{ "cells": [ { "attachments": { "image-2.png": { "image/png": "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" }, "image.png": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzgAAAGyCAYAAAAoOGEVAAAgAElEQVR4Aeydh5sURfe2v//kfYUl5yA5So6i5KQoSDQQDOQgOStJRUGiIiCgIBkFARFBQIKISFaSApIkc77rKX897+yyu2yYme2eueu6YGd6uivc1T1TT9Wpc/6fkSAAAQhAAAIQgAAEIAABCMQJgf8XJ+2gGRCAAAQgAAEIQAACEIAABAyBw00AAQhAAAIQgAAEIAABCMQNAQRO3HQlDYEABCAAAQhAAAIQgAAEEDjcAxCAAAQgAAEIQAACEIBA3BBA4MRNV9IQCEAAAhCAAAQgAAEIQACBwz0AAQhAAAIQgAAEIAABCMQNAQRO3HQlDYEABCAAAQhAAAIQgAAEEDjcAxCAAAQgAAEIQAACEIBA3BBA4MRNV9IQCEAAAhCAAAQgAAEIQACBwz0AAQhAAAIQgAAEIAABCMQNgQwJnMGDB1vlypWtSpUq/IMB9wD3APcA9wD3APcA9wD3APcA90CO3gPNmjWzhw8fpirKMiRwGjVqZBMmTLBdu3bxDwbcA9wD3APcA9wD3APcA9wD3APcAzl6D/znP/+xM2fOZF3gtG7d2lasWJFqBhyEAAQgAAEIQAACEIAABCAQSwJ58+ZF4MQSOGVBAAIQgAAEIAABCEAAAtEjgMCJHltyhgAEIAABCEAAAhCAAARiTACBE2PgFAcBCEAAAhCAAAQgAAEIRI8AAid6bMkZAhCAAAQgAAEIQAACEIgxAQROjIFTHAQgAAEIQAACEIAABCAQPQIInOixJWcIQAACEIAABCAAAQhAIMYEEDgxBk5xEIAABCAAAQhAAAIQgED0CCBwoseWnCEAAQhAAAIQgAAEIACBGBNA4MQYOMVBAAIZI3D37l27fv26/fnnn/b777/bsWPH7NChQ7Znzx77fscO2/zNN7Zxw0bb+u1W+2HnD7b/p/125JcjdvLESTt39pxduXzZbt68affv389YgZwFAQhAAAIQgEBcEEDgxEU30ggIBIuAxMvp06dt586dtnzZcpsxfboN7D/AOr7wotWvU9eKFCpkuf7z34j8y/3fJ6xCuXLW7Nmm9urLr9joUaNs7py5tmH9ejt44IBdvnQ5WPCoLQQgAAEIQAAC6RJA4KSLhw8hAIHsEvj7779t+7Zt9v6M9+zl7t2tZvWnLE+u3GmKlxJFi7lznm7U2Fo1b2EdnnveunXpar179rQB/fvbiLeH28TxE2z6tGk28/0PbMo779rYMWNs6OAh9tYbbzoR07lTJ2vftp01feZZa1C3nlWuUNHy58mbZpnFChdxAmjIoMG2ZPFi++XwL3b/His/2e17rocABCAAAQjkBAEETk5Qp0wIxCmBhw8f2vHjx51I6PdWX6tdo6ZpBSV8NUarMxIdXV7q7MTK3I/n2KaNm+zXI0ecSVm00KhuFy9etD0//mhfrFhh06ZOtb5vvmVtW7exqpUqPyK6Chco6D6bNHGibdm82a5duxatqpEvBCAAAQhAAAIRJIDAiSBMsoJAIhK4dOmSffnFF/Z67z5WvkzZR8RM6xYtbdSIkbbyyy/d/hgJDT+mf/75x37cvdvmzP7Y+vTq5cRZ0hO5Qu3RqtOzTzcxCZ7du3axwuPHTqROEIAABCAAATND4HAbQAACmSYgUbNwwQJr07KVhYuAMqVKW49u3UyrMnII8ODBg0zn7acLrl69at98/bUzgdMennxJeUKCp1TxEta/b1/7bvt2HBn4qdOoCwQgAAEIJDwBBE7C3wIAgEDGCci867VXXrW8SUluoC9x07TJM87cS4LGr6szGW9h+mfevHHT1q9b50zbyj1ZJiR2KpWvYO/NmOE8t6WfA59CAAIQgAAEIBBtAgicaBMmfwgEnIBWYTSo14Z9by9N9arV3CZ/uWNO1CQuWr3p9VpPK5gvv2NTKH8B5+xAHuJIEIAABCAAAQjkDAEETs5wp1QI+J6AVmPWrlljdWvXCa3WdHqxo3377bdxv1KT2c65cuWK8+hWpWIlxypv7iS3yqP4PSQIQAACEIAABGJLAIETW96UBoFAENi3b19oxUbmaG/0ed1+++23QNQ9JyupoKLy0Favzr+iUK6p5cL6xo0bOVktyoYABCAAAQgkFAEETkJ1N42FQPoEbt68acOGDHGOA+TeWYExT508mf5FfPoIAa1+yWtctSpV3YqOAo3KWQEJAhCAAAQgAIHoE0DgRJ8xJUAgEASO/HLEBdjUPpv6deo6l8mBqLiPK3n37l2bNmVqaI/OsKFDTcdIEIAABCAAAQhEjwACJ3psyRkCgSGwfds2UwBOeUUbM3q03bt3LzB1D0JFj/76qxONEo/t27bDZC0InUYdIQABCEAgsAQQOIHtOioOgcgQkLgpkDefW2VYs3p1ZDIll0cIKJBol5c6O5O1ls2bm96TIAABCEAAAhCIPAEETuSZkiMEAkPg1yNHrGihwk7cSOhEIinIZ+0aNV2ecpvcoG49q1iuvGlQr+CgiZzkhKDnq685kdOtS9fAB0JN5L6k7RCAAAQg4F8CCBz/9g01g0BUCdy6dcsJEZmlrVu7NqJlffThh24Q37VzF5evvIg93aixO7Z0yZIslSVxkDKldizlOX57f//efXu+fXvHQpxIEIAABCAAAQhElgACJ7I8yQ0CgSEwccIEN8gePXJkxOss72vab7L4s89CeXui55UeL4eOZfSFRNE7kycnOz21Y8lO8PGbK5cvW/kyZd0q1x9//OHjmlI1CEAAAhCAQPAIIHCC12fUGALZJvD3339b4YKFTO6LtZITyfTgwQMrVbyEEzjnz58PZT108GB3bPDAQaFj3gtdc+bMGRdA9PKly/bnn3+6j7Ta8fGs2c75wXfbt6d5zMvH+6v2Xbt2zXvr/spxwvHjx91rtfnixYvutco+eeJksuClCtAZbUcLK5YvdzwG9h+QrJ68gQAEIAABCEAgewQQONnjx9UQCCSBTxd+4gbXH7z3fsTr/9O+n1ze9WrXcXlLpKxaucoU9LJm9adMAsZLEhcKhNmoQUOTAHqm8dNOzMx8/wO3P6XpM8+6vGRGp/yWfb4sFIDUO7Z82XIvO2dq17xpM+vTu7crb8d335nEjgKV5kvKY7Vr1rI9e/ZYyWLFLU/u3DZh/HgrUbSYK0N1VLp69aopBlCfXr1C+UbjhczrKpWv4PZA3blzJxpFkCcEIAABCEAgIQkgcBKy22l0ohPwTMi8FY1I8pj67hQnGCQcJEokLGSuJpM47cXxkgb12mhf66kaoeNvvfGmO/fnn392pylgZtnST1rnjp28y9xKS8pj+nDcmLFOrMhxws0bNy1v7iR7b8aMUD4SWL179rR2bdra+LHjrHXLVqY6HD161NUxvAyZ7XV84cVQmdF6MWTQv6taP+7eHa0iyBcCEIAABCCQcAQQOAnX5TQYAmbPPt3E8iYlJTPLihQXraBI0GxYv95k6rXyyy+teJGiVqRgITt06FComNGjRjlhIYHhpZc6dnSCRsJGSUJHec2fN887JdVjy5Z+7s7TCo9MyyRkypct58oPz6dUiZJ26ODBUF7eCwktucr2XDer7lpFinZSu9S+L1asiHZR5A8BCEAAAhBIGAIInITpahoKgf8RkElY4QIF/3cgQq+070WmX3IPHW52NeWdd91AXkJCSSIjT67c9ubrb4RKvn79uhUrXCSZaZhM6CQATp08GTov5TGZvEk8ad+PVo+qValq3bt2c/tqvIskVpRP504veYeS/ZXDAn2++Ztv3PEe3bonM6VLdnIE33y2aJErN6ue5SJYFbKCAAQgAAEIxA0BBE7cdCUNgUDGCXR47nk3sJY3r0imtWvWuHxf7PBCsmznzpnrjjesV98dH/H2cPd+/0/7Q+fJnEwiI3xPTZtWrZ1gCZ1kZimPKTiprtOq1OdLl5qEUsrUtnUbt7cnLY9lOq48Jk2caNpD9PawYSmziMp7eYYLF1ZRKYRMIQABCEAAAglGAIGTYB1OcyEgAhrIa2C9+qvVEQXSv29fl++c2R8ny1eCR+WNGvGvS+pmzzZ1guPu3bvuPJmp6XP9kwe1Cxcu2M2bN50Jm7yunTh+wpmPpXbM2/OzZcuWZGVeuXLFvfeuea5du2Sfp3yjlR+t8MhMTo4JYpEU/FQODf76669YFEcZEIAABCAAgYQggMBJiG6mkRBITuCXw784MaEN95FKWjnRHheJlE0bN7lsJWAmjv833k7tGjXt9u3b7vhrr7zqztMKzt69e92G/8YNG1nlChVt1kcf2cIFC1weykvxc17v3cfkjU35pjy2e9cud0zxdZS/VqXGjB7t8lFhX2/695olixen21R5TVPeWm2KRTryyxEnbrQiRYIABCAAAQhAIHIEEDiRY0lOEAgUgRee7+AG9J4YyU7ltUdGqzISCPqnvTRPVa1m5Z4sYzJLe3fyO8lWRXbu3BkSQ02bPONWaLR6Io9r3uZ+OQxQXrreW41J7Zjq3eu1nm7vj/YVSUjJSYCXZA6n/T6PM8ebMX26c1ctIRWL9Hz79q593r6fWJRJGRCAAAQgAIFEIIDASYRepo0QSIXAb7/95jyHyeVyeEDOVE6NyiEF25TpmecxTa6dw03DdFyfh6fUjnmfy8zr3Nlz3ttM/VW+Hdo/51xGZ+rCLJ48e9YsJ25kDkeCAAQgAAEIQCCyBBA4keVJbhAIFIEF8+e7gXaj+g1cgMtAVT6ClZ08aZIt/uyzCOaYdlbr1q51MXrkxlr7jUgQgAAEIAABCESWAAInsjzJDQKBIzBs6FAncpo0bhwT18h+ASRHCCWLFbeunbs499KxqJc8vskMr2ihwnbwwIFYFEkZEIAABCAAgYQjgMBJuC6nwRBITuDBgwfmeT+rVrlKqoEwk18RH+9+3L3bFO9GoiPaSYzl7S3piVxO3Oz6YVe0iyR/CEAAAhCAQMISQOAkbNfTcAj8j4D2oHgD8Px58tr0adOc17L/ncGrrBI4eeKktW7R0q2SValYyQ4fPpzVrLgOAhCAAAQgAIEMEEDgZAASp0AgUQhs27rNKpQr5wbjdWrVti2bNydK0yPezhs3btj4seOcIwd5g5Mba88bXMQLI0MIQAACEIAABEIEEDghFLyAAARE4Nq1a6Z9OXlzJzmh06ZlK/t+xw7gZJCAvMG9P+M9K/1/MYG0arNhw4YMXs1pEIAABCAAAQhklwACJ7sEuR4CcUrg6K+/WpdOLzmRoxUIxav5atVXmK6l0d9yta0VGzkuEC/9VUwfucMmQQACEIAABCAQOwIInNixpiQIBJLA/p/2O09j2iCvgXul8hVs2pSpduHChUC2J5KV1t6lHd9958zP8ib9u+KllRsFDdVKGAkCEIAABCAAgdgTQODEnjklQiCQBI4dO2aDBgy0gvnyO6GTJ1dua9u6jc2bOzdHAoXmFER5RJMXtBFvD7fKFSqGVrhq16hpny78hBWbnOoYyoUABCAAAQj8HwEEDrcCBCCQYQIKTFm/Tl03qC/xf6ZYWtXJ/d8n3PHhw962jRs22vXr1zOcZxBOlLhTUNQe3bpZqf/bW6N2Fy5YyN7o87p9//33ptUcEgQgAAEIQAACOU8AgZPzfUANIBAIAqdOnrRqVao6caO4Offv37fTp0/b3I/nWIf2z5ncS2vQr39a3ZEQ0nmfLVpkR345Ylr5CEKSONu+bZtzld25UycrU6p0qF1qW/my5WxA//62aeMm++eff4LQJOoIAQhAAAIQSCgCCJyE6m4aC4GsETh08KCVLf2kG+hPnDAh1Uw02JcwmDxpkov7UiBvvmTCQKsdzZs2syGDBjtRJBfUp0+dckIp1QyjfFBunA8eOGArv/zSxQCSG+ca1aq7YJyeUNNfuc3u8lJnkzc0vW9Yr76rd5SrR/YQgAAEIAABCGSRAAIni+C4DAKJQkCb6IsWKuwG/nPnzM1ws+/evWs/7fvJ5s+b58y4ates9Yh4kGDQ5vyK5crbs083sW5dutrQwUPc6olWhrT68+UXX9iG9ett67db7cfdu+3QoUN2/PhxO3f2nMlk7syZMyaPbypLpmKbv/nG1qxebcs+X2YLFyywjz780CaOn2Bvvv6GW2mqW7tOyNNZuJDRa7WzTavWNnbMGJfHH3/8EWrvnTt3bEC/fk7klChazL7etCn0GS8gAAEIQAACEPAPAQSOf/qCmkDAdwRWf7XamZ7J/GzVylXZrp9ixGg1SPl+8N77TjC0a9PWqlaq7MzaUgqOaLwvUqiQW4WRmBo9apQTQRJPWk3KiBndksWLnaMFeZXTalVGrsk2ODKAAAQgAAEIQCDDBBA4GUbFiRBILALaVK+9NBIEMj2Ldrp/777zxnZg/36T+draNWvcKswnCxfarI8+cq6pFWfm7WHDrN9bfa3nq6/Zy927W5/evZ13t1EjRtqkiRNdkE1v9UfmZwqyqVWoo0eP2tWrVyPSDJm2SZRJgHV47nm7cuVKRPIlEwhAAAIQgAAEsk8AgZN9huQAgbgj8M7kyW7w/mTJUibBQXqUgETNC893cJy0P0eihwQBCEAAAhCAQM4TQODkfB9QAwj4hoA8ow3sP8AN2qtVrmInT5z0Td38WBGZp0kMylxNThUWf/aZH6tJnSAAAQhAAAIJRQCBk1DdTWMhkDYBbaLv3rWbEzfyFHbhwoW0T+aTZAS++fprk+MBmazJNbZYkiAAAQhAAAIQyBkCCJyc4U6pEPAVgWvXrjnvYRqgt2nZyvSelDkCclLQqH4DJ3KaNG5s4R7YMpcTZ0MAAhCAAAQgkB0CCJzs0ONaCMQBgYsXLzqvYhI38ix2+/btOGhVzjTh1q1bziW2WJYqUdK5ts6ZmlAqBCAAAQhAIHEJIHASt+9pOQTcHhvttdGAfED//jkWdDPeukLxd+RaW17opk2dag8fPoy3JtIeCEAAAhCAgG8JIHB82zVUDALRJSCvX2VKlXbiRvFcSJElsG/fPhfAVOKxc6dOEXNRHdlakhsEIAABCEAg/gggcOKvT2kRBB5L4Lvt261oocLO+9f8efMeez4nZI3ApUuXTIFMJXKqV61mhw8fzlpGXAUBCEAAAhCAQIYJIHAyjIoTIRAfBL5a9ZUzn5IJlV6ToktArrfHjRlruf/7hBXKX8CWL1se3QLJHQIQgAAEIJDgBBA4CX4D0PzEIjBv7ly3aqPVG63ikGJHYP26dVascBG3mjN08BC7e/du7AqnJAhAAAIQgEACEUDgJFBn09TEJjBp4kQ3uNa+m0MHDyY2jBxq/fHjx61u7TquH5o+86ydP3cuh2pCsRCAAAQgAIH4JYDAid++pWUQcARkIjWgXz83qK5WpaqdOnkSMjlI4ObNm/baK6+GxOb3O3bkYG0oGgIQgAAEIBB/BBA48dentAgCIQKKaaPYNtrkriCUinlD8geBObM/trxJSZY3d5LN/GAmrqT90S3UAgIQgAAE4oAAAicOOpEmQCA1AteuXbPWLVo6cdOmVWu7fv16aqdxLAcJ7N61y8qXKev6qEe3bnbjxo0crA1FQwACEIAABOKDAAInPvqRVkAgGYELFy5Yg7r1QgPnO3fuJPucN/4hoFW1Vs1buL6q9VQNO/rrr/6pHDWBAAQgAAEIBJAAAieAnUaVIZAegRMnTljVSpXdgHnQgIGmPTgkfxO4f+++jRw+wrmSLlywkK1aucrfFaZ2EIAABCAAAR8TQOD4uHOoGgQyS+DA/v32ZMlSTty8O/mdzF7O+TlMQMJGAkcxcyR4JHxIEIAABCAAAQhkjgACJ3O8OBsCviWwbes2K1KokOXJldsWLljg23pSsfQJyERNpmpyDCHTNRxDpM+LTyEAAQhAAAIpCSBwUhLhPQQCSGDVypWWLymP5c+T19asXh3AFlDlcAJyNtC9azcncuSEQM4ISBCAAAQgAAEIZIwAAidjnDgLAr4lMHfOXEt6IpcVK1zEdnz3nW/rScUyR+Dhw4fOfbTcSMudtNxKkyAAAQhAAAIQeDwBBM7jGXEGBHxJQAPgieMnuFn+sqWftEOHDvmynlQqewQUCLRMqdKunxUgVIFCSRCAAAQgAAEIpE0AgZM2Gz6BgG8JyDNa/7593aC3etVqdvrUKd/WlYpln8D5c+es6TPPuv6uW7uOHT9+PPuZkgMEIAABCEAgTgkgcOK0Y2lW8Als2bw51Ubcvn3burzU2Q12GzVoaH/++Weq53EwvgjcvXvXhg4e4vpd5ojr162LrwbSGghAAAIQgECECCBwIgSSbCAQSQLz5syx3P/NZVPeeTdZtlevXg0FhWzbuo1dv3492ee8iX8Cy5ctt0L5CzhX0uPGjCXOUfx3OS2EAAQgAIFMEkDgZBIYp0Mg2gS0IqON5XITrH+fLFzoijx//rzVr1PXHXu5e3e7c+dOtKtC/j4lcPjwYZNpou6Pdm3a2qVLl3xaU6oFAQhAAAIQiD0BBE7smVMiBNIlUO//RIwncBTXZt7cuValYiU3oB0yaLA9ePAg3Tz4MP4JaDWvc6dO7p6oWK687du3L81G37xx02TaSIIABCAAAQgkAgEETiL0Mm0MDIHZs2a5Aasnbry/imyv11PfnRKYtlDR6BOQJ71pU6e64K6KgZRagFed06NbN+vWpUv0K0QJEIAABCAAAR8QQOD4oBOoAgRE4M+LyU3TPHHj/S2YLz+uoLlVUiWw9dutVqpESSeC3+jzut26dSt03sz3PwiJ5rfeeDN0nBcQgAAEIACBeCWAwInXnqVdgSNQr3ad0EDUEzUp/yreDS6hA9e1Manw77//bk0aN3b3UKP6Ddx98t327cn2c+l+WvnFFzGpD4VAAAIQgAAEcooAAienyFMuBMIIpGWallLg6H2VSpVxDR3Gjpf/IyDHE158JLmS1r+U95AcWJw+ffp/F/EKAhCAAAQgEGcEEDhx1qE0J3gEHmealnKAqvcD+vcPXkOpccwIyPOet28rtfunTKnSOKqIWW9QEAQgAAEIxJoAAifWxCkPAikIZMQ0zRuk5kvKY2NGjzZtHCdBIC0C3iqOd9+k9rd923ZpXc5xCEAAAhCAQKAJIHAC3X1UPugEZn340SMmRKkNRpNy5bbevXrbndvEvgl6n0e7/os+XZShe0r32Xsz3ot2dcgfAhCAAAQgEHMCCJyYI6dACPxLICOmaTIz6vDcc3b58mWwQeCxBH7a95NplS81kZzaMd1f+/bufWy+nAABCEAAAhAIEgEETpB6i7rGFYHHmabJI9apkyfjqs00JroE5s+bZ4qHk5qYSetY4QIF7ebNm9GtGLlDAAIQgAAEYkgAgRND2BQFAY9AeqZpNapVZ1bdA8XfTBPQat9rr7yaqZWcurVqZ7ocLoAABCAAAQj4lQACx689Q72yReD+/ft248YN505ZLnF/PXLEZL7z/fff2+ZvvrE1q1fb8mXLbeWXX9qG9etNgRJ379plhw4etGPHjtnZs2ftyuXLLmBipDf0yzQtT67cj8yyK8bNpg0bstVuLoaAR+DevXs2YfwEK16k6CP3WmqrOQP7D/Au5S8EIAABCEAg0AQQOIHuvsStvATMH3/8YTt37rRlSz+3dye/Y4rg3qZlK6tSsZIlPZErQ4O61AZ6KY8VLljI6tepa507dbJhQ4eaYtasX7fODh06ZFevXs10J8hFb3gZRQsXNrn1JUEgWgSWf77MKleomOy+C78Hvddr16yNVhXIFwIQgAAEIBAzAgicmKGmoKwS0ArK0aNHbcnixTagXz9r3LCRFcibL9XBWt6kJKtUvoKL6N62dRt7qWNHe/XlV6zvm285cTJ+7DibNnWqfTx7tn36yae2YP58m/nBTCeQRo8caYMGDLQ+vXtb967drMNzz1vL5s2tbu06VrJY8VTL08BQKy86953Jk23L5s32999/p9nUD2d+GMpHARcnTZyU5rl8AIFIE5BDgSaNGqcZIydvUh63ehnpcskPAhCAAAQgEEsCCJxY0qasDBF48OCB/fzzz06EdO3cxUoVLxESBRIU8hJV66ka1unFjjbi7eFOpMjE7MyZM6aVnWglmbzJhO2rVV/Z9GnT7M3X37AWzZo5gePNgOuvPFPVq1PHhg0Z4kzhLl265KrkeU2TeZpWmhR1ngSBnCBw4cIF6/JSZ5PIDr939bp0iZJZDgIqcS9z0G1bt9nqr1bb50uXuufzw5kzbco779rYMWPcc/HWG2+6iQetiuqfJiF0TM+MztG5ukYTEMpDeSlP5Z3eBEJOsKRMCEAAAhDwHwEEjv/6JCFrJFOvVStXudUTDbDCB10yrXmlx8tO8Ozdu9fu3r3rO0YXL150ZmsanLVq3sIK5S8QaoMEz9ONGlulChWsZYsWDNB813uJWyHFVRo+7G2TJ7XwZ06OLlJLWk3VRIImFBYuWGBa9dQkhEw4ZcoZnkc0X3tmoypbdVBdvEmOSO+ZS40DxyAQNAL63dTe0sOHD9uePXts+7ZttnHDRlu1cqWzjpg7Z6598N77zhJBz9SoESNt0sSJ9v6M92zux3Pss0WL7MsvvrANGza4yYYfd+92E5H6Prh9+3bQcFDfBCCAwEmATvZrE2/duuVEjczIZFrmDYjKly1nfXr1sqVLlrh9Nn6tf3r10gZvOS2Y+u4Ua92iZTLXvRXKlbMxo0e7H5r08uAzCMSSwML5861ImEiRpz8Jdw2CJk6YYB3aP5emqaZMOLVqKVNNrcRoYKQBk8xKNYBSHhpQaWClAdbJEyft/Pnzbg+bJjf0WsfSG3wpT+WteqistMxGdVzn6HyV++eff8YSI2VBIEcIyPLh3Nlz9sPOH9yqp0ymZW4tM2v9pkZyX6r3W+391SSe9pY2bfKM8+A4cfwEJ4i+2779X8uKe9GzrMgR2BQaCAIInEB0U3xVUuYxE8aNTzZAaVC3nps50sb9eEw3b9x05mq9e/a0EkWLhcRc02eedcejaVoXjzxpU3QI/P777zZm9CXm4s0AACAASURBVBh3j2rQ4g1g9Ffvq1aq7PanyamHPBAePHDAeSuMTm0en6vMRg/s3+9mllUn7Z1THVPWvXrVam4fnhySqI0kCASZgFYpT5w4YV+sWOHMtGUqnXIV1nt2C+bLb9WqVHVC55UePWzo4H/NQDX5Fm4GKtPrb77+2r7fscP27dv3iNdRPTtaKf3oww9t2pSppv2scrrT89XXnMm1Vn2LFEp9FVexuZ5p/LQNHjjITXoc+eVIVM3Jg9y31D1yBBA4kWNJTo8hoOXxIYMGhxwEaG+N9tAc/fXXx1wZXx/LVEDmeO3btgvNqtWs/pQt+3yZ3WemK7462+et0f0m1+kyR9E96A2K9FdCXCshkydNcq7V5TY9KEl11WBNdVcbwicV1Da1VWY4ajvPXFB6NXHrqXtUYQ4kSLp0eumRfana11m7Zi1nyq3VVu1bkwVBTqxeXrlyxWRKvmL5cue8R5N6jeo3eCQul8SQnk05/dGqE2ZuiXt/R6vlCJxokSXfEIHLly47O38vwnq1ylVMEddlopboSd7h5KxAjhO8gdfaNWuMfQSJfmdEt/2aQR05fIQ9WbJUSNRowPFihxfcDK1WUmXyEi9JbVGbNEBUG8NnmsVALMSEBAG/EJB5qEw8e3TrZsUKFwk9p/qd0L7Ul7t3d/ezxMHNmzf9Uu006yGnOloZ0n4eiR6t+ISvtGqlSc/mvLlzTbHrSBDILgEETnYJcn2aBGR2pS8rb/ZUy+SaWWLG9FFkiukzoH//kNDRvh0GXI9y4kjWCWiQr/hNurc0SNK/UiVKOtMtBb/1o/OOrLc2/SvVVq3waE9PuJdGsVHg33gSd+mT4FM/Ebh+/boTNfKyGb5nRpOCCnWg4NTnz53zU5WzVRdNfirotiw76tSqnUzwyDHPnNkfm+eFNFsFcXFCEkDgJGS3R7/Rx44ds2efbvLvIKp4CfdFpY33pPQJaKN1ty5dHTc5XtC+Arilz4xP0yeg+0cOOzwTNM2aKkaUXC9zb5ljoP0HYuLNKIuVJmPgk/69xaeRIaD9NNob43kilLjR/kx5NdNniZLkJEGTojLf9hwPybpBzhK0348EgcwQQOBkhhbnZoiAZpk8N8nyhqZZGlLmCGzZssWqVKzkhE6zZ5vG1axd5khwdlYJyMxRjgC0wV6rNTIR1YoFK4NpExUbMfLMacVOXuAwGU2bGZ9knYAcZMgxhrdaU75MWef9D0cYZn/99ZfNfP8De6rav99f+g5r16atyTMbCQIZIYDAyQglzskQAQ0C5B5SX0QyS1u3dm2GruOk1AnIQ5RslcVTP3xyoUuCQEYIKFCuhLHunQJ58zlvR3LFTMoYAbFS0FGxE0OxFFMSBCJBQEJaAW51b+mfPIwpxgwrho/Slbno15s2uVUdb4VVrq/lRIEEgfQIIHDSo8NnmSIwetQo92WtGReZWpEiQ2DWRx+5GT7F92D2PTJM4zUX7W+TWWPe3EnO3Oq1V14NbCwpP/SR9saJoQZWYjrlnXfZQ+iHjgloHeRhbNCAgSavZxI22mvDikTGO1Nmal1e6hwyJZWjBXlnJUEgNQIInNSocCzTBGQ3qy/s2jVq5ohrykxXOGAXaA+FzBgqlivvgi8GrPpUNwYE5Bq5TavW7jnUpuQd330Xg1ITowgNQsVU33HaqxMkl9mJ0UP+b+WGDRtcMEzdQ/Xr1LVvv/3W/5X2aQ3lMrtV8xbueZRHxEWfLvJpTalWThJA4OQk/TgpWz7vtSGwbOknAz9brKjqP+7ebQvmz082iPGDDb42nLpZv1at8fIUJ89OpJqh4LmaXND9IbeyMm8kRZaAmGq/hBiLtZiTIPA4AlpVlRty3TcKxqlAmQR2fhy1jH2u2HHyBCm2r/R4ORDusjPWMs6KBAEETiQoJnAecreqH3stuStoXjSTzEW6du5i8pevf4oFoBgW2oyvjcGRcCep+DxyGysnCfLbr6TZIe0p8kNAUm+ANXfO3GiiJu8AEfj777+tbu067kdeZqJ+EONp4ctM3TJzblrlRfq46qQAoRpQ1atTxzQhQoJAWgT0GyKTKt0vekZ/++23tE7leBYJaKLBc33ftMkzPJNZ5BiPlyFw4rFXY9gmCQJ9ecvFZSzSzRs3XXky11KUZs2OKRKy6iDXrtlNGsBINCngmJdmfjDTCbj9P+33DuXYX4k4iS2JMGbpc6wbfFOwNuAqGrju/1EjRmarXoo8XuupGqEJBO09ieRMszZQKxZWv7feemw9/TSpkFplvRn5Ds89z2pqaoA45p4dz+X/8+3bm367/JT03XH8+HEnurzn/J9//jHF4gla0jjg9d593Peg3GsHIfBp0BgHsb4InCD2mk/qLDEgN6pa7YjE6klGmqVVIg3mFGPHS/pCzpP7302b2XVuILedyl8BxsKTn74wp035V9CxihPeQ4n5evasWe5+7dLppYis3EjM6/6XO9ZIp1u3bjlzks6dXnoka2+A5X3gp0kFr07hf/XdJ+Zi9fHs2eEf8RoCjoC30vdcu3YhawA/oFEYB+0jk2mXnnN5JCv3ZBnThv16tevY9zt2+KGama6Dnsk3X3/DPZMy09V7UmITQOAkdv9nq/XyaKIf+J6vvpatfDJz8fix41yZkydNCl2mlRzVQ56OZK6TMslzjUxJZE53+tSpVGdcvXOmvjvF5aVApV7SdWlFj9asnM6N5ZfpxYsX3YqSNlmSEpeANroXLVTYrThGatO77Nj1LC3+7LOogL19+/Yjz58caLwzefIj5flpUuGRypm5PXqlS5R0fRAp/qmVw7HgEZADAf0e1alV2zcrN3r2tHqq5/v9Ge8lW+XQSk7VylWcW3SdF9SklRwFCVUbP1m4MKjNoN4RIoDAiRDIRMzG85ymAUqsUqMGDd2XlxwBKP1y+BeTmYi+0D6cOTNZNQ4dOmQtmjUzzRhr74726mhAqC9BLx09etTNYHV84UWXT/EiRd3eHn0ukzTZ9iqScv++fb1L3N9DBw9a86bN3KyXZsHq1q5te378Mdk50XzToG4992OUcuY7mmWSt78ITJ82zd33kVpBkMmKXJHrWVJE8ZRJgkPH0xLzCsznJU0YyNwlZZI410qOkp7Dj2fNdt4BU7rKzeqkgszgvH0xak+0Y/94K2gzpk9P2VTeJygB3YMKlaDfDf0++SHpd0K/V3q2J4wbn2qVPv3kU+eFMdUPA3RQE576HtNvub6HSIlLAIGTuH2f7ZaPHTPGfWHu+iE2Abc0gPICfSkwmry26b1WMrTsHp40YJKY8dxHagVGX+7a8OmlnTt3uv0sGqQoSdDonP79+nmnuJlsHVuzenXo2KqVq5y48K7TB5qp6/Tii6Fzov3CczagASMpMQk0qt/A8ufJa9euXYsIgH379rn7X/dyeNIKpYT+040aOxMQ7QHTfp9vvv7a2evL9r1S+QrunzZV6732yGlPnCfAtfKq51XPkkSHxIds5fVe58o0Rs9wVicVvlixwg3gFJhTq08yZVVwXOWf8rshvG3ZfS326gNNvJAgIAIK2Kn7Llb7UjNCXZ7bVCeZpqWVLl+6bF+t+irVjz0Lh7SsILTRX8++LBr0bOd0Uuw4tVf7c0mJSwCBk7h9n+2Wextt5ZM+FkkDFX1pde7YySRO9Fq2w94gyquDBlDFChex3j17eofMM6dbuGCBO6ZZHm3U1yZQL23csNHluXbNGu+Qy0Me4rxBpPb4aM/RSx07hs7RCw3WNLhKa3Y72ckReCOzQLWfIGcRgBnALDTQ0H2p+y5SyTPP1HPtJd3PTRo3duYrnsmYYjHJEccPO39wp+l5072oiQGtlkpseLbw4StBuk4rnV5S3hI9ep7DkwSK8svspIJWWTXhoQGmVmTlvl759H3z8U4NwsvP7Gt5blIQUKLQZ5ZcfJ6v3wbddzL78kPS86nfLNXp602bMlWlx1lBnDlzxn0/9OnVy00waNXk9OnTmSojGifLAY88raacrIlGWeTpXwIIHP/2je9r5nkvkzCIRZJg0Ze0ltKVvCX3lF/aEi0a/IV/0SoCua7VHhwlrYBoMBTutnPE28OdswJPzGgAVqZUaVeOu8jMDZyUjwZPXtJ5Eku1a9byDkX9r2biVA9v0Bn1AinAVwS0cqf+12baSKVmzzZ1eYYHINy0cZM7Nmb0aFeMZnK14hLu5MObGNAqhlY3lbSqKvHipVMnT7p8tPHaSz///LM7Jk+M4UnPeVYmFVS+RNTz7dq72WQFOhUjb1IjvIxIvvZWUzVpQoKANu/LHNovSRMOeg7Kly2XqdWVjFhByIpDsX30GyjTU/1eppxwzCkO+o3Ub3wQvcLlFLN4KxeBE289GsP2eAObieMnRL1UT2zoi/r333935XkzvRpgeEn29/pS0wyulyQCJEDkolZJX3gapMm7jZd0TCZt8ijjJW8AppltJS2968s8pTvqrd9udT8gw4e97V0a1b/au6AVKq89US2MzH1JQM409Cx0ejH5SmJWK6vnRqJCs57hm4w1yFE5nunK50uXuvfLln4eKmrwwEHumGZxlfS8af+BTNW8pMC5ymfb1m3eIfMC10r8eMl7zjV54SU9y7o2vUmFcPNVb9JCew10nWaZo5m8+nl7f6JZFnn7m4C+m3XPtWnZyjcVlTmp6qS9qimTrC90/8rMUv9kKqrgmRmxglBe48aMdXl7kwgrv/wyZRE59l6x8dRuv6yk5RiIBC4YgZPAnZ/dpnvLwHIVHW27W23q15dVjWrVQ9VW+RIcGkzJfljJExsTJ/xPdHn+8Qf2H+A2IGvvgPIKn01+9eVX3DGtSml2XCZA3gBMPwIyTZMrbF0Xvo9HZSoOiWaOYzVT5AlLP9l4hzqFFzEhICGgvTBVK1WOSHkyB9O9rXs5POm51sCofp267nmQ17CU8XYktPUces/g5m++cXnJVMxLembCg+fqeJtWrR8R6VmdVPDMV72VJuWvfXrh3xdeXSL9V7P12tSsPiElNgGtXmiCLXyiLKeJKJ6Vnu1BAwamWhXPC6kmN7xVyIxYQSgzWUToOj3/nnVEqoXkwEHvdz+7oSNyoOoUGSECCJwIgUzUbLxZklUrV0YVwbChQ92XdPissAr0vsTenfyOK1+zNfoy114AmdPI/aw2/+uYVmLk69/bTK1gnpr5lfmaNlDrHM1My+OaNksqD63qaPA3aeJEl79mlmXK4yXFotF+hPDZZe+zaPzVIEr7LvQj6hcPPdFoJ3k+noAXRFDiP7tJ+1d0/2tzbnjSjLT2FOg51763lO6QNXjQdUOH/C/Qr/bwaIVUgkcOCpSHniM9bzL/1HFvlUerPyeOnwh5XMvqpEKv13o681LPk5u3IqXvDZmqasIiGsnb2xe+ihyNcsgzOARkChZunpnTNffMs/WMpJY2rN/gnmEvuHVGrCDC8/EcGKScHAk/Jyde63daq9KpeXPMifpQZuwJIHBizzyuStSsjZa2tYLh7V2JdANlaqJNvBpIaQZZX6he8gJ/qg5aLleSiNG52vQ/8/0PzFv90QZnL56NZzevHyINqtavW+eu0TmeCZxiBugLUn+9FSqtEMlFs2bFtOoj8RS+kdqrV7T+emZ5aiMpsQl4K5Gv9OiRLRBaedS+AT0zKV2da3+bjmvzsJ4v7/nxCvRcxYd7UtReGD0jcp2sVRxtVFYeWlXVc6Pnxdvbo2dZkxQSQUpZmVSQ6NckQ3hwUm+VU6ZxytNzTe3VO1J/ve8arVqRICACnjWAgkb7IckzoSYc9BstT2cpkyYn9HwquK5SRqwg9J3hreTqt1GrpcrDL26ZNdGRNynJ1Stle3mfOAQQOInT11Fr6bQpU92Xm0SDX8w0NGsbLrhS8zamwZq3IVL11myzJ2QES4Mu2SKnlrRvIFqDptTK07FfjxyxwgULucFmLEVVWvXheM4S0D2rWUqt5mV1gK372HMuoAGKTGvCB2aKLaVYUvJGpM9lDiobfS9ptVPiKPy5kQMCmawoxo2SJgw0USBzut27/nUprzyUX8N69ZMNirIyqXD48GGX15zZH3vVsi2bN7tjaltqz37oxGy8kMAUe63q+uV7LxvN4dIIEfAmHiTm/ZK0P1TPm8y0w739ySRNk3z6TKuRShmxgti/f79zKOAJpj69ezszTVk++CFpX7DapAkYUuISQOAkbt9HrOUSAp5Xr9GjRkUsXzL6HwGJMdn6a0C1bu3a/33Aq4QmcOSXI26lUiIj0jbwMst84fkOIb5yC609LXJw4U0MhD4MeyETl5QzueGTCTpVgkCmaSlTJCcVIs0jvK4ShmKu/QeaeCBBwCOge1srGvqulic/vySt/rt9e5WrmMzVJMDkfEcTD1ptDZ+k8FYm07KCkLMQtU+/+7Kw0IqtrCD8kDRRqeeyQrlymKf5oUNysA4InByEH09FyzZf3sU0ayLXkcxoRq53NQOuyNhiq+j1JAiEE/A8m2mzfyRXK2TSEu4oQGVqj47MwcJngcPrkgivxbha5SrueQz3JpcIbaeNGSOg1RCZSGk/TlpWABnLKbJn6XdZwl8rvj/u3u0c56RVQnpWENpHJ0+OEvp+EvhaQZJDFP1WbtiwIa2mcTxBCCBwEqSjY9FMmU1phldfLq/0eNlt1I9FufFchmbNPRMCzZSRIJAaAS8mVeUKFSM24JATA7lS10BGAyPZ8j9VtZoL5JlaHRLhmFbMKpWvwGRDInR2Nts49+M57j6pV6dOyMNgNrPk8nQIyGS8fdt2jrli2pEggMDhHogoAblSVpA/iRyJHc0SkTJPQF6ftClbzhX0D1vizDNMtCvkLENmI0UKFXLeALPbfnkfUpBABd7s+eprJo9kKZ0QZLeMIF2v1RqxFWM5LyFB4HEE5FFQv4V1a9eJ6Orq48pNtM9lFqvYQ2Kt4MfpmdAmGptEbi8CJ5F7P0ptl/nK+LHjnOcWbS4e0L//I+5lo1R0XGS7fds2q12zlvuyln00IjEuujUmjdD+LO2R0Q+93DtHO8hlTBqVw4WIoQKqiqm8yfllr0EOY6H4DBDQyqcXaLNMqdLOQ1kGLuOUTBCQOaBnwi2PktFyCZ+JKnGqTwggcHzSEfFYjT179piW5zUw0ObG92bMYNNfOh2tIIdeVHQJw7eHDTMFMyVBIDME/vjjDxewU8+dNtsq+KXMzEiZIyBmYieGYqmAp2JLgkBmCSxcsMCFU5C7Zk34acWBlD0Ct2/fdlYO8uwornKKEu4oIXu5c3U8EEDgxEMv+rgN8oqkYJhenA1tUJbQCXfh7OPqx6RqP+37yQUX1Ze0BlKK5yGxQ4JAdgh8teor53lP95RWdbSq6gXCzE6+8X6tGImVtxIm74Wrv1od782mfVEmoO90uUXX86h4bvpdTGRnHVnFrVUxmc7KukEs9ddP3uqy2i6uizwBBE7kmZJjKgQkaN6d/E5o0KDBgzYCRtOVayrV8M0h2QjL1MWzG9YXteKHfPvtt76pIxUJPgHNcio+TLkny7jBgFYj3nrjTRd8M/iti2wLFBD4zdffcG639TyKmTaKe7E+IlsauSUiAQma2bNmOVNH3WMSz1rd4R57/N2g38yVX34ZsgqRC2vFu7l50x+xdx7fAs6INQEETqyJJ3h5WppXYFBvRUerFq1btHRf8nJQEM9JM0979+41BV2T+1D9wOmf2o+wieeez/m2ycOQBlLVq/7rblz3nQS1jiWyuYzaLgZeJHZxESMdi3Ug35y/S6hBrAjIzbJixnnmj/KUqQlABd4kJSegyVG5p/dcs8vpTt8338JcNDkm3qVCAIGTChQORZ+ABg+KZq7I6fJKpIGF9p0o8viUd951QiAePKEo4OGqlavczHC4qNEK1uCBg0yzxiQIxIqAnqkN69c7M0jvucufJ69zB/3x7Nl28sTJWFUlx8o5ceKEm0WXC2y1Xd89YiEXs4qdEQ/fOzkGl4IzRUCCRuaQcl6h+1D7SbRRXhNeib6fRB4btdpcuGAhx0ZiUL+ZiWr1kakbi5MdAQQON0KOE5CXIi3by720RI6+6PVPX/ovdnjBRVlWPBiZ2/g9KWL7qpUrbejgIS66s7evRu3RqpVMYDZu2BiItvidNfXLHgEF8nt/xnvWqH6D0DOn+1SxdOQa+tOFn9jRo0cDHbRXq6Zqg9qiNqlt3veL/qrtYiAWJAjkFAE5k5E5pOcNTPemVnX6vdXXiW65bI/3JPO9bVu3OXf03v4acShfpqyz+oh3C49479+caB8CJyeoU2aaBK5cvuw29MrTTLg5jb7oNLvVpHFjGzJosDMh2blzZ44FUJPzhOPHj5vc8irIonzvewEAVVf90+xwm1atbfq0abZv3z5mhtPsdT7IaQK///67Lfp0kb3cvbuVLFY8mQjQe612jBsz1tasXm06169JkyWqo+qqOqfWFrVRbcUjml97MXHrpVUb/a4N7D/AShUvEXoOtd9EpszyFKYwAvEgeOTOefeuXe73sUP750IrNfrt1KqNJiQ2f/ON6beWBIGsEEDgZIUa18SMwLmzWhFZ5VwmS9zkTUoKfel7QkLmXnJHrdUe/TBIUCxdssTWrlnjlvoVR+aXw7+4WVrNAqW0rZdJyvXr1+38+fN27NgxO7B/v33//ff29aZNblPj/Hnz3ICp12s9nUmdNoamVg95xunS6SVTwMVdP+xilSZmdwkFRZKABlmHDx92kwgaZHi2797zpr9FCxW2+nXquvtd7sxl3ibzLl0XTdfmek5VhszsVKbK1jOnuqhO4XXUa9W9T69eri36Dkh0s59I3ifkFV0CGtjLcmHUyJFWrMi/sa28+1u/P1p9HNCvnxPr8tDmZyGg504rqTJLHzZkiDVt8kzImYfXpto1ajrHQzLPC4K1RnR7n9wjQQCBEwmK5BEzAhInEiByEzl50iR79eVX3Bd94QIFHxnceF+cqf2V6ZhWWFITKqmd7x2Trb720rRt3cYGDRjoTOs0yyQTF5nDkCAQjwTkOln3uRyEdO3cxa2uhpuTes+H91diQ+JCEw9yZqCVTMV40v6CN/q87mzpR48c6Z5hPcd6Lft6faZzdK6u0bXKQ3mlJmC88vRcVihbztVNddyyebNh0hKPd2JitUmrpRLvus/lcfPLL75wzgmaPvOss2jw7n/91R4ViR5ZEyh+0ycLFzqTL+1ZiYX4kYjRquj3O3bY4s8+cx7O9PssBx5FCv27j8arr747GjVo6MzRNIGpyUUSBCJNAIETaaLklyMEJC40oNGmfe1xWbJ4sYszoJg7EydMcDND/fv2tZ6vvuZmfLWhWD8ScnLwwvMd3I/C6737uEGWfhzk6ODDmTPdj4TElL60tQE75epPjjSWQiHgAwIyMdHgSaudy5ctd3vlJPo7vdjR7T/zNk57g5rs/FVeDerWc3mrjBnTp7sy9VzKbEcTFRo06TgTDT64OahCtgkoUHaZUqWduJH4TylStMoh02dZGGhvp4RQWhN2ejbk9lyiQuZg3bp0dSZgWgGSV0+5W5blg/bCar+anmf97smUUyuleq70Ozpy+AhnJaFV0e5du7nfTllWVChXLt2yaz1Vw2QBIW9oWpW6eQPXztm+QcjgsQQQOI9FxAkQgAAEIJAVAhqUaTCjvXVywHHq5Ek7+uuvdvDAAZPpqAL06Z9e65g+0zk6V9coxkXKgV1q9dD1GmRJRElgyXshCQJBJaCVGu27kUtkOR/IaNKzolUfiQiJFIkWiZgOzz1vMgHLrKVDRiYlVE85R9CkobyeaXJQJuJ6rvUsayKEBIGcIIDAyQnqlAkBCEAAAhEloBVcORbQoEz75Pb/tD+i+ZMZBKJNQKuPMtmUyaX2lm7ZsiXiRWrlR5MHZ8+edXtONbGgPaPa+yKnOSuWL7dPP/nUWS9IJGkvq0w+JZpkHq69NDJFUywfOTtgxTTiXUSGESKAwIkQSLKBAAQgAIGcJaB9AO9MnmzeHrsF8+fnbIUoHQIZJCDzZ+0/k0DXnjOtZpIgAIGsE0DgZJ0dV0IAAhCAgA8JaOZbcac0WJTtv0zdSBDwK4ELFy64EAi6X7UvVKsjJAhAIHsEEDjZ48fVEIAABCDgQwIywZE7Wg0aa9es5UxrfFhNqpTgBOQYp2K58u4+laObO3fuJDgRmg+ByBBA4ESGI7lAAAIQgIDPCGiD87ChQ93gUcEDtXmbBAG/EFi/bp3b+C+TyvdnvOeXalEPCMQFAQROXHQjjYAABCAAgbQIKNaGF4tjyKDBzJKnBYrjMSOggNASNvJsps39JAhAILIEEDiR5UluEIAABCDgQwLHjh2zurXruNUcBR+UO10SBGJNQKuKCmgr00m5NpeJGgkCEIg8AQRO5JmSIwQgAAEI+JCA3Nr26d3bDS5LFS9h33z9tQ9rSZXilYCcB7Rq3sLdfwqQef78+XhtKu2CQI4TQODkeBdQAQhAAAIQiCUBRWtXgEKZCCmK+/3792NZPGUlIAHFj6lWpaoTNy937+5iyCQgBpoMgZgRQODEDDUFQQACEICAXwgowKHijchUqF2btvbXX3/5pWrUI84IyG25AncqgOekiRMJjhln/Utz/EkAgePPfqFWEIAABCAQZQJXr161zp06OZFTvkxZF9E9ykWSfYIRmDtnruXNneRWDL9YsSLBWk9zIZBzBBA4OceekiEAAQhAIIcJPHz40OTRSoPQvElJ9uHMmcyw53CfxEPx9+/dN3ns0wphmVKlbc+PP8ZDs2gDBAJDAIETmK6iohCAAAQgEC0CO3futHJPlnED0q6du9i1a9eiVRT5xjkBrQw+3769u5fq1amDx74472+a508CCBx/9gu1ggAEIACBGBO4ePGitWnZyg1Mq1etZocOHYpxDSgu6AROnTxptWvUdPdQpxc72o0bN4LeJOoPgUASQOAEstuoNAQgAAEIRIOAPKqNHzvObQgvmC+/fbZoUTSKIc84JKBVYV0I6AAAIABJREFUwFIlSjpxM3L4CLzzxWEf06TgEEDgBKevqCkEIAABCMSIwKaNm6xkseJusKrAjIqhQ4JAWgSWLF5s+ZLyuH1cn37yaVqncRwCEIgRAQROjEBTDAQgAAEIBIvAmTNn7OlGjZ3IqV+nrp04cSJYDaC2USfw4MEDGzN6tLtHJIh3fPdd1MukAAhA4PEEEDiPZ8QZEIAABCCQoATu3LljA/sPcAPYooUK25rVqxOUBM1OSeDmjZvWpdNL7t6oWf0pBHBKQLyHQA4SQODkIHyKhgAEIACBYBBYsXy5FS5Q0O3NGfH2cLt3714wKk4to0Lg7Nmz1rBefSduFCj277//jko5ZAoBCGSNAAIna9y4CgIQgAAEEozAr0eOhDxkNXu2qZ07ey7BCNBcEdi3b1/IpfiA/v0Ru9wWEPAhAQSODzuFKkEAAhCAgD8JyCzp1ZdfcTP3pUuUtK3fbvVnRalVVAisWrnS5F0vT67cNnvWrKiUQaYQgED2CSBwss+QHCAAAQhAIMEIzJs713nN0kB3yjvvmjabk+KXwMOHD10/5/7vE6a9WN98/XX8NpaWQSAOCCBw4qATaQIEIAABCMSegEyVKleo6FZzOrR/zi5fuhz7SlBi1Ancvn3bXnvlVdfPVSpWsiO/HIl6mRQAAQhkjwACJ3v8uBoCEIAABBKYwJXLl+2F5zu4wW+l8hVsz549CUwj/pp+8eJFa9rkGde/2nf1119/xV8jaREE4pAAAicOO5UmQQACEIBA7AjIPG3alKluX4aCPc6Z/XHsCqekqBE4fPiwSbTm+s9/rXfPniaX4SQIQCAYBBA4wegnagkBCEAAAj4nsH3bNnuyZCk3IH6lRw+7ceOGz2tM9dIisHHDRitcsJBzCz5t6lTTHhwSBCAQHAIInOD0FTWFAAQgAAGfEzh/7py1aNbMiRwFf2S/hs87LJXqfThzpiU9kcsK5S9AYNdU+HAIAkEggMAJQi9RRwhAAAIQCAwBBQEdNWKkm/1XcNBlny8LTN3jvaI///yz3b9/P9Vm3r171/q++ZYTp+XLlrMD+/eneh4HIQAB/xNA4Pi/j6ghBCAAAQgEkMC6tWutWOEibsA8oF8/kzeu1NKWzZtt7OgxqX3EsQgS+O2336xE0WI2ZNDgR3KVs4g2LVu5vmrcsJFpJY4EAQgElwACJ7h9R80hAAEIQMDnBE6eOGkN69V3A+dGDRra6VOnktX41MmTIRE0YfyEZJ/xJrIEypZ+0vWDnAbIDM1LEj7Vq1Zzn3Xr0tX++ecf7yP+QgACASWAwAlox1FtCEAAAhAIBoFbt26FTJ+0grBhwwZX8Zs3b1r9OnVDg24Fkfx+x45gNCpgtezTu0+IswSO9tisWb3atm3dZsWLFHWfTRg3noCtAetXqguBtAggcNIiw3EIQAACEIBABAksWbzYCubL7/bmjB0zxl7u3iPZoFsD74L5C9jVq1cjWCpZfbd9u+X67xOPsJZL7zy5clv+PHnZJ8VtAoE4I4DAibMOpTkQgAAEIOBfAtrk/lS1f82hJGhS+1e7Rk3/NiBgNbtz+44VKVgoVc5ir1Wzr1auClirqC4EIPA4AgicxxHicwhAAAIQgEAECWxYv8ENrFMTN96x/n37RbDExM2qbavWaYobj3WNatVNTgZIEIBA/BBA4MRPX9ISCEAAAhDwOYEzZ85YqeIlHjvo1uB73dp1Pm+Nv6u3ZMmSDHEW6+ZNm9qdO3f83SBqBwEIZJgAAifDqDgRAhCAAAQgkHUC8s7VoG69DA+6tUfkwoULWS8wga/866+/TPy8VZqM/H3z9TcSmBhNh0B8EUDgxFd/0hoIQAACEPApgblz5j7WNC3lQLxi+fI+bY2/q1W7Zs1MiZsmjRsjJv3dpdQOApkigMDJFC5OhgAEIAABCGSdwNq1a61alSqZGnx379I16wUm4JWTJ07KMN96tevYgf37E5ASTYZAfBNA4MR3/9I6CEAAAhDwIYGf9v1kTRo/neEVnUWLFvmwFf6r0tFff3UxblKuhKV8rxWevXv2+q8B1AgCEIgIAQRORDCSCQQgAAEIQCDzBM6fO2edOnZ08VhSDsLD3ytey/FjxzNfQIJdUaZ06XRXb2pUf8p2/bArwajQXAgkHgEETuL1OS2GAAQgAAGfEbh165YN7D/ABQINFzbhr+V97d69ezGp+d27d+3s2bO2b98+27Z1m21Yv96+/OIL+2zRIpv78Rx7b8YMmzRxoo0aMdLVu0/v3qZN+kMGDbYxo0fbu5PfsZnvf2Dz582zpUuW2KqVq2zTxk2247vvTLGA5ATgwYMHEW1L79d6piluqlep6sqOaIFkBgEI+JYAAse3XUPFIAABCEAg0Qho0D9j+gwrWbx4qoP11i1bRgzJ5UuXbe/evfbFihU2bcpUJ1Bat2hpFcqVy5CZV7j4ysrr/HnymmLQvPB8ByeMPvrwQ1u/bp39cvgXk8e5zKRtW7emyqtqpcq29dtvM5MV50IAAnFAAIETB51IEyAAAQhAIP4IfLVqlWmAnlI8DBsyNNONvXr1qluJmTF9unXt3MWJmJT56r1Eh8ps3rSZvdy9uw0f9rYTP7M++sg+WbjQln2+zNasXm2bv/nGdu7caft/2m/a96L4PqdOnnTiRKJJKzVasdHKjVZwtJIz84OZ9s7kyW7Fp3OnTta4YSMr92SZVM3zZJJXt3YdJ7oWzJ/vytGqUmrp9u3bjzCqVKGiff3116mdzjEIQCABCCBwEqCTaSIEIAABCASXwL69e61RgwbJBvFyUpBeunjxohMXMhmrV6fOIysyZUqVtg7PPW+jR460Txd+4gTJubPn7OHDh+llG5XPZHZ37NgxJ4gkpGSq1+zZpla4QMFkbS6QN59phUmmcTKbu3nzpqtPy+YtQuepXQRIjUo3kSkEAkUAgROo7qKyEIAABCCQqAS0J6ZBvfqWlCu3FSlU2LRvx0v379+3PT/+aBPGjXfBRHP/94nQoF9CoVXzFjZ61Chb/dVqt7fGu87Pf9Ummast+nSR9e/b1+rXqZtstUeBPGvXrOXaKZM+7Q0iQQACEBABBA73AQQgAAEIQCBABLRy0a9vX3vzjTeceZg29ZcvUzYkaArmy2/t2rS1aVOnOtFz/979ALUu/apev37drfSMeHu4NZTYeyJXqN2NGjR0Ikd7i0gQgEBiE0DgJHb/03oIQAACEAgYATki2LBhg7Vp2So0uC9RtJj16dXLbdIPX9kJWNMyXV2Z4i1csMCeb9/e8iYlOR7aRySPbocPH850flwAAQjEBwEETnz0I62AAAQgAIE4J6C9KtqwX7P6U24gLzM0iZyVX35pd+7cifPWP755ly5dsg/ee9+qVaka4qN9Rj/s/OHxF3MGBCAQVwQQOHHVnTQGAhCAAATijYBWbOTK2Ru4s0KRfg+ntsL1XLt2dmD//vQv5FMIQCBuCCBw4qYraQgEIAABCMQbAQ3Kn2n8tFuR0N6aYUOH2vnz5+OtmVFrj1xWK86OXGBrxUuma1rpIUEAAvFNAIET3/1L6yAAAQhAIIAEFPNl3JixzmuYNtJrf43cOJOyRkBmak0aN3ZCp1SJki6WT9Zy4ioIQCAIBBA4Qegl6ggBCEAAAglD4MKFC9b0mWfdYFz7bdhDEpmul+navLlzrUihQo6tVsPiycNcZCiRCwTigwACJz76kVZAAAIQgEAcEDh96pRVqVjJDcD79O5t//zzTxy0yl9NOHnipIsVJLO1zp064aDBX91DbSAQEQIInIhgJBMIQAACEIBA9ghob03VSpWduJk+bVr2MuPqdAkollCH9s851j26dTMFFSVBAALxQwCBEz99SUsgAAEIQCCgBOTm2XMm8N6MGTFthQb3f/zxR2glQ4EytQco3pOYt2/bzomc8WPHxXtzaR8EEooAAiehupvGQgACEICAHwmMGT3aDbT79+0bs+o9fPjQJo6fYAXy5rNaT9WwOrVqW/06da1MqdL222+/xaweOVnQ9evXXdvlYe37HTtysiqUDQEIRJAAAieCMMkKAhCAAAQgkFkCJ06csLy5k+ypatXs1q1bmb08y+cv+nSRE1Vz58wN5TF0yBArUrCQaUN+eIqGCVc08gyvc0ZfHzxwwPGvW7sOpmoZhcZ5EPA5AQSOzzuI6kEAAhCAQHwTGNh/gBMaa1avjmlDvfgw4e6nNdhv3bJVsnosXbLE3pk8Odmx7L6JRp7ZqdNbb7zp+mDjho3ZyYZrIQABnxBA4PikI6gGBCAAAQgkHgGtlCguS7knyzyyahJtGq+98qob1E+bMjVU1O3bt235suXuvVwofzxrtikOz3fbt4fO8V6o7mfOnDGZumnfzp9//ul9ZPfu3bPjx4+791qVunjxonudXp4q28tDpmOxTAqoKq9qvV7rGctiKQsCEIgSAQROlMCSLQQgAAEIQOBxBGSepoF1z1dfe9ypEf98y+bNrmwJmHVr1ybLX+LFi8Wjz+vVrhMSPvps7Jgx1qhBQxs6eLBzjqBzZr7/gf3999/2Rp/XLV9SHqtds5bt2bPHShYrbnly53YiJ6085TVO+Wk1q3zZcvZ67z7J6hPtNxJpJYoWc2aC0S6L/CEAgegTQOBEnzElQAACEIAABFIlsHvXLicyxo0Zm+rn0T4ooSKBlT9P3kdWaTToL1v6SevcsVOoGvI81q1LV7cx/8aNG+64Z971888/u/e6Tvn17tnT2rVpa/JQJrM3XZtanlevXnWODj547313vbzITX13SqjMWL2Qg4WihQrHqjjKgQAEokgAgRNFuGQNAQhAAAIQSI/A3r17ncAYOXxEeqdF7TMJDq0eSeRocP/777+HypJg0fH58+aFjo0eNcqtzhw9ejR07KWOHZ0QUl5K3nUyvTt08GDovPDPwvO8eeOmW+FRDCAFNj19+rTt27cv2XWxeCNPcqWKl4hFUZQBAQhEmQACJ8qAyR4CEIAABCCQFgHtTZGI0Ib/nEraL9O2dRtXj/79+oWqoRUV1e3UyZPumMRKnly57c3X3wido70yxQoXsT69eoWOyVRN13Xu9FLomPciZZ7e8QH9+rtrBg0Y6B2K6V/t/9Gqk8zkSBCAQPAJIHCC34e0AAIQgAAEAkxAKweF8hewa9euxaQVP+7ebZ8vXZqsrJMnTppiwTSq3yB0vE2r1latStXQ+xFvD3ciZP9P+0PHZE4mMeM5JtAHEkvak6PgoSlTyjy9z9X2iuXKuzqofrFOG9avd+3IqZW0WLeX8iAQ7wQQOPHew7QPAhCAAAR8TcBb1Zg29X/ezKJZ4U8XfvLIipH2wcgRwPPt2ruib9686UzRBg8cZCeOn3CmY82ebeqEy927d905MlOTuNE/eT+7cOGCedc9167dI03wPgvP88OZM50nNp28aeMml5di8cQ6NW/azImrXw7/EuuiKQ8CEIgCAQROFKCSJQQgAAEIQCCjBLRZv0yp0la4YKGQOVhGr83KeVqlkJgJ32/z0YcfOnGxZPFil6UnNnRcHs3k3tlzK60VHO0dkuOAxg0bWeUKFW3WRx/ZwgUL7OtN/4oUL5/w+qWWp9wyz541y52mvTcSSzOmTw+/LOqvVVeV26Nbt6iXRQEQgEBsCCBwYsOZUiAAAQhAAAJpElj91Wo3yJaJmFY6opk6vfiiVa9a1UqXKGlaLfFcN8vls+coYNnny1x9Gtarb1euXHHV2blzp4vZIzHQtMkzbmVH+2zkElr7bpRkxqZ9OlcuX36kCanlqZWTwgUK2uRJk6xD++echzZ5W4tVOnTokBOWchF9/ty5WBVLORCAQJQJIHCiDJjsIQABCEAAAhkhMGzIECcq2rdtZwqOGa10+PBhl/X58+fdisvKL78MBeX0ypTQkWlayqR66bgnhOQBTbFvMpJSy1MODLT/RoE2Y7UHyavrsWPHXIBVCTKtPJEgAIH4IYDAiZ++pCUQgAAEIBBgAjIDU4wZrZC0at4iw8IhwE3OsarLzE5mgXKs8MnChTlWDwqGAASiQwCBEx2u5AoBCEAAAhDINAFt4H/15VecyKlWuUqOxIPJdKUDdoEETcF8+Z0pnRwukCAAgfgjgMCJvz6lRRCAAAQgEGACDx48sCnvvOs8luXNnWQKrhntfTkBxpXhqsscTvt8tEKmIKRbtmzJ8LWcCAEIBIsAAidY/UVtIQABCEAgQQjs+mGXPVWtmhuQly9bzj5btMju37+fIK2PXDPlJGH0yJFWIG8+x/Kljh1N+49IEIBA/BJA4MRv39IyCEAAAhAIOIHbt2+b4uPI05hWHiR4JHS8WDQBb15Uq//XX3/Z+LHjrFjhIo6dTP7WrV0b1TLJHAIQ8AcBBI4/+oFaQAACEIAABNIkcPHiRZOXtfx58v67olOmrE2bMtU0iCclJ/Dzzz9b3zffcvtsJArLPVnG5s6Za7F0P528RryDAARiTQCBE2vilAcBCEAAAhDIIgHFapG5VdFChZ3QkeB5uXt3t58kkc3XFCx10aeLQjF9JGyqVanqgo9G0+V2FruRyyAAgSgTQOBEGTDZQwACEIAABCJNQDFjZs+a5QbxGszrn9wev/n6G7Z2zRrTgD/e09mzZ52A6fjCi6HVGnFo06q1rV+3jv1K8X4D0D4IpEMAgZMOHD6CAAQgAAEI+JmAPK7t3LnTBg0YaKVLlHRCR4P8vElJbjVjzOjRLojl1atX/dyMDNXt999/t2WfL7N+b/W1mtWfCrVV7a1Tq7Yz2Tt54mSG8uIkCEAgvgkgcOK7f2kdBCAAAQgkCAGZqO358Ud7Z/Jka9rkGRfnRYN//Ut6IpfVrlHT+vTqZXNmf+zO87Pp1qVLl2zzN9/Y1HenWOdOnax8mbLJBE3hgoVM3tDmzZ1riJoEucFpJgQyQQCBkwlYnAoBCEAAAhAICgGZsSnWy6SJE61t6zYhT2ye6FGMnepVq9nz7dvbgP797f0Z79lXq76y/T/ttyuXL0e1mVp5Onf2nFt9+nzpUifKJL5aNGv2iJhRfcuWftK6d+1msz76yAU/vXfvXlTrR+YQgECwCSBwgt1/1B4CEIAABCCQIQL37923w4cP25LFi23wwEFuladIoULJVkY88aO/ck2tjfrPNH7a2rRsZS92eMF6dOtmr/fu40ziRo0Y6cTTezNmuFUh7QmSS+sJ48bb8GFv24B+/ax3z57WtXMXF2CzZfPm1qhBQ6tQrpwzoQsvK/y1xIxEl1w8az+RTNNIEIAABDJDAIGTGVqcCwEIQAACEIgjAg8fPnSuprVqoxgxH8+ebSOHj3BC5tmnm7iVk3Dxkd3XeXLlNsWjad2ipTOXmzh+gn2ycKEzR/v1yJGEcI4QR7cPTYGAbwkgcHzbNVQMAhCAAAQgkPMEtPIjr2yKuaPVlN9++80OHTrk9vF8t327jR83zq0CKfbMls2b7fsdO2zv3r1utejEiRPOFE0mb//884/JNI0EAQhAINoEEDjRJkz+EIAABCAAgTgmsGb1aidw5LyABAEIQMAPBBA4fugF6gABCEAAAhAIKAEETkA7jmpDII4JIHDiuHNpGgQgAAEIQCDaBBA40SZM/hCAQGYJIHAyS4zzIQABCEAAAhAIEUDghFDwAgIQ8AkBBI5POoJqQAACEIAABIJIAIETxF6jzhCIbwIInPjuX1oHAQhAAAIQiCoBBE5U8ZI5BCCQBQIInCxA4xIIQAACEIAABP4lgMDhToAABPxGAIHjtx6hPhCAAAQgAIEAEUDgBKizqCoEEoQAAidBOppmQgACEIAABKJBAIETDarkCQEIZIcAAic79LgWAhCAAAQgkOAEEDgJfgPQfAj4kAACx4edQpUgAAEIQAACQSGAwAlKT1FPCCQOAQRO4vQ1LYUABCAAAQhEnAACJ+JIyRACEMgmAQRONgFyOQQgAAEIQCCRCSBwErn3aTsE/EkAgePPfqFWEIAABCAAgUAQQOAEopuoJAQSigACJ6G6m8ZCAAIQgAAEIksAgRNZnuQGAQhknwACJ/sMyQECEIAABCCQsAQQOAnb9TQcAr4lgMDxbddQMQhAAAIQgID/CSBw/N9H1BACiUYAgZNoPU57IQABCEAAAhEkgMCJIEyyggAEIkIAgRMRjGQCAQhAAAIQSEwCCJzE7HdaDQE/E0Dg+Ll3qBsEIAABCEDA5wQQOD7vIKoHgQQkgMBJwE6nyRCAAAQgAIFIEUDgRIok+UAAApEigMCJFEnygQAEIAABCCQgAQROAnY6TYaAzwkgcHzeQVQPAhCAAAQg4GcCCBw/9w51g0BiEkDgJGa/02oIQAACEIBARAggcCKCkUwgAIEIEkDgRBAmWUEAAhCAAAQSjQACJ9F6nPZCwP8EEDj+7yNqCAEIQAACEPAtAQSOb7uGikEgYQkgcBK262k4BCAAAQhAIPsEEDjZZ0gOEIBAZAkgcCLLk9wgAAEIQAACCUUAgZNQ3U1jIRAIAgicQHQTlYQABCAAAQj4kwACx5/9Qq0gkMgEEDiJ3Pu0HQIQgAAEIJBNAgicbALkcghAIOIEEDgRR0qGEIAABCAAgcQhgMBJnL6mpRAICgEETlB6inpCAAIQgAAEfEgAgePDTqFKEEhwAgicBL8BaD4EIAABCEAgOwQQONmhx7UQgEA0CCBwokGVPCEAAQhAAAIJQgCBkyAdTTMhECACCJwAdRZVhQAEIAABCPiNAALHbz1CfSAAAQQO9wAEIAABCEAAAhkm8NmiRTZ3ztzQv75vvmm5/vNf69G1W+iYPp83d67dv38/w/lyIgQgAIFIEUDgRIok+UAAAhCAAAQSgEDD+g2coJGoSe9f+TJlE4AGTYQABPxIAIHjx16hThCAAAQgAAGfEmjXpm26wsYTPXVq1vJpC6gWBCAQ7wQQOPHew7QPAhCAAAQgEEEC8+fOy5DAeXvYsAiWSlYQgAAEMk4AgZNxVpwJAQhAAAIQSHgCN27cyJDAOXz4cMKzAgAEIJAzBBA4OcOdUiEAAQhAAAKBJVCiWPF0RU7+PHkD2zYqDgEIBJ8AAif4fUgLIAABCEAAAjEl8Lh9OOy/iWl3UBgEIJCCAAInBRDeQgACEIAABCCQPoHH7cNh/036/PgUAhCILgEETnT5kjsEIAABCEAg7gg8bh8O+2/irstpEAQCRQCBE6juorIQgAAEIAABfxBIax8O+2/80T/UAgKJTACBk8i9T9shAAEIQAACWSTQPo14OHVr1c5ijlwGAQhAIDIEEDiR4UguEIAABCAAgYQiMH/e/FQ9qbH/JqFuAxoLAV8SQOD4sluoFAQgAAEIQMDfBG7evJmqwDly5Ii/K07tIACBuCeAwIn7LqaBEIAABCAAgegQKJkiHg77b6LDmVwhAIHMEUDgZI4XZ0MAAhCAAAQg8H8EUu7DYf8NtwYEIOAHAggcP/QCdYAABCAAAQgEkEDKfTjDh70dwFZQZQhAIN4IIHDirUdpDwQgAAEIQCBGBFLuw2H/TYzAUwwEIJAuAQROunj4EAIQgAAEIACB9AgULlDQORtIeiJXeqfxGQQgAIGYEUDgxAw1BUEAAhCAAATij0DlihWdwCmUv0D8NY4WQQACgSSAwAlkt1FpCEAAAhCAgD8IzJs71wmct4cO80eFqAUEIJDwBBA4CX8LAAACEIAABCCQdQLePhz232SdIVdCAAKRJYDAiSxPcoMABCAAAQgkHIEGdeslXJtpMAQg4F8CCBz/9g01gwAEIAABCASCwMGDBwNRTyoJAQgkBgEETmL0M62EAAQgAAEIPJbA/fv37fz587b/p/22YcMGW7hggc2YPt0mTphgI94ebgP697c+vXpZty5drcNzz1ur5i2sccNGVrtGTffv6UaN3TF91r1rN3eurhk5fITLQ3l9snChbdyw0Q7s328XLlwwlUmCAAQgEEkCCJxI0iQvCEAAAhCAQAAIXLp0ybZ+u9Xmzplrbw8bZi92eMGeqlbN8iYlOYcBuf7z33T/5v7vE5YvKY8VLljIShQtZmVKlXb/9Fpuo/WZznlcPvpcZdaoVt06vvCiKVDo/HnzbPu2bXbl8uUAkKSKEICAHwkgcPzYK9QJAhCAAAQgECECt27dst27dtlHH35or/R42apUrPSI8JAYebJkKWvUoKF17tTJBg8cZNOnTbNlSz+3b7/91vbs2WNHf/3Vzp09Z9evX7cHDx48tnY659q1a3b27Fl3rfLYsmWLfb50qU2bOtUGDRhoL3XsaI3qN7DSJUqmKoiqVa5ir73yqs2eNcv2/Pij3b59+7HlcgIEIAABBA73AAQgAAEIQCCOCEjQfLd9uzMJa9GsmVtNCV9JKVWipD3fvr2NGzPWvvziCzt06JD9888/OU5A3tgOHjhgX6xYYWNGj7b2bdtZyWLFk4mx/HnyOhO4dyZPtu937EDw5HivUQEI+JMAAsef/UKtIAABCEAAAhkm8Oeff9pnixa5FZGC+fKHREHe3Eluj8yQQYNtxfLldvrUqQzn6YcTHz58aCdPnLRlny9zKz4N69U3tckTbAou2uWlzrZ0yRKT2R0JAhCAgAggcLgPIAABCEAAAgEkcP/efduwfr3bu5InV+7QoL92zVpuX82mjZvsxo0bAWxZ+lWWiZwcIAwdPMRqPVUj1G4Jny6dXrKvN23CcUH6CPkUAnFPAIET911MAyEAAQhAIJ4IXL161WZ+MNMqla8QGtxr78z7M94L3ApNJPpFKzzaL6RYPN7KTtVKld2+HYkhEgQgkHgEEDiJ1+e0GAIQgAAEAkjg8qXLNn7sOCtaqLAbyMuD2cD+A+yXw78EsDXRqfKhgwet31t9TaZrEjvFixS1yZMm2ZUrV6JTILlCAAK+JIDA8WW3UCkIQAACEIDAvwS0+X7qu1OsSKFCbtBevkxZt1qjlRxS6gTkYnralKnOdbWETrHCRey9GTNMDhhIEIBA/BNA4MR/ND1fAAAgAElEQVR/H9NCCEAAAhAIKIF1a9daxXLlQ8JGcWvu3LkT0NbEvtoSNHIxrTg9Ejpyka09OiQIQCC+CSBw4rt/aR0EIAABCASQgOLHKP6LBuUyt5JbZD+4cg4gSlflmzdu2oRx461A3nyO6Rt9Xo9LBwxB7R/qDYFIE0DgRJoo+UEAAhCAAASyQeD48eNWo1p1NxBv2by5nTp5Mhu5cWk4gWPHjlnTJs84tvI2d/r06fCPeQ0BCMQJAQROnHQkzYAABCAAgeATkMOA0iVKWu7/PuEcCjx48CD4jfJZC+Ree+TwEU7klHuyjP32228+qyHVgQAEsksAgZNdglwPAQhAAAIQiACBM2fOWNnST1rSE7lc0M4IZEkW6RCYN3euE5La43T+3Ll0zuQjCEAgaAQQOEHrMeoLAQhAAAJxR0Cb4b04LgsXLIha+9auWWMN69W3gvnyO3fT9+/fT7UsBQhVEE2dV7VyFftixYpUzwv6wVkffeRWcp5p/LTdvXs36M2h/hCAwP8RQOBwK0AAAhCAAARymMDoUaPcQHvYkCFRr8mnn3zqypIDg9OnTqVaXu+ePUPnbNywMdVz4uWg4uaIheLlkCAAgfgggMCJj36kFRCAAAQgEFAC2uieNynJnqpWzW7fvh31Vnje2TSo//bbbx8pb/my5aFAmYofk9YqzyMXBvSA4gxVrlDReVg7f/58QFtBtSEAgXACCJxwGryGAAQgAAEIxJjA6JEj3QpCLMzA5LSgVImS1qNbd1em9qGEJ3lsK1G0mA3sP8B93r1rt/CPQ6+1X+jypcuh93rx8OHDRzy+KeBmWunvv/82ucNOmSTy/vzzT3f4+vXrKT+OyvvPFi1y7Z00cWJU8idTCEAgtgQQOLHlTWkQgAAEIACBZASqV63m9sPEYg/I/p/2W/WqVe2ThQvdgH74sLdDdbl37541adzYmWr1eu1fEzWZs3lJ3sfGjRnrzhk8cJDz9jZ08GD38Yrly0PBNP/44w93TOJG3uCGpjC7U/DS5k2bWZ/evS1/nry247vvvCJs+rRp1qhBQyewypctZ6/37hP6LJovtIqj/Ub16tSJZjHkDQEIxIgAAidGoCkGAhCAAAQgkJKABtYyFWvbuk3Kj6LyftrUqU48bN+2zZXb8YUXQ+WMGT3axYiR0HmyZCn3uSdWFCizdYuWVrVSZbty5Yq75qtVX7lzDuzf796vWb3avd+wfkMozw7PPe9Wi7wDEkhaIfr1yBFTnnlzJ9l7M2a4j69everMxD547333XsenvjvFuzTqf5s+86zlyZXb1H4SBCAQbAIInGD3H7WHAAQgAIEAEzh79qwTBT1ffS0mrVDg0NVfrTYJFwmrmtWfcuVu27rNrSLJRO3ggQPuM3lR81L/fv3cMU/M6Lj27ygPT6DIZC38vc4ZNnSorV+3zmWzbOnn7vNlny9zIkKODLRK8/vvv7vPJXjy5M7tRNQ///zjgnDu27fPfRaL/7q81NnVL6XpXSzKpgwIQCCyBBA4keVJbhCAAAQgAIEME5AZl0RBpxc7ZviarJ6o/Sz5kvK4FRjtxZF5mFZQLl686OLvLFm82GU9Y/p0V6e3hw1z73/a95N7r9WY8DRn9sfuuFZyvFS8SFHr06uXe3vyxEnTCpH25kg0FClYyEoVL+FWZapVqWra36NzwtOAfv1dnoMGDAw/HJPX7dq0dWXLZTcJAhAINgEETrD7j9pDAAIQgECACWjwr03/FcqVi3ortPelccNGoXJq16jpBvSKvyOnA16SKZpE1+ZvvnGHZDKm9yu//NI7xf1t9mxTZ1ImgeSlOrVqO3M7rcC0btnKzp39N4CmZ7727NNN7POlSy0t5wFyOqDAm9q78+Pu3V62Uf/rOV9Q2SQIQCD4BBA4we9DWgABCEAAAgEmIPM0CYjdu3ZFtRVvvv6GjR0zJlRG546dXLkyE/P21Xj7YrS6461kyKGA6vfzzz+Hrt3z44/u2OxZs0LH9EKrIBJMclIgUeMl7aVRHlu2bPEOub9euR/OnGnyzKa0aeMmd25K5wTJLozwG5noqX6KiUOCAASCTwCBE/w+pAUQgAAEIBBgAjt37nSD6w7tn4taKyRctLl/1cqVoTJGDh/hVkrkcMBLC+bPd3V5ulFj75B5g/+t3251x7T6UrVyFXujz+vO/Cx0opm90qOHuz6lcwCJNwmIV3q87GL9yDRPTg1mffSRu1yCyBNLigukc2UqF6vUqnkLV6bM8UgQgEDwCSBwgt+HtAACEIAABAJO4MUOL7gBtjbgRzr99ttvVqViJZe/TMg8QSMxM2rESFecTMr69+3rXCVLXGizvxwL3Lhxw2S+JXfNdWvXNu3LkRhIGT/Hq7NEj/bRpJYkYpRv4QIFTeZxG9avD50mt9E6PnnSJJPQ69alq925cyf0eTRffLrwE8cmrZg/0SybvCEAgegQQOBEhyu5QgACEIAABDJMQF7NtMKiQb5i1fgxaRXIMyNLq37pBfbUNX/99VdoX054HvLepv038tKWWvDP8HMj+Vr7fArkzedi+ly4cCGSWZMXBCCQgwQQODkIn6IhAAEIQAACHoEtmzc7r2alS5S0w4cPe4f5GyUCElMSlXmTkpIFG41ScWQLAQjEkAACJ4awKQoCEIAABCCQHoGlS5ZY0hO53MDbMyVL73w+yxoBeYgrWqiwC+yZ0jtc1nLkKghAwE8EEDh+6g3qAgEIQAACCU9AA24vRs27k9+x+/fuJzyTSAG4d++eTRg33gmbgvnym1xnkyAAgfgjgMCJvz6lRRCAAAQgEHACe/fuDTkGaFivfkxjwgQcXZrV//77761u7TrOoUD1qtXs4IEDaZ7LBxCAQLAJIHCC3X/UHgIQgAAE4pSA3DEP7D/Amawp8OXL3bvb/2/vLKAsKa7/HwhOcLcFFjg4C4trsIXF3V2Ce/DgsLguFiQBAgR3d1s8wRYIDsni7gn8IPU/nzr/70tNUd1d/ebNzJuZe8+Z6ff6VZd861bV/Vbdqn7zzTf7aGm7rlivvfqq2/F3O/gjsXH/O/KIIxwHJpgYAoZA30XACE7frVsrmSFgCBgChkAfQOC5555zHKPsj28eOIvbY7fd3UsvvtgHSta1ReCdNhz9DKkBu0032thw61rILXZDoG0QMILTNlVhGTEEDAFDwBAwBNII/Pe//3X33XufW3P1NbyxzorOphtv4u684w7300+2R0eosV/p1ltudRttsIHHCWKz7lprO06oMzEEDIH+g4ARnP5T11ZSQ8AQMAQMgV6OAETnoYcecltstnnDgF98kUXdccOG9eujpVnROuaoo92iCy3cIIDbbLV146WmvbzaLfuGgCFQEwEjODUBs+CGgCFgCBgChkA7IPDKP17xe3TmmWvuBtlh8/yRhx/uHrj/fvf999+3Qza7JA/fffedX9E67JBD3bJLL9MoP0c/H3HY4e71117rknQtUkPAEOgdCBjB6R31ZLk0BAwBQ8AQMASSCGDs45a1+667unnmnKth7HPUNHt3jj/uOHfvPfe6Lz7/PPl8b7j52WefubvvusuvVK239jpu9llna5Rz3rnndnvuvoe74/bb+zSp6w31ZHk0BNoFASM47VITlg9DwBAwBAwBQ6CTCPznP//xblknnXCiW3vNNf37XtiHwh/7dljh2X3X3dx55/7Rh/viiy86mWLrH4eIjXhkhPvjOef6k+NYoSHvKsdsA2fx+2pOOelk98Tjj7sffvih9ZmwGA0BQ6BXI2AEp1dXn2XeEDAEDAFDwBAoRuDrr792Dz/8sOOFoVtvsaWbf975GkRBhGHwoPndGquu5nbc/nfeve388873K0LPPPOM+/DDD93PP/9cnEDNX4jrww8+cM/8/e/u1ltucef/8TzvUva77bZzqw1dxS0w36Bf5I977Kc584zhnvhwfLaJIWAIGAJlCBjBKUPHfjMEDAFDwBAwBPoQAhCMt956y918003e3Wvbrbfxe1h0lLJIT3hlxYR9PgsPXtC/fHSlFYf409w4qQzStNMOO7q999zL7bXHnv59M9zjN058W2mFFf0zPEscxBXGHX7mt2WX+a3bftvtvFsdBOjtt992HKxgYggYAoZAHQSM4NRBy8IaAoaAIWAIGAJ9EIEff/zR/etf/3JPPfmUJz+srPBCzJ133MltvMGGbtWVh7qll1jSrwCVkaGQsPAZ0sKq0TJLLuVXaIiLd9McdeSRjpWiW26+2T391NNu1KhRjjyYGAKGgCHQCgSM4LQCRYvDEDAEDAFDwBDoJwiwogIZ+e7b7xx7eD7++GP33nvv+T8+c4/fCGOrL/1EKayYhkCbIWAEp80qxLJjCBgChoAhYAgYAoaAIWAIGALNI2AEp3ns7ElDwBAwBAwBQ8AQMAQMAUPAEGgzBIzgtFmFWHYMAUPAEDAEDAFDwBAwBAwBQ6B5BIzgNI+dPWkIGAKGgCFgCBgChoAhYAgYAm2GgBGcNqsQy44hYAgYAoaAIWAIGAKGgCFgCDSPgBGc5rGzJw0BQ8AQMAQMAUPAEDAEDAFDoM0Q6DTBGThwoJt88sndgAED7M8wMB0wHTAdMB0wHTAdMB0wHTAdMB3oUR0YZ5xx/IuCU7zrV6mb8b0ZZpjB7bXXXu6TTz6xP8PAdMB0wHTAdMB0wHTAdMB0wHTAdKBHdWDMMcd0Tz31VExb/PcsgjPttNO6zTff3D300EP2ZxiYDpgOmA6YDpgOmA6YDpgOmA6YDvSoDowxxhidIziDBg1ygwcPdkOHDrU/w8B0wHTAdMB0wHTAdMB0wHTAdMB0oEd1YMIJJ3SjRo1qfgVnk002cTfeeGMyArtpCBgChoAhYAgYAoaAIWAIGAKGQHci0OlDBozgdGd1WVqGgCFgCBgChoAhYAgYAoaAIVCGgBGcMnTsN0PAEDAEDAFDwBAwBAwBQ8AQ6FUIGMHpVdVlmTUEDAFDwBAwBAwBQ8AQMAQMgTIEjOCUoWO/GQKGgCFgCBgChoAhYAgYAoZAr0LACE6vqi7LrCFgCBgChoAhYAgYAoaAIWAIlCFgBKcMHfvNEDAEDAFDwBAwBAwBQ8AQMAR6FQJGcHpVdVlmDQFDwBAwBAwBQ8AQMAQMAUOgDAEjOGXo2G+GgCFgCBgChoAhYAgYAoaAIdCrEDCC06uqyzJrCBgChoAhYAgYAoaAIWAIGAJlCBjBKUPHfjMEDAFDwBAwBAwBQ8AQMAQMgV6FgBGcXlVdlllDwBAwBAwBQ8AQMAQMAUPAEChDwAhOGTr2myFgCBgChoAhYAgYAoaAIWAI9CoE+gXBeeqpp9zqq6+e/ff666/7SvzLX/7ipphiCrfYYou5zz77rFdVbFFmv/nmG3fWWWe5ueaay913331FwRr3r776ajfRRBO5RRddtHGv7MMVV1zhVl11VTf11FO75Zdf3l144YWFwevmpTCiLv7h4Ycfduecc05Tqdx8883uzDPP/MXfRRdd5Pjt/fffbyre7n7oH//4h/vtb3/r/vrXv3Z30n02vf/+97/u+eefdyeccILbaaedWlbOH374wT344INuv/32c6effnrL4u1sRLEOUf5nnnnGHXvssW7HHXesjP7QQw91K6+8svv3v/9dGdYCFCPw5JNPuvXXX98tsMACbuONN+41fVBxibrvF3Tv6KOPdksttZT/O//887sv8S5I6Y033nBbbrll0+NbnSz95z//cSNGjHC043333bfOo10WNu6TWpnQv/71L3fppZe6DTfc0I0cObKVUVtcmQj0C4Lzf//3f96QwKj/1a9+1fj705/+5O6//3532WWXuR122MGNOeaY/jcMWmTOOedshCVMb5Y333zT7b333p6sCIO77rqrtEivvfaam2CCCTwGYFclRx55ZAMvpcH1wAMP7PBoM3npEEE3f4EcTzbZZO7777+vnfKrr77qTj311AYuM8wwg9tuu+28YTH++OP7+8sss4wbNWpU7bi78wGIKnW52mqrdWeyfTot+h5II7jOM888LSvr8OHD3cwzz+zj3X333VsWb2cjinXojjvu8EY25V9kkUUqo59++ul9mZ5++unKsBYgjcA777zjfvOb37hHH33UHXPMMR5PyI5JHgI777yzW2ONNXx/LX188cUX8x5uw1BHHHGE1wHaIJMiXSmQwSWWWMKnt9xyy3VlUtlxx31S9oMVASE3v/vd73xbA9uHHnqo4gn7uSsQ6BcER8DttddejcaMwRrL7bff7n8Xwdlggw3897HGGssxMPRWYdZk6aWXdmuttVaj/DS6MoKDMT/ffPM1wlcRHAbM0Ucf3a/aMACMN954jWcnnHBCB8lEmslLT+IOGaNc4NWZ2TqR5eOOO65RnE8//dStueaaPu4ZZ5zRMaPdrsLs28UXX9yr24Gwvffeex3Esx3kn//8p6//VhIcynXBBRf4eNuJ4KR06Nlnn/X5zCE4EJvrr7++Haqt1+Zho402ctNNN53PP/0NbfqFF15oWXno588777yWxddOEUFkfv3rX/u2Rb6Y/WeS9KeffmqnbNbKy9tvv+1XGCaffPJuqTfsBMbSriI4dfUv1SfVArAi8JAhQ3x5243gtNMYWAFhp37uVwQHlw0aF380aAkuaZoVZOlZBAflRxE++ugjBe31V63IgEEZwdl2220bs8CErSI4uKP9+c9/buDz2GOP+cFAeNOxxZKbl/i57vzOUjqDGuWYe+65m0568ODBPg7ckULBXUQY/f3vfw9/ss9dgADEZpJJJvEz2F0Qfe0ocVGk/ltNcC6//HIfbzsRnBQ4uMhQ/hyCk3re7tVDgNWbgQMH1nuoRuhdd93VYdT1RTn55JO9ruK63pfkyy+/dNNMM4176623urxYuOTS3ruK4LSb/q2zzjq+vO1EcNptDOxKpTOC45zbY489nGbWb7vtNt/Q2Xty9tlnN/5CF6Iff/zR+1b+/ve/d1tvvbU75ZRT/H6WJ554wvHHoB0+y8oQszzhPT5DnJhxCO+zN4N77PnYaqut3FVXXdWh/m+66Sa3zz77uN12283deOON7rvvvuvwe9WXqaaayjc4OpkigoNxhIsLaRGOvyrjHtxioRPT83fffXf8s8vJyy8e6sYbrGJNOumkvg7GHntsX5ZUOXKyVERwvv766wZGkMJYWDm89dZb/X4dZvsl7LNgBjH8028MVLqPLkm++OILR/7Rm88//1y3O1x59pprrvFt4OOPP+7wG3n929/+1uEeX4ryqIAMoCob6ZI+++LqCHs1aC/MOkME0U1WwGL5+eef3UsvveRov3EazFQzkKOT7CUCI3zqadvCiyvtm0mP8B5lQHA94H68AvTBBx84XK7Y16awcd6I891333Xgeu2117oPP/zQ738gPyI45CdMl8+5M8SffPKJx+Xll1/2brfEGxOcMnyUX3Trzjvv9JM76My3336rn/wVnXruueeSGBOAiaHHH3/ch33kkUd8PIog1qGY4PAd/cMQSgn3wTqUXN3gGXQV/aNNFbWBMO7wcxUuhM3RA3QHXaYuaE+UN8wLpJeVqiKDM6cdh/nmMzou4xI3WelYWLc58Ra1ddoluobO4YZE/KRJeKWlsYpVA90Ly00+U21EZcnJX1n/pXhSV9o8/coNN9zg8xaupqtN4nJE+Y4//ngfhrLlSBFmOc8Shv6CvgWdjftkfgdX+jr6PNpb2OcrjSJc6UMXWmihDpOTPJOj64o7vFb1L9LBFMHJqd+ivqdI/5S3On2SnilKS79zrarbZghOSoe5pzbDVXrAPmbdj/vFVDxFY6DKVFUHtAUtBpAH9hGrrycOxirIHO7Xzbj0Kx+tuvZ7gkOFTjvttA2CI2AxgGTU0qmxkoMwMM0xxxy+o8O9iHDaS0E4/hhAl112Wf+Z72uvvbYfzPCND+OkU6JDCFeWxhlnHIcbgeLiisGC4rBZje/s4cANYLTRRnPs36AzypUqUoEBh/sermQPPPBAIx9VBCeVPoczkF/c1TBsYqnKSxy+u7/jn6uVK8gmZWl2D0qK4NCBHnzwwT5eDG90QcJnXCpnnXVWv4cHAxhXyeuuu84HofPi4AfyhE6hW3r+j3/8o99Pxu8yKthsPu644/oDIPAdJz2ItwRDA8MEXWXPFMSOdoFg7OLeiG6ywimpyiOrU0OHDvX5XnLJJf0+tzHGGMPnmXzHq1mKN7wecMABbqaZZvLPEF5+7zxPGWi/EgabBRdc0M0yyyw+XcJQbyIce+65Z2MP2uKLL+6xYOBnI6j237FyibFz2GGHNe4RB+EQOnTwZsUSwUDcfPPNPVa77LKLT592SVrgw9+wYcPc/PPP78vAiiCrx+QNXYpXcGgnav+kceKJJ1ZuqkeP2KTPwR7sEVhhhRUabpUhwanCh/LQr6BzRx11lI+HfIaumRhatNtNNtnEY4tOLLzwwj6P9Gebbrqp37eHEc1sKs/zx0EbKR0SwWHDO7qn8Fy32GILjx/1d9BBB7nZZpvN/66+uI5uUDYmsli9Y78lLqHUOa679KsQzjKpwqVKD4ibdol+UjY2Wksn+I4uo2Prrbee79eFw7nnntshW1XtuEPg4AtGOfpEvOoHOAxGq8ZV8Va1dYwelQ39Jm72d7D/UCv12uPBJJ72iJ1xxhmVbYRiVOWvrP8KYEh+ZNJnyimn9IcQoXO4VKO/kHiEMZHyUEfgN2jQIP+dicYyqcKs7Fl+U1+AkUZ/gss47Y3+SgJhoE9kwpO2zhhBv4+9UNX3KI140qpK15V2fM3pX4oITlX9klZZ31Okf830SVVp8Xtu3dYhOGU6zPguT5Kw3TJZwD5OftNkeFk8RWMgZSqrA/ppJvNpG7jb03+FtizjBWMjh3Kp75p99tkbYy/x94T0W4JDJ49BwkBHhWgFJ6wEbYjjdw2qMnS5BwlAGOz5zl4N2DQGB6eJcY8/Bm4JnaPu0/gQDCoZWPxGuuuuu24j3CuvvOJXdPQcJAvRAIkBlStlpIJZEAbBP/zhDz66zhAcOmeViYEuJWV5SYXv7nvgy4lzCB0o+GO8xrP3OfkSwcGgwliDHNNZECdkmI4qFEgyv3HCFMJsKN/D0+xYDaCTQe/C09gYkMFeKz7MEKPvDEDIe++95yAalEWzPhhAfBcRZW+ECA5hwIH0Q4JTlUdmcDBseY4/9JTTCDGqSAtMqoSy0FHyPBMLrJAyG6f9cXSqCAPOvPPO641qPiO0b57D6JdwYAT3YpdJtWH2rki22WYbHzY8QQ9ChZFBvhCeoywMAJLNNtvMP8cJVeQFokfeSJf64sASBj7wiAkO2GBcMXGSK5STPNH3ILRjkV8RnBx8MIqYrAlPOKLvEsEBe8qqNkFaECvKhSGLfklPuMdEzEknneQHYIwo/RbqkAgO4TlJjtk/Blr1HRj46BGrcppYUl+cqxvkE6OaNHRYDH2q9tahx2H9eRCDf1W4ELRKDwjDivmAAQN8PtgHQ//KjCiEi7yhG2BEG0SvwRpdkOS0Y4VNXSGKpBO7qOXEW9XWSY86Jv7YRU3ESgSHsBhahBXBKWsjOfkr679SWOgek4cYhxAbCbPctCeImWar+U17eHNd1HIwU5qpK4Yskw2aoKG/J69MQGJjIIxRkBoJbQ5cIQNVfY+eCa85uh6G1+ec/oWwKYKTU79VfQ9xp/SvmT4pJ63cuq1DcKp0WBNG8Ymb1Dl9iKQqntQYWFUH2AxKH/2C3OOBwJjDBDZ9KW7GfGdMQ3cJxx61npR+S3CoECqsjOCoY6aiNKhqkOUep4whnE7Gd/5YJkZwMdC9kOCwHKz7IjiEDzfl4+ZCB8aeFhliMvLodCXMABNXjqGoZ8pIxWmnneZnY9V5dobg7L///j5vKLriUx50LcuLwvTUFfIK1jL4yYeMRvSmrojgQGYgONtvv31jRpqZOQywUDAGGNxkFGC0YvCAWSjEgw6wCihhpYPZcgnGNbMul1xySeMP8sJzMl5VX+SNtJDQ0NXAFBqnOXnEYCcdZplCwXCjDX711Vfh7eRnDq0gDvRRQsfJPeWRlS2+M7OkcqpDxphUmVKdO3FS3zxPm5fccsst/h6rTxLwUnvG6KZO5F6mMLiKYYjwm1wJRASY6QolJDisqDEbW8dfm1l/yGqMr05HEsHJwQd3B+qEPlEknv2IIpErrriiTytsExzCwQoW+oFAhsGRtqPVQ5U3pUMiOKw+YSRJDj/8cB8PdS+RsaC+mPs5ukE4rX7jOiHRZJNW53Q/vlbhUkcPMKTBR+MEackVmCOwQ9HYJLxz2nH4fPy5iODkxJvT1lMGJnlQvakv455O3ITgSIraSE7+qvovpRFfpRfxCh6rfdRT2NfXJTg5mMX50Xf1R6xShoI7PH2chIlQ/iRqN+HKXxGueia8Vul6GDb8nNO/EF59QOiillO/OX1Pkf7V7ZNy0sqtW+l+Tp9epcOQNfp6JisZYxAm+yaeeGLHhKSkKp7UGJhTB/T1tAl5tSg9Vt+5H7qqaUKpapVTcXTVtd8SHB0ywIwsLi2pFZwUwZHCUqFa3sethe/86ZSfzhAcOplQcEHDYFIaVBp/zDJxD3KUK0WkgsZDGhjH+CHzJyOJNDBImbVghqdKGPCZgcXoCn284+eK8hKH64nvzL4zI8F+JP3hPgIWzHKHrlE5+RPBid2yWDFQ3YYz44oT/DAC5P5B2qFgiGKU8q4iGUIYbhhNCLPfGNoQEwa/+E97p5i1kvsYq0xhh0k8zHRS9pDgKB9ledSABrELRUQfV7sqUZsLDSSRD97hgOBWR/7i8um7jO1U5670ccMEKxm8MnKIlxl/BDKDyx6iTpwZtFhwgeM5VnIRuaHGM1oiOIQFf9KsI+zf49lDDjmkw2OsVHBfBKcuPmxGp0+U+ysEkX4Gt9MyAWfSDVceFD6lQyI48SEDOm0JvVafI2M0JDg5ukH6WnkPDWqttGk1TvlMXQG1OyEAACAASURBVKU3MS6EraMHWq0JDWoMAzBbZZVVOiSNIcF9Zkpz23GHCKIvKYJTN96ytl5kYKbqKEVwUm0kN39V/VcEReOrSCQr5KGw2gb2TCxK6hIcPVeGmcLEV2bhSV99Tfx7/J3JUsiz3OVZOZWkcNVvqWuZrqfCcy+3f9F4IIKTU7+5fU+R/tXpk3LTEg5VdSvdzyE4OTqsPov2g2CnQTBCqYpH9avJ85w6IH6NVfGEHhMz6CqrsBImcLgXrzbp9+669nuCA9B0JrkEh5kVrbaw4oGLgVZ1cD/QcnJnCI6MMSkBAxzKoj8UE/cj/aF4uVJEKticqPjLrnKPK0oPwx+lYu8FDUfC7HT4nftFedEzPXVlxodlf1bImCEO/1IDSE4+iwgOz2q/VjwzgpsSft+4MjFjgwtLTHB4XsSLU344ypTZcK2aQYCoTzbIVgmkgdUOwmNsyz2O51LGKfer8qgBrdUEhw235FMERy5mLKWXSdy5h2HxcyZOZsDYfMsqhPZIcY82gnuPVoNYjSE8+05iYVWD33DdQoqMDA0auCsyEwdZzTVqiFeuPhCdUOJT1HLxYUVNqwzkH7IM4aPd850VpjKpY0wQTxHBIR6wQA9FsnIJTqwbpMM9yCsTBcTHJBLGa+jyWVauIlx4po4epAgOBgnYstodCoSb+/RHddpxGEf4OUVw6sRb1daLDEwZeeEERS7BqZO/sv4rxEGfacea1JE3hn6TXtIuJc0QnCrMFHd8xa2WumcfaJng8ss4xSoznh/02TzXGYJTputFecntXzQeiODk1G9u31Okf3X6pNy0wCGnbqX7OQSHOKt0GPdJ6pe9LtieuGqnJkbL4onHwJw6IG8aq2KCg61HnkKPJFzeuMfqYU+KERznvH936P6iCkmt4PAbhg4zeaxSYIBiFGLQhLPRrSQ4dMRarUFpYO3NShGpqEtwaGihQpMfZlpRdpbMZZRwn82aNMjwHveL8tJs2Vr1HEYthzekRG5PzPxpZjkVLr5XRnC01wOdktB5Utds+JQUERzqgbDMmmNIY4xL2N8FIWcTbYw/YTCEEVYocBHC8GMgxxgkTlbtkBTBycmjBrSuJjh6aSH7N2Jhf5Nmq+LOPQwLOWf5H6zYh8YLECHlbEyHNGJghxMhci1ik30sch/FFx6pIjgMGvLr5oCHeE9WHL++sz+IepKrnu7HBCcHH0ixVq/kgkHc+PnTB2kDqTZfKy2uGOlIHWOC8DIk4xUcCD1ph4ebdIbgkJZWu+izmYzC+GBfQ5WU4cKzdfSgWYKT247LypIiOLnx5rT1IgNTRl4zBCc3f1X9VxEucq/RSrbCac9lSIDrEpwczJRefNV+NbnDhr8zsQnppb/G9ZhDWHBVQuifaDfNEpwqXQ/zEX7O6V8Ir/FABCenfnP7niL9q9Mn5aaVW7fS/RyCk6vD2t/CATRMinGoQChV8cRjYE4dEL8RnBDlNvyMEUDj5y/1os84yymCw0wPrBAjRP718XN8ZxZWaYW+1epQ+U1GAeG1KsR9uRqF8eLupfjCDZEYpAya8epI+Gz4WSc4EZfc6fidDhKCFv5pnwNhMej5DaNeM93cD/cUaD8I+zswIDC4wZlw4UCh/BTlRb/3xBXs6TTCd/qE+WBw0ayfTi0Jfy/6XERw6Fy0Aojvr4QBANwgnhJWlVIrOPyu8DxDBxfKSiut5OMKiQ+/MzvI6TsIOhSuHLDyQFyQPSRFcJRmWR41oHU1weEYavILEcFXWMJsJG5VIgx6seo999yjIB2unBRDPPzJBTV0VQtXMCFErDJABkM3JwZJSBFESW05h+CQEfVRkAoG5ioRQaDNoUsSGUhyUcvBhwEsdEGjfLRjygc51sEOrFiF+2U4SEL7AuoYE+RV+Y8JjkgDq+uSzhAc6o09bbgeUZYw/4q/6FqFSx09aJbgkLecdlxUBu6nCE5uvDltneO6aTfhnjXiV5sKN+fjm09YjDRJURvJKXdV/6U04qvyEbuZ4p1B/kL9q0twcjCL86PvuHmTPnYBhruEFRvwxasDo5kwnJooYbWfe80SnCpdVzrxNad/4RmNB2AjyanfnL6nSP/q9kk5aeXWbR2Ck6vDqnfqGVf6WKriSY2BOXVgBCdGus2+a1YVxWDQjv1u4+wyIBKWPxg7wqyunkd5cf3BiMAQZAZWrkE0Kq26YDBztKM2+ylOjBl8OFEczZjzm94ZEuaHoyzDMOSDDYgct0nHmyMYqRhkSp/3+JQJs1oKG86k0qHqvjY3kxfdS11laCm9unnRc119hQSQf51IlUoPY4wwYJJaFYmfgTyKzKEzPMM9DGhm6IgLHaHzl+iIbYjBlVde6U9do+7Yr8Osfawj8hmPjQvi43nVCStskFL20rD3DP1D6BT5TYYfKw88o2OpIQR8Z7ZQkpNH5QsSF4qOe9bKSvhb/Fn7WSiHhL1L5EcnNtHu2CjJPUgOB3+w2gbBDjfcypUCfcSIxpAJRe5C4aQEbn/Ey/HNsYjsE14revI/Dg+OgBQQh3ynFY9W39APsKcc1I3KhhtClWizPAdLQBhwo9UxvBytzMEIOfhoAAt1i4kJcEXYoM8KF3lj1QqDFB3A7Uvkigkgfscwi/Oe0iGIIeHRByZrEJ7DgIDkhe0L3SbsxRdf7MPxL0c3CEf/RP/J5BD6DyljlYsySecbkUYfqnAheK4e6FAE0pdo5S70pYckq8/Qfricdqw4U9eRI0d6/FgpDifEcuLNaetydSF+jHL87zHIOfSDekOPaBvsi+M7f5wYSvtCitpITv6q+q8UHtxjkpJVECatdAAF2KDT9FmhDssV+Oijjy6KrsP9HMw6PBB9kesPrrKs8kPCmDhRn6xJVFb2OXyA1WtNKGLEar9ZEa5Rcv5rjq6nnsvpX3hONgX9kiSnfnP6niL9q9sn5aSVW7f0k+i5PCVU5tS1jg6r30tN1FXFkxoDc+pABwaxkk//JKGfpoyMpxL1h+FhR/qtO6/9wkUNIwqjR52qrgzWnGBFJxyLDM8wLA1F76jQ/fhKpyThRCr9TgcK26YzJV0GMyofg4qVDoXjCjGKNyMTJzNg6sAIx2eIVdUAzcCGoSLjJEyLfISKqbxzVQdKeHCS0JlyD4ObfGqTbBhv/FnHszabF6XdldfwsAjIS3gqCOkyAwpJDkkigzbuAkXCvhit0ISYYGxRfxinGF+sDIXCniXt92HfB3Uk8gUxQRdjwVUypTeEY0afgVJ5wIiGZEowRsgnBgcdJIcAaOM6bnKhjkK6WCWqyiP6KkObdBl0IXYauLnHQBca1MqPrrh+Cm/Kx4lerJzJvZF2JT9fMCRvKiOGFjOwYWeMeyd+9cRJGxfBU3pcqZPQnYZ7GNcygMKwfAZbyoHrE66NfNbqKAM/kxEYIeSLtHWyDAcQULfKL2lAcuWCwH0GEwzEMmGihnQVDySUSQ/6EQYzDYJV+GDcUAbqDIMKXYdAMjMqYbWO1WulRbpyzaXMOu2R3xncdcxukQ4RL4YZz3GgAAdckH/aouqG9gUm0gNWxiApdXQDEqHnlXdd5YKnMsbXHFx4pkwP+B3SreOvqVfqn4kEVsnIC30Cq1QY1azmKn+0S83iV7XjOO/6DqEKdY32HxLFqnir2jr9EeOQ2h/l0+l77GfD0KA8lJ/2gPFN+2Q8ZfKuqI0o/1X5K+u/FEfRlX17HPBA/0gbRBepB1bmENoNbj2qO/QUbwVWUcokB7Oy55k00KZysAMvkRaeg/yrL2V1n/aA6zjjChNhbPyvwjVOP1fX4+f4XtW/YPRqjyflwUNG3gZV9Uv8ZX0Pv6f0r9k+qSqtqrpl/GDSS5PSjFdVxLiODjPBh1dNOLapTqriKRoDy+qAfjy0YxgXGMvRL5WRfgybkcUA2r/ae92Dc1SOVlz7BcFpBVCKA/cXjBQqr+gvND4xPlhSxFUG4flwVlLx5l6ZXcIgZFYvpdy58XQ2HB1T1QpYZ9Po78/TgWhPhLDgtDutFOgeV8Khl5oFD3/TZ4xtSE2K0GsGHv1i9SpXt+rkUfno6iuzspQhhRNpY7iErmZxflKGS+pe/ByrEWVkNw7f6u8YGKzgIJQPQp6SInyoc3QEQc8wTFMCruCro0pTYZq5R9+Ia05RvTUTp55hb4JWuCALbMhmNQFXTIyueDJDz3HNxUXPdLUelLVj5aGZa1W8uW0dHYwn3ahTxgz1T5DXcHUkJ79l+Wu2/wrTpXz0j4or/K3Zz7mYlcVPO6S9qW3GYWmrcoXlN3CtOmwljkPf6+q6nguvRf1LGCb1uax+FT6n70npn56vc61KqxV1G+ZHepczBkNmi94vmBNP0RiYUwdhntv9sxGcmjWk420hLSii/lhm1+yl/P1rRm3BDYGmEWDGO3yhZdMR2YOGQB9DQO/ooM9OCbPkkEMTQ8AQMATaHQEIHKt5NsFcXVNGcKoxaoSA3eoFiSyrh8LsI0uGRadvhWHtsyHQCgTYT8CyMRsEcT8MN7q3In6LwxDoCwiwkZ3Vdty+8CNn9pIVOdxQ2EMpF8e+UFYrgyFgCPQ9BHjhMhPouFLiRhy+gLbvlbZ1JTKCUxNLZgHZWI9/O4cQsNmMK8e8suem7rJ7zeQtuCHQQID9XBhu+F9r42njR/tgCBgCHgHcorSxNnQrps8u2rNm0BkChoAh0C4IcAiA9rqwTxHPIZNqBIzgVGNUGIKZQPzQi3xjCx+0HwyBFiCAHzsvMIzPwW9B1BaFIdDnEGAvAwdFsPrOiruJIWAIGAK9BQFWn+N3D/aWvPdUPo3g9BTylq4hYAgYAoaAIWAIGAKGgCFgCLQcASM4LYfUIjQEDAFDwBAwBAwBQ8AQMAQMgZ5CwAhOTyFv6RoChoAhYAgYAoaAIWAIGAKGQMsRMILTckgtQkPAEDAEDAFDwBAwBAwBQ8AQ6CkEjOD0FPKWriFgCBgChoAhYAgYAoaAIWAItBwBIzgtgpQXxV144YVujTXW6PBW4RZF35JoeBsyR1z/9a9/bUl8rYzkiiuucGeeeWbl33nnndfKZN1nn33m32jOMbK84byd5dBDD3Urr7xyraPIOWHt2muvdbz5mPd+FAlvZR4xYoQjjX333bcoWO37zeQ5lcjNN9+c1I2LLrrI8dv777+feqzl93jX0KWXXuo23HBDN3LkyJbH3xsj/OGHH9yDDz7o9ttvP3f66afXLgJvT+eEoKOPPtrtsccetZ+3B8oR6EmdbfWYU6RrrU6nHNHu/7Xdxilrs92vA61Isb+1HyM4LdCaDz74wK266qqNc8rpjNpRIGC8B2K11VZru+xx/OHGG2/s80ce5557brf11ls3/uabbz7/24QTTtjSvPMWc95pRJoYae0s008/vc/n008/nZ1NXgY63njj+eduv/32wufOP/98t8QSS/hwyy23XGG4uj80k+dUGq+++qo79dRTff6oqxlmmMFtt912XmfGH398f5+X7Hbl250xFHkxJG+RJg+8E8vEueHDh7uZZ57ZY7L77rvXhuSOO+7w7xED02WXXbb2880+APmH+DM51Velp3W21WNOka61Op1204d2G6d6qs22W730tvz0t/ZjBKeFGjrddNP5Qb5dCQ6z9BdffLF75513Wljq1kX1yiuvePwwdJ5//vlfRHz55Ze7VhMcEuEN56TZ7gQHYnP99df/ApeqG2uttZYvXxnBIY5HH33Uh2slwWk2z0Vl0stNjzvuuEYQ3m+y5ppr+rzPOOOMjtnFrpQhQ4b4tIzg/A/lCy64wGOSS3DuvfdeB2mVsHrY3QRHRnGqr1G++sq1p3S2K8aclK51RTph3fOuu1Z7D4Txx59T6bV6nIrbYJyH+HscvifabJwn+14fgZ5oP/Vz2ZonjOC0Bkcfy2yzzeYH6XYlOC0sapdEBfHCyOHv9ddfT6bRSvcpJbDOOuv4NNud4Ci/da/MUoNpFcHB0CNcKwlO3bxWhR88eLDP4wknnNAh6JNPPunvk3/cnbpSpC9GcP6HMpMPYJ9DcCA2k0wyiSfUikG6150rOKxuko+ff/5Z2eiz176ks3V0rVUVuuuuuzpIYndJKj3VYSvGqVQbLCtbKnxPtNmyPNpveQj0RPvJy1nrQ/U7goMRzdvf8dtPuSZ8+eWX7rHHHvNI48Jw4403Fr49loGRGerbbrvNfffdd64uweH5l156ye8BKXpD7TPPPOM++ugjPyuN4XbTTTc5ZqxTQtnIb9nb7b/++utf7DWpU+YvvvjCvwmcdMCnlVJFcFKrF+AOdldffbXfY8LMV0o+/vhjx7I62PA5lHDg4HkMV+q0COfwWfDA/1t/uIQg1K3uhTPVOfmF3L377rs+n+yf+fDDDxtJMqjgEhlKVZwhwfn+++/dfffd5/Xk22+/DaPxq2ZFBKcz9Z7KM77Ad955p2NWkLjjvHTIWPCliOCg1+SdP7Xf4DH3448/euJzww03+HopWuXJCSd9iQlOTnuuqivyXKfNh2Ws+tzqtD/55BPfH7388svusssuyyI4L7zwgptmmml8WPYC0kb+/e9/N3RPBAf9v+666wrJag7WVXgMGDDAsboZCzoQyk8//RR+9Z9T934RKHEj97nXXnut0X+AEe0WYSxQvxJOpFWNa7HOEl7xoBfI22+/3bgX9+2dwbuzYw55y9G1VDo8S5/+3HPPlY6zRfjRT0Da6Vdw4QWz0A02B5e33nrLXXPNNY5rPPZ44IN/ZempDiE4VeNUWVsvaoNBNjp8LAofE5yubrNlY7gyzNjIOM8Yh12TkqLxlVXAxx9/3D/CflXGJklOPVeNaeggGNFu7777bj/hyNiXkpxy1Bknmm0/dWxD8GNV76qrrirst1Nl7cp7/YbgoKB77bWXm3XWWb0v/zzzzOPGGmssP4gCMDPAQ4cO9feWXHJJt8MOO7gxxhjDd2x0bvGMMUb1tNNO62d1dtttt8agTdhw4CmqPDrUBRdc0M0yyyw+XZ7baqutGo3ygAMOcDPNNJNPn7S1l4FwGAhxw2BzLrOR5Bs3nTHHHNMtvfTSfjM0RjLGJIM5+02WWmopn626ZWYD8bjjjuv3G5Ef8lG1KlBU/tR9MKF8/MUrOJDReeedt8NjdLDkA/wZhKjPRRdd1IVGBIPeRhtt5FD0Pffc07GXBwzYKC7RwMEm58knn7yRh6mmmipJgvUcV9zq2C9EnsEGAxpB33baaSd/H/9ppCy/hB82bJibf/75/TOsVCkvHCxw0EEHNQh02PGWxekTdc7vMyB/+++/v9N+Fb5POeWUyVn0eAWnmXqnYyzKM64etMOjjjrKrbDCCr687AHKkRTBYbA/+OCDfTzoJFiGwmBCWVdffXXHrD1ujuzhYcAJJTec9CUkOFXtmXSq6qpumw/zXvW5lWmD94477uimnnpqt/POO/s6HH300T3+VSs4tMGJJprIh1188cV9X0JbJ3/oJEYkhHy00Ubz37l34IEHdiheDtYdHgi+UOeDBg3y/QFxs5+KPgH9o09dZJFFfJ+CznPwCbP2jAuLLbaYPzyGfOIqxJ6jP//5z42YIei009TsOgdgoHf0x/ytv/76lQeF0BeRP/5wv5RhTJ9NnzfFFFN4Il81rimDsc6yn22CCSbw8SvPHNihvVRnnHGGHvUuzWVjVSNg9KEVY06OrqXSUVYwdunHOUSG8ZW+f+GFF27gX4UfE5iUnXqgP2av7RFHHOGjr9JDSCL6vPbaa3sdnnTSSb3NoLylrmXpqQ6rxqmqtl7UBlP54V5R+O5qszljOO2PfgObbJdddvF1Rh9C3qlj/orGV+y9TTfd1LcHxgVWz9T2qI+qegajojGNyRvsANlvHE6jPZykwXh81113NaCvKgcB64wTzbafurYhk+8DBw70tqX2UjOBhO5jf/WU9BuCw4oDCnXsscd6rJmF4TsGMcIMGad4SbFpDBAVKo6GgmElYaaLjdtsgpdg6OvZKoJDY8NYpyHxGaFR8jwGA8JKwOyzz+7vzTHHHO6JJ55wMOQNNtjA32MFSsLAxLPMoiIY3TI4KPcbb7zhZ/3POussH04Ep06ZmYHCgKexI++9954ngGATryj4AE38I25hyAZyjPx99tnHrbLKKm7iiSf2RkkYLWQAUiPhGZ5nUJMwIGFMazaHGZRf//rXbrLJJvOzYITTwAHe1De4aN+K9EXxpa7M6oE3hDis+z/96U+elGi1oCy/6AGdCnpBGcYee2xfn+SNAZWVPvSA30KCUxan8qoVHDpZ9mDdc889fjM3cTEgSAc1YIUEp9l6B8NUniGfdOqhqyGdYF2Cg6EIgcL4g7BQFmb/aZuhsLpAfWNgSpiRxNDBwGMAQ3LDEVb6IoKT0555rqqu6rR5n+ka/1qZNn0V+KH3CPpNP0odVBEcwkM0CcueL4l0j/tM6EA2tCcMfWGgRnKxVrypK/3oOeec4/NA/fMdOfLII/3f8ccf73UGAvbNN9+4k08+2Ydl9RcyxIosgziGsoSVdfIOSQ6FGWEmniCEYT/ACnGVYHQR5957790hKIaDJmiqxjU9GOss95dffnkfvwgO9zAISVMEpzN4My50ZswhPzm6lkqHZxkzGZ/Ig4R6oHwQPCQHP07X5JnQRS0HF/SI9DHQkWeffbaS4BAulR73VYdV41RVWyeuVBvkfpGkwndXm80ZwyGwYI2tI2FikXqjrVJfReMrpz9KTwmPHXHSSSf5E2cZT6pstbIxjTbPyt0CCyzg8zLXXHP5VW9WaMkz6TH5BhFCqspBmDrjRLPtp45tCLZMjGBnSdR3QW5y+jo91+prvyE4dOJUgDpzFI8GweyOBOMWheMo5VBg9RiwX331lb/NTBDhHnjggUYwFBQCwP3QyG0ECD7gekE4jtC95JJL/J9mDTioQAMhR07H6WA0cy80EJkV4N7999/fSIVZSu6FKyHqkERwCJxbZho5G7yVX64Yx6SRa5w2MlfwISQ4rMocfvjh7pBDDnHrrbeenz2jYw9l3XXXdfxJCE9+zj33XH9LmyCZ8QjllFNO8djrngaOED9mZIiL2ZccwUgnfHhMLjO/4feq/JKOBmBWN2JRPkOCkxOnCM7ZZ5/diBIDEtJIntEBRPoREpzO1nucZwxG2hKrjHLde/jhh73LaCNzJR+0ggOZgeBsv/32jZUtjE/IfihqG6xihsKKJ2Wn3SG54QirMong5LbnnLrKbfNhWXI+typt+hOIfNxHQsLBs7MEh5PwQpE7m9w0c7EO40h9hvAyo66+ljCs6tMHMXHFCrj6TowRDH5WZ3mOZzBKWImRQEJ4JnS1RNeZ1WSQVTqsxEDYco6kxyAGU9LC/QXBaA+/54xrPBfrbHhPYyL3IHmkKYLTWbzVpzQz5tTRtVQ6rLShqyIYlO/NN9/0M/2ER3LwSxGOHFxYMQdL+inVfzhu+wwk/qXSI5jqsGqcymnrKcKSyErjViq8MO/KNpszhjORhi1H+w0Ftywmt/hNK6BF4yskmbpi0ksum8SVU885Yxr9BvETn4SJFXlpMGFdpxw540Rn20+ubcjkIGUL9YC+knvyXlGZu/vabwiOgGUAovPWsjODjUQNVn7gur/QQgv5yoKJI7gHMJjBckOBQFGpVQQHlwvCYZCn/tTA1KGFA9Att9zin91yyy0bSYtwaVDiB81eaE8I96SI4WCTU2bKSSfBc6n8toqhhwRHxoUKyYoRBmxKcBekA6EuwZXZF4QZNL7jwlAmuTiXxcGgQ1qssiCUhfzEroT8VpRffmM2iXggsrHICA8JjsKUxSmCE7sT6mQl4SVdEMFpRb2n8qzBkqV6TkOT8aaylF1FcGKXUU6GYTADu3DGFiLFvdBvnvhxC+C+SHNuOJ6Vvojg5LZnlausrhR3VZtXXHWvnU0bkgxuTDyEkrsHh2dU/6kVnLjvVT+tvrcu1mEew89MWjEpEYpWRZiNZNY4FgZ8+iX888GASR4JM7S4I4VCf0w4ZpBxd8PgZVKI09tyBaOBOK688kr/yDbbbPMLlz1+KBvX+F16JZ0N74W6FhOczuLd7JhD/uroWpwOhAIvC1wLc6QMvxThyMEFMio3d1acIaw5kkqP51SHYX2l7AGlUdbWU21Qz6WuqfAaL7qyzeaM4fJgYeUlFlZhaT+0P6RofMXmIhz9Qig59Ux44VM0ptFuiZ+V3lBWWmklf5+J0DrlyNGFzrQf8lhUv7E9zCoXZWPCSMKkEPe23XZb3eqRa78iOGxqZUYQ9zPYPW5AdQkOBisVB8mJJfeQAS1NYrSXSUqJcZMg/ZDgcA8CgjGAsciMAoab3O+URjwIcD9HiZlpJ03eAdKVUkZwSDf0VeU7RJJ3+uBDy6ZC3MnIpwx23P34XmVQ5OJcVXZmkEiPAQhDIXRhzMkvYYo6YH5LkYUqDHiuiOCwskV+DzvsMII1dEEEpxX1nsozK6Ga0SJ9VhtjQuszlPhXRHAIykBLfLgBIBg5MjDocEPBlYGwuLflhtPz0hcZi7ntOaeuFHdoxKTavPKSe21V2nJhClcDyUOdk3lkDOQQnHgwzcW6DBdm8an7cHVV4dnrx2/qQ3Q/vLK6zNihFX32WrAqyT6wUNTuINyQJ+pUk1dhuLLPrDySH3QbIxx9FdnTc1XjGuGkV9LZ8F6oazHB6SzezY455K+OrsXpcBgDuOGWWyVV+KUIRy4uEBC9PoK+KMflOZVeUX2l+oactp5qg2U4pcLn2A7EmYtVKv2cMRxvB+oat6hY1Ablklg0vhYRnNy8V41pRQQHF3zyzmRInXKoPYdtN9aFzrQfcMytX8Lirks55Cmho6jjSdW4frr6e78hOHRiVEB4ln0zBAdjCLc24oJIhJJLcI455hj/fGqAhQ1r4M9RYqUvtg6Bw0ebZ9lvEko8CPBbjhJzqhCzYaFrRBgvxk0rpIrgkAb+rhgS+H3iMsdBDPKhZzWAepFxIr/aeKaWeDBkQg1j5AAAHqdJREFUtHeoDs5l5cRVj/Qx6pkFZgZPkpNfwhZ1wPwWk4XcONXJx52N3CLZZ4NIF0RwWlHvcZ7ZSyEyQ+esVU/8xnOkjOBoEGEWTSLf53iVkb0X1JUmAXLDEa/0RcZiTnvOrSvFXTZwqWy511amrb0rsatNdxGcHKyrcNH7b3TIBHuJIA8IqzLohfZmpeKCkKuN8Hu4/wbyoRPI0Kl4RjgVX9k9+jv6c/LEbDJur6HkjGuEl15JZ8N7oa7FBKezeDc75pC/OroWp8M4zSQkuKmeQ9zUN+fglyIcObiwF5a2h53AAUdMQpIf9v2USSo9wqsOw/qKjdrctp4iLGV5SoXXeBGv4MSTEjlYFaWdM4ar/YV74hQfE6Bgrn25ReNrEcHJyXvOmKaxKV7B0fjI3r065cjRhc60H/DLrV/CMvGPzQOJ59AlPoeHsKg+uvvabwgOAxKKjnuBhA3qdVdweFYGY+gKQ8ci4sMMSplwjC154RQiZhMlzAKwpK6N0jlKzLPMVmEossLBCg55SUk8CBAmV4m1lIqbRSgYC8xoSvCVD93idD/nmkNwmOmgI2OgBkNO4ZKwMsc9ERx8WvkOOWPQkFA/rPrIrz8XZz1fdKWTZJmWNDFuQsnJL+GLOmB+U2coF7XcOKWvMcEhj+isZqKlC6HxllvvYVnDz3GeOVEqdBuhY8SAY/DPcVUrIjiQMR3CgO+9RDNksfvCaaed5usJFwgkNxxhpS8yFnPac25dKe4yI4Y8UF50WptTfSEK/rUyba184WpFHiQyRHL24OilrBx2IZHuVRlLOVgrzqIrLr3af4MbJpMkmuzAEGGVBGKREvpWxg0MVgmftf8G1zYdNcsqO3vYYsFlln1nuSI3HfqV2EDKGddIR3olneUeK8zE+Ze//KWRFbWDE0880d/rLN6dGXPq6FoqHR3Iw8x+OCZymBD7X5Ec/DiOF5wYMyQ5uHCiaegezSoC8cQrfYpT11R6/KY6LOsbctt6qg0q/dQ1Fb472mzOGM4YwgoqY0hoe0ByOeCDiVntwyoaX4sITk4954xpIjjhqy5EwpmUJv065cjRhc62n9z6RV+wubAVIPOacE7pUXff6zcEB6OKzoUBFH9mGiyNAr99mC6KLL987aNQZeiIP62saAMVM8Wc+c1KCT7pxM8fBv/IkSP1+C+uMH4GPsJCctigymw6J3tpgzwPyX9U/tfc09JfeKILrkY0btx+WOakA2dGlX0hYceOQUGaDOiS3DKTB5WPWUTSYU8Ox1xr9hMcRBpFHpROzhU3M6XBzBSNhT8aPgPYH/7wB19fDBAMHIRlFY5DA1gNAz/uUbfaj0ReucfmQToZDF06vXCzXy7OOWXQxtJwpZDncvOr00eYSY2FAZaycBJanTjxg+W5cMUQsoNRJt9k4mOVg3CsREpy6l1hU9c4zwwGpEF7k7CKkjIE9buu4aZMOngIEffYG6FDHjg4gY5ZwuZSVvqYWdLmXIxa3Dlp5yIIueGIV8vxWrnMac+59Z+ri9pkim9/lbQ6bR1gwkmDDKJsBGbGTrpTdeiI3D4gQxjskM3cfigH6yo8wE4bYjkcISQrlCO1/yaMk8kkNo4jGFQYUOgYKzfs5VKfy74k+if0TcI9Dh5gMidXeB8XB9gwDsXEK2dcI51YZ7mn1SraHu8RY38ldcgf+4lefPFFf3pdzlhVVJbOjDnEmatrqXRo7zr4h/Jj3IIXbV/kPAc/ueoy3jOpwMEzTCpW4QLBYfyRPrCKALbh2JPCLZUek3I5fUNuW0+1wVRedC8VvrvabM4YPnz4cI8tR7WrjaDT4B0ePFM0vmrPCJOhGhMoe05/kzOmieCwH484EfKF3RZOMOSWI0cXSKMz7Se3ftFN2hl2A6vMkB0mviFzIeH0he7mf/2G4GDQaRM6AwwDq4xRjHSUTIM0jQIjGeNJjYt7VKAMM4xFzdZjKNLp8TwDHRug2eNTJrhIQbaIlz86T2bqYPUIs+4QMH7Dh5dTNiBTWiXCYONEEIQlb4VVfLri+kOcGNy4S+g+aTPA1Skzs7R6dwLxQHAgHhL2FDGgk5c6AzjPQ/B0oojyWHRl4MK4Vd1AqsCL438hOZBWzZIxkOnABeIDZ5Ef0q2Ds8pZdqXc5AFiFkpVfukYOJUJ/Mgns8jMpiLMLmN0qY4xqCCYVXEKA+qIjp24WT5mgGefSnikLR2rfMVJnyNkcbFAqurdB4r+FeWZwYAZK/SOTh8XAsg6s5ZlwlG9WqEJ9YIBArzpyCH6tKtY0EuOGkd3IVzsT2NlCeIcSlU44mYAJU3yQFvknRRIVXvOqas6usjxm8Khqq9pddoc2KDN7+SBCRNIAkdHYwiFKzMhvvrMBBH6jT5DTOmb6/RDVVgrnaIrhg86yIQQeiBjl5VM6jbsH1JxMHijc0wcYDgzoEOsOVmOyTIJ4wdEHBLK5Az9JTpPG6gr7H+kzcdSNa4x21+ksxAnDADqkDGM/oey00eiX0w4Ic3i3YoxJ0fXUumo78JjQ+M05URvwz1MVfhh+EJQNFbj9qZXNFThgk1AnwVZhOzguhUfzhHXJ99T6eX2DTltnTTiNqhJylR+UuG7s81WjeHKM+MUNhptm3rms1ZMIBVF4ythGBPUn9KmQxfVqnrOGdNEcBh7GTe4Mhki93CVgWtZOfg9VxcI22z7qWMbgq0IlzDUFdssnKAPy9kdn/sNwQFMBhz5/wtcDFIxft3LvcL0MarVOTDzUleYOcYHvNk8kB57TzSbygwTAxODOEviGK1ymaibt1R4lBmDucgNjwEhHEBScbTyHvWn5WfipU5ShzcwmIOzZk9amYc4Lh1JGd/ne25+U88W3cuNk3cBoa9VBnEqnap6Tz2TugfZVh2Qb+qlu4T2j+7KoC1KNzdc6vmq9pxbV6m4w3tgiJsTpDWccQzDxJ9blbbiZeBnBQdhRlvvmtLvZVfIJc90RqqwLoubvMd7FAkvw7jsWX6DwLPCoQkpjBziTAkux7jaSO9TYaru0ccV1XNnxjXGHcqsCRnGsqJ0OoN3Vfmqfu+MrlFG+v6ifi8XP3RdqzFhfotwUT/DCh7pS1fCZ8s+F6VX9ox+y2nrddtg3fDKS3gtwioMU/Q5dwxn1UAup0VxNXO/KO85Y5oIDhPr9DtFfUWYr1aWozPtJ8xT6jP9GyfcsuKD5xIr+njgsEIFGWOCpaekXxGcngK5K9PVOe2hb3WYHqsXOY0pfMY+GwKGQHsjgFHG+6Hi47LbO9eWO0PAEDAE+h8CIcHpS6Vn8oC9vOG+3bB8THj35FHRRnDC2uiFn9kMynIgG6vZi8AsC/tfWJrHrUFubL2waJZlQ8AQKECAfVg6TKMgiN02BAwBQ8AQaAME5FLMqX19SZhoY08z2wI4NY3VKVbyWX3kXX6LLLKI9xrpqTIbwekp5FuULq4F2gAov0euvJMl9bLIFiVr0RgChoAhYAgYAoaAIWAIFCDACgf7u7UPjL0q2sNV8Eivu83hROwLDu1PDmvAPc0OGeh11dmeGcY/lc33bBwPj55uz9xargwBQ8AQMAQMAUPAEOi7CHDoA9sIwr+q9yD1RjTYX8g+Q8jbU0891diX3tNlsRWcnq4BS98QMAQMAUPAEDAEDAFDwBAwBFqGgBGclkFpERkChoAhYAgYAoaAIWAIGAKGQE8jYASnp2vA0jcEDAFDwBAwBAwBQ8AQMAQMgZYhYASnZVBaRIaAIWAIGAKGgCFgCBgChoAh0NMIGMHp6Rqw9A0BQ8AQMAQMAUPAEDAEDAFDoGUIGMFpGZS9JyLeVH300Ue7pZZayv+df/75WZnnjb28a4dn99hjj6xneirQZ5995q6++mp/hPbf/va30mzwlunf/va3ru4Z9RyBeOmll7oNN9zQv8G3NJGMHw899FD/1t+iN4lnROGDNFue3Pi7Kxz69vzzz/uXWe60006VybYKv8qEujgAJ+88+OCDbr/99nOnn356F6dm0RsChoAhYAgYAn0PASM4fa9OK0u08847uzXWWMONGjXKTT/99P788hdffLHyuTvuuMO/X4fzzpdddtnK8D0ZYLPNNnPjjDOOLxvGYplceOGFPtxqq61WFqzDb5AbXqT6m9/8xj/70EMPdfi9mS+qi6effrqZxxvPNFOexsNt9IFjzyGeeq9TVdZahV9VOl39+/Dhw93MM8/sy7377rt3dXIWvyFgCBgChoAh0OcQMILT5lV67733uldffbVluYTI8NbZCy64wMfJbD8vBOWFVCmJ0x8xYoQ3vNqd4FCWFVdc0ee1iuDwNl7eDP/OO++kICi9N2TIEJ9GKwgOxOb6668vTS/nx86UJyf+7gzzz3/+0+PLi2urpFX4VaXTHb/TPiF2RnC6A21LwxAwBAwBQ6CvIWAEp41rFGIzySSTuEcffbRluTz55JO94cTbdasklT4uQ71hBYeyrbPOOj6vVQSnCoey35VGKwhOWTr99bf333/f12EOwelLGF1++eW+3EZw+lKtWlkMAUPAEDAEuguBfkdwmKW/9dZb/RtXmR1OyVtvveWuueYax/Xjjz/uEOTLL790Dz/8sL+HsX/ttdf6/RfsF4jliy++cHfffbfjzbWff/55/LP/zhtgn3vuOb9fhDfASl544QU3zTTTeCOHvSGstFTtzfjxxx/9HpkbbrjBhw/zxLPEgVsVBOX444/334tWLYrSjwnOu+++69/Sy96clORgoOcISx71hxsY8vPPPzfuhatZ3Oftuey1CbFTfCIfEBxwhoTcdttt7tNPP1WQxvXrr792qb06RfWjB5VGTHCq8qbn4yv4fvDBBx1usyfjzjvvdKymgdG3337b4ffUl1R50N3HHnvMB0cf0csUbmF80hvVCYSDePSd6+uvv+4f+f777xv32QMlKdNLheFKPOgTbY529eGHH7qY4MT5If1w9TGF3zPPPOM++ugjpz1kN910U1IHyAPtAVzoI4rabJhnfc4pIytrjz/+uH/kkUce8fWp57l+8sknjry9/PLL7rLLLksSnBy9SuEYpmOfDQFDwBAwBAyBvo5AvyE4GAZ77bWXm3XWWd2pp57q95KMNdZY3jhXJWPQLLHEEm7ttdd2Bx54oJt00kndtNNO63/GMF5++eXdmGOO6WaaaSb/GaKgP/a0hASEzcHjjjuuW3XVVf0+F8jK7bffrqT8lT0tU001ld8Iv9VWW/k9IwsvvLCPZ88993QTTTSRj3/xxRf38ciQ7BDJ//8CkZpyyind6quv7rbYYgs34YQTuhlmmMGTJ4JAFsiLSNOgQYP893322ScVnStKXwQHnDbffHM32mijNTAAs1ByMAjDv/LKK27uuef28YEdRA2h7thkDtbsrUEwRBdccEE3yyyzuKFDh/rfwBDjWyLywaEIk08+eSOfYC5yC3FYa621PPYcuhBKWf0onNIICU5O3vQ8V/J80EEHudlmm83nESIjOe+887zOHnXUUW6FFVbwv5cdCpEqz5NPPukxQt+XXHJJt8MOO7gxxhijgccJJ5yg5H5x/eqrr9yiiy7qw04xxRTeQA/riXusNiCQGtoBbeS+++7z96r0krodNmyYm3/++X0a++67b6Ou2BMVExyI20YbbeTD0h5PPPFET4RS+B1wwAG+raI3lFF7dPhOO4AshsLBGayYgs+MM87oy7H00kv7QyQgXEVSVUZI5KabbuommGAC3yZ33XXXBva41UGid9xxRzf11FM79sdRz6OPProPE67glOlVFY5Febf7hoAhYAgYAoZAX0Sg3xAcZmUxbI499lhfj2yw5zvGm4RVDQx2jCjk2WefbRCcb775xmFY8gx/GPMQDmZcMVy4B3FCWP3BQMcgQd577z1vUBK3ZuefeOIJn9ZZZ53lw/APIyeMB7LC9yoXNWZ82VcDsZGwAsMme/IWblqH5BFnjotaKn0RHOLAaMRIJH98H3/88b2xRh5yMFBewysz8hh3GODhKgD7hCAAzMJjzM0777wOQ5HPyC677OLzgIEoEfmYffbZ/coWKwyQGfIqPaA+qAPuhQQnp35IR2mI4OTmTXnkSr5YiZpjjjl8PkRwWJkAU4x+CeS7jOCkykP8Z555po+bckIowJZVN3Ry8ODBij55hdAQDoIgQbe5B6kOhbxCEJAcvQQvCBj1Sd7GHntsv3oBruQzJjjkGeIennhXhB+knronXrClTllF2WCDDfy9m2++uZH1iy66yN9j5QShzCIZ9B1vvPFGI2z4IaeMYCUdIy/bbbedO+mkk/wBChBtdJe2iu4j6LhIpQhOlV5V4Rjm2T4bAoaAIWAIGAJ9HYF+Q3BwU2L1RvsxMCIw0JjNl+y///7eyGE2mN+R0Lh88803/e8YY6FoX4sMRX6fc8453SWXXNL4YyUI40bGKRvgMeJFpoiP+FkVgUQgKYIRpqvPHFNM3PEsMzPS3IcISFpFcJZZZhlF6a9aGcKtCMnBoEMEwReMePIdHpHLKo2+X3fddf53jgUWxpoVn2666Rp1J/LBaVwSVkSIOzx2WKQtJDg59UOcSkMEJzdvyk94VVwiOJBqjGxWE+Sah3tkaJiHz+tzqjwQA8rNqWShQBZIg5WaMtGBDXLPJCwraMR5zz33+EdZiSCvuFwidfRS5J6VqlBCgsMKKCRLWIfh+Bzjxz1WlMjjAw880AgOWeZe2LaV11BXWOUkXNnKqZ6ransQT+JiwuG7775r5IW46QfiejniiCN8eBGcXL0qwrGRoH0wBAwBQ8AQMAT6AQL9huCoLtm/cMYZZzSMM2bIJczwynUH1xRWcELRiU4LLLBAeNvva8B4mXjiif1sPMQJY/nwww//xR+ubpCn8cYbzy222GId4om/5BIcjErSZ1UqlLvuusvfZxZb0iqCE5+iJmOXfUvMqFdhoPykrhiZlIdZd4QZcOpJLkWsnvF7Cl/uyYCUwStSS1y33HKLf3bLLbdsJM0sPPGJ4OTWDxEoDRnduXlrJB58kLEsgsNP0gGOoz7uuOMc+3GqJC4P4UV64npbaKGFfNmptzJhRQ6MtEoog517G2+8sX8UIz/U6Tp6yTtfiAvyEYoIDr/RNsvev5TCT/VTpQO4N5IGfYMEd0juaS+Y7ofX3DKik8QFoQzl7LPP9vcPOeSQ8PYv9uDk6lURjh0ity+GgCFgCBgChkAfR6BfERzcWlhpwPWFDb24w4QEh7rGAGYVQAaVXJn4rYjgsApDePz3mWnnM5v5i4QNz4QJXX5SYWXclrmoYYyLlL322msdosGthnTYjyPpKoITGso5GCg/RVdOzSLvGKZHHnmk23rrrRtBN9lkE/8brn9lkjJu2TxOvGUEJ7d+SFtpiODk5i2V75SBzsoKpII888eqQtmKAvF2BcFhEz2rnbhSsVeN/S2UlT1QtCPusXeE47aRunpZZJiL4KDDTCCw2sQ+o5Sk8FP9hAQnpQPcg5RD1CGRrJ4xMRC6sMZp1iljEcFhrxv1CtEJJT5FLVevinAM47bPhoAhYAgYAoZAX0eg3xAcyA2GBC5Kkpjg4HePLzvGDUQAg4dn8MFHigiODEo23mMIsjrD3oTUbDuGC4YRm7OJW+48yhNXVpKQHIJDOFaUiIvVoVDYe8P90EjrDoKTg0GYz9Rn7XfCaOWlh8KEsMccc4wvl1zWwufffvvtxp6lXONW9Reu4OTUD+kqDRGc3LyFedbn2EDH5UtkBgMdF0vqkw35ZRKXh7CdXcEhDlw3SZ9DGyAbI0eO9KfxcQ9XKg7lYPVOUkcviwxzERwIr/bRkQ71HEuMH7+rfqoIDmG1msIkyIABA/yznOpWJrllLCI455xzjsc0dJcjvZjg5OpVEY5lZbDfDAFDwBAwBAyBvoZAvyE4yy23nDckOJ5VwqlS4QoOm6PD2WEODcB4O/jgg/0jIjjMooeiDdw6ZGCllVbyz7GnJxTeML/bbrv5W9rozOlKkCoJcbG3BFlzzTV9PNrjoDDxlZPQyCebl0M57bTT/H0OT5DUITip9HMN5RwMlKfUFYMQQ5ZyFbkEcuoU+5YkrHbgIiXjN9e4TRGCnPohXaUhgsMxzOS5Km/Kc3iNDXSM+9DlCxc9DG+Id4o8K65UeXLrTXGkrrixaeM9e1sQ9lxxahplZjUilDp6WWSYhwSHuCECpAXJQ0dCifHjN9VPFcFh1Q4CiVsn2IZtMkwj/pxbxiKCo1VW9ugxMSDRoQTag5OrVykcmVDh0JEwfqVjV0PAEDAEDAFDoC8i0G8IDoYihhF7EK688kpPHjDWOH2MWVQMCAgOm9ll3HBMMM+wwRcRwYEY6Z0pHPEL4ZlrrrkaRifx8xx/xMfGaVYHcOfRO0zYZ8JJa4ThaGgME/KIi4wMEbmlYORwWhuEJSW8N4RDDXBV0yZpZtKJi30s4fHV6623nk+TWfgqSaWvfT3aH6M4dASv3OlyMNCzRVcd+hCuuhGWlQ2dugWR2Hvvvf1BCpNNNpk799xzG9FxvDb4kheJ3hA/ZMgQ3fKb5AnH8d+SnPohLHXHszoqOTdvSie8coQzccnNS8a93l1DWFbj4kMuwjj4DCGOy5Nbb3Fc8Xcdya33ufD7+uuv79ODWIVSRy8h+uQZd8RQOGKZ+xzzTbsEX9oS96jDULdj/IgnVwdwuYM44g5Ie2WigTpFD9QfhPnS59wy4j5KnlndDfNMPDrMgBP+IDwjRozwq5aE5+RAVjNz9SqFow7g+P3vf69s29UQMAQMAUPAEOjTCPQbgsMJTKzWYDQMHDjQEwYZ0BAPDBBO1sJwx9UMssO+knDzrwgOJyHxvhZWFjDs11133cYGeGkLM7A6Ppo0McpiA5DVJK1SEIaTycLN3rwHhr0HEDFOFhM5Uhrhlf0oq6yyik8TQ4/9A8xoa2M+ecflTbPtvGNn++239zPwYTzh5zh9MMRdjLzyxwoPx+7K6OUeBpkM8hwMwvTizxwuAGnBZTAWygNZVV7YhM9KFbPVCKfRabWBPVWcPHbVVVf5fSQ8AxnkxCnIExu/FQ9x4qqIlNUP6a+88soNN0b2p4g0VuXNRx78Y8M+7yhSfnFvxMiG4OAuBebbbLON470wGPW8uLJIUuXhpLk69VYUN/fRifigAghVfKqe4qjSSwx3CBLuotQB+q53M11xxRW+rapu0GtWosBK93AlHD58eBK/XB0gr7i/CX/FrSurRdIrlSu8VpXx+uuvbxxXTZyQ4vDodg4HAT+lB9FmpZX9TkwyaAW3TK/KcGSChLjjFd6wDPbZEDAEDAFDwBDoSwj0G4JDpWGMa0+DKhEjWm9C18oJqx+8kyI2ajAwMBQgNhgUuH0QZ5EQBlITvs8lDkvapMWhBymBoOA+kyvkhzRVltznisLVTT+OJweD+JnwOzPkZcLv4Kc6LAvbzG9V9VMWZ2fzhv6BH4Kefvrpp2XJdctvrGbEdUI+dTx4USZarZdF6TR7nxPqtILC6iwvKr366qv9u60gyOGKVVEanS0j/Yvet0ObD19aG6ZZV6/IF5MOreoTwrzYZ0PAEDAEDAFDoB0R6FcEp7MVEBKczsZlzxsChkB7IKB3zGgfVZwrjoum7ZsYAoaAIWAIGAKGQO9AwAhOjXpipYAVHEAzMQQMgb6BwIknnujbNS8z5YWorFqyIoWLIse948poYggYAoaAIWAIGAK9BwEjOJl1BbnRpntIDvs9yl4AmBmtBTMEDIEeRoA9XmHbpn3zx9HU8YtHezirlrwhYAgYAoaAIWAIZCBgBCcDJILwvhpcWcI/XmhpYggYAn0DAfY4cWra3Xff3eH48b5ROiuFIWAIGAKGgCHQfxAwgtN/6tpKaggYAoaAIWAIGAKGgCFgCPR5BIzg9PkqtgIaAoaAIWAIGAKGgCFgCBgC/QcBIzj9p66tpIaAIWAIGAKGgCFgCBgChkCfR8AITp+vYiugIWAIGAKGgCFgCBgChoAh0H8QMILTf+raSmoIGAKGgCFgCBgChoAhYAj0eQSM4PT5KrYCGgKGgCFgCBgChoAhYAgYAv0HASM4/aeuraSGgCFgCBgChoAhYAgYAoZAn0fACE6fr2IroCFgCBgChoAhYAgYAoaAIdB/EOg0wVl99dXd8ccf70aMGGF/hoHpgOmA6YDpgOmA6YDpgOmA6YDpQI/qwIABA9yoUaOSjO5XybvRzWHDhrkVV1zRDRkyxP4MA9MB0wHTAdMB0wHTAdMB0wHTAdOBHtWBzTffPGIs//uaRXD+F9w+GQKGgCFgCBgChoAhYAgYAoaAIdC+CBjBad+6sZwZAoaAIWAIGAKGgCFgCBgChkBNBIzg1ATMghsChoAhYAgYAoaAIWAIGAKGQPsiYASnfevGcmYIGAKGgCFgCBgChoAhYAgYAjURMIJTEzALbggYAoaAIWAIGAKGgCFgCBgC7YuAEZz2rRvLmSFgCBgChoAhYAgYAoaAIWAI1ETACE5NwCy4IWAIGAKGgCFgCBgChoAhYAi0LwL/DykZ0AGhL93wAAAAAElFTkSuQmCC" } }, "cell_type": "markdown", "metadata": { "button": false, "new_sheet": false, "run_control": { "read_only": false } }, "source": [ "# Week 6 Tutorial\n", "\n", "\n", "### Q1 \n", "\n", "Consider the network for car diagnosis shown below.\n", "![image.png](attachment:image.png)\n", "\n", "- **a**. Extend the network with the Boolean variables $IcyWeather$ and $StarterMotor$.\n", "- **b**. Give reasonable conditional probability tables for all the nodes.\n", "- **c**. How many independent values are contained in the joint probability distribution for eight Boolean nodes, assuming that no conditional independence relations are known to hold among them?\n", "- **d**. How many independent probability values do your network tables contain?\n", "\n", "### Q2 \n", "\n", "In your local nuclear power station, there is an alarm that senses when a temperature gauge exceeds a given threshold. The gauge measures the temperature of the core. Consider the Boolean variables A (alarm sounds), FA (alarm is faulty), and FG (gauge is faulty) and the multivalued nodes G (gauge reading) and T (actual core temperature).\n", "- **a**. Draw a Bayesian network for this domain, given that the gauge is more likely to fail when the core temperature gets too high.\n", "- **b**. Suppose there are just two possible actual and measured temperatures, normal and high; the probability that the gauge gives the correct temperature is x when it is working, but y when it is faulty. Give the conditional probability table associated with G.\n", "- **c**. Suppose the alarm works correctly unless it is faulty, in which case it never sounds. Give the conditional probability table associated with A.\n", "\n", "\n", "### Q3\n", "\n", "Consider the Bayes net shown below.\n", "\n", "![image-2.png](attachment:image-2.png)\n", "\n", "- **a**. Which of the following are asserted by the network structure?\n", " - (1) $P(B,I,M)=P(B)P(I)P(M)$\n", " - (2) $P(J|G)=P(J|G,I)$\n", " - (3) $P(M|G,B,I)=P(M|G,B,I,J)$\n", "- **b**. calculate the value of $P(b,i,\\neg m, g, j)$\n", "- **c**. Calculate the probability that someone goes to jail given that they broke the law, have been indicted, and face a politically motivated prosecutor.\n", "- **d**. Suppose we want to add the variable $P = PresidentialPardon$ to the network; draw the new network and briefly explain any links you add.\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "### PROBABILITY DISTRIBUTION\n", "Let us begin by specifying discrete probability distributions. The class **ProbDist** defines a discrete probability distribution. We name our random variable and then assign probabilities to the different values of the random variable. Assigning probabilities to the values works similar to that of using a dictionary with keys being the Value and we assign to it the probability. This is possible because of the magic methods _ getitem _ and _ setitem _ which store the probabilities in the prob dict of the object. You can keep the source window open alongside while playing with the rest of the code to get a better understanding." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "class ProbDist:\n", " \"\"\"A discrete probability distribution. You name the random variable\n", " in the constructor, then assign and query probability of values.\n", " >>> P = ProbDist('Flip'); P['H'], P['T'] = 0.25, 0.75; P['H']\n", " 0.25\n", " >>> P = ProbDist('X', {'lo': 125, 'med': 375, 'hi': 500})\n", " >>> P['lo'], P['med'], P['hi']\n", " (0.125, 0.375, 0.5)\n", " \"\"\"\n", "\n", " def __init__(self, varname='?', freqs=None):\n", " \"\"\"If freqs is given, it is a dictionary of values - frequency pairs,\n", " then ProbDist is normalized.\"\"\"\n", " self.prob = {}\n", " self.varname = varname\n", " self.values = []\n", " if freqs:\n", " for (v, p) in freqs.items():\n", " self[v] = p\n", " self.normalize()\n", "\n", " def __getitem__(self, val):\n", " \"\"\"Given a value, return P(value).\"\"\"\n", " try:\n", " return self.prob[val]\n", " except KeyError:\n", " return 0\n", "\n", " def __setitem__(self, val, p):\n", " \"\"\"Set P(val) = p.\"\"\"\n", " if val not in self.values:\n", " self.values.append(val)\n", " self.prob[val] = p\n", "\n", " def normalize(self):\n", " \"\"\"Make sure the probabilities of all values sum to 1.\n", " Returns the normalized distribution.\n", " Raises a ZeroDivisionError if the sum of the values is 0.\"\"\"\n", " total = sum(self.prob.values())\n", " if not np.isclose(total, 1.0):\n", " for val in self.prob:\n", " self.prob[val] /= total\n", " return self\n", "\n", " def show_approx(self, numfmt='{:.3g}'):\n", " \"\"\"Show the probabilities rounded and sorted by key, for the\n", " sake of portable doctests.\"\"\"\n", " return ', '.join([('{}: ' + numfmt).format(v, p)\n", " for (v, p) in sorted(self.prob.items())])\n", "\n", " def __repr__(self):\n", " return \"P({})\".format(self.varname)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Joint Probability Distribution\n", "The helper function **event_values** returns a tuple of the values of variables in event. An event is specified by a dict where the keys are the names of variables and the corresponding values are the value of the variable. Variables are specified with a list. The ordering of the returned tuple is same as those of the variables.\n", "\n", "Alternatively if the event is specified by a list or tuple of equal length of the variables. Then the events tuple is returned as it is." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "def event_values(event, variables):\n", " \"\"\"Return a tuple of the values of variables in event.\n", " >>> event_values ({'A': 10, 'B': 9, 'C': 8}, ['C', 'A'])\n", " (8, 10)\n", " >>> event_values ((1, 2), ['C', 'A'])\n", " (1, 2)\n", " \"\"\"\n", " if isinstance(event, tuple) and len(event) == len(variables):\n", " return event\n", " else:\n", " return tuple([event[var] for var in variables])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A probability model is completely determined by the joint distribution for all of the random variables. (Section 13.3) The probability module implements these as the class **JointProbDist** which inherits from the ProbDist class. This class specifies a discrete probability distribute over a set of variables." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "class JointProbDist(ProbDist):\n", " \"\"\"A discrete probability distribute over a set of variables.\n", " >>> P = JointProbDist(['X', 'Y']); P[1, 1] = 0.25\n", " >>> P[1, 1]\n", " 0.25\n", " >>> P[dict(X=0, Y=1)] = 0.5\n", " >>> P[dict(X=0, Y=1)]\n", " 0.5\"\"\"\n", "\n", " def __init__(self, variables):\n", " self.prob = {}\n", " self.variables = variables\n", " self.vals = defaultdict(list)\n", "\n", " def __getitem__(self, values):\n", " \"\"\"Given a tuple or dict of values, return P(values).\"\"\"\n", " values = event_values(values, self.variables)\n", " return ProbDist.__getitem__(self, values)\n", "\n", " def __setitem__(self, values, p):\n", " \"\"\"Set P(values) = p. Values can be a tuple or a dict; it must\n", " have a value for each of the variables in the joint. Also keep track\n", " of the values we have seen so far for each variable.\"\"\"\n", " values = event_values(values, self.variables)\n", " self.prob[values] = p\n", " for var, val in zip(self.variables, values):\n", " if val not in self.vals[var]:\n", " self.vals[var].append(val)\n", "\n", " def values(self, var):\n", " \"\"\"Return the set of possible values for a variable.\"\"\"\n", " return self.vals[var]\n", "\n", " def __repr__(self):\n", " return \"P({})\".format(self.variables)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### BAYESIAN NETWORKS\n", "A Bayesian network is a representation of the joint probability distribution encoding a collection of conditional independence statements.\n", "\n", "A Bayes Network is implemented as the class BayesNet. It consisits of a collection of nodes implemented by the class BayesNode. The implementation in the above mentioned classes focuses only on boolean variables. Each node is associated with a variable and it contains a conditional probabilty table (cpt). The cpt represents the probability distribution of the variable conditioned on its parents P(X | parents).\n", "\n", "Let us dive into the **BayesNode** implementation." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "button": false, "new_sheet": false, "run_control": { "read_only": false } }, "outputs": [], "source": [ "class BayesNode:\n", " \"\"\"A conditional probability distribution for a boolean variable,\n", " P(X | parents). Part of a BayesNet.\"\"\"\n", "\n", " def __init__(self, X, parents, cpt):\n", " \"\"\"X is a variable name, and parents a sequence of variable\n", " names or a space-separated string. cpt, the conditional\n", " probability table, takes one of these forms:\n", "\n", " * A number, the unconditional probability P(X=true). You can\n", " use this form when there are no parents.\n", "\n", " * A dict {v: p, ...}, the conditional probability distribution\n", " P(X=true | parent=v) = p. When there's just one parent.\n", "\n", " * A dict {(v1, v2, ...): p, ...}, the distribution P(X=true |\n", " parent1=v1, parent2=v2, ...) = p. Each key must have as many\n", " values as there are parents. You can use this form always;\n", " the first two are just conveniences.\n", "\n", " In all cases the probability of X being false is left implicit,\n", " since it follows from P(X=true).\n", "\n", " >>> X = BayesNode('X', '', 0.2)\n", " >>> Y = BayesNode('Y', 'P', {T: 0.2, F: 0.7})\n", " >>> Z = BayesNode('Z', 'P Q',\n", " ... {(T, T): 0.2, (T, F): 0.3, (F, T): 0.5, (F, F): 0.7})\n", " \"\"\"\n", " if isinstance(parents, str):\n", " parents = parents.split()\n", "\n", " # We store the table always in the third form above.\n", " if isinstance(cpt, (float, int)): # no parents, 0-tuple\n", " cpt = {(): cpt}\n", " elif isinstance(cpt, dict):\n", " # one parent, 1-tuple\n", " if cpt and isinstance(list(cpt.keys())[0], bool):\n", " cpt = {(v,): p for v, p in cpt.items()}\n", "\n", " assert isinstance(cpt, dict)\n", " for vs, p in cpt.items():\n", " assert isinstance(vs, tuple) and len(vs) == len(parents)\n", " assert all(isinstance(v, bool) for v in vs)\n", " assert 0 <= p <= 1\n", "\n", " self.variable = X\n", " self.parents = parents\n", " self.cpt = cpt\n", " self.children = []\n", "\n", " def p(self, value, event):\n", " \"\"\"Return the conditional probability\n", " P(X=value | parents=parent_values), where parent_values\n", " are the values of parents in event. (event must assign each\n", " parent a value.)\n", " >>> bn = BayesNode('X', 'Burglary', {T: 0.2, F: 0.625})\n", " >>> bn.p(False, {'Burglary': False, 'Earthquake': True})\n", " 0.375\"\"\"\n", " assert isinstance(value, bool)\n", " ptrue = self.cpt[event_values(event, self.parents)]\n", " return ptrue if value else 1 - ptrue\n", " def sample(self, event):\n", " \"\"\"Sample from the distribution for this variable conditioned\n", " on event's values for parent_variables. That is, return True/False\n", " at random according with the conditional probability given the\n", " parents.\"\"\"\n", " return probability(self.p(True, event))\n", "\n", " def __repr__(self):\n", " return repr((self.variable, ' '.join(self.parents)))" ] }, { "attachments": { "image.png": { "image/png": "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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "The constructor takes in the name of **variable**, **parents** and **cpt**. Here variable is a the name of the variable like 'Earthquake'. parents should a list or space separate string with variable names of parents. The conditional probability table is a dict {(v1, v2, ...): p, ...}, the distribution P(X=true | parent1=v1, parent2=v2, ...) = p. Here the keys are combination of boolean values that the parents take. The length and order of the values in keys should be same as the supplied parent list/string. In all cases the probability of X being false is left implicit, since it follows from P(X=true).\n", "\n", "The example below where we implement the network shown in Figure 14.3 of the book will make this more clear.\n", "![image.png](attachment:image.png)\n", "\n", "The alarm node can be made as follows:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "alarm_node = BayesNode('Alarm', ['Burglary', 'Earthquake'], \n", " {(True, True): 0.95,(True, False): 0.94, (False, True): 0.29, (False, False): 0.001})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is possible to avoid using a tuple when there is only a single parent. So an alternative format for the cpt is" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "john_node = BayesNode('JohnCalls', ['Alarm'], {True: 0.90, False: 0.05})\n", "mary_node = BayesNode('MaryCalls', 'Alarm', {(True, ): 0.70, (False, ): 0.01}) # Using string for parents.\n", "# Equivalant to john_node definition." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The general format used for the alarm node always holds. For nodes with no parents we can also use." ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "burglary_node = BayesNode('Burglary', '', 0.001)\n", "earthquake_node = BayesNode('Earthquake', '', 0.002)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is possible to use the node for lookup function using the p method. The method takes in two arguments value and event. Event must be a dict of the type {variable:values, ..} The value corresponds to the value of the variable we are interested in (False or True).The method returns the conditional probability P(X=value | parents=parent_values), where parent_values are the values of parents in event. (event must assign each parent a value.)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.09999999999999998" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "john_node.p(False, {'Alarm': True, 'Burglary': True}) # P(JohnCalls=False | Alarm=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With all the information about nodes present it is possible to construct a Bayes Network using **BayesNet**. The BayesNet class does not take in nodes as input but instead takes a list of node_specs. An entry in node_specs is a tuple of the parameters we use to construct a BayesNode namely (X, parents, cpt). node_specs must be ordered with parents before children.\n", "\n" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "class BayesNet:\n", " \"\"\"Bayesian network containing only boolean-variable nodes.\"\"\"\n", "\n", " def __init__(self, node_specs=None):\n", " \"\"\"Nodes must be ordered with parents before children.\"\"\"\n", " self.nodes = []\n", " self.variables = []\n", " node_specs = node_specs or []\n", " for node_spec in node_specs:\n", " self.add(node_spec)\n", "\n", " def add(self, node_spec):\n", " \"\"\"Add a node to the net. Its parents must already be in the\n", " net, and its variable must not.\"\"\"\n", " node = BayesNode(*node_spec)\n", " assert node.variable not in self.variables\n", " assert all((parent in self.variables) for parent in node.parents)\n", " self.nodes.append(node)\n", " self.variables.append(node.variable)\n", " for parent in node.parents:\n", " self.variable_node(parent).children.append(node)\n", "\n", " def variable_node(self, var):\n", " \"\"\"Return the node for the variable named var.\n", " >>> burglary.variable_node('Burglary').variable\n", " 'Burglary'\"\"\"\n", " for n in self.nodes:\n", " if n.variable == var:\n", " return n\n", " raise Exception(\"No such variable: {}\".format(var))\n", "\n", " def variable_values(self, var):\n", " \"\"\"Return the domain of var.\"\"\"\n", " return [True, False]\n", "\n", " def __repr__(self):\n", " return 'BayesNet({0!r})'.format(self.nodes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The constructor of BayesNet takes each item in **node_specs** and adds a **BayesNode** to its nodes object variable by calling the add method. add in turn adds node to the net. Its parents must already be in the net, and its variable must not. Thus add allows us to grow a BayesNet given its parents are already present.\n", "\n", "burglary global is an instance of BayesNet corresponding to the above example." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "T, F = True, False\n", "\n", "burglary = BayesNet([\n", " ('Burglary', '', 0.001),\n", " ('Earthquake', '', 0.002),\n", " ('Alarm', 'Burglary Earthquake',\n", " {(T, T): 0.95, (T, F): 0.94, (F, T): 0.29, (F, F): 0.001}),\n", " ('JohnCalls', 'Alarm', {T: 0.90, F: 0.05}),\n", " ('MaryCalls', 'Alarm', {T: 0.70, F: 0.01})\n", "])" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "BayesNet([('Burglary', ''), ('Earthquake', ''), ('Alarm', 'Burglary Earthquake'), ('JohnCalls', 'Alarm'), ('MaryCalls', 'Alarm')])" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "burglary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**BayesNet** method variable_node allows to reach BayesNode instances inside a Bayes Net. It is possible to modify the cpt of the nodes directly using this method." ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "__main__.BayesNode" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(burglary.variable_node('Alarm'))" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{(True, True): 0.95,\n", " (True, False): 0.94,\n", " (False, True): 0.29,\n", " (False, False): 0.001}" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "burglary.variable_node('Alarm').cpt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exact Inference in Bayesian Networks\n", "A Bayes Network is a more compact representation of the full joint distribution and like full joint distributions allows us to do inference i.e. answer questions about probability distributions of random variables given some evidence.\n", "\n", "Exact algorithms don't scale well for larger networks. Approximate algorithms are explained in the next section.\n", "\n", "#### Inference by Enumeration\n", "We apply techniques similar to those used for **enumerate_joint_ask** and **enumerate_joint** to draw inference from Bayesian Networks. enumeration_ask and enumerate_all implement the algorithm described in Figure 14.9 of the book." ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "def enumerate_all(variables, e, bn):\n", " \"\"\"Return the sum of those entries in P(variables | e{others})\n", " consistent with e, where P is the joint distribution represented\n", " by bn, and e{others} means e restricted to bn's other variables\n", " (the ones other than variables). Parents must precede children in variables.\"\"\"\n", " if not variables:\n", " return 1.0\n", " Y, rest = variables[0], variables[1:]\n", " Ynode = bn.variable_node(Y)\n", " if Y in e:\n", " return Ynode.p(e[Y], e) * enumerate_all(rest, e, bn)\n", " else:\n", " return sum(Ynode.p(y, e) * enumerate_all(rest, extend(e, Y, y), bn)\n", " for y in bn.variable_values(Y))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "enumerate_all recursively evaluates a general form of the Equation 14.4 in the book.\n", "\n", "$$\\textbf{P}(X | \\textbf{e}) = α \\textbf{P}(X, \\textbf{e}) = α \\sum_{y} \\textbf{P}(X, \\textbf{e}, \\textbf{y})$$\n", "such that P(X, e, y) is written in the form of product of conditional probabilities P(variable | parents(variable)) from the Bayesian Network.\n", "\n", "enumeration_ask calls enumerate_all on each value of query variable X and finally normalizes them." ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "def enumeration_ask(X, e, bn):\n", " \"\"\"Return the conditional probability distribution of variable X\n", " given evidence e, from BayesNet bn. [Figure 14.9]\n", " >>> enumeration_ask('Burglary', dict(JohnCalls=T, MaryCalls=T), burglary\n", " ... ).show_approx()\n", " 'False: 0.716, True: 0.284'\"\"\"\n", " assert X not in e, \"Query variable must be distinct from evidence\"\n", " Q = ProbDist(X)\n", " for xi in bn.variable_values(X):\n", " Q[xi] = enumerate_all(bn.variables, extend(e, X, xi), bn)\n", " return Q.normalize()\n", "\n", "def extend(s, var, val):\n", " \"\"\"Copy dict s and extend it by setting var to val; return copy.\"\"\"\n", " return {**s, var: val}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us solve the problem of finding out **P(Burglary=True | JohnCalls=True, MaryCalls=True)** using the burglary network. enumeration_ask takes three arguments X = variable name, e = Evidence (in form a dict like previously explained), bn = The Bayes Net to do inference on." ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.2841718353643929" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ans_dist = enumeration_ask('Burglary', {'JohnCalls': True, 'MaryCalls': True}, burglary)\n", "ans_dist[True]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Variable Elimination\n", "\n", "The enumeration algorithm can be improved substantially by eliminating repeated calculations. In enumeration we join the joint of all hidden variables. This is of exponential size for the number of hidden variables. Variable elimination employes interleaving join and marginalization.\n", "\n", "Before we look into the implementation of Variable Elimination we must first familiarize ourselves with Factors.\n", "\n", "In general we call a multidimensional array of type P(Y1 ... Yn | X1 ... Xm) a factor where some of Xs and Ys maybe assigned values. Factors are implemented in the probability module as the class **Factor**. They take as input variables and cpt.\n", "\n", "##### Helper Functions\n", "There are certain helper functions that help creating the cpt for the Factor given the evidence. Let us explore them one by one." ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "class Factor:\n", " \"\"\"A factor in a joint distribution.\"\"\"\n", "\n", " def __init__(self, variables, cpt):\n", " self.variables = variables\n", " self.cpt = cpt\n", "\n", " def pointwise_product(self, other, bn):\n", " \"\"\"Multiply two factors, combining their variables.\"\"\"\n", " variables = list(set(self.variables) | set(other.variables))\n", " cpt = {event_values(e, variables): self.p(e) * other.p(e) for e in all_events(variables, bn, {})}\n", " return Factor(variables, cpt)\n", "\n", " def sum_out(self, var, bn):\n", " \"\"\"Make a factor eliminating var by summing over its values.\"\"\"\n", " variables = [X for X in self.variables if X != var]\n", " cpt = {event_values(e, variables): sum(self.p(extend(e, var, val)) for val in bn.variable_values(var))\n", " for e in all_events(variables, bn, {})}\n", " return Factor(variables, cpt)\n", "\n", " def normalize(self):\n", " \"\"\"Return my probabilities; must be down to one variable.\"\"\"\n", " assert len(self.variables) == 1\n", " return ProbDist(self.variables[0], {k: v for ((k,), v) in self.cpt.items()})\n", "\n", " def p(self, e):\n", " \"\"\"Look up my value tabulated for e.\"\"\"\n", " return self.cpt[event_values(e, self.variables)]\n", "\n", "def make_factor(var, e, bn):\n", " \"\"\"Return the factor for var in bn's joint distribution given e.\n", " That is, bn's full joint distribution, projected to accord with e,\n", " is the pointwise product of these factors for bn's variables.\"\"\"\n", " node = bn.variable_node(var)\n", " variables = [X for X in [var] + node.parents if X not in e]\n", " cpt = {event_values(e1, variables): node.p(e1[var], e1)\n", " for e1 in all_events(variables, bn, e)}\n", " return Factor(variables, cpt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**make_factor** is used to create the cpt and variables that will be passed to the constructor of **Factor**. We use make_factor for each variable. It takes in the arguments var the particular variable, e the evidence we want to do inference on, bn the bayes network.\n", "\n", "Here variables for each node refers to a list consisting of the variable itself and the parents minus any variables that are part of the evidence. This is created by finding the node.parents and filtering out those that are not part of the evidence.\n", "\n", "The **cpt** created is the one similar to the original cpt of the node with only rows that agree with the evidence." ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "def all_events(variables, bn, e):\n", " \"\"\"Yield every way of extending e with values for all variables.\"\"\"\n", " if not variables:\n", " yield e\n", " else:\n", " X, rest = variables[0], variables[1:]\n", " for e1 in all_events(rest, bn, e):\n", " for x in bn.variable_values(X):\n", " yield extend(e1, X, x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The **all_events** function is a recursive generator function which yields a key for the orignal cpt which is part of the node. This works by extending evidence related to the node, thus all the output from all_events only includes events that support the evidence. Given all_events is a generator function one such event is returned on every call.\n", "\n", "We can try this out using the example on Page 524 of the book. We will make f5(A) = P(m | A)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "f5 = make_factor('MaryCalls', {'JohnCalls': True, 'MaryCalls': True}, burglary)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<__main__.Factor at 0x7fe8802dcb20>" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f5" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{(True,): 0.7, (False,): 0.01}" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f5.cpt" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Alarm']" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f5.variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here f5.cpt False key gives probability for **P(MaryCalls=True | Alarm = False)**. Due to our representation where we only store probabilities for only in cases where the node variable is True this is the same as the cpt of the BayesNode. Let us try a somewhat different example from the book where evidence is that the Alarm = True" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "new_factor = make_factor('MaryCalls', {'Alarm': True}, burglary)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{(True,): 0.7, (False,): 0.30000000000000004}" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_factor.cpt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here the cpt is for **P(MaryCalls | Alarm = True)**. Therefore the probabilities for True and False sum up to one. Note the difference between both the cases. Again the only rows included are those consistent with the evidence.\n", "\n", "##### Operations on Factors\n", "We are interested in two kinds of operations on factors. **Pointwise Product** which is used to created joint distributions and **Summing Out** which is used for marginalization.\n", "\n", "**Factor.pointwise_product** implements a method of creating a joint via combining two factors. We take the union of **variables** of both the factors and then generate the **cpt** for the new factor using **all_events** function. Note that the given we have eliminated rows that are not consistent with the evidence. Pointwise product assigns new probabilities by multiplying rows similar to that in a database join.\n", "\n", "**Factor.sum_out** makes a factor eliminating a variable by summing over its values. Again **events_all** is used to generate combinations for the rest of the variables.\n", "\n", "In the next, **pointwise_product** extends the operation of **Factor.pointwise_product** to more than two operands where it is done sequentially in pairs of two.\n", "\n", "**sum_out** uses both **Factor.sum_out** and **pointwise_product** to finally eliminate a particular variable from all factors by summing over its values." ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "def pointwise_product(factors, bn):\n", " return reduce(lambda f, g: f.pointwise_product(g, bn), factors)\n", "\n", "def sum_out(var, factors, bn):\n", " \"\"\"Eliminate var from all factors by summing over its values.\"\"\"\n", " result, var_factors = [], []\n", " for f in factors:\n", " (var_factors if var in f.variables else result).append(f)\n", " result.append(pointwise_product(var_factors, bn).sum_out(var, bn))\n", " return result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Elimination Ask\n", "The algorithm described in Figure 14.11 of the book is implemented by the function **elimination_ask**. We use this for inference. The key idea is that we eliminate the hidden variables by interleaving joining and marginalization. It takes in 3 arguments X the query variable, e the evidence variable and bn the Bayes network.\n", "\n", "The algorithm creates factors out of Bayes Nodes in reverse order and eliminates hidden variables using sum_out. Finally it takes a point wise product of all factors and normalizes. Let us finally solve the problem of inferring\n", "\n", "**P(Burglary=True | JohnCalls=True, MaryCalls=True)** using variable elimination." ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "from functools import reduce\n", "\n", "def is_hidden(var, X, e):\n", " \"\"\"Is var a hidden variable when querying P(X|e)?\"\"\"\n", " return var != X and var not in e\n", "\n", "def elimination_ask(X, e, bn):\n", " \"\"\"Compute bn's P(X|e) by variable elimination. [Figure 14.11]\n", " >>> elimination_ask('Burglary', dict(JohnCalls=T, MaryCalls=T), burglary\n", " ... ).show_approx()\n", " 'False: 0.716, True: 0.284'\"\"\"\n", " assert X not in e, \"Query variable must be distinct from evidence\"\n", " factors = []\n", " for var in reversed(bn.variables):\n", " factors.append(make_factor(var, e, bn))\n", " if is_hidden(var, X, e):\n", " factors = sum_out(var, factors, bn)\n", " return pointwise_product(factors, bn).normalize()" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'False: 0.716, True: 0.284'" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "elimination_ask('Burglary', dict(JohnCalls=True, MaryCalls=True), burglary).show_approx()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Approximate Inference in Bayesian Networks\n", "\n", "Exact inference fails to scale for very large and complex Bayesian Networks. This section covers implementation of randomized sampling algorithms, also called Monte Carlo algorithms.\n", "\n", "\n", "Before we consider the different algorithms in this section let us look at the **BayesNode.sample** method. It samples from the distribution for this variable conditioned on event's values for parent_variables. That is, return True/False at random according to with the conditional probability given the parents. The **probability** function is a simple helper from utils module which returns True with the probability passed to it.\n", "\n", "#### Prior Sampling\n", "The idea of Prior Sampling is to sample from the Bayesian Network in a topological order. We start at the top of the network and sample as per **P(Xi | parents(Xi))** i.e. the probability distribution from which the value is sampled is conditioned on the values already assigned to the variable's parents. This can be thought of as a simulation." ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [], "source": [ "import random \n", "\n", "def probability(p):\n", " \"\"\"Return true with probability p.\"\"\"\n", " return p > random.uniform(0.0, 1.0)\n", "\n", "def prior_sample(bn):\n", " \"\"\"Randomly sample from bn's full joint distribution. The result\n", " is a {variable: value} dict. [Figure 14.13]\"\"\"\n", " event = {}\n", " for node in bn.nodes:\n", " event[node.variable] = node.sample(event)\n", " return event" ] }, { "attachments": { "image.png": { "image/png": "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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "The function **prior_sample** implements the algorithm described in Figure 14.13 of the book. Nodes are sampled in the topological order. The old value of the event is passed as evidence for parent values. We will use the Bayesian Network in Figure 14.12 to try out the prior_sample\n", "\n", "![image.png](attachment:image.png)\n", "\n", "Traversing the graph in topological order is important. There are two possible topological orderings for this particular directed acyclic graph.\n", "\n", "- Cloudy -> Sprinkler -> Rain -> Wet Grass\n", "- Cloudy -> Rain -> Sprinkler -> Wet Grass\n", "\n", "We can follow any of the two orderings to sample from the network. Any ordering other than these two, however, cannot be used.\n", "One way to think about this is that Cloudy can be seen as a precondition of both Rain and Sprinkler and just like we have seen in planning, preconditions need to be satisfied before a certain action can be executed.\n", "We store the samples on the observations. Let us find **P(Rain=True)** by taking 1000 random samples from the network." ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [], "source": [ "N = 1000\n", "\n", "sprinkler = BayesNet([('Cloudy', '', 0.5),\n", " ('Sprinkler', 'Cloudy', {T: 0.10, F: 0.50}),\n", " ('Rain', 'Cloudy', {T: 0.80, F: 0.20}),\n", " ('WetGrass', 'Sprinkler Rain',\n", " {(T, T): 0.99, (T, F): 0.90, (F, T): 0.90, (F, F): 0.00})])\n", "\n", "all_observations = [prior_sample(sprinkler) for x in range(N)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we filter to get the observations where Rain = True" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [], "source": [ "rain_true = [observation for observation in all_observations if observation['Rain'] == True]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we can find **P(Rain=True)**" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5\n" ] } ], "source": [ "answer = len(rain_true) / N\n", "print(answer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sampling this another time might give different results as we have no control over the distribution of the random samples" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.486\n" ] } ], "source": [ "N = 1000\n", "all_observations = [prior_sample(sprinkler) for x in range(N)]\n", "rain_true = [observation for observation in all_observations if observation['Rain'] == True]\n", "answer = len(rain_true) / N\n", "print(answer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To evaluate a conditional distribution. We can use a two-step filtering process. We first separate out the variables that are consistent with the evidence. Then for each value of query variable, we can find probabilities. For example to find **P(Cloudy=True | Rain=True)**. We have already filtered out the values consistent with our evidence in **rain_true**. Now we apply a second filtering step on rain_true to find **P(Rain=True and Cloudy=True)**" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.8065843621399177\n" ] } ], "source": [ "rain_and_cloudy = [observation for observation in rain_true if observation['Cloudy'] == True]\n", "answer = len(rain_and_cloudy) / len(rain_true)\n", "print(answer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Rejection Sampling\n", "\n", "Rejection Sampling is based on an idea similar to what we did just now. First, it generates samples from the prior distribution specified by the network. Then, it rejects all those that do not match the evidence.\n", "Rejection sampling is advantageous only when we know the query beforehand. While prior sampling generally works for any query, it might fail in some scenarios.\n", "Let's say we have a generic Bayesian network and we have evidence e, and we want to know how many times a state A is true, given evidence e is true. Normally, prior sampling can answer this question, but let's assume that the probability of evidence e being true in our actual probability distribution is very small. In this situation, it might be possible that sampling never encounters a data-point where e is true. If our sampled data has no instance of e being true, P(e) = 0, and therefore P(A | e) / P(e) = 0/0, which is undefined. We cannot find the required value using this sample.\n", "We can definitely increase the number of sample points, but we can never guarantee that we will encounter the case where e is non-zero (assuming our actual probability distribution has atleast one case where e is true). To guarantee this, we would have to consider every single data point, which means we lose the speed advantage that approximation provides us and we essentially have to calculate the exact inference model of the Bayesian network.\n", "\n", "Rejection sampling will be useful in this situation, as we already know the query.\n", "While sampling from the network, we will reject any sample which is inconsistent with the evidence variables of the given query (in this example, the only evidence variable is e). We will only consider samples that do not violate any of the evidence variables. In this way, we will have enough data with the required evidence to infer queries involving a subset of that evidence.\n", "\n", "The function rejection_sampling implements the algorithm described by Figure 14.14" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [], "source": [ "def rejection_sampling(X, e, bn, N=10000):\n", " \"\"\"Estimate the probability distribution of variable X given\n", " evidence e in BayesNet bn, using N samples. [Figure 14.14]\n", " Raises a ZeroDivisionError if all the N samples are rejected,\n", " i.e., inconsistent with e.\n", " >>> random.seed(47)\n", " >>> rejection_sampling('Burglary', dict(JohnCalls=T, MaryCalls=T),\n", " ... burglary, 10000).show_approx()\n", " 'False: 0.7, True: 0.3'\n", " \"\"\"\n", " counts = {x: 0 for x in bn.variable_values(X)} # bold N in [Figure 14.14]\n", " for j in range(N):\n", " sample = prior_sample(bn) # boldface x in [Figure 14.14]\n", " if consistent_with(sample, e):\n", " counts[sample[X]] += 1\n", " return ProbDist(X, counts)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function keeps counts of each of the possible values of the Query variable and increases the count when we see an observation consistent with the evidence. It takes in input parameters X - The Query Variable, e - evidence, bn - Bayes net and N - number of prior samples to generate.\n", "\n", "**consistent_with** is used to check consistency." ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [], "source": [ "def consistent_with(event, evidence):\n", " \"\"\"Is event consistent with the given evidence?\"\"\"\n", " return all(evidence.get(k, v) == v\n", " for k, v in event.items())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To answer **P(Cloudy=True | Rain=True)**" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7995991983967936" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p = rejection_sampling('Cloudy', dict(Rain=True), sprinkler, 1000)\n", "p[True]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Likelihood Weighting\n", "\n", "Rejection sampling takes a long time to run when the probability of finding consistent evidence is low. It is also slow for larger networks and more evidence variables. Rejection sampling tends to reject a lot of samples if our evidence consists of a large number of variables. Likelihood Weighting solves this by fixing the evidence (i.e. not sampling it) and then using weights to make sure that our overall sampling is still consistent.\n", "\n", "The pseudocode in Figure 14.15 is implemented as **likelihood_weighting** and **weighted_sample**." ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [], "source": [ "def weighted_sample(bn, e):\n", " \"\"\"Sample an event from bn that's consistent with the evidence e;\n", " return the event and its weight, the likelihood that the event\n", " accords to the evidence.\"\"\"\n", " w = 1\n", " event = dict(e) # boldface x in [Figure 14.15]\n", " for node in bn.nodes:\n", " Xi = node.variable\n", " if Xi in e:\n", " w *= node.p(e[Xi], event)\n", " else:\n", " event[Xi] = node.sample(event)\n", " return event, w" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**weighted_sample** samples an event from Bayesian Network that's consistent with the evidence e and returns the event and its weight, the likelihood that the event accords to the evidence. It takes in two parameters bn the Bayesian Network and e the evidence.\n", "\n", "The weight is obtained by multiplying **P(xi | parents(xi))** for each node in evidence. We set the values of **event = evidence** at the start of the function." ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "({'Rain': True, 'Cloudy': True, 'Sprinkler': False, 'WetGrass': True}, 0.8)" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weighted_sample(sprinkler, dict(Rain=True))\n" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [], "source": [ "def likelihood_weighting(X, e, bn, N=10000):\n", " \"\"\"Estimate the probability distribution of variable X given\n", " evidence e in BayesNet bn. [Figure 14.15]\n", " >>> random.seed(1017)\n", " >>> likelihood_weighting('Burglary', dict(JohnCalls=T, MaryCalls=T),\n", " ... burglary, 10000).show_approx()\n", " 'False: 0.702, True: 0.298'\n", " \"\"\"\n", " W = {x: 0 for x in bn.variable_values(X)}\n", " for j in range(N):\n", " sample, weight = weighted_sample(bn, e) # boldface x, w in [Figure 14.15]\n", " W[sample[X]] += weight\n", " return ProbDist(X, W)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**likelihood_weighting** implements the algorithm to solve our inference problem. The code is similar to **rejection_sampling** but instead of adding one for each sample we add the weight obtained from **weighted_sampling**." ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'False: 0.216, True: 0.784'" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "likelihood_weighting('Cloudy', dict(Rain=True), sprinkler, 200).show_approx()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Gibbs Sampling\n", "\n", "In likelihood sampling, it is possible to obtain low weights in cases where the evidence variables reside at the bottom of the Bayesian Network. This can happen because influence only propagates downwards in likelihood sampling.\n", "\n", "Gibbs Sampling solves this. The implementation of Figure 14.16 is provided in the function **gibbs_ask**" ] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [], "source": [ "def gibbs_ask(X, e, bn, N=1000):\n", " \"\"\"[Figure 14.16]\"\"\"\n", " assert X not in e, \"Query variable must be distinct from evidence\"\n", " counts = {x: 0 for x in bn.variable_values(X)} # bold N in [Figure 14.16]\n", " Z = [var for var in bn.variables if var not in e]\n", " state = dict(e) # boldface x in [Figure 14.16]\n", " for Zi in Z:\n", " state[Zi] = random.choice(bn.variable_values(Zi))\n", " for j in range(N):\n", " for Zi in Z:\n", " state[Zi] = markov_blanket_sample(Zi, state, bn)\n", " counts[state[X]] += 1\n", " return ProbDist(X, counts)\n", "\n", "def product(numbers):\n", " \"\"\"Return the product of the numbers, e.g. product([2, 3, 10]) == 60\"\"\"\n", " result = 1\n", " for x in numbers:\n", " result *= x\n", " return result\n", "\n", "def markov_blanket_sample(X, e, bn):\n", " \"\"\"Return a sample from P(X | mb) where mb denotes that the\n", " variables in the Markov blanket of X take their values from event\n", " e (which must assign a value to each). The Markov blanket of X is\n", " X's parents, children, and children's parents.\"\"\"\n", " Xnode = bn.variable_node(X)\n", " Q = ProbDist(X)\n", " for xi in bn.variable_values(X):\n", " ei = extend(e, X, xi)\n", " # [Equation 14.12]\n", " Q[xi] = Xnode.p(xi, e) * product(Yj.p(ei[Yj.variable], ei) for Yj in Xnode.children)\n", " # (assuming a Boolean variable here)\n", " return probability(Q.normalize()[True])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In **gibbs_ask** we initialize the non-evidence variables to random values. And then select non-evidence variables and sample it from **P(Variable | value in the current state of all remaining vars)** repeatedly sample. In practice, we speed this up by using **markov_blanket_sample** instead. This works because terms not involving the variable get canceled in the calculation. The arguments for **gibbs_ask** are similar to **likelihood_weighting**" ] }, { "cell_type": "code", "execution_count": 133, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'False: 0.225, True: 0.775'" ] }, "execution_count": 133, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gibbs_ask('Cloudy', dict(Rain=True), sprinkler, 200).show_approx()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 1 }