{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2025-12-14T01:33:56.000Z","description":"Problems submitted by members of the MATLAB Central community.","is_default":true,"created_by":161519,"badge_id":null,"featured":false,"trending":false,"solution_count_in_trending_period":0,"trending_last_calculated":"2025-12-14T00:00:00.000Z","image_id":null,"published":true,"community_created":false,"status_id":2,"is_default_group_for_player":false,"deleted_by":null,"deleted_at":null,"restored_by":null,"restored_at":null,"description_opc":null,"description_html":null,"published_at":null},"problems":[{"id":58892,"title":"Neural Net: Calculate Perceptron","description":"This challenge is to calculate the Neural Net Perceptron value,P, given X, WH, and WP using ReLU on the hidden layer. This example is for two inputs, two hidden nodes, and a single output. No offset terms are applied.\r\n \r\nThe function template includes the code to create this figure using Matlab Latex","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 626.85px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 313.425px; transform-origin: 407px 313.425px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 375.5px 8px; transform-origin: 375.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to calculate the Neural Net Perceptron value,P, given X, WH, and WP using ReLU on the hidden layer. This example is for two inputs, two hidden nodes, and a single output. No offset terms are applied.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 545.85px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 272.925px; text-align: left; transform-origin: 384px 272.925px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" 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aWmpw+HIyspSppkFAACwLEImRLiPDgSQnZ0tIiUlJSIyaNCgxMRE33WGDh0qImvWrBGRwYMHx64Yp9PpcDiiXweGwuEXABB/CJmARmjMNKPrr78+JSVFiW1+n34p54ZuKusMGzYsdsWMGDGioKAg+nUAAABiipAJ7qMDwQwfPlz5IVB4s9lsSjdau90eKIhGb/bs2coMt1GuA0Ph8AsAiEsJLs5vVuJuSfP8vXOVo6WEBOFLh7C0tLRMmTJl1apVIpKbm+v32XShrAMDMunhNyFBzFh2HOPMAgTh9+oXsUZLptWZ9BIHsIgVK1ZkZGSsWrVq4sSJ0awDAACgGUImoClGZiIspaWlycnJa9euXbJkSTTrwIC4xwcAiFd2vQuAnrjEAQxuzpw51113nc0W7IZgKOsAAABohpAJAMbVr18/VdaB0XCPDwAQx7jzDWiNHrMAAACIY4RM6+I+OgAAAADVETIBHdCYCVgZ9/gAAPGNkAkAAAAAUA0h06K4jw4AAAAgFgiZAABoh3t8AIC4R8gE9MGwTAAAAMQlQiYAAAAAQDV2vQuAPgzeWauiQk6fFhHJy5OUlICrHTggR4+KiOTkyMUXa1QboBdXCN/bUNYBAACIKVoyYUSffCKjRsmoUfLggwHXOX5csrNl1CgpKSFhAjAHBmQCAKwggdveluIeBGj8X/uoUbJmjYjI2rVSXOy9tL1d+veXAwfkiiukutrEITMhgaYnwELiI2QmJJjgJGIpnEqAIDyufvmaaIeQaS0mCpnNzXL11dLYKGlpcvCgdO16wdJ77pH588Vmk6oqGTBApxLVwJUBYClhhcw9e+TEiWi32Lu39O4d7Yd4IWQaDacSIAhCpi4ImdZiopApIps3y5AhIiK5uVJZef71N96Qu+4SEZk7V2bP1qc2tXBlAFhKWCFzxAh5551ot3j33bJgQbQf4oWQaTScSoAgCJm6YEymFZnlKzZ4sDz8sIjIhg0yb97ZF48ckXvvFREpLjZ9wgSAIH71qwv++vbbcuaM/z/ffCNffy0HD8q778oLL8iNN55/15dfalw1AAC0ZFqMci/HRL9zp1N++lOprpbERNm3T3r1kmuvlcOHJT1d9u+XLl30ri9q3H4GLCXcMZm//a388Y9nf+7WTQ4elNTUkN5YUSFjx8qpU9K3r+zbF3adwWnZkllWJu3t/hfZbJKYKP37h/pvEoQyBcCVV0pWVqglXX65XH99wHWamqSqSkTkhhukW7doy+sQpxIgCFoydUHItBbThUwROXJErr1WTp+Wvn0lL0+eey4ehmK6cWUAWEcEs/44nXLNNVJTc/avt98ub74Z6nuVEQdJSfLtt+FttENahsxLLpHm5g7WycyUP/5Riooi34pyCfrAA/K3v4Va0tixsmxZwHUqK2XoUBGR1atl5MjICwsRpxIgCEKmLuguC6PLyJC5c0VEqqvluedERP785zhJmABMIcGDxpu22eTNNyUx8exf33pLliwJ9b2DB8tdd0lbW8CWQBPp2lVyc73/ZGaK3S4iUlMjI0bI66/rXSUA4BxCJkzg3nvPP8UkO1seekjXalTlcon2l60AQuf1DdU+cPbuLc88c/6v06dLfX2o773vPhGR6mr1q9LY4MFSWen95+BBOXNG5s+XpCQRkfvvl5YWvQsFAIgIIROm4HTKyZNnfz58+PzPAKAjzQLnQw9JdvbZn1tbZdy4UN84cKAkJcmnn8aoLv3ZbDJtmvz5zyIira1SVqZ3QQAAESFkwhQeeUR27RKbTUSkqUkmTdK7IAC4UKwD59Kl0rnz2Z+rquRPfwr1jYMHx3/73uTJZ3/YuNHPUodDNm6Ud96RjRvF4dCyLgCwLkImjK6s7OyYzN/8RmbOFBGpqJDnn9e3KABW4XK5wp0rIhaBMz39gjlp5syRAwdCemNOTscT55jdN9+c/eE///OC15ua5J575KKLJDdXRoyQ3Fy56CK55574/wcBAN3Z9S4ACKah4Wy7ZZ8+8vjj4nBIWZnU1clvfytDh4Y017wpMCwTiFcqzmo4frysXi1vvSUi4nTK2LGyb9/Z4YhBPPJIlJs1gb///ewPubnnXzx+XG6+WRoaREQGDZLLL5cvv5TKSpk/XyoqZOdOLZ4sAgCWRUsmDG3cOGlulsTEs/MrJidLaanYbNLeLmPHSlub3vUBgIZee+18NKqttUSADK69XZ5/Xn7/exGRnj3PTxHndEpRkTQ0SLdusm+fbN0qy5bJ+vXy8cdyxRVy/LiMGaNj1TGhy+zHABAIIRPG9cQTsm2biMhf/iJ9+px9sV8/+cMfRERqa2XGDN1qAwDtdekiixad/+uLL/ofhRh/tm6V4cMv+DNsmPzwh3LRRfLww+J0SlqarFlzdui+iCxbdvbhouvWSd++5z+nVy955x0RkZ07pbJS892IPXImAIMgZMKgNm8+GyaLi+X++y9Y9NhjcuONIiJ//7usW6dDbSriegBAWPLzL7i/NmGCfPmlftVopbFRysou+FNeLsePn42Xv/ylHDp0QZhcuVJEZNAg6dfP+6P69JEbbhARWbZMq+pjzOs8Qs4EYASMyYQRNTXJHXeIiHTrJgsX+llh+XLp00daW2XyZKmtla5d/azjdIrTefZR3eYS/dgtAKqL+Npd9W/0s89KRYUcOSIi0tgou3dLfr66WzCcG26QBx44+7PDIRUV8uab4nTKyJGycKF06eK9vtLAa7fLmjV+Pi0xUUSkri529YrTGcMP9+Jy+cmZnEcA6MuEF+CwgHHjpKlJROT11yU11c8KykSLkyfLqVMydqxs3uxnnREj5MwZo3eI8r04EK4PACOJLFvG9CuclCTLl8tPfypOpzz6aPwnTBH54Q8veHjV1KkyY4YMGyZr1si+fX5m8VGe2rJpk2zaFPAzlf60Efvuu2BL3c9KsenUY4zzCAB9ETJhOE8+KRs2iIj85jeSkxNwtUmT5O235a23ZMsW+dOf5Ne/vmDp7NlSVnbBTIPmwvUBYDpafmcdDklOluHD5X//V7NtGsvAgVJaKiNGyPHjkp8vO3dKSsr5pUpD4o03Sq9eAT+hU6cIN52ZKVu2SHt7sHXcS5VWUw24XC6vGyKcRwDoiJAJw3n8cXn88ZDWfPNNPy+2tMiUKbJqlbpFxZbvxYFwfQCYgS5f0oYGKSqSa6+V11/XfuMGUlQkM2bIiy9KTY3MmHHBlEgpKdLaKjfeGJOHKiu9c/fvD7bO8eNnf7jqKvULCIScCcA4mPgHcWXFCsnIkFWrZOJEvUsJTULC2YtUv9cBzN8AGJDLg/Zb//JLyc+XLl1k7dqQxpwvWHBB+ooz//u/0rOniMjixRdMtHvLLSIiO3f6f1dZmSxbJnv2RLjRa68VEamvP58k/W5CRJKSJD09wq1Exvf/JOcRALogZCKulJZKcrKsXStLluhdSvjImYABuW8D6Rgs3drb5bbbpKlJKiv9zHbj17vvxvP0s8nJ8tprZ3+eNu38w5OLikREdu7084iX+nq57TYZN05KSiLcqPtpnO65iLxs3SoVFSIi48ZFuIlokDMBGAEhE3Flzhypqzt/BWA65EzAgNQKli6XK8pv8113yb59UlYm3buH+pZdu8JY2Yzy889muX/9S37/+7Mv3n23XHGFiMidd16QMxsbZcyYsyNaf/lL7486dsz7QSmef9z69pWRI0VEyspkwAB5552zIzCdTjlwQJ54QoYOFRFJTpbf/jYmu9whciYA3dFZ31qU04xFfucJCZKba/TZZd3dZS980c/VAF9VIA4kJCRE/FV+5BGZO1feey+M6WSPHJEf/1jWr1d5BtqEBO3OI5dcIs3NMnZssMdaNjfLj38szc1is8m+fZKVJSJy+LAMHnx2ovIbbpBeveTrr6Ws7Oy8r2vXXnA7MpQI5rnLX34peXny0UfnX7Hbz88oKyIpKbJypXYT/4Z4KuE8Amtyfxf4CmiJlkzAcGjPBODlr3+V556T+fPDyy3KaMyf/jQ2NRlGaqr85S8iIk7n+QH5ffrIwYMyebLY7bJrl5SUyLp14nDIzTfLzp3Rdnjp0kV275Y///n87LXuhGm3y5Qpsn+//o+WoT0TgI5oybQWj3s5+haiBfO2ZJ5bRHsmEG8ia8l85x0ZMUJ+9zt58skw3lVfL717S1ubfP992FsMTsuWTIQirFMJ5xFYDS2ZuqAlEzAo2jMBiMiOHfKzn8nkyeElzIYGyc+X06dDnR8I8Yr2TAC6IGQCugly71lBzgQs7vhxGTlSbr5ZFiwIaX2nU7ZulUcekYwMqa0VEbnhhpgWCBMgZwLQXgjP2AKgH9+HawvP1wZMS/lGh/j1bWqSIUOkqUn27pUePQKu5nSKwyHffy/t7XL6tPfSH/wg8moRN3xPJZxHAMQUIRMwOnImYE2/+pX8618iIqdOyalTEX5IWpqKFcHEyJkAtMTxxVo8TzBx/5s3/sQ/HXaXvXBl5gEC4kE0TzExCCb+MZrQzybMAwQLYuIfXTAmE9BHWAlTGJ8JAIga4zMBaIPusohb8Xe7in6zQBwIa1gmoDr6zQLQAC2ZFuVyubh3aUa0ZwIAokR7JoBYI2QCJkPOBABEiZwJIKYImYAOwh2Q6YWcCZgafUlgBORMALFDyLQurnJMjZwJAIgSORNAjBAyAbMiZwIAokTOBBALhExAa1H2lfVEzgRMir4kMA5yJgDVETItjaucOEDOBABEiZwJQF2ETEBTKjZjupEzAQBRImcCUBEh0+pozIwP5EzAdDj8wmjImQDUQsgEtBOLZkw3ciYAIErkTACqIGQC8YOcCZgLjZkwIHImgOgRMsFVjkZi2ozpRs4EAESJnAkgSoRMIN6QMwET4TYfjImcCSAahEyIcJUTe9o0Y7qRMwEAUSJnAogYIRNnkTPjDDkTMAsOvzAsciaAyBAygZjTuBnTjZwJmAU5E4ZFzgQQAUImzuMqJ/6QMwEAUSJnAggXIROILb2aMd3ImYApcJsPRkbOBBAWQiYuwFWOunRPmApyJgAgSuRMAKEjZMIbOTMukTMB4+PwC4MjZwIIESETiBWDNGO6kTMB4yNnwuDImQBCQciEH1zlRM9oCVNBzgSMjyMwDI6cCaBDhEz4x1VONIyZMBXkTABAlMiZAIIjZCIgcma8ImcCBsfhF8ZHzgQQBCETwXChEwEjN2O6kTMBg+PwC+MjZwIIhJAJqMkUCVNBzgQMjpwJ4yNnAvCLkIkOcJUTOhMlTAU5EzA4jsAwPnImAF+ETHSMq5xQmC5hKsiZgMFxBIbxkTMBeCFkIiRc5QRn0oSpIGcChpWQkJCQkMARGMZHzgTgiZCJUHGVE4ipE6aCnAkYjRIv3T9zBIbxkTMBuBEyEQaucnzFQcJUkDMB4/D71eMIDOMjZwJQEDIRHuUqh1OGIm4SpoKcCejOswHTFzkTxkfOBCCETETA5XJxoSNxlzAV5ExAL8HjpRuHXxgfORMAIRMRsvKFjtKWG38JU0HOBLQX/Cvm9a208uEXZkHOBCyOkInIWfNCR4mX8ZowFeRMQDMhNmB6YeQCjI+cCVgZIRNRsdqFThw3YHohZwKxFnr/2ECvW/NOH0yEnAlYFiET0bLIhU58d5H1i5wJxE6IX6UOjzlWu9MH0yFnAtZEyIQ64vtCxwpdZP0iZwKqi6x/bBAWudMH8yJnAhZEyIRq4vJCx4INmF7ImYBawo2XYR154vtOH8yOnAlYDSETKoubCx13vLRywlSQM4HoafCVcd/p49sJAyJnApZCyIT6zH6hQ7z0Rc4EIhbx/LGRbS4uO5UgPpAzAesgZCJWzBg1iZdBkDMBzUR/CHIffvmOwlDImYBFEDIRW2aJmsTLUJAzgQjodVRxnWP8IzAshZwJWAEhE1rwvNAx1KnEXRLxMkTkTCAaoRxnVD8WETVhNORMIO7Z9S4AFuI+qbjPJTrGOqUEgmUEXC6X79VAQkIC/5hAIL5HP71qMMLhFxB/pxLOI0A8IWRCB3qlTc/TGWeyaJAzgQiEkjBj/SXyG3f54kIX5EwgjhEyoadAd/dVPMUQLGOEnAmExff74vsl0vLr47ktAif0Qs4E4hUhE4bgdUYJdL8/yHknUAsB56rYIWcCIQolYeooUOC8cB2tqoHFaJ8z9+zZc+LECZvNVlxc7HeFHTt2fP755yLSv3//7t27+65Qn6wRXAAAIABJREFUX1+/d+9eESkoKEhKSnrnnXccDofvana7PTExccCAARdffLGqewCYAJeD1uLRPdWUv/cg12Qm3aM44PeXwq8DcPObMH1fN/i3xjiRGH4Z/P9PhwJ9TWJh3rx5Dz74oIgcO3asZ8+eXkudTmenTp1Onz4tIjNnzpw3b57vJ9x///2vvPJKcnLyN998IyKdOnVqbW0NssWsrKw5c+bccccdqu0DwmH2q1+TYnZZmIkrML1Lsy7mmwWCCDFhGl+Qwy+MQO//INHy3YXYfUdyc3OVH3bu3Om7dOvWrUrCFJGysjK/n7B9+3YRGTFihOeLNpst0Yey6MCBA2PHjn3mmWfU2gXA+AiZAKLl9/rGdNfQgOqCNM54fWviICQAUdIsZ2ZmZqalpYlIZWWl79KKigoRGTJkiIjU1dXV19d7rdDa2lpTU+Nex+2uu+464+P7778vKSlRNve73/2utrY2FnsEGBAhE4AKyJmAlw67/7kboEiYgEKznJmTkyMiu3bt8l20fv16Ebnnnnt69eol/hozlRQqIoWFhR1uyGazjR8//o033hARp9P58ssvR1c4YBqETADqIGcCbqEPMCNhAp60yZlKPjx8+HBbW5vn6ydPnvzoo49EJDc3Nz8/X0TKy8u93rtp0yYR6dWrV3p6eoiby8vLU1Y+evRo1LUD5kDIBKAaciYg2k5hAsQfDXKmMizT6XR69ZjdsGGDiGRlZaWlpQ0dOlREysvLnU6n5zo7duwQkYKCgrC2qLSLtre3R1c4YBqETABqImfC4kiYQPRinTO7d++upL5t27Z5vr5u3To5FyCLiopsNltbW9vGjRvdK7S3t1dXV4vH7EGhcDqdyiRDKSkpapQPmAAhE4DKyJmwLBImoJZY50xl2h7lcZdu7733nogobZh2uz07O1vOjdJUKA2bdru9qKgo9G09//zzSr/cW265RY3aARMgZAJQHzkTFkTCBNQV05ypNFdu2bLF3Rv2gw8+aGlpSUpKUqYFEpFhw4aJx0w/IlJVVSUigwYNstvtXh/ocDhaL1RdXb1mzZoJEyY8+uijItK9e/e7775brfoBg/P+hgCAKlwul+/VQEJCApfdiEskTCAWfE8lap1H8vPzbTabw+H48MMPBwwYIOfm+CkoKLDZzrbBKK2dBw4caGxs7Natm5wLmcqcQF5KS0tLS0sDba5z586rVq2iuyysg5ZMALFCeyYsgoQJxE6M2jNTUlL69+8vHg8yUVosPQNkv379UlNT5dyEQA6HQxlamZeXF+JWbDZb7969H3744cOHD19//fXRlw2YBSETQAyRMxH3SJhArMUoZyrdYrdv3y4iLS0tSoD0mtFHyZPK0zIrKiqcTmfnzp379evn+2njxo37+kLffPPNd999949//OPZZ59VGkIB6yBkAogtcibiGAkT0EYscqYSMpWnmCgx8oorrsjIyPBcp7i4WES2bt0q5x5ecuutt/r9NLvdnnKh5ORkd89bwGr4rw8g5siZiEskTEBLqufMnJwcu91+6tSp48ePK31lfQOk0rDZ0NDQ1NS0e/duEQlrXlnAsgiZALRAzkScIWEC2lM3Z9psNqUxc9euXUpfWd+Q2bVr1759+4rItm3blAdmhvWETMCyCJkANELORNwgYQJ6UTdnuodc1tbW2u32wsJC33WUqYBKS0sdDkdWVhajK4FQEDIBaIeciThAwgT0pWLOzM7OFpGSkhIRGTRoUGJiou86Q4cOFZE1a9aIyODBgyPbUHBOp9PhcES/DmAchEwAmiJnwtRImIARqJUzr7/++pSUFCW8+X36pZwbuqmsM2zYsAi20qERI0YUFBREvw5gHIRMAFojZ8KkSJiAcaiVM4cPH678ECjC2Ww2pRut3W4PFESjMXv2bGVu2yjXAQzFrncBgNXt2bPnxIkTNptNmSfd144dOz7//HMR6d+/f/fu3X1XqK+v37t3r4gUFBQkJSWVl5e3tbVddtllAwYMCLTRpqamqqoqEbnhhht0GV7icrl8rwYSEhK4ZIdhkTA18MEHH3zxxRcikpiYGPrz7qFoaGjYv3+/8nP//v3T0tL0rUcDvqeSCM4jy5YtW7ZsWfB11q5dG2Tp119/HdYW3VpaWqZMmbJq1aoo1wEMiJAJ6KyqqurBBx8UkWPHjvXs2dNrqdPpHDp06OnTp0Vk5syZ8+bN8/2EZ5555pVXXklOTv7mm29EZOrUqY2NjSNHjly9enWgje7fv3/UqFEisnr16pEjR6q4O6EjZ8JESJjaeOyxx5qbm1NTUzt16kTIDNc//vGPuXPnikh1dfWSJUv8zmETf1TJmbpYsWLFzJkzT548OXHixKVLl0a8DmBMhExAZ+7J0Hfu3OkbMrdu3aokTBEpKyvzGzK3b98uIiNGjIhlmTFBzoQpmDphVldXHzhwwPf17Ozsnj17Op3O119/3XfpJZdcoldEefrpp0PZtOn2S4OC8/LylGTu7v9pESbNmaWlpcnJyWvXri0uLg4UIENZBzAmQiags8zMzLS0tKampsrKyjvvvNNrqfJ46CFDhmzatKmurq6+vj49Pd1zhdbW1pqaGmUdzWpWETkTBmfqhCkiX3zxxb59+5qampT5M/v06XPzzTf36NHDZrOJiMPhOHHiRH19/fr16//1r3/ZbLZx48alpqb6Pi3QaEy3X6Yr2FzMmDPnzJlz3XXXKf8BolkHMCgXrITfuzGNHTtWRDIzM30XXXfddSJSWlraq1cvEXnllVe8Vli5cqXyO/3kk0+UV5QxliNHjgyyxffff1951+rVq9Xai2hwdIIxxc1/S/dQvUOHDvldYfTo0SJy++23a1yYl9zc3HfffTf09c2yX27aFFxYWBjWP2PcMO8XVkRyc3OjXwd+me7/Q3zg1gigP6U31OHDh9va2jxfP3ny5EcffSQiubm5yox25eXlXu/dtGmTiPTq1curhdNcXMw3C+Mxexumpy1btohI586d+/Tp43cF5dhiumGQptsv0xVsLr7fUM4jgF4ImYD+lGGZTqezsrLS8/UNGzaISFZWVlpamvIw6PLycqfT6bnOjh07JPDE6yZCzoShxFPCFJGtW7eKSKC+l9XV1crYb+XB9CZiuv0yXcGmQ84EDIKQCeive/fuSm/Ybdu2eb6+bt06ORcgi4qKbDZbW1vbxo0b3Su0t7dXV1eLx+xBpkbOhEHEWcIUj9HdfpcqR57U1NTevXtrWlbUTLdfpivYjMiZgBEw8Q9gCEOGDKmrq1Med+n23nvviYjShmm327Ozszdt2rR+/Xp3TyqlYdNutxcVFXl9YENDQ5AHfylzBRmQi3mAoLf4S5g1NTUtLS0iMmjQIL8rbN68WUzYRdN0+2W6gs3L91TCeQTQGCETMISCgoL58+dv2bLF6XQq88h98MEHLS0tSUlJOTk5yjrDhg3btGlTRUXFs88+q7xSVVUlIoMGDbLbvb/Lu3fvHjdunIZ7oBpyJnQUfwlTzrWPdTgO0HQzVJtuv0xXsKmRMwF9ETIBQ8jPz7fZbA6H48MPPxwwYICcu9ooKChwz12uXHkcOHCgsbFRmUJWCZnKnEBeEhMTO3XqFGhzZ86caW1tjcF+qIOcCV3EZcIUEaWPfV5eXnt7u+9S844DNN1+ma5gsyNnAjoiZAKGkJKS0r9//127du3atUsJmcrQHc8A2a9fv9TU1Obm5g0bNkyYMMHhcOzcuVMC9K0qLCxcvXp1oM1VVlYqvXANi5wJjcVrwhQRZUax8vLyyy+/3Hfp119/LWGOA1yxYsXdd98dbhklJSXFxcXhvisI1fcr1kxXcBwgZwJ6IWQCRpGTk7Nr167t27fPmjWrpaVFCZBeM/rk5eUtX768rKxswoQJFRUVTqezc+fO/fr106nk2CJnQjNxnDBrampOnTolIlVVVVlZWb4rjBkzZtWqVWGNAxw9evRll10WbiU33XRTuG8JIsr9OnLkyIYNG+6//34VSwou4oJPnjz58ssv19XV9ejRY/z48ZmZmVqUG0fImYAuCJmAUeTk5Pzxj39UbnWXlZWJyBVXXJGRkeG5TnFx8fLly5VJ8JWHlwSaCj8+kDOhgThOmHJuHGBKSorfYCPnjjZhhUy73X7LLbeoUl7EItuv+vr6kpKSw4cPr1y5csyYMVqGzMgKPnz48HPPPffUU09169Zt4cKFV1999Ysvvjh9+nQNCo4nxs+ZoRRjqIKBDvEIE8AocnJy7Hb7qVOnjh8/rvSV9Q2QSsNmQ0NDU1PT7t27RcR3Xtk4w3NNEFPxnTDl3DjAQI/S3bNnT1tbm4gMHDhQ07KiFtl+JSUlFRUVLVq06KKLLtKgSE+RFTxjxoynnnqqe/fuNptt2rRpDzzwwIwZMww7PbiR8VwTQGOETMAobDabMpHsrl27lL6yviGza9euffv2FZFt27Yplyzx8YTM4MiZiJG4T5hybhzg4MGD/S5VJg8z4zjAyPYrLS0tMzPTPZualiIreMuWLddcc437r8pY+jVr1sSuzjhGzgS0RHdZwEDy8vIqKirKyspqa2vtdnthYaHvOvn5+dXV1aWlpQ6HIysrS5lmNu7Rbxaqs0LCPHz4sDIOMNCDGZW+9+E+mHHHjh0vvPBCuMXMnDlTrWGZMdqv2Im44ClTpvhGYqfTGYMaLcH4/WaBuEHIBAxEmbm+pKRERAYNGpSYmOi7ztChQ//85z8rd7ID3RRXhdPpdDqdvk/gdHM4HCISZAV1kTOhIiskTBHZsmWLBB0HqDwqKdwwlp6e7vcWWIfvCvctgcRov2In4oIXLFjg+dc9e/aISAT/+HAjZwLaIGQCBnL99denpKQoT7D0+/RLOTd0Uwl4w4YNi10xI0aMOHPmjNLFy9PJkyfnzJlTUlKijCC64oorpk+f/tBDD8WuEjdyJlRhkYQpHY0DdD+YMdwBmenp6ZMmTYq+vIjFaL9iR5WCW1paXnnllSlTpsTrjOKaIWcCGmBMJmAsw4cPV34IdDlis9mU29h2uz1QEI3e7NmzlakOvTQ1NV177bV///vfc3JyXnrppd///vf/8R//8fDDD0+dOjVGlXhhfCaiZJ2E2djYqEwhdtVVV/kudTqdS5cuFRG73e41i7XBmW6/1Cr4vvvuu/nmm1977bUY1WkpjM8EYs4FK+H3jg599dVXo0ePVv6f5Obmei2dMWOGiPzhD39wv/L1118rz23buXOnZkVyNENkLPLfplu3bl792O12e7du3ZSlL7zwgldXfJvNlpiYuHz5cn3Ldrlcubm57777bqClKu5XamrqxIkTY7UbMSj46aefvvvuu0PcbmFhYZB/RrhZ5IAAfr+6oCUTwHkrVqzIyMhYtWrVxIkT/a6wcuXKxMTExx57zP1KSkrK7Nmz5dyYIm24aM9E+KzThvnpp59+9913nif777777tNPP1WW/uIXvzhz5ozn0u+///7MmTN33HGHvmV3yHT7pVbBCxcubGtrU8ZnOp1OHmGiFt8jAOcRQC2ETADnlZaWJicnr127dsmSJX5XeOWVVxYvXuw126Fyq769vV2LEs8hZyIs1kmYiDNlZWXNzc1PPvmk8tfa2lpCporImUCMMPEPgPPmzJlz3XXXBXmIXHFxse+LSiK95ZZbYliZPy7mAUJoSJgwqR07dsyaNSs/P3/69OlOp/O7776rq6t7/PHH9a4rrvieSjiPANEjZAI4L4JJCxcuXLhhw4asrKxAMxXFFDkTHSJhwq2mpuYPf/hDXV1dc3NzSUnJiRMnLr300mXLluldV0DFxcXNzc11dXWeLxpn1ty4Qc4EVEfIBBC5devW3XvvvZ07d169erVeNZAzEQQJE54yMzPffPNNvasIw7///W+9S7AKciagLkImgAgtWLDgv//7v1NTU8vKyq688kodKyFnwi8Sphnt2LGjvb09MTFReVYTQldfX793714R+eyzz/SuxZTImYCKCJkAIjFr1qwXXnihe/fulZWVvXv31rsccia8kTDNaODAgfv37z906FCnTp0ImeH65z//uXjxYhHp0aPHpZdeqnc5pkTOBNTCN8da3IdOfu/oUEJCQm5ubmVlpdfrDodj1KhR77zzTv/+/d999920tDRdyvPL76yA/G+3IBImgIhxAIkzXP3qgkeYAAjPbbfd9s477xQVFW3dutVQCVN4rglEhAtEANHhuSZA9OguCyAM//M//1NWVlZYWPj222/rXYt/9Ju1OBImgOjRbxaIEiETQKiam5ufeuop5Yfhw4d7Lc3Pz581a5YedXkjZ1oWCROAWsiZQDQImQBCtW3btvb2dhHZtWuX79Ju3bppXlFA5EwLImECUBc5E4gYXxVrYegzLIV5gKyDhAkgRji8mB1Xv7pg4h8AcYt5gCyCS0AAscM8QEAECJkA4hk5M+6RMAHEGjkTCBchE0CcI2fGMRImAG2QM4GwEDIBxD9yZlwiYQLQEjkTCB0hE4AlkDPjDAkTgPbImUCICJkArIKcGTdImAD0Qs4EQkHIBGAh5Mw4QMIEoC9yJtAhQiYAayFnmhoJE4ARkDOB4AiZACyHnGlSJEwAxhF6zkxISOAUA6shZAKwInKm6ZAwARhNhzmTeAnLImQCsChypomQMAEYU1jtmbEvBzAKQiYA6yJnmgIJE4CRkTMBX4RMAJZGzjQ4EiYA4wuUMzlewbIImQCsjpxpWCRMdOjw4cOqf2ZDQ0Nra6vqH4v4FmJ7JicXWAQhEwDImUZEwkSHXn755fnz56v+sd9+++3IkSObmppU/2TEN7850+AHroTo6F0+jMvo//WhLvfhgN874Mvv+ZIviy5ImGo5fPjwnj17/C6y2WwpKSnZ2dldunSJZhPV1dUHDhzwu2jSpEler2zcuPHEiRO+a06YMMFmO3vje8WKFW1tbZ5Le/fuff3113u9ZdGiRW+//fbKlSvDqio7O7tnz55Op/P111/3XXrJJZcUFhaKyPbt26dPn15VVZWSkuL387WxZMkS3xeVXfB8pbGx8f333/d8JSMjY8CAAbEtDgH4PXx5vajjAc2nkig/7YK/GvNAzdWvLux6FwAARuF7HSBmuA8df0iYKvr3v/998ODB5ubm//u//xORG2+8sbCwsEuXLl999dXRo0fr6urGjBmTn58/b968jIyMyDbxxRdf7Nu3r6mpqaSkREQyMjIGDRp00UUX9enTx3flY8eO7d279+DBg9u2bROR/Pz8K6+8snfv3u6EKSI1NTUHDx5cs2aNiNx8881XX321b20HDhx47LHHgvSV9aqqT58+N998c48ePZQNORyOEydO1NfXr1+//l//+pfNZhs3blxqauqtt96qvH3gwIGjRo2aMmXKm2++Gdk/S/QcDseRI0c+++yzdevWKc2q+fn5mZmZ2dnZXmt+++23GzduXLx4sYhkZWX179+/V69eOlRsbe5BmL6nEiOcWTyClpof6/VpnnvKcdvqXLASfu9AhzhU6ot//Fj45JNPlH/M//f//p/Xovfee89ms3Xu3PngwYPRbGL//v3KJg4dOtThyi+88IKIpKamBlmnqKjol7/8ZaClffr0mT9/fvRVjR49WkRuv/1230VnzpxJT09fuXJlh1uJNeUGgYhs2rQp0Dr//Oc/k5KSFi9erGFduIDXUcsgV+Aem9P6j3GO4capxFIYkwkAF3AxPlM/tGHGiNJs2L17d98GroKCgqKiolOnTs2cOTOaTWzZskVEOnfu7LcB08vmzZtFJC8vL8g6n3zySaCSFixY8NVXX02dOjX6qsrLywNVkpiY+Ktf/erhhx92Op0dbiimxo8fb7fbJUDvWRFpb2//+c9/vnbtWt/+ydCGb9Nlh8eumJ5W3GMmfSOfZtybZgCnNREyAcAbOVMXJMzYKSsrE5HBgwf7XZqYmCgiu3fvjmYTW7duFRF3d9PglGg3ZMiQQCu0trY2Nzd7jTx0e/bZZ++55x7PHraRVVVdXX369GkR8e2AqpgyZUp9ff0bb7zR4YZiKjExcdy4cSJSWlra3t7uu8K4ceOmTZuWn5+veWkQMdIsskbIlr680qbe5UAjhEwA8IOcqTESZkwpWStQCKmpqRGRHj16RLOJiooKCZzWPHUY7ZRPCxSJt2/ffuTIkTvuuCP0qgKlWaWBNzU1tXfv3n5XSE5OLiwsXLhwYSjbiimlibKtrW3ZsmVeix566KGrrrqKNkwdKZ0DI3ijiucUA2ZLX0RNSyFkAoB/5EzNkDBjqr6+vr6+XkQGDhzou7S6urq2tlZEHnjggYg3UVNT09LSIqGFzA6jnYhs2rSpoKDA76I333yza9euQd7rW9WgQYP8rhBKr92CgoItW7Yon6OjvLy8rl27iogyj5HbggULGhoannzySZ3qwnl65UzPeGkKRE2LIGQCQEDkTA2QMGPNPSDzyiuv9FrkcDhmzJghIg888MD06dO9lm7evPnOO+8cPHjwiBEjhg0b9uGHHwbfhIoDMrdu3RooGW7cuDHQonCr6rDXrohcffXVTqdz48aNoWwxpu666y4RqaysPHnypPJKRUXFkiVL/D6LBbqIuEkzYuaKl56ImnGPkAkAwZAzY4qEqYFAAzJra2vz8vKOHDny97///W9/+5vnoubm5ttuu23ChAkzZszYvHnz22+/PW/evOLi4kANekoGC3FAZvAurCLS2tr65Zdf+h2Q6XA4Dh8+HGLPXqWqvLy8dn8+/PDDDnvtiojyfM4gAVszkydPFhH3Ez6rq6t/85vfrF27VpkTCMYRbtSM7ITibsA0NaJmHOPABAAdcPH8zNggYWpDGZB59OjRadOmKa+0tbUdOnQoNTV13LhxkydPTkpK8ly/paVl8ODBx44d279/v7vxMyMj49prr/3www/9tkBWVlZK4F6png4cONDa2iqRDsj86KOPnE7nNddc0+GG3FWVl5dffvnlvku//vpr6ajXrogkJiYmJSUdPXq0w82tWLHi7rvvDqUwTyUlJcXFxaGsmZWVlZmZWVNTs2jRovHjx0+cOLGsrKxLly7hbhHa8HviUMW5B3LG4rP1oewLZ9U4Q8gEgI6RM1VHwtSGe0DmSy+95H5+yUcfffT4448fOHDg0Ucf9UqYInLffffV1NQ8/fTTnt1rGxsbd+/efcstt/huoqam5tSpUxJayFSeKRLxgMzPPvtMRFJSUjrckLuqqqqqrKws3xXGjBmzatWq4L12Ff/1X//11Vdfdbja6NGjL7vssg5X83LTTTeFvvKkSZMeffTRmpqa/Pz8+fPnp6enh7s5aEk5poUSNUM/m8RB62UgSpOmcC6IF4RMAAgJOVNFJEzNKOMS09LS+vbt637xlltuKS8v79Gjx2233Xbo0CHPMFlTU1NaWmq325WxmiJSV1e3du1aZeyf8rATv5tISUnxm+UUTqdTeeJIKAMyt2/f/uijj/pd1NbWJiKhdBDtsCqlF3EoITMrK+vjjz/ucDW73e43hKvozjvvVP5lHnnkkQEDBsR0W1BLiFGzw7NJ/DVg+qJJM54QMgEgVORMVZAwtaREqZycHK/Xk5KS+vfvX1ZWtnbt2tmzZ7tfX7x4sYikpqbec88933//vdPpTE1NzcnJ2bt3b6Bopwx9DNT2KCJ/+tOf+vTpo3QKDWVAZnNzc6A2OqfTKaGFzOBV7dmzR8mrfmfc9WKz2ZSVdXfo0CERsdvtEyZM0LsWhCfK3rNx3IDpiybN+EDIBIAwkDOjRMLUmDIg02973e7du0Xk22+/9XxReZzJfffd98QTT4S4CWXoY6BRlCKyfPnyvXv3isjRo0eVAZk33nhjoJVXrVoVJK8qTant7e1RVlVVVSUhDMhUNDY2+k7Mq4vt27eLSHZ2ttIsDHPpsEkz0KnEUglTQZNmHCBkAkB4yJkRI2FqLMgTMo8cOdLU1CQiP/zhDz1fV/KbZ9/a4A4fPhx8QOaKFSsGDhyohCJl+hybzRakY+2LL7742muvBVqanJwsIsqssNFUFSR7+zpx4oR7OGsQO3bseOGFF0L5QE8zZ84MfVjmjh07RCTWnXIRU6EP1FRYMGG6KU2anCZMipAJAGEjZ0aAhKk9ZVyi3/Y69zM5Lr74Ys/XlTl1/I69FJH6+nqvjqzKRD6Bhj62trb++te/XrZsmfLXn/70pyISpBVu0aJFvXr1ChJxlU9obGwMtEIoVcm5J2SGGDJbW1t/8IMfdLhaenp6YWFhKB/o9a4Q13Q4HMovNJQuvjC4QL1nPc8jVhiE2SFypnkRMgEgEuTMsJAwdRFkbptdu3YpP7jz5Jo1ay655JK8vLxVq1b5HYL4i1/84tprr506darni0GGPra0tIwcOfI///M/lUdNikiXLl369u1bXV195MiRjIwMr/V37Njx/PPPK22MgaSlpSUnJ+/fvz/IOsGrEpHq6mqlLTSUtNbQ0OB0OoP073VLT0+fNGlSh6tFbPPmzQ6Hw263B+mZDBMJ3qRp5QZML+RMk6JPPwBEKNDgGe0rMTgSpi6ampqUrNW/f3/fpX369PF6Ze7cuX379r377rt79eo1f/58z0XHjx+/7bbbevXq5ZUwGxsblYl8vGJPU1PTq6++mpmZuWnTprvuustz0V//+lebzfarX/3Ka+sLFy689957y8vLO3zwY0FBgTJwNBB3VVdddZXvUqfTuXTpUhGx2+2+QdeXksaVFlQdOZ3ON954Q6mEAZnxxOVyeR4P3cmTY6Qn91RAMBFuDFiL+yvK7x1Qi98zH18xNxKm9n70ox8dO3bM4XC4X7Hb7d27dz927Jj7ldbW1pycnN27dz/66KMzZ878zW9+U1RUdMcdd4hIfX39mDFjrrzyysLCwpMnT27durVLly6//vWvPXPp5Zdf3tTU5N6EzWZzz/jqdDo9N33s2LGePXt6lldWVjZ16tQePXpMmTKlS5cutbW1K1euvPHGG5977jn3f5yDAAAgAElEQVSvvrt+vfrqqw888MC3337r26fXqyplx9PS0j799FMR+etf//rwww97ThqklL106VJlx/2aNWtWaWnp559/3mFhMfLb3/72+eef9y37/fffZ3BmXCJhBpKQEOHpg6tfXRAyrYWvGRAL5MxASJgGV1ZWtmHDhvb29smTJ/fr189zUU1NzSeffHLxxRffdNNNqjedtbe3r1mzZtu2bWfOnLnppptuvfXWbt26hfjeL7/88tJLLy0pKQmSDFX04x//uLi4+Nlnn9VgWwAJM7jIciZXv7ogZFoLXzMgRsiZvkiYiJFp06bV19evX78+1hv64IMPbr755o8//jiU2WWBKJEwQxFBzuTqVxeETGvhawbEDjnTEwkTsXP06NEf/ehHhw4dCuUpl9EYM2bMD37wg4ULF8Z0K4CQMMMRbs7k6lcXhExr4WsGxBQ5U0HCRKw9/vjj+/fvX7t2bew2UV1dPWTIkNra2q5du8ZuK4CQMMMXVs7k6lcXTFAGAKphvlkhYUITTzzxRHNzszLhaiy0t7dPnDhx8eLFJEzAgJhv1vgImQCgJovnTBImtGGz2VavXv3iiy/u2bMnFp8/derUe+65p7i4OBYfDniiGTMy5EyDI2QCgMosmzNJmNBSWlra22+/vXz5ctU/eceOHfn5+b/4xS9U/2TACwkzGuRMI2NMprXQKx3QjNXGZ5IwASAsJMzohTI4k6tfXdCSCQAxYan2TBImAEB7NGYaFiETAGLFIjmThAkA4aIZUy3kTGMiZAJADMV9ziRhAkC4SJiIe4RMAIitOM6ZJEwAgO5ozDQgQiYAxFxc5kwSJgBEgGbMWCBnGg0hEwC0EGc5k4QJABEgYcIiCJkAoJG4yZkkTACA0dCYaSiETADQThzkTBImAESGZkxYByETADRl6pxJwgQAGBaNmcZByAQArZk0Z5IwASBiNGPCUgiZAKAD0+VMEiYAwPhozDQIQiYA6MNEOZOECQDRoBkTVkPIBADdmCJnkjABACZCY6YREDIBQE8Gz5kkTACIEs2YsCBCJgDozLA5k4QJADAjGjN1R8gEAP0ZMGeSMAEAQGQImQBgCIbKmSRMAFAFfWVhTYRMADAKg+RMEiYAwOzoMasvu94FAADOc7lcvifFhIQEzWIeCRPa++CDD7744gsRSUxMzMvL07scQ2hoaNi/f7/yc//+/dPS0vStBwDCot2FC4zAffnI7x0wMr83XzX42pIwoYu8vLzm5ubU1NROnTqtXr1a73IMobKy8k9/+pOIVFdXL1mypLCwUO+KEAn6yuorIeGCW7ec0bRESyYAGI4u7ZkkzLixZMkS3xezs7N79uzp+UpjY+P777/v+UpGRsaAAQNiW1wATz/9dKAcZfDdiVF5eXl5SqPu8OHDVaoUsBx6zOqIkAkARqRxziRhxg2Hw3HkyJHPPvts3bp1TU1NIpKfn5+ZmZmdne215rfffrtx48bFixeLSFZWVv/+/Xv16qVDxUEZfHcMXh4A6IXustZChwHAXLTpN0vCjEuLFi2aMmWKiGzatGnw4MF+1zl69OhVV1316quvTpo0SdPiLpSXl/fLX/4yeI9Qg+9O7MobPnz49OnT6S5rRvSVNQLP8xunNi0xuywAGJcG882SMOPV+PHj7Xa7BOjPKSLt7e0///nP165dq2/CDJHBd8fg5QGAxgiZML0PPvigrKysrKyssrJS71pMpqGhoewcpaMXDCimOZOEGccSExPHjRsnIqWlpe3t7b4rjBs3btq0afn5+ZqXFgmD747BywMsi3OaXgiZML3HHntszpw5c+fO/dvf/qZ3LSbzj3/8Y+7cuXPnzp00adLu3bv1LgcBxShnkjDjntJo1tbWtmzZMq9FDz300FVXXWWuVjWD747BywMALTHxD/yoq6vbsWOH14uJiYl33nmn8nNNTc1HH33kudRms02YMEGj+nwEmZbQk+n2K9YFM3uhiag+DxAJ0wry8vK6du168uTJkpISz4SzYMGChoYG3yxkcAbfHYOXB40xIBMWR8iEH59//vmuXbtOnDixbt065ZWRI0d6PiD7008/Xbhw4ZYtW0QkPT198ODBffr00afWcJhuv0xXMGJKxZxJwrSOu+66a+7cuZWVlSdPnuzatauIVFRULFmyZOPGjXqXFgmD747BywMA7bhgJeH+3ocMGSIi6enpvou2bdtmt9tffPFFVQuMRG5u7rvvvhvWW0yxX55iXXBhYWG4/4bQS/RHck4ElrJ//37lt/zcc8+5XK59+/Zdd911X3zxhd51XSD0w7jBdycW5XF8NikRcbn4Y4g/nOx0wZhMBPPggw+KSH19vdecOl9++eWsWbPWr18/ffp0nUqLiun2y3QFI3ZcrqjGZ9KGaTVZWVmZmZkismjRosbGxokTJ65Zs6ZLly4RfNSKFSs6hc/dEcNouxMLBi8PsCbOcrqguyyCKSoq6t69e0NDw/PPP+/upXn69Onhw4f//ve/z8nJ0be8iJluv0xXMGLKFWm/WRKmNU2aNOnRRx+tqanJz8+fP39+enp6ZJ8zevToyy67LNx33XTTTZFtLpAQd+fIkSMbNmy4//771d169OWdPHny5Zdfrqur69Gjx/jx45VQCujinXfE4fDzut0uiYkyYIBcfLHmNSFu6NqOCq1F8Hv/wx/+oLzl2LFjLpfru+++y8/PX7p0acxqDFsE3WVdZtgvLzEtmO5YZhTuIZ3jv2V98sknym988eLFetfiX1iH8eC788knn/zxj3+cOHFicnLyxIkTVS1ThfIOHTo0ZcqUEydOfP/99/PnzxeRDgc7cHw2KTFDd9mUlA5iQlaWLF+uf51q9ZjV+z+FtdBdFh249957bTabiLz66qsiMnbs2MLCQh0nXFWL6fbLdAUj1lzh9JulDdPKDh06JCJ2uz0+jhjBdycpKamoqGjRokUXXXSR5qWJdFTejBkznnrqqe7du9tstmnTpj3wwAMzZsyoqanRvEzgPJtNEhO9/ygOHJCxY+WZZ3StD+ZEyEQHunbt+rOf/UxEXnvttalTp1599dWzZs3SuygVmG6/TFcwNBBiziRhWtz27dtFJDs7W7lRZXbBdyctLS0zM1PHPQ1e3pYtW6655hr3X4cOHSoia9as0aw8aMNczy+56y45c8b7z/ffS0mJpKWJiPzud1Jbq3eVMBvGZKJjM2bMWL58eXNz8+nTp5944gm9y1GN6fbLdAVDA66OxmeSMKE8bveWW26J/nNeeOGFcN81c+ZMdYdlqrU7MRK8vClTpviGT6fTGfOygDDZbDJ+vFx6qQwdKk6nvPyyzJund00wFUImOjZw4MDevXvX1tYeO3Ys4g9ZsWLF3XffHe67SkpKiouLI95ocKrsl5ZMVzC0ESRnkjDhcDi2bdsmIgMHDozyo9LT0wsLCyN4V5Tb9aTi7sRCh+UtWLDA86979uwRkQj+VQFt5OVJerrU18vRo3qXArMhZKJjy5Yt++qrr0Rk165de/bs6devXwQfYpBpCT2Fu196zVXoFnrBzF5oNYFypu9qWlUEo9i8ebPD4bDb7YMHD47yo9LT0ydNmqRGUZFTcXdiIazyWlpaXnnllSlTpkR2VgW00auX1NdLe7vedcBsCJnoQHl5+UsvvVRbW9u9e/fW1tbXXnststOh3W43VO+m0Pervr6+pKTk8OHDK1f+f/buPT6K6v7/+Iftso00CIgRQ76RwoMH+EDIFypgEIFcIMQQAhErUqF9iIgXwFTFtiBfELBaK9QbtVQxWjQGRAG5xMA33BJITEFBjMgP80UwgTTGVBpDDHFdfn8MLMvesrvZ3bns6/ngj83M7MzZDZlz3nPOnHlv0qRJaoVM3wt85MiRZcuWLV26NDY2Njc3d8CAAStWrOBBmobnNmc6bRC2wkAjbDbb22+/LSI33nijAW7I1PjH8bd4999///Dhw1955ZXQFw0IkM0mZWUi0vo8tIATzZ2joSmlpaVLlix5//33r7zyypkzZ4rI66+/3tDQoHa52sqvz6X6XIXiZ4GZvTBieYmRJMxIM3/+/J/+9Kc/+clPXn/9dREpLy//yU9+8tOf/rS4uFjtogVC4x8ngOI99dRTHTp0eP/9981mLvdDu5Yvl+ZmEREtdRNAHwiZ8OjQoUOzZs3asGFDly5dRETpwbNarcqjvfTL38+l+lyF/haY2QsjGWESiqeeeurcuXOOjyz78ccfz507p6kRJb7T+Mfxt3i5ubnNzc3K/Zk2m42LgFCX1SqNjZf9O3RINm6UqVPld78TEYmLE/9n1UCk4/oZ3Dt58uS0adMKCgq6deumLOndu3dycvKuXbtWrlz56KOP+rtDjUxLGPTPFWoBFJjZCyOZl+dkkj8BLSgoKKivr1+yZIny49GjRysqKrhzHirKz5f8fI9rO3eW9esZLgu/ETLhRk1NTWZmZn5+vtOsgI888siuXbsqKyuLiopGjx7t1z61MC1hKD5XSAVWYGYvjFje78kkZwKqKy0tzcnJSUtLmzVrls1m++GHHyorKxcuXKh2uQBnJpP06SOZmfLIIxIbq3ZpoEOETDg7efJkcnLy6tWrXS+sZmZmxsfHV1VVPffccwGETHWnJQzR5wqdoBSY2Qsjh/eEad+GnAljq6ioWLx4cWVlZX19fV5eXnV19TXXXLNmzRq1y3VBVlZWfX19ZWWl40JtPo4FkWPKFHGaf8pkkqgo0d7sWtAT/vvgklOnTv3pT39KSEiora0dOHCg6wYNDQ0///nPRaSwsHD37t1hLl7AdPe5glhgZi+MEG6fVuI2T/qSRQH96t+//7p16w4ePKjcGLlz507tJEwR+eabb867sFgsapcLEc1slujoy/516EDCRFvxPwgiIqWlpe3bt/+v//qvefPmNTQ0NDU1derUae/evY7bzJgxo0uXLspjpm02W3Jy8k9+8pOpU6eqVGSf6O5zBbfAzF4YIbw8D5OcCb0oLS3duHFjQUGB2gXRiqqqqo0bN27cuPFf//qX2mUBAL/R9ISIyM033/zDDz9432bVqlVON/tpn+4+VxAL7DR74ZEjR5hYwpC8JEz7j67bMG4WmnLLLbd88sknn332WceOHbmBXPF///d///jHP0Tkuuuuu+aaa9QuDvyjnHg5yyKSETIBA2L2wgjRasK0LyRnQsueeOIJtYugOUlJSUlJSWqXAgACRMgEjIbZCyOEjwnTvoqcCQAAwoOQCbRC43MVumL2wkjgV8K0b0DOBAAAYUDIBFqhzFWodin88M0336hdBIRWAAnTvhk5EwAAhBohE0ZQWlra0tJisViYMcIvVVVVH330kYgwe6GOBJww7RuTMwEAIvLdd2qXICyYUl0VhEzoHtMSBozZC3WnjQnT/hZyJgAACB1aFZHF3rLk9w7oTlASppe9tXGHAABHPMVEI+zVHXVcOJnULgAAoHXBTZie3u42eQIAAPiFkAkAWhf0hOllJ+RMAADQRoRMANC0ECVML7siZwIAjIEKTS2ETADQrpAmTC87JGcCAICAETIBQKPCkDC97JacCQBtcf78ec6jiFiETADQorAlTC87J2cCAIAAEDIBQHPCnDC9HIKcCQDQqXbteGyJagiZAKAtqiRMLwciZwJAYBgxi4hFyAQADVExYXo5HDkTAAD4jpAJAFqhesL0clByJgBARxgrqy5CJgBogkYSppdDkzMBwF+MmEVkImQCgPo0lTC9FICcCQDQProxVUfIBACVaTBhKsiZANB2dGYiAhEyAUBNmk2YCnImAADwFyETAFSj8YSpIGcCAHSEsbJaQMgEAHXoImEqyJkA0BaMmEWkIWQCgAp0lDAV5EwAgPbRjakRhEwACDfdJUwFORMAAkZnJiIKIRMAwkqnCVNBzgSAgJEzQ41uTO0gZAJA+Og6YSrImQAAwDtCJgCEiQESpoKcCQCBoTMzdOjG1BRCJgCEg2ESpoKcCQDQDhKm1hAyASDkDJYwFeRMAAgAnZmIBIRMAAgtQyZMBTkTAAJAzgwuujE1iJAJACFk4ISpIGcCAFREwtQmQiYAhIrhE6aCnAkA/qIzE8ZGyASAkIiQhKkgZwKAv8iZbUc3pmYRMgEg+CIqYSrImQCAcCJhahkhEwCCLAITpoKcCQB+oTMzYCRMjSNkAkAwRWzCVJAzAcAv5EwYEiETAIImwhOmgpwJAH4hZ/qLbkztI2QCQHCQMO3ImQDgF3Km70iYukDIBIAgIGE6IWcCgF/Imb4gYeoFIRMA2oqE6RY5EwD8Qs70joSpI4RMAGgTEqYX5EwA8IX9xEjO9ISEqS+ETAAIHAmzVeRMAPCiXbt2yimRnOkFCVN3CJkAECASpo/ImQDgyh4vHZcoL8iZjkiYekTIBIBAkDD9Qs4EADvXeOmKnCki7dqRMPWKkAkAfiNhBoCcCQDi9bzndJ6M8JypxEuqV50iZAKAf0iYASNnAohk3jsw3Z4hlZwZgadJOjD1jpAJAH4gYbYRORNABPJlfKwnSm9e5JwmGSJrDIRMAPAVCTMoyJkAIoov57dWa5MI6dJkiKxhEDIBwCckzCAiZwKIBG3pwHRl7C5NOjANhpAJAK0jYQYdOROAgfkVL/2qUAzZpUkHpvEQMgGgFSTMECFnAjAkv85jAVQoRurSpAPTqMxqFwAANI2EGVLnz593/YbbtWvHlwxAj8J5mcx+/tTp+VL5qjjbGxUhEwA8ImGGATkTgAEEFi/beKJT3q67qEm8jASETABwj4QZNuRMALqm7jh/x6gp2k6bxMvIQcgEADdImGFGzgQQaYJ7frPvTYMdm/azO6f0yEHIBABnJExVkDMBGIPbs5nrNqE7umimY5Ouy4hFyASAy5AwVUTOBKBHjucuXxJmGDh1bEoY06bjp+fsHbEImQBwCQlTdeRMAHrkGup82TgMPBUsuEUgWMIJIRNQ2YEDB6qrq00mU1ZWltsNSktLv/76axEZMmRIXFyc6wZVVVUfffSRiKSnp0dFRRUWFjY3N1977bWJiYmeDlpXV7dv3z4Ruemmm2JjY4PzSfSPhKkR5EwAOqWFPkxPnE6hbovqy1nW7Ufk/Awn1NmRxXEsh7olgd0LL7zw29/+VkROnDjRo0cPp7U2m61jx45NTU0i8tBDD73wwguue3jggQdWrlzZoUOHs2fPikj37t1ramomTpy4YcMGTwctKioaM2aMiGzYsGHixIlB/Dj6RcLUGg8NIH4pADTK01nLpQtRu+cxrXXDBgWtX1WY1C4AEOlSU1OVF2VlZa5ri4uLlYQpIgUFBW73sHfvXhEZP358aAoYEUiYGuT2V6DlXgIAkcwACVNEzvtA7TJCHwiZgMr69+8fExMjIkVFRa5rt2/fLiLJyckiUllZWVVV5bRBY2NjRUWFfRsEgISpWeRMALrgY8IEIgchE1BfSkqKiJSXl7uu2rZtm4jMnDmzd+/e4q4zU0mhIpKRkRHaUhoUCVPjyJkANM73sf3UL4gchExAfUo+PHLkSHNzs+Py2trajz/+WERSU1PT0tJEpLCw0Om9u3btEpHevXvHx8eHqbgGQsLUBXImAM3i7nHALUImoD7ltkybzeY0YnbHjh0ikpCQEBMTo8zTU1hYaLPZHLcpLS0VkfT09PAV1yhImDpCzgSgQa0mTE+vAcMjZALqi4uLU0bDlpSUOC7ftGmTXAyQmZmZJpOpubl5586d9g1aWloOHTokDrMHwUckTN0hZwLQFB/7MJkvB5GJ52QCmpCcnFxZWak87tLugw8+EBGlD9NsNo8aNWrXrl3btm0bPXq0soHSsWk2mzMzM512eOrUqTVr1ng6nDJXUMQiYeoUz88EoBGMkgW8I2QCmpCenv7qq6/u2bPHZrOZTCYR+fDDDxsaGqKiopRpgUTk1ltv3bVr1/bt25999lllyb59+0RkxIgRZrPz3/L+/funTJkSxk+gGyRMXSNnAlAdCRNoFSET0IS0tDSTyWS1Wv/5z38mJibKxTl+0tPTlcwpFx9Scvjw4ZqamtjYWLkYMpU5gZxYLJaOHTt6Oty5c+caGxtD8Dm0joRpAORMACoiYQK+4J5MQBOio6OHDBkiDg8yUZ5N4hggBw8e3LVrV7k4IZDVai0rKxMR++hZRxkZGd94tmHDhtB/Js0hYRoG92cCUAUJE/ARIRPQCmVY7N69e0WkoaFBCZBOM/ooeVJ5Wub27dttNlvnzp0HDx6sQnH1hoRpMORMAGFGwgR8R8gEtEIJmcpTTJQY2bNnzz59+jhuk5WVJSLFxcVy8eElY8eODX9RdYeEaUjkTABhQ8IE/ELIBLQiJSXFbDafOXPm5MmTylhZ1wCpdGyeOnWqrq5u//79IuI6ryyckDANjJwJIAxImIC/CJmAVphMJqUzs7y8XBkr6xoyu3XrNnDgQBEpKSlRHpjJEzK9I2EaHjkTQEiRMIEAEDIBDbHfcnn06FGz2ZyRkeG6jTIVUH5+vtVqTUhIUKaZhVskzAhBzgQQIiRMIDCETEBDRo0aJSJ5eXkiMmLECIvF4rrNmDFjRGTjxo0ikpSUFKKSWK1Wq9Xa6mY2m82XzVRBwowo5EwAQUfCBAJGyAQ0ZOjQodHR0Upsc/v0S7l466ayza233hrcAtTW1s6YMeOKK65o3759+/bte/XqtXz5ci/bjx8/Pj09PbhlCAoSZgQiZwIIIhIm0BaETEBbxo0bp7zwFN5MJpMyjNZsNnsKooGpq6sbNGjQa6+9lpKS8vLLLy9atKh9+/Zz586dPn262+0ffvhhZRZcrSFhRixyJoCgIGECbdSOP5iIYj9p8nuHqzlz5qxYsWLx4sULFy5UljQ2Ng4bNqyioqKsrCwxMdG+ZUNDw913371+/XoRSU1NVR67ohEkTNA6BNAWnEMMhtavKujJBHDBe++9Z7FYFixYYF8SHR398MMPi0hhYaF94TvvvNOnT5/169dPmzZNhVJ6RcKE0J8JoA1ImEBQmNUuAACtWLlyZVNTk8l02bUns9ksIi0tLfYl+fn5HTp0eP/997Oyst58881wl9IzEibszp8/7/r/oV07Bu8A8IaECQQLIRPABVlZWa4LV69eLSIjR460L3n88cd/8YtfOGVR1ZEw4YScCcAvJEwgiLTVTASgKbm5uTt27EhISHCchWjw4MEkTOgC42YB+IiECQSXtlqKALRj06ZN9913X+fOnTds2KB2WbwhYcILciaAVpEwgaAjZAJwY9WqVRMmTOjUqdO2bdt69eqldnE8ImGiVeRMAF6QMIFQ4J5MAM5ycnJefPHFuLi4oqKi66+/Xu3ieETChI+MfX9mYWFhc3Pztdde6/icISd1dXX79u0TkZtuuik2NjaMpQM0jYQJhAghE8AlVqs1Ozt7y5YtQ4YM2bp1a0xMjNol8oiECb8YOGdOnz69pqZm4sSJXka2f/LJJ9nZ2SKyYcOGiRMnhrF0gHaRMIHQYbgsgEsmTJiwZcuWzMzM4uJiEiYMhnGzAOxImEBI0ZMJ4IInn3yyoKAgIyNj8+bNapfFGxImAmbg/kwAviNhAqFGyAQgIlJfX7906VLlxbhx45zWpqWl5eTkqFEuZyRMtBE5E4hwJEwgDAiZAERESkpKWlpaRKS8vNx1rUZmCiFhIijImUDEImEC4UHIBCAiMnHixABq2XBWzCRMBBE5E4hAJEwgbJj4B4AOkDARdMwDBEQUEiYQTvRkAtA6EiZCxEj9madOnVqzZo2ntRUVFeEsDKA1JEwgzAiZADSNhImQMkzO3L9//5QpU9QuBaBFJEwg/AiZALSLhIkwMEbOtFgsHTt29LT23LlzjY2N4SwPoBEkTEAVhEwAGkXCRNgYIGdmZGRs2LDB09qioqIxY8aEszyAFpAwAbUw8Q8ALSJhIsyYBwgwGBImoCJ6MgFoDgkTqjBAfybg3e7du8+cOSMiw4YN69atm5cta2try8rKRKR79+5Dhw4NU/mCh4QJqIuQCUBbSJhQETkTxlZdXT1t2jQRycjI2Lp1q5ctp0+fXlBQYDKZ9u3bF67SBQ0JE1Adw2UBaAgJE6pj3CwMbOrUqRMnThSRgoKCt956y9Nmq1evLigoEJHHH388MTExfOULBhImoAWETABaQcKERpAzYWCvvPJK165dRWTOnDm1tbWuG9TV1eXk5IjIwIEDn3jiiTAXr41ImIBGEDIBaAIJE5oSaTnTarVarVa1S4FwiImJefnll0XkzJkzs2fPdt3g/vvvP3PmjMViWbt2rcmkp4YiCRPQDj2dOwAYFQkTGhQJObO2tnbGjBlXXHFF+/bt27dv36tXr+XLl6tdKITcHXfccdttt4nIu+++u379esdV77zzjrJk2bJlffr0Uad8ASFhAppCyASgMhImNMvYObOurm7QoEGvvfZaSkrKyy+/vGjRovbt28+dO3f69OlqFw0ht3LlypiYGBG5//77v/32W2VhfX290reZmpo6Z84cNcvnJxImoDXMmBdZ7Gdhfu/QCBImtM+o7dc5c+asWLFi8eLFCxcuVJY0NjYOGzasoqKirKxMd9O9wF/vvPPO5MmTReSee+5ZtWqViEydOjUvL69z584VFRVxcXFqF9BXRv0LRbDQ+lUFITOy8GcGTSFhQi8M2Yrt3r17fX39999/73jfXW5u7j333LNo0SLdzfiCAPzyl7989913RWT//v2NjY3Jyckikp+ff+edd6pdNF8Z8m8TwUXrVxU8JxOAOkiY0BFDPj9z5cqVTU1NTjO7mM1mEWlpaVGpUAirlStX7tmzp66u7t57721ubhaRyZMnkzABtJ2+K0j4i2s50AgSJvQoElq0o0eP3rFjxwcffJCenq52WRAO69evnzRpkvI6Njb2008/VR5won2R8PeIoKD1qwom/gEQbiRM6JSx5wESkdzc3B07diQkJJAwI8dtt912++23K6/feOMNEiaAoEvGQnIAACAASURBVCBkAggrEiZ0zcA5c9OmTffdd1/nzp03bNigdlkQVmPHjlVeJCUlqVoQX5EwAe0jZAIIHxImDMCQOXPVqlUTJkzo1KnTtm3bevXqpXZxAI9ImIAuEDIBhAkJE4ZhsJyZk5Nz7733xsXF7d27d+jQoWoXB/CIhAnoBbPLAggHEiYMxhjzzVqt1uzs7C1btgwZMmTr1q0xMTFqlwjwiIQJ6Ag9mQBCjoQJQzJAf+aECRO2bNmSmZlZXFxMwoSWkTABfaEnE0BokTBhYLruz3zyyScLCgoyMjI2b96sdlmgJ1arVS4+UjU8SJiA7hAyAYQQCROGp9OcWV9fv3TpUuXFuHHjnNampaXl5OSoUS5oV21t7eOPP56Xl9fc3CwiPXv2nDVr1qOPPhrq45IwAT0iZAIIFRImIoQec2ZJSUlLS4uIlJeXu66NjY0Ne4mgaXV1dYMGDaqpqcnIyMjMzKytrc3Pz587d+5nn32Wm5sbuuOSMAGd0nQViKCzn6z5vSPUSJiINLSGYWBz5sxZsWLF4sWLFy5cqCxpbGwcNmxYRUVFWVlZYmJiKA7K3xSCgtavKpj4B0DwkTARgQwwDxDgyXvvvWexWBYsWGBfEh0d/fDDD4tIYWFhKI5IwgR0jeGyAIKMhImIpcdxs4AvVq5c2dTUZDJd1jmhzP2jjLsOLhImoHeETADBRMJEhCNnwpCysrJcF65evVpERo4cGdxjkTABA2C4LICgIWECwrhZRIbc3NwdO3YkJCSkp6cHcbckTMAYuLYaWbj1GaFDwgQc+dJW5pwMndq0adOkSZOio6M/+uijXr16BWu3JEyEAmdaVdCTCSAISJiAE+/9me3ataNvEzq1atWqCRMmdOrUadu2bSRMAG5xTyaAtiJhAm75fn8mN21CL3Jycl588cW4uLiioqLrr78+WLsNZ8I8cOBAdXW1yWRye6OpiJSWln799dciMmTIkLi4ONcNqqqqPvroIxFJT0+PiorasmWL1Wp13cxsNlsslsTExCuvvDKonwDQAWq1yKK7AQMBX+nXywc0ABIm4J2n1rPTcq394dDRqi9h+P9jtVqzs7O3bNkyZMiQrVu3xsTEBGvPYe7DfOGFF37729+KyIkTJ3r06OG01mazdezYsampSUQeeuihF154wXUPDzzwwMqVKzt06HD27FkR6dixY2Njo5cjJiQkPP7443fccUfQPgP8obvWrzEwXBYa0s7F+fMS2D+n/aj9yQyLhAm0ytO4WU/3Z6rF99OvSIBnZv6F6F94TJgwYcuWLZmZmcXFxfpNmCKSmpqqvCgrK3NdW1xcrCRMESkoKHC7h71794rI+PHjHReaTCaLC2XV4cOHJ0+e/NRTTwXrIwDaR8iE+ry0aQLmtB8CZyiQMAEf+TjfbPhPUF5SJeDoySefLCgoyMjI2Lx5c1RUVLB2q8p9mP3791dCclFRkeva7du3i0hycrKIVFZWVlVVOW3Q2NhYUVFh38burrvuOufixx9/zMvLUw73P//zP0ePHg3NZwI0h5AJdbht2YSO28AZwuNFABIm4BdN/YG4DZaAJ/X19UuXLlVejHPhdkCpL1Sc6SclJUVEysvLXVdt27ZNRGbOnNm7d29x15mppFARycjIaPVAJpPpV7/61dtvvy0iNpvtb3/7W9sKDugGE/8g3JRKRcUGjf3QjNEPGAkT8MvF856beYBctwzdX5PDSS9ER4AxlZSUtLS0iIdUFhsbG8A+1Z1LNiMjY+3atUeOHGlubnbsmK2trf34449FJDU1taSkpLKysrCw8L777nN8765du0Skd+/e8fHxPh5u9OjR8fHxVVVVx48fD96HADSNnkyEidOFcy1w6ttUuzi6QcIEAqPieSZsw0ZgSBMnTjzv2apVq/zdoboJUy7elmmz2ZxGzO7YsUNEEhISYmJixowZIyKFhYU2m81xm9LSUhFJT0/364hKv6iS1YFIQMhEyGm/cUPU9B0JE/CXvyeWIJ6INHh1D1A9YYpIXFyckvpKSkocl2/atEkuBsjMzEyTydTc3Lxz5077Bi0tLYcOHRKH2YN8YbPZlEmGoqOjg1F8QAcImQghfTVuiJqtImEC4dH2s5D2r+4hMmkhYSqUaXuUx13affDBByKi9GGazeZRo0bJxbs0FUrHptlszszM9P1Yy5cvb25uFpGRI0cGo+yADhAyERL6ipeOiJqekDCBwIT/L0Wnp18YnnYSplzsrtyzZ499NOyHH37Y0NAQFRWlTAskIrfeeqs4zPQjIvv27RORESNGmM3O05pYrdbGyx06dGjjxo1Tp0793e9+JyJxcXH33HNP6D8ZoAlM/IPgU9o3uqaU3z5Xh8ql0QASJtAWvkz54ySwGYBUn1kN8ERTCVNE0tLSTCaT1Wr95z//mZiYKCKFhYUikp6ebjJd6INRejsPHz5cU1OjzG+khMy0tDTXHebn5+fn53s6XOfOndevX89wWUQOejIRTPYOTGOw92qqXRCVkTCBtlOmSAnd/vU7fgSRQGsJU0Sio6OHDBkiDlPmKj2WjgFy8ODBXbt2lYsTAlmtVuXWytGjR/t4FJPJdP3118+dO/fIkSNDhw4N6icANI2QieAwcPsmwkfPkjCBIPIravp+2jHq6RfGoMGEqVCGxe7du1dEGhoalADpNKOPkieVp2Vu377dZrN17tx58ODBrnubMmXKd5c7e/bsDz/88Pnnnz/77LOBPegF0C9CJoLA8O2biO3SJGECoRDEnGmw8SMwHs0mTLkYMpWnmCgxsmfPnn369HHcJisrS0SKi4vl4sNLxo4d63ZvZrM5+nIdOnSwj7wFIg33ZKKtIqd9Y8+ZGqkdQ42ECYSO8tfUxktXkXP6hU5pOWGKSEpKitlsPnPmzMmTJ5Wxsq4BUunYPHXqVF1d3f79+0XEr3llgYjF9RUELgKvoEdOlyYJEwgDX0bPejrhRNrpF7qj8YQpIiaTSenMLC8vV8bKuobMbt26DRw4UERKSkqUB2b69YRMIGIRMhEgww+R9cLwOZOECYSTv39fEXiBD7qj/YSpsN9yefToUbPZnJGR4bqNMhVQfn6+1WpNSEjg7krAF4RMBIL2jYFzJgkTCD/vXZqOf5WRfIEPeqGXhCkio0aNEpG8vDwRGTFihMVicd1mzJgxIrJx40YRSUpKCnoZrFar1WptdTObzebLZoBGEDLhNxKmwpCzzpIwARW1OnqW0y+0T0cJU0SGDh0aHR2thDe3T7+Ui7duKtvceuutwTp0bW3tjBkzrrjiivbt27dv375Xr17Lly/3sv348ePT09ODdXQg1AiZ8A9NHEcGu0WThAlogae/O06/0D59JUzFuHHjlBeeIpzJZFKG0ZrNZk9B1F91dXWDBg167bXXUlJSXn755UWLFrVv337u3LnTp093u/3DDz+szH8L6EU7jf/lI7jsZ//Afu80cTxp107rlWirSJh2Bw4cqK6uNplMysz1rkpLS7/++msRGTJkSFxcnOsGVVVVH330kYikp6dHRUUVFhY2Nzdfe+21iYmJng5aV1e3b98+Ebnpppu44QcKp79K7fxFtmunocJANFMH6TFhqmXOnDkrVqxYvHjxwoULlSWNjY3Dhg2rqKgoKytzrCwaGhruvvvu9evXi0hqaqrywBX4pY2tXwSGnkz4ioTphd77M0mYjvbt25ednT1hwoSTJ0+6rrXZbGPGjMnOzs7Ozv7zn//sdg9PPfVUdnb2XXfdFRUVJSLTp0/Pzs5+5plnvBz0k08+UfZZXl4elE8BAzh/kWgpYQJukTD98t5771kslgULFtiXREdHP/zwwyJSWFhoX/jOO+/06dNn/fr106ZNU6GUQBsQMuETEmar9JszSZhO7NPTKzPaOykuLm5qalJeexq8tHfvXhEZP358aAqIyMLpF9pHwvTXypUr//GPf5hMl7XDzWaziLS0tNiX5Ofnd+jQ4f3331+9enW4iwi0DSETraOJ4yM95kwSpqv+/fvHxMSIiNtRScoDu5OTk0WksrKyqqrKaYPGxsaKigr7NkBbcPqF9pEwA5CVlXXnnXc6LVSS5MiRI+1LHn/88crKSk/3bgBaRshEK2jiGBgJ0xP747ldV23btk1EZs6c2bt3b3HXmamkUBFx+7w1wHecfqF9JMxgyc3N3bFjR0JCguP8Q4MHD3bq7QT0gv+48IYmjr901JlJwvRCyYdHjhxpbm52XF5bW/vxxx+LSGpqqjLHoOPNM4pdu3aJSO/evePj48NUXABQAwkzWDZt2nTfffd17tx5w4YNapcFCA5CJjwiYQZGFzmThOmdclumzWZzGjG7Y8cOEUlISIiJiVEez11YWGiz2Ry3KS0tFc9T4QM+4gwMjSNhBsuqVasmTJjQqVOnbdu29erVS+3iAMFByASCT+M5k4TZqri4OGU0bElJiePyTZs2ycUAmZmZaTKZmpubd+7cad+gpaXl0KFD4jB7EBAAEiY0joQZLDk5Offee29cXNzevXuHDh2qdnGAoDGrXQBoFE2cNlJypgZrXBKmj5KTkysrK5XHXdp98MEHIqL0YZrN5lGjRu3atWvbtm2jR49WNlA6Ns1mc2ZmptMOT506tWbNGk+HU+YKAoTTLzSPhBkUVqs1Ozt7y5YtQ4YM2bp1qzLhHGAYhEy4QRPHqEiYvktPT3/11Vf37Nljs9mUeRc+/PDDhoaGqKgoZVogEbn11lt37dq1ffv2Z599Vlmyb98+ERkxYoQyE72j/fv3T5kyJYyfALrE6RcaR8IMlgkTJhQUFGRmZq5bt055qDJgJIRMOKOJEyxa68wkYfolLS3NZDJZrdZ//vOfiYmJcnGOn/T0dPtcf8pDSg4fPlxTUxMbGysXQ6YyJ5ATi8XSsWNHT4c7d+5cY2NjCD4HAAQNCTNYnnzyyYKCgoyMjM2bN6tdFiAkCJlACGknZ5Iw/RUdHT1kyJDy8vLy8nIlZCrPJnEMkIMHD+7atWt9ff2OHTumTp1qtVrLyspExD561lFGRoaXaQOLioqUUbiIZFzjg5aRMIOlvr5+6dKlyotx48Y5rU1LS8vJyVGjXEAwETJxGZo4hkTCDExKSkp5efnevXtzcnIaGhqUAOk0o8/o0aPXrl1bUFAwderU7du322y2zp07Dx48WKUiQ8c4/ULLSJhBVFJS0tLSIh6exqyMiwH0jtllcQlNnFBQfaZZEmbAlHsvlaeYFBQUiEjPnj379OnjuE1WVpaIFBcXy8WHl4wdOzb8RQWA0CFhBtfEiRPPe7Zq1Sq37zp//rzTU7UALSNkAiGnYs4kYbZFSkqK2Ww+c+bMyZMnlbGyrgFS6dg8depUXV3d/v37RcR1XlmgVVzjg2aRMAEEgJCJC2jiGA8Js41MJpPSmVleXq6MlXUNmd26dRs4cKCIlJSUKA/M5AmZAAyDhAkgMIRMIBzC35lJwgwKZQqfgoKCo0ePms3mjIwM122UqYDy8/OtVmtCQgK308BfXOODNpEwAQSMkAkRmjiGQ8IMllGjRolIXl6eiIwYMcJisbhuo8wKu3HjRhFJSkoKUUmsVqvVam37NgDgCxImgLYgZAJhErbOTBJmEA0dOjQ6OlpJbm6ffikXb91Utrn11luDW4Da2toZM2ZcccUV7du3b9++fa9evZYvXx7ANtAsrvFBg0iYANqIkAmaOIZCwgw6+0PM0tPT3W5gMpmUYbRms9lTEA1MXV3doEGDXnvttZSUlJdffnnRokXt27efO3fu9OnT/doGAHxHwgTQdpp4TDzCxl5zOP7eCZnh1K5dCKtqEqbBzJkzZ8WKFYsXL164cKGypLGxcdiwYRUVFWVlZYmJiT5uA83S6em3XTvRY7ENLIg1CwkTxuO29YtQoycz0um0iQNXJEzjee+99ywWy4IFC+xLoqOjH374YREpLCz0fRsA8AUJE0CwmNUuABBZlDszg15nkzANaeXKlU1NTSbTZVcDzWaziLS0tPi+DbSJa3zQFBImgCAiZEY0mjjGQMI0qqysLNeFq1evFpGRI0f6vg0AeEfCBBBcDJcF9I2EGVFyc3N37NiRkJDgaRYiH7eB6rjGB+0gYQIIOib+iSxOtz7TylFLsCZpIGFGlE2bNk2aNCk6Ovqjjz7q1atXwNtAC3R9+mXiH61pS51CwoThMfGPKujJjFy6buJASJgRZtWqVRMmTOjUqdO2bds8pUdftgEAOxImgBAhZAK6RMKMKDk5Offee29cXNzevXuHDh0a8DbQCK7xQQtImABCh4l/ABW0cY5ZEmbksFqt2dnZW7ZsGTJkyNatW2NiYgLbBgAckTABhBQ9mRGK6+j6RcKMKBMmTNiyZUtmZmZxcbGn9OjLNgBgR8IEEGr0ZAJ6QsKMKE8++WRBQUFGRsbmzZvbsg00hWt8UBcJE0AYMLtsZHGsWvjNq87f+QBJmBGlvr6+e/fuLS0tN910U9euXZ3WpqWl5eTk+LJNuMoLXxkgZDK7rNb4XpuQMBGBmF1WFfRkAvpAwow0JSUlLS0tIlJeXu66NjY21sdtAEBBwgQQNvRkRhaHaznqFqQV27dLU5OIyOjREh3tcbPDh+X4cRGRlBS58sowlS2I2nLtmb9cQKfoyUTQ+VKbkDARsejJVAUT/0CLvvpKsrMlO1t++1uP25w8KaNGSXa25OXpMmH6joQJGIYBEib0iIQJIMzoyYwseunJFJHsbNm4UUTk/fclK8t5bUuLDBkihw9Lz55y6JCOQ2arl59JmICRGCNk0pOpNd6rEhImIhw9maogZEYWHYXM+noZMEBqaiQmRj79VLp1u2ztzJny6qtiMsm+fZKYqFIRg8HflgF/sICu+RUyDxyQ6uq2HvH66+X669u6EyeETK3xUpWQMAFCpioImZFFRyFTRHbvluRkEZHUVCkqurT87bflrrtERP7yF3n4YXXKFix+tQz4awX0zq+QOX68bNnS1iPec4+sWtXWnTghZGqNp6qEhAkIIVMl3JMZifTyJ5aUJHPniojs2CEvvHBh4bFjct99IiJZWbpPmF6QMAH8/veX/bh5s5w75/7f2bPy3Xfy6aeydau8+KIMG3bpXd9+G+ZSQytImABURE9mZFGqHB39zm02ufFGOXRILBY5eFB695ZBg+TIEYmPl08+kS5d1C5fm7m9/EzCBIzK33sy58+Xp5++8Do2Vj79VFyeh+re9u0yebKcOSMDB8rBg36X07tw9mQWFEhLi/tVJpNYLDJkiK/fiRfKFAC9eklCgq9F6t5dhg71uE1dnezbJyJy000ShmcJuVYlJEzAjp5MVRAyI4vuQqaIHDsmgwZJU5MMHCijR8uyZUa4FdPOl5YBf6SAMQQw64/NJv/931JRceHH22+Xdet8fa9yx0FUlHz/vX8HbVU4Q+bVV0t9fSvb9O8vTz8tmZmBH0U57z74oPz1r74WafJkWbPG4zZFRTJmjIjIhg0ycWLgBfORU1VCwgQcETJVwXBZaF2fPvKXv4iIHDoky5aJiPz5zwZJmK5ImIAGtbso/Ic2mWTdOrFYLvz47ruyerWv701KkrvukuZmjz2BOtKtm6SmOv/r31/MZhGRigoZP17eekvtUmoDCROAFhAyoQP33XfpKSajRsmjj6pamqA6f/5Sg4CECWiQ4x9mOwdhK8D118tTT136cdYsqary9b333y8icuhQ8EsVZklJUlTk/O/TT+XcOXn1VYmKEhF54AFpaFC7oGojYQLQCEImdMBmk9raC6+PHLn02khImIC+hDNwPvqojBp14XVjo0yZ4usbb7lFoqLk9OkQlUt9JpPMmCF//rOISGOjFBSoXSBVkTABaAchEzrw2GNSXi4mk4hIXZ38+tdqFyjYSJiAroUhcL75pnTufOH1vn3ypz/5+sakJOP37/3mNxde7NzpZq3VKjt3ypYtsnOnWK3hLFdYkTABaAohE1pXUHDhnsx58+Shh0REtm+X5cvVLVRo0SwAtMPfv8cQBc74+MvmpHn8cTl82Kc3pqS0PnGO3p09e+HFT3962fK6Opk5U664QlJTZfx4SU2VK66QmTON/4UoqEoAqMisdgEAb06dutBv2a+fLFwoVqsUFEhlpcyfL2PG+DTXvMa5bYWqMr8IgKAL7pSGv/qVbNgg774rImKzyeTJcvDghdsRvXjssbYfWetee+3Ci9TUSwtPnpThw+XUKRGRESOke3f59lspKpJXX5Xt26WsLBxPFgkPtzUGCROAuujJhKZNmSL19WKxXJhfsUMHyc8Xk0laWmTyZGluVrt8AOCDYF05euWVS9Ho6NGICJDetbTI8uWyaJGISI8el6aIs9kkM1NOnZLYWDl4UIqLZc0a2bZN/t//k5495eRJmTRJxVIHmWucJGECUB0hE9r1xBNSUiIi8vzz0q/fhYWDB8vixSIiR4/K7NmqlS1YaAkA8F2XLvLGG5d+XLHC/V2IxlNcLOPGXfbv1lvl5z+XK66QuXPFZpOYGNm48cKt+yKyZs2Fh4tu2iQDB17aT+/esmWLiEhZmRQVhf1jhAUJE4AWEDKhUbt3XwiTWVnywAOXrVqwQIYNExF57TXZtEmFsgGAWtLSLru+NnWqfPuteqUJl5oaKSi47F9hoZw8eSFePvKIfPbZZWHyvfdEREaMkMGDnXfVr5/cdJOIyJo14Sp9WCjZkoQJQCO4JxNaVFcnd9whIhIbK7m5bjZYu1b69ZPGRvnNb+ToUenWzXkDZQpBM//BAWhAcJv+zz4r27fLsWMiIjU1sn+/pKUFcfdadNNN8uCDF15brbJ9u6xbJzabTJwoubnSpYvz9koHr9ksGze62ZvFIiJSWRm68orNFsKde0LCBKAdtMGhRVOmSF2diMhbb0nXrm42UCZa/M1v5MwZmTxZdu++sLy2Vh5/XPLyLtyu2bOnzJoljz4apmIHjJYBoFkB304Zur/rqChZu1ZuvFFsNvnd74yfMEXk5z+/7OFV06fL7Nly662ycaMcPOhmFh/lqS27dsmuXR73qYynDdgPP3hba39WiokRYwAiEiETmrNkiezYISIyb56kpHjc7Ne/ls2b5d13Zc8e+dOf5A9/kLo6GTRIamokI0MyM6W2VvLzZe5c+ewz992hABBcYbtgZLVKhw4ybpw880x4Dqg5t9wi+fkyfrycPClpaVJWJtHRl9YqHYnDhknv3h730LFjgIfu31/27JGWFm/b2NcqvaYAEGkImdCchQtl4UKftly37rIflyyRmhpZvPjS2+fOlWHD5PXXZeZMSUwMcjkBQNQYiXDqlGRmyqBB8tZbYT6ytmRmyuzZsmKFVFTI7NmXTYkUHS2NjTJsWEgeqqyMzv3kE2/bnDx54cUNNwS/AACgfQzjgHG8955YLLJgwaUl0dHy8MMiIoWFahWqFe3aMVYW0J/zDsJ86G+/lbQ06dJF3n/fp9vOV626LH0ZzDPPSI8eIiL/+MdlE+2OHCkiUlbm/l0FBbJmjRw4EOBBBw0SEamqupQk3R5CRKKiJD4+wKMAgK4RMmEcK1fKP/7hfAOM0gjzPq4JADyxx0gVg6VdS4tMmCB1dVJU5Ga2G7e2bjXy9LMdOsgrr1x4PWPGpYcnZ2aKiJSVuXnES1WVTJggU6ZIXl6AB7U/jdM+F5GT4mLZvl1EZMqUAA8BAHpHyIRxZGXJnXc6L1y9WuTiVW0ACIC6wdLRXXfJwYNSUCBxcb6+pbzcj431KC3tQpb78ktZtOjCwnvukZ49RUTuvPOynFlTI5MmXbij9ZFHnHd14oTzg1Ic/9kNHCgTJ4qIFBRIYqJs2XLhOqbNJocPyxNPyJgxIiIdOsj8+SH5yACgfe00UnEiPJRpEiPnd56bK/fcIwkJrdw8oyKGywIRpV27dgH/xT/2mPzlL/LBB35MJ3vsmPTtK9u2BXkG2nbtwlePXH211NfL5MneHmtZXy99+0p9vZhMcvCgJCSIiBw5IklJFyYqv+km6d1bvvtOCgouzPv6/vuXOiRFxJcphB0/8rffyujR8vHHl5aYzZdmlBWR6Gh5773wTfxLVQJ4YZ8knD+TcKInE4a1aZPcd5907iwbNqhdFABom5dekmXL5NVX/cstyt2YN94YmjJpRteu8vzzIiI2m0ybdmFhv37y6afym9+I2Szl5ZKXJ5s2idUqw4dLWdllCTMAXbrI/v3y5z9fmr3WnjDNZrn7bvnkk4h4tAwAeEJPZmRxuJajbkFCbtUqufde6dpVCgpk6FC1S+MZl5+BiBJYT+aWLTJ+vPzP/8iSJX68q6pKrr9empvlxx/9PqJ34ezJhC+oSgAv6MlUBT2ZMKCcHLn3XomLk717NZ0wAaBVpaXyy1/Kb37jX8I8dUrS0qSpydf5gQAACCKekwlDsVolO1u2bJEhQ2TrVomJUbtAXnHtGYB3J0/KxIkyfLisWuXT9jab7N0rmzfLyy9LU5OIyE03hbSAAAC4QciEoUyYIAUFkpkp69ZJVJTapQGAy50/f973EbN1dZKcLHV18tFHct11Hjez2cRqlR9/lJaWC8HSUadOgZcWAIDAEDJhHE8+KQUFkpEhmzerXRQAaLPf/16+/FJE5MwZOXMmwJ1ofEAHAMCQmPgnsrRzmKbdYL/5+nrp3l1aWuSmm6RrV+e1aWmSk6NGsbxiuCwQgdryFBONYOIfraE2Abxg4h9V0JMJgygpufA47PJyN2tjY8NcHAAAACBC0ZMZWQzck6k7XHgGIhM9mQg6KhTAC3oyVcEjTAAACJ/z5887XO4DAMCACJkRilYOAAAAgFAgZAIqYGgTAAAAjIqQCQBAWDGWBABgbITMyEUrBwAAAEDQETKBcGOsLAAAAAyMkBnR6MwEAFVw+gUAGBghEwgrujEBAABgbIRMAABUQGcmAMCoCJmRjlYOAAAAgCAiZALhw1hZAI64zAcAMCRCJmjlAAAAAAgaQiYQJnRjdwMrzgAAIABJREFUAnDFZT4AgPEQMiFCKwcAAABAkBAygXCgGxOAJ1zmAwAYDCETF9DKCR0SJgAAACIHIROXkDMBQBWcfgEARkLIBEKLbkwAviBnAgAMg5CJy9DKAQAAANAWhEw4I2cGEd2YAHzH6RcAYAyETCBUSJgA/EXOBAAYACETbtDKaTsSJgAAACITIRPukTMBQBWcfgEAekfIhEc0dAJGNyaAtuD0CwDQNUImEGQkTABtR84EAOgXIRPe0MrxFwkTQLBwBgYA6BQhE62gleM7EiYAAABAyETryJm+IGECCDpOvwAAPSJkwic0dLwjYQIIEU6/AADdIWTCVzR0PCFhAggpTr8AAH0hZMIPNHRckTABhAGnXwCAjhAy4R8aOo5ImADCRjn9cgYGAGgfIRN+I2cqSJgAwuz8+fOcgQEA2kfIRCC4oE7CBKAWciYAQOMImQhQxF5QV9I1CROAiiLz9AsA0AtCJtok0ho6SrwkYQJQHSNKAACaRchEW0VOQ4cOTACaErEjSgAAGmdWuwAwAiV6tWvXzqgRTGnDkTABaND58+fbtWsnIpyiAAAaQchE0Bi1oUMHJgCNM/yVPgCAvhAyEUz2ho4YImrSgQlAR4x6pQ8AoDuETASfAaIm8RKAHhng9AsAMABCJkJFp20d4iUAvdPp6RcAYBiETISWjto6xEsARqKj0y8AwGAImQgHLbd17LP/Ey8BGI/j6Ve0dwYGABgSIRPho6m2DtkSQOSwn+i0ebEPAGAwhEyEm1NbR8Lb3CFbAohkThf7hMAJAAgBQiZU45o2JTTNHYfdky0B4LIzIYETABB0hEyoz1Nzx2EDP/bmugOCJQB44v0MzOkTABAAQia0xW0gdJs8/doDAKBVrudPT6dff87KAICIQ8iEDqibG202m9VqdV1usVjCXxhonNVqtdlsTgtNJpPZzMkWusRlO+hUxNbdVEPQCJPaBQC0rrCw8Kcuunfvrna5oEXTp093/d+Sk5OjdrkAILJEbN1NNQSN4KoG4JOHHnpowIAB9h8NfykUgZk+ffrIkSMdl9x7771qFQYAIlwE1t1UQ9AIQibgk7Fjx2ZkZKhdCmhdUlJSUlKS4xJqdwBQSwTW3VRD0AiGywIAAAAAgoaeTCA4jh8/vnfvXqeFffr0SUxMFJHKysrS0lJPa0WkoqLi448/dlybmpoaFxfnuOTQoUOHDx92PfSoUaN69Ohhs9neeust17VXX321dq7j8i0BADQlDBXTkSNHDhw44KkAJpPpqquuSktLY24eGAn/m4Hg+Prrr8vKynbu3Hns2DERSU5O7tu37/XXX6+sra6uLisrKyoqqqystK/t06eP/e3V1dVFRUXr1q1rbm5OSEhISkoaO3as0yH+/e9/Hzx4sK6uLi8vT0T69es3fPjw6667zmQyiYjVaq2urq6qqtq2bduXX35pMpmmTJnStWtX1/2oiG8JAKApYaiYvvnmm08//bS+vv71118XkWHDhmVkZHTp0kVETpw4UVNTU1hY+P3338+bN2/BggXh+dRAyJ1HJOH3HoCtW7eKyNatW33Z+M033xQRk8nkdq1Su1gsFk9vnzZt2oMPPuj9EJ988onyS/zss8/cbnDbbbeJyO233+5LgVURUd+SiLRaWgBAcPlVd58PS8X01VdfKRXTF1984bTq7NmzI0aMEJHZs2f7WGDfUQ3R+lUF92QCwdShQwfx/HguZW1LS4vb9zY2Np4+ffqll17yfog9e/aISOfOnfv16+d2g8LCQhEZPXq0PwUPK74lAICmhKFiKikpEZG4uLjevXu77n/27NkismLFilOnTvlffEBzCJlAMCn1kIg0NTW5rj1y5Ijyorm52XXt0qVL58+fr4zq9KK4uFhEPA3vPHTokHLoUaNG+VzqcONbAgBoShgqpoKCAhFxmvrVzn5Dpn0kDqBrhEwgmK666irlhest/lVVVbm5ucrrEydOOK09fvz4sWPHUlJSWj3E9u3bRSQ5OdntWuVCadeuXe33k2gQ3xIAQFPCUDEpVz89jaCx51h7SQBdI2QCwdS/f3/lhevFznnz5r3xxhvKa/uNGXaPPfbYs88+2+r+KyoqGhoaRES5ecPV7t27RfOjQPmWAACaEuqKqaqqqqqqSkRuueUWtxso/Zw9e/a0z1sL6BohEwimqKgo5YVTLfXhhx/GxcXdfPPNbtcWFxf37dvX9SYNV0oXXKu3GnrqwdMIviUAgKaEp2Lq1q2b241LS0vLyspE5JVXXgmo+IDm8AgTIJhMJpPZbLZarU43dSxatGjdunVRUVEmk8lmszU2NjqufeKJJzZt2uTL/nfu3Ckio0ePdjv9gF5uNeRbAgBoSqgrJqWj0u2o2mPHjt1xxx3R0dH5+fkMsYFhEDKBIOvQoUNDQ0Ntba19SW5ublZW1pVXXikiUVFRTU1NjpdC//73v0+ePDk6OtqXnRcVFYlIYWFh9+7dXdd+9913opNbDfmWAACaEtKKSbkhMyYmRqmhRESJrAUFBXl5edOmTVu8eHFsbGwwPw+gKkImEGR9+/bdv3+/8tRmEWlqanr77bftlUrXrl2bmpoOHjyo/NjY2Lh27Vql561VFRUVZ86cEZF9+/YlJCS4bjBp0qT169d7ug567NixHTt2PPDAAwGsDTp9fUu1tbV/+9vfKisrr7vuul/96lf2W3cAAIYRuorJfkNmdXX1mjVr7MuvueaatLS0559/3impeqmRw1xZAwHjnkwgyLp16yYi33//vfLjU089NX/+fPvaG264QUTOnj2r/Lh06dInnnjCxz0rd3RER0e7zU5ycTSOU3yqqqr605/+9Otf/3rQoEHKLR++rw0dHX1LR44cmTdv3r333rt69epevXoNGDDgr3/9q4+FAQDoRagrJovFsm7dulUOnnrqKWWgrLKZl5pIrcoaCBghEwgyZfIApR46fvz4559/7ngPhvIkLmW8zfHjx48fPz5y5Egf96xcMU1PT3e79sCBA8punWaui4qKyszMfOONN6644gq3pfWyNnR09C3Nnj176dKlcXFxJpNpxowZDz744OzZsysqKnwsDwBAF0JXMdlvyPT+LE0vNZFalTUQMEImEGQ/+9nP5GI9NH/+/GeeecZxrVI9KHXYo48+umzZMt/3rAza8fQc53379om7Ww1jYmL69+/vqWLzvjZ0dPQt7dmz57//+7/tP44ZM0ZENm7c6HuRAADaF7qKSbkhc/jw4d4381ITqVVZAwHjnkwgyDp27CgilZWVxcXF8fHxTpOVK5dC6+rqdu/ePWDAgB49evi42yNHjii3Gnp69qP3pzxrjY6+pbvvvtu1XrfZbL7vAQCgfSGqmOw3ZPre8wkYACETCLJBgwaJyOnTp5csWbJ+/XqntX379hWRzz//fMmSJT7Oe67Ys2ePeL3VUHn2o15Cpo6+pVWrVjn+eODAARHJyMjwfQ8IrsrKytLSUqeFFovlzjvvVF5XVFR8/PHHjmtNJtPUqVODW4zjx4/v3bvXaWGfPn2UB6m7LaR9rdtCpqamxsXFOS45dOjQ4cOHXQ89atSoHj162Gy2t956y3Xt1VdfreL/Tz2WGVCEqGJSbsg0m81Ot2kAxkbIxAU0mIJFuamjoaEhOztbmffckTKvwJkzZ6ZOnerjvOcK77ca2p/9qJc6TKffUkNDw8qVK+++++7BgwcHtge03ddff11eXl5dXW1v502cONHxwsHp06dzc3OVKw7x8fFJSUn9+vULRTHKysp27tx57NgxEUlOTu7bt699GHZ1dXVZWVlRUZEyU6Wytk+fPva3V1dXFxUVrVu3rrm5OSEhISkpaezYsU6H+Pe//33w4MG6urq8vDwR6dev3/Dhw6+77jqla91qtVZXV1dVVW3btu3LL780mUxTpkzp2rWr637CSY9lNoww1ONHjhxRLrS5ZTKZrrrqqrS0NLNZl83LEFVMPt6QCRiMLs8CCAUaTMGi1D39+/efNWuW61plvE1CQsL06dN932dNTc327dvl4ux2Tmw225tvvikiZrPZ8ZeiZTr9lu6///7hw4e/8sorgb0dQXHzzTfffPPNIpKSkrJr1674+PgNGzY4bpCWltahQ4fk5OTnn3/e7X+woEhMTExMTHzrrbemTZtmMpmcnmSQlJSUlJT0xhtv3H333RaLxfU5B+np6crVkI4dO3qarzglJSUlJeXw4cPKOXPdunWOadlisShTX06aNOnLL7+87bbb3F6nCzM9ltkwwlCPf/PNN59++ml9ff3rr78uIsOGDcvIyOjSpYuInDhxoqamprCw8Pvvv583b96CBQvC86mDKBQVU11dnfLnHx8fH6RiAjpxHpGk1d+70go3mUxu1yqVisVi8fT2adOmPfjgg97L8Mknnyhl+Oyzz9xucNttt4nI7bff7n0/YbN161YR2bp1q4/bf/DBByKyY8cOL3srKSnxcW+xsbFOl4TNZnNsbKyy9sUXX7RYLI5rTSaTxWJZu3at6666du06bdo0Twfyvjbo9Pgt/fGPf7znnnt8LJKdiLT6R4HAvP/++8ov9H//938dl//73//+xS9+4el/V3C99957Shl++OEH17Vr1671csr97rvvUlNTf/zxR++HePHFF0Wkc+fOnjZQ2r4rV670q+QhpccyG0YY6vGvvvpK+Y/9xRdfOK06e/asckv87Nmz/S15cPlbd58PdsXUu3dv14rpyiuv9OW9XmqiACprqiFSjyrouMdllIrfZrNZrVZPa1taWty+t7Gx8fTp0y+99JL3Qyhj2Dp37uxpAJu+7i10NWDAgKefftpx3nOntcuWLfN9uObp06edGq8//PDD6dOnlbVz5sw5d+6c49off/zx3Llzd9xxR3A+TMjo7lvKzc1tbm5W7s+02Ww8wkQLMjMzlbF8y5cvty9samoaN27cokWLPP3vCi7lrKgc13XtkSNHlBfKfJVOli5dOn/+/FZH0CmzVXka02EfBD5q1CifSx1yeiyzYYShHlduMoyLi3OaGkfZ/+zZs0VkxYoVp06d8r/4agpuxfTFF1+4Vkz/+c9/gldeQNMImbgMDaa2i4uL+8Mf/uBpbXx8/KOPPhrO8miTvr6lgoKC+vr6JUuWKD8ePXqUkKkFJpNp5syZIlJYWHjy5EkRsVqt2dnZDz74YFZWVnjKcNVVVykvXG9Uq6qqys3NVV6fOHHCae3x48ePHTvmSxJWBoEnJye7Xas0912fyqMuPZbZMMJQjys3GXp6UpS9+84+cEkv9FUxARpHyMRlaDABTkpLS3Nyck6cODFr1qwHHnhgxowZDz744DXXXKN2uSAict999ykN4r///e8iMnny5IyMjLZMJPvcc88dP37c9+379++vvHBtss+bN++NN95QXtuHF9o99thjzz77bKv7r6ioaGhoEM9P5dm9e7dobOiHHstsJGGox70/C8qeY+0lARCBmPgHl2m1wZSamioiX331lVMINHCDSe8qKioWL15cWVlZX1+fl5dXXV19zTXXrFmzxpe1kcPL95CVlVVfX69MlWGnl1l8Da9bt26//OUv165d+8orr/zrX/8aMGBATk5OW3Y4d+5ci8Xi+3RBynSU4nLO/PDDD+Pi4pQJilzXFhcX9+3b13WooSvlulur9xd4umynCj2W2UhCXY/bn/ro6TSo9HP27NnTPm8tfOSlJqKyhu4QMnEZGkzG079//3Xr1gW2NnJ4+R6++eabMBcGfpk9e/batWvr6+ubmpqeeOKJNu4tOjq6a9euvm9vMpnMZrPVanUamrho0aJ169ZFRUWZTCabzdbY2Oi49oknnvDxOXvKvJSjR492exOdNu8v0GOZjSQ89Xi3bt3cblxaWlpWViYizMIdAC81EZU1dIeQicvQYAKgL7fccsv1119/9OhR1+F/vjh69OigQYOefPJJ5W6rG264QXmMwbFjx7Kzs0eOHPm3v/3N+x46dOjQ0NBQW1trX5Kbm5uVlaU8Zy8qKqqpqcmxQf/3v/998uTJPj5nr6ioSEQKCwu7d+/uuva7774TP+8veOedd+655x4fN7bLy8vz/TbXoJcZfgl1PW5/6qPrqmPHjt1xxx3R0dH5+fmMSAIiHCETzmgwAdCRNWvWKBM2lpeXHzhwYPDgwX69/brrrps5c+b8+fO3bt2an58fExMTHR2dm5s7Z86cPn36/PKXv2x1D3379t2/f799QHVTU9Pbb7+tnOtEpGvXrk1NTQcPHlR+bGxsXLt2retjM92qqKg4c+aMiOzbty8hIcF1g0mTJq1fv96v1vxtt9127bXX+r69wt791apQlBn+Cmk9rtyQGRMTY/9PrkTWgoKCvLy8adOmLV68ODY21vtOqLsBwyNkwhkNJgB6UVhY+PLLLx89ejQuLq6xsfGVV17xN2R26NDhhRdeWLBgwUsvvTRy5EiLxfLcc899++23mzdv9vEhKN26dROR77//Xvnxqaeemj9/vn3tDTfcUFVVdfbsWeXHpUuX+j6mVxmXGB0d7faEKRf7lPw6Z5rN5pEjR/q+vb9CUWb4K3T1uP2GzOrqascbAq+55pq0tLTnn3/ex6RK3Q0YHiETzmgwAdCF0tLSJUuWbN269corr5w5c+Zf/vKX119/fdmyZUp3jV9iYmLmzp1rtVqffvppm8326quv+t6cVW6BU86Kx48f//zzz5988kn7WuV5Ekqv0fHjx48fP+77KUtp96enp7tde+DAAWW3mpqGSo9lNp5Q1+MWi2XdunWtPunEC+puwPB4hAmcuTaYHC/nR1qDCYA2HTp0aNasWRs2bOjSpYuIPPDAAyJitVpfffVVf3dVV1e3cOHCuLi4c+fO3XbbbY8//vjUqVNvueUWe8+Pdz/72c/k4llx/vz5zzzzjOPaK664Qi6eUR999NFly5b5XjClAJ6eRrhv3z7R3s2Neiyz8YSuHrffkNmWhAkgEtCTCWd+NZief/553/dM4wNAUJw8eXLatGkFBQVKj42I9O7dOzk5edeuXStXrvTrgelHjhy58cYbO3Xq9P7776ekpIwbN+6qq646fPjwzJkzx4wZM3v27Jdeesn7Hjp27CgilZWVxcXF8fHxTlNuKg36urq63bt3DxgwoEePHr4XTLm/wNMDn7w/q9CT0tLSF1980a+3iMhDDz3kS9duiMoMf4WuHld+g8OHDw9iaQEYEiETzmgwAdCympqazMzM/Pz8+Ph4x+WPPPLIrl27Kisri4qKfD+T9O7d+/e///2sWbNiYmJE5LPPPmtubo6Ojn777bczMjJ8uW1s0KBBInL69OklS5asX7/eaW3fvn1F5PPPP1+yZImPs3cq9uzZI17vL1Ae+OTvOTM+Pj4jI8Ovtyjv8mWzEJUZ/gpRPW6/IbPtI12puwHDI2TCGQ0mAJp18uTJ5OTk1atX2584b5eZmRkfH19VVfXcc8/5fiaxWCyON6TV1tY2NDQor6dOnerLHpShiQ0NDdnZ2a63gyp9rWfOnJk6daqPc6IovN9fYH/gk7/3F8THx//617/26y2+C1GZ4a8Q1ePKDZlms7ntv0HqbsDwCJlwRoMpMMePH9+7d6/Twj59+iQmJopIZWVlaWmpp7UiUlFR8fHHHzuuTU1NjYuLc1xy6NChw4cPux561KhRPXr0sNlsb731luvaq6++OoC6PET4lhCwU6dOvfnmm08//bTVah04cKDrBg0NDT//+c+rqqoKCwt3797taWS+d1FRUVar1a+3KGfC/v37z5o1y3Wt0muUkJAwffp03/dZU1Ozfft2Ebnhhhtc19pstjfffFNEzGZznz59/Cpt6OixzEYVono8iDdkaqfulrBUTEeOHDlw4ICnAphMpquuuiotLc1splkO4+B/M5zRYArM119/XVZWtnPnzmPHjolIcnJy37597feXVldXl5WVFRUVKXPKK2sdP2x1dXVRUdG6deuam5sTEhKSkpLGjh3rdIh///vfBw8erKury8vLE5F+/foNHz78uuuuU+p7q9VaXV1dVVW1bdu2L7/80mQyTZkypWvXrq77URHfEgJQWlo6atQox+zXqVOnPXv2OF6TmjFjxuuvv26z2UTEZrMlJycrv1y31xS8qK+v97cBbbFYROSFF15wu1Zp7v/1r3/1cW/du3evq6uzf9jFixf/8Y9/jImJOX36tIi89NJLc+fObWlpUdZardb27dubzeY333zzjjvu8KvYQaTHMhtbKOrxuro65WKx8boTw1AxffPNN59++ml9ff3rr78uIsOGDcvIyFAmLTtx4kRNTU1hYeH3338/b968BQsWhOdTAyF3HpHEl9/7Bx98ICI7duxwu3br1q0iUlJS4uMRY2Njna7Mmc3m2NhYZe2LL76otM/sTCaTxWJZu3atX58rpJSPvHXrVl82VtKyyWRyu1apXSwWi6e3T5s27cEHH/R+iE8++UT5rj777DO3G9x2220icvvtt/tSYFVE1LckIq2WFrpWXV399NNPe1r71VdfLVu2LJzlAYJbj/fu3du1Hr/yyiuDV96Q8KvuPh+Wiumrr75SvsAvvvjCadXZs2eVGStmz57tY4F9RzVE6lEFM1DD2YABA55++mlPTyEfMGDAsmXLfB/Uevr06R9++MHx/9wPP/ygXN4WkTlz5pw7d85x7Y8//nju3Dn9Xt5WrhDbbDa3I+6UtfYr+k4aGxtPnz7d6lSWyt2tnTt37tevn9sNtD+1Bt8SjCQuLu4Pf/iDp7Xx8fF+zXYLtF1w6/EvvvjCtR7/z3/+8//bu9vYKsv7D+CXtZaK9W8GAQZNNS6OJqN0ErZExwKyzW5hCwm4wURg82Fkk7q9QJ10ZiI43DKjKBqnbBVhPElChgbmHDhkWlwkc5u1uFobsvIwVslYVwtKV/8v7njSnD4dytWHUz6fV+ec6/Tu1YuHX7/nvn/3FW++g0I/FKakqbWwsDDtVkzJ8cvLy0MIjz766OHDh898+jDoCJmk8wvT2UjqUAghaTFNU1NTkzxI7iyfZsWKFRUVFT1eqpfchreryztT3a3Tpk3LeNb9zSoB9B11vBf6oTAlTa1dtYunThenrsSBrCZkQkwjRoxIHnRs8W9oaKisrEweHzx4MG20vr6+tra2qw+e20u6W6dPn97paPJB6SDfbtQqATCo9ENh6n6rtlSOTc0EspqQCTGltlXo+GHn0qVL165dmzxONWak3HHHHT//+c97PH51dXWyv0JX243u2bMnDPqrQK0SAINKXxem1C6jXV2onJznvPzyy1P3rYWsJmRCTMmdJEOHKvXqq68WFhamdpFOG927d29xcXHHJo2OklNwPbYadnUGb5CwSgAMKv1TmMaMGdPpm6uqqvbt2xdCePLJJ3s1fRh0bGECMeXk5OTm5ra2tqY1ddxzzz1bt27Nz8/Pyclpa2trbm5uP7ps2bIMd8RO7iD/pS99qdPbD2RLq6FVAmBQ6evClNpltONQbW3tnDlzCgoKNm3a5BIbhgwhEyIbPnx4U1PTsWPHUq9UVlbOnDkz2RE7Pz+/paWl/UehTzzxxNy5czPcEXvXrl0hhOeff37cuHEdR//73/+GLGk1tEoADCp9WpiShsxRo0YlFSqEkETWnTt3btiwYcGCBffee+/YsWNj/jwwoIRMiKy4uPi1115Ldm0OIbS0tGzcuDFVVEaOHNnS0vL6668nT5ubm7ds2ZKceetRdXX1iRMnQgivvPJKaWlpxzdcd91127Zt6/g56LFjxx5//PG6urpLL7103rx5qc6T9mpra3fv3v29730vs5/ybGXXKmWygABktb4rTKmGzEOHDm3evDn1+ujRo8vKylatWpVKqj2Wm34u1tBrejIhsjFjxoQQTp48mTxduXJlRUVFanTChAkhhPfeey95umLFimXLlmV45KSjo6CgoNPsFD66GictPtXU1CxduvQ73/nOunXrPvGJT0ycOPGxxx5LjTY0NPz0pz9duHDhpEmTkoaQ/pFFq9T9AgIwNPR1YcrLy9u6desv21m5cmVyoWzytm7KzUAVa+g1IRMiS24ekNSh+vr6AwcOtO/BSHbiSq63qa+vr6+vnzp1aoZHTj4x/cpXvtLp6P79+5PDpt25rry8fMWKFYWFhTk5Obfccsutt95aXl5eXV2dmu3Xvva1tWvXXnjhhWf4g56VLFql7hcQgKGh7wpTqiGz+700uyk3A1WsodeETIjsoosuCh/VoYqKip/97GftR5PykNSwJUuWPPDAA5kfOblop6t9nF955ZXQWavhSy+99OlPfzr19Nprrw0h/OY3v0mejho1qqSkpMctpKPLolXqfgEBGBr6rjAlDZlTpkzp/m3dlJuBKtbQa3oyIbKLL744hFBXV7d3796ioqK0m5UnH4U2Njbu2bNn4sSJl112WYaHrampSVoNu9r7satdnm+88caOZamtrS3D79tHsmiVBucCAhBXHxWmVENmj2c+lRuGEiETIps0aVII4ciRI8uXL9+2bVvaaHFxcQjhwIEDy5cvz/C+54mXXnopdNtqmOz92DE+/fKXv2z/dP/+/SGEGTNmZP6t+0IWrdLgXEAA4uqjwpQ0ZObm5qa1aXSk3DCUCJkQWdLU0dTUNGvWrOS+5+0l9xU4ceLE/PnzM7zveaL7VsPU3o/d17CmpqZf/OIXN95442c+85nMv3VfyNJVGjwLeK5Zt25dxxenTZuWdjLh6NGjv//979u/Mn78+KuuuiruZOrr619++eW0F1PfqK6urqqqqqvREEJ1dfWf//zn9qNf/OIXCwsLU09ramqSXy47lZOTM2LEiLKystzcga/gWTRV6FEfFaYMGzLTKDdkO//vQ2RJ7SkpKVm8eHHH0eR6m9LS0ptuuinzYx49evSFF14IH93dLk1bW9v69etDCLm5uePHj+/mON/97nenTJny5JNPZv6t+0iWrtLgWcDxYt3aAAANLElEQVRzSmtra21t7T//+c9nn322sbExhFBWVlZSUjJt2rS0d548efLFF198+umnQwilpaWf/exn0y54i+Jf//rXvn37Xnzxxdra2hDC9OnTi4uLU12+hw4d2rdv365du5KNEJLR9n/lDh06tGvXrq1bt546daq0tPSaa6758pe/3P7477777htvvHH8+PGnnnoqhHD11VfPmDHjYx/7WAjh4MGDR48eff7550+ePLl06dK77747+k93RrJoqtCjvihMjY2NyaefRUVFZzQZ5Yas9yHnEn/uvbBjx44Qwo4dOzJ8/29/+9sQwu7du7s52h//+McMjzZ27Ni0kwC5ubljx45NRh955JG8vLz2ozk5OXl5eVu2bOl4qJ/85Cc333xzV99o5MiRCxYsyHBWZy8bV6n7BexKCOHWW28906+iU0mSCSH84Q9/6Oo977zzTn5+/tNPP93Xk0k+s8jJyel0NJlqXl5eV1++YMGC7v9i/OMf/0h+2Lfffjtt6L333ku6jsvLy3sx8+iyaKqcO860dn8YuzBdccUVHQvT//3f/2Xytd2Um14Ua2XIb78Dwl2qILKJEyfef//97e97njb6wAMP9NiYkXLkyJHTp0+3/0d7+vTpI0eOJKO33Xbb+++/3370f//73/vvvz9nzpy041RWVp46dSrp92hraxvwHTiybpUG2wKem+bNm5f80tbp1bMhhA8++ODb3/729u3bFy5c2NeTSU5rtLW1tba2djX6wQcfdPq1zc3NR44cWb16dTfHT/q4CgsLO56MHT58eHl5eQjh0UcfPXz4cK+mH1MWTRW6Ebcwvf322x0L03/+858ev1C5YWgQMiGywsLCu+66q6vRoqKiJUuW9Od8Qgg7d+48fvz48uXLk6dvvfXWgBet7FqlQbiA56a8vLzrr78+hLBp06ZO89v1119/yy23lJWV9cNkkhgZQkgafdPU1NQkD5LtENKsWLGioqKi+watpI+rq814UmdI/vrXv2Y64z6TRVOFbgyGwqTcMGToyYQhrqqq6gc/+EFZWdnixYvb2tpOnz5dV1f34x//eKDnNbh0s0oWcFBZuHDh+vXrT506tXnz5rTTlUuWLJkwYUI/nMNMjBgxInmwf//+tFMfDQ0NlZWVyeODBw+mbcpaX19fW1ubtgVfR11tt5NIhdjUNAZQFk0VBjPlhiEl9vW3DGr+3HuhF30dg8rIkSM7/sNPXT76xhtvfP3rX7/yyitDCDk5OdOnT587d+7ATnhAdLNK3S9gj8I53wwTXXKPx7KysvYvrlmz5mz+6j744IPvvPPOGX1Jsid7p/853HDDDbt3705Gf/e736WNzp49u2PvYppuuhwTV199dQjh8ssvP6M594UsmirnlGys3d2Um7Mp1sqQ334HhDOZMMS9++673YyWlJRs3bq13yYzaHWzSt0vIP3vhhtuePDBB3ft2nXs2LEkcL7wwgvr1q1LbuHYO7fffnteXl6nt5TsSrLbQehwQeyrr75aWFj4uc99rtPRvXv3FhcX93jP26TLccyYMZ2+s6qqat++fSGEwXDnySyaKgxy3ZQbxZqsoycTgGzyrW99K4TQ1tb261//OoTwl7/8ZenSpdu3bz+bzRgLCgo6PYfQjZycnOQ7pvVk3nPPPT/60Y/y8/OTlsvm5ub2o8uWLauoqOjx4KmN9ToO1dbWzpkzp6Cg4LnnnuvqCtX+lEVTBaDfOJMJQDYpLS0tKSmprq5eu3btvHnzFixYsHPnzmRvxsy99dZbkyZNuu+++5I7eUyYMCHZIq+2tnbWrFlTp059/PHHezzI8OHDm5qajh07lnqlsrJy5syZyTbu+fn5LS0t7c9kPvHEE3Pnzs1kG/eky3HUqFG7du1KXmlra2tubt65c+eGDRsWLFhw7733jh07tsfjPPPMMzfffHOPb0uzYcOGmTNnZvjmWFMFYCgRMgHIMgsXLrzzzjurq6vLysrWrFlzpruchxAuvfTSRYsWVVRU7NixY9OmTaNGjSooKKisrLztttvGjx//jW98I5ODFBcXv/baa3V1dcnTlpaWjRs3prLWyJEjW1paXn/99eRpc3Pzli1bMrmmt6GhoaGhIYRw6NChzZs3p14fPXp0WVnZqlWrMompidmzZ3/84x/P8M0pqWt9+3OqAAwlQiZkZO7cucOGDUs9HTly5N///vcBnA+D0+LFi7ds2TLQsxj6vvnNb955550hhDvuuOOqq67qxRGGDx/+8MMP33333atXr546dWpeXt5DDz3073//+7nnnutql7yOko7QkydPJk9XrlzZ/lLYCRMmNDQ0pO4PtGLFimXLlmVy2KTLMS8vb+vWrd1vc9Kj3NzcqVOnns0RuhdxqtAXzsHarQwxSAiZ0INx48bNnTs37UUfz9OpT33qUx17zyZPnjwgkxnC3nzzzRBCbm7u/Pnzz+Y4o0aNuv3221tbW++///62trY1a9ZkfhIvfHTvnyRG1tfXHzhw4L777kuNJhtpJpfL1tfX19fXZ5j3Ul2Ogz+2ZdFUOdecs7VbGWKQEDKhB1deeWX7y8CgG4sXLz6jO5TSOy+//HIIYdq0aWeTbRobG1evXv3QQw8tWrRo9uzZ11133fz588eNG7ds2bIM71Jz0UUXhY9iZEVFRdrWlxdeeGH4KIIuWbJk1apVGU4s6XKcMmXKmfw0AyOLpsq55pyt3coQg4SQCUCWqaqqCiGczYWgNTU1kydPvuSSS7Zv3/6FL3zhq1/96ogRI/72t78tWrTo2muvLS8vX716dY8Hufjii0MIdXV1e/fuLSoqStvDIzmT2djYuGfPnokTJ1522WWZTCzV5RjlMteqqqpHHnnkTL/q+9//fiZndONOFYChRMgEIJu0trYmrYCf//zne32QK6644oc//OHixYtHjRoVQnjzzTdPnTpVUFCwcePGGTNmZHiznEmTJoUQjhw5snz58m3btqWNFhcXhxAOHDiwfPnyZ599NsOJJT9abm7u2fx0KUVFRTNmzOjFV2XytrhTBWAoETIByCZ79uxpbW3Nzc295ppren2QvLy89rfhOXbsWFNTU/I48z7PpCezqalp1qxZybYl7SW3BTpx4sT8+fMz7wSL2+VYVFS0cOHCsz9OpzRkAtAVhQGArNHW1rZx48YQwuTJkyNmm/z8/NbW1jP9qiQ6lpSUdNoBlVwuW1paetNNN2V4wMbGxmSPk15sytLPsmiqAPQ/IROALFBRUTFs2LDzzz//qaeeCiH86U9/Ov/884cNG5bce+YsHT9+PPMomJKXlxdCePjhhzsdTc5zPvbYY5kc6pOf/OQFF1wwevToo0ePhhB+9atfXXDBBZdccsmZTqkfZNFUARgo53344YcDPQf6z3nnnZc88OcOcJYOHz68fv36u+66q9PRhoaGZ555ZsmSJf08KwDa89vvgBAyzy3+mQEAcO7w2++AcLksAAAA0QiZAAAARCNkAgAAEI2QCQAAQDRCJgAAANEImQAAAEQjZAIAABCNkAkAAEA0QiYAAADRCJkAAABEI2QCAAAQjZAJAABANEImAAAA0QiZAAAARCNkAgAAEI2QCQAAQDRCJgAAANEImQAAAEQjZAIAABCNkAkAAEA0QiYAAADRCJkAAABEI2QCAAAQjZAJAABANEImAAAA0eQO9AQYGOedd95ATwEAABiCnMk8t3z44YcDPQUAAGAoO0/qAAAAIBZnMgEAAIhGyAQAACAaIRMAAIBohEwAAACiETIBAACIRsgEAAAgGiETAACAaIRMAAAAohEyAQAAiEbIBAAAIBohEwAAgGiETAAAAKIRMgEAAIhGyAQAACAaIRMAAIBohEwAAACiETIBAACIRsgEAAAgGiETAACAaIRMAAAAohEyAQAAiEbIBAAAIBohEwAAgGiETAAAAKIRMgEAAIhGyAQAACAaIRMAAIBohEwAAACiETIBAACIRsgEAAAgGiETAACAaIRMAAAAohEyAQAAiEbIBAAAIBohEwAAgGiETAAAAKIRMgEAAIhGyAQAACAaIRMAAIBohEwAAACiETIBAACIRsgEAAAgGiETAACAaIRMAAAAohEyAQAAiEbIBAAAIBohEwAAgGiETAAAAKIRMgEAAIhGyAQAACAaIRMAAIBohEwAAACiETIBAACIRsgEAAAgGiETAACAaIRMAAAAohEyAQAAiEbIBAAAIBohEwAAgGiETAAAAKIRMgEAAIhGyAQAACAaIRMAAIBohEwAAACiETIBAACIRsgEAAAgGiETAACAaIRMAAAAohEyAQAAiEbIBAAAIBohEwAAgGiETAAAAKIRMgEAAIhGyAQAACAaIRMAAIBohEwAAACiETIBAACIRsgEAAAgGiETAACAaIRMAAAAohEyAQAAiEbIBAAAIBohEwAAgGiETAAAAKIRMgEAAIhGyAQAACAaIRMAAIBohEwAAACiETIBAACIRsgEAAAgGiETAACAaIRMAAAAohEyAQAAiOb/ATR5Wtu1QDvSAAAAAElFTkSuQmCC\" data-image-state=\"image-loaded\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 250px 8px; transform-origin: 250px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe function template includes the code to create this figure using Matlab Latex\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function P=Calc_Perceptron(X,WH,WP)\r\n%The P value in this case is a single value\r\n%No ReLU,Softmax, or Sigmoid applied to the output P\r\n  P=0;\r\nend\r\n\r\n%The figure was generated using Matlab and Latex\r\n%{\r\nfunction perceptron\r\n% put figure code into cody\r\nfigure(1);clf;\r\nplot([0;140;140;0;0],[-5;-5;100;100;-5],'k','linewidth',2);hold on\r\nxticks([]);yticks([]); %Tciks/Labels off\r\n\r\naxis equal;\r\naxis tight\r\n\r\n\r\nr = 10;\r\nphi = 0:0.01:2*pi;\r\ncxA = r*cos(phi);\r\ncyA = r*sin(phi);\r\n\r\nrxA=[0 2*r 2*r   0 0]';\r\nryA=[0   0 2*r 2*r 0]';\r\n\r\npatch(cxA+10,cyA+80,'y');text(10-2,80,'X_{1}','Fontsize',20);\r\npatch(cxA+10,cyA+20,'y');text(10-2,20,'X_{2}','Fontsize',20);\r\n\r\npatch(cxA+80,cyA+80,'y');text(80-6,94,'H_{1}','Fontsize',20);text(80-8,80,'\\Sigma','Fontsize',30);\r\npatch(cxA+80,cyA+20,'y');text(80-6,34,'H_{2}','Fontsize',20);text(80-8,20,'\\Sigma','Fontsize',30);\r\n\r\npatch(cxA+130,cyA+50,'y');text(130-2,50,'P','Fontsize',20);\r\n\r\npatch(rxA+80,ryA+70,'y');text(85,80,'ReLU','Fontsize',20);text(102,88,'Y_{1}','Fontsize',20);\r\npatch(rxA+80,ryA+10,'y');text(85,20,'ReLU','Fontsize',20);text(102,32,'Y_{2}','Fontsize',20);\r\n\r\nquiver(20,20,55,0,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,80,55,0,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,20,58.5,58.5,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,80,58.5,-58.5,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\n\r\nquiver(100,20,25,25,'k','LineWidth',3.5,'MaxHeadSize',.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(100,80,25,-25,'k','LineWidth',3.5,'MaxHeadSize',.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\n\r\ntext(40,85,'WH_{11}','Fontsize',20);\r\ntext(20,65,'WH_{12}','Fontsize',20);\r\ntext(40,25,'WH_{22}','Fontsize',20);\r\ntext(20,38,'WH_{21}','Fontsize',20);\r\n\r\ntext(115,68,'WP_{11}','Fontsize',20);\r\ntext(115,28,'WP_{21}','Fontsize',20);\r\n\r\nstr='$$ X=\\left[\\matrix{ X_{1} \u0026 X_{2}} \\right]  $$'; %valid\r\ntext(1,50,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ WH=\\left[\\matrix{ WH_{11} \u0026 WH_{12}\\cr WH_{21} \u0026 WH_{22} } \\right]  $$'; %valid\r\ntext(1,5,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ H=\\left[\\matrix{ H_{1} \u0026 H_{2}} \\right]  $$'; %valid\r\ntext(75,65,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ ReLU(H)=Y  $$'; %valid\r\ntext(75,58,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ Y=\\left[\\matrix{ Y_{1} \u0026 Y_{2}} \\right]  $$'; %valid\r\ntext(75,51,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ X*WH=H  $$'; %valid\r\ntext(60,5,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ Y*WP=P  $$'; %valid\r\ntext(60,0,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ WP=\\left[\\matrix{ WP_{11}\\cr WP_{21} } \\right]  $$'; %valid\r\ntext(100,5,str,'Interpreter','latex','Fontsize',20);\r\n\r\n\r\n\r\nend % Perceptron \r\n%}\r\n","test_suite":"%%\r\nX=[1 2]; WH=[1 2;3 4];WP=[.5; -1];\r\nP=Calc_Perceptron(X,WH,WP);\r\nPexp=-6.5;\r\nvalid=isequal(P,Pexp);\r\nassert(valid)\r\n%%\r\nX=[3 1.5]; WH=[1 -3;3 4];WP=[1; 2];\r\nP=Calc_Perceptron(X,WH,WP);\r\nPexp=7.5;\r\nvalid=isequal(P,Pexp);\r\nassert(valid)\r\n%%\r\nX=[-.5 .5]; WH=[4 2;1 4];WP=[.1; .4];\r\nP=Calc_Perceptron(X,WH,WP);\r\nPexp=0.4;\r\nvalid=isequal(P,Pexp);\r\nassert(valid)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2023-08-20T22:48:06.000Z","deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-08-20T22:16:25.000Z","updated_at":"2026-03-30T12:52:26.000Z","published_at":"2023-08-20T22:48:07.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to calculate the Neural Net Perceptron value,P, given X, WH, and WP using ReLU on the hidden layer. This example is for two inputs, two hidden nodes, and a single output. No offset terms are applied.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"864\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"1228\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe function template includes the code to create this figure using Matlab 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OnTtFxN171lNhYeG/A1u9enXs9wlGxz0+AEB8IGTiAlziqI6caV5Kt9jt27eLSEtLixIgvWb0UfKk8rTMiooKp9PZuXPnfv366VAuTI7DL0zE5XJ5Tb4NAJ4ImTiPSxzAkxIylQspJUZeccUVGRkZnusUFxeLyNatW+Xcw0tuvfVW7UsFAAAwDkImEHM0ZppUTk6O3W4/derU8ePHlb6yvgFSadhsaGhoamravXu3iPjOKwt0iHt8AIB4QsjEWVziAF5sNpvSmLlr1y6lr6xvyOzatWvfvn1FZNu2bcoDM3lCJsLF4RcAEGcImYAWaMw0KfeQy9raWrvdXlhY6LuOMhVQaWmpw+HIyspSppkFAACwLEImRLiPDgSQnZ0tIiUlJSIyaNCgxMRE33WGDh0qImvWrBGRwYMHx64Yp9PpcDiiXweGwuEXABB/CJmARmjMNKPrr78+JSVFiW1+n34p54ZuKusMGzYsdsWMGDGioKAg+nUAAABiipAJ7qMDwQwfPlz5IVB4s9lsSjdau90eKIhGb/bs2coMt1GuA0Ph8AsAiEsJLs5vVuJuSfP8vXOVo6WEBOFLh7C0tLRMmTJl1apVIpKbm+v32XShrAMDMunhNyFBzFh2HOPMAgTh9+oXsUZLptWZ9BIHsIgVK1ZkZGSsWrVq4sSJ0awDAACgGUImoClGZiIspaWlycnJa9euXbJkSTTrwIC4xwcAiFd2vQuAnrjEAQxuzpw51113nc0W7IZgKOsAAABohpAJAMbVr18/VdaB0XCPDwAQx7jzDWiNHrMAAACIY4RM6+I+OgAAAADVETIBHdCYCVgZ9/gAAPGNkAkAAAAAUA0h06K4jw4AAAAgFgiZAABoh3t8AIC4R8gE9MGwTAAAAMQlQiYAAAAAQDV2vQuAPgzeWauiQk6fFhHJy5OUlICrHTggR4+KiOTkyMUXa1QboBdXCN/bUNYBAACIKVoyYUSffCKjRsmoUfLggwHXOX5csrNl1CgpKSFhAjAHBmQCAKwggdveluIeBGj8X/uoUbJmjYjI2rVSXOy9tL1d+veXAwfkiiukutrEITMhgaYnwELiI2QmJJjgJGIpnEqAIDyufvmaaIeQaS0mCpnNzXL11dLYKGlpcvCgdO16wdJ77pH588Vmk6oqGTBApxLVwJUBYClhhcw9e+TEiWi32Lu39O4d7Yd4IWQaDacSIAhCpi4ImdZiopApIps3y5AhIiK5uVJZef71N96Qu+4SEZk7V2bP1qc2tXBlAFhKWCFzxAh5551ot3j33bJgQbQf4oWQaTScSoAgCJm6YEymFZnlKzZ4sDz8sIjIhg0yb97ZF48ckXvvFREpLjZ9wgSAIH71qwv++vbbcuaM/z/ffCNffy0HD8q778oLL8iNN55/15dfalw1AAC0ZFqMci/HRL9zp1N++lOprpbERNm3T3r1kmuvlcOHJT1d9u+XLl30ri9q3H4GLCXcMZm//a388Y9nf+7WTQ4elNTUkN5YUSFjx8qpU9K3r+zbF3adwWnZkllWJu3t/hfZbJKYKP37h/pvEoQyBcCVV0pWVqglXX65XH99wHWamqSqSkTkhhukW7doy+sQpxIgCFoydUHItBbThUwROXJErr1WTp+Wvn0lL0+eey4ehmK6cWUAWEcEs/44nXLNNVJTc/avt98ub74Z6nuVEQdJSfLtt+FttENahsxLLpHm5g7WycyUP/5Riooi34pyCfrAA/K3v4Va0tixsmxZwHUqK2XoUBGR1atl5MjICwsRpxIgCEKmLuguC6PLyJC5c0VEqqvluedERP785zhJmABMIcGDxpu22eTNNyUx8exf33pLliwJ9b2DB8tdd0lbW8CWQBPp2lVyc73/ZGaK3S4iUlMjI0bI66/rXSUA4BxCJkzg3nvPP8UkO1seekjXalTlcon2l60AQuf1DdU+cPbuLc88c/6v06dLfX2o773vPhGR6mr1q9LY4MFSWen95+BBOXNG5s+XpCQRkfvvl5YWvQsFAIgIIROm4HTKyZNnfz58+PzPAKAjzQLnQw9JdvbZn1tbZdy4UN84cKAkJcmnn8aoLv3ZbDJtmvz5zyIira1SVqZ3QQAAESFkwhQeeUR27RKbTUSkqUkmTdK7IAC4UKwD59Kl0rnz2Z+rquRPfwr1jYMHx3/73uTJZ3/YuNHPUodDNm6Ud96RjRvF4dCyLgCwLkImjK6s7OyYzN/8RmbOFBGpqJDnn9e3KABW4XK5wp0rIhaBMz39gjlp5syRAwdCemNOTscT55jdN9+c/eE///OC15ua5J575KKLJDdXRoyQ3Fy56CK55574/wcBAN3Z9S4ACKah4Wy7ZZ8+8vjj4nBIWZnU1clvfytDh4Y017wpMCwTiFcqzmo4frysXi1vvSUi4nTK2LGyb9/Z4YhBPPJIlJs1gb///ewPubnnXzx+XG6+WRoaREQGDZLLL5cvv5TKSpk/XyoqZOdOLZ4sAgCWRUsmDG3cOGlulsTEs/MrJidLaanYbNLeLmPHSlub3vUBgIZee+18NKqttUSADK69XZ5/Xn7/exGRnj3PTxHndEpRkTQ0SLdusm+fbN0qy5bJ+vXy8cdyxRVy/LiMGaNj1TGhy+zHABAIIRPG9cQTsm2biMhf/iJ9+px9sV8/+cMfRERqa2XGDN1qAwDtdekiixad/+uLL/ofhRh/tm6V4cMv+DNsmPzwh3LRRfLww+J0SlqarFlzdui+iCxbdvbhouvWSd++5z+nVy955x0RkZ07pbJS892IPXImAIMgZMKgNm8+GyaLi+X++y9Y9NhjcuONIiJ//7usW6dDbSriegBAWPLzL7i/NmGCfPmlftVopbFRysou+FNeLsePn42Xv/ylHDp0QZhcuVJEZNAg6dfP+6P69JEbbhARWbZMq+pjzOs8Qs4EYASMyYQRNTXJHXeIiHTrJgsX+llh+XLp00daW2XyZKmtla5d/azjdIrTefZR3eYS/dgtAKqL+Npd9W/0s89KRYUcOSIi0tgou3dLfr66WzCcG26QBx44+7PDIRUV8uab4nTKyJGycKF06eK9vtLAa7fLmjV+Pi0xUUSkri529YrTGcMP9+Jy+cmZnEcA6MuEF+CwgHHjpKlJROT11yU11c8KykSLkyfLqVMydqxs3uxnnREj5MwZo3eI8r04EK4PACOJLFvG9CuclCTLl8tPfypOpzz6aPwnTBH54Q8veHjV1KkyY4YMGyZr1si+fX5m8VGe2rJpk2zaFPAzlf60Efvuu2BL3c9KsenUY4zzCAB9ETJhOE8+KRs2iIj85jeSkxNwtUmT5O235a23ZMsW+dOf5Ne/vmDp7NlSVnbBTIPmwvUBYDpafmcdDklOluHD5X//V7NtGsvAgVJaKiNGyPHjkp8vO3dKSsr5pUpD4o03Sq9eAT+hU6cIN52ZKVu2SHt7sHXcS5VWUw24XC6vGyKcRwDoiJAJw3n8cXn88ZDWfPNNPy+2tMiUKbJqlbpFxZbvxYFwfQCYgS5f0oYGKSqSa6+V11/XfuMGUlQkM2bIiy9KTY3MmHHBlEgpKdLaKjfeGJOHKiu9c/fvD7bO8eNnf7jqKvULCIScCcA4mPgHcWXFCsnIkFWrZOJEvUsJTULC2YtUv9cBzN8AGJDLg/Zb//JLyc+XLl1k7dqQxpwvWHBB+ooz//u/0rOniMjixRdMtHvLLSIiO3f6f1dZmSxbJnv2RLjRa68VEamvP58k/W5CRJKSJD09wq1Exvf/JOcRALogZCKulJZKcrKsXStLluhdSvjImYABuW8D6Rgs3drb5bbbpKlJKiv9zHbj17vvxvP0s8nJ8tprZ3+eNu38w5OLikREdu7084iX+nq57TYZN05KSiLcqPtpnO65iLxs3SoVFSIi48ZFuIlokDMBGAEhE3Flzhypqzt/BWA65EzAgNQKli6XK8pv8113yb59UlYm3buH+pZdu8JY2Yzy889muX/9S37/+7Mv3n23XHGFiMidd16QMxsbZcyYsyNaf/lL7486dsz7QSmef9z69pWRI0VEyspkwAB5552zIzCdTjlwQJ54QoYOFRFJTpbf/jYmu9whciYA3dFZ31qU04xFfucJCZKba/TZZd3dZS980c/VAF9VIA4kJCRE/FV+5BGZO1feey+M6WSPHJEf/1jWr1d5BtqEBO3OI5dcIs3NMnZssMdaNjfLj38szc1is8m+fZKVJSJy+LAMHnx2ovIbbpBeveTrr6Ws7Oy8r2vXXnA7MpQI5rnLX34peXny0UfnX7Hbz88oKyIpKbJypXYT/4Z4KuE8Amtyfxf4CmiJlkzAcGjPBODlr3+V556T+fPDyy3KaMyf/jQ2NRlGaqr85S8iIk7n+QH5ffrIwYMyebLY7bJrl5SUyLp14nDIzTfLzp3Rdnjp0kV275Y///n87LXuhGm3y5Qpsn+//o+WoT0TgI5oybQWj3s5+haiBfO2ZJ5bRHsmEG8ia8l85x0ZMUJ+9zt58skw3lVfL717S1ubfP992FsMTsuWTIQirFMJ5xFYDS2ZuqAlEzAo2jMBiMiOHfKzn8nkyeElzIYGyc+X06dDnR8I8Yr2TAC6IGQCugly71lBzgQs7vhxGTlSbr5ZFiwIaX2nU7ZulUcekYwMqa0VEbnhhpgWCBMgZwLQXgjP2AKgH9+HawvP1wZMS/lGh/j1bWqSIUOkqUn27pUePQKu5nSKwyHffy/t7XL6tPfSH/wg8moRN3xPJZxHAMQUIRMwOnImYE2/+pX8618iIqdOyalTEX5IWpqKFcHEyJkAtMTxxVo8TzBx/5s3/sQ/HXaXvXBl5gEC4kE0TzExCCb+MZrQzybMAwQLYuIfXTAmE9BHWAlTGJ8JAIga4zMBaIPusohb8Xe7in6zQBwIa1gmoDr6zQLQAC2ZFuVyubh3aUa0ZwIAokR7JoBYI2QCJkPOBABEiZwJIKYImYAOwh2Q6YWcCZgafUlgBORMALFDyLQurnJMjZwJAIgSORNAjBAyAbMiZwIAokTOBBALhExAa1H2lfVEzgRMir4kMA5yJgDVETItjaucOEDOBABEiZwJQF2ETEBTKjZjupEzAQBRImcCUBEh0+pozIwP5EzAdDj8wmjImQDUQsgEtBOLZkw3ciYAIErkTACqIGQC8YOcCZgLjZkwIHImgOgRMsFVjkZi2ozpRs4EAESJnAkgSoRMIN6QMwET4TYfjImcCSAahEyIcJUTe9o0Y7qRMwEAUSJnAogYIRNnkTPjDDkTMAsOvzAsciaAyBAygZjTuBnTjZwJmAU5E4ZFzgQQAUImzuMqJ/6QMwEAUSJnAggXIROILb2aMd3ImYApcJsPRkbOBBAWQiYuwFWOunRPmApyJgAgSuRMAKEjZMIbOTMukTMB4+PwC4MjZwIIESETiBWDNGO6kTMB4yNnwuDImQBCQciEH1zlRM9oCVNBzgSMjyMwDI6cCaBDhEz4x1VONIyZMBXkTABAlMiZAIIjZCIgcma8ImcCBsfhF8ZHzgQQBCETwXChEwEjN2O6kTMBg+PwC+MjZwIIhJAJqMkUCVNBzgQMjpwJ4yNnAvCLkIkOcJUTOhMlTAU5EzA4jsAwPnImAF+ETHSMq5xQmC5hKsiZgMFxBIbxkTMBeCFkIiRc5QRn0oSpIGcChpWQkJCQkMARGMZHzgTgiZCJUHGVE4ipE6aCnAkYjRIv3T9zBIbxkTMBuBEyEQaucnzFQcJUkDMB4/D71eMIDOMjZwJQEDIRHuUqh1OGIm4SpoKcCejOswHTFzkTxkfOBCCETETA5XJxoSNxlzAV5ExAL8HjpRuHXxgfORMAIRMRsvKFjtKWG38JU0HOBLQX/Cvm9a208uEXZkHOBCyOkInIWfNCR4mX8ZowFeRMQDMhNmB6YeQCjI+cCVgZIRNRsdqFThw3YHohZwKxFnr/2ECvW/NOH0yEnAlYFiET0bLIhU58d5H1i5wJxE6IX6UOjzlWu9MH0yFnAtZEyIQ64vtCxwpdZP0iZwKqi6x/bBAWudMH8yJnAhZEyIRq4vJCx4INmF7ImYBawo2XYR154vtOH8yOnAlYDSETKoubCx13vLRywlSQM4HoafCVcd/p49sJAyJnApZCyIT6zH6hQ7z0Rc4EIhbx/LGRbS4uO5UgPpAzAesgZCJWzBg1iZdBkDMBzUR/CHIffvmOwlDImYBFEDIRW2aJmsTLUJAzgQjodVRxnWP8IzAshZwJWAEhE1rwvNAx1KnEXRLxMkTkTCAaoRxnVD8WETVhNORMIO7Z9S4AFuI+qbjPJTrGOqUEgmUEXC6X79VAQkIC/5hAIL5HP71qMMLhFxB/pxLOI0A8IWRCB3qlTc/TGWeyaJAzgQiEkjBj/SXyG3f54kIX5EwgjhEyoadAd/dVPMUQLGOEnAmExff74vsl0vLr47ktAif0Qs4E4hUhE4bgdUYJdL8/yHknUAsB56rYIWcCIQolYeooUOC8cB2tqoHFaJ8z9+zZc+LECZvNVlxc7HeFHTt2fP755yLSv3//7t27+65Qn6wRXAAAIABJREFUX1+/d+9eESkoKEhKSnrnnXccDofvana7PTExccCAARdffLGqewCYAJeD1uLRPdWUv/cg12Qm3aM44PeXwq8DcPObMH1fN/i3xjiRGH4Z/P9PhwJ9TWJh3rx5Dz74oIgcO3asZ8+eXkudTmenTp1Onz4tIjNnzpw3b57vJ9x///2vvPJKcnLyN998IyKdOnVqbW0NssWsrKw5c+bccccdqu0DwmH2q1+TYnZZmIkrML1Lsy7mmwWCCDFhGl+Qwy+MQO//INHy3YXYfUdyc3OVH3bu3Om7dOvWrUrCFJGysjK/n7B9+3YRGTFihOeLNpst0Yey6MCBA2PHjn3mmWfU2gXA+AiZAKLl9/rGdNfQgOqCNM54fWviICQAUdIsZ2ZmZqalpYlIZWWl79KKigoRGTJkiIjU1dXV19d7rdDa2lpTU+Nex+2uu+464+P7778vKSlRNve73/2utrY2FnsEGBAhE4AKyJmAlw67/7kboEiYgEKznJmTkyMiu3bt8l20fv16Ebnnnnt69eol/hozlRQqIoWFhR1uyGazjR8//o033hARp9P58ssvR1c4YBqETADqIGcCbqEPMCNhAp60yZlKPjx8+HBbW5vn6ydPnvzoo49EJDc3Nz8/X0TKy8u93rtp0yYR6dWrV3p6eoiby8vLU1Y+evRo1LUD5kDIBKAaciYg2k5hAsQfDXKmMizT6XR69ZjdsGGDiGRlZaWlpQ0dOlREysvLnU6n5zo7duwQkYKCgrC2qLSLtre3R1c4YBqETABqImfC4kiYQPRinTO7d++upL5t27Z5vr5u3To5FyCLiopsNltbW9vGjRvdK7S3t1dXV4vH7EGhcDqdyiRDKSkpapQPmAAhE4DKyJmwLBImoJZY50xl2h7lcZdu7733nogobZh2uz07O1vOjdJUKA2bdru9qKgo9G09//zzSr/cW265RY3aARMgZAJQHzkTFkTCBNQV05ypNFdu2bLF3Rv2gw8+aGlpSUpKUqYFEpFhw4aJx0w/IlJVVSUigwYNstvtXh/ocDhaL1RdXb1mzZoJEyY8+uijItK9e/e7775brfoBg/P+hgCAKlwul+/VQEJCApfdiEskTCAWfE8lap1H8vPzbTabw+H48MMPBwwYIOfm+CkoKLDZzrbBKK2dBw4caGxs7Natm5wLmcqcQF5KS0tLS0sDba5z586rVq2iuyysg5ZMALFCeyYsgoQJxE6M2jNTUlL69+8vHg8yUVosPQNkv379UlNT5dyEQA6HQxlamZeXF+JWbDZb7969H3744cOHD19//fXRlw2YBSETQAyRMxH3SJhArMUoZyrdYrdv3y4iLS0tSoD0mtFHyZPK0zIrKiqcTmfnzp379evn+2njxo37+kLffPPNd999949//OPZZ59VGkIB6yBkAogtcibiGAkT0EYscqYSMpWnmCgx8oorrsjIyPBcp7i4WES2bt0q5x5ecuutt/r9NLvdnnKh5ORkd89bwGr4rw8g5siZiEskTEBLqufMnJwcu91+6tSp48ePK31lfQOk0rDZ0NDQ1NS0e/duEQlrXlnAsgiZALRAzkScIWEC2lM3Z9psNqUxc9euXUpfWd+Q2bVr1759+4rItm3blAdmhvWETMCyCJkANELORNwgYQJ6UTdnuodc1tbW2u32wsJC33WUqYBKS0sdDkdWVhajK4FQEDIBaIeciThAwgT0pWLOzM7OFpGSkhIRGTRoUGJiou86Q4cOFZE1a9aIyODBgyPbUHBOp9PhcES/DmAchEwAmiJnwtRImIARqJUzr7/++pSUFCW8+X36pZwbuqmsM2zYsAi20qERI0YUFBREvw5gHIRMAFojZ8KkSJiAcaiVM4cPH678ECjC2Ww2pRut3W4PFESjMXv2bGVu2yjXAQzFrncBgNXt2bPnxIkTNptNmSfd144dOz7//HMR6d+/f/fu3X1XqK+v37t3r4gUFBQkJSWVl5e3tbVddtllAwYMCLTRpqamqqoqEbnhhht0GV7icrl8rwYSEhK4ZIdhkTA18MEHH3zxxRcikpiYGPrz7qFoaGjYv3+/8nP//v3T0tL0rUcDvqeSCM4jy5YtW7ZsWfB11q5dG2Tp119/HdYW3VpaWqZMmbJq1aoo1wEMiJAJ6KyqqurBBx8UkWPHjvXs2dNrqdPpHDp06OnTp0Vk5syZ8+bN8/2EZ5555pVXXklOTv7mm29EZOrUqY2NjSNHjly9enWgje7fv3/UqFEisnr16pEjR6q4O6EjZ8JESJjaeOyxx5qbm1NTUzt16kTIDNc//vGPuXPnikh1dfWSJUv8zmETf1TJmbpYsWLFzJkzT548OXHixKVLl0a8DmBMhExAZ+7J0Hfu3OkbMrdu3aokTBEpKyvzGzK3b98uIiNGjIhlmTFBzoQpmDphVldXHzhwwPf17Ozsnj17Op3O119/3XfpJZdcoldEefrpp0PZtOn2S4OC8/LylGTu7v9pESbNmaWlpcnJyWvXri0uLg4UIENZBzAmQiags8zMzLS0tKampsrKyjvvvNNrqfJ46CFDhmzatKmurq6+vj49Pd1zhdbW1pqaGmUdzWpWETkTBmfqhCkiX3zxxb59+5qampT5M/v06XPzzTf36NHDZrOJiMPhOHHiRH19/fr16//1r3/ZbLZx48alpqb6Pi3QaEy3X6Yr2FzMmDPnzJlz3XXXKf8BolkHMCgXrITfuzGNHTtWRDIzM30XXXfddSJSWlraq1cvEXnllVe8Vli5cqXyO/3kk0+UV5QxliNHjgyyxffff1951+rVq9Xai2hwdIIxxc1/S/dQvUOHDvldYfTo0SJy++23a1yYl9zc3HfffTf09c2yX27aFFxYWBjWP2PcMO8XVkRyc3OjXwd+me7/Q3zg1gigP6U31OHDh9va2jxfP3ny5EcffSQiubm5yox25eXlXu/dtGmTiPTq1curhdNcXMw3C+Mxexumpy1btohI586d+/Tp43cF5dhiumGQptsv0xVsLr7fUM4jgF4ImYD+lGGZTqezsrLS8/UNGzaISFZWVlpamvIw6PLycqfT6bnOjh07JPDE6yZCzoShxFPCFJGtW7eKSKC+l9XV1crYb+XB9CZiuv0yXcGmQ84EDIKQCeive/fuSm/Ybdu2eb6+bt06ORcgi4qKbDZbW1vbxo0b3Su0t7dXV1eLx+xBpkbOhEHEWcIUj9HdfpcqR57U1NTevXtrWlbUTLdfpivYjMiZgBEw8Q9gCEOGDKmrq1Med+n23nvviYjShmm327Ozszdt2rR+/Xp3TyqlYdNutxcVFXl9YENDQ5AHfylzBRmQi3mAoLf4S5g1NTUtLS0iMmjQIL8rbN68WUzYRdN0+2W6gs3L91TCeQTQGCETMISCgoL58+dv2bLF6XQq88h98MEHLS0tSUlJOTk5yjrDhg3btGlTRUXFs88+q7xSVVUlIoMGDbLbvb/Lu3fvHjdunIZ7oBpyJnQUfwlTzrWPdTgO0HQzVJtuv0xXsKmRMwF9ETIBQ8jPz7fZbA6H48MPPxwwYICcu9ooKChwz12uXHkcOHCgsbFRmUJWCZnKnEBeEhMTO3XqFGhzZ86caW1tjcF+qIOcCV3EZcIUEaWPfV5eXnt7u+9S844DNN1+ma5gsyNnAjoiZAKGkJKS0r9//127du3atUsJmcrQHc8A2a9fv9TU1Obm5g0bNkyYMMHhcOzcuVMC9K0qLCxcvXp1oM1VVlYqvXANi5wJjcVrwhQRZUax8vLyyy+/3Hfp119/LWGOA1yxYsXdd98dbhklJSXFxcXhvisI1fcr1kxXcBwgZwJ6IWQCRpGTk7Nr167t27fPmjWrpaVFCZBeM/rk5eUtX768rKxswoQJFRUVTqezc+fO/fr106nk2CJnQjNxnDBrampOnTolIlVVVVlZWb4rjBkzZtWqVWGNAxw9evRll10WbiU33XRTuG8JIsr9OnLkyIYNG+6//34VSwou4oJPnjz58ssv19XV9ejRY/z48ZmZmVqUG0fImYAuCJmAUeTk5Pzxj39UbnWXlZWJyBVXXJGRkeG5TnFx8fLly5VJ8JWHlwSaCj8+kDOhgThOmHJuHGBKSorfYCPnjjZhhUy73X7LLbeoUl7EItuv+vr6kpKSw4cPr1y5csyYMVqGzMgKPnz48HPPPffUU09169Zt4cKFV1999Ysvvjh9+nQNCo4nxs+ZoRRjqIKBDvEIE8AocnJy7Hb7qVOnjh8/rvSV9Q2QSsNmQ0NDU1PT7t27RcR3Xtk4w3NNEFPxnTDl3DjAQI/S3bNnT1tbm4gMHDhQ07KiFtl+JSUlFRUVLVq06KKLLtKgSE+RFTxjxoynnnqqe/fuNptt2rRpDzzwwIwZMww7PbiR8VwTQGOETMAobDabMpHsrl27lL6yviGza9euffv2FZFt27Yplyzx8YTM4MiZiJG4T5hybhzg4MGD/S5VJg8z4zjAyPYrLS0tMzPTPZualiIreMuWLddcc437r8pY+jVr1sSuzjhGzgS0RHdZwEDy8vIqKirKyspqa2vtdnthYaHvOvn5+dXV1aWlpQ6HIysrS5lmNu7Rbxaqs0LCPHz4sDIOMNCDGZW+9+E+mHHHjh0vvPBCuMXMnDlTrWGZMdqv2Im44ClTpvhGYqfTGYMaLcH4/WaBuEHIBAxEmbm+pKRERAYNGpSYmOi7ztChQ//85z8rd7ID3RRXhdPpdDqdvk/gdHM4HCISZAV1kTOhIiskTBHZsmWLBB0HqDwqKdwwlp6e7vcWWIfvCvctgcRov2In4oIXLFjg+dc9e/aISAT/+HAjZwLaIGQCBnL99denpKQoT7D0+/RLOTd0Uwl4w4YNi10xI0aMOHPmjNLFy9PJkyfnzJlTUlKijCC64oorpk+f/tBDD8WuEjdyJlRhkYQpHY0DdD+YMdwBmenp6ZMmTYq+vIjFaL9iR5WCW1paXnnllSlTpsTrjOKaIWcCGmBMJmAsw4cPV34IdDlis9mU29h2uz1QEI3e7NmzlakOvTQ1NV177bV///vfc3JyXnrppd///vf/8R//8fDDD0+dOjVGlXhhfCaiZJ2E2djYqEwhdtVVV/kudTqdS5cuFRG73e41i7XBmW6/1Cr4vvvuu/nmm1977bUY1WkpjM8EYs4FK+H3jg599dVXo0ePVv6f5Obmei2dMWOGiPzhD39wv/L1118rz23buXOnZkVyNENkLPLfplu3bl792O12e7du3ZSlL7zwgldXfJvNlpiYuHz5cn3Ldrlcubm57777bqClKu5XamrqxIkTY7UbMSj46aefvvvuu0PcbmFhYZB/RrhZ5IAAfr+6oCUTwHkrVqzIyMhYtWrVxIkT/a6wcuXKxMTExx57zP1KSkrK7Nmz5dyYIm24aM9E+KzThvnpp59+9913nif777777tNPP1WW/uIXvzhz5ozn0u+///7MmTN33HGHvmV3yHT7pVbBCxcubGtrU8ZnOp1OHmGiFt8jAOcRQC2ETADnlZaWJicnr127dsmSJX5XeOWVVxYvXuw126Fyq769vV2LEs8hZyIs1kmYiDNlZWXNzc1PPvmk8tfa2lpCporImUCMMPEPgPPmzJlz3XXXBXmIXHFxse+LSiK95ZZbYliZPy7mAUJoSJgwqR07dsyaNSs/P3/69OlOp/O7776rq6t7/PHH9a4rrvieSjiPANEjZAI4L4JJCxcuXLhhw4asrKxAMxXFFDkTHSJhwq2mpuYPf/hDXV1dc3NzSUnJiRMnLr300mXLluldV0DFxcXNzc11dXWeLxpn1ty4Qc4EVEfIBBC5devW3XvvvZ07d169erVeNZAzEQQJE54yMzPffPNNvasIw7///W+9S7AKciagLkImgAgtWLDgv//7v1NTU8vKyq688kodKyFnwi8Sphnt2LGjvb09MTFReVYTQldfX793714R+eyzz/SuxZTImYCKCJkAIjFr1qwXXnihe/fulZWVvXv31rsccia8kTDNaODAgfv37z906FCnTp0ImeH65z//uXjxYhHp0aPHpZdeqnc5pkTOBNTCN8da3IdOfu/oUEJCQm5ubmVlpdfrDodj1KhR77zzTv/+/d999920tDRdyvPL76yA/G+3IBImgIhxAIkzXP3qgkeYAAjPbbfd9s477xQVFW3dutVQCVN4rglEhAtEANHhuSZA9OguCyAM//M//1NWVlZYWPj222/rXYt/9Ju1OBImgOjRbxaIEiETQKiam5ufeuop5Yfhw4d7Lc3Pz581a5YedXkjZ1oWCROAWsiZQDQImQBCtW3btvb2dhHZtWuX79Ju3bppXlFA5EwLImECUBc5E4gYXxVrYegzLIV5gKyDhAkgRji8mB1Xv7pg4h8AcYt5gCyCS0AAscM8QEAECJkA4hk5M+6RMAHEGjkTCBchE0CcI2fGMRImAG2QM4GwEDIBxD9yZlwiYQLQEjkTCB0hE4AlkDPjDAkTgPbImUCICJkArIKcGTdImAD0Qs4EQkHIBGAh5Mw4QMIEoC9yJtAhQiYAayFnmhoJE4ARkDOB4AiZACyHnGlSJEwAxhF6zkxISOAUA6shZAKwInKm6ZAwARhNhzmTeAnLImQCsChypomQMAEYU1jtmbEvBzAKQiYA6yJnmgIJE4CRkTMBX4RMAJZGzjQ4EiYA4wuUMzlewbIImQCsjpxpWCRMdOjw4cOqf2ZDQ0Nra6vqH4v4FmJ7JicXWAQhEwDImUZEwkSHXn755fnz56v+sd9+++3IkSObmppU/2TEN7850+AHroTo6F0+jMvo//WhLvfhgN874Mvv+ZIviy5ImGo5fPjwnj17/C6y2WwpKSnZ2dldunSJZhPV1dUHDhzwu2jSpEler2zcuPHEiRO+a06YMMFmO3vje8WKFW1tbZ5Le/fuff3113u9ZdGiRW+//fbKlSvDqio7O7tnz55Op/P111/3XXrJJZcUFhaKyPbt26dPn15VVZWSkuL387WxZMkS3xeVXfB8pbGx8f333/d8JSMjY8CAAbEtDgH4PXx5vajjAc2nkig/7YK/GvNAzdWvLux6FwAARuF7HSBmuA8df0iYKvr3v/998ODB5ubm//u//xORG2+8sbCwsEuXLl999dXRo0fr6urGjBmTn58/b968jIyMyDbxxRdf7Nu3r6mpqaSkREQyMjIGDRp00UUX9enTx3flY8eO7d279+DBg9u2bROR/Pz8K6+8snfv3u6EKSI1NTUHDx5cs2aNiNx8881XX321b20HDhx47LHHgvSV9aqqT58+N998c48ePZQNORyOEydO1NfXr1+//l//+pfNZhs3blxqauqtt96qvH3gwIGjRo2aMmXKm2++Gdk/S/QcDseRI0c+++yzdevWKc2q+fn5mZmZ2dnZXmt+++23GzduXLx4sYhkZWX179+/V69eOlRsbe5BmL6nEiOcWTyClpof6/VpnnvKcdvqXLASfu9AhzhU6ot//Fj45JNPlH/M//f//p/Xovfee89ms3Xu3PngwYPRbGL//v3KJg4dOtThyi+88IKIpKamBlmnqKjol7/8ZaClffr0mT9/fvRVjR49WkRuv/1230VnzpxJT09fuXJlh1uJNeUGgYhs2rQp0Dr//Oc/k5KSFi9erGFduIDXUcsgV+Aem9P6j3GO4capxFIYkwkAF3AxPlM/tGHGiNJs2L17d98GroKCgqKiolOnTs2cOTOaTWzZskVEOnfu7LcB08vmzZtFJC8vL8g6n3zySaCSFixY8NVXX02dOjX6qsrLywNVkpiY+Ktf/erhhx92Op0dbiimxo8fb7fbJUDvWRFpb2//+c9/vnbtWt/+ydCGb9Nlh8eumJ5W3GMmfSOfZtybZgCnNREyAcAbOVMXJMzYKSsrE5HBgwf7XZqYmCgiu3fvjmYTW7duFRF3d9PglGg3ZMiQQCu0trY2Nzd7jTx0e/bZZ++55x7PHraRVVVdXX369GkR8e2AqpgyZUp9ff0bb7zR4YZiKjExcdy4cSJSWlra3t7uu8K4ceOmTZuWn5+veWkQMdIsskbIlr680qbe5UAjhEwA8IOcqTESZkwpWStQCKmpqRGRHj16RLOJiooKCZzWPHUY7ZRPCxSJt2/ffuTIkTvuuCP0qgKlWaWBNzU1tXfv3n5XSE5OLiwsXLhwYSjbiimlibKtrW3ZsmVeix566KGrrrqKNkwdKZ0DI3ijiucUA2ZLX0RNSyFkAoB/5EzNkDBjqr6+vr6+XkQGDhzou7S6urq2tlZEHnjggYg3UVNT09LSIqGFzA6jnYhs2rSpoKDA76I333yza9euQd7rW9WgQYP8rhBKr92CgoItW7Yon6OjvLy8rl27iogyj5HbggULGhoannzySZ3qwnl65UzPeGkKRE2LIGQCQEDkTA2QMGPNPSDzyiuv9FrkcDhmzJghIg888MD06dO9lm7evPnOO+8cPHjwiBEjhg0b9uGHHwbfhIoDMrdu3RooGW7cuDHQonCr6rDXrohcffXVTqdz48aNoWwxpu666y4RqaysPHnypPJKRUXFkiVL/D6LBbqIuEkzYuaKl56ImnGPkAkAwZAzY4qEqYFAAzJra2vz8vKOHDny97///W9/+5vnoubm5ttuu23ChAkzZszYvHnz22+/PW/evOLi4kANekoGC3FAZvAurCLS2tr65Zdf+h2Q6XA4Dh8+HGLPXqWqvLy8dn8+/PDDDnvtiojyfM4gAVszkydPFhH3Ez6rq6t/85vfrF27VpkTCMYRbtSM7ITibsA0NaJmHOPABAAdcPH8zNggYWpDGZB59OjRadOmKa+0tbUdOnQoNTV13LhxkydPTkpK8ly/paVl8ODBx44d279/v7vxMyMj49prr/3www/9tkBWVlZK4F6png4cONDa2iqRDsj86KOPnE7nNddc0+GG3FWVl5dffvnlvku//vpr6ajXrogkJiYmJSUdPXq0w82tWLHi7rvvDqUwTyUlJcXFxaGsmZWVlZmZWVNTs2jRovHjx0+cOLGsrKxLly7hbhHa8HviUMW5B3LG4rP1oewLZ9U4Q8gEgI6RM1VHwtSGe0DmSy+95H5+yUcfffT4448fOHDg0Ucf9UqYInLffffV1NQ8/fTTnt1rGxsbd+/efcstt/huoqam5tSpUxJayFSeKRLxgMzPPvtMRFJSUjrckLuqqqqqrKws3xXGjBmzatWq4L12Ff/1X//11Vdfdbja6NGjL7vssg5X83LTTTeFvvKkSZMeffTRmpqa/Pz8+fPnp6enh7s5aEk5poUSNUM/m8RB62UgSpOmcC6IF4RMAAgJOVNFJEzNKOMS09LS+vbt637xlltuKS8v79Gjx2233Xbo0CHPMFlTU1NaWmq325WxmiJSV1e3du1aZeyf8rATv5tISUnxm+UUTqdTeeJIKAMyt2/f/uijj/pd1NbWJiKhdBDtsCqlF3EoITMrK+vjjz/ucDW73e43hKvozjvvVP5lHnnkkQEDBsR0W1BLiFGzw7NJ/DVg+qJJM54QMgEgVORMVZAwtaREqZycHK/Xk5KS+vfvX1ZWtnbt2tmzZ7tfX7x4sYikpqbec88933//vdPpTE1NzcnJ2bt3b6Bopwx9DNT2KCJ/+tOf+vTpo3QKDWVAZnNzc6A2OqfTKaGFzOBV7dmzR8mrfmfc9WKz2ZSVdXfo0CERsdvtEyZM0LsWhCfK3rNx3IDpiybN+EDIBIAwkDOjRMLUmDIg02973e7du0Xk22+/9XxReZzJfffd98QTT4S4CWXoY6BRlCKyfPnyvXv3isjRo0eVAZk33nhjoJVXrVoVJK8qTant7e1RVlVVVSUhDMhUNDY2+k7Mq4vt27eLSHZ2ttIsDHPpsEkz0KnEUglTQZNmHCBkAkB4yJkRI2FqLMgTMo8cOdLU1CQiP/zhDz1fV/KbZ9/a4A4fPhx8QOaKFSsGDhyohCJl+hybzRakY+2LL7742muvBVqanJwsIsqssNFUFSR7+zpx4oR7OGsQO3bseOGFF0L5QE8zZ84MfVjmjh07RCTWnXIRU6EP1FRYMGG6KU2anCZMipAJAGEjZ0aAhKk9ZVyi3/Y69zM5Lr74Ys/XlTl1/I69FJH6+nqvjqzKRD6Bhj62trb++te/XrZsmfLXn/70pyISpBVu0aJFvXr1ChJxlU9obGwMtEIoVcm5J2SGGDJbW1t/8IMfdLhaenp6YWFhKB/o9a4Q13Q4HMovNJQuvjC4QL1nPc8jVhiE2SFypnkRMgEgEuTMsJAwdRFkbptdu3YpP7jz5Jo1ay655JK8vLxVq1b5HYL4i1/84tprr506darni0GGPra0tIwcOfI///M/lUdNikiXLl369u1bXV195MiRjIwMr/V37Njx/PPPK22MgaSlpSUnJ+/fvz/IOsGrEpHq6mqlLTSUtNbQ0OB0OoP073VLT0+fNGlSh6tFbPPmzQ6Hw263B+mZDBMJ3qRp5QZML+RMk6JPPwBEKNDgGe0rMTgSpi6ampqUrNW/f3/fpX369PF6Ze7cuX379r377rt79eo1f/58z0XHjx+/7bbbevXq5ZUwGxsblYl8vGJPU1PTq6++mpmZuWnTprvuustz0V//+lebzfarX/3Ka+sLFy689957y8vLO3zwY0FBgTJwNBB3VVdddZXvUqfTuXTpUhGx2+2+QdeXksaVFlQdOZ3ON954Q6mEAZnxxOVyeR4P3cmTY6Qn91RAMBFuDFiL+yvK7x1Qi98zH18xNxKm9n70ox8dO3bM4XC4X7Hb7d27dz927Jj7ldbW1pycnN27dz/66KMzZ878zW9+U1RUdMcdd4hIfX39mDFjrrzyysLCwpMnT27durVLly6//vWvPXPp5Zdf3tTU5N6EzWZzz/jqdDo9N33s2LGePXt6lldWVjZ16tQePXpMmTKlS5cutbW1K1euvPHGG5977jn3f5yDAAAgAElEQVSvvrt+vfrqqw888MC3337r26fXqyplx9PS0j799FMR+etf//rwww97ThqklL106VJlx/2aNWtWaWnp559/3mFhMfLb3/72+eef9y37/fffZ3BmXCJhBpKQEOHpg6tfXRAyrYWvGRAL5MxASJgGV1ZWtmHDhvb29smTJ/fr189zUU1NzSeffHLxxRffdNNNqjedtbe3r1mzZtu2bWfOnLnppptuvfXWbt26hfjeL7/88tJLLy0pKQmSDFX04x//uLi4+Nlnn9VgWwAJM7jIciZXv7ogZFoLXzMgRsiZvkiYiJFp06bV19evX78+1hv64IMPbr755o8//jiU2WWBKJEwQxFBzuTqVxeETGvhawbEDjnTEwkTsXP06NEf/ehHhw4dCuUpl9EYM2bMD37wg4ULF8Z0K4CQMMMRbs7k6lcXhExr4WsGxBQ5U0HCRKw9/vjj+/fvX7t2bew2UV1dPWTIkNra2q5du8ZuK4CQMMMXVs7k6lcXTFAGAKphvlkhYUITTzzxRHNzszLhaiy0t7dPnDhx8eLFJEzAgJhv1vgImQCgJovnTBImtGGz2VavXv3iiy/u2bMnFp8/derUe+65p7i4OBYfDniiGTMy5EyDI2QCgMosmzNJmNBSWlra22+/vXz5ctU/eceOHfn5+b/4xS9U/2TACwkzGuRMI2NMprXQKx3QjNXGZ5IwASAsJMzohTI4k6tfXdCSCQAxYan2TBImAEB7NGYaFiETAGLFIjmThAkA4aIZUy3kTGMiZAJADMV9ziRhAkC4SJiIe4RMAIitOM6ZJEwAgO5ozDQgQiYAxFxc5kwSJgBEgGbMWCBnGg0hEwC0EGc5k4QJABEgYcIiCJkAoJG4yZkkTACA0dCYaSiETADQThzkTBImAESGZkxYByETADRl6pxJwgQAGBaNmcZByAQArZk0Z5IwASBiNGPCUgiZAKAD0+VMEiYAwPhozDQIQiYA6MNEOZOECQDRoBkTVkPIBADdmCJnkjABACZCY6YREDIBQE8Gz5kkTACIEs2YsCBCJgDozLA5k4QJADAjGjN1R8gEAP0ZMGeSMAEAQGQImQBgCIbKmSRMAFAFfWVhTYRMADAKg+RMEiYAwOzoMasvu94FAADOc7lcvifFhIQEzWIeCRPa++CDD7744gsRSUxMzMvL07scQ2hoaNi/f7/yc//+/dPS0vStBwDCot2FC4zAffnI7x0wMr83XzX42pIwoYu8vLzm5ubU1NROnTqtXr1a73IMobKy8k9/+pOIVFdXL1mypLCwUO+KEAn6yuorIeGCW7ec0bRESyYAGI4u7ZkkzLixZMkS3xezs7N79uzp+UpjY+P777/v+UpGRsaAAQNiW1wATz/9dKAcZfDdiVF5eXl5SqPu8OHDVaoUsBx6zOqIkAkARqRxziRhxg2Hw3HkyJHPPvts3bp1TU1NIpKfn5+ZmZmdne215rfffrtx48bFixeLSFZWVv/+/Xv16qVDxUEZfHcMXh4A6IXustZChwHAXLTpN0vCjEuLFi2aMmWKiGzatGnw4MF+1zl69OhVV1316quvTpo0SdPiLpSXl/fLX/4yeI9Qg+9O7MobPnz49OnT6S5rRvSVNQLP8xunNi0xuywAGJcG882SMOPV+PHj7Xa7BOjPKSLt7e0///nP165dq2/CDJHBd8fg5QGAxgiZML0PPvigrKysrKyssrJS71pMpqGhoewcpaMXDCimOZOEGccSExPHjRsnIqWlpe3t7b4rjBs3btq0afn5+ZqXFgmD747BywMsi3OaXgiZML3HHntszpw5c+fO/dvf/qZ3LSbzj3/8Y+7cuXPnzp00adLu3bv1LgcBxShnkjDjntJo1tbWtmzZMq9FDz300FVXXWWuVjWD747BywMALTHxD/yoq6vbsWOH14uJiYl33nmn8nNNTc1HH33kudRms02YMEGj+nwEmZbQk+n2K9YFM3uhiag+DxAJ0wry8vK6du168uTJkpISz4SzYMGChoYG3yxkcAbfHYOXB40xIBMWR8iEH59//vmuXbtOnDixbt065ZWRI0d6PiD7008/Xbhw4ZYtW0QkPT198ODBffr00afWcJhuv0xXMGJKxZxJwrSOu+66a+7cuZWVlSdPnuzatauIVFRULFmyZOPGjXqXFgmD747BywMA7bhgJeH+3ocMGSIi6enpvou2bdtmt9tffPFFVQuMRG5u7rvvvhvWW0yxX55iXXBhYWG4/4bQS/RHck4ElrJ//37lt/zcc8+5XK59+/Zdd911X3zxhd51XSD0w7jBdycW5XF8NikRcbn4Y4g/nOx0wZhMBPPggw+KSH19vdecOl9++eWsWbPWr18/ffp0nUqLiun2y3QFI3ZcrqjGZ9KGaTVZWVmZmZkismjRosbGxokTJ65Zs6ZLly4RfNSKFSs6hc/dEcNouxMLBi8PsCbOcrqguyyCKSoq6t69e0NDw/PPP+/upXn69Onhw4f//ve/z8nJ0be8iJluv0xXMGLKFWm/WRKmNU2aNOnRRx+tqanJz8+fP39+enp6ZJ8zevToyy67LNx33XTTTZFtLpAQd+fIkSMbNmy4//771d169OWdPHny5Zdfrqur69Gjx/jx45VQCujinXfE4fDzut0uiYkyYIBcfLHmNSFu6NqOCq1F8Hv/wx/+oLzl2LFjLpfru+++y8/PX7p0acxqDFsE3WVdZtgvLzEtmO5YZhTuIZ3jv2V98sknym988eLFetfiX1iH8eC788knn/zxj3+cOHFicnLyxIkTVS1ThfIOHTo0ZcqUEydOfP/99/PnzxeRDgc7cHw2KTFDd9mUlA5iQlaWLF+uf51q9ZjV+z+FtdBdFh249957bTabiLz66qsiMnbs2MLCQh0nXFWL6fbLdAUj1lzh9JulDdPKDh06JCJ2uz0+jhjBdycpKamoqGjRokUXXXSR5qWJdFTejBkznnrqqe7du9tstmnTpj3wwAMzZsyoqanRvEzgPJtNEhO9/ygOHJCxY+WZZ3StD+ZEyEQHunbt+rOf/UxEXnvttalTp1599dWzZs3SuygVmG6/TFcwNBBiziRhWtz27dtFJDs7W7lRZXbBdyctLS0zM1PHPQ1e3pYtW6655hr3X4cOHSoia9as0aw8aMNczy+56y45c8b7z/ffS0mJpKWJiPzud1Jbq3eVMBvGZKJjM2bMWL58eXNz8+nTp5944gm9y1GN6fbLdAVDA66OxmeSMKE8bveWW26J/nNeeOGFcN81c+ZMdYdlqrU7MRK8vClTpviGT6fTGfOygDDZbDJ+vFx6qQwdKk6nvPyyzJund00wFUImOjZw4MDevXvX1tYeO3Ys4g9ZsWLF3XffHe67SkpKiouLI95ocKrsl5ZMVzC0ESRnkjDhcDi2bdsmIgMHDozyo9LT0wsLCyN4V5Tb9aTi7sRCh+UtWLDA86979uwRkQj+VQFt5OVJerrU18vRo3qXArMhZKJjy5Yt++qrr0Rk165de/bs6devXwQfYpBpCT2Fu196zVXoFnrBzF5oNYFypu9qWlUEo9i8ebPD4bDb7YMHD47yo9LT0ydNmqRGUZFTcXdiIazyWlpaXnnllSlTpkR2VgW00auX1NdLe7vedcBsCJnoQHl5+UsvvVRbW9u9e/fW1tbXXnststOh3W43VO+m0Pervr6+pKTk8OHDK1f+f/buPT6K6v7/+Iftso00CIgRQ76RwoMH+EDIFypgEIFcIMQQAhErUqF9iIgXwFTFtiBfELBaK9QbtVQxWjQGRAG5xMA33BJITEFBjMgP80UwgTTGVBpDDHFdfn8MLMvesrvZ3bns6/ngj83M7MzZDZlz3nPOnHlv0qRJaoVM3wt85MiRZcuWLV26NDY2Njc3d8CAAStWrOBBmobnNmc6bRC2wkAjbDbb22+/LSI33nijAW7I1PjH8bd4999///Dhw1955ZXQFw0IkM0mZWUi0vo8tIATzZ2joSmlpaVLlix5//33r7zyypkzZ4rI66+/3tDQoHa52sqvz6X6XIXiZ4GZvTBieYmRJMxIM3/+/J/+9Kc/+clPXn/9dREpLy//yU9+8tOf/rS4uFjtogVC4x8ngOI99dRTHTp0eP/9981mLvdDu5Yvl+ZmEREtdRNAHwiZ8OjQoUOzZs3asGFDly5dRETpwbNarcqjvfTL38+l+lyF/haY2QsjGWESiqeeeurcuXOOjyz78ccfz507p6kRJb7T+Mfxt3i5ubnNzc3K/Zk2m42LgFCX1SqNjZf9O3RINm6UqVPld78TEYmLE/9n1UCk4/oZ3Dt58uS0adMKCgq6deumLOndu3dycvKuXbtWrlz56KOP+rtDjUxLGPTPFWoBFJjZCyOZl+dkkj8BLSgoKKivr1+yZIny49GjRysqKrhzHirKz5f8fI9rO3eW9esZLgu/ETLhRk1NTWZmZn5+vtOsgI888siuXbsqKyuLiopGjx7t1z61MC1hKD5XSAVWYGYvjFje78kkZwKqKy0tzcnJSUtLmzVrls1m++GHHyorKxcuXKh2uQBnJpP06SOZmfLIIxIbq3ZpoEOETDg7efJkcnLy6tWrXS+sZmZmxsfHV1VVPffccwGETHWnJQzR5wqdoBSY2Qsjh/eEad+GnAljq6ioWLx4cWVlZX19fV5eXnV19TXXXLNmzRq1y3VBVlZWfX19ZWWl40JtPo4FkWPKFHGaf8pkkqgo0d7sWtAT/vvgklOnTv3pT39KSEiora0dOHCg6wYNDQ0///nPRaSwsHD37t1hLl7AdPe5glhgZi+MEG6fVuI2T/qSRQH96t+//7p16w4ePKjcGLlz507tJEwR+eabb867sFgsapcLEc1slujoy/516EDCRFvxPwgiIqWlpe3bt/+v//qvefPmNTQ0NDU1derUae/evY7bzJgxo0uXLspjpm02W3Jy8k9+8pOpU6eqVGSf6O5zBbfAzF4YIbw8D5OcCb0oLS3duHFjQUGB2gXRiqqqqo0bN27cuPFf//qX2mUBAL/R9ISIyM033/zDDz9432bVqlVON/tpn+4+VxAL7DR74ZEjR5hYwpC8JEz7j67bMG4WmnLLLbd88sknn332WceOHbmBXPF///d///jHP0Tkuuuuu+aaa9QuDvyjnHg5yyKSETIBA2L2wgjRasK0LyRnQsueeOIJtYugOUlJSUlJSWqXAgACRMgEjIbZCyOEjwnTvoqcCQAAwoOQCbRC43MVumL2wkjgV8K0b0DOBAAAYUDIBFqhzFWodin88M0336hdBIRWAAnTvhk5EwAAhBohE0ZQWlra0tJisViYMcIvVVVVH330kYgwe6GOBJww7RuTMwEAIvLdd2qXICyYUl0VhEzoHtMSBozZC3WnjQnT/hZyJgAACB1aFZHF3rLk9w7oTlASppe9tXGHAABHPMVEI+zVHXVcOJnULgAAoHXBTZie3u42eQIAAPiFkAkAWhf0hOllJ+RMAADQRoRMANC0ECVML7siZwIAjIEKTS2ETADQrpAmTC87JGcCAICAETIBQKPCkDC97JacCQBtcf78ec6jiFiETADQorAlTC87J2cCAIAAEDIBQHPCnDC9HIKcCQDQqXbteGyJagiZAKAtqiRMLwciZwJAYBgxi4hFyAQADVExYXo5HDkTAAD4jpAJAFqhesL0clByJgBARxgrqy5CJgBogkYSppdDkzMBwF+MmEVkImQCgPo0lTC9FICcCQDQProxVUfIBACVaTBhKsiZANB2dGYiAhEyAUBNmk2YCnImAADwFyETAFSj8YSpIGcCAHSEsbJaQMgEAHXoImEqyJkA0BaMmEWkIWQCgAp0lDAV5EwAgPbRjakRhEwACDfdJUwFORMAAkZnJiIKIRMAwkqnCVNBzgSAgJEzQ41uTO0gZAJA+Og6YSrImQAAwDtCJgCEiQESpoKcCQCBoTMzdOjG1BRCJgCEg2ESpoKcCQDQDhKm1hAyASDkDJYwFeRMAAgAnZmIBIRMAAgtQyZMBTkTAAJAzgwuujE1iJAJACFk4ISpIGcCAFREwtQmQiYAhIrhE6aCnAkA/qIzE8ZGyASAkIiQhKkgZwKAv8iZbUc3pmYRMgEg+CIqYSrImQCAcCJhahkhEwCCLAITpoKcCQB+oTMzYCRMjSNkAkAwRWzCVJAzAcAv5EwYEiETAIImwhOmgpwJAH4hZ/qLbkztI2QCQHCQMO3ImQDgF3Km70iYukDIBIAgIGE6IWcCgF/Imb4gYeoFIRMA2oqE6RY5EwD8Qs70joSpI4RMAGgTEqYX5EwA8IX9xEjO9ISEqS+ETAAIHAmzVeRMAPCiXbt2yimRnOkFCVN3CJkAECASpo/ImQDgyh4vHZcoL8iZjkiYekTIBIBAkDD9Qs4EADvXeOmKnCki7dqRMPWKkAkAfiNhBoCcCQDi9bzndJ6M8JypxEuqV50iZAKAf0iYASNnAohk3jsw3Z4hlZwZgadJOjD1jpAJAH4gYbYRORNABPJlfKwnSm9e5JwmGSJrDIRMAPAVCTMoyJkAIoov57dWa5MI6dJkiKxhEDIBwCckzCAiZwKIBG3pwHRl7C5NOjANhpAJAK0jYQYdOROAgfkVL/2qUAzZpUkHpvEQMgGgFSTMECFnAjAkv85jAVQoRurSpAPTqMxqFwAANI2EGVLnz593/YbbtWvHlwxAj8J5mcx+/tTp+VL5qjjbGxUhEwA8ImGGATkTgAEEFi/beKJT3q67qEm8jASETABwj4QZNuRMALqm7jh/x6gp2k6bxMvIQcgEADdImGFGzgQQaYJ7frPvTYMdm/azO6f0yEHIBABnJExVkDMBGIPbs5nrNqE7umimY5Ouy4hFyASAy5AwVUTOBKBHjucuXxJmGDh1bEoY06bjp+fsHbEImQBwCQlTdeRMAHrkGup82TgMPBUsuEUgWMIJIRNQ2YEDB6qrq00mU1ZWltsNSktLv/76axEZMmRIXFyc6wZVVVUfffSRiKSnp0dFRRUWFjY3N1977bWJiYmeDlpXV7dv3z4Ruemmm2JjY4PzSfSPhKkR5EwAOqWFPkxPnE6hbovqy1nW7Ufk/Awn1NmRxXEsh7olgd0LL7zw29/+VkROnDjRo0cPp7U2m61jx45NTU0i8tBDD73wwguue3jggQdWrlzZoUOHs2fPikj37t1ramomTpy4YcMGTwctKioaM2aMiGzYsGHixIlB/Dj6RcLUGg8NIH4pADTK01nLpQtRu+cxrXXDBgWtX1WY1C4AEOlSU1OVF2VlZa5ri4uLlYQpIgUFBW73sHfvXhEZP358aAoYEUiYGuT2V6DlXgIAkcwACVNEzvtA7TJCHwiZgMr69+8fExMjIkVFRa5rt2/fLiLJyckiUllZWVVV5bRBY2NjRUWFfRsEgISpWeRMALrgY8IEIgchE1BfSkqKiJSXl7uu2rZtm4jMnDmzd+/e4q4zU0mhIpKRkRHaUhoUCVPjyJkANM73sf3UL4gchExAfUo+PHLkSHNzs+Py2trajz/+WERSU1PT0tJEpLCw0Om9u3btEpHevXvHx8eHqbgGQsLUBXImAM3i7nHALUImoD7ltkybzeY0YnbHjh0ikpCQEBMTo8zTU1hYaLPZHLcpLS0VkfT09PAV1yhImDpCzgSgQa0mTE+vAcMjZALqi4uLU0bDlpSUOC7ftGmTXAyQmZmZJpOpubl5586d9g1aWloOHTokDrMHwUckTN0hZwLQFB/7MJkvB5GJ52QCmpCcnFxZWak87tLugw8+EBGlD9NsNo8aNWrXrl3btm0bPXq0soHSsWk2mzMzM512eOrUqTVr1ng6nDJXUMQiYeoUz88EoBGMkgW8I2QCmpCenv7qq6/u2bPHZrOZTCYR+fDDDxsaGqKiopRpgUTk1ltv3bVr1/bt25999lllyb59+0RkxIgRZrPz3/L+/funTJkSxk+gGyRMXSNnAlAdCRNoFSET0IS0tDSTyWS1Wv/5z38mJibKxTl+0tPTlcwpFx9Scvjw4ZqamtjYWLkYMpU5gZxYLJaOHTt6Oty5c+caGxtD8Dm0joRpAORMACoiYQK+4J5MQBOio6OHDBkiDg8yUZ5N4hggBw8e3LVrV7k4IZDVai0rKxMR++hZRxkZGd94tmHDhtB/Js0hYRoG92cCUAUJE/ARIRPQCmVY7N69e0WkoaFBCZBOM/ooeVJ5Wub27dttNlvnzp0HDx6sQnH1hoRpMORMAGFGwgR8R8gEtEIJmcpTTJQY2bNnzz59+jhuk5WVJSLFxcVy8eElY8eODX9RdYeEaUjkTABhQ8IE/ELIBLQiJSXFbDafOXPm5MmTylhZ1wCpdGyeOnWqrq5u//79IuI6ryyckDANjJwJIAxImIC/CJmAVphMJqUzs7y8XBkr6xoyu3XrNnDgQBEpKSlRHpjJEzK9I2EaHjkTQEiRMIEAEDIBDbHfcnn06FGz2ZyRkeG6jTIVUH5+vtVqTUhIUKaZhVskzAhBzgQQIiRMIDCETEBDRo0aJSJ5eXkiMmLECIvF4rrNmDFjRGTjxo0ikpSUFKKSWK1Wq9Xa6mY2m82XzVRBwowo5EwAQUfCBAJGyAQ0ZOjQodHR0Upsc/v0S7l466ayza233hrcAtTW1s6YMeOKK65o3759+/bte/XqtXz5ci/bjx8/Pj09PbhlCAoSZgQiZwIIIhIm0BaETEBbxo0bp7zwFN5MJpMyjNZsNnsKooGpq6sbNGjQa6+9lpKS8vLLLy9atKh9+/Zz586dPn262+0ffvhhZRZcrSFhRixyJoCgIGECbdSOP5iIYj9p8nuHqzlz5qxYsWLx4sULFy5UljQ2Ng4bNqyioqKsrCwxMdG+ZUNDw913371+/XoRSU1NVR67ohEkTNA6BNAWnEMMhtavKujJBHDBe++9Z7FYFixYYF8SHR398MMPi0hhYaF94TvvvNOnT5/169dPmzZNhVJ6RcKE0J8JoA1ImEBQmNUuAACtWLlyZVNTk8l02bUns9ksIi0tLfYl+fn5HTp0eP/997Oyst58881wl9IzEibszp8/7/r/oV07Bu8A8IaECQQLIRPABVlZWa4LV69eLSIjR460L3n88cd/8YtfOGVR1ZEw4YScCcAvJEwgiLTVTASgKbm5uTt27EhISHCchWjw4MEkTOgC42YB+IiECQSXtlqKALRj06ZN9913X+fOnTds2KB2WbwhYcILciaAVpEwgaAjZAJwY9WqVRMmTOjUqdO2bdt69eqldnE8ImGiVeRMAF6QMIFQ4J5MAM5ycnJefPHFuLi4oqKi66+/Xu3ieETChI+MfX9mYWFhc3Pztdde6/icISd1dXX79u0TkZtuuik2NjaMpQM0jYQJhAghE8AlVqs1Ozt7y5YtQ4YM2bp1a0xMjNol8oiECb8YOGdOnz69pqZm4sSJXka2f/LJJ9nZ2SKyYcOGiRMnhrF0gHaRMIHQYbgsgEsmTJiwZcuWzMzM4uJiEiYMhnGzAOxImEBI0ZMJ4IInn3yyoKAgIyNj8+bNapfFGxImAmbg/kwAviNhAqFGyAQgIlJfX7906VLlxbhx45zWpqWl5eTkqFEuZyRMtBE5E4hwJEwgDAiZAERESkpKWlpaRKS8vNx1rUZmCiFhIijImUDEImEC4UHIBCAiMnHixABq2XBWzCRMBBE5E4hAJEwgbJj4B4AOkDARdMwDBEQUEiYQTvRkAtA6EiZCxEj9madOnVqzZo2ntRUVFeEsDKA1JEwgzAiZADSNhImQMkzO3L9//5QpU9QuBaBFJEwg/AiZALSLhIkwMEbOtFgsHTt29LT23LlzjY2N4SwPoBEkTEAVhEwAGkXCRNgYIGdmZGRs2LDB09qioqIxY8aEszyAFpAwAbUw8Q8ALSJhIsyYBwgwGBImoCJ6MgFoDgkTqjBAfybg3e7du8+cOSMiw4YN69atm5cta2try8rKRKR79+5Dhw4NU/mCh4QJqIuQCUBbSJhQETkTxlZdXT1t2jQRycjI2Lp1q5ctp0+fXlBQYDKZ9u3bF67SBQ0JE1Adw2UBaAgJE6pj3CwMbOrUqRMnThSRgoKCt956y9Nmq1evLigoEJHHH388MTExfOULBhImoAWETABaQcKERpAzYWCvvPJK165dRWTOnDm1tbWuG9TV1eXk5IjIwIEDn3jiiTAXr41ImIBGEDIBaAIJE5oSaTnTarVarVa1S4FwiImJefnll0XkzJkzs2fPdt3g/vvvP3PmjMViWbt2rcmkp4YiCRPQDj2dOwAYFQkTGhQJObO2tnbGjBlXXHFF+/bt27dv36tXr+XLl6tdKITcHXfccdttt4nIu+++u379esdV77zzjrJk2bJlffr0Uad8ASFhAppCyASgMhImNMvYObOurm7QoEGvvfZaSkrKyy+/vGjRovbt28+dO3f69OlqFw0ht3LlypiYGBG5//77v/32W2VhfX290reZmpo6Z84cNcvnJxImoDXMmBdZ7Gdhfu/QCBImtM+o7dc5c+asWLFi8eLFCxcuVJY0NjYOGzasoqKirKxMd9O9wF/vvPPO5MmTReSee+5ZtWqViEydOjUvL69z584VFRVxcXFqF9BXRv0LRbDQ+lUFITOy8GcGTSFhQi8M2Yrt3r17fX39999/73jfXW5u7j333LNo0SLdzfiCAPzyl7989913RWT//v2NjY3Jyckikp+ff+edd6pdNF8Z8m8TwUXrVxU8JxOAOkiY0BFDPj9z5cqVTU1NTjO7mM1mEWlpaVGpUAirlStX7tmzp66u7t57721ubhaRyZMnkzABtJ2+K0j4i2s50AgSJvQoElq0o0eP3rFjxwcffJCenq52WRAO69evnzRpkvI6Njb2008/VR5won2R8PeIoKD1qwom/gEQbiRM6JSx5wESkdzc3B07diQkJJAwI8dtt912++23K6/feOMNEiaAoEvGQnIAACAASURBVCBkAggrEiZ0zcA5c9OmTffdd1/nzp03bNigdlkQVmPHjlVeJCUlqVoQX5EwAe0jZAIIHxImDMCQOXPVqlUTJkzo1KnTtm3bevXqpXZxAI9ImIAuEDIBhAkJE4ZhsJyZk5Nz7733xsXF7d27d+jQoWoXB/CIhAnoBbPLAggHEiYMxhjzzVqt1uzs7C1btgwZMmTr1q0xMTFqlwjwiIQJ6Ag9mQBCjoQJQzJAf+aECRO2bNmSmZlZXFxMwoSWkTABfaEnE0BokTBhYLruz3zyyScLCgoyMjI2b96sdlmgJ1arVS4+UjU8SJiA7hAyAYQQCROGp9OcWV9fv3TpUuXFuHHjnNampaXl5OSoUS5oV21t7eOPP56Xl9fc3CwiPXv2nDVr1qOPPhrq45IwAT0iZAIIFRImIoQec2ZJSUlLS4uIlJeXu66NjY0Ne4mgaXV1dYMGDaqpqcnIyMjMzKytrc3Pz587d+5nn32Wm5sbuuOSMAGd0nQViKCzn6z5vSPUSJiINLSGYWBz5sxZsWLF4sWLFy5cqCxpbGwcNmxYRUVFWVlZYmJiKA7K3xSCgtavKpj4B0DwkTARgQwwDxDgyXvvvWexWBYsWGBfEh0d/fDDD4tIYWFhKI5IwgR0jeGyAIKMhImIpcdxs4AvVq5c2dTUZDJd1jmhzP2jjLsOLhImoHeETADBRMJEhCNnwpCysrJcF65evVpERo4cGdxjkTABA2C4LICgIWECwrhZRIbc3NwdO3YkJCSkp6cHcbckTMAYuLYaWbj1GaFDwgQc+dJW5pwMndq0adOkSZOio6M/+uijXr16BWu3JEyEAmdaVdCTCSAISJiAE+/9me3ataNvEzq1atWqCRMmdOrUadu2bSRMAG5xTyaAtiJhAm75fn8mN21CL3Jycl588cW4uLiioqLrr78+WLsNZ8I8cOBAdXW1yWRye6OpiJSWln799dciMmTIkLi4ONcNqqqqPvroIxFJT0+PiorasmWL1Wp13cxsNlsslsTExCuvvDKonwDQAWq1yKK7AQMBX+nXywc0ABIm4J2n1rPTcq394dDRqi9h+P9jtVqzs7O3bNkyZMiQrVu3xsTEBGvPYe7DfOGFF37729+KyIkTJ3r06OG01mazdezYsampSUQeeuihF154wXUPDzzwwMqVKzt06HD27FkR6dixY2Njo5cjJiQkPP7443fccUfQPgP8obvWrzEwXBYa0s7F+fMS2D+n/aj9yQyLhAm0ytO4WU/3Z6rF99OvSIBnZv6F6F94TJgwYcuWLZmZmcXFxfpNmCKSmpqqvCgrK3NdW1xcrCRMESkoKHC7h71794rI+PHjHReaTCaLC2XV4cOHJ0+e/NRTTwXrIwDaR8iE+ry0aQLmtB8CZyiQMAEf+TjfbPhPUF5SJeDoySefLCgoyMjI2Lx5c1RUVLB2q8p9mP3791dCclFRkeva7du3i0hycrKIVFZWVlVVOW3Q2NhYUVFh38burrvuOufixx9/zMvLUw73P//zP0ePHg3NZwI0h5AJdbht2YSO28AZwuNFABIm4BdN/YG4DZaAJ/X19UuXLlVejHPhdkCpL1Sc6SclJUVEysvLXVdt27ZNRGbOnNm7d29x15mppFARycjIaPVAJpPpV7/61dtvvy0iNpvtb3/7W9sKDugGE/8g3JRKRcUGjf3QjNEPGAkT8MvF856beYBctwzdX5PDSS9ER4AxlZSUtLS0iIdUFhsbG8A+1Z1LNiMjY+3atUeOHGlubnbsmK2trf34449FJDU1taSkpLKysrCw8L777nN8765du0Skd+/e8fHxPh5u9OjR8fHxVVVVx48fD96HADSNnkyEidOFcy1w6ttUuzi6QcIEAqPieSZsw0ZgSBMnTjzv2apVq/zdoboJUy7elmmz2ZxGzO7YsUNEEhISYmJixowZIyKFhYU2m81xm9LSUhFJT0/364hKv6iS1YFIQMhEyGm/cUPU9B0JE/CXvyeWIJ6INHh1D1A9YYpIXFyckvpKSkocl2/atEkuBsjMzEyTydTc3Lxz5077Bi0tLYcOHRKH2YN8YbPZlEmGoqOjg1F8QAcImQghfTVuiJqtImEC4dH2s5D2r+4hMmkhYSqUaXuUx13affDBByKi9GGazeZRo0bJxbs0FUrHptlszszM9P1Yy5cvb25uFpGRI0cGo+yADhAyERL6ipeOiJqekDCBwIT/L0Wnp18YnnYSplzsrtyzZ499NOyHH37Y0NAQFRWlTAskIrfeeqs4zPQjIvv27RORESNGmM3O05pYrdbGyx06dGjjxo1Tp0793e9+JyJxcXH33HNP6D8ZoAlM/IPgU9o3uqaU3z5Xh8ql0QASJtAWvkz54ySwGYBUn1kN8ERTCVNE0tLSTCaT1Wr95z//mZiYKCKFhYUikp6ebjJd6INRejsPHz5cU1OjzG+khMy0tDTXHebn5+fn53s6XOfOndevX89wWUQOejIRTPYOTGOw92qqXRCVkTCBtlOmSAnd/vU7fgSRQGsJU0Sio6OHDBkiDlPmKj2WjgFy8ODBXbt2lYsTAlmtVuXWytGjR/t4FJPJdP3118+dO/fIkSNDhw4N6icANI2QieAwcPsmwkfPkjCBIPIravp+2jHq6RfGoMGEqVCGxe7du1dEGhoalADpNKOPkieVp2Vu377dZrN17tx58ODBrnubMmXKd5c7e/bsDz/88Pnnnz/77LOBPegF0C9CJoLA8O2biO3SJGECoRDEnGmw8SMwHs0mTLkYMpWnmCgxsmfPnn369HHcJisrS0SKi4vl4sNLxo4d63ZvZrM5+nIdOnSwj7wFIg33ZKKtIqd9Y8+ZGqkdQ42ECYSO8tfUxktXkXP6hU5pOWGKSEpKitlsPnPmzMmTJ5Wxsq4BUunYPHXqVF1d3f79+0XEr3llgYjF9RUELgKvoEdOlyYJEwgDX0bPejrhRNrpF7qj8YQpIiaTSenMLC8vV8bKuobMbt26DRw4UERKSkqUB2b69YRMIGIRMhEgww+R9cLwOZOECYSTv39fEXiBD7qj/YSpsN9yefToUbPZnJGR4bqNMhVQfn6+1WpNSEjg7krAF4RMBIL2jYFzJgkTCD/vXZqOf5WRfIEPeqGXhCkio0aNEpG8vDwRGTFihMVicd1mzJgxIrJx40YRSUpKCnoZrFar1WptdTObzebLZoBGEDLhNxKmwpCzzpIwARW1OnqW0y+0T0cJU0SGDh0aHR2thDe3T7+Ui7duKtvceuutwTp0bW3tjBkzrrjiivbt27dv375Xr17Lly/3sv348ePT09ODdXQg1AiZ8A9NHEcGu0WThAlogae/O06/0D59JUzFuHHjlBeeIpzJZFKG0ZrNZk9B1F91dXWDBg167bXXUlJSXn755UWLFrVv337u3LnTp093u/3DDz+szH8L6EU7jf/lI7jsZ//Afu80cTxp107rlWirSJh2Bw4cqK6uNplMysz1rkpLS7/++msRGTJkSFxcnOsGVVVVH330kYikp6dHRUUVFhY2Nzdfe+21iYmJng5aV1e3b98+Ebnpppu44QcKp79K7fxFtmunocJANFMH6TFhqmXOnDkrVqxYvHjxwoULlSWNjY3Dhg2rqKgoKytzrCwaGhruvvvu9evXi0hqaqrywBX4pY2tXwSGnkz4ioTphd77M0mYjvbt25ednT1hwoSTJ0+6rrXZbGPGjMnOzs7Ozv7zn//sdg9PPfVUdnb2XXfdFRUVJSLTp0/Pzs5+5plnvBz0k08+UfZZXl4elE8BAzh/kWgpYQJukTD98t5771kslgULFtiXREdHP/zwwyJSWFhoX/jOO+/06dNn/fr106ZNU6GUQBsQMuETEmar9JszSZhO7NPTKzPaOykuLm5qalJeexq8tHfvXhEZP358aAqIyMLpF9pHwvTXypUr//GPf5hMl7XDzWaziLS0tNiX5Ofnd+jQ4f3331+9enW4iwi0DSETraOJ4yM95kwSpqv+/fvHxMSIiNtRScoDu5OTk0WksrKyqqrKaYPGxsaKigr7NkBbcPqF9pEwA5CVlXXnnXc6LVSS5MiRI+1LHn/88crKSk/3bgBaRshEK2jiGBgJ0xP747ldV23btk1EZs6c2bt3b3HXmamkUBFx+7w1wHecfqF9JMxgyc3N3bFjR0JCguP8Q4MHD3bq7QT0gv+48IYmjr901JlJwvRCyYdHjhxpbm52XF5bW/vxxx+LSGpqqjLHoOPNM4pdu3aJSO/evePj48NUXABQAwkzWDZt2nTfffd17tx5w4YNapcFCA5CJjwiYQZGFzmThOmdclumzWZzGjG7Y8cOEUlISIiJiVEez11YWGiz2Ry3KS0tFc9T4QM+4gwMjSNhBsuqVasmTJjQqVOnbdu29erVS+3iAMFByASCT+M5k4TZqri4OGU0bElJiePyTZs2ycUAmZmZaTKZmpubd+7cad+gpaXl0KFD4jB7EBAAEiY0joQZLDk5Offee29cXNzevXuHDh2qdnGAoDGrXQBoFE2cNlJypgZrXBKmj5KTkysrK5XHXdp98MEHIqL0YZrN5lGjRu3atWvbtm2jR49WNlA6Ns1mc2ZmptMOT506tWbNGk+HU+YKAoTTLzSPhBkUVqs1Ozt7y5YtQ4YM2bp1qzLhHGAYhEy4QRPHqEiYvktPT3/11Vf37Nljs9mUeRc+/PDDhoaGqKgoZVogEbn11lt37dq1ffv2Z599Vlmyb98+ERkxYoQyE72j/fv3T5kyJYyfALrE6RcaR8IMlgkTJhQUFGRmZq5bt055qDJgJIRMOKOJEyxa68wkYfolLS3NZDJZrdZ//vOfiYmJcnGOn/T0dPtcf8pDSg4fPlxTUxMbGysXQ6YyJ5ATi8XSsWNHT4c7d+5cY2NjCD4HAAQNCTNYnnzyyYKCgoyMjM2bN6tdFiAkCJlACGknZ5Iw/RUdHT1kyJDy8vLy8nIlZCrPJnEMkIMHD+7atWt9ff2OHTumTp1qtVrLyspExD561lFGRoaXaQOLioqUUbiIZFzjg5aRMIOlvr5+6dKlyotx48Y5rU1LS8vJyVGjXEAwETJxGZo4hkTCDExKSkp5efnevXtzcnIaGhqUAOk0o8/o0aPXrl1bUFAwderU7du322y2zp07Dx48WKUiQ8c4/ULLSJhBVFJS0tLSIh6exqyMiwH0jtllcQlNnFBQfaZZEmbAlHsvlaeYFBQUiEjPnj379OnjuE1WVpaIFBcXy8WHl4wdOzb8RQWA0CFhBtfEiRPPe7Zq1Sq37zp//rzTU7UALSNkAiGnYs4kYbZFSkqK2Ww+c+bMyZMnlbGyrgFS6dg8depUXV3d/v37RcR1XlmgVVzjg2aRMAEEgJCJC2jiGA8Js41MJpPSmVleXq6MlXUNmd26dRs4cKCIlJSUKA/M5AmZAAyDhAkgMIRMIBzC35lJwgwKZQqfgoKCo0ePms3mjIwM122UqYDy8/OtVmtCQgK308BfXOODNpEwAQSMkAkRmjiGQ8IMllGjRolIXl6eiIwYMcJisbhuo8wKu3HjRhFJSkoKUUmsVqvVam37NgDgCxImgLYgZAJhErbOTBJmEA0dOjQ6OlpJbm6ffikXb91Utrn11luDW4Da2toZM2ZcccUV7du3b9++fa9evZYvXx7ANtAsrvFBg0iYANqIkAmaOIZCwgw6+0PM0tPT3W5gMpmUYbRms9lTEA1MXV3doEGDXnvttZSUlJdffnnRokXt27efO3fu9OnT/doGAHxHwgTQdpp4TDzCxl5zOP7eCZnh1K5dCKtqEqbBzJkzZ8WKFYsXL164cKGypLGxcdiwYRUVFWVlZYmJiT5uA83S6em3XTvRY7ENLIg1CwkTxuO29YtQoycz0um0iQNXJEzjee+99ywWy4IFC+xLoqOjH374YREpLCz0fRsA8AUJE0CwmNUuABBZlDszg15nkzANaeXKlU1NTSbTZVcDzWaziLS0tPi+DbSJa3zQFBImgCAiZEY0mjjGQMI0qqysLNeFq1evFpGRI0f6vg0AeEfCBBBcDJcF9I2EGVFyc3N37NiRkJDgaRYiH7eB6rjGB+0gYQIIOib+iSxOtz7TylFLsCZpIGFGlE2bNk2aNCk6Ovqjjz7q1atXwNtAC3R9+mXiH61pS51CwoThMfGPKujJjFy6buJASJgRZtWqVRMmTOjUqdO2bds8pUdftgEAOxImgBAhZAK6RMKMKDk5Offee29cXNzevXuHDh0a8DbQCK7xQQtImABCh4l/ABW0cY5ZEmbksFqt2dnZW7ZsGTJkyNatW2NiYgLbBgAckTABhBQ9mRGK6+j6RcKMKBMmTNiyZUtmZmZxcbGn9OjLNgBgR8IEEGr0ZAJ6QsKMKE8++WRBQUFGRsbmzZvbsg00hWt8UBcJE0AYMLtsZHGsWvjNq87f+QBJmBGlvr6+e/fuLS0tN910U9euXZ3WpqWl5eTk+LJNuMoLXxkgZDK7rNb4XpuQMBGBmF1WFfRkAvpAwow0JSUlLS0tIlJeXu66NjY21sdtAEBBwgQQNvRkRhaHaznqFqQV27dLU5OIyOjREh3tcbPDh+X4cRGRlBS58sowlS2I2nLtmb9cQKfoyUTQ+VKbkDARsejJVAUT/0CLvvpKsrMlO1t++1uP25w8KaNGSXa25OXpMmH6joQJGIYBEib0iIQJIMzoyYwseunJFJHsbNm4UUTk/fclK8t5bUuLDBkihw9Lz55y6JCOQ2arl59JmICRGCNk0pOpNd6rEhImIhw9maogZEYWHYXM+noZMEBqaiQmRj79VLp1u2ztzJny6qtiMsm+fZKYqFIRg8HflgF/sICu+RUyDxyQ6uq2HvH66+X669u6EyeETK3xUpWQMAFCpioImZFFRyFTRHbvluRkEZHUVCkqurT87bflrrtERP7yF3n4YXXKFix+tQz4awX0zq+QOX68bNnS1iPec4+sWtXWnTghZGqNp6qEhAkIIVMl3JMZifTyJ5aUJHPniojs2CEvvHBh4bFjct99IiJZWbpPmF6QMAH8/veX/bh5s5w75/7f2bPy3Xfy6aeydau8+KIMG3bpXd9+G+ZSQytImABURE9mZFGqHB39zm02ufFGOXRILBY5eFB695ZBg+TIEYmPl08+kS5d1C5fm7m9/EzCBIzK33sy58+Xp5++8Do2Vj79VFyeh+re9u0yebKcOSMDB8rBg36X07tw9mQWFEhLi/tVJpNYLDJkiK/fiRfKFAC9eklCgq9F6t5dhg71uE1dnezbJyJy000ShmcJuVYlJEzAjp5MVRAyI4vuQqaIHDsmgwZJU5MMHCijR8uyZUa4FdPOl5YBf6SAMQQw64/NJv/931JRceHH22+Xdet8fa9yx0FUlHz/vX8HbVU4Q+bVV0t9fSvb9O8vTz8tmZmBH0U57z74oPz1r74WafJkWbPG4zZFRTJmjIjIhg0ycWLgBfORU1VCwgQcETJVwXBZaF2fPvKXv4iIHDoky5aJiPz5zwZJmK5ImIAGtbso/Ic2mWTdOrFYLvz47ruyerWv701KkrvukuZmjz2BOtKtm6SmOv/r31/MZhGRigoZP17eekvtUmoDCROAFhAyoQP33XfpKSajRsmjj6pamqA6f/5Sg4CECWiQ4x9mOwdhK8D118tTT136cdYsqary9b333y8icuhQ8EsVZklJUlTk/O/TT+XcOXn1VYmKEhF54AFpaFC7oGojYQLQCEImdMBmk9raC6+PHLn02khImIC+hDNwPvqojBp14XVjo0yZ4usbb7lFoqLk9OkQlUt9JpPMmCF//rOISGOjFBSoXSBVkTABaAchEzrw2GNSXi4mk4hIXZ38+tdqFyjYSJiAroUhcL75pnTufOH1vn3ypz/5+sakJOP37/3mNxde7NzpZq3VKjt3ypYtsnOnWK3hLFdYkTABaAohE1pXUHDhnsx58+Shh0REtm+X5cvVLVRo0SwAtMPfv8cQBc74+MvmpHn8cTl82Kc3pqS0PnGO3p09e+HFT3962fK6Opk5U664QlJTZfx4SU2VK66QmTON/4UoqEoAqMisdgEAb06dutBv2a+fLFwoVqsUFEhlpcyfL2PG+DTXvMa5bYWqMr8IgKAL7pSGv/qVbNgg774rImKzyeTJcvDghdsRvXjssbYfWetee+3Ci9TUSwtPnpThw+XUKRGRESOke3f59lspKpJXX5Xt26WsLBxPFgkPtzUGCROAuujJhKZNmSL19WKxXJhfsUMHyc8Xk0laWmTyZGluVrt8AOCDYF05euWVS9Ho6NGICJDetbTI8uWyaJGISI8el6aIs9kkM1NOnZLYWDl4UIqLZc0a2bZN/t//k5495eRJmTRJxVIHmWucJGECUB0hE9r1xBNSUiIi8vzz0q/fhYWDB8vixSIiR4/K7NmqlS1YaAkA8F2XLvLGG5d+XLHC/V2IxlNcLOPGXfbv1lvl5z+XK66QuXPFZpOYGNm48cKt+yKyZs2Fh4tu2iQDB17aT+/esmWLiEhZmRQVhf1jhAUJE4AWEDKhUbt3XwiTWVnywAOXrVqwQIYNExF57TXZtEmFsgGAWtLSLru+NnWqfPuteqUJl5oaKSi47F9hoZw8eSFePvKIfPbZZWHyvfdEREaMkMGDnXfVr5/cdJOIyJo14Sp9WCjZkoQJQCO4JxNaVFcnd9whIhIbK7m5bjZYu1b69ZPGRvnNb+ToUenWzXkDZQpBM//BAWhAcJv+zz4r27fLsWMiIjU1sn+/pKUFcfdadNNN8uCDF15brbJ9u6xbJzabTJwoubnSpYvz9koHr9ksGze62ZvFIiJSWRm68orNFsKde0LCBKAdtMGhRVOmSF2diMhbb0nXrm42UCZa/M1v5MwZmTxZdu++sLy2Vh5/XPLyLtyu2bOnzJoljz4apmIHjJYBoFkB304Zur/rqChZu1ZuvFFsNvnd74yfMEXk5z+/7OFV06fL7Nly662ycaMcPOhmFh/lqS27dsmuXR73qYynDdgPP3hba39WiokRYwAiEiETmrNkiezYISIyb56kpHjc7Ne/ls2b5d13Zc8e+dOf5A9/kLo6GTRIamokI0MyM6W2VvLzZe5c+ewz992hABBcYbtgZLVKhw4ybpw880x4Dqg5t9wi+fkyfrycPClpaVJWJtHRl9YqHYnDhknv3h730LFjgIfu31/27JGWFm/b2NcqvaYAEGkImdCchQtl4UKftly37rIflyyRmhpZvPjS2+fOlWHD5PXXZeZMSUwMcjkBQNQYiXDqlGRmyqBB8tZbYT6ytmRmyuzZsmKFVFTI7NmXTYkUHS2NjTJsWEgeqqyMzv3kE2/bnDx54cUNNwS/AACgfQzjgHG8955YLLJgwaUl0dHy8MMiIoWFahWqFe3aMVYW0J/zDsJ86G+/lbQ06dJF3n/fp9vOV626LH0ZzDPPSI8eIiL/+MdlE+2OHCkiUlbm/l0FBbJmjRw4EOBBBw0SEamqupQk3R5CRKKiJD4+wKMAgK4RMmEcK1fKP/7hfAOM0gjzPq4JADyxx0gVg6VdS4tMmCB1dVJU5Ga2G7e2bjXy9LMdOsgrr1x4PWPGpYcnZ2aKiJSVuXnES1WVTJggU6ZIXl6AB7U/jdM+F5GT4mLZvl1EZMqUAA8BAHpHyIRxZGXJnXc6L1y9WuTiVW0ACIC6wdLRXXfJwYNSUCBxcb6+pbzcj431KC3tQpb78ktZtOjCwnvukZ49RUTuvPOynFlTI5MmXbij9ZFHnHd14oTzg1Ic/9kNHCgTJ4qIFBRIYqJs2XLhOqbNJocPyxNPyJgxIiIdOsj8+SH5yACgfe00UnEiPJRpEiPnd56bK/fcIwkJrdw8oyKGywIRpV27dgH/xT/2mPzlL/LBB35MJ3vsmPTtK9u2BXkG2nbtwlePXH211NfL5MneHmtZXy99+0p9vZhMcvCgJCSIiBw5IklJFyYqv+km6d1bvvtOCgouzPv6/vuXOiRFxJcphB0/8rffyujR8vHHl5aYzZdmlBWR6Gh5773wTfxLVQJ4YZ8knD+TcKInE4a1aZPcd5907iwbNqhdFABom5dekmXL5NVX/cstyt2YN94YmjJpRteu8vzzIiI2m0ybdmFhv37y6afym9+I2Szl5ZKXJ5s2idUqw4dLWdllCTMAXbrI/v3y5z9fmr3WnjDNZrn7bvnkk4h4tAwAeEJPZmRxuJajbkFCbtUqufde6dpVCgpk6FC1S+MZl5+BiBJYT+aWLTJ+vPzP/8iSJX68q6pKrr9empvlxx/9PqJ34ezJhC+oSgAv6MlUBT2ZMKCcHLn3XomLk717NZ0wAaBVpaXyy1/Kb37jX8I8dUrS0qSpydf5gQAACCKekwlDsVolO1u2bJEhQ2TrVomJUbtAXnHtGYB3J0/KxIkyfLisWuXT9jab7N0rmzfLyy9LU5OIyE03hbSAAAC4QciEoUyYIAUFkpkp69ZJVJTapQGAy50/f973EbN1dZKcLHV18tFHct11Hjez2cRqlR9/lJaWC8HSUadOgZcWAIDAEDJhHE8+KQUFkpEhmzerXRQAaLPf/16+/FJE5MwZOXMmwJ1ofEAHAMCQmPgnsrRzmKbdYL/5+nrp3l1aWuSmm6RrV+e1aWmSk6NGsbxiuCwQgdryFBONYOIfraE2Abxg4h9V0JMJgygpufA47PJyN2tjY8NcHAAAACBC0ZMZWQzck6k7XHgGIhM9mQg6KhTAC3oyVcEjTAAACJ/z5887XO4DAMCACJkRilYOAAAAgFAgZAIqYGgTAAAAjIqQCQBAWDGWBABgbITMyEUrBwAAAEDQETKBcGOsLAAAAAyMkBnR6MwEAFVw+gUAGBghEwgrujEBAABgbIRMAABUQGcmAMCoCJmRjlYOAAAAgCAiZALhw1hZAI64zAcAMCRCJmjlAAAAAAgaQiYQJnRjdwMrzgAAIABJREFUAnDFZT4AgPEQMiFCKwcAAABAkBAygXCgGxOAJ1zmAwAYDCETF9DKCR0SJgAAACIHIROXkDMBQBWcfgEARkLIBEKLbkwAviBnAgAMg5CJy9DKAQAAANAWhEw4I2cGEd2YAHzH6RcAYAyETCBUSJgA/EXOBAAYACETbtDKaTsSJgAAACITIRPukTMBQBWcfgEAekfIhEc0dAJGNyaAtuD0CwDQNUImEGQkTABtR84EAOgXIRPe0MrxFwkTQLBwBgYA6BQhE62gleM7EiYAAABAyETryJm+IGECCDpOvwAAPSJkwic0dLwjYQIIEU6/AADdIWTCVzR0PCFhAggpTr8AAH0hZMIPNHRckTABhAGnXwCAjhAy4R8aOo5ImADCRjn9cgYGAGgfIRN+I2cqSJgAwuz8+fOcgQEA2kfIRCC4oE7CBKAWciYAQOMImQhQxF5QV9I1CROAiiLz9AsA0AtCJtok0ho6SrwkYQJQHSNKAACaRchEW0VOQ4cOTACaErEjSgAAGmdWuwAwAiV6tWvXzqgRTGnDkTABaND58+fbtWsnIpyiAAAaQchE0Bi1oUMHJgCNM/yVPgCAvhAyEUz2ho4YImrSgQlAR4x6pQ8AoDuETASfAaIm8RKAHhng9AsAMABCJkJFp20d4iUAvdPp6RcAYBiETISWjto6xEsARqKj0y8AwGAImQgHLbd17LP/Ey8BGI/j6Ve0dwYGABgSIRPho6m2DtkSQOSwn+i0ebEPAGAwhEyEm1NbR8Lb3CFbAohkThf7hMAJAAgBQiZU45o2JTTNHYfdky0B4LIzIYETABB0hEyoz1Nzx2EDP/bmugOCJQB44v0MzOkTABAAQia0xW0gdJs8/doDAKBVrudPT6dff87KAICIQ8iEDqibG202m9VqdV1usVjCXxhonNVqtdlsTgtNJpPZzMkWusRlO+hUxNbdVEPQCJPaBQC0rrCw8Kcuunfvrna5oEXTp093/d+Sk5OjdrkAILJEbN1NNQSN4KoG4JOHHnpowIAB9h8NfykUgZk+ffrIkSMdl9x7771qFQYAIlwE1t1UQ9AIQibgk7Fjx2ZkZKhdCmhdUlJSUlKS4xJqdwBQSwTW3VRD0AiGywIAAAAAgoaeTCA4jh8/vnfvXqeFffr0SUxMFJHKysrS0lJPa0WkoqLi448/dlybmpoaFxfnuOTQoUOHDx92PfSoUaN69Ohhs9neeust17VXX321dq7j8i0BADQlDBXTkSNHDhw44KkAJpPpqquuSktLY24eGAn/m4Hg+Prrr8vKynbu3Hns2DERSU5O7tu37/XXX6+sra6uLisrKyoqqqystK/t06eP/e3V1dVFRUXr1q1rbm5OSEhISkoaO3as0yH+/e9/Hzx4sK6uLi8vT0T69es3fPjw6667zmQyiYjVaq2urq6qqtq2bduXX35pMpmmTJnStWtX1/2oiG8JAKApYaiYvvnmm08//bS+vv71118XkWHDhmVkZHTp0kVETpw4UVNTU1hY+P3338+bN2/BggXh+dRAyJ1HJOH3HoCtW7eKyNatW33Z+M033xQRk8nkdq1Su1gsFk9vnzZt2oMPPuj9EJ988onyS/zss8/cbnDbbbeJyO233+5LgVURUd+SiLRaWgBAcPlVd58PS8X01VdfKRXTF1984bTq7NmzI0aMEJHZs2f7WGDfUQ3R+lUF92QCwdShQwfx/HguZW1LS4vb9zY2Np4+ffqll17yfog9e/aISOfOnfv16+d2g8LCQhEZPXq0PwUPK74lAICmhKFiKikpEZG4uLjevXu77n/27NkismLFilOnTvlffEBzCJlAMCn1kIg0NTW5rj1y5Ijyorm52XXt0qVL58+fr4zq9KK4uFhEPA3vPHTokHLoUaNG+VzqcONbAgBoShgqpoKCAhFxmvrVzn5Dpn0kDqBrhEwgmK666irlhest/lVVVbm5ucrrEydOOK09fvz4sWPHUlJSWj3E9u3bRSQ5OdntWuVCadeuXe33k2gQ3xIAQFPCUDEpVz89jaCx51h7SQBdI2QCwdS/f3/lhevFznnz5r3xxhvKa/uNGXaPPfbYs88+2+r+KyoqGhoaRES5ecPV7t27RfOjQPmWAACaEuqKqaqqqqqqSkRuueUWtxso/Zw9e/a0z1sL6BohEwimqKgo5YVTLfXhhx/GxcXdfPPNbtcWFxf37dvX9SYNV0oXXKu3GnrqwdMIviUAgKaEp2Lq1q2b241LS0vLyspE5JVXXgmo+IDm8AgTIJhMJpPZbLZarU43dSxatGjdunVRUVEmk8lmszU2NjqufeKJJzZt2uTL/nfu3Ckio0ePdjv9gF5uNeRbAgBoSqgrJqWj0u2o2mPHjt1xxx3R0dH5+fkMsYFhEDKBIOvQoUNDQ0Ntba19SW5ublZW1pVXXikiUVFRTU1NjpdC//73v0+ePDk6OtqXnRcVFYlIYWFh9+7dXdd+9913opNbDfmWAACaEtKKSbkhMyYmRqmhRESJrAUFBXl5edOmTVu8eHFsbGwwPw+gKkImEGR9+/bdv3+/8tRmEWlqanr77bftlUrXrl2bmpoOHjyo/NjY2Lh27Vql561VFRUVZ86cEZF9+/YlJCS4bjBp0qT169d7ug567NixHTt2PPDAAwGsDTp9fUu1tbV/+9vfKisrr7vuul/96lf2W3cAAIYRuorJfkNmdXX1mjVr7MuvueaatLS0559/3impeqmRw1xZAwHjnkwgyLp16yYi33//vfLjU089NX/+fPvaG264QUTOnj2r/Lh06dInnnjCxz0rd3RER0e7zU5ycTSOU3yqqqr605/+9Otf/3rQoEHKLR++rw0dHX1LR44cmTdv3r333rt69epevXoNGDDgr3/9q4+FAQDoRagrJovFsm7dulUOnnrqKWWgrLKZl5pIrcoaCBghEwgyZfIApR46fvz4559/7ngPhvIkLmW8zfHjx48fPz5y5Egf96xcMU1PT3e79sCBA8punWaui4qKyszMfOONN6644gq3pfWyNnR09C3Nnj176dKlcXFxJpNpxowZDz744OzZsysqKnwsDwBAF0JXMdlvyPT+LE0vNZFalTUQMEImEGQ/+9nP5GI9NH/+/GeeecZxrVI9KHXYo48+umzZMt/3rAza8fQc53379om7Ww1jYmL69+/vqWLzvjZ0dPQt7dmz57//+7/tP44ZM0ZENm7c6HuRAADaF7qKSbkhc/jw4d4381ITqVVZAwHjnkwgyDp27CgilZWVxcXF8fHxTpOVK5dC6+rqdu/ePWDAgB49evi42yNHjii3Gnp69qP3pzxrjY6+pbvvvtu1XrfZbL7vAQCgfSGqmOw3ZPre8wkYACETCLJBgwaJyOnTp5csWbJ+/XqntX379hWRzz//fMmSJT7Oe67Ys2ePeL3VUHn2o15Cpo6+pVWrVjn+eODAARHJyMjwfQ8IrsrKytLSUqeFFovlzjvvVF5XVFR8/PHHjmtNJtPUqVODW4zjx4/v3bvXaWGfPn2UB6m7LaR9rdtCpqamxsXFOS45dOjQ4cOHXQ89atSoHj162Gy2t956y3Xt1VdfreL/Tz2WGVCEqGJSbsg0m81Ot2kAxkbIxAU0mIJFuamjoaEhOztbmffckTKvwJkzZ6ZOnerjvOcK77ca2p/9qJc6TKffUkNDw8qVK+++++7BgwcHtge03ddff11eXl5dXW1v502cONHxwsHp06dzc3OVKw7x8fFJSUn9+vULRTHKysp27tx57NgxEUlOTu7bt699GHZ1dXVZWVlRUZEyU6Wytk+fPva3V1dXFxUVrVu3rrm5OSEhISkpaezYsU6H+Pe//33w4MG6urq8vDwR6dev3/Dhw6+77jqla91qtVZXV1dVVW3btu3LL780mUxTpkzp2rWr637CSY9lNoww1ONHjhxRLrS5ZTKZrrrqqrS0NLNZl83LEFVMPt6QCRiMLs8CCAUaTMGi1D39+/efNWuW61plvE1CQsL06dN932dNTc327dvl4ux2Tmw225tvvikiZrPZ8ZeiZTr9lu6///7hw4e/8sorgb0dQXHzzTfffPPNIpKSkrJr1674+PgNGzY4bpCWltahQ4fk5OTnn3/e7X+woEhMTExMTHzrrbemTZtmMpmcnmSQlJSUlJT0xhtv3H333RaLxfU5B+np6crVkI4dO3qarzglJSUlJeXw4cPKOXPdunWOadlisShTX06aNOnLL7+87bbb3F6nCzM9ltkwwlCPf/PNN59++ml9ff3rr78uIsOGDcvIyOjSpYuInDhxoqamprCw8Pvvv583b96CBQvC86mDKBQVU11dnfLnHx8fH6RiAjpxHpGk1d+70go3mUxu1yqVisVi8fT2adOmPfjgg97L8Mknnyhl+Oyzz9xucNttt4nI7bff7n0/YbN161YR2bp1q4/bf/DBByKyY8cOL3srKSnxcW+xsbFOl4TNZnNsbKyy9sUXX7RYLI5rTSaTxWJZu3at6666du06bdo0Twfyvjbo9Pgt/fGPf7znnnt8LJKdiLT6R4HAvP/++8ov9H//938dl//73//+xS9+4el/V3C99957Shl++OEH17Vr1671csr97rvvUlNTf/zxR++HePHFF0Wkc+fOnjZQ2r4rV670q+QhpccyG0YY6vGvvvpK+Y/9xRdfOK06e/asckv87Nmz/S15cPlbd58PdsXUu3dv14rpyiuv9OW9XmqiACprqiFSjyrouMdllIrfZrNZrVZPa1taWty+t7Gx8fTp0y+99JL3Qyhj2Dp37uxpAJu+7i10NWDAgKefftpx3nOntcuWLfN9uObp06edGq8//PDD6dOnlbVz5sw5d+6c49off/zx3Llzd9xxR3A+TMjo7lvKzc1tbm5W7s+02Ww8wkQLMjMzlbF8y5cvty9samoaN27cokWLPP3vCi7lrKgc13XtkSNHlBfKfJVOli5dOn/+/FZH0CmzVXka02EfBD5q1CifSx1yeiyzYYShHlduMoyLi3OaGkfZ/+zZs0VkxYoVp06d8r/4agpuxfTFF1+4Vkz/+c9/gldeQNMImbgMDaa2i4uL+8Mf/uBpbXx8/KOPPhrO8miTvr6lgoKC+vr6JUuWKD8ePXqUkKkFJpNp5syZIlJYWHjy5EkRsVqt2dnZDz74YFZWVnjKcNVVVykvXG9Uq6qqys3NVV6fOHHCae3x48ePHTvmSxJWBoEnJye7Xas0912fyqMuPZbZMMJQjys3GXp6UpS9+84+cEkv9FUxARpHyMRlaDABTkpLS3Nyck6cODFr1qwHHnhgxowZDz744DXXXKN2uSAict999ykN4r///e8iMnny5IyMjLZMJPvcc88dP37c9+379++vvHBtss+bN++NN95QXtuHF9o99thjzz77bKv7r6ioaGhoEM9P5dm9e7dobOiHHstsJGGox70/C8qeY+0lARCBmPgHl2m1wZSamioiX331lVMINHCDSe8qKioWL15cWVlZX1+fl5dXXV19zTXXrFmzxpe1kcPL95CVlVVfX69MlWGnl1l8Da9bt26//OUv165d+8orr/zrX/8aMGBATk5OW3Y4d+5ci8Xi+3RBynSU4nLO/PDDD+Pi4pQJilzXFhcX9+3b13WooSvlulur9xd4umynCj2W2UhCXY/bn/ro6TSo9HP27NnTPm8tfOSlJqKyhu4QMnEZGkzG079//3Xr1gW2NnJ4+R6++eabMBcGfpk9e/batWvr6+ubmpqeeOKJNu4tOjq6a9euvm9vMpnMZrPVanUamrho0aJ169ZFRUWZTCabzdbY2Oi49oknnvDxOXvKvJSjR492exOdNu8v0GOZjSQ89Xi3bt3cblxaWlpWViYizMIdAC81EZU1dIeQicvQYAKgL7fccsv1119/9OhR1+F/vjh69OigQYOefPJJ5W6rG264QXmMwbFjx7Kzs0eOHPm3v/3N+x46dOjQ0NBQW1trX5Kbm5uVlaU8Zy8qKqqpqcmxQf/3v/998uTJPj5nr6ioSEQKCwu7d+/uuva7774TP+8veOedd+655x4fN7bLy8vz/TbXoJcZfgl1PW5/6qPrqmPHjt1xxx3R0dH5+fmMSAIiHCETzmgwAdCRNWvWKBM2lpeXHzhwYPDgwX69/brrrps5c+b8+fO3bt2an58fExMTHR2dm5s7Z86cPn36/PKXv2x1D3379t2/f799QHVTU9Pbb7+tnOtEpGvXrk1NTQcPHlR+bGxsXLt2retjM92qqKg4c+aMiOzbty8hIcF1g0mTJq1fv96v1vxtt9127bXX+r69wt791apQlBn+Cmk9rtyQGRMTY/9PrkTWgoKCvLy8adOmLV68ODY21vtOqLsBwyNkwhkNJgB6UVhY+PLLLx89ejQuLq6xsfGVV17xN2R26NDhhRdeWLBgwUsvvTRy5EiLxfLcc899++23mzdv9vEhKN26dROR77//Xvnxqaeemj9/vn3tDTfcUFVVdfbsWeXHpUuX+j6mVxmXGB0d7faEKRf7lPw6Z5rN5pEjR/q+vb9CUWb4K3T1uP2GzOrqascbAq+55pq0tLTnn3/ex6RK3Q0YHiETzmgwAdCF0tLSJUuWbN269corr5w5c+Zf/vKX119/fdmyZUp3jV9iYmLmzp1rtVqffvppm8326quv+t6cVW6BU86Kx48f//zzz5988kn7WuV5Ekqv0fHjx48fP+77KUtp96enp7tde+DAAWW3mpqGSo9lNp5Q1+MWi2XdunWtPunEC+puwPB4hAmcuTaYHC/nR1qDCYA2HTp0aNasWRs2bOjSpYuIPPDAAyJitVpfffVVf3dVV1e3cOHCuLi4c+fO3XbbbY8//vjUqVNvueUWe8+Pdz/72c/k4llx/vz5zzzzjOPaK664Qi6eUR999NFly5b5XjClAJ6eRrhv3z7R3s2Neiyz8YSuHrffkNmWhAkgEtCTCWd+NZief/553/dM4wNAUJw8eXLatGkFBQVKj42I9O7dOzk5edeuXStXrvTrgelHjhy58cYbO3Xq9P7776ekpIwbN+6qq646fPjwzJkzx4wZM3v27Jdeesn7Hjp27CgilZWVxcXF8fHxTlNuKg36urq63bt3DxgwoEePHr4XTLm/wNMDn7w/q9CT0tLSF1980a+3iMhDDz3kS9duiMoMf4WuHld+g8OHDw9iaQEYEiETzmgwAdCympqazMzM/Pz8+Ph4x+WPPPLIrl27Kisri4qKfD+T9O7d+/e///2sWbNiYmJE5LPPPmtubo6Ojn777bczMjJ8uW1s0KBBInL69OklS5asX7/eaW3fvn1F5PPPP1+yZImPs3cq9uzZI17vL1Ae+OTvOTM+Pj4jI8Ovtyjv8mWzEJUZ/gpRPW6/IbPtI12puwHDI2TCGQ0mAJp18uTJ5OTk1atX2584b5eZmRkfH19VVfXcc8/5fiaxWCyON6TV1tY2NDQor6dOnerLHpShiQ0NDdnZ2a63gyp9rWfOnJk6daqPc6IovN9fYH/gk7/3F8THx//617/26y2+C1GZ4a8Q1ePKDZlms7ntv0HqbsDwCJlwRoMpMMePH9+7d6/Twj59+iQmJopIZWVlaWmpp7UiUlFR8fHHHzuuTU1NjYuLc1xy6NChw4cPux561KhRPXr0sNlsb731luvaq6++OoC6PET4lhCwU6dOvfnmm08//bTVah04cKDrBg0NDT//+c+rqqoKCwt3797taWS+d1FRUVar1a+3KGfC/v37z5o1y3Wt0muUkJAwffp03/dZU1Ozfft2Ebnhhhtc19pstjfffFNEzGZznz59/Cpt6OixzEYVono8iDdkaqfulrBUTEeOHDlw4ICnAphMpquuuiotLc1splkO4+B/M5zRYArM119/XVZWtnPnzmPHjolIcnJy37597feXVldXl5WVFRUVKXPKK2sdP2x1dXVRUdG6deuam5sTEhKSkpLGjh3rdIh///vfBw8erKury8vLE5F+/foNHz78uuuuU+p7q9VaXV1dVVW1bdu2L7/80mQyTZkypWvXrq77URHfEgJQWlo6atQox+zXqVOnPXv2OF6TmjFjxuuvv26z2UTEZrMlJycrv1y31xS8qK+v97cBbbFYROSFF15wu1Zp7v/1r3/1cW/du3evq6uzf9jFixf/8Y9/jImJOX36tIi89NJLc+fObWlpUdZardb27dubzeY333zzjjvu8KvYQaTHMhtbKOrxuro65WKx8boTw1AxffPNN59++ml9ff3rr78uIsOGDcvIyFAmLTtx4kRNTU1hYeH3338/b968BQsWhOdTAyF3HpHEl9/7Bx98ICI7duxwu3br1q0iUlJS4uMRY2Njna7Mmc3m2NhYZe2LL76otM/sTCaTxWJZu3atX58rpJSPvHXrVl82VtKyyWRyu1apXSwWi6e3T5s27cEHH/R+iE8++UT5rj777DO3G9x2220icvvtt/tSYFVE1LckIq2WFrpWXV399NNPe1r71VdfLVu2LJzlAYJbj/fu3du1Hr/yyiuDV96Q8KvuPh+Wiumrr75SvsAvvvjCadXZs2eVGStmz57tY4F9RzVE6lEFM1DD2YABA55++mlPTyEfMGDAsmXLfB/Uevr06R9++MHx/9wPP/ygXN4WkTlz5pw7d85x7Y8//nju3Dn9Xt5WrhDbbDa3I+6UtfYr+k4aGxtPnz7d6lSWyt2tnTt37tevn9sNtD+1Bt8SjCQuLu4Pf/iDp7Xx8fF+zXYLtF1w6/EvvvjCtR7/z3/+8//bu9vYKsv7D+CXtZaK9W8GAQZNNS6OJqN0ErZExwKyzW5hCwm4wURg82Fkk7q9QJ10ZiI43DKjKBqnbBVhPElChgbmHDhkWlwkc5u1uFobsvIwVslYVwtKV/8v7njSnD4dytWHUz6fV+ec6/Tu1YuHX7/nvn/3FW++g0I/FKakqbWwsDDtVkzJ8cvLy0MIjz766OHDh898+jDoCJmk8wvT2UjqUAghaTFNU1NTkzxI7iyfZsWKFRUVFT1eqpfchreryztT3a3Tpk3LeNb9zSoB9B11vBf6oTAlTa1dtYunThenrsSBrCZkQkwjRoxIHnRs8W9oaKisrEweHzx4MG20vr6+tra2qw+e20u6W6dPn97paPJB6SDfbtQqATCo9ENh6n6rtlSOTc0EspqQCTGltlXo+GHn0qVL165dmzxONWak3HHHHT//+c97PH51dXWyv0JX243u2bMnDPqrQK0SAINKXxem1C6jXV2onJznvPzyy1P3rYWsJmRCTMmdJEOHKvXqq68WFhamdpFOG927d29xcXHHJo2OklNwPbYadnUGb5CwSgAMKv1TmMaMGdPpm6uqqvbt2xdCePLJJ3s1fRh0bGECMeXk5OTm5ra2tqY1ddxzzz1bt27Nz8/Pyclpa2trbm5uP7ps2bIMd8RO7iD/pS99qdPbD2RLq6FVAmBQ6evClNpltONQbW3tnDlzCgoKNm3a5BIbhgwhEyIbPnx4U1PTsWPHUq9UVlbOnDkz2RE7Pz+/paWl/UehTzzxxNy5czPcEXvXrl0hhOeff37cuHEdR//73/+GLGk1tEoADCp9WpiShsxRo0YlFSqEkETWnTt3btiwYcGCBffee+/YsWNj/jwwoIRMiKy4uPi1115Ldm0OIbS0tGzcuDFVVEaOHNnS0vL6668nT5ubm7ds2ZKceetRdXX1iRMnQgivvPJKaWlpxzdcd91127Zt6/g56LFjxx5//PG6urpLL7103rx5qc6T9mpra3fv3v29730vs5/ybGXXKmWygABktb4rTKmGzEOHDm3evDn1+ujRo8vKylatWpVKqj2Wm34u1tBrejIhsjFjxoQQTp48mTxduXJlRUVFanTChAkhhPfeey95umLFimXLlmV45KSjo6CgoNPsFD66GictPtXU1CxduvQ73/nOunXrPvGJT0ycOPGxxx5LjTY0NPz0pz9duHDhpEmTkoaQ/pFFq9T9AgIwNPR1YcrLy9u6desv21m5cmVyoWzytm7KzUAVa+g1IRMiS24ekNSh+vr6AwcOtO/BSHbiSq63qa+vr6+vnzp1aoZHTj4x/cpXvtLp6P79+5PDpt25rry8fMWKFYWFhTk5Obfccsutt95aXl5eXV2dmu3Xvva1tWvXXnjhhWf4g56VLFql7hcQgKGh7wpTqiGz+700uyk3A1WsodeETIjsoosuCh/VoYqKip/97GftR5PykNSwJUuWPPDAA5kfOblop6t9nF955ZXQWavhSy+99OlPfzr19Nprrw0h/OY3v0mejho1qqSkpMctpKPLolXqfgEBGBr6rjAlDZlTpkzp/m3dlJuBKtbQa3oyIbKLL744hFBXV7d3796ioqK0m5UnH4U2Njbu2bNn4sSJl112WYaHrampSVoNu9r7satdnm+88caOZamtrS3D79tHsmiVBucCAhBXHxWmVENmj2c+lRuGEiETIps0aVII4ciRI8uXL9+2bVvaaHFxcQjhwIEDy5cvz/C+54mXXnopdNtqmOz92DE+/fKXv2z/dP/+/SGEGTNmZP6t+0IWrdLgXEAA4uqjwpQ0ZObm5qa1aXSk3DCUCJkQWdLU0dTUNGvWrOS+5+0l9xU4ceLE/PnzM7zveaL7VsPU3o/d17CmpqZf/OIXN95442c+85nMv3VfyNJVGjwLeK5Zt25dxxenTZuWdjLh6NGjv//979u/Mn78+KuuuiruZOrr619++eW0F1PfqK6urqqqqqvREEJ1dfWf//zn9qNf/OIXCwsLU09ramqSXy47lZOTM2LEiLKystzcga/gWTRV6FEfFaYMGzLTKDdkO//vQ2RJ7SkpKVm8eHHH0eR6m9LS0ptuuinzYx49evSFF14IH93dLk1bW9v69etDCLm5uePHj+/mON/97nenTJny5JNPZv6t+0iWrtLgWcDxYt3aAAANLElEQVRzSmtra21t7T//+c9nn322sbExhFBWVlZSUjJt2rS0d548efLFF198+umnQwilpaWf/exn0y54i+Jf//rXvn37Xnzxxdra2hDC9OnTi4uLU12+hw4d2rdv365du5KNEJLR9n/lDh06tGvXrq1bt546daq0tPSaa6758pe/3P7477777htvvHH8+PGnnnoqhHD11VfPmDHjYx/7WAjh4MGDR48eff7550+ePLl06dK77747+k93RrJoqtCjvihMjY2NyaefRUVFZzQZ5Yas9yHnEn/uvbBjx44Qwo4dOzJ8/29/+9sQwu7du7s52h//+McMjzZ27Ni0kwC5ubljx45NRh955JG8vLz2ozk5OXl5eVu2bOl4qJ/85Cc333xzV99o5MiRCxYsyHBWZy8bV6n7BexKCOHWW28906+iU0mSCSH84Q9/6Oo977zzTn5+/tNPP93Xk0k+s8jJyel0NJlqXl5eV1++YMGC7v9i/OMf/0h+2Lfffjtt6L333ku6jsvLy3sx8+iyaKqcO860dn8YuzBdccUVHQvT//3f/2Xytd2Um14Ua2XIb78Dwl2qILKJEyfef//97e97njb6wAMP9NiYkXLkyJHTp0+3/0d7+vTpI0eOJKO33Xbb+++/3370f//73/vvvz9nzpy041RWVp46dSrp92hraxvwHTiybpUG2wKem+bNm5f80tbp1bMhhA8++ODb3/729u3bFy5c2NeTSU5rtLW1tba2djX6wQcfdPq1zc3NR44cWb16dTfHT/q4CgsLO56MHT58eHl5eQjh0UcfPXz4cK+mH1MWTRW6Ebcwvf322x0L03/+858ev1C5YWgQMiGywsLCu+66q6vRoqKiJUuW9Od8Qgg7d+48fvz48uXLk6dvvfXWgBet7FqlQbiA56a8vLzrr78+hLBp06ZO89v1119/yy23lJWV9cNkkhgZQkgafdPU1NQkD5LtENKsWLGioqKi+watpI+rq814UmdI/vrXv2Y64z6TRVOFbgyGwqTcMGToyYQhrqqq6gc/+EFZWdnixYvb2tpOnz5dV1f34x//eKDnNbh0s0oWcFBZuHDh+vXrT506tXnz5rTTlUuWLJkwYUI/nMNMjBgxInmwf//+tFMfDQ0NlZWVyeODBw+mbcpaX19fW1ubtgVfR11tt5NIhdjUNAZQFk0VBjPlhiEl9vW3DGr+3HuhF30dg8rIkSM7/sNPXT76xhtvfP3rX7/yyitDCDk5OdOnT587d+7ATnhAdLNK3S9gj8I53wwTXXKPx7KysvYvrlmz5mz+6j744IPvvPPOGX1Jsid7p/853HDDDbt3705Gf/e736WNzp49u2PvYppuuhwTV199dQjh8ssvP6M594UsmirnlGys3d2Um7Mp1sqQ334HhDOZMMS9++673YyWlJRs3bq13yYzaHWzSt0vIP3vhhtuePDBB3ft2nXs2LEkcL7wwgvr1q1LbuHYO7fffnteXl6nt5TsSrLbQehwQeyrr75aWFj4uc99rtPRvXv3FhcX93jP26TLccyYMZ2+s6qqat++fSGEwXDnySyaKgxy3ZQbxZqsoycTgGzyrW99K4TQ1tb261//OoTwl7/8ZenSpdu3bz+bzRgLCgo6PYfQjZycnOQ7pvVk3nPPPT/60Y/y8/OTlsvm5ub2o8uWLauoqOjx4KmN9ToO1dbWzpkzp6Cg4LnnnuvqCtX+lEVTBaDfOJMJQDYpLS0tKSmprq5eu3btvHnzFixYsHPnzmRvxsy99dZbkyZNuu+++5I7eUyYMCHZIq+2tnbWrFlTp059/PHHezzI8OHDm5qajh07lnqlsrJy5syZyTbu+fn5LS0t7c9kPvHEE3Pnzs1kG/eky3HUqFG7du1KXmlra2tubt65c+eGDRsWLFhw7733jh07tsfjPPPMMzfffHOPb0uzYcOGmTNnZvjmWFMFYCgRMgHIMgsXLrzzzjurq6vLysrWrFlzpruchxAuvfTSRYsWVVRU7NixY9OmTaNGjSooKKisrLztttvGjx//jW98I5ODFBcXv/baa3V1dcnTlpaWjRs3prLWyJEjW1paXn/99eRpc3Pzli1bMrmmt6GhoaGhIYRw6NChzZs3p14fPXp0WVnZqlWrMompidmzZ3/84x/P8M0pqWt9+3OqAAwlQiZkZO7cucOGDUs9HTly5N///vcBnA+D0+LFi7ds2TLQsxj6vvnNb955550hhDvuuOOqq67qxRGGDx/+8MMP33333atXr546dWpeXt5DDz3073//+7nnnutql7yOko7QkydPJk9XrlzZ/lLYCRMmNDQ0pO4PtGLFimXLlmVy2KTLMS8vb+vWrd1vc9Kj3NzcqVOnns0RuhdxqtAXzsHarQwxSAiZ0INx48bNnTs37UUfz9OpT33qUx17zyZPnjwgkxnC3nzzzRBCbm7u/Pnzz+Y4o0aNuv3221tbW++///62trY1a9ZkfhIvfHTvnyRG1tfXHzhw4L777kuNJhtpJpfL1tfX19fXZ5j3Ul2Ogz+2ZdFUOdecs7VbGWKQEDKhB1deeWX7y8CgG4sXLz6jO5TSOy+//HIIYdq0aWeTbRobG1evXv3QQw8tWrRo9uzZ11133fz588eNG7ds2bIM71Jz0UUXhY9iZEVFRdrWlxdeeGH4KIIuWbJk1apVGU4s6XKcMmXKmfw0AyOLpsq55pyt3coQg4SQCUCWqaqqCiGczYWgNTU1kydPvuSSS7Zv3/6FL3zhq1/96ogRI/72t78tWrTo2muvLS8vX716dY8Hufjii0MIdXV1e/fuLSoqStvDIzmT2djYuGfPnokTJ1522WWZTCzV5RjlMteqqqpHHnnkTL/q+9//fiZndONOFYChRMgEIJu0trYmrYCf//zne32QK6644oc//OHixYtHjRoVQnjzzTdPnTpVUFCwcePGGTNmZHiznEmTJoUQjhw5snz58m3btqWNFhcXhxAOHDiwfPnyZ599NsOJJT9abm7u2fx0KUVFRTNmzOjFV2XytrhTBWAoETIByCZ79uxpbW3Nzc295ppren2QvLy89rfhOXbsWFNTU/I48z7PpCezqalp1qxZybYl7SW3BTpx4sT8+fMz7wSL2+VYVFS0cOHCsz9OpzRkAtAVhQGArNHW1rZx48YQwuTJkyNmm/z8/NbW1jP9qiQ6lpSUdNoBlVwuW1paetNNN2V4wMbGxmSPk15sytLPsmiqAPQ/IROALFBRUTFs2LDzzz//qaeeCiH86U9/Ov/884cNG5bce+YsHT9+PPMomJKXlxdCePjhhzsdTc5zPvbYY5kc6pOf/OQFF1wwevToo0ePhhB+9atfXXDBBZdccsmZTqkfZNFUARgo53344YcDPQf6z3nnnZc88OcOcJYOHz68fv36u+66q9PRhoaGZ555ZsmSJf08KwDa89vvgBAyzy3+mQEAcO7w2++AcLksAAAA0QiZAAAARCNkAgAAEI2QCQAAQDRCJgAAANEImQAAAEQjZAIAABCNkAkAAEA0QiYAAADRCJkAAABEI2QCAAAQjZAJAABANEImAAAA0QiZAAAARCNkAgAAEI2QCQAAQDRCJgAAANEImQAAAEQjZAIAABCNkAkAAEA0QiYAAADRCJkAAABEI2QCAAAQjZAJAABANEImAAAA0eQO9AQYGOedd95ATwEAABiCnMk8t3z44YcDPQUAAGAoO0/qAAAAIBZnMgEAAIhGyAQAACAaIRMAAIBohEwAAACiETIBAACIRsgEAAAgGiETAACAaIRMAAAAohEyAQAAiEbIBAAAIBohEwAAgGiETAAAAKIRMgEAAIhGyAQAACAaIRMAAIBohEwAAACiETIBAACIRsgEAAAgGiETAACAaIRMAAAAohEyAQAAiEbIBAAAIBohEwAAgGiETAAAAKIRMgEAAIhGyAQAACAaIRMAAIBohEwAAACiETIBAACIRsgEAAAgGiETAACAaIRMAAAAohEyAQAAiEbIBAAAIBohEwAAgGiETAAAAKIRMgEAAIhGyAQAACAaIRMAAIBohEwAAACiETIBAACIRsgEAAAgGiETAACAaIRMAAAAohEyAQAAiEbIBAAAIBohEwAAgGiETAAAAKIRMgEAAIhGyAQAACAaIRMAAIBohEwAAACiETIBAACIRsgEAAAgGiETAACAaIRMAAAAohEyAQAAiEbIBAAAIBohEwAAgGiETAAAAKIRMgEAAIhGyAQAACAaIRMAAIBohEwAAACiETIBAACIRsgEAAAgGiETAACAaIRMAAAAohEyAQAAiEbIBAAAIBohEwAAgGiETAAAAKIRMgEAAIhGyAQAACAaIRMAAIBohEwAAACiETIBAACIRsgEAAAgGiETAACAaIRMAAAAohEyAQAAiEbIBAAAIBohEwAAgGiETAAAAKIRMgEAAIhGyAQAACAaIRMAAIBohEwAAACiETIBAACIRsgEAAAgGiETAACAaIRMAAAAohEyAQAAiOb/ATR5Wtu1QDvSAAAAAElFTkSuQmCC\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"rela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Nets: Activation functions","description":"Return values of selected Activation function type for value,vector, and matrices.\r\ny=Activation(x,id); where id is 1:4 for ReLU, sigmoid, hyperbolic_tan, Softmax\r\nReLU: Rectified Linear Unit, clips negatives  max(0,x)   Trains faster than sigmoid\r\nSigmoid: Exponential normalization [0:1]      \r\nHyperTan: Normalization[-1:1]   tanh(x)\r\nSoftmax: Normalizes output sum to 1, individual values [0:1]      Used on Output node\r\n\r\nWorking though a series of Neural Net challenges from Perceptron, Hidden Layers, Back Propogation, ..., to the Convolutional Neural Net/Training for Handwritten Digits from Mnist. \r\nMight take a day or two  to completely cover Neural Nets in a Matlab centric fashion. \r\nEssentially Out=Softmax(ReLU(X*W)*WP)","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 312px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 156px; transform-origin: 407px 156px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 252.5px 8px; transform-origin: 252.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eReturn values of selected Activation function type for value,vector, and matrices.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 240.5px 8px; transform-origin: 240.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ey=Activation(x,id); where id is 1:4 for ReLU, sigmoid, hyperbolic_tan, Softmax\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 253.5px 8px; transform-origin: 253.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eReLU: Rectified Linear Unit, clips negatives  max(0,x)   Trains faster than sigmoid\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137px 8px; transform-origin: 137px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSigmoid: Exponential normalization [0:1]      \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 72.5px; height: 19.5px;\" width=\"72.5\" height=\"19.5\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 120.5px 8px; transform-origin: 120.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eHyperTan: Normalization[-1:1]   tanh(x)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 192px 8px; transform-origin: 192px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSoftmax: Normalizes output sum to 1, individual values [0:1]   \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 43px; height: 19.5px;\" width=\"43\" height=\"19.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 73.5px 8px; transform-origin: 73.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e   Used on Output node\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 352.5px 8px; transform-origin: 352.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWorking though a series of Neural Net challenges from Perceptron, Hidden Layers, Back Propogation, ..., to the Convolutional Neural Net/Training for Handwritten Digits from Mnist. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 265px 8px; transform-origin: 265px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eMight take a day or two  to completely cover Neural Nets in a Matlab centric fashion. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 130px 8px; transform-origin: 130px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eEssentially Out=Softmax(ReLU(X*W)*WP)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = Activation(x,id)\r\n%id/function 1/ReLU 2/Sigmoid 3/Hyperbolictan 4/Softmax\r\n%x may be a point, vector or matrix\r\n  y = x;\r\nend","test_suite":"%%\r\nvalid=1;\r\nx = 2;\r\ny = Activation(x,1);\r\nif y~=max(0,x),valid=0;end\r\ny = Activation(x,2);\r\nif y~=1./(1+exp(-x)),valid=0;end\r\ny = Activation(x,3);\r\nif y~=tanh(x),valid=0;end\r\ny = Activation(x,4);\r\nif y~=1,valid=0;end\r\nassert(valid)\r\n%%\r\nvalid=1;\r\nx = [-1 0 1 2];\r\ny = Activation(x,1);\r\nif y~=max(0,x),valid=0;end\r\ny = Activation(x,2);\r\nif y~=1./(1+exp(-x)),valid=0;end\r\ny = Activation(x,3);\r\nif y~=tanh(x),valid=0;end\r\ny = Activation(x,4);\r\nif y~=exp(x)./sum(exp(x)),valid=0;end\r\nassert(valid)\r\n%%\r\nvalid=1;\r\nx = [-1 0 1 2;.5 .25 -2 5];\r\ny = Activation(x,1);\r\nif y~=max(0,x),valid=0;end\r\ny = Activation(x,2);\r\nif y~=1./(1+exp(-x)),valid=0;end\r\ny = Activation(x,3);\r\nif y~=tanh(x),valid=0;end\r\ny = Activation(x,4);\r\nif y~=exp(x)./sum(sum(exp(x))),valid=0;end\r\nassert(valid)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":3097,"edited_by":3097,"edited_at":"2023-08-19T14:59:54.000Z","deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-08-19T14:02:05.000Z","updated_at":"2026-03-04T20:22:48.000Z","published_at":"2023-08-19T14:59:56.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eReturn values of selected Activation function type for value,vector, and matrices.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ey=Activation(x,id); where id is 1:4 for ReLU, sigmoid, hyperbolic_tan, Softmax\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eReLU: Rectified Linear Unit, clips negatives  max(0,x)   Trains faster than sigmoid\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSigmoid: Exponential normalization [0:1]      \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e1/(1+e^{-x})\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHyperTan: Normalization[-1:1]   tanh(x)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSoftmax: Normalizes output sum to 1, individual values [0:1]   \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ee^x/\\\\Sigma e^x\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e   Used on Output node\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWorking though a series of Neural Net challenges from Perceptron, Hidden Layers, Back Propogation, ..., to the Convolutional Neural Net/Training for Handwritten Digits from Mnist. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMight take a day or two  to completely cover Neural Nets in a Matlab centric fashion. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEssentially Out=Softmax(ReLU(X*W)*WP)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":58907,"title":"Neural Net: Calculate Bias Quad Output Perceptron (Counter, Mux, Subtraction)","description":"This challenge is to calculate the Neural Net Bias Quad Output Perceptron vector, PY,  and Pclass, given X, WH, and WP using ReLU on the hidden layer and Softmax on the output layer. Test Cases will be all two input conditions for functions  Counter, 2 bit Mux, and Subtraction 3-X_value.  With four output classes ReLU performed better than Sigmoid in back propagation in ability to solve from random and prediction deltas between classes.\r\nX wll be a 4x3 matrix creating a Quad output  PY 4x4 matrix. PY(:,N) is Expectation of class N for each case. PY values shall all be \u003c0.25 or \u003e.75 The Pclass must match the Logical Function expectation where Pclass is the column of PY with max value per row, col1=Class1,  col2=Class2, col3=Class3,  col2=Class4.\r\n[PY,Pclass]=Calc_Quad_Bias_Perceptron(X,WH,WP)\r\n \r\nThe function template includes the code to create this figure using Matlab Latex","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 754.5px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 377.25px; transform-origin: 407px 377.25px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 380px 8px; transform-origin: 380px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to calculate the Neural Net Bias Quad Output Perceptron vector, PY,  and Pclass, given X, WH, and WP using ReLU on the hidden layer and Softmax on the output layer. Test Cases will be all two input conditions for functions  Counter, 2 bit Mux, and Subtraction 3-X_value.  With four output classes ReLU performed better than Sigmoid in back propagation in ability to solve from random and prediction deltas between classes.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 371.5px 8px; transform-origin: 371.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eX wll be a 4x3 matrix creating a Quad output  PY 4x4 matrix. PY(:,N) is Expectation of class N for each case. PY values shall all be \u0026lt;0.25 or \u0026gt;.75 The Pclass must match the Logical Function expectation where Pclass is the column of PY with max value per row, col1=Class1,  col2=Class2, col3=Class3,  col2=Class4.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 165.5px 8px; 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\" data-image-state=\"image-loaded\" width=\"875\" height=\"524\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 250px 8px; transform-origin: 250px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe function template includes the code to create this figure using Matlab Latex\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function[PY,Pclass]=Calc_Quad_Bias_Perceptron(X,WH,WP)\r\n%ReLU applied to X*WH\r\n%  X 4x3\r\n% WH 3x3\r\n% WP 3x4\r\n%The PY value is four values for each case, [Class-1,Class-2,Class-3,Class-4 ]\r\n%Pclass is 1, 2, 3 or 4 based on determination col max per row. Prediction for each input case\r\n%Softmax applied to produce the output PY\r\n\r\n  PY=zeros(4,4); % Four rows since four cases in X; four columns due to four output classes\r\n  Pclass=ones(4,1); % Values 1 to 4\r\nend\r\n\r\n%The figure was generated using Matlab and Latex\r\n%{\r\nfunction perceptron_bias_quad_ReLU_Soft\r\n%Quad output classifier\r\n%Softmax y=exp(x)./sum(exp(x),2)\r\n\r\nf=figure(1);clf;\r\nf.Position=[1935 122 1459 873]; % Dual Monitor setting\r\n\r\nplot([0;160;160;0;0],[-5;-5;100;100;-5],'k','linewidth',2);hold on\r\nxticks([]);yticks([]); %Ticks/Labels off\r\n\r\naxis equal;\r\naxis tight\r\n\r\nr = 10;\r\nphi = 0:0.01:2*pi;\r\ncxA = r*cos(phi);\r\ncyA = r*sin(phi);\r\n\r\nr5 = 5;\r\nphi = 0:0.01:2*pi;\r\ncx5 = r5*cos(phi);\r\ncy5 = r5*sin(phi);\r\n\r\nrxA=[0 2*r 2*r   0 0]';rx5=[0 2*r5 2*r5   0 0]';\r\nryA=[0   0 2*r 2*r 0]';ry5=[0   0 2*r5 2*r5 0]';\r\n\r\n\r\ntext(1,97,'Bias Quad Out Perceptron','Fontsize',20);\r\n\r\npatch(cxA+10,cyA+80,'y');text(10-2,80,'X_{1}','Fontsize',20);\r\npatch(cxA+10,cyA+20,'y');text(10-2,20,'X_{2}','Fontsize',20);\r\n\r\npatch(cxA+80,cyA+80,'y');text(80-8,93,'H_{1}','Fontsize',20);text(80-8,80,'\\Sigma','Fontsize',30);\r\npatch(cxA+80,cyA+20,'y');text(80-8,33,'H_{2}','Fontsize',20);text(80-8,20,'\\Sigma','Fontsize',30);\r\npatch(cxA+135,cyA+80,'y');text(135-6,94,'P_{1}','Fontsize',20);text(135-8,80,'\\Sigma','Fontsize',30);\r\n\r\npatch(cx5+135,cy5+55,'y');text(135-8,60,'P_{2}','Fontsize',16);text(135-4,55,'\\Sigma','Fontsize',20);\r\npatch(cx5+135,cy5+43,'y');text(135-8,48,'P_{3}','Fontsize',16);text(135-4,43,'\\Sigma','Fontsize',20);\r\n\r\npatch(cxA+135,cyA+20,'y');text(135-6,34,'P_{N}','Fontsize',20);text(135-8,20,'\\Sigma','Fontsize',30);\r\n\r\npatch(rxA+80,ryA+70,'y');text(82,80,'ReLU','Fontsize',20);text(90,93,'Y_{1}','Fontsize',20);\r\npatch(rxA+80,ryA+10,'y');text(82,20,'ReLU','Fontsize',20);text(90,33,'Y_{2}','Fontsize',20);\r\npatch(rxA+135,ryA+70,'y');text(137,80,'Softmax','Fontsize',20);text(147,94,'PY_{1}','Fontsize',20);\r\n\r\npatch(rx5+135,ry5+50,'y');text(135.5,55,'Softmax','Fontsize',12);text(147,60,'PY_{2}','Fontsize',16);\r\npatch(rx5+135,ry5+38,'y');text(135.5,43,'Softmax','Fontsize',12);text(147,48,'PY_{3}','Fontsize',16);\r\n\r\npatch(rxA+135,ryA+10,'y');text(137,20,'Softmax','Fontsize',20);text(147,34,'PY_{N}','Fontsize',20);\r\n\r\nquiver(20,20,55,0,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,80,55,0,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,20,58.5,58.5,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,80,58.5,-58.5,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(58,93,14,-10,'k','LineWidth',3.5,'MaxHeadSize',.7); text(58-8,92-1,'bH_{31}','Fontsize',20);\r\nquiver(60,5,13,10,'k','LineWidth',3.5,'MaxHeadSize',.7); text(60-6,4,'bH_{32}','Fontsize',20);\r\n\r\nquiver(100,80,27.5,0,'k','LineWidth',3.5,'MaxHeadSize',.3); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(100,20,27.5,0,'k','LineWidth',3.5,'MaxHeadSize',.3); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(100,80,31,-58.5,'k','LineWidth',3.5,'MaxHeadSize',.2); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(100,20,31,58.5,'k','LineWidth',3.5,'MaxHeadSize',.2); % line arrowhead x,y,dx,dy,  VectorLine draw\r\n\r\nquiver(133,63,0,8,'k','LineWidth',3.5,'MaxHeadSize',.9); text(135-1,63+1,'bP_{31}','Fontsize',20);\r\nquiver(133,3,0,8,'k','LineWidth',3.5,'MaxHeadSize',.9); text(135-1,3+1,'bP_{3N}','Fontsize',20);\r\n\r\ntext(30,85,'WH_{11}','Fontsize',20); %Thicker Bold\r\ntext(20,65,'WH_{12}','Fontsize',20);\r\ntext(40,24,'WH_{22}','Fontsize',20);\r\ntext(20,38,'WH_{21}','Fontsize',20);\r\n\r\ntext(107,84,'WP_{11}','Fontsize',18);\r\ntext(107,24,'WP_{2N}','Fontsize',18);\r\ntext(114,30,'WP_{1N}','Fontsize',18);\r\ntext(114,70,'WP_{21}','Fontsize',18);\r\n\r\n\r\nstr='$$ X=\\left[\\matrix{ X_{1} \u0026 X_{2} \u0026 1} \\right]  $$';\r\ntext(3,55,str,'Interpreter','latex','Fontsize',16);\r\n\r\nstr='$$ X*WH=H  $$'; %valid\r\ntext(3,50,str,'Interpreter','latex','Fontsize',16);\r\n\r\nstr='$$ H=\\left[\\matrix{ H_{1} \u0026 H_{2} \u0026 1} \\right]  $$'; \r\ntext(75,67,str,'Interpreter','latex','Fontsize',16);\r\n\r\nstr='$$ ReLU(H)=Y  $$'; \r\ntext(75,62,str,'Interpreter','latex','Fontsize',16);\r\n\r\ntext(34,77,'ReLU(H)=max(0,H)','Fontsize',18,'Color','b');\r\ntext(34,71,'Softmax(P)=e^{P}/\\Sigmae^{P}','Fontsize',18,'Color','b');\r\n\r\nstr='$$ Y=\\left[\\matrix{ Y_{1} \u0026 Y_{2} \u0026 1} \\right]  $$'; \r\ntext(75,57,str,'Interpreter','latex','Fontsize',16);\r\n\r\nstr='$$ Y*WP=P  $$'; %valid\r\ntext(75,52,str,'Interpreter','latex','Fontsize',16);\r\n\r\nstr='$$ P=\\left[\\matrix{ P_{1} \u0026 P_{2} \u0026 P_{3} \u0026 P_{N} } \\right]  $$'; \r\ntext(75,47,str,'Interpreter','latex','Fontsize',16);\r\n\r\nstr='$$ Softmax(P)=PY  $$'; \r\ntext(75,43,str,'Interpreter','latex','Fontsize',16);\r\n\r\n\r\nstr='$$ PY=\\left[\\matrix{ PY_{1} \u0026 PY_{2} \u0026 PY_{3} \u0026 PY_{N}} \\right]  $$'; \r\ntext(63,39,str,'Interpreter','latex','Fontsize',16);\r\n\r\n\r\nstr='$$ WH=\\left[\\matrix{ WH_{11} \u0026 WH_{12} \u0026 0 \\cr WH_{21} \u0026 WH_{22} \u0026 0 \\cr bH_{31} \u0026 bH_{32} \u0026 1 } \\right]  $$'; %valid\r\ntext(1,3,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ WP=\\left[\\matrix{ WP_{11} \u0026 WP_{12} \u0026 WP_{13} \u0026 WP_{1N} \\cr WP_{21} \u0026 WP_{22} \u0026 WP_{23} \u0026 WP_{2N} \\cr bP_{31} \u0026 bP_{32} \u0026 bP_{33} \u0026 bP_{3N} } \\right]  $$'; %valid\r\ntext(66,3,str,'Interpreter','latex','Fontsize',18);\r\n\r\n\r\nend %perceptron_bias_quad_ReLU_Soft\r\n\r\n%}\r\n\r\n","test_suite":"%%\r\n% All of the WH/WP arrays were created from a matlab centric back propagation algorithm.\r\n% The back propagation algorithm is for a near future fun challenge.\r\n%Counter\r\n\r\nX= [0 0 1; %[x1 x2 1] four cases\r\n    0 1 1;\r\n    1 0 1;\r\n    1 1 1];\r\n\r\nWH= [1.11   0 0; %wh11 wh12 0\r\n     0.00   1 0; %wh21 wh22 0\r\n    -0.11   0 1];%bh31 bh32 1\r\n\r\nWP=[ 6.02 -1.47 -1.68  6.69; %wp11 wp12 wp13 wp14\r\n     6.00 -1.33  6.92 -1.68; %wp21 wp22\r\n     1.16  7.99  3.99  4.17];%bp31 bp32\r\n\r\n[PY,Pclass]=Calc_Quad_Bias_Perceptron(X,WH,WP);\r\n[PY Pclass Pclass-1]\r\n%Verify PY used softmax\r\nvalid=sum(abs(sum(PY,2)-1))\u003c1e-6;\r\nif ~isequal(ones(4,4), PY\u003e.85 +PY\u003c.15) %Hi and Low Thresholds met\r\n valid=0;\r\nend\r\nif ~isequal([1;2;3;0],Pclass-1)\r\n valid=0;\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\n%2 Bit Mux 00:0 01:1 10:2 11:3\r\n\r\nX= [0 0 1; %[x1 x2 1] four cases\r\n    0 1 1;\r\n    1 0 1;\r\n    1 1 1];\r\n\r\nWH= [0.00   1 0; %wh11 wh12 0\r\n     1.03   0 0; %wh21 wh22 0\r\n    -0.03   0 1];%bh31 bh32 1\r\n\r\nWP=[-1.67  6.60 -1.76  5.91; %wp11 wp12 wp13 wp14\r\n    -0.98 -1.38  7.20  6.33; %wp21 wp22\r\n     8.08  4.22  4.12  1.23];%bp31 bp32\r\n\r\n[PY,Pclass]=Calc_Quad_Bias_Perceptron(X,WH,WP);\r\n[PY Pclass Pclass-1]\r\n%Verify PY used softmax\r\nvalid=sum(abs(sum(PY,2)-1))\u003c1e-6;\r\nif ~isequal(ones(4,4), PY\u003e.85 +PY\u003c.15) %Hi and Low Thresholds met\r\n valid=0;\r\nend\r\nif ~isequal([0;1;2;3],Pclass-1)\r\n valid=0;\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\n%Subtractor  3-X  00:3 01:2 10:1 11:0\r\n\r\nX= [0 0 1; %[x1 x2 1] four cases\r\n    0 1 1;\r\n    1 0 1;\r\n    1 1 1];\r\n\r\nWH= [0.00   1 0; %wh11 wh12 0\r\n     1.06   0 0; %wh21 wh22 0\r\n    -0.06   0 1];%bh31 bh32 1\r\n\r\nWP=[ 5.96 -1.71  6.66 -1.61; %wp11 wp12 wp13 wp14\r\n     6.02  6.90 -1.68 -1.29; %wp21 wp22\r\n     1.32  4.20  4.30  8.17];%bp31 bp32\r\n\r\n[PY,Pclass]=Calc_Quad_Bias_Perceptron(X,WH,WP);\r\n[PY Pclass Pclass-1]\r\n%Verify PY used softmax\r\nvalid=sum(abs(sum(PY,2)-1))\u003c1e-6;\r\nif ~isequal(ones(4,4), PY\u003e.85 +PY\u003c.15) %Hi and Low Thresholds met\r\n valid=0;\r\nend\r\nif ~isequal([3;2;1;0],Pclass-1)\r\n valid=0;\r\nend\r\n\r\nassert(valid)\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2023-08-23T19:37:45.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-08-23T18:47:21.000Z","updated_at":"2023-08-23T19:37:46.000Z","published_at":"2023-08-23T19:37:46.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to calculate the Neural Net Bias Quad Output Perceptron vector, PY,  and Pclass, given X, WH, and WP using ReLU on the hidden layer and Softmax on the output layer. Test Cases will be all two input conditions for functions  Counter, 2 bit Mux, and Subtraction 3-X_value.  With four output classes ReLU performed better than Sigmoid in back propagation in ability to solve from random and prediction deltas between classes.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eX wll be a 4x3 matrix creating a Quad output  PY 4x4 matrix. PY(:,N) is Expectation of class N for each case. PY values shall all be \u0026lt;0.25 or \u0026gt;.75 The Pclass must match the Logical Function expectation where Pclass is the column of PY with max value per row, col1=Class1,  col2=Class2, col3=Class3,  col2=Class4.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[PY,Pclass]=Calc_Quad_Bias_Perceptron(X,WH,WP)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"524\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"875\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe function template includes the code to create this figure using Matlab 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Net: Calculate Perceptron","description":"This challenge is to calculate the Neural Net Perceptron value,P, given X, WH, and WP using ReLU on the hidden layer. This example is for two inputs, two hidden nodes, and a single output. No offset terms are applied.\r\n \r\nThe function template includes the code to create this figure using Matlab Latex","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 626.85px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 313.425px; transform-origin: 407px 313.425px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 375.5px 8px; transform-origin: 375.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to calculate the Neural Net Perceptron value,P, given X, WH, and WP using ReLU on the hidden layer. This example is for two inputs, two hidden nodes, and a single output. No offset terms are applied.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 545.85px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 272.925px; text-align: left; transform-origin: 384px 272.925px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" 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\" data-image-state=\"image-loaded\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 250px 8px; transform-origin: 250px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe function template includes the code to create this figure using Matlab Latex\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function P=Calc_Perceptron(X,WH,WP)\r\n%The P value in this case is a single value\r\n%No ReLU,Softmax, or Sigmoid applied to the output P\r\n  P=0;\r\nend\r\n\r\n%The figure was generated using Matlab and Latex\r\n%{\r\nfunction perceptron\r\n% put figure code into cody\r\nfigure(1);clf;\r\nplot([0;140;140;0;0],[-5;-5;100;100;-5],'k','linewidth',2);hold on\r\nxticks([]);yticks([]); %Tciks/Labels off\r\n\r\naxis equal;\r\naxis tight\r\n\r\n\r\nr = 10;\r\nphi = 0:0.01:2*pi;\r\ncxA = r*cos(phi);\r\ncyA = r*sin(phi);\r\n\r\nrxA=[0 2*r 2*r   0 0]';\r\nryA=[0   0 2*r 2*r 0]';\r\n\r\npatch(cxA+10,cyA+80,'y');text(10-2,80,'X_{1}','Fontsize',20);\r\npatch(cxA+10,cyA+20,'y');text(10-2,20,'X_{2}','Fontsize',20);\r\n\r\npatch(cxA+80,cyA+80,'y');text(80-6,94,'H_{1}','Fontsize',20);text(80-8,80,'\\Sigma','Fontsize',30);\r\npatch(cxA+80,cyA+20,'y');text(80-6,34,'H_{2}','Fontsize',20);text(80-8,20,'\\Sigma','Fontsize',30);\r\n\r\npatch(cxA+130,cyA+50,'y');text(130-2,50,'P','Fontsize',20);\r\n\r\npatch(rxA+80,ryA+70,'y');text(85,80,'ReLU','Fontsize',20);text(102,88,'Y_{1}','Fontsize',20);\r\npatch(rxA+80,ryA+10,'y');text(85,20,'ReLU','Fontsize',20);text(102,32,'Y_{2}','Fontsize',20);\r\n\r\nquiver(20,20,55,0,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,80,55,0,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,20,58.5,58.5,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,80,58.5,-58.5,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\n\r\nquiver(100,20,25,25,'k','LineWidth',3.5,'MaxHeadSize',.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(100,80,25,-25,'k','LineWidth',3.5,'MaxHeadSize',.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\n\r\ntext(40,85,'WH_{11}','Fontsize',20);\r\ntext(20,65,'WH_{12}','Fontsize',20);\r\ntext(40,25,'WH_{22}','Fontsize',20);\r\ntext(20,38,'WH_{21}','Fontsize',20);\r\n\r\ntext(115,68,'WP_{11}','Fontsize',20);\r\ntext(115,28,'WP_{21}','Fontsize',20);\r\n\r\nstr='$$ X=\\left[\\matrix{ X_{1} \u0026 X_{2}} \\right]  $$'; %valid\r\ntext(1,50,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ WH=\\left[\\matrix{ WH_{11} \u0026 WH_{12}\\cr WH_{21} \u0026 WH_{22} } \\right]  $$'; %valid\r\ntext(1,5,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ H=\\left[\\matrix{ H_{1} \u0026 H_{2}} \\right]  $$'; %valid\r\ntext(75,65,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ ReLU(H)=Y  $$'; %valid\r\ntext(75,58,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ Y=\\left[\\matrix{ Y_{1} \u0026 Y_{2}} \\right]  $$'; %valid\r\ntext(75,51,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ X*WH=H  $$'; %valid\r\ntext(60,5,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ Y*WP=P  $$'; %valid\r\ntext(60,0,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ WP=\\left[\\matrix{ WP_{11}\\cr WP_{21} } \\right]  $$'; %valid\r\ntext(100,5,str,'Interpreter','latex','Fontsize',20);\r\n\r\n\r\n\r\nend % Perceptron \r\n%}\r\n","test_suite":"%%\r\nX=[1 2]; WH=[1 2;3 4];WP=[.5; -1];\r\nP=Calc_Perceptron(X,WH,WP);\r\nPexp=-6.5;\r\nvalid=isequal(P,Pexp);\r\nassert(valid)\r\n%%\r\nX=[3 1.5]; WH=[1 -3;3 4];WP=[1; 2];\r\nP=Calc_Perceptron(X,WH,WP);\r\nPexp=7.5;\r\nvalid=isequal(P,Pexp);\r\nassert(valid)\r\n%%\r\nX=[-.5 .5]; WH=[4 2;1 4];WP=[.1; .4];\r\nP=Calc_Perceptron(X,WH,WP);\r\nPexp=0.4;\r\nvalid=isequal(P,Pexp);\r\nassert(valid)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2023-08-20T22:48:06.000Z","deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-08-20T22:16:25.000Z","updated_at":"2026-03-30T12:52:26.000Z","published_at":"2023-08-20T22:48:07.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to calculate the Neural Net Perceptron value,P, given X, WH, and WP using ReLU on the hidden layer. This example is for two inputs, two hidden nodes, and a single output. No offset terms are applied.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"864\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"1228\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe function template includes the code to create this figure using Matlab 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"rela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Nets: Activation functions","description":"Return values of selected Activation function type for value,vector, and matrices.\r\ny=Activation(x,id); where id is 1:4 for ReLU, sigmoid, hyperbolic_tan, Softmax\r\nReLU: Rectified Linear Unit, clips negatives  max(0,x)   Trains faster than sigmoid\r\nSigmoid: Exponential normalization [0:1]      \r\nHyperTan: Normalization[-1:1]   tanh(x)\r\nSoftmax: Normalizes output sum to 1, individual values [0:1]      Used on Output node\r\n\r\nWorking though a series of Neural Net challenges from Perceptron, Hidden Layers, Back Propogation, ..., to the Convolutional Neural Net/Training for Handwritten Digits from Mnist. \r\nMight take a day or two  to completely cover Neural Nets in a Matlab centric fashion. \r\nEssentially Out=Softmax(ReLU(X*W)*WP)","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 312px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 156px; transform-origin: 407px 156px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 252.5px 8px; transform-origin: 252.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eReturn values of selected Activation function type for value,vector, and matrices.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 240.5px 8px; transform-origin: 240.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ey=Activation(x,id); where id is 1:4 for ReLU, sigmoid, hyperbolic_tan, Softmax\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 253.5px 8px; transform-origin: 253.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eReLU: Rectified Linear Unit, clips negatives  max(0,x)   Trains faster than sigmoid\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137px 8px; transform-origin: 137px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSigmoid: Exponential normalization [0:1]      \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAJEAAAAnCAYAAAAPS6pLAAAFo0lEQVR4Xu2bSeseRRDGkw8g4vIJooKCEMFEQfQiuMZbFE0iIiiuJ8ENl0MwEc1F8OACHnIwqEguguICCi4XNxS8uRxUPLniB9DnB1NhHHupfnvemf+b6YaHP0kv01X9dHVVdb/bt7XSNFCpge2V/Vv3poFtjUSNBNUaaCSqVmEboJGocaBaA3OS6CbN/j3ht2op2gCzamAuEu2Q1N8LFwhfOzRwhtpcL7zoaNua1GmgeHPPRaI7Jeftwm6HvBDomPCQk3COIVuThAZ2qu7pEn2XkggL8tcIR9BnGuMlh2WBQG93hPNYLNMNiihp31j1Xw2gv+PCXo8evSSCPPcLdwveIyi2MEzwK+Es4YfM6kGg94UjzlW+VO0eEy4STnf2ac3CGkCXzwhX54xGjkRYgic68tinakl0WANd2E0utYAPqvIO4WzHKpsJvqpr+0cj0f+0xlpeIdwqoKd9wqsC6/GI8JqAP9Qv1OO7PppagxSJrlXH84VvhIPCrm6gWhJ9p3GwFkwwVhD4W+GeTDv641+d2s3zZf09TWgkiusWC/NRR5ovu2bvdH+HLoAFQJep/uPYkDlLZP2wCjhblBoSmQBnapxUaF9ihfqycfyxy9ZFItNDaNcm9sSWq/qnm5FHDnT6pzC0UieEmppEz3VfxsKkyu+qfF5ImtHAAEsgkRHAw8wnIzo0PXkMAuR5RYj6sFOTCHLcLLyV0ADH6JvCnky70BBLIBHW3Fv+VsNQlMpmJkjy6NiOtLvUPpinm5JEMJrJ56Imc/RyR97JTiKChVOEX4RcFOslFe0g4dHOssQs1XA8Nv+7QvBIm5JELk9fEzVr4p1bX+BNt0SWSmGxOM6vFAhovIudI5Pl3e5TQ5zrzwUSvlj/1OmAXomSg5Gyd6FqHWsm/6vgOYNh/acC+YnSsskkIsp8oVtYy81YIIIeVrHMpj/G/km4TvhQYEMTJePncKTdIkQdZ9UlN/ZUJEKIB2JMHjAFx5GQc0kkMh+FnMzFgkWuRqzaaJMbAgjzlGCJWxsbi/d475uhjWtGJOhcT0Ui7zUHAsxJIizmeQnzt191OKSQ/FCiXYkfY4vJcH1Lbbky8l5RpzYxhzGrjETBfNEUJDLv3nPNMTeJ+sdHzSJ4F910w7f6ORvzXfCHsBS5lEjNXD19ZyeR95rDhJnTEtn1SUyxOJZsBo4X/LZYeVYVKUfV+uGb3Nj9Ayv0s3Cb8LDA0YYDHM0Ue1Z/pDbJiHkKS+S55ujLSntwsvtEfSsEKSEN5XWB43IrvUKY1bH2XnP0SbSUEN+SqsjuPf5GMizFw7AmHLHB91/rtkSWXEyFj0OJkpFARvxNCvH7aRNP5rh45UfsgIsR9c3WTSLPNcdQVntvtIpiN4lE/ajMnmWEdMGFsvc91Yi8OTGUrUdsju7fna2SbMRc8zQjd80REhyfiDR7aVSySSTq+0RkjnGw+9cb9pKhnzdaB0lyYzIPHP1zhODLC48lQlgWlKiEwltnz84g8sASlRKBb2QnHpB8GJ4n38DkNBepH/spiEU9fA7HmjCfQi4Knd8bW7gV579Kt+yGTpEI8hBu8gpxWNgxRA+xX1+UXHOEBCt5lAZ5SAIy32H5Qv/xieAJtz0KHptEfJP5X9PpmXc7Hwj8lGrMS1ePbKE2zO0NgXxVdD4eS7TKBHCkyeh6nrbGxrfjMGpGV5lYZR+Ueonwo5B6mVn5mS3TnZsGUg7Jk2ddJHJ93KEq7yM2x1CtSaEGsLqXC9l83TpIVHrNkZMNZ/noQnZ+ThdT1bt/6cGE1kEiGHyD4PlholcpWCR7wuDt09qtpgG7+jmg7q6fuK+DRKXXHF5Rcw+nvOO0dmkNFOt5bBJZYqrmAVVb5A3TwNgkwh86VxgrpN4wdS5zumOTaJlaXLjUjUQLJ8AY4jcSjaHFhY/xLzQKbDe+g/quAAAAAElFTkSuQmCC\" style=\"width: 72.5px; height: 19.5px;\" width=\"72.5\" height=\"19.5\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 120.5px 8px; transform-origin: 120.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eHyperTan: Normalization[-1:1]   tanh(x)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 192px 8px; transform-origin: 192px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSoftmax: Normalizes output sum to 1, individual values [0:1]   \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" style=\"width: 43px; height: 19.5px;\" width=\"43\" height=\"19.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 73.5px 8px; transform-origin: 73.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e   Used on Output node\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 352.5px 8px; transform-origin: 352.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWorking though a series of Neural Net challenges from Perceptron, Hidden Layers, Back Propogation, ..., to the Convolutional Neural Net/Training for Handwritten Digits from Mnist. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 265px 8px; transform-origin: 265px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eMight take a day or two  to completely cover Neural Nets in a Matlab centric fashion. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 130px 8px; transform-origin: 130px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eEssentially Out=Softmax(ReLU(X*W)*WP)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = Activation(x,id)\r\n%id/function 1/ReLU 2/Sigmoid 3/Hyperbolictan 4/Softmax\r\n%x may be a point, vector or matrix\r\n  y = x;\r\nend","test_suite":"%%\r\nvalid=1;\r\nx = 2;\r\ny = Activation(x,1);\r\nif y~=max(0,x),valid=0;end\r\ny = Activation(x,2);\r\nif y~=1./(1+exp(-x)),valid=0;end\r\ny = Activation(x,3);\r\nif y~=tanh(x),valid=0;end\r\ny = Activation(x,4);\r\nif y~=1,valid=0;end\r\nassert(valid)\r\n%%\r\nvalid=1;\r\nx = [-1 0 1 2];\r\ny = Activation(x,1);\r\nif y~=max(0,x),valid=0;end\r\ny = Activation(x,2);\r\nif y~=1./(1+exp(-x)),valid=0;end\r\ny = Activation(x,3);\r\nif y~=tanh(x),valid=0;end\r\ny = Activation(x,4);\r\nif y~=exp(x)./sum(exp(x)),valid=0;end\r\nassert(valid)\r\n%%\r\nvalid=1;\r\nx = [-1 0 1 2;.5 .25 -2 5];\r\ny = Activation(x,1);\r\nif y~=max(0,x),valid=0;end\r\ny = Activation(x,2);\r\nif y~=1./(1+exp(-x)),valid=0;end\r\ny = Activation(x,3);\r\nif y~=tanh(x),valid=0;end\r\ny = Activation(x,4);\r\nif y~=exp(x)./sum(sum(exp(x))),valid=0;end\r\nassert(valid)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":3097,"edited_by":3097,"edited_at":"2023-08-19T14:59:54.000Z","deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-08-19T14:02:05.000Z","updated_at":"2026-03-04T20:22:48.000Z","published_at":"2023-08-19T14:59:56.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eReturn values of selected Activation function type for value,vector, and matrices.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ey=Activation(x,id); where id is 1:4 for ReLU, sigmoid, hyperbolic_tan, Softmax\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eReLU: Rectified Linear Unit, clips negatives  max(0,x)   Trains faster than sigmoid\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSigmoid: Exponential normalization [0:1]      \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e1/(1+e^{-x})\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHyperTan: Normalization[-1:1]   tanh(x)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSoftmax: Normalizes output sum to 1, individual values [0:1]   \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ee^x/\\\\Sigma e^x\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e   Used on Output node\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWorking though a series of Neural Net challenges from Perceptron, Hidden Layers, Back Propogation, ..., to the Convolutional Neural Net/Training for Handwritten Digits from Mnist. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMight take a day or two  to completely cover Neural Nets in a Matlab centric fashion. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEssentially Out=Softmax(ReLU(X*W)*WP)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":58907,"title":"Neural Net: Calculate Bias Quad Output Perceptron (Counter, Mux, Subtraction)","description":"This challenge is to calculate the Neural Net Bias Quad Output Perceptron vector, PY,  and Pclass, given X, WH, and WP using ReLU on the hidden layer and Softmax on the output layer. Test Cases will be all two input conditions for functions  Counter, 2 bit Mux, and Subtraction 3-X_value.  With four output classes ReLU performed better than Sigmoid in back propagation in ability to solve from random and prediction deltas between classes.\r\nX wll be a 4x3 matrix creating a Quad output  PY 4x4 matrix. PY(:,N) is Expectation of class N for each case. PY values shall all be \u003c0.25 or \u003e.75 The Pclass must match the Logical Function expectation where Pclass is the column of PY with max value per row, col1=Class1,  col2=Class2, col3=Class3,  col2=Class4.\r\n[PY,Pclass]=Calc_Quad_Bias_Perceptron(X,WH,WP)\r\n \r\nThe function template includes the code to create this figure using Matlab Latex","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 754.5px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 377.25px; transform-origin: 407px 377.25px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 380px 8px; transform-origin: 380px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to calculate the Neural Net Bias Quad Output Perceptron vector, PY,  and Pclass, given X, WH, and WP using ReLU on the hidden layer and Softmax on the output layer. Test Cases will be all two input conditions for functions  Counter, 2 bit Mux, and Subtraction 3-X_value.  With four output classes ReLU performed better than Sigmoid in back propagation in ability to solve from random and prediction deltas between classes.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 371.5px 8px; transform-origin: 371.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eX wll be a 4x3 matrix creating a Quad output  PY 4x4 matrix. PY(:,N) is Expectation of class N for each case. PY values shall all be \u0026lt;0.25 or \u0026gt;.75 The Pclass must match the Logical Function expectation where Pclass is the column of PY with max value per row, col1=Class1,  col2=Class2, col3=Class3,  col2=Class4.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 165.5px 8px; 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\" data-image-state=\"image-loaded\" width=\"875\" height=\"524\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 250px 8px; transform-origin: 250px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe function template includes the code to create this figure using Matlab Latex\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function[PY,Pclass]=Calc_Quad_Bias_Perceptron(X,WH,WP)\r\n%ReLU applied to X*WH\r\n%  X 4x3\r\n% WH 3x3\r\n% WP 3x4\r\n%The PY value is four values for each case, [Class-1,Class-2,Class-3,Class-4 ]\r\n%Pclass is 1, 2, 3 or 4 based on determination col max per row. Prediction for each input case\r\n%Softmax applied to produce the output PY\r\n\r\n  PY=zeros(4,4); % Four rows since four cases in X; four columns due to four output classes\r\n  Pclass=ones(4,1); % Values 1 to 4\r\nend\r\n\r\n%The figure was generated using Matlab and Latex\r\n%{\r\nfunction perceptron_bias_quad_ReLU_Soft\r\n%Quad output classifier\r\n%Softmax y=exp(x)./sum(exp(x),2)\r\n\r\nf=figure(1);clf;\r\nf.Position=[1935 122 1459 873]; % Dual Monitor setting\r\n\r\nplot([0;160;160;0;0],[-5;-5;100;100;-5],'k','linewidth',2);hold on\r\nxticks([]);yticks([]); %Ticks/Labels off\r\n\r\naxis equal;\r\naxis tight\r\n\r\nr = 10;\r\nphi = 0:0.01:2*pi;\r\ncxA = r*cos(phi);\r\ncyA = r*sin(phi);\r\n\r\nr5 = 5;\r\nphi = 0:0.01:2*pi;\r\ncx5 = r5*cos(phi);\r\ncy5 = r5*sin(phi);\r\n\r\nrxA=[0 2*r 2*r   0 0]';rx5=[0 2*r5 2*r5   0 0]';\r\nryA=[0   0 2*r 2*r 0]';ry5=[0   0 2*r5 2*r5 0]';\r\n\r\n\r\ntext(1,97,'Bias Quad Out Perceptron','Fontsize',20);\r\n\r\npatch(cxA+10,cyA+80,'y');text(10-2,80,'X_{1}','Fontsize',20);\r\npatch(cxA+10,cyA+20,'y');text(10-2,20,'X_{2}','Fontsize',20);\r\n\r\npatch(cxA+80,cyA+80,'y');text(80-8,93,'H_{1}','Fontsize',20);text(80-8,80,'\\Sigma','Fontsize',30);\r\npatch(cxA+80,cyA+20,'y');text(80-8,33,'H_{2}','Fontsize',20);text(80-8,20,'\\Sigma','Fontsize',30);\r\npatch(cxA+135,cyA+80,'y');text(135-6,94,'P_{1}','Fontsize',20);text(135-8,80,'\\Sigma','Fontsize',30);\r\n\r\npatch(cx5+135,cy5+55,'y');text(135-8,60,'P_{2}','Fontsize',16);text(135-4,55,'\\Sigma','Fontsize',20);\r\npatch(cx5+135,cy5+43,'y');text(135-8,48,'P_{3}','Fontsize',16);text(135-4,43,'\\Sigma','Fontsize',20);\r\n\r\npatch(cxA+135,cyA+20,'y');text(135-6,34,'P_{N}','Fontsize',20);text(135-8,20,'\\Sigma','Fontsize',30);\r\n\r\npatch(rxA+80,ryA+70,'y');text(82,80,'ReLU','Fontsize',20);text(90,93,'Y_{1}','Fontsize',20);\r\npatch(rxA+80,ryA+10,'y');text(82,20,'ReLU','Fontsize',20);text(90,33,'Y_{2}','Fontsize',20);\r\npatch(rxA+135,ryA+70,'y');text(137,80,'Softmax','Fontsize',20);text(147,94,'PY_{1}','Fontsize',20);\r\n\r\npatch(rx5+135,ry5+50,'y');text(135.5,55,'Softmax','Fontsize',12);text(147,60,'PY_{2}','Fontsize',16);\r\npatch(rx5+135,ry5+38,'y');text(135.5,43,'Softmax','Fontsize',12);text(147,48,'PY_{3}','Fontsize',16);\r\n\r\npatch(rxA+135,ryA+10,'y');text(137,20,'Softmax','Fontsize',20);text(147,34,'PY_{N}','Fontsize',20);\r\n\r\nquiver(20,20,55,0,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,80,55,0,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,20,58.5,58.5,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(20,80,58.5,-58.5,'k','LineWidth',3.5); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(58,93,14,-10,'k','LineWidth',3.5,'MaxHeadSize',.7); text(58-8,92-1,'bH_{31}','Fontsize',20);\r\nquiver(60,5,13,10,'k','LineWidth',3.5,'MaxHeadSize',.7); text(60-6,4,'bH_{32}','Fontsize',20);\r\n\r\nquiver(100,80,27.5,0,'k','LineWidth',3.5,'MaxHeadSize',.3); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(100,20,27.5,0,'k','LineWidth',3.5,'MaxHeadSize',.3); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(100,80,31,-58.5,'k','LineWidth',3.5,'MaxHeadSize',.2); % line arrowhead x,y,dx,dy,  VectorLine draw\r\nquiver(100,20,31,58.5,'k','LineWidth',3.5,'MaxHeadSize',.2); % line arrowhead x,y,dx,dy,  VectorLine draw\r\n\r\nquiver(133,63,0,8,'k','LineWidth',3.5,'MaxHeadSize',.9); text(135-1,63+1,'bP_{31}','Fontsize',20);\r\nquiver(133,3,0,8,'k','LineWidth',3.5,'MaxHeadSize',.9); text(135-1,3+1,'bP_{3N}','Fontsize',20);\r\n\r\ntext(30,85,'WH_{11}','Fontsize',20); %Thicker Bold\r\ntext(20,65,'WH_{12}','Fontsize',20);\r\ntext(40,24,'WH_{22}','Fontsize',20);\r\ntext(20,38,'WH_{21}','Fontsize',20);\r\n\r\ntext(107,84,'WP_{11}','Fontsize',18);\r\ntext(107,24,'WP_{2N}','Fontsize',18);\r\ntext(114,30,'WP_{1N}','Fontsize',18);\r\ntext(114,70,'WP_{21}','Fontsize',18);\r\n\r\n\r\nstr='$$ X=\\left[\\matrix{ X_{1} \u0026 X_{2} \u0026 1} \\right]  $$';\r\ntext(3,55,str,'Interpreter','latex','Fontsize',16);\r\n\r\nstr='$$ X*WH=H  $$'; %valid\r\ntext(3,50,str,'Interpreter','latex','Fontsize',16);\r\n\r\nstr='$$ H=\\left[\\matrix{ H_{1} \u0026 H_{2} \u0026 1} \\right]  $$'; \r\ntext(75,67,str,'Interpreter','latex','Fontsize',16);\r\n\r\nstr='$$ ReLU(H)=Y  $$'; \r\ntext(75,62,str,'Interpreter','latex','Fontsize',16);\r\n\r\ntext(34,77,'ReLU(H)=max(0,H)','Fontsize',18,'Color','b');\r\ntext(34,71,'Softmax(P)=e^{P}/\\Sigmae^{P}','Fontsize',18,'Color','b');\r\n\r\nstr='$$ Y=\\left[\\matrix{ Y_{1} \u0026 Y_{2} \u0026 1} \\right]  $$'; \r\ntext(75,57,str,'Interpreter','latex','Fontsize',16);\r\n\r\nstr='$$ Y*WP=P  $$'; %valid\r\ntext(75,52,str,'Interpreter','latex','Fontsize',16);\r\n\r\nstr='$$ P=\\left[\\matrix{ P_{1} \u0026 P_{2} \u0026 P_{3} \u0026 P_{N} } \\right]  $$'; \r\ntext(75,47,str,'Interpreter','latex','Fontsize',16);\r\n\r\nstr='$$ Softmax(P)=PY  $$'; \r\ntext(75,43,str,'Interpreter','latex','Fontsize',16);\r\n\r\n\r\nstr='$$ PY=\\left[\\matrix{ PY_{1} \u0026 PY_{2} \u0026 PY_{3} \u0026 PY_{N}} \\right]  $$'; \r\ntext(63,39,str,'Interpreter','latex','Fontsize',16);\r\n\r\n\r\nstr='$$ WH=\\left[\\matrix{ WH_{11} \u0026 WH_{12} \u0026 0 \\cr WH_{21} \u0026 WH_{22} \u0026 0 \\cr bH_{31} \u0026 bH_{32} \u0026 1 } \\right]  $$'; %valid\r\ntext(1,3,str,'Interpreter','latex','Fontsize',20);\r\n\r\nstr='$$ WP=\\left[\\matrix{ WP_{11} \u0026 WP_{12} \u0026 WP_{13} \u0026 WP_{1N} \\cr WP_{21} \u0026 WP_{22} \u0026 WP_{23} \u0026 WP_{2N} \\cr bP_{31} \u0026 bP_{32} \u0026 bP_{33} \u0026 bP_{3N} } \\right]  $$'; %valid\r\ntext(66,3,str,'Interpreter','latex','Fontsize',18);\r\n\r\n\r\nend %perceptron_bias_quad_ReLU_Soft\r\n\r\n%}\r\n\r\n","test_suite":"%%\r\n% All of the WH/WP arrays were created from a matlab centric back propagation algorithm.\r\n% The back propagation algorithm is for a near future fun challenge.\r\n%Counter\r\n\r\nX= [0 0 1; %[x1 x2 1] four cases\r\n    0 1 1;\r\n    1 0 1;\r\n    1 1 1];\r\n\r\nWH= [1.11   0 0; %wh11 wh12 0\r\n     0.00   1 0; %wh21 wh22 0\r\n    -0.11   0 1];%bh31 bh32 1\r\n\r\nWP=[ 6.02 -1.47 -1.68  6.69; %wp11 wp12 wp13 wp14\r\n     6.00 -1.33  6.92 -1.68; %wp21 wp22\r\n     1.16  7.99  3.99  4.17];%bp31 bp32\r\n\r\n[PY,Pclass]=Calc_Quad_Bias_Perceptron(X,WH,WP);\r\n[PY Pclass Pclass-1]\r\n%Verify PY used softmax\r\nvalid=sum(abs(sum(PY,2)-1))\u003c1e-6;\r\nif ~isequal(ones(4,4), PY\u003e.85 +PY\u003c.15) %Hi and Low Thresholds met\r\n valid=0;\r\nend\r\nif ~isequal([1;2;3;0],Pclass-1)\r\n valid=0;\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\n%2 Bit Mux 00:0 01:1 10:2 11:3\r\n\r\nX= [0 0 1; %[x1 x2 1] four cases\r\n    0 1 1;\r\n    1 0 1;\r\n    1 1 1];\r\n\r\nWH= [0.00   1 0; %wh11 wh12 0\r\n     1.03   0 0; %wh21 wh22 0\r\n    -0.03   0 1];%bh31 bh32 1\r\n\r\nWP=[-1.67  6.60 -1.76  5.91; %wp11 wp12 wp13 wp14\r\n    -0.98 -1.38  7.20  6.33; %wp21 wp22\r\n     8.08  4.22  4.12  1.23];%bp31 bp32\r\n\r\n[PY,Pclass]=Calc_Quad_Bias_Perceptron(X,WH,WP);\r\n[PY Pclass Pclass-1]\r\n%Verify PY used softmax\r\nvalid=sum(abs(sum(PY,2)-1))\u003c1e-6;\r\nif ~isequal(ones(4,4), PY\u003e.85 +PY\u003c.15) %Hi and Low Thresholds met\r\n valid=0;\r\nend\r\nif ~isequal([0;1;2;3],Pclass-1)\r\n valid=0;\r\nend\r\n\r\nassert(valid)\r\n\r\n%%\r\n%Subtractor  3-X  00:3 01:2 10:1 11:0\r\n\r\nX= [0 0 1; %[x1 x2 1] four cases\r\n    0 1 1;\r\n    1 0 1;\r\n    1 1 1];\r\n\r\nWH= [0.00   1 0; %wh11 wh12 0\r\n     1.06   0 0; %wh21 wh22 0\r\n    -0.06   0 1];%bh31 bh32 1\r\n\r\nWP=[ 5.96 -1.71  6.66 -1.61; %wp11 wp12 wp13 wp14\r\n     6.02  6.90 -1.68 -1.29; %wp21 wp22\r\n     1.32  4.20  4.30  8.17];%bp31 bp32\r\n\r\n[PY,Pclass]=Calc_Quad_Bias_Perceptron(X,WH,WP);\r\n[PY Pclass Pclass-1]\r\n%Verify PY used softmax\r\nvalid=sum(abs(sum(PY,2)-1))\u003c1e-6;\r\nif ~isequal(ones(4,4), PY\u003e.85 +PY\u003c.15) %Hi and Low Thresholds met\r\n valid=0;\r\nend\r\nif ~isequal([3;2;1;0],Pclass-1)\r\n valid=0;\r\nend\r\n\r\nassert(valid)\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2023-08-23T19:37:45.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-08-23T18:47:21.000Z","updated_at":"2023-08-23T19:37:46.000Z","published_at":"2023-08-23T19:37:46.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to calculate the Neural Net Bias Quad Output Perceptron vector, PY,  and Pclass, given X, WH, and WP using ReLU on the hidden layer and Softmax on the output layer. Test Cases will be all two input conditions for functions  Counter, 2 bit Mux, and Subtraction 3-X_value.  With four output classes ReLU performed better than Sigmoid in back propagation in ability to solve from random and prediction deltas between classes.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eX wll be a 4x3 matrix creating a Quad output  PY 4x4 matrix. PY(:,N) is Expectation of class N for each case. PY values shall all be \u0026lt;0.25 or \u0026gt;.75 The Pclass must match the Logical Function expectation where Pclass is the column of PY with max value per row, col1=Class1,  col2=Class2, col3=Class3,  col2=Class4.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[PY,Pclass]=Calc_Quad_Bias_Perceptron(X,WH,WP)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"524\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"875\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe function template includes the code to create this figure using Matlab 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