFëͪgÕ®÷sÜ®ûoWîWôï¯è_ÖÛŸ$ú7b/Öˆ½X#öbø1?b/Öˆ½X#öb}ý = z€±kÄ^¬¯Ÿ¡çŒ–ŒØ‹5b/Öˆ½X_?|®ø =À‰Ø»5bïÖˆ½[#önØ»5bïÖˆ½[#önØ»5 ð£? ð£? p¢' p¢' p¢' p¢' p½×‹@p½× p½×‹PüøŸà‚ÿÀû¼/Àû¼/Àû¼/Àû¼/Àû¼/ÀõR §@Ož=…zàú—  ú—× p½  ú—¼ö¯Oƒá«-ü|“ùúñÜߎþÕÒêcþ¨Üêcœ÷ëð¬ïéœUùÕëMÞåô¨é’UùÙ×€U»£rÞÉk:ÂjgVß³ŽZþgë÷W½&[íy¿Ï⟗}«Ü³]ÇU÷‹ÙéÎÝ÷ýGOkjßNCyM‹öô¬Ž/V»³ö½ÇM¯éÔÙü£vV¯cV¾§ÇêŸW»º?oÍ¿Ú?¼ì¯ê³&ïv»«¯úãUλک5ïûÙë-g«^íüªŸ«r«÷KkÚ}½ï{~Ìֻ׸»êGOΫý¬ŽC£þôìZõíª_kÚegô9ÑË]÷#kº—×¢QÕèÕæTõ)ø×-úW±×Å¡ÊõjXúC¹(ØüÕ¢;ѺÚÞÄšÿ*vîDÿš÷&VêANî`âiü‹½#öþØû7bï߈½#öþØû7bï߈½#öþØË7b/߈½|#öòØË7fàSìå±—oÄ^¾{ùFìå±—oÄ^¾{ùFìå±—oÄ^¾{ùFìå±—oÄÞ¼{óFìͱ7oÄÞ¼{íFìµ±×nÄ^»{íFì±wnÄÞ¹{çFìûúåƒhçŒhçŒhgì±wnÄÞ¹{çFì±wnÄ^¸{áFì…±nÌÀ§XÌ'|Îø|êÁâä‹“G,N±8yücqòOƒÿøüc!ç7üûÛï¾âü-â÷ãÛâÏß´èß]¯ß³ú¼_Ûg“÷´ÅUùv?þ^eÇ»~½§Á¬ú½§#véÙ]ï^ý÷êv3ª÷ªþ3›vO­Úõî§WéY½/zMKÚí¥]åë=m6+ÿèqèjÿvM#÷¦‰f§ó¬ç¯ª«ìÏNÃir»ÛÃìtâhZžô~-—ÑþáÝgýñ²3*·«[óÚ·kÉ»}x?WXÓj9[ýÙu¼?öº¿ÍŽ£ãój¹ìº_¯öÍžW½Yõkiu¼ð.çÕûÝj=Œ~ïÝN49ïqsv¼^7®ÿ¼üаïŒ8…Û[ ùkª8ypqå{ÿµ¨Þ‹Ekø²·˜´u1ä{»Ê¢Î.Uõu0ì½>- W«ÿQàÆrÑÊ÷^ß4þÅ^µ{ÕFìU±WmÄ^µ{ÕFìUûúX ø´ŸàSìm±·mÄÞ¶{ÛFìmûúv±¸tn-À­Ø 7b/܈½p#öÂØ ÷õ3ì"ºµÏàYì±wnÄÞ¹{çFìûúvÝZ€s p.öÚØk7b¯Ýˆ½v#öÚ}ý »ˆ†-À¿ø{óFìͱ7oÄÞ¼{ó¾~†]àßü[=‹½|#öòØË7b/ß×ÏÐü‹½|#öòØË7b/ß×Ïð¿Áÿÿ‹±÷oÄÞ¿{ÿÆB\ŒŸW`ï߈½#öþØû7~ª½oѽ?‹ú5ü½þVX•³êñ~=•[Õ³ëõÎKŸ5Ÿ÷4À¬Ü®×F«>oùg™–YM»¯×ªïªþÿ(»«z½_û­zwµçgiw«z½ýµ;[ŽÏöüð,÷goW£çG§3wMS­NWõô]Õß´ï½Ê¹gOKÞö®šÆ´Úõïþx¶]iÉ»g¯WÓg=ß+‡U»Ö´ûùêQõÛÓ?;nìòkT~v<è国íwWû«wý®¶‡Q;«õáUN½4;¾{•›—Ül{Ñä®®_«_^úzß{»£r½4{¿²ê³Ê«ç‹0[C¶b? —jrpãdô¯†AÕÅ‹SkQÅÚu˜ÙîDájQÙªÖÅ©µ=‘ïÊU]üÙ->+¢+¢+¢+O®X<¹":±":±b1äŠÅ+C®X ¹"š·GWàè ]£+ptŽ®ÀÑ8º/Wàå ¼\—+ðr.®ÀŸ¸Wàâ ü[+ðoþ­À¿8·çVàÜ œ[s+ðlž­À³x¶ÏVàÖ ÜZ[+¢s+¢s+pkn­À­¸µ·VàÓ |ZO+ði>mhÏ í¹¡=7´ç†öÜОÚsC{nhÏ í¹¡}6´Ï†öÙÐ>ÚgÃÏÚgCûlhŸí3íýû#Š÷Côïo?9¾É};ÏýíÅŸõú=û¸¶j×[ÏèkÁ¬ÝYy·Ç·Íç{É«½xûéýú¦ÉïžæÚmgTÿê¸òè~畼_c{ù½ú‡W»òž>˜•_ÍgÕ3ûúîÕÞ½ôyû³[ÿU÷Io}»ÇS«ÞÕñÃK¦ïQr«Ó•£ÓŽ=}=ý=»³öFËetšÕêG/yµ‡Ùò·–ˬ_£i¶|WËg4ß®v0{<úý측%ïñª—Ôî¬>¯ñJË×ógUÏlý>ªß_ýü¸ÚþFÓè}fTΪÇû¹iö:fÇ+-Í>çXõõ伞Ã4½£~hç{ù½ô­Úµêù_‰ªíFsj8TÁ¯³{úšÞäŸy^ct²¶8ö»(amOÝÞbÒªœqQls´³‚‡ï¯cÿ6DÃ6àͼـ7ðfÞlˆ†mˆ†mˆ†mˆ†mÀ¡ 8´‡6àÐÚ Û Û Û Û€Oði>mÀ§ ø´!¶!¶!¶!¶·6àÖÜÚ€[pkC4lC4lC4lC4lžmÀ³ x¶Ï6àنœOnˆžmˆžmÀ¹ 8·ç6àÜœÛ=Û=Û=Û=Û€ðoþm'þ•ï'þ}ý|àsÀçˆÏ‚Ï Ÿ3>|®ø »ì°{Àî»ì°{Àî»ì°¾¿|*ü{ûŒìýÉ>¿?;ÿsü»úøà-ïýš1«÷ªÇ«Ù|W½Í&ïúðÒëÝNŸ¥žv•÷n;ÞòöOË·»~¯ÖÓÓçõš;k¿'÷h¿½ôï²»k|^µ»«ý>zÜðnÞù½¦E­ivüôžîìéͯéëéñžÆÕ7[¯V}æéÃE}=ýÚùÙû‡÷´ò®ëõs6í¬úGËÙšVÇ¿^û¶ÊkÇ£v¼Þ¼î ½´ÚŽgÇ—Ñ뜵ÛK^Ï3^í`ö¾5ëÇh>-­Ž=}Þý´§gÕ®UßhþYý³ùGÛñh?•³ž×äžmêÉ›6FsZ£a§£\µEŽÿEÁ«ÖèZEîC°‚W»Ñ¿Ö½u•ëì-ÖÜÅç=Œµ½z»õÖYÜ»==‹å{¶ ÀVØ*[`«l€­°U¶ŠÀev#ìFØ°a7Ân„Ý»vvzzzzzüIГ 'AO‚žŒëÊГ¡'CO†žL=ð§àº ʳ < ôè)ÐS¡§BO…?z*ôTèiÐÓp] zô4èÎ=€gàÙxöž=€gàÖ¸õn=€[àÖíÿ@û?Ðþ´ÿíÿ@{>О´çíù@{>"õÀ´çíùxmÏŸÿbß7üååý¿ŒþúþÿŸïý»+íz¬ÜmwÕþn{^z½ëÇ»¾fåFí^}½£ù¯–»ºZó]=n̾îXõ¯ÊYÓ®iÝi¶¿ÍN3>k?»J諾«§‹¼úçÕ÷yïü£ú­Ó±Z~ëùÙz™íç«ÓÇ£z{úV§w?Çîš^nìÉ=ª\v×Ûî÷ÕéâÙïw½g\}³Çš¾ÕñoT~µßÝÏ^§·=û½ó»žWïK«íª'gÕ3:þYí®Ž›»ê·—¼Ê¯—O³ç%·ÚÞfå¼ÆMïj­7«žU»ÞÏq»î¿]¹ûÅ­‹÷uî`X+Þsø^^ÃÉŠŸ]}÷×aÅâJtuw±æÞõ*‹6Ê}ˆÖì*ØWžî`ìiü+Àü(Àü(Àü(Àü(gtëëgÁgØ-° \)À•\)À•\)v+ìVØ­° ¼)À›¼)À›¼) vì6Øm° *À¡*À¡šÝšÝšÝšÝš€Oði>MÀ§ ø4!º5!º5!º5!º5·&àÖÜš€[pkBtkBtk °‹Ÿ'$àÙ<›€gðlžMøyBÂÏ~žð󄜛€spnÎMÀ¹ 87ç&àÜœ›ðó„$°+°+°+Ÿhïß{¬ûåÇçÛÿÄ÷óÀÂ?Ç¿»¦Mfõy?¾Ì¦«§k¼ò­–˳ØÙõÚ¶ëú®žÎñÖ³»Þ½úïÕífTïUýg6ížîZµëÝO¯Ò³z_\æšµÛK»Êw÷t“UþÑãÐÕþ­NGZåWë{ôüUõq•ýÞtšµ~GåGýº?ßûÞkZUÓçÝn¬þ¬^ÿj9­¾G=jºy´¼Ú±5ߨ}ë±–¼Û‡÷s…5­–³ÕŸ]÷Çûc¯ûÛìø7:>¯–Ë®ûõj?ÐìyÕ›U¿–VÇ ïr^½ß­ÖÃè÷ÞíD“ó7gÇëÕqãªñÏËsôï(†í-†|µªaÓÑÅ•{{×Z£ŽÍ‹I[CÖ¢f•èßîb×ÿêýõ©QÑJ4o/šXý€±\¬{@Oãß \–Ë2pY.ËÀe¸,—eಠ\–Ë2pY.ËÀe¸,—eಠ\–Ë2pYØEôoFôoFôoFôoFôoN°›`7Án‚]D gD gD gD gD ç»v3ìfØEtqFtqÞÏÀûx?ïgàý ¼Ÿ÷3ð~ÞÏÀûx?ïgàý ¼Ÿ÷3ð~ÞÏÀûx?ïgàý ¼Ÿ÷3ð~ÞÏÀûx?ïgàý ¼Ÿ÷3ð~ÞÏÀûx¿ïàý¼_€÷Ë÷O†oѾ_ñùg BÃçç~íý»SÏ®×p/ùÙi¤«Ï÷Òî×™U½^rš¼W{”QýÞÓ³z5.ê[½^ïþñè×ÕY½»òYõxOî.÷]ù¯ÖÕ}Ò[ßîñÔª×kzm×tè£ä¼§ÿvÓ½é>ïúµ»ë> é[m³åo-—Y¿FÓlù®–Ïh¾]í`öxôûÙqCKÞãU/ÿ¨ÝY}^ã•–¯çϪžÙú}T¿¿úùqµý¦Ñų̂œU÷sÓìuÌŽWZš}αêëÉy=‡izGýÐÎ÷ò{é[µkÕó!ÿ¯½íýûò“(á'ßûW pY.+Àe¸¬—à²\V€Ë pY.+Àe¸¬—à²\VË*pY.«ÀeÑ°Ñ°Ñ°Ñ°Ñ°Ñ°Ñ°Ñ°Ñ°Ñ°Ñ°Ñ°Ñ°Ñ°Ñ°Ñ°Ñ°Ñ°Ñ°Ñ°Ñ°x¿ïWàý ¼_÷+p}®¯Àõ¸¾×Wà÷ ü^ß+ð{~¯Àé8½§Wàô œ^õÀàô œ^Ó+ðx¯Àãx¼Wàî Ü]»+pwî®Àݸ»wWàîZ?þýãïÛËûHàûÈßûÏ?ñ¯½gôí~¼šÍwÕkÑlò®/½ÞíôYêiWyï¶ã-ÿhÿ´|»ë÷j==}^¯¹³ö{röÛKÿ.»»ÆçU»»Úï£Ç ïöèßkZÔšfÇOïéΞžÑüš¾žïiÌQ}³õjÕgž>\Ô×Ó¯Ÿ½xO+ïºÞY?gÓ®ñǪ´œ­iuüëµo«¼v|®ø »ì°{Àî»ì°{Àî»ì°`7Àn€Ý»vìØ °`7Àn„Ý»v#ìFØ°a7Ân„Ý»»»»»»»»»òÉðïmÑç[0÷÷Å>¿Pð·_{ÿŽ%ïi˜«òy=Æ?ÚŽwýŽ¾ÖzMÓ\õz¹ªgw½{OÓÌžŸMÞå³{üò²ë=mhµçUŽWéY½/îžf˜M»ÊwuÚÚu¿ºýZó­N“YåWë{ôüUõq•}ë´h/ÿ¨ü¨_÷ç{߶«Q}ÞíÆêÏêõ¯–Óê{”÷{˜—Ü®vlÍ7jßz¬%ïöáý\aM«ålõg×ýñþØëþ6;þŽÏ«å²ë~½Ú4{^õfÕ¯¥Õñ»œWïw«õ0ú½w;Ñä¼ÇÍÙñzuܸjüóòã×Þ¿¿öþý‹ÜŸlïßô]€­°U¶JÀV Ø*[%`«l•€­pY‚Ý »v3ìfØÍ°›a7Ãn†Ý »v ìØ-°[`·ÀnÝ»v ìØ­°[a·Ân…Ý »v+ìVØ­°[a·ÁnƒÝ» vì6Øm°Û`·Á.p÷Ü}wÀÝp÷Ü}wÀÝp÷Ü}wÀÝp÷Ü}wÀÝp÷Ü}wÀÝp÷Ü}wÀÝp÷Ü}wÀÝp÷>þýýåŒüå>¿¿áÿÛ¾ÀüþÇßûÅŸg§Vå¬z¼_FåVõìz½óÒgÍç= 0+·ëµÑªÏ[þY¦eVÓîëµê»ªÿ?Êîª^ï×~«Þ]íùYÚݪ^oÿFíΖã³=?<ËýãÙÛÕèùÑéÌ]ÓT«ÓU=}Wõ7í{¯rîÙÓ’·½«¦1­öGý»?žmWZò.ÇÙëÕôYÏ÷ÊaÕ®5í~¾zTýöôÏŽ»ü•ŸzùfïG»ÇÝÕþê]¿«íaÔÎj}x•S/ÍŽï^åæ%7Û^4¹«ë×ê——¾Þ÷Þãî¨\/ÍÞ¯¬ú¬òêyã"½ÖhN+öÓpél”«†‰{[ëZ¬u`mqê.^]»·—poÑn#>ÿ°8µ²W´u±ðiü{ Zò@´ähÉÑ’¢%D?ˆ~<ýx úñ@ôãèÇÑ¢D?ˆ~<v¯àëøú¾>€£àè8úŽ>€£àåxù^>€—àå¸ø.>€‹àâ¸ø.>€‹àâ¸ø.>€àßø÷þ=€àßø÷þ=€àßø7ÿàßü€ðoþ À¿ø7ÿàÜœ€spnÎ õÀàÜœ€sðl@4r@ÿ è_ý+ º8 º8 ô¯ðÚ¿> þ½Eô2ª—{üþv'Ã}‚¿j{ÿö’÷í¿'wÕ4Àìc¢U~ö:¼ýÐävM—ô쎞ïÉ_U^«ò^¯m«z¯N«åâ]nÞ¯¡VyïqíÙúýêx÷,å}õ´ç®äöº¾x~5y÷Ë«§ÿ¬i×ýÁËþèô–U~vZÌ{úmÕî¬}ïqsvÚÕ+ÿ¨¯i\¯ûœµ~½ÛÕýykþÕþáeUŸ5y·Û]íxÕ¯rÞÕN­ù{ßÏ^ßh9[õjçWý\•[½_ZÓîëì}ßóc¶Þ½ÆÝU?zr^íguõ§gתoWýZÓ.;£Ï‰^~ìºYÓ½¼¶(pwf-ZWÓwuÑÄ°i'ÖÜ[ìZÁ¾ÊÅŠ‰ïüéîÁ«`ï{}Ö=›µE›Õúí`ç^Ô±¶7°×Þ¿)·àÖÜ€[pkn À­¸5 Z8 Z8 Z8 Z8ÏàÙ<€gðl@´p@´p@´p@´pÎ À¹87çàÜ€há€há€há€háü€ðoþ À¿ø7ÿàßü€¢…¢…¢…¢…pq.ÀŸ8àâ\€‹pq.ÀŸ8Gàâ\‹#pq.ŽÀŸ8"Z8"Z8"Z8"Z8/Gàå¼—#ðr^ŽÀËx9/GàåˆháäåSáß?þ~ÿÉ#~¿Ý}÷#jøçø×ûui—œWò²wu9x¿†[å¼^—¼íÚÙõ:¼kZmWºjºnw{]Õ·kšè*=Z¾Õé0M慨«^Ãgí>kýÊíÒ¿«z럮µ3ªw—ý]íÐ{ZÊú}/­¶ƒÑûª×4—Vn³ÓÖz˜®»ª?Ž^GÏþ¬_»žw¯z~éÉ­öï«Úiï{¯þ¬å÷Ò§éŸM³õÑÓ3ë‡vü(¹]×ïu?ßUoWÝßfû¯UWÿµÛÓoMW=oŒê¿jüÕ·ÚŽ´|»Êûªþ¡é[½oí÷¦Û5zU׳xP±§ádmcMÎÝ? ‡k¶â_mOb+‹h¸cTôýu«ÑÓ÷QÀ½hç;½g œÄ¿‹ÍF,6ÍÍÍÍÍ--±·jÄÞªÑÑÑÑÑÂø:_Gàë|£#ptŽŽÀÑ8:/Gàå¼—#ðr.ŽÀŸ8Gàâü#ðoþÀ¿±RüþÀ¿ø7çFàÜœs#pnÎà\Îà\Îà\Îà\Îà\žàYžàYž•ƒzà?ð¬Ï 𬠿ú‹ ¿ú‹ ¿Ú¿ ý Ú¿ ýËgŠþ½íûËý~oQ¾·ÿ¿àóÝñ¯ÅŸgôÎêñöWyôòyMc¬Êíz²êó–ŸõïªúðÖ·ëõÏËg·»ªwõutVïîé—Y¹]v½Æ‹]íå^¿×45ߪÜlþ«îÏÞ®FÏÏN7y=GxMwöô]Õß´ï½Ê¹gOKÞöF§%wß—Gý»?žmWZò.ÇÙëÕôYÏ÷ÊaÕ®5í~¾zTýöôÏŽ»ü•ŸzùfïG»ÇÝÕþê]¿«íaÔÎj}x•S/ÍŽï^åæ%7Û^4¹«ë×ê——¾Þ÷Þãî¨\/ÍÞ¯¬ú¬òêù_‹?ÿZüù¹?ÛâÏ‚Å™‹3 gàVnàVnàVA´° ZX-,ˆàYžàYžàYA´° ZX-,ˆà\Îà\Îà\A´° ZX-,ˆà_þà_þà_A´° ZX-,ˆàb.àb.àb.àb.àb.D ¢…Ñ‚ha^àe^àe^à弜€—ðr^NÀË x9/'à弜-œ-œ-œ-œ€£ptŽNÀÑ 8:G'à辿|üû ¿ÿøŸ @½;¾íýû# x ÿ^õ:>û¸æ-ÿ,ÓÞ««vWëÇ[ŸW»õ–M»Ëõú;ªowºª_înwÞÓ ³vgÓUõ=j×»w¿³öÝßuŸÞ=í²ëybÔ®÷}u×tñ¬_ÖéµÕi¸Ývw?ßí*_«|OnuÚöêñnô:z~xµ¿ž¾Ñvçõ<9Z¿ÞÏ9^ãâì8ç5þôòy?Ÿkv{þ¬¶SM¯Õî¬Ü¨_½ïGËÅûzGõjiö:FõõäzúFíÏæÍç]~«ÉköÊoÕÕs̨¾Õç8¯qC;¶ú¡ß~¿Tp¤Š/•(Ö79#FÔ¢oÍ‹kØù~‘ãv¾÷÷ƒ>Ÿ~ëE +ØÙê߇r¾¾óW-?m±kå¼­àgÕ?ïèß„hÆ„hÆ„ÅfS¶B´pB´pBôcBôcBôcBôcBôcB´pB´pB´pB´pB´dB´dB´dB´dÂ^Ä ø:_'àë|€¯ðu¾NÀ× ø:_'àë|€¯ðu¾NÀ× ø:_'àë|€¯ðu¾NÀ× ø:_'àë|€¯ðu¾NÀ× ø:_'àë|€¯ðu¾NÀ× ø:_'àë|€¯ðu¾NÀ× ø:_'àè £3ptŽÎÀÑ8:Ggàèü½¾|ü{û·Ež¿Ü»ûLT¬.þ|K«¡³ùg7Vízëñz\µ£É{?&î:ßK^íÅÛO¯òíÉïz]¼ÊΨþÕqåÑýÎ+y¿†õò{õ¯våý:<+¿šÏªguÄúýn}»§½®ºÞQ9ïv¸«?z%ëôUÏ/=š¾GÉY§×¼¦•{úzú{vgí–ËîiQ-yµ‡Ùò·–ˬ_£iuZx¶|Fóíj³Ç£ßÏŽZò¯zùGíÎêó¯´|=VõÌÖï£úýÕÏ«ío4ÞgFå¬z¼Ÿ›f¯cv¼ÒÒìsŽU_OÎë9LÓ;ê‡v¾—ßKߪ]«žùï°®Í9Šýº˜Ø{ïßMþõCÖ¢ŽU k]<»ƒãUÜÝÙKX•3í%Ü«íz§ñoE´aE´aE´aÅâ·¸¸Wàâ \\‹+pq.®ÀŸ¸Wàâ ü[+ðoþ­À¿ÑÎÑÎÑÎÑθ¸Wàâ \\‹+pq.®ÀŸ¸Wàâ ü[+ðoþ­À¿5Süþ­À¿ø·ÿVàß ü[+ðoέÀ¹8·çVàÜ œ[s+pnέÀ¹8·çVàÜ œ[s+pnE4rE4rE4rE4rþmÀ¿ ø·ÿ6à߆èâ†èâ†èâ†èâv„—O…o‹:ߢ{½ü|ñço¿¢ÿ¶üU×ë¥Çû:že:e4ÿèù]r£ÉûuÕªÿÙ¦sv§«úåîv7ûººÚ¿®×¼Óî×ÛY9k¾«ÛÝjÚÕ½õÜŸ¿ºÞVÓêôÕ¬¾Õç„Y¿vOÇ^ew÷óÝ®òµÊ÷äV§_¯ïF¯£ç‡Wûëémw^Ï“£õëýœã5.ÎŽs^ãO/Ÿ÷ó¹f·çÏj;ÕôZíÎÊúÕû~´\¼¯wT¯–f¯cT_O®§oÔþlþÑ|Þå·š¼Æa¯üVýW=ÇŒê[}Žó7´c«Úùí÷Ë_Ñ¿¿¢_þ„Ñ¿ ‹Ó6,NÛ°8mÃâ´ ¸¸7àâ\Ü€‹pq.nÀÅ ¸¸7àâ\Ü€‹pq.nˆ.nˆ.nˆ.nˆ.nÀË x¹/7àå¼Ü€—ðr^nÀË x¹/7àå¼Ü€—ðr^nÀË x¹/7àå†hä†hä†hä†häÝ€£ptŽnÀÑ 8ºG7àèÝ€£ptŽnÀÑ 8ºG7D#7D#7D#7D#7àë|Ý€¯ðu¾nÀ× øº_7àë|Ý€¯ðu¾níáßÛbη¿Þ½ÿî^îÝÞ¿»7oiõqwÖO¯ÇìÕrÚeÏK¯wýx×׬ܨݫ¯w4ÿÕrW·Ck¾«ÇÑéˆG•«·>¯ëXM«¯¡w•ãÕõãÕþ{ú¼§{¬rWßç½óêß5½;›Ï«ŸïšöóžÆôž~í¥Õrñw½§™{z¬~ŒÚõ®·Ýï;Öëðz_ðª¯g¹ŽÙcMßêø7*¿Úïîg¯ÓÛžýÞù]ÏÇ«÷¥ÕvÕ“³êÿ¬vWÇÍ]õÛK^å×˧Ùó’[mo³r^ã¦w5Ö›UϪ]ïç¸]÷ß®Ü=–´îIÛÛÓ·ƒý¬{[£„Õ½‰{¶ƒ§»{÷¸Õ¢«»{õö®WÁí£r*vÖðo'ZüCýþËÏëwÿ"ú±!ú±!ú±!ú±!ú±!ú±ÑùûýøúùÀç€ÏŸ+>CÏ=ôœ8úõ3ôÐs@O€ž/¿~†ž=zôDè‰ð'âºN¼üúYðv#ìFØ°aW G G G G 'ÁŸ= zô$èɸ® =z2ôdèÉÔ2®« < ʳÀnÝ»v ìVè©ÐS¡§BO…žô4èiÐÓ íÿ@û?Ðþ´ÿãŒþ}ý,øœð9ãsÁçO´øó-â÷>k‘¿ßpüÓèßû´ú:›oõ±m×´ÊjºêõfÔQù]vF“÷4ÒªÝÓ4»ä½“w¹Zó_5ýsU?{´Þ«ÆíÙòÞ=yOì²ó¨þæ%ÕýhTþj¹gy¾òšæêÙï{ÓPV»£ç½Ÿ÷Vû¯5Í>oXõx½Wô’uútWXÕ?:Í::]ìÝ߬ù¼Þ{fÇ™Ñó³ö¬ßæ÷~ð'µãQ»=9kšõߪ¯'ç¥ÏªßZ®Ú÷Þãó£ÊûªvßK»ÆñGùç¼ü•ó./«½ÙúÝ­ï^^;îÉ÷ì~Я,æ;»x±†?èS¢G{‹!÷ä>`b%:ùƒ¾ÎõjQ½Ö(f ›[£zU¹{ûZTtgQg3ÖUÚɬÝYü›#pe®ŒÀ•¸2WFàÊ\ÏhØ×Ï>|†]àͼ7#ðfÞŒ» vì&ØÀ¡84‡FàИ`7Án†Ý »À§ø4ŸFàÓ|O#ði>À§ø4ŸFàÓ|O#ði,°[`·Àn…]àÖÜ[#pknv+ìVØ­° <g#ðlžÀ³±ÁnƒÝ» vs8W€s8W€s8W€s8W€s?üüAðóÁÏä³-þ|üD»_^Þ£áÛ‚Ï¿½¼[,úoã_-;†ÎÊízýèÉy½ŽÍêÛå‡&·ëñ¾gwô|OþªòZ•ß5=°ëz¼Òj¹\5Mó¬ã–ÿÙú½×t—=ïñ÷Yüó²o•{¶ë¸ê~a&ë峦ÝÓÇ«ö½§ŸGËÓkÚÌš¼¦c­çG“µÜGí®æµ³z³ò==£Ó¬³úµóÖü^ÓÀ«öWõY“w»ÝÕŽWýñ*ç]íÔš¿÷ýìõ–³U¯v~ÕÏU¹Õû¥5í¾ÎÞ÷=?fëÝkÜ]õ£'çÕ~VÇ¡Qzv­úvÕ¯5í²3úœèåÇ®û‘5ÝËkXÕºˆ°ªí‰û¦Ï{ï_Å^7ÖˆCUL¬DMÏbg #kQǽŚ‡ñ¾õ|_oöÖÊOÃÉÓøW`«Øê¶B´­ ÚVm+ˆ¶DÛJ¶ Àevì":W+ˆÎDç ¢sѹ‚è\At®w p·w p·w p·w p·w p·w p·w p·w p·w p·w p·w p·w p·w p·w p·w p·w p·w p·w p·w p·w p·w p·w p·w p·w p·w p·w p·w p·w p·w p·wËg\üùöÿWß0ƒþ¢á_ï×¥]r^ÉËÞÕåàýn•óz]ò¶?jg×ëð®iµ]éªéºÝíuUß®i¢«ôhùV§Ã4½£ú®z Ÿµû¬õ?*·Kÿ®vê­vºrÔΨÞ]öwµCïi)ë÷½´ÚFï«^Ó\Z¹ÍNwZëavºîªþ8z=û³~ízÞ½êù¥'·Ú¿¯j§½ï½ú³–ßKŸ¦6ÍÖGOϬÚñ£äv]¿×ý|W½]u›í¿V=^ýwÔnO¿5]õ¼1ªÿªñsTßj;Òòí*ï«ú‡¦oõ¾µ{Ü›nÊbðŸ1ú׌{Öņ{¸ÑÝ? ‡*xSðݨcmñìÎÞÄZ½Y±¸uÑéQÛJùiØÞkïß,ˆ–DK ¢%Ñ’‚hIA´¤ Z2!Z2!Z2!Z2!ú1!ú1!ú1!ú1!ú1Ð|€¯ðu¾NÀÑ 8:G'àèõÀà耣ðr^NÀË x9/'à弜€—ðr^NÀË x9/'à弜€—ðr^NÀË x9/'à弜€—ðr^NÀË x9/'à弜€—ðr^NÀË ¸8'àâ\œ€‹pq.NÀÅ ¸8'àâ\œ€‹pqªŸhñçß;·ˆàÛgFÿþkñçkÓìknï{ëùQ9M~—ÑäõêegW;ßõ:¿+y—«5ÿjÿðžØÝvë½jÜÞ=MpÕ}kVÏîû]OîÙÚݨü£ýò’{–ç«ÕûÇèsÎèôUO¯¦Õîèyïç=ïéR-yM®–Ëj>µê™µ?*gõ×ÚžG¯ßë¹×{Ïì83z~ÖžõûÑüÞïÞã¤v þ½EýÞ¢{¹Ø3£}o‘À__ÞE ÿÿîz¬»O»^‹wÛ]µ¿Ûž÷k£—z¬ŸM³Ó+Úñ¬ýg“»ºZó]=n¬N§õô¯ÊY“w==êþñ¨éµ]r»ÆË«ÛWÿœ­¯Uÿ®¾Ï{çÕ?:¸:ÔËçÕÏ­~Œúå5ÖógtZÑšVËÅ{ܵëÝWíz×Ûî÷ÕéâÙïw½g\}³Çš¾ÕñoT~µßÝÏ^§·=û½ó»žWïK«íª'gÕ3:þYí®Ž›»ê·—¼Ê¯—O³ç%·ÚÞfå¼ÆMïj­7«žU»ÞÏq»î¿]¹;¬©bD sÞ/J¬E¯*‹?wM6.­-ZÜÃÄ^Uõ)‹0÷ÅÖ°j/:Ù¼ˆµ²ø³UîÖÕìZ1ñ}ýþËÏëwÿV௠üU¿*ðWþªÀ_ø«U௠üU³*¢+¢+¢+pq­Ôÿ‹+pqÅÞ°ÑÎÑÎÑÎx¹/Wàå ¼Ü€—¢¢ö†mضG7àèÝ€£ptCttCttCttCtt¾nÀ× øº_7DG7àë|Ý€¯ðu ÔÿÝÝ°XwînÀÝ ¸»w7àî†hê†hê†hê†hê<Þ€ÇðxoÀã x¼7àñ<Þ€ÇðxCttCttCttCttËß_>þ%Ö½aßÛÏ¿ÿäÿo?þW¾¥ÕÇ'¯é‹]¯¿»õ̾ví¾ÞÕ×´«Ï÷’W{ñöÓûuI“÷~½ÚΨþ]¯_Wõ;¯´Z^³¯¡£vzr^÷ /ýÞþxéñžžÜ]î»ò_­ÿªû¤·¾Ýã©Uïêøá¥GÓ÷(¹ÕiW«>-_/Y§‹½ëcÔî®û€¦oµ=¬NÿZ§UGýM«Ódzå3šoW;˜=ý~vÜÐ’÷xÕË?jwVŸ×x¥åëù³ªg¶~Õï¯~~\m£iô>3*gÕãýÜ4{³ã•–fŸs¬úzr^ÏašÞQ?´ó½ü^úVíZõ|ȇ55œ§-6¬E‡jQ¶orÞ{ÿnòÏŒa•r|Ó7ºx¶=ûáz;QÛf9ãžÈZ´x¯>´ëÆ¿ Ñ• Ñ• Ñ• Ñ• Ñ• Ñ• Ñ• Ñ• x¹/7àå¼Ü€—ðr^nÀË x¹/7DW6DW6DW6DW6àèÝ€£ptŽnÀÑ 8ºG7àèÝ€£ptŽnÀÑ 8ºG·G—ï'Ž~ý|àsÀçˆÏ‚Ïéü|@Ï=ôÐs@ÏA=Ÿ >W|†ÿzôè Р'@O„ÿþGøá„ž=====ÿþ üøŸ 'AO‚ž=)¿|ü{ÛÛ÷†v¿û|;æÂÏ@À¿öþõ´wu9x¿~Y弧m¼ìÚY-¿«³w÷‡Õò¼ª¼½Òj½Y埥ŸÏNöôŽêó¶k=¿Ë¿«ûÅ£Æ{¯ó»íßËín/£zwÙßÕW§;­zFÓj;ðž¦ëåÓôŒúgÕ§ù1{ݳç5û½ó£ÓÌ^~ízÞ½êù¥'·Ú¿¯j§½ï½ú³–ßKŸ¦6ÍÖGOϬÚñ£äv]¿×ý|W½]u›í¿V=^ýwÔnO¿5]õ¼1ªÿªñsTßj;Òòí*ï«ú‡¦oõ¾µ{Ü›n¿öþýµ÷ïßhϺ÷oùžˆ¡€¿ðWþÊÀ_Ø*[e`« l•¿2ìfØÍ°[`·ÀnÝ»v ìØ­ÐS¡§BO…ž = þ4èiÐÓ §AO£øÓP åœ{ÏÀ³Ç-üú¹àó©çž=€gàÙxöž=Îèß×ÏÐ<{ÏÀ³G ø<{ÏÀ³ðì<{DØ°a7Â.pîœ{çÀ¹pîœ{çÀ¹pî‘ 8÷Î=€sàÜ#QüG<ÐÏýûåå}Ä/ÿ¿¡aìõûöý׿žûùâϳ)ÖïGõ­¾6z¥Ý¯ »òyM;<ÚŽwýîzîé÷ê_£i—ÝÝý|Õþ®qáªé£ÝÉ»]xO³x÷Ó«ôxM“]m·—v•¯×tàªü£Ç¡«ýóž>ÕäWë{ôüUõq•}ëô_/ÿ£§;W§ŸGõy·«?«×¿ZN«ïQÞïa^r»Ú±5ߨ}ë±–¼Û‡÷s…5­–³ÕŸ]÷Çûc¯ûÛìø7:>¯–Ë®ûõj?ÐìyÕ›U¿–VÇ ïr^½ß­ÖÃè÷ÞíD“ó7gÇëÕqãªñÏËxP[xtæ^4¬‚-W£kµ½‚ïõu÷ôµF[£a•=zgqòÛ÷Œ­ák5*Z‰>-­|ÝðoÎÀ¹87çFàÜœs#pnÎÀ¹87çFàÜœs#pn,°[`·Àn…]àßü#ðoþv+ìVØ­° \‹#pq.ŽÀŸ8Gàâ\‹Ñ¿‚è_Aô¯ úW€—xY€—xY€—å;ì"ZX-,ˆàhŽàhŽàhA´° ZXìG p´G p´G p´G p´G p´G p´G p´G p´G p´G‹|¢½o¾7Ì{C»·Å ùÝ·»ÿ¿Žîý»:ý°*gMÞúfõî.§Ýåû¨ë]}]µ7*¿:íð¨~ôìíåÏ2ÌÚñz÷ÒדÛ5M¸k\y”ý«ûgOþÙîÏÏ6îXó=ªwM›Žê³N;ígG§mwO³®–§WýíºÜ˯NçZåF§G§Ow×ûj¾Uÿ¬É{ºØZþ={³õÛK£ãÁ¬Qû»Û•µœ½ì¬Öûª]¯öÒócvÜõn÷»êÇ«öìhÇ£v½ûïêx:›oµ¿ôìÌ>ͦÑçÑç¦Ñþ´j÷þ{MOÏNïûÙzðî/Wµ뢿VL¬í»ºØð}¬º·ngO_«œu¯c-JWÛÙºW¯†Y?èS¢qßänÝYÝÛ¹SÖ½“§ñ¯ šQÍ(XœVØJ€­Í(ˆfD3 ¢ÑŒ¼,À˼,À˼,™zàð²/ ð²/ ð²/ ð²/ ð²/ ð²/ ð²/ ð²/ ð²/ ð²/ ð²/ ð²/ ð²/ ð²/ ð²/'à弜€—ðr^NÀË x9/'à弜€—ðr^NÀË x9/'à弜€—ðr^Nv€£ptŽNÀÑ 8:G'àèÃ˧Á¿Ä½7Ô{ÃÁ÷ >ßÐïïçwc‹?ï~,¾ú1þªi„U»Ö|»í¯&ïúðÒëÝNŸ¥žv•÷n;ÞòöOË·»~¯ÖÓÓçýÚ¼*¿úZ¿»]=íµKïêxwÕ8v¯ïÑã†w{ôÎï= gµ«[óÝ?:Í×Ó3š_Ó×ÓcÝ¥o¶^½¦w½ôõôkçgï£íd¶Ÿy¾ßÝË­¶“žÞÑó³z­ù­úVûÏŽ×û×}¡—VÛñìø2z³v{ÉëyÆ«ÌÞ·fýͧ¥Õñ£§Ï»Ÿöô¬ÚµêÍ?«6ÿh;í§£rÖóšÜ³C=ysô¯²¨²uamQâ^îèbÃ0ç=®5âU3ÆVì©‹g+Xôƒœ²gïýõvñï}´vo1éÞ"ÛZ³Ý­-Ê}¶ÀIü›͘͘͘͘͘͘͘͘͘͘͘°¸nŽNÀÑ 8:G'àè”`‹ñ&,Æ›°o¾NÀ× ø:_'àë„ÅxÇNˆ¦Nˆ¦NÀ× ø:_'àë|€¯ðu¾NÀ× ø:_'àë|€¯ðu¾NÀ× ø:_'àë|€¯ðu¾NÀ× 8:G'àè£3ptŽÎÀÑ8:Ggàè £3ptŽÎÀËx9/gàå ¼œ—3ðr^ÎÀË9|¢èßÛ¾¾·Ÿo˜ @ßdˆ‰œÿµ÷¯§½«ËÁk:aTÎë5ÃÛþ¨ÕòÛõšdµã•¼§Vå¼òêÛ5p•-ßê´‡¦wTŸ·]ëù]þ]Ý/5Þ{ßmÿ^nw{Õ»Ëþ®v8:};jÿªö<:ífÕcͧéõovn´¾4{£ç5û½óÖöâ5ÞÏ>Ï=êùÍ«?ÖÇUí´÷½WÖò{éÓôϦÙúèé™õC;~”Ü®ë÷ºŸïª·«îo³ýתǫÿŽÚíé·¦«ž7Fõ_5~Žê[mGZ¾]å}UÿÐô­Þ·v{Óíà×Þ¿¿öþýíài÷þ͈6̈6̈6ÌXü6gàâ \œ‹3pq.ÎÀŸ8gàâ \œ‹3pq.ÎÀŸ8gàâ \œ‹3pq.ÎÀŸ8gàâ \œ‹3pq.ΈŽÎˆŽÎˆŽÎˆŽÎÀËx9/gàå ¼œ—3ðr^ÎÀËx9/gàå ¼œ—3ðr^ÎÀËx9/gàå ¼œ—3ðr^ÎÀËÑÑÑÑÑÑÑÑ8:Ggàè ]€£ ptŽ.ÀÑ8ºGàè]¾×—OƒoûûÞð·»cF{ù°8ô{ü{ÕëÐhš}]}”Ÿ^v®ÖgÍç5±*·ëuʪÏ[~Ö¿«êÃ[ß®×?/?žÝîªÞÕ×ÑY½»§_fåvÙõ/vµ—{ý^Ó@Ö|«r³ù¯º<{»=?;Ýäõá5ÝÙÓwUÓ¾÷*çž=-yÛ–Ü}_õïþx¶]iÉ»g¯WÓg=ß+‡U»Ö´ûùêQõÛÓ?;nìòkT~v<è国íwWû«wý®¶‡Q;«õáUN½4;¾{•›—Ül{Ñä®®_«_^úzß{»£r½4{¿²ê³Ê«çQšÖÅ|­{¾j{åÎ.r<Š9{{õj‹ ˆÂµâsëÞº³QÑFÜ­Fm÷Nþ€}ïôëØƿьь‹ë,®[°¸nÁâºÑÑÑÑÑÑÑÑÑ‹ë,®[°¸nÁ⺋ëàîÜ]€» pwî.Àݸ»wàîÜ]€» pwî.Àݸ»wàîÜ]€» pwî.Àݸ»wàîÜ]€» pwî.Àݸ»wàîÜ]€» pwî.Àݸ»wàîÜ]€» pwî.Àݸ»wàîÜ]€» pwî.Àݸ»wàîÜ]€»KýDø—Ø÷ËÝñ}´ïM{ÿ<ú÷–¼¦c¼_Wóïšè¥Ý¯)Þ¯ šü.;£ÉûunÕήv>û¸ý,í|Tnõu|ÔÞýùgégÖ{Õ¸=[Þ»Ç!¯þ³û~ô¨þæ%ÕýhTþj¹gy¾Z½Œ>çŒN›õôjúW펞÷~Þ[í¿Öä5½ç=;šF§©­zfíÊYýµ¶çÑë÷zîßõÞ3;ÎŒžŸµgý~4¿÷{€÷8©ÚíÉYÓ¬ÿV}=9/}VýÖrÕ¾÷ŸUÞWµû^Ú5Ž?Ê?ïäå÷¨œwyYíÍÖïn}÷òÚqO¾g÷ƒþûè[-*Õý«Ej‹›ñåàÞµ*®µFá*þwCÖöNVöÖu~óOðF|þ!zºçŸͬÙUÏ6ÚÇ¿ˆ–,ˆ–,ˆ–,ˆ–,ˆ–,ˆ–,ˆ–,ˆ–,ˆ–,ˆ–,ˆ–¬ˆ–¬ˆ–¬ˆ–¬ˆ–¬ˆ–¬ˆ–¬ˆ–¬ˆ–¬X¼·wWàî Ü]»+pwî®Àݸ»wWàî Ü]»+pwî®Àݸ»wWàî Ü]»+pwî®Àݸ»wWàî Ü]»+pwî®Àݸ»wWàî Ü]»+pwî®Àݸ»wWàî Ü]»+pwî®Àݸ»wWàî Ü]»+pwî®Àݸ»wWàî Ü]ó'¿·}}o‘¾D¿DÀ¿¿œxøvîCôï-y=.y¿ÎŒ&¯×³{ù]¯÷«ùvÛ_MÞõá¥w÷k÷¬½]¯Ý^úwÙñ–´Z¾Ýõ{µžž¾ÙqsWýÌNYí<ªÿ>zºn6ߣÊYÓ÷èqû=z矞šM³ã笳å1š_Ó×Ó3:ýç­o¶^­ú¬ítU_O¿v~öþ1;½ºZo=ý½ï}¿»—[m'=½£çgõZó[õ­öÿž¯÷¯ûB/­¶ãÙñeô:gíö’×óŒW;˜½oÍú1šOK«ãGOŸw?íéYµkÕ7šVÿlþÑv<ÚOGå¬ç5¹g‡zò¿öþýµ÷/¯ãO³÷oþjÀ_ ø«5à¯üÕ€¿ðWþjÀ_ 8«!z³!z³!z³!z³!z³!z³!z³!z³!z³!z³!z³!z³!z³!z³!z³!z³!z³!z³!z³!z³a±âüÞ€ßð{~oÀï ø½¿7à÷üÞ€ßÛ‰ßë÷¿¿~>ð9àsÄgÁç„ÏŸ >W|†ÝvØ= ç€žzè Р'Àÿÿüð?Àn€Ý»v#ìFØ°a7Ân„Ý»v#ìFØ•ï/Ÿÿ2ª÷7üóÞ>ÿñýïïÿŠw={¿îÎÊy¥]Ó»ó]=-³ËŽwýzM“ê÷~íÜ¥gw½{õß«ÛͨޫúÏlzÔ4ˆ÷ô•UïUz5òèé—ÝÓ_³ö¬ò‡®öovÇjovMÓg=U}\evN“ÛÝFŸÛ½Ÿ#¼¦;gýY½þÕrZ}ò~ó’ÛÕŽ­ùFí[µäÝ>¼Ÿ+¬iµœ­þìº?Þ{ÝßfÇ¿Ññyµ\vݯWûfÏ«Þ¬úµ´:^x—óêýnµF¿÷n'šœ÷¸9;^¯ŽW^~˜ñ¯]k\„¹ý«îýkÕg\¼ØŠ5<¬E÷“V±éä^Âoßwö0Öpn'w£‰;åb]|ÿÖïl%ÀVl%ÀVl%ÀVl%ÀVluFc¾~†Ý» vì&ØM°›`7Án‚Ý »v3ìfØÍ°›a7Ãn†Ý »v ìØ-°[`·ÀnÝ»v ìØ­°[a·Ân…Ý »v+ìVØ­°[a·ÁnƒÝ» vì6Øm°Û`·Á.ðõ|}_À×ðõ|}_ÀÑpôq@Ï=ôÔ“ñ¹àsÅgø|}_À×ðõäåÓàß?Pî-ú÷wÃßmáçÇ?Ç¿»ÒêëÚîÇš]öwÛóÒë]?Þõ5+7j÷êë͵ÜÕíКïêqcöµÒªUΚvM3íN^Ó/³övËí/¯n?^ýÓ{ZÊ*wõ}Þ;ÿ¨~ë´¨–ßz~¶^®šf]ÕÛÓ7:Ý·:ª¥]Ó¶šÜhyyM ï.—ÝõvÕ4®u<˜µÓ³×Ëד{ÔuÌkúVÇ¿QùÕ~w<{Þ~ôì÷Îïz>^½/­¶«žœUÏèøgµ»:nîªß^ò*¿^>Íž—Üj{›•ó4½«i´Þ¬zVíz?Çíºÿvåî± uOÚÞž¾(\ktè0v¾—ï,ÝÃÄÝ=Œ­QÑ ÖíîÕÛ»^{Ê}XÔY³k]$ú¾~ì<D?ˆ~<ýx úñ@ôãèÇÑ¢D?ˆ~<ÍxôGÀÑpô}GÀÑpô}/ÀËðò¼|/ÀÅpñ\|ÀÅðïü{ÿÀ¿ðïœ{çÀ¹pîœ{ÏÀ³ðì<{ÏÀ­pkn À­¸5 ú7 ú7 ú7·àÖ|€Oði> À§í9 =´ç€öОÚs@{hÏí9 =DóDóDóDó´ÿ€öОÚs@{ò‰æ^¿·ÈÞ/øÿËï|ßþ¾ööþÕÒêcþ¨Üêcœ÷ëð¬ïéœUùÕëMÞåô¨é’UùÙ×€U»£rÞÉk:ÂjgVß³ŽZþgë÷W½&[íy¿Ï⟗}«Ü³]ÇU÷‹ÙéÎÝ÷ýGOkjßNCyM‹öô¬Ž/V»³ö½ÇM¯éÔÙü£vV¯cV¾§ÇêŸW»º?oÍ¿Ú?¼ì¯ê³&ïv»«¯úãUλک5ïûÙë-g«^íüªŸ«r«÷KkÚ}½ï{~Ìֻ׸»êGOΫý¬ŽC£þôìZõíª_kÚegô9ÑË]÷#kº—×öÔU÷Âí`NUŸ‚{X÷CTj'ÖŒ‰÷ÂU÷V°ùìžÃÚÂv¶.ÚmÞÛ¹·8µRjôtOã_^à5^à5At¢ :Q(X\W€ã8N€ã8N€ã8N€ã8N€ã8N€ã8N€—xY€—8N€ãD¨þ/ ¢Ñ΂hgA´³G p´G p´G ¢Ñ΂hgA´³_ ðµ_ ðµ_ ¢Ñ΂hgA´³_ ¢—ÑË‚èeAô²w p·w p·w p·w p·w p·w pwîNÀÝ ¸;w'àîÜ€»pwîNè ý1¡?¦×þøiðïo/gDï×—sè/øûªÿýZüy$yO[\•o÷ãïUv¼ë×{̪ß{:b—žÝõîÕ¯n7£z¯ê?³i÷ôت]ï~z•žÕû¢×´Ô¨Ý^ÚU¾ÞÓf³ò‡®öo×4rošhv:Ïzþªú¸Êþì4œ&·»=ÌN'Ž¦ÕéIï÷ˆÑríÞíqÖ/;£r»Ú±5ߨ}ë±–¼Û‡÷s…5­–³ÕŸ]÷Çûc¯ûÛìø7:>¯–Ë®ûõj?ÐìyÕ›U¿–VÇ ïr^½ß­ÖÃè÷ÞíD“ó7gÇëÕqãªñÏË_‹?ÿZüù/r¶ÅŸ3pVÎÊX,7c±ÜŒÅr3özÍÀ_ø+e௠ü•±¸nÆ⺋ëf,®›Ë2pY.ËÀeø:_gàë |¯3ðu¾ÎÀ×ø:_gàë |¯3ðu¾ÎÀ×ø:#š4#š4#š4#š:wgàî Ü»3pwîÎÀݸ;wgàî Ü»3pwîÎÀݸ;wgàî Ü»3pwîÎÀݸ;wgàî Ü»3pwîΈîΈîΈîΈîÎÀãx<gàñ <žë÷—O…oŒúýãø¶ßï7}/ûóè_ï×]r^ÉËÞÕåàý˜g•»jú`·ï×aoû«¯íÖäõz¹jÇ;ߨ¾]Ó/WéÑòy½jù¯×wMkŽÊYåŸå¾xU;|Týjr»Û˨Þ]öwµCë´ÓÕå¼ÚvM'jù4=£þYõi~Ì^÷ìyÍ~ïü®ie/»V;»žß¼úóh}\ÕN{ß{õg-¿—>Mÿlš­žžY?´ãGÉíº~¯ûù®z»êþ6Û­z¼úï¨Ýž~kºêycTÿUã稾Õv¤åÛUÞWõMßê}k÷¸7Ý”ņ5Œ¨E÷júÔèPÅžu±á^´©»Z4¬×Z£Õųµ(a£¸Ž^”µqÑéØWÃÎJÔ¶þEteFteFteFteÆÞ°{Ãfì ›±7lF4fF4fF4fF4fF4fÆÞ°{Ãfì ›±7lFôfFôfAôfî.Àݸ»wàîÜ]€» pwî.Àݸ»wàîÜ]€» pwî.Àݸ»wàîÜ]€» pwî.Àݸ»wàîÜ]€» pwî.Àݸ»wàîÜ]€» pwî.Àݸ»wàîÜ]€» pwî.Àݸ»wàîÜ]€» pwî.Àݸ»äï/Ÿÿþv¿¾œØ÷†woØ÷gè÷–çËèÞ¿W¿6Í&o}³zw—Óîò}ÔõzM—XíÊïzýµ7*÷ìíåÏ2ÌÚY­ŸÑòòš¶Õë=½rUÿÝ­ï*¿žíþülãŽ5ߣÊÑûþè5­¬Éïg­Ó\£Ó`Z>«£r«Ó“½ïw/£íª·÷ýìt§µ~½ë}5ߪÖäõÜ5Zþ={³õÛK£ãÁ¬Qû»Û•µœ½ì¬Öûª]¯öÒócvÜõn÷»êÇ«öìhÇ£v½ûïêx:›oµ¿ôìÌ>ͦÑçÑç¦Ñþ´j÷þ{MOÏNïûÙzðî/WµóbÈÆ(amqhU_Ç®¶(qw‘c%ú×*gÝëXôڞÈÖ½z5¼ûAŸ‚cßä”hhëžÈêÞÎú°î< ¢% ¢% ¢% ¢% ¢% ¢% ¢% ¢% ¢% ¢% ¢% ¢% ¢% ¢% ¢% ¢% ¢% ¢% ¢% .Àݸ»wàîÜ]€» pwî.Àݸ»wàîÜ]€» pwî.Àݸ»wWàî Ü]»+pwî®Àݸ»wWàî Ü]»+pwî®Àݸ»wWàî Ü]»+pwî®Àݸ»wWàî Ü]»+pwî®Àݸ»wWàî Ü]»+pwî®Àݸ»wWùþò©ð/€¾}þÙÿ÷‹?ÿ6ŠWÓêcº÷ãüUù»íyéõ®ŸÝ¯_Þívô5w×ëÒ³É]Ý­ù®7vM›ú±š¼ëéQ÷ï×âž½Ýr»ÆË«ÛWÿœ­/¯éØÙôèü£úg§×¬úGóyõó«§O{ú¬×ç=íh•÷¶7[^»¦'{iÕîUÓŽä}^ï ^õõ,×1{¬é[ÿFåWûÝýñìuzûѳß;¿ëùxõ¾´Ú®zrV=£ãŸÕî긹«~{É«üzù4{^r«ímVÎk<Ðô®¦Ñz³êYµëý·ëþÛ•3b?+U£W•½»{æŽ.:}/oÄœþíáKëžÈZ”p/:Ù¼‡qÃvä>`]Í®qOßõ«,:=‹Û÷¾~øñYð9ásÆç‚ÏŸÛùùă¯Ÿa÷€ÝvØ=`÷€ÝvØ=`7Àn€Ý»vìØ °`7Àn€Ý»v#ìFØ°a7Ân„Ý»vvvvvvvvvvvì&ØM°›`7Án‚Ý» vì&ØÍ°›a7Ãn†Ý »v3ìfØÍ°›a·ÀnÝ»v ìØ-°[`·ÀnÝ϶øómßû`â`FãøýâÏ»ïVӮײQ¹U=»^+½ôYó­–—W½Ì>FÏêÙ-¿úZ;›®~îÉ]ÝN½ËïêqgVïl;öšNš=¿šow»[Õëíߨ]¯é;k¾U¹ÙüWÝ?ž½]ž·N‡ÍN_õÒh}xMŸz§Ñr›-çž=-yÛ®Ý}_^&mWZò.ÇÙëÕôYÏ÷ÊaÕ®5í~¾zTýöôÏŽ»ü•ŸzùfïG»ÇÝÕþê]¿«íaÔÎj}x•S/ÍŽï^åæ%7Û^4¹«ë×ê——¾Þ÷Þãî¨\/ÍÞ¯¬ú¬òêy#†µâP랯Ú^¹³Q®£‹÷öêÕp¸Š9{‹g[÷Ö5îM¬í½ÛÅÉV<݉ž¾÷O•ëØÇ¿ت[U`« lU­*°U¶ªÀVت—5Øm°Û`·ÁnƒÝ» vìžÑ°í8£a_?øð9â³àsÂçŒÏŸ+>Ã.p÷Ü}wÀÝp÷Ü}wÀÝp÷Ü}wÀÝp÷Ü}wÀÝp÷Ü}wÀÝp÷Ü}wÀÝp÷Ü}wÀÝp÷Ü}wÀÝp÷Ü}wÀÝp÷Ü}wÀÝp÷Ü}wÀÝp÷Ü}wÀÝp÷Ü}|¦ÅŸá{ûÌžoÿÿŽÏ·¿/÷ø÷>yMÇx¿®æß5-ÐK»_S¼_4ù]vF“÷ëܪ]í|öqûYÚù¨Üêëø¨½ûóÏÒÏ­÷ªq{¶¼wC^ýg÷ýèQýÍKþªûѨüÕrÏò|µzÿ}Î6ëéÕô¯Ú=ïý¼·Ú­ÉkzÏ{:v4NS[õÌÚ•³úkmÏ£×ïõÜ¿ë½gvœ=?kÏúýh~ï÷ïqR;µÛ“³¦Yÿ­úzr^ú¬ú­åª}ï=>?ª¼¯j÷½´k”ÞÉËïQ9ïò²Ú›­ßÝúîåµãž|Ïîý÷‹!k{Ò÷þÕ¢G?èÅ—ƒ‹«¸Öº¯â¿v½¢¢µ½z{þÝG%kÖˆÏ?DëöüëEõ*ídÖî4þ=-y Zò@´ähÉÑ’¢%DKˆ–<-y Zò@´ähÉÑ’¢%DKˆ–<-y Zò@´äQa¸ûî>€»àî¸ûî>€»àî¸ûî>€»àî¸ûî>€»àî¸ûî>€»pwîÀݸ;wàîÜ€»pwîÀݸ;wàîÜ€»ðu¾À×ø:_‡@=ðø:_àë|€¯ðu¾À×ø:_àë|€¯ðu¾À×ø:ˆ¼|üûêýýÇw¿ãø¶ïïíû¯ø8x,úw×k@OÎëµhTþªëõÒ³û1WûÞ»œ¼ë{µüfåF“W9^5-µëµswºª_înw«¯¡Ö´«ž®ªïQ»Þå¸{üµ÷èþö¨ûôì´àªþÝåí=kÕ·úœ0ë×è´Ó¨ž«ìî~¾ÛU¾VùžœuºùQã°¦gµ´ãQ»=ýZþÕqpµ}xµƒÑþ½Zþ½ïWý³ú±:>¦Ñvµ:.=j¼]í_=»³íaTϨ^-Í^Ǩ¾ž\OߨýÙü£ù¼Ëo5yÃ^ù­ú¯zŽÕ·úç5nhÇV?´óÛï—J4ª½ªàÓ79c©›j{ÿª¸QYÄøþ:´E‘?èë,Ö¬aÓî¢ÓƒþY1q·üV÷vVö$îú§\ï4þ ˆ~ ˆ~ ˆ~ ˆ~ ˆ~ ˆ~ ˆ~ ˆ~ ˆ~ ˆ~ ˆf X¼7Gà耣ptŽÀÑ8:/àå¼€—ðr(Ôÿ—ðr^ÀŸ8àâ\€‹pq.ÀŸ8àâ\‹#pq.ŽÀŸ8Gàâü#ðoþÀ¿ñ ž‚ÏŸá?pq.ŽÀŸ8Gàâ\‹#pq.ŽÀŸ8Gàâ\‹#ðoþÀ¿ø7~&üû 7Ì{[èùþûß_N4üíý»ú˜³*gM»Ÿg?wÉí.ßG]ïUÓ³ò«¯mêGÏÞ^þ,ãÀ¬ïi§Ýõ½:½3{~·ü£ô]å׳ݟŸmܱæ{T9zß½§y¬ÓR^É:¶:]æUî«þiúzßï_f§GýðšµÖ¯w½¯æ[õÏš¼ž»FË¿go¶~{it<˜µ3jw»²–³—Õz_µëÕ^z~ÌŽ»Þí~WýxµÓžíxÔ®wÿ]Ogó­ö—žÙç¢Ù4ú¼3úÜ4ÚŸVíÞ¯ééÙé}?[Þýåªö¢-Z<•Å~U}»þU/îìék•³îu¬E«‹X÷êÕ0ë}E±g£§{‹q÷êúXø4þà/þà/þà/þà/þà/þà/A´¤ ZR-)ˆ–à2.à2.,ö+À×|-À×|-À×|-X¼W°x¯`ñ^Áâ½Ü-ÀÝÜ-ÀÝÜ-ÀÝÜ-ÀÝÜ-ÀÝ‚èkAôµ úZ}-Àã<.Àã<.Àã<.ˆ¦DS ¢©ÑÔ<.•zà?ð¸ ð¸w p·w'àîÜ€¯¢¢¢¢pwîNÀÝ ¸;w'àîÜ€»pw ß_> þ½íùËÈÞ[Ô/÷úý‚ó¿çíý;£wV·ÿÏòš²ªoVÎë±xw9®^¯w;ðÎ?«o÷kûUýÿQvWõ^=íèu~5ßÕÓE^ãÅ®ör¯¶ŸíùáYîÏÞ®FÏ{Mí–‹×tš·Ö4Zn«ÓÞã×ôà®òŸmV=³íJKÞå¸:­<ÚîzvFŸží¹ö^ï£ê·§vÜØåרüìxÐË7{?Ú=î®öWïú]m£vVëëœziv|÷*7/¹Ùö¢É]]¿V¿¼ôõ¾÷wGåziö~eÕg•WÏÿÚû÷×Þ¿ÈýÙöþÍÀ­¸5·fàÖ Üš[3pkn͈.Έ.Έ.Έ.ÎÀ³x6ÏfàÙ <›g3ðlžÍÀ³x6ÏfàÙ <›g3ðlžÍÀ³x6ÏfàÙ <›g3ðlžÍÀ³x6ÏfàÙ <›g3¢—3¢—3¢—3¢—3pnÎÍÀ¹87çfàÜ œ›s3pnÎ͈vΈvΈvΈvÎÀ¿ø7ÿàßü[í\í\í\í\€‹ pq..ÀŸ¸àâ\\€‹ pq9ò˧Á¿Œèå¢Ï?öö};ü#Jøç{ÿ^õš·Ë®5߬Þ¯£öWõîšYµû¨rñn§ÏRO»Ê{·]ÓIWÙ³æÛ]¿Wëéé[[µß“{´ß^úwÙÝ5>¯ÚÝÕ~=n\59›tšË»]z]ïèôdOÏh~M_OÏì4«—¾Ùzõš>õÒ×Ó¯Ÿ½Œ¶“Ù~æ=<ú~w/·ÚNzzGÏÏêµæ·ê[íÿ=;^ï?^÷…^ZmdzãËèuÎÚí%¯ç¯v0{ßšõc4Ÿ–VÇž>ï~ÚÓ³jתo4ÿ¬þÙü£íx´ŸŽÊYÏkrÏ6õä­Q½VœgÅ¡Ó˜ó‹‚9‹ [£kÍQÌŠ½þi‹XkrÊbÖ÷×ÛþUö¶îýÛ­7eñlëÏ ¦ñoA´aA´aA´aÁ⺋ëD D D D D DÿDÿàè]€£ ptŽ.ÀÑ8ºGàå¼\€— ðr^.ÀËx¹/àå¼\€— ðr^.ÀËx¹/àå¼\€— ðr^.ÀËx¹/àå\\€‹ pq..ÀÅø·ÿàßü[€ pnÎ-À¹8·çVàÜ œ[s+pnέÀ³x¶ÏVàÙ <[Ñ_*úKE©XŒºb1êŠÅ¨+¢ñ+úWEÿª¯ýëÓà_F÷þlàÛù[Ô/ÿý;§wVÏ®× /}Ö|WOóxéñz-¹º>Ë´›·>ï×ÄÙvzÕ뤷ÝU½³íØ{iôüj¾ÝínUïîé“ž]¯é+k¾U¹ÙüWÝ?ž½]ž·NÿÌNïöÒh}xM×y§Ñr›-çž=-yÛ[fM³íÀªguz·g§§W“õ¯çÏj½­NϦÝÏWªßžþÙqc—_£ò³ãA/ßìýh÷¸»Ú_½ëwµ=ŒÚY­¯rê¥ÙñݫܼäfÛ‹&wuýZýòÒ×ûÞ{Ü•ë¥Ùû•UŸU^=ÿ+ú÷Wôïr¶èß<Û€gsnX̹a1ç†ÅœpnÎmÀ¹ 8·ç6àܜۀspnÎmˆ.nˆ.nˆ.nˆ.nÀ¹ 8·ç6àÜœÛõÀàܜۀsðlžmÀ³ x¶Ï6àÙ<ÛýÛýÛýÛ€gðlC4oC4oC4o«ÔÿspnÎmÀ¹ Ѽ Ѽ Ѽ Ѽ ø·ÿ6àßö†?¾ã¿98xxy Ùç"û\dŸ‹ìs‘89'Gö¹È>Ùçb ¶ /Ÿ'ßB‰o«FcóàwáÆ¿A²ÅÉ^¯½´û1g—ÝUû»íyéõ®ïúš•µ{õõŽæ¿ZîêvhÍwõ¸1; gÕ¿*gMÞõô¨û‡×k»ÕÞn¹]ãåÕíÇ«ÎÖתWßç½óêŸv´êÍçÕÏG§?WõöôN[®NÛiiµ\¼ÇÝ]ÓÖ»Ëew½í~ß±^‡×û®éîG]Ç챦ouü•_íw÷dz×éíGÏ~ïü®çãÕûÒj»êÉYõŒŽV»«ãæ®úí%¯òëåÓìyÉ­¶·Y9¯ñ@Ó»šFëͪgÕ®÷sÜ®ûoWî>äÖúÚ î¬ôl m¶bg5Z ÍUüì껿ëÊÛ ®ï†÷®W •û€›5»ÊÊÒ*Žï¬”=“cx£ˆÃˆy#1o$æļ‘˜7F¬H€‰Ãˆy#1ojjj#æļ1Q[¢¶Dm‰Úˆ†#Ñp$ŽDÑh8&zéA¦™'GâäHœ‰“#qr$NŽÄÉ‘89'GâäXèA¡…z@‰“#qr$NŽÄÉ‘89ÖwÚx=•×Óx=DБ:AG"èH‰ …8Yˆ“…8Yˆ“…8Y¾¿ÓVxPy€ëâd!F #…?áþ„Cˆ“…8Yˆ“…8Y>NæJÔÄÇßî>ß62¾E-«ÑÉW½¾®Êy%ïÇΫ伧›¬r«¯sÖ´Ûή×ïúÝݼ¦VíxçÕ·ké*=Z¾ÕéIMï¨>o»Öó»ü»º_Ì^÷ìyÍ~ï¼÷4©Õ¯]Ï»W=¿ôäVû÷Uí´÷½WÖò{éÓôϦÙúèé™õC;~”Ü®ë÷ºŸïª·«îo³ýתǫÿŽÚíé·¦«ž7Fõ_5~Žê[mGZ¾]å}UÿÐô­Þ·v{ÓíÀºâòht­‚Ÿorš=+¾T£uWp6û×Ù`¸=­–ß}ô¯†ãµ¨ã»hlõ:Œ+y÷pòèÒšÞ³Îâd!Nâd!Nâd!Nâd!Nâd!Nâd‰ô€‘ÆÂHca¤±A ´A ´A‹ÐF' £“…ÑÉBl-ÄÖB-DÐB-DÐB-DÐB-DÐB-DÐB-DÐB-DÐB-DÐB-DÐB-DÐB-DÐB-DÐÂ(ha´0 Z-ÄÖBl-ÄÖBl-ÄÖBl-ÄÖBl-ÄÖBl-ÄÖBl-ÄÖBl-ï°5#§…‘Ó‰Ø:['bëDlˆ­Ó÷wÚp=‰Ø:1 :áåÓàä2&:¾}yÅüãÜØb×^¯ŸÞò£ù½§w¬iöµ¹÷½õü¨œ&¿ËÎhòz­õ²³«ïšØ•¼ËÕšµxO7ìn»õ^5nïžv¸ê¾5«g÷ý®'÷línTþÑ~yÉ=ËóÕêýcô9gt:¬§WÓ¿jwô¼÷óž÷ô«–¼¦WËe5NÇZõÌÚ•³úkmÏ£×ïõÜ¿ë½gvœ=?kÏúýh~ï÷ïqR;µÛ“³¦Yÿ­úzr^ú¬ú­åª}ï=>?ª¼¯j÷½´k”ÞÉËïQ9ïò²Ú›­ßÝúîåµãž|ÏîýÊâų‹5k8òƒ¾Ñ |û6ílàk½^ckQÛE§{‹q¿ù§-ÆmÜ@Z[4[õ¯u¬´“Y»ó89Õe¢ºLT—‰ê2Q]&ªËDu™¨.Õe¢ºLT—‰ê2Q]&ªËDu™¨.Õe¢ºÌEŽ 9.ŒJ-ŒJ-Ä{…x¯ïâ½B¼Wˆ÷ ñ^!Þ+Ä{…‹.r\¸Èqa$ka$ka$ka$ka$ka$ka$ka$ka$k ô€?E(ü)BáO ŠPøS„Ÿ"þ¡ð§…?E(ü)BáO VPø³‚Ÿþ¬ ðg…?(Œl/Œl/Œl/Œl/ü‰@áO "PøŸþD ð'…?(ü‰@áO "PøŸþD ”O„“¿þ8æ×<¾-€}¿wòÙ_{'ïÔ³úæeG“_}¿ê|/yµo?½Ê·'ÕkÞ.;£ú½¦•Võ>j\Õ÷¬Óg£ù¬öuÿØ}ßÙ5í8š¼ÚÓlþ«õ_uŸôÖ·{<µê]?¼ôhú%g¶]®ÓòõÒ®éÉžQ»»îš¾Õö0[þÖr™õk4Í–ïjùŒæÛÕfG¿Ÿ7´ä=^õòÚÕç5^iùzþ¬ê™­ßGõû«ŸWÛßh½ÏŒÊYõx?7Í^Çìx¥¥Ù竾žœ×s˜¦wÔí|/¿—¾U»V=òÿÚ;ù×ÞÉ/z´øóî\‰Ã*£l+£l+£l+£l+Z%B«Dh•­¡U"´J„V‰Ð*Z%B«Dh•­¡U"´Ê(ÛÊ(ÛÊ(ÛÊ(ÛÊ(ÛÊ(ÛÊ(ÛÊ(ÛÊ(ÛJt_‰î+Ñ}%º¯D÷•è¾ÝW¢ûJt_‰î+Ñ}%º¯D÷•è¾ÝW¢ûJt_‰î+Ñ}#ºoD÷è¾Ý7¢ûFt߈îÑ}#ºoD÷è¾Ý7¢ûFt߈îÑ}#ºoD÷è¾Ý7¢ûFt߈îÑ}#ºoD÷è¾Ý7¢ûFt߈îÑ}‹Ÿ 'ߢ¿àø†”ŽßÐò·Sflïä]ó^¯9»ôÍêÝ]N»Ë÷Q×»{ÚkU~÷ôªÞÝz¼òyOóõôïJWÕÏìëm/­¾zŸß-ÿ(}Wùõl÷çgw¬ùUŽWO«ÎNzߧµ4:ý7:­6:]¹Zž^õ·ë~p/¿:}h•³NçÎNï®÷Õ|«þY“×s×hù÷ìÍÖo/Ž³vFíïnWÖrö²³Zï«v½ÚKÏÙq×»Ý絛vÚ³£Úõî¿«ãél¾ÕþÒ³3û\4›FŸwFŸ›FûÓªÝûï5==;½ïgëÁ»¿\Õ^¬ñ6ííÕ{¿ó"¾¼Ç êÞÄ=‘­rÖ½¢µ(gmOië^Çÿ OY|úMn2º[‹vÖ°´y/k·Å®#?#?#?¡n\„ºqêÆE¨£E£E£E£E£E¡n\„ºqêÆE¨#L#L#L#L‘z#RoDêH½©7"õF¤ÞˆÔ‘z#RoDêH½©7"õF¤ÞˆÔ‘z#RoDêH½©7"õF¤ÞˆÔ‘z#RoDêH½©7"õF¤ÞˆÔ‘z#RoDêH½©7"õF¤ÞˆÔ‘z#RoDêH½©7"õ¤~|R=8xxy ÖìxO_íšžµ£~¿Ú­iöyêÇ{ÚZK½ú]nµ?*7:½m½^«^Ïý»Þ{fÇ™Ñó³ö¬ßæ÷~ð'µãQ»=9kšõߪ¯'ç¥ÏªßZ®Ú÷Þãó£ÊûªvßK»ÆñGùç¼ü•ó./«½ÙúÝ­ï^^;îÉ÷ì~Ðÿk±ë_‹]ÿLîÙ»>bªƒ˜ê ¦:ˆ©bªƒ˜ê ¦:ùùš"„ôà =8èÁA=ô Ѓ@=ô Ѓ@=ô ÒƒH"=ˆô ÒƒH"=ˆô ÒƒH„=z ô@èСB„=Hô уD=Hô уD=Hô уL2=Èô ÓƒL2=Èô ÓƒL2=(ô ÐƒB =(ô ÐƒB =(ô ÐƒJ*=¨ô Òƒ*/Ÿ'ß"‘ï0ñ;|üÛÝùßÎÏ?ÇÉ^_Þ¯G£Éëuï^~×tÁj¾ÝöW“w}xéÝý?ko×k¼—þ]v¼åퟖowý^­§§ovÜÜU?³ÓmV;ê¿žþ›Í÷¨rÖô=zÜðnÞùg§»fÓìø9ëßlyŒæ×ôõôŒN'zë›­W«>k;]Õ×Ó¯Ÿ½ÌN×®Ö[OïûGßïîåVÛIOïèùY½ÖüV}«ý¿gÇëýÇë¾ÐK«íxv|½ÎY»½äõ<ãÕfï[³~ŒæÓÒêøÑÓçÝO{zVíZõæŸÕ?›´öÓQ9ëyMîÙÆ¡ž¼5êXþj4¬}|o·‡kµèÛrJ´ñÌiŒþ5GY+ö>ø§DY«rÊ"Õ÷×ÛNVðµ'wëM[Ü[Y´ûþ:æq² Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° Ñ° K¥•TzPéA­/Ÿ'ÿ~÷w‹T¾ÿî^îV[ìz5ízLÝmwÕþn{^z½ëÇ»¾fåFí^}½£ù¯–»ºZó]=n̾>Yõ¯ÊYÓ®i”Ýi¶¿ÍN[>k?»J諾«§Ÿ¼úçÕ÷yïü£ú­Ó»Z~ëùÙz™íç«ÓÑ£z{úV§w?ÇîšV^¾ìÉ=ª\v×Ûî÷ÕéçÙïw½g\}³Çš¾ÕñoT~µßÝÏ^§·=û½ó»žWïK«íª'gÕ3:þYí®Ž›»ê·—¼Ê¯—O³ç%·ÚÞfå¼ÆMïj­7«žU»ÞÏq»î¿]9ã"ÇÖÅŸ­X׊­{,«‹+uÖ»î-ÖlÆìJÔ¯mݳٸxvOîCÔ±f×Å|_¿ _ÀÉ•«`5¬F€Õ°qøM\à7qßÄ~£ £ Qw"êND݉Ø:['bëDlˆ­t"‚NDЉ:A'"èDˆ t"‚NDЉ:A'"èDˆ“qr"NNÄɉ89 '¢áD4œˆ†Ñp"æMļ‰˜7ó&bÞ”ßi£oļ‰˜7ó&"ÛDd›ˆl‘m"²Mį‰ø5¿¦Jmì?©ñJµ5jkÔÆþ“¹Øuæb×™«d®"Ùò÷wÚà[fÿÉì?ù³-v}¿?òíø‰üÛÝw7™o£ÑÉZÚõ˜yUò~ì¼JÎ{ºÉ*·ú:gM»íìz-ð®ßÝýÁkºaÕŽw¾Q}»¦‘®Ò£å[žÔôŽêó¶k=¿Ë¿«ûÅ£Æ{¯ó»íßËín/£zwÙßÕ­ÓuW—ój;XÖ³¦ž]¯é]ëóÈìuÏž×ì÷Î{O“ZýÚõ¼{ÕóKOnµ_ÕN{ß{õg-¿—>Mÿlš­žžY?´ãGÉíº~¯ûù®z»êþ6Û­z¼úï¨Ýž~kºêycTÿUã稾Õv¤åÛUÞWõMßê}k÷¸7ÝŒÑÄÖÅ®ïõiÑÄš=+¾ü OÁÒnþu¢‰5½Ýò»êÕp¼M|¬^GÇ[1öh´¸¦÷l³893Ò83Ò83Ò8Ùf"ÛLd›‰l3‘mf¤qf¤qf¤qf¤q&æÍļ™˜7ófbÞLÌ›‰y31o&æÍļ™˜7ófbÞLÌ›‰y3#3#3#3#3Ñp&ÎDÙh8 gFgFgFgFgâäLœœ‰“3qr&NÎŒ4ÎŒ4ÎŒ4ÎŒ4ÎDЙ:Ag"èL‰ 3t&‚ÎDЙ:3Ò83Ò83Ò83Ò8[gbëLl‰­3±uf¤qf¤qf¤qf¤q&êÎDÝ™¨;ug¢îÌŸŠdþT$·O„“9£‘öw©<|õkØlòÖ7«ww9í.ßG]¯×ô‹ÕÞ¨ü®×éQ{£rÏÞ^þ,ãÀ¬Õú-/¯i°Q½ÞÓ5WõßÝú®òëÙîÏÏ6îXó=ª½ï^ÓÔšüîqÖ:m6:­¦å³ú1*·:ÝÙû~×ø2Ú.¬z{ßÏNŸZë×»ÞWó­úgM^Ï]£åß³7[¿½4:ÌÚµ¿»]YËÙËÎj½¯Úõj/=?fÇ]ïv¿«~¼ÚiÏŽv¯×‚GÛñ®ßÑ×d¯iŸ«^WWõì®wïiŸÙó³É»|v_^v½§!­ö¼Êñ*=«÷ÅÝÓ³iWù®N£XÓ£îãW·_k¾Õi7«üj}ž¿ª>®²ofíå•õëþ|ïûÑv5ªÏ»ÝXýY½þÕrZ}ò~ó’ÛÕŽ­ùFí[µäÝ>¼Ÿ+¬iµœ­þìº?Þ{ÝßfÇ¿Ññyµ\vݯWûfÏ«Þ¬úµ´:^x—óêýnµF¿÷n'šœ÷¸9;^¯ŽW^~˜÷N]tZÁ§ôõ¢“­úLÛÅĽhç{\ÛÃÓJt²ºçðd´óÛ÷(k-ºº·³ºè¹±\´òuÃÉáv{=È<(<¨¼Ê©—fÇw¯ró’›m/šÜÕõkõËK_ï{ïqwT®—fïWV}Vyõ¼ûY£u­‹k‹CÏî¬-ŠÝ[´[[LZ‹ÖV£p{8Ùº˜ô,f·F;[£§;{Oßû§ÊuìÎãäB„VˆÐ Z!B+Dh…­¡"´B„VˆÐ Z!B+Dh…­¡"´B„VˆÐ Za„ia„ia„ia„ia„ia„iá"á…¦…¦…¦…¦…¦…¦ÜÛ:poëÀ½­C!î/ÄýÜÛ:poëÀ½­÷¶ÜÛ:poëÀ½­÷¶…è¾ÝsoëÀ½­÷¶ܧ:pŸêÀ}ª_2 ÞiƒoÜÛ:poëÀ½­_¨H{[îm¸·uàÞÖ{[¿ðzˆÔ+‘:÷¶ÜÛúõ€ÚˆÇ+ñ8÷¶ÜÛúõ€ÚØOëk?ý48ù™|CÅ·(äowÿßE%ß¾ûytò-yMïx¿n®æß5ÍÐK»_{¼_?4ù]vF“÷ëáª]í|öñýYÚù¨Üêëý¨½ûóÏÒÏ­÷ªq{¶¼wC^ýg÷ýèQýÍKþªûѨüÕrÏò|µzÿ}ΆëéÕô¯Ú=ïý¼·Ú­ÉkºÐ{zw4N{[õÌÚ•³úkmÏ£×ïõÜ¿ë½gvœ=?kÏúýh~ï÷ïqR;µÛ“³¦Yÿ­úzr^ú¬ú­åª}ï=>?ª¼¯j÷½´k”ÞÉËïQ9ïò²Ú›­ßÝúîåµãž|ÏîýÊÞ¿ÚÞÄ¢q•èU ;¾éÅ¡»ê¢Øj½^s´î6~“S ×¢ŽßüÓ°®Çk{5[£“{vÕèn£Ýyœ\‰l+‘m%²­D¶•È¶ÙV"ÛJd[‰l+‘m%²­D¶•È¶rQàšé‘m%²­D¶•È¶ÙV"ÛJd[‰l+÷u®Œê®Œê®Œê®Ä¼•˜·óVbÞJÌ[‰y+1o%æ­Ä¼•˜·2ª»2ª»2ª»2ª» W¢áJ4\‰†ë;4̨îƨîƨîƨîFœÜˆ“qr#NnÄÉ8¹'7âäFœÜˆ“£º£º£º£ºt#‚nDкA7"èF݈ t#‚nŒênŒênŒênŒênÄÖ-†—Oƒ“¿½¼P¾!äÛ÷\èš{,ÿ‹NÞõZÑ“ózÍ•¿êz½ôì~lÖ¾÷.'ïú^-¿Y¹ÑäUŽWMsízݮꗻÛÝêk­5íª§«ê{Ô®w9îgí=º¿=ê>=;͸ªwy{O [õ­>'Ìú5:5ªç*»»Ÿïv•¯U¾'g¾~Ô8¬éY-íxÔnO¿–u\m^í`´¯–ïûUÿ¬~¬ŽÏ£i´]­ŽKoWûWÏîl{Õ3ªWK³×1ª¯'×Ó7j6ÿh>ïò[M^ã°W~«þ«žcFõ­>ÇyÚ±Õíüöû¥-«F×*8öMÎåjÅ°ê"ѾTö¾¿mOáú:{[‰Ö¢¢­þY±s·ü4œl\ô\[$¼ëŸr½Ó89~B{=$dT4¡½<< zèA zéA¤‘DzéA¤‘DzéA¤B„=z ô@èСB„$zèA¢‰$zèA¢‰$zèA¦™dzéA¦™dzéA¦™zPèA¡…zPèA¡…zPèA¥•TzPéA¥•TzPéA¥•4zÐèAûD8ù4|¿°õïøþ†ùÝí󯽓õz½<úzv¿®ÌÚ}T¹x·Óg©§]å½ÛŽ·ü£ýÓòí®ß«õôô­N?­ÚïÉ=Úo/ý»ìîŸWíîj¿7¼Û£wþÑé`ïvéu½^Óf^ã»÷4õ.}«Ó¼£v{ùgõõôkçgï£íd¶Ÿ¹Oûvô]õ<ãÕNzzGÏÏêµæ·ê[íÿ=;^ï?^÷…^ZmdzãËèuÎÚí%¯ç¯v0{ßšõc4Ÿ–VÇž>ï~ÚÓ³jתo4ÿ¬þÙü£íx´ŸŽÊYÏkrÏ6õäíükïd^ÇŸfïäx‡ÄaqØAv‡ÄaqØAv‡ÄaÑÖѨ­Q[£¶Fmí6^Oãõ b6D̾$dTP1| †Äð>Ã"õ@¤ˆÔ‘z Rá6úF¤ˆÔ‘z Äãx<âñ@<ˆÇñx Äãx<âñ@<ˆÇQw êDݨ;u¢î@ÔˆºQw êÄÖØ:[bë@lØçû\`Ÿ ìs}.°Ïö¹À>ˆ C­/Ÿ'#}ùˆ•oÊ÷ÅÉ»_çni×cên»«öwÛóÒë]?Þõ5+7j÷êë͵ÜÕíКïêqcöõɪUΚvM£ìN³ýmvÚòYûÙUzWí]=ýäÕ?¯¾Ï{çÕoÞÕò[ÏÏÖËl?_ŽÕÛÓ·:͸û9v×´òêôeOîQå²»Þv¿ï¬N?Ï~¿ë=ãêë˜=Öô­Ž£ò«ýîþxö:½ýèÙïßõ|¼z_ZmW=9«žÑñÏjwuÜÜU¿½äU~½|š=/¹Õö6+ç5hzWÓh½Yõ¬Úõ~ŽÛuÿíÊÝGÍZ÷ôíí‰ÜÁºVܨ.b­í|/ß‹îDOw÷€¶bveéî^ǽëU¢ÁGåÔ¨h-:¹·(ö}ý*X|'F0F0bÞ@̈y1o æ ļ˜7óF,rüz ÃgbøL Ÿ‰á3Ï\X<3Ú:3Ú:Ýg¢ûLtŸ‰î3Ñ}f´uÎòòipò-êøöÿýâ×_~üÿ³hå/£‹]kivzcUÎ+yÙ»º¼­rWMGì¶ãýzímuÀš¼^WWíxçÕ·k:ç*=Z>¯×M-ÿUãú®iÒQ9«ü³Ü¯j‡ª_Mnw{Õ»Ëþ®vhƺºœWÛÁ®éI-Ÿ¦gÔ?«>ÍÙëž=¯Ùïß5Míe×jg×ó›W­«Úiï{¯þ¬å÷Ò§éŸM³õÑÓ3ë‡vü(¹]×ïu?ßUoWÝßfû¯UWÿµÛÓoMW=oŒê¿jüÕ·ÚŽ´|»Êûªþ¡é[½oí÷¦ÛqqjmÏä^t­†95{ÖhØÞÆîþu§îá_µüÝÝÙÛ¹·¨xïgÖ¨èÑÅÇ5½g œÅɱ#6bÄFŒØˆ1b#FlĈ±#6"ÁF$؈‘`#lD‚H° 6"ÁF$؈÷ñ^#ÞkÄ{x¯¥wÚx=Ä{x¯ï5Fæ6Fæ6Fæ6Fæ6Fæ6Fæ6Fæ6Fæ6Fæ6Fæ6þ¬ ñg?+hüYAãÏ Z}§×ß4þ¬ ñg?hü‰@ãO"ÐðùŽÈö׃ẵƒŠà~á>âÂ}Ä…ûˆ ÷î .Ü\¸'¸pOpážàÂý½…û{ ÷÷îï-Üß[¸¿·poáþÞÂý½åý½? Nþ#êø}ÌÈäß^Î}“•üß}]ìúê×°Ùä­oVïîrÚ]¾º^¯é«½Qù]¯Ó£öFåž½½üYÆY;«õ3Z^^Ó`£z½§k®ê¿»õ]å׳ݟŸmܱæ{T9zß½¦©5ùÝã¬uÚltZMËgõcTnuº³÷ý®ñe´]Xõö¾Ÿ>µÖ¯w½¯æ[õÏš¼ž»FË¿go¶~{it<˜µ3jw»²–³—Õz_µëÕ^z~ÌŽ»Þí~WýxµÓžíxÔ®wÿ]Ogó­ö—žÙç¢Ù4ú¼3úÜ4ÚŸVíÞ¯ééÙé}?[Þýåªö¢aÝøÒÅlÞsØhWÛóW]Lº³ˆµUκ¸·†}µEÀ­‹S÷öv6ï¬Dk[W3ïÔ‡u±ðiœ,ß…K°„K°„K°„K°„ Qœ¯ô уD=Hô уD=Hô ÓƒL2=Èô ÓƒL2=Èô ÓƒL =(ô ÐƒB =(ô ÐƒB =(ô ÒƒJ*=¨ô ÒƒJ*=¨ô ÒƒJ=hô ÑƒF=hô ÑƒF= ‚>¥þzpð ð ò@xxyPxPy@zpЃƒuDÝQ÷AÔ}u=8èAøþòipòo/'.þ‚Ïßîþÿò“Ïjtò-y¿–í’Íï5M0šVK½®ëªé§Ýå¼ú¹*ç5Íe•Û%ï¼ËÕšµ¬¾ŽÍÊ=«ÞGM«XÏúÏîûÑ£ú›—üU÷£Qù«åžåùj÷ô±fÇ{új×ôä¨íxôûÕþkM³ÏV=ÞÓÖZêÕïêtó¨ýQ¹ÑémëõZýózîßõÞ3;ÎŒžŸµgý~4¿÷{€÷8©ÚíÉYÓ¬ÿV}=9/}VýÖrÕ¾÷ŸUÞWµû^Ú5Ž?Ê?ïäå÷¨œwyYíÍÖïn}÷òÚqO¾g÷ƒ~e±fëv°¤ÝÚ[ìºM<ˆC{‹Sw£„ÿµëý€µhâž÷˜[ÃâÊâÚêâÔ½Ÿ X÷DVÚɬÝyœ|,FWŒ®<]y0ºò`tå°V ÀŠDhŒÈ<‘y0"ó`DæÁˆÌƒ™#2FdŒÈ<„©Dê‘úA¤~©Dê‘úA¤~©Dê‘úA¤~©Dê‘úA¤~©Dê‘úA¤~©Dê‘úA¤~©Dê‘úA¤~©Dê‘úA¤~©Dê‘úA¤~©Dê‘úA¤~©Dê‘úA¤~©Dê‘úA¤~©Dê‘úA¤~©Dê‘úA¤~©Dê‘zøþýåÓàä[Ôñ-☋[Ågþ};ÏýÚ;y$y½>^o×ëàÕv¼ëwvzfU¿WÿM»ìîîç«öý|Uÿ™MÞíâÑÓmÞv½Ïïžñ¾{ÙïÉÍŽ»Úïhz–v7šo´¿ÎöÛÕú¾jÌKn÷øç=íس7*7úÜîý±kúÔêÏêõ¯–Óê{”÷{˜—Ü®vlÍ7jßz¬%ïöáý\aM«ålõg×ýñþØëþ6;þŽÏ«å²ë~½Ú4{^õfÕ¯¥Õñ»œWïw«õ0ú½w;Ñä¼ÇÍÙñzuܸjüóòã×ÞÉ¿öNþ‹ÜŸmïd ŒÈ ŒÈ ŒÈ ŒÈ ŒÈ ŒÈ ŒÈ ŒÈ ŒÈ ŒÈ ŒÈ ŒÈ ŒÈ ŒÈ ŒÈ ŒÈ ŒÈ ŒÈ ŒÈ ©"õ@¤ˆÔ‘z RDêH=©"õ@<ˆÇñx Äã¨;u¢î@ÔˆºQw êDݨ;ubë@lˆ­±u ¶DÐ:A"è@ˆ“qr NÄÉ89 ¢á@4ˆ†Ñp æ ļ˜7óbÞ@Ì9ÙO#ûid?집ý4²ŸFöÓøÚO? N¾íüŸo‹]ßþnßߎœßG'ÏNS¬ÊYõx¿nÊ­êÙõºè¥ÏšÏ{ZaVn×k¨UŸ·ü³Ló¬¦Ý×kÕwUÿ”ÝU½ÞÓV½»Úó³´»U½ÞþÚ-Çg{~x–ûdz·«Ñó£Ó£»¦½V§¿zú®êoÚ÷^åܳ§%o{WM‹Zíúw<Û®´ä]Ž³×«é³žï•Ãª]kÚý|õ¨úíéŸ7vù5*?;ôòÍÞv»«ýÕ»~WÛèÕúð*§^šß½ÊÍKn¶½hrWׯÕ//}½ï½ÇÝQ¹^š½_YõYåÕó >Ô0lZ1¢5ºÖº¨óèÁ=ü«-úüÃZ÷ž¶âZ#îŽîV!“3âx-j[•ëØÇÉ‘È6ÙF"ÛHd‰l#ñk$~į‘ø5¿ÆðN[åáñkdDsdDsdDsdDs$²D¶‘ÑÉ‘ÑÉ‘ÑÉ‘È6ÙFy§×Cd‰l#‘m$²D¶‘È62:92:92:92:9óFbÞH̉y#1odtrdtrdtrdtr$ŽDÑh8 GFG¢áH4‰†#Ñp$ŽŒ4ŽŒ4ŽŒ4ŽŒ4ŽÄÉ‘89'GâäHœiiii‰ #t$‚ŽDБ:A q²' q²'ËgÂÉ·=’oÑÈ7\üåî»ßîŽÿëÿc{'¯&ïÇù«ì®ÚßmÏKï®×­Ñ´»=ôô]}½£ù¯–»ºZó]=nx¿>º{É?êþá5bµ·[n×xyuûñꟳõµêßÕ÷yïü£úg§Ã¬úGóyõs¯éÊÑé¸ÑiDë}f÷s¬·½ÙòšÆíÉ÷Òª]ïzÛý¾c½¯÷¯úz–ë˜=Öô­Ž£ò«ýîþxö:½ýèÙïßõ|¼z_ZmW=9«žÑñÏjwuÜÜU¿½äU~½|š=/¹Õö6+ç5hzWÓh½Yõ¬Úõ~ŽÛuÿíÊ÷̵î%¬íIüAΈ±‡£¢ïå{Ïâs+6Õ¢Ž»ø·w½Æ½˜{r±ÖìZž¯_%*z' q²' q²' q²0X,ŒF ´A ´A ´A q²' q²' q²ÄwÚx=Œ.-DÐB-DÐB-DÐB-DÐB-DÐB-DÐB-DÐB-DÐB-ÄÉBœ,ÄÉBœ,ÄÉ’ßiãõ' q²' #…‹] ».v-DÐB-DÐB-DÐB-ÄÉBœ,ÄÉBœ,ÄÉBœ,ÄÉBœ,ÄÉBœ,ÄÉBœ,ÄÉBœ,Äɉ89''âäDœœ>NæþÉ·…¯o__Þï§ücÏäÛñXtòêã™÷küU×ë¥Çû:¼C­vv½Î¯ê•M^åèý®Éy½^ú±š®ê—»Û÷t̬ÝÙtU}Úõ.ÇÝã﬽G÷·GݧwO;îzžµë}_ÿwGÖéD¯iÇ]vw?ßí*_«|Onu:ûêñnô:z~xµ¿ž¾Ñvçõ<¹<ÝÃgbøL Ÿ‰á31|&†ÏÄð™>ÃgbøL Ÿ‰á31|&†ÏÄð™>ÃgbøL Ÿ‰á31|f$xf$xf$xf$x&ºÏD÷™è>Ýg¢ûLtŸ‰î3Ñ}&ºÏD÷™è>—ðòipò×Ç·eF*ÿˆ@~‡–­üõ'ß’×c®—üìãõêk£&¿ê磯g÷ëʬÝG•‹w;}–zÚUÞ»íxË?Ú?-ßîú½ZOOßêôÓªýžÜ£ýöÒ¿Ëî®ñyÕî®öûèqû=zçön—^×ë5mæ5¾{OSïÒ·:Í;j·—V_O¿v~öþ1ÚNfû™û´oGßUÏ3^í¤§wôü¬^k~«¾Õþß³ãõþãu_è¥Õv<;¾Œ^ç¬Ý^òzžñj³÷­Y?Fóiiuüèéóî§==«v­úFóÏêŸÍ?ÚŽGû騜õ¼&÷lãPOÞ¼w²5 ׸øóô¢ÎÚÁF|©ÚUä>Dm+ع»Øµ†Å5¹NT¯uð?øWû:zX\ÝZÙ»}>“3#?3#?3#?3#?3#?3#?3#?3#?3Î\|8sñáÌŇ3£E3£E3£E3£E3£E3£E3£E3£E3ñx&ÏÄã…x¼âñB<^ˆÇ ñx!/DÝ…¨»u¢îBÔ]ŽwÚ *x=D݅غ[bëBl]ˆ­ ±u!¶.Äօغ[bëBl]ˆ­ ±u!¶.Äօغ[bëBl]ˆ­ ±u!¶.Äօغ[bëBl]ˆ­ ±u!¶.Äօغ[bëBl]ˆ­ ±u!¶.Y^> Nþý'ŒD¾?Ljå{'[“÷ôLOîêiŠY»»¦[¼ýÐäv¿Ö[í¬^ïUåµ*ï=0«÷êä=­Ñ“›Õ÷¬ã–ÿÙúýêx÷,åíu_ÚÝVí[åží:®º_ŒNêŸÍ7Û_Ví¯NÃiò³Ó¶V=«ã‹Õî¬}ïqsuºøêéæÕ똕ïé¶Õ¯·æ÷šV^µ¿ªÏš¼Ûí®v¼êW9ïj§Öü½ïg¯o´œ­zµó«~®Ê­Þ/­i÷uö¾ïù1[ï^ãîª=9¯ö³:úÓ³kÕ·«~­i—ÑçD/?vݬé^þµF +8YÕ§,vÝÝëøs+{[÷NÖ°o¯jּDzÕ­ÚUpñ½>ëÏ>D=[£À;‹qkxߺ(öWù³Z¿¿|œ|Ã÷¬¹w2Ñ1ÃÆÊ¿öNöг+ÿ®rñ~ÍÓä¼ëkVnÔîÕ×;šÿj¹«Û¡5ßÕãƮ׺Q?VÓ®éµÝiušæÑãï®r¼º~¼§W¼ïÏ»§­~<*ÿ¨~mzÈkšu4ŸW?ß5ºzýWMûjÉ{Úºgg´¼f§5{ò½´j×»Þv¿ïX¯Ãë}Á«¾žå:f5}«ãߨüj¿»?ž½No?zö{çw=¯Þ—VÛUOΪgtü³Ú]7wÕo/y•_/ŸfÏKnµ½ÍÊyšÞÕ4ZoV=«v½ŸãvÝ»r¿öNþµw2ëíϲwrúT÷zyPxPyÐp€ˆÌ׃ƒ‘ÂzèA¢‰$zéA¦™dzéA¦™dzéA¦…zPèA¡…zPèA¡…zPéA¥•TzPéA¥•TzPéA¥4zÐèA£4zÐèA£ z<ˆ=8xxy ßK^íÅÛOï×/MÞûõøj;£úw½Î]Õï¼Òjy;֎ÚéÉyÝ'¼ô{ûã¥Ç{ºsw¹ïʵþ«î“Þúv§V½«ã‡—MߣäV§q­ú´|½d~ö®Q»»îš¾Õö°:l¦õk4­NGÏ–Ïh¾]í`öxôûÙqCKÞãU/ÿ¨ÝY}^ã•–¯çϪžÙú}T¿¿úùqµý¦Ñų̂œU÷sÓìuÌŽWZš}αêëÉy=‡izGýÐÎ÷ò{é[µkÕó!ÿ&Õðàèž¹Ý=–½£“7ùgƺJ9¾éÓ¢“5œÜÙËZÝ+ºí¬Ê£¶µhç^}h×;“qX  Äa8,‡â°€(Û׃̃ƒÊz@„ˆÐZ B Dh-¡"´èA ‘DzéA¤‘DzéA¤‘Dz ô@èСBøÀŸþD ð'?ü‰@àO"øÀŸþD ð'?ü‰@àO"øÀŸþD ð'?ü‰@àO"øÀŸþD ð'?ü‰@àO"øÀŸþD ð'?ü‰@àO"øÀŸþD ÔOŠ“oÑÈŒXþ‚óÄÉ?¾ÿµwòŒ¾Ýk³ù®zÍšMÞõá¥×»>K=í*ïÝv¼åퟖowý^­§§ÏëµyÖ~OîÑ~{éßew×ø¼jwWû}ô¸áݽó{M³ZÓìøé=}ÚÓ3š_Ó×Óã=-:ªo¶^­úÌÓ‘‹úzúµó³÷ïiê]×;ëçlÚ5þXõ–³5­Ž½öm•×ŽGíx½ÿxÝziµÏŽ/£×9k·—¼žg¼ÚÁì}kÖÑ|ZZ?zú¼ûiOϪ]«¾Ñü³úgó¶ãÑ~:*g=¯É=Û8Ô“ÿµwò¯½“yš½“S`„i`„i`„i`„i`„i`„i`„i`„i`„i`„i`„id„id„id„id„id„id„id„id„id„i$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=æO†“ï#‰–oçÁ ¤<†“µä5M3k5]ý˜ë=­5šv½xû»ÛÎjùízí²ÚñJÞÓ «r^ùFõíš^¸J–ouEÓ;ªÏÛ®õü.ÿ®îï½Îï¶/·»½ŒêÝeW;µU{Ƴê±æÓôŒú7;­7Z_š½ÑóšýÞyk{ñïgŸçõüæÕŸGëãªvÚûÞ«?kù½ôiúgÓl}ôôÌú¡?Jn×õ{ÝÏwÕÛU÷·ÙþkÕãÕGíöô[ÓUÏ£ú¯?Gõ­¶#-ß®ò¾ªhúVï[»Ç½év`Ä¿Ãѵ½½u{ÖÅ•{{õºû×Á¿½èiëÞÉ*×ðïNV¯£nŒÆÅûšÞ³ÎâäȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌÈE›#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDêB¤.DêB¤.DêB¤.DêB¤.DêB¤.DêB¤.DêB¤.DêB¤.DêB¤.DêB¤.DêB¤.DêB¤.DêB¤.DêB¤.DêB¤.DêB¤.DêB¤.DêB¤.DêB¤.DêB¤.DêB¤.DêB¤.DêB¤.DêB¤.Dê"Ÿ'ÿ…ÿñÇäÛçß_N´Ì…®ì¥ü·»ÖÒ£^Çf“×còª¯×w/ùÕëMÞå´»Þvéõ~ÝŸÕ{uZ-—]Ó$½ïŸe<Ðò?[¿÷ž–Yµç=þ>‹^ö­rÏvWÝ/¼§ÍFíõä¼Ë}uÜ´–óè4àèôš5/]5­ç=nŽÖCïûU¿gÛÁh¿õºÏYë×»]ÝŸ·æ_í^öWõY“w»ÝÕŽWýñ*ç]íÔš¿÷ýìõ–³U¯v~ÕÏU¹Õû¥5í¾ÎÞ÷=?fëÝkÜ]õ£'çÕ~VÇ¡Qzv­úvÕ¯5í²3úœèåÇ®û‘5ÝËkxX]¬¹¬êSöNv[ìÚˆM­‹:«xºƒÕE¶8ùÃÞÔJô´æ¿ºgsg±ksô´Ç÷XžÇɈLaD¦0"S‘)ŒÈFd #2…™ÂˆLaD¦0ºR])Œ®FW £+…¨[ˆº…¨[ˆº…¨[Ê;mô¨[ˆº…¨[ˆº…¨[ˆº…¨[ˆº…¨[ˆº…¨[ˆº…¨[ˆº…¨[ˆº…¨[ˆº…¨[ˆº…¨;u'¢îDlˆ­±u"¶NÄÖ‰Ø:['bëDlˆ­±u"¶NÄÖ‰Ø:['bëDlˆ­±u"¶NÄÖ‰Ø:['bëDlˆ­±u"¶NÄÖ‰Ø:['bëDlˆ­“|ù48ù~Aë6þ\û÷—wú¯8yôq×kbTΚ¼õÍêÝ]N»Ë÷Q×ëõ`µ7*?ûúy/wu?zööògfí¬ÖÏìët/íšö™=¿[þQú®òëÙîÏÏ6îXó=ª½ï»¦—¼ïÓZÒìÍNsY§+{~ŒÊÍ–›÷´®5yM?[åF§•­þYëCK»¦‹5ùÑüzî-ÿž½Ùúí¥Ññ`ÖΨýÝíÊZÎ^vVë}Õ®W{éù1;îz·û]õãÕN{v´ãQ»Þýwu<Í·Ú_zvfŸ‹fÓèóÎèsÓhZµ{ÿ½¦§g§÷ýl=x÷—«Ú‹¯Å6/=¸Wï=æìbSmOd£œ†K?,Š­àáøWÛ£Z)¿ÞbÜæÅ®ïìô¢¶5<~o·W–ö‹NÎŒzÌŒzÌŒzÌŒzÌŒzÌŒzÌŒzÌ\H8[gbëLl‰­3±u&¶ÎÄÖ™Ø:[gbëÌ…„3Î\H8s!áLÔ‰º3Qw&êÎDÝ™{ófF‚gF‚gF‚gâñL<ž‰Ç3ñx&êÎDÝ™¨;ug¢îLl‰­3±u&¶ÎÄÖ™:A"èB]m]m]m]m]ˆ­ ±u!¶.Äօغ[bëBl]ˆ­ ±u!¶.ÄÖ…ºA"èB]ˆ Kx§×C]ˆ  t!N.ÄÉ…8¹'—Ï„“ïQñ=JþßßÎÝd¿üÂÉ{õî.§Ýåû¨ë]}}µ7*¿úúú¨~ôìíåÏ2ÌÚY­ŸÙi„^mÏÞ×ÑKWõßÝú®òëÙîÏÏ6îXó=ª½ï^ÓÍšüîqÖ:¸:mêUî«þiúzßï_¼¦SGóÏN+N ïz]­çÕéáQ;£i´ü{öfë·—FǃY;£öw·+k9{ÙY­÷U»^í¥çÇì¸ëÝîwÕW;íÙÑŽGíz÷ßÕñt6ßjéÙ™}.šM£Ï;£ÏM£ýiÕîý÷šžžÞ÷³õàÝ_®j/¿pò/œL¹?N.DÃ…h¸ ¢áB4\ˆ† Ñp!.DÃ…h¸ ¢áB4\ˆ† Ñp!.DÃ…h¸ ¢áBÌ[ˆy 1o!æ-ļ…˜·óFAFAFAbÞBÌ[Ñ\Ñ\Ñ\ˆy 1oatratratr%æ­Ä¼•˜·óVbÞJd[‰l+‘m%²­D¶•QÕQÕQÕȶÙV"ÛJd[‰l+‘m%²­D¶•È¶ÙVF WF W"ÛJd[¹puåO8*ÂQùŽÊŸpTöÓÊ~ZÙO+û\eŸ«ìs•}®¾ö¹Oƒ“¹¸5¹¾{9¸¾}¾í§¬î|K^“«ù®z¼÷ÖsÕkÐÕÓ\WŸï¥ÝÓM«z½ä4y¯öþ(;£ú½§‡fõ>j\Õ·z½ÞýÃ{ºÏ[ÿUÓP£zf§õ®šn»ª=^­ÿªû¤·¾Ýã©U¯×4—×8´:Íè%g®-ïvaÞµ7Z.£ÓÊV?zÉ«=Ì–¿µ\fýM³å»Z>£ùvµƒÙãÑïgÇ -yW½ü£vgõyWZ¾ž?«zfë÷QýþêçÇÕö7šFï3£rV=ÞÏM³×1;^iiö9Ǫ¯'çõ¦éõC;ßËï¥oÕ®Uχüæ¼Ç« Þü€k•Å¦?Èy/v½É¿Þ^ÂÚ"Û*·î=}¿X´¶˜tgk³Ü Þ¿×׫ízçqrMï`q‘m%²­D¶•È¶ÙV"ÛÊhÞÊhÞÊhÞÊhÞJÌ[‰y+1o%æ­Ä¼•˜·óVbÞJÌ[‰y+»®Œ®Œ®Œ®DÕh¸ W¢áJ4\¹ØueÔpeÔpeÔp%N®ÄÉ•8¹'×w8™‹]WFWF7F7"èF݈ t#‚nŒ4nŒ4nŒ4nŒ4nÄÖغ[7bëFlÝiÜiÜiܸ@v#ênDݨ»u7¢îFÔ݈ºQw#ênDݨ»u7¢îFÔ݈ºÈn\ »ÅO„“¹'ò óï7ÈÜïŸükïäŽÜ¨]¯ÇÝÕ|W]ïlò.§Ýõ¶K¯÷ôϬޫÓj¹ìš6ë}ÿ,ã–ÿÙúýêx÷,åíu_ÚÝVí[åží:®º_xM›YÓ®ûƒ—ýÑé¿ÕéX«þÕi]/»«ÓÊ£iuZtõ>èõœï=½k•ïé±úçÕ®îÏ[ó¯ö/û«ú¬É»ÝîjÇ«þx•ó®vjÍßû~öúFËÙªW;¿êçªÜêýÒšv_gïûž³õî5î®úÑ“ój?«ãШ?=»V}»êךvÙ}Nôòc×ýÈšîåíükïäŸÖïÓïܸ nctectectectectectectectectectectctctctcDfcDfcDfcDfct#RoDêH½©7"õF¤ÞˆÔ‘z#RoDêH½©7"õF¤ÞˆÔ‘z#RoDêH½©7"õF¤ÞˆÔ‘z#RoDêH½©7"õF¤ÞˆÔ‘z#RoDêH½©7"õF¤ÞˆÔ‘z#RoDê H=R=8xxy Mÿlš­žžY?´ãGÉíº~¯ûù®z»êþ6Û­z¼úï¨Ýž~kºêycTÿUã稾Õv¤åÛUÞWõMßê}k÷¸7Ý”=‚5,iÞû··²bO†íDÿª‹S{ù§Eñ¯u±k5ª·=­î±l\ܺWôŒ¬ale‘r?œ‰œ"‘S$rŠŒ®ŒŒ®ŒŒ®ŒŒ®FW £+…Ñ•ÂèJat¥0ºR])Œ®. ,ÄãB<.ÄãB<.ÄãB<.ÄãB<.ÄãB<.ÄãB<.ÄãB<.ÄãB<.ÄãB<.ÄãB<.ÄãB<.ÄãB<.ÄãB<.ÄãB<.ÄãB<.ÄãB<.ÄãB<.ÄãB<.ÄãB<.ÄãB<.ÄãB<.ÄãB<.Äã’è£Ç…ÑãÂèq!R"u!R"u!R"u!R"u!R"u!R"u!R"u)áåÓáä*¾aäowÿ)c忽wòìã~O·üh~ïékš}mî}o=?*§Éï²3š¼^k½ììj绦v%ïrµæ_íÞÓ »ÛÃn½WÛ»§®ºoÍêÙ}¿ëÉ=[»•´_^rÏò|µzÿ}ÎëéÕô¯Ú=ïý¼ç=ýª%¯iÇÕrYM£Ó±V=³öGå¬þZÛóèõ{=÷ïzï™gFÏÏÚ³~?šßû=À{œÔŽGíöä¬iÖ«¾žœ—>«~k¹jß{Ï*ï«Ú}/íÇåŸwòò{Tλ¼¬öfëw·¾{yí¸'ß³ûAÿèÞºœ¬áÈ^trÿ1öl:¹‡±æ¿v½½E»?œ\ÄZ•S¢ž?ØÜ+ZÛëXk'³vçq²0"S‘)ŒÈFd #2…‹ –J€U °Å)ŒâFq £8…QœÂ(Na§0ŠSÅ)ŒâFq ‘º© ‘º© ‘º© ‘z"RODê‰H=©'"õD¤žˆÔ‘z"RODê‰H=©'"õD¤žˆÔ‘z"RODê‰H=©'"õD¤žˆÔ‘z"RODê‰H=©'"õD¤žˆÔ‘z"RODê‰H=©'"õD¤žˆÔ‘z"RODê‰H=©'"õD¤žˆÔ‘z"RODê‰H=©§ô‰pòm‘kF+}y‘|Åükïä¿%ÕõzéÙýج}ï]NÞõ½Z~³r£É«¯šæÚõ»;]Õ/w·»Õ×ZkÚUOWÕ÷¨]ïrÜ=þÎÚ{t{Ô}zvšqUÿîòöž¶ê[}N˜õktkTÏUvw?ßí*_«|OÎ:}ý¨qXÓ³ZÚñ¨Ýž~-ÿê8¸Ú>¼ÚÁhÿ^-ÿÞ÷«þYýXŸGÓh»Z—5Þ®ö¯žÝÙö0ªgT¯–f¯cT_O®§oÔþlþÑ|Þå·š¼Æa¯üVýW=ÇŒê[}Žó7´c«Úùí÷Ë_{'ÿÚ;ùå'QÌO¿wr!F,Ĉ…±#bÄBŒXˆ 1b!F,Ĉ…±#bÄBŒXˆ 1b!F,Ĉ…±#bÄBŒXˆ 1b!F,Ĉ…±#bÄBŒXˆ 1b!F,Ĉ…±#bÄBŒXˆ #s #s #s #s #s #s #s #s #s #s #s #s #s #s #s #s #s #s #s #s ».üiEáO+ ZQøӊŸVþ´¢ð§…?­(üiEáO+ ZQøӊŸVþ´¢ð§…?­(üiEáO+ ZQÚ'ÅÉ\øúWtòFý³iöñ¼÷½õü¨œ&¿ËÎhZ}Û=äm—¼wò.WkþÝÓWV=£rϪ÷ªqÛkúnV¿w¾Q=»ïw=¹gkw£òöË{úf4y_ÿêýãªiV¯é¿Õimë´ÛêóÞUÓÏ«Ó£==^ï½Ô«ßÑû½÷tvOÎ꯵=^¿×sÿ®÷žÙqfôü¬=ë÷£ù½ß¼ÇIíxÔnOΚfý·êëÉyé³ê·–«ö½÷øü¨ò¾ªÝ÷Ò®qüQþy'/¿Gå¼ËËjo¶~w뻗׎{ò=»ôÿŠNþü3¹§NnŒ˜mŒ˜mŒ˜mŒ˜mŒ˜mŒ˜mŒ˜mŒ˜mŒ˜mŒ˜mŒ˜mŒ˜mDݨ»u7¢îFÔ݈ºQw#ênDݨ»u7¢îFÔ݈ºQw#ênDݨ»u7¢îFÔ݈ºQw#ênDݨ»u7¢îFÔ݈ºQw#ênDݨ»u7¢îFÔ݈ºQw#ênDݨ»u7¢îFÔ݈ºQw#ênDݨ»u7¢îFÔ݈ºQw#ênDݨ»u7¢îFÔ݈ºQw#ênDݨ»u·úýåSáäßñÿ_üÿåîÜײ'kiöµvVn×ëLOÎëõnVß.?4¹]¯ =»£ç{òW•×ªü®é†]×ã•VËåªiŸg´üÏÖｦÿ¼ìy¿Ï⟗}«Ü³]ÇU÷ ë´[/Ÿ5힎^µï==Zž^ÓpÖä5½k=?š¬å>jw5ÿ¨Õ똕ïé¶Õ¯·æ÷šV^µ¿ªÏš¼Ûí®v¼êW9ïj§Öü½ïg¯o´œ­zµó«~®Ê­Þ/­i÷uö¾ïù1[ï^ãîª=9¯ö³:úÓ³kÕ·«~­i—ÑçD/?vݬé^^ôÖ(W-zUÛ³÷MŸ†ï÷êUöàý`׸G°¶gs¯Z÷:^ÅØ–þP~ŠÿÚžÍæŸ ÜcâÙ½§xz'3R²1R²1R²1R²1R²1R²1R²1R²1R²1R²qAéÆ¥”n\PºaAéò J¿<<ˆ<PÛAmµÔvP°õëµj Ô¨-P[ oÀÖ¯™‘Ú"µEj‹Ô©-¾ÓÆ뉼áõ¯G¨M¨M¨M¨-Q[¢¶ÄëI¼žÄëI¼žLm™Ú2µejËÔ–ßiãõd^Oæõd^O¡¶Bm…Ú µj+ÔVy=•×Sy=•×S©­R[£¶FmŸ):ùË¿[t2»þãïwüÝľ¡åßF»Þ%gÕãýZ8*·ªg×k­—>k¾ÝçV¹Q=³¯WÉ{½ÖŽ&¯ö?ªÏ«>´|WõÿGÙ]Õ»k:q÷ùÕ|»Ûݪ^oÿFíΖã³=?<ËýãÙÛÕèyëtROnµ\V§'{ú®êoÚ÷^åܳ§%o{V}=ykšmV=³íJKÞå8{½š>ëùÕéçÙ÷=Mw½žû|½ïWï3³ãÆ.¿Fågǃ^¾ÙûÑîqwµ¿z×ïj{µ³Z^åÔK³ã»W¹yÉͶMîêúµú奯÷½÷¸;*×K³÷+«>«¼z^YìZ[œZÃÀorJ4ooQg-ºVê³8Y[tZÃØ]L|¿vo±p¥¼ï¯£=ÝÝù€u‘r-j[•ëØÆÉå{#Új„QD¶‘íAd{ÙD¶‘íˆæ׃̃ƒÊz@Ì{óļ1ïqЃƒôà DÃÑðA4| DÃÑðA4| DÃÑðèA ‘Dz@œ|'ÄÉqòAœ|'ÄÉqòAœ|'ÄÉqòAœ|'ÄÉÑð‘¨-Q[¢¶DmDÃÑðA4| DÃÑðQè[á•–uaY'ÄÉqòAœ|'DÃG¥¶Jm•Ú*µÕwÚx=ÄÉqòñ™p2qñ—»ÿo¿ýä»ßÿ'ïzí¾OÞóWÙ]µ¿Ûž—Þ]¯[£iw{èé»úzGó_-wu;´æ»zÜð~ |t;÷’ÔýÃkÅjo·Ü®ñòêöãÕ?gëkÕ¿«ïóÞùGõÏN‡Yõæóêç^Ó•£Óq£ÓˆÖûÌîçXo{³å5;Û“ï¥U»Þõ¶û}Çz^ï ^õõ,×1{¬é[ÿFåWûÝýñìuzûѳß;¿ëùxõ¾´Ú®zrV=£ãŸÕî긹«~{É«üzù4{^r«ímVÎk<Ðô®¦Ñz³êYµëý·ëþÛ•»Ç«VÌÙÃÄ=s­ÛŬâîÞƽ»X܈Mµ(Ý.þµFYk{åÔ=›µ½“{‹bß×ï¿ü¼~çqòÑ°V#ÀjXDÐ89'âä@œˆ“Ñp DÃh8 bÞ@̈y1o æ á6úF̈y1o ² D¶È6Ù"Û@dˆl‘m^ðzˆl‘m ² ŒŒD¶È6ÙFF"Û@dˆl‘m ² D¶È6Ù"Û@dˆl‘m ² ŒŒD¶È6Ù"Û@dØûO`ÿ ïúÂÙ"ûOdÿ‰ì?‘?Ljì?‘ý'²ÿDöŸxä—Oƒ“¹/òíÿ//oŸoçð÷sœ¼kfVŸ÷ãÐlºzúÇ+ßj¹<‹]¯»®ïêé!o=»ëÝ«ÿ^ÝnFõ^ÕfÓîé³U»Þýô*=«÷ÅÕi³Y»½´«|wO_Yå=]íßêô¦U~µ¾GÏ_UWÙïMÏYëwT~Ô¯ûó½ï½¦i5}ÞíÆêÏêõ¯–Óê{Ô£¦¯GËÁ«[óÚ·kÉ»}x?WXÓj9[ýÙu¼?öº¿ÍŽ£ãój¹ìº_¯öÍžW½Yõkiu¼ð.çÕûÝj=Œ~ïÝN49ïqsv¼^7®ÿ¼üйîEõŽF ÷öNV϶ê3F×Z£?œï,bÝ‹vÖ¢°?àdÓªú:‹NßëÓ°®9zÚX.Ö(õyœ,ŒÌFæ #s…ÈVˆl…ÈVˆl…ÈVˆl…ÈVˆl…ÈVˆl…‘¹ÂÈ\ad®02Wˆy…˜Wˆy…˜Wˆy…˜Wˆy…˜Wˆy…˜W„=`4¯0šWˆ†…hXˆ†…hXˆ†…‹C ‡F #€…8Yˆ“…8Yˆ“…8Y¸ ´pAiaÔ°0jXˆ …Zˆ …Zˆ …Zˆ …Zˆ …Zˆ …Zˆ …Zˆ …‹P ¡F #…ØZˆ­…ØZˆ­…ØZi,Œ4–FøS!ê¢niŸ'ßò- ™ù†˜¿àÿj~·Øõêk¢5­¾þí~LÚe·=/½Þõã]_³r£v¯¾ÞÑüWË]Ý­ù®7f_S­úWå¬i×´Õîä53ko·Ü®ñòêöãÕ?½§¹¬rWßç½óê·N³jù­çgëåªiÛU½=}£Ó‡«Ó²ZÚ5 ¬É–—×4óîrÙ]oWM [ǃY;={½|=¹G]Ç챦ouü•_íw÷dz×éíGÏ~ïü®çãÕûÒj»êÉYõŒŽV»«ãæ®úí%¯òëåÓìyÉ­¶·Y9¯ñ@Ó»šFëͪgÕ®÷sÜ®ûoWîWtò¯èdÖÛŸ&:Y))Œ”.v¸Øuâb׉Ñɉѕ‰Ñ•‰Ñ•‰Ñ•‰Ñ•‰‹]'.v¸ØubDsâÂÕ‰x<'âñD<žˆÇñx"OÄã‰x<'âñD<žˆÇñx"OÄã‰x<'âñD<žˆÇñx"OÄã‰x<'âñD<žˆÇñx"OÄã‰x<'âñD<žˆÇñx"OÄã‰x<'âñD<žˆÇñx"OÄã‰x<'âñD<žˆÇñx"OÄã‰x<'âñD<žˆÇñx"Oõû˧ÂÉ7DÌÏ·Å­¿ý8¾_èúÇw¿¢“GÒÕÓ?^ùVËåYììz Üu}WOyëÙ]ï^ý÷êv3ª÷ªþ3›vOŸ­Úõî§WéY½/®N›ÍÚí¥]å»{úÊ*ÿèqèjÿV§7­ò«õ=zþªú¸Ê~ozÎZ¿£ò£~ÝŸï}ï5M«éón7VV¯µœVߣ5}=Z^íØšoÔ¾õXKÞíÃû¹ÂšVËÙêÏ®ûãý±×ýmvüŸWËe×ýzµhö¼êͪ_K«ã…w9¯ÞïVëaô{ïv¢Éy›³ãõê¸qÕøçåǯèä_ÑÉ‘ûÓE''FW&FW&FW&FW&FW&FW&FW&FW&FW&FW&FW&FW&FW&FW&FW&.$œˆÇñx"ÏÄã™x<gâñL<ž‰Ç3ñx&ÏÄã™x<gâñÌ‹3,Î\°8sÁâL¤ž‰Ô3‘z&RÏDê™x<gâñL<ž‰Ç3ñx&ÏÄã™x<gâñL<ž‰Ç3ñx&ÏDÝ™¨;ug¢îLÔ‰­3±u&¶ÎÄÖ™Ø:Ag"èL‰ 3t&NÎÄÉ™89'gâä\Þi£oÄÉ™896œÌE®oÊß^N”̱‰–¿Þpò‹’v?¦xÉæ_Õ?›¼³GÏÊiò»ìŒ¦Õé„U9¯×2«Ü.yïä]®ÖüWMo\ÕÏ­÷ªq{×ôçÿŸ½·Û²…­“N÷÷<áÞÿ…ÎŹÜ_õÙYéY®¨‘@x­®ƨ‘° ’ŒÛšXËyÕ³Ê9}¿›•{µ~g-ÿl»¼Ê½Êó•—ûy¦Oë޵ʕäïêÝuëy»#g¿¯¦Õç ­¯÷ŠYš]ßU·óª~k9«ÛV{¾Zû¼žûO½÷¬Î3Öã«ú´¿[ë{¿xÏ“RÞªwVN›Ví×Ê›•ó’§•¯mWéwïùùYí}W¿Ÿ¥Sóø³ìóN^v[Ëy·—Vßêõ=-ïZ^ÊÏÊÏô~/àP ×~ˆ:¾bX!ºu†“§Ñº½"îðïy“ó±³µ}‘'EuKX÷§}—ðµˆ/×Y´ou,ô“U½ë8¹95"§FäÔ¸Ào#‚nDкA7"èF݈ t#‚nDкA7"èF݈ t#‚nDкAw"èN݉ ;t'‚îDкAw"èN݉ ;t'‚îDкAw"èN݉ ;tgTwgTwgTwgTw'¶îÄÖغ[wbëNl݉­;±u'¶îÄÖغ[wbëNl݉­;£º;£º;£º;£º;Qw'êîDÝÚÚ¨»uw¢îÎhëÎhëNÔ݉º{Í_> N~ bâc"æoøûÇ~äm‹]¯¾Îz•Ó¦Wyì=ÝN§Û÷Yç»ûzdÕg-×ëñªÜÓr¼ê~]=5¬êÙ½>^¯yÚr«ýÜË®SåŸ%ï.»^íþüjóŽ¶Þ³ÚÑûþ¸ëV•?=ÏžrÃIõ´vXË­¶›õ<¬rfå­ýB+wö»¶ý¬õf¿Kvyµß¬¼µþ³ž»¬í?Ó·z}gÉ:¬ê±ê?ݯ´íì¥g÷ºïêõê/3;Vç]ï~êúxõÓ™)oÕë=~wçÓÕz»ãe¦gõ¹h5YŸw¬ÏMÖñ´«÷ú»$g¦göûêuð/wõ—‹!K¸QÅ,-ú,Ê›èý€›¥èi!ÊyM¬b¢k%,--®]œZŠþ ïŠå…¨r+Ž—ð´„“Õ‹»E'÷úFmq±ÞÎÅz;ëí\¬·3º²3º²3º²3º²3º²3º²uw¢îNÔ݉º;Qw'êîDݨ»uw¢îNÔ݉º;Qw'êîDݨ»uw¢îNÔ݉º;Qw'êîD݃¨{u¢îAÔ=ˆºQ÷ êD݃¨{u¢îAÔ=ˆºQ÷ êD݃¨{u¢îAÔ=ˆºQ÷ êD݃¨{u¢îAÔ=ˆºQ÷ êD݃¨{u¢îAÔ=ˆºQ÷ êD݃¨{u¢îAÔ=ò'ÂÉ\Àú뵸5ÿ{-ÿaN¶¦SÙ§ôz˹ë5èn7×ÝÇgé´»iW®W9©¼W–«|o÷ЪÜgÍKVy»çë=>¼Ý}ÞòïrCY嬺õîr·ÝÕï–×}Ò[ÞéùT+×ËÍå5íº½ÊiÝ•ÖvñîZ÷îª>k»XÝÊZ;fÉ«?¬¶¿¶]Ví²¦ÕöÝmk½Sý`5oý}uÞ’÷|5«oÕ»*Ïk¾’êÍìÙ•³z}Ÿ5îï~~ÜíÖd½ÏXËiåx?7­žÇê|%¥Õç­¼Y9¯ç0I®Õéø¬¾—¼]½Z9êK˜s¶xñ$ºVŠþYN¹xö|--þ|È>õÆÊŸµXw¶(¶´'ñ,*Z,gÄûÓŽ/×C:ßuœ<)9)9¸(ðà¢ÀƒûßFJFJ. <¸(ðà^¶ƒ‘’ƒ‹¢îAÔ=ˆº±õ ¶ÄÖƒØz[öNm#¶ÄÖƒØzA"èA=ˆ ô ‚DЃzA èþ8ù-˜‰Ì$f23™@iÒ¥Jæ}ËPZ¤´Hi‘Ò€lß2”)-QZ¢4à×· ¥%JK”–)-³E3¥eJË”–)­°E ¥J+”V(­PZ¥m•gZÙn•W¡RZ¥´FiÒÞÆϧÁÉ£a.vý ÿþuÉ?"“±¯ò{œìõ¸¶[÷õß«ü]çë%Çû<^Å=c­o=~ªœ5y¿þjå¿š{ètºk\žîw«¯¿»ãëîyÍ;~]^-§­ww¿ÛM§ú¡·œëñ»¯ÛnÚu‡­ÊÛ}NXµë´{÷.½§ŸïNµ¯¶ü¬Ü®;÷îùÎz3;¼úßLžµßy=OZ¯¯÷sŽ×¼¸:ÏyÍ?³zÞÏç’Þ™=»ýT’«Õ»ZÎj×ìwk»xŸ¯U®”VÏÃ*oVn&Ϫµ¾µžwûí&¯yØ«¾Vþ]Ï1Vy»Ïq^ó†”×Ú!?~¿TâK5†]ÝÃØ,.®|Ŧ³èZÿjq¨´Hô4ú×hŸvQìiûíFŸ±ý|îÜ¿7¨FÖˆÃqX#kX«`u¬NÖiA§tZÐiA§ Z0hÁ ƒ ZðÙZ0hÁ ˆî˜7óbÞ@̈y¢†ß2•™ÆLg† ¢á@4¥ ¢á@4ˆ†Ñpˆ<ŸÈó‰<ŸÈó!NÄÉ89'âähA¢‰$Z@ˆ t ‚DÐ!Ó‚L 2-È´€Ø:[bë@lˆ­C¡…[bë@lˆ­CýþåSâä+B¾þF¤ü#’ùߣ“¥´ûød-·ûxíýz°ªgõ<¼íÊ~ÑêÙ=ß»Úk·¼Uï]ãít:åΑÊy»‡´éÔ| Õµq¿;ß½J{{Ý—N÷‡]ýÚr¯vwÝ/VÝH§ïû«ãeW¿Õýgu·ÎôjÝpÚú³dÕ»ªß{Þ\u {Õ·êÙ=Õò39Zû¼úÕõ¸¶þîøðÒ¿+O›¼ûí©~¼kW;Ÿê§Úú³ßWÏÏÚÎZ¹Òñ];wËíÞ/µéôyÎ~ŸÙ±zݽæÝ];få¼úÏî¶wÒx>D÷‘è>ÝG¢ûHt‰î#Ñ}$ºD÷‘è>ÝG¢ûHt‰î#Ñ}$ºD÷‘è>Ã'bøD Ÿˆá1|"†OÄð‰>Ã'bøD Ÿˆá1|"†OŒÐNZhÑ}"ºOD÷‰è>Ý'¢ûDtŸˆîÑ}"ºOD÷)ö/Ÿ 'ÿŒP~,jýøû“åN½†Z“·R¹ÝvÖ¦ÓÚk·¼Uï]ãítºËÝèåv³êÕ–÷ž×^mÜŸv_Yõvƒ®ÊY•gM^ó«Ç]÷ ­›kVO›NݼôkÝ`Öò^ne«}³dÕ»ªß{Þ¼Ë}ìõœ¿{«ågr´ör+këïŽ/ý»ò´É»ßžêÇ»öxµó©~ª­?û}õü¬í¬•+ßµs·ÜîýR›NŸçì÷™«×ÝkÞݵcVΫÿìÎCV{fzµòN]_m:¥ÇúœèeÇ©û‘6]ËÿÆÉ¿qò/¯ïËãäJ”Z‰R+Qj%J­D©‹P¿e3€²JüZ‰_+ñk%~­Ä¯•øµ¿Vâ×JüZ‰_+ñk%~­Ä¯•øµ¿Vâ×JüZ‰_+ñk%~­Ä¯•øµ¿Vâ×JüZ9]9]9]9]‰l‘m#²mD¶È¶Ù6"ÛFüÚˆ_ñk#~mį(µ¥6¢ÔF”ÚˆR#š#š#š#š#š#š#š#š#š#š#š#š#š?áhü„£ñŽÆO8?áhü„£ñŽÆO8?áhü„£ñŽV>Ñb×ý’ÿþêïq캶ˆ“½_¿N•óJ^úînï×zm9¯×/oýV=§^¯O¹éN¥»Ü§û뮼Sn§»äHõvÝk’\«¼»^ëWõ¾êõ·–;%ÿT?õ–¿êþ´ê±Ê=¥ÿT?ôvsiŸ¥Ý~`½¯z¹Í¤v[uŸj¯Ãªûï®ñh=™þU»N=ïÞõü2+·;¾ï꧳߽ƳTßKž$5­^™œU;¤ü³Ê:¯ûù©ëv×ýmuüjåx_«Þ™|mºëyÃ*ÿ®ùÓ*o·IõNµ÷]ãC’·{ß:=ï-÷aàѺ÷ï 7 úÄÅ•'‹I‹ø×Ë> ¯ ¸Tº3œ,.=YŒ[ÜcY‰Ùµ{EXäZh?é3¿Å®;qX'ëÄa8¬‡uâ°NÖ‰Ã:qX'ëD[ÑÉÑÉÑÉÑɽ¼“Æóatrgtrgtr'ïÄãx¼wâñNÔ݉º;Qw'êîDÝغ[wbëNl݉­;t'‚îDкAâäAœ<ˆ“qò NÄɃ8y'âäAœ<Â;i´Ñ¼ƒÑ¼ƒz02w02w02w02wA"èA=ˆ ô ‚DЃzAŽ¹Á178~Ç÷8ïÜã¼sóÎ=Î;÷8»ò<Ž¹ñ™pòÿñ埽‘ÿ 1ÿ(ûïÑÉ^¯¿»õVÃvÓéó÷~ì´–÷z¬<}|–¼ú‹·ÞíRy¯þþ,=VùÞ¯û«rŸ5/YårÿìʳÖÓê{Öýãô}Ç:okåY“WZ­·ü»î“ÞòNϧZ¹^îE¯yè”ûÙZn憓òVyR½Y²º1WõYÛÅê>ÕÚ1Kw»•WÛeÕ.kZmßÝö±Ö;ÕVóÖßWç )yÏW³úV½«ò¼æ+©ÞÌž]9«×÷YãþîçÇÝþgMÖûŒµœVŽ÷sÓêy¬ÎWRZ}ÎÑÊ›•óz“äZíŽÏê{ÉÛÕ«•ó¡þ3JQ¤Ö=s§{,{G'²o†W%\+Fÿjqüd/kq¯èI´³XNµ-E;Ï®‡t¾ë8y¿â×Aü:ˆ_#šñë ~įƒøu0:y¿â×Aü:ˆ_#ñë ~įƒøu0jx¿â×Aü:ÞáWFñNÚ?¶ïˆ~Ëf"3‰™ÌLa¦2Ó ”(-PZ ´@iá´Î Ï'ò|"Ï'RZ¤´Hi‘Ò¥%Ú–(-QZ¢´Di™gš)-SZ¦´Li…íV(­PZ¡´Bi•W¡RZ¥´Ji•Ò[´QZ£´FiÒ:[´SZ§´NioãçÓàä+2þúåœü«åËÂ×ÿ‹“½^3féôcÎ)½»úOëó’ë}}¼¯×j9«Þ»Ï×Zÿîrw÷Cm½»çU7œVþn9mò¾NϺx½¶kõ.wj¾¼»ÿxÏÕëµkßÝ÷yïúVù«nG­|k=¯qnuîÊɳº-wÝvRÚmïy÷”Ûút»œ¾n§ßw´çáõ¾pÊÝý¬óXÍKòvç?kùÝqwͯž§·3ý³ã§žwïK»ýjVN+Ç:ÿiõîΛ§®ï,yµß¬ž¤Ï«Ün[-ç5Hrw“õºiåìêõ~Ž;uÿ–»`Ý+Æ|”“¢y?,Â,E× ‹]O‰Vbgi‘f ×JvNå ‹NÏ—pý,zZ½h·°Øµ¶ÜÜ,éU.býáúþϯ¯ï2NÜûupï×Á½_÷~Üûupï×Á½_÷~Üûupï×Á}\G"zLD‰è1%J#zLD‰è1=¦LiD‰è1=&¢Ç”)­PZ¡m…gJ\™ˆ+qe"®LÄ•©Ð‚B ˆ+qe"®L•Ò*¥W&âÊD\™¥5J#®LÄ•©SZ§´Niƒí6(mPÚ ´Aiã4Ú6x¦Äý™¸?Ýg¢ûLtŸ±ßò[†Ò8~2ÇOæøÉ?™ã'cñî· ¥EJ#ºÏ?9¾“FÛ8~2ÇONŸ8:ùßþ¸èõßÞG'Ÿz\ÜM§^ó¬åvåœzMõ’§­·Û^^×eõ±|UÎéò»¯É«éî×ëY¹»û©wûÝ=ï¬Ê]íÇ^î©Õã»õN÷»]¹ÞöYõz¹µõvË­Ö¿ëþñêýÊz\ë^[u‡Í’õzx¹c½“µÝVÛy¦OJÞú¬îßÓ÷å]·ëj¿’’w;®ž¯$O{|Ö»zµéôóÕ³®ïLþê¼qÊ.kùÕù`Voõ~tzÞݯÞ×w·?Xõì^¯vš¥Õùݫݼʭö©ÜÝ×Wk——¼ÙïÞó®µÜ,­Þ¯´ò´åÅãJ¬«Å«¢„gØùŠ©…rÒ"ËìS.ê<‹®U],\ØãxŠí'{[÷Vãn)jûºÈµ„“•˜}'âäBVˆÃ qX!+DÃ…8¬‡â°BVˆ¶J¢´Di‰ÒÉZˆ†K¦´Li™ÒÉZˆ† Ñp!.DÃ…h¸óbÞR)­R#Y 1o!æ-•Ò¥1’µóbÞÒ(­Q#Y 1o!æ-Ò:¥uJ#æ-ļ…˜·óbÞÊíÊíJÌ[‰y+1oe„ve¿®ìוýº²_×ðNmãg•ŸIT~&QÙ¯+1o%æ­Ä¼•˜·r,TŽ…ʱP9*ÇBåX¨ •c¡r,Ôü‰¢“ÿüò¿˜øÏ¿ÿýÿo?òü÷/”ùãŸc¶Å®WC¼_7wëŸr3ÌÒé×ïשü)=Öäýz¸«çT?_}|•~n-·ûzoÕw=þ*ãìÙrïš·WÛûô<ä5~Nßž5Þ¼Êßu?²–¿»Ü«<_íÞ?¬Ï9V7ÜL®$W¯õ¸÷óÞîøÕ&/w¡·{ך¬no­œUýÖrZ{µýÙzþ^Ïý§Þ{VçëñU}Úß­õ½ß¼çI)oÕ;+§M«ökåÍÊyÉÓÊ׶«ô»÷üü¬ö¾«ßÏÒ©yüYöy'/»­å¼ÛK«oõúž–w-/ågågz?È/^]¬YŠný ÏŠC•‹DÀɪ=_qÏf)j{²èôl1îŸöIXW‰ã¥E³EûfQÇB?YÕ»Ž“+Qj%J­D©•(µ¥VFÙVFÙVFÙVFÙVâ×JüZ‰_+ñk%~­\H¸22·22·22·ÙV"ÛJd[‰l+‘måâÕѼ•Ñ¼•Ñ¼•˜·óVbÞJÌ[‰y+,®Œ®Œ®Œ®DÕh¸ W¢áJ4\-`ÔpeÔpeÔp%N®ÄÉ•8¹'×w8™QÃQË„7.Þˆ £†£†[ ´@iDкA7"èF݈ t#‚nDкñsŒÆÏ1?Çhü£[7bëFl݈­±u#¶nŸ):ùÛüŸø÷+~ÿú‹2üóÛ¯»ö~­±&¯× ïäõúxw½S¯ƒwëñ¾¾«î™]ù^ãËšNé==Îwõ?û5ø®ñ³š¼ûųÝmÞz½Ÿvƒxß½ôÏʭΧú¯5½J¿³Ö³Ž×Õq»{½ïrƒy•;=ÿy»gú¬å¬ÏíÞϧܧZ{vÏ·vߣ¼ßüÊêÇÚzVýÚ¼”¼û‡÷s…6í¶³ÖžS÷ÇkÞëþ¶:ÿYççÝv9u¿Þ’>¯ë¦•/¥Ýù»wïw»×Áú»w?‘ÊyÏ›«óõî¼q×üçeLJ¨^i1dë¢Ó³½„/RŠ^µîM<‹®ÕFÿª÷bÖî%,`ßX\À¹¢<gKò¤E§ÕÑÓÊvÑF©¯ãäƨÇƨÇƨÇÆ¥”n\PºqAéÆHÉÆHÉÆHÉÆHÉƨáÆE¨¡n\„ºqêF<ÞˆÇñx#oÄãx¼7âñF<ÞˆÇñx#oÄãx¼7âñF<ÞˆÇñx#oÄãx¼7âñF<ÞˆÇñx#oÄãx¼7âñF<ÞˆÇñx#oÄãx¼7âñF<ÞˆÇñx#oÄãx¼7âñF<ÞˆÇñx'ïÄãx¼wFhwFhwFhwFhw"õN¤Þ‰Ô;‘zŸ'ÅßcQë?ñÿûû|÷뤶œ÷k–¶ü]çë%çٯ׫éôõÞm¿ÕrÖäÕŽ§Ü/»¯yZy§Ó]ãòt¿Û} Õ¦S×é®ëmÕëÝŽ§çßU}ÏoϺO{»9µòO·÷ê|°+ï”[QJZ½ÖrÏÒ{úùî.wñê¸òr«Î’÷}k·¤¼UïL¾TwÜí^ýÀ:¾wÛöû®}Z;vçgk²ö«ÝyéYóíîøšé]íV9V¹RZ=«¼Y¹™<«þÕúÖzÞí·›¼æa¯úZùw=ÇXåí>ÇyÍR^k‡tüøýRÀ‡âbÍBtïÏr«XWˆ^ý½<‹†ö¾ž‡´§ðy“½Ž¥¨ÞéžÍFû´QÌÓö“¢“•Ÿ¨1»ríuœÜ‘ّّّّّّّّّّّّّّّّّّٹp'ïÄãx¼wâñNÔ݉º;Qw'êîDݨ»uw¢îNÔ݉º;±u'¶îÄÖغ[w"èN݉ ;t'‚îÄÉ8¹'wâäNœÜ‰†;Ñp'îDÃh¸óvbÞNÌÛ‰y1ï ²D¶ƒÈvÙ"ÛÁñ38~ÇÏàø?ƒcap, Ž…Á±08ûõà§"ƒŸŠ ~*2Rýò©pòŸ_þYÔúë—"ÑÉÄÍŒXþ&E'ŸJ»Ï«vz=¶ï¶Ó)}^r½¯÷õZ-gÕ{÷ùZëß]îî~¨­w÷¼auo<«]½åyÇnÚ}­}öü{ªï¾>^ý&ÏÛ}¤-w÷}Þ»¾Uþ)wñj=¯q~ÊèíõvçÎÒn»xÏ»Þnë™­V½Þ×íôûŽö<¼Þ¼®×«œÇj^’·;ÿYËk~õ<½í˜éŸ?õ|¼{_ÚíW³rZ9ÖùO«wwÞßÝ×¾»Ï’Wñ¶ÓûõK*ïýz|·«üS¯sw;¯´Û^«¯µV=³r^÷ /ùÞöxÉñvwžn÷Sõï–×}Ò[ÞéùT+wwþð’#É{V¹]7®VžTo–´îgïëaÕ{ê> ÉÛí»îd­›Öj—5íº£WÛÇZïT?XÍ[_7¤ä=_Íê[õ®Ê󚯤z3{vå¬^ßgû»ŸwûŸ5Yï3ÖrZ9ÞÏM«ç±:_Iiõ9G+oVÎë9L’kµC:>«ï%oW¯V·úÂ"ÒÓÅ‹'Q©Ó(f%&þ-+-þ|È>5ÖU.Æ­Ýz-.FcOölË]±ó$Z|º¸·2º{'ÿÍ’°"V$ÀŠX‘+`E¬H€ °Z¢‰$ZhA¢‰$ZhA¢™dZiA¦™dZiA¦™dZPhA¡…ZPhA¡…ZPhA¡•TZPiA¥•TZPiA¥•TZÐhA£4ZÐhA£4ZÐhA£tZÐiA§tZÐiA§tZ0hÁ ƒ Z0hÁ ïðø ƒ X¾ÿò©pò?þÿˆTþUÔ2}•pòîãˆwyï×–U¹w=®­Ö»ë5k5y_/¹ÞýôU®Ó©ö>­Ç»ü³í“ê¾¾wË™Éózm^Õ?+÷l»½äŸÒ{j~ÞÕ{ªÿ>{ÞðîÞõ½Ü¬Ú´:z»Ogr¬õ%y39ÞnQ«¼Õ몕§vGnʛɗŽ¯Þ?¼ÝÔ§ÎwÕÎÕtjþÑÊ·¶³6íγþ­-/å­z¼Þ¼î ³´ÛWçëy®ê%¯ç¯~°zßZµÃZOJ»óÇLž÷8ÉÙÕ«•g­¿*µ¾µ[Ç©µœö¸TîÕæ¡YyõÞÉÚ(\Ã~À¡Ê=Œ?àai`eT´¨W(÷!j[Àµ³óí–ÊM¢z¯×kŠãý×ó˜-Ú-î-ì=‹>_ÇÉ™™™™™™™™™™™™™™™™™™™!Ò"õ@¤ˆÔ‘z RDêH=©"õ@¤ˆÔ‘z RDêH=©"õ@¤ˆÔ‘z RDêH=©"õ@¤ˆÔ‘z RDêH=©"õ@¤ˆÔ‘z RDêH=©"õ@¤ˆÔ‘z RDêH=©"õ@¤ˆÔ‘z RDêH=©"õ@¤ˆÔCÿþåÓàäÇ>Ɉ8~‡’Ǿá÷¿¾ü\üúߣ“¥äíž™•»ÛM±ª÷”»ÅÛ©Üé×z­žÝ󽫽vË{»Vå޼ݳr«ò^u>ê¿Ú¸ßï^¥½½îK§ûî~m¹W;»îVwUþj½Õñ²«× '•_uÛjåìÎ/Z½«ú½çÍ]wñÝîæÝóX-?“cuۮʗŽkë{¹•wõïÊÓ&ï~{ªïÚãÕΧú©¶þì÷Õ󳶳V®t|×ÎÝr»÷Km:}ž³ßgv¬^w¯yw׎Y9¯þ³;Yí™éÕÊ;u}µé”ës¢—§îGÚt-/EËj÷à•¢R¥¨ÝŸò¼£“}ÓÅŸ¥½„ ú¡]„EÂ¥=¥?´Ï$šXÂÝ’ýâ"Û“èd5vOi?œ‘‘‘‘‘‘‘‘‘‘]]]]]™¾g&0™IÌdf 3•™ÆLg†'âñD<žˆÇñx"OÄã‰x<'âñD<žˆÇñx"OÄã‰x<'âñD<žˆÇQw"êND݉¨;u'¢îDÔˆºQw"êND݉¨;u'¢îDÔˆºQw"êND݉¨;u'¢îDÔˆºQw"¶NÄÖ‰Ø:['bëTßIãù['bëÔâ—O…“¯‘ÈÀÅ?1óc!ì¯ï󶽓½ÝÖrÚä-oUîév:ݾÏ:_¯×­>kùÕ×Ïk¹»ÇÑ«÷—ÿÊ<°ªg÷ú¬¾NÏÒ)·ÏêñÓåŸ%ï.»^íþüjóŽ¶Þ³ÚÑûþxʽä}Ÿ–’¤oÕÍ¥uWÎì°–[m7o·®6y¹Ÿµå¬ne­}Úë!¥Sîb©¼µþ³ž»¬í?Ó·z}gÉ:¬ê±ê?ݯ´íì¥g÷ºïêõê/3;Vç]ï~êúxõÓ™)oÕë=~wçÓÕz»ãe¦gõ¹h5YŸw¬ÏMÖñ´«÷ú»$g¦göûêuð/wõ5^UF1«£z‹+_1ç›J‹X+Ëi÷Š–ð°´§´v¯ãYô´::ù¢g¶Èölm K«÷²¾È[ÇɉŒ‰Œ‰Œ‰‹'"èDˆ t"‚NDЉ:A'"èD¸(pâ¢À‰‹'. œˆ­±u"¶NÄÖ‰Ø:[gbëLl‰­3±u&¶ÎÄÖ™:Ag"èL‰ sx'­1Ó™áùAgâäLœœ‰“3qr&NÎŒ¶ÎŒ¶ÎŒ¶ÎŒ¶ÎDЙ:Ag"èL‰ 3t&‚ÎDЙ:Ag"èL‰ 3t&‚ÎDЙ:Ag"èL‰ 3t&‚ÎDЙÑÖ™ÑÖ™ÑÖ™ÑÖ™Ø:×üåÓàd.pýø—û(%?Žáï×8ÙëqúÔc¶µœWòvëÜUÏëµàÙz¼¯¯õ5ÙËís×ëꮜÓ×ÝÛí³z|5y·ÏéùËK¯·R«Ï«ï’³{_<í¶XM§Úw×¢MϺßݵõvÝnÚò»×Ûzü®ëq—~­›uVßZÞj×õøìwk¿²Êóî7Z{vÏ·vߣ¼ßüÊêÇÚzVýÚ¼”¼û‡÷s…6í¶³ÖžS÷ÇkÞëþ¶:ÿYççÝv9u¿Þ’>¯ë¦•/¥Ýù»wïw»×Áú»w?‘ÊyÏ›«óõî¼q×üçe‡z±ke4±z/á £{Køô*o¶È¶z/fí^ÂÒ"ÑÂb×ÚhìY”°´xõlñl 'kÛEj_?œœõ˜õ˜õ˜õ˜õ˜õ˜õ˜¹Xoæb½™‹õf.Ö›¹Xo&êÎDÝ™¨;ug¢îLÔ‰º3Qw&êÎDÝ™¨;ug¢îLÔ‰º3Qw&êÎDÝ™¨;ug¢îLÔ‰º Qw!ê.DÝ…¨»u¢îBÔ]¡]¡]¡]¡]ˆÇ ñx!/Äã…x¼âñB<^ˆÇ ñxa„va„va„va„v!R/Dê…H½©"õB¤^ˆÔ ‘z!R/Dê…H½©"õB¤^ˆÔ ‘z!R/Dê…H½äO„“¹ õ_øcÔ2£•/ÿ¾ØõéǯòÖú»òW“÷c¶õ¸µœTþ”kÚu'ì–óz-Ó–;UÞ;y·«¶þ]î»ÆÙ³åÞ5oŸrZËyÕ³Ê9}¿›•{µ~g-ÿl»¼Ê½Êó•—ûy¦Oë޵ʕäïêÝuëy»#g¿¯¦Õç ­¯÷ŠYš]ßU·óª~k9«ÛV{¾Zû¼žûO½÷¬Î3Öã«ú´¿[ë{¿xÏ“RÞªwVN›Ví×Ê›•ó’§•¯mWéwïùùYí}W¿Ÿ¥Sóø³ìóN^v[Ëy·—Vßêõ=-ïZ^ÊÏÊÏô~o] yåz:–¢“%).®lŒ®Ý]tZ²º—ðlñì™}רi)ÊZÀ×"þ5.î--N-õ“U½ë8¹¡U"´J„V‰Ð*Z%B«Dh•­¡U"´JV‰Ã*qX%«Äa•¦•¦•¦•¦•¦•Ñ¢•Ñ¢•Ñ¢•Ñ¢•Ñ¢•‘Ÿ•‘Ÿ• W¢ûJt_‰á+1|%†¯Ä𕾶wÒh1|%†¯Äð•¾ÃWbøJ _‰á+1|%†¯Äð•¾ÃW"õJ¤^‰Ô+‘z%RoDêH½©7"õF¤ÞˆÇñx#oÄãx¼u7¢îFÔ݈ºQwã˜ksc®qÌ5Ž¹F݈ tã˜kocîSáäëb×­¹gòïÅ®Ÿ ÷t;nßgïîë‘UŸµü]¯Ç«rOËñªwúuõÔ<°ªg÷úx½æiË­ös/»N•–¼»ìzµûó«Í;ÚzÏjGïûã®[uVþô<{Ê 'ÕÓÚa-·ÚnÖó°Ê™•·ö ­ÜÙïÚö³Ö›ý.ÙåÕ~³òÖúÏzÿLßêõ%ë|°ªÇªÿt¿Ò¶³—žÝ뾫׫¿ÌìXw½ûý©ëãÕOgz¤¼U¯÷øÝOWë펗™žÕç¢Õd}Þ±>7YÇÓ®Þëï’œ™žÙï«×Á{¼ÜÕ_~/vý{±k–ûï,v݉©:1U'¦êDÃh¸ wFWv¢­N´Õ‰¶:ÑV'NîÄÉ8¹'wâäNœÜ‰“;qr'Nî\°¸sÁâ΋;,îDкAw"èNݹ`qç‚Å w.X܉­;±u'¶îÄÖغ3â¼3â¼3â¼3â¼uw¢îNÔ݉º;Qw'êîDݨ»uw¢îNÔ݉º;Qw'êîDݨ»uw¢îNÔ݉º;#Î;#Î;#Î;#Î;ñx'ïÄãx¼âñA<>ˆÇñø Œ8Œ8Œ8ßû—O‰“ÿúò~¡ëÇØ_ñû×/ïò¿G'Ki×Mb-çõXk-çýZñj¯7Þ¯Úôjî1ï~¬-ïýš°*÷î´Û.Ï~½Ò¦SóTÿÕƽ·oWŸ÷üû*öyé×–{µó¸ë~±ëε¦S÷/ýV7ðÝî¿]7õ®ÞUýÞóæ®;Ô˪­¿{«ågr´öyõ«ëqmýÝñá¥Wž6y÷ÛSýxׯv>ÕOµõg¿¯žŸµµr¥ã»vî–Û½_jÓéóœý>³cõº{Í»»vÌÊyõŸÝyÈjÏL¯VÞ©ë«M§ôXŸ½ì8u?Ò¦ky 3N±®´8µ$ïŠ%•‹gÀדşՋq£uÅ(f¿®FEKQÎÚO°ŠÙgÑؾ^·Ù"ÛÚhçuœ<‘9‘9‘9‘9‘9¸`ñà‚Ń .X<Å9Å9Å9Å9Å9¸`ñà‚ŃÑÖƒÑÖƒ‘Ÿƒ‘Ÿƒx|âñA<>ˆÇGz'çC<>ˆÇñø Äãƒx|âñA<>ˆÇñø Äãƒx|âñA<>ˆÇñø Äãƒx|âñA<>ˆÇñø Äãƒx|âñA<>ˆÇñø Äãƒx|âñA<>ˆÇñø Äãƒx|âñA<>ˆÇñø #~ù48ù¹/2÷SæžÊ_/Çþp²÷ëשr^ÉKßÝíàýZ¯-çõúå­ßªçÔëõ)7Ý©t—ûïtÝ•wÊít—©Þ®{M’k•w×kýªÞW½þÖr§äŸê§ÞòWÝŸV=V¹§ôŸê‡Þn.íï³´Û¬÷U/·™Ôn«îSíuXuÿÝ5­ç1Ó¿jשçÝ»ž_fåvÇ÷]ýtö»×x–ê{ɓ䯦Õë1“³j‡”V¹Sçïu??uÝ­Ž_­¯ñkÕ;“¯Mw=oXåß5Zåíö#©Þ©ö¾k|Hòvï[§ç½å~pÁ|³=}µQ®SÜ(è“ð´´°TÎÝ> ¯*÷:V/²-DM‹ØY؃úÃyÌ°½ÐÄ=´¯‹_ÛEø À/:y0ºr0ºr0ºr0ºr0ºr0ºr º2|Gtå[&0™IÌdf 3•™ÆLg†ZhA ZhA ZhA¤‘DZiA¤‘DZiA¤‘$ZhA¢‰$ZhA¢‰$ZhA¦™dZiA¦™dZiA¦™ZPhA¡…ZPhA¡…ZPhA¥•TZPiA¥•TZPiA¥•4ZÐhAûD8™ÇDÅßðïã÷?.ǾZqòêãÐny¯×Þ]¹Þ¯SÖäu>»nÕtú5î.½^¯«z´éT{ŸÖã]þÙöIõN_ß»åÌä­Î›§®ÏªS«çYã÷”ÞSóó®ÞSý÷Ùó†wô®ou_y÷K¯óõro{ÍïÖyÉz^òV¯«—[ÔKÞL¾t|õþaí'«ãÌ{>xöýîZn·ŸÌäZ¯ÊÕÖ×ÊÛÿ3=^ï?^÷…YÚíÇ«ó‹õ ÉÛí«í¯m—U»¬iµ}wÛÇZïT?XÍ[_7¤ä=_Íê[õ®Ê󚯤z3{vå¬^ßgû»ŸwûŸ5Yï3ÖrZ9ÞÏM«ç±:_Iiõ9G+oVÎë9L’kµC:>«ï%oW¯V·úÒž¾×½„ü*bN!÷g9ïÅ®Ù7‹Ö•¢ÅŤµÑÝW\+EcO±V—3îe}•7»Òù®ãäÀˆÌÀˆÌÀˆÌÀˆÌÀˆÌÀˆÌÀˆÌÀˆÌÀˆÌÀˆÌÀˆÌÀˆÌÀˆÌÀˆÌÀˆÌÀˆÌÀˆÌÀˆÌÀˆÌÐh‘z RDêH=©"õ@¤ˆÔ‘z RDêH=©"õ@¤ˆÔ‘z RDêH=©"õ@¤ˆÔ‘z RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê1}"œ|]Ôúo\ü×—÷ÑÊDÎ_ß—{“½ÓŸýúò,;½ôÜ-O[ÏÛ³Zîn·ÀéòÞî5mòêÿVy^×Cªw·Ûán½»rWûñn<ÕŸ_¥ßíÊõ¶Ïªwµ_íùáUî¯Þ¯¬ÇO»'gÉz=´yïçœYºË <Ó'%o}Vwñéûòª[Ôê~ö¾/y7ëxןµÃ®^m:ý|õ¬ë;“¿:oœ²ËZ~u>˜Õ[½žwwÇ«÷õÝíV=»×ëfiu~÷j7¯r«ýE*w÷õÕÚå%oö»÷¼k-7K«÷+­?ÐF³¯ãäȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌÈEŽ#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RÄã‘x<GâñH<‰Ç#ñx$Äã‘x<GâñH<‰Ç#ñx$Äã‘x<GâñH<‰Ç#ñx$Äã‘x<GâñH<‰Ç#ñx$Äã‰x<'âñD<žˆÇñx"OÄã‰x<'âñD<žˆÇñx"OÄã)Ô/Ÿ'sQë?¿¼GËà÷Çÿ¿½ÿÿïÅ®Wäy¿–XõïÊ=åfÙÕû¬vñrNµ÷i=§ÜSwéÓÖ;}}ï–3“·ënÛÕ?+÷l»½äŸÒ{j~ÞÕ{ªÿ>{Þ¸Ë-ºZßê6óî—^çkuwÎäXëKòfrVݶ^òV¯«—;ÖKÞL¾t|õþaí'«ãÌ{>xöýîZn·ŸÌäZ¯ÊÕÖ×ÊÛÿ3=^ï?^÷…YÚíÇ«ó‹õ N¾¢äGôñ_?òýøûUtòÏÅ®½_K®å½ÊiÓ©×Sï×Ýr§Û÷Yçëíf8õÚ¸ªçYãèÕûËeXÕs—;G{|VÎË=a=~ºü³äÝe׫ݟ_mÞÑÖ{V;žv[jåYÝÁ§’Õýlu¯®ºÅ¬åVÛÍzV9³òÏr7kÛo×½{êyt÷:¯ºWõX“µýgúV¯ï,YçƒU=Vý§û•¶½ôì^÷]½^ýefÇê¼ëÝïO]¯~:Ó#å­z½Çïî|ºZow¼Ìô¬>­&ëóŽõ¹É:žvõ^—äÌôÌ~_½Þãå®þ¢]äX‹ÕÖ¸¸òƒŠÑµV¶–û€M'ÑØ0±e-D‹zgòf8Yˆ6±½•}Õ;»Úhöuœœ]™]™]™]™]™]™]™]™]™]™]™]™]™]™]™¹øpæâÙ‹g.>œ¹øp&RÏDê™H=©g"õL¤ž‰Ô3‘z&RÏDê™H=©g"õL¤ž‰Ô3ñx&ÏÄã™x<çüNχx<gâñLÔ‰º3Qw&êÎDÝ™Ø:[gbëLl‰­s}'¶[gbëLl‰­3±u&¶ÎÄÖ™Ø:[gbëLl‰­3±u&¶ÎÄÖ™Ø:[gbëLl‰­3±u&¶Îã“-výØ™øøñÿÇ‚×ßð;þo[ìÚëñÒZîôãý)w‚·;bµœ÷kթ׎Uy^ýÖ»œ5y¿¾iåïŽËS¯Ó§Ò]ãòt¿órGìê]Mw]o«^ïv<=ÿ®ê{öx{Ö}z×ý»*ÿt{{¹¥¬òvŸVíÚu'Z¯ó)½§ŸïNµ¯¶ü¬œÖmû¬yX’³ÛRÞªw&_ª¿;îö¯~`ß»í?û}×>­»ó³5YûÕî¼ô¬ùvw|Íô®ö««\)­ž‡UÞ¬ÜLžUÿj}k=ïöÛM^ó°W}­ü»žc¬òvŸã¼æ )¯µC:~ü~©Ü«W½ç°rÑd×*q²¸ÈöwÎp¨]«ÝûWÕ+-²­µOŠNþÙ¾¶—¢Ê—?0îQ=‹ßÀÉŒzÌŒzÌŒzÌŒzÌŒzÌŒzÌŒz,\¸[bëBl]ˆ­ ±u!¶.Äօغ[bëBl]ˆ­ ±u!¶.Äօغ["èB]ˆ  t!‚.ÄÉ…8¹'âäBœ\Ò;i´Ú…Ú…º'âäBœ\ˆ“ qra´ua´ua´ua´u!‚.DÐ…ºA"èÂhëÂhëÂhëÂhëBl]ˆ­ t!‚.DÐ…ºA"èB]ˆ  t!‚.DÐ…ºA"èB]ˆ“ qr!N.ÄÉå3áä¿ð÷Àļü«(å¿PæÃÞÉ×´ûX»ZõñeW¯·¯×Ò]=RyïÇÎSÇgÉ«¿xÛéÕ¾³ò§^?ïÒc•¿;¯<{Üy%ï׺Y}¯ñáÕ¯¼_¯WËïÖÓÊÙu«h?-ï´í®óµ–óƣWÒºÃfvxÉ‘ä=«œÖ]ç妞ɛɟé]Õgm—ÓnV)yõ‡Õö׶˪]Ö´ëf^mk½Sý`5oý}uÞ’÷|5«oÕ»*Ïk¾’êÍìÙ•³z}Ÿ5îï~~ÜíÖd½ÏXËiåx?7­žÇê|%¥Õç­¼Y9¯ç0I®Õéø¬¾—¼]½Z9êKQ³WükĈSì,D­Š‹5_ìÚÅœZû>`]åžÍ"ÖUF Ïð¾ˆÏ%,>+gŒŸí=­Åñë8¹SubªNLÕ)Ù)Ù‰©:1Uçb½Øº[w"èA=ˆ ô ‚ÄɃ8y'âäAœ<ˆ“qò NÄɃ8y'âäAœ<ˆ“qòˆï¤ñ|Ñ<Ñ<ˆ ô ‚DЃzA"èA=ˆ ô`ô ‚DЃzA"èA=ˆ ô ‚DЃ8y'âäAœ<ˆ“qò NÄɃ8y'¢áA4<ˆ†ÇÜà˜Œ4DÃhxpÌ Ž¹Á178æÇÜà§"c|¢½“û ÿˆQÈÜ;ùñûŸ8þ(#âdëc¸÷ã½×k6r¯œ.çý:§-çíòÒoÕ³Û~w=¶Ÿ»íyW{{¥Ýë¦-ÿ*ã|Õí8“k•ç­W{ü”}w‹gÍ÷^ÇOë¿–;Ý_¬rOé?ÕwݧZ9Ö´Û¼Ý~³z’«}Zy’«ç½z\Ò?;nu[{Ùuêy÷®ç—Y¹Ýñ}W?ýî5ž¥ú^ò$ù«iõzÌä¬Ú!åŸUîÔù{ÝÏO]·»îo«ãW+ÇküZõÎäkÓ]ÏVùwÍŸVy»ýHªwª½ï’¼ÝûÖéyo¹(÷:6ãP!úøQNÒ'bÉÙž¾Â½nöMö:žán±ý®¸VŠêDOÏö¨žb{%>·îe-Éý§.âäøhø-S™iÌtf2@[o™ÀLd&1“™¡ƒ Z0ht ˆh~Ëf*3™Î ¥A¿e3‘™Ä -´ Ђ@ -´ Ò‚H "-ˆ´ Ò‚H "-ˆ´ Ò‚H -H´ Ñ‚D ¥%JË”–)-SZ¦´ÌóÉ<ŸÌóÉ<ŸL 2-(´ Ð‚B -(´ Ð‚B -(´ Ð‚J *-¨´ Ò‚J *-¨´ Ò‚J *-h´ Ñ‚ö‰¢“ÿüòÏBרäë^Ê®‰š åN–Ò³^ÇV“×cò®¯×w¯ò»ç»š¼Ûéôu;%×ûuUîÝi·]N¹If¿¿Ê| Õµqïí–ÙÕç=ÿ¾Š}^úµå^í<îº_x»Í¬úfå¼Û}wÞÔ¶³Õ hu¯iëÏÒ]n=ïyÓzf¿ïÚ½Ú¬ãÖë>§½¾Þýêz\[w|xéß•§MÞýöT?޵ǫOõSmýÙï«çgmg­\éø®»åvï—ÚtúÝg¢ûLtŸ‰á31|&†ÏÄð™>©g"õL¤ž‰Ô3‘z&RÏDê™H=©g"õL¤ž‰Ô3‘z&RÏDê™H=©g"õL¤žÓ'ÂÉ…®ÿºüÿñ÷õ¿áï÷bמúîn‡W{ ÷¶÷´/÷Ä)ý»¯‰Ú´Ûžwµ·Wòz½¼«]¼û©·{è®y}÷µvW¾µü«Üïê‡Ïº¾R¹ÓýÅ*÷”þSýÐê&²ê¿«?kÝ¥V9Úz’«}ÞnÇYºkæ~ÉâØã_F+ÿ^ìZQnõño׎Ýzwïjòn§Ó×í”\ï×ýU¹w§Ýv9å&™ýþ*óTÿÕƽ·[fWŸ÷üû*öyé×–{µó¸ë~áí6³ê›•ón÷ÝySÛÎV7 Õ½¦­?Kw¹õ¼çMëu˜ý¾k÷j?°Ž[¯ûœöúz÷«ëqmýÝñá¥Wž6y÷ÛSýxׯv>ÕOµõg¿¯žŸµµr¥ã»vî–Û½_jÓéóœý>³cõº{Í»»vÌÊyõŸÝyÈjÏL¯VÞ©ë«M§ôXŸ½ì8u?Ò¦kùß‹]ÿ^ìú—×÷廮Ģ•Ñ¼•Ñ¼•Ñ¼•ûÅV¢ÔJ”Z‰R+Qj%J­D©•(µ¥V¢ÔJ”Z‰R+Qj%J­D©•(µ2š·2š·2š·2š·¿Vâ×JüZ‰_+ñk%~­\ˆ»r!îÊ…¸+â®Ä¯øµ¿6â×FüÚ¸wãBÜ q7.Ä݈l‘mã¢Ú‹j7.ªÝˆl‘m#²mD¶È¶ñ“‡ÆO‘m#²mD¶Ñ¼Ñ¼Ñ¼Ñ¼˜·ó6bÞFÌÛˆy£û£û£û1oã'Ÿ<4~òÐøÉCã'c»ql7Žíö™»þ?»þŠü·ËÿY aÿ/N>õúåõÚéíXMÞn»ê~œ¾K÷õõv«iå{»7NÉ9}ݽÆïÝýÆ*÷®ñ³šN»ÛvõzÓ»äìÞ½Ü\V½³tª}½Ýp«åŸ=Ýmß)·ôÌí´êÔ¿ëzÜ¥Õ­'•;ÝVÝ“Ö´ëîô~°¶‹u|x÷ÇU{¼ôXËêÇÚzVýÚ¼”¼û‡÷s…6í¶³ÖžS÷ÇkÞëþ¶:ÿYççÝv9u¿Þ’>¯ë¦•/¥Ýù»wïw»×Áú»w?‘ÊyÏ›«óõî¼q×üçe‡„‘ŨT-®½”›-v-âi­¼+¶‰þ€AgÑ΢Üô ‹O‹z'QàZ|>úRtµ´ˆõµýÄE¬'í"µ¯_tr#nDÃh¸ 7¢áF4ܸ8tãâЋC7.݈“qr#NnÄÉ8¹5ZÐhA£\PºA7"èF݈ t#‚nDкA7"èF݈ t#‚nDÐÀÀÀÀغ[7bëFl݈­;±u'¶îÄÖغ[wbëNl݉­;±u'¶îŒ4îŒ4îŒ4îÜ?ºuw¢îNÔ݉º;Qw'êîDݨ»uw¢îÎèþÎèþΈæΈæN<Þ‰Ç;ñx'ïÄãx¼wâñþÙ»þóÇoèäÇÿ¿âÿDÊ(kÛ;Ùš¼k¼_³î’sêµÞ«üª[êîã³túõhW®W9©¼W–«|o·ÈªÜgÍKVy»çë=>žýú»*÷T=­o·ãév?UÿnùwÝ'½åžOµr½Üu§Ü«Ï*çíN<5OÏ܇Þ×ê÷Ô}@’·ÛVÛ_Û.«vYÓjûީ~°š·þ¾:oHÉ{¾šÕ·ê]•ç5_IõföìÊY½¾Ï÷w??îö?k²Þg¬å´r¼Ÿ›VÏcu¾’ÒêsŽVÞ¬œ×s˜$×j‡t|VßKÞ®^­œõL;Ń“½¥hÜŸå¼£“Ù7[üÙŠ»Õ‹…O°ók£Årʨm5>¿\é|×qrgtegtegtegtegtegteçÞÉ ïv.¼Û¹ðngDfgDfgDfgDfgDfgDfgDfgDfgDfg´u'RïDêH½©w"õN¤Þ‰Ô;‘z'RïDêH½©w"õN¤Þ‰Ô;‘z'RïDêH½©w"õN¤Þ‰Ô;‘z'RïDêH½©w"õN¤Þ‰Ô;‘z'RïDêH½©w"õN¤Þ‰Ô;‘z'RïDêH}©"õA¤>ˆÔ‘ú RDêƒH}©"õA¤>ˆÔ‘ú RDêƒH}„O„“‹[sìoø—ÈùÇ‚×ïq²÷cú³__že§—ž»åiëy»cVËÝí8]ÞÛ½¦M^ýß*ÏëzHõîv;Ü­wWîj?Þí§úó«ô»]¹ÞöYõ®¶ã«=?¼ÊýãÕû•õøi÷ä,Y¯‡6ïýœ3Kw¹gú¤ä­Ïê.>}_^u‹ZÝÏÞ÷%¯ñfïÚã³vØÕ«M§Ÿ¯žu}gòWçSvY˯γz«÷£Óóîîxõ¾¾»ýÁªg÷zxµÓ,­Îï^íæUnµ¿Håî¾¾Z»¼äÍ~÷žw­åfiõ~¥•§-/ð¥Õ;‹6ÕF¥J8R*'íÙûÁ>åÁ3ª^ÄZ»÷´6úW=-aóÙâÔR¸6ºûÞÉÂçÚÅÑ×qò`Dæ`Dæ`Dæ`Dæ`Dæ`Dæ`Dæ`Dæ`Dæ`Dæ`tå`tå`tå`tå`tåHï¤ñ|ˆÇñø Äãƒx|âñA<>ˆÇñø Äãƒx|âñA<>ˆÇñø êD݃¨{u¢îAl=ˆ­±õ ¶ÄÖƒØz[bëAl=ˆ­ô ‚DЃzAþNχzA"èA=ˆ ô ‚DЃzA§ï@Ðo™ÀLd&1“™)ÌTf2Ò¥J ”ö™pòWü]÷GÆ¢Ö?ÿ}D'ÿ8nÃÉw½Þ¯>þy—w†÷ãï®ÞÝëã-Ï«ßz—³¦ÓùÏz¶Ê;î—§û·[eUïjºëz[õz·ãéùwUß³ÇÛ³îÓ§Ý8§ž'¬z½ï«§ÜÏ«viÝu»n½ÓzO?ßj_mùY¹]7ðÝóõ_«\)­ž‡UÞ¬ÜLžUÿj}k=ïöÛM^ó°W}­ü»žc¬òvŸã¼æ )¯µC:~ü~©Œ†UGõ®.:­Ý³YØKXŒÂ`l)šX]+í,âßEû¤(ëŸí{±Wl?i¯hå¢çÖ(ðÑØ^‹]§ïáŒê̇Eâ°H °"V$ÀŠX‘8,Ò‚H "-H´ Ñ‚D -H´ Ñ‚D 2¥eJË”–)-SZ~'ç“y>™çSx>…ZPhA¡…TJ«”V)­RZ¥´úNϧò|*ϧò|-h´ Ñ‚F -h´ Ñ‚F -h´ Ó‚N :-è´ Ó‚N :-è´ Ó‚N -´`ЂA -´`ЂA - ¶DÐ:A"èð½ù48ù±ˆõuAkbäÇï\Ž}ˆN~$¯Ç\¯ò«×»¯Rù];Ÿ}>§_WVõ>«]¼ûé«\§Sí}ZwùgÛ'Õ;}}ï–3“·ë~ÚÕ?+÷l»½äŸÒ{j~ÞÕ{ªÿ>{ÞðîÞõ­î`ï~éu¾^n3¯ùÝÛM}JÞ®›×ªwVUÞL¾t|õþaí'«ãÌÝí;‘w×óŒW?™Éµ_•«­¯•·;þgz¼Þ¼î ³´ÛWçëy®ê%¯ç¯~°zßZµÃZOJ»óÇLž÷8ÉÙÕ«•g­¿*µ¾µ[Ç©µœö¸TîÕæ¡YyõÞÉÊhSm´îr®´G°_Šz…r¢”ì­&ëóŽõ¹É:žvõ^—äÌôÌ~_½Þãå®þ¢Ý3W½ªÅ°Æ½z¯T\¬YÀÊÖrbô´=‹ž÷¨Úo†»Õ8YˆŸFOOŸ]íâè8™Ñ•Ñ•Ñ•Ñ•Ñ•Ñ•Ñ•Ñ•Ñ•Ñ•Ñ•‘‹G. ¹(pä¢À‘™‘™‘™‘™ûÒ¾eh‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#ñx$Äã‘x<GâñH<‰Ç#ñx$DÝ‘¨;uG¢îHÔË;i<¢îHÔ‰º#±u$¶ŽÄÖ‘Ø:[GbëHl‰­#±u$¶ŽÄÖ‘Ø:[GbëØ¿ù48ùü=P2£¿3ül‹N–Òª»d·œWòvÜUîôë’·ûÅ«_yéñv3xëß}צÝö¼«½½’—ûá®v9í[•k•ç­W{ü”}w‹gÍ÷^ÇOë¿–»Û i­ÿêó·»Nûû,íöë}uõ>¼êÕÎۧܫ’>ëqIÿìø®ûrÕ®SÏ»w=¿ÌÊíŽï»úéìw¯ñ,Õ÷’'É_M«×c&gÕ)ÿ¬r§Îßë~~êºÝu[¿Z9^ãתw&_›îzÞ°Ê¿kþ´ÊÛíGR½Sí}×øäíÞ·NÏ{Ëý@»÷¯°Èõl¯^)šXÒ'.®<‹š¢`Ýì›DKr§íwÅçR”õd1îY¸vOé‹lK{O+£Å%¹ÿôÀUœÁÁÁ¹Ào$‚ŽDБ:AG"èH‰ #t$‚ŽDБ:A'"èDˆ t"‚NDЉ:A'"èDˆ t"‚NDЉ:A'"èDˆ t"‚NDЉ:A'"èDˆ t"‚NDЉ:A'"èĨîĨîĨîĨîDlˆ­±u"¶NÄÖ‰Ø:['bëDlˆ­£º£º£º£ºQw"êND݉¨;u'¢îDÔˆºQw"êNŒêNŒêN5ùT8™ÑɈäG$2ÿ‹b?öVþãwtòY¹§Ûétû>ë|½Ü/Z}Öò§^§­ú¬å^½¿üWæU=»×ÇÚ^^n0«\owÍ]ã÷´¼»ìzµûó«Í;ÚzÏjGïû£—›Z*zžÕºÍ¬n5©žÖk¹]wçì÷Só‹µ_håÎ~_uŸj¯¯÷uß­·kŸ6y=wYÛ¦oõúÎ’u>XÕcÕº_iÛÙKÏîußÕëÕ_fv¬Î»ÞýþÔõñê§3=RÞª×{üîΧ«õvÇËLÏêsÑj²>ïXŸ›¬ãiWïõwIÎLÏì÷Õëà=^îê/¿£“G'³Ü':9qQàÄèÊÄèÊÄèÊÄèÊÄèÊÄèÊÄèÊÄèÊÄèÊÄèÊÄèÊÄèÊÄèÊÄèÊÄEN\8qQàÄE‘z"RODê‰H=©'"õD¤žˆÔ‘z"ROÄã™x<gâñL<ž‰Ç3ñx&ÏÄã™x<ug¢îLÔ‰º3Qw&¶ÎÄÖ™Ø:[gbëL‰ 3t&‚ÎDЙ89'gâäLœœ‰“s~'¶'gâäLœœ‰“3qr&NÎÄÉ™89'gâäLœœ‰“3qræ‚ß™c;slgŽí\?Ñb×ÄÆÜ#ùWù²Ðõ?{'{½6ÏÒîc¿÷ëÁ]¯ §õyÉõ¾>§_ç¼û­õµùÔë׫•»»jëÝ=oœrÃYíØMÞ×éY÷ï×왾ÓåNÍ—w÷¯ñ¹z½¼Ü»«éÙõ­òWÝuZùÖz^ãünwìLžöü¼Ý˜ÚòÞúVÛë”»s–võÞå~ž%ïóðz_ðº^¯r«yIÞîüg-¿;î®ùÕóô¶c¦vüÔóñî}i·_ÍÊiåXç?­ÞÝyóÔõ%¯ö›Õ“ôy•Ûío«å¼æIîn²^7­œ]½ÞÏq§î¿ÓrJŒ¨Å«Ú=Œµ{ëJ8R/•ØTÂÉ3ªÞSZˆbžEOOÏW‰§gå>`bI¯-.]·ÙÐë8¹#VbÄJŒX‰+1beÄleÄleÄleÄl%z¬Œ~­Œ~­Œ~­Œ~­D•‘¬•‘¬•‘¬•‘¬•è±=V¢ÇJôX‰+Ñc%z¬D•è±=V¢ÇJôX‰+Ñc%z¬D•Q©•Q©•Q©•Q©•è±òÊO*?¨üD òJÜ_‰û+q%î¯Äý•¸¿÷WâþJÜ_‰û+q%î¯Äý•¸¿÷WFÃWFÃWâþFÜ߈ûq#îoÄý¸¿÷7âþFÜß ß ß ß8NÇiã8m§ímœ~œüÀÃâßG”2±òø¿ÆɧÜ0«ò¼‡VÓÝî¯z»íò*zN½ž:¿»ÝCÞrN_w¯ñ{w¿±Ê½kü¬¦Óî³]½Þãô.9»÷Å]·ÙªÞY:Õ¾§ÝWÚòÏž‡î¶o×½©-¿{½­Çïºw韹ç´××ZÞj×õøìw/7­$Ï»ßhíÙ=ÿÝvÚ}z–ûÚÚ^ýX[Ϫ_›—’wÿð~®Ð¦ÝvÖÚsêþxÍ{ÝßVç?ëü¼Û.§î×»ã@ÒçuÝ´ò¥´;_x·óîýn÷:X÷î'R9ïysu¾Þ7îšÿ¼ìÐF›±®ûAÞl±k­<—N£Žg‹g_£gÑÎÂb×ôNŸ-žýó÷É¢ÝÒbÝÎC¸þR4ö¬]¤öõ‹Nnļ˜·1´1´1´1´ 7¢áF4܈†ÑpcTjcTjcTjcTj#NnÄÉ8¹'7âäFœÜˆ“qr#NnÄÉ8¹'7âäFœÜˆ“qr#NnÄÉ8¹'7F²6F²6F²6F²6"èF݈ t#‚nŒ~mDкA7"èÆ(õF݈ t#‚nDкA7"èF݈ £Ç£Çdo\½A·ñNΧAw"èN݉ ;t'‚îDкAw"èN݉ {ˆ_>Nft2ÿÿçåwî©ü.:ùË%y½¦y•÷~{ÞðîÞõµÏCVwàL¯”×Ö»þ¾ë;íÖ³ÚïåžœÉ[½®Zy^îM«V7ô¬Þêõ˜é“Ž{磌éÔü£•omgmÚÿfý[[^Ê[õx½ÿxÝfi·¯Î/Öó\Õ;K^Ï3^ý`õ¾µj‡µž”vç™<ïq:“³«W+ÏZUþj}k?¶ŽSk9íq©Ü«ÍC³òjœ¬ÂUîŬźֽzgQÑÚŤՋv úĽ§…¨çå&Q½êÅ®¯ø¶ólj ‹ Ÿ Ì¢Ï×qr'BëDh­¡u"´N„Ö‰ÐÚ BDhƒ8l‡ â°A6ˆÃ#2#2#2#2#§‘ú RDêƒH}©âñA<>ˆÇñø D݃¨{u¢îAÔ=ˆºQ÷ êD݃¨{u¢îAÔ=ˆºQ÷ ¶ÄÖƒØz[bëAl=ˆ­±õà¢Íƒ‹6bëAl=ˆ­W\\}´wÒx>D݃¨{u¢îAÔ=ˆºQ÷ êD݃¨{0Úz0Úz0ÚzpœŽÓÁq:0Nó÷·qúipòc1ëGt2÷R~àäBþ†ÿýß“gÉÛ=3+w·›bUï)w‹·R¹Ó¯õZ=»ç{W{í–÷v¬Ê½;y»5fåVå½ê| Õµq¿;ß½J{{Ý—N÷‡]ýÚr¯vwÝ/¬î «üÕz«ãeWÿ®N*¿ê¶ÕÊÙ_´zWõ{Ï›»îâ»ÝÍ»ç±Z~&Çê¶]•/×Ö÷r+ïêß•§MÞýöT?޵ǫOõSmýÙï«çgmg­\éø®»åvï—Útú•çSy>•çSiA¥•TZPiA¥4ZÐhA£4ZÐhA£4ZÐiA§tZÐiA§tZÐ?ÙÞÉŒü@Å”ü@Ë_/¿û§ü{œìýš¿Zÿôë ¶ü]çë%Çû<¼Ý/Z=§^ÿwåXËY“W;z¿ÆKå¼ÝK§Úu¦÷”œÓýΚ·ê³Êµ¦»®·U¯w;žžWõ={¼=ë>}—ûÕªw7­Î»ò¼Ý ³ä决^çSzO?ßj_mùY9­{óYó°$g·¤¼UïL¾TwÜí^ýÀ:¾wÛöû®}Z;vçgk²ö«ÝyéYóíîøšé]íV9V¹RZ=«¼Y¹™<«þÕúÖzÞí·›¼æa¯úZùw=ÇXåí>ÇyÍR^k‡tüøýRˆ"•¢%,ú³Üê¢ÓZŒ-E ѼâÞΞÂäMö:ÖFõJ‹Mkí“¢“¶¯=-}ðá|•QêêEÀ•Ñâ8¹` ¬A€5°Ö ÀXƒk` "4FJD4¿e3‘™ÄLf¦0S™iÌtfhñx Äãx<âñ@<ˆÇñx Äãx<âñ@<ˆÇñx Äãx<âñ@<ˆÇñx Äãx<âñ@<ˆÇñx Äãx<âñ@<ˆÇC¡…ZPh‘z RDêH=©"õ@¤ˆÔ‘z RDêH=©"õP?Næ^È…­µö+3‚ùÃb×Þn™Óî/ý§_·WõÜ-O[o·½¼wµr¼^ßîî‡ÞýÀ»þª<ï×éS¯U¯ªwW®÷ë¹Vî©þü*ýnWî³ÝLÞî´Y½Ýr«õﺼz¿²_u{=Gìº×¼ÜŸ»ÉÛý|×sŽ·›óTû¯ö­œÕ~%%ïv\=_Ižöø¬võjÓéç«g]ß™üÕyã”]Öò«óÁ¬Þêýèô¼»;^½¯ïn°êÙ½^í4K«ó»W»y•[í/R¹»¯¯Ö./y³ß½ç]k¹YZ½_iåiË‹Ç•‹KQ«Ó½u¥èda¯a©ÜlQgmt²„u¥¨èiÔ±rqoõÞÄ«‹€ ¸[Âábóä3€‹q QÇÚhöuœ‘‘‘‘‘‘‘‘‘‘‘‘‘‘‘‘‘‘‘:- Äãx<âñ0ÞIÃùDâñH<‰Ç#ñx$Äã‘x<G¢îHÔ‰º#Qw$êŽÄÖ‘Ø:[GbëHlã;i´Ø:[GbëH‰ #t$‚ŽDБ:AG"èH‰ #t$‚ŽDБ:AG"èH‰ #t$‚ŽDБ:AG"èH‰ #t$‚ŽDбæ/Ÿ'?°ñß¿=pñ_øûvù÷rü7N^‘»*çÔk·—_­¼»Æÿ³ôîÊ]íÇ»ýñT~•~·+×Û>«Þ]÷šµÞn¹ÕúwÝ?^½_Y¯ºÍ¼ž#¬×C›÷~Ι%k»­¶óLŸ”¼õYÝ«§ïË»îíÕ~%%ïv\=_Ižöø¬võjÓéç«g]ß™üÕyã”]Öò«óÁ¬Þêýèô¼»;^½¯ïn°êÙ½^í4K«ó»W»y•[í/R¹»¯¯Ö./y³ß½ç]k¹YZ½_iåiË‹Çãäß8ùïrÿ9œœˆ“qr"NNÄɉ89 '¢áD4œˆ†Ñp"æMļ‰‘Ó‰‘Ó‰‘Ó‰‘Ó‰h8 '¢áD4œˆ†Ñp"ÎDÙh8 g¢áL4œ‰†3Ñpft&ÎDÙh8 gF4g¢áL4œ‰†3Ñp&æÍļ™ÑÉ™ÑÉ™ÑÉ™ÑÉ™h8 g¢áL4œ‰y31o&æÍļ™˜7çwÒx>ļ™˜7óf"ÛLd›‰l3‘m&²ÍD¶™È63j83j83j8sÌeŽ¹Ì1—ù Gæ'™c.sÌeŽ¹ÌÏ1rÿ„8ùŠ‘¿^þÿXüú¯÷¿Þ;ÙûõðT9¯ä¥ïîvXµÛûqZ+׫_yéÙm?ï×S«¯´Ûžwµ·WºÛ-v—©ÞêëôL®Už·^íñSöÝ=.ž5ß{?­ÿZît±Ê=¥ÿT?ÔºÃîng/÷¢w’êIr¬öiåIv¸»rví[uGkí:õ¼{×óˬÜîø¾«ŸÎ~÷ÏR}/y’üÕ´z=frVíòÏ*wêü½î秮Û]÷·Õñ«•ã5~­zgòµé®ç «ü»æO«¼Ý~$Õ;ÕÞwIÞî}ëô¼·Ü´8TÚûwuo]AŸ¸¸òd1iq¯c/û$¬+ì ,áÚÙÞÉâ"ѓŸ¥ëfÅìÙ–pü+Ïð¹ûb×™È6Ùf"ÛLd›‰l3ñk&~-į…øµ¿FÙ.B]¸uá"Ô…øµ¿â×BüZˆ_ #s ñk!~-į…øµ02·¿â×BüZˆ_ ñka”ma”ma”ma”m!~-į…øµ¿â×BüZ1[1[1[1[ˆ_ Qj!J-D©…(µ¥–úNmã'…Ÿ<â×BüZˆ_ ñk!~-Œ /Œ /üL¢ð3‰Bd[ˆl £á ?“(üL¢pÌŽ¹ÂO ?y(üä¡pÌŽ¹ÊÏ*?_¨ü|¡rÌÕïŸh±kF'ÿeÿû½wò¿•¿ë|½äœz<ŸýîÝNÞ×û.·Ánòv7iåŸ~·Ê;î—§û5oÕg•kMw]o«^ïv<=ÿ®ê{öx{Ö}ÚÛݬ•º½Wçƒ]y»Ï »n¶Ùqo÷š·ÞÓÏw§ÚW[~Vnö»”Ÿý¾ZN+g·¤¼UïL¾TwÜí^ýÀ:¾wÛöû®}Z;vçgk²ö«ÝyéYóíîøšé]íV9V¹RZ=«¼Y¹™<«þÕúÖzÞí·›¼æa¯úZùw=ÇXåí>ÇyÍR^k‡tüøýò÷ÞÉ¿÷Nþò_Ü;¹¿Vâ×JüZ‰_+÷®Ä¯•øµ¿Vâ×Ê…‘+ñk%~­Ä¯•øµ2úµ¦wÒh£_+‘m%²­Ä¯•øµ¿Vâ×Êè×JüZ‰_+ñk%~­Œ~­Ä¯•øµ¿Vâ×JüZ‰R+Qj%J­D©•(µ‹VF²VF²V.,^¹°x%­ü¡òS„ÊO*£Ç+±h㧟"4~ŠÐ Þø)B㧟"4~ŠÐø)Bc¿nÜÛºqoëÆHðÆOûuã‚ß ~7F‚7~ŠÐدûuc¿nì×ýºñ³‚Æ~ÝدûuËŸ'?¹~ìŸüç—½þ†ßþøò~AìÇþ'¯>.Ìʯ>ï>ÆžJ»¯¡Ïª·Û.¯¢ÇûúÊ÷_ÖtJïéq¾«ÿÔ¼àÝ>§ç//½»n@«^ïqz—œÝûâ®»nUï,jßSnkùgÏCwÛg¯«ãv÷zŸv+{—;=ÿYÝ«å­v]Ï~_ukåy÷­=»çÚm½k—k¹SýX[Ϫ_›—’wÿð~®Ð¦ÝvÖÚsêþxÍ{ÝßVç?ëü¼Û.§î×»ã@ÒçuÝ´ò¥´;_x·óîýn÷:X÷î'R9ïysu¾Þ7îšÿ¼ìÐbDiñjí¢Ó0§U»,ocÏ¢gØôj¿,-ê½íüó÷I”µ„¹%ü;]Œ[Ù.ÚEÏ×qrcTj#mÄ¢X´‹6bÑÆEn\¸1’µ¥6¢ÔF”ÚˆRQjc$kc$kc$kc$k#~mįøµ¿6â×ÆHÖÆHÖÆHÖÆHÖFdÛˆl‘m#²mD¶Ñ¯Ñ¯Ñ¯Ñ¯˜·ó6bÞFÌÛˆy#f#f#f#fÑp'îDÃh¸ wFÙvFÙvFÙvFÙvâäNœÜ‰“;qr'Nîü´¢óÓŠÎO+:?­èDкAw"èNÝù9FççŸct~ŽÑ‰­;±u'¶îÄÖغóŽžâ—Oƒ“¯‹Z?Ðòu?eâåÇÿ¿J8ùTÚ}ý;ý˜tJÿi}^r½¯÷õZ-gÕ{÷ùZëß]îî~¨­w÷¼±úšª•¿[N›N¹­N'/wΪ¾ÓåNÍ—w÷¯ñéíæÒ–»û>ï]ß*_ëf•êk¯^—»Ü¶»rgò¬îÃ]·¬”N¹¥rÖöòr3Ÿn—Ó×í.·°v>XÕ3Ó7«7+÷¬óXÍKòvç?kùÝqwͯž§·3ý³ã§žwïK»ýjVN+Ç:ÿiõîΛ§®ï,yµß¬ž¤Ï«Ün[-ç5Hrw“õºiåìêõ~Ž;uÿ–»bFíž¾³=‘'Q©Ú=›ÍûZ~%<ÁÎÓ= •{K˜xº×ñì|Œn-'FE+÷”¢Æÿé«8¹3¸3¸3R²3R²3R²3R²3R²3¸sÑæÎE›;#%;#€;#€;÷¿íÜÿ¶uw¢îNÔ݉º;Qw'êîDݨ»uw¢îNÔ݉º;Qw'êîDݨ»uw¢îNÔ݉º;±u'¶îÄÖغ[÷þNχغ[wbëNl݉­;±u'¶îÄÖغ[wbëNl݉­±õ ¶ÄÖƒØz[bëAl=ˆ­±õ ¶ÄÖƒØz[bëAl=ˆ­±õ ¶ÄÖƒØz[bëAl=ˆ­Gü„{'ÿêïÏKþþÿCÿzïäYÚ}m°–Û},ô~½^ÕãíÚ-¿{¾«É»žå~Ù-¿úZ±«×ZÎ;y¹7´zVå½ê| Õµq×k·VŸ÷üû*öyé×–{µó¸ë~±ê>=}߶›TúÝêÖòr³ÎäìÎ/Z½«ú½çM/÷ìj}«žÝóX-?“£µÏ«_]këïŽ/ý»ò´É»ßžêÇ»öxµó©~ª­?û}õü¬í¬•+ßµs·ÜîýR›NŸçì÷™«×ÝkÞݵcVΫÿìÎCV{fzµòN]_m:¥ÇúœèeÇ©û‘6]ËK{!kM–p£UüSžK{ðj£˜§xUZüYÚ‹y²×ñ,ŠY½'òÅ. cO£Ê…èiõ¢â«{O+±ó:NŒzŒzŒzŒzŒzDЃzA"èA=¸ðàb½ƒ‹õ.Ö;ˆ G!(c´õ`´õ`´õ ‚ÄɃ8y'âäAœ<ˆ†Ñð DÃhx0¢y ¢áA4<ˆ†1ï æļƒ˜wóbÞAÌ;ˆy1ïæ-ßlß2‰™ÌLa¦"üú–¡´@iÒ¥¥¾e(-RZ¤´Hiñ4Úy¦ o™L¢´Di‰Ò¥%JË´-SZ¦´Li™Òò;i´­°u [§|¢èä"æ¢×ŒT¾âå?qüÏN>ý¦­çý:x—œÝ×}/=RyowÒ©ã³äÕ_îrsyËõêïÏÒc•Ê-r׸óJÞnŠY}¯ñáÕ¯¼ÜZ¹§êiåx½¾Ï~?-ÏÛžÓòïºOzË;=ŸjåîÎ^r$yÏ*§uëYÛÅ»_캽ÛE«ßûùÅ«?¬¶¿¶]Ví²¦ÕöÝmk½Sý`5oý}uÞ’÷|5«oÕ»*Ïk¾’êÍìÙ•³z}Ÿ5îï~~ÜíÖd½ÏXËiåx?7­žÇê|%¥Õç­¼Y9¯ç0I®Õéø¬¾—¼]½Z9ê ‹HO/žì™;ÝcÙ'²O%¬\Œ[ÂíôNÅ÷Šžài±œ³«÷Vî=½Œ“Ë÷BU£ aT%Œª„Q•¨®‡Uâ°JV‰Ã¥5Jk”Ö(­QZ{'çÓx>çÓy>tZÐiA§tZÐiA§ Z0(mPÚ;d;( ‘¹% 2÷-˜‰Ì$f23…™ÊLc¦3C ˆy1o”(-P1o æ ļ˜7ó†Ä3M”F̈y1oHï¤Ñ¶Ä3Ílë̶δ€h8 ¢á@4ˆyC¡´Bi…Ò ¥•wÒx>Ûc;¼íOƒ“xøŠ¯™‘ÉôŒ2¶Å®O=Î{½æœ’·*÷t;nßgïi·×nùÓî…]¹§åxÕóvóÍäŸJw]ŸÕ×ÛYÚ}ô>~ºü³äÝe׫ݟ_mÞÑÖ{V;ÞíV]uzߧ¥duÿYÝjVwån{z]¿S÷ƒkù]÷¡¶œÖ»ê>}ÝwëíÚ§M^Ï]ÖöŸé[½¾³dVõXõŸîWÚvöÒ³{Ýwõzõ—™«ó®w¿?u}¼úéL”·êõ¿»óéj½Ýñ2Ó³ú\´š¬Ï;Öç&ëxÚÕ{ý]’3Ó3û}õ:x—»ú‹:ZW¹Ç²½¼‹/¯t…+abe9íâÞR´±´¸vqj wÅçž×ZŒ=[ô\ÂÒêÅǽöN.‘Œ‘Œ‘h+mE¢­H´‰¶"ÑV$ÚŠD[‘:AG"èH+¥ÕwÒx>çCl‰­#±u$¶ŽÄÖ‘Ø:[GbëHl‰­#±u$¶ŽDБ:AG"è8(:AG"èHˆ qr"NNÄɉ89''âäDœœˆ“qr"NNÄɉ89''âäDœœâ;i´-ñL±oð[&2C ˆ t"‚NDЉ89''âäDœœˆ“S~'çÃqš8NÇiâ˜Ks‰c.'§ÏŠ“QÊŒX~{àärþ½Øµ¢œ÷kÅ«½Þx¿>hÓ«¹Ç¼û±¶¼÷kªܻÓn»<ûõJ›NÍRýW÷Þn¼]}Þóï«Øç¥_[îÕÎã®ûÅ®;ךNݼô[ÝÀw»ÿvÝÔ»zWõ{Ï›»îP/wª¶þîy¬–ŸÉÑÚçÕ¯®ÇµõwLJ—þ]yÚäÝoOõã]{¼ÚùT?ÕÖŸý¾z~ÖvÖÊ•ŽïÚ¹[n÷~©M§ÏsöûÌŽÕëî5ïîÚ1+çÕvç!«=3½Zy§®¯6Òc}Nô²ãÔýH›®å/vý{±ë_^ßW_ìº$"ÛDd›ˆl‘m"²MD¶‰È6Ù&"ÛDd›ˆ_ñk"~Mį‰QÉQÉQÉQÉÈ6Ù¦AiƒÒ5œˆl‘mï¤á|2‘mfÔpfÔpfÔpfÔp&æÍļ™˜7sqèÌÅ¡31o&æÍļ9¼“Ö™áùpAéÌ¥31o&æÍļ™˜7ófbÞLÌ›‰y31o&æÍļ™˜73Ò83Ò83Ò83Ò8 g¢áL4œ‰†3Ñp&ÎDÙh8 g¢áÌO82?áÈü„#óŽLœœ‰“3qr&NΟ'sÁëÇÿ¹ð5Ë ZÙ¼›VÛ­å¼õîê?­ï”ûe×ïëµZΪ÷îóµÖ¿»ÜÝýP[ïîyãÔkÕŽÝtʽv:íºiž=ÿžjÇ»¯·{Åûþ|Ú¨µãYõ­ò%÷—›ÕZÏkœŸr‡îžÿ]n_)y»­gz¬íµêÖœ•Ÿ¥]½Þ×íôûŽö<¼Þ¼®×«œÇj^’·;ÿYËk~õ<½í˜éŸ?õ|¼{_ÚíW³rZ9ÖùO«wwާ㴽ÓO…“QÇDÇ¿}ù'bùñÛ#BùÏß‹]Ÿ•{ºN·ï³Î÷Ôë€Wy/wØÝãèÕûËeXÕãåÖñ’7+·ë¶Y=~ºü³äÝe׫ݟ_mÞÑÖ{V;z߽ܖRùÓóìª;ÛêFój÷]û$y³ßOÍ/»î]m9«;rÛ)ü.ÙåÕ~³òÖúÏzÿLßêõ%ë|°ªÇªÿt¿Ò¶³—žÝ뾫׫¿ÌìXw½ûý©ëãÕOgz¤¼U¯÷øÝOWë펗™žÕç¢Õd}Þ±>7YÇÓ®Þëï’œ™žÙï«×Á{¼ÜÕ_~/vý{±k–ûï,v݈y1ocDfcDfcDfcDf#nDÃh¸ 7¢áƈÌƈÌƈÌƈÌFœÜˆ“qr#NnÄÉh¸ 7¢áF4܈†Ñpc„vc„vc„vc„v#nDÃh¸ 7¢áF4܈†Ñp#nDÃÚh¸ 7¢áF4ÜÆ;i8ŸN4܉†;Ñp'îŒÐîŒÐîŒÐîŒÐîÄÉ8¹'wâäNœÜÕÝÕÝÕÝÕ݉ ;qr'NîÄÉ8¹'÷øNχQÝQݺAw"èN݉ {ª_> N~,lÍ(ebä?qìÛåø7i±k¯Çs¯ò§^ îrKìêÕÖ;­7y_/¹ÞýôU®Ó©ö>­Ç»ü³í“ê¾¾wË™Éó~ ß-¿ë&8=¾îv£’»;ßÝ5]å={ÞðîÞõ½ÝzZ½R^[ïú»Õm8“c­/É›ÉѺYOÉ[½®^îb/y3ùÒñÕû‡µŸ¬Ž3ïùàÙ÷»k¹Ý~2“k=¾*W[_+owüÏôx½ÿxÝfi·¯Î/Öó\Õ;K^Ï3^ý`õ¾µj‡µž”vç™<ïq:“³«W+ÏZUþj}k?¶ŽSk9íq©Ü«ÍC³òZL¬ŽÂ¢k?,†,aS s^pé‡rʨh-®UcqAßû´‹I çù‹kqò5ª{¶ÈöwO¯Û¥ü‡~0‰>_ÇÉŒŒŒ‘ƺAw"èN݉ ;t'NîÄÉ8¹'wâä^ÞIãù'wâäNœÜ‰“;qr'NîÄÉ8¹ wFwFwFwFw¢áN4܉†;Ñp'îý4ÚÆý–;÷[îƒmMœÜ‰“;qr'NîÄÉ8¹'âäAœ<ˆ“qò NÄɃ8y'¢áA4<ˆ†Ñð á´Î χÑɃÑɃhx ¢áA4<ˆ†1ï`¤ñà8§ƒãtpœŽÓÁq:8NÇÛ8ý48ùG”ñÏE¬‹\cäÿ>þÿÇlïä»Ü³zÞýwÉY}};}¾^ŽÏ’Wñ¶Ó«}gåO¹cîÒc•¿;¯<{Üy¥»^ï½Ç‡W¿Úu7Y垪§•³ê~õvïüWÝM§¯ëÝ÷SãÑ+íº±¼åHòžUnÕ]i•'Õ›¥SîÉ™«ÞS÷IÞnXmm»¬ÚeM«í»Û>Öz§úÁjÞúûê¼!%ïùjVߪwUž×|%Õ›Ù³+gõú>kÜßýü¸Ûÿ¬ÉzŸ±–ÓÊñ~nZ=ÕùJJ«Ï9Zy³r^Ïa’\«ÒñY}/y»zµr>Ô—¢f¯Ñº~1§°WðÏrÞ{'²O½è´ÐŽ?åI{'K‹]_q­´7ñdOdu9c´øUÞìzH绌“ëwDJ¾e*3™ÎÌ@8ì-˜‰Ì$f23´ Ñ‚D -H´ Ó‚L 2-È´ Ó‚Li…Ò ¥J+”V(­ð| ϧð| ϧЂJ *-¨´ Ò‚J *-¨´ Ò‚J *-h´ Ñ‚F -h”Ö(­SZ§´NiÒ:ϧó|:ϧó|¥ J”6(mPÚx'ç<^ðø[&0S™iÌtf(-PP÷[†Ò¥J ”)-Ò6DN¿e3Ÿ's±kîü«¿+fþã÷b×gåžn§Óíû¬ó=íöÚ-Ú½°+÷´¯zÞn¾™üSé®ë³úz;K»¯ƒÞÇO—–¼»ìzµûó«Í;ÚzÏjǻݪ«îCïû´”¬î?«[Íê®ÜmO¯ëwê~p-¿ë>Ô–ÓºsWÝÀ§¯ûn½]û´Éë¹ËÚþ3}«×w–¬óÁª«þÓýJÛÎ^zv¯û®^¯þ2³cuÞõî÷§®W?é‘òV½Þãww>]­·;^fzVŸ‹V“õyÇúÜdO»z¯¿Krfzf¿¯^ïñrWù½ØõïÅ®Yî?³Øu ÄÉ89'âä@œˆ“qr NÄÉ89'âä@œˆ“qr NÄÉ89'âäiA¦™dZ@ˆ t ‚DÐ:'âä@œˆ“qr DÃh8 ¢áÐx¦DÃh8 ¢á@̈yC§´NiƒÒˆy1o æ ļ˜7"¢ù-S˜©Ì4f:3”F̱@ö[&2“˜¡DÑh8 G¢áH4‰†#Ñp$ŽDÑã4rœFŽÓÈqßÆé§ÁÉ<üÀÉÜ+ù±_ò5bÿìí¶X­ïå>Ø-×ùzɹëµêÔkǪ<¯~ë]Κ¼_ß´òwÇå©×éSé®qyºßy¹#võ®¦»®·U¯w;žžWõ={¼=ë>½ëþ]•º½½ÜRVy»Ï «víº­×ù”ÞÓÏw§ÚW[~VNë¶}Ö<,ÉÙm)oÕ;“/Õßwû‡W?°ŽïÝöŸý¾kŸÖŽÝùÙš¬ýjw^zÖ|»;¾fzWûƒUŽU®”VÏÃ*oVn&Ϫµ¾µžwûí&¯yØ«¾Vþ]Ï1Vy»Ïq^ó†”×Ú!?~¿TFê£z\ªÆµJœ,aݸs†C…hbmt­v‘è¸ÖhŸeý³}l/-R¾ü¹€1 |¶øø:NŽÄ¼‘˜7óFbÞH̉y#1o$æļ‘˜7óFbÞH̉y#1o$æļ‘˜7óFbÞH̉y#########Ñp$ŽDÑh8 GFGFGFGFGâäHœ‰“#qr$NŽŒ4Ž4ZÐht$‚ŽDБ:AGF'GF'GF'GF'GbëHl‰­#±u$¶Žƒ0¢92¢92¢9uG¢îHÔ‰º#QwbDsbDs‚ßo™ÄLf¦0S™iÌtfhñx"OááäGtò3Rù±wò×/ïQò·ŽÛöN–’·;çôãô)}w·ÃªÝÞƒZ¹^ýÊKÏnûí¾îºS¼Ò)÷Êj9¯zVyÞóÞ«ŒóÕ×Á™\«$y»÷­ÓóÞr?Pîul^¬YÀÅr’>_Îöôöèu³o²×ñl1n±ý®øW°Ü=Û£zº¨¸rqoë^Ö’Üzà*NNŒ®LŒ®LŒ®LŒ®LŒ®LŒ®LŒ®LŒ®LŒ®LŒ®LŒ®L‘0r:1r:1r:1"31"31"31"31r:©'"õD¤žˆÔ‘z"RODê‰H=©'"õD¤žˆÔ‘z"RODê‰H=©'"õD¤žˆÔ‘z"RODê‰H=©'"õD¤žˆÔ‘z"RODê‰H=©'"õD¤žˆÔ‘z"RODê‰H=©'"õD¤žˆÔ‘z"RODê‰H=©'"õD¤žˆÔ‘z"RODê‰H=©'"õD¤žÆ'ÂÉ£aiqkþ ‹`ÿÆÉžúîn/w€µÜêã²W¿òÒãýÚè­ßË åeÇêqk9¯zVyÞóÞ«ŒóU·ÍL®Už·^íñSöÝ=.ž5ß{?­ÿZînw–µþ«ÏCZ·Ô³Ü†Úr§Ý»³z’«}Zy’ÞîÄSîZmñšïWŸçžõüæ5ž­×ã®~:ûÝk$y»÷­ÓóÞr?ø“ãäé/Œ“‘™‘™‘™‘™‘™‘™‘™‘™‘™‘™]™]™]™]™]™‰º3Qw&êÎDÝ™¨;[gbëLl‰­3±u&‚ÎDЙ:Ag"èLœœ‰“3qr&NÎÄÉ™h8 g¢áL4œ‰†31o&æÍļ™˜7óf"ÛLd›‰l3‘m&²Íį™ø5¿fâ×Lüš‰_3ñk&~Íį™ø5s,dŽ…̱92ÇBa¿.Œ4.Œ4.Œ4.Œ4.ì×…‹j~öQøÙGág…ýºðŽÂO8 ?á(ŸiïäÇB׌BþÿrÏäGó__~F3ÛöNÞM^¯!wëÝÕZŸ—\ïëã}½VËYõÞ}¾Öúw—»»jëÝ=o<ËmqªŸ{•ÖýÃÛ]6ÓwºÜ©ùòîþã5>wÝ/«åî¾Ï{×·Ê_uKiå[ëyóU÷êªÜ™<íùYÛáÔóŸ×ó µ½¬ö=«]N_·Óï;V7õªž™¾Y½Y¹gÇj^’·;ÿYËk~õ<½í˜éŸ?õ|¼{_ÚíW³rZ9ÖùO«wwÞ N–·þó_Ž‰‹]ßý:©-çýš¥-×ùzÉyöëõj:}½wÛoµœ5yµã)÷ËîkžVÞét׸<Ýïv_CµéÔuºëz[õz·ãéùwUß³ÇÛ³îÓÞnN­üÓí½:ìÊ;åV”’V¯µÜ³ôž~¾»Ë]¼:®¼Üª³ä}ßÚm)oÕ;“/Õßwû‡W?°ŽïÝöŸý¾kŸÖŽÝùÙš¬ýjw^zÖ|»;¾fzWûƒUŽU®”VÏÃ*oVn&Ϫµ¾µžwûí&¯yØ«¾Vþ]Ï1Vy»Ïq^ó†”×Ú!?~¿ð¡¸x±°hóÏr«X÷þˆ–0§e{=)j÷ƒ¼I4±v1n çjí“ðïÏö½Ø+¶ŸÐÎÚÏ Ô˜]¥¾Ž“Z#BkDh­¡5â°FÖˆÃqX#kD[QœQœQœ wFqvFqvFqvFqvFqv"õN¤Þ‰Ô;‘z'RïÄã dw.Ý Þ Þ‰Ç;ñx'ïÄãx¼wâñN<Þ‰Ç;ñx'êîDݨ»uw¢îNl݉­;±u'¶îÄÖºAw"èN݉ ;qr'NîÄÉ8¹'wöëN4܉†;Ñp'îìט·óvbÞNÌ;دÈ\ {pìÁˆæÁ~=<<<>Stò#2ùßðñøÿeeN>UN+gõqÒ«Ü®oûOµÇ¬Þn{y?îjåx½¾ÝݽûwýUyÞ¯Ó§^«^Uï®\ï×s­ÜSýùUúÝ®Üg»™¼Ýi³z»åVëßuÿxõ~e=¾êözŽØu¯y¹?w“·ûù®ço7ç©ö_íZ9«ýJJÞí¸z¾’<íñY;ìêÕ¦ÓÏWϺ¾3ù«óÆ)»¬åWçƒY½ÕûÑéyww¼z_ßÝþ`Õ³{=¼Úi–Vçw¯vó*·Ú_¤rw__­]^òf¿{Ï»Ör³´z¿ÒÊÓ–+Ö.þ¬J•¢y¥rÒž½ìSî<‹&–ÿuží=­þUFOK8wºx¶v1îÉçnÿPn¢w'bÑÁHãÁHãÁHãÁHãA,:i<i<i<i<Ò;ieD©ƒ(u¥¢ÔA”:ˆRQê JD©ƒ(u¥¢ÔA”:ˆRQê JD©ƒ(u¥¢ÔA”:ˆRQê JD©ƒ(u¥¢ÔA”:ˆRQê JD©ƒ(u¥¢ÔA”:Þ¡T|Vо㳂·L`&2“˜éÌPZ ´@iÒðYÁ[†Ò¥EJ‹”)-Ò6ŒŸ·La¦"“(-QZ¢´Di‰Ò2mË”–)-SZþD{'?PñcOä?/yþ}ûøÿ÷8ù‘îzm<¥W[oÕ>ï׫þ]¹§Ü,»zŸÕ.ÞýôU®Ó©ö>­ç”{ê.}Úz§¯ïÝrfòvÝm»úgåžm·—üSzOÍÏ»zOõßgÏw¹EWë[ÝfÞýÒë|­îΙk}IÞLΪÛÖKÞêuõrÇzɛɗŽ¯Þ?¬ýduœyÏϾß]Ëíö“™\ëñU¹ÚúZy»ã¦ÇëýÇë¾0K»ýxu~±žçªÞYòzžñê«÷­U;¬õ¤´;ÌäyÓ™œ]½ZyÖú«òWë[û±uœZËiKå^mš•W/v­Ý#X‰W—±©´¨³2VÔ+”û€•…(æ)N–¢¬¥r“=‡µ˜ý^ÿÿþýçÓx>Ò¥5Jë”Ö)­SZçùtžOçùtžÏ ´AiƒÒ¥ Jï¤ñ|χÈ6ÙD¿¾e*3™Î ¥!úõ-CiÒˆl‘m ~ įø5¿†Hiįø5¿â×(-½“FÛÏ”È6Ù†Li™Ò2¥qÌŽ¹Àñ8~ÇOàø …Ò*¥UÚVÙ:s¡~2œüÿ>þ®{'_÷Mþqì×8ù‘v¿WëvKœ~}Õêµ>v{×îõ8¥ÇšN½ö¬ê9ÕÏO¹N¥»^;wÝDÒñWgÏ–{×¼½Úާ硻ܧïw³r¯Öï¬åŸm—W¹Wy¾Ú½¬ºë¬nÑÕóXÕëív´þ~ÚM{•ç5¯¬¶Ën²ºYµrVõ[ËiíÕögëù{=÷ŸzïYg¬ÇWõi·Ö÷~ðž'¥¼U﬜6­Ú¯•7+ç%O+_Û®ÒïÞóó³Úû®~?K§æñgÙç¼ì¶–ón/­¾Õë{ZÞµ¼”Ÿ•Ÿéý _ØûW\4ùŠOÜ(E±þ”g®è÷vžD×jÏWW¥Å¤¯Ç‹X‹å&ÑÝÒ"æZœ<Ó;]||¢w'â×@üˆ_ñkh„Q¨®‡5â0"Û@dˆl‘m ² D¶È6Ù"Û@d:-è´ Ó‚A ˆy1o æ ļ˜7óbÞ@̈y#1oddnddnddnÄÂÈo™ÂLe¦1Ó™¡ŒæŒæŒæ'GâäHœ‰“#qrdpdpdpdp$‚ŽDБ:AG"èiA¤‰0j8[GbëHl‰­#±u$¶ŽÄÖ‘Ø:[GbëÈHãÈHã˜iA¦DÝ‘¨;æþåÓàäo?òÄÊÌÿùå=b¾”µádk:õ¸vJ¯·œÝÇ0/=RùÝÇñ»ŽÏ’Wñ¶Ó«}gåïzÍ;¥Ç*ßË­´+÷Yó’UÞ«ºÏ¬õ´úžuÿ8}ß9åv´&¯þ´ZÿnùwÝ'½åžOµrwç/9’¼g•ÓºmwÝuR½Y:åžœé±ê=uäíö‡Õö׶˪]Ö´Ú¾»íc­wª¬æ­¿¯ÎRòž¯fõ­zWåyÍWR½™=»rV¯ï³ÆýÝÏ»ýÏš¬÷k9­ïç¦ÕóX¯¤´úœ£•7+çõ&ɵÚ!ŸÕ÷’·«W+çCý Þ”0¢uÏÜéËþ•¢k/víî髵OÂã?õ ‹l¯âxí^Öâ^ÑŒ-–¢«¥ó˜-®Ý{z'g¢­L´•‰¶2ÑV&ÚÊD[™h+me¢­L´•‰¶2£83£83£83#§3#§3#§3#§3#?3#?3#?3#?3#?3#?3#?3#?3#?3£­3£­3£­3£­3£­3q&îÏÄý™¸?÷gâþLÜŸ‰û3q&îÏÄý™¸?÷gâþLÜŸ‰û3q&îÏÄý™¸?÷gâþLÜŸ‰û3q&îÏÄý™¸?÷gâþLÜ_ˆû q!î/Äý…¸¿÷âþBÜ_ˆû q!î/Äý…¸¿÷âþBÜ_>ÓÞɽÿ~»ä¹o2ÿþ'[ýï½^{´é”{åt9ï×9m9o7—~«žÝö»ë±ýôxØmÏ»ÚÛ+í^7mùWç«nÇ™\«Õʱ¦Ý~àíö›Õ“äXíÓÊ“ìX=ïÕã’þÙq«ÛÚË®SÏ»w=¿ÌÊíŽï»úéìw¯ñ,Õ÷’'É_M«×c&gÕ)ÿ¬r§Îßë~~êºÝu[¿Z9^ãתw&_›îzÞ°Ê¿kþ´ÊÛíGR½Sí}×øäíÞ·NÏ{Ëý@»÷¯‡ Q»r’>KÎaUv³o²8õ w«£¢¥E»%L|Áçây(÷”žásëâã’Üzà*N®Än•Ø­»Ub·JìV‰Ý*±[#vkÄnØ­¡5"´F„ÖˆÐZ#BkDh­¡5FÌ6F¿6F¿6F¿6F¿6F¿¶øNχѯѯѯ‘¬‘¬¸¿÷7âþFÜ߈ûq#îoÄýè¾Ý7¢ûFt߈îÑ}#ºoD÷è¾Ý7bøF ߈á1|#†oDêH½©7"õF¤ÞˆÇñx#oÄãx¼u7¢îFÔÝ8æÇ\ãøé?ã§sütbëNl݉­;±uç˜ëß?Qtò#"™‹ZK `ÿ…2?òÿâdow‡”¼^CîÖ»«ÿ´>/¹Þ×Çûz­–³ê½û|­õï.ww?ÔÖ»{Þx–ÛâT?÷*ÿ¬û‡·»l¦ït¹SóåÝýÇk|îº_VËÝ}Ÿ÷®o•¿ê–ÒÊ·Öóç«îÕU¹3yÚó³¶Ã©ç?¯çAk{Yí{V»œ¾n§ßw¬nêU=3}³z³rÏ:Õ¼$owþ³–ßw×üêyzÛ1Ó?;~êùx÷¾´Û¯få´r¬óŸVïî¼yêúÎ’WûÍêIú¼Êíö·Õr^ó$w7Y¯›Vή^ïç¸S÷ßi¹ &M–uþ¿¿Æˆ³=Œµ{ëj£˜¥=‰g‹bKÑÉ¢¼ëyh÷”°ó wOÏ÷*w±Ü‡èeI¯vQìëõö€^ÇÉȶÙv"ÛNdÛ‰l;‘m'²íD¶‹w.rܹÈqç"ǘ·óvbÞNÌÛ‰y;1o'æíļ˜·óv.rܹÈqç"Ç‹w¢áN4܉†;Ñp'îDÃh¸ w¢áN4Ü Þ Þ Þ Þ‰“;qr'NîÄÉ8¹'wâäNœÜ‰“;qrg$xg$xg$xg$x'‚îDкAw"èÎHðN݉ ;t'‚îŒêîí4ž£º;±u'¶îÄÖغ[wFuwFuwbëNl݉­;?éã“E'c/äÿwì±ö__þÁÌŒXzþ5N>å†Y•çý8´šîvÿxÕÛm—WÑsê5ðÔùÝíò–súº{ß»ûUî]ãg5vŸíêõ§wÉÙ½/îºÍVõÎÒ©ö=í¾Ò–ö¯ë¦•/¥Ýù»wïw»×Áú»w?‘ÊyÏ›«óõî¼q×üçeLJèU 7Z±îlñg!ªvw1ii¯å«¼éžÈÚųµ‹? {¯F;ÿü}e-EW‹‹€ ˜ßÚ.RûºE'÷ïÀTo™ÊLc¦33ADæ[&0™IÌdfhÁ ƒ Z€(ÎÅù– ÌDf3™™ÂLe¦1Ó™¡ZhA ZhA ZiA¤‘DZiA¤´Di‰Ò¥%JK”–x>‰ç“x>‰ç“hA¦™dZiA¦´Li…Ò ¥J+”Vx>…çSx>…çS)­RZ¥´Ji•Òê;i<ŸÊói<ŸÆói´ Ñ‚F -hŸ'ÿùå<üˆFþ†‰—¹ò½”ÿ}ïd)íº5¬å¼_cµåN½ÞY“·R¹ÝvÖ&ïv:}ÝNÉõ~=Z•{wòz=ÖêñzM|•ù@ªÿjãÞÛ}µ«ïôköªœUyÖä5/¼ÚyÜu¿8åîÛ­·ë.^ÕïåæZuëiÝÅÚú³dÕ»ªß{Þ<åÞÕÖ·êÙ=Õò39Zû¼úÕõ¸¶þîøðÒ¿+O›¼ûí©~¼kW;Ÿê§Úú³ßWÏÏÚÎZ¹Òñ];wËíÞ/µéôyÎ~ŸÙ±zݽæÝ];få¼úÏîü– ÌDf3™™ÂLe†0"33"33"33"33"33"33"33"37Z@¤ž‰Ô3‘z&RÏDê™x<gâñL<ž‰Ç3Qw&êÎDÝ™¨;u¢îBÔ]ˆº Qw!ê.DÝ…¨»u¢îBÔ]ˆº Qw!ê.DÝ…¨»u¢îBÔ]ˆº Qw!¶.Äօغ[bëBl]ˆ­ ±u!¶.Äօغ[bëBl]ˆ­ ±u!¶.Äօغ[bëBl]ˆ­ ±uÉŸhïäXøN~üö8ί±¶mïä»_ÃV“·¼U¹§Ûétû>ë|½Ü/Z}Öò§^§­ú¬å^½¿üWæU=»×ÇÚ^^n0«\owÍ]ã÷´¼»ìzµûó«Í;ÚzÏjGïû£—›Z*zžÕºÍ¬n5©žÖk¹]wçì÷Só‹µ_håÎ~_uŸj¯¯÷uß­·kŸ6y=wYÛ¦oõúÎ’u>XÕcÕº_iÛÙKÏîußÕëÕ_fv¬Î»ÞýþÔõñê§3=RÞª×{üîΧ«õvÇËLÏêsÑj²>ïXŸ›¬ãiWïõwIÎLÏì÷Õëà=^îê/êh]å¢ØRô²(o¢WŠšFá ‹]kËi÷Š–°¯´§´v¯c 'àÝŸå„Å¿µ{J‹{cO®‡vïéuœ\ÁXÁXÁX¸Ào!‚.DÐ…ºA"èB]ˆ“ qr!N.ÄÉ…8¹'âäBœ\ˆ“ qr!N.ÄÉ…8¹'âäBœ\ˆ“ qr!N.ÄÉ…8¹'âäBœ\ˆ“+Ñp%®DÕh¸ W¢áJ4\‰†+Ñp%®DÕh¸ W¢áJ4\Ñ\‰†+Ñp%®DÃ5¾“Æó!®DÕh¸óVbÞJÌ[‰y+1o%æ­Ä¼•˜·óVbÞJÌ[\\\\\9¶+ÇvåØ®Ÿ):ù‰ÿúòNþzÉóÿ¼ÿû½w²%y»uîªçõZðl=Þ××úšìåö¹ëuuWÎéëîíöY=¾š¼Ûçôüå¥×Û ©ÕçÕŽwÉÙ½/žv[¬¦Sí»ëFѦgÝÇïî¿Úz»n7mùÝëm=~×õ¸K¿ÖÍ:«o-oµëz|ö»µ_Yåy÷­=»ç¿ÛN»ïQÞïa^åNõcm=«~m^JÞýÃû¹B›vÛYkÏ©ûã5ïu[ÿ¬óón»œº_ïŽIŸ×uÓÊ—Òî|áÝλ÷»Ýë`ýÝ»ŸHå¼çÍÕùzwÞ¸kþó²ã÷ÞÉ¿÷Nþåþs{'WbÞJÌ[i\i\i\i\‰†+Ñp%®DÕ˜·óVbÞJÌ[‰y+Õ®\T»óVbÞJÌ[‰y+1oå¢Ú•‹jW.ª]‰y+1ocÔpcÔpcÔpcÔp#nDÃh¸ 7bÞFÌÛˆy1o#æmá´Î χ‹]7.v݈y1o#æmļ˜·1š·1š·ó6bÞFÌÛˆy1o#æmļ˜·Ù6"ÛFdÛˆl‘m#²mD¶Ÿc4~ŽÑø9FãçŸc4ŽÓÆqÚ8NÇiã8m§ã´ÕOüˆ<~D!_ÿ¾âÿß>ÿ½wò¿•;õzgMÞvHåvÛY›¼Ûéôu;%×ûõhUîÝÉëõX«Çë5ñUæ©þ«{o÷Õ®¾Ó¯Ù«rVåY“×¼ðjçq×ýâ”»o·Þ®»xU¿—›kÕ­§ukëÏ’Uïª~ïyó”{W[ߪg÷'zÙqê~¤M×ò¿÷Nþ½wò/¯ïËï܉©:1U'¦êÄT˜ª wF wF wF wF ÷öNZe¦1Ó™vëŒîŒîÜ×¹s_çN݉“;qr'NîÄÉ8¹'wâäAœ<ˆ“qò ŒŒŒŒDÃhx ¢áA4<<ˆ†Ñð DÃhx0x0xpqèÁÅ¡Ñð DÃhx Fó¢áA4<ˆ†Ñð DÃhx ¢áÁñ38~ÇÏàøļƒãgpü ŽŸÁñ38~ÇÂàX ƒca¼…Oƒ“ÿFÄÜù±Èõ¿!æ)N^}¼>UÞûõuUîîëǪ^m½Óúw“÷õð’ëÝO_å:jïÓzN¹îÒ§­wúúÞ-g&ÏËm³ªVîÙv{É?¥÷Ôü¼«÷Tÿ}ö¼áݽë{»Ã´z¥¼¶Þõw/7°×ün—¬çá%Ï˨Õ;«¿*o&_:¾ëŽÝÕ'÷:ßU;WÓ©ùG+ßÚÎÚ´;ÿÍú·¶¼”·êñzÿñº/ÌÒn?^_¬ç¹ªw–¼žg¼úÁê}kÕk=)íÎ3yÞãt&gW¯VžµþªüÕúÖ~l§ÖrÚãR¹W›‡fåÕÑÉJœ¬Ý‹Y%lÝ«÷®½F×*“V/Ú-è÷žžáZá¸÷øàÞãƒ{î=>¸÷øàÞãƒ{î=þ–áù4ZÀhëÊhkîW>þÞ¯üÓàdâã…?[t²×㥵ÜéÇûSîowÄj9ïתS¯«ò¼ú­w9kò~}ÓÊß—§^§O¥»Æåé~çåŽØÕ»šîºÞV½Þíxzþ]Õ÷ìñö¬ûô®ûwUþéöörKYåí>'¬ÚµëN´^çSzO?ßj_mùY9­ÛöYó°$g·¤¼UïL¾TwÜí^ýÀ:¾wÛöû®}Z;vçgk²ö«ÝyéYóíîøšé]íV9V¹RZ=«¼Y¹™<«þÕúÖzÞí·›¼æa¯úZùw=ÇXåí>ÇyÍR^k‡tüøýRˆ–£k…=€–SF¹Š¸V»§¯}Å3*,Ö,Eßj÷žâZ£}Òb×?ÛWÀöR”ðòçæžÚ'œï:N®Œz¬Œz¬Œz¬Œz¬Œz¬Œz¬Œz¬\à·[WbëJ]‰ +t%‚®DÐ8¹'7âäFœÜˆ“Ñp#nDÃh¸ 7F'7¢áF4܈†Ñpc¤q#nDÃh¸ 7bÞFÌÛˆy1o#æmD¶È¶Ù6"ÛFdÛˆ_ñk#~mįQÃøµ¿6â×FüÚˆ_Qj#JmD©(µ1¸±_7öëÆ~Ýدûuã§ýº±_7öëÆ~Ýø™Dg¿îì×ýº³_w~òÐÙ¯;ûug¿îoýúÓàäG¤ñcÁëÇ>ʨäGôòã·GþGDó¯÷N~¤ÕLj™ïòÖú§×µzwÝ4ÖãÖrRùSz¬i÷uì´É[ÿ©òÞÉ»]µõO»¯´r¬å^Uî]ó¶—ûnU¾w=«œÓ÷»Y¹WëwÖò϶ËÛ}cMÞç¿{ÿ¸ËÍêåþÛukkÝn»Ï{w¹ŸwÝ£39^ï³4»¾Öû½·;{VNk¯¶?[Ïßë¹ÿÔ{Ïêïäe·µœw{iõ­^ßÓò®å¥ü¬üLïù×h]ao] #K{ëJ‹"ÿ”g]¬Ù]+bNé<&ç«ÞKXµB”³‹å&‹…KŸ h÷Nžéâý‰ÞuœÜ¹hs'íÄ¢X´‹vbÑΈÙÎE›;mî\´¹¥v¢ÔN”Ú‰R;Qjg”mg”mg”mg”m'~íįøµ¿vâ×Î(ÛÎ(ÛÎ(ÛÎ(ÛNdÛ‰l;‘m'²íD¶Q¶Q¶Q¶Q¶˜·óvbÞNÌÛ‰y;‡î\ºsqèÎÈÜN4܉†;Ñp'îDѹ‘¹‘¹‘¹8¹'wâäNœÜ‰“;?­èü´¢óÓŠÎO+:t'‚îDкAw~Zѹ"@çŠ+tbëNl݉­;±u'¶îü£Oü@Ä=“‰–‰—Ç _ÿ(k[ìz7zÍ>­wWÿi}Þ¯¡^v<ë5a5­ºk¤üªþW+ww?ÔÖ»{ÞØuÏÍäï–Ó&ïëô¬ûdzÜu§Êš/ïî?^ãsõzíÚw÷}Þ»¾U¾Õ=¹ë^šÕóçZ;¬vy¹×föXݔڴÛ.Þóîª^ïq¼«×ûº~ßÙu?¯þ~ê=ãîóXÍKòvç?kùÝqwͯž§·3ý³ã§žwïK»ýjVN+Ç:ÿiõîΛ§®ï,yµß¬ž¤Ï«Ün[-ç5Hrw“õºiåìêõ~Ž;uÿ–S.r¬]üY•ªÝ³YÄÄÚÅ••‹:¯î­Ý#X´ӽŽgç«\<{VNŒŠ–¢“gØùz}ÿç××w'sAéÎ¥;#%#%#%#%#%#€”\PzpAéÁèÊÁèÊÁèÊÁèÊÁ¨áÁ¨áÁ¥÷AÜy©"õA¤>ˆÔ‘ú RDêƒH}©"õA¤>ˆÔ‘ú RDêƒH}©"õA¤>ˆÔ‘ú RDêƒH}©"õA¤>ˆÔ‘ú RDêƒH}©"õA¤>ˆÔ‘ú RDêƒH}©"õA¤>ˆÔ‘ú RDêƒH}©"õA¤>ˆÔ‘ú RDêƒH}©"õA¤>>Ób×ÿÿï…«¿ýø—Èü_—2øÿ¯£“O»g¬å½Ý*«rO¹C¬úwåz¿Ni“÷õð’ëÝO_å:jïÓzN¹sîÒ§­wúúÞ-g&o× ´«VîÙv{É?¥÷Ôü¼«÷Tÿ}ö¼áݽëkŸ‡¼Ü{ÞîÍUwÝLŽµ¾$o&gÕë%oõºjå­ºA­òfò¥ã^nÛU}Òq¯ó]µs5š´ò­í¬M»ó߬kËKy«¯÷¯ûÂ,íöãÕùÅzž«zgÉëyÆ«¬Þ·Ví°Ö“Òîü1“ç=NgrvõjåYë¯Ê_­oíÇÖqj-§=.•{µyhV^»È±› 8tÕ;[$ú-,,Æ=ÃØÚ½‰Õ{@ ú>Ø'-Ú-•›`XõÞÉB4´6:yz݄ŵŸ ¬ãäÁˆÌÁˆÌÁˆÌÁˆÌÁˆÌÁˆÌÁˆÌÁˆÌÁˆÌÁˆÌÁèÊÁèÊÁèÊÁèÊÁèÊ1ÞIãùðxúþý<þw¦0S™iÌtf(-PÚ?¨ûïLd&1“™¡ZhA ‘DZiA¤‘DJK”–(-QZ¢´Di‰ç“x>‰ç“x>™Ò2¥eJË”–)­Ð¶Bi…Ò ¥J+ï¤Ñ¶ÂÖ©lÊÖ©”V)­RZ¥´Fi¶5Jk”Ö(­QZ§´NÛ:[§³uÞÆé§ÁÉ\Èúº õ#:ùñþþãï×ÑÉ^Ùޯϫå¼Ò)wÊézw»yNéñ¾¾^n7«|ï×ØSrN_w¯ñ{w¿±Ê½kü¬¦g¹U¼ÝaZ¹wÉy–[æÙîœÓî´U}ÚòÏž‡î¶oÕ-¤Õ·ê–“äißu=îÒ¿êÖ“ÊîÖçvïç/÷éª=»ç¿ÛN»ïQÞïa^åNõcm=«~m^JÞýÃû¹B›vÛYkÏ©ûã5ïu[ÿ¬óón»œº_ïŽIŸ×uÓÊ—Òî|áÝλ÷»Ýë`ýÝ»ŸHå¼çÍÕùzwÞ¸kþó²C“­Q—r³Å®%<­–·ºg³„Mµ{ ‹…Kç1Ý£Zˆúå Qã’<-žž.ž=i©}½öNþ›%F ¨A5£aÔ ªÄaƒ8l‡ â0bÞ@̾f"3‰™ÌLa¦2Ó˜éÌТá@4ˆ†Ñp DÃh8 ¢á@4ˆ†Ñp DÃh8DZiA¤‘'âä@œˆ“qr NÄÉ89'âähA¦™dZ@ˆ t ‚DÐ!Ó‚L -(´€Ø:[bë@lˆ­±u ¶ÄÖØ:[‡J *-¨´ Ò¢î@Ôjÿòipòc!ë¿ðï¯þ‹\?°òåß;YJ»n k9ï×Xm¹S¯wÖäm‡Tn·µÉ»N_·Sr½_VåÞ¼^µz¼^_e>ê¿Ú¸÷v_íê;ýš½*gUž5yÍ ¯vwÝ/N¹ûvëíº‹Wõ{¹¹VÝzZw±¶þ,Yõ®ê÷ž7O¹wµõ­zvÏcµüLŽÖ>¯~u=®­¿;>¼ôïÊÓ&ï~{ªïÚãÕΧú©¶þì÷Õ󳶳V®t|×ÎÝr»÷Km:}ž³ßgv¬^w¯yw׎Y9¯þ³;Yí™éÕÊ;u}µé”ës¢—§îGÚt-/íI,î%<‰Nå ‹]ÏðôÌ9Á«jÜmÜKXÜcYX$|uÏfifi‘mmT¹zoìYô´pD?Y{'FJFJ†F€Õ°V#Àbte`te`te`te`te`te`te`te`teè”F<ˆÇñx Äãx<âñ@<‰º#Qw$êŽDÝ‘¨;[GbëHl‰­#±u$‚ŽDБ:AG"èHœ‰“#qr$NŽÄÉ‘h8 G¢áH4‰†#1o$æļ‘˜7óFbÞH̉y#1o$æļ‘˜7óFbÞH̉y#1o$æļ‘˜7óFF4GŽÓÈq9N#Ç\䘋s‘c.¾¹Oƒ“È@Ä?Ñ1óÜüù?¯8Ùû5µþé×Amù»Î×KŽ÷yx»_´zN½þïʱ–³&¯vô~—Êy»—NµëLï)9§û5oÕg•kMw]o«^ïv<=ÿ®ê{öx{Ö}ú.÷«UïnZvåy»AgÉË g½Î§ôž~¾;Õ¾Úò³rZ÷æ³æaIÎn;Hy«Þ™|©þî<¸Û?¼úu|ï¶ÿì÷]û´vìÎÏÖdíW»óÒ³æÛÝñ5Ó»Ú¬r¬r¥´zVy³r3yVý«õ­õ¼Ûo7yÍÃ^õµòïzŽ±ÊÛ}Žóš7¤¼Öéøñû¥*-&-aÑŸåV£„µ[ZœZXZÜÛYØSøƒ¼É^ÇÚE¢¥è`­}R4ñÏöã–>øp¾Ê(uuÔ¶rññuœœˆ_ñk"~Mį‰ø5¿&â×Düšˆ_£“£“3£“3£“3‘m&²ÍD¶™È6ÙfF'gF'g"ÛLd›‰l3#3#sx'çCÌ›‰y31o&æÍļ™‘Æ™‘Æ™‘Æ™‘Æ™h8 g¢áL4œ5œ‰†3Ñp&ÎDÙh83j83j83j83j8'gâäLœœ‰“3qr&ÎDÙh8 g¢áL4œ‰†3Ñp&ÎDÙh8 g¢áL4œ‰†3Ñpæ'™Ÿpd~‘ù Gæ癟cd~Ž‘ù9FþL‹]s¡kââÇßWüKÔüãï׋]ŸJ»Ï«vz=¶ï¶Ó)}^r½¯÷õZ-gÕ{÷ùZëß]îî~¨­w÷¼auo<«]½åyÇnÚ}­}öü{ªï¾>^ý&ÏÛ}¤-w÷}Þ»¾Uþ)wñj=¯q~ÊèíõvçÎÒn»xÏ»Þnë™­V½Þ×íôûŽö<¼Þ¼®×«œÇj^’·;ÿYËk~õ<½í˜éŸ?õ|¼{_ÚíW³rZ9ÖùO«wwÞíž±–÷v«¬Ê=å±êß•ëý:¥MÞ×ÃK®w?}•ëtª½Oë9åιKŸ¶Þéë{·œ™¼]7ЮþY¹gÛí%ÿ”ÞSóó®ÞSý÷Ùó†wô®¯}òrïy»7WÝu39Öú’¼™œUw¬—¼Õ몕·êµÊ›É—Ž{¹mWõIǽÎwÕÎÕtjþÑÊ·¶³6íγþ­-/å­z¼Þ¼î ³´ÛWçëy®ê%¯ç¯~°zßZµÃZOJ»óÇLž÷8ÉÙÕ«•g­¿*µ¾µ[Ç©µœö¸TîÕæ¡YyõÞÉZl*àÐQ½Ê=Œ?DÁJ{+1¶¨W(÷³ Q̳óU/&-œçl¯ãébá“E»µQÖâÐÂÞسÏÖqrateateateateateateateateateateá»…Ñ•…Ñ•…Ñ•…Ñ•¥Sñx!/Äã…x¼âñB<^ˆÇ ñx!/Äã…x¼âñB<^‰Ç+ñx%¯Äã•x¼WâñJ<^‰Ç+ñx%¯Äã•x¼W¢îJÔ]‰º+Qw%ê®DÝ•¨»uW¢îJÔ]‰­+±u%¶®Ä֕غ¦wÒx>Ä֕غ[W"èJ]‰ +t%‚®DЕºAW"èJ]‰ +t%‚®Dе~ÿòip2£‘ÿ äÇ¿ŒLþe¾JÑÉ^Ùޯϫå¼Ò)wÊézw»yNéñ¾¾^n7«|ï×ØSrN_w¯ñ{w¿±Ê½kü¬¦g¹U¼ÝaZ¹wÉy–[æÙîœÓî´U}ÚòÏž‡î¶oÕ-¤Õ·ê–“äißu=îÒ¿êÖ“ÊîÖçvïç/÷éª=»ç¿ÛN»ïQÞïa^åNõcm=«~m^JÞýÃû¹B›vÛYkÏ©ûã5ïu[ÿ¬óón»œº_ïŽIŸ×uÓÊ—Òî|áÝλ÷»Ýë`ýÝ»ŸHå¼çÍÕùzwÞ¸kþó²C“­Q—rnü°·ïª¼ÕE¶%lª]ÔYØ›X:é¢âBÔ¯(o²‡±„‡gxZŒRW¶‹Ô¾~‹]WF0VF0VF0V.ð[‰ +t%‚®DЕºAW"èJ]‰ +tå¿•ûÅVî[¹_l%¶®Ä֕غ[WbëJl]‰­+±u%¶®Ä֕غ[WbëJl]‰­+±u%¶®Ä֕غ[WbëFl݈­±u#¶nÄÖغ[7bëFl݈­±u#¶nÄÖغ1ª»1ª»1ª»1ª»u7¢îFÔ݈ºQw#ênDݨ»u7¢îƨîƨîƨîƨîF<ÞˆÇñx#oÄãx¼7âñ–¿ù48ù‰‹™û*!ÿüû&E'ÏÒ®[ÃZÎû5V[îÔë5yÛ!•Ûmgmòn§Ó×í”\ï×£U¹w'¯×c­¯×ÄW™¤ú¯6î½ÝW»úN¿f¯ÊY•gM^ó«Ç]÷‹Sî¾Ýz»îâUý^n®U·žÖ]¬­?KV½«ú½çÍSî]m}«žÝóX-?“£µÏ«_]këïŽ/ý»ò´É»ßžêÇ»öxµó©~ª­?û}õü¬í¬•+ßµs·ÜîýR›NŸçì÷™«×ÝkÞݵcVΫÿìÎCV{fzµòN]_m:¥ÇúœèeÇ©û‘6]ˈ®Õîa,á_Iž°Øõ OÀœ¼ªÆÝœ;‹Ö•¢z¥EÂ%ìü¡}$½BôñUž6ªüÃbÖÚ=ª'‹qK[»(ö:NnŒ®l\¸qQàÆEnŒÈlŒÈlŒÈlŒÈlŒÈlŒÈlŒÈlŒÈlŒÈlŒÈlŒÈlŒÈlŒÈlŒÈl\¸©7"õF¤ÞˆÔ‘z#RoDêH½©7"õF¤ÞˆÔ‘z#RoDêH½©7"õF¤ÞˆÔ‘z#RoDêH½©7"õF¤ÞˆÔ‘z#RoDêH½©7"õF¤ÞˆÔ‘z#RoDêH½©w"õN¤Þ‰Ô;‘z'RïDêH½©w"õN¤Þ‰Ô;‘z'RïDêH½©w"õN¤Þã÷/Ÿ'_£“±õÏ<£“/eÞãd/÷‚µœVŽ÷k¡µÜ®œS¯µ^ò´õN?žkËY嬾6ÞUÞëµÖš¼ú¿Už×õêÝ5þŸ¥wWî)wâéã»õN÷»]¹ÞöYõ®¶ã«=?¼ÊýãÕû•õ¸Ö4+·Û.»îÉ™¼»Æ›ô»W;ÏôIÉ[ŸVÞ¬¼6­ö­œÕ~%%ïv\=_Ižöø®ûyõ}O’ã¬çs­7û}÷>³:oœ²ËZ~u>˜Õ[½žwwÇ«÷õÝíV=»×ëfiu~÷j7¯r«ýE*w÷õÕÚå%oö»÷¼k-7K«÷+­­&ëóŽõ¹É:žvõ^—äÌôÌ~_½Þãå®þ¢ŽÖU.Š­ÞsX©WŠšFáNp謜}+áó¸]Ââ¶õÎäÍöN𰈧Œ~Õ;»ÚÏ6p2£R£R£R£R£R£R£R£R£R£R£R£R£R£R£R£R£R£R¢RÃwD¥¾e3‘™ÄLf¦0S™iÌtfhA Ò¥J ”(-Ò¶Hi‘Ò"¥EJ‹”–h[bë$¶Nbë$ZhA¢‰$Z)-SZ¦´Li™Ò m+”V(­PZ¡´òNm+lÊÖ©lJ *-¨´ Ò‚J *-¨´ Ò‚F -h´ Ñ‚F -hõ˧ÁÉ\äš‘ÊWtüøÿŸ_~“¿Éëqr·Þ]÷Þrîz ºÛÍu÷ñY:ínÚ•ëUN*ïÕߟ¥Ç*ßÛ=´*÷Yó’UÞîùzowŸ·ü»ÜPV9«n½»ÜmwõÇ»åßuŸô–wz>ÕÊõrsyÍC»nF¯rZw¥µ]¼û…Ö½»ªÏÚ.V·²ÖŽYòê«í¯m—U»¬iµ}wÛÇZïT?XÍ[_7¤ä=_Íê[õ®Ê󚯤z3{vå¬^ßgû»ŸwûŸ5Yï3ÖrZ9ÞÏM«ç±:_Iiõ9G+oVÎë9L’kµC:>«ï%oW¯V·úæœí…+Eë ÑÁRT¯Ûbׇì»bïݽµXw¶ÇòskÏËñþt¯h!zùzË89|oX«`u¬N€Õ °:V'ÀêX­Ó‚N :-´`ЂA -´`ЂA -´€:A"è@ˆ t ‚DÐ:A"è@-´ Ђ@ ˆ­±u ¶ÄÖØ:DZiA¤‘u¢î@ÔˆºQw êDݨ;u¢î@ÔˆºQw êDÝ!Ñ‚L 2-È´€x<âñ@<ˆÇC¦™ZPh‘z RDêH=”O„“¯ÑÉ_‘6þ¹èõøýÏßÑÉgåžn§Óíû¬ó=íöÚ-Ú½°+÷´¯zÞn¾™üSé®ë³úz;K»¯ƒÞÇO—–¼»ìzµûó«Í;ÚzÏjǻݪ«îCïû´”¬î?«[Íê®ÜmO¯ëwê~p-¿ë>Ô–ÓºsWÝÀ§¯ûn½]û´Éë¹ËÚþ3}«×w–¬óÁª«þÓýJÛÎ^zv¯û®^¯þ2³cuÞõî÷§®W?é‘òV½Þãww>]­·;^fzVŸ‹V“õyÇúÜdO»z¯¿Krfzf¿¯^ïñrWùü;:™åþ3ÑÉ!0"30"30"30"30"30"30"30"30"30"30"30"30"30"30"30"30"30"30"34Z@¤ˆÔ‘z RDêH=©"õ@¤ˆÔ‘z RDêH=©"õ@¤ˆÔ‘z RDêH=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘zLŸ's¯d"cbä+jþúÏŸ 'ï¦ÝÇ~ï׃»^Nëó’ë}}N¿Îy÷[ëkó©×¯W+ww?ÔÖ»{Þ8决ڱ›¼¯Ó³îÞ¯Ù3}§Ëš/ïî?^ãsõzy¹wWÓ³ë[寺ë´ò­õ¼ÆùÝîØ™<íùy»1µå½õ­¶×)wç,íê½Ëýå†Y•çý8´šîvÿxÕÛm—WÑsê5ðÔùÝíò–súº{ß»ûUî]ãg5vŸíêõ§wÉÙ½/îºÍVõÎÒ©ö=í¾Ò–ö¯ë¦•/¥Ýù»wïw»×Áú»w?‘ÊyÏ›«óõî¼q×üçe‡6êØŒug‹?_¢j% k]LZŠÆ½Êûý+-&=[<[»øóU¯°ˆµ„iEyæ—äIXWºþR4ö¬]¤öõ‹NNŒJMŒJMŒJÍŒJÍŒJÍŒJÍŒJÍŒJÍŒJÍŒJÍŒJÍŒJÍŒJÍŒJÍŒJÍŒJÍŒJÍŒJÍŒJÍŒJÍDê™H=©g"õL¤ž‰Ô3‘z&RÏDê™H=©g"õL¤ž‰Ô3‘z&RÏDê™H=©g"õL¤ž‰Ô3‘z&RÏDê™H=©g"õL¤ž‰Ô3‘z&RÏDê™H=©g"õL¤ž‰Ô3‘z&RÏDê™H=©g"õL¤ž‰Ô3‘z&RÏDê™H=©g"õL¤ž‰Ô3‘z&RÏDê™H=©çÏ´w2÷K¾î£üõòÿ¿¾¼Tþ½wòa9§^ë½Ê¯º¥î>>K§_våz•“Ê{õ÷gé±Ê÷v‹¬Ê}Ö¼d•·{¾ÞããÙ¯¿«rOÕÓÊñv;žn÷Sõï–×}Ò[ÞéùT+×Ë]wʽú¬rÞîÄSóôÌ}è}=¬zOÝ$y»ýaµýµí²j—5­¶ïnûXëê«yëï«ó†”¼ç«Y}«ÞUy^ó•TofÏ®œÕëû¬q÷óãnÿ³&ë}ÆZN+Çû¹iõçhåÍÊy=‡Ir­vHÇgõ½äíêÕÊùPÿ÷ÞÉ¿÷Nþò‹(æ—ß;¹ó6bÞFÌÛˆy1o#æmļ˜·ó6bÞFÌÛˆy1o#æmļ˜·ó6bÞFÌÛˆy1o#æmļ˜·ó6bÞFÌÛˆy1o#æmļ˜·ó6bÞFÌÛˆy1o#æmļ˜·ó6bÞFÌÛˆy1o#æmļ˜·ó6bÞFÌÛˆy1o#æmļ˜·ó6bÞFÌÛˆy‘m#²mD¶È¶Ù6"ÛFdÛÝÝÝÝÝÝÝÝÝÝÝÝzÿòipòßxø¯üø÷•üírü6œìýúàUÞZßëµÊš¼ìǭå¤òw½Î­Ê÷¶ï”›ÊÛý³ZÞ;y·«¶þîø8õÚªM¯&÷®y{×]²+ß»žUÎéûݬܫõ;kùgÛuÊM£MÞçïí.övwÎäJòwõZ»»ÝŒõWÓêó†VŽ×{Å,iÝŧÆî|­½Úþ¼ë~özŽðzïYg¬ÇWõi·Ö÷~ðž'¥¼U﬜6­Ú¯•7+ç%O+_Û®ÒïÞóó³Úû®~?K§æñgÙç¼ì¶–ón/­¾Õë{ZÞµ¼”Ÿ•Ÿéý _Àƒ«8T¯³èd-^Õîý;‹&–ö<–ʉQÌÒ"à¬;ÃÝ?í“p·€sÅhâÙÞØÚE¬…~²ªw'sÝÆEtÑm\D·qÝÆEtÑmÜ›·1 º1 º3¢¹3¢¹uw¢îNÔ݉­;±u'¶îÄÖغ‡wÒh±u'¶îÄÖºAw"èN݉ ;t'‚îDкAw"èN݉ ;t'‚îDкAw"èN݉ ;t'‚îDк'wâäNœÜ‰“;qr'îŒîŒîŒîŒîDÃh¸ w¢áN4܉y;1o'æíļ˜·sÌuŽ¹Î1×9æ:ÇÜà*ƒ« ®"08æÆÛ˜ûT8™ ]3Jù±Èõ?öMÆïÿŽ“¥´úZ»ZîÔë̬œ×ëݪ¼SvHåN½.ÌôZÏÊßÕ^»åO¹NWÚm—»Ü>¯:Hõ_mÜ{¹ÿ¼ôyÏ¿¯bŸ—~m¹W;»îZ·Û¬ž6vGïê÷vg[ÛÓË §M^î]íqkÒ¶»Uïn}«žÝóX-?“cuۮʗŽkë{¹•wõïÊÓ&ï~{ªïÚãÕΧú©¶þì÷Õ󳶳V®t|×ÎÝr»÷Km:}ž³ßgv¬^w¯yw׎Y9¯þ³;Yí™éÕÊ;u}µé”ës¢—§îGÚt-/aZ¯Nöå {'»E' ú¦{ +ñªˆ…=§W1¶„¥¥=›§‹”[?öŒ¾^·Ù"ÛZ<½Ž“###‘í ²D¶ƒÈvÙ"ÛAd;ˆl‘í ²Œ4Œ4Œ4Œ4ļƒ˜wóbÞAÌ;ˆy1ï æļƒ˜wóbÞAÌ;ˆy1ï æļƒ˜wóbÞAÌ;ˆy1ï æļƒ‘ƃ‘ƃ‘ƃ‘ƃhx ¢áA4<5<ˆ†Ñð DÃQÃhx ¢áA4<Ú;i´QÃQÃQÃ8y'âäAœ<ˆ“£†÷Nüìcð³A=ˆ ô ‚ïôgÚ;ùˆuLÄüØC™Ç±¿ò¯qòêãõ©òÞ¯¯«rw_?VõjëÖ¿›¼¯‡—\ï~ú*×éT{ŸÖsÊýs—>m½Ó×÷n93y^n›Uý³r϶ÛKþ)½§æç]½§úï³ç ïþè]ßÛ¦Õ+åµõ®¿{¹½æwë¼d=/y^îD­ÞYýUy3ùÒñ]wì®>é¸×ù®Ú¹šNÍ?ZùÖvÖ¦ÝùoÖ¿µå¥¼U×û×}a–vûñêüb=ÏU½³äõ<ãÕVï[«vXëIiwþ˜Éó§39»zµò¬õWå¯Ö·öcë8µ–Ó—ʽÚ<4+¯Þ;Y‰“¥E¢§Q½|ù!ZøZîÕ{Ñ?Õ+”û€‹ü;;_5®Îs¶×ñÇO°¸„§×m²7ö ³/ãäøQÃo™ÎÌ?+D=¾e3‘™ÄLf¦0S™iÌtfhA J ”(-PZ¤´HÛ"¥EJ‹”)-ñL¥%JK”–(-QZ¦m™gšÙÖ™miA¦™dZiA¦…ZPhA¡…ZPhA¡…ZPiA¥•TZP)­RZ£´FiÒmk”Ö(­SZ§´Î3í”Ö)­SÚ ´Aiƒ¶ žé`[¶õ Ûc;plÇ·±ýip2÷N~D(?ó7üÿ’“½Ü-ÖrZ9ÞÿÖr»rN½¾xÉÓÖ»Ûmä%Çë5çî~ø*n¯:Hõ_mÜ{¹ÿ¼ôyÏ¿¯bŸ—~m¹W;»îZ·Û¬ž6vGïê÷vg[ÛÓË §M^î]íqkÒ¶»Uïn}«žÝóX-?“cuۮʗŽkë{¹•wõïÊÓ&ï~{ªïÚãÕΧú©¶þì÷Õ󳶳V®t|×ÎÝr»÷Km:}ž³ßgv¬^w¯yw׎Y9¯þ³;Yí™éÕÊ;u}µé”ës¢—§îGÚt-ÿ{ïäß{'ÿòú¾úÞÉ19e"§L䔉“3qr&NÎÄÉ™89'gâäLœœ‰“3qr&NÎÄÉ™89'gâäLœ\ˆ“ qr!N.ÄÉ…8¹'âäBœ\ˆ“ qr!N.ÄÉ…8¹'âäBœ\ˆ“ qr!N.ÄÉ…8¹'âäBœ\ˆ“ qra„va„va„va„v!‚.DÐ…ºA"èÂíÂíÂíÂíBl]ˆ­ ±u!¶.ÄÖ…Ú…Ú…Ú…Ú…¨»u¢îBÔ]ˆº Qw!ê.DÝ…¨»uFhFh—úý˧ÂÉ×}‘ÿøñÿë1þý8f‹N¶&¯×Nï×Á»äì¾î{é‘Ê{»“NŸ%¯þr—›Ë[®W–«üSn‘»ÆWòvSÌê{¯~ååîÐÊ=UO+Çëõ}öûiyÞöœ–×}Ò[ÞéùT+wwþð’#É{V9­[ÏÚ.Þýb×èÝ.ZýÞÏ/^ýaµýµí²j—5­¶ïnûXëê«yëï«ó†”¼ç«Y}«ÞUy^ó•TofÏ®œÕëû¬q÷óãnÿ³&ë}ÆZN+Çû¹iõçhåÍÊy=‡Ir­vHÇgõ½äíêÕÊùPÿ‚_%ì'í5,îé+àÓŸå¼qò!û¤hëŸzŒ½Ý--†-âÚ &V—Sbv OÏ®‡t¾ë8¹pßÂèÊÂèÊÂèÊÂèÊÂèÊÂ~ ø-\à·pßÂèÊÂÅz ë-\¬·p±ÞBÔ]ˆº Qw!ê.D݅غ[bëBl]ˆ­Ëx' ¶UbëJl]‰­+±u%¶®Ä֕غ[W"èJ]‰ +t%‚®á´Î χºAWâäJœ\‰“+qr%N®DÕh¸ W¢áJ4\‰y+1o%æ­Ä¼•˜·ÙV"ÛJd[‰l+‘måø©?•ã§rüTŽŸÊèäÊèäÊèäÊèäÊñSÛ;i´ã§rüÔÏ´ØõcAkî•ü×—÷ ^_½þãŸÿÿ'ï>Žx—÷~mY•{×ãÚj½»^³V“÷õð’ëÝO_å:jïÓz¼Ë?Û>©Þéë{·œ™<¯×æUý³r϶ÛKþ)½§æç]½§úï³ç ïþè]ßËͪM«ó§·ût&ÇZ_’7“ãíµÊ[½®Zyjw䦼™|éøêýÃÛM}ê|Wí\M§æ­|k;kÓîü7ëßÚòRÞªÇëýÇë¾0K»ýxu~±žçªÞYòzžñê«÷­U;¬õ¤´;ÌäyÓ™œ]½ZyÖú«òWë[û±uœZËiKå^mš•—¢’¥(×é¼Ê½„—÷¢a?,}Ñ?Õ+”û},àÚéÞÉÚè_áÙSZZ\[»ØõôºIø\¹'÷:NnD[h« 7¢áF4܈†qX#kÄa8¬m5.ÖÛ9Ý9Ý9݈“qr#NnÄÉ8¹1rº1rº1rº1rºA7"èF݈ t#‚îÄÉ8¹'wâäNœÜ¿¿“†ó錂îDкAw"èN݉ ;t'‚îDк3 º3 º3 º3 º[wbëΈæΈæΈæNl݉­;£“;£“;£“;±u'¶îŒ4îŒ4îŒ4îÄÖغ—wÒh±ug¤qg¤q'¶îÛc»sl÷·±ýipò¯¢ŽÿøÅï׿ˆùqòé×¹G:õ˜zZï®þÓú¼äz_ïëµZΪ÷îóµÖ¿»ÜÝýP[ïîycõõI+·œ6r£œN«ãmÕmùªãì.¹»úîv?yÏ»ïóÞõ­òµî]©¾öøêuYç»îh«Ü™¼]7ãéçØSnå]÷å¬Ü³Úåôu;ý¾³ë~^ýýÔ{ÆÝ籚—äíÎÖò»ãîš_=Oo;fúgÇO=ïÞ—vûÕ¬œVŽuþÓêÝ7O]ßYòj¿Y=IŸW¹Ýþ¶ZÎk>äî&ëuÓÊÙÕëýwêþ;-wÁ†â¼ÒÁ×E˜•XW‹µQ̦í±,E'‹ò„E§§˜]ˆþž-&­^´[ø @[îCô²¤W»Çòõú X|'wbÞNÌÛõØõØõ؉y;1o'æíļ˜·Ùv"ÛNdÛ‰l;‘í`ð`ð`ð ²D¶ƒøu¿â×Aü:ˆ_ñë ~įƒøu¿â×Aü:ˆ_ñë ~ŒŒŒ\Pz¿FFF.=ˆ_#€#€#€zįƒ(u¥¢ÔA”:ˆRQê JD©ƒ(uð3‰Á±08ÇÂàX ƒcap, Ž…Á±08ÇÂàX ƒca¼ ã4ÚÆÏ"èÓ÷·ñóép²baë/^~÷N¾ëõu·œWò~켫œ·»I[n÷uN›Në9õZà}}O/wîïzVy§ÜHwÉ‘êíº'%¹VyÞzµÇOÙw÷¸xÖ|ïuü´þk¹ÓýÅ*÷”þSýPë®»»wûÁ®[O›fz½Ü»Úç‘Õó^=.éŸ÷v“jí:õ¼{×óˬÜîø¾«ŸÎ~÷ÏR}/y’üÕ´z=frVíòÏ*wêü½î秮Û]÷·Õñ«•ã5~­zgòµé®ç «ü»æO«¼Ý~$Õ;ÕÞwIÞî}ëô¼·Ü”ÑÄæÅšg{ë ú´ør¶W¯»}“hâÙbÜÚ½“E/E_¢“Åó˜-*®ÄØÖhqIî?=p'§ïˆ~}ËTf3Â( Ô· ¥J ”( Xô-Ci‘Ò"¥EJ‹ï¤Ñ6 Ô·L`&2Ci‰Ò¥eJË”–i[¦´Li™Ò ¥ži¡´Bi…Ò ¥U¶[¥´Ji•Ò*¥5^…FiÒ¥5JëlÑNiÒ:¥uJëï¤Ñ¶Î3l·Á«0(mPÚ 4¢Ô€OÞ2™ÈLb&3ƒó Ò¥J ”Æñm‹”)-RÇOàX c!p,„·±ðipò#ùŠ¯‹[ó÷Çÿ±·ò{œ|×ë•5­¾þ>ËN/=wËÓÖór‹ì–;õz¦•ç]~Õ¾»®‡·¼S¯“^v¼ºÞ]¹»¯·«rO»sVËÒë5_œê/Wù^n%m½Ýr«õﺼz¿²_u_y=Gx¹OgòîoÒï^í<Ó'%o}V7çéû²Õ¾k~µ_IÉ»WÏW’§=>k‡]½ÚtúùêY×w&uÞ8e—µüê|0«·z?:=ïîŽWïë»Û¬zv¯‡W;ÍÒêüîÕn^åVû‹Tîî뫵ËKÞìwïy×Zn–VïWZyÚòâqe”°6ZWZ¼ZŒbO¾–›-êlŦ³½¥hmíËS¼ª×.®vÖFOK{J_1²¬ŒÚ^ÇÉ!F%¢­DP†Eß2,â×@üˆ_C&Ë´ Ó‚L ˆl‘m ² D¶È6ZPh‘m ² D¶¡RZ¥´Ji•çC̈y1o æ ļ¡Ò‚F - æ ļ¡QZ£´Niļ˜7óbÞ@̈y1olÁÖlbÞH̉y#1o$æļ‘˜7ò3‰ÈÏ$"?“ˆüL" G¢áH4‰†#?“ˆDÑh8 G¢áH4ùiE䧑ŸVD~Zù™DäØŽÛ‘c;¾íOƒ“øø±wòl‘ëË߯£“É˽ãýº¹[ÿ”›a–N¿öx¿~HåOé±&ï×Ã]=§úùêãû«ôsk¹Ý×{«¾ëñWgÏ–{×¼½ÚÞ§ç!¯ñsú~ô¬ñæUþ®û‘µüÝå^åùj÷þa}αºáfr%ù»z­Ç½Ÿ÷vǯ6y¹ ½Ý»Ödu{kå¬ê·–ÓÚ«íÏÖó÷zî?õÞ³:ÏX¯êÓþn­ïýà=OJy«ÞY9mZµ_+oVÎKžV¾¶]¥ß½ççgµ÷]ý~–NÍãϲÏ;yÙm-çÝ^Z}«×÷´¼ky)?+?ÓûAþ5:XŠšUF'KÑ­ÒÞºjªŒŠ–ö žžÇä|Õ{ _°ñÏrÂ"áÒžÈ?í“°®ÇˆîžÙ7[ÄZè'«z×qrÂþÄo™ÈLb&3S˜©Ì4f:3€k‰è1=&¢ÇDô˜ˆÓ ŒJMŒJMŒJMÄ•™¸2WfâÊL\™‰+3qe&zÌD™è1=fF¥f¢ÇLô˜‰3Ñc&zÌŒJÍŒJÍŒJÍŒJÍD™Ÿd~"ù‰@æ'™ŸdâþLÜŸ‰û3q&îÏŒÐÎŒÐÎÄý™¸?÷g¢ûLtŸ‰î3Ñ}&ºÏŒÐÎŒÐÎŒÐÎD÷™è>südŽŸÌñ“9~2ÇOæXÈ ™c!s,dŽ…Â~]د ûua¿.oýúSáäæžÉ_ñÛŸ?þþøòzþqü÷bמúîn‡W{ ÷¶÷´/÷Ä)ý»¯‰Ú´Ûžwµ·Wòz½¼«]¼û©·{è®y}÷µvW¾µü«Üïê‡Ïº¾R¹ÓýÅ*÷”þSýÐê&²ê¿«?kÝ¥V9Úz’«}ÞnÇYºkõ˜m­¿úšá]þ®óõ’sêñ|ö»w;y_ï»Ü»ÉÛݤ•ú5Þ*ïtºk\žîwÖ¼UŸU®5Ýu½­z½Ûñôü»ªïÙãíY÷iow³Vþéö^våí>'ìºÙfǽÝkÞzO?ßj_mùY¹ÙïR~öûj9­œÝvòV½3ùRýÝyp·xõëøÞmÿÙï»öiíØŸ­ÉÚ¯vç¥gÍ·»ãk¦wµ?XåXåJiõ<¬òfåfò¬úWë[ëy·ßnòš‡½êkåßõc•·ûç5oHy­Òñã÷Kañe Xôg9å¢ÉÚ¨Þxx {ž,²}µ÷ƒþ†¿G¤2¢”mÑÉ«s»åW¯w_¥ò»v>û|N¿®¬ê}V»x÷ÓW¹N§Úû´ïò϶OªwúúÞ-g&o×ý´«VîÙv{É?¥÷Ôü¼«÷Tÿ}ö¼áݽë[ÝÁÞýÒë|½Üf^ó»·›ú”¼]7¯Uï¬þª¼™|éøêýÃÚOVÇ™»Ûw"ï®ç¯~2“k=¾*W[_+owüÏôx½ÿxÝfi·¯Î/Öó\Õ;K^Ï3^ý`õ¾µj‡µž”vç™<ïq:“³«W+ÏZUþj}k?¶ŽSk9íq©Ü«ÍC³òZŒ¨Ý Wí¼¼Gð$v†/E½B¹{( ØYÝ-ìAý¡œ€Ç¯ç;ÛSZZ\[»Øõôº 8^»'÷:NŽD[‘h+"‚ñ-Ó™\‹ˆh~Ëf"3‰™Ì -h´€¨;uG¢îH„‰Ð"Z$B‹DÝ‘¨;uÇN :-è´`ÐâñH<‰Ç#ñx$ƒ Z0h"§s"RODê‰H=©'"õ„Å»ß2•™ÆLg†Ã'bøD Ÿˆá1|"†OÄð‰>ç@ "-ˆ´ Ò¢ûDtŸˆîÑ}"ºOD÷‰è>Ý'¢ûDtŸ-H´ Ñ‚D ˆûq"îOÄý‰¸?÷'âþ”?Nþúåý"Öÿ?þþüò3ÿßþxàäSÙޯϫå¼Ò)wÊézw»yNéñ¾¾^n7«|ï×ØSrN_w¯ñ{w¿±Ê½kü¬¦g¹U¼ÝaZ¹wÉy–[æÙîœÓî´U}ÚòÏž‡î¶oÕ-¤Õ·ê–“äißu=îÒ¿êÖ“ÊîÖçvïç/÷éª=»ç¿ÛN»ïQÞïa^åNõcm=«~m^JÞýÃû¹B›vÛYkÏ©ûã5ïu[ÿ¬óón»œº_ïŽIŸ×uÓÊ—Òî|áÝλ÷»Ýë`ýÝ»ŸHå¼çÍÕùzwÞ¸kþó²C“¥½‰¯Ñ«Ââʳ½“ÅÅ®µòV£¢g‹gÏ¢p…½§¥ó˜F+ÏþùûdÑn Ïðôt/æI»Híëœ-š-š-š-š-š-š-š-š-š-š-š-š-š-š-š-š-š-š-š-šˆÔ‘z"RODê‰H=©'"õD¤žˆÔ‘z"RODê‰H=©'"õD¤Îý°3÷ÃÎÜ;s?ìÌý°3÷ÃÎÜ;s?ìÌý°3÷ÃÎÜ;s?ìÌý°3÷ÃÎÜ;s?ìÌý°3÷ÃÎÜ;s?ìÌý°3÷ÃÎÜ;s?ìÌý°ß2•™ÆLg†©síÌ=´3÷ÐÎÜCû-C ˆÔ3‘z&Rç¾Û™ûngïvæ¾Ûo™O„“±€õÏE®ؘ‹a?òïÅ®åï:_/9Ï~½^M§¯÷nû­–³&¯v<å~Ù}ÍÓÊ;î—§ûÝîk¨6ºNw]o«^ïv<=ÿ®ê{öx{Ö}ÚÛÍ©•º½Wçƒ]y§ÜŠRÒêµ–{–ÞÓÏww¹‹WÇ•—[u–¼ï[»í å­zgò¥ú»óànÿðêÖñ½Ûþ³ßwíÓÚ±;?[“µ_íÎKÏšowÇ×Lïj°Ê±Ê•ÒêyXåÍÊÍäYõ¯Ö·Öón¿Ýä5{Õ×Ê¿ë9Æ*o÷9ÎkÞòZ;¤ãÇï—¿»þ½Øõ—ÿâb×…8¬‡â°BVˆÃ qX!+Äa…8¬‡â°BVˆÃ qX!+Œ-Œ-Œ-Œ-Œ-Œü,Œü,Œü,Œü,Œü,Œü,Œü,\è¹Ý¢ûB _ˆá 1|!†/ÿ{ÿ‚,Ǫ+\£^¯þ˜7ô¿C¾¸q½Ïqy ×´ªö 1ÃÎJ”¼2S#Äð¥ü$×C _ˆá 1|!†/Äð…¾ÃbøB _ˆá 1|!†/Äð…H½©"õB¤^ˆÔ ñx!/Äã…x¼¢îBÔ]ˆº Qw!ê.ã'i°­uW¢îJÔ]‰º+Qw%ê®DÝ•¨»rœVŽÓÊqZ9Në·qúip21ñþx™QÊDÍÅ®¿Ÿÿõb×´ú1“ãßZþôãºVï®›ÆzÞšOÊJ5í¾Žv#yë?•ß;y׫¶üi÷•VŽ5߻ʽ5o{¹ïVå{—³Ê9}¿›å{·~gÍÿj»¼Ý7Öä}ý»÷[nV/÷ß®[[ëvÛ}Þ»å~ÞuÎäx½WÌÒ¬}­÷{owö,ŸÖ^m¶^¿×sÿ©÷žÕyÆz~UŸöwkyï÷ïyR:¶êåÓ¦Uûµòfù¼äiåkëUúÝ{~~U}ßê÷³tj•}ÞÉËnk>ïúÒê[mßÓòžóKdzü3½ä ѵ"–|ƃÂbÈmžD'Kò$,=õ³hâò&׫^üYµÏç˜XÌ'D=ÐûÔÎÚÅ®gz§x¢w'×ðŒê< #æ­Ä¼•‹W. \‰y+1o%æ­\à·rßJÌ[‰y+1o%æ­Ä¼•˜·óVbÞJÌ[‰y+#´+#´+#´+#´+Ñp%®DÕh¸ W¢áJ4\‰†+Ñp%®DÕh¸ W¢áJ4\‰†+Ñp%®DÕh¸2B»2B»2B»2B» WF[WF[WF[WF[W¢áÊÈéÊÈéÊÈéÊÈéJœ\‰“+qr%N®ÄÉ•8¹'7âäFœÜˆ“qr#NnÄÉ8¹'7FN7~*Òø©Hã§"-Ä/Ÿ'?öIæ¿~ù¸6÷OÆñ¯»>•N½fŸÖ»«ÿ´>ï×P/;^õš°šVÝ5ÒñªþwËw»jËÝž7vÝs3ù»ù´É»^uÿx•»îT¾Sóåíþã5>WÛk×¾Û÷yïòVùV÷ä®{iVÎkœkí°Úåå^›ÙcuSjÓn½xÏ»«z½Çñ®^ïv;ý¾³ë~^ýýÔ{ÆíëX=–äíÎÖü»ãîùxõ:½í˜éŸ?õ|¼{_ÚíW³|Z9ÖùO«wwÞ<Õ¾³äU³r’>¯|»ým5Ÿ×| ÉÝMÖvÓÊÙÕëýwêþ;Í÷5+aI ›Jѵ“¨TíÞºæE¶ŸóÏ¢„'ÑӳŚÕ{J ˜VÄç–ÿ`Ÿ nÍ'FEKÑÉ3ìüܾÂ"Ûë8¹1‚±1‚±1‚±1Ò¸A7"èF݈ tc¤qc¤qã^¶{Ù6bëFl݈­±u#¶nŒNnŒNnÜ˶q/ÛFÔ݈ºQw#ênDݨ»u7¢îFÔ݈ºQw#ênDݨ»u7¢îFÔ݈ºQw#ênDݨ»u7¢îFÔ݈ºQw#ênDݨ»u7¢îFÔ݈ºQw#ênDݨ»u7¢îÆÈéÆÈéÆÈéÆÈéF<ÞˆÇñx#oÄãÑÖÑÖÑÖÑÖH½©7"õF¤ÞÆ'ÃÉÿàß¿püÀÇŒJf$óŸRtò­××Ý|^Éû±óV>ow“6ßîëœ6ÖsêµÀ»}O/wîïrVy§ÜH·äHåvÝ“’\«çhåÍòy=‡Ir­vHçgå½äíêÕÊùPþ 3JѦÖ=s§{,+£Ž%,¹»§¯Ö>u”°P?äYqüd/kq¯èÉ"Ûb>ïK×!-¶-µ‡t½ë8¹3‚±3‚±3‚±sQàN݉ ;t'‚îDкA"èA=ˆ \xpQàÁE±õ ¶ÄÖƒØz[bëAl=ˆ­±õ ¶ŒêŒêŒêŒêD݃¨{u¢îAÔ=ˆºQ÷ êD݃¨{0ª{0ª{0ª{0ª{âñA<>ˆÇñø Äãƒx|âñA<>ˆÇñø Äãƒx|âñA<>ˆÇñø Äãƒx|âñA<>ˆÇñø Äãƒx|ú‰p2qñ¯Ð2÷S~œÃ‚׿^ìÚú¾ûø2+wËM0K^³·Ëy»O^¥Ç»}O¿žîº}Vó{É9Ýî^ã÷v¿±Ê½5~V“w¿8ýú¯Íï¥×ûü®þÓzgéTý®Î§ú¯5½K¿³–³Ž×]·d÷x»Õ·ôÏ܇§ÜÊV»žÏÏ~·ö+«<ï~£µg÷úwëi÷=Êû=Ì+ß©~¬-gÕ¯=–’wÿð~®Ð¦ÝzÖÚsêþø|ìu[ÿ¬óón½œº_ïŽIŸW»iåKiw¾ð®çÝûÝn;X÷î'R>ïysu¾Þ7nÍ^v|À¡ÒbÈÖE§gxUˆªÝŵÒ^ËÏò¦{"kñ´¯ {¯F;ÿø}e-EW‹˜]ÀÙÖz‘ê×-:¹|Å¿ß2 *:]ùí ð ò€tZÐiA§tZÐiÁ ƒ Z0hÁ ƒ Z0hÁ Àã%;<ˆÑb×ÿ|ùÿƒ¹ö3óøŸÿýû9:yÕM±›O+ÇûuËšoWΩ×E/yÚrÞn…Õ|§^Cµò¼ó¿‹›g7¾^­¼[ãÿUzwåz»´rOõçwéw»r½í³ê]­Çw{~x—ûÇ»÷+ëy«{ô”Ûk×ý5“wk¼I¿{ÕóLŸ”¼õÝr‹jõ[í{>^íWRò®ÇÕë•äiÏÏêaW¯6~¾zUûÎä¯Î§ì²æ_fåVïG§çÝÝñêݾ»ýÁªg·=¼êi–Vçw¯zóÊ·Ú_¤|·ÛWk——¼ÙïÞó®5ß,­Þ¯´ò´ùÅóÊ=Œµ{ k1¢„_¥|Ò"ËìS.ê<ÿÒ^ÒÚ(æéâÏÚ=ª¥E¶œ;Ý‹Y»·³µý¼Èµ´w²rOéuœ‰E#±h$MÄ¢‰X4q&"ÎDÄ™ˆ8g"âLDœ‰ˆ3q&"ÎDÄ™ˆ8g"âLDœ‰¸2W&âÊD\™ˆ+qe"®LÄ•‰¸2W&¢ÇDô˜ˆÑc"zLD‰è1=&¢ÇDô˜ ¥J+”V(­PZùI¯§òz*¯§òz*-¨´ Ò‚J *-¨´ Ò‚F -h´€ŸI$~&‘ø™Dâg‰ŸI$~&‘ø™Dâg‰ŸI$~&‘ø™Dâg‰ŸI$~&‘ø™Dâ'‰c;ql'Žíümlœü«E®µÐõþÿˆNþëËｓè»]«v{?Nkåzõ+/=»õçýzjÕã•vëóV}{¥Ûn±[r¤r«¯Ó3¹VyÞzµçOÙw{\¼j¾÷:Zÿs¾ÓýÅ*÷”þSýPë»]Ï^îEïþ$•“äXíÓÊ“ìpwåìÚ·êŽÖÚuêy÷ÖóË,ßîø¾ÕOg¿{g©¼—$y»÷­ÓóÞr?ø½wòｓÿ~ð¶{'—Ìè×L4œ‰†3Ñp&ÎDÙh8 g¢áL4œ‰†3Ñp&ÎDÙh8 gF¿fF¿fF¿fF¿fâäLœœ‰“3qr&NÎÄÉ™89'gâäLœœýšýšýšýš‰ 3t&‚ÎDЙ:Ag"èL‰ 3tfôkfôkfôk.´€Ø:[gbëLl‰­3±u&¶ÎÄÖ™Ø:[gbëLl‰­3±u&¶ÎÄÖ™Ø:[gbëLl¹"@抙+d®‰º3Qw&êÎDÝ™¨;ug¢îÜó—Oƒ“¿G3âø§ßþúÅïø³E'{½[ó­>†íæ¿u½^rN=žÏ~÷®'ïö¾å6ØMÞî&­üÓ¯ñVy§Ó­qyºßY­ú¬r­éV{[õz×ãéùwUß«ÇÛ«îÓÞîf­üÓõ½:ìÊÛ}NØu³ÍÎ{»×¼õž~¾;U¿Úü³|³ß¥ãÙï«ù´rvëA:¶êÉ—Êï΃»ýëXÇ÷nýÏ~ßµOkÇîülMÖ~µ;/½j¾Ý_3½«ýÁ*Ç*WJ«×a•7Ë7“gÕ¿ZÞZλþv“×<ìU^+ÿÖsŒUÞîsœ×¼!kíο_*÷êUï9¼ºèôd¯ÞÑÑRTôóÁ“¨hiñlíÞ¿ZÜ-EEk퓧þQ¿ xZÀëâÞÉ“EÏ­{TÀân8¹¡"´B„VˆÐ Z!+Äa…8¬‡â°B´UÅYÅYÅYÅYúOÒh£8 £8 ».Œâ,Œâ,Œâ¬ŒÐ®ŒÐ®Äð•¾ÃWbøJ _‰Ô+‘z%R¯Dê•H½WâñJ<^‰Ç+ñx%¯Äã•x¼WâñJ<^‰Ç+ñx%¯Ä㕨»uW¢îJÔ]‰º+±u%¶®Ä֕غ[W"èJ]‰ +tåø©ÄÉ•8¹'WâäÊñS‰†+Ñp%®DÕã§r,TŽ…ʱP9êøD‹]?¢“QÈÜ+ù‘ÌÈ忾ü¥üÇïÅ®×ä®Êñ¶ÿT}ÌÊíÖ—÷ã®VŽ×ëÛí~èݼ˯Êó~>õZõ®zwåz¿žkåžêÏïÒïvå¾ÚÍäíN›•ÛÍ·ZþÖýãÝû•õüª;Øë9b×½æåþÜMÞîç[Ï9ÞnÎSõ¿Ú´rVû•”¼ëqõz%yÚó³zØÕ«M§Ÿ¯^Õ¾3ù«óÆ)»¬ùWçƒY¹ÕûÑéyww¼z·ïn°êÙm¯zš¥ÕùÝ«Þ¼ò­ö)ßíöÕÚå%oö»÷¼kÍ7K«÷+­:Qw'êîíádF?pòó>Ê¿Š\þŽ™ÿ'Ÿzí~NÞó·ôîê?­ÏKî©×-k:Ýfòn_¯µüí|·û¡¶ÜíyÃû5ðÕýÜ+ÿ«î^n­¾ÓùNÍ—·û×ø\m¯]ûnßç½Ë[寺ôò­å¼Æ¹—»Òꎳºµ÷™ÓϱÞúVëkÕ;Ë?K»z½ÛíôûŽö:¼Þ¼Úë]®cõX’·;ÿYóçãÕëô¶c¦vþÔóñî}i·_ÍòiåXç?­ÞÝyóTûÎ’WýÍÊIú¼òíö·Õ|^ó$w7YÛM+gW¯÷sÜ©ûï4ß3^ÕbÎ&žD¥j1¶„¥Eœüœ%<‰žžbq%6•ÅžâßÙõ ØšOŒŠV.>.bv!jü߸Š“;#%;#%;#;#;#;#%;#%;#;#;#;#%;£†;Qw'êîD݃Øz[bëAl=ˆ­Çן¤U4tÕ bëAl=ˆ­±õ ¶ÄÖƒØz[bëAl=ˆ ô ‚DЃzA"èA=ˆ ô NÄɃ8y'âäAœ<ˆ“qò NÄɃhx ¢áA4<ˆ†1ï æļƒ˜wó"ÛAd;<8æÇÜ`tò`tò`tòà˜sƒcnpÌ Ž¹ÁèþÑû—Oƒ“™˜øWÿ9ÿ^ìZ™ÿÖõzÉñ¾ïÇP­žS¯ó»r¬ù¬É«½_Ã¥|^¯WV;vÓ­qyºßy»cVõ®¦[ímÕë]§çßU}¯o¯ºOŸv;žzž°êõ¾¯ZçÿÓó‘Öèåv<¥÷ôóÝ©úÕæŸåÛugßžï¬×1³Ã«ÿÍäYû×ó䶻Ùx¼+OJ«óœ×ü3+çý|.éÙ³ÛO%¹Z½«ù¬vÍ~·Ö‹÷õZåJiõ:¬òfùfò¬úWË[Ëy×ßnòš‡½ÊkåßzŽ±ÊÛ}Žóš7¤c­Òùã÷Ëß‹]ÿ^ìúËãb׃ â.ˆ;ˆy1ï æļã'ÌˈæÁˆæÁˆæˆæúhøÛAàAäAâAç¥J ”( ÈöÛ¥J‹”)-RZ¤mÀ¼ß *¥%JK”–(-QZúI¯'ñz¯'óz2¥eJË”–)-SZ¡´Âë)¼žÂë)¼žBi•Ò*¥UJ«”Vi[¥´FiÒ¥5^i£´FiÒ:¥uJë´­óJ;뺳®¥ J”6( c®ŽŸÀñ8~ÇOøú‰öNæ×E¯ÿúòïB×\ðšÌßÆÉÏiõ1b&Ç;¿µüéÇu­Þ]7õ¼5Ÿ”ÿ”kÚ};íFòÖ*¿wò®WmùÓî+­k¾w•{kÞörß­Ê÷.g•sú~7Ë÷nýΚÿÕvy»o¬Éûúwï·Ü¬^î¿]·¶Öí¶û¼wËý¼ëÉñz¯˜¥YûZï÷ÞîìY>­½Úþl½~¯çþSï=«óŒõüª>íïÖòÞïÞó¤tlÕ;˧M«ökåÍòyÉÓÊ×Ö«ô»÷üüªú¾ÕïgéÔ<þ*û¼“—ÝÖ|Þõ¥Õ·Ú¾§å=ç—Žgùgz?ÈÂÇÏØö9ß<øŒW…èÖò¬‹5OôŠQÌ“Åšµ×+ab-—¢ºµ˜XÌ7Y,\úL@´ou,ô“U½Ë8¹â×@üˆ_ñk„Qˆ²ýv@´…(Ûo´€È6Ù"Û@dˆl‘m ² ‘DZiA¢ļ˜7óbÞ@̈y1o æ ļ˜7dZiA¦™ ¢á@4ˆ†Ñp DÃh8 ¢áPhA¡…Z@œˆ“qr NÄÉ¡Ò‚J *-¨´€:4Jk”Ö(­Qth?IãõA"è@ˆ t ‚DСӂN :-´€Ø:|¦½“ÿƒ…{!?Ðñ_¿øýñïÓÞÊÿ7N–Òêkíj¾S¯3³|^¯w«òNÙ!å;õº0Ók=?Ë«¾vóŸr7œº¯´[/·Ü>ï:HåßmÜ{¹ÿ¼ôyÏ¿ïbŸ—~m¾w»Ž[÷ ­ÛmVN›N»£wõ{»³­õéå†Ó&/÷®ö¼5iëݪw·¼UÏîu¬æŸÉ±ºmWåKçµå½ÜÊ»úwåi“w¿=ÕwíñªçSýT[~öûêõYëY+W:¿kçn¾Ýû¥6¾ÎÙï3;VÛÝkÞݵc–Ï«ÿìÎCV{fzµòNµ¯6Òc}Nô²ãÔýH›žóK˜Vå*E¯J{ÿ§\ÄúC´¬°×±vïdiã^±³ve%Æ–°´´Èö,šØü¹€°È÷s»I]ª? OoàäA5£5õõõõ± ô·ƒÂƒÊƒÆƒÎZ@Ô‰º#Qw$êŽDÝ‘¨;uG¢îHÔ‰º#Qw$êŽDÝ‘¨;uG¢îHÔ‰º#Qw$êŽDÝ‘¨;uG¢îHÔ‰º#Qw$êŽDÝ‘¨;uG¢îHÔ‰º#Qw$êŽDÝ‘¨;uG¢îHÔ‰º#Qw$êŽDÝ‘¨;uG¢îHÔ‰º#Qw$êŽDÝ‘¨;uG¢îHÔ‰º#Qwd´ud´ud´ud´u$Äã‘x<¶úåÓàäÿDÿýý_î“ü´¨õ/¾6G'[“×k§÷ëà-9»¯û^z¤üÞî¤SçgÉ«¿ÜrsyËõêï¯Òc•Ê-rkÜy%o7Ŭ¼×øðêW^î­ÜSå´r¼^ßg¿Ÿ–çmÏiù·î“ÞòNϧZ¹»ó‡—IÞ«òiÝzÖzñî»îDïzÑê÷~~ñê«õ¯­—U»¬iµ~wëÇZîT?X=¶þ¾:oHÉ{¾š•·ê]•ç5_IåföìÊYmßWûÛÏ»ýÏš¬÷k>­ïç¦ÕëX¯¤´úœ£•7Ëçõ&ɵÚ!Ÿ•÷’·«W+çCù'ü*a?ë"ÇÓE±½qò!ûÔ{ õøCž2º[»ø¸¸¸÷O‹ù”˜]ÂÓ³ö®w'gbÑL,š‰E3±h&ÍDœ™ˆ3qf"ÎLÄ™ÓOÒ*:×2qe&®ÌÄ•™¸2Wf¢ÇLô˜‰3Ñc&zÌå'i´è1=f¢ÇLŒ˜‰31b&FÌĈ™1#fbÄLŒ˜e›1›1›1›1›1›ýšýš¹hsæ¢Í™‹6g~¾PøùBáç …Ÿ/~¾Pø)BᧅŸ"~ŠPø)BᧅŸ"~ŠPø)Bᧅã§püŽŸÂñSøYAáø)?…ã§püŽŸBÜ_ˆû q!î/ù-vÍÈãÿüû+ŒüX› cÇÐÿ‹“½_CŸó{åÓ&oy«rO×Óéú}Õõžv{íæ?í^Ø•{ZŽW9o7ßLþ©t«}V_ogi÷uÐûüéü¯’wË®w»?¿Û¼£-÷ªz¼íV]uzߧ¥duÿYÝjVwån}zµß©ûÁsþ]÷¡6ŸÖ»ê>Ýî»åvíÓ&¯ç.kýÏô­¶ï,YçƒU=Vý§û•¶ž½ôì¶û®^¯þ2³cuÞõî÷§ÚÇ«ŸÎôHÇV½Þãww>]-·;^fzVŸ‹V“õyÇúÜdO»zŸ—äÌôÌ~_mïñr«¿¨£uµ{ë ÑË»øòƒN£pg{;Oò‰‹X ‹:ϱ½Pÿ ïŸ?×ËâbáÒâÙ–ûðÙÀ¿=p'WbªJLU‰©*1o%æ­Ä¼•˜·mU¢­J´U‰¶*Ñp%®DÕh¸ WF²VF²VF²VF²VâäJœ\‰“+qr%N®ÄÉ•8¹'WâäJœ\‰“+qr%N®ÄÉ•8¹ W.À\¹såÌ• 0W¢áÊÅ”+S®\L¹r1åJ4\¹Ÿo”ÆhøÊhøJœÜˆ“qr#NnÄÉÑðÑðÑðÑðºA7"èF݈ t#‚nDкA7FÃ7FÃ7FÃ7~öш­±ucd{cd{ãgغ¥O´Ø5÷F~üûç—ˆùñû__~ZÛ†“wÓîc¿÷ëÁ­×†Óú¼äz·Ïé×9ï~k}m>õúõnùn÷Cm¹ÛóÆ)7œÕŽÝäÝN¯ºx¿fÏôÎwj¾¼Ý¼Æçj{y¹wWÓ«Ë[寺ë´ò­å¼ÆùmwìLžöú¼Ý˜ÚüÞúVëë”»s–võÞr?Ï’÷ux½/xµ×»\Çê±$owþ³æßwÏÇ«×émÇLÿìü©çãÝûÒn¿šåÓʱÎZ½»óæ©ö%¯ú›•“ôyåÛío«ù¼æIîn²¶›Vή^ïç¸S÷ßi>%FÔâU ÓJ‹]O‰VF1‹øR‰M%œ<áÚEÀ¥èïÙbÒêE»gXw’ï&–ôj÷X~nßÿ÷ëö]ÆÉí+PÝ·ƒÈƒÄƒÌƒÂƒÊƒÆƒÎƒ ºo´ Ђ@ -´ Ђ@ -´ Ò‚H "-ˆ´ Ò‚H "-ˆ´ Ò‚H -H´ Ñ‚D -H´ Ñ‚D -H´ Ó‚L 2-È´ Ó‚L 2-È´ Ó‚L -(´ Ð‚B -(´ Ð‚B -(´ Ò‚J *-¨´ Ò‚J *-¨´ Ò‚J -h´ Ñ‚F -h´ Ñ‚F -h´ ýòép2´~ìüÏ—Ÿq3ÁþŽ–½Øõ­××Ý|^Éû±óV>ow“6ßîëœ6ÖsêµÀ»}O/wîïrVy§ÜH·äHåvÝ“’\«†yé‘òï>Žß:?K^ýÅÛN¯úå¿õšwJU¾—[iWî«æ%«¼wuŸYËiõ½êþqú¾sÊíhM^ýiµümù·î“ÞòNϧZ¹»ó‡—IÞ«òiݶ»î:©Ü,rOÎôXõžºHòvûÃjýkëeÕ.kZ­ßÝú±–;ÕV­¿¯ÎRòž¯få­zWåyÍWR¹™=»rVÛ÷Uãþöóãnÿ³&ë}ÆšO+Çû¹iõ:Vç+)­>çhåÍòy=‡Ir­vHçgå½äíêÕÊùP^ÚÓ÷yñg!V¶ý‘O¹ˆµå*F1;Û§^tZ¨Çò¬ÑÝŽýp½“ÏÔùŒ{YK8[jéz×qrb¤db¤dâ‚ʼn '.Xœ¸`qbtebtebtebteb¤dââɨ;u'¢îDÔˆºQw"êND݉¨;u'¢îDÔˆºQw"êND݉¨;u'¢îDÔˆºQw"êND݉¨;u'¢îDÔˆºQw"êND݉¨;u'¢îDÔˆºQw"êÎDÝ™¨;ug¢îLÔ‰º3Qw&êÎDÝ™Ø:[gbëLl‰­søI¯‡Ø:3B;3B;3B;3B;ug¢îLÔ‰º3Qw&êÎDÝù3íü«½’µ—òãÿOÇ?ãdïÇôW¿¾¼ÊN/=·åiËy»cVóÝv œÎïí^Ó&¯þo•çÕR¹Ûn‡Ûzwå®öãÝþxª?¿K¿Û•ëmŸUïj=¾ÛóûÜ?Þ½_YÏŸvOÎ’µ=´ÇÞÏ9³tË <Ó'%o}Vwñéûòª[Ôê~ö¾/y7ëxמŸÕî^m:ý|õªöÉ_7NÙeÍ¿:ÌÊ­ÞNÏ»»ãÕ»}wûƒUÏn{xÕÓ,­Îï^õæ•oµ¿Hùn·¯Ö./y³ß½ç]k¾YZ½_iåió‹ç•{k÷–¢yÅ=–'ѵÏXÓŠuÅÅš'8TÚKZ‹§‹?k÷¨–Ù¶Fw[÷v–0»µ-æ›è]ÇÉ™‘’™‘’™‘’™‘’™‘’™‘’™‘’™‘’™‘’™‘’™‘’™‹g¢îLÔ‰º3Qw&êÎDÝ™¨;ug¢îLÔ¹pæB™ g.$œ‰Ç3ñx&ÏÄã™x<gâñL<ž‰Ç3ñx&ÏÄã™x<gâñL<ž‰Ç3ñx&ÏÄã™x<gâñL<ž‰Ç3ñx&ÏÄã™x<gâñL<ž‰Ç3ñx&ÏÄã™x<gâñL<ž‰Ç ñx!/Äã…x¼âñB<^ˆÇ ñxa$xa$xa$x Ÿ'?¢’ÿóÛ#Jùü=~D%sÿäfÑÉRò~œ˜å»åVX}ìÔæ_½o;¤|§Ü/3½Öó³ü·êk7¿×kà®ÜÛi·^¼ëÍûµV›ß{^{·q¿;ß½K}ßv£žJn¯ÿ›çw“÷¸¼íNÔ¦S÷/ýVw™6ÿª›ÍÛ·«wU¿÷¼¹êÆõ*oÕãåöºÏiÛ×»_=Ÿ×–ß^úwåi“w¿=ÕwíñªçSýT[~öûêõYëY+W:¿kçn¾Ýû¥6¾ÎÙï3;VÛÝkÞݵc–Ï«ÿìÎCV{fzµòNµ¯6Òc}Nô²ãÔýH›žóKѲbtíŠòž1±wt²'k£pŽ˜'{ï.²--šý¡þûEL¬]Ì\ølà¹Ý¦{O+Ï^ÇÉ(µ¥v¢ÔN”Ú‰R;Qj'JíD©(µ¥v¢ÔN”Ú‰R;Qj'JíD©(µ¥v¢ÔN”Ú‰E;±h'íÄ¢X´‹vbÑN,Ú‰E;±h'âìDœˆ³qv"ÎA\9ˆ+qå ®Ä•ƒèq=¢ÇÁE¨£y£y£y£y£y£y#s#s#s#s#sGüI¯‡‘¹ƒ‘¹ƒ‹P~Z1øiÅৃŸV ~Z1ø™ÄàgƒŸI ~&1ø™ÄÈ?Iãõð3‰ÁÏ$?“§ƒãtpœŽÓñ™öN~àdî—ü@Æ~ù ÿ8Fôòÿâdëã®—šO›¼å­Ê=]O§ë÷U×ëõ ÕgÍ¿úúùœïö8z÷þòß2¬êÙmŸÕ×éY:åöY=:ÿ«äݲëÝîÏï6ïh˽ª½ï§ÜKÞ÷i)IúVÝ\ZwåÌk¾Õzóvëj“—ûY›ÏêVÖÚ§m)rKù­å_õÜe­ÿ™¾Õö%ë|°ªÇªÿt¿ÒÖ³—žÝvßÕëÕ_fv¬Î»ÞýþTûxõÓ™éت×{üîΧ«åvÇËLÏêsÑj²>ïXŸ›¬ãiWïóï’œ™žÙï«íà=^nõ5^pó ëJ{'[W~ÆœSl:‰®å÷D¢±g{ ‹‹Š õ'-ÞýAÞ$j{uïéY´ø¬=´Ñìë8yÙ"ÛAd;ˆl‘í ²D¶ƒÈv0úu0úu0úu0úuóbÞAÌ;ˆy1ï æļƒ˜wóbÞÁè×Áè×Áè×Áè×A4<ˆ†Ñð ˆdí_†¿D$dT4tЂ@ -´ Ђ@ -´ Ђ@ "¥EJ‹”)-RZ¢m‰Ò¥%JK”–~’FÛk'³v2k'Ó‚L 2-È´ Ó‚L 2-È´ Ð‚B -(´ Ð‚B Jýò)qòcëN~ æ_E'hþ“OÊ=]O§ë÷U×»ûújÕgÍ¿ûúúªqôîýå¿eXÕ³Û>«n„Y²ögï똥[ã÷´¼[v½ÛýùÝæm¹WÕ£÷ýÑËÝ,å?=Ïj݉»nS¯zßµO’7ûýÔüâåNµ–_u+[ݧžGwÛy×=lÕcMÖúŸé[mßY²Î«z¬úO÷+m={éÙm÷]½^ýefÇê¼ëÝïOµW?é‘Ž­z½Çïî|ºZnw¼Ìô¬>­&ëóŽõ¹É:žvõ>ÿ.É™é™ý¾ÚÞãåVù“ãdæû¯ÁÉýk!À*X…«`U¬J€U °*V%ÀªDh•TZPiA£4ZÐhA£4ZÐhA£tZÐiA§tZÐiA§ƒÒ¥ J”6(mü$×3x=ÄÖØ:[bë@lˆ­±u ¶ÄÖØ:[bë@ˆ t ‚Ò"¥E^Oäõ[bë@lˆ­±uˆ´ Òbë@lˆ­±u ¶ÄÖØ:[bë@lˆ­±u ¶ÄÖ!÷/Ÿ'sßä¿žþÏ}’ÿúÅoK‹]{=ž{å?õZpË-±«W[î´þÝäÝ^r½ûé»´Ó©ú>­Ç;ÿ«í“ÊnßÛrfò¼_Ãwóïº N¯Ûn´Srwç»[óس¼WÏÞýÑ»¼·[O«W:Ö–{þÝê6œÉ±–—äÍähݬ§ä­¶«—»ØKÞL¾t~õþaí'«ãÌ{>xõýî9ßn?™Éµž_•«-¯•·;þgz¼Þ¼î ³´ÛWçëu®ê%¯ç¯~°zßZµÃZNJ»óÇLž÷8ÉÙÕ«•g-¿*µ¼µ[Ç©5Ÿö¼”ïÝæ¡Y~-&Ö.v­ÝKxy`aqe «÷&òM±¸ ïƒ}ÚŤ…ëü€Åµ8ùyñïÙ"ÛO¸{ÚnOù?ôƒÉ"àë890ê10ê10ê10ê10ê10ê10ê1,bë@lˆ­±u ¶ÄÖØ:[bë@lˆ­±u ¶ÄÖØ:[bë@lˆ­±u ¶ÄÖØ:[bë@lˆ­±u ¶ÄÖ¡Ó‚N :-´€¨;u¢î@ÔˆºQw êDݨ;uG¢îHÔ‰º#Qw$êŽDÝ‘¨;uG¢îHÔ¡¡¡¡‰Ç#ñx$Äã‘x<GâñH<‰Ç#ñx$Äã‘x<ÆO†“ÿ~ú÷üˆB~`f.~ýýߟq²—»ÅšO+ÇûñßšoWΩ×/yÚr·ÝF^r¼^sn÷ÃwqãyËó~í\í§·^O½õîÊ]íÇÞn)ëùÝr§ûÝ®ÜÓ^/w˜¶Ün¾Õò·îïÞ¯¬çµî¤Uwñ,YÛÃËýç¬õ¶ZÏ3}Ròַ붵¦Õ~ •³ë.žé™É•òYí›Ù³Ûn»îçÕtúùêUí;“¿:oœ²Ëšu>˜•[½žwwÇ«wûîö«žÝöðª§YZß½êÍ+ßj‘òÝn_­]^òf¿{Ï»Ö|³´z¿ÒÊÓæÏ+±Ÿ6ÚTÂó(×Y4ì ›j÷Nþ…+ìŬÞY‰Ï%\¯ÆÝ3Ì>‰:ž~ DMÏö€–öDÖ~~°Ž“#£+c"ÀJX\H8r!áȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌȈÌÈ…„#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RÄã‘x<GâñH<ëOÒx=Äã‘x<GâñH<‰Ç#ñx$Äã‘x<GâñH<‰Ç#ñx$Äã‘x<GâñH<‰Ç#ñx$Äã‘x<GâñH<‰Çñx"OÄ㉨;u'¢îDÔˆºQw"êND݉¨;…O¶Øõcaë2þEò¬Ì=–ÿ’¢“gÉûqb–ï–[aõ±S›õ:¼íòr¿ÌôZÏÏòߪ¯Ýü^¯»ro§Ýzñ®7ï×Zm~ïyíÝÆýî|÷.õ}Ûz*¹½þožßMÞãò¶;Q›Nݼô[ÝeÚü«n6owÞ®ÞUýÞóæª׫¼U—[Øë>§m_ï~õ|^[~w|xéß•§MÞýöT?޵ǫžOõSmùÙï«×g­g­\éü®»ùvï—Útú:g¿ÏìXmw¯yw׎Y>¯þ³;Yí™éÕÊ;Õ¾ÚtJõ9ÑËŽS÷#mzÎ/-‚<]tZÀƒ¢¼gL,àf1ºö [®âdõâÞFþP/ZìüdÏ4JXÀèÏò´QåÒ"ÕbûN0¶´¸Tv^ÇɉQ‰Q‰Q‰Q‰Q‰Q‰Q‰‹'bëDlˆ­±u"¶NÄÖ‰Ø:['bëDlˆ­±u"¶NÄÖ‰Ø:['bëDlˆ­±u"¶NÄÖ‰Ø:['bëDlˆ­±u"¶NÄÖ‰‘à‰‘à‰‘à‰‘à‰¨;u'¢îDÔˆºQw"êND݉¨;u'¢îDÔˆºQw"êND݉¨;u'¢îDÔˆºQw"êND݉¨;u'¢îDÔˆºQw"êND݉¨;u'¢îDÔˆºóׯ_> N~D!?cäG´2÷Jþy¾ÿnÛ;y7­>¶[óyëÝÕZß)÷Ë®ÞíµšÏª÷öõZËßÎw»jËÝž7N½ÖYíØM§Ük§Ó®›æÕóï©z¼Ý>Þîïûói7¢ÖŽW•·Ê—ÜC^nVk9¯q~ʺ{ý·Ü¾Ròv[ÏôXëkÕ­9Ë?K»z½ÛíôûŽö:¼Þ¼Úë]®cõX’·;ÿYóçãÕëô¶c¦vþÔóñî}i·_ÍòiåXç?­ÞÝyóTûÎ’WýÍÊIú¼òíö·Õ|^ó$w7YÛM+gW¯÷sÜ©ûï4ŸrÏ\í^ÂÚE§µQÑZì,FO+÷^ÆV/.`Õi4±‹Ïö0žäû€›%½JLü¡}…E»×qrf¤df¤dæB™ g.$œ¹pfteftefteftefteftefteftefteæB™ g.$œ¹pæB™H=©g"õL¤ž‰Ô3‘z&RÏDê™H=©g"õL¤ž‰Ô3‘z&RÏDê™H=©g"õL¤ž‰Ô3‘z&RÏDê™H=gâñL<ž‰Ç3ñx.?IãõgâñL<ž‰Ç3ñx&ÏÄã™x<ug¢îLÔ‰º3Qw&êÎDÝ™¨;ug¢îLÔ‰º3Qw&êÎDÝ™Ø:[gbëLlGüò)qòìÿÜ[ù;rþ¿£“wǼÜ!§^§OËY};}½»¯}·ÏÏ’Wñ¶ÓûõKÊïýz|[Uþ©×¹[ãÎ+íÖ×êk­UÏ,Ÿ×}ÂK¾·=^r¼Ý§ëýTùÛòoÝ'½åžOµrwç/9’¼WåÛuãjåIåfIë~ön«ÞS÷IÞnØu'kÝ´V»¬i×½Z?Ör§úÁê±õ÷ÕyCJÞóÕ¬¼Uïª<¯ùJ*7³gWÎjû¾jÜß~~ÜíÖd½ÏXóiåx?7­^Çê|%¥Õç­¼Y>¯ç0I®Õéü¬¼—¼]½Z9Ê?aR J{ ‹{ú Ѹ?òyG'²Ou…zü!OŠN–pò3¶•¢'QÇê|ʨm)ÚyÖÒõnàdF=fF=fF=fF=fF=fF=fF=.ð[ˆ­ ±u!¶.Äօغ[bëBl]ˆ­ ±u!¶.Äօغ[bëBl]ˆ­ ±u!¶.Äօغ[bëBl]ˆ­ ±u!¶.Äօغ[bëBl]ˆ­ ±u!¶.Äօغ[bëBl]ˆ­ ±u!¶.Äօغ0¼0¼0¼0¼u¢îBÔ]ˆº Qw!ê.DÝ…¨»u¢îBÔ]ˆº Qw!ê.DÝ…‘à…‘à…‘à…‘à…x¼âñÒ>N~D ?¢{'ÿsÏ‹]?"”/v=ÉgÕëõ¸»[îÖõ®&ïz:Ýn§äz»VåÞN»õrÊm6ûý]æ©ü»ûÝùî]êÛë¾tº?ìê×æ{·ë¸u¿ðr›iÓ©ûƒ—~«ûo׫•¿ëÖõÒ»ëV¶¦]·èî}Ðë9ßÛ½«Í?“£µÏ«_=Ÿ×–ß^úwåi“w¿=ÕwíñªçSýT[~öûêõYëY+W:¿kçn¾Ýû¥6¾ÎÙï3;VÛÝkÞݵc–Ï«ÿìÎCV{fzµòNµ¯6Òc}Nô²ãÔýH›žóÿ^ìú÷b׿lß·_ìº1º²1º²1º²1º²1º²1º²1º²1º²1º²1º²qQàÆèÊÆèÊÆèÊÆèÊFÔ݈ºQw#êîDݨ»uw¢îNÔ݉º;Qw'êîDݨ»uwbëNl݉­;±u'¶îÄÖغ[wbëNl݉­;±u'¶îÄÖغAw"èN݉ ;t'‚îDкAw"èN݉ ;t'‚îDкAw"èN݉ ;t'‚îDкAw"èN݉ ;t'‚îDкAw"è^û—Oƒ“¹oò?_~^èúy¯ä¿ŸÎý#-v}êu~Už·`5y»An•;ý8}Kwûz»Õ´ò½Ý§äœnw¯ñ{»ßXåÞ?«é´»mW¯÷8½%g÷¾èåæ²ê¥Sõëí†[Íÿêyè¶}§ÜÒ3·Óª{P{þV{ÜÒ¿êÖ“òî«îIkÚuwz¿GXëÅ:>¼ûãª=^z¬ùNõcm9«~í±”¼û‡÷s…6íÖ³ÖžS÷Ççc¯ûÛêügŸwëåÔýzwHú¼ÚM+_J»ó…w=ïÞïvÛÁú»w?‘òyÏ›«óõî¼qkþó²CÂÈ°¤uÑéY´î&ñ´1ú÷Ù~iëÑÌE;k£uŸõ QÇ~åM°î³ N~ âGÄñßÂÿÿùÅŸy±kïǯüÖò»òW“÷c¶õ¼5Ÿ”ÿ”kÚu'ìæóz-Óæ;•ß;y׫¶ü-÷Æ­qöj¹·æíSîOk>¯rV9§ïw³|ïÖï¬ù_m—W¾wy¾òr?ÏôiÝ»V¹’ü]½»n=owäì÷Õ´ú¼¡•ãõ^1K³ö]u;¯ê·æ³ºmµ×«µÏë¹ÿÔ{Ïêïäe·5Ÿw}iõ­¶ïiyÏù¥ãYþ™Þò­Ñ«“Åš¥Å¡g8Y»øó,ßܽ%,Ù/]¯Uü#Ÿ°ç´„uØ'-Æ-àkÿ£±Å=‘…~²ªw'‡NÕ £:aT'Œê„Q8¬‡uâ°N6ˆÃ-´€¨;u¢î@ÔˆºQw êŽDÝ‘¨;uG¢îHÔ‰º#Qw$êŽDÝ‘¨;uG¢îHÔ-´ Ђ@ ˆÇ#ñx$Äã‘x<GâñH<‰Ç#ñx$Äã‘x<GâñH<‰Ç#ñx$DÝ‘¨;uG¢îHÔ‰­#±u$¶ŽÄÖ‘Ø:AG"èH‰ #t$NŽÄÉ‘89'GâäØØ Ò¥5Jë_¿|*œüˆBþ2žüý::YJ«¯³^ù´é]{O×Óéú}Õõî¾YõYóßz=^•{ZŽW¹Ó¯«§æU=»íãõš§Í·ÚϽì:•ÿUònÙõn÷çw›w´å^UÞ÷Ç]·ê,ÿéyö”N*§µÃšoµÞ¬×a•3ËoíZ¹³ßµõg-7û]²Ë«þfù­å_õÜe­ÿ™¾Õö%ë|°ªÇªÿt¿ÒÖ³—žÝvßÕëÕ_fv¬Î»ÞýþTûxõÓ™éت×{üîΧ«åvÇËLÏêsÑj²>ïXŸ›¬ãiWïóï’œ™žÙï«íà=^nõõâÏÊ(fi1ìݽzŸñåtQg Ã*ó‰˜XÀ¦3¼*îQ=‹bžÉ{ÆòBT¹ÇKxZÂÉb?8‰“#qr$NŽÄÉ‘89'GâäHœ‰“#qr$NŽÄÉ‘89'GâäHœ‰“#qr$NNÄɉh8 '¢áD4œˆ†1o"æMļ‰˜7ó&"ÛDd›ˆl‘m"²Mį‰ø5¿&â×ÄèäDüšˆ_ñk"~MŒ4Nį‰ø5¿&â×ĨáDüšˆ_ñk"~MŒNį‰ø5¿&â×ÄhÞDüšˆ_ñkê”Æñ“8ÇBâXH ‰c!±_'öëÄ~دûuæ'™ý:³_göëüõ-výØùü}ëÇ|‘£“ÿ‘¢“O=N{çóJÞnƒ[ùN¿.y»_¼ú•—o7ƒ·þÝ×xmÚ­Ï[õ핼Ü·êå´;lU®Už·^íùSöݯšï½ÎŸÖÿœï¶ÒZþÝç!owö÷YÚíÖûêê}xÕ-ª·O¹W%}Öó’þÙù]÷åª]§žwo=¿ÌòíŽï[ýtö»×x–Ê{ɓ䯦Õö˜ÉYµC:~U¾S×ïu??Õn·îo«ãW+ÇküZõÎäkÓ­ç «ü[ó§UÞn?’ʪï[ãC’·{ß:=ï-÷ƒ§(ÔÙ"ÌZ,9Ý[WÐ'íÅ,-Ú,ås·OÚKX¹8µ:*ZÀÜb4±°hø‡ë˜íQ-ôqÑóçèíçzö¼öÃÉ™È6Ùf¢­L´•‰¶ £y £y ÑV!Ú*D[åëOÒ:Ê £y £y £y £y 1o!æ-ļ…‘¹…‘¹…˜·óbÞ’Æëadnadnadn!æ-ļ…‹P.B]¸uadn!.DÃ…h¸ Fæ¢áB4\ˆ† Ñp!.\PºpAéÂ¥ £y qr!N.ÄÉ…8¹'âäBœ\ˆ“ qr!N.ÄÉ…8¹'âäBœ\ˆ“ qr!N.ÄÉ…8¹pÌŽ¹òÓ˜#N.ÄÉ•c®rÌUŽ¹Ê1W?N~ âÇÞɼü7~ÞGù¯óýŒ“O=f[˯¾fxç¿u½^rN=žÏ~÷®'ïö¾å6ØMÞî&­üÓ¯ñVy§Ó­qyºßY­ú¬r­éV{[õz×ãéùwUß«ÇÛ«îÓÞîf­üÓõ½:ìÊÛ}NØu³ÍÎ{»×¼õž~¾;U¿Úü³|³ß¥ãÙï«ù´rvëA:¶êÉ—Êï΃»ýëXÇ÷nýÏ~ßµOkÇîülMÖ~µ;/½j¾Ý_3½«ýÁ*Ç*WJ«×a•7Ë7“gÕ¿ZÞZλþv“×<ìU^+ÿÖsŒUÞîsœ×¼!kíο_ {åŠxPÀ¢?ò­îalŒNW~ŽžE× {1?ãZqqo%î–ö.ÖÚ§]{Z»ÑçÚ=¥%,+ñk%~­Ä¯•øµ2ʶ2ʶr1åÊÅ”+‘m%²­Ä¯•øµ¿Vâ×Ê…‘+#s+#s+#s+‘m%²­Ä¯•øµ¿Vâ×ÊÈÜJüZ‰_+ñk%~­Ä¯µü$¶¿Vâ×JüZ‰_+ñk%~­Ä¯•ûùVâ×JüZ‰_+ñkåÞ¼•{óVîÍ[¹s%²­D¶•È¶ÙV"ÛÊúÊúÊúÊúJÌ[‰y+1o%æ­Ä¼•ô•ô•ŸcT~ŽQ‰†+Ñp%®DÕh¸1‚¾qAöÆO8?áhü£ñsŒÆÏ1?Çh!~ùT8ùˆÿú~îñ/ññß8÷8ÿ‡´Øõêc›öw«<ïǨմûúªr»õò.z¼Ûw÷5`U¾×ø²¦SzOó]ý§æïú9=yéÝuZõzÓ[rvï‹»îºU½³tª~O¹E¬ù_=ݶÏ:^WÇín{Ÿv+{ç;=ÿYÝ«ù­v=ŸŸý¾ê.ÖÊóî7Z{v¯ÿ´Ûz×/=Ö|§ú±¶œU¿öXJÞýÃû¹B›vëYkÏ©ûãó±×ýmuþ³ÎÏ»õrê~½;$}^í¦•/¥Ýù»žwïw»í`ýÝ»ŸHù¼çÍÕùzwÞ¸5ÿy١ňâÞÄ“Å®%\+EÕ.Ë[Åسhç6öžž.b=‰ŸE;ÿø}e-an ÿJŸXëEª_?œÜ‰“;qr'NîÄÉ8¹'wâäNœÜ‰“;qrgpgpgpgp'‚îDкAw"èN݉ ;t'‚îDÐQÃQÃQÃQÃغ[wbëNl݉­{¦™dZÀHãNÔ݉º;Qw'êîDݨ»uw¢îNÔ݉º;Qw'êîDݨ»u÷J *-à^ÃÑÉx¼wâñN<Þ‰Ç;ñx'ïÄãx¼wâñN<Þ‰Ç;ñx'ïÄãx¼wâñN<Þ‰Ç;ñx'ïãáä*~,jý'þ¨ùüÿñ÷=ϯ»~$ï×ÇÕrÞ¯Y·äœz­÷Ê¿ê–º}~–N¿íÊõÊ'å÷êï¯Òc•ïíY•ûªyÉ*o÷z½ÇÇ«_Wåž*§•ãív<]ï§Êß–ë>é-ïô|ª•ëå®;å^}U>owâ©yzæ>ôn«ÞS÷IÞnX­m½¬ÚeM«õ»[?Ör§úÁê±õ÷ÕyCJÞóÕ¬¼Uïª<¯ùJ*7³gWÎjû¾jÜß~~ÜíÖd½ÏXóiåx?7­^Çê|%¥Õç­¼Y>¯ç0I®Õéü¬¼—¼]½Z9ÊK{úÎðà$*uŬÄÓҢɻQ³Zûf‹?[q·z±ð v£±¥(ëY>ã^ÖS|®ŒîÞÀÉŒ®ìŒ®ìŒ®ìŒ®ìŒ®ìŒ®ìŒ®Œ®Œ®Œ®Œ®\¬wp±ÞÁÅzëŒÈŒÈŒÈŒÈ\ {©"õA¤>ˆÔ‘ú RDêƒH}©"õA¤>ˆÔ‘ú RDêƒH}©"õA¤>ˆÔ‘ú RDêƒH}©"õA¤>ˆÔ‘ú RÄãƒx|âñA<>ˆÇñø Äãƒx|âñA<>ˆÇñø Äãƒx|âñA<>ˆÇñø Äãƒx|âñA<>Zýòipòc±k âÿ,~ͱÿúù÷_G'ŸJ§sNéÝÕZŸ—\ïöñn¯Õ|V½·¯×Zþv¾ÛýP[îö¼±ê†ÓÊßͧMÞíôªû‡×k»Vßé|§æËÛýÇk|®¶×®}·ïóÞå­òWÝŽZùÖr^ãÜêþÜ•;“gu[îºí¤´[/Þóî)·õéz9Ýn§ßw´×áõ¾pÊÝýªëX=–äíÎÖü»ãîùxõ:½í˜éŸ?õ|¼{_ÚíW³|Z9ÖùO«wwÞ<Õ¾³äU³r’>¯|»ým5Ÿ×| ÉÝMÖvÓÊÙÕëýwêþ;Í÷µ*E¥J‹SK‹5O9–°é*vþ°öÿû5†•®WŠbžE×J‹IK{'Ïðªc QÙÖ|â"Ûʽ¬E/àì{à*NŒ®Œ®Œ®Œ®Œ®Œ®Œ®Œ®Œ®Œ®Œ”\Hxu¢îAÔ=ÆOÒx=DÝã_Ô¿~ýuÿç ð ò ñ ó ð ò ñ ó€ZhA ZhA ZhA¤‘Ò"¥EJ‹”)-ѶDi‰Ò¥%JK”–i[fídÖNfídJË”V(­PZ¡´Bi…×Sx=…×Sx=…ZPiA¥•TJ«”V)­QZ£´FÛ¥5Jk”ömlœüÀÃØùËßø?ññã¨ù×ÑɧÝ3ÖüÞn•U¹§Ü!Vý»r½_§´É»=¼äz÷Ówi§Sõ}ZÏ)wÎ-}Úr§Û÷¶œ™¼]7ЮþY¾WÛí%ÿ”ÞSóó®ÞSý÷Õó†wô.¯}òrïy»7WÝu39Öò’¼™œUw¬—¼ÕvÕÊ[uƒZåÍäKç½Ü¶«ú¤ó^×»jçj:5ÿhå[ëY›vç¿YÿÖæ—Ž­z¼Þ¼î ³´ÛWçëu®ê%¯ç¯~°zßZµÃZNJ»óÇLž÷8ÉÙÕ«•g-¿*µ¼µ[Ç©5Ÿö¼”ïÝæ¡Y~õb×Zl*àÐѵÊE§?DK‹:+1¶¨WÈ÷³ Ñ¿³ë÷€–òM0ìs{M£»ýÏ×1ÛZ\´[XÌ|ö¹À*NþK"ŒêD[p­®uµN€Õ °:V'ÀÄaƒÒ¥ J”F4ˆ†Ñp DÃh8óbÞ@̈y1o æ ļ˜7óbÞ@̈yC¤‘DZ@̈yC¢´Di‰Ò¥ ¢á@4ˆ†Ñp DÃh8 ‡LiDÃh8 ¢á@̈y1o æ ļ¡òJ+¥óbÞ@Ìë­Q1o æ ļ˜7tÚÆq8NÇià˜ sc. Jûl‹]sÁëç=’¨ùÑËßû½w²%r§œ.wÛÍsJwûz¹Ý¬ò½_cOÉ9Ýî^ã÷v¿±Ê½5~VÓ«Ü*Þî0­Ü[r^å–yµ;ç´;mUŸ6ÿ«ç¡Ûö­º…´úVÝr’<íù[íqKÿª[OÊwº?XŸÛ½Ÿ#¼Ü§«öì^ÿn=í¾Gy¿‡yå;Õµå¬úµÇRòîÞÏÚ´[ÏZ{NÝŸ½îo«óŸu~Þ­—S÷ëÝq éój7­|)íÎÞõ¼{¿ÛmëïÞýDÊç=o®Î×»óÆ­ùÏËŽß{'ÿÞ;ùòý—íü–D´Edˆl#‘m$²D¶‘È6ÙFFóFFóFFóFFóFbÞH̉y#1o$æļ‘˜7óFbÞH̉y#1o$æļ‘˜7FZÀàÈàÈàH4‰†#Ñp$ŽDÑh8 G¢áH4‰†#Ñp$ŽDÑh8fJ#ŽDÑh8 G¢áȨáȨáȨáȨáHœ‰“#qr$NŽÄÉ‘89'GâäHœ‰“#£†#£†cãõ4^t$‚ŽDБ:AG"èH‰ #t$‚Ž=ù48ù†8ù’Øøüû´Ðµï×Xm¾S¯wÖäm‡”o·žµÉ»žN·Û)¹Þ¯G«ro'¯×c­¯×Äw™¤òï6î½ÝW»úN¿f¯ÊY•gM^ó»]Ç­ûÅ)wßn¹]wñª~/7ת[Oë.Ö–Ÿ%«ÞUýÞóæ)÷®¶¼UÏîu¬æŸÉÑÚçÕ¯žÏkËïŽ/ý»ò´É»ßžêÇ»öxÕó©~ª-?û}õú¬õ¬•+ßµs7ßîýR›N_çì÷™«íî5ïîÚ1ËçÕvç!«=3½Zy§ÚW›Né±>'zÙqê~¤MÏù¥½|Ž'ÑÉ¢­½Úþl½~¯çþSï=«óŒõüª>íïÖòÞïÞó¤tlÕ;˧M«ökåÍòyÉÓÊ×Ö«ô»÷üüªú¾ÕïgéÔ<þ*û¼“—ÝÖ|Þõ¥Õ·Ú¾§å=ç—Žgùgz?È·.†<ÁÉŽœí<Å¿JŒý›..:-Ù/]¯´Hõ|Ïç%ûžq²%¬Œî–öjž-î-F ýdUï:N.ŒÈ,ŒÈ,ŒÈ,ŒÈ,ŒÈ,ŒÈ,ŒÈ,ŒÈ,ŒÈ,ŒÈ,ŒÈ,ŒÈ,ŒÈ,ŒÈ,ŒÈ,ŒÈ,ŒÈ,ŒÈ,ŒÈ,\|¸©"õB¤^ˆÔ ‘z!/Äã…x¼âñB<^ˆÇ ñx!/Äã…x¼âñB<^ˆÇ ñx!/Äã…x¼âñB<^ˆÇ ñx!/Äã•x¼WâñJ<^‰º+Qw%ê®DÝ•¨»uW¢îJÔ]‰º+Qw%ê®DÝ•¨»uW¢îJl]‰­+±u%¶®Ä֕غ[WbëJl]Sýò©pò?_þÝ+™èøü+Ìüç—_àd¯×£Ýò»±Þ¯e«Éëñû]›¥ß½ëÉ»½wëo5Ÿ5yÕã-7ש×ØÓéÖ¸<Ýïv_kµéT;Ýjo«^ïz<=ÿ®ê{õx{Õ}zÕ͸+ÿt}{»…µòvŸVí²º±¬rné=ý|wª~µùgù´îëWÍÃ’œÝzŽ­zgò¥ò»óànÿðêÖñ½[ÿ³ßwíÓÚ±;?[“µ_íÎK¯šowÇ×Lïj°Ê±Ê•ÒêuXåÍòÍäYõ¯–·–ó®¿Ýä5{•×Ê¿õc•·ûç5oHÇZ;¤óÇï—ÂâËb´©€cäS.š¬Å°âž¾¾|Æ“E¶Ÿíý OX,úC¾Y³°È¶Ö>-vÖîyýáz…ób´¸ð9€hŸ{trecececå¢À•ºAW"èJ]‰ +t%N®ÄÉ•8¹'WâäJœ\‰“+qr%N®ÄÉ•8¹'WâäJœ\‰“+#´+#´+#´+#´+t%N®ÄÉ•8¹'WâäJœ\‰“+qr%N®ÄÉ•8¹'WâäJœ\‰“+qr%N®ÄÉ•8¹'WâäFœÜˆ“qr#NnÄÉ8¹'7âäFœÜˆ“qr#NnÄÉ8¹'7âäFœÜˆ“qrcätcätcätcät#‚nDкA7"è–â—O…“‘ÉXÄúÇßóy.„ý×'Ÿ~|}¤ÝÇçU;½Ûwëé”>/¹ÞíãÝ^«ù¬zo_¯µüí|·û¡¶ÜíyÃêÞxU½zËóºŽÝ´ûZûêù÷T=Þn¯þ?“çí>Òæ»}Ÿ÷.o•Ê]¼ZÎkœŸr#z»E½Ý¹³´[/Þó®·Ûz&Gk‡U¯w»~ßÑ^‡×û‚W{½Ëu¬Kòvç?kþÝq÷|¼zÞvÌôÏΟz>Þ½/íö«Y>­ëü§Õ»;ožjßYòª¿Y9IŸW¾Ýþ¶šÏk>äî&k»iåìêõ~Ž;uÿæ{ÆœÚ=}g{"O0¢vÏf1êXÚ;ù9ÿ ëNp÷thåÁRÔït¯ãÙõ øÞšOÄØÊÅÇÅ=¥Ìÿo\ÅÉ8¹'wâäNœÜ‰“;qr'NîÄÉ8¹'wF4wF4wF4wF4w"èN݉ ;t'‚îDкAw"èN݉ ;t'‚îDкAw"èN݉ ;tgtgtgtgt'¶îÄÖغ[wbëNl݉­;±u'¶îÄÖغ[wbëNl݉­;±u'¶îÄÖغ[wbëAl=ˆ­±õ ¶\$|p‘ðÁE D݃¨{u¢îAÔ=ˆºQ÷ êD݃¨{u¢î¿~ù48ùˆÿ|úÿcìžþÊß#šm‹][“—;äÔëôi9«¯q§¯w÷µïöùYòê/Þvz¿~Iù½_oë±Ê?õ:wkÜy¥ÝúZ}­µê™åóºOxÉ÷¶ÇKŽ·»ót½Ÿ*[þ­û¤·¼Óó©Vîîüá%G’÷ª|»n\­<©Ü,iÝÏÞíaÕ{ê> ÉÛí»îd­›Öj—5íº£WëÇZîT?X=¶þ¾:oHÉ{¾š•·ê]•ç5_IåföìÊYmßWûÛÏ»ýÏš¬÷k>­ïç¦ÕëX¯¤´úœ£•7Ëçõ&ɵÚ!Ÿ•÷’·«W+çCù'L*áAkTê4ŠY‰‰¥=xw£fµö©±®P?äY÷žžD‹‹ÑØ“E¶Å|Â^ÖÒuH‹mKí!]ï:NŒ”Œ”Œ”Œ”Œ”Œ”Œ”Œ”Œ”Œ”Œ”\¬wp±ÞÁÅzëÜcvpÙÁ=f÷˜Ücv©"õA¤>ˆÔ‘ú RDêƒH}©"õA¤>ˆÔ‘ú RDêƒH}©"õA¤>ˆÔ‘ú RDêƒH}©âñA<>ˆÇñø D݃¨{u¢îAÔ=ˆ­±õ ¶ÄÖƒØzA"è¾A;<ˆ†{?Þ{½öhÓ)÷Êé|Þ¯sÚ|Þn /ýV=»õwë±ýôxØ­Ï[õí•vÛM›ÿ]ÆùªÛq&×*Ï[¯öü)ûn‹WÍ÷^çOëÎwº¿XåžÒªîºOµr¬i·x»ýfå$9Vû´ò$;V¯{õ¼¤vÞê¶ö²ëÔóî­ç—Y¾Ýñ}«ŸÎ~÷ÏRy/y’üÕ´Ú39«vHǯÊwêú½îç§ÚíÖýmuüjåx_«Þ™|mºõ¼a•kþ´ÊÛíGR¹Sõ}k|Hòvï[§ç½å~ `Ó˜ÓŠC…hæG>IŸ6v¶è´»}Ò"Ö†—¢“§{6KÑݓŸEì,àîö)ñù\/ÔŸô€Ûb×ák$ŒŠÄa‘8,‡Eâ°H€ °"V$ÀJÄa‰$ZhA¢‰$ZhA¢‰dZiA¦™dZiA¦™dZiA¡…ZPhA¡…ZPhA¡…TZPiA¥•TZPiA¥•TZPiA£4ZÐhA£4ZÐhA£tZÐiA§tZÐiA§tZÐiÁ ƒ Z0hÁ ƒ Z0hÁ Äãáë×/Ÿ'?0ñc¯ä_E$ÿ*Bùû¢Ø¶Å®o¿†­&oy«rO×Óéú}Õõz¹_´ú¬ùO½N[õYó½{ùo™Võ춵¾¼Ü`V¹Þîš[ã÷´¼[v½ÛýùÝæm¹WÕ£÷ýÑËM-å?=ÏjÝfV·šTNk‡5ß®»söû©ùÅÚ/´rg¿¯ºOµíëÝî»åvíÓ&¯ç.kýÏô­¶ï,YçƒU=Vý§û•¶ž½ôì¶û®^¯þ2³cuÞõî÷§ÚÇ«ŸÎôHÇV½Þãww>]-·;^fzVŸ‹V“õyÇúÜdO»zŸ—äÌôÌ~_mïñr«¿h£RÕ{ë ‹>ïFÃ>cUq1éÉ"ÖÚ|ÚŽ%ì+-®]œZÂÅä x÷G>akí"àÓ½gý`²Xø:NND‚‰H0 &"ÁD$˜ˆ‘`"LD‚‰H0 &"ÁD$˜ˆ‘`"LD‚‰H0 &"ÁD$˜ˆ‘`"LD‚‰H0 &"ÁD$˜ˆñ^fôkfôkfôkfôkf$kf$kf$kf$k”ÆHÖÌHÖÌHÖ)Ÿ"d~VùYAæg™Ÿd~Vù‰@æ'™Ÿd~"ù‰@&îÏÄý™¸?÷gâþLtŸ‰î3Ñ}&ºÏD÷™>ÃgbøÌ1—9ærýImã˜Ës™c.sÌeŽ¹Ì1—9æò·1÷ipò÷E«ÿçïWèøœãØýï¿?G'¯º=vóiåœzÌ÷¶ÓKÏmyÚr^¯»ù¼³O×ãîõz÷ïò«òN»nÿWéÝ•{Ûéu~·Üm÷“×|qª¿<Ë_­Çw{~x—ûÇ»÷+ëy/·Þn½x¹ç¼íÓ&k½íºO½ç!/wã©ú_íZ9«ýJJÞõ¸ë¦¶ö»™ëóû=×>Ë}UûÎä¯Î§ì²æ_fåVïG§çÝÝñêݾ»ýÁªg·=¼êi–Vçw¯zóÊ·Ú_¤|·ÛWk——¼ÙïÞó®5ß,­Þ¯´ò´ùÅóÊ=Œµxðí±üŒO±©5 w¶X³„Uµ‹bO£uµ{TKQÑ¢ÞÓ½˜µ{;Op¼´˜õ‡|½ë8¹‡F²F²F²F²â°BVˆÃ qX!+Äa…8¬‡â°BVˆ  t!‚.DÐ…º'âäBœ\ˆ“ qr!.Œ-Œ-Œ-Œ­DÕ #W.Œ\¹0råÂÈ•h¸J ”( #W¢áÊ(õÊ(õÊ(õÊ(õ’FÛˆ“+qr%N®ŒR¯ŒR¯ŒR¯ŒR¯DЕç•ç•ç•ç•ºæŸ¤ñzˆ +teÄyeÄyeÄyeÄy%¶®ÄÖ•c®rÌUŽ¹Jl]‰­+±u%¶®§•ã´~§Ÿ'ÇÂ_û!óïOœ'^qò#Ýzm<¥W[nÕ>ï׫þ]¹§Ü,»z_U/Þýô]ÚéT}ŸÖsÊ=uKŸ¶Üéö½-g&o×ݶ«–ïÕv{É?¥÷Ôü¼«÷Tÿ}õ¼qË-ºZÞê6óî—^×kuwÎäXËKòfrVݶ^òVÛÕËë%o&_:¿zÿ°ö“Õqæ=¼ú~÷œo·ŸÌäZϯÊÕ–×ÊÛÿ3=^ï?^÷…YÚíÇ«ó‹õ:WõÎ’×óŒW?X½o­Úa-'¥Ýùc&Ï{œÎäìêÕʳ–_•¿ZÞÚ­ãÔšO{^Ê÷nóÐ,¿6êX‹µxu›NWþ…«ÅµB¾i”µ O\,\Àòò {?_ï4:ù¯ÏÏž-*.àÿQåÊÏÖqr%²­D¶•È¶ÙV"ÛJd[‰l+‘meÔpeÔp%²­D¶•È¶2¸2¸2¸2¸óVbÞJÌÛÜ܈y1o#æmŒnŒnļ˜·ó6F7F7bÞFÌÛˆy‘m#²mD¶È¶Ù6"ÛFdÛˆl£†£†£†£†1o#æmļȶÙ6"ÛFdÛˆl£†?“hüL¢Ù6"ÛFüÚˆ_ñkãgŸI4~&Ñø™Dãgc®qÌ5ŽŸÆñÓ8~ÇOcÔ}cÔ}ãgŸI4Ž¹Ö?N~`äÇ¿DÈù's_åïXù×8y–¼Ý3³|·Ý«zO¹[¼íò~­×êÙ½Þ[õµ›ßÛ°*÷vòvkÌò­Ê{×ù@*ÿnã~w¾{—úöº/î»úµùÞí:nÝ/¬î «üÕr«ãeWÿ®NÊ¿ê¶ÕÊÙ_´zWõ{Ï›»îâÛîæÝëXÍ?“cuۮʗÎkË{¹•wõïÊÓ&ï~{ªïÚãUϧú©¶üì÷Õë³Ö³V®t~×ÎÝ|»÷Km:}³ßgv¬¶»×¼»kÇ,ŸWÿÙ‡¬öÌôjåj_m:¥ÇúœèeÇ©û‘6=ç—Až.:=ÙëXŠþ!Oˆ^qèdobmt²zqoi/æÉ^Çb³rïä‹kK‹] öO—®C‹»µ{O ¸Ù'7â×FüÚˆ_ÛOø•Q¶Q¶‹6w.Ú܉l;‘mg”mç¢Í‹6w"ÛNdÛ‰l;‘m'²íŒÌíŒÌíD¶È¶ÙvFævFæöø“4^1o'æíļ˜·óvbÞNÌÛ‰y;‘m'²íD¶‘¹‘¹=ÿ$×CÌÛ‰y;1o'æíļ‘¹‘¹‘¹˜·óvFævFævFævbÞNÌÛ‰y;1o'æíļ˜·óÓŠÎO+:?­èü´¢ w¢áN4܉†;Ñp'îDÃh¸ w~ŽÑù9FççŸcô¿|œ<Û'Yúû¾ö¯÷N>õ:¿*ÏÛ °š¼Ý ·Ê~œ¾¥Ç»}½ÝjZùÞîSrN·»×ø½Ýo¬roŸÕtÚݶ«×{œÞ’³{_ôrsYõÎÒ©úõvíæõŸa])Úxí,.Æ­¬í¢çë8¹ w¢áN4<ˆ†Ñðà̃hx ¢áA4<ˆy1ïà~¾ƒûùFóbÞAÌ;ˆy1ï æD¶ƒÈvÙFæFæ"ÛAd;ˆl‘í ²įƒøu¿â×Áý|ñë ~įƒøupaäAü:ˆ_ñë ~įƒ(u¥¢ÔA”:ˆR±è Ä¢ƒXt‹~ò0دûõ@¿Ž_ѯ¿T4tP>_øv@iÒ¥JÃç ß(-RZ¤´Hiñ'i´-ñJñù·ƒO„“{#ÿùýÿˆdþýñýß¿¿ÿýñoþß‹]¯ÈÛµóÕ׳ëXM§Ü^¯[»ú¼^ÿ¬ùVË{õƒ[õ{Jÿiû¤r§Û÷¶œ™¼[nÌÕñõj»½äŸÒ{j~ÞÕ{ªÿ¾zÞðîÞåµÏCVwàL¯t¬-÷üû®;ì´[Ïj¿—{r&oµ]µò¼Ü›V;­nèY¹Õö˜é“Î{]磌éÔü£•o­gmÚÿfý[›_:¶êñzÿñº/ÌÒn?^_¬×¹ªw–¼žg¼úÁê}kÕk9)íÎ3yÞãt&gW¯VžµüªüÕòÖ~l§Ö|ÚóR¾w›‡fù/vý{±k^ÇÍb×ñk"ŒJ„Q‰0*FeÂ( ÔoÄa™8,‡eâ°L 2-È´ Ð‚B -(´ Ð‚B -(´ Ð‚B *-¨´ Ò‚J *-¨´ Ò‚J *-¨´ Ñ‚F -h´ Ñ‚F -h´ Ñ‚F :-è´ Ó‚N :-è´ Ó‚N :-è´`ЂA -´`ЂA -´`Ð"è@ðiÅ·ƒÈƒÄƒÌƒÂƒÊƒÆƒÎZhA Z@ÔˆºC¨_> N¶D$ÿ…/v­ÈwêuÚ¼íò~­×êÙ½Þ[õµ›ßÛ°*÷vòvkÌò­Ê{×ù@*ÿnã~w¾{—úöº/î»úµùÞí:nÝ/¬î «üÕr«ãeWÿ®NÊ¿ê¶ÕÊÙ_´zWõ{Ï›»îâÛîæÝëXÍ?“cuۮʗÎkË{¹•wõïÊÓ&ï~{ªïÚãUϧú©¶üì÷Õë³Ö³V®t~×ÎÝ|»÷Km:}³ßgv¬¶»×¼»kÇ,ŸWÿÙ‡¬öÌôjåj_m:¥ÇúœèeÇ©û‘6=çÿ½ØõïŮپï¾Øu Œ” Œ” +`E,FJFJFJFJFJ†D´•(¨;u¢î@lˆ­±u ¶ÄÖ:A"è@ˆ qr NÄÉ89'¢á@4ˆ†Ñp ļ˜7óbÞ@̈l‘m ² D¶È6¿â×Hü‰_#ñk$JD©‘(5¥F¢ÔÈàÈ~Ù¯#ûud¿Žì×1Ò6F GF GF GöëÈà˜(-QZ¢´ô“4^ÇBäXˆ ‘Ÿ}D~öùÙGägñÛøù48ù‘üˆ@Fäñ}”ÿ½Øõ©´úØnÍç­wWÿi}§Ü/»vx·×j>«ÞÛ×k-;ßí~¨-w{Þ8õZgµc7r¯N»nšWÏ¿§êñvûx»W¼ïϧ݈Z;^UÞ*_ry¹Y­å¼Æù)wèîõßrûJÉÛm=Óc­¯U·æ,ÿ,íêõn·Óï;Úëðz_ðj¯w¹ŽÕcIÞîügÍ¿;îžW¯ÓÛŽ™þÙùSÏÇ»÷¥Ý~5˧•cÿ´zwçÍSí;K^õ7+'éóÊ·ÛßVóyÍ’ÜÝdm7­œ]½ÞÏq§î¿Ó|Ï‹ K‹ øðæTF k£WÍ‹l?ç—¢;§ò¬;Ú°ê ת±¸€“µù>àfI¯h_!Êz'WFÙVbÑJ,Z‰E+±h%­Œ²­Œ²­Œ²­Œ²­D©•(µ¥V¢ÔJ”Ze[e[e[e[‰_+ñk%~­Ä¯•øµ2ʶ2ʶ2ʶ2ʶÙV"ÛJd[‰l+‘me”me”me”me”m%æ­Ä¼˜·ó6bÞÆ(ÛÆ(ÛÆ(ÛÆ(ÛF4܈†Ñp#nDÃQ¶Q¶Q¶Q¶8¹'7âäFœÜˆ“qr#NnÄÉ8¹'7~ZÑøiE㧟V4"èF݈ t#‚nDкA7"èFÝò×/Ÿ'ÿ%ÿƒÈÿ×ß=›qòîëÂn>mò–·*÷t=®ßW]ï©×¯ü^î°ÛãèÝûËË<°ªÇË­ã%o–o×m³zþtþWÉ»e׻ݟßmÞÑ–{U=z߽ܖRþÓóìª;ÛêFóª÷]û$y³ßOÍ/»î]m>«;rÛ)ü.ÙåU³üÖò¯zî²ÖÿLßjûÎ’u>XÕcÕº_iëÙKÏn»ïêõê/3;Vç]ï~ª}¼úéLtlÕë=~wçÓÕr»ãe¦gõ¹h5YŸw¬ÏMÖñ´«÷ùwIÎLÏì÷Õvð/·ú‹:ZW¹(¶z‘h¥^)jv…+`bm>->—¢%̮ſ³Å¸Õ‹] {Ik1»ø¹À¤=´8~'7F06F06F06F7F7F7F7F7.\ݸpucpcpcpã"Ô‹P7.B݈Çñx#oÄ㨻u7¢îFÔ݈ºQw#ênDݨ»u7bëFl݈­±u#¶nDкA7"èF݉“;qr'NîÄÉ8¹ w¢áN4܉†;Ñp'æíļ˜·óvbÞNdÛ‰l;‘m'²íD¶øµ¿vâ×ÎàÎñÓ9:ÇBçXè c¡s÷ÎEÜ;#è;#è;ÇOçøé?ã§süôoãçÓàd"biakþË|?/víí¶X-ïå>ØÍëz½äÜz­:õÚ±*Ï«ßzç³&ï×7­üÝqyêuúTº5.O÷;/wÄ®ÞÕt«½­z½ëñôü»ªïÕãíU÷é]÷ïªüÓõíå–²ÊÛ}NXµk×hmçSzO?ߪ_mþY>­ÛöUó°$g·¤c«Þ™|©üî<¸Û?¼úu|ïÖÿì÷]û´vìÎÏÖdíW»óÒ«æÛÝñ5Ó»Ú¬r¬r¥´zVy³|3yVý«å­å¼ëo7yÍÃ^åµòo=ÇXåí>ÇyÍÒ±Öéüñû¥%,íM,-Úü#Ÿ€KÕ¸V»³€u¥(Ûçë¢v?È›Dk÷þ€köI{'ÿ¨_ÛKÑÓËŸ ˜{jŸp½Ë89}üvPyÐxÐy0p$øí€Ò¥J ”$øí€Ò"¥EJ‹”’FÛ€¿DPZ¢´Di™Ò2¥eÚ–)-SZ¦´Bi…WZ(­PZ¡´Bi•Ò*m«¼Òʺ®¬ëJiÒ¥5Jk”Ö(­ñz¯§ñz¯§SZ§´NiÒ:¥õŸ¤ñz¯gðz¯gPÚ 4Du'wrâÞɉ{''wrâÞɉû '¸râ>ȉû '.JŸ¸(ý·J㘠ñ“-výXÈú?ÊߣŽÿçßÇñ8~üöýø7Nþ¿òߺ^/9Þ×áýøªÕãÝÞ^×Ö|ÖäUÖ~°;.½ú×é×ho=¯îw«n‘Ýñu{^óN^î©ÓóÅ®»nU¾w:Õ½å<Ÿ¿Ýn»ÉËMj•·ûœpÊMèå®>­÷ôóÝ-7¬·;ö]æaIÎn=HÇV½3ùRùÝyp·xõëøÞ­ÿÙï»öiíØŸ­ÉÚ¯vç¥WÍ·»ãk¦wµ?XåXåJiõ:¬òfùfò¬úWË[Ëy×ßnòš‡½ÊkåßzŽ±ÊÛ}Žóš7¤c­Òùã÷Ëß8ù7Nþò߈“¹÷뷃ʃƃÎÀ5_lâ~±‰ûÅ&_lâ~±‰ûÅ&_lâ~±‰ûÅ&_lŠ™dZ@4‰†#Ñp$ŽDñЂB -(´€89'GâäHœ‰“#qr$NŽÄÉ‘89'ÇJ *-¨´ Ò"èH‰ #t$‚ŽDБ:AG"èH-è´ Ó‚N ˆ­#±u$¶ŽÄÖ‘Ø:[GbëHl‰­#±u´`ЂA - êŽDÝ‘¨;u'¢îDÔˆºÓ×O„“xøŠŸ£”­üØSxù÷b×'åž®§Óõûªë½åYÍ¿ûøªqôîýå¿eXÕãíÆ:ÝÞ»î¢Õó§ó¿JÞ-»ÞíþünóŽ¶Ü«êÑûþèí6Òº¹¼’Ö½¶ë~óª÷]û$y³ßOÍ/«ne«^nVmûz·ûn¹]û´Éë¹ËZÿ3}«í;KÖù`UUÿé~¥­g/=»í¾«×«¿ÌìXw½ûý©öñê§3=Ò±U¯÷øÝOWË펗™žÕç¢Õd}Þ±>7YÇÓ®Þçß%93=³ßWÛÁ{¼Üê/¿»þ½Ø5óý×,v#%#%£“£“£“S ÀbtebtebtebtebtebDsbDsbDs ´€™‰™‰™‰™‰QЉH=©'"õD¤žˆÔ‘z"RODê‰H=©'âñD<žˆÇñx"OD݉¨;u'¢îDÔˆ­±u"¶NÄÖ‰Ø:A'"èDˆ t"NNÄɉ89''âäDœœˆ“qr"NNÄɉ89''âäDœœˆ“qr"NNÄɉ89''âäDœœˆ“qr"NÎÄÉ™89'gâäü™pòÐðßß{^èš{*?ò<þ¾G-ÿ'{=NŸz̶æóJÞn[å¼^ ^­Ç»}­¯É^nŸ[¯«»rN·»·Ûgõüjò®ŸÓó——^o7¤VŸW=Þ’³{_<í¶XM§êw×¢M¯ºßî¿Úr»n7mþÝö¶ž¿Õ·ôkݬ³òÖüV»žÏÏ~·ö+«<ï~£µg÷úwëi÷=Êû=Ì+ß©~¬-gÕ¯=–’wÿð~®Ð¦ÝzÖÚsêþø|ìu[ÿ¬óón½œº_ïŽIŸW»iåKiw¾ð®çÝûÝn;X÷î'R>ïysu¾Þ7nÍ^vh1±„g8YŠþ}Æ ÏÓ,o5*Zˆ&VGá>Ûÿ$_Ô+E?_ÇLž€½%yÏvþÈ'´¿>©©~ýpr&NÎÄÉ™89'gâäLœœ‰“3qr&NÎÄÉ™89'gâäLœœ‰“3qr&NÎÄÉ™89'g¢áL4œ‰†3Ñp&ÎŒœÎDÙh8 g¢áL4œ9999‰†3£ 3£ 3£ 3£ 3ÑpfDsfDsfDsfDs®?I£mÄÉ™89'gâäLœœ‰“3qr&NÎÄÉ™89'gâäLœœ‰“3qr&NÎÄÉ™89'gâäLœœ‰“3qr&NÎÄÉ™89'gâäBœ\ˆ“ qr!N.Ÿ '3*ù±°õ# ù’¹ ö%ÿŸ8ùTÚ}ý;ý˜tJÿi}^r½ÛÇ»½VóYõÞ¾^kùÛùn÷Cm¹ÛóÆêkªVþn>m:å¶:¼Ü9«úNç;5_Þî?^ãÓÛÍ¥Íwû>ï]Þ*_ëf•Êkϯ¶Ë-·í®Ü™<«ûp×-+¥Sn`)Ÿµ¾¼Ü̧ëåt»Ýr kçƒU=3}³r³|¯ºŽÕcIÞîügÍ¿;îžW¯ÓÛŽ™þÙùSÏÇ»÷¥Ý~5˧•cÿ´zwçÍSí;K^õ7+'éóÊ·ÛßVóyÍ’ÜÝdm7­œ]½ÞÏq§î¿Ó|ʨTm´®v‘c [~ÀœVŒýœ_…+aç•“£§'‹g8?»^e´ó,Ÿ¸È¶´Øµp=bû {'âäBœ\ˆ“ qr!N.ÄÉ…8¹'âäBœ\ˆ“ qr!N.ÄÉ…8¹'âäBœ\ˆ“ qratratratratr!‚.DÐ…ºA"èÂèä¿ ü.\ð»[bëBl]ˆ­ ±u!¶.Äօغ[bëÂ(èÂ(èBl]ˆ­ ±uatatatat!ê.DÝ…¨»u¢îBÔ]ˆº Qw!ê.DÝ…‹w.Þ]¸xwáâÝ…x¼âñB<^ˆÇ ñx!/Äã…x¼âñB<^ˆÇKï_> NþãËϸ˜‹Z?þÿ@ÎfäÿyïäGºõúº›Ï+y?vÞÊçínÒæÛ}Ó¦ÓzN½x·ïéñàånØÕã]Î*ï”é–©Ü®{R’k•ç­W{þ”}·ÇÅ«æ{¯ó§õ?ç;Ý_¬rOé?ÕµîºÛõ¼ÛvÝzÚ4ÓëåÞÕ>¬^÷êyIÿì¼·›TkשçÝ[Ï/³|»ãûV?ýî5ž¥ò^ò$ù«iµ=frVíŽ_•ïÔõ{ÝÏOµÛ­ûÛêøÕÊñ¿V½3ùÚtëyÃ*ÿÖüi•·Û¤r§êûÖøäíÞ·NÏ{Ëý@À—R”«vÑäYÔ¬¤O‹/g{»Û'퉬Œ&ëï9ÊZÂñ“½¥v“¢»ÅE±'8YZ\[Ük[ûo\ÆÉŒ®,Œ®,Œ®,Œ®,Œ®,Œ®,Œ®,Œ®,Œ®,Œ®,Œ®¬Œ®¬Œ®¬Œ®¬Œ®¬_3 *:hñx%¯Äã•x¼WâñJ<^‰Ç+ñx%¯Äã•x¼WâñJ<^‰Ç+ñx%¯Äã•x¼WâñJ<^‰Ç+ñx%¯Äã•x¼WâñJ<^‰Ç+ñx%¯Äã•x¼WâñJ<^‰Ç+ ¯\$¼2¼2¼WâñJ<^‰Ç+ñx%¯Äã•x¼WâñJ<^‰Ç+ñx%¯-~ù48ù±oò¿øÿŸ_~ÞWùùüVœ¼ú8´›ßëµwW®÷ë”5y]Ï®[c5~»¥×ëµcU6ªïÓz¼ó¿Ú>©Üéö½-g&ouÞ<Õ>«nL­žWßSzOÍÏ»zOõßWÏÞýÑ»¼Õ}åÝ/½®×˽í5¿[ç%ëuxÉ[mW/·¨—¼™|éüêýÃÚOVÇ™÷|ðêûÝs¾Ý~2“k=¿*W[^+owüÏôx½ÿxÝfi·¯Î/Öë\Õ;K^Ï3^ý`õ¾µj‡µœ”vç™<ïq:“³«W+ÏZ~Uþjyk?¶ŽSk>íy)ß»ÍC³üêÅ®µQ¸J<­]tÚŠ/gQÑÚ½‰Õ{@ úD/D=È7‰êUïüŒ‘gxz†í¥E»…ÅÌgÑçë8¹2º²2º²2º²2º²2º²2º²2º²2º²2º²2º²2º²2º²2º²2º²2º²rñáJ<^‰Ç+ñx%¯Äã•x¼WâñJ<^‰Ç+ñx#oÄãx¼7âñF<ÞˆÇñx#oÄãx¼7âñF<ÞˆÇñx#oÄãx¼7âñF<ÞˆÇñx#oÄãx¼7âñF<ÞˆÇñx#oÄãx¼7âñF<ÞˆÇñx#oÄãx¼7âñF<ÞˆÇñx#oÄãx¼·ò‰pòsïäGò?ÂöVþ5N~¤ÝÇïÕr§Ý§__µz­Ý^×µÛ§ôXөמU=§úù)÷Á©tëµs×M$—qöj¹·æíÕú>=Ýrœ¾ßÍò½[¿³æµ]^ùÞåùj÷þ±ê®³ºEW¯cU¯·ÛÑúûi7í³<¯yeµ^v“Õͪ•³ªßšOk¯¶?[¯ßë¹ÿÔ{Ïêïäe·5Ÿw}iõ­¶ïiyÏù¥ãYþ™Þò…½¥½‰gÑÉRtëlïäi4±2*Z§Óë˜\¯¯þ¿_ãUi‘p ÿ°OXŒ[Ú«YÜëø©µ8y¦WÄñJ½ë8¹‹vbÑN,:ˆE±è Ä¢ƒXt‹bÑA,:ˆE±è Ä¢ƒXt‹bÑA,:ˆEç âDœƒˆsqâÊA\9ˆ+qå ®Dƒèq=¢ÇAô8ˆÑã zDƒèq0ÊvpáêÁ…«®\¸z0Êv0Êv0Êv0Êv0Êv0bvpAéÁO?yüäaðó…ÁÏ?_ü|aðó…ÁO?EüaðS„ÁOÇÏÀøÉ_1~¾D$dT4J ”(-PÚ·±ðipò3*~ÆÊÏÇOçl8ÙšN=®Òë-g÷1ÌK”÷qüÖùYòê/ÞvzÕï,ÿ­×¼Sz¬ò½ÜJ»r_5/Y彫ûÌZN«ïU÷Ó÷SnGkòêO«åoË¿uŸô–wz>ÕÊÝ?¼äHò^•Oë¶Ýu×Iåfé”{r¦Çª÷Ô}@’·ÛVë_[/«vYÓjýîֵܩ~°zlý}uÞ’÷|5+oÕ»*Ïk¾’ÊÍìÙ•³Ú¾¯÷·ŸwûŸ5Yï3Ö|Z9ÞÏM«×±:_Iiõ9G+o–Ïë9L’kµC:?+ï%oW¯V·òOxSˆR4¯5+`Ûù”{"K‹&‹‹b;Û§ÞÃX¨Çò¬‹… 8öÃõN>P碫¥ëp¶ÔÒõ.ãäü5mE¨H £"aT"(K”–(-QZ¢´Dì–)-SZ¦´LiÀ¢ß(­PZ¡´Bi…@®PZ¡´Ji•Ò*¥UÚVy¥•õV‰¥5Jk”Ö(­QZ§mÒ:¥uJë”Öy¥ƒÒ¥ J”6(mжÁ+¬kà׈RQj J D©(5¥¢Ô@”ˆRC 4Ž…À±8ÇBàX c!p,Ž…À±8ÇBàX c!p,Ž…À±8ÂgŠN~ìü«½“ÿ~ú{^üúïß{'ûê»]Þ¯sÚ|Þn /ýV=»õwë±ýôxØ­Ï[õí•vÛM›ÿ]ÆùªÛq&×*Ï[¯öü)ûn‹WÍ÷^çOëÎwº¿XåžÒªîºOµr¬i·x»ýfå$9Vû´ò$;V¯{õ¼¤vÞê¶ö²ëÔóî­ç—Y¾Ýñ}«ŸÎ~÷ÏRy/y’üÕ´Ú39«vHǯÊwêú½îç§ÚíÖýmuüjåx_«Þ™|mºõ¼a•kþ´ÊÛíGR¹Sõ}k|Hòvï[§ç½å~ð{ïäß{'ÿýàm÷NΡF¢­BPVˆÝˆ_ñk ~ įø5¿â×@üˆ_ñk¨´ Ò‚J - ² D¶È6Ù"ÛÐhA£4Z@̈y1o æ ļ¡Ó‚N :-è´€h8 ¢á@4ˆ†Ñp DÃh8ü„†™™™™‰“#qr$NŽÄÉ‘89"²ýÛ-´ Ð"èH‰ #t$‚ŽZhA¤ÄÖ‘Ø:[GbëHl#-ˆ´ Ò‚H ˆº#QwLŸ '?ïüÇ—Ÿqòã<1ówìü¿8Ùûµã9¿W>mò–·*÷t=®ßW]¯—ûE«ÏšÿÔë´UŸ5ß»÷—ÿ–y`UÏnûXëËË f•ëí®¹5~OË»e׻ݟßmÞÑ–{U=zß½ÜÔRþÓó¬Ömfu«Iå´vXóíº;g¿Ÿš_¬ýB+wöûªûTÛ¾Þí¾[n×>mòzî²ÖÿLßjûÎ’u>XÕcÕº_iëÙKÏn»ïêõê/3;Vç]ï~ª}¼úéLtlÕë=~wçÓÕr»ãe¦gõ¹h5YŸw¬ÏMÖñ´«÷ùwIÎLÏì÷Õvð/·ú‹6*U»Ç²zÏac4ì3V•p­„c­ùÄE¬…Eg‹?‹QàBýÍövVＸX¸´x¶å,ö! ý߸Š“##%##%c"ÀJX‰+`1º22º22º22º22º2fZiA¦™0"32"32"32"32:9©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‰H=©'"õD¤žˆÔ‘z"RODê‰H=©'"õD¤žÂ'ÂÉÿÁÃÏÉÄüøýï_üû1ÿ'{=NŸz̶æóJÞn[å¼^ ^­Ç»}­¯É^nŸ[¯«»rN·»·Ûgõüjò®ŸÓó——^o7¤VŸW=Þ’³{_<í¶XM§êw×¢M¯ºßî¿Úr»n7mþÝö¶ž¿Õ·ôkݬ³òÖüV»žÏÏ~·ö+«<ï~£µg÷úwëi÷=Êû=Ì+ß©~¬-gÕ¯=–’wÿð~®Ð¦ÝzÖÚsêþø|ìu[ÿ¬óón½œº_ïŽIŸW»iåKiw¾ð®çÝûÝn;X÷î'R>ïysu¾Þ7nÍ^vh1±vqjõâÏBTíîbÒ>}–7ÝY»x¶vñgaãÕhç¿O¢¬¥èjqpa±mk½Hõ뇓3ÑV&ÚÊD[™h+me¢­L´•‰¶2ÑV&ÚÊD[™Ñ¢™Ñ¢™Ñ¢™Ñ¢™Ñ¢™Ñ¢™Ñ¢™Ñ¢™Ñ¢™Ñ¢™Ñ¢™Ñ¢™Ñ¢™Ñ¢™Ñ¢™Ñ¢™Ñ¢™Ñ¢™Ñ¢™Ñ¢™ #gâþLÜŸ‰û3q&îÏÄý™¸?÷gâþLÜŸ‰û3q&îÏÄý™¸?÷gâþLÜŸ‰û3q&îÏÄý™¸?÷gâþLÜŸ‰û3q&îÏÄý™¸?÷gâþLÜŸ‰û3q&îÏÄý™¸?÷gâþLÜŸ‰û3q&îÏÄý™¸?÷¯_>Næ~ÈÏÇÏ¿=¢—¿ÿk‹NÞM»¯§“Né?­ÏK®wûx·×j>«ÞÛ×k-;ßí~¨-w{ÞX}MÕÊßͧM§ÜV§“—;gUßé|§æËÛýÇk|z»¹´ùnßç½Ë[åkݬRyíùÕv¹å¶Ý•;“guîºe¥tÊ ,å³Ö——›ùt½œn·[naí|°ªg¦oVn–ïU×±z,ÉÛÿ¬ùwÇÝóñêuzÛ1Ó?;êùx÷¾´Û¯fù´r¬óŸVïî¼yª}gÉ«þfå$}^ùvûÛj>¯ù@’»›¬í¦•³«×û9îÔýwšO•ªÖk~Î'`ËÕ=–ÅhXe®„g8TZ\[Œžžì%lÞ³Yí<Ë÷!êXÒ+,>.î=-ìÝüo\ÆɌ͌͌͌͌͌͌͌͌͌͌üÌŒüÌŒüÌŒüÌŒü,DÝ…¨»u¢îBÔ]ˆ­ ±u!¶.Äօغ„Ÿ¤Ñ6bëBl]ˆ­ t!‚.DÐ…ºAâäBœ\ˆ“ qr!N.DÃ…h¸ ¢áB4\ˆy 1o!æ-ļ…˜·óbÞBÌ[ˆy 1o!²-D¶…ȶÙ"ÛBüZˆ_ ñka´uáø)?…ã§püŽŸÂñS8~ ÇOáø)?…ÑÖ…ÑÖ…ÑÖ…ÑÖ…c®|sŸ'ÿ‰¿?ðbd.~ýþý±Øõ!í>Žy¹CN½NŸ–³úwúzw_ûnŸŸ%¯þâm§÷ë—”ßûõø¶«üS¯s·ÆWÚ­¯Õ×Z«žY>¯û„—|o{¼äx»;O×û©ò·åߺOzË;=ŸjåîÎ^r$y¯Ê·ëÆÕÊ“ÊÍ’ÖýìÝV½§î’¼Ýþ°ëNÖºi­vYÓ®;zµ~¬åNõƒÕcëï«ó†”¼ç«Yy«ÞUy^ó•TnfÏ®œÕö}Õ¸¿ýü¸Ûÿ¬ÉzŸ±æÓÊñ~nZ½ŽÕùJJ«Ï9Zy³|^Ïa’\«ÒùYy/y»zµr>”—ažE›JøWÀ¾«˜XÚƒWÄœÎö©±®2zZ½÷ô$ŠY´â˳|ÆÅǧÑØOí!]ï:N.D¶…ȶÙV.Ö[Ñ\Ñ\Ñ\‰y+1o%æ­Ä¼•˜·2¢¹2¢¹2¢¹2¢¹ W¢áJ4\‰†+Ñp%®DÕh¸ W¢áʈæʈæʈæʈæJœ\‰“+qr%N®ÄÉ•8¹'WâäJœ\‰“+£“+£“+£“+£“+te¤qe¤qe¤qe¤q%‚®å'i¼"èJ]‰ +t%‚®DЕº2Ò¸2Ò¸2Ò¸2Ò¸[WbëʨáʨáʨáJl]‰­+±u%¶®Ä֕غ[W"èJ]‰ +tŸh±k.býˆ:~ì™Ìå?ñÛÏ8Ùû1ýÕ¯/¯²ÓKÏmyÚrÞî˜Õ|·Ý§ó{»×´É«ÿ[åyµ‡Tî¶Ûá¶Þ]¹«ýx·?žêÏïÒïvåzÛgÕ»Zïöüð.÷wïWÖó§Ý“³dmí±÷sÎ,ÝrÏôIÉ[ŸÕ]|ú¾¼êµºŸ½ïK^ãÍ:Þµçgõ°«W›N?_½ª}gòWçSvYó¯Î³r«÷£ÓóîîxõnßÝþ`Õ³Û^õ4K«ó»W½yå[í/R¾Ûí«µËKÞìwïyךo–VïWZyÚüâye”°6ZW»È±„#W÷–°îlÑn ‡JÑÚZìük“V.Æ-aói´³6zz²÷´„‹?ä›è]ÇɃèq=¢ÇAô8ˆÑã zDƒèq0úu0úu0úu0úuWâÊA\9ˆ+qå ®Ä•ƒ¸rWâÊA\9ˆ+qå ®Ä•ƒ¸rWâÊA\9ˆ+£l£l£lFDœƒˆsqâÊA\9ˆ+£l£lqå ®Œ˜Œ˜Œ˜ø|¡|Åç ß"2 *:hA ZhA ´@i‘Ò"¥EJ‹´-RZ¤´Di‰Ò¯4QZ¢´DißÆö§ÁÉŒ<~üÿ”ùï¿ø¿9:yõ1Äûus·ü)7Ã,~íñ~ýòŸÒcMÞ¯‡»zNõóÕÇ÷wéçÖ|»¯÷V}Ïçßeœ½Zî­y{µ¾OÏC^ãçôýèUãÍ+ÿ­û‘5ÿí|ïò|µ{ÿ°>çXÝp3¹’ü]½ÖóÞÏ{»ãW›¼Ü…Þî]k²º½µrVõ[óiíÕögëõ{=÷ŸzïYg¬çWõi·–÷~ðž'¥c«ÞY>mZµ_+o–ÏKžV¾¶^¥ß½ççWÕ÷­~?K§æñWÙç¼ì¶æó®/­¾Õö=-ï9¿t<Ë?ÓûA¾mº]+EóÎöNÖFëÎò}Àɪ½^qeiðI”ð,zú‡}ÖUâx)ÊY´o¶ˆµÐOVõ.ãäò5Fe¢­LP–‰ê2Q]&ÀÊX™+`â°B -(´ Ð‚B -(´ Ð‚B *-¨´ Ò‚J *-¨´ Ò‚J *-¨´ Ñ‚F -h´ Ñ‚F -h´ Ñ‚F :-è´ Ó‚N :-è´ Ó‚N :-è´`ЂA -´`ЂA -´`Ðbë@lˆ­±u ¶ÄÖØ:[bë@lˆ­±u ¶ÄÖØ:ZhA u¢î¿~ùT8ù± õ?»~D,s¡ë§½”ïì!çTùSõrË òª×„մꮑŽWõ¿[¾ÛýP[îö¼±ëž›ÉßͧMÞíôªûÇ«Üu§òš/o÷¯ñ¹Ú^»öݾÏ{—·Ê·º'wÝK³r^ã\k‡Õ./÷ÚÌ«›R›vëÅ{Þ]Õë=Žwõz·Ûé÷]÷óêï§Þ3n_Çê±$owþ³æßwÏÇ«×émÇLÿìü©çãÝûÒn¿šåÓʱÎZ½»óæ©ö%¯ú›•“ôyåÛío«ù¼æIîn²¶›Vή^ïç¸S÷ßi¾ß{'ÿÞ;™íöß²wr Œ” Œ” ‘+`E¬H€ÅèÊÀèÊÀèÊÀèÊ(Ñ•Ñ•Ñ•Ñ•!Sñx Äãx<âñ@<ˆÇñx Äãx<âñ@<ˆÇñx Äã¨;u¢î@ÔˆºQw êDݨ;u¢î@ÔˆºQw êDݨ;u¢î@ÔˆºQw êDݨ;[bë@lˆ­±u$‚ŽDБ:AG"èøõ'i¨H‰ #qr$NŽÄÉ‘89~&œÌhcDÿÏïÿùûçû¿\ûñïŸN>å†Y•çý8´šn»¼ÊíÖË»è9õxêún»‡¼åœnw¯ñ{»ßXåÞ?«é´ûlW¯÷8½%g÷¾¸ë6[Õ;K§ê÷´ûJ›ÿÕóÐmûvÝ›Úü»ím=«=n韹ç´íkÍoµëùüìw/7­$Ï»ßhíÙ½þÝzÚ}z•ûÚZ^ýX[Ϊ_{,%ïþáý\¡M»õ¬µçÔýñùØëþ¶:ÿYççÝz9u¿Þ’>¯vÓÊ—Òî|á]Ï»÷»Ýv°þîÝO¤|Þóæê|½;oÜšÿ¼ìø°H´„­Xw¶—ðST«„a­{ÏkÖ.&­Þ‹Y»—°Eü' ˜V”'`~Iž„uÕ‹q+ëE»èù:NŽDÑh8 G¢áH4‰†#Þ‰p-®%Â5âäHœ‰“#qr$NŽÄÉ‘89'GâäHœ‰“#qr$NŽÄÉ‘89'GâäHœ‰“#qr$NŽÄÉ‘89'GâäÈhëÈhëÈhëÈhëH‰ #t$‚ŽDБ:'GâäHœ‰“#qr$NŽÄÉ‘89'GâäHœ‰“#qr$NŽÄÉ‘‘Ó‘‘Ó‘‘Ó‘‘Ó‘:AG"èHBÐŒœŽŒœNŒœNŒœNÄÖ‰Ø:['bëDlˆ­Ó×þåÓàäG421ñ1?p2ûÇ?¢“¿|O«nŠÝ|Z9Þ¯[Ö|»rN½.zÉÓ–óv+¬æ;õª•çÿ]Ü<»éôõjåÝÿ¯Ò»+×Û •{ª?¿K¿Û•ëmŸUïj=¾ÛóûÜ?Þ½_YÏ[Ý£§Ü^»î¯™¼[ãMúÝ«žgú¤ä­ï–[T«ßjßóñj¿’’w=®^¯$O{~V»zµéôóÕ«Úw&uÞ8e—5ÿê|0+·z?:=ïîŽWïöÝíV=»íáUO³´:¿{Õ›W¾Õþ"å»Ý¾Z»¼äÍ~÷žw­ùfiõ~¥•§Í/žÿ½wòｓÿ“ï¿mïä’õ˜õ˜õ˜¸ðn⻉ ï&.¼›)™)™)™)™¸Çlbätbätbätbät"OÄã‰x<'âñD<žˆÇñx"OÄã‰x<'âñD<žˆÇñx"OÄã‰x<'âñD<žˆÇñx"OÄã‰x<'âñDÔˆºQw"êND݉¨;u'¢îDÔˆºQw"êNŒ¶NŒ¶NŒ¶NŒ¶NÄã‰x<'âñD<žˆÇñx"OÄã‰x<'âñD<žˆÇñx"OÄã‰x<øåÓàdFsodî•Ìhå§ý•m‹]{?FXóiÓi7‚wþÝ×/ù»åÜ/ÿ¿5ý«ú¬ù½^Ko£wï/ÿ-óÀªžÝöYu¯Í’·ûÅË®Sù_%ï–]ïv~·yG[îÕn¿ÙùÓò´î°Óó¬Õ=©uçIå´vXó­Ö›—Ûuu~Yu[íк­njm{HiµŸï¶³Õ>mòzî²ÖÿLßjûÎ’u>XÕcÕº_iëÙKÏn»ïêõê/3;Vç]ï~ª}¼úéLtlÕë=~wçÓÕr»ãe¦gõ¹h5YŸw¬ÏMÖñ´«÷ùwIÎLÏì÷Õvð/·ú‹„A?ì­«Œb–ð¡(o¢÷–”¢§…(çi4±6Šy‚Ïg{ KÑÕÓ(晼 f_Ý{z†÷gí¡ýü`'gbªLL•‰©21U&¦ÊÄT™˜*3Š33Š33Š33Š33Š33Š33Š33Š³0Š³0Š³0Š³0Š³0Š³0Š³0Š³0Š³pñáB _ˆá 1|!†/Äð…¾ÃbøB _ˆá 1|!†/Äð…¾ÃbøB _ˆá 1|!†/Äð…¾ÃbøB _ˆá 1|!†/Äð…¾ÃbøB _ˆá 1|!†/Äð…¾ÃbøB _ˆá 1|!†/Äð…Qê…Qê…Qê…Qê…è¾Ý¢ûBt_ˆî Ñ}©ù˧ÁÉÏ \?"•‰Ì}•™çÏçèäçäõ8¹[îÖã½·œ[¯A·Ý\·ÏÏÒiwÓ®\¯|R~¯þþ*=VùÞî¡U¹¯š—¬òv¯×{|x»û¼åßrCY嬺õn¹ÛnõÇÛòoÝ'½åžOµr½Ü\^óЮ›Ñ+ŸÖ]i­ï~¡uï®ê³Ö‹Õ­¬µc–¼úÃjýkëeÕ.kZ­ßÝú±–;ÕV­¿¯ÎRòž¯få­zWåyÍWR¹™=»rVÛ÷Uãþöóãnÿ³&ë}ÆšO+Çû¹iõ:Vç+)­>çhåÍòy=‡Ir­vHçgå½äíêÕÊùP^Âœ³Å‹¥h]!:xuOäøZZüù}ê=Œ•‹qk±îlQliOâØ^›Ïˆ÷§‹{ ÑËÏ×±Ž“ #? #? #? #? #? #? #? #? #? #? #? #? #? #? #? #? #? #? #? #? ‘z!R/Dê…H½©"õB¤^ˆÔ ‘z!R/Dê…H½©"õB¤^‰Ô+‘z%R¯Dê•H½©W"õJ¤^‰Ô+‘z%R¯Dê•H½©W"õJ¤^‰Ô+‘z%R¯Dê•H½©W"õJ¤^‰Ô+‘z%R¯Dê•H½©W"õJ¤^‰Ô+‘z%R¯Dê•H½©W"õJ¤^‰Ô+‘z%R¯Dê5"œÌý‘¹ò_ø—‹^ÿƒ¿?$œ¼û8âßûµeUî­ÇµÕr·^³V“w{xÉõî§ïÒN§êû´ïü¯¶O*wº}oË™Éózm^Õ?Ë÷j»½äŸÒ{j~ÞÕ{ªÿ¾zÞðîÞå½Ü¬Ú´:z»Ogr¬å%y39ÞnQ«¼ÕvÕÊS»#7åÍäKçWïÞnêS×»jçj:5ÿhå[ëY›vç¿YÿÖæ—Ž­z¼Þ¼î ³´ÛWçëu®ê%¯ç¯~°zßZµÃZNJ»óÇLž÷8ÉÙÕ«•g-¿*µ¼µ[Ç©5Ÿö¼”ïÝæ¡Y~õÞÉÊÅ‹µ‹?//ê,í,D«“òM£¬}ÒÆRÔî‡|~¾Þitò3>Ÿaga¯e±þ&{cÏ _ÇÉX´‹6bÑF,ÚˆE±h#mÄ¢X´‹v"ÎNÄÙ‰8;g'âì_’†ëéDœˆ³qv"ÎNÄÙ‰8;g'âìDœˆ³qv"ÎNÄÙ‰8;g'âìDœˆ³qv"ÎNÄÙ‰8;g'âìDœˆ³qv"ÎNÄÙ‰8;g'âìDœˆ³3j¸3j¸3j¸3j¸3¸3¸3¸3¸3¸—Ÿ¤ñzÜÜÜù9FççŸct~ŽÑù9F租Vt~ZÑùiE租It~&Ñù™Dçg½¢½“¸ø±Ø5"ÂÉŒPÆbØÿwt²”¼Ý3³|·Ý«zO¹[¼íò~­×êÙ½Þ[õµ›ßÛ°*÷vòvkÌò­Ê{×ù@*ÿnã~w¾{—úöº/î»úµùÞí:nÝ/¬î «üÕr«ãeWÿ®NÊ¿ê¶ÕÊÙ_´zWõ{Ï›»îâÛîæÝëXÍ?“cuۮʗÎkË{¹•wõïÊÓ&ï~{ªïÚãUϧú©¶üì÷Õë³Ö³V®t~×ÎÝ|»÷Km:}³ßgv¬¶»×¼»kÇ,ŸWÿÙ‡¬öÌôjåj_m:¥ÇúœèeÇ©û‘6=ç—¢e§‹N 8Y”÷¼h²wt²rïdõâÞýP/Ú=–¥èäI4±¸Øµ`ÿtpé:´¸[h1zzõ¾Ž“;qr'NîÄÉ8¹'wâäNœÜ‰“;qrÿ '3Êv0Êv0Êv0ÊvA"èA=ˆ ô ‚DЃzA"èA=ˆ ô ‚DЃzA"èA=ˆ ô ‚DЃzA"èA=ˆ ô ‚DЃzA"èA=ˆ ô ‚DЃzA"èA=ˆ ôàÂÕƒ W.\=¸põ ¶ÄÖƒØz[bëAl=ˆ­±õ ¶ÄÖƒ W.\=¸çôàʃ¨{uú‰p2£ÿ~:æß_¿þýgœìýš¿Zþôë 6ÿ­ëõ’ã}Þî­žS¯ÿ»r¬ù¬É«½_ã¥|Þî¥Sõ:Ó{JÎé~g=¶ê³Êµ¦[ímÕë]§çßU}¯o¯ºOßr¿Zõî¦Õù`Wž·t–¼ÜpÖv>¥÷ôóÝ©úÕæŸåÓº7_5KrvëA:¶êÉ—Êï΃»ýëXÇ÷nýÏ~ßµOkÇîülMÖ~µ;/½j¾Ý_3½«ýÁ*Ç*WJ«×a•7Ë7“gÕ¿ZÞZλþv“×<ìU^+ÿÖsŒUÞîsœ×¼!kíο_*WV/-ì ¬Þ›X‹9…=Œ?`ÓÙÞ¿Bt­v±fmT‰öIÑÉ?êWˆž–>XÞÛ¸¨ø,Z|'FJFJ.\=¸põàÂÕƒ‘’ƒ‘’ƒ‹P.B=¸õ`¤äà‚Òƒ¨{u¢îAl=ˆ­±õ¶®_­¿D$dJ ”(-PZ ´@Û"¥EJ‹”) høÛ¥%JK”–( ˜÷Û¥%JË”–) ÈöÛ¥eJË”V(­°F ¥J+”V(­RZ¥m•WZYo•­P)­QZ£´FiÒmk”Ö)­SZ§´Î+í”Ö)mPÚ ´Áz”6(c!| Ÿ'?·~D?íüÓÞÉãG4³¸wòêcÛí|^éôëö©|«v{?jåzõ+/=»õ·û:¸ëNñJ§Ü+«ù¼ÊYåyÏ{ï2ÎW_gr­ò¼õjÏŸ²ïö¸xÕ|ïuþ´þç|§û‹Uî)ý§ú¡Õ=jÕ«?{¹ÿvÛEë&ÔÎÛVwã®ÛÓz^Ò?;¿ë6]µëÔóî­ç—Y¾Ýñ}«ŸÎ~÷ÏRy/y’üÕ´Ú39«vHǯÊwêú½îç§ÚíÖýmuüjåx_«Þ™|mºõ¼a•kþ´ÊÛíGR¹Sõ}k|Hòvï[§ç½å~ \œÚ¼÷ï,zUÐ'âËÙ"ÌR4±—}“Å©gÑÓÚèdÃNp÷lQñi¸r¯hëâã’Ü{à"N®X4 š÷ÛAãAçaQj J D©(5¥†@ -´ Ðâ×@üˆ_ñk ~ ‘DZiA¤D¶È6Ù"Û@d-H´ Ñ‚D ˆy1o æ ļ˜7dZiA¦™ ¢á@4ˆ†Ñp(´ Ð‚B - NÄÉ89'âä@œˆ“qr NÄÉ¡Ò‚J *-¨´€:A"è@ˆ C£4ZÐh±u ¶ÄÖØ:[‡N ú'Ú;ùŠ [ÿñå#Jþ¿3Ͻ“½ÝRòz ¹­wWÿi}^r½ÛÇ»½VóYõÞ¾^kùÛùn÷Cm¹Ûóƫܧú¹WþWÝ?¼Ýe3}§óš/o÷¯ñ¹ë~YÍwû>ï]Þ*Õ-¥•o-ç5ÎWÝ««rgò´×g­‡SÏ^σÖú²Ú÷ªz9Ýn§ßw¬nêU=3}³r³|¯ºŽÕcIÞîügÍ¿;îžW¯ÓÛŽ™þÙùSÏÇ»÷¥Ý~5˧•cÿ´zwçÍSí;K^õ7+'éóÊ·ÛßVóyÍ’ÜÝdm7­œ]½ÞÏq§î¿Ó|ÏQ³þû‰ÑÄRtí$*U»·®vQli‘æi”ð$zZÒÿCžvOi;Ïp·zÑn)šX™OŒŠVâ}©Ýf{@oàäN€Õ °)))))Ö ÀDhƒ0º20º20º20º22Ò82Ò82Ò82Ò82Ò8©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤ë'ÂɨãÇÞÉcbæ?Ÿ~ÿÏß?_€“¿|O§wÓ©×–¯Ê9÷5y5Ý~½žå»ÝO½ëïö¼³*wµ{¹§VÏï–;ÝïvåzÛgÕëåÔ–ÛÍ·ZþÖýãÝû•õ¼Ö½¶ê›%k{x¹c½“µÞVëy¦OJÞú¬îßÓ÷å]·ëj¿’’w=®^¯$O{~V»zµéôóÕ«Úw&uÞ8e—5ÿê|0+·z?:=ïîŽWïöÝíV=»íáUO³´:¿{Õ›W¾Õþ"å»Ý¾Z»¼äÍ~÷žw­ùfiõ~¥•§Í/žWb]-^ý5;ÃÎϘZÈ'EÅ~°O…+éÿ`ŸwO£»…E¤§Ø~¶x¶1[»'‹™K‹È7Ñ»Ž“##2##2##2##2##2##2##2##2##2##2##2##2##2##2##2##2##2##2##2c§Dê‘H=©G"õH¤‰Ô#‘z$RDê‘H=©G"õH¤‰Ô#‘z"RODê‰H=©'"õD¤žˆÔ‘z"RODê‰H=©'"õD¤žˆÔ‘z"RODê‰H=©'"õD¤žˆÔ‘z"RODê‰H=©'"õD¤žˆÔ‘z"RODê‰H=©'"õD¤žˆÔ‘z"OÄã‰x<§ü‰öN~,tý¼àõßOÿ>öV~?÷ëÅ®É˽ãýº¹[þ”›a–N¿öx¿~HùOé±&ï×Ã]=§úùêãû»ôsk¾Ý×{«¾çóï2Î^-÷Ö¼½Zߧç!¯ñsú~ôªñæ•ÿÖýÈšÿv¾wy¾Ú½XŸs¬n¸™\Iþ®^ëyïç½Ýñ«M^îBo÷®5YÝÞZ9«ú­ù´öjû³õú½žûO½÷¬Î3Öó«ú´¿[Ë{¿xÏ“Ò±Uï,Ÿ6­Ú¯•7Ëç%O+_[¯ÒïÞóó«êûV¿Ÿ¥Sóø«ìóN^v[óy×—Vßjûž–÷œ_:žåŸéý ÿy±iifåb×RtëyVª\dûNžàPíõŠ‹l+÷€–¢ºµ{"‹ù&8^Ú[´ou,ô“U½ë891º21º21º21º21º21º21º21º21º21º21º21º21º21º21º2qÁâD<žˆÇñx"OÄã‰x<'âñD<žˆÇñx"OÄã‰x<'âñD<žˆÇñx"OÄã‰x<'âñD<žˆÇñx"OÄã‰x<'âñD<žˆÇñx&ÏÄã™x<g¢îLÔ‰º3Qw&êÎDÝ™¨;ug¢îLÔ‰º3Qw&êÎDÝ™¨;ug¢îLÔ‰º3Qw&êÎDÝ™¨;uç¿|œüþ¸ 5Ñ1#”ÿùþ÷=ïÿ“¥´úZ»šïÔëÌ,Ÿ×ëݪ¼SvHùN½.ÌôZÏÏòߪ¯Ýü§Ü §®Ç+íÖË-·Ï»ÎRùw÷^î?/}Þóï»Øç¥_›ïÝ®ãÖýBëv›•Ó¦Óîè]ýÞîlk}z¹á´É˽«=oMÚz·êÝ-oÕ³{«ùgr¬nÛUùÒymy/·ò®þ]yÚäÝoOõã]{¼êùT?Õ–Ÿý¾z}ÖzÖÊ•ÎïÚ¹›o÷~©M§¯söûÌŽÕv÷šwwí˜åóê?»óÕž™^­¼Sí«M§ôXŸ½ì8u?Ò¦çü¦ñê$ÚT”',v=&öà]N–¢ˆgxU»×ñ.Æ–°´´È¶õ,å›âs! ý¹Ý¦{O+ñô:NÎŒ”ÌŒ”ÌŒ”ÌŒ”ÌŒ”ÌŒ”ÌŒ”Ì\|8sñáÌŇ3ÎŒ®ÌŒ®ÌŒ®ÌŒ®Ì\|8gâñL<ž‰Ç3ñx&ÏÄã™x<gâñL<ž‰Ç3ñx&ÏÄã™x<gâñL<ž‰Ç3ñx&ÏÄã™x<gâñL<ž‰Ç3ñx&ÏÄã™x<gâñL<ž‰Ç3ñx&ÏÄã™x<gâñL<ž‰Ç3ñx&ÏÄã™x<¢îBÔ] ^ ^ ^ˆº Qw!ê.DÝ…¨»„Ÿ¤Ñ6¢îBÔ]â×/Ÿ'?¢‘Ÿ÷M~üöÎýýýË¿Þ;ùÔëüª­&ëóŽõ¹É:žvõ>ÿ.É™é™ý¾ÚÞãåVQGë*±³½,ʛ蕢f§Q¸“½gùÄE¬¼:[üYÂáSì<“7ÙSzu±ðÙ^Ö³öÐîµ½Ž“+£+£+£+£+£+£+£+ø­Ä֕غAW"èJ]‰ +t%‚®DЕºAW"èJ]‰ +t%‚®DЕ8¹'WâäJœ\‰“+Ñp%®DÕh¸ WbÞJÌ[Ñ\Ñ\Ñ\‰y+1o%æ­Ä¼•˜·ÙV"ÛJd[‰l+‘mí?I£mD¶•‘Æ•‘ƕȶÙV"ÛJdÛˆl‘m#²mD¶È¶Ù6"ÛFdÛ¸wãBÜ q7Ž¹Æ1×8æÇ\ã˜küì£ñ³ÆÏ>Ç\‹Ÿ'ÿ‰¿ðÿÇ>Ê¿BÌ~Q.víõ8¹[îÖã½·œ[¯A·Ý\·ÏÏÒiwÓ®\¯|R~¯þþ*=VùÞî¡U¹¯š—¬òv¯×{|x»û¼åßrCY嬺õn¹ÛnõÇÛòoÝ'½åžOµr½Ü\^óЮ›Ñ+ŸÖ]i­ï~¡uï®ê³Ö‹Õ­¬µc–¼úÃjýkëeÕ.kZ­ßÝú±–;ÕV­¿¯ÎRòž¯få­zWåyÍWR¹™=»rVÛ÷Uãþöóãnÿ³&ë}ÆšO+Çû¹iõ:Vç+)­>çhåÍòy=‡Ir­vHçgå½äíêÕÊùP^Âœ³hÓÉÞ¿ÒâÎ?òy/v}È>i/èzÑÓZ¬;‹b~ÆÜÚųÅ|F¼?Æ~jéz×qr#²mD¶È¶Ù6"ÛFdÛˆl‘m#²mD¶È¶Ù6"ÛƨáƨáƨáƨáFÌÛˆy1ocpcp#æmļ˜·•Ÿ¤ñzÜÜÜ܈†Ñp#nDÃh¸q±ëƨáƨáƨáFœÜˆ“qr#NnÄÉQÃQÃQÃQú'7âäFœÜˆ“qr#NnÄÉ8¹'wâäN4܉†;Ñp'îDßct¢áN4܉†;Ñp'îü£ósŒÎÏ1:WèÄÉ8¹'wâäþ™p2÷GÆ"Ö?ýqáëfþëËïèäãrO×Óéú}Õõžv{íæ?í^Ø•{ZŽW9o7ßLþ©t«}V_ogi÷uÐûüéü¯’wË®w»?¿Û¼£-÷ªz¼íV]uzߧ¥duÿYÝjVwån}zµß©ûÁsþ]÷¡6ŸÖ»ê>Ýî»åvíÓ&¯ç.kýÏô­¶ï,YçƒU=Vý§û•¶ž½ôì¶û®^¯þ2³cuÞõî÷§ÚÇ«ŸÎôHÇV½Þãww>]-·;^fzVŸ‹V“õyÇúÜdO»zŸ—äÌôÌ~_mïñr«¿üŽNþÌ|ÿ5ÑÉí+ðÞ·ƒÈƒÄƒÌƒÂƒÊƒÆƒÎƒ Áo´`ЂA -´`ЂA -FlñÛAàAäAâAæAáAåAãAç-´ PZ ´@iÒ¥EÚ)-RZ¤´Hi‰Wš(-QZ¢´Di™õ–)-SZ¦´Liù'i´-óJ ë­°å -(´ Ð‚B -¨”V)­RZ¥´Jiõ'i¼žÊ멼žÆëi´ Ñ‚F -h´ Ñ‚F -h´ ýòépòŸ_þEÊdü8~D#?°òßÿžÿ¿£“¥´ë&±æóz¬µæó~­x·×ï×mz7÷˜w?Öæ÷~MX•{;íÖË«_¯´éÔ| •·qïíÆÛÕç=ÿ¾‹}^úµùÞí:nÝ/vݹÖtêþà¥ßê¾íþÛuSïê]Õï=oîºC½Ü©Úò»×±š&GkŸW¿z>¯-¿;>¼ôïÊÓ&ï~{ªïÚãUϧú©¶üì÷Õë³Ö³V®t~×ÎÝ|»÷Km:}³ßgv¬¶»×¼»kÇ,ŸWÿÙ‡¬öÌôjåj_m:¥ÇúœèeÇ©û‘6=ç—0ãëJ{Kòž±¤wt² OÌ7ÃçO8UŠê•ð«„±%üüA¯€‹ŸåM÷¼¢‰µØ^ŒžžìÙü!ºZˆz_ÇÉ89'âä@œˆ“qr NÄÉ89'âä@œˆ“qr NÄÉ89'âäHœ‰“#qr$NŽÄÉ‘89'GâäHœ‰“#qr ´ Ђ@ - ‚ŽDБ:AG"èiA¤‘DZ@l‰­#±u$¶ŽÄÖ1Ò‚D -H´€¨;uG¢îHÔ‰ºc¢‰dZiñx$Äã‘x<GâñH<‰Ç#ñx$Äã‘x<GâñH< -(´ Ð‚úõ˧ÁÉ­¹Øõ# ùyïäçˆåžq²—{ÁšO+ÇûµÐšoWΩ×Z/yÚr§ϵù¬rV_oå÷z­µ&¯þo•çÕR¹[ãÿUzwåžr'ž>¿[ît¿Û•ëmŸUïj=¾ÛóûÜ?Þ½_YÏkÝI³|»õ²ëžœÉ»5ޤ߽êy¦OJÞú´òfùµiµhå¬ö+)y×ãêõJò´çwÝÏ«ï{’ïd½žçr³ßwï3«óÆ)»¬ùWçƒY¹ÕûÑéyww¼z·ïn°êÙm¯zš¥ÕùÝ«Þ¼ò­ö)ßíöÕÚå%oö»÷¼kÍ7K«÷+­ ÉÛí«õ¯­—U»¬iµ~wëÇZîT?X=¶þ¾:oHÉ{¾š•·ê]•ç5_IåföìÊYmßWûÛÏ»ýÏš¬÷k>­ïç¦ÕëX¯¤´úœ£•7Ëçõ&ɵÚ!Ÿ•÷’·«W+çCyiOßçèd¿Š˜SXÜùG>ïÅ®Ù÷Œ‰µQÑâbÒÊ=Œ?àZi1éÉ"Öê|ƽ¬gÑÝRôòóu¬ãäÊÓJ,Z‰E+±h%­Ä¢•Q©•Q©•Q©•Q©•(µ¥V¢ÔJ”Z‰R+Qj%J­D©•(µ¥Öô“4^F®\¹¿Vâ×JüZ‰_+ñkåÂÈ• #W.Œ\¹0r%²­D¶•È¶ÙV"ÛÊ…‘+F®ŒR¯ŒR¯Ä¼•˜·óVbÞJÌ[1[1[1[‰y+1oå'•ŸÇyÍÒ±Öéüñû¥2VÕ»Šu'Ñ°‰–0çsîdÏf Ok£kµ‹DK8WkŸ„Ô¯°·´H¹<ù¬À>[||'FbÑA,:ˆE±è`ð Ä¢ƒXt‹bÑÁàÁàÁàÁàA”:ˆRQê JD©ƒÀƒÀƒÀƒÀƒøu0šw0šw0šw0šw¿îe;¸hóà¢Íƒ‹6â×ÁÅÈm\´ypÑæAü:ˆ_ñë ~įƒŸ" ~Š0ø)ÂৃÈvÙFæFæFæ"ÛdÛ¿b¡ço…•ˆÔÿv@iÒ¥JCÔý·J‹”)-RZ < ´Di‰Ò¥aü|; ´Li™Òòotò—ÿßÿ§&w(¾6'bayesm/data/margarine.rda0000644000176000001440000005546412516003354015145 0ustar ripleyusersý7zXZi"Þ6!ÏXÌç~ÜZ÷])TW"änRÊŸ’Øáà[áß^ ·ÖnŒÍV9/¡±vc1k8tNùk†æeho耄£j/z·ƒ¸HóÓ‡¶ÒŸ33ÍgdçØ‹8B£M[í'äš¹ŒÉ ¶=’hO¯{ÔDzÊË&½R?úÂYÍKkX§me¯Hî éï16Â:õbSe€ÌH“œ!÷ D‚ÍøŽÐ æŽM\˽±i^UËŽãˆn fHžö;x`È;¹þWŽk4i$'!)ð­ÝÝ,mƒ¢¯óÈ_È?™SÜyM’Ug}¡^TÙ}¾¤W‹2sÛ߈R„HÑØ*ùh]Àª»¥¯†¸‡€¸³¼ÄËÂ7ïÊÎ<.í î°íXQøEñúÌ'Ž&ÜõŠfSè¸þ^ÏNéìgÊI“†ÒI]oºËßvõ|9%”zÞ–Ëè®D1¥©³G£‡Ñ¹"̨“¤0“e@×£EVÂ/&K¶ÐFÕÁ:mïõ-PW< -´ œ¦^M›`ªDÖÿÜiDrïc™çÜ9f S®."‹r,QXÝ@-©Ùº0=“Ë6þ[ÿõzÞY4Ä_{9”³ªÔ-¢5Æ.|n4gèŠ9Ø òs­¹ T‹º2ßàn0¾›}ß ZtI: ´4_ÁüËfFlÚ‰T`9žÃö‰4#€‹¨ã iÊ!NJý›×[ŠÌvBkjøá™ +ˆqÿ£˜Œ45µðþ§‚mñ7´>rëcKì›üz‚󢥧:S‘‡ÇÁ’fº]u’ç³oâ¿1ŨՑ§Þä‘Í©¦ÓŠ„ÑT“¿lRë;pÎÜ'õªz.¯$i DÌ ¥ÊòþPh6K&TÌÑ2Uâ,ìþ`’ÌäæÔ…•ûÎâfÞi)%VFchÒ5|Ûl&Øvµô-0(ÙøIœ€¯)Ê©=hÀp›œG¿6 j*–÷¤ T²JÌs¥ÕAó±ÊÕNË>¡[ïÌ| ¶2õ^¾¿c ~§DÛZx˜Au ~x³÷¯¹«Žr’j¾ÿÞž/-Wï€8¿o;)/üô%ø3G»äj¹Ù¬†È–ÏÅ•è *P†ÖD'éÙÃK‘0é@ ÙbS%Z{MÆN$WcÌ1 SÜ” [L*ò%Q”ö¿îs`o»¸ ꟷ=âY°ÀúÆï0À1<ñÒ¹\Xùb%ðz hà7ú@ËêÈÃE³i@¹¯MÇ—ó ãv`²J˹w3¡´ûjŽMQ R›?c˜ÆþˆÐ^ŸQ¦¹§…”‚få:²ZW´—®Ë”¯÷|ŸÂàLl4 $ÏͬQ ¨XáîVÎØ6´,,›7Ñ$ÞÝi<-­àÁÎBd¶¦N,ïFò îPÈÝn)\n4S4ïÁ~×6*SÌràuݵ‚³¤@݈d/÷‰ØžBR‡:±ePPj ÖÓê°2%‹”¤»… rxƃpÄÚƒ­˜Gè¢8còÛkGF.JU<3GíaéCãƒq‡£XÃÞ-/%©1Mlð–ø®Mž•.#«1I]1¡T üU_w$?ÿÎYz’ˆî÷gÈGÂxä †}‰ÆÓ?‰Ê”ØÐén•B}.Q=-Üâ†oz¶—/L"E·Þã#Eï&{à6M_Od;ÞÉ”ROƒ¹N1ŠŒ¶‹*½&¶ò­¤/Ñof»WÔ.bÖÊY‡qNã *0ðãàÖÀ 5tÊ°‰m‘Ÿ:ƒˆ¹¦“4[b(ˆŠZ]Ô–æqG+ÀÒâ¼ÝÖ*èueømz˜›},„ ‚³à ãN,Q|è²'àiÚQM=UW}Šz0l£y=ô)ÃÛLɾyj? ¥e&d~kµ¢sã5zŒOõ÷#”êIƒG=zRw±¡î€k´^ʶªàõÞ@q…Mf"³EÄMH¡–ÙYfõzîÑᄨa†iv{µ ÚP»tg€”ß…9ª2áÍÜäà”º6ûvÖË×*vc#£Æºücûk3MÜ8d?Ñc¯‘ øÅ¥ÇÎ÷fÀüpÌ.€~õŸãдNóirþËX¯¥¨CR?ÐÁì‘tT;«ê¿Ä¤%Wn3&H À”ùŸsÂÍ :®H`=ª<‘¥ÆQÍ¢e¹(R‹x*¥o¯?vG,UŒîaGõ|ÌÅ ´$à zV锼ýH*ú€{~LËH¢uÂØ\4?ùY8ÇŠ-ãŠaÆQÌ&OøÀ[Çer‡c~•¦¤Ï´ö’åH`Áæô]jWíR;5À†¥aâ;Ó )Ü º÷qþGð¶ÕåÅ3åà lIf”a´ÏÎý-Jm {(î ð«§qKmoC§0Û7ñÕn3–…Ä£^‘9×ßØξ¸o]°Rç,ÝW¶Ã)õ&¶¡,Èœf5%fî´™~î:ꢮñ*^ú3éf‡²EV ¯ê2`Zdzvت g™´[£!u¸&ÿÞÓTÔtf&%O¶µùô¤ƒŸ %9ª€CÑi ™aG3ß0¤:7É«ZZKdiá&ÑoÅÈ£`¹|PùÔd‰¸ÝÓŠŸæh7-rÉ·‚˜ýHõL'ͯþ`©"´ÝNþDò€& -î"p”m¦£51FHIV~è<{ÑÁ|=ìÍö©UÀþQ Î.Ve÷E–öW÷›üþÀ¾-OGr‡£(‰¦¸–ÁLJar¯Ÿ×Ûò•ÞëJ S¹ÎKeûm‡`¥HÖY÷ågêÀɳ¢ýìÝ'ÓúA‹‘Äê¹S'£Q\ÙÜõ2ÛÈÖáiàsM–Ÿªao§ F‹¸vª\)3Íwc] f5+Žœ¼Á‚òðĉ•¦ZëŘ2Èygò?–5% ­•éZÅú?nÁ䃾Vn.$I"†´ÁÂî x+žLCôr€ù×4*fNÚù{¼µdÌHE&ÀN ¯ÐeK/ß/àvDEñÈÃh_ú•÷5+h^Âah}TEÚ¯¬;‚bwèA««¨ø‹·ëèbÿ)Íñ: õNŽöÉ#Í1k"bˆÔ9€Ó(7šâðV%õA¹ý•-ϯµyîBaAÓÉwƒ\çš$„P;ZoCÑ0M]€„”̧¯Á9."¼2«W1“‘ 9›žÈÑÊxÞuT[Û8 Pó0Âìå?P…“WdöSý Í™èˆþp/\ýr§yfðàýÏîºl‰3æÎ\Ëë9U…nFottoݳNò1 Õ!4GA¥¨k#»'•šqy«’[™—×ÅÇwàq~¥G1cp5ÂnF!oÚî@±øÁü”j&ä[^É/Ëÿ8Z0gÌê2D×á Rî}}»·ú41£Ñ‰ÙOô[Òù’…«æä<$Ž­õ\h﬎¶„M°ök¥…à˜.ª'¡é \3 rð(FëA»j!¸jr7z·¹þ§¹$œÉq-²ƒæª%²"„ÿ–ÏRÑo ëmÞV!I’¤ÁúÝçQ|,§dŸPÞïBÑŠîgÒ.Rw4jô½ù+} :úì,úæ^©”GÌöíh jE§nè‹(¯H–@¶ÖËøn&‹Y( H~6Ïh‘I‹o móÎyM…'B×­=bsÇK†èèÙLçbÈÒŽj¹U­‚I§T‰F÷Ñðèè&ñc,,%ÛU´£ìòüÀç$úñ`?ß<é<99ê8±í:îæyE«XÅ<¶Nƒ—Ø]PmõÏùøÝZ§ÊùˆƒH3ÍõRÙÜä‘£ÖK‚ûÝ Š7:2_`xÄ…íy¼9•®b‚Q¾—l)¬ô¨à³p‡ã™Û"±ä‰ ó¥R >—Ù4ÿ[Rö#‹‚ù»°ö˜£ôp¨Rèvz‘n´´÷ùG!¿WRƒYÑ≩Û"Ì#¥Çm»Ÿ9cèšäý8G%Ìúï­$]ܱi§lØÀòv ûb|u%gýõ2¥Ñ³µìÄŒQÿ©îŠ$UÖUtiúã?Ø*IžB..:Y›ôÃc§þÇÏJ;;ÒtZlþ†0^?n:xé : R]ªyÔIÝÏz"šÄ‚R0¦°äÌFŒgìê†ñ@xuŠëÚò¿íK¹ß~é2 ÝLZX?ÃecÜX…ekšÑTmÊ8¢èÕXVÙð7µ(ð®¯.©Õñ{ðˆ⧬¬Cv©‚ôµK;ÁÍ´:ëäA.àSþ¸ÊñDºgýOÚœË — ?ÀœQPˆžûê£ü¤+Îw +Sê¾ÄlŠãƒw§)œôl[¯Åd§Œâ·ÙÂhT~÷ON¤…d. 43D˜³}ýW…u¢¬­¶ÿ—ÙM Ga©kÕ±4^ÂAp·@e†äXsN‰°žHÕ›qö­âFÁ•ã×¤È ~¦¡y9\ Á1«9é7f u²h1 *¬&C56û]ÚìŠ7çfÄ"ÌNB2Ô¤Ú+R4ëõÇÉc ʤKÐ÷³?Cg°_è‰1´Òžt–-(XBÈS²ÖÃmAÈ"r(¤Måæœv&CU” _ç¹!ñ’^¨?¼Rò]Ú²„¤kÑË&°ŠÔ¤2òÄ›Ãìÿiquz]LP@>`Þ!G"ò4­ÕÒ.¬7_ýÂŽ£ô16Mz¢‹Ç™â«ÂÛM׺êÐÃÞSÐЯêd˜&ClxÑôb[pRðêõÀ‹v]ÄÃ&DQbËÇy”¸rXýï(ð@¡p±Ó”ÙÏgÐ1¸r›dB©G4í˜9ÝåÑ9VõlB¹^ÈÄ$ Ó±%ÿ–ÕÜ|ÞÃELÉIBÀù`þ Ô <½L‰h5ÏDÜ4Çßî~ʼnØrÃÍS~›Û|tHqçƒ×žqÞS&NŸS~žiŠTÐ5dåd[UȱzE2F25Qÿ”ä“`Ö˜¿sm¶}Ób¿ßõ^4©dòK^ØÒª*ÿüšÔžà.xË“bõv²‹uë2s›:ùÿŽ0QN'Œßd&f®‹@W§Áû+d©È‡dèLcŸkä_‚XƒBl‡=˜¹Q;Hu¢3YUeÑ#ýf§—ª_ùgaëÚ¨¶ÇâË»aŸ®„pþºÄÍõ^ò…ÿRT¢^ʉü9N°h*Ï­9×57¨°äÔ "£S¡:*ƒçHÔnXÊè_á¦.¬‹¹U/ ékø™+·m…ÞÒC!³’¼>’y ’2Sû¬Š¸Ó†û-ƒK¾×-Âv‚®KT4~´‰À+$£Ã 6 þó ³ÇQÓ±–YBbª¡³™¿R ÷Í•né^–ƒÛbL(QÇÓì9UôŒ‹ øv£ùFæHÝçÔM§º+¨øûuQÅ!(îW|”ïrz"Šziù£,ìí”5ý?Åæûò”÷lÀ㎇”›ª×>¨crùxó:ƒ;“_VÎqÄ0É]ŽžT+ºü—’uþ.í^-}z“¦µÜ²´Šœ¢ wÑç EY:ôŠâj9,âO F*-vøæÑÉ·^ŒAEóqWÀÕê‚Ý Šv~ ‰rDQnùÄQ?£ö6üÂPdÚ*æ¥|áÔ6•µÍùʃŠ½“Ùê€8ô&>6iÙ§2ÆAó=ƒÖð˨ ¡ÃåPI8åÄ+ÔMmG¡5Ïa«¿c.Ù•³òTÉtÝÖ?Sö*´ ã§ÇDîûßL%'ƒŒmÆ'ŠQÛªr„ù#}o-6ÍÀºÞ©”Qmª¨(‹óõUòßðÒ5X׊=°ÓZñÃñïa0Ô–ÄXan¿_~C‡‘„•j‰ tõ^™ÙPBš8íÞ\ž&šÂi܇AÜš`wË’…äô ³¸uRq3gY³U`|ïÈïâWtW/ùd ×hÌìX#¸ü3Ù Î(yV®9´Õ¨e‹?qï`²cÔÒÙÛá§lÁxÌ}vãÂbÄʹÝÏPý»h€þUhK™><>2AÆa¯È ö‰È‚t÷²W= v€lƒeËJ âWËë3$'eØá„pØ« Á²è~¡Ý×å/[3°Á1¼¨!Xoˆhb@/A¯%¾nVÁšÇÆ#Q©p  ?ÈÄRóšaf»I—•¶)•ºoõâorýV­”±ïF†¦šY:õîÝjß‘êÐxŸX³¡9\&œÉcÊ6=+áãŸ'þlƒ©Ø€6•~¹Ó½È–Ñ¿Z¸ÄÂ,UDao¿X=î ŽÝIurª.Ά$ù5 ´½šµMÖ0ŶÑÕkçv:ÆP€‰oÆ *”UýLL Éfœê ÿðÜ“8//РýK– C&Ð ~&v:SC‚µqrÚ»}/_]äí}ñz£’¬Ë½‘DðîwÌ;ÐÀPÚ½’ËÍcj…ñù§¶Æ7%ø÷ײùj—»o%’*nŒâÍ¥E®`e¦ëÏZ”´–,e~Šœ8c^lg݃ÁM‚ǽV0÷Æô„·â÷€Úyì+ƈ0~pyâ7Gݧ€^ ‡Þ-^Fy™Oµ?VÍ/5 NÌ÷‹˜9$Ò —Û?HeE_ Ï9Úâ¯ähÓþ’î/ªËî˜Zÿ‚›´Å‚\§z¨¿Í*þɨØD>—ìàCýöwB·sìÈ…³”uFtMÚ”²ŸŸŽž3¥ÓÇNs£¿™SIî"㉠Z´ÞË®@ƒe§oxm‡•Tú³ÂÔ†RÔO PüÛ Ä4bë§4)‹–úGÕzdc}{Àµ}õÛ¾oZñË)È9K%†Ú"œ¤sæUàNŽ‚Ó¹ð‚°¯ŽÂÆ€ñ`‰eQ+ÒæøÑ›íB¯±Md‚س^Þ¬$ NÁõР(RoÇM5iCð`ówx¦Œ’ÒÛù&"8²ô±z gŠÔº!k˜ ª´þÜå‘c¥±+g[©1àjë¡ 4C¾ÝhUl{?Ûµ¡üÉn<«&zQ…ÓwTŸ·jW›éóŠÚgãͤ®§ÈÞ׆¬E)Á0Ð;›ßÔ¤‰w”â¶lö«Ñ¿E£Ïl¿ïîtµ}qáÞ9ÐÕYqá˳\Œ€/¡ùÁ´Æe„ÏÌÚ¹tß\&ƒÞ ¼3z—ˆ?pÖí嵺‚¡äzÍ zæ M›<»%8{Ρ1<ÝTö±LÛ>ˆ ¡aO  ðQ¢—b¨‰©0hUp…¶û¸ænÒ±–úaæKíó,KX¹ÿï7¶%8&ªpY’ñD{TÔu›ÛÀõCdDЫ×狉n‡?4 Wʶ5Þ-ùÙµÙ[·ßnhŠ"L«M>IôÒ#†wÂ6´?ªsja°®'œÄ]t3Õž<I ‚ ¬¢r^~ÈïOºµ! ÐŽsÊ´3yÌÄÜÿÒ €Z4 ‘]Âé2-‰5™å¬¿¦ÎøJÝæ>ã/Aöb"é%I¡ˆí)pA& Ñæ`Ã) ¸ãád·…›Û…kq> ³eà« Óþ„Ñ¥¹=eYX温¸LïÿûÞíÇÖÇsÞÆ7/xqAmó;Åè i_Î$šØŠz@g›ªžN‡LžÊÆÍmóáCJ£[IW.T©5l©×*²ÊÆÁïß\Û~­/bà (¶Ë0B;ˆÓIŽ/#¨ïe'_ìXd$•Úº ·>Oðx–gîƒ Xh“p-Ç[Ž`1VBËM)íÛ¢[¡õA9r6Þ†%ËJ>µUÚ&h ]Ì:ž"ÙÆÍ’ºÆ‹ø”OA=ˆJðü\HUÚ>ôS.¤†Þ DÂF²râÓzß=žìzÚèÛ;µþኂF‰èˤÌ÷ÀVéëµtqDEfçOÍœÑ`i/¦vÊ)I¯z[ׄÆ]Ê•ý£¥xDÍ$…VdðùÌø;ÉŠÀH†Ðq}ŽKÌŽzU›¶,“—=kDRR—}/:Š„Wp4±á¼=„W¹è _³àwü6e9;66ø“ç“E’ƒ7Ót7„1}Ô7š§ãPW艀Ð)¹p îªHÄUäèÃF³Ý¡”ŠpÃÞèdU!½ #Ü•“„¸uñ v[ú>_ÐÒf#±–³×vrùÜeÝðnú4âïe»ƒÚ—¾öÁÈO#ª°~Â]‹‹z%R•ÞhQœ!¹¸cDüj.]±ùô@ëLW0*Ru–¥ø{^‹‚/—]’oÇkÛtƒ~¡áD(¤îÔÁƒ`BÁë™\ã`96Ì6,— ìæè'*ü|›GÖ Î»ƒL.R6„›‚4ÁFŒèÿ“7Q’šýëzƒÿwùN%ü®‰-¿U‰e>äkz (Ý—áÁr"²…ˆ (¦yËÀÓZƒ¿Oe‘1ÉCï󯋵î:ºÅÌa¶Q‰™w±¡²ƒðvsŽcÔìÈÂ)ùŠFNuÓ[íW(¬ãƒÕ{óØ-[`ÏíÀ„Âá3åÈ —ºƒšsð¯ÀAW¸äË%îYzõfž8Ó} {2Ô·êæOIyE•®>¥Û4£”€xeÉ®¶ ÀU]çc#µxìíÃ…Ps96g _'³“)ÀÆÉÌ:JÑÉ(׉ڻãü‰ cŽ<Ýx‡n¹~²*Ã÷¨¿bÉ…µðÊ;ã·¸¥³D­2±þxõë”É/ƒÔ€dL›€\Rèëˆ:ȶpÊ'Þåtã ¸‚Ìä›÷Ý &cªAzP¾—Lj^¦J»áÕwèEÅ ¨ti貄òØWëÞ«ëfK£–«Ûm5Õfi¨N2?ÂE±c{\z7U|¹ÑÔCÌQw.Â/\Š³¤wÜWÓ”±Ý´!«cÉjY:âY,C• [bgŒtœíìd‘"×8{võ«0Åݯ€…(4D+J´êŽaPü²r'£ ù ^ÊÀÄü€¹üñ“Yõø‡&^–HR›_ ª2üٟæžiƱ–ŽÝ¨EIpÆe‘Ëà&LШ…[sGXåžµ4êÁbÁ !þIÙt;ì&í,© £A/ˆeÙT==ãµe–rhG¢xK:Ü|‡ó"½“³<(– FZ|ñ{]áMÚ[jü¥¢¥W[Œz¢¥J±»£Ð>œ§¢Èu![Ü¯Ý Á˜µšå’ÿkêD8nì~χÇGA^˜ì~ š‹Ð’9(]ƒØ—A¸Ú9E®MA¶N“YÀ˜Í™éóÍKÃyúš2¢n|Ý?uÚûEu.ÃäfUh\26Ë\Ò*f·b1¨"v\„à£(kX/í׎‘æO¥«9Í—v3¯8Mäè„Ž1B„¹0›•“w`*Ú[jSwnMÞö­{\ÃÛÔrÿŒ¦3R  ³` V×$2±lì§ÝD-Ž\‰bÊrïkzêšeSRò³`‹…A23ö§J8_5} ÅMÅ…ªœ4 ‘ïÑ›CxÇXm Q½ u-4s`×GŨLÄŒ“ \»[$o­ÜŽdOØ&fQÏüWÈ% jù«èæJ­SÓ]ö ˆ\ï.JL²¯bûc¿µ|*Úá‚–†œI«K>´½Ýxߦ­ø`–-©‚àØÜâÂ$åWÈÅVj×ì ÝËë&.ÅŒË`#¡CFJµ£ñ¸*#”óØ_̘6gF=C8Ò‚L‚«¡ôÍya–†C[üX5víðl¸¥b¿=‰K&˹ÚZNií"nÛÆÅ ‹µc ÅG/Ú9N2Qí€uDZw§éhëW7"ë_ ¥ØÍŠ¢ö]ºùÉ}unI!þ³üWÀ¤Ä-QQå:š0¦vSiŸ7]Ê9r¥¬˜2´‰-¾ìÌíõTEñnܘŸ%ê†Òl¬Ê g ª*S×i-më}sÇÆ&ÉÊ@»±÷¥ÃqX›cæšóÏ ™ÂA²£üu²N2K”÷nEÊ® Á c.…øöüB¯ãBK @_¦/ÃþDè“óŒMi&êò1§r¤C'X‚ àçZØ–‡òsQð„2­Œ:•®b ƒt°­n’n³‹uYÑ~Ëbwú¦ #½?pBŠÈÔàtG">[†$¼üÆÜŒÓèóRâö­eýÁ\ƒAë»»c`?­ÉªŽ{y·æ€+Æ’z³‹Ù_qã/“w—G–U?"?¦ª[›ê)£Å[ ë-쌕³ ?.NœqкÛŽÿ©oô¿¬BeY¢~ +Þ¦©•ùàVòüØR/FêõæPóMÁäA¹ ‘’¥5%§&* Ϫ/»}.ÌÀÔŽˆJTÓ¯1þñçR¦Dl]7 #cû>Cª¦—Ñ8EÄ5¯ª?4Ä—ø¬ÐK¢E úœ2؆7<5›ˆïãv¢P œÄ'üÇg×Æ•Ö‹]}ÑäŒz Ú“«%û+æ'ðʰ؉‚P ]1Ñß“\®täwZô¤š¼½Åg´þz séþ¹-·®ã6ØêóŽtÆ@ÎY!5Æv#„bg1²Ðý ^K=G%+E,ƒ,åý±0|{rº^k%d£gJ|†ºâ,í½w"ºéTÂùNÙ©'Yâ¦åóšWçz@B©)‰^” µì+®%}U=d½±€ý‡nw¯”çë ëÀ*ŸÛCGÉ‘DW{÷“¬oö•wÈzñR;7UlKÉ¢!¶»¡J˜±4¢½Ù ^×V n\r¶{ºˆPȇ)#MrìybŒ Ô›&í ò›·[KˆŒ»}idÛ( ›÷Ì…áßrÓB\¯MîÓ.ìLXâµMpÓT2Ú´½Ÿ”tYN::v»2ýÚg;¦å\>üB|§âÈÔ²¨ËêO,µ 0`0PÁí†"Lîäa6¢qè6è°/?ªX%3„••qéP?gŠ™>ÐlçàdÖwjUÅSü4; ÖZ‰è­1Â’›AUŽàcMû³ e« ²f¥ãGú—ŒÿŸÂ,®€dR^¢Áýõ0ÆŇ Ô¢xóaÑ#5!iž˜/ÉGôPÇtz{@ŠõÙ £pVmµ™x#SŠÈ¥âŠ|m¼HHÙê¼nMÍ^ñ’ÅE1±ßYÒ…Œw¾?ö4 ʵsQ8 ÿúYýËÊÝZ“fµ¥&¬ÉÈÄØ»††ƒÙn´Þ_ÆÙ.Cœr½ŸuÙfÆÜûé|®ü@­”~Œˆ¬yO¹oIT²ßõ/UˆEueÙ1>kÛ§s{1ßD\IÆa0³Êîõc¼3W¤,#Àf>ÏøÕ‰r¶ÅÅÚ¿tŒÐœŒ EØß`cÔïºZ!Â,XÇÝ¿åzéóÍêg‹¡¦o¥â-ÆvíôES[³.¡‰‡â_R:¬* ¸Ä?DFH4B=Ö†rŽæúh;þÛ¹Bˆ±&k°4´¬ôc²ÈªoŽ<Öžò°RÖb‹IXm\īاGý¹eô*¸œÈš§ à±õ)ÝÕ”’6:˜£+›ªz$Cí—Õ¤R,ÃŒ$òõV×5!-§Ù™Äþ~‡v)à$ßxÏ{¨ÑùŒS4±Îâ:‚íÒW½e<¡ž9St% (‹ oŠý8\m` F®á¼0¹vAË¡ÁY ³‹ŽC´+¸õ—c––(¤ÿ!•b"õp¾ ]]˜hà{[Bl§@å1Cex#Á+uu½ã^Ռ(‚ÁMb:CÔèÎ?äÏÒ§üEþâëjm:+B}d@s¦ña…"ÎusÊO þŸ›D˜ÔÐ-|Âßb‰Å/ªZÆØ=oÎ#5^4#ÍOÚrÔM¶÷ã)5ÜÞ5g]XemîðÇʎGi„/@ñ˦Uˆ¥Šûg×Ó×q¤Æ¨ô=XG(ìMõÿP3˃ ¹Û¡r)~ªµEP¦É hÆ }`¹r›Wd“wô¾ª¸ea„meÚfíÝú»¯bá,PÊóbVFeW¤[r$Žd±`©|’ׇËÜT qßÇ$YG>ˆÜãSz ®\‚þ-DkÌ[GìN‡¬ÉÆl5'E§ÛÆêØ許õÔ\REꨢ,2ÖÀ½…FX2¯¶)ƒ4ºÔ“ŠwÿéÚ -‚ý—ïP¨E2VSª“7zÊÒé’ H7±uÝAGíß'-ŠÅ Íhš#Üj—37¹†¶\¦[‘ۜ膗iºTè–d®^A¼,&pmg]S®ß¾†Ö"'½ÿ' ¶Fµ_m±Ÿ»¨ÔêÑÏh YyõÇ‹wnuMÁ tqJ‡–žA,ù¸ë±`Ôœk’¡6Øa´V5úbÑ|ÝšGÌNjoS(Ê) ýY]e*ªbH/½ppùÙ’>ÎÐyõ§­²,O\,½vJØ;£å<Åfå±Zã%`=zåf ßV³°[…;Û(v5¤º¬ÆÄ[Š’&OÑ6;Çc)®²e’>jOÈzÕøzuá³ì/\*ÎW0Bääh2Pâ/gt‘ß÷^¶Íf>q.G79ÌÖÎËs(€*¡g"~IC³ Sr¸#œÙ©×ŬY¨9²x;’hˆÜÇõŒÛ'Ž¸øÂq¿1ï½ Õþ•L;›J˜6œ æ[X+°_lÖ~¥Ë§nücÓJóÝV.±pñBÞb‹‡bÜ0ª_•O„—Y•¸þ®ŒoýtP§AžªÆƒRfX{¯ý®šË¥:ÀÆ^ÇåGœ <²/ …àíèÌm))ÞíM#©;*Ï-¢Ã[Æ©™óǯüˆÛUf—Óz"J÷c|'OUÕsÑPô –ºK"ªöõÖ§*ͨ>7WB±[Þe³7£ ]uIñieƒep«3³%ƒ‡¼x?z|®K\…6€ëΞ¬À2¤†žHM+¥Ú0‹´õÑŠ˜D)ñîƒGìïàžÙE`Zâf)óaYÂݪ¸7Ëþ¿ðY3Ÿ 1ã~Óh/˜†×*­¦¦gRn¼Ÿiû%>ÄÊ|¯b´ÂÄ[—߇%:°+8è´ýMB&¤ƒÙ¼’WÀ™° §Oª qk°ßM«[~³ &9ÓE¨‚G2˜B²;¨²SNt"Q­ÖÍŸµNkWä´ä6—£H·ïNÙ_Ý/èOÆíEIÜ®Ô@”ªXÛ]„À3½úxbð[èÅäþKpfÑà‘57#Ð8Äƺ:zBØcÒ1•Œ¿º8auã¨ÀWBd^³µ!TºîÈaô×<2‘û2eÈ4þ§ê9¿GÀ`\ÄïÑ%\Ü.$ ·hBšO>J#¶Q×ðˆl܈üèAÁž5ۜǔà £>p¾-µšS£Û‹•ù`±i$UžË§ÞÁ|€ZR›©¨‰/=è›ø»Ëé–ýà"Õlǧ%N8ïD»b×#à”œ¯±ÞØ ñ¥…/ci  #ÃO‘º6…ä¢,ms3°P•ø&üAfO¤x=Ïs–qßšT‚¬‡È^WÊÆ Õö‹ÉòêiÆÛêÞ”fdµxÙÇÎTÓmfÿu •¶¸?z¶êŠ0[uf±0Šjì‘ÑóâÃw™9†óz&¯a)½®%ì7×í4A¼X1Ø«††m°íbØÙ”€°ÐÄ^1ÒlÍ°K­-'ó¡Á?”-ÚbeÁÜ’4g}ÃxPîŽÍ¸0; •û®ÝÇ,Ç}-SÑ÷„Tv}£”ɤöY’Vz„¶¦·T;(ùIÄtÊ#–X¬¬2üà`y¼Õä,µUÂÂ7ì¡Ð±›ao¸{þX‰õ»nçèHƒ£¼]ª|*Kï|JýÑò:=z°A‚[OÔdž…<Ú b*é¡òÌ”œã~±–|¼ÿÇ|ëÖÆUÈd¢Ð%FÆfŸÇÜË„Ap`P¡¡eè.^i“67úR*Ü銧ïÒ)/!›ÊÑ妯kT5¨4HíóÙU`BÄoŠÝ¦<¼cì¿9©ÑÝ_íæ”›ò;3Ö•‡ýFýƶƒy²thÒ¯<ÜuÂÝE=à YnÚƒ Y7ü°RD>O7(åcºkœöËyµ;½˜\ËO}»N¿ëœ{½[f~ŸDk™€ÚEÿ’füNRÔvx§…O`rØTÐA¬ªÿvÛWU©_ ïÍ5¿NI³hµPhüƤ&¢ÚIß®©‹"cG"Íܯ®O¿ádƧq9øHí§hº *S9è¸ùÚ[¿å N¹’9]ò²ÿ›q(ŽîX«<}?f\â„ÿºq4PͶ`ÍFb‚ª;SüŠÅÂëoç¦~[XP’_ÿ­Ñ—CXM8Õcsã©6¿ÝåO Í Kì®çBèíl·>Gj3½89rP°jÔ{M *¹;egCÿøRñ–7Ý!˜©„/PœõËK-WÔJþpdåb9:|?©ØL@HQ¸j£60è“ÎdéÄs¤k+ùi\<l¯®DÅ!;lfX“ãæžÓsÊ8îÇ,¶²Èh›9NR%¦À;K­#ó_¿—»©=п³%BºÆ8ƒ‘:™7Tâ»úƒµ%z€pX颤KEùªûáfTÎcGÆb¸ì±Ä r3í¬ý -Bm3¯PfŽË•‡ îË4­=ÀH÷äçm©œ!CŠÝãÖ˯t„™²dvU@˜DŽ¿‡ÞGø8ñní VÒ¡vd ìÌäU8FY)$gð}$¥V÷¯™waRƉk û˜aãÜÔ了ÐáïvÅxÞ äü‚flJº·tYÕ'=73¤ËfЮ(C†è1g´ï—¿Y† &ëÐPù-"2>šä§Kz ê®ÐVzK»¿0Рî%èæmâHÀàÁ´åb¹ o7£QÕœOó‰åž"„½xî£.œ)›¸×˜üg > 3öÂðc°²K*ŸD;cúR«‰“xÚSgŸNÙÏŽP•íÈ‹þ)ÞÞÿßöah?Ó`‡ý =¬pJÊ>ý…ïÉÈeµ…T-Rþ1LO³OŽòº§/pµ‚5ï,Ò©3kÜ° ½¦®ëœ^Ïl©m8»åZVa¯=ƒO¹ˆ4ÎãsUúÃö‰ë!F3§ÒZâèÍ.uÌ.¿ÂŠJÛ©ûÞùæ$›T-~¾grpb‰"5â‚W‹„Ewð%îUªò²äÇì ät“Œ{•ç—]~{jäDdÃß:ÞòªFKïJìæVüßùÛq=Dž”õó­· ihGYÏx€¡ÛÎ×j£à]ý$ŸOOs`éìÇ*P7¾sï‰,x›7~µ•‰-ÑÃ]—)/:þ'ÕºN$r_›égÜ2M¦ú'óoK~hÐ,ÅÎ+W2ÖHú$ÆCôÂUöÊÃÊg2â¥m‘8Fîšúº<;>>ŽÀ—ê”ýÂD[¾ q—væ­l#w;Si$>5ÙužŒ~>Ó†5)£%„¶lo†÷’Í£L·B¤¿Ý²WÎ|™{:·ÚyR£8oBל¦ØõƼ)ÒŠÿºOî.OLZ‰…“ð””¡íîÉp»Zè‰ý¿Ç¹¯×É= lé ZÛÆ{o¶ «¿U¥!¥ÂôŽ’¥• Ôy¢þ€-rš@©ø㲃ùC-p„™õ@úF-u‚Y…®éO†dÄ]J}—vº¶ Ø?“å$ß¹háA¹¹4kåu›é§Áa{¥KáÏ›o˶ö ð¹+½Š¿¶’Y¢Ë‚x˜ç;tóÁk<¥íœ¸˜åá¤O—ýÔ¼²ãè¡ßÅ¡2¹¦ä’QÕÉÛ ¬š´\{*Û \`˜ÂC×–#µ‹þæökÚ×r2@α‘‹ý ì¢Ùö"âLcòò•ù.8¯³¬¸5$01M}FŸfߊÁ;ö+f¶¦y~!Clf bsm{ús¹°KëLn7|Ð9žš#t•ÊÊœ~wó<¬¬“Ï쑤Lõ æà;N ÚÚ“XÁËY( OûWYgD?P¥yõ畵U=jLù•Iù¬Á™ßWúÇ%À[!c5ÛÜXÇ@ØÀ/®_Ydö£õhO„VD–µéž»uÑ8¬T¯˜,L=]ªusYòów³÷ÈK¡%„jŠaJËÁ€ËÈá$sÁ Â–=ÔAP™Ñ+CK¿÷»{Εþ¸7ѵ`ÕxÌ»á?ö$«èÂD›šªç¤¢fˆÉ’^´b'o’·–šÕ ÉuÑ´’Iö'Óó"QûêÄÄŽÅÂ}S\_>¸õÙšÍÙŒjìOtRßû«:©uÞ;Ä·ésßþË_­ ꚜЇhæìè ÄP¢Û‚§ù°énÓÙš²ä¹[¥ðAΘ„¯ê{‰¯‘×5—±…Üä Íú(˜w—®?¼)w0ò6b6ùùN¯ˆ¨}òÁoƯү,†tÚ®Ä󘢶‹”pÇÓlu»Ä÷ú«Éê"ngöïô¤!Ë‘'¡n&ØÝ×CE}®bh¡ñrXî^{À‰c‹ 19#^ÙÚìÔ]§$ã:U~äçl¼'VþÐÝŸÍ\:N½”çx5›©°~͆Tëqx|óüétMÖÎ)bpïõBø1QÝ®ïá×`…©gmS#zˆŸî2ôYzJ€J¨1{Ù¥žÇšÆÁeÝ5Û·J#œ·J«{¡ aeÃñXîОè%BETâáɧ §ÀkFàtŒGnßžüáóøÊ`Z–OÀB"à%™´I2Á…šæ]È™Èf8¼"³ÂÐ ¬ãÕç·p¡B䲎RfÙ*çZK¬’‘a@IH!ß2¿Ü5'T"a™æB*TúdåÆ´µº,]ÉSׅĦ*1²URòZ­·äùð}Èüçv‡˜µŸw¾ úŽý6Š¾|¼›Í21¼€} ˜:´¥[ÇÙ‘)åu\É#TQö/ãèL|xÑŠ1]ŽeNŠ§ÚLlö¨@›9vZñQ»Å‹>}olœtÎÀun²™¢°oa~‰ÙX:ë= µNrœ;èËÂý ¤Â ˆ[×’8à+~Ù¥Ìù…»[Ãþ*˜ª,hrt‰ÏÚ!dõ©~DQ§—Ô·?ÅŸêøi¿¦ÎÉ…-TÉ"†Æ)¹ößÐ{6ýVÖFÇ“ùÎÃQ)Í ”A®¬ÊwO1/y=<ˆjO ¾dü|Ãï˜ß\kí§÷ö°nPA´Þcý?hŒr£¤ v#šF×”ÈSaÄ.ÔjSÅoi£àÍL|ü°Å9=íOó‚ªr@Æ2¤ÉR:Hý”QÅ6ɱyÃ(%1¯d´lèWKIö9ŠhÎãÍàø˜g²òR2¿%€£;N‘Å’føçy°bDƒ¹›”¯wÎŽùðöODû ÇöGJCÂq®+ ºÑ„æùE^™aè˜ÆÂߧ\O—à}“:Oäb™=ˆtzc@æúÊÚãBÆ¿§þ9]ÏØÁáʲ§‹ãûBª8¥"²ð _é3¹1ÀÊŠGQ%„‹v€nÌAKCüýWÛ4A¨â*óžîµøî÷‹ŒXzS—¡ÔÙ]†ö±%V ²Äà c5Ž‹³²¿È“Â~7÷[E†¿¿‹ýê(täý9/Knû¿~Û˜“±ÖœÑ  ilßGfA79üdµn} Ò(‹Xn ™Xó;Š45žˆÅ(^b!à×"·ì&Éùœ f%+¥?·|†bÎi\FFF\«ýÌ; ë·¡ z2¥áÐÜÖ»QÚlÎàV ñÉ¿Ù{Ø1.¼4Ò™.Älë+¦ØG|­¤NXx³þ²â2×,QÄ ùV]&ÙdØõT®@y8¢MšÂŸá”{{éÙØMW7yYÐàÀ¶­“1 )x)v]Ï·Î}¶[ã ëýb¥4•}òyJqÑT\­@žKöÊeÖÞ¢åHéªv~Ò˜O@)L(ú¯.w­#´I‚Ñbfñ‚‡%Ü•°…äY&·3øÁs½ø‹í_d®lÞ¬êÁm±TŽüƒd‰Dºï$ØÀŽ©8"GRd„GQ—˜˜íƒ+Gš 'S9²Ê-t9bÎÈ´F¿%|¬›Qªiœ·R¹à•0¾rwù9]?…ì².`+enä)T¿Ñ‡|Êžr¯?ÊÉgÿ”Õjí-TñYaKxÃÔQwº $“7N"ô1(2)›MÈ`óê׎Üt¼Y¼åªúÞ,Îrò{ƒ«A_žô!$SJm:ŸÇ:íZöj+h#²çÉ’·#§pÈ°HÑXêÙô¿õ8q9pD8’Ím§ z±ÅdN €<Ÿ]Š}§¥O§ â-J[dçªèOö<}3dϲ<…y\_ÜÅNìg„³âÒPˆŠàððâ“]uòœ0›R>Î4 Õg³"ˆ±æœðTKz˜ íí­Î5õ(R§®’<¥ˆtÍ9{±¦CQèò.w’r÷ÚÄŠ¶=ª%xP,w´:H¯oŸYuþz‚^¨£¸"úÕ{FYÂ$gtBô¬¤J&@ê×Õï ¬l“‰FkÉ­•é÷È“Í[±¬ yM*ÆŸ>‘kýŒqlºÕÅoê†ìð¶½ê2yzKök_™Ž˜³›è÷ÌÇWlµJ_íZÉQââ°¿±<¿‘Ô] ¿Ê¿0P󉚥#Hb:8 …nÜÐ"$e€ÇŒü7°gŽÒ4ܦ´£þb ]~s…÷¾zÒðdž»åèp»ÁÜê)?:\§¬¤«%Ö¹„Ïã…æÞ[“´åf~ë/ÆSÙ]K漪®b˜;N›2šI:S´­w«8NÊÕÀ¯ÿñÃT ÁU ?fÒo€øóÀAˆc±«Ñ¢—¤T³AÌMG·¢“˜±.ƒhNN¨gá‘}ìt/|øÂÆS¾ýÇkÍBÒ€ܲóð«¸TÓA‹°úë B±â>Y{ðOW Ḃ:]AYZ )RkFz7pdá¼4¡ÿ‚ö A^[vEÄ J†nKÌò˜ ˜PI°K š—»Ï…ˆh±óH·ÅiEzaÎá~S(ô jY®Q"!ø5¾ÃXv.üë„XëS»Yü ¦bã#cR‡yÑS F´ÓÑ°üÔÒ3ð$jD}­Ì„Ë#Î?nÁ÷Èý³U³….ÝÒ€iZŽ¯lÎW{—:Z¶r”(||A²E+>0ëŠÉ¥Ú Ý9ŽM“nO§WÐÄݸn¶³ÈUeLÑi©)ÊÁ)¸3g-£ge°kíáËÏ ¨pcÒEðÔóJˆAc‚û+䪺TQ«¢ážÃ©@ øëIÎSmò¿ ¤ÿÉ2B\‘A BáãGWöÈ(EO­Òyú±^FmZÄ–ÜajÈ2.Üe3uªt€ä1fGàõ…FÛejpìx])={ ÐRx¿õ:Pþ¸׬òê#G°Á4 ³Tø R«¾ÂËßømEò8~÷2h•Šçkkƒ·µÞ—L±=¶Ðÿ ŠÇÍËs¨%LxúÁʆ¼Òs¸·Éõ yeZsp…„Ì 6õ…á:ú0É:›Ïü¦EØóuBÉõKh&SÞØùϺãË·Škô2ˆû;ÿŒn˶77ÈÉ°ÑMsP×I WŽð3h¢ÖR9ÌÂD°ªéaºM®FˆMt#s¸Âÿ[ª˜ˆñçáÑLb{:±&IÝ:xžÙnß}ÀÕi¢…–1nµsv‹Èb¿×ÍÂd|pÿáΆ!ÅþƒŽ;)ÜþÖ)C ¤³Nlª,Þ˜N¢í`ÎqN¡EÞëîÃc·úV¾á‹¦ª€¶•dlÆøñdôcV‡6ò úiFõŽŸ‰öŸLYaÕo/~–¬Ž•ŠÚŸjçš(üêä3=kú*bÝ8ìˆÛ ºíŠ›‹C(=nA÷‹p×iج~“ábk*á«1êo웼¯ö¨%GÙ•|5ß—"ÍTyò‘7Þƒ´2œ…¡aáÚo×ø©b%Ú…%Ì¥FF{î¬î-]]òuÆ—2’Ó„¾ ä\2¥P´¶ £Ò3{¡¤æ(‰Ø…ŽÂŒ”Ø7•°üðŽQÓ^Jk=Ú!LØÈ#ãx?I)Àdt›±vMÙ_].œ˜É¡78‰ÝO“½Æë7”’gÚåâàå•ÝËø-îI“Jzrß?^ìœc‚ ¿¸LÝ´yï+ZS@äx6(E6ÀW7R½Ñ©Ñè[Cq[0AšSÝn˯ý›–Ž7#‹8\щ¬-çh3‰=ÒT…‹ƒR8ÿãø`cáÉØÎæ!Cï°?Ýø™VÇš «UØgeþ"'n%Ûöç20Jý9—2·Ïa?—ÏsŒ—ç5û ëRgâØþ‡)%=ýèÅ÷¼COK梿é2-ŸÂYŸùC6›Ôkçj PÖ+3 íë÷)^7†]‚:;6·¤¤bêÿa¢£·ûu–(|#=»¤‘þÇDrÉ{gدë縀˜:CÓ^ï¼k!/Hò¥Ë7”`ÿ"ð /å>Š¤rÌ Ì¾Í8–o8ÇziLð;]¹N½§¦MòÌX6Ñá™ÝÍ<Çç·MÝ/™›8Ê4¯bC“¸E I¦J©t¶ƒ@ ㌧2{Mõõßü~ÀZªŒÈAi¥Ón‚q׊ï¦Sj¥‚ì :Êü*}³Î¼i‘1¢R²?ðñd᤭ÄæÍ?ÖÖÍÞÒ¯†sÕ—Ž€¶¿´K'°åî°®|͸RÕ“…²µÖmÐwp{5ÊŒw¹âÝõ= å´ÝÃA=ò9G]`›.¤N£$ÔmLQáo [ð{KIT. A¶‹ä§™¸ ¥Ûܹ_ûM+âè«šwœ•5µà¯ûXècq5îœs(¤×!ÈË!DóH6pµ„ý]¬€¢²æ~L ý,¾úŽË¢Õ¼»Bø0áªÅÔ¢-…؃“ÎsF¬ƒðWb-”Œ„ŸÂí¥X§ÖðŸÄñ, KÇŽ5Ñšjøéqhgu@ã¡/ƒîâÀXwbê^_nG0hד¦í11Vc­ æ¶aŽëÝ_#Sµ†á½qa@í*)1llÃÈE`6¬\A¨v†-±¸Ãªpæ5ùSî³*õÓc*Àæ“óÔÃIâKë×á‰ÊG“”=$8' ª}Œc¹=€”5¾…x·û^Å‚Zú=éÞú…:ºqR†òwu{Fpk+‰M_³Ø}½¹À" ¾%á™®žÈ æ—¾bFº¼2G.K!9i;\‚6Ê‚ÐòóÈytäÀ¨ÆgCž÷õ›+ôt7Æè°öøðŠV**ãåºñ¸²ˆû±;<°gà ðÅk{í a&)a³ .96ПžìÝ\ªïÞ<ÎL° '`”¸ÄÔ™f™z’‘g={£¶O­øS60ö>ƒ‰g*+[7•øÊ9B“+6£‰~S_É+Q^u%+U‡À#ä#./!sKzi;ÀGü—”ÉIlœ³„þ„î9 P†lWëoRE 3ú¢Y-u0kÏ^°Ü‹höŸƒÐˆÒêÉQ¨ÇIþ€ñФïÛµÙ~·£5%‡L‡e‡ ¾·ó?iUp­¼5c8O7¸¼Øž©*awÙ\]'?Æÿ¼®ÖƳ6N9Œ$U¾&€5µûÑkï‚´ðJñ= ¼?ÅúòÕ*€ÚR¨=t00jÜ¥RÔDN´ìú­„XLÊþÏ/ù×ÿݱ©`Š§Áúuº¾x²ê?´]Ùág'˜à þ¦’–0½2:—[K‰Ï6à¹]Y PaX¬·ˆG/óôì N•ß Šo.8ïÅù¨TkT}#³‘o¸"±2 É‹x÷ªâÀOûÕ£ñ$çTiÏâv×Jaÿ2'QÀPÍèFšñÐŽ£M¤I.»B+öV´ñ}Ý!Ù¾»’°­·ZŠ%›I›Ë}nÉw¶—V;Ñ'ýE1Ð27»¤ˆrW[f$®ÊÊ aáÚÎ¥B:|„#„RÁX°Wˆ²‡¥!î‡îÕ—>È”:ÅKX¹S•˜°¼6¤‹¼ôD¦ì>$ÂFÙüKtÇè!AUþ¬š0J† $­âŒì¢iâêÈÅÿ]7=æ%­mK9I€ÖغxÏJ¨TžÉÙSbŸ¤O¶Žë9C‘” ñ2çIï( ÄV< ±Zàà|í có£9Â647Z†Bš‘‘5æ Ô#+/_õJv•Ò÷— ¹úÂce‚…µ.¨»deB–·¸×àÀ £µíG‰]ÊzS #C¶#6]>}*åR‰ð@xüò`ò즿nI]É«Ù’ete(¾¥éwô- ²µ{þ™¶xÃæv5½ßyçÿàl©÷5é¸l3嬌׽ñó~u1®ùEN^„0 Éëq­ áÇ%†ü»(»*´?Ãvþ|X30Œ7t]®Ä1;й ‹X¼oijªý5¤5Â5xßák -O–n Ìî3ìEVŸ©¤ÿë"Å þ«ñ®˜šýñÚW²jºÙØÚ–× ²æt-¢³›J­Gx¶äøî»úÅL|¤V›È|ê`#Dã‡H³²…^+,nåp݉©“%81}-®<7ÊüN­K„°ß·ÁZ‚žÃæDÿ¤ì¶j_ôhèüšòy³7Ï€^1í÷ÎO„ÍÎì‡ÛX›ÝWwÀR=6Í!å—–U­:%qš‡ÏNOn~éã ǧºW¡¼“SÈBm`ˆ¨X€c°w¼#¦Ð¬òÿçn.ß”ï¡ufûOùÑò¼ž€…ê+@e ¬ÏÄ|)àÛÄ«†¼`Bgã xøGõô'Ëu®êeÔù|«P‘IA~NãwÖ¬ ê+ƒúk³™.Òq·8W›1yÞ_`M´“•–Šó¿Ý¦sƒ+>~]ý鉀ƒWo²ÇïJl ©”¦DÊøv„î]É«Bd?ÊÓí ï[4&éZÂD%Óò ƒ,½¯í; Ù‚>_xï¢ékz/Óþ*k;§½ÙE‘„ºr0mÜD.ŸÅÌ=J)Ð*²nûS`É®ëG»tk²#:ù—¿ü]–[µ³}w¯ÉÈ÷§–Š/»ÒΕ dÙo•P‘ï"È"Ït1úõä扂|ÅãÈËÃ…QÔ ÿ*³_ •÷n#íS• •išËBÁŸáQŽ ÑwÁJ8܆óF™ÂßðS+xaÎ$RŠgmÞ\ÀA›]¤§Ø ía9ÎO’€ô5¦–ΞÜ?K\²ž\©Ѻû¤jÀŽW1 Åè[† &¥XRkŸYÍÌ ´Èf“Â;wT¼TnOBÌ[é Bå }?´ž‡ä²ŽËàH¡½}·r&SãätbVŽKl.PÒ‡ß9KzI7(ƒëØðKÀ½'zÕ4¥ŸHöÕ\Rf²7ŽÒ†€>äÄ-¢k¥MQÂYiÉ>ª?"˜UTxÀj¤dDq+3pKõ`?-ðOæTÓÓþ†úØ_zÎ`Ô§mKa›QŽã³¥—ʵyýò”º±¹Èž¡"j&Õ²'ç%·€M‹.Kûâ—³'¢6Å0wï‚KrqÙXÒÄdLâV†³Ÿ…r¢(dÞÏœY@¢ÈeD!àQ9˜y^\i¢"ËÄ”[O›þCªâ¾®aNG h*(–DI ŽáØ_ñ–©^­,5 ˜fÃá‡kmµ”ÆE!¯®³MS„HØÄ\öùxE—ä¹@—íNò€0ð.¸2ȘÚÙÁuĦ½Ë6{"5ò××4_$’kŸ,NË “ÂG]Ò'hkDÞyÛŠ&úû,f]ÁßlÐ?q¼n54a©e\`µM±óKR|qÎ¥ø§‡s2sD)â:fÒHMÀ­’æ’½z‘§g.œ}ÈgIœêQäHÒÞì”Ý#ç(á±äKãµê¡–qø3Ðþñ‘YÓcItB_íVH¨.¶YÑÀ‹„ôÕ鵫Næ?V +‰R!|*\ˆlƒ+­ÚåÙ ‡KÐBkhk‹óçP¹žÌ,ýU)‚ˆâÂ3ÿ8{Ò`&°ëÎKøÒõ‚xÚ¼é?;d­{Z¨^7¶¡³FïSx´¬Ì¿Ä£2s ñiI¸ àšÕÝ0&\¹É˜8ÝdÓþb²abþu7Jóný0“쑵ãŠÒJZ"KðI:±Å.²Æ½]x †nkø!½ a#æíÓ50üWtªúý_¾Ótyq…3 ¤‡ŒòuGã9Çþ÷Zä(–©‡4tZAÝþiÑU7æ"¬pä±Ýî‚ßDeq¦¦6ãÌz‰yû A¶s÷>‹¾ØÜÄi×Ûƒi,#Ù4!­Á_/šº:% þˆýÚë¶þ?(F‡l’U6#38‰¥]«VP‰§›fsÌxT[¾_Ϥ~žþÜ3ð¿°§b㜸¤Óàk«ÿ…¹:äM9ŽˆºÌ¯w‡öIT1¯†¬wÉy#+ε°…6”´3G€¸á&1´0ñÛõÊ.+ß^—¬·ö‹}Ì·–F¡p‹•E „Ôå‰\jX6¡ôœ¦øu$|Íи Ò²7ÉrÐÝ ¤ê™Gÿâ³Ë“kùfs]°àUŒŒ"Eó²EB©+„É4)Þ fƒ®R1Mý‘0GŸøÿ[)Q®ó< _Þà© hÛ¡™_ñ«öG]¡‡=ú䣩Ôéüî¾íÜÓ!³B¾r|sÈ—}ãû¼WŘôù&oÙFâí­¾‘¹«’<ôJ­aaäÒ¬(“×ÜçxÜ:" C£&Ôƒ››½Þm9÷é@¢Okb\†$cëôºšákunÝõ¡á Ó çƒéQ,5Ãù,Nq•ó@Jr·¿—KÇRedz¼Ë/¢ty¤ÃlÍ)A `“ÄD{Õ>®(Yß…½<+ÊÀ >Ø—!ú¦!^Ìt™-°š@<ôÔÑ$ )àoðóGŽnMPºtøn¢FL»zç§wŒ”ÿhŽªˆè*Æ Y3H±ù¼Ü;š6/"xË–TöÐíûèêkS{(¿Ð:åßã$Ímæ#¶ÝýK½ÍL>0 ‹YZbayesm/data/Scotch.rda0000644000176000001440000001372512516003352014413 0ustar ripleyusersBZh91AY&SY>µVêj÷ÿÿÿÎÜÎÉÿèÞþþÜÿÿßìÌÌÈÌȘŒèȉˆÈ̈ˆ‰`'>à°" D*¢B@öp¼ƒ †@€!B„ºjdh#@LMÓM5= z™©¦¦˜òOF†¦™¢iÍ5h ™Ðhhiµ1ˆ›ÕdÁ0˜'¤4É€LL†ŒLŒdÄ !€ €2M=Òf‰£&ôÊ4ÓF&#hCÐŒ@Éɦ@2dÁ4 ši‘¦Œ&ƒ&’ßú¢‘@ €Ðbh†£SSD"bb!¦PÀƒ@2Ðdh €õ 2Ð4B§ÿÿªª”Ðÿꪩÿþª*˜ ddÁ1L™4CÂhƒ!“LA„hÈÈb0†FƒÿˆD@üÈ’0€L" B $ À(€! ‚D ˆD ¢D H)H ‚(IEA("@  „(¢"H!€$@(„A„H€$$I"HHDB ¤ER$HA@ˆD$D ˆ $$ €„D" „ D„‚@‚ I€D €H¤!P@’@$ ‚@„I)R €$DH@„‰H$ ™ËdÈH $€ J:‚@!€‚  ”(ãÑ2Í(‚Hôj ‚‚ß}‚'ñ ö‘ùkå }B¸ €$öD’º>:úÏ}ïþÁø^?áÝÝÝÝÝÝØ @Ýêp‡€Í~Ïxÿd}í³ÑN©a­(¥ @$y½ç»÷±%¯j$“sïå7’Kñ"øQN YHý¦]{Õ›¯€ô3<ç*g/…öÕŠÕ—Øóˬó™¤ü¼ÇKj±ÙÇĺ.QØOßÇKÖ¾Nèq>éH˜˜‡ßŽj“ŽózŸ·†¼;XÂá@èg¯ôïÖ>*‘Þ䮩®Ê†'¥!©ðÕL÷~ù§ŸºÍâzó•íˆUêgªâC…9¹R£éòÓ¨·† Raˆ¨ZÀ›Ú¦!HÏ}L¬5Þ”)Wc:•³Íáxss\Æ2¶ã(Lá:N¦¨sée¬ñ¬ê¾Ï! MõŒ â¸Wè׉mcŠÍ5m5µl™! vPoìfx‹“2 *Wu.“ËC$MHÖ.úÈq4&'ËŸžb5§5B­]uoK>·PÒöݱ"9è8âD:ô˜Žô’—–c;3Úf²Ã—|kG4N†•Ê$‚@ŽZëEAzìE­ÌnÅ×TãhÑ“Yà~LòQž·3ëµ~œÞ $Îo±,h¢à`ŽÃ6 ‹ÐkZ-°vãdsÈâèA'~Hh’ö¤å–9bÆfnG=ïFÇv-Æs‹Ö_–»ÊQé¡9Ç+Üów£‹{žiõ1à„u†I=ê”)¨Fú+¬òÕíZ}d5˜\c„& r«®öë˨òÓp—mç™ÂMÒqX¥=²¼öïvâ;qÄ©ß²ü§½I]ò:Ÿguœy; xV8¬|~óÅ\fž—¡ws庘\'ÅBÈXkN½_^¥Ë¿4¦Ì­`ð|3@ƒë[j$­h §5¤Å®ÂhD$¡‚+͇Šº¸—é|ü Õä%?KÇ.± ãæqZœ5F¦2ëR1†«Ï‰Å¡ÙEN/,àcË qÀÇňÒÅ(R±^Å9¥¸ã:YˆÃë{{/ ‚ó7á½ó‡æWìy½‹ôóâÇ<àqߧÒÏY‰Œ¬¼Vµ7«œFo´Èœr:?’ ×>ÉLc;4Î~áZ1Éc(¦6²öê(Û™=üVï'øKøC^*R•‡ÊáHâ=ק«_]l(–CÃå=¼'½çÀdd”°pB‰¨ÏÁ嵺6¼ÕÆ}Œ\ìñ´BûÛ¡ú·Û»>|-¢­½¦³…ÍôSíü¹ã¼QÇŸŸ’ÊΉ³šQ‡4ÔiìÙØž[çÌæmØéa†sv(eô5®8$­6ÓÒÕ——MÚ¡Ì_O­ÑªK¥ÉÝÃnN‰ù»ú³öu`¯jŽã%Á¹§§Ùàk´I$3,_¯vz´öµA¦9rkßåãéKk63z8îç_R¬C’µáË&Zðþ_woU8ûÜ)÷ù4O V:ž“pí'»ýèrõéÔáoæ~Ù|9 ¨×tífÛMÓó­À#íåÔÖ’:yljlÖÿçw¯½Ý{0x«ÕÒaýG*kîmÝsÀ¾î¯KØ·7OÃÖ³^¼ºlaop×¼#¿±öYåÑÏåmwHïÕ—«èúÞ‹þRÓænóÙ6Ž»„¨¾bX3kP6ã'(|8¡æI55ä…ÁY|ŒÆ®‰F=úBÄ EÑ™A†R:\!€( ^Ç7è¤ ¿<â‘æeâîu{‰®‚9Ü™ý¬ÛÄÌÜy/¶áàÓäõ+Ík£Øǯn¾û1lôÛŠ0ye×ûUyÅÚ¿çùæyýgsw‡:õô.q-hkÌ0F`œ'4D#-å´0ËÒˆÄ`8HzŸI.cXÍ(‚S…1MÔó›÷X÷˜ÄJ»RR@aê¨q§kšÈ8É]¢šðc‰×8j $ÃÞ9Tª…ÄÉŠQÄ˦0¥û˜<5PÜÛqŽ&5CR§¾^§,aóbE¤»Lf~8^XŸYi :ËyUãUU\6Ч©p°áöƒŒÃ&*ž„Ó‚óôæª5®âàªÒzQ¬ÖM*möiˆÌBÀÎTL û÷ñíœgJí(C¨¬öŠ¬¼ÈÌө߇…vú¼ò'Mvø,M´z/©ëªÐ‘n[çYŒŒw—˜ç¼Èññß—7žªALÃãr:¥å™k((ƒZÖ<ÞKQᤇ”Ÿ$ùˆ^nÏ©`7 ó6ð«}b^¤] N6ï|Ôö¯V㶓€°§ÎUVœôíV0½_™O¼c©Þ×~9Äü™õÌ/6ù v¢¦¦ðŒ‘Љ^œeéb#U„8zf3xÇ©Xã¿„ec¼ îêqSPŸj‘4 D¨Añ3>}1¥¬F)z:MÖóÂ\vã¥5¥•ÔPÔˆq3,B‘Úk8àkÓZ˜Þ jP‡ÒõÕqœL,uC4šˆQ5ÑÎ’?¹@+Þ%(@‡÷ ”¦ïÒ}îr[¾Y½/o[m²2g@‚€@ö¿ãÇç{hX_Õ?*?³èããDœ¿ûù8ì ‹QwŠËÝìî°W^ýPŠ(¦"""" ªªªª *ªªªª€ªªªªªªªªª¨ ªªªª *ªªªª€ªªªªªªªªª¨ ªªªª |ZlÄDDDUUUUUU@¹¦""""fÎV‹6Ø!¶Á ¶m°Cm‚lÛ`†Û6Ø"6ž÷½ï{Þµ­kZÖ®÷°D6ÁÛ6Ø!¶Á ¶m°Cm‚lÛ`·½ï{Þ÷wwwwww¸‚lÛ`†Û6Ø!¶Á ¶m°Cm‚l¶÷½ï{Þîîîîîî÷°Cm‚lÛ`†Û6Ø!¶Á ¶m°Cm‚6Þ÷½ï{ÝÝÝÝÝÝÞâ6m°Cm‚lÛ`†Û6Ø!¶Á ¶m°FÛÞ÷½ï{»»»»»»ÜFÁ ¶m°Cm‚lÛ`†Û6Ø!¶Á ¶Û{Þ÷½ïwwwwww{ˆØ!¶Á ¶m°Cm‚lÛ`†Û6Ø!¶Áo{Þ÷½îîîîîîïq6Ø!¶Á ¶m°Cm‚lÛ`†Û6Ø!mÆ÷½ï{ÝÝÝÝÝÝíq°Cm‚lÛ`†Û6Ø!¶Á ¶m°Cm‚6Þ÷½ï{ÝÝÝÝÝÝíq°Cm‚lÛ`†Û6Ø!¶Á ¶m°Cm‚;\\W]u×]u€ ›vffffd[¦-Ñ`6Ø!¶Á ¶m°Cm‚lÛ`†Û6Ø#mï{Þ÷½ÝÝÝÝÝÞ×6Ø!¶Á ¶m°Cm‚lÛ`„ÛD@#q;Þ÷½ïwwwwwwµ°DD"I$88páÇ8UUU€m¶Ûjªª°Í¶ÛmUUV¶ÛmªªªÀ6ÛmµUUXfÛm¶ªª« Ûm¶ÕUU`îé™™™™|±E,Z³3333 ÐͶÛmUUV¶ÛmªªªÀ6ÛmµUUXfÛm¶ªª« Ûm¶ÕUU`›m¶Úªª¬3m¶ÛUUU€m¶Ûjªª°Í¶ÛmUUVZfffdWLDDDD 馚i¦š@m¶Ûjªª°Í¶ÛmUUV¶ÛmªªªÀ6ÛmµUUXfÛm¶ªª« Ûm¶ÕUU`›m¶Úªª¬3m¶ÛUUU€m¶Ûjªª°àÛm¶ÀUUqäãÊÛm¶ÕUUq¶ÛmªªªÀ6ÛmµUUXfÛm¶ªª« Ûm¶ÕUU`›m¶Úªª¬3m¶ÛUUU€m¶Ûjªª°Í¶ÛmUUV¶ÛmªªªÀ‹m¶ÛUPUÇ+m¶ÛUUUÄfÛm¶ªª« Ûm¶ÕUU`›m¶Úªª¬3m¶ÛUUU€m¶Ûjªª°Í¶ÛmUUV¶ÛmªªªÀ6ÛmµUUXfÛm¶ªª«-¶ÛmUUUjÇ'''''VÛm¶ªª«ˆÍ¶ÛmUUV¶ÛmªªªÀ6ÛmµUUXfÛm¶ªª« Ûm¶ÕUU`›m¶Úªª¬3m¶ÛUUU€m¶Ûjªª°Í¶ÛmUUVœ[m¶Úªªª«S¶Ûmªªªâ3m¶ÛUUU€m¶Ûjªª°Í¶ÛmUUV¶ÛmªªªÀ6ÛmµUUXfÛm¶ªª« Ûm¶ÕUU`›m¶Úªª¬3m¶ÛUUU€Ûm¶ªªªªª€\DDDDEUU\m¶Ûjªª°Í¶ÛmUUV¶ÛmªªªÀ6ÛmµUUXfÛm¶ªª« Ûm¶ÕUU`›m¶Úªª¬3m¶ÛUUU€m¶Ûjªª°DDDDS(Yóx -@+GЋWšj ¿G‡Õ'ƒÅÀµr€¢—êAôŒD} 0I ¿0áD¨ðùP€öûªÿcç=œ"ß+|8lY—7/c=®†ÅsìPQ‹8çXõ÷bÅòÀx«9¦W’áXo֪ŭ"%‰µÕ¨ÔÂÄ N5wp‹8ä±Ã W=„¥*…,R YBè$õiÞ Uں傘œ÷P["”zâɨÜüGkÆ:èTØ°Ú²Cæ5aÆ'ªYTûZÎ7 c¸\®WܹåñÏ•àÔ¡ÒØ’}ð$ ÿø»’)„õª·Pbayesm/data/cheese.rda0000644000176000001440000017447012516003353014432 0ustar ripleyusersý7zXZi"Þ6!ÏXÌâËïþ])TW"änRÊŸ’Øáà[áß^ ·ÖnŒÊt¯D6Mï” ÷ä1—áý]*ýyž{ØÅÃÈE!“,'| ÛŤb÷mËbùfoô¬ œ©¾p­‡&r†Y^J¬8Ù}ñßnéÝr”Ò1øi÷,j´?‘Ŷ³h_ ŒÒ¯•uÐÙI¾ûQµ5&š-›À´À†“¹qÃÀîEJUq&íÇá|±µfôfX3^È[ÚñkäÝ.DG˜ßÿq^¡‚O.rq ÕlÅøëF²l`—™’N?Ô®/¡uXÀï³I¢s0NPÖDܯú€îÕÎÀ}À$Æ=œbŸ]¸YÙ˜üþÚ¬A[ ¿wïÔB.™ÞT"0Tªa¥2?FaöÃõÜ<˜B"È}Hø…Yã®… >e£¤2zK³Ç…««ÃklÆ=nœ¢Y¤³ÒEƒ ìÔâ•WqwvªnOu×)t0ÑGD±ABNà+èÿßÒ¦.ÄÚ±¹ùà‚5ÝEÌ#ÕK©r…Í #C{Çj¦ÃB\ÜÑCþ^¯¯†¿Î%"”m([Ü3¡\µSÒ™’XBøî; JTé5\ÛÊ¢ ×çcѭχs»P]¯$ÞÞP³Ö9>Ž‚ÑPÔ9u‰’4‰´„ã”Ï{/&ÈùSr ý¼S q™¶¿‡(4ö¯‚ŸÌ-‘"@1î>­¼Êé£(*° 3ã[b±ï¤XÓ;Ê#5÷æ©×„“{˜]rÙ’ _ûó2o «“ ðe 1?%4^…X| ×@\ù%¤eži¶¡HI ÂÓbÜ3Ë+QÎ?“(Ì‹¸j»à{/+ÿר¶‚‰:À".éXû»;D¤i |zضä[b}Òè3ƒkÛî·‰*B!B¨W±Ʀ?d ¥TÜÄÉH£‰}8¾wüS ÿ~²ÄVtˆúEL"ËbQ¨dhÖìü—•Æ®³ ÌTÀÓWŸÞkÂñ3vÔwc¡ÿ|gq7WÞy½ÔÍ,•vi]l Šbp¯Ä£_Á2œò%ÅtjîN,m̺–=ÔgŠQqhOœ,,ñ~ eŸY°•\¯”q ºEþÄü¶HáO0ò’“O½D]7³•úD¦®B£Æˆ˜ŠTNq<9ëªöÑBývÜZó˜8]æ·X!0½_ulL½îztIEðFsæž‘¾`_}«Ú3mÜtÖ|GîÀàÍ n š7–w9½îMi¬¼4ñ47Û•ðíÒ:tA7ò}„P³<ç ®[fUÝ\F>X´VL¨|´øÓæù!Bæz½¥7S·ÿHb >®\ YÝ7ar¶¡h½ÏM5²o.Õ³·„ ‘ð)´·…AÄQ2ÁÉ¿¡W·Cî.y쥑zÂцÊ9üZ€€å„“cQ«6Æ\•†vóU1# ìÚ¥ŒÁGŠO‚TÑe6÷Á·¨6ÍçúÏ= ¥¸3l„™®pÁZ˯ǃ&#…Üè¥d2Ë=ñ\©<È’Û¿ ä2±“3ÉàÑO!e3œtzl÷?—«gˆ3ãë:ý…7¤Ö¥€Zi‰jžãBꢖ™ñ‚¥ß%b¿$nê ŸP;á¡ 6¯‡˜&´Ù«Û7Í[2â&ˆ0Ú½}àÛÊÇÛR3åºsþ rá^"R»zL'Þ^T®cÒ} è4¼×{Æ>ܘ+­d„UAÞºí6ƒÁçæ¤mlÔAÛ‡Ü7Øñ ¿lºØͦö7Ð&t^èj¢§ñÇúÊ.W½ ýJÕm­—¸ð@­K×>ý©Ëbb´@k¿rxG­U ¿»sªY‰¤Ò23UyçëšüÅ»åü‚³KÒ€w/™’ÂÓ3ÖVör7=Þè?RƦ2ÕhV?”ðîƒÐÐuL»Zð?@ÌéÕ¦+Ù¹ M2³¦5­õ+’¤Ãܬ’GÍü^p¼òŸTìPº÷Ç Õ/{,ó5ºÇil#´”FÕÛ)ð¼F*‡m§ßSÇ¥“ïÁivÄÔÄôµÐW4=X÷Á° éyM½Eu#ò?Yyœ4\ü#Ñ™˜ˆlC>èΗ2úZu°i°®w8u6FÁkéT„Æ66vê%y%"|s…ËÔ ¶;x‘/–ªé8fÀÑ÷õÞ‰eàÁ¹%ΖéñÃßø†Û‹±ž‡]·ß0ì¬voãÔ[ÊõÿÝ^ÇŸ‘qIÄi¦žL¨ñ§‡«(açg)þT©j2üp}zΟ'*C¸õõë€z›Y=—<|g·§ &W‘t…ºm—Íò˜³l\&~¾q_Wr¶ÕIbª /ݦ\odèG]häåYå#ï˜^Äî…lºà9ÇV6´RÅße§v‡-›C¨½àС3zº£ÿ¡T‘>±üQÅ:XÂë÷ôä¯Ò¬Lþ• ;ÖÄ£rªhø ­wrçU#ð7ÏG<¶€SF ®?á§0Ö3À>®í$Ûj‰r?E²áP‹æÉøºP„µ‚º›M?´ý¢¯Ö4x) vpÖ0ªäwê5æ Üq ŽhàjîÁí7 ¨_².{ ‚Ë[ÈÉK¥7õ+·ò›¼êÑa‡g‰eú;lщÛ8Ñ ˜wÙÊq A“«Æi„½H°é…·uNúwË„í&HxRÐD¥8„P$‘™p[É$ƒ-{eÚѪãzsæÓæ•Ñý,ÁÈb³ÀgÓm/Lâ¦HòxYGjLÖ^KG§Ÿ¢Špå<±ÐïɘNW;¤C”@w°ª! :+#ÏaYDâ èÚó.ŠØù‹ æc —õ1x‹~ÝÑø1&`ò€êdª9w¢¿÷Õ-Ÿìé ²TPÙùÙÙsN ýEõ)Ñ@¼I*KJdßÿqã–Gçæ18ø}Û)@Â%Jlc^øy{>.%e_GÉâ-rÁѯïzQÍ☤Eà5'\+•ò ZΠxH§¡LŒøÃVh¨¼vâ7 Ê£ñ[Y7>E ç±Ä‘\ù|ôœàM¬  €é‹ÁÖÍ"R«=œך÷T‘®AÁ|òvßM¦Î8H¼±´Ö®fC´ÍÏäŽÌ§7åúèŽÚ-0à|ÊèHéþX—„›ðp 2Á±\Ð.“ ®ÿ9™GñúÿBÞJ³IÊxöÓ°·Ày±õÊóµuy+¬÷|Ô(n”‰œØÐ÷îd,ÁˆSÆ¡üÿăºÊ´€Ú±‚zlÉûš¢V¿|‚´QÏOè~¶‰¤·gc$Ñr(5>YqOI.È¥–Ý'3jÓ-B>Àª«LpœÈÈ£Ÿ:»³¬ÐÕ\"©CeëÃ)LÛ®ØùDì×9\•Y3‚؆I"8¯0WÞ5í®\â¿wCÃ%1x»<Žáö07]ô3{½>aõÇW'õŒùÊÜTßZ¸¼­15›©‚°CWo?Úòº$^#] Ól¸¬ï;—¥ªCQò¾ó! ž3\¼à¥!‡çK}\"6A£º’0͇lWÿ>éLbb)1p²®¯:Ę¡=N7gÍ»àù—?]6oÿÔÇÑ¢­ñ´>‚cqYÕôï!ºØ4?üBn>÷4Eݱ-ilDCRÉ`¯®bŽÆö.fàÀi ü '´Êâ8úh<ºËñχlõÑ ~Ñ8û”6».ãÙ¡GçÀ\¿N¾êÐÅy³#Ê(]DÏΑs¬gbODÒA2}Jwà%ª!)¡Î;ºg ZÈ´I=2üŸ~ôÖ½Æ b¬ÆöAÕMÃLøÉhþedË=WXkœˆ1'm^Z™X®†È¹Vÿ²¨Î0ùfS!ÖðÉh`âdcpAÇÈÊ7‹•?ì-Ù]¼ z ^á}QU´Láröê/EÉÖ¹µt ûm_Ž‰¶ Ž‰€°Èzn&Â;§q0ëÅFB@‰íçcÙÄ|‹9o\¦a¨ñ58ˆmÊ’îXID¡ø¸P/†N|TxS>8!à fiÝ•PI3ÜðÁ—^w¹¤Ñ+tDÊàX3ŠäœµÐ¿"ç}­­‹vÃœ÷£sUèv_‘2bó†˜ fÅ`ÃŽtZadý Mç+‰oÇã¹°ÈÆõ}qFƒ|8èëòÔÄÇvõ©h-Ë_zÿ•þŸwgâGþÃ⊿;[‘=ìž<ÊÍÌÎpœÑäà¹(>|ª\â_HÓÊyT+*K7ÏÊÖáOÕOnOù虆#"iºÛÏ康@ÊK›¡/Ï+ÉäœjõkÖ±ñGFR¸É¬E>D¾©ïeyöö*¥[1ñ Ç ¯¡dæd+Š­:Á½®¿Ñœ± ¾fH è; F{¿ô‘3VP¥6D:Týg"ZWͼ!bÉ™ñ•¸I¦êùHŽÚâN³ý½”»°ß#·p½…Ð…–RJ oûÒ÷ÉÐð“‚®„Ñ_ ÍåɹêüB¶2¨£Ãl`jvÛÈ ²‡«tçSÒœ½…yNNRMr X,œYjIv x$Hžª#ŒAŒ¹åOáÆ;ÒB³ô§M3½hb$6xIä÷k“vš´¡D“zrŽ—ô}¾ùÃhr?N`EK¸ª>Jw6ËØI.QªŠj.ñX]0›}¨¨*8\[ŒKŒÍµÿƒGfÒètCÅö¤z à}€¬–ÂÃ,§+ACY \¾“œoz+ÃP¯ÿ–ûoËJM!3•–©ŠÜÖÿ!°Äƒ¹jöa -["§ù6ÚG* @H㬎 +j+³-_„59äõŒ?¼™kÇ]?…ô(RW ÂoÝ¢CRLizÅâSwçs‹CºvS‹îÒçÁ7ëC OS[™ü ´ÉÆtHß[,€AEñI`;Z¦8@_ÃðO~X ÂfØ”^44"-[¨±ö“Õç;ÒZ 2•6 Ù0Å’»Ú–вÂ’PL„ÕEŠ/y^ÕswNžÆÀ÷Þé°Û ˆÇ_¦3}úf.ö”gáLçP’"æ¥î‘Ê @̵£xQµ=1ûbܹ wg^ÒZ{ìPâ: Œ“F“‡(ôýøɾVd×%ARNKOÀ$uÂÊœèÇóº˜{üQxï-òC?FÌJœoš©DÄ„ú}L_pžì€é1yœN>JÇ£K[.è’z‡7i…wŒ‹v¹X*¦³ÍýžÕoŒ¼ÛÒ)îžTÏn]œ¸ªÐeœaçÚûWû×{ûŠDÖ e "(AÑܳ¶J`ÄruaïScóº`8œë4äË ù÷˜PØø – éÈ/É.)øPé»Z]Áv]aóŸï“:ðâ»–Jb[(°á‹Äš-ó/¯™³::£ˆí¬¾DóÚ2³üèé±µÖt¿Ü%ð¿kˆ§K¼ Ëu, 3³eMÅI B‚§J»‰} ¿í~@˜©ÅìÖ° nÈ÷wh †Fs^4õÖz~}»gÌxÙ)dö´¡„¯€®Â&/E * ö ÷8>/–G0ATsþv-ƒß‰igD'åE&A e ]‹êN€¼æ+w]ÀÉ®k÷iáÅ+^Ñg­–Œä'`5±©$öq À]€Y&íj˜»~Ô*kZ  Dž3»2óþ¯Bã#´åY‚ ê¥è/V§,õ4G»þ•B‰]¨~%;*ïˆæ-ýF‡ªx.M7$)ãN-Uk²ÆÍ, ;‚çIæ«ÜÔåh%ß²÷j"Ê`ä¢å„½‚êK,aŠ±Û¾Ž¤1-ž?ÿ¬–?b¢–X ¤ÚcQÐv±>]_Ž=OÚïfxfëé(Z<´„~‹Ùd`Óð¢‡\®mFI°/ÇWŽƒ;J±Ï×Z^uÛ |-~/?|y?“-7ãê@"âý:ÁJ'|¤ÅèûÜ$Þà®Ç㓦51˜”ÝXÿ”ZÀ`ŸÅp¶×’/EÞ—µ=hóÖG¸¢Åf‚ð‡Hp‘Bª#ÌO½\y ]È®Ü8äê[€—[l9H5Ápoi1¬#¿ü½}í"—›'S–jùf‚â8âP׬¾ák øÖ[OI 0–ñz§Ib« »#È«í±¹Í<‡Ã·*û)ÁÂ5ÞàO£YMM$À€9þOè¥ÕÑAÚžû<õçq‹X‡ÃExÝÚA?—(”"Ž¼Ôñ¡ßlÖs²;&µ¤!­6O Äî(¿Œ@&Št#ûË™ 96uàŽùV„:ÕƒÛØ4ï¬àjM•£gDHTÚëàSAFðÛë( þùâþÔyžjîZŽ ­ŒSs(3€e©?"´Ÿø%š2/kâƒPOÞP§"JYŸ‚ZÖ3–9á«¢þÛÏç@” "s»8þ߶玛èJ§Ï¤…5˜T­1µ;Ÿ8¥†s®_/aøs‚”(ÎA¸üNŒ•VX _.=ƒ?õ,Ï;(…™¢!1*Y²U $5/ÔÐîÂ]Èÿ£GÂ2ÛHçøá”hå¤,Ûîyü d{,–S¹kKÓ\Ý6”™þ–¬Šr‚7ê¶yÜEÝf³¨P|†´ýY'#Š¤rBJà7õûUŸÙS[”Ñ[Ûe™Óä@›žýŸ…ŽA[ÏmêÏ¡ïÚ!«Ñ0›®{V k&Ì0ß×/1Ô‘ßXL «™{¬M®2uY©ìÏHçÿG/UÿU] rQO#òH¡áJÝOB—àkMS|abúy|¥Ð»c1mHØâA™æ½|˜Ä¡™&ôJßÜý„ôƒ‘ô{JÅÅÆøÁ¼ÔšIœ(Úïí À¼-%ïw¥ YãñíÍGkÖØŸšN†LölW~¯Ì“!™Ã€Ü.7 Dj0¢‰¸ö¿?‘¤EÞäÜÚž[ãs|›LeÍÄɯ֦¦ù~Àê€ÞyîU:H‚Šƒ¿õÃ×j387Lkv‚šØ t„žêÐÞów./ êºt§:…u„(€xq Iyyé‚ ¡Hz‹õX„¾¢*‘û·zì„Gñçíü}¨õ1F¡-/3Æu £’?#kg©À’FÕ„ú¸_”¡L:þ¿þÚRœ¦»”(|ZW\¿ß.šë¢¡,Í[0gø\&ø)|Œ~ y „mìxÔÑì&(¤%G˜–ÆåÞFë#’–\鮺B!0¯­$ ŽNS’:@Áð–¸@Cc&Æo}´õÒ@^ãµ mŠ(Ï<÷ð9—x–ÿNW çϤ陉n÷̈Õdæ”_´*Ž.úþ!—amLÑï.㥠AÀ¯\½±ë*"j­¯:4ÅïHÄ PX Ðh»‹Pg~îH’ Éi©/z~fÚ‰l;JÜ,[WÒˆ7û=í^|yyÕî%WE Û¸a´|‚­Àµ@yø" i…Éj‰h¾6¶—¶ùé4úÆyTwñå› ¹¥Ûñ1—[)q Æ14¤Mk„ŒùÞãEèÙO…¥^A °Òì9ìXp9ñ•ÈâïÔ$•¶tDnxEdE°áiÝ?ó“»™X–õ¾eÑlQè, QY¤î!ô;…0,n°è¢#•~Âo%cÀÀNÚ¨eF3枪ésEW3K¯EÆ5óÇïoWkìÒK$û¡L¿™¾utþÖF}˜Sš u _r?Wiý;Í”œšÄÖW8}r$h#"èRƧ²¡é¾ pƒ¿àÄÄ·+ùg×_â…³á×Hñ§'ðöL£±>P°f}¼žÝG£õBàyQÖ ö½2=Ó—¶¾-CÛ ¨ÉøƒŒZ4C^j2Ìït‘i,lÔ´$ŸŽY×úî÷â“L0ƒA ï=« •py âiÛÒ9ÇF±oè]ñÉk"ž¬óNBÕEÓV¼Ëä/™&UPÂuP§‰Ê{ÙMi`‡áKàÉÄŽ]‡Dcdù S+Û‡£v(ÞD{lE² –<_PdžKœÐŸÈ êåºk9N•˜Pí—>¬û˜u›%Ð °¼×ýXÛ|²·³=Ô÷£Ê!’À0KÊ›? SGé«P;¹5áàwö.„¨¦ÎŽKÌŽ%°œn|¨1kŸ‰†okÐ'”Ÿr“SÈ…VìÖãd!|7€í©žMù >Ýÿ!ă$çuÛ ª(>¹Qú#©žYN÷IjÁû†¼…ôóDó;o.¿Ó^ŽÒ“íÚþòêè'ó¯4õ¦ôᎺ gØï†Nõ…'’$:tp½ûÌòÌL*Og}“ >7cnù÷³3 —cŽK×ómFøz0Fpf–IºÈ¥Ì ó¯; ÜmSMÿöG“º åk`Ÿ¿jɉ4«†9•Ð0jo­ÝØÐSêÔîQÚó\èä§4ü÷ë^ÎIÿ–S:Ó~|åÍçasF“èóËÎhZSZ¥Ê¤ŒÈÊ[<¥x›ŠÓÄG=RÇgõ¤œ3ºB&*e÷‰]|}9ûô¼˜äL¨n`ëD@ºáÚÔ¬¶²»þ@=ù÷>ßµ9¢%9L+\3ω××?‚ÓÈ䩇…ßÓµÖcM-G°þ§YÜÒ»¡ˆ‹ªÙûˆÐ쎶Ʋ4èv~ƒ»'Ä¿½ŠÙ"G]CÞx#ªÌ¯,š¤3¢»-‚½j;ö:ˆVêgLµKѺD¯)pðú¿B'.FL.äþÇüQeÜ1Š™!·¸Wþ åúuóe¨#+Yâöº[þ9¸Š$«ù`Ã.õÎ̸º$û‡ÿ…'³‰š• E³›6 8ýàìDŠ* Výœ±½ÃìÅ1*£òø}ísâ‚–)ë)¦ü8:·®ÿŠâß%Ó¢ƒå6ŃéòªâÄ m­³â‚<–™‰d¨×’ŽD¡—ÍJ Mtß± ½Ä;N Ì0ä]”QíÐJw郅¨”bAsÐåRy Ó9Äè4’†J7ÊøýŒÃ¯ci3Bí•[s2òµ|˜!gzèBà]¿«üçYžk}ñx¨ÔO)8êïþ=9' OQ8­$€éÖu©hi kÒpâ\‰úößþØÙ„#þTÁÛÜáÙ‡ùQ±Umˆeæ8HGº,-Šò*Í5?ð7 n¨’½C™ebU’C2EÃ)JðèF 1ϳÜTjè°÷—eÌ{ÿ…gØán~¼[tÞpEË7‡ÄÑ’þ§=Å9„}!üT‰]Ö£V’ea¢øB‡l!Ò5ð6 b‹¥’»‡{-ÙÛ1áV¥lÝ.‚aùâá”Ihá.Ä ‰6ŸH ݳú†d!ñ>>Òøßõ ùòêàs¹ÒÊ<ùCœ‹>*[í‹+Ïø‚`²Ìû º6OOPÜ\)‚2T$ ‰™ Í¿ ÷Ÿïm7ìå;äòÀA¿Õ=’¼Í¯œ]—£*QP¦ªÎgâ6Ô@f6ÂhODUpÙƒÖ÷ž)°êѼ\Ñúj/Lмc54Jyk¶Ï=rt–Çwµúï)ó X¹¨‘Î)õ7"­Î,#ýXQx€Ò€Ã$6©ÈàHäD­+‘Zç|ùˆÎFöc{Âù€X¹OPê÷ñ Ò¿„7œÀ‘õI¹Ç$16ÝqÔñe¶Š¦ÍÑ–kšßž/»HØ¢4q£)”½£6p¦ÖbÖõpíE2WHŠ¾‰ö*AS•²h XreÿЄ…jKC‘7u¨Ö¤#ŒÚ— J©æLÒÍ  µ<ª@ âƒÎÿŠ›ôîü]½˜”æ—Ój¹DÜMå™7¡byâCbQ¥üÐr¯ %GQÁÓu éÍœÂ9ì„‚ç|h´7¹=!uoð­x~éñÈŠ£%B!Ӓɹǽ½#oJÑ´gÔ “žPcp}|²Ò­VóêdYÓxh#&?g±¨ ™Úãß𩦢+xð9‹â Öö¨ó= ô/-‘øZþBü¿‚‘¡½lGK8˜¯dF¸¢ÑüËöÿ ?›ï€¿=pj*T˜ëñŒ­¤Ö( €08u EoxG“w¯Hm¶„ ë©ÃžI1VïOá¼ éRýÞ]ñrÏIv +“a8õøg5 "ã†å ¼P¯7»MìÇ‘·Qî3c˨Ԡ2?±-¿pîˆ\ÛºÅ;åTIÌ×è~bÑ+Ÿ5MQœHñ"ó÷#“‘œu|Qù¢·Eý)«ü[ô[FÔüE† æuÞÓ€ÂëˆÀæ˜!>#žZ¤y¥$è¦çœϸP&’¥¶ŸIô;ù½Ý­5(ÌT4Odñ°ß£úñ€_e &)È5 ¸ý©ZŠQÿ‘ yÀÛÐÍëUu#¶<ÓÌ^ˆ :—V\âÓâ è2ue·‘g9# XŒãàE ‰±jê ôîÔ‘8SUÐPÈhËõ?” ©d´j:qQ”î!3îáeúƒ j_ˆÁ¼ yO qX¶?"ýH#Avÿ¬•D%¶CÏêíyÕ?µMTKMRøÆÍÎR{HÚoSË=ÞQQŒhÏ‚Eõ3º:¡_d¦UÿúAÍÜúÇÊwÙp[w‡À­çi”›u[>Òšr̼³tD³J SLoëb2>#F£ãšôóù7ZØúöŒ/a|Å'YÜð®Qþ`Ê;CÅŠ%ÇeiéÁ)<ÊÛXâ$‘1Ç íRhÜŠñ°[p99ñí) úÃÇ’ óC ¬æ£öͱ«<¦hÈ»äð‰pAã*•4õù¬ã¾aó‘üÃá‚: ƒÌO€“[«2hÿ8¶Õâåó`šÙÌeÅ3MDÕÁNÈ«4_)øQh¢VÿŸ¥]›M" ü]AŠA™ÈžS¢MYÑÀh~ø;òp¿sœ&•Éš³ÙI‡Få²lrbôfž:2Ž} Œ+ñù!SϚƂ17|óÎTË-äòV=¹¬…tâùóv¶Ÿ²“Ò ±äë²Ä~0›DÚ 5‡ñO2 rt„ÌÚKž÷$ÆôGd:ÜÖfMÔ¶NŽTË_ÛÍÀ=D=éJðèB_ô€šÕ©ÕÈîõ,¹ÐÐ@i „j›®4(k—rò} ·éò4¿ipRãÙµZ˜ý=ÑÓ’ˠ߬”Z¼ ²kž¼RàÅž<ž]QoFŒØèçÙüJ Æf$/;‰¯“YÆGDÙ$ü÷æ°Ø¥ÊÝÉ#.O è¯×,>vGpvlþ¨nŸdIñI[ó8ðìË\°S¤‚‚un7‡v>[±•ˆS¥•1 úPCMç]!Ü®h:ú¥U¿—këqJÇé Õê„1ì_F=±Óɸk‹íM$^—ª×˜˜ð“¼DñÒç†NCoí5šµAG¸²QŠ®8è/4ËîñË>¥„\Œ¡7ö¡¦c¹wªï—Ì„`e`ã›v›9ˆœ C‰¼Ã4q– {ЪDÒœ>f¦CñDM0›¤s}î³í¡®ôäÂR±`*ÙxÒïèѳC©rTt"raÝÙJê»á‚h¥,Ä“, iíöòYç鉌ñ…0¹AÜûf:öÁã¤sØKÈ~fûÓiLPÝá–…o·!Ò <Ðà/Ò8E¨¬Ã‹s7£„˹³Ê UŒ܇ýp«ëwq»0;j[NÙ•¦¦âJ›ÉÂíBð·û6Ås‡!Áãå…ºu£i^ÚÃT$D–šŽ­0¤pÐ … ^nOý+(»€Žþœ _gˆi¢ œ¢ŸðDw*jH±R-§˜D·¦®¨[~ýÃÏÖÁ,Œ6'èHH"&Ôâ¡2Ûo¦åPlÝ ‹Îs!qÓùŒ-€Âð„Ø]P_¢-\f `ôB«S‰˜g¡Í¤}Oäõµ·×5¨Ý¼þþ§'m¹ß1ó(`½“êIæs÷'¤šM'Gâ4ÕS¯Žûë´PbÏÑ©¾ºâ’¶·¹?[¸è •ùÝJ9m¤€ÎÖ‰v¯Ñ¹GÉsþ—#,]Ñ Aª¸Wj%?+mÝ°S¥{A÷5Ž°7æ:‘T8ÔýOù꧷ E}.hÅ—'{±š(˜ £½ô‘”9–þÅù—ä³_,Òœ4ÙÌÓ6Ç&sæD¸` NNO‹CñÜ•Ôr®Ü“F0õË\/¶¬Ð¥` Y¦ŠJ”çBÆＪ ;{ÖQå1>ª¶Ø¥É`!€À\ðÉD¾v!âÌï³^Ãþ¨¡A:ã3+Bœ5êÍÓ"§„ Keƒ/Ã*g²a®5õI{§ ·Ø ƒM(¹öc÷o­Ï|ýfƒªËÊ‹w“W¼D> m—7õö]W’Eí–\6w_g{š*/³´ÔnðG»G‡?c_>¥Å8¼ j ¼/£¸8°*Ž^a׋Krôp,<³g¾+üߧ£Ké5 á—NÁ§â⎉.¿³(Ÿþ0c8×ãz’-‰ßYëöü”.›OjjÅüÀÀüà$½p‚6úTSÁXVÓm„O¾¡k|Š·¢òvaŒ'ÀãðkP`› 8\îµgò":Ó§¹âû=€¥c£:#v”u ÑBRñ{kQGÄ›hÒHVqÞÙB*;Äú;zæÅ7§c1Òn[@•sg®0x’DA¯Oæ#±"ø¿Uˆ¯®z>wyÚÌ·»–\þ˜Ÿ‹ÅR—0ðæø½€ŸŽ†S½EÌü¨Jt=¸^pç“û“®cŽ~;¦!¹`”³Ÿl ØnÖèyý"²amGÞ$ «xÝžxÁ?ÚV^ÇçtÜ …öy+2\t^4ŸL'ÀЃ@§þÇŠ+­pÐv'¡½!›²º’íˆ<’ÖXxÌ5õö€í gÇ© ›’Øy&êYqútäĹ1;#QWWA ²ÿâwÜû_+½|Ê4F[ÆiGw–4ËÔCnƒ×ãÝ؉S(R"ŽÕ†oª~K¡MÝÉ$8ˆÅ¦ÖPà?²gw8X¸·YRPÍKžéÌÌk ýH>U±‘Ù¢i ™ÛKlaÙãã;ÿV¡o>g|ø¿œb?Éõ±®ÚHûÿ¥›—[Ùþ³x”³ü®X¯Øq;u¥Ù†ZrÜÁâ”a õz•9ÑëÅ ˆéš ‰=ù#@ÇP.n×1)Wµ7’Q]3ÙªwòüRÄÛæZãöøÝñ[ãÈp‡à/;iíä û}‰ö|©ëÅ›2çO§Îü§ñ!„0D Ä¿^.I!Étkë ÚÝ\è ¬)A÷·>©ÁšZâe’š~öŒ·tu¸8O °ÏÑÛã‡ÌÕÅJ5jžïBJZ&ã M§ëQW!k¢-ô„ }æø‰gYXôhVôq¾ïõå¤Õ¦Ld1¶…é`ý 0Î}1Ó%<Æÿ½„7=O$|ò. ¦åÈõQ²*d°¶^9edM¦“i_ã©N¸ëå §+"¶ ‰ùœ@‚Bnk KØu½ëížÃÎŽÿPÜl=nH]†â‹— a°KŒñ«œ£xg¿µºÚV›Àl>‚Øt”Û¸U’ƒý ò/oaÀÊÉ´ÎQbÀÕ—â©ïí†NndžÅ¿oµp Ña«ãäµEMÃf Òc€XEëÔÍKËç¼¾Võ®6)~¼ösyÓ±êvO£rL‡¬¿ólÝZªv¼`©ÚøF/Q mÚ úW,.Òä]·äÎÆÊ"Õ:ëÉ4úË[Á&SŒ%AIéJÈk†¡À­7¸.!ÝGÌ·õú‘ÞhEÔ5/3K$†,­Ö¾€q¢ßv QW<Õª ¸Ñ5é¬2#÷ÌÚeIýu™Š®Ó@Õ‹Ë-¥þ0½SåEºÄÑV–l¯cd> -vF9©wRo­2Öx0 WrüØ/ùg#Õ¬X¦æ¢?«7<ß2‡ÿ$üHYQ¤¬üLØ4…ö7ìv§g`“ý”{Ñ}þÕ“Ê_ºî„@îéTeŒ]p¤/út‚¢ MPã)¡#¼. œéì´¾žÆTi Žl%!Ï©òqwÎמ³œ#ƒ$.§$ÒÕÉ5ÃéUHr¶‚Möš"Ûžó;ùPL‰Ó9•–[?äg „èÛK¬‹¬®GÇž)óäj¨Z}?¶4Óì[JªOÐÕ™ÒëŸ_ îû¥iT¶¤pè\ ²Ñ—;‰)ÇáÎÁµ­Z¥cŠŠC@ƺ ÖÊŒÂ)4º¯Ö2¿ÇSæÙ”ÆâõgÖy‡v?.³ö#ÞŦ%„öF2Ý‹Á Æâ4£·Bæ1 §û`Ò L!ê–À‚§Ð·z"€ö6Ï&NÙæf«t?)Èê߃à‘VN)Tš|8ÞûÛdôYrF¨é~TÞ%øFµÇx'™•…AùŒ´7<dxµ÷äâ7L*«€•k¼!Km hŠŒ¯ÿU:e¦›Ã£o7c÷ÿ~¤ÍNžŒÓUÜGx=.ÅÁ¯L¢Ìp|+U Õ4MX¦ Ðm¤Ô–¬RŠ? V÷á(LÃS|‡ùÛ‘2›€Ä1[ªFµ¶ŸÁ´Ýi™F‰k¦G0 p›ÒÓ“f¾ç7šuéÚ£ *œ¡/VÒ‘™‡›‘p^i´^n‚x æ_}2)NîÁgxǃ’u„•ï[rý8B ·úƒ¥ÐDº¨¥m¹*ñÚÑÜíô&yTÍ…ØA¦;Z]öÛûõˆE€1¶«õˆRóºüƈI£F8@Ãe¯@cÑQb·êpïÂVé&Á¸(U°ßwõöfƒ;ÐnWÓý·ªÒôËB†[ @O‹¡ ç„‘\·ìr cÔQ,£‰òjsÈë×( àä‘Ä ¾(„ö'JST6 ©€TüÀÁKÖ,»d“Ñ„ˆ t^H:É p°È‘f•ÿ&‰XoK)_à Uc_‹îîõWžõöó üRé|2ep¤Í‰¿ÙÉ7FžBÜrè=Ó=jgÁ^'mÅ–ˆh:¾®ihù&‚3P–ÔÂkŸ3´¨Òqzȇè–Úp¸2ä#lïsF˜3ú¨Áñ3½ Œþ‚V—/ª…Iå¸_žÂÉ5d¤áIl˜ÝÍeÞljPož9›3X1ƒ³QÃfÚGOøBÆîò£+:h#K“Ê@1uù®f‘õÒÔñmÄbˆ$ ÕvCL&ËÖ8ÿ‡žlÂò-øhî¡Ùph (ÁÄ+ gqíϬµ1L{"2ËÅ€²­†i$k#]W^Þ9UÞ‹H¹Q7¯hÝ,Ugë7l“¤9󭣄eOÜûß´éz‹ß0€æzPÏrðëg’È9…?Åô“o´Áåaæ‡#´*új¿êHÀÕ)…p¯Öý{¨J4×±î š¸´± N€…Õ¯]ˆ/µŽ-œS1 N³E:q­Û”ÊÖŽ¾.‡7%è0Zå?×]=‰±Íì°È§¥ÔDÇ(ÛCHPmP¹¸è]·Å9ê¸jOš—°ð´—?NS,3l`qƒî4QMäd·œQüþã_ˆN4¶L(UÇ®\Y*Vx×s_<_Ö˜¥Ç&Å¢¤½±Uá EèùøݧÞV³½Š)»ECŒíÄ‹x_Óâc>á ,&y´­ûÜkW'~¿üÚÍû$ot{>챋9út:«í Ÿ³ÑF1Q”‚×v×.@6 µ¾½=* ¬CïÿI4±£ìb«SQàA odÈà¬Ì¹“.º‹¤öï6VD„ǯ—R$X³£ä¶÷ŸÎÆ ïãïÿ8]ÃY­ku¶õJzU…ào#}NZ»ìÓ¸~;¬§&7—°šcQÖ˜Ùùú=ò¿df:•îµÃdÞÞbŸ5ý¼TŽû©jÒÎHú–°Ã—mœ ¦ÒW~€p[ÿ¿•Ã”ÇÃÙÈd'K©<@'7éA‘Mœ· aCõpV#z‹zÞ¶XYé²ÝF‘† {¡ú…ýœ™/·ç×BlÄÇ úòV¯ê’äå›`Ãf¹ó® Í‹‰IàµlìRÎ’“B“èåÿ1OÒ”‘zœg§„8nâóNáÛV½È’.V<wí"¾¯Hiùö=>öiÇñqºìu¸ß3Ô|é×æ’çß4©ø:Ò3‹ÁüUÞqäø=Ž½D?…ŠáwdH=¹îøin8½ J¢Š-Õá†eÌy5cB‰Ò°œÐÌÚÚÿ7áý˜•Fôvç{A2SõABÉ"JɆߤŽçî¬à`š~zH$ý‰©þ=ÕäO ‡ àÕD’÷®.$>3 UŠb2B–&ÆR,¨9ÛjAîϬ´$ï32·LrÅEõ „f¥ QNÞÎfCdÇ%’‹Ÿw^@J_3"°«Ò–TÓæ‚Ûï1ÑóYœáczuXæÞy"TµÎbv('þ˜°ÙÝê~÷jÈPrR%F¨ä½èÙp^±…¹«á­ Z~›ã ²-ŸaÜ-ÕQ{œ0Ê[?Éë¦Ñ®ñ¤Kŵ¶Øè*_Êg[œlKPà‚§Zp¨»Í {]°=ús5©æó¦ë„òƒd•¸–°=ÓO6È¥Óf¤ë83ØLB6ÒL«Jµ¹œdŠê“Å\#Üû›K 9k˜0m Ýåö ,dx}ãäwf ––®Öë¶B®v¾sØ@ßÌ+)ñ®d–íÝc,uÓ%À¦E¹RdJ”ڸ˾­š*ÅTåÃ4Çä{¯)€pNÁ*^-ùâC󬫆úQ¯U«lRPéðâç,F d/QYK‰Þ²žnû­~ºšÂŠ…Ä3 È;†K¿E‘ù%¬x dñM‚]ÙÞâ^2­²~>U oS‰;‰¡[EZ™QE—6—Ø÷9uÁåWPò¤â~Æýõ•”/7;r¨tż’UŒ»¾¥`>j”ÔñyÀä7kJù¤v°ÿ¢)Læ“6íœi³C‰&9×f4DU—Ï;ô¢Bò¬»™­ Žä 9 ºªB‡¨B§OîóÔ÷|‰?Hüï:·$ÚxÝM¸¯Ó§‹!ùÊ;ä(™ÅYÍ*±ÅSÑ'ôG%A(ã ç0¸ÜEàÉ\³†¢åGhæàžÍ‰%§€,Ôê 88Ž7Ù¯ÙèBF§üö‹­ôvÁd¯.VÆDYKþä K·¹Ë£vhÄ;#ö0©K·ùÚ+Ã}årJC–J(²È1Úßöø ÿ²Ž–k¤ÜßK&eò«±€©„w˜è†«H¼.c%AX\›ù°xï§V§Éö?è$'V€™\Z{Y­2Uì¨:mí3× ëæZR¾ã[^¯óF‚€F€»‡‘$´…L2êL-7¿Œ¢¹?B6ØN—-6LïU4ȉ1­/wT¿¶%Û[4¡JùÆRógÞ-ÖeÄDû›ÐZ!§˜ ä8[kï$g/о`Ù7Pnœ^Vt©ŠIÙšå@…£qH›®•9Æ÷ ¶äÆx˜’ð¨Ì&²PFÂÌê ·Bþ¹*¹(™x{òxÉ‘8?·Dýf(ûʧæþ!B¼¹¸¢@7ƒâäƉ‰pÃÎpö9x?S–(¾µ@ë¿á‰õðûóÆ0}²#o=ÿTÛŠŸ¦¨H¥…õ¥ªL-}ŒL嵯X7ádRÖ‡!᪂ØÖb Cû·¿}!·âçÁMVŠ”+ëÚ“ÁZ„E¯ÖÆc²)2úC¢8k/Òþ5¥›CÚ×w’½?.Ú_¹Q„“Únü¨„Þj»€ƒÜËü±Ík{вæ“(xUD„ÖÌ ½!©çù" I ¡ŒÂâ-uG£÷º0óBatûvìÛâÞê¾8õ^CÅ ƒ-¶ækÙC`ì!]9ÇB(QJÿ²CxÍýÀðô(ïêZÅ Ë—²Ãu¨F Q~Üè6A\cÏ®zC?j¢£??›„Ýo%¬‰*H«P“ÏHí‚Á•'ØGHï¸pvÊÿlÿ8ùBÍ¿~ÒIým;ôÆý5·0ªÛ(CªÐêê~›A@ÂSßCì.{ 6!‚©dÕD9êAZ}âPë!J¾øCø£(o´®+È¡Ý×{dÎÌHU+Vy›mŠ\/}ñªë+Møm!ø‚až»üÒÙvÂõ=úôã«  †5y8Š•¦ˆÞ‡V}žýJJ`¹ {£ìQ‘™#vhf5ŽyA\¨ìl%óo„/~qn;A˜è ùŒšÐ;kÌ;«¬…ɶã³ùe¼À¦RƒKʹJU_ȱ—ÞP¬®Ìæ?3ºAš^owTóQ³˜Ë–úrpHŽ8ì¨ÞãuçŒuT¹Ì¨~sÞà¼Uë×d¶YùYj°Û¡žjéçå¯~®œc(É’š“÷%1n‰Ó  G“ýia¿°æeÙôÙÅ~äDõ¼ò7;+f­’±%,ƒ\”XRÝcD#ük3  ,¤öqP^¢ãC4|ï«­ˆìi%è¼½)=Š}Ћ˜£GÒº¯ð]¥È¬pêp Å8$8<_£ø'G šñúÎfÔçðÓ ¹dŽ)Ø)óVØ$à [^=åhöÕÕÏ‘œÔÇIýñIàÿþÀ ¢–t³@9ÁÖøAŽ·Ë>WFr ¼}YïJ–—Š€8‹d,dQR63hû’ð76tüÏ0ì| Ž¯ù³8ýBñS:žgpöy{OèŒþ„ê% ÃÍN¸[¼ˆRSžác–à½è¡g>äi˜—lpâ)´5”çµç2®vç¼2²¦Ý3@ÀO©J2ÜtY†˜mœtÜÍ…÷ˆ…D‹Ÿ×Aáé-þ.çÀå{Oˆ¸ ’c­kuÙ˜y³®©AA!É!-š»ˆ­™±+”Y G^°ç›#}sßa{·ï˜Œ¸ØHºƒ7€Ø1ÙÏ€éj•°IrCë®®Õó˜‚m­6§3~ÞƒÑÖ´^ƒ&2ü}í }%ÐGÜ %VK“þB 5¼Vê?ÁaÃŇÇô´›å¹:0 çÀy šæm›e,Ôí¦”¡[uÃÆ¥')A‡… txh~ÈÑ<÷ëÍ*ð—k¤Úa^eî\7JÉŽ2,W:ˆœ–y¹‹1:ÑzÝóÙ"7͉:JîxH–Ëå»}ìF¿[úÇ Ï„‹ysvQýÙÂ$~Ÿò«ÌRPjŸ¬¹ ûy=ç-‚±ùƒV¤\nS,O‘Y|"xÈ—¬Í«ÿìQ$¿ÿ 50ÖúöˆèáÑ`HD¹fA$ZïñÔ[WÄa÷±hÀª½O ^¨j3)æÒNV!…vªîöó\a3&—ã¦üÏRâNŠÎ(׈uÁåÈ[¥Õï„ ökFG»çé|÷wñ= JÆ­gè ÁÎÄoÿ· ÖŸ]q >Mƒš½ Y¹'?‘À†iõd5¯ N+s¬.„±)exõÄ{Š·°A°…Ÿç^»*sˆ{ÊNl7ƒÁæÿ˜”COÝy"ò•EQ±˜íÉ\v†]™(Ög|^W…Òed…ð¤?Õ_.`)Jˆ4hDç¥Á+@üÒBp«³Þñ¼ƒ§ÌDL•eœ)´_¹MY΂•,I÷d $à:܉7@¶Ê$Ý»ë4åǃ+m¿:–£l\ñôòo}µfš}6Ö>¾/Zªh¢¥”;1-ëJTÞ…òpºÄÚ}–¢¡2ü•çV«f_ßÏÁ#MöП³-YÌ€Û˜BQË‘ˆÇ¿‹`úw®‰HÁÉA‰ŠTô ÖÖÇQNŽ‹>w4}îëïs“ ˆhï§#IŽ'7R Y xRÑêyѦH¸¶·£âóJCP 7Ez€B©Ç7È v4]ü†)- l8¾èM ?!¥Ü3²z­œ¶Œ³:ä$ÓqZX#¤3LR«‹¹æÂC4ä„hÁ¬í%µhL"°w'Ýí^]¾müde’AŽtP#¯-ìÊZa.Ñí?@ i¨¶6Õ€–Y{¤ºðGßèP¤EJÁ<¶Ž¼bK´¦¯ãóð›RÔ=ƒ¼€ÛÜØþ¼¼­ÍÜÚqò”ßêÛwgˆ²ÿ†-ˤq†HTæ3lÆ60<$YµìÃç3#Þ5DZ{<X“Ô½Ø82+\[aѸ¡#1RÇbó ׺ªæë(#^zX©#¿b5›ªrRŽ©éY@ц¡…bõƒà ®a6ÊÛeè_”æ–,ŸfÁÆIµÃh‚kD†™p-ȸ¢ýÈÔpD6ÌVMÚ—‚ÌÉÕžwM-Þ|Ó®jG¨t°NîGCÇ¢ðo5á‡-‹Qàù8Y­NÎÈøª@ÝìKͲ•Ïf‹ð*f´ŒÁMh… +FŽhŒ^¤Å&ÐY†~#W, 6„Û¥ÞÇ=&ª–¤Ÿ 4âÓcë9ï›’Úü¼<æãÉ"MòÏè"Dº=ç©@0vÈPÏÏŪzJòq:C• ðÓt¡_U)ää²Ζ·Ù:õ{ój„Ù†0ÉxÄÒ3¬ âŒCF sѯIú¬À¹jn~šóµdÔ×^u5¸‰ ôZW ×iÿ ÚÒBqr¾_{¸Åð°ˆ Öa̘Ò×Ó—È|Vt™ûS}…žnSp³£Òd½³V{4Ú-Xíoi™'?´ßðsã“ñ¹7é ?2pEø )9#ú,ÃS™W9Ö%7O€E¡]n#ÿTd±\ NœTá€ñ2¢ö„)Ÿ0‰7¹âñ‚umhcÛ ”ðûlÛþ^ÔTËô.¾ï"¸ †(ðšü*‡ÃTÁêÆ«Ÿý¢¦ùeT¨t/JH:ßí//S;¤Œg¬øKfp+žœxÞ_I;‡×ê㡤záÃèÛG¯º³T0ð²ÒÉOu À]ÑSR+DÙUVè—&Œ>ûEåh:ÂdÆŸFwâƒUW¥žTX÷A‚¡þþßÍËâ+-‘]iyb´µ²Œ¹Ì5çY«l£(ol=ÑZ:òu¹Uf½q PºC=œî°î¿|i±²ƒärà˜¹Á-òHlÂËÏó(îfÊkIø‹‚–îZï †ö¢q0êÇòh‹Bt5ÐZ3ˆtUc︲¸bx2Ÿén4å×.´²˜Z¾ ±¢tFxrgwÿ®³¾=ñ}ÔÑÂ^/t »Õ YCÓĬ§$áFL’åk¤"yáÛò 48B°dä/&=yÐp–ð‡}yS„ö$í%gø_KŸöW¹þæXBÞØцè\!Ñ<¹(ä[/IÅ *$¨ËÔ ºŠø~r|o“*ž1e0ʦ‚µÎ+1ç$4O{­©&YîháÈ[ƒ6VK É­È>÷ÜòÁi{ŒWÿžÏ9&æZñ¨µC3EÆ:Ê ïdæüRÑÂX庈üž$ùþ×Ðt¡¶n~³·;\ÜKúÕèÁ¨ÊZè}g«v`vm¦ÇÙ&˨÷ÞðúôîiéÇw2 üd—Y4«--™x*÷b¸ý±~²"@)b¼‘£¹®šªCGÌîÔÍÞIÀ‘[_Ô £äR¾¿.Œÿ~Vþ/V¤•ê;Ó?“îlù’ä6ï&[¤Âü ~ãG¯ž*ªŒ'”„ªj»|pVŠî]]îQo%"ZIóØ·Ð>oø´˜‚ÿ¶O+žE¯>P;XJE6•œBÅ쯿KéÉv_éèßhtbÅMæ- . UÿõÆ-ƒÀõhi§;½µ.Ç´8‰ÿžIÝ ÛÂôÒNO G\kªÙTNÇödLb'ƒ·ÚWG’÷ù< â<¶à]4Ö%Ò¥Efs0Ãé©/*„ Ån‹ñY_q?ù²UhñAÇÚ¡S‰ sÙF+¼éî£ç®ÙA㎠d-ÂrØ¿”HŸ/¶¯)é*¤Ö4ä¹ò±çå$g©HbUUá±Éw)<êcFhˆÝ.´sXÉ߆ï…ãõMÊJ!T´ùŽ‚¿ÔÒ"5ÒñÛÓû¿{ñ#Ʋú»2ŠS”ûIð·m¸Î@ÙåZÇ'ÓÃ{\ÍçÅO¢ß~Œ*q>ÿ:”RÕb2X@ô&w˜ÐÐ"}¦«µ‚ñÅù€}aö¼Õ(îêäKЀãÿ½4‹T0{Э°«_¼é†Ë,¨m°2Ë?¢¼=êðÑ­+êŠt‚EfJbpKa c°Z$lv¬íÔŒ9He  Ã{4Ç'¦cÍæ–™Áñ^|¿¤iÀ¦rýÆîJ`1ÍD•º3ó Úz¨÷м,Ì¥ø„Ùd]^Iãˆ=.SØ:à[ ôƒð0¹Åh0ÐC‰Ž²èt&Ã#CiG½Ø7/{_kÖ²iØׂ´dŽÄ%yYË õ¾HQ‘ÃÇÀjÌcJ‚äkt!ÈŸóu÷xµÅϸ9Üi6)WGLœ÷¹§Vƒ/ô;ËÀ'Ò©„vC Ÿÿʨ–ª**ÿ0^-»=é ¼“œ»]*½Ž.nF)—_(ž×3•¿Ý¿ÓQw =4käiÉÓ…hôþ<‚·3‡x}SÙ¨H·#ÌöjÛY¥_Õ/çï5L;§«Ž<œþ‘·"&l€â å¶ë«¾ç(êG‰gÈÕƒ)¯a™¬³¯²—°Ì¹‹ù8ú.QŒôa‰û¿†Ž@)„TÕÙ_±¨è+ÎÈz'ó ð¸Àz_z kxš?dxÊ~Ó¯Ý%euW¯\åZ ò=Y@ädñ÷œYh­ÿ5Õ§ZC¡ÀNõ\½¸‹ÀÍ:F¢ÊGåF­C«Ò Óµï–Ö,þÌ7˜Óc²£~TrvÁ/*È[cˈòµdºÆë%¾ïjN6>%KʃÃîDV+w ‚c?ùn ¥ªŸ?Ö×–60¿¢‹-]©ç8×zH€_:—Vµ^N¡ßpBY·R"9bÓcÚt!z¬jY6 0TϹ‘^³»ÙÇà÷™F}Иá…Ø üúš×™WŸG¡KØ[ú熃օÐz ³×Ú}zÚ8{Ö‘ZD?ØpÌ_°Ý×/e®(³ç»øÂ)ëûEÐâËßGbiu¨ú¦ÜÑÜò$ŸŠtíÑÓˆ²•îBø[ä¡[Î>Bÿpg £ƒSKF{Þ( šÇPaìSSŒÅD>—õBq5åZb„hPf»È¬Ì¾/C’AÙÍøC5Ðg©g1·¼ô÷îùýxg–v9ßä ¥ŸÔÓV{¯èà>óz{I×{"ÑÂd"·bª'êH¶'j±:yF:}p”y)Y:hAe]Ë`Ž'1m~;Š G¸´|:˺/75Å 9±1†9ý±~ø‚˜9Ì]î]¡#vöÛd”tûar´yûÄå·ãª‰¢C-–zü/LCŒ©ñѤ8ãJ¿®qÄ0…BäÍ¥øe.Ê=JùÅNXj³Ï;«m€’Ým…¢Ó ç&Q°"G“LéÛn¾ÕQ4:%n³'Ël¾ùRfÁ?jI[WçïÛ¼M˜Y\L ±†š>ª?Î8 "«ƒËN'ÔÆz²;ØÜÒ"W©Gk㩠͉û9èAéßÖA]Èz%bu—8­”¿pïªVz²óD0!á_g•:²_=̵9£…÷=‘Ê ÷ ×\K€Á8 @ ³×2dy™ëZ¤M²º ja;ðê|§eeT¹7s¢Œ[½}·ÜÛSgòÜ›/hÙ2g™ûð0Q*ô"[ÀÖö0óÂJ}.=‚T«¬·À#°ÅàT0”x«šï\¸‰œJœ`¸¹^¤½zùû”ϱ Êž¹þ¿ ð$¤.÷[~DX¦X p_‰Ì0Òý(½Y'5ßSïÿÝñd6êHv¦T¤ a@y¯ôQ;*f!Uûà)@4~éùÂÓsÒ_3ëH75=—n°T¤ú ŧVÒŸòx-¿DmOçéå,Ïæ%‹˜–)HV¯ïh¸N•½ÂùîÍôN<¾_R_ZWÛCrB¤¼ß¶ÇŸÌj[ôƒDë/á# è±|åÒ¯¨‰°}#;à>Î~å–ŽKÈ dÔì#håÌR™ÏþÛý±!žž¾‚®©L–ŸÅ¦Ñ"=’{1 ˜ü=ÇéI?ŒL‡ =oH¼ÅMJÔ¤|T›_oÜú¯c»m)vÄ Ä:5_ ýòÑúÂÁ(‹;—×f!‘Κoô ûEÀ1iDPA´èwœbÆÖ‘É.d«wó}ôê5J¨ÂõFÉá…J;ñ#4£M"‘€ü2ØØ2ƪe!Yó˜Âh4ä–Gsx…ÄȽþAÌ]?þTìh©í¡»Ü8|=^•ú ÞâLv/WUœµŽ¨üýøxÄ_4ÆÈþŽà ¦Ž×耴^Fµ$î21×#Ô,ßåï}«hó§¶4›Ê6/ì6‡*¦/5´¢‡ÁµÍ8C뙃Î|UÌ®8tX¾ˆÈÏèT K$²Ÿ-\= ,?¤…Ä´‰²7?g€7€³yt ®‚§sL×uÏeéõß7óiHi@‰Ô$gžNq»ÑŠ €y³f¬º¾˜[=M Ñð8÷¿}¨üÐ*‚É@†`ïžÄòn~ý¢+Ôôï*åøûƒh.¥KW#:`W;a›h6¹mëÒÁn~Vt ®M ±ã–ÓNZlV^Ý~õG“"ÆÒ´—¤ë²¸ «í€Œ2ÖQG2t“¦z.M…}L[OÞàQEkžÓyPÝ’,sç”ØXÖLFÍ1 MnQKßék¨‚< öÆë¦PF÷cˆW®KÝu$vPÇì¯Ø‚˜Q”úçuȲ8ª€U«Á†n 6™v¢Œ¨ÝªÏ$Á‹(¼®\D RÆ‘i E)0K =ñŸ °‹\\pjLsH®8CÓq>`ŸËk'Ó(§¾>ö3‘ô2L \MgUv¶Sþj“šâÒöÌê¤À©¨‘®EÞè ‡('KzìT½úV:\{þãÂæ‰3–5ü«-¾€™v³Äáõš:¨o„uÃ?ã|#:×6¦Ww7åÓ¨"ºÁ„ªNDE7ÓNTQýwiy²K©pªaŒk§¨4–çB á‘ê'Ërˆwâl„ïÐÛ“´ÈãLàs‘°µI]ÿËŽÂ5Ö~G¤ŒZ;À³ïœµt‡ì—}@±òÔÍU~pê<VYŒSÛjö3±iößÁžm/›TììÊ…×ý7OF ó%ŸSžª’¶Óœ7ÂÏ®Éá +êx|­ê"©PF'ç·_„õÈÓ" GÜËm9Aü!ì²,BõáëÆt•Wpæ‹…«• ]Ê…nñÜ–‚)ßwÂtMÑT¾J g@TÙvJæ 2ỈÛfCÝvt¥ý<¿Å„8Ø™8ÏÆ^•\ÇØb3b˜ê‰,™ä·L-¯…mÌ A½ñMEÑ÷žêÔÏþÚ#ÎoýSÆKW*Á‹iüßžínö2-1Ä%[8ÜL^GøÎy»7ôkä­Ó;Õþ; È{3ý¨"nQAègÝAäòêÈ´–l›Q}—1afŒ‡¥ZúÕþD\SŽ3á-3•ƒ{šîNóÓTGuîl[d>£×7Û2qÉ{ËF>:Ï`\¿/¯wBâˆÛ½ ¾…´ºà Û¾5ÂtÉôG"£1°^™˜Ï¯ÚHw|õâ?÷Pu ǦÙ4­Šµº­9#ís1Ò‚Õ?ÕÊ}|ˆ³wÚ¼p:v› ôÝŽÛJBUzOÈ~ÿ®/u²^£K=úóÈ»sÍqkAX&´*ì{ß}H$œÑkŠÆùÌiv&rLÔabÕlË–ÉQïcfžxrÚXE”}Ž#Jž6Ò&GœË\##o8¡˜7ùÖµ…U7a/Ñ%¶Ž6äk/Nå;ÕÕê&KqÀšÕÁ/õÍlÈ^R)¼&#ÚéZ¯$ðO¤@¿LD’yMõ$}ªŽ­&\¬V þ]\¾©V¿,G:Q[d«8¨Ã¬ {‡ U ]]Ђ—ïhé’ ÁZŒ toxJZWj¬ GO8ê½Ê =“wˆÆ3rÅ›þRi—×½Ëõ⨘CÊ Š#´»:Øóª*XUsCÅ¢v±³kÀ™Ît}-uÉ°Òa§¿¯G?²;fQ Æõ#g^Ýô®Z5!*ð¢ÒP‰9|äƒßY¸h÷«ŽdP¿tåvzVºY;…k€æu‚X¶7¶¨”Ã\TéµwЩɋ,q|ÿÔ¾BðÂL^֔宄& PÄc;ö?,ïš>%Ñ\Ç £Ø0A}äcô_˜Y$zH®äkc…¼1Ñ3n ƒ]ɧ€þ$o3 ,FxNÚÁš’Ägzd¬+v†}A8I ã-ŠøO£WŠºË]áwlV¶Àºsò8¦µxñÖÌ’?ªnh/Ëu92 ÷ŠÌßœîèÜ~:tk4±m^'J:ìÅ8R(ã\°&ÙÅ-Åú¾kÄA¶&ÌXPâyyßÉȽ¯³£Ê}âƒÇYý.æ ƒ‘ß\[@C”9ᨯƫüìÊZ´ÒDF % +½kÛX?=0ÿÔ tºB2áÉͧsNS‡“ÓBóJEÜûÕ—ÿQ1ôhÍN × yiRfà-6ØŽE] ñÙ¿é ešÙ †òsó®…ò–hŠ±ºŽíŒð~ÝA7"7°T3·åˆãW_õÐõ@úZ\Q:“¤2¨)UŠú¬8WWº…H,Li®í³3ù_?iJq}SOƈC1ÊJý>õÉ+v®FXXmš’¸O„ð •#ËâǬ½+^U´.sÌ«7\r›» ×W[8ü¡Õ× ã‰ŽYõ@_„´‹Æ|n#÷7 w-q[ɵÌ?´ A(ðxû×tJ´ø|ÖÆÝzÏÎO¦I8p¨? ³¾äéqà–E€|7.̶‘ AKêÄm§¸B³°f7ÝK¦÷eKåÇM'àÿ[ú›„õ8õ‹ôeñ ÷°¿©¡ª~;Θƶ®r*!¼y]A@dlI~åUmˆ^U]ƒöäª\NÐ@½¤ù‘ó¢³ÛàÑ|šœ©GYÀŽäTf²£Åz""Q-ĘÀ Ââkž9ÉÒ õp|ù˦çH‘>_³¨.¢¯~¯@&Úà‡aÊ®#×LÚoCÚwdbª6/iÅJË@ÈøÄ«Lºj”EeKZ0]÷åYf‰Šùj­Ë„b•(œÅ‚î"„^¡e~ l“þ·2°w¯¨k|ZŠØÌêN[k’$È!gðSÛNÉõ”qÞd&ðv¸ˆæò êØ¢O×6TýÀq¬Ò=±Þû™$ Ǧü›,bGÜwD6w|tʘO àòÊM~òãß2wÀ&j¨œýâ¬Îë‰c’Cé¥@Êü8/öïÖšmÑé½À•cæ§Lôì}2¢í÷ïSroÖÏ0¦¡ ):ÜMö^©äOtï ÓðB iúáêÚ çâØû1‰¶>¨O"О JÅs÷kþÕïýé>í¢òmô~!‹>:C¾3Ú áÛ²"n×wŠ,ÀìÍ®ïl«Ùk[§Œç {75‚lR°«+a”•02S¬Ä³â^Ñ#Ð 3(ŒŽÌ†_׺킆 @¦‹¢dqñ ?`h^|2™¹“ß­áÊê®»O{F#ƒ\YÆ.áÒ–lc÷ö•µ æ¸nÈÇbÐHLŸè ¼ÑyüÖ…{ågëãÑ܉Œ|†£õ hA4‡¼ÉQí¯"±(ßàVÅ~Ÿ*#&×élf‰£ØM×cêú]öÏFwÛc´§öú*áÈÙXÕ6@(9JË'w^ˆ>uä›DÕ#)o0Cj8›ŒÙÔ­893Þ'Ë‚?«¦æ4sRºb8µ²/lVëŠÐêi ή”÷\d¡%‘2s|ƒPç;„ÈŽÆÔ_¿NîwÎ…¸K &@Ÿ7FMFoaùÍ.ˆüִ‡Eæm¯–jìwdà‚øÛÿG‰5òaxÇðO7¼¹K€O—$ð/+…*KjëäeÓy\vË‘SÑ‹‚Uk<º´¬å7#=õ•ýÈÏ]+BÀYÕ׺¾Ç[Þ “ÿšÒ|Ô³‚M_ŽŒ6¬€q”Pü]gfR¡Ã“¤ ÜöLd£µD\šeö"(í)±©ÿŽÈYÿFM$Ú¥jé¾N°O—¿ ?ZZ¸3HóTPy > ÒÏ”d[ ·C³ï˜”k_]Ýf=#" á°Æb—'s ‘ºÙùšH’³kL%Ò\Bú7õ|:W6¤ç¥\ÄE5„X:iÔA´gç:¶”VÊÀ§M  ß- /¬t7°Ý~\C]·†Y,‹>2”Ægë(²þQYvôøKùFjÔI‹öYÓž›½·#T³$ÜLÀV5 •C¶}¿ÄWdÔVb7Å(.Í0à*'þXO9¼ôdŽ¯§Å™=FjN¬ÍMeæ䇜½ô½ò´%dù§¨‰ÁÙ3ÿ#êŒ#É dz˜|,ÙI F²¢ÂÏÝ]7¥?÷ëô]t÷‰kAƒM™–¹TÎàPWvy½—º’Jý‚š…í}QU¸.Õ0 Û^{ï8Ul™¼N#+G¾ô¼Ü°Ø·L5‚J’ã?AxhëñµkÇúx¬pÚ0°GŽÚª%;8þ+˜VMþÁ2¤_ú›vÃUÙX’ž¤8Ä h©~K†-)…HuZaÞäs•ú§v°7$Xc£.@‚ñ¹ü²Ü ’6ó~r g¹³+GUH™¿¾yçœîµÕJá§îržsê€õÂOB`¿ñ r ×àR¡³gþãU¬ú 2‰æZL,™JšjbIÊÀŶm R–7.CnŽÝ½ò'ááÒ;˜:ùÌaÖÖÈŽ ã•+†½(²¦18'ÿÊ!Ž•Ì~d¢š’ýýÞ‡2³ä¶§¶bÀô§ª;àÓÅ¡DskXG©Ë;ÉÍ2.1×L¡à£6c?Y¼wð5ü•46-îxÌ소4üÚÁz›¥ÀóVA¯*É`påtäJÔ¿ N5÷)zV±øCý¨Gzrß춾y Ò¿ªx9ª9®â¡l„ŽÌÅÚñI,J•¦ fò,µF!Èi V`yêM¿ÈÅŠLÙ®žùÌp"ieàê´fî2n± ÄX$ûÉΑÀÙdÊïÁ;9™M €K;=Ó.Òx[?½¢Ç-xed:‹’¹NíÔÙa!.qƒ<=ö ÆË"ŸJ|熮.°.ÑMGxßÎÅK -OHÓÿ­@Sø(Òvê7 ÛD(&ã:ÜÖ6V)-›øó@Ho1]h} låZ¨€8¡brê=rÁ®8üL¦sò”Û¤ÜCîãmئ!Ù9ä¡+lPÞ@(Z­³Û(­| ]ïb¬Ñ­ÿ_N²ÁSM¢B pßå·¾Äpú^4quð³e“PÑÒÁ¨}ä«'´l¼Ýzmåì¾é†ŒÐÓLìxÑwΕEŠ×s’®§Õ\å&×1Ž&p ¨Þ¿ ”vÐF:h sÛÄJ²Þ‡Ï ²·x¼Êfûû»â4Ì`ÐÑ3œ÷Ôa)LYKs»ßÊèŒPµ°%KÐ,QÒ3<ñÈäì÷´ÅLû¯pS»¯€×]n<~À8iѸcŽì}yàmOYG)QÎÚãñöÑXz[—©LõsÜè{ï¬$ ‡Ã¹û•C1 2›7r–)¨×Z$Œ3å¬Üó¸Ù^¿Äð[‰úæ‰; Æž|æ‰ì¡N#0¯ðbµ`¸¸H¨ö§¥—ôà?õöÌèˆïSÕÀL¨¢åÍ7^Û»Ò:þ¡I;˜ M±‹u¢Se½ý|¤ Ä:r¢Ž=¶;P·´Ͼ<`Ò•ê=J1÷ÑØ–¾œŠKªË´ÿì4cç§2ajWÎê|`Á{e.GèiLþS ìˆÃõÞðvàš1);kØÎs'Iåoè—HŽßmûH€s®lá}€,k8E”LvÐ)„;ÝJ˹)E4‘2š«?mÿ£Œ#Ù{Mw|p)}BE“’D°&lÜ3Ù-”UÇÑî—¼PÚî¾"êà|An„>æ^âÐ"’Ã;ñ¢Üï[Èù“–.[%€46½ü¤@¾çÑï2¬Ó 8§À…æ3Ú¡MÝ]™Ï4$x»ÙÅ"•³O ”œ‘!ŽžjKº53A/cAxkÏ vòiÑ*صv˜§>åz¬! > ëɧ®®ó]…ZÛÌÓ ¶6Íá5R¨*|¢!Ž[¹·Î_?L@ýt!w¦°ŽËþPdõ¼Æ{².œ ´U“ª4 ÈëE6‹BVÊ%îð´&› Lé-ÒT4¼û“s7¶µþ¹¹Ád|/K¹Š›6Y\!¿7€¯ÕN -{fl×™¥ L¼{UUæSëÑjV0Hhí b-#lüÕ:»ÊCJMý¶Žs«"£·g¯Üt6Ÿ‰`O î9ù^§±JÁ UOGŒ©è¬qY”í1Ò®©3¾äV̉~5x Leàé²x4ÜÑäÃ4mrhyá[½»=I¦¹™9öqxaüW©å¼ žPkÛ픊hýfÆAìç7"¶'kWùóç™qË+þ¬˜Û­âJA-*~nQ;¬s±öà=¡újOÞ²VKÎ>û%šè†;2Z5rÁQžW¡’mOl&ç¼÷‘¯´GHY K:gΔG. (ó4UƒÞ‰ì§Êœî±Á´ˆ9TJƒF5QoV{y€åÑzl(UªŽR¿G³Ÿ'ËŸ0ã|Øšx(5;YcDôÄ Ÿ+m^ ßÊê)½¢s¼bßbB=ðóš*×&ýA5wŸNÈç ”†¯4«Þ+*XÅCÚU¤ˆhsH•Äô΀ ÛêQ°|§‡1j£7 ’Ù\|TØ ²É2ŠŸ[hYçÜ Ò,=Hàþž«U-(ߊ’‚‹ô?k9ZæúÔƒäí²nÛ³[·¼4ÁÆj,Ï]?ac ˜¥¡1üºà%¢AÆ€¾®3Ørúcÿ½VS®ëžK ɹ~²èx”Ñuõ<¶ëعSÉ|©Ð=Æš!š¬îVŠõÛ"%‡¤9]ì*^ç\@©íGÂô–†èêÕâãQ w(—ï86­“JŽ´:ôS_)ÏŽåÐÉ®ÿ+µ5y:lQOÑ–Û—h²#ÐBjOWË3â®óf ã⦳aðjZ'i\ZÃýF¸ ÆC?šRÕ¨ú™»ÇθÊGzUÆ\Qˆó dÉvy•Ç}ÐXUž¶Ey,ÄWm™Ê¿pÇ6Jøç`‰Æ&U/¼­*üÐK ,ûÙAñÕÜÍùÞP¤‚ÙÉ" ‚Í,Åá6«üí­‹¦N=òîé9C®ÿAÔ¸ ]¿¨,ê€ÖËü10dydR 5L&ÂØ‹¼0OË£2êMª&X€ê;P• Ý,¹sÖ8ã |BýT¸†ùøËêyä!Q*q«5u’+£îí=,nÞ|X¸/¿š¼ƒp?P=ýŽÕŠ«7/@8¸“×_ÜÉÙyåO}ïÙÙø•€7ÇÅÍŸ<^âIË|ô†Î=g#Ú„1¨CGz˜yœZ|é·KºzÛUAŠh'¡BßaWÈ15¯àA0ƒÁŽOb×®m_ÊÓéX²£!Õ¬R%™s{01Ôc0Öt·ìàW¼WÑwq&–‚iOœð´¿q®†ÕÙ¡*Riñ. ‹½Y³a+¡kQú鹌CÙIâ×¾v e÷ˆÊΆ]©Î6ª‘IªûÒŒ¤gUª°BOÉEËÔ¨¦ÑuÅ“Œ°•š~jýdÊy;JfbzhÈ  ÈèXTF±òI iæ´€þä”×VÊ =qÊ-0æ`Ø@ß²c9šŽáyøáqy¥Ý‚ä¼qIPñt™D`$ä/4°&O±âkþªíÔrƒ· –(Æ•NáuÄ6BÜ,’,|g V±rÈK\~£}Uý Ô/Ó~xù?4öOeì¹lÍ4Úɬz<à;Jº–Œ·/÷7¬.Þå”@àÝ.æ$X R7tŽ*ÂîŸbõÙàÔ¡¶-ß¡¸·Ï”SŠ\Û¶î”K”P¼q©º¼Cf2û#i«C¸NÔAÈÜ¿©®*¢'lÞºˆÑa ËâR%Š& {þï+òÐlnU¹I2—±$X×$£üµÒ«Ý€œ',OÞ±LTlˆuä—öN.óJPªÁpœvÙÌwKkrß"¾Ô"´ôôßOëÈZ´,iàu$z¤áœó$WXÐd ¤Õí1N6ø|ˆRONUWÏÀ¸î;·eã3T\? YªÃA± 7REWG4 ÓW›èÝîÏb¯ÅÔŽ¦orÁ¥­¢uÁ†SÜ?Á¦}‰IÔ¹ ûÂÕ‰_!á0P(G>/äÁ†ßÜåq˜LV˜=ÄýÙ ‘kµo—e‘GA²€ÈP·=ｻŋ!’+÷Ý›ØáÚ”å­o9˜°ªÌËâ+~mè7bËß j,*¦ßá “ Eòÿ¹µÞY"¤¨¶eÙ ù×dóÙP"p„ÅV™^fìWoºþlǵõ]B…â/ÖñfâÌ#>O…ÿþ“ùRƒ5–˜“ÏâYÞ¬ú”;AçkñÎ`äI›ä Y I7¦§h}}iÛ»¨dÙ·[*—y̽Ü[ðÏŸ![Ù„ÿ¦åÿo*äïMÓC´G?~ê„ðIR¨]ïu!©šÓvyuT±õ™ëâŸ$.ϲnÇ÷Ù›|Ûù»¯â›Èãª}^B;ƒ7ãO‰Ò$8·ÁE{Ëÿ©UMhzs¦M4ãv~ãhM¥cvµB‘ªLD\xè±~çÌÒµ=Ýà¿€6Ë+„}œýóêy·´Û+‚dä53áò‘‚@á°%°¿ù;;³;,ÚͤêsæêenÇì¯ë@kf¬âü¥Ûè2¹½7ñL\ýâx+Y{< "??zŠ. Ø1ÜóÍ“MI7TÈ ”%¤ Gû½1–ÑÛRFåÐy¾B¤˜n•Ý‚Qšª¸æˆ,Ê"ÙjKŒZ\ÇßVïhh…/–#æSô£éjF`b'8ùÓµýà‹DR—Rè‹Hí­•VçK«ú#‰®Q‰@6ÜOQ–©½xî.LþîÁ½ÕÎéì¯u]6*ø\P}á–ô*—ÈÒƒ¥±™Ô›K eÙ ëÁ 7J¨ØŠtÏ—£dȪ"7´o'åG{¤ÊaÌý}LHFêçÝÀ†nÛ›T%b€7‰Ï–vþIœZ‚Œ€)Ï/»šŒ®¶O—6üüÚÄd—NðxîÏà $7øÓ‚1vû'5êôô¼%v•¼SÞÒ5£e¢±¶ØASJì¬+½”ª+_;¡$X©ä jÒðsÓ+°Ùׇ &MéE÷ZÝIk‡¶nŽhG‚"H–––²²úü§j4Ä{@÷ÁTs”<×^³8[¥RjËAJà¼)¨ ´~¥c§ˆy~Bú²õ‡ñòIhZ‘;ÆüGaCšH2ã»Ñ4·d¡¬IfŠ/F ƒ£zв³éÛ3wÈØì '@/#šì=hvð,ÒH'þð4‹áÙ9…¦øæü…ð”™ë„'Ǹֳ]×]@•e]Àz(º"<§ðÍÀB„´•i8¸ía½o¹HðT–.ÿæ:ÌfÉCÀÈýÓj}!3ûªdØß”Š¥¡öµŠzó.Ik¥@ïÇ"Ñ™EŒ[Ô¹Ö¿4pP·T½ú”F6„LšS OUãˆ+æ¬ìKÕ˜ÚŸ{ú~ª!U.ì1—)>‚Á”æAK=•¾é ·”ßžº[K2§€Mì¼užÔšÛˆt°$µ×Xêø1ÙÖìµÝ`HwÇOÏY­Ø -o‡Û‚¡Ýi¥X Ó$g2¦w¸.#»Æ{ª;€µmIJµÿ¼:Vd°ÙÜÞ(=ÿ_âé“.€UžÃÏÝ ^­1´× ðY6é´¬½X ŽW¡¢…:ê»Ì¿WgÍ'f17ó[M.F‚ÌlôÚåí.›^ Õ¹²TB¥NP*>¥pÞzsA„gl(SÖÚÁŠ1ÐóªÊ¤ƒß6ô›1 y4›ù»r` ¥¥,ÿG½ §ÐLÝäÚË»#R†¨D‚TD3ãž?H»HC(ÁAt¶¿Ý •HÚÏŠoNÏqÔ{ú”Š‘t/KYJ‚0%üõÛç Ó£ 8F]jÔêàù·nœêCææÕ±üTñ¬Ã‡'3éâ&f‰¨^øþä?eï>Ù§jrIuõwa:Ž?Z1.· ÍV­F¹±Ê4V©æÛí͸f…q@fK5ò“ܹ¿P„XúÒ¸üÚÌ‘—€†¯òhb“Æ4×¢O–ËÑÒñ;yE IqØ(…ÐÀ“0¨2³*6 N¨$ÿ§‰¡h-{¢ª=W4‡pv®ì~@ÝÔÇùwñ&³™ªiÜ(+Q#ÍðïÈëQü¡j*ÍÍqzñ5•?WaºÝ{QǾȜ’<Üó0ðYý€ÓÅi£•tU''}żS˜%Jµ¸ì§EÄ© fW*³µè㦠ŒƒHëð³jR+v‡Éx­7Ñ%ÏvŠÄÉþ!û,Ì«7`×mõ-ƒçGÑÆì¿$}7ýÜõOÇÃ:Ï5AÀáFͽ£µüŽ×Ÿÿlæ3+ÁÏLVùZ§¢ƒÊœY¹í‡†}/?ÊšøöV-MU 3‰?\rס¯ÿÈjІÄTg¿ÿ“dëŠl¦½ ÐdÈèîÂd εlI¬œô{ðšaùžÇ”û\uà âoôÑI÷Åd¢îø#‚k¼&àn²çËUä©ÓÔ% ;Ó;i»èH*…"7nàï”òiûí¯2V5š¹ýßnÊþØ…ÓÖCCˆ²ja©š3qì$†ÿ÷TÍÒfà實ÃОw¦.ì —jEV}ÿ²ä¡,ÿ¦yÜ?fÍSžˆÃÉŸ¹';ÝÉ#€s8\”`ªf¼ß5àÚ›‚ü»ýß‹2ºØY¥ m¥çcÕ„ôÚ™'›`/ i±òK™B“Ú75ì»´¸>ò©ðC•6&­™KºA˜”˜¤VêMY¸aÖÁ«ÎQ×b)Í”Ù/îYöš²¦löyÍâS§½è´è_ŒÃ~] à=ºsBUÕèõȧi†®r´ø6‹™ì0%‡Åöf[ÇZ•n,…óþ½–0YåQáÀe2æôÿ¸"¼¼Ã„¬²‚¥€Ë]öG[´zÞ © ÝSZÞëó›5çÑ ‘-­¢‘-]ÉfiL4fcÊG»8–õOï¶Ò‹[%QØé tÍ_ªz ~N”fòÏ¥ÙÉÕg³ãX|9'´‘•¤.ø!™.xàe—s‘\W h~Ä~óÄÄ'“å¶Gÿª¯ð¹WQ+¦e”¥‹&åß溫ÊóBª~ C6o”•Ò‘Ç’m‡…eáãHYPó‚Æø肘¦¸CiFĤÙ!–¥W6>½Ú“ͯ82Nïõ°lØb—±Âœò#º Ó&µI vl¥rW­àÖRO]ù´€j•FhÝêÝ~LÞÒ÷'vCÇ˧X¥}MW¦@“[šK¦…[‰Ï2Nël‘û‹÷"·Ž²g‹†s¶ìD¸vWW/®>”Aç½ÙÂ¥ØÅÿ›‹åÇ.¾I1ï`>ž§8fÄ{Ù aÅ—´,L¹ÿ“ÊíÀí¨«ŠÇØK¿ƒÉi}À}ª9 ¸3Èt }ÅÝì`ÚFè¦k^ôÎwÈk¥S­ß¬yáîŠäEéÁ¯•A§:æ\©¨`t\uŒâog¬;NŠs;K¤Gö.,dûAC‹ÃŽþ)˽·f:[­P”_ŠrÿÓf ƒ‘ò¸*«fy}DÛ%ªÿmÀªØß± žñˆHégÛmSß6dF{£~0‚™a“6· SÚ1åÛÚwæÛ¿ÆÌ¿9@$ J·@^¹Ñ$†QÇükiü¿dâˆãWÜR)M&Ì ÁØ,ß{ðéËòw9ÎÖ=ŸÂ ÖÌ]Ì>mCÕ^]G8½C¨1”šôfh³)˜µö }4|5P¬&$W˜jïºdsö#€ª2bZ. žc°ÄN#Ñ ô QÎßn¦à¨)rÈJïDkQàqÈ-©—Åoq€dÓ0øP­*ÃùÌdÞnû[>ÿæµ± 5—n´_w4 ¯Û¿­üÏ!Ôþ±:©X|E 3åUZ<[ƒ>3²X–¡‰Æ»Éoô ?+ª‚Ë© Àæ?•]#¹‚MEÒßâÓ¨MÆ!øfÅD'™ÕˆöíÈ;Õ‰¸LRÃJªÇê|5…G~BTÄgõ%JF碯 =tN¾&øhŲaÉ‚ä¿6kBω°\–¹¡8‹Më÷ÖQm}p´U)‘Ó:rQ Qμ4yÞh¼µ`•œ2*blü«öêùêu{  w+#¹ÓÓkqO¦í\>or}Âw~¦ÏüÊh¹$»ƒÍÅFÍI9?‡«Ú€šŠe¦Úgo(ÿ&g2.\ø‚äDw!š ¦GÞ‚áôŠÈBv`?ÊHÓׂ ¨Ô[zöž­ü¶„¨ÈðûšðÞRWøÏšç”~ÃÉ©ý…©›Ò—i*È€.Δ8p*eç©]îM‰BbI'dßCéo&ª59´¼8/gzoKÊ¢ù£KE±ñmÂz÷#ÚÃgŽ¶ò„w† ‰ÃÎ;VY µ¤ñ…òtQõ×]ùØ3§íùÕ'wî[ÝõÊR©øŸŠïÆJ®0<Óˆ€u‚!ŸxL®CÄȦÞý|’ožö¤MKâ»´1*XáXÜ8¶Ž ¼ÅõÿÊ`2«¬Û¨¨9VC:çò*Y·¼úb%lÄð kjgÉC5²&+(Ùm°ï°QŸÏô4øûÞy³F§y¡èh35Øbʦ§’Wÿoð®\éÕ ìÞ¸³RmEurgÛ’çÌCËá,FK/óEž þ`(¢¶v¨,É€ƒöõ…†¥_l„ù[—Új¨ê©¤ÝçÍ0S#†[‘ÝbŠl‹¶_Ù_®/º5ÃÝë´Âññ:ÁÖ«–•ÏØ}bÁs“ã7O8RûšÆQ ˜^h¢A¯*/dªyç{¾òS[^øn , ë", –°q©¯|Rˆó™Ü»çi št1ÿU\>€¾ ©„Y5N ø3$ SNþýàÏÝÑšTÿÞ¡Òõ#—pƒ³´«e¸ ÏÃáòK§‰+ [ªæ|À}|Ѥ]Óøä®-2ÓžJ6SB©®²I(Ȳ²Þä7îöX˜ʺMŽÀÿíöÓ©cŠ i«Ý ÙQ\E¦4âdP<%¯–Æ€à¾]íTHÛœÀÓýó\Y„×q'¨<`݃?\RóÒG5$HIÎ÷ò1Óåü#$µ”ãÞ––ÐápZЯÊð½>2ûîÍÒ+Æø ÷N$Û~–Åvéš"±ˆ»¨÷ÔgÍ4^>no½ð%tö)ð—_éš[Ùw…«“ÊÑŽnØôNKD[ ¦±/ÉÌõGx¨¹ëÿ^¯‹ëöÌù'¯#Ö|D ¡d"y0tZKï @‡&‚ZÊñé^ºÿ]|Ë潆¡[¨­€<Š,7¦“hûݸŒ¯L`°yëί3=6£ÝLîT”…”+å“Ò˜˜þ~Õ›Èf%è§q hC‹ÉÏ,|Av]Ÿ«%@?ÇƘKÒ`f̦Õ|ãOS"ÖåÿÞA당±Í„‘9ǘ…t/ÐTÍf ª *Õ¬JƒK¯£Oû®°`E_ÄÕÄe3·Y½­„̨âúÞ²v<õÌC,X‹o¦H¶ƒÓž5ÜÑ¢Ué´Â#]|U¡ø!ŠØÁÎä末¥½ÚÿZk þbK@oÛ+Xt+‹zÆ9½"N3udŽÏ¬:]C\ñ$ÌäHâ*M°ôÚ‹qÒwÒ‘6»KS¼v„…ú.OÅ \T ºžÁJA@Nà¡CëA娣sé<@aÿ›¬œÍoW±R…×ÞzN¤±kOΗ@g¤ôM¾a¥v>7‚k•¼®µ¹û_•ÞM£˜i%‘ìB1vƒ&|ÓB¤ùÆma,Bþ t$S']FK¤ÿ|u‹Ý±7\­G'ô@*ñ-èOQ@kŒîÌÞ%¤«}‡p‚ø‘¹ñü$ðÔ ÏôIA»‰7,±×gƒ¬_õ ^+©.ñÚcÅB秙Ú3Àæcë4†—+Õó¢æ¼.^™ô3™Qit”QKÝ—N!…ÞáOn{F–º>l4$Hס™8Px z0›í4ÿ ò¸¿ÆÃFhÛñïã÷ì<â,üWÁAÖ*Æ]ÀÁ›¨¥ÎëÐX*ÁwCœ´…È´ÙL˜L;¦l `Øiüš½û!l6ÿ=r°_nÖVs’£#­a³m@hsë-^A˜–g©1P¾Š©3ú=©¶Op9ëWûQ­'1QÔíÿ“ž@Ǻ®éº¢.èõ@sîcßô*/zñAñ¡~0¶ç¦þÙEBT¦5¾ÄyŒãÆd„óÞu 0Ç ©9¤Œ ÿ¶qf°3Lœ.©3téöÊ@K&9b^Îög™aS£¸x£}€¶ó0ü¸ÍêÒä5N!ŒÇô#ìâìMüÈ6a´Ãù |¸ÇÛÿK3¸à¶ R¹-ÇSyx¼ vAýÝv‰ÜÒÏÙ7´ø6TœOo’ ¼žp-ó„" »Í²áŠ+yÆOzm8p–é˜e‡ÝJë—Çdî#Aª/A'ÈwL%)þäÑ ;"»£%çY×kOëŽÏ‚;¡Æè™×úJûøq?$bį̮cö½Ø«tõå6†ÿÚKO8°{¾žrÜ "M£æÙç½ÌÙ`ÞSÒ oR-Ÿ/½ŠV}%áŠdjÚÄ_ºd'Œ1˜A8k”XšÑ鶄š ®€;M²á²zYò€%5XƦSðÈ0Þ–lÖ㢤f½Ô¯)+î%£îA‡JC¬®Q\ñJY\óMÓe …"Íë¦þ'rØ«Ëâv[«>Bå€bæÙd!yûTõ›%7þ¢1úÁS³°j‰ha\7V:‘û!ÒÖþ/‰€ÉdÁUƒí†Y#Ï 4ñ³I/o2îçŽL bÃÎ}–óvX‹Óñ»Ãâ|Ì°· –ܺ5—ÖÌIPo­%¿Yëà—'Ò‹åÐXqæÒ.—çŠ#Þ…­°Ô­vP‚ù¶œ3‚\7GW$`>rOÏŒQ"§ô_%Õ€²Üý¬6¤|/K›“Øê]ô†Òã¿JÛÃÛ:¬…„œÀˆ·eRßÄ¡ùîüîm”/?йâ°L•<¡û6 Fã¦Ë‹™ý½ /¬òZœ.ó©£Õ¨UÊW‘ÕÌ$1Ûÿ6á0€BÒ]/Ü°Ëu\ $JÈùù×€:↚4_R¡m¨AŒaàX2 uzÑ¡K(Ö8‰ZN–ì©@Ð~%OTtã!r£^¢5yúY)éáÒ¾`Š“N³Ùîý[ýï¿\n°—ÁjÓR9ÔÄ.K;ãá2Ux¡¢;#’lÿÑ­ L¯XÚꬔ]É'tM“J•¹W¾êð!­=1¾{ §ÛV“.¤…?op䳞)wøO«¨þkFHÿã—)²ßU)_ôüR©6Ð"~óvOjT(ŠÕ+\‡XºóeÁh’uj¹âqIb' ¥pÅþÄþ‡Cë—††‡ëÛ­çȾ˜]áƒHo«\Ί"ˆIÞ’»Ò“b;v {¹ï)OEáXÎÉ.«zf"EÝNÊT”g©tgg ù:Ñ0õ™$¯àk¼“ ª`L{®—{›_“\D8þ‚'lAFXje*í$ÈßÏ9”B«!R(Jiýùƒ”y“Õ|HöݸœÃ3Wp:'Û¢e‡¥üóÅ| Dµ\ÞÂ#LÙ>©¸™#åT¼­ÌÞùp]<äòÎU¨²3»/Äì_¤ x”M(câÎÁ‘Àþ<2C‰‘ ÒM’bìZôõÊÚÜ´Ó¾ÉØg¿âD5%#„Uývdøëe¶?CzÛ‰ÈÊÊ"®[aX„qÂám/Æ7›ç¼Yy¦„òã[wžÃ©¶ÕE`›|TPl?ƒ¦Èmb9_-ŽDªÛdî² ·Êá‡FœApÔÂ|C_„‡øÕÿS¦»›\—),O”›”•}o’d~R×’å{²¶AÌ¿·7˜Ê/©…^žcשÕÄG{1÷ÄÛÒ«C Àä¨Z&-êhô5²l"%ú8ó(8J{¶›=Â~ÌjŽcöµîu ~Ù5‰"àǸ.TåƒL†KeQyRѨFo¿$%¯×äúL&Žd~‚2ÿ1´ž)š–*Yˆ²ÀÕ1x…ͧ‹°I¦?ë= :öÙ’Rd)5…y_¼ëN@÷5$'ž–`³, &„åy AÅHùÉt&„œÉ¾Ú­æj‘Á˜LͦKÃarÜD¸,>U_+PÓ€]Ðòn¼çe}Õ5`¸!:Æ´¤æ'h>Õ2IÞ"FéåTðm¯—„4Ò%*rztPñßo á=¨§SÉ9?È [øµ‚ŸÎi¦TË“r]ÈÕ_ÖŒ‘­¹ÊÃefri,š´dkà?Ñò5  pu}ýÚǺºéè¬ÿmn:6÷£–e½KE f ¼åÆÞ>q‚¶Š¯Ê™ÉûÉ\ß°¤÷åN†ËWf2{Ü)ÚÆ*îI¾gf™$üó>oÔ-/"¿âyØ:‰¥ÚtÓŸKÝ!¸šœŸ¿Øð×ק¼>¸Sò„1ñåáYâ è$¯#Zù»ˆÔÖDËùî3>2`tÌcEZ aug\ôrÿ®sl(~OÉƆè‚ËzÈè8­­ ýfÝf æçáˆð{Wªs‡‚½a©gHÙ=ÈH¦¢£!Â[ÅQE“‰Ü´ŸRŒe5Â7OÜ ŠŽ©äJ©f<È@ÓihùRžK8y”˜ z~Ñ‹ìÿ‡(‹µ¾ÈE”4;p@Ez›šóá:ÿS˜ôçZ Ñ׉ÍÃë0¥Ìy/áø‰yc "hêåº[þŽ“@1öØ ,A[yë>ØÈF–XåÊÙùvng(lðdF¢zÓ¦“ïL?‹ŸOŸ;½-ЯJ‹iÝÜÄðØæo=n%O$°ÞeqŸDÍzMúÊ. žŽŽãÿg3¹£ÏÿeÅ)DuÀè¼Ñd\&ÇÅ›ÞÖä[`¥`ÓH#Y¸HwZ<*wF4€T<>;Ìšî5øXoÂÌËñõÚä’ çëPÉÉ{iå% 4߀ïá(œ‹%؉ûÊö )AÕ60ˆˆ×-w°Ä¯€Ëi}H Bñ´4>”6É6¸FÖ|öŸR$"´ü%7æÛtý™vGÒð8fñÙeè™T>unäÍ$Œñÿ¯¬ñlãÈÍ&YúÛu–2Ù®µ/_vz%þåu¥Õvë±µ?Š#ö9XŠ½2€±q:N‘þÚcŽ:{I¶$ÿ1´êúkÃàK„*á(åƃ6|Õê‘zçun´âuFr¬¢ùGheyŠ‘$}@äÛšE{}¹ï$™RF#ú$ËìV„‰†ÙS1&£¨KjbÓ|‹B#I¿ž2 ¶­|c¬&n­2éèEô¤i œÀ‰8*SE'»:;™qŽ{ø,³ÎºÏ'ˆïgƒ1;:Õ‚t"xž~¹4V¢‘¤duJ3QféüÜ_è‡ÅiÛ!RíÆ'‡®Ri» ‡Eë_!-´¯MZ/—þ,z›­Ì‰âwDÈä$Žc ~Îén5¾ù¥\ÐI?ã!&ã]ÚÁ~‚:=‹¡¢õ£½T Q Û:\¸M|t{ºˆ:²$oÆΡ˂Uðã½9O^MºkÃÔ_˜ô!òD÷ ‘ñœ/{Þ…~éx/w&ñÿÝÈwm,0.¾àc‹"g¢/ôµK[ñE$¹èAQv÷ó0+t×BÕ¼b/i€Î°[sÔ¦ÙŸêƒø†'£þ÷óÏB@W*€”FÊàJ+h§‚Š µHÆôº‹½hØd@CErfÎÃ匀¯õZ¾…ghëÍñLl´QŒS_þóø)9똩ø2åÇÿoÆAîpl©2“„hŽ^ã!æmúÝ"úì#Ö¶(˜Êd’åË¥âV²Äº´¶Ü7ÂmOI6/‘•¦ƒÀÙ›!+`Ê:¼OÔïne˜!øâ¢øc­é³ººÛ°*Ð [ÒXÝ /DS•'bõeíH±ÍŒ¬² Ü @LsÚ­h&§j­ÒY¶mà÷ÒZŠ¢¸ò^L*µ9T+²£†«ÓŽ—Â$Y‘9ÍU|Æ.õ;3. ›”±6°Ä'¢A..Öx{ÝUÂCP†DBÆAG@Dö…üê£å-úyÞg²`Àì›*<ÑæVˆr߀!;PÔJÄI²_z“@ë´°¯†0‘7¤Ÿê¢vÚ¢-«2iËhÀ‰ˆ'AîQÂUþ§§Ní-·) ÌŽœó`¿- Üp Ò™}Ñ [Ðò§së"ñûh;v E]r/›4ÿë(Ñ«û3g÷#×x«Ók00ë›5StÃ#ÐewŒSƒP‰aâ^`^•œ†íp£–MJ=y²·Õ€±âOÚSAÿ;’ÇÄÙ±‡ƒ"îŽ%ÿ/’•|©P,*€b΋`„ °“qV£6°i b?tõvE&[9›kÖOío-gR@üïhÍÝù;Ç.ã§~.Fµ‰iˆ£²ƒ½Ÿl,P×LÚ«ƒ÷&Óôªƒ§¨ê ñp$“NEÐï“hº´ éjç)ßIönv½W¼î3É ÔU0Y-u”Ò×—4âY•§­d˜0N0©èß»Iã#"/9ÙÑ.Îï ´)g>ÕhI ­&÷Š–VÛ‚€{ ¦]ä¬*ʼ4}u7ϱ877¹H²r^:Â/ð3Eñ¡Xã;»¾”íNÿÏ÷â#R.X*ŠlL?<—¯iñ2’ÎÏ«ºcðòòœ†9áYvBÔâÐz.wöÂè9"‰é{5hW/Ùx{!­êh¥Þè3å#XÉ©Å¡ïI©£/C7¸žäP ^8q=$ÙÍñeï ™A¿ÓÓÏgJߺ‰G#};0–á@ÏvgK֕ʺN¿ë6žwPJýSc/E®s"”³‹neiÛ>ï[îó‹Æ¥Ò{(ÿOè\Y¸‘Ï’/A²L¾ J¼ mŠ´–-cBj·!縃EwTÂKcp9ÙÏ¿Žå0ÌÆ9ÜÿzrüÙq–±<ÚÞP5œÙ‹÷hH§~`,¼=c©å¨ð¯•Ù…ƒð'!ª m±…¾þëêÜI˱ 7¸«ÃõsSiµ­5!ð"p€«p&¢ö[ »·ÒésÓ¡ežü²2Ã3Œo€b=mHsñ¨¸¨y\=õ "õ¨Šn¿eGÇL{$S§KÿE×dúòŽaA»]M:& úžœTøâp7ä‰DN¤Bp»{ë‚Õ+лkåF»çÎæÓBÚžàã¢ãfüŒè ÀŒÍ{Ìü“Ž¡£)þE"8eU³ÖL›¡—Gµ‹C+f-ÇèÁFnèàá&óo#È;µŠÜÙ°®ñÜva¾³Yͦ1ùõÑkÈ\H’–Ö¥×#<ƯrŽnˆÖXºaYÊ/Ô* )¥jø! v´±³rò•gÁ«´õË­ìHŸ„‚Q<4iûÒ¼ˆP§íÜ>˜›¥ŠÊ:s'™û¨¹|{àÊ(Cà‹1OoÚµÐ.&èz3J5¹€âøT‚zk‡îäiª†¨£‡Vg¤/åX©A¢J "=Ì*eT‘ž²ŸzùG´‡»À±T2—:‚tó·YÇ ŽA["ÆÈ­Wõ­ȱÚ‹3‚O ^D5L.ýèáPIÿöQÞÖšž<=ÊÅOiZlžÎ$Ù>R8ˆ¸!œSù©·Ë%kø¼Þ_çºk¶áëu+Ë >'Ìò #ï»°m–³_éíĤ¤¥8œ<—Y˜n:žCUíĵ®»‰[/¢ýYúõxD-å#äY}•@U•¾Àša£ŠòðF\ÍgiÄn{×™ìnÕmGc‹"ž?Zéw¯ê¢ù èâËgÂ!ý©XÔc!zƒˆ_Î +¬u¨Òü™§JDxYëûÍÄ p΄v,¦ça;ë²ïp…,\&t3òzUÁÙ°–>ã7OöSŒ½ýoÊ,õA}˜Yä$áÉFÞ?Téxt 8´¿;¼¥_3½ @mU‹WËvºÚ«a:’ÊHAs妄Áïîñ?œ9yïÉ(zÊG*ºÓÔ£…Tzþ <|Mbµ[‰Ã!ß@YðöÄatÐå…ø½®÷—4› CCòbû¾«[sêF®Êç úÇ>ƒõ› û$Ÿ§IˆÆÌq l«á:¯¥¦(VŠã®1< wÊwfþ¤ö™#Ç¢£ßç8Š5¾+®ÝSr…ûc‘Ø\™‡Õiá3&•‘nºš(P.[MmpIæ=[_o)LЫŠÓä&¼±àPÛ²h7oQuÐÈZBž¦7¨âu®qVu˜ø‹ÇK›$ ó|w½ñŸ6ÄXú#ʧ—Ë|wÎFÝhÎ%‡ž Q9C'Sg'7¨ZO„—ý»­<ýVÜ‘±úÔŒ¢´RÀ ±¾Š(ÿ9$£ ƒ–nìlDÚn- ÿi¼ô\ŠvÄ;­[í´Ž|0ÙGšÜ á|"2HÕ°}N¶Tî%à.w•ÏàXÿ‚Ú'N_‡«FcÑ÷a¤GoRèüÁ| |ÖºS¡9øšÇ]Ô©OGlé;63U/~KRqOxK=·M›¯e£ƒ”üsBÿâ%år€jWÕšnöQ̵I]éa’VÒv…2øa8ÛoÁ½ã*ÿ)§oÁßh1Ue‰Î!k¦.!ª…ÊDCÁ÷ß]c¤Ç+c(ðË5´>> »ÐDïô)¼ Å‰ÐMM\ûÛÙ¥œ•Y”èö$¦¾môÖzîÛ™æ‡s9\#eÚU #Ðþh)ØêüPŽ¤ ø3Æj‡®s) ‡®E*–±¾”ÛYM”œñÕûsžrKÕbÑ÷F-×þ2Å)ê%S¡AŠ0o±ðÊL…·¯l‹EjD¶#Ðo›¨ìÓÒB„™DÚV/_¥{Þx¿o(à·ÌJ{(“ì)<3ùÞ¬ ëD§‰'˜µ@XÁ ô0þb"]à¢,ë®ÓH{ÄûÚðäÖK‡­ÿ½Š§íúè ¼0°ßÒ©š›,¥Ý3âÙB «uÌù\†æÚ ´SÙÆS üŸ„ø»ž“z3°µÚ¢mµåš7ÓÚRØ>Ž\ð©kI,Ô”¨@özµ8__ý(Š…=ô‚eWûä-!˜Y>6yÇ9V ì.¡å2w”‰kFù™˜u£áøBüû‰ŒïN­¯nCåÈÆTñea?n`ç¦<ó©0p£!òÅzƒ6z:”™uz{ÿB=ßÿ°7Úv×CT¹ é—¹]ô&‰ÁiµÃþuQ£}½'™>|f”ŒuÕÁ‘Ùº^ÉO™Ã lõb˜s+ªß4æ7Ÿ&ž€xÉE…nœÇ*äDX‘úÌ$ßÑ—Êo±MÝÑqû§ª-«VðWo{\õн#¼ø;2cœRb–bΈ¡@bÀç9y#ôútµô¼»“×é £N ÙŽ·G†VÙiãg8›f‹§: ˜·M*š«ˆüÆñÓLÁ04!J±‚cŽj\Wïï[¥ÒhÁ;iHsQcvÔë½DlûŸOÇiЧß´íÕ@‡è¯~H‰Ä âcÃÝ4.ÜmÎÃ{v„nßY¥Uâ¾-(”š…G¿»ª!&Õ*nà)Þ:Ʊ"Õ.§ŽäL° Êw 3Aˆ!â#VÐÈÎçšgw—TÑICûn´2­]\¢¹ÅÀ†w:œÆµÓcÚ¯|ïµÒ|£)uÈ5$À–Q´½ÛÜÖíÀÀYVX”Þ2ð×VpŠÏ\ù#º¨m#¢ñ!¥ÂsŽç²Ü?U´_,¥w€3åx¶¡mIú‹*òþØg_qø\ä9>õGÇˆð…œˆHpá3»Ü¹ýöïMµ®µœnHŸsÃ*xå¬^dðÙÚ¸!bh}è{¿šƒì_NB£%ù„ûþùººçëÖ„ÔúYB‡«BÈ!&"¢äOLF ÒÂ+4!>‡±ŒyÂX»+oaó¦ÞúûÍÅÈM“¹ò¾ÙÞPÅ}~Ëù$Äàe(S¢¾d´ÐÜu‡Vz¢Ç†$h‘q¡¼ï¨¤^ PåíOÊ?©ÿÄ,õM^”†œj`²¿Èt溱Q4K£÷¢8ŒÍB¢í)A,£‰§WñI¹wOJ<¾D~™¨ Ñzokh'<ËD ªõÄè‰ãq‹Žb3zÁˆ.2ÎÓWG¡—'ÿé+ãkšÜÈM6é É‚bëæÃ.ƒ»FO|€aèQh1¨Á™ìû2¾ëV…ÛQ2Ñ×X?¼…í”{Gœ+¤¹nÐ~ZNÆâ(䀱 ‡Â§lGà„rVǘ-IË„eºÓZÆ3ƒbØWÙ•ÄÜŸU É 5hÎÒY§í¥ºñ´a^ GÖG³\¸¡H&ÔSzëšùo%>†Š@įZŸn¤¤sûÑ>çv]›ÑçßLdMª˜µ;8;vo¡=öã0²÷è±.\šËát°6Nø ­Îé'ßîò>Pm¹ÁñWrå4&åÒ“ñÍ&E/ìêy7áÑoÜ/ÏÅ­Y`q?gY¤’fƒ7÷$¼`*`vŠtU×8s!ÙŸwDoÁ KIÐÞê¥ØÝÄìƒô¾·f«G±Â*Ô!v¢aÇ9ˆŽ`<:©â†#£ëJ]°òÆW…|f°"ˆfcüÛ™wA4³¦óÕXuÉRøpð}ýÄí§È´mêvëwiÐwLää/àØqõãfnu‹´Í³šÑžä A*åÅŸ.ª7­ƒÆ ×÷ãâ´º’BVn_4©[Öh*Ô>dG.Ÿؽðpåâ3vÛ£«zç‰Þ+žî¼}£!÷D ºƒÇ:¾…iÕЬ£¡‘ýL!ô>ÿ¯Ñ]ÏÐûë¸öÓ&¨íg'‘'+ª;²›:>ÿè;3¾DOIŽpy 9€Q›âˆ )’)[u-[ á ð"“ëøþ¯wÕ;®Ø=w"µ#×¼Ûäú¼\Ù3 ä Ž¤{0CCº7‡ƒì˜!{f OîÞlš´ãW³Rö¬§E³™› 6ƒÜøbº ìÞ ›:jú08?‡ùowï¸dù4³¢X:%=u䧺H³ß…âŒÅáÞó®µxÇ×%Fò¼¯•̹õ -JÅæ–³ñž÷¢]²9WÕ‡4ó‡qÍ)° 4ø‡Mÿ"#øÅ»Ze0=¬˜s©3pö9Ötç1̾â•ÑÊ`!vJÆò²›'R¨^r£*eœ»pKg‰ç3ûµã^:»ƒ¥?ð½ÌOW ,?hé¡wu÷£´šÅt¥´í‡ëõ\WÞ?J$ÿµïËí +x¡ÑbTæg ³ ‘¤R2¾%:ÇΣÈQÃ}9ñF ‰¿EÞÓY X8]Ý€GD­Â]ìYR ô÷ÿJ2'L +:'¢5W°pæx4Ñ[*µýŸÊÀ;ÚÆ:¿ãÓpêëɤ¶äÁ¡S¤À)?>;ægÿ]­·É 9ÑB]ëÉ8UKa©ùóèéö¥X†DéÃÕþ‡ãb¦Õ Úû@EŸ[,Ù}(*\€VÎ8Ò÷`Â}ñ«I>Ë€—glÏà0²¹ÄR´n€ˆ¡‰ ªº¸ó¤ÿkG-¸iU‹>€´ž::Ó$(ž;6¥ú –ȉ¢ †ÞxÚ ÄÝëùÏ¿ZÃhãfg¶9}x.€3Él´1ñѬ.©{ìgg¾žla A…ô&„ZPŠŠÞT*"¸‘úÄTÇC’¨Q«1°áâ¤óp³çrëßQÃÜÄÚ,ÊVa—íQc-Í8{q:読'‹ˆw©rÛŽ!^ÌqÕEðÒ9„”g_ÕãF‡dŠ4š Š`×=“!ž«Ð nÚ´c^WÀ褬µÏIiFn= <äÀNRìKNöºŽàÕzÆ&W}Ý{ãÄÊlý)Ê´ÞÃIúÙb̬2R6§õÁ%0Ý—¦6] ¼ãî5©Ø ÄíŽWƒ6· ÂËBGç8“5›ÁXz#> a1ž×¤RéqÒæE¬F%4{HIÌ©Þ®ð(m•Â+GÊÊ{î]^:·ìŸ²\R‘¸”HÒ(Zés†É«°ZMÐ"h Å!r/3É}îYœÍô‚»= b€qqÒ&«Ž9µ¡îKxËÁ"$›ÛÓ•”c“Òú5‘ùF¢nHgT\ŽÑ…ágn[®£L%ì ˜báýà*{¾Ö’mɶsR _:jC¡%Ý·ýÓEê&=¶ {¡ØÑÁ‡ Xî7Éìd½ìŠ¥ÀáöAÎI º¡šµ’?åù˜{¿?½Ñ{ÊQ9º–Ýø÷ãp˜('ä2Èßñ™ôB†Ò¹’‰Éey^õ0©€ ?<Ô±½#šsÚ^X##9E[&²Í™´¢sÜæ¨ÙàYyŒ¤Yeì¬àIxAf—'  ­ Q5{–ýt ãæ¨mÊ<¡+`ÇbnŸ¯ü”ï!zUž ºI=»{:§ö¯½ZÿcQ(b 8%pè½eI«(ܱÑØúG·¼¸ê–ÌxB_Ã伺é‰ý´ž»æXb8p'<·KØxA™Dÿür¦ó•BN8ñïÇme©þ÷º~ýûÀOŠfUb{ŠPÐ{§~«ôɸd.­ä‡Ü›ø‹>sœT6O‚[?üžjE£ 9ª‚›§¿Ç&jøUÊuQ•0røR…Ùþ:”»£m_†×› \Ú–±ðŒ&‡EÅ`ºÁºbŃ⃯ý}byÔ)–¥bÕó’#Î…Ó‚΢ØèNælƒJ{d!²º’&À0Œ«ÍÌ¥pÀð™oMºÈåÝ!Vwɽ÷7Á>ÏC vƱ1b4CôÛyÌÍ/%·¡0½ñV ›[„tíK|$ÉpGCM¶çPä5Üy¡i-cë@ÄXå@ªë ÃìaÕAèh{°¼º§.ÁMcÏ“®Ài2û„±«§Sž0g{³;ŽÎªñ×+-)²MÚ „>ÏòÖ»F·qÖÚ dóÞ¹È+ú˜XL9Ο…pö²´37Œ^ã ì -4Á»ñ/’œ,)-¤›Ï  êL¢ø‰µ:ó!ÞÒÝjªÍ¬>ê)¡!:„„l,° ÏÄ~n ×ä–Þ´l±gÉäÿØM`>û–—#Õ™$UR£ ªM•ÇP£: c1o–é&8Îñ™ï­VZ´Ç½L³óRËLEm “s’[l":#¯<‚oG<ÜÄŠ¤ž wÀÂêR{lU>ÑrÕÒÌ.v{<î‚ò5éª&/WÏM;=/S%»Ê£;fY™²äŒ(kØ€wôcü¨šBDE·é±6‰Èh+^¤V”æ“ðþJ÷ <·Ð°q;`ø^ûû[Í·Åmvç9ÝœSÙ?Tj¡óËø„àÂ'31{~B5™=ĦÈÞYä·¦Z˜4:×Jy4®·Ù1´okè¿©Ò6;“ç¼;n¾Oæ„Œ–Š•<ïxœinËI”-½U,\áÓèÿo½ïË™ª¾ÇH¯X#’DíC*tÄ%<¹§¶9¡äÁLs‡©nôiú­QÇ‘?÷Ù.ܦ‚ÎeçNâ ÈLæá¿Y¤÷UBKÑžÏêêÉÖà+Ô:€^V:íÊC‰[<ç·ýÑ›5ºö‚5Øeã®G.;>²™V1Y Ú3Òª©çL$œüˆw†@ –©Õ}(Gˆ}c‹yV8Á¦Zü÷Š.BÁ¯>Œ~ûBR±^‘×#êâõ("'¢CÉpN8Û åX–ü&±£8á8Ã?ÕrX¼š”V–¨ÝŽîo‚^C‹|`(»×?ó?©¯ž ?£n¾§œÂ,ÔN#ºfOé—#Ö„ƒ0'£oH HöÑje¾©ž TÅcS0;ƒ¨REDž½v+ê)Š׫pwZäÔ«»£¢ Y–’Î÷+^c‘ÄKà>{ÇÛ©CÀãú5åcX£TÅY1PU_2=áä½>Iôy$ÿås÷µ›†_Î(µYG#0/ïLßÀšuß7~Õ ðËV£d<$ž!Ç^Á'HÅoNk:$¥†TžŽ-guMî秃¢¹ ”0QÍx>Û`ĸÐÒílªh«ßU ‚~VÕ4å•*óßn‹ÉIRJïaàx–†JêÍX7ÿÒ}H _íSÊ!B?è< Öÿº™e<i<ü2ØšœäÀ›£Ôûõ>},íò>ubK\(Ø+<ç¶X[‚ ¶ àöúH®Èõ›/üÆ_;72‚m¦ä9úÀaQ4¯žU…2¾¡o}d-¢Òh/GÞòÐVgŠñJÑÞÝA•¿=Ä < ’\ïÎäB(™çifµòw ÓGÖ­P5•ÑP-ÏѢ̓˜MH<,o/Ž •šù˜–Z_< ¬NÖn\ E«5Éw&új£9ŸWº½«ºu›þIˆ¦-¡GÕòGÓq„ʳÐIsP._&6éÅúîH—”aoÇñ!÷>†¢©oâ~97±&-õ°ªV@°M$h)ž Õ»þH°Êû*uÆ •–¸TÆwH9“2r±v”F«Ý Œ ¼ãJF¥-+ç)¹ºsR©:¹‰V‚E?÷½„N¤ëîºìú%Y"è`ùêÍ{’®ÎÒW.G¨I Ù™›ÜYÝ®\Kaeqãwª™C´<ê²8ǧ¬$ž*òÖN!H ªÿCx³µ¦PãÐÐl—&“ná$ðÅ6tÛåº%î÷3SŽ³`ð©”Ù_±ÎDA% ÷äM½ÿ1mÀi¤Áåw–ßæêIvìš%"Q՞λ?dœP*©1Dew‘7.kÄ»§eáû¬X–;T˜ž€Àxwñðéµ9ÆV#úA3äF3ÉŸ°$aZUáíÍwlè¹Ý© ÚsÜ¥qb6ä~8På´ëòÎ2‰‰à^Ôy|m'ëîã-€ˆc± exR«2±3Óù¢›—G"íÝ—oGÿ»—–w¼ `>rÀŽ8¶Ê Dúû¼ŽàSLj£òH[š ¾ï 4\˜ä[+g@HæE1*x: Üúe#Íòñ¼õ—£SÿÈ43€‘ÍɉÐIÙÍ}½(Ë þ:í,eœL‡ÛH+˜ üé¡›i&à~½îÈ࿽•œå£6Ì «‚Žü4xÊP#÷då¨ë«ŠìÙÝ+ªšJ¶Á=Q(\êÆŠ ªŽeøɉ%Ú©öÇ#^1-7ˆÑ¼FëD Ž5sW(ï¼J’VpÝïu®|·< 5º}÷Î…ÌóCÈo»áb²öëc©Ö øŽ¿C3ØÀôäÞ.]Ý‹TísîëyÔ¶@~Ô,J˜–A˜O¾šˆšzà·[À¸6;ºÄÑëïCù/JF…ó¸­"k }sdéñöÊ’ Flâ0›<{‹·•<ÌzÙúªšÐúT{ñe-÷%ÎKõHŠNw®€–øËØ{G³ ªØqáü8Cºqþ>ËЯ߷µþòÙbQj°‚%ålöÜàS—^nË %½² ln?Ÿ¥AÏÈ™.ÇsõPõ"=!ü¸‚݉;Ÿ¶w®ŸpòqCªþŒÔ “šüb¡µØá,¯­iç6.Ñ\êŸò{þ‡û›v¥D¹GtàÃɉ˜ÈÅHÅš}Ue… ÃtW‚ý‹ÿÇü€dýÌûøÌsqA—ŠÉî,¹‚ºøé<Æ;>wßê©<¦*qÛ(f «–ïò05:÷ðž“…<˜<¯–L½&‡f¥y/¶º»ê-Û#.ª.£j‡þÑt¿#: ?Âp05«%†tnû™!”ôOp,)‘çm®Š5)ôä6Á4™ÖÁ„m}\½ïù³ã¼ù&ž"K½ÝL;ª“ÿM ]A]*L€K8 &/çVœ)›¸÷+>…µ-oªÃ(â¬r½v1æ[â ^Ìãp0êý©#¼Ú¤õ¡qþÕ²S,S¨ö¤‘}ÊþÂ_CÔᇠ_o²oÎrÓA«büu^sü…X·)ŽÓ»¹ÜÆ€ûj²Â^N¬‹Ÿâ-Æ—(C×2h?`ýi+Rüýê ¿qhœœT ‹¥Ä³k¹A¥)}ìM7B/ïËPñx'{vI?SJ'éÐ0¡Åºû£0iM¤R™ÐÏ™=(2‘»2ä”5e ‰"<Êròn‰Þ|‡NŒŽ±£,ÕBOíq§Å’ïë&ÆK…5M'˜jesSJþ&•g uéûZ^A’)ˉ©zR®?Ô+ÑmÝ{.ôwœØ—QÒ(éçÑô13úý«±4ÚÛ¬0\Ü$êúÚƒ®Ñ¶Pä¼Ìãn­ÿnkì IÂ14×Ï„ UwTØî„Pv$Tííc†…$8ÃZ"EßÕ_ᱸî?ÉTÌ`صü}/LQÆŸr±)–ÿLâùÔW;\¬ýtɘWà²ôz³¸»•ðr>ôPƒ„³½]Û˜Ìq3S½JÙÃ,÷nú¤”¡7“Ø,a$êÀaaéçÛu²ëá.d¡ó,¥r%aW f’.$©¾p玴:€Ú(>“Š“ óÑ?êàiØù Ý¿,Oă™×²+ðÚjKv\ײbܺ;Õ=HåRjÀÓ”Ò[FÀˆlß*äbç! è£T;ò3$èñè/5ãÅšÎhËôÍO—ªÉn³F{óo…4Þ.y<áÖÙÌÍm47žéïW6´)@úQa—Aùrw9•°›ìµŒ@zã5`UQ”g!ç)ßïftR¸‹¿öÁÂ6:‹„×-—`šmûP)1õ¦ÚÆ ¥ŽON¹d?‹Jj,•”™å¨ñÕKw6÷h ©UïUãqÞBœÒ=É5tBpöx7€“QàŒ†oÈÿÆéC-¹± 8>Á[ªòÁ˜:¿Nfëi÷ ðÜð{í+¯€ù‡°LdoŽ_wa×ÊÐ…íiÛsŒˆÝÆlÝACÓ¶®õh£…JÕ!¶NO4¶óp(Ñ{¢Ð ‚\á í’ø¯àóΜןìÓ³¢T}ãÎFIEÍ”î爨€¥œ°zè+pµþÝ^…ŒáRççž38ÔHðº±ZN†Ø÷]Ô®Ü4¹:ö¾‚;,Ú„ŒÀßÖß×Í tÎðdDdB«d`p‡ÀáN!œð”ËÖyڦϕŸa÷µˆ ÏüvE‚Ÿªbb5HÅF|ô©Ù_ÏîKKp‚]p¸Ûj%!ÑCÜŸ3 Ad5I)¥v›’gª öó_4(ˆ>Þa×Ó…ìm“8å%Ç›)hÛjpד£G~lØÌT¼óLñfà ‚ìQ•‹R¸%þO¿úrFÔ‰ßwüÔègb*Œd²i§Ø™›bÝÉaíS]¼C‡ÏÈl’¯VîA ÅÐ(Ñ~üÆ<±²PÐO%ô®ÎR^e’ÌùçÉ<%*1LŒ-%ª·ž¬usˆN#$¤ _÷:QB4Ã7+UÍýy¢ Kæо­@-w­Ã:ãäl ²€6—d<Rüê’¥OäàW„‡å*JÂ.£ßL{„F­Gò<â¯ñØv˜sÐbc·Iä;J6¤s<·HÏf4œv¿¹ÒZ !ÞU‚/}çÿú8«DÃ7í5eÃ\hâÝj+ž¿õú*»«8½Æ®¨?ºNõ¹(ü?ì®{ Oª}`&­çŒ<îϼ<¹Ð òêôÚ•ŠJÙy¥c¬„¦i%Éíi`Zk˜“›Üá'AŽÏÒÁ°á®£ý}"t!MEK­ì V"¼øڌܮîò÷JVeiê4ÅR2ÿ@’¼¬3¼+ꃣ·ô>í˜'H…OEWJØ—u>#U“[ T‘"¶ŸÝèß™NECD£Gýï#;‹¬ßA‚¾û\ƒY¹µÊ9Æ bŒN0ÉDO¤2ÛÈ=ºõ+åwÆ×Âý š³•NDSèÿ ="TŒ‚Š`p"j¿Lk×,ãt= &(%ˆÂ,[בÐE®‡æº©pÕVžw"=W§Ö2”u¡¹¼5:(ÁjÓs2|ÉÑ—Ø*ÿÌÊàßosæµí´CˆÎ"*$M„.VÁ|áðMÏœ§.¡ö^…ÜDnÌŸæêç~ t.f‰k¼öb¾×è·XÛëZÜ”2Ô•i Ù½ˆ;æOŠä]4!?°–IŒ¹Aÿ¸Ñ'R¶lû‘¥ÔõqFE‘Œ®â÷ÚO8}W:ð‹éU•Ÿ|ùYi’§‰H€T¥KÆÅPN«ÆùlÜñbâœgmJ"‚Pž¦~ûÎ_ýukuükù®cçÛ6JŸ !:é;þ65<÷²=[—-;ÿ,ArO ¯?xŽHa™†Óñ&€ŠoĪ¦x»Œ[hú[­cƒ%¶#JÒdãÞ-Ó¶á·Úæ™p—´išè~÷ å¼Dħõž˜…üYnä/CñOì“nQÏ¥²>‘Ô‹R8® ôW"­½®½ã0·åf‰ûÜo°ÌaEåencfVƒ"v‚:f Û†^Cm„Ç eœØE†©:vÿs¢Ë\®Ë‘lÜž±!ü^ÓUâØ]„ÝñÁ·v‚eÙ?Náz¾ZC—}&˜ou H úòòÆÇ‘G»ü.ÎTnìæ3ÊÒ[ߟh4*j¤T%¥“&ƒÁÙýTŒ Hf ÚPà8¸Ï°-ÊW&{˜˜ÕˆÀ|†ƒ[¡=p×oøMß”Æaé‘Bò->ÅHµÊïòörø¦‰ãa[Û| SGhr˱}*_Ÿä_ɦ¬üÿáZ*ð¡ý Ôø™»Ÿ<•Q$~ÓÀ²ùí{0Êw–žI:yœâ샜ðFiìÅš¡Ù—G$yc¦ÕØ·±‹³çaVSv¦ÛWB«Æz ¶áDè6àc^<„´”&ž[ZÙëÑ[ÿ'xMº‚)ì'„ù Û)¶ Îyø1¬íÆÞËY?¯?„\/…%= ä¿,ëç×ùíÏö™é¨;üùà^´±f¼Gƒ§¬þn ÝÂkJ~ÐY%kFn>ç45ÌYGF2Þ#G &bµ§ÊÕ] Þ%³¤ =2ûÑx©À®qBàV¢[W# çÝK³ìZöàféRj ÝÜaFÏô-ÆŽ¡3núšÇ?Þî² øiï¾™iÝ»ž3f§ud¸–]?jje¸Ò,ÔÚ‰ÞÌVŠšßä 0\É0ê¼öï#庵oúì]­º-‚¯q™3‹R²q$ÁJV(¤[ˆ¨ö&/lf ²˜Ñ¹ò¾9qï5!æÇLí–¤5£K•Öüâ`–3²W"‡‡%»jìyËT‹æ]ŠÝÞíÏLÆÒ§¨EîFÃ(Ìo2o~è Ýÿœm´€ AZ€£iàoÜà¼_òÌ_ëM’!~µ‚eï Fd8åUy…å¼oõ8Ýé4ªú¦–Þ`µß!ûÂRäÅã°³~\)[Jfßµ°·kšµ“žŽH­‚š Ýk”ùTý¹ ¬£@1uB »Î*|·²EÔå4±‰¾(úÜ£Ó¤›µîäž»ÅIĶ„“â\kOJ× ëš{¸ÙüG¸q\Ê1!œ€4!Oüux§{ò›‡˜óºÿÖÑ0ù4¯¦`Ÿþ  0 ¡¤ºˆ`øüs÷˜é7RbPÛËžçç¸Î —¦9t¯7ùlmZkã™\ýÑl£ð }KÛ£DÅÉWFëÒc…•®(4û²§5ïBØüû°ÆèùýO^Aü(èJàÆ+Úy6"ºüþ€¡VÌv¼<Ð×M „éÃê`zê' *Tk¤„G`ÌÀ^gdj#ê1…c涥JPÕ´zB<ÚcÝ󚃰•Úú“ËŽRÈxð즮}'4… Ë`Œ yËÉÎnaÔ(Z¥% DùJñý2‡Áä¥v>!Kê™.*®&òÄÙ™óÁ“Ûi— ‚3=Í$Bà‚Áç')Ô«Ù&ï´Zt¸÷ñxâœü:,òüA÷2BZa¬°Q?£”YÙWóÐç| z?ª  HœÔ¸KKŒ(FØ÷†©†ûƒÊ·íšÕŠº—¤«ÚhüƒI` ä9|tø‹JåO™Îß²áWÐT ÖV¯L˜ª–œ£Á„vÊ;vÈ!çß¿±mÏ‚ð‹Û¡™”ª¦ÖãÐÅò}¶py‘0Wls> ˆ‹'ƒq‚ѬeNöÕK ø2ÌEÃ7¤¹ÂckÅPMT@*䪫ÍöQ‰êoÜZ@‚¢v¥Ž§ ±Àcÿú’ádC­’WJùÖC7Õ­‰‡yïŸ;‹$de?ù›+Ê%N0˜*¨èÚ”Otn&ŒÔˆL=Í%¿ÈÔC†\ÕrZk†ß„Õ¼€Kž—ŠaLL. Ï[V?KW-gß­â£ÖvH°p(0‹Ÿ ¾²NÁX¦á-êM­X,_>ãk^Òmëåi?ø0l[:L¼3½ÑM˜¤^ñð´ÊBAœ |TM¸ðê~dx—»é1BÞEH®#…3TsçÈpýxQòr¿*ï<ì‚Ê%)ãÃy{†(#§[:6¡nÉB"ý·nW¯^eÃöÓ9z¼5¶•‹\fYç–’îÝÿ-ü W¯X*E‹:׬¢õþ5¥ ˜·Ä½ÊJ¦º£7(¢ÆŠšÔŠKl3nWh»V%]ÌÿÏF ·É‘æ¬5 í$M1O²®A¸zKÍâä‹äøcRåâ§Kv¹+|ÒX÷7{q©–¿@¬5°qz¸]F3XpAš³P[P8‡ ©súÜM±t=lÉ¿s¸YÃ:êÁ„œeq÷OMr ó#YíáêtuïîüÍ7ßÎXš—"(¤¦áN¥Aü0š‰Ÿ jѳP¨ÉÄ9eÁ´‡•c|ž{Z©VA{þ13C§¥ˆ!ôǬWÞ—Ñ?âØ„GŸèü×.‚Òø®Ó\‹Q¿XƒrfH“ NS E"F0¿ü¶Ý“ø"Æ k–ⱇ,5Ko”Þÿ›×׳×rMMÖLxÈj]8~ýà’`o™|Ê“H{H÷à¾mdKGôb‹òqÃæõ'#–T×àÙ`ŽÔÖ6ma¯€[L!I†ºüׯGÖñÎÈŒ ìŽ6x žýàUHÕÝ1KP—u{ ÞÕ]:CryÑ`ox »1ÐJÁÉÒSŠ¡}…Rå+ªL}"Ô´…2AT/¥r±§´µýõ¯~Ž ª„3Ò­‹[ñ½LÿÌŽ¯Ö> ™\½Æðø=ìÉì.IŠ+ëq» < °ÙjªðÄIüªÙ>RrmoÒô<·¦Qõ‰Ç4 nÖëð÷Oå%®cJdd¾“r^q‰†mØà-™Šâ®[¢X,ãÊHv|‹Bð¾»¹$CÑV§îŸÎßè €ÃaO´C¿mBû0- ¢M45³,ödi Îá÷©Û“ç¦3¾¾([( UÚ7œHÑWÕ?º9ŽäyH]ô('Õø7ÈÿÁÚhî§øç­ðôrr4=ŒÅšîýLR!>Ÿ,ûaF²ô¯ªœ¼Ò’—iíÛÖÅã+픕͡² ®‚Æ­pˆÕÆíÇå©ÜeðX݃Ø›šDºnHw眥ÁBÁá(1ŒÉ£Éld`ž@ÏØQ?<1‹üôKˆì¯úëoL&ÞNÔUx¤»Voä\‘4¾Œpž¬&Ë3æ:ú—z¤ÀÆ\²Ê ļ&|º+»TñäV!ÞCMà^“6©—'èíôf|kiͦÙIJlÎçï "V‚'ùäÓú“­1­GP¶º- Så–ö ¯M¼GáM(¾h¹èVÓñ’„’¬óÐÈu¤< '7Ô¾´UâEѭ弧TgrOÓÙÅg3­äw‘‚™ªñbpÏ`K^®œ1Vu†jöŒk—qòt>^UåémÁw쪪oÃAQ&@ 0Oð[Í%èÞË«hR¥µŽ¢V'o_`Ýú6æ!b“­1fIUqê¢ï/9/Ò*f [1ñ+·=BãR~&Û¿(¶Áfª—»µÔ¤v›à2CýÞÍeeNÊÉJ âe«ásêÆümµW~Ç×ü 8¥ T-}¶‡öò8tÒmuÏ] ¯æÅ !ࢠvý‘÷xŽØkÌGé‰öŠOÊÝ$Dªaš$ƒ³ëc\C’ãFîïJG4ä­%Œ8©Ø¯Ád\ ¸Sh£gNÚ‚ ï _v ºƒ!!—°;´¨8q¹]¶Æ-|Dí‚”ùÃSæ¸I&ÍÖ7è—hÇ1³ð§î'3ɺ溕j..CjÌåìyä%cFïœhO8¾ÎÛ岬˜b¡<—žKaiõ#,9Óî¨*ØÚÑtKZÛ=Â=™Ä± 3‚7•ÝÉ> ‡,ÎÌ“‚p¯bê‘áÀ’ Ÿ¡” ¦[«t69¯ yïx:UÝ[ÓÚÚwî‹q< NWþ™‘ìÅ”ËÎ ×p˜Ú‚Œ …®ˆóc°[ªrêÜèÊT»‚(tnëÑÆ7Û7a àÉ“ªÌuSU7Gû-D‘nµþT§c.A8U]’m-c÷qÔ¤'RÅ¢Ñ@‘ÚÉÆÏ—o^‚žû“ªâ Q®-6ö 1œon"6Ïäd.]傃ϑˆñ6Â<««”ƒ³ÙgHhEœBHŒ\^R_L$…vÕ’Ù++Ê>|me3l&d9UéÜžØw i¹ d]{(ˆt³ŠVLÜ|U^“É\‡cô÷Ä6õ<×ßJS„ ­›-ܤϽðÉF÷>Z‹ÂBr¹`î4béÿû— ¤AƒüY?oŠt"ñ¡vmMÞ\oo­èæÛSÌ~µÅqZË“ )a8äj“y–‚Ñ+bÓ¬J ÷?Û-ÆtÓíÞîÉ»B™ûi?™ÙìfˆëÇXóG¢}š±t.'Q|µÄ~p:­âò wžß|þV³[˜yÀ¯t‘~Ttö•œ¨§kMH‹sqùJš$cãÛj†d~UjÎÛ|_Ÿ|6§+kŽ+ÆM°º”C4pÛ¯'¸`ÓK<Ô’=:ÿ8ÜßLoæ–.JýXÔÓ÷þtQöspJzkõ•G «‰l×­~§C}Ø¥ Ö‡gÊ’|È7<ñOóœ!¦bV¾ù!Êó2Žj™‘<2RDŒP *-»Î!! ¹°NT”ZÃÌD진d‡¿Îý$ë¥ö?"ºmü´Óƒó§‡À¶Î¶äá^i‰«ø@‹DB4ûªíN!¤›í½ jZõý"© ã ÊPïu e€íÔHE=Hdª¦tm°*oÁ„íé¦f~èÛ]+/ÂÒGa…àd °‘ñœ´Ðe,éó®Z…n’€ý´ü’öW—Ý{RÔ|¬ á5•Â[`äR×FÏV\¶r¡Û³¥ãŸêXc4‰ ætý<Ýž¬u—Æ7y<ˆ&„Áz~ð‚PÐonîÈ꺮S <ÚPý8ruòú\â¤} 25>Ézòëä͹ú8Ö¤=#͹áSÊ÷žD6û6ØdÃvÙ Ø+@MD§ÜŸç﫯]x‹¯2†hñO³‘{÷Ô è|Ôá¡Š¢ä#¢üæ\:K÷‰­¢hZ˜HD\J‰“hc7³;úLµeÒHx´Ê4‹¸ w½N†"ìôg;ß’ïëŽ(~QÛÆnLX¼µhri”ðZ, þÓ[‡Ü—¹ÎøሠƱo™s[|& uEϦxc®„³m|ä›ü`›Cy|NµÁµ%ŸBF’Ž|hÑ×ûurXäɳˆ—^Ý8ÁäNÄn¶VË7sæV àÃB}ËgÝZß•†q3—m@762ŽŠ .ðŽ¾ûÚ`ŒOMÐ"²[‹WœóY¯ÁÆlÝn¶Í¢ò\ÛHSßtRq(Êì$OS”–ÂyßI']s­óÞs³ÆÂÜzÊ>¦æTEýpâr®9†£ÿà†Jh7a‹6<ˆ÷÷ûdÞÖ1¢‚@ƒž‘oaQy|1k*׿ÂÖ²,¼cuóu¾&ñB%émý9”c ?;:rÙgûR9Ü@€‘®jH²×Æ' % ­äTÔë…·ö°QDJ¦.¸è€™ó™ò3¼Œ"qà.ØÐ>ÏÚöQ‚ù ܬAe¯Ñ̸LÄ•ÞJî©bæCŠ§]UóÙÆ,Á·þ¨7ò‹‹ŽC1A×CCܽÃ(Rd@MIFÁ•õ±»Í3Ú‚`d¯£(«þÕ´Þ)‡&Y¿+‚ÃFå°Z%®!I?Ùγ‘Ÿ5ÄÁ·àÐ*D—áNì ÓŸ{×0᫧dPV^ü1sŽÁþ{Ȭ9Í0¯Cun`I4¼i"ÆÀùCŒ>B„4gˆ±J¬+fÓ²ÔÈ£«ÏÃ{`‘ž"‚ X|#BåØÈä-]ê$[m˜ùé¹ÝÔ3ÜÎCÃ}ÂØž®8”ó vÓÓébx{4AozÌâ³^vP"ÌÞn—¾_é§`z°{ L-qV/0Vìâï²ø?0Ñ+¡ W yJßÇ{Ruͨ-éïý´êÒ­ÂÕÓ¨=i.Îà !2#Þ|›_B††IÄž"›¼Q_Q[:*Õ£p¡*§ñGàáƧPKÉbÔ ˜ÍÌò³µÑ8Ÿt¦Ø" ¯mìkWa\_D Ž€=_ð÷/  cãt=4K p.úÀJØ Ò€)òa6?=ý#u}Âô[¦ˆÂGä¦@‚`ÅÙ±l‘àtêÕqïbEk1N­xr!6iN#?ÁG…8>%™w ãoK¤‡Qj0¬Í‘ ÑJ†sHjÌk”q dⳂ|„úøCŸùXàïD["ÜI‹LÚÿH€$ ÖþÃ6JîtÐH£¤ôž=„;7¿ ƒ„8_ ÙfMzÝ<ÈN€„ôÖõæ>³É°e§£TEç¸W»]!Ç£ sý¸"Kz¿ü5¬k‰7ï€ñįÒµ¶ü~éÿD{(³x{9X LNrÊ5&:»´ôZ1êŒè­ßXq1ÑߊøÀ•Ë`ò|ì^Lù¹wv›<Än85Ì›ÿ”°n;/×èÜ÷ÿDgé  „†¼cúzl÷ׇh£zá/rýªÞóŒ×8eR÷{ÄêëbŸ+–š+m QÓ„FR xZ™XáF —Vݱ Å$·{@€àônyæ®Hî°ÖS¨r®WH"Áý•ñd¡cž¨@qAè?ƒocê¯ö„µí`Xã¬+C™ã¹+<›3„Ãú_ŠÉ˜†ea;¢±ÈÒW®À+ï$û8+Ž' u›mÏŽkq|Ê’ç]e=i`—ãêzP³Kî{•Ý}uS!øЂŸÎµ Í••„ö‰r»À9û±kðÖ8^‡8,@qõàȯù^d=ÛÁܯžüH‘Õj`ü*F¥(¬-Á¼Hú¦f"”xvm[ºÖš-Ï6‰*Àe`óó©\i7öÇD€£þŽ0 U’Œ=;G‰±þ"ƒÖDåˆO'›ByHKwW(úㆠãTÚá`T®§Ãûñz×ʶ…px/H’#XMf3·¦_5ŠÑ¡w(§Že¿§³” ãðgõJ  =ž vh”Ù‚we~âbXê”ul{é¤U?TïühÍÖ0 éÆϧ^”ˆ¯¹y`ÊÚ'‡Þ•Â] ÎmýK}šcÎÀ í »a% ¢jïE”3qµhá6nTb3 ºä‘™?Z#2ôaqY»d%HFÒ[âwªd£Z+,J"øý‚H^ A"V£È©ÈÊk.¨(KOé€ܾ™Ç`TH@ø±îÊ6x4s¯mÔ/ù~j J£ólY†^¥yuAŸÔJÒÑ‘Ð?ú¸M‚‰Ó²-'$&…FÖèúܸõ¢ß-9²¬ +Ô:|÷n7îk¶æ–æyßñ–ÈG€edùS.™â ™•3”‡ü¶Åã¬Ú‹£‚ze–n(,+¡ü¶ï&¤¤€Sf=ÀyÑø¶ïzàYÜ8¨HáþKöDŒiÅýéuYWþòÝ Ü ZiÚ·ÛK=QU­Þ©Q퀴‘ÖÏ) Weœó}@ôAYÀYÂÄß nÝØg—q|š_€º?8èÖÕ$ë“:U×v¥‚”ó" …Ú/„¼F6‰8¬2󱟢ô¯Y*´Þ^Wë:ãêRžã2š“Êh5øRBI¹D)+êg t±ƒ<µ¶5€•ÝÖ’Æ*0ž³ÒÅŽºh`Žç‹vTü9käc:Q›^ …ôŸh98äÏvªD‘W„ÆâÅvjj0­½%²rÈœìKH'_ ’æbŸ c'bn¹ó4’àW¤ò !‡²[Ü÷üÃ)Ymµ=ë`•E*:²Ü áçoÚw؇š>/š–´ .‹×w™`ðÖ €Qð‹mýçcÖ…ü‹½¤¶ü™ aã=–$‡µ„hó¡&ÿÚ¿5Ÿ\b– ¡F>A>w ÐIwb¥bäld½8Zý—l§¹9°|•{½>àpòõ ~8þaäÔôÛ™ó ¶‹¾«±µ¹Ö!ÍùD2‹IÓËËÛfÝA6à4úsª{Pú$Ä>„¸¦xmöõg2ÁŽñ-}ª»²háyÀ c™ÿ„¦lWÌ ÖšÈ¬[a” OWíüD©0NͨG‡pº4øíÅT…’5¯ìyëÊ}ª#ëdß´ßš±±ŒÈƒ6Qcï†ïcH0ði ØÒsa7Ãb•ÓüJ̧ÓùŠìãÕe?9¿û<Ý ã¤Â G ˜\ô(O)Asoô¯ÔŠä+Œõ -&á{k«-ô¸îL¯ø(çÙÕß k;šW§KêeMcNÐÁGÌÒ¤µI£Ae)¯/ „À£wúªÖÆÊŠÉ­î 8ÃS× ªZ5ê_3âèBçzýÂ$]i9Ⱦ‚‰÷]2cØÿh|~i-}òé0Õ‹€9û‹zŒ¿Æ¿h‡Ìèy«ñªHã‚txÑÈ›£¨~±)îú²£ŽHKþÙÏ*˜´ÒD,Q¤›u„ìÈh½Îˆø” §y)…E$)¹êó;æ˜ñRÍñÂuH€öY:LžØ¤ÞP £YÉ¿²ª¿3?¯ú‚i œ£/TëR ´UíÃJËQ¡,¼AªÍy“'âýdÅ&ó–æûøbЄŒ}µ]YoÎÆ„* ñLî¨U𥹓ÝCö†ƒ¨B“àöÜuLÜz‡ù`Ù-â04o®Þ_v+ਊ#÷44æ×¼´Ž‚ýˆg'T#Ø-ö)²’Uûµ…êße€2ÈI蘚>ÌY?fnÂtH®žMHɼ¸5öX`Íá´sœvn'$ÊåÉòa§Ë…þ_œç˜ù Ûî6妙¶‹ƒA!Ìò  öõq…öJ,Ø ŸñÕlh9xó¤V,aªåµ½<Ôpû;ÌÕßy4ðÔÄ]fʪ*PâB"Ž7<«jaAàÎf”Ö–Ç ,mâè¦oŸS†~‡Å~‰,aMб8P?ß@çdfýØjÔ8æ9ùéÕC嵑Íaò‚¯ç?µ½Ü[ ‚¤öyKè +HŽüðÚËÎ,õÀ<›œrz!ýYW²L>… qÐ ‡¢p‡7b È&ÉÛ,i¼$˜ì²öíí•"UÏõ©9’êüçÿŠô9u‡TítÙ1R§ Õ„pO1Oý›âXð¾ogÞsÅ¢™.z:È)ÆÂê ë ñ¸½+3FVD°m]”@þAÑIK^Ì)[ÀJBþ‚>á:· ;Ⱥ`“Ê93«G»ã+·"zhb.HA‘±Ÿ¼ö…°6$ð’N¨Àîç]¼Lr»¡€]Ÿgf¬–g̳¥‘é‰é>Œk“lw“ݪ³èîØA ŸIl­\l ¨ñ{Ǻ(Ó`&ê¬Ñ’sUœ Ç/+TV×2hIãž{ôUái×Ýœ£I¯›º6[;ùóZö­bAÊmS¹¬Û ›£'eJžx Ö#C³¹Ê(ÎýôàtÁ)WY]ÀZÓð.ÈÕü";Š)tÀ½—í0:9á(sztû•n*1àD³BClãWŸ ,ê›g¤*-ÙÖ¹ƒß/\³ÿ¡œìƳ‚“[6ˆÍúuÑ?ÄóÙe»Q—4IÀ$™Ýw_¹>“ž##§qÁ{Z”:§}±¤Õê!~zª'ÊÊÑêkÆÃtðPÜZɤaÓEšµnùmÒo5 À$íþm¾Å®9]ÁûÒL”{‡ãtymk{Ô÷†4qŒo²¯uëk¸ó‰#; Œgz}_èËŸŽ±T}5òΪ{øŸº4Óqoûìí jé;鸗P ;/} Õªòpm3ŒtnBòàíSèoö;âTFuTì§aáCÂn‰sÇãY>ÅšùG£ã=eôE{"9x2 w$ {çå×£°3üŒ»z °aØ+j˜hä~üv½!Ä~ý?4Yêâú›R”ÈtXó»1ùF£¡¥–ÙpàRŠtÓ7d6TÿO  `Èyƒú#ÑHÉ1à^ä=‹<Žd¹ðy¢©âyô öÓá¿p.rNªd1F\„ü1Ûk¨3vÊ+Í€V¶\Žä:fQÇyR“?‘ô0âzÈoâX¿i„«¼ÖpåiOu3uø¿G²¸ã|0RúN€˜kõo6ÏwFF|~º'‚µ oí»´bŸ·#]×I®Jç“»î/63>ù§/ø+n±£½,kl ,°R iŠ¸uʘ4O‘ŽX+x˜´pWç¤ë9ì…§'žB:ýÔ|C£PHÝú{šíñlX€¹›Ož^1sªçZ ýâÙ®9×ʲSFw¢I(NbEõðUàt×’ëÝñ¿3Ë(ŸØåi©PMå¾aê¼l)AMÇŸU*Ç"²›mEÒþ,ºw^õç>F$¢V,ÃR})-7véÃ/éà…: „jôÔìzŠÚËZjª…|D)&¶!NÇ#UG€WºÁã>ºYâq öIÙÌjíì¥L'Ê À<ú÷yC>ØÛ–’ë¡GC2;‹ºÅ¬yÚ*ê\>º ñxóœpHJº6Ÿ:É?óLjú9¶Ì1œ²¤‘ïoe÷ãÁ+kïL3·¬"Ø¿Y.~ŽHñÂ=>V‘0+©Á%D5ß*f†Û#£ž úõ<‚_™+pnÐ$ò*—Ž"oÏLxyH§ow,ÇË-°1Vd.pÃMmÑÕœë.ƒñFw1’ðŠ‡K%Ó¦«Z…¼0ìnvF½Yºª›UÀáñï;i³|ØÌ;eø B±H¶U½lX†³€8ùéNÇø=~ñJ*–”]g1à䞧àô†„1¦båˆÒ£%Ç¡æ«iÒ-cÁŠ*;5ÿ’vGE2uedøŠ˜rÚpÑèæ{^ŸRKüѸÉÓó]YÖÀÍbËëÿK8»G*ÁÆ/çSz¦FWû…o¾iµêš´qrÔw òY‡äÞ¸¼6m¯k8§‰'p¤ž"°«¤Œ·Ä tÐ-©¨ú^sh–¯3BMGê­GÄDúÎÉÀ<’2zùb1kž•žþ¢N Öì9½c†G£0P×pËë§ëbeôì ¾… ‹Øéo~•º2O)ÞÖi-"€q›`»˜8qÇ¢Ô/eZˆæú&µ [‚ùÛv–¦ÏÏIêàxäFƒë$²¶9ÍÕ+ìûbfëä+Œº‚¬6«{SK¬lî±›ö´B êÅIvuÜa8\09ŸýT Õ œ“4$ˆV*ÃÎÎ0Ø)²J³íš7Šþx½ø˜q»öpq"Ü©«‘µËÁOÈ&ã™n{„¤Îš“f`ÊÿoXçHRÓw Å¥2(†žFÓ¿©GiŠ‰qZÀÅ!b?AiÓÓ¿V”àæÜÃ99þÿýh(¿ÒA¯cTª÷š¥AŸÖ<GÕáÒzƒG´¾ÀPíÁævðC IC¥ŽR6>ÐëÜ–¨“ÌB Ñ] GúäÚ×,÷Æò\Ž(lŽãÇ)Íb×Zè3¾¦ãYÓÁû‡¶Ç@¢Ï7V×´o¬"8Æ””Î|Ö©ºf}Àç°ôƒSB]¬[ï( Â(Õ†×ÿdG[bDÕ BL)AZ®4lˆ"Uq]LÄl YQ)–€Ð@‡Zƒ„ɘ,³$ØõvçÀÆHÚÕ[±j2K¶[ºn)€NÙ´¡_6@ÏÝ£ ¿?õ$ë.ß‘ îÜ@c!M­« ˆr£A\63°QùO‘ƒô¨Û€¢Â|J™3ªP$ÙðÌ{êœ3dmòl_ž®)ï)DÛ¶ès³Å¯óbPÌAÎêàÚ}oP½zM†»­÷ëï&¹+Òg¹"Õn•TÓ í¸hDk…o ûÊLèEê"÷1^˜¼Ì ‹T߯b+äóI þAJýcPzBàϧâóˆPÀ p­Œ1{ox ´Í‹2- y#1&ìQ¬AS̉.nÒ ¦=eÎ=‚Íûñâ‰ôÏ:Ò2Ùë‘eZ.ÜŸÌÛ´À7e)˜m‚Î>÷ò™æ<–Á3­ÐÔhk>Žzéi|þÞ”&xØHõQDi)ö€('ÔUZÌþXí^sÁ¸¹ÎmÚ¨_ ²ž‰û Q¢%Çgž8ò¼© |¶×?¦I›®=¥t+4‘§Š²3 õ¸ =±ÐsK\ͪµeã5÷d”VêÒ-ƒ†Dù]/=¢Õ¼ˆX1®½/å::m/ ¯_I`ÓÐå«J’ƒ=­¢@_AkáE†Ò8;ðÝOn¨G–à• æxÝ-1|ÂUùKtSY@ò½gc‘a¨€܃’o›pÏe_ÜžURPÖùDøò"â jÙîVYŠÄöB—sY÷É®xnïDcgÑ/Œ˜xë-Ö‚ïg,*0j××B@RÐtúÍ€ÂÔÊí Âcl=%6qü:ðM `æ;Ééæìúd%x ¥Æ´˜ Ö3ÛÈP€I¬æËz€õZ[bùéÜ|¦`è‘?›RŸ/Ùâ5V°ûïúýÀšƒØÿßæ äÎ 8?’7hW/ÁÍ;Ý •ãVX˜äBÝ%Ž¸ùæ:Fõn L` NÛ.þŽy4P»…»Ù¼¥<£ÛÔóÆO®\x3³ÑVü/!CQï‚sÆÈm:e­ÙÉ£÷ÿûP{YÏ~ƒ~ðsº;VÁÆ=« ´_µa Ꜩ©whÎèßnÛ•$kL6¸ÃJLîhòìG– 8I)Ë—ÚßܶÀ 5ÿ¡®[É*þÒ–îù]ïá®0ОÿØ2€-üî$W5ƒZ`¾ŸF¦0Räµ_^ógB>Š6 —Žß´þR.ø|@@ZÛӀŴFUIðq'YŸ·2E¢ž:\5+îlÐ_-’ÒfÞÔ!¶×hGVîèuÈÀKþ'¬7†•í’FÀ˜¼KÞ·½=¥^¯j [(ìm·uí:²mG³ V*z²ªº`r-)‰øuüT£ùÏ—Iߘ°ºü¶qÜ?ïI˜=‹¢f,-oDœHË>½•ŠÅ$ G£›y{ïŸ^Soj] Ó q´'ù§AÐë(>Ö˜“у£)­Hs&ªË”e΀H~;d£}PiM»·è«Û6û­¿î{ªø÷kªÇÁÊ3‘AÀäøu»Ê€Ì¨.ÁÑ¥WBmúµda'„ÒØî%=%¯€“e!,ÁÆ_°Í¯ Œ÷ËRT¯•¿ke+ÊÉ*Yšl߆*Qãiéñyj/΢\ÊnK÷›#z󊦼u®yâZ¡‹ÃÇöú÷}¤ÞT­~X‚x…E‹&êvyäZõ.ÄóÖÓõsGûK¬Ãû6ý?ÞQͨ5p!'Wýl%¾gܹšŒ–åéŠ]È/ì+cMQ;rï_ÙEER‘¬©ùD‘òö© ùò>0 ‹YZbayesm/data/customerSat.rda0000644000176000001440000002107012516003353015472 0ustar ripleyusersý7zXZi"Þ6!ÏXÌálè!ú])TW"änRÊŸ’Øáà[áß^ ·ÖnŒÊt± #ù4A#âžc.bËãÎ㞥ژö”Ø´x.rŽfºï²ÌP8HêǺì–)²_5*ЀÑâç—2Kc³÷°ì‡ëÃÌxòuúó èÞJy³ ¼ùoØy¼qç …ÃNgPšIW´«/“(Y+ß1ÀÒo;|»ó7Y¤ð#m‹*÷Âùع£âΓßÏ‚¶ˆJ­›.ié¾ù{c”ÌDŒ1àŒá¿HžpŽ= º*똓´’¾ÿäjÝ#ª¤,"üÂØ~ù'To„Ò@èÂç,T¥·ÿ§ 6Û—§vq v÷ìN7҇%¤ûh‘(ÒG'§++¯(öÀJñÉÖØ”‰Ê¶ºÀ…Éx.¨Òs5í‘P§ZIȨ¿Nt§ õ%ä´qËbPŤ Ìk¹%VzpwSɲڅ°*À×&] V¸ù”¼ €'ÈÚ΢Ä4!ì{$ |¬NBÜnm|RmuO++¶ o»¼½,Í{#gÈÍAš‡§»$*dÜ­¦e2‡Ññ{Ãù+„IP¤˜fru×Ã0´¯8ü=0­|Ž’Æ`uýBno’"Îa³A]çUÔ¸|żð«´Á1_Áá%~™C³ïd·}øÿÿ¥÷³Ú»ç* –¬¯CÚ«”f¥ Ô¨!ž£Ÿ¦š­%r5-Ñá8TÛL%p"*PF fy q“Dæf*øâ,IŒÅ°ÞÝÈ÷veT4IÀ¥ù»žµçÎ;·ˆtÉnJùÜñcRJ«{÷Å,/Õ°DHt¼9àŽªÍeÔ»6“7^S‘»˜RXD×àö-˜¯:>ú6IþÞKdDJ6³ €tÉá‘”±ãV‘ÀbžâôúèÁèáQ¦P2]/“Ö£a­sN$ó1ûäG€’®$YÄž\ôô‚tú¯e‘nÎgÅ£±‘*€ÁI68Ñ!A¢E”¸ÅI,'ÇŽH×O±g•ŽE ?7a?;Ô·% %}z¨’r˜¹Y ÃÂë}R.J‹$9yÒ6{kÐ.æý@+7‡ý¦}x’ü\ºe5‡®ÊÈ„OÃê~„Z·QªàihdïÒwjE£ŠäIÃ6²™ÐµZP°()”h.¹Ã4h}IÜàI#D)'ÿ‚n»’‚ÀW-V0 ~Äñ/y;ªº7îIÂ÷Æ£³³°w -*#߃ýÝeŸßZY„ÿ†ÄŠ¨!›ð¸R³B¯ÉZ±UD![ñÝ­Ìk¶E#?µÂmÙe¶ÿ¸óÒû½ÓÅ‹mëŒZ‚ìOyå 6Í‘¬•>b1Œ–ÅfïC8¶†n:†rbTNªZÃÖH‰ê¯Ñ#³tÎ(“g¨¦¾ì³ n(»z+ªU&áGEw®CùòÊK±ç?“Ò²ÅÅWwÚiŒ±RÎô ò62¸úªõ»Çý»– BLǽd½ž-R·ELõ²jù€Î_E$Í£~"ýbaÞ5•‚øÁØM]2Ù¹¿O½ñéY¯‡J eãçsÚob!ÑDå‚á •#éz?¬Xª€7K>€SU65|>¤zZÂœgøð.£dÊÓÒyKoÝZë}aO_Àƒ>#èYÄ.pˆÇŒó›+\û@[÷ ßï¶Öºl~«ƒ´z¹ç+[>º¡ žF¨UÿJ?Õš°%Púì4h¹UÍîÖÖ…œÔ¼Œë¨ñß baØ·(M¾§!páNò95ð.ˆa°ø_½ieÞ(ŸR§™ jÍtêÿ eÜ9Ø÷Æ©Oèݳñ c‚MìU3Ø] ô7¤pSÞÜ›=Šð\j8wˆL›è4²cå¾”t+|?$˜»pLš~â®^³*°áô[ïÏ04},X“‚ƒ– ÿÀÃbæå}œPÑÌÄ»D. ¥Üµ !.3ÑkØ@Ta-ÈîZŠf›<Ûqôì*ÂDAj)Ь--”Èdè¤0‹”f ¯=´[Åïì×e†Dhƒg<æ.Ýk‰ƒå‚lrHÛ:k€\}”ÏÑù õ„™3ç{^%:5Š1åwŠe<°œì·fì÷üÕÔ¼‰¼ÔpWæ»Y ºT8W碟ZiJÝÃ@«bmÑ›ƒiì~UítgEÇ›Uä—õ{fû:ÑÈ‚:)%M;Άä®_[(bÿlÃîÛ8º–®6ÌêA ±·bOçÌDlŸC%²™Ó„§G¨ŠijcORS=è[ÚÇ/Ös¿wG.¤Î׋ÖVW=rMY½&ÐÔ{ÿ €?ÄàNÖ½úpq‚eÃ_‡§ ÍfÅ'“ï[5ÓäŠâxß~È̵qö*µ:¿oîoOÆ&Dµ ãa„Ððt’ÀÓ¾çm}^bKSt 0MµsÏ•õìoäDoû3)Ì1-QXþ¤fŸxºAØ«™!|¨°;RtÔÜ‚Tô(ÿžê,‚îé¡_dÞÕ^Oä{ÃÃÇ6Wå@kš§…ýÒGI.!ð_ôÇ8‡Â%€@ÈÿìþÂÁËðdŽÚòþ5ñ7“‡"’zIønéœýÓ€¸Ö ÿ¬a’T“,€¨g°´gÆCÄú™þ¶¿€ññ]}n¢C;i æDY}J3ñ‹o:ó*n)ø~Ù‚±Aº0üÁá5Šñ ˜Æ唹«”‡!ß6ìò»ç÷‹ýž wI h™7Â͹šè(r*kEŒAëa ô˨r2¤(‹4ñÏê¦6ÒÁ îoSP.¹ Mv2Æ°á, kÊÓZãv¸Weà('|§÷ȯ蓲‡?… 8„'À-ÿ\O¹‰ôû—3Þ (l+ä¹ÌçoÌ&žÚ¾œÌ—#G‡’çW`[ô=šïi6”o×â;qê›´`5Í4ÏÓ8kUÒ@ݯzC‹°+vÃßç_·çsÓ™=¶®dÕ‹°ÚadS¿`&vq+ÓÕ:F*É=`>H/ºÇË®X„EÅúídØ`…xnÌOØöªD;Ê3[R‡¦ß?à §ˆ/dŠ)Ó“~±~êAÀÇâx¢[$·˜ÏèMÊCÂ"ôšSú&Ä[¡¬Û¸%£—!·,ÃÍ%Ì‚;XäœÈ –Q,¤‰×ü§D¾™æ/h©ÕRr´A—Qáb™Šr#é¥h]Èïïã­Ï¸y ê¦? Gb (cIïÿSjQXÝk¨ùr•÷Ú‘‹Å­aü?¦Õ­­¹Æ`¢Ìe4º’t-Û¾IªÿöUe]Ôñ{¿tj¦ãå—¶bJ''§y´òPÿAu!ßf8.u†h,§±Ò«y¢þ…­á8Dj!€S¶öðe®do´ß1”Œ}Ï>Wa~· `pqš‘-3ÓÛ4§hý?l Ë’%9ûæ/ÍÐÜ—9ѳŽUjú ðì®ñGÜÑÄ/Ä( ‘› EŠîƧ»Ÿ—ƒR.Ÿ,Ò„ZíêEfdheÚ;Pûnì·^‡B˜N»¿bƒ ûªüå;ññJgw;Œƒ¤¸«dÜ vI°ˆf•Ñ?ùˆ6¹{¼Nª$¬œ¥,ËÖP¾:nLÀ¥fOS®ÒKw-QâÚ0¹€‰á›¾ÄϬÍZÃÄ}"¡f‚ã ¾£Föc43p ƒŠÈúó»†&µÖúÎ[ä1ZVt'DM·Þ•lŸ·B©ã~Äp ñ¢´P0³SÛ„Ûn²á¡Nð..•<› ãÅè‰ ¡^9ÍuÎy’öÝÛ1c¯;|tyùXÐgvoòn«ÞCl$‚¯—Hî§"½x ¤L«­ÊÞn§Y³:"&º‘oYW1cCéˆb¢mG*‚Óß³“óž6 ¤ô¸ÌŽé˜öÍ.8µ2k>î^¯¢RSšOÑÁSäjBؾÔ)¦¸4>Ç42,ËoŽÐ.$Õ³|¹¾†!¯½ Rf“òD¨ÜUïÇRÜdîƉ¦ƒ§\¤:JáF’Âm öúË!¿y)+qÍß*©¯IÉ/he ©^·æ;“æ×%hËD‡×¼aD m¿²â—¬\)ͧ„qSG¨S_Ëìâm)ÈmntkÛ¹1²ä €; ‚»÷…ßÛò8Ü;Ld)fixœ°*A‚/þî™K¤[ûʆŻTž1Cí´ZÂ)›èéMþóüSvE·Ò°ZÙüJ$Ž<é-\ø3 `·aC]§/nuös³—™wf…!vKóƒåš©Ëìs·uIÓ̆ýÌBÔP¦¸–?5º„¥WÐÿànÃ6|ì?‰œÑgž+€U?YÛžLhÑb1å#×ãë4ÁEϺ6 Æý¢.Ãxa”J}ÅàŽóºR\˳ދÒçÿŠc,§-×ø™ ¨ïj½98æ§ÕB-W›³å..MeÕ8µ³²‹öÿÛ t) •ÃB{Ã;˜VÒÕæÛéÝÏ&ëÂÄ9št{ÿ˸f{Q0¶Æ1ƒÊÛ{„Yü E@w“âã¡íñk¸ûá¨XW%ÊÕúRDÞWAGw•20Òs F‰t£Ëë÷{,O}Õ˜a(I)2 Ø„‹¦óå\*Îÿ=­æ¬îHc1ÖfPtx¾ª½Pàν®áI—³D×Q}³B­ü®àa4¹€Æ `„}"Œ#¹×”—úë‘(öp#|Œ¶tŒÔ[µ¦7A$°ç3)ñvƒ\¼Ô\e[1ðØ 3›Ñs†¬*”kÍí¡Ò3ÁXÛ{ËyŸj|\ ™‰ËŠy?¹Bv~«ÙÂú !†„ÂíÓŠAS’ì—0Y|Ÿ„œƒ2,ƒ>¢'ÉÆÝïpvœk­wgËÙvÜïîI@ØxEõ]‰¨ÛT® ®„v*J×[ÏUu¨ì ê;Ì"4+9GIÞ,[óZÖâ…gÀöèF=%‹ÁëC9<¯x£$þdsP®¼Aw$åV„«ZqÚ‰_áµà׌$Žœ|§T“6×ç¾g¿kÇ#AúÝK3=A×18>ѺZ¿ó µËéîWëÌÎôí·É\a@Ýš~ŒÁ~ðY‡Ãul¯¼y©&$5< ¢&P dÆHpÝÒ`‘—¢¿\Ù׺ÀÉÛ]aÕ {Ò6ë"º™k&yÇžjƒ+2ªÍ»(ݵs¹YW¸OñmѬíü’”óz…ÁZí9æï9³Ú/9}ÈÜ’£Gàt–BHdd9$’gQmØâ yû;ù¶q¾ÐýQ«O½s,~Tü(ÿõ¶b&è²îÁÑ!ª;–¼Hçqtø  GÃ_,wøc?GQ:ø˜WC®ð¼(¬ íÖÎ[«2ç® AgÓ‹Ò_u‰[ÔˆÌl¼Ó¿I˜1f“:?I­8,äå6c·ô£`A„—ub¨ÞÏn¾†©Â"Í×Ê•Mƒxe±déÛŒ<3í<'0¼ò5§dQÍ€4(½ž*æZ¢ ÝÝ —ÈR™Á‘†˜¨B»µspe¤Ÿ?ÐÚòŠŸn;gÜ]Ùn¤qÍ7Æu{è–z²ó1/Ð4ÙŠø‰$¨æ·%"\pEêg;¿Íé×vL_¬Xkb!@¯±•™Þ05ïÉ›ÊÄF¦cÑQ´nò5F°«Ð‹µ<ÙE¹ÏËR¢µf}ðîa®ó‰È*á·ü©Ï(.98érDχvÚ£sS?d@¨F&m£ÿªØõö â™:KÅÍ4që „¼ãaEr©¬âC¶d$½¡æ=8ºüT` Y9—a%•†DIìÈ~bËš~ô0xä”A²Ã&–&‹€Ö­&Š]j½¥Vð¡Lö2‚ 1q4ÇOÚ¢e» '¨x%ãzˆþ%Ass‰ÆS,.å1$ÕéVÊæ¡pdv… ÉkíBævÆàdôÑëçZ/ &#„!á¡F©=Eã©#@¥clöaá·•1êž‚ÆÕv$–Ê0™<¹fëM¹´OíJYÄ÷g\ ¡âðø–ÀOÄ)íÇœ„†ƒS ²k¿¾±~Ðv|‘îÎ!‡br§®G™0á´ŸÛlw ‡SV9Áþ¨6¥ìn°jAoŽYªO±¸šKV]ô¯TS|uï õ±ð/¦áZQÆôDNûÊ7OGeò½­”·.Ìš@vµbúRe8kê¹/;æE»ķЮ 0€‡º&Ë÷£ h‰Ò ¤ïë°@G X¬F0c×ÑØCM'n ¼7=ÔL6å.iÅ –8„Š§KžY«+!ìó½Øa¢ÙkOw­Öžf‹þtîU£ÚªÃ÷ñøte-†lÒñ¨äªPka: Ô]æî’<[ý5ôoS>Û¹fC§t É@bªBÏëTv,ðäzQ‡õåúÒK%•Ødá•aŸJÑQD9‰gý¶5žÚF]xÊøôû›Ç›,ôxa÷],™|ÜneÄÇ/5A³µwÏGF³0l˜j–_€Òa\5Ç:Ŭ¨íÒ,*+ä9•_bٞΌJ\æ£d ÓÈ3g(!&ðh·é±OKç'ˆ\È…)°ýó˜·’“Elh š!ÄRVA¡¥?ˆ^à¡‹S÷yî‘õýŽª6|’ÔKkÇÉB`Äp(Õîs1‰ a;Oä­þ7zµÄ‡[½r±ÞÒ~<Îõ«¦/uä{Ï©Ü™ñJ[”7ñ#Œ™«øÚAä vПn_ Æ÷šò*|>Ì¢-Ôø¾ðkt8ñtUŒÙÊ{­³Ó¾n'TëÏ% *1iˆÓ– Z ‰8°¯Çݼ W›X˜Z>Ðü–=*R¸í”åÀ$ÊWÕÐs`Ÿ5íîìÇã1·ìƆs1N”o¿ džxQ´ÜJ’73hd×RíRIsz¶(çdÎâ&yhIÆsL°Å5ÖÐâÝœÖÓƒ“˜$<>¨Þê.×BÜ¿OÚ]„’Éøƒù}“ZëËÃUßNל[h³TÁÚÅ‰ÊÛVÔ3¡BYÎK–6ÔEhÉ·uELyµ)gã\¶ÎUå 6á˜;»õoMmc …ùzè«™<øSLp bŒ&熦gSóÍ’Å.Ác<«uôomzÀÿ#ŒZYÀðIC¶ÔTî5úO¾ˆ‡óÕBцÛÀ?Å_T;«Äð÷V.ókºs<ÍBE³Bê¡|0@Õ}µü™æsí—ê&tÉ·“W¥/-)Ñ»ÿ?ážÚ4 |¥!½¯éCjˆbîL\#ƒ^¤Rqº *1¢©ae̶<¢1ÈÇÍH5µŒÑwGOÝ*OCHHn*\ŸR+vf]’šzæÿAÀ²¯¥HÖ&s† Ž@âëÙOÁ…ÜC·~aï3¡)"`(»#éûèÛñàÇÝgN›Â­¾Ño¦"téÅAa§([)gJÃqLØ…® xG$£nÅÞÉL`„¯³/ÏЃE=¢nÒµØR.k,Cìié*5C„‚£¶[Ó¿ÅYì´°0„¾whw’ØÜÉQhËîõ¡›HFÄÉ'o­¦âƒ¾WPëyB-*y†ži>G'ŠõªÉ¢ééR´Ç¹!ìÙò}þ–ª™¶).†Žµ–ëgyÖ'Qmðnã=oM±è†-›’˜cIÈÏA[äõ¹I!À*)A·“ô;õåÀt¸0¸Ò[tÇ­ô)èô†é,¤ ëõuø–ýsãß!¾[Î^Áá6bÜÔJìB¼G“j¾? só[Q°ä#§¼L¹xÛ°^/äŒx³§­Ñ¥H0?eqÛD—5bqî]ŸAÎ µ©½Öik]\Ã7ò¿CÞ6`{°,¬1R2S¨•MgRäûŠrè¹2Ž'‘r"Œ¤yöž*°+lwóÃÔ]T°h ¯rlÇ=ÞÌÜ`¤!¿Ò÷MŽXmí×dÿjTyë RK•á‡Î¥o2—à=Ç!U'Ï1o2†zôÏ1·RXbæ$IT,Õ%W2¾cþšÊæÚ{K c5¡äŒ4a¨§üY£ÀûJ"íbÃÖ†ÿmÇZÜÔ)»6GÆn¾"¬½ý‹#u0S£R=•×n„èБ~§ªœ>m½;¿‘OØg˜#ñ<ÅþD‰¦»@6n¬àâ«\ò\ƒÈ­hšSêi<¹ŒTǨý:0UCOi^¿øØ25ŒŒ-ûXDêŸ^C‚}²#Ý©VÃÂ@‘i9½”D‘¥£ÒuCD4ºL„@È ÷úü?’(çÖ|ag6ª¬ü°±Q¼Œà s.Žw׃0¨iˆbU¸o4ñn|–±/åèÔFT%uJ©‡èÙ¯:út7Øpw ¬òšyKºG²KÕB²¡‰IK®g…„5H˜L 7M,ˆq\Y{Ú]›ˆŠmªvÏÀ^Ç· Ô[fMü§„¯8­¹9.:‘ˆÿõÀÏbÝ>àŽL„Íd(q¼´¿ÞÔ,C»fqÌŽiìv©Š,W ‰‹¬iÀû4éêô½ÈlDݿ䢅ÝY0ætr5™3¼JÝuÄ~&h—¢‘nza$@2'ßkƒ15Uš£bU¡\´$±-u¯ Šåµ?æ!.´êÊÔeî.±Š·ãZc í¿ìë×ûî ÝA_OÚqÛ¤$ÿú™©‘l¡ý]Î$]補5{Užj¹ÊoW&9^Ý€c²À§ H‡GÛi'é»( êëÝÿ‚hÞîÿÁc¤°ÅüÈÔÛ ©béÏ-–b·þ|ï>{“·¿ÂJ6¾î¯±JQ¢S’&¾X7 “hÔÄyj;–ߪ ¡?Â9”ìå’í3_@¨]ržÍ$MYsŒ"YM‹ía `F­›°VDB’>Ôú-ÿ?ÂÚIqQ'#‡8{²þGªýß7?ÇÛÊ|®åï›Ào…àe%Eãl£Õ'UÃÚiý¸¢X,ú*³R*Fo‡x RÕ²Pm¬Å$Ä5å!|„ ð×¹Þφ¦æóö¼riëÌRFf“(8Š½·ƒâÁ¨ÀñÎÀ϶¼¨²uð>]$Ðp«FéØìA+ß3ÁI’f¾Ou6!â¹{3thò¦=©ŸßÕíw1Êʪ-§çë¨üí÷¼Ñ!yZüôÛÃ#gÄ;/TMd‘2øšb×Ä¢¦{¹‚ÔOÛ¤/.’ÛÜ6öØón–˜¤ßÔ"ªÑ!¿n.:9qÙbäWö0¤Ñò)iÞÓ´ aQr.Ùq…k$ Í>xÞ¢æº Æ’DéÙTiÐj>0 ‹YZbayesm/R/0000755000176000001440000000000013123305446011764 5ustar ripleyusersbayesm/R/runireggibbs_rcpp.r0000644000176000001440000000671312566706215015675 0ustar ripleyusersruniregGibbs= function(Data,Prior,Mcmc) { # # revision history: # P. Rossi 1/17/05 # 3/07 added classes # W. Taylor 4/15 - added nprint option to MCMC argument # Purpose: # perform Gibbs iterations for Univ Regression Model using # prior with beta, sigma-sq indep # # Arguments: # Data -- list of data # y,X # Prior -- list of prior hyperparameters # betabar,A prior mean, prior precision # nu, ssq prior on sigmasq # Mcmc -- list of MCMC parms # sigmasq=initial value for sigmasq # R number of draws # keep -- thinning parameter # nprint - print estimated time remaining on every nprint'th draw # # Output: # list of beta, sigmasq # # Model: # y = Xbeta + e e ~N(0,sigmasq) # y is n x 1 # X is n x k # beta is k x 1 vector of coefficients # # Priors: beta ~ N(betabar,A^-1) # sigmasq ~ (nu*ssq)/chisq_nu # # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of y and X")} if(is.null(Data$X)) {pandterm("Requires Data element X")} X=Data$X if(is.null(Data$y)) {pandterm("Requires Data element y")} y=Data$y nvar=ncol(X) nobs=length(y) # # check data for validity # if(nobs != nrow(X) ) {pandterm("length(y) ne nrow(X)")} # # check for Prior # if(missing(Prior)) { betabar=c(rep(0,nvar)); A=.01*diag(nvar); nu=3; ssq=var(y)} else { if(is.null(Prior$betabar)) {betabar=c(rep(0,nvar))} else {betabar=Prior$betabar} if(is.null(Prior$A)) {A=.01*diag(nvar)} else {A=Prior$A} if(is.null(Prior$nu)) {nu=3} else {nu=Prior$nu} if(is.null(Prior$ssq)) {ssq=var(y)} else {ssq=Prior$ssq} } # # check dimensions of Priors # if(ncol(A) != nrow(A) || ncol(A) != nvar || nrow(A) != nvar) {pandterm(paste("bad dimensions for A",dim(A)))} if(length(betabar) != nvar) {pandterm(paste("betabar wrong length, length= ",length(betabar)))} # # check MCMC argument # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} else { if(is.null(Mcmc$R)) {pandterm("requires Mcmc element R")} else {R=Mcmc$R} if(is.null(Mcmc$keep)) {keep=1} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=100} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} if(is.null(Mcmc$sigmasq)) {sigmasq=var(y)} else {sigmasq=Mcmc$sigmasq} } # # print out problem # cat(" ", fill=TRUE) cat("Starting Gibbs Sampler for Univariate Regression Model",fill=TRUE) cat(" with ",nobs," observations",fill=TRUE) cat(" ", fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("betabar",fill=TRUE) print(betabar) cat("A",fill=TRUE) print(A) cat("nu = ",nu," ssq= ",ssq,fill=TRUE) cat(" ", fill=TRUE) cat("MCMC parms: ",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint,fill=TRUE) cat(" ",fill=TRUE) ################################################################### # Keunwoo Kim # 08/05/2014 ################################################################### draws = runiregGibbs_rcpp_loop(y, X, betabar, A, nu, ssq, sigmasq, R, keep, nprint) ################################################################### attributes(draws$betadraw)$class=c("bayesm.mat","mcmc") attributes(draws$betadraw)$mcpar=c(1,R,keep) attributes(draws$sigmasqdraw)$class=c("bayesm.mat","mcmc") attributes(draws$sigmasqdraw)$mcpar=c(1,R,keep) return(draws) } bayesm/R/nmat.R0000755000176000001440000000042710225356251013054 0ustar ripleyusersnmat=function(vec) { # # function to take var-cov matrix in vector form and create correlation matrix # and store in vector form # p=as.integer(sqrt(length(vec))) sigma=matrix(vec,ncol=p) nsig=1/sqrt(diag(sigma)) return(as.vector(nsig*(t(nsig*sigma)))) } bayesm/R/condMom.R0000755000176000001440000000117610227517711013515 0ustar ripleyuserscondMom= function(x,mu,sigi,i) { # # revision history: # rossi modified allenby code 4/05 # # purpose:compute moments of conditional distribution of ith element of normal given # all others # # arguments: # x: vector of values to condition on # mu: mean vector of length(x)-dim MVN # sigi: inverse of covariance matrix # i: element to condition on # # output: # list with conditional mean and variance # # Model: x ~MVN(mu,Sigma) # computes moments of x_i given x_{-1} # sig=1./sigi[i,i] m=mu[i] - as.vector(x[-i]-mu[-i])%*%as.vector(sigi[-i,i])*sig return(list(cmean=as.vector(m),cvar=sig)) } bayesm/R/runireg_rcpp.r0000644000176000001440000000673712566706215014674 0ustar ripleyusersrunireg= function(Data,Prior,Mcmc) { # # revision history: # P. Rossi 1/17/05 # revised 9/05 to put in Data,Prior,Mcmc calling convention # 3/07 added classes # W. Taylor 4/15 - added nprint option to MCMC argument # Purpose: # perform iid draws from posterior of regression model using # conjugate prior # # Arguments: # Data -- list of data # y,X # Prior -- list of prior hyperparameters # betabar,A prior mean, prior precision # nu, ssq prior on sigmasq # Mcmc -- list of MCMC parms # R number of draws # keep -- thinning parameter # nprint - print estimated time remaining on every nprint'th draw # # Output: # list of beta, sigmasq # # Model: # y = Xbeta + e e ~N(0,sigmasq) # y is n x 1 # X is n x k # beta is k x 1 vector of coefficients # # Priors: beta ~ N(betabar,sigmasq*A^-1) # sigmasq ~ (nu*ssq)/chisq_nu # # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of y and X")} if(is.null(Data$X)) {pandterm("Requires Data element X")} X=Data$X if(is.null(Data$y)) {pandterm("Requires Data element y")} y=Data$y nvar=ncol(X) nobs=length(y) # # check data for validity # if(nobs != nrow(X) ) {pandterm("length(y) ne nrow(X)")} # # check for Prior # if(missing(Prior)) { betabar=c(rep(0,nvar)); A=BayesmConstant.A*diag(nvar); nu=BayesmConstant.nu; ssq=var(y)} else { if(is.null(Prior$betabar)) {betabar=c(rep(0,nvar))} else {betabar=Prior$betabar} if(is.null(Prior$A)) {A=BayesmConstant.A*diag(nvar)} else {A=Prior$A} if(is.null(Prior$nu)) {nu=BayesmConstant.nu} else {nu=Prior$nu} if(is.null(Prior$ssq)) {ssq=var(y)} else {ssq=Prior$ssq} } # # check dimensions of Priors # if(ncol(A) != nrow(A) || ncol(A) != nvar || nrow(A) != nvar) {pandterm(paste("bad dimensions for A",dim(A)))} if(length(betabar) != nvar) {pandterm(paste("betabar wrong length, length= ",length(betabar)))} # # check MCMC argument # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} else { if(is.null(Mcmc$R)) {pandterm("requires Mcmc element R")} else {R=Mcmc$R} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} } # # print out problem # cat(" ", fill=TRUE) cat("Starting IID Sampler for Univariate Regression Model",fill=TRUE) cat(" with ",nobs," observations",fill=TRUE) cat(" ", fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("betabar",fill=TRUE) print(betabar) cat("A",fill=TRUE) print(A) cat("nu = ",nu," ssq= ",ssq,fill=TRUE) cat(" ", fill=TRUE) cat("MCMC parms: ",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint,fill=TRUE) cat(" ",fill=TRUE) ################################################################### # Keunwoo Kim # 08/05/2014 ################################################################### draws = runireg_rcpp_loop(y, X, betabar, A, nu, ssq, R, keep, nprint) ################################################################### attributes(draws$betadraw)$class=c("bayesm.mat","mcmc") attributes(draws$betadraw)$mcpar=c(1,R,keep) attributes(draws$sigmasqdraw)$class=c("bayesm.mat","mcmc") attributes(draws$sigmasqdraw)$mcpar=c(1,R,keep) return(draws) } bayesm/R/momMix.R0000755000176000001440000000413110307453451013360 0ustar ripleyusersmomMix= function(probdraw,compdraw) { # # Revision History: # R. McCulloch 11/04 # P. Rossi 3/05 put in backsolve fixed documentation # P. Rossi 9/05 fixed error in mom -- return var not sigma # # purpose: compute moments of normal mixture averaged over MCMC draws # # arguments: # probdraw -- ith row is ith draw of probabilities of mixture comp # compdraw -- list of lists of draws of mixture comp moments (each sublist is from mixgibbs) # # output: # a list with the mean vector, covar matrix, vector of std deve, and corr matrix # # ---------------------------------------------------------------------------------- # define function needed mom=function(prob,comps){ # purpose: obtain mu and cov from list of normal components # # arguments: # prob: vector of mixture probs # comps: list, each member is a list comp with ith normal component ~N(comp[[1]],Sigma), # Sigma = t(R)%*%R, R^{-1} = comp[[2]] # returns: # a list with [[1]]=$mu a vector # [[2]]=$sigma a matrix # nc = length(comps) dim = length(comps[[1]][[1]]) mu = double(dim) sigma = matrix(0.0,dim,dim) for(i in 1:nc) { mu = mu+ prob[i]*comps[[i]][[1]] } var=matrix(double(dim*dim),ncol=dim) for(i in 1:nc) { mui=comps[[i]][[1]] # root = solve(comps[[i]][[2]]) root=backsolve(comps[[i]][[2]],diag(rep(1,dim))) sigma=t(root)%*%root var=var+prob[i]*sigma+prob[i]*(mui-mu)%o%(mui-mu) } list(mu=mu,sigma=var) } #--------------------------------------------------------------------------------------- dim=length(compdraw[[1]][[1]][[1]]) nc=length(compdraw[[1]]) dim(probdraw)=c(length(compdraw),nc) mu=double(dim) sigma=matrix(double(dim*dim),ncol=dim) sd=double(dim) corr=matrix(double(dim*dim),ncol=dim) for(i in 1:length(compdraw)) { out=mom(probdraw[i,],compdraw[[i]]) sd=sd+sqrt(diag(out$sigma)) corr=corr+matrix(nmat(out$sigma),ncol=dim) mu=mu+out$mu sigma=sigma+out$sigma } mu=mu/length(compdraw) sigma=sigma/length(compdraw) sd=sd/length(compdraw) corr=corr/length(compdraw) return(list(mu=mu,sigma=sigma,sd=sd,corr=corr)) } bayesm/R/rnegbinrw_rcpp.r0000755000176000001440000001413713114117322015173 0ustar ripleyusersrnegbinRw=function(Data, Prior, Mcmc){ # Revision History # Sridhar Narayanan - 05/2005 # P. Rossi 6/05 # 3/07 added classes # Keunwoo Kim 11/2014 # 1. added "alphafix" argument # 2. corrected code in more clear way (in Cpp) # W. Taylor 4/15 - added nprint option to MCMC argument # # Model # (y|lambda,alpha) ~ Negative Binomial(Mean = lambda, Overdispersion par = alpha) # # ln(lambda) = X * beta # # Priors # beta ~ N(betabar, A^-1) # alpha ~ Gamma(a,b) where mean = a/b and variance = a/(b^2) # # Arguments # Data = list of y, X # e.g. regdata[[i]]=list(y=y,X=X) # X has nvar columns including a first column of ones # # Prior - list containing the prior parameters # betabar, A - mean of beta prior, inverse of variance covariance of beta prior # a, b - parameters of alpha prior # # Mcmc - list containing # R is number of draws # keep is thinning parameter (def = 1) # nprint - print estimated time remaining on every nprint'th draw (def = 100) # s_beta - scaling parameter for beta RW (def = 2.93/sqrt(nvar)) # s_alpha - scaling parameter for alpha RW (def = 2.93) # beta0 - initial guesses for parameters, if not supplied default values are used # alpha - value of alpha fixed. If it is given, draw only beta # # # Definitions of functions used within rhierNegbinRw # llnegbin = function(par,X,y, nvar) { # Computes the log-likelihood beta = par[1:nvar] alpha = exp(par[nvar+1])+1.0e-50 mean=exp(X%*%beta) prob=alpha/(alpha+mean) prob=ifelse(prob<1.0e-100,1.0e-100,prob) out=dnbinom(y,size=alpha,prob=prob,log=TRUE) return(sum(out)) } # # Error Checking # if(missing(Data)) {pandterm("Requires Data argument -- list of X and y")} if(is.null(Data$X)) {pandterm("Requires Data element X")} else {X=Data$X} if(is.null(Data$y)) {pandterm("Requires Data element y")} else {y=Data$y} nvar = ncol(X) if (length(y) != nrow(X)) {pandterm("Mismatch in the number of observations in X and y")} nobs=length(y) # # check for prior elements # if(missing(Prior)) { betabar=rep(0,nvar); A=BayesmConstant.A*diag(nvar) ; a=BayesmConstant.agammaprior; b=BayesmConstant.bgammaprior; } else { if(is.null(Prior$betabar)) {betabar=rep(0,nvar)} else {betabar=Prior$betabar} if(is.null(Prior$A)) {A=BayesmConstant.A*diag(nvar)} else {A=Prior$A} if(is.null(Prior$a)) {a=BayesmConstant.agammaprior} else {a=Prior$a} if(is.null(Prior$b)) {b=BayesmConstant.bgammaprior} else {b=Prior$b} } if(length(betabar) != nvar) pandterm("betabar is of incorrect dimension") if(sum(dim(A)==c(nvar,nvar)) != 2) pandterm("A is of incorrect dimension") if((length(a) != 1) | (a <=0)) pandterm("a should be a positive number") if((length(b) != 1) | (b <=0)) pandterm("b should be a positive number") # # check for Mcmc # if(missing(Mcmc)) pandterm("Requires Mcmc argument -- at least R") if(is.null(Mcmc$R)) {pandterm("Requires element R of Mcmc")} else {R=Mcmc$R} if(is.null(Mcmc$beta0)) {beta0=rep(0,nvar)} else {beta0=Mcmc$beta0} if(length(beta0) !=nvar) pandterm("beta0 is not of dimension nvar") if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} if(is.null(Mcmc$s_alpha)) {cat("Using default s_alpha = 2.93",fill=TRUE); s_alpha=BayesmConstant.RRScaling} else {s_alpha = Mcmc$s_alpha} if(is.null(Mcmc$s_beta)) {cat("Using default s_beta = 2.93/sqrt(nvar)",fill=TRUE); s_beta=BayesmConstant.RRScaling/sqrt(nvar)} else {s_beta = Mcmc$s_beta} # Keunwoo Kim 11/2014 ############################################# if(is.null(Mcmc$alpha)) {fixalpha=FALSE} else {fixalpha=TRUE; alpha=Mcmc$alpha} if(fixalpha & alpha<=0) pandterm("alpha is not positive") ################################################################### # # print out problem # cat(" ",fill=TRUE) cat("Starting Random Walk Metropolis Sampler for Negative Binomial Regression",fill=TRUE) cat(" ",nobs," obs; ",nvar," covariates (including intercept); ",fill=TRUE) cat("Prior Parameters:",fill=TRUE) cat("betabar",fill=TRUE) print(betabar) cat("A",fill=TRUE) print(A) if (!fixalpha) { cat("a",fill=TRUE) print(a) cat("b",fill=TRUE) print(b) } cat(" ",fill=TRUE) cat("MCMC Parms: ",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint,fill=TRUE) cat("s_alpha = ",s_alpha,fill=TRUE) cat("s_beta = ",s_beta,fill=TRUE) if (fixalpha) { cat("alpha",fill=TRUE) print(alpha) } cat(" ",fill=TRUE) par = rep(0,(nvar+1)) cat(" Initializing RW Increment Covariance Matrix...",fill=TRUE) fsh() mle = optim(par,llnegbin, X=X, y=y, nvar=nvar, method="L-BFGS-B", upper=c(Inf,Inf,Inf,log(100000000)), hessian=TRUE, control=list(fnscale=-1)) fsh() beta_mle=mle$par[1:nvar] alpha_mle = exp(mle$par[nvar+1]) varcovinv = -mle$hessian beta = beta0 betacvar = s_beta*solve(varcovinv[1:nvar,1:nvar]) betaroot = t(chol(betacvar)) if(!fixalpha) {alpha = alpha_mle} alphacvar = s_alpha/varcovinv[nvar+1,nvar+1] alphacroot = sqrt(alphacvar) cat("beta_mle = ",beta_mle,fill=TRUE) cat("alpha_mle = ",alpha_mle, fill = TRUE) fsh() ################################################################### # Keunwoo Kim # 09/03/2014 ################################################################### if (fixalpha) {alpha=Mcmc$alpha} draws=rnegbinRw_rcpp_loop(y, X, betabar, chol(A), a, b, beta, alpha, fixalpha, betaroot, alphacroot, R, keep, nprint) ################################################################### attributes(draws$betadraw)$class=c("bayesm.mat","mcmc") attributes(draws$betadraw)$mcpar=c(1,R,keep) attributes(draws$alphadraw)$class=c("bayesm.mat","mcmc") attributes(draws$alphadraw)$mcpar=c(1,R,keep) return(list(betadraw=draws$betadraw,alphadraw=draws$alphadraw, acceptrbeta=draws$nacceptbeta/R*keep,acceptralpha=draws$nacceptalpha/R*keep)) } bayesm/R/simnhlogit.R0000755000176000001440000000241612524673031014274 0ustar ripleyuserssimnhlogit=function(theta,lnprices,Xexpend) { # function to simulate non-homothetic logit model # creates y a n x 1 vector with indicator of choice (1,...,m) # lnprices is n x m array of log-prices faced # Xexpend is n x d array of variables predicting expenditure # # non-homothetic model specifies ln(psi_i(u))= alpha_i - exp(k_i)u # # structure of theta vector: # alpha (m x 1) # k (m x1 ) # gamma (k x 1) expenditure function coefficients # tau -- scaling of v # m=ncol(lnprices) n=nrow(lnprices) d=ncol(Xexpend) alpha=theta[1:m] k=theta[(m+1):(2*m)] gamma=theta[(2*m+1):(2*m+d)] tau=theta[length(theta)] iotam=c(rep(1,m)) c1=as.vector(Xexpend%*%gamma)%x%iotam-as.vector(t(lnprices))+alpha c2=c(rep(exp(k),n)) u=callroot(c1,c2,.0000001,20) v=alpha - u*exp(k)-as.vector(t(lnprices)) vmat=matrix(v,ncol=m,byrow=TRUE) vmat=tau*vmat Prob=exp(vmat) denom=Prob%*%iotam Prob=Prob/as.vector(denom) # draw y y=vector("double",n) ind=1:m for (i in 1:n) { yvec=rmultinom(1,1,Prob[i,]) y[i]=ind%*%yvec } return(list(y=y,Xexpend=Xexpend,lnprices=lnprices,theta=theta,prob=Prob)) }bayesm/R/llmnp.R0000755000176000001440000000541713114117322013235 0ustar ripleyusersllmnp= function(beta,Sigma,X,y,r) { # # revision history: # edited by rossi 2/8/05 # adde 1.0e-50 before taking log to avoid -Inf 6/05 # changed order of arguments to put beta first 9/05 # # purpose: # function to evaluate MNP likelihood using GHK # # arguments: # X is n*(p-1) x k array of covariates (including intercepts) # note: X is from the "differenced" system # y is vector of n indicators of multinomial response # beta is k x 1 with k = ncol(X) # Sigma is p-1 x p-1 # r is the number of random draws to use in GHK # # output -- value of log-likelihood # for each observation w = Xbeta + e e ~N(0,Sigma) # if y=j (j max(w_-j) and w_j >0 # if y=p, w < 0 # # to use GHK we must transform so that these are rectangular regions # e.g. if y=1, w_1 > 0 and w_1 - w_-1 > 0 # # define Aj such that if j=1,..,p-1, Ajw = Ajmu + Aje > 0 is equivalent to y=j # implies Aje > -Ajmu # lower truncation is -Ajmu and cov = AjSigma t(Aj) # # for p, e < - mu # #################################################################################### # 08/2016 # Keunwoo Kim # We do not use this anymore, do call directly from rcpp ghkvec function. # # # # define functions needed # # # ghkvec = function(L,trunpt,above,r){ # dim=length(above) # n=length(trunpt)/dim # .C('ghk_vec',as.integer(n),as.double(L),as.double(trunpt),as.integer(above),as.integer(dim), # as.integer(r),res=double(n))$res} #################################################################################### # # compute means for each observation # pm1=ncol(Sigma) k=length(beta) mu=matrix(X%*%beta,nrow=pm1) logl=0.0 above=rep(0,pm1) for (j in 1:pm1) { muj=mu[,y==j] Aj=-diag(pm1) Aj[,j]=rep(1,pm1) trunpt=as.vector(-Aj%*%muj) Lj=t(chol(Aj%*%Sigma%*%t(Aj))) # note: rob's routine expects lower triangular root #################################################################################### # 08/2016 # Keunwoo Kim # now refers rcpp version directly. #logl=logl + sum(log(ghkvec(Lj,trunpt,above,r)+1.0e-50)) logl=logl + sum(log(ghkvec(Lj,trunpt,above,r,HALTON=FALSE,0)+1.0e-50)) #################################################################################### # note: ghkvec does an entire vector of n probs each with different truncation points but the # same cov matrix. } # # now do obs for y=p # trunpt=as.vector(-mu[,y==(pm1+1)]) Lj=t(chol(Sigma)) above=rep(1,pm1) #################################################################################### # 08/2016 # Keunwoo Kim # now refers rcpp version directly. #logl=logl + sum(log(ghkvec(Lj,trunpt,above,r)+1.0e-50)) logl=logl + sum(log(ghkvec(Lj,trunpt,above,r,HALTON=FALSE,0)+1.0e-50)) #################################################################################### return(logl) } bayesm/R/rordprobitgibbs_rcpp.r0000755000176000001440000001404212566706215016405 0ustar ripleyusersrordprobitGibbs=function(Data,Prior,Mcmc){ # # revision history: # 3/07 Hsiu-Wen Liu # 3/07 fixed naming of dstardraw rossi # # purpose: # draw from posterior for ordered probit using Gibbs Sampler # and metropolis RW # # Arguments: # Data - list of X,y,k # X is nobs x nvar, y is nobs vector of 1,2,.,k (ordinal variable) # Prior - list of A, betabar # A is nvar x nvar prior preci matrix # betabar is nvar x 1 prior mean # Ad is ndstar x ndstar prior preci matrix of dstar (ncut is number of cut-offs being estimated) # dstarbar is ndstar x 1 prior mean of dstar # Mcmc # R is number of draws # keep is thinning parameter # nprint - print estimated time remaining on every nprint'th draw # s is scale parameter of random work Metropolis # # Output: # list of betadraws and cutdraws # # Model: # z=Xbeta + e < 0 e ~N(0,1) # y=1,..,k, if z~c(c[k], c[k+1]) # # cutoffs = c[1],..,c[k+1] # dstar = dstar[1],dstar[k-2] # set c[1]=-100, c[2]=0, ...,c[k+1]=100 # # c[3]=exp(dstar[1]),c[4]=c[3]+exp(dstar[2]),..., # c[k]=c[k-1]+exp(datsr[k-2]) # # Note: 1. length of dstar = length of cutoffs - 3 # 2. Be careful in assessing prior parameter, Ad. .1 is too small for many applications. # # Prior: beta ~ N(betabar,A^-1) # dstar ~ N(dstarbar, Ad^-1) # # # ---------------------------------------------------------------------- # define functions needed # dstartoc is a fuction to transfer dstar to its cut-off value dstartoc=function(dstar) {c(-100, 0, cumsum(exp(dstar)), 100)} # compute conditional likelihood of data given cut-offs # lldstar=function(dstar,y,mu){ gamma=dstartoc(dstar) arg = pnorm(gamma[y+1]-mu)-pnorm(gamma[y]-mu) epsilon=1.0e-50 arg=ifelse(arg < epsilon,epsilon,arg) return(sum(log(arg))) } # # ---------------------------------------------------------------------- # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of y and X")} if(is.null(Data$X)) {pandterm("Requires Data element X")} X=Data$X if(is.null(Data$y)) {pandterm("Requires Data element y")} y=Data$y if(is.null(Data$k)) {pandterm("Requires Data element k")} k=Data$k nvar=ncol(X) nobs=length(y) ndstar = k-2 # number of dstar being estimated ncuts = k+1 # number of cut-offs (including zero and two ends) ncut = ncuts-3 # number of cut-offs being estimated c[1]=-100, c[2]=0, c[k+1]=100 # # check data for validity # if(length(y) != nrow(X) ) {pandterm("y and X not of same row dim")} if( sum(unique(y) %in% (1:k) ) < length(unique(y)) ) {pandterm("some value of y is not vaild")} # # check for Prior # if(missing(Prior)) { betabar=c(rep(0,nvar)); A=BayesmConstant.A*diag(nvar); Ad=diag(ndstar); dstarbar=c(rep(0,ndstar))} else { if(is.null(Prior$betabar)) {betabar=c(rep(0,nvar))} else {betabar=Prior$betabar} if(is.null(Prior$A)) {A=BayesmConstant.A*diag(nvar)} else {A=Prior$A} if(is.null(Prior$Ad)) {Ad=diag(ndstar)} else {Ad=Prior$Ad} if(is.null(Prior$dstarbar)) {dstarbar=c(rep(0,ndstar))} else {dstarbar=Prior$dstarbar} } # # check dimensions of Priors # if(ncol(A) != nrow(A) || ncol(A) != nvar || nrow(A) != nvar) {pandterm(paste("bad dimensions for A",dim(A)))} if(length(betabar) != nvar) {pandterm(paste("betabar wrong length, length= ",length(betabar)))} if(ncol(Ad) != nrow(Ad) || ncol(Ad) != ndstar || nrow(Ad) != ndstar) {pandterm(paste("bad dimensions for Ad",dim(Ad)))} if(length(dstarbar) != ndstar) {pandterm(paste("dstarbar wrong length, length= ",length(dstarbar)))} # # check MCMC argument # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} else { if(is.null(Mcmc$R)) {pandterm("requires Mcmc element R")} else {R=Mcmc$R} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} if(is.null(Mcmc$s)) {s=BayesmConstant.RRScaling/sqrt(ndstar)} else {s=Mcmc$s} } # # print out problem # cat(" ", fill=TRUE) cat("Starting Gibbs Sampler for Ordered Probit Model",fill=TRUE) cat(" with ",nobs,"observations",fill=TRUE) cat(" ", fill=TRUE) cat("Table of y values",fill=TRUE) print(table(y)) cat(" ",fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("betabar",fill=TRUE) print(betabar) cat(" ", fill=TRUE) cat("A",fill=TRUE) print(A) cat(" ", fill=TRUE) cat("dstarbar",fill=TRUE) print(dstarbar) cat(" ", fill=TRUE) cat("Ad",fill=TRUE) print(Ad) cat(" ", fill=TRUE) cat("MCMC parms: ",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint,"s= ",s, fill=TRUE) cat(" ",fill=TRUE) # use (-Hessian+Ad)^(-1) evaluated at betahat as the basis of the # covariance matrix for the random walk Metropolis increments betahat = chol2inv(chol(crossprod(X,X)))%*% crossprod(X,y) dstarini = c(cumsum(c( rep(0.1, ndstar)))) # set initial value for dstar dstarout = optim(dstarini, lldstar, method = "BFGS", hessian=T, control = list(fnscale = -1,maxit=500, reltol = 1e-06, trace=0), mu=X%*%betahat, y=y) inc.root=chol(chol2inv(chol((-dstarout$hessian+Ad)))) # chol((H+Ad)^-1) ################################################################### # Keunwoo Kim # 08/20/2014 ################################################################### draws=rordprobitGibbs_rcpp_loop(y,X,k,A,betabar,Ad,s,inc.root,dstarbar,betahat,R,keep,nprint) ################################################################### draws$cutdraw=draws$cutdraw[,2:k] attributes(draws$cutdraw)$class="bayesm.mat" attributes(draws$betadraw)$class="bayesm.mat" attributes(draws$dstardraw)$class="bayesm.mat" attributes(draws$cutdraw)$mcpar=c(1,R,keep) attributes(draws$betadraw)$mcpar=c(1,R,keep) attributes(draws$dstardraw)$mcpar=c(1,R,keep) return(draws) } bayesm/R/rhiernegbinrw_rcpp.r0000644000176000001440000002316313117541521016044 0ustar ripleyusersrhierNegbinRw= function(Data, Prior, Mcmc) { # Revision History # Sridhar Narayanan - 05/2005 # P. Rossi 6/05 # fixed error with nobs not specified and changed llnegbinFract 9/05 # 3/07 added classes # 3/08 fixed fractional likelihood # W. Taylor 4/15 - added nprint option to MCMC argument # # Model # (y_i|lambda_i,alpha) ~ Negative Binomial(Mean = lambda_i, Overdispersion par = alpha) # # ln(lambda_i) = X_i * beta_i # # beta_i = Delta'*z_i + nu_i # nu_i~N(0,Vbeta) # # Priors # vec(Delta|Vbeta) ~ N(vec(Deltabar), Vbeta (x) (Adelta^-1)) # Vbeta ~ Inv Wishart(nu, V) # alpha ~ Gamma(a,b) where mean = a/b and variance = a/(b^2) # # Arguments # Data = list of regdata,Z # regdata is a list of lists each list with members y, X # e.g. regdata[[i]]=list(y=y,X=X) # X has nvar columns including a first column of ones # Z is nreg=length(regdata) x nz with a first column of ones # # Prior - list containing the prior parameters # Deltabar, Adelta - mean of Delta prior, inverse of variance covariance of Delta prior # nu, V - parameters of Vbeta prior # a, b - parameters of alpha prior # # Mcmc - list containing # R is number of draws # keep is thinning parameter (def = 1) # nprint - print estimated time remaining on every nprint'th draw (def = 100) # s_beta - scaling parameter for beta RW (def = 2.93/sqrt(nvar)) # s_alpha - scaling parameter for alpha RW (def = 2.93) # w - fractional weighting parameter (def = .1) # Vbeta0, Delta0 - initial guesses for parameters, if not supplied default values are used # alpha - value of alpha fixed. If it is given, draw only beta # # Definitions of functions used within rhierNegbinRw (but outside of Rcpp loop) # llnegbinR = function(par,X,y, nvar) { # Computes the log-likelihood beta = par[1:nvar] alpha = exp(par[nvar+1])+1.0e-50 mean=exp(X%*%beta) prob=alpha/(alpha+mean) prob=ifelse(prob<1.0e-100,1.0e-100,prob) out=dnbinom(y,size=alpha,prob=prob,log=TRUE) return(sum(out)) } llnegbinFract = function(par,X,y,Xpooled, ypooled, w,wgt, nvar,lnalpha) { # Computes the fractional log-likelihood at the unit level theta = c(par,lnalpha) (1-w)*llnegbinR(theta,X,y,nvar) + w*wgt*llnegbinR(theta,Xpooled,ypooled, nvar) } # # Error Checking # if(missing(Data)) {pandterm("Requires Data argument -- list of regdata and (possibly) Z")} if(is.null(Data$regdata)) { pandterm("Requires Data element regdata -- list of data for each unit : y and X") } regdata=Data$regdata nreg = length(regdata) if (is.null(Data$Z)) { cat("Z not specified - using a column of ones instead", fill = TRUE) Z = matrix(rep(1,nreg),ncol=1) } else { if (!is.matrix(Data$Z)) { pandterm("Z must be a matrix") } else { if (nrow(Data$Z) != nreg) { pandterm(paste("Nrow(Z) ", nrow(Z), "ne number units ",nreg)) } else { Z = Data$Z } } } nz = ncol(Z) for (i in 1:nreg) { if(!is.matrix(regdata[[i]]$X)) {pandterm(paste0("regdata[[",i,"]]$X must be a matrix"))} if(!is.vector(regdata[[i]]$y, mode = "numeric") & !is.vector(regdata[[i]]$y, mode = "logical") & !is.matrix(regdata[[i]]$y)) {pandterm(paste0("regdata[[",i,"]]$y must be a numeric or logical vector or matrix"))} if(is.matrix(regdata[[i]]$y)) { if(ncol(regdata[[i]]$y)>1) {pandterm(paste0("regdata[[",i,"]]$y must be a vector or one-column matrix"))}} } dimfun = function(l) { c(length(l$y),dim(l$X)) } dims=sapply(regdata,dimfun) dims = t(dims) nvar = quantile(dims[,3],prob=0.5) for (i in 1:nreg) { if (dims[i, 1] != dims[i, 2] || dims[i, 3] != nvar) { pandterm(paste("Bad Data dimensions for unit", i, "dims(y,X) =", dims[i, ])) } } ypooled = NULL Xpooled = NULL for (i in 1:nreg) { ypooled = c(ypooled,regdata[[i]]$y) Xpooled = rbind(Xpooled,regdata[[i]]$X) } nobs= length(ypooled) nvar=ncol(Xpooled) # # check for prior elements # if(missing(Prior)) { Deltabar=matrix(rep(0,nvar*nz),nrow=nz) ; Adelta=BayesmConstant.A*diag(nz) ; nu=nvar+BayesmConstant.nuInc; V=nu*diag(nvar); a=0.5; b=0.1; } else { if(is.null(Prior$Deltabar)) {Deltabar=matrix(rep(0,nvar*nz),nrow=nz)} else {Deltabar=Prior$Deltabar} if(is.null(Prior$Adelta)) {Adelta=BayesmConstant.A*diag(nz)} else {Adelta=Prior$Adelta} if(is.null(Prior$nu)) {nu=nvar+BayesmConstant.nuInc} else {nu=Prior$nu} if(is.null(Prior$V)) {V=nu*diag(nvar)} else {V=Prior$V} if(is.null(Prior$a)) {a=BayesmConstant.agammaprior} else {a=Prior$a} if(is.null(Prior$b)) {b=BayesmConstant.bgammaprior} else {b=Prior$b} } if(sum(dim(Deltabar) == c(nz,nvar)) != 2) pandterm("Deltabar is of incorrect dimension") if(sum(dim(Adelta)==c(nz,nz)) != 2) pandterm("Adelta is of incorrect dimension") if(nu < nvar) pandterm("invalid nu value") if(sum(dim(V)==c(nvar,nvar)) != 2) pandterm("V is of incorrect dimension") if((length(a) != 1) | (a <=0)) pandterm("a should be a positive number") if((length(b) != 1) | (b <=0)) pandterm("b should be a positive number") # # check for Mcmc # if(missing(Mcmc)) pandterm("Requires Mcmc argument -- at least R") if(is.null(Mcmc$R)) {pandterm("Requires element R of Mcmc")} else {R=Mcmc$R} if(is.null(Mcmc$Vbeta0)) {Vbeta0=diag(nvar)} else {Vbeta0=Mcmc$Vbeta0} if(sum(dim(Vbeta0) == c(nvar,nvar)) !=2) pandterm("Vbeta0 is not of dimension nvar") if(is.null(Mcmc$Delta0)) {Delta0=matrix(rep(0,nz*nvar),nrow=nz)} else {Delta0=Mcmc$Delta0} if(sum(dim(Delta0) == c(nz,nvar)) !=2) pandterm("Delta0 is not of dimension nvar by nz") if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} if(is.null(Mcmc$s_alpha)) { s_alpha=BayesmConstant.RRScaling} else {s_alpha= Mcmc$s_alpha } if(is.null(Mcmc$s_beta)) { s_beta=BayesmConstant.RRScaling/sqrt(nvar)} else {s_beta=Mcmc$s_beta } if(is.null(Mcmc$w)) { w=BayesmConstant.w} else {w = Mcmc$w} # Wayne Taylor 12/2014 ############################################# if(is.null(Mcmc$alpha)) {fixalpha=FALSE} else {fixalpha=TRUE; alpha=Mcmc$alpha} if(fixalpha & alpha<=0) pandterm("alpha is not positive") ################################################################### # print out problem # cat(" ",fill=TRUE) cat("Starting Random Walk Metropolis Sampler for Hierarchical Negative Binomial Regression",fill=TRUE) cat(" ",nobs," obs; ",nvar," covariates (including the intercept); ",fill=TRUE) cat(" ",nz," individual characteristics (including the intercept) ",fill=TRUE) cat(" ",fill=TRUE) cat("Prior Parameters:",fill=TRUE) cat("Deltabar",fill=TRUE) print(Deltabar) cat("Adelta",fill=TRUE) print(Adelta) cat("nu",fill=TRUE) print(nu) cat("V",fill=TRUE) print(V) if (!fixalpha) { cat("a",fill=TRUE) print(a) cat("b",fill=TRUE) print(b) } cat(" ",fill=TRUE) cat("MCMC Parameters:",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint,fill=TRUE) cat("s_alpha = ",s_alpha,fill=TRUE) cat("s_beta = ",s_beta,fill=TRUE) if (fixalpha) { cat("alpha",fill=TRUE) print(alpha) } cat("Fractional Likelihood Weight Parameter = ",w,fill=TRUE) cat(" ",fill=TRUE) par = rep(0,(nvar+1)) cat("initializing Metropolis candidate densities for ",nreg,"units ...",fill=TRUE) fsh() mle = optim(par,llnegbinR, X=Xpooled, y=ypooled, nvar=nvar, method="L-BFGS-B", upper=c(Inf,Inf,Inf,log(100000000)), hessian=TRUE, control=list(fnscale=-1)) fsh() beta_mle=mle$par[1:nvar] alpha_mle = exp(mle$par[nvar+1]) varcovinv = -mle$hessian Delta = Delta0 Beta = t(matrix(rep(beta_mle,nreg),ncol=nreg)) Vbetainv = chol2inv(chol(Vbeta0)) #Wayne: replaced "solve" function Vbeta = Vbeta0 if(!fixalpha) {alpha = alpha_mle} #Dan (7/16): add "if(!fixalpha)" alphacvar = s_alpha/varcovinv[nvar+1,nvar+1] alphacroot = sqrt(alphacvar) cat("beta_mle = ",beta_mle,fill=TRUE) cat("alpha_mle = ",alpha_mle, fill = TRUE) fsh() hess_i=NULL if(nobs > 1000){ sind=sample(c(1:nobs),size=1000) ypooleds=ypooled[sind] Xpooleds=Xpooled[sind,] } else{ ypooleds=ypooled Xpooleds=Xpooled } # Find the individual candidate hessian for (i in 1:nreg) { wgt = length(regdata[[i]]$y)/length(ypooleds) mle2 = optim(mle$par[1:nvar],llnegbinFract, X=regdata[[i]]$X, y=regdata[[i]]$y, Xpooled=Xpooleds, ypooled=ypooleds, w=w,wgt=wgt, nvar=nvar, lnalpha=mle$par[nvar+1], method="BFGS", hessian=TRUE, control=list(fnscale=-1, trace=0,reltol=1e-6)) if (mle2$convergence==0) hess_i[[i]] = list(hess=-mle2$hessian) else hess_i[[i]] = diag(rep(1,nvar)) if(i%%50 ==0) cat(" completed unit #",i,fill=TRUE) fsh() } ################################################################### # Wayne Taylor # 12/01/2014 ################################################################### if (fixalpha) {alpha=Mcmc$alpha} rootA = chol(Vbetainv) draws=rhierNegbinRw_rcpp_loop(regdata, hess_i, Z, Beta, Delta, Deltabar, Adelta, nu, V, a, b, R, keep, s_beta, alphacroot, 1000, rootA, alpha, fixalpha) ################################################################### attributes(draws$alphadraw)$class=c("bayesm.mat","mcmc") attributes(draws$alphadraw)$mcpar=c(1,R,keep) attributes(draws$Deltadraw)$class=c("bayesm.mat","mcmc") attributes(draws$Deltadraw)$mcpar=c(1,R,keep) attributes(draws$Vbetadraw)$class=c("bayesm.var","bayesm.mat","mcmc") attributes(draws$Vbetadraw)$mcpar=c(1,R,keep) attributes(draws$Betadraw)$class=c("bayesm.hcoef") return(draws) }bayesm/R/mixDenBi.R0000755000176000001440000000426610545276104013624 0ustar ripleyusersmixDenBi= function(i,j,xi,xj,pvec,comps) { # Revision History: # P. Rossi 6/05 # vectorized evaluation of bi-variate normal density 12/06 # # purpose: compute marg bivariate density implied by mixture of multivariate normals specified # by pvec,comps # # arguments: # i,j: index of two variables # xi specifies a grid of points for var i # xj specifies a grid of points for var j # pvec: prior probabilities of normal components # comps: list, each member is a list comp with ith normal component ~ N(comp[[1]],Sigma), # Sigma = t(R)%*%R, R^{-1} = comp[[2]] # Output: # matrix with values of density on grid # # --------------------------------------------------------------------------------------------- # define function needed # bivcomps=function(i,j,comps) { # purpose: obtain marginal means and standard deviations from list of normal components # arguments: # i,j: index of elements for bivariate marginal # comps: list, each member is a list comp with ith normal component ~N(comp[[1]],Sigma), # Sigma = t(R)%*%R, R^{-1} = comp[[2]] # returns: # a list with relevant mean vectors and rooti for each compenent # [[2]]=$sigma a matrix whose ith row is the standard deviations for the ith component # result=NULL nc = length(comps) dim = length(comps[[1]][[1]]) ind=matrix(c(i,j,i,j,i,i,j,j),ncol=2) for(comp in 1:nc) { mu = comps[[comp]][[1]][c(i,j)] root= backsolve(comps[[comp]][[2]],diag(dim)) Sigma=crossprod(root) sigma=matrix(Sigma[ind],ncol=2) rooti=backsolve(chol(sigma),diag(2)) result[[comp]]=list(mu=mu,rooti=rooti) } return(result) } # ---------------------------------------------------------------------------------------------- nc = length(comps) marmoms=bivcomps(i,j,comps) ngridxi=length(xi); ngridxj=length(xj) z=cbind(rep(xi,ngridxj),rep(xj,each=ngridxi)) den = matrix(0.0,nrow=ngridxi,ncol=ngridxj) for(comp in 1:nc) { quads=colSums((crossprod(marmoms[[comp]]$rooti,(t(z)-marmoms[[comp]]$mu)))^2) dencomp=exp(-(2/2)*log(2*pi)+sum(log(diag(marmoms[[comp]]$rooti)))-.5*quads) dim(dencomp)=c(ngridxi,ngridxj) den=den+dencomp*pvec[comp] } return(den) } bayesm/R/createX.R0000755000176000001440000000513412566706215013521 0ustar ripleyuserscreateX= function(p,na,nd,Xa,Xd,INT=TRUE,DIFF=FALSE,base=p) { # # Revision History: # P. Rossi 3/05 # # purpose: # function to create X array in format needed MNL and MNP routines # # Arguments: # p is number of choices # na is number of choice attribute variables (choice-specific characteristics) # nd is number of "demo" variables or characteristics of choosers # Xa is a n x (nx*p) matrix of choice attributes. First p cols are # values of attribute #1 for each of p chocies, second p for attribute # # 2 ... # Xd is an n x nd matrix of values of "demo" variables # INT is a logical flag for intercepts # DIFF is a logical flag for differencing wrt to base alternative # (required for MNP) # base is base alternative (default is p) # # note: if either you don't have any attributes or "demos", set # corresponding na, XA or nd,XD to NULL # YOU must specify p,na,nd,XA,XD for the function to work # # Output: # modified X matrix with n*p rows and INT*(p-1)+nd*(p-1) + na cols # # # check arguments # if(missing(p)) pandterm("requires p (# choice alternatives)") if(missing(na)) pandterm("requires na arg (use na=NULL if none)") if(missing(nd)) pandterm("requires nd arg (use nd=NULL if none)") if(missing(Xa)) pandterm("requires Xa arg (use Xa=NULL if none)") if(missing(Xd)) pandterm("requires Xd arg (use Xd=NULL if none)") if(is.null(Xa) && is.null(Xd)) pandterm("both Xa and Xd NULL -- requires one non-null") if(!is.null(na) && !is.null(Xa)) {if(ncol(Xa) != p*na) pandterm(paste("bad Xa dim, dim=",dim(Xa)))} if(!is.null(nd) && !is.null(Xd)) {if(ncol(Xd) != nd) pandterm(paste("ncol(Xd) ne nd, ncol(Xd)=",ncol(Xd)))} if(!is.null(Xa) && !is.null(Xd)) {if(nrow(Xa) != nrow(Xd)) {pandterm(paste("nrow(Xa) ne nrow(Xd),nrow(Xa)= ",nrow(Xa)," nrow(Xd)= ",nrow(Xd)))}} if(is.null(Xa)) {n=nrow(Xd)} else {n=nrow(Xa)} if(INT) {Xd=cbind(c(rep(1,n)),Xd)} if(DIFF) {Imod=diag(p-1)} else {Imod=matrix(0,p,p-1); Imod[-base,]=diag(p-1)} if(!is.null(Xd)) Xone=Xd %x%Imod else Xone=NULL Xtwo=NULL if(!is.null(Xa)) {if(DIFF) {tXa=matrix(t(Xa),nrow=p) Idiff=diag(p); Idiff[,base]=c(rep(-1,p));Idiff=Idiff[-base,] tXa=Idiff%*%tXa Xa=matrix(as.vector(tXa),ncol=(p-1)*na,byrow=TRUE) for (i in 1:na) {Xext=Xa[,((i-1)*(p-1)+1):((i-1)*(p-1)+p-1)] Xtwo=cbind(Xtwo,as.vector(t(Xext)))} } else { for (i in 1:na) { Xext=Xa[,((i-1)*p+1):((i-1)*p+p)] Xtwo=cbind(Xtwo,as.vector(t(Xext)))} } } return(cbind(Xone,Xtwo)) } bayesm/R/plot.bayesm.nmix.R0000755000176000001440000000773211754251324015335 0ustar ripleyusersplot.bayesm.nmix=function(x,names,burnin=trunc(.1*nrow(probdraw)),Grid,bi.sel,nstd=2,marg=TRUE, Data,ngrid=50,ndraw=200,...){ # # S3 method to plot normal mixture marginal and bivariate densities # nmixlist is a list of 3 components, nmixlist[[1]]: array of mix comp prob draws, # mmixlist[[2]] is not used, nmixlist[[3]] is list of draws of components # P. Rossi 2/07 # P. Rossi 3/07 fixed problem with dropping dimensions on probdraw (if ncomp=1) # P. Rossi 2/08 added marg flag to plot marginals # P. Rossi 3/08 added Data argument to paint histograms on the marginal plots # nmixlist=x if(mode(nmixlist) != "list") stop(" Argument must be a list \n") probdraw=nmixlist[[1]]; compdraw=nmixlist[[3]] if(!is.matrix(probdraw)) stop(" First element of list (probdraw) must be a matrix \n") if(mode(compdraw) != "list") stop(" Third element of list (compdraw) must be a list \n") op=par(no.readonly=TRUE) on.exit(par(op)) R=nrow(probdraw) if(R < 100) {cat(" fewer than 100 draws submitted \n"); return(invisible())} datad=length(compdraw[[1]][[1]]$mu) OneDimData=(datad==1) if(missing(bi.sel)) bi.sel=list(c(1,2)) # default to the first pair of variables ind=as.integer(seq(from=(burnin+1),to=R,length.out=max(ndraw,trunc(.05*R)))) if(missing(names)) {names=as.character(1:datad)} if(!missing(Data)){ if(!is.matrix(Data)) stop("Data argument must be a matrix \n") if(ncol(Data)!= datad) stop("Data matrix is of wrong dimension \n") } if(mode(bi.sel) != "list") stop("bi.sel must be as list, e.g. bi.sel=list(c(1,2),c(3,4)) \n") if(missing(Grid)){ Grid=matrix(0,nrow=ngrid,ncol=datad) if(!missing(Data)) {for(i in 1:datad) Grid[,i]=c(seq(from=range(Data[,i])[1],to=range(Data[,i])[2],length=ngrid))} else { out=momMix(probdraw[ind,,drop=FALSE],compdraw[ind]) mu=out$mu sd=out$sd for(i in 1:datad ) Grid[,i]=c(seq(from=(mu[i]-nstd*sd[i]), to=(mu[i]+nstd*sd[i]),length=ngrid)) } } # # plot posterior mean of marginal densities # if(marg){ mden=eMixMargDen(Grid,probdraw[ind,,drop=FALSE],compdraw[ind]) nx=datad if(nx==1) par(mfrow=c(1,1)) if(nx==2) par(mfrow=c(2,1)) if(nx==3) par(mfrow=c(3,1)) if(nx==4) par(mfrow=c(2,2)) if(nx>=5) par(mfrow=c(3,2)) for(index in 1:nx){ if(index == 2) par(ask=dev.interactive()) plot(range(Grid[,index]),c(0,1.1*max(mden[,index])),type="n",xlab="",ylab="density") title(names[index]) if(!missing(Data)){ deltax=(range(Grid[,index])[2]-range(Grid[,index])[1])/nrow(Grid) hist(Data[,index],xlim=range(Grid[,index]), freq=FALSE,col="yellow",breaks=max(20,.1*nrow(Data)),add=TRUE) lines(Grid[,index],mden[,index]/(sum(mden[,index])*deltax),col="red",lwd=2)} else {lines(Grid[,index],mden[,index],col="black",lwd=2) polygon(c(Grid[1,index],Grid[,index],Grid[nrow(Grid),index]),c(0,mden[,index],0),col="magenta")} } } # # now plot bivariates in list bi.sel # if(!OneDimData){ par(ask=dev.interactive()) nsel=length(bi.sel) den=array(0,dim=c(ngrid,ngrid,nsel)) lstxixj=NULL for(sel in 1:nsel){ i=bi.sel[[sel]][1] j=bi.sel[[sel]][2] xi=Grid[,i] xj=Grid[,j] lstxixj[[sel]]=list(xi,xj) for(elt in ind){ den[,,sel]=den[,,sel]+mixDenBi(i,j,xi,xj,probdraw[elt,,drop=FALSE],compdraw[[elt]]) } den[,,sel]=den[,,sel]/sum(den[,,sel]) } nx=nsel par(mfrow=c(1,1)) for(index in 1:nx){ xi=unlist(lstxixj[[index]][1]) xj=unlist(lstxixj[[index]][2]) xlabtxt=names[bi.sel[[index]][1]] ylabtxt=names[bi.sel[[index]][2]] image(xi,xj,den[,,index],col=terrain.colors(100),xlab=xlabtxt,ylab=ylabtxt) contour(xi,xj,den[,,index],add=TRUE,drawlabels=FALSE) } } invisible() } bayesm/R/logMargDenNR.R0000755000176000001440000000071110227516062014370 0ustar ripleyuserslogMargDenNR = function(ll) { # # purpose: compute log marginal density using Newton-Raftery # importance sampling estimator: 1/ (1/g sum_g exp(-log like) ) # where log like is the likelihood of the model evaluated as the # posterior draws (x). # # arguments: # ll -- vector of log-likelihood values evaluated at posterior draws # # output: # estimated log-marginal density med=median(ll) return(med-log(mean(exp(-ll+med)))) } bayesm/R/mnpProb.R0000644000176000001440000000275312533663350013540 0ustar ripleyusersmnpProb= function(beta,Sigma,X,r=100) { # # revision history: # written by Rossi 9/05 # W. Taylor 4/15 - replaced ghkvec call with rcpp version # # purpose: # function to MNP probabilities for a given X matrix (corresponding # to "one" observation # # arguments: # X is p-1 x k array of covariates (including intercepts) # note: X is from the "differenced" system # beta is k x 1 with k = ncol(X) # Sigma is p-1 x p-1 # r is the number of random draws to use in GHK # # output -- probabilities # for each observation w = Xbeta + e e ~N(0,Sigma) # if y=j (j max(w_-j) and w_j >0 # if y=p, w < 0 # # to use GHK we must transform so that these are rectangular regions # e.g. if y=1, w_1 > 0 and w_1 - w_-1 > 0 # # define Aj such that if j=1,..,p-1, Ajw = Ajmu + Aje > 0 is equivalent to y=j # implies Aje > -Ajmu # lower truncation is -Ajmu and cov = AjSigma t(Aj) # # for p, e < - mu # # pm1=ncol(Sigma) k=length(beta) mu=matrix(X%*%beta,nrow=pm1) above=rep(0,pm1) prob=double(pm1+1) for (j in 1:pm1) { Aj=-diag(pm1) Aj[,j]=rep(1,pm1) trunpt=as.vector(-Aj%*%mu) Lj=t(chol(Aj%*%Sigma%*%t(Aj))) # note: rob's routine expects lower triangular root prob[j]=ghkvec(Lj,trunpt,above,r) # note: ghkvec does an entire vector of n probs each with different truncation points but the # same cov matrix. } # # now do pth alternative # prob[pm1+1]=1-sum(prob[1:pm1]) return(prob) } bayesm/R/BayesmFunctions.R0000644000176000001440000000007012524505375015224 0ustar ripleyuserspandterm=function(message) { stop(message,call.=FALSE) }bayesm/R/rdpgibbs_rcpp.r0000644000176000001440000001614312566706215015005 0ustar ripleyusersrDPGibbs=function(Prior,Data,Mcmc){ # # Revision History: # 5/06 add rthetaDP # 7/06 include rthetaDP in main body to avoid copy overhead # 1/08 add scaling # 2/08 add draw of lambda # 3/08 changed nu prior support to dim(y) + exp(unif gird on nulim[1],nulim[2]) # W. Taylor 4/15 - added nprint option to MCMC argument # # purpose: do Gibbs sampling for density estimation using Dirichlet process model # # arguments: # Data is a list of y which is an n x k matrix of data # Prior is a list of (alpha,lambda,Prioralpha) # alpha: starting value # lambda_hyper: hyperparms of prior on lambda # Prioralpha: hyperparms of alpha prior; a list of (Istarmin,Istarmax,power) # if elements of the prior don't exist, defaults are assumed # Mcmc is a list of (R,keep,maxuniq) # R: number of draws # keep: thinning parameter # maxuniq: the maximum number of unique thetaStar values # nprint - print estimated time remaining on every nprint'th draw # # Output: # list with elements # alphadraw: vector of length R/keep, [i] is ith draw of alpha # Istardraw: vector of length R/keep, [i] is the number of unique theta's drawn from ith iteration # adraw # nudraw # vdraw # thetaNp1draws: list, [[i]] is ith draw of theta_{n+1} # inddraw: R x n matrix, [,i] is indicators of identity for each obs in ith iteration # # Model: # y_i ~ f(y|thetai) # thetai|G ~ G # G|lambda,alpha ~ DP(G|G0(lambda),alpha) # # Priors: # alpha: starting value # # lambda: # G0 ~ N(mubar,Sigma (x) Amu^-1) # mubar=vec(mubar) # Sigma ~ IW(nu,nu*v*I) note: mode(Sigma)=nu/(nu+2)*v*I # mubar=0 # amu is uniform on grid specified by alim # nu is log uniform, nu=d-1+exp(Z) z is uniform on seq defined bvy nulim # v is uniform on sequence specificd by vlim # # Prioralpha: # alpha ~ (1-(alpha-alphamin)/(alphamax-alphamin))^power # alphamin=exp(digamma(Istarmin)-log(gamma+log(N))) # alphamax=exp(digamma(Istarmax)-log(gamma+log(N))) # gamma= .5772156649015328606 # # # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of y")} if(is.null(Data$y)) {pandterm("Requires Data element y")} y=Data$y # # check data for validity # if(!is.matrix(y)) {pandterm("y must be a matrix")} nobs=nrow(y) dimy=ncol(y) # # check for Prior # alimdef=BayesmConstant.DPalimdef nulimdef=BayesmConstant.DPnulimdef vlimdef=BayesmConstant.DPvlimdef if(missing(Prior)) {pandterm("requires Prior argument ")} else { if(is.null(Prior$lambda_hyper)) {lambda_hyper=list(alim=alimdef,nulim=nulimdef,vlim=vlimdef)} else {lambda_hyper=Prior$lambda_hyper; if(is.null(lambda_hyper$alim)) {lambda_hyper$alim=alimdef} if(is.null(lambda_hyper$nulim)) {lambda_hyper$nulim=nulimdef} if(is.null(lambda_hyper$vlim)) {lambda_hyper$vlim=vlimdef} } if(is.null(Prior$Prioralpha)) {Prioralpha=list(Istarmin=BayesmConstant.DPIstarmin,Istarmax=min(50,0.1*nobs),power=BayesmConstant.DPpower)} else {Prioralpha=Prior$Prioralpha; if(is.null(Prioralpha$Istarmin)) {Prioralpha$Istarmin=BayesmConstant.DPIstarmin} else {Prioralpha$Istarmin=Prioralpha$Istarmin} if(is.null(Prioralpha$Istarmax)) {Prioralpha$Istarmax=min(50,0.1*nobs)} else {Prioralpha$Istarmax=Prioralpha$Istarmax} if(is.null(Prioralpha$power)) {Prioralpha$power=BayesmConstant.DPpower} } } gamma= BayesmConstant.gamma Prioralpha$alphamin=exp(digamma(Prioralpha$Istarmin)-log(gamma+log(nobs))) Prioralpha$alphamax=exp(digamma(Prioralpha$Istarmax)-log(gamma+log(nobs))) Prioralpha$n=nobs # # check Prior arguments for valdity # if(lambda_hyper$alim[1]<0) {pandterm("alim[1] must be >0")} if(lambda_hyper$nulim[1]<0) {pandterm("nulim[1] must be >0")} if(lambda_hyper$vlim[1]<0) {pandterm("vlim[1] must be >0")} if(Prioralpha$Istarmin <1){pandterm("Prioralpha$Istarmin must be >= 1")} if(Prioralpha$Istarmax <= Prioralpha$Istarmin){pandterm("Prioralpha$Istarmin must be > Prioralpha$Istarmax")} # # check MCMC argument # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} else { if(is.null(Mcmc$R)) {pandterm("requires Mcmc element R")} else {R=Mcmc$R} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} if(is.null(Mcmc$maxuniq)) {maxuniq=BayesmConstant.DPmaxuniq} else {maxuniq=Mcmc$maxuniq} if(is.null(Mcmc$SCALE)) {SCALE=BayesmConstant.DPSCALE} else {SCALE=Mcmc$SCALE} if(is.null(Mcmc$gridsize)) {gridsize=BayesmConstant.DPgridsize} else {gridsize=Mcmc$gridsize} } # # print out the problem # cat(" Starting Gibbs Sampler for Density Estimation Using Dirichlet Process Model",fill=TRUE) cat(" ",nobs," observations on ",dimy," dimensional data",fill=TRUE) cat(" ",fill=TRUE) cat(" SCALE=",SCALE,fill=TRUE) cat(" ",fill=TRUE) cat(" Prior Parms: ",fill=TRUE) cat(" G0 ~ N(mubar,Sigma (x) Amu^-1)",fill=TRUE) cat(" mubar = ",0,fill=TRUE) cat(" Sigma ~ IW(nu,nu*v*I)",fill=TRUE) cat(" Amu ~ uniform[",lambda_hyper$alim[1],",",lambda_hyper$alim[2],"]",fill=TRUE) cat(" nu ~ uniform on log grid on [",dimy-1+exp(lambda_hyper$nulim[1]), ",",dimy-1+exp(lambda_hyper$nulim[2]),"]",fill=TRUE) cat(" v ~ uniform[",lambda_hyper$vlim[1],",",lambda_hyper$vlim[2],"]",fill=TRUE) cat(" ",fill=TRUE) cat(" alpha ~ (1-(alpha-alphamin)/(alphamax-alphamin))^power",fill=TRUE) cat(" Istarmin = ",Prioralpha$Istarmin,fill=TRUE) cat(" Istarmax = ",Prioralpha$Istarmax,fill=TRUE) cat(" alphamin = ",Prioralpha$alphamin,fill=TRUE) cat(" alphamax = ",Prioralpha$alphamax,fill=TRUE) cat(" power = ",Prioralpha$power,fill=TRUE) cat(" ",fill=TRUE) cat(" Mcmc Parms: R= ",R," keep= ",keep," nprint= ",nprint," maxuniq= ",maxuniq," gridsize for lambda hyperparms= ",gridsize, fill=TRUE) cat(" ",fill=TRUE) ################################################################### # Wayne Taylor # 1/29/2015 ################################################################### out = rDPGibbs_rcpp_loop(R,keep,nprint, y, lambda_hyper, SCALE, maxuniq, Prioralpha, gridsize, BayesmConstant.A,BayesmConstant.nuInc,BayesmConstant.DPalpha) ################################################################### nmix=list(probdraw=matrix(c(rep(1,nrow(out$inddraw))),ncol=1),zdraw=out$inddraw,compdraw=out$thetaNp1draw) attributes(nmix)$class="bayesm.nmix" attributes(out$alphadraw)$class=c("bayesm.mat","mcmc") attributes(out$Istardraw)$class=c("bayesm.mat","mcmc") attributes(out$adraw)$class=c("bayesm.mat","mcmc") attributes(out$nudraw)$class=c("bayesm.mat","mcmc") attributes(out$vdraw)$class=c("bayesm.mat","mcmc") return(list(alphadraw=out$alphadraw,Istardraw=out$Istardraw,adraw=out$adraw,nudraw=out$nudraw, vdraw=out$vdraw,nmix=nmix)) } bayesm/R/rmnlIndepMetrop_rcpp.R0000644000176000001440000001042512566706215016265 0ustar ripleyusersrmnlIndepMetrop=function(Data,Prior,Mcmc){ # # revision history: # p. rossi 1/05 # 2/9/05 fixed error in Metrop eval # changed to reflect new argument order in llmnl,mnlHess 9/05 # added return for log-like 11/05 # W. Taylor 4/15 - added nprint option to MCMC argument # # purpose: # draw from posterior for MNL using Independence Metropolis # # Arguments: # Data - list of p,y,X # p is number of alternatives # X is nobs*p x nvar matrix # y is nobs vector of values from 1 to p # Prior - list of A, betabar # A is nvar x nvar prior preci matrix # betabar is nvar x 1 prior mean # Mcmc # R is number of draws # keep is thinning parameter # nprint - print estimated time remaining on every nprint'th draw # nu degrees of freedom parameter for independence # sampling density # # Output: # list of betadraws # # Model: Pr(y=j) = exp(x_j'beta)/sum(exp(x_k'beta) # # Prior: beta ~ N(betabar,A^-1) # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of p, y, X")} if(is.null(Data$X)) {pandterm("Requires Data element X")} X=Data$X if(is.null(Data$y)) {pandterm("Requires Data element y")} y=Data$y if(is.null(Data$p)) {pandterm("Requires Data element p")} p=Data$p nvar=ncol(X) nobs=length(y) # # check data for validity # if(length(y) != (nrow(X)/p) ) {pandterm("length(y) ne nrow(X)/p")} if(sum(y %in% (1:p)) < nobs) {pandterm("invalid values in y vector -- must be integers in 1:p")} cat(" table of y values",fill=TRUE) print(table(y)) # # check for Prior # if(missing(Prior)) { betabar=c(rep(0,nvar)); A=BayesmConstant.A*diag(nvar)} else { if(is.null(Prior$betabar)) {betabar=c(rep(0,nvar))} else {betabar=Prior$betabar} if(is.null(Prior$A)) {A=BayesmConstant.A*diag(nvar)} else {A=Prior$A} } # # check dimensions of Priors # if(ncol(A) != nrow(A) || ncol(A) != nvar || nrow(A) != nvar) {pandterm(paste("bad dimensions for A",dim(A)))} if(length(betabar) != nvar) {pandterm(paste("betabar wrong length, length= ",length(betabar)))} # # check MCMC argument # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} else { if(is.null(Mcmc$R)) {pandterm("requires Mcmc element R")} else {R=Mcmc$R} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} if(is.null(Mcmc$nu)) {nu=6} else {nu=Mcmc$nu} } # # print out problem # cat(" ", fill=TRUE) cat("Starting Independence Metropolis Sampler for Multinomial Logit Model",fill=TRUE) cat(" ",length(y)," obs with ",p," alternatives",fill=TRUE) cat(" ", fill=TRUE) cat("Table of y Values",fill=TRUE) print(table(y)) cat("Prior Parms: ",fill=TRUE) cat("betabar",fill=TRUE) print(betabar) cat("A",fill=TRUE) print(A) cat(" ", fill=TRUE) cat("MCMC parms: ",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint," nu (df for st candidates) = ",nu,fill=TRUE) cat(" ",fill=TRUE) # # compute required quantities for indep candidates # beta=c(rep(0,nvar)) mle=optim(beta,llmnl,X=X,y=y,method="BFGS",hessian=TRUE,control=list(fnscale=-1)) beta=mle$par betastar=mle$par mhess=mnlHess(beta,y,X) candcov=chol2inv(chol(mhess)) root=chol(candcov) rooti=backsolve(root,diag(nvar)) priorcov=chol2inv(chol(A)) rootp=chol(priorcov) rootpi=backsolve(rootp,diag(nvar)) oldloglike=llmnl(beta,y,X) oldlpost=oldloglike+lndMvn(beta,betabar,rootpi) oldlimp=lndMvst(beta,nu,betastar,rooti) # note: we don't need the determinants as they cancel in # computation of acceptance prob ################################################################### # Wayne Taylor # 08/21/2014 ################################################################### loopout = rmnlIndepMetrop_rcpp_loop(R,keep,nu,betastar,root,y,X,betabar,rootpi,rooti,oldlimp,oldlpost,nprint); ################################################################### attributes(loopout$betadraw)$class=c("bayesm.mat","mcmc") attributes(loopout$betadraw)$mcpar=c(1,R,keep) return(list(betadraw=loopout$betadraw,loglike=loopout$loglike,acceptr=loopout$naccept/R)) } bayesm/R/rbayesBLP_rcpp.R0000644000176000001440000002405013114117322014751 0ustar ripleyusersrbayesBLP=function(Data, Prior, Mcmc){ # # Keunwoo Kim 02/06/2014 # # Purpose: # draw theta_bar and Sigma via hybrid Gibbs sampler (Jiang, Manchanda, and Rossi, 2009) # # Arguments: # Data # X: J*T by H (if IV is used, the last column is endogeneous variable.) # share: vector of length J*T # J: number of alternatives (excluding outside option) # Z: instrumental variables (optional) # # Prior # sigmasqR # theta_hat # A # deltabar # Ad # nu0 # s0_sq # VOmega # # Mcmc # R: number of MCMC draws # H: number of draws for Monte-Carlo integration # # s: scaling parameter of MH increment # cand_cov: var-cov matrix of MH increment # (minaccep: lower bound of target range of acceptance rate) # (maxaccep: upper bound of target range of acceptance rate) # # theta_bar_initial # r_initial # tau_sq_initial # Omega_initial # delta_initial # # tol: convergence tolerance for the contraction mapping # # Output: # a List of tau_sq (or Omega and delta), # theta_bar, r (equivalent to Sigma) draws, Sigma draws, # relative numerical efficiency of r draws, tunned parameters for MH, and # acceptance rate # pandterm=function(message) {stop(message,call.=FALSE)} # # check for data # if(missing(Data)) {pandterm("Requires Data argument -- list of X and share")} if(is.null(Data$X)) {pandterm("Requires Data element X")} else {X=Data$X} if(is.null(Data$share)) {pandterm("Requires Data element share")} else {share=Data$share} if(is.null(Data$J)) {pandterm("Requires Data element J")} else {J=Data$J} if(is.null(Data$Z)) {IV=FALSE; Z=matrix(0); I=1} else {IV=TRUE; I=ncol(Z)} if(!is.matrix(X)) {pandterm("X must be a matrix")} if(!is.vector(share, mode = "numeric")) {pandterm("share must be a numeric vector")} if(!is.matrix(Z)) {pandterm("Z must be a matrix")} if(length(J) > 1 | floor(J) != J) {pandterm("J must be an integer")} K=ncol(X) if (length(share) != nrow(X)) {pandterm("Mismatch in the number of observations in X and share")} T=length(share)/J # # check for prior # if(missing(Prior)) { c=50 sigmasqRoff=1 sigmasqRdiag=log((1+sqrt(1-4*(2*(c(1:K)-1)*sigmasqRoff-c)))/2)/4 sigmasqR=c(sigmasqRdiag, rep(1, K*(K-1)/2)) A=BayesmConstant.A*diag(K) theta_hat=rep(0,K) nu0=K+1 s0_sq=1 deltabar=rep(0,I) Ad=BayesmConstant.A*diag(I) VOmega=BayesmConstant.BLPVOmega } else { if(is.null(Prior$sigmasqR)) { c=50 sigmasqRoff=1 sigmasqRdiag=log((1+sqrt(1-4*(2*(c(1:K)-1)*sigmasqRoff-c)))/2)/4 sigmasqR=c(sigmasqRdiag, rep(1, K*(K-1)/2)) } else { sigmasqR=Prior$sigmasqR } if(is.null(Prior$A)) {A=BayesmConstant.A*diag(K)} else {A=Prior$A} if(is.null(Prior$theta_hat)) {theta_hat=rep(0,K)} else {theta_hat=Prior$theta_hat} if(is.null(Prior$nu0)) {nu0=K+1} else {nu0=Prior$nu0} if(is.null(Prior$s0_sq)) {s0_sq=1} else {s0_sq=Prior$s0_sq} if(is.null(Prior$deltabar)) {deltabar=rep(0,I)} else {deltabar=Prior$deltabar} if(is.null(Prior$Ad)) {Ad=BayesmConstant.A*diag(I)} else {Ad=Prior$Ad} if(is.null(Prior$VOmega)) {VOmega=BayesmConstant.BLPVOmega} else {VOmega=Prior$VOmega} } if(length(sigmasqR) != K*(K+1)/2) pandterm("sigmasqR is of incorrect dimension") if(sum(dim(A)==c(K,K)) != 2) pandterm("A is of incorrect dimension") if(length(theta_hat) != K) pandterm("theta_hat is of incorrect dimension") if((length(nu0) != 1) | (nu0 <=0)) pandterm("nu0 should be a positive number") if((length(s0_sq) != 1) | (s0_sq <=0)) pandterm("s0_sq should be a positive number") if(length(deltabar) != I) pandterm("deltabar is of incorrect dimension") if(sum(dim(Ad)==c(I,I)) != 2) pandterm("Ad is of incorrect dimension") if(sum(dim(VOmega)==c(2,2)) != 2) pandterm("VOmega is of incorrect dimension") # # check for Mcmc # if(missing(Mcmc)) pandterm("Requires Mcmc argument -- at least R and H") if(is.null(Mcmc$R)) {pandterm("Requires element R of Mcmc")} else {R=Mcmc$R} if(is.null(Mcmc$H)) {pandterm("Requires element H of Mcmc")} else {H=Mcmc$H} if(is.null(Mcmc$initial_theta_bar)) {initial_theta_bar=rep(0,K)} else {initial_theta_bar=Mcmc$initial_theta_bar} if(is.null(Mcmc$initial_r)) {initial_r=rep(0,K*(K+1)/2)} else {initial_r=Mcmc$initial_r} if(is.null(Mcmc$initial_tau_sq)) {initial_tau_sq=0.1} else {initial_tau_sq=Mcmc$initial_tau_sq} if(is.null(Mcmc$initial_Omega)) {initial_Omega=diag(2)} else {initial_Omega=Mcmc$initial_Omega} if(is.null(Mcmc$initial_delta)) {initial_delta=rep(0,I)} else {initial_delta=Mcmc$initial_tau_sq} if(is.null(Mcmc$keep)) {keep=1} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(is.null(Mcmc$s)+is.null(Mcmc$cand_cov)==0){ s=Mcmc$s cand_cov=Mcmc$cand_cov tuning_auto=FALSE } if(is.null(Mcmc$s)+is.null(Mcmc$cand_cov)==1) pandterm("If you want to control tuning parameters, write both parameters.") if(is.null(Mcmc$s)+is.null(Mcmc$cand_cov)==2){ s=BayesmConstant.RRScaling/sqrt(K*(K+1)/2) cand_cov=diag(c(rep(0.1,K),rep(1,K*(K-1)/2))) tuning_auto=TRUE } if(is.null(Mcmc$tol)) {tol=BayesmConstant.BLPtol} else {tol=Mcmc$tol} minaccep=0.3 maxaccep=0.5 if(length(initial_theta_bar)!=K) pandterm("initial_theta_bar is of incorrect dimension") if(length(initial_r)!=(K*(K+1)/2)) pandterm("initial_r is of incorrect dimension") if(initial_tau_sq<0) pandterm("initial_tau_sq should be positive") if(sum(dim(initial_Omega)==c(2,2))!=2) pandterm("initial_Omega is of incorrect dimension") if(length(initial_delta)!=I) pandterm("initial_delta is of incorrect dimension") if(nprint<0) { pandterm('nprint must be >=0') } # # print out problem # cat(" ",fill=TRUE) cat("Data Dimensions:",fill=TRUE) cat(" ",T," market(time); ",J+1," alternatives (including outside option); ",fill=TRUE) cat(" ",fill=TRUE) if (IV==TRUE){ cat(" ",I," instrumental variable(s) ",fill=TRUE) cat(" ",fill=TRUE) } cat("Prior Parameters:",fill=TRUE) cat(" thetahat",fill=TRUE) print(theta_hat) cat(" A",fill=TRUE) print(A) cat(" sigmasqR",fill=TRUE) print(sigmasqR) cat(" nu0",fill=TRUE) print(nu0) if (IV==TRUE){ cat(" VOmega",fill=TRUE) print(VOmega) cat(" deltabar",fill=TRUE) print(deltabar) cat(" Ad",fill=TRUE) print(Ad) } if (IV==FALSE){ cat(" s0_sq",fill=TRUE) print(s0_sq) } cat(" ",fill=TRUE) cat("MCMC Parmameters: ",fill=TRUE) cat(" ",R," reps; keeping every ",keep,"th draw; printing every ",nprint,"th draw",fill=TRUE) cat(" ",H," draws for Monte-Carlo integration",fill=TRUE) cat(" ",fill=TRUE) cat("Contraction Mapping Tolerance: ",fill=TRUE) cat(" until max(abs((mu1-mu0)/mu0)) <",tol,fill=TRUE) cat(" ",fill=TRUE) if (tuning_auto){ cat(" automatically tuning parameters of RW M-H increment",fill=TRUE) cat(" ",fill=TRUE) cat(" target acceptance rate is between ",minaccep*100,"% and ",maxaccep*100,"%",fill=TRUE) cat(" ",fill=TRUE) } else{ cat(" scaling parameter of RW M-H increment is given as",fill=TRUE) print(s) cat(" ",fill=TRUE) cat(" var-cov matrix of RW M-H increment is given as",fill=TRUE) print(cand_cov) cat(" ",fill=TRUE) } # draw for MC integration draw <- matrix(rnorm(K*H), K, H) # # tuning RW Metropolis-Hastings # # if auto-tuning complete1 <- 0 initial_theta_bar2 <- initial_theta_bar initial_r2 <- initial_r initial_tau_sq2 <- initial_tau_sq initial_Omega2 <- initial_Omega initial_delta2 <- initial_delta rdraws <- NULL if (tuning_auto){ cat("Tuning RW Metropolis-Hastings Increment...",fill=TRUE) cat(" ",fill=TRUE) cat("-If acceptance rate < ",minaccep*100,"% => s/5",fill=TRUE) cat("-If acceptance rate > ",maxaccep*100,"% => s*3",fill=TRUE) cat("-If acceptance rate is ",minaccep*100,"~",maxaccep*100,"% => complete tuning",fill=TRUE) cat(" ",fill=TRUE) while (complete1==0){ cat(" try s=",s,fill=TRUE) out1 <- bayesBLP_rcpp_loop(IV, X, Z, share, J, T, draw, 500, sigmasqR, A, theta_hat, deltabar, Ad, nu0, s0_sq, VOmega, s^2, cand_cov, initial_theta_bar2, initial_r2, initial_tau_sq2, initial_Omega2, initial_delta2, tol, 1, 0) initial_theta_bar2 <- as.vector(out1$thetabardraw[,500]) initial_r2 <- as.vector(out1$rdraw[,500]) initial_tau_sq2 <- out1$tausqdraw[500] if (IV==TRUE) {initial_Omega2 <- matrix(out1$Omegadraw[,500],2,2)} if (IV==TRUE) {initial_delta2 <- as.vector(out1$deltadraw[,500])} cat(" acceptance rate is ",out1$acceptrate,fill=TRUE) if (out1$acceptrate>0.20 & out1$acceptrate<0.80){ rdraws <- cbind(rdraws, out1$rdraw) cat(" (r draws stored)",fill=TRUE) } if (out1$acceptratemaxaccep){ s <- s*3 }else{ complete1 <- 1 cat(" ",fill=TRUE) cat(" (tuning completed.)",fill=TRUE) } } # scaling tunned var-cov matrix from r draws scale_opt <- s*sqrt(diag(cand_cov)) Omega <- cov(t(rdraws)) scale_Omega <- sqrt(diag(Omega)) corr_opt <- Omega / (scale_Omega%*%t(scale_Omega)) s <- 1 cand_cov <- corr_opt * (scale_opt%*%t(scale_opt)) cat(" ",fill=TRUE) cat("Tuning Completed:",fill=TRUE) cat(" s=",s,fill=TRUE) cat(" var-cov=",fill=TRUE) print(cand_cov) cat(" ",fill=TRUE) } # # main run # cat("Starting Random Walk Metropolis-Hastings Sampler for BLP",fill=TRUE) out <- bayesBLP_rcpp_loop(IV, X, Z, share, J, T, draw, R, sigmasqR, A, theta_hat, deltabar, Ad, nu0, s0_sq, VOmega, s^2, cand_cov, initial_theta_bar, initial_r, initial_tau_sq, initial_Omega, initial_delta, tol, keep, nprint) out$s <- s out$cand_cov <- cand_cov attributes(out$tausqdraw)$class=c("bayesm.mat","mcmc") attributes(out$tausqdraw)$mcpar=c(1,R,keep) attributes(out$thetabardraw)$class=c("bayesm.mat","mcmc") attributes(out$thetabardraw)$mcpar=c(1,R,keep) attributes(out$rdraw)$class=c("bayesm.mat","mcmc") attributes(out$rdraw)$mcpar=c(1,R,keep) attributes(out$Sigmadraw)$class=c("bayesm.mat","mcmc") attributes(out$Sigmadraw)$mcpar=c(1,R,keep) attributes(out$Omegadraw)$class=c("bayesm.mat","mcmc") attributes(out$Omegadraw)$mcpar=c(1,R,keep) attributes(out$deltadraw)$class=c("bayesm.mat","mcmc") attributes(out$deltadraw)$mcpar=c(1,R,keep) return(out) } bayesm/R/numEff.R0000755000176000001440000000114110240734574013334 0ustar ripleyusersnumEff= function(x,m=as.integer(min(length(x),(100/sqrt(5000))*sqrt(length(x))))) { # # P. Rossi # revision history: 3/27/05 # # purpose: # compute N-W std error and relative numerical efficiency # # Arguments: # x is vector of draws # m is number of lags to truncate acf # def is such that m=100 if length(x)= 5000 and grows with sqrt(length) # # Output: # list with numerical std error and variance multiple (f) # wgt=as.vector(seq(m,1,-1))/(m+1) z=acf(x,lag.max=m,plot=FALSE) f=1+2*wgt%*%as.vector(z$acf[-1]) stderr=sqrt(var(x)*f/length(x)) list(stderr=stderr,f=f,m=m) } bayesm/R/eMixMargDen.R0000755000176000001440000000123410224401101014232 0ustar ripleyuserseMixMargDen= function(grid,probdraw,compdraw) { # # Revision History: # R. McCulloch 11/04 # # purpose: plot the marginal density of a normal mixture averaged over MCMC draws # # arguments: # grid -- array of grid points, grid[,i] are ordinates for ith component # probdraw -- ith row is ith draw of probabilities of mixture comp # compdraw -- list of lists of draws of mixture comp moments (each sublist is from mixgibbs) # # output: # array of same dim as grid with density values # # den=matrix(0,nrow(grid),ncol(grid)) for(i in 1:length(compdraw)) den=den+mixDen(grid,probdraw[i,],compdraw[[i]]) return(den/length(compdraw)) } bayesm/R/mixDen.R0000755000176000001440000000352510554234436013350 0ustar ripleyusersmixDen= function(x,pvec,comps) { # Revision History: # R. McCulloch 11/04 # P. Rossi 3/05 -- put in backsolve # P. Rossi 1/06 -- put in crossprod # # purpose: compute marginal densities for multivariate mixture of normals (given by p and comps) at x # # arguments: # x: ith columns gives evaluations for density of ith variable # pvec: prior probabilities of normal components # comps: list, each member is a list comp with ith normal component ~ N(comp[[1]],Sigma), # Sigma = t(R)%*%R, R^{-1} = comp[[2]] # Output: # matrix with same shape as input, x, ith column gives margial density of ith variable # # --------------------------------------------------------------------------------------------- # define function needed # ums=function(comps) { # purpose: obtain marginal means and standard deviations from list of normal components # arguments: # comps: list, each member is a list comp with ith normal component ~N(comp[[1]],Sigma), # Sigma = t(R)%*%R, R^{-1} = comp[[2]] # returns: # a list with [[1]]=$mu a matrix whose ith row is the means for ith component # [[2]]=$sigma a matrix whose ith row is the standard deviations for the ith component # nc = length(comps) dim = length(comps[[1]][[1]]) mu = matrix(0.0,nc,dim) sigma = matrix(0.0,nc,dim) for(i in 1:nc) { mu[i,] = comps[[i]][[1]] # root = solve(comps[[i]][[2]]) root= backsolve(comps[[i]][[2]],diag(rep(1,dim))) sigma[i,] = sqrt(diag(crossprod(root))) } return(list(mu=mu,sigma=sigma)) } # ---------------------------------------------------------------------------------------------- nc = length(comps) mars = ums(comps) den = matrix(0.0,nrow(x),ncol(x)) for(i in 1:ncol(x)) { for(j in 1:nc) den[,i] = den[,i] + dnorm(x[,i],mean = mars$mu[j,i],sd=mars$sigma[j,i])*pvec[j] } return(den) } bayesm/R/fsh.R0000755000176000001440000000031510225316753012674 0ustar ripleyusersfsh=function() { # # P. Rossi # revision history: 3/27/05 # # Purpose: # function to flush console (needed only under windows) # if (Sys.info()[1] == "Windows") flush.console() return() } bayesm/R/rbprobitgibbs_rcpp.r0000644000176000001440000000654613114117322016032 0ustar ripleyusersrbprobitGibbs= function(Data,Prior,Mcmc) { # # revision history: # p. rossi 1/05 # 3/07 added validity check of values of y and classes # 3/07 fixed error with betabar supplied # W. Taylor 4/15 - added nprint option to MCMC argument # # purpose: # draw from posterior for binary probit using Gibbs Sampler # # Arguments: # Data - list of X,y # X is nobs x nvar, y is nobs vector of 0,1 # Prior - list of A, betabar # A is nvar x nvar prior preci matrix # betabar is nvar x 1 prior mean # Mcmc # R is number of draws # keep is thinning parameter # nprint - print estimated time remaining on every nprint'th draw # # Output: # list of betadraws # # Model: y = 1 if w=Xbeta + e > 0 e ~N(0,1) # # Prior: beta ~ N(betabar,A^-1) # # # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of y and X")} if(is.null(Data$X)) {pandterm("Requires Data element X")} X=Data$X if(is.null(Data$y)) {pandterm("Requires Data element y")} y=Data$y nvar=ncol(X) nobs=length(y) # # check data for validity # if(length(y) != nrow(X) ) {pandterm("y and X not of same row dim")} if(sum(unique(y) %in% c(0:1)) < length(unique(y))) {pandterm("Invalid y, must be 0,1")} # # check for Prior # if(missing(Prior)) { betabar=c(rep(0,nvar)); A=BayesmConstant.A*diag(nvar)} else { if(is.null(Prior$betabar)) {betabar=c(rep(0,nvar))} else {betabar=Prior$betabar} if(is.null(Prior$A)) {A=BayesmConstant.A*diag(nvar)} else {A=Prior$A} } # # check dimensions of Priors # if(ncol(A) != nrow(A) || ncol(A) != nvar || nrow(A) != nvar) {pandterm(paste("bad dimensions for A",dim(A)))} if(length(betabar) != nvar) {pandterm(paste("betabar wrong length, length= ",length(betabar)))} # # check MCMC argument # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} else { if(is.null(Mcmc$R)) {pandterm("requires Mcmc element R")} else {R=Mcmc$R} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} } # # print out problem # cat(" ", fill=TRUE) cat("Starting Gibbs Sampler for Binary Probit Model",fill=TRUE) cat(" with ",length(y)," observations",fill=TRUE) cat("Table of y Values",fill=TRUE) print(table(y)) cat(" ", fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("betabar",fill=TRUE) print(betabar) cat("A",fill=TRUE) print(A) cat(" ", fill=TRUE) cat("MCMC parms: ",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint,fill=TRUE) cat(" ",fill=TRUE) beta=c(rep(0,nvar)) sigma=c(rep(1,nrow(X))) root=chol(chol2inv(chol((crossprod(X,X)+A)))) Abetabar=crossprod(A,betabar) #a=ifelse(y == 0,-100, 0) #b=ifelse(y == 0, 0, 100) above=ifelse(y == 0, 1, 0) trunpt=0 ################################################################### # Keunwoo Kim # 08/05/2014 ################################################################### #draws=rbprobitGibbs_rcpp_loop(y,X,Abetabar,root,beta,sigma,a,b,R,keep,nprint) draws=rbprobitGibbs_rcpp_loop(y,X,Abetabar,root,beta,sigma,trunpt,above,R,keep,nprint) ################################################################### attributes(draws$betadraw)$class=c("bayesm.mat","mcmc") attributes(draws$betadraw)$mcpar=c(1,R,keep) return(draws) } bayesm/R/summary.bayesm.mat.R0000755000176000001440000000356011754537723015667 0ustar ripleyuserssummary.bayesm.mat=function(object,names,burnin=trunc(.1*nrow(X)),tvalues,QUANTILES=TRUE,TRAILER=TRUE,...){ # # S3 method to compute and print posterior summaries for a matrix of draws # P. Rossi 2/07 # X=object if(mode(X) == "list") stop("list entered \n Possible Fixup: extract from list \n") if(mode(X) !="numeric") stop("Requires numeric argument \n") if(is.null(attributes(X)$dim)) X=as.matrix(X) nx=ncol(X) if(missing(names)) names=as.character(1:nx) if(nrow(X) < 100) {cat("fewer than 100 draws submitted \n"); return(invisible())} X=X[(burnin+1):nrow(X),,drop=FALSE] mat=matrix(apply(X,2,mean),nrow=1) mat=rbind(mat,sqrt(matrix(apply(X,2,var),nrow=1))) num_se=double(nx); rel_eff=double(nx); eff_s_size=double(nx) for(i in 1:nx) {out=numEff(X[,i]) if(is.nan(out$stderr)) {num_se[i]=-9999; rel_eff[i]=-9999; eff_s_size[i]=-9999} else {num_se[i]=out$stderr; rel_eff[i]=out$f; eff_s_size[i]=nrow(X)/ceiling(out$f)} } mat=rbind(mat,num_se,rel_eff,eff_s_size) colnames(mat)=names rownames(mat)[1]="mean" rownames(mat)[2]="std dev" rownames(mat)[3]="num se" rownames(mat)[4]="rel eff" rownames(mat)[5]="sam size" if(!missing(tvalues)) {if(mode(tvalues)!="numeric") stop("true values arguments must be numeric \n") if(length(tvalues) != nx) stop("true values argument is wrong length \n") mat=rbind(tvalues,mat) } cat("Summary of Posterior Marginal Distributions ") cat("\nMoments \n") print(t(mat),digits=2) if(QUANTILES){ qmat=apply(X,2,quantile,probs=c(.025,.05,.5,.95,.975)) colnames(qmat)=names if(!missing(tvalues)) { qmat=rbind(tvalues,qmat)} cat("\nQuantiles \n") print(t(qmat),digits=2)} if(TRAILER) cat(paste(" based on ",nrow(X)," valid draws (burn-in=",burnin,") \n",sep="")) invisible(t(mat)) } bayesm/R/rsurgibbs_rcpp.r0000644000176000001440000001165712566706215015220 0ustar ripleyusersrsurGibbs= function(Data,Prior,Mcmc) { # # revision history: # P. Rossi 9/05 # 3/07 added classes # 9/14 changed to improve computations by avoiding Kronecker products # W. Taylor 4/15 - added nprint option to MCMC argument # Purpose: # implement Gibbs Sampler for SUR # # Arguments: # Data -- regdata # regdata is a list of lists of data for each regression # regdata[[i]] contains data for regression equation i # regdata[[i]]$y is y, regdata[[i]]$X is X # note: each regression can have differing numbers of X vars # but you must have same no of obs in each equation. # Prior -- list of prior hyperparameters # betabar,A prior mean, prior precision # nu, V prior on Sigma # Mcmc -- list of MCMC parms # R number of draws # keep -- thinning parameter # nprint - print estimated time remaining on every nprint'th draw # # Output: # list of betadraw,Sigmadraw # # Model: # y_i = X_ibeta + e_i # y is nobs x 1 # X is nobs x k_i # beta is k_i x 1 vector of coefficients # i=1,nreg total regressions # # (e_1,k,...,e_nreg,k) ~ N(0,Sigma) k=1,...,nobs # # we can also write as stacked regression # y = Xbeta+e # y is nobs*nreg x 1,X is nobs*nreg x (sum(k_i)) # routine draws beta -- the stacked vector of all coefficients # # Priors: beta ~ N(betabar,A^-1) # Sigma ~ IW(nu,V) # # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of regdata")} if(is.null(Data$regdata)) {pandterm("Requires Data element regdata")} regdata=Data$regdata # # check regdata for validity # nreg=length(regdata) nobs=length(regdata[[1]]$y) nvar=0 indreg=double(nreg+1) y=NULL for (reg in 1:nreg) { if(length(regdata[[reg]]$y) != nobs || nrow(regdata[[reg]]$X) != nobs) {pandterm(paste("incorrect dimensions for regression",reg))} else {indreg[reg]=nvar+1 nvar=nvar+ncol(regdata[[reg]]$X); y=c(y,regdata[[reg]]$y)} } indreg[nreg+1]=nvar+1 # # check for Prior # if(missing(Prior)) { betabar=c(rep(0,nvar)); A=BayesmConstant.A*diag(nvar); nu=nreg+BayesmConstant.nuInc; V=nu*diag(nreg)} else { if(is.null(Prior$betabar)) {betabar=c(rep(0,nvar))} else {betabar=Prior$betabar} if(is.null(Prior$A)) {A=BayesmConstant.A*diag(nvar)} else {A=Prior$A} if(is.null(Prior$nu)) {nu=nreg+BayesmConstant.nuInc} else {nu=Prior$nu} if(is.null(Prior$V)) {V=nu*diag(nreg)} else {ssq=Prior$V} } # # check dimensions of Priors # if(ncol(A) != nrow(A) || ncol(A) != nvar || nrow(A) != nvar) {pandterm(paste("bad dimensions for A",dim(A)))} if(length(betabar) != nvar) {pandterm(paste("betabar wrong length, length= ",length(betabar)))} # # check MCMC argument # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} else { if(is.null(Mcmc$R)) {pandterm("requires Mcmc element R")} else {R=Mcmc$R} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} } # # print out problem # cat(" ", fill=TRUE) cat("Starting Gibbs Sampler for SUR Regression Model",fill=TRUE) cat(" with ",nreg," regressions",fill=TRUE) cat(" and ",nobs," observations for each regression",fill=TRUE) cat(" ", fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("betabar",fill=TRUE) print(betabar) cat("A",fill=TRUE) print(A) cat("nu = ",nu,fill=TRUE) cat("V = ",fill=TRUE) print(V) cat(" ", fill=TRUE) cat("MCMC parms: ",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint,fill=TRUE) cat(" ",fill=TRUE) # # set initial value of Sigma # E=matrix(double(nobs*nreg),ncol=nreg) for (reg in 1:nreg) { E[,reg]=lm(y~.-1,data=data.frame(y=regdata[[reg]]$y,regdata[[reg]]$X))$residuals } Sigma=(crossprod(E)+diag(.01,nreg))/nobs Sigmainv=chol2inv(chol(Sigma)) # # precompute various moments and indices into moment matrix and Abetabar nk=integer(nreg) Xstar=NULL Y=NULL for(i in 1:nreg){ nk[i]=ncol(regdata[[i]]$X) Xstar=cbind(Xstar,regdata[[i]]$X) Y=cbind(Y,regdata[[i]]$y) } cumnk=cumsum(nk) XspXs=crossprod(Xstar) Abetabar=A%*%betabar ################################################################### # Keunwoo Kim # 09/19/2014 ################################################################### draws=rsurGibbs_rcpp_loop(regdata,indreg,cumnk,nk,XspXs,Sigmainv,A,Abetabar,nu,V,nvar,E,Y,R,keep,nprint) ################################################################### attributes(draws$betadraw)$class=c("bayesm.mat","mcmc") attributes(draws$betadraw)$mcpar=c(1,R,keep) attributes(draws$Sigmadraw)$class=c("bayesm.var","bayesm.mat","mcmc") attributes(draws$Sigmadraw)$mcpar=c(1,R,keep) return(draws) } bayesm/R/rivGibbs_rcpp.R0000755000176000001440000001222012566706215014713 0ustar ripleyusersrivGibbs=function(Data,Prior,Mcmc) { # # revision history: # R. McCulloch original version 2/05 # p. rossi 3/05 # p. rossi 1/06 -- fixed error in nins # p. rossi 1/06 -- fixed def Prior settings for nu,V # 3/07 added classes # W. Taylor 4/15 - added nprint option to MCMC argument # # # purpose: # draw from posterior for linear I.V. model # # Arguments: # Data -- list of z,w,x,y # y is vector of obs on lhs var in structural equation # x is "endogenous" var in structural eqn # w is matrix of obs on "exogenous" vars in the structural eqn # z is matrix of obs on instruments # Prior -- list of md,Ad,mbg,Abg,nu,V # md is prior mean of delta # Ad is prior prec # mbg is prior mean vector for beta,gamma # Abg is prior prec of same # nu,V parms for IW on Sigma # # Mcmc -- list of R,keep # R is number of draws # keep is thinning parameter # nprint - print estimated time remaining on every nprint'th draw # # Output: # list of draws of delta,beta,gamma and Sigma # # Model: # # x=z'delta + e1 # y=beta*x + w'gamma + e2 # e1,e2 ~ N(0,Sigma) # # Priors # delta ~ N(md,Ad^-1) # vec(beta,gamma) ~ N(mbg,Abg^-1) # Sigma ~ IW(nu,V) # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of z,w,x,y")} if(is.null(Data$z)) {pandterm("Requires Data element z")} z=Data$z if(is.null(Data$w)) {pandterm("Requires Data element w")} w=Data$w if(is.null(Data$x)) {pandterm("Requires Data element x")} x=Data$x if(is.null(Data$y)) {pandterm("Requires Data element y")} y=Data$y # # check data for validity # if(!is.vector(x)) {pandterm("x must be a vector")} if(!is.vector(y)) {pandterm("y must be a vector")} n=length(y) if(!is.matrix(w)) {pandterm("w is not a matrix")} if(!is.matrix(z)) {pandterm("z is not a matrix")} dimd=ncol(z) dimg=ncol(w) if(n != length(x) ) {pandterm("length(y) ne length(x)")} if(n != nrow(w) ) {pandterm("length(y) ne nrow(w)")} if(n != nrow(z) ) {pandterm("length(y) ne nrow(z)")} # # check for Prior # if(missing(Prior)) { md=c(rep(0,dimd));Ad=BayesmConstant.A*diag(dimd); mbg=c(rep(0,(1+dimg))); Abg=BayesmConstant.A*diag((1+dimg)); nu=3; V=diag(2)} else { if(is.null(Prior$md)) {md=c(rep(0,dimd))} else {md=Prior$md} if(is.null(Prior$Ad)) {Ad=BayesmConstant.A*diag(dimd)} else {Ad=Prior$Ad} if(is.null(Prior$mbg)) {mbg=c(rep(0,(1+dimg)))} else {mbg=Prior$mbg} if(is.null(Prior$Abg)) {Abg=BayesmConstant.A*diag((1+dimg))} else {Abg=Prior$Abg} if(is.null(Prior$nu)) {nu=3} else {nu=Prior$nu} if(is.null(Prior$V)) {V=nu*diag(2)} else {V=Prior$V} } # # check dimensions of Priors # if(ncol(Ad) != nrow(Ad) || ncol(Ad) != dimd || nrow(Ad) != dimd) {pandterm(paste("bad dimensions for Ad",dim(Ad)))} if(length(md) != dimd) {pandterm(paste("md wrong length, length= ",length(md)))} if(ncol(Abg) != nrow(Abg) || ncol(Abg) != (1+dimg) || nrow(Abg) != (1+dimg)) {pandterm(paste("bad dimensions for Abg",dim(Abg)))} if(length(mbg) != (1+dimg)) {pandterm(paste("mbg wrong length, length= ",length(mbg)))} # # check MCMC argument # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} else { if(is.null(Mcmc$R)) {pandterm("requires Mcmc element R")} else {R=Mcmc$R} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} } # # print out model # cat(" ",fill=TRUE) cat("Starting Gibbs Sampler for Linear IV Model",fill=TRUE) cat(" ",fill=TRUE) cat(" nobs= ",n,"; ",ncol(z)," instruments; ",ncol(w)," included exog vars",fill=TRUE) cat(" Note: the numbers above include intercepts if in z or w",fill=TRUE) cat(" ",fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("mean of delta ",fill=TRUE) print(md) cat("Adelta",fill=TRUE) print(Ad) cat("mean of beta/gamma",fill=TRUE) print(mbg) cat("Abeta/gamma",fill=TRUE) print(Abg) cat("Sigma Prior Parms",fill=TRUE) cat("nu= ",nu," V=",fill=TRUE) print(V) cat(" ",fill=TRUE) cat("MCMC parms: ",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint,fill=TRUE) cat(" ",fill=TRUE) ################################################################### # Keunwoo Kim # 09/03/2014 ################################################################### draws=rivGibbs_rcpp_loop(y, x, z, w, mbg, Abg, md, Ad, V, nu, R, keep, nprint) ################################################################### attributes(draws$deltadraw)$class=c("bayesm.mat","mcmc") attributes(draws$deltadraw)$mcpar=c(1,R,keep) attributes(draws$betadraw)$class=c("bayesm.mat","mcmc") attributes(draws$betadraw)$mcpar=c(1,R,keep) attributes(draws$gammadraw)$class=c("bayesm.mat","mcmc") attributes(draws$gammadraw)$mcpar=c(1,R,keep) attributes(draws$Sigmadraw)$class=c("bayesm.var","bayesm.mat","mcmc") attributes(draws$Sigmadraw)$mcpar=c(1,R,keep) return(draws) } bayesm/R/rmnpgibbs_rcpp.r0000644000176000001440000001123412566706215015170 0ustar ripleyusersrmnpGibbs=function(Data,Prior,Mcmc) { # # Revision History: # modified by rossi 12/18/04 to include error checking # 3/07 added classes # W. Taylor 4/15 - added nprint option to MCMC argument # # purpose: Gibbs MNP model with full covariance matrix # # Arguments: # Data contains # p the number of choice alternatives # y -- a vector of length n with choices (takes on values from 1, .., p) # X -- n(p-1) x k matrix of covariates (including intercepts) # note: X is the differenced matrix unlike MNL X=stack(X_1,..,X_n) # each X_i is (p-1) x nvar # # Prior contains a list of (betabar, A, nu, V) # if elements of prior do not exist, defaults are used # # Mcmc is a list of (beta0,sigma0,R,keep) # beta0,sigma0 are intial values, if not supplied defaults are used # R is number of draws # keep is thinning parm, keep every keepth draw # nprint - print estimated time remaining on every nprint'th draw # # Output: a list of every keepth betadraw and sigmsdraw # # model: # w_i = X_ibeta + e e~N(0,Sigma) note w_i,e are (p-1) x 1 # y_i = j if w_ij > w_i-j j=1,...,p-1 # y_i = p if all w_i < 0 # # priors: # beta ~ N(betabar,A^-1) # Sigma ~ IW(nu,V) # # Check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of p, y, X")} if(is.null(Data$p)) {pandterm("Requires Data element p -- number of alternatives")} p=Data$p if(is.null(Data$y)) {pandterm("Requires Data element y -- number of alternatives")} y=Data$y if(is.null(Data$X)) {pandterm("Requires Data element X -- matrix of covariates")} X=Data$X # # check data for validity # levely=as.numeric(levels(as.factor(y))) if(length(levely) != p) {pandterm(paste("y takes on ",length(levely), " values -- must be ",p))} bady=FALSE for (i in 1:p) { if(levely[i] != i) bady=TRUE } cat("Table of y values",fill=TRUE) print(table(y)) if (bady) {pandterm("Invalid y")} n=length(y) k=ncol(X) pm1=p-1 if(nrow(X)/n != pm1) {pandterm(paste("X has ",nrow(X)," rows; must be = (p-1)n"))} # # check for prior elements # if(missing(Prior)) { betabar=rep(0,k) ; A=BayesmConstant.A*diag(k) ; nu=pm1+3; V=nu*diag(pm1)} else {if(is.null(Prior$betabar)) {betabar=rep(0,k)} else {betabar=Prior$betabar} if(is.null(Prior$A)) {A=BayesmConstant.A*diag(k)} else {A=Prior$A} if(is.null(Prior$nu)) {nu=pm1+BayesmConstant.nuInc} else {nu=Prior$nu} if(is.null(Prior$V)) {V=nu*diag(pm1)} else {V=Prior$V}} if(length(betabar) != k) pandterm("length betabar ne k") if(sum(dim(A)==c(k,k)) != 2) pandterm("A is of incorrect dimension") if(nu < 1) pandterm("invalid nu value") if(sum(dim(V)==c(pm1,pm1)) != 2) pandterm("V is of incorrect dimension") # # check for Mcmc # if(missing(Mcmc)) pandterm("Requires Mcmc argument -- at least R must be included") if(is.null(Mcmc$R)) {pandterm("Requires element R of Mcmc")} else {R=Mcmc$R} if(is.null(Mcmc$beta0)) {beta0=rep(0,k)} else {beta0=Mcmc$beta0} if(is.null(Mcmc$sigma0)) {sigma0=diag(pm1)} else {sigma0=Mcmc$sigma0} if(length(beta0) != k) pandterm("beta0 is not of length k") if(sum(dim(sigma0) == c(pm1,pm1)) != 2) pandterm("sigma0 is of incorrect dimension") if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} # # print out problem # cat(" ",fill=TRUE) cat("Starting Gibbs Sampler for MNP",fill=TRUE) cat(" ",n," obs; ",p," choice alternatives; ",k," indep vars (including intercepts)",fill=TRUE) cat(" ",fill=TRUE) cat("Table of y values",fill=TRUE) print(table(y)) cat("Prior Parms:",fill=TRUE) cat("betabar",fill=TRUE) print(betabar) cat("A",fill=TRUE) print(A) cat("nu",fill=TRUE) print(nu) cat("V",fill=TRUE) print(V) cat(" ",fill=TRUE) cat("MCMC Parms:",fill=TRUE) cat(" ",R," reps; keeping every ",keep,"th draw"," nprint= ",nprint,fill=TRUE) cat("initial beta= ",beta0,fill=TRUE) cat("initial sigma= ",sigma0,fill=TRUE) cat(" ",fill=TRUE) ################################################################### # Wayne Taylor # 09/03/2014 ################################################################### loopout = rmnpGibbs_rcpp_loop(R,keep,nprint,pm1,y,X,beta0,sigma0,V,nu,betabar,A); ################################################################### attributes(loopout$betadraw)$class=c("bayesm.mat","mcmc") attributes(loopout$betadraw)$mcpar=c(1,R,keep) attributes(loopout$sigmadraw)$class=c("bayesm.var","bayesm.mat","mcmc") attributes(loopout$sigmadraw)$mcpar=c(1,R,keep) return(loopout) } bayesm/R/BayesmConstants.R0000644000176000001440000000345212566706215015241 0ustar ripleyusers#MCMC BayesmConstant.keep = 1 #keep every keepth draw for MCMC routines BayesmConstant.nprint = 100 #print the remaining time on every nprint'th draw BayesmConstant.RRScaling = 2.38 #Roberts and Rosenthal optimal scaling constant BayesmConstant.w = .1 #fractional likelihood weighting parameter #Priors BayesmConstant.A = .01 #scaling factor for the prior precision matrix BayesmConstant.nuInc = 3 #Increment for nu BayesmConstant.a = 5 #Dirichlet parameter for mixture models BayesmConstant.nu.e = 3.0 #degrees of freedom parameter for regression error variance prior BayesmConstant.nu = 3.0 #degrees of freedom parameter for Inverted Wishart prior BayesmConstant.agammaprior = .5 #Gamma prior parameter BayesmConstant.bgammaprior = .1 #Gamma prior parameter #DP BayesmConstant.DPalimdef=c(.01,10) #defines support of 'a' distribution BayesmConstant.DPnulimdef=c(.01,3) #defines support of nu distribution BayesmConstant.DPvlimdef=c(.1,4) #defines support of v distribution BayesmConstant.DPIstarmin = 1 #expected number of components at lower bound of support of alpha BayesmConstant.DPpower = .8 #power parameter for alpha prior BayesmConstant.DPalpha = 1.0 #intitalized value for alpha draws BayesmConstant.DPmaxuniq = 200 #storage constraint on the number of unique components BayesmConstant.DPSCALE = TRUE #should data be scaled by mean,std deviation before posterior draws BayesmConstant.DPgridsize = 20 #number of discrete points for hyperparameter priors #Mathematical Constants BayesmConstant.gamma = .5772156649015328606 #BayesBLP BayesmConstant.BLPVOmega = matrix(c(1,0.5,0.5,1),2,2) #IW prior parameter of correlated shocks in IV bayesBLP BayesmConstant.BLPtol = 1e-6bayesm/R/RcppExports.R0000644000176000001440000002013013117541521014373 0ustar ripleyusers# Generated by using Rcpp::compileAttributes() -> do not edit by hand # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 bayesBLP_rcpp_loop <- function(IV, X, Z, share, J, T, v, R, sigmasqR, A, theta_hat, deltabar, Ad, nu0, s0_sq, VOmega, ssq, cand_cov, theta_bar_initial, r_initial, tau_sq_initial, Omega_initial, delta_initial, tol, keep, nprint) { .Call('bayesm_bayesBLP_rcpp_loop', PACKAGE = 'bayesm', IV, X, Z, share, J, T, v, R, sigmasqR, A, theta_hat, deltabar, Ad, nu0, s0_sq, VOmega, ssq, cand_cov, theta_bar_initial, r_initial, tau_sq_initial, Omega_initial, delta_initial, tol, keep, nprint) } breg <- function(y, X, betabar, A) { .Call('bayesm_breg', PACKAGE = 'bayesm', y, X, betabar, A) } cgetC <- function(e, k) { .Call('bayesm_cgetC', PACKAGE = 'bayesm', e, k) } clusterMix_rcpp_loop <- function(zdraw, cutoff, SILENT, nprint) { .Call('bayesm_clusterMix_rcpp_loop', PACKAGE = 'bayesm', zdraw, cutoff, SILENT, nprint) } ghkvec <- function(L, trunpt, above, r, HALTON = TRUE, pn = as.integer( c(0))) { .Call('bayesm_ghkvec', PACKAGE = 'bayesm', L, trunpt, above, r, HALTON, pn) } llmnl <- function(beta, y, X) { .Call('bayesm_llmnl', PACKAGE = 'bayesm', beta, y, X) } lndIChisq <- function(nu, ssq, X) { .Call('bayesm_lndIChisq', PACKAGE = 'bayesm', nu, ssq, X) } lndIWishart <- function(nu, V, IW) { .Call('bayesm_lndIWishart', PACKAGE = 'bayesm', nu, V, IW) } lndMvn <- function(x, mu, rooti) { .Call('bayesm_lndMvn', PACKAGE = 'bayesm', x, mu, rooti) } lndMvst <- function(x, nu, mu, rooti, NORMC = FALSE) { .Call('bayesm_lndMvst', PACKAGE = 'bayesm', x, nu, mu, rooti, NORMC) } rbprobitGibbs_rcpp_loop <- function(y, X, Abetabar, root, beta, sigma, trunpt, above, R, keep, nprint) { .Call('bayesm_rbprobitGibbs_rcpp_loop', PACKAGE = 'bayesm', y, X, Abetabar, root, beta, sigma, trunpt, above, R, keep, nprint) } rdirichlet <- function(alpha) { .Call('bayesm_rdirichlet', PACKAGE = 'bayesm', alpha) } rDPGibbs_rcpp_loop <- function(R, keep, nprint, y, lambda_hyper, SCALE, maxuniq, PrioralphaList, gridsize, BayesmConstantA, BayesmConstantnuInc, BayesmConstantDPalpha) { .Call('bayesm_rDPGibbs_rcpp_loop', PACKAGE = 'bayesm', R, keep, nprint, y, lambda_hyper, SCALE, maxuniq, PrioralphaList, gridsize, BayesmConstantA, BayesmConstantnuInc, BayesmConstantDPalpha) } rhierLinearMixture_rcpp_loop <- function(regdata, Z, deltabar, Ad, mubar, Amu, nu, V, nu_e, ssq, R, keep, nprint, drawdelta, olddelta, a, oldprob, ind, tau) { .Call('bayesm_rhierLinearMixture_rcpp_loop', PACKAGE = 'bayesm', regdata, Z, deltabar, Ad, mubar, Amu, nu, V, nu_e, ssq, R, keep, nprint, drawdelta, olddelta, a, oldprob, ind, tau) } rhierLinearModel_rcpp_loop <- function(regdata, Z, Deltabar, A, nu, V, nu_e, ssq, tau, Delta, Vbeta, R, keep, nprint) { .Call('bayesm_rhierLinearModel_rcpp_loop', PACKAGE = 'bayesm', regdata, Z, Deltabar, A, nu, V, nu_e, ssq, tau, Delta, Vbeta, R, keep, nprint) } rhierMnlDP_rcpp_loop <- function(R, keep, nprint, lgtdata, Z, deltabar, Ad, PrioralphaList, lambda_hyper, drawdelta, nvar, oldbetas, s, maxuniq, gridsize, BayesmConstantA, BayesmConstantnuInc, BayesmConstantDPalpha) { .Call('bayesm_rhierMnlDP_rcpp_loop', PACKAGE = 'bayesm', R, keep, nprint, lgtdata, Z, deltabar, Ad, PrioralphaList, lambda_hyper, drawdelta, nvar, oldbetas, s, maxuniq, gridsize, BayesmConstantA, BayesmConstantnuInc, BayesmConstantDPalpha) } llmnl_con <- function(betastar, y, X, SignRes = as.numeric( c(0))) { .Call('bayesm_llmnl_con', PACKAGE = 'bayesm', betastar, y, X, SignRes) } rhierMnlRwMixture_rcpp_loop <- function(lgtdata, Z, deltabar, Ad, mubar, Amu, nu, V, s, R, keep, nprint, drawdelta, olddelta, a, oldprob, oldbetas, ind, SignRes) { .Call('bayesm_rhierMnlRwMixture_rcpp_loop', PACKAGE = 'bayesm', lgtdata, Z, deltabar, Ad, mubar, Amu, nu, V, s, R, keep, nprint, drawdelta, olddelta, a, oldprob, oldbetas, ind, SignRes) } rhierNegbinRw_rcpp_loop <- function(regdata, hessdata, Z, Beta, Delta, Deltabar, Adelta, nu, V, a, b, R, keep, sbeta, alphacroot, nprint, rootA, alpha, fixalpha) { .Call('bayesm_rhierNegbinRw_rcpp_loop', PACKAGE = 'bayesm', regdata, hessdata, Z, Beta, Delta, Deltabar, Adelta, nu, V, a, b, R, keep, sbeta, alphacroot, nprint, rootA, alpha, fixalpha) } rivDP_rcpp_loop <- function(R, keep, nprint, dimd, mbg, Abg, md, Ad, y, isgamma, z, x, w, delta, PrioralphaList, gridsize, SCALE, maxuniq, scalex, scaley, lambda_hyper, BayesmConstantA, BayesmConstantnu) { .Call('bayesm_rivDP_rcpp_loop', PACKAGE = 'bayesm', R, keep, nprint, dimd, mbg, Abg, md, Ad, y, isgamma, z, x, w, delta, PrioralphaList, gridsize, SCALE, maxuniq, scalex, scaley, lambda_hyper, BayesmConstantA, BayesmConstantnu) } rivGibbs_rcpp_loop <- function(y, x, z, w, mbg, Abg, md, Ad, V, nu, R, keep, nprint) { .Call('bayesm_rivGibbs_rcpp_loop', PACKAGE = 'bayesm', y, x, z, w, mbg, Abg, md, Ad, V, nu, R, keep, nprint) } rmixGibbs <- function(y, Bbar, A, nu, V, a, p, z) { .Call('bayesm_rmixGibbs', PACKAGE = 'bayesm', y, Bbar, A, nu, V, a, p, z) } rmixture <- function(n, pvec, comps) { .Call('bayesm_rmixture', PACKAGE = 'bayesm', n, pvec, comps) } rmnlIndepMetrop_rcpp_loop <- function(R, keep, nu, betastar, root, y, X, betabar, rootpi, rooti, oldlimp, oldlpost, nprint) { .Call('bayesm_rmnlIndepMetrop_rcpp_loop', PACKAGE = 'bayesm', R, keep, nu, betastar, root, y, X, betabar, rootpi, rooti, oldlimp, oldlpost, nprint) } rmnpGibbs_rcpp_loop <- function(R, keep, nprint, pm1, y, X, beta0, sigma0, V, nu, betabar, A) { .Call('bayesm_rmnpGibbs_rcpp_loop', PACKAGE = 'bayesm', R, keep, nprint, pm1, y, X, beta0, sigma0, V, nu, betabar, A) } rmultireg <- function(Y, X, Bbar, A, nu, V) { .Call('bayesm_rmultireg', PACKAGE = 'bayesm', Y, X, Bbar, A, nu, V) } rmvpGibbs_rcpp_loop <- function(R, keep, nprint, p, y, X, beta0, sigma0, V, nu, betabar, A) { .Call('bayesm_rmvpGibbs_rcpp_loop', PACKAGE = 'bayesm', R, keep, nprint, p, y, X, beta0, sigma0, V, nu, betabar, A) } rmvst <- function(nu, mu, root) { .Call('bayesm_rmvst', PACKAGE = 'bayesm', nu, mu, root) } rnegbinRw_rcpp_loop <- function(y, X, betabar, rootA, a, b, beta, alpha, fixalpha, betaroot, alphacroot, R, keep, nprint) { .Call('bayesm_rnegbinRw_rcpp_loop', PACKAGE = 'bayesm', y, X, betabar, rootA, a, b, beta, alpha, fixalpha, betaroot, alphacroot, R, keep, nprint) } rnmixGibbs_rcpp_loop <- function(y, Mubar, A, nu, V, a, p, z, R, keep, nprint) { .Call('bayesm_rnmixGibbs_rcpp_loop', PACKAGE = 'bayesm', y, Mubar, A, nu, V, a, p, z, R, keep, nprint) } rordprobitGibbs_rcpp_loop <- function(y, X, k, A, betabar, Ad, s, inc_root, dstarbar, betahat, R, keep, nprint) { .Call('bayesm_rordprobitGibbs_rcpp_loop', PACKAGE = 'bayesm', y, X, k, A, betabar, Ad, s, inc_root, dstarbar, betahat, R, keep, nprint) } rscaleUsage_rcpp_loop <- function(k, x, p, n, R, keep, ndghk, nprint, y, mu, Sigma, tau, sigma, Lambda, e, domu, doSigma, dosigma, dotau, doLambda, doe, nu, V, mubar, Am, gsigma, gl11, gl22, gl12, nuL, VL, ge) { .Call('bayesm_rscaleUsage_rcpp_loop', PACKAGE = 'bayesm', k, x, p, n, R, keep, ndghk, nprint, y, mu, Sigma, tau, sigma, Lambda, e, domu, doSigma, dosigma, dotau, doLambda, doe, nu, V, mubar, Am, gsigma, gl11, gl22, gl12, nuL, VL, ge) } rsurGibbs_rcpp_loop <- function(regdata, indreg, cumnk, nk, XspXs, Sigmainv, A, Abetabar, nu, V, nvar, E, Y, R, keep, nprint) { .Call('bayesm_rsurGibbs_rcpp_loop', PACKAGE = 'bayesm', regdata, indreg, cumnk, nk, XspXs, Sigmainv, A, Abetabar, nu, V, nvar, E, Y, R, keep, nprint) } rtrun <- function(mu, sigma, a, b) { .Call('bayesm_rtrun', PACKAGE = 'bayesm', mu, sigma, a, b) } runireg_rcpp_loop <- function(y, X, betabar, A, nu, ssq, R, keep, nprint) { .Call('bayesm_runireg_rcpp_loop', PACKAGE = 'bayesm', y, X, betabar, A, nu, ssq, R, keep, nprint) } runiregGibbs_rcpp_loop <- function(y, X, betabar, A, nu, ssq, sigmasq, R, keep, nprint) { .Call('bayesm_runiregGibbs_rcpp_loop', PACKAGE = 'bayesm', y, X, betabar, A, nu, ssq, sigmasq, R, keep, nprint) } rwishart <- function(nu, V) { .Call('bayesm_rwishart', PACKAGE = 'bayesm', nu, V) } callroot <- function(c1, c2, tol, iterlim) { .Call('bayesm_callroot', PACKAGE = 'bayesm', c1, c2, tol, iterlim) } bayesm/R/plot.bayesm.hcoef.R0000755000176000001440000000317512566706215015451 0ustar ripleyusersplot.bayesm.hcoef=function(x,names,burnin=trunc(.1*R),...){ # # S3 method to plot arrays of draws of coefs in hier models # 3 dimensional arrays: unit x var x draw # P. Rossi 2/07 # X=x if(mode(X) == "list") stop("list entered \n Possible Fixup: extract from list \n") if(mode(X) !="numeric") stop("Requires numeric argument \n") d=dim(X) if(length(d) !=3) stop("Requires 3-dim array \n") op=par(no.readonly=TRUE) on.exit(par(op)) on.exit(devAskNewPage(FALSE),add=TRUE) nunits=d[1] nvar=d[2] R=d[3] if(missing(names)) {names=as.character(1:nvar)} if(R < 100) {cat("fewer than 100 draws submitted \n"); return(invisible())} # # plot posterior distributions of nvar coef for 30 rand units # rsam=sort(sample(c(1:nunits),30)) # randomly sample 30 cross-sectional units par(mfrow=c(1,1)) par(las=3) # horizontal labeling devAskNewPage(TRUE) for(var in 1:nvar){ ext=X[rsam,var,(burnin+1):R]; ext=data.frame(t(ext)) colnames(ext)=as.character(rsam) out=boxplot(ext,plot=FALSE,...) out$stats=apply(ext,2,quantile,probs=c(0,.05,.95,1)) bxp(out,xlab="Cross-sectional Unit",main=paste("Coefficients on Var ",names[var],sep=""),boxfill="magenta",...) } # # plot posterior means for each var # par(las=1) pmeans=matrix(0,nrow=nunits,ncol=nvar) for(i in 1:nunits) pmeans[i,]=apply(X[i,,(burnin+1):R],1,mean) attributes(pmeans)$class="bayesm.mat" for(var in 1:nvar) names[var]=paste("Posterior Means of Coef ",names[var],sep="") plot(pmeans,names,TRACEPLOT=FALSE,INT=FALSE,DEN=FALSE,CHECK_NDRAWS=FALSE,...) invisible() } bayesm/R/plot.bayesm.mat.R0000755000176000001440000000437712566706215015153 0ustar ripleyusersplot.bayesm.mat=function(x,names,burnin=trunc(.1*nrow(X)),tvalues,TRACEPLOT=TRUE,DEN=TRUE,INT=TRUE, CHECK_NDRAWS=TRUE,...){ # # S3 method to print matrices of draws the object X is of class "bayesm.mat" # # P. Rossi 2/07 # X=x if(mode(X) == "list") stop("list entered \n Possible Fixup: extract from list \n") if(mode(X) !="numeric") stop("Requires numeric argument \n") op=par(no.readonly=TRUE) on.exit(par(op)) on.exit(devAskNewPage(FALSE),add=TRUE) if(is.null(attributes(X)$dim)) X=as.matrix(X) nx=ncol(X) if(nrow(X) < 100 & CHECK_NDRAWS) {cat("fewer than 100 draws submitted \n"); return(invisible())} if(!missing(tvalues)){ if(mode(tvalues) !="numeric") {stop("tvalues must be a numeric vector \n")} else {if(length(tvalues)!=nx) stop("tvalues are wrong length \n")} } if(nx==1) par(mfrow=c(1,1)) if(nx==2) par(mfrow=c(2,1)) if(nx==3) par(mfrow=c(3,1)) if(nx==4) par(mfrow=c(2,2)) if(nx>=5) par(mfrow=c(3,2)) if(missing(names)) {names=as.character(1:nx)} if (DEN) ylabtxt="density" else ylabtxt="freq" devAskNewPage(TRUE) for(index in 1:nx){ hist(X[(burnin+1):nrow(X),index],xlab="",ylab=ylabtxt,main=names[index],freq=!DEN,col="magenta",...) if(!missing(tvalues)) abline(v=tvalues[index],lwd=2,col="blue") if(INT){ quants=quantile(X[(burnin+1):nrow(X),index],prob=c(.025,.975)) mean=mean(X[(burnin+1):nrow(X),index]) semean=numEff(X[(burnin+1):nrow(X),index])$stderr text(quants[1],0,"|",cex=3.0,col="green") text(quants[2],0,"|",cex=3.0,col="green") text(mean,0,"|",cex=3.0,col="red") text(mean-2*semean,0,"|",cex=2,col="yellow") text(mean+2*semean,0,"|",cex=2,col="yellow") } } if(TRACEPLOT){ if(nx==1) par(mfrow=c(1,2)) if(nx==2) par(mfrow=c(2,2)) if(nx>=3) par(mfrow=c(3,2)) for(index in 1:nx){ plot(as.vector(X[,index]),xlab="",ylab="",main=names[index],type="l",col="red") if(!missing(tvalues)) abline(h=tvalues[index],lwd=2,col="blue") if(var(X[,index])>1.0e-20) {acf(as.vector(X[,index]),xlab="",ylab="",main="")} else {plot.default(X[,index],xlab="",ylab="",type="n",main="No ACF Produced")} } } invisible() } bayesm/R/rhierLinearModel_rcpp.R0000644000176000001440000001560013117541521016361 0ustar ripleyusersrhierLinearModel= function(Data,Prior,Mcmc) { # # Revision History # 1/17/05 P. Rossi # 10/05 fixed error in setting prior if Prior argument is missing # 3/07 added classes # W. Taylor 4/15 - added nprint option to MCMC argument # # Purpose: # run hiearchical regression model # # Arguments: # Data list of regdata,Z # regdata is a list of lists each list with members y, X # e.g. regdata[[i]]=list(y=y,X=X) # X has nvar columns # Z is nreg=length(regdata) x nz # Prior list of prior hyperparameters # Deltabar,A, nu.e,ssq,nu,V # note: ssq is a nreg x 1 vector! # Mcmc # list of Mcmc parameters # R is number of draws # keep is thining parameter -- keep every keepth draw # nprint - print estimated time remaining on every nprint'th draw # # Output: # list of # betadraw -- nreg x nvar x R/keep array of individual regression betas # taudraw -- R/keep x nreg array of error variances for each regression # Deltadraw -- R/keep x nz x nvar array of Delta draws # Vbetadraw -- R/keep x nvar*nvar array of Vbeta draws # # Model: # nreg regression equations # y_i = X_ibeta_i + epsilon_i # epsilon_i ~ N(0,tau_i) # nvar X vars in each equation # # Priors: # tau_i ~ nu.e*ssq_i/chisq(nu.e) tau_i is the variance of epsilon_i # beta_i ~ N(ZDelta[i,],V_beta) # Note: ZDelta is the matrix Z * Delta; [i,] refers to ith row of this product! # # vec(Delta) | V_beta ~ N(vec(Deltabar),Vbeta (x) A^-1) # V_beta ~ IW(nu,V) or V_beta^-1 ~ W(nu,V^-1) # Delta, Deltabar are nz x nvar # A is nz x nz # Vbeta is nvar x nvar # # NOTE: if you don't have any z vars, set Z=iota (nreg x 1) # # # create needed functions # #------------------------------------------------------------------------------ append=function(l) { l=c(l,list(XpX=crossprod(l$X),Xpy=crossprod(l$X,l$y)))} # getvar=function(l) { v=var(l$y) if(is.na(v)) return(1) if(v>0) return (v) else return (1)} # #------------------------------------------------------------------------------ # # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of regdata and Z")} if(is.null(Data$regdata)) {pandterm("Requires Data element regdata")} regdata=Data$regdata nreg=length(regdata) if(is.null(Data$Z)) { cat("Z not specified -- putting in iota",fill=TRUE); fsh() ; Z=matrix(rep(1,nreg),ncol=1)} else {if (!is.matrix(Data$Z)) {pandterm("Z must be a matrix")} else {if (nrow(Data$Z) != nreg) {pandterm(paste("Nrow(Z) ",nrow(Z),"ne number regressions ",nreg))} else {Z=Data$Z}}} nz=ncol(Z) # # check data for validity # for (i in 1:nreg) { if(!is.matrix(regdata[[i]]$X)) {pandterm(paste0("regdata[[",i,"]]$X must be a matrix"))} if(!is.vector(regdata[[i]]$y, mode = "numeric") & !is.vector(regdata[[i]]$y, mode = "logical") & !is.matrix(regdata[[i]]$y)) {pandterm(paste0("regdata[[",i,"]]$y must be a numeric or logical vector or matrix"))} if(is.matrix(regdata[[i]]$y)){ if(ncol(regdata[[i]]$y)>1) {pandterm(paste0("regdata[[",i,"]]$y must be a vector or one-column matrix"))}} } dimfun=function(l) {c(length(l$y),dim(l$X))} dims=sapply(regdata,dimfun) dims=t(dims) nvar=quantile(dims[,3],prob=.5) for (i in 1:nreg) { if(dims[i,1] != dims[i,2] || dims[i,3] !=nvar) {pandterm(paste("Bad Data dimensions for unit ",i," dims(y,X) =",dims[i,]))} } # # check for Prior # if(missing(Prior)) { Deltabar=matrix(rep(0,nz*nvar),ncol=nvar); A=BayesmConstant.A*diag(nz); nu.e=BayesmConstant.nu.e; ssq=sapply(regdata,getvar) ; nu=nvar+BayesmConstant.nuInc ; V= nu*diag(nvar)} else { if(is.null(Prior$Deltabar)) {Deltabar=matrix(rep(0,nz*nvar),ncol=nvar)} else {Deltabar=Prior$Deltabar} if(is.null(Prior$A)) {A=BayesmConstant.A*diag(nz)} else {A=Prior$A} if(is.null(Prior$nu.e)) {nu.e=BayesmConstant.nu.e} else {nu.e=Prior$nu.e} if(is.null(Prior$ssq)) {ssq=sapply(regdata,getvar)} else {ssq=Prior$ssq} if(is.null(Prior$nu)) {nu=nvar+BayesmConstant.nuInc} else {nu=Prior$nu} if(is.null(Prior$V)) {V=nu*diag(nvar)} else {V=Prior$V} } # # check dimensions of Priors # if(ncol(A) != nrow(A) || ncol(A) != nz || nrow(A) != nz) {pandterm(paste("bad dimensions for A",dim(A)))} if(nrow(Deltabar) != nz || ncol(Deltabar) != nvar) {pandterm(paste("bad dimensions for Deltabar ",dim(Deltabar)))} if(length(ssq) != nreg) {pandterm(paste("bad length for ssq ",length(ssq)))} if(ncol(V) != nvar || nrow(V) != nvar) {pandterm(paste("bad dimensions for V ",dim(V)))} # # check MCMC argument # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} else { if(is.null(Mcmc$R)) {pandterm("requires Mcmc element R")} else {R=Mcmc$R} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} } # # print out problem # cat(" ", fill=TRUE) cat("Starting Gibbs Sampler for Linear Hierarchical Model",fill=TRUE) cat(" ",nreg," Regressions",fill=TRUE) cat(" ",ncol(Z)," Variables in Z (if 1, then only intercept)",fill=TRUE) cat(" ", fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("Deltabar",fill=TRUE) print(Deltabar) cat("A",fill=TRUE) print(A) cat("nu.e (d.f. parm for regression error variances)= ",nu.e,fill=TRUE) cat("Vbeta ~ IW(nu,V)",fill=TRUE) cat("nu = ",nu,fill=TRUE) cat("V ",fill=TRUE) print(V) cat(" ", fill=TRUE) cat("MCMC parms: ",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint,fill=TRUE) cat(" ",fill=TRUE) # # allocate space for the draws and set initial values of Vbeta and Delta # tau=double(nreg) Delta=matrix(0,nz,nvar) Vbeta=diag(nvar) # # set up fixed parms for the draw of Vbeta,Delta # # note: in the notation of the MVR Y = X B # n x m n x k k x m # "n" = nreg # "m" = nvar # "k" = nz # general model: Beta = Z Delta + U # # Create XpX elements of regdata and initialize tau # regdata=lapply(regdata,append) tau=sapply(regdata,getvar) ################################################################### # Keunwoo Kim # 08/20/2014 ################################################################### draws=rhierLinearModel_rcpp_loop(regdata,Z,Deltabar,A,nu,V,nu.e,ssq,tau,Delta,Vbeta,R,keep,nprint) ################################################################### attributes(draws$taudraw)$class=c("bayesm.mat","mcmc") attributes(draws$taudraw)$mcpar=c(1,R,keep) attributes(draws$Deltadraw)$class=c("bayesm.mat","mcmc") attributes(draws$Deltadraw)$mcpar=c(1,R,keep) attributes(draws$Vbetadraw)$class=c("bayesm.var","bayesm.mat","mcmc") attributes(draws$Vbetadraw)$mcpar=c(1,R,keep) attributes(draws$betadraw)$class=c("bayesm.hcoef") return(draws) }bayesm/R/rnmixgibbs_rcpp.r0000644000176000001440000001343312566706215015354 0ustar ripleyusersrnmixGibbs= function(Data,Prior,Mcmc){ # # Revision History: # P. Rossi 3/05 # add check to see if Mubar is a vector 9/05 # fixed bug in saving comps draw comps[[mkeep]]= 9/05 # fixed so that ncomp can be =1; added check that nobs >= 2*ncomp 12/06 # 3/07 added classes # added log-likelihood 9/08 # W. Taylor 4/15 - added nprint option to MCMC argument # # purpose: do Gibbs sampling inference for a mixture of multivariate normals # # arguments: # Data is a list of y which is an n x k matrix of data -- each row # is an iid draw from the normal mixture # Prior is a list of (Mubar,A,nu,V,a,ncomp) # ncomp is required # if elements of the prior don't exist, defaults are assumed # Mcmc is a list of R, keep (thinning parameter), and nprint # Output: # list with elements # pdraw -- R/keep x ncomp array of mixture prob draws # zdraw -- R/keep x nobs array of indicators of mixture comp identity for each obs # compsdraw -- list of R/keep lists of lists of comp parm draws # e.g. compsdraw[[i]] is ith draw -- list of ncomp lists # compsdraw[[i]][[j]] is list of parms for jth normal component # if jcomp=compsdraw[[i]][j]] # ~N(jcomp[[1]],Sigma), Sigma = t(R)%*%R, R^{-1} = jcomp[[2]] # # Model: # y_i ~ N(mu_ind,Sigma_ind) # ind ~ iid multinomial(p) p is a 1x ncomp vector of probs # Priors: # mu_j ~ N(mubar,Sigma (x) A^-1) # mubar=vec(Mubar) # Sigma_j ~ IW(nu,V) # note: this is the natural conjugate prior -- a special case of multivariate # regression # p ~ Dirchlet(a) # # check arguments # # # ----------------------------------------------------------------------------------------- llnmix=function(Y,z,comps){ # # evaluate likelihood for mixture of normals # zu=unique(z) ll=0.0 for(i in 1:length(zu)){ Ysel=Y[z==zu[i],,drop=FALSE] ll=ll+sum(apply(Ysel,1,lndMvn,mu=comps[[zu[i]]]$mu,rooti=comps[[zu[i]]]$rooti)) } return(ll) } # ----------------------------------------------------------------------------------------- if(missing(Data)) {pandterm("Requires Data argument -- list of y")} if(is.null(Data$y)) {pandterm("Requires Data element y")} y=Data$y # # check data for validity # if(!is.matrix(y)) {pandterm("y must be a matrix")} nobs=nrow(y) dimy=ncol(y) # # check for Prior # if(missing(Prior)) {pandterm("requires Prior argument ")} else { if(is.null(Prior$ncomp)) {pandterm("requires number of mix comps -- Prior$ncomp")} else {ncomp=Prior$ncomp} if(is.null(Prior$Mubar)) {Mubar=matrix(rep(0,dimy),nrow=1)} else {Mubar=Prior$Mubar; if(is.vector(Mubar)) {Mubar=matrix(Mubar,nrow=1)}} if(is.null(Prior$A)) {A=matrix(BayesmConstant.A,ncol=1)} else {A=Prior$A} if(is.null(Prior$nu)) {nu=dimy+BayesmConstant.nuInc} else {nu=Prior$nu} if(is.null(Prior$V)) {V=nu*diag(dimy)} else {V=Prior$V} if(is.null(Prior$a)) {a=c(rep(BayesmConstant.a,ncomp))} else {a=Prior$a} } # # check for adequate no. of observations # if(nobs<2*ncomp) {pandterm("too few obs, nobs should be >= 2*ncomp")} # # check dimensions of Priors # if(ncol(A) != nrow(A) || ncol(A) != 1) {pandterm(paste("bad dimensions for A",dim(A)))} if(!is.matrix(Mubar)) {pandterm("Mubar must be a matrix")} if(nrow(Mubar) != 1 || ncol(Mubar) != dimy) {pandterm(paste("bad dimensions for Mubar",dim(Mubar)))} if(ncol(V) != nrow(V) || ncol(V) != dimy) {pandterm(paste("bad dimensions for V",dim(V)))} if(length(a) != ncomp) {pandterm(paste("a wrong length, length= ",length(a)))} bada=FALSE for(i in 1:ncomp){if(a[i] < 0) bada=TRUE} if(bada) pandterm("invalid values in a vector") # # check MCMC argument # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} else { if(is.null(Mcmc$R)) {pandterm("requires Mcmc element R")} else {R=Mcmc$R} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} if(is.null(Mcmc$LogLike)) {LogLike=FALSE} else {LogLike=Mcmc$LogLike} } # # print out the problem # cat(" Starting Gibbs Sampler for Mixture of Normals",fill=TRUE) cat(" ",nobs," observations on ",dimy," dimensional data",fill=TRUE) cat(" using ",ncomp," mixture components",fill=TRUE) cat(" ",fill=TRUE) cat(" Prior Parms: ",fill=TRUE) cat(" mu_j ~ N(mubar,Sigma (x) A^-1)",fill=TRUE) cat(" mubar = ",fill=TRUE) print(Mubar) cat(" precision parm for prior variance of mu vectors (A)= ",A,fill=TRUE) cat(" Sigma_j ~ IW(nu,V) nu= ",nu,fill=TRUE) cat(" V =",fill=TRUE) print(V) cat(" Dirichlet parameters ",fill=TRUE) print(a) cat(" ",fill=TRUE) cat(" Mcmc Parms: R= ",R," keep= ",keep," nprint= ",nprint," LogLike= ",LogLike,fill=TRUE) # pdraw=matrix(double(floor(R/keep)*ncomp),ncol=ncomp) # zdraw=matrix(double(floor(R/keep)*nobs),ncol=nobs) # compdraw=list() compsd=list() if(LogLike) ll=double(floor(R/keep)) # # set initial values of z # z=rep(c(1:ncomp),(floor(nobs/ncomp)+1)) z=z[1:nobs] cat(" ",fill=TRUE) cat("starting value for z",fill=TRUE) print(table(z)) cat(" ",fill=TRUE) p=c(rep(1,ncomp))/ncomp # note this is not used fsh() #Wayne Taylor 8/18/14##################################################### nmix = rnmixGibbs_rcpp_loop(y, Mubar, A, nu, V, a, p, z, R, keep, nprint); ########################################################################## attributes(nmix)$class="bayesm.nmix" if(LogLike){ zdraw = nmix$zdraw compdraw = nmix$compdraw ll = lapply(seq_along(compdraw), function(i) llnmix(y, zdraw[i,], compdraw[[i]])) return(list(ll=ll,nmix=nmix)) }else{ return(list(nmix=nmix)) } }bayesm/R/rbiNormGibbs.R0000755000176000001440000000635712566706215014515 0ustar ripleyusersrbiNormGibbs=function(initx=2,inity=-2,rho,burnin=100,R=500) { # # revision history: # P. Rossi 1/05 # # purpose: # illustrate the function of bivariate normal gibbs sampler # # arguments: # initx,inity initial values for draw sequence # rho correlation # burnin draws to be discarded in final paint # R -- number of draws # # output: # opens graph window and paints all moves and normal contours # list containing draw matrix # # model: # theta is bivariate normal with zero means, unit variances and correlation rho # # define needed functions # kernel= function(x,mu,rooti){ # function to evaluate -.5*log of MV NOrmal density kernel with mean mu, var Sigma # and with sigma^-1=rooti%*%t(rooti) # rooti is in the inverse of upper triangular chol root of sigma # note: this is the UL decomp of sigmai not LU! # Sigma=root'root root=inv(rooti) z=as.vector(t(rooti)%*%(x-mu)) (z%*%z) } # # check input arguments # if(missing(rho)) {pandterm("Requires rho argument ")} # # print out settings # cat("Bivariate Normal Gibbs Sampler",fill=TRUE) cat("rho= ",rho,fill=TRUE) cat("initial x,y coordinates= (",initx,",",inity,")",fill=TRUE) cat("burn-in= ",burnin," R= ",R,fill=TRUE) cat(" ",fill=TRUE) cat(" ",fill=TRUE) sd=(1-rho**2)**(.5) sigma=matrix(c(1,rho,rho,1),ncol=2) rooti=backsolve(chol(sigma),diag(2)) mu=c(0,0) x=seq(-3.5,3.5,length=100) y=x z=matrix(double(100*100),ncol=100) for (i in 1:length(x)) { for(j in 1:length(y)) { z[i,j]=kernel(c(x[i],y[j]),mu,rooti) } } prob=c(.1,.3,.5,.7,.9,.99) lev=qchisq(prob,2) par(mfrow=c(1,1)) contour(x,y,z,levels=lev,labels=prob, xlab="theta1",ylab="theta2",drawlabels=TRUE,col="green",labcex=1.3,lwd=2.0) title(paste("Gibbs Sampler with Intermediate Moves: Rho =",rho)) points(initx,inity,pch="B",cex=1.5) oldx=initx oldy=inity continue="y" r=0 draws=matrix(double(R*2),ncol=2) draws[1,]=c(initx,inity) cat(" ") cat("Starting Gibbs Sampler ....",fill=TRUE) cat("(hit enter or y to display moves one-at-a-time)",fill=TRUE) cat("('go' to paint all moves without stopping to prompt)",fill=TRUE) cat(" ",fill=TRUE) while(continue != "n"&& r < R) { if(continue != "go") continue=readline("cont?") newy=sd*rnorm(1) + rho*oldx lines(c(oldx,oldx),c(oldy,newy),col="magenta",lwd=1.5) newx=sd*rnorm(1)+rho*newy lines(c(oldx,newx),c(newy,newy),col="magenta",lwd=1.5) oldy=newy oldx=newx r=r+1 draws[r,]=c(newx,newy) } continue=readline("Show Comparison to iid Sampler?") if(continue != "n" & continue != "No" & continue != "no"){ par(mfrow=c(1,2)) contour(x,y,z,levels=lev, xlab="theta1",ylab="theta2",drawlabels=TRUE,labels=prob,labcex=1.1,col="green",lwd=2.0) title(paste("Gibbs Draws: Rho =",rho)) points(draws[(burnin+1):R,],pch=20,col="magenta",cex=.7) idraws=t(chol(sigma))%*%matrix(rnorm(2*(R-burnin)),nrow=2) idraws=t(idraws) contour(x,y,z,levels=lev, xlab="theta1",ylab="theta2",drawlabels=TRUE,labels=prob,labcex=1.1,col="green",lwd=2.0) title(paste("IID draws: Rho =",rho)) points(idraws,pch=20,col="magenta",cex=.7) } attributes(draws)$class=c("bayesm.mat","mcmc") attributes(draws)$mcpar=c(1,R,1) return(draws) } bayesm/R/summary.bayesm.var.R0000755000176000001440000000272412135054024015655 0ustar ripleyuserssummary.bayesm.var=function(object,names,burnin=trunc(.1*nrow(Vard)),tvalues,QUANTILES=FALSE,...){ # # S3 method to summarize draws of var-cov matrix (stored as a vector) # Vard is R x d**2 array of draws # P. Rossi 2/07 # Vard=object if(mode(Vard) == "list") stop("list entered \n Possible Fixup: extract from list \n") if(!is.matrix(Vard)) stop("Requires matrix argument \n") if(trunc(sqrt(ncol(Vard)))!=sqrt(ncol(Vard))) stop("Argument cannot be draws from a square matrix \n") if(nrow(Vard) < 100) {cat("fewer than 100 draws submitted \n"); return(invisible())} d=sqrt(ncol(Vard)) corrd=t(apply(Vard[(burnin+1):nrow(Vard),],1,nmat)) pmeancorr=apply(corrd,2,mean) dim(pmeancorr)=c(d,d) indexdiag=(0:(d-1))*d+1:d var=Vard[(burnin+1):nrow(Vard),indexdiag] sdd=sqrt(var) pmeansd=apply(sdd,2,mean) mat=cbind(pmeansd,pmeancorr) if(missing(names)) names=as.character(1:d) cat("Posterior Means of Std Deviations and Correlation Matrix \n") rownames(mat)=names colnames(mat)=c("Std Dev",names) print(mat,digits=2) cat("\nUpper Triangle of Var-Cov Matrix \n") ind=as.vector(upper.tri(matrix(0,ncol=d,nrow=d),diag=TRUE)) labels=cbind(rep(c(1:d),d),rep(c(1:d),each=d)) labels=labels[ind,] plabels=paste(labels[,1],labels[,2],sep=",") uppertri=as.matrix(Vard[,ind]) attributes(uppertri)$class="bayesm.mat" summary(uppertri,names=plabels,burnin=burnin,tvalues=tvalues,QUANTILES=QUANTILES) invisible() } bayesm/R/rhierLinearMixture_rcpp.r0000644000176000001440000002024613117541521017020 0ustar ripleyusersrhierLinearMixture=function(Data,Prior,Mcmc){ # # revision history: # changed 12/17/04 by rossi to fix bug in drawdelta when there is zero/one unit # in a mixture component # adapted to linear model by Vicky Chen 6/06 # put in classes 3/07 # changed a check 9/08 # W. Taylor 4/15 - added nprint option to MCMC argument # # purpose: run hierarchical linear model with mixture of normals # # Arguments: # Data contains a list of (regdata, and possibly Z) # regdata is a list of lists (one list per unit) # regdata[[i]]=list(y,X) # y is a vector of observations # X is a length(y) x nvar matrix of values of # X vars including intercepts # Z is an nreg x nz matrix of values of variables # note: Z should NOT contain an intercept # Prior contains a list of (nu.e,ssq,deltabar,Ad,mubar,Amu,nu,V,ncomp,a) # ncomp is the number of components in normal mixture # if elements of Prior (other than ncomp) do not exist, defaults are used # Mcmc contains a list of (s,c,R,keep,nprint) # # Output: as list containing # taodraw is R/keep x nreg array of error variances for each regression # Deltadraw R/keep x nz*nvar matrix of draws of Delta, first row is initial value # betadraw is nreg x nvar x R/keep array of draws of betas # probdraw is R/keep x ncomp matrix of draws of probs of mixture components # compdraw is a list of list of lists (length R/keep) # compdraw[[rep]] is the repth draw of components for mixtures # # Priors: # tau_i ~ nu.e*ssq_i/chisq(nu.e) tau_i is the variance of epsilon_i # beta_i = delta %*% z[i,] + u_i # u_i ~ N(mu_ind[i],Sigma_ind[i]) # ind[i] ~multinomial(p) # p ~ dirichlet (a) # a: Dirichlet parameters for prior on p # delta is a k x nz array # delta= vec(D) ~ N(deltabar,A_d^-1) # mu_j ~ N(mubar,A_mu^-1(x)Sigma_j) # Sigma_j ~ IW(nu,V^-1) # ncomp is number of components # # MCMC parameters # R is number of draws # keep is thinning parameter, keep every keepth draw # nprint - print estimated time remaining on every nprint'th draw # # check arguments # #-------------------------------------------------------------------------------------------------- # # create functions needed # append=function(l) { l=c(l,list(XpX=crossprod(l$X),Xpy=crossprod(l$X,l$y)))} # getvar=function(l) { v=var(l$y) if(is.na(v)) return(1) if(v>0) return (v) else return (1)} # if(missing(Data)) {pandterm("Requires Data argument -- list of regdata, and (possibly) Z")} if(is.null(Data$regdata)) {pandterm("Requires Data element regdata (list of data for each unit)")} regdata=Data$regdata nreg=length(regdata) drawdelta=TRUE if(is.null(Data$Z)) { cat("Z not specified",fill=TRUE); fsh() ; drawdelta=FALSE} else {if (!is.matrix(Data$Z)) {pandterm("Z must be a matrix")} else {if (nrow(Data$Z) != nreg) {pandterm(paste("Nrow(Z) ",nrow(Z),"ne number regressions ",nreg))} else {Z=Data$Z}}} if(drawdelta) { nz=ncol(Z) colmeans=apply(Z,2,mean) if(sum(colmeans) > .00001) {pandterm(paste("Z does not appear to be de-meaned: colmeans= ",colmeans))} } # # check regdata for validity # for (i in 1:nreg) { if(!is.matrix(regdata[[i]]$X)) {pandterm(paste0("regdata[[",i,"]]$X must be a matrix"))} if(!is.vector(regdata[[i]]$y, mode = "numeric") & !is.vector(regdata[[i]]$y, mode = "logical") & !is.matrix(regdata[[i]]$y)) {pandterm(paste0("regdata[[",i,"]]$y must be a numeric or logical vector or matrix"))} if(is.matrix(regdata[[i]]$y)){ if(ncol(regdata[[i]]$y)>1) {pandterm(paste0("regdata[[",i,"]]$y must be a vector or one-column matrix"))}} } dimfun=function(l) {c(length(l$y),dim(l$X))} dims=sapply(regdata,dimfun) dims=t(dims) nvar=quantile(dims[,3],prob=.5) for (i in 1:nreg) { if(dims[i,1] != dims[i,2] || dims[i,3] !=nvar) {pandterm(paste("Bad Data dimensions for unit ",i," dims(y,X) =",dims[i,]))} } # # check on prior # if(missing(Prior)) {pandterm("Requires Prior list argument (at least ncomp)")} if(is.null(Prior$nu.e)) {nu.e=BayesmConstant.nu.e} else {nu.e=Prior$nu.e} if(is.null(Prior$ssq)) {ssq=sapply(regdata,getvar)} else {ssq=Prior$ssq} if(is.null(Prior$ncomp)) {pandterm("Requires Prior element ncomp (num of mixture components)")} else {ncomp=Prior$ncomp} if(is.null(Prior$mubar)) {mubar=matrix(rep(0,nvar),nrow=1)} else { mubar=matrix(Prior$mubar,nrow=1)} if(ncol(mubar) != nvar) {pandterm(paste("mubar must have ncomp cols, ncol(mubar)= ",ncol(mubar)))} if(is.null(Prior$Amu)) {Amu=matrix(BayesmConstant.A,ncol=1)} else {Amu=matrix(Prior$Amu,ncol=1)} if(ncol(Amu) != 1 | nrow(Amu) != 1) {pandterm("Am must be a 1 x 1 array")} if(is.null(Prior$nu)) {nu=nvar+BayesmConstant.nuInc} else {nu=Prior$nu} if(nu < 1) {pandterm("invalid nu value")} if(is.null(Prior$V)) {V=nu*diag(nvar)} else {V=Prior$V} if(sum(dim(V)==c(nvar,nvar)) !=2) pandterm("Invalid V in prior") if(is.null(Prior$Ad) & drawdelta) {Ad=BayesmConstant.A*diag(nvar*nz)} else {Ad=Prior$Ad} if(drawdelta) {if(ncol(Ad) != nvar*nz | nrow(Ad) != nvar*nz) {pandterm("Ad must be nvar*nz x nvar*nz")}} if(is.null(Prior$deltabar)& drawdelta) {deltabar=rep(0,nz*nvar)} else {deltabar=Prior$deltabar} if(drawdelta) {if(length(deltabar) != nz*nvar) {pandterm("deltabar must be of length nvar*nz")}} if(is.null(Prior$a)) { a=rep(BayesmConstant.a,ncomp)} else {a=Prior$a} if(length(a) != ncomp) {pandterm("Requires dim(a)= ncomp (no of components)")} bada=FALSE for(i in 1:ncomp) { if(a[i] < 0) bada=TRUE} if(bada) pandterm("invalid values in a vector") # # check on Mcmc # if(missing(Mcmc)) {pandterm("Requires Mcmc list argument")} else { if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$R)) {pandterm("Requires R argument in Mcmc list")} else {R=Mcmc$R} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} } # # print out problem # cat(" ",fill=TRUE) cat("Starting MCMC Inference for Hierarchical Linear Model:",fill=TRUE) cat(" Normal Mixture with",ncomp,"components for first stage prior",fill=TRUE) cat(paste(" for ",nreg," cross-sectional units"),fill=TRUE) cat(" ",fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("nu.e =",nu.e,fill=TRUE) cat("nu =",nu,fill=TRUE) cat("V ",fill=TRUE) print(V) cat("mubar ",fill=TRUE) print(mubar) cat("Amu ", fill=TRUE) print(Amu) cat("a ",fill=TRUE) print(a) if(drawdelta) { cat("deltabar",fill=TRUE) print(deltabar) cat("Ad",fill=TRUE) print(Ad) } cat(" ",fill=TRUE) cat("MCMC Parms: ",fill=TRUE) cat("R= ",R," keep= ",keep," nprint= ",nprint,fill=TRUE) cat("",fill=TRUE) # initialize values # # Create XpX elements of regdata and initialize tau # regdata=lapply(regdata,append) tau=sapply(regdata,getvar) # # set initial values for the indicators # ind is of length(nreg) and indicates which mixture component this obs # belongs to. # ind=NULL ninc=floor(nreg/ncomp) for (i in 1:(ncomp-1)) {ind=c(ind,rep(i,ninc))} if(ncomp != 1) {ind = c(ind,rep(ncomp,nreg-length(ind)))} else {ind=rep(1,nreg)} # #initialize delta # if (drawdelta){ olddelta = rep(0,nz*nvar) } else { #send placeholders to the _loop function if there is no Z matrix olddelta = 0 Z = matrix(0) deltabar = 0 Ad = matrix(0) } # # initialize probs # oldprob=rep(1/ncomp,ncomp) ################################################################### # Wayne Taylor # 09/19/2014 ################################################################### draws = rhierLinearMixture_rcpp_loop(regdata, Z, deltabar, Ad, mubar, Amu, nu, V, nu.e, ssq, R, keep, nprint, drawdelta, as.matrix(olddelta), a, oldprob, ind, tau) #################################################################### attributes(draws$taudraw)$class=c("bayesm.mat","mcmc") attributes(draws$taudraw)$mcpar=c(1,R,keep) if(drawdelta){ attributes(draws$Deltadraw)$class=c("bayesm.mat","mcmc") attributes(draws$Deltadraw)$mcpar=c(1,R,keep)} attributes(draws$betadraw)$class=c("bayesm.hcoef") attributes(draws$nmix)$class="bayesm.nmix" return(draws) }bayesm/R/llnhlogit.R0000755000176000001440000000213712524673066014123 0ustar ripleyusersllnhlogit=function(theta,choice,lnprices,Xexpend) { # function to evaluate non-homothetic logit likelihood # choice is a n x 1 vector with indicator of choice (1,...,m) # lnprices is n x m array of log-prices faced # Xexpend is n x d array of variables predicting expenditure # # non-homothetic model specifies ln(psi_i(u))= alpha_i - exp(k_i)u # # structure of theta vector: # alpha (m x 1) # k (m x 1) # gamma (k x 1) expenditure function coefficients # tau scaling of v # m=ncol(lnprices) n=length(choice) d=ncol(Xexpend) alpha=theta[1:m] k=theta[(m+1):(2*m)] gamma=theta[(2*m+1):(2*m+d)] tau=theta[length(theta)] iotam=c(rep(1,m)) c1=as.vector(Xexpend%*%gamma)%x%iotam-as.vector(t(lnprices))+alpha c2=c(rep(exp(k),n)) u=callroot(c1,c2,.0000001,20) v=alpha - u*exp(k)-as.vector(t(lnprices)) vmat=matrix(v,ncol=m,byrow=TRUE) vmat=tau*vmat ind=seq(1,n) vchosen=vmat[cbind(ind,choice)] lnprob=vchosen-log((exp(vmat))%*%iotam) return(sum(lnprob)) } bayesm/R/rmvpgibbs_rcpp.r0000644000176000001440000001074512566706215015206 0ustar ripleyusersrmvpGibbs=function(Data,Prior,Mcmc){ # # Revision History: # modified by rossi 12/18/04 to include error checking # 3/07 added classes # W. Taylor 4/15 - added nprint option to MCMC argument # # # purpose: Gibbs MVP model with full covariance matrix # # Arguments: # Data contains # p the number of alternatives (could be time or could be from pick j of p survey) # y -- a vector of length n*p of indicators (1 if "chosen" if not) # X -- np x k matrix of covariates (including intercepts) # each X_i is p x nvar # # Prior contains a list of (betabar, A, nu, V) # if elements of prior do not exist, defaults are used # # Mcmc is a list of (beta0,sigma0,R,keep) # beta0,sigma0 are intial values, if not supplied defaults are used # R is number of draws # keep is thinning parm, keep every keepth draw # nprint - print estimated time remaining on every nprint'th draw # # Output: a list of every keepth betadraw and sigmsdraw # # model: # w_i = X_ibeta + e e~N(0,Sigma) note w_i,e are p x 1 # y_ij = 1 if w_ij > 0 else y_ij = 0 # # priors: # beta ~ N(betabar,A^-1) in prior # Sigma ~ IW(nu,V) # # Check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of p, y, X")} if(is.null(Data$p)) {pandterm("Requires Data element p -- number of binary indicators")} p=Data$p if(is.null(Data$y)) {pandterm("Requires Data element y -- values of binary indicators")} y=Data$y if(is.null(Data$X)) {pandterm("Requires Data element X -- matrix of covariates")} X=Data$X # # check data for validity # levely=as.numeric(levels(as.factor(y))) bady=FALSE for (i in 0:1) { if(levely[i+1] != i) {bady=TRUE} } cat("Table of y values",fill=TRUE) print(table(y)) if (bady) {pandterm("Invalid y")} if (length(y)%%p !=0) {pandterm("length of y is not a multiple of p")} n=length(y)/p k=ncol(X) if(nrow(X) != (n*p)) {pandterm(paste("X has ",nrow(X)," rows; must be = p*n"))} # # check for prior elements # if(missing(Prior)) { betabar=rep(0,k) ; A=BayesmConstant.A*diag(k) ; nu=p+BayesmConstant.nuInc; V=nu*diag(p)} else {if(is.null(Prior$betabar)) {betabar=rep(0,k)} else {betabar=Prior$betabar} if(is.null(Prior$A)) {A=BayesmConstant.A*diag(k)} else {A=Prior$A} if(is.null(Prior$nu)) {nu=p+BayesmConstant.nuInc} else {nu=Prior$nu} if(is.null(Prior$V)) {V=nu*diag(p)} else {V=Prior$V}} if(length(betabar) != k) pandterm("length betabar ne k") if(sum(dim(A)==c(k,k)) != 2) pandterm("A is of incorrect dimension") if(nu < 1) pandterm("invalid nu value") if(sum(dim(V)==c(p,p)) != 2) pandterm("V is of incorrect dimension") # # check for Mcmc # if(missing(Mcmc)) pandterm("Requires Mcmc argument -- at least R must be included") if(is.null(Mcmc$R)) {pandterm("Requires element R of Mcmc")} else {R=Mcmc$R} if(is.null(Mcmc$beta0)) {beta0=rep(0,k)} else {beta0=Mcmc$beta0} if(is.null(Mcmc$sigma0)) {sigma0=diag(p)} else {sigma0=Mcmc$sigma0} if(length(beta0) != k) pandterm("beta0 is not of length k") if(sum(dim(sigma0) == c(p,p)) != 2) pandterm("sigma0 is of incorrect dimension") if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} # # print out problem # cat(" ",fill=TRUE) cat("Starting Gibbs Sampler for MVP",fill=TRUE) cat(" ",n," obs of ",p," binary indicators; ",k," indep vars (including intercepts)",fill=TRUE) cat(" ",fill=TRUE) cat("Prior Parms:",fill=TRUE) cat("betabar",fill=TRUE) print(betabar) cat("A",fill=TRUE) print(A) cat("nu",fill=TRUE) print(nu) cat("V",fill=TRUE) print(V) cat(" ",fill=TRUE) cat("MCMC Parms:",fill=TRUE) cat(" ",R," reps; keeping every ",keep,"th draw"," nprint= ",nprint,fill=TRUE) cat("initial beta= ",beta0,fill=TRUE) cat("initial sigma= ",fill=TRUE) print(sigma0) cat(" ",fill=TRUE) ################################################################### # Wayne Taylor # 09/03/2014 ################################################################### loopout = rmvpGibbs_rcpp_loop(R,keep,nprint,p,y,X,beta0,sigma0,V,nu,betabar,A); ################################################################### attributes(loopout$betadraw)$class=c("bayesm.mat","mcmc") attributes(loopout$betadraw)$mcpar=c(1,R,keep) attributes(loopout$sigmadraw)$class=c("bayesm.var","bayesm.mat","mcmc") attributes(loopout$sigmadraw)$mcpar=c(1,R,keep) return(loopout) }bayesm/R/rhierBinLogit.R0000755000176000001440000001715413114117322014655 0ustar ripleyusers# # ----------------------------------------------------------------------------- # rhierBinLogit = function(Data,Prior,Mcmc){ .Deprecated(msg = "'rhierBinLogit' is depricated \nUse 'rhierMnlRwMixture' instead") # # revision history: # changed 5/12/05 by Rossi to add error checking # 1/07 removed init.rmultiregfp # 3/07 added classes # # purpose: run binary heterogeneous logit model # # Arguments: # Data contains a list of (lgtdata[[i]],Z) # lgtdata[[i]]=list(y,X) # y is index of brand chosen, y=1 is exp[X'beta]/(1+exp[X'beta]) # X is a matrix that is n_i x by nvar # Z is a matrix of demographic variables nlgt*nz that have been # mean centered so that the intercept is interpretable # Prior contains a list of (nu,V,Deltabar,ADelta) # beta_i ~ N(Z%*%Delta,Vbeta) # vec(Delta) ~ N(vec(Deltabar),Vbeta (x) ADelta^-1) # Vbeta ~ IW(nu,V) # Mcmc is a list of (sbeta,R,keep) # sbeta is scale factor for RW increment for beta_is # R is number of draws # keep every keepth draw # # Output: # a list of Deltadraw (R/keep x nvar x nz), Vbetadraw (R/keep x nvar**2), # llike (R/keep), betadraw is a nlgt x nvar x nz x R/keep array of draws of betas # nunits=length(lgtdata) # # define functions needed # # ------------------------------------------------------------------------ # loglike= function(y,X,beta) { # function computes log likelihood of data for binomial logit model # Pr(y=1) = 1 - Pr(y=0) = exp[X'beta]/(1+exp[X'beta]) prob = exp(X%*%beta)/(1+exp(X%*%beta)) prob = prob*y + (1-prob)*(1-y) sum(log(prob)) } # # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of m, lgtdata, and (possibly) Z")} if(is.null(Data$lgtdata)) {pandterm("Requires Data element lgtdata (list of data for each unit)")} lgtdata=Data$lgtdata nlgt=length(lgtdata) if(is.null(Data$Z)) { cat("Z not specified -- putting in iota",fill=TRUE); fsh() ; Z=matrix(rep(1,nlgt),ncol=1)} else {if (!is.matrix(Data$Z)) {pandterm("Z must be a matrix")} else {if (nrow(Data$Z) != nlgt) {pandterm(paste("nrow(Z) ",nrow(Z),"ne number logits ",nlgt))} else {Z=Data$Z}}} nz=ncol(Z) # # check lgtdata for validity # m=2 # set two choice alternatives for Greg's code ypooled=NULL Xpooled=NULL if(!is.null(lgtdata[[1]]$X & is.matrix(lgtdata[[1]]$X))) {oldncol=ncol(lgtdata[[1]]$X)} for (i in 1:nlgt) { if(is.null(lgtdata[[i]]$y)) {pandterm(paste0("Requires element y of lgtdata[[",i,"]]"))} if(is.null(lgtdata[[i]]$X)) {pandterm(paste0("Requires element X of lgtdata[[",i,"]]"))} if(!is.matrix(lgtdata[[i]]$X)) {pandterm(paste0("lgtdata[[",i,"]]$X must be a matrix"))} if(!is.vector(lgtdata[[i]]$y, mode = "numeric") & !is.vector(lgtdata[[i]]$y, mode = "logical") & !is.matrix(lgtdata[[i]]$y)) {pandterm(paste0("lgtdata[[",i,"]]$y must be a numeric or logical vector or matrix"))} if(is.matrix(lgtdata[[i]]$y) & ncol(lgtdata[[i]]$y)>1) {pandterm(paste0("lgtdata[[",i,"]]$y must be a vector or one-column matrix"))} ypooled=c(ypooled,lgtdata[[i]]$y) nrowX=nrow(lgtdata[[i]]$X) if((nrowX) !=length(lgtdata[[i]]$y)) {pandterm(paste("nrow(X) ne length(yi); exception at unit",i))} newncol=ncol(lgtdata[[i]]$X) if(newncol != oldncol) {pandterm(paste("All X elements must have same # of cols; exception at unit",i))} Xpooled=rbind(Xpooled,lgtdata[[i]]$X) oldncol=newncol } nvar=ncol(Xpooled) levely=as.numeric(levels(as.factor(ypooled))) if(length(levely) != m) {pandterm(paste("y takes on ",length(levely)," values -- must be = m"))} bady=FALSE for (i in 0:1 ) { if(levely[i+1] != i) bady=TRUE } cat("Table of Y values pooled over all units",fill=TRUE) print(table(ypooled)) if (bady) {pandterm("Invalid Y")} # # check on prior # if(missing(Prior)){ nu=nvar+3 V=nu*diag(nvar) Deltabar=matrix(rep(0,nz*nvar),ncol=nvar) ADelta=.01*diag(nz) } else { if(is.null(Prior$nu)) {nu=nvar+3} else {nu=Prior$nu} if(nu < 1) {pandterm("invalid nu value")} if(is.null(Prior$V)) {V=nu*diag(rep(1,nvar))} else {V=Prior$V} if(sum(dim(V)==c(nvar,nvar)) !=2) pandterm("Invalid V in prior") if(is.null(Prior$ADelta) ) {ADelta=.01*diag(nz)} else {ADelta=Prior$ADelta} if(ncol(ADelta) != nz | nrow(ADelta) != nz) {pandterm("ADelta must be nz x nz")} if(is.null(Prior$Deltabar) ) {Deltabar=matrix(rep(0,nz*nvar),ncol=nvar)} else {Deltabar=Prior$Deltabar} } # # check on Mcmc # if(missing(Mcmc)) {pandterm("Requires Mcmc list argument")} else { if(is.null(Mcmc$sbeta)) {sbeta=.2} else {sbeta=Mcmc$sbeta} if(is.null(Mcmc$keep)) {keep=1} else {keep=Mcmc$keep} if(is.null(Mcmc$R)) {pandterm("Requires R argument in Mcmc list")} else {R=Mcmc$R} } # # print out problem # cat(" ",fill=TRUE) cat("Attempting MCMC Inference for Hierarchical Binary Logit:",fill=TRUE) cat(paste(" ",nvar," variables in X"),fill=TRUE) cat(paste(" ",nz," variables in Z"),fill=TRUE) cat(paste(" for ",nlgt," cross-sectional units"),fill=TRUE) cat(" ",fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("nu =",nu,fill=TRUE) cat("V ",fill=TRUE) print(V) cat("Deltabar",fill=TRUE) print(Deltabar) cat("ADelta",fill=TRUE) print(ADelta) cat(" ",fill=TRUE) cat("MCMC Parms: ",fill=TRUE) cat(paste("sbeta=",round(sbeta,3)," R= ",R," keep= ",keep),fill=TRUE) cat("",fill=TRUE) nlgt=length(lgtdata) nvar=ncol(lgtdata[[1]]$X) nz=ncol(Z) # # initialize storage for draws # Vbetadraw=matrix(double(floor(R/keep)*nvar*nvar),ncol=nvar*nvar) betadraw=array(double(floor(R/keep)*nlgt*nvar),dim=c(nlgt,nvar,floor(R/keep))) Deltadraw=matrix(double(floor(R/keep)*nvar*nz),ncol=nvar*nz) oldbetas=matrix(double(nlgt*nvar),ncol=nvar) oldVbeta=diag(nvar) oldVbetai=diag(nvar) oldDelta=matrix(double(nvar*nz),ncol=nvar) betad = array(0,dim=c(nvar)) betan = array(0,dim=c(nvar)) reject = array(0,dim=c(R/keep)) llike=array(0,dim=c(R/keep)) itime=proc.time()[3] cat("MCMC Iteration (est time to end - min)",fill=TRUE) fsh() for (j in 1:R) { rej = 0 logl = 0 sV = sbeta*oldVbeta root=t(chol(sV)) # Draw B-h|B-bar, V for (i in 1:nlgt) { betad = oldbetas[i,] betan = betad + root%*%rnorm(nvar) # data lognew = loglike(lgtdata[[i]]$y,lgtdata[[i]]$X,betan) logold = loglike(lgtdata[[i]]$y,lgtdata[[i]]$X,betad) # heterogeneity logknew = -.5*(t(betan)-Z[i,]%*%oldDelta) %*% oldVbetai %*% (betan-t(Z[i,]%*%oldDelta)) logkold = -.5*(t(betad)-Z[i,]%*%oldDelta) %*% oldVbetai %*% (betad-t(Z[i,]%*%oldDelta)) # MH step alpha = exp(lognew + logknew - logold - logkold) if(alpha=="NaN") alpha=-1 u = runif(n=1,min=0, max=1) if(u < alpha) { oldbetas[i,] = betan logl = logl + lognew } else { logl = logl + logold rej = rej+1 } } # Draw B-bar and V as a multivariate regression out=rmultireg(oldbetas,Z,Deltabar,ADelta,nu,V) oldDelta=out$B oldVbeta=out$Sigma oldVbetai=chol2inv(chol(oldVbeta)) if((j%%100)==0) { ctime=proc.time()[3] timetoend=((ctime-itime)/j)*(R-j) cat(" ",j," (",round(timetoend/60,1),")",fill=TRUE) fsh() } mkeep=j/keep if(mkeep*keep == (floor(mkeep)*keep)) {Deltadraw[mkeep,]=as.vector(oldDelta) Vbetadraw[mkeep,]=as.vector(oldVbeta) betadraw[,,mkeep]=oldbetas llike[mkeep]=logl reject[mkeep]=rej/nlgt } } ctime=proc.time()[3] cat(" Total Time Elapsed: ",round((ctime-itime)/60,2),fill=TRUE) attributes(betadraw)$class=c("bayesm.hcoef") attributes(Deltadraw)$class=c("bayesm.mat","mcmc") attributes(Deltadraw)$mcpar=c(1,R,keep) attributes(Vbetadraw)$class=c("bayesm.var","bayesm.mat","mcmc") attributes(Vbetadraw)$mcpar=c(1,R,keep) return(list(betadraw=betadraw,Vbetadraw=Vbetadraw,Deltadraw=Deltadraw,llike=llike,reject=reject)) } bayesm/R/rscaleusage_rcpp.r0000644000176000001440000002323613117541521015476 0ustar ripleyusersrscaleUsage= function(Data,Prior,Mcmc) { # # purpose: run scale-usage mcmc # draws y,Sigma,mu,tau,sigma,Lambda,e # R. McCulloch 12/28/04 # added classes 3/07 # # arguments: # Data: # all components are required: # k: integer giving the scale of the responses, each observation is an integer from 1,2,...k # x: data, num rows=number of respondents, num columns = number of questions # Prior: # all components are optional # nu,V: Sigma ~ IW(nu,V) # mubar,Am: mu ~N(mubar,Am^{-1}) # gsigma: grid for sigma # gl11,gl22,gl12: grids for ij element of Lambda # Lambdanu,LambdaV: Lambda ~ IW(Lambdanu,LambdaV) # ge: grid for e # Mcmc: # all components are optional (but you would typically want to specify R= number of draws) # R: number of mcmc iterations # keep: frequency with which draw is kept # ndghk: number of draws for ghk # nprint - print estimated time remaining on every nprint'th draw # e,y,mu,Sigma,sigma,tau,Lambda: initial values for the state # doe, ...doLambda: indicates whether draw should be made # output: # List with draws of each of Sigma,mu,tau,sigma,Lambda,e # eg. result$Sigma is the draws of Sigma # Each component is a matrix expept e, which is a vector # for the matrices Sigma and Lambda each row transpose of the Vec # eg. result$Lambda has rows (Lambda11,Lambda21,Lambda12,Lambda22) # # define functions needed # # ----------------------------------------------------------------------------------- myin = function(i,ind) {i %in% ind} ispd = function(mat,d=nrow(mat)) { if(!is.matrix(mat)) { res = FALSE } else if(!((nrow(mat)==d) & (ncol(mat)==d))) { res = FALSE } else { diff = (t(mat)+mat)/2 - mat perdiff = sum(diff^2)/sum(mat^2) res = ((det(mat)>0) & (perdiff < 1e-10)) } res } #+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # print out components of inputs ---------------------------------------------- cat('\nIn function rscaleUsage\n\n') if(!missing(Data)) { cat(' Data has components: ') cat(paste(names(Data),collapse=' ')[1],'\n') } if(!missing(Prior)) { cat(' Prior has components: ') cat(paste(names(Prior),collapse=' ')[1],'\n') } if(!missing(Mcmc)) { cat(' Mcmc has components: ') cat(paste(names(Mcmc),collapse=' ')[1],'\n') } cat('\n') # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ # process Data argument -------------------------- if(missing(Data)) {pandterm("Requires Data argument - list of k=question scale and x = data")} if(is.null(Data$k)) { pandterm("k not specified") } else { k = as.integer(Data$k) if(!((k>0) & (k<50))) {pandterm("Data$k must be integer between 1 and 50")} } if(is.null(Data$x)) { pandterm('x (the data), not specified') } else { if(!is.matrix(Data$x)) {pandterm('Data$x must be a matrix')} x = matrix(as.integer(Data$x),nrow=nrow(Data$x)) checkx = sum(sapply(as.vector(x),myin,1:k)) if(!(checkx == nrow(x)*ncol(x))) {pandterm('each element of Data$x must be in 1,2...k')} p = ncol(x) n = nrow(x) if((p<2) | (n<1)) {pandterm(paste('invalid dimensions for x: nrow,ncol: ',n,p))} } # ++++++++++++++++++++++++++++++++++++++++++++++++ # process Mcmc argument --------------------- #run mcmc R = 1000 keep = BayesmConstant.keep ndghk= 100 nprint = BayesmConstant.nprint if(!missing(Mcmc)) { if(!is.null(Mcmc$R)) { R = as.integer(Mcmc$R) } if(!is.null(Mcmc$keep)) { keep = as.integer(Mcmc$keep) } if(!is.null(Mcmc$ndghk)) { ndghk = as.integer(Mcmc$ndghk) } if(!is.null(Mcmc$nprint)) { nprint = as.integer(Mcmc$nprint) } } if(R<1) { pandterm('R must be positive')} if(keep<1) { pandterm('keep must be positive') } if(ndghk<1) { pandterm('ndghk must be positive') } if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} #state y = matrix(as.double(x),nrow=nrow(x)) mu = apply(y,2,mean) Sigma = var(y) tau = rep(0,n) sigma = rep(1,n) #Lambda = matrix(c(3.7,-.22,-.22,.32),ncol=2) #Lambda = matrix(c((k/4)^2,(k/4)*.5*(-.2),0,.25),nrow=2); Lambda[1,2]=Lambda[2,1] Lambda = matrix(c(4,0,0,.5),ncol=2) e=0 if(!missing(Mcmc)) { if(!is.null(Mcmc$y)) { y = Mcmc$y } if(!is.null(Mcmc$mu)) { mu = Mcmc$mu } if(!is.null(Mcmc$Sigma)) { Sigma = Mcmc$Sigma } if(!is.null(Mcmc$tau)) { tau = Mcmc$tau } if(!is.null(Mcmc$sigma)) { sigma = Mcmc$sigma } if(!is.null(Mcmc$Lambda)) { Lambda = Mcmc$Lambda } if(!is.null(Mcmc$e)) { e = Mcmc$e } } if(!ispd(Sigma,p)) { pandterm(paste('Sigma must be positive definite with dimension ',p)) } if(!ispd(Lambda,2)) { pandterm(paste('Lambda must be positive definite with dimension ',2)) } if(!is.vector(mu)) { pandterm('mu must be a vector') } if(length(mu) != p) { pandterm(paste('mu must have length ',p)) } if(length(tau) != n) { pandterm(paste('tau must have length ',n)) } if(!is.vector(sigma)) { pandterm('sigma must be a vector') } if(length(sigma) != n) { pandterm(paste('sigma must have length ',n)) } if(!is.matrix(y)) { pandterm('y must be a matrix') } if(nrow(y) != n) { pandterm(paste('y must have',n,'rows')) } if(ncol(y) != p) { pandterm(paste('y must have',p,'columns')) } #do draws domu=TRUE doSigma=TRUE dosigma=TRUE dotau=TRUE doLambda=TRUE doe=TRUE if(!missing(Mcmc)) { if(!is.null(Mcmc$domu)) { domu = Mcmc$domu } if(!is.null(Mcmc$doSigma)) { doSigma = Mcmc$doSigma } if(!is.null(Mcmc$dotau)) { dotau = Mcmc$dotau } if(!is.null(Mcmc$dosigma)) { dosigma = Mcmc$dosigma } if(!is.null(Mcmc$doLambda)) { doLambda = Mcmc$doLambda } if(!is.null(Mcmc$doe)) { doe = Mcmc$doe } } #++++++++++++++++++++++++++++++++++++++ #process Prior argument ---------------------------------- nu = p+BayesmConstant.nuInc V= nu*diag(p) mubar = matrix(rep(k/2,p),ncol=1) Am = BayesmConstant.A*diag(p) gs = 100 #changed from 200 to 100 by Dan Yavorsky (dyavorsky@gmail.com) Oct 2016 if(!missing(Prior)) { if(!is.null(Prior$gs)) { gs = Prior$gs } } #allow user to modify gs; Dan Yavorsky (dyavorsky@gmail.com) Oct 2016 gsigma = 6*(1:gs)/gs gl11 = .1 + 5.9*(1:gs)/gs gl22 = .1 + 2.0*(1:gs)/gs #gl12 = -.8 + 1.6*(1:gs)/gs gl12 = -2.0 + 4*(1:gs)/gs nuL=20 VL = (nuL-3)*Lambda ge = -.1+.2*(0:gs)/gs if(!missing(Prior)) { if(!is.null(Prior$nu)) { nu = Prior$nu; V = nu*diag(p) } if(!is.null(Prior$V)) { V = Prior$V } if(!is.null(Prior$mubar)) { mubar = matrix(Prior$mubar,ncol=1) } if(!is.null(Prior$Am)) { Am = Prior$Am } if(!is.null(Prior$gsigma)) { gsigma = Prior$gsigma } if(!is.null(Prior$gl11)) { gl11 = Prior$gl11 } if(!is.null(Prior$gl22)) { gl22 = Prior$gl22 } if(!is.null(Prior$gl12)) { gl12 = Prior$gl12 } if(!is.null(Prior$Lambdanu)) { nuL = Prior$Lambdanu; VL = (nuL-3)*Lambda } if(!is.null(Prior$LambdaV)) { VL = Prior$LambdaV } if(!is.null(Prior$ge)) { ge = Prior$ge } } if(!ispd(V,p)) { pandterm(paste('V must be positive definite with dimension ',p)) } if(!ispd(Am,p)) { pandterm(paste('Am must be positive definite with dimension ',p)) } if(!ispd(VL,2)) { pandterm(paste('VL must be positive definite with dimension ',2)) } if(nrow(mubar) != p) { pandterm(paste('mubar must have length',p)) } #++++++++++++++++++++++++++++++++++++++++ #print out run info ------------------------- # # note in the documentation and in BSM, m is used instead of p # for print-out purposes I'm using m P. Rossi 12/06 cat(' n,m,k: ', n,p,k,'\n') cat(' R,keep,ndghk,nprint: ', R,keep,ndghk,nprint,'\n') cat('\n') cat(' Data:\n') cat(' x[1,1],x[n,1],x[1,m],x[n,m]: ',x[1,1],x[n,1],x[1,p],x[n,p],'\n\n') cat(' Prior:\n') cat(' ','nu: ',nu,'\n') cat(' ','V[1,1]/nu,V[m,m]/nu: ',V[1,1]/nu,V[p,p]/nu,'\n') cat(' ','mubar[1],mubar[m]: ',mubar[1],mubar[p],'\n') cat(' ','Am[1,1],Am[m,m]: ',Am[1,1],Am[p,p],'\n') cat(' ','Lambdanu: ',nuL,'\n') cat(' ','LambdaV11,22/(Lambdanu-3): ',VL[1,1]/(nuL-3),VL[2,2]/(nuL-3),'\n') cat(' ','sigma grid, 1,',length(gsigma),': ',gsigma[1],', ',gsigma[length(gsigma)],'\n') cat(' ','Lambda11 grid, 1,',length(gl11),': ',gl11[1],', ',gl11[length(gl11)],'\n') cat(' ','Lambda12 grid, 1,',length(gl12),': ',gl12[1],', ',gl12[length(gl12)],'\n') cat(' ','Lambda22 grid, 1,',length(gl22),': ',gl22[1],', ',gl22[length(gl22)],'\n') cat(' ','e grid, 1,',length(ge),': ',ge[1],', ',ge[length(ge)],'\n') cat(' ','draw e: ',doe,'\n') cat(' ','draw Lambda: ',doLambda,'\n') #++++++++++++++++++++++++++++++++++++++++++++ ################################################################### # Wayne Taylor # 3/14/2015 ################################################################### out = rscaleUsage_rcpp_loop(k,x,p,n, R,keep,ndghk,nprint, y,mu,Sigma,tau,sigma,Lambda,e, domu,doSigma,dosigma,dotau,doLambda,doe, nu,V,mubar,Am, gsigma,gl11,gl22,gl12, nuL,VL,ge) R = out$ndpost ################################################################### attributes(out$drmu)$class=c("bayesm.mat","mcmc") attributes(out$drmu)$mcpar=c(1,R,keep) attributes(out$drtau)$class=c("bayesm.mat","mcmc") attributes(out$drtau)$mcpar=c(1,R,keep) attributes(out$drsigma)$class=c("bayesm.mat","mcmc") attributes(out$drsigma)$mcpar=c(1,R,keep) attributes(out$drLambda)$class=c("bayesm.mat","mcmc") attributes(out$drLambda)$mcpar=c(1,R,keep) attributes(out$dre)$class=c("bayesm.mat","mcmc") attributes(out$dre)$mcpar=c(1,R,keep) attributes(out$drSigma)$class=c("bayesm.var","bayesm.mat","mcmc") attributes(out$drSigma)$mcpar=c(1,R,keep) return(list(Sigmadraw=out$drSigma,mudraw=out$drmu,taudraw = out$drtau, sigmadraw=out$drsigma,Lambdadraw=out$drLambda,edraw=out$dre)) } bayesm/R/summary.bayesm.nmix.R0000755000176000001440000000261710647461730016055 0ustar ripleyuserssummary.bayesm.nmix=function(object,names,burnin=trunc(.1*nrow(probdraw)),...){ nmixlist=object if(mode(nmixlist) != "list") stop(" Argument must be a list \n") probdraw=nmixlist[[1]]; compdraw=nmixlist[[3]] if(!is.matrix(probdraw)) stop(" First Element of List (probdraw) must be a matrix \n") if(mode(compdraw) != "list") stop(" Third Element of List (compdraw) must be a list \n") ncomp=length(compdraw[[1]]) if(ncol(probdraw) != ncomp) stop(" Dim of First Element of List not compatible with Dim of Second \n") # # function to summarize draws of normal mixture components # R=nrow(probdraw) if(R < 100) {cat("fewer than 100 draws submitted \n"); return(invisible())} datad=length(compdraw[[1]][[1]]$mu) mumat=matrix(0,nrow=R,ncol=datad) sigmat=matrix(0,nrow=R,ncol=(datad*datad)) if(missing(names)) names=as.character(1:datad) for(i in (burnin+1):R){ if(i%%500 ==0) cat("processing draw ",i,"\n",sep="");fsh() out=momMix(probdraw[i,,drop=FALSE],compdraw[i]) mumat[i,]=out$mu sigmat[i,]=out$sigma } cat("\nNormal Mixture Moments\n Mean\n") attributes(mumat)$class="bayesm.mat" attributes(sigmat)$class="bayesm.var" summary(mumat,names,burnin=burnin,QUANTILES=FALSE,TRAILER=FALSE) cat(" \n") summary(sigmat,burnin=burnin) cat("note: 1st and 2nd Moments for a Normal Mixture \n") cat(" may not be interpretable, consider plots\n") invisible() } bayesm/R/rhierMnlDP_rcpp.r0000644000176000001440000003027413117541521015204 0ustar ripleyusersrhierMnlDP=function(Data,Prior,Mcmc){ # # created 3/08 by Rossi from rhierMnlRwMixture adding DP draw for to replace finite mixture of normals # # revision history: # changed 12/17/04 by rossi to fix bug in drawdelta when there is zero/one unit # in a mixture component # added loglike output, changed to reflect new argument order in llmnl, mnlHess 9/05 # changed weighting scheme to (1-w)logl_i + w*Lbar (normalized) 12/05 # 3/07 added classes # W. Taylor 4/15 - added nprint option to MCMC argument # # purpose: run hierarchical mnl logit model with mixture of normals # using RW and cov(RW inc) = (hess_i + Vbeta^-1)^-1 # uses normal approximation to pooled likelihood # # Arguments: # Data contains a list of (p,lgtdata, and possibly Z) # p is number of choice alternatives # lgtdata is a list of lists (one list per unit) # lgtdata[[i]]=list(y,X) # y is a vector indicating alternative chosen # integers 1:p indicate alternative # X is a length(y)*p x nvar matrix of values of # X vars including intercepts # Z is an length(lgtdata) x nz matrix of values of variables # note: Z should NOT contain an intercept # Prior contains a list of (deltabar,Ad,lambda_hyper,Prioralpha) # alpha: starting value # lambda_hyper: hyperparms of prior on lambda # Prioralpha: hyperparms of alpha prior; a list of (Istarmin,Istarmax,power) # if elements of the prior don't exist, defaults are assumed # Mcmc contains a list of (s,c,R,keep,nprint) # # Output: as list containing # Deltadraw R/keep x nz*nvar matrix of draws of Delta, first row is initial value # betadraw is nlgt x nvar x R/keep array of draws of betas # probdraw is R/keep x 1 matrix of draws of probs of mixture components # compdraw is a list of list of lists (length R/keep) # compdraw[[rep]] is the repth draw of components for mixtures # loglike log-likelikelhood at each kept draw # # Priors: # beta_i = D %*% z[i,] + u_i # vec(D)~N(deltabar) # u_i ~ N(theta_i) # theta_i~G # G|lambda,alpha ~ DP(G|G0(lambda),alpha) # # lambda: # G0 ~ N(mubar,Sigma (x) Amu^-1) # mubar=vec(mubar) # Sigma ~ IW(nu,nu*v*I) note: mode(Sigma)=nu/(nu+2)*v*I # mubar=0 # amu is uniform on grid specified by alim # nu is log uniform, nu=d-1+exp(Z) z is uniform on seq defined bvy nulim # v is uniform on sequence specificd by vlim # # Prioralpha: # alpha ~ (1-(alpha-alphamin)/(alphamax-alphamin))^power # alphamin=exp(digamma(Istarmin)-log(gamma+log(N))) # alphamax=exp(digamma(Istarmax)-log(gamma+log(N))) # gamma= .5772156649015328606 # # MCMC parameters # s is the scaling parameter for the RW inc covariance matrix; s^2 Var is inc cov # matrix # w is parameter for weighting function in fractional likelihood # w is the weight on the normalized pooled likelihood # R is number of draws # keep is thinning parameter, keep every keepth draw # nprint - print estimated time remaining on every nprint'th draw #-------------------------------------------------------------------------------------------------- llmnlFract= function(beta,y,X,betapooled,rootH,w,wgt){ z=as.vector(rootH%*%(beta-betapooled)) return((1-w)*llmnl(beta,y,X)+w*wgt*(-.5*(z%*%z))) } # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of p,lgtdata, and (possibly) Z")} if(is.null(Data$p)) {pandterm("Requires Data element p (# chce alternatives)") } p=Data$p if(is.null(Data$lgtdata)) {pandterm("Requires Data element lgtdata (list of data for each unit)")} lgtdata=Data$lgtdata nlgt=length(lgtdata) drawdelta=TRUE if(is.null(Data$Z)) { cat("Z not specified",fill=TRUE); fsh() ; drawdelta=FALSE} else {if (!is.matrix(Data$Z)) {pandterm("Z must be a matrix")} else {if (nrow(Data$Z) != nlgt) {pandterm(paste("Nrow(Z) ",nrow(Z),"ne number logits ",nlgt))} else {Z=Data$Z}}} if(drawdelta) { nz=ncol(Z) colmeans=apply(Z,2,mean) if(sum(colmeans) > .00001) {pandterm(paste("Z does not appear to be de-meaned: colmeans= ",colmeans))} } # # check lgtdata for validity # ypooled=NULL Xpooled=NULL if(!is.null(lgtdata[[1]]$X & is.matrix(lgtdata[[1]]$X))) {oldncol=ncol(lgtdata[[1]]$X)} for (i in 1:nlgt) { if(is.null(lgtdata[[i]]$y)) {pandterm(paste0("Requires element y of lgtdata[[",i,"]]"))} if(is.null(lgtdata[[i]]$X)) {pandterm(paste0("Requires element X of lgtdata[[",i,"]]"))} if(!is.matrix(lgtdata[[i]]$X)) {pandterm(paste0("lgtdata[[",i,"]]$X must be a matrix"))} if(!is.vector(lgtdata[[i]]$y, mode = "numeric") & !is.vector(lgtdata[[i]]$y, mode = "logical") & !is.matrix(lgtdata[[i]]$y)) {pandterm(paste0("lgtdata[[",i,"]]$y must be a numeric or logical vector or matrix"))} if(is.matrix(lgtdata[[i]]$y)){ if(ncol(lgtdata[[i]]$y)>1) {pandterm(paste0("lgtdata[[",i,"]]$y must be a vector or one-column matrix"))}} ypooled=c(ypooled,lgtdata[[i]]$y) nrowX=nrow(lgtdata[[i]]$X) if((nrowX/p) !=length(lgtdata[[i]]$y)) {pandterm(paste("nrow(X) ne p*length(yi); exception at unit",i))} newncol=ncol(lgtdata[[i]]$X) if(newncol != oldncol) {pandterm(paste("All X elements must have same # of cols; exception at unit",i))} Xpooled=rbind(Xpooled,lgtdata[[i]]$X) oldncol=newncol } nvar=ncol(Xpooled) levely=as.numeric(levels(as.factor(ypooled))) if(length(levely) != p) {pandterm(paste("y takes on ",length(levely)," values -- must be = p"))} bady=FALSE for (i in 1:p ) { if(levely[i] != i) bady=TRUE } cat("Table of Y values pooled over all units",fill=TRUE) print(table(ypooled)) if (bady) {pandterm("Invalid Y")} # # check on prior # alimdef=BayesmConstant.DPalimdef nulimdef=BayesmConstant.DPnulimdef vlimdef=BayesmConstant.DPvlimdef if(missing(Prior)) {Prior=NULL} if(is.null(Prior$lambda_hyper)) {lambda_hyper=list(alim=alimdef,nulim=nulimdef,vlim=vlimdef)} else {lambda_hyper=Prior$lambda_hyper; if(is.null(lambda_hyper$alim)) {lambda_hyper$alim=alimdef} if(is.null(lambda_hyper$nulim)) {lambda_hyper$nulim=nulimdef} if(is.null(lambda_hyper$vlim)) {lambda_hyper$vlim=vlimdef} } if(is.null(Prior$Prioralpha)) {Prioralpha=list(Istarmin=BayesmConstant.DPIstarmin,Istarmax=min(50,0.1*nlgt),power=BayesmConstant.DPpower)} else {Prioralpha=Prior$Prioralpha; if(is.null(Prioralpha$Istarmin)) {Prioralpha$Istarmin=BayesmConstant.DPIstarmin} else {Prioralpha$Istarmin=Prioralpha$Istarmin} if(is.null(Prioralpha$Istarmax)) {Prioralpha$Istarmax=min(50,0.1*nlgt)} else {Prioralpha$Istarmax=Prioralpha$Istarmax} if(is.null(Prioralpha$power)) {Prioralpha$power=BayesmConstant.DPpower} } gamma= BayesmConstant.gamma Prioralpha$alphamin=exp(digamma(Prioralpha$Istarmin)-log(gamma+log(nlgt))) Prioralpha$alphamax=exp(digamma(Prioralpha$Istarmax)-log(gamma+log(nlgt))) Prioralpha$n=nlgt # # check Prior arguments for valdity # if(lambda_hyper$alim[1]<0) {pandterm("alim[1] must be >0")} if(lambda_hyper$nulim[1]<0) {pandterm("nulim[1] must be >0")} if(lambda_hyper$vlim[1]<0) {pandterm("vlim[1] must be >0")} if(Prioralpha$Istarmin <1){pandterm("Prioralpha$Istarmin must be >= 1")} if(Prioralpha$Istarmax <= Prioralpha$Istarmin){pandterm("Prioralpha$Istarmin must be < Prioralpha$Istarmax")} if(is.null(Prior$Ad) & drawdelta) {Ad=BayesmConstant.A*diag(nvar*nz)} else {Ad=Prior$Ad} if(drawdelta) {if(ncol(Ad) != nvar*nz | nrow(Ad) != nvar*nz) {pandterm("Ad must be nvar*nz x nvar*nz")}} if(is.null(Prior$deltabar)& drawdelta) {deltabar=rep(0,nz*nvar)} else {deltabar=Prior$deltabar} if(drawdelta) {if(length(deltabar) != nz*nvar) {pandterm("deltabar must be of length nvar*nz")}} # # check on Mcmc # if(missing(Mcmc)) {pandterm("Requires Mcmc list argument")} else { if(is.null(Mcmc$s)) {s=BayesmConstant.RRScaling/sqrt(nvar)} else {s=Mcmc$s} if(is.null(Mcmc$w)) {w=BayesmConstant.w} else {w=Mcmc$w} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} if(is.null(Mcmc$maxuniq)) {maxuniq=BayesmConstant.DPmaxuniq} else {keep=Mcmc$maxuniq} if(is.null(Mcmc$gridsize)) {gridsize=BayesmConstant.DPgridsize} else {gridsize=Mcmc$gridsize} if(is.null(Mcmc$R)) {pandterm("Requires R argument in Mcmc list")} else {R=Mcmc$R} } # # print out problem # cat(" ",fill=TRUE) cat("Starting MCMC Inference for Hierarchical Logit:",fill=TRUE) cat(" Dirichlet Process Prior",fill=TRUE) cat(paste(" ",p," alternatives; ",nvar," variables in X"),fill=TRUE) cat(paste(" for ",nlgt," cross-sectional units"),fill=TRUE) cat(" ",fill=TRUE) cat(" Prior Parms: ",fill=TRUE) cat(" G0 ~ N(mubar,Sigma (x) Amu^-1)",fill=TRUE) cat(" mubar = ",0,fill=TRUE) cat(" Sigma ~ IW(nu,nu*v*I)",fill=TRUE) cat(" Amu ~ uniform[",lambda_hyper$alim[1],",",lambda_hyper$alim[2],"]",fill=TRUE) cat(" nu ~ uniform on log grid [",nvar-1+exp(lambda_hyper$nulim[1]), ",",nvar-1+exp(lambda_hyper$nulim[2]),"]",fill=TRUE) cat(" v ~ uniform[",lambda_hyper$vlim[1],",",lambda_hyper$vlim[2],"]",fill=TRUE) cat(" ",fill=TRUE) cat(" alpha ~ (1-(alpha-alphamin)/(alphamax-alphamin))^power",fill=TRUE) cat(" Istarmin = ",Prioralpha$Istarmin,fill=TRUE) cat(" Istarmax = ",Prioralpha$Istarmax,fill=TRUE) cat(" alphamin = ",Prioralpha$alphamin,fill=TRUE) cat(" alphamax = ",Prioralpha$alphamax,fill=TRUE) cat(" power = ",Prioralpha$power,fill=TRUE) cat(" ",fill=TRUE) if(drawdelta) { cat("deltabar",fill=TRUE) print(deltabar) cat("Ad",fill=TRUE) print(Ad) } cat(" ",fill=TRUE) cat("MCMC Parms: ",fill=TRUE) cat(paste("s=",round(s,3)," w= ",w," R= ",R," keep= ",keep," nprint= ",nprint," maxuniq= ",maxuniq, " gridsize for lambda hyperparms= ",gridsize),fill=TRUE) cat("",fill=TRUE) # # allocate space for draws # oldbetas=matrix(double(nlgt*nvar),ncol=nvar) # # intialize compute quantities for Metropolis # cat("initializing Metropolis candidate densities for ",nlgt," units ...",fill=TRUE) fsh() # # now go thru and computed fraction likelihood estimates and hessians # # Lbar=log(pooled likelihood^(n_i/N)) # # fraction loglike = (1-w)*loglike_i + w*Lbar # betainit=c(rep(0,nvar)) # # compute pooled optimum # out=optim(betainit,llmnl,method="BFGS",control=list( fnscale=-1,trace=0,reltol=1e-6), X=Xpooled,y=ypooled) betapooled=out$par H=mnlHess(betapooled,ypooled,Xpooled) rootH=chol(H) # # initialize betas for all units # for (i in 1:nlgt) { wgt=length(lgtdata[[i]]$y)/length(ypooled) out=optim(betapooled,llmnlFract,method="BFGS",control=list( fnscale=-1,trace=0,reltol=1e-4), X=lgtdata[[i]]$X,y=lgtdata[[i]]$y,betapooled=betapooled,rootH=rootH,w=w,wgt=wgt) if(out$convergence == 0) { hess=mnlHess(out$par,lgtdata[[i]]$y,lgtdata[[i]]$X) lgtdata[[i]]=c(lgtdata[[i]],list(converge=1,betafmle=out$par,hess=hess)) } else { lgtdata[[i]]=c(lgtdata[[i]],list(converge=0,betafmle=c(rep(0,nvar)), hess=diag(nvar))) } oldbetas[i,]=lgtdata[[i]]$betafmle if(i%%50 ==0) cat(" completed unit #",i,fill=TRUE) fsh() } #Initialize placeholders when drawdelta == FALSE if (drawdelta==FALSE){ Z = matrix(0) deltabar = 0 Ad = matrix(0) } ################################################################### # Wayne Taylor # 2/21/2015 ################################################################### out = rhierMnlDP_rcpp_loop(R,keep,nprint, lgtdata,Z,deltabar,Ad,Prioralpha,lambda_hyper, drawdelta,nvar,oldbetas,s,maxuniq,gridsize, BayesmConstant.A,BayesmConstant.nuInc,BayesmConstant.DPalpha) ################################################################### if(drawdelta){ attributes(out$Deltadraw)$class=c("bayesm.mat","mcmc") attributes(out$Deltadraw)$mcpar=c(1,R,keep)} attributes(out$betadraw)$class=c("bayesm.hcoef") attributes(out$nmix)$class="bayesm.nmix" attributes(out$adraw)$class=c("bayesm.mat","mcmc") attributes(out$nudraw)$class=c("bayesm.mat","mcmc") attributes(out$vdraw)$class=c("bayesm.mat","mcmc") attributes(out$Istardraw)$class=c("bayesm.mat","mcmc") attributes(out$alphadraw)$class=c("bayesm.mat","mcmc") return(out) }bayesm/R/rhierMnlRwMixture_rcpp.r0000644000176000001440000003136413114117322016643 0ustar ripleyusersrhierMnlRwMixture=function(Data,Prior,Mcmc){ # # revision history: # 12/04 changed by rossi to fix bug in drawdelta when there is zero/one unit in a mixture component # 09/05 added loglike output, changed to reflect new argument order in llmnl, mnlHess # 12/05 changed weighting scheme to (1-w)logl_i + w*Lbar (normalized) # 03/07 added classes # 09/08 changed Dirichlet a check # 04/15 by Wayne Taylor: added nprint option to MCMC argument # 07/16 by Wayne Taylor: added sign restrictions # 10/10 by Dan Yavorsky: changed default priors when sign restrictions imposed # # purpose: run hierarchical mnl logit model with mixture of normals # using RW and cov(RW inc) = (hess_i + Vbeta^-1)^-1 # uses normal approximation to pooled likelihood # # Arguments: # Data contains a list of (p,lgtdata, and possibly Z) # p is number of choice alternatives # lgtdata is a list of lists (one list per unit) # lgtdata[[i]]=list(y,X) # y is a vector indicating alternative chosen # integers 1:p indicate alternative # X is a length(y)*p x nvar matrix of values of # X vars including intercepts # Z is an length(lgtdata) x nz matrix of values of variables # note: Z should NOT contain an intercept # Prior contains a list of (deltabar,Ad,mubar,Amu,nu,V,ncomp,SignRes) # ncomp is the number of components in normal mixture # if elements of Prior (other than ncomp) do not exist, defaults are used # SignRes is a vector of sign restrictions # Mcmc contains a list of (s,c,R,keep,nprint) # # Output: as list containing # Deltadraw R/keep x nz*nvar matrix of draws of Delta, first row is initial value # betadraw is nlgt x nvar x R/keep array of draws of betas # probdraw is R/keep x ncomp matrix of draws of probs of mixture components # compdraw is a list of list of lists (length R/keep) # compdraw[[rep]] is the repth draw of components for mixtures # loglike log-likelikelhood at each kept draw # # Priors: # beta_i = D %*% z[i,] + u_i # u_i ~ N(mu_ind[i],Sigma_ind[i]) # ind[i] ~multinomial(p) # p ~ dirichlet (a) # D is a k x nz array # delta= vec(D) ~ N(deltabar,A_d^-1) # mu_j ~ N(mubar,A_mu^-1(x)Sigma_j) # Sigma_j ~ IW(nu,V^-1) # ncomp is number of components # # MCMC parameters # s is the scaling parameter for the RW inc covariance matrix; s^2 Var is inc cov # matrix # w is parameter for weighting function in fractional likelihood # w is the weight on the normalized pooled likelihood # R is number of draws # keep is thinning parameter, keep every keepth draw # nprint - print estimated time remaining on every nprint'th draw # # check arguments # if(missing(Data)) {pandterm("Requires Data argument -- list of p,lgtdata, and (possibly) Z")} if(is.null(Data$p)) {pandterm("Requires Data element p (# choice alternatives)") } p=Data$p if(is.null(Data$lgtdata)) {pandterm("Requires Data element lgtdata (list of data for each unit)")} lgtdata=Data$lgtdata nlgt=length(lgtdata) drawdelta=TRUE if(is.null(Data$Z)) { cat("Z not specified",fill=TRUE); fsh() ; drawdelta=FALSE} else {if (!is.matrix(Data$Z)) {pandterm("Z must be a matrix")} else {if (nrow(Data$Z) != nlgt) {pandterm(paste("Nrow(Z) ",nrow(Z),"ne number logits ",nlgt))} else {Z=Data$Z}}} if(drawdelta) { nz=ncol(Z) colmeans=apply(Z,2,mean) if(sum(colmeans) > .00001) {pandterm(paste("Z does not appear to be de-meaned: colmeans= ",colmeans))} } # # check lgtdata for validity # ypooled=NULL Xpooled=NULL if(!is.null(lgtdata[[1]]$X & is.matrix(lgtdata[[1]]$X))) {oldncol=ncol(lgtdata[[1]]$X)} for (i in 1:nlgt) { if(is.null(lgtdata[[i]]$y)) {pandterm(paste0("Requires element y of lgtdata[[",i,"]]"))} if(is.null(lgtdata[[i]]$X)) {pandterm(paste0("Requires element X of lgtdata[[",i,"]]"))} if(!is.matrix(lgtdata[[i]]$X)) {pandterm(paste0("lgtdata[[",i,"]]$X must be a matrix"))} if(!is.vector(lgtdata[[i]]$y, mode = "numeric") & !is.vector(lgtdata[[i]]$y, mode = "logical") & !is.matrix(lgtdata[[i]]$y)) {pandterm(paste0("lgtdata[[",i,"]]$y must be a numeric or logical vector or matrix"))} if(is.matrix(lgtdata[[i]]$y)) { if(ncol(lgtdata[[i]]$y)>1) { pandterm(paste0("lgtdata[[",i,"]]$y must be a vector or one-column matrix")) } } ypooled=c(ypooled,lgtdata[[i]]$y) nrowX=nrow(lgtdata[[i]]$X) if((nrowX/p) !=length(lgtdata[[i]]$y)) {pandterm(paste("nrow(X) ne p*length(yi); exception at unit",i))} newncol=ncol(lgtdata[[i]]$X) if(newncol != oldncol) {pandterm(paste("All X elements must have same # of cols; exception at unit",i))} Xpooled=rbind(Xpooled,lgtdata[[i]]$X) oldncol=newncol } nvar=ncol(Xpooled) levely=as.numeric(levels(as.factor(ypooled))) if(length(levely) != p) {pandterm(paste("y takes on ",length(levely)," values -- must be = p"))} bady=FALSE for (i in 1:p ) { if(levely[i] != i) bady=TRUE } cat("Table of Y values pooled over all units",fill=TRUE) print(table(ypooled)) if (bady) {pandterm("Invalid Y")} # # check on prior # if(missing(Prior)) {pandterm("Requires Prior list argument (at least ncomp)")} if(is.null(Prior$ncomp)) {pandterm("Requires Prior element ncomp (num of mixture components)")} else {ncomp=Prior$ncomp} if(is.null(Prior$SignRes)) {SignRes=rep(0,nvar)} else {SignRes=Prior$SignRes} if(length(SignRes) != nvar) {pandterm("The length SignRes must be equal to the dimension of X")} if(sum(!(SignRes %in% c(-1,0,1))>0)) {pandterm("All elements of SignRes must be equal to -1, 0, or 1")} if(is.null(Prior$mubar) & sum(abs(SignRes))==0) { mubar=matrix(rep(0,nvar),nrow=1) } else { if(is.null(Prior$mubar) & sum(abs(SignRes)) >0) { mubar=matrix(rep(0,nvar)+2*abs(SignRes),nrow=1) } else { mubar=matrix(Prior$mubar,nrow=1) } } if(ncol(mubar) != nvar) {pandterm(paste("mubar must have ncomp cols, ncol(mubar)= ",ncol(mubar)))} if(is.null(Prior$Amu) & sum(abs(SignRes))==0) { Amu=matrix(BayesmConstant.A,ncol=1) } else { if(is.null(Prior$Amu) & sum(abs(SignRes)) >0) { Amu=matrix(BayesmConstant.A*10,ncol=1) } else {Amu=matrix(Prior$Amu,ncol=1) } } if(ncol(Amu) != 1 | nrow(Amu) != 1) {pandterm("Am must be a 1 x 1 array")} if(is.null(Prior$nu) & sum(abs(SignRes))==0) { nu=nvar+BayesmConstant.nuInc } else { if(is.null(Prior$nu) & sum(abs(SignRes)) >0) { nu=nvar+BayesmConstant.nuInc+12 } else { nu=Prior$nu } } if(nu < 1) {pandterm("invalid nu value")} if(is.null(Prior$V) & sum(abs(SignRes))==0) { V=nu*diag(nvar) } else { if(is.null(Prior$V) & sum(abs(SignRes)) >0) { V=nu*diag(abs(SignRes)*0.1+(!abs(SignRes))*4) } else { V=Prior$V } } if(sum(dim(V)==c(nvar,nvar)) !=2) pandterm("Invalid V in prior") if(is.null(Prior$Ad) & drawdelta) {Ad=BayesmConstant.A*diag(nvar*nz)} else {Ad=Prior$Ad} if(drawdelta) {if(ncol(Ad) != nvar*nz | nrow(Ad) != nvar*nz) {pandterm("Ad must be nvar*nz x nvar*nz")}} if(is.null(Prior$deltabar)& drawdelta) {deltabar=rep(0,nz*nvar)} else {deltabar=Prior$deltabar} if(drawdelta) {if(length(deltabar) != nz*nvar) {pandterm("deltabar must be of length nvar*nz")}} if(is.null(Prior$a)) { a=rep(BayesmConstant.a,ncomp)} else {a=Prior$a} if(length(a) != ncomp) {pandterm("Requires dim(a)= ncomp (no of components)")} bada=FALSE for(i in 1:ncomp) { if(a[i] < 0) bada=TRUE} if(bada) pandterm("invalid values in a vector") if(is.null(Prior$nu)&sum(abs(SignRes))>0) {nu = nvar+15} if(is.null(Prior$Amu)&sum(abs(SignRes))>0) {Amu = matrix(.1)} if(is.null(Prior$V)&sum(abs(SignRes))>0) {V = nu*(diag(nvar)-diag(abs(SignRes)>0)*.8)} # # check on Mcmc # if(missing(Mcmc)) {pandterm("Requires Mcmc list argument")} else { if(is.null(Mcmc$s)) {s=BayesmConstant.RRScaling/sqrt(nvar)} else {s=Mcmc$s} if(is.null(Mcmc$w)) {w=BayesmConstant.w} else {w=Mcmc$w} if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$R)) {pandterm("Requires R argument in Mcmc list")} else {R=Mcmc$R} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} } # # print out problem # cat(" ",fill=TRUE) cat("Starting MCMC Inference for Hierarchical Logit:",fill=TRUE) cat(" Normal Mixture with",ncomp,"components for first stage prior",fill=TRUE) cat(paste(" ",p," alternatives; ",nvar," variables in X"),fill=TRUE) cat(paste(" for ",nlgt," cross-sectional units"),fill=TRUE) cat(" ",fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("nu =",nu,fill=TRUE) cat("V ",fill=TRUE) print(V) cat("mubar ",fill=TRUE) print(mubar) cat("Amu ", fill=TRUE) print(Amu) cat("a ",fill=TRUE) print(a) if(drawdelta) { cat("deltabar",fill=TRUE) print(deltabar) cat("Ad",fill=TRUE) print(Ad) } cat(" ",fill=TRUE) cat("MCMC Parms: ",fill=TRUE) cat(paste("s=",round(s,3)," w= ",w," R= ",R," keep= ",keep," nprint= ",nprint),fill=TRUE) cat("",fill=TRUE) oldbetas = matrix(double(nlgt * nvar), ncol = nvar) #-------------------------------------------------------------------------------------------------- # # create functions needed # llmnlFract= function(beta,y,X,betapooled,rootH,w,wgt,SignRes = rep(0,ncol(X))){ z=as.vector(rootH%*%(beta-betapooled)) return((1-w)*llmnl_con(beta,y,X,SignRes)+w*wgt*(-.5*(z%*%z))) } mnlHess_con=function (betastar, y, X, SignRes = rep(0,ncol(X))) { #Reparameterize beta to betastar to allow for sign restrictions beta = betastar beta[SignRes!=0] = SignRes[SignRes!=0]*exp(beta[SignRes!=0]) n = length(y) j = nrow(X)/n k = ncol(X) Xbeta = X %*% beta Xbeta = matrix(Xbeta, byrow = T, ncol = j) Xbeta = exp(Xbeta) iota = c(rep(1, j)) denom = Xbeta %*% iota Prob = Xbeta/as.vector(denom) Hess = matrix(double(k * k), ncol = k) for (i in 1:n) { p = as.vector(Prob[i, ]) A = diag(p) - outer(p, p) Xt = X[(j * (i - 1) + 1):(j * i), ] Hess = Hess + crossprod(Xt, A) %*% Xt } return(Hess) } #------------------------------------------------------------------------------------------------------- # # intialize compute quantities for Metropolis # cat("initializing Metropolis candidate densities for ",nlgt," units ...",fill=TRUE) fsh() # # now go thru and computed fraction likelihood estimates and hessians # # Lbar=log(pooled likelihood^(n_i/N)) # # fraction loglike = (1-w)*loglike_i + w*Lbar # betainit=c(rep(0,nvar)) # # compute pooled optimum # out=optim(betainit,llmnl_con,method="BFGS",control=list( fnscale=-1,trace=0,reltol=1e-6), X=Xpooled,y=ypooled,SignRes=SignRes) betapooled=out$par H=mnlHess_con(betapooled,ypooled,Xpooled,SignRes) rootH=chol(H) for (i in 1:nlgt) { wgt=length(lgtdata[[i]]$y)/length(ypooled) out=optim(betapooled,llmnlFract,method="BFGS",control=list( fnscale=-1,trace=0,reltol=1e-4), X=lgtdata[[i]]$X,y=lgtdata[[i]]$y,betapooled=betapooled,rootH=rootH,w=w,wgt=wgt,SignRes=SignRes) if(out$convergence == 0) { hess=mnlHess_con(out$par,lgtdata[[i]]$y,lgtdata[[i]]$X,SignRes) lgtdata[[i]]=c(lgtdata[[i]],list(converge=1,betafmle=out$par,hess=hess)) } else { lgtdata[[i]]=c(lgtdata[[i]],list(converge=0,betafmle=c(rep(0,nvar)), hess=diag(nvar))) } oldbetas[i,]=lgtdata[[i]]$betafmle if(i%%50 ==0) cat(" completed unit #",i,fill=TRUE) fsh() } # # initialize values # # set initial values for the indicators # ind is of length(nlgt) and indicates which mixture component this obs # belongs to. # ind=NULL ninc=floor(nlgt/ncomp) for (i in 1:(ncomp-1)) {ind=c(ind,rep(i,ninc))} if(ncomp != 1) {ind = c(ind,rep(ncomp,nlgt-length(ind)))} else {ind=rep(1,nlgt)} # # initialize probs # oldprob=rep(1/ncomp,ncomp) # #initialize delta # if (drawdelta){ olddelta = rep(0,nz*nvar) } else { #send placeholders to the _loop function if there is no Z matrix olddelta = 0 Z = matrix(0) deltabar = 0 Ad = matrix(0) } ################################################################### # Wayne Taylor # 09/22/2014 ################################################################### draws = rhierMnlRwMixture_rcpp_loop(lgtdata, Z, deltabar, Ad, mubar, Amu, nu, V, s, R, keep, nprint, drawdelta, as.matrix(olddelta), a, oldprob, oldbetas, ind, SignRes) #################################################################### if(drawdelta){ attributes(draws$Deltadraw)$class=c("bayesm.mat","mcmc") attributes(draws$Deltadraw)$mcpar=c(1,R,keep)} attributes(draws$betadraw)$class=c("bayesm.hcoef") attributes(draws$nmix)$class="bayesm.nmix" return(draws) }bayesm/R/clusterMix_rcpp.R0000644000176000001440000000447112566706215015311 0ustar ripleyusersclusterMix=function(zdraw,cutoff=.9,SILENT=FALSE,nprint=BayesmConstant.nprint){ # # # revision history: # written by p. rossi 9/05 # # purpose: cluster observations based on draws of indicators of # normal mixture components # # arguments: # zdraw is a R x nobs matrix of draws of indicators (typically output from rnmixGibbs) # the rth row of zdraw contains rth draw of indicators for each observations # each element of zdraw takes on up to p values for up to p groups. The maximum # number of groups is nobs. Typically, however, the number of groups will be small # and equal to the number of components used in the normal mixture fit. # # cutoff is a cutoff used in determining one clustering scheme it must be # a number between .5 and 1. # # nprint - print every nprint'th draw # # output: # two clustering schemes each with a vector of length nobs which gives the assignment # of each observation to a cluster # # clustera (finds zdraw with similarity matrix closest to posterior mean of similarity) # clusterb (finds clustering scheme by assigning ones if posterior mean of similarity matrix # > cutoff and computing associated z ) # # define needed functions # # ------------------------------------------------------------------------------------------ # # check arguments # if(missing(zdraw)) {pandterm("Requires zdraw argument -- R x n matrix of indicator draws")} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} # # check validity of zdraw rows -- must be integers in the range 1:nobs # nobs=ncol(zdraw) R=nrow(zdraw) if(sum(zdraw %in% (1:nobs)) < ncol(zdraw)*nrow(zdraw)) {pandterm("Bad zdraw argument -- all elements must be integers in 1:nobs")} cat("Table of zdraw values pooled over all rows",fill=TRUE) print(table(zdraw)) # # check validity of cuttoff if(cutoff > 1 || cutoff < .5) {pandterm(paste("cutoff invalid, = ",cutoff))} ################################################################### # Keunwoo Kim # 10/06/2014 ################################################################### out=clusterMix_rcpp_loop(zdraw, cutoff, SILENT, nprint) ################################################################### return(list(clustera=as.vector(out$clustera),clusterb=as.vector(out$clusterb))) }bayesm/R/mnlHess.R0000755000176000001440000000136010316323040013512 0ustar ripleyusersmnlHess = function(beta,y,X) { # p.rossi 2004 # changed argument order 9/05 # # Purpose: compute mnl -Expected[Hessian] # # Arguments: # beta is k vector of coefs # y is n vector with element = 1,...,j indicating which alt chosen # X is nj x k matrix of xvalues for each of j alt on each of n occasions # # Output: -Hess evaluated at beta # n=length(y) j=nrow(X)/n k=ncol(X) Xbeta=X%*%beta Xbeta=matrix(Xbeta,byrow=T,ncol=j) Xbeta=exp(Xbeta) iota=c(rep(1,j)) denom=Xbeta%*%iota Prob=Xbeta/as.vector(denom) Hess=matrix(double(k*k),ncol=k) for (i in 1:n) { p=as.vector(Prob[i,]) A=diag(p)-outer(p,p) Xt=X[(j*(i-1)+1):(j*i),] Hess=Hess+crossprod(Xt,A)%*%Xt } return(Hess) } bayesm/R/rivDP_rcpp.R0000644000176000001440000002305213114116632014156 0ustar ripleyusersrivDP = function(Data,Prior,Mcmc) { # # revision history: # P. Rossi 1/06 # added draw of alpha 2/06 # added automatic scaling 2/06 # removed reqfun 7/07 -- now functions are in rthetaDP # fixed initialization of theta 3/09 # fixed error in assigning user defined prior parms # W. Taylor 4/15 - added nprint option to MCMC argument # W. Taylor 7/16 - corrected bug for colAllw.set_size(ncolw) (see email to Peter and Keunwoo on 11/30/15 for more details) # # purpose: # draw from posterior for linear I.V. model with DP process for errors # # Arguments: # Data -- list of z,w,x,y # y is vector of obs on lhs var in structural equation # x is "endogenous" var in structural eqn # w is matrix of obs on "exogenous" vars in the structural eqn # z is matrix of obs on instruments # Prior -- list of md,Ad,mbg,Abg,mubar,Amu,nuV # md is prior mean of delta # Ad is prior prec # mbg is prior mean vector for beta,gamma # Abg is prior prec of same # lamda is a list of prior parms for DP draw # mubar is prior mean of means for "errors" # Amu is scale precision parm for means # nu,V parms for IW on Sigma (idential priors for each normal comp # alpha prior parm for DP process (weight on base measure) # or starting value if there is a prior on alpha (requires element Prioralpha) # Prioralpha list of hyperparms for draw of alpha (alphamin,alphamax,power,n) # # Mcmc -- list of R,keep,starting values for delta,beta,gamma,theta # maxuniq is maximum number of unique theta values # R is number of draws # keep is thinning parameter # nprint - print estimated time remaining on every nprint'th draw # SCALE if scale data, def: TRUE # gridsize is the gridsize parm for alpha draws # # Output: # list of draws of delta,beta,gamma and thetaNp1 which is used for # predictive distribution of errors (density estimation) # # Model: # # x=z'delta + e1 # y=beta*x + w'gamma + e2 # e1,e2 ~ N(theta_i) # # Priors # delta ~ N(md,Ad^-1) # vec(beta,gamma) ~ N(mbg,Abg^-1) # theta ~ DPP(alpha|lambda) # # # extract data and check dimensios # if(missing(Data)) {pandterm("Requires Data argument -- list of z,w,x,y")} if(is.null(Data$w)) isgamma=FALSE else isgamma=TRUE if(isgamma) w = Data$w #matrix if(is.null(Data$z)) {pandterm("Requires Data element z")} z=Data$z if(is.null(Data$x)) {pandterm("Requires Data element x")} x=as.vector(Data$x) if(is.null(Data$y)) {pandterm("Requires Data element y")} y=as.vector(Data$y) # # check data for validity # n=length(y) if(isgamma) {if(!is.matrix(w)) {pandterm("w is not a matrix")} dimg=ncol(w) if(n != nrow(w) ) {pandterm("length(y) ne nrow(w)")}} if(!is.matrix(z)) {pandterm("z is not a matrix")} dimd=ncol(z) if(n != length(x) ) {pandterm("length(y) ne length(x)")} if(n != nrow(z) ) {pandterm("length(y) ne nrow(z)")} # # extract elements corresponding to the prior # alimdef=BayesmConstant.DPalimdef nulimdef=BayesmConstant.DPnulimdef vlimdef=BayesmConstant.DPvlimdef if(missing(Prior)) { md=c(rep(0,dimd)) Ad=diag(BayesmConstant.A,dimd) if(isgamma) dimbg=1+dimg else dimbg=1 mbg=c(rep(0,dimbg)) Abg=diag(BayesmConstant.A,dimbg) gamma= BayesmConstant.gamma Istarmin=BayesmConstant.DPIstarmin alphamin=exp(digamma(Istarmin)-log(gamma+log(n))) Istarmax=floor(.1*n) alphamax=exp(digamma(Istarmax)-log(gamma+log(n))) power=BayesmConstant.DPpower Prioralpha=list(n=n,alphamin=alphamin,alphamax=alphamax,power=power) lambda_hyper=list(alim=alimdef,nulim=nulimdef,vlim=vlimdef) } else { if(is.null(Prior$md)) md=c(rep(0,dimd)) else md=Prior$md if(is.null(Prior$Ad)) Ad=diag(BayesmConstant.A,dimd) else Ad=Prior$Ad if(isgamma) dimbg=1+dimg else dimbg=1 if(is.null(Prior$mbg)) mbg=c(rep(0,dimbg)) else mbg=Prior$mbg if(is.null(Prior$Abg)) Abg=diag(BayesmConstant.A,dimbg) else Abg=Prior$Abg if(!is.null(Prior$Prioralpha)) {Prioralpha=Prior$Prioralpha} else {gamma= BayesmConstant.gamma Istarmin=BayesmConstant.DPIstarmin alphamin=exp(digamma(Istarmin)-log(gamma+log(n))) Istarmax=floor(.1*n) alphamax=exp(digamma(Istarmax)-log(gamma+log(n))) power=BayesmConstant.DPpower Prioralpha=list(n=n,alphamin=alphamin,alphamax=alphamax,power=power)} if(is.null(Prior$lambda_hyper)) {lambda_hyper=Prior$lambda_hyper} else {lambda_hyper=Prior$lambda_hyper; if(is.null(lambda_hyper$alim)) {lambda_hyper$alim=alimdef} if(is.null(lambda_hyper$nulim)) {lambda_hyper$nulim=nulimdef} if(is.null(lambda_hyper$vlim)) {lambda_hyper$vlim=vlimdef} } } # # check Prior arguments for valdity # if(lambda_hyper$alim[1]<0) {pandterm("alim[1] must be >0")} if(lambda_hyper$nulim[1]<0) {pandterm("nulim[1] must be >0")} if(lambda_hyper$vlim[1]<0) {pandterm("vlim[1] must be >0")} # # obtain starting values for MCMC # # we draw need inital values of delta, theta and indic # if(missing(Mcmc)) {pandterm("requires Mcmc argument")} theta=NULL if(!is.null(Mcmc$delta)) {delta = Mcmc$delta} else {lmxz = lm(x~z,data.frame(x=x,z=z)) delta = lmxz$coef[2:(ncol(z)+1)]} if(!is.null(Mcmc$theta)) {theta=Mcmc$theta } else {onecomp=list(mu=c(0,0),rooti=diag(2)) theta=vector("list",length(y)) for(i in 1:n) {theta[[i]]=onecomp} } dimd = length(delta) if(is.null(Mcmc$maxuniq)) {maxuniq=BayesmConstant.DPmaxuniq} else {maxuniq=Mcmc$maxuniq} if(is.null(Mcmc$R)) {pandterm("requres Mcmc argument, R")} R = Mcmc$R if(is.null(Mcmc$keep)) {keep=BayesmConstant.keep} else {keep=Mcmc$keep} if(is.null(Mcmc$nprint)) {nprint=BayesmConstant.nprint} else {nprint=Mcmc$nprint} if(nprint<0) {pandterm('nprint must be an integer greater than or equal to 0')} if(is.null(Mcmc$gridsize)) {gridsize=BayesmConstant.DPgridsize} else {gridsize=Mcmc$gridsize} if(is.null(Mcmc$SCALE)) {SCALE=BayesmConstant.DPSCALE} else {SCALE=Mcmc$SCALE} # # scale and center # if(SCALE){ scaley=sqrt(var(y)) scalex=sqrt(var(x)) meany=mean(y) meanx=mean(x) meanz=apply(z,2,mean) y=(y-meany)/scaley; x=(x-meanx)/scalex z=scale(z,center=TRUE,scale=FALSE) if(isgamma) {meanw=apply(w,2,mean); w=scale(w,center=TRUE,scale=FALSE)} } # # print out model # cat(" ",fill=TRUE) cat("Starting Gibbs Sampler for Linear IV Model With DP Process Errors",fill=TRUE) cat(" ",fill=TRUE) cat(" nobs= ",n,"; ",ncol(z)," instruments",fill=TRUE) cat(" ",fill=TRUE) cat("Prior Parms: ",fill=TRUE) cat("mean of delta ",fill=TRUE) print(md) cat(" ",fill=TRUE) cat("Adelta",fill=TRUE) print(Ad) cat(" ",fill=TRUE) cat("mean of beta/gamma",fill=TRUE) print(mbg) cat(" ",fill=TRUE) cat("Abeta/gamma",fill=TRUE) print(Abg) cat(" ",fill=TRUE) cat("G0 ~ N(mubar,Sigma (x) Amu^-1)",fill=TRUE) cat(" mubar = ",0,fill=TRUE) cat(" Sigma ~ IW(nu,nu*v*I)",fill=TRUE) cat(" Amu ~ uniform[",lambda_hyper$alim[1],",",lambda_hyper$alim[2],"]",fill=TRUE) cat(" nu ~ uniform on log grid [",2-1+exp(lambda_hyper$nulim[1]), ",",2-1+exp(lambda_hyper$nulim[2]),"]",fill=TRUE) cat(" v ~ uniform[",lambda_hyper$vlim[1],",",lambda_hyper$vlim[2],"]",fill=TRUE) cat(" ",fill=TRUE) cat("Parameters of Prior on Dirichlet Process parm (alpha)",fill=TRUE) cat("alphamin= ",Prioralpha$alphamin," alphamax= ",Prioralpha$alphamax," power=", Prioralpha$power,fill=TRUE) cat("alpha values correspond to Istarmin = ",Istarmin," Istarmax = ",Istarmax,fill=TRUE) cat(" ",fill=TRUE) cat("MCMC parms: R= ",R," keep= ",keep," nprint= ",nprint,fill=TRUE) cat(" maximum number of unique thetas= ",maxuniq,fill=TRUE) cat(" gridsize for alpha draws= ",gridsize,fill=TRUE) cat(" SCALE data= ",SCALE,fill=TRUE) cat(" ",fill=TRUE) ################################################################### # Wayne Taylor # 3/14/2015 ################################################################### if(isgamma == FALSE) w=matrix() out = rivDP_rcpp_loop(R,keep,nprint,dimd,mbg,Abg,md,Ad,y,isgamma,z,x,w, delta,PrioralphaList=Prioralpha,gridsize,SCALE,maxuniq,scalex,scaley,lambda_hyper, BayesmConstant.A,BayesmConstant.nu) ################################################################### nmix=list(probdraw=matrix(c(rep(1,length(out$thetaNp1draw))),ncol=1),zdraw=NULL,compdraw=out$thetaNp1draw) # # densitymix is in the format to be used with the generic mixture of normals plotting # methods (plot.bayesm.nmix) # attributes(nmix)$class=c("bayesm.nmix") attributes(out$deltadraw)$class=c("bayesm.mat","mcmc") attributes(out$deltadraw)$mcpar=c(1,R,keep) attributes(out$betadraw)$class=c("bayesm.mat","mcmc") attributes(out$betadraw)$mcpar=c(1,R,keep) attributes(out$alphadraw)$class=c("bayesm.mat","mcmc") attributes(out$alphadraw)$mcpar=c(1,R,keep) attributes(out$Istardraw)$class=c("bayesm.mat","mcmc") attributes(out$Istardraw)$mcpar=c(1,R,keep) if(isgamma){ attributes(out$gammadraw)$class=c("bayesm.mat","mcmc") attributes(out$gammadraw)$mcpar=c(1,R,keep)} if(isgamma) { return(list(deltadraw=out$deltadraw,betadraw=out$betadraw,alphadraw=out$alphadraw,Istardraw=out$Istardraw, gammadraw=out$gammadraw,nmix=nmix))} else { return(list(deltadraw=out$deltadraw,betadraw=out$betadraw,alphadraw=out$alphadraw,Istardraw=out$Istardraw, nmix=nmix))} }bayesm/vignettes/0000755000176000001440000000000013123305446013573 5ustar ripleyusersbayesm/vignettes/Constrained_MNL_Vignette.Rmd0000644000176000001440000003260313117570164021073 0ustar ripleyusers--- title: "Hierarchical Multinomial Logit with Sign Constraints" output: rmarkdown::html_document: theme: spacelab highlight: pygments toc: true toc_float: true toc_depth: 3 number_sections: no fig_caption: yes vignette: > %\VignetteIndexEntry{Hierarchical Multinomial Logit with Sign Constraints} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, echo = FALSE} library(bayesm) knitr::opts_chunk$set(fig.align = "center", fig.height = 3.5, warning = FALSE, error = FALSE, message = FALSE) ``` ***** # Introduction `bayesm`'s posterior sampling function `rhierMnlRwMixture` permits the imposition of sign constraints on the individual-specific parameters of a hierarchical multinomial logit model. This may be desired if, for example, the researcher believes there are heterogenous effects from, say, price, but that all responses should be negative (i.e., sign-constrained). This vignette provides exposition of the model, discussion of prior specification, and an example. # Model The model follows the hierarchical multinomial logit specification given in Example 3 of the "`bayesm` Overview" Vignette, but will be repeated here succinctly. Individuals are assumed to be rational economic agents that make utility-maximizing choices. Utility is modeled as the sum of deterministic and stochastic components, where the inverse-logit of the probability of chosing an alternative is linear in the parameters and the error is assumed to follow a Type I Extreme Value distribution: $$ U_{ij} = X_{ij}\beta_i + \varepsilon_{ij} \hspace{0.8em} \text{with} \hspace{0.8em} \varepsilon_{ij}\ \sim \text{ iid Type I EV} $$ These assumptions yield choice probabilities of: $$ \text{Pr}(y_i=j) = \frac{\exp \{x_{ij}'\beta_i\}}{\sum_{k=1}^p\exp\{x_{ik}'\beta_i\}} $$ $x_i$ is $n_i \times k$ and $i = 1, \ldots, N$. There are $p$ alternatives, $j = 1, \ldots, p$. An outside option, often denoted $j=0$ can be introduced by assigning $0$'s to that option's covariate ($x$) values. We impose sign constraints by defining a $k$-length constraint vector `SignRes` that takes values from the set $\{-1, 0, 1\}$ to define $\beta_{ik} = f(\beta_{ik}^*)$ where $f(\cdot)$ is as follows: $$ \beta_{ik} = f(\beta_{ik}^*) = \left\{ \begin{array}{lcl} \exp(\beta_{ik}^*) & \text{if} & \texttt{SignRes[k]} = 1 \\ \beta_{ik}^* & \text{if} & \texttt{SignRes[k]} = 0 \\ -\exp(\beta_{ik}^*) & \text{if} & \texttt{SignRes[k]} = -1 \\ \end{array} \right. $$ The "deep" individual-specific parameters ($\beta_i^*$) are assumed to be drawn from a mixture of $M$ normal distributions with mean values driven by cross-sectional unit characteristics $Z$. That is, $\beta_i^* = z_i' \Delta + u_i$ where $u_i$ has a mixture-of-normals distribution.^[As documented in the helpfile for this function (accessible by `?bayesm::rhierMnlRwMixture`), draws from the posterior of the constrained parameters ($\beta$) can be found in the output `$betadraw` while draws from the posterior of the unconstrained parameters ($\beta^*$) are available in `$nmix$compdraw`.] Considering $\beta_i^*$ a length-$k$ row vector, we will stack the $N$ $\beta_i^*$'s vertically and write: $$ B=Z\Delta + U $$ Thus we have $\beta_i$, $z_i$, and $u_i$ as the $i^\text{th}$ rows of $B$, $Z$, and $U$. $B$ is $N \times k$, $Z$ is $N \times M$, $\Delta$ is $M \times k$, and $U$ is $N \times k$ where the distribution on $U$ is such that: $$ \Pr(\beta_{ik}^*) = \sum_{m=1}^M \pi_m \phi(z_i' \Delta \vert \mu_j, \Sigma_j) $$ $\phi$ is the normal pdf. # Priors Natural conjugate priors are specified: $$ \pi \sim \text{Dirichlet}(a) $$ $$ \text{vec}(\Delta) = \delta \sim MVN(\bar{\delta}, A_\delta^{-1}) $$ $$ \mu_m \sim MVN(\bar{\mu}, \Sigma_m \otimes a_\mu^{-1}) $$ $$ \Sigma_m \sim IW(\nu, V) $$ This specification of priors assumes that $\mu_m$ is independent of $\Sigma_m$ and that, conditional on the hyperparameters, the $\beta_i$'s are _a priori_ independent. $a$ implements prior beliefs on the number of normal components in the mixture with a default of 5. $\nu$ is a "tightness" parameter of the inverted-Wishart distribution and $V$ is its location matrix. Without sign constraints, they default to $\nu=k+3$ and $V=\nu I$, which has the effect of centering the prior on $I$ and making it "barely proper". $a_\mu$ is a tightness parameter for the priors on $\mu$, and when no sign constraints are imposed it defaults to an extremely diffuse prior of 0.01. These defaults assume the logit coefficients ($\beta_{ik}$'s) are on the order of approximately 1 and, if so, are typically reasonable hyperprior values. However, when sign constraints are imposed, say, `SignRes[k]=-1` such that $\beta_{ik} = -\exp\{\beta_{ik}^*\}$, then these hyperprior defults pile up mass near zero --- a result that follows from the nature of the exponential function and the fact that the $\beta_{ik}^*$'s are on the log scale. Let's show this graphically. ```{r} # define function drawprior <- function (mubar_betak, nvar, ncomp, a, nu, Amu, V, ndraw) { betakstar <- double(ndraw) betak <- double(ndraw) otherbeta <- double(ndraw) mubar <- c(rep(0, nvar-1), mubar_betak) for(i in 1:ndraw) { comps=list() for(k in 1:ncomp) { Sigma <- rwishart(nu,chol2inv(chol(V)))$IW comps[[k]] <- list(mubar + t(chol(Sigma/Amu)) %*% rnorm(nvar), backsolve(chol(Sigma), diag(1,nvar)) ) } pvec <- rdirichlet(a) beta <- rmixture(1,pvec,comps)$x betakstar[i] <- beta[nvar] betak[i] <- -exp(beta[nvar]) otherbeta[i] <- beta[1] } return(list(betakstar=betakstar, betak=betak, otherbeta=otherbeta)) } set.seed(1234) ``` ```{r} # specify rhierMnlRwMixture defaults mubar_betak <- 0 nvar <- 10 ncomp <- 3 a <- rep(5, ncomp) nu <- nvar + 3 Amu <- 0.01 V <- nu*diag(c(rep(1,nvar-1),1)) ndraw <- 10000 defaultprior <- drawprior(mubar_betak, nvar, ncomp, a, nu, Amu, V, ndraw) ``` ```{r, fig.align='center', fig.height=3.5, results='hold'} # plot priors under defaults par(mfrow=c(1,3)) trimhist <- -20 hist(defaultprior$betakstar, breaks=40, col="magenta", main="Beta_k_star", xlab="", ylab="", yaxt="n") hist(defaultprior$betak[defaultprior$betak>trimhist], breaks=40, col="magenta", main="Beta_k", xlab="", ylab="", yaxt="n", xlim=c(trimhist,0)) hist(defaultprior$otherbeta, breaks=40, col="magenta", main="Other Beta", xlab="", ylab="", yaxt="n") ``` We see that the hyperprior values for constrained logit parameters are far from uninformative. As a result, `rhierMnlRwMixture` implements different default priors for parameters when sign constraints are imposed. In particular, $a_\mu=0.1$, $\nu = k + 15$, and $V = \nu*\text{diag}(d)$ where $d_i=4$ if $\beta_{ik}$ is unconstrained and $d_i=0.1$ if $\beta_{ik}$ is constrained. Additionally, $\bar{\mu}_m = 0$ if unconstrained and $\bar{\mu}_m = 2$ otherwise. As the following plots show, this yields substantially less informative hyperpriors on $\beta_{ik}^*$ without significantly affecting the hyperpriors on $\beta_{ik}$ or $\beta_{ij}$ ($j \ne k$). ```{r} # adjust priors for constraints mubar_betak <- 2 nvar <- 10 ncomp <- 3 a <- rep(5, ncomp) nu <- nvar + 15 Amu <- 0.1 V <- nu*diag(c(rep(4,nvar-1),0.1)) ndraw <- 10000 tightprior <- drawprior(mubar_betak, nvar, ncomp, a, nu, Amu, V, ndraw) ``` ```{r, fig.align='center', fig.height=3.5, results='hold'} # plot priors under adjusted values par(mfrow=c(1,3)) trimhist <- -20 hist(tightprior$betakstar, breaks=40, col="magenta", main="Beta_k_star", xlab="", ylab="", yaxt="n") hist(tightprior$betak[tightprior$betak>trimhist], breaks=40, col="magenta", main="Beta_k", xlab="", ylab="", yaxt="n", xlim=c(trimhist,0)) hist(tightprior$otherbeta, breaks=40, col="magenta", main="Other Beta", xlab="", ylab="", yaxt="n") ``` # Example Here we demonstrate the implementation of the hierarchical multinomial logit model with sign-constrained parameters. We return to the `camera` data used in Example 3 of the "`bayesm` Overview" Vignette. This dataset contains conjoint choice data for 332 respondents who evaluated digital cameras. The data are stored in a lists-of-lists format with one list per respondent, and each respondent's list having two elements: a vector of choices (`y`) and a matrix of covariates (`X`). Notice the dimensions: there is one value for each choice occasion in each individual's `y` vector but one row per alternative in each individual's `X` matrix, making `nrow(x)` = 5 $\times$ `length(y)` because there are 5 alternatives per choice occasion. ```{r} library(bayesm) data(camera) length(camera) str(camera[[1]]) ``` As shown next, the first 4 covariates are binary indicators for the brands Canon, Sony, Nikon, and Panasonic. These correspond to choice (`y`) values of 1, 2, 3, and 4. `y` can also take the value 5, indicating that the respondent chose "none". The data include binary indicators for two levels of pixel count, zoom strength, swivel video display capability, and wifi connectivity. The last covaritate is price, recorded in hundreds of U.S. dollars so that the magnitude of the expected price coefficient is such that the default prior settings in `rhierMnlRwMixture` do not need to be adjusted. ```{r} str(camera[[1]]$y) str(as.data.frame(camera[[1]]$X)) ``` Let's say we would like to estimate the part-worths of the various attributes of these digital cameras using a multinomial logit model. To incorporate individual-level heterogeneous effects, we elect to use a hierarchical (i.e., random coefficient) specification. Further, we believe that despite the heterogeneity, each consumer's estimate price response ($\beta_{i,\text{price}}$) should be negative, which we will impose with a sign constraint. Following the above discussion, we use the default priors, which "adjust" automatically when sign constraints are imposed. ```{r, echo = FALSE} nvar <- 10 mubar <- c(rep(0,nvar-1),2) Amu <- 0.1 ncomp <- 5 a <- rep(5, ncomp) nu <- 25 V <- nu*diag(c(rep(4,nvar-1),0.1)) ``` ```{r, eval = FALSE} SignRes <- c(rep(0,nvar-1),-1) data <- list(lgtdata=camera, p=5) prior <- list(mubar=mubar, Amu=Amu, ncomp=ncomp, a=a, nu=nu, V=V, SignRes=SignRes) mcmc <- list(R=1e4, nprint=0) out <- rhierMnlRwMixture(Data=data, Prior=prior, Mcmc=mcmc) ``` ```{r, echo = FALSE} temp <- capture.output( {SignRes <- c(rep(0,nvar-1),-1); data <- list(lgtdata = camera, p = 5); prior <- list(mubar=mubar, Amu=Amu, ncomp=ncomp, a=a, nu=nu, V=V, SignRes=SignRes); mcmc <- list(R = 1e4, nprint = 0); out <- rhierMnlRwMixture(Data = data, Prior = prior, Mcmc = mcmc)}, file = NULL) ``` While much can be done to analyze the output, we will focus here on the constrained parameters on price. We first plot the posterior distributions for the price parameter for individuals $i=1,2,3$. Notice that the posterior distributions for the selected individual's price parameters lie entirely below zero. \begin{center} \textbf{Posterior Distributions for beta\_price} \end{center} ```{r} par(mfrow=c(1,3)) ind_hist <- function(mod, i) { hist(mod$betadraw[i , 10, ], breaks = seq(-14,0,0.5), col = "dodgerblue3", border = "grey", yaxt = "n", xlim = c(-14,0), xlab = "", ylab = "", main = paste("Ind.",i)) } ind_hist(out,1) ind_hist(out,2) ind_hist(out,3) ``` Next we plot a histogram of the posterior means for the 332 individual price paramters ($\beta_{i,\text{price}}$): ```{r} par(mfrow=c(1,1)) hist(apply(out$betadraw[ , 10, ], 1, mean), xlim = c(-20,0), breaks = 20, col = "firebrick2", border = "gray", xlab = "", ylab = "", main = "Posterior Means for Ind. Price Params, With Sign Constraint") ``` As a point of comparison, we re-run the model without the sign constraint using the default priors (output omitted) and provide the same set of plots. Note now that the right tail of the posterior distribution of $\beta_2^\text{price}$ extends to the right of zero. ```{r} data0 <- list(lgtdata = camera, p = 5) prior0 <- list(ncomp = 5) mcmc0 <- list(R = 1e4, nprint = 0) ``` ```{r, eval = FALSE} out0 <- rhierMnlRwMixture(Data = data0, Prior = prior0, Mcmc = mcmc0) ``` ```{r, echo = FALSE} temp <- capture.output( {out0 <- rhierMnlRwMixture(Data = data0, Prior = prior0, Mcmc = mcmc0)}, file = NULL) ``` ```{r} par(mfrow=c(1,3)) ind_hist <- function(mod, i) { hist(mod$betadraw[i , 10, ], breaks = seq(-12,2,0.5), col = "dodgerblue4", border = "grey", yaxt = "n", xlim = c(-12,2), xlab = "", ylab = "", main = paste("Ind.",i)) } ind_hist(out0,1) ind_hist(out0,2) ind_hist(out0,3) ``` ```{r} par(mfrow=c(1,1)) hist(apply(out0$betadraw[ , 10, ], 1, mean), xlim = c(-15,5), breaks = 20, col = "firebrick3", border = "gray", xlab = "", ylab = "", main = "Posterior Means for Ind. Price Params, No Sign Constraint") ``` ***** _Authored by [Dan Yavorsky](dyavorsky@gmail.com) and [Peter Rossi](perossichi@gmail.com). Last updated June 2017._ bayesm/vignettes/bayesm_Overview_Vignette.Rmd0000644000176000001440000015530213117606056021264 0ustar ripleyusers--- title: "bayesm Overview" output: rmarkdown::html_document : theme: spacelab highlight: pygments toc: true toc_float: true toc_depth: 3 number_sections: yes fig_caption: yes vignette: > %\VignetteIndexEntry{bayesm Overview} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, echo = FALSE} library(bayesm) knitr::opts_chunk$set(fig.align = "center", fig.height = 3.5, warning = FALSE, error = FALSE, message = FALSE) ``` ***** # Introduction `bayesm` is an R package that facilitates statistical analysis using Bayesian methods. The package provides a set of functions for commonly used models in applied microeconomics and quantitative marketing. The goal of this vignette is to make it easier for users to adopt `bayesm` by providing a comprehensive overview of the package's contents and detailed examples of certain functions. We begin with the overview, followed by a discussion of how to work with `bayesm`. The discussion covers the structure of function arguments, the required input data formats, and the various output formats. The final section provides detailed examples to demonstrate Bayesian inference with the linear normal, multinomial logit, and hierarchical multinomial logit regression models. # Package Contents For ease of exposition, we have grouped the package contents into: - Posterior sampling functions - Utility functions - S3 methods^[For example, calling the generic function `summary(x)` on an object `x` with class `bayesm.mat` actually dispatches the method `summary.bayesm.mat` on `x`, which is equivalent to calling `summary.bayes.mat(x)`. For a nice introduction, see _Advanced R_ by Hadley Wickham, available [online](http://adv-r.had.co.nz/OO-essentials.html).] - Datasets Because the first two groups contain many functions, we organize them into subgroups by purpose. Below, we display each group of functions in a table with one column per subgroup. ----------------------------------------------------------------------------- Linear Models Limited Dependent Hierarchical Models Density Estimation \ Variable Models \ \ --------------- -------------------- -------------------- ------------------- `runireg`* `rbprobitGibbs`** `rhierLinearModel` `rnmixGibbs`* `runiregGibbs` `rmnpGibbs` `rhierLinearMixture` `rDPGibbs` `rsurGibbs`* `rmvpGibbs` `rhierMnlRwMixture`* \ `rivGibbs` `rmnlIndepMetrop` `rhierMnlDP` \ `rivDP` `rscaleUsage` `rbayesBLP` \ \ `rnegbinRw` `rhierNegbinRw` \ \ `rordprobitGibbs` ----------------------------------------------------------------------------- Table: Posterior Sampling Functions *`bayesm` offers the utility function `breg` with a related but limited set of capabilities as `runireg` --- similarly with `rmultireg` for `rsurGibbs`, `rmixGibbs` for `rnmixGibbs`, and `rhierBinLogit` for `rhierMnlRwMixture`. **`rbiNormGibbs` provides a tutorial-like example of Gibbs Sampling from a bivariate normal distribution. ------------------------------------------------------------------------------------ Log-Likelihood\ \ Log Density Random Draws Mixture-of-Normals Miscellaneous (data vector) (univariate) \ \ \ ------------------ --------------- ------------- ------------------- --------------- `llmnl` `lndIChisq` `rdirichlet` `clusterMix` `cgetC` `llmnp` `lndIWishart` `rmixture` `eMixMargDen` `condMom` `llnhlogit` `lndMvn` `rmvst` `mixDen` `createX` \ `lndMvst` `rtrun` `mixDenBi` `ghkvec` \ \ `rwishart` `momMix` `logMargDenNR` \ \ \ \ `mnlHess` \ \ \ \ `mnpProb` \ \ \ \ `nmat` \ \ \ \ `numEff` \ \ \ \ `simnhlogit` ------------------------------------------------------------------------------------ Table: Utility Functions ------------------------------------------- Plot Methods Summary Methods -------------------- ---------------------- `plot.bayesm.mat` `summary.bayesm.mat` `plot.bayesm.nmix` `summary.bayesm.nmix` `plot.bayesm.hcoef` `summary.bayesm.var` ------------------------------------------- Table: S3 Methods --------------------------------------- \ \ \ --------- -------------- -------------- `bank` `customerSat` `orangeJuice` `camera` `detailing` `Scotch` `cheese` `margarine` `tuna` --------------------------------------- Table: Datasets Some discussion of the naming conventions may be warranted. All functions use CamelCase but begin lowercase. Posterior sampling functions begin with `r` to match R's style of naming random number generation functions since these functions all draw from (posterior) distributions. Common abbreviations include _DP_ for Dirichlet Process, _IV_ for instrumental variables, _MNL_ and _MNP_ for multinomial logit and probit, _SUR_ for seemingly unrelated regression, and _hier_ for hierarchical. Utility functions that begin with `ll` calculate the log-likelihood of a data vector, while those that begin with `lnd` provide the log-density. Other abbreviations should be straighforward; please see the help file for a specific function if its name is unclear. # Working with `bayesm` We expect most users of the package to interact primarily with the posterior sampling functions. These functions take a consistent set of arguments as inputs (`Data`, `Prior`, and `Mcmc` --- each is a list) and they return output in a consistent format (always a list). `summary` and `plot` generic functions can then be used to facilitate analysis of the output because of the classes and methods defined in `bayesm`. The following subsections describe the format of the standardized function arguments as well as the required format of the data inputs and the format of the output from `bayesm`'s posterior sampling functions. ## Input: Function Arguments The posterior sampling functions take three arguments: `Data`, `Prior`, and `Mcmc`. Each argument is a **list**. As a minimal example, assume you'd like to perform a linear regression and that you have in your work space `y` (a vector of length $n$) and `X` (a matrix of dimension $n \times p$). For this example, we utilize the default values for `Prior` and so we do not specify the `Prior` argument. These components (`Data`, `Prior`, and `Mcmc` as well as their arguments including `R` and `nprint`) are discussed in the subsections that follow. Then the `bayesm` syntax is simply: ```{r, eval = FALSE} mydata <- list(y = y, X = X) mymcmc <- list(R = 1e6, nprint = 0) out <- runireg(Data = mydata, Mcmc = mymcmc) ``` The list elements of `Data`, `Prior`, and `Mcmc` must be named. For example, you could not use the following code because the `Data` argument `mydata2` has unnamed elements. ```{r, eval=FALSE} mydata2 <- list(y, X) out <- runireg(Data = mydata2, Mcmc = mymcmc) ``` ### `Data` Argument `bayesm`'s posterior sampling functions do not accept data stored in dataframes; data must be stored as vectors or matrices. For **non-hierarchical** models, the list of input data simply contains the approprate data vectors or matrices from the set \{`y`, `X`, `w`, `z`\}^[Functions such as `rivGibbs` only permit one endogenous variable and so the function argument is lowercase: `x`.] and possibly a scalar (length one vector) from the set \{`k`, `p`\}. - For functions that require only a single data argument (such as the two density estimation functions, `rnmixGibbs` and `rDPGibbs`), `y` is that argument. More typically, `y` is used for LHS^[LHS and RHS stand for left-hand side and right-hand side.] variable(s) and `X` for RHS variables. For estimation using instrumental variables, variables in the structural equation are separated into "exogenous" variables `w` and an "edogenous" variable `x`; `z` is a matrix of instruments. - For the scalars, `p` indicates the number of choice alternatives in discrete choice models and `k` is used as the maximum ordinate in models for ordinal data (e.g., `rordprobitGibbs`). For **hierarchical** models, the input data has up to 3 components. We'll discuss these components using the mixed logit model (`rhierMnlRwMixture`) as an example. For `rhierMnlRwMixture`, the `Data` argument is `list(lgtdata, Z, p)`. 1. The first component for all hierarchical models is required. It is a list of lists named either `regdata` or `lgtdata`, depending on the function. In `rhierMnlRwMixture`, `lgtdata` is a list of lists, with each interior list containing a vector or one-column matrix of multinomial outcomes `y` and a design matrix of covariates `X`. In Example 3 below, we show how to convert data from a data frame to this required list-of-lists format. 1. The second component, `Z`, is present but optional for all hierarchical models. `Z` is a matrix of cross-sectional unit characteristics that drive the mean responses; that is, a matrix of covariates for the individual parameters (e.g. $\beta_i$'s). For example, the model (omitting the priors) for `rhierMnlRwMixture` is: $$ y_i \sim \text{MNL}(X_i'\beta_i) \hspace{1em} \text{with} \hspace{1em} \beta_i = Z \Delta_i + u_i \hspace{1em} \text{and} \hspace{1em} u_i \sim N(\mu_j, \Sigma_j) $$ where $i$ indexes individuals and $j$ indexes cross-sectional unit characteristics. 1. The third component (if accepted) is a scalar, such as `p` or `k`, and like the arguments by the same names in the non-hierarchical models, is used to indicate the size of the choice set or the maximum value of a scale or count variable. In `rhierMnlRwMixture`, the argument is `p`, which is used to indicate the number of choice alternatives. Note that `rbayesBLP` (the hierarchical logit model with _aggregate_ data as in Berry, Levinsohn, and Pakes (1995) and Jiang, Manchanda, and Rossi (2009)) deviates slightly from the standard data input. `rbayesBLP` uses `j` instead of `p` to be consistent with the literature and calls the LHS variable `share` rather than `y` to emphasize that aggregate (market share instead of individual choice) data are required. ### `Prior` Argument Specification of prior distributions is model-specific, so our discussion here is brief. All posterior sampling functions offer default values for parameters of prior distributions. These defaults were selected to yield proper yet reasonably-diffuse prior distributions (assuming the data are in approximately unit scale). In addition, these defaults are consistent across functions. For example, normal priors have default values of mean 0 with value 0.01 for the scaling factor of the prior precision matrix. Specification of the prior is important. Significantly more detail can be found in chapters 2--5 of BSM[^BSM] and the help files for the posterior sampling functions. We strongly recommend you consult them before accepting the default values. [^BSM]: Rossi, Peter, Greg Allenby and Robert McCulloch, _Bayesian Statistics and Marketing_, John Wiley \& Sons, 2005. [Website](http://www.perossi.org/home/bsm-1). ### `Mcmc` Argument {#mcmcarg} The `Mcmc` argument controls parameters of the sampling algorithm. The most common components of this list include: - `R`: the number of MCMC draws - `keep`: a thinning parameter indicating that every `keep`$^\text{th}$ draw should be retained - `nprint`: an option to print the estimated time remaining to the console after each `nprint`$^\text{th}$ draw MCMC methods are non-iid. As a result, a large simulation size may be required to get reliable results. We recommend setting `R` large enough to yield an adequate effective sample size and letting `keep` default to the value 1 unless doing so imposes memory constraints. A careful reader of the `bayesm` help files will notice that many of the examples set `R` equal to 1000 or less. This low number of draws was chosen for speed, as the examples are meant to demonstrate _how_ to run the code and do not necessarily suggest best practices for a proper statistical analysis. `nprint` defaults to 100, but for large `R`, you may want to increase the `nprint` option. Alternatively, you can set `nprint=0`, in which case the priors will still be printed to the console, but the estimated time remaining will not. Additional components of the `Mcmc` argument are function-specific, but typically include starting values for the algorithm. For example, the `Mcmc` argument for `runiregGibbs` takes `sigmasq` as a scalar element of the list. The Gibbs Sampler for `runiregGibbs` first draws $\beta | \sigma^2$, then draws $\sigma^2 | \beta$, and then repeats. For the first draw of $\beta$ in the MCMC chain, a value of $\sigma^2$ is required. The user can specify a value using `Mcmc$sigmasq`, or the user can omit the argument and the function will use its default (`sigmasq = var(Data$y)`). ## Output: Returned Results `bayesm` posterior sampling functions return a consistent set of results and output to the user. At a minimum, this output includes draws from the posterior distribution. `bayesm` provides a set of `summary` and `plot` methods to facilitate analysis of this output, but the user is free to analyze the results as he or she sees fit. ### Output Formats All `bayesm` posterior sampling functions return a list. The elements of that list include a set of vectors, matrices, and/or arrays (and possibly a list), the exact set of which depend on the function. - Vectors are returned for draws of parameters with no natural grouping. For example, `runireg` implements and iid sampler to draw from the posterior of a homoskedastic univariate regression with a conjugate prior (i.e., a Bayesian analog to OLS regression). One output list element is `sigmasqdraw`, a length `R/keep` vector for the scalar parameter $\sigma^2$. - Matrices are returned for draws of parameters with a natural grouping. Again using `runireg` as the example, the output list includes `betadraw`, an `R/keep` $\times$ `ncol(X)` matrix for the vector of $\beta$ parameters. Contrary to the next bullet, draws for the parameters in a variance-covariance matrix are returned in matrix form. For example, `rmnpGibbs` implements a Gibbs Sampler for a multinomial probit model where one set of parameters is the $(p-1) \times (p-1)$ matrix $\Sigma$. The output list for `rmnpGibbs` includes the list element `sigmadraw`, which is a matrix of dimension `R/keep`$\times (p-1)*(p-1)$ with each row containing a draw (in vector form) for all the elements of the matrix $\Sigma$. `bayesm`'s `summary` and `plot` methods (see below) are designed to handle this format. - Arrays are used when parameters have a natural matrix-grouping, such that the MCMC algorithm returns `R/keep` draws of the matrix. For example, `rsurGibbs` returns a list that includes `Sigmadraw`, an $m \times m \times$`R/keep` array, where $m$ is the number of regression equations. As a second example, `rhierLinearModel` estimates a hierarchical linear regression model with a normal prior, and returns a list that includes `betadraw`, an $n \times k \times$`R/keep` array, where $n$ signifies the number of individuals (each with their own $\beta_i$) and $k$ signifies the number of covariates (`ncol(X)` = $k$). - For functions that use a mixture-of-normals or Dirichlet Process prior, the output list includes a list (`nmix`) pertaining to that prior. `nmix` is itself a list with 3 components: `probdraw`, `zdraw`, and `compdraw`. `probdraw` reports the probability that each draw came from a particular normal component; `zdraw` indicates which mixture-of-normals component each draw is assigned to; and `compdraw` provides draws for the mixture-of-normals components (i.e., mean vectors and Cholesky roots of covariance matrices). Note that if you specify a "mixture" with only one normal component, there will be no useful information in `probdraw`. Also note that `zdraw` is not relevant for density estimation and will be null except in `rnmixGibbs` and `rDPGibbs`. ### Classes and Methods In R generally, objects can be assigned a _class_ and then a _generic_ function can be used to run a _method_ on an object with that class. The list elements in the output from `bayesm` posterior sampling functions are assigned special `bayesm` classes. The `bayesm` package includes `summary` and `plot` methods for use with these classes (see the table in Section 2 above). This means you can call the generic function (e.g., `summary`) on individual list elements of `bayesm` output and it will return specially-formatted summary results, including the effective sample size. To see this, the code below provides an example using `runireg`. Here the generic function `summary` dispatches the method `summary.bayesm.mat` because the `betadraw` element of `runireg`'s output has class `bayesm.mat`. This example also shows the information about the prior that is printed to the console during the call to a posterior sampling function. Notice, however, that no remaining time is printed because `nprint` is set to zero. ```{r} set.seed(66) R <- 2000 n <- 200 X <- cbind(rep(1,n), runif(n)) beta <- c(1,2) sigsq <- 0.25 y <- X %*% beta + rnorm(n, sd = sqrt(sigsq)) out <- runireg(Data = list(y = y, X = X), Mcmc = list(R = R, nprint = 0)) summary(out$betadraw, tvalues = beta) ``` ## Access to Code `bayesm` was originally created as a companion to BSM, at which time most functions were written in R. The package has since been expanded to include additional functionality and most code has been converted to C++ via `Rcpp` for faster performance. However, for users interested in obtaining the original implementation of a posterior sampling function (in R instead of C++), you may still access the last version (2.2-5) of `bayesm` prior to the C++/`Rcpp` conversion from the [package archive](https://cran.r-project.org/src/contrib/Archive/bayesm/) on CRAN. To access the `R` code in the current version of `bayesm`, the user can simply call a function without parenthesis. For example, `bayesm::runireg`. However, most posterior sampling functions only perform basic checks in `R` and then call an unexported C++ function to do the heavy lifting (i.e., the MCMC draws). This C++ source code is not available to the user via the installed `bayesm` package because C++ code is compiled upon package installation on Linux machines and pre-compiled by CRAN for Mac and Windows. To access this source code, the user must download the "package source" from CRAN. This can be accomplished by clicking on the appropriate link at the `bayesm` [package archive](https://cran.r-project.org/src/contrib/Archive/bayesm/) or by executing the `R` command `download.packages(pkgs="bayesm", destdir=".", type="source")`. Either of these methods will provide you with a compressed file "bayesm_version.tar.gz" that can be uncompressed. The C++ code can then be found in the "src" subdirectory. # Examples We begin with a brief introduction to regression and Bayesian estimation. This will help set the notation and provide background for the examples that follow. We do not claim that this will be a sufficient introduction to the reader for which these ideas are new. We refer that reader to excellent texts on regression analysis by Cameron \& Trivedi, Davidson \& MacKinnon, Goldberger, Greene, Wasserman, and Wooldridge.[^1] For Bayesian methods, we recommend Gelman et al., Jackman, Marin \& Robert, Rossi et al., and Zellner.[^2] [^1]: Cameron, Colin and Pravin Trivedi, _Microeconometrics: Methods and Applications_, Cambridge University Press, 2005. Davidson, Russell and James MacKinnon, _Estimation and Inference in Econometrics_, Oxford University Press, 1993. Goldberger, Arthur, _A Course in Econometrics_, Harvard University Press, 1991. Greene, William, _Econometric Analysis_, Prentice Hall, 2012 (7th edition). Wasserman, Larry, _All of Statistics: A Concise Course in Statistical Inferece_, Springer, 2004. Wooldridge, Jeffrey, _Econometric Analysis of Cross Section and Panel Data_, MIT Press, 2010 (2nd edition). [^2]: Gelman, Andrew, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin, _Bayesian Data Analysis_, CRC Press, 2013 (3rd edition). Jackman, Simon, _Bayesian Analysis for the Social Sciences_, Wiley, 2009. Marin, Jean-Michel and Christian Robert, _Bayesian Essentials with R_, Springer, 2014 (2nd edition). Rossi, Peter, Greg Allenby, and Robert McCulloch, _Bayesian Statistics and Marketing_, Wiley, 2005. Zellner, Arnold, _An Introduction to Bayesian Inference in Economics_, Wiley, 1971. ## What is Regression Suppose you believe a variable $y$ varies with (or is caused by) a set of variables $x_1, x_2, \ldots, x_k$. For notational convenience, we'll collect the set of $x$ variables into $X$. These variables $y$ and $X$ have a joint distribution $f(y, X)$. Typically, interest will not fall on this joint distribution, but rather on the conditional distribution of the "outcome" variable $y$ given the "explanatory" variables (or "covariates") $x_1, x_2, \ldots, x_k$; this conditional distribution being $f(y|X)$. To carry out inference on the relationship between $y$ and $X$, the researcher then often focuses attention on one aspect of the conditional distribution, most commonly its expected value. This conditional mean is assumed to be a function $g$ of the covariates such that $\mathbb{E}[y|X] = g(X, \beta)$ where $\beta$ is a vector of parameters. A function for the conditional mean is known as a "regression" function. The canonical introductory regression model is the normal linear regression model, which assumes that $y \sim N(X\beta, \sigma^2)$. Most students of regression will have first encountered this model as a combination of deterministic and stochastic components. There, the stochastic component is defined as deviations from the conditional mean, $\varepsilon = y - \mathbb{E}[y|X]$, such that $y = \mathbb{E}[y|X] + \varepsilon$ or that $y = g(X, \beta) + \varepsilon$. The model is then augmented with the assumptions that $g(X, \beta) = X \beta$ and $\varepsilon \sim N(0,\sigma^2)$ so that the normal linear regression model is: $$ y = X \beta + \varepsilon \text{ with } \varepsilon \sim N(0,\sigma^2) \hspace{1em} \text{or} \hspace{1em} y \sim N(X\beta, \sigma^2) $$ When taken to data, additional assumptions are made which include a full-rank condition on $X$ and often that $\varepsilon_i$ for $i=1,\ldots,n$ are independent and identically distributed. Our first example will demonstrate how to estimate the parameters of the normal linear regression model using Bayesian methods made available by the posterior sampling function `runireg`. We then provide an example to estimate the parameters of a model when $y$ is a categorical variable. This second example is called a multinomial logit model and uses the logistic "link" function $g(X, \beta) = [1 + exp(-X\beta)]^{-1}$. Our third and final example will extend the multinomial logit model to permit individual-level parameters. This is known as a hierarchical model and requires panel data to perform the estimation. Before launching into the examples, we briefly introduce Bayesian methodology and contrast it with classical methods. ## What is Bayesian Inference Under classical econometric methods, $\beta$ is most commonly estimated by minimizing the sum of squared residuals, maximizing the likelihood, or matching sample moments to population moments. The distribution of the estimators (e.g., $\hat{\beta}$) and test statistics derived from these methods rely on asymptotic concepts and are based on imaginary samples not observed. In contrast, Bayesian inference provides the benefits of (a) exact sample results, (b) integration of descision-making, estimation, testing, and model selection, and (c) a full accounting of uncertainty. These benefits from Bayesian inference rely heavily on probability theory and, in particular, distributional theory, some elements of which we now briefly review. Recall the relationship between the joint and conditional densities for random variables $W$ and $Z$: $$ P_{A|B}(A=a|B=b) = \frac{P_{A,B}(A=a, B=b)}{P_B(B=b)} $$ This relationship can be used to derive Bayes' Theorem, which we write with $D$ for "data" and $\theta$ as the parameters (and with implied subscripts): $$ P(\theta|D) = \frac{P(D|\theta)P(\theta)}{P(D)} $$ Noticing that $P(D)$ does not contain the parameters of interest ($\theta$) and is therefore simply a normalizing constant, we can instead write: $$ P(\theta|D) \propto P(D|\theta)P(\theta) $$ Introducing Bayesian terminology, we have that the "Posterior" is proportional to the Likelihood times the Prior. Thus, given (1) a dataset ($D$), (2) an assumption on the data generating process (the likelihood, $P(D|\theta)$), and (3) a specification of the prior distribution of the parameters ($P(\theta)$), we can find the _exact_ (posterior) distribution of the parameters given the observed data. This is in stark contrast to classical econometric methods, which typically only provide the _asymptotic_ distributions of estimators. However, for any problem of practical interest, the posterior distribution is a high-dimensional object. Additionally, it may not be possible to analytically calculate the posterior or its features (e.g., marginal distributions or moments such as the mean). To handle these issues, the modern approach to Bayesian inference relies on simulation methods to sample from the (high-dimensional) posterior distribution and then construct marginal distributions (or their features) from the sampled draws of the posterior. As a result, simulation and summaries of the posterior play important roles in modern Bayesian statistics. `bayesm`'s posterior sampling functions (as their name suggests) sample from posterior distributions. `bayesm`'s `summary` and `plot` methods can be used to analyze those draws. Unlike most classical econometric methods, the MCMC methods implemented in `bayesm`'s posterior sampling functions provide an estimate of the entire posterior distribution, not just a few moments. Given this "rich" result from Bayesian methods, it is best to summarize posterior distributions using histograms or quantiles. We advise that you resist the temptation to simply report the posterior mean and standard deviation; for non-normal distributions, those moments may have little meaning. In the examples that follow, we will describe the data we use, present the model, demonstrate how to estimate it using the appropriate posterior sampling function, and provide various ways to summarize the output. ## Example 1: Linear Normal Regression ### Data For our first example, we will use the `cheese` dataset, which provides `r data(cheese); format(nrow(cheese), big.mark=",")` observations of weekly sales volume for a package of Borden sliced cheese, as well as a measure of promotional display activity and price. The data are aggregated to the "key" account (i.e., retailer-market) level. ```{r} data(cheese) names(cheese) <- tolower(names(cheese)) str(cheese) ``` Suppose we want to assess the relationship between sales volume and price and promotional display activity. For this example, we will abstract from whether these relationships vary by retailer or whether prices are set endogenously. Simple statistics show a negative correlation between volume and price, and a positive correlation between volume and promotional activity, as we would expect. ```{r} options(digits=3) cor(cheese$volume, cheese$price) cor(cheese$volume, cheese$disp) ``` ### Model We model the expected log sales volume as a linear function of log(price) and promotional activity. Specifically, we assume $y_i$ to be iid with $p(y_i|x_i,\beta)$ normally distributed with a mean linear in $x$ and a variance of $\sigma^2$. We will denote observations with the index $i = 1, \ldots, n$ and covariates with the index $j = 1, \ldots, k$. The model can be written as: $$ y_i = \sum_{j=1}^k \beta_j x_{ij} + \varepsilon_i = x_i'\beta + \varepsilon_i \hspace{1em} \text{with} \hspace{1em} \varepsilon_i \sim iid\ N(0,\sigma^2) $$ or equivalently but more compactly as: $$ y \sim MVN(X\beta,\ \sigma^2I_n) $$ Here, the notation $N(0, \sigma^2)$ indicates a univariate normal distribution with mean $0$ and variance $\sigma^2$, while $MVN(X\beta,\ \sigma^2I_n)$ indicates a multivariate normal distribution with mean vector $X\beta$ and variance-covariance matrix $\sigma^2I_n$. In addition, $y_i$, $x_{ij}$, $\varepsilon_i$, and $\sigma^2$ are scalars while $x_i$ and $\beta$ are $k \times 1$ dimensional vectors. In the more compact notation, $y$ is an $n \times 1$ dimensional vector, $X$ is an $n \times k$ dimensional matrix with row $x_i$, and $I_n$ is an $n \times n$ dimensional identity matrix. With regard to the `cheese` dataset, $k = 2$ and $n = 5,555$. When employing Bayesian methods, the model is incomplete until the prior is specified. For our example, we elect to use natural conjugate priors, meaning the family of distributions for the prior is chosen such that, when combined with the likelihood, the posterior will be of the same distributional family. Specifically, we first factor the joint prior into marginal and conditional prior distributions: $$ p(\beta,\sigma^2) = p(\beta|\sigma^2)p(\sigma^2) $$ We then specify the prior for $\sigma^2$ as inverse-gamma (written in terms of a chi-squared random variable) and the prior for $\beta|\sigma^2$ as multivariate normal: $$ \sigma^2 \sim \frac{\nu s^2}{\chi^2_{\nu}} \hspace{1em} \text{and} \hspace{1em} \beta|\sigma^2 \sim MVN(\bar{\beta},\sigma^2A^{-1}) $$ Other than convenience, we have little reason to specify priors from these distributional families; however, we will select diffuse priors so as not to impose restrictions on the model. To do so, we must pick values for $\nu$ and $s^2$ (the degrees of freedom and scale parameters for the inverted chi-squared prior on $\sigma^2$) as well as $\bar{\beta}$ and $A^{-1}$ (the mean vector and variance-covariance matrix for the multivariate normal prior on the $\beta$ vector). The `bayesm` posterior sampling function for this model, `runireg`, defaults to the following values: - $\nu = 3$ - $s^2 =$ `var(y)` - $\bar{\beta} = 0$ - $A = 0.01*I$ We will use these defaults, as they are chosen to be diffuse for data with a unit scale. Thus, for each $\beta_j | \sigma^2$ we have specified a normal prior with mean 0 and variance $100\sigma^2$, and for $\sigma^2$ we have specified an inverse-gamma prior with $\nu = 3$ and $s^2 = \text{var}(y)$. We graph these prior distributions below. ```{r fig.show = "hold"} par(mfrow = c(1,2)) curve(dnorm(x,0,10), xlab = "", ylab = "", xlim = c(-30,30), main = expression(paste("Prior for ", beta[j])), col = "dodgerblue4") nu <- 3 ssq <- var(log(cheese$volume)) curve(nu*ssq/dchisq(x,nu), xlab = "", ylab = "", xlim = c(0,1), main = expression(paste("Prior for ", sigma^2)), col = "darkred") par(mfrow = c(1,1)) ``` ### Bayesian Estimation Although this model involves nontrivial natural conjugate priors, the posterior is available in closed form: $$ p(\beta, \sigma^2 | y, X) \propto (\sigma^2)^{-k/2} \exp \left\{ -\frac{1}{2\sigma^2}(\beta - \bar{\beta})'(X'X+A)(\beta - \bar{\beta}) \right\} \times (\sigma^2)^{-((n+\nu_0)/2+1)} \exp \left\{ -\frac{\nu_0s_0^2 + ns^2}{2\sigma^2} \right\} $$ or \begin{align*} \beta | \sigma^2, y, X &\sim N(\bar{\beta}, \sigma^2(X'X+A)^{-1}) \\ \\ \sigma^2 | y, X &\sim \frac{\nu_1s_1^2}{\chi^2_{\nu_1}}, \hspace{3em} \text{with} \> \nu_1 = \nu_0+n \hspace{1em} \text{and} \hspace{1em} s_1^2 = \frac{\nu_0s_0^2 + ns^2}{\nu_0+n} \end{align*} The latter representation suggests a simulation strategy for making draws from the posterior. We draw a value of $\sigma^2$ from its marginal posterior distribution, insert this value into the expression for the covariance matrix of the conditional normal distribution of $\beta|\{\sigma^2,y\}$ and draw from this multivariate normal. This simulation strategy is implemented by `runireg`, using the defaults for `Prior` specified above. The code is quite simple. ```{r, eval = FALSE} dat <- list(y = log(cheese$volume), X = model.matrix( ~ price + disp, data = cheese)) out <- runireg(Data = dat, Mcmc = list(R=1e4, nprint=1e3)) ``` ```{r, echo = FALSE} temp <- capture.output( {set.seed(1234); dat <- list(y = log(cheese$volume), X = model.matrix( ~ price + disp, data = cheese)); out <- runireg(Data = dat, Mcmc = list(R=1e4, nprint=1e3))}, file = NULL) ``` Note that `bayesm` posterior sampling functions print out information about the prior and about MCMC progress during the function call (unless `nprint` is set to 0), but for presentation purposes we suppressed that output here. `runireg` returns a list that we have saved in `out`. The list contains two elements, `betadraw` and `sigmasqdraw`, which you can verify by running `str(out)`. `betadraw` is an `R/keep` $\times$ `ncol(X)` ($10,000 \times 3$ with a column for each of the intercept, price, and display) dimension matrix with class `bayesm.mat`. We can analyze or summarize the marginal posterior distributions for any $\beta$ parameter or the $\sigma^2$ parameter. For example, we can plot histograms of the price coefficient (even though it is known to follow a t-distrbution, see BSM Ch. 2.8) and for $\sigma^2$. Notice how concentrated the posterior distributions are compared to their priors above. ```{r, fig.show = "hold"} B <- 1000+1 #burn in draws to discard R <- 10000 par(mfrow = c(1,2)) hist(out$betadraw[B:R,2], breaks = 30, main = "Posterior Dist. of Price Coef.", yaxt = "n", yaxs="i", xlab = "", ylab = "", col = "dodgerblue4", border = "gray") hist(out$sigmasqdraw[B:R], breaks = 30, main = "Posterior Dist. of Sigma2", yaxt = "n", yaxs="i", xlab = "", ylab = "", col = "darkred", border = "gray") par(mfrow = c(1,1)) ``` Additionally, we can compute features of these posterior distributions. For example, the posterior means price and display are: ```{r} apply(out$betadraw[B:R,2:3], 2, mean) ``` Conveniently, `bayesm` offers this functionality (and more) with its `summary` and `plot` methods. Notice that `bayesm`'s methods use a default burn-in length, calculated as `trunc(0.1*nrow(X))`. You can override the default by specifying the `burnin` argument. We see that the means for the first few retail fixed effects in the summary information below match those calculated "by hand" above. ```{r} summary(out$betadraw) ``` The same can be done with the `plot` generic function. However, we reference the method directly (`plot.bayesm.mat`) to plot a subset of the `bayesm` output -- specifically we plot the price coefficient. In the histogram, note that the green bars delimit a 95\% Bayesian credibility interval, yellow bars shows +/- 2 numerical standard errors for the posterior mean, and the red bar indicates the posterior mean. Also notice that this pink histogram of the posterior distribution on price, which was created by calling the `plot` generic, matches the blue one we created "by hand" above. The `plot` generic function also provides a trace plot and and ACF plot. In many applications (although not in this simple model), we cannot be certain that our draws from the posterior distribution adequately represent all areas of the posterior with nontrivial mass. This may occur, for instance, when using a "slow mixing" Markov Chain Monte Carlo (MCMC) algorithm to draw from the posterior. In such a case, we might see patterns in the trace plot and non-zero autocorrelations in the ACF plot; these will coincide with values for the Effective Sample Size less than `R`. (Effective Sample Size prints out with the `summary` generic function, as above.) Here, however, we are able to sample from the posterior distribution by taking iid draws, and so we see large Effective Sample Sizes in the `summary` output above, good mixing the trace plot below, and virtually no autocorrelation between draws in the ACF plot below. ```{r} plot.bayesm.mat(out$betadraw[,2]) ``` ## Example 2: Multinomial Logistic Regression ### Data Linear regression models like the one in the previous example are best suited for continuous outcome variables. Different models (known as limited dependent variable models) have been developed for binary or multinomial outcome variables, the most popular of which --- the multinomial logit --- will be the subject of this section. For this example, we analyze the `margarine` dataset, which provides panel data on purchases of margarine. The data are stored in two dataframes. The first, `choicePrice`, lists the outcome of `r data(margarine); format(nrow(margarine$choicePrice), big.mark=",")` choice occasions as well as the choosing household and the prices of the `r max(margarine$choicePrice[,2])` choice alternatives. The second, `demos`, provides demographic information about the choosing households, such as their income and family size. We begin by merging the information from these two dataframes: ```{r, results = "hold"} data(margarine) str(margarine) marg <- merge(margarine$choicePrice, margarine$demos, by = "hhid") ``` Compared to the standard $n \times k$ rectangular format for data to be used in a linear regression model, choice data may be stored in various formats, including a rectangular format where the $p$ choice alternatives are allocated across columns or rows, or a list-of-lists format as used in `bayesm`'s hierarchical models, which we demonstrate in Example 3 below. For all functions --- and notably those that implement multinomial logit and probit models --- the data must be in the format expected by the function, and `bayesm`'s posterior sampling funtions are no exception. In this example, we will implement a multinomial logit model using `rmnlIndepMetrop`. This posterior sampling function requires `y` to be a length-$n$ vector (or an $n \times 1$ matrix) of multinomial outcomes ($1, \dots, p$). That is, each element of `y` corresponds to a choice occasion $i$ with the value of the element $y_i$ indicating the choice that was made. So if the fourth alternative was chosen on the seventh choice occasion, then $y_7 = 4$. The `margarine` data are stored in that format, and so we easily specify `y` with the following code: ```{r} y <- marg[,2] ``` `rmnlIndepMetrop` requires `X` to be an $np \times k$ matrix. That is, each alternative is listed on its own row, with a group of $p$ rows together corresponding to the alternatives available on one choice occasion. However, the `margarine` data are stored with the various choice alternatives in columns rather than rows, so reformatting is necessary. `bayesm` provides the utility function `createX` to assist with the conversion. `createX` requires the user to specify the number of choice alternatives `p` as well as the number of alternative-specific variables `na` and an $n \times$ `na` matrix of alternative-specific data `Xa` ("a" for alternative-specific). Here, we have $p=10$ choice alternatives with `na` $=1$ alternative-specific variable (price). If we were only interested in using price as a covariate, we would code: ```{r} X1 <- createX(p=10, na=1, Xa=marg[,3:12], nd=NULL, Xd=NULL, base=1) colnames(X1) <- c(names(marg[,3:11]), "price") head(X1, n=10) ``` Notice that `createX` uses $p-1$ dummy variables to distinguish the $p$ choice alternatives. As with factor variables in linear regression, one factor must be the base; the coefficients on the other factors report deviations from the base. The user may specify the base alternative using the `base` argument (as we have done above), or let it default to the alternative with the highest index. For our example, we might also like to include some "demographic" variables. These are variables that do not vary with the choice alternatives. For example, with the `margarine` data we might want to include family size. Here again we turn to `createX`, this time specifying the `nd` and `Xd` arguments ("d" for demographic): ```{r, results = "hold"} X2 <- createX(p=10, na=NULL, Xa=NULL, nd=2, Xd=as.matrix(marg[,c(13,16)]), base=1) print(X2[1:10,1:9]); cat("\n") print(X2[1:10,10:18]) ``` Notice that `createX` again uses $p-1$ dummy variables to distinguish the $p$ choice alternatives. However, for demographic variables, the value of the demographic variable is spread across $p-1$ columns and $p-1$ rows. ### Model The logit specification was originally derived by Luce (1959) from assumptions on characteristics of choice probabilities. McFadden (1974) tied the model to rational economic theory by showing how the multinomial logit specification models choices made by a utility-maximizing consumer, assuming that the unobserved utility component is distributed Type I Extreme Value. We motivate use of this model following McFadden by assuming the decision maker chooses the alternative providing him with the highest utility, where the utility $U_{ij}$ from the choice $y_{ij}$ made by a decision maker in choice situation $i$ for product $j = 1, \ldots, p$ is modeled as the sum of a deterministic and a stochastic component: \[ U_{ij} = V_{ij} + \varepsilon_{ij} \hspace{2em} \text{with } \varepsilon_{ij}\ \sim \text{ iid T1EV} \] Regressors (both demographic and alternative-specific) are included in the model by assuming $V_{ij} = x_{ij}'\beta$. These assumptions result in choice probabilities of: $$ \text{Pr}(y_i=j) = \frac{\exp \{x_{ij}'\beta\}}{\sum_{k=1}^p\exp\{x_{ik}'\beta\}} $$ Independent priors for the components of the `beta` vector are specified as normal distributions. Using notation for the multivariate normal distribution, we have: $$ \beta \sim MVN(\bar{\beta},\ A^{-1}) $$ We use `bayesm`'s default values for the parameters of the priors: $\bar{\beta} = 0$ and $A = 0.01I$. ### Bayesian Estimation Experience with the MNL likelihood is that the asymptotic normal approximation is excellent. `rmnlIndepMetrop` implements an independent Metropolis algorithm to sample from the normal approximation to the posterior distribution of $\beta$: $$ p(\beta | X, y) \overset{\cdot}{\propto} |H|^{1/2} \exp \left\{ \frac{1}{2}(\beta - \hat{\beta})'H(\beta - \hat{\beta}) \right\} $$ where $\hat{\beta}$ is the MLE, $H = \sum_i x_i A_i x_i'$, and the candidate distribution used in the Metropolis algorithm is the multivariate student t. For more detail, see Section 11 of BSM Chapter 3. We sample from the normal approximation to the posterior as follows: ```{r, eval = FALSE} X <- cbind(X1, X2[,10:ncol(X2)]) out <- rmnlIndepMetrop(Data = list(y=y, X=X, p=10), Mcmc = list(R=1e4, nprint=1e3)) ``` ```{r, echo = FALSE} temp <- capture.output( {set.seed(1234); X <- cbind(X1, X2[,10:ncol(X2)]); out <- rmnlIndepMetrop(Data = list(y=y, X=X, p=10), Mcmc = list(R=1e4, nprint=1e3))}, file = NULL) ``` `rmnlIndepMetrop` returns a list that we have saved in `out`. The list contains 3 elements, `betadraw`, `loglike`, and `acceptr`, which you can verify by running `str(out)`. `betadraw` is a $10,000 \times 28$ dimension matrix with class `bayesm.mat`. As with the linear regression of Example 1 above, we can plot or summarize features of the the posterior distribution in many ways. For information on each marginal posterior distribution, call `summary(out)` or `plot(out)`. Because we have 28 covariates (intercepts and demographic variables make up 9 columns each and there is one column for the price variable) we omit the full set of results to save space and instead, we only present summary statistics for the marginal posterior distribution for $\beta_\text{price}$: ```{r} summary.bayesm.mat(out$betadraw[,10], names = "Price") ``` In addition to summary information for a marginal posterior distribution, we can plot it. We use `bayesm`'s `plot` generic function (calling `plot(out$betadraw)` would provide the same plots for all 28 $X$ variables). In the histogram, the green bars delimit a 95\% Bayesian credibility interval, yellow bars shows +/- 2 numerical standard errors for the posterior mean, and the red bar indicates the posterior mean. The subsequent two plots are a trace plot and and ACF plot. ```{r} plot.bayesm.mat(out$betadraw[,10], names = "Price") ``` We see that the posterior is approximately normally distributed with a mean of `r format(round(mean(out$betadraw[,10]),1), big.mark=",")` and a standard deviation of `r round(sd(out$betadraw[,10]), 2)`. The trace plot shows good mixing. The ACF plot shows a fair amount of correlation such that, even though the algorithm took `R = 10,000` draws, the Effective Sample Size (as reported in the summary stats above) is only `r format(round(summary.bayesm.mat(out$betadraw[,10])[1,5],0), big.mark = ",")`. Because of the nonlinearity in this model, interpreting the results is more difficult than with linear regression models. We do not elaborate further on the interpretation of coefficients and methods of displaying results from multinomial logit models, but for the uninitiated reader, we refer you to excellent sources for this information authored by Kenneth Train and Gary King.[^3] [^3]: Train, Kenneth, _Discrete Choice Models with Simulation_ Cambridge University Press, 2009. King, Gary _Unifying Political Methodlogy: The Likelihood Theory of Statistical Inference_, University of Michigan Press (1998) p. 108. King, Gary, Michael Tomz, and Jason Wittenberg, "Making the Most of Statistical Analyses: Improving Interpretation and Presentation" American Journal of Political Science, Vol. 44 (April 2000) pp. 347--361 at p. 355. ## Example 3: Hierarchical Logit ### Data While we could add individual-specific parameters to the previous model and use the same dataset, we elect to provide the reader with greater variety. For this example, we use the `camera` dataset in `bayesm`, which contains conjoint choice data for 332 respondents who evaluated digital cameras. These data have already been processed to exclude respondents that always answered "none", always picked the same brand, always selected the highest priced offering, or who appeared to be answering randomly. ```{r} data(camera) length(camera) str(camera[[1]]) colnames(camera[[1]]$X) ``` The `camera` data is stored in a list-of-lists format, which is the format required by `bayesm`'s posterior sampling functions for hierarchical models. This format has one list per individual with each list containing a vector `y` of choice outcomes and a matrix `X` of covariates. As with the multinomial logit model of the last example, `y` is a length-$n_i$ vector (or one-column matrix) and `X` has dimensions $n_ij \times k$ where $n_i$ is the number of choice occasions faced by individual $i$, $j$ is the number of choice alternatives, and $k$ is the number of covariates. For the `camera` data, $N=332$, $n_i=16$ for all $i$, $j=5$, and $k=10$. If your data were not in this format, it could be easily converted with a `for` loop and `createX`. For example, we can format `margarine` data from Example 2 above into a list-of-lists format with the following code, which simply loops over individuals, extracting and storing their `y` and `X` data one individual at a time: ```{r, eval = FALSE} data(margarine) chpr <- margarine$choicePrice chpr$hhid <- as.factor(chpr$hhid) N <- nlevels(chpr$hhid) dat <- vector(mode = "list", length = N) for (i in 1:N) { dat[[i]]$y <- chpr[chpr$hhid==levels(chpr$hhid)[i], "choice"] dat[[i]]$X <- createX(p=10, na=1, Xa=chpr[chpr$hhid==levels(chpr$hhid)[i],3:12], nd=NULL, Xd=NULL) } ``` Returning to the `camera` data, the first 4 covariates are binary indicators for the brands Canon, Sony, Nikon, and Panasonic. These correspond to choice (`y`) values of 1, 2, 3, and 4. `y` can also take the value 5, indicating that the respondent chose "none". The data include binary indicators for two levels of pixel count, zoom strength, swivel video display capability, and wifi connectivity. The last covaritate is price, recorded in hundreds of U.S. dollars (we leave price in these units so that we do not need to adjust the default prior settings). When we look at overall choice outcomes, we see that the brands and the outside alternative ("none") are chosen roughly in fifths, with specific implied market shares ranging from 17\%--25\%: ```{r} N <- length(camera) dat <- matrix(NA, N*16, 2) for (i in 1:length(camera)) { Ni <- length(camera[[i]]$y) dat[((i-1)*Ni+1):(i*Ni),1] <- i dat[((i-1)*Ni+1):(i*Ni),2] <- camera[[i]]$y } round(prop.table(table(dat[,2])), 3) ``` However, when we look at a few individuals' choices, we see much greater variability: ```{r} round(prop.table(table(dat[,1], dat[,2])[41:50,], 1), 3) ``` It is this heterogeneity in individual choice that motivates us to employ a hierarchical model. ### Model Hierarchical (also known as multi-level, random-coefficient, or mixed) models allow each respondent to have his or her own coefficients. Different people have different preferences, and models that estimate individual-level coefficients can fit data better and make more accurate predictions than single-level models. These models are quite popular in marketing, as they allow, for example, promotions to be targeted to individuals with high promotional part worths --- meaning those inviduals who are most likely to respond to the promotion. For more information, see Rossi et al. (1996).^[Rossi, McCulloch, and Allenby "The Value of Purchase History Data in Target Marketing" Marketing Science (1996).] The model follows the multinomial logit specification given in Example 2 above where individuals are assumed to be rational economic agents that make utility-maximizing choices. Now, however, the model includes individual-level parameters ($\beta_i$) assumed to be drawn from a normal distribution and with mean values driven by cross-sectional unit characteristics $Z$: $$ y_i \sim \text{MNL}(x_i'\beta_i) \hspace{1em} \text{with} \hspace{1em} \beta_i = z_i' \Delta + u_i \hspace{1em} \text{and} \hspace{1em} u_i \sim MVN(\mu, \Sigma) $$ $x_i$ is $n_i \times k$ and $i = 1, \ldots, N$. We can alternatively write the middle equation as $B=Z\Delta + U$ where $\beta_i$, $z_i$, and $u_i$ are the $i^\text{th}$ rows of $B$, $Z$, and $U$. $B$ is $N \times k$, $Z$ is $N \times m$, $\Delta$ is $m \times k$, and $U$ is $N \times k$. Note that we do not have any cross-sectional unit characteristics in the `camera` dataset and thus $Z$ will be omitted. The priors are: $$ \text{vec}(\Delta) = \delta \sim MVN(\bar{\delta}, A_\delta^{-1}) \hspace{2em} \mu \sim MVN(\bar{\mu}, \Sigma \otimes a^{-1}) \hspace{2em} \Sigma \sim IW(\nu, V) $$ This specification of priors assumes that, conditional on the hyperparameters (that is, the parameters of the prior distribution), the $\beta$'s are _a priori_ independent. This means that inference for each unit can be conducted independently of all other units, conditional on the hyperparameters, which is the Bayesian analogue of the fixed effects approach in classical statistics. Note also that we have assumed a normal "first-stage" prior distribution over the $\beta$'s. `rhierMnlRwMixture` permits a more-flexible mixture-of-normals first-stage prior (hence the "mixture" in the function name). However, for our example, we will not include this added flexibility (`Prior$ncomp = 1` below). ### Bayesian Estimation Although the model is more complex than the models used in the two previous examples, the increased programming difficulty for the researcher is minimal. As before, we specify `Data`, `Prior`, and `Mcmc` arguments, and call the posterior sampling function: ```{r} data <- list(lgtdata = camera, p = 5) prior <- list(ncomp = 1) mcmc <- list(R = 1e4, nprint = 0) out <- rhierMnlRwMixture(Data = data, Prior = prior, Mcmc = mcmc) ``` We store the results in `out`, which is a list of length 4. The list elements are `betadraw`, `nmix`, `loglike`, and `SignRes`. - `betadraw` is a $332 \times 10 \times 10,000$ array. These dimensions correspond to the number of individuals, the number of covariates, and the number of MCMC draws. - `nmix` is a list with elements `probdraw`, `zdraw`, and `compdraw`. - `probdraw` tells us the probability that each draw came from a particular normal component. This is relevant when there is a mixture-of-normals first-stage prior. However, since our specified prior over the $\beta$ vector is one normal distribution, `probdraw` is a $10,000 \times 1$ vector of all 1's. - `zdraw` is `NULL` as it is not relevant for this function. - `compdraw` provides draws for the mixture-of-normals components. Here, `compdraw` is a list of 10,000 lists. The $r^\text{th}$ of the 10,000 lists contains the $r^\text{th}$ draw of the $\mu$ vector (dim $1 \times 10$) and the Cholesky root of the $r^\text{th}$ draw for the $10 \times 10$ covariance matrix. - `loglike` is a $10,000 \times 1$ vector that provides the log-likelihood of each draw. - `SignRes` relates to whether any sign restrictions were placed on the model. This is discussed in detail in a separate vignette detailing a contrainsted hierarchical multinomial logit model; it is not relevant here. We can summarize results as before. `plot(out$betadraw)` provides plots for each variable that summarize the distributions of the individual parameters. For brevity, we provide just a histogram of posterior means for the 332 individual coefficients on wifi capability. ```{r} hist(apply(out$betadraw, 1:2, mean)[,9], col = "dodgerblue4", xlab = "", ylab = "", yaxt="n", xlim = c(-4,6), breaks = 20, main = "Histogram of Posterior Means For Individual Wifi Coefs") ``` We see that the distribution of individual posterior means is skewed, suggesting that our assumption of a normal first-stage prior may be incorrect. We could improve this model by using the more flexible mixture-of-normals prior or, if we believe all consumers value wifi connectivity positively, we could impose a sign constraint on that set of parameters --- both of which are demonstrated in the vignette for sign-constrained hierarchical multinomial logit. # Conclusion We hope that this vignette has provided the reader with a thorough introduction to the `bayesm` package and is sufficient to enable immediate use of its posterior sampling functions for bayesian estimation. Comments or edits can be sent to the vignette authors at the email addresses below. ***** _Authored by [Dan Yavorsky](dyavorsky@gmail.com) and [Peter Rossi](perossichi@gmail.com). Last updated June 2017._ bayesm/MD50000644000176000001440000002263713134414120012075 0ustar ripleyusers8c81678af6546a1c2ef87e249e6de183 *DESCRIPTION 21a335b1d91a250bd8e5ca7a97774e78 *NAMESPACE 722ef18fc061d3605cf687388e4be2a1 *R/BayesmConstants.R 750237445e7fb47a0c72db9038bad9b3 *R/BayesmFunctions.R e448ac43733eae3181a9c0bb1c62260f *R/RcppExports.R e33f545ae8ab84bdf86aa1840b426f58 *R/clusterMix_rcpp.R 0207b70adcd95129ba38b6177b235d51 *R/condMom.R 9ac8fed10a2ce38bb6f384f512891b86 *R/createX.R f0ab1f89e4c7b93a3abf2969c94b4b19 *R/eMixMargDen.R a6774f78661955079a5fae59bb3be712 *R/fsh.R 6f46c7edcc98677e73e38a52c8b95176 *R/llmnp.R 4af4b88d141c001e1a076bacb1059ade *R/llnhlogit.R a6170041c44b4b577f7d54a606d47b66 *R/logMargDenNR.R 527cad4b4579e5b54a81980efd32d5b9 *R/mixDen.R 0d7e8a1405b2ea906f5e253f419e285a *R/mixDenBi.R b707216d4a165cafcfd77ff74ca32d4e *R/mnlHess.R 94567c1ff99a7aec5b4fb8a884001520 *R/mnpProb.R e8c68b7db1d51d1ce270e09648db3112 *R/momMix.R dd4a04551d37b77645a4cb38b3feb114 *R/nmat.R 72dbf320a442a6ee65a3de8bc4e6f238 *R/numEff.R e846262147a6cac35a4363b1fc77dadf *R/plot.bayesm.hcoef.R 2761d0e0e3bc3b97179790616880c5e1 *R/plot.bayesm.mat.R d14eae20edc467e1557775af62c45eb0 *R/plot.bayesm.nmix.R 3b35e7b69751438d7c06ed92717892c2 *R/rbayesBLP_rcpp.R e7be9effe9226ccf40154f86491d3e89 *R/rbiNormGibbs.R ab27c52c104047c56b03e9488fb3af2b *R/rbprobitgibbs_rcpp.r 8ae0fff41ac30695ba99ed9b69fa54d4 *R/rdpgibbs_rcpp.r b51fe0e4c2a296988a3847bdc23f14d7 *R/rhierBinLogit.R d6f886dd9321369927df7167ebd966ba *R/rhierLinearMixture_rcpp.r ca0ff52fd2ef0ea9eb6a1055e67fa37f *R/rhierLinearModel_rcpp.R d2b0844bb4cede20ba0a1880013b0c78 *R/rhierMnlDP_rcpp.r 197cd52a3d0111781e1028724f92d6ff *R/rhierMnlRwMixture_rcpp.r 185015f355711febecec70841089280a *R/rhiernegbinrw_rcpp.r ec71cae23dc6dc1a5d23bccaa4eefaa2 *R/rivDP_rcpp.R f4903ad0daae8987f84926f1aff5a034 *R/rivGibbs_rcpp.R 652d928d65799efac1472647005856a6 *R/rmnlIndepMetrop_rcpp.R 80e51f00ef260180307f1e766023df3d *R/rmnpgibbs_rcpp.r 6342bcea7a0aadc3dcb702798996cb45 *R/rmvpgibbs_rcpp.r 59e00dd8645e33a1821a6942cd0e5d6d *R/rnegbinrw_rcpp.r b3c94e27e90c8a3de878e9e2dec28ec6 *R/rnmixgibbs_rcpp.r a91df673c2d3a496d059f0aeda8abd2b *R/rordprobitgibbs_rcpp.r 4ea0aa22b4d07c120dbe9147102bc8c2 *R/rscaleusage_rcpp.r 69ef61896e667add5decbc6110ab6c5a *R/rsurgibbs_rcpp.r 2c1b75af6274d8843d626d5492db68db *R/runireg_rcpp.r 55d3a319658b97ea46997f392d992fe2 *R/runireggibbs_rcpp.r 88c70d97e33d665f93f08e5c5a7e31e8 *R/simnhlogit.R f6bbbc1dd104aa996c7ba62f17c1460b *R/summary.bayesm.mat.R 14b1a1bf0cd8784439a7c16d2408ddde *R/summary.bayesm.nmix.R 8fa4d18ae9076fb6a474d7483386f432 *R/summary.bayesm.var.R f281a40aac3327926171ddaa72cc306d *build/vignette.rds fa58bfb68b79925644d958bdd659b14c *data/Scotch.rda 8233d19a217afa65d03bbd5e1f95cec8 *data/bank.rda b95835dcc2899199080a62c9c5b5cd90 *data/camera.RData 970c1954f36021804256b1cb160e2f9b *data/cheese.rda 12629326a348c7901248e33e76987858 *data/customerSat.rda b64a79aed4d1f4e6f64593aba5aba322 *data/detailing.rda 0bb56ee15246d15c8b7931f98e332693 *data/margarine.rda 506ede0b22d91973c03085ca17ba9d8e *data/orangeJuice.rda dbce99be695403a0df5c8874b8f6ddcd *data/tuna.rda 53e5c8b98d6b710c656fd969bf6d71b6 *inst/doc/Constrained_MNL_Vignette.R 7dd837d812cf03c9a5c260d5334ff525 *inst/doc/Constrained_MNL_Vignette.Rmd 3e0abd84f0a418a738135795ea4b5a22 *inst/doc/Constrained_MNL_Vignette.html 6072b88a2b44bb212e221fe47a977045 *inst/doc/bayesm_Overview_Vignette.R 6b8bc3010578ff01c1fa1240035bdfcc *inst/doc/bayesm_Overview_Vignette.Rmd 5b5ebd82c458a060148b887ea4ad62f9 *inst/doc/bayesm_Overview_Vignette.html f6ce98a7f6aa9e853a3b4cb4d3db254f *inst/include/bayesm.h c702dda44e13952523d39224e34928f6 *man/Scotch.Rd 73c5d32b59a0824091f6a8f104826943 *man/bank.Rd c06c7bff1ae48639df2df727f1d70861 *man/breg.Rd 7484f5a9741d05e5540210bded466166 *man/camera.Rd 7ef60a7a41a7669cd44887a7204cc5c2 *man/cgetC.Rd 69376efb73d26ceadc25dd0c377999be *man/cheese.Rd 1cf6002b2e9c500f4cf41e83dc333218 *man/clusterMix.Rd be009d68ff2b2da8c8e9632d8eadf5d9 *man/condMom.Rd 9fd280ad2f5b5edfc626489cbeead42b *man/createX.Rd 2b7fe05f7e8ffb5adc8fd5971c262977 *man/customerSat.Rd 887b42f64672d979e3608be3b381f2d3 *man/detailing.Rd 7eb036793dcb08d4b102bc5cc0a869d5 *man/eMixMargDen.Rd 2e1e09913a3159e942e92cfe7d102cce *man/ghkvec.Rd f01a0195aeeb6ac5bf0dc52214523985 *man/llmnl.Rd 391a5122525429e0be3d1cd3de47821e *man/llmnp.Rd 39e61dc24d2ae8b82a5472deac63c728 *man/llnhlogit.Rd 97fc7ca4efe7b61f405ac483b0e0ecc2 *man/lndIChisq.Rd ff73ac4b41d4136209f75634be95600f *man/lndIWishart.Rd a50c2c98243f56356a9f1d6540eba780 *man/lndMvn.Rd d3ea604953ac4d24c7409cffc21a1dd7 *man/lndMvst.Rd 328888e84e1a4aa5cab540d004487dac *man/logMargDenNR.Rd a071eb9332275b78b49f48c508e7a600 *man/margarine.Rd 534ed2cb79d27ad767466b3d3e8fe66b *man/mixDen.Rd 0d9239abdc91532de7f78ad7c632f19b *man/mixDenBi.Rd a819e8fd506d7b124db9126525836bc5 *man/mnlHess.Rd 3c6784f34e4e43abc7a96f91174a5073 *man/mnpProb.Rd 0f21fc3ee72154a522de572f1f490961 *man/momMix.Rd b1e2a66ae5fd0b20b8e303f2143546f4 *man/nmat.Rd fa6d6c450229bbaf42f09b708717f8d1 *man/numEff.Rd 23440b16399038f11f11b7e33c3963da *man/orangeJuice.Rd b914f7b0ae5577aa7025bb873fbc8543 *man/plot.bayesm.hcoef.Rd 378003d8aeceeb033be1855c4fc9f006 *man/plot.bayesm.mat.Rd d6194bc76d2014e7937a62e1d77d1ca3 *man/plot.bayesm.nmix.Rd ff56b11aea220dd888c6ba8c49c136b4 *man/rDPGibbs.Rd b8658b4e2e44c44dfe4b3d23d34fe9ae *man/rbayesBLP.Rd 8604546a510dd5fb9f1c8644fbc6c533 *man/rbiNormGibbs.Rd 5ff1b6bc2e6c0cba3c3f25cece7f70c8 *man/rbprobitGibbs.Rd b5d6cd8b9c59f80ad749ca0fc4f9b605 *man/rdirichlet.Rd 2693ec08cd0a69b667b90c1708773791 *man/rhierBinLogit.Rd c228939ac8c6945db2d24712d6034464 *man/rhierLinearMixture.Rd 38927e370dd8d319a34e8f51ffcb46b3 *man/rhierLinearModel.Rd b8e31619d5eb3ddc72ad15b6f7927ee7 *man/rhierMnlDP.Rd e890a313bc537e0e55e5b9f09bd02f4b *man/rhierMnlRwMixture.Rd a8232995d82663f97caeeae8bc1ced4c *man/rhierNegbinRw.Rd bd39023454af40ece1a9168cdba8237c *man/rivDP.Rd 553f7cda6df5c263580f60f2a4071204 *man/rivGibbs.Rd 2e65701d6264f7999da7be9983013c87 *man/rmixGibbs.Rd d9631bf2e6c4050b84f5f03730ab741f *man/rmixture.Rd cb52c2777fcd6ef8404b2ccc9a93ea60 *man/rmnlIndepMetrop.Rd 310fdeed512e5c4a9771ebde2b8ddce6 *man/rmnpGibbs.Rd 273f6daa0555e23b33bfd5da0d75a196 *man/rmultireg.Rd dba5af7dbe63f06aad5e35f5c9196ae7 *man/rmvpGibbs.Rd f314137b7e884b1faaa97c84770c8eb3 *man/rmvst.Rd 058969e02775b10e9cd58cf232c6b508 *man/rnegbinRw.Rd 6e134f7bc56100344519393f5d8fa39b *man/rnmixGibbs.Rd f31d8647cf2245acd4e7bd401705b6d6 *man/rordprobitGibbs.Rd 659ea8d49652ab790049f06e5775776f *man/rscaleUsage.Rd 4af41699e7914803f542c6a7d42daa87 *man/rsurGibbs.Rd b9aed5ca264b5af93a7d7cd2f2fc476a *man/rtrun.Rd e7f9d91b8dded8110981f6d107b3d2b1 *man/runireg.Rd 079779aa45ccdb95e83234fa26cb63c0 *man/runiregGibbs.Rd 0531ccc121b27439301e8febaebbbaa3 *man/rwishart.Rd b42a88a4ca09db682a2c977be803764e *man/simnhlogit.Rd 53e079e589ed7e43eee0cc1c94da69f4 *man/summary.bayesm.mat.Rd 37aeaac9d62c8f400dc0b87ae18bed8f *man/summary.bayesm.nmix.Rd 77121d7aa528ed72e5dbca961484c373 *man/summary.bayesm.var.Rd 63f119507bf3a2f9f8c10dd746459da8 *man/tuna.Rd 7cc14aaf4e9b43167d156ac973e01c68 *src/Makevars 7cc14aaf4e9b43167d156ac973e01c68 *src/Makevars.win c6e72fea6ca36d84f36f1bd1fc73e464 *src/RcppExports.cpp 38906292e8329c759e77c1982939169d *src/bayesBLP_rcpp_loop.cpp 0fa53738f4a680174a18f4bf4c4c9181 *src/breg_rcpp.cpp c4cea898c0b3b51fd3731caee125569a *src/cgetC_rcpp.cpp 1ae014e74b5f4c73354a40218e2a612d *src/clusterMix_rcpp_loop.cpp c32622825b8839daad7ffe3a7c202a03 *src/functionTiming.cpp 379eb42a07c86700af57a4437cd66d27 *src/ghkvec_rcpp.cpp ae68b66eaf3c0b468904456d044925a4 *src/llmnl_rcpp.cpp 607d592ac59d827b9132d7ca42abadc0 *src/lndIChisq_rcpp.cpp b3e4382526389879999985aa463f40c7 *src/lndIWishart_rcpp.cpp 9760670d64f12c4739ad92caecd1f452 *src/lndMvn_rcpp.cpp c5a3b79616c2e1755ba1e921b90a271a *src/lndMvst_rcpp.cpp a64df3d8c5ce90ced6f2559aab6723f4 *src/rDPGibbs_rcpp_loop.cpp b05f091395ad4570ce822dbe62c47d9d *src/rbprobitGibbs_rcpp_loop.cpp 3ddd1fb8a9ca3a6fb02008e12c1079aa *src/rdirichlet_rcpp.cpp d16f181a8b010ef48c8b99b92f796c44 *src/rhierLinearMixture_rcpp_loop.cpp 675918add3060f982f45c40bb3c42e34 *src/rhierLinearModel_rcpp_loop.cpp 2a6c4e09e078e40768659cb69e940c49 *src/rhierMnlDP_rcpp_loop.cpp 56e81ad73fc758bad5c694bca2a59623 *src/rhierMnlRwMixture_rcpp_loop.cpp df282cc42237495151715b946e719577 *src/rhierNegbinRw_rcpp_loop.cpp ec21162ad271a9adf1f05ea6c91fd877 *src/rivDP_rcpp_loop.cpp 87a21cd755d2f3461bbb6ae4e1552b35 *src/rivgibbs_rcpp_loop.cpp f326109666682ed0844daad5a093652e *src/rmixGibbs_rcpp.cpp e5ca228c396cb5f46a8e1f1f96002dc1 *src/rmixture_rcpp.cpp f0cf94cb44640ddbc3daa72c12e21c28 *src/rmnlIndepMetrop_rcpp_loop.cpp d269d1987d35ceda0f37d1e7a974bce3 *src/rmnpGibbs_rcpp_loop.cpp 0d522901127a1ffd9a5246c45edf10de *src/rmultireg_rcpp.cpp a33cf7ed340dbb759dba2cca369fbd44 *src/rmvpGibbs_rcpp_loop.cpp 1015db5ea007f193c9b3e010ab2551d3 *src/rmvst_rcpp.cpp d98f8385126b91ba2044ca09ede98339 *src/rnegbinRw_rcpp_loop.cpp 756517253a6b9d509255ddc93138d80c *src/rnmixGibbs_rcpp_loop.cpp 65554b346373018519da4beb78784bd8 *src/rordprobitGibbs_rcpp_loop.cpp b250e5ef3755130722e0e42233543ccb *src/rscaleUsage_rcpp_loop.cpp f8bb2a7066819e4498e8202464e934b7 *src/rsurGibbs_rcpp_loop.cpp efcf97fd8da23a7f47f60d8cc346c079 *src/rtrun_rcpp.cpp fb98b48e6d1247550bf0820b1fb7ff1d *src/runiregGibbs_rcpp_loop.cpp e0dc5bd40feb6ba71b1a6dd4e0dbd84a *src/runireg_rcpp_loop.cpp 1524fe60c6fea0029759326afe3a163c *src/rwishart_rcpp.cpp f00ee55ec4e2ec8ebf0e33ca555f8c6f *src/trunNorm.cpp 28301b73be4deb83a4955b68bfc8371f *src/utilityFunctions.cpp 7dd837d812cf03c9a5c260d5334ff525 *vignettes/Constrained_MNL_Vignette.Rmd 6b8bc3010578ff01c1fa1240035bdfcc *vignettes/bayesm_Overview_Vignette.Rmd bayesm/build/0000755000176000001440000000000013123305446012662 5ustar ripleyusersbayesm/build/vignette.rds0000644000176000001440000000042513123305446015222 0ustar ripleyusers‹uPÑJÃ0Íln {…|AŸüÇÜT¦ˆo%¶×õB’JWöæ—¯ÞuÉ°ƒ<ôæÞ“sÏ9éǘ1vÂ’”jBm2¡2¤ïªÃS6¢óæ¾ÖÖÊ|ñ4Ïßq¥Á9È–ª œO±«òç5˜5BÓçôõîŒ0E……|ñ#êZ!õóz…Ž7è*þJûü`ì¬ß½Üûðàs$}Z9%)šµ#õ¯ãŒøÓévÒií5Ù¿3‚§;là•OµP`ýåЃé %ºüLg¾„lgSø]†¿wþ›¦64÷F¦n²`v±KöK¥mÛíq¢B p\ '²/Cû4mÿ*àÙRTbayesm/DESCRIPTION0000644000176000001440000000371013134414120013262 0ustar ripleyusersPackage: bayesm Version: 3.1-0.1 Type: Package Title: Bayesian Inference for Marketing/Micro-Econometrics Depends: R (>= 3.2.0) Date: 2017-06-23 Author: Peter Rossi Maintainer: Peter Rossi License: GPL (>= 2) Imports: Rcpp (>= 0.12.0), utils, stats, graphics, grDevices LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown VignetteBuilder: knitr URL: http://www.perossi.org/home/bsm-1 Description: Covers many important models used in marketing and micro-econometrics applications. The package includes: Bayes Regression (univariate or multivariate dep var), Bayes Seemingly Unrelated Regression (SUR), Binary and Ordinal Probit, Multinomial Logit (MNL) and Multinomial Probit (MNP), Multivariate Probit, Negative Binomial (Poisson) Regression, Multivariate Mixtures of Normals (including clustering), Dirichlet Process Prior Density Estimation with normal base, Hierarchical Linear Models with normal prior and covariates, Hierarchical Linear Models with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates, Hierarchical Negative Binomial Regression Models, Bayesian analysis of choice-based conjoint data, Bayesian treatment of linear instrumental variables models, Analysis of Multivariate Ordinal survey data with scale usage heterogeneity (as in Rossi et al, JASA (01)), Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see Jiang, Manchanda, Rossi 2009) For further reference, consult our book, Bayesian Statistics and Marketing by Rossi, Allenby and McCulloch (Wiley 2005) and Bayesian Non- and Semi-Parametric Methods and Applications (Princeton U Press 2014). RoxygenNote: 6.0.1 NeedsCompilation: yes Packaged: 2017-07-21 14:37:52 UTC; ripley Repository: CRAN Date/Publication: 2017-07-21 15:05:52 UTC bayesm/man/0000755000176000001440000000000013123305446012336 5ustar ripleyusersbayesm/man/simnhlogit.Rd0000755000176000001440000000265213117541521015011 0ustar ripleyusers\name{simnhlogit} \alias{simnhlogit} \concept{logit} \concept{non-homothetic} \title{Simulate from Non-homothetic Logit Model} \description{\code{simnhlogit} simulates from the non-homothetic logit model.} \usage{simnhlogit(theta, lnprices, Xexpend)} \arguments{ \item{theta }{ coefficient vector } \item{lnprices }{ \eqn{n x p} array of prices } \item{Xexpend }{ \eqn{n x k} array of values of expenditure variables} } \details{For details on parameterization, see \code{llnhlogit}.} \value{ A list containing: \item{y }{\eqn{n x 1} vector of multinomial outcomes (\eqn{1,\ldots,p})} \item{Xexpend }{ expenditure variables} \item{lnprices }{ price array } \item{theta }{ coefficients} \item{prob }{ \eqn{n x p} array of choice probabilities} } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 4, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{llnhlogit}} } \examples{ N = 1000 p = 3 k = 1 theta = c(rep(1,p), seq(from=-1,to=1,length=p), rep(2,k), 0.5) lnprices = matrix(runif(N*p), ncol=p) Xexpend = matrix(runif(N*k), ncol=k) simdata = simnhlogit(theta, lnprices, Xexpend) } \keyword{models} bayesm/man/rmnlIndepMetrop.Rd0000644000176000001440000000624613117541521015753 0ustar ripleyusers\name{rmnlIndepMetrop} \alias{rmnlIndepMetrop} \concept{MCMC} \concept{multinomial logit} \concept{Metropolis algorithm} \concept{bayes} \title{MCMC Algorithm for Multinomial Logit Model} \description{ \code{rmnlIndepMetrop} implements Independence Metropolis algorithm for the multinomial logit (MNL) model. } \usage{rmnlIndepMetrop(Data, Prior, Mcmc)} \arguments{ \item{Data }{list(y, X, p)} \item{Prior}{list(A, betabar)} \item{Mcmc }{list(R, keep, nprint, nu)} } \details{ \subsection{Model and Priors}{ y \eqn{\sim}{~} MNL(X, \eqn{\beta}) \cr \eqn{Pr(y=j) = exp(x_j'\beta)/\sum_k{e^{x_k'\beta}}} \eqn{\beta} \eqn{\sim}{~} \eqn{N(betabar, A^{-1})} } \subsection{Argument Details}{ \emph{\code{Data = list(y, X, p)}} \tabular{ll}{ \code{y: } \tab \eqn{n x 1} vector of multinomial outcomes (1, \ldots, p) \cr \code{X: } \tab \eqn{n*p x k} matrix \cr \code{p: } \tab number of alternatives } \emph{\code{Prior = list(A, betabar)} [optional]} \tabular{ll}{ \code{A: } \tab \eqn{k x k} prior precision matrix (def: 0.01*I) \cr \code{betabar: } \tab \eqn{k x 1} prior mean (def: 0) } \emph{\code{Mcmc = list(R, keep, nprint, nu)} [only \code{R} required]} \tabular{ll}{ \code{R: } \tab number of MCMC draws \cr \code{keep: } \tab MCMC thinning parameter -- keep every \code{keep}th draw (def: 1) \cr \code{nprint: } \tab print the estimated time remaining for every \code{nprint}'th draw (def: 100, set to 0 for no print) \cr \code{nu: } \tab d.f. parameter for independent t density (def: 6) } } } \value{ A list containing: \item{betadraw }{\eqn{R/keep x k} matrix of beta draws} \item{loglike }{\eqn{R/keep x 1} vector of log-likelihood values evaluated at each draw} \item{acceptr }{acceptance rate of Metropolis draws} } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 3, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{rhierMnlRwMixture}} } \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} set.seed(66) simmnl = function(p, n, beta) { ## note: create X array with 2 alt.spec vars k = length(beta) X1 = matrix(runif(n*p,min=-1,max=1), ncol=p) X2 = matrix(runif(n*p,min=-1,max=1), ncol=p) X = createX(p, na=2, nd=NULL, Xd=NULL, Xa=cbind(X1,X2), base=1) Xbeta = X\%*\%beta ## now do probs p = nrow(Xbeta) / n Xbeta = matrix(Xbeta, byrow=TRUE, ncol=p) Prob = exp(Xbeta) iota = c(rep(1,p)) denom = Prob\%*\%iota Prob = Prob / as.vector(denom) ## draw y y = vector("double",n) ind = 1:p for (i in 1:n) { yvec = rmultinom(1, 1, Prob[i,]) y[i] = ind\%*\%yvec } return(list(y=y, X=X, beta=beta, prob=Prob)) } n = 200 p = 3 beta = c(1, -1, 1.5, 0.5) simout = simmnl(p,n,beta) Data1 = list(y=simout$y, X=simout$X, p=p) Mcmc1 = list(R=R, keep=1) out = rmnlIndepMetrop(Data=Data1, Mcmc=Mcmc1) cat("Summary of beta draws", fill=TRUE) summary(out$betadraw, tvalues=beta) ## plotting examples if(0){plot(out$betadraw)} } \keyword{models} bayesm/man/summary.bayesm.mat.Rd0000644000176000001440000000365713117541521016373 0ustar ripleyusers\name{summary.bayesm.mat} \alias{summary.bayesm.mat} \title{Summarize Mcmc Parameter Draws } \description{ \code{summary.bayesm.mat} is an S3 method to summarize marginal distributions given an array of draws } \usage{ \method{summary}{bayesm.mat}(object, names, burnin = trunc(0.1 * nrow(X)), tvalues, QUANTILES = TRUE, TRAILER = TRUE,...) } \arguments{ \item{object }{ \code{object} (hereafter \code{X}) is an array of draws, usually an object of class \code{bayesm.mat}} \item{names }{ optional character vector of names for the columns of \code{X}} \item{burnin }{ number of draws to burn-in (def: \eqn{0.1*nrow(X)})} \item{tvalues }{ optional vector of "true" values for use in simulation examples } \item{QUANTILES }{ logical for should quantiles be displayed (def: \code{TRUE})} \item{TRAILER }{ logical for should a trailer be displayed (def: \code{TRUE})} \item{... }{ optional arguments for generic function} } \details{ Typically, \code{summary.bayesm.nmix} will be invoked by a call to the generic summary function as in \code{summary(object)} where object is of class \code{bayesm.mat}. Mean, Std Dev, Numerical Standard error (of estimate of posterior mean), relative numerical efficiency (see \code{numEff}), and effective sample size are displayed. If \code{QUANTILES=TRUE}, quantiles of marginal distirbutions in the columns of \eqn{X} are displayed.\cr \code{summary.bayesm.mat} is also exported for direct use as a standard function, as in \code{summary.bayesm.mat(matrix)}.\cr \code{summary.bayesm.mat(matrix)} returns (invisibly) the array of the various summary statistics for further use. To assess this array use\code{stats=summary(Drawmat)}. } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \seealso{ \code{\link{summary.bayesm.var}}, \code{\link{summary.bayesm.nmix}}} \examples{ \dontrun{out=rmnpGibbs(Data,Prior,Mcmc); summary(out$betadraw)} } \keyword{univar} bayesm/man/ghkvec.Rd0000755000176000001440000000606313117541521014103 0ustar ripleyusers\name{ghkvec} \alias{ghkvec} \concept{multivariate normal distribution} \concept{GHK method} \concept{integral} \title{Compute GHK approximation to Multivariate Normal Integrals} \description{ \code{ghkvec} computes the GHK approximation to the integral of a multivariate normal density over a half plane defined by a set of truncation points. } \usage{ghkvec(L, trunpt, above, r, HALTON=TRUE, pn)} \arguments{ \item{L }{ lower triangular Cholesky root of covariance matrix } \item{trunpt}{ vector of truncation points} \item{above }{ vector of indicators for truncation above(1) or below(0) } \item{r }{ number of draws to use in GHK } \item{HALTON}{ if \code{TRUE}, uses Halton sequence. If \code{FALSE}, uses \code{R::runif} random number generator (def: \code{TRUE})} \item{pn }{ prime number used for Halton sequence (def: the smallest prime numbers, i.e. 2, 3, 5, ...)} } \value{Approximation to integral} \note{ \code{ghkvec} can accept a vector of truncations and compute more than one integral. That is, \code{length(trunpt)/length(above)} number of different integrals, each with the same variance and mean 0 but different truncation points. See 'examples' below for an example with two integrals at different truncation points. The user can choose between two random number generators for the numerical integration: psuedo-random numbers by \code{R::runif} or quasi-random numbers by a Halton sequence. Generally, the quasi-random (Halton) sequence is more uniformly distributed within domain, so it shows lower error and improved convergence than the psuedo-random (\code{runif}) sequence (Morokoff and Caflisch, 1995). For the prime numbers generating Halton sequence, we suggest to use the first smallest prime numbers. Halton (1960) and Kocis and Whiten (1997) prove that their discrepancy measures (how uniformly the sample points are distributed) have the upper bounds, which decrease as the generating prime number decreases. Note: For a high dimensional integration (10 or more dimension), we suggest use of the psuedo-random number generator (\code{R::runif}) because, according to Kocis and Whiten (1997), Halton sequences may be highly correlated when the dimension is 10 or more. } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.\cr Keunwoo Kim, Anderson School, UCLA, \email{keunwoo.kim@gmail.com}. } \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch, Chapter 2. \cr \url{http://www.perossi.org/home/bsm-1} For Halton sequence, see Halton (1960, Numerische Mathematik), Morokoff and Caflisch (1995, Journal of Computational Physics), and Kocis and Whiten (1997, ACM Transactions on Mathematical Software). } \examples{ Sigma = matrix(c(1, 0.5, 0.5, 1), ncol=2) L = t(chol(Sigma)) trunpt = c(0,0,1,1) above = c(1,1) # drawn by Halton sequence ghkvec(L, trunpt, above, r=100) # use prime number 11 and 13 ghkvec(L, trunpt, above, r=100, HALTON=TRUE, pn=c(11,13)) # drawn by R::runif ghkvec(L, trunpt, above, r=100, HALTON=FALSE) } \keyword{distribution} bayesm/man/clusterMix.Rd0000644000176000001440000000534013117541521014765 0ustar ripleyusers\name{clusterMix} \alias{clusterMix} \concept{normal mixture} \concept{clustering} \title{Cluster Observations Based on Indicator MCMC Draws} \description{ \code{clusterMix} uses MCMC draws of indicator variables from a normal component mixture model to cluster observations based on a similarity matrix. } \usage{clusterMix(zdraw, cutoff=0.9, SILENT=FALSE, nprint=BayesmConstant.nprint)} \arguments{ \item{zdraw}{ \eqn{R x nobs} array of draws of indicators} \item{cutoff}{ cutoff probability for similarity (def: \code{0.9})} \item{SILENT}{ logical flag for silent operation (def: \code{FALSE})} \item{nprint}{ print every nprint'th draw (def: \code{100})} } \details{ Define a similarity matrix, \eqn{Sim} with \code{Sim[i,j]=1} if observations \eqn{i} and \eqn{j} are in same component. Compute the posterior mean of Sim over indicator draws. Clustering is achieved by two means: Method A: Find the indicator draw whose similarity matrix minimizes \eqn{loss(E[Sim]-Sim(z))}, where loss is absolute deviation. Method B: Define a Similarity matrix by setting any element of \eqn{E[Sim] = 1} if \eqn{E[Sim] > cutoff}. Compute the clustering scheme associated with this "windsorized" Similarity matrix. } \value{ A list containing: \item{clustera:}{indicator function for clustering based on method A above} \item{clusterb:}{indicator function for clustering based on method B above} } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch Chapter 3. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{rnmixGibbs}} } \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) { ## simulate data from mixture of normals n = 500 pvec = c(.5,.5) mu1 = c(2,2) mu2 = c(-2,-2) Sigma1 = matrix(c(1,0.5,0.5,1), ncol=2) Sigma2 = matrix(c(1,0.5,0.5,1), ncol=2) comps = NULL comps[[1]] = list(mu1, backsolve(chol(Sigma1),diag(2))) comps[[2]] = list(mu2, backsolve(chol(Sigma2),diag(2))) dm = rmixture(n, pvec, comps) ## run MCMC on normal mixture Data = list(y=dm$x) ncomp = 2 Prior = list(ncomp=ncomp, a=c(rep(100,ncomp))) R = 2000 Mcmc = list(R=R, keep=1) out = rnmixGibbs(Data=Data, Prior=Prior, Mcmc=Mcmc) ## find clusters begin = 500 end = R outclusterMix = clusterMix(out$nmix$zdraw[begin:end,]) ## check on clustering versus "truth" ## note: there could be switched labels table(outclusterMix$clustera, dm$z) table(outclusterMix$clusterb, dm$z) } } \keyword{models} \keyword{multivariate} bayesm/man/mixDenBi.Rd0000644000176000001440000000301613117541521014323 0ustar ripleyusers\name{mixDenBi} \alias{mixDenBi} \concept{normal mixture} \concept{marginal distribution} \concept{density} \title{Compute Bivariate Marginal Density for a Normal Mixture} \description{ \code{mixDenBi} computes the implied bivariate marginal density from a mixture of normals with specified mixture probabilities and component parameters. } \usage{mixDenBi(i, j, xi, xj, pvec, comps)} \arguments{ \item{i}{ index of first variable } \item{j}{ index of second variable } \item{xi}{ grid of values of first variable } \item{xj}{ grid of values of second variable } \item{pvec}{ normal mixture probabilities } \item{comps}{ list of lists of components } } \details{ \tabular{ll}{ \code{length(comps) } \tab is the number of mixture components \cr \code{comps[[j]] } \tab is a list of parameters of the \eqn{j}th component \cr \code{comps[[j]]$mu } \tab is mean vector \cr \code{comps[[j]]$rooti} \tab is the UL decomp of \eqn{\Sigma^{-1}} } } \value{An array (\code{length(xi)=length(xj) x 2}) with density value} \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 3, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{rnmixGibbs}}, \code{\link{mixDen}} } \keyword{models} \keyword{multivariate} bayesm/man/rbayesBLP.Rd0000644000176000001440000002422513117541521014454 0ustar ripleyusers\name{rbayesBLP} \alias{rbayesBLP} \concept{bayes} \concept{random coefficient logit} \concept{BLP} \concept{Metropolis Hasting} \title{Bayesian Analysis of Random Coefficient Logit Models Using Aggregate Data} \description{ \code{rbayesBLP} implements a hybrid MCMC algorithm for aggregate level sales data in a market with differentiated products. bayesm version 3.1-0 and prior verions contain an error when using instruments with this function; this will be fixed in a future version. } \usage{rbayesBLP(Data, Prior, Mcmc)} \arguments{ \item{Data }{list(X, share, J, Z)} \item{Prior}{list(sigmasqR, theta_hat, A, deltabar, Ad, nu0, s0_sq, VOmega)} \item{Mcmc }{list(R, keep, nprint, H, initial_theta_bar, initial_r, initial_tau_sq, initial_Omega, initial_delta, s, cand_cov, tol)} } \value{ A list containing: \item{thetabardraw }{\eqn{K x R/keep} matrix of random coefficient mean draws} \item{Sigmadraw }{\eqn{K*K x R/keep} matrix of random coefficient variance draws} \item{rdraw }{\eqn{K*K x R/keep} matrix of \eqn{r} draws (same information as in \code{Sigmadraw})} \item{tausqdraw }{\eqn{R/keep x 1} vector of aggregate demand shock variance draws} \item{Omegadraw }{\eqn{2*2 x R/keep} matrix of correlated endogenous shock variance draws} \item{deltadraw }{\eqn{I x R/keep} matrix of endogenous structural equation coefficient draws} \item{acceptrate }{scalor of acceptance rate of Metropolis-Hasting} \item{s }{scale parameter used for Metropolis-Hasting} \item{cand_cov }{var-cov matrix used for Metropolis-Hasting} } \section{Argument Details}{ \emph{\code{Data = list(X, share, J, Z)}} [\code{Z} optional] \tabular{ll}{ \code{J: } \tab number of alternatives, excluding an outside option \cr \code{X: } \tab \eqn{J*T x K} matrix (no outside option, which is normalized to 0). \cr \code{ } \tab If IV is used, the last column of \code{X} is the endogeneous variable. \cr \code{share: } \tab \eqn{J*T} vector (no outside option). \cr \code{ } \tab Note that both the \code{share} vector and the \code{X} matrix are organized by the \eqn{jt} index. \cr \code{ } \tab \eqn{j} varies faster than \eqn{t}, i.e. \eqn{(j=1,t=1), (j=2,t=1), ..., (j=J,T=1), ..., (j=J,t=T)} \cr \code{Z: } \tab \eqn{J*T x I} matrix of instrumental variables (optional) } \emph{\code{Prior = list(sigmasqR, theta_hat, A, deltabar, Ad, nu0, s0_sq, VOmega)} [optional]} \tabular{ll}{ \code{sigmasqR: } \tab \eqn{K*(K+1)/2} vector for \eqn{r} prior variance (def: diffuse prior for \eqn{\Sigma}) \cr \code{theta_hat: } \tab \eqn{K} vector for \eqn{\theta_bar} prior mean (def: 0 vector) \cr \code{A: } \tab \eqn{K x K} matrix for \eqn{\theta_bar} prior precision (def: \code{0.01*diag(K)}) \cr \code{deltabar: } \tab \eqn{I} vector for \eqn{\delta} prior mean (def: 0 vector) \cr \code{Ad: } \tab \eqn{I x I} matrix for \eqn{\delta} prior precision (def: \code{0.01*diag(I)}) \cr \code{nu0: } \tab d.f. parameter for \eqn{\tau_sq} and \eqn{\Omega} prior (def: K+1) \cr \code{s0_sq: } \tab scale parameter for \eqn{\tau_sq} prior (def: 1) \cr \code{VOmega: } \tab \eqn{2 x 2} matrix parameter for \eqn{\Omega} prior (def: \code{matrix(c(1,0.5,0.5,1),2,2)}) } \emph{\code{Mcmc = list(R, keep, nprint, H, initial_theta_bar, initial_r, initial_tau_sq, initial_Omega, initial_delta, s, cand_cov, tol)} [only \code{R} and \code{H} required]} \tabular{ll}{ \code{R: } \tab number of MCMC draws \cr \code{keep: } \tab MCMC thinning parameter -- keep every \code{keep}th draw (def: 1) \cr \code{nprint: } \tab print the estimated time remaining for every \code{nprint}'th draw (def: 100, set to 0 for no print) \cr \code{H: } \tab number of random draws used for Monte-Carlo integration \cr \code{initial_theta_bar: } \tab initial value of \eqn{\theta_bar} (def: 0 vector) \cr \code{initial_r: } \tab initial value of \eqn{r} (def: 0 vector) \cr \code{initial_tau_sq: } \tab initial value of \eqn{\tau_sq} (def: 0.1) \cr \code{initial_Omega: } \tab initial value of \eqn{\Omega} (def: \code{diag(2)}) \cr \code{initial_delta: } \tab initial value of \eqn{\delta} (def: 0 vector) \cr \code{s: } \tab scale parameter of Metropolis-Hasting increment (def: automatically tuned) \cr \code{cand_cov: } \tab var-cov matrix of Metropolis-Hasting increment (def: automatically tuned) \cr \code{tol: } \tab convergence tolerance for the contraction mapping (def: 1e-6) } } \section{Model Details}{ \subsection{Model and Priors (without IV):}{ \eqn{u_ijt = X_jt \theta_i + \eta_jt + e_ijt}\cr \eqn{e_ijt} \eqn{\sim}{~} type I Extreme Value (logit)\cr \eqn{\theta_i} \eqn{\sim}{~} \eqn{N(\theta_bar, \Sigma)}\cr \eqn{\eta_jt} \eqn{\sim}{~} \eqn{N(0, \tau_sq)}\cr This structure implies a logit model for each consumer (\eqn{\theta}). Aggregate shares (\code{share}) are produced by integrating this consumer level logit model over the assumed normal distribution of \eqn{\theta}. \eqn{r} \eqn{\sim}{~} \eqn{N(0, diag(sigmasqR))}\cr \eqn{\theta_bar} \eqn{\sim}{~} \eqn{N(\theta_hat, A^-1)}\cr \eqn{\tau_sq} \eqn{\sim}{~} \eqn{nu0*s0_sq / \chi^2 (nu0)}\cr Note: we observe the aggregate level market share, not individual level choices.\cr Note: \eqn{r} is the vector of nonzero elements of cholesky root of \eqn{\Sigma}. Instead of \eqn{\Sigma} we draw \eqn{r}, which is one-to-one correspondence with the positive-definite \eqn{\Sigma}. } \subsection{Model and Priors (with IV):}{ \eqn{u_ijt = X_jt \theta_i + \eta_jt + e_ijt}\cr \eqn{e_ijt} \eqn{\sim}{~} type I Extreme Value (logit)\cr \eqn{\theta_i} \eqn{\sim}{~} \eqn{N(\theta_bar, \Sigma)}\cr \eqn{X_jt = [X_exo_jt, X_endo_jt]}\cr \eqn{X_endo_jt = Z_jt \delta_jt + \zeta_jt}\cr \eqn{vec(\zeta_jt, \eta_jt)} \eqn{\sim}{~} \eqn{N(0, \Omega)}\cr \eqn{r} \eqn{\sim}{~} \eqn{N(0, diag(sigmasqR))}\cr \eqn{\theta_bar} \eqn{\sim}{~} \eqn{N(\theta_hat, A^-1)}\cr \eqn{\delta} \eqn{\sim}{~} \eqn{N(deltabar, Ad^-1)}\cr \eqn{\Omega} \eqn{\sim}{~} \eqn{IW(nu0, VOmega)}\cr } } \section{MCMC and Tuning Details:}{ \subsection{MCMC Algorithm:}{ Step 1 (\eqn{\Sigma}):\cr Given \eqn{\theta_bar} and \eqn{\tau_sq}, draw \eqn{r} via Metropolis-Hasting.\cr Covert the drawn \eqn{r} to \eqn{\Sigma}.\cr Note: if user does not specify the Metropolis-Hasting increment parameters (\code{s} and \code{cand_cov}), \code{rbayesBLP} automatically tunes the parameters. Step 2 without IV (\eqn{\theta_bar}, \eqn{\tau_sq}):\cr Given \eqn{\Sigma}, draw \eqn{\theta_bar} and \eqn{\tau_sq} via Gibbs sampler.\cr Step 2 with IV (\eqn{\theta_bar}, \eqn{\delta}, \eqn{\Omega}):\cr Given \eqn{\Sigma}, draw \eqn{\theta_bar}, \eqn{\delta}, and \eqn{\Omega} via IV Gibbs sampler.\cr } \subsection{Tuning Metropolis-Hastings algorithm:}{ r_cand = r_old + s*N(0,cand_cov)\cr Fix the candidate covariance matrix as cand_cov0 = diag(rep(0.1, K), rep(1, K*(K-1)/2)).\cr Start from s0 = 2.38/sqrt(dim(r))\cr Repeat\{\cr Run 500 MCMC chain.\cr If acceptance rate < 30\% => update s1 = s0/5.\cr If acceptance rate > 50\% => update s1 = s0*3.\cr (Store r draws if acceptance rate is 20~80\%.)\cr s0 = s1\cr \} until acceptance rate is 30~50\% Scale matrix C = s1*sqrt(cand_cov0)\cr Correlation matrix R = Corr(r draws)\cr Use C*R*C as s^2*cand_cov. } } \references{For further discussion, see \emph{Bayesian Analysis of Random Coefficient Logit Models Using Aggregate Data} by Jiang, Manchanda, and Rossi, \emph{Journal of Econometrics}, 2009. \cr \url{http://www.sciencedirect.com/science/article/pii/S0304407608002297} } \author{Keunwoo Kim, Anderson School, UCLA, \email{keunwoo.kim@gmail.com}.} \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) { ## Simulate aggregate level data simulData <- function(para, others, Hbatch) { # Hbatch does the integration for computing market shares # in batches of size Hbatch ## parameters theta_bar <- para$theta_bar Sigma <- para$Sigma tau_sq <- para$tau_sq T <- others$T J <- others$J p <- others$p H <- others$H K <- J + p ## build X X <- matrix(runif(T*J*p), T*J, p) inter <- NULL for (t in 1:T) { inter <- rbind(inter, diag(J)) } X <- cbind(inter, X) ## draw eta ~ N(0, tau_sq) eta <- rnorm(T*J)*sqrt(tau_sq) X <- cbind(X, eta) share <- rep(0, J*T) for (HH in 1:(H/Hbatch)){ ## draw theta ~ N(theta_bar, Sigma) cho <- chol(Sigma) theta <- matrix(rnorm(K*Hbatch), nrow=K, ncol=Hbatch) theta <- t(cho)\%*\%theta + theta_bar ## utility V <- X\%*\%rbind(theta, 1) expV <- exp(V) expSum <- matrix(colSums(matrix(expV, J, T*Hbatch)), T, Hbatch) expSum <- expSum \%x\% matrix(1, J, 1) choiceProb <- expV / (1 + expSum) share <- share + rowSums(choiceProb) / H } ## the last K+1'th column is eta, which is unobservable. X <- X[,c(1:K)] return (list(X=X, share=share)) } ## true parameter theta_bar_true <- c(-2, -3, -4, -5) Sigma_true <- rbind(c(3,2,1.5,1), c(2,4,-1,1.5), c(1.5,-1,4,-0.5), c(1,1.5,-0.5,3)) cho <- chol(Sigma_true) r_true <- c(log(diag(cho)), cho[1,2:4], cho[2,3:4], cho[3,4]) tau_sq_true <- 1 ## simulate data set.seed(66) T <- 300 J <- 3 p <- 1 K <- 4 H <- 1000000 Hbatch <- 5000 dat <- simulData(para=list(theta_bar=theta_bar_true, Sigma=Sigma_true, tau_sq=tau_sq_true), others=list(T=T, J=J, p=p, H=H), Hbatch) X <- dat$X share <- dat$share ## Mcmc run R <- 2000 H <- 50 Data1 <- list(X=X, share=share, J=J) Mcmc1 <- list(R=R, H=H, nprint=0) set.seed(66) out <- rbayesBLP(Data=Data1, Mcmc=Mcmc1) ## acceptance rate out$acceptrate ## summary of draws summary(out$thetabardraw) summary(out$Sigmadraw) summary(out$tausqdraw) ### plotting draws plot(out$thetabardraw) plot(out$Sigmadraw) plot(out$tausqdraw) } } \keyword{models}bayesm/man/rnegbinRw.Rd0000644000176000001440000001014213117541521014557 0ustar ripleyusers\name{rnegbinRw} \alias{rnegbinRw} \concept{MCMC} \concept{NBD regression} \concept{Negative Binomial regression} \concept{Poisson regression} \concept{Metropolis algorithm} \concept{bayes} \title{MCMC Algorithm for Negative Binomial Regression} \description{ \code{rnegbinRw} implements a Random Walk Metropolis Algorithm for the Negative Binomial (NBD) regression model where \eqn{\beta|\alpha} and \eqn{\alpha|\beta} are drawn with two different random walks. } \usage{rnegbinRw(Data, Prior, Mcmc)} \arguments{ \item{Data }{list(y, X)} \item{Prior}{list(betabar, A, a, b)} \item{Mcmc }{list(R, keep, nprint, s_beta, s_alpha, beta0, alpha)} } \details{ \subsection{Model and Priors}{ \eqn{y} \eqn{\sim}{~} \eqn{NBD(mean=\lambda, over-dispersion=alpha)} \cr \eqn{\lambda = exp(x'\beta)} \eqn{\beta} \eqn{\sim}{~} \eqn{N(betabar, A^{-1})} \cr \eqn{alpha} \eqn{\sim}{~} \eqn{Gamma(a, b)} (unless \code{Mcmc$alpha} specified) \cr Note: prior mean of \eqn{alpha = a/b}, \eqn{variance = a/(b^2)} } \subsection{Argument Details}{ \emph{\code{Data = list(y, X)}} \tabular{ll}{ \code{y: } \tab \eqn{n x 1} vector of counts (\eqn{0,1,2,\ldots}) \cr \code{X: } \tab \eqn{n x k} design matrix } \emph{\code{Prior = list(betabar, A, a, b)} [optional]} \tabular{ll}{ \code{betabar: } \tab \eqn{k x 1} prior mean (def: 0) \cr \code{A: } \tab \eqn{k x k} PDS prior precision matrix (def: 0.01*I) \cr \code{a: } \tab Gamma prior parameter (not used if \code{Mcmc$alpha} specified) (def: 0.5) \cr \code{b: } \tab Gamma prior parameter (not used if \code{Mcmc$alpha} specified) (def: 0.1) } \emph{\code{Mcmc = list(R, keep, nprint, s_beta, s_alpha, beta0, alpha)} [only \code{R} required]} \tabular{ll}{ \code{R: } \tab number of MCMC draws \cr \code{keep: } \tab MCMC thinning parameter -- keep every \code{keep}th draw (def: 1) \cr \code{nprint: } \tab print the estimated time remaining for every \code{nprint}'th draw (def: 100, set to 0 for no print) \cr \code{s_beta: } \tab scaling for beta | alpha RW inc cov matrix (def: 2.93/\code{sqrt(k)}) \cr \code{s_alpha: } \tab scaling for alpha | beta RW inc cov matrix (def: 2.93) \cr \code{alpha: } \tab over-dispersion parameter (def: alpha ~ Gamma(a,b)) } } } \value{ A list containing: \item{betadraw }{\eqn{R/keep x k} matrix of beta draws} \item{alphadraw }{\eqn{R/keep x 1} vector of alpha draws} \item{llike }{\eqn{R/keep x 1} vector of log-likelihood values evaluated at each draw} \item{acceptrbeta }{acceptance rate of the beta draws} \item{acceptralpha }{acceptance rate of the alpha draws} } \note{ The NBD regression encompasses Poisson regression in the sense that as alpha goes to infinity the NBD distribution tends toward the Poisson. For "small" values of alpha, the dependent variable can be extremely variable so that a large number of observations may be required to obtain precise inferences. } \author{Sridhar Narayanan (Stanford GSB) and Peter Rossi (Anderson School, UCLA), \email{perossichi@gmail.com}.} \references{For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{rhierNegbinRw}} } \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=1000} else {R=10} set.seed(66) simnegbin = function(X, beta, alpha) { # Simulate from the Negative Binomial Regression lambda = exp(X\%*\%beta) y = NULL for (j in 1:length(lambda)) { y = c(y, rnbinom(1, mu=lambda[j], size=alpha)) } return(y) } nobs = 500 nvar = 2 # Number of X variables alpha = 5 Vbeta = diag(nvar)*0.01 # Construct the regdata (containing X) simnegbindata = NULL beta = c(0.6, 0.2) X = cbind(rep(1,nobs), rnorm(nobs,mean=2,sd=0.5)) simnegbindata = list(y=simnegbin(X,beta,alpha), X=X, beta=beta) Data1 = simnegbindata Mcmc1 = list(R=R) out = rnegbinRw(Data=Data1, Mcmc=list(R=R)) cat("Summary of alpha/beta draw", fill=TRUE) summary(out$alphadraw, tvalues=alpha) summary(out$betadraw, tvalues=beta) ## plotting examples if(0){plot(out$betadraw)} } \keyword{models} bayesm/man/rhierNegbinRw.Rd0000644000176000001440000001353713117541521015402 0ustar ripleyusers\name{rhierNegbinRw} \alias{rhierNegbinRw} \concept{MCMC} \concept{hierarchical NBD regression} \concept{Negative Binomial regression} \concept{Poisson regression} \concept{Metropolis algorithm} \concept{bayes} \title{MCMC Algorithm for Hierarchical Negative Binomial Regression} \description{ \code{rhierNegbinRw} implements an MCMC algorithm for the hierarchical Negative Binomial (NBD) regression model. Metropolis steps for each unit-level set of regression parameters are automatically tuned by optimization. Over-dispersion parameter (alpha) is common across units. } \usage{rhierNegbinRw(Data, Prior, Mcmc)} \arguments{ \item{Data }{list(regdata, Z)} \item{Prior}{list(Deltabar, Adelta, nu, V, a, b)} \item{Mcmc }{list(R, keep, nprint, s_beta, s_alpha, alpha, c, Vbeta0, Delta0)} } \details{ \subsection{Model and Priors}{ \eqn{y_i} \eqn{\sim}{~} NBD(mean=\eqn{\lambda}, over-dispersion=alpha) \cr \eqn{\lambda = exp(X_i\beta_i)} \eqn{\beta_i} \eqn{\sim}{~} \eqn{N(\Delta'z_i,Vbeta)} \eqn{vec(\Delta|Vbeta)} \eqn{\sim}{~} \eqn{N(vec(Deltabar), Vbeta (x) Adelta)} \cr \eqn{Vbeta} \eqn{\sim}{~} \eqn{IW(nu, V)} \cr \eqn{alpha} \eqn{\sim}{~} \eqn{Gamma(a, b)} (unless \code{Mcmc$alpha} specified) \cr Note: prior mean of \eqn{alpha = a/b}, variance \eqn{= a/(b^2)} } \subsection{Argument Details}{ \emph{\code{Data = list(regdata, Z)} [\code{Z} optional]} \tabular{ll}{ \code{regdata: } \tab list of lists with data on each of \code{nreg} units \cr \code{regdata[[i]]$X: } \tab \eqn{nobs_i x nvar} matrix of \eqn{X} variables \cr \code{regdata[[i]]$y: } \tab \eqn{nobs_i x 1} vector of count responses \cr \code{Z: } \tab \eqn{nreg x nz} matrix of unit characteristics (def: vector of ones) } \emph{\code{Prior = list(Deltabar, Adelta, nu, V, a, b)} [optional]} \tabular{ll}{ \code{Deltabar: } \tab \eqn{nz x nvar} prior mean matrix (def: 0) \cr \code{Adelta: } \tab \eqn{nz x nz} PDS prior precision matrix (def: 0.01*I) \cr \code{nu: } \tab d.f. parameter for Inverted Wishart prior (def: nvar+3) \cr \code{V: } \tab location matrix of Inverted Wishart prior (def: nu*I) \cr \code{a: } \tab Gamma prior parameter (def: 0.5) \cr \code{b: } \tab Gamma prior parameter (def: 0.1) } \emph{\code{Mcmc = list(R, keep, nprint, s_beta, s_alpha, alpha, c, Vbeta0, Delta0)} [only \code{R} required]} \tabular{ll}{ \code{R: } \tab number of MCMC draws \cr \code{keep: } \tab MCMC thinning parameter -- keep every \code{keep}th draw (def: 1) \cr \code{nprint: } \tab print the estimated time remaining for every \code{nprint}'th draw (def: 100, set to 0 for no print) \cr \code{s_beta: } \tab scaling for beta | alpha RW inc cov (def: 2.93/\code{sqrt(nvar)}) \cr \code{s_alpha: } \tab scaling for alpha | beta RW inc cov (def: 2.93) \cr \code{alpha: } \tab over-dispersion parameter (def: alpha ~ Gamma(a,b)) \cr \code{c: } \tab fractional likelihood weighting parm (def: 2) \cr \code{Vbeta0: } \tab starting value for Vbeta (def: I) \cr \code{Delta0: } \tab starting value for Delta (def: 0) } } } \value{ A list containing: \item{llike }{ \eqn{R/keep x 1} vector of values of log-likelihood} \item{betadraw }{ \eqn{nreg x nvar x R/keep} array of beta draws} \item{alphadraw }{ \eqn{R/keep x 1} vector of alpha draws} \item{acceptrbeta }{ acceptance rate of the beta draws} \item{acceptralpha }{ acceptance rate of the alpha draws} } \note{ The NBD regression encompasses Poisson regression in the sense that as alpha goes to infinity the NBD distribution tends to the Poisson. For "small" values of alpha, the dependent variable can be extremely variable so that a large number of observations may be required to obtain precise inferences. For ease of interpretation, we recommend demeaning \eqn{Z} variables. } \seealso{ \code{\link{rnegbinRw}} } \author{Sridhar Narayanan (Stanford GSB) and Peter Rossi (Anderson School, UCLA), \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 5, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} set.seed(66) # Simulate from the Negative Binomial Regression simnegbin = function(X, beta, alpha) { lambda = exp(X\%*\%beta) y = NULL for (j in 1:length(lambda)) {y = c(y, rnbinom(1, mu=lambda[j], size=alpha)) } return(y) } nreg = 100 # Number of cross sectional units T = 50 # Number of observations per unit nobs = nreg*T nvar = 2 # Number of X variables nz = 2 # Number of Z variables ## Construct the Z matrix Z = cbind(rep(1,nreg), rnorm(nreg,mean=1,sd=0.125)) Delta = cbind(c(4,2), c(0.1,-1)) alpha = 5 Vbeta = rbind(c(2,1), c(1,2)) ## Construct the regdata (containing X) simnegbindata = NULL for (i in 1:nreg) { betai = as.vector(Z[i,]\%*\%Delta) + chol(Vbeta)\%*\%rnorm(nvar) X = cbind(rep(1,T),rnorm(T,mean=2,sd=0.25)) simnegbindata[[i]] = list(y=simnegbin(X,betai,alpha), X=X, beta=betai) } Beta = NULL for (i in 1:nreg) {Beta = rbind(Beta,matrix(simnegbindata[[i]]$beta,nrow=1))} Data1 = list(regdata=simnegbindata, Z=Z) Mcmc1 = list(R=R) out = rhierNegbinRw(Data=Data1, Mcmc=Mcmc1) cat("Summary of Delta draws", fill=TRUE) summary(out$Deltadraw, tvalues=as.vector(Delta)) cat("Summary of Vbeta draws", fill=TRUE) summary(out$Vbetadraw, tvalues=as.vector(Vbeta[upper.tri(Vbeta,diag=TRUE)])) cat("Summary of alpha draws", fill=TRUE) summary(out$alpha, tvalues=alpha) ## plotting examples if(0){ plot(out$betadraw) plot(out$alpha,tvalues=alpha) plot(out$Deltadraw,tvalues=as.vector(Delta)) } } \keyword{models} bayesm/man/rhierLinearModel.Rd0000644000176000001440000001130313117541521016047 0ustar ripleyusers\name{rhierLinearModel} \alias{rhierLinearModel} \concept{bayes} \concept{MCMC} \concept{Gibbs Sampling} \concept{hierarchical models} \concept{linear model} \title{Gibbs Sampler for Hierarchical Linear Model with Normal Heterogeneity} \description{ \code{rhierLinearModel} implements a Gibbs Sampler for hierarchical linear models with a normal prior. } \usage{rhierLinearModel(Data, Prior, Mcmc)} \arguments{ \item{Data }{list(regdata, Z)} \item{Prior}{list(Deltabar, A, nu.e, ssq, nu, V)} \item{Mcmc }{list(R, keep, nprint)} } \details{ \subsection{Model and Priors}{ \code{nreg} regression equations with \eqn{nvar} \eqn{X} variables in each equation \cr \eqn{y_i = X_i\beta_i + e_i} with \eqn{e_i} \eqn{\sim}{~} \eqn{N(0, \tau_i)} \eqn{\tau_i} \eqn{\sim}{~} nu.e*\eqn{ssq_i/\chi^2_{nu.e}} where \eqn{\tau_i} is the variance of \eqn{e_i}\cr \eqn{\beta_i} \eqn{\sim}{~} N(Z\eqn{\Delta}[i,], \eqn{V_{\beta}}) \cr Note: Z\eqn{\Delta} is the matrix \eqn{Z * \Delta} and [i,] refers to \eqn{i}th row of this product\cr \eqn{vec(\Delta)} given \eqn{V_{\beta}} \eqn{\sim}{~} \eqn{N(vec(Deltabar), V_{\beta}(x) A^{-1})}\cr \eqn{V_{\beta}} \eqn{\sim}{~} \eqn{IW(nu,V)} \cr \eqn{Delta, Deltabar} are \eqn{nz x nvar}; \eqn{A} is \eqn{nz x nz}; \eqn{V_{\beta}} is \eqn{nvar x nvar}. Note: if you don't have any \eqn{Z} variables, omit \eqn{Z} in the \code{Data} argument and a vector of ones will be inserted; the matrix \eqn{\Delta} will be \eqn{1 x nvar} and should be interpreted as the mean of all unit \eqn{\beta}s. } \subsection{Argument Details}{ \emph{\code{Data = list(regdata, Z)} [\code{Z} optional]} \tabular{ll}{ \code{regdata: } \tab list of lists with \eqn{X} and \eqn{y} matrices for each of \code{nreg=length(regdata)} regressions \cr \code{regdata[[i]]$X: } \tab \eqn{n_i x nvar} design matrix for equation \eqn{i} \cr \code{regdata[[i]]$y: } \tab \eqn{n_i x 1} vector of observations for equation \eqn{i} \cr \code{Z: } \tab \eqn{nreg x nz} matrix of unit characteristics (def: vector of ones) } \emph{\code{Prior = list(Deltabar, A, nu.e, ssq, nu, V)} [optional]} \tabular{ll}{ \code{Deltabar: } \tab \eqn{nz x nvar} matrix of prior means (def: 0) \cr \code{A: } \tab \eqn{nz x nz} matrix for prior precision (def: 0.01I) \cr \code{nu.e: } \tab d.f. parameter for regression error variance prior (def: 3) \cr \code{ssq: } \tab scale parameter for regression error var prior (def: \code{var(y_i)}) \cr \code{nu: } \tab d.f. parameter for Vbeta prior (def: nvar+3) \cr \code{V: } \tab Scale location matrix for Vbeta prior (def: nu*I) } \emph{\code{Mcmc = list(R, keep, nprint)} [only \code{R} required]} \tabular{ll}{ \code{R: } \tab number of MCMC draws \cr \code{keep: } \tab MCMC thinning parm -- keep every \code{keep}th draw (def: 1) \cr \code{nprint: } \tab print the estimated time remaining for every \code{nprint}'th draw (def: 100, set to 0 for no print) } } } \value{ A list containing: \item{betadraw }{\eqn{nreg x nvar x R/keep} array of individual regression coef draws} \item{taudraw }{\eqn{R/keep x nreg} matrix of error variance draws} \item{Deltadraw }{\eqn{R/keep x nz*nvar} matrix of Deltadraws} \item{Vbetadraw }{\eqn{R/keep x nvar*nvar} matrix of Vbeta draws} } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 3, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{rhierLinearMixture}} } \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} set.seed(66) nreg = 100 nobs = 100 nvar = 3 Vbeta = matrix(c(1, 0.5, 0, 0.5, 2, 0.7, 0, 0.7, 1), ncol=3) Z = cbind(c(rep(1,nreg)), 3*runif(nreg)) Z[,2] = Z[,2] - mean(Z[,2]) nz = ncol(Z) Delta = matrix(c(1,-1,2,0,1,0), ncol=2) Delta = t(Delta) # first row of Delta is means of betas Beta = matrix(rnorm(nreg*nvar),nrow=nreg)\%*\%chol(Vbeta) + Z\%*\%Delta tau = 0.1 iota = c(rep(1,nobs)) regdata = NULL for (reg in 1:nreg) { X = cbind(iota, matrix(runif(nobs*(nvar-1)),ncol=(nvar-1))) y = X\%*\%Beta[reg,] + sqrt(tau)*rnorm(nobs) regdata[[reg]] = list(y=y, X=X) } Data1 = list(regdata=regdata, Z=Z) Mcmc1 = list(R=R, keep=1) out = rhierLinearModel(Data=Data1, Mcmc=Mcmc1) cat("Summary of Delta draws", fill=TRUE) summary(out$Deltadraw, tvalues=as.vector(Delta)) cat("Summary of Vbeta draws", fill=TRUE) summary(out$Vbetadraw, tvalues=as.vector(Vbeta[upper.tri(Vbeta,diag=TRUE)])) ## plotting examples if(0){ plot(out$betadraw) plot(out$Deltadraw) } } \keyword{regression} bayesm/man/llnhlogit.Rd0000644000176000001440000000402113117541521014615 0ustar ripleyusers\name{llnhlogit} \alias{llnhlogit} \concept{multinomial logit} \concept{non-homothetic utility} \title{Evaluate Log Likelihood for non-homothetic Logit Model} \description{ \code{llnhlogit} evaluates log-likelihood for the Non-homothetic Logit model. } \usage{llnhlogit(theta, choice, lnprices, Xexpend)} \arguments{ \item{theta }{ parameter vector (see details section) } \item{choice }{ \eqn{n x 1} vector of choice (1,\ldots,p) } \item{lnprices}{ \eqn{n x p} array of log-prices} \item{Xexpend }{ \eqn{n x d} array of vars predicting expenditure } } \details{ Non-homothetic logit model, \eqn{Pr(i) = exp(tau v_i) / sum_j exp(tau v_j)} \cr \eqn{v_i = alpha_i - e^{kappaStar_i}u^i - lnp_i} \cr tau is the scale parameter of extreme value error distribution.\cr \eqn{u^i} solves \eqn{u^i = psi_i(u^i)E/p_i}.\cr \eqn{ln(psi_i(U)) = alpha_i - e^{kappaStar_i}U}. \cr \eqn{ln(E) = gamma'Xexpend}.\cr Structure of theta vector: \cr alpha: \eqn{p x 1} vector of utility intercepts.\cr kappaStar: \eqn{p x 1} vector of utility rotation parms expressed on natural log scale. \cr gamma: \eqn{k x 1} -- expenditure variable coefs.\cr tau: \eqn{1 x 1} -- logit scale parameter.\cr } \value{Value of log-likelihood (sum of log prob of observed multinomial outcomes).} \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 4, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{\code{\link{simnhlogit}}} \examples{ N=1000; p=3; k=1 theta = c(rep(1,p), seq(from=-1,to=1,length=p), rep(2,k), 0.5) lnprices = matrix(runif(N*p), ncol=p) Xexpend = matrix(runif(N*k), ncol=k) simdata = simnhlogit(theta, lnprices, Xexpend) ## evaluate likelihood at true theta llstar = llnhlogit(theta, simdata$y, simdata$lnprices, simdata$Xexpend) } \keyword{models}bayesm/man/rbprobitGibbs.Rd0000644000176000001440000000455613117541521015430 0ustar ripleyusers\name{rbprobitGibbs} \alias{rbprobitGibbs} \concept{bayes} \concept{MCMC} \concept{probit} \concept{Gibbs Sampling} \title{Gibbs Sampler (Albert and Chib) for Binary Probit} \description{ \code{rbprobitGibbs} implements the Albert and Chib Gibbs Sampler for the binary probit model. } \usage{rbprobitGibbs(Data, Prior, Mcmc)} \arguments{ \item{Data }{list(y, X)} \item{Prior}{list(betabar, A)} \item{Mcmc }{list(R, keep, nprint)} } \details{ \subsection{Model and Priors}{ \eqn{z = X\beta + e} with \eqn{e} \eqn{\sim}{~} \eqn{N(0, I)} \cr \eqn{y = 1} if \eqn{z > 0} \eqn{\beta} \eqn{\sim}{~} \eqn{N(betabar, A^{-1})} } \subsection{Argument Details}{ \emph{\code{Data = list(y, X)}} \tabular{ll}{ \code{y: } \tab \eqn{n x 1} vector of 0/1 outcomes \cr \code{X: } \tab \eqn{n x k} design matrix } \emph{\code{Prior = list(betabar, A)} [optional]} \tabular{ll}{ \code{betabar: } \tab \eqn{k x 1} prior mean (def: 0) \cr \code{A: } \tab \eqn{k x k} prior precision matrix (def: 0.01*I) } \emph{\code{Mcmc = list(R, keep, nprint)} [only \code{R} required]} \tabular{ll}{ \code{R: } \tab number of MCMC draws \cr \code{keep: } \tab MCMC thinning parameter -- keep every \code{keep}th draw (def: 1) \cr \code{nprint: } \tab print the estimated time remaining for every \code{nprint}'th draw (def: 100, set to 0 for no print) } } } \value{ A list containing: \item{betadraw }{ \eqn{R/keep x k} matrix of betadraws} } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 3, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{rmnpGibbs}} } \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} set.seed(66) ## function to simulate from binary probit including x variable simbprobit = function(X, beta) { y = ifelse((X\%*\%beta + rnorm(nrow(X)))<0, 0, 1) list(X=X, y=y, beta=beta) } nobs = 200 X = cbind(rep(1,nobs), runif(nobs), runif(nobs)) beta = c(0,1,-1) nvar = ncol(X) simout = simbprobit(X, beta) Data1 = list(X=simout$X, y=simout$y) Mcmc1 = list(R=R, keep=1) out = rbprobitGibbs(Data=Data1, Mcmc=Mcmc1) summary(out$betadraw, tvalues=beta) ## plotting example if(0){plot(out$betadraw, tvalues=beta)} } \keyword{models} bayesm/man/rmvst.Rd0000755000176000001440000000170513117541521014005 0ustar ripleyusers\name{rmvst} \alias{rmvst} \concept{multivariate t distribution} \concept{student-t} \concept{simulation} \title{ Draw from Multivariate Student-t } \description{ \code{rmvst} draws from a multivariate student-t distribution. } \usage{rmvst(nu, mu, root)} \arguments{ \item{nu}{ d.f. parameter } \item{mu}{ mean vector } \item{root}{ Upper Tri Cholesky Root of Sigma } } \value{length(mu) draw vector} \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{lndMvst}}} \examples{ set.seed(66) rmvst(nu=5, mu=c(rep(0,2)), root=chol(matrix(c(2,1,1,2), ncol=2))) } \keyword{multivariate} \keyword{distribution} bayesm/man/rmultireg.Rd0000644000176000001440000000507213117541521014642 0ustar ripleyusers\name{rmultireg} \alias{rmultireg} \concept{bayes} \concept{multivariate regression} \concept{simulation} \title{Draw from the Posterior of a Multivariate Regression} \description{ \code{ rmultireg} draws from the posterior of a Multivariate Regression model with a natural conjugate prior. } \usage{rmultireg(Y, X, Bbar, A, nu, V)} \arguments{ \item{Y }{ \eqn{n x m} matrix of observations on m dep vars } \item{X }{ \eqn{n x k} matrix of observations on indep vars (supply intercept) } \item{Bbar }{ \eqn{k x m} matrix of prior mean of regression coefficients } \item{A }{ \eqn{k x k} Prior precision matrix } \item{nu }{ d.f. parameter for Sigma } \item{V }{ \eqn{m x m} pdf location parameter for prior on Sigma } } \details{ Model: \cr \eqn{Y = XB + U} with \eqn{cov(u_i) = \Sigma} \cr \eqn{B} is \eqn{k x m} matrix of coefficients; \eqn{\Sigma} is \eqn{m x m} covariance matrix. Priors: \cr \eqn{\beta} | \eqn{\Sigma} \eqn{\sim}{~} \eqn{N(betabar, \Sigma(x) A^{-1})} \cr \eqn{betabar = vec(Bbar)}; \eqn{\beta = vec(B)} \cr \eqn{\Sigma} \eqn{\sim}{~} IW(nu, V) } \value{ A list of the components of a draw from the posterior \item{B }{ draw of regression coefficient matrix } \item{Sigma }{ draw of Sigma } } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 2, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} set.seed(66) n =200 m = 2 X = cbind(rep(1,n),runif(n)) k = ncol(X) B = matrix(c(1,2,-1,3), ncol=m) Sigma = matrix(c(1, 0.5, 0.5, 1), ncol=m) RSigma = chol(Sigma) Y = X\%*\%B + matrix(rnorm(m*n),ncol=m)\%*\%RSigma betabar = rep(0,k*m) Bbar = matrix(betabar, ncol=m) A = diag(rep(0.01,k)) nu = 3 V = nu*diag(m) betadraw = matrix(double(R*k*m), ncol=k*m) Sigmadraw = matrix(double(R*m*m), ncol=m*m) for (rep in 1:R) { out = rmultireg(Y, X, Bbar, A, nu, V) betadraw[rep,] = out$B Sigmadraw[rep,] = out$Sigma } cat(" Betadraws ", fill=TRUE) mat = apply(betadraw, 2, quantile, probs=c(0.01, 0.05, 0.5, 0.95, 0.99)) mat = rbind(as.vector(B),mat) rownames(mat)[1] = "beta" print(mat) cat(" Sigma draws", fill=TRUE) mat = apply(Sigmadraw, 2 ,quantile, probs=c(0.01, 0.05, 0.5, 0.95, 0.99)) mat = rbind(as.vector(Sigma),mat); rownames(mat)[1]="Sigma" print(mat) } \keyword{regression} bayesm/man/runireg.Rd0000644000176000001440000000472713117541521014311 0ustar ripleyusers\name{runireg} \alias{runireg} \concept{bayes} \concept{regression} \title{IID Sampler for Univariate Regression} \description{ \code{runireg} implements an iid sampler to draw from the posterior of a univariate regression with a conjugate prior. } \usage{runireg(Data, Prior, Mcmc)} \arguments{ \item{Data }{list(y, X)} \item{Prior}{list(betabar, A, nu, ssq)} \item{Mcmc }{list(R, keep, nprint)} } \details{ \subsection{Model and Priors}{ \eqn{y = X\beta + e} with \eqn{e} \eqn{\sim}{~} \eqn{N(0, \sigma^2)} \eqn{\beta} \eqn{\sim}{~} \eqn{N(betabar, \sigma^2*A^{-1})} \cr \eqn{\sigma^2} \eqn{\sim}{~} \eqn{(nu*ssq)/\chi^2_{nu}} } \subsection{Argument Details}{ \emph{\code{Data = list(y, X)}} \tabular{ll}{ \code{y: } \tab \eqn{n x 1} vector of observations \cr \code{X: } \tab \eqn{n x k} design matrix } \emph{\code{Prior = list(betabar, A, nu, ssq)} [optional]} \tabular{ll}{ \code{betabar: } \tab \eqn{k x 1} prior mean (def: 0) \cr \code{A: } \tab \eqn{k x k} prior precision matrix (def: 0.01*I) \cr \code{nu: } \tab d.f. parameter for Inverted Chi-square prior (def: 3) \cr \code{ssq: } \tab scale parameter for Inverted Chi-square prior (def: \code{var(y)}) } \emph{\code{Mcmc = list(R, keep, nprint)} [only \code{R} required]} \tabular{ll}{ \code{R: } \tab number of draws \cr \code{keep: } \tab thinning parameter -- keep every \code{keep}th draw (def: 1) \cr \code{nprint: } \tab print the estimated time remaining for every \code{nprint}'th draw (def: 100, set to 0 for no print) } } } \value{ A list containing: \item{betadraw }{ \eqn{R x k} matrix of betadraws } \item{sigmasqdraw }{ \eqn{R x 1} vector of sigma-sq draws} } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 2, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{runiregGibbs}} } \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} set.seed(66) n = 200 X = cbind(rep(1,n), runif(n)) beta = c(1,2) sigsq = 0.25 y = X\%*\%beta + rnorm(n,sd=sqrt(sigsq)) out = runireg(Data=list(y=y,X=X), Mcmc=list(R=R)) cat("Summary of beta and Sigmasq draws", fill=TRUE) summary(out$betadraw, tvalues=beta) summary(out$sigmasqdraw, tvalues=sigsq) ## plotting examples if(0){plot(out$betadraw)} } \keyword{regression} bayesm/man/cgetC.Rd0000644000176000001440000000176413117541521013661 0ustar ripleyusers\name{cgetC} \alias{cgetC} \title{Obtain A List of Cut-offs for Scale Usage Problems} \description{ \code{cgetC} obtains a list of censoring points, or cut-offs, used in the ordinal multivariate probit model of Rossi et al (2001). This approach uses a quadratic parameterization of the cut-offs. The model is useful for modeling correlated ordinal data on a scale from \eqn{1} to \eqn{k} with different scale usage patterns. } \usage{cgetC(e, k)} \arguments{ \item{e}{ quadratic parameter (\eqn{0 <} \code{e} \eqn{< 1}) } \item{k}{ items are on a scale from \eqn{1,\ldots, k} } } \section{Warning}{This is a utility function which implements \strong{no} error-checking.} \value{A vector of \eqn{k+1} cut-offs.} \author{Rob McCulloch and Peter Rossi, Anderson School, UCLA. \email{perossichi@gmail.com}.} \references{Rossi et al (2001), \dQuote{Overcoming Scale Usage Heterogeneity,} \emph{JASA} 96, 20--31.} \seealso{ \code{\link{rscaleUsage}} } \examples{ cgetC(0.1, 10) } \keyword{ utilities } bayesm/man/lndMvn.Rd0000644000176000001440000000205113117541521014060 0ustar ripleyusers\name{lndMvn} \alias{lndMvn} \concept{multivariate normal distribution} \concept{density} \title{ Compute Log of Multivariate Normal Density } \description{ \code{lndMvn} computes the log of a Multivariate Normal Density. } \usage{lndMvn(x, mu, rooti)} \arguments{ \item{x }{ density ordinate } \item{mu }{ mu vector } \item{rooti}{ inv of upper triangular Cholesky root of \eqn{\Sigma} } } \details{ \eqn{z} \eqn{\sim}{~} \eqn{N(mu,\Sigma)} } \value{Log density value} \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 2, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{lndMvst}} } \examples{ Sigma = matrix(c(1, 0.5, 0.5, 1), ncol=2) lndMvn(x=c(rep(0,2)), mu=c(rep(0,2)), rooti=backsolve(chol(Sigma),diag(2))) } \keyword{distribution} bayesm/man/mixDen.Rd0000644000176000001440000000261313117541521014052 0ustar ripleyusers\name{mixDen} \alias{mixDen} \concept{normal mixture} \concept{marginal distribution} \concept{density} \title{Compute Marginal Density for Multivariate Normal Mixture} \description{ \code{mixDen} computes the marginal density for each dimension of a normal mixture at each of the points on a user-specifed grid.} \usage{mixDen(x, pvec, comps)} \arguments{ \item{x}{ array where \eqn{i}th column gives grid points for \eqn{i}th variable } \item{pvec}{ vector of mixture component probabilites } \item{comps}{ list of lists of components for normal mixture } } \details{ \tabular{ll}{ \code{length(comps) } \tab is the number of mixture components \cr \code{comps[[j]] } \tab is a list of parameters of the \eqn{j}th component \cr \code{comps[[j]]$mu } \tab is mean vector \cr \code{comps[[j]]$rooti} \tab is the UL decomp of \eqn{\Sigma^{-1}} } } \value{An array of the same dimension as grid with density values} \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 3, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{rnmixGibbs}} } \keyword{models} \keyword{multivariate} bayesm/man/lndIWishart.Rd0000755000176000001440000000223013117541521015054 0ustar ripleyusers\name{lndIWishart} \alias{lndIWishart} \concept{Inverted Wishart distribution} \concept{density} \title{Compute Log of Inverted Wishart Density} \description{ \code{lndIWishart} computes the log of an Inverted Wishart density. } \usage{lndIWishart(nu, V, IW)} \arguments{ \item{nu }{ d.f. parameter } \item{V }{ "location" parameter } \item{IW }{ ordinate for density evaluation } } \details{ \eqn{Z} \eqn{\sim}{~} Inverted Wishart(nu,V). \cr In this parameterization, \eqn{E[Z] = 1/(nu-k-1) V}, where \eqn{V} is a \eqn{k x k} matrix \cr \code{lndIWishart} computes the complete log-density, including normalizing constants. } \value{Log density value} \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 2, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{rwishart}} } \examples{ lndIWishart(5, diag(3), diag(3)+0.5) } \keyword{ distribution } bayesm/man/lndMvst.Rd0000644000176000001440000000227213117541521014256 0ustar ripleyusers\name{lndMvst} \alias{lndMvst} \concept{multivariate t distribution} \concept{student-t distribution} \concept{density} \title{Compute Log of Multivariate Student-t Density} \description{ \code{lndMvst} computes the log of a Multivariate Student-t Density. } \usage{lndMvst(x, nu, mu, rooti, NORMC)} \arguments{ \item{x }{ density ordinate } \item{nu }{ d.f. parameter } \item{mu }{ mu vector } \item{rooti}{ inv of Cholesky root of \eqn{\Sigma} } \item{NORMC}{ include normalizing constant (def: \code{FALSE}) } } \details{ \eqn{z} \eqn{\sim}{~} \eqn{MVst(mu, nu, \Sigma)} } \value{Log density value} \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 2, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{lndMvn}} } \examples{ Sigma = matrix(c(1, 0.5, 0.5, 1), ncol=2) lndMvst(x=c(rep(0,2)), nu=4,mu=c(rep(0,2)), rooti=backsolve(chol(Sigma),diag(2))) } \keyword{distribution} bayesm/man/lndIChisq.Rd0000644000176000001440000000213613117541521014504 0ustar ripleyusers\name{lndIChisq} \alias{lndIChisq} \concept{Inverted Chi-squared Distribution} \concept{density} \title{Compute Log of Inverted Chi-Squared Density} \description{ \code{lndIChisq} computes the log of an Inverted Chi-Squared Density. } \usage{lndIChisq(nu, ssq, X)} \arguments{ \item{nu }{ d.f. parameter } \item{ssq }{ scale parameter } \item{X }{ ordinate for density evaluation (this must be a matrix)} } \details{ \eqn{Z = nu*ssq / \chi^2_{nu}} with \eqn{Z} \eqn{\sim}{~} Inverted Chi-Squared. \cr \code{lndIChisq} computes the complete log-density, including normalizing constants. } \value{Log density value} \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 2, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{dchisq}} } \examples{ lndIChisq(3, 1, matrix(2)) } \keyword{distribution} bayesm/man/rmnpGibbs.Rd0000644000176000001440000001011213117541521014542 0ustar ripleyusers\name{rmnpGibbs} \alias{rmnpGibbs} \concept{bayes} \concept{multinomial probit} \concept{MCMC} \concept{Gibbs Sampling} \title{Gibbs Sampler for Multinomial Probit} \description{ \code{rmnpGibbs} implements the McCulloch/Rossi Gibbs Sampler for the multinomial probit model. } \usage{rmnpGibbs(Data, Prior, Mcmc)} \arguments{ \item{Data }{list(y, X, p)} \item{Prior}{list(betabar, A, nu, V)} \item{Mcmc }{list(R, keep, nprint, beta0, sigma0)} } \details{ \subsection{Model and Priors}{ \eqn{w_i = X_i\beta + e} with \eqn{e} \eqn{\sim}{~} \eqn{N(0, \Sigma)}. Note: \eqn{w_i} and \eqn{e} are \eqn{(p-1) x 1}.\cr \eqn{y_i = j} if \eqn{w_{ij} > max(0,w_{i,-j})} for \eqn{j=1, \ldots, p-1} and where \eqn{w_{i,-j}} means elements of \eqn{w_i} other than the \eqn{j}th. \cr \eqn{y_i = p}, if all \eqn{w_i < 0} \eqn{\beta} is not identified. However, \eqn{\beta}/sqrt(\eqn{\sigma_{11}}) and \eqn{\Sigma}/\eqn{\sigma_{11}} are identified. See reference or example below for details. \eqn{\beta} \eqn{\sim}{~} \eqn{N(betabar,A^{-1})} \cr \eqn{\Sigma} \eqn{\sim}{~} \eqn{IW(nu,V)} \cr } \subsection{Argument Details}{ \emph{\code{Data = list(y, X, p)}} \tabular{ll}{ \code{y: } \tab \eqn{n x 1} vector of multinomial outcomes (1, \ldots, p) \cr \code{X: } \tab \eqn{n*(p-1) x k} design matrix. To make \eqn{X} matrix use \code{\link{createX}} with \code{DIFF=TRUE} \cr \code{p: } \tab number of alternatives } \emph{\code{Prior = list(betabar, A, nu, V)} [optional]} \tabular{ll}{ \code{betabar: } \tab \eqn{k x 1} prior mean (def: 0) \cr \code{A: } \tab \eqn{k x k} prior precision matrix (def: 0.01*I) \cr \code{nu: } \tab d.f. parameter for Inverted Wishart prior (def: (p-1)+3) \cr \code{V: } \tab PDS location parameter for Inverted Wishart prior (def: nu*I) } \emph{\code{Mcmc = list(R, keep, nprint, beta0, sigma0)} [only \code{R} required]} \tabular{ll}{ \code{R: } \tab number of MCMC draws \cr \code{keep: } \tab MCMC thinning parameter -- keep every \code{keep}th draw (def: 1) \cr \code{nprint: } \tab print the estimated time remaining for every \code{nprint}'th draw (def: 100, set to 0 for no print) \cr \code{beta0: } \tab initial value for beta (def: 0) \cr \code{sigma0: } \tab initial value for sigma (def: I) } } } \value{ A list containing: \item{betadraw }{\eqn{R/keep x k} matrix of betadraws} \item{sigmadraw }{\eqn{R/keep x (p-1)*(p-1)} matrix of sigma draws -- each row is the vector form of sigma} } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 4, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{rmvpGibbs}} } \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} set.seed(66) simmnp = function(X, p, n, beta, sigma) { indmax = function(x) {which(max(x)==x)} Xbeta = X\%*\%beta w = as.vector(crossprod(chol(sigma),matrix(rnorm((p-1)*n),ncol=n))) + Xbeta w = matrix(w, ncol=(p-1), byrow=TRUE) maxw = apply(w, 1, max) y = apply(w, 1, indmax) y = ifelse(maxw < 0, p, y) return(list(y=y, X=X, beta=beta, sigma=sigma)) } p = 3 n = 500 beta = c(-1,1,1,2) Sigma = matrix(c(1, 0.5, 0.5, 1), ncol=2) k = length(beta) X1 = matrix(runif(n*p,min=0,max=2),ncol=p) X2 = matrix(runif(n*p,min=0,max=2),ncol=p) X = createX(p, na=2, nd=NULL, Xa=cbind(X1,X2), Xd=NULL, DIFF=TRUE, base=p) simout = simmnp(X,p,500,beta,Sigma) Data1 = list(p=p, y=simout$y, X=simout$X) Mcmc1 = list(R=R, keep=1) out = rmnpGibbs(Data=Data1, Mcmc=Mcmc1) cat(" Summary of Betadraws ", fill=TRUE) betatilde = out$betadraw / sqrt(out$sigmadraw[,1]) attributes(betatilde)$class = "bayesm.mat" summary(betatilde, tvalues=beta) cat(" Summary of Sigmadraws ", fill=TRUE) sigmadraw = out$sigmadraw / out$sigmadraw[,1] attributes(sigmadraw)$class = "bayesm.var" summary(sigmadraw, tvalues=as.vector(Sigma[upper.tri(Sigma,diag=TRUE)])) ## plotting examples if(0){plot(betatilde,tvalues=beta)} } \keyword{models} bayesm/man/nmat.Rd0000755000176000001440000000164213117541521013571 0ustar ripleyusers\name{nmat} \alias{nmat} \title{Convert Covariance Matrix to a Correlation Matrix} \description{ \code{nmat} converts a covariance matrix (stored as a vector, col by col) to a correlation matrix (also stored as a vector). } \usage{nmat(vec)} \arguments{ \item{vec}{ \eqn{k x k} Cov matrix stored as a \eqn{k*k x 1} vector (col by col) } } \details{ This routine is often used with apply to convert an \eqn{R x (k*k)} array of covariance MCMC draws to correlations. As in \code{corrdraws = apply(vardraws, 1, nmat)}. } \value{\eqn{k*k x 1} vector with correlation matrix} \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \examples{ set.seed(66) X = matrix(rnorm(200,4), ncol=2) Varmat = var(X) nmat(as.vector(Varmat)) } \keyword{utilities} \keyword{array} bayesm/man/orangeJuice.Rd0000644000176000001440000001132513117541521015061 0ustar ripleyusers\name{orangeJuice} \alias{orangeJuice} \docType{data} \title{Store-level Panel Data on Orange Juice Sales} \description{ Weekly sales of refrigerated orange juice at 83 stores. Contains demographic information on those stores. } \usage{data(orangeJuice)} \format{ The \code{orangeJuice} object is a list containing two data frames, \code{yx} and \code{storedemo}. } \details{ In the \code{yx} data frame: \tabular{ll}{ \ldots\code{$store } \tab store number \cr \ldots\code{$brand } \tab brand indicator \cr \ldots\code{$week } \tab week number \cr \ldots\code{$logmove } \tab log of the number of units sold \cr \ldots\code{$constant } \tab a vector of 1s \cr \ldots\code{$price# } \tab price of brand # \cr \ldots\code{$deal } \tab in-store coupon activity \cr \ldots\code{$feature } \tab feature advertisement \cr \ldots\code{$profit } \tab profit } The price variables correspond to the following brands: \tabular{ll}{ 1 \tab Tropicana Premium 64 oz \cr 2 \tab Tropicana Premium 96 oz \cr 3 \tab Florida's Natural 64 oz \cr 4 \tab Tropicana 64 oz \cr 5 \tab Minute Maid 64 oz \cr 6 \tab Minute Maid 96 oz \cr 7 \tab Citrus Hill 64 oz \cr 8 \tab Tree Fresh 64 oz \cr 9 \tab Florida Gold 64 oz \cr 10 \tab Dominicks 64 oz \cr 11 \tab Dominicks 128 oz } In the \code{storedemo} data frame: \tabular{ll}{ \ldots\code{$STORE } \tab store number \cr \ldots\code{$AGE60 } \tab percentage of the population that is aged 60 or older \cr \ldots\code{$EDUC } \tab percentage of the population that has a college degree \cr \ldots\code{$ETHNIC } \tab percent of the population that is black or Hispanic \cr \ldots\code{$INCOME } \tab median income \cr \ldots\code{$HHLARGE } \tab percentage of households with 5 or more persons \cr \ldots\code{$WORKWOM } \tab percentage of women with full-time jobs \cr \ldots\code{$HVAL150 } \tab percentage of households worth more than $150,000 \cr \ldots\code{$SSTRDIST } \tab distance to the nearest warehouse store \cr \ldots\code{$SSTRVOL } \tab ratio of sales of this store to the nearest warehouse store \cr \ldots\code{$CPDIST5 } \tab average distance in miles to the nearest 5 supermarkets \cr \ldots\code{$CPWVOL5 } \tab ratio of sales of this store to the average of the nearest five stores } } \source{Alan L. Montgomery (1997), "Creating Micro-Marketing Pricing Strategies Using Supermarket Scanner Data," \emph{Marketing Science} 16(4) 315--337.} \references{Chapter 5, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch\cr \url{http://www.perossi.org/home/bsm-1}} \examples{ ## load data data(orangeJuice) ## print some quantiles of yx data cat("Quantiles of the Variables in yx data",fill=TRUE) mat = apply(as.matrix(orangeJuice$yx), 2, quantile) print(mat) ## print some quantiles of storedemo data cat("Quantiles of the Variables in storedemo data",fill=TRUE) mat = apply(as.matrix(orangeJuice$storedemo), 2, quantile) print(mat) ## processing for use with rhierLinearModel if(0) { ## select brand 1 for analysis brand1 = orangeJuice$yx[(orangeJuice$yx$brand==1),] store = sort(unique(brand1$store)) nreg = length(store) nvar = 14 regdata=NULL for (reg in 1:nreg) { y = brand1$logmove[brand1$store==store[reg]] iota = c(rep(1,length(y))) X = cbind(iota,log(brand1$price1[brand1$store==store[reg]]), log(brand1$price2[brand1$store==store[reg]]), log(brand1$price3[brand1$store==store[reg]]), log(brand1$price4[brand1$store==store[reg]]), log(brand1$price5[brand1$store==store[reg]]), log(brand1$price6[brand1$store==store[reg]]), log(brand1$price7[brand1$store==store[reg]]), log(brand1$price8[brand1$store==store[reg]]), log(brand1$price9[brand1$store==store[reg]]), log(brand1$price10[brand1$store==store[reg]]), log(brand1$price11[brand1$store==store[reg]]), brand1$deal[brand1$store==store[reg]], brand1$feat[brand1$store==store[reg]] ) regdata[[reg]] = list(y=y, X=X) } ## storedemo is standardized to zero mean. Z = as.matrix(orangeJuice$storedemo[,2:12]) dmean = apply(Z, 2, mean) for (s in 1:nreg) {Z[s,] = Z[s,] - dmean} iotaz = c(rep(1,nrow(Z))) Z = cbind(iotaz, Z) nz = ncol(Z) Data = list(regdata=regdata, Z=Z) Mcmc = list(R=R, keep=1) out = rhierLinearModel(Data=Data, Mcmc=Mcmc) summary(out$Deltadraw) summary(out$Vbetadraw) ## plotting examples if(0){ plot(out$betadraw) } } } \keyword{datasets} bayesm/man/rdirichlet.Rd0000755000176000001440000000142413117541521014761 0ustar ripleyusers\name{rdirichlet} \alias{rdirichlet} \concept{dirichlet distribution} \concept{simulation} \title{Draw From Dirichlet Distribution} \description{ \code{rdirichlet} draws from Dirichlet } \usage{rdirichlet(alpha)} \arguments{ \item{alpha}{vector of Dirichlet parms (must be > 0)} } \value{Vector of draws from Dirichlet} \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 2, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \examples{ set.seed(66) rdirichlet(c(rep(3,5))) } \keyword{distribution} bayesm/man/momMix.Rd0000644000176000001440000000343413117541521014076 0ustar ripleyusers\name{momMix} \alias{momMix} \concept{mcmc} \concept{normal mixture} \concept{posterior moments} \title{Compute Posterior Expectation of Normal Mixture Model Moments} \description{ \code{momMix} averages the moments of a normal mixture model over MCMC draws. } \usage{momMix(probdraw, compdraw)} \arguments{ \item{probdraw}{ \eqn{R x ncomp} list of draws of mixture probs } \item{compdraw}{ list of length \eqn{R} of draws of mixture component moments } } \details{ \tabular{ll}{ \code{R } \tab is the number of MCMC draws in argument list above. \cr \code{ncomp } \tab is the number of mixture components fitted.\cr \code{compdraw } \tab is a list of lists of lists with mixture components. \cr \code{compdraw[[i]] } \tab is \eqn{i}th draw. \cr \code{compdraw[[i]][[j]][[1]] } \tab is the mean parameter vector for the \eqn{j}th component, \eqn{i}th MCMC draw. \cr \code{compdraw[[i]][[j]][[2]] } \tab is the UL decomposition of \eqn{\Sigma^{-1}} for the \eqn{j}th component, \eqn{i}th MCMC draw } } \value{ A list containing: \item{mu }{posterior expectation of mean} \item{sigma }{posterior expectation of covariance matrix} \item{sd }{posterior expectation of vector of standard deviations} \item{corr }{posterior expectation of correlation matrix} } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 5, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{rmixGibbs}}} \keyword{multivariate} bayesm/man/mnpProb.Rd0000755000176000001440000000326313117541521014250 0ustar ripleyusers\name{mnpProb} \alias{mnpProb} \concept{MNP} \concept{Multinomial Probit Model} \concept{GHK} \concept{market share simulator} \title{Compute MNP Probabilities} \description{ \code{mnpProb} computes MNP probabilities for a given \eqn{X} matrix corresponding to one observation. This function can be used with output from \code{rmnpGibbs} to simulate the posterior distribution of market shares or fitted probabilties. } \usage{mnpProb(beta, Sigma, X, r)} \arguments{ \item{beta}{ MNP coefficients } \item{Sigma}{ Covariance matrix of latents } \item{X}{ \eqn{X} array for one observation -- use \code{createX} to make } \item{r}{ number of draws used in GHK (def: 100)} } \details{ See \code{\link{rmnpGibbs}} for definition of the model and the interpretation of the beta and Sigma parameters. Uses the GHK method to compute choice probabilities. To simulate a distribution of probabilities, loop over the beta and Sigma draws from \code{rmnpGibbs} output. } \value{\eqn{p x 1} vector of choice probabilites} \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{ For further discussion, see Chapters 2 and 4, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{rmnpGibbs}}, \code{\link{createX}} } \examples{ ## example of computing MNP probabilites ## here Xa has the prices of each of the 3 alternatives Xa = matrix(c(1,.5,1.5), nrow=1) X = createX(p=3, na=1, nd=NULL, Xa=Xa, Xd=NULL, DIFF=TRUE) beta = c(1,-1,-2) ## beta contains two intercepts and the price coefficient Sigma = matrix(c(1, 0.5, 0.5, 1), ncol=2) mnpProb(beta, Sigma, X) } \keyword{models} bayesm/man/tuna.Rd0000644000176000001440000000543413117541521013601 0ustar ripleyusers\name{tuna} \alias{tuna} \docType{data} \title{Canned Tuna Sales Data} \description{ Volume of canned tuna sales as well as a measure of display activity, log price, and log wholesale price. Weekly data aggregated to the chain level. This data is extracted from the Dominick's Finer Foods database maintained by the Kilts Center for Marketing at the University of Chicago's Booth School of Business. Brands are seven of the top 10 UPCs in the canned tuna product category. } \usage{data(tuna)} \format{ A data frame with 338 observations on 30 variables. \tabular{ll}{ \ldots\code{$WEEK } \tab a numeric vector \cr \ldots\code{$MOVE# } \tab unit sales of brand # \cr \ldots\code{$NSALE# } \tab a measure of display activity of brand # \cr \ldots\code{$LPRICE# } \tab log of price of brand # \cr \ldots\code{$LWHPRIC# } \tab log of wholesale price of brand # \cr \ldots\code{$FULLCUST } \tab total customers visits } The brands are: \tabular{ll}{ 1. \tab Star Kist 6 oz. \cr 2. \tab Chicken of the Sea 6 oz. \cr 3. \tab Bumble Bee Solid 6.12 oz. \cr 4. \tab Bumble Bee Chunk 6.12 oz. \cr 5. \tab Geisha 6 oz. \cr 6. \tab Bumble Bee Large Cans. \cr 7. \tab HH Chunk Lite 6.5 oz. } } \source{Chevalier, Judith, Anil Kashyap, and Peter Rossi (2003), "Why Don't Prices Rise During Periods of Peak Demand? Evidence from Scanner Data," \emph{The American Economic Review} , 93(1), 15--37.} \references{Chapter 7, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \examples{ data(tuna) cat(" Quantiles of sales", fill=TRUE) mat = apply(as.matrix(tuna[,2:5]), 2, quantile) print(mat) ## example of processing for use with rivGibbs if(0) { data(tuna) t = dim(tuna)[1] customers = tuna[,30] sales = tuna[,2:8] lnprice = tuna[,16:22] lnwhPrice = tuna[,23:29] share = sales/mean(customers) shareout = as.vector(1-rowSums(share)) lnprob = log(share/shareout) ## create w matrix I1 = as.matrix(rep(1,t)) I0 = as.matrix(rep(0,t)) intercept = rep(I1,4) brand1 = rbind(I1, I0, I0, I0) brand2 = rbind(I0, I1, I0, I0) brand3 = rbind(I0, I0, I1, I0) w = cbind(intercept, brand1, brand2, brand3) ## choose brand 1 to 4 y = as.vector(as.matrix(lnprob[,1:4])) X = as.vector(as.matrix(lnprice[,1:4])) lnwhPrice = as.vector(as.matrix(lnwhPrice[1:4])) z = cbind(w, lnwhPrice) Data = list(z=z, w=w, x=X, y=y) Mcmc = list(R=R, keep=1) set.seed(66) out = rivGibbs(Data=Data, Mcmc=Mcmc) cat(" betadraws ", fill=TRUE) summary(out$betadraw) ## plotting examples if(0){plot(out$betadraw)} } } \keyword{datasets} bayesm/man/mnlHess.Rd0000644000176000001440000000213513117541521014236 0ustar ripleyusers\name{mnlHess} \alias{mnlHess} \concept{multinomial logit} \concept{hessian} \title{ Computes --Expected Hessian for Multinomial Logit} \description{\code{mnlHess} computes expected Hessian (\eqn{E[H]}) for Multinomial Logit Model.} \usage{mnlHess(beta, y, X)} \arguments{ \item{beta}{ \eqn{k x 1} vector of coefficients } \item{y}{ \eqn{n x 1} vector of choices, (\eqn{1,\ldots,p}) } \item{X}{ \eqn{n*p x k} Design matrix } } \details{ See \code{\link{llmnl}} for information on structure of X array. Use \code{\link{createX}} to make X. } \value{\eqn{k x k} matrix} \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 3, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{llmnl}}, \code{\link{createX}}, \code{\link{rmnlIndepMetrop}} } \examples{ \dontrun{mnlHess(beta, y, X)} } \keyword{models} bayesm/man/Scotch.Rd0000755000176000001440000000313613117541521014055 0ustar ripleyusers\name{Scotch} \alias{Scotch} \docType{data} \title{Survey Data on Brands of Scotch Consumed} \description{ Data from Simmons Survey. Brands used in last year for those respondents who report consuming scotch. } \usage{data(Scotch)} \format{ A data frame with 2218 observations on 21 brand variables. \cr All variables are numeric vectors that are coded 1 if consumed in last year, 0 if not. } \source{Edwards, Yancy and Greg Allenby (2003), "Multivariate Analysis of Multiple Response Data," \emph{Journal of Marketing Research} 40, 321--334.} \references{ Chapter 4, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch\cr \url{http://www.perossi.org/home/bsm-1}} \examples{ data(Scotch) cat(" Frequencies of Brands", fill=TRUE) mat = apply(as.matrix(Scotch), 2, mean) print(mat) ## use Scotch data to run Multivariate Probit Model if(0) { y = as.matrix(Scotch) p = ncol(y) n = nrow(y) dimnames(y) = NULL y = as.vector(t(y)) y = as.integer(y) I_p = diag(p) X = rep(I_p,n) X = matrix(X, nrow=p) X = t(X) R = 2000 Data = list(p=p, X=X, y=y) Mcmc = list(R=R) set.seed(66) out = rmvpGibbs(Data=Data, Mcmc=Mcmc) ind = (0:(p-1))*p + (1:p) cat(" Betadraws ", fill=TRUE) mat = apply(out$betadraw/sqrt(out$sigmadraw[,ind]), 2 , quantile, probs=c(0.01, 0.05, 0.5, 0.95, 0.99)) attributes(mat)$class = "bayesm.mat" summary(mat) rdraw = matrix(double((R)*p*p), ncol=p*p) rdraw = t(apply(out$sigmadraw, 1, nmat)) attributes(rdraw)$class = "bayesm.var" cat(" Draws of Correlation Matrix ", fill=TRUE) summary(rdraw) } } \keyword{datasets} bayesm/man/rnmixGibbs.Rd0000644000176000001440000001163113117570164014737 0ustar ripleyusers\name{rnmixGibbs} \alias{rnmixGibbs} \concept{bayes} \concept{MCMC} \concept{normal mixtures} \concept{Gibbs Sampling} \title{Gibbs Sampler for Normal Mixtures} \description{ \code{rnmixGibbs} implements a Gibbs Sampler for normal mixtures. } \usage{rnmixGibbs(Data, Prior, Mcmc)} \arguments{ \item{Data }{list(y)} \item{Prior}{list(Mubar, A, nu, V, a, ncomp)} \item{Mcmc }{list(R, keep, nprint, Loglike)} } \details{ \subsection{Model and Priors}{ \eqn{y_i} \eqn{\sim}{~} \eqn{N(\mu_{ind_i}, \Sigma_{ind_i})} \cr ind \eqn{\sim}{~} iid multinomial(p) with \eqn{p} an \eqn{ncomp x 1} vector of probs \eqn{\mu_j} \eqn{\sim}{~} \eqn{N(mubar, \Sigma_j (x) A^{-1})} with \eqn{mubar=vec(Mubar)} \cr \eqn{\Sigma_j} \eqn{\sim}{~} \eqn{IW(nu, V)} \cr Note: this is the natural conjugate prior -- a special case of multivariate regression \cr \eqn{p} \eqn{\sim}{~} Dirchlet(a) } \subsection{Argument Details}{ \emph{\code{Data = list(y)}} \tabular{ll}{ \code{y: } \tab \eqn{n x k} matrix of data (rows are obs) } \emph{\code{Prior = list(Mubar, A, nu, V, a, ncomp)} [only \code{ncomp} required]} \tabular{ll}{ \code{Mubar: } \tab \eqn{1 x k} vector with prior mean of normal component means (def: 0) \cr \code{A: } \tab \eqn{1 x 1} precision parameter for prior on mean of normal component (def: 0.01) \cr \code{nu: } \tab d.f. parameter for prior on Sigma (normal component cov matrix) (def: k+3) \cr \code{V: } \tab \eqn{k x k} location matrix of IW prior on Sigma (def: nu*I) \cr \code{a: } \tab \eqn{ncomp x 1} vector of Dirichlet prior parameters (def: \code{rep(5,ncomp)}) \cr \code{ncomp: } \tab number of normal components to be included } \emph{\code{Mcmc = list(R, keep, nprint, Loglike)} [only \code{R} required]} \tabular{ll}{ \code{R: } \tab number of MCMC draws \cr \code{keep: } \tab MCMC thinning parameter -- keep every \code{keep}th draw (def: 1) \cr \code{nprint: } \tab print the estimated time remaining for every \code{nprint}'th draw (def: 100, set to 0 for no print) \cr \code{LogLike: } \tab logical flag for whether to compute the log-likelihood (def: \code{FALSE}) } } \subsection{\code{nmix} Details}{ \code{nmix} is a list with 3 components. Several functions in the \code{bayesm} package that involve a Dirichlet Process or mixture-of-normals return \code{nmix}. Across these functions, a common structure is used for \code{nmix} in order to utilize generic summary and plotting functions. \tabular{ll}{ \code{probdraw:} \tab \eqn{ncomp x R/keep} matrix that reports the probability that each draw came from a particular component \cr \code{zdraw: } \tab \eqn{R/keep x nobs} matrix that indicates which component each draw is assigned to \cr \code{compdraw:} \tab A list of \eqn{R/keep} lists of \eqn{ncomp} lists. Each of the inner-most lists has 2 elemens: a vector of draws for \code{mu} and a matrix of draws for the Cholesky root of \code{Sigma}. } } } \value{ A list containing: \item{ll }{ \eqn{R/keep x 1} vector of log-likelihood values} \item{nmix }{ a list containing: \code{probdraw}, \code{zdraw}, \code{compdraw} (see \dQuote{\code{nmix} Details} section)} } \note{ In this model, the component normal parameters are not-identified due to label-switching. However, the fitted mixture of normals density is identified as it is invariant to label-switching. See chapter 5 of Rossi et al below for details. Use \code{eMixMargDen} or \code{momMix} to compute posterior expectation or distribution of various identified parameters. } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 3, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{rmixture}}, \code{\link{rmixGibbs}} ,\code{\link{eMixMargDen}}, \code{\link{momMix}}, \code{\link{mixDen}}, \code{\link{mixDenBi}}} \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} set.seed(66) dim = 5 k = 3 # dimension of simulated data and number of "true" components sigma = matrix(rep(0.5,dim^2), nrow=dim) diag(sigma) = 1 sigfac = c(1,1,1) mufac = c(1,2,3) compsmv = list() for(i in 1:k) compsmv[[i]] = list(mu=mufac[i]*1:dim, sigma=sigfac[i]*sigma) # change to "rooti" scale comps = list() for(i in 1:k) comps[[i]] = list(mu=compsmv[[i]][[1]], rooti=solve(chol(compsmv[[i]][[2]]))) pvec = (1:k) / sum(1:k) nobs = 500 dm = rmixture(nobs, pvec, comps) Data1 = list(y=dm$x) ncomp = 9 Prior1 = list(ncomp=ncomp) Mcmc1 = list(R=R, keep=1) out = rnmixGibbs(Data=Data1, Prior=Prior1, Mcmc=Mcmc1) cat("Summary of Normal Mixture Distribution", fill=TRUE) summary(out$nmix) tmom = momMix(matrix(pvec,nrow=1), list(comps)) mat = rbind(tmom$mu, tmom$sd) cat(" True Mean/Std Dev", fill=TRUE) print(mat) ## plotting examples if(0){plot(out$nmix,Data=dm$x)} } \keyword{multivariate} bayesm/man/rtrun.Rd0000755000176000001440000000266013117541521014005 0ustar ripleyusers\name{rtrun} \alias{rtrun} \concept{truncated normal} \concept{simulation} \title{Draw from Truncated Univariate Normal} \description{ \code{rtrun} draws from a truncated univariate normal distribution. } \usage{rtrun(mu, sigma, a, b)} \arguments{ \item{mu}{ mean } \item{sigma}{ standard deviation } \item{a}{ lower bound } \item{b}{ upper bound } } \details{ Note that due to the vectorization of the \code{rnorm} and \code{qnorm} commands in R, all arguments can be vectors of equal length. This makes the inverse CDF method the most efficient to use in R. } \value{Draw (possibly a vector)} \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. **Note also that \code{rtrun} is currently affected by the numerical accuracy of the inverse CDF function when trunctation points are far out in the tails of the distribution, where \dQuote{far out} means \eqn{|a - \mu| / \sigma > 6} and/or \eqn{|b - \mu| / \sigma > 6}. A fix will be implemented in a future version of \code{bayesm}. } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 2, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \examples{ set.seed(66) rtrun(mu=c(rep(0,10)), sigma=c(rep(1,10)), a=c(rep(0,10)), b=c(rep(2,10))) } \keyword{distribution} bayesm/man/rmixGibbs.Rd0000644000176000001440000000270113117541521014552 0ustar ripleyusers\name{rmixGibbs} \alias{rmixGibbs} \title{ Gibbs Sampler for Normal Mixtures w/o Error Checking} \description{ \code{rmixGibbs} makes one draw using the Gibbs Sampler for a mixture of multivariate normals. \code{rmixGibbs} is not designed to be called directly. Instead, use \code{rnmixGibbs} wrapper function. } \usage{ rmixGibbs(y, Bbar, A, nu, V, a, p, z) } \arguments{ \item{y }{ data array where rows are obs } \item{Bbar }{ prior mean for mean vector of each norm comp } \item{A }{ prior precision parameter} \item{nu }{ prior d.f. parm } \item{V }{ prior location matrix for covariance prior } \item{a }{ Dirichlet prior parms } \item{p }{ prior prob of each mixture component } \item{z }{ component identities for each observation -- "indicators" } } \value{ a list containing: \item{p}{ draw of mixture probabilities} \item{z}{ draw of indicators of each component} \item{comps}{ new draw of normal component parameters } } \author{Rob McCulloch (Arizona State University) and Peter Rossi (Anderson School, UCLA), \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 5 \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \seealso{ \code{\link{rnmixGibbs}} } \keyword{multivariate} bayesm/man/rbiNormGibbs.Rd0000644000176000001440000000240213117541521015201 0ustar ripleyusers\name{rbiNormGibbs} \alias{rbiNormGibbs} \concept{bayes} \concept{Gibbs Sampling} \concept{MCMC} \concept{normal distribution} \title{Illustrate Bivariate Normal Gibbs Sampler} \description{ \code{rbiNormGibbs} implements a Gibbs Sampler for the bivariate normal distribution. Intermediate moves are plotted and the output is contrasted with the iid sampler. This function is designed for illustrative/teaching purposes.} \usage{rbiNormGibbs(initx=2, inity=-2, rho, burnin=100, R=500)} \arguments{ \item{initx }{ initial value of parameter on x axis (def: 2)} \item{inity }{ initial value of parameter on y axis (def: -2)} \item{rho }{ correlation for bivariate normals} \item{burnin }{ burn-in number of draws (def: 100)} \item{R }{ number of MCMC draws (def: 500)} } \details{ \eqn{(\theta_1, \theta_2) ~ N( (0,0), \Sigma}) with \eqn{\Sigma} = \code{matrix(c(1,rho,rho,1),ncol=2)} } \value{ \eqn{R x 2} matrix of draws } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapters 2 and 3, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \examples{ \dontrun{out=rbiNormGibbs(rho=0.95)} } \keyword{distribution} bayesm/man/rhierMnlDP.Rd0000644000176000001440000002464713117570164014652 0ustar ripleyusers\name{rhierMnlDP} \alias{rhierMnlDP} \concept{bayes} \concept{MCMC} \concept{Multinomial Logit} \concept{normal mixture} \concept{Dirichlet Process Prior} \concept{heterogeneity} \concept{hierarchical models} \title{MCMC Algorithm for Hierarchical Multinomial Logit with Dirichlet Process Prior Heterogeneity} \description{ \code{rhierMnlDP} is a MCMC algorithm for a hierarchical multinomial logit with a Dirichlet Process prior for the distribution of heteorogeneity. A base normal model is used so that the DP can be interpreted as allowing for a mixture of normals with as many components as there are panel units. This is a hybrid Gibbs Sampler with a RW Metropolis step for the MNL coefficients for each panel unit. This procedure can be interpreted as a Bayesian semi-parameteric method in the sense that the DP prior can accomodate heterogeniety of an unknown form. } \usage{rhierMnlDP(Data, Prior, Mcmc)} \arguments{ \item{Data }{list(lgtdata, Z, p)} \item{Prior}{list(deltabar, Ad, Prioralpha, lambda_hyper)} \item{Mcmc }{list(R, keep, nprint, s, w, maxunique, gridsize)} } \details{ \subsection{Model and Priors}{ \eqn{y_i} \eqn{\sim}{~} \eqn{MNL(X_i, \beta_i)} with \eqn{i = 1, \ldots, length(lgtdata)} and where \eqn{\theta_i} is \eqn{nvar x 1} \eqn{\beta_i = Z\Delta}[i,] + \eqn{u_i} \cr Note: Z\eqn{\Delta} is the matrix \eqn{Z * \Delta}; [i,] refers to \eqn{i}th row of this product \cr Delta is an \eqn{nz x nvar} matrix \eqn{\beta_i} \eqn{\sim}{~} \eqn{N(\mu_i, \Sigma_i)} \eqn{\theta_i = (\mu_i, \Sigma_i)} \eqn{\sim}{~} \eqn{DP(G_0(\lambda), alpha)}\cr \eqn{G_0(\lambda):}\cr \eqn{\mu_i | \Sigma_i} \eqn{\sim}{~} \eqn{N(0, \Sigma_i (x) a^{-1})}\cr \eqn{\Sigma_i} \eqn{\sim}{~} \eqn{IW(nu, nu*v*I)}\cr \eqn{delta = vec(\Delta)} \eqn{\sim}{~} \eqn{N(deltabar, A_d^{-1})}\cr \eqn{\lambda(a, nu, v):}\cr \eqn{a} \eqn{\sim}{~} uniform[alim[1], alimb[2]]\cr \eqn{nu} \eqn{\sim}{~} dim(data)-1 + exp(z) \cr \eqn{z} \eqn{\sim}{~} uniform[dim(data)-1+nulim[1], nulim[2]]\cr \eqn{v} \eqn{\sim}{~} uniform[vlim[1], vlim[2]] \eqn{alpha} \eqn{\sim}{~} \eqn{(1-(alpha-alphamin) / (alphamax-alphamin))^{power}} \cr alpha = alphamin then expected number of components = \code{Istarmin} \cr alpha = alphamax then expected number of components = \code{Istarmax} \eqn{Z} should NOT include an intercept and is centered for ease of interpretation. The mean of each of the \code{nlgt} \eqn{\beta}s is the mean of the normal mixture. Use \code{summary()} to compute this mean from the \code{compdraw} output. We parameterize the prior on \eqn{\Sigma_i} such that \eqn{mode(\Sigma) = nu/(nu+2) vI}. The support of nu enforces a non-degenerate IW density; \eqn{nulim[1] > 0}. The default choices of alim, nulim, and vlim determine the location and approximate size of candidate "atoms" or possible normal components. The defaults are sensible given a reasonable scaling of the X variables. You want to insure that alim is set for a wide enough range of values (remember a is a precision parameter) and the v is big enough to propose Sigma matrices wide enough to cover the data range. A careful analyst should look at the posterior distribution of a, nu, v to make sure that the support is set correctly in alim, nulim, vlim. In other words, if we see the posterior bunched up at one end of these support ranges, we should widen the range and rerun. If you want to force the procedure to use many small atoms, then set nulim to consider only large values and set vlim to consider only small scaling constants. Set alphamax to a large number. This will create a very "lumpy" density estimate somewhat like the classical Kernel density estimates. Of course, this is not advised if you have a prior belief that densities are relatively smooth. } \subsection{Argument Details}{ \emph{\code{Data = list(lgtdata, Z, p)} [\code{Z} optional]} \tabular{ll}{ \code{lgtdata: } \tab list of lists with each cross-section unit MNL data \cr \code{lgtdata[[i]]$y: } \tab \eqn{n_i x 1} vector of multinomial outcomes (1, \ldots, m) \cr \code{lgtdata[[i]]$X: } \tab \eqn{n_i x nvar} design matrix for \eqn{i}th unit \cr \code{Z: } \tab \eqn{nreg x nz} matrix of unit characteristics (def: vector of ones) \cr \code{p: } \tab number of choice alternatives } \emph{\code{Prior = list(deltabar, Ad, Prioralpha, lambda_hyper)} [optional]} \tabular{ll}{ \code{deltabar: } \tab \eqn{nz*nvar x 1} vector of prior means (def: 0) \cr \code{Ad: } \tab prior precision matrix for vec(D) (def: 0.01*I) \cr \code{Prioralpha: } \tab \code{list(Istarmin, Istarmax, power)} \cr \code{$Istarmin: } \tab expected number of components at lower bound of support of alpha def(1) \cr \code{$Istarmax: } \tab expected number of components at upper bound of support of alpha (def: min(50, 0.1*nlgt)) \cr \code{$power: } \tab power parameter for alpha prior (def: 0.8) \cr \code{lambda_hyper: } \tab \code{list(alim, nulim, vlim)} \cr \code{$alim: } \tab defines support of a distribution (def: \code{c(0.01, 2)}) \cr \code{$nulim: } \tab defines support of nu distribution (def: \code{c(0.001, 3)}) \cr \code{$vlim: } \tab defines support of v distribution (def: \code{c(0.1, 4)}) } \emph{\code{Mcmc = list(R, keep, nprint, s, w, maxunique, gridsize)} [only \code{R} required]} \tabular{ll}{ \code{R: } \tab number of MCMC draws \cr \code{keep: } \tab MCMC thinning parameter -- keep every \code{keep}th draw (def: 1) \cr \code{nprint: } \tab print the estimated time remaining for every \code{nprint}'th draw (def: 100, set to 0 for no print) \cr \code{s: } \tab scaling parameter for RW Metropolis (def: 2.93/\code{sqrt(nvar)}) \cr \code{w: } \tab fractional likelihood weighting parameter (def: 0.1) \cr \code{maxuniq: } \tab storage constraint on the number of unique components (def: 200) \cr \code{gridsize: } \tab number of discrete points for hyperparameter priors, (def: 20) } } \subsection{\code{nmix} Details}{ \code{nmix} is a list with 3 components. Several functions in the \code{bayesm} package that involve a Dirichlet Process or mixture-of-normals return \code{nmix}. Across these functions, a common structure is used for \code{nmix} in order to utilize generic summary and plotting functions. \tabular{ll}{ \code{probdraw:} \tab \eqn{ncomp x R/keep} matrix that reports the probability that each draw came from a particular component (here, a one-column matrix of 1s) \cr \code{zdraw: } \tab \eqn{R/keep x nobs} matrix that indicates which component each draw is assigned to (here, null) \cr \code{compdraw:} \tab A list of \eqn{R/keep} lists of \eqn{ncomp} lists. Each of the inner-most lists has 2 elemens: a vector of draws for \code{mu} and a matrix of draws for the Cholesky root of \code{Sigma}. } } } \value{ A list containing: \item{Deltadraw }{ \eqn{R/keep x nz*nvar} matrix of draws of Delta, first row is initial value} \item{betadraw }{ \eqn{nlgt x nvar x R/keep} array of draws of betas} \item{nmix }{ a list containing: \code{probdraw}, \code{zdraw}, \code{compdraw} (see \dQuote{\code{nmix} Details} section)} \item{adraw }{ \eqn{R/keep} draws of hyperparm a} \item{vdraw }{ \eqn{R/keep} draws of hyperparm v} \item{nudraw }{ \eqn{R/keep} draws of hyperparm nu} \item{Istardraw }{ \eqn{R/keep} draws of number of unique components} \item{alphadraw }{ \eqn{R/keep} draws of number of DP tightness parameter} \item{loglike }{ \eqn{R/keep} draws of log-likelihood} } \note{ As is well known, Bayesian density estimation involves computing the predictive distribution of a "new" unit parameter, \eqn{\theta_{n+1}} (here "n"=nlgt). This is done by averaging the normal base distribution over draws from the distribution of \eqn{\theta_{n+1}} given \eqn{\theta_1}, ..., \eqn{\theta_n}, alpha, lambda, data. To facilitate this, we store those draws from the predictive distribution of \eqn{\theta_{n+1}} in a list structure compatible with other \code{bayesm} routines that implement a finite mixture of normals. Large \code{R} values may be required (>20,000). } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 5, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{rhierMnlRwMixture}} } \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=20000} else {R=10} set.seed(66) p = 3 # num of choice alterns ncoef = 3 nlgt = 300 # num of cross sectional units nz = 2 Z = matrix(runif(nz*nlgt),ncol=nz) Z = t(t(Z)-apply(Z,2,mean)) # demean Z ncomp = 3 # no of mixture components Delta=matrix(c(1,0,1,0,1,2),ncol=2) comps = NULL comps[[1]] = list(mu=c(0,-1,-2), rooti=diag(rep(2,3))) comps[[2]] = list(mu=c(0,-1,-2)*2, rooti=diag(rep(2,3))) comps[[3]] = list(mu=c(0,-1,-2)*4, rooti=diag(rep(2,3))) pvec=c(0.4, 0.2, 0.4) ## simulate from MNL model conditional on X matrix simmnlwX = function(n,X,beta) { k = length(beta) Xbeta = X\%*\%beta j = nrow(Xbeta) / n Xbeta = matrix(Xbeta, byrow=TRUE, ncol=j) Prob = exp(Xbeta) iota = c(rep(1,j)) denom = Prob\%*\%iota Prob = Prob / as.vector(denom) y = vector("double", n) ind = 1:j for (i in 1:n) { yvec = rmultinom(1, 1, Prob[i,]) y[i] = ind\%*\%yvec} return(list(y=y, X=X, beta=beta, prob=Prob)) } ## simulate data with a mixture of 3 normals simlgtdata = NULL ni = rep(50,300) for (i in 1:nlgt) { betai = Delta\%*\%Z[i,] + as.vector(rmixture(1,pvec,comps)$x) Xa = matrix(runif(ni[i]*p,min=-1.5,max=0), ncol=p) X = createX(p, na=1, nd=NULL, Xa=Xa, Xd=NULL, base=1) outa = simmnlwX(ni[i], X, betai) simlgtdata[[i]] = list(y=outa$y, X=X, beta=betai) } ## plot betas if(0){ bmat = matrix(0, nlgt, ncoef) for(i in 1:nlgt) { bmat[i,] = simlgtdata[[i]]$beta } par(mfrow = c(ncoef,1)) for(i in 1:ncoef) { hist(bmat[,i], breaks=30, col="magenta") } } ## set Data and Mcmc lists keep = 5 Mcmc1 = list(R=R, keep=keep) Data1 = list(p=p, lgtdata=simlgtdata, Z=Z) out = rhierMnlDP(Data=Data1, Mcmc=Mcmc1) cat("Summary of Delta draws", fill=TRUE) summary(out$Deltadraw, tvalues=as.vector(Delta)) ## plotting examples if(0) { plot(out$betadraw) plot(out$nmix) } } \keyword{models} bayesm/man/rhierBinLogit.Rd0000644000176000001440000001136513117541521015373 0ustar ripleyusers\name{rhierBinLogit} \alias{rhierBinLogit} \concept{bayes} \concept{MCMC} \concept{hierarchical models} \concept{binary logit} \title{MCMC Algorithm for Hierarchical Binary Logit} \description{ \bold{This function has been deprecated. Please use \code{rhierMnlRwMixture} instead.} \code{rhierBinLogit} implements an MCMC algorithm for hierarchical binary logits with a normal heterogeneity distribution. This is a hybrid sampler with a RW Metropolis step for unit-level logit parameters. \code{rhierBinLogit} is designed for use on choice-based conjoint data with partial profiles. The Design matrix is based on differences of characteristics between two alternatives. See Appendix A of \emph{Bayesian Statistics and Marketing} for details. } \usage{rhierBinLogit(Data, Prior, Mcmc)} \arguments{ \item{Data }{list(lgtdata, Z)} \item{Prior}{list(Deltabar, ADelta, nu, V)} \item{Mcmc }{list(R, keep, sbeta)} } \details{ \subsection{Model and Priors}{ \eqn{y_{hi} = 1} with \eqn{Pr = exp(x_{hi}'\beta_h) / (1+exp(x_{hi}'\beta_h)} and \eqn{\beta_h} is \eqn{nvar x 1} \cr \eqn{h = 1, \ldots, length(lgtdata)} units (or "respondents" for survey data) \eqn{\beta_h} = ZDelta[h,] + \eqn{u_h} \cr Note: here ZDelta refers to \code{Z\%*\%Delta} with ZDelta[h,] the \eqn{h}th row of this product\cr Delta is an \eqn{nz x nvar} array \eqn{u_h} \eqn{\sim}{~} \eqn{N(0, V_{beta})}. \cr \eqn{delta = vec(Delta)} \eqn{\sim}{~} \eqn{N(vec(Deltabar), V_{beta}(x) ADelta^{-1})}\cr \eqn{V_{beta}} \eqn{\sim}{~} \eqn{IW(nu, V)} } \subsection{Argument Details}{ \emph{\code{Data = list(lgtdata, Z)} [\code{Z} optional]} \tabular{ll}{ \code{lgtdata: } \tab list of lists with each cross-section unit MNL data \cr \code{lgtdata[[h]]$y:} \tab \eqn{n_h x 1} vector of binary outcomes (0,1) \cr \code{lgtdata[[h]]$X:} \tab \eqn{n_h x nvar} design matrix for h'th unit \cr \code{Z: } \tab \eqn{nreg x nz} mat of unit chars (def: vector of ones) } \emph{\code{Prior = list(Deltabar, ADelta, nu, V)} [optional]} \tabular{ll}{ \code{Deltabar:} \tab \eqn{nz x nvar} matrix of prior means (def: 0) \cr \code{ADelta: } \tab prior precision matrix (def: 0.01I) \cr \code{nu: } \tab d.f. parameter for IW prior on normal component Sigma (def: nvar+3) \cr \code{V: } \tab pds location parm for IW prior on normal component Sigma (def: nuI) } \emph{\code{Mcmc = list(R, keep, sbeta)} [only \code{R} required]} \tabular{ll}{ \code{R: } \tab number of MCMC draws \cr \code{keep: } \tab MCMC thinning parm -- keep every \code{keep}th draw (def: 1) \cr \code{sbeta: } \tab scaling parm for RW Metropolis (def: 0.2) } } } \value{ A list containing: \item{Deltadraw }{ \eqn{R/keep x nz*nvar} matrix of draws of Delta} \item{betadraw }{ \eqn{nlgt x nvar x R/keep} array of draws of betas} \item{Vbetadraw }{ \eqn{R/keep x nvar*nvar} matrix of draws of Vbeta} \item{llike }{ \eqn{R/keep x 1} vector of log-like values} \item{reject }{ \eqn{R/keep x 1} vector of reject rates over nlgt units} } \note{Some experimentation with the Metropolis scaling paramter (\code{sbeta}) may be required.} \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 5, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=10000} else {R=10} set.seed(66) nvar = 5 ## number of coefficients nlgt = 1000 ## number of cross-sectional units nobs = 10 ## number of observations per unit nz = 2 ## number of regressors in mixing distribution Z = matrix(c(rep(1,nlgt),runif(nlgt,min=-1,max=1)), nrow=nlgt, ncol=nz) Delta = matrix(c(-2, -1, 0, 1, 2, -1, 1, -0.5, 0.5, 0), nrow=nz, ncol=nvar) iota = matrix(1, nrow=nvar, ncol=1) Vbeta = diag(nvar) + 0.5*iota\%*\%t(iota) lgtdata=NULL for (i in 1:nlgt) { beta = t(Delta)\%*\%Z[i,] + as.vector(t(chol(Vbeta))\%*\%rnorm(nvar)) X = matrix(runif(nobs*nvar), nrow=nobs, ncol=nvar) prob = exp(X\%*\%beta) / (1+exp(X\%*\%beta)) unif = runif(nobs, 0, 1) y = ifelse(unif max(w_{-j})} and \eqn{w_j >0} \cr if \eqn{y=p, w < 0} \cr To use GHK, we must transform so that these are rectangular regions e.g. if \eqn{y=1, w_1 > 0} and \eqn{w_1 - w_{-1} > 0}. Define \eqn{A_j} such that if \eqn{j=1,\ldots,p-1} then \eqn{A_jw = A_jmu + A_je > 0} is equivalent to \eqn{y=j}. Thus, if \eqn{y=j}, we have \eqn{A_je > -A_jmu}. Lower truncation is \eqn{-A_jmu} and \eqn{cov = A_jSigmat(A_j)}. For \eqn{j=p}, \eqn{e < - mu}. } \value{Value of log-likelihood (sum of log prob of observed multinomial outcomes)} \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{ For further discussion, see Chapters 2 and 4, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{createX}}, \code{\link{rmnpGibbs}} } \examples{ \dontrun{ll=llmnp(beta,Sigma,X,y,r)} } \keyword{models} bayesm/man/rmvpGibbs.Rd0000644000176000001440000000760013117541521014562 0ustar ripleyusers\name{rmvpGibbs} \alias{rmvpGibbs} \concept{bayes} \concept{multivariate probit} \concept{MCMC} \concept{Gibbs Sampling} \title{Gibbs Sampler for Multivariate Probit} \description{ \code{rmvpGibbs} implements the Edwards/Allenby Gibbs Sampler for the multivariate probit model. } \usage{rmvpGibbs(Data, Prior, Mcmc)} \arguments{ \item{Data }{list(y, X, p)} \item{Prior}{list(betabar, A, nu, V)} \item{Mcmc }{list(R, keep, nprint, beta0 ,sigma0)} } \details{ \subsection{Model and Priors}{ \eqn{w_i = X_i\beta + e} with \eqn{e} \eqn{\sim}{~} N(0,\eqn{\Sigma}). Note: \eqn{w_i} is \eqn{p x 1}. \cr \eqn{y_{ij} = 1} if \eqn{w_{ij} > 0}, else \eqn{y_i = 0}. \eqn{j = 1, \ldots, p} \cr beta and Sigma are not identifed. Correlation matrix and the betas divided by the appropriate standard deviation are. See reference or example below for details. \eqn{\beta} \eqn{\sim}{~} \eqn{N(betabar, A^{-1})} \cr \eqn{\Sigma} \eqn{\sim}{~} \eqn{IW(nu, V)} \cr To make \eqn{X} matrix use \code{createX} } \subsection{Argument Details}{ \emph{\code{Data = list(y, X, p)}} \tabular{ll}{ \code{X: } \tab \eqn{n*p x k} Design Matrix \cr \code{y: } \tab \eqn{n*p x 1} vector of 0/1 outcomes \cr \code{p: } \tab dimension of multivariate probit } \emph{\code{Prior = list(betabar, A, nu, V)} [optional]} \tabular{ll}{ \code{betabar: } \tab \eqn{k x 1} prior mean (def: 0) \cr \code{A: } \tab \eqn{k x k} prior precision matrix (def: 0.01*I) \cr \code{nu: } \tab d.f. parameter for Inverted Wishart prior (def: (p-1)+3) \cr \code{V: } \tab PDS location parameter for Inverted Wishart prior (def: nu*I) } \emph{\code{Mcmc = list(R, keep, nprint, beta0 ,sigma0)} [only \code{R} required]} \tabular{ll}{ \code{R: } \tab number of MCMC draws \cr \code{keep: } \tab MCMC thinning parameter -- keep every \code{keep}th draw (def: 1) \cr \code{nprint: } \tab print the estimated time remaining for every \code{nprint}'th draw (def: 100, set to 0 for no print) \cr \code{beta0: } \tab initial value for beta \cr \code{sigma0: } \tab initial value for sigma } } } \value{ A list containing: \item{betadraw }{\eqn{R/keep x k} matrix of betadraws} \item{sigmadraw }{\eqn{R/keep x p*p} matrix of sigma draws -- each row is the vector form of sigma} } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 4, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{rmnpGibbs}} } \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} set.seed(66) simmvp = function(X, p, n, beta, sigma) { w = as.vector(crossprod(chol(sigma),matrix(rnorm(p*n),ncol=n))) + X\%*\%beta y = ifelse(w<0, 0, 1) return(list(y=y, X=X, beta=beta, sigma=sigma)) } p = 3 n = 500 beta = c(-2,0,2) Sigma = matrix(c(1, 0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 1), ncol=3) k = length(beta) I2 = diag(rep(1,p)) xadd = rbind(I2) for(i in 2:n) { xadd=rbind(xadd,I2) } X = xadd simout = simmvp(X,p,500,beta,Sigma) Data1 = list(p=p, y=simout$y, X=simout$X) Mcmc1 = list(R=R, keep=1) out = rmvpGibbs(Data=Data1, Mcmc=Mcmc1) ind = seq(from=0, by=p, length=k) inda = 1:3 ind = ind + inda cat(" Betadraws ", fill=TRUE) betatilde = out$betadraw / sqrt(out$sigmadraw[,ind]) attributes(betatilde)$class = "bayesm.mat" summary(betatilde, tvalues=beta/sqrt(diag(Sigma))) rdraw = matrix(double((R)*p*p), ncol=p*p) rdraw = t(apply(out$sigmadraw, 1, nmat)) attributes(rdraw)$class = "bayesm.var" tvalue = nmat(as.vector(Sigma)) dim(tvalue) = c(p,p) tvalue = as.vector(tvalue[upper.tri(tvalue,diag=TRUE)]) cat(" Draws of Correlation Matrix ", fill=TRUE) summary(rdraw, tvalues=tvalue) ## plotting examples if(0){plot(betatilde, tvalues=beta/sqrt(diag(Sigma)))} } \keyword{models} \keyword{multivariate} bayesm/man/logMargDenNR.Rd0000755000176000001440000000176713117541521015121 0ustar ripleyusers\name{logMargDenNR} \alias{logMargDenNR} \concept{Newton-Raftery approximation} \concept{bayes} \concept{marginal likelihood} \concept{density} \title{Compute Log Marginal Density Using Newton-Raftery Approx} \description{ \code{logMargDenNR} computes log marginal density using the Newton-Raftery approximation. } \usage{logMargDenNR(ll)} \arguments{ \item{ll}{ vector of log-likelihoods evaluated at \code{length(ll)} MCMC draws } } \value{Approximation to log marginal density value.} \section{Warning}{ This approximation can be influenced by outliers in the vector of log-likelihoods; use with \strong{care}. This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 6, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \keyword{distribution} bayesm/man/rhierMnlRwMixture.Rd0000644000176000001440000002354313117570164016307 0ustar ripleyusers\name{rhierMnlRwMixture} \alias{rhierMnlRwMixture} \concept{bayes} \concept{MCMC} \concept{Multinomial Logit} \concept{mixture of normals} \concept{normal mixture} \concept{heterogeneity} \concept{hierarchical models} \title{MCMC Algorithm for Hierarchical Multinomial Logit with Mixture-of-Normals Heterogeneity} \description{ \code{rhierMnlRwMixture} is a MCMC algorithm for a hierarchical multinomial logit with a mixture of normals heterogeneity distribution. This is a hybrid Gibbs Sampler with a RW Metropolis step for the MNL coefficients for each panel unit. } \usage{rhierMnlRwMixture(Data, Prior, Mcmc)} \arguments{ \item{Data }{list(lgtdata, Z, p)} \item{Prior}{list(a, deltabar, Ad, mubar, Amu, nu, V, a, ncomp, SignRes)} \item{Mcmc }{list(R, keep, nprint, s, w)} } \details{ \subsection{Model and Priors}{ \eqn{y_i} \eqn{\sim}{~} \eqn{MNL(X_i,\beta_i)} with \eqn{i = 1, \ldots,} length(lgtdata) and where \eqn{\beta_i} is \eqn{nvar x 1} \eqn{\beta_i} = \eqn{Z\Delta}[i,] + \eqn{u_i} \cr Note: Z\eqn{\Delta} is the matrix Z * \eqn{\Delta} and [i,] refers to \eqn{i}th row of this product \cr Delta is an \eqn{nz x nvar} array \eqn{u_i} \eqn{\sim}{~} \eqn{N(\mu_{ind},\Sigma_{ind})} with \eqn{ind} \eqn{\sim}{~} multinomial(pvec) \eqn{pvec} \eqn{\sim}{~} dirichlet(a) \cr \eqn{delta = vec(\Delta)} \eqn{\sim}{~} \eqn{N(deltabar, A_d^{-1})} \cr \eqn{\mu_j} \eqn{\sim}{~} \eqn{N(mubar, \Sigma_j (x) Amu^{-1})} \cr \eqn{\Sigma_j} \eqn{\sim}{~} \eqn{IW(nu, V)} Note: \eqn{Z} should NOT include an intercept and is centered for ease of interpretation. The mean of each of the \code{nlgt} \eqn{\beta}s is the mean of the normal mixture. Use \code{summary()} to compute this mean from the \code{compdraw} output.\cr Be careful in assessing prior parameter \code{Amu}: 0.01 is too small for many applications. See chapter 5 of Rossi et al for full discussion. } \subsection{Argument Details}{ \emph{\code{Data = list(lgtdata, Z, p)} [\code{Z} optional]} \tabular{ll}{ \code{lgtdata: } \tab list of \code{nlgt=length(lgtdata)} lists with each cross-section unit MNL data \cr \code{lgtdata[[i]]$y: } \tab \eqn{n_i x 1} vector of multinomial outcomes (1, \ldots, m) \cr \code{lgtdata[[i]]$X: } \tab \eqn{n_i*p x nvar} design matrix for \eqn{i}th unit \cr \code{Z: } \tab \eqn{nreg x nz} matrix of unit chars (def: vector of ones) \cr \code{p: } \tab number of choice alternatives } \emph{\code{Prior = list(a, deltabar, Ad, mubar, Amu, nu, V, a, ncomp, SignRes)} [all but \code{ncomp} are optional]} \tabular{ll}{ \code{a: } \tab \eqn{ncomp x 1} vector of Dirichlet prior parameters (def: \code{rep(5,ncomp)}) \cr \code{deltabar: } \tab \eqn{nz*nvar x 1} vector of prior means (def: 0) \cr \code{Ad: } \tab prior precision matrix for vec(D) (def: 0.01*I) \cr \code{mubar: } \tab \eqn{nvar x 1} prior mean vector for normal component mean (def: 0 if unrestricted; 2 if restricted) \cr \code{Amu: } \tab prior precision for normal component mean (def: 0.01 if unrestricted; 0.1 if restricted) \cr \code{nu: } \tab d.f. parameter for IW prior on normal component Sigma (def: nvar+3 if unrestricted; nvar+15 if restricted) \cr \code{V: } \tab PDS location parameter for IW prior on normal component Sigma (def: nu*I if unrestricted; nu*D if restricted with d_pp = 4 if unrestricted and d_pp = 0.01 if restricted) \cr \code{ncomp: } \tab number of components used in normal mixture \cr \code{SignRes: } \tab \eqn{nvar x 1} vector of sign restrictions on the coefficient estimates (def: \code{rep(0,nvar)}) } \emph{\code{Mcmc = list(R, keep, nprint, s, w)} [only \code{R} required]} \tabular{ll}{ \code{R: } \tab number of MCMC draws \cr \code{keep: } \tab MCMC thinning parameter -- keep every \code{keep}th draw (def: 1) \cr \code{nprint: } \tab print the estimated time remaining for every \code{nprint}'th draw (def: 100, set to 0 for no print) \cr \code{s: } \tab scaling parameter for RW Metropolis (def: 2.93/\code{sqrt(nvar)}) \cr \code{w: } \tab fractional likelihood weighting parameter (def: 0.1) } } \subsection{Sign Restrictions}{ If \eqn{\beta_ik} has a sign restriction: \eqn{\beta_ik = SignRes[k] * exp(\beta*_ik)} To use sign restrictions on the coefficients, \code{SignRes} must be an \eqn{nvar x 1} vector containing values of either 0, -1, or 1. The value 0 means there is no sign restriction, -1 ensures that the coefficient is \emph{negative}, and 1 ensures that the coefficient is \emph{positive}. For example, if \code{SignRes = c(0,1,-1)}, the first coefficient is unconstrained, the second will be positive, and the third will be negative. The sign restriction is implemented such that if the the \eqn{k}'th \eqn{\beta} has a non-zero sign restriction (i.e., it is constrained), we have \eqn{\beta_k = SignRes[k] * exp(\beta*_k)}. The sign restrictions (if used) will be reflected in the \code{betadraw} output. However, the unconstrained mixture components are available in \code{nmix}. \bold{Important:} Note that draws from \code{nmix} are distributed according to the mixture of normals but \bold{not} the coefficients in \code{betadraw}. Care should be taken when selecting priors on any sign restricted coefficients. See related vignette for additional information. } \subsection{\code{nmix} Details}{ \code{nmix} is a list with 3 components. Several functions in the \code{bayesm} package that involve a Dirichlet Process or mixture-of-normals return \code{nmix}. Across these functions, a common structure is used for \code{nmix} in order to utilize generic summary and plotting functions. \tabular{ll}{ \code{probdraw:} \tab \eqn{ncomp x R/keep} matrix that reports the probability that each draw came from a particular component \cr \code{zdraw: } \tab \eqn{R/keep x nobs} matrix that indicates which component each draw is assigned to (here, null) \cr \code{compdraw:} \tab A list of \eqn{R/keep} lists of \eqn{ncomp} lists. Each of the inner-most lists has 2 elemens: a vector of draws for \code{mu} and a matrix of draws for the Cholesky root of \code{Sigma}. } } } \value{ A list containing: \item{Deltadraw }{ \eqn{R/keep x nz*nvar} matrix of draws of Delta, first row is initial value} \item{betadraw }{ \eqn{nlgt x nvar x R/keep} array of beta draws} \item{nmix }{ a list containing: \code{probdraw}, \code{zdraw}, \code{compdraw} (see \dQuote{\code{nmix} Details} section)} \item{loglike }{ \eqn{R/keep x 1} vector of log-likelihood for each kept draw} \item{SignRes }{ \eqn{nvar x 1} vector of sign restrictions} } \note{ Note: as of version 2.0-2 of \code{bayesm}, the fractional weight parameter has been changed to a weight between 0 and 1. \eqn{w} is the fractional weight on the normalized pooled likelihood. This differs from what is in Rossi et al chapter 5, i.e. \eqn{like_i^{(1-w)} x like_pooled^{((n_i/N)*w)}} Large \code{R} values may be required (>20,000). } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 5, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{rmnlIndepMetrop}} } \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=10000} else {R=10} set.seed(66) p = 3 # num of choice alterns ncoef = 3 nlgt = 300 # num of cross sectional units nz = 2 Z = matrix(runif(nz*nlgt),ncol=nz) Z = t(t(Z) - apply(Z,2,mean)) # demean Z ncomp = 3 # num of mixture components Delta = matrix(c(1,0,1,0,1,2),ncol=2) comps=NULL comps[[1]] = list(mu=c(0,-1,-2), rooti=diag(rep(1,3))) comps[[2]] = list(mu=c(0,-1,-2)*2, rooti=diag(rep(1,3))) comps[[3]] = list(mu=c(0,-1,-2)*4, rooti=diag(rep(1,3))) pvec = c(0.4, 0.2, 0.4) ## simulate from MNL model conditional on X matrix simmnlwX= function(n,X,beta) { k = length(beta) Xbeta = X\%*\%beta j = nrow(Xbeta) / n Xbeta = matrix(Xbeta, byrow=TRUE, ncol=j) Prob = exp(Xbeta) iota = c(rep(1,j)) denom = Prob\%*\%iota Prob = Prob/as.vector(denom) y = vector("double",n) ind = 1:j for (i in 1:n) { yvec = rmultinom(1, 1, Prob[i,]) y[i] = ind\%*\%yvec } return(list(y=y, X=X, beta=beta, prob=Prob)) } ## simulate data simlgtdata = NULL ni = rep(50, 300) for (i in 1:nlgt) { betai = Delta\%*\%Z[i,] + as.vector(rmixture(1,pvec,comps)$x) Xa = matrix(runif(ni[i]*p,min=-1.5,max=0), ncol=p) X = createX(p, na=1, nd=NULL, Xa=Xa, Xd=NULL, base=1) outa = simmnlwX(ni[i], X, betai) simlgtdata[[i]] = list(y=outa$y, X=X, beta=betai) } ## plot betas if(0){ bmat = matrix(0, nlgt, ncoef) for(i in 1:nlgt) {bmat[i,] = simlgtdata[[i]]$beta} par(mfrow = c(ncoef,1)) for(i in 1:ncoef) { hist(bmat[,i], breaks=30, col="magenta") } } ## set parms for priors and Z Prior1 = list(ncomp=5) keep = 5 Mcmc1 = list(R=R, keep=keep) Data1 = list(p=p, lgtdata=simlgtdata, Z=Z) ## fit model without sign constraints out1 = rhierMnlRwMixture(Data=Data1, Prior=Prior1, Mcmc=Mcmc1) cat("Summary of Delta draws", fill=TRUE) summary(out1$Deltadraw, tvalues=as.vector(Delta)) cat("Summary of Normal Mixture Distribution", fill=TRUE) summary(out1$nmix) ## plotting examples if(0) { plot(out1$betadraw) plot(out1$nmix) } ## fit model with constraint that beta_i2 < 0 forall i Prior2 = list(ncomp=5, SignRes=c(0,-1,0)) out2 = rhierMnlRwMixture(Data=Data1, Prior=Prior2, Mcmc=Mcmc1) cat("Summary of Delta draws", fill=TRUE) summary(out2$Deltadraw, tvalues=as.vector(Delta)) cat("Summary of Normal Mixture Distribution", fill=TRUE) summary(out2$nmix) ## plotting examples if(0) { plot(out2$betadraw) plot(out2$nmix) } } \keyword{models} bayesm/man/customerSat.Rd0000644000176000001440000000262513117541521015142 0ustar ripleyusers\name{customerSat} \alias{customerSat} \docType{data} \title{Customer Satisfaction Data} \description{ Responses to a satisfaction survey for a Yellow Pages advertising product. All responses are on a 10 point scale from 1 to 10 (1 is "Poor" and 10 is "Excellent"). } \usage{data(customerSat)} \format{ A data frame with 1811 observations on the following 10 variables: \tabular{ll}{ \ldots\code{$q1 } \tab Overall Satisfaction \cr \ldots\code{$q2 } \tab Setting Competitive Prices \cr \ldots\code{$q3 } \tab Holding Price Increase to a Minimum \cr \ldots\code{$q4 } \tab Appropriate Pricing given Volume \cr \ldots\code{$q5 } \tab Demonstrating Effectiveness of Purchase \cr \ldots\code{$q6 } \tab Reach a Large Number of Customers \cr \ldots\code{$q7 } \tab Reach of Advertising \cr \ldots\code{$q8 } \tab Long-term Exposure \cr \ldots\code{$q9 } \tab Distribution \cr \ldots\code{$q10} \tab Distribution to Right Geographic Areas } } \source{Rossi, Peter, Zvi Gilula, and Greg Allenby (2001), "Overcoming Scale Usage Heterogeneity," \emph{Journal of the American Statistical Association} 96, 20--31.} \references{Case Study 3, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch.\cr \url{http://www.perossi.org/home/bsm-1}} \examples{ data(customerSat) apply(as.matrix(customerSat),2,table) ## see also examples for 'rscaleUsage' } \keyword{datasets} bayesm/man/margarine.Rd0000644000176000001440000000703113117541521014572 0ustar ripleyusers\name{margarine} \alias{margarine} \docType{data} \title{Household Panel Data on Margarine Purchases} \description{ Panel data on purchases of margarine by 516 households. Demographic variables are included. } \usage{data(margarine)} \format{ The \code{detailing} object is a list containing two data frames, \code{choicePrice} and \code{demos}. } \details{ In the \code{choicePrice} data frame: \tabular{ll}{ \ldots\code{$hhid } \tab household ID \cr \ldots\code{$choice} \tab multinomial indicator of one of the 10 products } The products are indicated by brand and type. Brands: \tabular{ll}{ \ldots\code{$Pk } \tab Parkay \cr \ldots\code{$BB } \tab BlueBonnett \cr \ldots\code{$Fl } \tab Fleischmanns \cr \ldots\code{$Hse } \tab house \cr \ldots\code{$Gen } \tab generic \cr \ldots\code{$Imp } \tab Imperial \cr \ldots\code{$SS } \tab Shed Spread } Product type: \tabular{ll}{ \ldots\code{$_Stk} \tab stick \cr \ldots\code{$_Tub} \tab tub } In the \code{demos} data frame: \tabular{ll}{ \ldots\code{$Fs3_4 } \tab dummy for family size 3-4 \cr \ldots\code{$Fs5 } \tab dummy for family size >= 5 \cr \ldots\code{$college } \tab dummy for education status \cr \ldots\code{$whtcollar} \tab dummy for job status \cr \ldots\code{$retired } \tab dummy for retirement status } All prices are in U.S. dollars. } \source{Allenby, Greg and Peter Rossi (1991), "Quality Perceptions and Asymmetric Switching Between Brands," \emph{Marketing Science} 10, 185--205. } \references{ Chapter 5, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \examples{ data(margarine) cat(" Table of Choice Variable ", fill=TRUE) print(table(margarine$choicePrice[,2])) cat(" Means of Prices", fill=TRUE) mat=apply(as.matrix(margarine$choicePrice[,3:12]), 2, mean) print(mat) cat(" Quantiles of Demographic Variables", fill=TRUE) mat=apply(as.matrix(margarine$demos[,2:8]), 2, quantile) print(mat) ## example of processing for use with 'rhierMnlRwMixture' if(0) { select = c(1:5,7) ## select brands chPr = as.matrix(margarine$choicePrice) ## make sure to log prices chPr = cbind(chPr[,1], chPr[,2], log(chPr[,2+select])) demos = as.matrix(margarine$demos[,c(1,2,5)]) ## remove obs for other alts chPr = chPr[chPr[,2] <= 7,] chPr = chPr[chPr[,2] != 6,] ## recode choice chPr[chPr[,2] == 7,2] = 6 hhidl = levels(as.factor(chPr[,1])) lgtdata = NULL nlgt = length(hhidl) p = length(select) ## number of choice alts ind = 1 for (i in 1:nlgt) { nobs = sum(chPr[,1]==hhidl[i]) if(nobs >=5) { data = chPr[chPr[,1]==hhidl[i],] y = data[,2] names(y) = NULL X = createX(p=p, na=1, Xa=data[,3:8], nd=NULL, Xd=NULL, INT=TRUE, base=1) lgtdata[[ind]] = list(y=y, X=X, hhid=hhidl[i]) ind = ind+1 } } nlgt = length(lgtdata) ## now extract demos corresponding to hhs in lgtdata Z = NULL nlgt = length(lgtdata) for(i in 1:nlgt){ Z = rbind(Z, demos[demos[,1]==lgtdata[[i]]$hhid, 2:3]) } ## take log of income and family size and demean Z = log(Z) Z[,1] = Z[,1] - mean(Z[,1]) Z[,2] = Z[,2] - mean(Z[,2]) keep = 5 R = 20000 mcmc1 = list(keep=keep, R=R) out = rhierMnlRwMixture(Data=list(p=p,lgtdata=lgtdata, Z=Z), Prior=list(ncomp=1), Mcmc=mcmc1) summary(out$Deltadraw) summary(out$nmix) ## plotting examples if(0){ plot(out$nmix) plot(out$Deltadraw) } } } \keyword{datasets} bayesm/man/summary.bayesm.var.Rd0000644000176000001440000000340013117541521016364 0ustar ripleyusers\name{summary.bayesm.var} \alias{summary.bayesm.var} \title{Summarize Draws of Var-Cov Matrices} \description{ \code{summary.bayesm.var} is an S3 method to summarize marginal distributions given an array of draws } \usage{\method{summary}{bayesm.var}(object, names, burnin = trunc(0.1 * nrow(Vard)), tvalues, QUANTILES = FALSE , ...)} \arguments{ \item{object }{ \code{object} (herafter, \code{Vard}) is an array of draws of a covariance matrix } \item{names }{ optional character vector of names for the columns of \code{Vard}} \item{burnin }{ number of draws to burn-in (def: \eqn{0.1*nrow(Vard)})} \item{tvalues }{ optional vector of "true" values for use in simulation examples } \item{QUANTILES }{ logical for should quantiles be displayed (def: \code{TRUE})} \item{... }{ optional arguments for generic function } } \details{ Typically, \code{summary.bayesm.var} will be invoked by a call to the generic summary function as in \code{summary(object)} where \code{object} is of class \code{bayesm.var}. Mean, Std Dev, Numerical Standard error (of estimate of posterior mean), relative numerical efficiency (see \code{numEff}), and effective sample size are displayed. If \code{QUANTILES=TRUE}, quantiles of marginal distirbutions in the columns of \code{Vard} are displayed. \cr \code{Vard} is an array of draws of a covariance matrix stored as vectors. Each row is a different draw. \cr The posterior mean of the vector of standard deviations and the correlation matrix are also displayed } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \seealso{ \code{\link{summary.bayesm.mat}}, \code{\link{summary.bayesm.nmix}}} \examples{ \dontrun{out=rmnpGibbs(Data,Prior,Mcmc); summary(out$sigmadraw)} } \keyword{ univar } bayesm/man/summary.bayesm.nmix.Rd0000644000176000001440000000266213117541521016560 0ustar ripleyusers\name{summary.bayesm.nmix} \alias{summary.bayesm.nmix} \title{Summarize Draws of Normal Mixture Components} \description{ \code{summary.bayesm.nmix} is an S3 method to display summaries of the distribution implied by draws of Normal Mixture Components. Posterior means and variance-covariance matrices are displayed.\cr Note: 1st and 2nd moments may not be very interpretable for mixtures of normals. This summary function can take a minute or so. The current implementation is not efficient. } \usage{\method{summary}{bayesm.nmix}(object, names, burnin=trunc(0.1*nrow(probdraw)), ...)} \arguments{ \item{object }{ an object of class \code{bayesm.nmix}, a list of lists of draws} \item{names }{ optional character vector of names fo reach dimension of the density} \item{burnin }{ number of draws to burn-in (def: \eqn{0.1*nrow(probdraw)})} \item{... }{ parms to send to summary} } \details{ An object of class \code{bayesm.nmix} is a list of three components: \describe{ \item{probdraw }{ a matrix of \eqn{R/keep} rows by dim of normal mix of mixture prob draws} \item{second comp }{ not used} \item{compdraw }{ list of list of lists with draws of mixture comp parms} } } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \seealso{ \code{\link{summary.bayesm.mat}}, \code{\link{summary.bayesm.var}}} \examples{ \dontrun{out=rnmix(Data,Prior,Mcmc); summary(out)} } \keyword{plot} bayesm/man/plot.bayesm.mat.Rd0000644000176000001440000000407613117541521015650 0ustar ripleyusers\name{plot.bayesm.mat} \alias{plot.bayesm.mat} \concept{MCMC} \concept{S3 method} \concept{plot} \title{Plot Method for Arrays of MCMC Draws} \description{ \code{plot.bayesm.mat} is an S3 method to plot arrays of MCMC draws. The columns in the array correspond to parameters and the rows to MCMC draws. } \usage{\method{plot}{bayesm.mat}(x,names,burnin,tvalues,TRACEPLOT,DEN,INT,CHECK_NDRAWS, ...)} \arguments{ \item{x }{ An object of either S3 class, \code{bayesm.mat}, or S3 class, \code{mcmc} } \item{names }{ optional character vector of names for coefficients} \item{burnin }{ number of draws to discard for burn-in (def: \eqn{0.1*nrow(X))}} \item{tvalues }{ vector of true values} \item{TRACEPLOT }{ logical, \code{TRUE} provide sequence plots of draws and acfs (def: \code{TRUE})} \item{DEN }{ logical, \code{TRUE} use density scale on histograms (def: \code{TRUE})} \item{INT }{ logical, \code{TRUE} put various intervals and points on graph (def: \code{TRUE})} \item{CHECK_NDRAWS }{ logical, \code{TRUE} check that there are at least 100 draws (def: \code{TRUE})} \item{... }{ standard graphics parameters } } \details{ Typically, \code{plot.bayesm.mat} will be invoked by a call to the generic plot function as in \code{plot(object)} where object is of class \code{bayesm.mat}. All of the \code{bayesm} MCMC routines return draws in this class (see example below). One can also simply invoke \code{plot.bayesm.mat} on any valid 2-dim array as in \code{plot.bayesm.mat(betadraws)}. \cr \cr \code{plot.bayesm.mat} paints (by default) on the histogram: \cr \cr green "[]" delimiting 95\% Bayesian Credibility Interval \cr yellow "()" showing +/- 2 numerical standard errors \cr red "|" showing posterior mean \cr \cr \code{plot.bayesm.mat} is also exported for use as a standard function, as in \code{plot.bayesm.mat(matrix)} } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \examples{ \dontrun{out=runiregGibbs(Data,Prior,Mcmc); plot(out$betadraw)} } \keyword{hplot} bayesm/man/rsurGibbs.Rd0000644000176000001440000000672513117541521014600 0ustar ripleyusers\name{rsurGibbs} \alias{rsurGibbs} \concept{bayes} \concept{Gibbs Sampler} \concept{regression} \concept{SUR model} \concept{Seemingly Unrelated Regression} \concept{MCMC} \title{Gibbs Sampler for Seemingly Unrelated Regressions (SUR)} \description{ \code{rsurGibbs} implements a Gibbs Sampler to draw from the posterior of the Seemingly Unrelated Regression (SUR) Model of Zellner. } \usage{rsurGibbs(Data, Prior, Mcmc)} \arguments{ \item{Data }{list(regdata)} \item{Prior}{list(betabar, A, nu, V)} \item{Mcmc }{list(R, keep)} } \details{ \subsection{Model and Priors}{ \eqn{y_i = X_i\beta_i + e_i} with \eqn{i=1,\ldots,m} for \eqn{m} regressions \cr (\eqn{e(k,1), \ldots, e(k,m)})' \eqn{\sim}{~} \eqn{N(0, \Sigma)} with \eqn{k=1, \ldots, n} Can be written as a stacked model: \cr \eqn{y = X\beta + e} where \eqn{y} is a \eqn{nobs*m} vector and \eqn{p} = \code{length(beta)} = \code{sum(length(beta_i))} Note: must have the same number of observations (\eqn{n}) in each equation but can have a different number of \eqn{X} variables (\eqn{p_i}) for each equation where \eqn{p = \sum p_i}. \eqn{\beta} \eqn{\sim}{~} \eqn{N(betabar, A^{-1})} \cr \eqn{\Sigma} \eqn{\sim}{~} \eqn{IW(nu,V)} } \subsection{Argument Details}{ \emph{\code{Data = list(regdata)}} \tabular{ll}{ \code{regdata: } \tab list of lists, \code{regdata[[i]] = list(y=y_i, X=X_i)}, where \code{y_i} is \eqn{n x 1} and \code{X_i} is \eqn{n x p_i} } \emph{\code{Prior = list(betabar, A, nu, V)} [optional]} \tabular{ll}{ \code{betabar: } \tab \eqn{p x 1} prior mean (def: 0) \cr \code{A: } \tab \eqn{p x p} prior precision matrix (def: 0.01*I) \cr \code{nu: } \tab d.f. parameter for Inverted Wishart prior (def: m+3) \cr \code{V: } \tab \eqn{m x m} scale parameter for Inverted Wishart prior (def: nu*I) } \emph{\code{Mcmc = list(R, keep)} [only \code{R} required]} \tabular{ll}{ \code{R: }\tab number of MCMC draws \cr \code{keep: }\tab MCMC thinning parameter -- keep every \code{keep}th draw (def: 1) \cr \code{nprint: }\tab print the estimated time remaining for every \code{nprint}'th draw (def: 100, set to 0 for no print) } } } \value{ A list containing: \item{betadraw }{ \eqn{R x p} matrix of betadraws} \item{Sigmadraw }{ \eqn{R x (m*m)} array of Sigma draws} } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 3, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{rmultireg}} } \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=1000} else {R=10} set.seed(66) ## simulate data from SUR beta1 = c(1,2) beta2 = c(1,-1,-2) nobs = 100 nreg = 2 iota = c(rep(1, nobs)) X1 = cbind(iota, runif(nobs)) X2 = cbind(iota, runif(nobs), runif(nobs)) Sigma = matrix(c(0.5, 0.2, 0.2, 0.5), ncol=2) U = chol(Sigma) E = matrix(rnorm(2*nobs),ncol=2)\%*\%U y1 = X1\%*\%beta1 + E[,1] y2 = X2\%*\%beta2 + E[,2] ## run Gibbs Sampler regdata = NULL regdata[[1]] = list(y=y1, X=X1) regdata[[2]] = list(y=y2, X=X2) out = rsurGibbs(Data=list(regdata=regdata), Mcmc=list(R=R)) cat("Summary of beta draws", fill=TRUE) summary(out$betadraw, tvalues=c(beta1,beta2)) cat("Summary of Sigmadraws", fill=TRUE) summary(out$Sigmadraw, tvalues=as.vector(Sigma[upper.tri(Sigma,diag=TRUE)])) ## plotting examples if(0){plot(out$betadraw, tvalues=c(beta1,beta2))} } \keyword{regression} bayesm/man/llmnl.Rd0000644000176000001440000000245213117541521013745 0ustar ripleyusers\name{llmnl} \alias{llmnl} \concept{multinomial logit} \concept{likelihood} \title{Evaluate Log Likelihood for Multinomial Logit Model} \description{\code{llmnl} evaluates log-likelihood for the multinomial logit model.} \usage{llmnl(beta, y, X)} \arguments{ \item{beta}{ \eqn{k x 1} coefficient vector } \item{y }{ \eqn{n x 1} vector of obs on y (1,\ldots, p) } \item{X }{ \eqn{n*p x k} design matrix (use \code{createX} to create \eqn{X}) } } \details{ Let \eqn{\mu_i = X_i beta}, then \eqn{Pr(y_i=j) = exp(\mu_{i,j}) / \sum_k exp(\mu_{i,k})}.\cr \eqn{X_i} is the submatrix of \eqn{X} corresponding to the \eqn{i}th observation. \eqn{X} has \eqn{n*p} rows. Use \code{\link{createX}} to create \eqn{X}. } \value{Value of log-likelihood (sum of log prob of observed multinomial outcomes).} \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type.} \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1} } \seealso{ \code{\link{createX}}, \code{\link{rmnlIndepMetrop}} } \examples{ \dontrun{ll=llmnl(beta,y,X)} } \keyword{models} bayesm/man/bank.Rd0000644000176000001440000001032213117541521013535 0ustar ripleyusers\name{bank} \alias{bank} \docType{data} \title{Bank Card Conjoint Data} \description{ A panel dataset from a conjoint experiment in which two partial profiles of credit cards were presented to 946 respondents from a regional bank wanting to offer credit cards to customers outside of its normal operating region. Each respondent was presented with between 13 and 17 paired comparisons. The bank and attribute levels are disguised to protect the proprietary interests of the cooperating firm. } \usage{data(bank)} \format{ The \code{bank} object is a list containing two data frames. The first, \code{choiceAtt}, provides choice attributes for the partial credit card profiles. The second, \code{demo}, provides demographic information on the respondents. } \details{ In the \code{choiceAtt} data frame: \tabular{ll}{ \ldots\code{$id } \tab respondent id \cr \ldots\code{$choice } \tab profile chosen \cr \ldots\code{$Med_FInt } \tab medium fixed interest rate \cr \ldots\code{$Low_FInt } \tab low fixed interest rate\cr \ldots\code{$Med_VInt } \tab variable interest rate\cr \ldots\code{$Rewrd_2 } \tab reward level 2 \cr \ldots\code{$Rewrd_3 } \tab reward level 3 \cr \ldots\code{$Rewrd_4 } \tab reward level 4 \cr \ldots\code{$Med_Fee } \tab medium annual fee level \cr \ldots\code{$Low_Fee } \tab low annual fee level \cr \ldots\code{$Bank_B } \tab bank offering the credit card \cr \ldots\code{$Out_State } \tab location of the bank offering the credit card \cr \ldots\code{$Med_Rebate } \tab medium rebate level \cr \ldots\code{$High_Rebate } \tab high rebate level \cr \ldots\code{$High_CredLine} \tab high credit line level \cr \ldots\code{$Long_Grace } \tab grace period } The profiles are coded as the difference in attribute levels. Thus, that a "-1" means the profile coded as a choice of "0" has the attribute. A value of 0 means that the attribute was not present in the comparison. In the \code{demo} data frame: \tabular{ll}{ \ldots\code{$id } \tab respondent id \cr \ldots\code{$age } \tab respondent age in years \cr \ldots\code{$income} \tab respondent income category \cr \ldots\code{$gender} \tab female=1 } } \source{Allenby, Gregg and James Ginter (1995), "Using Extremes to Design Products and Segment Markets," \emph{Journal of Marketing Research}, 392--403.} \references{Appendix A, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \examples{ data(bank) cat(" table of Binary Dep Var", fill=TRUE) print(table(bank$choiceAtt[,2])) cat(" table of Attribute Variables", fill=TRUE) mat = apply(as.matrix(bank$choiceAtt[,3:16]), 2, table) print(mat) cat(" means of Demographic Variables", fill=TRUE) mat=apply(as.matrix(bank$demo[,2:3]), 2, mean) print(mat) ## example of processing for use with rhierBinLogit if(0) { choiceAtt = bank$choiceAtt Z = bank$demo ## center demo data so that mean of random-effects ## distribution can be interpreted as the average respondent Z[,1] = rep(1,nrow(Z)) Z[,2] = Z[,2] - mean(Z[,2]) Z[,3] = Z[,3] - mean(Z[,3]) Z[,4] = Z[,4] - mean(Z[,4]) Z = as.matrix(Z) hh = levels(factor(choiceAtt$id)) nhh = length(hh) lgtdata = NULL for (i in 1:nhh) { y = choiceAtt[choiceAtt[,1]==hh[i], 2] nobs = length(y) X = as.matrix(choiceAtt[choiceAtt[,1]==hh[i], c(3:16)]) lgtdata[[i]] = list(y=y, X=X) } cat("Finished Reading data", fill=TRUE) Data = list(lgtdata=lgtdata, Z=Z) Mcmc = list(R=10000, sbeta=0.2, keep=20) set.seed(66) out = rhierBinLogit(Data=Data, Mcmc=Mcmc) begin = 5000/20 summary(out$Deltadraw, burnin=begin) summary(out$Vbetadraw, burnin=begin) ## plotting examples if(0){ ## plot grand means of random effects distribution (first row of Delta) index = 4*c(0:13)+1 matplot(out$Deltadraw[,index], type="l", xlab="Iterations/20", ylab="", main="Average Respondent Part-Worths") ## plot hierarchical coefs plot(out$betadraw) ## plot log-likelihood plot(out$llike, type="l", xlab="Iterations/20", ylab="", main="Log Likelihood") } } } \keyword{datasets} bayesm/man/rmixture.Rd0000644000176000001440000000231713117541521014506 0ustar ripleyusers\name{rmixture} \alias{rmixture} \concept{mixture of normals} \concept{simulation} \title{Draw from Mixture of Normals} \description{ \code{rmixture} simulates iid draws from a Multivariate Mixture of Normals } \usage{rmixture(n, pvec, comps)} \arguments{ \item{n }{ number of observations } \item{pvec }{ \eqn{ncomp x 1} vector of prior probabilities for each mixture component } \item{comps }{ list of mixture component parameters } } \details{ \code{comps} is a list of length \code{ncomp} with \code{ncomp = length(pvec)}. \cr \code{comps[[j]][[1]]} is mean vector for the \eqn{j}th component. \cr \code{comps[[j]][[2]]} is the inverse of the cholesky root of \eqn{\Sigma} for \eqn{j}th component } \value{ A list containing: \item{x: }{ an \eqn{n x} \code{length(comps[[1]][[1]])} array of iid draws } \item{z: }{ an \eqn{n x 1} vector of indicators of which component each draw is taken from } } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \seealso{ \code{\link{rnmixGibbs}} } \keyword{multivariate} \keyword{distribution} bayesm/man/rDPGibbs.Rd0000644000176000001440000002115013117570164014264 0ustar ripleyusers\name{rDPGibbs} \alias{rDPGibbs} \concept{bayes} \concept{MCMC} \concept{normal mixtures} \concept{Dirichlet Process} \concept{Gibbs Sampling} \title{ Density Estimation with Dirichlet Process Prior and Normal Base } \description{ \code{rDPGibbs} implements a Gibbs Sampler to draw from the posterior for a normal mixture problem with a Dirichlet Process prior. A natural conjugate base prior is used along with priors on the hyper parameters of this distribution. One interpretation of this model is as a normal mixture with a random number of components that can grow with the sample size. } \usage{rDPGibbs(Prior, Data, Mcmc)} \arguments{ \item{Data }{list(y)} \item{Prior}{list(Prioralpha, lambda_hyper)} \item{Mcmc }{list(R, keep, nprint, maxuniq, SCALE, gridsize)} } \details{ \subsection{Model and Priors}{ \eqn{y_i} \eqn{\sim}{~} \eqn{N(\mu_i, \Sigma_i)} \eqn{\theta_i=(\mu_i,\Sigma_i)} \eqn{\sim}{~} \eqn{DP(G_0(\lambda),alpha)}\cr \eqn{G_0(\lambda):}\cr \eqn{\mu_i | \Sigma_i} \eqn{\sim}{~} \eqn{N(0,\Sigma_i (x) a^{-1})}\cr \eqn{\Sigma_i} \eqn{\sim}{~} \eqn{IW(nu,nu*v*I)} \eqn{\lambda(a,nu,v):}\cr \eqn{a} \eqn{\sim}{~} uniform on grid[alim[1], alimb[2]]\cr \eqn{nu} \eqn{\sim}{~} uniform on grid[dim(data)-1 + exp(nulim[1]), dim(data)-1 + exp(nulim[2])]\cr \eqn{v} \eqn{\sim}{~} uniform on grid[vlim[1], vlim[2]] \eqn{alpha} \eqn{\sim}{~} \eqn{(1-(\alpha-alphamin)/(alphamax-alphamin))^{power}} \cr \eqn{alpha} = alphamin then expected number of components = \code{Istarmin} \cr \eqn{alpha} = alphamax then expected number of components = \code{Istarmax} We parameterize the prior on \eqn{\Sigma_i} such that \eqn{mode(\Sigma)= nu/(nu+2) vI}. The support of nu enforces valid IW density; \eqn{nulim[1] > 0} We use the structure for \code{nmix} that is compatible with the \code{bayesm} routines for finite mixtures of normals. This allows us to use the same summary and plotting methods. The default choices of \code{alim}, \code{nulim}, and \code{vlim} determine the location and approximate size of candidate "atoms" or possible normal components. The defaults are sensible given that we scale the data. Without scaling, you want to insure that \code{alim} is set for a wide enough range of values (remember a is a precision parameter) and the \code{v} is big enough to propose \code{Sigma} matrices wide enough to cover the data range. A careful analyst should look at the posterior distribution of \code{a}, \code{nu}, \code{v} to make sure that the support is set correctly in \code{alim}, \code{nulim}, \code{vlim}. In other words, if we see the posterior bunched up at one end of these support ranges, we should widen the range and rerun. If you want to force the procedure to use many small atoms, then set \code{nulim} to consider only large values and set \code{vlim} to consider only small scaling constants. Set \code{Istarmax} to a large number. This will create a very "lumpy" density estimate somewhat like the classical Kernel density estimates. Of course, this is not advised if you have a prior belief that densities are relatively smooth. } \subsection{Argument Details}{ \emph{\code{Data = list(y)}} \tabular{ll}{ \code{y: } \tab \eqn{n x k} matrix of observations on \eqn{k} dimensional data } \emph{\code{Prior = list(Prioralpha, lambda_hyper)} [optional]} \tabular{ll}{ \code{Prioralpha: } \tab \code{list(Istarmin, Istarmax, power)} \cr \code{$Istarmin: } \tab is expected number of components at lower bound of support of alpha (def: 1) \cr \code{$Istarmax: } \tab is expected number of components at upper bound of support of alpha (def: \code{min(50, 0.1*nrow(y))}) \cr \code{$power: } \tab is the power parameter for alpha prior (def: 0.8) \cr \code{lambda_hyper:} \tab \code{list(alim, nulim, vlim)} \cr \code{$alim: } \tab defines support of a distribution (def: \code{c(0.01, 10)}) \cr \code{$nulim: } \tab defines support of nu distribution (def: \code{c(0.01, 3)}) \cr \code{$vlim: } \tab defines support of v distribution (def: \code{c(0.1, 4)}) } \emph{\code{Mcmc = list(R, keep, nprint, maxuniq, SCALE, gridsize)} [only \code{R} required]} \tabular{ll}{ \code{R: } \tab number of MCMC draws \cr \code{keep: } \tab MCMC thinning parameter -- keep every \code{keep}th draw (def: 1) \cr \code{nprint: } \tab print the estimated time remaining for every \code{nprint}'th draw (def: 100, set to 0 for no print) \cr \code{maxuniq: } \tab storage constraint on the number of unique components (def: 200) \cr \code{SCALE: } \tab should data be scaled by mean,std deviation before posterior draws (def: \code{TRUE}) \cr \code{gridsize: } \tab number of discrete points for hyperparameter priors (def: 20) } } \subsection{\code{nmix} Details}{ \code{nmix} is a list with 3 components. Several functions in the \code{bayesm} package that involve a Dirichlet Process or mixture-of-normals return \code{nmix}. Across these functions, a common structure is used for \code{nmix} in order to utilize generic summary and plotting functions. \tabular{ll}{ \code{probdraw:} \tab \eqn{ncomp x R/keep} matrix that reports the probability that each draw came from a particular component \cr \code{zdraw: } \tab \eqn{R/keep x nobs} matrix that indicates which component each draw is assigned to \cr \code{compdraw:} \tab A list of \eqn{R/keep} lists of \eqn{ncomp} lists. Each of the inner-most lists has 2 elemens: a vector of draws for \code{mu} and a matrix of draws for the Cholesky root of \code{Sigma}. } } } \value{ A list containing: \item{nmix }{ a list containing: \code{probdraw}, \code{zdraw}, \code{compdraw} (see \dQuote{\code{nmix} Details} section)} \item{alphadraw }{ \eqn{R/keep x 1} vector of alpha draws} \item{nudraw }{ \eqn{R/keep x 1} vector of nu draws} \item{adraw }{ \eqn{R/keep x 1} vector of a draws} \item{vdraw }{ \eqn{R/keep x 1} vector of v draws} } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \seealso{ \code{\link{rnmixGibbs}}, \code{\link{rmixture}}, \code{\link{rmixGibbs}}, \code{\link{eMixMargDen}}, \code{\link{momMix}}, \code{\link{mixDen}}, \code{\link{mixDenBi}}} \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} set.seed(66) ## simulate univariate data from Chi-Sq N = 200 chisqdf = 8 y1 = as.matrix(rchisq(N,df=chisqdf)) ## set arguments for rDPGibbs Data1 = list(y=y1) Prioralpha = list(Istarmin=1, Istarmax=10, power=0.8) Prior1 = list(Prioralpha=Prioralpha) Mcmc = list(R=R, keep=1, maxuniq=200) out1 = rDPGibbs(Prior=Prior1, Data=Data1, Mcmc=Mcmc) if(0){ ## plotting examples rgi = c(0,20) grid = matrix(seq(from=rgi[1],to=rgi[2],length.out=50), ncol=1) deltax = (rgi[2]-rgi[1]) / nrow(grid) plot(out1$nmix, Grid=grid, Data=y1) ## plot true density with historgram plot(range(grid[,1]), 1.5*range(dchisq(grid[,1],df=chisqdf)), type="n", xlab=paste("Chisq ; ",N," obs",sep=""), ylab="") hist(y1, xlim=rgi, freq=FALSE, col="yellow", breaks=20, add=TRUE) lines(grid[,1], dchisq(grid[,1],df=chisqdf) / (sum(dchisq(grid[,1],df=chisqdf))*deltax), col="blue", lwd=2) } ## simulate bivariate data from the "Banana" distribution (Meng and Barnard) banana = function(A, B, C1, C2, N, keep=10, init=10) { R = init*keep + N*keep x1 = x2 = 0 bimat = matrix(double(2*N), ncol=2) for (r in 1:R) { x1 = rnorm(1,mean=(B*x2+C1) / (A*(x2^2)+1), sd=sqrt(1/(A*(x2^2)+1))) x2 = rnorm(1,mean=(B*x2+C2) / (A*(x1^2)+1), sd=sqrt(1/(A*(x1^2)+1))) if (r>init*keep && r\%\%keep==0) { mkeep = r/keep bimat[mkeep-init,] = c(x1,x2) } } return(bimat) } set.seed(66) nvar2 = 2 A = 0.5 B = 0 C1 = C2 = 3 y2 = banana(A=A, B=B, C1=C1, C2=C2, 1000) Data2 = list(y=y2) Prioralpha = list(Istarmin=1, Istarmax=10, power=0.8) Prior2 = list(Prioralpha=Prioralpha) Mcmc = list(R=R, keep=1, maxuniq=200) out2 = rDPGibbs(Prior=Prior2, Data=Data2, Mcmc=Mcmc) if(0){ ## plotting examples rx1 = range(y2[,1]) rx2 = range(y2[,2]) x1 = seq(from=rx1[1], to=rx1[2], length.out=50) x2 = seq(from=rx2[1], to=rx2[2], length.out=50) grid = cbind(x1,x2) plot(out2$nmix, Grid=grid, Data=y2) ## plot true bivariate density tden = matrix(double(50*50), ncol=50) for (i in 1:50) { for (j in 1:50) { tden[i,j] = exp(-0.5*(A*(x1[i]^2)*(x2[j]^2) + (x1[i]^2) + (x2[j]^2) - 2*B*x1[i]*x2[j] - 2*C1*x1[i] - 2*C2*x2[j])) }} tden = tden / sum(tden) image(x1, x2, tden, col=terrain.colors(100), xlab="", ylab="") contour(x1, x2, tden, add=TRUE, drawlabels=FALSE) title("True Density") } } \keyword{multivariate} bayesm/man/createX.Rd0000755000176000001440000000433213117541521014224 0ustar ripleyusers\name{createX} \alias{createX} \concept{multinomial logit} \concept{multinomial probit} \title{Create X Matrix for Use in Multinomial Logit and Probit Routines} \description{ \code{createX} makes up an X matrix in the form expected by Multinomial Logit (\code{\link{rmnlIndepMetrop}} and \code{\link{rhierMnlRwMixture}}) and Probit (\code{\link{rmnpGibbs}} and \code{\link{rmvpGibbs}}) routines. Requires an array of alternative-specific variables and/or an array of "demographics" (or variables constant across alternatives) which may vary across choice occasions. } \usage{createX(p, na, nd, Xa, Xd, INT = TRUE, DIFF = FALSE, base=p)} \arguments{ \item{p}{ integer number of choice alternatives } \item{na}{ integer number of alternative-specific vars in \code{Xa} } \item{nd}{ integer number of non-alternative specific vars } \item{Xa}{ \eqn{n x p*na} matrix of alternative-specific vars } \item{Xd}{ \eqn{n x nd} matrix of non-alternative specific vars } \item{INT}{ logical flag for inclusion of intercepts } \item{DIFF}{ logical flag for differencing wrt to base alternative } \item{base}{ integer index of base choice alternative } Note: \code{na}, \code{nd}, \code{Xa}, \code{Xd} can be \code{NULL} to indicate lack of \code{Xa} or \code{Xd} variables. } \value{\code{X} matrix of dimension \eqn{n*(p-DIFF) x [(INT+nd)*(p-1) + na]}.} \note{\code{\link{rmnpGibbs}} assumes that the \code{base} alternative is the default.} \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{ For further discussion, see \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{\code{\link{rmnlIndepMetrop}}, \code{\link{rmnpGibbs}} } \examples{ na=2; nd=1; p=3 vec = c(1, 1.5, 0.5, 2, 3, 1, 3, 4.5, 1.5) Xa = matrix(vec, byrow=TRUE, ncol=3) Xa = cbind(Xa,-Xa) Xd = matrix(c(-1,-2,-3), ncol=1) createX(p=p, na=na, nd=nd, Xa=Xa, Xd=Xd) createX(p=p, na=na, nd=nd, Xa=Xa, Xd=Xd, base=1) createX(p=p, na=na, nd=nd, Xa=Xa, Xd=Xd, DIFF=TRUE) createX(p=p, na=na, nd=nd, Xa=Xa, Xd=Xd, DIFF=TRUE, base=2) createX(p=p, na=na, nd=NULL, Xa=Xa, Xd=NULL) createX(p=p, na=NULL, nd=nd, Xa=NULL, Xd=Xd) } \keyword{array} \keyword{utilities} bayesm/man/plot.bayesm.nmix.Rd0000644000176000001440000000441413117541521016036 0ustar ripleyusers\name{plot.bayesm.nmix} \alias{plot.bayesm.nmix} \concept{MCMC} \concept{S3 method} \concept{plot} \title{Plot Method for MCMC Draws of Normal Mixtures} \description{ \code{plot.bayesm.nmix} is an S3 method to plot aspects of the fitted density from a list of MCMC draws of normal mixture components. Plots of marginal univariate and bivariate densities are produced. } \usage{\method{plot}{bayesm.nmix}(x, names, burnin, Grid, bi.sel, nstd, marg, Data, ngrid, ndraw, ...)} \arguments{ \item{x }{ An object of S3 class \code{bayesm.nmix}} \item{names }{ optional character vector of names for each of the dimensions} \item{burnin }{ number of draws to discard for burn-in (def: \eqn{0.1*nrow(X)})} \item{Grid }{ matrix of grid points for densities, def: mean +/- nstd std deviations (if Data no supplied), range of Data if supplied)} \item{bi.sel }{ list of vectors, each giving pairs for bivariate distributions (def: \code{list(c(1,2))})} \item{nstd }{ number of standard deviations for default Grid (def: 2)} \item{marg }{ logical, if TRUE display marginals (def: \code{TRUE})} \item{Data }{ matrix of data points, used to paint histograms on marginals and for grid} \item{ngrid }{ number of grid points for density estimates (def: 50)} \item{ndraw }{ number of draws to average Mcmc estimates over (def: 200)} \item{... }{ standard graphics parameters} } \details{ Typically, \code{plot.bayesm.nmix} will be invoked by a call to the generic plot function as in \code{plot(object)} where object is of class bayesm.nmix. These objects are lists of three components. The first component is an array of draws of mixture component probabilties. The second component is not used. The third is a lists of lists of lists with draws of each of the normal components.\cr \cr \code{plot.bayesm.nmix} can also be used as a standard function, as in \code{plot.bayesm.nmix(list)}. } \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \seealso{ \code{\link{rnmixGibbs}}, \code{\link{rhierMnlRwMixture}}, \code{\link{rhierLinearMixture}}, \code{\link{rDPGibbs}}} \examples{ ## not run # out = rnmixGibbs(Data, Prior, Mcmc) ## plot bivariate distributions for dimension 1,2; 3,4; and 1,3 # plot(out,bi.sel=list(c(1,2),c(3,4),c(1,3))) } \keyword{hplot} bayesm/man/rordprobitGibbs.Rd0000644000176000001440000000756413117541521015775 0ustar ripleyusers\name{rordprobitGibbs} \alias{rordprobitGibbs} \concept{bayes} \concept{MCMC} \concept{probit} \concept{Gibbs Sampling} \title{Gibbs Sampler for Ordered Probit} \description{ \code{rordprobitGibbs} implements a Gibbs Sampler for the ordered probit model with a RW Metropolis step for the cut-offs. } \usage{rordprobitGibbs(Data, Prior, Mcmc)} \arguments{ \item{Data }{list(y, X, k)} \item{Prior}{list(betabar, A, dstarbar, Ad)} \item{Mcmc }{list(R, keep, nprint, s)} } \details{ \subsection{Model and Priors}{ \eqn{z = X\beta + e} with \eqn{e} \eqn{\sim}{~} \eqn{N(0, I)}\cr \eqn{y = k} if c[k] \eqn{\le z <} c[k+1] with \eqn{k = 1,\ldots,K} \cr cutoffs = \{c[1], \eqn{\ldots}, c[k+1]\} \eqn{\beta} \eqn{\sim}{~} \eqn{N(betabar, A^{-1})} \cr \eqn{dstar} \eqn{\sim}{~} \eqn{N(dstarbar, Ad^{-1})} Be careful in assessing prior parameter \code{Ad}: 0.1 is too small for many applications. } \subsection{Argument Details}{ \emph{\code{Data = list(y, X, k)}} \tabular{ll}{ \code{y: } \tab \eqn{n x 1} vector of observations, (\eqn{1, \ldots, k}) \cr \code{X: } \tab \eqn{n x p} Design Matrix \cr \code{k: } \tab the largest possible value of y } \emph{\code{Prior = list(betabar, A, dstarbar, Ad)} [optional]} \tabular{ll}{ \code{betabar: } \tab \eqn{p x 1} prior mean (def: 0) \cr \code{A: } \tab \eqn{p x p} prior precision matrix (def: 0.01*I) \cr \code{dstarbar: } \tab \eqn{ndstar x 1} prior mean, where \eqn{ndstar=k-2} (def: 0) \cr \code{Ad: } \tab \eqn{ndstar x ndstar} prior precision matrix (def: I) } \emph{\code{Mcmc = list(R, keep, nprint, s)} [only \code{R} required]} \tabular{ll}{ \code{R: } \tab number of MCMC draws \cr \code{keep: } \tab MCMC thinning parameter -- keep every \code{keep}th draw (def: 1) \cr \code{nprint: } \tab print the estimated time remaining for every \code{nprint}'th draw (def: 100, set to 0 for no print) \cr \code{s: } \tab scaling parameter for RW Metropolis (def: 2.93/\code{sqrt(p)}) } } } \value{ A list containing: \item{betadraw }{ \eqn{R/keep x p} matrix of betadraws} \item{cutdraw }{ \eqn{R/keep x (k-1)} matrix of cutdraws} \item{dstardraw }{ \eqn{R/keep x (k-2)} matrix of dstardraws} \item{accept }{ acceptance rate of Metropolis draws for cut-offs} } \note{ set c[1] = -100 and c[k+1] = 100. c[2] is set to 0 for identification. \cr The relationship between cut-offs and dstar is: \cr c[3] = exp(dstar[1]), \cr c[4] = c[3] + exp(dstar[2]), ..., \cr c[k] = c[k-1] + exp(dstar[k-2]) } \references{\emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch \cr \url{http://www.perossi.org/home/bsm-1}} \author{ Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \seealso{ \code{\link{rbprobitGibbs}} } \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} set.seed(66) ## simulate data for ordered probit model simordprobit=function(X, betas, cutoff){ z = X\%*\%betas + rnorm(nobs) y = cut(z, br = cutoff, right=TRUE, include.lowest = TRUE, labels = FALSE) return(list(y = y, X = X, k=(length(cutoff)-1), betas= betas, cutoff=cutoff )) } nobs = 300 X = cbind(rep(1,nobs),runif(nobs, min=0, max=5),runif(nobs,min=0, max=5)) k = 5 betas = c(0.5, 1, -0.5) cutoff = c(-100, 0, 1.0, 1.8, 3.2, 100) simout = simordprobit(X, betas, cutoff) Data=list(X=simout$X, y=simout$y, k=k) ## set Mcmc for ordered probit model Mcmc = list(R=R) out = rordprobitGibbs(Data=Data, Mcmc=Mcmc) cat(" ", fill=TRUE) cat("acceptance rate= ", accept=out$accept, fill=TRUE) ## outputs of betadraw and cut-off draws cat(" Summary of betadraws", fill=TRUE) summary(out$betadraw, tvalues=betas) cat(" Summary of cut-off draws", fill=TRUE) summary(out$cutdraw, tvalues=cutoff[2:k]) ## plotting examples if(0){plot(out$cutdraw)} } \keyword{models} bayesm/man/numEff.Rd0000755000176000001440000000243513117541521014053 0ustar ripleyusers\name{numEff} \alias{numEff} \concept{numerical efficiency} \title{Compute Numerical Standard Error and Relative Numerical Efficiency} \description{ \code{numEff} computes the numerical standard error for the mean of a vector of draws as well as the relative numerical efficiency (ratio of variance of mean of this time series process relative to iid sequence). } \usage{numEff(x, m = as.integer(min(length(x),(100/sqrt(5000))*sqrt(length(x)))))} \arguments{ \item{x}{ \eqn{R x 1} vector of draws } \item{m}{ number of lags for autocorrelations } } \details{ default for number of lags is chosen so that if \eqn{R=5000}, \eqn{m=100} and increases as the \eqn{sqrt(R)}. } \value{ A list containing: \item{stderr }{standard error of the mean of \eqn{x}} \item{f }{ variance ratio (relative numerical efficiency) } } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 3, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \examples{ numEff(rnorm(1000), m=20) numEff(rnorm(1000)) } \keyword{ts} \keyword{utilities} bayesm/man/cheese.Rd0000644000176000001440000000440713117541521014065 0ustar ripleyusers\name{cheese} \alias{cheese} \docType{data} \title{Sliced Cheese Data} \description{ Panel data with sales volume for a package of Borden Sliced Cheese as well as a measure of display activity and price. Weekly data aggregated to the "key" account or retailer/market level. } \usage{data(cheese)} \format{ A data frame with 5555 observations on the following 4 variables: \tabular{ll}{ \ldots\code{$RETAILER} \tab a list of 88 retailers \cr \ldots\code{$VOLUME } \tab unit sales \cr \ldots\code{$DISP } \tab percent ACV on display (a measure of advertising display activity) \cr \ldots\code{$PRICE } \tab in U.S. dollars } } \source{Boatwright, Peter, Robert McCulloch, and Peter Rossi (1999), "Account-Level Modeling for Trade Promotion," \emph{Journal of the American Statistical Association} 94, 1063--1073.} \references{Chapter 3, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \examples{ data(cheese) cat(" Quantiles of the Variables ",fill=TRUE) mat = apply(as.matrix(cheese[,2:4]), 2, quantile) print(mat) ## example of processing for use with rhierLinearModel if(0) { retailer = levels(cheese$RETAILER) nreg = length(retailer) nvar = 3 regdata = NULL for (reg in 1:nreg) { y = log(cheese$VOLUME[cheese$RETAILER==retailer[reg]]) iota = c(rep(1,length(y))) X = cbind(iota, cheese$DISP[cheese$RETAILER==retailer[reg]], log(cheese$PRICE[cheese$RETAILER==retailer[reg]])) regdata[[reg]] = list(y=y, X=X) } Z = matrix(c(rep(1,nreg)), ncol=1) nz = ncol(Z) ## run each individual regression and store results lscoef = matrix(double(nreg*nvar), ncol=nvar) for (reg in 1:nreg) { coef = lsfit(regdata[[reg]]$X, regdata[[reg]]$y, intercept=FALSE)$coef if (var(regdata[[reg]]$X[,2])==0) { lscoef[reg,1]=coef[1] lscoef[reg,3]=coef[2] } else {lscoef[reg,]=coef} } R = 2000 Data = list(regdata=regdata, Z=Z) Mcmc = list(R=R, keep=1) set.seed(66) out = rhierLinearModel(Data=Data, Mcmc=Mcmc) cat("Summary of Delta Draws", fill=TRUE) summary(out$Deltadraw) cat("Summary of Vbeta Draws", fill=TRUE) summary(out$Vbetadraw) # plot hier coefs if(0) {plot(out$betadraw)} } } \keyword{datasets} bayesm/man/camera.Rd0000644000176000001440000000402013117541521014050 0ustar ripleyusers\name{camera} \alias{camera} \docType{data} \title{Conjoint Survey Data for Digital Cameras} \description{ Panel dataset from a conjoint survey for digital cameras with 332 respondents. Data exclude respondents that always answered none, always picked the same brand, always selected the highest priced offering, or who appeared to be answering randomly. } \usage{data(camera)} \format{ A list of lists. Each inner list corresponds to one survey respondent and contains a numeric vector (\code{y}) of choice indicators and a numeric matrix (\code{X}) of covariates. Each respondent participated in 16 choice scenarios each including 4 camera options (and an outside option) for a total of 80 rows per respondent. } \details{ The covariates included in each \code{X} matrix are: \tabular{ll}{ \ldots\code{$canon } \tab an indicator for brand Canon \cr \ldots\code{$sony } \tab an indicator for brand Sony \cr \ldots\code{$nikon } \tab an indicator for brand Nikon \cr \ldots\code{$panasonic } \tab an indicator for brand Panasonic \cr \ldots\code{$pixels } \tab an indicator for a higher pixel count \cr \ldots\code{$zoom } \tab an indicator for a higher level of zoom \cr \ldots\code{$video } \tab an indicator for the ability to capture video \cr \ldots\code{$swivel } \tab an indicator for a swivel video display \cr \ldots\code{$wifi } \tab an indicator for wifi capability \cr \ldots\code{$price } \tab in hundreds of U.S. dollars } } \source{ Allenby, Greg, Jeff Brazell, John Howell, and Peter Rossi (2014), "Economic Valuation of Product Features," \emph{Quantitative Marketing and Economics} 12, 421--456. Allenby, Greg, Jeff Brazell, John Howell, and Peter Rossi (2014), "Valuation of Patented Product Features," \emph{Journal of Law and Economics} 57, 629--663. } \references{ For analysis of a similar dataset, see Case Study 4, \emph{Bayesian Statistics and Marketing} Rossi, Allenby, and McCulloch \url{http://www.perossi.org/home/bsm-1} } \keyword{datasets} bayesm/man/detailing.Rd0000644000176000001440000000642613117541521014574 0ustar ripleyusers\name{detailing} \alias{detailing} \docType{data} \title{Physician Detailing Data} \description{ Monthly data on physician detailing (sales calls). 23 months of data for each of 1000 physicians; includes physician covariates. } \usage{data(detailing)} \format{ The \code{detailing} object is a list containing two data frames, \code{counts} and \code{demo}. } \details{ In the \code{counts} data frame: \tabular{ll}{ \ldots\code{$id } \tab identifies the physician \cr \ldots\code{$scrips } \tab the number of new presectiptions ordered by the physician for the drug detailed \cr \ldots\code{$detailing } \tab the number of sales called made to each physician per month \cr \ldots\code{$lagged_scripts} \tab scrips value for prior month } In the \code{demo} data frame: \tabular{ll}{ \ldots\code{$$id } \tab identifies the physician \cr \ldots\code{$generalphys } \tab dummy for if doctor is a "general practitioner" \cr \ldots\code{$specialist } \tab dummy for if the physician is a specialist in the theraputic class for which the drug is intended \cr \ldots\code{$mean_samples} \tab the mean number of free drug samples given the doctor over the sample period } } \source{Manchanda, Puneet, Pradeep Chintagunta, and Peter Rossi (2004), "Response Modeling with Non-Random Marketing Mix Variables," \emph{Journal of Marketing Research} 41, 467--478.} \examples{ data(detailing) cat(" table of Counts Dep Var", fill=TRUE) print(table(detailing$counts[,2])) cat(" means of Demographic Variables",fill=TRUE) mat = apply(as.matrix(detailing$demo[,2:4]), 2, mean) print(mat) ## example of processing for use with 'rhierNegbinRw' if(0) { data(detailing) counts = detailing$counts Z = detailing$demo # Construct the Z matrix Z[,1] = 1 Z[,2] = Z[,2] - mean(Z[,2]) Z[,3] = Z[,3] - mean(Z[,3]) Z[,4] = Z[,4] - mean(Z[,4]) Z = as.matrix(Z) id = levels(factor(counts$id)) nreg = length(id) nobs = nrow(counts$id) regdata = NULL for (i in 1:nreg) { X = counts[counts[,1] == id[i], c(3:4)] X = cbind(rep(1, nrow(X)), X) y = counts[counts[,1] == id[i], 2] X = as.matrix(X) regdata[[i]] = list(X=X, y=y) } rm(detailing, counts) cat("Finished reading data", fill=TRUE) fsh() Data = list(regdata=regdata, Z=Z) nvar = ncol(X) # Number of X variables nz = ncol(Z) # Number of Z variables deltabar = matrix(rep(0,nvar*nz), nrow=nz) Vdelta = 0.01*diag(nz) nu = nvar+3 V = 0.01*diag(nvar) a = 0.5 b = 0.1 Prior = list(deltabar=deltabar, Vdelta=Vdelta, nu=nu, V=V, a=a, b=b) R = 10000 keep = 1 s_beta = 2.93/sqrt(nvar) s_alpha = 2.93 c = 2 Mcmc = list(R=R, keep=keep, s_beta=s_beta, s_alpha=s_alpha, c=c) out = rhierNegbinRw(Data, Prior, Mcmc) ## Unit level mean beta parameters Mbeta = matrix(rep(0,nreg*nvar), nrow=nreg) ndraws = length(out$alphadraw) for (i in 1:nreg) { Mbeta[i,] = rowSums(out$Betadraw[i,,])/ndraws } cat(" Deltadraws ", fill=TRUE) summary(out$Deltadraw) cat(" Vbetadraws ", fill=TRUE) summary(out$Vbetadraw) cat(" alphadraws ", fill=TRUE) summary(out$alphadraw) ## plotting examples if(0){ plot(out$betadraw) plot(out$alphadraw) plot(out$Deltadraw) } } } \keyword{datasets} bayesm/man/runiregGibbs.Rd0000644000176000001440000000520613117541521015251 0ustar ripleyusers\name{runiregGibbs} \alias{runiregGibbs} \concept{bayes} \concept{Gibbs Sampler} \concept{regression} \concept{MCMC} \title{Gibbs Sampler for Univariate Regression} \description{ \code{runiregGibbs} implements a Gibbs Sampler to draw from posterior of a univariate regression with a conditionally conjugate prior. } \usage{runiregGibbs(Data, Prior, Mcmc)} \arguments{ \item{Data }{list(y, X)} \item{Prior}{list(betabar, A, nu, ssq)} \item{Mcmc }{list(sigmasq, R, keep, nprint)} } \details{ \subsection{Model and Priors}{ \eqn{y = X\beta + e} with \eqn{e} \eqn{\sim}{~} \eqn{N(0, \sigma^2)} \eqn{\beta} \eqn{\sim}{~} \eqn{N(betabar, A^{-1})}\cr \eqn{\sigma^2} \eqn{\sim}{~} \eqn{(nu*ssq)/\chi^2_{nu}} } \subsection{Argument Details}{ \emph{\code{Data = list(y, X)}} \tabular{ll}{ \code{y: } \tab \eqn{n x 1} vector of observations \cr \code{X: } \tab \eqn{n x k} design matrix } \emph{\code{Prior = list(betabar, A, nu, ssq)} [optional]} \tabular{ll}{ \code{betabar: } \tab \eqn{k x 1} prior mean (def: 0) \cr \code{A: } \tab \eqn{k x k} prior precision matrix (def: 0.01*I) \cr \code{nu: } \tab d.f. parameter for Inverted Chi-square prior (def: 3) \cr \code{ssq: } \tab scale parameter for Inverted Chi-square prior (def: \code{var(y)}) } \emph{\code{Mcmc = list(sigmasq, R, keep, nprint)} [only \code{R} required]} \tabular{ll}{ \code{sigmasq: } \tab value for \eqn{\sigma^2} for first Gibbs sampler draw of \eqn{\beta}|\eqn{\sigma^2} \cr \code{R: } \tab number of MCMC draws \cr \code{keep: } \tab MCMC thinning parameter -- keep every \code{keep}th draw (def: 1) \cr \code{nprint: } \tab print the estimated time remaining for every \code{nprint}'th draw (def: 100, set to 0 for no print) } } } \value{ A list containing: \item{betadraw }{ \eqn{R x k} matrix of betadraws } \item{sigmasqdraw }{ \eqn{R x 1} vector of sigma-sq draws} } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 3, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \seealso{ \code{\link{runireg}} } \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=1000} else {R=10} set.seed(66) n = 200 X = cbind(rep(1,n), runif(n)) beta = c(1,2) sigsq = 0.25 y = X\%*\%beta + rnorm(n,sd=sqrt(sigsq)) out = runiregGibbs(Data=list(y=y, X=X), Mcmc=list(R=R)) cat("Summary of beta and Sigmasq draws", fill=TRUE) summary(out$betadraw, tvalues=beta) summary(out$sigmasqdraw, tvalues=sigsq) ## plotting examples if(0){plot(out$betadraw)} } \keyword{regression} bayesm/man/rwishart.Rd0000644000176000001440000000253613117541521014475 0ustar ripleyusers\name{rwishart} \alias{rwishart} \concept{Wishart distribution} \concept{Inverted Wishart} \concept{simulation} \title{ Draw from Wishart and Inverted Wishart Distribution } \description{ \code{rwishart} draws from the Wishart and Inverted Wishart distributions. } \usage{rwishart(nu, V)} \arguments{ \item{nu}{ d.f. parameter} \item{V}{ pds location matrix} } \details{ In the parameterization used here, \eqn{W} \eqn{\sim}{~} \eqn{W(nu,V)} with \eqn{E[W]=nuV}. \cr If you want to use an Inverted Wishart prior, you \emph{must invert the location matrix} before calling \code{rwishart}, e.g. \cr \eqn{\Sigma} \eqn{\sim}{~} IW(nu ,V); \eqn{\Sigma^{-1}} \eqn{\sim}{~} \eqn{W(nu, V^{-1})}. } \value{ A list containing: \item{W: }{ Wishart draw } \item{IW: }{Inverted Wishart draw} \item{C: }{ Upper tri root of W} \item{CI: }{ inv(C), \eqn{W^{-1}} = CICI'} } \section{Warning}{ This routine is a utility routine that does \strong{not} check the input arguments for proper dimensions and type. } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{For further discussion, see Chapter 2, \emph{Bayesian Statistics and Marketing} by Rossi, Allenby, and McCulloch. \cr \url{http://www.perossi.org/home/bsm-1}} \examples{ set.seed(66) rwishart(5,diag(3))$IW } \keyword{multivariate} \keyword{distribution} bayesm/man/rivDP.Rd0000644000176000001440000001700413117570164013657 0ustar ripleyusers\name{rivDP} \alias{rivDP} \concept{Instrumental Variables} \concept{Gibbs Sampler} \concept{Dirichlet Process} \concept{bayes} \concept{endogeneity} \concept{simultaneity} \concept{MCMC} \title{Linear "IV" Model with DP Process Prior for Errors} \description{ \code{rivDP} is a Gibbs Sampler for a linear structural equation with an arbitrary number of instruments. \code{rivDP} uses a mixture-of-normals for the structural and reduced form equations implemented with a Dirichlet Process prior. } \usage{rivDP(Data, Prior, Mcmc)} \arguments{ \item{Data }{list(y, x, w, z)} \item{Prior}{list(md, Ad, mbg, Abg, lambda, Prioralpha, lambda_hyper)} \item{Mcmc }{list(R, keep, nprint, maxuniq, SCALE, gridsize)} } \details{ \subsection{Model and Priors}{ \eqn{x = z'\delta + e1} \cr \eqn{y = \beta*x + w'\gamma + e2} \cr \eqn{e1,e2} \eqn{\sim}{~} \eqn{N(\theta_{i})} where \eqn{\theta_{i}} represents \eqn{\mu_{i}, \Sigma_{i}} Note: Error terms have non-zero means. DO NOT include intercepts in the \eqn{z} or \eqn{w} matrices. This is different from \code{rivGibbs} which requires intercepts to be included explicitly. \eqn{\delta} \eqn{\sim}{~} \eqn{N(md, Ad^{-1})} \cr \eqn{vec(\beta, \gamma)} \eqn{\sim}{~} \eqn{N(mbg, Abg^{-1})} \cr \eqn{\theta_{i}} \eqn{\sim}{~} \eqn{G} \cr \eqn{G} \eqn{\sim}{~} \eqn{DP(alpha, G_0)} \eqn{alpha} \eqn{\sim}{~} \eqn{(1-(alpha-alpha_{min})/(alpha_{max}-alpha{min}))^{power}} \cr where \eqn{alpha_{min}} and \eqn{alpha_{max}} are set using the arguments in the reference below. It is highly recommended that you use the default values for the hyperparameters of the prior on alpha. \eqn{G_0} is the natural conjugate prior for \eqn{(\mu,\Sigma)}: \eqn{\Sigma} \eqn{\sim}{~} \eqn{IW(nu, vI)} and \eqn{\mu|\Sigma} \eqn{\sim}{~} \eqn{N(0, \Sigma(x) a^{-1})} \cr These parameters are collected together in the list \eqn{\lambda}. It is highly recommended that you use the default settings for these hyper-parameters.\cr \eqn{\lambda(a, nu, v):}\cr \eqn{a} \eqn{\sim}{~} uniform[alim[1], alimb[2]]\cr \eqn{nu} \eqn{\sim}{~} dim(data)-1 + exp(z) \cr \eqn{z} \eqn{\sim}{~} uniform[dim(data)-1+nulim[1], nulim[2]]\cr \eqn{v} \eqn{\sim}{~} uniform[vlim[1], vlim[2]] } \subsection{Argument Details}{ \emph{\code{Data = list(y, x, w, z)}} \tabular{ll}{ \code{y: } \tab \eqn{n x 1} vector of obs on LHS variable in structural equation \cr \code{x: } \tab \eqn{n x 1} vector of obs on "endogenous" variable in structural equation \cr \code{w: } \tab \eqn{n x j} matrix of obs on "exogenous" variables in the structural equation \cr \code{z: } \tab \eqn{n x p} matrix of obs on instruments } \emph{\code{Prior = list(md, Ad, mbg, Abg, lambda, Prioralpha, lambda_hyper)} [optional]} \tabular{ll}{ \code{md: } \tab \eqn{p}-length prior mean of delta (def: 0) \cr \code{Ad: } \tab \eqn{p x p} PDS prior precision matrix for prior on delta (def: 0.01*I) \cr \code{mbg: } \tab \eqn{(j+1)}-length prior mean vector for prior on beta,gamma (def: 0) \cr \code{Abg: } \tab \eqn{(j+1)x(j+1)} PDS prior precision matrix for prior on beta,gamma (def: 0.01*I) \cr \code{Prioralpha:} \tab \code{list(Istarmin, Istarmax, power)} \cr \code{$Istarmin: } \tab is expected number of components at lower bound of support of alpha (def: 1) \cr \code{$Istarmax: } \tab is expected number of components at upper bound of support of alpha (def: \code{floor(0.1*length(y))}) \cr \code{$power: } \tab is the power parameter for alpha prior (def: 0.8) \cr \code{lambda_hyper:} \tab \code{list(alim, nulim, vlim)} \cr \code{$alim: } \tab defines support of a distribution (def: \code{c(0.01, 10)}) \cr \code{$nulim: } \tab defines support of nu distribution (def: \code{c(0.01, 3)}) \cr \code{$vlim: } \tab defines support of v distribution (def: \code{c(0.1, 4)}) } \emph{\code{Mcmc = list(R, keep, nprint, maxuniq, SCALE, gridsize)} [only \code{R} required]} \tabular{ll}{ \code{R: } \tab number of MCMC draws \cr \code{keep: } \tab MCMC thinning parameter: keep every keepth draw (def: 1) \cr \code{nprint: } \tab print the estimated time remaining for every nprint'th draw (def: 100, set to 0 for no print) \cr \code{maxuniq: } \tab storage constraint on the number of unique components (def: 200) \cr \code{SCALE: } \tab scale data (def: \code{TRUE}) \cr \code{gridsize: } \tab gridsize parameter for alpha draws (def: 20) } } \subsection{\code{nmix} Details}{ \code{nmix} is a list with 3 components. Several functions in the \code{bayesm} package that involve a Dirichlet Process or mixture-of-normals return \code{nmix}. Across these functions, a common structure is used for \code{nmix} in order to utilize generic summary and plotting functions. \tabular{ll}{ \code{probdraw:} \tab \eqn{ncomp x R/keep} matrix that reports the probability that each draw came from a particular component (here, a one-column matrix of 1s) \cr \code{zdraw: } \tab \eqn{R/keep x nobs} matrix that indicates which component each draw is assigned to (here, null) \cr \code{compdraw:} \tab A list of \eqn{R/keep} lists of \eqn{ncomp} lists. Each of the inner-most lists has 2 elemens: a vector of draws for \code{mu} and a matrix of draws for the Cholesky root of \code{Sigma}. } } } \value{ A list containing: \item{\code{deltadraw }}{ \eqn{R/keep x p} array of delta draws} \item{\code{betadraw }}{ \eqn{R/keep x 1} vector of beta draws} \item{\code{alphadraw }}{ \eqn{R/keep x 1} vector of draws of Dirichlet Process tightness parameter} \item{\code{Istardraw }}{ \eqn{R/keep x 1} vector of draws of the number of unique normal components} \item{\code{gammadraw }}{ \eqn{R/keep x j} array of gamma draws} \item{\code{nmix }}{ a list containing: \code{probdraw}, \code{zdraw}, \code{compdraw} (see \dQuote{\code{nmix} Details} section)} } \author{Peter Rossi, Anderson School, UCLA, \email{perossichi@gmail.com}.} \references{ For further discussion, see "A Semi-Parametric Bayesian Approach to the Instrumental Variable Problem," by Conley, Hansen, McCulloch and Rossi, \emph{Journal of Econometrics} (2008). See also, Chapter 4, \emph{Bayesian Non- and Semi-parametric Methods and Applications} by Peter Rossi. } \seealso{\code{rivGibbs}} \examples{ if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=2000} else {R=10} set.seed(66) ## simulate scaled log-normal errors and run k = 10 delta = 1.5 Sigma = matrix(c(1, 0.6, 0.6, 1), ncol=2) N = 1000 tbeta = 4 scalefactor = 0.6 root = chol(scalefactor*Sigma) mu = c(1,1) ## compute interquartile ranges ninterq = qnorm(0.75) - qnorm(0.25) error = matrix(rnorm(100000*2), ncol=2)\%*\%root error = t(t(error)+mu) Err = t(t(exp(error))-exp(mu+0.5*scalefactor*diag(Sigma))) lnNinterq = quantile(Err[,1], prob=0.75) - quantile(Err[,1], prob=0.25) ## simulate data error = matrix(rnorm(N*2), ncol=2)\%*\%root error = t(t(error)+mu) Err = t(t(exp(error))-exp(mu+0.5*scalefactor*diag(Sigma))) ## scale appropriately Err[,1] = Err[,1]*ninterq/lnNinterq Err[,2] = Err[,2]*ninterq/lnNinterq z = matrix(runif(k*N), ncol=k) x = z\%*\%(delta*c(rep(1,k))) + Err[,1] y = x*tbeta + Err[,2] ## specify data input and mcmc parameters Data = list(); Data$z = z Data$x = x Data$y = y Mcmc = list() Mcmc$maxuniq = 100 Mcmc$R = R end = Mcmc$R out = rivDP(Data=Data, Mcmc=Mcmc) cat("Summary of Beta draws", fill=TRUE) summary(out$betadraw, tvalues=tbeta) ## plotting examples if(0){ plot(out$betadraw, tvalues=tbeta) plot(out$nmix) # plot "fitted" density of the errors } } \keyword{models}