foreach/0000755000175100001440000000000012607127365011710 5ustar hornikusersforeach/inst/0000755000175100001440000000000012606615125012660 5ustar hornikusersforeach/inst/examples/0000755000175100001440000000000012606614324014476 5ustar hornikusersforeach/inst/examples/germandata.txt0000644000175100001440000030716011472542406017352 0ustar hornikusers 1 6 4 12 5 5 3 4 1 67 3 2 1 2 1 0 0 1 0 0 1 0 0 1 1 2 48 2 60 1 3 2 2 1 22 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 4 12 4 21 1 4 3 3 1 49 3 1 2 1 1 0 0 1 0 0 1 0 1 0 1 1 42 2 79 1 4 3 4 2 45 3 1 2 1 1 0 0 0 0 0 0 0 0 1 1 1 24 3 49 1 3 3 4 4 53 3 2 2 1 1 1 0 1 0 0 0 0 0 1 2 4 36 2 91 5 3 3 4 4 35 3 1 2 2 1 0 0 1 0 0 0 0 1 0 1 4 24 2 28 3 5 3 4 2 53 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 2 36 2 69 1 3 3 2 3 35 3 1 1 2 1 0 1 1 0 1 0 0 0 0 1 4 12 2 31 4 4 1 4 1 61 3 1 1 1 1 0 0 1 0 0 1 0 1 0 1 2 30 4 52 1 1 4 2 3 28 3 2 1 1 1 1 0 1 0 0 1 0 0 0 2 2 12 2 13 1 2 2 1 3 25 3 1 1 1 1 1 0 1 0 1 0 0 0 1 2 1 48 2 43 1 2 2 4 2 24 3 1 1 1 1 0 0 1 0 1 0 0 0 1 2 2 12 2 16 1 3 2 1 3 22 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 1 24 4 12 1 5 3 4 3 60 3 2 1 1 1 1 0 1 0 0 1 0 1 0 2 1 15 2 14 1 3 2 4 3 28 3 1 1 1 1 1 0 1 0 1 0 0 0 1 1 1 24 2 13 2 3 2 2 3 32 3 1 1 1 1 0 0 1 0 0 1 0 1 0 2 4 24 4 24 5 5 3 4 2 53 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 1 30 0 81 5 2 3 3 3 25 1 3 1 1 1 0 0 1 0 0 1 0 0 1 1 2 24 2 126 1 5 2 2 4 44 3 1 1 2 1 0 1 1 0 0 0 0 0 0 2 4 24 2 34 3 5 3 2 3 31 3 1 2 2 1 0 0 1 0 0 1 0 0 1 1 4 9 4 21 1 3 3 4 3 48 3 3 1 2 1 1 0 1 0 0 1 0 0 1 1 1 6 2 26 3 3 3 3 1 44 3 1 2 1 1 0 0 1 0 1 0 0 0 1 1 1 10 4 22 1 2 3 3 1 48 3 2 2 1 2 1 0 1 0 1 0 0 1 0 1 2 12 4 18 2 2 3 4 2 44 3 1 1 1 1 0 1 1 0 0 1 0 0 1 1 4 10 4 21 5 3 4 1 3 26 3 2 1 1 2 0 0 1 0 0 1 0 0 1 1 1 6 2 14 1 3 3 2 1 36 1 1 1 2 1 0 0 1 0 0 1 0 1 0 1 4 6 0 4 1 5 4 4 3 39 3 1 1 1 1 0 0 1 0 0 1 0 1 0 1 3 12 1 4 4 3 2 3 1 42 3 2 1 1 1 0 0 1 0 1 0 0 0 1 1 2 7 2 24 1 3 3 2 1 34 3 1 1 1 1 0 0 0 0 0 1 0 0 1 1 1 60 3 68 1 5 3 4 4 63 3 2 1 2 1 0 0 1 0 0 1 0 0 1 2 2 18 2 19 4 2 4 3 1 36 1 1 1 2 1 0 0 1 0 0 1 0 0 1 1 1 24 2 40 1 3 3 2 3 27 2 1 1 1 1 0 0 1 0 0 1 0 0 1 1 2 18 2 59 2 3 3 2 3 30 3 2 1 2 1 1 0 1 0 0 1 0 0 1 1 4 12 4 13 5 5 3 4 4 57 3 1 1 1 1 0 0 1 0 1 0 0 1 0 1 3 12 2 15 1 2 2 1 2 33 1 1 1 2 1 0 0 1 0 0 1 0 0 0 1 2 45 4 47 1 2 3 2 2 25 3 2 1 1 1 0 0 1 0 0 1 0 1 0 2 4 48 4 61 1 3 3 3 4 31 1 1 1 2 1 0 0 1 0 0 0 0 0 1 1 3 18 2 21 1 3 3 2 1 37 2 1 1 1 1 0 0 0 1 0 1 0 0 1 2 3 10 2 12 1 3 3 2 3 37 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 2 9 2 5 1 3 3 3 1 24 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 30 2 23 3 5 3 2 3 30 1 1 1 1 1 0 0 1 0 0 1 0 0 0 1 2 12 2 12 3 3 1 1 3 26 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 2 18 3 62 1 3 3 4 1 44 3 1 2 2 1 0 0 1 0 0 1 0 1 0 1 1 30 4 62 2 4 4 4 3 24 3 2 1 1 1 0 1 1 0 1 0 0 0 1 1 1 48 4 61 1 5 2 4 4 58 2 2 1 1 1 0 1 1 0 0 0 0 1 0 2 4 11 4 14 1 2 2 4 3 35 3 2 1 1 1 1 0 1 0 0 1 0 0 0 1 4 36 2 23 3 5 3 4 3 39 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 6 2 14 3 1 2 2 2 23 3 1 1 2 1 0 1 1 0 1 0 1 0 0 1 4 11 4 72 1 3 3 4 2 39 3 2 1 1 1 1 0 1 0 0 1 0 1 0 1 4 12 2 21 2 3 2 2 1 28 3 1 1 1 1 0 0 0 1 0 1 0 0 1 1 2 24 3 23 5 2 3 2 2 29 1 1 1 1 1 0 0 1 0 0 1 0 1 0 1 2 27 3 60 1 5 3 2 3 30 3 2 1 2 1 0 1 1 0 0 1 0 0 0 1 4 12 2 13 1 3 3 2 3 25 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 18 2 34 5 3 3 1 2 31 3 1 1 2 1 0 1 1 0 0 1 0 0 1 1 2 36 3 22 1 5 3 4 4 57 1 2 1 2 1 1 0 1 0 0 0 0 0 1 2 4 6 1 8 5 3 3 2 1 26 2 1 2 1 1 1 0 0 0 0 1 0 1 0 1 2 12 2 65 5 1 3 1 4 52 3 1 1 2 1 0 0 1 0 0 1 0 0 0 2 4 36 4 96 1 3 2 2 3 31 2 2 1 1 1 0 0 1 0 0 1 0 0 1 1 3 18 2 20 1 5 2 2 3 23 3 1 1 1 1 1 0 1 0 0 1 0 0 0 1 1 36 4 62 1 2 2 4 4 23 3 2 1 2 1 0 0 0 1 1 0 0 1 0 2 2 9 2 14 1 3 4 1 1 27 1 1 1 2 1 0 0 1 0 0 1 0 0 1 1 2 15 4 15 5 5 3 4 1 50 3 2 1 2 1 0 0 0 0 0 1 0 0 1 1 2 36 0 20 1 5 3 4 4 61 3 1 1 2 1 0 0 1 0 0 0 0 0 0 2 2 48 0 144 1 3 3 2 3 25 3 1 1 2 1 0 0 1 0 0 1 0 0 1 2 4 24 2 32 1 2 2 4 2 26 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 4 27 2 52 5 5 3 4 2 48 3 4 2 2 1 0 0 1 0 0 1 0 0 1 1 4 12 2 22 1 2 2 2 3 29 1 1 1 1 1 0 0 1 0 0 1 0 0 1 1 2 12 2 10 4 3 4 1 1 22 3 1 1 1 1 1 0 1 0 0 1 0 0 1 1 4 36 2 18 1 3 3 4 4 37 2 1 1 2 1 0 0 1 0 0 0 0 0 1 2 4 36 2 24 5 3 2 4 3 25 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 36 2 81 1 3 2 2 2 30 1 1 1 1 1 0 1 1 0 0 1 0 0 1 1 4 7 4 7 5 5 3 2 2 46 3 2 1 2 1 0 0 1 0 1 0 0 1 0 1 1 8 4 12 1 5 3 4 4 51 1 2 2 2 1 0 0 1 0 0 0 0 0 0 1 2 42 4 60 1 4 2 1 1 41 1 2 1 1 1 0 0 1 0 0 1 0 1 0 1 1 36 2 20 5 5 3 4 4 40 3 1 1 2 1 0 0 1 0 0 1 0 0 0 2 1 12 4 15 1 5 3 4 4 66 3 2 1 1 1 0 1 1 0 0 0 0 0 0 1 1 42 2 40 1 2 3 3 3 34 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 2 11 3 48 1 4 3 4 2 51 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 54 0 94 5 3 3 2 2 39 3 1 2 1 1 0 1 1 0 0 1 0 1 0 1 2 30 2 38 1 2 4 1 2 22 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 24 2 59 5 2 2 1 3 44 3 2 1 2 1 0 0 1 0 0 1 0 0 1 2 4 15 2 12 3 5 3 3 2 47 2 1 1 2 1 0 0 1 0 0 1 0 0 1 1 4 18 2 16 2 3 2 4 2 24 3 1 1 1 1 0 0 1 0 1 0 0 1 0 1 1 24 2 18 1 5 2 4 1 58 3 1 1 2 1 0 0 0 0 0 1 0 1 0 1 1 10 2 23 1 5 3 4 1 52 3 1 1 1 1 0 0 1 0 0 1 0 1 0 1 4 12 4 14 1 3 2 2 1 29 3 2 1 2 1 0 0 0 0 0 1 0 0 0 1 2 18 4 13 1 2 2 1 2 27 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 2 36 2 126 2 3 3 4 4 47 3 1 2 2 1 0 0 1 0 0 0 0 0 1 2 1 18 2 22 2 4 3 3 3 30 3 1 2 2 1 1 0 1 0 0 1 0 0 0 1 1 12 0 11 1 4 3 3 1 28 3 2 1 1 1 0 0 1 0 0 1 0 0 1 2 4 12 4 6 1 5 3 4 1 56 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 12 4 14 1 5 3 3 1 54 3 1 1 1 1 0 1 1 0 0 1 0 0 1 1 4 12 4 8 5 5 2 3 2 33 1 1 2 1 1 0 0 1 0 0 1 0 1 0 2 3 24 4 36 5 5 3 4 4 20 3 2 1 1 1 0 0 0 1 1 0 0 0 1 1 2 12 2 13 4 5 3 4 1 54 3 1 1 2 1 1 0 1 0 0 1 0 0 1 1 2 54 0 159 1 2 3 4 4 58 3 1 1 2 1 0 0 1 0 1 0 0 0 1 2 4 12 4 20 5 4 2 2 3 61 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 2 18 2 26 2 3 3 4 3 34 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 2 36 4 23 1 5 3 4 1 36 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 2 20 3 71 5 4 3 4 2 36 1 2 2 2 1 0 1 1 0 1 0 0 0 0 1 4 24 2 15 2 5 4 4 1 41 3 1 1 1 1 1 0 1 0 1 0 0 1 0 1 2 36 2 23 1 4 3 4 3 24 3 1 1 1 1 0 0 1 0 1 0 0 0 1 1 4 6 3 9 1 3 2 2 1 24 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 2 9 4 19 1 4 3 3 3 35 3 1 1 2 1 0 0 1 0 1 0 0 0 1 1 4 12 2 24 5 2 4 4 3 26 3 1 1 2 1 0 1 1 0 1 0 0 0 1 1 2 24 4 119 1 3 3 3 3 39 3 2 2 2 1 0 0 0 1 0 1 0 0 0 2 4 18 1 65 1 5 3 4 4 39 1 2 2 2 1 1 0 1 0 0 1 0 0 0 2 2 12 2 61 1 4 3 2 3 32 3 1 1 1 1 1 0 1 0 0 1 0 0 1 1 1 24 2 77 5 2 2 2 2 30 3 1 1 2 2 0 0 1 0 0 1 0 0 1 1 2 14 2 14 3 5 4 2 1 35 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 2 6 3 14 2 5 1 2 3 31 1 2 2 1 1 0 0 1 0 0 1 0 0 1 1 3 15 2 4 1 2 2 4 2 23 3 1 1 2 1 0 0 1 0 1 0 0 0 1 1 2 18 2 63 1 4 3 3 1 28 3 1 1 1 1 1 0 1 0 1 0 0 1 0 1 4 36 4 79 1 3 2 2 1 25 2 2 1 2 1 1 0 1 0 0 1 0 0 1 2 1 12 2 17 3 5 4 1 1 35 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 48 4 36 5 5 3 1 1 47 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 1 42 2 72 5 4 2 3 3 30 3 1 1 2 1 0 0 1 0 0 1 0 0 0 2 1 10 4 21 5 2 2 3 1 27 3 2 1 1 2 0 0 0 1 1 0 0 0 1 1 1 33 4 43 3 3 2 4 3 23 3 2 1 1 1 0 0 1 0 0 1 0 0 1 2 2 12 4 24 3 4 1 3 3 36 3 1 1 2 1 1 0 1 0 0 1 0 0 0 1 1 21 2 18 1 3 2 2 1 25 3 2 1 2 1 0 0 1 0 0 1 0 0 1 2 4 24 4 39 1 5 2 2 3 41 3 2 1 2 1 0 1 1 0 1 0 0 0 0 1 4 12 2 18 1 3 3 2 1 24 3 1 1 1 1 0 0 1 0 1 0 0 1 0 1 3 10 4 8 1 5 3 4 4 63 3 2 1 2 1 1 0 1 0 0 0 0 0 1 1 2 18 2 19 5 2 2 3 1 27 3 1 1 1 1 0 0 1 0 1 0 0 0 1 2 1 12 4 21 1 3 3 2 2 30 3 2 1 1 1 1 0 1 0 0 1 0 0 1 1 1 12 2 7 1 3 4 2 1 40 3 1 1 1 1 0 0 1 0 0 1 0 1 0 1 2 12 2 6 1 3 3 2 3 30 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 2 12 4 19 1 1 3 2 3 34 3 2 1 2 1 0 1 1 0 0 1 0 0 0 1 1 12 4 35 1 3 2 2 1 29 3 2 1 1 1 1 0 0 1 0 1 0 0 1 2 2 48 2 85 5 4 2 2 3 24 3 1 1 1 1 1 0 1 0 0 1 0 0 1 1 1 36 3 69 1 3 3 3 2 29 2 1 1 2 1 0 0 1 0 0 1 0 0 1 2 4 15 2 27 1 2 3 3 2 27 1 2 1 1 1 0 0 1 0 0 1 0 1 0 1 4 18 2 20 1 3 3 4 4 47 1 2 1 1 1 0 0 1 0 0 0 0 0 1 1 4 60 2 101 2 4 2 4 1 21 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 4 12 4 12 5 5 2 2 1 38 3 2 1 2 1 0 0 1 0 0 1 0 0 1 1 4 27 3 86 4 3 3 2 3 27 3 2 1 1 1 0 1 1 0 0 1 0 0 1 1 2 12 2 8 3 3 3 3 1 66 3 1 1 1 1 0 0 1 0 0 1 0 1 0 2 2 15 4 27 5 4 3 2 1 35 1 3 1 2 1 0 0 0 0 0 1 0 0 1 1 3 12 2 19 1 3 2 2 3 44 3 1 1 2 1 0 0 1 0 1 0 0 1 0 1 3 6 2 7 4 2 4 2 1 27 3 1 1 1 2 1 0 1 0 0 1 1 0 0 1 2 36 2 48 1 2 2 1 4 30 3 1 1 2 1 0 0 1 0 0 1 0 0 0 1 1 27 2 34 1 3 3 2 3 27 3 1 1 1 1 0 0 1 0 0 1 0 0 0 1 1 18 2 25 1 3 3 2 3 22 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 4 21 4 23 1 2 2 4 2 23 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 2 48 1 36 2 4 3 2 3 30 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 6 4 9 1 5 2 4 4 39 3 2 1 2 1 1 0 1 0 0 1 0 0 1 1 4 12 4 7 2 4 2 3 3 51 3 2 1 2 1 1 0 1 0 0 1 0 0 1 1 1 36 4 54 1 3 3 2 2 28 3 2 1 1 1 0 0 0 0 0 1 0 0 1 1 4 18 4 16 4 5 3 4 3 46 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 6 2 13 2 5 3 4 4 42 1 1 2 2 1 0 0 1 0 0 0 0 0 1 1 4 10 2 19 1 3 3 4 2 38 3 1 1 2 2 0 0 1 0 0 1 0 0 1 1 3 36 2 58 1 3 3 1 3 24 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 2 24 4 78 4 5 2 4 4 29 3 1 1 1 1 0 1 1 0 1 0 0 0 1 1 2 24 3 70 2 4 3 4 3 36 3 1 1 2 1 0 0 1 0 1 0 0 0 0 1 1 12 2 13 1 3 2 4 3 20 3 1 1 1 1 0 0 1 0 1 0 0 0 1 2 1 9 4 13 2 5 3 4 1 48 3 2 2 1 2 0 0 0 0 0 1 0 0 1 1 1 12 1 3 1 5 4 1 3 45 1 1 1 1 1 0 0 1 0 0 1 0 1 0 1 2 24 2 35 2 4 3 3 3 38 1 2 1 2 1 1 0 1 0 0 1 0 0 1 1 4 6 4 19 5 3 3 2 1 34 3 2 2 1 1 0 0 1 0 0 1 0 1 0 1 4 24 4 29 2 5 3 4 1 36 3 1 2 2 1 0 0 1 0 0 1 0 0 1 1 4 18 4 11 1 2 2 1 2 30 3 2 1 1 1 1 0 1 0 0 1 0 0 1 1 4 15 2 13 3 4 3 3 2 36 3 2 1 2 1 0 0 1 0 0 1 0 0 1 1 2 10 2 73 1 1 3 4 4 70 1 1 1 2 1 1 0 1 0 0 0 0 0 0 1 4 36 2 9 3 5 3 4 2 36 3 1 1 1 1 1 0 1 0 0 1 0 0 1 1 4 6 2 30 3 3 3 2 3 32 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 1 18 2 11 1 1 2 2 3 33 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 2 11 2 16 4 2 2 1 1 20 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 24 2 40 1 4 2 4 2 25 3 1 1 2 1 0 0 1 0 1 0 0 0 1 1 2 24 4 19 1 5 1 4 1 31 3 2 1 2 1 0 0 1 0 0 1 0 0 1 2 1 15 0 10 1 5 3 3 3 33 3 2 2 1 1 1 0 1 0 1 0 0 0 1 2 4 12 2 8 1 3 2 1 1 26 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 2 24 3 21 1 1 2 2 2 34 3 1 1 2 1 0 0 1 0 0 1 0 0 0 2 2 8 2 14 1 3 3 2 1 33 3 1 1 1 2 0 0 0 0 0 1 0 0 1 1 1 21 3 34 1 2 3 1 2 26 3 2 1 1 1 0 0 1 0 0 1 0 0 1 2 4 30 1 75 5 1 2 1 1 53 1 1 1 2 1 0 1 1 0 0 1 0 0 0 2 1 12 2 26 1 3 1 1 3 42 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 6 4 3 3 5 3 4 3 52 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 12 2 20 1 4 3 2 3 31 3 2 2 2 1 0 0 1 0 1 0 0 0 0 1 1 21 4 6 1 5 3 4 1 65 3 2 1 1 1 1 0 1 0 0 1 0 0 1 1 4 36 3 96 1 2 1 1 3 28 3 2 1 1 1 0 0 1 0 0 1 0 0 1 2 2 36 3 45 1 3 1 2 1 30 2 2 1 2 1 0 0 1 0 0 1 0 0 0 2 1 21 1 16 5 3 3 2 2 40 3 2 2 1 1 1 0 1 0 0 1 0 1 0 2 4 24 4 38 4 3 3 4 1 50 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 2 18 4 9 1 5 3 4 3 36 1 1 2 2 1 1 0 1 0 0 1 0 0 1 2 4 15 4 14 1 3 3 2 2 31 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 2 9 1 51 1 5 2 4 4 74 1 1 2 2 1 0 1 1 0 0 0 0 0 0 2 2 16 4 12 1 1 3 3 3 68 3 3 1 2 1 1 0 1 0 0 0 1 0 0 1 1 12 2 7 2 4 4 1 2 20 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 2 18 0 32 1 3 2 4 3 33 1 2 1 2 1 0 0 1 0 0 1 0 0 1 1 4 24 2 46 4 3 3 3 2 54 3 3 1 2 1 0 0 1 0 0 1 0 0 0 2 2 48 0 38 2 4 3 4 4 34 3 1 2 1 1 0 0 1 0 0 0 0 1 0 2 2 27 2 39 1 3 3 2 3 36 3 1 2 2 1 0 0 1 0 0 1 0 0 1 2 4 6 2 21 1 4 4 2 1 29 3 1 1 1 1 0 0 1 0 1 0 0 0 1 1 2 45 2 30 2 3 3 4 2 21 3 1 1 1 1 0 0 0 0 1 0 0 0 1 2 2 9 4 15 1 5 2 3 3 34 3 2 1 2 1 0 0 1 0 0 1 0 0 0 2 4 6 4 14 1 3 2 1 3 28 3 2 1 2 1 0 0 1 0 0 1 0 0 1 1 2 12 2 10 2 2 2 4 3 27 1 4 1 1 1 0 0 1 0 1 0 0 0 1 2 2 24 2 28 5 5 3 4 4 36 1 1 1 2 1 0 1 1 0 0 0 0 0 1 1 2 18 3 43 1 5 1 3 4 40 3 1 1 2 1 0 0 1 0 0 1 0 0 0 2 4 9 4 9 3 5 3 2 3 52 3 2 1 2 1 0 0 1 0 0 1 0 0 1 1 1 12 2 12 1 3 4 3 1 27 3 1 1 1 1 1 0 1 0 0 1 0 1 0 1 4 27 3 51 1 4 3 4 3 26 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 1 12 2 9 1 4 4 4 2 21 3 1 1 1 1 0 0 1 0 1 0 0 0 1 2 4 12 4 15 1 5 3 1 1 38 3 2 2 1 1 1 0 1 0 0 1 0 1 0 1 1 30 4 106 1 5 3 4 4 38 3 3 2 2 1 0 1 1 0 0 0 0 0 0 1 4 12 4 19 1 5 3 4 1 43 3 3 1 2 1 0 0 1 0 0 1 0 0 1 1 2 12 4 14 1 4 3 3 2 26 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 24 2 66 1 3 4 2 3 21 2 1 1 1 1 0 0 1 0 0 1 0 1 0 1 4 12 2 14 4 4 3 2 2 55 3 1 1 1 2 0 1 1 0 0 1 0 0 1 1 4 9 4 31 5 3 3 2 1 33 3 2 2 1 1 0 0 1 0 0 1 0 0 1 1 4 36 2 38 5 5 2 4 1 45 3 1 1 2 1 0 0 1 0 0 1 0 1 0 1 1 27 0 53 1 1 3 4 2 50 2 2 1 2 1 0 0 1 0 0 1 0 0 1 2 3 30 3 19 1 5 3 4 1 66 3 1 1 2 1 0 0 1 0 0 1 0 0 0 2 4 36 4 33 5 5 3 2 3 51 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 2 6 4 9 5 4 2 3 2 39 3 2 1 1 1 0 0 1 0 0 1 0 1 0 1 1 18 0 31 1 4 3 1 2 31 1 1 1 2 1 0 0 1 0 0 1 0 0 1 1 3 36 2 39 1 3 3 2 1 23 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 1 24 2 30 1 3 1 2 1 24 3 1 1 1 1 0 0 1 0 1 0 0 1 0 1 4 10 2 14 1 3 2 4 3 64 3 1 1 2 1 1 0 1 0 0 1 0 0 1 1 2 12 2 6 1 2 4 1 1 26 1 1 1 1 1 0 0 0 0 0 1 0 1 0 1 1 12 2 12 5 3 2 4 2 23 1 1 1 2 1 0 0 1 0 1 0 0 0 1 1 4 12 2 7 1 3 3 2 1 30 1 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 24 3 30 5 3 3 4 1 32 3 2 2 2 1 0 0 1 0 0 1 0 0 1 1 4 15 2 47 1 3 3 2 3 30 3 1 1 2 1 0 1 1 0 0 1 0 0 1 1 4 36 0 26 1 3 3 2 3 27 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 2 48 2 110 4 4 3 2 4 27 1 2 1 2 1 0 0 0 1 0 1 0 0 1 2 1 12 2 79 1 5 3 4 4 53 3 1 1 2 1 0 0 1 0 0 0 0 0 0 2 4 9 2 15 1 4 3 2 3 22 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 1 24 2 31 1 2 3 1 4 22 1 1 1 1 1 0 0 1 0 0 0 0 0 1 1 3 36 2 42 1 3 3 2 3 26 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 4 9 2 25 3 5 3 4 4 51 3 1 1 1 1 1 0 1 0 0 0 0 1 0 1 4 12 2 21 2 4 3 1 4 35 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 2 18 2 9 1 3 4 2 1 25 3 1 1 1 1 0 0 0 0 0 1 0 1 0 1 4 4 4 15 1 4 3 1 1 42 3 3 2 1 1 0 0 1 0 0 1 0 1 0 1 1 24 2 18 1 1 3 2 3 30 2 1 2 1 1 0 0 1 0 0 1 0 0 0 2 2 6 2 146 5 1 3 2 2 23 3 1 1 2 1 1 0 1 0 0 1 1 0 0 2 2 21 2 28 2 5 1 2 3 61 1 2 1 1 1 0 0 1 0 1 0 0 1 0 2 4 12 4 13 1 3 2 2 2 35 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 1 30 2 25 1 5 3 3 2 39 3 1 2 1 1 0 0 0 0 0 1 0 0 1 1 1 24 2 9 5 5 2 2 3 29 1 1 1 1 1 1 0 1 0 0 1 0 0 1 2 4 6 2 16 1 4 3 2 2 51 3 1 2 1 1 0 0 1 0 0 1 0 0 1 1 1 48 0 46 1 5 3 4 4 24 3 2 2 1 1 0 1 1 0 0 0 0 0 1 2 4 12 4 12 1 3 2 2 1 27 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 12 1 34 3 3 2 3 1 35 3 1 2 1 1 0 0 1 0 0 1 0 1 0 1 4 24 2 13 1 4 3 1 1 25 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 4 12 4 7 1 5 3 4 1 52 3 3 1 1 1 0 0 1 0 0 1 0 0 1 1 4 6 0 12 2 3 3 1 4 35 1 1 1 1 2 1 0 1 0 1 0 0 0 1 1 3 24 2 19 1 3 3 2 1 26 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 18 2 4 1 1 2 4 1 22 3 1 1 1 1 0 0 0 1 1 0 0 0 1 2 1 6 4 7 4 4 2 4 1 39 3 2 1 2 1 1 0 1 0 0 1 0 1 0 1 3 12 2 23 1 3 2 2 3 46 3 1 1 1 1 0 0 1 0 0 1 0 1 0 1 2 30 2 22 1 3 2 2 4 24 1 1 1 1 1 1 0 0 0 0 1 0 0 1 2 4 24 3 42 2 3 3 3 2 35 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 2 9 2 20 5 4 3 1 3 24 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 2 60 3 74 5 3 3 1 1 27 3 1 1 1 1 0 0 1 0 0 1 0 1 0 1 4 24 4 27 1 3 3 2 1 35 3 2 1 1 1 0 0 1 0 0 1 0 1 0 1 1 12 1 21 1 3 1 1 4 29 3 1 1 1 1 0 0 1 0 0 0 0 0 1 2 4 15 2 38 2 2 2 4 3 23 3 1 1 2 1 0 1 1 0 0 1 0 0 1 1 4 11 4 12 2 1 2 4 1 57 3 3 1 1 1 0 0 1 0 0 1 0 1 0 1 1 12 2 17 1 3 3 2 1 27 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 24 2 16 1 5 2 4 3 55 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 18 4 53 1 5 3 4 4 36 3 3 1 2 1 1 0 1 0 0 0 0 0 0 1 4 12 4 27 1 5 2 4 4 57 1 3 1 1 1 0 0 1 0 0 0 0 1 0 1 4 10 4 12 1 5 3 4 1 32 3 2 2 1 2 1 0 1 0 0 1 0 1 0 1 2 15 2 8 1 5 3 3 3 37 3 1 2 1 1 0 0 1 0 0 1 0 0 1 2 4 36 4 63 5 5 3 4 1 36 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 24 2 15 1 2 2 3 3 38 2 1 1 2 1 0 0 1 0 0 1 0 0 1 1 1 14 2 90 1 5 1 4 2 45 3 1 1 2 2 1 0 1 0 0 1 0 0 0 2 4 24 2 10 5 5 3 2 3 25 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 18 2 27 5 4 3 3 2 32 3 1 1 1 2 1 0 1 0 0 1 0 0 1 1 4 12 4 14 3 4 2 4 3 37 3 1 1 2 1 0 0 1 0 1 0 0 0 1 1 2 48 1 122 5 1 3 4 4 36 3 1 1 2 1 1 0 0 1 0 0 0 0 0 1 2 48 2 31 1 4 3 4 1 28 3 2 1 1 1 0 0 1 0 0 1 0 0 1 2 1 30 2 120 1 2 1 1 4 34 3 1 1 2 1 0 0 1 0 0 1 0 1 0 2 4 9 2 27 1 3 3 2 1 32 3 1 2 1 1 0 0 1 0 0 1 0 0 1 1 4 18 4 24 1 3 2 2 3 26 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 1 12 2 13 5 5 1 4 2 49 3 1 1 2 1 0 0 1 0 0 1 0 1 0 1 4 6 2 46 1 2 2 4 2 32 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 4 24 2 19 2 3 3 4 3 29 3 1 1 2 1 0 0 1 0 1 0 0 0 0 1 4 15 4 34 4 5 3 4 4 23 3 2 1 2 1 0 1 1 0 1 0 0 0 1 1 4 12 2 16 1 3 3 2 1 50 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 3 18 1 14 5 4 3 4 3 49 1 1 1 1 1 0 0 1 0 0 1 0 1 0 1 4 15 4 15 5 5 3 4 2 63 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 2 24 4 39 2 2 1 2 3 37 3 1 1 2 1 1 0 1 0 0 1 0 0 1 1 1 47 2 107 1 2 2 1 1 35 3 1 1 2 1 1 0 1 0 0 1 0 1 0 1 1 48 2 48 1 4 3 3 2 26 3 1 2 1 1 0 1 1 0 0 1 0 0 1 1 2 48 3 76 2 1 3 4 4 31 3 1 1 2 1 0 0 1 0 0 0 0 0 0 1 2 12 2 11 1 3 2 4 1 49 3 2 1 2 1 0 0 0 0 0 1 0 0 1 1 1 24 3 10 1 2 4 4 1 48 2 1 1 1 1 0 0 1 0 0 1 0 0 1 2 4 12 2 11 1 3 4 2 1 26 3 1 1 2 2 0 0 1 0 0 1 0 0 1 1 2 36 2 94 1 2 4 4 3 28 3 1 1 2 1 0 1 1 0 1 0 0 0 0 2 1 24 4 64 1 5 2 4 4 44 3 2 2 2 1 0 1 1 0 0 0 0 0 0 1 3 42 4 48 1 5 3 4 4 56 3 1 1 1 1 0 1 1 0 0 0 0 0 1 1 4 48 4 76 5 5 1 2 3 46 1 2 2 1 1 0 0 1 0 0 1 0 0 0 1 2 48 2 100 1 2 2 2 3 26 3 1 1 2 1 0 0 1 0 0 1 0 0 1 2 4 12 2 47 5 2 2 4 3 20 3 1 1 1 1 0 1 1 0 1 0 0 0 1 1 4 10 2 13 5 5 3 2 2 45 3 1 1 1 2 1 0 0 1 0 1 0 1 0 1 4 18 2 25 1 3 3 4 1 43 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 2 21 4 27 4 4 3 2 3 32 3 2 1 2 1 0 0 1 0 0 1 0 0 1 1 4 6 2 7 1 1 2 4 1 54 3 1 1 2 1 1 0 1 0 0 1 1 0 0 1 2 36 0 38 1 3 2 1 3 42 3 1 1 2 1 0 0 1 0 0 1 0 0 1 2 3 24 4 13 5 4 3 2 1 37 1 2 2 1 1 1 0 1 0 0 1 0 1 0 2 1 10 4 10 1 4 3 3 2 49 3 2 1 2 1 1 0 0 1 0 1 0 0 1 1 4 48 4 101 3 3 3 2 4 44 1 1 1 1 1 1 0 1 0 0 0 0 0 1 2 4 6 2 15 4 3 1 2 1 33 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 30 2 48 5 4 2 4 2 24 2 1 1 1 1 0 1 1 0 1 0 0 1 0 1 1 12 2 7 2 2 4 3 4 33 3 1 1 2 1 0 0 1 0 0 1 0 1 0 2 2 8 2 12 1 3 2 4 1 24 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 2 9 2 3 1 3 4 4 1 22 3 1 1 1 1 1 0 1 0 1 0 0 1 0 1 2 48 2 54 5 1 3 4 4 40 1 1 1 2 1 0 0 1 0 0 0 1 0 0 1 4 24 2 55 2 3 3 1 3 25 2 1 1 1 1 0 0 1 0 0 1 0 0 1 1 3 24 2 37 1 2 2 4 3 26 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 2 12 2 7 1 4 4 3 3 25 1 1 1 1 1 1 0 1 0 0 1 0 1 0 2 3 4 2 15 5 2 3 2 1 29 3 1 2 1 2 1 0 1 0 0 1 0 1 0 1 1 36 1 27 1 5 3 4 3 31 1 1 1 1 1 0 0 1 0 0 1 0 0 1 2 1 12 2 7 1 3 3 3 2 38 3 1 2 1 1 0 0 0 0 0 1 0 1 0 1 2 24 2 44 5 3 2 4 2 48 3 1 1 2 1 0 0 1 0 0 1 0 1 0 1 4 12 4 7 1 3 3 2 3 32 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 1 15 3 36 1 5 2 4 2 27 3 2 1 1 1 0 0 1 0 0 1 0 1 0 1 2 30 4 42 1 1 4 2 3 28 3 2 1 1 1 1 0 1 0 0 1 0 0 0 2 1 24 2 19 1 2 1 3 2 32 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 1 24 2 29 1 4 3 1 4 34 3 1 1 2 1 0 1 1 0 0 0 0 0 0 1 1 18 2 27 4 3 3 2 3 28 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 18 4 10 1 3 2 3 1 36 3 2 1 1 1 1 0 1 0 0 1 0 0 1 1 1 8 4 34 1 4 3 4 1 39 3 2 1 1 2 1 0 1 0 0 1 0 1 0 1 4 12 4 58 5 5 3 4 2 49 3 1 1 2 1 0 0 1 0 1 0 0 0 1 1 4 24 2 15 4 4 2 3 3 34 3 1 2 2 1 1 0 1 0 0 1 0 0 1 1 3 36 2 45 1 5 3 2 3 31 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 2 6 2 11 1 5 3 4 3 28 3 1 2 1 1 0 0 1 0 0 1 0 0 1 1 1 24 4 66 1 1 3 4 4 75 3 2 1 2 1 0 1 1 0 0 0 0 0 0 1 4 18 4 19 2 3 2 2 1 30 3 2 1 1 1 0 0 1 0 0 1 0 0 1 2 2 60 2 74 2 2 2 2 2 24 3 1 1 1 1 1 0 1 0 0 1 0 0 0 2 4 48 4 116 2 3 2 4 3 24 1 2 1 1 1 0 1 1 0 1 0 0 1 0 2 1 24 0 41 1 5 3 4 4 23 1 2 2 1 1 0 0 1 0 1 0 0 0 1 2 1 6 4 34 1 3 1 4 1 44 3 1 1 2 1 0 0 1 0 1 0 0 0 0 2 2 13 2 21 1 2 2 4 2 23 3 1 1 1 1 0 0 0 0 0 1 0 1 0 1 1 15 2 13 5 3 2 2 3 24 3 1 1 1 1 0 0 1 0 1 0 0 0 1 2 1 24 2 42 1 3 3 4 2 28 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 2 10 2 15 1 3 1 2 3 31 3 1 1 1 1 0 0 1 0 0 1 0 1 0 1 2 24 4 57 1 2 2 4 4 24 3 2 1 2 1 0 0 1 0 0 0 0 0 1 1 1 21 2 36 1 4 2 4 3 26 3 1 1 1 1 0 0 1 0 1 0 0 1 0 1 2 18 2 32 3 2 4 3 1 25 3 1 1 1 1 0 0 1 0 1 0 0 0 1 1 2 18 2 44 1 5 3 1 1 33 1 1 1 2 1 0 0 0 1 0 1 0 0 0 1 3 10 2 39 1 2 3 1 2 37 3 1 2 1 1 1 0 0 0 0 1 0 1 0 1 4 15 4 15 1 3 2 2 3 43 3 1 1 1 1 0 0 1 0 0 1 0 1 0 1 2 13 4 9 1 2 3 4 1 23 3 2 1 1 1 0 0 0 0 0 1 0 0 1 1 2 24 2 38 3 1 2 4 4 23 3 1 1 1 1 0 0 1 0 1 0 1 0 0 1 4 6 3 17 2 3 3 2 1 34 3 2 1 1 1 0 0 1 0 0 1 0 1 0 1 2 9 4 11 4 5 3 3 4 32 3 2 2 1 1 0 0 1 0 0 0 0 0 1 2 4 9 2 12 1 2 2 4 1 23 3 1 1 2 1 0 0 1 0 1 0 0 0 1 1 2 9 2 10 1 3 2 2 3 29 3 1 1 1 2 0 0 1 0 0 1 0 0 1 2 4 18 4 32 5 1 3 4 4 38 3 1 1 2 1 0 1 1 0 0 1 0 0 0 1 1 12 0 62 1 3 3 2 2 28 3 2 1 2 1 0 0 1 0 1 0 0 0 1 2 4 10 2 7 3 5 3 4 4 46 3 1 1 2 1 0 0 1 0 0 0 0 0 1 1 2 24 2 12 1 2 3 2 1 23 2 1 1 1 1 1 0 1 0 0 1 0 1 0 2 4 12 4 23 5 5 3 4 1 49 3 1 1 2 1 0 0 0 1 0 1 0 0 1 1 4 36 3 45 1 3 3 2 3 26 3 2 1 2 1 0 0 1 0 0 1 0 0 0 2 4 12 2 8 1 3 4 2 1 28 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 30 2 24 1 4 2 4 1 23 3 1 1 1 1 0 0 1 0 1 0 0 0 1 2 2 18 2 12 5 3 3 4 4 61 3 1 1 1 1 0 0 1 0 0 0 0 0 1 1 3 12 2 34 5 5 3 3 3 37 3 1 1 1 1 0 0 1 0 0 1 0 0 0 1 3 12 3 22 1 3 2 2 3 36 2 2 1 2 1 1 0 1 0 0 1 0 0 1 1 4 6 2 18 1 3 4 2 2 21 3 1 1 1 1 0 0 1 0 1 0 0 0 1 1 1 18 2 25 1 1 3 1 3 25 3 1 1 1 1 0 0 1 0 0 1 1 0 0 2 4 12 2 15 1 4 3 4 3 36 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 4 18 4 38 1 4 3 1 3 27 3 2 1 1 1 0 1 1 0 0 1 0 0 1 1 1 18 2 36 1 2 2 4 3 22 3 1 1 1 1 0 0 1 0 1 0 0 0 1 1 1 36 2 34 1 5 3 2 3 42 3 1 2 1 1 0 0 1 0 0 1 0 0 1 2 2 18 2 30 1 4 2 4 1 40 3 1 1 1 1 0 0 1 0 1 0 0 0 1 1 4 36 2 31 5 3 3 4 1 36 3 1 1 1 1 1 0 1 0 0 1 0 0 1 1 4 18 4 61 1 5 3 4 3 33 3 2 1 2 1 0 0 1 0 0 1 0 0 1 1 4 10 4 21 1 2 2 3 1 23 3 2 1 1 1 0 0 1 0 1 0 0 0 1 1 4 60 4 138 5 5 3 4 4 63 1 1 1 2 1 1 0 1 0 0 0 0 0 0 1 2 60 1 148 2 5 2 4 4 60 1 2 1 2 1 0 0 1 0 0 0 0 0 0 2 1 48 1 77 1 4 2 4 3 37 3 1 1 1 1 0 0 0 0 1 0 0 0 1 2 4 18 3 23 1 1 4 3 1 34 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 7 3 8 5 5 3 4 4 36 3 1 1 1 1 0 0 1 0 0 0 0 0 1 1 2 36 2 143 1 5 3 2 4 57 3 1 1 2 1 1 0 1 0 0 0 0 0 0 2 4 6 4 4 2 3 2 4 3 52 3 2 1 1 1 1 0 1 0 0 1 0 1 0 1 1 20 2 22 5 4 3 4 3 39 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 2 18 2 130 1 1 2 4 4 38 3 1 1 2 1 0 1 1 0 0 0 0 0 0 2 4 22 2 13 5 4 2 4 2 25 3 1 1 1 1 1 0 1 0 1 0 0 0 1 1 3 12 2 13 1 2 3 1 1 26 3 1 1 1 1 1 0 1 0 0 1 0 0 1 1 4 30 3 43 2 3 3 2 2 26 3 2 1 1 1 0 0 1 0 0 1 0 1 0 1 4 18 4 22 1 3 2 1 3 25 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 18 2 11 5 2 2 2 1 21 3 1 1 2 1 0 0 1 0 1 0 0 0 1 1 2 18 4 74 1 1 3 4 2 40 2 2 1 2 1 0 0 1 0 0 1 0 0 0 1 2 15 4 23 3 3 3 4 3 27 1 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 9 2 14 1 4 2 2 3 27 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 18 2 18 1 3 4 2 2 30 3 1 1 2 1 1 0 1 0 0 1 0 0 0 1 2 12 2 10 4 2 2 4 1 19 3 1 1 1 1 0 0 1 0 1 0 0 1 0 1 1 36 2 32 1 4 3 4 4 39 1 1 2 2 1 1 0 1 0 0 0 0 0 0 1 1 6 4 20 1 4 2 4 3 31 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 9 4 24 1 1 3 3 3 31 3 1 1 1 1 0 0 1 0 0 1 0 0 0 1 2 39 3 118 2 4 3 3 4 32 3 1 1 2 1 0 0 1 0 1 0 0 0 1 1 1 12 2 26 1 1 2 4 4 55 3 1 1 1 1 0 0 1 0 0 0 0 0 0 1 1 36 4 23 1 3 4 2 2 46 3 2 1 2 1 0 0 1 0 0 1 0 0 1 1 2 12 2 12 1 5 1 1 1 46 3 2 1 1 1 1 0 1 0 1 0 0 0 1 2 4 24 4 15 4 3 2 1 1 43 3 2 1 1 1 0 0 1 0 0 1 0 1 0 1 4 18 2 15 1 2 4 4 1 39 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 2 18 4 19 5 3 4 4 1 28 1 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 24 3 86 1 2 3 2 3 27 1 2 1 2 1 0 0 1 0 0 1 0 0 1 2 4 14 3 8 1 3 3 2 3 27 3 2 1 1 1 1 0 1 0 0 1 0 1 0 1 2 18 3 29 5 5 3 4 3 43 3 1 2 1 1 1 0 1 0 0 1 0 0 1 1 2 24 2 20 1 2 4 1 2 22 3 1 1 2 1 0 0 1 0 0 1 0 0 1 2 4 24 4 22 5 4 3 4 3 43 3 2 2 2 1 0 1 1 0 0 1 0 0 1 1 1 15 2 11 1 2 4 2 1 27 3 1 1 1 2 0 0 1 0 0 1 0 0 1 1 4 24 2 32 3 5 1 2 3 26 3 1 1 2 1 0 0 1 0 0 1 0 0 0 1 3 12 4 9 3 4 4 2 1 28 3 3 1 2 1 1 0 1 0 0 1 0 0 1 2 2 24 2 20 1 5 2 4 3 20 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 4 33 4 73 1 4 3 2 3 35 3 2 1 2 1 0 1 1 0 0 1 0 0 0 1 4 12 4 23 1 1 3 2 3 42 2 2 1 2 1 0 0 1 0 0 1 0 0 0 2 4 10 2 16 3 3 3 2 4 40 3 1 2 1 2 1 0 1 0 1 0 0 1 0 1 1 24 2 14 5 3 2 2 2 35 3 1 1 1 1 1 0 1 0 0 1 0 0 1 2 4 36 4 58 1 5 3 2 2 35 3 2 2 2 1 0 1 1 0 0 1 0 0 1 1 1 12 2 26 1 2 3 1 1 33 3 1 2 1 1 1 0 1 0 0 1 0 1 0 2 1 18 3 85 5 3 2 2 3 23 3 2 1 2 1 0 0 1 0 1 0 0 0 1 1 4 21 2 28 3 4 2 2 3 31 1 1 1 1 1 1 0 1 0 0 1 0 0 0 1 2 18 2 10 5 3 2 2 2 33 3 1 1 1 1 1 0 1 0 0 1 0 0 1 2 4 15 2 32 4 4 2 3 3 20 3 1 1 1 1 1 0 1 0 1 0 0 0 1 1 2 12 2 20 5 3 3 2 3 30 3 1 1 1 1 0 1 1 0 0 1 0 0 1 1 2 12 4 10 1 4 3 3 1 47 3 2 2 1 1 1 0 1 0 0 1 0 1 0 1 4 21 3 16 2 4 3 3 1 34 3 2 1 1 1 0 0 1 0 0 1 0 0 0 1 2 12 2 28 5 5 2 2 2 25 1 1 1 2 1 0 0 1 0 0 1 0 0 1 2 2 18 2 28 1 3 4 3 3 21 3 1 1 2 1 0 1 1 0 1 0 0 0 1 1 4 28 4 27 1 5 3 2 3 29 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 18 4 11 4 3 3 3 1 46 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 9 2 13 1 5 3 4 3 20 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 18 4 12 1 1 2 4 4 55 3 3 2 1 1 0 0 1 0 0 0 1 0 0 2 4 5 2 34 1 4 3 4 1 74 3 1 1 1 1 0 0 1 0 0 1 0 1 0 1 2 24 2 113 1 3 3 3 3 29 1 2 1 2 1 0 0 0 1 0 1 0 0 0 2 1 6 4 19 1 1 3 4 4 36 3 3 1 2 1 0 0 1 0 0 0 0 0 0 1 4 24 4 21 1 3 1 2 1 33 3 2 1 2 1 0 0 1 0 0 1 0 0 1 1 1 9 2 21 1 3 3 2 1 25 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 2 12 2 15 5 3 4 1 1 25 3 1 1 2 1 0 0 1 0 0 1 0 0 1 2 4 6 2 7 3 4 4 4 1 23 3 1 1 1 1 0 0 1 0 1 0 0 1 0 1 4 24 4 13 4 5 2 4 1 37 3 2 1 2 1 1 0 1 0 0 1 0 0 1 1 1 42 4 34 1 1 3 4 3 65 3 2 1 1 1 0 0 0 1 0 1 1 0 0 1 3 12 1 6 1 2 2 1 1 26 3 1 1 1 1 0 0 1 0 0 1 1 0 0 2 4 12 2 19 1 5 3 4 3 39 3 1 1 2 1 1 0 1 0 0 1 0 0 0 1 1 12 2 16 1 3 2 3 2 30 3 1 1 1 1 0 0 0 1 0 1 0 0 1 1 2 20 3 26 1 3 3 3 3 29 1 2 1 2 1 0 0 1 0 0 1 0 0 1 1 4 12 2 7 1 5 3 4 3 41 1 1 2 1 1 0 0 1 0 0 1 0 1 0 2 2 48 4 51 1 3 2 3 3 30 3 1 1 2 1 0 0 1 0 0 1 0 0 0 2 4 9 4 12 5 5 2 4 2 41 3 2 1 1 1 0 0 1 0 1 0 0 1 0 1 1 36 2 18 1 2 2 4 3 34 3 1 1 2 1 1 0 1 0 0 1 0 0 1 2 2 7 2 26 1 3 3 2 1 35 3 1 1 1 1 0 0 0 0 0 1 0 0 1 1 3 12 2 14 5 5 2 4 1 55 3 1 1 2 1 0 0 1 0 0 1 0 0 0 1 2 15 3 15 4 3 4 3 2 61 2 2 1 1 1 0 0 1 0 0 1 0 0 1 2 4 36 4 111 5 3 3 2 3 30 3 1 1 2 1 0 1 1 0 0 1 0 0 0 1 4 6 2 5 1 3 2 1 1 29 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 12 0 28 1 5 3 4 2 34 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 24 2 27 1 5 3 4 3 35 3 1 1 2 1 0 1 1 0 0 1 0 0 0 1 1 24 2 48 1 4 3 3 2 31 3 1 1 2 1 1 0 0 1 0 1 0 0 1 2 4 24 2 27 1 2 2 1 4 29 3 1 1 2 1 0 1 1 0 0 1 0 0 0 1 1 11 4 39 1 3 3 2 1 36 3 2 2 1 1 1 0 1 0 1 0 0 0 1 1 1 12 2 34 1 5 3 4 4 35 3 1 1 2 1 0 1 1 0 0 0 0 0 1 2 1 6 2 3 1 2 2 1 1 27 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 18 2 46 1 2 3 2 3 32 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 1 36 2 36 1 3 3 2 2 37 3 1 2 1 1 0 0 0 0 0 1 0 0 1 1 1 15 2 17 1 2 3 3 1 36 3 1 1 1 1 1 0 1 0 0 1 0 0 1 1 2 12 2 30 1 2 2 1 1 34 3 1 1 1 1 0 0 1 0 1 0 0 0 0 1 2 12 2 8 5 5 3 4 2 38 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 18 2 20 1 4 3 1 3 34 2 2 1 2 1 0 0 1 0 0 1 0 0 1 1 1 24 2 29 1 3 3 4 4 63 1 1 2 2 1 0 1 0 0 0 1 0 0 1 1 1 24 3 17 1 2 2 2 3 29 3 1 1 2 1 0 0 1 0 1 0 0 1 0 2 4 48 3 72 5 5 3 3 3 32 1 2 2 1 1 0 0 1 0 0 1 0 0 1 1 4 33 3 28 1 3 2 2 3 26 3 2 1 2 1 0 0 1 0 0 1 0 0 1 1 4 24 3 47 1 4 3 3 3 35 3 2 1 2 1 0 1 1 0 0 1 0 1 0 1 2 24 2 31 2 2 4 2 3 22 3 1 1 2 1 0 0 1 0 1 0 0 0 1 2 1 6 2 4 1 2 2 4 2 23 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 1 9 2 7 1 3 3 3 3 28 3 1 1 1 1 1 0 1 0 0 1 0 1 0 2 4 6 2 12 5 1 3 4 2 36 3 1 2 2 1 0 0 1 0 0 1 0 0 0 1 2 18 4 12 1 3 4 2 3 33 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 1 18 0 31 1 2 2 4 2 26 3 1 1 1 1 0 0 1 0 1 0 0 0 1 2 4 39 2 26 3 3 3 4 3 24 3 1 1 1 1 0 1 1 0 0 1 0 0 1 1 3 24 2 52 1 4 3 2 3 25 1 1 1 1 1 0 0 1 0 0 1 0 0 1 1 2 12 2 10 2 4 3 4 1 39 3 1 1 1 1 0 0 1 0 0 1 0 1 0 1 1 15 4 15 1 5 3 4 3 44 3 2 2 2 1 0 0 1 0 0 1 0 0 1 1 2 12 4 36 1 3 2 1 1 23 3 1 1 1 1 0 0 1 0 0 1 0 1 0 1 2 24 2 12 1 2 3 1 2 26 3 1 1 1 1 1 0 1 0 0 1 0 0 1 1 1 30 2 36 4 5 2 4 2 57 3 2 1 2 1 0 0 1 0 1 0 0 0 1 1 4 15 3 10 4 4 2 2 2 30 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 12 4 12 3 3 3 4 1 44 3 1 1 2 1 1 0 1 0 0 1 0 0 1 1 2 6 3 12 1 1 3 4 2 47 3 1 1 2 1 1 0 1 0 0 1 0 0 0 2 4 12 2 31 1 3 3 4 3 52 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 4 24 2 38 1 5 2 4 4 62 3 1 1 2 1 1 0 0 1 0 0 0 0 1 1 4 10 2 14 2 3 3 2 1 35 3 1 1 1 2 1 0 1 0 1 0 0 1 0 1 4 6 2 35 1 3 3 3 2 26 3 1 1 1 1 1 0 0 0 1 0 0 0 1 1 4 12 4 19 1 5 3 2 4 26 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 2 27 0 83 1 5 2 4 4 42 3 2 1 2 1 0 0 1 0 0 0 0 0 0 2 4 6 4 12 2 3 2 1 2 27 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 2 6 2 4 5 5 3 4 2 38 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 12 4 21 1 3 3 2 1 39 3 2 2 1 2 1 0 1 0 1 0 0 1 0 1 1 24 2 30 5 3 4 4 3 20 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 2 36 2 90 2 2 3 1 4 29 3 1 1 2 1 0 0 0 1 1 0 0 0 0 2 4 24 4 16 1 4 3 3 2 40 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 2 18 2 13 1 5 4 2 1 32 3 1 1 1 1 0 0 0 0 0 1 0 1 0 1 3 6 4 13 2 5 1 4 3 28 3 2 2 2 1 1 0 1 0 0 1 0 0 1 1 1 24 2 31 1 2 2 1 2 27 3 1 1 1 1 1 0 1 0 0 1 0 0 1 2 1 36 2 55 1 5 3 4 4 42 3 1 2 1 1 0 1 1 0 0 0 0 0 1 1 3 9 2 11 2 5 1 4 1 49 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 2 24 4 12 2 2 3 4 4 38 1 2 2 1 1 0 0 1 0 0 1 0 0 1 2 1 24 2 12 1 2 2 4 2 24 3 1 1 1 1 1 0 1 0 1 0 0 0 1 2 4 10 2 13 5 3 3 4 2 27 3 1 1 1 1 1 0 0 0 0 1 0 1 0 2 3 15 4 24 3 3 3 2 3 36 3 1 1 2 1 0 1 1 0 0 1 0 0 1 1 2 15 1 68 2 1 3 2 2 34 3 1 2 2 1 1 0 1 0 0 1 0 0 0 2 4 24 2 14 1 3 4 2 2 28 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 39 2 86 2 5 3 2 3 45 3 1 1 2 1 0 1 1 0 0 1 0 0 0 1 1 12 2 8 1 4 3 2 1 26 3 1 1 1 1 1 0 1 0 0 1 0 0 1 2 4 36 2 47 1 3 3 2 4 32 3 1 1 2 1 0 1 1 0 0 0 0 0 0 1 3 15 2 27 1 4 3 4 2 26 3 1 1 2 1 0 0 1 0 1 0 0 0 1 1 2 12 3 6 1 3 4 4 1 20 3 2 1 1 1 0 0 0 1 1 0 0 0 1 1 4 24 2 23 5 2 3 1 2 54 3 1 1 1 1 1 0 1 0 0 1 0 0 1 1 1 6 4 6 1 4 2 3 2 37 3 2 1 1 2 1 0 1 0 0 1 0 0 1 1 1 6 4 14 1 2 3 4 1 40 3 1 2 1 2 1 0 1 0 0 1 0 1 0 1 4 36 4 71 1 2 2 4 2 23 3 2 1 2 1 0 0 1 0 1 0 0 0 1 2 1 6 2 12 2 5 3 2 2 43 3 1 1 2 1 1 0 1 0 0 1 0 0 1 1 4 6 4 7 5 5 3 4 4 36 3 2 1 1 1 0 0 1 0 0 0 0 0 1 1 4 24 4 55 1 5 3 4 4 44 3 2 1 1 1 0 0 1 0 0 0 0 0 1 1 1 18 2 32 1 3 2 2 1 24 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 1 48 0 71 1 3 3 4 4 53 3 2 2 1 1 0 0 1 0 0 0 0 0 1 2 4 24 2 35 2 4 2 4 3 23 3 1 1 1 1 0 1 1 0 0 1 0 0 1 1 2 18 2 11 1 3 2 4 1 26 3 1 2 1 1 0 0 0 0 0 1 0 1 0 1 2 26 2 80 1 2 3 3 3 30 3 2 1 1 1 0 1 1 0 0 1 0 0 1 1 4 15 4 15 2 3 2 3 3 31 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 4 4 15 1 4 3 1 1 42 3 2 2 1 1 0 0 1 0 0 1 0 1 0 1 1 36 2 23 1 3 1 4 3 31 3 1 1 1 1 0 0 1 0 1 0 0 0 1 2 1 6 2 7 1 2 3 4 1 41 3 1 2 2 1 1 0 1 0 0 1 0 1 0 1 2 36 2 23 1 4 3 1 3 32 3 2 2 1 1 0 0 1 0 0 1 0 0 1 1 2 15 2 26 2 3 2 4 3 28 3 2 1 2 1 1 0 1 0 1 0 0 0 1 2 4 12 3 15 1 3 4 4 1 41 3 1 1 1 1 0 1 1 0 1 0 0 0 1 1 4 24 2 13 2 4 4 3 2 26 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 4 24 2 31 5 2 3 2 3 25 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 3 21 4 23 1 2 1 1 3 33 3 1 1 1 1 0 0 1 0 1 0 0 0 1 2 1 6 2 14 5 1 2 3 2 75 3 1 1 2 1 1 0 1 0 0 1 0 0 0 1 2 18 4 36 1 5 2 4 2 37 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 1 48 2 78 1 5 3 4 4 42 1 1 1 1 1 1 0 1 0 0 0 0 0 0 2 3 18 2 30 1 2 2 1 2 45 2 1 1 1 1 0 0 1 0 0 1 0 1 0 1 2 12 2 15 1 2 4 1 1 23 3 1 1 1 1 0 0 1 0 1 0 0 0 1 2 4 24 3 20 1 5 3 4 4 60 3 2 1 2 1 1 0 1 0 0 0 0 0 1 1 1 30 2 64 5 5 3 4 2 31 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 3 18 2 29 1 3 3 1 1 34 3 1 2 1 1 0 0 1 0 0 1 0 1 0 2 4 12 4 13 1 5 3 4 1 61 3 2 1 1 1 1 0 1 0 0 1 0 1 0 1 1 24 3 13 1 1 3 2 1 43 3 2 2 1 1 1 0 1 0 0 0 0 0 1 2 4 24 4 20 1 3 2 4 3 37 3 1 1 2 1 1 0 1 0 0 1 0 0 1 1 4 24 2 16 1 4 3 1 3 32 1 1 2 1 1 0 0 1 0 0 1 0 0 1 1 1 12 1 6 1 3 2 4 1 24 1 1 1 1 1 0 0 1 0 0 1 0 1 0 2 4 48 4 89 5 4 3 1 4 35 3 2 1 2 1 0 1 1 0 0 0 0 0 1 1 4 12 4 10 5 4 2 4 1 23 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 6 1 18 3 5 3 4 2 45 1 1 2 1 1 0 0 1 0 0 1 0 1 0 1 1 48 2 70 1 4 4 1 1 34 3 2 1 2 1 0 0 0 0 0 1 0 0 1 2 2 12 4 20 2 2 3 1 3 27 3 1 1 1 1 1 0 1 0 0 1 0 0 1 1 2 9 2 12 1 4 2 4 2 67 3 2 1 2 1 0 0 1 0 0 1 0 0 0 1 2 12 2 13 1 2 3 1 3 22 2 1 1 1 1 0 0 1 0 0 1 0 0 1 2 2 18 0 23 2 2 2 3 3 28 3 2 1 1 1 1 0 1 0 0 1 0 0 1 2 4 21 0 50 5 3 2 4 2 29 1 2 1 2 1 1 0 1 0 0 1 0 0 1 2 1 24 1 36 1 4 3 4 3 27 1 1 1 1 1 0 0 1 0 0 1 0 0 1 2 2 18 4 19 1 2 3 2 1 31 3 2 1 1 1 0 0 1 0 0 1 0 1 0 2 1 24 2 30 5 5 3 4 4 49 1 1 2 2 1 0 1 1 0 0 0 0 0 1 1 1 24 1 15 1 4 3 4 3 24 1 1 1 1 1 0 0 0 0 1 0 0 1 0 2 3 6 3 7 1 2 2 1 2 29 1 1 1 1 1 0 0 1 0 0 1 0 0 1 1 2 36 2 124 5 3 3 4 4 37 3 1 1 2 1 1 0 1 0 0 0 0 0 1 2 2 24 3 47 5 3 3 2 2 37 1 2 1 2 1 0 0 1 0 0 1 0 0 0 1 2 24 3 16 2 4 2 2 2 23 3 2 1 2 1 0 0 1 0 1 0 0 0 1 1 1 12 2 14 1 4 1 3 3 36 3 1 1 1 1 1 0 1 0 0 1 0 0 1 2 4 24 4 26 4 5 3 2 3 34 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 2 48 2 40 5 4 3 1 3 41 3 2 2 2 1 0 0 1 0 0 1 0 0 1 1 1 48 2 68 1 3 2 2 3 31 3 1 1 2 1 0 0 1 0 0 1 0 0 1 2 1 24 2 32 1 2 2 4 1 23 3 1 1 2 1 0 0 1 0 1 0 0 1 0 2 4 30 4 60 1 4 3 2 3 38 3 1 1 1 1 0 0 0 1 0 1 0 0 1 1 4 24 2 54 5 1 2 4 2 26 3 1 1 2 1 0 1 1 0 1 0 0 0 0 1 1 15 2 8 1 3 2 4 2 22 3 1 1 1 1 0 0 1 0 0 1 0 1 0 1 2 9 2 11 1 5 3 4 3 27 3 2 1 1 1 0 0 1 0 0 1 0 1 0 1 4 15 4 28 1 4 2 3 3 24 1 2 1 1 1 0 0 0 1 0 1 0 0 1 1 2 12 2 29 1 4 2 1 1 27 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 24 4 19 5 3 2 2 3 33 3 2 1 2 1 0 0 1 0 0 1 0 0 1 1 2 36 4 28 1 2 1 4 3 27 3 2 1 1 1 1 0 1 0 0 1 0 0 1 2 4 24 2 9 1 2 4 3 3 27 3 2 1 1 1 0 0 1 0 0 1 0 1 0 1 2 18 4 11 1 5 3 3 1 30 1 2 1 1 1 1 0 0 0 0 1 0 0 1 2 2 12 4 31 1 2 3 3 1 49 1 2 2 1 1 1 0 1 0 0 1 0 1 0 1 4 9 2 14 1 3 2 2 1 26 3 1 1 1 1 0 0 1 0 1 0 0 0 1 1 2 36 2 24 1 2 3 1 4 33 3 1 1 1 1 0 0 1 0 1 0 0 1 0 2 4 12 2 21 5 5 2 4 4 52 3 1 1 2 1 1 0 1 0 0 0 0 0 0 1 1 18 2 20 1 3 2 4 1 20 1 1 1 1 1 0 0 1 0 1 0 0 0 1 2 1 9 4 28 1 3 3 2 1 36 3 2 2 1 1 1 0 1 0 1 0 0 0 1 1 1 12 2 13 1 3 3 1 2 21 3 1 1 1 1 0 0 0 0 0 1 0 1 0 1 1 18 2 12 1 3 4 3 1 47 3 1 1 2 1 0 0 1 0 0 1 0 1 0 2 1 12 4 22 1 5 3 3 2 60 3 2 1 1 1 0 0 1 0 0 1 0 0 1 2 1 12 4 4 1 4 2 3 1 58 3 4 1 2 1 0 0 1 0 0 1 0 1 0 1 2 24 3 20 5 3 2 4 3 42 3 2 1 2 1 1 0 1 0 1 0 0 0 1 1 4 21 2 16 4 5 2 4 1 36 1 1 1 1 1 0 0 1 0 0 1 0 1 0 1 2 24 2 27 1 3 2 4 2 20 3 1 1 2 1 1 0 1 0 1 0 0 1 0 2 1 24 1 14 5 5 3 3 3 40 2 1 1 2 1 0 0 1 0 0 1 0 0 0 2 2 6 1 9 2 2 2 1 2 32 2 1 1 1 1 1 0 1 0 0 1 0 1 0 2 1 24 2 14 1 4 2 4 3 23 3 2 1 1 1 1 0 1 0 1 0 0 0 1 2 2 24 0 42 1 3 3 4 1 36 3 3 1 2 1 0 0 1 0 0 1 0 1 0 2 4 18 4 28 1 4 3 2 2 31 1 2 1 1 1 1 0 1 0 0 1 0 0 1 2 4 24 3 39 1 3 3 2 4 32 3 1 1 1 1 0 0 1 0 0 0 0 0 1 1 2 7 2 23 1 2 2 1 1 45 3 1 1 1 1 0 0 0 0 0 1 0 0 1 1 2 9 2 9 1 3 2 1 2 30 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 2 24 1 18 1 4 2 4 4 34 1 1 1 1 1 0 0 1 0 0 0 0 1 0 2 4 36 2 33 1 3 2 2 3 28 3 1 1 2 1 0 0 1 0 0 1 0 0 0 2 3 10 2 13 1 2 2 2 2 23 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 24 1 28 3 3 3 4 1 22 2 1 1 2 1 0 0 1 0 0 1 0 0 1 1 4 24 4 45 1 3 3 2 1 74 3 1 1 2 1 0 0 1 0 0 1 0 0 0 1 2 36 2 27 2 3 2 4 4 50 3 1 1 1 1 0 0 0 1 0 0 0 0 1 2 4 18 2 21 1 2 3 1 1 33 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 15 2 13 5 5 3 4 4 45 1 1 2 1 1 0 1 1 0 0 0 0 0 1 1 1 12 2 7 2 1 2 3 2 22 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 3 10 2 12 2 5 2 4 4 48 3 1 2 1 1 1 0 1 0 0 0 0 1 0 2 1 21 2 34 4 2 2 2 3 29 1 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 24 1 36 1 3 2 4 3 22 1 1 1 1 2 0 1 0 0 1 0 0 0 1 1 4 18 3 18 1 4 2 1 1 22 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 2 48 0 122 5 3 3 2 3 48 1 1 1 2 1 0 0 1 0 0 1 0 0 0 1 2 60 3 92 5 3 3 2 4 27 3 1 1 1 1 0 0 1 0 0 0 0 0 0 1 1 6 4 37 1 3 3 3 1 37 3 3 2 1 1 1 0 1 0 1 0 0 0 1 1 2 30 2 34 2 3 2 4 3 21 3 1 1 1 1 0 0 0 1 1 0 0 0 1 2 4 12 2 6 1 3 1 2 1 49 3 1 1 1 1 1 0 1 0 0 1 0 1 0 1 2 21 4 37 1 4 3 3 2 27 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 18 4 15 1 3 3 2 2 32 1 2 1 1 1 1 0 1 0 0 1 0 0 1 2 4 48 2 39 5 3 1 2 1 38 1 1 1 1 1 0 0 1 0 0 1 0 0 1 2 1 12 2 19 1 2 2 1 3 22 3 1 1 1 1 0 0 1 0 1 0 0 0 1 1 1 18 2 26 1 3 3 4 4 65 3 2 1 1 1 0 0 1 0 0 0 0 0 1 2 4 15 2 20 5 5 3 2 3 35 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 3 6 2 21 1 3 3 2 1 41 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 2 9 1 14 2 4 3 3 4 29 3 1 1 1 1 1 0 1 0 0 1 0 0 1 2 4 42 4 40 3 3 3 4 1 36 3 2 1 2 1 0 0 1 0 0 1 0 0 1 1 4 9 2 38 5 5 3 4 1 64 3 1 1 1 1 0 0 1 0 0 1 0 1 0 1 1 24 2 37 1 3 2 4 3 28 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 18 1 16 1 3 3 3 3 44 1 1 1 1 1 0 0 1 0 0 1 0 0 1 2 2 15 2 14 5 2 3 1 2 23 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 9 2 20 1 2 2 2 3 19 3 2 1 1 1 0 0 0 1 1 0 0 0 1 2 2 24 2 14 1 2 2 4 3 25 3 1 1 2 1 1 0 1 0 0 1 0 1 0 2 4 12 2 14 1 5 3 4 2 47 1 3 2 2 1 0 0 1 0 0 1 0 0 1 1 4 24 2 14 3 4 2 1 3 28 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 60 3 157 1 4 3 4 3 21 3 2 1 2 1 0 0 1 0 0 1 0 0 1 1 4 12 2 15 1 2 2 3 3 34 3 1 2 1 1 0 0 1 0 0 1 0 0 1 1 1 42 3 44 1 4 3 2 2 26 1 2 2 2 1 0 0 1 0 0 1 0 0 1 2 1 18 2 8 1 1 2 1 1 27 3 1 1 1 1 0 0 1 0 0 1 1 0 0 2 2 15 2 13 1 5 3 4 3 38 3 2 1 1 1 0 0 1 0 0 1 0 1 0 1 4 15 2 46 2 3 3 2 2 40 3 1 1 2 1 0 0 1 0 0 1 0 0 0 2 4 24 4 19 1 4 4 2 3 33 3 2 1 2 1 0 0 0 0 0 1 0 0 1 1 1 18 4 19 1 4 4 1 2 32 3 2 1 2 1 0 0 1 0 0 1 0 0 0 1 4 36 3 80 5 2 3 4 3 27 3 2 1 2 1 0 0 1 0 1 0 0 0 1 2 1 30 0 46 1 3 1 2 1 32 3 2 1 1 1 0 0 0 0 0 1 0 0 1 1 4 12 2 14 3 3 2 2 2 26 3 1 1 1 1 1 0 1 0 0 1 0 0 1 2 3 24 2 9 1 4 3 3 4 38 1 1 2 1 1 1 0 1 0 0 0 0 0 1 2 1 12 2 7 1 3 3 4 3 40 3 1 2 1 1 0 0 1 0 1 0 0 1 0 2 1 48 2 75 1 4 3 1 4 50 3 1 1 2 1 0 0 1 0 0 0 0 0 0 1 2 12 2 19 1 3 3 2 2 37 3 1 1 1 1 0 0 1 0 0 1 0 1 0 2 1 24 2 23 1 5 3 1 1 45 3 1 1 1 1 1 0 0 1 0 1 0 0 1 2 2 36 3 81 2 5 3 4 3 42 3 4 1 2 1 1 0 1 0 0 1 0 0 0 2 4 24 4 23 1 4 3 3 3 35 3 2 1 2 1 0 1 1 0 0 1 0 0 1 1 1 14 2 40 1 1 3 4 4 22 3 1 1 1 1 1 0 1 0 0 0 0 0 1 1 2 12 2 9 1 5 3 4 3 41 1 1 2 1 1 1 0 1 0 0 1 0 1 0 2 4 48 2 102 5 4 3 3 3 37 2 1 1 2 1 0 0 1 0 0 1 0 0 1 1 2 30 0 42 1 3 2 1 3 28 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 2 18 4 64 1 5 3 1 4 41 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 3 12 2 13 1 3 4 4 1 23 3 1 1 1 1 0 0 1 0 1 0 0 0 1 1 1 12 2 9 5 3 4 2 3 23 3 1 1 1 1 1 0 1 0 0 1 0 0 1 2 4 21 2 22 1 5 3 2 1 50 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 2 6 3 10 1 1 3 1 2 35 2 2 1 2 1 0 0 1 0 0 1 0 0 0 1 3 6 4 10 1 3 2 4 2 50 3 1 1 1 1 0 0 1 0 0 1 0 1 0 1 4 24 4 63 1 1 3 2 4 27 1 2 1 2 1 0 0 0 1 0 1 0 0 0 1 2 30 1 35 4 3 3 2 3 34 2 1 2 2 1 0 0 1 0 0 1 0 0 1 1 4 48 1 36 1 3 2 1 1 27 2 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 12 4 48 1 5 3 4 2 43 3 2 1 2 1 1 0 0 1 1 0 0 0 1 2 3 30 4 30 1 5 3 4 2 47 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 24 4 41 2 3 3 3 2 27 3 2 1 2 1 0 0 1 0 0 1 0 1 0 1 4 36 2 57 2 4 3 2 3 31 3 2 1 2 1 0 0 1 0 0 1 0 0 1 1 4 60 2 104 1 5 3 4 2 42 3 1 1 2 1 1 0 1 0 0 1 0 0 0 1 4 6 4 21 3 3 4 2 3 24 3 1 1 1 1 1 0 1 0 0 1 0 0 1 1 4 21 3 26 3 2 3 2 1 41 1 1 2 1 1 0 0 1 0 0 1 0 1 0 2 4 30 4 45 1 4 2 4 3 26 3 1 1 2 1 0 0 1 0 1 0 0 0 0 1 4 24 4 52 1 5 3 4 3 33 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 2 72 2 56 2 3 4 2 3 24 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 1 24 2 24 1 5 3 4 1 64 1 1 1 1 1 0 0 1 0 1 0 0 1 0 1 4 18 2 15 1 2 2 1 1 26 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 6 2 15 1 2 2 2 4 56 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 12 2 23 5 3 3 4 4 37 3 1 1 2 1 0 0 1 0 0 0 0 0 1 1 4 15 3 15 1 3 4 3 1 33 1 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 24 4 51 1 2 4 3 4 47 3 3 1 2 1 0 0 1 0 0 0 0 0 1 1 2 36 3 99 2 4 3 3 2 31 3 2 2 2 1 0 0 1 0 0 1 0 1 0 1 4 60 2 65 5 3 3 4 4 34 3 1 2 2 1 1 0 1 0 0 0 0 0 1 1 3 10 4 13 5 4 3 2 2 27 3 2 1 2 1 0 0 1 0 0 1 0 0 1 1 2 36 3 29 2 5 3 3 4 30 3 1 1 1 1 1 0 1 0 0 0 0 0 1 1 4 9 2 28 2 5 3 4 3 35 3 1 1 2 1 0 0 0 1 0 1 0 0 1 1 1 12 2 37 4 3 3 3 2 31 3 1 2 1 1 1 0 1 0 0 1 0 0 1 1 1 15 4 10 1 3 1 3 2 25 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 2 15 2 26 2 3 2 2 1 25 3 1 1 1 1 0 0 1 0 0 1 0 1 0 1 2 24 2 29 2 2 3 1 3 29 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 6 4 47 5 2 3 3 1 44 3 2 2 1 1 1 0 1 0 0 1 0 1 0 1 4 24 2 23 1 4 3 2 3 28 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 4 6 2 12 3 3 3 4 2 50 3 1 1 1 1 0 1 1 0 1 0 0 0 1 1 2 12 2 11 1 4 3 3 1 29 3 2 1 1 2 0 0 0 0 0 1 0 0 1 1 4 12 4 9 1 1 2 2 2 38 3 1 1 1 1 1 0 1 0 0 1 1 0 0 1 4 18 4 18 1 3 3 2 3 24 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 3 15 2 19 1 5 3 4 3 40 3 1 1 2 1 0 0 1 0 1 0 0 0 0 1 4 12 2 11 3 3 2 4 3 29 3 1 1 1 1 0 0 1 0 1 0 0 1 0 2 1 48 4 63 1 5 3 4 4 46 3 2 1 2 1 0 1 1 0 0 0 0 0 1 2 3 24 2 14 2 5 2 2 4 47 3 1 1 2 1 0 0 1 0 0 0 0 0 1 1 2 30 3 25 2 5 3 2 2 41 2 2 1 1 1 0 0 1 0 0 1 0 0 1 1 2 27 2 25 1 2 2 1 2 32 3 1 2 2 1 0 0 1 0 0 1 0 0 1 1 4 15 2 53 3 5 2 4 4 35 3 1 1 1 1 1 0 1 0 0 0 0 0 1 1 2 48 2 66 2 4 3 2 2 24 3 1 1 1 1 1 0 1 0 0 1 0 0 1 2 2 12 0 30 1 2 2 3 2 25 3 2 1 1 1 0 0 1 0 1 0 0 0 1 2 2 9 2 12 1 5 2 4 1 25 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 2 9 2 21 1 3 3 2 1 37 3 1 2 1 1 0 0 1 0 0 1 0 1 0 1 4 18 4 6 3 5 3 3 2 32 1 2 1 2 1 0 0 1 0 0 1 0 0 0 1 1 6 1 12 1 5 2 4 4 35 3 1 1 1 1 0 0 1 0 0 0 0 0 1 2 4 21 2 25 5 5 3 4 1 46 3 1 1 2 1 0 1 1 0 0 1 0 0 0 1 1 9 4 11 1 3 3 4 1 25 3 2 1 1 1 0 0 1 0 0 1 0 1 0 1 2 60 2 140 1 4 3 2 4 27 3 1 1 2 1 1 0 1 0 0 1 0 0 0 2 4 30 4 76 5 5 3 4 3 63 3 2 1 1 1 0 1 1 0 0 1 0 0 1 1 4 30 4 31 5 5 3 2 3 40 3 2 2 2 1 0 0 1 0 0 1 0 0 1 1 4 18 2 15 1 3 3 2 4 32 3 1 1 2 1 0 0 1 0 0 0 0 0 0 1 3 24 4 31 5 3 3 2 3 31 3 2 1 2 1 0 0 1 0 0 1 0 0 1 1 2 20 0 61 2 5 4 4 3 31 1 2 1 2 1 0 1 1 0 0 1 0 0 1 1 3 9 0 13 1 2 3 2 3 34 3 2 1 2 1 0 0 1 0 0 1 0 0 0 2 2 6 1 4 4 2 2 2 2 24 1 1 2 1 1 0 0 1 0 1 0 0 0 1 2 1 12 2 12 1 3 2 2 1 24 3 1 1 1 1 1 0 1 0 0 1 0 1 0 2 2 9 2 8 3 3 2 3 1 66 3 1 1 1 1 0 0 1 0 0 1 0 1 0 1 4 27 2 26 1 3 2 3 1 21 3 1 1 1 1 1 0 1 0 1 0 0 0 1 2 4 6 4 2 4 3 2 2 1 41 1 2 1 1 1 1 0 1 0 0 1 0 1 0 1 4 15 4 13 3 3 4 2 2 47 3 2 1 1 1 0 0 1 0 0 1 0 1 0 1 1 18 2 19 1 3 2 4 3 25 1 2 1 1 1 0 0 1 0 1 0 0 0 1 2 2 48 1 64 1 5 2 3 4 59 3 1 1 1 1 0 0 1 0 1 0 0 0 1 2 3 24 4 13 4 3 1 4 1 36 3 2 1 2 1 0 0 1 0 0 1 0 0 1 1 2 24 3 64 1 2 3 2 3 33 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 24 2 20 1 3 3 4 1 21 3 1 2 1 1 0 0 1 0 1 0 0 1 0 2 2 8 2 8 1 4 2 2 1 44 3 1 1 1 1 0 0 0 0 0 1 0 1 0 1 4 24 2 26 4 3 2 4 3 28 3 1 1 2 1 0 1 1 0 1 0 0 0 1 1 4 4 4 34 1 4 2 1 1 37 3 1 2 1 1 1 0 1 0 0 1 0 0 1 1 2 36 1 40 5 2 2 2 4 29 1 1 1 1 1 0 0 1 0 0 1 1 0 0 1 2 24 2 116 1 3 2 4 3 23 3 2 1 1 1 0 1 1 0 1 0 0 0 0 2 1 18 2 44 2 3 3 4 3 35 3 1 2 2 1 1 0 1 0 0 1 0 1 0 1 4 6 4 68 1 4 3 3 4 45 3 2 2 2 1 1 0 1 0 0 1 0 0 0 1 2 30 0 43 2 3 2 4 3 26 3 2 1 1 1 0 0 1 0 1 0 0 1 0 2 1 24 1 23 2 4 3 3 3 32 1 1 1 1 1 1 0 1 0 0 1 0 0 1 1 2 10 1 10 1 3 3 4 1 23 2 1 1 1 1 0 0 1 0 0 1 0 1 0 1 4 21 2 32 5 5 3 3 2 41 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 1 24 1 25 3 3 3 4 1 22 2 1 1 2 1 0 0 1 0 0 1 0 0 1 1 1 39 4 142 5 4 3 4 2 30 3 2 1 2 1 0 0 1 0 0 1 0 0 0 1 1 13 4 18 1 2 3 1 2 28 1 2 1 1 1 0 0 1 0 0 1 0 1 0 1 1 15 2 25 1 1 2 4 3 23 3 1 1 1 1 1 0 1 0 1 0 0 0 1 1 1 12 2 13 1 2 2 1 1 37 3 1 1 1 1 1 0 1 0 0 1 0 1 0 2 4 21 2 52 5 3 3 3 3 26 3 1 1 1 1 0 1 1 0 0 1 0 0 1 1 4 15 2 30 1 4 3 2 3 33 3 1 1 1 1 0 1 1 0 0 1 0 0 1 1 1 6 2 4 1 5 2 1 2 49 1 1 1 2 1 0 0 1 0 0 1 0 0 1 1 1 18 2 10 1 2 2 2 3 23 3 1 1 1 1 1 0 1 0 0 1 0 1 0 2 2 12 2 8 2 4 2 4 1 23 3 1 1 1 1 0 0 1 0 1 0 0 1 0 1 4 30 4 58 1 4 2 2 3 25 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 12 3 16 4 5 3 4 4 55 3 2 2 1 1 0 0 1 0 0 0 0 0 1 2 1 24 2 13 5 4 2 4 4 32 3 1 1 1 1 1 0 1 0 1 0 0 0 1 2 3 6 4 13 1 3 3 1 1 74 3 3 2 1 2 1 0 1 0 0 1 1 0 0 1 3 15 4 13 5 3 3 4 4 39 3 2 1 2 1 0 0 1 0 0 0 0 0 1 2 4 24 2 14 1 3 3 2 1 31 3 1 1 2 1 1 0 0 0 0 1 0 0 1 1 1 12 4 7 1 5 3 3 2 35 3 2 1 1 1 1 0 1 0 0 1 0 0 1 2 4 15 4 50 5 5 2 4 3 59 3 1 1 2 1 1 0 1 0 0 1 0 0 1 1 1 18 4 21 1 3 2 4 1 24 3 2 1 1 1 0 0 1 0 1 0 0 0 1 2 1 12 2 22 1 3 3 3 2 24 3 1 1 1 1 0 0 1 0 0 1 0 1 0 1 4 21 4 127 5 5 3 4 4 30 3 1 1 2 1 1 0 1 0 0 0 0 0 0 2 4 24 4 25 2 4 4 3 2 27 3 2 1 2 1 1 0 1 0 0 1 0 0 1 1 2 12 2 12 1 5 4 3 1 40 1 2 1 1 1 0 0 0 0 0 1 0 1 0 1 1 30 2 31 1 2 1 4 2 31 3 1 1 1 1 0 0 1 0 0 1 0 1 0 2 4 10 2 29 5 2 2 4 1 31 3 1 1 1 1 0 1 1 0 1 0 0 0 1 1 2 12 4 36 1 5 3 4 3 28 3 3 1 2 1 0 0 1 0 1 0 0 0 1 1 4 12 4 17 1 5 3 4 1 63 3 2 1 2 1 0 0 1 0 0 1 0 1 0 1 1 24 2 28 5 5 2 4 1 26 3 1 1 1 1 0 1 1 0 1 0 0 0 1 1 1 36 4 81 1 3 2 2 4 25 3 2 1 2 1 0 0 1 0 0 1 0 0 0 2 4 21 4 33 1 5 3 4 3 36 3 1 1 2 1 0 1 1 0 0 1 0 0 0 1 4 24 4 22 2 5 3 4 2 52 1 2 1 1 1 0 0 1 0 0 1 0 0 1 1 3 12 4 15 3 1 3 4 4 66 1 3 1 1 1 1 0 1 0 0 0 1 0 0 1 1 24 2 14 5 3 2 4 1 25 3 1 1 1 1 1 0 1 0 1 0 0 0 1 2 4 36 4 35 1 4 3 4 3 37 3 2 1 2 1 1 0 1 0 0 1 0 0 1 1 1 18 2 35 1 4 2 1 1 25 3 1 1 1 1 0 0 0 0 0 1 0 0 1 1 4 36 4 57 4 5 3 2 3 38 3 2 1 2 1 0 1 1 0 0 1 0 0 0 1 2 18 2 39 1 1 2 4 3 67 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 2 39 4 49 1 4 3 2 1 25 3 2 1 1 1 0 0 0 0 0 1 0 0 1 2 4 24 4 19 4 5 3 4 1 60 3 1 1 2 1 1 0 1 0 0 1 0 0 1 1 2 12 0 14 1 3 3 2 1 31 3 1 1 2 1 0 0 1 0 0 1 0 1 0 1 2 12 2 8 2 2 2 2 2 23 1 1 1 1 1 1 0 1 0 0 1 0 1 0 2 2 20 2 65 5 1 1 4 1 60 3 1 1 2 1 0 1 1 0 0 1 0 0 0 1 2 18 2 19 4 3 3 2 2 35 3 1 1 2 1 0 0 1 0 0 1 0 1 0 1 4 22 2 27 3 5 3 4 3 40 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 48 4 28 5 5 3 3 3 38 3 2 2 2 1 0 1 1 0 0 1 0 0 1 1 2 48 3 62 1 5 3 4 4 50 3 1 1 1 1 0 0 1 0 0 0 0 0 1 2 1 40 4 60 1 3 3 3 4 27 1 1 1 2 1 0 0 1 0 0 1 0 0 1 2 2 21 2 12 1 5 2 4 2 39 3 1 2 1 1 0 0 1 0 0 1 0 0 1 2 4 24 2 63 5 5 3 4 3 41 3 1 2 2 1 0 1 1 0 0 1 0 0 0 1 4 6 4 12 5 3 4 2 2 27 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 3 24 2 29 1 5 1 4 4 51 3 1 1 1 1 0 0 1 0 0 0 0 0 1 1 4 24 2 31 3 5 3 3 4 32 3 1 1 2 1 0 0 1 0 1 0 0 0 1 1 4 9 2 23 2 2 2 4 2 22 3 1 1 1 1 0 0 1 0 1 0 0 0 1 1 1 18 2 75 5 5 3 4 2 51 3 1 2 2 1 0 1 1 0 0 0 0 0 1 2 4 12 4 13 1 2 2 4 2 22 3 2 1 1 1 0 0 1 0 1 0 0 1 0 1 4 24 3 7 5 5 4 4 3 54 3 2 1 2 1 1 0 1 0 0 1 0 0 1 1 2 9 2 15 5 2 3 2 1 35 3 1 1 1 1 1 0 1 0 0 1 1 0 0 1 4 24 4 16 1 5 3 4 4 54 3 2 2 1 1 0 0 1 0 0 0 0 0 1 1 2 18 4 18 1 5 2 4 1 48 1 2 1 2 1 0 0 0 0 1 0 0 1 0 1 1 20 4 43 1 5 2 4 2 24 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 12 4 10 5 5 3 4 3 35 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 2 12 2 75 5 1 2 2 1 24 3 1 1 1 1 1 0 1 0 1 0 1 0 0 1 1 36 2 93 1 4 3 1 3 24 3 1 1 2 1 1 0 1 0 0 1 0 0 1 2 2 6 2 6 1 2 4 3 1 26 3 1 1 1 2 0 0 1 0 0 1 0 1 0 1 4 12 4 9 5 5 3 4 1 65 3 4 1 1 1 0 0 1 0 0 1 0 0 1 1 2 42 1 93 1 1 3 2 4 55 1 1 1 2 1 0 1 1 0 0 0 0 0 0 1 2 15 0 18 1 2 2 1 1 26 3 2 1 1 1 1 0 1 0 1 0 1 0 0 2 2 8 2 9 1 2 4 2 1 26 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 2 6 2 5 1 4 4 3 1 28 1 1 1 1 1 0 0 0 0 0 1 0 1 0 1 1 36 4 96 1 4 3 4 3 24 3 2 1 2 1 0 1 1 0 0 1 0 0 1 2 1 48 2 31 1 3 3 4 3 54 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 1 48 2 39 1 4 3 4 4 46 3 1 2 1 1 1 0 1 0 0 0 0 0 1 2 2 36 3 74 1 3 2 2 2 54 3 1 1 1 1 1 0 1 0 1 0 0 0 1 1 4 6 2 13 3 3 1 4 1 62 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 6 4 16 1 4 2 2 3 24 3 2 1 2 1 0 0 1 0 1 0 0 0 1 1 1 36 2 159 1 1 1 3 3 43 3 1 1 1 1 0 0 0 1 0 1 0 0 0 1 1 18 2 13 1 3 4 3 1 26 1 1 1 1 1 0 0 1 0 0 1 0 0 1 2 4 12 2 11 1 3 4 2 1 27 3 2 1 2 1 1 0 1 0 0 1 0 0 1 1 3 12 2 30 1 3 4 1 3 24 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 36 2 27 1 5 3 2 2 41 1 1 2 1 1 0 0 1 0 0 1 0 0 1 2 1 8 4 7 1 5 3 4 1 47 3 2 1 1 1 1 0 1 0 0 1 0 1 0 1 4 18 4 38 1 2 1 2 3 35 3 2 1 2 1 0 0 1 0 0 1 0 0 0 1 1 21 4 16 1 5 4 3 3 30 3 2 1 2 1 1 0 1 0 0 1 0 0 1 1 1 18 4 40 1 5 2 4 1 33 1 3 1 2 1 1 0 1 0 1 0 0 0 1 2 4 18 0 42 1 3 3 2 3 36 2 2 2 1 1 0 0 1 0 0 1 0 0 1 2 1 36 2 83 5 5 3 4 4 47 3 1 1 1 1 0 1 1 0 0 0 0 0 1 2 2 48 3 67 5 3 3 4 4 38 3 1 2 2 1 0 0 1 0 0 0 0 0 1 1 4 24 3 24 3 3 3 2 3 44 3 2 2 2 1 0 0 1 0 0 1 0 0 1 1 1 18 2 12 1 2 2 3 3 23 3 1 1 2 1 1 0 1 0 1 0 0 0 1 2 1 45 0 118 1 5 3 4 3 29 3 2 1 1 1 0 0 1 0 1 0 0 0 1 2 2 24 2 51 5 5 2 4 3 42 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 3 15 2 23 1 2 2 3 1 25 3 1 1 1 1 0 0 1 0 0 1 0 1 0 2 1 12 0 11 1 3 3 4 3 48 1 2 1 1 1 1 0 1 0 0 1 0 0 1 2 4 12 2 9 5 3 2 2 3 21 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 4 2 6 1 2 2 3 1 23 3 1 2 1 1 0 0 1 0 1 0 0 1 0 1 1 24 4 30 1 5 3 4 2 63 3 2 1 2 1 0 1 1 0 0 1 0 0 1 1 4 24 4 26 1 5 4 3 1 46 3 2 1 1 1 0 0 0 1 0 1 0 0 1 1 1 36 2 52 1 4 3 2 2 29 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 4 21 3 30 1 3 3 2 1 28 2 2 1 1 1 0 1 1 0 0 1 0 1 0 1 4 18 2 19 1 2 2 4 1 23 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 4 24 1 16 1 4 3 4 3 50 1 1 1 2 1 0 0 1 0 0 1 0 0 1 1 4 18 2 34 1 5 3 4 2 47 1 3 2 2 1 0 0 1 0 0 1 0 0 1 1 2 21 2 40 5 4 3 3 3 35 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 4 18 2 68 5 3 3 4 3 68 3 2 1 1 1 1 0 1 0 1 0 0 0 1 2 4 24 2 12 1 2 4 2 1 28 3 1 1 1 1 1 0 1 0 0 1 0 0 1 1 1 9 2 14 1 4 3 4 1 59 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 12 2 7 1 5 3 4 1 57 2 1 1 1 1 0 0 1 0 0 1 0 1 0 2 1 20 4 22 1 3 4 2 2 33 1 2 1 1 2 1 0 0 0 1 0 0 0 1 2 4 24 4 40 5 4 3 4 2 43 3 2 1 2 1 0 1 1 0 0 1 0 0 1 1 4 15 4 15 1 3 3 4 4 35 3 2 1 2 1 0 0 1 0 0 0 0 0 1 1 1 18 1 14 1 4 3 4 4 32 3 2 2 1 1 1 0 1 0 0 0 0 1 0 2 4 36 3 109 1 5 3 2 3 45 3 2 2 2 1 1 0 1 0 0 1 0 0 1 1 4 24 2 15 2 2 4 3 1 33 3 1 1 2 1 1 0 1 0 0 1 0 0 1 1 4 10 2 9 5 4 2 3 2 40 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 4 15 4 33 1 3 3 2 4 28 3 1 1 2 1 0 0 1 0 0 0 0 0 1 1 1 15 2 40 1 3 2 2 2 29 3 1 1 2 1 1 0 1 0 0 1 0 0 1 2 4 9 2 36 2 3 3 2 1 26 3 1 2 1 2 1 0 0 0 1 0 0 0 1 1 4 24 4 58 4 3 3 2 1 27 3 2 1 1 1 0 1 1 0 0 1 0 0 1 1 4 18 3 22 1 3 4 2 3 28 3 1 1 2 1 0 0 1 0 0 1 0 0 1 2 1 24 2 24 1 2 2 4 1 35 3 1 1 2 1 0 0 1 0 0 1 0 0 1 2 4 27 4 45 4 2 3 2 1 32 2 2 2 2 1 0 0 1 0 0 1 0 1 0 1 4 10 2 22 1 3 3 2 1 25 1 1 1 1 1 0 0 1 0 1 0 0 1 0 2 4 15 2 22 3 3 2 4 3 20 3 1 1 1 1 0 0 1 0 1 0 0 0 1 1 1 18 2 24 1 2 2 1 3 27 2 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 12 4 33 1 5 3 4 2 42 2 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 36 2 74 5 5 3 2 2 37 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 1 12 2 7 1 5 2 4 2 24 3 1 1 1 1 0 0 1 0 1 0 0 0 1 1 4 36 3 77 3 4 2 4 3 40 3 2 1 2 1 0 0 1 0 0 1 0 0 1 1 3 6 4 13 1 5 3 4 1 46 3 2 2 1 2 1 0 1 0 0 1 0 0 1 1 1 24 4 14 2 4 3 1 1 26 3 2 1 2 1 0 0 1 0 0 1 0 0 1 1 4 15 2 9 5 2 2 1 1 24 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 12 2 36 1 3 3 2 2 29 3 1 2 1 1 0 0 0 1 0 1 0 1 0 1 2 11 4 13 4 3 2 4 3 40 3 2 1 1 1 1 0 1 0 0 1 0 0 1 1 1 18 1 19 1 2 3 4 4 36 1 1 1 2 1 0 0 0 1 0 0 0 0 0 1 4 36 2 36 1 5 3 2 3 28 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 9 2 14 1 2 3 2 4 27 3 1 1 2 1 1 0 1 0 0 0 0 0 0 2 4 30 4 67 5 4 3 3 2 36 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 24 2 78 1 4 3 3 3 38 3 1 1 2 1 0 1 1 0 0 1 0 0 0 1 4 24 2 93 5 3 1 4 4 48 3 1 1 2 1 0 1 1 0 0 0 0 0 1 1 2 30 4 22 5 5 3 4 1 36 3 2 1 1 1 1 0 1 0 0 1 0 0 1 1 4 18 4 11 1 1 2 4 3 65 3 2 1 1 1 0 0 1 0 0 1 1 0 0 1 2 24 2 41 1 4 1 3 3 43 3 1 1 2 1 0 0 1 0 0 1 0 0 1 2 1 12 2 8 1 2 2 4 2 53 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 2 24 4 28 5 4 3 3 4 34 3 2 2 2 1 0 0 1 0 0 1 0 0 1 1 2 48 2 157 1 3 3 2 3 23 3 1 1 2 1 0 0 1 0 0 1 0 0 1 2 4 36 4 66 1 5 3 4 3 34 3 2 1 2 1 1 0 1 0 0 1 0 0 0 1 4 28 1 78 5 2 3 4 1 40 1 2 2 2 1 0 1 0 0 1 0 0 0 1 1 1 27 4 24 1 5 3 4 3 43 2 4 2 2 1 0 0 1 0 0 1 0 0 0 1 4 15 4 18 1 5 3 4 3 46 3 2 1 2 1 0 0 1 0 0 1 0 0 1 1 1 12 4 22 1 3 3 4 2 38 1 2 1 1 2 1 0 1 0 0 1 0 1 0 1 2 36 4 58 1 3 3 4 3 34 3 2 1 2 1 0 1 1 0 0 1 0 0 1 1 4 18 4 12 5 3 3 3 2 29 3 2 1 2 1 0 0 1 0 0 1 0 0 1 1 4 36 3 89 5 4 3 2 3 31 2 1 2 2 1 0 1 1 0 0 1 0 0 0 1 1 21 2 26 1 2 2 4 2 28 3 1 1 2 1 0 0 1 0 1 0 0 0 0 1 4 12 4 16 4 4 2 2 2 35 3 1 1 1 2 0 0 1 0 0 1 0 0 1 1 4 15 2 22 5 4 2 4 1 33 1 1 1 1 1 0 0 1 0 1 0 0 1 0 1 1 18 2 42 1 3 3 3 3 42 3 1 1 1 1 0 0 0 1 0 1 0 0 1 2 1 16 4 26 1 5 3 4 2 43 1 1 1 2 1 1 0 0 0 1 0 0 0 1 2 4 20 4 35 5 2 1 4 1 44 3 2 1 2 1 1 0 1 0 0 1 0 0 1 1 4 36 4 105 5 5 3 4 4 42 3 2 1 1 1 0 1 1 0 0 0 0 0 1 1 4 15 2 14 5 3 4 2 1 40 3 1 1 2 1 0 0 1 0 1 0 0 0 1 1 4 24 2 13 1 5 3 1 1 36 3 1 1 2 1 0 0 1 0 0 1 0 0 0 1 1 12 2 11 1 3 3 2 1 20 3 1 2 2 1 0 0 1 0 1 0 0 0 0 1 1 21 2 38 5 4 3 2 1 24 3 1 1 1 2 1 0 0 1 0 1 0 1 0 1 2 36 2 37 5 3 4 2 3 27 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 15 3 36 1 2 2 2 2 46 3 2 1 1 1 0 1 1 0 0 1 0 1 0 1 2 9 2 32 5 3 2 2 1 33 3 1 1 1 1 1 0 1 0 0 1 0 1 0 1 4 36 3 45 1 3 2 4 1 34 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 2 24 4 47 1 2 2 4 3 25 1 1 1 1 1 0 0 1 0 0 1 0 1 0 2 2 30 2 30 5 5 2 4 3 25 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 4 11 2 21 4 5 1 2 1 28 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 1 24 1 32 1 3 3 2 2 31 3 1 1 2 1 0 0 1 0 1 0 0 0 1 2 2 48 0 184 1 3 2 2 2 32 1 1 1 2 2 0 0 1 0 0 1 0 0 0 2 4 10 2 28 2 3 3 2 1 32 3 1 2 1 1 0 1 0 1 0 1 0 0 1 1 1 6 2 149 1 5 3 4 4 68 1 1 1 2 1 1 0 1 0 0 1 0 0 0 2 1 24 2 24 2 1 1 1 2 33 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 1 24 2 33 1 5 3 2 2 39 3 1 1 2 1 0 0 1 0 1 0 0 0 0 2 4 18 4 18 1 3 2 2 4 28 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 48 3 127 3 4 3 1 3 37 3 1 1 2 1 0 0 1 0 0 1 0 0 0 1 1 9 2 14 1 2 2 4 2 22 3 1 1 1 1 0 0 1 0 1 0 0 0 1 2 2 12 2 20 1 4 3 4 2 30 3 1 2 2 1 1 0 1 0 1 0 0 0 1 1 1 24 1 69 1 2 1 1 2 55 1 1 1 2 1 0 0 1 0 0 1 0 0 1 2 1 12 1 7 1 2 3 2 3 46 1 2 1 2 1 1 0 1 0 0 1 0 0 1 2 1 18 4 10 1 2 2 4 2 21 3 1 1 1 1 0 0 1 0 1 0 0 0 1 1 1 48 2 103 1 4 3 4 4 39 2 3 2 2 1 0 1 1 0 0 0 0 0 1 2 4 30 2 19 5 5 3 4 3 58 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 1 12 3 13 1 3 3 2 1 43 3 2 2 1 1 1 0 1 0 0 1 0 1 0 1 1 24 2 17 1 2 3 1 2 24 3 1 1 1 2 0 0 0 1 0 1 0 1 0 1 2 9 2 17 1 2 2 2 3 22 3 1 1 2 1 0 0 1 0 0 1 0 0 1 2 4 9 4 12 1 3 3 1 1 30 3 2 1 1 1 1 0 1 0 0 1 0 0 1 1 4 12 4 5 3 5 3 4 2 42 3 2 2 2 1 0 0 1 0 0 1 0 0 1 1 1 12 2 15 1 3 2 1 3 23 1 1 1 1 1 0 0 1 0 0 1 0 0 1 1 2 30 3 19 2 2 3 3 4 30 2 2 1 1 1 0 0 1 0 0 1 0 0 0 2 3 9 2 7 1 3 2 2 1 28 3 1 1 1 1 0 0 1 0 0 1 0 1 0 2 2 6 2 21 1 2 4 3 3 30 3 1 1 2 1 0 0 1 0 1 0 0 0 0 1 2 60 2 63 1 3 3 4 4 42 3 1 1 1 1 0 0 1 0 0 0 0 0 1 2 4 24 4 68 5 3 3 4 2 46 3 2 2 2 1 0 1 1 0 0 1 0 0 0 1 4 12 2 35 5 2 3 3 2 45 3 1 2 2 1 1 0 1 0 0 1 0 0 0 1 4 10 2 15 1 3 3 2 1 31 3 1 2 1 2 1 0 1 0 0 1 0 1 0 1 4 24 2 9 5 4 3 2 3 31 2 1 1 2 1 0 0 1 0 0 1 0 0 1 1 4 4 4 15 1 4 3 1 1 42 3 3 2 1 1 1 0 1 0 0 1 0 1 0 1 1 15 2 18 1 2 2 1 2 46 3 1 1 1 1 0 0 0 0 1 0 0 0 1 1 2 48 0 84 3 2 2 1 3 30 3 2 1 1 1 1 0 1 0 0 1 0 0 1 1 1 24 1 33 3 2 3 4 4 30 3 1 2 2 1 0 0 1 0 0 0 0 0 1 2 4 12 2 29 5 1 3 4 4 38 3 1 1 2 1 1 0 1 0 0 1 0 0 0 1 4 18 2 15 1 2 4 1 2 43 3 1 2 1 1 0 0 0 1 0 1 0 1 0 2 4 24 2 36 2 5 3 4 3 31 3 2 1 1 1 0 0 1 0 0 1 0 0 1 2 2 18 4 36 1 1 4 3 3 40 3 3 2 2 1 0 0 1 0 0 1 1 0 0 1 1 36 3 21 1 4 3 1 3 24 3 2 1 2 1 0 0 1 0 0 1 0 0 1 2 2 24 2 41 3 2 2 4 3 28 3 1 1 1 1 0 1 1 0 1 0 0 0 1 2 4 36 2 110 1 1 2 2 3 26 3 2 1 2 1 0 0 1 0 0 1 0 0 0 2 1 12 2 19 1 3 2 4 2 29 3 1 1 2 1 1 0 0 0 0 1 0 0 1 1 1 24 4 12 4 5 2 4 2 57 3 2 1 2 1 0 0 1 0 1 0 0 0 0 1 3 30 4 37 5 5 3 4 2 49 2 2 1 1 1 0 0 1 0 0 1 0 1 0 1 2 9 4 12 1 5 3 4 1 37 3 3 1 1 1 0 0 1 0 0 1 0 1 0 1 1 28 2 40 1 3 3 2 3 45 3 1 1 1 1 1 0 1 0 0 1 0 1 0 2 2 24 2 31 2 5 3 4 4 30 3 1 1 1 1 0 0 1 0 0 0 0 0 1 1 4 6 4 17 1 5 4 2 1 30 3 2 1 1 1 0 0 1 0 1 0 0 0 1 1 2 21 3 24 1 3 1 4 2 47 3 2 1 1 1 1 0 1 0 0 1 0 0 1 1 4 15 2 36 5 3 3 2 4 29 3 1 1 1 1 1 0 1 0 0 1 0 0 1 1 4 24 2 24 3 5 3 2 3 35 1 2 1 2 1 0 0 1 0 0 1 0 0 1 2 2 6 2 5 1 2 4 1 2 22 3 1 1 1 1 0 0 1 0 0 1 0 1 0 1 2 30 2 17 5 3 2 1 3 26 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 2 27 4 25 3 3 3 2 2 23 3 2 1 1 1 0 0 1 0 0 1 0 1 0 2 4 15 2 36 1 5 2 2 3 54 1 1 1 2 1 0 0 1 0 1 0 0 0 0 1 4 42 2 72 5 4 4 4 2 29 3 1 1 2 1 0 0 1 0 1 0 0 0 1 1 1 11 4 39 1 3 3 2 1 40 3 2 2 1 1 1 0 1 0 0 1 0 1 0 1 2 15 2 15 2 3 3 2 1 22 3 1 1 1 1 0 0 0 0 0 1 0 0 1 1 4 24 2 74 1 3 3 4 2 43 3 1 2 1 1 1 0 1 0 0 1 0 1 0 1 1 24 1 12 1 1 2 4 4 29 3 2 1 1 1 1 0 0 1 1 0 1 0 0 2 1 60 2 73 1 5 3 4 4 36 3 1 1 1 1 0 0 0 1 1 0 0 0 1 2 4 30 4 28 1 3 2 2 3 33 3 1 1 2 1 0 0 1 0 0 1 0 0 1 1 3 24 2 13 3 3 2 3 3 57 3 1 1 1 1 0 0 1 0 0 1 0 1 0 1 2 6 2 8 1 3 2 3 1 64 3 1 1 1 1 0 0 0 0 0 1 0 0 1 1 2 18 3 24 5 5 3 2 2 42 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 24 3 25 1 5 3 4 3 47 3 2 2 1 1 1 0 1 0 0 1 0 1 0 2 2 15 1 13 2 3 4 2 2 25 3 1 1 1 1 1 0 1 0 1 0 0 0 1 2 2 30 4 84 1 4 3 2 2 49 3 1 1 1 1 0 0 1 0 0 1 0 0 1 2 4 48 2 48 1 1 3 2 3 33 1 1 1 2 1 0 0 1 0 1 0 0 0 0 2 3 21 2 29 2 3 2 1 3 28 1 1 1 2 1 1 0 1 0 0 1 0 0 0 1 1 36 2 82 1 3 3 2 2 26 3 1 2 1 1 0 1 1 0 0 1 0 0 1 2 4 24 4 20 1 4 3 2 2 30 3 2 1 1 1 0 0 1 0 0 1 0 1 0 1 1 15 4 14 1 3 2 3 2 25 3 2 1 1 1 0 0 1 0 1 0 0 0 1 1 3 42 0 63 1 2 1 1 2 33 3 2 1 1 1 0 0 1 0 0 1 0 0 1 1 4 13 2 14 2 1 2 4 1 64 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 24 2 66 1 1 3 2 4 29 3 1 1 2 1 0 1 1 0 0 0 0 0 0 1 2 24 4 17 1 5 3 2 2 48 3 2 1 1 1 0 0 1 0 0 1 0 1 0 1 4 12 4 36 5 2 3 1 2 37 3 2 2 1 1 0 0 1 0 0 1 0 1 0 1 4 15 1 16 2 5 3 4 3 34 1 1 2 1 1 0 0 1 0 0 1 0 1 0 1 1 18 2 19 5 4 4 4 3 23 3 2 1 1 1 0 0 1 0 1 0 0 1 0 1 1 36 2 40 1 1 3 3 2 30 3 1 1 2 1 0 0 1 0 0 1 0 0 0 1 4 12 2 24 5 5 3 3 3 50 3 1 1 2 1 1 0 1 0 0 1 0 0 1 1 4 12 2 17 1 4 2 4 1 31 3 1 1 1 1 0 0 1 0 0 1 0 1 0 1 1 30 2 39 1 3 1 4 2 40 3 1 1 2 1 0 1 1 0 0 1 0 0 0 1 4 12 2 8 1 5 3 4 3 38 3 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 45 2 18 1 3 3 4 4 23 3 1 1 2 1 0 0 1 0 0 0 0 0 1 2 2 45 4 46 2 1 3 4 3 27 3 1 1 1 1 0 1 1 0 0 1 0 0 1 1 foreach/inst/examples/pi.R0000644000175100001440000000031711472542406015233 0ustar hornikuserslibrary(foreach) w <- getDoParWorkers() n <- 10000000 h <- 1 / n pi <- foreach(i=1:w, .combine='+') %dopar% { x <- h * (seq(i, n, by=w) - 0.5) h * sum(4 / (1 + x * x)) } cat(sprintf('pi = %f\n', pi)) foreach/inst/examples/bootpar.R0000644000175100001440000000104411472542406016267 0ustar hornikusers# foreach version based on for-loop version from Wikipedia # http://en.wikipedia.org/wiki/Bootstrapping_(statistics) library(foreach) data(iris) x <- iris[which(iris[,5] != "setosa"), c(1,5)] trials <- 10000 opts <- list(chunkSize=150) print(system.time( r <- foreach(icount(trials), .combine=cbind, .options.nws=opts, .options.smp=opts) %dopar% { ind <- sample(100, 100, replace=TRUE) result1 <- glm(x[ind,2]~x[ind,1], family=binomial(logit)) coefficients(result1) } )) hist(r[1,], breaks=40) dev.new() hist(r[2,], breaks=40) foreach/inst/examples/for.R0000644000175100001440000000557311472542406015422 0ustar hornikuserslibrary(foreach) n <- 10 nrows <- 5 ncols <- 5 # vector example set.seed(17) x <- numeric(n) for (i in seq(along=x)) x[i] <- rnorm(1) set.seed(17) y <- foreach(icount(n), .combine='c') %do% rnorm(1) cat('results of vector example:\n') print(identical(x, y)) # list example set.seed(17) x <- vector('list', length=n) for (i in seq(length=n)) x[i] <- list(rnorm(10)) set.seed(17) y <- foreach(icount(n)) %do% rnorm(10) cat('results of list example:\n') print(identical(x, y)) # matrix example set.seed(17) cols <- vector('list', length=ncols) for (i in seq(along=cols)) cols[i] <- list(rnorm(nrows)) x <- do.call('cbind', cols) set.seed(17) y <- foreach(icount(ncols), .combine='cbind') %do% rnorm(nrows) cat('results of matrix example:\n') dimnames(y) <- NULL print(identical(x, y)) # another matrix example set.seed(17) cols <- vector('list', length=ncols) for (i in seq(along=cols)) { r <- numeric(nrows) for (j in seq(along=r)) r[j] <- rnorm(1) cols[i] <- list(r) } x <- do.call('cbind', cols) set.seed(17) y <- foreach(icount(ncols), .combine='cbind') %:% foreach(icount(nrows), .combine='c') %do% rnorm(1) cat('results of another matrix example:\n') dimnames(y) <- NULL print(identical(x, y)) # ragged matrix example set.seed(17) x <- vector('list', length=ncols) for (i in seq(along=x)) x[i] <- list(rnorm(i)) set.seed(17) y <- foreach(i=icount(ncols)) %do% rnorm(i) cat('results of ragged matrix example:\n') print(identical(x, y)) # another ragged matrix example set.seed(17) x <- vector('list', length=ncols) for (i in seq(along=x)) { r <- numeric(i) for (j in seq(along=r)) r[j] <- rnorm(1) x[i] <- list(r) } set.seed(17) y <- foreach(i=icount(ncols)) %:% foreach(icount(i), .combine='c') %do% rnorm(1) cat('results of another ragged matrix example:\n') print(identical(x, y)) # filtering example set.seed(17) a <- rnorm(10) # C-style approach x <- numeric(length(a)) n <- 0 for (i in a) { if (i > 0) { n <- n + 1 x[n] <- i } } length(x) <- n # Vector approach y <- a[a > 0] # foreach approach z <- foreach(i=a, .combine='c') %:% when(i > 0) %do% i cat('results of filtering example:\n') print(identical(x, y)) print(identical(x, z)) # Define a function that creates an iterator that returns chunks of a vecto ivector <- function(x, chunksize) { n <- length(x) i <- 1 nextEl <- function() { if (n <= 0) stop('StopIteration') chunks <- ceiling(n / chunksize) m <- ceiling(n / chunks) r <- seq(i, length=m) i <<- i + m n <<- n - m x[r] } obj <- list(nextElem=nextEl) class(obj) <- c('abstractiter', 'iter') obj } # another filtering example set.seed(17) a <- rnorm(10000) # Vector approach x <- a[a > 0] # foreach with vectorization, limiting vector lengths to 1000 y <- foreach(a=ivector(a, 1000), .combine='c') %do% a[a > 0] cat('results of another filtering example:\n') print(identical(x, y)) foreach/inst/examples/output.R0000644000175100001440000000154211472542406016164 0ustar hornikuserslibrary(foreach) # define a combine function that writes the results to a file. # note that the first argument is not a result, but the file # object, and must be specified via the .init argument and # returned as the value of this function. output <- function(fobj, ...) { lines <- list(...) cat(sprintf('writing %d line(s)\n', length(lines))) writeLines(unlist(lines), con=fobj) fobj } # create a temporary file to write the results to fname <- tempfile('foreach') fobj <- file(fname, 'w') # use ireadLines to create an iterator over the lines of the input file, # which are converted to upper case, and processed by the output function foreach(input=ireadLines('output.R'), .combine=output, .init=fobj, .multicombine=TRUE, .maxcombine=5) %do% toupper(input) # display the results and clean up close(fobj) file.show(fname) file.remove(fname) foreach/inst/examples/bootseq.R0000644000175100001440000000100211472542406016267 0ustar hornikusers# for-loop version from Wikipedia # http://en.wikipedia.org/wiki/Bootstrapping_(statistics) data(iris) x <- iris[which(iris[,5] != "setosa"), c(1,5)] trials <- 10000 intercept1 <- rep(0, trials) slope1 <- rep(0, trials) print(system.time( for (B in 1:trials) { ind <- sample(100, 100, replace=TRUE) result1 <- glm(x[ind,2]~x[ind,1], family=binomial(logit)) intercept1[B] <- coefficients(result1)[1] slope1[B] <- coefficients(result1)[2] } )) hist(intercept1, breaks=40) dev.new() hist(slope1, breaks=40) foreach/inst/examples/sqlite.R0000644000175100001440000000202511472542406016122 0ustar hornikuserslibrary(foreach) library(RSQLite) # Define a simple iterator for a query result, which is # just a wrapper around the fetch function iquery <- function(con, statement, ..., n=1) { rs <- dbSendQuery(con, statement, ...) nextEl <- function() { r <- fetch(rs, n) if (nrow(r) == 0) { dbClearResult(rs) stop('StopIteration') } r } obj <- list(nextElem=nextEl) class(obj) <- c('abstractiter', 'iter') obj } # create a SQLite instance and create one connection. m <- dbDriver('SQLite') # initialize a new database to a tempfile and copy some data.frame # from the base package into it tfile <- tempfile() con <- dbConnect(m, dbname=tfile) data(USArrests) dbWriteTable(con, 'USArrests', USArrests) # issue the query, and then iterate over the results it <- iquery(con, 'select * from USArrests', n=10) r <- foreach(r=it, .combine='rbind') %do% { state <- r$row_names crime <- r$Murder + r$Assault + r$Rape data.frame(state=state, crime=crime) } print(r) # clean up dbDisconnect(con) file.remove(tfile) foreach/inst/examples/comprehensions.R0000644000175100001440000000132311472542406017655 0ustar hornikuserslibrary(foreach) a <- foreach(x=1:4, .combine='c') %do% (x + 2 * x + x / 2) print(a) a <- foreach(x=1:9, .combine='c') %do% (x %% 2 == 1) print(a) a <- foreach(x=1:4, .combine='c') %:% foreach(y=c(3,5,7,9), .combine='c') %do% (x * y) print(a) a <- foreach(x=c(1,5,12,3,23,11,7,2), .combine='c') %:% when(x > 10) %do% x print(a) a <- foreach(x=c(1,3,5), .combine='c') %:% foreach(y=c(2,4,6)) %:% when(x < y) %do% c(x, y) print(a) n <- 30 s <- seq(length=n) a <- foreach(x=s, .combine='c') %:% foreach(y=s, .combine='c') %:% foreach(z=s) %:% when(x + y + z <= n) %:% when(x * x + y * y == z * z) %do% c(x, y, z) print(a) foreach/inst/examples/feapply.R0000644000175100001440000000114111472542406016257 0ustar hornikuserslibrary(foreach) feapply <- function(X, MARGIN, FUN, ...) { FUN <- match.fun(FUN) r <- foreach(x=iapply(X, MARGIN)) %do% { x <- FUN(x, ...) dim(x) <- NULL x } n <- unlist(lapply(r, length)) if (all(n[1] == n)) { r <- unlist(r) dim(r) <- if (n[1] == 1) dim(X)[MARGIN] else c(n[1], dim(X)[MARGIN]) } else if (length(MARGIN) > 1) { dim(r) <- dim(X)[MARGIN] } r } a <- array(rnorm(24), c(2, 3, 4)) m <- diag(2, 3, 2) MARGIN <- 3 fun <- function(x, m) x %*% m expected <- apply(a, MARGIN, fun, m) actual <- feapply(a, MARGIN, fun, m) print(identical(expected, actual)) foreach/inst/examples/tuneRF.R0000644000175100001440000000231011472542406016021 0ustar hornikusers# tuning random forest over mtry parameter in parallel library(foreach) library(randomForest) # a simple iterator over different values for the mtry argument mtryiter <- function(from, to, stepFactor=2) { nextEl <- function() { if (from > to) stop('StopIteration') i <- from from <<- ceiling(from * stepFactor) i } obj <- list(nextElem=nextEl) class(obj) <- c('abstractiter', 'iter') obj } # vector of ntree values that we're interested in vntree <- c(25, 50, 100, 200, 500, 1000) # function that gets random forest error information for different values of mtry tune <- function(x, y, ntree=vntree, mtry=NULL, keep.forest=FALSE, ...) { comb <- if (is.factor(y)) function(a, b) rbind(a, data.frame(ntree=ntree, mtry=b$mtry, error=b$err.rate[ntree, 1])) else function(a, b) rbind(a, data.frame(ntree=ntree, mtry=b$mtry, error=b$mse[ntree])) foreach(mtry=mtryiter(1, ncol(x)), .combine=comb, .init=NULL, .packages='randomForest') %dopar% { randomForest(x, y, ntree=max(ntree), mtry=mtry, keep.forest=FALSE, ...) } } # generate the inputs x <- matrix(runif(2000), 100) y <- gl(2, 50) # execute randomForest results <- tune(x, y) # print the result print(results) foreach/inst/examples/bootpar2.R0000644000175100001440000000121711472542406016353 0ustar hornikusers# foreach version based on for-loop version from Wikipedia # http://en.wikipedia.org/wiki/Bootstrapping_(statistics) library(foreach) data(iris) x <- iris[which(iris[,5] != "setosa"), c(1,5)] trials <- 10000 nwsopts <- list(chunkSize=150) # Can use the following "final" function instead of # using cbind as the "combine" function. final <- function(a) do.call('cbind', a) print(system.time( r <- foreach(icount(trials), .final=final, .options.nws=nwsopts) %dopar% { ind <- sample(100, 100, replace=TRUE) result1 <- glm(x[ind,2]~x[ind,1], family=binomial(logit)) coefficients(result1) } )) hist(r[1,], breaks=40) dev.new() hist(r[2,], breaks=40) foreach/inst/examples/matmul2.R0000644000175100001440000000142511472542406016205 0ustar hornikusers# Less inefficient parallel matrix multiply using custom matrix iterator library(foreach) iblkcol <- function(a, chunks) { n <- ncol(a) i <- 1 nextEl <- function() { if (chunks <= 0 || n <= 0) stop('StopIteration') m <- ceiling(n / chunks) r <- seq(i, length=m) i <<- i + m n <<- n - m chunks <<- chunks - 1 a[,r, drop=FALSE] } obj <- list(nextElem=nextEl) class(obj) <- c('abstractiter', 'iter') obj } # generate the input matrices x <- matrix(rnorm(100), 10) y <- matrix(rnorm(100), 10) # multiply the matrices nw <- getDoParWorkers() cat(sprintf('Running with %d worker(s)\n', nw)) mit <- iblkcol(y, nw) z <- foreach(y=mit, .combine=cbind) %dopar% (x %*% y) # print the results print(z) # check the results print(all.equal(z, x %*% y)) foreach/inst/examples/cross.R0000644000175100001440000000144711472542406015761 0ustar hornikuserslibrary(foreach) NUMROWS <- 500 NUMCOLS <- 100 NUMFOLDS <- 10 CHUNKSIZE <- 50 nwsopts <- list(chunkSize=CHUNKSIZE) xv <- matrix(rnorm(NUMROWS * NUMCOLS), NUMROWS, NUMCOLS) beta <- c(rnorm(NUMCOLS / 2, 0, 5), rnorm(NUMCOLS / 2, 0, 0.25)) yv <- xv %*% beta + rnorm(NUMROWS, 0, 20) dat <- data.frame(y=yv, x=xv) fold <- sample(rep(1:NUMFOLDS, length=NUMROWS)) # the variables dat, fold, and NUMCOLS are automatically exported print(system.time( prss <- foreach(foldnumber=1:NUMFOLDS, .combine='c', .options.nws=nwsopts) %:% foreach(i=2:NUMCOLS, .combine='c', .final=mean) %dopar% { glmfit <- glm(y ~ ., data=dat[fold != foldnumber, 1:i]) yhat <- predict(glmfit, newdata=dat[fold == foldnumber, 1:i]) sum((yhat - dat[fold == foldnumber, 1]) ^ 2) } )) cat('Results:', prss, '\n') foreach/inst/examples/matmul.R0000644000175100001440000000050611472542406016122 0ustar hornikusers# simple (and inefficient) parallel matrix multiply library(foreach) # generate the input matrices x <- matrix(rnorm(16), 4) y <- matrix(rnorm(16), 4) # multiply the matrices z <- foreach(y=iter(y, by='col'), .combine=cbind) %dopar% (x %*% y) # print the results print(z) # check the results print(all.equal(z, x %*% y)) foreach/inst/examples/apply.R0000644000175100001440000001066511472542406015757 0ustar hornikusers# File src/library/base/R/apply.R # Part of the R package, http://www.R-project.org # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # http://www.r-project.org/Licenses/ applyPar <- function(X, MARGIN, FUN, ...) { FUN <- match.fun(FUN) ## Ensure that X is an array object d <- dim(X) dl <- length(d) if(dl == 0) stop("dim(X) must have a positive length") ds <- 1:dl if(length(oldClass(X)) > 0) X <- if(dl == 2) as.matrix(X) else as.array(X) ## now recompute things as coercion can change dims ## (e.g. when a data frame contains a matrix). d <- dim(X) dn <- dimnames(X) ## Extract the margins and associated dimnames s.call <- ds[-MARGIN] s.ans <- ds[MARGIN] d.call <- d[-MARGIN] d.ans <- d[MARGIN] dn.call<- dn[-MARGIN] dn.ans <- dn[MARGIN] ## dimnames(X) <- NULL ## do the calls d2 <- prod(d.ans) if(d2 == 0) { ## arrays with some 0 extents: return ``empty result'' trying ## to use proper mode and dimension: ## The following is still a bit `hackish': use non-empty X newX <- array(vector(typeof(X), 1), dim = c(prod(d.call), 1)) ans <- FUN(if(length(d.call) < 2) newX[,1] else array(newX[,1], d.call, dn.call), ...) return(if(is.null(ans)) ans else if(length(d.ans) < 2) ans[1][-1] else array(ans, d.ans, dn.ans)) } ## else newX <- aperm(X, c(s.call, s.ans)) dim(newX) <- c(prod(d.call), d2) #### ans <- vector("list", d2) nw <- getDoParWorkers() if(length(d.call) < 2) {# vector if (length(dn.call)) dimnames(newX) <- c(dn.call, list(NULL)) #### for(i in 1:d2) { #### tmp <- FUN(newX[,i], ...) #### if(!is.null(tmp)) ans[[i]] <- tmp #### } ans <- foreach(x=iblkcol(newX, nw), .combine='c', .packages='foreach') %dopar% { foreach(i=1:ncol(x)) %do% FUN(x[,i], ...) } } else { #### for(i in 1:d2) { #### tmp <- FUN(array(newX[,i], d.call, dn.call), ...) #### if(!is.null(tmp)) ans[[i]] <- tmp #### } ans <- foreach(x=iblkcol(newX, nw), .combine='c', .packages='foreach') %dopar% { foreach(y=1:ncol(x)) %do% FUN(array(x[,i], d.call, dn.call), ...) } } ## answer dims and dimnames ans.list <- is.recursive(ans[[1]]) l.ans <- length(ans[[1]]) ans.names <- names(ans[[1]]) if(!ans.list) ans.list <- any(unlist(lapply(ans, length)) != l.ans) if(!ans.list && length(ans.names)) { all.same <- sapply(ans, function(x) identical(names(x), ans.names)) if (!all(all.same)) ans.names <- NULL } len.a <- if(ans.list) d2 else length(ans <- unlist(ans, recursive = FALSE)) if(length(MARGIN) == 1 && len.a == d2) { names(ans) <- if(length(dn.ans[[1]])) dn.ans[[1]] # else NULL return(ans) } if(len.a == d2) return(array(ans, d.ans, dn.ans)) if(len.a > 0 && len.a %% d2 == 0) { if(is.null(dn.ans)) dn.ans <- vector(mode="list", length(d.ans)) dn.ans <- c(list(ans.names), dn.ans) return(array(ans, c(len.a %/% d2, d.ans), if(!all(sapply(dn.ans, is.null))) dn.ans)) } return(ans) } ############################################################################## # # Something like this will be added to the iterators package. # This creates an iterator over block columns of a matrix. iblkcol <- function(a, chunks) { n <- ncol(a) i <- 1 nextEl <- function() { if (chunks <= 0 || n <= 0) stop('StopIteration') m <- ceiling(n / chunks) r <- seq(i, length=m) i <<- i + m n <<- n - m chunks <<- chunks - 1 a[,r, drop=FALSE] } obj <- list(nextElem=nextEl) class(obj) <- c('abstractiter', 'iter') obj } # Simple test program for applyPar library(foreach) x <- matrix(rnorm(16000000), 4000) actual <- applyPar(x, 2, mean) expected <- apply(x, 2, mean) cat(sprintf('Result correct: %s\n', identical(actual, expected))) foreach/inst/examples/bigmean2.R0000644000175100001440000000157411472542406016315 0ustar hornikuserslibrary(foreach) # Define a combine function for the partial results comb <- function(...) { n <- foreach(a=list(...), .combine='+') %do% a$n means <- foreach(a=list(...), .combine='+') %do% ((a$n / n) * a$means) list(n=n, means=means) } # initialize some parameters datafile <- 'germandata.txt' nrows <- 100 # germandata.txt only has 1000 rows of data # create an iterator over the data in the file it <- iread.table(datafile, nrows=nrows, header=FALSE, row.names=NULL) # Compute the mean of each of those fields, nrows records at a time print(system.time( r <- foreach(d=it, .combine=comb, .multicombine=TRUE, .final=function(a) a$mean) %do% list(n=nrow(d), means=mean(d)) )) print(r) # This is faster for small problems (when it may not matter), # but becomes slower (or fails) for big problems print(system.time({ d <- read.table(datafile) r <- mean(d) })) print(r) foreach/inst/examples/bigmean.R0000644000175100001440000000255611472542406016234 0ustar hornikuserslibrary(foreach) library(RSQLite) # Define a simple iterator for a query result, which is # just a wrapper around the fetch function iquery <- function(con, statement, ..., n=1) { rs <- dbSendQuery(con, statement, ...) nextEl <- function() { d <- fetch(rs, n) if (nrow(d) == 0) { dbClearResult(rs) stop('StopIteration') } d } obj <- list(nextElem=nextEl) class(obj) <- c('abstractiter', 'iter') obj } # Create an SQLite instance m <- dbDriver('SQLite') # Initialize a new database to a tempfile and copy a data frame # into it repeatedly to get more data to process tfile <- tempfile() con <- dbConnect(m, dbname=tfile) data(USArrests) dbWriteTable(con, 'USArrests', USArrests) for (i in 1:99) dbWriteTable(con, 'USArrests', USArrests, append=TRUE) # Create an iterator to issue the query, selecting the fields of interest qit <- iquery(con, 'select Murder, Assault, Rape from USArrests', n=50) # Define a combine function for the partial results comb <- function(...) { n <- foreach(a=list(...), .combine='+') %do% a$n means <- foreach(a=list(...), .combine='+') %do% ((a$n / n) * a$means) list(n=n, means=means) } # Compute the mean of each of those fields, 50 records at a time r <- foreach(d=qit, .combine=comb, .multicombine=TRUE) %dopar% list(n=nrow(d), means=mean(d)) print(r) # Clean up dbDisconnect(con) file.remove(tfile) foreach/inst/examples/bigmax.R0000644000175100001440000000227111472542406016073 0ustar hornikuserslibrary(foreach) library(RSQLite) # Define a simple iterator for a query result, which is # just a wrapper around the fetch function. iquery <- function(con, statement, ..., n=1) { rs <- dbSendQuery(con, statement, ...) nextEl <- function() { d <- fetch(rs, n) if (nrow(d) == 0) { dbClearResult(rs) stop('StopIteration') } d } obj <- list(nextElem=nextEl) class(obj) <- c('abstractiter', 'iter') obj } # Create an SQLite instance. m <- dbDriver('SQLite') # Initialize a new database to a tempfile and copy a data frame # into it repeatedly to get more data to process. tfile <- tempfile() con <- dbConnect(m, dbname=tfile) data(USArrests) dbWriteTable(con, 'USArrests', USArrests) for (i in 1:99) dbWriteTable(con, 'USArrests', USArrests, append=TRUE) # Create an iterator to issue the query, selecting the fields of interest. # We then compute the maximum of each of those fields, 100 records at a time. qit <- iquery(con, 'select Murder, Assault, Rape from USArrests', n=100) r <- foreach(d=qit, .combine='pmax', .packages='foreach') %dopar% { foreach(x=iter(d, by='col'), .combine='c') %do% max(x) } print(r) # Clean up dbDisconnect(con) file.remove(tfile) foreach/inst/examples/isplit.R0000644000175100001440000000156211472542406016132 0ustar hornikusers# iterator for splitting data using a factor library(foreach) # let's use isplit on a data frame a <- foreach(i=isplit(airquality, airquality$Month), .combine=rbind) %do% quantile(i$value, na.rm=TRUE) # make it pretty and print it rownames(a) <- levels(as.factor(airquality$Month)) print(a) # use a list of factors to do an aggregated operation it <- isplit(as.data.frame(state.x77), list(Region=state.region, Cold=state.x77[,'Frost'] > 130), drop=TRUE) a <- foreach(i=it, .combine=rbind) %do% { x <- mean(i$value) dim(x) <- c(1, length(x)) colnames(x) <- names(i$value) cbind(i$key, as.data.frame(x)) } print(a) # compare with the standard aggregate function b <- aggregate(state.x77, list(Region=state.region, Cold=state.x77[,'Frost'] > 130), mean) print(b) cat('results identical:\n') print(identical(a, b)) foreach/inst/examples/sinc.R0000644000175100001440000000123311472542406015555 0ustar hornikusers# simple foreach example that plots the sinc function library(foreach) # Define the coordinate grid to use x <- seq(-10, 10, by=0.1) # Compute starting indices for each task nw <- getDoParWorkers() cat(sprintf('Running with %d worker(s)\n', nw)) n <- ceiling(length(x) / nw) ind <- seq(by=n, length=nw) # Compute the value of the sinc function at each point in the grid z <- foreach(i=ind, .combine=cbind) %dopar% { j <- min(i + n - 1, length(x)) d <- expand.grid(x=x, y=x[i:j]) r <- sqrt(d$x^2 + d$y^2) matrix(10 * sin(r) / r, length(x)) } # Plot the results as a perspective plot persp(x, x, z, ylab='y', theta=30, phi=30, expand=0.5, col="lightblue") foreach/inst/examples/rf.R0000644000175100001440000000116011472542406015227 0ustar hornikusers# a simple parallel random forest library(foreach) library(randomForest) # generate the inputs nr <- 1000 x <- matrix(runif(100000), nr) y <- gl(2, nr/2) # split the total number of trees by the number of parallel execution workers nw <- getDoParWorkers() cat(sprintf('Running with %d worker(s)\n', nw)) it <- idiv(1000, chunks=nw) # run the randomForest jobs, and combine the results print(system.time({ rf <- foreach(ntree=it, .combine=combine, .multicombine=TRUE, .inorder=FALSE, .packages='randomForest') %dopar% { randomForest(x, y, ntree=ntree, importance=TRUE) } })) # print the result print(rf) foreach/inst/examples/colMeans.R0000644000175100001440000000076211472542406016370 0ustar hornikusers# compute the mean of the columns and the rows of a matrix library(foreach) # generate the input matrix x <- matrix(rnorm(100 * 100), 100) # compute the mean of each column of x cmeans <- foreach(i=1:ncol(x), .combine=c) %do% mean(x[,i]) # check the results expected <- colMeans(x) print(all.equal(cmeans, expected)) # compute the mean of each row of x rmeans <- foreach(i=1:nrow(x), .combine=c) %do% mean(x[i,]) # check the results expected <- rowMeans(x) print(all.equal(rmeans, expected)) foreach/inst/examples/sinc2.R0000644000175100001440000000165011472542406015642 0ustar hornikuserslibrary(foreach) # Define a function that creates an iterator that returns subvectors ivector <- function(x, chunks) { n <- length(x) i <- 1 nextEl <- function() { if (chunks <= 0 || n <= 0) stop('StopIteration') m <- ceiling(n / chunks) r <- seq(i, length=m) i <<- i + m n <<- n - m chunks <<- chunks - 1 x[r] } obj <- list(nextElem=nextEl) class(obj) <- c('abstractiter', 'iter') obj } # Define the coordinate grid and figure out how to split up the work x <- seq(-10, 10, by=0.1) nw <- getDoParWorkers() cat(sprintf('Running with %d worker(s)\n', nw)) # Compute the value of the sinc function at each point in the grid z <- foreach(y=ivector(x, nw), .combine=cbind) %dopar% { y <- rep(y, each=length(x)) r <- sqrt(x ^ 2 + y ^ 2) matrix(10 * sin(r) / r, length(x)) } # Plot the results as a perspective plot persp(x, x, z, ylab='y', theta=30, phi=30, expand=0.5, col="lightblue") foreach/inst/examples/qsort.R0000644000175100001440000000055711472542406016001 0ustar hornikuserslibrary(foreach) qsort <- function(x) { n <- length(x) if (n == 0) { x } else { p <- sample(n, 1) smaller <- foreach(y=x[-p], .combine=c) %:% when(y <= x[p]) %do% y larger <- foreach(y=x[-p], .combine=c) %:% when(y > x[p]) %do% y c(qsort(smaller), x[p], qsort(larger)) } } x <- runif(100) a <- qsort(x) b <- sort(x) print(all(a == b)) foreach/inst/doc/0000755000175100001440000000000012606615125013425 5ustar hornikusersforeach/inst/doc/nested.Rnw0000644000175100001440000003250012606615125015377 0ustar hornikusers% \VignetteIndexEntry{Nesting Foreach Loops} % \VignetteDepends{foreach} % \VignettePackage{foreach} \documentclass[12pt]{article} \usepackage{amsmath} \usepackage[pdftex]{graphicx} \usepackage{color} \usepackage{xspace} \usepackage{fancyvrb} \usepackage{fancyhdr} \usepackage[ colorlinks=true, linkcolor=blue, citecolor=blue, urlcolor=blue] {hyperref} \usepackage{lscape} \usepackage{Sweave} \usepackage{float} \floatstyle{plain} \newfloat{example}{thp}{lop} \floatname{example}{Example} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % define new colors for use \definecolor{darkgreen}{rgb}{0,0.6,0} \definecolor{darkred}{rgb}{0.6,0.0,0} \definecolor{lightbrown}{rgb}{1,0.9,0.8} \definecolor{brown}{rgb}{0.6,0.3,0.3} \definecolor{darkblue}{rgb}{0,0,0.8} \definecolor{darkmagenta}{rgb}{0.5,0,0.5} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \newcommand{\bld}[1]{\mbox{\boldmath $#1$}} \newcommand{\shell}[1]{\mbox{$#1$}} \renewcommand{\vec}[1]{\mbox{\bf {#1}}} \newcommand{\ReallySmallSpacing}{\renewcommand{\baselinestretch}{.6}\Large\normalsize} \newcommand{\SmallSpacing}{\renewcommand{\baselinestretch}{1.1}\Large\normalsize} \newcommand{\halfs}{\frac{1}{2}} \setlength{\oddsidemargin}{-.25 truein} \setlength{\evensidemargin}{0truein} \setlength{\topmargin}{-0.2truein} \setlength{\textwidth}{7 truein} \setlength{\textheight}{8.5 truein} \setlength{\parindent}{0.20truein} \setlength{\parskip}{0.10truein} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \pagestyle{fancy} \lhead{} \chead{Nesting {\tt Foreach} Loops} \rhead{} \lfoot{} \cfoot{} \rfoot{\thepage} \renewcommand{\headrulewidth}{1pt} \renewcommand{\footrulewidth}{1pt} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \title{Nesting {\tt Foreach} Loops} \author{Steve Weston \\ doc@revolutionanalytics.com} \begin{document} \maketitle \thispagestyle{empty} \section{Introduction} <>= library(foreach) registerDoSEQ() @ The \texttt{foreach} package provides a looping construct for executing R code repeatedly. It is similar to the standard \texttt{for} loop, which makes it easy to convert a \texttt{for} loop to a \texttt{foreach} loop. Unlike many parallel programming packages for R, \texttt{foreach} doesn't require the body of the \texttt{for} loop to be turned into a function. \texttt{foreach} differs from a \texttt{for} loop in that its return is a list of values, whereas a \texttt{for} loop has no value and uses side effects to convey its result. Because of this, \texttt{foreach} loops have a few advantages over \texttt{for} loops when the purpose of the loop is to create a data structure such as a vector, list, or matrix: First, there is less code duplication, and hence, less chance for an error because the initialization of the vector or matrix is unnecessary. Second, a \texttt{foreach} loop may be easily parallelized by changing only a single keyword. \section{The nesting operator: \%:\%} An important feature of \texttt{foreach} is the \texttt{\%:\%} operator. I call this the {\em nesting} operator because it is used to create nested \texttt{foreach} loops. Like the \texttt{\%do\%} and \texttt{\%dopar\%} operators, it is a binary operator, but it operates on two \texttt{foreach} objects. It also returns a \texttt{foreach} object, which is essentially a special merger of its operands. Let's say that we want to perform a Monte Carlo simulation using a function called \texttt{sim}.\footnote{Remember that \texttt{sim} needs to be rather compute intensive to be worth executing in parallel.} The \texttt{sim} function takes two arguments, and we want to call it with all combinations of the values that are stored in the vectors \texttt{avec} and \texttt{bvec}. The following doubly-nested \texttt{for} loop does that. For testing purposes, the \texttt{sim} function is defined to return $10 a + b$:\footnote{Of course, an operation this trivial is not worth executing in parallel.} <>= sim <- function(a, b) 10 * a + b avec <- 1:2 bvec <- 1:4 @ <>= x <- matrix(0, length(avec), length(bvec)) for (j in 1:length(bvec)) { for (i in 1:length(avec)) { x[i,j] <- sim(avec[i], bvec[j]) } } x @ In this case, it makes sense to store the results in a matrix, so we create one of the proper size called \texttt{x}, and assign the return value of \texttt{sim} to the appropriate element of \texttt{x} each time through the inner loop. When using \texttt{foreach}, we don't create a matrix and assign values into it. Instead, the inner loop returns the columns of the result matrix as vectors, which are combined in the outer loop into a matrix. Here's how to do that using the \texttt{\%:\%} operator:\footnote{Due to operator precedence, you cannot put braces around the inner \texttt{foreach} loop. Unfortunately, that causes Sweave to format this example rather badly, in my opinion.} <>= x <- foreach(b=bvec, .combine='cbind') %:% foreach(a=avec, .combine='c') %do% { sim(a, b) } x @ This is structured very much like the nested \texttt{for} loop. The outer \texttt{foreach} is iterating over the values in ``bvec'', passing them to the inner \texttt{foreach}, which iterates over the values in ``avec'' for each value of ``bvec''. Thus, the ``sim'' function is called in the same way in both cases. The code is slightly cleaner in this version, and has the advantage of being easily parallelized. \section{Using \texttt{\%:\%} with \texttt{\%dopar\%}} When parallelizing nested \texttt{for} loops, there is always a question of which loop to parallelize. The standard advice is to parallelize the outer loop. This results in larger individual tasks, and larger tasks can often be performed more efficiently than smaller tasks. However, if the outer loop doesn't have many iterations and the tasks are already large, parallelizing the outer loop results in a small number of huge tasks, which may not allow you to use all of your processors, and can also result in load balancing problems. You could parallelize an inner loop instead, but that could be inefficient because you're repeatedly waiting for all the results to be returned every time through the outer loop. And if the tasks and number of iterations vary in size, then it's really hard to know which loop to parallelize. But in our Monte Carlo example, all of the tasks are completely independent of each other, and so they can all be executed in parallel. You really want to think of the loops as specifying a single stream of tasks. You just need to be careful to process all of the results correctly, depending on which iteration of the inner loop they came from. That is exactly what the \texttt{\%:\%} operator does: it turns multiple \texttt{foreach} loops into a single loop. That is why there is only one \texttt{\%do\%} operator in the example above. And when we parallelize that nested \texttt{foreach} loop by changing the \texttt{\%do\%} into a \texttt{\%dopar\%}, we are creating a single stream of tasks that can all be executed in parallel: <>= x <- foreach(b=bvec, .combine='cbind') %:% foreach(a=avec, .combine='c') %dopar% { sim(a, b) } x @ Of course, we'll actually only run as many tasks in parallel as we have processors, but the parallel backend takes care of all that. The point is that the \texttt{\%:\%} operator makes it easy to specify the stream of tasks to be executed, and the \texttt{.combine} argument to \texttt{foreach} allows us to specify how the results should be processed. The backend handles executing the tasks in parallel. \section{Chunking tasks} Of course, there has to be a snag to this somewhere. What if the tasks are quite small, so that you really might want to execute the entire inner loop as a single task? Well, small tasks are a problem even for a singly-nested loop. The solution to this problem, whether you have a single loop or nested loops, is to use {\em task chunking}. Task chunking allows you to send multiple tasks to the workers at once. This can be much more efficient, especially for short tasks. Currently, only the \texttt{doNWS} backend supports task chunking. Here's how it's done with \texttt{doNWS}: <>= opts <- list(chunkSize=2) x <- foreach(b=bvec, .combine='cbind', .options.nws=opts) %:% foreach(a=avec, .combine='c') %dopar% { sim(a, b) } x @ If you're not using \texttt{doNWS}, then this argument is ignored, which allows you to write code that is backend-independent. You can also specify options for multiple backends, and only the option list that matches the registered backend will be used. It would be nice if the chunk size could be picked automatically, but I haven't figured out a good, safe way to do that. So for now, you need to specify the chunk size manually.\footnote{In the future, the backend might decide that it will execute the tasks in parallel. That could be very useful when running on a cluster with multiprocessor nodes. Multiple tasks are sent across the network to each node, which then executes them in parallel on its cores. Maybe in the next release...} The point is that by using the \texttt{\%:\%} operator, you can convert a nested \texttt{for} loop to a nested \texttt{foreach} loop, use \texttt{\%dopar\%} to run in parallel, and then tune the size of the tasks using the ``chunkSize'' option so that they are big enough to be executed efficiently, but not so big that they cause load balancing problems. You don't have to worry about which loop to parallelize, because you're turning the nested loops into a single stream of tasks that can all be executed in parallel by the parallel backend. \section{Another example} Now let's imagine that the ``sim'' function returns a object that includes an error estimate. We want to return the result with the lowest error for each value of b, along with the arguments that generated that result. Here's how that might be done with nested \texttt{for} loops: <>= sim <- function(a, b) { x <- 10 * a + b err <- abs(a - b) list(x=x, err=err) } @ <>= n <- length(bvec) d <- data.frame(x=numeric(n), a=numeric(n), b=numeric(n), err=numeric(n)) for (j in 1:n) { err <- Inf best <- NULL for (i in 1:length(avec)) { obj <- sim(avec[i], bvec[j]) if (obj$err < err) { err <- obj$err best <- data.frame(x=obj$x, a=avec[i], b=bvec[j], err=obj$err) } } d[j,] <- best } d @ This is also quite simple to convert to \texttt{foreach}. We just need to supply the appropriate ``.combine'' functions. For the outer \texttt{foreach}, we can use the standard ``rbind'' function which can be used with data frames. For the inner \texttt{foreach}, we write a function that compares two data frames, each with a single row, returning the one with a smaller error estimate: <>= comb <- function(d1, d2) if (d1$err < d2$err) d1 else d2 @ Now we specify it with the ``.combine'' argument to the inner \texttt{foreach}: <>= opts <- list(chunkSize=2) d <- foreach(b=bvec, .combine='rbind', .options.nws=opts) %:% foreach(a=avec, .combine='comb', .inorder=FALSE) %dopar% { obj <- sim(a, b) data.frame(x=obj$x, a=a, b=b, err=obj$err) } d @ Note that since the order of the arguments to the ``comb'' function is unimportant, I have set the ``.inorder'' argument to \texttt{FALSE}. This reduces the number of results that need to be saved on the master before they can be combined in case they are returned out of order. But even with niceties such as parallelization, backend-specific options, and the ``.inorder'' argument, the nested \texttt{foreach} version is quite readable. But what if we would like to return the indices into ``avec'' and ``bvec'', rather than the data itself? A simple way to do that is to create a couple of counting iterators that we pass to the \texttt{foreach} functions:\footnote{It is very important that the call to icount is passed as the argument to \texttt{foreach}. If the iterators were created and passed to \texttt{foreach} using a variable, for example, we would not get the desired effect. This is not a bug or a limitation, but an important aspect of the design of the \texttt{foreach} function.} <>= library(iterators) opts <- list(chunkSize=2) d <- foreach(b=bvec, j=icount(), .combine='rbind', .options.nws=opts) %:% foreach(a=avec, i=icount(), .combine='comb', .inorder=FALSE) %dopar% { obj <- sim(a, b) data.frame(x=obj$x, i=i, j=j, err=obj$err) } d @ These new iterators are infinite iterators, but that's no problem since we have ``bvec'' and ``avec'' to control the number of iterations of the loops. Making them infinite means we don't have to keep them in sync with ``bvec'' and ``avec''. \section{Conclusion} Nested \texttt{for} loops are a common construct, and are often the most time consuming part of R scripts, so they are prime candidates for parallelization. The usual approach is to parallelize the outer loop, but as we've seen, that can lead to suboptimal performance due to an imbalance between the size and the number of tasks. By using the \texttt{\%:\%} operator with \texttt{foreach}, and by using chunking techniques, many of these problems can be overcome. The resulting code is often clearer and more readable than the original R code, since \texttt{foreach} was designed to deal with exactly this kind of problem. \end{document} foreach/inst/doc/nested.R0000644000175100001440000000616712606615125015044 0ustar hornikusers### R code from vignette source 'nested.Rnw' ################################################### ### code chunk number 1: loadLibs ################################################### library(foreach) registerDoSEQ() ################################################### ### code chunk number 2: init1 ################################################### sim <- function(a, b) 10 * a + b avec <- 1:2 bvec <- 1:4 ################################################### ### code chunk number 3: for1 ################################################### x <- matrix(0, length(avec), length(bvec)) for (j in 1:length(bvec)) { for (i in 1:length(avec)) { x[i,j] <- sim(avec[i], bvec[j]) } } x ################################################### ### code chunk number 4: foreach1 ################################################### x <- foreach(b=bvec, .combine='cbind') %:% foreach(a=avec, .combine='c') %do% { sim(a, b) } x ################################################### ### code chunk number 5: foreach2 ################################################### x <- foreach(b=bvec, .combine='cbind') %:% foreach(a=avec, .combine='c') %dopar% { sim(a, b) } x ################################################### ### code chunk number 6: foreach3 ################################################### opts <- list(chunkSize=2) x <- foreach(b=bvec, .combine='cbind', .options.nws=opts) %:% foreach(a=avec, .combine='c') %dopar% { sim(a, b) } x ################################################### ### code chunk number 7: init2 ################################################### sim <- function(a, b) { x <- 10 * a + b err <- abs(a - b) list(x=x, err=err) } ################################################### ### code chunk number 8: for2 ################################################### n <- length(bvec) d <- data.frame(x=numeric(n), a=numeric(n), b=numeric(n), err=numeric(n)) for (j in 1:n) { err <- Inf best <- NULL for (i in 1:length(avec)) { obj <- sim(avec[i], bvec[j]) if (obj$err < err) { err <- obj$err best <- data.frame(x=obj$x, a=avec[i], b=bvec[j], err=obj$err) } } d[j,] <- best } d ################################################### ### code chunk number 9: innercombine ################################################### comb <- function(d1, d2) if (d1$err < d2$err) d1 else d2 ################################################### ### code chunk number 10: foreach4 ################################################### opts <- list(chunkSize=2) d <- foreach(b=bvec, .combine='rbind', .options.nws=opts) %:% foreach(a=avec, .combine='comb', .inorder=FALSE) %dopar% { obj <- sim(a, b) data.frame(x=obj$x, a=a, b=b, err=obj$err) } d ################################################### ### code chunk number 11: foreach5 ################################################### library(iterators) opts <- list(chunkSize=2) d <- foreach(b=bvec, j=icount(), .combine='rbind', .options.nws=opts) %:% foreach(a=avec, i=icount(), .combine='comb', .inorder=FALSE) %dopar% { obj <- sim(a, b) data.frame(x=obj$x, i=i, j=j, err=obj$err) } d foreach/inst/doc/nested.pdf0000644000175100001440000043111612606615125015410 0ustar hornikusers%PDF-1.5 %ÐÔÅØ 31 0 obj << /Length 2622 /Filter /FlateDecode >> stream xÚ­Ë’ÛÆñ¾_‹Ë`EaÌø”8•uÉe'iS9Ø>@$¸d´\® ®ÖÊ×§§»ç ”Vª´ fúý†šê¶jª®š ¿ßß\½¼6®R°VµÕÍ–Ö¶²ºʺêf]ýRÿ}XÈz<íàï=ü»]üvóãËëV—­®i,Þ¸†c‡#üzø³‚[º4ÅæÑZ/ý.-–ª­!ÝP²’FhcU¸±ôRhoª¥QÂ:OßÀéS@ö~¡\=,–ZÙúß ;ã Â\ë„”¾ZÊNx­éúšñÚzõçc.ÝÁ¿G¸?ö{CÞ~(Þ®Fø#Ââ°GlÚŠd¹Ô­-Ëòá=õQÇ@n[KØR/h­`ÝÀ?Ù²¤» èU²5(é½h]W-mð%Á•‹¥”ÊÔ¯: T‰-X®ã.ɤ*…o[”j4ja:u³ BHJ.Î)#:'£¾6—”<"·É0X€«…êêw‹¶­û[ĶÔÎá["ð>Èt/ƒ1à>üt¨— ±µôÀò'ÛÄ«?£þ¬F¾Ýp×ÃêÔrç5¡]%|ëHlWÄJìÑüÖl2N!,­tõ+ƹËLŒŒgÏ¿áJ$ûì”0Z‚Ž0Æ’ÌN¬H¯ëÓ–ñÃYëïYH=ó¹žÓ›k…«ŸêmFUJ i’?Þ%¿@ã4²©ŸPÏlñA‹["'pÓ¿Cig‡2Å%ZÈø™`fXIAÕxí˜Ï÷³Ö±ÉÙgraf¹ üYž³xZ%dã¾ÜÊ;e.ˆ.Ø‚¶õ¿îYÛA2ï‚ìŽJÚ‰Ö»©Ú÷)Ö€p@t ¬è¡Ð4ÆŸ;ö»üþÈïöȇ÷…­±m—° W /ƒö”³à$GZ¼FÝÏéÂÉ1ÿ¹‚²Àb/åØ;Œ¶oÉ^õ;»n  ?±ò#ÅQ²FïÃë‡Íùé$„PeŸcSL¯›·©€m*Ó5 Æä]û±C?²¦î)pÀMÇ m'hñÐÍ#Ÿ]¥\Äb–/é…ÿ*Õ@š-TýÚH¾9E›h`ûHá ]#œ”Ï“«Ó—äÚ±HH‘[6ØS–Ö)“…ìM%›Ž…,]òÀñD€/h,»AÌüÃaOù¶~Ú²§‘GR¯j=¦à‰zg"!½{†@¤X{!xY]G!„k ZÀÞífJ;‰õÊšŽ…7ãÀ×c*Zçãê;ØÈi¤'Fp¯?Ðö®8Š9q|d"NúZÓÕß«žÉ‹=,OENgcöP9ÿåAY[{!(±"„úD{VaKGIÊÄiG _Qñ4Oñ™GF“—¢hL÷*3]Êps–n×ÈîYy­iôEnY2Ó{"6 ðî¡pŽš¬$“ÔË"À†Çój ï2N´ØZ%…œòåž~b‰rb!ž×e™2œÒ‚g©©c¡a¥šC7É4á’3ØG*ZÂÛäòÜÃ÷xól$' áwPìƒ}ÌŽ_!É /IèÈœê,ÍYyä‡UNM|rÍì>åÀª?ì/"cÙ}uÔ ç‚¡`o³0VA ÛX®"f‹„G-Q3•1rtŒÇ“ÒY ñ‘E 5íÄNà1Ö»¢–î ÿ{Æ"‚f÷1¸R•\¯‡óü»OÉ T¾ŠŠ1kb–]¡SŽX?ÅÝooðelïÖ,úæBfƒX´ò+R«mý…°žØÁBëhT;3ÕMTö·TRïRËcª£ÜÑ2’¶#8ù–«Ê°f—bs¹=kƒB?~?EÓÓϘŽÝ±Ú`“ëZ8û„mô‘e*æzÙ†;râOq{³ °º¶¾¢ßíî)!q&ŽúGà±mÓÔß„å7ŸorÿrO^± 5Ft<¶b”ÈqŠ¢Úï\XWºùr;ñ6• EHùd«aéS¹,(.tN¢‡Ü¿#w(€à³_ë«lKø£™™9ð¥Õ¤¼˜ ÌÊ»X°ÑÜÏY’tURD‰_5ð(«cøJ¯¸zy­›ª¾K O(S¡µÓoºFß5>BшÎk(5ò…Ë3î¥qZ¸öÜÌ.ÒNÞÚܘ^˜Ó€Ø¨À-F¤¸“#nôÑd¹.ÉÎ&¤ÜÀdZqëFÉ 'yÜä@îE‡Ø2Ó“«C´¤Iü=oX'›*zc'=<ÎLW¦çÈ@i²Bbtå=RTR ZÓŠŒÊ]oØ>ïÇù :Ï“‚€è=¶<³÷Âf‹ýtʰ.}ûi°F´.ûÁ² •p QšâÁ]rsPöÓ´X‡Ö¬’G Ê1Êzùœ"ÆÁúy3vãå|?¤^Îui§ßÉÈÞõ‚ÇX¥y¡‚§žOKé;ÞYW’æå0#Vðþtyì+ÃØW}>8…‚Y«Ï;7çÎ'Øêÿ Å1ßE‚Øa97­ÄÏ€ ‘HKz[â.Ln†TWfñ?ÍB‘¢³2ã\Z‡›lð»gÇbˆª!¶ºØÆ•Á8þþíæê÷+ÉP û .'½¯Vû«_~kª5ìÿX5ò}õ„§ö•j¤h±¯¹«Þ\ý“¾Ww$–.àÚT¶ƒÎJÕBwÝ©à嵫ʟ :Þƒ‡HNšZò¨ª@”ö)ë`³»¥úÌ;} Þ–°áò!Vßô jà Ã~© ¥‹ %›ÚÖqQ?mþJ¾!d8˜y<ÆÉSœŠ1)‡ó2=•)…"wS±ÇqÑûT/jUžÁ™úé#)qz„á?¦‚ Â9”‚Þè‚”.þ— ð’ÿ‚9Ì© endstream endobj 50 0 obj << /Length 1984 /Filter /FlateDecode >> stream xÚ­YKo7¾ûW,‘P‹ásAÛCѺMPhã¢LJµ´²Z–«‡í¤ÈïpfÈå®V‰ãô°—Î çÅoÖ2»ÈdöóÁ'/ŽµÊ”•s:;™gÚæBê"+´º,³“Yv:ú­YoÆjtÏ <㳓×/ŽI7Z-rY[Üq dËüijø3…ç’6u¥°Jé°éW¿i<ÑüU£[xÖ~ÓÁO'ÿ( ’™ÊœÍ U ëòlº88=“Ù æ_gRTUžÝ#Õ"sÒÂïuöæà÷™ž¶(SùÀ,/p¥! ¾Oœ6£úù4˜ÐpCo†›âž·ÒIøñÏQ^ûCGCÑŽË–Ò3¹c’)M«Go>ÞŒ?`(8åDYamÞ9ÍÍêõÆWæõŽÞȧ8ô¬^ÂóJ Ÿ‡tùf:ðé×ϳ{êW[ÆÉCWXapjê=ºnŽü[Ž Z‰ÿ÷X—£©=!G$ pbC•ËoXoB„7]iLŠEq½åÐÞ´*pðûaÈîWVqÍâîÇP.YÒ” î&“£åM2žêsËü—·>)0\W|/òCC'k¦þF“]‡ †ž²ªByòR.8ž)üÙ´E{{Ågí땹`Ádz íȪ~nËÇa²»±s£ è¶¥÷š©¬r8bY0÷CŠk-ŒÊ]€]Íj¾Ê;·Å+¥¡} ÚqëÄÛ†V–Ú:+r˜ú”u•„Ë<*Éw1Ìe«j¨lMWûÕ’ v½“Q¨|È»Q0ÞŠLpÍ÷zN‡¸J+‘‡­!oï8éPôú“€eòx¶ù#D¤²i¼ÉªÍ!™CÌQöz±Ï¹ €:ÓÈÝ%æ(Ðw€å(/·1̯݆INÛ Î51½j€Â ˆ¯XˆO,¨û Ëé̸2ÀiÒø‹¼Ø!œÜ0øƒ“’Ü©ãÐÌ¢’UºŽ¨ÒަËpEJg9Û†¶ô«!NÚ´4£šÙÝa-žn‚Ë×þäU9ºE}ê .‰¸¥DÝ`ñ<³ÍŒVCI-©ZlY! j î5nÚñJ¸­ãùÒºîýjÌè—Mòœì/‘°½§÷Xjkœ½ŒvÃÕ^ uÈš¡ŒÒ²•ŒåÐC°Ã}µÏDØN„—Ç*ÜXx¾DôN°}uÁøýŸ…™¢*¤â&ÀHgSGà ·÷A;4T>(<ô5ù~èþ(JiÑVÐýnð2Àüºäxï„nEËy§ —PðâíòœIÊÞèT ˜öφ˜%ŒÖŸeZp|È ûÒ_k'te†±ò# V³ÁêÿÓ`•ªÅ±Ø—\èÜ>Ñ@áF8L ¸¸´(r»c+¸’Š}CÒìkÙ‚µ¿åIéöºlÔŠðýªáÛƒV¼gôÞ“®ÄR-"~ÿl7`‡nÆv4 ö‘À;mR'¬t`¥V_МtNO€H~µå¨Ý„û¯ X¦[Èϼp&¶)Ä‚ÉkfOýCÛ-$ã… £hm´ÐÆöÎpYÎÛ²¼ƒº&N–t^Ü»ÖD—8¦øRŒU­b>öl˲ø¢Jo'ߢx#Ýã6DõQ§•xÊv{F‚ù-ÆG‘@ît]䣷ÊX¬:$Ã#ˆgcæ9òˆJ"@oakee¢À¢}_ò­éÇ=ä;äH] S¸§@Ö´EòÊ2ÔÑ1Ö‚­}7 (ýÊÒS…€íŠ^¡h{ªhÀºc=œžÓ­@U±dPuÓ·×u¸g!0aˆ¨Aä^G!D²£Ts ±ŸîàÊÀ#­ÂÏhz¾eMCë³ ½¢‹‘ ’ú½m\¿Ù‘æÉ×uÚDåÔP ©ÞÓD²õ<‚îM謠½¨×hs' ï)å𬹠Fû…rŠÍE°Ð†ÀjLÉê´èê×ÿ8‚ LŠßŽÚ7vëŽ@(oܤ}ð Q ¼v5ëb`žš¨éA׆“É¡BÞbÐ_'Ÿ ƒ5>4³ÐuFLÙÿ¤¬r-J½OÊÐUÄOÊ?xµ†ÏÊô9€CU˜!D!,`;åœ0²Lì‹ã2«D•C¯ìó¨/Êbô§—;Çš‰éM¬sÐpu/šé%Ö[J2-9—*¬´$ò ¬ÓÇ€º-˜ÀÀ˜ècÚº½@îF“´0ðÞø†®^ÜÆ`Ä}щ—!ó ÁzÖQßÄkË,B1^专°Öû»ºö±ÿ0ø·ü@¶ð K÷ê?aeð³ endstream endobj 57 0 obj << /Length 2806 /Filter /FlateDecode >> stream xÚ­Zëoã¸ÿž¿Â_k£H‘µè½C÷ŠC(šâPìíÅVb7±½kÙÉ¥Eÿ÷΋ÉJ“-ú!E‡Ãß g†#—³ÛY9ûþâÛ«‹o>h5Sªh¬Õ³«›™6®(u=«µ)´÷³«ÕìãüO]\¨ùþvðw»øtõÃ7l•O4ºp¥¶4ãíð¯káßþÖ"¡|´…¿þqóÑšjæßÑ&E*¤ê~–õ·rºA¨Û`ñÂý†»sKPÍy UMÄRHÈ V8†­>åû`OÎm|—]Œå[Ÿ#‹ÂIfƒQ2y~N¢ Ñ\Mäúu“î (UUÆLÇ":Kòˆ ì–y€_ñÈ&Í~>@—o¬¸üSÇ;mn¦À0z¬ˆ „Ï,rG“æÀª¡w¨Î?kZyöÈó’'x9I¤åÅûÔBÚÄq ˆ7·Å3+Yˆ¤ë ‹Óz è'Lô²m±ïôñlyŸ;\IØiÙ›SðáêÉüç2¿a’Œ¯ç‰[ÏSÈø‡`$’ÍCë4e5iòƒHäÅNzž“i;ò©Yl£»«NþW½ºvç9POû0šôÄïË6 O@wBÓ–9íÅ®ò´¡òñfXeNiyÌíßóv³9Çüv|^FPEe'k(geãU Û'£1)$Õð–ó(÷QâOÒe7Û ï3U|ÎÉ™äÎͨüñb‘«.J÷\)$!žûZ~L_ê?w¬NÌc ÕTCk¨Ô¦J‹u¨9¹$@ÿ°$cÑìt Üï2•[Si ¬+|cÏJZÓjö…¯ÜkÔl]ªAØaÁÃ=1y »¯åF˜q*‡i•ÎÒ*+E£Nø?†[00xEaˆ cÞ}”˜æmQWj|÷C—$©ï|•+´r_kh®p¾™´4¸)9ظ Öè•|(W°m厭¬_8Àª,”ñ¯Ö®¶&О™z‰qs ]4þÊš¨©ŠRE«{ÇWڨƒCôAÚËéñ|ÿ-?ú|$ØöŸÅ˲Náz2É tS•2ç,±•ä®%äá ^SØÔ.$6§t}Šáe—e°Ï™ë{†®ö9t¿t]˜F±h¿æ³ñ3?~ ³.§ÂÜ/—ÊÖŠM4¦–#dw?•¶¤ã§æ¿’çƒ-9Ä% y Ñé:C¾£©,´¢‡¸U}¿Ú—+”!d9b¼šbŠÑIë™Øžâ-P ¦ pj[èè‰mL;H‘_X+€µÿOÀÑ¡ùÄ&¸€wræh%"çú†‡ÿ%¸)]ÔΜZå\:Û!N‚É5÷ªgL•ÿ÷Ô`4÷)OTµ¶Ãã‘rÊ;¸GU¡wá<™GPKúÙ‘*‰™Ž‰æ²v.ëGVïàß'*–û¹Âj×s¥CƒÙG3µmd¥sV:c¥+±ÒÕØõJ™aôg)[;™vH¿úNêÚtSGÇü69:íª”]žr×õă!çˆ]†˜ž¤ÊÓ{îÞÆ//ô°o|ƾM˜jþ›Ï¤ñ6Í !ÛgEóÀë#ð²ëûÃ{Ú¼ÍËuA¶PØwϺ{æš|=ˆpGÉ$5Ú–qßw, ïªìöÅï¿°™EŸÀaÝYðj\zN+eòçX/ÏBI~PA×µ iü䦂à37Š!@bô覶pK=~ƒ9…ÛeTw­Dü‰µ„C¯¿>©Õu¾pöÑ¢ž? d§Iô&ª$$á:«†)A‹µž¬³3鍊‘7î×Yµ7ÖÇ Û¨ŒA ô¤@€a§„`œÓòcª>–Žøþ´çþG,Yv9r`¡#Di™üö&‹ ä¥ küaÑø÷IΪ¬^ûó¤‰aÁ£Àce+x4á§I£%ÿ^|´X endstream endobj 62 0 obj << /Length 2809 /Filter /FlateDecode >> stream xÚ­ZëÛ¸ÿ¾…p@‘Ô3èh‹æšC/@›E’Z[k»ëÇÖ’³Ùîï¼øV›M€~%‘Ãr8œùÍÈi²NÒä§‹¿\^¼|mt¢µªóÜ$—×‰É •š2)M¦LU%—«äýìmÛõs=ÛÂu€k=ÿxùóË×¹fFiliÄk ;žà§màg ׆ ¥°Z7è8h¾0%üêÙ-\ºøÛåÅ/4P¥‰Nò,)u­²¼H–û‹÷Ódí?'©ªë"¹#ª}’§ÜwÉ»‹^¤¯˜•Uimx(g}¼‚ŸN®=L©²³öÓÜT³ö€ofv}$RêiøÖÅ*ÂÁ÷p-Â…4Ø®˜t'+-üJÕ|‘ézv¹!ræw”Iœáê·4¹“üþ(÷Híx_\~{øyýµžÝ‘€ž6NÌå~.J?ó;ö5ØÆk.3˜ÓÑ`Mn÷`ç@í ©,+X½¢3 ë~ kò, û÷È qdÏDØŠŠéZ6¬¢ˆ7VÛTeÐ$–Õ7Ý ÉÓÙrs>Ülë)k,jemíÆ(ZÁBW¹ÊÒ…  Ç£¬cÙ¢™‚â/#«C‚eÖ‰®È®…¿Ÿ®(HÖàœƒ¨Ldh(í‡T8j+fr@>=CVÀ,»[‘!Óê=déìšÐs' &=·‘‚°ã¦ã£W–Õì¯rÒN43èÙ™áÇÃPVPè„S4u¡ªàW8·ðóë»)û³9ø0ï ¯dIs7â{ÈhYgÙ÷[ïFNbRèh‘<â s·Ö¨j¸'«hŸ‡#¸a®À|è}+’#’•wWlkÐtçÈ6SªÒÖªÚèoUUª B9âWLRV1ÉBœX8¹6Sy]2éŸæ‹ÜXï_D_ØôGôÖü¸óÖÍÒ<õaLô÷NH¿ˆ¡þ—á!zÊŠüÏ±Ì ²?Ì:/5[²ìmë ‚§!³º©xÿ$dKvø(BÉë‘"“mýÆàPÖÝÐ}VàìJ§ÞçSê-”Îýn-GŒWSL­VÖ˜'™:‚hþñnm#ÃâÓËÏwòþ㈾s›AÜžÁÛ+¸ž±µ“+SÛº7ÄâoÐ}#›ÿ§îëLéºþåOp)”)²oÖõPA«H…(àwÿ&zÓF•Eö@o¶(JƒöC=‰N®¦G>Ôüï_=9΃Ü;éZiÛp.g*®QÛÙ©»àEGö±÷øÀ‹=™xL‘Ïõ=tëðóQG]Í46Ðs9ÓÆ=0{OM-Y™˜•‰XÇÊD¬Œc5všgcoøæ]s£öâ'ç¶k2Ö£` ÝW3ºÀT~#ϼ•¸»á(“¥±i@D¸¤zð cÅÆˆtwŽ«‡'Ðø(Ln÷(äˆ Ú S!—“péƒÖ– ïW¡Å-¢ ºÜº·ÜªÜšáÞ?  jW òMÀ d+ÍìßYiæ¶ x͆åv¸[“ù ?W×÷LçêÐÓ— !Ý“èiaK$¼Á¥Å'ÄŸ™YãÛ¼ôy!“ÊÂëƒ !ëó‰æ7¦ç¬bï7ÉÛyŸ}l¦!ƒZ; =§T!¡yÃa/"¶4¡vÈng™2™+ƒ3>¿qò¦ÇlI Œ—ó»c ¦Œ°8¾¼­k×DûËÑÂðu -R‡óî_áSbo˶[(—Þâ†ÅЧñI¼GûÅ$¶â8Ê£ˆÉ¾IÒZÍ>yE?DQdâ ÎÎÁ­X{GÏKS’ Ôë(AÇ|”}Æ#ÙJ‡ó¼v(WHÓ¸ç]õùªžUŒŽ¨µ„%¥ûz”*3Ú–d˜äŠðNp™\$nò$G²eÆ“[t]¶DHûsp„£Í1l¤\¥9­¥\ó¯Ÿ.^¾¶iRªºL5y|ȉŠJ'$Ûª3ÉušqˆXØÊ(Øv0}HªLRpg¬€B´õ¹"öø@€Dñ1Ç®+Ù#|µýdb§³ZÆ×V>›ŠopZÓܱcØ‘“Û|Úi4l…œ”sÒô|t ûäóôžçÙ0«¦L͸´ª  QRä'gUªnYØwˆ†d"MÇ’Ÿ* B²Ânã6˜ûƒÒê@£ªd©Š:·iE{`ítUR[Ÿ´»õ<â,›R¤blÔCe_håN³Ì∆£cCïç.#ËØ!bž_äå(~û!Y~°ái£( m1û mݨ:à³Ûö¤ÎYëÃ4Œœb ÆeGy S§cà7µxþ*As-÷¹$iÍ]äÇÉC¯i5rõ³Ž l?#Ç¥ '\k­Gµ¤ÉºŽ©‹ALfêÅ×Î “‹—Ö˼âuÕ)Yw#ÖI ;aÛ¬?çH›ƒœ‡º£•GJÔ ¢ˆ­kzRË…„)®Ä¸xÍq´,I…Æz[76@ vÂõ×WÞÓž«EW´EÆŽ‡11s:.§QiwÞÚpO2ë¶w¨Ecã(éJ4ÝìëvgÅdâ·p­«YŒµ=ò7]¨wyC’ˆÝ„˜;øІöÞWöÔz²#sl¼ðm4a´@NT:(žŽóP‘æÙ‚`‚Ê}‘!ޱl5xgX× °4¥›|HÇÏË£¯Ï£FìýB8¸ç`(žd9àbÃQÃfr@ŸÅABßÉY)á«®UòiH <Ècž¤¨”Ní·þ¡dê$¹Qˆlr ·zªª"ÿ¿ß%| endstream endobj 67 0 obj << /Length 1413 /Filter /FlateDecode >> stream xÚµkoÛ6ð»…P,˜Å,ŸzK h†E€¡ö!Í?cgq”JΜnèß‘¡¤(Òdk Ö‰¢æÛäcïõ¥Írÿ| –æ‚Ђ[Þ†Š ÒL?C»¼Õ¢4â³þÆHÄúŸ¨¢0M`üƒ S»Í4ëpîI"eПíПÁrŒtõL`,`Tø¾FâxÞ#Œ3dçÁû\¡•¦-èc‡õOíYã— O^‚ì«gÙQª ”º0Þ£‰˜WÄ¿±oÖuÍR“zãS6»ÿÅÌõÓ`ÈTÆ<¾»vû -¡OP¼Ú¸Kâ–Âø`Æ2qW{|ÂgÇ1Ÿ=F%)ëo> stream xÚÅÙŽäDò½¿Âí—–Êqž¶‡@šFƒ†•–é7àÁutwÍÔÑ”Ý4ÃjÿȈÈtf•{h»<¸l§ãʸ2"ª*nŠªøúâ««‹—JRŠÖZU\]Ê8Q©º¨•ªiŠ«Uñ}ù¯u?Ìd¹k×ÍìÇ«o^\Z"%\ÕYĸ°Ã~Öü,áº%¤œ0k¥ H¯=Òl®jø•å\½GºxyuñÓ…¨ª…5E-[a¬+–»‹ï¬Š¬ST¢m]ñ€P»ÂVîÛâÍÅ¿/ªt·¹ÐµFÛÂ5ZT­")~™Í­Ôeço¦\ÐÛÚïåè¥)æ²A¤¹4ÂGH’Àd¸2ܤ5²¬±bK(Š@U¸ŠšDa.šAu†¢EN¡5Ša5e^̵¶ ö÷öÖ³¹n›r@cÊÒv ¥>qŽå ¿¢3¬üûÑ/€‰¯'á:Ô3øØ=üìpÝÓUØ¢10¢ªuùƒÔƳ? À/àá}»¾Ây™7HÀ/ ‘ ú’`æ}èŽDxÇ$Êdz¹‘¶|Eh¤ XÿÙÿðFúõÀî‚êLm’lY¶.H.%¦ÚÂ]ÈÖN¨†„~ªbµL¦ÔpF§‘ù¥´7/§Œ¯+Q+€ѾºEÙêI‘k/Þ=¼%–T¬©ûÑ^•h|õê¿NhõLkë5}B®ã îÙ€q²a|dâD°OL±ÙíO…$ã¸Vh×䑹c[cÒ#‘Mä‚Sb£åHï=½{ë—NÅ2'Ž Mj~'@d=I²RÂÖÜ1¨gE¼>Œ¯×ããqÅÐG°°Ñªü*ÂB®#Ýñ&6ìý·„âc¸oÈ€¹§G÷˜A>÷¸V•7­Õ˜Úq'ÛÜ_ë-ûµË£T–nÚU¨¸.|ÇBRœÌ½Î(\×KúC%1ÃÃo"¥Ú3ÙŽßW›¸±Rí“¢S(¸ŸÖzQü4>¬‰þL^¶š‡:’w¢k0”ï1CÚj6zØ:ɕޠ“Å%s Õú@6òKŸbNÎdè‚ù.ÙŠA_ï|IN–ç³™/Q'øÛŒÿ*å9!æ?}µ[K2,okσ¥Z0Wÿ™Á–”Â=‹·ü9€‡dÉÞPÞˆ$àÓŽç?Dïª]Ö‡Y'c‚O~Ä0™å±R;ž^MÕRÀ‘þdš‰ü©‰3§æ£Ñ??ðûg'ðуÚsxû®ç”ÅtU ãê3ƒAòkŸb°ŽvÓÛüÿ æ+ÉúéÊÍLz€¨ló»ô䄵b¡Âº[%ñíµ=\¯9æ^žYh•Ø0$çôù?ÁpÒŽ:3œvŽÎs¯µ·gÙ OÚ«Ä„,üâÉ&ÐÎOIr¿ë$y…¦‘©ÿ’x$‰$Ëðú„“œúÛq)(ðøÁôûc +8ö?M'+Á0sH»rTl¨¥û×*ÜTØ¿wP*v“¾5…ì=ç……I @GGDCˆûˆ}Zóç-ÏËch)€Ò"é¾à5™HæÈsÏ <Þ±4‡´ÌÞ¿>¯ÀB,zdÍjš*ȉ—ÓJ¶UIɱdzM.¼´UEE\¬ x®Ú6Ne<ÔcC‡ ªš°K8dö¬§jœTYÕì¿lCS@]^ï;ZèS¿í¸¨HËË*év#÷)C v¡Lĉê‡çUõ#î|`o·y1²fíQW³¾›Š„LBîϸUëßÇAökis®€,›óÄîþKÇFO ÎŒ.ƒÈjÍÓÉ«tNTîlòª!þÂäUUⱎÓWœºÖpTµµ¯gáЪñÀ—¶î’ ÷ÅeS´¢uÊa PP[ká$'•W>š¬Œ ‰UISûžÒÉ]Lîö~›ŒŸõ€“õåŠ&8Ûí{I/ܶDb‰0xVö}ÂÄy‡#¯#¼q~9Ñ!o=h?ìnTÖœL–DgÔNØqîaa!x^]'JXóbýß™Œ¤}îqL”¶~\i‚¡j ‰ÙOÒ0AÓàuºµå]×Ó`+®<¶e•’bÇjÉ&îø>Ì:ö7Ä;TNc™Áì[&ŒC]Ú?ÄXû*¡C‡B(I¢@h~‘ /üâþ0Pÿy³’¦•Q°CÉŽaèyQÒëÐ<‚Åœ–aøÊí/¤¾ãÉ1$;³åâ>´Ñ6n¡ã\^YáÿÉÌZ³]H#hÔÍ!ÌÜ´"’=ã\î›F˜F¿¼\Æ„¯Õ¨ x^­ƒ•nºÎ¡Æ–=óe¬hjóg‚¢Ë|ë>ïÖ÷¨ù4 >– ]#d¥Ÿú/ÔÄmpƒÞVí%‰Üiþý Âó?w endstream endobj 77 0 obj << /Length 985 /Filter /FlateDecode >> stream xÚ•VÉŽÛF½ë+úb„FíÞ¸åè À$ÑÍðCR#Á’8©L&_ŸÚš¤f;T³kyõêu‰F=+£~X}Ø®Þ?:«¬Õeš:µÝ)2m\®r´+ µmÔ§ä§¶Ö69Àu†ëyýyûñýcêçŽÁéÌ–<Á¬»À­­àVõg§Ûl¬´.:ýˆNëËán“¸ztZ}¿]}]Y°2ʪ4¨Ü–:¤™ªO«OŸjàýGetYfꕬN*5žGõëêç•™W›æÊæÚYùáž^›Ò1‚l½±Ö…ä;p®×˦Å|£6Þëß°Ô6Kôø²øXéŽéYé¬ÎÍÈÈQÉ’Žér|‚”·¸ °„‡Kj„y’«;Ï^bÏÚå*Í`»pI…[ ›RÔ–3 ×ŽŠ‘8upLI)8ê¸}ÔbR3 °¹Ò†›É‡r`A–!1º¿ÿ…—}}‘ /d0µIK—©ÚØ CH™¨X ¾˜p¾ñºž‚/)Îå(šÔBoà³’„mÏŽ»Ž<ÉúEö/x;ÊÕÅûèzˆÕëõ&Ïm²ÝO8®‘Á8¥NÂØÉ^½†£°gƒ·ýňæ2†ºGÓNû©àîë#¢Š‰ÊlQs o]ò?Ùoã àíñY&¯±ý6~[»3àÛ¾m¥×âT 8€v uŒS¢á=)â`Æ'Æ2Bl`uä/dÑ"ܪ“0‚jÁÔH²$›Q U˪™ªìøgóH¯™l{^·¿Ç€A˜z»eê0åœ+¹aþ@y¤h—9í²ì¶£ú¦&pÿ4}kÅ¥ûo®¬ÐÖøÿúɵða _]©‡GÉXòû”sásÓ endstream endobj 93 0 obj << /Length1 1846 /Length2 11740 /Length3 0 /Length 12900 /Filter /FlateDecode >> stream xÚ¸TœÙ- !8‚¬±àîww÷·àîÜ ÜÝÝÝÝ‚kpç‘™¹3sïÿ¯õÞ굺¿]µ«Î®sªN¯nRB9E~Ck}ˆµ•= -='@PZ@@OÏDKOÏGJªdjoúŽTdkgjmÅù/† -hÿfÚ¿¥­­&+''==€‘žžã?Dk[N€ÐÑÔ M °¶ÙÁ‘ Z۸ؚ›Ø¿­óŸG¹€ƒƒúp¿%ÈÖÔhÚ›€,ßV4Z­ LAö.ÿ•‚œËÄÞÞ†“ŽÎÉɉhiGkmkÌCA p2µ7(€ì@¶Ž CÀï’2@KÐ_¥Ñ‘”LLíþt(ZÙ;mA€7ƒ…©ÈÊî-ÄÁÊd x[ (.µYýI–ú“@ øks ´ §û+úw"S«?‚Ö–6@+S+c€‘© +"EkïlO Zþ&-ì¬ßâŽ@S  þáé@€¿<øVá_õÙØšÚØÛÑÚ™Zü®‘îwš·m¶2´¶´YÙÛÁýÖ'dj 2xÛwº¿×ÜÊÚÉÊí?ÈÈÔÊÐèw†6tÊV¦_@âBqÞLpÿØŒAözzz6Vè+äl`B÷{%ÐN†ßæ·<Ül¬mFoe€ù3¤™úi³XDPö4x0”(çÎH3×7Ó(éã¯ÑTpÇáL¹nŒ÷³®«%JŒvØ$ØXx×(#“ŸÝö$0çÝ ò"%CóŒðtæêyZ‘€_™+ Û¹Š†Ç7‡±t}‹¶'# ä·H,ÓÛ°P2ªÙò§žt³áìäÛöž1\CKgRmýxûÌíO䃒ºc,ðý¯Éy¡ÚöƒöæY°³úz9<§Èjb^½ø¢õ3í5;݀Έ'mïØî~yŒ‹ZÁüмRR?áQšÃ$1˜¦Ò;ù¡:ÐW†òjÎŽS>ùNÒl¤r]¢ÝõÃk·Ì#?Þ%®Ý{>„wó_•vü ‹4A“ÉfœVï‡%Cˆ³*4ãŽzÓôQxGJ¤Á}­ý«ØÊ €Àœ„à,²O AÆWù·rxœc5“…Sßà Ë cÜ'Å‹&NÄtœ¾I#•ƒˆ…„ÎÒŠ™Tc³;‚iG&.Û%h1XçËf𸕰¥‰jyüm±È±Kd|ûÑ$ lù§ðÔk˜uÞ˜d ÖR%*¿8•¿þÚ†{ª!‰úÙ=±¥µ©*C+˜K®2¥u&³»‹›ÑÚÕÀôñä ?ï…….²]g¡t \ým|[„‹‘¢KÁæ\»þÆÇÑn=<›ˆ¾¹­¨ Û9 )n“ˆYß©¡±Å̦N¹™P|¬úÈŽŽ¸Ÿ4c®Ê”£ËøL%{ž7Ûb¹N˜~®;òœïf¡› >ÌmŽÒÆM¢87ad;b}/úDžì‹Â¥ÁºÌ"¹<“\ >MåúôQ´5“œ¢ÃËV½$6„s¾mÐ C±0“M\eÖçrn'Cõµ1EA±6•£RC#ÏÒÝÓÚ?¨2ÊgÐW\Ë2P,ÐÓ*)îpçá&›^ ûÙH !ÅöýÂÜûøÙžWO˜¥ÕÑ“ˆ”Ò$pˆ/àþäCdK»>ß;*ýŸèr T¿û %ö?c°ƒû =(ô‘k!ø7¡f½ïYœjO~"±9G%œ Â[¹ä¨Sp³i\x¦®DiS¹‚°T 5“†`?AYÖÒ´Q®owl0ç¡‘,Æ©iД't–%ͨ.I&ØÔ–#œf5Ôez*9ÜæžÏÉU3 G—ÞûàzS>³1çøíA†¬Lµ÷BSs7Q ©ˆ`ì2õ2U c÷³›÷›ëBž@Y4£ïñ[ÁD­“wj}ëod:úgÚ9Ï3ó5“HFÓ 9'L Ðç’Ìž¥ê°Ø–IE^rfcý¬ÎxØ(mõÜx1È@™—Öƒ-ØF%4âÐå‘;,÷€É)¼)å\ØMD¯K„ÞaŽmÁcw½S†¡’À:¼Ç6ÿˆÀÅC-æY¦S<®ÌSîùeÍ“â=f‚h: š®?ÕøLÔwÆ»–(ËdÓÝû±40ÛÊøÂã«^âÓ‘5ÞñˆQö-9 pËý((bKÜÕdrÁ7Ö&œè¸{Ž—¨@™»DhrŽOù{˜†,s\YkjÌEÍ,¨„‘çj‚)PÂø7ÅmõÈZ 3Új™hXM ø5e)O‹” 3#8œê¸µ4<¸6ƒl7ñ RM5ý ?ú=«û-®ï¯KBPš “rF9é|Æ„Ôhýp­+á™äǨΡ9˜TÜmñòásG¢eµu[ˆl`„_Þ»|¥ÚÜ-þ…8%QïÅ »ëxkFg;1»”ê§'‡LÁ\a4G%k£¦ûÍÞÀ’ © AÂe’!¸æ”ØJðë‘YŽ5ÎÉD¿ÝKìžÉTØÃß·V)×òØõ6^ýŽoú BS8ƒÙ 5 ÇÆUNõþà l³OPWr·6ÖÒ$¶dtAÖÒཧù5¥>†6mNÞW9ëï¸õsÑÎ25ÿ$¬ s5ÉZ.ÏTcN"7qˆO’Rõ]‹pp¹ý{ÝA‰Ö¸~x}¼±ì ìK ÃÈóB“ „¨ªÐÈg™“¬5›íH;ˆ¼´FQ)ïEÙ\žËôiëð«é£Œ‚´O`K_Î%¨´,áÆp%°Y±†ºÌ±Q#æ,}8ˆ dzBSš­ç€}V'7~$.îµ2G”$90ÁÜu˜)Êw;þùïÇF’}TW¥ „>¥áù±ŠV÷9½ÛžÄá^º)µñ~Ôj™ž¢ßKŒš¦}N´”HVŒZ3¾¹P*Þƒð¸ 6UauÖƒcn’XV:r!»jÈãèHÍîÑË*|M¾!\¦dt’mãÖ÷õ<}Ò]•€}ôçåbù• ¯„àj—_sâá ¥— ½à]ÔFÄöC²mâôv¯ùâgà*»‹, Ôíâ"*Ç=ÃQ¿‹V(—)sq7^’$-{6©@ÍäU•œ0µj×&„Ù¼1­ð›Û¥„Xç=bP ]ÌxíGêóä"¤%]óQ‘Clá\>¼e6ÍÖèJ4_çI‹…%(žx{Ñ]3@}¼†}Cò§b®n§UcR©ô'µGjöÏÙl@Ä>C’—ï«ñð:ötª(> .ãí^í=Xù0—%Ì ò’n‹Ü£`ªB-†<ÍÖŸ@‡K)6‹ñŸLýi@H|r1R¯ú‘î%7Ä6D¬í’á¤äÔ†×]õ?ä¥kLÝz¼úœPbJ-HÉ2"­–ùlmÈefj”£Êij@¯­¶^éa Å.aJæ=ì†ñªËÜ…J£S6!VÈ@šžJ]‰ÌÐåóÂgƒÄY#èG 3V·íµ¯]}µZ1S KÖâè’l™sŽö0¤ 0fÁRæEœ¼Î@,dÚ^5r£ø6àóóuy¶å¼rQ@ÿA¤1©X2œ^¸qôE¡+·L¢¹JàØ{dÉK™‘VÄ:<ï"§2æÈ2hRÞ{¯y¯ˆÜµ9IË*E• ÙI$Ëã4Õú´*­µÕ_1–©`G—ÉØ­OE<^ yµ‘ ø-™„],“àÎSo^ò¤ï}º´>ü¾D$–6^þa)ÐPÌ8&€\ <ÊF&QI^w֌ן2yQjÀ '¾µyKk‘!?n6(Bj–^ WÇpÚìªÏ8XV©ê½&Me)Yïߨ×oDM´BÇä™ÝSþåñ&-‹Ž­³”{ØšfOÔ;dwÃ*WB —Ïjü••Ÿþ Ë¤MA„£"]iÁ-Š<ësâ9Éz@”çð•Iû§Û×ëHéW³Ž=è‚t„œcÒ(‘w˜*þ¯KßD·ë}¡xõ97Ž8%@6 }#uƒHw^[)Å`aë\ê·šVßëJ¯ú×ÞEÁ Mo´mÎù±’<´`&¸QfÍñàñ~­ùg&D†¥סúÖ È9á×rä_“È4û/qe=¼~#2SƒlV³ƒx–¶ý´p(\^sœ6+õD´OxLi„E•B®Ì=>ôš— ¥cÛ™-á+1Nû+ð2„ûÛ6ñêã3ü`ü C.f¨~V]ìºh{õ 1@LîV«PKOs{–䯖»b”7Š.ïÓ-GWŠ5ª€2òáÑ"[mKá6–@ݶ‚8 ©:—H„¥BˆºýÏù#ƒñnþQÉ×Dóxî„Àã0€CK`xy˪˜æYYòO D“¢#ßT¶éAÓÞ” ñ³aòë»®á³@dR»¡uÏ-Ð}ðÝ´6yÄÙçα)‡wueÅ- cg[4}î _g_¦EÉC¯p—}±M´iJ%ĨùJâcKæJÛQîˆ88—^ãi ïéµöšË# (QE| ;Âí¦5Â|¹Ù™S òsùw0‘ÃDÿ5›îF|´ÆØqʵLÁqÞdYÝApÈæï(Oœ[ñ•úÂ{*Úþ uS*ç÷úÔ~DA’Ö"ô†úQÀOMÇØ¯#ékFÌ¢:#%æ1än{Ý£œ+p0Iµ/kÚ4 ‰úýOñÃ>³¼è•éO#&ƒ·vT(cY^!}ºµv)?ú«0Óöñ¨ö.¦à•¿&Gl_Ik.D˜°ŸŒ‘sÓs×B?gI{,, 0¥ ™˜è…k¥Û:ò¬ÿúrkÚÈ£ÛvŒg-¢;™d`~-“2zG`¥—õà8>KÁ˜¿œó“„ø‘õ~Òíå æ©xo«¬NKÄñZùX¾ÝÛâŽÔRÓ¸fù$ÀŒÕ3E~}…(&FÇÜB°êÌ©i…÷…€b‚ÔªNuV$U– M9É¥ÔkmfÏóRãkëIá©xV€I^ºà°R ¥³_Q¯öcÑ^Åi:8¸=³FJ‚ ØŒ¶åN°Ê4Xô’ìc¬wÙu …1˜éK#Ü)Í; å¾ M£ %5¶:ÚOA¡>0Êï¿Qßš°±Kg1âºîspnToš½XÚ:^§È]E#òÄdÀè{;_4oëÞ6ç Ku7ô¤Ã»ÅúU\³ M*ðѹÉ þò(6ꀘ¿ñ\ض­> ,‡`n~NŸŒºn3’˶®?ñn—ž¦Ò!£NüpʪK·¬êf²0:x6H<™|8b¶W­è1õr"ä ?i2[÷3dsÎ ¶`­ÚÓ5EÊí+¿˜Â” E(§«Œ¢„Ôª ®™Îž¸þ¼Zð¨>\|Š)¥ Ï I ~ô9zÈH_( 9 OrOŽ­ÏݹkŽ[:¯D0~m­Aj,{8Ü&2S¾@ä…ªBÒpÈ­9G(†cGÞ=ofÖj6±›hŒ#—ËZ™«çN³¬-Yf¼‚V´Å6ýÕלÆppûL‘ —‹Ýêë_ŸÏÜ.ä5©4B$b²¾6ÀQ5nòÁŸm[ZQ¯›I[ ‰#XÂ(ˆâ•+¿/­QÿPÎÞ•|'€Yýš&‰‰£Ý“#@XÔ‹)Ãë-r¶G6ÏO{cÞáÆ%7Ðõw‡„N?k›Ô@¡Æ'pÓª…ð=¥ßöEr&{ÁŠ ’ƒ®RÚa2CÁçSJHPŽŒÎç|q÷ë{ª×9úÂZ"xNxð(CLEèóÒâ.Ö2e§kY"áùG/¼‰ó‹#n‘Èn®>‚ ¤IM¢gz4°ÛÏðI×nn¾áÝWk´þX'ìÃýdu°5£º@RcCDÏf¯(ýí}IÕ™â#å ×À ð›¬Ð!™´À¬jqÀèÔvïOÝÓ^é¦Ávv`$…@Ƥ'„kÀ-Ín|› dª*:ªªA³mäêA@0f¯W— µ¡7¸É—¼{\%…R4iöN¡c¯† ©t;u·`¡bfø;?!7`¡–Ù—z«\dát²ÑQIa„2›Øà4þÍŠ®àGLp†9Þ†ž€ÂÂV˜‰Úó¸4K< Ù¼ÙHš’ö¼ÍéÅë̃ÉJó ô¹FâQnÜû¿¨oE×µ[tˆ¸-Q£ß¤îærÀ¡’QóøYáfk|ýÊW¡‡Œ…&ZqJý{ÉýB{¿J“±¿Ðí®adg×íJ·7ä­¹-ÒZ4;ȯW„K¶¢R?×HÒÒÛP”äF!_ÏÎ\²ÅCEXLZ%K;í¨q¥Â»"¶ße6œ¯9a17qºO´°®eÎ3‹ÝWäΘÛþò÷ŽÈµ¼šù•ìX\®OÝ¥™ì\äë³9€¬ÀWŸ¸a‹+…YßAö@À4Ì©—ËÏ&é©F¥»›Ø¦@,ú“_¥Ç^;Dhͱ­†vŽÞ{‹ÒÇarÙõJÿ'f…obÎQ±­úäI^•LHEzéå$¶1 ØÅÀ'eµÏ&£ßX£dÛö.6fTÇ+u–°î¢À?¸Š–Ÿ<Le2¦ÆŠŸŸ]ŒZu_‰yiõn> °ŠîÁ= Äã? ªFÕ¢]G4ª {€U¦žì‚TQšu÷Ó-,ä QYŸ¡L­ ü}bD¸ª£>1Í$=i@¼¬nÉb¬ÉCé8a­ú…v»¥|. ãÌ2ç àüJÆ(¨¬wχK÷œ]J)7’,45wÎDžºvZÖ6à§™«=-OÌá²ir½ê·m›Å*ì–‚—ž4òwJŠhßk¿tjcfqÖ¡›PŒºÝSŒ4ÅЋ,V™–}†4׃\û>ôÒ òÏüÔŸó›¨÷ 'ƒ´.ÛY­ÃOÊg:ö? w±ƒ‰Îj"O¡~0ªÉwáófSÓÌ@@‡>0DQ%cm–à/x½– ;ƒ'ñÄ‹ ¬ÙòÈ@Üö~/-Y Ï_¯Áy“ÝÖ‰mí6R&"Ÿ¹Æª†[/g¢1¶»äØøœ¬œmZ Î^ŒµtVê­sw.>ÒÝRëç€@™¬ÀÕ¹~±UGtÆI qä9–tó¹ô1p±V 6L G†£ZÔ“› 8Ö Ì¤{˜l&¯{…k‡š~µå(2iØÅÛʶj"…—ôäZvNÜrpÊÿ’7ÀdŸ°º'IÐÔÍóí*mzq VÖì”6Ü;9’oKzY3ª †ž¨uâ\]%þðY{_ºéK e=kôõÓ–Žtהˣ¨aãµn>+a)¾äšÍF4uò¶•M'¾”§ª•»Ý¡ª(kY• ´~1´3¤¤hnqÏ)ÛäÓ,-2r43‘â‚Äå" Tü&úZOðH!ÉYðák_¼‰/ÙNúΗ-G~„Oþ]ªt–ñ]Cí}8ß…7ZìÏ0Ð1µá˜B¥gÏ©tXoÆoM~ô¶Ö(z‰oH)5äaú>0ºøÒ˜À BÍ”ÓV¿ÿ¦­_æhû¥u“%øªr«É52 §é@ögjm£%rËÄSúWr*YÉÒ:šè Þ£1ÝÀŸmï  ²{<èv—Õ0¡î-¶éˆ‚Ë< áÉãêíwã/zuTø-‹ö;¤egÚå44Ú2hsguQˆª6ÆÝÚ–RŸŸKjNÀr'½õ²uÒÙ”ÉÒƒm‚D}Ið+ß ç+š?´F) ¿ŒOþ”+¶­zÔéÕ⫆Ê;í•Ð\ßLµ ÒéP£@-ºd¬˜RÊj…¯\£& q,“/m‘–¡‰ËÚpŠ–ÖNÝ…töc#%*Zµ\î:ŸtŠÁÔ6/õtl-pªC—/!E½ÂÂN taˆxöª¢5UÞ®‚ƒ˜ëÙ7d‘ùDàž‘@9©â‚ !œ!8izŒí@—çxƸ0›{ºN+ŸmS2¸Sã­=Žq…1š›Å!9>rª |P“»ï'õ€¨—}À/ñõ\^ÍsF±äÌ>²¢Õ‡ ñP ¹ÊVu Éß´[G°/PÅ0ZÛô§jjTÒ·ÄøÁ½v| Ê«ÐLP¦f¡¯Å{¾oB–Œ$¹atë5ò ³ C[âŽÜÙ‚yÏr~ÎZ!†@@׿¤+7 æ–ú–R{h¯a‘Ý)ê™´*| ȹ¡×©("T–N]E.·Š^in6²ŽøMéçi^ª+䤹sížÓU?ô~!v#†ÁE4‰#s˜…ˆA=^»Ð1¼u´HÑ’;˺üÿ•€ƒJ¶·{`l=N~L!¡\ÒA…9:•6Ñhv â¹ð|ʺ¶wX¦puQÎm>º½2&qCá䡹Šö-Iσ:;tÙhø·º!Î#î­ÉþY^µÂé8²ôl0K3¼uÑa8õüRž¯Í‡FäeNÐG s |gí5û‘‡&Ü¿Y'\)¬ãE §T ÑV„W”×hü§{UÑž0€€Ó¦‰’*B«Ÿ€Wpºóvøl±¤”çZ2úSî¿ÿ`¶Œ½lEÏd]ÊzJhªÙY—S1‰|Z·¬ >—æK!+{•ÝüšÙÈPè¢wSp}0ïÌG!’®Ûªèy}¡Qì!ì‚|¿–MÉ1Þïû h’(é=Kÿf…> .·‡ÄxûR«©•s"CïlzTÝéÄ·!»Ô¬M[ÁÖN¤yΆÑ*dyj:âšY³›Ðê‡ Ø¥Ñ‹ñ°,UÉòö4ƒ}M¦µ’ƒ‰V‚±¹ß€sÇ5¨lç4&€7 ¤Â ñ™ƒ_DÉ;wN£$§¬MÞr€ûxðž0éó²·‹ˆŒµDÕ7b*3çû 'Uwrúà{ q†ù—[SÛA ‚Wâw8T¬¥Å½Y+‰¹O$´ y]áãZübSX¿°K#K»²××z½`GrÞ›ÛÍ®Á•/vÒÂ2 Єõ™0¶O÷hdÔ$ õÁZ>—£x|ûz ¨[SøD’#kºãÁkÝfá¹öu¸Ó•ÖvˆÔõä› ï0ì,`u«äq—©Ši£8ò6ð5ÄÕV ûƒ´ŽÕíî5h€tJ……_~-vÌKt÷ÖØ106wSpßî¥Ø’óc1/M`!®aIÜ‹ø>¸íª~a*u9j=¶ ƒ(úÙƒ7á÷kuÛ·ñ3ágÿR£Gõ3Éo%_ è -‰(Q>ªç$óEùLŸ7Ñ@}ªÒë§ŒuE¥0 –iÐâ ¿B±¸Ë¨EŸ~"(å±›ñ.òŽ©lê€ô–µ%GŒyïýŽ5,‚,¤ršgêü€@Ìζ\ 5Æ }ø‡ñ':t߆ŸY ¡žòè±­‡òS`ÎÇÓ(‚=“xþ'Æ,ΕÅ"^dúmWDäå*ÅaŽÒüØ(ö]- x—c²ÁpîÄ_fÎò£<ôôKCŒøÎu f¢I[mª­/Fk ;o¬o’n:r-f{Ú ~#{®ôpk]ŸÄ>…ÂØ¿6öë1Rpbû‚Š"[Ð gø¸¸\œˆIdh ù}6RÂOœ?Z50ì£*dçå TÇdÍhúU"ëlU&D)håCwE?[j‰b ’.xóÂXõP(JƘúØ<¥4G–aƒ w ëiDÁ^zpÅãŽ÷‡§Óþ‹þ7ÑÍ‘²ÀÝ”nó[üôÝ`/ì4éýã˜lŸ)Ðäõž&ÙÎñ=N^ »n§K(S¸d ùFN Fv¸¤j¶²Qj7kÙP>‡‰ªe‘%!V7Ž•¥L÷Á¡{ýR§­‚ ¿4¾¦LBðnJ•¸²n·­Ãž –¼—€ª€·®<‹´Åð{¬ò­•*Ò¯”hMσ»"¯| !Ö)¼ƒö)ª¸à`×I }nß×§ïð’‹µ‰ ïýZêP 9”ŠâÃïËâɱ¿.ÓFë–!Pg×tNú •cÞ£yêT4ƒ¶¤š¶ά1ï¿ãg]Vså#îbd?@Ÿb-tÕu•™<×"K>ãƒGUdö¶;ÅÆÍf–^HòSïsÓt–7ì1H4©{£^U&¡¿†=¹ãEÊ0¡àÒ€Þ}„jzšÂg0ò[8czÞh ïÌßÀ"ƒbœ#ãç±–9À³"9Žî¯—~ÜÝêf;³¼†Rì3†šY‹À½Æˆ»RµºØa¹r”.ë|[€39ò wü2Êœn‹ Hô#oqßHŠc(lcûðYHcüû 2ú†¦í‚ŒØª_ô«E3îìÖGÂ[¸€Þ|u[ûøª~3¶CeŽb ¥üÌÝrg_ vÀ‹ÂHÉȼ~É£÷0/gq¹ñ GºöjÉsxénK™±•›o´¦Ð/ýV ñBk£$VCLÙÉ=§`œxž¡´ÓALY.!vT“έæ?¹Vr0&ü)[ÃPÃDBav¥È-p„ÈŒD»ì'¨²Å«ÐUÅ– ¿–϶Jj̬Üt6ŸÈêë\ž$©ÄV†Üí?Çx?܉ s¥=ðÉã¯z9ã.ìn~‚ØþìvLr\ XÓ‘ˆíˆ/Æë¡-±®-F2©½wšV“)YuC"Ôóó«,¾e?u‹?Á†Þ)ŒI©kqɼ+‚b»OÜxçW"^[L '¿‘ÐSÇ:´‰A»ü-¬dÍl¶FîèçPz#]ïí¿"5‡|ƒ"ˆ\ÎÍxß°”™‘«‹] òÿ|N9ˆÕ4Í­´Oï±ÐƒU’Š+12ðU¡(P}7üÀÖ£©ýv)Ðc¿˜ä^¾¤ñlㄘšé%6¯¬9T«¼¤ŽXï6~‚)™Å_«Ð¥Ž9¿vôEå§&CY¨#lr¸¡Šu|^°{l²Àíw\º¨–áv¨°ˆS¼lI§“šæµwœ"X§µ¼Ñ] œÓí‰K²U‚’Õ‡…µ ˆçË=ØšÑfä@à°æ᡹(e(n4ø¼V§ÛùÛTÂAqŸ+êØM˜ÜMrÅcãô*!ÖÙ%^óË“{ãϦRÖø—úFÿ¸ÆAæêÅs Åkæ:1§k|Sœ˜Õj ͨ3ÁuÁÔˆ/+Œæû® d‘êx^ª[‚,{ÆlU®Ê+åÓò×’yçãVPO­Ìë*ËSéS¶cfÄMk"L¾ð€nóJ† ¶‚w\H<È铦˜Ç¾G܇žæÙjq,央¾ÜLÁÊâÀȹõÌ4^# pŠæøƒ2‡GÒè,")ŸûûF³L)âà »é]Ïúúã˜d§F·ïyü·ÙlÏN·¶‘0ëèË—IÑÑèÞß!]Zý—ø›9+õ¥Yû:.•°ÏÈ¥gܬÉÕiT³…Tb[5±Ìö¿àt$“¤îOSÍ1d¶Ãš¼4Ý òb¢>K› m׊¤ßÅþróÑïq»¤þÅ›¯Bõ\1‡|'+g6À —û– …Ý€Y2h—ØD7å·¦-Zmäwólç5æôðáùÅoå Y0dů2Êmó8rDL/ï;sʼð$4ÉÞû÷½—Kî“^yœ5™t uïÈ^ø×wÒ÷–¢ K·Ì3Ö‰’bO’gàó©­6½Úsɯ”3T‹C;W©e”Îå¯k¤Xo<ÝX5#±µ‹‹RÞ¡Ü>(Ý¡Ÿ›ípzhé%Ö\ÀÕGÔDV}è†ód‹cr­—"Ïki‰ñ¶Ó@θ@ÐC#Ö<åäœFtKfº ~ÂÛ¢@HËWÕÖ|ºz}ÌT7k,ï¯Þ6æêñ!Ô:Ó·áËÿBÍH {¢RQ Ýœ¦Æ ¹)jl±q«'—ÓŒ=pPWéBÍÔ³jóÍ10€«ØÔñ¦.›É^3 p´Kè~OÉ ¥Â}‡Ëè”ø0k½XDþv§×©ñ2«®»Œ:ŠÕKç^c8è’¨«×c§.Ãa—H‘"á–·Š¨aêƒlaAÁ –{ÑK©õòHpõK;yaüôSê8]dìÆ.A¾^¨Db‚ÞöJL­–<¼ò>_X4¥m2ǧgôô‡ zü48B!úŠI(ÍÞ*€BOÃÛ7Íòt…Ö¯2óú¢SttËïä 3ÿ‘Î1jæêªn™¯Ê'o6Ç÷‰·~½Õð,ü«Áˈ}ä>Yúê(r´×ñÍßÙò<Û[&ã´ ë¢øë$‰c]â –&…ò@’$ —Û8Ä~—¹ó$5A„;'tG dIï3~$cUÕ&›î¤ "ø¬=s þ ð@ýTKa]̰«Ñ ½‹CŸ~q+UŠgpg9ÿC;ÐðÜ›1Ü.ª$&àå‰òØ‘›£®)ùÚ‚To£½ãÛA©Æ‚eÏ€¯:kz£gÜùHÙ—ÉsÃvIB©e)ƒÃ.PŒ2wg9Ù‚/¼ì¦ш(²Î·¸ÖXÅÝ»iPi'^É’f”*†!uv¯çFî‘:ËÒîÛ,Î e_ŽGäX‘üvδ̭Ѧ1íA#[¹ÐÏŸ™²&ðÏðäpO·Ö\¾èX‚ЉßÙá}íÝéÀ¨H…ÜE"s‰k½ êv‡[×ÊeNw]ÔabwÖЇ]oÓS1Ç ªÁíEÒ’ôÊûR2r,­XuZ§•ãz_æE@©!Ýë]d‰o'v|íÉSº’W©‰Q­Aà¥Aâ#òâóǽ,ÄîØš,U$=¿ÑvÕ‡GLðÃ9£ñ<©NLßx#„*¯XsFÆ› ëÏHQèÛ%çÉè}(¸ÛzžÝƒÔbÇŒšÓнùCŒ;VE@£ró0E?æÀÙf»¼›‚ky_IõsGÖþž•]+'©oæA+¹9²g`ÕéhÉõÐfßM0`Šg=5¬’ø6ƒp£°À"}ô¶×8¸tš/ï}úôÂuÌd—/zoÖz0íÎ,@ :€ã,œÓEÄ¥¼Ž+òæyÿu£{”èÐÌÔJ7™c7F´¸+(‡Þé ?3Ò1ºãt<™‰ù}ì úˆòÏl<±h¤i`¨2!#š´b„A…n¯‰9­óUdøwb ª ‰O7jèÆ~ëØqÖýl›(È·ïÞ~c Š—¸â*ÚÁwvy>ÀD;È7ŸHîÄqàûÂÕ°p¥Ùj H'OÒ7;¨ä;3¤ Q?SwR”¿Ôã${Q†…å!I@¢~¬‹Ú”³“˜>¸î1g)µL]4ãšt_”ò*(ŸÆ;AŒ®Œ…”»÷*d¿,$Wï8K‘ò|/¿±‘'1"/5¦4¾âtºÁ•­˜ÂäÌŽGBÅ´ ¼akžO×8O{_zà«·:ÃQuþŽú£y¯‰ðá§Ãxñø×šï7f ‚¬òUi&ýÇ«G÷Rw9¥¦·˜´³qp?Ñ¢çyRM†FïIæ³F­‚f¥À6Å÷8Çû–-aæh•è±c:Ÿ²8ÊÇ%4óIæsñˆý¯~ìŽ "S¼/ëÄYÕ¢™Wæâ»”[¼s  ´áÀ‹’:_æ¬[o£ÂW~-³Þ¬,BQ¨TÌ-#r2¥EWƒ!‘|åÀ«œÚsMU6z|X«\«5íÏGáø¦vof¹å›,ƒüÕØdöIùŽÈ¥£¬Ö¬yP²;Ï`ÁátÒY3 â‚óÐGÑà×ÕI—B{úଦÉWc†ýÌ9ù3©._×)¿Š‘dét“§KâSTw Þ¾›aµÜ†Õ+óSÕ‚JÂü̼ú=FŸ¥[ë}k»BA½4²GL59¶®f­îÃX·›¾hGfëóºfÀÇ÷qÍ·`|NêW­:¨0DÈ6"K’Œ°_¿0s¯¸÷l3¹±Ž¯Ð±¢ŸŒ°ÛÇàŒ*ƒK‚%U“\VS*‰ðŒ"@h?ºÃ/u~…@y)`ªÇÞ©ÜV Q+(¶‡@ÀW¤Ëaº¯ù¶Í†±h¼…‰üZ=ƒkÒGìBÆ¿zPšïð¡$gmUc2<áƒVèLü<žéAÌúÚ'éë¢ñŒ+½J§Ãï"cŸØäÞ'î:ݪ’¡ÁvµD͈qOÉ7Åõl<¢¬ùÁÇé]¾o!{æñ¬dئ&ÑG @Ǧ¿ Ki^ÃŒw¹UÝ¡8i¥\íu€VIéçŒ"Ž&ÆÝEpr9ulÜÍn¥g/ö{ì-´X¢£ ò"üƇ'ïçpØE)ÈR2¤@†¬B›êº§Û‘ÞÂA(A%ŒiÍhËt¶Nø'>¾¤]$)ÉJ¬êb?$Òî÷“ªáÒûRÏ’Ò@xv {Êäœ&a÷¤[¢Ì‰¤è°ðàŽpõŒI[€àqáÃ~åµÍlT |—tV&™;í¶ñü@ëò7¢ËÔe&þ~þk†Ž2L±—¯X’µó6 TGTá†õ¹r íêë»ð`ƒŸ?]Ù'»lì¡tþ€'‰0_„´#-_3!îë|o'À¢.\\û=³g,÷¨ß"ÎÃV€ãAÂôµ`'tÊ†Êø¼º]ÙÙ‡Œ‚ñ¡^eqHŠå®{:Uã¡s³­d-ëªxh–Ú×õÖòˆ(8] R•ñ¢kVÏ@©;g %ÖŒÙI0w‹2]ÑâJIWÑÎË/èÞò)»õ£ã!ÙžIÎ`>RÞÎãëõ›P8F Wß#™T”Ä¥Ä+y–"ÿ:spÿÐs-™IÒþ~02a}˜½ зpÝRÉ``ºh'Œg®*2½–ìÜ·Ñ¢g® Øêëº,8ùrqÒf |&<ð€ÔF'( œ¹LhNuPMŸ»°Ï×ÂÑ\ö3\w]Zºß¸ø½˜ŸŠøÅÊÛ|}“ú²Oè«zBý¾ë¤XÕÒøwºKæ×a” HDÊy£Ãz56S•í´VF&B“Ó¬Ôjç87Ùö²5Ë–Ø™¹‘¼ÓÛtÛ+JùÄE{ýmBºuã÷‰8i§/ìýªçAì"ŒšÝAKbÏçý%û„ÈÉó;¿b}]‘¹+¹)TyRw:G“•žô³žÍÀ:‰Ž¢_QN‚‚uíÚ‹-qÖyEÕp¾Î¥ÚàI—dHyZU˜š+Ïjkïʇ5¾{W|ÅTäJôÐ݉;xþš%š0 Nƒ‚¶Ê¯B&kD‚Nß%¾B~Ž—×9G4`Xº–Gý¸Õ&ãÔ mº5ƒîxëü²€Sô•oóõ{¾ÚËã´rª?]¬¿Ÿ,&±ª»f=¶]>Ç.÷ȃ> ãÃêŠ$vÆeÌq*¿¥]–cLJžoC‡6Õ‰gwÏ^„@Iü©¨ÃSËÙ¼ÏHò?3[cí„À…MW„ò>G±êzW`)áP y5mØÖ¬©¤ÎâLKo›Œüí ¶pœlBŠ_>f’![É›VÅ“³s\v¶V¹ÜýSÿÞ.¿ÚFUÓºN»¼U–c—íYërÚ<Ên{x`ŒªÓ3vb»] [Bß]ß®óÌDØ[êé¥ÛGDy c'TÅtÏ ö ÌõE‰i³wR¢w}™>¯õ6žuÐM¥}ô縸DsMÖÙ|S¹_Р‡$y¹)ÅßcVÁ™úíÆ‘|õpш.:Ô¿œ]˜ÿ¦~eùâ4™£k§°–®õ®$V endstream endobj 95 0 obj << /Length1 1407 /Length2 7301 /Length3 0 /Length 8265 /Filter /FlateDecode >> stream xÚtT”k×6(RJ#C)ÍÐ)ÒH·t Ã0ÌÐ „€tww Ò" Ò) Ò! ê9ïùÎûÿkýÿšµžyöÞ×Þ÷Þ÷¾®‡‰N]‹CÒn‘ƒÃÜœ@€´ŠŠ7äåy°™˜´¡H[Èß~l¦—Gù_iGyç“!ï€*p@ÑÉÀÍ àá<@ ðß@¸£@ä µ¨pá0›Inïæµ²FÞó÷+€Ìàdÿ´ƒ8BÁ @„´†ØÝÙ´à`(éö¯ÌbÖH¤½—‹‹ 'ÈÁ w´ga¸@‘ÖMâè ±ü  ²ƒü5'6@ÛŠøЂ["]@ŽÀà †Àw)N0 ˆ#àît€–‚2@ÍûVþ`üu9nNîÿ”û+ûW!(ìw2 †ÛÙƒ`nP˜Àj ¨É)s"]‘ìÌâd‹€ß僜AP[ùàwë €œ¤t7á_ó!ÀŽP{$‚µý5#ׯ2w×, ³†ÛÙA`Hö¯þd Žðݽ»qýµÜW0¸ ÌãoË ³°ü5†…“=— êàQù sçÂþÇgAøB‚¼Bˆâ ¶æúu€¶›=äwû—ûn/{¸=Àòn ˆÔr÷‡í9CHG'ˆ—ÇÿüÛÂææX@ÁH€9Ä Ãþ§úbùǾۿ#Ô`¼£7øë÷Ÿ7ã;†YÀa¶nÿÀ¯˜KWWYS_—í¯‘ÿ”’‚»<8x¹<ü@7G x÷âõï:ê è_}ÿÉU€YÂï2þô{wQ÷ìü ˜ÿR àßÅTáwÔ…˜ÿaº¾{pÿóýwÊÿ濪ü?™þßÉ9ÙÚþŽ3ÿüqÔÖí/Äuw2P߉ößP]Èíª@, NvÿU@‚îä ³º£47'ºB,Ô¡H°õÚüñëüœ-Q‡# ¿>1wY@àÅîT~u÷AÜqóO„¸“ò÷"Ù;Qý»YnñK}<ü£#È ûnùw?ÀƒûN¦×ßìpqÂàÈ»ÀÝÌ^K¸#ö¯E ¸@¿\,!—ùoë_‡€ïºøÍŽ»þ¶ëq…€±§Æá`Ñ›ê€æ³JIJŽÕA±{‰gz<ƒ¹&˜È.ÙÓïÑZi©ÓJÅrSÜr&6U¥ÎÒç'Ž-ˆ°°’å†â¦p:8©Ò­CÛ è(zyc™2ã²õ*®ùÉ4Ü¢NÜy¬D¢äù¤ªHÇþèÂq0Æä¬ÅTd’òÙR{®0º’ï–‹ü2ù¶ö7ÌÆÂdê}Ù]ytÊ~Š2¶YùôC,àâîÑöœË=Çi0­`q…gQUb?d—¶¯›ð_©ô ÌS>ä¡Ý×—ý)Õ2¦%¯mÒ/ú71iprNRòg…›'?úNç½:hÒß',c»‰^#îˆïd¶œ›­öÆTY[°2 HÎ4˜†´Á™XÊ äGHSC+¥c:Ø<¤Àòº(AP¤ØmÍø²]Ž1/ã!ˆ)ƒaÇšh^¿*ÚX쇣ðt"qÌØ)«¯dV¯¸^aª¶ò¿P7\èÚ'"©J1|V5‡]nÙÌë÷Ú©º*¼úÒ K9ú˜êŒŸY´|U¢«ÌÿhQÖž¨µ ;Xsåp+T=Èû&†ˆTfó3´`ÄúJæ'ï¿=Jˆè|_Hë¡ÊÓ 'Rdú¥ÊF¸7Õ• íÅØÖÛ––Úýu·jJNùúZySRbíw_Šu\]<}F »lZ'Å!NÜØ9õ)?"ùÀõ:¹•)GÖ*ݬðžã™4ÍÔömUC ‘,žuY2 ïyzOÁ€8­»Æ]Ö‡z޼ï”Å¥n ™¤.e¬Òfõ^5%oZç±6jCÕ‚m&Çf£2;ÓT!¢ÕKÅ„¤‰Þ(¦ƒÕAÅ1ó,ïRv‰×N_6׿ÈØPÛ_™”ãÛ*aj8ÌG?[@{“PË/95ª+Ôê°îiƒfV_”l}Z´ò»>è6 Ú^«s&ä~±R£}cb˼­ÝÎl¤fš x¥ e |…Ï-pøÍlLeãhF<µ¢mÚí¼I|ø‘¶ã}<^¼ÇféŒF]dy‘ãýÝUǵŠÛ¸^÷üû­ð–0$^vš§-…*ÀYØ¿W*,§{\`éKðˆÓ–úʸ¼I‚½§ý\uuÖ2}¥ð”unÞ*zûç\‚AƒQC@óäf•\7]Wÿr<:)ùÌ”€¢P¡÷pÍ1¹ËXœ9ƒïŒ"ò¹û˜ÛÀÌólHÆáÖÍx0ýÒ™ý£7 g²Knšî´ší‚ ¤1”'…náì±|$«íXþݺ|{CÓ"6EêÑ®i‘œT_£Ï¥–>T¶Ù}Ä’Fhv¡z-l¾9“d…PÎë4'\ŽKä&¡ËÙ~ç:¶‹D¬ŒÃ?ƒYû‘©c0?æ±Øñâùø[e%åêsçã}¯ð/5>er—²äãúúcXG/Ù=¡ƒ7+£~=ÚÓÜ™> °s)ãý/®þ{kÑœ;f´• á:‰—køèÌÑzþ†yžËÊ"ù=Ñ eiõO=ò´Ãb”ΘñÔ\£q‚­ž¥8a±£¯ç\LùlÛih%º—$̵•(+m“×?Å]QºÀk¬PEöËd÷¬ã•‹²p¼Ðö‚}t9¦,åÀ)Ûigzžˆ‡[‚‹7æþøœ'"¯X8T,Ú/Ößa²*9¼7nò„»n5W†ÈTùr”$v˜ÇŠÆŸa´€…ÌÚ¥SŠ9!H[ÏÍ`bj-Þ‡¾W¦b‰¼¾aÏ’„´HÈh9 ö|-~;ÜM÷ØB³×Ø1®!QÕÌQedf‹š#²¸Ìhžÿó=ö>'ªºá' ‘iŠý®e.]Iòž*þÈ€Žô >U9“YýI¢oˆ¨ ¹ÉJĘN.qxþ®Ï·· ¤Ÿyjkµº#Ù7^µDv•µ›¬ü8‘!2¿RÊåϘgILS6ÈÅO¬3ïµf"ü?âÐ7dÁß¶Ð]­ßtNaRèA-eúÕâÔÑû hç>ÿ™_xOCCBó“­“9ªÚ£!Aüg}#÷œ¯àLk`ž FKÛ`Ó{•å*m%R‚˜“ ǼÞ0Îë$©)’ŸàŒÓàô;m_»"×Ü æÇ¾íYúµrÔjxûeÈYq ‘ÅÙùè¨nV KÍÌ81EJ¹âòêÚƒ‹‹ ü·¯ùøy0”¼švÙNbV] 3kÙú§%ÇñÓ–rQÐ'§òcmÐT¯íqýä4;¾(ûÉ7Àùè’»Ç+/H@ÓîPä^‚:XW¸a¢y0ÕZjð³uO»_9!É‹3^ÑúϤYr*¡>v—˽Jƒç®÷ˆ„7®Ï˜•G«ðãÐ@O±õ˜9÷bÚFù‹=}¢0*xUù~µ´†`¸KBÞ£¶ÊFJï¹ï.}y !ì+díW‘H(ð)LDoh£„ÞÜX)Ý®âóÈâtÈÞ°ÌÅÔj•kìë䯮½Q­÷0ÛñÑš›.Ú™23w~“?—3ªìëjLÎQÏÎ×QîÔY£v\÷¹´²?q…½¸öö‚pzÃ}5¡±•hN[Ròšý´$‡'òÈÞ“jo—:úÜØ ït}ÕŸðØ6˜Ž@ÙpG õy:‹ž’Z6UbcOé‰{žM)A¥Ã€/¬ßÑSôœUuêõ¡Ø‹— òå†xjÀ§ ÄðäœL–!ßLÈæIûüAv˜ÿsMý>ü”[´Ï •?7Ù¸Äså'pŒO ø«‚EÍFÝðO„7D¢‹7Û÷ò¿ p JŒED3:nì]T-}IL,ž=ÜÓÙî9†.–_2;v‡ïf¨\…O­åÖ0.¿•Ö\¸ž*.E`"Œž$õ+²mßû¾hà¼Ií-båõ«i±œ1£üÑ6/E™üNn‡É{šh [aâäú¨››å9v¡¬ÇÊæä<ºå³-[ϱ¢L¡]ÓÈ4pwZÄ Øk˷Ѧâ¨"‰pzg&öȤ¼˜Ú²¼º?Š(|FóÕ¬D°¡O±ñ{×—¬þA…_pÚÑvèƒBqüçDäüi4o\ÚÆtyFñ ža×@VvQ£$si«L9³×É0«! rÄÇyå…aÆ¡Z|l+~5–GùîÁ:ÉTü;ëé(E`žê ZjU×—û¨L¦(þ7±˜rÙ¯çOm&£VMöñUIW%4–.ËÖã±¶ÞÖ‚åkA¸{ü&»ty¨sÒ:# X¥¦Úé£)'‘N\.æ«‚Ê)ßçm¾ò³ú¸ôOÕ2ç¸KÒ Ä5 Êfw¹v(ž®ÐÈnÔ+ƒW |uí¼ŠñM>J6NŒú^ì§'×ä“kŠ'bí ?hZÀm£¤¬Uõ–nߊ¤òrz0%7W…ÞñJÒÇ[ÙÄ~´ö!Ÿ²êœNÈ]:qͽ‚ázlçÍ –Øúú—Gïc Ÿ3s@¹¾›X8b /ôô.s¬ lÛqÊ.xúFÅôi¸6Z×ò7i_Q4c6“Þ𥎈}W¬yøø±µ+ÝÍpY¨ëëç×ôêòÄji(²žnî¤Ò/ãÑ4v"´&=ò·»„ƒž/†ˆ (Ôgž²ÜnªÛ\̤ÿØC× ‘j`ñ“b˜¤šú„ ~fr½@vâÆÙÃË—_óKM,Å×xº‹;ªH-°@(Ô%âZ™—Sõ»ZRˆï#¹(~ ˆ†ôéh£„dÙépL 0ß¶½•øFB”˜þI¥FŸ²;g¤';€úþX·BÓÚÉaz¸VÍXCof¡åWOeØOÑÏ¡æ©þ¾þP'¨8¦Y<Ñas–±(’©äã1~xð ~•Õ–xÚ¦Ÿ;µª'ÁtûÁ˜´Q|¥@¾ ¡¢¶Ý³’'MLÎ!tÂ+ò‰|ˆ²ŠÞ k\üȤlSÁ§`µpûKÕ,ÉѦ'æ9%A\øÐnä5¢½L`‡EÍš¯½8Ýöè#÷¤RMÝ|/ïÞRK1NÛÐvNM‘x»Øƒ7®?¦t9&Þü``ª,UÚÌ/#¯(Á*À€>uÞÓÖì•òðø„R nÿsBE"ðUÎ…NëØ¾söϽûn»È·éÛîãEœO8hØg˜ð¾É @Y2wf³QèAöpÝ‘Ò7‹ Ê1ºA˜4~G ‡\Ý'.ù{ÉMO̧R.ºI½‚Ä5”bSFò²Çoßèmêtéß;lî㾌"‹ù4G= ZUTö£râ8^Cs~­1Œ†È©Â¦h¾@ÙòCphÑjLΊQkÅÒÖ)AæÁPñû}Î$ܱV”äIŸà¦P°*1ŠË©ÈÑýµ¶jùÃÒ<¦±Q>-¡G‚XÍË„êF§7 CÕˆõ‰§c2¢ßG“¡AUNÅëŽc(×§ þ t–-Gèøá‹(?' wšïÏì a~ýŠùF‚hÄÏ–à-9³± ÚœCAÇ6Å-ÌW–L¢/ðÞsA˶èÑu£Èîƒ8”[&6´̊Eâäh”7S²Á¨¸N¾d=M3ôC®‡Bé=5lñccÓ‹u½[3ìzµ*+ÊZi}(*†½XÙÌ!2³>Àï!Õ ;ÎßJo)ø<šÅ4§tM)WZÐS /ÑŽó]hP½H&uoÛë )ü²ñ£•×t Â~-_uÉ%^ÅWÙOx³l'®1/½¦v£]Í›¤HÝ÷ìF´¶çä³ë¾o]Ñ}`þAn¹‰ÀÚ–QNà‰Zב°¢nRUè6­gÄ&õ4æÒMï%!Æá3Eõfß*^-Gs z/¿ÏÛcÁ"9+ŸÓ–ÞmñÚ]Óý¨Â1óQq3Ù£ÂXòKnKFö°˜OIÂ’,ø>ÁTåÕ(7ñã|~Þèx“Ûµ&îtÂÅ[Ýí8à …_†`ɼM©w³ë Ïq¼×ëÕ«ËJ±_k­ŒäÇ¥Þs¬B}ÿEÊ2?YÿD0òÑ7z8|; ¢ã§Þ=L²ÝqN `\ùœÈAñ”>¶{›®:îö«åÙ)ENóÜ7?Îežè$”ç›i5z; ŽóÊ“Ðïé« 5o\=ÝŸVP È É_²[¿ÏDú1v€ÞMæÆÔòÛ ›Û&õû\'êgu™7s÷Nžµêy•œ*‡X„.Ž{$z´9ˆw7|Õ(Yf`2ö5üÁng„XKwòwwðârŠgìÎ!£ÁÇdhU¦ãÞ£u³,SÆ3r礄’of’}×›·9æÕŒ¬âM0§ë5¼šÔ»ÝÉÈÜŽõk§Þ%û¬ƒån^nïÊÁÌÚý‚îå鬯E]¨_¦Fþ´“OQì³k°-~áÜŒ jz}ïÜ-rÑûÅ•˜9êãË(ÈvJÙyn&ZP¼DTzëÕkØNÎãî,Æ$¢z8‚ÄóýAi«>W/2¬u›ë°ÞÔ4çéKÏwٌժ"£„?ö?¨…T¬_½Áun9ÿ~£Õ?OîAu4Û` omÈŽ[‰ïöü?}h᧺D”ÏG×ìüIôµÁlÁu¬ÔR:-*ÿˆ$5àÞgÇ.to, ¶ì9¯AJ²ñyߦÉ(‡L!?‘â#øÏ{m-‘De‹E¹è›ÈW_ç³Èv÷Þ® zΟ²Ôö•e=+€Ì]å[´¿‰ßUIgOÏ%¥!cñݶpwYvà›¾i•µ8  ä¶dí¾ö·vö~ÀL.ïC¢VBß2&t^*hž]xowÿjðMé¦híBžƒÁÅm Áû{LÏÚƒ£ ÑžpŠÝÍ4y”öÁ,•ò—ÂÁŽ‘™ –·&ÎÑí¯†Ê¾¢=ýŸé…¢´-“Ðc•`%PØ5dS%*uäM*âl.¦ß˜gÝK‹|*¬œŸNn‡ ƒ•þ|Bú§OþþÕuVdð Ý•\N‡U¤ÊÇ–ó…ȃå=²44¯_s®Í®=¿OfÃÒë4fåFò.2sø6ï+‘°9 -šçù°‚aÒç"›gê0=õÊ2âÊ—-/wN.Ðe…+"íQÍâéóo:´}sîm `zE¬~®Ç®¹dgñˆzÛWȹx„¦Jé~áÊWdúè¥U(mùpð…È4¤î îBy†Ÿ1VðJ=k‚"å–ÎÈå» ÃbLÌ|Tm¤=y?p3'Ò[—@§2h9šc•¨zb»”þý!´,\_× ôl-ý ê\4›@¨ð¤öF™@.F£HuV§•€q¹ÕîÒp©úV]f`dâ…N¤äârÝ«¿Æ íLU¼_1=ãW_Rxb·"f’ìÅWúPž6-%xã¿z5'è:µ6†ûEPF÷Ž·„lúÍ#„…'™ÇuJÅÜY2k¥ptËÚ£bh^£­½Ó~çŠØ¡™t íº;«F:Ø]Ï¥\?2A!McÊñ)‘ZíÌ8b[*^ªn'¦á'­‰ú²­u~Øô^˜¸{Ø _ˆW裡x§ÐèÂJT›°ºšÇóŽŸ:“‹íp9>Ť°Ä…âK)–íÜj‰@Y#ÔGx„Ãïs£]Ô2Pçn|v¡ Çó\y ®:ͪ›ß¶³tÆ'µj¹òù¬×çʹ¼-ë°GÛ²¼ÝåµOì­u*mÌÃCùeL.£jΜðB{ë/„%…ô6ŠØÑ¡=ñÆm“gõ–Ñß4‚„U±‰BºBy»<Îü]܇+oÇz?)óu€Ã&k–÷ÌOO—]í3…FóNæ¬T?•f&ˆS¤Kp‹¾Œò«4ºšÇ*qÓW´›_LŸ<6L³?¿ü˜Vqö=`vPf^w˜…Žðû2™úÍtáù¾ÉðÒ; L„7pàXOÀmðÓH5‚u³¤z‡!&D£-çªÝ˜Ÿ–Í3øa4¤g·Ð|j'×ÀÙ6ÕªÏ@#TUC¥Rò! 1JÜÖ“BYGâ ·'YµmÚ_1›X'=ØOĽP d{¬$ÓÀàóDÁ¼ÉYK=`Ë"Š/Xýqû¢ |VNì1~ßu›:çÖ;€’Š {ÖYcï;˜ìS‘ŒEó;vdã,ûüîE)úÙßïY¾×‰×‘as£ì à21]˜Ï¤0™p°:&²[‹¨ß­àß¶~C‚þmלӠ±,öúä a¦rÕ^¼Ü7]E¥ÎÝÞy=î×y¨ù$ù|øc¯…Àû¾„š›gA¢Ã7/[4Ä 71vš B¶íÅÍÔšÏ|è Uh† ì¿e!X5G¯òçSq,•¹Ÿ²Ì·i~XÅ)&+‚¹nšrX,@EM4† RôÐk¦Ž.ýö2/ï©¡VÂ|1m#à¦óS"ªq½ýÝ®ïuR¢QX¢*‡Àͅˈ-ßNÃáð·ØXJ‹TSb E¤v²æÑ«¹‹{Ê,ü Ú˜„ †9.ï‘Å7Í5´œx>y›aõž‘&“Á8ËSÊmúÙõf·ucyò¾ONKZgeˆLî«ø¯¨3ÂqÓìîÏ$–>5–è{Fp]I›iFÕXò`¯c½:È–yiiË,§®í GK{¢…næ-BÿZí՜ٸ‘ûZŠgŸ§%¤FGpqz­7é9,5Ý©,­ù1Ô=}˜gÂkH/¯V/›¹ú•¥ÎÃ5[_ïl£º#ÎâFÒfmãkZÈèñªç‹ó(”…Éþº„á©Öì¯jÐKv³[Q] ,È&ÌQu‘ø-¹åÑ¿ï/>]Ä ¾é…E@¨4°£[®“Y¢¾ 1¿%ôhÍô³“Çâ íÿî ‚}û 8 L xG²r±Lí¥}-*,£‡C0D0m;‚Ò[X&v€œœñòH}ÊôÒ`<|`#Q´´#º3)x^`0EÖÅìŠ~k@2ñî,aˆÌYÄê<¨¬lŸðÍݕ֡(\[ÁË`m_)/w8 ýz©_-_uSd‘i6‘’¡ç”M’'`ÙÈŒÒj@êesæò$ baÕ•bÇi»«XîÜ2 =nšã’8Dc?]ûIzt¶Q^,2ÞmXDf/+Ì.âà,T¯‹vX(~”[jsµÊÊ4Uò´ð=¾qÐ}æ¹Y*Âc} Ųº[Ÿ|êý ï—ÿOI9ð endstream endobj 97 0 obj << /Length1 1855 /Length2 14761 /Length3 0 /Length 15916 /Filter /FlateDecode >> stream xÚ·Pœ[Ó® Á%¸†ÁÝÝÝ]ƒËàîÜ‚wîîîîîî~Øò½{¿ßÿWSSÅü¤­­ŽzF= =+€ŽŽý­í8BzN¦†i€„µÐ–XÐÚÆÕÎÔØÄác›ÿyèÙÙY©þ ð[íL ô¬Òz&@Ë ô,ŠÖ¦@×ÿJAÆeâà`ÃAKëììL£giOcmgÌCNp6u0(ívN@CÀdô,UFK P21µÿË®hmäà¬g|,L €VöŽV†@;ÀÇæEq)€¬ Ðê/g©¿¨Ÿ €ž†þ?éþŽþ#‘©ÕŸÁzÖ–6zV®¦VÆ#S @VDŠÆÁÅ  geø‡£ž…½õG¼ž“ž©…žþ‡ÃŸÊõ"üò½ÿ.ÏÞÀÎÔÆÁžÆÞÔâiÿHóqÊÂV†‚Ö––@+{Ø?ô ™Ú >ŽÝ•ö¯›5·²v¶rÿŒL­ þ(ÂÐцVÙÊÔÖ(.ô·Ë‡ ö›1ÐÀLGGÇÊÎÚ€.&´¤Wrµþ¹Hÿ‡ù£Owk€ÑG@OS#àǬ»½žà`çôtÿ÷Â,==ÀÐÔÀ 46µ‚ý'û‡hô\¾© @ƒî£÷èt|þó¤õÑ^†ÖV®ÿ¸ÿy¿´‚üJb*”UüŸ5k€;5€š™@ÿG“±~κ­–*1Øw`aÇ»E™$q8HÀœw/Ì[é[piäÊÃÏøL•¸BDsÀZaªfUß]é4²ØF¿“ùàOŠÓ‡ºWYT¦cG(‹^Ѭ†êPÉhÄ=h3Xm¹Sé42KüºÄ>Uý˜ù¬nrìÝÐéþ\c»:8ÔG}˜Øô0X?´Ýé©ZÀÛËûÕØ™žBØD#te™#7¢ØaôS‡*P ‰jÉé'Û „^—»ï¥ÀÓú­\:ºïlÝ #_Ϙ3=.#=Ëj/dÚ6fç-œQ´üÞýXdÞõÛ/.r±M…u’e¥ w†d­E‘ÊsVkgüŠi/6Ê0h„Š\ñ¥b·˜.û‰ò˜ÄE»wçËC5}]ÍÙshÎTÊ”›£™sä vN¨Gj}üzØï ¶îJJ´Ñ­U/Êœƒ#7•Xý³BÚ¼3:ìe“EùÃ>åßoÏvÃè;Up"°¬BTUâ~ÚíÈq¦*"&Í[”î•<ä òXÔZÊëÆþí͇r[ïÍPÕ*û@ÿ i5’ø×Þ2Ø7ÖÁz•Ë·/8ŠJÙßÜ6@ºç§F· †'·‰Ü-Ê4 ˜zœÌáÉ`1$óÛÞ)×ëÜdá¡oF¡ kÊÎü5K_+Sgú€ë'#àß ZéÐxÆNĹE¤¿Ö¤ò(ÑÞçâÒͶÜÒøžð£¸Ëí5«ÖÐn¹J¸5;ÔA‰Ö…xƒ•`&ö^X~fL¹ˆfÉ[õ—ऄbï˜VÏŧÌOÂJ1€`Vj¤§ ßlçý '7 Pó…68@ƒÙæš%E¨ŠJ¸T@•cÎ"ð§æ¨D&EÞx›àG@¬ Xì©”‘p(!évg…Œf©„ýæ£2Ñ=Ñq<;'6‹wF[4„ 2oìL(Âå|©LäQñîc-Ï5¶?–7©ëUÇ•†GOWäüÎÕ|hy^Sÿ¸ðkmÐäb-Ó½ìк¶ö÷ïTÞAl]ÞkÖ„c 8‘LŸm×l.r\Lô³¸×Üãb KˆŸ9Y’<í$éŸS”A LS0jÄʪ&§=ï\±ðæÞ¼ ðˆ5×mϽ5›šö˜d™œ4RuîÅ›²ŸWW¼ ÷í„}¡[Ç-¹’–x”ÎP§+‰rrgYÇŠŸÓ|è|²Ë°L!dÄæ¾‹OSšR—¤Y‘Ä:$ï¤Â’t× ü. šÞ ‹‡ëºL†*IØÍ K7­’XÎ".GDdñ|øTÚ@‡¹¡Ñu< {èÿ-92OÂ@l¯_át/1‚Låhí%]ÊÐƒå¶æ›œ4ÃB‘ÒL‘ÿ¹·ësÉä;{ûXá:oOŽd<†9Öðøý¦æ…q†C oÑväxÏͳ¤Ò±skÜ^ Q¦¡Œ¦`CjîØ’ìÝÜ8_ž*ƒ þ›-”áTù6Žõ±×½ mø€7_åÍÓ)]l§z–µÛ#ÑéãÄ}¿7qOYûä ºóæÚ&ƒ–uq÷: \ç•WÜdÑñ KÀòL“£–z!ÁºhÀ•åÞ·ci €+ò¹ÐKÊŠYñÊ™¤ˆúxúwEº¨ÛC!û÷iìÙÈk‹ø‹‰ÌSÍœûŽ, !‰LÊÉ`Ö´6¿ƒd€PúäȲ‡¯^u6° +·ÌrEŸ˜n1 ‚âI7åª'+n=ŽÖè¶;Í¢•XN˜!‹O…Ÿ\Q@Šv«ãŒµX0?gý>Í‚³Ü}¸Žì[Ó‚„•d,!tHˆ?›»Â‚u,rg&ÙŽŽêÅ'}þ·’ÖÒp:¨sÓ‰ªÈëo_cZ?»îH¼³x—dVj·Þë9¤ãCóÉÒgѦˆ\âÇ×1ºâ}KÞ6@”–žŸíýþºªfYúÞä6.òÅ¢ø±„°K‚§¾ "¥*ðÕs_…<|Eq`Ù›*ëÜg­à\£#¯µY$¢¬¸U.æ U |ÒÛå<KGcLM%³ÿúF>³â³’Üû­“_æ#,pˆ*Öæm鋟Ý“¸§†8GŒËhÿp]J ÷/Ð÷@ùû¹h>#LÆ/bërzN“nØÊ£è:¿3FÑú¾—ÐãQïôG TDISöëÛxrº¯Àø”É3b¨¨p.dµŒO#|F)—¥‘Mß0¥jac+™Ãó;é 0ÎàÖäÞà#Ùx’yPÊÒNŠiSñ‰´ ³0.ïL[ÒŸ*‹X%h8(žM˜ÖÜ|}Ôc’§Çü=â(aß²UÉk(ÉÄÚk›ŸfÛF¦Z!¸ÅtBC•åìçrÌ}•JwÃÖŠD¾(¶ƒ ÍË.ös’j “ee'—2}#‰ã_0ÆÙ´²`q ’€‰‘­½â’„Ù=–Í•äçÇr Œû±6e‘M²_û ¾;_Cº8¢º4±ëGÒ‘Mç# xÃFO¡5…1]œr„ÑOÿÒ%ËÖßãÆ/–7›Á ëJs¥‘éÒ¸µê@ÏïXVœoÃ?N£Á”£¸À ïOip(Ê=“~Á2ù*4Rµ ×K˜L€çµSÂIÉj£™ëì‚¢¾% «½Ê,\>ºr‡Žò5Cg¥1A´ö~óâЛ Þ¢uòŒ^w…Í‘æÚ‘òl{‘ð隇ó‚ê‡Û`Í$1¬usÅz¦ Ø]’½/òÜ6Š*½ˆËøÙÌAâ:ÀøÍ2&¢ÖEÅõ¶v y2Pë››VbÓUçç4ÜÀ˜ä»…ÞB;ö"t«uÁ2Aú§‡bC¾o÷…l æ2A3a’¿A3§9s:Žfâ,|DžÈ áÂe®CóA976ó ßJ»žOAcË^Ö)ZB¹úò³~n÷¢ú£ƒÙwä³sÕŠF}Ö P9Ì6Ùhì!F—ɳ–kíÙšDöûATÝJ8ÉÈØr/Ç>Ï~ò=ž=”Tw» ïó‰Ý’ê#‡L|ÞÉ€ýôm|=>¤‘ü/m\ÕÕ&g”" 7¸=¢ÕsÔ*çÕ€¾Yvý÷œ]BÙü Ô¡QÕn ­UÚ|]×âpùJ˜àÁxMùŸ]8"Å+¦J—Ú2€;Ci]¿Ü@Eb¥j™&IÖ¨ hJ…ä¹ »¼Ô—õÛ^ÔSu[ gKåÂWø¼°ûõ ¹§Ø1Peáì“ë·ð‹iI¿ŽYD±pÒªZÐïò|cˆâ‹Ä9;ï ¿:4 ÉY{HÚ^2MƬÒà+±ÕIg–õºê”º/IÕì~̬'ÓZéXý“Ê€ƒ&Kàƒ­ÚÏžH-«•Áu—©]_¾Ð6OÕ_ë²:òÂ8Õ•ªÈÑ‚^ÖËUÎmìu FqÚ:—Z_iÚËfŠ»¡O"újgÉÿ~fÿ­«ïúÊêû:Er3wÑýýí…u*š>Ÿ{Œë4  ~˜;¦4êïu'çx{t˜Vpú6ÆörI†DÔ“šwc3©$R\6íà( ˆ¨ð^©5lO4ƒ= u”Fcýn®…N~ø›:UnåCA7 ¼N3-ÐŸŽ®[n烺Õ@õrذgs-a–ƒ°Œ  `¡XwE`Ôù\µ¼.2>%—=ZX Ì–ÆÁà'¤9Ð=Ô‡ŠÄû¯®? ¯ëiû=ȹ0y‹d¢{A©¾Ôp.‹âÍ¡!©£2 Ü0/^¿ì=lŠiN—h@>Ð'Œ%Ÿ0Ë<°Ûð0°J4 ¼^×ÚÓ‚…֓ݹ ËS!8ðh”o±Æ‰Ú÷6øpà+±k± ‘?Yº–“›Öb‡: šuÝ©†Iõ(hã*èY´»TëdΫ§qT1KT€¯ï-£‡B¡F6—!Öâü(|¦5Ë Øè]Ì"ð\Ó?;"Qýê=P¼3'¯ÖH)¶^°8â ž[Öóc`–fQýë%ß‚z‰]’Î͉ՊOôç6Ù—m~j©ÏV a‰¼:kÚ:ËÆh±µ^é}Qœºx¿'N %‚Ö.X€=u+ø1 °<ñíò61_J²L“wÖ¥=Öæ]ÑFpB)b“Âk7Þrì8€KTšñ¡¹”Tû “щqŽ5¡/ËÛ~Š,ê7W"m¼HGâL状űZØqôq°¤Së0~I„µ™ï±ÊóáÝ ›p£T¤ìøš#Gßí…ü¤È0 ÇX"‘ ½4äÿÓ‚ìrº–¯ 8(¿Kki#Ã,É ªú õ ï^^XÓ K(ÛøÞ8]Áä¡ìÙÞÛ³ß}S‰¦ ˜$NPÿyròH0*=£¾[8-I|`'\Ñp‚÷œÊ-°8"®…ã­Iøv=Ý„l‚0¢!(eR¬N½ëë¥h÷æªÇ –S-šàÄcøª^4‰©‚ÀÇ컺>IööáW \Z·_ý ˜YP6Ò¶=ºº¯ËJìMÏÒÄ\EEÖ¸ybù’E'IBÅ7®ý¥”ßWÀhr£É·TÝúþzÞìmq>E1’®wˆ–Ø3p “ªOšˆïWÍm•ÊbÆÞk½2~W×–mŽËiJ´t‚— ˆ_ýqé·¹MZ#OÆõ„ÔãáüþmBÑ¢ä²}R#iûI¨|5UåWÏ—»ÚúõžÚm8ï™yb³Ñ~±¶ó0Y±wèÁÑH|Ǻ0΃ ª±«1[ NYU>Z+ß`‰¡Š—IMx}˜“¹ü±^q›O‡w^òÅ•a¥Á¤è•ÖxS£%óÿ0 <´°V­‘&»Š‚´©/ø¶¬;?a¥în¶¬ËèañÉ ·\·‰TÜ"óõÇF%ÁnÍKÖí´\×Љ{|6¤pEÒ9÷˜Þ6œÏ‚ɱ¬9˜nZýYê¶ÍÃ|›£ë ‘ÝUÂêõOGX#OÛnJæNW/‚×U­Ù´'œ‡Žeª2Ð6‡Lò=S±«Ì6Ù€že²r ¬Ï!®ˆJÛ›í$O©ãÁFµ(Gðæe›_z@ªØwMâ(åS§…¼ÏnÛÚXîÄÞ2J[ uŒÜXåjǼ-–h±ÔÓŒþ÷¸¦Ûób|+Ñ>Fã~¯æ 4Ç©·¯N¤Þý–4(¤£ŠÛ]BÍ#‹®/øX¬qÙðê·s‚Õe½–‡µÚu¢ŽÌ:¢©Pµ‹ôùψ´á3po#[°L!•ôeVÁ:ÏB<ž6”{FÅÆì&D¸O…½ Z|çÒº ÚS¢’wœÌ%¹ÎýÇÍà;âv¶çü…ŒFü¾ëêÈ¦Ž¥Þ]«;ÝW­“ç'S(êvH‡¸B·wQK†xP y`«€8~døcêâh*4ò­&½âÜÁ&“­qv™ÇwhÅ},vÉXlä%É"=5¢gãÁ¸:Y?­O.a8¶ý~9®àâd ï-fHÌRÅⵞ&xD1nB[¨Êã]ÑÎÍê”­¯¥ß9ÆÎ÷{Æs{Ñ·_ÕZüXvàôƒê:>ð}úâ^z‚>·|P“Ò§½Ä‚sF›î:ù¬«w‘…’?ð9T£b,IwªmD8=§ËùK'ĉ|–Ûžé•”ž?&ЧÙÐïýA¾çœÐ]ÏÞ.ˆ{çªuPÉ»VAö‘ItVî¼ß‹Ü®Ã~¡„ñ4ì³âÛPKM’ ä<ƒKêòtk ‰»t²ÏVÛí‹y›œ†›û´5_B(bÑç^å‡6 eô(—[ÚÖüÓ­ a" ”aZÜ&¯ŒGõv"£kD2'“¹ºQÕž\WŠK'>=Q´|0¾{gâUcÐêŠf*KÍN¿JÑ•ZWýJR>½ö‚¿Ï‹`å9!ï˽N³ŠNÿÔTXÆÏJxè.¨ÀEËàð*ÝÛóh¿›ïýlÎ.®vk”Sôklë}üõ™u1_Mø¤ i€æh“ c¢›BbY‘¡cI¯úëĹÙòg\’©Eþ xÊ’½àöÆfh”%îÛ™¬]"§*Å/JêÈ!ß–±Ê¥ß™Æ ÎöySAÆIïN`ªˆJÍQQîcØ£—оånhÓ6ÙÖtƒÞš„÷"–>>~Ç+uó*ràƒg¼¢¤¤fH/ðñ˜‘s©šèîH·ó¼Ïg¾{m7NU„$f=›šÔ=' íB0û”øàŒ°^?%åÄÆËyrB„N·HÞM‹QʬG¹ÒÜÿòz¯=†û Ž3¦ÃãH ¤ÙãûYÌq†å]¾÷u"‰4Mžq@ŸÓå[C$>ã5¾âG±™>òé2qƒƒD -ÏP–ìûÙ/\;•5Ÿ.l­ß-<ä[çµZÞi`ÌÖmHûY"œHT2$…¤¨¦E¡š•2òâWb3£7ø¼J§| "ç˜û2ïpPŠj«t•\¥äÍ&ªs>­LF"Bõ&QË4 ÕJŸÈµ!&©ñ¬œ=_©à‚¿Ë4£O‚ÞTÜéÃE(@kݺfî.[¦4aëNßÞÔ7™u£0E¼tR]:#üÀ”Ó=ÂHÕbkûƒüEù•çXÄá4ñ“ý÷Wîx^8Utž? òÌæ³SˆÓybL×jº/ CŸ3*/¹õ]:îˆü2YÌ ¸•[/"ŽfCô}QÎûõäL^~aŒ:Œn;ñahö-¯±b=Øʶ.ïW‹½õgêF¾XXI0QÖ¨mÂ2ù‰ O{ÅO»€ïWnö¾ž9=R³5Ѝ¡yÞFų–-]üŒçQsŠp =ÜØ1sÕ†õ q/¿4Wª1ðʾ^aœ,ay¿<"z@;´™ÑOö¥òïÛ½…¢—wL–¿aH8uQIâ{€Â\ÕŒÿTY‹Žþ›ô’*ï_£±²Æ¥Ü©ÆñÜwi™×€òŽx£æT¦yE@‘™=ðÁ¯³4¢8°{®çWµñÒaËꎶ~‘j/4ä2Y ܲôá€=4wî—goÓTB%E“ ‹ó9 ‘˜Í:ûI¿šûÀ߇°.ý~¯¶¢Ô=gUE..mÔj/q<2“ µâK:5eR~ …‰s¢½kC´§U^!,íÆÖŽæ«_ë8hà{ô.=~µÉ•ŸiNŸŽóš^áá¹F]²zQž®@ª… Ï·jˆ< ç†úþõ^®tÔN,jqwAß)²DÃÚ…Kœ5¾EŽüERÆ`â LÏÌ!ÁÓØ¦ÙÕS™*«‰TÕ½°Ç;&I®\ÔÿÁFpÞÒ8>{ Ðïã¿åÚлð·az^Uáß”’uˆHžûê}-É.!|'Ãf÷¥S¨(nâÒYz¢j)ÔÜek©•ù|¢G€Õ­©×£W±Í_XHÓïpãVÞV`VÚ¹'­. a?·?39kß}U‡õµMY:à#$ÇzÕ~»&Ù6®?¹]ß’µÒ8e·6Éå"iGõý´Gíï Þ+Yÿ8tŸ†û‚ù cf ™È¢Šº“4Ø-ã°zŸ_[÷•isÌvÌ\…ª¥CŸ]!T$‡õHq#£®SŽÓ‹‰c'þ§¦D3±ÌsGßU‹\í×°¹’þÔN=%&¢LÆ=‚t°¯ljyS%>± ™ì—9?·Íj‡o¹‡KZ…ÉB˜_vüFÛªÓÐŽ6ùô™ÿ¨à®³¨Î·Z¨~%¾ø´ìý¶îÏÒR2ŸVIľçìôû(¥– k’úÌìœú½VN)åCÑ^ ’K¢T±á¬7·–qTA©–ô¾W!!Ôaÿu¢½A$çÔë†'f‹˜eè©’:Šmi§w¼ná ŸÂÌC… ÷*ka±(LgÖYa£â É®õ>0À@$‘ž´3·+¨]ÆZ”ØiA|î*–µî°o‡¾q¹Od)v ÙCc¤tç&£‹ôºÂQ69\¨Ê\pË^!agÜ>„=rg”›5¿.®¡ éR’Ús ªfÂÚ¡J|Åÿîp;¿É‚E‹?Í=b«,]ÈŠ‘T­hq÷gÄ»¡ýIÍõÂÍ=æÀ/¦ëGmX× ÊËù39ÿÕ=ê¾ ùp>h¦YxÄŸ5w!øI6×™L8 )\Y¬pxÝPèøÓݨєYR_}9+ë—€5=ʦƒ“.-·û÷|³~U:$ó%Ûú’•MîT¶ñæÕpƒ:ຄđÂÓ#9f˜[i9.+ ™®¹š‹WËP¢Ïâw!tæð¢¦çж+¨ôC¯²䚉¯÷1‰üî±(ÏMïÈé«þoU5ÒÍP`moËboßTâsPzJe†/làRèê°b+(ÍÉKû7­ÅC@îFí¤=²KÜáÆ 7d*Ë—€×¶'§ˆsÆ0»i´ô  |zªÔÌtq±=\Z÷ £y,8æÞòˆØ-î b#Mdz4”y>F{wô1û©tÙ\t+ä×9ß;6¯ª•_ô¢RbÎ;Uñ©+°G¤F²”r¤"j‹íâÄ8ý«Á;5Ê€6³å3"W—-/ w&™#¶Î€Tâ´aÇiKS4yžJ\(ô"¬à´Eùý-fíµp'à‹ˆ—š*•¹W¢‡Ï¢gôgvBÁ #ᬳ?“ö“c¬½»_ÀjñÒSìk}[þ,¡õ")fAvÔDùêíR¸¬ñæwüZüvÑaµýtlé—©y¨êVù¶Y ÄÍÀµKoÌUìpOÈ<É7[.ý͘‘X, §%(Õ¶ŸºÅê#8h.$£oËT‹—DZX”šÀRpíW²àpÐ$l5÷†(×±8,NdÄÄÅÉHùΊ¨Ç„VËOºDz§ð¹ÙöÅ=0ªSm$á)Ì*ó4× ÏñøÂ§;ÄïV¡£7Ô®{û„µ¹ ¶ò+9”­lùд¸‘@¯_åéH5ýñ6´¾ù˜z&áÉVÄG;²ÂíÌÈý B$öp^o–4ŒnÎs°œ=S ŠªŽÂ!Û|È®{ÜMœ¤2~ÁÃið;¡ZÑ‘O.6I)SºÁy‡ÒÀ\¿b{ǹœ4ø€ ”*Wg¿­Œ(â ¹F»3ì–ÕÑ÷MíÍŒöæË•ŽÕÞÒÃïAIÙ¥èÔOLAz¿V‚åäm5NWíg¤hï p“ø†É›m•ä:…p*q:‚s+UP#f·‹!2ÃtQÑf{¹2zµÃÅGÒŠDùѼ¡Ó›ÍgvºÁŽhUÔÄ÷—òö‹OðE˜Õäi2d¯¥ŠQ—eW“ˆµ8_ šØØoʆFÏ¥?Öµë+wÈòãós#Ï<eÔÝÿÆ­+M+ðã àÍ€^ïOM‚Böc]ù¼?òµhÔøº]–·oÂÃ52ª1W*‰OS¶ ÎÀŒ_M¬‰ñÞPÔˤ³È‡Åšl‰R¼[–`Ör´lƒ/5%K’mßzB‚™pœ€MÞK•Ëìà°yªé$˜Õ LiÞ £‘lè~ðê’í–—7Å<¸AGPæÄÃ÷¢£Ç°ÍÕôÜ[ Ejb3_ä¸9'5û³Å}¸¨±z¨‹$Šê…Ñ’f9ÕR¬‚åéÐgOù>â¶ò4p¥±O½p+‘ Ñú3˜(RèÏÂè¥G³8¨vÞò[Ÿ¨ú7_ޒčÐ,ÂTÞ/…, $F "M/³ŽÎã= z¿·QÙœL «l›b\R°|m³òûã§Ú×;ȇޠËàJ±2W§L·©˜ GŸxí¯°ì(®¨¬ åÈpØ^*ËvÑ…ÚužT,KèY/›ˆ!2¿ õÈ2Ä®-´°¡°ôx‰ãˆNXDyIâò%Ý›‘2 ÝuÑ7Èj¡@”\ÖL*ÒÉ0³ðØr‘œ X>I ×íÐÐ^FÏÙü¾ÆC‰mjË üešg«¢âZNRË=)Ñ+#@âµGBöyÐQBQå¡Ú,ö5åM9“«/6ôîŒég âbü”ré°¿)Ij†j, 1ÑÞ«^%¼ù­’”êZÃ[þg-‘ÞVµ†¨pïáÏÊÔvpsQŒæ0Þ+”Ö/q˜ÌaÇ9I“5ˆ‡0q‰®ÍSªP‰¸”&=2P~åŸë,x‰ £å$E¯µBB¦†„ÚÖÌÃ23äq¾Jo7qŽ³Ù…x«__B%Æ=û,“·Ð«¥¾0K6¸(:=C+%àáíª L#Ý^1tÃU7¤8[«Mê”]#U("¦q££õtÂ,Ê?.ò¡é¸×Äòœ¥{ ÷p«èìÍ %Rá…õAâS@íÁó’p² ÷ëÙ{ C‰oåo]°R籑M[ê0‹ö!¬‰ÛC c£››ê&ädiâѯi‡=Þö°"cBsw!ìUá°ò)ñ¡ÚJAiSx"pqjT3•U0ž§^m´›U>õPc¼Wpôgª «žf«„¥”.`š$ÓôD¹¤Ÿ?kýÒCãñ(4vJ饋kdÁ\Цô›Ô>P̼iVøŒ:yûVƒ™´˜x¥Pþ(ü"}B3‹¹,ôóà)V<ºŽS¹@h_è´“—gˇKKkú4µÞRº´´lbºÀa†ÆôÛV¯¥¸x’ Óטý5޾´M"—DœUC×RWĈ¸{*xN ªÍ$EoZa+‚,*БWÇ%‘aÒ…ØRŸ ^йÀ—YôìÞNF®,Hâ~Þ¹~»z£&ÞûH`g#“Ù"$ŸuCá,¬x9P¬£ÜŸeŸy´- /}K9îŠdâeʘW§ÉDGàà%D_•:ïNÎ °Ÿ‹Á!yÙSÊÊ@8ÙŸ³$ %,óv]m @^³©²†{kIÃÎw:Op\zhþÍ;7lÞv/éwüмɔ=Z/ÝZõ”*Ø|¸u–çô%O:tŠ4³kíËFj…nËsñ¦V%˜ákð_ÚøTËGTö ¡¼¬Äܘ™^Uöšßd×Á~mÞÖuµõ–Øø¡âÑ9š<—Ç¡;Žæ¬š3G*c+®úÒó® çÁdŸAõËÀƒöúÙØµè¬mz{6 ¬0i¼f^8ï!§–]ºÂx.ÒŠFDþ0È%„ìæ²æ1"ìñLÕR©Ú‰fðŠ5Ámdì2ÈÞj=Ó6–÷«ãõ¯Ù›q½Ë0jú¼)„£dsäKuSŸž¶x{ƒèª¯íx6‚]õ3Ò:ŠSéªöËnç±›Æ?ËUâcèó”­ìÀr‘ìo™&Ì­–ÒÓ%I›ˆ]¯ú7|Ð^›R~VOvP,ä«|"5ÍTܧËsÀ¦q;ý©ñbÇ8RΗ¿ÍGŒ´qVžû)~Çùe΢¡I{.e‹§Þ~^#D`Xг Q'Ú¨P–)áKT•ƒ8ÑŒ¼¢fï ”ƳP–ZDc»ÚéË·j¸eq÷ñ®º›Uý(áÈ  NÖÈ RÃó0-”k©g)cãñ¨;+˜’þ=È¢½dø–Ã>`¼ÞÖq«Çﻹm—q`-è ­KÞ!&ù>w/Éå&+ÅÁÀB§S Ù%ª…Hedõá‚}{tÄ1žÃe™Þx•wE?æ xè¼C§LFF! ‹ôinYœ”d}ï\Ù¤xmaˆ°î‹}¾.°ì9WèÛæµp¤6®H´‹ßýmý9,¸¥b´$·~kF»ÎO°%‡úÖÚ”!G£,H6Æ´°„‰ÍXÖùºö]ê,ùÔ>+Q)=Úü•ÈïBl6”m”õÜ…¦ßòÆ‚ž´å=éF*€´Hµ=va¥ÅF g¤}ýÏ;–• 9A›‘Šn¹ÃcÚMÞ2ÇóåT×À×ÑÞÊoÇ’…ߎiø„ƒÒ‰h‘È/³¬Wr®¼qê±(Óì¦A­B±Œ·)Y£QƒÔüËïÿ2V›åÌ=Ï´˜ÑªD¾Kì÷–VE!øª%!¨3Lù„‚ö6'²1úEÆYýÙ&@=íG{6.yî[¨D6 ‰ÒV8ì$LVÿ§ ¬û]n‘˜¾fÓöš'³çC¨y9/; §¹±€Oös2Âë~šÓ¸ö¡gk–ÕX¼|ùfÁ~hYÔ€¹Œú\êÚ³G÷èçëŒÓkä:Ú Þ%’64x ßüÁ±é>3WE‚|¾K1üd[PCyö”õúSyMDÒ 3º¨ô²•¤#ç\)2Á›:ƒ’Œ§t̓‰áx ÒÄ"‹¬iË0P±âô¬bäDAçà­Í *³TÃ+ã'j<š$A…æõÓQfqj­¼sÀ©Û\oƒ¯Äºteoüo’Òv}³Á÷ åßAƒwÓ'”d%¸P'€þ«zYa ‹µ@ƒˆg8åæ9Cþe²¢m?2¿àȇxßa“iÙ³òÏZ³­º|$„SÇÉ뇗ˆÀ_‹‚”¨ê{½ òît{Aš»V8^:S~šPsŠ_I¾à³j±2vAííÍKƒÝ[ 31f¤Q 1­‚KEØú9 GîʺØxþ°lê¶Ç¹ÐBh3Æ @ú挋KéI¤HÂ@I‡Ð ´”ø2¹ÃOÂ,©G_A.ŠËO|vþ‹ÝHó ϰ l1*K+Ò¼ý¹“âÕëê±f2~{z†Ò þ [ê^ å7>Ð )»˜Æ·¾"¾pƹËbœ»o#Åè—eèÃŒê`2)ÒÈO{[¹„¦svèšd`¥›PlÖŽÓœ®JÑzª¹ž¼Ý¥5È U©S!êàŒ:æÔI¢¸cb.‚“™­eQÙ¨ XòBòî°Ì±Œ– éžÇŽjö¬ —ðŒéˆ¨ L‚ÃÍ]~Y1°è£ºiÆYÑ£ÄoP ¿Wº·Ñ´3á$Ç)W–ÐkñÙMp§¹[†o£³È?­P,]ž§P2Y&¯þþä>öªMÀnh‡'‘smC¦¾ ͺëZÈˈè-£dô}–+|ç¹FØeÜò«ýʃJ‡Ø'ñ·@Ó}¯žc•̱™ñJäÂÿÒ6bˆoÛB® 3¬ÖP…Œ}<õ1‚ÓCUA¸ø)hŽR ÂÐO<áŲjMûlwë±,Î 7ˆ|_d9@ˆeË”ôt”z¾ëãÔ? ܶ2”»è}êXÀX¸ìR)5²e&X )C?ãt—’außIE!NÜTF{4l…¢4ƒÐC`™ ‚Ã%%Ø¥N!-àë#ÇZ YZDyì¯tœL(Ù½nӬʆ]nrO‘v…W‹´]ï„О®ÎÀÅBî!‚e·)vúY†áC4p—(ùFj@Š×tvÅŸÀsÞ#¨Þ ¹ÒZÈKOŸê€×Að[ÞML­þ‡б=e#I’Uð þ‹RQeõ¥Öx‡•1¢ªÕÚƒ ºõ|W5³ à®w{ãd‘Ü~+e‰Ñ”ŸÙ§j%_L¤, Ô¾d€vDãºkoå2W¦=ª7ÃÊà˜®4ë€kîªØN`f=û^=våg™ôÈD¼7ð·òÒDÔE‹øuý—ùû×W×¶»ëëÕÛÖÜæ(¨ÁO|pk÷)¿OÄÀü8 ™w‘ð9%?ÞoSßt²gøÒ®=|éf„߈¢l½É56«ø¥Qý.I·£ÆbGZ—:°fa·¿Ò’‹T7ÛIê•òhÚéžéB¸Ly¢bK)‘ÇàæÝIRŽÒ20b,áÓ9×[Ÿs¥ÅmâØS n®/Ú Adý4â}æ@aÖvë¶0wU5q‡Tµ'›oF}¼ÁCð¥hƒG‹¸7;Á·äû™h…É„: ÎoÃ\ÈlÓ Ÿjþ ²Ïx?pÏîѵ(C­ΤƟӒ·)ó8€¦oÈ~ïå8ã=DÝ®ò aƒèÈr>ÍÈŒâ$MØëIËÀºø©ô—…‹j‡æ‰Pþ<Ä!„¶­MBñÄŒ‹5<3Ò»tA«ë…Kd'ðT<Û­6ê™]€¤Ý€¡lø[¢Ì#X.©w8š¿ììš\bm'¼þ :¶C«ÊM~Úê‰ßZo8T;Ûª+¿|a!’‘Äák6óõ]©Æ½C×V¯|KY[\ÒRúIk5ßfkSÓÎÆx! ¤ú[0ÂAdˆŒo*=Ãl‹Ÿó†F˜~áˆ(î‚AÝp›î´-dào¾¼¯ßó]¶Û1ç}üI²¢›o¤îM·gº¹Úˆ‡ÿÔyá)FƒÔk8ÖÉPÖÒçRéŒW![pJƒýd†q/wèÝ~M žÍµìÃVÐ-̈·11‚6:Žœ ÀËi›Ëlÿæé÷¼è{زúϤ®ÞŽžÈ•9e;Ü’­IÂztM3ÉÚDv¡䇃.ŸÛZ‰Î»Ùý† !û ~n0ÝK‰^|ûÌô;•¾¿³ë!iìÑY…/•9 ž¥Gš\SN’ÒQ‰ÃcÀ@áK<—’›~B__rwRkʪôŒù®Æürí~¥`¾9j , }ÿ•ÑJ[¦g» ²Dmý\f‚UX·­B ý­=D‘ßüT*JrtI™dQ橬ï[¢@Ö'Ü8´É=YbÖöˆUøWfGRúä׋ ȷؾÊ_8è¿„yÎN’Q°«‚bbЇ˜< ™ ‚½w=ËÏŒ’¬T:QIÑp£×'ÖWçsòkTqŒåKòEe§k¤ À-{é}ž—Yêø6fvï9Ö$«‚ö$9æšÌi‹õ0zFØlY¡O­ž0ª¯=ð‘öâMáÓ «¥l¾;Gœg½ÞE½¼¿×,`/†XòH¸Öd¤D?ÖÜõö›`^Ôkà°{!WmGuiÈwgv Ó‚›¼Pûˆz}àdý¡È,Ð>QAý¾$¶ÑÒ{/n$ÏW›`¶ÑRN4ûøÅæõ÷S¹·¯"ÕEå0èp±­fšYݳjÔ“ö|—£â ;‹“¹èH…›Ì1LÝ.ÜõoóÍkÒ)KK.•ÐÀóaŸ¥aõ|€„} ¬,”l¤-Ûr¢½55l ànëßòÛCjV'}Ÿ‚ eý”iBË?ª\ í™ú[ÆËFÓ玬kù—ß)rV$+ßo…Œ_îâú)ÊL ñø±ñ`¯â=_M9"¾ )é:Y4u¨Qon†A\ ŠwÅL—÷²èÙc7³vÌNÁUîQZ=ÛXî!\:ñŒ§÷ÊòˆŽÁ­š¼¦=õ‚=ú6B´z¯ Ú%‹K^-¶\b…H8}NÀîÿÄPN.s3(0¼Ã K¯ÅHðö+nÚ ™C sÚ¯† `ñ¯ZµÿÞŽD»óîú½Ž½Á!pÜrSåeRš[²wв€?{6öËHhu…2uNöDÅw󯬡 ]¸»¤?vУ¨Ž/ Xºãa…Ìè2¥ºî³WI›­âœÐT˜3C(zf0ï$¡êFÇŸ‚{k¶@wÃçt…y"<>Þ¯4Ê$û~–YI²[NnM¡0oÊ™ª_qŠbz¸2†n*>‘Ý2ÏìüãókoE.H¤ÿZD>„Õß-«~zWœçÁ—0íñ^»œxY‹Ûß½›† µá+k€MaR†ŸdzÂ6ú|Ì&¨aØ–ÝÍà ¾CreR½–®§Žôxl&¦Âw?†ÜÛÊwS$uûе„skÌ3‚áÆ“WKÆ<­í.ï8gÑÔú™™Ý¤%ã éZÉ\2GÕõ©T{Mî¡KWÁ ,¾¹ )kÍSA¶Kí:'ª‚ó©Þo~[î1$,JvRa~ä⪨Æ՞Œö4X)19‘’c¯K/¨Ü‹ÙéAãm •%ä1vv‰céW¤æ,¦>ÑJt‰”—„Ìöó¤Ô™å]š:NN ßYÛý=-ÌÍ«!è¨#…^@‰Qú]“c[Ó†Èä?™!R4ÕUålÙ¸…Ü¢Îwä»Òö«VEb< • =&±¢«{Oâ|áj)·.êî†ÿpþ÷nÝdÉMA¨;$•üœÊB2ŒTшYjÄ$b£(0Á†#ÏVd¢Ï<÷¯ùÏOÑí>s_Øõ}ð†Æîç|oªm]Òä…ç`Vס“ó7— ÖŸuÙûI0´~ßÀ;ñÔœ–Ä]&+ÛîX?Aj¶ îË©.h`Éå)¤SÞ;úFRÞ«ú_úóGöü´–tÆ1ú~bF«îµ¶îAÝØcÇÕf\³´@Ò”Zd·–/ÉŠ=t‘/¯Ü.whÁ'‹ÀÒ¬Þ‡Dßç ·ª¾Àeµ¼ö¦ Ù3ªEáÔP¦q"Ч’H›×&›ÞcÕ;Pg«å€ÍÁ_þ©Ó€Àl¢ì§?Yk²á¦nß½Hpæ {íe5€û8ë=p«"Oß;Š=9аà€bÃVgÑ[r‚Z´M ®Mâ0 _¹ÿ4Äd·±M± \6AÂ[ô-ÞyëÊ_|õ!ä8j¿Ò¶â›±R1fû™Óp¼à¯eø4?¬©wÑK½@ž5è€:œ¥¦ù¼›ÇQz9›âò9¼„P9LóŸí$™Û´7Ånö½ž–É9]I^1ϸºD:•<šêœ¦hNÄSzĶuó“ØSZÌ¿ùaÖã¦:ÕønÞãÓû„›?ÿ ò,zË¡fµÍx—MgÛV„‹’x!Õ•4´Rà#eT©³P–Kk©é# 1w$¦ê ó~'ÒØ ([¬ÕaÄ`çV™ jsNA§£{î“[nX}gçFA#U[áKJæ=â€.žj¡ ©Ý%EW}A¹bÏ;ææÏ%_>6ùïáÍvÂßÀ¸I–ÎgB–ýÌÄKË^² · œ–/ÖÀžÕw¤;µ"¶aJêÜä¦á¸¥«ÈJ“eK®sïäP‘ÂÛ,³sàr8§^Ǿ#x\˜RÂS6;ÐÇ£‹ùM)¨Æ  ³´œÌpì¶ÿÓ“JyDåÑ âM§ä†vV.·6娄†çd¶_¯•Y³/¯óhJ<OÎnÒn3W¶ÔÖ.ïªEó­®±¦Ó㾆„`cº…ÿHEÓs'Œ×áî$<õ‡Ë»¡U¤ ‘ÛE¨šPóðÛË“g‰Ç£œkç‰q|\%€Ü„y·XÙQV¢ˆØ!®Ö•F(‚4S²Áü^ ·z…°æñ½&F¶½Ú'’hVŸlŸdå&Ê™³ƒC¬@·ì›Ò›ŸÄˆ-¬SëÊÎ\2RÝ>»¨È¶£ù‹?'LCR~®Õò3ȹ}§ô]5é£ä‚ göŒìù› |26$~oµ#5šM¤wš¹lèº;gÈ«ÜwH1•Z§§ØÚÚ"<³æÙw d2jøæ929ÿ{Á•BÐQ³À;ÙYcÙ”,â< Ùª¥QÁï‡ñœûZ9(äN Ë‘ø~Æ&´bÔU I‹ƒž^!OoSB¯¯”U:æ!Õâàdˆú«T=,jívWgü’ó÷"XLE’ú߈֑©··öKÙ#y‚œmøõr´ÌÞGû@R‰°,‘¦-±d½ÂŽ´í{l°¸’t6^FÏŸ=áÜw:ÅØ‘Ê%B“lͧ‘uo…\à‡ò*C…RÙ¼‡üÅÐî(,7p¬ñRÐÑý=6š±4y&”¯(Gü•”Aw²¡’EÓed9¤I·¤ EKòÄ$Da‚{!À!< Or:²v;Š‘—° óËûBuÎî‹ÍëõR_³Co›A|y0[!‚Ôo»·k?E2œ2›B'ö=ę̈{wå2y&¾ÁêS…ŤŒ·4sapšx<‘¶ƒ‡ÅÉOYþÆ1\ìÿ¾ý£‹kûçõ ÎàA ’27S…åÆ xuÄýÒ'åhH¦'d$Ô}Àtn}òÿ²% endstream endobj 99 0 obj << /Length1 2341 /Length2 17531 /Length3 0 /Length 18905 /Filter /FlateDecode >> stream xÚŒõPÚ¶Šâî.!4îîîÜݧqww·à 8Á5¸»{n NpxdïsOrïÿUïUM)cúj %U3{ ¤=È……‘™ &¯Â `ffcdffE  P³r±þGŒ@¡tr¶²ñþe æ4vy“‰»¼ÙÉÛƒ²®¶6 '/ /33€•™™ç íxâÆnVfyF€¬=èŒ@!fïàédeaéò澨Mi,<<\ôÿ¸Dì€NV¦Æ €¼±‹%Ðî-¢©±-@ÕÞÔ èâù¿(¨ù-]\x™˜ÜÝÝíœí,ièîV.– 3ÐÉ hø]0@ÁØøoeŒ5K+çåªöæ.îÆN@À›ÀÖÊr~óp™oÁª2rE è_c¹ èÿé €…‘å¿tÿñþMdúÇÙØÔÔÞÎÁäi²˜[ÙŠ’rŒ..ôcÙoCc[gû7c7c+[c“7ƒ27HŠ(Œß üOyΦNV.ÎŒÎV¶¿KdúMóÖe ™˜½äâŒð;?q+' é[Û=™þ¬ ÈÞäý`n23ÿ]„™«“:ÈÊÑ(#þ“7™ÐÀÁÌÌÌÅÃ:€¦–L¿éÕ<€ÿ(Y~‹ß*ðõv°w˜¿ôµ2¾ýCðv6v\œ\¾Þ+þ7B`a˜Y™ºL€V „?ìob ù¿ømøNV]æ·Ýc0ÿþûï7ý·õ2³Ùzþ1ÿg¾L2âꊊZtÿVü_¨¨½À›ÀÀÊÁàáæpq0|ÿ7‹’±Õ²`þã*2·ðü›ì[—þ'a·ÿÌŸú?·Aøß\ öoK PÿÙq=ffÓ·–ÿÏ›þËÿ¿ÿÍòÿ¶ãÿ7!IW[ÛÔÔÿèÿÔÆvV¶žÿ1xÛYW—·ý—·»Ðÿ5Õþ{³ò@3+W»ÿ«•q1~»…íÛhå,iå4S²r1µüwYþ•«ÿ>2[+PÉÞÙê÷«``afþ?º·Ë2µy{9œß6òðípþwH ©½Ùï cåà;9{"0¿-+À›åíÍ€ÿì0€‰dïòæx+Ï`nï„ð{¢œ&‘ߢ'€Iôâ0‰ýq1˜$ÿ V“ôÄö¶|ЋÜôÆ"ÿq˜þ “â÷§ÊôÆ©ú±˜Ôþ ·¬Õÿ ·šÐ§öÏ›ÎøOEo,Æ.”oɘüAoަÿEo:S{Û·¹ü„ý·ÄÎîÝï1™ýYLÀ¿à[=æà›Òü/ÈþZýÁ¿ÿ@¶ßÐíÇo½½«Ó_ôo&Á7BË?ɾuÈÒÓÁúËâMöW@æ·1Yÿß:eó|«ßö/øÖ»¿jy+ü/æßµÙÿ‰ýfûö{÷—ú-w‡?ê·¸o—oÿ§sìo¥8غ:ÿåò&qü3·ßÈèüÏüw^¬¿…ö.@3[[ ùŸ±¾½tÿUüûüüWÃóÍÿ³üÆ_eykÔŸ\8~# Û_äx3w~{ÿ8¼•ô‡îí•cr±tþ5¼·v¹¸ÛÿåðÖl׿à[³Ýþ‚oiºÿµoÞc}£÷ü ¾µÁëO'ߘ¼€Nÿ†ú_/…©«“ÓÛoå?oùÛ3ò?øŸf Ðhа²hoÊb]ÒqW+Bèΰ7)0G±§ù‘†Á{Å©Óõ6•¦&;hÓé—HêH/ÚúŽõð*ñ³÷Iklx[²rû£Ï“a¢ÊÌ^;Âò4ÎàÔ§‘ú"øw jÂû>ÏŽ>6­à]²ùŽ®Ü(J…˜wîýRõåkãa‹{Êû5œŸÊgbÕcôKç) LrðHa\ˆàh1Î=Pço~ÍaäM½Ë&Ò!øþˆe+öÖùÎw¿àµQ©ÆêÜOޝƒGyƒ1>Cé-z˜&‹»äý¹xÓi<}§sbË.…ÅöÚkOAåÎéèÒ€’bŠ— K&:f³M:É®”$ †=ç|]vÓCæ’«ÕÄ™^1ÉêUÑ–«žC‘g¯ç!¯Þë–»N‹6\Å;µžÔÇNžWO‰Q´g1ô‹ûMoÈtct«±…ÅH‚¨µ3`Û"»Î=ðhCddÝcXfþBÖk6Âíh‡ÖëüÚ4Ë~°kÂÙÌß<•¢±1Hà°¶-5O‘Ž0§>‡ÈõO+!4A,Yjvf~+[£â¡~ÝÞ "MVÍaû€;Â]ÄÀñV¹zù«HMU>w6ú'xiØø¸ûfˆð­ W©é-^ñoá[²q/Ôa…WšU½W_¯Îô‹˜ùË-TEQ‘lS0uräÎæ â8 xú°^xK?´ÑBûœJ"…¼z÷EVÃÊ%è7ñs; p;­™ Ò=NÒê”^¨´úŽ~ú…FvXá#ðá{êŒ(ݸ´@íBà¾~Y^w8xϨªQ î?Ä[Òý­ÜøÊŒÙ²ÉÛߟßfÕýçx^ÚCv¨Ç dPÚðÂ}ð;B© Ë€¿©ŸWó4ülª3xÈ¡f]²}`‹¸rïi£oº(CwøÝ©Xò¯ ÄÝŠšbrѲ¥úTA:g§Ÿ’p¼æ²sT£“寉iÂÖøGšcNÝž•æ,ÈøYdÈ ÷œ7>µ÷KÞZ¸ÖöíüÄ1ùn½Fçv÷šœTpþˆœg§TAãAõ°OÉÊémä`¾h‰‰{;õ„kõlqA$_1¥×®[y…rñþ‹FæçY\Ù,-à©)¡ƒUÉ褞 Ô£t®õ^!å”8Yìý:ýó4é/Ÿè"í”û©0y8·–e¸` ¹÷¨Ä³!fH‰µ˜‹.F[2ï鎧Ì6ôEÒ°f‘…†’,J=›ÎDJêf v›Ë`âoxp29¬Ré‚c¾ÊB{Ô ñöí¯OiûªLY íñøÕL¹Ùª?`{‘”ëS…; = ÑšoÞù$,üš¡.”¸–êì†nGBÙdùy®F )þìû¶#›S/’G“;ù±"\·¦½+“H=²OàÚó,ãÛ!å¼E%Oý¶å“rˆ¹£í3fBUÃ÷¬²:nÌÔ™WÚñ"ò-’u bH<éU°w|*=`?Õ¿ü"±-‰•_©œaµÀT<\cÄ UëçJÆ{hrŸJö§j…,ƒÛÌOÓ0Áâ2ضÉÊ-»‘å«4EFPèN¡½B^‰•z×]òéûŠOVÒ|ßûõO¯¦Õ›…‡(#Ÿmçý·’9g-39¡uáYW6§óÞë|wS¿ÎܶíPò¡ ÑjA±;o[ªÈ!ÂÀ ì0F8¨Ä¹–ësþbHT4^—”íç¿Bàã­TËÙß;š.šÏe-sKˆ0ü‘5à¹Þ+ò©”éõ£0;¢ð+ö1;uÒ»FðSÀ±Qî¬%•CåìGªÛ„–oXPô8ºÔ"º8ozAS|cÁ\^-e+û…òӶGìÓò}~¼%j–ð…ªáxܱ˜­̸ïjƒ#pÛÌ&ðõ¼ð—ö—ØüfŸ(wù(§dS,žM%lêÝ\~îvؾÔr®!i¦ä¢föt•Ui¼vޝUs0‹{êØKô’ï|&h׃cB 1+£Œ‰÷"Qð‚T‘¦uI(« 1Þí ûû,ÜÚQ@ªuNÙÔnÁ9¹†Öj ˃¢š]'5(„P•¹‰®Ý¥Ø,ˆ^ø:ÍŠaZÖmGíá'ZYŸ‘’Õ·®n\»9·?Š-FS h«ÝP©\Ç XÏ‚åÙ/²É—ƒ|å^âéÀΓy_™Æn…ƒæòæâö°Á°GØ[â*dPBD‘¢Ç‡É+´j¥±áEý-˜qd”Sð¯ø¿ñÁ¤?ºùðÿä„—ZÙv‰uÔ ‹Ü‰wÁ­¢¨¥~¯uí–:ºÏåËÒÀn[!Róaµ:œ˜³.-M?¹.óáûÛÄkCNì+ª4ü“i¶u#Z5¤&êê2ýÜ>TN•eÝj‰ª"²$lμÐ@Z‘Âãõ¢q$h—òa ç…™»Óéò FƵ{A¤ðÞˆ¹ËlAå®pÊV´!꽓ñV/ÐG”®ˆ0±ÏµæŽš +©ÏÁœŽ‘ö°´èYXÛêä;½·t!ŒŸbãe-vY¹Xñ¤$”Ì qçÿåH|†ò³¶]’Q~ůl‹/S„ô`’T-äÖ©­0I'›>¸Sƒ9ÀI™Oþ#ë1Ñã˜ÌõvªD|m]˨¶›ŠvŽwHYν7Ÿçõe GÊ>‚§{|žÔ·ê>@í¦G¸‰ÁêWw ã%ó/³§#ÍVv¯Ù±ìì„L&Õ ¯?¨2~A~XëeÛëD¿¸<úX¡h9%+2”‘Yß!hPŠÁã–7—àIKK%cçÒ2WÍk¦@v–T”"7G¸o¬â‘ßGúdFÒ©‚ž’å‘ÙH{‘ص˳Ÿ«Z= n†i³ÜGdB^û q1Vnà’ÆîkÉLü&­áÔ/]%SL¯Ø ËIõ½k ݶ°ž8, !ä5LAño•Ìà ~TQ-Nc¡º™D¶±Yç«§Ö&|çgý¯¥uá%Ʀ€²{l$ŽŸ‘`S ñÉâr †N+Ò{GUŽA¥5LïJƒÐ-&j”(’£=•fÇ]iÝùó"Ϧ'GÛ¦ Ÿ´Í6¨ChysïìcÍŒ" Eä”LBbÂ@ŸÃj\ ’ôÉ‹#m-(G怟ӫPÓL8Ì0gh%à9¿¨«Y>àoغuñ´Ò€ßÏã“;ˆeež¡ÑS·ÈÇÁ/t¬*=jD'):Œ¹˜Šµ-äo3gÅõ×TssTj‹PP%E°Èf"Ò Ô`¦Ì Œ‚„$ââ¶ŒÑ £ôjî©hÆ ÙWˆ¾ú~Ð.¨¡k4[40ý¦Í9ˆÁ,¡3èB?üŒ…‰OSiõ èP$®¯šËã¡—Zq‹r¼Ð{|u ÷H\\\="$®Õ#¬JXß.d‘­­˜Â “Ý­o<ï¾zé»h!rÀÅeÆž`Mj…Êb '~àÒ‘'5.J½6ß—aùüNhÞÚ¥j„›—‰:F&Wª ol´‘3j ?ü#20]A]™Ó/À]¶Gî²uà˜@DƒM;OמWŸÌW¨’Á&'t ”B—–‚ƒHd;Œº³¾ ŸE"øûX9Þƒ˜[&ìÊÕêqLãå48TÞiû¡”Ëá]£DîÔ J ˆ¼@¡&S4<”Å;,$±µô¡ .®:£¡r¨š*y·MØc¢cŸúPJçþ˜I¤jN-’~ 2ÙT¸n²;únH ˆ:Äl%.Ý~Þžø¶Ô0ôGŠ=+ÊÿF{9OÉN‚“t{§1 Žüp¾Ìˆ«G¥]øk0îZ'(*3–(2bÑgƒ‡6»s囹êVS€r‡¨™ Ç©Œ±÷“I7 ¢óžnrP€¢Š+°÷IpOª ±Ø ì¡ÆZi¬†ÞZó 0GeµÚ•«ÁùGûµ-BXT&[Éú9>SRÈ A¬üš‘k?7I9A½)Ôç{uõŒ'ÅQ>?ߵϨ-èwf‚!QÃp£(ãi÷ÈÄm_a¡ÁmÕŸìÝŠŸ£òèÐëI•)¼úÉ?«ƒ»ò7cËrªH¥ÿmXw³û¶[KqsßמW?‹¥_) $æò´nȺ>4oó|ƒ…Iy2xÄü"ŵ”],“vÓɬlÞÃÔ–ø >ê‘ó,+H¢ºí×,9Í»˜Â`㽑²N¿¥ñ³IÍ_‘§‰.]S{×¶)oàû f&'êÚBíþqx€° %K‹z6ÿU5–ÙëPÌcо«ØJ!&àƒ°‚ðǘ¬·æ5ý»÷Á¬ßj?õZ-:pÍ@‡Q¼+Äôˆš™`/æŠþÉ=Öü3$àKøp!‡ßïKgޏ»iŸõE­ÔCü¤‘ªŠ^“6ÊÆYw…ï¸Ïb_mö<1+ü¾¹J“Qs£L)pÕÜP7™dpß#Zð&3Ë›x/'=J²Á'Ç«ºKƒ|Å¿0ÿ„}8¹ö¼Ù56ÿð9[„MHâW£ Zž( ø&ô^`:´é”ºâågºVu"ò§•ðÈ„jBPÂsÇÑxjý÷-¦ùs'>!žÑåsTAål…%)+ØàÊÍÛìD‹ô0ï°9CÔá æyé Aê±WÍιb/Ÿ,µñ† ùsÚÍ¢Î^lØqÓƒêUþôlŽ 7÷jÚ=yÔåøˆû«4– Â$«p¡Ý YQóÐ|üœ"•X+ï]i²vKDêPð|B‹¿î¯ ‹qKÇ¡XÂwÔ¾?²ðž"÷qÔ²aÔåÓ³C”ª¾9ÌGyÅx¿PeeGx EžaÓe&!ŒáòŸò»,%ñ(d®ôŒoÚÊHåo8çô œ9QkÇ·Ç¡pùb‘,/£]ªí*“ÓÍhîu¦5µ¸^ö³³1wû,ƽ1:„†ŒÌ£æ #o€<>Y’oÅåÐ\ÁÚ”¤,WQÏU÷É¡¹ÇLø€FE¿çÕRhU×.ÔAˆì‡GQ:3=”û¾½WýeVó:œÌ¶«‚¨4*Õðúó8Ü Írì-Îy`¦Vû“¿¯óçDNæOê5_–ÈŸÌÝ»Î+#úan]¡#÷¯qj‡Š¤=#ô>áŠ]Ê‚åÓRnvÛÉGRBíäwÁfêA‰tõL_r9ˆUæÉ•bËŠ¶2ßüB¿ÉRñ»/²{åä Ž«‚wSÚ¦Á”ªyÚÙS£@­3×§ˆWt ôoÝÛÁ³5ï±Ý.ÀYÌ Ýj$ömŽ0œj®Ûy….»ÒÄ¢<øLMv?¹P›³øHW€K>’Àºoe˜Q&šãåufôN_ìhHß”î0 p&¦-¤š6xô¾±zOž¹µ#ì¶`IobøÆ~xí*îÊ®M¡Œ¾XM·ã ¦heÑ8äŒjÀ)nõëJó¤]>ã‘‹aã,=U@I¯ÍêÕti®íú»f¨Ãy>â;uo"†˜¦Ï3A¢‘„W€¼˜0ÇÕüŒBc#ÅqÇH³Ðß¶&ÙŽ çúÚÈ·Ž™®|fåUšŒb¿× •ñqîË?ø äõYø]Üj\Ëu¿Ø^RÛw¬¼ÂÌZdØJ þpOnnéY ¡ ˆñw¸6[­×²ÅŠ×r%Æ*Ì•Bl}oÂ*¬BÞ‹¤.-ÝmZ©¬iyp·Š¦K€T1RõT° `ò=Ó%æ¶P³z!3zƒ ­§žÔxXðΔ’KjšV®õÕ|úg\™3¼¾kÊG©Ô&$C^Á7‚gm²î³^úcá€ó±ô‡±ý wÄ"E‹’9Ë6 L&z7;‡un2·û#͈Êß0®rÀÅ¿Ì_=ÚÜ9§Z 6e…&æÇ–)ÕRûYS(U‡µ ¢"4JiÚ¦ÊÎAdä„ï6žß—ü‚ÚH,£ì‰²t½£ì|/FŽ;OñÇcª§„D*EMú i`»üþù¦1lëôHwVê,˜W(Ô»˜¼t:ÒäݬՖàrm騌¤›UüÆX©Õ¼[i¨mmÔŽ#µº‰ÙŠpç†>Õ˜Š±úº|G^„ H )Œ¾Êœ·—ØÈð6!OÅ6ÕEÆ}ºïjdFOF¤Ë€@øA›ÄlV‘,Á=ÊwÁu‡ø*^3æè‘Gi4wÖ"ÉTá&¢Jfü5;ųqôs$¨}n {vˈ²$‰_hù«¿0©ÖA ÷BÈóøj鮩°m=ˆ°Ò)›n»IrÂKÜ$éž1¢LÎé((‰íô[âaÇùcœ8ä˜fä þ°õ€='HLÇÞî¹ üûtNÜ´è©®6qKGÊ |%ƒÑzLjdJâÅD"Iq)ƒOÙŠ†¼'¼9ºuìm…{5 ¯­*ö'5£ã¯Kd—îd#¼‹aÿˆÜÃ_ ñ –=K*Èkf ¨’ó=€ to·‹õM±âe% Ͻ×]Ñ•VaèÅÌÃ4*'ãU£h¾‡§Rº×‘»–uäÓ:ÛEáäåå’ÿBÖua0c j& Ý0®0%jgT¢^ÿŒ~wމæfI¡Â·b‡lã¾Sæ$óøÛø Pû‚„x,Rƒ졘ïEíúÞüe4(¢]°ÄC¬íñÍFBHnï÷‰vbêËÍ|ÍÊ./„;9ºÕQ:›åèÖƒ™ >ÚÊìÄÃûbüØ:À©åS\ûݽñPç«Enð)銇œ5¨QùkéôÃÖóü6°ŸÖgíU>¡}Üç¡‹įŽèB³¡˜Í(ÀiHÁmÎܾæ¬îƉúÆ‚‚Ö’#Í_B_ÒsÓËŸ;B›¦ÁZ.ᣒ†a¿>i$Ds$Ct>Ž}êç£M˜_ªV»ý´|càöœKøò½éåô½À¹•óZw ±uòŒ›J#¢õA'RŽ™‹;Cyˆ:"%\~…´¼'9=¨MÎZc²¡ 4‘ñ˜P¯]ëò5ù^ÆéØú«Ê§ôNi’æ&lúÍ4ù1_C-FÒY¬ )‡—Ðzã½vcj’§„RºÚMc,u‰–' åñ°²ö‹ôº>7D>†Ã÷&»›ÎÜ•JN™ª Uê(„ÒèÀz ê.VøUä[Ì3)åǯò`J ÍZz°‚ñÊS•3j`ÆÃäOˆ“$fs-æ¨~`i¾‚Å©{Âtø¹¹nûók½eøãTo@ð K£ÐÔJÁ‡‹%,0Z›J‹U‰‡î/l¹¦…›pj«¨ä¨6  ‚k³‹Ð"gZ>«Ügâ‰|ZD•ç8Lthd.i)Ø5|mŽ™¢/`töà‘âÍx7ÛF@Lf¢œ:’ Q«úòt4Åâ%“âZö•åJ¢›¼íQGÖe.ÁàÈ0 k©8>)#YÂ/¬àÌÜ$Í1'ÏÜqwIœKˆSîos$äàÆR [ír{é® <å…!ÒìzäÌRÀä=ÁÚ[ÚÈ>>Ai†{¢cDâŸ^”šQA¸·íù®;ïÇï9ÑPåÆn"h4.O|ò”§"˜„¡†÷ƒâüÇõŠÒø’}ˆ\Œ“ýZÈîŽIoÇÁ‘¥ñŠg#.õ¾Ijj™…|ÌD?“»ZYؼوD/FI½m?úŠ=Ê|;îÈëuwßÎ3‰äÛÆ–QŒW•.Ë‹² ^Üi›#ßæwÎ%ìÆƒ¶ÉM¾æ-Å 褻£ŠÍâÄÏäáj*„›ï–9èg ©ºMa(쑚äãÀëöª8xéÓ¶ÖÐ “Ò2»Ûƒ­ópñ5`±5Pc$¯içzÐÝ&Úæ²!‰ P:>rh %¬C-±[xtZ#` pw>¶¦úšô«p)ƒ‰Â|ôù¹á»f—n*ß­gõ¼ŠYb¦§ùÃ9~¢ø‹Ó/ÌRΈéMÙv ?,GŒ EKUuÊÔkZP‰õȇÖ\J2)¾ðžEÞ¦IìG©Yéjærô fÎ4•“f4/ä¤ä»ï½yŽ8ˆyŒbqÈU «–Z™È§ž‹«AI“¨˜äXï%ÍQs ‡ įMN’>Ï©*bKˆ™ %Ù˜ÍÖ÷®Z'ÊáK%Æ7â+_k'+[K¿O¤è\sµÂ¿R cTˆ¼öªÝó£ÙÙó`‡|§(‘äaŸs ûl…Âræi`’Ñ‹ÜnO:¯l°Z;v§$•°³Q—÷ùú¥j¾Œ]Û„ H{‘GýrÁ­9Ò@ì·¶7£N CÓ€‘æÈe¦ M¹H!.‚·í«¶!˜ÎB0AòÜé_ÎQèL|3HÕÞhå¸Û>[«¢-x<3¯ôÉ•Á)Ý>,6·HùcfŽêtóYp%²M<ÎÝ­c¢lžI&éð¨•ã$¯¸›•®5CÏHµêPV6ÊB39‹¯¢¯ó×Ì·žQ“Ã8_ÒUÄsÄB!ë·¼díôŽ€%?{C(*œ-Lʉ#F¤} —æna«ÔvâöQQȘ1g+$Ú^î¥"žm_05÷¶fw¾¼ãÍvÄ3À_Ê%+\ßÕ¬pÃ÷–$7ýUC•f°› ÈXí‚,Õ*C¢ªIÐAê×ó¡OA™÷H”ͤ*q¦Ó|ûÀŸKɼíR§ÑE³*¸Jtaé¯mâvpçÚa‘ +,îá;Ã=ýHŠLVÑïå‡fŸ˜*ýüÅÙ>^,,pBÿk$A^VS‰ß<”)&–%Ⱥ—¦Âƒàæ‚j#èþ@iÈÙ‹÷Ý$‰Q vÿH…1­»ÊüPàLÜ‚±ëé; ãÜŸÿæ o~`®!Q-ãN~= ,Ÿ™a>]ì¢UÜD³†¨;ý¬`°Û¯)¯¿>»‘ëŠÕ—A õS¦ebN9¶|’ƒi–š-Å<³ÂÝ?¡KZŸÛúšwf”6ß!ßû˜ãbÞ.ûÂPÜó9ö‘`ú%ë}ûùcƒtµÜ7uª Ï\(Û‰•/)’·7qÕ—ÌŒˆ7NÉØ~шj°¿®³8ÉûÔˆðÂ5?!ÙœŸó»F¤êÞ qJ´—§‰YZ?–ÙM°Ý·^¢ÌŽŠëcTø/FhׯãõryU¥ø³Àa á!˜´~J^zœK»’y_¾jšîB¬öH3p|3rq[ŠÔíi°‹°H‘Ë5:Zex¹çD©}SéK䔪³ÁôH/0÷½¾ÿËî3ÕÂij%k¾òra®û½H®ÂÇxò¨q¢¦€{Öh”¹epàr”Y[€Ž X€réñÓ¡#]3p¨N0Yæ•ÇüU¡o„åƒFW›,ÿÉ= ¶KÇ¿ï}·Ç/ø%4mKu–ÐIÁ_ÙÖöåÔüúåíµ9A‹[·ma¬Ôú0Ñû÷ck}ÃC‘²O_H1f×B³eDŒ…¥N8ª‚?»}LzÚ-ŠÌTdŠñaÅoóGÀœ;÷‰ROcbPæ€%m!TG%ôêåݘèKâ‡Q¯µ•hJÛæ^æK \­‰ÿ>lÚ .¹ÎÒ{·iãï}Ÿ¥¤¾ e‹bm‰ Èûô—±C 2£Nl•1t½†ú–̵Ä[»Û 8h1'I~°;Ÿ‘U3Šæ’xëQ¡'ë¾ötìùÎ+{˜/˜Æî¡Ò©A[6é$/ØoßVÜj^byЙî9_)/G/WtAâ—ÜHm€pI‹·Œž.Æñ~P‡¾||ºÇ~5 z<Á^¾Ï2L™T÷„¢ –û)'‹Þ¾i‹ÇÃ<Ò¡kÎúÎjMQ¸/IJˆƒó«‹ÕÍ·ýä?SõœÆ=2õó³áïYn… u#û3øÇ©t~ U0VÕƒÒÆ¹Ü/_pêvœê 351¸údº6kât€Œ;3ºd×Ï>„N¶»6$Õ‡ mTRU¾:~¿l5¿)àôÉð¾®›ã›¦ÃØ Ú•¤ïMm³q[×—ÝXÏK­m.œ}Ìp‹¬t }¸D2×FKÅ$*ae‚pÈœV+N$PUáᛜÓðÀàøW#|‘«ÆÅ,ÐcÊ¡T?ªãM½“n£–ôt·ãô~1&hßñÏ·ÍåeY*u ±¿«‡ž`©¤‚Z£Yè2+d’$Ç‚$VR·ú¨ßl‘ë 0ø ìAuʳÄl¼>ÕIì9H×X@ý)! Ó˜@UŠÖ¤j6ûœFk^àøkYpL[` ¯¾ƒ =í*Éœ3+ ØW\Ù ê ¸óð)W pU'–««ÝšCGgíÐìõ±`É´Ðgxxh³lŠL°ÝÕqg‡Ë[ïKYpÐ꽬úvk_Zè ¢|¹à,ïƒTo¢K7^G§å°)~dÆîS?*.­Øé£>¨¤,Èå3FCDÝù]_ <Ôû¦ï§K|Öw!ψ›ëöbH@gäú@ÿø9±ö"_¯å‡ZÜp §Æ[+qev‘r‘"@Ó¯FÎj!wQÃoÛ°AUT™=IºË]{ߟüô~˜qòì]’²#Å(ËFY~º6{>­Ü¸di@9¶ß&wö2é;§ª™vkn§‘ÝÆ]Üž=ýýUÅ «þíÍfÛê [[8#pü.ý—þ¬(7Ï]×õ+@vS+MÌ&ÁÆRü‘BÜâô2GJŒNR|˜íÍÖ´Àå€P¼€Ñ˜Ä…½&—D fŽK 7Ž_àñ²rƒ6q[0èNÕˆBÍzôöÄPœ»ô°°XIÓØ®¸"SÖC>‡H.öÒÐBo_>Ѩšó%|u,šÚg˜¡vÞoÖ ù±ØXÝ¢qkè0E%d'îW›£±%ÑuEÁ ·0þƶ5[Ã+fŸÀ×ÐF÷ä½®E8´0D' h«JƒŒ¦aÒÎìy•;rt¥¶ ‘ ˜y,öšöØav…xTD%‰“¿Ù¡E7ÌB]‹,SWáÚ“FXŠÇ ‹aZŽÃ—cÊ»c”zphS‡Xú^ãa‘ðÙcF€~’Cñ_ kí&}„½å¤È«6Á=ÏYÊùä×£ŒG¹µ&¶Öì+ÒÍkÀl\ÝØáqx«…¨‰~ŒÎÔëË­îËSsÓiƒItî@ô·‚å’¡7‰„@•Óª"ÔB¢«”IIܬӡCá5Kñ9ãßF‹u[Xçq#UO.”}b–L ‰YˆŽŽMµÉ$}uõÃTx€ñ¦;\ã^¾šS¨ý °¸p­gÏ×"GžZãüªå´„|CaCh1(‹.C:b!³†*#aŽñ @ˆÚ^)›œA¯ V:ö§ì—‰Ï^Y´$šRlp»˜çevÁQMáÄ.IŽ[Oå#ò Ñûô¯›û!DTKÊý¼|ɰ$š}æ‡Ea•e¥}®ÂÙ8¯ùIF†‡?:†“u’ɱ¡bö3¨HX2¤HòGoxÄ?¥¢Å…–jœL¬Â’)iTÕ°A~½™fâúÐîc«ˆÚa't¢‘Pù©ÙœgàÖC7ÓúÁ]õt Ê(Ñ zž5©w&ÕþŽÃ6¯1LôÂÀ2Nò6V„¤‹=ýMHœ^âœîí||ÒdRÍ׈G òŸt¾¹óÁ•iî©Ô€Ý(o’w<\ !_êð¶‹_>Á±s¦M&ŽîXHLh•‹^Éú냑mzÚÅ#ªè8¼WÀ‚+†ÑÙ⹌Êa³í)kUÒ„m@F&&m6—„‘fáWÑ;;¦ku"€ÉŒe‚ø¡ ¡ã:à&ë•ÞËS¶ÅZò¯DïÖZw¤õÑ›^W‘Ríå¨áò¥!€“ƒ'ñN4,à‰¯áß’By­sØöŸX?‹àA®X˜¢Ñ5UŒÃ\J\¨š—žÆùºí¦Â¨ïz*ÕtS'/J¨ +§Ä{=X JM:M16½QÏ×™Ä+2Jí Ùµ]UÈ\Úofµ†‘fÜPŽÿ”ÌOi3 ñþ¸¹@ˆl´£ú°ò#&jaK÷¾ÁZm1‡ls~ØrÌÌ™ƒÞU4èø˜hºH¢Á\,û øÉÝ´®ÍlÙ"gQÕÈ«3þ›Ÿ½<®Ay)>^3ˆU'·³ƒ“K?ïòf¬DûQZðøJz›B"ŇíÊ9e¬óÌY¨n±@ÉŒóôž£&ÛgŸ{œÑ‚oÔ¤ dÿq¬£)NÚmør=#¬á¦PD•f_N³î×ùjˆ²žÄCÊ‚ŒBšÊÁ¤‡¬÷—MÏ£E8’_³=‘¾‰Ùhë®^“ÏQ p^Ý3?=Ì?úƒ“|¨¬÷¶?ÉËÎ!Èmóª”J9Ë ÏªÛÛœ”[%r«Kñ‹Ã‘ó?ûšÆyFŠ®#p«ó“›~\Ûuq©‚I@-sÜŽÆEEɼ”"ÉÞxûÞ4_çµÌÆÈÈM@z6‚^bí¯cÜÉÕLäØ1”*ƒM Ì,VÁ,€SÜ8\´/d„ô¥N ziýÊàÕÑܯ¬¡4¥BÖ«¶ðÇlÉW+6ï âb\ºpwýr3=Ù¦h õlùdº%Á±ÔäyË‹Cðùƒ0¾{óYÄÒÅ s] Ùx¬!9Г \é•7¥ÏÂ*BB]ÈÑÊZÐï‹k3Œ1\;S È 4ÄìóùAKOßSPzEÎýð°e‡¡·´8Í¿ÚA}€ÖÎí+ž×Šè-xñ‹•’Qr"ÿ‰,…b8ªÖó{†“8Äô†¹ô6Ÿò ck´Êwi‰=—Üë{B-ÎûÐ6yþTŽfx°ã…žå¾ëHÞ€d'’††¥åàUfŸvÜǧ+ÅËBÕ)ÅtÝëIÃ-PWD.s«¢]b®EíGôònTדó…Ö_×M^¾½ƒ4Ée0Ñ@í‰Srú¯ݲÜï Û3hyÈi„ÜÙ2€|ÂZ‘«¦¯®ßM~)feá+>Û˦Î/àÅu¢˜¿–p-㬱R§Q€³1`+ŽXuQŸø(oùtxHŠŸcÑ»áQYÌ턬­ûîÌðXÙvïa4ã©eÕ~O“yÙ£áV–úP 8sᾞÇŽ\h\«V°ç8/›¼ÇÁÿ ¢Ùïcô„eXÈ[±ÑaÇÆQìÏ4ô½Ú}[$ŸÜ?^V>¦)1OZÄW¤#{½ó&#%}aõH}ô±ígÁ} YGÊÿÜ"å%ÙŸWÉ]$!\gÐy¦|Ã<¬X%=rµB® {`ì"ý­¯ëõx‡{ç£{cU/¡¥ÿŸîl jgÍôî=&¸Ÿ¬Èã½O÷Êç¡çI˜$$O‰%ë>|ƒàïTÙùІ“ÒEÁõ«ë1 F'u=Mº®îy,ަoíy—†f1„ÎÃð00^¤î]O•!$=õP.Gª°+ÄúG®Ö§@ ÐæƒÁïÈ‘Ît§B°Ô= ×ñ_–,Ÿ&*ž™G÷—®£êíÒâäsK`¯pÛ; =ÁàÇKÙ¢#ksî2§1eßñ„º?ó KÓÌNÐʈؠ¥p$¿[Ô2Ñ8)”ȸθþeëÛûŽT§>¹óé ̦ÎN‹Z‘Ѥ€ ïg~]À¼¥Y±²™Äµs>Ô¯gºkÇœ¥ gëCêž Œ×­3ÙÈÞsf ¥}&*‡B¼ê[Wf½3>â$dÇÄ-5È4*òìLmµy‚GÂ’JskÐ×AÿÝwæ4y»Mw…Fh“»2ùÍPs¥þ€ôÇ#.àsµ–“ÍÔøa@¨© ÊãÚiÀçV>‹-²,ó>1Í…XtöM¹žnÉŸ&pÛ6¤r«à_pæˆÔ™{¶tieáôbk¬}«?ÜK‡3•NgdDp9Íq‰ß £ª°‰ý½8 1±íÕÇö<ðÞðñÂ:¥¥I¹N‡vϘmp“#Ýtú; -ïíV€Óiâ7NWõe މ÷2ööaüjg/˜Ð"qjñ8VA1BéƒÅ.ªjšF»;—¥èìÎuó—Ï]£¦béNü´PË­xß{ÅN¥˜‘Q|™?Gàó^¬*Æ`aÕn¬Ð;@ßíÞx±Î$íC¸#{vm.}U°JAÆe@ŒIŒ*Ùkb=pG¯cØ!*Ýߪť>^Sé†ì‚ŠÁU£"Ñ\Š(QDÇA+·M®ˆp>±ˆz¾AY6•n Uœ„ûQtו/¢F‰rÊL/XŸöL˜Ë…w°–ÛÁƼؽ /RS£73¡N™œUõ­W®½¸Ò^/ºå†¯8ê5X#~ST"D±R^Z(÷^?|c®†í¦ŠnéózlÒ5±ùÖÁ¯^\ìŸYÁND¢î™,Oáú6pu[h–V(¶Õ™ *£åè“Îþ/ ’)M©ª œM™¿6°d”O-™ÕBðäÕ% ó¢úrxí¬8qJC€ÛÞw¡BiQ>}¨XMN{EA/—ã—Bºp–²f*tÉWT©ÍÁRL̆€nQ-ò;½o:×å‘Èè‚ÒŸnȶôb'ÆHÚ£'cH4`‘6j‚baÛ;Žóëm®h•›%R Qr´qd¬A8VD¸àOõE‰;ÏwjÂÂhub"Ÿ‘(ÌÑSí¼9Ö•‘c êKØÅƒBbè†v#¸¦do‡•ÓNwò~¶·ý¼ç‰›=^á:¿¡ ¯»Gl4 16é % ¢²oP8©Ï.íÈGCÑ ¤ç;A,lwª-%ãx7Ób2Yuš õ®¼¹ŠƒE\ï‚ØÁ»Ÿ6–ⓌœŸå‰QÔÆ…W·#ßzÈk¿-ÓϬj$ö´ÂªÆú}@@"-¸§ðwYîbsòDyNUx#’g}ôÎQ‚‚VÔ ØŒóœ!Ûœ/·þïD~Äyåz|—§÷iÕ/+ÝsGUlSššú*bÕÖ¬°]ù*v>l£.*1÷ý¶#‡_~‹jî\yóW6Ï‚±¹O‚U4¢ÃA›±bu Éqž@¹þØ(GÛÓàNù·5 Áè¡OÝe2¸bxAæ=+!Á=ÑŒ¦áqCßÂ9~jn>±ØJ§%Í\a¶~GÃÜ”î÷!$Ü 4Þ÷rAÒÞÂ[ó4~¤ Ý^Bƈ^…œVƆ î.)y ŽŠmÚ5õ1 l¥ù®6Há¿èí>F´òJ¦,’q3´ Ø2¢¡ªǰòÎ> ªîOÕƒÛ"gu¸R°êA:–¦þ˜N<æÁ+ÅúÍ®”áÝ®µ‹:¿—]€A½±>ÜꌌÎÏq‚¤0JîûzÌ6ü$FS‹~ò¶Ê«®ÐÄ>13â?Ù|is‰õÂ^´~pµ±g²iÆ=T›˜nÜ8à˜®¬ š±;8ò² Ÿì‹UÂ/d4C„K(‘µ™G™@­Q±*ë~éVt (ü|xðM Ÿãþ=Îì„/¶¡L§ä“çÙëô5¿û Ý>.óþþކÜükT^hzQ™v‹ŠQ]´½ñpðFÍé÷`<@4?ŽåÙêý’Ö¡ ¤-­ Ûû'M€À ¢7ÅVþ/ýä}îÆóª¹Ù÷¯‰~`4Ü⚣*‰i=a…gƒn´­Ÿ#}'Ã+Ñ=Ó°€s©¸a^P¼ÄëÂÁNÙIÚ¬Dò#ÍçSPž]®’rˆÒŒ–ŸÖñú˯Ü6ÁNã. `›œ4;ÕŸ–¬T8n•}¸žâ³DªÐÛµ¯Ó«9p.’ÚÄ”gäuì[!¦ p©9 ]Œ øæNª<â/‰“á8ùãAO±‰kï"=& ¸JL§|‘T©¹Þûaaº–†¸Üaüòo»ãGõGò€, øHWz`Jé…ÕÞyZ¾¥SŠéÿ#ÏÇÛó¦‹L65–1ÇRÕÉéS—Ðþòá@ñ­ÉŸd¿Ž–ìŒÏÙœe&§Œùp¼¥6î³JÁiZŠpª„´¿Z—ë"°vözÍ(ݬƒS ¿D¤ãPî=¸± ¾`P»só`tpggÓ£¦*i«ðùeÍ+ɬM}óÓL#œx#…Á˜ÕC 䣌GøÕ"ŋġÌûãÈà>&`~°üuølƒ=¿½óÜkàºBÒ)‘qR §¨9tôµ7­‘¿,xÿ·\ÊËËqG>M–ä7˧ºÅ+уrüƒ*a™ðXô&ª¼•ûpÒ&Ë.„´;§.æeJ´vpòæ«Ü^‡9sLø[ïñO[M5ëÆyïíÜfžRÃôä8ÝŽm¢öÖÌ!Ű¹’¿`0'º¡Ð 07hPòx}¢#ù™0èU [`dI&“­|…ÃħÞk#< Še/‰^2ØëíTEAîl YE'¶$·=;#sCûZE£â'>ýäöIá¨Z’ŠÓí¡ Ï#_ÊãQ£©Ïc1šßä-ó<ªÊ_¢Kq!ž%, ð´dOëײ®±h«âeö£{ùݼ"»e 'áBG¦§ƳªMÓg]ÍØä£åáœ'•B[ÅîèÆó>‚H-Ñ:´îm† u¯ïßG|ÉJ*EBÒ²ûÚÅêM$x2´h’}0á_'Ç…™5of<“úU|ㇷ@Ã6vfÄ‚B-Òm =ÔKN}Ô't5Z/Üù´Ä½y&sŸ‰ ú‰Èõ#ôå6‡—ˆª¸»ÏÄc-Îë“"WE˜[Ñ©»Eß+ãYvDA£ '“®²nr™Ü{½gLJآ<\#.»È¼RZ±£r {—&cà=@ü#Súíà¶ý˜W×EÏ]w3×·™¬–sHHÚVìzWG9 }ŸÛï=©…ø£Öç¡s BÄoå$ÜqÊPZb}ð¾T{˜XžhÛ}Ñr>~øQ¯¬‰ã°iŽ0´Á^ôí;@Õšø¶¾p“ÜþgPiéž?¤é*‚¨«_Ÿ"³éwæ©çœ\ËÊK‘Gm±"ÇJ±@剨ԓ¼9©‰âÞH7[þ<ã_eRcVïnN2n–ZyDN¾?žÀíJ# …½î¾;Ý-¶»*{$b`KpÎfÓ@R:¸ÛΫ‰’å`{‚R‚ϺS °|ç4]q¶g\£tÎYªVL\ ‰"ÿl€]ÈÙyŸäfoLj‚8‹;ÆVríýbî"ø*yé¾·_’‘Læ¾Õ¢ó@æO9>šÀɫן¹èÔ,ukâ}ë#JÍuª*ãZ*GªºÞw«’YäÝ;;vBIt¥öÈèÁEv¾›¦ÕõÕÞ|¦ìA5^¶5t£° ÄIƒ@ <à ¯D‹µÆ3LîÄ´ ý“‚×<î•¡åûýO׀C–ŸÚ1› ÙKЦ²žl .Íš«ÃÀc¢‹ÊŽ õ]6ro(ƤGQ8óÕ&”´Ð‡cN§‰3…w¼¹8–J*í…îÀØQ?±môo¿FB$v¨ ¾ˆÆ©Á‘¶üÜè: +:¼M¡Ë¿zǵN3:j.®Ò‘æèÜ{Õ¼çàá#E>Íób$l}˜Ò×”;ŸóÒ™"™Nغ'Ú­ÄÄö¢ëà’`¿$º•Øÿï§—ý)‚StÒ~~‹yQ“Ñ{ʼ |žùäªd‰¸ýJÿªÉí:¤`‚øÚOÃNŸOê䂵–7nc“H¸†N—¿N¤èLÈ+‚}mÈì®æ¯ˆù~À5¥¢ÊÈôrWDG¹üÑ5…5;ýžE¿¢%鱤.LÚq¸^/?x«±VêýJ@uÛéš(¦ö H-†¦õVó+¦üWð\+g\;Þx˜6qßåÛ4ˆ;«"·@ת1ìÔOÊ4=.h?›^öAŠqÔ;a„îø’V¢êûþ湑#Rð/âÝ>=ŸÙ1?H;Õ£tF¯[—j‡†T©¯Ç ÉuóJöòBW’-ÛÖ¹ªÚä=hwRvê}Kz°4MÚÚêXbSФGàtÐzëKŸ^ñ× ±ÆY+#Š2± éO-a^1-)/“*ëà‡LåK¯¯ÀýŸAÁÕÔb"{:wAŸ2ƒ ƒÇŽ,2¢?*C‹w ÊÞEµ ú*ÆvZw w>噣ön6šG'/*Y²£Ê®)h×"a%sâžD·€j²j.˃b(‡' iI7ö×?×"†Ût))!!|=…òPôKIÞúhƒC"]ypi–êÎõ…?®2!“ìhG“šÜÕ| Úñ^šTÿ°¸ÿ«î>ÑŒgÍ•L¡ú…>¾Àâßâ[ÅXˆ\Ë0•®f-V€Î&ráÜQtåµ^hÏð”cì„»ÝÀh ŠU(›¬‚E‰CçXnädGØÏëÝü{w7\¤Ÿ.‰ƒ;`Ó \A]úH/AßÑDëíÿ«¢öïqxjçÌÕŒI‘šzíCÞtN•žØ!5½mß·h) D×oŸÿT!›Ù·É‰SÒLøÎ_n¨•&žo×Çè6€Òu,Ò°á—ÆÆ_e|z8®ïå=—÷¢š~ò¹;g~Mr¤;qÛéÈ‚”]®×m%ÿB'¼ãºÆ xYXkÉA>«-Æ{¸œˆfIÓÅø`g-ŠWÍÑ@%­¹¦ëA°ãá(c»Ò\¶ZlXßôÓÝüŠ9Z!?bVaòvÊSw·Þ>£ÇOE!GmîZ“âmYRøÓ”¬>–Žú’Ù©T"(.€¹÷úf9•Ÿc+DÒ4s(ïù×Ô9UÉÞ`È¢wqhÊ侦ü ˆÔ?üa}‚CWb%…L¼/g^D·~ŸJAE8xú‘ŸûW„ì`.%½ýƒkk”AKC4Ò‘Z¥!¶+Þ6¾;o‰d¡›HŽoÚmxNË0…;…¹5êIEqìßøùÅØý´^õΚ{†ïŒ´|¹­í1F ÇîêÕ9†©DŒ>U]Ê9'Î’ÇÓó„oø¯z»N²•Ó/½¬åQüDÑŽ§tj³µNÙ¿lO?ÜŲ%Y… /Ãû†(E8s³Ф¶Êå3•÷ß58$cKûèm·Âèn¹Êôtjw¾;X¼.Àœšm.ççé±íàï|]?i㜚¢Ø/„>ÝæaêR×K*ü˜rC–:¡âãœRRçrÓ'D ðâ«o¿&3r=Æêˆ¿I\²bäPãr¢yV=NÅ–?Peˆ8ÔÙX‘!> Å/†Þ%^þö+Ý | ¼…æv o™öC„WáLFÓ±"¤äÈÚ.†4“—l¯1K͹$*oâ =\EÞ:íólÖãÊ´©šH0/|¥±ZŠº =eÙ_ƒØN\ì}I–F™‘‘Ý>K]S é!DåUwïèb…ç¶Èˆìo•ÿt!sH ½;8cUÝéÊ«J.UPm sšvHIÚûzå±D=’’œÂÕ˜áÉVvåìÆŒ¿ÖèŠm¯ë‰ýYôNmÅÛ>O‰ñÒ¦áwWmöíK{jçl:¼ò0E±ÿðû}ëkâ$ºýb3d¶ƒs*.€‰"žò·ËÜiÓþ[ïÕ&²—X–Á0¯¦èߤ⥶Í>+e˜´sÌ[vɹ¥µbô©¥õQ3ÊêÍüè2—£}çÙZ:º´Ù´ç'T/å?ÄÙ)¬ˆÓ§ª—ͬ³èר”ÊIi…kÌ~>ÔÞ7æ#BkŠÏ"ºb¶)±Àc7ò@¶Ú)hh±þI²;q#dãeø´ë,_N Çuyzz“Ð -¹JWeÏUçàÜxcë‰é†gº‚Ìá? ¢½ ýëIÅ":&‘ ­ &'T"œ2h•×øIú†ìFö€¯½º D×…_-‹*8ÅÊÞ48¡§Í´Š [<ð7›*•2“ãÅ­,{ •tGÎÒdÃuÙü?ù1?ü‚TGn-ÂC{%Í 9„7NFfdÉx>tÃdCÉz¯vý·þå±ï‚M3hÚ&ëzK®›ûg3åñÚ·¤•‰ýJÄ ~òFš4ÔÆ}÷L0R¼f1Pª¼ ˆ˜W®N]> stream xÚ¶T”[6ŒtIKH Ý0”„tww×à Ò=€„t—‚JwKIwI‡"’Ò%êyßsÎûÿk}ßzÖšç¹îÚ÷µ÷uï5 4Ú’60+ êÎÁÍ H«jq €@^N “AìýeÆdйºaPáH»‚,Ýl2–îqª0(@Éàæp?æ<@ Ða®ÂKO° @• ƒ‚Ü0¤aÎ>®`;{÷‡eþó `¶fp °ÿNH:\ÁÖ–P€ª¥»=ÈéaEkK@f ¹ûü«³ˆ½»»³0———§¥“'ÌÕNŒ…àv·hÜ@®ž À/Â5K'Ðfœ˜ {°Û»6ÌÖÝËÒx0@ÀÖ ¨ÛC†Ôä xX ­¨PwAÿ«ü `üµ7nNîÿ–û+ûW!0ôw²¥µ5ÌÉÙê†ÚlÁ@]N…ÓÝÛ` µùh qƒ=ä[zZ‚!–V¿;·ÈIj,þEÏÍÚììîÆé†ü¢Èõ«ÌÃ.ËBm¤aNN ¨»æ¯þdÀ® ë‡m÷áús²ŽP˜Ôï/` †ÚØþ"aãáÌ¥ »x€eþ y0aþm³¹ø@ €?äy[Ûsý*¯ãã úíäþe~`àïç sØ>ùƒmA/L?7KOÀÝÕäï÷OÇ¿&77Àlí°Ù¡˜W0ƒlÿà‡Ãw{ŒÚã=ÿý2}— ñù;ü÷ùr)êJ¨I±ýaü_Ÿ”ÌàÇÁË ààá„øø|BÿWѰÿÕðïTE¨- ô§Ù‡]úOÞ?ó_³Áøw-5؃hAæ¿5näZ?üpÿ?+ýwÊÿŸÀUù¿iü’ó€@~»™ûÿ?nK'0Ä節Íz¸?è_ö0Ðÿ Õý™YU ØÃ齊î–s µƒüwÁnr`oØÝÚþXþØu  iÀÜÀ¿n7ø?¾‡É²v|¸9ÜùÛzœ/) µ†Ùüš0þgKWWKLàƒxøù~Ü£hòþ­a'æþx ç°…¹bþ:Qg.•_¦?HÀ¥ö_Äý 7.Ð? /€ËîÀþr\ÐÀ‡\Ø? €Ëùð!×íð¡ ÷ßð_ä¬=\]Æû·ü˜ÿÿ¾K@ o5æÂ,Ìúy˜CuXëe¥$¹Çö¨èö~: ‡ß‚k›Ç5Z ˇìU×sÉ”OÝxK›²Ìg‹Ôw~ß›jÑ"›_i¶Ü¼¸5OÔšØnÁœ'î+þ.YÓK‰AÁ¡#ñåÅË ½`G¤¦GJ ù.‚8…„—^=òÞ5½eŸ‡#f·5¿|x¦Œu[6ɧk\2ÍP`•3CJ‹êÎA‰ÎJpä;}v>E7vO­”Ȇé¿ÇûÚÏhçåÕŒïr¹['=™)%ÒÁð£ŸÔNªÉœ_雼FÎ!žÂ;¥Ó½hÌy%f³í ~£Ê]h2{çkpFoPDÛЗ‹ Tô#_¹VéZô˜/úá†òÞ]¢ÍNš°äOøœÖˆxFç/.g<ö•&J2‰ÁVA‡í+Mð—O\Óˆ0™…õŒrËüÙ=¼µç‹»Úì e{£]? ÖIââì®jL‰§È/æã¡|ÖÈhÇ]Îêú’&fcŽþ£µ…Œ î9ŽSˆÈe쬜ŽêÆÍ˜ÞëRUÄuº­¹ #ù¶dëãXJƒõ´ ;G¡’½Çĉg~w\’~Q—b—ZáûWD©ÝŒ›ú3»g|Á;ç¬Þö“­«|‘u”Š^©Î“Dreg yõHv<3û'Þß)RqÞ†ßÂÆ—£t«MUå>ֳ߮îö7ë·Ç†³ëÔÌP[õ£[¬pêØ¢’?»Æg¶¹¦PÔBa)›½-úÉFÑ©ûê"¨–-ÏÎrtQë€ö•È‘“|PK ÅÔz îÓònsI4Â^ï²`JNY»½“µ_³£5ܾ>Gr²«úКÝw°è-«Ÿ‘|Úé_Ho— aÛ¤ÇÉÚ°|C hb7ìÎæU#0´šuÒ+nøW äë¾ç"*Dl½üb÷•ªMcæ6Óü.êûÖ”ºiyr’hÒ‡u÷úU¢\~àb~…+ê¹À!öé±Aÿ-A€D#ZŒ5^†éˆ©ÒÓUÒ¡—mµ¢|˜ùÇó^à£9*…Ç%g™<Äj]‘–g²6¶‡‘1ðB7Òœ™tB¼1?³Íf¢äM' \ê ¯yÙÀ5lá¶ÌO…;Í–žœÉùš+zåÎÛIÜ|Y ¬¸eI‹×ÿ ×f¡X9vÚK~õ "fÔÍ‹f,Y”@UHup’±ªÀSŠ0öì}ﺋmI*ÔÔßf">Zj±æ)øg47y–PÁáyr¬»…¤Z˜¶ÿ“­ðÀ·…¤fÅZüÓ,ÆÊÍ•+Íx%Ñ-QŠdžg›nšÅõD‘ˆ9¾Ô­y!zZ\3ï_öðõ=¤A›o#ÃÙÈÎ l ÞK°  Äçyó\ãV¦7ëXâ…»B8ÊÓIŠqwS"Ç.]‰Ü\[^gU™tœ1Yjã{Çܲ*£Q§ïÒ ÜärêbXX':L¦U “ÀÔ¡ÅÅ:!­YBì 󹮘ÄyɶóÜĢןZRJ½Jô uºWúB7ƒÜi~¼˜µ`CceŸ¸›Â5°÷áhÍËðß>sŸ|EÀ]Ý{*²ál)]Ç#ªq±]ù£¦fŠ?Ó‰¢îk„xç’ÒÇAI³)]oïú&›a-çÎKc䪻ÁžÉ7в¥¾™=ýÈH›VâZŽéØòE’ \Ÿ°þ¬òÀ¡WƒAìTá±¥Š•”ºåŸc_hQòÏ.*”H؇!_¡]ìÒ~ìÚf-ì ÕÏ*ÉDèd¼ÃªY “:YÒ™ =LÛr¼D˜û8ÍQðæg'ÒÏ:uÛ,)–*$Ý i[YAΚNJú@¿•~ùé7áÉ;u„RUð‘p)B-2g:û–eT|œÝŽ eóÕȳÕÓó…º2ð•Œw31¬#û¬½ G]“ÞNí¹•®T»OÖcǸ uÈž´i…Iqya{]ð_èêÅTšCÍ.ìeÀ»?j»:@ÙÔ:ÏKwŠ¸Û©f6%Û•_jrWÞ ¯„Xµè! MƒµÖëú~ o!Å ÝÓ1‘£š”4¼ÍÅYÄrÝ-OTâçW‰jç‹$l'¯P5&Íüraêf±6œ“£@Ad }„ 7Z°EFieÁÈj¾&P~Ú›D±å)$üS¯aߦè~ßíœäîc´F Ðg=39§¥hÑ¢ƒ0êàëëKÔØ’LÇaÎóÛm™7ÌÀRÒe> Ñ  ^ë-÷Ë‚ÕCµÛ莴ݗvr³ù-ÖD„^¾m÷)&LHE¬‰kz¤Z¼7ùgFLß<ã4·ð{w#î•IUk²m0­†3Úº„ª5z¼ŠvÓ1¼µW,Z ¤r 7µYîyÍGD@X1~Vv#ú³¨é<×£É`TT v2mQ‚öu%ç½ÞÕÌÓ×ô„\b¾õV ¹wL_ÉPÙ£éÁÛ°¾›‰ÍîvÎÅ©ažb ýŠ"&5Ýw›á Ïá'‹Iál@QY–2V´ã";QH-ë¬ö2ÂÉU¿Wk¶aqDSÚ‡õÄÈBàßpg© X«(ݘ´Á¤”í÷—2š³'ÖœHœåf4N-Ôú*%‘˃ñÙö¸ƒ$‹pî°$ûN÷c«Ð%b( `£HîÚî6î팑IàØ†êrË×ç©`ÃÇÞÊ-Ý©‰ïöb²§4Gª7žñ||eí¥ó|Ä×aÄ ªlÇ*OL}ú¤aÓŒnn=Jw¬Ð­•0j¦Fî''ã5ó4'í¿üàÜÒÐà\·Tž1m‘•HµPñ]yBys½QŠà$©Š*¤Ú3õ%óൠÆlÏ3³=+âmÐŽAe©Ð]ö£‰§hTòêrYeyuI¾t9àcÇf„@&©¥•—ø°£¸TºY·[wåElHiW},rû,Â(T7©¹ p<ïʰ†ôO½^Ž#”_«qŸ¼]%H.‰L¶F~_QzŠ„.Y+Ö3“†©õ¤043`@¨->BZ»Eµ³ªùì«PØ"Î Š’QÑÀ„ÈSyu]>›ŠÖÁO2Û&ëஆw¼Âiõijiˆ¢«§mñ3·žF¯dÕ‘P›£?Ù­?R$ý†kûíÄ`‚gã=—‹P½cíZõˆœÙÆ«ŠU\Ó:âLc%ÓÉF¶öã(à©Zv骴x]ÓšZv0³,eLœˆHŸjâúÎÙ‹V,A[+ýR¸õfÉ}o0¼dÿ3|ï±F#gòàôŇeyéübHQ²€f;¨ªy‹­1DE‚ÇÝ~‘|™× 1t®äyƸ•鬙¤7Ÿñnz©íËÕ·EïÕ›&W'2\ÇÈ#ô}¿,ÌH ذ£ùšTÝ¡÷^9ÔxFW÷ùY™Æ3nŒÍ¦"8™ç×µÁ¾d4êvgZef4¨½_0ï,“ÐJ±Ðâ°@šv@,¨pó2O,'•€|sDûü¤ŒŸ©”xô(Ð]K“©—É»MŸr¬9F·³8up{w0j¿EΠ¢®éž€N¦eˆužþZW‹L‘S¼æAa‡ªÁg¢•¬JWİA÷­7ñ»úÑ~H‘ÔÛFO›_fšõŸvcY¬‚©¤Ïa‡›ðP*ªÒ¾¤Õxz—Yì¾IÔö\«ç–SLgd¤±Õ‚íÐ!§¢˜æÖO÷ß6wé0VȨΖìQ¬n3:µ¿sfmù*"Üs‰¦¶˜YºúÖ[à¤RÝ=Ë÷,‘b*}سÚIlÞÜÇFQ#z¡Uw‡æDÒš·†W}’Ä ŸU”[½òÏK†¶ÈEÄ+ºÝ}Û÷(¯¢WtàsÝi̤ÐB<ˆêôJ¤é‚B}¤ÏE¦r3«/W”µÞúÌìÙHQœ é• uƒ³r1IÚ{VPt­n3L³Q®mXmÿpÜXñÚÞîÑFúíQµöc¿`Ó–äõ ¡zÉSj òwtœ£a{薻ͅÙÖ4%: Ž€es iÞ‚‰Ä9éTÎ'·Iª´ûwHÚJ„.¤?œ’õ+Ó³%è&æ…ûjI–¡#Gäm÷™ÖׄºÅÁÚ.ân8H»´÷~Õß8ôóÙèeqQ/œŒç¨âÌ0gF¢&n#D´LKli$öÝ”Æ:F¤{Î;¨Û‘ù^þùÊ£àJvÚãLq‚j~vñ µrÄOÌÈýѤwX-ÄCaÝÅ`þÇI§*ÃîŸ'¹Ô‰Çk î3¿ðø”êò¡/²\z¹…ÿ­L¡A®$¶ þbÂ-6Æ”uý*gz½ÓSY®À¸+'3ÇZKµ¨R£Þ@àŒ¿—îLG\:7ŠÀK1SÆ8]ëÑÎUÎ A¡•/^„¾}³¡6GÙ­Ú#¦£éä‰×Z2¥MR(É"rÐr:œ¦°güôEUÎa8Esš¹W7«æ‰A 먳?ÕíðÞ(δàŒvêDº´O &zyåHù¢Í¤—ÚVxkµv>Ý”í¬|bÖïÕ°ä†j .û ™~òT'-‚úú93yŽ.ìIw¨<|Â\ÎN`˱®!ÊE¼Ì´ü„W s©[i"ë\KVÅaQ½åÙU(1˜¯OÞ‘ðž)޳®3,F^újs½v£$ãm9ÏÛÝkÖ/òŸã0B½í xñhomo˜ ‡Ñƒ™2ôñÂö‚}ó­öa1kBAºBðÅHO|ËžÄöþNMj…œþrñ€•ä{•10ê #}K…{‹x8»{Õä {•çi'A|kß1î0(VýeÈ}×çÚYùB–_¬jÙà§/å¤ÌLª(;2‰ërRyV÷$*•ˆ ÄÈHOâQœçW‡Ê9§â:aǸý¤Š´ cCš*’ÖNëÈo~‰«þˆe ´VqÂ…3EHïö®M³ýûí0Rüx‘GøD]¸™¢  ´l !£Jg<‡ Y¢Ò÷’y%o¦UjusºTZ£ÅÃbŠƒN9!¼p{»”)–Â?*™]Ÿç|r–ֹø¹Ôü€*tãúq·?ÌaäòЭ\xÖ1·‹.> ë¯UàÕÌòX£ø†‹:ð…§”gªtÙg]c[dpˆ­a²ýÄŒô×X‹¿w%ç­^=Ó•vu=Ñ'3h†û{NE¨Ê®É:ä%ÔI¡ð¤$)Þe¢eÏ2áÛ]õe`? 쥤´k£Ö%Æ(á“©Le4ä,©[2ºÄä7Ÿdÿd2u£ð¤‰–Ï*ñ}=Æ‚"f€íÇg8Þæ|N«ÕÁªŸqö{¥8§?m|upwX@¼/°á˜‡kÔ0—YM LŸ/ͩڰÜ$Žû„KÅû’ÅÜWÝô1f³)‘-ÍqJÄeuÂâAD¬&¸G¤Õ-"¥T¨ÉÂß.k0®Tg:‹…Çå ÀPÎ"Û€×ÜTÙ\'ØÎ»èXéhŽ*L8ÔTÙÚ¹Se÷£ .¹3.âGË‚5ñKHØíà-t1´É¨?Œl’O|—?µê(-w™×%2¤š±cîÑg"dz)ÏòPEÈÚÖÅ #¼Å %VËÉE /]}YÊÒ‚l$ÿYm:?:v+dÚ1ïG¹¬©ÿº³§óˆ«ÓGÚ ÜñýG"E™üÇa“7a„½Kæ³ sÙoB,t½ê„ó :x rSi¡ó? ä9zëy¡&- [ ñ™¸ãÐÏWT×áA†î-<„Î7úRÚCÓ»#â½)ᨠA]»üg<ÎþhÇ Î×ê9Òþ»Sê¼uß³–ˆê0 ƒ'É—ºN%dF-7?™ê|»§¹ç¥%âû²¥·N4úŠaÌ´I^81 &“Rgósé­¹iýû¼Ò¹7ÝÄ¿µH µLŸ:]Ñã‡EÀç¦4Í’=o)|(ÍýØ Ü®Éß1Þ–NÆéš^GŠÅ Ö¶ì»GÒ2ÄõÁŠÂƒ«¥Ä~ŽôŸÌ,b&xÏ”»ôM¶ƒ¨D§DGÃÞ6P¨SdðxŠlÜ y;št0¼¼±2*±ßL ~³)±íìÆVwg)Í4õ¦ÆxŽòd$Œª™½d û4sÀ@³1ëhÄ„QØMÿqQÚߘŠåÇRúh Æs_y“oÊñ¬ÊÒB©ÏšVgÝÞS1`’Ÿ Áú{ÁpA(2Åî¡„N¸·ùSo{|€Ü]†Ui™—1¯qš?ž4­B‘Ê.¿¦’‹aά3ŽP¼«…\zKƒ¤UëòÒNíN/‡Ì dØ‚äïì‡ý"zý³;‘ªˆlÏÈŸ&‡[§†o“pÖœøšže!Ÿ-ë-îAÖ^fÔOBžÛâ옺ßò¼!÷ö 5¥é.9V~&ÆÙÆz(`…@rÕ5 gwoEK¥Ty_¾>tónoÚ-³Ó-|ÏÈí[Ÿ ú‡¬¼ŸÂo¥ÈAÏäqTIq‘ ‚5¸ÎÛJAb–Ì6„NëþàTÈv+‰ÈH݈e Í… WC¼ ãó®]qP¯#C3,’&ŒÑ(ŽýÒpÌ®&e+Â^VSP2êŠþ‰ñŒfý2í+…ý¸¤jC<>Q¹3Åj”:¤«plZ2n×\¸?'e+¾'Ò{béV}'[ŸöÃWˆ´Ã¯4;;Q ¦ù3XÖxLÀqÌL¹Þ^CÇx3g&›»ÞnÛa-H%>;ºð>¥§vúÏÚ TÍ IJá±7”Œç¼”½ƒ¿·²S`~]jì)c¸ž*÷ˆ¨Ì ¼¡ná8íÏŸuœHºW÷?<ÇÔnÄÒ×ñ8ÙU !´-º=॓.OØ2ß<ÆÊ×"ª‘\;M´½¥2Íä«Å5bÁ>yá2îü£lé>9\’ÃNÉÉ\³‰f=_RòØ4c¨¿ý\ë`+Ú£×û):tä½Ò~„Û"®V ÆñŒ«³{ϳò¶BqGÊR­–Ò[køxMç‚xÀ%Rµq£áÛk ;{dd¡,\RKÍ—bû}k6Uäú'H§Ô’+Éõ#±ï`Ÿª\»^þKuÊ endstream endobj 103 0 obj << /Length1 1425 /Length2 6643 /Length3 0 /Length 7613 /Filter /FlateDecode >> stream xÚvTÚ²6MJ@:Ò%R”NBéUz¯ÒBJB¨¤(M&¢T¥‹(Ò{iR¤÷¦T¥HçE=÷ž{îÿ¯õÞÊZÉÞß73{fÏ7{…熡‰²Ò¦D`„À Y ªž±D<<¦pŒì à1‡¡½áH„ìðªhƒÃÔ œ™Ôöñ‚Å€`IY°”,dþeˆDËÕ ¾p' ž0P‰€yxT‘¨4ÜŃ;å_K /”–‘‘üíTö„¡áP¨Á¸Â'ˆ;h¢¥ 4@ÁŒuÿÿº Xüïpyÿ Güv†@¡HOG¸á0 †®0Æ#„ œ~B<¼‘8ˆ/îqÄüNÔP6BpõýU7 Ga¼…½á¿*ùwÉê'U¤§' ñüÊO ކAq· ò»­î¤ûgí G89ÿ*ÁÉ%b†€{ùÀ´Ôþ²ÀA€¿1(¤d@@˜æuùÜ4ûM‚Á¸üƒ±($ èŒ+ w†á~Xoˆ/ ˆAûÀ‚±ÿIüsƒNp(ès#GÇÁ0ç?{\çÑpà]Nx` è×çß+[œ¶œ€¿Í7WÄR_ÍÀ\MàwÁÿ¦TTþ@¬¨PHTƒEER¸Eð?£BàeúÛW áŒÊüIwKÿJØ÷¯îóþ5|ÀÆÒGâ òþ-pŠûÿŸeþÛåÿ§î_Qþÿw>>¿YÞ_ôÿÃB<áñ8½ú`pÚ×Câ&ñߦ°?ãªs‚ûxþ7«…àf@áâñïK„{kÀýaN†p ÔõTþàf¿ÌŽ€"½á¿ ú/7UPwÜ£áÓão †š©Ž€"~M—¨„$‚FC¸ãv@,7†N0ÿß Š#œ W^0Љüê§„PÄŽ“,ýˆâ¤ú߀¸ P÷pýmŠ`\Ѱÿ@@8Äù{ÿä >h4n4‹—ù¿ö¿ßÌLŒ!¡·ï»½»ßpT®Ìê'´Ò/?̳bñ”O;nô9¡$Iá{ó<|}¨œÒÓF=½¤Î{ 4ÉqŽÝ¨}OU—dTtfŸh<´R¼öaàå†rE';›©ÒjйWy˜;a-~³6O¶—4¥a.Ý‘_‡¦Egñԧȱ£Õ7’:ägÅŸ…™ÅÙ„Œðä8¾eâ$Ʊ“òÓîúSÓf \rh' ‚7‰½ÂZÏ‹>> œ)5õnaæf¶fb'< ý4t«²žªÍø[øj6oÄÆ'?FlºÏV3dl‘LJ.ž(ýV±›ðƪËÂÏÍfòš^„ ftÙdêËÕsÁ’{e(‰Õµ3ÐAV÷=êmì{1mÕ;Îæ³—RªGïÓ%cBµ¹n°ÈËÜJæØE?œôåP˜äÐÜgN-»)r•Ã誥ÞûNgÏë¬ü9ð‡¼™œ7àE­YP :ŠÎ¼32§$Am»’ʬÌD‚»¶áv¹€À5FF–Ïü¬d?*¢Ý]‹† Ü üÖ§½6 ÈvÝo &­Ú+†¦ºè‰†^çLB(æ÷ZD'ñe&¥Ž>ä kaég'Ì'{äÞžº›â°õÈúuur»È~¼ íUòTþ>Å”Jâ{Ð ’åä‹NdSâvâù(ËDzwŽ­piW˜÷QS¹üä~O8ŸFNâ1oÍÇÃg¡†æÛ+…ׯZPp÷Ä·ÑÝe‡–ŒÊVõ3>$éö·J‹]r7}燭S-5†=¦qãÄuùŸ!öÜ;_©__íñQs¢{lDz˜¦Âlmâà#N±žYèß6Ÿ_·07¤,w‘gý¼˜c¹àž"Å8¬•Q«ÀÃz¶<|0~kjÅ4ý’¯(„[‹§ÿÒkþ‰¡W…mÒÀº¹¿{?e›ç¦ÒlÃÈÔs:ë9mI×Ð׋“ò®Ê“åsýŸˆe©›¹Žc¾u³ô X“Í9Ç>UÎ~[N';(©gýÊÔðJ ¶“%×O~î¨Ê ûmFóÙ-l;øK‹’S¿ZH€%º)E­ÌÀ:¡P½Gx aZ~#‰—Ç®°QDÔ¼¥†‘¯¦‰åÅ·Ï]˼s»[ž;uÌÎuÌ$WhÙŒ‹‚˜fcªMu=÷Eð—èë>ùÈ~5Pg\Ýó-Gß«·¯žïž“ Ø}­óU®h Õ{/4ý9Š}Ù6·,«µü• zö,Þg½$é>UðŸTm+nÞ"ŠHÞß¹á—3l÷5‰â˜~¼ãÕ¸o2/áþ¸ P^8¬í{Ãp}©¢ˆqÌÒfšâcµ /˜WkjÍ‹G%l¤B9Ù…¤þê[Û@îƒé§®%äò‹!Êž†[€°úÂ'ç•¡ ÑÑ·ä³Ãª»´´OÙÚζŸ¢T9½QˆðòlÑp0VìD^ýlÒ­lƒ@ü`ÄZÿ£(ïÍN¿_Xâþþµ—ëÛ\©ï³#‰dŠYnïÞ)_á{f@I.àgÙKNõWÒTšÔG9«ËÕM*Ñn”k ±í&Olì¨X.Ÿ ¼²Íä†tZÞ2TuKžÔ$Q† d¬¸™O‹«¾ô¦<Ý¡¤Êú²ÁvgÕå‹óU»€|1MÀÎef¥÷ÓsÛ$±¯äéfÁƒÈÓ¥8²½»”š3¶þyÝ2ïžUÃQ}’Óì£ÎÍÀI:§N2{ÛÌ!X 6×4Å>zʹŸÅ¾71_òf‹¥Ëz)æmÊ‘Âr ‡øMRÍNEô+› ÉŒBÊôn]>˜_[>SâtûSôˆJtm*3[ìA«/ #mþÆûRYÖ‡Yp½=wPù:É”[0ÖKÌÂN¯èÖ‹Þs:&«WxÝ®ИE‡ñz¼É‚"ží1ÞdÅ9Áfšu'GyUJt<<ñ~iô£üDcÍdv×S¾$l¥÷ùuO›ð°ð”f;MWýnŽqb¥Óy^¶´ï«þDõ'’ Ilo‰Oô¢ÂnÑò#­ºEXœß)¹¢¸¼²hVήÅ>Ï,Oç¬OGc3Ô£7…ÇŒý=õÅ™ëÜçVyéñüè2j=Ä@zÇÔÅ„ÍYS« 6êx±ÌhÀå:Áû9ÒôÔùVf+`†Œe¹E¥p ÀÇ\@ìÓÞT›¶´—§nc†´hž2©7nã÷’ÇØ†=rîÚÐëi”:Y+»Ï\“ùúÛz$KPl­ñs¢·R«›ãnÛHïHø„l¯Ü•åÐT •ݧ!À©O f¹·eýÔ“9M£d2´y8å9% _^lÞAw‡Ø"Ûpp^ìGÐ)A6Ê3ÄYÿªÉÌIj­êKs÷]sËÌá|LãMÁèѪ€Z«ì¼÷LΓ•ƒ£< ÝG4õæBFäöî¨ xà-ïëÒ˜¢º¬> ´—)˜£í¡³F/d·çðÜ’¾.ÎÉŽžûK2³c¨×{m$de,͆€n±Æs.\¶;^•Ïw¬k —êCŸµö"†‡³U _¬ïpfÆn¼sãèZìê¶IÃ&õ%ß HY+N±l¤28Êg™òS‡=A‘šŸîßiÙZ;•®=UV"›V̵¹•Ê‚v^oŒdŒ#çÿVÈÒ”à·{휚†B'‹jàÆÜQJPÙú†Ø¡—/Qš°F™=»¥rjŽFðáC •;êà8 …‘aÑ@ƒ¯U¢‚‚{^åyq¬œ™Àh­žžiîïU’Ù­ƒ<â+ r+¥ÂãºîB‰“¢uêj¬VããqºOWÖÂß!UßÏlFp(‹Ê·¨+nç5´rGÓ”Z]ì{CªNz «ÈOi¦’·áŠ–±ŽŸÄ#föƒ1?a™-„L#“—Õºà›IºNÖ}Áû¢‘{4Ââ#„·ëíxúø_ ²W\ëU›Ú±V¬Øº/uÿ±xÿ5ÿU59Ã%p¯›íqÌbä\ñ—*ÛºúŒ}ßH=¤RwÄ ï\­³– —g9PhÏ8N#Ißí{N”ÇÅPïjBk¬\±© Ú1KAj0ªèAÅé5ËK5¤öÍ 2êÀÇý€PÜ>˜cª+“«ˆ~sô H¿„$œºÞ^u¥tBEnQ¾ÄeCÇ€,ri¼±u#j$‰×A»÷õfá¨.ã¼ÞÂ0‰IþwDšnÅO‡çãk¡ÅáL¯ÇBúŠ}êcµßºù© 0ev»uì,¤t/ñy+?7»ßY—FwÞcÜT‰»ÖElOú äëÐÒ :ŠÆû‘`—v`õgÇïWª|RɪNͦµö%êÜ‘‘À!Z]kÕ0Ço”óÔýñ L|\>°¼õ™y’u~¹\jOâÎ`VâËëÁ1åîY¿¯S–î¥ØJU¥-!Öœ§¬6SÊSKÆn@C®s9¼&Bê9ÔhÍ/?ÑTe†vÎk=2nãu¦ÁO¦R\Lùª @b¥CªjMÿ€§8Ù›žõyàç¬ÿîáåF5ã/®÷uO b⢦h‹Š‚ß•¦ªžuµ{-òøSÒUZÌû¬}Ð%h1MËN–‚d÷IV†˜»/bå‘GC¯ÕB=\VцV^UÕìtmά Yãbf°üL#I·'$>ÔÛ17*¯&æcî­Xé r©»ðè;¯‘V ¥R”ûJsé©X´Ó~ÿÙb[Íùp³Õvc‹Ô¨(ªë»žCÓ„¥àDÏ!’ 0ÿÅ”¾mdáÅô)1¥ªÉn.Êq¬w¸¹ûÒ¦{}ÜÉ$*žË3½©m¼£ês“ì\DíW9¬©?)¨÷4øºöéý¯‹ÃBÈLýV¦©ª¶«ð‘.‘“ßN:Ìv•ùQFÄnÕëðlñmü…—E´$÷ ¢¶n”*,TgzºÇˆZÝ™§NNÐäÞОè8IJ3¨TлÊ& 1µ'LΣ3½•{ùÅb’Šrò묅õŠ(›uîé¨ñcúü„5Ó—àÌÊ—À…ôzÄaFêVlÜmÁëšcßo7±Ë¾ë,Êsbؼf5€Òj8°í+Ìó÷Yc“FÕyÃóõ¹Q©¡ŸQÕrfŽ ®žÄ¢;»Ç·º¦ñ­ãßt ôæäu\ûÞfšKº¾•òÒèR ‚&Ó!O¬|N$C:rkLÕÑZ„D&ÿ"q“Û½³L7ë=ôh-)rÁŸoü: о 2zÃf«…cе ¿v¿/ÍMO8À¼JFæc=¥×Dä«úÎ>÷BÄ49xMÈB {ÿ2u¡¨J¾³uÌïPx0λ³ÍxA(•=:Ü2X>;ã,×Ë'î[àTÈ‘0ÛܪA7‘¶¹{wk·Éa0/6êÛrw;›ºÉÊ™¾ù9Ýècºãî²ðôû”¼Hï‚ÚU¤#û¦·¿ýããõ ‘‡®S63×v¸=oZ»ê‡‘Ü&©¨âz¯¬)©¢XÅÑÙ%B{ ³»A3Ÿðl_ ƾhs rB¹¿õN~ë¶é=ª@ê ‡áu)e(à$ŠŽòú#™jŒ ¤:U@2þ$u´4wÒÍo pv]˜æt¢Ob•üf!׳orÓ£wÍǬŠ¡méìU‘ð&gLí« _aE6´c®1GžK(á±Å2 ” ,@Ô(º“ÀÝ„ó‡;ŠªÊ»Ž• dþ–·À°O’‹¯B‡eŒt×r®x@ 5ÔÛF“g2,ZÿL|ÇW/ Ï¿Eé÷µ¦Ÿ·„³þîÍŸ\Ù~Âèg>ÛB}d±òO +9Ѫ*o—QÖÃû»Ïû\†Õ'åßõ—@È÷'¢Yhßáþã\¼™ÑOJXߎ”¯•,E¾-,eüqür”²ŒÝy¤Ï>)Áj£dy§ˆG¼\@oê!Æo‹ 8×úp””7øØ#t¯c²¢’wnçMC` ±OöR@í…ÏÊ ‘ñZtàe·ü%›kÔ´„®µdfCnd¯îà÷vÅ'ké†=Êqå[¿§ßß_8um©XGT‰É·_ºÊï`P@*+Ñ@£·™ùl)81èhÅZ¿Þ¬ëçã\sW>7R©–§fj5jCV‘¬?ÉA™D±‹Ü:`:DW¿Ìóºè­Q™— $?°ÄJÇ+Œ:Œ"Î4žCÄL‚ xÅ3gT¡§t‚d µY7“ Á7ê§ÂkšN¹ë”C9X”µ×xÄÉä¦C =†ÇèìE{·’-*ÝŠëô Ú›9–ñbWL¦'S£ÖJWÆO_âP'ôL*ð–°õGZÆS™[çà§©;?ö,06q?·? þÐlÑŽpö`Þ\iÚPNÿá¼ï¬‘PS¥æ.’¬]ôpIh:O©âßÉá§{ʶĭ7ÏZ’á¹èÄMR‘TÈM Š#‚­ŽyHA ÓgeÑWyY·ýÄ¢m¾¦ÛXf.)w7gÜÏpødûf+5(É7r)bRÜÌOžyþ Ë©Q’HXJ£Î]uUd<®-á”±Ê{®Ùþ6æ„%Â+¼,µ6”cÏ7Ü”u®e.S¿k`=Hɦ‘‡ðI$LÕ~S¡ŽÛò@ªò£æ–3–âU"Ëèú;kýh2ãŠxDV~°qcx–D%ðêê×tFûÁçq_ž­µ­ Q “ªç¸»­lL{"Ü9Ÿ°~‘(p; )]r| ^ö@f/š«U#ÕÓjªh-$H_ õ:Í”ÍTÒšçûéà2(õY'Æ­øG XÖóI 2u…¨Ží•Œ1ûÝŒ¹bí£"óЯâcÅ[‰x:³goì*î‘/ˆ9îð¨°(që9UmrKónx'=4ühÍ·ºÜ•MÒ¿ùˆ¨oZüéAw ÐÜÊ·¢íÃ*âZF`8³&š\@1¾¤H²gYaÑ»{³º¬Héªe Åòé;®eÚ;wÔxâ|­C, )š“ŠuŠê“k<{.3ˆO’ ¸ËIÁY_‰ö,…=NÛxÚ‹¹Óå`‘Š_syþ¢«"­aØÈÁHGú-‡ÍÑîödJ¨3Èõ û-0T®ìRš>çÁÕ€ˆñ™è^›ùãMµÈZ»×]`ÝYð§û6¸ñ¨ËU›4wö<ôŒð sÔõ.æìjM½Á+—®qö¥(šèŸ'à™Ì|e6¬bjþ„ˆñNÑ”£@ J;Ø0×óÎÕ¤Í,YØcõà’ëM1“ÕFTÉobd¥[§bh7üŽ‘ m‡‘‹ó*Ða·‰-„•¦ðñæ]ÿOS„ñ"D¨%@¢o6œ¼F!#Ùÿ#íx׊Ï`/Ê®£I)ËÇoPN|É‘ZðŠ'p1êuÛýDl½ó~¶zh´’³~ö¼ eŽ0»ƒgge-Sa)š|q…fœÅ]'.Ág"ÔnFßCÍhïÿŒ`þÒá“åÜJs¶/^Îl¶Ðì•7ì?Ç ò­NûÐT>¯»CyèÉÛjÏÂrÙ£¥Pç9ìãä…>1I¡(:’Eư݉ÛG£øï÷nÆFƒ÷jû´„MôÒm aow=É‘É,Ë[ë\?>Og'ß;)B ß]Þ6{ËåÂE¥(bÅ+Kh;6:'Á}2ÃÕ»Ý á–<‚2µäy =Ú<²äȃê‡?=—Tã½%=§Œ…E1× GºõurðQ„sk9BíDã…xH`”>0A/fätñnX°Êu6$8HéÑÎNTŒË»Ä54ÈʹÓ>`ØQjêbUàÍdÓ¸V—w2=nw¸*Âb|€EÑ™Îí·,uÝUVôjålÎ}q­]ÑuWSæpdd3ï;qÐd¶‚¶·;+ÞkѸ’øv™ûV o"è&Ý™Âõ½éf©‹;œÏ4iÔ´eÎßyWÅÛÍ50e0®= s˲*>¶ªÕT?5œ·ß I+ ʲ5=Ôe9¥Xºç¸ºu}$uæ5 &4¡®7<>f€JšðŸ8°¨Ç}ïwäW]ºÿc(¦Õ7FMqZ÷›É”ÐZn&´])Á„ªÉKðªv@;›§”B?˜MzLÒÔ¶ó8ƒ\>ÞЧ‹zCvû¦Œú§à–´g¦é€Ù.pKûh¥±tpãõ$þÀÜKd`*1Ö|Fp!6²›×ŽI®-!5÷y %»8Wj>¢fH¨Ž¹¢yo¶ò“·¼)ý"ÜaÅnóÕ}˜ƒf(5߉ ÿ²µzooèF#ÛtolåFKI¶s—˜EAÛíìh“÷Û—9·µÅ$†æ¬÷œzÕèéXr‹¯öÒw©JœÜšix`ö¢Ñ8iÒ`ûwö“Ϙ~¦Í7òåri/Ó§ECM››š»¥?3ºIå(Ü—›ß9†¸—ŠV¦«¢ ZÚÝ †â·ŽP­gÏñ3E,Z±)?úý Ý"Ú«JF*§/”›s*7-§?]-~ôYÞߘ¦ý)þ5Y×’Š\§’ì¹,l°_MRHÊZ¥ÿqŒÉ|¤×gÃñ6ž:[6Y\…ßDfö÷ó/㊅;4ç®kÝœcµ¯Ç>*Te½#Iåí?ÔØ‡*ú¨Eº· Gø/]¼Ú<øÊh`ðøa‰v5t°„÷ÜäÖ8I«GÚí`G2Zïáôð™€f¢ªyRu™º´ VÔ:b‹² J6ô´dõèòÉă1Çr^RA’C¼$žÇ½Í\{5GÝiþk*¢KoYÅ2 ¡ •ýÉ‹€â¶±©¾D’ÀxÍ8Uü»Ê@¦$†¡»ÕhØë†I¾¹äA•Ío*rÚ°#KW¾žñ³ ø€Tüþ›-ìrÈI­C|/wWÙIr•O1¶Â„œT¯ë[pÑæøÅ®JŽMè­”C“CÔù^ÐK¨z]—’üÔEI¶÷Š2ZÈ2ˆæÎ\1¾¸Û‹Çwp[ŠÿÈé¯s endstream endobj 105 0 obj << /Length1 1425 /Length2 6638 /Length3 0 /Length 7608 /Filter /FlateDecode >> stream xÚtTÔÝÖ>]Ò%)Cw ) ÒÝ)’ 30 Ý"Ý%’" ]Ò)!H·€t# JK}£¾÷¾÷½ÿÿZß·f­ßœó<{ï³÷ÙÏ>¬Œ:ú¼²¶0k Šàò Hä5õ„øñXY ÀèŠÇj‚»aP‰ÿàåá +S°B Í4aP€š;E%€bAñÂà+°-@“ ƒ‚ÜðXåa.Þp°½yÊ¿–NP\\Œç·;@ÖÛXAšV3òD+@f!¼ÿ‚ã‘á"ÁÏïééÉgåìÆƒÛ?æäx‚=î²ü* eå ú]+ÀÀìöÖ‡Ù!<­à €€m@P7¤ƒ;Ô Ïè«j´]@Ð?Æ x] Èüw¸¿¼C;[ÙØÀœ]¬ Þ`¨=À ´•4ø^€Ôö—¡Ä †ô·ò°C¬¬‘¿·(Éê¬õýU› ì‚pãsC~UÈÿ+ ò’¡¶ò0ggá†÷+?0dƒ¼uoþßmu‚Â<¡¾Öv`¨­Ý¯lÝ]ø ¡`WwªÂ_HïoÌ„ˆˆ‰ WÈËÆÿWpoÐoò7ŒÌßß׿°C–òÛx¾nV îò÷ýOâŸ;< ` ¶A¬Aö`(ÞßÑ‘0ÈîÏÙy8Ø `*€ ðë÷ï•9R[¶0(ÄûoóßÍåW3Q7~ªËý»àSrr0/€/¯(€WPDˆ!þÿŒ¢cþ+ ¿}U¡v0€øŸd‘·ô¯„=þê>Ç_ƒÁ øg,-R± Çß7°A~€ÿg™ÿvùÿ©ûW”ÿEàÿ’;ò›åøEÿ?¬•3âýÔ«;©}Mr ÿmúôg\5A¶`wçÿfUVÈ…ÚCþ}‰`7%°ÈVŒ°qø#•?¸á¯ƒ€¡ ˜ø×ƒà ü‡œ*'ä£á†Ôão „š©µÙþš.AQ€nå‡l1r'ð"ÇÐäõ[Á~>( t ËóØÁàx¿ú)"à·#%‹Dÿ‚Hæÿ7 ,àG>\ü8è?$â û½ÿGr6îp8r4‹™ù¿ö¿ßÈ dƒ77³‘|áXó¢õ¢Z–Ö“wsDj’uóÉ+N^ß9x›ûOBìTΪ¬çËð3ÙÔÄ_Ö9Neæn|¿6Õa‡7'ë¶\ù][&êo¶à}£ì-ø*[ÛKKÇk ³åwãêgì„Þ„Ú¡ÆšëêþP'ì³GÙ«¶·ta(lfSw«JTýÞuéo¬aŒYpÑëëìi*&,/=é‘ÑÔéÙ$éëÑ;µDn<ÿýX¡·¾&+‚q—Ó>‹å‚nÔ,Ô&Tô觤Cãl¾r;/ÕîÏú¿]†¥MS¶ ¯:§!;>›ZzðÝl¬£ÔTüèäªÑ1ËÍ*IÎEŒIXÂÙG_Ô–½Tˆ5Y»ñh'ï´!b5„–=Çö㵯ÚÜD%·R?è¤e§Õ5Vø#H½êx:U=Ô¢„zž¢äì*3Çúw%õå  ´ž!b;[e ‘TÁ•âðJÔá׸J¹ÓH§ýñq8VÿB•ÝÍz‰¶%6 0ËO§Ý%•RËF›¢±²Bv¸èôô\µQßz7§Cbᨅ< ‘ÚK*½uïTžN7Nn•åhÉug”\÷¥·«dµ1…±jŶH>d·>žÇQÆ"‹5éNíL¶þh»B`Þg©hIÓXƒÎRÜrz–VC”8!þ6Œñ1 è5=.ЋY•µŽÚg{ïóL¶ñ<‹?)X‰Ÿ—^¼ùÝDh§ñOZ†TQ‡·¾ÚÛ3Â- V'XK²hjì {8ÞòˆS³¢34&lýšó2Fl±äÐöËDº$¢ m[ ¹‰þùög»aáê©fNúWþš‘™:…;ö‚c5 }ìКÒéÈná·¥<‚4#”ÿ1¾õELI(èDÀjÚOKO‹ “G‘š´Ó±ìŸ-E’¾‰¡®4Qaï˜ä  Ñ•̧Žð†*t;K£ Ôí—¼`ÇÙ¿¤ŽhÏoZ+&3"åÖ›£ T¾qÉñV¸O§2êáåëDj£A‰‘wôlÝùо8AJÓC‰Û¨´«©eÏ¡µ—žâÄØo––l•Ñ$Ê?ÓZ.Wn7>WíÁСø:•sú­ô0M¾žÅÄÛ4=Dduu“½3´ –N<ú40n'Õ˜3ø]ìü,’9ÈëÀe§»Í¹9®íô™ã[›§:w‰F,½Rò¹Ð(°â¶a3qËô›â×ö[×ê§ù,ž²½{µF—[¢w{\@p9ØÝҞؼ‰Wõб݇ZuraW>Ö2y]—¡ }HûCÏžù¥¤BëËAÚœ1bÁD5îÁc¿¹ñ.å¬ä¢éƒ¬¯ä+Nâó53!äÇ ï<’›bþ~qn^k!Ï&ñ' bhþÙ³{ B’ƒ¯>Ó«ùMtoo‡'Kh ½o \~v"f¯ã"¿[^§ o[‰N¸'…oS2bMig˜‡À³1,Kµf+²Ç.p–O‘”b3¢AHDnÄS(нÀÅÖôpðâÔ…ÉÑÛßðø-céJÚºcù€…ËÈôÛV²ŠÉMιzÕÜÌóôMkR ;ë¡eM†Žft©ü ”ÄòÔ©ìþ0ù0»¶W¬¼³ µz[×¢tÑÑFˆ2'ƒ{5…ËÃ뮩V}ò·6’ÆC_qü«ö$¨pGQ¤ƒBÛOí)ñ¥HŽ»æwè­Ï˜BǪ] ´QÍ5°Ì‚ÚñÈe‡2/ÆË±&z4‡“O5ãRCae ¯½4šÛ£#'ܘ‡´XŽÏo ’‹ñ ”ÊL6<È þÈ»@íËQe¡5Íûò”G%,Šôm{¯êücÁŒ9|ί¹×Í%•÷;ÞÖõøD{ňv:'‡RbŸ)E¥œÉ+j{©…ýÄsTr.|áÀIm<Іþ†nz,<úa}ÈGúèùÝ—¾ëîØÝÑ¡6ù\›’Ń éyëÍå(£ò[¿ó™‚žq^’<:ǯ²vkâïÛ®­OÞÔâ’>D3¸Z°­q¤fÜ5jm~µª9…G™xL™ð€Œ 'Â0¤ðLОô*`]zÜ-@êÈvûæ"ªMEàáþ®8GÎŽò3›eÖš=¤ö9«]Ýág¸=FÚ_ÉÁ#[ÛÉó¥D*˜ýƽÏy…‡yy¿gݳ U:ózò“0¶­¬í}¬‹[£1c¬¨+oü M"öC²›7½ñóËLÀ}<ÇÍn^ïUõóÓ02{².Hî˜,Å6…²?ya¥§s`äXªŸñæ9IÇ|ø–1™Ä·*iëË9½ê‚#çp‡[¸|'ÞÝ÷WMÙÚªÄOçä·’èYÝž¿cäÅ{»6ðÐ8s˜Û…MøÌ1bƒºþLÛ¢øî­‹G¬B¨EZyO6CLÆÁ‰Ë«÷Üv“ ¯¤Dê7[b]URkÔ@}ÑÇ´GpŠŸš´á>á•eó;‚´üaãôPùÂ¥Á/ d{]¸‚Ô˜}Í–nw"î å?Ù¥×—qoA®”;Ø]?O‹0ñ«[9Èîã´]N&Ïé*ºÛ±z}ØápfÂAE•»Ça w}êJíŽj>R¦Boÿq®…ªÀE,79¯BâÖœ®,…-jëAØôôÓÔïÁßt8ü(Æ>Xžêçó÷ž?´J6ºìge‡Í{0 Æ1–ï9Ñ;ˆèJÇ¿)åÜŠJµgS{ÃrÈPDN45ȳòqÁQßœ‚íp_Éulˆ«kaÀ8ƒ+.\ ÿ¡o§•B–æëjB‡[ÅŽ.óqÅã ¡oô½aRïQ8ÉC½ëÍÀF,<'º/žqgì/ìÒŇtYŠÉ¤˜Ðö”´ó¿•µiæ#C)‘í2¦—Ôšô­Ãb¾;xs!rZ¾Ñ©QÕ5'Åh$Âw¯=ÛŽª¾zEOùÖõù†~::»ÞO© ‰ö„ª¥°¶/Õ!uÐñM†R¢Ô÷µ8zI.“×-±/–ºr£ˆ•a1 s<ŸYY–}Àèñ«©Ë½"dkº{©×ˆ>tg†à¨‰‘`È­Rl ág}ÆãLYV ÎÙ'ÞÒ_Ys¢¤i>†/n%äÿ4Y›~M‰µo‰PË.£pý˜Þ…­v’ó&Åɽ_놙»´º¨²¯aõÕú“F e32=ã{Èéfß½'Ö Õæœº±$³ŽvPìøA‡`âMDEºäˆY—˜³‡{<¾55R7èjDŠ!‚¦O*š}zž´Ð‹wѲ«-åœm^1OFŽiãPîSLå<žÙÞQžTŠM²@ˆÛ­Pëâs%Õ;••xºÐgI(gYí©_K6¥X'ù¬¶¼QÕÃâ#+¼5å.¡ŒmúîNÞL"ï|ðfŠ¾Ð² Ó œ/ ¼Ðõ#`›)nîïœlhÖ£²rb›lSËÍÅv!86ýÜišÜïà7bNŸE&²±<„'®VßÛô ÿtzfO ‚pzýµý ê.K<:Oèj‡ïn«®l&½¨¤IÙ>YUåeŒ9¶"Áe5áq",°×(û®a3¨UX‚#gÐlÝ à)¶Û£ŽÚÜÆ•—ÝZMÒ+xš¯ûñvÙ«?YÖóf°a8Ë›y¸¬Ò_”ŠÔW¥[ÎP«Üñ¹Ëj Þÿ/\9޹Þyëž‘·õìéòeË'Ù»Õ ch~ëjyg|¥òз±Qžrç´uÅ¢½FQñÇ©“×Åb*a4²iÓTm ñ“ ßõ±ÊÜXi£ÉA­|3ƒä%ÈÈë{vö9’ª·mË]©ÒyLé r“3“ Þc5øûšñyz…m gºüvñhü #17¥~nW“×{QX« ^¢µF]YlZ<9Ç0”5«ôÒ Q0*ª Ùñ‹ ê á;f.}$¬›‚Ê )Me¨²QØú5§r#4k‹°f­bŽl}À¼ÉiÆÖgòG³ÔQ/Bʃš3å®ãü˜Ä ”IKBÒ³ñ}ؘ3dd´n ÕÙ=ñúþFYbÜIW„?ƒ=Fqn„/:­~)ùÎØßAõúÓ¿@JÌä‡éǬºÝs›—…_gJc¥ýÙȉ­ ^5ºÎ‚£…y%:ߌÄ_^™ˆN*žˆåÕÊorK¨'NíE&¿[½\ P²Åв/â(4o±ž¡'¡ ϧ¼ß_]Úè]z¯ÕÞ_ÚÄ*# öl“‡`l•–¿Ër*uéÚå1Vz[¢SÅÍR¿íÉ|hÍÜf@›ßäè «·ˆÉ S¤Ç«½am|Ço§æ9´nñà^f{jm ÚQ Np8>ÌêE+åǼ礄3a&oNnLèB&ÇíCm»q Å'ù×ýÞœL¶X’R.²Ñ°Xf–S†JÓ¯|ÈF”g•W½>îàó£E2ÕÔV)ŸÂ0‹kñp%ÆVÝÿ|F4Ç<ûô;lVbÙŠ-ÁQ~‘*¸¿ª÷­ûÕ©zÛ³ÒêA÷q˜óãtþ³,÷ŸžVŠˆØœV3„1ÈÿùqBÙ½Õ›œ&Ô˜ëPÇÏáaY”Eì\ =‡M^¢X¾äÅ’±Xšoa}ŸËmªâ‰FÏz½~r0;©¢<ï5R\j 5º¯¥~&8{`pxŠ“ UŒÚ,j0Ju[ÀRƒsdJ—d—°1r0³ÿò§Œ©ÛŒJ๰“ZË‘ ¥¾vé(ô©š“s?Íæ¡/ðM-&mlÊû[̬M.t̵kõù$? Žö* È%?Q¼¬Jš€Cn¸‚‡;¢Ù¤=qm?GI›ª‚¹Ú®Næ?õ<<#Ψ«ûBì á–)Ýyæ_8åvêiÂÝŸTÏ$ã¾vkvãæ¸BU¬Hír‡i&T³n²ÃÊâ‡ø|ûô’¸&X*¨yFjÛ ÷6HçtúºÂi ±R”$†jìh0pM¾ù> y2x=Û¼I@+jÅ6}Ÿr×qëÁ¤D©óð›y7ºO€Vpû·”\`¤&%ɹ§Ý öùc‘«ÕÛðQúå²@Ùù Uµhöx_^YH ‹ƒä7gæ ÝÆ" ¶¶4îBÜ¥ 3ò6Á¥§&“„ãÑS~À¹sÉ-¢úÕ\ƒˆÕÍãþíÑ‹Á­{Ǿ\' ¯ë±µ-Dv $ ;E—û¦zTcÒyû¼dŽÑ&>Å»M˜v¢´FâÆhÑÅËÏY¿|ÅÞ lÉÌ㈹(›‡’¸û¯d¿Ž©<Š9t„´’§¤(¼—°DÏÁñœkúÂŒ’^/€‹LU%?Ò÷~ofm8`оÖiï3éÔõ‰kÐÏPÙÞG/·œxUú%œõPO2ô/ÄÌé~¢«’¶¸«®Ëÿ(lu>zöC1ŠÿIfOËXßQ}Þz{ÒŠ«rGA­ )ˆØì±.PÕî¯"[äüìrt£|#óåŸyqoŒ.5šõEçÑLw$¯â$šIçÌB0VÝ3ÜcêRVRuibNªþE7 H¹Ø¶g7dnßjBãå(©tbÈ /_sã&‹«âU  bçGp°–¾*Ç’ÂiŽ™EбÓQaZ[Ѷ#%òxÃìÓÛwaý¨rywt‘Ë‚ÂLß”˜ –Ô³*4î¹ävKè¾xÞË[´á¨vàyýˆÙ®Å‹õ–ûÁ²†ˆpnª÷Õn‡ N,s\Ú±¾[c\(÷ö‰c)k[_ÿêÌÕìN7ÜuóµZuvpGBÍój¢®à ÂOçÔ›Øç*ûi•ÁϾ`‘èû¥ž²[Q0Š™žÍ˜°ñÉy¨‹‡û W±+¦Ü‡lœ./^ʉã‹Úˆ‚ê±ù )idŠÀþÆ;Ù-Ò’£*«–éu´¹…Y-'‡1‚u½#ÉøÁÜÀ6Ô£OþKÁ§’Ã#fú¯ êŠ2åŸXïcõè2Mao,°)—D–GÇ5gR„Ml5y0FH P9>ã+ahHÝ㸯ù²vm‹ËX”" ÿ’ßµ'úø1³+lÍ6Î<[ûq'–”/gÙ]Y½-ýE¡ýˆ4Gcº÷ûºR¼¢ÊD¦Y„öŽpâEÒ yÓê÷o#› îW뢆% ÂÞëÀCåJ¹vhCüuÍ¡Èìkmú«˜ ÐÉõ"fNÐdpâ×n#i˜žˆ÷ éÅtÜžF“[öºCîNgïW"2$\r&(zvq\[ Ÿy㠮༹  f“®ÓBH†ÄäÞ^H¡Tß;ŠŸ?…}íêxÊ]ªÞ·¥ÔÝ‹õéáoðÑs ¼4÷áÉt‹ }`«»g‡_F{ç}ÕÃÖXn†÷&¼Îƒ;ßÃx¬ù«»¼àOtÕÊ\Â}¬€×'4ö!éInRL.›º­Eg¹Lzº»Eú®@¯È¡Bží] Ý@„©òóvs/6†áT’@§×¯šE¯‹Ùñ¯}├Ó~¦›WD}q6èö}î ì1‰J|ÅÑ÷ ŠôÆ# ¡ÝIó@âÓXu¦p Œú¨Kç.`*¿lçÝkˆäZôùbíâí °ÿY퉷ªLFT£ÝQƒFØI‘¢hþ,É£îÅuÏ¬Ü ~Ëà·ÜÆZÇaF#»ªÒOû·²¨ÝDúà+öÅÍV)ḔW©KâmM©ª[éŸîÛÂbVŽÊw%ž¨3/à ó§Ó«o¬~öÛíÜÙ³$K%íA•ýññ½ Ûc™øg§86èæÔH8-´b®ãÙyôh«­º‚–]h.CÕF ¨@_0s ›hòi­°øråÐäíéð 麱r¥¶á~•QˆxI{›R ó‘á誌QÌI[ã¤Å¹„(Êød\:z ÌpŽˆ<0›ô1'§Š*ô^û¬âŸÞ0vp›å¯\=5¸ZË="W¦±´Æ¡úš)’pð©eçb…ƒ'Û£Aã­xRjúËÈ©ØnL´\¡Q*™UFë 5±Ðñ²oj8ýàf: 3šæÀnÐ1 5XPßÌPsóɇtí ‘ÆÉOMe/« ™¸Úì²;|Pt`ßÙ…{pý`ê. †õƒû¦¨[˜HX`»<úTQGàê¨`ZKØrni“UD-;þ+¯vžBŒ7JfØÅy¹§¾uì†ûÍv.ù­_£o,õ„·ÊÍê°RR6­n'ùûHÓ$åÓ,ñOoQTÎÚ§7š¢”Dêìn¾|n)5ë!ýŒÅBj»‡ï(áMzj,]•r”Ñy§_•oU´ž5·~b‘I+anú’Z>úÝn›ø±,ã …j}Qü†L¯”dÚý›]z¤Åu·…ˆ°W¿*ö“­•ÔÊ'øò\ÈNÁtu?B°K‰Ë¸B¬MY#;«õ0ÃiÄ’^KaOz̶Nµ«¯SÚƒU‡d­MÕJ„%pç[ÞÑ´9Y•Œgj6§†ZƒžÚ¥ÚÉ™y3ªsck€æî¨Õ…Ä´ïœ ïç³I \jg¿20Så¢e²á×0+²Ãèg ùé-÷á”’¯?õIØ®d%íZrû¬±°n°üøÙ#¼…ÙööþíîâÁ‘OðÆr¿ü_Ï+ endstream endobj 107 0 obj << /Length1 2234 /Length2 13568 /Length3 0 /Length 14895 /Filter /FlateDecode >> stream xÚ¶uT›ëòŠ×âÜ!¸»S´¸kpîîP\ +îN±¢ÅÝ).Å­(Zäfï}ÎnÏïÞ?îÊZIž™gôùÞ†BUEÜl’;º±°³’JêŠì@ÈÉ r ÓÐhظك~ki´@.®6`G?8’. S7ˆLÊÔ BU;Üíìœvv^ Àòÿ—vH™zØX”X `G+2$ØÉÛÅÆÊÚ é¿ôæ v~~^æ¿Íâ sSG€’©›5ÈÑÜÔ 6·¹yÿ z!k77'66OOOVSWV°‹•3ÀÓÆÍðä rñYþ* lêú·8Vd€†µë?*u°¥›§© ØÛ˜ƒ]!FîŽ $>@]^ ârü‡¬øðŸöØYÙÿu÷ë¿Ù8þmljnvp2uô¶q´XÚØƒ*2Ьn^nÌSG‹¿ˆ¦ö®`ˆ½©‡©½©„ðwò¦q5€)¤ÆÿTèjîbãäæÊêjcÿW•l¹4ZÚÑBìàrtsEþ+?)9¤óÞlÿ±#ØÓÑ÷7¶´q´°ü« w'6MGgw¼ÔXòo™È À ù  r€¼Ì­Ùþ ¢áíú[Éþ—R‡¿¯Ø ` )äoc ‚ü ûºšz€n.î ß?ÿ‹ÙÙ6æn3•#òoï1Èò ™/€>2„ìà_ŸÿBæÌìhïý›þ÷A³É©KŠkh1ý[ô¿j °À—…`áàäð°óxøyþÿëHÕÔæ?‰üa*ïh ðÿ“/¤QÿÍÙã?ƒ@ÿŸ=aü¯/e0d€AúßónäšC¾ØÿOýß&ÿ_Ãþ——ÿóþs’q··ÿ›Aÿ_Êÿ‹aê`cïýd„ÝÝ ë †,…ãÿ¥jƒþÙb%…»ÃÿÕÊ»™BÖBÜÑ 2Ú,ü¬\<ÿˆm\el¼@ª6næÖÉ?bÍ¿ÖÎÞÆ¤ vµùëQ`aÿ²kævlj+d:ÿV «ô¿Q¥ÍÁí7ÀÔÅÅÔ*nn€/;d9-@^Ï3€Õì1@*ôX‚]ÿ:Zn›ø_¢?€Mú_Ä °ÉüFœ6ù߈À¦øñØ”ÿE|¦úo‰ ù/âç°™þF;³;ÄÐÌÅÔd²tûCÌýñ?Cõ¯1û?b;Ûÿðù9ÿ•ÿHuæÿ"nHls°=¤ñÿ•pqý%qpøß_'Âfñ/ä„m¶·7uùƒÉô»!gwÈ<ÿ&pØ,ÿ€ì¬~ó!J«¿®П.!yXÿÎ R½µ·“5ÈñDfó„äeû„ôØî)ôw><Šìÿš¨ßzH[~CÈ3„íPçøw2.ä~ûC )Àé·bë¹`ÿç@¸Øÿ#ýßãà„¤ér\$Pyþ–Ù€7 Ò0'{÷?f‡ôçv±Cšñ§ââ·CÈÞ°¹y‚ÿPC¢ºÿ!ÍñøBêóücò Ö^@H!Þ@Hí>¿ó„xò¹üêvÔÜÝR¼ÛßSÈÿÿ}I‚@^ säåE°¹`˜mCXÇ]8±'ËÞ$gæÍ‹;9rœ=ô+Ìe»rb®ük¾´)Õ1k‘}X%XèˆDbé8^O;~‰NV„רá¯a䢉NY…”P ÒO_ûˆ’b ÒóŽ$½åÎ)*otiD$'^«G€qIïôUà‰É* «‘TøñB¾«ÈÙ¨ ÀÏtImC"ÓQº›gîéôPšOo±«;_›,,öK qÎGÈ"»5ü0#Ö-çèšèæx°î|#ÖÝÃ\ó‘îàŠ¹xävY69€ŠZØ…W ïs ÀýKj‰”ÌâÙGûµ|œŠ¡“§ÈÔ ö“³sÔ99hÿ6½^¨— ñqzï8‘óô*d&ÑýûY8‹U:Zó%}"‹õ§è#LÐt>“É) qù pë&.sÙÓyó„• Ï.élºX¦ö< ¯  ý+Á+„ýYl4ïc ?Šº*/ƒôÁ&òÎPÆbŠOt$«Q~M{˜ü˜½èè#ªÈDÓýÑžø±XìŸ ’¤žÈcæÚ³’ÞIŽÓ‹„f¢*$AÜ›˯ÏwöåóVæ$<ýùߌ"‹ÁQqhjéZ›~¥õ3[ý¾FÞð,œ†iÞ£yÍf87?ÌJHC Îj1oï:¤HÑÕõ ø|ÎKAV9Æu]ÁòzoËÇ Û“û¼ƒÒŠ‹ÈI0Y3™Ø¥/Èü¦á*p/½¾Ô–›©¦åôŠMv M@ONãAÍPR‰ý£/|vz1œJV6¯“M~b…ë"};:¼]«8ƒ+ºá]oã[]£9ZÛM¯š×·†ïÍ+KÞâzìtô‰ëK¸–ö‡|ík`í¹#%¬¿0!»¥ãfhœ¥ñô…Ø<Òfx|XÅC’äÊ,ê¡ã¢µ :=H #@’Ƨæ* ­Ê›üÂâ¬&›»ˆCÿÊ>fð³ŒE¿Î6¼Ïc-ˆ}µä•¤DWXÔll£ÚPW*«MB)J¦zðù~³íYiùË•rg[I¶hósE/7bIT¬+U†‹0[>´e§Ã¸õ;tý‰9‹õ¯ŽãQØW MÀ¸0~fƳprbUS§ó áô•DWfxË£” “µ°Ë™è"pKH7ëÅÝö[¢\Ÿ„^ŽÌcö€3‰ ÝŸ» 1MÊj©lXUñW‡òìñ‡±Vžz°ÆËªâjm†(â™"Zœ–G™ÕC)Tw2¼q¥›¦òÔ¢š’Þ‘½Íp—z©èü沊ìÚ?) J ánØ©Ž 0D1ñcAdô]N]§—+:„üÏ›wƒMÛ*kŸ3K½3³©Y W¸ÇftoOX}ĉp`òa–<ÌuîU_kiÆO±’yhè÷ìŸWÄmÉ«\lÒÆ©Z‘‹®<•}[·Vá:mhÒ‚söG=IW»ÆÊQ–hÈúúË‹l†ìù×§=.ø§[~%‰g£×"B#ð[c>’™ o¸¸s¼øôéÎj¿TnÜþ‚«º¢^}ùîgïÔE•Là­²ú> ÷ƒVZ§‡ú󘹭 9#ÛXÃýZ&¢Ì£¾~Að¡³Qs"=ûk³’§ c©÷õÇ’”bj¯¯?x—ܬeÐÊSÁ&û´ŸÞb©yKVÉíy¦mR²áçWhè™Cu`Z$i˜ÇZ†ÄE’åù°VæÈ‹ÉÃÍ4t.sûDÄU/ÛÉáJÞñø-TñY£Š?åø„‹õ¬´úû¡y‘x/5ÿLÞÛ$˜¤uéÎ^ ÄaÕ×Á¡xy…-èÖs/šqx3Ó«/aSÿˆ¬QÄÚÐúY׸Xô0Î3ë^®‹üHZ)›7¶õ*—¦é|aPÂ~Ÿ5½w©…« n‚ûÇK§G;GÏ‚ò'’U*³±GmÓ‹v*Q­SG-hŠ#Êøþ†Òé~©ÇEã~¤Û4TÇŒ83§O÷ç0 $;œhpëÌŠ¦LŸ ©é=¼žÞÞs¢Õ2ã¼â3ÖÄlÙu«F-<º;J£RD\6xÏ8myõÊsS²”˜®x÷ 19®™Å/½]ÁòQR13pÀæB7‰ª]Sdº™J¶©Ï2SÏÖfYG~¨aIƒý¡+28p»y æ ׇòý@ˆ}þ¼MôñaÛâšþ¬®?ñ‰®¦`šàMƒ€fôe¯eq°ˆÆqkøUnz9ê¢V15x-Z岄ã¢4Å}3­Ó;¯û`髱©ÞêäÜF…Ò‚kþàªh`ÑÂʧ~— 5Ã}O>3>ÙùÐK*Ó䌹]Ù9ñW"”©›rRÂ5݇füšÌKÒÁÛ=*•ðo/ŒHø×“±[:†K/)\Ûe?úé7™Þ³E¸ ×G/Ko7cäcê`•‘¤X®G7ÂdÇQ-åŠÆjódqΕÅåŸ)õƒ˜CËá'ÃãЙ Ô) “+0»á§DX9›è`®ˆ7à·1ÎÅüÚαL6G›MˆZ$Dæ2-.ÙôNkD– ø?$œ>uAÞidÚ·]PL`)Õ^ª<´Ó[¢½—·¢ÆyE3«¾ .¬±¹µ5 Õ­Ÿ‚Y† Ww Õݘ-º–×vŽÜBwÚ[ô{”ó1†¸Ó€i–œkd‡@‡ÐöÝct¦d9c®‰ÊNØ¡fë%â1/iS¼—wúÖFÆÎtf•EÝïÛdR®'îŽLq%b) ÈR ­¸”ïVªfš•¿å¸<É©ìmÍaBâ"Ï]ÎYX×Çb`#°½­TuÄ`*üö0»¤¦c®ä†’þV4ÉŠ˜ƒ—“Y›3 &hµ7zPzŠvwm1',m\™)Á:7ɬ7`ƒr!Öµ†J[fºRšÞ+ÿ8 ‹"ÕL¨}çjö••{C–<ä³î܃&Ë&ìítÛû½n,eÆB•S{ ìkáW=ÏØQ†´¬Áæ#Ïf7¥n¤m1(¨ý ¡¦áo6˜*ðdŽO×H’¡wùmƒÅTc… Þ™}`€ãù´Y1“š¦ÕKdœë íBö†3Mó èúÅ]^¾u2–²UÚb#ŠÄžÑÜd¶Îp4þn¦åH˜b­63½À^nHês¥YdÊ Ñ;œ´æ0Á2·!¶Í5¬ïq®r”HЍNùôNºžÜ¡æË¾¦Œû¼[œLH4$íö’/˜»R‘ÐT^8…ÛapÓ!feˆÃëÒ™·Î ?·»8qÂљ˔‚Ôî-øQšù/ËPm .t*ߟñÔÈ;MIŸ•§²únÍu·?g Ì2¼.T3“f–G…càôsÊfæÚÕ¨zˆhá:y!‰]‚õ]×$½®qyó 9lŸàõ”2q†u.’¢þ•ÈûÍ«T´·™ÁÙYÓ4Þ!!žñ¼ŸTß½nË$þ@¨Øî3Û„:Š*ðì–´êöhœÙëÕ)¿‡’Ê—2T”„¢Žê™ ÇÛá¾[,:ü‘V~gmW~ó£ï)ÍÓªùä£X¾äg7î¸è¤§W˜pvþ+ ›‰ v_z™Ñ²þ;öoÜ@¹”Ïa!?ÐŽM°ðå¿I.î†‡Þ œ€Š–ž{º/S­ïttZ^åúøŽƒv–V«ÃeONœÅjY½\ˆ µ×¢À ë v ¸c¢\sGjçj.œê]ÿr„×8¡ÊU`Äò×/!i½pژÆ×-µ¤”€rD¶k¨më¡Et[˱Žð¬§.˜P…¡½Ø½ äi\YJ‘ŸúÚ°¬>B=—FI³Oþ^õ"µŠ8câIï:tI̧ÐÝ¢«Ír¬ ý¢œcX«sËÿ»Š÷Rœº@ ËžyùV©¨ÜËN$ ö<qT<4ç¯×¶;é¦Opº¶Ig{…E ²ZîÁ2K–‹5ïÙ¢1-&Gë·™oz­"88%ƒ9%z:2pýɵVåêÂo›×wQ'Dq6HŸ].^O¬_µ&_Ѷ€ÛïÜmV¦k[px9³6;+xbt¿7Cï%sr‚H^õwFU&“lYëOà"õqð¬·n¤N*.^ÀèšßDzþ¬d1à°Ø¾˜ûVê™±ÅÀ… ïž´ÊòH¨ÿÖø¹™×k1÷ªá@¿=éêWî7<ûÂâÚ| —àKò¦r<«ö‰qPaÇTÐGêÓç¨s4 tÙQ!çøóMöÏ“’凒á=@Æ.•GõØÀQ¾G«¿Xx‰>sf½ñ­¶‰£öáŠû†dŒû5åÉùÊ2²ýV…t™%ê×MÁZëÍ4]ÐQqüˆûÖ^ÀÛø÷­bÐmj§g`Þ¯W«0q‹®`|úäÍnØà5ZŸÂ¯ÄPû˜;} u.ø¯"U™p=©œÅ¿’d/yOà:Ô¾0.RæŸôj±I¼bµ‹s›ú0¾‘jä£A$äÅj"tyilÌ·t^P9x+hðÊ›Gǵ+ùtkyÔ×ÀùÇüd²‹$‹^½G.ß!úE ±Ò ŒÇ›¸;Ñ+q{÷ÌŠÍäJ²úOq.ÎôJj]PÃG€]á\¸on³_‘"Yäã•uË“3íંLàö¨Ö/‡úÚ^j H¾†f°H-ÍlΩȎ gjRÁ-MÂE½¶àomLê3sÓÛÎE‹iéÖ="lØæ}_5œÕÂÙy>ÃF.“ ׈ÿZu·×M0(lº/@ÕÉ•;wš‚´ÏÍœæÝýhØÀ§·¥õá1˧GÈV~uï½K=jÖ·4"7qmVN¨|™ï"Õáº)ø——_ÁWå‰xúJâ¶,žxUvhàÈ~ˆî’é×LxEüÂ[ôà;¬÷²ÃÌô;JÞ²žf¨/j—€a©3”c¬ Ôk*9àiÉžz7‰Â·ÆZöë)à™/caŽ´·ÿ Ó†BË'päZ¸À‹aŽÝ2‹…2^K礯µ¸¼píX ’Ü}~û R9¹OyuÅ¢â›l„PûpªCÚ ú@¿\Ó×OÍuè|î°t1-Åü9IÚù˺jÊ"ܽì6ø_Ì¢Q–m£ÜRIä‹ï‚ó\K&h_ñ9¿1×´uq7Ÿ)·å+ÀÍ.Q´Vœ¤l9ÖÍÌþÞ[¼ƒÆ=jïýBÏ·ÙK8!ø:0žc‰êE(Õ%˜êÅq†}œRÁ)RÁ$X-O”ÊøZx N>ƒd¶à®Þè´ý•êH~_>™þk ë¼4¨ì!ÜF#уÁßMœ5Çs!o—•¡jûÂ9$ž?Ô ƒ6kXº± ¢ºÜLx?xñ1<ÑžRqó Gâ[à‘‰¶QW’Rzân‰3ÃvšÔÊ×ÿ\ÑÚ%’ÙÍÀ’"ÈX¥ßɆ«)I €»ž,÷Di‘JÒîîe e«ÙU É.ÇÑlh.íZÉtË̦Š$8µX$J©GþíËÕõÒLŸkQ:gÑJ£¼°°¸èò4<4ïuðõ>Opr¿ç\O™ÉˆüÙå]§ï$áv¨ÒŠïz¸ äV²Ûé:XtŠ’„ïì¼^|ž °_x[åBú¢o<+wTSxZÖ£3Xðž AÜþF+íL½âõ&ZfˆB^sSÆ¿õ¶e%Ü-\€Èœ® 3º1³xl¯N'}Y—a%gE ÇèY¬÷'ʪlª»­O„ñ¹šËmÁ¨v‘Xá9ÒWF€ͯu Û^Š ‡‹šý¯Kl{«eµ)°;œƒ[þ0$øô˜}·Y^+­Ï3ØV%)wþ\1ã?ƒ¦Ðw×›%–Kx,q(Ø¢Rïóñ¦^î‚Þ•êWZ16zBw‰Mœ†ÀáõcŠí¢ÃëÑK ‘z²#Tó{Ò6÷b—½©dÜÛk&ë.”LL:¶/‡¸Oú•CõuMêy’Nýs{Í@*÷Þ»¨ÛÙNPè‚)HËB´#¤Æ¯ ihqæt›DU7ýw1) >è¼§y˜486>K§;¹×Kî·È¶p³z[gô6Nù<““u$:^kú´ÔÐï°g§ƒñÆôH ·#2Ò¥ Þ™ Ú Kß„pÁ^%–£­X5Vµ´ zo­Ÿ%ÄïUËÛôÒ;1Mà<ÿì×a˜º5ÙLÊNÉìõq_Ä`ìä´OjP ‡SÒ1éON¸Ì_<HiåÍ¿·s¬ØÄ,"èQ°GŒ½¡_râä‘ð甌;{'¥nuîû3(9ï5·t\‚Só‹ûGã¨{ÌJ+­*ÔG”¶÷fEåÃÛ¶«q&–¦Ð¹a—ux‹‹IRÐ14ùî&ØVzºÁK÷F´óêJz0ßnì>™ô*‚IKD2-äËñÛ<0ŽÒç@=›úvÕ8¯øYtFÌío‰¥"-+;§©E)(c©¸»Š¸§$1fŠÒu5¡Õíë¹ï|—§æ8}•ùËôØWŠ-­5Ä?*üº1Gq}Ê«OUpþðí’"‚¸z&µ¼ § ÜÂÞ¤«ž7 ué{Cjiá¹B_F=ð¾€Õq™C™uzéÙªÎI][†ùç•ÁZ“?ÓbiÊ2ŽÒž¿”sÅ V gÀty)ï)lêóCÄ[@«´Jâ—¸€ôÄúö”9ž<3¢Ð\]WܪA´.¥N©±À½·à·,®šõ m$ðúIOè;ºÇxË$¹È¸Ú8š+ ÞÛ­Ÿut^×¼z¥þëÅ?ÜO¹PuXüMt-¢|D(²7ºëx“S™õDVÞ¼.m HÊãpyÑü4ÝCì8ú{Ñ&¶«*ƒŒ¯¿K¹I‚ÞP¦-(ÊÔâr•èOÚZ‡Édbû «d[à—_«ÂöYt¸Jkû:zFø Á£Ú7È'zšpDšxÚL ð~†ghˆ¾”‰HWÛ(保,´’:f}}þRŸfG Ìñ_Äé |®~yÔ4»·ž¸%Þ ße] ùŽ^¹gŠTðŒqÍ¿ŒÝ¾.a€ÛûckóÁ©$>y)»è#ŠÀ¾ îª%êæ€Ý?SYIè£À,¿²®ÍþøfÑ“fª¢3º‹,ÝØoRàÛ©Ý4]|9wýl[×f~@$‹FÓwÜb'#hIuRhúûé8i^ñ€k6ü¹è4©sM¹ƒvŠ]ÃO$ÓU:‹Ãуš]j*øF¨L„u‡+/ÇîL'Ýn‰vnܰ5ƒöÍA×âõÏ*Æ-½‡ŽÛ☡ƣ÷‡›ÇZ»ÅA;tÛ.Z—îàª&Éÿ:…!1‰ÒRzÝý)ˆ.jl÷Îýå’’·IÉ”Œâã@ÈN‰÷îÈIâö¹Ý%ÉðýÏ€\5ùV^ÎQ¢$yVö`Íd"7 ÕO¾â&#—£ê¹ðõ1*iKŒ#*$CÒ‡ÏsÖ׌ò¤ˆ]^øMüB¥hÚSá2{<ýŸßŸOÇÞºtt†ö¾eEw×l}ÍvµÆ+¯ÛÏ·Ü_ôIÅj>Œ%˜¢™à+ªdìð!«g]·°…O͉äfêÙâY[0ïÐ.GW'‡ÂǾ£°:»ͽÁ• –=?•~gŠuùå}ìDÉÆÔ 3øµL›aw¡{¸H’mïQêÿ\³½›À»Ñ Åøà=EáIÔŸϯÞ+ähûƒƒb[é¿ÀÒêÁº¸' Ý|è–÷TCÖúôˆ{sy¢,4›Ð9A]˜™'q–yY¢í1öµj—h4†û%ö=Œ­k~}-hØíHôxt6J¼cºóµ”•ì ù¯£:^ÑoŤÏdÕ±oô®åbå­[ó§,“×oZÕ£¾ùmðäöOÀ³¼ÇT_ϵv|ݰcí€ö*X³¥é Ž¾Í­3–ìŸ6Ðê=aºT¼ ê|×tÁnâLŠÊ*q›Wyó.©ÄI–cÜÉè¿û¡\¢—’Þy\j6N%{p9ú=£@&ì-ƒR՞ܗƒY´”…”Y¦xÝ¢]aÁP¦;z¨ÔeLá4_i âìFƒCdТ$˜~.pâÉDƒ/}¸)d%ïÍÆèÄ´°kmçQ‹eùa%Šê×”R$i…§¯Yð“Æ¬apcƒAu‡æU£½ýŸ[5¸æÈ‘ø‘WÌOÉhV•(RáHÐí­odñXëaÄ{ºŠ”Þj¤³Ävú¤Sß¿‘Ý“%‹Ò<†<Š+rý²ñ"IŸ&î¼°Ú Ù–¤ôì}»4&ÊÁyªc壓ÏÙŠaùirp†$õË¥¹žï¡ƒö”B¹)ÕsîH…@þrM¸Íþɹ&i£ ”·™@,’Äæ$¿^¡òã¥#sa4£ã±ï™PÆeP5aû¦Ö/VDz}2þ¼I„ú‹LGÃÛ˜nu1úT|•#ÏuêÂ!IcÊ]éü”Ì:#ha¬¤ ÖÐcj\My-bâيL•šýoáÓ¾jý2ÅçÂiÕ PÆú|zó«ÂÛ èŽBxÌ…ÖŸ-¶š'‹N2¸ÛÇ5_%´ß6%½aðéðë×yó«l]/2²è6Ï3êñmBžMOÌ? ‹ÎºIÙ›Rð¹¢å[j 춇´'®z1é®_Êøžû`ÈÅ)+‰«n×11èš±œ‘~pºú€o½ãGu†¦lóâÅPköœ³¡'ùðAÏ"Z’`ÑYiéNܨÛÝ48üSž6VÆãœÌ·µ³NªjÐ%¿Å :)ç¼%hûL¤Å‡ÃEÂS“¥)¤Ly×%l ·Æù‹ * #I)Ñy4*‡ͽÌü‹*;Æ{¿šÏÚ#œœÕ&ƒ’Nm<2ÒÆD'L„Ç'(`“È|/!¡õ3zÜŽÖBMñá%RšpÑ€6pµ„õÅ®´Çx×PŒõr‹–l%÷Ò¶`K0,xd½r{b¤êešÁsâÙøq#± ý¹HöÓ- “£¤¨Ï0KÑ)nËvßç ¼¸¥Nuûg­‘:†ÑC]Rc«o ·Óï³–¯/•4ÖoÍ;x9Õy8­Hy·êŸÊrw¯" bGŸ€Ã€ñÇg§ˆâæ¨I^Âù€‚›ìc^Gog¼>¦bDiõÁ"#¹zÌñ“øÀŠ”AÖGÏ/,[øHD¨z,ØI5·=áç" ‰ô¥uid$Kũљ¸HÕª ">rÑÉR4»ÞUï3Ç®á8VÑÅyÕ1ûæÕÒ®®tœÙS¾P1G,†:w˜ÿá6œi•ÄËõDò˜É(ŠŸ»#Ž!yX(ÄZßSk) 6/2Æ?[~ë‘­'¢*G‚ï˜'ÍÆï5Õw‰9çɶ{fºM]7Óg5fÁ»esȈ•OuŠpªÎ–ø&Ñr ¸ýTx_¦ýpšþ™ÆU? p„óå’ÊÌIŠ=¡Ì·Ül¦ixaOk@˜»· ?¨bîGÚ~nsÅék­}å«Rè@£xp+'ïÅíÍMæh&·<9ê}4~ï÷DVŒý¯§£«/ÃéE¿¨/ø»¨KÍ{L?µ––fÆ€Ò‰Mtûœ^_|Ùˆõ'_qÖ©95²¾åBk³­<Ô ¢Zó·ÝmÌ#ùʹ}Ë»Ë ,wY;oØ ¥Ámøi㥲ôÒ¢¨As0Ýàðy¹Œgd—¼ù¶üùYM÷)ΈqC´ô•ÚKvŒõ$.#6‚ié°)µþwš-V ÛÆ†ci€âbjÑ]«QßÊeXò÷¼l:ÆŠl$Ö)Aꛩüµ^¥øÍ²eª÷ïHÄvxÝÛ ç}"L¢4Oæ2÷ù –Ó‰‹*¹6ïÂ+ýÒ@Ä–úW”’®.•¡>¢j‡¨@!ìPG ùN§o…Yã>ô‹’Ã' ÷oʯ¥ØùKÏÞØ|<¸£-┋°1þ9æ¿.M1æI'SH‹‹s¦¼·lNº2LTDM$~zé_íÑ&>Å\Ö¹¥Šš¾{É."ÎÉû(Ì*5Ð-ÄÌ8ÀyªKÍ3A‚·×(L{’/×P*ñDª,1ã–Ýß·ÒúãÑZb¨íÍ„Á·Þõ‘œ†;ZÝY$ïr«ý)EýƒBÈžŠ.ö»#JÄ/QÇ"cä”tcÁ"|džhX‘Xéf4B°*O&·ríxRØ}q¥bxH5§Ú‹Í+ áá³co :L9ŠÊDb7¯Õ®;:4ñmLßnµ£ ÎR1«úÓNÝ4–^?8 U¬ôÁûª=~H,tË r 0ÀϨ–~뾌Þ2)2¼w"9*nž¬Õ7mb\D¬-þ9pQþ¦ÚÿFéG¨Qq•¿ll´¶Mçr1¸À€a¡ï×5\MÔ=Á¡}j׃ª‹v½X<±…wšÐyÐG[ -(x^k¤ðÞ•â2“E©žG>“ÍÒ[ $ÙdV9VfL Ðz§¯ÂÔ^NÖY¾s†Íï£Ldþ^UÝÚ6(Æq^ÈàBĤñÅÎÛ".v[ÂUqž+ˆµïeîœ>m¢wºê/Í-WRô³ïðÞøDf’=ï äŒ3?¬¸¸q0àõAQ½ùjü*¢¹Ì$½•v@ Wx;zëTä4o§Æ™Ë÷ºiQâ3`Ï…ˆnͨüÍHÈÑ\ΡÇ\œxÙø2òœŠ«œ¼ZcõR³ÔâGÄ(UÒ_‚#"œž;Â.ñÈDá†~8\E¶4|ûåþÛ,¼Y%"\V¬íRi«¹þ²¬eEëg"“÷=ÌV!·°èâSñÒ•ÒòÉ>%£·œ2Þ²Á$±Ä^ÍnËrœTìk•¨,ì.›¿þøþÞb3㜯B¤­œÑŠƒA»Lôæóˆs» ÐA^–à,LÅ£ÏÀ}ðºUžGI¬”1ÅvÓÄ«æ yŸ$É«ö“a úfi%¼j0 Kž<à©E<&ÿ*o€»å[9b§AQOÁ ‚¾™…cvGæ¯bÏ‚¼'¶õ$:BêiÙAd3m•Þ|‹c‰ÒÃC|§É ö˜pµe55D?7êþ¯Iƒ)m/U Ñ#Q}*®†¨ÂEÝ©êæœ W¼ŸÞï^$m¬í_HH®š©ÌF8>ÜÃ÷Æ7|ÏûôÓé1pÓ~ÑØA^žÆV~E½Ÿ`íj1)åÌ—£(˜¯Ü‚Àp-A$ɇϱøâºhI­]ëú| 6)u£Éªb½ôƒ4 –bWþ«1¦F­³ŸŸéù{²ÊNiN¹Kô[×ÈY.S·ŸwÑÒ—ÌP|tqÚE¢JCÞd2é{(îNY^æJÚöå|S/çŽ/SD»_SjQâ¡|ïôªÑÅ;dÚÐ=ƒÇÝ)fEÄÃÍœA¸ŠKúªVêipô,ÙN@ÿõz~¸¨ g¯iwUJ+‰eýsûàâÆÙ\VÿõClˆØÑ<@fg=ð!p&üeṚ_ï!¥ç+ïEæÝOÊF/   ²¼Ú‰Êf#|C j}í0r¶i› ¾?¼‹z¾4ZþR<4­o©;).!{ÝI><˜Aó ËÞç—èpfË`þ¡JâÓÀùrG9ö›3^Ì,by¸sr[>Y঻^tÂ×B2¬âº{…Ý3¹‘D>»´FÑèæî`²í i’£\€e ôWcB4P²fÍÅ5Wn’Ê–ö•©.TÒëøÞ´Îñ^—ld0ñ§¬@Û'CfÑ‘GJz#žH”ЙYƒ4r¡¨G»­NcM]Ά^  kúѬW-m‘'­P™€«ÓÈüM6ïû•õ?oF^Âa —½¼Ý„ÔåŒ6”¸NäTöÇEæ[|jŽG]~~Ür_c×bƒzêa/Úøl½‰½ÅQx\G§äœÉËQó¡Ÿ„;j›QÉcQ mKtœ|ñíóшBᨓ·ëþþEUƼ܆§hvJËçn‚óævQyl¹Íž©¤x¤QݘÂÑÀÆ,Ù‡P™wÒ÷Ý×%LŽyàv„ãý€½&î1CÊ:ö¸ægÜ+÷_{ ‰zn¼Qj¼±ŸæU¡2©,¡‹L}[Ï¿¯À½*|(nÜÆÞçù»Ž~el5ii’ªVŸØõm¾X-u[ÈjHÜÿHôŽ}¬þà¡cWÚJ:NðBAHø™¾Jò‰À®_¬V÷à"*Æóp…1f_ÉŸ| K~AêcåY¶ñ¥¡ÐÞuònûãºìàCšœpÕÏ6XâsR.€´û. Æ ì× W*häõE…]AT䬖F˜¶Nµèž6r§¶7ëíÈÙe“ó…ŸU”¦nYÒéVbnÌ f\b?ã8X}õÀn!Û‘†"ì莡ІÝòÊËù0Ù¨pƒå 7Zã]÷ëÐùçêÁý}è¶¾ô¢Ë©2DÅ/ÓbΤ¢‘"Ç7©DñFƒl¥)ËŒ)Í…Üd2ê• ¹r}ÌlìØÚ°оÞOÁ3~›ZË”½ëG›÷ãpéuß®-ҧדs?å3€GSJoè;Îä’l­ø‘íÀQÑûÖa(pÚOMn•Ž×¶ÁŒ K¾ËÑÕ¬ƒÚÞt&"ì×¹s^„ši…3>¥@4I¤µ{6*x¹bHÞ£H·àåhüú¥î‘£‹&|ðNå‰mßÇäÙ¼¤NàÖT”C["5S<*øélg·Z(ˆv~”ý~­.²‘bUÔÞ¾ý ~GOæÅóo$ƒþ©ýòÞI_D5WúVý‚û¯:uùü–Pø‡¡J-¼›Î–¶—Ÿ…ŒôËœ~d!‘ufoÿƒ¥ª©[v@oü¢_Ù7GhÑž}^ãÞõ°,Ñrrë»Ôa©Eô­h<Û³Šò~Mó£¢óoe#Z’[ºèda´¢£Òü_±e§F­„»cÒǰÄÁ¯I]@ƒÓcí˜`i2YT>f]S™j"ï~ìÿ®›õ…[?!5ˆé;…›2zb À°c@‘7£Ô‚¡M149Ôŵgf¿1¸¹*§KÀà°á¨‡$/mO0.y‚²\4/;lÄ^ }õTŒÚú2˜åY[ÈjmÙŸh¶l*!õ¥î| _×À±ÁÑ ï.×H\xg'ÚÛBMv:YÞf ˦1퉷Œ¿›®*(xšDMw±$ì'xɇÚe-½‘C*cxIO®g;À´l¾µÃùŽNÉ3ËÿFÀ³&æêGCbÅXµG:’1vì{‹ù±ŒFÚ½Ÿf½[V;‰‘`å„à[N¯¼'mË{áw'š¶¼añìÆáP_?ÏÛøUO´=ÿàG®­©`Þ^‘öuñ ŽQ—ß\gU"U‡W—,‰ªáÇâåŒí‘ÇfqÖ*è+JÌ]ª›1€÷XÄTö{î$ øå´ãó é6¥ñ%ï­• 1„NÛN ÑþGŽÌ[f½ ³ŽqiµÂHÁnžgæD¥NéÅP÷M{Z+yŒ-WG¢6¿¸øU¹!ðéøµîû­ž$KžuheZ¿˜i¹>œâ:‘<:Ú˜£ï‡ƒ¯ˆp¦¼ÝDZY` zV :œ‚ÏkŠžìʘ`\Ô®ˆ0“º"= Ëú^ÖŒâ5Q?×K·i4‚^‹§³µ¨ÂUWŸ7KP–Oy+ËXÂÈ×ErnÞës”…±KÁ„ÆÈQsZ…|ø*û”ôÜ,$C~ltEE$~´ÁÇÈF±R³€yEIŽšÊVX"Œ {«Æ‘N'Á€ð”—MBÔIÒK3bYíî9àÝj·ÎagžãGãZP0«ã榉pv ¬ ²”D¶žÓ³ž1Û6@ªòÑaš,|ˆZ¥CäJÍ"䦫è7+Œ\ذï¡6ŽÒÑg•ÜvãyöŠCBÌbúû7ø¥Y&UEøàsrfÉçäDë㨡2üÇ’‹¢›ŽIØJÕ2bd‘P_˜–˜ŽmÇõ« KÆp ëF£ Y£4eNÌA’KX@Bô¥hO×få/¥Œï¢5tŒÌï(Ç_Á“¿Dg5à]ÌxÿåŠ5QòGª3ñ€Žò@кsÔÑpÅ xÑÛ Ïå“· ­]è³y›šÞRàeÔÕÜQZØu\>F¼(Úaè—-„Â12/C˜5\-SnªVïºÉö+Ê;dmÃNíÄ 5Û~Ï<‚Yꔺw•¡€Ô°–…,¾lX2ÎúÓ}Â_\ˆ5ÜGÆ!lÝv¡€] ´hC¨({gOÿõ uÑîŒ_: ?=Ê(åþéƒî çp2=èOùnÙ"xœZ^‹dæSæ†à&äj ´ˆtÉÔÕájšŽÝÐ| ü2™ M㜫܊R3""ïà`”Ê©4¸IAªÎsµÐþÚŸ¦Ø+§9+9N=Ïâ‹;›¬Å-'ßl½$´sìf‹Ð!ìá,.¨68?7ÁfÝòªuX'(gû*¦nSEçµ Üú²î‹°ZäMUCN©áž5ØÅ}7ÌlµG¡¯Á^¿EÁ:\Õ|£ÚQ¤^áMliE6jÌu·Q²š¼) QaÂâõ“Ž$¦æ×3îЫZ™áâÛ|ÏaM˜ ÆI<ï°ª}>—Üg7jøQÃ0MFzöݪÕÂJêËt˜i±ð.÷ßÐ@ß=МáDØ,Ôæ;ár¨%~¡ÉÇ׊æ©yÝœvc/×ì•9ªŒwLU*¸(÷…ÒC“òÜÁ8æ*¯°kO̓bðDZ\lmÖèÏ ( I_*­H¹Þï‹6¢b'¼×gëb·,þáZ”Ý»ú0*˜ÖÏœ+s€u¤e«ãŒªfD EºÊx‘ख†¾ÚÙ­»?X…öHïsÒïQU±6¡{šÎ‹aPµÓ~÷Ãki¶9¡ÔÀýçb}_3عHÍH’œ¥|©Ó_…‹N/ïòðòí|ªíº-?%uÛu9’ƒ/tƒ—k(Oˆêtrl@©Z(uÏ%†-›ý —ã/*)C\òiþàBK<”g–ŽñÒioÇì0@²mI=×öj‚5†éÙjLÝLÆ@Ãñ×-èz¡< 8f‚âÈSbÎJ+\`ÿ~ÝE.Ú©êÁ¹= {'¼Ý–¡¹Ã,.²‡¦D>aÃGèڃ̧/“‘"ËÖã^&'ïØáZ´®ÉDX_ LU}”P}¯šóâ«N§ú<é_(vq¥P“”^Š›öÐÆ$ö 6©7#Y‡c/™àéÞå|•¾¸{þˆ¼eôQ``~ý0ü2@l=— y/Zˆ4š u•™9*Œ¡lq1±aº26$ûè-åñÈ B{:Uõ]³üƒ3[Ô(²Q––µ©"뀿‹œjtg½&[ä¢é5·–ÀTv¼¢u^³§Ü=:9 Åx,”ÒCÃ&vþKqV¾V^-›¼§IqQRá²Ò£)yäi® q#Mkÿ0aµIŽõ‹KkÛÚ„èp#¿ÎöwBª—h“;/&VB ‚²z“¸ä–USrpxûnÒ```\ŒŽåM×ræz‘ýyª_ïç;€oþ‰‰ý endstream endobj 109 0 obj << /Length1 1525 /Length2 7891 /Length3 0 /Length 8910 /Filter /FlateDecode >> stream xÚ·4œ]6ŒhÑ»eôÎèè½-È0ƒQf0£÷]ôB”hÑ[t"A¢÷.ZôDÎ'OyŸç}ÿ­ï[³ÖÌ}í}í}ö>çÚç^àkÀ#†[C”á0$?/P  e¨Æ/y@C(Ò ò·‡å)Ä …Ã$þÅPpƒ€÷6Eòž¨‡ÔÝü‚~ ~Q  ŠÿM„»IAP0@‹ ‡A8, po7¨=ò~¿ì6~qqQî?ÂrÎ7¨ Ð!í!Î÷+Ú€œp(éý_)Ø¥ì‘H >>OOO^3‚îf'ÃÁ ð„"íúÄÍün  r†üÕ/ ÀЊøÓa·Ez‚Ü €{ƒÔC܇¸ÃÀ7Àýê5M€Ž ö'YóO7à¯Íðóòÿ'Ý_Ñ¿AaƒllàÎ. ˜7f°…:A:Êš¼H/$7ÿ&‚œðûxê²¾'üQ: ,§ÝwøW7¨ Á‹€:ýî‘ïwšûmV‚àÎÎó»>E¨Äæ~ß½ùþ:\GÜæû7²…ÂÀ¶¿Û»»ðÁ ®î5Å¿8÷&œlv$@Š ˆ ®ˆ—=ßï ½] 8ù›ï{ð÷u»lïÛ€øCm!÷?8¾€ts‡øûþÛñ߇Ÿ†Ú Ö;( çŸì÷fˆíŸøþüÝ ^sà½üøÀߟÿè¿à}bÇ ?Àû¼'#þE|ÈÁûX÷?àõjãîæv?øˆò~#þÆÜ2ˆÄgv n#æPÖz^%÷Ø“çû€~ÏQô ¶ñº•Ǧ^ˆ‚Ôű`~ÌŒ\çÄFÌÅ2É®@ñ]££ ÐÑò›_FŒÅl]+†­õ·ß‰ôå.BÓ]`C££Ãì¹ lG±ÿA¿ÏóäÉ8%·ëá÷ɬm)ÖgåöûXl {¼xhCq1XÄÄñÐÂÚä²Ú*ä«åÆ'ÚÕ˜GO†~$Ó*o_d|î^í™+åF…®d<ÞÙÁwÑ8ÉÚ‘yµõQ„Zãù`œeý£Ò£«—ì!'­aÁìÖˆ©úú ¼šÉà–Ï€uµ—!.ª‚b\±›é‡Içæ¡ó …Õ¡ÜvŽJa—)þ± 6ïäš±<3-†ã×v(eSUö˜Y»«ˆmî`îBNÕ¹aÚ"¥¯ÛÂÎø´›–d§—œú&$ºåîÜ÷·Ù¦¯EíˆuÔ“ÙäQüRüa Âò¢RíP¬Ã<; "’g‡ß1Vô‰!O13h3&Ó\" c²=nÃЉ;÷;¡|œkòòz4±ã+¬|µrƒ1YûWôŒ²´Ô‹à-û¡í9ɇyu†ªm{:XO…¢|ÇŸ0¼›ÉWgÞ‘`2êË )Jn¬¡nl=0Â~7šHRψÉHR%Áz…eóXォ{°Ö˜ÎG£36um‰5Cl¯œ¨;¦{oK¹ÚAØŽŠ_žÕjìc½‰„ýA:Ó„Òné7Ø9DâŒËÇ !‡ üôçmòKX)o[®ùhe½Ð’šÕÄ„T·Ý¶ÐÞ- ¾^ðRŽâ DAó§4½µƒ¬šØ7ž­Yi³eˆˆYƒâ¨àKÌ 5\ sÈg<³BŸ¡®Ãu¯¤0(6/~ó½R°ÂR%Ö·-» Û8æQkÜ»È „=£öë=ôÐ/ëäôÆ?«/ëè™ÔôN#ÄßÌ‚—ü|SëµìRLõÅO½%9U½™U‡äª £¡ÿûæ.¶‘yHCqÚ¥¡ˆ4NKÚÉvvƒ Ssý©Ô •:üˆƒÈlç”·…Ù¨4„¨Ø¤@úu“ Ô„¦%À[¸0².»ÆäFõ6¡§ø˜©O*¶xÊÙì’?D†{´Ñ¤_ ðØý›Ã²ìÈ‚Zú^u½"&h$‰Ÿ%G(‰•®Ÿ¹õ'LŸ/k’žä3å¬\eô#+OÉ{»ºƒÞBŒ «¢g²ÝV•‰eíØ¹AmNn'ÞÓ_”,Piø%ù-’¨ÒIG#µì‡1-s¿tRç,dÌ$4o^¡Õ1ÉRÓâr=¦µY{fG€öýQü4j§VgØœrMÞ8`r®n„It%[Crc+N¦ ¥aaŽy¸ÐJŒ6òc(¤/ f+‘¶vP¦¸{.V‘§…«¤ø]Õx-,„•úX¨Ñµ°xp­@®7Ì¢<®N2ÇÌ<¦ëUCÁ↚3^/ñì„ùã¡¶À¯N®RœªŸQ.¦m+ÉÕmÜïžã¿~/FŒ³9œu7þ(ä‹¥“KOùRôZ ¥<í÷A 4ª8­i‰`Ò é‰ùñwÑÂþ¡1\•xO"…C¸¿›ßX“£‚ÞÖxµãɨ;nV}g±R-V9Ò¢ÇgYŠøV·Ls¢«çPd:?Îß-]P‘¹£úµHÕ¬0á3æÀkO¾Ð|EôŒµÙ0bâ …d±‹;n›Gr³i²à˲ ?ÇÞ´´;t]ÈJש½§Ì•S)~¹jªÛ¡¡Aþ¥hö®©{Kþaµ4j,Þ!÷¢RQ@MéÛÞñ/ íœjNA¨ 2$„ ·¬©›î…·ÒÍÔÇŠjÐl¯ˆÂx\&ì£î‰ùUÅ"éË+-5[éo!—œ¶}ÙXnÅ;—~ñ릀W"xuÊÓcÿNM*8Šv±F ût|„+hçKdlúû9±Ä“*}Ü57´Ê`g„4-Ahh’l÷R} c˜: õêîUlŠ8ñÁ{Õš(ŽR•AC¹aA)IGúÀ³kÆ×=Íä85Œâ¹“è-èÆò¢¥¾¦fîa2õ=Ò©öó !õÙT•L ÓõIýûÚÅ'½ÒCbTvŽMG–[fC!ƒ`âܯVôÍ ìx³cº¢<Ü®·2Øòã|0»Ì:Õf~6et)¶ ç~4xÍ0RËòçPŽ`8­„ÚlŽU¡“Ŭõ6y]ÀÊ¢GëA« è›áÔ `ºs»ä ükÿ.œÖ©…ŽÅw˜âϾîäN;…í=¬#ظÝzò±%'E’$ݨ£êÍŸ%a¸AJ;`%-,6ŒêHmû‘þåϤüeÀYXƒ9µ]ý)’¼i¾Ë#5nAr¼×)4€îŸÃfÒ”ÝàÞŒ‰¡©FàG^­“¸ ° ìæ¡åŽúŒ‰ë¼ïéçr`í2cQ¯ççsÇ^¬ºñ©ðãøã %JIµ¥±ãGxA>O6èöæ©"ôš åÖʧÖÛ˜rRJŒI~R î¯ìûǵR&Wb;DÇSÙ7w ÍÚ³=ð%Ú2÷<.ú²*Sèú¬ø®î qšñØ—ÛtètHã†Âd…zähÚÂ®àˆªöêRÜj"d…óKnW„ãSUu"ÉwÔEyVçš§F§5óÒêE™dÑâM>ŽH<ÑXôéAŸ6—;9ÛŠu¨³á¤¦h%ïü鳸 5 º½ÌG¦^èD•y¢cõƒ¾+¼™—øs˜ZE ¡4ôÃkŸS7ç#RNk÷éŒö®\诇Nƒ‡Ü{úq›;'}KÑaƒ„‘[ÚØì(ìí€"mê´äs»kÎ[ÒZdkÎô³"¾…v\¢Ø:«(ÚOs®Ééù Í|Ç'|… «ÖÆs.ÃéK\Þß>ЏY|£Ë‚'«02ð‹üÍCñÅÀ.¹§'.>9Ç+_THºÕ‚XÌLl*ÔqvËÝh½zH^Ù™j¿½v³­³ñ¤^H/o •/•Å–[ŒÉÊŠ-œY~(ˉq»Ü­#-PD!¼QfU›8ò¤…\f²ìÙBÉ×öqVÃË„GÑCS¤Î2>Æå)H+OÙ2Ó±I":Ó Ÿ}Q.›z‹Ù~»îÓÒc>§_¶‚?/„aN']µÀ±¥ôÉ ~=È:e<œHÍ'„kj©sÓMÔq€÷†Ö^ ³ÓOS²ŠsA£%RÞ;3…ÕÂÝX\DÇ1RüÛXü:ãÖ ®RÜÌ&ù¶¹Ëº™0´ßñ£­-¨~œOà}¡?­õÆQðëÛÏ¡¢h5z¢ ¶/ŽKݾ¼rœØÒk¿ü9µÛ}Öðì ÞÏ“&²Þ¡öµø„Ißó=šBïëU×ýKzðÊÁÕYgG™Å¾YBŠxr­aÒçÒ™9É·9Š¹Ë™àPéh&ýž÷BÆŸ Ž;†dò5#,®Cç>ŸãŒ<‹®OÚ¦³;ßÜaÈÈež¹BsÞ¢ìÄ ÁIpm Ü¯øð`1_Κïþy3£_¸BþðÇgâÎÍȬÁÞ2uÐ(ÅXÇ·‡Òëælá¶ø%úþÙçRhÚ«m¹> #Á“ÓL?€®ý-Q*‚1}l€0;V»¹›6ÈéÔüÇ_¨r±—•0 Pá[tµ«ôgúJë¿æ¿î»ˆ=¡ ÈþVžLøÚ=3lYˆ ÙÓ X«av…2¦_ÙN(UHR«,4ä°Þue­ô”ˆE¹øãEÖ í®mã°dtÛ)ûñõri`EúTPÃ^<•1kù]ôÛ— 6çì¢6Þ}ÿ/áât—k¦ÕÛóiÃh¯x ò×—Ñ)§œ\»àö• `Jý°~5|3ì£Ø¢¯‚ÊÊEÚ[·.Á üœ{úO‹jpœ™L^üªU£õd•òЦ6Œ"þ2u¡›Ás¦@pI 0ÎØtô™6ȼ¦d«Ô'Bd÷ÚN<ÕèùŽþ$ÙÁª+ås·Òù\QøÌ«ÜT$ó¾ññ²ÞŸšsIÖüX„A -o~幩°²`ü¬5õL«Þ¨|ãGð¡Vð=ž¦ùìöÊ r›æ°D)üÞ\yÓ ‘óyF¥•7Ù`¸J6¾d&%‘÷…Ž»”÷-ó¦Á!ò@ºØÀAyÆŠjÄE euÃgxÉ-Ä¿Bþ Þ{¬ý ßÛòùÑ §àšVJ ú.£Û+7ßN>lØ…Xïåg|\²ó¶ÉùIɤu4÷íïå@²v*šV=WÁYÆÞþJ„îgypÒÃùoœkËËÓ°€©2ŽŒ´3ÈüEwR`»yŽ7g%†ìðbm’ÒØ›z>«/fÚ› àäwJ6Œ1YÛ¾%Í+hî‚(ê°ýÑý¼4”áXÔŽtî§?¨5 $Í2ÀìŒ.ZÔ‡5Šßi¤:F½zýÒý¤ÿ«œµºHAùq]¿Çþ#$ÏÒU§meƒû)ë6ˇõD"}já(½¥‰ú;,Š€6ÚÎèPWù¼ø,”Øéòº‚.jv% šÖK†¯§7Ã{‰¼WOM’ŸèØLÍ?ÏÍ+fMì±’Ç,§F^¿‰ÑI°Ó¿…%òZ'!v8¸¹¾øn9ϳ½‚½Sàˆ tZæþTÉúè½ftN6®¶*ZÞ;FF<¶¦Q›º“R¾٢³5ZNæH¿É|4„R¬¿£uAÛw?IJ/8·ˆû#ë Ê>1í_tCô)ÖsÝZq’‰xþpDg/³V¸ñFzæû(Þ|æ ȪYÂ'å.ŒãHì#õëãã&õä)Üç©rÝEE€Ô!àÉøùZ{š¾-!’vè¬ ð¤š„—É'§ÜhúÚƒ)©w6rcVÁ~-ž,ÑvžäÑóB¿×R7¸4)¬WÄ™[9k9 ßóê«Õµ ô9n#ѻβ·;Cî7nªc7SU*lï“õ²X™®ËÛ?¹w ¤FkÈ®ôëŒ×œŽ±7¤Û”b^{Éc›ÉjB¯·¢Û.tLpD@ÍMU×±·U.y²÷h^4ßÖ+ Üàh+¸âîSñ阣·ç‘ÌEŠ­,A>Þ*ä‘4¥é!ßÎY˜çÏ™2¦ÞÆýñQlϽ³ ÉøÂø#¢^òì`8–ˆž“BX (Ãeͼã-žÍ>óž5Fiœî`íç˜ÁvÄ…øÞÿÄæ#WÆݱ¨¡-¢×Öétp®É—ƒ·Ê± žñµ';Ï„æïʵu9°M6ã~UQ[Ìël ô´"²‡Û+Sá‹'+X˜°Eº ùQ÷õÏa—J€äE™4D£ûkˆaë¨g;©Œ»bµA}ýü9o¶^,ð`úæj¯Q¤UH"Áøµœ ÆØ}Â@ù­Îj5¦¼&S;Þ<_ÌyyÃxZÁq£ø¬Ýrd³}½`Ja€Š¾õ:±ç¢¼ir¸“!#v#•"·†Xotѧ£G£·»q¹Øïµúž³i™4ömQh Uä{`„ÜÞ¿cÙHÇ4u8j_+ì}F TÄä(÷},©F¢. õŸ8¨Ö‡g)_6Fß’VˆQòO\t%̦œ°*IŠÒq­ .&s狨t×ú <8¼V%šž3&ðiVs1Þâu%EqoÐKÍX,­AMÔ]#©I(@Í£%Doµ”ÚõtÌ×~{9qúX)öËR‚÷ÄùÌô¡íêÆü‘kj³±Qa"ÏV„WSó1š¾`ŒL¸ÜètQGŒO¥¾&*ÚsûÑmñt˜˜»ð8e•ùlârá§w8ÙÔrÀ6 .× ~DHD-urwýpZ[ûKWŸÉä1ºbbê’2ã>òVàˆü Þ®¼!{ëW¡äï2K¡öÁ‹å®—]gjK=;9¯E‹ç×…ƒ}ŠTòçŽ%3‡®wß±Ÿ™ˆz8ç;?HãÐvÌ!D©RÝ¡>Ý!%B¸4|vÒ¨ŒFC [){p1Ù³¹ÛAKÀÚ{öX= GFGÛ,7§$ö>ƒqKxh6?;¢f@_I\žßê½¶Žy.Ý;O‡3è,!x1—hš¦ÁAämo[kW8h?_x¦+G²8oÃÖj@oDɃžè—®RÛ%Ö—ìÇ×ÃH×Ò©E«QçØ¥@KÉ îtƒZú:Ð+J-Jº„Ô5„wûÜ:È}aJô*ª’ÜÙò+? dzõ™­Ú}=;2ONéC%£†æ Wè»w˜}ø·Ü¡A¡ ‡è÷‘éÈly9§?ŽŸˆÊŸeÛÓyÇÀQä?[Ûö¬6=ÛØy)F™®b@øF0©+«¼?!6(ê—öR=Ø„»Î÷±Ð“PV£Þ’^~r'Ö7ºm¸óSïõ|§Ù®_Øœ£‡TR­°¸#¨Ý(IE†›™ªŽÈsòB&6a‹¢Õ´÷NòÌ?šWÅTF3‹Ÿ‡2'‹¤qKÏßvu÷¹’ãsCRG–ÍØÁΤ¬¨å²„Dur µg㢣À„ºw å©®–›„(‡·4ï“’ÛX¼}^X“°znªPOZDV¬¶»|CÁ<šŠ­˜,3 Sz6~¯®3U} |@óÄ’>ÂF´®<á±~;‰NåIORo+Ûªiþï°ë—,DQhm4à”µ•ÃZŸ¸J<åtï%²o*â”() º/=kc~vA~¸ ’fŸ Gtßd«åôWÀú¶é£A1§úÔû{RW^•dYìÙ”×l"*.fƒ¯WŒ¡.üMèUدÑÝöÔ4ÿ'›O\«…Xí¼#Ïßç.Ïõ5¯u»ÒÔ %e ‹^Wcqß(bà„óV rªy–!'âšÎbBH)w µ¾uÓ†Á­]¶L2NB6FÇõ¡#YM—ÊD=2̨2³ønJRfBqz}†¹)W"Ï—Reð’’õÎo N?%Y5žŒ1££6¬?rº>½J0×­}0ûœWX·:ÂÆ6ì¾kÿéi¢YÐËÚÇ~`.u]ÚomðwÓ^ZÏôIàÙs>Ƨù+ÍSâZQv‰TðOy»øjó)W&S*M=yr‡ Æð™¥ƒ&J ÜÈ¢ØÔÞÛj$a«)ž+>–N†›“­pmÊ*!¨ü•É¥n:Ó< Ìé#ÕÃ’~ýøZ  Uq0l|_¤ª/¦4bjÕÉã+–&S¦ßÞ¿;«¾i·‹Z¯Ñ€ß-Meél;!ËxåÎŒ%ÿÁ£KJ—pxÌž.|ÉYë±^g"(š×MH—\½Å=Mö(>9fGÅYSžúXü©èBÒ5o l‡·}–üzrkðõW$…g¥!@JB¸±<µŸi.·?qvh”|@6äº9D¯¥¹Í¨ á8דØ&·ÃŸ{)r•Vz}âç^­KSg3,Äc‰V hMÍÌÕà_‹¿I$%ñe[×Þ’ÿJ§Ô} ˆ ø$Ê9¬o~PßÎ*òöå;Vø.9ëÜT$TS>_xzÀFí9)7÷L52½•@áùÇsžàÜÖ§¬ÑK¥7‡éå‰úÙÏø·IãuõUª©Ÿ óáküÂG•˜b^i%¤î^‹÷p,oTgÿŽ—hÞZ¯NAÚ2ލëYÈO©UTfLÆÜmú—ŸŸ§‡WðÐÓ‡_×óƒÑÊ+ÇWÑýÏ{ý[÷Ø,Oj 5Ø{üÂàîc~R‚&t2ºÕw}š)%MÈ2ì.ÀùóZš+qº»Ø¢ßõB¤XS}¦ÒÆ6÷kp“mIÎY‚íjw‹LëÆ±}¹º‘è„."Ї»:8 º:é£Ö`ÿpy¾ž`# YópX’G|ð[(yYÖ®ÀBˆd*U]ÊÐ ²|þ|Œ¤ò¹G Nó»äNâÍ .=±¡Ê”õù×Mßzëšá§x Ssí<þ;lqÐÉåOôÌèÄV½ª@¾Ö ÷jˆ*ÉL°ñQ—­µÃÓ¨‘h€²4áU´ì·ÌêÐú•q8à %ã…•ÓŽ²<·^(-Ùáz8W_ãâyÕ’ùÓ×Ó»y™ ý}#²:  C šêuKÿœJÅÜÑÍ×0ü”T©iÚŒüE¥M²Ïð`&[ OYv›-ö(‘,-ŸÙꆆs$lëÖNÅãùõ5Þ.,ÝyÂ(ºi/Pˤ¶q—Í».›³b ÜzQq/!G,œ(ØkÆY½o«CµñGkpE©ê9_o~§"­¡â›DR|å×:":*.hÔûÔÍ:PæP:½œ¨~JÛw)›')˜…ç ]x3¹øÁ&úRVC;³î31„ ´&AíÄòj”^“~qàèÆÌ0"¼Hïmú P<ë[¬ š ì6Äàîxüléƒé»œZjóX—A ³vŒ>œâ+qbù9¯s‚˜&ÆL>ˆ—м¸•šÕÜ| O­÷ÐÖò(p^×”¸[ÌØµÔ“±¯TJ,ç…Z øøb^QžÀÙåEÃÚ ½,p~ ¢ OT¯…аÞAyf¦GYzé¬:ÙwÉïÞä«Ðx™ 1Å„­Í<,ž49‹½nQ2}YïƒA«N=åÔ†Äø,\Q€qÉdÚ:Vs‘¬œ&¼êÞš²·ë›Pïº7Ì­Ây‡QÖ– ­‚W¾MûÐf6 ÓµhÕf!>A—žgãD¨*ZIÞÆà=äšFvZ}^„pк©ßœ¿ÿ¿ñà…ôMþøžÅÒҳΙ­É´¦mÏ4OWߺTn¡®žîJj¦áë§¿`6Ǿ‘Ø‹ù1<y}6{8׸`T£@çI3(Á…­_wóc|æÇ^DTI„à—¥0­Îù]wÙŠ[on*èæñþ¡½l½V)¾]Ã×÷žS Þ†kºbcEÈÓü„$ñ—‡îIiÔØÝïówr ù~ÝŽe Ó”Å8¿&ß`EföYËTKÐr(âzy«•%\Z^þÑ ‰y„I÷vBåT±#¡_hsJv³?P’{€Æz÷ ï…‚I¿SögY3N¼¨æooË´Œ„ØËÅ@ã»4ê8Íç㢖¡ú„-S'U:·Ägo’Ìh± p}òË,U?^¢ëáê?¡´¶êdÐë–ÑÞšÓoWÉDC7ëH[™EE|ÀB·$¥c¡bÛBÈ[C˜ XqG\AÀ<Óð!ËþäŒ5Ýôæò]‚1å+¼mðq ,aqùÍ—Š»S©âAN]dŸ©¤¬^“¶jšœÀw®QýuQ!ü×ò)>šœ)3¢Ê ‰K«G2óê&_±ñŽ`]…ù˜â)™ìg ;Y§H çZ|¡<¶cÐŽCƒWþ¥ÌÕÁeKî…‰¼$À o{$Kw,Q¡ìr¦®V§Ár…àYÁ³ˆöðàñO: Wª'díŸå÷J»ÿv+-› endstream endobj 111 0 obj << /Length1 1502 /Length2 8759 /Length3 0 /Length 9768 /Filter /FlateDecode >> stream xÚ´T”í6L(HwÒÝ]Ò)Ý0   Cƒ¤€”t7HwHI#Ý©H#”Òú¾çœ÷œÿ_ëûÖ¬õÌsí}í}ß×}_ûa¤ÓÒ᱆Y`P'·(@N]W—‡ÀÍÍÇÉÍÍ‹ÉȨ A8€ÿÇdÔÃ] 0¨è?rp0ñ{D<ÕaP€Š«€‡À#(Ê#$ÊÍ àåæù¼ºA¬êœì‚É(sò„ClíëüëÀ bðˆˆ±ÿ)È8‚á P"ìÀŽ+‚€FxþW fq;ÂI”‹ËÝÝèè ƒÛJ²°Ü!;€6Ø w[~KhÁKãÄdèÚA\þJèÀlî@8ðp€€ÀP—‡W¨5xX £¬ÐtCÿ"«ýE`ü}8Nž·û»úw#ôO1‚9:¡ž¨-Àâh*¨q"<ì Ôú7èà{¨º!@«Ÿ­ 2/À…ësÁ!NNˆÃo\¿Û<³<ÔZæè†"\0ïï=œ»'×ß—û s‡zÿ Ù@ Ö6¿eX»:qéA!ή`åsB˜ÿ‰Ù‚nnna>^ØöÙqý^@×Ó ü'Éó;ü Á×Û æ°yö…Ø€þ0½]€n`î öõþgâ¿&ÀB¬À¶(æº?„Á6á‡û‡C<&ÜöãpÿþýûÍìÁaÖ0¨ƒç讘ËPõ…¢’<Ûß’ÿ”•…y¼9ø¼||>!€ ˆ À÷¿»h!ïâ•ÊP@ä¯Í>œÒ¿6ìö·˜ÿÀ÷Ò€=ø `þÍM¹¸Ažÿg³ÿ)ùÿóøï.ÿW›ÿïŽ\þä™ÿ"üò@Gˆƒçߌߺ"f@ö0 Ðÿ¥€ÿ\u°5ÄÕñ³ÊàÃ,È@mþ}ˆØZ ‚ÙýñÆ_a½ßs悵`.ß_7÷ÿä† ôêáëáò`É?)ðÃìü÷ŠòPÌú÷ñ €p8ГûÁI¼ož‡i´{ü11€‹ C<”Ôùl`pÌßW*"àþý…D\ #žÃqÿy\6ÿ€ü.»@äð¡•ãàƒç¹`ÿ€µðÀ‡Z—?ð¿Ä\áð‡ ÿã¿åÿÂ>'`°„ùe ¶¯ þxY-Céα9&1͸iÂÂáýÞæzƒžÈR•¸?—IìÆ[Üg>“^ ýå½ß\Úÿ²õÆçÖ"V{r³óóIßxÁ¾LÝ'j *]é-Ÿ_Î>ú¯P›‘;Tsœ]…q´ò/Ý{=ê>•~ ™Û|¹U%¨Šu[:Å¥iP4Øk•9KF†à ~ÂJpì;sv>M=~O«ˆé{Å÷ÞÛx•7újÖk©\—×¥“œÜ˜ŒõŒ`dò¹·ìN’ é¼wqaäŠÄ|7 'ÒÊÈ'ZŸáºï¢&jʤÜð/"Š# ¾­u\†=O126u,XköqæÉØlÜÝó4› {Í·B1.ÂàlÖênïÐ5<½·Ö¾<:3Ú?SÝÿ^;nâ5db³Ž¹N¼Ø(»ôâW¨³/‘:ÕÏ-áNŸûô†ïûñ§i-¶¾2ùCWLô‚ÄT\<‚´BŒƒ±g5„ü¦U"­VH¦],&ƒä z_9O¶îø¿çާ«Þx¹»S5x:$Œ* X4ÒÚƒb¿—£à¤†³Ç eÌ –ªÖˆ^>“Vùj])=Ùªp€<ªº}ÑeQš{cÿÌ–­öz_¢qGr?üÑŒqf(Я©1Ù€*\ãŒ(\IÀˆkaCܸ¶¯`§ 7ôS<2 ¥üè{“á±ç9QÊXÝü;³çØ‘ßÐ’pÉìœ^Ù4†i˜Td0’ÈÞV)tKÙj§ª–Ô™½×™Ö lê¼Ç}Wþ^â¬tÝd„ª'þ‹Yõ³×)#ÆÓ]ÆŸ=‡<ª"kCkú ž¿0‰ÞEJNаÙ2+F/BÕúFË÷Ê·=v¥£gx6gÏiñk,*gaX‰¶a ºe¾wÙçþnpZ½qšÕñi–d˨2™hDò`›¾IXãdãÍý¢ÓGPºl;{7SÌl«ªÝÎg|‚ó²H[æÑò`µê ªc†Ã¹ùâsƒEÒˆ¥~ošÆ¼ã_Ž ½KGeÔ™õÉ” ³©ƒ|1 ª¯•IBÞ I“è É,ªßhjá„sŠE/ÅD* ±AB k²Ä|kã.³ˆ¶ì?žÉÏ»–Ñ~Ìèouú#«ü±p-7\@r[’#äô²-ŠŸ„¬”:3Ëìjr€ínÔÖ“”–µ™ÉûÑ1158”´YŸ!e÷35¼É*cíŽõ 5 ƒ?¦uß{Ëp€Ö›§oÒߊàÑ朔°D—øèMõ•ìGÚ¢]•¾h(@\âÍX'wåH”§gŒþøèÇFù¡àÐ^¨‹q"}ŸL²N8€ÏBŽ<6¬Ë…4áF¡·8¡°[sÓÌ“€œj\ʓƞÌó'å!ïɯU0×–JâÈLÌ<í.t& F7Ï »‰Daå±u¿ØæíÌåç*ÌÉnâë.[7­òú ôÚ›ä¡ÚeyÛȹšzü# BÞ#Ј©e ôbO Lä'µoç]o›E›íĸó<º½Ùáó·ÊV6 Íïòlªù†m3—[²¸¿¹wRxSlc{ÀØá”{#9¡Õ¤afò.y7[;Œã^v§³¥Ng~q2Ö¹åKs¡Pá/;[ ›Žj‰ýNhþ(¨+ùи0 kÿË=SbLE²¦÷¤BÊÛàZºÁ@ÀÍ帘ÀRF–£ÌÌ[Žþü×ÒP~\:ˆ„6”Ì’Ï\3[Ë ó9EuõF×^y¬Ù…¶vz©Ü{¹GæU¿Džý”ù¾Æ&Õ/»Ï°½ØÏ²'W+ZÄlÀ´ÅŽŠt'ûI±ô)9äá·ÆF_ôäX,Ïm¤øŸÏ/˜ŠiœÂVp˜%•ñ4o—½ÍÑë˜,/æ:м~¬‹Éhùü€Ê;˜™Lõ“(§ÜdTU$¯ÃCé¡Ûºüí ¿‡…MØæ»œ/-·ái½ì?³Š¬×˜¬†­–œÙ“”Î9šñÕ;ŸÖ044 Æ½Š§Ñ]GiûkæÔPÚvÙÞìÎXs¡¢p¢Ã¾¿fímK"XkiÇê9]7`òÖúÂpýU‘˜“޼e(&\Ô7¨-¥øO³†}Éñ|N3ŠÍ§Š˜ox:ܲՑ o?ËØÇÐé?úôJ@b¯ïyºQî„æ‹ûaÒb²Ì7¹ª-°×†Â£šå¨]K… utMºÎ²;¯¾Üþž|yNá|™ „%[òlïË^Â“Ê lÿ /"Ë-:Ÿ;¿ kå¨õ¶VÌ#tN„2•sìü] +7jŽó¡K•äv°q¿Æ'¾>Úw«B¸½þ"šˆ&|>QÇpnÔÍN}Ÿïn> £¯$¤P9¿PûÔö1Ú{ñ»e-Qü€µ{¥Ï|0¶¢Ê½WcÆŽS»1CðÛ/H©ŸÕÇ „£ V£ßõã„E#<Zñ®RsZÆï¹Ðga$$XÛy5´È|ª +¸•ƒúòK6ßê½[á¤]zÆ÷"ò¥Ùs3Š•k®±ü" ’«–0rçöQ®À‚µ×»8ëIïtÞød؈ÐΗKužpºÈzØ(ZÉßrºˆÝ¼I…±wb¾—U{õJµÁ]Äæ‰­í-;²Ã®OÙó”þSd8ê@Öü†ó·ÔnÏ?Hé€výF{> —!15¢…B›4p'RÇx"Ó¶ –ûÊîЋ¿Üë/>-IÑpEc¹_ÀåÂðTÄežì¾[YÁ.Àîî §¯ðZÇiv[$ø”ƒŒï¨¼d ¶‚¤ýeh—Ú¯V¥ù`¤MJ+©˜mIú1âDäûAt£Î7G‡s¤owu meë~‰Ñû¥°§R%EO`âdÖµðxÈiÌ.òsWÎXg\ñIˆаÿøl¼Wîör»ZˆTÿ5J<é­(j„XåÙu®Á=ãˆaBðÙ#†Ï¡¢¤Vƒ"Nõ æÖi²c4s*ÚÌÈ‚&º¬ßùé2¢&=,™Ô~1£bU<©ú>¤NX¦%ó¬Ï&Ö_—Þ†*’þ‘ø"ùΰèyœv{¯|ÿ>–Y1¯šÿ^H/’ࣧ'Rv,ÂÏGˆ¹dx6AçIñt¤-Üa ǼFuñO¨†ÜÈ^m<+ ‚ ľ7â+<ö›u!Áí_ íü5`äêú:Àv‰Ú&»ÛX®®ëËX³èҩĉ/uƒ|ýp1ó†ÎÐð(7g|GU4Ï2 –õ IDгCÊh£b­þZf¤mƒ 'Í$ÚÈw‰·;‰-ʨì ôRé™ñçV²Ùãc·óÂ>‹_dz–—ŽLÅZ”žbBûÖÐ@¼a+U´†chD9M¸«.µÿVÃ]ï"ã€?ùóB/{Æ*¤0ÔÈr¥™üÙ̈-Êù§zlkcæëÊN¯ÅñeϽ-¹ éd£üÊìf-Ã+Ž®™!c‚7¾š»äòœºV Æø³Í¯Óð-øÂõóêÉsÐê)ùµwéßQ“ÔN4ì¯áà`—ÀBT’£ ºžw7û3_ïM(©a Jo…Ž Yköäo ;D„cOÉ«+H³D]HÑÝhXéÕÅGÐhRúáwÝ% Ø‰ ƳˆŽ™cQÒZ`X'³9”éÒ{Ó@«-Üàúš_IòBž ÇØ %mg–Ú­Ør¥z´æVkL~ï›Öã•:µ•;$zPFôÌ Ó×H]Õ$ðêS{ûô…LÊ‘¶Ö-ºÖ+ˆø….òf|A öê¾ebL3,N«/Íaäݹ®7«)·.Ö( ã€ìÂfŽFÎÓZ©ÇÜ ÉNä÷ßïÔŽë ëk°–¯^$5Ð6%ðüóñ³ÒäWÖí¥É] ÛnUDu({-`âÞ6Úl -ʽKÙWÃXaê»´™ GÇÿ´¯bòUj{½fJ>:ñÝÕ5¡€ggèEÇ—ÃÔõE›ÛÕ­}¸¥Û8gµ”W©€‹âÓ`A’«ð­cpÁaW²ÿY#][¼”Gh h5¾$º¹ºÀ”±°À°ì²qâôÛìÂÖ™»cÃB 6DO./‹ÌŽ÷JñåÑ(h¾:'¶¬ØK‹õåpI²Î¼ö¦5+í¶ƒÆwÒÏï¬íCKˆPÜÛ\->¡’Qq¯ñ½·èpì‘@åÚYñ{yò³ÎÕæçzæ “2†Æ£¼Öu©ABgûÒ“TÌ¡èý´éZ¶¬¹2ý“Ôð¼õÖÁçJ r"sŽðÇ>=BðZ‚ŠWŒ¢¡§*ð»âËÌe¶ŠæÝ½õu¯«<´vÑÌýU×—gÈI»V®Þ¼ާ¿lJ` ’²i¢I^H[ñÅoŽ3þÛ Ûæ»Í`ZgÓ}ö:{íq)¢Ð7‡6H$rÅ9JI€l<±CÂÑ@à“Sǽ lu…€¶\F¿©ø»c‚Œ}>t=f0™¤„σƒ°¦è/njWÛûÄ(ø|db½ÖŒøÏ Ð?ëP) ¥¨¬eí²¼¾'bÌ(²2éÀ×”ê9ù$Fœ­íºÒÜ/_!/EŸe°éã/6[©,x…’`ˆÂdj¾£zõÓÅ—7’Üû¥ ‹;…ÊÝp5šÂç©MÀ‡¢›î,kåCózÿ:N|—÷¦SˆÜJÆ÷M@v*Å{5ÃMÁí.<&KQÚñ™“6”úŽúØbšdâaÛHed†3EöÄûuKO_Ž Ò!)PÜâãåIi÷BkªXt²CmÑȃœ‹OQ`g4ã$ ³®an˜ªÁ:R£ÜÆâ¥ÔOÕͳ`ò(ksG Øâ†aÖ@°*¶ùëþÕF6 :œöUqGÅf·(O[nÉ¢zzžÝÂï¦à±råOu\ðe•u#ggÜN ¾€wri1´[_¹90øT@.¸ŽÛ0_z MÂaJ×/ÃêRݼN¶Ó?ƒÃ¾œð¿³,î—X]¤´H•é3U=މižv³®’`ôç"·¾ŽPß”“û™÷¶2áŒ{ŠžFé’>¦#W¹…"Ñ=å9ôôÆdæfÆ\œŽ£‡¡Z@-aY_™CòŽd6Aù‰'ÔúYŠÈáóTåw¨ožÕÆ?ÙF¯|±¶Êâ#³W/*@­CÔ}ǵ\S­´P+ÜÒ¶AR¶ê¯Œa½°%¢oTÅÙuAS69!¨¯IW¾+vKÎÜ~nØ cÏÆË)OÙ°}5h†"/LØ(…š’ýÙ¦Áeé„V„»=¶¾ËË A-¾F c0s™ôJoøˆÉ® ™á‚³†gØÁMì(å"‘4IÎß¾š½Ÿ«4S½^°·ˆËÍf `áûuo‘aÉYïGL—±¤´ûj!q9mcËüëéŠ÷e_ËÝõJí›bþWÅÏ»2k5Ç“K8’8ô ê«47:¾×ù£ÀÞ^…ïD¹Ì“ª{øYºŠÞ.tRæé/»ÜFlñ%ì½¥š¡)dwdè:Æ!?§(¢||ºÅò¡.®™D˜"éB¡;ã´´¸@s¦úž#y¥pvy¿Øq_v–³ÇÀ"Q[õž‡Ð»Hª–¾4ĸ“W¹I¢µ‹Çb™&{Žû‘G9„^*«÷« Îé‘Åá!ÅÇMÞ(©| v{È=,³u,ÚãÃ'mÅtš—ýšbk0‘ΆL<¿nªghS¾ºóm” ”#ö'kl‰ömÚ F&|¢h*²}£)¹TgÖÇr—ÌòG÷ô0ƒßñ™ˆ c ·°3 §©È¾M0ñwèÒ$jñŠ”ÞdäOÄoY%gGH¢©è¼3ð«ý‰ pÄ¢×A'žUYÈÌpO­ìYZšûe ÃSK+·:„ÎÞÊG-‘´·ÅÆõ¾d˜eæ‹i6ûtÚùqö—•†"n­ÑéÏy5 Þ®%*é^ËLP!Ÿç™XÎlj“=T¹ÞYü‰°~q¾iÎ;3zfß®±Mn¢ egߘÏð©H;ß] ¹ë2ÅPM«±®(6 ƒ#XÖª_çm½…o¤u;ñ%ééñ-ü^»¸`€|’\"‚eä.͵ƒ•ÙøÊ@;(–L˜dþ,"ÅðáèÝÖÔ"Ù¾J_ÇXÏ6 `ë‹¿Âa‘¦7kÿÒO¸Æø±ÅjûŒ·8…µ™ÇÐÊž°:b’6ÎE ÚÏÿ¥º¬¥˜|ÿ,Ø—X—ÝÒî,Yñú)L\£Vk”âd9ÉOá½÷›¬ãÀ±:Ð=¨(ÇýÛGàqJ«·sÏik·ù£HñnŽwZÓ¨‚ƒS¢—ª;yËqÆ¥¿bùf((ÓµJ À‚Ê£Cø¹÷LUE÷di^êëaè×äÌ­µ[ݧä¥svj̈jÚ’ÿâµjÄOÕØFî¸H†Á£86UÒâýœêKD’rö½ø eVzN ‘è#rØNûÈ‘‚¥B‚¢/±¹Û.Hõ®´AƲ%ŽiçkŸÄG~ šÞ§í$^ È>·- êœåBv•Z4ÿØ$ìO”à{ú}ÄadÏϹ, nD5}")ÙEIú² +,OûjØhQ÷02Þ àé€1Œ¨Ûé`Ù|$”ñœ‘¬]ã-’.ÝŒ@^°Çqö‚ë6‘Ê¥©AéÞÇ 7»c%_Ú÷Ô*~‡:±É¥ZFçΞ—»±ý LÛ&Gkï½°ïx·’ÂHÐÙtXMÇÈ쯶„câÀ%ÎÕú%tbìF¾¸›¢ d𬾩òl¼I.ì& Eµ–\ŽÔe=–bý‹5¶M€Š ع~oh€Vš©& ÎÉgŠüùí{"Õå§¶7Ì©lÕµÌ[”» žm3ÎP³™¯ö‹ZŠ’ÒÙ_ù=+ ÐÝ›¬®Ü›îa{C¢þÒh©¥Ðº—[Š¢,I‚ô!«ª½ÁVSÁÖ«{é§lK1åãu á,µ×ÜK×\ø9Ÿ3Ïó²-¶­>âÝÉÑíYHÉ•»>‘ªû^ ’7vO˜öGl{VvH‰37hø6ÈlT©Q©v-ºŽYÅœØlìâ²(€ü['E@5µØÜWÒÑe9þŒ:£M_’á‘ë΄'Ë“ß>´:NRhGâU׬ݰ¾$®Ü½’IÂwïì¢^àpQ£R•í}M$Ì¿zSgý껿ÿLÄ^_År}b“Ò j“DŸnŸôÚ ¹ËŽ ¶JI¤‹Z¢—E;à¼Y¤>ûeN¨ÇʉÊPnÈÅ:{Åô"ý×kœþý_=R<û|•.Äwbǽ֞ÅTn_3ä)6²‘x¥1åš\$÷Hø£—Ø‹³é!9– \Ê,péÅ!!p%bW Ñ¡‰BY Í \”',’!‡ÎéíÅ sGåŸfšÖC³m^7IÊ÷}7Íá»_”–›R­¬‘ç9-¢¢äXOD·ŸR€:ó¿|óÁ;M~"ºòyg‘Ë‘Ž>­õ‚ÀÒ±±²ò´š€–ê0´1öådª–om8QêðV†$ª¨êZ6(£ÿ—“„I‚³DÖ™ÿ=Kú“Š+½ªÕ¹±ƒª>±µÁí~ÏŸ";İX¦kqœw³ |!¯¥Òð¡ö_¡†åQî|wøH\RÈÁEŠÉ"à§¡]+BQë3·ˆ:?!·F•Ä»­£.tTÑdSoŸx¹S$Æè¶™ýA¬4þíèÑ2Š© Ï–/ðs¢wÈY«Ø^bwm ÝŒŸÐTáf‘{4n°$©ýŽ \œ79«úËcd¹§ø±†×t_CÿÍÂiÓáÈs7U#[\´þå#ÎígQs ñ½SÄTÝ„³ 0áqmäÏ%xtÍò*ÞV´ºCúI¿](}"Â)ta[Œ. d­Ue/ÒèZÊ‘Š@µý¬,Ÿš§!¾p—]—)t0q­Çüㆎ—£eou0´=´A,Åf ˆ6LµÙ‹Ÿ®?9ɵû6Õ¡\ÒÞ$vþöVE§¼V\Ü_y+mª$S³‚¼]Û ä‰Kúȹ³8ÁÆßì–/…eàŒOŠÑÚsï=Ömß äë”I±*½D2„¢J‘ZUõóŠíû­ ªKj‹Sô®`Ï0¥Â¡ésƒû~AFl¬;8]Š¡–D¹ŠÛù›Â?ž”á_BÆ2;eïÝ<²oÓšµæôu°× .(}p}H¡KÜ¡Qk5V AÌ™¬Ôß…TS?>ÆmòwÈw?uÄ™R÷ OÀø¥âÎ|±••`]yôîW|·ÆÏP•q¿òrÇäÒÌ£Û¡R+yÍ'W|ĺ* 0uõ2]âó‚¶Dò¯’o=Šÿ©ÿ}»­ŒûrÛY _³â¾¥GuzlH·]CæÅP:š†ç×+Xðå›ÊÆf}ð$,2‡Ø½eïV¼Q…õ„Óc°g+ùÜpÈÎòfy^Qó ZÉGŽÔî•TÁJ- È@­_Íõª¶Q çTµB’mŒé\¢br3IKJT+´¿ÊÕ%q;,|ãá>*†+)òI¼‚Yò>é>õ‰âº+¡Ë^Ÿ›ë¼š8öOrß" ›4h™°qŒîŒáî‰?صíûe}É€H{/]¹l0ð$¼*Öo‹p½ù®-\7ÑØ[š€ª›žÿmŸ6žD•Î)÷¶)ةߤÓ†èG!aX›ÁOR½IÎEÕ ñ*­¨*·pÃÄÚô !“y*Ç×·¾W R,ï1O’?¢%fê¿paA~õ1ó°bè:ÌP wÕIÃî›MûýS®@l"ĶâMòqoÈiE?æÐ‹=ÑñÛlÖ†^Ÿ°¹ùOo±Ÿ¡1´.Iô{‡üô\Äê/´ýŠÑ ðü(ðn@’À¯0Zzz-ùc˜0¢:ªÔhå Zÿßl'jwÎBÍb¢L],@²èî§V·_á63\A"á±öƒè\R½ßÓ¥‡ó‚4MKÕ^¼â¨ÔLàFÎ!”M ùW(™Ì¤jUû9vÍ(²É©ÿõÿ'ë endstream endobj 113 0 obj << /Length1 1957 /Length2 6403 /Length3 0 /Length 7586 /Filter /FlateDecode >> stream xÚµT”k6¬„„Ò "Ý -ÝÝRÒ0 ŒÀ Cw(HI*HIw#J ‚( RÒ "©‚À7Æ9zÞÿ_ëûÖ¬53×®ûÚû¹öý°2êðÊÛ!l¡*8ŠW€_PÔ64øù…øøùñYY a(gè?v|Vc(Ò†€Kü¡ˆ„‚Qh›…ÔFÀ g@@•¸-ÁÏòó‹ÿˆ@JJ`O˜ Íh àPw|VE„«æàˆBŸóÏ_€ ˆ‹ßæù™È»@‘0hƒQŽPô‰°3`€€À (Ÿÿ”àrD¡\%@ ///>°‹;é ÃÉxÁPŽ€>ÔŠô„Ú?ZtÀ.Ðß­ñ᳆Ž0÷_„=Ê Œ„hƒ3 …»£S<àvP$€>0P×t]¡ð_ÁZ¿x€ßÃøþ-÷;ûG!üg2A¸¸‚á>0¸`s†º*Z|(o†Ûý;»#Ðù`O0Ìl‹øI ¨ÈßÀè÷çAÂ\Qî|î0ç=‚~”AYn§ˆpqÂQîø?ø)ÁPzî> ß× Žð‚ûýƒìap;ûmØy¸‚Œà07¨ºÒï´ ÿÍŠDøùùÅ„„¨õ†8‚~`èã ýéøaF÷àçŠpìÑm@`öPô¾Ÿ;Ø  п¿ÿEø€ ‚l¡08þŸêh3ÔþF?$Ì0çGËOàÿñù÷Ÿ%Zav¸³ÏŸðŸd¨­` ©Èý»å oÀWàD…Qq à¿UôÀ°ß,øÿdªÃí€ø/²è)ýCØó·8~¯'ðßZ:´n¡Ç™[ð‹ðCÐ_ÿÏbÿ™òÿ§ñUþ¯2ÿ_F*ÎÎ?ý¿þ?~° ÌÙçwZ·(ôh#ЛÿßлÐ_‹« µƒy¸ü¯WFï‚<ÜÁùßAÂÜU`ÞP;= âøS¿ÌF?ö̇ê!Üa?n€W€Ÿÿ|èå‚8¡ow´$º èÝùï‰ÊpÂîÇ’ Šˆ`$ìƒÏV’ ˆà'€ÞF;¨÷O >8…NÐÝö$þG**€ä˜~!q¤ü/ºÍ€Tþ Q¤õ‰ ‘˜2øƒn »ÿ"q4ÿAè<Û?H`ˆ}ÿÚ£þØ…þµÿп4AÈ¿H] ‚pFÏî‹°ð‹‹ËŸ d÷D ý  û? „ò/?º ã_=.Ø_=“{A4ç¿ š¬Ë(€&ÿ ¢‰ þGǢ߹ÑT\ÿ…Bè!ºB‘è[öÏ,„EÚ`ˆ¿º@Óý‹½š®û_ñ'½ ”#úçP´È@(/Ä_ èc=þ‚èþ<ÿ@At¸÷:h§/ù+û?…x ‘hâ?¯´€ÿÁ?ß P¨7‚?3‰€H†ß« oûZ-OçÅ»6"(LÐwu„{wU™ÆsãN¨¢Ô·C¡¼èiù®ñõèoK¤;‚Å—N&”üNV‹þiÑ”3õmØö¶+<þ£¤Ò§;îZ‚ëšf6Bp;ʽ&ý¯#¹1N*·ýµ‰Œ-U)¶¦Ê/Ž{8ì ùn`ŒÄFãP‹‰ ÝÀo—Ïh¯T¨‘ïPgq?ÙM¢WÙú–Ö;$(ò¡o¶”ç*l9n{›ÀUó(c[&uó…(­¦Í(0ƺúB™ú,ž#ô¨-<„ÃÖýµ^|0­½áAãN‡.Sx²i·vUíMY¹àÊ;Ù#Cý·Ã¹ÌÖøˆfµp+[> pãõ8Ú¤Ly•#G*‡O d™MÝåž©û™tî³ß™œ2åà !qÚÕŸ€0DÂò÷>åVcGÆ,œNûŸæ¸Êej¨Ö5¬ ¢/ʃãæ®Áågg ï¼?`Ûš¦òÛï1<2ù’›û>dÆUý±Ð¤ÞbìÛ})’(oj•‘›ÙìR2'CÝ”L\©º^Ò1ÖRQUúä=×Õ>{½XRHI½ÃÒt8Ïu†8#P-%{Øõ4ÓK8an" ì9d=ßôÀª|Œµ©‚ Ù÷}Êfªk0zwr'Gº«×˜ñ¨“ïÆ,mM³ÆöqGä&£IÃiæ‡êt“*ºˆ7Ôsxn}ÝÌîe[Ô)zTbè¾Í]‰Ûr$ï³X"Aðƒ¾IÚpYT3ÙÊ¥²¯z2yñV¦šYžES@ï*ªyx?Üþi¸Ûõà)Hd®Á‚ø&‘„Ù {zsÑá[È"9ÞF‡öÁúóó£"cäñû5ÜIZ­lqóºr<¶8dc°Ö££zOÉóåü¦0wÜ+ÄÐÿJîÛOœ ¬9µïlSÊõ*4[*v¶dÙ<©¹sU’Þn}OŽO5òHtð|/§­Éܼª«L R•ß"uŸ€¾õ¹ö~xbÁп&ß›´÷„6q¨#³ÕžmQBK$¿¦w}·]!U¥Qñåö‹Ö'úîäç¡]-Ò\_í2Ö@ó y¢P)FöÎ̾ҽéõ]©塪<×)ýìdõÏÐG›íòEaµìÃÇ>ƒw“5˜jݪރh²åD_Ì3»+­¥ÓŒû¬óéŠ5ËvG_U’V3k¾¢¿rJÊ>Õ¸u¥¶ »T“D4½C¦bWÛ¹ ÂO©‹ø »ºà8ÔÍ‚••ƒ9üh&=úÑÀMߤÙ/[ÍgEi¶°îb<¨’K¶ž¾7蟺½º¦Óר˜/b/@KŸAaT[Fìl$*îvUl.͆·ë!k{‹æAvŠßÞM„¸¢[ÕS£Lüj›{*4¬™m»ig þ“Kß5$­[ Å™½ÖzŬ²~Oe'Øü oÌþÒMsÍ7X÷d÷þŠ+ÿF`gЧ8ÿ¦í<öø«¢YÐkæïûwª,›Å*Iv<÷k¿­¯j6$ÊÑ_LˆVÍc 3œ¦X[q›N¾–„TbÔÊ+U[÷$郎¾ÖS‡Ï„)2 Ññlô$¯³rYȼC!å@âai·ß+­@B¦ç½[…{¿õNzûÙ‚µúîäßn4[ǪáŽEä$BÇfë±­²taÐi®ð"•mÕ« …~DC¯×wJÿ…n•–Éç ï6œX±±{à6Å‚‡ä™SùjÃ2Žy£Ìl]]Üb«2fÚû+Ô#-‘µµŸ WÖ†€ —¥ã”ÃR£W€W뻪.ºiþÚ¶¶§%W ;;¯¶Z×ã7dbÜU‹lÅ`ï0پϑJWÉ=ÀŒ£µOy5ªx‡[‚ÞD¢ž¼`jÔYIô{¬¡ì¦c¡˜ì>“Žgs­‡Ë×Þ޷DZ6kk›!ºÃXs“ì'Áˆ…çlüqžú^ÊdG›k¸x¥(Û±}µ0Â{þ( ¨~ÓÀ]î-CIS5Å¢µÈA>b['wyYªt¥æÍÜ'ŸoëÈÒG|-j6-^(æ–5 ±ä+V|aõ¦âädm†'ètH²Â“vœVÁ­yYͰc¾[>Ú"1‚Ì7½G²¢ ÇÕªJsmy¢ÃiöêlsÐ ž÷ ÌSÿ;i¡³†@#s]¶µæÀêPÝ÷âÆ²jˆy#/üÖü®iã: 8íkœ[¤¾øƒ½;õ+·,ÌǯD~)$a9åâôRý&’ÜœŸs_íÛꂘA<žZŒQü›r¬ìhw’…hé‹/”Ã+Ñ/õ–M^}°äÍÙW¯cÂH×ÞRWEöoÎ`ycÝ×G­Rðåu3<¸-Õ#ø®ÒË¡‡žût`ïå|Ž}òäÇ(ã Èo¢6í­jÙWv+ Ò”—0Īf“Dº¡¬á9à 1˜ Z#F¥?ØFÁ+MûÝ ·UF¤,)\Z¨9æ1>ÜŸ4—[ èd"•?çßéšµôˆûäÁ8kv ˆ5 |U­x¸¦kÄ3 mN‹Št©?]{Bsïº?P:©þTñ¹¶eª¦ýhe¾ñ5iÚ´¤Úò›ÙXG§Þ› ­ÞKŒ ™Ë³N VŽLØ—[؃ß&#'¬¨¼ð½‘lcVfýö%~×5KbՠƯÙEÏ#àæ:žÛËkuZwÛÇmœÕkÒ3Çlq+’µ*¸P<㢸á6#5DUÙšÉaI!¼-¶ýê›+c*õå ‡-Ê+>:ŠÀÁn©sQ)gÇzIo¤ã]·,ØB±pjù®âº^lÜÔHdÖ“½ù»j ãù<‘£IÝV‚Êî§s¬JmyýBÒ/ÃîYnº½ðˆÂ*øÜJßq׌cZjSûæIÔ–Çäž(‡ŽšcÚ-¬l7ÚžÛôL§¼—Ç3uçëõKôA]IbÔ‡œƒöX+QãÑޱFˆ2Wáèæù=±$m™=Ø ÓøqÚ—FL]á§X°,¡{Xž"nýhHDä)æXÀAÌ[«›ö<í M‰iÅ.S•Zô‘þ©ŸýjDy­6ÈyÜLªzTP“12xëz×IwSŽÊn/O¼t»iÛ˜8d¾.ÓwL>ª$¾-ÕŽ­?iÐ:TV’çxXˆJ;ˆ&«Ž!WãÎáš¿h/#¨dù‚]s}òR°®Âê©*s¼Õ-¹×Obó^ “ W¬´šÔ>ÝœcpäH±T_¹íÖÑNÏùü‚)Å_’ñ¸3›ãÓꤹŽ_ÎÓÑÛž?¼­±h7Q¯ÿù šâqŒYUÛ´î5ƒœxnÃfÊl×ÛwtŸŸÒ]2˜ë¼Ï3æÐ¶cÄzAÆì…ÚmÞÂëFú}ŸX³åÒ6¥¢›ˆ«¾’¸i6²öÞÇIRGQð„S·ä:1:æøî|E¬¬Ý°ý ~°ºµQÇ• zS>F™“ý(ýxIH˜hZhä„©…˜.²›šè$‹°‘eS¢ÀƒZ‡³9iK’VÝ¡ðJÙùð—Ò+]ŽF­Êñ6•]Aȵ$¢ýG|"j1YŸö |pô9d ‰ûI°6`ôzj‚}/øoÌ40±·„™iùwøÑâv¿ó½t–rÑY‹GõÎÇaÏpIÑÚ_[¸º¼OdÄN4`¹fœú¬œØÑ‰ž ['|sŠAg®ÂÅzÔð­óöú¥E}í,²çʽ‚Å"r½Î¨—FwK–;Iº>¸è!ÿÿÕól®Š¼4†ÕÛõAUÌ*z^¥¹·<ª¸¹eÓMÄv)Ť¼—¤¬ÈÆlöU﮾‹ã̆,eèo-ÜjáPéŠÝÔrD*ËÞçÞóŒS!ëzϲiP6ï›>ºgçìæ¹/§$VB`MœÕ}êr ÷n½äneËhÓ•\¼qú¾fa“kGÿàõÔVâ£4-—y«!‹‘V9'ηh[?¥R“æ‰SoeéÔ0>«„9ÿ¼Ø‡ýÙÌt¿•ZÆêžŠïøÊñ^ô1YNNÚD¨Åó9Ûy[ÕÚö±é®û¹›y+‚Læü¹a-Ň›ÊXWzw¾}ÇÞWIOî³·)#-cÁ>¡ `߈y¨—® ˆôM fÛ7ÊÛ(‰9¡-cEzuÏê”ÇR #ÞȨÂBFæq´+ÜN¨í¢¬KŠçÌ ªï6§õ¶Ô îµ ä-~1b¡ïk‚\ÑÑm‡%ÓŸs<+#-è;ØkÅŽXë #0 tÐ$Ôpæ‹zïD%6 Þ ¦hæ x”?ßU«ÉÉ©TD±–rÏìphƒÅf[EzÇ3LÙD>¼Êª ‡]`St%<*=ˆ†œ†f2ÙÍ”©iX§œ–Ù®ïR+#´M÷”DØ–Ö… :¾¬Éï(\ôÕ"JCBõT]¯Ô¬Ä:>ôÓ²’®MGføÑ|f¼qšVÿÞLX¹³9ú⥠"Ãá…“JUcÏûØ'¶PfLûWO_=§$Ø®Å2ŒIÕÒÈL¾}iz§å„“h-ôY}ÉTˆ)ôt+>ÕÑâwSÊäÓÃŽü‚F›ªÏˆ‰Üb#ælí˜ïAßG,w…p<Ê qe)¾ÄsfƒR‘*¹eiCUÜí[᥌²K¦v `ƒõ1d¬25Ób?÷«ŠÅÞÈ$h[׎˜ ÏìuÕ ú`Üç\Ÿß2¶Õ8]ž%9eÕ5±[+ 6]í -+ép"’›Æ?ûØâ°•™À Q¿úÎŒã êæÒ–ÞíyÜ…Ùrë5Á!] 8˜üƒ‡§¹Þ}mÊ»’´ç9̨iláY¡O5áRZÛ—Ùã7ƒX0Âðï¼Ô¨[|P¾„×gþvžÀ‡JÓ›ŗœ¼ñ6Ê×õ%yƒ¹—N/ôÆ‘;b ôeÀHv‚Ðé{„ò2ÝÍ,­Çîð™|uŽªñüXû²ŸwåHð8´,÷™·+ãƒT凃Mø$ˆ=¬eJ¦åE"Q¨Ï²©ÇιÐWåÚ‘’¼jög'汞·Þ’r?ކ=%oÎ>a)ÃI™ÀDKãûƒhD64îešw•+}ð•þ̦t/¼¥æÝó¾‹Á‰àÈÞ‚‘¢¶@7žãç™ìcÏ<æÄf çÚ‰+vK@ÓdóŠ‚ó¾RÈ»§]™^ ;çsGŸ% ‡æúöKsêE©$.o• 6Ï9æwøoTwI)y³¾bÉSÉ[’Aªw¥½.ˆjæ^Ü0ã Øf4$Û÷ŠYác²N”û|‹Ÿ)ýÐçÝCWuR6ŠÏcúI ”LÏísÂל´0oGédéç3v cšïgÈ ’ôÚ¿Õ[Ù"žÿÑé{*^‰]ÞWßÀñF\™7¹„*!:Õ·O³»U˜Ù›é¨VyÞ4$òð¦TZš|wK•ì|;Çœb9m¿ÅS˜vRâ(t¾149¹2‰õ‰”Ý)þþ„E“&{^R…A ¡®ÕhƒPäyÛúUÝoXüäËä,„éõwu`i:úúiU^†¹ÅÅ®Gc4q‚)B>{-Ü.±Õ¿®)ä8åÒqÆ‹™ÓÂÊëYüUε{°CKö–QbŸŠ¹l(¦¶põ“¤a ”™ô$iÕモiúËåýnv™°Á¡R¬>?n'Ùûò阧&¦¦G}<ÚOR£à½²×XKm©ópëi¿_HFŽg‰°±RóÈɬŽ{fÒÔP¿dÀ Üí'+&5Š5¸r¢P|éshX¯J~sj7FšÞ¸ÛÇâ\½áTn¹ò6kr±Í šºøÑ{Nœ»x¼¢-äÝrŽn€ê'Ýê%yÄ+wtB C¥mñ™‡ÐåFWá þ”5iÓ|žÕÌ2zj·8óà>j|c£ÇÇõT+VÙF™ôÙ:ÅøOƒô^}1>·¾E—u‡ÓtNÏ2+æBðJ8ß³ö¸©qD:—zõ°ŽŒø“ýãP¥ £,ˆ$&-TŽ«ªÐ|W‹ê=y]#WÃÛè¯QSQeœ^GŒË ‡¸ÖAOæ$E{ “9·¥ou<8»bTßaƒé«cʤ}ÂÐ"ku TíóJ‘ ÷9–,¢¥æý[Q”¸²}„¢ÛÛx~ /øµ(&Í^¡qXÄ$1åÓgC1¤z/’à&ô†Ï¶3÷|CkŸµi2~µßÀ~Sš˜·ñå‹èqàµ^B7ƒO¾½Õ¶ÎœøKŽ'x¥¹=_“¢¹QoîU¾½aðV† V€ã¬¿%ÒÀ»¨ðí«V&¨#3/xˬÅçÙùku<¬yëo©ö"ÔpGŸ™¨v*ÍÒ1QAIŽ/pšá¶Iü‡§ßªÍß*ß1fé°Þ%âd1e8OLXÅr’¿mëɪ­³L›‚\>—"%Oß›í#© 'Ú—„]g1-w›íhRÓ,ª™ÄÐ Ñ(ÏË'j<‰ÕŠJÎÚl[‹yPËô&ÌoïÔex1ñ„óæþ—¤,ÍÕš9ª;¤u%Žú]9¾ê4˼Mßá(¥Q—˜Dwt칤G:|â7ç¦: u:óÏ=1z§½qªË{oqí.·NÄŒ~çÉGörЦÍ£Hsš{»+-žáˆ‚†dE6®ýC¶0°sí‡Ý•, Èe¨¶åõ)è« }ö´åç{“#3$…Ń=V zí@<¸ëØZÝEc 3¨n_EËX§“l–6ÜuÀÛ(ù²Ýtü{,ՂŪYo\÷Í (§ýP•Ø2ÜFáüWÅDzÜƨwÁ ºŸ»ã¿ßÿÆUò¬çƒ •{]`¶WúgLF2¨éUnžâö²ö;yÿYéLý;ªn-.»>3˜á—IQ·¨˜Mhœž™ñ©q "!-Ü"~RÖ !UÞêøš”–z É×bÏ›aåÉoJ!M7T’¼¿ôÚÛŸèÖ€3A\Csq´I+òóP¯LštN”qàZd5[‚ÄÕ+¼€Ë-åëj…ùW³hËðn…¯Þ òê͹4s’©™´“_7š÷=Ƀ.ïó¶pUE.²ö¬SðÀÓðZ"ÊÔœvëe­ƒˆàól8 ù\ºŒºçk¶ÂÖóZ•Nà?µôp³«Ðê‡O™ 8™q» Rö¿_Ã^†>Ô=³]êëU¶v>›—!-ï®"Sç¬fbÚuËÖ ËË ¬˜ôwRzÇÛ}¥8†b¦¹(u{ì„Í 3h_±.Š}Ï­™¬ Mô|Cä¸é{E“´¢v^-ù’úµeãË̆ÜA^ߌãÂ7ºyÄóV1'y¨ãˆŒ’ÐüØU@kàð [óÉÁÓ %Êà:L&ÃÈ*‡o1Åh?"!+²KœÀ¼AòuÝѸBád8ö;§/okªX.ßc ÎÜõë‡e2ØVvÜc!Vì-wC·2ª%ªnœ^kÜg²Ù£6“º Û[ª¥ÿö>8‹ÈôHÓ¼·u?7ã7|É–Ó¤'wÇy*Ͱ5Ê’…DûÙüÅ™]X-‚pƒ±Ç÷XïÌž¾Î]F¡ð‰æ2@ÞÜ¢D¥Výˆè~µUnHgܲ'ètú²¹Ë6îqq\N¦çöÛ>ÔGˆ´Ñÿîõ³ endstream endobj 115 0 obj << /Length1 737 /Length2 969 /Length3 0 /Length 1537 /Filter /FlateDecode >> stream xÚmT{4”i’š¦\ÊQ/Ž—1“ä’eBŠAEªÏÌ‹¯ù>¾ùf\:B±érdS«ë¬Ë¶®©t¿œR)*ÅÒE-V´)É%i?¦töœ=ï?Ïó{žç}ÏïyÎkbèAà” >"V›ã<ˆ ‡8l[†‰ „(…øJ”‚ Š@Œˆ  S† àÑñ$E3!kAbT„I0bBŽ £€“\.ÿ^.•±IÙ 6]! ¢ ˆÀÄð|ýÖ{ñ=™'?xB’¨øÉÂŘxcBˆK! D$+ $p6ÎIÊž €Ë!IÑÄ"HB|Ü®¾|pçY xÅEÀ{]ŽSR: ¥Ôxw–€š´Äè¤>iMFÉ8¥Åf aB „ÃH gXëæ…GÀN ‹dÑ_C4!)͘Ѳ€FÐÙ2±˜J 0ã’hIàCˆ ‰ƒqícIl £8ÝÉ×T‚‰ãÿ·èK†…Ò¢¸â‘´ŒVËÙ\;%ŒI=°8(òÃ(Z{Š”A% •sò"L&Q^i}è ‹\ÿåUº­‰7­ƒB<¼CB,&b"ìŽ †G‚ŠÖ%E“ÀDØÅ”Û8ß®›ð‘o¾J‘Xå°Ç×…3~¾Zaß²Ü܈¸­VÈR.°²µãÄv)ìm–'þ‡®PF’§&@÷òÕŸX,ã ñ´‘:¦nɾðcÑ6÷üºb5‹º›-•¦û×{Bµ°ÛQ¢Míïôúz9±Lƒ‡)áÎ-oÿôê;o„îƒÌŽšÚ½†éazC'Œ‹#·•½ñÏ>“¾±{Z®DÈŸÖ³«—:üìév?óZîQÅ›²Õ1ÎZžÈÞÍ…Wnªi¾Ì|Rÿ÷d…™j cÂ6‹“šuQiyÝá.C­OUwæìhaö˜ènŠÊ+J‰nß’Óö½¢ÕYªó¾mêP¦¬¨Ê×T5Ô$ëVYœ.ªva;žCæJí´—Í~•têŠéÚÒì©¢§ù)½¥Q:ç{™¹·O£>XqÂkÇùI®kæF6]¾åÞ^8ܦÛU·X¡­yò’[s6!áÌ%lº;¦.¬_¹.6î~¡^Šÿšä¬Îdc–ý¥¸ë±ªþϪ ÏfáùÙêF üÓs1ˆQô³>úQXé¼Õ03L8À u“'™ ªî2Æë$YbŠ·Ý¦ZÉ–ý«fÌãgg ì±v}5çãœGƒ]•‹ßœÖã½ú¾ë+IÓ¼çó蔿àn©°Ïm<`~e!iÝÄÜ_“ZÇ <¦‡ŠúÎ\‘æ‹úm?_ì-¢îõ[[\Ôþ+÷ÓÑk£Ä›¿³5?:¿Öì6¾¦xtv¡ÇÇX0e»Æ…»&õªe‡fŽt*’G»™~‡ÿغÁd¸Ã*#çR /)xi`˜7ovåmÇLs®Úäïæ.²|Õ²Žë)CÿÙÄ* endstream endobj 2 0 obj << /Type /ObjStm /N 100 /First 798 /Length 3306 /Filter /FlateDecode >> stream xÚí[[S9~÷¯Ðc¨©´Zwi*5U\†I6ä²$$,4౉Ý$™ùõû©ÛînÛ€yÚIÅj]ŽÎýIÝB°œ¦rf™6,0g˜Èñ?0¡ðÍ„³ø1™ãá™”ŠIÁ¤Q=)™tT<,S€–Ž)m™ôL[´³yΔdNa@1ç4SÌ»cÞÈž–̉+MŒç˜]xí˜Ú3#À‹×Ì€ âè„Ý€#BoжAöàH CLcž>wÌ¢?@& …„°$MF[ňQ mŽ'T`Á¿–ªÒ™5xÈ@R‚c‹yÎ@àñ!gŽ…àó Gª€ºœÇ3‰‚”éµ’öª>/ñQqmBDå!7LkzÂ*|y¨ÿ˜‡R5û…YÝi“+Ï @c¡ÜÖÈhACD ¢€X°Þ“]Á›0Ðja ņI€—Øõ¹=‘{ð[A ª8ðUÀ¼P£DÅ‚ œÁ*HãðA¶ ÚŒ+K4í¾\@n Sä ÄT$cA"'íªÈPjöCÞHx Ï ù±CÜ“‹È`asGÎB¦Ä«Îa,â^[¸'dŒ šg`j¹‡A Ö?õ9Í&Œž\…fÉ\ÙÞ³gŒï3þÇø`Ìø{2-NÊÁx”‰ öÛo½'/Fåd|zû6VË|pQ°Q1-£s6¾*&ýr<ù•ý'׿W*VÏWiþû)ÍœÁ³ƒò"6OÇWýÉÍ(tB±}q=úJXÊþôët5¸Ià›£qyQLXñ³y5,VÃÛ ýxt2¼ž.SÆ‚#gïß”ÇüìYüyU0þ¶^ô8æ–Ũœ"^°ÇßÓñõ䤘RNˆ=¯ŠÓAkü“Q‡äò¸Ì$ψ`›#°=eG”ˆ$<ŒǽÕÕãû×_ÊØÞŒ¾öøÖxrZL"þü˜?ç/øö‘ˆ bè¤dGFÚ,·ð8¯2ƒtc”ÌÂQ—I°MÖÕÒó³ñ¸µ¢r›GáC9•y_Öy7¦´‘™/6èj¹‰Ùd¤aÃOŸYLk&Ólt=¯B‚Ä!{ ˜É]扷€)!³1}˜YP´*©, ³ÝT+2N t®u³‹Ü†¼”üb#Û¤:ò…²u¿£ä–êX”¯êðJ꺤ėêŽ2\ªÆ4XõjJjTüíd|²_ÀŠðþ]ÆŠŸe×A;aaònXh¿°Ðî1ãA©<ƒZ´ð0Ô£\F V"sÁÜ↪ã†:4-¸Â Û@ÉÌVe´P­0sËœ 3cuƪ³hNÈaÔßlN×5§1ë˜s®cïº- ]ƒ°p·a£°º·«µ1MÏÕzO¥YÙUš½çÒ`uMÐ:d[6Š,ÌØ™e:§]°Î°+½%t'¬¸C 4`(ìÖ²\Þ 6Kž&SN¬kÓfæ[nÓvµ|`IÝ×ÂÆÞ3lì­a³^þx°s»çv÷tnû¨Î“BFG7íE¦mÌœç€þ oñnÓñnwïvKÜÖÚÌãÔ¶¦…ZY}…ÛþMÞ-¸ª»›«ºÐqUw— ïîá×IÝ7K}d„ΰºc;lØ?¿¿ý÷¸š&l8¾(ì9Lf«Ò+êI¥t.ÃÖ8£ôz¥ós†Îè:Ó©…M6|8Ρv@Æ “¼À6½ê–’@™y­ ¥¼Ç¼TFôͱ{Ë[õ“ \$¬Úë©L=$šË=ñ”IJ$…ÅéÊb­!Áœ×™‹pÊŠ 'À†œ ÂVãU_œ[aW–fD…WøMR'Ò쌼¡¬@¨ç‚vk Dd•5õ†õbo˦rŽ£2…iC'°‹Gñ%ƒ4F“©Kò+2øØòò8æ ¤[›)ªãŒÊËK7Ú¤_]Wù¼­òy[âç#÷s£Ç¹8VbGß@Ö¾pF1ÌÓ»¸4Û©4V¶ÆÔ¬–FRVS©%Ű7%§¶×¤/Ù&Âz̦´ïa‡Ó4½ŒÓ:µ¬âúrr6g2z¯fÜ'x¸üÓzBë^Œ’¯:/Ô[ሰVVÕTZïf\¦6šm)PHËÐv| ò,¥|(ÁSo-ì Š¼,ö$«Äy3Ø4ŽÝÙGÇú*Ž% Å:y v ðH­ãÏ [+ }"Ð}ÔØŠÒ)ø&×.éÚ!Èkï‰fd»Ôq»zQ›×æ*Ú&•îù˜Äi¡ã!eÍ ý*¾-8•©‡¤´ÐWíKNÇ$…ý•dV%GQJEÚÌUœ)cN‚ÆCœ¥j|if…[Rª#ÝW¸ *¾ùÍí¬a‚o Ü®Í[„&™c^kš‘zç¦5H*>¶IõqÆ *ÕF vS}>–ØMõTFz)a–Êÿ¯2&„1-Å!îPDˆë»'·Nuzã èœÒò­­ SŒ¦DÕè1ØI`I×1\'_¨7{fSzhÖ%R¼oÁTsSܤ‡}¨ç6qF‘Α\uª73tTØ)¦'“ÁU9ž¤£Ãëþ%F^~¿·ûû/Û¯¶…ÄÀ°>e:AlÅSÍSœ(žJúdGŸ¢°›¡3âô„Î66r»õ¼œ_ é-/E{*hðEÙN6GçÂå8®–Åå:söøa5 Ûà¸èOèhò„oòmœZø{Þç'üd<ø)/øÙà; ½ø9¿àþ•ù%ñ1 ~ŝР‘å>åÓÁO^òòbR¼ü1æ×üÿ¹‘dÚ€“ šg²ÛTôñãÞ»O¡¢W/V©H RQÜöÙ[T¤nRÑS¡k%YÓБMõù—–4fi¶·6ž€4ïD¾\×Âäá!ÂÌìmÃJQv`ìWü Ç÷+£‰f¿¼ì'³ó³³‹GkÆ§ÑØ%Ìû Ìÿl)Å­£”;ïß¼9ŒJYea‚ xÿ8:YmÞM¾… دoö × ý|䟠£~Y©‰¢££¬³³ÿ›ñrñçÕE1‚ÿ»,pH•WÃë)ÿÆ¿]Ó'M`ýv=.‹Ó/ÃaqVÎb7µR5F[ñ¨c®2 ÿ‹ÿULÆ-Û„µló~óðõV´[a•6hlu¸³mÌ ÛÕ´M޴ͬQ@¥ƒÊáMÉpJ^G´Ã×;o>ìh+$“® E!壸[Šs!ǘY²-ÞZ‰ó_Ÿ_~ú7‰çWÎÖ™Æå#ž}˜xkeÒçûÛ›”I÷÷V%SIÉj…ƒP¶!¡- ©9“PJÙYÂ’é&ÿiãEtÒý*™~™ôOŠı––ª_‹rÖzèæ”ÓñpØŸÀã‹o×ý!§T|>)úÿŤ2,¦Óvf¡ ‘F¬% õ:ÝL;)ß–5à$ëXçíþÛ÷(m¬^µm½±¡CÑã¬Úê†Uû$¦Ž´–ÒÒÕp­¼xørçç´s[í|:ùžQò½»¬ãI¸³(Ü%<&m‰&ÖÊ‹¯¶ö_n'ÑVØŽ6\tú”ñåøÃE3·DUŠ©³˜ºSøÄ%™RÎEŒ’å;ÖÖ^¦½€þ\ ±Vþðywïóç_öw÷¡È|…—?¡kkB!¼l, yS—ù\“ùªìdò•KgÜ6Ð¥'ºzÔ”hIÒý}t2>((ÎÎ dúâr¤BËñ¢ĨvÜêO‹ø¡dá¤ÓR]±‹_sv“iIL3…5y¯_5 ½ÿ88-/¦t[o© n"Þ=CtˆÛ.ñÐ$üœ¶]ŸvgÇß!í»¤)br‹9m½>íÎÆºM›®/ÞL\Έ‡üÄÛ;Ç.qÙ%N»‡9qaçÄåúÄÛ{».íoÓ¡A›6 µÒÍú¤Ûû®.i{gÒn}Ò‹{¢.ùS-­K3§­wWýu‘ßh‚ü¤¦îïaóÎ’Ü¥.o¡Þ]­O½»jv©ß=ñ>õÅ¥¦KÁñTÓñ¨Q _Ý­Wº–¼À}ŸŸÒúkZõgŸÛéúpœürp:]"¬.™U¢ª{4Õ‘t=áøìböô•þ¦©®Õ±°dÆ›ër8Ѥ¨VŠTCWŸc£–³ž8Àöƒnê§»"LV®5ã³ ³·“â;Ýšnª/MõÔ販µÑ^Ó]©ñÔ Ùdµ-Ç#›x–ðSíkO~ß@#ì"šPc±7 ÑM$rÉL«b9’8±•h·#?ÙŸ<Ý/û“rƒþ6‚ Þ¾.¬e·SnÐßJt:Õ]úètê ú»†N§Ù ?Vˆ.Å÷—ƒ²ÃE ô¸Ëî\•ØR[± ŽŒh´‰ßhªÑ ¶9n7èï(:,Õ”jÈÊÿD‡ endstream endobj 127 0 obj << /Author()/Title()/Subject()/Creator(LaTeX with hyperref package)/Producer(pdfTeX-1.40.15)/Keywords() /CreationDate (D:20151012042628+02'00') /ModDate (D:20151012042628+02'00') /Trapped /False /PTEX.Fullbanner (This is pdfTeX, Version 3.14159265-2.6-1.40.15 (TeX Live 2015/dev/Debian) kpathsea version 6.2.1dev) >> endobj 122 0 obj << /Type /ObjStm /N 5 /First 38 /Length 234 /Filter /FlateDecode >> stream xÚm‘MoÂ0 †ïù>ÒÃH4P¤ i7 'Ä!¢ŠmE³ÿ~N?PÃ8ÙyüÚŽm1 €J‘M@x_(±·"aYÆøZßL ÇQ¥/f<`šRâFµ9;[cŒ@†D($2Ä%„Tˆ¡™G'ÆWöfݰí@ubóyðµ>4‰@È׃àÀÖXÚœTØ6¦ÁãÎbgÿÕ[”ç§ïî}½…©IæJy=Ü?*üK;}-/Œoi&Ò`³%Æ7¿îj‹†¤-ió×hž^ÿ]æ†jÓ‹)­2ÅgÓè\]·?KÅ}‰ endstream endobj 128 0 obj << /Type /XRef /Index [0 129] /Size 129 /W [1 3 1] /Root 126 0 R /Info 127 0 R /ID [ ] /Length 314 /Filter /FlateDecode >> stream xÚ%Ò§RC…áÝK j „Þ{ 5zï½…Þ<q5žáÀ x48À0ƒA@þÅ|sfÎÎΊùuDQ§6(¤b8Jà 2ÀÕp ™5p^ȆF8†ðAA.äA>@!”ªH¡T~X‡]Xƒe؆MX À*ì@R‚ (SñÝÛú0lÁ”C-Tª?ÙHÔC3Ô©T}YÑMÐ-*mÃV´BD¡]%æ·¢"Ð 1•ăq؇%è^èƒ~HÀ B†`F`TeèÍö©\=ZW¹½´4¡òîµ4©JYšRMÞYšVM½ZšQ½þ¶4«úüßœêǧ¥yÕŸK ê„ÿÛE8€C¸€Ë4nú¯¢/Œ¸|“Ë7¹üëQ'r•nãù§¼+] endstream endobj startxref 143380 %%EOF foreach/inst/doc/foreach.R0000644000175100001440000001532112606615125015161 0ustar hornikusers### R code from vignette source 'foreach.Rnw' ################################################### ### code chunk number 1: loadLibs ################################################### library(foreach) ################################################### ### code chunk number 2: ex1 ################################################### x <- foreach(i=1:3) %do% sqrt(i) x ################################################### ### code chunk number 3: ex2 ################################################### x <- foreach(a=1:3, b=rep(10, 3)) %do% (a + b) x ################################################### ### code chunk number 4: ex3 ################################################### x <- foreach(a=1:3, b=rep(10, 3)) %do% { a + b } x ################################################### ### code chunk number 5: ex4 ################################################### x <- foreach(a=1:1000, b=rep(10, 2)) %do% { a + b } x ################################################### ### code chunk number 6: ex5 ################################################### x <- foreach(i=1:3, .combine='c') %do% exp(i) x ################################################### ### code chunk number 7: ex6 ################################################### x <- foreach(i=1:4, .combine='cbind') %do% rnorm(4) x ################################################### ### code chunk number 8: ex7 ################################################### x <- foreach(i=1:4, .combine='+') %do% rnorm(4) x ################################################### ### code chunk number 9: ex7.1 ################################################### cfun <- function(a, b) NULL x <- foreach(i=1:4, .combine='cfun') %do% rnorm(4) x ################################################### ### code chunk number 10: ex7.2 ################################################### cfun <- function(...) NULL x <- foreach(i=1:4, .combine='cfun', .multicombine=TRUE) %do% rnorm(4) x ################################################### ### code chunk number 11: ex7.2 ################################################### cfun <- function(...) NULL x <- foreach(i=1:4, .combine='cfun', .multicombine=TRUE, .maxcombine=10) %do% rnorm(4) x ################################################### ### code chunk number 12: ex7.3 ################################################### foreach(i=4:1, .combine='c') %dopar% { Sys.sleep(3 * i) i } foreach(i=4:1, .combine='c', .inorder=FALSE) %dopar% { Sys.sleep(3 * i) i } ################################################### ### code chunk number 13: ex8 ################################################### library(iterators) x <- foreach(a=irnorm(4, count=4), .combine='cbind') %do% a x ################################################### ### code chunk number 14: ex9 ################################################### set.seed(123) x <- foreach(a=irnorm(4, count=1000), .combine='+') %do% a x ################################################### ### code chunk number 15: ex10 ################################################### set.seed(123) x <- numeric(4) i <- 0 while (i < 1000) { x <- x + rnorm(4) i <- i + 1 } x ################################################### ### code chunk number 16: ex11 ################################################### set.seed(123) x <- foreach(icount(1000), .combine='+') %do% rnorm(4) x ################################################### ### code chunk number 17: ex12.data ################################################### x <- matrix(runif(500), 100) y <- gl(2, 50) ################################################### ### code chunk number 18: ex12.load ################################################### library(randomForest) ################################################### ### code chunk number 19: ex12.seq ################################################### rf <- foreach(ntree=rep(250, 4), .combine=combine) %do% randomForest(x, y, ntree=ntree) rf ################################################### ### code chunk number 20: ex12.par ################################################### rf <- foreach(ntree=rep(250, 4), .combine=combine, .packages='randomForest') %dopar% randomForest(x, y, ntree=ntree) rf ################################################### ### code chunk number 21: ex13.orig ################################################### applyKernel <- function(newX, FUN, d2, d.call, dn.call=NULL, ...) { ans <- vector("list", d2) for(i in 1:d2) { tmp <- FUN(array(newX[,i], d.call, dn.call), ...) if(!is.null(tmp)) ans[[i]] <- tmp } ans } applyKernel(matrix(1:16, 4), mean, 4, 4) ################################################### ### code chunk number 22: ex13.first ################################################### applyKernel <- function(newX, FUN, d2, d.call, dn.call=NULL, ...) { foreach(i=1:d2) %dopar% FUN(array(newX[,i], d.call, dn.call), ...) } applyKernel(matrix(1:16, 4), mean, 4, 4) ################################################### ### code chunk number 23: ex13.second ################################################### applyKernel <- function(newX, FUN, d2, d.call, dn.call=NULL, ...) { foreach(x=iter(newX, by='col')) %dopar% FUN(array(x, d.call, dn.call), ...) } applyKernel(matrix(1:16, 4), mean, 4, 4) ################################################### ### code chunk number 24: ex13.iter ################################################### iblkcol <- function(a, chunks) { n <- ncol(a) i <- 1 nextElem <- function() { if (chunks <= 0 || n <= 0) stop('StopIteration') m <- ceiling(n / chunks) r <- seq(i, length=m) i <<- i + m n <<- n - m chunks <<- chunks - 1 a[,r, drop=FALSE] } structure(list(nextElem=nextElem), class=c('iblkcol', 'iter')) } nextElem.iblkcol <- function(obj) obj$nextElem() ################################################### ### code chunk number 25: ex13.third ################################################### applyKernel <- function(newX, FUN, d2, d.call, dn.call=NULL, ...) { foreach(x=iblkcol(newX, 3), .combine='c', .packages='foreach') %dopar% { foreach(i=1:ncol(x)) %do% FUN(array(x[,i], d.call, dn.call), ...) } } applyKernel(matrix(1:16, 4), mean, 4, 4) ################################################### ### code chunk number 26: when ################################################### x <- foreach(a=irnorm(1, count=10), .combine='c') %:% when(a >= 0) %do% sqrt(a) x ################################################### ### code chunk number 27: qsort ################################################### qsort <- function(x) { n <- length(x) if (n == 0) { x } else { p <- sample(n, 1) smaller <- foreach(y=x[-p], .combine=c) %:% when(y <= x[p]) %do% y larger <- foreach(y=x[-p], .combine=c) %:% when(y > x[p]) %do% y c(qsort(smaller), x[p], qsort(larger)) } } qsort(runif(12)) foreach/inst/doc/foreach.pdf0000644000175100001440000044401212606615125015534 0ustar hornikusers%PDF-1.5 %ÐÔÅØ 41 0 obj << /Length 2516 /Filter /FlateDecode >> stream xÚ­MÛÊíž_aôòl V4’f$ÝŠM‘^^±ÝâÚ[^»¶¶–}ù÷åç|ز“àõàÕh†ä’CR›Ïžfùì¯ïòÏï>|*ͬÈ3ç ;{ÜÌLã²:w3WÖYášÙãzö¯ù?Ç…™ïðçi±,ó|þ¸…q¿øÏãßßÅøm‘å%'¼ @ GíàÏ ~[ÁIölò̺B‘þ¾(Üœáað¼°å¼{ Ûffª¬¬P—m•9ÛÌ–U‘¹¦e ÿèb|] à-K ôÛ¢™çø Õ™1íliê¬-KF_ÈpV>z2ˆô¿³ß!ª¤#jðÄÕoÑê µ–á`ØÓn¥ËrçfËÒfÆŠjÅubê mÙ‘];70U¼çqã~Ʋ ª|üÆV¤Ó¶™mêÙÒVYQTL×,–ÆÕü3qWWG †ë3í¸óz`­š¬µ–´šeVÕÅÀ²¬,)bƒÃjþÃ_à7òÊ^t J†÷’T5ö›3iŽf6d'Q#)wdR»@f' ÑÊO€Žô(_©–ápjfm ôU·ñ3ÙbY5 [ð.삼wÏd/Ѿ"ÛёΗÖHŒQ^N¢X|![?4Ñ¡âÖtú T0ܪr¬ÕþwbOΜyA&zªù›êbËï,? ™Ý ±xo#ÎZàSLöÌP'µi+§¢ôhêEöÁ¹Ž!V_e@'8^/I‹8¸æ]/lâdFdõ®ÂøCª’îÒÕFpˆ¢vä¡tv¯ž'`ü[¢fœ™ètìK#í9Šg=±ÂMºŸDŒW¯X˜F_$9S¯|t‹÷>€àî=Ò3b9›- “9u®¨­ IÀnƒ>k2ª–jààKu<Ëñ€lÛçëqö8­’±êò‚K=™ï¥ "°d®¨,—CD†Á7*ÛÆ^9e׿J(@DL<ÕN}¨A záZ/¡Š6Òµ'¶ú¦¬çŸPïzœ"W&!úäh@‚æ@2,úàºBŸQö¾B` ÓÖ3KëÝaʃ&R•²ís~Ⱦn$/º'¥‚–2©²*8 ŽŒ™ä ¥äV†ûÙÅԥצO»HdÁOš@‡8ßɾ»°O¬÷&O?;µAµîî”RŸ²fZ£´õ™ mB¯c|<€ ö»Wúsê"ÁŠhõ ­´Vº:”(`àáÜŠAàwLežíR'Émts\¹µ“´e^SFuå™8i²°:d~nçB/‘õÅp»€JåÈ W9 {¶CWWóGïÖZWG%YYWYE^ZYOÔnE™­/¦AI¸ˆ›$æÂë&å»—ˆ×u›ZŠ–zGÊ2PebŠ{½Ó¥¬Í¥‹-ÂÉ!|Ž“è“íÚÆ!Ižîé¨(ò¬mTt1XŸžOèª2YQùnEˆ‘S´A­¡±Æ¶%ÿï§èBŸ·­'5–~2ýpÜîPU’oŒš(ºôÊÒ¼Îi±^ÝÓ[JÅOGW‚BlaÓ£ë$ª$Á^Má½ä©[Éñˆ)ë­¦´.É/áuÓ‰P»(X§×PQBbÊJ3ÌWeæÂåœH“Âè:¤”~ëëDÐÛ6°DXÚ•Ys4˜ržÒe®ŽÏù•øø6yxyV•¿Ÿ¢Wg¶q±‹±h,×I—×½IÑzƒùQô<ö?FѵwI.mºq×‘à“®3S&žû“$ýmÕæƒ·ÔcHj—ÎeÊß)·C HZ¶ßíg^„•:+sôª+¼WŸéVÒdmP ¸úUÌ|­1Ì€ðÑpœ~ã©—¨CpWàf\%R¾p°íeÁ–ÐièÆFrQñÁ3?TÈÛ²œÿÊ:Ò~ã2S57 xŽ=’º¿E!e­‡˜Üw«CL„7 E•ÉNoô„„¹“ àêÊG¢TžtÚ%ª é}e_ô"3i½÷Ç  ºÎh—")½ Gé 3ÒŸ¦ò:uŒØ#–UUß“°n[u ‚&Ñ$ÑÚ\Tvã 3µçÇtAÅ™ÌÝ¿¾kpÉøxpálMVºp¶ªj|ļï²àW_©‘H'mŽ|ød“í 8{ùým>ïŽôêtx¼@iÄy”ƒ°Ð–io¥‡’€VçÓn8܈%EQD»1óØWÛiéhŒo[·ÖÜr]½"„žÇ =aœ»áÛ÷{|‡ÐsdǤ™¦€zýË^!úa4[QصÈ?¬¼ Œ õÙïø3‚~ÊAM%†ïÓÒרuF.†ä¶™k»å¬µhóïò Ù5;SŠ#ÏjÊÓ’¿âÃç&còý¡(柰‹Ø˜èGÍ>Ñ|ò™?}æyà\¿UÄlì}dшQAtk"lòå 5×íTå`¸ùU#13ìë‹Mâp«,hq{Ô«ƒ¾\„¼Ñ9¯ü½D=nÿÇQD0"±~¡¯ÇsÜ)´ˆuBêŠ.þüA^¡©°cZ™rþ1#þKJˆpS‰gc²ÊÚŸ ie–›(M„îÂþw¼‘=³wQ¹y©kš–ørÕ»e“€•b°ÃvÇ‹–v$qq ûg\s¥‰ÏûîÏraóޱ&œ uÔX¾Öz‹Ew¢ÅVĽhVÒ>—¶Bš¢]*f¿ößßm]wÃùnöX?žC¿©í²@ãó¹ åæ§qœkÁ+Åm-ÚZiòPÓ'"哌Ö]]ê)Í)ÈÛ̹ŸO k[ïNǶIe%C9¡:?f뉒ØëêŸ þòøîVùÙ endstream endobj 53 0 obj << /Length 2301 /Filter /FlateDecode >> stream xÚ¥Ûnã¸õ=_¡—Ee æð"ê²h ìA‹¶;›¢(fó ØNœŽe,g<é×﹑¢Å“ilJäáá¹_(Ýd:{wöãÅÙ›·ÖdƨÆ{›]\gÖ9Ui›U¶P¶®³‹uö!ÿg»0ù=ünKg}~±…çÍâòâ§7o]™î÷¥jt Øiã5€u{„máo¿-oZÀ&cæ,l•3<<|\xŸ·7á¼³¿\œ}:3«3“ù"«L£ _f«»³—:[ÃüO™VMSfG‚ºË¼.`Üe¿žýr¦_f•µW¾vLG¤»ïõX/l~ &LÞøýia뼃‰G~¿ƒG”ÖÍh…ϸ«[,'ï7‚¯ãñ1™Ãý‡á8‚pÀ¿É¿àßê‘ÖQ"´ð‰NçcEf´wóa"tªCXE"€œ2ßì™é¬?G¸’4ÑóAGD.4vG?zï7²° È-( ¤½4…*Š’¥ÊTQâF c-ò)™Aäæž…hrÅvS€Ö+e è 4¨‹JØíbiŒ-òwÜp ƒ×Êç¿â 2²'ÂÖsFÈÀö‹Š‘ýŒpüa¿azÉÇ †¦€wv#Ý~ k¬ÆæËøŽdÏòÛð)«_‹¶˜ºkáÍx¡õµ2…ùV7¬T劰éAè™z¡Z,K×ä?ô}ÐRjX e­ð>òx§5œûœÈñ$;K§½ª+?¶¤ÿ)¸¼Ä›`पÞE qž³Ù2?üò›6Úo¢)D°A?]‹™w<îĹZ™¿¿üž©.ëËB6¸”òÄá?/–Þ:B„›¯äàVÆ'øý¦½†áZÎ"ñD–YÄbæ…²•óÄ þ†(ac+x„Í68?#öõ·Ë ¶>l Iœä„ SsÒû€2Æ aŸöƒÆ&åC­Ü ¯Gq§Õ8·!ûD/m f-b|ZÔ–Ü h# êûIœ…%Az‡`q£ŠãÄãÉÐ`ÖðÝw²~$º* Å?2žypƒß·÷ƒOïCzx'»ŸÐ÷ÜöL oÙ Ç}wÄ´h&ø8n8¨í6‰Hs Ï»X}¡’‰k¥ýMïßA郕“j*mpW ÉÂg…rN‘}\+=3.¨|>‚Y%$ç¹ö…‡?#K~üJÄÒ"?ÁÏPÐ4¤ f„à;ñQDòO‘­ ÎÃO‚Óò$Ò7#\=få ù€´\^Ž0ùaÙ\:ãòxøGÝØd¿}¾¿œÙ¯àó %kèí%”îu$!Ê ¥ˆ$hOr=„o*þoÅxËÑcËÕ w–Z £µxLìb¥×}ù ûZÙ„¼jÅ_¨ú)¬fŠD(øS y?“xè-Wou¾ ôÎùP š€qÄ vVU>ÖK =)#Ì€ˆ"ÊÊÎÔÓšw#â"¤Á%PŽk¡Œ«Æ–s•È”j”€wÏ*~$+¸Ü9µš=sIršibn!·cŸ›ØM‚BÓ«òvغóÁ{9t¤" zVE54§•}•†ÊÒŸÒŽ&gÍFuT—ç @¢}Å#L¼o‡­i’.L@$…8ÕÒ¹iÛ>îÁÔ0”¦j¾ÉråC z@ÖË5 0Rák\¢çNÌï>´¬µ ],`vÕ“M+Ænùy¨O{AªÃZ$Ü2 öÚA×v/TìS”J`Æ\c•ìðõ=Žob1s *Ù΂J9í^iè2¾ °,…Xmèz/|¡Ï (±©°™uVª§5ÐëÒ'·oÞÖxšÒ–|P”PÓ8UQËß¹È+¬§Û÷‰‘Veº¹Q®GïØ¤?;Éú´Ê“¬^Xl è0¹D–Ñø/ê×1î<Žcˆh w!Œï áµ.î:ÊK4[œ¼Çcây{ž¤’¨ÝI£‰3ë'©=\ã€sTÐxÿ(í ‚¦ßJ"MÇp~zÄÙó¥2=o;„8òË¡“QÉ— r(]ƒ·Yä«Ö‹ÅbÐioÐ(ãFÁY‰Œ+êqŠ÷:¾©¡À¼ –â÷1‡ßˆ`")©Gš7•VM1Î ³w>ÏÌÀ•Ênˆ3©¼äp˜ ¼òËàÜAh»ðS“÷*ø‚zàï¦9· endstream endobj 63 0 obj << /Length 1112 /Filter /FlateDecode >> stream xÚÕËnÛFð®¯ØK µ6ûä#h{(ÐÉ¡hQ=8:P2e •EW¤œAÿ½³3³|(¬#$ŠHîÎÎûµC%n…?Í~XΞ¿4Zh- ïXn…±VfʈÌ8iò\,oÄuò{³›ëäÏí|aO–w°®æ«åëç/m:¤÷©,T Ü‘p hõ1à–ðÚÀsGDc¡ˆ´‰D¿ÌM–>,þœ{Ÿ”·QÞìÇå쯙\%´ðNdºÎ§bs?»^)qð×BÉ¢HÅ[ĺ^9øîÅo³_gjhzšTw"Í­T…!=¾Ÿ/¼±É;ú| ,h‰VÁƒ†ÁÓÛ¦“7Ê+}†ç<ž+¢^óQ¤~è©¶ê1-èøAà3ØÝ°ÏÔÉÄÝ7½¤§Å í¤sé™eTk¤J¡^›ë Áj5Å)ë°Ó6Ñ,“ËÄ£Äàæ&(¶jè8)¢Süp*£ªBTà…Lî8¸Çð’ó…³6ùcžw› qË=¬›š6§°f¤5KÁüªšdÜ8i[÷¿ÉÛSñ„ôÕ>û8›‡¨¾C gnŠû/Mqý¥)n¾ZŠwÉ–çcL²ý”7´<_Äæ[N²W2ËMBwÁÍ7c¥K»þ¿þ4·®¾Õo#%ó¬CݵÕqîÁ–vWCí8•%åqW®÷P—‚ #±‘ú*P˜¤»Ê6ì¨@+`Ãú ðö¯$F ìÊ5:—ûCž{JN·Ù±¢5ÞjñïÔáå ¡JNîtòba´ß¹q µ+òä>t­+Ô2‡¨ áTïÂzÂ"R$5ЭR—Cl{ü("šTdÉÛ`ÝÛó¬°yµ#3¶*>ƒ@‰À6öÌ{Úï¸C²kÉ%›¦ì©ã8í§ô äÍÙqõžÎÊNÙ Rt­¶nãlr^|O‡«P½L5Žˆ/Ñšn= àfàüê0&Eµ ˶©ØJ•F!þ9ïCŒ:†ŸŒªŽ„2{Kh]êî9ö …PGg¦6Þ°¢o”vñÒƒãm£©>éÖé©9æ d_d;v~_dà›œØûÞõIЇçªÇû:¢ûh"m:×Âïæ&È“¼bçù´Oy@ªÙ3A¡÷jN\h±Øvð œÁšÉ“zhß( `Nµi§%\ŒOvÖTjh }¿ Næ$ØpŒ£Lß!°³8—I sÿ(Hhp=@æé¦wÎB‹7º™ÏÝüå£[úߎnÝÿßùo$XC€/ü‹œøq†Ô§Þ§ ùö\ä?­xw” endstream endobj 67 0 obj << /Length 2098 /Filter /FlateDecode >> stream xÚ½YKã6¾÷¯‘7cF|J ²»@€Í.rÈìN:˜Ã¤n[Ý=;~ôZîôäßo½HQyÎwõë«ÿ]i­ ]xWÔºU·b½»zsShÿ¾¨Tۆ♤v…¯Ü·ÅWÿ¹ªò¥OLwE .°o`Î7.7783¨-µSÎù¡[ß,–^ÛR›¹e¡tx©¶6¬óÔNÝbél[žMK=qÓ¯ Ó”‡'|iÊõŠ@§ŽGx|ŠrM¹C9|ßR›.Ñ?òŽÃ»ªì±k%ýÝ/{TÃמG½],M(»6?#Ø]·ç ¢m]Ôå‘\Òá°/ 5€0"6¬pÖò'š.Y…==…Ó½„Tn¼dØjñDŠ0uZŠm+z<ÀcÓ–ý —ò tÜÉÑ+ 8Äq'ÁnÅ=$ý Eœ€‰æw2:ïŽ×1dpŒ ò– á¥õj± °]þuf»Ž‚båIáŽ-O âKÎ(bϪïe¢ûà°Â$ÝÇÇG¬{´ÖÏH”ðS(n_­üd÷^(J[s)”*g/‡Ò C:’3Z¼âÛùÍe>Ü\Ü9Сm\“F 8Äü£«9:Ó+ç)dà·ï²ˆŒîyÿ÷sçö—cÂ2>tš —#ÌahÍa|β@¿Ìrt€"6ðÜY·´´Í\|[§‚1—â[Ÿ¡ º­•­›?Žƒ¸?ŸƒŒGÞÌjµ²h¿”ÄêÙ `¸19±µj`ÖO&'®Ú1C±¸(0 έ’)†.%|eèJ™ŒØËù.ÇöÆéss‰ ½ÀÏ‹Hw»Z!OªµÐ"L¾²DŒ¡°šL‚ ©aZRw@…x Ÿân+Ó´¨X5dþÌ©­3ëH5šZÞ^СcëÚh½ Óé[!Ó43yÖäÝN\Ý’a±Æ3ÖÙ9ëh>“#P72>6¢KÚ Ð‘ š&Qd0XOøH[3©HU À ogAtsfvµ N³˜v0ÓªÜ:rNã æf€•Nî-Y!^ h­gCK¨~m޹Özª£TP©§oÉÒ{—c±nTMŽÃ ±@Ïhs¾Ë¹ŒÚ3@ÅŒ¸( ‰Ò†ôå…)~Xõzî•ièthsH3ò°”mÔ‘ZTÛ‡/„OÌ6§åNþã><ž;¾í"³K¥3~˜g‘Ï^¯MªœùgȘ{)—ùwbs‰WjTÊÖŸÜ—ŸÍ’`£ìÀ9χ…|¸¿|Âpç+k?ááœwîÃi†dÂxTªÓŸ>ÓÿŽB£te?õ¯£™Ëà¦ðC§·”ßiun:åÿ6Ù-( endstream endobj 72 0 obj << /Length 2194 /Filter /FlateDecode >> stream xÚíZ[o7~÷¯("¡;¼Í¥Ø.ÐÉ6Å¢Øm܇"̓,b#–”jä:þ÷{näpÆ”í)v±»ÔÃÃÃÃsùxÈQY¼+ÊâogߟŸ}ýÒèBkÕzoŠóMa¬UuiŠÚ8eš¦8_of¿ôWs=ÛAy7_Xãgç—PïæoÏüú¥­Òñ¾RmYw¸²ýi—ð³‚rɃƓ:¤Mô¹©gL•÷sïgËwa¾³çg¿Ÿi - ]xWÔºUÎWÅj{öæmY¬áýE©Ú¶*n‰j[øÒÁóºx}öϳ2]zÕŒDwEÕXU¶†åøë|á}äÇ_@‚WiUPhaP†µéÙo¥/áúúІò å9VB¶P.„|'ܾe Õm*›nÒmTôŒiÆòWJ{(¾Êq©”©Ü£<,E³È_‚XkùK~u‰ƒ¶ÃÚW5ò/FUì í”sÕDµ*aL¾ç˜ô jñí|¡Kp£D§ÆÃÅÙxFìn¨®!ijøi±ÕÀlÚÎÔ ¥¢1È¡b:îÖ¡ÇÐJ,”ºÊy,.f,ç¯ó†µq3_8[ÎVKÒÕ±z ¥ßs»‡ú‡ùÂT³n…¦ßܺ…3-pÒµº¸=ˆw¥tÂrC3± VG!Úï˜Á‘ Ãbì·sÐÌØÉ¨ç(+Íú^#’bg¯æ‹ÚÕ³°‡ºŸá[$gŸQ°çãR|àƒ0“\J|ÙXo©+1$3Øø¶gŒYRƒ±VpÇo©-èBp,õ79¿NåGþ‡a8(2æQÅÂýJ\~7¸ûrí‹àùÔú Z¿@ù;\~)K÷ÿ3Ö]†£ñªôõÓ~&¸±Zyß|:܈MÈhÑb‡ýO(ѽѶÑ9ÙáG¯®(t2óוjœi5QédsnTe#>oîÇ?û±µÍìˆ2¼ÇÀéú 4(žöL²<ІÎ&Ü2¶I}WÛöyÝ8e!?øÄ¾Veý$ùß³µQäþQåŠú7i´.ج×Ê‚è÷0Gðò„UPÓÕ`”ÌRÀw]4Åó¬e•®GárAQ¸Îq³¥ªMÂÀ­ŒÅXÈIتÖD°wªr1¸qùKö žh;€€–''ÉxɸÌÂQýöJ0øšÛ«eÚnSmz~IíÞ±zÜóSóöþ¨4L!hpÁKàÍÆÖÊÔÛ#°t›¥¸}Ø5|lÝRBµf‡&ú¿}5…ý.n]ìïØ·!Ô” ÚA€ù¶œ}/r®®.£1P’¨>‘ätì—ž«}H3„ê·R#^vkî¥ÑºÂù.eòSqá*¯¬ŽžôÐ}õE)åì8•¤ ʶ!Ÿ!9H¸k?I hö>Z3Žò^ü»²FÔ6e4è÷&nY06”Á‹‡Ü ,T5föŠM’JÐqó¤ prÞ¿à<9èôZÄcTcÓÈËe&‘‡ ޭᆣm—»\‚1µþ&$Q)ŽB›ÎÉïxúaŠzÀ>L7¶KA +Ê‚ÊÉFܜތŽ6¥DQæð†§ñíùyÁÑ/ì† Yð…HwahïdƒúíÈaéUD`™G‡ý¹|<[(Ì¡«„M¥9+.­¨Å|²]Z7B.”üctERõn>¶n­•kã=Õ^¶ÿc 'à ß®Üÿáê?òú™ñjÂŽí l´\jý›ÏÃîáNàñô‰ÑX¥í(k @K“ìÌ Þ¤ʄܸ6d¼Æ—ry¶æ÷’°ãû€2h !É- œæ×a `1„»„ÁU¸®¾ÌŽè¨Põ ä_§k%ã6­ªšÉùf8˜àrÉ?Ži’y$”€Ä± ¶®–‡jŸŒ¨ïº‚—w7Ø“wº*é#‚Xƒ£"w©áÔSäûD<©"ï(¿º&ü§ ­Œ*Ë Ç¤e…Ó:›»ÇÆ«nh"Ez/iWûäR",—R=‰!p|¯}·A»Aš;»‰Ê »ï~@!åj¢²;âL#8s5È«–÷°#‹E‘»êú>hô*1ª„þ!"P&›‰V‡Ýè«)à7T?ˆä‡ÔmÅ€ ÓZÒÌ>^žv|És…Äö•0Â\dI l5õ¶€”„û>«ô}šÑõùíðaê;~±®u,½¡ÞkÁtZw ç¾ý†ŸÇ^2CEF¹}vÏ£„ðqÙ¯HØ¡ï!Þ×ç'NàãìyH·ßSºñÖÌ&©«Ù¬ŒgÃüKf´ÚG ÆJ]3·ü·¬ÉZ—rý> stream xÚåÛnÛFöÝ_Á—"T7b87Y´R )Zôa‹º‹4´DÛBeÉå8FÑï¹’C™NÝ],°À>ŒHΜ9÷ËœQ™]eeöÝÙ7çg¯ÞX“S4!Øìü2³Î±´Y´¾°u¯³·ù/ýfaòŒ«ÅÒÙŸ_Ã{·xwþë7®J÷‡ªhÊ °ÓÆKÛ¶…ŸŒkÞ4%êa“±ºéŸ s†‡—ß!äí•Ò;ûöüìý™Ø23YðY4MáC•­nÎÞ¾+³5Ìÿ•EÓTÙ=AÝd¡ôðÜf?ŸýtV¦¢Wõ„uŸUµ+ÊÆ2_/–Á:–‰c”Ää¿–¡„jç+Æ0 Œ—¼»hÄpãBÀw‚í+ÖGlRNLe‹:FUÈ †™r[&(ÀjIUØ*<Hb˜ãÏ€«µp|+ò¢ìŸñòïˆ)[šÚÞ[xñð¬É?K¢Éð=ŒBž[¸´¢:ÇX?çdž Ñ(y˜"ßLGʸÿ9—PÁ+ƒ¾E½xãÀ`ððÄ|Y~˜9] ù¿ò„]eO…^ «‘ò Œ×0~\°|«ýßt­êS®þk®5Í~HlŠGÒëÒU]«¿1$2- /ùõ˜ÀÿZ/jí8å󽂉gv}§¶Îï1Ëî²ûˆV¹=’^…Úf$Œ¹ùib¢Aä]·f‚©ôÝVŒ•ÛÉ:ÇÛ1©þh߯N2ÀÒ4Η…uñï6±Á ›îä²"䶘J3Ī2+†#£ŽœŽ&¶X ,µB™i‘8ÿã¤5 /L˜>ZÞø¨Ú±êBœF:ÍU.GÃÅ#ü@ÈL ÏS—‡¤®u²EÓ(ò3SߥЖMþ¯EM~F+u”b+­=“9ÑΠœ™„ô”ï,mEUÄ.}·ü<%5g¨ Ô•¬—|kYìü½’Ãⶨ:Lªiš‰”› #@ɶ*TC…Öú}rXQ©£&1è“…¡·å'ºd_†¢²ÿ^‚ª‹èí_%(vmy°:ñm:KÞ&.°Q/Éø# ùI#¬¬À5¶‰ó¬g°³Eé\*ßa§bÝÌFxY„±p<²ÓUâü©lä¿'ñf+4b¢BtG¬À‹ÍoÕ„DZÑÂzôSœb;gzÔžF)¼wí(ŸO£-Âo¤ÇÐü” 7ãQ‹ˆ­¦ ¬[sðzç ñOWºÃwé_<¿¯U·¸H"á WÒדîoð\m>>Ño •üø —Ïî§[é47ƒáÇ-7#˜Ûlm±Õ§IG.}ÄÒCô¦ªþ²›6cG>żžÃêLá¬}>Òù&[ëV* =­tƒ)æ²”+ n¦Âí(jSÛÑè8"be³ó*Óbcí, MlÁÝùSK^²—O…:ì—ðóŽˆÖy)÷»Cƒ_8b-ý63aò:âl$º&oN678i+AQ* 1®7¸1òrÎ¥‘1;Çõ\M-|” 3F¨‘˜Gª¡Bæd³Åƒ 4Ëeæïè4r)IÛxèg©¨u;þXëj›d‚+L;3øÞêeË•” Ð"¹ý]zû"´µ€5Õx(i¹PTÞåß?jµ %´IyÙcÕºgÒÛE¬OYÞOUòÄeÌë =RUv%÷ÉËAtXXBÝTvøçœÍ[äÔ™¡>ºÑ °J˜;½ÎXóú…öQÁûx˜£¹ýíXÂOîäÚ0§‹×Ì?mÖojœ”®÷B`“œâÕÀÎ$Zfú—STSL¬7,ïU¢QÉ'וO)˜îƒÆ[§éí•à»JXÝ¥lÓÆƒ)*½šž:8>>ºX“‹¯­žÙ^èy&àµ8ÙÔ©O¼ ã»yé}#MªR×ÑÕþJ öÒK1¹vS?èǃÞpçמül˜4Ô|Õ…wúø¯”þ­túï4ú’Ç3ÿœšù?ÞÁ¹=«NIþ Î>à endstream endobj 80 0 obj << /Length 1459 /Filter /FlateDecode >> stream xÚÝXIÛ6¾ûW茌©‘")©hz(ÐÉ©‹{JsðØò‚Úž‰å‰3úßû6J”£‰'hm´$êm|Ë÷žœ%«$K~ý0=it¢µªœ3Ét™˜™ïFoÞfÉö_'™ª*Ÿœˆj—¸ÌÂu›ü6úe”ÅG÷eÏt›ø2WYeØŽïÇgò´!Õ:=ÂR°Â3®¬?2—ÁEÃ2°rÞÒh,hšh«¬õ=‰øòPNø–üë r;oµòqë¬í#–]Gfa}ÃBçBq/ÔG‘€–fÑ"k[.%ªoEîèÜ‹i/8€E».Ï ‚mÔ¯˜¤ï]¯´ ×C2¼2Þ=YM?“8 ÁÏxkFÎÇLUÎ8‰;‹Á@òB¸r«\%iÿõv<ÑÎê´R⮪€O? Jçé½—«àYQ((&:ƒ ÖV2'#&CœHŒ¯Kb"áøÂáOáHÕ@¡àAú6R%n(-἞‚M*ÏïÇ ¢Æ$yàÒÝ =fÃqKA¥}Œu½ õú0. f¾i‹.¤ÌNâ[Í.­ßI®áÞû±³”²¤aF™ìH¡"æ£dxÍT[á¹E–S;ÑÊ­UY‡5'”† ÛzÈuà4oZŒaÐz{·ß¥Û$¯ LÙwøWDwöâÐÈ=ˆsçýr¿€6›OäfŸ¡>IL6!v¼-êZYtzóqHÃ5VR¡*;×|¹€n7ˆÿô¼›žøÇ˜ñÝ_ŸÏÿ1Š2‡±¥Ô…½‚2`†Ý—რo¨^j¨f¢ ˜»— .ª|âi¡(£:𠮸º…IùMqËš´‚ÊlGŒÐÞæGÑŒBøªt/£i¢X³ý«:´´ÃL ¹nÎ ¦c½§D…Qh=(#¼ítq´ée4˜ËAN/õ•Ø.*›Z„mƒ H¹ÁžŒã´æÒ°t ð‹Àû_Ìnâ;‘àóòÓQ‘NViZ¬ï0yFÌ Yá䍸ÞTxx¢c†î “7´nÁ?’þÁ¬Â}úç•òÁŸôÆè|ÑŽ*S:Ó±Ç^k ©¸2È.u.w-^ö¿]úŒ­âü2¹nS¢9²‰ã™TÌ]WHP#-%w¹ª¬ïÏ*Ôë*ã @_Xßbô>’Ïd¾Í™UoÉÅuh1%:.Û.¬ø¸ë :°õ‰éHõŠ/çs2î5aN~j´xê6jˆ¥=k$ÇHo#CÑMŸa ÜïÛïa¡¥Ô@!Ñ ù,ÑÈŽ›èK[æmÿ„-¨ÐÎÚ.±}¬¹¾_ø\øCíü9_ æOý[nàŸH¸À,f€9í*¶¦8Wù7—YžÚ endstream endobj 85 0 obj << /Length 2233 /Filter /FlateDecode >> stream xÚ¥Y[sã¶~÷¯àËÎJÓC€/™¶3M'ÛI¦Ó´©;}Hò K”¥XG’×ö¿Ï¹ DÙÞìƒM888÷ó*²Û¬ÈþqõÍõÕW­ÉŒÉ[ïmv½ÈlYæua³ÚºÜ6Mv=Ï~ýï°›ÑþnÇ“ÒúÑõÆÝø—ëï¿úXVé~_åmQwÚ¸²Ýi§ðoKÞÔ?ÔÁ&cuӿǶ1= îÆÞ¦·zÞÕ·×W¿] -2“y—զͯ²Ùæê§_ŠlóßgEÞ¶UöHT›Ìžëì¿Wÿ¹*RÕð¨sk€ Šî²ª)ó¢µ,‡OŒ±ä) ɳÇküëh0)k?ú†Oø>{8¢•vÛ! ‹l†u5sþ29’5`ÃÙ5˜æÄžÜP¾=}œ0›’ó&¶­G7ð¨À’ðÒ€N4‡çºÅë„33Ö¦QH<gG^]Ev+ÑãÀ wcÛŒe ·­‚j°pü+lP\¤`uää{xøÑŽÀ=<½ƒ÷“ÇÝÖ*!Úò‹¬u?6ƒ/›¸ÊåeÕfãrçðDY¦k ¢nÍtdì ü»;Åp[1^>†c‹Ñõ¸ 7ÂðmÔñÜTmöÌïb3•J,®âY>ÉI;9Y”èž½ŠD²ÆE†-¿®tè.h)Oô}Éi½–ä-Ï(/$½,i?L|J´bƒ¢í 1zÅFWóš:Øß4Áþ†ã¯SÈ£ƒ!ӘܵN½ÿèæhŽwC‘b›¼¬C2>®BðµUöœï½ZhðåÇxÝ„Ž/‹Ñ7¤”„4S¯ÂëÝjl)fæÑ0üMü¶Š±Ìô ѶíE-“Ì\4ȯÇ$d¶}»/ÅÎ"ê#Ò½‡‰àO P·å|25To3äÙDˆgŠÑq)ñ´G^­ã4v×¢9îx/RM(xø²AAÔKždíâ\upEƒ †”Ýì<=! ·Á÷2$Eë.¦>¥uU6£/Ÿä5ý´–³VsHw¸´š˜‰$йÈÅÈú–cZß©*<È%~F‹|ªV œØˆãöRÜ«h¬NjUŠUµî%Ë 9M½¤é´?J}è4Ú;ñ'oé+ÑX¬J¶.å$Œb·’n#Ã#3%ï…n2vH…; Y|1ÕVŠUÐ&ñBìÞ‹Ó`a+g]0¦Êþi줗d F˜-C”Z—¶…šbæD¤Ük݆q”©Û3ah†SkÊo`'ªDe¬D¶T#‘:Z…`|Z/pC¬,pZ(,¥qÃVØ+Oy.°ê,çL¬gýåøÆÅcßBbø ¡> @ÄäÒ˜Ä}Tsá¹NJß-iø!2Ž.en‡c’üǃîø RîbްgÎ÷çµ7ç‚(P‰«i:“7N1,™7Ù)æ, Í|C-ÛqÁEK¡<ÃØÖ¶yéÜþÕ8JNÚC(8A±¡ÍJÄþÆxí m¤áº&Ã- á“Óv§´/÷¯áÞÕ&Sg²Oùñ¸V‰~4ÜQ™A$×B[ÜžCCÓ E‹!ˆ“ªzø@bŠTT`þ4[Lérã… OC¯€"€‰ÿ¤„R”/c*/¦hÎ#w†!V’²ÂïùÕ¿fŠªI)&b°°Ÿ€¾•‹ë_Çç‰ &< Æ“±=ÌèçÂÉ•G{Í".aFòS†Kò4§+©3}O ç3n%äÙú–§¶Aö}­ÿ7…ò²XÍû,hM›Î_ÍyþÅ›–,lÌg_µ|^—veWA…Ô'q"žT|"!¯‡¡Ãºn΋Ç?IÁé[k«ÜÙ X,TZ¢?¾^k`áÛ×ÔŽcW™ÜØv0Ž×Ž7 (åùÜܘ‹Z[Aê6SÙßUR׿ÃÊê¬ø!OSØ|iR+# gj?*ÆÂiÊ3ñ±/8HÀhta¸QItÔß.¹“¥lCjQ²u,a®ê•0 Ië®H}vM'ìä¤}˜p7ò‡,™VôYpn©m@/µ _TP¦¦lÄT³©Ð’ΧðÖ¸Õ!EZúaE/ü;‰µÞUêÞ¥‡=ô ý,µgš{9ˆ º }£ xáz„Ò\þ^µ¿-ý—%¢&~qÔ²=ëwzùzc!·N®›‰¿&ÎWý+‹„‡kš^h9ï_©"Ææ¶Ud«}·ë†T(¡¿Õ¡çRVߊP›äCǺc<Ç"c· ì„ÑÐûq.Í‚v)­¤‹ä0Rœ}vQ#ìts¾I\›Ð-ã+{J¯ãg¨ýÔJà‡&/›‡ü±(¨ó&~ná@ŸørôkGp wiOÁ¼}чϩ¿,6{R]ü´Ø/0”`gµÌp-3I„Ÿ^ÏÅÏ óá¶Ž;{];Ô¹ë¼hCK ù(‰ÕØÜ×õà:„ÞP}Оܘ†ÑUFšŽ5y¥<ΰ²Ôû]3§Iɤ‹Ut1¼þ\'åïP¡ œ<åÜ%´„nîxâmß̤zؤ½ï9mOáâ¤sG€šþ6A²üý%y¿ïKE”0Ý’Íåô~â3ï—Ö‰qz—ÀwC·›?ÁÝÔ×·‚Ù?(¦ÃKöš©³OÒ¨è»ðï!ñÈércù¼ºtšxã@`x”ACãèÖ3²ðüŸÛf’u•%MƒšM•¶íKvñzÏ[åÌöüU ÞÒq=¢}ͪ¯™z¶f Ä ­n5ë}âN ¯©N„ø¦ESÇÍîDŒcpÙAOs)ªc¤WÀ•}?"Û]Þ熿{LéÚ7UD$ß‚m¸Ê]çäÛnNn8´Dh,@11„¥ß*õÇÆÓß,+€,Þø“åÀ¯´ðÈñWK_£eE›Ó#kÆÙ÷ endstream endobj 89 0 obj << /Length 2395 /Filter /FlateDecode >> stream xÚ­ÙŽãÆñ}¾‚1°°„Xí>xq€$ÈÆk†ãŒ›yàêØV—Eiþw×Õ%J³äAb³º«ºª¨³™Îþ~ó—Û›¯ßZ“£š¢°Ùí"³Î©JÛ¬²¹²uÝβ÷£Ÿ»åØŒ6ðû8ž8[Œnïa<ßÝ~÷õ[W¦ç‹R5ºètpÛ¶{ÜÛÂß~÷|¨4‡CÆúC?Žm5âý0ø4.ŠQûÑã»ùÛíÍ/7öêÌdEžU¦QyQfÓõÍû;Í`þ»L«¦)³GڵΠÃs•ýëæŸ7ú2ëe­røsJ7–)¹×@ÿxb›f„\I ôz ^ÌÓP´-ãê†ÈjWð·"úùùnÏGÈÝœ¢hç¸>‹Ð·¼B¨ SËò¿ wkrUäA†oÖþÞ Ét[Tß+ô|L‚'ÇÄB'‚8£UæñlçlˆÄ4ßm*£rk_o-E¨G,(îÃRPnðL6É6 d‚Éó’7O[Ñéc6D“³Ê乇®:QóITúqN «¨•uþ hÐæ9 çŒ4¼_ö£J›¿V"…jÂo{gÞÈ‹€¼M÷è¡?ñìüIKè».Š—–7bÐ Dr[xf`¹ÞŒ^bÜæV¹(¿½·x²â5üÞâ`ÏÒGÚä0ÉV¶4¢ôâ%8[’% ävÆÜP|WÆáVD0c+¦9ïžpéy¿éQÌ]àvø>½Ýâ˜ØÝó¸¶`X@¬};'ñ~)QÎ%ÚÀ×+‘„Hy@!ÖŠì,›²Ne£3ðrU†…ò§ñ¤°ŽÀ.xøGNx¸™ìW4>3ú.´\‡d þ¾IÞwq«…_?ͱä¼jâŒ^Û$¶nØ×Ö0»„â൲­#P$€©Û7¹*Ë`~_‰°T¦èÙ§àŸ%t½=]b«}„U¡L•6>’±ø&Á˜Zlj{50WõãÞïÇSTæ•T >E?Çá%š'ò‘>Ý'¬g†QÁ[-ïþkp2q8yà‹q#°4)LôÂÃaæ™gdáù+~nCód=¼£ËºTÚ6}ÊnÑõwìÙxt+®Àö¤“¨ZNH¤ȯi÷%ÐvÂÐ÷-§>¤K@"\©ê²éÇÂð’'ÿ™Cp+d‚Æ:-ñ\­Ù¾Œm”Î]Ÿ7»U}hHB\¶â¯+Võò\J&sr'ááä¾£%$h·Ÿ?ˆ ìЀdõCÝ; È•%hBåV²Œ¸;yq®7ã§ÀŒ`ý_Š qƒp™¦Ûµ8£¿b¾Ìù^%öÉúÔ_ ._„n»ágËRùÑÛ…{š0E;×|ñV¥qÛÄåéÃ…û„»wQKÙ·!Dí$ÖTçgW¼«]¸a8‹C/IÓK›}=ºdp™N—"ŽÕ¸û“еº˜HhÌþ+8ŸÌÃ…¼3‚çTnBAó<ÃPÈ'¥.}…D¤s×±gÒŒ¯„Eâí€ïvt¸˜!¹¡;Ëðw½ à,5¬T©c†}V§Þ%¥CJÒœì“d¸R¬V…µýH’ÒmË*¼o}P¢e`²…äy½~ðšëøW{É>á s÷6BÙ»­šDW=¬>Å©{‰bÏ ñ1dH‚bÆ¡ÚäÞÝ–¯ßÀ>†irÓ½ÿdÉ‚‰_D\{ïeëdŸ¡DØ;¼J%7p#R‰Âu”‰w}ÿ¢§µÇeÈ)é½MÂÏvd"ºl{ ( o»÷…_R²­ñ]K}gzeBcb8ë•T«ô|ê)Jk¤ÜǤ=eh!ÅU‚ÕtŽ*o*¹ÎùèQn½9ïMhå‚qLÜ‘JxµtIátšmãµsRôx'Ùð©y0hÛ¾±W ußF‡BŒ…ж~}\¡ §×§ Ð6ÄM—e$öà{óSâíPÄHØOC*i»y™j%Æ*˜kL!]^rGÁi(§ª.A“9ÆjVY¨©ŒÍG?ŽÄæc|}ùÙŸ‘†Ýj0oUA2íŠJ’è-Úå#¸«–hz¬ã¹Câ%O´qï–ãø'œÑt)ã“/äc9ÎÓõpI_ê¸(y0šÆE\‘R†0ˆ¹à˜ä›æl$¥Ÿùi°ùÖÊÙ&MÌvHÓ°ü\i‰y)·¡XÍ•¦5õê“Jò%äy³/º¼‘†{_ÂøfQ²‡†Ãf“dÇ¿®’$z{” ã>IL§¼|iP>°©$ñfC™Ç¯«´½âa-ø9e[›ùð3„ÙQܲ±O#h|…1¯LK Y>„06aùö4rì—1g6q…TNWNðà*»aªŒ]­ÙI¡¦¸ñí ýÑ[{4{d¶´DâS ‡+;I³pž‹C)}?F„‚•^`@d”5³æ¥Vg}ò¡Æ¾Ue<"‰ hÇПu §óºfKÂë2¡~Ýë ±fjˆÏæ¤É:õí–p²ÎÃ<ÞlB%qîoUºLî“{ÃG²Ðõà8`½¢»Ù =» 3ú^”±Oš‡«Áhðé¤*é‚"äçß±3†í´Ÿá÷CœšI#4yõ-ÏÔf’åÍ…-ß`Dðù%ÝOÿ‹ Ãÿå¾#ØÆ¼õD›gß[°þE¢gß.üâœß³Æ_ÑÇ¿8‡¼dqh¨eú@¯òfKÃ_ÑØ$OyK$XÛ$ÁlÅV.hù=±É¬ßý_tzÞ ï©ñ ”­ER—hD Âc‘e­c—ÞôdM‚Y}Ÿ°zw&Êꊕýúy&8¼ ±ü:dC¯öòÀZû+ù).{{Ãgyí«Å:ù@²éo<=s¥¹-A‹dÀïî†$ËæN:ŸŠ¾©à÷pÿAûô»xY+£Ýç~øePáàÑ0 Í)ÊßJUk endstream endobj 93 0 obj << /Length 1189 /Filter /FlateDecode >> stream xÚíÛnÛ6ôÝ_A ("a“*R"% ë ,ÃÚa·ºÀ׊-ÇAb«ó¥v0ìßwÈsHQ²xhЧ=P&©s¿Ë »e ûqôz°w£ßG‰¯zOôŒ)UÄ*S(ÇxN<¦SÍÐ"žÅY&Û×|F’§Šá -”(⢑ô”ˆ:%Â8ÛNCß„Q–ÁM Ô³ŠØm‰½³¬j-ÃÁ0¬ ZüP"ú`–á8kVpøH—¬»ÂW;<­W¸7ܽJxjíh×W`_À#Z™­öG„võëlóÉÄðÉ3ÇÒÙÐ8ÓÕGR:6Ô˜à9û†³ô=©J$É PÃýQ?f{"]XOÛ,æR@ËÏJ㪕uÑ8?é €¬>la÷-Q…O”†ÊÃs QŠþïÁò"54?z.~„õÖ”/X14øw°p» [“ýüÈÄ# ƒëOŒT} Û÷°~i¯æ°ÝcL´+O[ïõú È+"¬üL‹Ðüà 9Þþ=TI¾#.s޾"sÔÄiF¡OJÞSn|À*-‡tÛ­+"øâ g¡xÙ1q²xÚ?ž·ôÄhÂMŸÅ®Fó¦ÖDƒþ3ôî?G ÓtEríDkxl_[/è_ÕÊ›h°ò<ºîöqR:éÖ|ÓNøE}Iœö$þt‡T_¶Cò§;¤úüùKh”æ§M0‡†g#bC9S™Ñe‰¿KÁ¹Ûï}‡Ó”Ù×®ŸÞ¹$ð¦HTœ¹1Ë&Ò€:¢Œ“”ûeyƒù\IjÞ°m{¶­8'€µÑHÏfÑÂx™ú±éåfM¶0m¯vê{¢é§"+o;NH"d<QgZ‡¥>!#Óœîñ­±^=GwjÛ0¿;W5g¶¡í)¿×HJ›@ÿZjØn ‰ÃB˜(å¨O}…-\cÚ!ážÆ1+pc‰™qÍ]‹Ô I èæ dkîæ¶UCùatZ™äm¯Ô$MÀîüny2¤_[3é¾ÀIžâ .løÀ²µ&tƒ'½ïÍ9.­nl謀ƒ‘Á®ñ"UÑF;;KV;× wéÊóàš¸˜Á¥µ¸^¤KWèÛ*Oc™µ€ŠœsƒÐiú zÆ)èHLà üiܽ_¡Ëò²Ó8rgí×ïÕ[UÌÝ\=#)‰q^fÓ¢YáÒ.ÒD>Ç wüBÚûtïÿßì> stream xÚÝÙn7ð]_±/†W¨ÅðXîQ4}PH‹ ‡pô H+Yб®$Ç6Šü{‡œá’+Q¶R¤}è%.9çâ y2OxòcïͰ÷êZŠDVi-“á,‘J±‚ˤ“e™ §ÉMú~·è‹tcÞ(©Óá-ÌëþhøöÕµÊC|³Šç@Ý"άÙØ1üL`Ü"R—iHB:¤_ú²H&˾Öéxîøõ~öþì €å‰Ht–¢b™Î“ɺw3âÉÖß&œUUž#ûZfŸÚüñ„+«ØõÅ)çvþc䀬±3x¯ÜŽ!K€ƒkñ‹oy þL8í¨â(‡¶—¾#ðã_Ãô=Œw~ɤ ÙýdD;Œì`{sä56 ~¦AhŒP˜+ ìê_1•C$t!°Ðk“±H}¹×ò‘˜.È*+ÊŠ>¯¼¤uT±Mt×hä¢ê”¸P£–Yæ’ÕeÌr&´˜ÄˆäLæúl¸wv–4Ÿ“¸»S"š‰âe‰E§îŽš#Bóè#.áÅqPª“{Kó§.Õëo(Èè«ÄÞ±wÂí™8û|bó¸QÑÿ¯F%ÿ}V£"¿V£"þ½F%o°Ÿ¹lKV/æ¯nœ›F¢ ±DU1¥\°…Q‘d¨<¬Kȶ ™z žå¦%“òˆÛO1ñg•wŠÐ9UoL§êgÔŸ MšHÉèP¬FÅ!Ês%ID ™q&!7w<â1vvÈÐY›¢MŸ‘É O•U§&W²tmVnÛï™°?ˆ$¤`ºgúAÉÀm¬½éÆÁ }…’-¨G”e«–¥n‡ Ø<»ÎGúÊ1> stream xÚÕÉŽãÆõÞ_A†BFt­\ŒÌld’1bÃŽÛ§ñ8lv·`-=¢ä!ð¿ûmEÙT/@.9P¢ªÞ¾Õ«'•Ü$*ùçÅ7—_½3:Ñ:«¼7Éåub¬Í e’¸̔ery•|HéV ná¹Y,­ñéå-¼·‹—ß}õÎæ1¾Ï³Jå@¯l·GØ>xniÌÔ’6éÇ…)R†‡—ßÞ§õMàwñË‹Ï`U¢ï’BW™óyÒl.>|Tɬ—¨¬ªòäž 6‰W¾×ÉÏ?]¨Xu4ŠÌh ƒ¢»$/m¦*Ãrä‹¥ÖÆ¥ÿFå;x |áÓoQ© |Üí[Ô¨Ýâæj‡&êæÔSɬê &ûþz±tZ§§…)SD:Âó ²ï]£úÈY¯á©÷¸¡ÒûÉv¤ßüÚãýxê·AÆ-ÃÜ mäuƒ/x` ŽEkÙæ%–ÿ,Ãv³0 .:1Ÿ[Â…ß¿£f"Ñn±4yÚ4G‚”¼„ßñë ñØ#½êÃTÏ™˜ûj—9ç_tEæ ‚îNsK[êÈ„¤›á ÷;èvbÏ6HgL98¥–8*BÐpG˽UÈ´p%Ô~UÚ¶¤× ,TPÛ=s'èiplI\ú‘¬·™’u½êÐÚ à„øÝ~áøj7ÚU•åe !D²ôJeE.>É%è·(ãÀnP<ˆ'[U;6·™µþ¥Å¤Èr]<ǯžv0ä»ÿNkdý8r3³ÇwßPòófS¯»%콌rPd{琢…}L•éÒö°!¶H¡[a‰J ûª-gá*. RÖ^óëÈ P®œËJgÆ>Š~0Ù‘³3VŸÂÙ©<½åfƒ¿}øwô`Ah·qŠ`mBË#î‰ñ6¸pŒ”Åŵ`þª ¬¤H€_µu+Ö,/Ó¿ðbC…í(…¼e1™?gÈõjÐ(0å ñdq>‚¹oÅád†UÈ=ä– …Mß-JC¤÷dc51î—¨0ß…zÍÇ-GþÑ„3cMáEkPœ¬ `CZè0úVD¼©ÅüMÛ2<8ØLq˜!RൖH7>] ì$X=‚ ¶êàL"Xë2÷ã‰PÂÚ'M-ÕX]ïDäu(Ñ÷Èùk&õ)"²´¹ÉJ¨N¾€3Ñz»XzcÓ/üõwÀ]òëu¯[n(³Ø Tذ… ¶Ýqà9«2•ãÀ»ß/ôP'C¥êI‡Ä¨ÚóögqÃjèZ~ׇ>W½¨„ }ÈVZ+šz® ˆÚ„6¿ë˜ ý«îøLåRJkȤ÷šúlYU³õ³œ‘ä3ö5]Ó†BRü˨4üwîýÜÖ|AOx¬£âsóSÚ•~© –°ys®|“Ñäºâ:tŽ×L  ­)»íIšwôîæ:ŠAïípÆè9ø1ýN(ÕÑYÛòqÿòCó$GÚþa²ˆ_rîñþKÏÓ ï›p*°A€GÏ Óc>iµ—HûhåŠÿ;½¹ÿwÖi C¿u>ΦÑT²×çŠNO{Þ?=ݧªÀg6‘ëìÞÛ'%ÚGõ‹Ì¨c4…{pGÎ4¤êÙF…Æaâ”´!ÐËŒ·‰¥1·в-.Ðõ{˜ªÂÇFÛ„+bUDÛØ9#\¨rÕd;WÒDj•Üœ)Q§"Ö‰‰­Þ:î¼p±,Md b©r\Pba²¯u“m"ŽÚk8l—0¤bç1q2‘lMÖ­æŽ23~âºv2_³f:2Â¥÷<„£¹c³Ûô×y «1žô{¸D½Y¸^‰pcÆ]šmÖ=É‘+üÏl[å+¸‹õó.4n‡3ŒÂõ=ع٠ݓ oÒo†6´8¾T©y¹¿wT¦6”@zÈÍ´íÓ{(]Ù,9Ÿq î³m|ž±K©2ýò•Íàú>¢MGÓKm,äy]fÁR1h!êoÉ‚Íú8Ì¡ŸCߌ+ÃßxÕÍè^ßá5Ã5»È@Ǩg“¶ß¸Jb³e¯!Å0Ô„½pdLPBÔ’½Úk> stream xÚ•UÉnÛ0½û+x)"5ÃUË5@S §uÑCšƒlÉŽÛr¢¸Iþ¾3œ!½ÔAÛƒ¤!5ë{á ¡ÄçÑÕdtym´ÐZÖÞ1™ c­,•¥qÒT•˜´â6û>,smàYäck|6¹¹Ëï&7—×¶8´÷…¬UÞƒáÔú'Ômà5ƒçžŒŽƒ:0Ò&}ÉM™‘>¹÷Y³ˆñFŸ&£Ç‘]%´ðN”º–Îb¶ÝÞ)ÑÂþP²® ñ´ÖÂ+ß•ø6ú:Rï—ΊÊJUÊk~ƸX@ƒR.†|ìl‘VðÚâÚgo¹©²~G Òâ°õÈcúM W[öÜOYµ[“~ümÙU7ëø|„ÝÂxT½ø¾XcyëÐÿÌ î«TVíφä)C‹6æÃëå^/±ßÄ~ø†ÿ W:¬”Z˜7!?Ô#Ý]Ü‹“ì ׳§ÆZ$Æýï4+¥ªRCl¹Kà ›×Ù}óÚ:[,‰¬*ë6´3ôŒpǤ¬¤Òúø€,ƒ‡X3VYCøö±áh÷ˆ@p¿Î Dÿgý1›:{¥Ž'Îk GˆEr.˜á_¥šZjOWªì*%U&æ.öî2F¼›%‡Ž<Âþ†à*Î ŒüÂêá,ø$€——eš'ˆ­:uÖÞ4…w –)â@F1¸—Ã]‘ý†¾/hÖ ÂȼÌI\sö'W,ß(6{Í„sØ2ËR, ÿÍw|<ÀtAŸùBÁnâ ·ìñ(tñΙcHuj“8ï÷b¼¦ã={z]•ÔÊþëm}ö–†)­*»Ú×D–†°ö4ìo÷# “ endstream endobj 120 0 obj << /Length1 1928 /Length2 12468 /Length3 0 /Length 13660 /Filter /FlateDecode >> stream xÚ¶PÚ-ŠwwÜÝ‚;w >À `pw‚„`ÁÝ]ƒ»»'8 Gîɹ÷ÿª÷Š*˜Õ¶W÷^½ZJ5MV {3¬=Ä…•“C ¥,©ÇÉàààfãààB¦¥Õ²v±ýÇŽL«rr¶¶‡þ+BÊ ty±I]^•í!W['7€“O“_ƒÀÅÁ!ðŸ@{'A€4ÐÍÚ ÌP°‡€œ‘i¥ì<¬-­\^ÎùÏGƒ9#€S@€ŸåÏt€„ÈÉÚ(]¬@v/'šmšöæÖ Ïÿ*Á låââ ÈÎîîîδsf³w²ed¸[»X4@Î '7à–*@;Ðß­±!Ó´¬¬ÿrhÚƒ]ÜN À‹ÁÖÚq~Iq…X€œ/§4啪 È_ÁJ°þ€“óŸrgÿQÈòg2ÐÜÜÞÎñ´†XÀÖ¶ €ª¬›‹‡ ±ø#hëlÿ’tZÛÍ^þ¤ÈJ¨€/þÝŸ³¹“µƒ‹3›³µí=²ÿQæeÌ2 ){;;ÄÅù~ÒÖN ó—¹{²ÿ}¹6{wˆ÷Øbþ£ Wvmˆµ£+H^úï˜òo›%ÈÀËÁÁÁÏ'9@æVì åéúÓÉù‡ù¥_o{ø¥ ¯5ôòÙÛè¸8¹‚|½ÿíøo„ÌÉ °°6w˜,­!È¿«¿˜Aà¿ðËý;Y{ 8^äÇ àøãçŸOF/ ³°‡ØzþÿóŠÙ¥TU•¤•™ÿnù§¤¤½À›•—ÀÊÅË àääðór|ÿ»ŽÐúo¿så!`{€À_t_æôÊnk€áïaüw-ûå‚ ¿…nÈÁËaþò‹óÿYî¦üÿ©ü*ÿW¡ÿ/#YW[Û?ý üü@;k[Ï¿#^”ëêò²Êö/»ùßP]Ð_«+iokñ¿>yàË.H@,mÿ£µ³¬µÈBÍÚÅÜê/¹üe×þcÑl­! 5{gë?ž+'Çÿø^¶ËÜæåùp~ÑäŸ.ÐËòü÷‘2s{‹?¶Œ‹—trz"s¼H‰‹—àÍù²Ž ?U `gƒØ»¼¤^šó€íÿ¸Q>^»Ä¦¿ÿ‹Œ~#»Ì?ˆŸÀ.ûqØå~#n»üoÄ`Wú^ª¨þƒ^¿TQû¸ì¿ÑKÍ߈À®õxaü^jšÿƒþ˜»Å¿à 7Ð?ðEøì`k·^Û»:ý+á%Äò_ðåd«Á—Yÿ ¾°Ûþ ¾P±û 9_¨@þ_¨Øÿy^b_¾ þå~¡âðÛý27‡—•°ÿW/œ/\þÅ”ó…‹óïNþ@ 7Ðïy_Â_^Žß /5]~»_è¸X9þ5Œ¾.îöÿJx´ëoÈõâÿW9®—žÂÿR¡¹«“ÓË[üç;ñ"Ñÿà?~Èd޼4oo.ò®&¤í®J‚Äuw\d†vW7•‘Õ{É©Ýõ':B2cå§  §‰ä¡ÌÕm†kñeŠ'ï£æ:„ð–DõÖŸG“x©ÝVäÅIüþ‰/Gµ}dH¤¬Zâ{>OŽ>:6°ÍÐ ´ÙŽ®¯ÑÕrqîÜ{åûÉ)X¬ßÆåD+<ík‚-yÈ5ƒ|ÇAøAÊß"QnŸ€ß4}]³¿xü"—YŽ‹A·7Ðí¤†ÿîÂwbB‚øÙàJë퉨Ö$é5p˜ÓŠ2KÞôä³î`ö³(4Iñ‡)Åw£Õ ^èÏ_U>™‹- ïÝ‹£Â|[pÔÚu Y´(1MŤ¿T膼Q|ˆ¤æÒÌ­2H:êË”4Ã-S†²©á¯,¿¤DäÒ7…[^Þª‘ Ž×MO¿GùnQ)i‘à3%_2yúÆØý½2FEéˆZZúŒ3³”[71¯+‚mtòG§>ßBÅ,™pª‘òÐ1U½hˆ!Q™JÐÞçÔ­`ê ÑFýwÝ7V¹Cõ̩ڿÜ+Ÿ/¶‘ëhâv¾NnnꪰIå3èLž©ìí‘f·÷4qc¡ÃÍJˆ]Úš`:w+‡©5Þ– tÄx‚5=‹6ç;;a}5p%sˆé¿QÛŒ«RqušgTqµ*Ž™ š”_ÊiéV›2.ô%ll¢€ëêšEÞa-÷Òf[!ç.Û÷»Ù~“ïN쵫.37„ÐJ>¿9Æ–4…gîÑR‚ŸçFø±„˜!=[X_8’o1Wqe6½z†ÙëK®=‡š±ËßémYb¤àB%îy.4¾$Zq¿¼Î\àÜün¶îss††Zý'^8}ý 2;sûðê¸À¡ yC»0¨0?HZÒáî êM6‡!ÑX 5ÃéÕâü«ä¶@ÕÞg?Äåµ±“˜Œò. hX!è†aúå=ŠÀÝàê`´Gö|sÝÁÒ©ODE¯¡ƒ‡jô3b…´Œãä¾ê]šîL¤q8ÇI¤œ¤"[ý!P­á׿ô-þ´gÄ|ä %ÔA0HF"†·«gí`ÚØîúÆS€K³”¤#­ÏZ™Ò]‘6«»¬˜âP_‰zšÛÔã§åz›>¯zTˉ_FyHêÌ ãçù¼ý}uº³E¤…y¤DG–À|»»j„hàµMŠÕ± Ü ¼m+Þ¾1n˜¾[Ôx£Ò5€6Û)xžShF3–e!8imŽ7ŸöîIêA™kÇt”שB)ö‡`áNí—ÁŸßÊ;Æü¾ý2ÏÆ^P6E*úC 0U;¬ôE”ýR|S. ·¸—Š“ÜŠ°‹È“ØFöÚ £È¬@xwOdƒE„*,ÊòƯ¸tZ[´Ò {Ýñ•AŠ\+®IóÄl8üG®»¶8»të½ûñLN(§êäâã«>QýÒÓÑu±‰˜±×[júÐvqðÔv iµ«©ô¢÷|-$ñI÷n"V2]&z ýžqa*w© Ä…û¼ó¹ëz<%p­¼8”±ç¨zRðU2ä 71¥˜†‹³Fz9¸„-)äuK¶LM³£$‚oIëYEI†øo’›”Zêõ?HùÕØ^ß_—Eb·Z¤q~dü °¤dÁ@n_õýƒ®>Î|Ž À­ã èHVÿ`;$WQß°…ÆE)Díéȼ¹Wz6­P—ì/q´çvûŽÝirnùS°>™&#OxžYÕAÏ佋€7Å VCŸFØ*[jÝ=µââGM'ÉÝʬÓÿÍ=·µŒoHP½F¾Ý±×m²Þ ÙÌ#¬L67Nh¯ƒ—»îýwtV¤ÖÀ" ‰<Ü^}¢Ub |IßÖ°oà˜ÑXˆÈ–9/ÅáJª{KvÜNW²»Â"1…äÎJÅÌSK³ž/:Ýü%U„º$®È¤ Ó&QéòÊdˆU¡=iÅŒlzJ*×ai ¼YÆèÒ¿N¿KmŒÜa:fa¡¡À©RlP-2„1®”HÏ KÒ‡_Ç*?¿ëÚG(ÊB]$9¦“…!Ð y^~/·5èÖ/fª)øíHPä Ñ?Ú0„qç¿%Q =²&üöÖ2ø±¡üj`&æwæ[Çæ|ðëCšŸÔT ÄIì92² ð:8$'%6:k¥»8¨UÍD¦^ ðz‚F¥5dY8÷©èù=Õ;=¸V=gØ'e'âÅC™ÊºãÌ9¥GªÊc&0!s%´¶DDüÄ0 *‹7v×¶Ê5Iç3òG¤ÈûmêAœ‡'”¡ä!×|‚(³Ü&öÄ ¹ÎÚUjÀµ[ÃbCS³Íí9š'‘Æ1±8öâ[á {ImÌã%þ ¶lÞâmBɆm y@Zm¾ŠP‡g›ŽÌ‹Ór¯ð¨Ì1Õ&Y$%ì8š ÏÙö¡²míÁYEúŽ,¶EÓM|:Ïú{ƒûþ´'õ“Eú3LÏÈY&­óð†_†!,è>ânƈ!ælYp÷ÈÕòEl guÕ;4{w[.kþ3¢x`ŽmÙÏ>gàÞ냥¹–iHpë•V[ÎjÇQþ¨<´°iói «Øéµ‘ë#|x9_Mr êí¦=êBƒ+ý™{æSåGÏ@(ÙÃÔu‡¯u\ä¸Í‰½¾òLëu’R¢žbéª&CÐpm<±·’«Ídö³uŒB8¦uÎïÍX‚©Âíe9,Ìâ€Ä-ÇDÏ£Yë`9ãÑ2›ïý¯c‚«Èˆiõ¿ÖXA ‹fÄÉ#æsbxÕY£VC·ÎÌØã¹þ‘?ûMê3>÷kÖd±]Š<­¡«/¦F~5ÓÖ]Êr=Zbægå¯GÑå*û.® rgIÛ Z™~0Ìrrµݸºµn– 1é8ÀC¶—5™J3·¹Vɘº£€˜æþt[À›cäJO^IKØ¡¡>A㻟òþu†øXº¿UÑ`(ëv­}¬K†Ò`{ŠLëg`Y7‹yúŽÏ/[ eo•*!ÁØÆVªæÌ£e•–ôã$-¤AwNö“*;®2fšg¹ÿîì¾ß}Çö“âe kùÜÐ@š‚,©­ &à’>£‡’ýÆ+ ë,hhýŒ¨Y#»Ýˆ@¨øeÕ‡ÄÀ€Šë6FK(ë_ÍÈ—´¬0 ÚýÙà*-=þYv6âPópü¨@DíWïYn­øÏ^+çòL’"ynÖn¾ûeçäv¡vC&šhàqÙºmrÛZ0¢ôµ©7 ų4¬$1¸êj‚cJCœÝ|¤.uá[j1Ü…8¸pã·¸íTû²–§õ)k*îº5¬–gßxЩ<ÃlLÏ’Š~Êg¾¢ëmµ86t6Ä1•~8ún¿VÓwú׉B¸Ê”sXä\ÃNäæü;¤¢õZ?¯ %oG C(i+Æ(A/M¥5OÒwÆŸ{“Èê¡ãúIɧµ&ý&õa%ð~ªqÀÅÁ+¨áû1ˆú lÑ}Í_wëÞ2~¦B †ë`l×ãÝ'±‚U™ª`.ÖÓ~à„Ûšwƒç\µtÛàá«å|sDùëG½ÊÕS÷»Ü-U²¢vÜ¥³µçÝ/Í ]rd/=/÷jw¯/èμ¸«0ëG*$ä:6!37oŠ£œmÛAX6Þ)CdÀò¨v(Erd•Ú¯ÊëÞ¢W¾îI¿S %(Ê{ÎTÄŒ$1êý"IYÒG " €y¶O¿ ÁvcÓå-¬6ØóQl—†Ý,w›Ö\£.0lÒFùŠ)xû2=G÷u$Ū †«‰Væa:g*Ý)苊1]¡«¼Ïõ}óóBo·âQ*:m»d½KXðn>íå ãгˆÛ‘æé;:ŠW™âGâJEU² ó-û\jÔp {ß5îîy_™Ô’F¾ˆÛ6³Í ‹á:uU¦l;4î]"%‡ËwƲü8LðùõÜìþdøÛ)Hº²û®žð'Ÿ!´Î»œ¦óuwB.êÖaAŸ‘É6¾õœž7÷Uù³6N~ò!P21ùvW³énå•f,=é%A›ƒ˜oCÅS¿9‘*4vÑÿ¤à4Í—à—ŠõÓc6ÙKíР–Û‘Ðéu1Š”^·@먣DšçØb tÝ€[ñº2Û!¨ J­"9*uz;uRP£Y•Õ@ûcŠÈêrX[Îjì=_Ü¢jÇþŸßfuǹª— ïâ Ñ½ä*OÖ*ÙÓã¥OOžàvÍgª%Ñ6KÓbI>¹}ä§Á#ÉdòÇA ½Ó¸zÜë˜f=_¨êO'¡¡{ ]ìV“ƒ,[[õb¾'xkû¢ÀYáÚ8bîÙ´G}Ø_k[*´øëêðÆîã„kÁQ_½3èdÊ£smDßI.ÒI%‹ªå}ò“‘³üæ–3*ÁŠÅÖ6ù€ØS¯n»ú&Ê ÷Ï¥Z?»d<Ö|ÿÆm§Ö7ÕGÎ)ËËú…»e%l¯Œ~uä S6àY1޾úd€ Þà•Ø®†q¯l„{õ£÷±à Ï‚ß>¶¦°XL…þèä³ÿzR9»ØuÐL,ÓóJnÎs\Wè)À¯gŠðÝ[—ž¯UA¢èAîZý …ÆOŠ,†²nË7m;à•²b-ŠD£¾àM6QG7Z|¼½/îh…¬zκ`”f4~b£š•&þøÞ²[óSºvžu-ôëRÂå³:>#ãÛ¤sùѯme‰Á®¡LéÂÝ—[ Tg‚l°G~ãiwˆtåaKõZHIPJ$*µr~"ÌÀñv`ûÏ…°V†:ägäNØá™g'«¦‰Á=²­¹’Ÿ®4³ó°…®_E''‚Y).‚œ¢\~ uï)ÿÔ>÷«>&f<m\"EáÇ6 |ò&ž›t¬´âôȪ}~Á¤Pž»™Ç¶#ü`ÊÇ]v»äžáÎ~’2ßÚ8]Îðq¥ÜŒ¹£”çΙùn'n­>÷uPÖiúËSÒj* úÉåÄj…ˆƒP2[ÉVû꽑Y…›“Pû&oÄUõV‹WlIËwÕOõÍv˜m“GÜYŽ Ìªd\(EWÑ!i WŒGF¸Îh)Q¼©¯ 1•ÌlÓ• lñšÆB¿wð;^QÀçõú²ï­è ¹ßÛn³SIFTøÂ¡0$5hº¹ì%_öëHØ•wi+ÎŒ*›YYTBqçÏâÐt,¿Ù)eÓ=•Õ@åOšægñkÓgJE8„ËúÝÞ[ùssŽlΜÓiø SÁ u?ïùLQ\ºRô:↠è—ÓH¨N{9IãÅÑ^-=ëëÀ­FååPÐèeÓ¦(Gà ÙÓ Ê™j5büCI]Á-øò„V‘þ!oOÎb}µ>S~ál`T\PcCUƵ;šJuòxÕFx±U}{ßâM{~ìjB†éP3÷Ê€ ¹¾ 7ÏÏ”ûˆSÔaãxTþîÁÂ^k|à0nÑè['¢‚Á௪5ƒƒñN:jÙ×ÖÛHÆ ‰ÑÐ!îS æÖBÛÆvÉ‹On8g¢;’Åq\›äTø?ß-û+Sk–'@Î6Á¿x8àqÊÅ‚0®ÊMì?çaOÅ+Ï"Ò°ÂPTƒ'—€•–û0 Ûê?ä l¯®ß{ʽ§ðzœX÷è]R+SÕÇßV‘yKàzo‰ /ñýÍå£éÜ>Ú+²·¢ngçT"ÍemB!Ÿ”$üï+ 9.,ÚÀAkm„`HM2:É6);‘­ÎôiöÛå/ŠÓ& ^]ùCjh"æ€woÙäº}_µ85 É ¶lZËÚÓv“'&,(ˆ¼%£kB2›÷ý·aöË€&Þþ×÷–¦ñv¢SׂÔ:O:ŸØ¡ìóo^ \0dàÞÞaTÃis´Š½²¥÷Zί#Ôl7´ù>2+Å(µÜM¤ÝpN'¯F”¤¨k×Ù§1L™zW$D÷\weKœ”È”¨m‚bÈþùG8l h,)½u©­©´Ê¨t›qÄORàZåã¼Õ2Z8í<‘ü©§Îßí¦L¯ Å —Þ.ìólÄDQr¦6™ÂjþŽS’ؤ:öÓúÑÍU‰¯ÿqz—jgéˆÜr•R{ú>×N-µ “<˜Iç8eao„(¬ÐŸ­XT+ÓÅzà1±“ÞýŠÍXÀ8`í" ^9…RF!d6\Ø`…6¥4’ÔL•\Œ{íB·wÒ¹Ý(@Nà±²ìû’V„Ì»…5uR’¨Ïcg6æ#Ðd*<y;K4è€ÑõˆŸ¹†$õ‘Äý€jšÔõÁ³“ãñ­D !™dÛß›T© Çи¶i3±fF{¯ãRYi«^*‹Óý1%/F@n´!*4c_hóýmÂŒZ]/ϯ)–ƒ©A :yUR|En¿Hœ‡d2¨rÉäƒ ÜÆSû½½Ì(MâÜTs@4Òâé×–JyΤ©”š7p cÉko¹OKñ0<ÔÊ-…+ { í(¹°öxš7©Î†‡X?ˆ½wæÒàÞ—kö39¦ÁståwÌ#wDÖôÒ ç®E³à1ˆ…ÛÀ‘q®å¥"ÁŸZñÿ4(c Hm[crÆSGšÀO4†`ìßB‹ GÒÈÙê{ó6öÒÒ¦Q÷y ïö·( ÁCët°¶Šh“CžŸ}âèµÀ?¶»Ýkà’§Nøæ.é Þ„KtEÔU qýšz–ÂëØÙ† :-]$|!l›|—ÝùÀ%|ûˤ5 ÈW[UI‹è‹wâäQP°aN•æïýDa ¥Ž¹;–8ƒïÛ;LgÅnœ6¼#;pƒñƒ†ÆÒê+¹¬Rõp´—.šm›ká !o}Îa~t Îgαâ upO1ñðH¤ÝÁÁ ºüqbººÄ¢‹WâÛƒ‡×\È·_Ÿ°pº¹Ï¯¹®oË¯ÏæÜ승ÀÄ€ü@¥Hw™î­}aºÒÜ•TÉÜ5 œ¡LHäÏàKйÜj€ˆýL¹ª„3cúJAj]µ´ÑÔà3ð¡uP'EÙ[€îéƒ6‹IýÙ$m¬½ø‡zÉ{³¤¤M*É4 PŽ€ÿײ8Øõ¬6âøb¯k:Îp4çÝB“$ã ò¾žÕî•ÝAøÄæ¼Þ&Ar™d ¥jz$Ž,ù‹ÿ%ÛI›^ÐrÆÏÝ·Jß3)•‰e¹§çËgèìÔµQ°„‚õX"žXÇ’î`’ª">LPëN,J~,+:Ã!©y½þ »ƒè£GcÞ¤œd¢<ì$ üÑËÕˆ>3üÜxàWGcŸÈ–”‡õ,ZyþÏ&qÔÞaæNIÆ^æO‰M×(‹ßíý(ú«±OÙß–Š¿S1kp\öÖR©FC>ÁvÆæ´'ïÞQXWÖïFÕ×t¨šžn7SjeÖ*ÁÇ%üI¤ 1¨èƒÅvñ1¢óIµoã2fGQx³ÝÇþÆD)­ŸÄTp®û¼ò•û å.å36´ ¤çL1RS`‰kpõÐTsÛ͕Ÿ²GÀ-f¾¨cCÙ;ê/_8…—´·¶Î*=ä]@P¢‰Æ8¬%ÊAXFi™€ÊeÕdaê_Ÿ<ÍH®}’wy_Æ×4'ÿØ·gNZVE]†\æ3"CJ©ajyãÝŽ6¿7å߯•µk¿­Ðâ€Y÷º[äÊÌçÁç53ú“f¹»V)ÊÛ‰±°V.œ§ð“·UŸˆ€Ôk*ë´ÏÑ\·Uê,[Uç1Y*ö´w^§Þ}NˆgÚ>…ã€Û>iÒž Æ;3“>ñ$øðN~¨tP…®ckÀÁ¤i'x–T_º‡õ7Öõ!t÷ë®Ó…ÕUǵm¢¬õZÆ_$"eÑšjóÒ5Mí‹rû{>#©”W¤ºÕ/=ILÀÑd›\¢:°#ïòÂßÕÎ1x æÒíÜ8Þ! )'}‘€)ÌŸ_0)s´îCGîív%?ˆNÀ™EÒhÊ Ê^9!žÉÞ†ä\ú{¼üWüNñ£êû±Z1GÌïvRß+XéÞ.]‚¯Êëßï½ûö‘ÿ‚¼™‰‰ì^bçso`/O‚cOŽYㄆ‡bq n ­m§fDWvZx¼CF¡NÖé“)Vßz”ú]εž½ÎÒ­Oÿ5E/ûÌò-@´¢´¿ÅI)˜ÉŸ{)“O1‚Ρۑ„=þ2í}©xI$CYëqEÒýîiV‹°ÅWµ2¼À´4~a· §IQ$õ6¡ní‹×ØŒÖ×·ÒA'óŸR0â òGxŸP™ÒBÝö9(ÓU<ˆoÔÊvÃá1dÇû«á*W¢¾°Uœ3‚€3$§;²š%œxSBÈô~ƒ´¢µÍÁ‡°Ž’ð|ÞS«×ÜýÏéôhÆÆÂç'Ǭçìa2!Ù‰z¶`ZΑ :7jÕ1j»"¦³ãBO6<á•™^1À¼þ÷†Ó"c7Dy;â2¿˜X¿sh‹¤EFCŽtÞHÐv-u}ºÓ½²*ƒ{8ŸÚˆqâo{ƒ_·ýT%?˜ì¦ž: Àí<-MÊ«§•k* Blmý?vPáYóë"nÙ Ö&š™Âg£+\˜æŠg>m]ó=£è£ð=ü$ç¢üãG㡯ÅoÆ-VÆâò³·»¬Ôo(ÈBMãÓù}šea¸cõûbžŽSÝÏ7k骨Șï=l†šVb Ax˜5_â9uT‚fíÕãP† Ý2õ(‡yq+ WV"Ú2•ì,¬Ä"!å·{w’häÙÁr—xAJÚ|J!é¹b„åçåßôÊz§í„|ÚÃ(¤P¾÷‹©pqò»õ‡T~Ï)‡ðÞ¢Äõbñךյdò±žÀþ‚)øþácl m´’äÖPäzhÊÓ¡¾ ÃìÓ0†ØÒóë¯L f9g†Þë0iVÇ>yº+%8ñCœ,úm“;‡æ»ÓîòœC[g(aÅ9÷G ’6§×xìlâÑa‚‹îtÃeŠ%9Ç¯ÏøÛà!ºZ>RáV&:õUíp46JCí¬¤¬ïef¯å ÕÀLöæñ¿&k¾“zcB¾YF–Üö †rZ“J×mÇ-„Ð nZëîM7¸tï$“S 8Z–Ȭg° ­Šz)ý †‚䈟ÒàKJ×ÒYÏ”_#Hù—T5ºø×“›NÉo¦M2×U:L.³´„fZm—y„|ƱÑ"Úæ•é!ã€óÅóyU6T¥€á¬?O=4U¹8&n²d•œæ|_ 3F8cÄU}ÁLeW)ÆR÷ó-ßöÌ2Qo7RK]4Ë[Ém8ûÒ–`bœÊCÊðŽ>œÎ¿Nž‰ÓÏ’ˆÝ{Ñ‘GÂݘ¦ J+pÊ›´úJ!žq·1ݯ«³púXÜùÑ‘J¤€”2U[/ʪ)OsNpÄjºªÅ^RYÙZZ„&’yÖ¡ªŒÏ­:ÿY±B^“Îé;cÌœÀšC»[Lv+#Å9$´ÏÇ$%FÚ>ž[qé^ßòÀ“ÍL}³KµÊ®Ô$¡„$þÐkׇ*£ÿ¢°éXz³lšía&tXÜì°„;¡«ýRð—½+Ö|K@Æ Ä„v!¹¤áR0òËÒ8]÷ÑZ[‘RRªCdyÒ3(w©W‡CR®gù‡d­ãíd›·÷i¼ÍÕ¢VÀ4‡—ø+ôå¯ÝlÓγ³Z½óö»ã±>Î œÆßÑbBõЩUÙMÃÀÇâ£Ü|n€iÃA<¿ð¨OîDÑBrnO¹l&YĘ8 XÅ+3xÀÞB³È5Ûcs>ì£ ¨,'x5kæ¾áŸ?»ïwˆ(Þ\°§*PN@µ’!Œpó÷Æk^~ƒ/„‰"dš§¿LUŒÆÝOï–fB}Vï ææ{™eMždʇw{zË»¢'s„>F•=T •›­’ðišíjþ…d \nñm.j;¸•3Çó^€uÃʵju±Uç\wUð¬Š|w¾¥‡Øš}Ô\f®˜*ô/MH]iŸÅÎ΋B‘>8ã†yÅa wjÚ^(D â®RžxŽ|e#¹øä”V¼Ý«0i¼?¹Â[©¬4ˆå,¹ÔŲúœ~"P‰ƒRˆþÙŽ?2IÿüNÿݪÝýuóo:‹‚æ–ÝÆsk}Ñ]í¶PC®ìb_ýˆ±Zç3X4ŠÊF`v…¦ÿ”pSù ã5<ÞCJ€ö.ù¶È+ýšXeÕ¯à–鎼¡Øs}7•Œ½-sÞe4š’Ë5"PáOjÃ2x8ږМ¦jºÂ£”±ÝìâúÞ9©7|ê*zNYK‰}X¼V©WäûæŠòŠÙ8Ô»­6÷t\MØSRŽ€œŠFÎÐÜNôÕ¬oì[Ë|AÇ”=áö¾"ß3YHò>¹S¦íµ#ÕóAddní~Ý9‹›Ê⃼ˆËÑÊCbwsLOaбS¾Ö)ã¿:j[Vƒ9üÀJDïfÉFºðãcÂmK'ƒ£.a_W ¿ gJö„iÅ(ì$Tž9](6)¼íà¯k-x5"Nôa5LÓcœˆÍìj<ȧêÃ’À^‚²œ¾ýK¸eÏyÚKo÷’ý$œ¯ï‰òb--¤&àëÔ[¸ÖÑöÃóÄé›Bphc|—°Óz‡›uŠf¶:\Èðy“‹Ü¬Û>þ\Z,]|2E.Vt'õmøÜZ€ë’¤Assé7ØÉ©«QŠ‘ÿY:©Òi@2¬±èvJn6Wúebo0ÉÜä D»Å„(õƲK•º°)ØT¹õôûe*I#ºE&ˆ•·J*ù »¥aÓöÔóðI[r#;,Çœ@Ç´ÅŸ£7½f0³iça¡èZý¹Ó)$w¹^½‰ÿy-ÉŸû¨ö“L] ÉXìôg–¾ï¨LÇýY®¾2æEO=Äëk]ìo‚WÂA&˜Öõ¯x;DL P\^5Œ"„-¹E:žå-.Örn¥/б2wx‡<¬2µÝIS‡ÜÐ Tûœ"åËDq'Xg‹Xž»/‚ž‹gV$>·V_fëóW‘1\Yæ„ÎÓfíhiaeVvëR ™ZŒ]5ó¢?ý“"ukf ¡#P´ùX¯×{§'ŸËpRßžH`kó~8*·£ÌùAââé4¹Š¬ÅV2ç'±´Õò‹_çl}‰`+\Ókö„ܱœôD'‚ÏQd3‹Ý tƒ'›Újmб2œãØÃqÍÏnVE\ïöõèâ¢iº6ÃuõLÙ8æ@ýÈ$±_¤7‘¤×†Ñ|³³³®;Ø£_ïð} **0,ÔgYÄx(BÞ ã`#ßÍ ëµbŠ”_MI‰5@¿½¡€Óõüùå¤ù<æ‚NæS‘p“¹¹«[çDÆW Ç%Êž¢ï_ãÇJ‚íÈG›ÎîO“õÆ“:›[_‰ø#¦dLÏÙdýÚ‰·¯Zq•th[Ã"YÍ Ÿüi^ #¾ùKaY§å(%²âƒž1ѤÀ2¤ª˜Í Ì‘åÅ˞Π+vFþ1Æ>Ií²Ÿ¤*ü@”ܪ/C`‰ Ï…åK½%ñ;㼤WŽø'nïO é3 /C»Ê>4Peµwä|1=X$[ÛjdÚ¼ˆTžñ,ýØGRV¾ ~ÁÇ\€i11$òFÜPJœ‡bŠíópö¥;2zÂGŸ¥ÄWÉÎ(èíb•ã k§çÎ~Pèû†„y¼ÎDƒ–ÓÚ=ˆ™ ÚC£u”þJ¥3NÅËvO²Œr.=ÆêB?”¿Ô§r¤×H#⦀v gœ[ j¢&k?]”Y¿Ò­§ãÔÔ‘7šÝ VðCƒB …ËÝÙÌÖwrÞŠ#­d‰/gzYëõ â_x£2}ð¹a01ÅuSîn56_3¨aqÙwë"^ ‰Ðä°¶ ";¶òPQ^êÕT"ÿ‹µkå’¥å¾:ñá5ú…ûê³!U„GŒIÛ¬…Ž¿­{©¨êæ1'o»‡ÕÃ7Öf1%š$ÂêŸg¸ax>¯RIð|yÚëÇõ”…1jH%º¤˜=Ýb—U9Uï±Ù²wàN²ù\âÂoöLYïÔ/ˆDL¶ ¹|-Ê2…§Saó?x/™¥eï–ÓG¿q‚¶ÙiæbRQKã„w:Ec^m2âÑfâ°up\€š‰åSD\½ÑÉ¿ý:‘P5º?¤ý±ÃÇë¡EVeJ7µC›ŧ•”vnöé«÷ùAk¢Ý0®y—™ÄÓ×uV#¥ô™z¢o ÌQd{‡!¯Î½±44Ûƒ¼V¤¦º©AòçhÒ'ºéSrãÚŸhtå¹£[1…\b0¹ÖÏå±µæóØ&D1dªkÚ¥H3}þÄ‹3YÈ•öÉågn—?6´$ŒHo81‘å{¯éôÙ…ÄvÞc²’ŽK/³ý±Š™ïŸÞ~­9¤·lüáô%hý˜y¥Y š…S; <Æjó s&h²§ê½êûÛ{Nò²Q²†“]nS—£õHØ iäÓ~[ fØ«®+|µ~üHóS&•¤ fIøG¯ÄU¯…£}¸O¯úU %ûë¦-êR =ûS‰âǸ—ºA­"ží4OY©Jén{GÖq~‚Êj‡Fû8û ü…Œ1¦_4PQC¥gýª‚H®7{¨fÀ…çU‚*)ê­Ô-¾‹Â Ÿ`réŠÙÌçÂc)@r‘¸E¯•CŽmôªóX|þ2LLÙe0¦Ý°(´q;I|fÓF?O“¤/‚9aÊi ´©[°g×2hLŸ=™ðçÑr’Ù°fT¦q%·8e ¿ß/Ô·o˜ü5_[-_3ÌnxT$)œã7§ ÅIZºÛ·àÆŽ·ë÷:cZ7É£ŠeiLÜ c3W Ƭ%Õ±Á´H1E…cFtB4+kÝ.ɤ/ŒŒÜjÙ õ"öŸ°ÒX². É.c'ø6×Õ“^w…·d¸ï^üTÿ; endstream endobj 122 0 obj << /Length1 1735 /Length2 12328 /Length3 0 /Length 13432 /Filter /FlateDecode >> stream xÚ·PœÛ¶-Œ»»Ó¸»»ww§qw·@$$¸»Ü‚»»CðàîòØrÏÞçþÕ{ÕUÝߘ¶Æ\s̯ª©ÈTÔ™Ä,Í€RŽ 7&6fV~€„¢+€••ƒ™••ŠJÃÆÍø·J èâjãâÿW€„ ÐÔíÝöÁÔí=NÑs·°qظùÙxøYY쬬|ÿèèÂø`êacPdÈ9‚€®TŽNÞ.6VÖnïÇüÏ#€ÖœÀÆÇÇÃøg:@Ìèbcn (šºYÞO47µ¨;šÛݼÿ«­ µ››? ‹§§'³©ƒ+³£‹•0#ÀÓÆÍ tºx-4 P2uþÕ3@ÃÚÆõ/»º£¥›§© ðn°·1‚\ß3ÜA@ÀûáuY€²ôW°Â_Œ€¿ïÀÆÌöŸrgÿQÈôg²©¹¹£ƒ“)ÈÛd°´±”¥˜Ý¼Ü¦ ‹?Mí]ßóM=LmìMÍÞþdn S˜¾7øw{®æ.6Nn®Ì®6ö´ÈòG™÷[–YH8:8An®ðû`ã4¿vo–¿&krôùþ ,m@–4aáîÄ¢ ²qvÊ~ø;äÝ„ðÍ èàbeeåáã@/sk–?Êkx;ÿt²ýa~ïÀß×ÉÑ `ùÞÐ߯øþƒàëj긹¸ý}ÿíøo„Àư°1w˜­l@ÿT7-ÿÂïÃw±ñè³¾k ÀúÇç?O†ïò²pÙ{ÿþç|Y$äUuÔÅþêø?>qqG/€/'+€‰‹Àö‡ÈxÞüÿ»ŒŠ©Íß4þ•+ ²tðýÅöýšþ‡±Çß ý{9èÿ]KÉñ]µ@í?"7`åb5ÿbû–úŸ)ÿ ÿ£ÊÿMäÿ›”»½ýŸnÚ?ýÿ·©ƒ½÷ßï¢uw{_EÇ÷5ýïPmà_K«´°qwøß^Y7Ó÷EYÙÿçm\¥l¼€*6næÖ©å/»æ[foª8ºÚüñZ0½æùÞWËÜîýÕáú.É?]À÷Íùï#%A掬;7ÀÔÅÅÔá}Èïˆ àËö¾‹@¯?E `a9º½§ÞÛóX:º ü1Qnn‹ø¦?€Eù?ˆÀbúz÷™ÿqr¾£÷õûÇÿ{‹A6 ð_Àbù/È`±ú|/gý/È`±ù|'b÷/È `±ÿ¾‹ô/ø~®ã?<ßûsz—™ã?Ì8Þqvw|ŸñŸ£ü'ñƒË¿à;×Á÷Bÿ~§äþ/øNÉã_ðýÏ û;%ïÁ÷«ðùþ×(ÍÝ]\Þßf.Ûûœÿÿùê½€æKóŽæa¶µam÷Õb„žL»ãB3T»Úßé˜|—\ÚÝQ`¿ÒU¥‡l¸ÜŠ}êF[Ý–¤½]&}ñ=j®ƒhITm}ò{6ŽW›ÚmEXœÄéŸÈ?ûÑG OĤ!ºç÷âì§lÙ þSŽ*ÛÙE%óÞ³WÚëG_éÊèÇù]Õ½*nyÄçÒi¦X̓à¢Yª³Œ9kåì®ø”øzxÄ7£SÔ¾âÉr¸ ¾Å«ý‹^Í‚¤YÈŒßV™Ð˜ØÓªlÔ¢A=5£Kl;Ù_a4„ý˜;‰5%XÆ.äÊØU­ÆÑ˜n5v@âƒKñµ¶[­.KÛoDË|Ó?޳ȺÖGAM]¾OuÎkCÃýLw¿µÜ5owùk‰ô‰Yy²ÑKZëG­®ð§ƒ.uƒèÔÊb¡1.{|å}†6 ô ¹$`ÛÊgeÕã…î%:åÊö»øì_Q®Ô±9‹¾`ï‰aúÊ­ôföóÛùy>¾5‡¤IúGÚœ4É®O9ëq4ªµÀúÙÐR–óÍ æ%Þ¤ [\‹”œ²Ò=» •CQ!Þ¶|y̧Zš­_¸òTÉvŽ£™ÆêƒÅCÃ{ùn-¶nËÊİ;j=œWxFŠ}þ¥Á–ó©C-(«ÓU9EZ,¢ðn{®ÞŒâDC܃̡1U[Cèq{¬³ðÙ“±TöMÁ’bŸüoÁ¿%ÃÅ=øÕA¢8ŸnƒØkÛ•ïiÁÒÑÖ>S¥ï­@R·5j]¼ˆªkÜçRøl‚õ,Lm“LmSúÚWqö6¸bR íã©É¶ƒR¯6„hc¢^-£šÖ5=Åê–‰jÒfûǃ£Pâ°í¬XÂãÇ2€üšôu…¬·ùÄLÛ-Ÿ ÑGÒ¸&¡JWƒÚu¬Áð2!ƒN]pÊ"‘š‡ð“{ÏÜ_³¦½¤sTAe„©Qø;6UTóI™d S ì`¸5ú™©è×Ûtd1–á:!pæèÑp`Û‚s4XƒµqÉj˜*’9daLü5è4蛯“b(r¨UÌÐK½µrã`rn¥`Ô=¾<0_¸k(‚µ&{&;ææe毢YJ†Q¤¬<É¥¸’â_ÏÛ¨øW__]&2„o"rÅ“|„ƒh<š ­ÿµãŠ×KE“mŽøJÈmâ^lY€;¥¹ÉßEÞí7ÇSª¦´M*NŒ[EíÔŒÿ­7Éük€9 •Á†óYAK˧2§™~šñlKnõÙªgìþ=óhÖ â²KE¹Å,]ÖòÑ8_î ¼¤yƒû„K–óP*9¾ÐdR†Æ´®<óª<Þá]#®ô°¼¯i¤„ž¸tf; ±÷ -J”<ÍÇveÖ­o•ܲ*””öO¿Ë›˜q7õ;ñŽfcCÑ÷pøò£ ÔìTVIfU>wr£ø)ŒîŽŠåì½x>ëî )Ù³Üô©­ô…È}ÝÈeSo|?Ç‹7Dzóä“pÌéðF&î~œ[e¹UP‹”lžè½~Áhy¶'îµQf[(H4¥å/+ßÎOˆh³{‘ð:“)XLWn:ì°i±Ä Š ‰Ö\?ž±&téæ8ú[+=ÖÍûî(cP¾³A*x˜Ï9`CÛÔvR’+:ôŠ6jÏ}¬À˯pX5£b]úMÿh’zÛ›“¸‘ÈbyÓ“aßN¥"ÉžÃO¢%F§.®À²'XŠsÊ…Y*‘)–·q—a/Pé;Ò‰HKѲ¨ ’‚I ‡‹ûÆë«¶è˜€ÞÕ«K»c¬}a&ö(ï稈hŒ9?óM}ã•(¾Ù{Gñà;¨,»Æ¨ýÎÔ-“NT™í(‡%Lê‚4©Ã›„"eÛUQqaRºOïeMÒ¡ü­Åg"F*þô¡Œ¼[N¸µ¨%§-N俯E÷jU}pÅŸ1ç,x½èL § § ½U*v²´]%ƒù ]j*ÈëªÏX\G+wàêZ5» YCåíÆ#4û8̘àôºLêhøØ”èŽsñ%,Æ„ÁÞ—î¨z7ÿÅXÔ—›ƒ@fCÅÔcÊ_s Û¸:k «_¯Œ„¾tg £*N‘aÀÌ)裀ïZé(|p…*Ζ–ÀbNÛXä 2F¥2³ræ¦ c/oÙñ]p°yûùüóD ™>ø†ŸÑÏK]£ rKC=”>tD+JóðQÊ*ؼR'Š-ZþÙs{ƒGÈÁÇI)×_ ÊÃ社å+Ãöø˜¸>mŸK½_¯»»PG³4¦½<¹JÝ™Ë,xÅÇNe±·Yšv‡7Ë$è–@àèõ“)˜ËâGðf¾ÔÏöjÞZscs¯(Í@r›`ÖçÌS_d7óLb0¸•äŸ* ð¬‰>ŒÖ®!ö‘§‘ì” 0ð8ä{zácènI#­qIVŽ­$bce¯6'K×ßý:ÿDK"†d߈9uÊf²ÊëÎ|åÎpº½Dþx%,pÎè3T7E…àØZµ‘í9ÈúÝ5}~C›MÊkâtöàÛÀêÕ!>¶ÞKËû¦~}*ƒÜÂ'ï[‘“ÇerñÇø”Ûžbfq¾O”Ø &]‰ ¶Ç^·R QŠ»b^°I;¥ˆÙhùjðì¼ÎÃÙDû`©GZÄÅ¥«¨BpÍ_¯åÝO'à hÏômQ‚ý…9_·û0Ã0K!]; ùë¥ã%$Mµ~çZo¶õRa«&ŽK8ª´÷nM¡‡R–~i'Ÿâàh»Sá[à;Ö‹uå‹§1Ù® A>vYÖ~àWJ*8t¹Hj$…± EJ7"Ö^ §fçôÄ(±÷AÜ£œe×>ìõ\ ï?Væ3{ËÛ%W.ÌÂÓî¡—Â0\c)4ñ.Q­J2PýÚM(E_ºj£s{`¤¸µ0ÎS4 Íÿ¨N¥ñC©Ež'® ŽA-eþXߥ íyP÷¦óD×èßV³H„T}·¨¡òx0.®)™{|õs>#Ú9‡*CS[/ ®§*:Ž*»´éF•·ó–œÞ©_LÇÓKÝñœm=Ê@ªÁÿ­K3»bÚÝ ÑsA£ã8»‘Â2}…”SÈÁA„£ME¨ÝÏ™LE¨¨W"ö•—ªßX97²K3[ïàœ˜(Tå­˜ækæ€7÷ù>Gbxl)l¬¶ìår&^³}§ Ätq{;uýŠÕÝu ÒÛ€¤Oi*¹v½9wLÃ2õ÷žG„#“ öÅ5‡cÖÔk8>#ÙcÅ!nš9…[¹ª|· dš2¸­ŸM£Vtú …Ñ„j‰ä×VÇ{ðÈ<Ô›ÜÀ`9>àã]ìJú˸Vܧr8"ëã5»ˆÇl ÄáUÛÍBD¸i/þ\¾BòJž•ç,Ü=Ï®¼®¨·ºaÀExDJѽ£øüïòDxÒä 2Ö‡ƒÆ(©¤¤µÇÓÉ SýÈË{/Î ™ðll¿CXíç:i’y,4]Lrñk®¥«ç½û_2ä3eú0÷lÉã)'æ\J÷|¥NÂìppŽyI—èBt„]æ’Œf|} Z î†CîP~ÞvS‚fR@%G6^Ç12_±ÂJ¨ÈÄéˆ5!©ž<)–X?çö6t­’VÅ7!'ØÿTuŠÿHP–8C×ù±!㡾à’%VVŸɨù†Ç J®ÆVôË«ìG¨$-«ô°Àx öŠª3Äç’$; šæóL4LÁ/΄ Å÷`7{ ¶D^×Á7çø…^çé‚`‘èI6 u†N¢<¶žà„:û"G™\²âòpØW{Ú‹™zJ8’àê.‹ƒ“—<¸ö 0Ê®geqÝ4J&¹bS¯y¦ŠÓ=VÓÿg_ï~Ïu1 –&`Šl$Y÷ëñ!ØÙ,¸,ܬînñL¾<ÕydUÓ1ÉSšøÒ¨¬!a: Û€âj¦ÝeT_õ£‚u©.ÓnH€ºË«·)@QEWºdnR€„*–Ȫd W ':\”+(amcŠ–á>=^1£'´q背mD.Ú¶+¶ vˆ×j2Âs·ðòä|UU΄ݷÊeû^êä* ÁýåÔêKà:Ë©)Ÿ4“ÆF‘ÜmYs0QuêdÁ7è¶„S( Œ38C*„È`[«¦”£ïÊ´BŒÌÛ»m›ÿb†+Óœ,Ïë:9ÄlBñuþÓ/KŽä´£‘Âmré’dôŠ}KE×)ØYRmÍÿçÛúÆ&ÝÞúX#Ä Ù*Û±2™Ž³he™7¸¡±Ï¤Æ ѵã—ñ[rM9(µÁ†«”K°¥¨+4Ö"Á\)‚aêx/Ä­'#Õ]¢‰xð©¦åuALX)bæQÛ;jƒ×)Ò^ÆÁ¸€5Q¬˜,L‚t}mWL8üì!<ˆ+MZhdí³_7kÈvëžsnfTº‡}“ra$«¾Ÿ ;™!/Z)ÛAšd4ž¦m;Ý?"Uðº{¯Rº\&¯]}uG°ô7wîaàêò {Y3œËx$¼ï‡ßõF UÒî\ÆÒi°÷õKl…O¨n,1³ˆ¯›±£[盜ŸjØ*@‘ÆO„ý`ö,K­ø¬)‰‹ûÀ EÏL¦›¥åo¸Ê`ò=ŽZ¡vd]œÏÄŠ9,ÅB6tÑmÜ˃º×vz.Û§ÎŽ§1t]Ð~¸¹å‹]¶ U+€\$й¡#1•~aÄ¢Ûj1-Íj±ÞšàÓŸ-rƒSßÇã“OÀCïÕ/1Õ¡Üñ´JlPÕß‚ðŠ&tÍó†’¥M~k³EãR(*•í4¯÷÷³&餌÷ù°…©9ÑýųU7–¡ý¥\ül¿w"¿/íçe½}à°“pÜÄý^‚Àªü˜{~5ò .µßh™›ð”%Ó{êÉÄô<£p9J¿j< ÍdºcT23¯Û“  úØ”„)ÇgÏæRÁ4 Ãßv¸zHô)/j׿/Ð uïL»6eáúù;+ˆÅ¥Ê^D¯Äç*úë0F´pÓ>ï ©“Âõ`Þ䙼‰0À¤®˜ªÛ$9÷tíçk©H‹ÇHk¿‘Áó'úìùÕá_ÅJf–á +mëa™Žäð±KEšðm>SWV‡Vc¦;Ÿ¿¸×IeO¥ö`ê¦ëÏ¿J‘}É„h)®ã!ÿíûᣚ »Û‹bßÀõƒënaГŸ¬Îu‚e^IúäÖÛÄËæR¡ŽäqQ2Ú óá/²ÎÉrz¹u”Îe]’*8¢è3Ûdbêé%±9$†²½ÈŸÍ­pËB7ÉJ9»”ܵêºèŸ(Vð*¾½qŽMÐÜßõ<JáZhI+Ì32ìã¸b—K¿æo±4•8×õ@ Ý‚[Çô¡–?<è‘”û„}(qEâ¸b¤¦e(.Š ÛÒ j[›ì(¶E ¿-ä½ünžh}›óûÖÒ¢ƒê?U$á5ˆÛ¯1(6x€h‰÷ñ=/!IÅ_:Š|¦Mþv†b<^#»EêîÑ`ŸàåÎh6’˜Q ¾ÓïP ðÁ 7YÆ}–ûMµïeò;M†*Ç ™€EÓgRŽ+RõQþR[3ô“ª&7¹xXááå·Ótb­õàn|/Ãêf˜6aº­³¬®zà ûŒH.Ç4ýýœI)4F%êbL›’(ƒ%Õ.ÙK™Ù±ëIÒN þÑE©3Ü}¥7DXu®5ÖÁrºVëMíy˜àvNËf©Öq+Ì‹?4 èŒÔ¡§˜HÂAžþ/Œˆ‘zJM’Ø“à×Uwˆºˆ±jÐÀzŸîÙÛs޶i„†E·“×WÝ_\&q¸RÆi^]±¡š™~Ñ4: õaÒ¢¨šóhܲˆ¤)aû«·ÂÏüCZÆOCÂs…|ô²¬þ83õ例ØpgšË>ýCî;Ré–£Kùá7*ÅÃt’_"yQCž5 Ò=é*Ï­0£F°gù7-l) š«V±#­a ÒÛ\‘™šEä!¥yâ¶É+üT'# öJwz—>®~á!þy½ suê˜QüA–¥smEÝbgqóçêˆì½Bøñóú?,ŸåÒ VàT]â˜/ã=? úÁŒ¸uزMõ§‰í»¼FaWvNU¾âÊyt3Ê“úÃ_Ö…O|ÕZÿò%áûsšjXþ꺠f—?TÈòŠˆ9Ã-Õf ü‰R몸:ßÇûЮà±¥™ä=ó½é?¬4~·­í™•h÷ÁÁ¬ÐÖÒSbRãº|³• 4a‰ öG`²€sûŒØ_¢Ž¾D¥ËsÐË3á/¬i¨ïªDUŠ•JÓ„öâô®>âþ˜œ•Œ‚Õ#ºS)s‘I€]$^4óø\¦ïè%(Ë“ôC…êY FÉ|úRŽÄÖ-ÙßʾÕÛ_“—±@°…FÛ·°'2.O§Y2ðÅIbÁÆ*)w…<ôýßr}ÔmÌë<›ˆ¶d5ƒ|*šÿ¾n[Š×'ÑûãÛ»òiL Ù"ÅSiT&¡Šõ´šB~QéCÀÚÖôËá‹Ì¯ä=Ä Ð}d”Üî óu™Ó“Êè–H!Ä9uù@”œïqÌèõŠzÛªñøfcKdÔS¶JnOÕC-Ø$ïÿö¤¢Ë=5Locÿ%¨ù¬ kÙu£©÷·s„ÑWK°ÿ´¡˜Újõ¸äÚ¸ÏÒôiBÀâq¨_:ìKú0ŒC%Ý'êÌZc#½–ÁåhåѾ£ %rðþqöƒÏ¿Á1‰µU“g¿Ý;–Å%Í×SÅ5êvîAçL&Ña}ðÜ©“âD¾~n ¼Êô‘S‹°†®¢™Ã”]Û~« ­¹¸ ”"LOÃ^çØó¥rSbYf–.]ýA‡÷­Ëô'Ï~äÓq·hI„³µ‰;sö¢þ/3ð:(XðTÚ†”/„Ž~6avd¥PßáùÂbÒìô|vÎÝ‚r­OļF1×":bÈŽøSbÞ•ôÚ>\sÉë_>žÈƒ]92î^[ÄŸ~Ô‚SÇîæhÇ¿ZëS*NåÍâœ$G](WhÅ6óÞèÀœû–ÿF0=âkA¹'ûðáfÀx-mt!_ÌÞ´£vÓ‡ÈÝIQ§WÖQr‡ÏL¹Ã},äZÞ ºFäžÁ–ð¢…g¨(@ å¤gpJÌ»cSV‚¾I|Ú´8‰·æòÑ·mú^küŒÐq-Ò3yj‰X¸Oôp(6°ÛÊ{ýCeÜ1Œr-Æ×¦"aöéHETåE¥Ê©´‰í¦ŸQÞÓ–"ìN¢ÃŽ ×v†PÔ•æÁdŽrd`6aÎ+¹ïDÛª½§‹SxëÊIo/3̱°¨Ì‹Ô6È’—/âyã…HÌÀãÜÇx@kñMU—K8V;c÷ÁS ðXWÏY©þAÍs0åõã®|ô Ö—r·°Ýñè ^5µÔn »¿+ ï²]|/‰t:_ÁuŠ3?1 uO9ûlÉrŒ1z»ä'Ñ\&:ßK®¡ã:Ò­J6ˆhJ‹!nT³Ã¿;ïFD©¾Ñ2ÿòúSC‘Õ爷‚­)ñCèQAÀLùÎ ÑÏ I~Ûû­$,Qä]«•C£mÝã1œÚï"˜ŠËxa}+mÀ5õ º¦¦÷71׆‡fÝ#˽ÿË@ÈïÑE†‚r3uI> üäW꘢¯¢u|š“ö¼k•òêá¹Èõ™rŒ­;—Þõòˆ¢¦·o+?®Á¥”1Œì<˜“…nçtÁænâxšEÏ"ÉU(Bš¶}¤«åPÛ‚ º¥ý½«RPÔ³ qZeŒ5ï(¤>%lê·²ÒQÒÂaHÄEà‹Yë÷`Ÿª™bÔÆªLàèÑ´œÿ¬i²g »C„áQcŸKØ‚dt:]NȨ¢t´ýÒ‰n×â}ïÆfHë›§ÏGç s`È@gT@¬qIãÜö¬=æ6 {—ΙÉßl•V|¯¾n/.KL@_ÿ,h͘,b7K"’x¬4"§š–¡vôY»^³0x$Ýè{;or ´šÓ©jzφÇRBÉœJ„ŒdX÷—]Òk<·™NÉãGвâ®2_Ї ËÍJÄ¡v‹ßRä–4ÉqÅŸ%Y+6½ÀóæÛ¤ž2´‘¯Áà$ESÐP ÂD®*߉âÎO­lÝÖ‚Ž—ú”0ZF-ÓQ$b³ä[õ°ÜEW1y†yâ„âófÜʨ~N oH:µàœÝÜ}UcÝ-Jª}«'7&º^É€šQJѧ0 ËJÏüF§ƒÓ»åÕiíÕ¯3â÷¸mšÄÛ‰¼O¥84¯JÌæ¸±ß»KQ W|í[¹ÈPúæ›O‘»2Kï2:%`w‹¶«`ŒÊVŠû v„Ö7)ÃYRjŒÁÅ„‰>ÎÃÔoíÙdÆÿÉùç$ZÅ¡ÊWÿ[>tõ¾ Ú¯ªÜ~Ï&OBGÓ±ûNã=Cµ›a™²¹¸Ãk+zÅHæ>6ÚÕ`%82WÈœòÆ·9t÷×oб+aÁŒ¨(~)¿ç¡DŽÙ>7V X9ò~Ö½P ³¦ÌûÉ'ž&U~I®!lÓ¥GT…*8!N)›!~) ¤©Ö®5¬…uˆ¶ØzpoÁÑOp‹Cù^:öîF}f™‹8I)í"Í­¶mq€ä%Ò‚TÅö{a€-Ûc–e"Ø­=äkhYyô¬ðÚMÀVJ {½s¨ÚθäGcB£˜2u(“|\#-77Õ¯Ð+œ¾Õ°i•!¼³ Ê97[÷YºŸòQwÊÏêr.Lå{€™P°zt‡‚ËÐi ?äl°~?ÚŠÒBH­‰]3ïE6ô‰Dó\òp…±€œl÷P?†îïH‘¢5uBêW{÷ñžj­Î 6óò×qp}7[ýXÀ/&`”¢ìÒÈ ó#[I€ÇàvwÚ\ç#‘5ü2Áá»è=P5È¥fóûxt‹³K÷*bÏEj³r‘ÕœÍ;f!‰!0´ ¼3_w5&AP^‚Ò!ë²=$ŽRfS—KÇÇ&™PA#K7å»ü(µÙvN1UÖŒ©YM1‹£  ‹“’d…Cˆxä#SŸÄ¸8ìH9†æ(Ù²‰w…ê_YÓ«¾Ž?&5œ]¸$u*y&èM£¢Ýâ‚ßËn]`Õ›Ó6†ªÈ“Œè…LýÁ²A.AsW½rä ™”D¾KìKÍßT®`ñFÖìã…‡Æ!Ž3Ëí‹JØõ Býl‰Ø‰÷™+˳ìKSÝO‚ ¹°uñ;:’ Ö.–Ÿ e]= PsÛ|݆ÇGŒ;¯pŠÁá÷p=?¡içͦ)ŒDÎ-JB×’žÀ.XâúÆY]‡†¾ÂEؽz‹Æ73^ }Z8Ž:a_°Ç²"wÂ…ÚÍžx]q¡+ì­!Ï ¢¿tø5dEœLb¯Ö{gu ÷Dõ£/ DíóH¦K]šþíWÅÏð²ZHúv{…£ É™­tat£Ö?lÞÉ4wÏ>¼ZÚÈÓçÎJ6ÿø\¨ï¿ÔskźW:›r¡6L^Ã7PFŽoÙ–â›f‡¶Œe©¨; 0Ä<ÖIÄi¨¹¦‡*…{¿¿‰—h³P‰¼¦èE‹sÓqòðöinTˆ>,+Óhµ½”9ÉŽ¤´šdãú!t gÖ)$Ä7UFuîtÁ­G£ŠöTꞬ²¢|S3#^VÒÍñ,]ÃKÁ03@ZÏŒíFf2 B%üJ‹¨¾îñ+u ’GôûÒo¹p!¢ç†ï{×-!§–Õч¬P8±yá5cPûÄ'=¨ž®"55õ‰¼ å[”%ǵ´£Ô)ÅåB)`?)À„’àCÊͺ{Êoû˜ºð`ãTN4^8¹¢Ç÷g÷“+w‹V«{ +|ö[biLÍ}±îÒÚ×¼ô håἫŽYFðÖõ&Þ$ÉDl uK‡8Ù;(ì‘Ä£x­µ”æÉ#öÔiÞ.T§b­ìZÅ)&¦¸ds?G4ÁQG.¯¨Î¼5 ÷4¼?ÍäwØkì¢D|¶øC ð¿ã=p˜Ëõîý~²hݽ¾÷]œoR£ù.E@T÷²«eÒ—³w °Ì•0{¸±9&•iKï U¢CÁýÀn7ˆ ¤¢,µ9GÐækGÜλk¼|Õ@Oçös¼äò IZ8‹—î]KÀuº…cF]ßæ†kj¬LZä;•¸‚ešÜâO«TM ý*Œ ò:1°Ú{@°á1³†Zç;å,¦ÅrwèÝv+]Ю‡°áÅ¥[>¼ËÝ>õû0Æ«š~×û[Á¶£•¿ÜÏn9v&—ypOÓÇ'sÙn?i§›)†Q¤çÞîbÜóȳ“e ñÍöîŸ ~æðšõ*¼ÌPnw ÀS…sJzïVl(*~¡ò.¸ÌBG\᪬Õé×uR±“õ±tÖÝþî7•A•H¾Òê`(Åbà2.F—/V§³Xßß9S¢ƒ3wއ v¦õ!ƒX‡„>å~Æ4Æ…È\8²ÉxUqeÚŸÖšbz¥œË&b%§‡®·/«þÞ9³X]Ô=[×¢`ä ×ú³ÅÿnäÉvŽSú§Ü ${-é"z:‹áã±B³—y[Ñ÷Ѧx0ìsf+ã Ä kH¤ið[0ð|˜³vÈA{Ñs Íb Ý6c2ÿÆþñ¥fbzT$ly"ƒ3¢üòé³û TB‡g’ôqwW?d@‰FLs?¿[/›Ý]×è¢ óRa6œ¥€ðR¯­´ýWº¡ÄúA ×Ú׫Í㽺+ Å´µž÷Ûéé³Ýw®«{÷=Só>7-¤ ±áX 3Ù'g™ƒ™t65ÏŽdsÁ©‡Ðض™æÏî†_JL<)]^çn1zú³ü:ʾN9»•Ró[½gH{ñç¼lQAfÄíÆ‡¡Íågwü!p£¦yöÓsºà=‰ )¬-¼×ͧëÔgú»U“&³ÁœæÖÕúÉj]M6¾AsR¿ÆD)<×®FTÅAöØy¥ÕÕwÓhK@ø-¨½×Ï“gŽñQ3cP?âzb·7ÜÙ¢±4B—@Êh ¢àsY"ÿÖh¢ ¢@%Ãp@p8NX[»°Ô½ÝǬkS®1‰ƒØ°—S²I;ºç/9,@m¨x ³¶y¿*ЏˆÎò7^Ý×¶%{ÜÌçÏ)\":dºïó‹²LÅ`\—D ;“ö½-:©·+WL㸂¸œ5áÔýid:ÂÜÆ˜²žbèNBè^u`×n «ý±¼oߢþM] ÿ3ïÒÏÈvæ±ÉÉA/c' éax2ûÔú¬ÃøÉÆOŒEy»aGKû‹ü.¬6ÙÊ•Ökû¿ >Kµ—[Ä,N4Ñe#‹ÞÃf' aÌ·øÅ‹b ŒµöÒ¿ÄžcF.ZKí =mþpí%yŒNK>‹¼+Äà"~ X×¾AŽ+Åî8ÄÆŽ{zÊl%Ò=}Và&­)álך¸åÕ—sŸÃ–mBÍüX‹Wë­ ài”zgvdz‘M¾¼!À¿YÑk„€ OåÏ(þiã%δ“U¡}tç ¦ŒïŽ4p‡i~Át|Ûg¸ iÓ¸à‹–y¤Í¡}Q«ícAxÅþàÖ‡ÝÚ–à¨WR´]´†+,´ß)¼ÆoùÈ󷇩ؾp éÆ\{†‰Ò\¬±L*ÛÙÔÎEãÍ¿çÞ˜ú¸^†d§ã\zK >©—@äl=À„ƒŽ(ŸZ*`2ð‡¡TP¶<É­Q«øOøé($§ ŽÌñ…d\–Ëíô›“Xc=¿PõÀ†¢Ó^ó£âaAƒ!UãòÎ b¦¿_ÁñêÞ™è/x6yJ–a]ª#ƒGˆG›ˆ.ƒÓ[-ñ¼”ÖyÜú¸þÞŸ‰uYJù./±ÎÜÒº—g±evÖÞÄîh¨‹±®‘ÄÇB¬]ª÷1‡¿‰§zðÜà wès7ªw;<ÎìËü’Ô䯲¹~Ð| Uã¡Íʨèª2™2"¦ëÍ!˜C„§ÌÌNÝ!§ÖT, tê– e> 7"¤žÃ.¶aBc¶Þ>‘HT›6·Ýp˜žu-ŒÂwšb{%'yîÕóAŒtl‚1vaœ5*‡Ý~®xPyS Í‘ÏÌÕÇ ppK{Ç]Z º.ͤ·|FÐÚò[y¾uŸJWpµ„‚ÉŠE?(\e¦| ¡ýýJÕêtý«ORFû œZ@$5˜ƒ.âÝ+“.ÙM¤úö·˜ ãœ7ˆ‚鈗+QlÕklÚi Ÿ—ë¼[ïÄ|ËÆt'\.‡ñ⋸@°¸Õ%—ª²é÷KrÍ»¯vÂøÎ–›Ôbóo«sÕh•ôJ?wŠêùý>U™l"û‰¾yª= dzw[á4&°ÒØ •D^cz"ž'wózý`ÛãѪ/Ê ¡E>·• ”•¥l±)Ô]HL,¥¾"_¼,3ú’•)|ÊØ&(_¢×ç «¥ F¦ýÚ‚SœÐ V‰H`r<Ý臮·Ë­GåÁ 8Œˆ\ ¶þ–áb×—k›Ñ8‰ï0ŽÌÐ;ÍPÄs + F¾µð’ÿxB=ú{(•Bô„~WGHKºdÎw Â=vyöŠž¯«|ÙëFÓê~Ÿ¯:±™**Bå$d½+QÀ¼­àK&šöî•ÑÒhÛQv<)åݽÅ6šw›WðT}h:(Y¯3Ø®R¢>™OâС8ttéA÷ÃÍÿ܇‚Ø>EŸ–ˆû¬.r€R“'âÁ‘ÛtóâR#í›äÏÝdýCÌ §½›äúâó[{ÉÞT32p©k‡ÖhûPÞ¾ª=¦@â”]gº)HðSiå´åj†<öÌ.VIõ,ö¢-P=¸!]*ÒN§: W~g"îðnw£]I_¢CFY{=:³llŸ‚‚:Ýï…ji˜’ ÂŽ™/•†Á<°âí„æv䔢`ÈcÊ–?PS€8N®Ê,“8¨ûl åѤk©dhq¥ÃŸ„998Ãé¼ÛP1¼ÿ$_’äì¸õôóbD€ÍÔÐh?FÇR]—j!yHæ¹^.Kóቫz­Û›q!4:ÂfhchÒ7AУƒãœ –ËÀ$ÇêÛš[œ÷¶¼cÇê&a{@몪Ù0/áè⾘È÷èG…^ ÿ@ •‘ˆâæ Ò•ÎbS/hª®È\/»w=ZñÑB‰ÜùÙè%³:T®í¢ôP¤úìQÑý&©rÈ×0;êÀýÜÉêgF@õÞö¼"˜.Ê^:°›ÂRÌO¶lw÷*6‘˘™šÔÙ顉õ¡Èœ‚^4ÃÃ>ÄÂåßúJæYê¥à„';>ø™h¹…ÊK ­0ÝÓz±s–AžöÉ›wŸœXŒŠBi4¦›¦ëR‘C‚ñ‹UÆ€(´pè­0‡A¼ŒžoYjçqí ÌÖ…}¼<â±ë“WþÑz޽*DøÜH«`cÔ-Æ%ÚðP è.{ÜJ¡iú$КT<ò-$ÆË…[ãv¾æ×è£Hš¬.ƒb«ˆ'œF_µð¨NI¾¤³ ¢|ûn)­ÿ˹š›=—'{Ð7SUÔ†ñzèÔ/\ÊÑMÜÂ…Ÿiɾ=oš±R3|ÞW£Õýµ{OP— ¦?}ÇÆ}åò ¸®Š}òÇd+4‹Ÿ`gxbœ•áŒJ{é ›¼»ãÂY¸OǦp2_ç³…Î-ñ©UE  ¯€9L‡·‰}]ëøõŒøuƒýÑöÉ£ÛG‚p¬ítõò9M±3mGÿÅØ¬œa¦!êêô=â¯.´³$ÔÁzjNõØê¢6riÂs[Þ·Z4òlöðΠe¡çLÃêÄfi‘O[01[ ož€0ÀìÉ–^@k; ØýFÍ•K;I©?“¥Œ~±Oëk×ÿÝ.ì endstream endobj 124 0 obj << /Length1 2456 /Length2 18731 /Length3 0 /Length 20153 /Filter /FlateDecode >> stream xÚŒ·Pœ[Ó-Œ»»3wwww œÁÜÝÝÝ-www @ÜÜå’sÎ÷æ¼ßÿWÝ[TÁ¬–Õ{u÷~ž’LEQÔÌÞ(eradebáˆ+ª±²XXØ™XXØ()5¬\lÿ˜(µ€NÎVö ¾ˆ;]ÞlÆ.oqŠö €œ«-€•ÀÊÅÇÊÍÇÂ`caáýŸ@{'>€„±›•@‘ g:#PŠÛ;x:YYXº¼•ùŸSZ+//7Ã_éQ; “•©1 hìb ´{«hjl P·7µºxþ€¥‹‹3³»»;“±3“½“…-ÀÝÊÅ t:¹Í¿”Œí€+cB hXZ9ÿmW·7wq7vÞ ¶V¦@ó[†+È èx+P—U(;A+üÀø§7V&ÖÿÐý“ý›È ôW²±©©½ƒ1ÈÓ d0·²”¥˜\<\Æ ³ßƶÎöoùÆnÆV¶Æ&oÜ %ª 0~øŸ„•Ðô­ížÌOÖdïòþ˜[ÌÌ‹0su`ÖY9ºe%þ y3!ü±Y]œ,,,ܼ #èajÉü›^ÃÓø—“õ·ùM¯·ƒ½ÀüMÐ×ÊøöÁÛÙØ pqrúzÿÛñß•`feê0ZXþ°¿™æã·á;YyôXÞvÀòûç?ŸôßÖËÌdëù'ü¯ù2+kiÊ‹kÐÿ­ø?>11{€7#;€‘“ÀËÃàædøþ7‹Š±Õ?§`ù“* 2·ðþ}Ø·.ýÏÝþ™?Í?wƒðß\JöoK ÐüÙñ,œ,¦o¿XÿŸ7ý¯”ÿ¿ÿÍòÛñÿ} )W[Û¿Ü4ùÿ?nc;+[ÏÞvÖÕåmÿíßn臾þ}gfV®vÿÛ+ëbüvDA¶ÿi£•³”•ÐLÅÊÅÔòïeùÛ®ùû’ÙZ€*öÎV¿Ÿ*FV–ÿå{»Y¦6oOç·üË|»8ÿ]Rdjoöû†±qrŒœŒ=XÞ‰“àÍúvÍ€í0€™ dïò–x“ç 0·wBø=Q.N³èoÓ߈ À,öÄÍ`–úƒXÌÒ€Yæb0ËþAo, 7€Yñâ0+ýA¼oKûÄóVOåz« ö½UPÿƒ8ÌЛÍ?è­Þû?è­‚Îï›ÏøÚ7c—?η£™üAo‰¦ÿAœo>S{Û·)ý…ƒã·ÅÎîÝïñ1›ý ¾5 ø‡á­)ïΟ€7…æà[¸ù¿ Çohõÿþ9ÙC·?ü¬¿ ¶ü¿Ãí]þUí-Àâ_ðßòš·Zz:XAÿŠx³ý«>Ë›ëÁ·VÚü ¾5Èö_ð­{vÿ’öÖ™?Ìœo© ·íþ—ÿM»ýŸÃ¼%Ûÿ—ûMŒÃ÷™ÃÛ; d 4ÿÓMÖ¬NÿÕdö·Z@§·wÓ¿B¹þ²YÙÿkX¿Ûçøg3~#W ó_÷ò?ÁöÛhï43±ý¯ÒoOÖÿ8þWuÞ<ÿmfý=܆õ­ÓÎÚóÝþ5 ηpç··Á¼‰r¶5v¶üÅ›¦?Þž³Ì.–NÀ­ß[Ó\Üíÿ•ðÆáú/ø6?·Á·ƒ»ÿk÷Þ²=þßè=ÿœæ-Õ èô7÷=œL]Þâò×ëãíÉõ?ø¯ï@ ÐayÑÞ”?ĺ!¤ë®N”ÐqwFpr÷}-£÷²S·ë l*mmNІÓhêøÚê¶$͵ÈwÒgïãö&ØðŽdÕÎGŸ'ÃDµ¹ÝN„oŸqFf?‹6Ã1jˆìù<;úhÚ@¶ƒ÷ÊQ8ºò ¨aÞ¹I{4W¬L…-îªîÕrÉ#>UÌ3ÆjÆ|,ýBYh’ûïŒ #1Æ™ê—뛌üÙWR¹Dzß“XöoÝM¶¸û¯^kUlÎ}øøºxÄ×SsTÞbir¸KÞe%NSé_qº§·ìRXmh¼v•Ôîœ/ ¨(gùhðñ˜!±d£c6:d’ìJÉ’`8rÏVå6ÚÆªÐ¬x­­.àÉAÿ/wß ¾5íŠ#ýy‹Ob#|K."î…&¬èò}õÀåtÏå/ýbF¦ u1T$%ÛLÝ<å…_ …qœ„¼MƒX/|¥òtÐ>?¥‚B^½#ßÃ*%6ðó» p…º­Y Ò=ŽÓT‡_¨µOýC#»¬ð‘Æùñ=uÇU®]Ú ¶‹ p„^ë¿­:ì“°ªkÕûÆò}ê[¯0¾4c±Zòö÷°ƒùÊæ~:•Ÿö†jÁ9â”6ö á>˜ˆW:È2 Æoöôò ­»úr¨Yo%„Ü Ø"®‰#]ôu/%aè¶€;5ks॒„[qKLZ“ôP“:¨ËC÷×ÏI8^ ÙL¹êÑÉŠW¤´a+ã­1?ÝžU,ÈXeÉ• w×>vIÝZ¸Ö nŸâ˜lZ¯Ð»À„ݽ&'ž="çÛ©TÒzP?lÇS±qy9˜/Z¢FâÞÎ>¡ÁÆZA=[œ+VÎ~èT‚­ºD9'©ÖÊ*›Çõ˜ÏÖþ4%t°ú4!ÔŸ‰z˜Î½: ¬šA…“Í1¤;ô…6ýå#=A¤ê5&/×Ö7Yn˜"cž]j‰ˆ¹w¤Ú,ÅçmY÷ôG³fkúb‡iXóÈ£I¥Æž-¿D?5Ìì´VÁÄ_óâdq2Z¥ÒÇôÈA{Ô"ñõï­ÎþÒñUÛŸµÝåõ«ý|èrh«ù€íEV¡Oî$ü(LgNºqç“ðõfަHòJº¤Wº d“íçu<ý5\¬|#4¸¾-—Ó(šO›7“Q®7ÓY…•E¬9(ø íù+óÛ!å¬M-_ó¶í“j”¥«£ 3!ˆ:q3»¼3uÎEöUÔƒnª˜b‹lÕ‚T O&@Œ¨’_­_ ìTs3àD˜ÌöS¬ârÕ›¦òÁ V¨Æw2ÞC‹ûl²?­p;d‰8ÜFAš– ·Á›ì¼òk9þ*Sd¥¾ºKäåXi¢¾O7—}²“¾ ’Dh~|5®Ù(:@/³ýâ¿•Ì5o™Å­϶¼Ñü9?„TwÓMó*ë‡m—Š%­ÖhŠÝYÇRe.1† U—1Â~Ε\Ü sFˆ!qñTCR&´gœÿ2·ZPQ,×PSÞDºXz<·µì-l nxÀX[:Ás£1VäS)%Òk†7¢È+öMQ“ øOÀ4©QÞ¼%µCÕü6½GªÛ´¶oXƒpôºô";º1_zAK|sáB~U;ǹÃ7§±O[œ@H’‚xKÔl‘sué¸]Rq[˜)ßï-ŒŽ¼"sÀ×ux‘úΗO°­>QîŠQNɦX¼*Ø4;y6<°µü©Ü£2ÌÉÅ­éjßeð:9{ª`w5›±—¤ˆ|¦éVƒcB 1«¢ŒIw"Qð‚•Ô‘>ë‘QUCc&íSøû|½µ£„Òèžµ©Û‚;tr ­ÓQEµºÎ¼7¢&@Uå!¾r—f· xáïB4+i[µ°‡Ÿ&hg{FJÖܺ¼víãú‘!¾MI0¬£qM­v3J`=–o¿ÈJ¬XòUx‰§;KæS~ifž2º Þ_È_ˆÛÅÃçh‹kª”C@ CŠž£¨Ô1þZ'ƒ /šèoÁ‚#«š‚)°Î“þèæ#pÊûEzyGÄ%ÖQ;,Fhk<ÞA·š²Ž†DûÊ-ubÛ—µ‰ܶR´Vþ{M8)W :\Zš~r?}ÖÃf½mâ•!ö%uþñgöU#: ¤ššrý¼AT.NµoŒzý4’Õ'¢rdìÎ|Ð@:Ñ¢£Õâ)$hÝ—Š1Iç&¥¹»&ÓÏçLL+÷BHá 9Bª½áTí„i£5»ÇSí^  ”Þˆ0ñ²:sÇ÷ŒË©ÏÁ\Ž‘ö°tôL7Ö¶ºN$–.„ñ3Âì|l%.ËçËžT„R™dî7ޤ¿PNë:¥˜P¼ñÊ·ø³DY@&I5ÂnC˜:J3ôré#Ûµ˜Ã\T'Ù ˆžG䨷³u ‚à+ë:&TD°ëp¼ªJôpžÝ/ù^õùÒö¼}S_ÞàVߨ¡}Äô÷Ñ%3Fíq—4^2¯Ÿÿ9Þje÷šËÁAÈlR£ôzBMy)¿2À¾Û~~qXX©l9+':š™ÕØ%dPŠÁë–¿àIGK-kçÒ¶PÃg¦Dþ+=¨8EapÏXÍ£`,ôÉŒ¬[ <%Û?"«™î¼&°w‡w¯W½fXÂ ÓæÛ A’ E݃ÖùhX…K‡¯%'0q]F)ÂiH¦Z¶„A¹–‹z³kݶ¨‘4,!ä5LIùï;¹‡1ü„²FœÖ.c'M+™\s«n§ö|w™~­×ÒªÈSK@ù=6çi$Ø,c|²„B§¡Ó²Ìîaµ£EPi-3‰Ó~iÚÅt­ er´ Êì¨7­Ï£à‹è³éñpÆDÇlaÑ“ŽÙM_Þ}¬™QDQ‘¨âƒŠIHL#ècbøa­ T’>ysI䈭Õø°,½õ Í„Ó sŽNYž3¹^SÃòÍÖ­—·“Ðüþ þ…ƒxvÖÈ11ýM›bü×®ï*ZÑIÊ…£.¦âF_ ~°dÇ 5ÔðpVéˆÒ S&E°Êe!Ò"Ôb¦,N€„%âⶌы¢>ÔÞQÓNr,÷øÊëÖÒ7›- ˜®kEs`°Hꎸ0Œ=caâÓVYÝ@ýÊ¤Õ ùœ ôO„V{¼T°9ÝÃ(ëæNܨ[-ª]~ VF€g¤*Æî)³^D7ž^rP€²š+°÷IhWzh±Ä ì¡ÖZe²–Áúý~` ŽÚ÷Wî&Gä“Î;k;[„°¨,ö|²Õ3|^§¤c‚X+øC4#×!²N ‚FS¨²{MÍÌ'å ~?ß•2Ô6ô;3¡¨1¸ ”©´{dR‹ŽXhp[Í'{·’ç¨|zôÆwª”^‚Ceš`à®­Ør\jÒé7M«Sánvë;¸”×÷ƒùóXúU‚*â¾!O«†l«£_lž¯±0©ŽG ùõÒÜKIÐ%²iw1Ý,ªæý̉7ðQ¤ø¿²ó€dê?|Øâ’¥>óÇ.&€°Øù®¥­ÂoiýlR³¦–©E£KWԄĉ:6 |ôÃÌÄ\ÛhÜ3Ɔ©_¥åèP}éQeñÁ:÷±ï-±RŠ QɈ9ÆÊ¸5¯ÚY»f[¯û8`µèTÈ=FIT„é57ÍQÂ}Ê3ÙzP>VÄiã÷•ï¥;WÂÝá]%¿õy(ÔC5üŒ‘ºÚ‡(”z¦yw¥MÜgñúD›]OÌJ¿uWòC”Y%îF€ê³,. ¢ÿA2‹¢9÷·¤G)vbøäØ`uwƒ¯D=Ë)ìÃñ•çõޱùÇà€²QvaÉ›8&5´|1ð èÝÀthÓYMå‹2úvMbŠ&§9ÁåðÈ„BPÂsÓ>§ÑTj1Ãæó—3'~NaÞ‰og¨Bª9JKÒºV°ÁU·9‰éa‚Þa †¨c™,_d¦…h&›\ßw/”xùdkL5mkÉ*nךÓm‡p `ÃN™î×|7HÏQãœvct¯¡ÛUDýq™Æ¤Uô É*\x§(HNÌ<´?÷˜Xí¬•÷Ž y‡%"M(x¡Å°_OãbÜÒQh÷4–ÈïI6ÞSäŽFŒ¢bzNˆJu}Ñ/‡/…F^1Þ/ÔÙ9H‘¿°é³’&q~ ¸,%ñ*e-÷OmØÊʬ9ç þr¢ÖMý˜‚½ñÿÈ*7SQN»T×[® —Ù:ëLgjq-òÍÏÎÆÜ­Lœgmb3$ ™WÃAW$Þ=xj²4¤ÀŠÛ¡µ’­%5HU¡²‘»á£Cr+Ž™È>­š~ÿ«¥ðw=»Paòât(÷=a{¯Æ‹ìÖU8Ù®Jb2¨Ô'àgq¸ÃZï+°cÈ´¹¾³´;/™ý}ï<§s³NiV|yY#O»Yúvœ—3"†`n]¡#÷®p÷ëœG‹¥‘=#>|Ä¿+  ¤Úè³SŒ¤$„Ú.˜ê…Íú%ÚÛÿyê‚ÛA¼ª X¡£DN¬åúý:[Íï¾Øî•‹?8®ÞMå-¦tíÓö® %jƒ¹>e¼²K |ûî6ž­y—¨ ìB ¡ñÅüЭfRß‘ÃÙÖ†íWèòË÷XTûe´Ðä÷3KuY1‹ô…¸ã l{V†™åb¹^nP¿ŒˆôÅGõMéçb:Bš é‚'î›kv YÚ»Ân —>L½ÀØ®\Æ]Úuh!”3”hèu]Â//‡ü¢vŠûÞ³Ü:#H_ÀtèbØ<Ï@ði@…öûåçÒ<'ºô-Ž÷¡KˆDšÞÄŒ1-esAb‘„—€ü˜0Çï™EÆFÊS>ŽÛ‘fÐ…ë[3ì‡ ƒ[GÌ—>óŠj­ŒF1„›µHåü\{Š>Ãùƒ~·:× ½zÛ û®åW˜/™¶’¨'îÉ­mý‹!ô1þWfßµm±â%´]I±Šò¤ÛILØ!€ÕÈ»‘tÀ¥¥» +µmžvуtI:Fê5ìB˜ÏtÉ…-ÔÀìÈÌ`BëÙ' ^V¼_*É¥Nµ-ËWú>Cs®,™^›ï£Ô÷ë’!/A„àkÁó6Ù÷Ù/C±pÀ/± ±C_ïHE‹¥r¿Ù41›|¸Þ>hp“]»Ý‡hE„,TÅXǸÌ—¨ÿrtùhsçœVd)Ô’eš˜o[®RGãgM©RÖ66.„ŠÐ,ýÞ6U!pN"37|§ùìþÓ ÔZb9U”¥ë5U7‰(xZLPìʇ8^Ó*Hï¤iÞ­# ÿ¨¸¾nÛ:ïëÇJGó …"ŠÉO§—¼@؆õ½-¸Bë‡LTfÒõwüæXVéïù·2Q?tP»5¦ç+ÇÁÔc`*ûÅ ù‚ yy=€¦0új Þ^âõážÊê‹L{ô›äFOFï¾áGlslØD³…v©ˆ‚ ÖðÕ¼æÍÑ#Óhï,¬E×’©=ÂMÄTÌj·K æãÈP ݾîÚ}C”#K¬§¨©gVo€€TœâõÕ&Ö[Qcßze£W 6;~÷à LŠ ^ò:IïjáÌ‚®’R!ñöÀ>©%vL±)Æ™ü¾ç|4'üÖö‚=lX0=G§çW‘2ìuèܸÏb_êé¶u¥\ÃW3=¡Ç¤F¦$žO'’•”2ú”_«i):qÁ›Q [ÇÞVº× ò;ªcOi(™Œ˜n.]ú’ŒðÎÇü#ònšâA¬»–T×Ü P-w3€to·ƒµ®\ñ²†ç>à®ìJ§4z‡bæa•›ùªUü¥Ÿ·JfÀ‘§Žmüã,:ûyÑÌÅÅ’ÿ×ì«¢`¦@õ,ú1\*Ôî¨ÄCsú}¹&ï7 ÉŠ”ÖK–±²$™ÇwÙÆ¯:$%b‘š`ÄøÎë.‰Ñw¿œ¨¢A ï€%`ý˜Ê}ßL‰ÃãM’h'®ù­•¿UÕå…p;W¯&Jw£Ýz$«ÉGR•ƒtlO\[8ûí'®ýÎîƒD¨óå"ø¬LåCî Ô„â•LúAûYÁ>;Øi¦õ¯ÎjŸÐAž³ÐEAÒWGtáù®PÌVAIIàgH¡\yò¾ælçîÆ‰úÆBBÖRã­õ¡/éyéÏÎ]¡-ŸÁÚ.࣒Æ`{ž´¢9“!º'?ñÓ¦M(.ÔkÜN-Ÿ&x<§'ê7[^~’žY9¯ôÅ:Ñä2#ÿ¢‚ñPkE´?èF*°pód*`#ÒD¤„+.¿«èONêP°ÖšiêMg>&4jA×¹ô$ßË:ùÑô¨}Lï–!ÛnmáÄöádØ(DóQœô…<Ðfz7O†U)íðÚÆa,?`7©!õ“PZO§e’µ!Ѳë¸P©"VÎ~‘AÏçšØGÄpì^ÐdgÙǠJÅ)K=¡Z…PùgH`V‡™£ØPã#6' ï/x[º* 3,p“Ó­–³¨ ÎUYw﯋~uÅYì>öòBN¸^C¤„Ï_­¡• pÄ·Á¤\Tͧù36U«#a_ð.cu–…Ö§}+f}âÔ}ÁJJ†a)c[¡B-«©-Ø)„¤UË"Ÿõ |:\­å4D=‡[{ g O$Óu–ñz˜ •/bö•ãjêÄé¿Ý“%¿ê°[Fà.ží ”FâjRpô/H)žæÚþèÜæBLU£ ·›ìòë€Â¥ZŠ{ºnÌ?•gN›=ö=Tâì¤Â¿\K£SXãÇ|æSx«’Ïyƒ\Š˜Ý&²Ä¥¡G'‘yø°mà ŸXŒXt¯`‘o@µ&ý™}Œ=Z¡n¼}&’ fYÚ8®1+б7ÀÏ©5ySd÷²ÜÅ …ß` “(*ôÄ×’;˜A\¥|ö(~`ÿe ÿu™gCVÝàª>^â‰dâ©dg9äƒωOPã#÷ÃÅhÿêÑi¾ÈmæJŸu}'ŽÞª~æ€6-ÇZèORà‰F_Ìë¿\ÈD¥Áª?õ)k÷ ÒÚ.¹Z—a1 îcxø\úî.\ºB!ãO)îB]ßC5DAÌUÉßÐû=MÒÇw/¬˜´ BïÆ"êï×[?QH9¾z/Õ7£¯PÈÁ™¬޼вô‘4›flÂc—J¾7ÄIvÚs¸ýPŽØ/Œ{°ñ—¶¾·øŽXâï7žíÔM(H’m·§šíáþ q^ðšW‘›ôT­Mµºa”² Ï‘Ò¨ñ¬}™}¼Ü|_\ö.ÍàIŸÕ‡È@–…I¡z¶vÉ€‘âCšlœ)q]¼²”òHæÆäUq6˜yõ}ñd빌_ÀpH‚Døx„UIZüê[et ipõƒ–ÁšžÂÉ+¦îšW+ ~XÜqcšZî4ú­A;Bøp.4£¢Ï/®ar¯nF‹GìWJÃd r–³©¹ÅÛKŠÕÇ+iöZä%ÐÅ5øÓæO‡àøk<æãõ™§a]=„˜{*êÏɲ¹[ò¦Ã±šì9»˜¦[\°¾F„Í&€³ Íñ¶°FºeHÃ0$˜É+ЪBI„¿RBés ·¬nïH¸4‹5Ъä×KOðÛKMÄÝtàvŸ‹=#ü|šŒ²— ÍúÑ£ì¨Å-i]p 79Ö â7YY˜)4%Äè|Œ 62ëGÆÎë–„º4Ó¸:vø.z@4—"6H´{¼Å¥þÉš}Ú§ý<Á¹ág¿±F% $}ŠQþ Ç%«c·®Û´§öqà)_¯TtG»E»ü=ÀEu÷Àâ(¯Šýã¯ZDÿ¸ìççºù‹,z×äª/x-MJ\’{бF÷,ï’¦¶>u:«ª€Ñg©Õê§vÝü[G»ËÒ+Ò5¯'9»šŒA‘Å×fs{±(7¼¶ú\ÿ‹}°LF\,­A%K3ôž ñžxÐeAÛW¶Gôh§ÙÓ÷䩊.+õï&ÈÇvªôB ­y½Ü.Ëýß}ìÊË›À×Z–0˜_¶5X»ú¶ÅΔ ýñGM±±pß´%b}Ðßa„áFî5g„ñ!ÜrCë°ƒÚRĹÒ"UC˜9=%øIÁ%2)õnÒdнwYÁ­öNŠ‹³÷®®èÃC?­ÄÐÑV˜ùXã;ÚP ðffÊ÷Hì„ðŒ;ˆFQ>ןÖ̹C¡«ä—/ó59êPgût½ÑdJbÖ‡öéM}CíØå­šÂÛ·S±ÛÔ<*O_RwôŽÞ áäö¹ëíÀÅÝ žhw+†OA‘àÜžWkæ­g§CƇD°T®ë˜^hØ &^›2RÎF÷vÓôã,Q¤ÕD~bwW   oI ÕÒ—L3•b% ÌM¸tš V/ÈÓ-´¾/¢„Ò#ßs{QÃw L2ƒZN“iv Ž…äaê²/¸³+ù!'œ’ !æè§üÓ+ÊÅF¡¯×ðwüôc6´•WÌ ¯QQOšÈ(x60I·ö´zÙxÖMTÒGu[´!u¾]O<|Æ‹±¡‡<~7étn8;ùÕeèzô³TÁˆ¢V^äžèÝs5Áh¢ V÷ôánþEÿX=MÆf)—ám§O`Z4¨Þ¹Ž7?k¯©Y¢q¬ã¿Ø*¹¸¥ôÜýjìË“õÊñQê߬Ëú ¡ÛF 4z.YÕ.B¦”ù–A'"84üaâÆ<êèÃãµ(åª\‹\Uá±M ‡ÄÞÌÏ+RÞßêƒ[ÖT9Ñ8?öXÕB·R¾9ª0Á{ãÚRö • Ä$ñäÑ Rý]QT~ß~Ú)̼°-½N°ÌhèÛNx6ïÂl?îCÇÞ6O«ìV5dù@uW@§")¦kM¤±|íši3EBœ×_ù:–Ï骈¾˜Š6 ï”`$7b|—`À*snë ÂÎÊœ|°¸{òe’FÛºíÆG)þÅ•ÐÇKgͯåùÜ"‘†E)%µ ;b’G©Ö¼ Gxk{þF$_š€ÚwêŽó¥¹B¿÷—£’–R*Ä%ÑB‡`Ænn£2¡²jˆ’£Yÿnmbqc{JŠ íwX9aŽO·mdâþœucç[þiî!gc"§Þ.j‘™ÉˆFTÁâh›ÞƒÖþÅ’Û× ©ÀÚ£Ò %„ÅháÃq¸lˆ~aVõĵQeQd`ÜÍ)àLVÇO9I§ˆK…!-¾û±üÉÎåH€%w!Äüåv ´Àíkyt ‰<=˜ÿ9î$ݯåôj²a­ŠžÞ‘‘¬½-ÆúrU±êzÄÚíÇ5L0¯•>ydZ‡ñÕ\e£üp†+’ M7áPT”T"%S6f ÚCH÷9ïEÉBIˆ n#e÷“5ܧ.ÖX©Ä×H+Ij¿éûzþ~CµÅ^´êP³êÐãE$mBþs¦äAC)(À±·EXÛ‡äÙ ìâ“mKK¢o¾ÈrîAÈ‘Põq¢ƒèÏÅîC‰$ʧ1m‹G1ÁšÎ×êÊuìÓ SÀÜhe¡Ú&&ûϾ·&ʦ{èøÐìNSá$-}Ž/Q‡dªÐ³Ü1ÇdHl_\à㦗”rIÐÔ–öÆ$nr”ïžX8ˆ~î¿Â"ÃìÉ9úöD‡5¥ãð(ùáûÁØËTÖŒIc©Ÿòe1ž+ÖKö1Ã7¿}ÎÇÊj…ªã¼ “ùåû³' â”Ùû³ž·fŬ§Úa¢˜Ô,;8­vRöpª8%LH¤È‘ÕŸô§K{í:a`›Ÿ—\·t¦â¥À *åƒ5ŽŸ=Æç™» „–[X†¼>¤[‹·{ÐÖ¿RieòÀñEªº­²hHQfâôȤ¿® ½Dtßqñœè?'†O“¿¿Ùf|¹­xª¥Êq-­/Bu¬.ÜL¡qN‰<ÅQ\%Ë|¿I€×££)‚ƒ1a’Ý¢5Ô{℆IÝ­óÍ—f’*—¨ûë”[€ÓŽü³®Ì@î•øttÂNœþ¯æÜ5C~~±»wl¼Þܺ¦dõîq@Â< ‚3Ív‹‘Õ:a¥ýþçu°A];}(–ÅM¢Òø;-´ø“¶Èˆ¼:TÕ¥Üx]Eÿ_¼½•p<Æ >rVΘ.퇲]‘okü´ôtÔ<ÖªŽ!?é°ÍèaÐÉÖJ›vŽ&`†îîdXܤ׌SFðŸ¥7ö:XÔg.&†ŸeÒDµªOŸ>óÖÀ‰u÷:åÔ¸q—Ñb €ÎdWÑ¥¸9.yŠÂñ¸e%ZÙ¥eÚóÀRÌvÏÈ¢^ÙËktR'4œ3\©e÷õïbñé[ôåœcuX”²¿€ÅÐ`@ Àsß;4Î¥F(Ñ,:rèOˆ ÑÓrCšªUÖ ±a'sGÊÏþ:- ð3ó‰Ô¦)¼å}å A$]뺂"ÆlëëjrÌ’ ÏÅV’¤‡XºŽï’ábÞžëÝ ZdÆ ‰bg /˜¶'B‹žfÒˆ7˜Ò»Á9šÙPîîö̸ZZJ±^“iD€€ô–j»ÅOÉà;»™Êîì“rÊ©…øLÙ5 _0"”°‹ÚLKËÃÀ„ð_sÔã £²ÂôŽÐã¹ü· 9qÏÓÒÕ®IòH—Ë•ÑýI×ȳ§å‰S|ÇÈ’ø”zÍí…×ÍÙ²]AEÈ£®„eå¹{ò`QÞ*»$FNz d5¬}U×(ÒÀ•û&Ù3S¤AÛ†µìSx«zUreß ~ˆÑ2×ó· ÷I.ÈÝ}.ù)†¢}¹óÚ=†R*4öœ2ÄDå4¥Õ‰1jb̦ÃÀ@UY·I6®–ïˆÚyS€…Ì“ÙL…w¾Ý¿ÈÝ5wíeOãdþÆO*o–ći`§Ôò ¹C€K2Û n!ZV®ë½©P'À$>x³8–i9>²‰ýB´–ý9å#ñó´"¶‚1íA<”ÒÏü KIµv<†*ŽÃé³UtVCqZ=…¤A04Êô‡+Ë÷ñmpi½LìÞ²&~µõí}ˆ¬Ý@¸Ç‹Û&Ù:„Æ·% ”Ñã‹rJ•m«RhXÔƒì%ümÁD£Îp £y`¦à[Á)ÇØhjŒ. |¶çâL’XoË3H_PÐó «­õ Côî,õÀû\ñ=»†)÷'AÖGPj=⨤Ð(A™hTªÁDÒºâÆ^JRÚ–o’±^Ü,ö¾ÁÃÖ0hI8ò*}9ùc=Û'F U<‚>‡ Œ;Ý‚;Úá‚bY{ÅwV?&²€ÍtîwÌ?#„+vj#£cÓ®ø|1siâÔÜ &ÛòMiÍ W${V]>a(ŠÖ1Å ®ûk+m}4€ço,Š£Ù_#Jw×~#ôaõþq¹Ç‹“‡m˜í=ĵĈ¡«3ëèÂåØÝbœì·¨#ñ”x.vXf™èe… â­“®÷ìBóc¦ÍêSÿâ§„ Ò÷>‘ŒF·$ÔW _væ¡Dn äò(¤Ìf–ÀÂU©ŸB~óÛ†NOf€³væÝ¯ãZè*‘8(ûFLÁeŒ¢ìŒ\ËÌø5ï†÷ ñ§¬ˆ¶Úv¬ûþ™7o:ˆ,îüÅU~§ÞÕR,ŒATÑ5¬xïh[ŸhŠd§”ÉJŒÊæ˜Xu+ðj“…r´?³™Œ „%¡ &¾ðpîÝXÚÄ(û沇£Í¥¢oœ·Úáå¶âúT¬¡1YFÕ©S#*l ºì¯ž*…¹~ò‚ðÕŸ–ÛXµhçQ»¬6‹Tõ8?×p–R—ÛNL1 'q“ÌÎtú#:ÜÛÚrõžÞ•Ñ8”xb Ñ›4Ëhª‡›Ç5­Çó6íkâê/k“˜ù‰¡«èÿê¾åÞ‡¹©x"|ðÈ…<½£]T–ôÄ„€\žÜka'Ún'œÿ¼¡6Òæÿ€¨妇´ EϤ]¿vå"iKPëPğέëmƒèæzoãµ§y&U–Ÿ–á*šùØì:WqÓõ«²ž×†9QGΠ‡eoçPhb3,þ]7ÿ@ÚA£ì$)qÓܘ(©£nj1:‹§Â¢läôIî¼æ¸ÔqÚÃb Nïâs$¹×Tl±Bî³lœ{Œª[¤_Úè‘þØ Ðü{¶öéÐ/Zi*òñ…ã”R´: h¾)0ÛR9‘‹ÎߢIɚɧiŠ×^.Þ$…ð™åk(Í— gW^GÞc|®‰8Õ ~o›‡po\#z„PŠBzÀä6¦J!ó-›m ,h ­ß]m¾Lö¦¢S†þ€$!ºXóQMbuIéìv‘§b0ìÜÁ™7©ch.•8ZÈiš¹c #A"[Ç™ÉMN·VšãíŸxˆ(¤†Ñq”A¿S¦“ŠpÛCƒ)PjW*®_çÖÖlW·K¢ï½Jyn:§Ã1¼ñ’H°¶) á‡aî—»ùNHõ°¬Ñ_%ák3îûëÆÊÔœ‘Í~¿¼¶…6×iˆ‡®1’}N]8qj ³ûÁÊ`íŸ×0[Ñz–’Âë®á×Ók­B$NžT[ÊDÝüD²ŠÙÇÔÁP.s Ó%÷K˦ÀoÉBz i-b½èÁ<$Û¿‘8?Ç}®“å¬açHd’ÕE9žÖ¦d?È 'óèN­A sÅTm}s.Ç•D²|¬søÚÓLn_cî+‹cw†é§óëšny=ƒœ»,¢gäùåt¥ºVét÷‡¦lFHs&Rý÷ Û¶“Ôg%gQ¢AÜÞµ_)w}z >í~™Gí\,µ<…\ÃçiÚœJ× µ´K}ë?Сˆ¦z2]FùÊdýáSx< fCÖÞ®Èû¹ Hø#~š)p4'h½ 6Úóë ¿¯¾Ïn…K4s¯-³§ö÷©Z\2²)#´Æòõ!®Y¢_²4bpànÆ909Àë¨ìDe¥¬ÅܰƒUJ—² áß´ì¬8…¬T4„V4ß-¤F9ÞAS4y)uºH5Ð,²5´mIŒ¥ŒYy\ oGÕY$²Â#ºˆe.ˆìÓÆwN!¸‹±^è¥W-<•‘ÿ@­Cö„ÿ!ÎimR7›pÎ*U ¹šŠ:lD2Ncï7ÒÕ$ðÉS;µËð »Å íÓV»\tbFþ¦´Dkyš®mÓL?ŸÞ÷¯¸”3<Ò”]J^2=ÍMlœ~½Å@Ý9Û6ö®°4믠íüÌ©:~¶\8–€jüûŽÓŸàºÊªÄ˜\aò,.$¬¹ä¼(8¯“RJnXÚ2á?Àà :µu›-:•欉…I«Ž[‹5aP ùò  ÁÃ6 ]"6¨Hg…¶ÒÆýÖ!§$\ÀzrñãfϲÖS9‚S½>tˆ‘ÉöMž…p ݦèÖ ­Â—ÔÒ R¬tÎú”œø¸÷Ë´ÄÞø´ „Gmï×纠ódØžQøñЋíaÇ;:Ò?µs­å4æ„„Ãé0å‚MÆØ$1T9aãv]‰,š¦r(ÙRH¤a÷©x¥2¿Ã8L,Þ[–‰S²¢Æá­e,u¦§/H ¡5æRßçÑvûñ-j—T6ž|m/O¶‚Ï*‡7°IºžÖ5òíz’dÅwSu¨¶Ô1= iõ”×t‡«“·Sæ®…«E¹ú&¯^É>Å=’JÄ'wÏ@ó(Æxwð£XÿŠ£†|"þÙ'Ìcûj†<­QzÂ#çgøâaX¶@UÒ§ô)ÎZ¤µ‹+´þµrRvØÒ µDOÄÂ}?¼2¤¥»(ŸÀQX§\=\ºbüûŠ0÷mM§ù´¶å™„V€Ñ/êäî˜pµ‰Ò”ç;ðc³¥c‰AðKÒ¼£ (Qï‘úÞ× «~¾å³öзlÂë hù†ySx’l1Ýå ¯2ÖU5ó‘_8ÿînÅÂG8"iƒ2´Ä ®ïÆD·×q|å&„eß[(|f$'Öõ¶b,Còˆ‘6¸Þ û±ƒøD2*Á­ ›<ç]©¯Žæ.šft>ªz— óÎè“O_àúú¹ÎàNÚÈÊàŸ\&®®*­W•CÕÊì´E’j¤÷ÝzûÃðY×î˜n‘4 °ÏšÄëÛT}Ü‚<±Aª®%d·lWÊ3¾6.wÕ‡º8øü §ºÌ©I'ÍÔs‰¦Ü~.ÌüµgHÅó€ ¿ûÌ}9…¸²æhºcŽÆ[¥šaßK¸ûh‘ïá‚öÁ“1Bñ_l›‰#ê,fíß÷~ý6cÚ,ä‰ý´¬„Ö^–_%­&e‰Yè™."٪Š1‰³¾§—ùi§’lAK·×ŽYŸÚ5K=sK2˜rÝLÑ·-ì²r îë·ëѸ…€™ù=\Ðv±šFvD¥+6vf1*ª¯V`x¬â³zX ´\p•pÑg_è!†wHé<­9ô•'±^mÇE¡—lH5ŽŽ¡ñOE䋌pzbéI»ô§@YdTìU”÷I8m²oÐpã–¬ñœuÂwJ{o¹¼_u—u”Jˆ ¡Ú\¶ÓD¨ŽoðÚ=Be„枯¶÷à+:=‡ wÄV£trïû©Ç¬ülÑ(}kƒbjp„¤\Oo^@Jz”ø*ê9ÿ‘8}¶±Ò9'ÛÇw}æ›OÜý9:çñ—Œ»[‡õç‚Â?Í3¨;¦„(÷ûlàiÏèV5©Bý§'…§ cðöç4½Ö!»¤DÓÜ'Ùq›{‰æ…y,=XÆ]Fz¿5šV°V4 ’°cGÏEÁÉ—j…×S{“èO‰böuYÍu  ÕBºSÞ•0½ß·w1`;>÷<:j ¹§0ï›ù˜Ø´¸dwÔ“LÛ†Öá)u‰Wk驺TŠšùÕú‘Û>.”_ç§´â™ |é+IÑ&™Ê!1n¤…À¹Ö4Jì&T”UüC/Ìå%E´††Œ»_¿U ‘>%…n¡>Ì-aÕæ¥-óh’'rQ—× ´p’üW\±RÖªc“¯ÙÝÈê ÛŒÀ¼åŽ˜Òýr™àT±ÄvpÊó îÓLÓ9„uÐaÑîՃ㬠\ˆeœ ƒBó Űˆ]'ãL‹eאָ|fáÐ2—ÜóNçêUð¯Gž-IÀ”®àfwz‘d‚ âuåPÀ;hØPaÊcåÝ5[6Ý×ßiÏôËØª:‚5μ©K¸{ev››¢>¸_—waëk†9¨bB=Xgm—Я†VuõadWuaŠˆîl‘Ι^~¢$5Æ ¡‘÷fÒ|ŸôèôÍ)T®fú02.Ri d0ºßEx­~¾'Ézó+WLõeéip”·w2]Ù9á[íçQ¡Ü×*Üù 2Á# ºÙ ÂU2Ïib‡}¸ñWä©*FƒzÄz#¨Þ‹[¯òâÃÛƒÊ/èJUÈí†í‰âO?ËŒú4( —Rvxº,Èœ33÷´›.÷rYÃdÅ )”$9aQKJ¦weÖÈ<ÈRZ¡Ç6ýTMãÚÃLôòcEņˆP¬vÈÆ/ÉÛXÉÜß.~MCy’Ã/«¬Aû}o—Š"@å+":oFÜ}Ü÷y®!±,Ký“^á¼9zË©°Q •±ÞE£¶fŠ}:x¾²qp 5Ì"É2ñ#bèÑ ÒÇí@>@AÚæxð¾0NË5–ïZ‚ÓáJ‰Þƒºäf»¥÷í£áô_ó Ô<Åà :)ÎÛúöÑ, ë/ßÏÓÃ9ä‰7åÍ l€iñEîrÔç̵÷YÓww. $fÀ,†Ò¸ßÏË{%},Ô£‹¤]îïæ(”`(lV ò&žáåõ†§À ÜÃ9›®Þ=d0£¿f~Yó£]‘b‹-RÞM³ˆ¶5Òx ZI p{f-Sq•C*Í›®h ý‰4wú]M"4ÊmR4â^'m£ +£L̺D Ì‘ìÛ8cs2l•îæG5•mÓûB«‘0‹½c]«p{sßë²¼OÃ~à™´,:Ð;᫜åñ«ô Yð‡“ë°°#ÁÍØÅïvv~RËþ¶ÏTÅçìÄ)üˆ'pÁÃïwaÚ>mUýpF:%¯X&}c¡’OéP;­•0òãáFÛšg>ÅJ?²FÚüÅ*‘¸<‚$›SºÝÞ{Ó­ T\ƒºuR_ZK4•zÓOîZê‰` Ž¢O…"Å£PFOÜ$eq ²ŒŠŸ¹Ï’E~³Zw#„Q˜-Ñí#Úda…È¢—êQu¦^$FºÃÙY´]bÅD½†¥Üýýݨ±b«P,´šz(lÕû‹µªìAìfb2öÐౡl¸CN\ØŸ¡œIêÆät©®Ú³ôA0“U¿Ú•)ÙÛ¨QÈÝCtÝCú“XôÓ úÂaÏ“þT!öR KSF¶ž’ÚM×- €Ùv??è`gVŸFâ­SGUU ÆÏTI~µJ˯2€8’§ØÏ\ãªñ‰=–qH.UîRN¼ûækK¾ôåö=?Ò¹µÊÉ:êsñÕ;ê’tzÓ¦m•~vÓ¬¢`6ÿrì.ˆÔݪ!Ù‬Yl¿JDa‡E"ÃÛ4éõžXNgûù©N”äŽÔL ˆ•Ó~ÃRþ„õ°yß`»( Ú»•Ç‚UIOÅv^¯1Ÿ"·›V}úhn,ðç²RÛç/Û‰5J˜0ÒƒIb’àæÐŒfL ¸é2$Ú2ÑT¾èûšÅ¡Òî6z^—TuWSÒ¥ÍU© #hi'JM±D>~’¥IšC¤yÎ^RŽÇÒp5 p—l»Ü×ôgîý!Û°o=OeÎàÿ£»i8*‰ DOrž?¸Óùs;‘îxß!þ2#ɈÇb^61<ôDÕWxxãŒà]Wnrw5:·TËš>Tâ¶–IVÍÉ]”磯M¸ Õ¡à¾à„+JÒLÆ÷7˜ñíœÅ#®Ÿ:úÉŠ! ÷û¬YøüR9ÔÈ v(…]o(ˆó¶tml»RáPÕI¯0ƒ^Tðý;ˈ(S>Gð­;5½/'µQ~P.¤bqdOÂWÞŒ]pí6{}5àò–qŠïs;ñ2-Í J9üטÓ+Ša×"nR°Šyòxr!+;Žå’·ï¶Õ^¬µ»tîsFj Ì¡`NÏxœêàPbƒ§ŽW}µÕèßCÇnÏT¼1»a¦¦ž?ýÀl— |BÒøÚË7ªŽia96²mÞ}mÁ8Šad,GW\÷ƒeœDÜQF|ÇîçÁ$ôgQ¼Ì–èHЗN„q3Xcš…o~§÷ãu¶7œråÚåµiÚÃÊèý 4’«Oá¸ûþfÅp3Î:—›Üº˜‰xyn1 ¶ù/‡x••'þ”’’ULSÚ! tŠœŽ@4-?Âø ´º„ª³³Y”á%¦²†®—¸ I½ú¢“Oêuã®Àü@pPSj9µÝœžÍ¢z—j¢;–7 ^ ÷­¢œX-'ÅeŠŸ×hù~Q†*ÞaÎd¼¡ ;wøì úñtÖÑÄœ‡%lFýwDÜ*ÙÂt¬0 ¹úÏjŠ<ª¢.O…E®/YvÎ+“ n §¸g }4äfª£„¨NĽ¼Èxà¤â«$mt1Ï'|ÎÐåx–²=&Ñi²sʶ¶ßÛ…Ö´Ög·Žç”–·\?Y¶e\Ó¶HÊ”®–—Q3»ë|è!îÆ¨²z ÀÔÝÈ[—”&_L‘˜ÔP¸æ¿°¹ì§‡˜Ý%)=Y—0M4©^Æ£³‡Á¸åÊCŸ8¨>7°«XH[Œý2¯sXèYÉ ?Ϭhi¡!9´f…=ðÑDákõC¦hżiÞÑÿŒ¶×jõ-‘ß!£’~üYvNËUßmÌû„ ~º¹€8} bh¦/‹r/MCSQÏmøulç´Îx‚_¸¯­:Öß¿N¹ÌóL'o–»åÏ)P™à„B²j‰øþ'Œ›]ý¨6*´°*¦¹¾¶Œ!åªwæ‹”Ëí¨­Ê¤u½8ôÉú>‘IºìGTÝŸ{BÎi/®Q‘-‡·DmÄÄð !Þ¸Á_†}Íqê\~I ïà¢Éè6#¸§VóØÏö׿½WotŸë±íó°ù°5‚h½`Åý›¡ ü´j[6z×3óƒçŠRxIôX˜i‚ù.ĵ õ^îƒÊ{¹£’ê×»Ìé§UßÍìÝ›'|KÍMèm%Ù™^·þÿ\”%*˜é ©‚7ô‰Z}ƒùD‚XF9T›fj9xª•šcqÿè Qÿ0Ë¿(ì;%‘«½´ïïBpúSåËF¦>¶Œ¤‰lÊ ±t:ÀÙµga÷>Yò˜F“ÕÉê·T†0ÎÙ´–m”ZÝЀb ÄÅïP’£©ÿ9lÿ©è7·ðA˽ÅÊÈ©ÂåhŠc@>]“~¹RXN¼í9n.kh¨Ó¿osÝÉ 6b‡Ñ@§¬KSǶ]û‹•!g_Ñ5Øðo ¡“\!¤JŠñy™YE[¢šÝOìn;g£¹l]˜`^­ÙçþŸ)ô±ªÚ P‹ì±‘J¨ÌoxšC{näkqrŽ+QÔ !°X{ú’ðÖ9*Šyôý¹ãêеÿ”èL^Zwt2`ÂIÇ\ÂJ¼É¤‘/ û™n Ã6àu «,´e—Rs-ðvºöEå· ~˜'õò¶ž|’ä…7½! ¾úvE˵öosp=æ q»LΘfœc%A÷ TöC{{O|’½‡'ââ”SkÅáuöz/ú?‡÷ØœQ+ôn^-ĆSÂîŸ3¼çJNU%Ïwt[µ^YU`³i”œ¹M.ч‘6¸ã'qëR9Dµ/‰n>G @ôwð»úÆÄ ü‹*NŸrH@’ ·J ÔÑ8#i¥ðö’¢5a£" $²’Bž ·$jâ G@ä¨ D%¾þ=Õü¿”_ •:Ì£°¹¦É¾Þ½)å@·ô#>»<É;K¡Ôj•©OÃ'rŽAèI|Åg-Ê5E4¬&ú|MÞÓ2„5Àô=ý3›PX­Aÿü¿A¨o é^G®ÀÌ Æ {‹W}£¤Ù¹Å*ˆ×ß….p ÿÁÔ‰½‘*g*f¼éKÊiõϘî=SR´ÆÞü³»’ŠÚ×&jT*#¨P©Z[QjŸÇs æíXÈÏÅKƒ—¹ùÚpî-ë­òXŽÝ¡¸È³¾Ì¾¼Û²<ïîZ\€Òõû ÇHÌ—Ø÷à?€§±’m{N3$csÝü¤Þ×§‚Þ€sx»àÆ|‚•Åo?‡k¢¤çì–z|2‘† ~_™¤û> ÛÎoÊËJô”å—k{l{ÝVM€S(µâ¾§g²_,šƒ^-ˆÏ¾ÀÓTŠ´;Q5ô» Ï ¾›|y©˜ÓÎ\”ÙtÖwŸN¾Ö†ˆt‡þS‘ÏsµŠ{íc  Î àÞ=d++õŒ–6Š9ØTè„“´¿ËPÅ!sf÷bn\ˆ{ßyËò…¢®JůÜÅJj…:ôÓæ~žˆ˜xé:7ƒr_‰·á'<<+,ú™ð»2)3~¤¡–Sy¾ìÛ•.QØ&ÝZQ€&ëÇø½¶Yö^GÔ–%窡ä?ÒÑ4‰xÃkC¬`«´lîG_ƒãF=#šu„¢gýC°8šD˜:Jª6›Så½fzº½ñÔ_ï<#pQÅ'A¿,È2©F ¾³úå>Æ ¸4qèà:†«ÇPÓnïVÂøYmÀ“¯M.ýo…!EÁÔÖØ FßéÖ †IˆVÕÚÓÛXé 2IÏÀ‹ÄÚ–û&?Öw™¥Ú±Uœê+ À?^*©ì“¡V°u”::*° ºà–^r¯ÒvÆóɤVW¬EëÃR 3E›ìF«‘Á^‰Ä”æcÔW N‰ôÍ–h1J89hQís£§ÀÛDÛ+¢È"v䥯['"î0²ì6'BY±®æ®7’>7 äÄ%ÆaÐõT·º³±ô”ó”JNVÿ{f>ÝÓ}H˜3|ÒÑÁ1@‘ ¡¯.Uø§É@:¡_»:,N¯oþ0VÉÐeˆñ÷8µ" ïO6ŽóZRåŒa«¹fÙøB@5ƒlÙ˜ Ç–Þ`«*ó4*¡ Ë“\hæ;šôãCË{œÄX +Ê/²ääx(à ùfËEÖÍžJ (ã>رUX S§k¿@´£¢Z]—ÒlAAF¸+ Aòñ„¹Iú<‡m&mSÜou&Då6QÔ­¡]çf ØVI\ں曼I:>ž¯{dr±Ç58‡¸›]8¤l¼´$T½]IvÉâÕ0 Ìš_@ÎóÕC6ÝŒ\ØG¢€ü+r­ìÁÔƒïA°·•4ã¡ë‘ú?­=äT飭»¹»ë ¸¥]A§ÒÉTFöÉ:É5æƒ÷"¶BU2cÜP£»Šl®Miãi˜bøÔO¼”@5PÑV~ðü¸ÄQy5’¼¹áʼ}„bB—;Iì`¶¨|%.$’—ˆjý£k²ýf¼÷.ó ïªÅv‰ùëõ¬ ,ƒmå$ün™ºéI¢—° a¹P 㓬ó_}÷ …Zò#p1«ƒù‚^vÛòiwÒÚ—IäÀ¸´r>ú’?䆋„4@ÀÂvCmßX8”ÞÎÉdãþhc^¤Î#^ÀÆRH’Y‰à½@uUôuàPðßQ¿¼bK<º!-0ùs3lJ~wð0GÃË"¶3·8Õõ‚¿jüÇDØî³o<³élæ}*ÉÒOêò?Úfµ¶Šuk[ú+ìZ„LŒŽßuè*phÅÝÌô úåÁS ¼±VHsX¸QŽ|¯½¡wÒN c‡xÏDLÒ«ð¨ÅuÇò‹1¦sm¢»ê¶×Æ¿tO0åwNaÚµ`æØàáza ¦ÒÇ®ãö•½ÒC1Q;(Tª'¡¬Â[ܪ¤iÃÑD?'¹êftSoyyåÀ AשoI˜¶¬þ¤ðõ»÷!S‚-‡bÊÅl¸ávÜJaFÂuø<|ÏØUϧFþ@hv®¬º¾šyCB6ïÉ3÷$FŒÕ9É Þ¡ÝPÎEŠ2%\O2K|˜°mðyy¼ÓÞê¡’4"² tüÜ_söï!5R¤<ÃN¦q8Æ’€UêÉFPk’ž>¹%’joÃÈY?9³Á@§S¨’#ÈiŠFcÜ£Hœô-×I“²:Q©õÏKNõ‘Þí‡Â'½DéQ' 37zÀ!ùZª,Ì\…•!|0".02`ÏMQ[h÷Òß$Ýi9„>}‰ÏÇhÍWít@jU¿—˂ժ¾—}‹ UÍp/n“ÁŸ#d×Ió+»–§›Ó{¬¶öX«ªÃ6Æá‡w†IŽÜò.3v×Ák@ªxûô’WCåj.ÀuØ_µ(wf–à»E¨Î* )…;%;ëàÂÇ/|Id½ž9p7Ìî„-Ç ¹¬Aðò-+\v^9wGEñÎ|L_YŒe4"]ÒËŠÙ% uå }šÝ¶Ÿ'zç¡‹†îß[ÓUz·32¬uÇÛ¥8fC,óÇX÷@#Bi7Òa£ÍV†å91;MûÆ*}ˆ,&8Þ]H>Àõ¦ÇGª.f¨ Ú67{5>=þWÙ‰5½í_=Q5ýÕpMÓ3¡³œÎ!HˆwËL7œ«”NÏʘSá×1Ý?t«ß‚×Þæ;ΆǴ,\ÚÕda´OÁ£Õ£d”žÌ:¼Õ.,­’×ßôZK{Ucm½x\¥ñÓ¨(ÈÖë%¼qŸ)*úædò9$d‰ç^ÍUÞŒþ=—6äçÇ¢—ÄÖ0¶³È…å}]Z_Mµogä2Å Ãè$ßiáIÇ}kÛDä»QekI‘ÉÏx­.׆}h"b3)ÄÛ/—1Ñ$„,¸¶5ø\§va½ßgEYY.±Þ?T§‰–Ãñ‘îºr¤ƒ öxXj‚Iž/ŒÇ=øý@¨I endstream endobj 126 0 obj << /Length1 1523 /Length2 8171 /Length3 0 /Length 9184 /Filter /FlateDecode >> stream xÚ¶Tœ[-ŒC 8Á¥±ÒXp·àîNÓ@#ݸ»»Ü Á!¸»÷ Á=y$÷ÎÜ™ùÿµÞ[½V÷·«vÕ©:g×ùšžZEEÌf–†AYØYü E5vÈÉ r ÓÓk@œmÁ›ÑéµÀŽN”ÿ?Ž`Sç'›¤©óOȹØØ9ìoøÙyø@È÷/"Ì‘ iê 1(²ä`P°:½ÌÞÃbiåü´Ì¿ F;óŸp€˜Ø2…M­ÀvO+‚Lmê0ììñ_)­œíùÙØÜÜÜXMíœXaŽ–ÂŒÌ7ˆ³@ ìvt›~7 P2µÿÕ+:=@à âô—]fáìfê<l! 0Ôé)Âjv<-P—U(Ûƒ¡‘þ"0þÞ;+û¿Óýý;ú'Ø‚ÙÙ›B= PK€Ä P–V`uvwf˜BÍMm`Oñ¦®¦[S³'ŸÊMÒbªÓ§ÿnÏ ä±wvbu‚Øþn‘íwš§]–‚šKÀììÀPg'ôßõIBÁ §m÷`ûëdm 07¨×ßÀ5·øÝ„¹‹=›&âà–•ü›òdBÿÇf vp@>nØvY±ýN¯áaþãdÿm~êÀÇËf°xjì±?ý {9™º‚ÎŽ.`¯ÿtü7Bgg˜C@Î3°%ŠþOö'3Øâ/ütøŽw€>ðI{ìàïÏ¿Ÿ ŸäeƒÚzüCÿs¾lò’âºz2¯ÿêøß>qq˜;À‹…“ÀÂÁ ðqqx¸ø>ÿEÅòwÀBe¡0ß_Å>íÒ¿ výûüþž FÀçR‚=‰ `øGã@n èé‹ýÿYéBþÿþ;ËÿMãÿ[´‹­í7ÃÿÿÇmj±õø›ð¤Yç'ý+ž¦ú¿Tmð_3«6‡¸Øý¯WÖÙôiÄ –¶ÿÞFˆ“4Äl®qYý%–¿ìš¿‡Ì«Àœ ¿o ;ø?¾§ÉÙ<ÝNOŠüã? Î/)ÁÌO÷€©££©:ðIHÜÜ/ö§Q4»ÿÑ0€ s~ <µç°€9¢ÿ>Q^ €Må·é/Ä`ÓøqØ4ÿøxl¦ÿ >è߈ýI™làÿ€œ6Ëÿ€Oi­þ>å…ü|Jlódªúð‰ìôþWß GǧÉÿ£Ì§MùþsÍ€Áî`úÂ, $l]ÜrS)FæÆ²=*4E¿­ÂÈâµàØêr‡…úžñsFàªã•Øû.œ¥M)†KÑEªG¯ƒ/µ¨aM‰ªÍ÷ÞÆ jÛÍèóㄽcb5=ÏÈY4Dw¼¼µl¿À·ËÑç8¸ðb©äá߸u˸×ô”~ÝVÝùüFã¡t’%F3Ú  hš>×,s†˜Å™… ï‡;öôåÕ^öØ/*¹„×è>‡1œ½ôÖ8bog<—?ip8uБèS ^â O¼ôßû G4çU\…ÓÈ:Ä‘÷(—ix>/GÍ`´íÿÕËzT¾Uòðj-ò¥;8´uhçÚß퇧t‹D-ZÔŽvˆ®Œ{§P“*9,i—„€£wÕî}3ãr$7Q”F1óǶ­|‰Œ}á˜L€ÎÀ¯¥—UïÃìâ®>_ÐÙöÃ2Oª'Âñ+o¸.®.ÖþªÊ”È{™Å¼äo*©4“ÖA ²7w¸ æwä²j|p¢‚Œ¥³ù?™ƒÉ;Ô£¯ýk_g[šŽ.ªÓ$ þ°“ñÏdô#ŸZ¯Á&ýÔe,‡ŠßãþY BaÀ*eyx6Y{¼›>¡â´+€hgYõ¹%£÷xÑ]J=.5é¢Ã'Î2^Ôâ‹t+}ôð…Y·+ƒVGßbÔ9J'»áSÕŸ£YÁF‡Ðr³c¹KÙªrøæ4Õèp)äYbHUޏ΋—‡Ñ{Õ&‚¤M;rl*·x,)¿µçü­{™èÞ¤üFK/.¥=eÝrç-üEïwÖüýÊè“ã´?K·†ÊVŽ]ôݾÁˆur£KzKŒ§Ìħ88IƒP•ë*Ž}Yѳ.ÌhQôjèc>!¾XC ùÙ ÍæLšÅk-íìB.¦¬îób+į$ب¾Ï‹7Ù”~쓱|¥ñ§š)r¾¤@†á¦QLw8‘°M^¬f·ÐEA.Ì8®oÙÅÓó„u¾•k#‹?Û¯5pø0Þ„œ—£P@åA²3ýTÔÛùm2é$ù¸³!M§¦hV§½¢ä1Ö˜•þ/›@ìÒ*½Q»‰·ì¢dÒÊÂç¯ «Ž'†ëøÔfñŸoÏuF%ÌȈµ^ca'äh~_ìV¤­«ÑµÒ´éìL}æ==jÕÿ•‰yâqž[Çʃ»ÌÄIÿ×*íˆl…ÇšYÙ}*¬ár)EÃ%¼q±Mþ«ªê{ŸWç:²šáâì}‰Š1ýß;ï,yìl‚5_Ù/‘)î¸&ÝCK—zgµÃÂÌ[k‘Y¦£?-m`»x÷¥òJôg¦ ‰.–­¤8þÜ%m#ðüZ‚{vñí§=ïø#Ò-êõ>Í×>ÿm¦¼Þ í  üd¨FjFÍb°ÌøÐù’ÆLÐIò–Í ÏÜ×i–ÜŸˆ?ë”-ÒÅ«5'$,¤xYk0eµ^+}2Ó…Ç!I{ÏêðÅ«®#GBÄñÕHìi­š—Qp±öÛ7äWÃ.Û/®¼5%#WRËf¢˜FŽ˜z~tNª¸ÛµeU:Rî¿XâcÓÔ yÁÓª,ÎæBðÜíF‡#àÚW{P+ª Ðdtm% Ù?«ílgPiëïå³·QÎlŠÉ¶ÉÄWe?­¼GZ 4kÖºDš†¨­ŸÔõþôÍÞBŒæûEûŠ Å ¨¡$ kÃ~tÿS‚7wY%Š¥'"O|€¥ÌÛòð1 †€Ø…©ûÅÚV–Ü·‚k$h#$ؼ͒r+ zfó5~2ÓîDÊ/›Czt{7…Öp{æÄö1=PÉÁß´Œ¤í–"„òƒ©îînP¢‹Òl†Y¯Nî·% €ÅÄË\¢:Bþœ ³æ_˼ÕCµÛh64]7–Ò³9Í |7ÏV–Æ_ï ^!æ3%¬ÁiéµopÞç\ê½úî‚£º…Û³úKžX±&ÃÝl8µµ“¯ ¾Ñ%1ÂIC÷ÁJ6ÍÒ.ÄÐ|¹K°â9~ùøeée\”ÐÏü/WY._tF…Ô`çÓ&E¨»+™Z·3¤MèðÙ„=ëÍx²_YïÊ’ 0GÐA¶a{\÷›]m¬‹SÃŒ¢Úåù¯”4Ë6Cè#Ïß…¼ I1–21ª$/èç[ ÙÖ2ͪ/Ãßö¹µdè¾#|g«ª=íÁôf¤Ãq@îÙÓ}MNäî Za¾ÖòVG˾©M“ñkvDöƒÒ3*&J½ñ •bHŸp_²û‹åc=>÷ëu ‚cëSLn¤±Ah´©òXŸ¡Ã’4ç©f@[‡u·:­ìl‚7­îõÛØ:¿$Ò²Ãt6i:„KR–sÙ¤!Ãýõ¨ŠnÜÆºI­T· ÑJ{ˆµY“ÚDó˜INrͨ!q<î»àH÷‚õW Ù'?æ^FuC9¶[¬‘ýÏT‹ÔgŽjOéGOäª$¦¿.ŸÉú(tÄE¨ë·•ßYɧ­î%s1(tS'í—G%ãg³}¢!âfÛ&[k…7MVƒeï™Ý;•xü¾~ÓJqÿŽ½Ü˜2Ð︬„]e ³A ¯oªK¥šˆ Œ Þª}ô—yÒ§rÉý>¯|©uu¿?wãœÐÈY×\çÁGšòê—Ý¥Rv¸Ô z¿P¾d‚q#€5‘fKš‘Êóñ1‡¢6ÑKy?:ïñ¹W,Í# ¥ÇÂpjU0g^Üg? ÔÖöȯ·ÚY‘š«²ÚHUÐç]ájB_eǹKõ¿hÚrl_»F³Ý&èë$G†~O•uUMLÅcÉ3ªÛeí`lI2°TÉ(ø”ÔpêÁxX‡Cô¬žéy¸ˆ~›e;ýxâ·(šˆ3À¢ïóHÛÑoåž ø)£ü÷NE Ö)”íd¡to8½_Ê$¯×*†·˜$ÀyiÆ´YÂ.>)H|K|Vë%|ª)xYOwøw4¤*#RÝ1F{T®U“¼’oN\MׇAìú7¢êêLq•]âà(ÛÔñ@¿#Šu'^ÛzÀv˜Ï;iŠÙpÑêK¼­_|#S%$Ä®ß4ÃTð–ˆ’obx°nw"àL[jåÏ¢4®±¡#æ-ЯSfk'؇ãyíÑÍgèDÓhbù™z”ã€ßåÀ‹Å‰ ]J™ªd6keû¡:Ùhþ¶MWº"™kÅC mŠ¥$®,›‹FŸeÁ'YÈP’EDjµžÇØ–ÉèÓdŽHÐuøå£”:#2Ô©N~ÄC7É]º¢QvÞa1vÛx‘– ã“mFG›Ê»Ög“H¸¶ÆS¡£N‡á×}î@rðbþ¹œe›áQE(9îÞÄKM×Ü‘gÁ1µýbQÏÌC€ç!Û§½„­ÖƒÝ;Íud|53Û¶Áñ^xø1èÇ"Þ›šÌô­Ëb\¯á@`Èes¦áË’IZÁ¾ŸF';tÍ—H”Þ&5ø?sÅLqf+×$ ÏÅ{MžÔ¹Ý¥ßÓ>Ä%ϲ Õd—©ÏVÀ8ë/‘^zTÙïx¡0F¬×Ÿqż¹iŒxZè„×C,™d©Žƒ^4ìA£šPÂ!M-?RlÊ5kEõ¨úð I8MMïŽb?Û774Ø×-}J6IO \(?Bšß\oÇ;WEáSìžÚI;þhþl¶ûÑ¡á6xO§²Øƒï–6~bÏ.…¬ú“”<ظº(Gâàk6Ëg8¿WâK+±¸°L½˜.´³¹_)—'ùò¬´§\­š»ÊÖáiU÷eôI°ÈÌôÜô:‚lrrã'ÿ—%»°—ÏæSBÆ]ö+0©íE²:A®:þÌÏF|­# +9Tû Ãgñ™‰(† O5½S/„Å˃pŽÔ¥‚ÜV…¾!ÓYÿZÑ<~ôøé¿%‰ü5×6ÅÚ$ׂKÇ]£}¸ùÚö¡ þ—ž0äŠpAIˆaÞ-à .š}ê´P·0ÈEòÉ—{ÿ½B½ïí÷ž(4„åGùâ%_õH›bÓŒú.:ô1LV!”â‹ W°“MžÈ J ÊâÞw«qtýRÏ{'QÍL§^]’GWó¶A‡ì¢¢š˜~}ßܧE·[!¡¼\²J@6{Hm·Q?`Mßò”…ûÅ&ô¡ÙÈÔѳ¾Àëå㛜²7 äÓP‰“îÕB㮤Þ×ä5B×ju¨vD-Ùk8ÕçïØ#çÀåŸÌ*‚¦|²“ ÍÒ¡q²N;ÂG.Ÿªð ˆ–Ð!íY Âe%×ÕÌ[gxq~b%ø8QÛdhQ¿®¼OøYîüùë`÷ëÏ×Pô&OŸ¬Í¸åæÕü±"~NZ¥ËÎ÷;{æÎÕÛÛÛ®l¥úönÌÊüBt¥ôŠïúJ8¡!û2<'¹„?:´ˆ/8á Þ+u3J¬EÝN»f'Ùß›&êÑQÊua;œöŽ|Ñê47£[£ÃVGN§¾‰/µ•B.Â7ùŽK<üÔ¡¿äuЇM¾Iÿ"Sà‘oÖgjÕ'μ*Í.§—rË#ûŸßõ˜S'YKŽ ¦_K§Ä"#„¦1_gÎäByUá+x=T;GîN±éì·1¬šz]òi!WšÓD3ÝoÊËQÁ·#åþ–ÎÑ'»ó&f¦PÚŸ?¼S¤9zDT—Ãw >³KÒ®LÉ¥˜Oø©â©B VІš½ÝkX_ àën„¨;ˆ8a!îÓüòª>8fÑÎyMÛ(…rm§?Gc„>3>Iÿ*¨fXdAýVôÈIn¬}D¢ûªª ‰ëã·Ÿ‰.¹·RÓ.—²”ó³‹/¨äC¢Ÿ„¾+Ãm& î*€pc¾»Pvþ6ɦL8^«‹÷+m‡Ã£X“ mÀ«—îÐÃÎÿçoå{j¤JB³€s0v>º”ã®´øã^wå§·/÷¥%ç#1Ö>˜T)QmÀ±Æý’èHAXºÒ }&üÞHþÙÅZ·z–|*ž *õÓâuPIá†ÒE—b·°v”ª+NKÑ”ömP`ž t$oÈZÍîdšÜêåÀŽ¢´Üð{Õi†uìôšDzaŒ}œN~E6ò@§Ó@ý¬vˆ7§ÖS»Ñ‘M«û8›÷R­L„oɤ“ Œ¹¯L±XkŒŒÞv¹ü¬Jk«e >jÌÃàZãE¹Æ+™ë!¤Hž—”FZDýÅh¿šçY Èš®âÃî¶;.H\” «l™#»’‰ÄÕdH¿ >ðæ®ieÓ5² T…0fÞ¸ÀßއíÜxØøÜô½Ì–/> pTÇŠæêŽUGk¼ì_2ªRáqø M6´‡§$tskCáÝjÐCeÁȶZ;ŸbøúòÓĬWâ°ØÖ3Å÷ØÌCD$ÚIS4´pÊChW d™šx°]A2‘ÆÒ–<[6u á"p$†Ÿ.À8Å0‡º•/$kIŠXŒŠûo?èÌ×'퉺ÏÄ€êt –vÍO wNÄcœÍWÙûûs-³ÃÚù>s,z(=¾ÞðÛ83¨‡Ã$h/-ž1Pa.l/hÓ5=¨Ÿ0Å禼 X ñçÈŒÜòŠ|÷3²LI|…Œîfñ˜‰è Êƒ¾ÃÅaC×íPîÜ,Âì\5éMo¥ |îϵvðì×gä«>’džësmL\AÖËÞ«jǸ)K™ïg&äm^‰h²RºVw'È ññDIJLâ_e_UIÛÀ¶{åô“2Ì"˜_q*_B=¹=§)[±`g¯jVƒ_ª`Ñëòûh‹¼¤É 8cP€ç0å¢ðú÷ƒŸ¡âAÄàtÂááÿe90ô(Y±Ê%­‘ÛYꢇh©Ï«í0£ šôŒ‡UÕ8»t¯‘ˆ%ÒeªÔ…å;1lžèzÃʯñè}õ3é¬yëÆçà YöL”0«Ç_kZÒÖ3n¤–ƒ‹†ÙÀæ‘„uèžýv›X4^L±P?ö„3YøÈâ\™]+yºªxÃ’=?3wí‹ÆxÇ/bœº«ãNÒøãÈj£žë…¥lF8&9­óúûΤ*lEHkÐâÎcM"Ȼԧ‚ fB¹€:Æ%CÞ¡“<µS¡fæüñÁ‚Ww†$eCª+™BÕ‹\!:ÈHîc’“^Ùë#}µÓ*?gôD„zÕá™7vÔ— ¿ò¢¦hºÁ/ú2ÙÙõ3ØMrWØCéubòv¡êbF\Ýø`¼Ù±`dïêuüÑ+A·e+*¶4Èוó]@‰Vü 6ÃyÃàÈê´^ÛzLعħ  ç‹‰À¥[§FS*@j6ì#4 @öú>®dGÇ~—òÛžEwÒÍWHþ/­Ã@ÿ‰•zuz]?¢ÝùÂI h¬o;ãû OKbVÕhJ&|Ð@¥+§¥‡kWRAûçËØ`kà° ¾Óç…éËíTZBÕŒU‹AR„ðŠ‰·,¡è—¬^¶GÚ.:,ˆ© :‰5ãŒ[”¸4ŒÑžÉšq/j•y^`V¾ÉH!ßu耻û’lŸÏ|žE)áa;Þ" X±ü¡«°N?ÍáCG µûŒ¯ 8Ò­«³Žû[”Ü©MòC1½ÓðUñ¬˜©l)Ýt»¿"IUÏÝöe Š?BqÀk,Ý…¤Rk ‰DÐCÇèvKop­•-²SØöžØu[1†_Ž>0§%¨&ãƒýÍ5šÖΪîLj1¥¬Ï‚æXh‹Ag’ŠÀ­ýQæÙ-G}1_ÔÒÍ%€7È”«Ê!w~G«âæ3Õx€IdÀ€¤™nݼ‹jRuñÝý`  Œz:ûâkÌh³Ç…<ê·rMÙÇßÑ\Zmt°×¿ÐÊRØ’õ«¹ „9âþ”¸–@æÄ0ºIx6 YÝ/!Å3;báÓ#îåã€À††·:P{IMŒƒŸIüM¼SÍðóíäýv1ß CÛº¤|œ*ð€ü{«§²3ÊvÆòG 2„E%Òå.P;hëÎD¿ëSÈ—4Å‹9ŸA Q¥¡Žš«»ºNþ~Xî“ MÞÞBìFþ±÷ú+ßu/ÝUHßW—Þ:Y£Ó7Úè°mŠ™À°_¦Þ2ÁºÕN â-öâ^×·Ùëã›5öÆ ~”N‚õ›{jƒ·7X¯ÏH…Ùòál[%~b…u7¸œñ??ðC) 0Sˆ¿Á+D¶ {c’«tÖ¸ ǯ]_Ž È½[b< ¾¦%4D|Ÿ¤ëMú^ã°”Ûs>”_h±ºŠËN4%®yeq›kÿó®|ÿ÷žx’%9Ïíïõj×Ñþ²†Ü›±dˆ¨0Í+¸FU,?Uê÷ž”µ;n¨_óøðä rÊw]2ýp? Ç>;—y®±zÉ]­ãÍ~ó¦Ýj endstream endobj 128 0 obj << /Length1 1357 /Length2 5946 /Length3 0 /Length 6879 /Filter /FlateDecode >> stream xÚVT“Û²¦I ½HG~)JOB”Þ«T)ÒB „PHQš¨ M©ÒQ¤K'"MŠ¥JQª HçE=÷žwî{k½·²V²÷|3³gö|ß^>oj!©îŠqAè`Ð8I¨D Ð46W )D$,l‰Äy#þXAÂ×X?$­ôßpM,†#Ú´`8¢›1 ø{P*¯UP‚@iDñ_ެ  @ºÆR€ð kb|‚±Hwñ”-¸(UTTø¨£X$†Œa8Šx"æ X`àH.ø)D®xàp>J`p``  å'…Áº«ˆJHœ`ŽðC`®À¯v ñ»1)0`éôûc¶À¸áaX@4x#á´1ÀíŠÀij }#àªýÇÙèƒð×ÕP)è¿Óýý+ý;‡cP>0t0í¸!½ÀU#)\N€¡]9¼ý0ÄxX é s!:ü.訛0buçÇ"}p~R~Hï_‚¥!^²6ÚUƒB!Ð8?Яú´XœxëÁàßcõBcÑø?k7$ÚÕíW ®þ>`+4Òס¯õ—ÑúÛæŽÀrDA |DÜü+¹e°â7ýe&Ö†÷ÁønÄaH7ñ„÷ƒ Ö†ÿïÀ?w (pEÂq€ ‰ýhF¸ýÙ'E×!DâAȯϿWDn¹bÐÞÁ»ÿ.XÛ@ÝÂÒHüwÃÿ†440A^RZ”–ƒP¨´4 @\„ý3‹) ùW¿cõÑn@ñO±Ä[úWÁM_ä/aˆÿÌe‚!2ˆüMp{ˆNü‚þ¿iþ;äc÷¯,ÿÁÿ³oïߨÈ/ø 0Ò;ø/œÈW‘ûÆ¢Ðÿéjø#Wc„+ÒõŸ¨>FÔ€:ÚÝûß—ˆôÓA!\M‘8¸Çªü±[ý˜70Åø!=(€$ùŒ¨*¸ñÑð#òñ7„ ŠæŸGj£á×_ê’–“`X,,D1q'à¡Dº"‚~3K¡18b@l/ pÃ`A¿æ)«€‰ÏÒ/#è‰áþX,QV¿O<õ_ûßF ‚pÐÄ~ù¦çË›¯÷*Õy%û•‡…­ˆJâ'°Mþ Ti¢Ï³¢f°»êi=íL´EvÔ&ùñ«õ¯¨bRÌCœ’͇Aãƒìož¬ªWøhx%-Õ–B}C¯Ez‘ד¶çúú_b0ÍgÝ ìÔ ª"”M½‹[4[z.oH{Tö^òŽU¢}dñˆpžËãQNJœ$µËVãÈÎî0KÎÀ)¿A²8(líŽL!ÞnVúîþhȧ§–Ò~­\B\vœ|ä;,ï†.à5VÒ 8>àK § Fìý‹âe>¶’8è†ÍÓ(\ F&_z¡ÚM~~É}îçZ m]/ZB7®b2ýÉÒ±Dù 2¹¥å#ÈNN÷ ¦ ü_KÍK–£' š{¯ž—0<Ï­¬¼s1• {{2€_e’_w›+½â| ˜žßŒ^‡ÁøU8Á Œ:Ç#–‡¼-’-pYÚ–2ŒcU¡#Ѹ¦H8–Wßâá¢ØpˆrÌ…,spp¿ã¡ù^-çåQ:Læy5p¥é£ï͖׉”%'Õˆ‡ïNzâàçRЪ»Eý£Öq)¢Ù)飷ùY[¹ûùÈ‹hîxu¤/%¤9¯ß±{V›ÚÞ¾gÁBO›.Ö§šVMyJõ9uÑšàŽiNÞH>å~[ñÒ¥ yÉá·×\iƈ<¸ÙÓ +ª“—¼ÏP÷v÷Q„)ݵÅöñ¤V¤Wr³çæèÖgçÖÌê6ír+Q õÆ×jë-ZO ·ÛmS­uѦ=–‰ã” EïaNB›_˜žÑ÷øk¹²ÅŒËÜ5£ÞÍÐಳp ö—¥[É. jŸ-j˜›2Ù¿r’“çy?Ÿg3畦À1¬ŸY¯"Ìs´6|4~qjÑòá©hiøY!}áþSßÙû¦ú‚U’Æ,)+ׂ¼úÚƒkjÓ¯G¦²Xíf ä="6ŸÍO*{¨OVÎô¿£TbjL¸Œ4L³AÍöÇüÛŒyë¤íy>HJÏÊ™¸és<;ßDyf¯Æšðõ“øè‡V5×~­ð`lsšVÅU»¤í©¤Ê«)"ÂŽ%M`ék­u¢uÍÜ¿¾'t}™ÙZGm6p¹5pQaá5¿+ átžŽ¯µ4BmƒIw?›±Þ5ºÿ–7Š>Äpܘõ‚¿¯ðEaÖÖÑ8¸ã—º±õª× Œvï‰nYïÚºM­~:YÏëW9O¨ºFU‚&5ÛËZVå†(¢S·7Ïæ ;~I¡Ûgï,H!ß’•,ˆB´{=ÜX@­ 6_XËP½«Uå‹ðmKog~|§œ—ºJ2/·„:H{}Úùøà{R¾˜ æõ\Ń(Ûq^ÜDêà¸:‚ 2wQ97²¶Kßà·äæð.VSÀÏU™+¥ÁË(kMzV¬’ÉîŒØ™¼•‘<ß1  ŒlP=!Ýf²²!˜þ*7†B±Œûò6è…úÑG¦Á ´ qa«Qþ<:Yã]Ò y©O+ƒÂQòí”W®9z“ó÷‡\»ü³â‰€vá%F]¦½¼%ÂçÎÚÎf8Ï&†å× œ“ì¹OŠ:d Á6M5=S'u©Ôâ E[!.—òšýƒi6s>¬òê-¹p;bìW.cÞ<Ä-ö¾ËrH‘ùrBûÐ*ls˜§–Hóã:ƒî'‡ ‚n9Å—jÂ>ŰmªÃܽp·-À$«+†ÙÉ!{‘†Ï·LsŠ›rëçåvêßJ.’¿Ðj#ɽ‰]ˆÑŸ¶§ò¹œ‘_öµ.A[h*ŸYÂð°ÛHØ^Ä™ü±ã†8vD# ¾<•+s«-”™1{þÕS%ÞŸËt®#PýÕ”gÞWÆÚQƒ¤ôâãO"Ǭœ¶…$ÝhȘu§ùÊ=‹9U–}’ÉNºc²µ ;eMZÜ=d2úÕÂè[剦ºÉÜ®¢«)øj¿çÊ+(û¨È¨´foG]“nþqJµÃÕYÞŒ$?ú ŠÆù³)¼/(\Œc#/²ˆ}¥®¹H^VD_Tý¼¸ŠnQÏ­ÇgeW>ä—h|ÃgjïÆ­I™¡Ld¹¼f–DØHY3ë½e ÆûLeä-9SKsöÚ$ \UXÐé Ù«ê‡;>Çë}¸õàO4Ü3˜uF•})¾øæ”ñ¼Öb·µ­ t]ëa¹TúnHŸù§öpÓi/m¼Cä7ÂÁòµ¯£)åÓž ×`N°žÖ;’xÁõáuŽ¥bÉȃ³ ]¥e^—«S׿‰2oè={M5¢nC6©½Ú˜ÚyZüÓÊ=çŠWµo1‰WG•FùÒØ[1ϧà Ì cú‰é„Î4«M*¡¤¤bÀ¾Âe>Þ€F˜‡ÞÞíË¢¿ü\FƒaÎâÖ#Ô;A(²T9ºõe1ìÆ^zmKâeûK›rþá‹×•øÅu5#”¶_êˆ ˜0‡¹o¬ÆÚ=@qe¨ãÔ,B×vrPSâÊ•e×:Yõ(­sMge¾‡’åú B ›­\–k>4su_¿f“½™&U„kz®ó¶9 ;Z\ož[ðŠÓm²ºipTølwô^ÿÎ-]ãY³ð廦½›ZÞ$Ÿ·˜-}ºlßø,ìÒqÅ9Á§Í+mÌx¦|Y(™Q=ú˳sã™Vzíå”m¬†Ï2óÌwA‡M2kßê¬M»º’ä…ÆˆGm½èáá\MòÇ+›ÁÙñÔ«/=Ż滺íŽáSúRo§-—¥9 61^Ý+âž ÔFÜ÷¡¾vøã0xz¤ï`çút9 R]i^r½f ¤Vºyû\S G"­Ø×îæÌàÀ-öc&f:ÃÆóßóG +«2{ä¾R:N|6êéy:a»·å¬5‚ɘ­UF†¥C®¦ù.Qßð­Œ.HäÈn`õ{Â{>ò «ø-QM¯ïPžTÚª•ì7t—È”®0Õâõ›îŽ“©°¾ >³õ3¨ùêÓZ4¿º´r«Æ¾êFÁë6ó8æJP›»SDxÍáO_)íPy7a)©j“àòN6úÓvî'"»õ„œ“ldò´Öh¾z!ÅÈÕ®/l[:ÆúG&³ÔªlñÝh©FGá>±ÂA¾*ö^­‰ÐM;Õªõ› 7ïZC·HŸÑ‰Ñk]1]€öz:ìÇÏÒ†4³î¥å~¢üz_…ï›wŽª©Óé&n“×÷Ã0!-É(á"¼,a§–dgšæ#ïLlîm'›¥ [›äãÄqÁÐä»t—oôÏ_7‡’/NæßNsÞU«©ajúÑ{¼[¯¥Fׯ>éˆ÷ʰ~ÊÉø,jnÃ(ÈWW´Ÿ¯Xè"ûÍ&^ॠËN£aòŒ„dΓúbæ›Ä\³„‘d\ý.ï¾4ÏËItø†Ôzy4p³¥—'-´Ì‰pŽu‹s&©îÄk@ä™Î) „^¸$–}ÄkK"³/ åNþEÆBcüéù¾Œï}~Ó—^Óˆ÷/|Š¡OOC>¨›ø¢¥PßîuÔ¸_ñCpñùÎT+FûÐØ2 ñ)E—½»­mÙ9Qq(>ΧþÖ‰¶Ñì'uH´yg<ý'R £q?K947-D.íröZGf³5ÿöf5›ù–ÿÖ#ǵz‡W Ò‚1ÏZb·ž‚‚Øk-´Ùå* Ò®fnZ½URÅ<ó¥«b²¶gŠ’ÆCyz.,þö•v¯j¡ O·÷Òj2Ð+·ŽÓ–Z”™äãwWááçŸQ`Œëôg?ß×ÕLà‚fõ‹¸1“¾¢ƒ2ªÎ§}Q§²5¤âÑ´c»%\–êÇ&êó~àçtÐÖî©ÞU-ó7Éâc§˜çKKUA:_wÕ¦j“uuxKÎ 1°V[Ïú/¿1"kµÌÈMS€åöɇ¸ºOâ]”ç1{CwÙëJàw²âa' %¬©­oM-k»ÏÙœq+DQ¶™¼ç}*¦øóÕôÉE¸‹¶F‚Ú_O¢{ÇŽë.©F0ª ^ùÂ|ŠR-Ýì¸ùh¾½îx¸Åv#ƒ£UaÔÑ ÛõÍØ¹yÂFb¢gCRôˆ†nÊÄ!¦ääã!%ƒ¦ÅV¾ËXïpK÷©}÷>nŸÀ)-)°¼``¾©éæ¬uG°ÑÜŸÚ{vÎàðæ—ùaIÌcÎÌ«ý¶–éšKÈ‘.ðÁ›¯‡€!—cuQ¬%¬[ó2ŸHy™tîI) ÕMçâØõË µ*kÍO½?½â¥mõf™R“t…V &:wFR2®V«ŠGÒóŠëLý¢6ü¸žúÁz’‘aòË´µÝº48¯Sþá¨ù]¶¢¤eË'Ðìê'À'•MìJônfúzBâeÁsºcÅß.7ó)½$”¸ž]c·‰@‡ê¿Þqè+)ò/sB©cüE&B.>éï}j¯X¹ì@{’KõtV·ö«.v}$µå¸÷¼{P¢¸7¯ “ýmT»eÑÅK=>iO¿´b0I¤b§2¥ú1…"õÈÅ1M;0•bÑI<ú‚¡Ê:í7tg9%f.Htü³[ lô¼ýz+ÿ G3iýv_†–üÓY = ¿Ý”q3E€æK§ü°e f]ÒZó4}®´F™Ð6¸+5˜èGh7Ÿ“Lç‹ †¶VNr»Ò+*PìZŸ4ÝÒ¦C™8‘±¶u}}«Ùy4+3к—x9Ÿ¾ÆÃ.ðp9òs>½ÚÇ©çå>÷àÛ”2¸wN‹ã‚'ºàç(ætw¥ |ÛcÊþû¦ꂇI$ÕeªªÁWêºòª5ü„.0Ë)Âñ<Û…"ò£mñx§Òµ † õ&Ò6½¢¶ ˌߦN*çïQ i>À¤+ù;е8 X)|ª8˜jü~úèÓüIÏÀÐÑ9)æÃ‰>¹%Úw¸¹|TßäšwﲿU Ç–Ð! &ôjÈùÑòÊH¤÷¿Ð…/ð`^wàØ¹bŽåÔ‡Hxï…pŠWŠÏÁ´ÏÒu§@»Égw7U5Õ·\ªÏÒÙ\„"ÞÉÏF +š-çñ†™ê8kÝuˆ£ Ë>;o÷3ù¥èÓ{—|¢Š®Ç2~©ë)h¼~¡Ü?_©GŒü1ö‘ÿ†dM‚òƒ’j¬¦ÚÏ}”g÷æVVŸûðÞúei™vÈä——ýå0ÚÁ!±ó<â,/‰ÿqNCLudS’V6b”ëåŸb^”<åø¾ÿd”¡‚Ïm¤Ï)%Évµ b£W*,[)n<¥GŽ \çÛnR‹„í{Güèœ| ×(8v ‚'Ë ñâ%ø.?Ò %*`1÷ôY¡Ÿ=‹ìÙ™XÈ=êi¬†ªødŽê;fšTír†ÃìÆŒ¢ä”€ÜS=rSô)A]J’”a²êïæt'ûÊô@jY×5…áv?·ºÁòYaYš+Sƒá%ÞÃc¬NÒ½ë©ÖÕže®ÆW;Zø?“$,ZL¡¥<íM3´Êù(Ò2Å”ø·—Ð[±ª3t‹i5 [>3JµÖh2‡ÌW\¦Ò…àÖ«Q¢6ߦZRlA=Gäß'Ä[S.£ ³fëŒ"ÛX+ÄØÊ$ï@é"Ä_·}ÏÈÜä¬ð$ ¾›ÒûþÃgŸøsãÍàwÝVƒh7o®µÅæUõ‡ßݶÝT`rZšLB¥òõóÞîIÍÇi5b²›à<1Ö¼ BƳ<åE'¨yW!ªª”!H"biÌ[ûxTG/³(gÿ塽Mö‚zwKæ ÙLçwÏ×ÓCSb¢'¥¡-¢÷h³oå¸6ÉSH)è4xi.gI ãr®™Kœˆ,ÝŽñÜѾQéõüs$Q–<3­3Ù&]+¡jöMÂäo¨bšNk*¬B6;—àßë.ºáé sÐ9fç^Ú™¼ÁÒ˜WÝCç…™7%A§©4Bè—¾<äpÌJüðh¹}Y’q˜Z»`ÇËsi`õ# í%pŸçƒ\±ç.X:1üé‚Ëí{;Ú·Ä ¶é¤£l§J—ÃCM´|žeXòZÊÛ ;Ô£3pžeßCdr²&íÏb©LU`Ho¡¢9ßõÌ7ù2£àYøÙ‰±²õdÃé£çŽU7hçd\6…5¸Õ„ÊŒ]kÖ„.‰¬ú¥Ü6}k /²=ÝR’N2¹ðÝ—™¢ï£ìƒ$¡ë‚MCßÝ´¿%DÂT©Ù¬ã²ùÃÛØŒ4äŽCï|Þ–þÔs’ÊKuntå’ý³d“مW#‡5,¯Ý§àÐ+²åoõ1³Íô¼Dù4pÉÄ”ôØÞ:†åûÑ}Êi§¨³0³5jÐ0uº¥gM¡ÁÒiæî¶8o5ó†ó0—Ü]»ônŠü˜‚,Øg”‹¤­SÉ á zË2Þµè?ØëãØÙ¬öPÐ?pðŠì‚ “Ä”XX™Ï³ö[&ÉøF·í\íˆ857“ÜYw†<)>gWBu4 gÉS,íü"ó8·¶AVŽ+ÑB²ÃŠ­‡‰Ã)èÙì©ó;A†™Å–$|?ɽ+VÓ%V…~ˆbüƒ˜†CSE"^Ε/jQáä•J{ ÑÂïüÝÔ¹>yÉXVïòUŽÈ­‰h‡;dt£¤„W?.$ÄAÔ÷éKY?´¿šŒx±É¢Å¤Vq^_üþ~Ê<7õÆA©ÔõÏV/=ÃUÁ¶"Jäc£3rBã{7Ú`Bò{pÎÖ?ÉCkG÷ûìò²Wôß7¼IþÊÃs¨O“ö•ç gõ÷Å‹ ÊÏõ”{ÉsM> ” û’`Sqf]s»UE'Í©¼Z¬ÑxS o¼€<¶`9jÞ1½f»Þ¡‰€P…¬ÜvyÍýSOÁ¼\X4Òd{!"ÖDàù‡§]¶±÷#°MÏÑRë6½“grh0A¹ âšÉ¢vþ¯lŽÝ×(ë ó¶ä8©ºª§`OÏeŠ› ºV}oÌíJM}oÜÇÒDâ„kÈsKOûµšQ‹÷ÈœŸ2N¯þ1™ÐW‚xÐ]øRdg`™¹\3¬0@ÊB€„޽Œô@QMŽ©Âíl/²Í:›o†W¡¹Þÿúï1¹ endstream endobj 130 0 obj << /Length1 1357 /Length2 5943 /Length3 0 /Length 6878 /Filter /FlateDecode >> stream xÚTT”]·¦¤”î’¥sèéîI``˜™¡[¤»DR¤”.ié¥ûŽúýÿw¿ÿÞµî]³Ö;çìgï}ö>ûy‹ž!Ÿ¼Ü¬‡¡ø€ü‚REm €  0¿  !›ÿ±²™€H&õßpE„BÛ”@(´›6Ðp‡€Â ˜P\JP $((ù/G8B  ò€Ø´ùpIȦwõF@QèSþµpÚr€’’⼿Ãò.`ÄhƒPŽ`ô‰¶ (Àn £¼ÿ‘‚ó# å*% àééÉrAòùxž”#ÀŒ#<Àv€_ít@.àßñ²Œ!È?fC¸=Ê„Ð(Ä C¢Üav`}6ÀP]  ë †ýqÖúãÀ øëj@~à¿Óýý+ö;dk wqÁ¼!0€= èªhñ£¼P¼Ìî—#Š„£ãA dƒvø]8 "¯¡ûû«;¤-âŠBò#!Ð_ üJƒ¾de˜"ÜÅ C! Õ§A€mÑ·î-ð{¬Î0¸'Ì÷ÏÚ³³ÿÕ‚»«€1 âæVWúËm"üÛæFDÅ%°ìeë(ð+¹‘·+ø7øÛŒ®ßß×î °G·ö‡ØƒÑ„¾H€B¸ƒý}ÿ;ðÏ!°ƒØ¢6`Œðïìh3ØþÏ=yÄ `.ˆ& øë÷ï•%š[vpÔûo÷ßÃ0ÒÑÔPÔåùÝð¿!¸À—OX À'$* Š ÄÑ ÿfÑAþªBðïXu˜= ù§Xô-ý«`¿¦Ïù—0¸ÿÌ¥G3 àü›à‚¢‚¶èðÿMóß!ÿ»eù?þŸõ¨¸C¡¿QÎ_ðÿ@A.¨÷_8š¯î(4÷µáhÀþÓõø\µÁvw—ÿDÕQ ´äaÐ_"©ñÛéAP¶Ž¨òÇnüK`P ¬GB~=(>  à`hUÙ:£ $š¿!0Z4ÿ8Æî«°ù\ƒfÆ·øõb0mŠºehÅ%ÝäôY×18Elý´bg‘⤣À¦TŽYjTKr)bIÂÉÞÿ¬±ä¥þS¼ÁÉ«›¹Ñ…ŠW[w8ŒÕ¼hAŠIo¤~ÐK ÊN«­¯ðG‘{Õò¶«{hD w=ÆÈÙùXf‰û“ðBæón@é{ætª¯Ùj«¨´Ô §W¢ž€^ÿEÊV:ÃÏÞ¡XÃSu¤Í"CSþL@`®Ÿ^¶k*µŽ­.Uý› ù¡¢££· U&=_:Y¹Œ G¬ÉH¼°žÓr|ñNåmGrñ¨-EKqÁøÀM#ûõ¡ZV kø ›Nl“´‡ÍÁ¾*.E¬Ygj{²M¯Ý2‘dç~Eý~ãa„D >NAæÖu-®„¨pA¥üïÞT ¯™&Þ"bÒö8Å.fSÕÙoé¦á/lx2yš¹ƒ­q‹4AWHXb.‚ ˆá³TW}j»Öb/¾6T8øShfˆ>q.ßß@|ýµ‡¨¤Sî€€Ý‹Š’‰z©†\‘ðÕ¬¯¨V”qI+¨wÊ“#GF·áP‹ÖÖáToõí¶G¢ä[•LŸs*6Ò§¥§dÏÀ~Wºi6{!•tá­Ÿ.¶·MÊ‚5‰V“¬ê»Â$&ÛpiW´‡Æ„}y€å²„[,=øõy"cI…¿®ÐÒÌp˜òë]½û!‘ªÉF.¦þÚ‘™z…ý›B£ÕP CìàªÊÑðVñ÷ÅšÅ&7"7ke º¿s¼Mú.޹Ð@‹·i–ƒÅX2—:²[HRÀgªÔ1æ"‹%<\»SòŒçŒ.¢5¿aµ˜Â„t„Ç`–!PõÊ5Ç[‰†QmÄÃË×5ˆÜV‹'ïèÙ¼ù¡uaœœ¾‹*’ ^eK[ÇÓ3j.;É…³Ó(+Ý,§M1˜¿« ³T®Újz¯!Ù†aÃîé½™5lþ`h:¯tËÜùÛqL¼C¯ù*«£ó€â­±lâþÇþ1{¡¨úœâ'Ç‘÷‚¼v16;[\ãZŽž8½¶íw®½ï.UkPJ9QþjÜHÚ4õj˜ôåƒæÕ÷SüVÙß¾˜cL€/2H" t6µ&6®VJ8µúЩOÌo)¦±Á›&.Ëâ2ta ? î=—Vj~>À3J*”È©Á2pà7;Ö¯š•\´9µ›õrÙYr®z:„òìGOvU,Ð'ÉÃËg#ìÙ ù(áTË?{f{@L¶ûÍgj%¿±þõõÐD ½±÷•‘ëy;jæ¢>.ò‡õeªHñWØ8²8)ü+5 î¤n&àÞ d&æþbŲü+â~‚§hJ±É€+4"7â1 è^àjg¾7° uî¸Å5ØYóø-cíJÞ¼i}÷>=®‰ù÷ Üd5³«œÍÊ „…çÑ«æ¤@w¶=ëê "«ÛØ2ùk‰›”©“Ù}aŠaö-/ØøfæëÚj 6¯Fécc “ äz»4„+"j/i—÷|ò7Ö’•ÆB_pRƒý+·¥hñ•G0d•ƒB[¨ïÈtÌm2Ù ²†ŽV¹ébZjáZµRÊfžŽ•ãŽwi%iÇ¥†Â˘_zi5¶FGޏ÷ßµ i²›ûJ„I)9Êß_*7Qw7ƒ!ºW~ŠÙ“£~ŸÁ<ïóc^µ°(ò×­Ýês…2fïp}˽l,yCÓöº¶;À'Ú+F¬­0Ð%9”ÏôXÅ$*åXQY×K#ìœÐIÅ¥ð™#i ö+Æ©Ñðh‰÷!½LÑs[Ï}¿¸ãuE‡Úæs¯K€eçlÖ—z1 &å×~'Ãdr]c|dyŒNßäíW%ßµš\Ú¾ª! —À2º˜·«v¢aÙ2in|±®>BD™yLšñ‚M ÇÃpd Ͱvu«àžyÜÌCk)¶özf#ªÌEZáþnøFû.NŠÓëe6Ú]äR9+»áÇ]&ºß(!Ã_“çJIîVÜóóv9áâãø‘uÛ>TåØëÑ9qlKYË»XWd½)K¬˜_ü3mŽ=Š«WÝñsK¬ÀÞÞƒF¤Wÿ;uÃü4œÌ®¬Sr“V«]'ñuá,å&¸ééœ8yFV{šÇ|yβ1?ú¿gL$ñ¯HÛùrM­¸â+8Òàóˆ”oÆ»Üæ®.ÛÄZ‘:wI~-ý€‰ ùô- áëÕ~ ÓÌ!Wv‘c§tª5º÷ǺVÍÀ·¯\=b•B­Ò(»ºð˜c2扽î¿x·Ëc=\óJJ¤{µ!ÞQ)³J4{Ȱ :×f÷ˆ,/ Zޥ噦‡*.|f¦Øî ¢ ¼ÕÓh¼uW*·:çý²Dpåv£>ÝÄë8?*ºu§ª™“‚¿ål"yV_ÙÝžÍëÃ&§ +>&¦ÂmN„Ûc7:wLËá25&‡ÞÙ. ÚWñÜä¼ ©·:³úòTv˜Í»aSSS×wæô£ý`}h˜/Ð7p"J69ëcã€Ïy° ű”ï939ŠêËÆ¼*åÚˆJu`×xu¹ˆ’,hr€w¹wÞÉÐ’Šý#pGÅmt»o›{¾ß4ƒ;.R ÿih¯“B‘æëfÆHPÉ-×»l‡s ƒ2Õû^±jv)æaÞtgL‹ æÇ“ÐHfܘú‹8‚õï@;¬ÅåRÈÌ»JZ^ËÛ6rR`”ÈwXˆ0IëLøÖâ2‹Ü쾺Ä=*_kת옕a1å¿ÝšíÀÔ\¹`¢~íötÍ0››Ãà\&‡8Hªµ¥‚ií¬kÂG»G4Kz•¡’(óc5ŽIšÛìeSì³ÅŽÜ(RUx ø„P9nF‰Wþþ`äàÅäÙö. z4Ýt«$:3ˆCðÕƒ$?K1çVªŒDÖÑ °=áu¡.«äìn.¯"iÚ¡C  ùçf«S/©qw¬QÙeTn½éx‡9¯RœÝû¼«ïñ”V•Õv´@ßl>jÅRÖ#Ó3~„­÷Ü~dÃ\eÉ¥K6ãdËpu &]GU¤K[$|Ž9–Úæõ¨®–¹ÂÖ Q2ôF©ÑW©[üs“dÇ1‡FÐbÎÉèúÅ=àáÐ)Mj ÆstK5w¦{„7•j"мn^xª¢~c¤¶¯Æú$ ã8Ã#«5õ[ɺ Û?hÃS3,>²Â[[á ÆÒbèîìíÁ*úv^ɇpºè3‡}ÿÉÒ0Æ3}?"öéâÆ¾ö‰ºFZ[agö‰æYÜÜ;".ÃDæŸÚÍ“ûý†-™²(D×– %5Þ÷Å6Üí;ššž ¬ žúòÒáç]‡5!£'l¥Íw«Y_¾î »¨¤AÕ!Y]íyŒ%ž2ÑYñA"<°Û$û¦n=¨YDŠ3Àâ‹QÁcf<䃶šÜúåç: ²Ë„Ú/ûîìmqT}´~Ï—aÄŽã¢hááºÂtZ*ú¾2ÝzšNˆì†ß]^g€æC¼È›1”Üåæk÷Œ¼'—&©ûš>Ê߬Àò»ÜVÊÛãߨ~/á-w©Æú¢\´]/&ù0uâ²X\-Œ^>mж¯«Þ±Qò"tøåm{‡iõë–¥ŽTÙ<Öôy…‰é %ïÑê;Z;Ú •x»El §;ü¶éý‰#ˆo­Ëœ­¢|?äEe£¹'t¹‡Õuaµ¾gõèÇXÞDü—~˜êÞž²Æ ½€(¦PѧÌÅ^âÚI˜– Éd†:;•_c*úA³± ëoÔ)æÌ6Ì9“el|¢|0Cõ,„¨<¨1Pî6&p‹´Ž:iQXv&¾'#  ïÖ4E2¡*»+Þ0Ðß$Kœ'邸ü Øc_b-|ÁyåsÉ–¾6Ú—wý½2âf?ýCÈ{³j·r,mŸ~c™v (•õg§$µ1-xQï6QަÁ¦ðJt¹Ž?»0›*T>Ï«Q\ç‘ÒLœÜŽL~»r¶ b‡£ãP ÄWnÜ`;ÆNš .Ny·3?²¸Ö=ô^­¡Y\Ç-£Œ ölQ„ÞÚÝ(-›3èTéзÏâ,w7E§JZ¤~ß–ûМù•kn³;‚ñ½UL£2ÎhÍ[ý[Î;ö>‘ƒ_¬îÞάåH­©ÆÇÚ¯#2Ô Å‡ž5S÷†äí=&'ž3«uxeÆ21æj×I`,9!ðÅïÕá„ÉH‹‘59õ;ý}ëÌrêP`ú…ŰêŒêŠWïæ¬HÖê÷…•*'Gð[Å5x¹c+i>“ÌÞ›yü>#µbOpR\  î«ì~í¾K{¤Ùò¤´jÀýCÜ­¹1¶Àq–û¹'H›Ó ¸e òzPv{å*§3æ2ÔéSxXVu7ØÀq$–ÿ®#e±t¬#®ökxϧrÛÊx’‘ãn¯sNæ}H;m”çízª3Áz÷ÕÔODÇwöŽðdŠ1Ŭ&é/® îWãÆì›3&Ù'¬ ïNïº.Æö¡äÔáÌî¼(ÅN²—ÁI†ÛT:îôü¡bT[±?£xI<;ŸÒuw²M{äÖz‚ ’5W‡0?p³[™Èì¹wLšQ[û™ ØÂ#WºòÄ¿pÆãÜÕ@°3¡™I4Ês‰lDòp^`*W¤öy´“wªØÖ9àeñƒü¾=IDÜãlJt¼Ã5­ÆÛkä³z=á …N¸)*Rƒ5 öô8æÎß}Ÿ„<¸œi\'b±DÓPoÙ£®=XU¨õ$¾ûQ&ñ`ûè·ÎóÈ(FjS“xÚâ<½X¹cZ* ”ŸËP׈æˆ÷哇Vßw”þîr¯B¿þ®(³ ½»0O)ó´¢mpé‘ÙñXô¤°M¡ËRzƒäýJ.‘QÄݪÆ1ÿÖè…àæí_îÄ—ïYðt­úE·ˆ¤ ÛÅ–z&»ÔcÒ]øz¼ä°&æ?Æ#ÇÍÛ„0š# btãgmž¿àɇl6eæñôÇœ–ÍÁÈÜý—³_ƼÙÙs‚6S¦¤(½“é·ÆÎÁ÷œmøÌ Œ–]•,éGˆŽïW&?0ô~gacÜo޽Ñkí1k×ó‰«3ÌWû¼ ‰^j)<ôzã—`tÜE7qo¸o>fVÿ#c¥¬5À]£ð‹âÏÂz1—ý'?EQcàøs ‹x–÷mU'ÍׇÍú!7TtºÐòˆˆõ.›uݾJŠÑ®OÎ`'gAyqzèwRq_ég·GS“ñ`YŸõLwv@ó*£Y 7­„b5=Ã=&Ïä¥ÕÇgeÞ?ë¤Ç«ûÏtÍ„Ìî€Æµž°“Ë&† ð¡îh¯]eñãV¼Èt@íýˆvWÓWîgcpYÞÊ"ÇÙl«0¯©hÙ”}¸fññõÛ°>L…¼ÆÈ%!‘~Öï*÷ 5³*´n”¹¶\J?{ÞÎ[°åí¯rä}ùàž}“Û5ÏÝ%-Q‘ÜTï‹­6 üØ{qi†Èú¸PžQ¼C§R¶–ž¾•é‹™ÍN„ÛúKªìà¶„ê§U$ÁkÄOèÖñNÔvÒÞ?ùŒKfè—zÄ¢b7?ž6câWðД ñ ªäPN¡®.-œ)HÞsø3$a÷ßTÑÊEýM7³›d¥GÔz+—˜ôt¹•Eج'†p‚õ½#)øÁ<ÀÌýþ‹ÁGÒCÆ/ šÊråÙhp» ôY'ñÖvÙL”J"Ë£ã3©ÂÆ7¼wY"¤©‹œžð—0×¥nsžIV^½´#`)J”Ž.`ŽÙ}ððž|Õ.Î2[÷a;®ŒWÙMÙ{;¦ÓB‡GP?X¾ÖT5Á/*ñÊjã™P†âñgI” +?¾¯'¸_|3.v_Qh…ÕÅÐԌ̾ÔeºˆÉ^.ÜÊ šNüÖi"ë7õ~&»î‘ÛUov-ÇQ»ÇÓîâýBtMî®”kÎ8U÷þ¾[“ñï!B¡eüW×tìrCµ:(é˜ÜëSìáªÛûñsGðomyJ5{æ3ƒã£T:»Q¡>]u>ÎAg–>¼9ÐNIáìµ·­âî”QÆÞx_t±×—[¾ ¯õàÉ÷0ݦhüæ®(tŽ­þ&—x7àå!½CHzR攕Óu]¿¹è8—Õ@¿}«ÈÐ è9XÈûu hßa®ú´ÕÒ‹y(•,Ðùå‹FQåËbŽ;—>q*.içé–‘ûŸ]Œ:}Gž:¦»Ì¢_pöÜ­$¿òÈBé¶Óß•zå<šGK—)§Ûïл ˜Ì»S¶ùö%Tz5úd¡fázØ÷¤æÐ[].#*ŽÅ~¿N+ì°HY,†ìAÈ0ÏÂÏ¬Ü ëà×<¦:a&ÃZ[íFê²û6²èÜŘ{ËÅ ”pj”›ÌéWm™ÊkÙs÷¯"Žâ 'Õ›OÌéçaùSéUW6ŸüÄ·:7·­)RÉ»0åö¾B>”ÛE|rŽc¿[Ÿg€U̶=¹ Œi¶ÓTÒ±Íe®\ ì ¹×Ä.–|T#"¹ôfpâúhh™ü‹©ê]ãÃJ“É’Ž v•˜{ûÆ#+r&1‡-œ3°×"ª(ã£iéÐ éaN†/“ÍJìѬ†){¢g9e°nžÙnE‘C׿ÊjDã€Âõ~iÝÕªT„P4™?IO…Ø ë_Ý^§~Ý%¼јÍþ=¦§&Ýc§¦‹²0‚°(Dt¤²a¥L¢˜·Må5^ã ê…çxÒ6àíûÊG-i²¬ûƒÄÕ`x ïÑåF&x™– "–ènp5½³lèõ³goŸbÓjûk郷°À —¸þ€È]‹ KJÚ¨BïÕOjþéu£»×YþjÁï/'Vjx†CÊÔÖÒè]#«¾! a»ÕF¿Ç™­ûÝ×çÙr?6Ñ‹4nÀ)ßÍ?¸>)“žØ6¦Ó¨{9Þ9.Pu:ŨáJ0gßYx懈 *Ó:µµ¸siӹĸÕ9ÇAŸcír™³ÄÐÙîÎÊ΢ÜrÛ3¿­™SJw÷åów»ê¬?ÄMsô:Jë;z¸µÕeÙµuz]njb°„ùx˜ñ“g’Xô™Ë¿V¦éÖl7}*Òú U¢Ÿ“fA «½ $óÙ­œEù„uÙï*Ûž [4çhNˆOÀnL.\°.âV¦H¸6 ¦\6u1‡sа]yöÐ|= QT 8ÌŠ|Û•Ó9¤)ñ¬õ¿ºg± endstream endobj 132 0 obj << /Length1 2371 /Length2 14887 /Length3 0 /Length 16281 /Filter /FlateDecode >> stream xÚ¶eTØÖˆw‚[áîÁàîZ…»»»»Cp—à$ „àîîÁ݆î~¯“÷Íü˜ÅZEísöñsï- Ea 1HÂÎÖ™…‘™ *§"«ªÊ `ffcdff…§ Pµp¶ýÖÀS¨ƒ,ìlyÿàˆ:‚ŒœßdbFÎoT9;[€Œ‹5€… ÀÂÉËÂÅËÌ `efæù/ÑΑ fäjÈ1dìlANð¢vöŽfæÎo‘þû@mB`ááá¢ÿÛ lr´01²È9›ƒlÞ"šYTìL,@Îÿã‚úƒ¹³³=/“››£‘££™ =ÀÍÂÙ  r9º‚€€¿ÊÈÙ€þ-Žž jnáôJÅÎÔÙÍÈxX[˜€lÞŒ\l GÀ[|€Š´,@ÁdûYö=à?í°0²üëî?Ö9²°ýÛØÈÄÄÎÆÞÈÖÃÂÖ `ja (HÈ2:»;ÓŒl¬ìÞì\,¬Œß'oV½ÕøŸ L-ì,¬ÿª’é/7o·ŠÚÙØ€làÿÊOÌÂdòÖy¦Glekçfëõ›ZØMÿ*èbϤfkáà’ûëMÿ[frp033s¿ ä¹›˜3ýDÕÃô·’å/ñ[>^övöÓ·R@>¦ ·ð^NF® €³£ ÈÇëOÅÿ"xÐÂÄ` 2³°…ÿíýM 2ý¿m£…;@‡ùm YÌýýûMïmÏ€v¶Ö¿éšIXDNR^›îߢÿU‹ˆØ¹¼X™ ¬llN.''Àç)Yü'‘?L¥mMí<ÿäûÖ¨ÿæìúŸE þÏ9¡ü¯/y»·¨ï».3³ÉÛËÿï­ÿÛäÿkÙÿòòÿcßÿoN.ÖÖ3¨ÿKù1Œl,¬=þÃy[aç·ã g÷v(lÿ/UôÏ)–-\lþ¯VÚÙèíXÛš½­6#;ç?b ' wPÑÂÙÄüï5ùG¬ö×±³¶°)Ú9YüuÕX˜™ÿîí¬™X½]'NoÛù· ôv”þ7ª¸­‰ð¯3ÇÊÁ 0rt4ò€g~[*V€ËÛá‚ÜÿÞg£­ó› à­B€©#ü_£åäx[¡¿Dÿ “ø¿ˆ‹À$ñ½1?þFœ&Ù߈À$ÿ/âf0)ÿFl&•߈À¤ú½ùTûÞ¼hþ‹x¸LFÿ"ö7ŸFNo£±p²úMy30þ±°¾E2v42YƒLÿsüGüÏNþkÌòØ äü?|¶åÿÇà­9&ÿ"Ž·Ø&vÖosû7Eö¿$66¿“þk LÀ?à[HÐïfÿ…\Þvÿ¿’·cÊô6+k#›?lÞ 7ýõajáúÛ Ç_j;Ç? Þ(f¿ƒ¼éÍþz£@RÞ25ÿ÷[Ì=ìÍA¶0ÞdÀ·YXýßjÿ5ç[‘Öíèoý[§þ(áíVbúÃ÷ÛMÈd÷;ú÷íÅüCý–±ýoõ›­ýÛ“eû?3bgùô'Äö–¦=ÈñíiúƒÊù·ÌÂî÷Øß:doíòGÂo;“Ão7oíqp±s­ÿà¼Iÿè!Ë[‡~{àxS:½ÝÝ¿Õoa'ñv29›;‚þ˜Û[]Înv¼åîò|k±ëð­Kn¬ô›õÁXßÜ{ü.ïÍÔäøïÿ¹,L\ßzæü÷½þv“üÿýZƒ@î ø…Y;¾`ËúàŽ»ÏÂøn ;?Ùz9Õn^]ˆÙác¬Á·!¾ÉãX•ã³ç_s§äˆ)Ž˜ Ôè@ÊA‚‡Æã‹Çp¹Yñˆt2 c„ CHEà36|çE<ƒ¥?gîÅKˆÆIût ê1 uJRy£E! :Š®j‡éGx§½·ï OTYX §°?KÃõøKAÊÚW‰zú{—³e`X‚LwóÄ=•Œ6Bóñ-Fu'ºáÌlŸ‡ßt¨$¼sý™1¾V9o×h7ëƒyçG¡.ð¯ôÅ‘yT{—ôÅßod!}ÉÈù¹”°¾D\¾%—ˆIÌžŒ²¶_KÇ(èÙ» ŒMÓóÎ"¦[™º›KýŠõÌå*?úîÏ9%E}ú }²Ûbò‡!ÙÔ'ù¨X¡ùÒäa¢ó‘cfR!Âm-Ǹ7¯Ö ;œ ú¸pZO3ɃÎëÈðCþª2ê•{h' z„wŽ b=S†t N¼éÆ©3Gö?n̾÷—PÕáî`i3rOÞ¢Z(œá5é›tÕÓ>ë©Ú­ñm“›`¡K(×ô<žhô#rcÓ ®Æ}ùBΟˆ*¬7>¾‡;]@ôp>%Cªî&AFÅ´ØoÚìUe9:9E쟒÷iÓî{­þAíÑ%Pƒ`’VO'¸{? \¢¢4™×y¯‹\yŽ8@Õ Jã6ü9Ƭ‰_¾È<ƒv“±àæ°vÄH†åM•p²^,Q yš?†U‹„ÜĈófwÉãPÕ›äs•»nÚ@#qgm1IÁR¸wã*j2òwE^x¼ñ¾·QôH4ç{Œ+QB7øÉ QÛñY\cAŽ5ÕôÓ­]éüúÅ)7ž(ÝaøÍ‘È÷ 6 ƒ-]ËãïÔ¯²‘ïk¤õíøSPM¾ª]3éMM1âRà¨0§­eÉÓ»º2Ù‘™_N¹Hˆ*Gh¯KBÐwÖ=Í0\Ø8N;HÍØñìùÕñ{ýMnê/ývÒj K½ 9pàjZŽŸÀ˜$ûSxµµ©T”ôDåX‘ó¼ ³ÓŠ¡²²¹ì-òã+œF`©Û‘¡­Z…ñˆhä‘õîz”µô§(-×ÜkÐoõR§åEo1]·:z…uDœJûçz[h[ú.p +¯tðÎi˜éªGþ)œ½×”½ NOFá ™©2`xL„N§k=!n(HÔàØD¤^y“_XœÕhqº/ãSÙK/`h÷"ìÓ<Á ‚ö|ª±,•¼é ŸŒnPìJf´ˆ+EÈP 8Ým¶<)-½”ïlë Èl~©èá€- v"KwägÊ×5í´ùa…ì¨3:\™³}BùŽqYßШÊÌCO{BŒ/£hdêÏŸ¶X‹áD¡Imzät£|1q^‚gרÍx¾gµ¡Œ— ã×ÚqÈâ{"2ªuµ]Ù(¯„’ÌD‚Võ{¹”fA‰Ý6sÓ†4XPVjÓCÎPg3í<ȨL"»“àŠ)]3’&TõàCéi†ºÐNFæ1‘”eѸ"Õ/(Å…†¹a!;ÔEDÅŽQwÙ¯v_,jöãò¼¬Ý 4n¤+,É(uÇÍÈ&g(\d§™Ðº=bô¦Å›y‘á?ð0ïj¢¹w¯ˆ®®;ÆH䪪óu÷´"f]Z±à|º0FÑŒXpñ¹lsÅ\F‚ý¸¾QÊÁñ(Mé-G^¤…7kîÑh‚èå±iÇ“úù–GN¤ÀA]àÃwèõÑOÑÔç :½vŽwnª“Úo•«7~PuÌNˆ—ß~y[Ûw„'âxÈ.¥úödz¢¥tºŠ!¿Œ˜XJÓ2Ôß/gÀJ<éèRéì;è7ÇS³ S—<ˆ¥ÖŠ’º )¡_gz ”Ü,§S K“0ÛòîœS6Á(£)yˆVIí¸¥¬‘2açW¨ê˜€u TM¢McýÈ>y2VæH ICéOÔw|)snÂc¯“ìdu"îxÚ ’}E©âI:—ËæŠn½Ì¥h<±ÞeL뙡kaï‚¥Ä8{ítmgý:ó¾‰`‰ÌxäIÃèlÖJ!¼uì Iö»<¶žxšwòaÑïôLñ§6UÅ}ÖYÚŒñcƒÝ)ÔÑ{ ÌÏÆEcF/8äT ®îÏÊ÷lHµôïßq¨¡¶lÛ Õ1û…Ï<¹ØŠ#’„^Ô{Lد»÷Hs2”ë-zôðÐÙë-Ç΄\Dó”S0ÖµÁ`G6 ¯ Z–ebv6mS™¦'Ÿ¬Í2ˬaHüåð)VUK`̯ÿvíÐŽ-DÌ+ãôe/ïaxM}ò¹/ö™ª¦`çc=¯ZÄEiq€€êakÈenZ9â¬z1¹Ýr„ÂE k¬8É}3¥7]”û}€øåÈXOu$üW̙҂kž€ª梙Ŧ>Çs%½]Í&Ï Ïì|ðy…qœtâ~ÚÜÑ®ìœØKÒä5)1þÖî}c5úyñ’¯ •ÐÊçú<Ëû‰-34C%~$Ní’yÞ:ÌkOt©L¡Nüu ‡âÍ(ù¨šheþ#DI¦+ Ù‘dó¹‚ÑœYlSúe1ù'r} ú r(ÛŸ!18Èôz8*¤¸û‰¨ÝÐcŒlT—ø«Ð(§BÞm§h†kÃ|͆x-"SÀ &«±SÕ°Ÿgݸãç®·ß4íOކ2¤J¯U2®i-óëá?¸2ª6ùf–™œÛ|‘>¯Û1 îã.më©8Ó»æã—]·Dã³H‡\eÌ̆¯ÖÌ×}r5¶dr‹øTÆØî{›u©aZü‰a>SUº"Û˜ÁÓgq{^¨†¶ÜÛc@÷‹VozH"ÉãšÍv,©E÷ò=5„¯ìŒMÞÚä:†<ÏĂ֌˜kï&½°1r¤fºS”‘ïNGhbŽ}Çr®ámül‚Ú·‘é¥ ØG+;!ëéÍçqðGÜŰ^£tÌõ t¨Œ+‹ºSÛ$’¯Vðã·¿±Ç£ÉÀH’ -:–oW*f—+³^òåTö´æÐÁ±ç.äGά蠱„2áXÞV*Ú¢Ðn>LÎ+išÈ9#¤åA &˜á³r±qÁk°%@¤3›í ï•#Ý]D§øÅ *3DØ#’§~rh÷˜ÀñµÌÁRè.åÆwÊóú%Äšq5ç9V%‰¿hMpÒ¨1¬AÞŽw1¥¶hw£ÉÓ*ËX«b\ó¿ûú‚®GÉ`òýÅø¦Ô™°-±¯>È(äã*]ŸÚ^Ë#ÈdÁËh•vØÁu¾ÎÖ@@‚Óxe)ù†º-æ Næ¾÷¾p#j<и vhE<ãÖäí|#ŒÛŸ$8q‘Tܽn=ìJ1ým¬­~Õ‘Já× –q§á‹üXöãÁ¾×úTwû~:Ì$ z¡’±8½4"c(›·}6=û– jÕChÃÈwö£WüïÑó^+j„×5Ž¿Ô†ÌóS|ÝŸ“FOÐN’tçð<>¾K¶ARÎÈΧðÀ t‹åjRŒBoËÀÏÄ•m÷œlDFä}qNXr~2Èèqï”ÞAHæNò/ñO@PÁ¾ rË€âêpÙ®; È£”ÞZÞ–^Ëó:¦˜¢[29ƒ?ÈäN|‘p戉Hxþî‰`—ÿNWÄb´‚Å‹Zb¸¬ïŽe“ƒÙµ@*éKpàÒ¡a¶ôæèì_HÐï±.¨hþ¥G¿û"IÖüNS³å]®§×ÐöãQaiµ TÖŸ£'Ñêf¯çpíµHºPüÚ.¨×lCÅa¹j3ÇÚ׶ЪGd¹ò¾´h>:%­çö«ã}wÂæØZ ù–¤PŽ lÜÞÃF ¹e¸€Vk9Ú–ùØ9 "?øcÑ«Þ;ÁH?ΆÅù$é±¹¾½••ïäS)¤»Ä©ŠçÉUøé£ÏÚàAóBž…ÖÈÀ®6Ó‘‚´órÖ!õÎuŸ_ ó1*¼~Ž;&å륂R¯—ïÃ0¦¡ðÃcÁÙÑy-·ÒŒž¡´,Nv ‹"†Dyê.ó¦³5©L¨ÀQ¿áº ú›³PV6ѶÑþ¯8阳>ÄêKR3ŸCn›W¶áGx1p_ÏÑGW.[ /)[ìÚï\,Çök[Þs±e­uVÎpFjýjßIdc¼ëë %ªL$X7×)Å„ëeå\i]Mþ);{¡erÍxUÉ Ë Ü8ŸÚ,uK_§a‡‡vIX bxÂÕQ6xiæÂtŸÍ½¬ßÓiO¸|ÌÝA³ÌÌ.Ç‘ösç›bH\“eÔ82ð/ì8Kå‘¿„Ç ˜ ‘ »NP}ÉÞn´~ù)Z¾/ò•™¶ @æZ=r°•þªÞWÌ?OÇ6±Ò ¬ah«±ƒ»èòŒ"ér Fz´E¼¸ÿz½B¼Ìqn¯Ö|-E tPûÝe}ÇW96µU¼MéøüÄŽkÎæ²c "fÖÉ›:q­2`™Ò³plu+ÏK—VóœÇá2L‘®ÓíÌAxŽP7{ÞcÓ¦ö•v–T/ÿ¨GIä¨]ŸÃÈ“ö£X7¹,.×{°F\Ç׆†|S‡…=tLñxÝwœ ïº×†½tΦ&:Š2h×¹ærï#Ÿ'ÑáËÝ@¸~Œ¹³¼¶vɨXK¬$ªkŠqt –Sê:lóçòBm:OÎÁ…Ñ2HÇÊk•Á'fXAUùBí­\ ö¶½ÖÒâÌÌ¥3ˆÍO¬M)HþàÏP#ƒšÿ ~‚äimˆûÐk(䬽‘‹ÙÒ­}€[¿û•z_6”ÕÂÖy2ÁD,™ Õ€®¸ÝãÌç<Þë«hïÄ‘;NBØëlBu?Üߤ\ZY°p|oæýY7µÁc¶ÔµfeƒF5l ÓbñˆÌ‹þ.Lª›„gaötU΀›—(fËîi滲}]‡Q Ë_§A°a%ƒöt»ù2=%WµXdé.²óúøÖ™ ó; ÁTeïOÝ+ü l–5͇¼´½KŸ9ýÙÖÙ´¼àóËS¦­p¯ ¹¼|©ç‚÷3Þžò²ýÜb#´QEÃŒÍ53ž<.)4üfq‡‘¨øG›LÉ̈.‰>mÀ¨{è#vð¬+÷_»j†WÌAâºù8M]Q»Ãgc>)Ú \íÆ’=Ζ챨ŸÜËŒe›ìϾ/Üé3S„=}ßèVeZšì–Cx_õr¬€òXŸ:z™ Kó׎ðÉIQèð0XŸËºNI5}ú\,(¼Æ„ ¶ ¥8¨1£Ã ãk„þÜü™›Õ’*ò¼¥˜''A#4ßwAK (/ÀÑÃ’¹Êój°` îì›LÂL8»ðÉ©d”òÕ›íi“¾¦­‹£ùD¾-_jrž¤µâ(iíÏöóÄ䙸Îì8æA½pç¾%H,î¼55iuÙ)v¶VʨóB^k*xlƒ<ØhÇ–/úŸKŒuùZ‡¿qí2ÆWýŠÿÈNúѧ‚¦*³êÛÁâ¾g¸(à󖘩óxŽ­—à|y-îGpÄ–°áYúÈQ;n((ÁOðÄé¤ âIt–"$Ò7x?„¾ü,6ònˆÜbÓÓóå¹ñ‚Ü‚-U„”¬[/;HIa‰ÓÇ=X•6wK§ßѼRuÍܸ*3úèñ\íçÎeIH…H# “­~é…Ò%‰ãrt1*3Ô|¾qŽpâ¶“:ÙŒ ˜Nc=d€ûy¶ë‘([8ö³#‹ý Ë€ú+¡Šéä\oã²ÖÉš©ÔJ׿ý Æ+^'a½P9vfmyd‘Ãa+#ØÉ~匲 ÂÊi¹]—7;è!F\üþî fý@°jl´ÎN#ƒÀ Âa }["ÊנЅͳïÃËmöüû@lM·‘x•"„êT•b€0ÍîûRÕ1B¾Hu7„ÊØZh6Í^ÝDM¦€®žˆ”ÝÅê0/n‰<Ÿ‡þå¸. D–@ýï®›²†j?rß~]V)íŽòç¸)û¯Õ0ìucàDÀt9r5ùºsÓ)·U’yžÒ¦Òñ¥€ÝØ¤ŽæŸ4礢ðVªçéüòZìkû<Æ ìÉU~ ¤Â:â ¯.Šr(âúÉÜs™;anùCrr=R Ï3¼K„«‹8QB¸ºº`Äå ⶘dg‘ÏÖm]“ùyÅ-EB‚š¨tÃJŸ7½UdæÅ±‚ª°ÞÁÝAvýôQ–'±ö´$+E€Åò9{Ë볘^‡$b` Á],ãïh…?É3<Ì…)lÑ{8øAaÏïÅ_ážPfV.U¶á¹# N騔ÙGqÍŒë¼ â*UfÏé…áÆÔáËEo«/Æ!!åï—Ävy‡ŒÔo%– äkžìm8k’³FÕNõ~E µÖvDgųy$/òÚŒó‘Çúº€wl!Ù×ÿ<êbÁâ+S²èdRs©ã<6û<®!­*eñbbXAééâSýE÷%HºßÀãcŠ1ý½ÆØÐrss-`Ó<¥F¸5w Íö埂ïòbðÚ¶')ð¢ÕrVi˜|)"#™¸bÓDK: Y£9÷àO“SProËnäçÒ¼kè-Ç"6bŽ…}?»ð؃)9ÉHº™¯vOžƒèÕ« œ ÝÌCÙS/Íä6æšùîÆ¶†>‘P¼!^\Ð'ž Q K×ÌW{.rÓ32ìÁU$ òØßWöZƒd•˜=Âàâƒÿ±¿… îD”crÕ.¿]¯¾M~dØ»lÅs.….ÆP=ÅHK±QÛ&*å®ÉÍhø\NµP¨¢bÒa^”ˆ-7xwèË•ÿ¥7B èûƒ5ñ‹ ]޳é¸m¾•ÉÆ3ÖQ¸ØdkÚ%$±ÇhÅÜU˜^ðO<‘x Sϧºì’«Ð–òíÚ;Ûs;¯VÕËH[c„©|Í?¬àåŽ6 ÔP_ÓWEÍ,ã‡% M9U(þJ"ÔàÉ6Š!“Ûo õ9‘ÚAêÒ+Œ±‡:² —f˜Ų‹]k& >Fú†òX »|ó ÑÀ(éš¿ ÙŒOqøë<1:Z»º2’5¬ö6£™¦¸^³²-Ã9êÁ¥°4ÓMªŸý=Ü2ºt=òÊ8¨YæE°ÁÛ„nú Ý‘•'6ê+{ŠÆ ~,±¯¡Úõ¬œ\ÜON‘ 77¤·"Mch«{†·]Û¢Éç» 2dÄN´US{§ ¿"+"i”ƒ¶I³–w• ä­)´øúù´\Æ/·³¯»¡ucãè‚ÙÓ|‹Láɳ‹õ‘%¦Lé^‘ée²iŸiX=Œ)Üæ£î{æbïlÒg4pN‰j PþµzÇÈâ17‡tçX-ûZ U%̃¸½(ñ nuÜ“OŸ+ô*f+¡G‰Ìï"_•°G-^c–K–W=§Kĸ]¾/èùT(Å6»Pà+]PEDö.È&Äþ\{5þݘ½ü‘Ê´T‡¢ó-ø¾ÀðñÏ4Æ%Å›L š&ZÂ¥W¯;1&²¥~íºOL@à¤õ¸VÎ>{:»a5AäÜåàú-yfÿUY5…ñEËhØB|1ËmNÚÅÜÁXvð=šLí¥v…Hú.T½ ;Ç­6²"x”̪DŒÅÙ`4sȇì$Wù=Q¹˜‡ðqFcÊkìËŠOHWPÎßk90šœ 预ÂÌÑù@Ù3ȳǡæIdØ*~Ëöù§ ÅúmGeX!—@F[O@vùD#îÉJµ|„Ãfµ» fÚ¥š¤¾àuK ®’™k¡_ÿXÙçøSÃ1º›³³‹xÍzYN5r3!$TÒ}êV÷&PÖr¡çb% Hõ+¶¸:ÙË¡1*DȽ٠»~s¾©”õ;ÒÚ(ØÊŽ?‰!å»$ds÷Œi¾dŽjn—Coí…,ÄZ3KÆõZ—=ÔH@º3×'>¹úmñºðèóÛB¡t툟à­Ù'VÍ›²"W+/ÓíÑ8_Ä—ãNØü—›š³b—ý„î½ááú,(Dm½ƒ5Ô+WÒA<ìǺWÄ50p¼]XÓœƒ4á£KÔÁÎ̓_ï6ú—Ü.W†?t5@tâQYÙ}¦¬¥@u`šv‡‚¢jñ8â¦|N–`ÝìöÛ±¾Qœø¼™{v±Hù•›ò£µÃ#­TM ÊYÙ$ ^êÝýéÀÀ÷ uªš<ûäR"èVi‡/-yH?š«WÂÇèÚ¶ÒmpŒæý_2ÌÐñMH=ä†Ï$ªÓîž޽½n¥&GîÓ/m‹8 ÎÌs é¸Änu ùÎÞÿ COè´äÂhÊ)R[°™]]X_\’l¥|ˆblê7 é¥vŒ|â÷n¿wUM|Œ=¶”=¯€’G.zL©bµ†PRgm |㓇I±8^Ícu¶Ê‰¾ŠERec‘Ê›b=À™°MÎØ ±: öçÊ»M5—=ŽtjFü èÀÕ 2¶ ë†[P™õï™*æ²ðà.WK±}5úó:ˆ“ºR÷•LD»8ÈQ«45ƒèœÎ>Ø‚/ù[áìu“_?Õêt N%wî_£¹›ŽpíÊ3´“ é]4oï‰PÔ½5Ùo*n'J;–# ž–7BUVú V$®ƒ¬¨·PÌ„ˆ¬ÒÛRèËC)©Ýe’¶*åŸíœ–ñÕ„_ñ”àé4‡†înñ¦l&˜Å‰¬Ç}²þuõhÀ®3U·CQä±óK_K|‰p¨`Ôüã£oQ]8æ–sÂN,Ãp‹u>–[ð€\(Ó,rKYwÅ£Á‡4šðt±t” E[þ´›í¢SlKÌ‚5r¹KeÛPŸ°²ìŒ‘¤Öñ,}/ððfîž@)=5@jq’ …¡#¢T$<¯Jò ¶Û3NÍ?­ãoEê]ǾX=| ¤òðìÓî³2‰L 2åÀ„ztc  R¾f?j ÓÖÊ…ý.òÁß·ý4Ì?žï€1"$'…¼t> èçþ=êgÔËkª‹|ULPÔsH!Áã¦ÞŽ´ë²+ªÆhZÆ‹/ˆRd¨ÄÀÕP_ÌY:|Å0Þj„œÒÀ)+#Ðn¿tØO â“;åùvËì{äk.Ï#á,Ñx9¹SÎ{UÔÎwm'&ð@÷ÎB°+çÕú­e·OûwzGGI­5'×ö'…îxð&:|lÆÂú¡îÄbnrÅq4üã%8ËѸŸYË…X“Q;k_2³øm8óíÈ}G§#…‚”u#¿ª Ñ{,|MÂDÃ0¢ãŸ—CFÈh‰H8<'}yôõ[‰|·Žñõ‚Íòo«%ÌÊ£©@¬æf9 õmåFEݶ/ðéMÃP&p·m„ͱ/â Í7árJ"'ûó:öj˜‘i­0ÏJ H¹u9ÛE9mÒå¦\ýæ6€¾í'ëIŸ”øñ³OÑÉbDu™åàf{ï5| f<nvxI[6J·l–ÖÉY½p®"Ê*mPõ¦ Ùà¶H ã*yõ?·…é”›½]Úec?må-ÏWãìùt²«Êo6Þ¼© ¶15#»¦2CÚ%О8Ò·d.?¢XÚ1gµ=´ºG¶¯TýhˆÀ¬Ñ0üèv®1†`ämo²Éë,{uþ´“ *±–Ž]ýÍW·'ÇZlÂwЩÚi•9S½ýbØJ`Üí× V%Ý@¯†nб$ÙUÃTe)åÝíT¯õÚ¯ôìå rö!âù“oC¥H¥Ç^ ¦f‰I]‹†XýÃ×1 4¢iÄP hÂoºݹäŠQ@"Úõra4”:ûêÞ¬öï¿‹½<d°nŠ”w×q½êáúUð¾ÍïC‘ée05)­·-Mspð"Y­q]d1ˆ±-ú¡x^Ô«äDûZ^œ“9åz7öe‰ªˆ•[$KABOT}^R¹Q4LÆÆú€+8 &®¨>в²MAP )ÆöÔ°fíN}O!pŽƒ þ“|\Ìg±ÉyÛGë~î|TNFŒƒ‚Ë^bº¦µà“ñ·ôá.鉮|Ôý0Ç­$ÔÁ.÷Ñž[›Éì®ò)8U‰á›«Ûät±D [Õ›ëV6‘ÓÄR1Ç\ÕØ p° ÀöeT’DQ",(bk€Dõ<š1Bûñágœ*¾‹ΜòF1Àò 2³5/)'*2‘àaKªp:(²§¾ÈYÜ¥a+Q5´Ÿ<ˆž¿:”mpv·-¡øÛNßЃµzVÕ= ”û˜Ò|ÇGvÛgž ñÒ~ÄûŽíe¾Ú`Y¸T3’Hq„²(_ ´Ý4Þ¡xPÇé’ ×£2ÖãÀÒoº/È Äá­29|² âä¿Ç+8½û:Þñ k÷5Íû½úFåÈö°ú&…êU¹ÍòÞ /ô÷·ñ|›,u•+ß # ˜!!aBÀIIhîø8‘ˆ}Š[Œ,+[†%3Ñ“Q½±ÊGè\i2¼*SO¼¿,îÔéS¾þË"ž^Ô'šÏu8¦î^¯³ý¬¦•pEn—gŒØúUEæ1fp—£Vèâ·FT ÅKVœ’òlnÁ6ø“ï ýoí£5?EÛ~Ññ¯Žq1fÚA«®ËC³«­ã“=ò{îê¥bIî¾bÄ“ûØ„#MæÇ}Ä`ì49H#ìïe(ôt›?—X6õs¿“ÊÉT>Œï Γrï?´¿qt¤¹ø'¸§Q%¸U­œîPßʺI Û¡N˜öh^……ט¢*9ªËÙ妦÷b¸kÕÝÕw¤0´h~ÇæÙÁFs[ã§’TGS¢ü…ÐdzúhÂÄ””Ã"dI{™Xï³tWÍàl˜hè\Dž©Œ‚õ‰Úmª¯Üı(EÛ[hpî(T)šØóü+)¥ö­ò“7€â0´Õ˜²†ö' °Óïihp­6zÕ°ÃàR8~Qü´«dPõ‘~bXÖ8xrGù᳞†˜÷Å9Kš=w–Š0®0Ùñ3w“Ö󸢈"ïC~t †c´¹ZUòËú²±8§ ¤µ‹fVð¦ÉˆepFÿ %_—ïåmÊpbªä¬Ø ˆ«€’¥'qŽQš˜*‚†·°ó›ÂöÛŠÎÞAy÷ ìö³‰Tô]êvs‰É5>?xkI©ÌöRž®Y«ÒÇöºíL WE^ T7¶›£‹§>ùL pÞ6ÏóvÉíé[Z4yxDkŠ^&ÑkÔ$½½“¥'Lr~[«~KÅù—ëÙ ž½{=±‰ç”«F‡ø=§^†ò[Q(̶ԂûM‚ Vœ¨!xS÷eV©Åkô¬[¢•þœ§W‡ÉÝ1Ó_Ú!g–/Sñ/[Š…¢ÛùâËól˜e8ÏþÁ³ÒÔ¹`j8 †ÝŠ&'(MÌÛôuq€övw úo»‘Ǹ‰£ºÇý×3l¶«)]ÔYE\ »Ê *ÌÉùb¬•\[JzƒœžÏ&%¥WõšygÔzn/›‘­]Ö èµSÚÆýô3¸$~GÓD{‚rÎ`M·êÉHåæÑ¯,9~à,Eu"ÆØæOÐŽLB;1Þ“{ჺtÑÑÅîd‚mœ"O æ*Lꔃ¬"¡As×@Åç­ Yøñ °K‰ùè%¯r¬Gñ_aqé/±È3Ea|ŸÝ™ÀT$%‰ËúÉqPl2×òïV}„:ÕÐæ‹‚ ?Qøæ0lðRÆ¥ã•QŽÖ][?jеxãú ¸]c¡MP—¡cĈ}žÐñbάˆiðŒ€ÉôKW¾³ÏÓ¸ÏpëtØœF¹òñ¼aè蕜ÀP™”ÿ’¼› ’N¦4£/KðžK.m ÅnmòIÈP;wmLAÞ4½ã¾éÝa"áW)éBÔñ},œ™Ã­yþµÎœ“æüs ‚ ì“ñO¤€ñÉúñT/¿Š€»ú[.SìxÙ¾A¢ 7ZžŸ»Â` qùêU/ÿ¥ÖqÖ"ÒlPv`߯æÐÔ' Æbô3¼¥âG™Bv†f“OúÍëôÅeË,æ\þkÆînªÖnûž«Iñ„#ØüÏJá¶·êWÇ@Õbcƒ(„þï£õDuí„o¯€«?ýa¾À7ºD¬H[èM®„&‚¾³ñª3ƒg°³ÂJ¹»9F+ë× ¤ô‡L¹¬ŸRŒ»\Økæcì½Ô›Q8E±iî*À^㻜Sð{ŸÆC ¡,†KÅ¢ôÊÌ"ö£[ÄHŽlÍð?të4H&£Œ³R:ïÙ~íÝÕÀCP ;M‘X;ݶTe}¹ßòRF†X$ î½-ŸdëúhågH‚v?ÂRðNy°¶+b1ҦνG¬ÊÝwÖmÚ§ðÓ“®P@²£Ø* x œùCÍ©¥Â+€µ7 ÕG¶Uä<Ëú;uÍ\[cY†[ÊÊGu×,Ц™YYàsk¨ÃÞˆ7‘ö­Rž¯"~̳qøxN”ƒ1ãÈÃè}¦…Í#©­ždTe¾nUŽÍ,ÿägÑ”E ;â¤ÄêÍváî¤ù˜xœöøEÚé*'x„q5cµ XŸ{a0?,vd˜VŽÞ²?ÏJJé39½6S\¿žŸ•¾–w«Ôl_ ZëƒÙ¼Á¬j2 å]ÉúŠ÷1":ÿ=ÇÀG¥½ÇQë‰íwUãPÚ NOÂÌ»÷ÑG[›$½Ëw“*êviçœ=-3Ä‚F,(îa¥pÓ{'ï'6x[˜ÇªJùv×Ô_A1tëÈWP ¹ °MБ6´¢Éו ¹?))<¿½“<˜!ĈÛc8ð‚ÙÔ* ¼ûû®öëq a"«A*™ˆ]Í£vþ¦ËKËÂxIMÄÏ175¤#\© EbÒ’”6OÚJ2N…kŒ½ë# µÇ³¨ç‹B²ç¥Á 6nQiTÕÑr²³Ä‡ËÌžÀª-èÜ9YK}dŸämg¨ÏEu¨ê›„¹ÚjÖw…*°ÎÌ>¤°¸L-­&"_ê>ÜT6¤GÌ¢dT7ªþjò›†Èpàé½3ßYÁM6p«|•"ù L|Vcví:N¿úù»Zi.½®%¿o4‡-¸}Þ¯iã¥HKª©OªæsÁ‚`äVPMÌk\¨ÞHs§»*‚c¨¸L/˜gÔwry×3Ñ‚ô–D5§RÍì'Ö:ˆ–I/ä˜_toŸ|ö6Ý-Æ¥µœ<ñIÎj€RÀ1¦zÔ([ F춘|ÿgZü÷(¡†RƒK‘S^9ê Q`t"*‹´…Ò:± °œ࣭?•„ú~ˆÞX«ŒuÎi:¶¡ÕàÎ éáI×*i«+¤˺O¢ì`çõne×±B§›ã¤Àf÷–X._÷6òÉAÈ©N˜Æ›p—±0m5K}•¶Q†|XÁB¦ÒnûÙÏùÝœ´/ˆM£,†ëà”rÈf$àYîþ;ö|f—8FA·±Aü/ïXßJ•E<!ó—$‰ÁåH^Ãúl0ãÔéÖ–ôüŽs€÷ÑfwüÅhP ]Öé´)U[×ÅSih8ѹòƒþºªì¶‡\ÇèUI~È-=28MSdÏm^K«^„(>ä¼}ü‘!‰=Rç'!â%OýÆí<–šÃ\{g£Ð|‹û &fø)Ý•“°©m<@ØÊÓk/kveó•üËÈJR€Ké]C¡dɪdÿ+UîˆLwiñÏ|Ý›èÂeÏž:CuÿI 5"[Yð¼)…R=}…‚”ê>úñ2¤¤º}ìà]M·e»!‚ô[êZSVo½'6qt5äSÿ_°Ï*äÌkéå,·#’É–óÁ`Þ¡5P­p”Rü±{ùç?"¼7‡85Ðê^7uP‹ZQÇ€EHà»›v¹Ê°ë1+!Ýû¦,Ÿ^o¼úÎ_oäðé@‚¦¬;mö<¸S“IL'NY-oíWþ¤å;i5W'¸ $/>à ²ÿ­{­|¾XáhQÕLz†—†å—)—a†~ÞâñÏfœ¤š.ŽþaŠ6-BäÊFàœ¤œŒ~ÖF¡ð3äBV«§n˜•Éd! ᯚ!þßWìSE`O„"Hw÷ëÕ¢>eÎØÓÂ@jKãìßúH¼÷Â>:ʽj2ÁBÓxä×îìuöÞSdÙÊU˜pMžQ‰etŒ˜UòØpÅßk"ï壧u˜‚^}=¯x.nS-ZY–ûq¤x’zx—÷xËñíÔÿ?d2Î: endstream endobj 134 0 obj << /Length1 1614 /Length2 8978 /Length3 0 /Length 10029 /Filter /FlateDecode >> stream xÚµT”k6L‹”tÇHww(ÝÝÄCÌ ÌÐ!Ý!-‚„ - Ý)ÝÝ)H+͇'ÞsÞ÷ÿ×ú¾õ¬5ÏsíÞû¾ö= Ï5u8¤l V@yÎÁÃÉ- QÓUâápsóqrsób00è‚àNÀ¿å ú@Wý—…Œ+Ðþ(“µ„?ªAÀe7'€GP”GH”›ÀËÍ-ò·!ÄU k鲨q”!` ƒAõrÙÙÃóüý `¶fðˆˆ±ÿárº‚¬-Á5K¸=Ðù1£µ¥@b ½þ+³¸=åâòððà´t†qB\í^°°<@p{€6tuÚ~· P·tþÕ'@×ûS¡±…{Xº'5 {tqÛ]Ù:Jª (ü§±êŸ쀿†àáäùO¸¿¼ÿp¶´¶†8C-Á^ °ÀähÈ«rÂ=áìK°ÍoCK'äÑßÒÝädiõhðGé–y)-€åc‡õ³vAá0NÈéw\¿Ã<ŽYl#qv‚á0ŒßõÉ‚\Ös÷âúëpÁ°Ïßȶ±ý݆”K rq*Éþeó(ÂøGf„¸¹¹…yE@ÐÓÚžëw]/(ð%Ïoñc~>P`ûØÐd ||aøÀ,ݸ«ÐÏçߊÿF<<5`´1þ‰þ(Úþ‰Ïßä 0á~¤€û÷óŸ/³G†Ù@ÀN^ÿ˜ÿqÄ\†ÊÒŠÚºlµü¥´4ÄàÃÁ'àààððpó„¸~ÿGÓôWÜÿø*m!‘?Ë}œÓß%»ÿÅæ¿„ðß±Ô!̘ÿ!º)··õãÏÿ3Ýÿpùÿcùï(ÿW¢ÿoEònNNè™ÿ4øÿè-AN^Y<2× þ¸jÇ]ÿ¯©ðÏÕUڀܜÿW«·|Ü)°Ý#£9xø9¹ùÿ”ƒ`ò O &nmÿ'kþ”ëýÞ7'¨ ~ß0^ÜÜÿ£{\2kÇÇ[öHÍ?TÀÇúï¼r`kˆÍïeãXººZzap?2ŠW@àÃó¸•6@Ï?È àâCà.€Çý¶WŒß+"à²ü-ú ¸¬þA".ëÿ žGrÿù\öÿ‚.пàc$§ÁÇPÎÿ@nø_ð12ä_Àý|Läú/ø˜ö/(à‚ÿ >öãö/øX†û?÷1¯çð¿†híæêúx£üÁöÇ ÿÿ¸¾€@O 5ÆÜ4ÄZ,Ä¡*¤é²RŠÂƒck˜—»ç4êÝ`SŽÌ}G+HFüêŒ//zVªmr;újÿ;oÑC£!1·£ùŠoz´(ñ\Mª­Õ»ï(¾Äõw˜ªï¶Jk«ñ+>°ñQ½vŸÇ¹ä »ËÉÖTæž‚8c}ù¯2û£'LµœXHñÑOH……ù°Bš¥2›Ë¥?KML¶(ÑÃN%‡“¨ä÷®Ò»yÖ{æ‹ÙAkéûûØP•óÌýow;ÉU^&6;äHoÞ07…2[Á¾éˆ×kkÇsª&½¶iÜ÷ðßTzUäf‹ÙI;I¼4 ^°”YÎia©€ž‹óŒ_á3y%Uç«=ß?˰;F(Q™®´GKϬû¾Ûcd/`Uœ¯Q,ÎhùÅ¥þuùå̲Sßd¼hû‰ÔƒÛÒQóóÓ´È}áÖ/„Ö¹Ä?euYÂ+ä‘N„[M²B€‚¹vØ­ã…]ÏseßìD¿3MEj‰]@ÕubÏÙz&ë’/¿º…çøVæIžR™ÎøKû·0”ô’Ô”«À]ûá½y±§¹5ºŠÍOôù£}&œ%ŸœÍS¥ß— Ñj¿@’ÛÞ@ÜÞEÖCÿ8–ôÿ --~¥èóà›'ÖZ¥j”.¢ÌjãÓ,u¾ôèäþTŸ<ÉŸÇôJ ¹¡y2÷6–)Ýé„ì+øæ*‘!“£ŸiMÆ QÅwJ¼GÏÆ¡]í,òÐ\6K/?IþÐxËEõòÄ)±AI˜_qÏuéãâðIÆ¢§|$ÇkÄX?—÷vÀuCûº_Vû£LêLé‚Âæ€XòCì•„ì`Ý¥‡<Ú_(³ä5˜n•|Ä:E&Eï·*øÊÍb|š³šÑ ¢I›b?F\ÁìiÕ3P‚û7‰h ~|¾r¬¡¡SÒºy?gCñéÇûjÏUhY¿–rcRŠr ¢þM~9jíàOæïèzjD–*²3ÐÚB‚XÍ'¾t¶sÛ8mª›úâwdÊtS\ãý Î6z½â fÜ"Ã|‰pò¯ ⓪æ0gÁâèæË :W\—!ùqÇ;mᥠÖhÞ0!æéöWí| ·L«/G•Ò>‘E³MäaÈæOd¤¤oúÖæÊœ¢Áï&™“ªð'u0r|*µ[¤>î=~†`Qhz<ù²Ó¢¢.¡¤=' ÙÉõÜk¦_Î ‘’GŒÇ,‘,`,¬NÍ~ÍFD{YÚ³ŸP0yOôÏ¢H1N Ñȹ ?3©¤ø½ëº2=Ö¥DøÏ»|NÑÀõiwÔ£AL$ñÀH"ðÕ¡ÿKý‰Q¶€ýþˆ˜´Òyá„óJmÌ W¤Š@g˜+(5žÿ·a’Ýå ÔrÿZÄ›‡·1É"xÇ¥ŠU‘,Å ßøt¥Fø¬ÅÅiÖ¦·´=Ç ¯‰0ªhEÂr¦PQ ¤…Š}ŒŒÝB^|1ë‘H±_¨ ú’EVA׺8ó%qðH½è¼WbX˜ÌÎñ멎ù®ñpÐ ^N•gÊNzœñõ1î>fû‡èÒ\`»w5ŠŸèy˜äQž‹»78"AªFàjæ?†³ùB©D•æ²- œÌæ¬öˆjüŸ–’ntTêÐ4@Èãm¨/Ýí’ònýÚ1š¦[—>¢ù‹¼ú6°Ÿ3ã8rð´gûþ{¤dGcv²~š^kå=’CüH­¸ºÿZRH.xÑ‘ÜV–ÖeXöÆØ8¤Ö—܉úË¡¸û§Ü®ëd=%v>"¬ŒdJ@Pçc œN›ÞôÉáé:îNµóØr>ðÝS~ó}åYC—Ÿ‹î2îÛUÚÂ^îKJÇÞ'šq)Àг¸³+91¥åñ3R¬oÉmꃲ0íÚ¯ReÓ›ÍtÙÉÅß ðó­àùEó7‘$U>CwˆÔ!Š#³ohž³gBöÁãß5ñ8+ìß ~QàbZôPs»H§°Ãd7¾V¹#6\C =1³ƒh{ø' *õƒ EM†ü?¥¾ ²tX|NÀßB\’ft®Ò×»¨ZP.|G%òÕÛŽ%ƒ2sŽÆ‰äRgs#bZ³ v¥×âGªà\˜$0-WâV¶±ÌòN+x²Ò$ÁŒö3rß Öììy4µB™`Jš‘î”…¡°ä‹ê#j½ƒ(ÍíðEà0Ž[Ï fƒcÛ”O1 xèYÄ®:2“±½= Pè§NžštiwËzOP oÊž1-ä w à߇ÖF2µUñ¶\dßÑpûLL-ú8TnŒ/e_Ÿ…Ò|‚–>¼& »[z¯É€õRfôÛO¢·”OE–^·KéŸC½³ÏÖúð;•Œ ­Ë•1¾—¹Ryöü¸±3Rÿpëj[cíA¾éŸVÖ,]ü]úC##|¬`l^_’ ¿þ^C/‹ðìNžQiòÛ©çðúÃ5шhãc=´D` %8ùXüWzGl® · AÅÓ»Ôtü°¶ÔÓ~ù’éh-÷›Þ=&óÚ%kØ ü¨f¡ÔåÁaOYvå^å¦ ½ß‰-2Rz®ü«À,Jjg6Õæ8 V¢ëˆv Ý‹’“vtŸ›ïmr“ÿ¥ .Æ;éôMe8 Ä ›pòËUT¨…ðl¨zoõfùnë°yZ¡mÀÈY|9/D¿Í æÄàÊ«ã4÷DêäÖ†=qƨ~¸¨ ”a"; 4»]ìgœ8{•n„Ù«Ÿˆyz6HâXê̸µP'½#™0<’1Ä8m0’@ÎÅid®q݉ÛzŒõ ŒÔ’ÿb¬*gë$ˆD…+ðË\ ®se€ M ¦,ú53ø¶éÉÆnæß$»Oqí±—„tÒ¡ªäAÚXTìXˆç —‰›Q ³äÿ\ù,„T¥%ôÜ6ü¬Øµÿ­ãŒÍ±-ú›Óß;Õšþ‚ r¤ n¶* Ói{˜Ò Éôf쯻]ÓØ¬ßü cõg)1;2ޝDIªÖMì.žû-›³úî500(X"ŠN»§”ß +ÿ¬uøEžj˜Ùmð|÷ùYÁƨiÔ—Ä=j»Ë}zÞôúÙ$ç]’6Ì Œx—Z’£òzä¥<)›à<·îôAré“Ãn¼¶ˆÌ¡ÞeË1âñÖ•§›&L¡¶ØŸ´ý²¶Í.ÃÄ‘Ô×›sˆ½ãG§fXè¹]ê#øx£û˜Â!vŒvówÍÀ‹é…ŽŸˆR1×`bË‚/P”nÒLµ‘‹¿ äeô _Åœãd­”%=Ëp{²ÊÏon lTÑ»€hÓnì.&åÊÅÈ•FW¿Õf3<´g®õ”áGBý°"ªF:«›YÌi]÷‹Æ®Çà:ú|*¨ö")´™ô£>¼±LfrÎ*læ<ò롾Þ0úœ¿·:‚äÿ–$}{•|ÁÊöݦe- ÐD;dR Ûý4(¦p€O^¾P}÷(ÃÂÃzu ­_X…áàLgþ³Z‰Ê[„QlØ]\7¯¥žÅr™!†±‰ñÒÎZ·öÕ¾È aªPÁ…eõÚá MŽåë«ôl!†>ÅOr°hGDèî”»œÅ }›“§¯çš˜Ø'ìý¡:Ÿ8iÅóäY@mãûŸ¹P=FÔÕF©Ÿ·+ÞûâÔWó•b©šÌ=¨¯ É7«Ž˜åBÅw @)oä2µÜ©ZÝu‰ecqÑÜ~jöbÎô;:'ðc‰"ùY ²Q¨Âú¶÷Ȳk_¹ô/Hï™z=N/ ;¶—ù«ÓYV¾ µäF èc:j§gNžtÈOë¤36&áeóÔB˜Xâ&’ÛÞV7a e“– _£ÌKÚÞÁ ˜f·´{(ÁÉ 0;ÞÊüú"ĺ„%=5Û¸p•Ê>˜øºÅ$˵õåÈRu¢Üøû/\ýÆê;µ6Iå¬i£3÷|>5¬!¹ñ!(ƒÆŽrSÎb;©Ý.ÉUóÅLüÓmP™i¡jä#*EURðžE¾Íxãv>8 e¥,˜_vV3è~D çX¾‰dµ­¨u»`Üc¨ßL0ÄÕ&ˆT9XžüDóð„Ø¿™ª-*ØE:7.!f¦¬&¿œYN”2ˆñC£vàânä a›óFß0IRÃzzáUNncB…4Z9üö}´F¼ö=8ãÄ*¶ÏÂÎÖﳫç<ÈôüQ†%òµÓ*{W#i©jTv¦º"Rî±jzºª^;“²“ \Ô¾Á¬­)Jê²¥öW“± áÁÖ¦EuŸ£Dq­ÀœBôÆY„anÈä‚·Z–_ánWŒÇ¦•ìÞÂÉ"¾ÆÁ»jº;‰YRæ1¬…´YJÁu™'„ñ]òí¨gè§Ê·gg_•“‚¦1_¥HuR†¹oð'ú¥«í)ûvùñ[@s†ÜçŸñ9é¼³ËôŽfnÝé{ç"¶çdì7âlç!‰î=[ÄÚ½æšÅ‰!½‚Îì*ð9óoÒ=o,nù@¯0ëp?¶•|Øv»sU¿›®T`úX—t¢•ÉHw[Ö,ÛåÖ «"¼Ñ®1Øp:C§¨M³.F»õ”F=3~© ºÝtl¾Ò0Ä< ?3RÜDßS¸æÈ:  o¸¯Æ[ùôz›—¥9ÿ†ç•wy×Koé‹«dÛ—8yXëÀp\ £®ý_!?fKèzëŽ&ÆÐ=~ˆÅÄâöReBžj9ÉÁxÒ¡&­°¬è¬P‹c5‡ª»£óùZ`W"Ë<“‡~ô§.´kšã‘û¸ViÔ¶©7C÷ò1ñ0wމ É}Sþ…‡2ugMtÃØŸ•ä+K¹­¯_û YàóÛCìåɰE’dÌ ™" ü¾ä}ƒ³§èÅ¢–ÒBt*BQƒUxàMÄ_û)´ß…«úy²ßï†/r ùä¨oŸ§–‹Ám°«Y-ic޼Ÿ½FpàµT#º×XÿÌ‚&­JׂUåƒ¶à ­GÅW%¢—ù½ Þ`ÿ…/¹À_AÛªžZø•gªâ¤û„½ŽL–]E¸7ª°ë”tì^ç{lz©Zß+&5ú¾]buþÊÑbJøÊ ¿É‹ÉÏÚLù뺨{‚ražÿ«öD¹äsF91‘1ªB¶µ¡¥$öpɽÌåºý—ÒrE›,ë­AѦ@ w¡BÞü)ìÓì‰Ëú©¡»sž3r*‹ºcö3„JÅ}ò‹}\!\”o¢×º¦çuòH”xàµaä«©žÕˆï­818Ï#‚{Q(‡`¼ÐP7ΉÀÀ(´.M§Ý@ž/Ì*Ûhkt&"‹EZÊ©ãðLRHú²0ËžÚ+‚ ÝmeÛ³þÕt{ÿ0Iš‚γ÷|‰í™eƒñ1‘?Õ—¿Ø²×øPðK3ê}âüÔËCäÄø^³YsaºTËÇy†é6Üú%¨‚lÁ FîJB 8Ò@Wyöœ(;7hJσsC2ºÑ ”êd¨JÎÆ¥Á Qÿcš½v§­ÍÒç8ˆ+Ÿ'™ÓBÌmô¯ëº¸bXwíê"¥×bÊu1èp–ý³³‡ªlP£â·I~É-ž¸Î#ÖnûÅñÖî'ì lM¸‹-nz«sWfyQ¼öîÑxþâ6¶ùŠ™ôJf-‹:o'Õ`æ½8îF_zKûh‘†h@½MoéÄ— „œðÛzµoCÇpQ"ÈXò¿:þì¤kôžäí¯ÿX,©úmP`7VÎWD&h¡‰8Œý'ŠßÞ{«²sCÑ:ìö¸* ´·íÔ1T¸¾U¸¦?™G;1• Ÿ§œ6óܳ`*ÏΞsïùèÚ mlÀ]¸ßêúß굸‰E¾ñ¥¡ãvZþæ£@8×”Þ·Î/^I7úÕd>«t>b囹J¯‚ÉÑÅ;s|V›oµØâ!ôK5Ê7¬ÎЋÖìA%oo3ñaæF_݉AÓ·ôŸ^} |ŽÌr1£…Éí—έ1 f XL` àŠGú8hQXËzg.¤Û$¬Í¯”ÇBd¿KÜdÔû öË/ŠSÁè…jÊ’8ŒLüº]-?Ûõï¯ìä_é82ìÄTñU±&fF®–Ä'(™)™NÔ qÇ×|´”Ÿno¼‹tø@Yš˜ÔÌèäåý6ð›>£ÇŽù”YDùz tÆvÿZdAÏkþ® ¹gûéÖç#eËzdJIsš0k¡š²x í|ŠóžÄÞ&¦z²ž ¯Û7þ ¸‘HÍ”6Ék'ÕÞ±Xòi^Ë„+ "$Éñšoê<ª£´¡ÃºY—awY+Õ¬~2OVv¼UˆÇBbõ½¶ÛK°*ÅJb~M{Î%X‚D^†XckU¢*û‡ "ñ¿ YæÔ{8EŸžáéò³îªc[ÊÏhçqYšýzhu¾¯a£Ó…² )1}¸Hñ­ïN#”ƒ ‘UÉ£>ûõWtÉw]µ•V*݈t×0ýÞ§åD¡û1÷?LK’¾˜?ÓCô5¬î&µ©èÇÎ0ôÈô éÖÞÖy¯9;“…QÓô€ô>†œ¶—ôË 4-²Ìâžs (H޵+‘8DˆÏPA¡Ü]îxøSœKwÜ–Á²|ÓþWPµØþù4#Exm>:‘Îy޲.¢ö¾Þ¸þõ¾…Ê®ca Ï?ùwðÝúËò'E™â F7“X²$UD+o"KD ÛÖ:žO3k#@=ØÅ´øM#m j;(ךBV*Lµæ{]²_«%dà$4Xªf…K•+†$ž}û?)NzÀðÀጾ2&ÇGþŠC¶µ¢¾®uñõè‹cC‚`¾8VÐYFgØ®Õh0|›Kk«=ÓGÚâöç:Ž£¾²£Y'Ë’‡AUŸãZ…&{&CD6à~ºÄûcÚ~JåRý ¯d3í1ÆJ2NŽ0ñÄ[#×sR”R'Ê2ßѬ,]‘¶ö8ì#ü©‘¸ØJ'Y²ÎíÊðMÓ.QϼkÓæÀÆvƒØ¼1ê>_‰;Ù°1Æd‡ð¬ ÌŒˆ;QÖµãÂâÿ .™ê6 Ä*2c™>ÛV›5_[÷m ¹ÝJ ŠÝ«­)ɧ[–y­áü­š>þQWoŒ3Ô÷€¯±}qtæh·Um†ü5fâÓCu¹$[–ñRpz4óBË‚µ4®(`–½l…¹Ø_”à¡¿oêð†õØåG$¿ó½PÆÌ³Ìå‡Ö7¹äÁCmVMcïWnUÞ\PSêzÙu:¿uÑA~S™Öo»šVÿÉ{e›Ĉ’±96?&naØRPbv<&lÙÀÉÌ*D·Â{Yá@«=/ÍÁ­G®“ö]Ü}‰_#%»Lá!n,¨–4ACÍG†oÀÄ[EÄ û«tÇwHø„ üÊþÈ_Þ¦®µÌÄ8†.®¼e˜_ªåu“” ï'ƒv–¶I=kd²ÂÜctð˜Pø_ãmÆ&Oò ÄT¯>ñÓ6wr6Ü2qãO r|‰Jp¥ÑPìâ!µL-‘p"« ƒé‹Ï¯¿ìGdK&Ž_yZÿ¹‘½IÑ G ܇”çÖƒF8–ªõñ›+!òCz£$b¨SJÛ³^2¦/Ý×ï <С,ábî1ü0Œv=Œx¾ÚÙëC4Øò$&™µ³C9lS^;ôr;™wˆÇš³gAî‚ ÑÌH³º¶Ój!K6µ²m‰Ü-x©p(ØnAßZ[šÄ‰™)⪣ /]¨ujrézö­ d™­å‹úËé€]vþ4œ/©u˜…'ÑK º›Ðo`Jî®~áM;õçݧÙû¥Ú¦Á]^Äçd(¡…7¶šâÉów}b^ÓÍŸðÂ^û¥$ æµ­hªˆÈEF˜çÃè5>ºa•¨þàqÏ51Ý”=[9„MÇÂ}«2ö‹ß@ÎyIB¦v ™Hí}-¨g_D/Ÿ¢oñ–©»ÜhÉÙèê®éƒêÍšo1ë^¢yìK°lÖfP;ÚIàŽõš®˜æÉl3ߦÉÿL€œÀüAþ©/Ýb ŸBlRºÛ¼KMÝÁ³»»[&º÷Jí¢÷0?œ¡®D„qÞ‹Ö & 2ôJ9±'–e—ó‰­#$…1C.L?ñã}°©?my«ì'öJdöcÎl»ˆó8=|Þ33;)oÒHèyÛŒBQ¶²½—ĘkÌ"šu¢dF²öÙTÕSAwR-¹¯[t˜çH à*o‹oÓUµVèÔK*=SâZŸ'‹ý‘‰è=VÉЪó­£Dþ¬ÒwmTÚÜÞr€sب\"H½ß!ŸcǶñT27I'¼Lø»{pÜáXÜ›'%€JHå,lSÏæv«Ü©œ†V?Eæ K–Ò¶Ðö•/‚B¶ ãtÕh[Ü, lm¼îáî8Þlln糊ÐÅ8¯ÔÖÛú'L‡y?äè1ò Y ôNõ-’SSÜ`wSb÷E£kü.v¦Q·×ôŒò˜á Ñ—ÃA&ÈŠ‘î Wä«®½‹-ÍÊþð+†4«h”Û<&ƒµF/V祕¦-°ˆJ_ÐÎayhlz¶4îB”©7»ýGöšUÂ*ü Ô×Ì1¦Éé½ÒZjñ «3™àÒ>ëLö^²'NgßSÁ´a¿õk•ŸÕõIFú_Dâ’Š.+À¼upÿ¯×ã3e&î¼0K>Á©¥óWéM8D»t—bâ)¢'£‹]ÈÂ!¥£T_Ci»±ÉlŸžñ§˜YžI~ ,å~6ž6ÀEÀP•Ú‘Ù‚FÕJ–¸éÎŽß–¬¼^ gŸ|º´?£ã¨ÄðE*¦ÍýÇrW®ïµ$‹÷C7å(Ï>ðcäs°¦gêZðxج ’ØÄn(lyªÈ? úSó锫›wë·ô½C˪suQ†ÃÖ{;÷Y2E 7&cyU&¦n¸BÇ 3U£ä;áš|N¼d)} ú¥xjÕ¿"a7fâª?&–ž3-àqóŠ  ÛO iØçŠÎyÅçųbÀ3wV¾uäú„*ˆ-X&V¶kÆ}w?3îC¦Rã ¢=ŽÞÓ÷”/˜¡DE](/{}O<ìÜhì Ù£ømçWFþœôçuES?¾‡~“t"Ef£/¬TëÛóè+ôïº}çÏâ"Îd­ý7£¨¾¡fø‰±[> stream xÚ¶T”]6LÒÒÒ=tIwKHI ÌCÌÀÐ%ÒÝŠ4Ò ÝÝ RRJׇO¼ïó¼ÿ¿Ö÷­{­¹ïkïkï³÷9×>kh5µ9¤Àpsˆ<æÌääȨéè¹ÜܼœÜÜ´ñZB^X^N WÀáññú§ãß €¡Îsˆ†õßìfˆåŸøáüPw€÷ƒü€îßϾŒ†Ãì<þKÿ㈹ T_É)j²ýÕòœÒÒpw€€ƒ‡—ÀÏ+øü;‹&úWÿˆT‚YÂÂû°Kìú—˜ÿÀ¿s©Ãt 0ÿW毹ù¹-~€ÿÏbÿ#äÿO㿳ü_eþ¿É»ØÙýágþ“ðÿñƒì¡v1tëâü0jð‡I€ý/UòçàªAÀPûÿõ*9ƒfA fe÷Ÿ„:ÉCÝ!`M¨³…õÚøÓ¬û{Îì 0ˆ&Ü úûfp¹¹ÿÇ÷0\¶·‡Óƒ$ÿpAfçß+ÊÁ,ààßCÆÃ/! ,î%ñðó¼€Ó†¸ÿ!b' îüxèÎ` G`ý>RaAè·éO$ à²ø>Ž òÈà²üäpYÿ>¨š þÈ àrü| #þù\Nÿ€.ç?࿚³pA &üý=tþ7þã:@Ü!Xó3p Ñ ›ª /Rn›#/&6õRY8¼æÍ.W¸I,å™+ˆ3©¤þާKŗ’ 4·^{ 5¡ /›®½oLã´Æ7›°æÆHzFóö¤ª»©0)9t$·¼o½_ùÛ¢6 ·*3ä8ºáj¾'¼pëRp¯î.^ ™Ù|¹U. òä¦x‚#J7òµÁC®ù»égtèÎTY ŽÜñ¦NÏ& ²Gïi”ãØ°|ö£x?z®òD_N{.—êð8µ‘Ñ“>£B=%gô’þž¬L:ëU˜¹òb6‹›…ie¨›Æ{°•ö‡ˆ‘ª)·b^XaˆÞ§©šË_¿3sSÛ̵Êawö›¥››¼Â{Ïù]&[}¡˜ça6°šk †º‡×ÖÚü£Sƒ½S•½U£FžF–ë˜XëÄKõÒ˲·¡Ž>Dj蔿¶„Ú¼ól2Šè+½:mF÷&ݷܵ›{Jä\p±0ò’Òðžæ¥× 2ôÇVò½Æ,n2GÂ|ÝÎbÔO¶ ¯»Èéx¼uÇ÷#wÈ0UHåÚÓͲÖÃ.q4_‰ßœ žÆÆ"î‡!q) nZ8{‚`vÞL±J¥ÈÅsIåEð'Éñ&ù}äaµÐíóvÓâÜk0þs+¶ª«½õßÅ÷ÂM¾ ù~®OÑ£ W?% Wä7àZØ3¬êÉûž—Ú€LB!7üÑhp„1'JéIß÷éó3œÈoèÉxϬl-ëÃÔÊ2H¤oúNÊå;$¬´ÒTŠªßÇŠiOj|n»Ç‹)ýøâ´xÝhˆ²3aÞ¸âù›Ô!ÃÉvÃ9÷¾òȪÐÊ^=FY£¸É¤ F‚ÚÍÆiQ:aʦ`MŸKŸ–¸•ÖÎqÐéŒ §éíHTΠ"Mí2l+Êd÷¢Ç-¦R­~’ÕÝþó4É–Á§¢!ñý%NÄ&ai¬ƒ¥·l›·€dÉvöÎ;Qã]®òkïÑ1΋-©G_û+T¦Pí3íÎL–õ–üH#–{½¨ëßÝÚËw‘/–P½«Ii¥X˜Nëç•Wy£DòQP’DGPjIíZC7œS4z96Rq€ ¢_™%êS‘E´eÓm *7ëRBóåo“Ãb¬´Ò—üµÜpmÉMQŽ ÃËæ(>n dÅ´©i¾#£}7ƒæÎäô¬Íw<_ŒœÇ&ú’>DÖdJXÿJ ÿlž¹vÇ:O…Bï‡îùhNÐ ÆÎx+âÿ”. 縈%ºÈ[wü°§h/Ò’ý²„P¶6Ïùâé8¥£ çEiF&ÿðÏ/¾luy6‚í {PÉû’uÂ>|2äñ°A.¤1Ç0rÝ¥1ùÊóØ`¢³ÜhÀDýò{Iœñ÷~¤@²ÎUò&ZÉï’Þtä;’a˜dÒ^G¢°­ÜηyÚrù¸òs²?ó¶J–¬¿®ô\yîŽ)wXÞÖs®¦ýôs0`jì+>ßå“·ð}³iáYožFŸ± úëÄŽº¹úÎëgž­dšÞîñ¹J$zèŽåLnÑÒnDÂæîqþu¡¥Õ>C«Cîµô˜Fƒš™É«(fºj×­äN{K’Á$${<8N±^Í3:Ò¶åC}._æ'=]ŸŒjŒûAhò(°=åÀ0?ýÉÞü=SRlY.²†×¸|êÛNÐZ†^ŸÿõŨ(ÿrfž¦½ÔT[ΫÙÏåñ$4¡ÏÌxMtp±²5]±É+*6ÚwKãŒÏµ´2Še>Ê<2)ç¿~þKêÇ›D¯ôýöR/Ë®d|•H³Ó;j(Òt·B16"~H'7$išÄê÷/HšÝöjá6ãSß‘9º —ã¦yƒÕ«"³Cqüq3j¤椲I&tû´+ª'*Lx*ë“8LâÙø™r«šêì"j߽µyx´p%^g¯JpM›èG1ù-~?õ•'át´NíD;7 È>¹¶ º Í«Òu.0++ûµž®&\‰þÍ”HÙjÂÜv={è‹S—ÿ€ÎD"R™lªy@¿ˆž#­±/´'×­)Áìþ{^Ñ#ŒH¦Í³Ukôl¥˜â[ˆÝ·º÷øÍÃñ'Ñç9fËg3þ©~g³Ë¦Bj‡°\æJ € [g%ost[ÇK ¹ö5®Ðt°Ì÷)½‚˜Ÿ©t‹pÊìC‡U„ß·º+vsº®ËÝLñ¹›òS‡mÆäÌ7Þ„§w±ÿÊ*¯Ûñ3™š/;²'+žq4*¾ u¯aª«ÃÎ »NÐ0]†©ë{+gÔQZ~µ[]ïL¹PQ81à?Þ°v5'¬5¶à ´Bˆ.k±€U>pvVí ÙSo‚“L$‚B“‰…æë`«k¶²þÍœ”M,í«G?mù_ìö0fäîC©çÝ~† z“>{â*S¡ ñÜTù5jÇL>p]Cƒ¶­äγ'··óƒ'‹Ð©Lg3¶”øîünâãOe8~žDf[´Þw¾e ÖOÃàmÝ'±0X8•(­ãfï¨Y¹Qsü+ýÊÅ·ƒÔ‰{u1»y{húbVñºü„5œëŒx½£Žøܨ?zæî®ç„0V’ôR)eU»›¿D{-ý0«"Jè»%RxÏá((ß{rÕg~Wdj1¤z;”6§6’'•·Ócøt(,9ﱘàŠW¹ªÃWüÎóW, „kßm–xƒöÓtày720>ñzÃ{D_…W‚´]×ð^X®8{fJáÓšKŸð‚øªœÌ±e˜+ oíÍ®ûzrŒv°w¦¥0Íl©D›þ1§KŸ´»¥‚¹Ü §›žèupœ½ “ô£´ª­­J­†°åc+«vd»ïÆ/~ÏpÕú@¬¦èÏÞRµº>_ªßÔÝ·Øñî¬Ã(AbªG! …}VÇKF¦oçë}í)¹Ã(œ¿µ„]”ªî‚Î&|¿€Ç'ˆá¡€Ç<Þq·²‚SèÓÑNWæ9°Ž ×è0Mô.µ0¼£ô”ÎÛ ”¬7ô“¢Yn¹\•}€¸'oR˜KÄn‹Ó'!ß÷c´dξÝÑÉ·’®±{D8%¬{+¿«\.AG`ä`ÜñdmÀaÄ:r®=g¤-¾ð8DEÈot:Á3wûk‹jˆDµßJéµJ„è§Ó«\½{†!ýÄ ÓGôs¡"¤æýÂ5ò&àtéêe-fd#Ö|´™QãHfLjµ·Ì¨OÊ—ÿP#,Ñ”zÞcç§CgII÷Hržr§_À¯ÕÒ%×»÷ĸGÕo7¤ Iàö±„5‹ã1—4è©eàYr-i#wXí‘óÓz5±îT}ndÏfà ½=q½Ïµ¿žØ Ðfº.(°}KßÂW A®¨©l©n²»Žä긼üoY>yqìCU+W3Xȼ¡=08ÌÍ™ÐZ üÊò|Møüç€ú°h“Ÿ¦1)Asíq‰rë]ÒÍ÷¤F%TöDº>‰Œw þgæÒÙ£#}7³BÞK‹ £Y_—_‹6*bcÁzÖÐ-xÂVÊÝiôGЉr€´"«Äkø É›OIœŸSw®’H04ŒŸ'Y…k·BŒ<Ó³!ÃHÁ>€k–”+Ù}lTŽ«àÇA]ûA®§ž.y±’æ˜ݘP÷‹že3Ä"#¢m›Wi †,f¡ÁTÿ±?P{WŒö éÙ¦ýA°K^GÙéx׳Y´p’ƒÖ$|ã'ZÆcòJŸtlÝ2l¬Ì³À9™E°Ú×K2)9#ÇPRò[‹‰“ø–Š›ìxº³/x™Ä[#2ÖàŸêCjNsû¾–:+&è,ÉÜŽÎ’4DÇ×ý….é‰<>×K‡ÁU8~^/,ÛêJé™òÝ>¬§ —¬¸WÅWÎ`§÷$2ÈêžuMé×ü o†}ç:>gA»<TSàJäð cs'O¦Ö>‘ÍQžTMqš8ß36¬[”|~—•ÊÔ:'We ŠaÄÈB…kõ$°Ï„c£^7•þx_.®g9ßOøhÞì':w¿/Pv¯Nší›Ô¢\\ã$}í5`æ²|oDFAS[ð‹a§·=,æ&X°ãõ4¢®LS»{1z’v›#_f[Zõ¤í¤Eæ5¥™üÓÊÂ[ †„|zÌ©HmúUJKñEjòÏöUŒbåcƒWǃB5ûmÀö[tðseU ‰¡ZMŸ!]w†yÁÐËv\ÊÖK‹ú±ÉlDMsù1+»7Q‡)qüå*£/üµ7÷ÀX’ay~¦üÓŠN«×§KÄ=»\'ëC¶¯(—]¶¬¾ù&ÞæFÌ<õaâ³KKÜ”¼,o5ÕPZi$ËåòŠ5yäéÁF ?sDZ¤Èm¢¹"T^Ÿ9¾šl;¯Ñ~W´Á”MNáéEÖ°g^ûqÒÂVÓüñ›¤9,îÜʎ΋üaOŠ ¥É2Ú ÀÏZÅô“ì© ÷¹r¾K#åí¯û3Ê¥¢ µW§õÞ¨aºéžgîó¥Ì-t±g®BóC ÌVX!sÆlQŽ¿ÔâòXëß½i¤hó\jûê᯻%3&’bð!„"[Ö¸qpÅÞå]ȈÀµÆ™§Žy­!þtÛt|SÞðWïkÈrÐk(ø´vèb¨HòªÆjwÖpqqŠà!Ê)ÑzíŒ­Ä Þz†Ì—A»cŠª˜ý’[¡#‚`Î[AŽz! ¸²ò¥2Ò,'R WjV:5±!têTÙuau¥×h©=üQÖÓËx¯Â/¿©R›O·³—[iÀ˜„ˆûoúŠË>Ñ’’ËM…0‰RÝI¡!™¾Ld?A†=»mÊÂÉÒ3ÁKºÜs? Édº—‰n#6Ä ÓÖúÀójQ¦ÓÂ1´“|G<±QæÃŸ¾Wíü mO‚ BOMcA#&O(hL1Á)lv%:t^ÔP SWÄ+E’÷!‘c-Ñ“·³Š­W¬¸ÒÜ›r+ÔÇôLêòHœXɽô/!zî€åc „¡bpÙÆÞ2y.•z¨¥9Dƒ¡i ;×ÓAÞLÈ‹ÅYÝ3 sÑ‹×ìI·Š9Óñb}Í­ód˜š¡Oza3G=»J;1ÅìþÇêQM¢~M%ÎÂ×KÙD:ú¦8ÈŸñ…o6aZ’ìÜRœÒž¸íZÎOT²Û˜É!î’µÔb«·x¡ E¹w*YÔ¢ºKŸ³·ðK_•++WÝí2ÎTô.ÓN訨 ­ø ñúƒßàßöyo2™ÖÙt‚!Þ‚§o²Ý/„å[âgÐID.XC‡©‰ÐÇÖHXÂêÎødTñÁÂM.P‹-§áoÊ~nX†ÞuíDhô2}FãÔÐðYÈQà“ ºs¤ëªÕ–Qr^o©8Où5¾3=Œ9mJÅT嵂¬–7÷D ™æF­øÇÝ¢ÄÙZn¡+ ½reü$è²ô6½ýD§?) D¡$ê£0½‡W¹üåäÃIæõRŠÅ\y×C5šÊë©EÀ‹¢“áÈgîMýfï*^l‡çºMÌ\Ê'Ø?;ü£ªþ”†Àö5×S&3šÑ©ãf”šÖš¸BêFdâc!«H%dúSö¤û!53…ÝüLÒ ‹ø%´¯?“Óï×TžÐ I4GÛ!÷s.a£ÀO©GIj§]Â\±Tꃴ%†¹ ÅŠ©¾œ¨™dÁåPÖfqÄôÃÀ ˆ ŽÉ›ÞÕz?6LZÜ–U1{…×(+nañ‚:jàNþ×e‘R¥:lm'ü~i%úÈé)ׂyÈ÷Ú\L­&[W;zï2èù>·£ÃQ3ÖKw¤q|CñêeXuš«çñvFwà$lþ˜/Ƭ°wRd~žÚ(QòŠ©ä~DLÝÁº6L‚Ù›‹Üô&BmSFæ×û·ŸO¹'èéN#Ú2Ÿ¶P^tLx `_M]O™ˆÑräé+øU¿ªáã+qˆß‘L'* CÁÏ3 ”…Ó”bPƒŸWF»­¢Wê,X›¤ñ‘٠ˈ—äõ`à5ŸQ-—4sMÔú€2×ôm S+5[~Cx|™èeavuà„eNêÒ• âS7sµ{ÃaìÙOsJS7¬lûQä„ë%PS³çÌ@aê\fèxÛ#ë;<ª[0—wù¾Ï>ò-f¿2Zeã|ol¾[Hï|V Üå¬óéŸél·¿)÷úV"&–Ếôy…dÈsæW¨ØÕ“a¤ãä¾±ËHÂCô¯Ïjýé2ÍœëÒRé=vÆŠgr_íŒõ’ŸuÌLL‰XÞàFÍÂ?õ¥ÑÌ“Ž¯×c¥2”éïÉ×ÈÚÕö÷ÃÜã®Þñ›Å6ŒÌ´5¢ojUò$sø¾ x<ÕC2ÏÑžÁJúYIæSŒ°¯!D*x Z?4dmãepDØ÷®ø¹p’yˆaqȨlÍS·Þ5æ<5\4ÑËq:º¹„uÍSô¥e芅u³¯–0YRû'›ÂT lHqèMýblÖþK Rù Ö½+#r)T1oï´j=Ýï›aHÔ\c/˜Yº+3âÔUßòXSÎ`Ê›G²bê–ÇÃÓà~¡BdmÔŸOk Ñ£I¼`ø‘)´E;Ë`"{òŸdé:sS±>)‚ÝÛñYºåÉÇÖz3ÈŠ¬ÇmlÔä7à¶åçŒÕ/"p&dåÝÄ–#TŽ #Iê'¯¼L¹+í—]zHJsbblEr¿ò£,ßÅåþÔüäè½ÅíJg•¦¦ŸÉö­µ×8B%i,vø>‰<ÔgÎÔá³Ú«gO½1Þ Û»æMðW|ÎzÉÆÁý”‰î#!S`ªFãŒ^]ÇáB2èæ¢}z¾uF ‹I>©mÄ̈~˜ÍS®ûÅ0ÔàB›«¦åM¸X‚êâLƒP#¬‚H¸yUú-¯Ý¸(\4L ›•3?I·2ÓÚåñ[Ìãc¨ï{CEß©ž%SöªÑÖ(îõìËÔ³š¹°iœùD‰ü–te¨ª>R ©ÆaÒ¥DèrøþeÓùhûýš4yÎ1r#³FºúÜì:.6ci)ÊûAŸ*?¼ŽÉ˜ Á±«ÉÌ oBkо'ò;Ô=ýjµìH(Ïë_Ø{h)ATéŸøŽ}z%9„uBÎ{Û€bmëßq³Ù°{øò”#¹½¯Ä&ýÓ»<õûf¬)ò¥„ظ&õ™GO*ù×$³6 žHa‚Gb†gü”±Í·•×göØjȇWY; V±¸|ZUd4áÌÔDlí¹I›Ãfº‡uŸZʾ6õ#®xÝðSÐs°âz°¡Ùd3õ)PfÒùðãÇdÆá…¡bõ&&Sït‘&ì¤z_Mr¢©l’¯Ûaž¹+ý@¡Tw)Ì´=Z63–Ý´Àˆø…Ñ m/q쯯ؒöGQÒmŸ}$˜ÄÞ{b³âwéñ~qu « Ó ¡0Gõ­½zZèè&ç W5¡-f8'ò&~>:ƒw-¿“ïÅá7¨¾“'ó†<]+¬ma®6È®û8A¦ˆ–8Et¯:‘d7•Ô.L¨4CðJóBͲW¢ò"Z@¾ÛÈ~Úz‡Ïæ»Fù­ayIv™A.jY8™Ð1?ƒCä‡ýqïl|öÔ$ûk#|w‰ÝüZW\_L>±?~Åræb u({Áƒù23—¿.ë‰åÄJê0¾˜4ôè°Ða§š'S¤`Žô¢”á^$ÃUC’ìi+³u1”›ì̉}Z™E0ªGhXèî`nGép㫵‰=®†!îŠi» §hE/¾nNúšÚ:6îµÎ…|9– `Ýáš§-=*Ÿe¢O83Ëᆹª‹6W ª„)\>ãq½ÍPçò$ãå¬ú!‰¢÷ÅQsÓ3¡{|Ù Çç.‚¯LðªÞ Ú©_=ºÍÒ*Z,háR<ö‹Ñ]’föÌ}~w"üdÈ;7wž¹šÿBbòcYr³0BsGPI@þS2ùc’ÔÄÚà"wÊ€8Ç£5íå¦[•Âòñ© NsSQ€'Î!ÿ‹3˜Úïð¼ ³ãÆ>û¡vÇMë6à˜iȤø8‡“g÷oq¾ìxýêÒ´ûÉI¿Y_DŒãW/dÏl)„©àŒpÜo+X% «z„Œ ’Ðeˆ[Âæ6”˜M å•Rhë¸#Oó‡jLÅ7™t‡INÍm$?dêôóT™2!sL¿¬Ð¡»Þ³†Oèî*Ô—õ})5Ò‹©4n’ÁJvx¹r=-w ­R–Éâ]€ñLßҔ𠔵õ qJ/C6Iîy<(ƒŒ75¬E›± ÃQ?ú=EilUL<¹d&80·`³Y‚O p×ÛáO([7õ›‚e¾Ð–Ò|i÷iÇ:¤¡×Cë àz„—Vö#¸ÛÅË;^Vøew}Ôý²6g;¹ïM‹8«¨Üµëþs>;¼…!Û¹¸ä¼6ÍDÜ‹°¿¯ßT±¬× Ïý$/ ŒÓhÀhŸ¤%Kš¨^œ§ºÓgã®×þ;–2œU–ÅÏ¢@Éý·Õï– ¾ÏÙ☄ìQoÿ쉰Wé¶­1yφyöRZ”²,Ž~˾„§Úä?µäær"cjÌK!C£Œ4J&P÷®fºÅ×MÛp“ÆæÇ™h¶ïs³¾ªÝ\AŒ† ªM‰}ÞÔ2ê—J¶Sä 3õœ©ëé*†Z P1ë1`iA7»"†ˆrrÚiLs­†Ûãµës,Ûê†i¯ÈsÆ ìà³ôÌ×P衤#î¢ï]¡7οâ²ÌùpE—ÀšR~^o:÷o°)1‡(jõ,­¿›$‡ÂÒ~Ê™dÝðe~¹{Ñ íCÁ7L-'€ìÍ»VR|³Ÿþ+`}J€ú©Ÿ4Ïû×+©>n|6êý° {é˜Ð(ÚÝI?•ãK.}ZnÀý!ôäÖm¼]« ‡—n¯dÏ|ÕTœvŸX–g ÕZ­¸ûU¹‰m+!🩠á=Á˜ÍÓÓ« Û¶Ž'>;eL¹ÖUߜ*1 —F9Ü~£(+~“¡¾r@ø’á‘nƒä¥‘hX#E\Ù˜)fŸæç¹_µáù# ¿Fk ~ª’ú—lØÈ*ŸÖ‘}„êžçNiã{1 ¦|¨,A ‚S^q¾¬T~{3–ez!j?–bé¢vn~Œ;.~§fyû„¡¥¦PæÜ ÆNbæbc’̓Ü[üjFÌóÐçEû'&ñDS&ô û¦…ëŒk´nã\c K¸¦’×@Œüoo½Ý–}÷½’”hEÁ¼yK¥·5 H\ 1…=|M­7Ì~sÞüŤÀ®÷ü­š2©CÎ+^pdiLA¹Ã­IÜyØ^´™Eë[]Á[?FoÞGÆ™gétsä§õMÝþG}‚¿´nÆjrø;‚‰ÈÊRfε‘ôq,0Bôv¯ƒo9^×Å4öüÒ±u}!ÛÃtX¶#³yo‚Lµw€8cú–ˆ²5)ÁY¥Ì—àtmnaôçÄà(M ÀŒR(Ë#oq®="ÒâðÜ ¬$Tµs2.%f¾£ñ]ûHOñJ1}½€¼4ÄõqÜãhÏ78%˜CøECGt*ãÝÿô†„ endstream endobj 138 0 obj << /Length1 2300 /Length2 9372 /Length3 0 /Length 10719 /Filter /FlateDecode >> stream xÚ÷T”ë÷ "ÝÄÐ CwK ÒÀ 1C‡tKIƒ’ÒÝ% HK—„ )t¾ã9çwðü¿o­÷]³ÖÌsí¸vÜ{ßÏFZ mN+˜XuãäáŠäÔttxx@ È‹ÁȨqsÿOŽÁ¨vq…À ¢Xȹ€AnpÙSÜP ¨¸;xø<‚¢aV0¨ƒ÷ƒù_GÌý\Ió¹¶"û?%ÿ«”•…y|8yœ¼|ü^~€ ˆÀï¿, È?Y<•¡Ö0€ÈßÉ»ô¿„=þ™–Öƒð_®ç0øÜ‚,cnZ¿xþ?û_.ÿÿfü7Ëÿë˜ÿߌÜþÒ³ümðÿ£9B¼ÿ±€Ï­»|Ô`ðM€þ_S}ðß‹«¶‚¸;þ_­²¾ 2P‡ qU€x­4 n–¶ÍÆßbÝß{æ‚5`®ß7 €“ü?:ørYÚÃoWøHþ¥Ãwç¿å¡–0«ßKÆ+ ¹¸€¼1€ðIâøðÀ·Ñ ìõ׸¹ 07¸ ^Àæ‚ñûHÜ2¿E#!·ÜpËÿ‹„€n…$àV}@Âðéû 󸵀[ûñ¸u<ºî‚³ü‹DเþEüpN+üX ®ö&p‹ÄG. K{0ü2·v{óý+ÿ{ÿUÀë³ü ÀÉ,aðƒø7&ÿo‰£ãC¿OˆÛê ~`€÷üŸ‚¿õÎîð±~pbýàÏÍâñÇo5ÌÝ帉͞”íCŠðÚz;Ù‚¡XÀe? ¼ö@x™¦ïãä×÷@%w…ÂÇð=¼ ØCt¸3ì?jxöNj8™üýýÏqðóü#ýïaðÁc9]àï‘?Lÿ’A`-ç‡wÃÉÁÝõ˜p‰ó ¼=Îî07°•ÅUòÀ¥´”Þ¡ßìñG àæ®ðë÷ÁžÈCZðkŒÛÍÖüÇ©Á+uó„ýá¯ÆýoºÇÞ7ÏÈ ÷þ#/œÞû¡`¸ëK°ËßÜÿY}KwxÝþºá÷Âÿð_/[0Ø l‰17 ³ µ« m»¨’yâɹ>ÂËÝ{u‚¦¿&O,'~ù‹/?zVæãäFôåw‚Þ÷ö$@{Óeß·Ñ¢$sum(Ö?8|G $®v\UÝy7žuv™óA­lHš´úùùÛéÙIæOÉ7[Ÿ)ž™&×>É“]dzŸ´…±X¸iÄðSXë7Ӆ¹4íÕ¬)}--ãý1.u¢£µ+Êfcœ°Î…;¯*‹VѲêÂÛ€¯ÁÒ&nÈ)ÿjÔ_Ö¦bø¬@ŠÞÐU:GÙÇ óˆ™zdzFw”…•Ï'ˆPÀ~OkÊ’æ$?ñèd?¯ åUÌÊñÕ¬ïU®“ô;ÅÚ†¸¿àïŸãÓcú+9×K,ã^«(†o€Ö4qçyy ¹ÜFlU»…u&—aâøQ^d #”»LV)旅ŸºÐ±½Q÷”ˆ1ªÔ"âéÆT:ôüô]6å&CÓ¯E¶kد lÅÂÈéï<ù¾Mù•ž¸ š-6…›–M06•c7¿\H9A®uû8½…}}¦éÏ!ñ±Gö¤“ kž¢ºYåç ^Ç«dZUü†«w«U•O"¾’}CwŽHµ}éldW@˜mR+ç^¤>–÷ãõ¶-Q¯Éw|»ïòD̛ͤŠÎBú óÆ~Úôݘd½ Ã3 D·B—_a¡Öé¡Î˜3–¯ò´—D¶pEΘ3š‹&pÆ,—‰Ð7;ÔŽ7êoOŠô\NÖÝѦ)T³EŒƒÜýž”± 3½vi T :©ó»]yßäb‚zéhc äí³rg}Sºaš‘¯S ¿‹ØV°² Ìæx“÷­RÌ˹7íôJ%ÿ9ÃÓ~·5™Sq®Aœ¸âò•z÷e¯SÏjN Í—u™ž”€ƒ4ŠÄÁŽw­ÖLË¢ªï«{6öÚeß(4Ê}þù)—1MË•è6øc‹Û…Uæ:÷"÷"n°8-sç»Þ’ƒÙ=ñjùÁÊ|§ ­ìdåอv™¢òæáSïýdº×¥ÊPy¶´à§Ez×§ë)¸€YÚ#ÆÅ ¹ÔcÛg'©R]шO%Tƒ6‘cÄùû‰ s#Фª9=26O2¦µìÓýÏ=÷NOª —ÚØ‡ºi¥MH­Ù­ÿöfŒï]þé¤RiŠm¥ÒµäÀ¦8É/äǾàn/*r…ˆ¤xÁ/mÃG9–wšúeT™™+‘ºþ^½dÛÉ„>½~¦ÔzsõCâäºÉ·Vnr–¥îˆ* j=b+OZWÚf¢›Jæ®1ôœÛ$)tæí© ®D±COi!w̦±GH¥A®’™>²˜ª-3‡¨!»‚a)wÙOš©Íã µ±>sD}ñíž¾Cã†:ƤS„¥hÑ÷®|dŠÒëýÒ¦@‡PVI‡Pî1ú%.·PB„wb©îö}%´'O¦D¬®ßÖó¾a~”Ö­†õ‚Ÿä‡ÙÓDGÁîSÆ­Ù]£ ø¬'›F´c+r$´K¦ž·S<ýQ§ùaÞ±ÇDÚ¶yã†ÄáÅš_ïº>¬€ŽßåÀö AyªèÏ&ŽŠ™°è†9‰®^¾ÎÇÓM¹¢::T² ÄýLc\Lé²>B!$šçüà ÃÍÒ^ ³®7—O €õUâ ù¥-UóÃï&ÿqóå£ütN‚óÜnb¤&;IU_Ar/’Æ…FÃ9˜äd,a´¦&ÄΦ¦~ÖžðõÈBÑýìËŸÏ}fA w'•ývÅGÈêíºò›M,SZÈsÒ¼Lú©QÊ>u<}IÔü5îJP ƒjñð0æ: (y¼¤½k}²Ù+$§Ü4<…F‰Æ÷pοo=}ŧôr‹&4ú“ qa ‚²¨, ßFîÄ#»ŠtÓ¼<½¨=|ß”>áBj9¬.ëWêÞ–ÿØ_Xh탴åàÒEÞ¯°wú–]^3.ñ¶#/sÞÒ?ÖÑ@Y3æW§ds ­9”J70Ö©˜3³h»Ý…ÒD •‰ªa¢ëøúE¥>›ŽAü¢ÌÊFâ?Y7"ºxª‰çêÐ##½Ç·a‹¯Ÿ qeOìzwq%…\Â’žôbÌí Ú²·jÁj_5¤üûG¯&ÕŽ9Q/smêëØ÷¡•h1¸È¤€„‚<ΈN%#‡„K››²•Žq8+Î=ªC·ãÄz‚cë}æu!¥>‚v9ú$öì0¬Ô”Å›8a½ÅþpokçËOæB.ÏGU*Vk—ÓS""ü>·²TéØaùšî/ÜŸ6§{–ä,§òÐ,y ‘kú3BØÖ‹n>¡ŒÈ4x!®JÃbEƒ‡"ŒÑ6ìæ^5nŒ¹Š¾ûØ¡».nñzjÅ#ë­àP`T…‚ÕÉð"U ¿‡@‘…RÚZh“K`Sõ.MÊùþ@ü,˜èÍ—å+©¤¶¦d.Ú8ÉÙÀBî>¿—ò°ë!…žþÖ "ž´-÷n-i•õ\ý¦÷}MßÖ]FäˆÂg+%M›Û|VDw†ÏÖbš•›zõ:AêueM ê>CTMìj>ï(‡ ÷•½ÄÒ­Áv­…S›.zŒTy‚U=(aøR•’K¨\±TAÒi6Uqì´„Œ*ãzªÝ9™tÓ£>¶E÷?ËãðäßÞ¤Ý9ÑÕ">Oø[]ˆ®“ÒÝ$xia¸ôäñ«Ò}EÔæu#ÍÂ!d_Ó{µÂ÷¯‘y½?M§xeÏv†ÔY_vòÆÈEz¿xkÃÇ(&:!Á«­{ŒÞdHÚ‹‚àуWpõØ’êø™ÑÑA£òk× ÀÞÔ¤}ës°W½q@hÒö‚„Qšo¦^JÂä]èõ1,(¿¾Ó¤}œc¥ãMÂþtwgN2›07¿ã^Í»'nGáÞŽÎDWþŽòjø»±û¹5óÛÓP×ÚHšDÁ¬$ï‘ ó eŒ_Ʀü¢ N>>Í6[Àà¶­e±Gövú¼Á‰—sagú¶6å•j³Ï#gï¦Ôêg…4ã±<¨éž …¢±W¿0gYþÊR‰Âo7ûVó­ÖË€ÊæÏ-b5Ö¬Tb;e(_»ë.æ9£|?¨°49&x¦)/|ËÈ$DÈή%ê¥Ìè2|Õ£GÜÐ7‡oZ „þÄ͆ócJëy&µ ˜xÃ]€µŸl¢õf¸Ú|@]I<~?„xÙß9X–u»ƒë}­ìª«òŒªcír÷G«À{«gÊ+©~_²L0ïøë7" ~òºÝ”?ðÝ`õ?ОaV›ÙÚ¿eª_ô*¼2kÛ¢GòeDe©‰/ˆ¹ŒñbìÏŸÇKšîðŒ714•B—˧Vá:òëC2?ü˜¬‰M…3$°M,†—ÇC;,}h§Úû±b‡÷GÀüÑÐ÷AÂýhÃ\GæÂk÷¦éC„.?zï¦l.…´ØžšÞ´«vnÏA¿¦ ÕϽŠ´ØO@m = ÍÀùT?6‘ú{M}Ñ*ã¯är$›Ÿ^šT5G¡áÅüîä^~ßb¥Å«âıé#á[¦Ý—¼?{XÌùùã›ôä ìÉÇçòh %€$•n}JŠåÈD¯Ç×\%ë,[šÜþÏl±¾ÁÆkì[Ϊ‹ÓýXËrÔ2uÆW$³_⛃A‚Í%*,»²]#^?u'1Ùµ$Í0< À*œý²S¹è.ÒmŸ2 ßÏÆÜäÎI`ⶉ|übnתìœïu.Çé¸ÐH²A~Ó°)`¦ÈW÷½ü^ên­;$9 Á´Jn³<`G†îôW÷ÏÊ_ǹÞB·¸\|w‚ØïŽ8†øÅ£?-8ø(~²]Ñb®t•…b[^s˜5Ô1ùãùðßóÔÉ¡¶3k?“9aR¢ÕgW+ØP Uû¡ô©lÎ%<R§ÚŸ4‡Píη¬È3ª›Ÿg°æ¶‘2öYÇvÉÅ~’¹—Éæ2öø^ëi0±µ -ß!a¡úg@Ñß èIˆ‘}Á‰÷uAa$ÌŮЈ:Ubä'Ÿu³ ¢¡†\pÚ*ÍȨƋ|´¨?·¦²–YyL×Ìñ(Y €–¦þ€ê"UWs}9r'£:sI‚‡*Øç „æËeð:<.É8)Ÿ÷jüÇ2ÝD‚_‹m{bcŒÔŸ1‰Š^¹sÓÎ2w÷­ùHrT9¸AuVo¿N‹¾PCõý䫨rŽÙj¼K9Q®>u„Xßê9÷©8÷œ²â+P8ÿ"ÌÙ ™™0Ô“¼n¡ÓÈž9ȤñŠ|Fâµ>õ=ˆAÙ`χ¤Ü—CtH#ÿIÝç´²d‘æ­³ñ¶wKÞÚÖc+wC*2ZíØcçHá¨0“ÅâOèS¬_\ã[Ïž¸U%Idî,\Ñ78â$ʪ¤ªQ>DíCEB*¶ŠU÷GÎ÷UÊãz.-—fÖòxËüõ-›ýtçcŽ<Ã6ͪ±) YXò&z< y¹^,$‚ƒ&:ñýO±6ý¥Æ˜töv=žÂ’A¤;òWÊ‘sça ûê-W§RVL žÍ—ÞÆëÍ…·+LI÷ü„u1מu¢¥Ì$Ds¬lˆQ*®#þ}Ä áÊÂù·ºÇ®·Ëö8s߬ÏÒQ#Ä‚[貟zQ›>:I-rÙèV²Žÿ¼¯.ù­H·VÓÔòãQüÅwω9š|¦êvß5ÇZï«F|Wwó-¤¢ê㩆šúÂ1ûáj-„6zEVüŠ éó†û§tz¡õ¸ÖØžúY¶ùq‘B÷9:H[⟬%â²´ôÑ“>*2ê/K!?ŸPtÀÚ$BíPÑ11¸£¨Æ¨Ýl´»´ë©ÀÕêï­Ad|ãK¨n[gù3åFU'Q˜(pN½g^Z\ÍÆç”zã8Z¥ŸŠ}ä-ÏaÊ”~-£Ç*k%aH–!Ç2«(Dªí¬î)@y‡g&T¨.Œ8 g`(ûöL:h¿»Òh‚Aèçb{°LÓŸæãû¸/¥%ϼ*ÂrÛ¦S”V¡¥ékù(w©£ç~­HÙ&5´øêÀåGS8%¹WÉH,‚_ÚékÖ¼³ M'§¬ñ(Ö#\o­ÚÈâgÂÑäê®,,3\Ÿ9pßÔJ>m]×IŒùÆêеK˽ð=>}fB"ò"ï³²©ùëOµ¥ÙuA¡ŠÒ‰ó í ƒ24w0—!S¨[¾vY¨€TŸôW2Kí=Io>—¥ß%€)¾¨Ö‚NcU ªq\};ÌD|žI¨U’r½´?LöÄ#ûþ\w¹d7bNÔųêJ¢´¤³Ù# )XÝL¾Ç’Ôá<¬wUúâC`‚>ÙËü%¤´g×C¨©U2G ›ìœG~ð©VUÇ´Dy¿ŒýnT&:hŠ1ŸèŠY%°6÷4„¯§øÙÊm7ê÷\Á¢^Ô¢ò(&QÜ $oâAáw²nf2¶¯òàAº”^Ö¼/™‡“<ûq·¼z¹ ›YGb( N²ï‡Ä¯†c%T*ÃöÍ!Ãå߮ģÛf&¤k`'_§ŠN”LÒwº,Ku²€eFRçïñ‹~¼÷ͽsDï±+¡iV¢X;ôcŸnß8&,r×É4¦ ÐÉPäéà¾3:‚‰×ÐÞ#úîÖÛ¥>÷ÑÚjk¢JmΠã¶Ý>V¼¢Š5IŒDHÃ-f1Ò½ìê z`¯¸IÌàr·Øüz ׂ'‡ŠåŒ~á+ Ç«³µÏD´Î§ØIo ÀÒi̯6Ì ßÐ8½ ÝÓwSJ2å!´?—` ©Íþ~ú¹¸TžKÎÕd5§;r8Ñ2¸|pž:Ï+ ÖÎ;Çm¤Òª©Ñ/;â&)èHG9¯XÃa`{ÕìþÔ•Ü¥I7íóz)СASl@=Çÿ °Ã1½³Ã›J(©‡Q[Ym£Ã¬€M1ÝR?†u×O©&œôÔ9¢®UNüú§³j©Î3¹Gþw~Ÿ½FEHʹyÉ­)QCgr›.§I^Ÿ°b¾–KÔú44”œCl;c÷¢î;ÒœKËÌ™“Š1seðÇ<“Q\ïÛ31@’·+­üpþ‹Ø[“N+ò¬¸-èâÖIJ¢^Ô»IµŸÆ˜7ºú‰W»\óÞ­SgÙ{™tècÕ<öxP*Qô»\{ãj3½Ÿ§>#Å’Ë$¦Yëꮲ}Ök=Ç)WaW ª¯æÚÓ.¯ÖWüU*ë© Þ< Ž,Ëà„ÀØäÚ$ @0Ýu)ÆOð¦Mßûõ1ð›§>B¬ÐF«Ô¬–Méã½/ˆ¼©ÃT•F9-…€á«Wú5ŠçÒû9¬|À|Ëfr1ÿº"fÔæ<ýÊo—  %M € n»ÐÇš¨.ü¶‰âB¼žefÒ©&. ˜Ó¶âdÏÇègwb1.j–ÃÉ‚,OHNfÈxñÂPޱIKÏojäÍ#F,õVƒŸ¼ûßí¨æ²s5Ñ®®— aåá³ç(¼ìo/.¤ëê Š´+PÞ~²Šskù"[(p¿"ý¦‘½L5º6àqIq‚qŽ#ú"/•-h…~«ÒXÈ^îÓBÅõöziu1Ôh‹ƒêÒ·©v·+®È Mdféu¤SƒÑÿ¥4Ë߈ 6ùâ¨%Y‘KûÇÉ>ê%ûÑË·oéÁÛ_›HJSdò®èB^ªÑW;ÄÙ´PúÕ ŸF{'›ã3\×åbY³Æ'ø™ ®j3mŠ Òì¤KÙæ¬ÂÉø„‘ 1¬ÆŒ€ xè4Ä©¯6-Ç õVSÙHU½6v˜1}¼C3ÜYÚ¿*¾ü`Ç ; 7âoD~ j\ÜpÝÀ u7µ¾Nˆrζ¿Ž“+ÁŒ $K œjª»Jù<“waû3%eùèZ®xÝm­l±ÉÄÇn“²›:wÚ÷d~*OÚçÍwòRl‹$4›úéˆV2Y+‚w6W§l¨ç¶c#Ö—®3åOA~[£…„ÂÍ1ÂãÄSd”[o½NßâŒtâû(˜DøñJ mÑFÙx:EP\±Ì `¥óÔˆ’qˆ£å°}ÇZåÚZ´ A££Zp–A­}ÇœÕo«ƒÞ£â–ó1Q…'•n|<†9XIµCÏ»æ1{ä-æ¾³²Œ[唕y,Ò° Y ! ‰Œ¯© ¡õz*bk—ÒR¿CV–ç¨8zK5v£¦~~õméöÀgœ!®÷ƒ¾4!)­ÅÞ– ’ªÌF³Ù&èËTëtYâ7Es8æ ²@k}£§± ·ÐYÝïQQ™€®{uþ(§oîûNá™9á‹:ø O“®ýgf~^œí†M–¤ÚJ îFæ¸y‰…¯t*³m5ÿÒÞ†¶¿Ó«|„qd·f°íÖ™w¢3¦Thã¾;˯/ê<ž“âí§Ô•jV‹ûBĻٰR×qïÉ[%øýÀÁ#5Ãè‘_ˆOneJh–GÙ¹‡o14Õ™°er<ï³ \îB§ÎN³BÆpèÎS"ýUŸ´Î^¹xxjª|ZȽöyy+fß‹hóêżŸëUbnì‹ë:3¹fˆÌö^½_ï›Â€··ØéËÏù¬ühÑ^z‡z‡*‘Sãvw]´lÕììÚV*Èrê|¹M‘¦)2rêmßÇ!ÆŽ†E` u»ó?z÷íY‘”ö ×ɶ¥‡˜Ó›—5å`Ÿê¤3Ø›— ŒgR/ªYx|Š+Y{\R^ WN&ÕŠŸ*Ø1CcS’4¯¸¸¬Qr¿jÜ0²žã{®‹ £ç}Eq™fðÚTxÏB²RAbøô0¥Â’‰ÍG2ŽŒÔkˆò™ëƼåKeNØ¡; _mæça¦œˆÐP»Äôƒš»Ÿêá†'m ï§jC™ª¼Pnq„¨Wù”­aÕ1hwϱL[H>G¡kž7»ësßîÒ‹ÿìJ3!¬ÒFÿó„P 9|»Xt˧%â:v1'0^¡ºIõ“¬*‚y`KcÐóm{ÿFÅ+uS‹ý&ÝÕ qŒÊèNºè('“ÃÂ×ôv~á(:H˜ñ¶’ß’-Ö=ÝóÙª|][&âÛk¼ä'0xÅöÆ?‘§##áó“à /¶Zاñ¯- Ej6%(•ºÅ%Èõ«²÷E‡ÕO˜ú€¥-ò‰]'Ž·Î„ÜÊ gLÁøø6B\þÕÊõÌ[¾k[&þ¼A/ ò¢%ãAð•Ex/ÊÓ‰ªü&[ǜ֟ѩÍ4MÓ µRdº›ÍšÔó[$†b2û|%Hàð¨Ù•ư&qQ—§ú%éÒ‘O«©¸EZHï lÓAic:WŠÕY\FU…ºiõÒæ_üÊC•z£Ž”„_,òÊïh)«Â™XeÂ?ô°‚ã3­0‡E4@þ_ìÝÝÍš‚…G~ E AórÇk RÞÒÞKÊèšW‘N5›Òï×ׇøÉ2®”»?:îÜÊÑ{DÐè¯¤ÝÆ{cU³ý¶˜ËŠFr@f m·û¨N‹ï¹Æà(-(Ûë0z5;ÝdÅ!Ì '–9ŽÞÕ™ÂÔÑg#ûCøWß·ˆ;úŠ)}Ÿ©·iDª=F÷ƒô§„fÌÌôÇ+DœÛûá«­YoÇŒ›ã§nW«IÔä~\¨š ý".wï„Gýž€²–ÔÔKøq_QxyýîSèþsnMÿ‘"{Ÿnb0ò)“ø)SZÌ:)£{]>ô%›2¼ªa!Fi¿±0Ñ'tîGБ™Q¤†’åæÓ™¹¿˜ ÜX"(¯E—Aj4SC¼{œõ†,ûŒ’ÚH93@0A7/)<êÆÀ€V ùá&|{¿™ˆA⇴säi›^ %µøÉÎ)[˜F×]~Hr´È¡æ‘fh©w­†´½j¹.¯Ÿ;x{XÖ°M§ÁVJE\ímw Ø:hÙXoBýL±Ÿð’Çß~Áé—d¡ÖÐÁ|kæn•;c\2~âÌÞü¨á¶ †‘ãº!ÝtÌî¶$ÝuFº þ¤Äñrf)‰ƒ*iQÛu)Á (\6ø8y‹?•‚/ÕpË6ltÿF) Å3L¶èX{,õ›3äkjФ¦MG×áVŠ·ü‡;¹ç˜¾Á&Ôã<*XßÛµ¨¯nm:mc¬" ®*û\ÇPâ"pb»¨—°}¬ØkBiç˜ê­F¡`L+¤q·®s£ô7zè†<¢6õøžo¬ß×!Ưi¨^¯|Ð,|]‚Áooù‰°¯9YÆÉ`Ãyœ¶ìƒº“õ4ÆŒ©]…x!1¡_6j8È/£¥œºD.½³­þ|ÎdV1O‚ÖÌ 9£FiKÄúÉ2bñ±ûBE¯^L|Íž3~O‰©pÑho¿R÷ŸfƈÝÔ4h…±ùC)F“0c‡¼ëÆÈBCS w¾ê~|ŠPòvRVèIÔ*@ö{—ÒÊæ#L}Uj¿`PÉ´ú¸El]‡däËT¨Á ­OîíWðý«ŽË$˜IÞµbNÿ*õCö×È ¿O’?—]tJ¿g¬‘½†9–™NS¯ãP5ãÚt_F§)~eÒx®›mOÔ]gµËß>ÙkߌÚ[º~RgQuiÿi™ª^¾ãÀ}$yí«_ým`k3™ÔL8y3É5Ík—™oÊÜ'Rúh°ÙÔãÔÑ0’úêeœ»÷Ó $Ë›FjÀ}!YøͺUqü€”±‰¥ÁóY—b´û Á3 ÕÈÉ,j >꫃yýG¶6“ÅõÞl\Ï4µJ7]ÁÑL/ü*Ó´b$ø}õcaÌ·jI³À¥¿nkœÇÄ#:“0qn¹«Sö´¢†ëLP½Úr&Åte–fNU+n‹BqW¸9Rî¬cqm;ZJèEùöfèÕå¿8µŽÒ­“b è–R©ª“h§ÚöÊfç× “5Q¹gý©+­ÇfA‚tÁz;޽Öí¹YÐÏ!W–òyÂjóöYîyF¾“5þÁ=Z,Æ6ów¿sïôsârŒYanSrreI)ìÇlÈ}ffï mÇ”DcqC'« £7G¦ôg¬$3…ÜÜ#'Ù8¾Ì÷FÎ.ĘòmÌÚH;tßVaA¾ã­âÉHé¤>‚éÅ÷¸¶q Ï7˜-?/;„ÿA?hy'Øþ„%ÝY'™¡œùñ÷DŒuTƒ¦˜¼F4JU÷y1ÒN=3 2‡J6i/»å8ò‰d噊ÌZ.Ðo…¾ƒñÅ·J!ãØ5í¨ ÞÖñ‡!ן. ¯Î°"37ŒÎ=w‡Äö q®¤©sÒÑÏMMÃN9Ì4< ö^fbYXuŸwé+ˆvY`jSÇAòæ"bû×ñ?RˆV—4“òç§öåvc¨Í7µs¬Eìd?¨(uÓBGì˜Ø(•žâŸ}·IïüaãS!ꪶHà£ÛÒÛó.Dô—ÿM8#Y endstream endobj 140 0 obj << /Length1 737 /Length2 969 /Length3 0 /Length 1537 /Filter /FlateDecode >> stream xÚmT{4”i’š¦\ÊQ/Ž—1“ä’eBŠAEªÏÌ‹¯ù>¾ùf\:B±érdS«ë¬Ë¶®©t¿œR)*ÅÒE-V´)É%i?¦töœ=ï?Ïó{žç}ÏïyÎkbèAà” >"V›ã<ˆ ‡8l[†‰ „(…øJ”‚ Š@Œˆ  S† àÑñ$E3!kAbT„I0bBŽ £€“\.ÿ^.•±IÙ 6]! ¢ ˆÀÄð|ýÖ{ñ=™'?xB’¨øÉÂŘxcBˆK! D$+ $p6ÎIÊž €Ë!IÑÄ"HB|Ü®¾|pçY xÅEÀ{]ŽSR: ¥Ôxw–€š´Äè¤>iMFÉ8¥Åf aB „ÃH gXëæ…GÀN ‹dÑ_C4!)͘Ѳ€FÐÙ2±˜J 0ã’hIàCˆ ‰ƒqícIl £8ÝÉ×T‚‰ãÿ·èK†…Ò¢¸â‘´ŒVËÙ\;%ŒI=°8(òÃ(Z{Š”A% •sò"L&Q^i}è ‹\ÿåUº­‰7­ƒB<¼CB,&b"ìŽ †G‚ŠÖ%E“ÀDØÅ”Û8ß®›ð‘o¾J‘Xå°Ç×…3~¾Zaß²Ü܈¸­VÈR.°²µãÄv)ìm–'þ‡®PF’§&@÷òÕŸX,ã ñ´‘:¦nɾðcÑ6÷üºb5‹º›-•¦û×{Bµ°ÛQ¢Míïôúz9±Lƒ‡)áÎ-oÿôê;o„îƒÌŽšÚ½†éazC'Œ‹#·•½ñÏ>“¾±{Z®DÈŸÖ³«—:üìév?óZîQÅ›²Õ1ÎZžÈÞÍ…Wnªi¾Ì|Rÿ÷d…™j cÂ6‹“šuQiyÝá.C­OUwæìhaö˜ènŠÊ+J‰nß’Óö½¢ÕYªó¾mêP¦¬¨Ê×T5Ô$ëVYœ.ªva;žCæJí´—Í~•têŠéÚÒì©¢§ù)½¥Q:ç{™¹·O£>XqÂkÇùI®kæF6]¾åÞ^8ܦÛU·X¡­yò’[s6!áÌ%lº;¦.¬_¹.6î~¡^Šÿšä¬Îdc–ý¥¸ë±ªþϪ ÏfáùÙêF üÓs1ˆQô³>úQXé¼Õ03L8À u“'™ ªî2Æë$YbŠ·Ý¦ZÉ–ý«fÌãgg ì±v}5çãœGƒ]•‹ßœÖã½ú¾ë+IÓ¼çó蔿àn©°Ïm<`~e!iÝÄÜ_“ZÇ <¦‡ŠúÎ\‘æ‹úm?_ì-¢îõ[[\Ôþ+÷ÓÑk£Ä›¿³5?:¿Öì6¾¦xtv¡ÇÇX0e»Æ…»&õªe‡fŽt*’G»™~‡ÿغÁd¸Ã*#çR /)xi`˜7ovåmÇLs®Úäïæ.²|Õ²Žë)CÿÙÄ* endstream endobj 2 0 obj << /Type /ObjStm /N 100 /First 798 /Length 2862 /Filter /FlateDecode >> stream xÚíZ[sÛ¶~ׯÀc;ĘÉtÆvâ8㤱›&ñøA‘i['²èŠtêœ_¾(‰’L[²=gúÐ \‹]ì·€¢Ë™a*g–iÃs†‰œÅ>_ú>¸x|“ד’Iå™4L:Ťe2à.0¥IT_PÍt.™rL+\<Ó©ÓÎõ´dÆ‚TÌæ–¡ÅÆz¼fF2ç-ƒ3XJIXJî3X‹ÀZÀÕ¨ž•X°˜µ{Á,ú5l-&Çü6ÏÁŒ+VbaŽ…M%œs &…8°„ÜöÈÜ,ÃÌ=&ÃL[ÆJ\`R:Ï J*L)uîÖ-5–‹…KƒõðYi{’ ‚R«Ë5ƒHé‚f!gÒ3ô0¾Æ•CX”0–+ƒ%)éUX+…E ©ŒÏ#äD`¦¡n̰äæ¸Æ‘_¡]yI8BV4\Áæ=‘ò ²šÐÆL A=!B(A„6ž`×DŸé`iˆ¼ òÖ È¹×=¬¥pBôX¯d /,<ÕœΑÃáD‘Ï=E¡`^;¼&]˜à…$|„—=@ :îfÁcª€»G 0"¡^‰Åh£+§pÒpœ!X 9‰J¯uïùsÆU•Œ¿`?UÅ –ãLüÌ~ýµ÷Óëq=)O¯cßÏÌ21¿*êz8>g‡uR§Ýü*ñ],”—_‡ã‚½»º[‡nT“~]NªnN“8ß÷'ýѨ±—7ÅàºCøõ×Ù¬©É³yúãÓò’í–“¢ªï™+—æn]]~t/Ñ&öýaU³òòjR\ã #w˜åÒœr<]W·™sŒpÈÙÆw‡õ 1?ÞãG?® Æß÷Ï‹Çܺ×Â{üCQ•דAQQ=Š=o‹Óa»¼aÇÔa‘.È“L09ÙÙ-•Ÿ>aÑ®L¦Ùøz4:ébÒÌ—”È{ØŒP™AF/°íÂFvï"oàÉ^k{¢‘|È€DÃ*äG¢QEµ4Dñ÷“rpXÔìؼØeü¨¸©ÙLË­ Á¼%ÐŒØ4¾5—sLåžÖ±¤.÷øáõ×:ÞïÇßz|»œœ“(8?á{ü5ß9ñ†V2€ÒËÌzÔ' XQ°EdT¦µê7ø¶ØrPí•e uEõs'`Gš9Çím3‘CÊh½‹Í‰,(*›!“¹îrjÛym§¶‰mÎLû)L¦üŽê\"=Uæע:?ÆívÅíV<,W¬¼/WîbÁøÇF´u+¦™šf׈š69θ̅§ŠH²á1`8¹ †{`MtâiýüxÓìŠiú¦™5üÜf2t€Ê³ÉO‚À×tŸ¯ ã×BÆËedÂ?ÌéÞ,›æÕÃLóz §·™âo2œ=ïc3ZepBšxæÜ‰CSTˆò’CZ£É€D§6j¦ÐÀæCD‰L=}¨üû]+È›8Çï”Vùü^åó{I?GïÏ“&Î2£_ÁÙȦ^›¤B‡ŠãÆ>¶MršVKĘÇq>ÆOÓҽ׈+TÀ@qy=fS‘óˆP—;Ä£Gzw LˆX/ЗS":“Ñ•Ö!µƒGÀ"w­'Q‘ö*`”òØy ¡ÞêÈGŠ…À’ÈÔZïf«&=m4Úm©ˆ‚@2^eÂ1Ô5üÔ;µ?öE{‚r Çœ7kCëÐ:Òs®8–Jtl)&œ œÕ:~½°ñ+ñ**J¡¸u´Ž^7`C™ò%ÄÊà”Ã{Òy0õGÙn:ƒ´Í©9‡ŠJm#{>&-1—ñ˜vºú6ë¶äæÔ¦²Ò¯iD9˸µ ­Já¢"—2ŠÐÄfM3e¬Úž^©Ð,5•—f6²%m„}#›¸èe@žÛÙ ~ÁàEj~Gb’;æTÛÔ;w­Á¾äã=AgÌ¸ÒØÔ ´ÜDÏÇÒrÛ“tÀüÛ[jVz jšsºœJj®I¥š6MŪÕcpÒô†’ÒxŠöºÝ“$뜊C›–(ó~§™›úãé-õ8œûÄtn[fäQX¡ ÷}t¤™Þéôü¢¨“áU]NÒiú ‰‘wïö_¼ýeçíö'!10êŸWL'Žíx††ù3ß«R¡”oU:ÉÛÎþÕ^1<¿À­·8Îiì™ Á×u4lÏGË{ü°..?¢n„ÿÔLÂñ 2.ú:¹ÿÄ·øÉwù+þšïówü=ÿÀùïó?å?~Gƒ'~Î/øø%ó’—ã‚_ñ«b2,Où„W¼*¾c^ oxÍë‹IQðúï’_óþãçdÞî‹2o?ŒÜ ×›ß?n®"¿-<}=“ñlœß‡–Z -ÛK‰6XÛˆ€”——ýOË7C°4€üu]Ö  "bS‡ïüoþƒÿw ¹ ï>þñfç(bÑ9ØbäïŸ Ó ÅÀ ¨Ù‹qó–´bçþ'ÿ  ú5ÿÑ•ãfQ;£ÏÇÙhg?®.MCþŸˆ,…Üxˆˆ›Å=ÙŽŠ³:Q i`OÍ|/½žLî8ý:Š3¦7iÒ’§æQ\úÕÅR,“oȋŤ\t¤ÞÄ‘o^lþò*:Òu8R¥ ¦7íN‡µi:iTÛ‘yÛ‘ï£Ã(ª‹Y,Eóì&æ½ümëðhŸÌë°Nº&e)ž"N]wÊ"^Mñ›˜rtðæ·wdŠïp”V—?)vmSÔF•tkûí«ƒ/0åpÿ訫˜J*¦>+ °-k„X°†ngÖH)­yÖ(¦[qßyƒêq0«Ÿ¨jT”Õ7ÔŽ¯“þ ˆI©”¦D~+êY7è4pK¥ùëº?âÅÍ`Ô¿¤‚ÓÚËÎ'EzÚµ†*ͨ¨ªÅnJs5º®b¹I…%’•°³x¨vO¿mï} ]àèuç6`§zèxxP>zæGÕéÇ~,òÅÂéવíÝ,»Q¥ü¼ÿñåÞ{2¶3duŠX£±ëìþ©FžÁÐÞŽ.š¶Q•<ØûýàðU2­Ã‚L£ÿáÅŸ°oÚ]»y:Þ“‹ë¦]ìOÉw¶t‚\ȶíë·f[Çá³;ç6Ú>~ÙÝÿòå—ÃÝCø*ïDaŠŠþ>§ ým®µkçmwåsgå]eÓäûu´»Ò„‹ô-»ÁËñ <+4ÏÎ Jï ŽUhK9YE¡õGêD¨l÷«"¾Xy€Y-þ½0¾“ØNªšVÍ4v´ý~sƒñÿsxZ_Tñ:áNõ‹ËÚÕ²v´Ë–v±¹ö¥#ø²v³¬2³Ãv·¹ö¥sã²v·¬Ýçmí´´‡Íµ/ë–•‡··§›™n¹¹îÅsØ’n%Ö×­6×½zpZÖ¿tjÁímàgÛßÿk]É endstream endobj 152 0 obj << /Author()/Title()/Subject()/Creator(LaTeX with hyperref package)/Producer(pdfTeX-1.40.15)/Keywords() /CreationDate (D:20151012042627+02'00') /ModDate (D:20151012042627+02'00') /Trapped /False /PTEX.Fullbanner (This is pdfTeX, Version 3.14159265-2.6-1.40.15 (TeX Live 2015/dev/Debian) kpathsea version 6.2.1dev) >> endobj 142 0 obj << /Type /ObjStm /N 25 /First 195 /Length 808 /Filter /FlateDecode >> stream xÚV_OÛ0ϧ¸G*Äâ³;–X·•ÂhÇ6&B›•H¬Am:mß~ç8 NŠØCåÜù~|±Ê(È5Èx$A1‚4Q2æ 82   µ%Ä"¡ 6<L„Àc*两‘88£0`\Ò$”¤'aH‚h¬6 cT’ÓOi PjÀe¨Ô(‚‘Ž’Š#œóAR‡‡A8ùû”Bxž/Š ¯ï‹2´I “d•Ú¿}<\Oöû“ȃ²ü4]M—ÙS‘/ÉRD½¸¦|¶\ý‡d Fá0©ä,¿f³âaE«rµGGoÿ>¼9\Yù ²y½[•'ÏÞ.?|ß;ù®Õ›My![«GO>~»üÍíùðöv|>&yÖ±~‰[ŒgÀµ>«¼ž-¦ù,[ÌínÛvt•ÌÓUöó5éSó®’eJOv —èOÙl?ܺi¯—ƒªUÝP¾ì»ÿa׎=–n(]‚q"Ɖ c¯ÑãKôÈä«XQËÊ ¯duôr] <3FF9C0C531B5C9A4AD20B1B775700B56>] /Length 400 /Filter /FlateDecode >> stream xÚ%Ò9OaÅñç ˆ ²‰È•Mv¹ È¦  Ü+‹l ‚ˆ¢HKm"•½f*?‚€Ž†* + ¾„¥ò?4¿œ9óÎ;Ûñ/‰HBÉàß ]?#­$ ¨£« @!´Ò•“.@tÑ•‘.Â%è£+%]†bè§»B*§ût>ôb󾑟 Oç­üW¡ jQîW¸ÕðŽÅ~…·ð†C–¤~…ÐEñ€¯­…xÛP[\æOÒŠªO¾â&´A³"Óé®…ÅþV· ]ÑôÃ': :Ù/î²Ð·ýµîî°?ìÜU 7øD/ Á "ßáî<„aÅÜ/wàŒ(ÝÂcS¬þq7S0¡Øêr7ÉÍýC_Ã4<˜…9˜‡x Ï`–ÛGÞê¹âÛw§eÅÏÏN+RÝÓ )Wí´*mî:½”>:­Iû¿Ö¥“NJ ³N¯”ôœÿ€M%ùSÿU¦.eêRÆ"e,R†!eROv d”侞üÔ^ü—ž;R endstream endobj startxref 148858 %%EOF foreach/inst/doc/foreach.Rnw0000644000175100001440000004775712606615125015550 0ustar hornikusers% \VignetteIndexEntry{foreach Manual} % \VignetteDepends{foreach} % \VignettePackage{foreach} \documentclass[12pt]{article} \usepackage{amsmath} \usepackage[pdftex]{graphicx} \usepackage{color} \usepackage{xspace} \usepackage{fancyvrb} \usepackage{fancyhdr} \usepackage[ colorlinks=true, linkcolor=blue, citecolor=blue, urlcolor=blue] {hyperref} \usepackage{lscape} \usepackage{Sweave} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % define new colors for use \definecolor{darkgreen}{rgb}{0,0.6,0} \definecolor{darkred}{rgb}{0.6,0.0,0} \definecolor{lightbrown}{rgb}{1,0.9,0.8} \definecolor{brown}{rgb}{0.6,0.3,0.3} \definecolor{darkblue}{rgb}{0,0,0.8} \definecolor{darkmagenta}{rgb}{0.5,0,0.5} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \newcommand{\bld}[1]{\mbox{\boldmath $#1$}} \newcommand{\shell}[1]{\mbox{$#1$}} \renewcommand{\vec}[1]{\mbox{\bf {#1}}} \newcommand{\ReallySmallSpacing}{\renewcommand{\baselinestretch}{.6}\Large\normalsize} \newcommand{\SmallSpacing}{\renewcommand{\baselinestretch}{1.1}\Large\normalsize} \newcommand{\halfs}{\frac{1}{2}} \setlength{\oddsidemargin}{-.25 truein} \setlength{\evensidemargin}{0truein} \setlength{\topmargin}{-0.2truein} \setlength{\textwidth}{7 truein} \setlength{\textheight}{8.5 truein} \setlength{\parindent}{0.20truein} \setlength{\parskip}{0.10truein} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \pagestyle{fancy} \lhead{} \chead{Using The {\tt foreach} Package} \rhead{} \lfoot{} \cfoot{} \rfoot{\thepage} \renewcommand{\headrulewidth}{1pt} \renewcommand{\footrulewidth}{1pt} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \title{Using The {\tt foreach} Package} \author{Steve Weston \\ doc@revolutionanalytics.com} \begin{document} \maketitle \thispagestyle{empty} \section{Introduction} One of R's most useful features is its interactive interpreter. This makes it very easy to learn and experiment with R. It allows you to use R like a calculator to perform arithmetic operations, display data sets, generate plots, and create models. Before too long, new R users will find a need to perform some operation repeatedly. Perhaps they want to run a simulation repeatedly in order to find the distribution of the results. Perhaps they need to execute a function with a variety a different arguments passed to it. Or maybe they need to create a model for many different data sets. Repeated executions can be done manually, but it becomes quite tedious to execute repeated operations, even with the use of command line editing. Fortunately, R is much more than an interactive calculator. It has its own built-in language that is intended to automate tedious tasks, such as repeatedly executing R calculations. R comes with various looping constructs that solve this problem. The \texttt{for} loop is one of the more common looping constructs, but the \texttt{repeat} and \texttt{while} statements are also quite useful. In addition, there is the family of ``apply'' functions, which includes \texttt{apply}, \texttt{lapply}, \texttt{sapply}, \texttt{eapply}, \texttt{mapply}, \texttt{rapply}, and others. The \texttt{foreach} package provides a new looping construct for executing R code repeatedly. With the bewildering variety of existing looping constructs, you may doubt that there is a need for yet another construct. The main reason for using the \texttt{foreach} package is that it supports {\em parallel execution}, that is, it can execute those repeated operations on multiple processors/cores on your computer, or on multiple nodes of a cluster. If each operation takes over a minute, and you want to execute it hundreds of times, the overall runtime can take hours. But using \texttt{foreach}, that operation can be executed in parallel on hundreds of processors on a cluster, reducing the execution time back down to minutes. But parallel execution is not the only reason for using the \texttt{foreach} package. There are other reasons that you might choose to use it to execute quick executing operations, as we will see later in the document. \section{Getting Started} Let's take a look at a simple example use of the \texttt{foreach} package. Assuming that you have the \texttt{foreach} package installed, you first need to load it: <>= library(foreach) @ Note that all of the packages that \texttt{foreach} depends on will be loaded as well. Now I can use \texttt{foreach} to execute the \texttt{sqrt} function repeatedly, passing it the values 1 through 3, and returning the results in a list, called \texttt{x}\footnote{Of course, \texttt{sqrt} is a vectorized function, so you would never really do this. But later, we'll see how to take advantage of vectorized functions with \texttt{foreach}.}: <>= x <- foreach(i=1:3) %do% sqrt(i) x @ This is a bit odd looking, because it looks vaguely like a \texttt{for} loop, but is implemented using a binary operator, called \texttt{\%do\%}. Also, unlike a \texttt{for} loop, it returns a value. This is quite important. The purpose of this statement is to compute the list of results. Generally, \texttt{foreach} with \texttt{\%do\%} is used to execute an R expression repeatedly, and return the results in some data structure or object, which is a list by default. You will note in the previous example that we used a variable \texttt{i} as the argument to the \texttt{sqrt} function. We specified the values of the \texttt{i} variable using a named argument to the \texttt{foreach} function. We could have called that variable anything we wanted, for example, \texttt{a}, or \texttt{b}. We could also specify other variables to be used in the R expression, as in the following example: <>= x <- foreach(a=1:3, b=rep(10, 3)) %do% (a + b) x @ Note that parentheses are needed here. We can also use braces: <>= x <- foreach(a=1:3, b=rep(10, 3)) %do% { a + b } x @ We call \texttt{a} and \texttt{b} the {\em iteration variables}, since those are the variables that are changing during the multiple executions. Note that we are iterating over them in parallel, that is, they are both changing at the same time. In this case, the same number of values are being specified for both iteration variables, but that need not be the case. If we only supplied two values for \texttt{b}, the result would be a list of length two, even if we specified a thousand values for \texttt{a}: <>= x <- foreach(a=1:1000, b=rep(10, 2)) %do% { a + b } x @ Note that you can put multiple statements between the braces, and you can use assignment statements to save intermediate values of computations. However, if you use an assignment as a way of communicating between the different executions of your loop, then your code won't work correctly in parallel, which we will discuss later. \section{The \texttt{.combine} Option} So far, all of our examples have returned a list of results. This is a good default, since a list can contain any R object. But sometimes we'd like the results to be returned in a numeric vector, for example. This can be done by using the \texttt{.combine} option to \texttt{foreach}: <>= x <- foreach(i=1:3, .combine='c') %do% exp(i) x @ The result is returned as a numeric vector, because the standard R \texttt{c} function is being used to concatenate all the results. Since the \texttt{exp} function returns numeric values, concatenating them with the \texttt{c} function will result in a numeric vector of length three. What if the R expression returns a vector, and we want to combine those vectors into a matrix? One way to do that is with the \texttt{cbind} function: <>= x <- foreach(i=1:4, .combine='cbind') %do% rnorm(4) x @ This generates four vectors of four random numbers, and combines them by column to produce a 4 by 4 matrix. We can also use the \texttt{"+"} or \texttt{"*"} functions to combine our results: <>= x <- foreach(i=1:4, .combine='+') %do% rnorm(4) x @ You can also specify a user-written function to combine the results. Here's an example that throws away the results: <>= cfun <- function(a, b) NULL x <- foreach(i=1:4, .combine='cfun') %do% rnorm(4) x @ Note that this \texttt{cfun} function takes two arguments. The \texttt{foreach} function knows that the functions \texttt{c}, \texttt{cbind}, and \texttt{rbind} take many arguments, and will call them with up to 100 arguments (by default) in order to improve performance. But if any other function is specified (such as \texttt{"+"}), it assumes that it only takes two arguments. If the function does allow many arguments, you can specify that using the \texttt{.multicombine} argument: <>= cfun <- function(...) NULL x <- foreach(i=1:4, .combine='cfun', .multicombine=TRUE) %do% rnorm(4) x @ If you want the combine function to be called with no more than 10 arguments, you can specify that using the \texttt{.maxcombine} option: <>= cfun <- function(...) NULL x <- foreach(i=1:4, .combine='cfun', .multicombine=TRUE, .maxcombine=10) %do% rnorm(4) x @ The \texttt{.inorder} option is used to specify whether the order in which the arguments are combined is important. The default value is \texttt{TRUE}, but if the combine function is \texttt{"+"}, you could specify \texttt{.inorder} to be \texttt{FALSE}. Actually, this option is important only when executing the R expression in parallel, since results are always computed in order when running sequentially. This is not necessarily true when executing in parallel, however. In fact, if the expressions take very different lengths of time to execute, the results could be returned in any order. Here's a contrived example, that executes the tasks in parallel to demonstrate the difference. The example uses the \texttt{Sys.sleep} function to cause the earlier tasks to take longer to execute: <>= foreach(i=4:1, .combine='c') %dopar% { Sys.sleep(3 * i) i } foreach(i=4:1, .combine='c', .inorder=FALSE) %dopar% { Sys.sleep(3 * i) i } @ The results of the first of these two examples is guaranteed to be the vector c(4, 3, 2, 1). The second example will return the same values, but they will probably be in a different order. \section{Iterators} The values for the iteration variables don't have to be specified with only vectors or lists. They can be specified with an {\em iterator}, many of which come with the \texttt{iterators} package. An iterator is an abstract source of data. A vector isn't itself an iterator, but the \texttt{foreach} function automatically creates an iterator from a vector, list, matrix, or data frame, for example. You can also create an iterator from a file or a data base query, which are natural sources of data. The \texttt{iterators} package supplies a function called \texttt{irnorm} which can return a specified number of random numbers for each time it is called. For example: <>= library(iterators) x <- foreach(a=irnorm(4, count=4), .combine='cbind') %do% a x @ This becomes useful when dealing with large amounts of data. Iterators allow the data to be generated on-the-fly, as it is needed by your operations, rather than requiring all of the data to be generated at the beginning. For example, let's say that we want to sum together a thousand random vectors: <>= set.seed(123) x <- foreach(a=irnorm(4, count=1000), .combine='+') %do% a x @ This uses very little memory, since it is equivalent to the following \texttt{while} loop: <>= set.seed(123) x <- numeric(4) i <- 0 while (i < 1000) { x <- x + rnorm(4) i <- i + 1 } x @ This could have been done using the \texttt{icount} function, which generates the values from one to 1000: <>= set.seed(123) x <- foreach(icount(1000), .combine='+') %do% rnorm(4) x @ but sometimes it's preferable to generate the actual data with the iterator (as we'll see later when we execute in parallel). In addition to introducing the \texttt{icount} function from the \texttt{iterators} package, the last example also used an unnamed argument to the \texttt{foreach} function. This can be useful when we're not intending to generate variable values, but only controlling the number of times that the R expression is executed. There's a lot more that I could say about iterators, but for now, let's move on to parallel execution. \section{Parallel Execution} Although \texttt{foreach} can be a useful construct in its own right, the real point of the \texttt{foreach} package is to do parallel computing. To make any of the previous examples run in parallel, all you have to do is to replace \texttt{\%do\%} with \texttt{\%dopar\%}. But for the kinds of quick running operations that we've been doing, there wouldn't be much point to executing them in parallel. Running many tiny tasks in parallel will usually take more time to execute than running them sequentially, and if it already runs fast, there's no motivation to make it run faster anyway. But if the operation that we're executing in parallel takes a minute or longer, there starts to be some motivation. \subsection{Parallel Random Forest} Let's take random forest as an example of an operation that can take a while to execute. Let's say our inputs are the matrix \texttt{x}, and the factor \texttt{y}: <>= x <- matrix(runif(500), 100) y <- gl(2, 50) @ We've already loaded the \texttt{foreach} package, but we'll also need to load the \texttt{randomForest} package: <>= library(randomForest) @ If we want want to create a random forest model with a 1000 trees, and our computer has four cores in it, we can split up the problem into four pieces by executing the \texttt{randomForest} function four times, with the \texttt{ntree} argument set to 250. Of course, we have to combine the resulting \texttt{randomForest} objects, but the \texttt{randomForest} package comes with a function called \texttt{combine} that does just that. Let's do that, but first, we'll do the work sequentially: <>= rf <- foreach(ntree=rep(250, 4), .combine=combine) %do% randomForest(x, y, ntree=ntree) rf @ To run this in parallel, we need to change \texttt{\%do\%}, but we also need to use another \texttt{foreach} option called \texttt{.packages} to tell the \texttt{foreach} package that the R expression needs to have the \texttt{randomForest} package loaded in order to execute successfully. Here's the parallel version: <>= rf <- foreach(ntree=rep(250, 4), .combine=combine, .packages='randomForest') %dopar% randomForest(x, y, ntree=ntree) rf @ If you've done any parallel computing, particularly on a cluster, you may wonder why I didn't have to do anything special to handle \texttt{x} and \texttt{y}. The reason is that the \texttt{\%dopar\%} function noticed that those variables were referenced, and that they were defined in the current environment. In that case \text{\%dopar\%} will automatically export them to the parallel execution workers once, and use them for all of the expression evaluations for that \texttt{foreach} execution. That is true for functions that are defined in the current environment as well, but in this case, the function is defined in a package, so we had to specify the package to load with the \texttt{.packages} option instead. \subsection{Parallel Apply} Now let's take a look at how to make a parallel version of the standard R \texttt{apply} function. The \texttt{apply} function is written in R, and although it's only about 100 lines of code, it's a bit difficult to understand on a first reading. However, it all really comes down two \texttt{for} loops, the slightly more complicated of which looks like: <>= applyKernel <- function(newX, FUN, d2, d.call, dn.call=NULL, ...) { ans <- vector("list", d2) for(i in 1:d2) { tmp <- FUN(array(newX[,i], d.call, dn.call), ...) if(!is.null(tmp)) ans[[i]] <- tmp } ans } applyKernel(matrix(1:16, 4), mean, 4, 4) @ I've turned this into a function, because otherwise, R will complain that I'm using ``...'' in an invalid context. This could be executed using \texttt{foreach} as follows: <>= applyKernel <- function(newX, FUN, d2, d.call, dn.call=NULL, ...) { foreach(i=1:d2) %dopar% FUN(array(newX[,i], d.call, dn.call), ...) } applyKernel(matrix(1:16, 4), mean, 4, 4) @ But this approach will cause the entire \texttt{newX} array to be sent to each of the parallel execution workers. Since each task needs only one column of the array, we'd like to avoid this extra data communication. One way to solve this problem is to use an iterator that iterates over the matrix by column: <>= applyKernel <- function(newX, FUN, d2, d.call, dn.call=NULL, ...) { foreach(x=iter(newX, by='col')) %dopar% FUN(array(x, d.call, dn.call), ...) } applyKernel(matrix(1:16, 4), mean, 4, 4) @ Now we're only sending any given column of the matrix to one parallel execution worker. But it would be even more efficient if we sent the matrix in bigger chunks. To do that, we use a function called \texttt{iblkcol} that returns an iterator that will return multiple columns of the original matrix. That means that the R expression will need to execute the user's function once for every column in its submatrix. <>= iblkcol <- function(a, chunks) { n <- ncol(a) i <- 1 nextElem <- function() { if (chunks <= 0 || n <= 0) stop('StopIteration') m <- ceiling(n / chunks) r <- seq(i, length=m) i <<- i + m n <<- n - m chunks <<- chunks - 1 a[,r, drop=FALSE] } structure(list(nextElem=nextElem), class=c('iblkcol', 'iter')) } nextElem.iblkcol <- function(obj) obj$nextElem() @ <>= applyKernel <- function(newX, FUN, d2, d.call, dn.call=NULL, ...) { foreach(x=iblkcol(newX, 3), .combine='c', .packages='foreach') %dopar% { foreach(i=1:ncol(x)) %do% FUN(array(x[,i], d.call, dn.call), ...) } } applyKernel(matrix(1:16, 4), mean, 4, 4) @ Note the use of the \texttt{\%do\%} inside the \texttt{\%dopar\%} to call the function on the columns of the submatrix \texttt{x}. Now that we're using \texttt{\%do\%} again, it makes sense for the iterator to be an index into the matrix \texttt{x}, since \texttt{\%do\%} doesn't need to copy \texttt{x} the way that \texttt{\%dopar\%} does. \section{List Comprehensions} If you're familar with the Python programming language, it may have occurred to you that the \texttt{foreach} package provides something that is not too different from Python's {\em list comprehensions}. In fact, the \texttt{foreach} package also includes a function called \texttt{when} which can prevent some of the evaluations from happening, very much like the ``if'' clause in Python's list comprehensions. For example, you could filter out negative values of an iterator using \texttt{when} as follows: <>= x <- foreach(a=irnorm(1, count=10), .combine='c') %:% when(a >= 0) %do% sqrt(a) x @ I won't say much on this topic, but I can't help showing how \texttt{foreach} with \texttt{when} can be used to write a simple quick sort function, in the classic Haskell fashion: <>= qsort <- function(x) { n <- length(x) if (n == 0) { x } else { p <- sample(n, 1) smaller <- foreach(y=x[-p], .combine=c) %:% when(y <= x[p]) %do% y larger <- foreach(y=x[-p], .combine=c) %:% when(y > x[p]) %do% y c(qsort(smaller), x[p], qsort(larger)) } } qsort(runif(12)) @ Not that I recommend this over the standard R \texttt{sort} function. But it's a pretty interesting example use of \texttt{foreach}. \section{Conclusion} Much of parallel computing comes to doing three things: splitting the problem into pieces, executing the pieces in parallel, and combining the results back together. Using the \texttt{foreach} package, the iterators help you to split the problem into pieces, the \texttt{\%dopar\%} function executes the pieces in parallel, and the specified \texttt{.combine} function puts the results back together. We've demonstrated how simple things can be done in parallel quite easily using the \texttt{foreach} package, and given some ideas about how more complex problems can be solved. But it's a fairly new package, and we will continue to work on ways of making it a more powerful system for doing parallel computing. \end{document} foreach/inst/unitTests/0000755000175100001440000000000012606614324014662 5ustar hornikusersforeach/inst/unitTests/iteratorTest.R0000644000175100001440000000376211472542406017507 0ustar hornikusers# test matrix iterator with foreach test01 <- function() { m <- matrix(rnorm(25 * 16), 25) x <- foreach(col=iter(m, by='col'), .combine='cbind') %do% col checkEquals(m, x) x <- foreach(col=iter(m, by='col'), .combine='cbind') %dopar% col checkEquals(m, x) x <- foreach(row=iter(m, by='row'), .combine='rbind') %do% row checkEquals(m, x) x <- foreach(row=iter(m, by='row'), .combine='rbind') %dopar% row checkEquals(m, x) } # test data.frame iterator with foreach test02 <- function() { d <- data.frame(a=1:10,b=11:20,c=21:30) ed <- data.matrix(d) x <- foreach(col=iter(d, by='col'), .combine='cbind') %do% col colnames(x) <- colnames(ed) checkEquals(ed, x) x <- foreach(col=iter(d, by='col'), .combine='cbind') %dopar% col colnames(x) <- colnames(ed) checkEquals(ed, x) x <- foreach(row=iter(d, by='row'), .combine='rbind') %do% row checkEquals(d, x) x <- foreach(row=iter(d, by='row'), .combine='rbind') %dopar% row checkEquals(d, x) } # test function iterator with foreach and %do% test03 <- function() { func <- function() { y = NULL repeat { x = rnorm(1) if (x < -3.0) stop('StopIteration') if (10 == length(y)) break else if (0 < x) y = c(y, x) } y } ## XXX mean is not a reasonable combine function ## XXX removed this for the moment - sbw ## r <- foreach(v=iter(func), .combine='mean') %do% mean(v) ## 'r' is NULL if iteration stops early. ## checkTrue(is.null(r) || 0 < r) } # test function iterator with foreach and %dopar% test04 <- function() { func <- function() { y = NULL repeat { x = rnorm(1) if (x < -3.0) stop('StopIteration') if (10 == length(y)) break else if (0 < x) y = c(y, x) } y } ## XXX mean is not a reasonable combine function ## XXX removed this for the moment - sbw ## r <- foreach(v=iter(func), .combine='mean') %dopar% mean(v) ## 'r' is NULL if iteration stops early. ## checkTrue(is.null(r) || 0 < r) } foreach/inst/unitTests/mergeTest.R0000644000175100001440000000260311472542406016746 0ustar hornikuserstest01 <- function() { f <- foreach(i=1:3, .packages='foo') %:% foreach(j=1:3, .packages='bar') checkEquals(sort(f$packages), c('bar', 'foo')) f <- foreach(i=1:3, .packages='foo') %:% foreach(j=1:3, .packages=c('bar', 'foo')) checkEquals(sort(f$packages), c('bar', 'foo')) f <- foreach(i=1:3, .packages='foo') %:% foreach(j=1:3, .packages=c('bar', 'baz')) checkEquals(sort(f$packages), c('bar', 'baz', 'foo')) f <- foreach(i=1:3, .packages='foo') %:% foreach(j=1:3) checkEquals(sort(f$packages), c('foo')) } test02 <- function() { f <- foreach(i=1:3, .export='foo') %:% foreach(j=1:3, .export='bar') checkEquals(sort(f$export), c('bar', 'foo')) f <- foreach(i=1:3, .export='foo') %:% foreach(j=1:3, .export=c('bar', 'foo')) checkEquals(sort(f$export), c('bar', 'foo')) f <- foreach(i=1:3, .export='foo') %:% foreach(j=1:3, .export=c('bar', 'baz')) checkEquals(sort(f$export), c('bar', 'baz', 'foo')) f <- foreach(i=1:3, .export='foo') %:% foreach(j=1:3) checkEquals(sort(f$export), c('foo')) f <- foreach(i=1:3, .noexport='foo') %:% foreach(j=1:3, .noexport=c('bar', 'foo')) checkEquals(sort(f$noexport), c('bar', 'foo')) f <- foreach(i=1:3, .noexport='foo') %:% foreach(j=1:3, .noexport=c('bar', 'baz')) checkEquals(sort(f$noexport), c('bar', 'baz', 'foo')) f <- foreach(i=1:3, .noexport='foo') %:% foreach(j=1:3) checkEquals(sort(f$noexport), c('foo')) } foreach/inst/unitTests/errorTest.R0000644000175100001440000000162011472542406016776 0ustar hornikuserstest01 <- function() { x <- 1:3 checkException(foreach(i=x) %do% if (i == 2) stop('error') else i) checkException( foreach(i=x, .errorhandling='stop') %do% if (i == 2) stop('error') else i) } test02 <- function() { x <- 1:3 actual <- foreach(i=x, .errorhandling='remove') %do% if (i == 2) stop('error') else i checkEquals(actual, list(1L, 3L)) actual <- foreach(i=x, .errorhandling='remove') %do% stop('remove') checkEquals(actual, list()) } test03 <- function() { x <- 1:3 actual <- foreach(i=x, .errorhandling='pass') %do% if (i == 2) stop('error') else i checkEquals(1L, actual[[1]]) checkTrue(inherits(actual[[2]], 'simpleError')) checkEquals(3L, actual[[3]]) } test04 <- function() { n <- 3 actual <- foreach(icount(n)) %:% foreach(icount(10), .errorhandling='remove') %do% stop('hello') checkEquals(actual, lapply(1:n, function(i) list())) } foreach/inst/unitTests/loadFactorTest.R0000644000175100001440000000071011472542406017722 0ustar hornikuserstest01 <- function() { x <- c(1,10, 100, 1000, 10000) y <- c(1,10, 100, 1000, 10000) d <- expand.grid(x=x, y=y) foreach (i=seq_along(d$x), .combine='c') %do% { r <- foreach(icount(10), .combine='c') %do% (3 + 8) foreach(i=seq_along(r)) %do% checkEquals(r[i], 11L) } foreach (i=seq_along(d$x), .combine='c') %do% { r <- foreach(icount(10), .combine='c') %dopar% (3 + 8) foreach(i=seq_along(r)) %do% checkEquals(r[i], 11L) } } foreach/inst/unitTests/nestedTest.R0000644000175100001440000000461011472542406017131 0ustar hornikusers# Test nesting of "%do% and %dopar% in 01, 02, 03, and 04. test01 <- function() { y <- foreach(j=seq(0,90,by=10), .combine='c', .packages='foreach') %do% { foreach(k=seq(1,10), .combine='c') %do% { (j+k) } } checkEquals(y,1:100) } test02 <- function() { y <- foreach(j=seq(0,90,by=10), .combine='c', .packages='foreach') %do% { foreach(k=seq(1,10), .combine='c') %dopar% { (j+k) } } checkEquals(y,1:100) } test03 <- function() { y <- foreach(j=seq(0,90,by=10), .combine='c', .packages='foreach') %dopar% { foreach(k=seq(1,10), .combine='c') %do% { (j+k) } } checkEquals(y,1:100) } test04 <- function() { y <- foreach(j=seq(0,90,by=10), .combine='c', .packages='foreach') %dopar% { foreach(k=seq(1,10), .combine='c') %dopar% { (j+k) } } checkEquals(y,1:100) } # test05 <- function() { # s <- getSleigh() # y <- eachWorker(s, eo=list(closure=TRUE), # function() { # library('foreach') # foreach(j=seq(0,90,by=10), .combine='c') %do% { # foreach(k=seq(1,10), .combine='c') %do% { # (j+k) # } # } # }) # wc <- workerCount(s) # checkEquals(length(y), wc) # foreach(i=1:wc) %do% checkEquals(y[[i]],1:100) # } # test06 <- function() { # s <- getSleigh() # y <- eachWorker(s, eo=list(closure=TRUE), # function() { # library('foreach') # foreach(j=seq(0,90,by=10), .combine='c') %do% { # foreach(k=seq(1,10), .combine='c') %dopar% { # (j+k) # } # } # }) # wc <- workerCount(s) # checkEquals(length(y), wc) # foreach(i=1:wc) %do% checkEquals(y[[i]],1:100) # } # test07 <- function() { # s <- getSleigh() # y <- eachWorker(s, eo=list(closure=TRUE), # function() { # library('foreach') # foreach(j=seq(0,90,by=10), .combine='c', .packages='foreach') %dopar% { # foreach(k=seq(1,10), .combine='c') %do% { # (j+k) # } # } # }) # wc <- workerCount(s) # checkEquals(length(y), wc) # foreach(i=1:wc) %do% checkEquals(y[[i]],1:100) # } foreach/inst/unitTests/combineTest.R0000644000175100001440000000245311472542406017266 0ustar hornikusers# test cbind and rbind via .combine option test01 <- function() { m <- matrix(rnorm(25 * 16), 25) x <- foreach(i=1:ncol(m), .combine='cbind') %do% m[,i] dimnames(x) <- NULL checkEquals(m, x) x <- foreach(i=1:ncol(m), .combine='cbind') %dopar% m[,i] dimnames(x) <- NULL checkEquals(m, x) x <- foreach(i=1:nrow(m), .combine='rbind') %do% m[i,] dimnames(x) <- NULL checkEquals(m, x) x <- foreach(i=1:nrow(m), .combine='rbind') %dopar% m[i,] dimnames(x) <- NULL checkEquals(m, x) } # test arithmetic operations via .combine option test02 <- function() { x <- rnorm(100) d <- foreach(i=x, .combine='+') %do% i checkEquals(d, sum(x)) d <- foreach(i=x, .combine='+') %dopar% i checkEquals(d, sum(x)) d <- foreach(i=x, .combine='*') %do% i checkEquals(d, prod(x)) d <- foreach(i=x, .combine='*') %dopar% i checkEquals(d, prod(x)) } test03 <- function() { x <- 1:10 adder <- function(...) { sum(...) } d <- foreach(i=x, .combine=adder, .multicombine=TRUE) %dopar% i checkEquals(d, sum(x)) d <- foreach(i=x, .combine=adder, .multicombine=FALSE) %dopar% i checkEquals(d, sum(x)) d <- foreach(i=x, .combine=adder, .multicombine=TRUE) %do% i checkEquals(d, sum(x)) d <- foreach(i=x, .combine=adder, .multicombine=FALSE) %do% i checkEquals(d, sum(x)) } foreach/inst/unitTests/stressTest.R0000644000175100001440000000042711472542406017174 0ustar hornikuserstest01 <- function() { m <- 1000 # number of vectors for (n in c(100, 1000, 4000, 10000)) { r <- foreach(x=irnorm(n, mean=1000, count=m), .combine='+') %dopar% sqrt(x) checkTrue(is.atomic(r)) checkTrue(inherits(r, 'numeric')) checkTrue(length(r) == n) } } foreach/inst/unitTests/packagesTest.R0000644000175100001440000000065711472542406017434 0ustar hornikusers# Try loading thye package splines and running a function from it. test01 <- function() { # First unload the package if it is already loaded. # eachWorker(getSleigh(), # function() { # pkg <- "package:splines" # if(pkg %in% search()) detach(pkg)}) d <- foreach(1:10, .packages='splines', .combine='c') %dopar% xyVector(c(1:3),c(4:6))[[1]] checkTrue(all(c(1:3)==d)) } foreach/inst/unitTests/foreachTest.R0000644000175100001440000000075311472542406017262 0ustar hornikuserstest01 <- function() { x <- 1:3 actual <- foreach(i=x) %do% i checkEquals(actual, as.list(x)) actual <- foreach(i=x, .combine='c') %do% i checkEquals(actual, x) } test02 <- function() { x <- 1:101 actual <- foreach(i=x, .combine='+') %dopar% i checkEquals(actual, sum(x)) } test03 <- function() { x <- 1:3 y <- 2:0 for (i in 1:3) { actual <- foreach(i=x, .combine='c', .inorder=TRUE) %dopar% { Sys.sleep(y[i]) i } checkEquals(actual, x) } } foreach/inst/unitTests/whenTest.R0000644000175100001440000000203611472542406016610 0ustar hornikuserstest01 <- function() { actual <- foreach(i=1:5) %:% when(i %% 2 == 1) %:% foreach(j=1:5) %:% when(j %% 2 == 1 && i != j) %do% c(i, j) expected <- list(list(c(1, 3), c(1, 5)), list(c(3, 1), c(3, 5)), list(c(5, 1), c(5, 3))) checkEquals(actual, expected) actual <- foreach(i=1:5, .combine='c') %:% when(i %% 2 == 1) %:% foreach(j=1:5) %:% when(j %% 2 == 1 && i != j) %do% c(i, j) expected <- list(c(1, 3), c(1, 5), c(3, 1), c(3, 5), c(5, 1), c(5, 3)) checkEquals(actual, expected) } test02 <- function() { qsort <- function(x) { n <- length(x) if (n == 0) { x } else { p <- sample(n, 1) smaller <- foreach(y=x[-p], .combine=c) %:% when(y <= x[p]) %do% y larger <- foreach(y=x[-p], .combine=c) %:% when(y > x[p]) %do% y c(qsort(smaller), x[p], qsort(larger)) } } x <- runif(100) a <- qsort(x) b <- sort(x) checkEquals(a, b) } foreach/inst/unitTests/runTestSuite.sh0000644000175100001440000000320211741344141017665 0ustar hornikusers#!/bin/sh LOGFILE=test.log R --vanilla --slave > ${LOGFILE} 2>&1 <<'EOF' library(foreach) library(RUnit) verbose <- as.logical(Sys.getenv('FOREACH_VERBOSE', 'FALSE')) method <- Sys.getenv('FOREACH_BACKEND', 'SEQ') if (method == 'SNOW') { cat('** Using SNOW backend\n') library(doSNOW) cl <- makeSOCKcluster(3) .Last <- function() { cat('shutting down SOCK cluster...\n') stopCluster(cl) cat('shutdown complete\n') } registerDoSNOW(cl) } else if (method == 'NWS') { cat('** Using NWS backend\n') library(doNWS) registerDoNWS() } else if (method == 'MC') { cat('** Using multicore backend\n') library(doMC) registerDoMC() } else if (method == 'SEQ') { cat('** Using sequential backend\n') registerDoSEQ() } else { stop('illegal backend specified: ', method) } options(warn=1) options(showWarnCalls=TRUE) cat('Starting test at', date(), '\n') cat(sprintf('doPar backend name: %s, version: %s\n', getDoParName(), getDoParVersion())) cat(sprintf('Running with %d worker(s)\n', getDoParWorkers())) tests <- c('foreachTest.R', 'errorTest.R', 'combineTest.R', 'iteratorTest.R', 'loadFactorTest.R', 'nestedTest.R', 'packagesTest.R', 'mergeTest.R', 'whenTest.R', 'stressTest.R') errcase <- list() for (f in tests) { cat('\nRunning test file:', f, '\n') t <- runTestFile(f) e <- getErrors(t) if (e$nErr + e$nFail > 0) { errcase <- c(errcase, t) print(t) } } if (length(errcase) == 0) { cat('*** Ran all tests successfully ***\n') } else { cat('!!! Encountered', length(errcase), 'problems !!!\n') for (t in errcase) { print(t) } } cat('Finished test at', date(), '\n') EOF foreach/tests/0000755000175100001440000000000012606614324013045 5ustar hornikusersforeach/tests/doRUnit.R0000644000175100001440000000634112321564330014553 0ustar hornikusers## unit tests will not be done if RUnit is not available if(require("RUnit", quietly=TRUE)) { ## --- Setup --- pkg <- "foreach" # <-- Change to package name! if(Sys.getenv("RCMDCHECK") == "FALSE") { ## Path to unit tests for standalone running under Makefile (not R CMD check) ## PKG/tests/../inst/unitTests path <- file.path(getwd(), "..", "inst", "unitTests") } else { ## Path to unit tests for R CMD check ## PKG.Rcheck/tests/../PKG/unitTests path <- system.file(package=pkg, "unitTests") } cat("\nRunning unit tests\n") print(list(pkg=pkg, getwd=getwd(), pathToUnitTests=path)) library(package=pkg, character.only=TRUE) ################################################################ ## BEGIN PACKAGE SPECIFIC CONFIGURATION # ################################################################ if (!identical(system.file("DESCRIPTION", package="doParallel") , "")){ library(doParallel) w <- makeCluster(2) .Last <- function(){ cat('shutting down cluster...\n') stopCluster(w) cat('shutdown complete\n') } registerDoParallel(cl=w) } else if (!identical(system.file("DESCRIPTION", package="doMC") , "")) { library(doMC) registerDoMC(2) } else { # default to sequential library(iterators) registerDoSEQ() } ################################################################ ## END PACKAGE SPECIFIC CONFIGURATION # ################################################################ ## If desired, load the name space to allow testing of private functions ## if (is.element(pkg, loadedNamespaces())) ## attach(loadNamespace(pkg), name=paste("namespace", pkg, sep=":"), pos=3) ## ## or simply call PKG:::myPrivateFunction() in tests ## --- Testing --- ## Define tests testSuite <- defineTestSuite(name=paste(pkg, "unit testing"), dirs=path, testFileRegexp = "^.+Test\\.R$") ## Run tests <- runTestSuite(testSuite) ## Default report name pathReport <- file.path(path, "report") ## Report to stdout and text files cat("------------------- UNIT TEST SUMMARY ---------------------\n\n") printTextProtocol(tests, showDetails=FALSE) printTextProtocol(tests, showDetails=FALSE, fileName=paste(pathReport, "Summary.txt", sep="")) printTextProtocol(tests, showDetails=TRUE, fileName=paste(pathReport, ".txt", sep="")) ## Report to HTML file printHTMLProtocol(tests, fileName=paste(pathReport, ".html", sep="")) # printHTMLProtocol(tests, fileName=file.path(dirname(dirname(getwd())),pkg,"gsDesign-RUnit-Test-Summary.html")) #paste(pathReport, ".html", sep="")) ## Return stop() to cause R CMD check stop in case of ## - failures i.e. FALSE to unit tests or ## - errors i.e. R errors tmp <- getErrors(tests) if(tmp$nFail > 0 | tmp$nErr > 0) { stop(paste("\n\nunit testing failed (#test failures: ", tmp$nFail, ", #R errors: ", tmp$nErr, ")\n\n", sep="")) } } else { warning("cannot run unit tests -- package RUnit is not available") } foreach/NAMESPACE0000644000175100001440000000205711741344141013122 0ustar hornikusersexport(foreach, when, times, "%do%", "%dopar%", "%:%", registerDoSEQ, getDoSeqRegistered, getDoSeqWorkers, getDoSeqName, getDoSeqVersion, setDoSeq, getDoParRegistered, getDoParWorkers, getDoParName, getDoParVersion, setDoPar, getResult, getErrorValue, getErrorIndex, accumulate, makeAccum, getexports) S3method("iter", "foreach") S3method("iter", "filteredforeach") S3method("iter", "xforeach") S3method("nextElem", "iforeach") S3method("nextElem", "ifilteredforeach") S3method("nextElem", "ixforeach") S3method("getResult", "iforeach") S3method("getResult", "ifilteredforeach") S3method("getResult", "ixforeach") S3method("getErrorValue", "iforeach") S3method("getErrorValue", "ifilteredforeach") S3method("getErrorValue", "ixforeach") S3method("getErrorIndex", "iforeach") S3method("getErrorIndex", "ifilteredforeach") S3method("getErrorIndex", "ixforeach") S3method("accumulate", "iforeach") S3method("accumulate", "ifilteredforeach") S3method("accumulate", "ixforeach") import(iterators) importFrom(codetools, "findGlobals") import(utils) foreach/demo/0000755000175100001440000000000012606614324012627 5ustar hornikusersforeach/demo/00Index0000644000175100001440000000006111472542406013757 0ustar hornikuserssincSEQ computation of the sinc function foreach/demo/sincSEQ.R0000644000175100001440000000161311472542406014261 0ustar hornikuserslibrary(foreach) # Define a function that creates an iterator that returns subvectors ivector <- function(x, chunks) { n <- length(x) i <- 1 nextEl <- function() { if (chunks <= 0 || n <= 0) stop('StopIteration') m <- ceiling(n / chunks) r <- seq(i, length=m) i <<- i + m n <<- n - m chunks <<- chunks - 1 x[r] } obj <- list(nextElem=nextEl) class(obj) <- c('abstractiter', 'iter') obj } # Define the coordinate grid and figure out how to split up the work x <- seq(-10, 10, by=0.1) cat('Running sequentially\n') ntasks <- 4 # Compute the value of the sinc function at each point in the grid z <- foreach(y=ivector(x, ntasks), .combine=cbind) %do% { y <- rep(y, each=length(x)) r <- sqrt(x ^ 2 + y ^ 2) matrix(10 * sin(r) / r, length(x)) } # Plot the results as a perspective plot persp(x, x, z, ylab='y', theta=30, phi=30, expand=0.5, col="lightblue") foreach/NEWS0000644000175100001440000000227112151466751012410 0ustar hornikusersNEWS/ChangeLog for foreach -------------------------- 1.4.1 2013-05-29 o Improved handling of implicitly exported objects, courtesy of Steve Weston. 1.4.0 2012-04-11 o Removed spurious warning from getDoSEQ. Bug report from Ben Barnes. o Moved welcome message from .onLoad to .onAttach. Bug report from Benilton Carvalho. o Modified setDoPar and setDoSeq to undo changes to .foreachGlobals on error. Bug report from Benilton Carvalho. o Moved vignettes from inst/doc to vignettes. o Modified DESCRIPTION file by moving codetools, iterators, and utils from Depends to Imports. Bug report from Suraj Gupta. 1.3.5 2012-03-14 o Cleanup from previous patch. Bug report from Brian Ripley. 1.3.4 2012-03-12 o Added support for multiple sequential backends. (Idea and patch from Tyler Pirtle, Matt Furia, and Joseph Hellerstein.) o Modified doRUnit.R to use no more than two cores during R CMD check. 1.3.2 2011-05-08 o Regularized unit tests so they can run through R CMD check o Added support for compiler package of 2.13.0 and later. 1.3.1 2010-11-22 o First R-forge release. foreach/R/0000755000175100001440000000000012606614324012104 5ustar hornikusersforeach/R/acc.R0000644000175100001440000000176711472542406012771 0ustar hornikusers# # Copyright (c) 2008-2010 Revolution Analytics # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # makeAccum <- function(it) { # define and return the accumulator function that will be # passed to eachElem function(results, tags) { if (identical(it$error.handling, 'stop') && !is.null(it$state$errorValue)) return(invisible(NULL)) for (i in seq(along=tags)) { if (it$verbose) cat(sprintf('got results for task %d\n', tags[i])) accumulate(it, results[[i]], tags[i]) } } } foreach/R/foreach.R0000644000175100001440000004531011722010246013630 0ustar hornikusers# # Copyright (c) 2008-2010 Revolution Analytics # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # accumulate <- function(obj, result, tag, ...) { UseMethod('accumulate') } getResult <- function(obj, ...) { UseMethod('getResult') } getErrorValue <- function(obj, ...) { UseMethod('getErrorValue') } getErrorIndex <- function(obj, ...) { UseMethod('getErrorIndex') } defcombine <- function(a, ...) c(a, list(...)) foreach <- function(..., .combine, .init, .final=NULL, .inorder=TRUE, .multicombine=FALSE, .maxcombine=if (.multicombine) 100 else 2, .errorhandling=c('stop', 'remove', 'pass'), .packages=NULL, .export=NULL, .noexport=NULL, .verbose=FALSE) { if (missing(.combine)) { if (!missing(.init)) stop('if .init is specified, then .combine must also be specified') .combine <- defcombine hasInit <- TRUE init <- quote(list()) } else { .combine <- match.fun(.combine) if (missing(.init)) { hasInit <- FALSE init <- NULL } else { hasInit <- TRUE init <- substitute(.init) } } # .multicombine defaults to TRUE if the .combine function is known to # take multiple arguments if (missing(.multicombine) && (identical(.combine, cbind) || identical(.combine, rbind) || identical(.combine, c) || identical(.combine, defcombine))) .multicombine <- TRUE # sanity check the arguments if (!is.null(.final) && !is.function(.final)) stop('.final must be a function') if (!is.logical(.inorder) || length(.inorder) > 1) stop('.inorder must be a logical value') if (!is.logical(.multicombine) || length(.multicombine) > 1) stop('.multicombine must be a logical value') if (!is.numeric(.maxcombine) || length(.maxcombine) > 1 || .maxcombine < 2) stop('.maxcombine must be a numeric value >= 2') if (!is.character(.errorhandling)) stop('.errorhandling must be a character string') if (!is.null(.packages) && !is.character(.packages)) stop('.packages must be a character vector') if (!is.null(.export) && !is.character(.export)) stop('.export must be a character vector') if (!is.null(.noexport) && !is.character(.noexport)) stop('.noexport must be a character vector') if (!is.logical(.verbose) || length(.verbose) > 1) stop('.verbose must be a logical value') specified <- c('errorHandling', 'verbose') specified <- specified[c(!missing(.errorhandling), !missing(.verbose))] args <- substitute(list(...))[-1] if (length(args) == 0) stop('no iteration arguments specified') argnames <- names(args) if (is.null(argnames)) argnames <- rep('', length(args)) # check for backend-specific options options <- list() opts <- grep('^\\.options\\.[A-Za-z][A-Za-z]*$', argnames) if (length(opts) > 0) { # put the specified options objects into the options list for (i in opts) { bname <- substr(argnames[i], 10, 100) options[[bname]] <- list(...)[[i]] } # remove the specified options objects from args and argnames args <- args[-opts] argnames <- argnames[-opts] } # check for arguments that start with a '.', and issue an error, # assuming that these are misspelled options unrecog <- grep('^\\.', argnames) if (length(unrecog) > 0) stop(sprintf('unrecognized argument(s): %s', paste(argnames[unrecog], collapse=', '))) # check for use of old-style arguments, and issue an error oldargs <- c('COMBINE', 'INIT', 'INORDER', 'MULTICOMBINE', 'MAXCOMBINE', 'ERRORHANDLING', 'PACKAGES', 'VERBOSE', 'EXPORT', 'NOEXPORT', 'LOADFACTOR', 'CHUNKSIZE') oldused <- argnames %in% oldargs if (any(oldused)) stop(sprintf('old style argument(s) specified: %s', paste(argnames[oldused], collapse=', '))) .errorhandling <- match.arg(.errorhandling) combineInfo <- list(fun=.combine, in.order=.inorder, has.init=hasInit, init=init, final=.final, multi.combine=.multicombine, max.combine=.maxcombine) iterable <- list(args=args, argnames=argnames, evalenv=parent.frame(), specified=specified, combineInfo=combineInfo, errorHandling=.errorhandling, packages=.packages, export=.export, noexport=.noexport, options=options, verbose=.verbose) class(iterable) <- 'foreach' iterable } iter.foreach <- function(obj, ..., extra=list()) { # evaluate the quoted iteration variables, and turn them into iterators iargs <- lapply(obj$args, function(a) iter(eval(a, envir=extra, enclos=obj$evalenv), ...)) # create the environment that will contain our dynamic state state <- new.env(parent=emptyenv()) # iterator state state$stopped <- FALSE state$numValues <- 0L # number of values that we've fired # accumulator state combineInfo <- obj$combineInfo if (combineInfo$has.init) { state$accum <- eval(combineInfo$init, envir=extra, enclos=obj$evalenv) state$first.time <- FALSE } else { state$accum <- NULL state$first.time <- TRUE } state$fun <- combineInfo$fun state$buffered <- rep(as.integer(NA), 2 * combineInfo$max.combine) state$next.tag <- 1L # only used when in.order is TRUE state$buf.off <- 0L # only used when in.order is TRUE state$nbuf <- 0L # only used when in.order is FALSE state$numResults <- 0L # number of results that we've received back state$errorValue <- NULL state$errorIndex <- -1L # package and return the iterator object iterator <- list(state=state, iargs=iargs, argnames=obj$argnames, combineInfo=combineInfo, errorHandling=obj$errorHandling, verbose=obj$verbose) class(iterator) <- c('iforeach', 'iter') iterator } nextElem.iforeach <- function(obj, ..., redo=FALSE) { if (redo) obj$state$numValues <- obj$state$numValues - 1L tryCatch({ # XXX this shouldn't be recomputed repeatedly ix <- which(!nzchar(obj$argnames)) elem <- if (length(ix) > 0) { lapply(obj$iargs[ix], nextElem) ix <- which(nzchar(obj$argnames)) if (length(ix) > 0) lapply(obj$iargs[ix], nextElem) else list() } else { lapply(obj$iargs, nextElem) } }, error=function(e) { if (identical(conditionMessage(e), 'StopIteration')) { obj$state$stopped <- TRUE if (complete(obj)) callCombine(obj, TRUE) } stop(e) }) obj$state$numValues <- obj$state$numValues + 1L elem } # XXX make this a method? complete <- function(obj) { stopifnot(class(obj)[1] == 'iforeach') if (obj$verbose) cat(sprintf('numValues: %d, numResults: %d, stopped: %s\n', obj$state$numValues, obj$state$numResults, obj$state$stopped)) obj$state$stopped && obj$state$numResults == obj$state$numValues } accumulate.iforeach <- function(obj, result, tag, ...) { obj$state$numResults <- obj$state$numResults + 1L # we can't receive more results than the number of tasks that we've fired stopifnot(obj$state$numResults <= obj$state$numValues) if (inherits(result, 'error') && is.null(obj$state$errorValue) && obj$errorHandling %in% c('stop', 'remove')) { if (obj$verbose) cat('accumulate got an error result\n') obj$state$errorValue <- result obj$state$errorIndex <- tag } # we can already tell what our status is going to be status <- complete(obj) # put the result in our buffer cache name <- paste('result', tag, sep='.') assign(name, result, obj$state, inherits=FALSE) ibuf <- if (obj$combineInfo$in.order) { tag - obj$state$buf.off } else { obj$state$nbuf <- obj$state$nbuf + 1L } # make sure we always have trailing NA's blen <- length(obj$state$buffered) while (ibuf >= blen) { length(obj$state$buffered) <- 2 * blen blen <- length(obj$state$buffered) } obj$state$buffered[ibuf] <- if (inherits(result, 'error') && obj$errorHandling %in% c('stop', 'remove')) -tag else tag # do any combining that needs to be done callCombine(obj, status) # return with apprpriate status if (obj$verbose) cat(sprintf('returning status %s\n', status)) status } callCombine <- function(obj, status) { if (obj$combineInfo$in.order) { repeat { needed <- obj$combineInfo$max.combine if (!obj$state$first.time) needed <- needed - 1 n <- which(is.na(obj$state$buffered))[1] - 1L stopifnot(!is.na(n)) n <- min(n, needed) if (n == needed || (status && n > 0)) { # get the names of the objects to be combined ind <- 1:n # filter out any errors (if error handling isn't 'pass') b <- obj$state$buffered[ind] allsyms <- paste('result', abs(b), sep='.') args <- b[b > 0] args <- if (length(args) > 0) paste('result', args, sep='.') else character(0) # XXX these operations won't be efficient for small values of max.combine blen <- length(obj$state$buffered) obj$state$buffered <- obj$state$buffered[(n+1):blen] length(obj$state$buffered) <- blen # XXX put this off? obj$state$buf.off <- obj$state$buf.off + n # create the call object to call the combine function callobj <- if (obj$state$first.time) { if (length(args) > 0) { if (obj$verbose) cat('first call to combine function\n') # not always true obj$state$first.time <- FALSE if (length(args) > 1) as.call(lapply(c('fun', args), as.name)) else as.name(args) # this evaluates to the value of the result } else { if (obj$verbose) cat('not calling combine function due to errors\n') NULL } } else { if (length(args) > 0) { if (obj$verbose) cat('calling combine function\n') as.call(lapply(c('fun', 'accum', args), as.name)) } else { if (obj$verbose) cat('not calling combine function due to errors\n') NULL } } # call the combine function if (!is.null(callobj)) { if (obj$verbose) { cat('evaluating call object to combine results:\n ') print(callobj) } obj$state$accum <- eval(callobj, obj$state) } # remove objects from buffer cache that we just processed # and all error objects remove(list=allsyms, pos=obj$state) } else { break } } } else { needed <- obj$combineInfo$max.combine if (!obj$state$first.time) needed <- needed - 1 stopifnot(obj$state$nbuf <= needed) # check if it's time to combine if (obj$state$nbuf == needed || (status && obj$state$nbuf > 0)) { # get the names of the objects to be combined ind <- 1:obj$state$nbuf # filter out any errors (if error handling isn't 'pass') b <- obj$state$buffered[ind] allsyms <- paste('result', abs(b), sep='.') args <- b[b > 0] args <- if (length(args) > 0) paste('result', args, sep='.') else character(0) obj$state$buffered[ind] <- as.integer(NA) obj$state$nbuf <- 0L # create the call object to call the combine function callobj <- if (obj$state$first.time) { if (length(args) > 0) { if (obj$verbose) cat('first call to combine function\n') obj$state$first.time <- FALSE if (length(args) > 1) as.call(lapply(c('fun', args), as.name)) else as.name(args) # this evaluates to the value of the result } else { if (obj$verbose) cat('not calling combine function due to errors\n') NULL } } else { if (length(args) > 0) { if (obj$verbose) cat('calling combine function\n') as.call(lapply(c('fun', 'accum', args), as.name)) } else { if (obj$verbose) cat('not calling combine function due to errors\n') NULL } } # call the combine function if (!is.null(callobj)) { if (obj$verbose) { cat('evaluating call object to combine results:\n ') print(callobj) } obj$state$accum <- eval(callobj, obj$state) } # remove objects from buffer cache that we just processed remove(list=allsyms, pos=obj$state) } } } getResult.iforeach <- function(obj, ...) { if (is.null(obj$combineInfo$final)) obj$state$accum else obj$combineInfo$final(obj$state$accum) } getErrorValue.iforeach <- function(obj, ...) { obj$state$errorValue } getErrorIndex.iforeach <- function(obj, ...) { obj$state$errorIndex } '%:%' <- function(e1, e2) { if (!inherits(e1, 'foreach')) stop('"%:%" was passed an illegal right operand') if (inherits(e2, 'foreach')) makeMerged(e1, e2) else if (inherits(e2, 'foreachCondition')) makeFiltered(e1, e2) else stop('"%:%" was passed an illegal right operand') } makeMerged <- function(e1, e2) { specified <- union(e1$specified, e2$specified) argnames <- union(e1$argnames, e2$argnames) packages <- union(e1$packages, e2$packages) export <- union(e1$export, e2$export) noexport <- union(e1$noexport, e2$noexport) options <- c(e1$options, e2$options) iterable <- list(e1=e1, e2=e2, specified=specified, argnames=argnames, packages=packages, export=export, noexport=noexport, options=options) # this gives precedence to the outer foreach inherit <- c('errorHandling', 'verbose') iterable[inherit] <- e2[inherit] iterable[e1$specified] <- e1[e1$specified] class(iterable) <- c('xforeach', 'foreach') iterable } iter.xforeach <- function(obj, ...) { state <- new.env(parent=emptyenv()) state$stopped <- FALSE state$fired <- integer(0) state$ie2 <- list() state$errorValue <- NULL state$errorIndex <- -1L ie1 <- iter(obj$e1, ...) iterator <- list(state=state, ie1=ie1, e2=obj$e2, argnames=obj$argnames, errorHandling=obj$errorHandling, verbose=obj$verbose) class(iterator) <- c('ixforeach', 'iter') iterator } nextElem.ixforeach <- function(obj, ..., redo=FALSE) { if (obj$verbose) cat(sprintf('nextElem.ixforeach called with redo %s\n', redo)) if (redo) { i <- length(obj$state$fired) if (obj$verbose) { cat('refiring iterator - fired was:\n') print(obj$state$fired) } obj$state$fired[i] <- obj$state$fired[i] - 1L if (obj$verbose) { cat('fired is now:\n') print(obj$state$fired) } } repeat { if (!exists('nextval', obj$state, inherits=FALSE)) { tryCatch({ obj$state$nextval <- nextElem(obj$ie1) }, error=function(e) { if (identical(conditionMessage(e), 'StopIteration')) obj$state$stopped <- TRUE stop(e) }) obj$state$ie2 <- c(obj$state$ie2, list(iter(obj$e2, extra=obj$state$nextval))) obj$state$fired <- c(obj$state$fired, 0L) } tryCatch({ i <- length(obj$state$fired) v2 <- nextElem(obj$state$ie2[[i]], redo=redo) obj$state$fired[i] <- obj$state$fired[i] + 1L break }, error=function(e) { if (!identical(conditionMessage(e), 'StopIteration')) stop(e) remove('nextval', pos=obj$state) if (complete(obj$state$ie2[[i]])) { callCombine(obj$state$ie2[[i]], TRUE) if (is.null(obj$state$errorValue)) { obj$state$errorValue <- getErrorValue(obj$state$ie2[[i]]) obj$state$errorIndex <- getErrorIndex(obj$state$ie2[[i]]) } accum <- getResult(obj$state$ie2[[i]]) if (obj$verbose) { cat('propagating accumulated result up to the next level from nextElem\n') print(accum) } accumulate(obj$ie1, accum, i) # XXX error handling? } }) redo <- FALSE } c(obj$state$nextval, v2) } accumulate.ixforeach <- function(obj, result, tag, ...) { if (obj$verbose) { cat(sprintf('accumulating result with tag %d\n', tag)) cat('fired:\n') print(obj$state$fired) } s <- cumsum(obj$state$fired) j <- 1L while (tag > s[[j]]) j <- j + 1L i <- if (j > 1) as.integer(tag) - s[[j - 1]] else as.integer(tag) ie2 <- obj$state$ie2[[j]] if (accumulate(ie2, result, i)) { if (is.null(obj$state$errorValue)) { obj$state$errorValue <- getErrorValue(ie2) obj$state$errorIndex <- getErrorIndex(ie2) } accum <- getResult(ie2) if (obj$verbose) { cat('propagating accumulated result up to the next level from accumulate\n') print(accum) } accumulate(obj$ie1, accum, j) # XXX error handling? } } getResult.ixforeach <- function(obj, ...) { getResult(obj$ie1, ...) } getErrorValue.ixforeach <- function(obj, ...) { obj$state$errorValue } getErrorIndex.ixforeach <- function(obj, ...) { obj$state$errorIndex } '%if%' <- function(e1, cond) { stop('obsolete') } when <- function(cond) { obj <- list(qcond=substitute(cond), evalenv=parent.frame()) class(obj) <- 'foreachCondition' obj } makeFiltered <- function(e1, cond) { iterable <- c(list(e1=e1), cond) inherit <- c('argnames', 'specified', 'errorHandling', 'packages', 'export', 'noexport', 'options', 'verbose') iterable[inherit] <- e1[inherit] class(iterable) <- c('filteredforeach', 'foreach') iterable } iter.filteredforeach <- function(obj, ...) { ie1 <- iter(obj$e1, ...) iterator <- list(ie1=ie1, qcond=obj$qcond, evalenv=obj$evalenv, argnames=obj$argnames, errorHandling=obj$errorHandling, verbose=obj$verbose) class(iterator) <- c('ifilteredforeach', 'iter') iterator } nextElem.ifilteredforeach <- function(obj, ..., redo=FALSE) { repeat { elem <- nextElem(obj$ie1, ..., redo=redo) if (eval(obj$qcond, envir=elem, enclos=obj$evalenv)) break redo <- TRUE } elem } accumulate.ifilteredforeach <- function(obj, result, tag, ...) { accumulate(obj$ie1, result, tag, ...) } getResult.ifilteredforeach <- function(obj, ...) { getResult(obj$ie1, ...) } getErrorValue.ifilteredforeach <- function(obj, ...) { getErrorValue(obj$ie1, ...) } getErrorIndex.ifilteredforeach <- function(obj, ...) { getErrorIndex(obj$ie1, ...) } foreach/R/do.R0000644000175100001440000001623711742054437012645 0ustar hornikusers# # Copyright (c) 2008-2010 Revolution Analytics # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # .foreachGlobals <- new.env(parent=emptyenv()) # this is called to register a parallel backend setDoPar <- function(fun, data=NULL, info=function(data, item) NULL) { tryCatch( { assign('fun', fun, pos=.foreachGlobals, inherits=FALSE) assign('data', data, pos=.foreachGlobals, inherits=FALSE) assign('info', info, pos=.foreachGlobals, inherits=FALSE) }, error = function(e) { if (exists('fun', where=.foreachGlobals, inherits=FALSE)) remove('fun', envir=.foreachGlobals) if (exists('data', where=.foreachGlobals, inherits=FALSE)) remove('data', envir=.foreachGlobals) if (exists('info', where=.foreachGlobals, inherits=FALSE)) remove('info', envir=.foreachGlobals) e }) } # this is called to register a sequential backend setDoSeq <- function(fun, data=NULL, info=function(data, item) NULL) { tryCatch( { assign('seqFun', fun, pos=.foreachGlobals, inherits=FALSE) assign('seqData', data, pos=.foreachGlobals, inherits=FALSE) assign('seqInfo', info, pos=.foreachGlobals, inherits=FALSE) }, error = function(e) { if (exists('fun', where=.foreachGlobals, inherits=FALSE)) remove('fun', envir = .foreachGlobals) if (exists('data', where=.foreachGlobals, inherits=FALSE)) remove('data', envir = .foreachGlobals) if (exists('info', where=.foreachGlobals, inherits=FALSE)) remove('info', envir = .foreachGlobals) e }) } # this explicitly registers a sequential backend for do and dopar. registerDoSEQ <- function() { setDoPar(doSEQ, NULL, info) setDoSeq(doSEQ, NULL, info) } # passed to setDoPar via registerDoSEQ, and called by getDoSeqWorkers, etc info <- function(data, item) { switch(item, workers=1L, name='doSEQ', version=packageDescription('foreach', fields='Version'), NULL) } # this returns a logical value indicating if a sequential backend # has been registered or not getDoSeqRegistered <- function() { exists('seqFun', where=.foreachGlobals, inherits=FALSE) } # this returns a logical value indicating if a parallel backend # has been registered or not getDoParRegistered <- function() { exists('fun', where=.foreachGlobals, inherits=FALSE) } # this returns the number of workers used by the currently registered # sequential backend getDoSeqWorkers <- function() { wc <- if (exists('seqInfo', where=.foreachGlobals, inherits=FALSE)) .foreachGlobals$seqInfo(.foreachGlobals$seqData, 'workers') else NULL # interpret a NULL as a single worker, but the backend # can return NA without interference if (is.null(wc)) 1L else wc } # this returns the number of workers used by the currently registered # parallel backend getDoParWorkers <- function() { wc <- if (exists('info', where=.foreachGlobals, inherits=FALSE)) .foreachGlobals$info(.foreachGlobals$data, 'workers') else NULL # interpret a NULL as a single worker, but the backend # can return NA without interference if (is.null(wc)) 1L else wc } # this returns the name of the currently registered sequential backend getDoSeqName <- function() { if (exists('seqInfo', where=.foreachGlobals, inherits=FALSE)) .foreachGlobals$seqInfo(.foreachGlobals$seqData, 'name') else NULL } # this returns the name of the currently registered parallel backend getDoParName <- function() { if (exists('info', where=.foreachGlobals, inherits=FALSE)) .foreachGlobals$info(.foreachGlobals$data, 'name') else NULL } # this returns the version of the currently registered sequential backend getDoSeqVersion <- function() { if (exists('seqInfo', where=.foreachGlobals, inherits=FALSE)) .foreachGlobals$seqInfo(.foreachGlobals$seqData, 'version') else NULL } # this returns the version of the currently registered parallel backend getDoParVersion <- function() { if (exists('info', where=.foreachGlobals, inherits=FALSE)) .foreachGlobals$info(.foreachGlobals$data, 'version') else NULL } # used internally to get the currently registered parallel backend getDoSeq <- function() { if (exists('seqFun', where=.foreachGlobals, inherits=FALSE)) { list(fun=.foreachGlobals$seqFun, data=.foreachGlobals$seqdata) } else { list(fun=doSEQ, data=NULL) } } # used internally to get the currently registered parallel backend getDoPar <- function() { if (exists('fun', where=.foreachGlobals, inherits=FALSE)) { list(fun=.foreachGlobals$fun, data=.foreachGlobals$data) } else { if (!exists('parWarningIssued', where=.foreachGlobals, inherits=FALSE)) { warning('executing %dopar% sequentially: no parallel backend registered', call.=FALSE) assign('parWarningIssued', TRUE, pos=.foreachGlobals, inherits=FALSE) } list(fun=doSEQ, data=NULL) } } '%do%' <- function(obj, ex) { e <- getDoSeq() e$fun(obj, substitute(ex), parent.frame(), e$data) } '%dopar%' <- function(obj, ex) { e <- getDoPar() e$fun(obj, substitute(ex), parent.frame(), e$data) } comp <- if (getRversion() < "2.13.0") { function(expr, ...) expr } else { compiler::compile } doSEQ <- function(obj, expr, envir, data) { # note that the "data" argument isn't used if (!inherits(obj, 'foreach')) stop('obj must be a foreach object') it <- iter(obj) accumulator <- makeAccum(it) for (p in obj$packages) library(p, character.only=TRUE) # compile the expression if we're using R 2.13.0 or greater xpr <- comp(expr, env=envir, options=list(suppressUndefined=TRUE)) i <- 1 tryCatch({ repeat { # get the next set of arguments args <- nextElem(it) if (obj$verbose) { cat(sprintf('evaluation # %d:\n', i)) print(args) } # assign arguments to local environment for (a in names(args)) assign(a, args[[a]], pos=envir, inherits=FALSE) # evaluate the expression r <- tryCatch(eval(xpr, envir=envir), error=function(e) e) if (obj$verbose) { cat('result of evaluating expression:\n') print(r) } # process the results tryCatch(accumulator(list(r), i), error=function(e) { cat('error calling combine function:\n') print(e) NULL }) i <- i + 1 } }, error=function(e) { if (!identical(conditionMessage(e), 'StopIteration')) stop(simpleError(conditionMessage(e), expr)) }) errorValue <- getErrorValue(it) errorIndex <- getErrorIndex(it) if (identical(obj$errorHandling, 'stop') && !is.null(errorValue)) { msg <- sprintf('task %d failed - "%s"', errorIndex, conditionMessage(errorValue)) stop(simpleError(msg, call=expr)) } else { getResult(it) } } foreach/R/zzz.R0000644000175100001440000000164311741344141013064 0ustar hornikusers# # Copyright (c) 2008-2010 Revolution Analytics # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # .onAttach <- function(lib, pkg) { if (interactive()) { packageStartupMessage('foreach: simple, scalable parallel programming from Revolution Analytics\n', 'Use Revolution R for scalability, fault tolerance and more.\n', 'http://www.revolutionanalytics.com', domain=NA, appendLF=TRUE) } } foreach/R/getsyms.R0000644000175100001440000000562012150757453013732 0ustar hornikusers# # Copyright (c) 2008-2010 Revolution Analytics # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # getsyms <- function(ex) { fun <- function(x) { if (is.symbol(x)) as.character(x) else if (is.call(x)) getsyms(x) else NULL } unlist(lapply(ex, fun)) } gather <- function(x) { fun <- function(a, b) unique(c(a, b)) accum <- list(good=character(0), bad=character(0)) for (e in x) { accum <- mapply(fun, e, accum, SIMPLIFY=FALSE) } accum } expandsyms <- function(syms, env, good, bad) { fun <- function(sym, good, bad) { if (sym %in% c(good, bad)) { # we already saw this symbol list(good=good, bad=bad) } else if (!nzchar(sym)) { # apparently a symbol can be converted into an empty string, # but it's an error to call "exists" with an empty string, # so we just declare it to be bad here list(good=good, bad=c(sym, bad)) } else if (exists(sym, env, mode='function', inherits=FALSE)) { # this is a function defined in this environment good <- c(sym, good) f <- get(sym, env, mode='function', inherits=FALSE) if (identical(environment(f), env)) { # it's a local function globs <- findGlobals(f) if (length(globs) > 0) { # it's got free variables, so let's check them out gather(lapply(globs, fun, good, bad)) } else { # it doesn't have free variables, so we're done list(good=good, bad=bad) } } else { # it's not a local function, so we're done list(good=good, bad=bad) } } else if (exists(sym, env, inherits=FALSE)) { # it's not a function, but it's defined in this environment list(good=c(sym, good), bad=bad) } else { # it's not defined in this environment list(good=good, bad=c(sym, bad)) } } gather(lapply(syms, fun, good, bad))$good } getexports <- function(ex, e, env, good=character(0), bad=character(0)) { syms <- getsyms(ex) syms <- expandsyms(syms, env, good, bad) for (s in syms) { if (s != '...') { val <- get(s, env, inherits=FALSE) # if this is a function, check if we should change the # enclosing environment to be this new environment fenv <- environment(val) if (is.function(val) && (identical(fenv, env) || identical(fenv, .GlobalEnv))) environment(val) <- e assign(s, val, e) } } invisible(NULL) } foreach/R/times.R0000644000175100001440000000151611472542406013354 0ustar hornikusers# # Copyright (c) 2008-2010 Revolution Analytics # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # a simple convenience function for use with %do% and %dopar% # inspired by Daniel Kaplan of Macalester College times <- function(n) { if (!is.numeric(n) || length(n) != 1) stop('n must be a numeric value') foreach(icount(n), .combine='c') } foreach/vignettes/0000755000175100001440000000000012606615125013713 5ustar hornikusersforeach/vignettes/nested.Rnw0000644000175100001440000003250011741344141015661 0ustar hornikusers% \VignetteIndexEntry{Nesting Foreach Loops} % \VignetteDepends{foreach} % \VignettePackage{foreach} \documentclass[12pt]{article} \usepackage{amsmath} \usepackage[pdftex]{graphicx} \usepackage{color} \usepackage{xspace} \usepackage{fancyvrb} \usepackage{fancyhdr} \usepackage[ colorlinks=true, linkcolor=blue, citecolor=blue, urlcolor=blue] {hyperref} \usepackage{lscape} \usepackage{Sweave} \usepackage{float} \floatstyle{plain} \newfloat{example}{thp}{lop} \floatname{example}{Example} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % define new colors for use \definecolor{darkgreen}{rgb}{0,0.6,0} \definecolor{darkred}{rgb}{0.6,0.0,0} \definecolor{lightbrown}{rgb}{1,0.9,0.8} \definecolor{brown}{rgb}{0.6,0.3,0.3} \definecolor{darkblue}{rgb}{0,0,0.8} \definecolor{darkmagenta}{rgb}{0.5,0,0.5} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \newcommand{\bld}[1]{\mbox{\boldmath $#1$}} \newcommand{\shell}[1]{\mbox{$#1$}} \renewcommand{\vec}[1]{\mbox{\bf {#1}}} \newcommand{\ReallySmallSpacing}{\renewcommand{\baselinestretch}{.6}\Large\normalsize} \newcommand{\SmallSpacing}{\renewcommand{\baselinestretch}{1.1}\Large\normalsize} \newcommand{\halfs}{\frac{1}{2}} \setlength{\oddsidemargin}{-.25 truein} \setlength{\evensidemargin}{0truein} \setlength{\topmargin}{-0.2truein} \setlength{\textwidth}{7 truein} \setlength{\textheight}{8.5 truein} \setlength{\parindent}{0.20truein} \setlength{\parskip}{0.10truein} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \pagestyle{fancy} \lhead{} \chead{Nesting {\tt Foreach} Loops} \rhead{} \lfoot{} \cfoot{} \rfoot{\thepage} \renewcommand{\headrulewidth}{1pt} \renewcommand{\footrulewidth}{1pt} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \title{Nesting {\tt Foreach} Loops} \author{Steve Weston \\ doc@revolutionanalytics.com} \begin{document} \maketitle \thispagestyle{empty} \section{Introduction} <>= library(foreach) registerDoSEQ() @ The \texttt{foreach} package provides a looping construct for executing R code repeatedly. It is similar to the standard \texttt{for} loop, which makes it easy to convert a \texttt{for} loop to a \texttt{foreach} loop. Unlike many parallel programming packages for R, \texttt{foreach} doesn't require the body of the \texttt{for} loop to be turned into a function. \texttt{foreach} differs from a \texttt{for} loop in that its return is a list of values, whereas a \texttt{for} loop has no value and uses side effects to convey its result. Because of this, \texttt{foreach} loops have a few advantages over \texttt{for} loops when the purpose of the loop is to create a data structure such as a vector, list, or matrix: First, there is less code duplication, and hence, less chance for an error because the initialization of the vector or matrix is unnecessary. Second, a \texttt{foreach} loop may be easily parallelized by changing only a single keyword. \section{The nesting operator: \%:\%} An important feature of \texttt{foreach} is the \texttt{\%:\%} operator. I call this the {\em nesting} operator because it is used to create nested \texttt{foreach} loops. Like the \texttt{\%do\%} and \texttt{\%dopar\%} operators, it is a binary operator, but it operates on two \texttt{foreach} objects. It also returns a \texttt{foreach} object, which is essentially a special merger of its operands. Let's say that we want to perform a Monte Carlo simulation using a function called \texttt{sim}.\footnote{Remember that \texttt{sim} needs to be rather compute intensive to be worth executing in parallel.} The \texttt{sim} function takes two arguments, and we want to call it with all combinations of the values that are stored in the vectors \texttt{avec} and \texttt{bvec}. The following doubly-nested \texttt{for} loop does that. For testing purposes, the \texttt{sim} function is defined to return $10 a + b$:\footnote{Of course, an operation this trivial is not worth executing in parallel.} <>= sim <- function(a, b) 10 * a + b avec <- 1:2 bvec <- 1:4 @ <>= x <- matrix(0, length(avec), length(bvec)) for (j in 1:length(bvec)) { for (i in 1:length(avec)) { x[i,j] <- sim(avec[i], bvec[j]) } } x @ In this case, it makes sense to store the results in a matrix, so we create one of the proper size called \texttt{x}, and assign the return value of \texttt{sim} to the appropriate element of \texttt{x} each time through the inner loop. When using \texttt{foreach}, we don't create a matrix and assign values into it. Instead, the inner loop returns the columns of the result matrix as vectors, which are combined in the outer loop into a matrix. Here's how to do that using the \texttt{\%:\%} operator:\footnote{Due to operator precedence, you cannot put braces around the inner \texttt{foreach} loop. Unfortunately, that causes Sweave to format this example rather badly, in my opinion.} <>= x <- foreach(b=bvec, .combine='cbind') %:% foreach(a=avec, .combine='c') %do% { sim(a, b) } x @ This is structured very much like the nested \texttt{for} loop. The outer \texttt{foreach} is iterating over the values in ``bvec'', passing them to the inner \texttt{foreach}, which iterates over the values in ``avec'' for each value of ``bvec''. Thus, the ``sim'' function is called in the same way in both cases. The code is slightly cleaner in this version, and has the advantage of being easily parallelized. \section{Using \texttt{\%:\%} with \texttt{\%dopar\%}} When parallelizing nested \texttt{for} loops, there is always a question of which loop to parallelize. The standard advice is to parallelize the outer loop. This results in larger individual tasks, and larger tasks can often be performed more efficiently than smaller tasks. However, if the outer loop doesn't have many iterations and the tasks are already large, parallelizing the outer loop results in a small number of huge tasks, which may not allow you to use all of your processors, and can also result in load balancing problems. You could parallelize an inner loop instead, but that could be inefficient because you're repeatedly waiting for all the results to be returned every time through the outer loop. And if the tasks and number of iterations vary in size, then it's really hard to know which loop to parallelize. But in our Monte Carlo example, all of the tasks are completely independent of each other, and so they can all be executed in parallel. You really want to think of the loops as specifying a single stream of tasks. You just need to be careful to process all of the results correctly, depending on which iteration of the inner loop they came from. That is exactly what the \texttt{\%:\%} operator does: it turns multiple \texttt{foreach} loops into a single loop. That is why there is only one \texttt{\%do\%} operator in the example above. And when we parallelize that nested \texttt{foreach} loop by changing the \texttt{\%do\%} into a \texttt{\%dopar\%}, we are creating a single stream of tasks that can all be executed in parallel: <>= x <- foreach(b=bvec, .combine='cbind') %:% foreach(a=avec, .combine='c') %dopar% { sim(a, b) } x @ Of course, we'll actually only run as many tasks in parallel as we have processors, but the parallel backend takes care of all that. The point is that the \texttt{\%:\%} operator makes it easy to specify the stream of tasks to be executed, and the \texttt{.combine} argument to \texttt{foreach} allows us to specify how the results should be processed. The backend handles executing the tasks in parallel. \section{Chunking tasks} Of course, there has to be a snag to this somewhere. What if the tasks are quite small, so that you really might want to execute the entire inner loop as a single task? Well, small tasks are a problem even for a singly-nested loop. The solution to this problem, whether you have a single loop or nested loops, is to use {\em task chunking}. Task chunking allows you to send multiple tasks to the workers at once. This can be much more efficient, especially for short tasks. Currently, only the \texttt{doNWS} backend supports task chunking. Here's how it's done with \texttt{doNWS}: <>= opts <- list(chunkSize=2) x <- foreach(b=bvec, .combine='cbind', .options.nws=opts) %:% foreach(a=avec, .combine='c') %dopar% { sim(a, b) } x @ If you're not using \texttt{doNWS}, then this argument is ignored, which allows you to write code that is backend-independent. You can also specify options for multiple backends, and only the option list that matches the registered backend will be used. It would be nice if the chunk size could be picked automatically, but I haven't figured out a good, safe way to do that. So for now, you need to specify the chunk size manually.\footnote{In the future, the backend might decide that it will execute the tasks in parallel. That could be very useful when running on a cluster with multiprocessor nodes. Multiple tasks are sent across the network to each node, which then executes them in parallel on its cores. Maybe in the next release...} The point is that by using the \texttt{\%:\%} operator, you can convert a nested \texttt{for} loop to a nested \texttt{foreach} loop, use \texttt{\%dopar\%} to run in parallel, and then tune the size of the tasks using the ``chunkSize'' option so that they are big enough to be executed efficiently, but not so big that they cause load balancing problems. You don't have to worry about which loop to parallelize, because you're turning the nested loops into a single stream of tasks that can all be executed in parallel by the parallel backend. \section{Another example} Now let's imagine that the ``sim'' function returns a object that includes an error estimate. We want to return the result with the lowest error for each value of b, along with the arguments that generated that result. Here's how that might be done with nested \texttt{for} loops: <>= sim <- function(a, b) { x <- 10 * a + b err <- abs(a - b) list(x=x, err=err) } @ <>= n <- length(bvec) d <- data.frame(x=numeric(n), a=numeric(n), b=numeric(n), err=numeric(n)) for (j in 1:n) { err <- Inf best <- NULL for (i in 1:length(avec)) { obj <- sim(avec[i], bvec[j]) if (obj$err < err) { err <- obj$err best <- data.frame(x=obj$x, a=avec[i], b=bvec[j], err=obj$err) } } d[j,] <- best } d @ This is also quite simple to convert to \texttt{foreach}. We just need to supply the appropriate ``.combine'' functions. For the outer \texttt{foreach}, we can use the standard ``rbind'' function which can be used with data frames. For the inner \texttt{foreach}, we write a function that compares two data frames, each with a single row, returning the one with a smaller error estimate: <>= comb <- function(d1, d2) if (d1$err < d2$err) d1 else d2 @ Now we specify it with the ``.combine'' argument to the inner \texttt{foreach}: <>= opts <- list(chunkSize=2) d <- foreach(b=bvec, .combine='rbind', .options.nws=opts) %:% foreach(a=avec, .combine='comb', .inorder=FALSE) %dopar% { obj <- sim(a, b) data.frame(x=obj$x, a=a, b=b, err=obj$err) } d @ Note that since the order of the arguments to the ``comb'' function is unimportant, I have set the ``.inorder'' argument to \texttt{FALSE}. This reduces the number of results that need to be saved on the master before they can be combined in case they are returned out of order. But even with niceties such as parallelization, backend-specific options, and the ``.inorder'' argument, the nested \texttt{foreach} version is quite readable. But what if we would like to return the indices into ``avec'' and ``bvec'', rather than the data itself? A simple way to do that is to create a couple of counting iterators that we pass to the \texttt{foreach} functions:\footnote{It is very important that the call to icount is passed as the argument to \texttt{foreach}. If the iterators were created and passed to \texttt{foreach} using a variable, for example, we would not get the desired effect. This is not a bug or a limitation, but an important aspect of the design of the \texttt{foreach} function.} <>= library(iterators) opts <- list(chunkSize=2) d <- foreach(b=bvec, j=icount(), .combine='rbind', .options.nws=opts) %:% foreach(a=avec, i=icount(), .combine='comb', .inorder=FALSE) %dopar% { obj <- sim(a, b) data.frame(x=obj$x, i=i, j=j, err=obj$err) } d @ These new iterators are infinite iterators, but that's no problem since we have ``bvec'' and ``avec'' to control the number of iterations of the loops. Making them infinite means we don't have to keep them in sync with ``bvec'' and ``avec''. \section{Conclusion} Nested \texttt{for} loops are a common construct, and are often the most time consuming part of R scripts, so they are prime candidates for parallelization. The usual approach is to parallelize the outer loop, but as we've seen, that can lead to suboptimal performance due to an imbalance between the size and the number of tasks. By using the \texttt{\%:\%} operator with \texttt{foreach}, and by using chunking techniques, many of these problems can be overcome. The resulting code is often clearer and more readable than the original R code, since \texttt{foreach} was designed to deal with exactly this kind of problem. \end{document} foreach/vignettes/foreach.Rnw0000644000175100001440000004775711741344141016032 0ustar hornikusers% \VignetteIndexEntry{foreach Manual} % \VignetteDepends{foreach} % \VignettePackage{foreach} \documentclass[12pt]{article} \usepackage{amsmath} \usepackage[pdftex]{graphicx} \usepackage{color} \usepackage{xspace} \usepackage{fancyvrb} \usepackage{fancyhdr} \usepackage[ colorlinks=true, linkcolor=blue, citecolor=blue, urlcolor=blue] {hyperref} \usepackage{lscape} \usepackage{Sweave} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % define new colors for use \definecolor{darkgreen}{rgb}{0,0.6,0} \definecolor{darkred}{rgb}{0.6,0.0,0} \definecolor{lightbrown}{rgb}{1,0.9,0.8} \definecolor{brown}{rgb}{0.6,0.3,0.3} \definecolor{darkblue}{rgb}{0,0,0.8} \definecolor{darkmagenta}{rgb}{0.5,0,0.5} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \newcommand{\bld}[1]{\mbox{\boldmath $#1$}} \newcommand{\shell}[1]{\mbox{$#1$}} \renewcommand{\vec}[1]{\mbox{\bf {#1}}} \newcommand{\ReallySmallSpacing}{\renewcommand{\baselinestretch}{.6}\Large\normalsize} \newcommand{\SmallSpacing}{\renewcommand{\baselinestretch}{1.1}\Large\normalsize} \newcommand{\halfs}{\frac{1}{2}} \setlength{\oddsidemargin}{-.25 truein} \setlength{\evensidemargin}{0truein} \setlength{\topmargin}{-0.2truein} \setlength{\textwidth}{7 truein} \setlength{\textheight}{8.5 truein} \setlength{\parindent}{0.20truein} \setlength{\parskip}{0.10truein} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \pagestyle{fancy} \lhead{} \chead{Using The {\tt foreach} Package} \rhead{} \lfoot{} \cfoot{} \rfoot{\thepage} \renewcommand{\headrulewidth}{1pt} \renewcommand{\footrulewidth}{1pt} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \title{Using The {\tt foreach} Package} \author{Steve Weston \\ doc@revolutionanalytics.com} \begin{document} \maketitle \thispagestyle{empty} \section{Introduction} One of R's most useful features is its interactive interpreter. This makes it very easy to learn and experiment with R. It allows you to use R like a calculator to perform arithmetic operations, display data sets, generate plots, and create models. Before too long, new R users will find a need to perform some operation repeatedly. Perhaps they want to run a simulation repeatedly in order to find the distribution of the results. Perhaps they need to execute a function with a variety a different arguments passed to it. Or maybe they need to create a model for many different data sets. Repeated executions can be done manually, but it becomes quite tedious to execute repeated operations, even with the use of command line editing. Fortunately, R is much more than an interactive calculator. It has its own built-in language that is intended to automate tedious tasks, such as repeatedly executing R calculations. R comes with various looping constructs that solve this problem. The \texttt{for} loop is one of the more common looping constructs, but the \texttt{repeat} and \texttt{while} statements are also quite useful. In addition, there is the family of ``apply'' functions, which includes \texttt{apply}, \texttt{lapply}, \texttt{sapply}, \texttt{eapply}, \texttt{mapply}, \texttt{rapply}, and others. The \texttt{foreach} package provides a new looping construct for executing R code repeatedly. With the bewildering variety of existing looping constructs, you may doubt that there is a need for yet another construct. The main reason for using the \texttt{foreach} package is that it supports {\em parallel execution}, that is, it can execute those repeated operations on multiple processors/cores on your computer, or on multiple nodes of a cluster. If each operation takes over a minute, and you want to execute it hundreds of times, the overall runtime can take hours. But using \texttt{foreach}, that operation can be executed in parallel on hundreds of processors on a cluster, reducing the execution time back down to minutes. But parallel execution is not the only reason for using the \texttt{foreach} package. There are other reasons that you might choose to use it to execute quick executing operations, as we will see later in the document. \section{Getting Started} Let's take a look at a simple example use of the \texttt{foreach} package. Assuming that you have the \texttt{foreach} package installed, you first need to load it: <>= library(foreach) @ Note that all of the packages that \texttt{foreach} depends on will be loaded as well. Now I can use \texttt{foreach} to execute the \texttt{sqrt} function repeatedly, passing it the values 1 through 3, and returning the results in a list, called \texttt{x}\footnote{Of course, \texttt{sqrt} is a vectorized function, so you would never really do this. But later, we'll see how to take advantage of vectorized functions with \texttt{foreach}.}: <>= x <- foreach(i=1:3) %do% sqrt(i) x @ This is a bit odd looking, because it looks vaguely like a \texttt{for} loop, but is implemented using a binary operator, called \texttt{\%do\%}. Also, unlike a \texttt{for} loop, it returns a value. This is quite important. The purpose of this statement is to compute the list of results. Generally, \texttt{foreach} with \texttt{\%do\%} is used to execute an R expression repeatedly, and return the results in some data structure or object, which is a list by default. You will note in the previous example that we used a variable \texttt{i} as the argument to the \texttt{sqrt} function. We specified the values of the \texttt{i} variable using a named argument to the \texttt{foreach} function. We could have called that variable anything we wanted, for example, \texttt{a}, or \texttt{b}. We could also specify other variables to be used in the R expression, as in the following example: <>= x <- foreach(a=1:3, b=rep(10, 3)) %do% (a + b) x @ Note that parentheses are needed here. We can also use braces: <>= x <- foreach(a=1:3, b=rep(10, 3)) %do% { a + b } x @ We call \texttt{a} and \texttt{b} the {\em iteration variables}, since those are the variables that are changing during the multiple executions. Note that we are iterating over them in parallel, that is, they are both changing at the same time. In this case, the same number of values are being specified for both iteration variables, but that need not be the case. If we only supplied two values for \texttt{b}, the result would be a list of length two, even if we specified a thousand values for \texttt{a}: <>= x <- foreach(a=1:1000, b=rep(10, 2)) %do% { a + b } x @ Note that you can put multiple statements between the braces, and you can use assignment statements to save intermediate values of computations. However, if you use an assignment as a way of communicating between the different executions of your loop, then your code won't work correctly in parallel, which we will discuss later. \section{The \texttt{.combine} Option} So far, all of our examples have returned a list of results. This is a good default, since a list can contain any R object. But sometimes we'd like the results to be returned in a numeric vector, for example. This can be done by using the \texttt{.combine} option to \texttt{foreach}: <>= x <- foreach(i=1:3, .combine='c') %do% exp(i) x @ The result is returned as a numeric vector, because the standard R \texttt{c} function is being used to concatenate all the results. Since the \texttt{exp} function returns numeric values, concatenating them with the \texttt{c} function will result in a numeric vector of length three. What if the R expression returns a vector, and we want to combine those vectors into a matrix? One way to do that is with the \texttt{cbind} function: <>= x <- foreach(i=1:4, .combine='cbind') %do% rnorm(4) x @ This generates four vectors of four random numbers, and combines them by column to produce a 4 by 4 matrix. We can also use the \texttt{"+"} or \texttt{"*"} functions to combine our results: <>= x <- foreach(i=1:4, .combine='+') %do% rnorm(4) x @ You can also specify a user-written function to combine the results. Here's an example that throws away the results: <>= cfun <- function(a, b) NULL x <- foreach(i=1:4, .combine='cfun') %do% rnorm(4) x @ Note that this \texttt{cfun} function takes two arguments. The \texttt{foreach} function knows that the functions \texttt{c}, \texttt{cbind}, and \texttt{rbind} take many arguments, and will call them with up to 100 arguments (by default) in order to improve performance. But if any other function is specified (such as \texttt{"+"}), it assumes that it only takes two arguments. If the function does allow many arguments, you can specify that using the \texttt{.multicombine} argument: <>= cfun <- function(...) NULL x <- foreach(i=1:4, .combine='cfun', .multicombine=TRUE) %do% rnorm(4) x @ If you want the combine function to be called with no more than 10 arguments, you can specify that using the \texttt{.maxcombine} option: <>= cfun <- function(...) NULL x <- foreach(i=1:4, .combine='cfun', .multicombine=TRUE, .maxcombine=10) %do% rnorm(4) x @ The \texttt{.inorder} option is used to specify whether the order in which the arguments are combined is important. The default value is \texttt{TRUE}, but if the combine function is \texttt{"+"}, you could specify \texttt{.inorder} to be \texttt{FALSE}. Actually, this option is important only when executing the R expression in parallel, since results are always computed in order when running sequentially. This is not necessarily true when executing in parallel, however. In fact, if the expressions take very different lengths of time to execute, the results could be returned in any order. Here's a contrived example, that executes the tasks in parallel to demonstrate the difference. The example uses the \texttt{Sys.sleep} function to cause the earlier tasks to take longer to execute: <>= foreach(i=4:1, .combine='c') %dopar% { Sys.sleep(3 * i) i } foreach(i=4:1, .combine='c', .inorder=FALSE) %dopar% { Sys.sleep(3 * i) i } @ The results of the first of these two examples is guaranteed to be the vector c(4, 3, 2, 1). The second example will return the same values, but they will probably be in a different order. \section{Iterators} The values for the iteration variables don't have to be specified with only vectors or lists. They can be specified with an {\em iterator}, many of which come with the \texttt{iterators} package. An iterator is an abstract source of data. A vector isn't itself an iterator, but the \texttt{foreach} function automatically creates an iterator from a vector, list, matrix, or data frame, for example. You can also create an iterator from a file or a data base query, which are natural sources of data. The \texttt{iterators} package supplies a function called \texttt{irnorm} which can return a specified number of random numbers for each time it is called. For example: <>= library(iterators) x <- foreach(a=irnorm(4, count=4), .combine='cbind') %do% a x @ This becomes useful when dealing with large amounts of data. Iterators allow the data to be generated on-the-fly, as it is needed by your operations, rather than requiring all of the data to be generated at the beginning. For example, let's say that we want to sum together a thousand random vectors: <>= set.seed(123) x <- foreach(a=irnorm(4, count=1000), .combine='+') %do% a x @ This uses very little memory, since it is equivalent to the following \texttt{while} loop: <>= set.seed(123) x <- numeric(4) i <- 0 while (i < 1000) { x <- x + rnorm(4) i <- i + 1 } x @ This could have been done using the \texttt{icount} function, which generates the values from one to 1000: <>= set.seed(123) x <- foreach(icount(1000), .combine='+') %do% rnorm(4) x @ but sometimes it's preferable to generate the actual data with the iterator (as we'll see later when we execute in parallel). In addition to introducing the \texttt{icount} function from the \texttt{iterators} package, the last example also used an unnamed argument to the \texttt{foreach} function. This can be useful when we're not intending to generate variable values, but only controlling the number of times that the R expression is executed. There's a lot more that I could say about iterators, but for now, let's move on to parallel execution. \section{Parallel Execution} Although \texttt{foreach} can be a useful construct in its own right, the real point of the \texttt{foreach} package is to do parallel computing. To make any of the previous examples run in parallel, all you have to do is to replace \texttt{\%do\%} with \texttt{\%dopar\%}. But for the kinds of quick running operations that we've been doing, there wouldn't be much point to executing them in parallel. Running many tiny tasks in parallel will usually take more time to execute than running them sequentially, and if it already runs fast, there's no motivation to make it run faster anyway. But if the operation that we're executing in parallel takes a minute or longer, there starts to be some motivation. \subsection{Parallel Random Forest} Let's take random forest as an example of an operation that can take a while to execute. Let's say our inputs are the matrix \texttt{x}, and the factor \texttt{y}: <>= x <- matrix(runif(500), 100) y <- gl(2, 50) @ We've already loaded the \texttt{foreach} package, but we'll also need to load the \texttt{randomForest} package: <>= library(randomForest) @ If we want want to create a random forest model with a 1000 trees, and our computer has four cores in it, we can split up the problem into four pieces by executing the \texttt{randomForest} function four times, with the \texttt{ntree} argument set to 250. Of course, we have to combine the resulting \texttt{randomForest} objects, but the \texttt{randomForest} package comes with a function called \texttt{combine} that does just that. Let's do that, but first, we'll do the work sequentially: <>= rf <- foreach(ntree=rep(250, 4), .combine=combine) %do% randomForest(x, y, ntree=ntree) rf @ To run this in parallel, we need to change \texttt{\%do\%}, but we also need to use another \texttt{foreach} option called \texttt{.packages} to tell the \texttt{foreach} package that the R expression needs to have the \texttt{randomForest} package loaded in order to execute successfully. Here's the parallel version: <>= rf <- foreach(ntree=rep(250, 4), .combine=combine, .packages='randomForest') %dopar% randomForest(x, y, ntree=ntree) rf @ If you've done any parallel computing, particularly on a cluster, you may wonder why I didn't have to do anything special to handle \texttt{x} and \texttt{y}. The reason is that the \texttt{\%dopar\%} function noticed that those variables were referenced, and that they were defined in the current environment. In that case \text{\%dopar\%} will automatically export them to the parallel execution workers once, and use them for all of the expression evaluations for that \texttt{foreach} execution. That is true for functions that are defined in the current environment as well, but in this case, the function is defined in a package, so we had to specify the package to load with the \texttt{.packages} option instead. \subsection{Parallel Apply} Now let's take a look at how to make a parallel version of the standard R \texttt{apply} function. The \texttt{apply} function is written in R, and although it's only about 100 lines of code, it's a bit difficult to understand on a first reading. However, it all really comes down two \texttt{for} loops, the slightly more complicated of which looks like: <>= applyKernel <- function(newX, FUN, d2, d.call, dn.call=NULL, ...) { ans <- vector("list", d2) for(i in 1:d2) { tmp <- FUN(array(newX[,i], d.call, dn.call), ...) if(!is.null(tmp)) ans[[i]] <- tmp } ans } applyKernel(matrix(1:16, 4), mean, 4, 4) @ I've turned this into a function, because otherwise, R will complain that I'm using ``...'' in an invalid context. This could be executed using \texttt{foreach} as follows: <>= applyKernel <- function(newX, FUN, d2, d.call, dn.call=NULL, ...) { foreach(i=1:d2) %dopar% FUN(array(newX[,i], d.call, dn.call), ...) } applyKernel(matrix(1:16, 4), mean, 4, 4) @ But this approach will cause the entire \texttt{newX} array to be sent to each of the parallel execution workers. Since each task needs only one column of the array, we'd like to avoid this extra data communication. One way to solve this problem is to use an iterator that iterates over the matrix by column: <>= applyKernel <- function(newX, FUN, d2, d.call, dn.call=NULL, ...) { foreach(x=iter(newX, by='col')) %dopar% FUN(array(x, d.call, dn.call), ...) } applyKernel(matrix(1:16, 4), mean, 4, 4) @ Now we're only sending any given column of the matrix to one parallel execution worker. But it would be even more efficient if we sent the matrix in bigger chunks. To do that, we use a function called \texttt{iblkcol} that returns an iterator that will return multiple columns of the original matrix. That means that the R expression will need to execute the user's function once for every column in its submatrix. <>= iblkcol <- function(a, chunks) { n <- ncol(a) i <- 1 nextElem <- function() { if (chunks <= 0 || n <= 0) stop('StopIteration') m <- ceiling(n / chunks) r <- seq(i, length=m) i <<- i + m n <<- n - m chunks <<- chunks - 1 a[,r, drop=FALSE] } structure(list(nextElem=nextElem), class=c('iblkcol', 'iter')) } nextElem.iblkcol <- function(obj) obj$nextElem() @ <>= applyKernel <- function(newX, FUN, d2, d.call, dn.call=NULL, ...) { foreach(x=iblkcol(newX, 3), .combine='c', .packages='foreach') %dopar% { foreach(i=1:ncol(x)) %do% FUN(array(x[,i], d.call, dn.call), ...) } } applyKernel(matrix(1:16, 4), mean, 4, 4) @ Note the use of the \texttt{\%do\%} inside the \texttt{\%dopar\%} to call the function on the columns of the submatrix \texttt{x}. Now that we're using \texttt{\%do\%} again, it makes sense for the iterator to be an index into the matrix \texttt{x}, since \texttt{\%do\%} doesn't need to copy \texttt{x} the way that \texttt{\%dopar\%} does. \section{List Comprehensions} If you're familar with the Python programming language, it may have occurred to you that the \texttt{foreach} package provides something that is not too different from Python's {\em list comprehensions}. In fact, the \texttt{foreach} package also includes a function called \texttt{when} which can prevent some of the evaluations from happening, very much like the ``if'' clause in Python's list comprehensions. For example, you could filter out negative values of an iterator using \texttt{when} as follows: <>= x <- foreach(a=irnorm(1, count=10), .combine='c') %:% when(a >= 0) %do% sqrt(a) x @ I won't say much on this topic, but I can't help showing how \texttt{foreach} with \texttt{when} can be used to write a simple quick sort function, in the classic Haskell fashion: <>= qsort <- function(x) { n <- length(x) if (n == 0) { x } else { p <- sample(n, 1) smaller <- foreach(y=x[-p], .combine=c) %:% when(y <= x[p]) %do% y larger <- foreach(y=x[-p], .combine=c) %:% when(y > x[p]) %do% y c(qsort(smaller), x[p], qsort(larger)) } } qsort(runif(12)) @ Not that I recommend this over the standard R \texttt{sort} function. But it's a pretty interesting example use of \texttt{foreach}. \section{Conclusion} Much of parallel computing comes to doing three things: splitting the problem into pieces, executing the pieces in parallel, and combining the results back together. Using the \texttt{foreach} package, the iterators help you to split the problem into pieces, the \texttt{\%dopar\%} function executes the pieces in parallel, and the specified \texttt{.combine} function puts the results back together. We've demonstrated how simple things can be done in parallel quite easily using the \texttt{foreach} package, and given some ideas about how more complex problems can be solved. But it's a fairly new package, and we will continue to work on ways of making it a more powerful system for doing parallel computing. \end{document} foreach/MD50000644000175100001440000000667612607127365012237 0ustar hornikusers6b046ed0adf6845065a91e10e391d317 *DESCRIPTION c32fc6487cdb79913750f7097de2075f *NAMESPACE c4b02ef0b5c0765c12c527e1ca1f70a9 *NEWS 47e33082104d2bc4bbf6903a8f5ee69a *R/acc.R 5027fbd0eb0875545deefc4e77df3085 *R/do.R a7eea424b2f51e3fdc52da6a58a0c278 *R/foreach.R e0b326499355bbf084ae160b01ea797f *R/getsyms.R ba7e6bee7fc596e6ee08feaa2ee44a62 *R/times.R 85b4a5712555f13aefa4d723ba78fcd9 *R/zzz.R b95c5f5e3b127affca4f431a96b59f71 *build/vignette.rds caa28a573b448cbd60f22052c2bca7a3 *demo/00Index 515798525e9e08e68a66ab24df3d17a0 *demo/sincSEQ.R 114454e06e155812fbd7de0001ef5dc2 *inst/doc/foreach.R f8ac22a80a28c04f29ae2a88686d07b7 *inst/doc/foreach.Rnw 172fc4f16cc9fc4ce6aa167f0a63ffb2 *inst/doc/foreach.pdf 509eb035fb2bee0b47433e2a00960b50 *inst/doc/nested.R b58f77e7ee3b70d93050f5bb19502b5a *inst/doc/nested.Rnw 6807760f25f55377e6c6192f2898aa86 *inst/doc/nested.pdf 3380c6bfe2789c1316d36c368d968bc1 *inst/examples/apply.R 500fc0fa2cb4b07b809e974bc99ba4b9 *inst/examples/bigmax.R 4549e5165479d323e9348b2d726de9da *inst/examples/bigmean.R 31be8c935f6fef084c3b9f69148d9d95 *inst/examples/bigmean2.R 20be562ced9134739ff69cbe09a20b7c *inst/examples/bootpar.R 53e2a90eb9cf9eb9f6a02f1658b87b7c *inst/examples/bootpar2.R 00f17395946b1090a308985cbca56515 *inst/examples/bootseq.R e0be352dcb9674bd2e97f522ae9a4bf6 *inst/examples/colMeans.R 40c1d5c69df84a9d96c21091999335bb *inst/examples/comprehensions.R 684705c4a63eeadc8cc37f25a2606269 *inst/examples/cross.R 4fb83e90b01ab5da4e25805cce489393 *inst/examples/feapply.R f99bbd4ecd00c5558e4c130c6b453161 *inst/examples/for.R dd6d1bd8f4bcc4555b900b9c00955c33 *inst/examples/germandata.txt b0684796a1576e974134f62b8fc5e6fc *inst/examples/isplit.R f0e946d73e7dea4c65dcd1d2754b7ad5 *inst/examples/matmul.R abf01cf248cf054f26c0a29ced984e8a *inst/examples/matmul2.R cadff38eb4c9fc2fa385d600e42046b3 *inst/examples/output.R acd652ebc9903fb4a039b09f3ac102a9 *inst/examples/pi.R 62900b80c46aa3fa40b69edef2e51291 *inst/examples/qsort.R 81fecace2a92963972db2adb2fc9fed7 *inst/examples/rf.R db5fcdbddb502aab39e82d17a10b0c1a *inst/examples/sinc.R 23d155b4116b87e6304a954d630371d3 *inst/examples/sinc2.R 4aaa9d9782f8d0b331e3dcc021bd9c14 *inst/examples/sqlite.R 80d22f8c75b2d99335d8f09c9c22dc34 *inst/examples/tuneRF.R 3d981a90b7471c26347ec0593ca55167 *inst/unitTests/combineTest.R bdc6faf27f9438670191fb0e9571bd01 *inst/unitTests/errorTest.R ff12ff3dd5c50845c81e15178de13b36 *inst/unitTests/foreachTest.R 1f4ee6f110624a1678ba8976ba7aec1d *inst/unitTests/iteratorTest.R c14fa871ff9d9fc719fdce467f0d717d *inst/unitTests/loadFactorTest.R 9321ccd8b46047e704893cab4c5c6795 *inst/unitTests/mergeTest.R e8cdce27b2b33bd51fcb021f9034d276 *inst/unitTests/nestedTest.R ddcfe0035e22f2d6c2f9e454b439cffa *inst/unitTests/packagesTest.R d96e58771409bee69f000df068219695 *inst/unitTests/runTestSuite.sh fb0831e84e6f8d7b062bc7d2c3cbd892 *inst/unitTests/stressTest.R 802149beb60028bba88d9206d6056118 *inst/unitTests/whenTest.R 3f8eb3a21cfa4ca1720d247a89dd671b *man/foreach-ext.Rd 6d93867afe82453e171d5dbea2814347 *man/foreach-package.Rd bd6f8b6d151c6bfb91f493c8d9d3beb2 *man/foreach.Rd ccb36ea1d3b7a8df1b3507663fc2a8b8 *man/getDoParWorkers.Rd 649d347d1fc6c8bfcce655b70e7ff43d *man/getDoSeqWorkers.Rd 807d977bb241d21ac67742178d24c4e0 *man/registerDoSEQ.Rd a2b2665ab547a5ceb315dd36a5dcdffa *man/setDoPar.Rd 6c3efff477172db6a6b8ac789c125f74 *man/setDoSeq.Rd e084981416d15aacdc13e2d10fef54ce *tests/doRUnit.R f8ac22a80a28c04f29ae2a88686d07b7 *vignettes/foreach.Rnw b58f77e7ee3b70d93050f5bb19502b5a *vignettes/nested.Rnw foreach/build/0000755000175100001440000000000012606615125013002 5ustar hornikusersforeach/build/vignette.rds0000644000175100001440000000034512606615125015343 0ustar hornikusers‹uŽM‚0…KAŒÆÄ¸çœ‚°ñ'ƸpÛHQlI‹!î<98âÅ„E§3ß¼—y'ŸB‰M)¡6´öŠ oÑr‡xðOS©8;_èù‚ë’'-é‹ç(¶LÜYŽtµ}&.AŒÛ”…ºS$éß7鋽.‚‰‰ý²~ îÇhÆ¿.òýú# vã&¸‹Ð‰³œÁ1+»ÁÞG1¶–É9ŽxÁE¢Mì5TRÁÜ?ä)Y…æØìì ¥išú?Ñ9gÚ$2ÐOXÉÂT¦úãô Ÿçforeach/DESCRIPTION0000644000175100001440000000252412607127365013421 0ustar hornikusersPackage: foreach Type: Package Title: Provides Foreach Looping Construct for R Version: 1.4.3 Authors@R: c(person("Rich", "Calaway", role="cre", email="richcala@microsoft.com"), person("Revolution", "Analytics", role=c("aut", "cph")), person("Steve", "Weston", role="aut")) Description: Support for the foreach looping construct. Foreach is an idiom that allows for iterating over elements in a collection, without the use of an explicit loop counter. This package in particular is intended to be used for its return value, rather than for its side effects. In that sense, it is similar to the standard lapply function, but doesn't require the evaluation of a function. Using foreach without side effects also facilitates executing the loop in parallel. Depends: R (>= 2.5.0) Imports: codetools, utils, iterators Suggests: randomForest Enhances: compiler, doMC, RUnit, doParallel License: Apache License (== 2.0) Author: Rich Calaway [cre], Revolution Analytics [aut, cph], Steve Weston [aut] Maintainer: Rich Calaway Repository: CRAN Repository/R-Forge/Project: foreach Repository/R-Forge/Revision: 27 Repository/R-Forge/DateTimeStamp: 2015-10-12 01:37:31 Date/Publication: 2015-10-13 09:12:53 NeedsCompilation: no Packaged: 2015-10-12 02:26:29 UTC; rforge foreach/man/0000755000175100001440000000000012606614324012456 5ustar hornikusersforeach/man/registerDoSEQ.Rd0000644000175100001440000000101111472542406015417 0ustar hornikusers\name{registerDoSEQ} \alias{registerDoSEQ} \title{registerDoSEQ} \description{ The \code{registerDoSEQ} function is used to explicitly register a sequential parallel backend with the foreach package. This will prevent a warning message from being issued if the \code{\%dopar\%} function is called and no parallel backend has been registered. } \usage{ registerDoSEQ() } \seealso{ \code{\link[doSNOW]{registerDoSNOW}} } \examples{ # specify that \%dopar\% should run sequentially registerDoSEQ() } \keyword{utilities} foreach/man/setDoSeq.Rd0000644000175100001440000000131311716316520014470 0ustar hornikusers\name{setDoSeq} \alias{setDoSeq} \title{setDoSeq} \description{ The \code{setDoSeq} function is used to register a sequential backend with the foreach package. This isn't normally executed by the user. Instead, packages that provide a sequential backend provide a function named \code{registerDoSeq} that calls \code{setDoSeq} using the appropriate arguments. } \usage{ setDoSeq(fun, data=NULL, info=function(data, item) NULL) } \arguments{ \item{fun}{A function that implements the functionality of \code{\%dopar\%}.} \item{data}{Data to be passed to the registered function.} \item{info}{Function that retrieves information about the backend.} } \seealso{ \code{\link{\%dopar\%}} } \keyword{utilities} foreach/man/getDoParWorkers.Rd0000644000175100001440000000237511472542406016037 0ustar hornikusers\name{getDoParWorkers} \alias{getDoParWorkers} \alias{getDoParRegistered} \alias{getDoParName} \alias{getDoParVersion} \title{Functions Providing Information on the doPar Backend} \description{ The \code{getDoParWorkers} function returns the number of execution workers there are in the currently registered doPar backend. It can be useful when determining how to split up the work to be executed in parallel. A \code{1} is returned by default. The \code{getDoParRegistered} function returns TRUE if a doPar backend has been registered, otherwise FALSE. The \code{getDoParName} function returns the name of the currently registered doPar backend. A \code{NULL} is returned if no backend is registered. The \code{getDoParVersion} function returns the version of the currently registered doPar backend. A \code{NULL} is returned if no backend is registered. } \usage{ getDoParWorkers() getDoParRegistered() getDoParName() getDoParVersion() } \examples{ cat(sprintf('\%s backend is registered\n', if(getDoParRegistered()) 'A' else 'No')) cat(sprintf('Running with \%d worker(s)\n', getDoParWorkers())) (name <- getDoParName()) (ver <- getDoParVersion()) if (getDoParRegistered()) cat(sprintf('Currently using \%s [\%s]\n', name, ver)) } \keyword{utilities} foreach/man/foreach-ext.Rd0000644000175100001440000000231711472542406015156 0ustar hornikusers\name{foreach-ext} \alias{foreach-ext} \alias{makeAccum} \alias{accumulate} \alias{getexports} \alias{getResult} \alias{getErrorValue} \alias{getErrorIndex} \title{Foreach Extension Functions} \description{ These functions are used to write parallel backends for the \code{foreach} package. They should not be used from normal scripts or packages that use the \code{foreach} package. } \usage{ makeAccum(it) accumulate(obj, result, tag, ...) getexports(ex, e, env, good=character(0), bad=character(0)) getResult(obj, \dots) getErrorValue(obj, \dots) getErrorIndex(obj, \dots) } \arguments{ \item{it}{foreach iterator.} \item{ex}{call object to analyze.} \item{e}{local environment of the call object.} \item{env}{exported environment in which call object will be evaluated.} \item{good}{names of symbols that are being exported.} \item{bad}{names of symbols that are not being exported.} \item{obj}{foreach iterator object.} \item{result}{task result to accumulate.} \item{tag}{tag of task result to accumulate.} \item{\dots}{unused.} } \note{ These functions are likely to change in future versions of the \code{foreach} package. When they become more stable, they will be documented. } \keyword{utilities} foreach/man/foreach-package.Rd0000644000175100001440000000264211472542406015752 0ustar hornikusers\name{foreach-package} \alias{foreach-package} \docType{package} \title{ The Foreach Package } \description{ The foreach package provides a new looping construct for executing R code repeatedly. The main reason for using the foreach package is that it supports parallel execution. The foreach package can be used with a variety of different parallel computing systems, include NetWorkSpaces and snow. In addition, foreach can be used with iterators, which allows the data to specified in a very flexible way. } \details{ Further information is available in the following help topics: \tabular{ll}{ \code{foreach} \tab Specify the variables to iterate over\cr \code{\%do\%} \tab Execute the R expression sequentially\cr \code{\%dopar\%} \tab Execute the R expression using the currently registered backend\cr } To see a tutorial introduction to the foreach package, use \code{vignette("foreach")}. To see a demo of foreach computing the sinc function, use \code{demo(sincSEQ)}. Some examples (in addition to those in the help pages) are included in the ``examples'' directory of the foreach package. To list the files in the examples directory, use \code{list.files(system.file("examples", package="foreach"))}. To run the bootstrap example, use \code{source(system.file("examples", "bootseq.R", package="foreach"))}. For a complete list of functions with individual help pages, use \code{library(help="foreach")}. } \keyword{package} foreach/man/setDoPar.Rd0000644000175100001440000000130411472542406014465 0ustar hornikusers\name{setDoPar} \alias{setDoPar} \title{setDoPar} \description{ The \code{setDoPar} function is used to register a parallel backend with the foreach package. This isn't normally executed by the user. Instead, packages that provide a parallel backend provide a function named \code{registerDoPar} that calls \code{setDoPar} using the appropriate arguments. } \usage{ setDoPar(fun, data=NULL, info=function(data, item) NULL) } \arguments{ \item{fun}{A function that implements the functionality of \code{\%dopar\%}.} \item{data}{Data to passed to the registered function.} \item{info}{Function that retrieves information about the backend.} } \seealso{ \code{\link{\%dopar\%}} } \keyword{utilities} foreach/man/foreach.Rd0000644000175100001440000001671611741344141014363 0ustar hornikusers\name{foreach} \alias{foreach} \alias{when} \alias{times} \alias{\%:\%} \alias{\%do\%} \alias{\%dopar\%} \title{foreach} \description{ \code{\%do\%} and \code{\%dopar\%} are binary operators that operate on a \code{foreach} object and an \code{R} expression. The expression, \code{ex}, is evaluated multiple times in an environment that is created by the \code{foreach} object, and that environment is modified for each evaluation as specified by the \code{foreach} object. \code{\%do\%} evaluates the expression sequentially, while \code{\%dopar\%} evalutes it in parallel. The results of evaluating \code{ex} are returned as a list by default, but this can be modified by means of the \code{.combine} argument. } \usage{ foreach(..., .combine, .init, .final=NULL, .inorder=TRUE, .multicombine=FALSE, .maxcombine=if (.multicombine) 100 else 2, .errorhandling=c('stop', 'remove', 'pass'), .packages=NULL, .export=NULL, .noexport=NULL, .verbose=FALSE) when(cond) e1 \%:\% e2 obj \%do\% ex obj \%dopar\% ex times(n) } \arguments{ \item{\dots}{one or more arguments that control how \code{ex} is evaluated. Named arguments specify the name and values of variables to be defined in the evaluation environment. An unnamed argument can be used to specify the number of times that \code{ex} should be evaluated. At least one argument must be specified in order to define the number of times \code{ex} should be executed.} \item{.combine}{function that is used to process the tasks results as they generated. This can be specified as either a function or a non-empty character string naming the function. Specifying 'c' is useful for concatenating the results into a vector, for example. The values 'cbind' and 'rbind' can combine vectors into a matrix. The values '+' and '*' can be used to process numeric data. By default, the results are returned in a list.} \item{.init}{initial value to pass as the first argument of the \code{.combine} function. This should not be specified unless \code{.combine} is also specified.} \item{.final}{function of one argument that is called to return final result.} \item{.inorder}{logical flag indicating whether the \code{.combine} function requires the task results to be combined in the same order that they were submitted. If the order is not important, then it setting \code{.inorder} to \code{FALSE} can give improved performance. The default value is \code{TRUE}.} \item{.multicombine}{logical flag indicating whether the \code{.combine} function can accept more than two arguments. If an arbitrary \code{.combine} function is specified, by default, that function will always be called with two arguments. If it can take more than two arguments, then setting \code{.multicombine} to \code{TRUE} could improve the performance. The default value is \code{FALSE} unless the \code{.combine} function is \code{cbind}, \code{rbind}, or \code{c}, which are known to take more than two arguments.} \item{.maxcombine}{maximum number of arguments to pass to the combine function. This is only relevant if \code{.multicombine} is \code{TRUE}.} \item{.errorhandling}{specifies how a task evalution error should be handled. If the value is "stop", then execution will be stopped via the \code{stop} function if an error occurs. If the value is "remove", the result for that task will not be returned, or passed to the \code{.combine} function. If it is "pass", then the error object generated by task evaluation will be included with the rest of the results. It is assumed that the combine function (if specified) will be able to deal with the error object. The default value is "stop".} \item{.packages}{character vector of packages that the tasks depend on. If \code{ex} requires a \code{R} package to be loaded, this option can be used to load that package on each of the workers. Ignored when used with \code{\%do\%}.} \item{.export}{character vector of variables to export. This can be useful when accessing a variable that isn't defined in the current environment. The default value in \code{NULL}.} \item{.noexport}{character vector of variables to exclude from exporting. This can be useful to prevent variables from being exported that aren't actually needed, perhaps because the symbol is used in a model formula. The default value in \code{NULL}.} \item{.verbose}{logical flag enabling verbose messages. This can be very useful for trouble shooting.} \item{obj}{\code{foreach} object used to control the evaluation of \code{ex}.} \item{e1}{\code{foreach} object to merge.} \item{e2}{\code{foreach} object to merge.} \item{ex}{the \code{R} expression to evaluate.} \item{cond}{condition to evaluate.} \item{n}{number of times to evaluate the \code{R} expression.} } \details{ The \code{foreach} and \code{\%do\%}/\code{\%dopar\%} operators provide a looping construct that can be viewed as a hybrid of the standard \code{for} loop and \code{lapply} function. It looks similar to the \code{for} loop, and it evaluates an expression, rather than a function (as in \code{lapply}), but it's purpose is to return a value (a list, by default), rather than to cause side-effects. This faciliates parallelization, but looks more natural to people that prefer \code{for} loops to \code{lapply}. The \code{\%:\%} operator is the \emph{nesting} operator, used for creating nested foreach loops. Type \code{vignette("nested")} at the R prompt for more details. Parallel computation depends upon a \emph{parallel backend} that must be registered before performing the computation. The parallel backends available will be system-specific, but include \code{doParallel}, which uses R's built-in \pkg{parallel} package, \pkg{doMC}, which uses the \pkg{multicore} package, and \pkg{doSNOW}. Each parallel backend has a specific registration function, such as \code{registerDoParallel} or \code{registerDoSNOW}. The \code{times} function is a simple convenience function that calls \code{foreach}. It is useful for evaluating an \code{R} expression multiple times when there are no varying arguments. This can be convenient for resampling, for example. } \seealso{ \code{\link[iterators]{iter}} } \examples{ # equivalent to rnorm(3) times(3) \%do\% rnorm(1) # equivalent to lapply(1:3, sqrt) foreach(i=1:3) \%do\% sqrt(i) # equivalent to colMeans(m) m <- matrix(rnorm(9), 3, 3) foreach(i=1:ncol(m), .combine=c) \%do\% mean(m[,i]) # normalize the rows of a matrix in parallel, with parenthesis used to # force proper operator precedence # Need to register a parallel backend before this example will run # in parallel foreach(i=1:nrow(m), .combine=rbind) \%dopar\% (m[i,] / mean(m[i,])) # simple (and inefficient) parallel matrix multiply library(iterators) a <- matrix(1:16, 4, 4) b <- t(a) foreach(b=iter(b, by='col'), .combine=cbind) \%dopar\% (a \%*\% b) # split a data frame by row, and put them back together again without # changing anything d <- data.frame(x=1:10, y=rnorm(10)) s <- foreach(d=iter(d, by='row'), .combine=rbind) \%dopar\% d identical(s, d) # a quick sort function qsort <- function(x) { n <- length(x) if (n == 0) { x } else { p <- sample(n, 1) smaller <- foreach(y=x[-p], .combine=c) \%:\% when(y <= x[p]) \%do\% y larger <- foreach(y=x[-p], .combine=c) \%:\% when(y > x[p]) \%do\% y c(qsort(smaller), x[p], qsort(larger)) } } qsort(runif(12)) } \keyword{utilities} foreach/man/getDoSeqWorkers.Rd0000644000175100001440000000224511716316520016036 0ustar hornikusers\name{getDoSeqWorkers} \alias{getDoSeqWorkers} \alias{getDoSeqRegistered} \alias{getDoSeqName} \alias{getDoSeqVersion} \title{Functions Providing Information on the doSeq Backend} \description{ The \code{getDoSeqWorkers} function returns the number of execution workers there are in the currently registered doSeq backend. A \code{1} is returned by default. The \code{getDoSeqRegistered} function returns TRUE if a doSeq backend has been registered, otherwise FALSE. The \code{getDoSeqName} function returns the name of the currently registered doSeq backend. A \code{NULL} is returned if no backend is registered. The \code{getDoSeqVersion} function returns the version of the currently registered doSeq backend. A \code{NULL} is returned if no backend is registered. } \usage{ getDoSeqWorkers() getDoSeqRegistered() getDoSeqName() getDoSeqVersion() } \examples{ cat(sprintf('\%s backend is registered\n', if(getDoSeqRegistered()) 'A' else 'No')) cat(sprintf('Running with \%d worker(s)\n', getDoSeqWorkers())) (name <- getDoSeqName()) (ver <- getDoSeqVersion()) if (getDoSeqRegistered()) cat(sprintf('Currently using \%s [\%s]\n', name, ver)) } \keyword{utilities}