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	<title>איך זה עובד · קופיילוט שם קוד</title>
	<meta name="description" content="צלילה לעומק לשיטות מאחורי קופיילוט שם קוד — וקטורים, גאומטריה, מורפולוגיה עברית, וללא LLM.">
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oBSySHp6dAc1vNgvB+tDgsMKQ2CUpOAQYpDcFex4s0hD+t1w9QB8IZj4cCtcsEMyoNB2mH3Jh+mKJD2+wfvB8EFySDGY910HYtvET+NyJG+EbOk2HKkHn0rIcEWfcvryxTF5SEa6on/SWXnviLb0v9FHP7CmL1hVbulr1rHlbIyjkSAn0dUUOuA9e+aBtb1+pRNljTktv1qZ9gyid5a6lee0irUbCgEAL6DCgxSiIH32E8cfKpge7sYIffWYiek34PsUo0+RJVGKTGbagsuyomfZc2UYSJ+9IjEyhoqahLd9uQS+ozAvEWqcfhOsvwgA2AznGkVOXehKMkmg0lzGSxuTR3cFYKVKNk+Zt45PeqWewPRcyATVRhsw+iQ1MbmWGJR+QLDNlm2W2Oeb3RYzPq1n8TJIlsNQyy5+jct5a6wNkChU1De0dFSy2CUGs+gnXX4QBbAZy/BM5I3UMWeCycQ72DIZeRBkmug+fsHQQU38eMbW1EjtcucRhToqVlZWVlZV1tRZaAiakSDVOmreNzzuDn5Ol6MwQ80GRZaZss8w2x/ysccla/ACKG1QMn94qiFmp0jF2spOImzNfznsA0V2p00ggU6ioaWhF1pDh4/Bp5WbEGDuhZxLX48fUfFbOfg9keLFs6FoXE2edoUWUYaL78OHtPjGo2AUSh9t0tqMwWUZynbNiqc9VjGZWF38wji8vGHaDzKaZ3s5FtquWvzDWg8JGAHumpNVdnHBVBj9G3WcyETa59/ZAlcRuosq6k5S88gj6Cwob0wnsoVRiNGBqM53iJlh3VKHpMrc6kd0lamA6k1n2Y0aiZFq0BjG1jChdjGnT8nrYMCau3OTrsnNG0SXOaG5DUCffjDpqKsqI0Q8HARHQAAgBSG2MHxpAybGVlY32aVRwnZ3VTN2JihKTrwn9lYT247NclM3CzGgj4OMGiWQj23KWllGWxTJGi9Mgs4BwkgAQF1UKjBcJJWflkOYOAWEhJB0iF3FaRgbuIhgBRk2xh3JqZ8/+UjjLgMu5qNqGERAYrM0ObrOvSQiwzl8igYpHiGrVbGEqaJnAyG2iBAGZ6RKTyEa2SRKHhAixQowoIypX4Y0bI56BQxNBTKOFpjuigKuo3X9OC5+3JxUZgopFMAkxdG0j2Mvox8DWLA/CoZytxuGMQLIU2h6th0ywj6KBN81cInXHp+zZJhkD7dECYzIXPsgpI0DBYIKUydS6tz+sQQAyHFvfO4Y0J5XSQFmR7EQDWSeEMYKuupqJbJlOJpuKIqNrz/p7/xAsdgsJIvxagWGdqQyvrsZZphENBoa3ecXOmKSTYelk9kqEA6COWPFgH0csdlAlQlyIpXM8Dwh65qAPgyHRfyhZJ+LrKIoQEs3UqDhm7K7XtDgN26/hLxUPt/aiuy9TI5+oxJHyGnhSFpeni4MkFDzKwDbbyafq6VQcVUMDGMODXw4kxvlzMqhyEj4ndEOczQ3hAVLuKHG+5mRO4WROKQgkNKd6QznPcyrFxkmczElzyQjMkoEPmVL2KNeikEkhHOPLIC/sNrsddUy1GhJbhmNgx95hAAbf3KcH/kYLaARuiEsYCDoSzP5PJUcmfHcQSIbx2OU5T+s6LP9L6WEI+bKwVIBRS9+YwlYAFQj06W972kbpmwUtIYSHS/TU1r7/Uy1ZLsMywjvavg4SE52zXvw4Mo18EgO1IqHtM2c2aKgNnzqBsM+cZhk1kITTfeoZT2LkUADtaHqbmgVmoEO5f/UjM5dxNqQHjLbCAI9R/s8pJMhQoIIaGmjhAx18oYcfDPBHAHogED1hRBBM6IVg9EYIQmFGH4ShLyx4A1b0Qzj6IwIDYMNA2DEIDgxGJIbAiaGIwjBEYzhiMAKxiEM8RiIBo5CI0XDBGEiCNyEZxkIKvAWpMA7S4G0YD+9AOrwLGfAeTICJkAmTYDJMgakwDabD+zADPoAsmAnZMAtmwxyYC/NgPiyAHFgIi2AxLIGlsAyWQy6sgJWwClbDHyAP1sBa+BDWwUfwMXwCn8Jn4EY+CrAeG/A5CvEFivBHFGMjSrAJpfgSHmxGGbZgK7ZhO3ZgJ3ZhN/ZgL/ahHPtRga9QiQM4iK9xCN+gCodxBEdxDMdRjROowZ9wEn/GKXyLWnyHOnyPevyABvyIRvyE0/gZTTiDs/gF5/ArmnEeF3ARl/AXXMZf0YIruIpruI6/4QZ+gxd/x03cQiv+C7fxD7ThDu7iHu7jn3iAf6Edv6MD/8ZD/AePCIhPAL3HBAEyEHtoWITg3e2cIvedpJ9T5n4JDBqj87COfLQkUXnGbIMc7AifzidP7cQrRYUkdiG721g5b/ESJJ+k/VJMFL/Yw5ivvTzbIXON+pjEved1jnOJPIN5IM2FCTyEZJ4ylHeaQAGZXUkuToK9FuNR0dGNX+RNJTC9ACAScqGf6+RyAeV7lQqcX/PQV+B1TlBFbxKKLIqyTWROBNhNe4KYf2H4nJX5o5kK0u0GvM0fuziGZygYKLixwHZpsqlvmYkX+1nvt736/7Zm0aK+D1wkiE4/Yp2FjHhoDoGR5pVjJq15qZrLAAxYxUiTZaVCEoAQNOOA54psSGGM58EUWisJWXikZaJT26pIU7FMKi45ldQACYsetaplCyi/A2RPqucRUXaFNac5DUB3dLPv/qSew/Sf7TL8nTO7NpDsbUbyiO9oipQNxLBQMPFjiDif6hYWQzXR53zTrRGyImvlfru8LMpIxrIqO3LNcmzPijkcXlsAflg4xauw+icsRpaXhGRBlg7b92PyO3gH0C3mdHi9/Trw+vqrb2c/XkPbd7R9EauiLgbFdf/40qIBAUSDDF6AKGNLicJjFLyr/UmZnbb6VpNmu3yl0kZneHxtmz2+tFujBj/a7lcCHb744Y9REJNezPoI05eFlZ3j9+b+8HYaLsYIseLtV6LCX+31wEhvSpYi1TvSvSvDZFNMNc10M8w13wI5Flks1worrZJnn1+U+95JW/zZd06pc84l/3TBh77xk0Mu+pfLzvq3h+565LxN7umwTpU/KlJsBzUSClpUaPBBT08BeggUKlhvIQy8IUI/4Wz6+9kA0YaKMkycQRYaLcEoYyRySTLWeOOkedskE0yU6S3vy/aBLLPNVG+W5ZZYapnV5vmDgeY46IBjjjtKAAEkBEZqdPcl5R4TW+a1CISQGoRGSKQDhFbIUSr4CAXuJ606oVr4FcJXqB1soX+NuyM3OTM8Bojb1RfafzkYfwPFBFINACqk7s3m+0KCx8im16NIijrR0CNEd5BHN0Wr0r68xN5wUmmUkXTkwsWQ6Nx8LnWLGUp3U2YUUVP8iBsoizdG3RNdS6vVgFR8Eo9c6gMqkOKd7lFG6RtHk/SmvIwos8Xa74ggXTnIalVG1FAYM3JOOOftzCMHOa6TIGsumGSYGGCOuh2nXdqhuwXdSMN63GKexT2nBnL2Ic8atuSK8/mRFmnK13avduMeq2+7lt4zDI4xxjmOUzwDptMYAZuQwIb0k9UHZDAkcDppHSPK/JRqNAaYcAcMhZiIYRveWyvhET0kALz/8e1Q79fJaOazumYo2qnqd64LtmTxOmubzkY2nAhwc+hxlCIhAn/Y9vKsdUJyJ7eTf4Pm0zWY62l0gHc6kDAYyJCafbbN2mlsjrEMBBvh+cWWSHHsMCQiPBO7I6qorsjXI3LZCVEEJemO/F69JhfAMAo9beT5CQ5QM3irbbblUZ5GtM+toN4FJGTjkrDBhmOyRWbCoCGWjGh2Zq5VTZtscLTXUB5kUSRfQBPdyKHFw1jLR119QO0OuydTJDzupWh/+ziw6uiXfIQp6/SAongzyBPfAFOdxuV6VGR6Z+sQ2akOyQjTL+9h+3qk3ZaTYRpbdirgbHvAsiqp0sLz+XBSXj+c7uZW/EfnsKnjLykODNa9uhDiMvfpy2J1xrzYlVh9GNDxfau8rRkMYpvQAbIG9R82ajIcEjO4+m2u3sHF6ziPb7oFAbdhyOPYyCdd84jWy6uN/CNUR4egxuPAZ7l/7vLBZUqzVyR1QORpH5sh2HTDL+Msvurq5zAYdybQgSEHZPSS80ewPko/KIJssviucpyUQVIT3YPlsVuDJfgGhL3bsPGm4br+pySOaeB6F9zeg+K5J51HDLoPEKLlctoJjejujmg51dDSDuKh0Su2W7l7pfEYXZKw3bgXWO3cyEGxHsOXKWSLmugcv6XmEyeVsLjDWeC6mKC1le2p98h9dUey16WIb4yJ8p4GpJUL2A7lR+2sp84oDM67DqD5U1CVw+jFTOcVeGaB4X46XmDVrz+Vgc6DxIk26V2zDx9yHMropW1oZfIe+3QFsX0JGAUg+meY2vBUorUgyQzNwmKTDRkNpqXyfbIkXnu41v/sHi11/RVNMJxOnWMYcZvFgbfK940DZGr+p1pxupvLscmSnzp7olp+sVfLfox+JT3KTpYJt0/eByc/mPPIRrK5c7IKIAR3hpTPx282SJsbGTx0jYtYDqCrR0WOZgCyrm7U9zrj44nn+vEKSFErGFe3aWZGofAOCkOm84WxOyTOMIw+pO+MgRFAZcZA4fvW2/gc6PBfj/ZHT+SsxYcy5IoI5dObGHpQN2hk866qr4M9ugbz9+/b9uHTCGebhFozMJDxP7/obKe7vZWnBAlaaxumDlL0CLoffnd0+aDhYYCDHNGq9BbH1iogJL9IollihPV1C+p/ubh/YJRS2+52fWIcnDFQXzaITG2FYup6tCSH2k8HoDo698jH74Kv3ykVjAG0XEfn0VvFe5XJUH5m1XFOLL77zrDFwHghtm77zPyeXbc4e2fPa/riCGWQ0zAm3yWbn7Zuhdt0axwz7Kb0PIfnQAIH2Roivm7nI5v+89vE2Tc9dV8iXYI+1Eu3dNRf72AtqqZxWNd20kqlRUgrdP3M4IOXis4D2bMWOK+fTwrfy9T2d3U6eOqmJjEHWEn5F6iE/qc865+e+mfH2/AIsvGGCqcCO3fykOxb8U3Cs/50NvrNh1L8yeanNlrfqx0MhG5NQjAkZIqNzuiYRRpZalZp4sOoaTeM9byMzsPC24kY/47aFaS1SkmWGtsVskV1/8l/6Mi7Amv5cBDUjLN7MnnxjE2rcj0KULKzWdA+MngiZFkS13sqQkcQa9WET/Us6oF40po+rZTkffQJV3350pSlQJj8rVSqFRTbPtJAuo6P+wOePQPin2NLiXgt+AwBIf/i3yWmSS2daFxfQxj7R13jwjBjhjdidAt3tsBpnrNAHKI6zstaGHNkBs8IUBdHcz4tYXnm6/eT0WFYnu2YZqwUjVpXG30xNUrKmdKtoLuWlOlqSmV06tA4Hjo4iIsU6pyQOr3rKkgINKjuVrijA4x7schxf1MuLnlR4vxVuRxFA/3uPPRG2FjtLWy2dF5fEBjKgirIr96aL50T50OVKyuz6txj5E5+y8BfZlKOq4iIxTazDNm8QIaOXibJH9rMOSc3gwHc4kAPagcYJ90og0bPnZXgwEAQOEX4lCh0YE4XAwHyHoy8Jw8o2s1MLy3H6vH9eUv+nNbZJ0AHRsg4f1RddtQalcJu6O3BXhACpOzFBDpAaeu83akDWrJqAlWY2W3cp6B33vo7Da22wUqWtHUx6gaN5lGByUZbLwlHih3VT59OU0fntvGuMyqX61mUShxqUtpEm62tcHegYjtHX/BMbWpPkAvNPmI23Up51WNsnTGg1U9ExoNHRWl/pOOp02zcwdpH4+jxYKNosyWnQtmstjI8wWsBivIPf/QRsZP2/ASFg6XQHaZg1CJJFe9oiiZdvcmHNoPSK9n2MvkV1vohHuvlkLucfRKXKKMbKIfXZivcXTw29eczW89Bk6Vkveo0x7lJkQQyNY3DhSB63RAFoP70hNZ3+dhy6NDnGARcpP3Dys4RVEntQ9uDOlVBdqR1rjssWxT5KEUZSrrj5fE+6M4dQNgZeVcuUMRq0tXu01A69D7Hsr611Ho0fUSRrPofYm+xZnFaAQiPC4RpMM03xWVuZDL1ZOmR1beW2CdL+oiWY73qszz2FQBFXabOkPQSPvHsxJ8+jS4u3/9Udfo4T9VJ5gIJ0ogy4280mtfMu37s0tjKYk/yorGNUtbWuX5P2h5aQ0HhLtoeiqq69iTWMJj7UaiYs3nYzVsIUaDlH2vcw8O2ryE+BOi9o3UQNvdP6E+1FooNUL4wD9Bub+z+EJoHgaaxc2SCQ9jVSqUHo5jT/AFbmT68CHXvYozMH/RWuvvK20egoYW1dqMraahfARzU4JJKy975PsEyZ7wZK9OFZVtVF518mWehW78lkdCNtHtdqqDISbiqFaBpbKxlTbB0tLGxdHxNRUsvo6uuz6/eFI2qN/Z56roA30+vmPU89IKftFrTWCNEzk2H0tUetTvrIVP+wULagZdauazSune+T7jMHW/DzGSlYotqxCXU+rvdJVsSCf3IIq8Xr4Q2vXcMeg68WS8ETWOwoEcAi2yoJ4Qu8xh4gar048sm9n4iLivxhj6CP1OyKqzy7rmWpfiXCt/5ceqw4MPNWye+Wm4PqVVgKrduf5fumqJcL3Q9t1eVZ7O+Sp/LmEtR7BjbxxnfR+ygGEn6q5dn5HA/dq+mZKRUY+dYl1WYsyKyK8BC9Q1J75pv3T0U8mkhZN09xd6hSi/TPt+rH/l0bJnvtiNqWMmS7Gde4M9J/HF05qyjaz+fjrbNFeR4YyM5ujnYthMMs+5jWGusDUs9+o3RqH7TUv/yQhx0z+dofGSZKtQqcq3hmQbaW1zhaJtpYY/+60dcrRxVn2uPnHtx1YPYYmpfpdMXX+dw5cKon3c9z86QFnxyZfmVDe+etM/36bdKq261f16DprEHzHVswmXUE746oXmn1vekWf60pbdiS02D0+NrKKvZAqicC7VVYV2zvCRQ6yuT5DakN0sfu/64XIbbKxBVSTX2hOrJAJ+wVVp1olDHgmap/0vERCgUvhoJ+GXtDehV6MYQ0FGFnoJbvCbPQXhnNdFSIcLlYZGypXon66CH3/SFpwB8dHsLZ+VwCavJQ/EUN0lWDsPTo+ChPaOc6PbJgvZJyiS9fap9O699+/WdZKh5x9IpG941Cq+c3pBl20DZkLOpYfk0yH4h/LCR0/vK9bw/lXuRqZ0X/HDZCxWcxe+8cKcP5Z/LvyVv8HLm1FpO2/Ucjo1LIfi2nxcVPhycCZTZG5tboG1iAUcgMEXlfxWTBnaFDmfNyXTjCiKINNzrKLHkIdofi4K4ml6W4o1Jb0uConsXcqhLhNUijQkDrw2iL1Fwm331WOCPbFMer8Rm3b07blVrfApRg+O+zMJQBpYM6AjRQ0emL36rlvzz3f9mujRnRW3ExZ1UKw8az1ruLNWYsB/urU16G62oXSGNgSwqxwOjKWX0t1k3ZiUSEanUXGkQLXIF9T0JY7vSr5e2WozFDXatgV+Bvigc9TMBlbFer+n0Bwxd9XqdwswbUA0cFBBBLd5EkpJkUKMRWdgb895z8cBfvvvaVy326rvqjXoJyfLMir/MepteljLjl/6FesWDwl4bU0AEDLLm0mRkCUijrFHrW8rNRLJGW4ZXYt8JRKaQQKTzqgQxwoBVeQmZvryM02ZqFI94mhs9K38tzwkFO7HRxoCiP1Z2OGdSemASZChpNOg7fT59V22JShNQiE3034QLIF8yWG9F/TjR6LHJGytVKtHvR/5+kWSCpkrZORonA7m3519TBmG1OpB4utCc+n8I/PG7Mcr+Wggvuo9C72ewPV9SKvhWMS52O8VY7m9jFVDfWLFZhIs9TgmW92ja3/eHMFo0CwJOQitUlTdiJoYuxL8No0d1HZQcQvM7A48YyNXcq35UbLzCU3tdZtR/FdS/zbqxsMWvkLtssVolCw+ovKVSp+z7DlB/h3UHML7MuHf0wzUPZKDLMRPLisXPshdzpBIkjZuZwaGlZ2dk5gz6RuKl3JPx9jw6yeYcT+tevw5sp2bUnC04yDqWcSEttas+HjgC6u/QU90ps/1yxPD8YweDSqHDLoOo9hp4LRMdr1daKxId5Pcm7JOrWSWNrl6v6/QHdGOiWJfQ67sCfn2n/O51mAhqsCRJYk1BLUEEtLImkpQlAxpgyQse/jV4+2valbNp6qgkuiSNoc1V6CqyJgIlTg7GMmRLdYRj56MmmwNFJL28wWViED6NNEmS0iafllD4tSTrL1zSr/mvWE4GG62oQ4HFQGLmMboxpaRSMg22rOO4iturro1/Ox2IIPqF8KpXunczu8R6t65m8wdmnpjVkB7AFN7xtJmZRSkG+mOPf+sX1Pq/IuMpS6068byKyaXXfkpPOrSQ3QPeoKqjgj81txw8dcmMEHa2P+xxFH8oUnicTp6dvq6gOy4oWcJl5pa/XOdk/SLjGmw80EpFLIyTBS9E2WqVWcj/Lj1O+44n+HT5xxwpXaF3RGrnsehGYaRcigt3/MlHvljxAcz6bG485VMx4vkYTFNJqcdo9RS6U1OH6SPboG3l2F/KdNHUHvsW8DnVCaD5+tU+o9Qpn2ph3aGnBlN0XgI8MznD6VZdLrI9SXlyo17ee8NXZ8Cp7TOcavMqqJOyHVoFP+to7dv+PhE3w8MzaFxzfyZ5mLI623olqgLp+2P8Hie48sqJZynvbHjGvxIe//ToPU588/O37yufPPpZdDMwz8xbv3ZpT+VcaZlXdHvqd+ti3eOQ4w3KkwWOA+06+En2NaH9cUDP5F+eA2FiwXAIDO9A68SqpLo9TfSQpHiaOskgmYhOaFG/nB+lmGtPF8YeWdiY5pPxOJ3g8g6hCRE4laqeNgmEhAQBlZKFLCZKfcayUg9BwCozggW+plYlLkxMJM5USTjjdSHOkFlt/E3JJH+D1SKDaoEMdZP9P5lAA+0W9uSHj7rpTdsggOa2d2w9bFuuK7WGvVKf7HqFIKzWaX1ioU9ldq3dtLc2eTjI+AwCn1IJFyOmVL1eobRZoyELhtmakSDLE0cQs1DntJZKvaLzqv/ukWpkbq7EgpvsPhNlkBQLbZhGHgiLgfb9PBmFcmX03Zd6r4BOZcrP5q4Cd6mEs8hBqF4Lym0St1QaVAdjNW7+4LMTBQgLSsn7dWI9Xx1FiZjdqqurxsEsuBshPIxORwt/2FQR4mPom0tUVUtgVbxMff9yH3eJx25EZjgeGc70/B3Ni+N7heIIgWt9UmHUpMNiHgI8RVmLvhG6ZqqKQWuydR73dvH9eROpteB7qj4uJfwKCWK/dpeSr3MQUrGtXCANxuocvO+PsCiHdFQxhrnCkmHczusQKPWmWpRrtFVoRSjpRup1V6ReMf/f6xJduUONe4NC8DAV/WCBqmpLccAdXuPuS75HSI8qLyruUWAjusBRavdZuLWAXfxVWlTkCuUfX/M3fQOblms1ReKIlSOliKDZafJ5X/FUJkMpKvH4Q5aX6eCiaZH8Cl2lfZfaTNFVeDvv0OdkTv7nFoIPdT/sgzSpVWroZkxykK2oyIuShaVmFYf7lajwH41cN8qDHd/C/Geoqh0oL3vv+VnXusarZxKqcJQ9WF00O0mf3VBdMBhlq8IJYxCYJ3JTW1h9JrDxQOHDT7DZTzxcyOcNDOMP/rhCfL0oPjstw5yd3UDL7WISSh/oPJt6tRQuXWelPkhLZucmaekVudkVoP3JY63wvFdGNa0pJ/tbNWTnVIK6oulKKJZ3uQqqKZqCmrGfpUNQ5TGOQFVF02i6Y5r5635r0n7QSnX9QT16trHIJzH91ReBN5Eo386ekL/hFhM4KeRYRCK2NV1qorvq0oLcqZde+Z5xkM8HILBztsmf8EiErT+mO/7t7OESQclmGbjvlryhqcHvw2Bmlzrk5qfaUi1ceJaqkGt/N5eXak218d0hNSsfxn9Vy2dXaVHOi5QXuSLN7CqF+lcczgdZu9pIEakREQ9RHlKgWk5usq66dSsv9107t1CZBXNTLZfhILmrZnf+KnzVVShn7a4BM+dLGu3vr1vXJXgwDLXGD4eGjB8Y6oDsvCHUGT9QP3Q6GcZGW0zqvQDyNfQm9Bb+z/iSdYsFrjo3H4XWYvnSiaWHgdNZBklp8uNrCBI/B9RChMih/fGrOWiWamhd2ry6rEw2cHz7oyUyP3/s8icys+CMLA+TENlBToWxXqlpdroMzfXEKS+YM/BYcUgui5dUJReFsgBvyKnFvugEdAIQ/SqzpaCEyzcWmK37vDXdZmXlItJa4OVzPff58uN3qJZbbFvxQA5iFgrKcVzgNIsQxCRCnDiOlJuE4GfftCU/qcg7FcLUpK1cpxKZSNTJIUUiEyERWu0CiZYUniMe68LVFjuB7SH5TjYpFZUbFFKHm29saGG1MD/vY/UBGZVsCKnGm5u1o/MD5VKHzFuRGOeZYk5sY02NeHPIbZCYRF8Gv/Io/LMLf6ZD8fZCc6oJAvrsVfhCvawoWhTBCMRVjhCEBXVVONeiJrNfKy0MFcZUJFKpoEigdoRunvURBN4/dQo65ToF7b8Y5iRfAjCZR1EVk5l8jeDL4YPQGdcZ6MwpYKNBfx3dmJ+/NKdaOwAxv54/zoL2nFrBAGAboDHopZdeut0hrfVjMWDvgr+vXUtQmrTtCY8e5oHJXZNEIxpnAuOdr/ZBY19czwUcm1VHT1fpoZ5FvGoBzz6yeMpo4rtwjSXpXGAyly9IWvRXsrunFbr6cERXq5DrayNhff1nyPcNB7c7X30EeuRVmElGoPlt4U34zezb0/NiOIk/2qEv6HfMrPeh9xnUW9AtsHxr+RAWjeIrgw2l+xd2jhi8ic1e13BdGZwgzB+wEKUg5ezDXClJWrgFXoy0iiSMXZ+nDNLZD3K2NsAWpbTZSQranM4WxGRbqWlu1e+ocqJ9/lCHxEqEGKqbe2voni/pqz8uvizlF8osel5MUlIM7lIVlajCr5GiDtLgVpOa8wJ2IYoRpEwp7nH0xFqeD9glXz/6tVO69s+advegZFCDwqZjAkJpVohEJKlza0idiBRhqMXOR2VBnDKhX5xtY+WWl6676oyrAPsPVydijLtcuqpWoVWyV+X8BP4EdFNNPVgsKl/iccsXx8I9mMncjcXCmnB75Eti0QUq2Yh5pbJYmV4eCkhkyKLBvIEbsDFQ6fB54mWCA/2/VjWpFulbxf6gtMtmlXb6A62oXt8lKZFltdmSJ/lF4FmbPCjDw3DwSBCXawHHI2rYZqehVlTownGh2ypGIYiFbtWF940C85sk80tBTidFHuUgXoVvPqvwzbxsG5MDiO3TzzpkorU/OZg/rbPRr66WIhboRF6vE1G/w+IO/+tg3tsyBHmbs+B1ws15MbzrcRwf5/G2moqdTBN/i1R9Z+xBK0CRNSb26sldj3Kb7wB5t0KiWl0cpTQmsVqiYJ1JTVDC06ykUnY1nfkgvwP6E+rIB7umIPpPPP7PdEgyf+bzfqI/zHubj7zNy8UI/21g4Y4v5jEBB68TaynSqwiE4zQOLZ2NTbPSadmP1HcMYvecHsbN471r90i1OKd7Qfec7t8BtWmLO1dTQBNiQVELQpHNzBY2u4UJDTAHgMfCXH7vlQvtLowv0Oc8mLnonh+E9s7Zwfg8DwwmoB8g4LwZN1P+zC+wr0fCENPyWxaLb0NA4iYNzsiAaemZuMPM1+tBIG9qeWe0f1CqdykdBb1zelU31K7/0t3uJAW8E/e/qB+1SxQKiX0UvdUMHuOm8wU0Locp4LPyCrBiJlxQBDOLMeB9izn0n24Ff3G76dsQ5qb/QljwNdFdlXXl/OnxNPEKcZ+8ALnr9FSxI/Pp/eFb976o/+eJAOsAMJkWXiDwOmc4ySne8JvWcRi8XWdnxyoSZzjpUxYZDKZXU5yJk5pKOYSqiTvXWuEMJznlN9Nyqg44w0mfUqlNc1P9/VXh5TVO8Apf89dVa28J6lrvwjrrrNu2weBybh2vxmtxQldyXyRBHdDVrfAaJ3jFX89zTpUAr3HCr+hD/VXa5okJtvlsgm31srFcjjWyZIXXo09Ft6TmIwYfD9ewDcLmAXzHQtQWEUCv0YB8fgFiqXINY5cg8T26YTI8jsbXKu6wxEahwB2BI7TgE3j1UZbglsBV+6ptIhTKGxpM0XfbwRdD5k+bSy20vS660vLyR/7xtUh8ptzf4r/q/w3+liivttHffnqHXiWetEMPK262tKFyfcq618GA+81pgA7E66TDn+JPnShY45/LdBE70zqpgrkStCjwp/qDyXQyQGtgGmoXwqYJdCJ9CzutSyWjp5ZhKdyfOlFwS3AxWzOSQv7U5mDKKyBqoCZgpbZahl+cqA+c4D7oCDE7BbwUGVpdRCG/2NxHdKSQpxtIUL2WPVVI0bBpAQ9CWuAgyrZFrbc8CrcV7m+TuLQlA6bCMaX4RQnxskHfPVN7KbIPK0SJBZm0Qwg5UqYusEIPmRwzJfEFZgZjFFyKzCdJZh7x3LU457OTZ7fpCEKLXVrSVU0MYLpX7L6Kiwzx6VSwmyw+j2w3EG/7y3GPzeRRK7DyplahKc4elv3gRhO10u3U7xSlIVGIqUstsrWPyO0+KpOWGPlTU1mfUoE/oborNd+3WdRkHXEEAQgEiE4eA0QX0FecMcZ5zzSzLLTCl35wT0e0YpBG6k29rYl6X4u0XTsU0H6F1awWtasbXpUSne0cl7nPW9zvAQt+EIPsyPh8np3h83tplYAKLkvZylVLa0v9UcEaqf+rbp8vLdNKWt3t09rntV9Z9ZUqWve/ujmHQZx1ZDnmO5Y58hy71Kg+6Ov1zfIt8q3ybfLtWr1es1bzj4avn6tfIw1yKbmW3EruXbNizYdrTjfcb4T+bf4La4t9ko3PnAx1qanUXGoptXedW7dTN+a84bztXGjyxp14tqO/Y9/6xetLfTf7/te02hyhd2xYu2GT/lgL6XQFdm5csPGoX7Tl/y1vtDxqmW0NM1OZjczpTR9v2myItL5oXWkLJHOd0zvnd17e7PFf79/v/39/um2m7Vbb07b5dsCSVIRV2TzrsENbTm75cuiX/W15X0jv0IN7t347/E842THbsdJJZCKcwZW4oW27t/00sjMyGBnunDjBCH1s+/ej6UUSf37H92PBWHTR+4vmF7flmJwUXh/es/PozvGdn9v5anx3PBiPLH5r8bwrki5dAqZhDq6HG+CIc4tzt3Pc+c3Ev+kNn74aboGR9AIA9VB9TibgCeCtmlZzP7pQko51auL/rsYGeTHYan2Pm8KvRqOJZ7D74CN+Y+Wvv4lfO4D+KoxWoh9lHsV8TJKk5J1P7+CzWeByC4S4PnDg4M6dBw/UUG7Wf7m+K/gyJD2xn/7PJYQLoSRYJ20+A8EXPXn9G7vPc7b7upBvlo696S58PoC+ihg4rK03+3+8jvlWxoWGu16CKxW7CjzNcVNuMpPSW90v/yKfLmzpw08GI7sWtxDByYBwRwc6jGgosNzhPXpFPzMcQmHiEh8ojeUAI06kTyACWKBTVpXzzotiQqpqJAr2Gj0v+1FnKHt1AQicQ82CGcpQEK7X/BvdxP4bVr5pFTR++atvPKG9zUHw0KwCAEiWYHtgRpEikpzYWndroIpx8pazBTsnDUoRdkDlda57jfD71PS1olg4KDNzvjHWQ36vuELpMiLSgvzRLW/vr/40rrvT/lE4uZ9xUPQu5oQ6l5rq2z4cl7pb7la76b3JleYkNABzzCUUaDFUPYqs0hktT4tcka9t3sXoWxIW5iZ8M0dOSkA95dmigwbrl6sTeCXi5icJopXZSfp1YHLumzIShy592mZjL8YvLxN4YDDijUdve/SNweufNov92laSRvLvKYOuYYXTQHPMJVBZkentY8LFOYkYowp6gskMAVe+X/OUE7qVhqf9s2h1unMAC6SYgfvwDd14+pGa/PLyXQrpd0jXDrjhlrL/8y9d0UHdUMhfp8mqua4bHF7W9U6KvoNeJdjrW29oWpcIb/kqP5eI2Q7Xz/esSQ8mZ0R6dSQNencKhdiBiursQKH8xJySp/yBbkddZrTgqMt4/gZBa5nVkCsFlACon4F4Cb71cSJxeKhEJC8PpG2cUEnB9mPn96AOVTV/HF4eWD342EzagCQf8CApN7OdW4EGo07GpdpmIdoU4nyJyGM4N9K/7tei23tyDTzU82PA4NzHLquoLZBsR6oYfPTNuZbZTW48zgqvPX239agluIAx5hI0iuuL73Xt20Y0dSKamENZm8gi7vnwDuLMTCZ/9LhkYWjoMaEkNzsdDYadFOnuCKiYSZxhXNR85S7J0wnvgK5N1vDiexFEh4XqedvxFRcKIrp/+7r6QVg9o2zh+6cKD1w8mceSjvOtDVshTph2KzgO/mVV1gMlgdE1WaQigSPgMYnL1M7iGKCop1dR+oPDqcA8SkMmNcVEh1J5AucxpwgfHNwW+wzjE7Zn+elmC/f5F+RijqamGfkA/bM9rKMYTLcaWEgWa3xOOvx9hn9DZw9OdOf+rTo47mwJru52M7+YbKJLaelvthzU9NYlksi52S1kc+sQUS+Sg99UEvm+hy4AzD14ZL/mcIQ1pO7ak839KlPul/zu1/gEx0PmNJWEnPtfy5GqBmSRy4emKQ+zEAcWZ/ts+ULJwmMSLrIjw/vqzsSJZA6RldX/BaSZT1xVnUJEVRMSjgd8HXWkdOV6OnTdWnmg11PoKuR3ItuNdM9yRgCFXiMS7YL/WRjdVAMTGwU6Z2N7eqD2m5i/qWqRrkY6vx/O7ok0+2VYgT7mOEjPLq7r27HBvokct6xuJEz6MpuYzu2L0CuNxtJSV5VB8WHY9rKp/q1+RE/2GEa3LD16pDeNRDHkh//dc9yTnbyIEM4Yn0TCP6CgQW/UFyxrQasQ3zADZmMK/lY6TZvTqFoOTRpzGFoI+Uw0t6PMuiU0RZL+cuneNSjAdvuzT7vTVrFYHqIJeIg8br8SEue5CEVFTlJ6+FyzqfJgaamsYOfTgk90f9Yi/x3hifXrrS751kfHHIMl91HC2R0MBgJdpRx/3ZY7XwjOG+H0gQxkXBnwwPW/Ztx3u9gpurzo7EldX6dG2O10hCor1UGFDzQ2FkFMEVGWjRYbsj+kqdTU2dF3w2Fs/IXXFKI4B7XHo5G42H69H+LH+kBPHH5+I57nXxp0vQ7GZ3L8f/RkkzqmnqyYsBQUfuJw8GNnx5ehlIWKhw/89JthidRTwyRJWMIBoRShF6Eo6tPNzuY4zrFAi5NqDnDFxdErexuawkNmH1+gwNrE71+cgXOzB4AnP39ABxIjKEbomOlreoVDhHaVTdDednY+srhhfe+ZLMhhOXOqOfpojByrzB1T29X3Aiy91uihMFZDfeuGsRRLv8/1qgofPCJvOxJ9DvcM73fq7zqEsZl3/DCgc0c4MDdLkrugQz1CbIAYoHXHD6XEyA5W/czccCB0gOsUU0RDkV3OyDGs3jMy+sDRo6ipRLSyL7WlERKInENqCEt2QXAC/e1AFXFGZ1pTmzyb+SuAzc6tQESb8qgG0zX0zcOJJB4OwwCPZZZTj1ypuVT+2HGyErBx9m1m6TDELcrMkPRlj5+KZ0DrThOvDh5y7UU7YbYgAEedIVGaLHtqCrDEYMO1ciyzQ4aItzNjh/ZHnqOZw3Ud+WzJ24pdGVB3A8x5UC1lWciBkfzkUIC4uuBr2zRWeDflJaMR3BUOzc/Ehyjwz1czHgiDV5x6pr2uhDARPS1XYGWzB/iHG/8npYwpDOUntShFV6eJENjat9q2j0Mq7On9iGcYtwLEeYNJw8o9CdwGwAmLgDXmOBiw/J2ZSc8eE01TLCqqnTo048R+SfZoR7vj+CWpDQHwizdzbHRMc2Z7Xw3HYi74c+3g186OsPyR4KwkZQHmJWlCYE3Vs0ERl8vjSTcRhshGdb2njsnBYKJMZOjU8/j+yJbJs2EUhweaeJRdSUUwa6fMQ5cl1AKqRnukUCAT9Cg7gkb0h61dhLgxv2ZA+MfPJrl6bg4nvFPs5JRcfcrJghKTzlYSnRDcSp5ZeMJzj1GrsKxXJAM6zDqlQezfClbI6gnwu8R0jMnxQS92KK1dmy/pHOvWSJLbkmHwlwyN3HRHDTeLLGE5qJYWuvhhLwlTObKxx8FHv4iHqRWjUu0NjUEA4Fu6LnuaNPYNlViPBw4RE6BG9nZaNWydfTs/bNQnlhO6cBvvMf2rIeCa5Iqsy83KFrrJWQdUpDOfk5U/tqPUfO+YL7tHHTNzVpKuYaHKvdvDxS5vjRfmXbt3P+AsOpWq68a8ftwSh2SkV+4/5ln2E0O7X7pOVcEmtbbucVsmmw5nYik+McUKYtDr5HQm3ZDltz1nXWdcRSInPxdMpSTIPSh6zCHdjTe/VL2ijkm4kzKdkEEQexO5oKrGdz4NlNBGF8oJu6kUUbSZnFog1DDTGN5CnJlNpPccdXquA0ic+wSwSw51MmKCi1emk/aG57LxJ9+ANOY42PwI6vPBi7xSVuyPss4jrR26yXQ7Hp603eiLihWNT8blmST+yhPhfMlNb1YRfMVZYqWPOZUBKGlQUrnYqJBqTaWWquFH9KHgqbbkQKNXk6FFH4bKIRr+41T32q6ZdjT4f2W7OPxVU6omDqbz881S3LvSsUBcuujQ0b237JW4GaG34spWYryQkDoqg2nJEWkJjaLpLo37vdu2qUvnp8o2CoEWQmZkKmV8oF5V3uczM4VTUG7KN3BLOtE2Triox1LS+t/D+FGxwiLPsKA8Gqi8a+9VvwpB6QjLHjZqVpAxYhZg8pJQ0mpcLh5lD+ydyOUey4hi8jecMHz1pbxaTl6AMexl9RBt6vOzmCgPSTQnGUE1WTmlspU7LBY3tpsQNSmfDflZeQHY3ImoPZuVYI6IPg/CjKx7BIJJ9Fvdqa3eRVhUVD0lY+gTAt9g1Q5SAfehGZZq9orjuWSmEFQFWpxRGlHQ+8NzbcU9CbjhaZbswteo3v832IfN+AJutvqoiI/lB82VTvOSz3FUImowk4Xi6EiopmpbUpQpNrY25psi0THJzwNzcLDx3LGsaUXi8Vgci9RAWZ9c2nlJ1UOADz8cZ05hCzwT5YVpzCbG6uptx4wumrnwnB63VrJyToKowLQTY3FKfSXeTHf4Pkk+JnwPRCmPxXD+TUZQG7G2zHkg/ILUdVGSCN2gOMa42P4A/wibtti+YzpIEBO9hdmPim5tHRgu+MFGKZ8vNYxBQfpZGmifrSh6T2KKogK+MnMlGqXph9kRNvjswE7LOtvuq1QGoyyBQuJTbuGCgyhEgXq7VlsIYD0KsVxB5xf2Y9XMUUY+pY+KbGN6+jYukDCs7fBYz4gz2WyCJtYkYTcqsdWqnF1wKpWNiSyBMnLVVnb7OV24lYmEwxGGzZr5LzWh+vuWHxq0gF3T+5LGDMXp6y1oNJg3wKW9bdrvOMWs8ey9AErhVq/lkEdeSWtrKEJxw9YeGkUQjsCDEpT+JUMrgeXR35PH+hHe6xcuBh8dFVfJ7Wk8dzPxo3IfgQZxF7Ny/G0mwnd8B+itEpYMlJCrqrmww+cfIF6Bq2rYbb4dAEYYdzH+QGjY9SAMeIg0xeqdNDlmIy9bXsxbAGS1E+lhmm6vMqgbIF5lmOxJGsa3iKuP3S9IWIwyDDkT44Kk2NtwXamGOkLFyVNbmRLKppweGQsB6uGiTpWtTivgePa5a2w8fTwjOU3meHp8jHGLs+mk1ZZGfax8A+mZoScRu3C+iYewr0QRsB4PHDF0sYBw5fe2KnBRprFqgfFJbHw99W8Z6354XHHc5IFCtq01qBQErw6p8IxUxGcZD/HOeXdzunznxCMv+bi0HuBgCg2wx3lRE0RqX535CBQ24yw/00yTPEwjmTvkjtZ5IXHAA95z5ClYjKyXOLm61Jo3K085jsBKsYREy63EMghJXkPIWz0aZsoOPmfcK8eZOW4MQpSkfCrc6ZZADS6rDHYdmuy9/CqH40sROUTv94ArDnZ15MzB6PFpJURdAEJECHWnERmTU4d8wjArlV+XnLJ6unKM88W+qqr9xQXOs/9FnLsprodRdi0fYz3qfLsLPLu+v3nfcXCaNq8fjKpWPeHpdylsX0aqsL7zFbRv1km1UsW/ZNQ3tFry40KLi2CdHWMxpAiccI3ziFt7okdDx5j5mzObgiKS1axByIRzXZFi8/ZUY69Ryaf85RqDHejmUuyYs0zOI5C0YVzylxCH7nQGnRME7eLW3Q7iC/CUBLsqDDmf3pCcS/OmyKG6fO11VCglFKXzrfuKAU9h4XP57YnL6oK+8DlnOw/9sPENmc/qWKaxXiFHmEgkHqUJb7fc9mgES21SdREXvNwLGmcdyFXs0OGSuuMrtm4O84YBIOaRFL2mFZwfx8SZDcG+g3RxL5ORoUtGBg/xg8lFOLqngsdyUZzrz3zJRqzVsTnt/Kyuhh4xEkKNMTirsxW6NXI3wUIdEdqENARVsYZSuTi1Fznt/ImGJpHZfWcy7CdO3ZKtucN4AF4ZV0JZKM/IgRFojpSQF0GgoD8DK0v5ffxIDTSd8np8zF5lKtsJoVNKvxnpSBUrXapmMs0FNzpTgXVsAF/jrHwojwnPUe8igIqgS4giI4CZXlDFI8GZz8SQ+UX1rGlAu8GIBXdDeDp1UBQwjKbD4TyJ4vw710NuKcOzxPwDQSVDqxPDOZR2dpJr+Cpu85rU2C6TthracplUk/HCM5QaJ5m5ChLkR/OYi2AdcaA2obFQsFCw7VErgA1bt/Say6900sE33DVVUMZYqbHCg4gka4scEXDgjNqF0QcSE2A8jWNxVcXSRGswXp226ZZK52rpg6kGgQL9JZ3oBru4UkSFe463K18qOfnPPYVjUoxqgQv859Q9ljc+xR7kfxHi98zIRcMRSrEhHZK/4NuYN8pbuPJI+MAe/gyTQRpHVR5LlNBokCl+dYdBfY0LvexEdvUkKzfQCPts6UVyfaUDeRszlx/RzGTonVs5xuVL6uJB1RV3t679lyGQltac07DnaQNMRpSFxvu2PC7OK/aVdtnuLHKJm27pA/tTSadWeWaq/DK7XA764xf1S+yEfRwtu4fr/MvkK84f3zRc11y18hEd2mofGHaXhaG19DpTiY3V5S+vDlOUb5gq/uo/4fqyi6613jyVhuNO099kT9G+IIBMaZTupHvrS3XTsZUxlpCcyK9AeZkTJae7Hcx6o4AvtlQlaaOswzDPWt8QiZir6vNaL1MRW43RmlcUmfxv8mUkyuPJ38v4dNh75FGpoHUoKW+xpFMX4ttvz47/RJA1MQkz0lOzctWww2ykxw0KHZ3q06gRRKc2rGo09ejpgkHReqBGVZLLW0x16lUSGx4eTujDWmfciNEaEV1SGBDiTZ2pC+ClMIOX0bsWj1yuZM3yLvGlvPNXARDQQ2C4/rfMcw+M0hGE/U6UuBTdvIJDHaPUurD4llQbr2QzlFrLchtsqVQX1IYUnKTppIJUSwhP5gP2yQ9syXIrMhRSXHdaTaJoW001C5qQHreUWcx92aqqWRSmatzBKYhyZmJu1RAFmPA8GSCf1BmMR2ckviBaUrFrstQKlj0TPQxtaa8rjzZISa3qy5pOoqGMh/giLwg0Va0mipJqWp1MCVCez/CBj461H9i7eM/4vhNEKLmC6tzcTM/Ms/yy5AcL4GUHOw7kSj19XZSrz0uUwnTCWijoajyU0UFwivgl4o5cJ+sRp2BrXtfm8Qn6WO54EDwZlByeAt0ufKIYSFUZ09hTyaMzm0XzTc/KNzGKNy4yjoQe5I6KA/7zYymEoKUVE5jcUCgvnMXo9RDs2Q6eAH2uurdmMOuaVyDQSbu1oT/iTi4+sME3Ohb1O0hsP2S5qk0bzcf9lSzCfe5+Ts8cciXFV73P2VuPeeEfJ49WuXKMtX9/VfSNmb0qzBVRyPZAYi6HC4WhVHzcEpab2Gc1TSM1xhmZzLePN3kadu3yKB5vGrcf6z/bSlwRAe/jirV70U54fRelM2PCqgfjzDbR2Z7TPa12/9MxbwLWA4OSdEBAMgvLTujS5CRxl/yvg3/hJmj1D595r/l6gtVqvbUFjLsmK5/mC7oeVWV6ZbEXs0xmB89lYOS49LaA04t7SvfWE15CT2G7qdaSGQU1TmZxgFmwY/qjp6RpvOZpd42gA9uZk3s9JSEiH9zFegVPJTawulmtFo77wTnmDMbNGvToUc9QWHzoyGgaMLVGWR9JbgkKRlUBF2aSEarXZNcTfSf5RLMIYkC5BA7tbI1obXuRpEaT8fS0DPZH+raVeP94teAMQyFpptgvGNdR8E6b7msKO532/LjXDxsKGPe4f6iU5Jw8Bo9KeBC1iytL6rJNdP2AMOXm7j+ri7aSwebiwIEyovq23TOqPieO+k+B1/VM4q6yHON7AuCAEwvMsinf0heXzjpuWjokWWrU0pb1fk2HxHqUbUOsp8T/NGisyYEZ/lCkXe4UaSKlWcHtyrbykshvG1uUBjRUNQfgrt0037Ed1NbEHvOa9nTZcPk9j60rSixEX3+MQIH9QLZSOaFDF4dKklv0wLXFPX2UYSeRbIL5S64teqA0rfzmJ6LMfFSUVS2TKzypix9r0RUkRcNcc1IFNb14niFjhaOJhpgS1AulTfutxSy/T+Pm7HVUMzmk2ID7D43zti1QW2BQXvMKSeaxXqszKQLuX64qvw4GfhOQmnO7TSx6IMgXddRAbe3JU/I66nwGKXouwRWLvFRzLsh0PWkhH0QZSqtdJnvPpIqypzj/s7hwrGTXTP12gTWC+bArrnrvYWFq7x+K66jlkVcAvXj+hKWJuc2YPFCMrLGCGl9x2dqNqe5hNxZG/xBj9BEG1pi4cGydqqmWWE2lNRzvpJWEqDq1Gu60c0koGnb/dhzaa8NF0bW3a3rqACvTjBCgdIzRGKWdvnIdZQ6lRLTNgmi8caJcDcGawwUgXbVlO3jQ4IqHWM/dFhjH/g6IE2AvjlOfLTuueqF5RcHYa1RQEyGzkPzaTMcNzuHvrxNOLPWITnMvjfiyiQYHN/yy17Ot9x76pRs76+1khyoSR+3hrH4c25Ti5ISpaVZKFzk8RaVbSyURUj+sLKVYYpiI8gjtccMMW+O+HEuN8bZZI7wNsoWnelRrY9GkOo91RvP8+ISBmKkHuNzYr2ariizOy7Kkhiog0Fsi46IoSU/OJ7vZxfxERo5QzQjriX6VzSWEa2P8pY4u3RmNs44vEs1xFnzbWXdXNN3sZCDKgY6Y7GzRTgufYSrSFkItEd9kVsQFh9h7iOZTquHLaL9Hj9EU22gHCpCvvVeZHQZuuTl/lDNFZQUWPLhJRbdQ4y80rEtV45yi4ZVqCW8kzYwriCrlxJEjlDBVxxklhpNjyC7ZNsRTvgia49MyrMxSQdTOPYgqVJGrD2a/cJg8DjRE1u+YMSr4vmsHm3ouTERbmwRJpHkyyJCUt4WYCpFpUXxYJDC9GXcv1zYCGnyLthke6rAKpWGuWDahqu1zE7wf0Pmja/3CQnYhpGGRhYa5cCxo2jQYodvxqAiqu+pe3sLiPDJNTQogVRA5zioWbNTm5DXTyvBhZ7WwK8pLRLv0fL9LePO6npL5lM7nbDKnC4Ld7xCDhBvictBSea+YUQY7xWjaTEu2gRcPsCRb9ZqTQsoKJ2QipkpSIUrr7lFtOvC0vrYqLcWwAsdjCsBPnZ3La15AcVaScjKznMN9u/jDEpIxJoOl2f1climI69RF/hQ1STUoMMNDRcO62hsTiTy/JpNlagv3gprRpZVROoDHCOIow01jGqztn6uO1HJFmLEDFGnMp0iaThsr80W+EcFlmSIOUxxP5eDeJkpChMGJGIJigj6adBkZuxj2lS35otapWVazfTSaSn0tBecai0VwAl1c7naJ9i/4Efr81KnOoJN7u8S1unxpXBerjrkRryoCRdTeOovDY2qCFxOqWWKbYFRo6L85JUoIMkhIYioWeupv1bjyiWmwAvHKezv00/zPboz1XUhGR75aUb4qLCzoIAO7I5knKk10SPkxceg9MihehuVs5MLXvVh2hkilSRJZZ9sxBiB1kCeLR8xioZvvXexL3p92Af2BqAv+p+xLcsVxguxoiHnRenbnHtkBnIf+JLI3rbNfMXUPRfMXZqQbofUBP8OADy+8CQ3nRSIxsdyy5OcANCMUcJOKaSpJWLHCxEyLEpVecKTrltKYw1ljerxQS2MEW4U0Ra9O8Utf9y6Vxo3nMoDXktUq2DuS2WKvLIFn4VUK3yVPV67b/AB5V8LFcxKkuBC8zR4lQlJxxoNS6hdi5Wt/2EwZYC4IoAcjsFoFVk9nVqDifCbtih2PUmExpdVIapsuQbAYcpyEaCm0SywnPv3jyfJJPLwUdzQYrrc372ml8549Cf31QEZ7sVlQkVIXSVJtbwqU5HHOg3jw4C7sVgQfRZom5TG9qViPFdLg/gMNdVCiwCsWMONskQcLFbEM/CbVr8u2LaZblhFhLLVaiTFmyCST6Ve09gjnpDSZXk27Mloi7RZpYzjFejOkDs8nR4bEN6HSKQST2eK6GEsN8giI6I/TfgZdHAi1eVp5ms7m0dYKeDNfiwbrn3iTosWUhH5X3DQrYlaW7US6u+fqwwlzAzVtP4rALnVeD9B4Cq32TQBmuB/8fePDsjjTuFJkeo36LdWjPXcbdI4isUCItxDy1GzNCUsmhSi2MmNCfAD8w1ldig+k7tVmI1lvaC0VfWLl/vovfcGmxM9mmvTDIefiRzRvONwjABnmv1qbO7s54Z8ly+0AV99rPAbc+jy85/Vmt6My8wpQIwEg4M0a0iifLvCvNym29P7p/bi5ZFeL7mlaOZyQJjK3UcSeaFP1amcJEXktyxO6IUXkGFqX0k4oIzADmYPoRTOORkThklYP/9qTKyIW4oOxJyHZMPgdmkl+MBWc1RLWPGbTnLmLWnpJWG4QmFMol1uRFEJ0HpDQeQJIjq3JTiMxCNTsGEnMbS6LHl5iTRtH6Mh2WwZKKCE5lZDF00YM6CzZHvAnYrsaTs5IjB6J1MnsbWaTBDWKw+LB2sDHN5Kw6plKHjmEXqogOEUYRJVOYL8lhOQm1tzdNB1H5pdwL1E5yQjNBgiDAAiBGNPRYjmk77GjY3y7IPPc9hSQlhwkWiQnANtFxv6dp50Cz5gqjxrhmkwx4bTn6GSQqUVzjg/Wik70wkA4INCcy2F6z7BjwZY2+COEDdhDMV5jD8Ukg1g9HrwWGVys0IwQwkuiaQEEmzES2HXrSJRGO9ZHnzDspUurbFyGYyW6UUTkKfbeJzpFWck2nooknDLyyeguZmiDnXAJPoaDkG0WtaHGdOug1fxUyx3rCUhGbjDUQyFshUoog+rQouSQDVFfbHbPQdIziQAe89CTTbl/G7KUDYpUnUXxAWmRU+tGga2WRgmDFNgok5jrlYquUUVI6hvV2FK2JwPRyWKAAL2YgEaBMYmNEjoRbcr9KBoVAhLeqEIrzI1qAqNr1BDF26jFpqbRB4cNjToiTW70JYJT0+c87JDGWKhaeZneVKAxpTTUy1eCPmPZTIYCUkONbBkLZWaiObogygaYkC9ZVLPOj/FJGK+sqc6YjJYwjTZujJePnLCN7qXlKFbTQ84hnceQ/CpFhp/RmWz0R9Rq8kOznKFkwLtosfDnJnImay7LYRlDNS7DRHLuI6vIkVLRGJkx8b7dMt+4y3jOoAbdkQyx4BLFeWQsMgFzozpOl8HQUDGnKOXGWEzPaAzY7mJL0lx4m1ugrMosksiYM1ttwCoUKBrOWllvbKCj1jVy3ixxDztUmxjUFrNZFKhL80Lk209UOYO8XjKnj2TAwIieO/dZtbhPbqaLA+YaV8M1KWNhCTKWOWpzSfHr65KuoB58/IBB7nWHyNFoQuAyXVM15HWn0+9b5bKcMmw8NHUwjsyUKiXGLuNo0RwMnRmFtiG7g2auTlSbZJjlQsk01dEAMnKyAQ/aV7HR2pgTlw5G50UN3f0kwvsb210T5S33GIQ7P/ar2W43xx08+oswV4ff2czzrAsuGshuEIdL/uKyv77t0tUfar5hLa5Y4A2bnVBjmP+IfkOmV851f5PjbXHijZTg30ZJVlZVU6Gpu8FYp/QN+V4qHSbm/JsdXVLMO1azLFl8C6f3lCRbypGOdoyj3OgTd5voZ5n2yTrTFPqa6lNu+T4zzXTvm+Gheh847Wf7lKdGKPR8Synl7Zj+GApl1qCT/6ULP0Y74wt9+HOUwgFqerCqpEJHmEBJsmRL5OKLnj87KSAqDjui0U9+ccq3an3nLFmo2eBHGqHhc9vNUpcGLN7Q10GPPUGLgS1matePD0U2KlaoxHpj4iN0wlfohZ8wCH8RoB4KVE8ZFSSTeilYvRXCJrd5lfp8C3gPzIBp8CM+dREDu+aYG3uaB5rnNGC9/V29czqfn+W+/o42HDdbX9o45Vvd0CVdCoVcha6LE7j22WrSWnsaBpqTnYsa0xqS/X1nsW5TUT0Oc1pf28KmzoJyXlNn38fyHLGiTaGhDr8BSzZ00YaOYmiVQDHSSkX70Gqf2aTxLaGqU2iyKZYQ) 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) 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W/B4Bh8Dn/xyihOx7nqNxmo7teY7dQL/ltfAgjdrex/w9anqUWhgX5tWl2ppIeGz8pC13jZ5I+Wg3Gf+OdeLnWisxPDXH0xCj5JSPiZ+m7Z0ImGfBiZEUWvJzGnGxh+byALxhsOzuTcFg/jCU1ic9RWg4fn9SOsV72BxkcPKu/+YjJYStzZiqBjs3NxKPfToRgZGn+2aypx+18yr2ynTe64nKzU8rdv4y+/A4IYaNWBobNb1+aG6SfpSqfVRW4aie85pLgIO5DGhPWfVW2z/fVT0j2ToVS5NGhtoJuctK0m/N4eCC93NcpoHt1S0R9GFHBXWiom9rzuTKm+YMfqGt18TDB5zDxjs8HzRpk3EHyHfK2piy+xtBk3aI6iA3cZ9/8hk73wSjCIygUFXmymCOPKPVuW5yLCBKzE+H5Xx0phr73yCgRJplIjvZSrhlfPVZ2gi+5XtvO9gQZ++souOHubeSS2OcfNkeEUCaZJ2b5p3ypNd/c6/DHt/AkTq/yelG/Bq79uPDslXtPjh/65WvnesWX/4zyG6OH+P3NaTv0Ky+HUI4nsanQlsKpX3oFK2LUflqq7Kmnz97b6Y8MEvWa999Z7RO0n5oBLcqMR+XDHFWCSw/qlsPIoyrgmZv1f/Iaxn1ptlJ+wfODEWkvI+HlMhSpOw9t0dS5L2ShnkZB/FWa9l+xVEdo24H3qPxEzbjGiNqZEQuVpoxoLjKD2TnQSZLlF4Bz2hv0umYyAijmlkTLZKD0nM1NKdxVeBaxaoW8jubE+YGJjvMbKJSAqINXbOoRhVLmrMPGhp5pWwyMl8iPVHwIY7PHV1XVW/2H7wqdd1k2jynjCBznmQmcOQ0+1TXKAa0mrfQwCLCO7nO5Ywg3BTrfSZTesYXqxrajdgQFTQmjZEqK4EM/xaOhuwyg+dM9P9jLkseb6fUKT+Tap4WqXUH4phWa1ewERd5IzH3krsX5L6wWCPs7r+/LWqtY1OkEgwCmqhX5He+ajrGvQR8xGy8f0r82Us4yeglldlTGhltqivPEFS5K84o2ZfMl1I/Y6SmbySuuwyxVcmximFtJbdQzd81EPfPa6cS4bon+Xrs+F/K3wgw0nG/eWqEoHSL38fsWlVVVdmo4CjsJU3yRGfR3pGZ141PheoXZnRlHX1XYIUj6kMbrWFtRHi/UmNsqycQAvKjmf9coZFbp5HOHmtDqqbV2Ebdc0ylrEpc8T5vtuy18U78e2Q1f5u9mNJluNSZnI5obMaaqk294tuDt1ho0z1Vev5NOpn385jTVcJBzAjsxRnfT22tK33TkOogrNbeCQ4tanEXJaJw/G4EiYrfaE6lrfIpKR92bk2Qb5SMVXNPufQ+zigRNthxfeG60DjmYhykgvNWFYZRjfC69I2OMh7B6jwOGrkv+OrVsKGcFrgjHuacSislqRv1mPUuUkq8I/U3LSi7sqfUqWHoX8b+1A5D6249VsN5miXqVe2RQwJD887Q42PQqstkvwOcO6UbH53H0ZF83zqiuHZ7Recy54JNnbRohMXPv0e1V2tYOcr32jJzT79RG2RX6qHVO1h/LJpgN2j4uvZMa4L2+KeShm/wCmq4GL39yPznI0pWzkhlfDY3ohnOrxM0nd/EqliyVjGbSJUxEhfKRyfi9L9FtU5GaS4bScxqNPbUCKkQExhZnodFVnMqL0f+/0IurlGi7xBm1rS5Rp4bDeWYCSOa+Qpwf7G5WT0K/vWvKU05ES6v7GyEOvKjbtmtfaL13I+OyqnkrPJbJhIpeRc0CNSM6E5VLbKR/Fk70+DECL5U5UVyDfk22Q51m6b05p/wqQi5ibRQ59iwiiRhB4BKHUY5aAZ3fcH+8j3O27tGknZdwzaxQ0h+PHc5wXGhQaNno5Ivn5ldmEVUL69+w9qMGNFjzHK9XqRX7xKDw95mcsS7rjjqU7OOA/Z/AOzzADjkAbDXA2DnB5CkQBBJviiXNzju/JAcMzhXuWDKFX9ZQYhHJGc5kAZKJN0QLaHECCFWeHH7TGY2mGkk0nkhsmSWWZz8suQsw3KFIOUvs0UlIZWKoExUqTeHVaiCVaeaQI1asDUaoK3XAWn7JbQdoukWW4/Qdotoj2j6lzkGDIPUL9ONmcAw6aFbdPeqQzfpjdxdYO5+BRAUMmRAkJCRkM3fb2DhrgXzdx0g/K0IkDztNrrRiXCbhA3I6ACNqSRUuSUB2QNR52qAocFowMCBBBcUNOiJ92cfkPibThlRZChVoMQXEixWBai1LIk/fwQIOlhTnVAGosSxdlMJIkF+Ki1SFZgWNCPZMkHLLc8DGBIYJhwcGDoGOgYKKgqqpFe7nbcKEQ33Af5+ika66L/X03Qvc3c7qpN11OfV69SRrhTVO+qI6uimAeVV/SDlLtBCUcByjLJk78n2Sf8zE7eeJCAhiK6K3AmR8Jxw+YtiMwiuCzaEW/g3q4+/S1jW41fwc3jXea8mqrinuKQzwPlN8XEWUzThL8/E6SOZhOu7PI6K/RFbxfqjuNwTMAeYJvoOellYzK9wQNAyXgDtLdoi7XHaNtoKWhPNQONRn6cyKH9TPqW8I8c59//JTnIO8XPiAfyn+BfphtNgDqJZlMtxNAn1t1uNf8HviOdpfgAlQ55D9iFIn0M5zKoMHA8vt7zcKmvc5Vnft2Ktu5xl+SHnnPewC370zhg/Ntc8sX7SR9LTLusZV/ef9al5z90fs/7db4z9iQeGOX4qM0b0IEGGAhUadBiwYMOBC6/ib/V/gCh0NoiRkIMWHYaq8jK5pezUBlCpn+C7CcCOEeyQ4r5ZUWBg4eAREJ8rKfZLUhwhpSnpL8MSWK/jElFvWknDH5Ph10x+UFBQUFBQUNBEzvjXTn7uzsxXtH59F/RMtZznEP5/DwwsHDwCYiU5jvgRle+9T6Wfr4vXecaRECJeSpR4SUjifg0PQEOI/FxjtpnUBM13C+JQXk0JPt3/woL3Qqj5MhEoWfg6dQi/hlCp3NtxRQfR4rzywgF1dJ+OhvsU0sBsB/K+6gNgKLdDv+vivXJW9FTomtE7vd2RIr+EUA8GFg4eAfFuSYF3XdIS3o+S1e2TFHJso9oW7LDHAUeccLZLIq644X6XR15bynnm+pSxzXtKDyKyLXZIyLE0l2UgR4ESFWo01oLOehXDuJExs2lmgnxqEXKxYms7GeGjXbgpwoMXH/52EHeLoeUwNqUQwTuKYxAnQfKhYvrNVARY4kXWMAMZ6RvjCcx1JFmohwY3JroJmmmhlTba6XAn+10BVbO7Mveo+72EL9MXG+9nfekB4qUHPYZYwvDlpZibGVnee4uj1OYbAy+Pw4Qnnacm5WlhhmUs3wEwOXajlBwM8OEnQJAQ4cIMxUzWlkBcCkxUTQIyFKjQoMMwMxEWbDjjXBBsmOEBWzjKHxCRbTFIyLE0l2UgR4ESFWo01oLOehVDdOOEX5/DvSMXK7aO+hX24KYID158+Auijy0oiCpAjLJDdUyIkyDZqRA09CsfzR4V/zd3JcbNgAY3JroJmmmhlTba6Vh1Et2YlUJRBYRaEJ/8bgry0YhXdnjczwM8WJjRKc4pnEEdrV9+DxOFGdCx+th6mGEZywtpKvWJj4kAFGiywIAFKkgJjwI2SLBqYKJqEpChQIUGHYaZibBgwxnngmidigfc30qBPyAi22KQkGNpLstAjgIlKtRorAWd9SqGcSMDPWpmfO9YhFys2NpOpuFdF26K8ODFh78dVInJUBX+lUcj00E2SsxxSJB8qJhi66E0AEBPNsjwB2XvJoH864jI8AQ0uDHdTdBMC6200U7HqpPoyVoW7etzj1o0a9jqQoeQF9Twz+GhoWMUVPasGlaNLilIsoVwTBTEmT6m5mlhhmUsPwRgekxtseGrrojy5JJviJCIx3n2RsIzmJoEYLYFAKRs1rJqGchRoESFGg36VxkB5Hx+wkwq22VMbIFcrNjWecxodlZjaN65aam+C2nvA8i57cJNER68+PC3gzrSDc3DXD2K1C01o6pjQpwEyacU0/RzI4DIZYRKVg9uNLgJmmmhlTba6XB30Vf1Bvg9mVTpAU7rQWGIJQyvl6rGcws312Poakdw7VfmAEIi1ocAeE/qeRgGGnhr0baqSPZ4v2Q5JOsdjENFnGWmSs25J6qugBvSgfY5SwFZDaM0kQMA/SOwAYKZ5ftIQIEmCwxYoKfgmFUsAROAbB44GTTKrgE3RXjw4sOvIJ3Qchjk+xGsozgGcRIkq5IuG68M0NHLI6/VFbSvFrKyngY3prgJmmmhlTba6Vh1Jsbyrfn8vib3qEWDLffF9gdEuzXoMcQShl9Ew7hWASBFDbUvugDPqEQTYLYFAHh+oaUgsxwUKFGhRjPRx/LK1KpGSC55eQJysWLryDuqNhW1jJxBOwjvYKgKj6zpCJvkEFUdgzgJkrti5jq/XDZUo7hJaKaFVtpop8Pdhd7eTx7+gS0VB2GIJQxfLJVZnnVwta55VNuJECY8VYenYYZlLN8AULvqjTtMDN2qRsnYCZE4G06SWAQ+fo2zAYvIqslkfWQoOal0d2xQ85YFukutrdjoZOSDyyK1MRVFpKi7GdSDgYWDR0B8E0kRl3lxwO4sJSWNimemgxJJwSqJaHGn/Tp4NwArZb2XHluT5XI9lGRorJ1potlHEn0UjnGcE3HlK2rpBzCwcPAIiG0FRxzkXZAXOwp2A7IWYVA49xc9Jlk7y9lR2lYc7kURKcXoQGFg4eAREGMm1NzGHmcXq3h+oiXKJaskotAiKLYr2HqCSNuYi6yZSadgaxOzFYaYiKeqVGdZLWszGXJkwxy8XvXAwMLBIyCWt4Lt9mVnsygheL6Slj4Ggzj+d/KmRiXaGGmC5w+MlLH1SEVOYcHQvk9tnKzcNEW2ustOXgna0UaVzDdxvdfTKWeDx938tRbLF1TbnBWbqPu5Td68cOtnDcHtnGTgtRNyD5e2gud0zAktBRlyK0CJCjWaib777yLA004X/Tp9aIuQixWb8hh9yGcgrczcGdJ2MnNTCi7cFOHBiw//PMgfhKpwaRcQ4RB1DOIkSFJCI00000IrbbTT4e4S470B4uhAgHtqEIZYMhmOWRiZ5NuE4XoX3otZBWP89sABVh+9yfVfhGMjwDOcYULyU5WNwf8UAQ5vgP/QxxpQvkLok9dYnr4V4CZ+OZ0ouqKtu60HFGiywIAFagsYHgbYM4f5ox4pvYxBVcBQeHh+atT3Mu5gGtXlZCy0uAbcFOHBi2/ij+2i7vogyKCSBp5QHRPiJEi6xOxGocnN0EIrbW6HDncnPtTL6ZbJAPfw0hjx6rMZ0Sfl6/o99BcGVWd9kGZko2KCVhMuw5Wn3FRe291GbyzuYjZ6Q4kepj3BjW9MLc8L1g5W3UNANUrNK379O1mVHoHJiJJhbXLD2E8chKwWweUnlibJTe9Kg+v39jo2MjnWPXjc0aC3Cmlkh4smmvcPmwNwJNFH4RjHOdHuE8WYfvRvYuCviowMc8lNQvzqNhC07oKXh2wepj2qG3PGmWH2vbXyQpsNpULkQIJ+ZIGGHcRoCG6L4KQorISWF5vOBsk8Blmmxqg1t3u4Ca9yfPWQGPiwAnaRpPdOZm3vZ6SJjq6pNs8vAhLUnttSYnGjhHQb05dwienrysEpFManOBoWgmCWG6xTHLQLeE5KNzJV47gqUSs6OMVF64FsdV9wAg+qG76glx8MW3XDeqkRlzgqpRmlgNF8wQ4E2NFAPlUdXLah1kvqwnW34gK5zfIr+wfiNf1Qwpx7TuKn5b9LSjmzr5X3eSartni5VapXS3Vyi2fTpq9O5+r+7ins/p0H4cN5cFnSO9/OW919Mh7Pmm13Dz8FPBiQOgJQB/lpXV9nY/dcI13lSE47MdhOleQc1eW0K4Lz2yqporqq2XIzqSWpfat+VimqTp9I368T5wYvfxTPSvSgIH2TNWxkU9o8cWhs64Y97lh2w185g918DXY02FE7buiFCiqporqq2bJHqA2tzuhVeLX7DVA2HmZ/H86HUptbamUH0ONsxy+XXI9Gx4k6Lfv2kVWD7HtCNqbKjKjZC8RNAglMfCi5JvG05ZIVHNsst1LoWRJ8Tm3vl2S1kr33C47LkZ0Fx08Pdm+MHRUG3uoh+rjQBseft4yuetC2fDPJsJlwO2F9N4GdNdi/XC7KerncaaNsp1HXa9SxBtnTRnU8LsPianhCddMSa09I8PJBWV53SqPvnh/1/9LVNHpr16PJmzTHivWSCdMGh1kPgNaO6AW9B2M7A1fwlnyqF7D3ZbrWYMlDZg9z2xocXgCk6+03j2hqySW9UOvzO/fo+BTy7d2rhGpuSKEzTj3jXgT2MEVu87Qzbl8Em0WEzWaRo0R1/VScHZLghn4KnXHVaWyvse2s78o33AFP0tJ1llUQtM1ZhfOzaYsCxGrqp7z5OjONTTdDuDG5xuYaXSoKGutvtL8Zpho93OhmY9Pdck9OqWyu3zOef6xrpTjJNyZartVmK7VDlpaqbg1qSs+Mn25nPPkxYLQf1O8m5R80XXiwJ7xm6n6Q7UFCkMIdG4CspZzU1zgqWB9ejcldS7vOtJ5ZfZWF3a/nXrrf9VsyjqErv+8/3eOqmXi2Gg4er/ZiD7bDr/7+De3TIG0CdNDE1cKu8eh6BjSsf5Jp3LPxaUWeZIObrXtJx82zwslhTpWb8wR9lhlVVxaJvsu2VdpeSNUvqpMVU2bJcWkr9KaPvB2oOgiCCSGUMMKJIPoiVkSJPXRt+38Py1jOipRyMFwqaaSTQSZZZCuXTt4mH2rnzphaClQXCkUUU3K5lE/XK9MPKb7Mqsqt3fOqhOqqZkHHqi2bqm6rJVbi9Vm8ATayic1sYSvbvLOy9W6DOr5XSFH7mAD2Cwc4ODkUx+9Z9weFdiNKZjXR7CPV+ygc4zgnxnVB+rEWgRmE9ordo8YmevtnJnktd9lxpT0SbD401p5Q0QOm2mbW1GIpwLoYKnBBnuz3JFTU/uZGw35bi0f0VA3/lfx57dxerxMeUb3M3nLdNgpAEynVMYUCAwsHj4A4LilT7YNkxernyAdckuLAztuBqoMgmBBCCSOcCKIvx9LHsBRvhympkEY6GWSSRXY7V2FIyavyr4xhKglT4EIoopiSdSkvPirTCdCDNX/BXGtwRxg09K4szjeTVg/Uvb33vMbstbDO6/N6A2xkE5vZwla2eXvi/g54CNtZt3e567uVAOyJRbfm0S16iIfHAQ5yqCDc2web5Qaon9MI0zRpjhtF9RDup+yjwjGOc+JCx+3tndvVobVduc+6aHpxUYeaQ1xOb7/qA+ilBPt9hvqYfq89APQFp6xXY7N73QEMRoefmNQHn8vCFQ/ZPMz4BHqomeS3ZlMh03fMUXq7eWhkbWZgC08DykdwLt86UUdlldqrcqdXVV/jjlIT1vB+N/puqA26h/YoC5TGH+iowSJo0wkUSwtbkPw+0W3UeazE8nge6IoGg/i7OXUcaZ4tc8z4jmbhECgdsZMG8qhKYuzxT1En3RFkKZgOOkHRsZJKPpMJyIMyWlpGt/ULXSUgdM0+QCVU3T1LipLGCVnc+n2zvSp/Avs1QDgOwiIFyyzqwcDCwSMgXsSZwD/3ZsxVTfxx+06qt1nlW3tI4RTK2dpPVPZKsZb1eRA/7B3NPmw40noEQJ8qfqtf0D80yFcoA5XnrmuymlO7vSKx7QwKHjJ7mPERskoYJcHaWMj4/gzUasxyZmXINZEMMtPK2xmRQhM/CgwsHDwC4oWkjEnhaKqm8SCOR1bt/S7TfvNqwSFe4ZYVcjNZH0CKqDoV0kgng0yyyG7n6tG4vCp/1XTszgJQoLpQKKKYkqrsh83TjdHrxRuEjWxiM1vYyjbvLPRQG18i2pmN9RSZucT0jsiiPqvkD/ploo+zV2pZXgRymiy2M+BKdGrCpDdPC0ev3Cd6WN45H4SnWGNZwCeHOGqSJU78DybSagjCIrqETdBgYKcyws3eV4IwCfDsAD1Ys7hJmBz/E5eU/hBHUQkbFY1/yKPWV/yb3VfNAF3svWwxRQ5rpxGhLpbYrgcpVuulCtxZBdF33mk92KAvRTmj5bjQTZBsXn4ZHWOIO5ix0GbxbC2xAXKn5Hj5TtMmewUJaZTY6EINwsKx67C6HvWOXr6YZoYa2+I2QODVRJXwMDcPlT7XaAjcOiBfWBRwfcggJA8PxFPqgC8z5n1AMU8xiczzbOnb8iiqnzJzAklOlpTytQxkUqP8JqT8Wyrfifl9PmkTVnx3WSonSKvQuQAJx0Klu6ulZERR67WwPhWuxDUvM2Kw9UCDvlAx8tBHkrlkpontJ/T5arcnJAC3DMnu9s5IqbGPqAcDCwePgDguKSN0NlkZAJAPuLQV+mw7bweqDoJgQggljHAiiH4oVsQ+UurylfP2DnaiFIJTIY10Msgki+x2Lp9ekjfJj4Vfs4996EKhiOJJSdxmqehRmf6Kv9kV1/SG3lHdkdnPT/lqnc2ti/fr6DRupWuv4TCJtazz+uzeABvZxGa2sJVt3p7oHU3wTrxLHXYfG8Ke2PheLp2yj8f69sMBDnLImppO6+F+H1WjYY0QDWgSmi8Oa/HF20RQKBTa8XirgEm0C/mIXg71nctU09d7PFCTT2G9um9QBmASLnPFQwYPu/akDnWYPgTiRtQQRCIFLCGBEpSEfDun94WJdmIuAl3jFFPzGtdQaYOlGUsV0Z0OINnGhr92UPiFdQ85W4UElYI06UiY6F9ffHgSTpijPWySb9wAVA9XAhhYOHgExHMJkOeQB+O0g3NnAQMHBVdIXSKzOsl9rS+rhhOcO2ipCfSCfmcQksKiTz/MJHydYSysPj1iaQg0BDLeT14FIonFFW8URHzb5+A71iOr5EIdsHQHfAVRtfDt9liBGC/3WJWiBucvqIof1Adk39d3K/i0k9J4P60Esa5nvPnVDQBdTKDLVi9vKBkvP0bi2QbN4vGMpvDgbZeMDinQjsk4klkH72fxCsP4ZgOkV+I0RzbuhOJHMB7448Q2Oz/mgXhG2feZJ3zraktKJJzveNVEU3eEkp3m+OR4cfQ2c8bNnEkza3rfs2c3ihOYGVNOSk+o65MpE0iNDlWcVJlO9BUVMTAzEp+vkGD0gvTkASQqELmKxYk8UUo6Pfu8kTVytWx7jJRRGJN7clo/DUGAbYVAIIDZB+sSUoCcv7h5RjzL6DRy44vGFuOLLS/qFseuuHz1SsvPrZs9LNrzaiamuLxurg6Ff9n0GTMdPLVXJyEjjfXkr5Go69UAC+NTRuzh+PIdd5Q1POwdv+XeuCrj2O8RaBEceracj7DnmTqRsG0o2PD6Sjaw6s2f8ah+2zYUlL8bssKWeB1niTl4RplRYuTBiTEP3jA7DeSMrRk4HOfD0ztPxjM3LaRQGp+0aXlin//ZHn/E4uqQhsCNLvRk+4qInTK+MKW/HC7pJ4QIBw3BCUirLTvMFaZspWagorWmsZVCIGYE/EmJaMwQwQPWWW+DjTbZbIuttrnuvrGNz23rzJ40Gv3Izd4VH1gx8NojfY37q+OGuK5JCBizriOEGGDyUEjEUw40giksmrj5FXMQte9ljvJ8RrPOmuJYR3mI1Bpqh5zfa0/ZYadddttjr332O2DRcxM821cc9D+HPOghD3vEo+Y874WBFCPTLZKTUQpI3qphVDMhZUHTxIZoGmK2vVXLR+qoPdxphlsGwSrMdZETnBewHSXOo3Ez4ct7DBbjasyrQhOtrMWdHe8S3BMM+apoQhPv4WI4OsY5xl5MLHlGCssjIWeGOZcHwiBQYaWFcBSfikwKMnoyshFRi/hwfGdYlTIlGeIxMDG9zu5GLwZisIobZklUCN3TkmFM1xDJLOjiim9moKOISuty4zUuVVQPbtDqmJEs+js3eiNpB8BIn9+00clbwfvSlsci91EyVvrHNbDTiHO29cKKYyDuaZaLGHObWS6HN5uuBZQhxL1wRH5UxOKLlsxjlC81FCUntzkU7UuOl0rKVshmZmGOlGyPRJbIuk5WIZ5PhzsWHi4OZhCZ9AjRiiqaigXKkiLocvx+prLiYFLhoxOgYehvPllZBJlxXsWfpfEvC3UULBSxVxfrCxcdWhQkvFJJMUQiLYiIGwQPbSG4RCqLyKN2HGm4yDQHN42hSbD4o2ftlL98eBDSUrMRi3JyR1QKTS4VglwACSocQZQwrxVYGojar7A0AywBpYwzYpeZb4OCzWCDgZH4S8R73DrL1qzM3zib7LDbPg94yLxjTrvgqlvusBaM8XKM9NDmDetxcZH33eG3mxSnaLaT0XTLDvhwnKIZlhMkWTdy5R8/Msdga1e8vkAwHInGfvQIl1LKKyqrfn6P1dQ1tLR1dHX39g8MDi0ZXjoyOjY+MfODSPj1Zjtj3EDb4J6BQV4jwnyOYRTwBTbtk64sSGfMCFjTP35sw1JBn5/0OGBXwBrw7/jlGwUv8nsb9z16Oajm8M6bwB8ESK30EGA6KOTfsgwj1Rxmgjz0k4UVA/DkY+7Y33gxewI6BQJS6nQNZ8OwdAEZJDTOA/KS+NbI8Wt4XZU2q23YrqvzdNFmPikfR4aiDTjBI4voe8wM6N6Z9gRcXmPEu/xHTEuwvRafsgGSOGwkJ5w2o9MWyYWIoRvfvkZ2y92Bi/mVb3GV7d7eA3OVW9O9Ai1wgiRoQlMYF/RFKKESvrWA6ZgUOliytcwMmHz9I6QFxl7j77z1AQq8a0Hn/nnQ37S/Un/Ffrff6afWYwSAoa8FVygSbrbO/f9nzuSgeBPEcsB6PgLiAskXESflt9gDzphx0W9FzJu9xZ79oL+1MOljMOBUOCKSs+bW/19AxoYdZ0wsbAIi2cQkpLT0DIxMzPLkK2DncJXR3XhXK8kpICgsqliJlFJpNWrVqdeoU7cevfoNWGrEqDGTLvvJnqjvnfULh8mlG664nzT4Os4lloR+ze9bz8QHWXC+UT1lhRs+MO+oc3gUS8QRSMg5oaCi4ePg4iEJoCQjp6ZwB8zGxiKXVSGdPh4ubj5FvPxCkmLiEqqUq1ApokGrJs3atQSwk2GDhiwxrssEjQ5fGjdkxKhhK4vcjJnYZjgd/kh0OVsgAWwxGc6DDL9yFWnDvY020yRqyNMFolmerNIKYwiIm3mYtEbmd3u6DwHM+g4A1chAHhtARksSklo1UC/JYUGLWRr2DBIXGgMpt5lJlOIyrXZDClQjyNRT1bV5XX6jiYuqpFkjmQLD3hclcVVcIZ+HAxWEj7SVa1RWKTVSZ6a4UolJ9bJ59KOBoXyp8inUcfKUyqfFNWm+0b8P9nKevC9+05+ORNyLsIlLK3xDQVQdtyUCMxJEJBORMD4ySAtsIS9K2fjEGFXw4oVKGCKsWK6M6wiC0AhvIC2dQCtoTMFpQhhu5bQhzlna7NLVznstiqIMRrkNydcUkQx7RQm1NpR9EtKlg+FmkfVVGIj84iAzgiLORHSvSOtMzQrDkDyGkaR6naaTBwUjzyF618fLsQQ/k5qx8j3MRdRzACVG6mY+v/ZdIS6iMuMEDaCcruEZB0WxEIkmKh6ZYSBIwitBPodu3xFT1Rk7rMOSJzA8trBWd793GJYhqF5FsZgkET8lQMSIZZLtsKKIBetgJ0VQsiYid5TL3WC9MROZVuXoz5TZH1JFuIeIRRMJVFHSpOuxXOXEwvrUCGHekkwIAyP5lD+3GwbrFQ6btFuS3ndQqg3aQUlBNGKaju5eTtQw7kbk90dLCYpSpEjGr/hbZRIigXFYJPfJLuqywU3B2los31IFGhqDGWM8/AGkbbrJuQRhn7FhpoCmoeKmU9fsIWVsQ0ei4vErzbaBQkEpG12zld97kLNFNptQKkgEhBASTWgDZdW/xEbMzHQPGjape+wzjtVGiMCWcEez8xVyuxUdVWLVCHxoGEa3HSVHxJcaMJFxG102Ra9N1SiaN1P37ua144oSJpgoCR7nhAisG+V8wiFj6pGE3fxBqbEyV+6OyMJStWyCd3vA3hpgEA92k2nS0Hu1g6wJG4iV4c27sjvJZ4uYslGakcasjMra3h0coruse2sn2a0FsULvofMQ4Wjq95SWwRo3il0+s4J3BysfYHQcRFm3VU4tTS85u2zrgZAJFs+b1vGdfWSPc8qWfuKIFcB/iMw0cBIERhtq5Lr4iolnMtGssw1uvpmydYt8SJetr7PLTrQD5IKkckQQxeVjryLhoiZhixk8Y0YGnd1CAY6zYpRXD7zFIbJ1mw0386BIFpSPFfQstMIWIZihutT63ovaHvGp72IWeZz+xTYw2WUVVUBqFWFfca9LOfRNaHDQyg8V/Ef6DWSwGlB5spGM8Yglsv3Dh137vFBJHBB5wiMkCK6WBcq7hhCsUF3uPihdx71jVN2VeQTdbdwOzK2+krghHhrv9ZGZLkK8Ccpf07ccVgTSgKC6t6JQtZw1ntG8276Wei+XTrl57QgRWdjLKZjs8qapMzwP2CQMISD/JgKF/WdaudwRBw/ep4GVMmWCKB4bu63tS+A70Yqi+FZugNQ4MXm4VSBlCEak9FL8xUYybCRmcu6vyKzZa/cI7MXibuqBTEPDAyJpi8Y0mLTIvYWl1R5QDmEwRqdG5851jpmOAW2jjfQ1XKgdQPfnCw0B1pdSKpykeeyFGaCwLGyRlKk1mupsg+FTc8zZ2xiJx/34H1KERT8s8oSUYD3wahEa5W7YkYe6luNVMwiYqZ5hKmGhvNcQrrj107wO/QRklYbalPxbq9YAm7ck50ZqGVJOIZcvLD1Gxnj1n1F4WDVNSGE5URJuc620/vJfPU8kkKbOIi8ws0WETaFw4q+0znhxKnCvnNeNucfrqg243ia1zLJUoqKR5hisHCUXk4AY9q6SDUozkgJGuzht5liZ1DGBUMO8tbG05LqU16F/TLy8SIUaXTlUVSyqCp8YidhPShjltKd5Ug/dE2JxTVWdqu93tqF9HWLn9WvBGf3HIw8VSkmumK/ZAtGXt/PXpuqwW64BkRGCGu5QxY5iS+zbVHI/m5yJNm1PmyPugDEhGII/KITsMQfP7zkI4VcDpJhs8wdIuXUjK0ZJUnZRQKrtkmVktN4OZQTSgdLC/GHu8OXaLR7Q1tnQv97K+9o8a5p5NiPFeZiH5sOsmzRXn8ylRdFWhZS/QasuKSW5B8MWJpwRndc069XZtoIRq6xcCOHT+T1D2SyVlyGTcY6mbPrMx6e9oFSL/BBVM0l2EHhf+uayxKHvLIlSbHNmW8h2yN3IcV2hGwpjgggXEqUCqT2zu9Hwslvd6bGAsMyk4i//chHHHhVxmqAY+xhxDkFhkvDix5zC/tmNnYzY4Sxl336XydnKa+xR+b0Oz0LjIJS9ySUC/MXPJMBUG2YdotQe1VR8pqJxsf4jwHr40QezIYPLmX0ZIi6zuXJNKUDdmChmaT5Oxel/mwDij+/hJrzGLG84LBXMWyTca7aUIPlC3RXNcDeJ6uK2WblqKQ/kVjvf2HILwpekklb73FIz0lh8xoWrpSpAlbFUrU0zJi6EPI9Y9rSciYSVTb2UBlJ4BMiVregwOg8scUMkvz0LKdA/bfS72y2hGdTwHaXifvdiKUSLNCUdGg2Z9m3stC8NrVHIYxtZFmn4SCJWKZwlOmMReD2yg7mz097vO5opEcl/QWzpB9xoc7MB8KAX8hA3u+Ued/OoigPuzeIvvm0c9vVBZJXVwl1yF1c2LDScp2XZCNVJCQWNiGWJw0qMmDpQhXAk1nSIwKUTHiLEQUnF7VmOCLJtZ8QoKXzEwRXm7E+X4eKrM4+upc8uLYvPvarf4lcxfIC5ZcD7+3TCL9x8I/lbHiX++lRmiu0MKMt0clpLaccRn8e4goFLat0cRXgJfPA7Ey2pMLhx3NrcWOzHI5c2fbyKQNJarUtaX9pAKGl91GhilJCYWOEcGX2Mn6aSRa7a4saNsPJGkWoA0JsvH/WehTTLnyUiIPwJXMvxLCq3/f9tual5P1oLQhitR66VCJXRuLYmrKuvB8VR97ipZWZuGap9YlOQOD/IxT5Neyqq2o7um2J+VHaSUEnquKLZvkOm0dGEG/TCLQMm+JwlOQgAVQlVNJnG4OtTwTGbAO04aWklWfidI9kr+CbnUprHOHg37kara6qUIMXUrqUf4vmAhb2zTaiG27M++GDuVw6p/sDeo6ED6tRSw7QF3cF8IHmqOz0H3d9e+pEMM1nzeCF6nNufy51hNlTuCpfoY3bxwFKxlTfaO9vmGPBUoSZpriUOj/ndS7ylXbkP0rATXH7lwyPt806vFt7mMToy8Cvb4NJbb403zjg8GkZsyfpqOIONLp/SKtve22SW+ksPPkckn+RMPhAwbl922OmCH/1dCQgl1qmk9vCqAzzpqtpf3j9uR6WBxCdZO9QAspjXndiERjDh2JnLchm2Tel00+rMERwP1EOcNLr443NAWC/hBZzbgLUZGqv5woAMm3BgB7KWOHpqFGkw98hjRS93uVP/bvN4MdpALxZmkexPe9yCeL4HU5QyRTzcuMn62cdpBiHx+IN48eU/8xB0cNFOPlrF+JPcNeRKfKJMhUflxQriXZmK9sz7afCngwT/jTHq9D+melC9OhVtopFUVLegEYHKcq1WGvfk1n03uJi3vcYhDKaQ3awf5aWR2eDdi+HqN/3gzFp1oRZqH4ZU77fdm5LeK4dd0/HakYhW6f5MuURLXIajFHEXBh9xFI5jxEU4j2ho77YvbXK3N3Oh4zMMDnD97SA81weDSGXOdW/NILZe0ddftaF7t1KxNwozNwr6yxTvhLaI8Swi3DSPi8IbZe0PmNrTkJuIeSV2kGqqf7pgUnaXi+4VNjYrnKd+Pv0RxjLJmeuEIKQSXyIfgy3OWcCRW6OjAxsRjU1TETqoFTR2IdIqtBS5v3G9kLOyNOI5a3y5VV2oMav/JxGqPPpzQ9GAuCaWMlfpEHYlY863ED9cptynqoV7Ff0s/qxQWbuJpyI+FfdnziBnsf/O7EHM2r2RJLG/NNpHWKzpN/vu2wXstY/+ve8l9lbEJDhJWICymP/e33idkEN43b748ayqmgHvobseaKFZUPNw77eWXasBV1kiJh9EAvpUyfqZK5n9Na9hH8Mv4MnwNlB/5/+va8K4dmEDLAfejYFl3n9tIO937OZvv/wJAow8+Dbi4f+szAplzOqUSXv0HvtSV7rIYqipKwh87vg4lko6XalE7GPnm7GE1+FIOGJvgllc7XAofxKZhBpgZDJ/ajhYG25Vuj2iGB06j2NAT/NpmS5vqranAzWELEVWooZ6OmpTXV5ahv80xMCdh+iimNvTqgThKycPKceu3vfxIIu6jU7bSqVEPh//KdikcIfhmohuCBmU8gHCg+V+vOSYTPalsZfs/Z5xl2UC3EoIEhC15vHRws6TjqOpOo2qAdQT9Toior+5a+JRMNXj1xUFIoUPr0jZ+oVrNeoQckc37w6pU713YmjDltlrqolBf1BCP7xAElc14nOOI5OENgKy2Dk26qsMNquuXvNDz8Is3EThgZvFMmf1QBNqCFmNrEYNDjRXO+p1trpI7nF4ESbhF+AP8Av4TMo9OeHvEWgDslnZL3EjiDR10Dhr1Mq0CZbo3z8xkC2aK9k+syLBwB2FGfARKNdYLDBkaoZrVywZ8M9yN34B/0hx/niPT84y0Aa8hU+hQAsQFCjTd8YqofRNpVL4Fr9S0l9/T7VdzKPwBeR/xlx4N/LzZ754hrYa5qiqI3p+3P4NgUX4OJqIohqVKjBHRhBDuWr5c22AF2EWvJo6aFZxXlIMV7600DWFrCcUE5B13qkRR3Gx1/NNIeoExQJEvXuqYQlljqqBTtSIICBAJK3NGZ29AFCcQCYJAYIy12AnuK1ztNA8jqj1gtwq88S4q+ddtQdsd/hf12TjXA/osqMxUDPR3IXoF+EJIkRS1VCnUTfv18s7DAW1gYBHsRw6D/XksarPpCvi4TAenD4zNgdPnmc3TG54/j0qJfE/Xba4mBriOPQGv6+oGsRjyeHfGh4o1XLub7pnWjtSJY0hykGqvPU4AuWn6gOEN2g6mFoXIPR9NpReAEaxdCjXUSqEFnAM+AjWsaNU5m2pDjlVnzYqYuwQq9akCebn2lIdxpgjnb3tKgG7D8/IusEvrfPEYvV2t2pLtSLEirGbjcZAvstZMagDRJ8QzwY+Mp6l82PqP9ODJwHVuNJhh2u6eYejVbuHurprqik1tBt/Adav+Y5m5/QSBzCH/7nmxt3RU8WE51GXW19D9D0wE1+RNrfjHxL4kHBed/8qFAbRTJ9uGLFP71DdIrJI11QJKgQMdPYwAoOifN15ApnwnePuh19Jx3cEYNEK0QrETd8neTZsPisH+0j4HPtVdvgRLGuZiLVdLbjhobLQ95VHQ2+JX3OHHsUCi7QA/5593yt/VzGwT+Z/1ASOMWCE0Uya6j4/xJN54EWYQT4lXW2YsgGc2JlBvCt0n+n+ELmEV3GmlNLs0wT6S9P4/UypzeMMW/88uUiiEPbhSNGng3yLI5F9SChKOpHKD9L42OkAZ7chrtSVFeWsoJbC5Ta1pPohcA1nSyl13AwVBrFKnvtDyjHnAriLH1zo7+ZEuYWxo8JUFKqwaEvIGQUZvgDXwAiPIRmU5CjjoiXTD+MJTAb+PlVMkButcQ2ebc/7S955on45y5+ItZUoAnJdUgDyRHdKt9Koo7Tn+/y3VqT0bkeJ2hxkV+JJPBKeUYpKeh67w58rLM/G47MjlF2wIz+Um5PEkkwkjjgVtAGmGGrOs3QiQiQLCRG2dCyXKGRLCEQ7YdwVHAm+Px6J6GyBTBmynkZD1mfKAraILhK/HyZZ3kNZ/4h3KAdoS9HbckkJsXIUUcc9DO8UNdfYFqB5oXZhSm0Osir5JB6Jz6pMUelNKnSn9Ctu2eEBGlemdJP2oE2SgkgmEjYnGcwtKAyMAMq+nQBFjDS7Tf2BoKm32RVR+eizrODWRlkLBprD0fG7T394ixXt7Ebj5nEMAqbdmP1TnXQ46/EF7nr8w0fMD4C7EAhEwNzX5ArbrUGjtNSCQCIsOaXbcm3WkDGn1IJEIYD0+qARUMXmJcuxHTQ32m/rbLB7Q80u69KUF1z6NiAkgTwVtJi0q0XhddvwFNx5iHguHOde1LnRFnlJ2GCxhAyyEgvYJS3AT1cHIoknV9v1nKhH67LScqy2iM1697P/7qfpYwW0PZ6U5EUKCXcVR4KedDXOn7DmR/mKUFFwZUrn/i4JQBx3DXskz9kAL8IU+CJ8wyPvbHKl8fagTZzikDQgaWC3Yl0X6E52KKH1BGoKdW0eQf0s7FSyJh6uqkmxnR90Crv6kP/u5w+TrIAXPVzcywWmAYB62KQctLr+fyLo/s8hZOTEPToXptzMpT67S+jLCkrNdEut2stpuT53+QxMx83j0LGRf1/Ab1+A8nQK+dUrG9cvg7ZFwz1mnI90YyDHPGSs78lYCm0VJlNjRPr+Abk+eeSaTHRNFfFx6zmlIWdGL4/btIyjNx9LjDQ5LJ3ZpDwSwmPpyN0RmtABjk3I1hDOF7aH0tXICpKOhKxMZ0KyKoOsFCqUQ4ECDsd1zj7Y4PDBsBe+vYBqkvSez49y9d7GwgdnZcwk/8VTZ+p8Lgz13Y4SldnPLseTZCR8iFJU8jhCdoanbsUkJ+DInO0L5YpryTwdb8EFHht1C1Mqs59VzifJSPwQpajUsBBHR87dqgej4yEgCfShU7oyW3IMNL/EIgdbhtYZDDrz/PEz+vsW1hj/j3Yc/DdPxqzqelHkVTmTer3gsyj31g+j6jGxVOkr1jjNAe7j/kh4EWRoh+2BhU9Vzk0K1pCX98P/o6obCpXOn1EBMf/TfFV9pjASzcTnU6bVLm4v+E+ZCkgl7JCuVOaIa6DylpWVO10+VOatXSg/Cq5wO/KSQY8nkSi+IpMBKC9ix43KYqZQ8ZzhobmZS86M1KbV59XcFNDFQKZQ017kncNHtocrb/VUP75pTAvQQ4mxp97MFnxzJ170jLRdC4UU8BHxSmIdE9D1ph6Y3cN8jsggXspK3FNrMkZvgWz5ziskKhHvXLb6lXL+QwL5yX2iKnTis0feD6nQMjoftm5/cfsLgV7G41eY++jEdd/+1ynLehgGc0s+shWJ08+/1JBVTZhjPrr2yFr5rtaxrAb+hjQAPKbkQBtD8vsYxrMUdc2GsPQ+U7UYPL1rcdlNFXMUrz3Ns2XQMAive64K2w+HxJa+4zn7SW9VcK9et/Itq+/9EwQ7/tEKslUv5d1EeYs5rOpv8FT4LA53kSm27DVueL8w7LJfmSZR9PX2+8j4GTD7H6snT4lVYpTBPKMxmIqYCVU/LXbBNTxWiMF9IhC8ycUIsfxLoCQGarR5CXYri4+FBBhOa452FqAjHMPg7QXweSl+R3XlKKKDkJsLI9oqx6szVeOxwkcihRCpGgXe8pEqTq0eRaSPpSmj7z3Y9hh2reeOFZ3f5PChq0hk2dLPs/2JeFjvlco9Nfi/OQY8DyG4kWSpwp6os6trbWp2wOjxfnNrqSx9cEKxIC14kwBw9+NI0A33/IYIRZZsicZimaJkhnsOFLNe+sWQ2cKoDhkTkb7zB2OiQzOc6fe56npNYP3sPqBKIQVp7jaeOslVY+jJ2m2mv/ZA8J1i/5F6D2CL0TabrUIIPYOjQ6+55+V+tsycUhqTBVZjJKUwy3++/SFCw12FhCH4CBwS8S2ZzfjskcsFliqBr0BWll/s6ezLBWox1evP63UKX3UF1r7KCLxBpOOO4tAK6vZ3372I4qxROL2iCr2zCTXqlTiMpUpLsd1uScZu429IcsV+sLtvDMUETZUGGmY/DQSD/d90/kE8sIq5NTy/VZGweFwZ5ZgxbnM5Uu1qt9uj2Oey1XxjSm2pHr+vdsgYcFjLpWav3mD2lOdYcyty0GfQo16F1NJ0MCI8o1A+Eqn0uqjwEWW29KgAvI47isuyIfZf+1B2ZpSFPkFKoxGUEspFZ41zLKlUoyw6cfJSjvUNkt1qLFEb41abd5DfWEwmmxXBfwCgBZdqAnwBJuP9yLxh06CPXXlqdX/PQV/Zbw0ws6t+2UYp0qfFbqP88/zpbnnK3/6HpD1dro9wkZUf3l3vpqjkZPNxBsb6/equ14nUX/M/QwKPN3vDMS1Oitf648GgT2j5Uq62eqk9+SLV70i+kEsM6HRWIU6Ip32lApv/ZLH++aGNmu5Oi9sbQzuIJBbLwmIBq/Z9gAod+Pj/iTwtn9fP4x98UF6zAyIzaGz+jMTu9enkfwQL7t3FYhFYLHCoWbfjXQbuIzQDO2etuvSUQPI6RzgiVkXoLGxPIJPawCqejfLxnvl4cPZfwv1AGLx3WHy2raJDRBrX3v/C1NdYXh48I23uk9qk6crZhvvAoWbhv8wsWfxFbJd1XXuaK6nkCopE/KkjMB9+E0fazHtj09zLg1yDjXsP81W9r8gEH6wCR5pl31cpBTfqj6a7g39CtAzg4nsChYHSf8bVyqqrYcW9AuE9Cvl2oWAHaD/MYGLzexE7RYmvRt9y2BJlH7rgOB7MN6sWKNm80btuV3L5z/Wq1dOX+Fzrn/1hXjZlYZzH4fNvC5g/eTSXu08wxmbfU8tsZ3MqwbjDRcWf6KTVGfB4Bfcr5FXX5fLrVXKhc/lEeUfwT4j/kA5+0LjJJnjRw8UlX4BBJAb1QrRx/nGCHjuJwRVohuzzeGujejKjsb71k03PhJy7lO51OktpAdCZbAHhpn7Hmza7G45sZLyZ+w/qJSdwCAZJkGOpdEd3h4XKwrSLkBB8BFFwz+Jw4vDDxSJTfirbyvblqOWePBlPNuIYbCzg+Iz/vt5RURTZ89XWUeUmxIU3sL+XH1rDv3yMLzh6mS84f1TAP3YePHH9Ifzj326P0myIyni4k2ASI5pXcM6w2c2I1lozrQOFQkCXcCrcJQiBQtE76yytiCVY9hnO0BKE2NRFTIQqEUHayO2l1BCCStc36htREkSeBDx9DG4Bos9mUCPjglemfgqrlCH2PpjWpPbR31L/mzBqb5kYW0mblTEq/DNsefsRQ/wRYLjJDwsUzFX59D0wNW9DK9dHzcNgzU3wHrpfiVZiVHgeBW2KrNsEWYtNfuM5aqiRJFtSckwXRfZXrix6lhn+N8nhg2IchlLj8VIyGEjMYeHxV4g50nRNjbQ6h3gFj2eB3IM5UL0irXDEEGbI5/UMz4PBAfI1RaX5uiXUk5OTC8qR0ryiSTY93CJWs0NaR94/Pm7k+E4NdtH5Ngjy3xXYYGfLDh+u6AeKuV1APpB85/ArsmNQT9D+1CnfXUwC867y048UBKs52VG/wpEly0eTlfsK3F9TKI13HMD/fewV8ODL4ue3AtSOEJ8rnzz9bE49qmc6BQ9UO6Sxjgfgy6OuhAdOW0OtBxwX0k7m4FDuUD6YS22knSXuMrBj7Ol5Xj09UkkXtdc3cva6s3DoTeEWdXQPngpNcw7dH/55wmn2XQLeFfI1RkvyeLLJ+QBhS8yTFQtP7E5hKdjU7o1uxZMY3DyOSli2e+7BWdm9jECtTRO5222DUC27Ie5Lp3knurOAftcBn9qkLvj8H2gKSzoEzeP0RwRoc9/YKWgX8xCgvuB3MtjyfNumQNk9cBNRZBUQsKHcocrkqCpetUKZSRiGkBFePow1Cj1NaslT06GZF1mKWHVXa2tNd1Rz52lv+L9HXzb2C0QvryPhEUFlVol7cNreVjpaGBjAlVCIJBphdMOKqUSa9/bTljkcD7eAQytoe2LGPmH5jw5tqcejLXPY9WUeOd33OkiLnlZzfrrAqAp4rUG5OqFDHS7WWnKT6nw6g0HnC1vGnn/WLCgtZzIE+DbDLMHsdDY33o/F7YZZ8Bl/NJrut0Ud+lpF59vnMxG/VbsQFtHbcm+Wig2/G7M+q5HxQQnoanV4qM0lRmOJ2byg8JtPV6XfJJLfKnkrjQPkM3OjnJsD3sGvLxr0lKN5tspYdFSbvXR+MMaqtWhCNu31258d4SmVsVKjK1ihWU8ovM6j8dItIdOzeia1eDRlMoq+wIlFD0TVUeZfdDM7f0wdYf4CfDG+Mr+lJX9lPL6gjNwoztKL5bn7jKXlhgG3O1CgaOSO9V1DlrBWHubBWCzMM3KWJS8ooXAkG79Xcd+CVnWQhN5hVxT5oEq7cJ9yz5SOQO3c0wJ8R1umWCwfi7VC8b5Fqxr5J9BHZWVBx/Ak+ONIeLh6iMVivpA2fMFif25IgzNnsFjXWSwS8RObq8XVYvuESNqKrLNS5QvvxUPwkfBGWCnuZHDRitDYoZTPumgdsLhM/nZcroi9vZzipZhCHn+pKmd3ziobtL0JL8Ayyfm0DDovJ5bH5KOGlLcywO3ozFAF1DiOaPFRhJsbJyqScyiys0MWkh6UIHwVXOpKVxrlzS5bCsIIycHugCy411mrMwsd1I7D2yOR7Yc3eMvkPVt/Xy97mFv3sJTDdqZnYGOjvYhQ/WKq3qP3gl/jVyoDao3Sn92arNWvUSsDQSAW9C0fQjdbcxM8L2FOuKutGRP0e76HQ76sdL4uhpovzC6wlrYYAmjxddeLdcPhvBlk8RyyeI22E+rCbbJvRf7scIH55rS0aLczVd/dhRg+iBj2hPpUt5MW1c79p2SL/N+eCG1EKU9qSs/UuHbrJwZeRCvue3l2J8/4axrdSGLj9mIRIX/kNgIfTCeLfaOEkGyW37C0NVwcb3VaWrC4RRwNe4VDiJaURVOp/ioWi/bu7r7z/uczxYTKz/0/u5CVRh/DcnF7IIQ84MsB/EJ9V0dmC4Hle8E/O7SCZb46QgktTvGSutIzdY5vlYG58hq68nGP6//4/SuQQ+9fjtouX+kHzRho5dlAZfv5FXYh7W5otPHsfnJMnl3g6ELqBw08WsaP7N0X7P+ZmteWm2nsr626oEYCPQNT4XVPiV+b/U+t287zDIT98wztwt+vkgjEM3/fCix/ArISe9NXt50hEkiv3rbvJrZkBBAfG/RL+/ce2r/nFHn3MOFA6OkXGVJL+oJA/T/Y3Vg28c8YI/523zw/O/9JcykKuh9iE/6q20TfrjsvSJrKwKbDAwe2VduWhvsvpMpNwkMLVRXz72Bh6J09VRULEJ5ExXxhKXHMaZYvMKD5tU/PQy7qfJshEVbJFSFVbmxzVCdWhnWOwiWw+fbe99Y3mD5KD2etfD5tv/3giwt6LDcB6behXogyiEsuU3Y1q4MWICRGuowsXYPYowgMPfvBwCDh4WreKjJ9D0zHv3DX+rYAbXAU0+ga6y874DcLfnmsLxPA0ukG8oeTgbIARKGApbcoFCgQKJv8kGyg07GBvszYyzAov6Xa83m6Q0+1CaA9ODJuD8RZLZev5my5scCGANs7JN9I/n84gQiIywNnNTY4GMHv34BnYFdjSEdCR8mcahthgwmaTC2EjoHa4xFZi/Raekbwm0ew4QtH+JAPyjZQP6SybEP1U1N2WNvrj+HzHUuyXxxqPyzb66hSaJ+37oX+dSHJke7a6/te5JQUqitmsE9sYKOix+jool78B0DOAhAACAUDnAGMNgus+wgGc7wioaBNvdHTSVU2ODGUOgfMgtxkgDnNaDUuYPUEXKGxEYz/BPOkoJ3O0TfFgiJaQRFIsJVSXPDSs2XRbhpY8UavdzVq251BRtxP8/nlscUE63TERm92fbE9EYtwzgXgYfkIhtARCl5S8L1CtDSXHY+j2XDcRy1AwKoIuBjK3gQEhPCQ4D2e488hkxOQUZ/L7o0bi+O0bzyLIni1gl9Sp+RT1blqDuA5P2DlBKydgCZxDIcwluHFqk1wbPL3k8d5gif5GE/xNB9n1b/C15ozR1uKouBTRcHnioIvFcV93RHaxyvvZQ3gt+EH78f4WerT4nB9ItivpmrtWu9IXrdm2I+76wkwzQwNU4cJmIgpNl1z/2pIWcfqlx3/908tLbVpaCk86994fBwBoSzMJxvBLwCitGA33McC6YAz5F/r1aK8nKomNPhKiOOmElLnxRTD/dcAarCFnJiu1uamDv0NR1KS65wEXweXlHC1XrhlV+7/yJCBq7SCoC4pA9JGEjTcV69tO41W03LE/I888eRUQryrsVYA/vNfDfgT+sLnSZeLweXbpEUJgVVI/+S+gi2slPBrX9RAaZHRBhKwphUEdXLgvRCXW+6Lwu6gsVpRAfd0UDYQ80kUTKINsNwL4AAUUQpUgAkNkIvuRuRLnPssVFWdfy9xmBNPriAbfyaLiknUAixmAQEyUQrYgAlZIBfNG9FLbn0uC5BeJD68zAna8nwVlShU3v1e9rNSuKdQZ0ppmu8AnlJfHTmu7g9e4C5x9fAKbWmWOcvnL/fzPrAVZ5uj//NoIPvMXbZvdBx+CBAopO2SB0g7AZhPAY+IlIwmXV7yS2XbK09MS09wyVWufmt28wBm/OasZo/CTsp4jKd5hSNioy2S6aT+pn9IlCJHGf0xgX1vUet3mCPdMXfa/d396GRX7X7VJLUfakc3yNh92E+xN7E/8X8KAGpBFGgUWoBuixW4HbjfpHy4CA7DJXAa3gn/LKvw5fh+/Ch+Gf4e/Hr8Wfzv8n+hfEIpYSfhPOEdYhaRSGQShUQ50UDMI24jXibJSJ2kfaSvyFbyCPkQeZ58ivws+WXyDYqQ0khZT9lO2U85S9VT99IotCraPto7tL/oTXoz/QQDxShkbGG8xwTMa5nrmJdYWaxS1kHWRbaIXc++h/02h8bhcsQcJcfAsXGcHD8nxinlVHOaOJ2cAc4oZ4azirOGs5mzi3OQc5LzDOcVzkXOX9wE18Ed5m7lXuTe5iG8OG+aN8tbzdvE28k7wHuYd5R3mvcs733eL3yS7+ffw3+G/4MgKTgueFZwTkgXtgqXC3cLjwlfEn4s/ER4WwSLlKKIqFu0TfRyNj27PntN9qnsa2K8WCVuFT8g/lj8hyQlqZEclLwm+SGHl2POeUiKkGqlm6WfywSyhKxXtkL2oOyW3C0/JL+jCYoziouK35VxpVs5pjygPKO8pVKpqlRLVTtUH6nd6oA6ri5Vp9X16lH1LvVx9ZvqmxqERqEJazKaVZpdmkXNR5pftaRWpa3Udmu3afdpv9EZPT1PWNoBiNQBkq+/0jQNZtKV9uXwaM/2Wq/Wki5durSzGt7+JZux3yTxai/3XI/01GTbQrBzOgRM8f8vmnppvwSQFSc2uE6/33HHx5kLZpSHngOCrJ2yvKIztecse7K9EXsZe6vtnd3qwHvLl4V/GfP0tmWVspkVmGc86aU5nh1/mINSmMTnc/1sxsqF39ZvWRoRB6yGR/uk7/qmL/uqT3uj1hkwPlYzHRwmf277vce7nrmLU3/5+WeV928d8653vAG+ub/DumLFwOMT0T/QCVzruQ20OrFNGv3+h3YgxqJRMYXPLH1rspKgOUZ+ybbdheUOaV2vW1aeY+gMjow2YFd4cDHJzsIRwMplRftNRe0LqeC0wIR6nipj85AZOh4QiV6UynleFvr0sUnueIt/17IP+MpgaVrGVHxHZOkom+imVLlUUxWaf/z5379Pyxg3Mohkk1mijQfvnFnx+1/Nc48sW9XqLyVwo4Dp4Y9gNKTIcsRqJ5seprh/18i+A2g4AXuH1Mn5njXKUWqcpVJBv+iN1cMz+qVjVEqV1mIxaRVStn5xyxR6g1anlAvEYu6/Ytkt8akNalNo55GBspUreHd6PljdIDCwkND/GL8UO54TMAeC/7vpuidSYSIe1jAG+R9UIFR4Uk21ZFNob9THjMbj0eBAMtuA8/En1/7008+//LwKbj2+eVbunMyxwPUhxcqBJvfPPAMMYLBYi6AqgpUKe8o2MG/sY+2Y9M9nOxcWFleWaVp26Umt/a6eL/SsWTGtWeppM/qSJKJrrL8ylZM/lezIr+CsCBO6SdZ640i74BThWeDgOveKiuLRmG19UD/JywqNK2H5jlYPlMIkuLm+Z6wcQzgq725pDZrWK/b+gIZl75C9jtZ5daWiyjdXm6CEvEUm9wxgMOX4W1dwTNCUyQymgE8+HWsu96dffnAJB/emMQa1IUu6CHlQYhQXhTAM/OCjr//9Z+YWalzNICMrVmf2738Q3bJK5tNPy9oc917FWG7rNbhD8LYrcQjAYSXE0cxuimSkUVr7/unPIXPA2mh90yQ2H7/fzdpWKB5XKKE0GnDdRiASlHtC0soXIzYboG9jNrb5C556qqLk/9THWm9dLi6WWcEhgbHUHPWPyWcO+yhpbCG0uVneOi1/dtl3EJFsmqXTKnipOLdbvoHbeC/sg3GNdJY9s3UdNF1QWKe3EMOyPHcJVI6UHPnovfrHZ4Yws75jC9iECUGz7nNwswgJCdlMRjRuwlRcVjo3WrfV6a90xWzoeirxPKwbjaDya3hQ0aBLmSJvg5xTNldrtau/NwoGOZ1qG8yBd2Kzytp4LKFRg8kEwpFlXLA+r0IWKJhBBFXCCws/GCAN7JWmkiCdekY97ccJJXMwWod4xIm0rUBzRP2C4bIAwz4o2e6t9+o8ek41QSn83VxKkYtpctpySKgDSW+FAjo/ScskUZ8ymg5wUJX8fw9SvjDyrsqg2MOIm3hM94KPYJs3/66rg+N02t9LTy09CIe2blJZejIW9xQYTASbRJkjnWqgrbvldOjzim0sPYgbg1fJCl2l+mvj7JI/J/3VRo05Dvi1Rp4eTL4bpwQPmWMQ5HuK4PmIGA6DwKsNk7rL/9MT6dbv2ld37tOtNGK7lxN1p5tV/cT1lGWvw16nvQX2Mq2DsCMthbOeAyxd+U0k/EHDRok3S1rGyiXr5E5+l7sS1xyzKM78VsLkMbjlhMMU+m7AulaW2sbv+I74xWAluAPOuEcFkPAgDazsWVgakVV4TE/AJWAmjK74vV+FDuzUhlHB8XOsFp1eKje52FZKUsL5luDe/ylOzg3qdXyTwdxrdEhkK3kOH+DGQ5yrRDz3JDppeNDs0/ChhEdkeRbetoypb2HFrLm3UolEIzw2vS6JWk/y8/SBKyXZLClR1806un1m8R8n/XMd0Wc5YNIi8jNI57twyin4cnIRreDfBwrJDmIIpjQJGj1xmcP7jC/pEhKA07NqWfrws59/GxbthlVgN3rPvxw8MhoXrDwzTjjcUsT6hl7cHMfg1GuphbPCAu36j8IDbI2H7PhQK4nJl0Cs6hpC2ZI3775/dDQ6bXHyYtkDncNCs3GJ6DS3nK+Mzp8xUzOvpBEQNTvdx/ZXoWYd74O7hSwUipLMFk0buM3uOZvrrvg7MkN/N6/cwIeKbA8rAgchaulpWET+ZbtWUBwrpF/nRKiRQEzBdvG0zQPHsDnuK8EGpSgDk3IquX1wMFq3ncGWJRPgDVgUGiSnyExAcjjMxAdPbu8lCuRpcvP68wf/wfbb73dc6Z8gdRec20mRwLONTgpcMyBG0XINTLLj4YFBYztdtZ84jbLs7bDX3voPRNSRl2fGlvT2kWmYFyHOwqSZ00n/nbFkzdOGK6jSLdet3nS5SykqiQi8z9OHzsdiOhG85bAAoIgkK/TlRV+LDS/o6tZIIf1CyDea+beu/keoO1fn87l///f3OXde7eoz0etT4InQedK/3EH7wEsalQz2CS4S4UkQMBW/De9cjqVCy0nGEkDDe+GA8D0nUaGIk9t7HixWDDOB+D6TmxDErIU5a9upRYlGneLNyDeTJfoaQAqV2DRlfKN3li6n2ZaeWnHRGGo1qJBdk2EjwAzwH9SBEpuyRieYjKwqRhjgnxU8O5NkIbmVoSALAqVK7K1ilnaW5ZckiBeBpXLZ87H/nzZ/1TOKyqimMyAIB9y1h+T4Lds39IBDGSSOvw8AvdtRyTV9xLVQHUv319sMWvYURCGPHetqB88l8G9EFyj58EwxiEnyO27VEEV4gsdO96s0NTb1zHBXIr4yCCDtjrf27IkTL6dBY+HArS5NBdDBleWhS6RDKfZPWjV1rlTFE8hDudQOMxR4soTyLIMLIh/xUjSGIydqy2Ru64B/CgA0A0+hmA20NvGruYRmRkxw56KOHc9Xmt18Z6E9xn5hB9zaKMuy12dvrb2TWwfhxUQr4vQDt9uDNXPNnz4xtwTZ2fxG/TD7hnyJJzmFiYSrhpY0bzYTBF7pYqR/8dfffPvddReZJ7nDxkIFCTjSfzxxMEtq+2kKHEkwC/f7p3dqrku4nMrJaXBOaiVHETYijfjT9nuceumdL5/0veJc4SwXIqB+6fj8S1v2Iv50h08ll70rOTwiepb32Zs7T5EglemP4wOv/SM13jCbgPcYWbZcwalJAzb02c5dE9/hj62nheuKl/fUVtV3doVOR11lrabg4vnEiukz71mr9LKxFbubc+doGcsMwLdrg5v4VRt56ZX4ckWkNANRXz8s2MQbX+XsJkEPLp3CjaZ1szUZF18w4oOBtIxAg+7v6gYXaKF5tWqf3tQ/+2XevFVMvZCBxeEi0Lk2b7zpXT00d9HiF97ZLSI2di6bOB94rKUVepWqEa7JMiQWqrXk/U7LQffq4gc6qnc1I0eRsyYLYWI4qV9UNFUzHSdvKiKusKTYh96rlirTHAv31KnnUcKY0LezblPMt4gEblUrBuIDLHBFfqpOfqW8jnnAAJwBAtOZzsHQ0/tQ0tl3je5aIr4pnXnBnmL6dZaosycn9u2IicfZzDblqzkIvSHzUhEh333uPpw4LEiBT0MlTELBCWF8k6YWETroq6B0zidbrHv5Vf1vNwX/HFwksoJTiZbNdBWaEwfaof/XLm+fnY3HZpNOxczEvIWLxi1Z8shrti12/iUGl+cLC00XimGahtLYtcPbt3i007dwQU7ZCxxQpbqwX6Q0ZHuR2YmeYbKkJOJ6V6aYjWUBwtizAeyznnoKI0RW1y5Eluk0itIkV7eiXsYAj17027sDqXBl/CsmLzbSMatvqzFehbBsIdQI7lMBUCUvTx8bzDU2nTBwSJ618WrylmfzsDoWLnr8cbdZ87ANp5uW2Yb0eQrorgQbClxrV/oY5wTKcVmzVSZZWZ47476KvG/oK7LEJkNAKgv+Tcu49yYibDdQPesbDbDsdbS2wJiCnnEHbd4A/NsO4PGT7LnUbgFHYMgTEnC3Iiklc/mcRFysJqWKlEDo2yz4/gduV0Ht62VV9NwP3n+bkVntipClSMVwJNGyotlu+3RTLPg2mmImqZNPHjsid81Gt3hNdfR0LxRQTox0ktWKmjear9ie5/vVl8XoLp2ZFGtvWbySA9fL4lSEy3c6GTMqxQQviGpMCGpufk6n5n3gz6O2wUGQA1+4cwuy56vU9/hsFlQf064bTz0hkTgdWPk2kSio3Efs8Bto84RMHrvs+54yjeLvubcjncbbNDBIm16Pvp1JkNM76SoWGhw3KVHB5amA2XJiCpFDPiWqVPet/iwOXu1iuVhot8DoNgXxsjMQ20g7A7I56bEr994rC4iLoTAVbcuXKwerhhRVeafc4OWYupZzo8pJoz8i29ew7Fn2hlvXQ3cM+YSs04Vsb3f+sJBU1EsQi16Fpk9PxXPn/RFP1vQrgMKQ4MYGydwTLiQ/r68cnSTjExTH5J5JeiWFihieRCB3iiUfbLK0+R0inSxKV+IPg0MmbvK7RE02gbI9lzsWSJSghqe676FvytRDDtwdTzYO4GLfStfgbLGUnoh2pN7dUMBpVvFG0sPXfG9YlgtPmK80HQNmypkUyZQOWcIlds4Na+zKbgKOzFcE7FPR97wABj4muH5rCVxwOLkqNV8GzEuPYwXMT+46zHrqzR9+xfaZzbDt0LLUyWgwxN/t/KdyqMtWsJt5ycqva79Whybpu2WH72TBkp1IRAXbDsIJ8/laAAhEFIKhQ4laJChb//OUuNVX69m2zV+uDzQc6leGR97jAOzVQowIW39BccNmmV+LT+f4x17KIkmOK5MqOCfhoUV8JWmdFS03Q12aYBHrCLAMNF9iNS4VFVVVxM6h5Zk3l9F58ISL2V6apxEn31xhhDcy7t8mEUkpliI0wG5PkfZwc0Dpplt4Ew1spZPfU8ddz5sbjbDsdbeu4jDvzOuBCK6+OTbMtbUPTg+1THzN1phdW19sYrE4HTPzCjX0yqV7ZDF7ZqX+73fpeP+Diy+ZvZlF1rYsRKP5Jpj7jhvOBRsvYmHftjLtdJNMbTf0ZyF8s8UsvUCWse7Yd0Inzt9bLr5RlVu/JqVy+8ptainde8EqD6u94Tf8NeuFltCpdflF9rxX7whrdsiG6T1ZzIIgTeOWUSDwymUcjVOXwxJCvCLGWCrIoN0HPQUPWbeDU3EbskuXb4iwiKzq1hpEfJFHORItFNbGid16b74kY9FvdMXNwfl8HQ67lK8kV86Y9TW02iifQLUeiQbLeKBiPS/zkzCjOC+nYqydrILAyfZ0Q76rdzGN6nCz9bzePsPPoT19vnh8yWa2KV3FgR9tMjc1wOe7Ts45DOIPxWy33oOMK2KRZcyH6sbDGy+d9YhG1//rstIYUrdROWPXgTbdVTr6eC/17He9lt6lx3mzN4b6PVtp9qM9Mv6A98k5/PufOv6/vC+8fy5jnvG0x6PBSVjeNL+D+1Nhm+zhcYofXdDSwe+9atvLzLP+FJ22ZlhsS1rbz882Lhb9k/zR1c2snWtfT+uCGp2eGQt2qMCCMX3pJZDPZRhRVjU/vFhe1oNg9kC9lElUAe4pxUHUy3MU1lphYnrzKXboOCFbeTFFOhcM23gtneAU1S/1M+Bo0pQrpyPK4iETu/VCK2PKupuQzRdbVWwbOPctF1Ze3Yv35uxSv4oFFZGbGfeMI2e+QQiVsDfSFVIagAiZEDBUKwl9Qpizi+1SK7GwpdJ7s6Kl3x75MDh/t/Gk6E25r3OrZ7Pnmu2u50chumPinWXomizZ8sXsBp8ja0AaiOTGQuRT1GQFkRLd8Gn2Wy90oYeU2Idg8saklNcXAcq2hD/UtCunRnYxxfZP967Gls297kVWz/1ZuxFvZYk7LPJ4FrueZeijkyztthB+5HdvU28EPpGedzQyWMZBDbgAvBnRg5ppKkOwJaB3KGAozPqpH7YY8RXHI+Ubhu1YYucOVnSQ7wUqdM0kGpPpqijw/BRYwQ1aUs4xHRmuCZSW9qITrYn/z2M6plitHf+gyYiPCxIPUF4sF/M/O5nsCdePxiOcFZSAO/sOdKUMkkP4IEADDMwd7AQ8m5hMqTClJPbLIjaBLQH9PE18EWIUOq25cCcrgmuBwlFRJoXlgdY3pthgpTeHOIY5fawOCSZYTbNKiCDoIxx4XnDL8x7+33pVCvzKrxBzPsQEqQShLxFu2ED86LBwiUCBqq6bln0RcAye0J2zU6iPvrPmAOhZkWgUkF/BHScCZK6JRbnB/ll7yXRUylggipznYpUEuiir1YFIb3p6adDxvr2w5UfnIuvLIoGR8baFNLSYA7EG0W5YEmP1IfsTSXOhxHvvSrFn2qjIejqV0yOcpzDr2zGfvIt93nYnGnV0yBPW0TAVH2NK5Tpz1HS0M/K1GnXMuOg592CmmBooEe8iTvOwHy5J1DIaW1lraykqalZGJXOzOpqMOK5ctqflQ1zmj0v7gSFPh6EVfA6LDIOKmQaNgv0cPanO6Ff91D+Fn+zo5jQROo6qkPRpONad6jwrlvYpWZXbj1XHamKXx6ioqY5K5w6khNhUI6yd0ZWeA81oYG8ZcWRVs9iiqTxdD1/znKKmlfjRC6ZecNKe435SFZVWDbTE3b0Sk+jWak01wDEi/S7JYWMUX8ix4tQ7DVJp8us7DvnBRs35/OCnSOUhih3Pw17Gl2nGJ5DWXAVznuWrEd9iPsgnNtx0FsoxAtlAYEjdUFhYLfdzyNXIhtF8hNEXy7glRcDvkQKrtoOlE7B0SgjgpENrn92jsZ4K+UKOMJGnF22sp2tAy9eMhXS+WJ2feWG0jkJvXowXV4bnzV+wpJnFuzkUzMZ8R6GTn8UtIbnZgTBtmE5DgorMxV5EbzXuuaBa933duWX+BiT5+oVbnwWLj2uT4GmOnNYGIwQEFcQLfc4leTjQOGbZblgWxStQplw1cRRjZTWZN4/0q2KZx1wWsIgekhEiFsaIsCUWOpcdZpxuX/D/iF0Q/MZxkT2nhhawfrY8WULmlMmmnctVtSZra/qSzAbpuufTsfx5qxp3rjJek8X6xdPTe9uCQGxV8kXbEWlpjYCO5v9oTvsOB2aC7hCB0ESEw2J6zZ91JSJOop6ty8CB+DCzplvEWehipOUKpY4OXssf4e9g26KCwHKXPOK9vq1HbGfMLZOOXE+E+diclDU8D80iuaGJXZGRCoXLDdWNsE0vXOJTmC253HX7XIaJheL2pCiMbz0yELG1h/5DvtNYzWUOX8kmoRwcDaMYaFfg/YoQTn9JyumE6o0DFzr/URcbz9KcbIGcuyGxJIKThMXoLW85P5Mg37viudcwnOC+ND177INJ9W0z4wV4RB1qX15RVTA+eglckZ2an4WYbyGvHM1HCh88UjeP6yOo/KHfMI3AQFwDO+bGdW/W4KdB0yUJBte5nOOEmPwYXlkJAq7gFFmUwtKKIBH1xlNx/HRsGVctWLMlELi2Y7GHXZHjAubjw5IYKUb67udEvKz1WE0EXsD2MZJ6xcQtcyi4c1fKxZRJo55KAXulXh6jnuJ7Zj1B3tVd2NDwIQVyPR6FBp0PLKD1Sqv9vvlkU1UlPWcx8VAG9fLsfvpcbFGkVmqFg/fOPpMVlMQSxy3Dntkgseg1oGXn6dlSSNlyBIyajAsVIhDY6qsBk1kXgWmKInjaBAPYgmCjCstH+mQNmSm2kkpZyF9j68KnLSEhVserk99/lNg9OnF3JL/AABMSNTbXPyKPFrQD2iU/aVHYLlLGclj10yGHChk8FHhepouzPjxxSNB83wM0xaHg6af+GFuvTnieLXa8cKl0iU2M7Jr552N2+LlllxvjZIZxBf+gUVP3anVo04gQ9vszQvm45ryE8ogOjubXukn6yssTcePF0oliTMQHDF3BgJdHvpH4fAceu5A2tymuma9KRuQrPCHOgJT7/7sqd232qbcj9q23+x3QT1Z2bOjOv8dQZrhWqougZa6jzxfk3Mrc1A1edcLA5Qs77vmhuRfHMmS4d3EmkhwWZ76O8zNADCV0xvCdHI2J45vvTqSSzydSGdsyjIvna90mXTe9GtIAhFIGgRhNdtXInnRKi19NMIuZzGYu3cspCrFw0Ws7LJc20GAOnXr7gv/zC1Joaguxjd1Z+wzpMzMExLSXHWTuED7ead1OYZ13np+Mc9MxAHbYP4xwzNKO9GWskM/JyrAtkod6Aujkdvvilr5JZ1FvPXNQCLRg6x0NmNsFcyt1pNqQ/srKO5Ehc+gzJE02kG/I8lwSiPF8oQoOhWKmuRxlQweBVtFKrTKY7PPCYSj+/G3ADb5fIx7uJhli2DOuRhxnGJbjmpw5gdl57sor2R1yT4xzVPeqLwgRCZG2cViUJNPq+eMdj/OH0tcUZBw8NWxoYCegraFfvWl87ustw4hSENfImCjintPeYoXLb1RMp2DoVMvZbpBrlUXkTuODLPLFxLj9Odlpn4IF/vLKX0XbKX6lnuPoqmw5NgMEQr1EWGWD5XMLUdOgrxJZQv0jVtkZ1gCScKFBYCDzFSqp4Ibu9Z4lrOuJOHp1kuLR6GlSjtx9Oe42BuQ0hZqcr39Knyh7B3N3d9ah+so3dJmQBmIMYUIh3y4XrVMoLFtyKCuH3S7mcLmqytR8TUEgw3lig3Yt5THI8Tz2cMYT0d71ixlQoui3BCrfDMSF3FYl20wtU+4OGQI3k+grdwDW2cHoeVFPqSfbc6Kn0ZPumWuhjPx/NUwHeFSBoEBOHYWMappuHNyxZOuKAkIpqQgIE7/lu2nhgy/ANEjgxTYu3tCCDy9TwB1w/QJ6NGD4WU3TuWi0QmBtHo7qTLViM7Y0R+nu1I4AsE4n4KmInyr7aHJs9cBZZTkGLB4M5zzJJniLkKvKDcrF02MSQBDi+TwRhyQIYRw75xP/mONdWrSYdkYKczdEgbUWW+xBWeiyETy63Gh2PxdqmsvDxebq3EjrTicgdZbxks4MDvstUH8Eb1iWuX5XDQtMGMoXvN/xS6ZKiZ6ng06cFDRLxcNrbWwuWu+7q217bdc4yqI0NOsJU4DtdPgQkeygeS37UA7u4S5m1zaMtlN8ImxvC2DTrdhjprmjlRQKvOKPO4BhKh0ilOvx+L5G067Se+SfwA55T3q7b0F5+wco8SHhLRutols/pWutc6KVc4pZBu0Cuq4P87lSY/1613HdY7tpVqlSsmxXsVlgvI8yEsmkwcrJWSdvIaLr5uUdy/xGvK09CryRAAVA8BrUJ0zYb3nqMK3skOGETuXyYE7VrDIFrwiWKKq3LlAHdGc5ALG/QMrUBgHyF2jNE3C8ifrPcwwHBDU1Fo4LpE/BqsNzvuEsc6U6iC96v2+NdvwEb6k0yM9ieDpZgTacrMxDdmBlofGiYNE0WbeaPAQOWOZDmbqFgvqqo0co6tOTAVSnE6UCJRwkzHV9RQ7mPEAghYhiUM6l/CsmOhFBuWgUapBnBPooqn1wa8A8DDYAGN3vZMJYQtI7DMvOtsfO0JaOLDfXkgHT0VlhwHzUj2zHLwsgjwQxZupUyeIUdvobb9NGX6jcDgzHa/yW6lAZBIzrdxuAPpY8oIrYVPDwbDE0m+IIeuNnuHtpioFiITDrpnZyrRZCnvustkLtMsx+CdSMHhdD+EqUWL8DFEVIsuxi9oj2kfz4UmLG5YKF54A4WZ7kR35V33szURk8NyjRld8pZzM0rVr3j+fOEl5V8+qxTjBpmE+Kig5fFMQqyxumNpH7lXl2+bpiJeLFQrORFhoT+fnGpCQVYw1zTpYHIaucVPGc4zkfhHM09K3kUHxE8nOaSVBTobLYiQGgwRNqmAqDF1z1V3BXyltIoGYg4tXAsPlzycSRMk8FqYHjIj8Vm5oU7+UoHr3FDnqgzDDU3gyQo/xnd0gqNwOekt4Lq0Us5DEKFEa4urSyiKZSkx37iq3ONCZTVCg6tp09Xkfh8UjyKE4Wt7uO1sOQNf/kWZ5BjqJRSyI12pauJm70/KvDAgzBopaPOkutQTRSRtK0SeXKFPcTb3wYiaRSlgxGODM1Is8qsjDMUk8ycdbXHnm3hgNJ+dt9BymWJqiiFipRO60DCeftkUpHXmnnsMeNvY5TCUXyrlUFT+culidHdnRxWVy2UKFajdr83T+RfXwc6rp3OZwwLwznbaE379mle4GeGWbSY0vXb2qSsgwuGzfx8ImqlESwijABnoyIfPDBh598N3Pxhg6X5anjeoB0Eqx8/1VWoRNHsUS4Pw+Ni4Hfy9cm/v7Dz7//kzF8FnImT9iwkEY3xHva2yt+DAlNsE5uZfiO7TcYyeNCpgDB0NRmtsuG/dLFV94GIJ97Nw5a2Ptgqc0IIidA72GO3d5acViHFvBTcznHPpQbti04wqFOm28lGoEVgH6KbPW17L1vZx4MuzehJLD1fWeqqG3O54OktzsOoFMnU/H/t6aSm964NsFnE1rPzk5PD5mRQd5srh3OoB20+0oHK6pWiAKnO5utmXR5HhRNg5V3eWZ6SD4l/x355eDQBylcZYDwP6z5/MmVWlEU+zZmLBkS4VqlvON4XBQHPsYlAx/iUhs/AkZVgbuuQzd7JOtzr4RRYE12PNvKRAVvXq7aztKWiQWbo2JTybRYhOlZ+znuei/4pWS6f/0bYmGA19FJjMIgYI9mpRlEkGOMRsIA5aHpSMhBkOTN2Ky3tbePvt9PSIIomjwiy8ppsq1vHrAbQ9v/A6dIguRhTZFNQ5uFQ7MLPPgpoosWLVueJFkUkvjiVMMpLyCP6o6z8nuqDA6fQNgkeQMvpyAIvnT6woVLl12aZKQQR08tbTgjsKtYq8AH0n4orShYFsMyC6KZHfoTAcd2Upxr2YzyRA2tSeUyM/SC3KpVGo1vc3H3wAL8/5YYBAaLJYyuylLYCp8SGsiv1FryFj2Tyb2/o484wlNmWy56Usm39p6BfXQhTryGX4ip+WQm6CEIUWE28dJFF7gjzDeAA/gwUTjffWvqG02pJIc0LgO7fwJM1xD4rv1mzBnKghnvJgUpCUxsNhQQCoUdQbFzbg/qHmoefxXb/4KYjnf9YEOAaV4h0XR2+bBcqumNqW+5PYUjAg/+/nePH+zKH4lCb916ch8dEFANmVJBJbLLJdngJeLg81CU+PMTJnmcREwLLGudSkNiOI1pMVKKLNf/zsyHyLcc0P1Hhoq5M0MxWBpwIcwUz4imizeamiZ/3S55s0D0mcZxtxFAh9fCIAhjCMKmhbU5vq530j0FRzletDqQrYCy7KWsJHfc3Wce9uY930usttnti9bcUDQi2rSenu05Pt1AsAc3uYJtLPKwmOBoFj+uaqqJNth0AgQhRFCEMwkJgK1H76zOR6AVSPKunyc3CnwA+H8PqCeEZEqHk91OeiUIMRtMhhOOC0+dolL0wj4vh7Yg7En2eAeEhb6v3vAV9Bo8OThYB24K2tOF33EYR1QrE7f1n3v2M7RB6do56oovprwYvDxqiPx0YYcdZSBaiTpFi04eYHZmKNWxGfC2jrk+d/pNk1tvg81DT4OgTSvBpJv6kmk2Na3jPKNa8QvFoem9Iqh2grC9WTRmOH3w9vlwaBhhGoR/bAHtSBBcepMTxjf3s3u/w8n67Mbp4IphQAncMukT7GR8FIU63286b9brgwjIzc/adS+tsok/kn+naBjDQT2PXc3GZTzh/nsR5jML6rqKmd/UeEVO2VxDtbNtQM9ZBO6Ved51M8BwljBIqJkH786XM7u5T2YYjLvgpdenWZRKkTsg2XzUViAE92ZQM1TqO4w2NopTJo9WS8cC5eRpfGq9sRHVuahhywnEblxjqKhauOdsbE7wimFCSAKORmKQaSg8LybDbZB6JpNfGTDQAZeZT4AywTYNw4ouv7u7JAOUdYbayWoFIwD3oEm0kDdopml//9PvfgeuOzv1Cb3Kd+FlAWWopetqfPkzh6Da4hLPNqv1Wq1ebbK81J/34PIvukiL2YaWE4cjkWg451xog/rMTlG3dgYDBL/709+1Y666+QJK0tRBgHClqip2KfbPTkuQ/aXQUpyWYzX1J1C9eFCB0m9Z4dC9+Fmr2R0lcDFTV7vSuc410NdLvv08YPCcBG/8c6edzyf3y+iLaV4C37j/9LdTXTVmOX/+qXQ+rQg4ecNRjZMcWGNdpgTszWyZwSMAIQu1CVDMDXF3kiOZRP9xhuPwiJ8KnD+HsZpdLa3ZLPs0t2JRODhwIoLJKSE0jcOUjiqWR5lYfz0XjyWv6AwHg36vnXqe6Z1V/JLGmPWOCoDrUEeUiOt3Cv6iZHSO9JnHulEgYKlICCm7DdvTXMUrAFvxST0C1wjH7Za6G0lUjPe7XeQqWp835co2xBMOyyaBKNg1lV4FBukWjbmAVrUkhRkJjAuf+vKrU9Z2pWX0OrHS1uXqCk+zN4MSbXk0VEgTEx4M/k67dGrnhfE89OL0wdcSqXSU4csDqxcddRUFQNTTo/HFOKwpq3L4ahoMfA8B+XE8FHJAc2JSJNb1uoxIkYx+w7dN/UHiFKSKiMgSHyrsLgSNZRC88Y146k7deXiDWVq7UiRfUOty4h9taz1sb6q2KIgzyBMd5jzTJ/E4+Sr8YLCJaDTcH+ICEgc9t+PEYRPHaydZaHbcJRJtv/xCVvCyqiJT/pEj6AL1jSCIfJG5cmySTv+305xqyoKkJ9V8qTN0papFZOVnrWa9CbYp3UZDIWr2ziuurcwlG7oQidrrNVhXN2XPi6u9jriJKzveQ8vXMkdxkQRJ0WARC3O9s36CbOgy06J4lpFViWrAnnKWaRloUaoi1db9rG+kTSNcPquYmigrbIbVbCoIGucD3HtoZnnzrz/M39JPHNguthqXa88quiDhQxRbRs6NhIgR8kCfXQidflSJxZROXuqMIfMci/Ky4SNM8OwZM2Zt6N/PThT+aHF1S5z96GNA2o73Y8eF7Hayxx8HnL+uIVGRS5UM616OZcqVitwDdBNwbKHAYihBX7Ij5KNCLZMliVtxkOE3rtVGPLpehW3DiDCW8jaxzp41e0/FjY/nRhasG2y+z4sx/zIXmKbjAKZQBgaCT+iVPAPtNvHooGCThCaDzUbTZliWbiVAQgVEzPKegtbCeUBVZwpBgA3QD8Dg5V0+x+3iU7yTmJNuSSIAKpiPO4VjdGSIs25BW5P1s+Pu0eXA1Klfzmrd9iE/si2++suprYCDXwKuLnwo3zecxv0NBzh8hwG4POKoSL53MPGMgz7rYBws3VjlBGuYFn4rK7JAUFJCxeVHeEHtoqI3U6RPvJ85czkyFplZMjDm0nbH3Am0rJoxd97Tuwnq1TEwxM8A7dyDXPTkwvgWCEC223cv73HLG+iDIk2/eA4tm0Us5kKBL4ItflpN8CHutSXuJ2iabUkMmYjnptLU5a1Ws5plFMhWP1/obDqFxuFS3dY0gYmdGlE7cqQ0N930hSQfgnqn1bqRX5KiMsimDdPzLxoaGqw7BgGnHi/YmzONdMYFXCQqSQS3ypQmxXTOP3swi4CT9fhmzqnbRCw72Lq4pUEQm9haRB80LQvjS6CDX4M//gSQql/Tj71aUWkSdGI34Yi+zvrTX+EnBEWjRsAAI8Lne9N+QyI2voSVb6GRD7vA/GRxSQPgRXl4TR/6uReWeC6ihBI5zr4QfoPVTazdfyTBfVrDqvBy+A2iW41MnFM2nJyfckzvEcMAG5lObmTk2itm/9y75r9mOc7tNHIVX2fJefL4k43VfzXPWXKgwHZH5MOTWLcN20dlnRmzNgsHUyxEs1buyS3iiszh/mIx74Fy/e9ajeG5g4m5I0nev/eBW/YpikW81wcrviZTnHwvJRv4RUdPcEq12Ww2qxUGCFw8nTFTrBQy6aKsK7Jo/SlQquAIgK5uGhmshrmt3hyzd1LL1/VNJxwbNl8bnz9r7vAx1zCeXKzRsf6c2kBk1qpjgGQ7fe6tZ2v0Iz4SSZAY9g3Q5fNATK470JxZhf5uTVcTbCEbqojuncK6svVg1WzC+o1Yclmi7uFkQNaH5U2Ofj6kZYdiqvJ87Ix6J+XE1k2bWN3j2P0BI+ZKBkdZbElVOJ3O+qw1vRaWbMUKmxv/7nniNShSrIRcX6pMEZcX6+MpWNE8bCosWVIjIdGoMHeoJkvfp75iLse9L7zm5hWmKn777a+/ozEMBYH//quR3t7OsBdQnAIU2aa1fomWNxBV1McWzjW/vM3vadvEwkXHHGvbfMSw/YauIV0D+w2GsM7WUvk4p+wQF3ZsYk7h7dIjl5kCRSx53NSwmdZWUGwYksGfL0jjba4DXUc/oFpG534dbVMHmlyVLZWfIUmCnJ4eF4nkyWQu4VhAJJHihaUsrrvTXFwiiJ6DIoA3VasVq1yplH8DBH4DSWQy/wnvew/2gA6Pe5edHFmebZa5YmbC1rJiWzh3RjjCmg28FaeuTecLJ5XrUsqlN/INErjhTipZSSJvFowfniWfUaoSKBUfq1UcNNBQVEI5pTSsqKIkS4PYAydxMjpnT7G7GbWt8/zlXFKOFfLSdOLMTvm2erPxqLYyzjyxsOX/u9pdn3w6u66aeoSTw+vydY4QLiBmi632n/y01tbSd7aK6cyEgzaB0iF5sCWAzT9DnX6xsF1ckI+3dfU2vu9fSbf33dj4suKxjrH4Mdn8yHsX/rBRkRkCKQjg3ajE0Sc/zBcdzOdcfq5dXM/kg3inyu75wWBY3cwN5bqZYVPtLFMPlkuLcPHjFbCXJfjw7G7h+um9FhH9iNz7Lijwqu45NEw5CIkQCbP03FSc8/UB1bLc9BDRiaNXxnKtoyttlnaaOFQajeZQUlWsD3Iz42pf2rJyJJ1OxpVkMIV/891XKj0tmfVjxNekcgYyVjmtPuhAvfb1kZylwRlmPmlZr4w0Hd89GI18E59OdJ7eValUQgIxh3GMotBarTxKAlqKGDKKeZdaLBQwFjphIUctfOnjoqSIZuPjDfva+erdTq9yGQjkSw/Z4FlKOhVzDmv+i04YCip4qoWQcvcEKPS4azwO6N2xNbhQgSlXMfjOMa6Xb1h4RWaCKLpgTLl7Rue1Ck1N9fX9XdXpqnQ6090/HAQKWEXGzcrD/ZkimxV2Lu6PgHfAw/V2FJl7wapj/73d8zCHtINr/0IqLdamIv7tfuP552xfdPll2wSrhga6+ff0ZI+5mzZoDHKUlgLcz1MgaAtwpI2fAwcKNgwdVdkcLEd48m5pZzagZdA7bdDcarLEpIVP2eimMsPgnsGUzHmzWUy8p10qFvcNlZ/K2Nghm1hwBYJ6HLXs2X5T5OcN/UKnVJOHw9sTOQ6msE8JBOAw8GL35ujBJ4g7j8x4ngSAtw2bd0gir6YQ5GEjNlwA2wOnelUEBigPfUJcS1E0DUNaP2ia1kV0vLOFt22Kpbg05XIS4HNtTdtxZercm6eCqk5TEs8fCxibLP17pUAdCB+DnbdKROU6BPEc5ZXA1t5zJh1rlXGcReN83wCecPhcBt3+jX1UvBIoAnVdzaOfiTrc322W2Hgpb21PFWOkjf9nXMcFJeGfMvfEKrDQPzI/aUhwXAoSQRZdIKzD5vD651vufNPFFdyO9eo7S7mlvYlzR2ITYj+NKuRLY86+T6gHL+FU/zmW3Hz5SGiPb59NIV/OnarvcW4V8nDn8rjY2MUfTJpboSHiViSmZa6cSHDLYqUemqdr+/PgMLl7nDhqCasxeckKU7FgKG4r5NLPVva4nNug2NRIpFn64JXH4ETGBvZl0RigMBfV5rBES6eSzmw6gfYlrY9Oo3/4w7SXGKUEbXrqClaRKkyN6cbbGcdP2uf3pL8a0QC8OcwHDzv2LLPllT29x47Tm/G2LmLXULlmBrcfOvGpVud9hl+w+rHrmzH0Nyg13VLKcbt5Q+bBWI1cfRMZSl/o4hzTtJuXKw2OJTOFUomzoRiMVUlsehbiQHEzhStbl2KqVQ21RLVceEWqnE8Nv/sQzyiK3H5bJzifJInfqAAJfeWwoRs40LNDCFDoQ+Skh2xd42XZdGnrxW8RSFAxOf+BJTOmhEeQAGYsfUga28yPBmhfPIr77b61x3B+zxdb/v+7WEIm+MFKTtiPLQh49+Nc9/OvxuLCEL5X5rg1F/kRECCt9d+R8hXBbWHnL335S+DDn3/Q/XP/ZSkFUx3kx3E0ATUKCHgSYn89Q8BJWNV8/gUcM89TUluDc2l6dslKZ2vYWt5BfLi+6UeQ03oagEK0afdtsd8/tF0D7Ou13UkvFsHJ2g5BOspLtIMv7dwtuJFMmrSKK/xzXc80ffydlnHJ5eSHkWQ3E22CjC+mn2acgrOEdgFlsAJsAXlTPJmcBetCX1D+SpT5RN61oqLtatB/Qmz6gsKk1i9U+uwxzoDSFb9Ehgqe5DzIvqCy39v51SntezErqKxOjEUlhp8ZJ9mhpR5G4BjDLH0rYrPoB3dxQVM/mqa9pA0qrJtzXfxtwJ8p6zyMKyeeM/NC8rvBez2cXVKzDAfQJEVCkEIReKAScs+2oB4qv8ipRHYpgmJYBYdfWqQJYpYmrxjf2snxW4N+7LJvOZzI7kjV2iJIuHR4X6hX4X9r/aDALUl+dnUoBbMW9Zl8H8vudlX91KhivM2o6XmOPcfaBV4s5Ml1HVJRwBsKl7nAC3cGf662RKyiPNi4Zcht/PQV2wTLeM19pLy/peS3cFSryNXT5H1ltYyQmGgEOVHB5U1Mp9C0Q3P2Hb+pMxwf+zDnlrA0xeaKsl4744lzzie8Crp9IHZbWBcjq/8SboR+ayQmzD9b0TlK9Kn8PhM6XcHlllxQokMzF3u39pUVgFUcrM6lUBmB7UQ5u78JWvJhH+hgGu7K9PDpuKxWXOesEBAzd4HgQ5VYt1ilfagRvsUgVRtckzJcQ7imW0PZxuQ1jCi/1LK2NZwkT6/huTq0xpZS3yMdwDblBPTUUhux4nJcILcUrqnYK1zTIFq6puZIvKZJynVNi5xUbGsh2MCgfs0GRI2cXft0a+WD5mSvoAnE4ZfsM/j2trkblmvXr9uJyQ7axFHLae3lsTt1GZEgF+TCIUsVMjKeyVbDuj/iyEs0WO7W59/5QcM6GZUISSgzqkW3Xvp8o1XaDSfNPmiAmIVBLtMtOiLoYIO1IeRaplmyf6ivN8vFSp47EuQoBGWosNGG1A7bNhSL/s/2IZOGVVxRoYmZjVgF0K5Jl1OGd2LPxFoTol6jDWKDk7pNMVWzHEA368oTNWidNxzB0xQ7FW3X0Qrcys1Gw+WG1JA+f1Flg6W+E2LmgJm8HnLWJse0Om4TOYU2St8I0+6cCwaF09DS/fZOtv9fdKjn227eweItJtu967FdS26uW5azPfn22b3nqh0+kKiQg9N3UgWqlPv0ePF+BwWFDAr7lspQMwaWiD5V9kf6UL0x40YlQ0fjebm/MSeUmTRtxpT/WeYx5X5QrVKV1dIyllthpbtUP1n3A/ter/ow0YyWPv1ecgD9+b35Z99+JT5BuP/3fVYMiaGoDyjbGJhY/33m4OLhExASySYmkUNKRk7Rot/ojHQqahpaOnoGRiZmFrmhYiE2efIVREXmTeQItDfkOOQVnYh4joqyK4ySo1NO8+XkCk9unSZuntyKetVpX3rCk55y2LznvSCLVCCcLvdaa411Vml0U5vnYGM599iRAskis/nEJLJt1ewh3nygWLFAwUKFixQtVrxEyYorKVVpZTbwW+8jd910y71uqqIfoPYWcjzcPtaOXyndJlN5xJTruKFwk6VU043VpI8fdGQ9wcZuL9CH/b2ZZ4x0YFSl/aXZcgYfWuh3UZp2sX+gIv5bjlmOuZzwhzjqvd+rUe+PshxYXM5+fYYLjb5pjIVH4+JGgZG1Ecb51jh/uREz9ks4hG8CgxECAKzA4D1Q+77FVHf8abosssQyK6yyxrprtEymUVp8bolNIybSoW8g4OcbEcPInHd4ATSEH1UDWOpl3QdJalWFEZ8pcQx+bwXfh1VBclaXAl26aP8EoRnerJeIWHCjH19JYgcsbmUKo03I7a6P/Hepvl1rpG240WR0xFltLfvanJG9ZXpFsXplqRaqQ4VIf15SPp4jJSk4w+uK2KNqHGGk9iUTjWVi45ZRP3Oq39sGX5nXHw9WGMaah719IyYz9lD7gAc=) format('woff2');unicode-range:U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD}
	:root{
	--bg:#070b14; --bg2:#0b1120; --glass:rgba(255,255,255,.035); --glass2:rgba(255,255,255,.055);
	--line:rgba(125,138,170,.22); --line2:rgba(125,138,170,.35);
	--ink:#e9ecf7; --mut:#9aa3bc; --dim:#6b7591;
	--cyan:#2ee6d6; --cyan2:#7df5e8; --blue:#4d7cff;
	--team:#36c9e6; --opp:#ff5a6a; --neu:#c9b48a; --ass:#0b0b12;
	--grad:linear-gradient(120deg,#7df5e8,#4d7cff);
	--maxw:none;
	}
	*{box-sizing:border-box}
	html{scroll-behavior:smooth}
	body{margin:0; background:
	radial-gradient(1200px 700px at 85% -8%, rgba(77,124,255,.14), transparent 60%),
	radial-gradient(1000px 600px at 5% 4%, rgba(46,230,214,.10), transparent 55%),
	var(--bg);
	color:var(--ink); font-family:'Assistant',system-ui,sans-serif; font-size:17px; line-height:1.75;
	-webkit-font-smoothing:antialiased; text-rendering:optimizeLegibility}
	.wrap{max-width:920px; margin:0 auto; padding:0 22px 120px}
	a{color:var(--cyan2); text-decoration:none; border-bottom:1px solid rgba(125,245,232,.3)}
	a:hover{border-color:var(--cyan2)}
	a:focus-visible,button:focus-visible{outline:2px solid var(--cyan); outline-offset:3px; border-radius:6px}

	/* ---- hero ---- */
	header.hero{padding:84px 22px 40px; max-width:920px; margin:0 auto}
	.kicker{font-family:'Rubik',sans-serif; font-weight:700; letter-spacing:.18em; font-size:12.5px;
	text-transform:uppercase; color:var(--cyan2); display:inline-flex; align-items:center; gap:9px}
	.kicker::before{content:""; width:26px; height:1px; background:var(--cyan2); display:inline-block; opacity:.8}
	h1{font-family:'Rubik',sans-serif; font-weight:800; font-size:clamp(34px,6vw,58px); line-height:1.05;
	margin:.34em 0 .2em; letter-spacing:-.01em; text-wrap:balance;
	background:linear-gradient(180deg,#fff, #cfe5ff 80%); -webkit-background-clip:text; background-clip:text; color:transparent}
	.lede{font-size:clamp(18px,2.4vw,22px); color:var(--mut); max-width:60ch; line-height:1.6}
	.herometa{margin-top:22px; display:flex; flex-wrap:wrap; gap:10px}
	.chip{font-family:'Rubik',sans-serif; font-size:12.5px; font-weight:600; color:var(--ink);
	background:var(--glass); border:1px solid var(--line); border-radius:999px; padding:7px 14px}
	.chip b{color:var(--cyan2)}

	/* ---- TOC ---- */
	nav.toc{margin:34px auto 0; max-width:920px; display:grid; grid-template-columns:repeat(auto-fit,minmax(220px,1fr));
	gap:10px}
	nav.toc a{display:flex; gap:12px; align-items:baseline; padding:13px 15px; background:var(--glass);
	border:1px solid var(--line); border-radius:14px; color:var(--ink); border-bottom:1px solid var(--line)}
	nav.toc a:hover{background:var(--glass2); border-color:var(--line2)}
	nav.toc .n{font-family:'Rubik',sans-serif; font-weight:800; color:var(--cyan2); font-size:13px; opacity:.9}
	nav.toc .t{font-weight:600; font-size:15px}

	/* ---- sections ---- */
	section{padding-top:64px; scroll-margin-top:24px}
	.eyebrow{font-family:'Rubik',sans-serif; font-weight:800; font-size:13px; letter-spacing:.06em;
	color:var(--cyan2); display:block; margin-bottom:8px}
	h2{font-family:'Rubik',sans-serif; font-weight:800; font-size:clamp(24px,3.6vw,34px); line-height:1.12;
	margin:0 0 6px; letter-spacing:-.01em; text-wrap:balance}
	h2 .sub{display:block; font-family:'Assistant',sans-serif; font-weight:500; font-size:16px; color:var(--mut);
	margin-top:7px; letter-spacing:0}
	section p{max-width:68ch}
	h3{font-family:'Rubik',sans-serif; font-weight:700; font-size:18px; margin:28px 0 6px; color:#fff}
	strong{color:#fff; font-weight:700}
	em{color:var(--cyan2); font-style:normal}
	ul,ol{max-width:68ch; padding-inline-start:1.3em}
	li{margin:6px 0}
	li::marker{color:var(--cyan)}

	/* ---- glass cards / callouts ---- */
	.card{background:var(--glass); border:1px solid var(--line); border-radius:18px; padding:22px 24px; margin:22px 0}
	.note{background:linear-gradient(120deg, rgba(46,230,214,.08), rgba(77,124,255,.06));
	border:1px solid rgba(46,230,214,.28); border-radius:16px; padding:16px 20px; margin:20px 0; font-size:16px}
	.note b{color:var(--cyan2)}

	/* ---- stat row ---- */
	.stats{display:grid; grid-template-columns:repeat(auto-fit,minmax(150px,1fr)); gap:12px; margin:24px 0}
	.stat{background:var(--glass); border:1px solid var(--line); border-radius:16px; padding:18px}
	.stat .v{font-family:'Rubik',sans-serif; font-weight:800; font-size:30px; line-height:1;
	background:var(--grad); -webkit-background-clip:text; background-clip:text; color:transparent; font-variant-numeric:tabular-nums}
	.stat .k{color:var(--mut); font-size:13.5px; margin-top:7px}

	/* ---- formula ---- */
	.formula{background:#05080f; border:1px solid var(--line2); border-radius:14px; padding:18px 20px; margin:18px 0;
	overflow-x:auto; direction:ltr; text-align:left}
	.formula code{font-family:'Rubik',ui-monospace,monospace; font-size:14.5px; color:#cfe0ff; white-space:pre; line-height:1.9}
	.formula .hl{color:var(--cyan2); font-weight:700}
	.formula .dim{color:var(--dim)}

	/* ---- tables ---- */
	.tablewrap{overflow-x:auto; margin:20px 0; border:1px solid var(--line); border-radius:16px}
	table{border-collapse:collapse; width:100%; font-size:15px; min-width:460px}
	caption{caption-side:bottom; color:var(--dim); font-size:13px; padding:10px 4px; text-align:start}
	th,td{padding:11px 15px; text-align:start; border-bottom:1px solid var(--line)}
	thead th{font-family:'Rubik',sans-serif; font-weight:700; font-size:13px; color:var(--mut); background:rgba(255,255,255,.02);
	text-transform:none; letter-spacing:.02em}
	tbody tr:last-child td{border-bottom:0}
	td.num,th.num{font-variant-numeric:tabular-nums; direction:ltr; text-align:left}
	tr.win td{background:linear-gradient(90deg, rgba(46,230,214,.10), transparent)}
	tr.win td:first-child{color:var(--cyan2); font-weight:700}
	.bad{color:var(--opp)} .good{color:var(--cyan2)}
	.tag{font-family:'Rubik',sans-serif; font-size:11px; font-weight:700; padding:2px 8px; border-radius:999px;
	border:1px solid var(--line2); color:var(--mut)}

	/* ---- pipeline ---- */
	.pipe{display:grid; gap:10px; margin:22px 0; counter-reset:step}
	.pipe .step{display:flex; gap:14px; align-items:flex-start; background:var(--glass); border:1px solid var(--line);
	border-radius:14px; padding:15px 18px}
	.pipe .step::before{counter-increment:step; content:counter(step); font-family:'Rubik',sans-serif; font-weight:800;
	color:#04141b; background:var(--grad); width:30px; height:30px; flex:0 0 30px; border-radius:9px;
	display:grid; place-items:center; font-size:15px; margin-top:2px}
	.pipe .step b{color:#fff}
	.pipe .step span{color:var(--mut); font-size:15px}

	/* role swatches */
	.legend{display:flex; gap:16px; flex-wrap:wrap; margin:14px 0; font-size:14px; color:var(--mut)}
	.legend i{width:13px; height:13px; border-radius:4px; display:inline-block; vertical-align:-2px; margin-inline-end:6px}

	footer{margin-top:90px; padding-top:30px; border-top:1px solid var(--line); color:var(--dim); font-size:14px}
	footer a{color:var(--mut)}
	.refs{font-size:14px; color:var(--mut)} .refs li{margin:8px 0}
	.backtop{display:inline-block; margin-top:10px}
	.cta-row{display:flex; gap:12px; flex-wrap:wrap; margin-top:26px}
	.btn{font-family:'Rubik',sans-serif; font-weight:700; font-size:15px; padding:12px 22px; border-radius:12px;
	border:1px solid var(--line2); color:var(--ink); background:var(--glass)}
	.btn.primary{background:var(--grad); color:#04141b; border:0; box-shadow:0 8px 30px rgba(46,230,214,.25)}
	@media (prefers-reduced-motion:reduce){html{scroll-behavior:auto}}
	</style>
	</head>
	<body>

	<header class="hero">
	<span class="kicker">קופיילוט · שם קוד · איך זה עובד</span>
	<h1>איך לרמות בשם קוד</h1>
	<p class="lede">קופיילוט שנותן רמזים טובים משלך — בעזרת וקטורים, קצת גאומטריה, ואפס מודלי שפה ענקיים.
	זו הצלילה לעומק: למה זו בכלל בעיית דמיון סמנטי, איך בוחרים את הרמז, ולמה דווקא עברית הקשתה.</p>
	<div class="herometa">
	<span class="chip">מנוע · <b>fastText</b></span>
	<span class="chip">ללא <b>LLM</b></span>
	<span class="chip">מקומי · <b>CPU</b></span>
	<span class="chip">המודל · <b>369MB</b> (היה 7.2GB)</span>
	</div>
	<div class="cta-row">
	<a class="btn primary" href="/">‹ נסו את הקופיילוט</a>
	<a class="btn" href="#refs">מקורות</a>
	</div>
	</header>

	<nav class="toc" aria-label="תוכן עניינים">
	<a href="#problem"><span class="n">01</span><span class="t">הבעיה: דמיון סמנטי</span></a>
	<a href="#score"><span class="n">02</span><span class="t">פונקציית הניקוד</span></a>
	<a href="#why"><span class="n">03</span><span class="t">למה fastText, לא LLM</span></a>
	<a href="#hebrew"><span class="n">04</span><span class="t">האתגר העברי</span></a>
	<a href="#map"><span class="n">05</span><span class="t">המפה הסמנטית</span></a>
	<a href="#compress"><span class="n">06</span><span class="t">דחיסה ללא אובדן</span></a>
	<a href="#risk"><span class="n">07</span><span class="t">חוגת הסיכון</span></a>
	<a href="#limits"><span class="n">08</span><span class="t">מגבלות וכנות</span></a>
	</nav>

	<div class="wrap">

	<section id="problem">
	<span class="eyebrow">01</span>
	<h2>הבעיה: דמיון סמנטי<span class="sub">"שם קוד" מחופש למשחק מילים — אבל הוא בעיית גאומטריה</span></h2>
	<p>ב"שם קוד" רב המרגלים נותן מילה אחת ומספר, והצוות מנחש את הקלפים שלו — בלי לגעת בקלפים של היריב,
	בניטרליים, ובמיוחד לא ב<strong>מתנקש</strong>. ברגע שמפסיקים לחשוב על זה כמשחק מילים ומתחילים לחשוב עליו
	כ<em>קרבה במרחב</em>, הפתרון מתבקש.</p>
	<p>כל מילה הופכת לווקטור — נקודה במרחב של 300 ממדים. "קרבה במשמעות" היא פשוט <strong>מרחק בין נקודות</strong>.
	רמז מצוין צריך לקיים תנאי אחד: להיות הכי קרוב למילים שלך, והכי רחוק מכל השאר. המילה שממקסמת את התנאי הזה — היא הרמז.</p>
	<div class="legend">
	<span><i style="background:var(--team)"></i>שלך</span>
	<span><i style="background:var(--opp)"></i>יריב</span>
	<span><i style="background:var(--neu)"></i>ניטרלי</span>
	<span><i style="background:var(--ass); border:1px solid var(--line2)"></i>מתנקש</span>
	</div>
	</section>

	<section id="score">
	<span class="eyebrow">02</span>
	<h2>פונקציית הניקוד<span class="sub">DETECT — קרבה מדורגת עם קנסות, אחרי תיקון אניזוטרופיה</span></h2>
	<p>הקופיילוט מדרג כל מילה־מועמדת מאוצר הרמזים לפי ציון מדורג: בונוס על קרבה למילים שלך, וקנס הולך וגדל ככל
	שמתקרבים ליריב, לניטרלי, ובעיקר למתנקש. זה הניסוח (s′ היא קוסינוס ממורכז סביב 25 מילות הלוח — תיקון אניזוטרופיה
	כך שמילים נפוצות "קרובות לכולם" לא מנצחות):</p>
	<div class="formula"><code>g(c) = <span class="hl">Σ</span> s′(c, b) <span class="dim">// top-m of your words — reward</span>
	<span class="bad">−</span> λₐ · max(0, s′(c, <span class="bad">assassin</span>)) <span class="dim">// the black card — avoid hardest</span>
	<span class="bad">−</span> λₒ · max(0, max s′(c, opp)) <span class="dim">// rival team — avoid strongly</span>
	<span class="bad">−</span> λₙ · max(0, max s′(c, neutral))<span class="dim">// bystanders — avoid mildly</span>
	<span class="hl">+</span> λ_f · FREQ(c) <span class="dim">// prefer mid-frequency words</span>

	where s′(c, w) = cos(c, w) − mean_b cos(c, b)</code></div>
	<p>שלושת הקנסות הם <em>מדורגים</em>: <code>λₐ ≫ λₒ > λₙ</code>. ככה הקופיילוט "מפחד" מהמתנקש הרבה יותר מאשר
	מקלף ניטרלי. הרמז שמנצח הוא לא בהכרח הכי קרוב למילים שלך — אלא זה שמשיג את <strong>ההפרדה הנקייה ביותר</strong>
	בין שלך לכל השאר.</p>
	<div class="note"><b>בטיחות לפני קרבה.</b> רמז שקרוב למילים שלך אבל גם מתקרב למילה של היריב הוא מסוכן.
	המנוע מדרג קודם לפי כמה קלפים אפשר לנחש בבטחה (ה"ריצה הבטוחה"), ורק אז לפי קרבה — ולפעמים פשוט פוסק "אין רמז בטוח".</p>
	</section>

	<section id="why">
	<span class="eyebrow">03</span>
	<h2>למה fastText, ולא LLM<span class="sub">מדדנו. הוותיק הקטן ניצח את המודלים הגדולים</span></h2>
	<p>זה לא היה ניחוש. ערכנו מבחן ראש־בראש: כל אנקודר שיחק רב מרגלים על אותם לוחות ואותו אוצר רמזים, ומדדנו
	כמה טוב מנחש עצמאי משחזר את המילים שהאנקודר התכוון אליהן (recovery), ועד כמה הרמז שומר מרחק מהמתנקש.</p>
	<div class="tablewrap">
	<table>
	<caption>12 לוחות · אותו אוצר רמזים (שמות עצם/תארים) · מנחש־שופט עצמאי. recovery וגם margin — גבוה יותר = טוב יותר.</caption>
	<thead><tr><th>אנקודר</th><th class="num">recovery</th><th class="num">margin (הפרדה)</th><th>סוג</th></tr></thead>
	<tbody>
	<tr class="win"><td>fastText</td><td class="num">0.375</td><td class="num">0.194</td><td><span class="tag">וקטורים סטטיים</span></td></tr>
	<tr><td>EmbeddingGemma‑300m</td><td class="num">0.292</td><td class="num">0.131</td><td><span class="tag">SOTA‑small 2025</span></td></tr>
	<tr><td>Qwen3‑Embedding‑0.6B</td><td class="num">0.125</td><td class="num">0.112</td><td><span class="tag">סנטנס‑אמבדינג</span></td></tr>
	</tbody>
	</table>
	</div>
	<p>למה דווקא הסטטי? כי "שם קוד" דורש <strong>אסוציאטיביות</strong> (מלך→כתר→טירה), לא <em>נרדפוּת</em>.
	וקטורים סטטיים מבוססי הופעה־משותפת לוכדים בדיוק את זה — וזו הסיבה שספרות ה‑AI של המשחק משתמשת בהם.
	מודלי סנטנס־אמבדינג מאומנים על דמיון משפטים, ולמילה בודדת הם דוחסים את כל הקוסינוסים לטווח צר וגבוה
	(qwen בקושי הבדיל בין "מלך·מלכה" ל"מלך·ים"). ועל הלוח אין הקשר — בדיוק היתרון היחיד של מודל קונטקסטואלי
	מתבטל.</p>
	<p>בנוסף: <strong>fastText חינם תפעולית</strong> — רץ על CPU, אופליין, מחשבים את כל אוצר הרמזים פעם אחת. גם אם
	טרנספורמר כבד היה מנצח בנקודה־שתיים, הוא הבחירה הלא־נכונה לדמו ציבורי תמידי. תיקו באיכות + ניצחון מוחץ בעלות.</p>
	</section>

	<section id="hebrew">
	<span class="eyebrow">04</span>
	<h2>האתגר העברי<span class="sub">שורשים, מורפולוגיה, ואניזוטרופיה</span></h2>
	<h3>חוקיות: שורש, לא מחרוזת</h3>
	<p>חוקי המשחק אוסרים רמז שחולק שורש עם מילה על הלוח. בעברית השוואת מחרוזות לא מספיקה — "קסם" ו"קוסם"
	חולקים שורש אבל נראים שונים, ו"בבית" הוא בכלל "בית". הפתרון בנוי בשכבות: <strong>למטיזציה מבוססת DictaBERT</strong>
	שמקלפת תחיליות (ב/כ/ל/מ/ה/ו) ונטיות; מעליה <strong>חתימת־שורש עיצורית</strong> שמנטרלת אותיות שימוש
	(מ/ה/נ בתחילית, אהו״י באמצע, וסיומות) — כך "רכבת/רכב", "שומר/שמירה" וגם "פחד/מפחיד/פחדן" נתפסים כאותו שורש;
	ולבסוף בדיקת <strong>הכלה</strong> שתופסת הכפלות והקטנות שהשוואת שורש מפספסת — "כלבלב" ליד "כלב", "חתלתול"
	ליד "חתול". זו הֵאוריסטיקה דטרמיניסטית, מהירה ואופליין, שמכוּונת לפסול־ביתר ולא להחמיץ (אין מודל מודרני
	שמחזיר שורש עברי כשדה — בדקנו).</p>
	<h3>אוצר וסינון</h3>
	<p>אוצר הרמזים הוא כ‑<strong>5,260</strong> שמות עצם ותארים בתדירות בינונית (לא מילות תפקוד, לא זבל נדיר) —
	כך שכמעט כל מילה עברית מוכרת היא מועמדת לגיטימית, לא רשימה קצרה וקבועה. מילים פוגעניות מסוננות מראש
	מהאוצר באמצעות רשימת חסימה.</p>
	<h3>מורפולוגיה: subwords</h3>
	<p>מכיוון ש‑fastText בנוי על n‑gramים של אותיות, הוא נותן וקטור הגיוני גם למילה שלא ראה מעולם — הטיה,
	מילה עם אות יחס, או רמז שהמשתמש הקליד. זה מה ששומר על הכיסוי בעברית בלי טבלת מילים ענקית.</p>
	<h3>אניזוטרופיה: ניקוי המפה</h3>
	<p>וקטורים בעברית נוטים להצטופף לפי תדירות ואורך, לא רק לפי משמעות — קונוס משותף שמעוות מרחקים.
	אנחנו מסירים את <em>k=3</em> הצירים הראשיים (all‑but‑the‑top, Mu & Viswanath) לפני ההקרנה, וזה פותח
	את המפה חזרה כדי שתראה פיזור <strong>סמנטי</strong> ולא פיזור־תדירות.</p>
	</section>

	<section id="map">
	<span class="eyebrow">05</span>
	<h2>המפה הסמנטית<span class="sub">למה הרמז יושב במרכז המילים שלך</span></h2>
	<p>המפה שמתחת ללוח היא ההקרנה ל‑2D של אותו מרחב. בונים מטריצת מרחקי קוסינוס בין כל המילים והרמז, ומקרינים
	אותה עם <strong>MDS קלאסי</strong> (Torgerson) — מירכוז כפול של מטריצת המרחקים בריבוע, ושני הווקטורים העצמיים
	הגדולים. הכול ב‑numpy טהור, בלי תלות נוספת.</p>
	<div class="pipe">
	<div class="step"><div><b>הטמעה</b> <span>— כל מילות הלוח + הרמז לווקטורים (fastText).</span></div></div>
	<div class="step"><div><b>ניקוי</b> <span>— הסרת קונוס התדירות (all‑but‑top k=3).</span></div></div>
	<div class="step"><div><b>מרחקים</b> <span>— מטריצת 1 − cos בין כל הזוגות.</span></div></div>
	<div class="step"><div><b>הקרנה</b> <span>— MDS קלאסי לשני ממדים.</span></div></div>
	<div class="step"><div><b>ציור</b> <span>— הרמז כצומת זוהר, קווי זהב למילות היעד.</span></div></div>
	</div>
	<p>התוצאה היא התמונה שמסבירה הכול במבט אחד: רמז טוב נוחת ב<strong>מרכז המסה</strong> של המילים שלך, ורחוק מהאשכול של היריב.</p>
	</section>

	<section id="compress">
	<span class="eyebrow">06</span>
	<h2>דחיסה ללא אובדן<span class="sub">7.2GB → 369MB, ואותם רמזים בדיוק</span></h2>
	<p>המודל המלא של fastText שוקל 7.2GB — רובו מטריצת אימון שמיותרת בהסקה, ומיליוני מילים שלעולם לא נשתמש בהן.
	דחסנו אותו עם <em>compress‑fasttext</em> (גיזום אוצר־מילים + n‑gramים + fp16), ובדקנו שזה לא פוגע: על 12 לוחות,
	המודל הדחוס בחר <strong>בדיוק את אותו רמז ב‑12 מתוך 12</strong>, עם recovery ו‑margin זהים לחלוטין.</p>
	<div class="stats">
	<div class="stat"><div class="v">20×</div><div class="k">קטן יותר (7.2GB→369MB)</div></div>
	<div class="stat"><div class="v">12/12</div><div class="k">אותו רמז כמו המלא</div></div>
	<div class="stat"><div class="v">0.9985</div><div class="k">קורלציית גאומטריה</div></div>
	</div>
	<div class="tablewrap">
	<table>
	<caption>וריאנטים של דחיסה מול המודל המלא. fp16 הוא זה ששוגר.</caption>
	<thead><tr><th>וריאנט</th><th class="num">גודל</th><th class="num">קורלציה</th><th class="num">אותו רמז</th><th class="num">recovery</th></tr></thead>
	<tbody>
	<tr><td>מלא</td><td class="num">7.2GB</td><td class="num">1.000</td><td class="num">—</td><td class="num">0.375</td></tr>
	<tr><td>ברירת־מחדל</td><td class="num">14MB</td><td class="num bad">0.64</td><td class="num">5/12</td><td class="num bad">0.250</td></tr>
	<tr><td>PQ גדול</td><td class="num">107MB</td><td class="num">0.96</td><td class="num">6/12</td><td class="num">0.250</td></tr>
	<tr class="win"><td>fp16 ✓</td><td class="num">369MB</td><td class="num good">0.9985</td><td class="num good">12/12</td><td class="num good">0.375</td></tr>
	</tbody>
	</table>
	</div>
	</section>

	<section id="risk">
	<span class="eyebrow">07</span>
	<h2>חוגת הסיכון<span class="sub">זהיר · מאוזן · נועז — אותה גאומטריה, תיאבון אחר</span></h2>
	<p>"סיכון" ב"שם קוד" הוא שני כפתורים: <strong>כמה מילים</strong> לכסות בבת אחת (m), ו<strong>כמה חזק</strong>
	להימנע מהקלפים של האחרים (הקנסות λ). החוגה פשוט מזיזה את שניהם:</p>
	<div class="tablewrap">
	<table>
	<thead><tr><th>מצב</th><th class="num">מכסה</th><th class="num">קנס מתנקש/יריב/ניטרלי</th><th>התנהגות</th></tr></thead>
	<tbody>
	<tr><td>זהיר</td><td class="num">2</td><td class="num">3.0 · 1.5 · 0.5</td><td>רק רמזים בטוחים, מסרב יותר</td></tr>
	<tr><td>מאוזן</td><td class="num">2</td><td class="num">2.0 · 1.0 · 0.3</td><td>ברירת המחדל</td></tr>
	<tr><td>נועז</td><td class="num">3</td><td class="num">1.5 · 0.7 · 0.2</td><td>מכסה יותר, סובל קרבה ליריב</td></tr>
	</tbody>
	</table>
	</div>
	<p>בנועז המנוע גם <em>לא קובר</em> רמז מסוכן בתחתית הרשימה — כי זו כל הנקודה של נועז.</p>
	</section>

	<section id="limits">
	<span class="eyebrow">08</span>
	<h2>מגבלות וכנות<span class="sub">איפה זה נשבר, ומה לא מדדנו</span></h2>
	<ul>
	<li><strong>רב‑משמעות (polysemy).</strong> "עלה" הוא צמח, מחיר, ופועל — וקטור סטטי אחד לא יודע להבדיל,
	ופה הגאומטריה קורסת. זו תקרת הזכוכית של embeddings קלאסיים (ומכיוון שאין הקשר על הלוח, גם מודל קונטקסטואלי
	לא היה מציל אותה כאן).</li>
	<li><strong>מילים שאולות.</strong> מילה כמו "ג'ודו" (עם גרש) מאבדת את הווקטור שלה בדחיסה ומתנהגת כניטרלית —
	מקרה־קצה אחד מתוך 573 מילות החפיסה.</li>
	<li><strong>שורשים חלשים.</strong> חתימת־השורש תופסת נטיות, תחיליות והכפלות — אבל שורשים שבהם אות נושרת או
	מתחלפת ("נ.פ.ל" → "מפלה", "ק.ו.ם" → "תקומה") עלולים לחמוק. אם המשוב יראה החמצות כאלה, נאמן מודל זוגי קטן
	שמכריע "האם X רמז חוקי כש‑Y על הלוח".</li>
	<li><strong>השופט הוא מודל.</strong> מדד ה‑recovery נמדד מול מנחש‑LLM כפרוקסי ל"האם אדם היה מבין" — אינדיקציה
	טובה, אבל לא בני אדם אמיתיים. לשם כך נאסוף משוב מהשחקנים.</li>
	<li><strong>אוצר המילים חשוב מהמודל.</strong> מאגר הרמזים הראשון (המילים השכיחות) נתן זבל; רק סינון לשמות עצם
	ותארים בתדירות בינונית הקפיץ את האיכות. הלקח: לפני שמשדרגים מודל — מנקים נתונים.</li>
	</ul>
	<div class="note"><b>הדרך השנייה לרמות.</b> אפשר גם בכוח גס: הריפו
	<a href="https://github.com/durfi/codenames">durfi/codenames</a> מתעלם מ‑embeddings ומנצל שיש כמות מוגבלת של
	מפות מפתח — ומסיק לאחור מהלוח איזו מפה שוחקה. ההגנה פשוטה: להגריל מפה אקראית בכל סבב.</div>
	</section>

	<footer id="refs">
	<h3>מקורות</h3>
	<ul class="refs">
	<li>Koyyalagunta et al. (2021), <em>DETECT: Codenames as a game of co‑occurrence counting</em>.</li>
	<li>Mu & Viswanath (2018), <em>All‑but‑the‑Top: Simple and Effective Postprocessing for Word Representations</em>.</li>
	<li>Bojanowski et al. (2017), <em>Enriching Word Vectors with Subword Information</em> (fastText).</li>
	<li>Dicta (DictaBERT) — למטיזציה ומורפולוגיה לעברית · <a href="https://dicta.org.il">dicta.org.il</a>.</li>
	<li>compress‑fasttext (avidale) — דחיסת מודלים.</li>
	</ul>
	<p>נבנה על‑ידי שמוליק · מנוע גאומטרי, ללא LLM. <a class="backtop" href="/">‹ חזרה לקופיילוט</a></p>
	</footer>

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