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    <title>topic Hi Vladimir, in Analyzers</title>
    <link>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173223#M18191</link>
    <description>&lt;P&gt;Hi Vladimir,&lt;/P&gt;&lt;P&gt;Thanks for the clear instructions. Unfortunately, in the sample collection I had with me I see 'No data to show' under the grouping you mentioned. Probably I missed something while configuring the run.&lt;/P&gt;&lt;P&gt;But I see the same suggestion from you as well - ITT Task API, which is what I am looking for. Though I am not in the state to experiment, I can see that it is what I was intending.&lt;/P&gt;&lt;P&gt;And as mentioned before, the screenshot has MPI stuff because of the sample trace file I downloaded from the vampir homepage.&amp;nbsp;&lt;/P&gt;&lt;P&gt;Thanks.&lt;/P&gt;&lt;P&gt;Uday&lt;/P&gt;</description>
    <pubDate>Tue, 25 Jun 2019 11:19:00 GMT</pubDate>
    <dc:creator>K__Uday_Kiran</dc:creator>
    <dc:date>2019-06-25T11:19:00Z</dc:date>
    <item>
      <title>Parallel behavior visualization</title>
      <link>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173214#M18182</link>
      <description>&lt;P&gt;Hi,&lt;/P&gt;&lt;P&gt;I tried searching for the topic, but couldn't find much information.&lt;/P&gt;&lt;P&gt;I am looking to achieve performance visualization such that I can view the parallel behavior of my application. For example, at a given point of time I would like to know which functions are being executed in each of the threads so that I can redesign my application for better usage of resources.&lt;/P&gt;&lt;P&gt;I have seen that kind of view with Vampir, which I am attaching for information.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&lt;IMG 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Oeeo99+O2G//X566qn377rr5ylT7jjrLDYQ4849d/3Pk/89evSyiRPv+d3vFGeQwTT//vvfveMO5efPP8d/O3dsx/833/iH++4Ziw9H9uo5+6O3P5rxxn+ffOzll6YdcXiN6/65F1y2fvLk0f/+58Q3l/zx94pfyYRPdmwuqy7dWb27qnqv9l2+XTIPQT6g0L5lyzeXlCPIB59btWpZ/eabha1bnTjk8mlzfrn3qQ8ef3vp7Q+/hghQevXVTRuj8nUzfbHtxgDC7CqsWH12XBP4ho6VM1zBERzHChnuQI6y9zXHDGMqNZ8cnUay2xIBiUDWIAA/Q9hssWkLtag5X+pE79OY8d7BNVeG+mTgZiktRTggzZ5dd9cuxPM0QdqM5cvpxhvp5ptpxYomTRoc3L59i4YNadkyevhhojU1Eq5YQc88g89Ll8xbt3YF812Af0HxBiXoa+/C1m323nv3rt0LFsybeNFF026+ecXaYlBuf3B3qDcbi9e9/ep/Z31fgSbbyqsf+XhHWYXSvnRn1fzVlTAlqdeVz+XlO2sVhrz8Vm06NW62x6/bquZ+/BZNmpQBrCRLiYBEQBiBPMfcbsKkZEWJgERAIiARyAAC77777ieffAJXTLgdhscennjIFYkASLh9hsfFSDn63vG53ViQj2kwp3humyjhkrzsEUByVERO8u7cSKMqQZMISARyBwGp+eTOWMueSgQkAlmLAPIiIPcg0l2G10Okievbty8cycJjYUU54t7pslpbSSU1n+hngn+OyBCrIyI1H/+oSgoSgQQhIDWfBA2WFFUiIBGQCEgEQkdAaj6hQ5w5BlLzyRz2krNEIBYISM0nFsMghZAISAQkAhKBmCAgqPnERFophkRAIiARkAiII5D33nvvideWNSUCEgGJgERAIiARkAhIBCQCEgGJQBIRkDafJI6alFkiIBGQCEgEJAISAYmAREAiIBFwh4DUfNzhJWtLBCQCEoEMIoCjeGRCzgziL1lLBCQC2Y0AMl7g8GIc7651s379+m3atOEjxETqZDdKie6d1HwSPXxSeImARCDWCEBRMcoH1cWzAsMaem4ea7CkcBIBiYBEIKMITHj00UYNG5588smrV6/etm3bzp07t27dipyZ69at61RUdNWVV0I61MkjwmHQJ5xwwiGHHIIra9eufeWVV5D7vm5BAasjS5wRkCeZxnl0pGwSAYlAshGAlsIKuqH77Kdj0uzjBz3ZViIgEZAImCLw26ZNw4YNa9269dKlS19//fUZM2Yg38mKFSt27dqFn1gTfGjSpAl+euihh77//vvi4uLx48fPnDmzsrJSqyPhjTMCeTKTfZyHR8omEZAIJBSBs846i5dcZ6XRTDdaHWbJYV81xYY3GbGLvM1HR8SmFSM7ffr0hIIpxZYISAQkAqEiwFbssWPH3n///Tt27CgtLX3kkUeg2DCme+65J3ze8Curc9VVV917772rVq1q165dw4YNly9fDmXpxhtvfO6551gdY5HLb6jD54p4Hn+SsauWsrJEQCIgEZAImCKAQ+J11600H15d0X22acLrPCKtmDDy5E05XSUCEgGJgBEBbcWG3nLfffdB7Vm/fv2ECRNWrlzJKu+///7YLWuaD+qsWbPm7rvvXrZsGX5FFNDtt99+0EEH3XzzzVaaj1x+4zPxZJxPfMZCSiIRkAhkLQIhaT6825tpCJCMCMraKSU7JhGQCASNAPQW6DOffvrpE08+8fOGnxHMgyX0559/Puyww8BK03xQB+5wMPswo9Dee+9900039e7dG/qPleYTtKSSnncEZJyPd+xkS4mAREAiECoCeOhqRZyRt1bi9GVNiYBEQCKQrQjMnz//n//85/Iflm8t3XrKKafAsQ1pDBo1asT3F6aeO+64Y9GiRS1btmzbtu2PP/4IhQcRQdmKSZb1S2o+WTagsjsSAYlA9iCgJUXQ0iSI9M1bKxHKso5EQCIgEchiBAoKCubOnYu4HTi8nT3k7H79+uFF0rXXXosP+Il1HB9efvnlJUuWNGvW7IYbbrjtttvat2+PmJ/JkydrdbIYoizomtR8smAQZRckAhKB7EfANEG2Y7e9tXIkKytIBCQCEoHsQ6BVq1YVFRWnnnrq8OHDr7/++q5du3bu3Bk5DL7++mv8xPqLD9BwkBHhrrvuOvHEE+EI98ADDwwePHivvfbS6mQfMtnUIxnnk02jKfsiEZAIxBQBD3E+6IlIbjetwxoLY6uYgiLFkghIBCQCMUMAKd02b96MFNWaXNBz4NV29dVXa1dE6sSsW1KcWgSk5iNng0RAIiARkAhIBCQCEgGJgERAIpD9CMis1tk/xrKHEgGJgERAIiARkAhIBCQC/hHgDy1gp/ToTm8zZSGzWvtHPigKMs4nKCQlHYmAREAiIBGQCEgEJAISgRxCYMSIETnU26zoqtR8smIYZSckAhIBiYBEQCIgEZAISAQkAhIBWwSk5iMniERAIiARkAhIBCQCEgGJgEQgYwjk5eUx3tqHjImS1YwBr9R8sm2EMaha0fpmetHYc+P9ZiSVbXgF0Z+gMOfpsM9BSJedNILCnD1m5DwXmSUScxGUgq3jDXNvrYKVPLnUvKFns4zIldxxMgSIeVLWc1ezQrc3cMQzzhVcdTykjkjNJyRgM0MWU6qaK2yGmV7UyWe6z+YbZqY/SeAaIOb82OFzEnqfGRkDxFzk7shMJ2PGVWIe/YB4w9xbq+h7F0+O3tCzWkZMH6zx7HgGpQoQ8yxez41buwwOWdJZS80n6SOYJr/n7TK7qXhabAWxR0e+KQc+jihZYWjEXAR/iXl4mFuNlMRcYp6R54S3tcVbK/aOTO7UvaFn1cp+kZeYs9sqQMxFSMVhnrP9lU/rh/ZqW6PDm4a0Jct40b6asaENKbdz2LTjjhLqgOL7qwnAhORXMKtWqCY1n4w80ZLB1PT24O8B7SWEzxs4GXDEQEr+hZbEPJoBkZhHg7NR7WfLi5zn0eAv53k0OMt5Hj3OWYy5dtuyTb+jy49INU2h0m3wHOl7W6t1ZI0S2k8YrTnbrwo+NaTmk9nbMETubBL4YSBuXfXJyI+QsWrrH3PtvYUjpI4VYoVMeML4xJztrbVij6rEXHx+BjXiEnM/mHu7OyTmEvOg7l+3dLzNWNNWjqQyNc81wVy92TF9ToXRBSNNQS6O1bx13H4K8UwdBdBISc3H7Y2ZgPqa7stk1e3tguoATzYomsmlIzGPfuwCwdz0PRbfFznPdW9M+f2Et7XFsZXE3D/murvD8faUmEvMHSdJqBW8reem89xm8id3nvNvon0OBK9EGXeJVldwXaShT9miaS41n2hwjo6LtpPT7d602yZAUXTG0AApJ4tUsJjbv6mSmLO5ESzm9vNNYh445o5PcYm5H8xN7w7HRVViLjF3nCQhVfC2npu2cpz8cZjnmgoREp6CTzQGhabqsK+QzfGKfUMb7pntuCaY1HwyMvHCYupo3uUraPPbmzQ+m3tjGsNWEvPoB0VinjWYmw6lXFv4Lbj9NkLbuGigOd4dpgQl5hLz6FeVwDF3nPwZn+c6LwNe08gI/gwQIyyOQFk1NKXGv6z0qXk6CiayvknNJyOTLUSmvDmSfxay61Z+kOxXNjvZB51HirEhX8GKbIj9jBPpoDDXVgerzknMNWSCwlzOc/E7KSjMtXXGakWS8zyQea6Nl1zPxSc5Pzm1p6HjjLVvxT9YeUnkPA98nlsNhMYoizE3fZbpLlotBUZY7K9oS7d/PB0l1Mksvtu0kU1qPq6WxLhX1pRp7QOTWPdVu8gvB3xbXSurqWZKNu4YBS1f4JjbCygx5+eznOdBT2dLeoHPc95fwshVznPP89xxpOR6bnPXOKKnU12Mz0p+YuuoyXluinyAmFuR0o2a/eIT6qJqvPu0K8YP/A7NdPLoLhp3cbplxGZrZ1xyba7wvRBcq+07bvUo5zHRRs0UKJ1Ixq0v30pqPqFO8iQR92ZDTFIP4yerxDz6MZGYS8yjRyB6jnKeS8yjRyB6jnKeR4+5PUcb36L4iCo1n/iMRYYlsXoRmGGxspq9xDz64ZWYS8yjRyB6jnKeS8yjRyB6jnKeR4+5PcdEjIjUfOI2baQ8EgGJgERAIiARkAhIBCQCCUCgpKQkAVJKETkE8rZs2SIBkQhIBCQCEgGJgERAIiARkAhIBMJAoHnz5mGQlTQ9ICBtPh5Ak00kAhIBiYBEQCIgEZAISAQkAhKBhCEgNZ+EDZgUVyIgEZAISAQkAhIBiYBEQCIgEfCAgNR8PIAmm0gEJAISAYmAREAiIBGQCEgEJAIJQ0BqPgkbMCmuREAiIBGQCEgEJAISAYmAREAi4AEBqfl4AE02kQhIBCQCEgGJgERAIiARkAhkFQI4kEcrph0zreDYKmKM7OXJS0Tu7Yghy012iTh/KsuGRmIe/YBKzCXm0SMQPUc5zyXm0SMQPceMzHM+jfX06dPR67POOsux74nI7abD0wivaQXHVo7gBFvBUR5p8wkW8ARTkzpw9IMnMZeYR49A9BzlPJeYR49A9BzlPM9NzEeMGBF9xzPF0fMkt7cjRdwdqflEDLhkJxGQCEgEJAISAYmAREAiIBHICQSYEYYVfM54n6Xmk/EhiIsAcZiOccEiKjkk5lEhXctHYi4xjx6B6DnKeS4xjx6B6DnKeR495uCoGXA8mIA8NAm8j1LzCRzSpBKMw3RMKnZe5ZaYe0XOezuJuXfsvLaUmHtFzns7ibl37Ly2lJh7Rc57O4m5d+x8tHRrwGGmHlZ8sA2sqdR8AoNSEpIISAQkAhIBiYBEQCIgEZAISAR4BNwqS6GiJzWfUOFNEvGY6OJJgsy3rBJz3xC6JiAxdw2Z7wYSc98QuiYgMXcNme8GEnPfELomIDF3DZmnBhrO3gD31sqTpHaNNDGk5hM4tkklKK3G0Y+cxFxiHj0C0XOU81xiHj0C0XOU8zw3MeeTXEePQLAcebc0Y0pr8DKt4KpVBLeJozy5fp5P3tW/BDtvJDWJgEQgKASqJxQGRUrSkQjkAgJ5VxVndzet1oSs73j2Davg8q4bWa2VtxGvfmQv/0h6U3UScZ6Pf3ASQUHafBIxTJEIuembSNhIJhwCEvPIp8PiyDlKhhLzDMwBubZED7rEXGIePQKSo3sEpM1H2nzczxrZQiIQCQKCLwUjkUUykQgkAAFvL8IT0LGUiNLmk6DBshdVcHmPrc1n0qRJGzZsKC8v17pZv379Nm3ajBw50rTj0uYTn6krbT7xGQspiURAIiARkAhIBCQCEgGJQKwRmPDoozt37oSec/HFFz+glmuvvXavvfYqLS3FT7EWXQpHJDUfOQtSCEhLffRzQWIeOebS8ypyyEliHj3mJNeW6EGXmOcM5r9t2tSkSZMZM2Y89NBD33//fXFx8fjx42fOnFlZWYmfoodBcnSFgPR2U7zddEFvLO0Bu6hLgWC8iCt8Hd1XRkcwj4KNGLpBBUFjoJ7xog1fEV5ac5+8XM1IWVkioCFQG8yqXqpOh4adiIaLuqPRWDXtoq6VRgMV+J/sv+pvwFRbXSs5dhKBzCLAXINMl3cmGP8Tv8Ibn3SmV0yfBaZdtnlqOD5VbTAM3NvN+ATnuYtDZErHam9gHAVXwFoNjeMgZnZy6rgn2ttt7NixV1111b333rtq1ap27do1bNhw+fLlrVu3vvHGG5977jn8aoQ6W73dTFPAxWqmGYUxsfloJ63G57xVXqTAAWULFv8XSPYPt3JaiaEJpmzmUnIy4jqxjRftO2LaZSscfPJyi4asLxFwhQB0GO1P04iUfZ4rKsKVQyIrzF9WlAhYImDzRNP95O1JZ/osMJVGvGashjPszYDVKCQUrliNXZTCdO7c+c9//nOnTp1WrFjx9ddft2zZ8pZbbjn00EOjlCEOvCLIUh14N8293bTDVllW7MC5uiLIFMooz3/VXpzorChW9hxvzw9HEAQtRY50RCtIS70oUsHVk5gLY6mz7Xg2tph6XjFqpisdLmp/mrCspvZ/hpdIYQwzVVF6u2UAeW5t0bwYdM8UU9+BsEV1fKqGLQBPnz3TbZ7gghAxOotGb+CJm7Y17iKiftBHiW8EvDL3DIVj2+bNmxHYs00t+LBp0yZctOr0grw8+RcTBETjfJj+w1uBTO0wxov21Wp3Eql6EdwpkoU5Aq26S2SiRkBiboX4+jn0y6IwhqOHS6JGa5JGgFl+WAVZbBBwi7kE0wMCs4lm8c38rS2x0k9quoU1AX+Gou+4B+wEmogA0qNHJme6iIQCHY2kisVQ6njrRzbVKu26v3nup7fLli274447Fi1aBGtP27Ztf/zxRzi5zZs3z4bm4UTZ91c3gZ0S1Xwwlprthf/MTDGaXsQbZ0SqGRtGb2Ji6wX/x09cbTWxCeARfC1kf4/Zi2Ha1lRs/qL9yyTTmlbNffLys77ItjmKwCe3tCYaQTQt1X/NLGM0+PCWGUE9xMrIoyxcUpnJ0TmX4G6/QbRAFd/Do8RVt20elzo6VjVtnqp2krToQjOu1FWYSjSRqK+rDmjrSSoE1+cTXNsbGOm4oiwOrKfuxqnRjo309SRHgWbplPntxbT0abTSX3ckFEKFgoKCl19+ecmSJc2aNbvhhhtuu+229u3bI+Zn8uTJ+CkEhvElmUl13ysq5poPb6jRfPjCcOYz0gyDiyM4Ov/akLzXAhfDMfbGniPfXKmpWo3ZRfaBb+6Tl2Pfc7RC5iz1cQe8bW86571viU4ielWV9QSif1gLzVtm7Ltm9LzS9BxTXcjU2y3u6MVMPuntFsGAlBI1TbFRluuhM7VF29sTzUo/MX0WmHZQvKYQPo0KqfUhb3JVoemNJ3pOqHEAlZwVNrP1XFz58Q+Xs4QBwBAEiZbdaPMy14Ra7ENbftS3ytAztFWrVtBwzjrrrLvuuuvEE0887LDDkNh68ODBSGyNn1x3TTaIFgHnOB+f8hi93ZiNiHecM17xyTTY5mw1MbWfaC9pguWYGWqc1Vh8sc6MqFnDNXOW+kRAuCfRxUTPq7LeTVRCdIhqkLmc6DUiS39q2765ekHFlCLtLxGgxVBIV5jHUP5EiMRrPorA8V5bbJ6qdmh3HwELDyvbVFPPfH9jE9QTvIbOtH7+xMmZ1i33pc0/uO5ti660Zbm+VYbm+ZVXXlmvXr3GjRu///77f1HLM888g+xt8HzDTyJdc3ynZqzAX2GfdSUjEadJfLHlwttNZCyNdXj/N958xLvJoVWUCQx0Qnp7H2aTP8cjUGoS7fgUqfzEZyykJEDgeKJ7qOZkmF5ETxE1VnH5V1ToZOShElXnJJ/EI6BpPlZPNOOSbvVGT8PCo34SHpadBsDOw9II7Ef0vQ9Ggk9wHURGQETo8MibjoKrfugoOEroinhElfPyqXmR6+EraEwFTdKSSEQkrjmbq6+++vbbb0dsj1bwFRdFhNK9UzPVYUxDTPmLOr/uTD2hkvhiK3TNh58EWlSPbmZEH9uTJpUhzsdo3jE1+FjNb2PIkFUQkVsxdBwDdgs2WI35FTZgXiJrQy7UyZClPtHQYrmHzWc6UYXajbXq/12J/kj0Ltcx9hgwPgz4F1TMvY1/fuiusK/szzR8SKuQaEjDFj6JLwXDxiRw+rCBNGFzXn2iIc+Ytmhrzy/2k/E6Wok8pJjM4s8C+5qunqoaXFcQPUYE2wqc3NoEDqKhg+6EFFvPrUZBHFiegjsJw0HMC9WW+8KZ2XVp0UWvL4lh7ppRphsIRqtqYtrHpmpPsUx3Ky78TU4yNT2WyHiRV1c0Y47xon01Y0PTOB9TIoFAmNRVI5DOSyISgXgjIHjUHTqxguht9e89olOJBqv/d4p376R0EoEAEThCjfU/Qj3JNIvL+gmFeM0BSy9UIL6wI1xlSQwC8/7+9y8fus1W3HHq+6Zx/MjO/tPEpVNg9tFfF+62Nx8fHfmSEnhe06RJkzZs2FBeXq79Wr9+/TZt2owcOdJUnOUtWiCxGys6RcVeb9Hq617kadqR1tyUDn/RkZEwkLUV8WIrcWYfE83HQ8+T2yQjmo/pvReeJBGzS+5kkJLHDQFxzUeTHA8D6D9vqf/DHQ76D/5ONOuY25dqcQNHyiMR4BHoRoT0bt0yoQBE+Yh5YELhI0QPEZ2WPvxZqflECWzUd9P3L1304e+nuNV8Fk+4cc5YZPLIuOYz4dFHGzVsePLJJ69evRqH+ezcuXPr1q04z2fdunWdioquMgv18aD5aKqO6dOKqTGOik0Y2k7UsyVoflLzSctgFjS8iaIHq3GGggUTBVOgwkrMbeH0oPnw9JamDEGfpVQgaEF4M5y4F1SBzrkMEEviS8EMwOSPZVs1q3VbTfPJyrVl9YzT3r5otGrdgprHl1hoPlmJub9padl64+LDXzrZPkGFic1HnQAwb9ZqPi4xD8rmc/fdd99000116tR5+umncaSPFspRt25dJDlAwI+x4x40H42IjTHH6NFtVJPsNaiQRjjOZCON84kzEFI2qfZkYA5IVTNM0A8k+hPRR0jYTjSM6FOinkSjiP5CNDdMvpK2DgGpakYwJZKV280LIFtW0JwxUHhg7dHyHHihE14buZ6LY9uyG95MuS7N99HH+WQI88rKSmg4SO82dOjQDh06lKll+/btSHWNn1z3y0cDXfZRq3hUVs2oJvngXNM0iWGcuW7z8T/qkoJEQCKQIAQWLlz41ltvvf32299+++2pasEhDC1atEhQF6SoEgEjAqYButkEFN9BZNPC1zFjxmRTB3OtL0VFRbNnz+7UyTIec9y4cQj8xljzyGDcccV4PUr0EOcDGe67777S0tL169dPmDBh5cqVTID999/fSjYRm49VxI7RsQ28jIagjMT5RIl8ULykzScoJBNPJ7MZ9hIPn6cOSMw9wearEY6cu+OOO+bMmYNnFXSed955p3Pnzr1798aBdF9++aUv0rKxBQJynoc9NRBp0KQJS+1WU7IM827dui1bVnv25ejRoydO1I72CRtdUfpZhrlot73WO/DAA5cudW342W+//RBOE4d5DtvOggUL7rzrzq+//nr37t1NmzaFzcc0R5cRIT5rqKm6YlrBsZXpUDhmKPU6gAlul+s2n/DyCiR4UkjRJQJxRcBn5I9Vt+D8xlLDrUvlhUNEUMO4giDlijkCGYg5KSumFwfQiK9ijoy3+/d0IsT26FIa6C5mAPOYYx1L8fgJAFdk5CW/MV3OtHGc/wCOeqReN6VVefsiqtd0zA+vjIXRw30+j6DifGDzGTJkyD333PPLL79UVVddPPzinj17vvzyy82aNYPbm85OxeTnbT6xHByPQiUxjFPafDwOtmwmEZAIZA0CxxDdqQYPYBE/lmgqUSv1zJD7iVy/k8waUGRHEoRAxTaql2bzSZDs9qJif9zXoPagCctzIEtyETiAyNORPl1p528Z7zXieebOnbt8+XI4vJ095Ox+/frBZfraa6/FB/yUcfGiFCCJYZxS84lyhsSbV5aeCBZr0CXmkQ+PfThmIdElRC8RlREhhmAj0XlERUQ4l/tNoqrIpc0OhkkMgU0Y8pXbcLx9msxZsbbg0FJkbtCZBVg345jnICswj2zmIwONl/dKLbrQzs21QmYI81atWlVUVCBMdPjw4ddff33Xrl3hNd2wYUN4vuGnyDCUjLwhIDUfb7hlY6sM5UjJRiiF+yQxF4YqqIriL6j6Et1H9A3RbKKD1RfMeJU3iOhh9eBUWcQREMdcnKasmYZA5XYqwPlVXEn+2oL77jlbww4OM4VqFKOSfMyjBNOrzQeaD2fzyRDmV155JRJY4+jS33777S9qgYfbQw89tGPHDvwUJYwZ55XEF1syzkee55PxG0cKIBEQRcBbnIAodYF676YigvLVM4IGE/UXaCWr5BQCHsIP/OKzegZ9M5lOfcYvnZDbi9+/G4hw4P16W3n4OhnAPGSsspK8bgKwQ6gQ7aMV5zgfhLQ913tM5baxGY3z8TA6iPPx0Eo2CQMBqfkomo8u6I2lPWAXdSkQjBdxha+j+8roCOZRsBFDN/YgaAzUM1604WvCa9M31dMQ2sCtQSoORnwYLKbShjFHs5mmy1PYshkKsb5pD052LoHu7ALtou7UAlbN8TQ3XY4d+684VgIZEd5SLUJQgV4jWkvU3izTqFjPsrlWEkNg/YwH273ZrJD8T9oqLfLsYHVMFv8BE2n1e9T/0Vqx1bXF5jHh+Gjzg4BVW3HNpykRFBvH0CUtz4E3zcf4+LZ6AhrhMnnup9Zz+32CcfRNh8kUQ6tJIjJ5whhQtzR1EwCvjW4jOonfdfBJC0wzHKDyxPa3VVX+nWk+Lp+hQWU4cNtx1G/evLmHVrJJGAiYeLshMyNfwuDqjWZIKSPZksH/BXJvuO2jlRiaYEzfYH+MuE5s40X7jui7rFqNrXDwycstGrlSP0OW+lyAlz/fjVeTTA968wDIfkTXEc0g2q6ejgrHG6RJYG5dn3ggl9VNctDbzeaxovvJ2+NGvyAb43xSa4vp0h3z6XYE0SwBtQe9CDzPga+dgMB6bjX6SRwm/7PIo8Nbw1a1aa0FMPcvpw2F6WoJlUXMiYe0Mw+11+ZxPkhJrpU49IppYqECwRPnVQv+sWTzuiUM2QQtRWGwljQlAvFHQHcitempCJ57oZ0cZ6SAn7Q//IrgnzPVeAPYfFhS4RPUCkOJnib61bMEsmF2IaC5EugWdlMbjuuuGzUfWxI6puKOCa4Fc9/gAjWlAVzdRIqfPAdWBjSNr+DQmNIxbWvEOcef8h6THDRoCZN7fMqIESPiI0z0kggeYRS9YDYcRTMcMMWD10BM7ULGi/bVapeYVD1TWZkaFivgslCYDOVIyUIkxbskMRfHCjXXz/nOVX2zyv7DMY3WJI0PW6TwPxJSnUP0IRFeah5JNJboc9+SJ5eAf8yT0ne4Ps4KTlYX+okxw0ES1hYNLpyj9T/1TQH8rZFBcZgbDCPIcyA6EJnDXFRCN8AGXHf9HCzgOpq8zcfFvbNjE14z1ZTMYR4wPpJchAiIaj5M7dE0EO0zu6LpRbyxiG9iVc3YMErbDsOZLRn8H4+/tqDY+NEKvhmyH1Z7MUzbmorNX7R/n6SvqVqNrZr75BXhlE4Uq0xb6hMFliLsENWjDB7erGhmH6PBh7fM8G9NbDyvdEaktHXAEFNkDx3iE84nmqzmxf6PagK6hqg1Ed4NTlP1opwquePt9h4RTsV1fKz4H339gmw8zye1tlg93Wwebf7Fs6JQQrSI6BWi8US/J7qXaABRPaKj1HiPl4kQXIsPrgoO/HEMBzIS1B7oPh/faXQMgbLirow2mxBXaMS3cula+u55nXi8zecDVft1LnPGUr3mHbR68hnqDFm4NaLftPvvj7nmwxtqNGNLGFYXI80wuDjCpHOxFV+tHCm7quBWDMfYG3vufHOtJrvIntxpO7/0UCj2kykFV12WlSUCLhBo2xsn301RQ2tQEAyAk0atCm+ZccGCTeyUnmOqC+m83RyJ91KPBpqnHtuHWN5XidoRHU/0j5R3nCMFWSEpCFSo+3jj8uh5e22ln+gXf2tvt8wEkFTvppIfae0sJePcZ3fS+5fRSyfTf7t1UoPinlWzF+BNG14H/FtVfn5So+OQJgTvBdzqyVB7TA/8CXbOeFMUxcfd/zB5kzBYlOyoNe1AUH7SCxTd3UQsaAcfkDDTocy+iRoX0r5n9XGqKH+PDIGMbNp99s45zscnA6O3G7MR8Y5zxis+mQbbnC0opvYT7T1NsBwzQ42zGouv15kRNWu4Sku9+6E8hOhutRUO2IFFBS//oIo8SPSzGCmfnldMKdL+xHjW1NqT6GIivPbcSnQPEd5/D1fTwcFdZzpRpStaiarsE/ME9bWcqH5GxPV0kqnNo81FJ5Bl+Ocv6PuXaME/aea19NoQmtKTJnagty6kxROU1Fv1W1KXM6nP/XThvC1EC1XbzgPq6cA4HQspCrALwTHB0ZegHt8ancWLc2emux+uph2pdI2xmebw5qz5fHA1tT6YelyVRkQ+Q90PhWzhwtvNG1i8/xtvPuL930A5gwkVvFl4bFL3eARKTaKdyZJuNZbKTxRjIS31PlBGDDROGmXP0pVEPYnYi8DatD9mxN2+UbYR0E/SleNU5QcbJdiCYBSCXxzOocTL738RLfOBSTybBoh5PDuoSaXZfKweK8Z11TG7gJB+Yu3tFhhiUK6wy1z5Di1+lD75s6LYPH8cPdaBXhxAc8fR2pm0u5LaHEO9bqIhb9KV6+mCuXTaNDrhPur5B+pyOrXuQfVbmAqD7CBQ/qMsgo9v3dAYB0JH59CJbYzjzo+46ei76riOgqOEroiHW7lpeyqFbU9fNIc3B83n3RHU6SQ6EO+O0ot8hoY7bM7Uk+jtZnKeD4vh0XVXd9H0q7GhYzVWga9myp0JY/OT8+BY1GCWHN1qpV20svOYPooEE7yY0mTSmYqhCa5b46xWWBv6PAamj1urWCafvDyPjmwoEdAhwJ/nY8x58rGq/LRSlZ/H1exqOBVEK5qiYp8sxTRkiG/C09Eq861YBQ8pWeDpjjOC8Af7D85IxTFBp8gZkCgE4MqFGTjK63k+fF/5ldx58W93rKJytO2tv18ML9TsXRgUptVVyrv5ratpa+r/UnxerSg2zTpRs461/zfF106Ur/n3OQ+V1Xk+Y1XLLZxCPRdX5/kIPumMD1PjQOjqaNsA3fApGxiz8/FMdyB8ZVNAtCeyo4Se8Qyl4TNHrCxdW5RO+kGi1eobn+uJ4Ax5vel5Pm+cSwddRp1Ty+H8B8Z8cb8yZ/jKYhJ7e9Oto11SAoN9TUFKa+R2469YCSLP8xEboihqOSs5TApTrUYTUNOUeOWPXTRe4S8aG5q6DOp0ygDdCgX1hCiGIuM8XJ4IlnF5s0EAibnLURQ8CREqBAIG8AfTCvQf/DVIMYKZJf4miB/VM1KhAiFiHvoP/qAIYVuQ0JIIzAPBFhmZcbbmBe43ZH65I4oG7mSwq2hFZG3ZsTFNw9m6StF5tq0nOCbxSg7TcBq09CskXgdMKLQi0pZoAVEbrzw8bIK9srJuJ4J58FyTQ/G1ITPXz+mbLu/7aroLLHQI+uqG/42az5oP6ajbqT1CI1OF13xcYi41n+RMlxAlNdF8QuQWP9IZ0XxM773wJImYXfwGWUqUPQgIaj5ah99JqUAnE/1OVYGYg6+po5oHK03YyEIkZgWCIgR3OKYF9Qubq6TvFYFz1YzM50av+UztvWze2/vth/N100rNYwU5r5kBp8aYo9pw8LmgaboZp0jReeCVFFqxuX8R6oPIvTe8so6F5uNVeNN2WfjgnnntU989PzK9t3CAwxnQSH2AuK+D8b9O8/n6v3Tqs7QXfJm5Im0+gc40n8TC8MbyKZJjc6n5pGUwc8RLVpAISAQyiIBbzUcT9XU1tQCsQGer+g92qIkrS1NaEPImM184/O2duG5ktcAw+CBk//ToNZ/Jh9B5M6hRYY0NR6fh7NpZ46VWY8xRbTj4XFczhUY0Kvb3L0MPh5N6KNmn+XgAIe5NUhqLTk74JCPXHzu+djR/7zy5L+13Dh2PLJjpRWo+cR/puMsXeoaDuAMg5dMQkDlSop8MEvOoMD+D6Dk1cSpODoH+g/SpcEyCOpSgglBgHF3yEdFm1bDwqZrU4TCiO1JnyMS2L7mT8YrPah3ucOz4lYq/pB9epS8folnXE76+PIjgS/bGebTwISpeQLDzdB5Ex99DQz+hy36k82fRKZPp2Dvp4Muo0wBquV/0ao8jINGnOnAUyV0FuZ7b49W0wyqzCizJgT7DwX+70QHDqAHCNm2LxNzdHJW1FQRy3eYjZ4FEQCKQgwhUVVVNmzbt+eeff//994eqZdAgpNhNXlm4cOHbb7/91ltvffvtt6emSsuWAYRkJA+LGEjcr1+/MWPG9O3bNyhZysrKfvzxx5UrV7L/tQ+NGzfu3LnzPvvsw/6/7LLL8FNRUVFQfDNFZ+zYsXCeAYaZEkDyDQ+B2bNnY2RnzZqlYzFy5EjcOLjep08ffK6oqGjatOmGDRsefvhhBHVjSujqjxs3zvR6eJLrKMsMBzwg0tstsoknGUkEJAISgQAQ2LlzJ1QglP/9739MBerfv38AdCMnsWXLFqhArOy///5MCTrsMNiEZIkOgd69e48fP/6YYxC54LqsWrVKU2w0bWfbtm2aegMlR9N2oPloDFCnTZs2paWlrlnGskHbtm0XLFiAHsVSOimUdwRWr14N3QbzXEfivvvu27Rp0/r16wcOHIhVi03mevXqWWk4UvPxPgaypYpArtt8wssrkLwJ5jJHSiI66DksJJre5U7Oq2jwFOFihTm2jSwdHE5aZOngThAhF0kd1zEMxfNp9QeEnEjbNlCn/spfx/6Z9G4KdG2J8019hBqmj3AFm/IrEXL34QQq/n98Re6+zkT7pP8vcsobYiTAcX06y+SuLaapDlzfApHcmHomgc7zjPRAY+rtLnMeJjhkspzvXJY/NuKI9sELg7HcgWzj1LMBcEVX2PVxLCLIJeYyt1tm51VMuMs4n5gMRAzEkCeCRT4I8U+vHDkkoTO0whzP3cuIZqgHiXYn+ouaYPe62IfQmONVeAQdeSudO4OGzqQ2RynRIE91o9eG0KL/0G+ZOCg1Z9aWcqL6qSHZoUYvICnfw0Q3EJ2lplPHNMOh9Ujg+yoRlG0cYnubOusQ5ADlB0FcTxDdrsZxHU0kovaAG+jw51Yx/sldW1iGA+yGk1dyZp57H5qmHagUidzSCu6Ib9Xwxb85nUNtwldi7n0wgmmZxJNMpeYTzNhLKhIBiUB2IICIWuRXna0af/AOHhmHOhLdrB42krzSsDXtP4wGPkmXr1ZOutyxiWZcTs8cTh/fQqtnKGdWyhIEAmuIZhE9pRpe/kyE80ShNmMinUc0gegHog443pRoiprDaiPRPDXT4N+JLic6SbXzmKZZFxTNVPMRbBvPaolPdRBPWOMglZnm04UIdxBStjwaBwmlDC4RCPCATZecvVeXmo937LKtpcyREvmI5k7Oq8ihtWQojjkSRl9LNEd9JLdW8+12Vd/HfxWfzriSpG1vOuavNPRjOvM1anUAfTOZJrajN39HS54gHGEZasmWtWUT0XyiF4juJbqSCIdE7avmCcQhi/DA+YQIquSJ6smMUJvLiL5RbRf/p55PfybRIURNQsB5mxlZ8XkegkR+SUJvvEKFNGElW+Z5iLA366gcLZVePiQqIIKnKG4l10Vi7hoy2aDmWD8JhEQAryjh4yNLpAgk1yMlUpgCZeYBc7ywv0nd8uKgccSVD1cdlsYSfReoYNERw1GV3UfSqc/QlRuox2gqWUlvDKWpvenTv9BPsHWFUJK2tsBpDYOLA2T/oxr9cAbUoUTN1TPmryJ6WfXMQUpxzAoclVtJtJpoJtF/iRqqEWIIV4jynKUs83Zj8w/3F2I/YCJLUknaPM8Atgabz2tE96jvBbYQ1fUgkMTcA2iBNpHeboHCKYlJBIJDAM4kRn8S7SL7oP0xtrqvRll0BO2/6pprlf14uQQHj6QkhAC8MuDLhLfpCNLAwA1Royngs7RCqLXfSojN5f94cqbXjbG8Jleg8xx3d3XJymXz3v7XqAMG7Hy84In2Z66/gpZOoW26mHmFoU4GRpBd1HXP9KJfCJza+7nNkUcZOu1xRO1UJQeDC7UH4Qf/VK9PVo+ZR3ICKMBIg4GzFWEDHKDafHjHCd15PtHc5tnn7cbG2Y/Pm25CWt07jveIKR3drad9Nd6GpveL6Sy2ksRRQqd7Ima/p2s+z6o+oh8Q4Uifrd5sPjHrXw6KkyXeblDg+BKHgYybPHHAJHgZcttqjFwx2h/br+Cr9n/gaDPiifZICRyTaAgGgvn+RNgoY1uMyI3tRANVV4371c1xSAUbICSi5P+0LZHuJ2/Ji7o92OL67y+Y0fWZyktWvlZ+Nm1cTK8Ophf60md304bP+E7pxBDqbwzWFlhpUF4kuk91VMOQ4TaHjw0Meigj1V0XLsKN7QuinergIjnBv9VfmYrbTKCrfIaD8FYPnSCmmk8g81ygxyFWCTbVgem9E6D0ym04dKbGRbsNvdwvAYoVN1Kc5gObHkym01UJYUWH06b0dovbcGWrPOZxPtDhtJJxSxY7Jik+8mTrVMh6bzcoG7yBBZ+Z+hFIYdRMDThGaxI4spqHqv+bvqgORCpJxIiAB283GxgRuXE30XLVMweR68eq0e0PEf0cPvQsIz9Te3hu+OpN+akhUqeAOp9CfR6g4QvppAlUvynNu4ee2Ifev4KWTfv1V5g9TIqOqV6qCD1SYHXBDfUu0SOqNxo+91Tlhb0OBeYaDBPmwPXqV6RfYzEHePH8V6KL1BFsaz12jrc5dm98mjV2m2v/h2TgzUpvNzYI3sw+bPrZ3AWC94gpHdO2NROem+fywAzz26hpRypV7jkWC/dMqhJsPh41nwjXlvAX9URyyLiO4AE10QwHrG/M9sLYmNphjBftq2kSa9WMfUiiKc3DSMgmwSOwfg5e3Ga8GK1JmkiaWSlAHSzj/c1NAXCgCowJeKTjib5S3W33UTffiIn3U2arScOCKg76iZENEiH0/COd9RqNWEJdBtO6Tw844AB6eSB9cT/9gn1LxooOFnwdkTp/iR3wCeUT2QUK1c9Pqv//pv7P23zw1UtcgUWn2W2OYnwDEvZtbprhIGNjEyjjsFMduL4jAu2dCLHMSLh+jttlx3ml2rWTtqygkh+p9Cd6a9j/0jO5weYD47kXm48IiLJOmAgkcYsuqvkAN832wn9mphhNL+KNMyLVjA2TqD6GOakipB0Dj5TAe3sO0cFEd6XoapsSo8GHt8wI6iFWRh5l5osZlLLAIyXwIQubYNiYI7r9QTUy+051842YeESD4JAWvJj3UBBk/7najG2ArOJ8PFDWNXEgXtCYupxJJ/7fxo0bP3/zsbGnNTly7V9bvXDgxdtvouWv4cB1TUJl628wQymnDQZa9iO6gCOIOxG5oWF8Q4HNB19h80GUDmw++MxsPqyEepvrgnwC7bEdsWz1dmN9Hssy11ViYyxUtOknaNixImpDx5Ky2TwP9bYVQsRDpV078B7HVZmlvaNBfODPX2BZUE4P+9/t9O4IerE/PXWAcqTYO8Np4cOUX4+2roaJVVcaEa0TY9mXCH81Jei1RUwEWSvZCJhrPryhRtPnwlDsjDTtuTDtK9mQx1b67LMat+2NkwSnEsH5HgUbIOSQsXzOcXE+bodI03NMdSFTbzfGIljPK7di52b9yDA/Qd18wznsFvX8FsTNn6oGyiOGRLwgMzI2BKxYxfmIU2NEtLQEvEOOeDTCkU91Glt81eeHv7HpjE+f/qkXrXyrXbt2NP0M+vKhr76yyPgd9NoCUwCMbNphl32JLlZTSzsW3gDrWFlXwfE2xxmmzNcu4pLF3m4MSaTXI+je4RSrO8Kem7nyYzbPxe8sK47eJPSF1l6HsRcuVgUWVLxCekNd4m5VX0MgseGDqD1hbyU+cO44LAvK6WHNO1O3YdTvXzTsU+VIsWFz6PQXCaE+zffh30cwLsiLqE93bcEe5nTc8jUl6LXFF2452TiJ5grnOB+fQ2n0dmM2It5xznjFyJTVl2qPz+HIweYHqW/fUeDKjy0C3P2xNUEc8+pIsGC7Je0vEp6SSbwQ6E/0uJq56Bo193FLNYUrFHKRY0QRiIINQUxLg5bU7Xwa8NjWrVvpqNvxVr7HicPavX5YXvfhtPIdqkK25xALUqshtsqqsHcQkb0kY7yg5baKkKnW92zN7eZ59mhmFs8UWMOg6PgUIwPNG7RsoSb5wCG8OGxnEtHf1FN3kQIEbmkwwSGlIfxLsayhDha004mwyo2CoFdtUOIDh7yJZUE5Pezgy5SgwT0PpgZ71Pai0V6008QLGC8OonkoZwDPrGaZxG25C283b2PH+7/x5iPeTU5ZYlLFVH3kHe28iSFbOSOQ7VbjQ9UExAhGR8G6i/dGR6mff3KGJpgaxpfBYXteBSN3dlHJIOaD1D0ElJlL1delSCx2HtFLtvBqNh+rcG3ju2cTZ7N0FqxJwBHYbY6io/5M58+cN28ete6JXAj0eOcBAwbQ4om0ZXng3m7oEBJ/LYjZeS/M2y0mNp8MzvMMLhg2KRB5qXTz33hHiNDhb72az9wz1G2WEft4nlDuWQ2R7RuoeAGteI0WT6D/3UHvjqSXTsZjEebxwaqjBGJ4dquPSxjfXiUqVh+gi4heV5MfwqY9jKijmgteaPo3bkPlJcZJAs1nlYepk+37Fg+QyCaOCJhYUUxNK7qLpl+NDR2rsQp8NUcijl1yVSHgx78r3rJy+AhUT2BBzuYvgGHQx2reXlV+HiQ6nwheNFrRdjD2b46Nr5Z1V3g62k98HVYhsvfT4aMuOYgiALUH2cZeUY+/xB9sQboyRH23OuQqbDaUN9D8r/zaxf+kXbevb9W85n5Jzxen8TXu52z2ZDPO/mrAn16i1R8q9p+O/alTf+p4oig01vW0m3qsel+PSb9nTe8jdruZWoGy4Dbvp4LQ1z+yMaaQp94CjsWo0lvlZ9OR0jW0oWO8cUzvOHbR9H6x6YVW37gz8fu2AsrGtnU1f6WpD+xKo0Jq0paatFf+b9qOfbjzpZOh3vzLEfFUhXHqLTZOZJiQKH/Z89VlaRkpQQZHBaDwx0MzmrjNTYvglDAOtHCfLCuWlNRqbtOnTx8xYgR/xapZ8+aqbph1JYneWB41H4wdb5zRjDnGi/bVjA2NhjOjFShA45rUfLLuNkzrkLZJsu/mXHUD+oLqC4cNKFQg/bmM2Q2T7F1GEYDbG6Yf/t5LqUCwDrEC9xK8Zx0osp/IaBccmG9dTWs+oNUf0JqZqv5zkvI/3P09Fe2mRhoJRPuYnLfqiaz/Rt+rbj/L/BNySQFnScHxD1BkcfG2zc1iQMy7hlcMyJyGHAPbUv9rSk6dutQEWo36p6g33F+eie/PnAmFWHnmCCPoQvNZ+gzNGVNdiZSEaQV+6biDYDjVitR8hOGXFd0hkOuRM1LzqZ0vsBpnXbCgoOajgYBUm0wFwvEC0H+gBTH3ZFMPFv9WGnikRBZw725hyN7accYcyQ+YCoR5yKxAePbD2+T4TGg+pu46HhfM2rWlWtV/PlT+r9tQ1YL6UzscouOi8Dc1NA0E/LAjL/0Xn7f516rbzxL/crikgPyBcJ5Evju+xHmeu+yfugJn4hZwLafYMzSAO6usmGrtNqqSw76Wb6GmqumGGXDwv6bkuEwRsWtCIdzPdglD4ELzWT+H3ji/2hAHyI70QSQkO3oLRUjzEcNc64dbL0RTAKTNR3hexLSi1Hz0JteYDpQUyxMCbjUfjcksVf/BHrRnSgXKTkO1J1Rlo7ARQMw6U4EwD+Hw9uKZ06kt0mVnV9n8vaoFfUA/z1dUIKYFIfrZqfA3NdK7wdyBfX8cCuKOoIbNj1wUHL0K1rynbuQihM4wGZpPsDBUbK2x3tQqOaoxB18b7snZbTQvtXbUsHVQIuAuc2VLdKH5QG2bfIjxvSEUeCTAvIF7kSGk+bjssNR8XALmXD1LvN2cO5pFNTy+wswiBLK7K541Hw0WZLZhKhAigpgjnHK4hCwSgUgQwG4As+5OqD04BLDrmdT1LFI2JNlVdpcrKhDTghBy0OkkRQXau5dVJ3U3dXz2/XCadeUgFNQoNlUzPWT3upS1mg9MH4pzmmqx0Zlx4Gqgs9toX/NCP/MTd9nV6ml4V4lNUxeaD45merLryuqqonTKXdVTuWDwuTl1XWo+YtjLWq4RyHWbj2vAsrdBEhX3KEfj/fffnzZt2gsvvHD88ccPHTr0/PPPb9jQb8JhiXmUI8h4JQvzzp07z5w5s6io6Oeff8b0Q/npp59+97vfYQYefnhiIjvEMV+4cOHbalm6dOmpqdKyZUubeTJ27FjQHzOGz3QQ/bRSOM6ePRtizJo1K2L2IkmJIhYpN9nZzHPcv2vXrl2jFvaB/b9p06YOHTp07NiR/599bdasWWZhnDRpEqb0U0/hPAjnMm7cOARg42Z0rqouwljW+vbty1fGWvf73//+m2++0TiK0BRfW0QEE6wjvd0EgYptNan5xHZopGAxReCdd95hKlD//v2h/6DUq4dktrJIBIJHoLCwcMmSJXvtVesDhg3T888/jxm4ZcsW6D8ohxxySPCMM00RvWMqEEq3bt0GDx4MPeiwww4zyrVhwwYogevXZz7TAV6OjB8//r33kKgiurJt27Y2bdqUlsJBUpYMI4BRMOo2TMlp1aoV0210Ss7ee++dYaGt2X/77bfnnHMO3kGISCiipWh08MR8+OGHR4+Gc2htAT4TJky48847lcz4anFFU0TIoOpIzYdHMiPKp8+hzHnNJ880qNUnqrJ5TiCA6AI4wuHv1FQsUOjHY+UErrKTtQjYODKtSMUCIRsSPOJ+l0oLm33wfUb0FtHbROvUew1/OGaEt7fq8hxkzDNq9Qz6ZjKd+kykQ4CoiRcH0IivImVqy8zoY6wbEf9OyBnsLE62WUO0lvtf+4okjTjWBn/IWqj9zz7g8K4kFnFHSnvPNF3fYcy6gmi8LmvF5EPorNfoxZPosh9r6s9/AEc9Uq+bAoROxvkECGZyScmtWnLHLmDJkQtIFlcIYL81hahcPXGFHUx5LoLR3ZCQmLtBK5i6ycIcx55auVTCIf7PROgOzhbE+xskQkCeQJzVC40obsUn5kcj0kkN4ged41V9rxVRP6L7ib5Ru4pXx8hzkPmCgKV8JMSKtlRso3pmMT7yhEd/44DcR0hWgbO2HlLDtxDhiXmIEHwMMMJR/qLmoIehDTfd74meJVpN9AkR8vtBRcdsvJ3oYvWQpX0Sq/YAP0S31thf/IGpa92YKKXccL9U76Z6Tal+cyXwSbzIeS6OlayZQkBqPnIu1CAg0yt7ngpnET2nnnKNnLbQfLANxTt4PDIdi8TcEaLAKyQIcxhzEMjsGMuMEwAR4/KtqodvV48AQg4EaAV4FR2TEhTmhUSj1FusTO3yRvVG66TuNXHwCCxCGS67Kyk/8pf7OBelwEzzybojCsIYXKRRhi8XdJjHif5KNJIIR+3uq6o3LLgfKs1KVeE5T1WBvlATPWPXPlu93fCi4UrVCInKSP4Z1DwPo6feaB5JhPO+Ay+w+UBR1BdoPsjc0HI/QtZH8SLnuThW4dQ0HrkZDp8gqUrNJ0g0Ja0cR+Ac1fkN+TrxAY/MukQXEr2W46DI7ntFwMbgY0oS4T53Ey1XXzlDK4B5pLe6XfvZqwBxbteX6D71FTtetGPfuafqU3QK0f9B6K2rMiN5VQXViTzkD5myXB7VkhlwMscVTmjMIIP3U/8gQsoymOuhpeCsNqQCh0rzL1Wlgc4KQyKMOe+oxpxi9eLLRA+qqZZRDQYQJBLMqRKS5oOMJSbvKaqrCGeqttyXNv+QUyAnvbPIbJG4LkjNJ3FDFpbAPj1SwhIrmXThGoFHJhzhEJCAzDh4gwjPB5w9oisS8+iHN0GYw7LRyBNASPoGrQC7vfHqG+ueRH2IHiHa5Ima/0ahYo44Crybn0WEUPHrmbPfG0Npam/69C/0E17NR1h2V1B+bDSf3PMCgrb/JdF0VftFaAjsgdD826sqDeY/3M8QLVai6slwj4TFBlMFGg4cJt8lekzVeeC3DOUH6ZW9jWKo8zzCeVzLKiRvN5w6BMVSX6p2UZ261LIbbV7morO5N89dgCOrWiAgNR/FNwl/uqJdZB+0P1ZN99WIrY6g/Vcja41LlPM2+yz1UaJnygt+SheoT2I8Xweob+Lh3wx3HbxTZEViHv0YJQhzz5qPhipOP31QPewFcTLY4eF0IMzDJ9QJGWWJAHO8vIe+V6nauOjCeTRoCjXtQAv/QxPb0bsjaOkU++ABXdwzvvJ/GlbG8Oi0K1UVfzgx7cRjRoSHmv9qZKHjaxONXftTKs6HXam9nvICCiSkO/DZ4uex+x3R+0RPqh6PWE77E+2nhsOh/5cTTVZVGjhGwvwOzR9hKvBDXqXafGCHv0fVk08jgoEUlgetaM9o42ZAvO8RzHNxYQKpidsKT7GfAqHFEUHiBOXI1VLkieCK9HYLGudo6GWJtxu6wZdosLPnkll5YMnT/tiyyGx7IVn4QiIbh3HMWRnwBnG4mgUBbyXx9vFhIjg6X0Y0I2cRkR0XQMCtt5sNyROI/kP0K9Et6l4QcQsITsAecaeAGEmpkpbnoEUXOuQKOv0Funw1dRtGGxfTq4Pphb702V20AbninAsOudb+RJWH3RX164tmOABNUxb8RaGDtq3ifJy7GOsasFj+L+WfhsfuGUSHqvLi5dEQVaXBubHYlGM5vU015mxWf0UajFdV7fdP6ldo/pjqIkU+dq1QCsPsA81HUTt1mk/VbqrjPs5HZHRlnTARyB5vN/REKxnX51iy8FDlwarHv+nB5wDXQUbN9E2S0ZqE+clqav/7eQXlarYvdlVbVvaEAJyXRqby8+KpjJhaPACwY/vIEzXZyAMCCZrn/m0+RnzwghzB3FuJriGaqU6/M4mmEiEWIrwSDeZ4kY+NLwxcaQX+M51PoT4P0PCFdNIEJXPUvHvoiX3o/Sto2TTaoTgAMiXERr2x/1Vj9/fT6o2fuUtHx7Qt48jLKaTkmI6QleYTey8g9lhcqEZC/lv9jMzsKIjXQkGUGrR0vC1iKg2MOXAbRkFOAiTzQE4CmC5ZTgJkWkNOggbp4GTksRvNPA/vPjWlHEaoDzQfxTa6NT0JC4vzadBSyXOwA29pxErs57lYN2StSBEQ9XZj+g+zvTABTe0wxov21bS+atWMvU+iQik4hkZrktZQMysFqIPZS5V9lnrBUchINSz9l6qvLVeqXjoITEeINrKjzs6INLnENEHzPECbj3GEBxFNIgILzEPsLxELgRjul8KZCZFhjkNCELBhWVodQD2vUc4MGbGEugymdZ/StOPp5YHKwfO/YAfuXHRqjE6BqahAhoPoc7ulZTiolVD1djOqWM6d9Flj/ZxZHAUsaPxXWj+HZl4Pl0sUFsOG6QeVhgV2QAlHwaqIwtt88BU5CQ71KViqeXiP3cjmeUBICJEJQ/NBLkLY7sy93XDdVXo3mdtNaBhDrJRx64iHvolqPiCt2V74z8wUo+lFvHFGpJqxYaZA1MwyRoMPb5kR1EOsjDwKJoEalDwMuWwSKwRaqGe6fag++7FVGaMGav9R9fSQJccRCMPmY4QUfkQsJzsiwnFUDgtOw2v4JBb4OJkleDZ0BcnQupxJJ/4fjVz6+ZuPjXu7jD65La/hHsO3/YmWv0aworgv0DHGvr4ZGQ6MRh5BkxHjaRViZCmR1Xk+7rsQTItt6xGBo5UPjEtZm14w5qDA3IYHJTTO6arNB5+ZzQc5CVDkYzeY4fBNJQxvt4Fq5v00zYcF+bDiSvPx3UFJwCcCSTROmGs+vKFG61UY3TPStOKiiRSGGPYDz78icjtFND3HVBcy9XZzyyKo+llpqQ8KnJDo8Ji3UtOtzlKPa4TzBpzX4aF+PZFQUEJI8mUj2QTN82g0H22Q2Tm8SBKA9+t4DY+Q8ZFcNg4/cyEyzKH23OhS0F69etERf6Jz3qVhcwcMGEAr36LJPY4//nj68iEyc6TR1BgTc4p1bjdx5cd/nE8Nr03fZMDgA/CbFynp9VIFGnVqP6teatub9r8ALpcomGA2JYmP3cjmucs57qs6hqmLmj4+wAJPb/impms+qqsbK3vsR78JH+kjvd0CHJicIWWu+ehMN37QMHq7MRsR7zhnvGLkGGqcD2PHlBNBq44fTFhbxkv780/QJ4WstNT7xCTs5qaYIysRYjCQiQinSXRSlR/k7cXLbHyVxT8CCZrnoXq7WSGJRwIO5J2uxlf0S2XjQJQFXt57LjHHvMbM8t9uw4cPpwGP0WUr7rnnHsI5OR9eQ08fSrNvpJXvKJ5sTuUPx+c/PKyVaDoEJ2qiv8ftJNNmtpqPoVfZ9NiN+TwXnVGGemGYfYrApZSL82HpDVhpsR9tEdZ8pLeb53ENqGGmHLX8iO/C280bG16J4s1HvJscKEeg2HiTP5pWNbFT0TCTXBKCAE7Nu071ev9UPXEPUUCdiW5Vz6yQJRcQiNjmo4MUIeMjUtk4sPW5NxWK9nEWQa9LsKZZZo477jg66s90/kd0zjvUuudZDabXb9iE3jiXFk+kLctZNT4nAfv6749KrnmpnBltjPoPf9FYwa2+VEtBPclUJ49RwugGreGemLeav6De5hOdHM6c5GPXGSO1RhihPnipR6Vcumze282VzUewD7JaaAhE74flvyuhaz68iFpUj05ue5UxSoVS0ODDVkz7ddP4Kkt3hX1lf6Z8tQr+h1mEQlZa6kU6nsE6gpgj2RF8eD5PJePCC3ictYeD+QSbZ7CDMWSdINAyYvMxDhmycbAM7CwUDQc+Qg9nOrlgSRDmWo9qDEGTe1TPumF6m4k0+ifqeS1tX0fvjKBnj7zmmmtoDaLz0ktVpXiGA6acaH+aHiUe58Mo9C2qmHlze2NquMWLM4Y6VqflKWBENJ+seexmDHHB+9BrtTA0H0UWnLilJbbmNZ9GhbRrB1UgA6VAkd5uAiDJKnqlw6iusUwGRuVEd5FXSLSfjBftqxkbmqqPpkSCGctUqrpgqEkqEoFIEIAnPYLRcVQfzmM5X/07KBK+kkmUCODURRw5+vcoWYrx+lmdfvjDO1tEpSM1AvITxqfkXWVyQHyQ4m1dTWs+oNUf0pqPqFN/6niS8j+2cR9cSZ0G0r5DguTlSOulk6nP/dQ6Rp5W50wohMPkOarkcNzF+bnXpI9I9QS49MqSJARwTBXWIhxMZ1XGqS9wx7rpUx6CvnrdpIR+oezcTFOPoVE4pVYtrwymY8fR2llwB1LqBFfcGldNOZeUlGjXp0+fPmLECP6KlbDNmzcPrh8xomSqMsRIPjNRTGw+prqHaSoCzUVNo2wMEDINGbJpaIpXgHFHgY8Hn6VA+xw4F0lQIsAjgJDTPxMtUnNwIY0u9hl4GY9nT+q5IdHKBgQy6+1mgyDSD15LNEf1w8RZ7DiQihkhv8oG1AX60KwTHXQpDX6OrtpA3UfQ5mX02hB6/jja+BWV8OH9AqQEquisQOxrbbv4nWSK1UlDQcTmI4CBvop87HoAzU+TUMw+zTrWHunD23wgqHR48zNa0baV3m7R4h0PbnyWgvikK/CATbZa6j1AEVkT/5jjfSq8j5aoWbkw/fC2+RD1dKAfIutD0hj5xzyyHsfE282mv/DDxPvY+erJkjigYzjRAep7X50GniDMXQ5uHnUaQMf/gy6aTwOfUpJT/fgWPdaR3ruEvn2OytLOKnVJuba6Ltsb+1r7s1VW68x5AUWg+cTzsZu985zCSHJg6e2GyS2e2Dpz89zzHS0bZhyBSON8Mt5bKYANAjHylsiZcQoQ8wPVHSdON39WPZ5yMFFPon8Q/ZgzYAp2NEDMBTl6rhZbm4+xR8wIiZ3fq2rgIjRw4Aw/PfbuP0GYex4sarkvNWlHx4yhS3+gfc+h4i/oxZPoxf70+T30c5h5GdUMByZiZy7nVQSaj/dhCrNlFs/zUGw+fJwPn9vNleaTuXke5lRKEu0oQ/GDwsUkpCco0pKOREAikEEEFi1aNG3atBdeeGGPPfY4Xy2dOikJdWRJCgKjRo3q06fPyJEjkyIwL+dXX32F6YfSokWLoWrp2BHp2bO59OvXb8yYMX379tU6uXDhwrfV8s033wwePPhUtbRs2TJAFGLoZL9q1SpAsXLlSnQz0XM4wGFKOil+TE37Mm7cOHg9jR07Vryns2fPxv0ya9YsNFm9ejXWOnBhzRk7LH1uaYpz91NTxvn4QS8ObaXNJw6jEAsZkqi4xwI4H0KEivmhhx6KY0lWrFgxYcKEjRs34rly9NFH//Of/1y3bp0PkRPfNFTMg0WnrKysUaNGwdKMjNohhxxy9913L1++fOLEiTfffDPOBu3du/dDDz3088/Ij5Cdpby8vH59hILXlp49e95+++2ffvopNnbQfN55550uXbocc8wxd95555dfBpCdftu2bU2a4PhWk5LBeV5UVLR27drduxHjQ7t27apbt252jrehVxnEPGyEMaZYjn75JRgHTiYtaGqqjm6e4Kfi4uLKSpyr7FCyGHOnrsvfvSOQ6zYfYzJQ71gG1fIROCs5lavtz79Wm+vo2DdxVdlJOvl7LQKmoykyfGGAWPwlrXiNlr9GTdtTlzOp65nUiAuVDoOjpOkHgbcvUgLoEUmSHaV4Pi2frky/Fl3U6XcWNQjS+pF5kATTrBUvoNUfKNnhtm2oTQ1XV2BJ53rI0qNtULPqrTfreegJ7uzhntqbBk1RBjqIfHd+csFlGIfMT8rgJLBfjuY/4CUPG6Yxy/u3ZQW9M5yGIW1Kqrx08hUbFyOB/jinxICuhljmdgtuQtRQiqHZ2bGP0ubjCJGsIBHIFgQKD6Pe4+jiRUo0QulqJQ5h+pn09X9px6Zs6WF29WP3TsrHgaLZUgqPoGPvohFLqNctSj40JLF94zz69llCdrLsKLvLKT/N5mPercLD6chb6NwZNHQmtTlKeRnx1AFKdrhF/6HfcGaSi4JEwzhtKY6lWRFtXaUIVlVFdeQ2I45D5FomzFso7cEWLdSnukpJEMKXlvv+GiwvSS0cBGRut3BwlVSjQWAxwuNliRaBTOWl2btXzR70qNto8w807Xh6/Wz6ZjKVb4m2/5nglinMPfS1cgcVuDMFeGASRRMd5m2PUfKh4eyOntcoe6nJPeitC2jZNILmkOji5iRTpaMNW9P+w+jkJ+jyVcqhJXgBMeNymnIYfXwzrZ5B2As6FaiM5r5uaJjZea5pPrpsxU49SvbvmcU8bOz26km/LAyYSa3ms5vy8tM1n/02CjDL4nx6Ar2XVTwiIF/GeAQuC5v1QEJaWaJFION5adocTcffQyOX0uE3KlulZ49SXsMvfYYq8DY5S0vGMRfHNWtsPlaYtz+B+v6TLltBB19K6z6lJ/eldy6mH14V2fSLoxhdzd0VlG9z2KOtIDjP8Zi/0tCPacgbBLjwGmJiO3rzd7TkcSpRUgWYFjubT2bnefMiKlmlyJxTmk9mMQ97ooeh+WhH+hjnSUshzSeL8+mFPZ5B0U9iqJXUfIIafUlHIpBkBNodSyfcR5csU17Db1xIU3oqu67vnqNKpFaWJUMI7NpBLsM/MiSob7YdT6IT/4+uWEMHXEir31M2/e9fRive8E03WgJ+NB9NUqTGRnDXqc/QlRuox2gqWU1vDSOEzXz6F/pptq4/8fV2a965RmGr2kV1ciXDQbSzLXJu9ZtTwz2VgJwAi2bzMc4TMc0nQFkkKW8ISG83b7jJVvFAQHq7RT8Om77RHdDORNAf025xUResaYzdFI/mrBXj9XOrv5lCly7HruuSzksaTz3o1NWjJh/97o5/NtOkMh4qr8ms/WSDpU2Xjc198jIRI0EeKVmj+YhjXjSQ+j+qbPqRAgEBMI+2UULkV74b/a3phWNVBdXxavMx5dehHx13F13wmZItAHvEhf9RdEKYxZY+/ZNa307zUTE3vdf4RUa31IisIayO6Y1Z24lab7eqt/+wh1EMwSXOyyj4a2NcVHnhNdrmQHHz3JQOLxpfwcjCAV6OkNWQiQyla6gCN/vYxPm06LJZPZ7bvkhvNyeE5O8mCJjYfGC64kusYEuiWS1WANoJI73dIh+q6mn9dAe0i+sqAQoLpiZidOj33/r3bL/4x7fzLvzwww8bNmmR13lg3oAJhGAGnFapHiSv/TFh+Cv2HTGtadXcJy89UAnySMkazccD5l1OVwJgRq+jTgNp2VR6rCN99Eda82GA0z54UoHYfEzFQpK0Q66g01+gy1crZrGNS44hOoRoMpFldohW3c3va5W+7idvy47pjVkjPuftlp+fb1cz+GEImKL4sqa4KbovVmMRR9CQIwcJQgMsNnE+RHsSOYb6SG+3AEfDG6kkbsvNvd1gvdJKfHoVH0m8zQ/ZSiLgiICWZh0fdC8FdRnY2fPS25ZFXAylZsf+kydPVj2RhtPqD+nxzmeffbaSnlggAtuRkaxgh0DWaD6ehxnpnvYdQqdMpkt/IDhkLnmSnuhCs26gnz72TDLEhuFpPprQ8ByDWazP/WuJniWCGxlCzpHkYKiqBTluE9kawpYOHofgV5I6BdSoNW1bjzgfaD6mmDsucSGOlAVpx0VVEChTOqZtTcci+o6LctzrMPolWM2nI5WuUbhX7aY6+nnSWmBKi0ou64WGQDZ7uzGtg9mCGICmdiHjRftq2lho1axGJ4kpw0ObaeEQlt5u4eBqQ3Xx4kTZ6otOpv6P0Oifhg8fTivfSQVjvB45bP4Yinte+eMTQOtdO6luVmS19o85skV3G0qDn6MRiwnZdRc+TP/tRp/cSuvnBoBzUCTc5nbzx/c3ImwNLyXCeSjnEM0kgsWhF9FIopdA2T/m7EHv9ArGshPM4a3KUvMR6v36OYQ/9wURUbPctxJvYQ5LQJiLi2FT0/vA2RAN3tutPZWqnpvGDAfr5yDV43KdMOvn8MOKUX4yELAkkRxDwEWGA6Z7MPVO+8yuaHoRbywSqWZsaGrYkWpPFNNSertFgXIaj0MntrFxxNceXcb3gtoVwXeQ9j1jREzd2U0bDhkypPqHV6urdr/4j4vPa/5+3qN704wrXn31VZ6C/RnBpjWtmpvKJs5L3wVPHimRTw2irFF7gF2AmBc0Udy9Tn+RLpxHLbvRF/fS5IPp0zsIx6RmtkSr9qCvS4lw4MlAosZE5xNNIoIdZwIRItCfVzF3e1+7xc9h0WAOb9W769ata1XTZomrEQYp75Y+PdWtZERXELV12SqARVWd5zZ0XC3XrtZkl331UT145acDla41sflsmIe07nsbJB3LXYEWBOOnLJlFIIneWOaaD2+o0SxZYZi0jDSNV6Tak9lpLbmHioBJgE2o/CyIuxWD1T/voxNe3PPf1YhH7zx4ypQpefkFefudk3fqFHu1ByLw7DSJ2EX2Ky+mqb+7KYVMIBcaT+nqZg9t/RZ00Cg6czqd9yE17USfjlESEs79G23MkB01Ale3dECQZwDHpiLghy9HEB1K1Dd1ye19bQq5lX5iemPWUuBsPg417Qe6/6PjiVydoInKOOB1v9BuTUbYWW0zE0Bc+fEFmg8JHWAL3uFN1XyMNp+8OsyNM6207a3PbxjyKEvyjgiEoRo4MvVZwTnOxycDo7cbsxHxjnPGKzqmfP0k6pc+MYyoufR2iwhojo2TdwR7TJoqEtobweilNnDMo65nvvLKK0r4dacB9O2z9FgH+uga5TRG90V8Z+CettrCCXOPZANvlk2aT6iYN9qLDrmczn6LhrxJDVvR7JuUY6nm/Z02wSgSYYlc8ylU/dyMZQeOSI3DPE/ZfKzifJjkNkuc1jWY8/ra5HIwQAAb0TBPI+93UU3Nc790PAkfUaPAkxywI31MNJ98zOQWhl5hJsziLpreAhFBIdkkFgEX3m7e+sj7v/HmI95NDpRtEiroPOiSqF96gy7qVtLbLWrECbndPPC0SdnkgZpy96lpagMoCL/e92wa9DRduoLaHa+cxvh4Ec28jtZ85Ip4uMpPgJ5XrnrltnI2aT7RYI6TcA79PZ37Pp0+jQoa04e/p6nH0Rf305Yf3GLvpX7gKa2dhMBNm2YbTdXHCVyN8FnN7WZKw3h/Wb1e0ZqL6Cd6XszmU11lr/k49bLm9+/d2HCeI7pAkG6qmuCiqgNKD4ttPj0dmOyr6Vi4kl1HwUFCV6RNK4fk7YZ8OUhnwpc6+TsFNB/o/7JkFoEkWiNC13z4IdGienTjlETgMjvVJPfsQMDoiG807zh6jvFQGGN1RKJ3RMTQAe7gg46T7Ludr5zGOOpbanOUcg79k13VlFyirgr84zym/u5hT8HdOyk/K9IbhA2UkT723IddS+fPpEFPISaV3hlFL/SjLx9SNuLhld2VlF8QHnkjZSvNp8bmk9pVm0bE6W55fpERWTGYMA43Zo3mo2Q4sK8pssS1IYI+I/KiCEsM/NxQP6jiPaRQTAKrsRBf93gKImCKyWVRCwnWyzZSxVZfRPjGLLG1MbdbXl0RzScwMSQhrwgk0RqhJC0w6iEiF3l1RatvvGhfzdjQHsTAY35CXyY8TKZH8ORyKlcr7gwORUfHvgkqw9tNM/uI0HfiL39XEDAdTQ1eeEdE8zo844NRUUorXqflrylhGF3OoK5nKUmKM1KSgjlC9hG7AieuLCgZxxyeb8jDjumHc+i7nqlMPxiIgi042/6d4TTMSyIyD4JUT1BediPOB4eZ6g5PRc6DG5H54PyPMr+2PLU/NdxTOZdpj/099FFrwjqL8pga8DPRltZoosPVDAes5F0VoT9Uxue5H5TF2745lHpcRR36prWY/wBcd6jXTeJkamoidx8MswdfSj+8SgO5VG1fP9nskz8jaK9z+ghiMiC/MNu2jiP6mehRN0MciINDSUmJ1s3p06ePGDGCv2KFQPPmzV2DIxuEg4CJzcdU9zBNRaD5odWuUJxrGruo81XTXTQ2tO9mEpXLcAYuBKrS2y0EUB1IRq726N4jsq/h9buW3RNdqmdeX7121m8bfqTWB9OC8fTUAfTJbRnIShw55h7hrdxBBQIvODxSj7ZZxjFvdSAd9WclHVyf+2nHJnr1dHplMH31OJWZ+ot5Aify3G6Q0tTso3m7eeqGr0a65eXIQ/Ytal5Jeebn+XjgBH0GqQuQ8MCmeHB18yCJeZOQ53nEq7clLME6vDVVj/Qxi/NBVusWZkL05UJ9pLdbYLPXK6EkOm1F6u3mFVjZTiIgEQgGAV2+IPY1GNJmVIzs9ri9kg68mM54hYZ9Si33VbISTzqI/ncH/fx5eGIkkrL0dgtj2Fr3oGP+SsO/pGPHUelqevEkeu0s+vop2rnZL7fd5YRDh6ItpkkONG+3aGVRuOnu9883t1v1yw59/IY/sR5Qd71vWhDBdWyLkRMsK0vEq7clhjhQq9hVsj3b0WiqHulTVUV10rejefkimk9WDnSyOpVEg4TUfJI1x8KUVuZ2CxNdc9qh5ryKvjuuODbYgw66RMlKPHQmNe9Ec++kpw+lOX8N8plqKk9SMN9VRnWVSPVsKDHEvPAIOvYuGrGEet1Cm5fR1GPojXOVtISV2zwCHjebTxwwR6gPFMI6gdl82NC8obrzIeeBsWTS4ANp4oC5x+nrplmwNh9wRqjPjo1622BVhc6NUxMRyu2s1JcIfRndQCTrxhsBkzifeAsspZMISASyE4F169a9oJb169eff/75Q4cOPeIIHE+So2Xy5MkzZ86cNGlSjvY/8m5/+OGHzz///LRp04477jjMPZQGDVxkmJg9e/aYMWNmzdK2ZFF0YOTIkf369UOYAc+sc+fOmDlFRUVRSGDLA7P3mmuuWbJkSRjCmAb9Bh4JnHEM4ykABhQTvlOnTpp448aNw7v/sWPHehAYc/joo4/esGEDv9yNHz8e1EpLEchmUthA+2HqQU6tiYzz4dFL4k0nbT5+5r9sKxGQCASGQLt27a6//vq5c+d+8sknhYWFV1111T777HPrrbcuXLgwMB7JIVRWVtaoUbbYfJIA+0knnfT4449v3boVm3VoDi1btjzzzDOnTp1aBT8cgVJeXl6/fuTeboWFxcX6t97xmTldu3atrKysW7euAH6uqyxbtqxbt258MwzWsGHeDvJxzT3HGxx11FHz5s0LCgToUZjGunmCG6qgwDJZYt++fSN+yxBUZ7OPThK93XLd5hNqkIPQFEfur5gkUsuRvDT8qLDEaxnEPwcxF7orUpVwwt2K15SUXPBBQkquLmcSQtV9lqRgvngCbS+m3mN9djcWzZOCuQ6sVe/R8leV6bfPYGXudTndBsw3JhQi5xgcsRxLkNnGTCfJE/soXnxI4R1ywL1jT6msWHFhvXgRNXIdiK7lc7PhgqgeHnMMDxK7naY2CBJk536maiR0not3UKtpnHiec7uB5vwHTvvi/rbI3cdnafv8Xlr6NI38xlw6xi4v78ov7pe53TwMYI43kTafHJ8AXPcz/pjMwaGQmNsPOo737nkNnfcBDZ5K+Q3VsymPpS/uo9+WeZ8sScF8106q68LbyjsgEbRMCuY6KIoGUv9H6coNitoDDfzRNvTBlbTyXVPAEI0dtcUHcjRsrQRI6AqbOXHAHAoPtqdVu0KaYlBy+hL9SaWO8KxZKbUnJHbOZOOAubOUQdTY6zD65csgCKk0mnb4lUgfDaZEiFkfkNW2NyEdNpFrlTowoSWhGgRkbjc5FSQCEgGJQAgINO9Mh1+nnk05WQmEnXE5TTuB5o+nLctDYBYPkrt2UN1syWodD0S9SwFrDw6lGb2OOg2kZVPpsY700R9pzYc8wQrDuTre2Ym3xGk5O7Bp5AoSLeTVCTCRtLgs5jUhDDJ3hVaQ6gCBIDjnJ8O5DULrYEwJFx5GxSFrPlDgbXJjpDSfmOKTS2Il0dtN2nxyaYba9zVH8tLEasAl5m6Ho0VXOuJGGvoxDXhcOQLinRH0Qj9a8CCVrBSllBTMs0nzSQrm9nMIm/h9h9Apk+nSH5RzeJc8SU90oVk30E8fo11cNB9t2sQEc4C2bZ3ovempHhzeoPZMIMp8iE9MMPcEo7tGsMa03C+wXHZNO/5mYvNBPnTbCDEoP6VrZW43dwMna6sISM1HToQUArljqY/PmEvMPY/FHt2o183KoUAn/Yd276C3htGL/Wnhw4TQIPuSFMyzSfNJCuaCsxHn9nQbSoOfoxGLCWebYNb9t9skIuzeoi6NWlNZurebNm3ig3np2rBhmUr0FVFauoOwWZrSjw/mEXQ/QLNP0/a4d/Rajr3NBx3E24eta6S3WwRD7fBGKC8v4zK4FUBmOAjxGEehwYhPhgMhcbOrUnqGA5yQzXePZb9gF3WZMIwXcYWvo/vK6Aim07ARQ4c+COoqM1FNKZiOnAgvTWyfvEKfOr8uUYLREY9Rfw8lHQL+mrQLnWl4DBDU1KEf7XdueBwk5cAQKN9y/n+7/Y8IaeCGqn/HWJNmwfc2Nyn/E3/3GVehvKs20MQOdOV6xk1ZZC76vMtnFyxfnuYFarXy2NzRjiueKHQT29P+Q6nPeNH6qXoiGQ40mqCOFx7/VT3fWHGb4cC4gBufBabLuP3Kb/+kMA606YhYLd0m80Fd/E2vu8Xfuf63z1HxF9T3XzU1/WQ4IGo5ofB85KvgMxy8O1JxZv4d7iqLgjifj/44pnTtOL6Vk9xGhJ1amPwus1p7AC1WTUxsPghX4kvGxdXJk8RoqoxjKCRA7ljqzeBgDwz+L5Al0gF5A+ZWYmiCKQ/1lJw1z/h0sY0X7Tti2mUrHHQQueUlNA89V9rzYDr6DrrwC+pzrxL2/erp9MqptHgibd+QRjIp8zybMhwkBXPPc69+iyNVbyvkX++snrPZkegmIquD7m1WG91PDqsQYt7qN6Odm2sF37WzYcOGyn06dKZuoTDtnOkd7RkG05XV2Qzrmx9sPherGQ4CPP/Ly7PAzTy3GujwR8Q33IxAgDYfaD5qjoq0AuslprdNkd5uAY1kDpIx93ZDxJJW4qBp8PIkMZoqGRMrpyz1AkOivTnTWVGsXqp50ZQEMBe0FAl0KGeqtD6UjhlDw7+kY++k7evo5UGKFrTk8ZosWAKYxwIp6e0Wi2EQFYLF+exNdC0Rck59qmadQpLlrkS3q75Y9kWzMOvud1O7bhopXZKDXTtqjoHyMc8dVzyn3nC/I7ebowOqC3ImVb9XTT2Hq3/QOS9wT40t6TYLuPMoqEyrp/UzNbkbKRsfIslb5/fYX8mcjhc0QZTmRFt1dCrLlFwd9qVph/TXWkGIImm4RCAOOoJLkYXjfFjfmPmF8TC1Cxkv2lfTxNWque2ArC8RkAhIBMwRQAxG778px4kc8xcqWU3T+tFrZ9HXT9HODERkuB6jbNJ8XHc+aQ3Wz8H+G5qPVjqo2ZbnE71H1JhoONEBRKOIXguuZzWb7FRi65rN9K4dsPkEx8Q3JaQhwf7Yd5mtmnRMCww+5xEB/w+IcEAbtsIelB9BAYPUCQVZuqwWqYTB5baG7rpU19NdAprP7vJFLvGR1QNHIInWCCeVmgMJygmzveCa9pld0fQi3jgjUs3Y0FR9jJX3XeDzJi4E3Vjq4yJzcHKwBwb/x9PWHic2XtSC7wXTRDZgbi+GaXdNxeYv2r9NNK1p1dwnr+CGyyWlvY+k4+6ikV9Tr1tow1zlUKDXz1aOySvf4pJQhNWzSfPJ+rVl23pkbu5vNju6EP2ZaDHRq0Q/EE1S63i4zS1nnu5IH9Xmg/t00egN2t1qM2utFj2bFc/FPQC1Bw5LzYr8Kz99VAMakujBnjaN6H6iPxKdRXQY0QNEiPPBMab3qqY21MRF+u07QTm1Jd3LAs7xUOgMnakNrunjQ0Qkm8eQSPNI6wTn8IZtpT5MHjYfw7W03r1/OdVvATc5WSQCbhEw13x4TUPT58JQ7Iw0Ta/EzfvOLcrJqO/DOyIZHXSSUudg7cV7zYmF/nczzN2K4Rh7Yy8U31yryS6yp7juEW6sb0rBLRIR1W97DA14jEZ9S4ffSBuX0LNH0Rvn07fPUoUWGh2RIM5ssknzyfq1pXnRdqLjbAd1f6KeRCel6ri9zU1pg8gfBnXAkT61b2RUzQfXD53YRmNhI5fp6uE8OQVrVFcpDkvNi6hklWALpRreR2z65g2i/xDdqhpwjifqpOqNA9ToKeiQyGe3r2pDe5II+exh6sHZxjPUr2PZ2abwxQqn2OmE1vNcXK3yPyLBaK0i6O3Vk35BaFsApQWRfsAqt+MVuyXp548nnLLV9cwzA2AuSfhCIHu83XSmGz+oGM01zEbEO84Zr/jhKNtKBMJAgD1OTO0nIu9WwxApbJriT+uwJQmePjKi9rmfLllGPX9PxQtoSk8lL/Z3Uwn6RkxKNmU4iAmk4YnRrGiFAHHMrcAd0QoLC9MSW6sZDgRkca5is+I5N2Y1qnYrh1Ga2nx2V1DJj/TTJ/Td88qRxDgW6c2hNPU4eqKz8j7iw2seJ/pWDXyHMeceok+IdhNdQ3Q20fNE96mfseuFMrmnqDSW9YJawIOi47tDUREIztsNYXL1dVJD8zG1+Wz+niYU0sAnqcsZUfVT8rFDIAyjSNiIu/B28yaKaXICY/qEWCVU8NbTxLfKeo8U2xHyZuGxydEkNB/McrsJNYyqUhYqPzrM2/ehvv+kS5fTwZfThnn01AH09kW07AXCziyzJZtsPlm/tjTcE645+uRUhvmDOo3Ui1arjfF2s3rbotH+ywcNrzh8e+0bGS3DQRwwV7zd6lCdurT6A1o8gf53ByFV8Usn06QD6cku9NaFtPAhxfsU1RCS1+MqGvQUjfhGeR9x/kevE/2b6BY1Yx6MaciVh/f/zLEtwKB2wQVcNwpGnbCGDpdPzzjE/OCaDrSr9UZHwVFCV8RFKzdpqyi3ZQGcJlqeHianCFBpdj+teJ3eu5SQxhrnqKolAN6ivZX1sgeB0DUfHiotqkeHn72xLImmtEROkKz3SLEdFaPnvdG8Y2rwsaJqDBkyCSIyYC4iho5j2H7h/CM2bF5R3DhW87zjidTvQbp8FXUfQes+UXZm71xMP7xCVbuikMrIY/dOym+QGdaBc82BtQU53NLO0DHDULP52Nzmup/4Ncc8CrHhnr/8wrmkqjYfpea0fiImCPs7WnTFKy+hTUtp9Qz6ZhJ9djd9cDVNP5OeOYKe7Krk/lo7k379mrYXU/NOygmwsLUO/YSuWEsXzKXTX6J+DylHEh9wIXXoSy32pQKmG1qW58JMYMBztYp1tBPOzTy3GmjxNZanIDpSgd/ahT2pOACHN5YasbZUbKWCxopKzBccGbTiDfod7H+1RZ5kGviQuiWYxC26yUmmLHuBUTnRXeR7q/1kvGhfzdjQ1HBmSsTt8JjWz9h6oUkjTzINZCC9EUk/ydQbDdkqaxFY9R4tn054y9jpZOp6FnWFc0WEh1XjCMjLV1KdgqyFN7s6ds6EQlgnzrHt1EA17fJANwcvOoNUPJ8+HUNnv1VT88uHCG5CRyGrQtClqpJKf6Jt62lb6v/SdbRN/YNVB6cGs7+mqQ/4XLcRPX8snTmdZoymoUjP5qLYnGSKVAdIYH2FLTG3J5m6kExW5RHQ5pu/k0zbTig8jegx7dbATHt1MBIY1E4b5DOAnacX4ry4Mv+BMV/cL08ylVPSLQImNh9T3cM+8YDG1RggZBoypF00NjTtQIBxR24ByqH6cfCOyCG4FY8XPv8Se9UXHgC6V4lhswuvI34pi8/zooHUfwKNXkfdzqOVb9GENoSnL146RlDwshPR4Vmj9ohjHgG24bBADjfHUB/N2y1IEQzn+VBdNc4nHXMXtz+cl4q/VOb54kfp0zvovUvopYE06SB6vLMSC7dgPK37lHaXU+ue1GO0Em4xYgld8j2dP5NOfYZOuJd6/pH2PYfaHE1Nkdm7OqjcbjxiE1UF0tG3MEiQBWmFM89djJ2gnAFWCyjUR+/tVlFCBU1qbT4sn4FO7VF7Ib3dAhzM3CFlYt7Jnc6jp9Lmk1PDre+stPnk9PC77zzsP/j78S0luLbrmdT5VPckxFrgtf3kg+myH8Vqy1qZR2DihMIFRNiU25Qj1ApHBGvz0U2VOWOpcaESM2Nf4E3ErDea3Ub7ClWq1m7TnhDLwb4ifbbbAiXqxQE04iuafAidN4MauXBNsrH5QIo3VSRtXkJIm4/bsfJYX5t+/mw+TScUwog3Xrs11s+huX8jzNJTJhHUHni4pQJ70uSUNh+PwxZkM1M3sSAZhEAr0jifEOSXJCUCEgGJQFQIQOE5+QnFCtR5EC17keCQhpCGVe8Hzz6b0hsEj04cKWbM5sPCIRBOwwo/c+CftnU1YRP5/Yu04EGafRO9dQFNO0EJv5lyuOKBtuQpxTQECh36KS/UT3+ZRq9VFJWz31Hmee9xdMjltM9gan2oF7UHwrDcbihuE1s7jTA8o5oS4QxTWTKMACYPdOPNSDnuq+htPuVbqV5Twv/p+Qx88ZCNw0Egibndct3mE85MSCTVJCruiQSaE1piHv0IBoh5ZWXlCy+8MG3atBkzZpx//vlDhw499dRgrECrV6/u06fPqlWroscnDI4BYh6GeIHQxGD169dv5UqcLmNZOnfuPHPmzKKiokA4akQ6dOjwn//8B7NxzZo1Tz75JDIc1KlT5/PPPy8oKMBPHTt25P9nX5s1axasDKbUtGk8cuRIgDNixIhgmcZtXsVNnmDRtqLGBhfzHzvgsWPHemMK6MaMGaM1nzx58n333Yf5s22bnVfjuHHj0CT6nXdJSYnWzenTp2Ni81esEGjevLk3cGSrwBGQNp/AIU0qweiXj6QiFZzcEvPgsBSlFCDm2FleeOGFr7/++ubNm0866STsPps0aXLppZe+9957otJY1CsrK8N5lD6JxKd5gJjHp1M6SaDPrF27dvfu9GxU6ZX8DGtpaek333zzzjvvPPbYY3fccQc2W9hudunSpV69esXFxbfddtvUqVMhAObkCSec8PDDD2/YsKGiomLFihXQtZ5++um77rpr9OjRgwYN6t69ezRqD7q+a9euunXr4kPXrl2XL3dMfed6bJ977rkLLsBJp3EpuTDPjVgfddRR8+bN8zMGUG8wjXkKDz74YP369Vu2xHlODgX6klMV+Xu4CGRJbrdwQYoZ9VDifDyka/PQJGZIuhYnB7vsGiPZIGkIVJbRium0/HVCxi0EAnU5k9qf4LYPiHBAxAgSWM3nWsq4BR5G+yAQt4AHVb+bGnlSc86IGVE4aOEsmibW/KA2rSFay/2vfa1Sz7TBH/IGaP+zDzjcE7MFDmAoOPdT+xxUvzzT+V6VZ5nqlgZkkJA68GLaX3mzBI6zHcGNi+FIOXbjYmQEFk+zxt/CuClw79xANFZl0139w9zGhFlv25NxSg4NF0xBLJA0QtLmE+kEC4GZtPmEAGpCSYaTlyahYEQktsQ8IqA5NqFijtNI9r+ATnuehi9Ukl8tfJj+240+vlmJtXBTtINf3DSKcd1QMY9Nvx1DfbRhxfk7UGtfIXpITVN2PtHRRO3UY+xPIvoLEYyGpUQ9iH5P9CzRaqKtRF8Tva1G9t9OdDFRX6J9iJD1HHkDtAxXGovFMYAFipwa5UOOyHgWFjoVkonHpeTGPNej3boHzmuCZu65YKozi89S9dyAF9V7oQWRnf00xUzmdvMMey43lJpPLo9+et/dnMImUQsGAYl5MDi6oRIN5gjPPfAiOv1FunAegeMX9yvn1n9yG234TETWUNIfizAOqU40mIckvDBZ3f4eAQrYyUGHeZzor6qugvfT+6vqzcFESL4GlQZRQdjknaeqQF/APYwI6fxw8M0Uor8TXUmEuDFUto8PQC587ShTbeZAa8p4iUDzgQFtvGrmikXJjXluAvVePdf5GABoPrgpcLPgRsA9ciDRFmHNx0W6QB8SyqY2CCTR201qPnJKSwQkAhKB0BDAYXzdR9CZryrn1rfcl+bdo6SrxjEpP2Oja1myzeYTGroZJ4xX3TDI4FR5eOasUpUc+F9B69iDqI26k/uXqtLAMtObCOfsvKMac/CiGhdfJnpQdfJBtaOI2nrtDK/5xGrmaJpPK1Wpq40K99pT03bIhgyHOnfnpAYqgCRGhb40H7wjgMb+jfrHCjQfhPiI2Hwk+BlHIInhbVLzyfi0iY0AuWmpzyz8EvPo8c8U5g1b0UGX0Fmv0XkfUtNONHccPX0ozRmjnBppKNlm88kU5sHNro1EGKfpRP9HhGPkh6qaTHtVpemjup+9RdRAfWMN+wMsNjjVFBoOdnLv4mR61YHtTCLkU+uacuwJTrQ0bzdt5sTB2w3ajpLfQC3oePApDlLE4+Lzlvx57nFa7nW4H5vPHapXJPR/rUDzwbsDzB/HIr3dHCGSFYwI5HpWa5nhIGN3BZfhQBd0yAaFXdQNkPEirvB1dF8ZHcFRthFDhxIIGgMljRdt+Irw0pr75JWxIZaM7RHYvoGWv0YrXqOyX5RcCF3PUA5OwYydUPgk0VyiJ7jmLGjbdNqwWvxP/Mwx3kGmV0znmKn4NrPR8W4NakbYZzhAqIACSDoz7SL7oBVWTbv4rVl2AaQcwHE5h3E5BqD8IHILQdjwVWPNQQdtz1GddvTLhfordCHs/nVSBQLIh0T3EH2g0kLCA8wcqGRxKJ8TXUPE0n5BVwQ4iGgKqcDnDYHyD7DhcHNWrPEJwksofiuZ0rF6NhnvVlc3oNUt7HizhwQ+bV21x7NHYaw9ZDhAMoMBRB+piTrGpuTrSfQoUT/VFmRTZIaDsAbUDd0kJnM3sfmgG3xxg0CIdTWRQuQhSWcCAfbA4P8Cyb7ititWYmiCKQ/UlJyMuE5s40X7jph22QoHn7zcoiHrR4FA4zbU40rl1MgzXyNYhGbfQs8eSZ/dvYjI1GfJ5k7R/eTtDjKdY6Y4iNeMAkb3POCf9j/VP+0fqtJyBtGhKpHGREPUuBEoD3gJDWPObaoxZ7P6KxLuvaoG5PxJ/XpMSu3R+FvF8TNtB2MaUjHNcBASL1dkNW83tAovyQETCYkiZqlj5L+E/TCyulsTfFs1K8KrAbczXMtngMCetJzWqrcbbD7S283/ZI6AQvZ4u6EnWolD9BLTKVmJYCBzlEVsLPXaiyudFcXKnuNtn+c4yoKWIkc6dhVig7mvXiSrcQwxb9qeDv09nfuekhGhftNReHWq7uGWOAGrWUd1c9XUJulEzO/vdndr5JjjOQGVZiHRa0T/Vj//Tu0fTCIoxxPdouZZZirN5URPqR8QbwC7DXISwNqGnAQj1Uxr+6pubHxhFh6d7QgVsHurUK+zP62wz0hUgKL7yS/oanvTDAdx8HaLUvMBDlBl3R7uw54pNk8QwVuJ0Vk0GmYnbtzNKBufYlE8aAKZZ7ZEYPx05fCGBG5aPgOW4YAvW4Q1H+ntFv7YZiEH0Tgfpv8wwwuDwdQuZLxoX6322ZCqZ8Q4iaa0RM6UnM1Lk8HRkphHD36cMW/WiXr+8UEinAGEaPgLiQ4ggiI0PTiUHN8mBMeKoxQC5ohon5UuK65cqnrOoLBTYPEVKg3C31EQZoMCYw4Kb/PBV+QkODSgbsNMBKmgFxlVIyYt+ynYYprhIFa53dDfsG0+YLGfmuEabw3CKCI3To8emURdRMIwkFForp+DWa3lJzDnUrULTnG07n/03fP01gV/4+rjjYPR5oOsGCI2H5nbLawxFaYbB+uIsLA1FUU1H1TXDC/8Z2aH0fQi3lgkUs3Y0BTEGHrfuQVa1rdCgK3X/B9fU1vNbQJ4BF/L2Q+BvRimbU3F5i/av8wzrWnV3CcvOf0ShMAq1fiAncFXqnvVd2r6Y2U5tb1T/HfQ5jbUEbeqaXO3+hdPRwHbXN0Lfuy9jlWNOSibVAUDNp/pqs0Hn5nNhwXAaGYZZr0RKaZGHtaQJ2ITx28fsSAig00dpvzEKrEbpI0sw4GGzFgi7JhpJzPmORTtmeLzCWJDxxVl8RvQqWeR/75hHraSLRnbiq20aSmtep++/i/N/RvNuIJeOZUmH0KPd6I3htKCf9GGuVRWjFtVK9p5PuxKSSqNOzxO7ZWfvuqpVrJkFoEkumKZaz68pqH1KozuGWmacomb911m51lY3CP3SNE6ovNvDsl7zRE3t2I4xt7Yc1SaD53JiOigwFej/xLPjtU3XnHso6xAmZvnguAXEZWrm3gUHP+Cv8Gplm6nqClHK/3EdD5bUXA390LAHDmjDyd6k5MPe6BLiPqrV2AxsynM8OLN/KLpOaa6EFJTXGbh0haB5sOnBIybtxt0Tixz8AYMu/wBDBrUbMKD5eWs2JvNc3HlR/wGtOqXs4QBIrL9Zyqer+RoWfhvWj0Dg4t3NPRkV5pyOH34e/p2Cm3+QQlf7DyYjr2TzptBo9cph5ud8TL1e4g6D9qbk0Tn7bZFPcwHxVHzgYk1lJEOECVJKpYImGs+OtONH8mN5hpmI+Id54xX/HCUbT0iEIJHikdJ0pux1dzUfqK9JAuEUQaIWGAu/rDMgMxJZxnXea7hWqSeDKMVbR+QCODN79ZwMEf+6Im2oDDlRNCq4x9e8Jqmpi8z1ancxn+7koclOeBtPpn0u0qJzsf54FoEDm+uQEPloJ4gNXSm9XMrQNzrV1VSyY/00yf03XP0xX0081p6/WwlEcvEdvTyQPp0DK18i8o3U6O9YGobic4MX0iX/kDnz6RBU+j4e5Twxa5nUuHh1MjOK01n89FWPEfNBwzjMM/jPoghy5fl3m7e0OOVKN58xLvJKQtQqiQRRG/IyFYMAW8WHps8V96A9SaGN14iraTyI4JSVtbppAaiaEXbB1hNUeNUsXpToNG0eZuQIEhPU/NApAWVZ1p6bXNvzH8Qsc0n00go/OOs+Qg+QXS3kvHGEaHD36Gmd6urwdJRcJRQiHj5FsUYvuo9WvKEctTY+5fTy6coZy4/3pneupAWPkQbvqC8fGpzDB1+o5KI5fLVdPFiOvstGvAYHf0XanMU8iIWgVO9pkLs0it5tvl44CWbBI5AGO5ggQupI+gizse/KFpUj46U1Hb8YxsAhRA8UkSkMkYvGM07pgYfK+LGkCGrICKegogYOo4BuGXbYs4/4QLgJTIYuVAnQ/PcFbTYQ6xKNdA0H5spqvuJv19EJj9jJT7H7Gua3K2hYX6Fek6oTRE0+DBFxaiu8JSNFiTdFXw9QlXGTA1NMMhcG05uNwhpjPOJm7cbhIyhzcd05ojHauqbi81zq7tV/AbkKbh6ONK29fTzF/TDq7Tw/+jjWxStZlofeqILPXsUfXgNffsslaykhq2py+mKxQZnLo/+iS6YS6e/RP3+RUfcSPv/jtodS0jEUkc7opbdOXUR06XLUiC+4ukyHGgrnojNJw7zXLynsmZMEDA5ydQ0l5rxIq+uaDqf8aJ9NWNDU/XRlEggCLpbNQRZcmd0CrYgD01ESce1Xg52Oa5DIeWKFQI4phNOM2NSwbudiWYSdXZzOGOsuhOGMNpJpjD4INpnfRg8vNLcU80mp8TZpxccNop323/3Sta+HSOOJHU41gZHrMakvEw0leillDRINQFkHg5fOFcnmYYvTuQcdpdT6U+07Sflf+0D+9p4b2rSjpBGv0kHaso+tFf+L2jiS8qvn9zvkz+PRdIRwWVq/gNjvrgf9VnB+wLcxYjcY1cmqyveJCKrW0knqqvhDsS5o6QEWRhqyvTp00eMGMFfsUKyefPmvkCOa+Mkpl82sflY5RjQwW4aC2S8aF9No6lVMx3cAOOO4jp5pFzRIaB7tca+hsc+YnbhdURSjgyBIjObT2Tca5fl9KSLYd8pHjpozHPggYh+I5We/I0lghMvVpYNPv2AODXBmszmEyoLQUn4anH2dvPQneibODw74KL26xJa+Q4teZzmjKH3LlVibyZ1pyf3pXeG08L/UPECyi+gdsdTr5vo9JeVHAOIwxnyJvV/lI6+nbqPpI79aY/9/ao9wCUv34/NR3q7RT+1AuQovd0CBFOSihwBMUt95GIFz1CXQod9DZ5NiqIdu5zBPDx4XVNOAuYx0XwCu1PCxNwxz4HbGcJnfvOQAs5K8wk15bQxw0EcvIByTvMJep7X3IAXL8wb8kbegEfzjr49r/sIHIZDzx9PT3RWXNRmXk/LplHJamrchvYdQsffS0Nn0xVraNgcOv0F6vtPOvwG6nY+te1NzTpSXmjRDarmozuNVPy+85PhIA7zXLynsmZMEAjtTohJ/6QY4giEk39JnH8u1pSYRz/qScBc03y0oy2ixylIjmFiHrc8BzGx+cQh51XOaT5+5vmunbTlB1o7k5Y+Q5/foySGfu0seuZwmrA3vXoazbsbmaOpcju1PpgOvpQGPkkjvqFLltF5H9Apk+i4u6jHlbTPabTXodQQPmKRl7x8jLXnOB8/Np84zPPI4Y4XwyQG6pvE+cQLVCmNREAiIBHIMQRmz549ZsyYWbNmrV69uk+fPqtWrcoxANx1d+zYsXj6AjF3zcKpPWnSJAzfU089pSM/atQoDOXIkSPDYIsZ0q9fP+Awc+ZMCBAGCw80J0+erJOnqKgI4HTqhPyFOVp+/fVX3NRr1qzB//yH7du3AxaUjh078h/wNf47S0y52267berUqX379hUZ13HjxsFFCtOVVUYH8Vm7ot0psZ0tMs5HZJTjXEfafOI8OpHKFv/lNVI4ImEmMY8E5jQmicAcj3ym7WzZsqVFixbRoxQsx7AxHz169MSJ9kf7BNshO2pdu3Zdvny5sUZZWVmjRo1CkqOwsLC4uJhnETbmIh3ZtWtX3bppScCswBGhFv86PObQaj755JNnnnnm7rvvxvw85ZRTDjjggMaNG+N/fJ0yZQoqQMO58MILMXW//fZbjB3+f/fddx977LHbb7/9oosuOuGEE1AhDuPoiDxGeffu3fXre/F327ZtW5MmafkVtEUPZDGF7LknAh9HAGWFiBHIeZtPnqvg1YhHR7KTCEgEchQBlhl5tprkbVaOYuCi20hrhoAfeL6x4irdkws2IlXLiunFATTiK33dty8iBGl0GiBCw0udJ/ahw66lnZup91gvzcNos3TKFbP/xKukGCNk8UIuct0Aacn6wpAiPJrbidaop2/hj/+Az7BqdVT/130IS/cNr5MClJHB7w9E76uDK1LGqYvbOJYIjt0v3S/GqY5KJgaU14YoHxCbNLW3ciJqCziQBlYCyWYkbT78eGRJbrfAppgkJBGQCEgEJAKeEChS07ttIWrhqXmuNQo8z4F3AHFcfUUJIWxDV3bvpPwG3sk6tsQxLDs3Ud0wWTjKoKtQvRtHsvAlKUf66PqBBDjziZCk+59E1xENITpMzbmMxBLnED1EtJAIR3kOIkJ68U/UbT1u3o+JphDdpWp6A4n2J8pKtQdYsYN3vFh80LhiG9VLz6ldsZXqNVOGACkZqqvcTjpZP2IEZG63iAGX7IJEQOZICRJNMVoSczGcgqyVFMyLskjziQDzeOU5aFZEW7H1TS+VO6igYZBTWUcLoe0w+NRNsQg6z5gXyROl+WCLjTGDlfVpVV25nOhkVV0BoAcTXaWeTbQOJ2sRjSB6Uj2bCEdwLiV6h+hRoj8THUR0nGrqybXCNB+PGQ4qt+nTapeXUH316Ju8fKoGYdsSh3mea+Od/P7KOJ/kj2FAPZA5UgIC0gUZibkLsAKqmhTMs0nziQZzvFl/LKBJ4pdM8yIqMWg+Ydt8Gu1JOOBF03z85Bnz2/9U+6qqGNp8oK58Q/S2qq7chsM3VXWlA1EBETtB+EOicqKjiOB69RrRZiJ4ZX2hHsk6nuhaorOIepodVhvNPA9qcAKkg1GG3uhV89lOBTCYcaXW5iOg+cRhngcIZQJJJTHUSmo+CZxoUmSJgEQg2xHIJs0nmrGKkcNbs860daW+17t21KolYSACb7fyreGycCt29a60/AZEXYlMkj+4JStWH+rK5yl15Y8pdWUPIpx+O5ToP0QwRcKygAixe4nmqlYLjNksoslEdxJdRoSQrG5EcXIfFOt55LUwyt693ZCqW6f5aDafOnWpyiHDQeR9lQz1CEhvt0TOCdMjurWL7IP2x3qo+2rsti5tgv1XXXOtcsS5FyLwSEnk/AhTaIl5mOia004K5tmk+USDOXa0CLB+08eU0kU/4yv/pxE2Bknrrjwy+mDe5sOIEKf58PWNLHR8bWKy036Ct1tF6ZTLEHtCyvV0L6BAArtdQ1ulj/NBPAdiXUzPjfbzIJ6pqit/I7pUVVf2U2NOYIGB3WYa0QaifYlGES0iWkGE02O+JnpLtfncSjSM6Fii9oa+eXgQRzPPXY9C+A182Xx0cT6awUfZaQnE+Uhvt/DHN/s4mNh8YLriS8b7rJMnessaf6Q3Ww1xRfs/cHwY8ehLzlrqo4da4ygxjx78pGCeTZpPZJgHbvbJu/oX7U9ceejSpYtJnI+ZzQc0TVnwF/FZ6DZp1BonXTZsmIrziYMXkCHOBx3xnOQAyfKg1sJWc7P68rE3UVsVl31UEw2UH5gdoMPcomo1UG9+Vn99kegBomuIzlS/thSC0uPzPbJ5LtyJiCoyzcdjhgNdnA/slvXV9AYoInE+cZjnEcEcUzbR78n9A2Hu7QbrlVYy3itemJDMalA2eAMLPgeofjBqpgYcozVJudnZLZ/6P2LLj/8pJSlIBCQC/hEoyqIMB/7REKTgJ88BU0Js1Bv7XzUJQWHg0y261FvLy4y2DfN26lzRGEddNcGemlSDzWfXjvCODPIiWLU+zgdEbDQf9qCcR/QC0f3qZ7iioTB15UKiCWqITiv1K0JuEHuDAjPOR0STkCWZ6BKi/qqFxxhzIh/EXkZQrA00H4yd1zif9AwHyItYT01vgFInn6qcMhyISShrhYdASNvy8ARWZpYgdab/MPMLa2JqFzJetK+mcdeq2cuTxMThNj0yWpO0yppZKUAdzB7bnLXUC94CYVSTmIeBanbM807qISFbsiKrdZTzPII8BzoVyKjAULOiFSuwIU8rO3YgzifMmBHE+ezeyWw+kHDRaDh51RQTCaO58bauStP/VKaICUEKgdqyfg7NvO4k9TvbOiOFAPJHb1Q/I8EaCguZ4m0++HpMyubjvytBPYijnOf+ex0gBV/ebro4Hy3IR5nHAhkOpLdbgAOZM6RENR9lEuYpx54y9U77zK5oehFvnxGpZmyYKROTZpYxGnx4y4ygHmJl5FEwCdSgFOBEzVlLfYAYuiUlMXeLmP/6CcK8SI2IaOG/z5mmECXmfyJKPxxEqPOabiBo2LEiWqtjNCtafiNcrlIFx/vUbSDuL2cVYmTXGUXzKddsPj16RIm6hVz1mm0x/AL1BpnT0kqbo25Xv+NgUDw6YfNBcA5sPvjMbD7sFoj/gzgGiAvN9sAr2Wx4THn1JcJfTdHF+fDebiI2H+ntFvhwuiSYqU27SzHTqptrPryhRrNkhWHSMtK04ZIpgw//Qsgt1pqeY7o0mHq7uWUh60sEJAJZiUAR0W9ZoflEOTpQe24Mn5+mIFmaU5qnm33UIB9xtcpLnA+83XZXapqPs4Tho0RtjtLldgNP+KrheJza0rY37T/sRPU78krbFPkgjmDEvLHA0Lh4iU7Uh9d8dHE+vLebiM3Hm8SyVXAIhKEaBCedOSXnOB+fEhi93ZiNiHecM17xydRbc6acCFp1vLHgWzFe2p9/gj4p5Kyl3idufppLzP2g561tgjCH5rM1KzSfRGCumVm8zSutFaNz1WkHpDm8hZ3SGuwbtMSxj/XqpaIt4uAF1LA1UkvrCuI2dIf86Cok90GciHnuc3qbNseYeg9I9pnhIA7zPAxMJc0wEXClqHsRxDQ/gTF9gmNChUwZfLz02X0b76uGe15WLXLWUh8chK4pScxdQ+a7QYIwh+ZTlhWaT8wx1yVYs7LM6Mw7rBqfooCnM2Fx4dUTl9S6t6U0H564kZG4Oxy7D3QU6tSp0+2On2pukVbdjRL6vntcEmi4pzEtnaPm45JHkNV9PohjPs+DRCqdFhK7eYdOn9Way3AgktVaeruFN65ilLPH202sv65raVE9upbxAU7Q4KMlXrOBwPjiSneFfWV/pny1Cq6Blg0kAhKB5COAk1k8pktKft9j0gM+3kY0uzQTvVlRWmLr3Tsp3yS9AVNOtD+NhXicD0+hqjoPia1jAp0ihlfNRz6IYzSIAqL40nxsMhyIxPkIiCerhIpA9ni7OcLE+6dp1hjdRRBxrMYg46uZghiqwcdqkWXXrXQS5qVmX4wVdFd4VzftJ76OCBcnKUR/z1lLvShAIdSTmIcAqgPJBGGO8+ZDN8pHMgAxx9yo0rArjsE2djmpm3emkpUaHf4YU0ZWA57nwi7q+DpqXFp95aFUmUqroHoBObYNd/zrN4fRsiKdxy41vZuxZMGDOObzPLyxDlLzSTvJtC5VY77YFuntFt64Zi9lkwerqe5hmopAc1HT8NHldlM0B64Yq9lc4TGPs07JZynQPidxwuSspT6DgyUxjx78BGGO8/ywpciCkiDMA0NbZ/Op3EEFqTNGPfHQWYHY1zRK1bvxuKWdW2ouxsMLCCLqHN5C8naLw4M4F+e5Ott8nbmjj/MpofrNa+awSIaDeMxzT/d0ljSKj9OWOKDZ8UpRvL/B19TsNvyH4NlIihIBiUCOIYCdsu59eY4BkOTu4ugeHMhYlorwt/B2E++h0RCkt+cgcXadurSDHYQTlwKPTV2Sg5A0H/kgzuCQ+8pwoM9qzWk+depQVXa8/Mng4ITOOs6WCavOS80n9GmRFAY5a6nP4ABJzKMHP0GYbyFqRLQqeoyC5pggzIPsevMiKkmN3q4yqovBDLMgiUJ+Pdrxaw2PeHgBRWbzCRNZUdo5Os+DtfmkebvJk0xF556s5woB5XBSVw1kZYmAREAiIBGIAIHJkyffeuutU6dO7du3bwTsJItgERg5cmS/fv1GjBgBshjKmTNnTpo0KVgWPLXVq1cffvjhp59++lNPPRUeF7eUeRBY21GjRvXp0wfX3ZKS9WOLwLRp0zCgO3bs8CAhi+IeN24c/h87dmznzp1xpxQVFYGUcfJ4oB9Gk5KSEo3s9OnTcY/zV6w4Nm+e8uILQ6bM0Qw1Dj+kbkmbT0jASrISAYmARMAXAlu2bNljjz1WrVrli4psnCEEunbtunz5csa8rKxMO2M0JHEYi+Ji4wk6ITEUIltYWKgTadeuXXXrmuY4ECIoK8UQgd27PUb6bNu2rUkTHD5cW7DotWjRgn3HPMFsiWF/pUg8Akk0n+S6zSfDqW+sbqBHBN6dXO0UL6sjYl8flRd/Sz0OqJHIkbi89cURMB1NhjA8UmSApjiSgdRMEObzH6B1nxIOue91UyBdzxiRBGEeIEY/vEqr36P+jyokF0+g7cXUe2xQ5KsnIIImrSwgGq66R85HSreriuOythg7/sGV1Gkg7TtEBApjN0VaaXUUHKIsuTnPgfCyF2j2jXTFWtdgIxDuxQE04ivCWgf/Iyx0mNhXFbNxH010OP5PH0R+SniY526PzDLtkbT5uB7omDWQNp+YDUgGxdHUngzKkGuspdoT/YgnCPPyEmq8N5W6309Ej6o9xwRhHiB0aXE+Owk5D8IseFuGl+e1O/2YYN6wtT7pAmLWEbmelSUmmEePrWPuaSuR9MeYbuUdwvJFIohyFvPoR9mCo8ztFpuhkIJIBCQCEoGkI4Bg3ybts0HzSfpAeJOfT2yN9AN1naz03rikWuHkHOwadSmk/ZEMonXDPWuTLjB6yL6NbMWyZBMCnjOw6VNab23BwSKk+WQTjMnsSxK93bL01UsyJ1CGpYa3mywRIxCP/EsRdzrD7BKEOWw+zTpR6ZoMI+affYIw999ZjUKDllS1m6C+ooSv+cDm05gI2lVN8HVMMM8pzScmmAc4hwVJwebjLVdW5XYqwLRNlYoS15pPzmIuODSymhkCUvOR8yKFgPR2i34uSEu9xNwGAWg+Lfah0p+iBylgjjk7z5t3ppKV0Wg+sPkgyKf2/JyYYN6oNZWlHzFUtUs5dygrS0wwjx5bjKm3otN8ytM0H8wSZ7o5i7k3wENoJb3dQgBVkpQIZDUCuqPZWV9NDmg3u6gL1jTGbopHc9qIYfzJeJy8JrP2k82gifDSmvvkley5w462aNpBOrwldRw1h7fwbT5M8+HPzzG90fgVRrfOiCwgrI7pXWk6RruePDC//Fe+PlVXvf2HPUx5ia9X4c0H46KaJnyKsSNWpnR4sfkKRhbiCFtJ4ihhkBjCtkmezkfRH2Mqvd2CHJZoaGWJtxsUOL5Eg509l7jJEwdMgpdBersFj6kDxUWjN+iOZs/Isx9MTcXQLqIb/Gf+K7vO+skTse+IaU2r5jrZ3PLSj0GCvCNg86nfPBs0nwRhHuwioCU52BVFhgO4umk2H5u1RXe/e1tzTO9KI3j5+fm7C5rlXbKsdgGp3o2LwcIcKjXxZU3Jp+e+WA2HIMLuGYbQoqrSq7fbNirgslpLb7cQBkeSNCJg7u0GHU4rGbdksWOS4iNP1k4j6e0W+dD26NFDx5PXInQvBXUZ2Nnz0tuuxbGjMc327ii3SIUEeUcwm0+zjrQ14aE+CcJcZAqJ18mczcdqbWHrBt+D8JaRGi66UB9V89ExNUoljnGANR0XVQesUvPclI5pW9PhCLBHEZGCt1t1lRde/jMc5Oza4gXuUNpkXEfw0CvROB/WN2Z7qXnnyhliNMZG44ypuUa7aGzooQ+yiURAIiARyEIEssbmk4VjI9alWptP8LndZhPN4qRAhgPYfPD/52KiidQKRj/RJbauErP5rJ9D+PNUdMh4ouHQKBhkwpAsRTNSCZWs1p683XRxPpu+Xgb5U+Ouz+3GTYkIhjjMwckq2lni7WY1JprthalARjuM7qJINU2hkiadzN8K0tst8jHIGzrTxhdfe3QZ3wtqVwJ5X8uImLqzW0FiWp+/aG81Mq1p1dwnL30XPHmkRD41SMkJBoMPShbE+SQF88CHOUybD04sfZMT+FiiAUSfEPVSL9qvLf47Kr5inH9c+2kXVGj1kdW6bl0lw4HN+qaIhwN8f3jlMU+CXoHW7hsGsKiq89yGjqvlWhxh930NusXuCsqro+Qrd1t0cT5Vu1aDwubvGZkTiU7gCUJNWvQIu/AGEY7uVUrOri1uoZb1OQTMbT68oUbT58JQ7Iw0Ta+YyiPHMWAEpLdbwIAKkGvVPQ5xPmwX4sKd3VCfdZWnYN9505rsIqPDN3eM8xEAmquSFO+I8q1UP1s0n6Rg7m4mCdRu0lbJbIYoiBAyHDQlKuVE6E/Umgivya9mFwNaW6z0E/EolBeWNh367xW1cT6CNh90oc8D2ODy2p0A4sqGGMjsJ1LVXx0TZATmubjyI46wVT8cdEt/3U9rDc2H8rw4vOm83ZALEQXukWqB2tOPZ9P6ENr4FbuAmY9RZvM8wH5IUh4QyB5vN96e4wEIvonR2w3Edd5uxis6CjLOx+coyOYJRYA9ukztJ7XvUBPaNwuxxXcG2dVvQ28qSqieeqB5047ZcKRPlo+WdfeYw1sIGQ50mg8keJnonBjirEtszWU4sFnfWD+g+YzV3u6LdW0q0TCxmsZaQS2qQdHx2o/I2zGbj5LhzWXRaT5E20EA7pGmpRHyd9AG9adazcclQ1k9cATCMIoELqSOoGicj2c5eKWFNx+x65qyKL3dPCMcWEPp7RYYlKKEkH9JtCpXzyoVmwdSrElIaRI8yxOu8pMU7wgW5IPStH3ij/RJCuaep6xNQ+bwFr7NByLwmo/V2mK8uRyzCzjqJw6w6TMcVLnK7QanvtNT+12R8XmO6AKReul1BBdVHVZ6ZDZ9I0KHHwLT4XAlvo6Cg4SuSItXroLmA5uPB80n/STTGs2nxuZjwr/1IczJrVbzyeW1RXyAZM10BELXfHh2WlSPbhSSaCzLwokkvd0iH9RDJ7bROXMbzTv2ATM6kY2xOiLRO+zZKR6lw5QlEcqeEeUf5wHzSop3hObtpig/CT/SJymYe56vNg2ZzWf3TspvECx5JAPexlFEgMSvRIenrtisLbr7nV9hxG9q8bvy1eu6nLVfKR/nw2s+IuvbeuG4HQS+w8+tTaBAu1gYPc1zq+EQR5inIIJnoPCoxODPGUicj6Pms9eh0IRRMPNrkmF7wjx4BHKYYhI38EqiAqMeInKR761W33jRvpqxoanhzJRIIDMtM8uEo+iPIEOPU7kaiXxsi46IfX1XlZ1Ek7+nIWA6mo7DJ0HMcQSWTaN1n9KJ/6fA8NoQ6nWTEvMtS+IQ+PpJ2ryClj5Nl6+kOgVBiV89oRAvv0cTsY0gynjVMPKA+jnvquKgGAVAp3g+fTqGzn6rhtS0PjRgIu2xvwhldJNVQ9eg1EEFsi8ABNWQ4YCVeOHgJHyCf//gSlr1Pl20gBq0dNeLl06mPvdT6x40/wHlRKDqqvwF/9x9VbE27vpBXD3jtLcvgg/kEUQT8b/7eR6Ig0NJSYnWzenTp48YMYK/YoVA8+aqDV+WGCBgYvMx1T1MEw9oLmpaR4wBQqYhQzYNTTEJMO4oBpjHVQTp7Rb9yERuqde9R2Rfw+t3xOyEOhI55kJSGSvxNp+kH+mTFMw9DpVtM8XbbaUS/B2c2sP46eJ89EE+kWNuebMbzvOhPNcnmcKMw/a79sWbq5sTVeHfQ8Y8jsspw2Y3bD75nrzd0k8y3VXW2B5s6e0mPBllRRsEIvV2kyMRawSkt1v0wxO5pV6XL4h9Da/fEbMT6kjkmAtJZaykZThQNrnS280jiplvBs2nZCXVDdjVTaf5wCSyiugYvreRz3PLm113ng+UQHhGuS8w5oxVY36sCrLA9dWcoNzTD6BFyJjHcTmt0XzKg/F2q9zuoPmkkhzI3G4BTNeASCTR283LAhQQXJKMREAiIBGQCFggoGU4yALNJ5cHGXE+W9dQXSfnZPcQ8TafmGZ1Y50qaKxYA5DdjhVkAKvj2ubDmp6maj5waTMtGTb4uB/B7GnB4ny85HZLz3BQ6WTzUb0Z4ecpc7vFZ/IkMbebSZxPfACVkkSJADuINkqOkpfEPPo5kBTMR40a1adPn5EjRwKi2bNnjxkzZtasWdHDFQjHpGAeSGeNRNq3b4+LP/30U+D0NWD79euHGdK3b1+NRawwLyoqwhzu1KkTxOvcufPMmTNxxTMaY8eORe/QXx2FjHc54wJ4htRnQ0y/b7/9dt68eWyIxYuG2Lhx47D9WLx48cqVKxctWmRDgY0+/mfblYxgLuN8xIc4njWlzSee45IBqaTaEz3oEnOJuRUCW7ZsadGiBfsV28RVq1ZFj1VQHHN8nnfo0MFVHmdx2Js0abJt27ZNmzYtWbKEV3tAIVaYFxYWFhfXJF3YtWtX3bp1xftorIld7/r16x977DH+p6lTpw4b5vkgHz/i1LaNFebBdEmMSnl5OcYUIytWvaYWpi4mMN9k+/btuitGgkccccRnn32mVctZzF1BHWrlJHq75fpr/lCDHFzMNpb+S+b7cgFZcFUl+MFhKSkFhoAunxuSXLlPZBSYMJKQewS0/FQ4XRT+OUxzDTjb2ORD6LwZSlqtjYupD8vrFsvy9kXUfQR1GqAIx2RWAzasCp/ay6oOc3uD/xsr/NeAQY4lovESCinadvxKp79ILbqIC7Z+QqGWr28cdHWicXv3UnwjT5tmR6SsmF44Cc511WXFHgY6kIw+0uYjPsrxrCltPvEcl0xIFXJemkx0KfY8JebRD1FSMK/YSvWa1cKT6CQHScE8nNmIPb77Ix7FRKnXhCq20Y9v0j6aCpBqGCvMG7Wmso01kiHmx31uNyMcSPU2VlUpUXC6yyxOCxLDLoRascI8hP5ZktxdTnXqKgkM3RSTWJ3K7VS3kQMNRWeuqk0ZkrOYu4Fa1tUhIDUfOSVSCIScl0YCbYKAxDz6aZEUzPkMB0Ap0ZpPUjAPZzbiRPrKcChTQRPasZF+/oI69NVziBXmfHq3gDQf9BdnGcHUg7x2ccltECvMQ5pypmSVDAeus1rXnkaq0awsowInzQeVW+xbmx4wZzGPcnxteSXR201qPrGZPlIQiYBEQCKgIaCz+ST9SJ8cHtk9iFJ5zYJGAa5Bqz80MfgEzccvPf5IH69ZrU1lwNmmbYmmEmU4xMcvQAlvv7tCydcHndZNMbH57CpztvmABfIlVrmLKXIjl6zrDoEkhlpJzcfdGGdzbWk1jn50JeYScysEssnmk9vzHCe3bw9pnkPzWfcxdTE75CZWmPM2H+xZ4RkVXFlI9LF6kk/mS6wwjxIORfMpcKuNmHm7webjcKKP0q0m7WqTpOcs5lGOb9bxkppP1g2p5w5Jq7EKne6cbAYnu6iD1nhRV8e0SRoRa8xtxDD+ZDzb26ojprNDhJfW0Ccvz9MzsIaJmOc6gw86L73dApsBURNC9EMBEX9gsOkdxy81urXFciWBa9CvX7PMAaxOLeVp/UxXLZEb2XGhc41gms0nmDgfTYYPiS4gOsK1TLUNjOs2P0A8YkZk+CvV0/qlfTU8NXhGRhamS6vVim0qifMTxwdKdk2roPkEEecDm4+It1ujvagSepNaErGehwR7PMhmibcbusGXOGAbN3nigImUIQwE8OTQnZMdSCoYt6JaiaHJBoL8Z/4ru8448n2x74hpTavmOojc8nKLRi7WL99K9bn0BoAg0ZpPLg5hbZ+RuBNmnxWpCzaLjO4n58Vnxyba8yAduqa3p+kIiNf0O4B8hgMfJ5maigFXt+vUbAdmli+PgouvnN4YWA10dCPiTW6rVrD5uI/z0dl8lPy2+fXx1HIWTcmQkYf4LlnigED2eLuhJ1rJuD7HTqqKjzxxmGqhyCCtxmaw8lqE7o2dLiU6e5g5b1Z4LsKYxyX9eigzL1qiwphHK1Y6t4oSqofdMleadqTSNZkUyQ/vRGDup4O2bcvwYprTfIx12d3NFhD+V+f1ZNt6armfOXOvmOuYGqXyglNocT7fE2EDjeTIyG3HElu7LY7rtvMoqCwVOkNn6tZ/07amA+1W7HjVr/F2cxfno8twoHiEihh8UK1yGzXa6yUGgdd5Hi8ApTTRIiDq7cb0H2Z7YRKa2mGMF+2raZ3VqkXbfcmNQ0BajaOfDhJzibkpArogH9Rp2p5Kf4oerWA45vA8n030LRHiWuCUFWCp2VKX/EjNOtVsu9O1pnh5AfnzdgOGsyywg8Hnd0Q/Es0kqkcERSi8VAfOOmGm57mzhAFOQZ4Ucrsp3m5uNJ/1c2ZAbeGIKJqPY0prVv+372jXjjnsc6YxDwnRBJHNuHXEA1aimg9TdZjhhf/Mrmh6EW+cEalmbJhEED3gLpvEEwH25DB18lbmc8qkY3xpp10RfEFo3317MUzbmorNX7S3GpnWtGruk1c8hz5eUhm93SCfdHiL1yAJSYP9Wbl6nk/HVHUPd7cpp5dffplaHaic55NebFYwwZo2C51Qn00rMeXHU0rrPmokD9wFlxK9Q/Qo0W3qlWOJHiS6mwhxTncSQUFCzWPAHee6ipWg1m0bOq6eCOJjJ9a/SGopak+BktsNfoziZcM86KuwhbLSl0jR4AVtPuvnUv0WZ4jzkjXDRCB7vN14Q43WqzC6Z6QZBpcwBz2LaEursTqYOk9rd95rbqeDNeZuxXCMvbEXjW+u1WQXGSZ8c5+83IIUcP1EzHOjtxtQSK7mkwjMA55nNeRgiEDa5VVEN3H03d7dpqKde9uzlw3pDc8f3YuYGuJDZ2r3r1XPTG/kcGAgqtF8qmpPYrHiVL4F6dpeVbWa64nOVp3ZoN51IzqP6GEiqDXwBIVv2/1EXxMhvTGUoo+InlKjff4Isq17hNQLB53QYp6LKz/+RyQUrdUeTbi65ddzHeeTpyT40/JSQGXFsVdCNp/pZyp2ni5n1Bj3cnhtCWmS5wJZc5uPznTjBwijtxuzEfGOc8YrPEf+V2kR8jMWDm2l1VgAXPZcMbWfaK/rBMikqiQBc/HHtouOZ7BqEjAnU5tPco/0SQTm4cxJhGzjzJkiZOINnP6KN0844QQl5sG0+MPcZqHz2A+W5IBPb7D9ZyqeT8un08KH6eNb6K0LadoJ9MQ+9OxRlxJNIVqt2gGGEz1OtJJoENG9RG8TTSC6VfVq603UzqM0tc28rNtmTBU6aj493xIljYBHzSefxb9ppRif7FNaw7j09KF01G20Z/faZv7medKwjqO8SdyWu/B28wY5r0Tx5iPeTQ6U7RMYBKiJeeuFbJUjCHh7bukS9fhXFbyJEd4Y+e9ReLJlJ+Uss/lk5yAJ9Qo2H4RnYY+uFau723iX2WUXWD2DOvS9+Pk6p3WrtHdkFZIyvEpwb9u6mtZ9SpXb6ZtJNPtG2l0J3YYmtqOXB9KnY2jlO4SotlYH0MGjaMDjNGIJXbLsS6JXiP6lJm0bQnSYukV+jOiKQOUUXLeNJjXdFRE6/OCaDrSrnukoOEroirjrykpKa9h86hDOqBUvdfKRzI3XfBS/Ahtvty0r6PHOdM471OZocSayZgQIJNFRK3TNh8ddi+rRDYa4yshijSIYy1xkIa3GqUge+/AYV/sMY8hQ2hVb7wjxKB3l3YF1eFIgk5l/1obNKxCBLYkkYp4bMxygP9LbLdyZEQp1aD5Iv6sEn6SKMc5HW1J0P/FLjX4l+fFN2uc0vCPfts08zmfR6A2siU2v7G9kVwsdVZbR5u9pzYf0zWT67G764Ep69TTlDf3EDvTGebRgvBKPhNNaCo+guvXp9Gl0+Wq6eDGd/RYNmEhH30HdR1LH/rRHN5u3/m1UzWdcKKOkJ+pq7a1tLLy2WA20+NLKU3A3UoEDCFU2332cT15dE83HKsPBmo/oneE0+idqjFmQXoQxD7zfkmByETBRJEy1C+NFXl3RtBHjRftqxoamio1GJHC1J8NLhjZxHlFy2dPVDZM7kxIsuQQ/wYOXpaLPvJbaHEP7I20VV9bPoS/upzMRASFLAhConlAIKZF8DKmW5xOx5NN5VylOPQEUeIXBPLJlOc2+ic59PwCCgiR2bqbStbTtJyXTID7gf+XzWtpdTk3aK8o5khDir0nqg7ZV/fIhxezT4yqaegyN+s6eG4POtCBoagGRYf+bVjcwkAUxyfFqsMZALdnjANp3iKKNC5avnyz45M+/cV6gsIvOPeACatyWevExcaSYCle9R4NxJ6XK/AcUN6H5D3gY6ED8KUpKSjRZpk+fPmLECP6KFQDNm6cfVCAIVOyrJdEgYWLzMdUuTFMRaE5o2tAY3dJMHdVsGpqOsrF+7CeDFFAiIISA7iWf42taIaLWlSJm51Pa3G1ubvNJ8pE+uTqWSOyGP4szd7yCsnYW7d1LMY8UNLGM8+Foe7nrjUE4zx9PT3RW9BZ4rC17UdF2oOd0O4/6jKdhc+mylfS7T2jwc3TCfdTzj8omGBLyb+gbtqYdG73lduNhCtznzesYRNTOy9hFJFqKDcvt5vYk02olBzYf/KZ4u9VtrJd97jja9G2a2hNx7yQ7JwQCN0g4MQzg91x3HpM2n9pJBKtxbgYLZtDmk7OYB7B2eSWRCMxfG6K8+GzLh4eo/cW78KCMBl7x89IuEZh76ZhdG2a4GEM0kejnVEUPb6lNeMz+k5K+7MDhVFZMLw6gEV+Z1BHBHJtPxXSjGm10ZhykYlNsOJwZB2fp4opg3mGjQAhMgiNc3/GWAvOqmrXNB7XYiaU2xoVgQPYwH0Qw90A25k2QQxyGxxb7UKeBisYrWBb9p9Hcvyln+KRKU2TwO+xaRYnSbD7vXUqFh9OhV+tJ8jYfl5hLm4/g+GR3tUjjfLIbysT3LjfVnswOm8Q8evwTgXnFVqrXzASbhIb6JALzcKYiztY0G0h/zFiQD4qNzYfH3DEIZ8NcuA9Rm6Po8Ostg3Ba7udd7YGoLKs1n9vNKwbxNfvk5jz3ZvPZtRNpD7UCd3/lMCBkx9bKSwMUPcqo9uimTW5i7vXeCaOdeKB+GNy90cx1m4831GQriYBEQCIQHgKdO3eeOXNmUVGRjkW/fv3GjBnTt2/f8FhLysEi0LNnTxBcuBBH1ARTZs+ejTkwa9YsRo53st+0adNqs7Jz585OZqVdO/9JoUU7tWrVKsxeiN2nTx98Fm1mUW/s2LHoOHDwSUc2948Am5BYrDC+iHgRJHjbbbdNmjRpwwak/1AKpi0mxqhRo+A6dd1112EB/PDDDw87DFn9TMq4ceNQDXNAkFfg1WScT+CQRkxQ2nwiBjy+7JKouMcXTTHJJOZiOAVZKxGYb9mypUWLFsZuY3vhf9cYJJpitBKBuVhXXNf65ptvGjRo4LqZdYOXXnppwIABc+fOff755//xj38UFBQMHDiwe/fuTZo06dat2+WXXz5lyhTsIx988MHhw4c//vjjy5YtQ/43iPH2229PmDDh1ltvHTZsWO/evaNUe9CbwsLC4uLiXbt21a2LEyz9Fux6J06cqO2b/ZILqH1uzvPy8vL69etjWDG44kCWlZXVq1dr4cHcwAxBc4wp1J6VK1daqT06FrmJuTjOsqYpArlu8wkyzieD4SIJmt1ASaawS9B4SVEzgsCEQtPk/Ujpi+tjA0wRlpHehcPUJiFYOAydqSILGQ7ibKTmdnNb4PyDozyNfyugRRDto57yWaQe6/kfItiV8NUQHu6WZ7j1EcjxCdFQomVB8HlTDaB6w0AqY0E+QXQqkTRWzzjt7YuQc+9wxF+JRSHiVr2caBbRD6kOs9HEnYIZgpN/7QtbBseJ8dKRknE+Tui6/j1Lcru57rdsIBGQCEgEJAJBIVCx1Sr7KXa6q4LiIumEj8BcokPV3G42BUHeS4neIXqU6DaiC4iOJWpPhCiIAUR3Es1W93nHE91O9A8inOOIfAlz1HzZ96haUC+iA2Ov9gABiIr8XfkBwc4yHGDHLEuGEdhdjrmKYVUCdYQLpn0BVxm53jE3vg76sFphcWRF7wgkMbeb9HbzPt7Z1lKeCBb9iErMJeZGBMq3trCApSihmk+uznPoJ0cQVaijuYnoSyIcxvQg0fVEZ6vvyPdU9YHziB4mWkwEjReJy+4nmkcEzyGYdz4iekq18o0iOpHoM6Jz0+cGDCmlZrMF1OJWcKjqxuA0H/QudqkOcnOe766E15pPzedx2LGJzvcwZXMTcw9AySYcAlLzkdMhhYDMkRL9XJCYS8yNCFSUZJvmk0vzfF3KIAP7DCwSLxKtVM8t6UYED58pqgMbPNPgBYfdHvy+tuGoRqK3Vb+1W4mGESGXuVXmgZeJzkmfMKAMCsbSI/o7y4kjNJ9fA9V8cJ7pFfB6cuIb3e+5NM9rUa2q8KD5YNJq8V6DVItl+rHNwoOWm5gLwxNBxSSGWknNJ4KJIVlIBCQCEgFhBMotNR/smLFvliXjCMCx50eimUST1J33JUQnEXVVN3NHEeEIesSfQAXC8/UPRNBj4cyDTT/Cfl4h+hfRdUQ49wSJq1q56Qmao35RehMrm48bwhHVZd5uAeQ34OQdq8aH1CQIi6gfkk06Arsr4O2GYXWR30A1VDK/R7wRuFa1fCr5DWRJIAJJ9HaTGQ6Ug4ODKUnPcODyRDCPoHEZDnSxhizbBLuoyzxhvIgrfB3dV0ZHMH2FjRi6PoKgMT7SeNGGr57X0Jk4PdZUAA0KXgZXvDwOUNY3i2ae+4Fx5btnvjtiugWFzuqGu7Ma3Ws1c3Q/aRNS5DZhdUznualENndE7Y2gYm56a/vBSdfWPsMBfGkUWNLbaBfZB62watpFGGR0mQYQaQMtFMrPSPUD+4Pyg82fFseC5rDPYF+OvR3UHv1ikhIG1UyzWRiR+TMR9BzEAvEFcUFwkIOlSFfg7RY3s89fVG83pPeGL1+ARZfqwG2GA+OjhJdN/N5R6KjrOWtu/8zi7xr+wWfKWoeV1V0scncHCHstqa+f/MMnf0ZyAqjlt4hlHcCtisi0hkRL1Nie/Yj6qxP7f6kMLvZypmU4cLmeywwHocyBpBE1sfnAdMWX+PRIZ1OLp5Dxgcu1JNFajdkyzf8FsiS57bWVGJpgygYoJScjrhPbeNG+I2ldntbPvrlPXm7RyIn60c5zL5Bae7uBWlEq1MfmDtL95O3OMp17pt1xrhl/zNMNMtBGEITDDDLYolkF4eBXPggHX3Xh+8eoiQpMMxwIajs84EZXN/xqZfOJm9oDUfFSH8FOQWU40JAJPNWB96eS2Dy3uj2d7yMvq0n4bVSbj9s4H7wLgM6DPAdQe1DwIsejzUcM8/BRyF0O2ePtBuuVVuLQK6bk8DOL5dGLlZC5O/ED6rn2dk33ytnmFVdAnNPICFqKwmAtaUoEFASsMxzwmo8RK+3lsW4Om9pwwoba8S4OWwAjfSgbeIqwrGgIwsFnBBigIFQGhQ/CwVcWhIMiGITDTDc62xEidhACgZAGXGd/WmGftf91DY3CY5sIUmybyJcEebvBmv9bCJoP0PCc6sDKwlk7TGb2T5OpZWYpNb3vjI+zxD9xdruO83lanQnIUqild4O/h+LsIUsCEUiit5tonA9TPHgNxNTkYrxoX612fUnVMx13puEkcEokSmSZIyXy4Vq8OIYZmCJHIWKGMZ/n6+fQpq9b2GIyKzjEItJPosJ8tnpICF9wZZSaFQ2FRZiwIJwS9fM16v+K42C6zQdf3QbhmI4JbD7ssEY8vdifqVsd+8m+mBp80MQqw0EMV5bwNJ8IUh2I3CmZXc9FJHSaZZ5+L/kRHoxr7bNaV+2iratp3af03fP01gUIdUOkHL/79K75RLW2eIJGNoopAqKaD8TXzCz8Z6aTaHoRb4cRqWZsGAcTU0zHKmyxorUas2Wa/+P7py3iNr7LgbzJthfDFHJTsfmL9u/w+JqHTsQjW90YcVDwzX3yCnvKJJJ+tPPcNUSla2nbhlOsm2leJR6mrithbG5PHR2rmrV38bR+0bzYhkkEcS98gUbRhwgRJijwOsNX2HyeU20++MxsPuwAUM0sIx54Y9RkNNYgArWHFSg/LLG1n/KSIZ81o5Ysbze86Q82w4EG6VhrJdAKdu3h4vNRotHBeq7zLHVFWfyO8zORQmibt1J1DT0BpCu30W/f0eoZ9M0k+uwumjGaXhlMT/egxzvRG+fRgvG0YS6VFcMVszmXEcGt2tOXCH81JebreQhwx41kEjft5poPb6jRjC1hWF2MNMPgEreJIuVhCOjcmr1FI/gH060YjrE39iLxzbWa7CLDhG/uk5d/cCSFqBFo2oGqKuEHYlVaqIdjBngHWb1lMJ17plKJ1wwbTLxIQHwOf7oldkgjiVg4nX2EiWaWcTS/GHuhKUu8LgRXN1ag9iAQQuft5gqK71UiB5m1SZa32xanUXAFi67yjSnHRT9EbNravI8TaeUolf/7yJuEjoKZVNjxK/2yiFa8QYsn0JblSGOIpIVKvvXJPRRt55vJtOlbqt+cOg+iY8fROe/R6HV04ed0xivU7yFcxKSF5qOdfOpW88G7DNzXssQEgSRu2s01H53pxg++Rm83ZiPiHeeMV/xwlG09IhAzqzFbxE1fFWvvxjz2ND7N0jF39YIwPp1ImCQxm+d69Jp2pNI1NpDCcIFtdFJKzV2MnFdRldFqOjWbwpQTD+qN2x4wmw944XU44rlFXNqsWFi5uqF+gk4yBQ4IeXLhZ+IWcff1g3qUMDqLRmdZeu1qKv2J1s+l71+kBf+iWTfQG+fT1GPpsY40rQ99cguteI22F1ODPbAi/VU9eJcuW0FDZ9Opz9AJ91LPa6jrWVR4BDXeWzcyO9I1H+/pDUA35uu5+zkpW0SAQOirEK9E8eYj3k0O/ZS5CiIYbAcW0VqNvVl4As8I502MwAbLgLlUfgLD1opQtPPcdXeatld2G9YF7/5rQkfU/O/GYpxCVm8QtLY2bxlcy2/aIELMkekL597EYQeq2Xw0bzfHNAYBaj4xzO2G3rWwjwYJZrYJURF8lOjuHeOdwtOBt5vpAs5fNL09hSROVdJRcJTQmfiuHbT5B1rzES2dQvP+Th9cTdPPoCk9aUIbeu1M+uJeWjubdpdT4eHU8/c0aDJd+gON/Eax5Jz8BPUeS22PqSI6lqilM6eaGmXpM8GtzSeNT4Rri3D/cqti9ni7hTRuWlSPjn4SgQsJotwha4xSMJp3XMUGGEOGrIKIeJBFxNANStje2PyDLWxeuTPfktTTph1WCWg+NlNX9xN/H4ncFIy5+Nyzr+nqLg5kmK5Qk33ZFEGDD59+zYqa0YLErnQiYjF8+Ao1rK21oYnVt1GKVqsmI3jxmRarDAeBIBk4kWaIBAmcaNAExYM2vXG2uj3F7ziegov7a+dm2vgVrXybvnqM5vyV3ruEXjqZnjpA+XtvFC15nDYupoLG1OkkOup2GvImXfUzXbSAzpxOJ/2bjryVDriQ2vehFl0pP93qnFcXmoyrzGyw+bTw4e3mDXbZKiQEkujtZnKSKctkoMPIeJFXV7T6xov21YwNTUHUqUasjillt0PrYtVwJC1PMnWECBW4k0xFqmd5HZensGU5GtF0L/6YvzZk5vo5fS3QYCdXXiB2YmA0iDpziQRz7SRTaBrQE9Y7ixVWDWTNRvo45FFgBSeZ4qsxIbUg+/Gq7vSARW0YuODgN9/wK3K7xdDsc6jqnveJYM89VXN7kqknJhaNIpnnzgJvW0/bflJMx0iXgv/ZZ/wPjQVhhE3aEwzL7H/lrwPc1Zxp2tT46vH8T+/AGb4oQuDPf+DkL+7HewFMXZxkiqId0TtO7CRTJksNL5eYB+LlUVLCckMqZfr06SNGjOCvWEHVvDmCm2SJBQImSk4s5IpKCKn5RIV0ik/mNB/TJS/ICZAOZcTsoh5HyS88BGZe+9R3z4+0oH8u0TCk+cqE5hPzKa1pPkAOyiH0AXbGZSDF1CZjZTtCsmxoO39IMT5YPUHIND+BiGzwmoPyo2WK0zVB8gN0lh09FP+C9F9w1/wsTEGFNt9hChAgbbs7bneFotgoWs261IeUqtOkTUqx6VCr5EDVKWgUoGy1pL58qMm8v5eq34XAn//AcV/cfwTRe0RL1VaXqdP7UiIvmo/LLknNxyVgztVNjSXOzTJaQ2o+aam0fI1F0m0+vjov3Dhzmo+wiLKiRCCjCMx/YMwX94+1EIHt6U/PhOaTUVCcmfOaD9K7Ic8BLC0ZKdjVgbvmn6b76kokR/uVYwVX7MKuDF0Utrgvw2QjtPkOU4CAaZeX1BpwtkHJSak3FSUpA067WksODDgw49j5TgYsnUJu3t/3/PKhjSphIfDnP3D4F/efSfRMSmPX3lNIzSeE4ZEkTRAIPcOBRD0xCMgcKdEPlcRcYm5EQCzOJ3rkvHOMfJ5nMM8BbBrw4eHDcvyc52OT1Y0NR4JyuzFp/R9t5H0eht3SzzwvK6biBbT8NVr4b/rkVnr7InqhLz3ZlZ7tRR9eQ98+SyUrqWFr6nI6HX8PIVniFWtp2Bw6/QXq+y86/Abqdj617a2oQBGrPYB0V1kDl8AiLggOdp6zWqdx84O5S7Fl9axBINdtPlkzkLIjEgGJQHYgMHv27DFjxsyaNcu0O/369cOvffv2zY7OhteLsWPHwg0DWIXHwpSycfgwWOPGjevTB8eQuC4iw50gb5PLL78cE/uHH35wDURWNNi1a9dqtaxZs4Z90Err1q07pZeOHTviQrNmyAoR6+J2THEvTJo06Z577rnttttWrsQhqNS5c+eZM2cWFRXhJ0Rx486NdYeJZJwPP0AJWn80saXNJ+a3mBRPIiARyC0EsANYtWqVVZ/Ly8vr10/QiT4ZG7vRo0dPnGh/tE8oss2ZM6d3by2jtcIC44VR88Bs06ZNS5YscdRymzRpsm0bTspJQGncuPHOnTsTIKg/EUtLS7/++uu33nrrkUceufXWW4cNG4Yp0a5du4YNGw4cOPDuu++Geowt/vHHH3/77be/9957O3bsWL9+/dy5c59//vl777336quvHjx48MEHHxx/tQc4Ye65XZHKysqg6UEPZDAXFxcXFhb6g1y2zhgCWZLbLWP4ZYRxnucjFjIibohM45kLKMQOx4C0xDz6QUgE5jZHbfoJGokebcYxU5jr8hwIBSH4xwh+St1HUKcBtZSMVwS54HwVJBruY5XXLUVl8iF03gxqlL53dJnzSlAiv9VSMSG6seBjtPyyiLA9ooSRc1z7g5fjFvUrdvRIa87+OnKfkdw8+4rb2C0E82BCbzvvQ3rrAhrxFVVup8kH02U/KsjMfwBnO1Kvm1yg5HKeywwHLrDN3qrS5pO9Y+uyZzFMgeqyB8mrLjGPfswSgXkR0SoLaGA7SJzFJ1OYIxVEBow+xfOVc+v5gmzCOAjSQ/nxTdpHIEFdvSZUYbD5xPOEx4LGibP4IIMfbsbZRFOI7iK6nOhkNXcfgluQte/3RNPUtOP7El1P9BTRCqKtaqwX0mz8h+gWot+pucuyUu3BpEZn2dnK4gXn+VDDVlStRvrs2KjEL3ku8ZznnruTwIZJPJBTaj4JnGhSZImARCCrEbDRfBAg7nafkdVQ2XUuA3kOtqxQTkdpkH6cfX4B7XZ/gCfehf/8BXXo6zx8BU2oMhneblS3kbLrjWVB2P23RO+q2vLtRBcRHa9abPKJ+hEhXGwGEfTXo4hgkkDaQBzpUkz0OdGLqhEDqczPIDqUKH3sY9nVQIWC5lPghiBWMEBK+Q1Sms+v1HBPNwRk3XghkERvN6n5xGsOZVAaeKTIEjECEvOIAQe7RGCeZZpPBjG/guixKCeZ0eAD7nXqUZX7lGaCBh/QL2isuAzpSlxzXtVVdYYMlk1qWu1XiR5UrTRnE8FCB6MD/s4heohoIVFjokFE9xB9SlRFhDD8WURPE92pHj4DR0acS2s0vWZwnmcQT7eaDzTMhhA3rw5VA1qisl+pkQ/NJ67zPIMjIlk7IiA1H0eIcqVCpjxScgVfs35KzKMf/URgbqP5SG83V3Mmaoc3WGn27qWXML8e4dxJt2XFG0oKY5FiqvnE0wuoehdO04SpJIKyluh/RM+pCsyVqiZzoKrSwFENHmtwXUNADuJwhqtGHlh7oDviYM13iB4l+jPRhUTHESFLtHhJxNoi3h3BmtBjoc2KF2g+yomqdepSlZrhQHq7iWMXy5rS2y2Ww+IkFIKJjVkOtIvsg/bHiOm+GjnoCNp/1TXXKhulcuqK/F0iIBHIBgSKrON8pLebqwFuox6tg4gLD0UXDI2v/J9GMK2aqvnoGv7hxOa8zYcR4eXhv7Jfyx9sUW/DbJYmQcfXJEQ7Fedj/IldsbruARO/Tap2Q/cwPT7cz4MYfmhPEP2VaARRX2RJxr6a6FjVXe0tolKinkTXquoNDtz8lWgB0Suqzec6oiHqDDEaHeSDWHCstzDvNeECd0dF88nLl95uwpjFumKWeLtBgeNLfCA31SwjUDcR4Kj9sdUQX7X/A8eHEY++5KalPnqceY4S8+jxTwTmRdmV4SCzmAdo9sm7+hftz0QDgctZ6VraY3/drFZy/orZfECT0a8/+KmKToM1FjxffNbfNaZxPvH0AqpWNB9vNp9FRK8R/R/RjerLR8TbsGR2cDy7n2ieGm1yEtE4opnqKZlr1F+fJfo7EebAKepXZc8tVjw8iDM7z8W6FXAtuA42UR0CxUuNt1udfKpSMxyUbaRGPjIcxHOei8Mha2YCAXNvN+hwWolAtXDsONPEdNVMLzqSMq2ANY6njs8eVj0r1oyaqQHHaE0CEVZT+z8yy09uWuq9TZigWknMg0JSnE4iMC/KLptPZjH3lueAKSE2OXBNfjVzdQOF8TN3/f20tLQUppQZx5qZrAb5mCg5phPdVPOJqbdbFTbKpjYf9Iw9KOeq2dLuUz8PVvvLzvKEPedJIpyBCjseysOpmL1lRO8TPU70F6KLifoQ4fYxPjcjeBBndp6Lr4EB1sRQtlD1TPGSsvmk4nx2+MtwEM95Lg5H8mvGQUdwi6JonA/rG69smNqFjBftq2niatVMO8DUMN1Pphfd9j+z9Y3WJE0ezawUoA6W2c5K7hIBiYAgAgg/WG1RVXq7CWLIVws1z0GtGqOmN0hTYJgQdQoqKlzG+YinNwB90zgfrv86RctEQg+YemuyddUWNR9abVk/h2ZeB1uN0g/1f5h0kH4AxgSUq9T/16n/L+JsPvh6ZMrm400QvpV8EHvGEJpPS5eaTyrDgebt5i+rtWfRZcOAEMgSbzcrNKCcaMqG9pld0fQi3lgEOo7VjA0zpT5qZhmjwYe3zAjqIVZGHgWTQA1KAU1dhUwOWuoDRM8bKYm5N9z8tEoK5kVmZp+Eqj0Zx/xPqk+OeNF0A3uzj56gweZTQye/3tjXN+vMR3aUDWqPVYhRjQCm5/nE1AuoDjyj9EmQ2xyFLNIosAbg0Qmbz/OqzQefmc2nqfp//B/EGZ/n4jM8qJpwXGylntwqXqAp1RjHWKiPzwwHMZ3n4njImhlAwNzmwxtqNH0uDMXO1JKTARhsWfIvhNzKpuk5prqQqbebWxZB1c9BS31Q0HmmIzH3DJ3nhknBPJs0n4xjDrUHloTwSo0a8/MXpVNPNXFRs8jtZqn8GDQfL3E+6V5AGq9MGnwwAM2LoMb05keibW/af9iJ6hX7FGHxfxBnfJ6HN8OtKMPms4dLmw9OgD2akWOJraW3W/TDFijHTJkr/HTCOc7HD3VlbnOFkWI2It5xznjFJ1NvzZlyImjV8caCb8V4aX/+CUoKEgGJQNYgUGRm80liSuuEjohmZhGU/+uvv6amHZo00duWQOfhYa3+cLyb9Fcr3qQuiE4SLgk6yTQvH4adFrY9kw9i4YHPfEVm83EV5wM/RjRRCpIcIPNH+Vb9yb+Z75aUwAUCYRhFXLD3VFU0zscT8Ro9R+cCx5Qf3k3O9IpnjklsGFkaAxtwctBSn/GpIjGPfgiSgrmp5iO93SKYMFqCNbs0bmqqaN68c/Af3rvi3BP4Kxqda14q//dHJaYWHv5izefVM6hDX4QGKY/F9OTXln1Pebulsdj0jU5C9qto1oSQgK6Tv9NJ8wmJswhZnw/ipKwtIlAI1oHNx7vmA2+3sl+ooY9jTCGl9HYTHCpZjUMgdM2HR1uL6tENQXyMZYIGHz79mtV0Mr640l1hX9mfKV+tQjQzNgct9dEAa8NFYh79ECQF82zSfJKCueNs5ONt9CpE8fxjjjnGnALUmKpKR+KoAJoj9/rgqbEXMEYaC4c4Hy7DAVNvlL9p/TKs5Jh2OK+uiOaT0Adx1sxzkbnK6kDzgeLi0eYDzUcJ8vGn+cjcbuKjFU7N+GzgxfvnUfPh/dNYGgPlBRXnxsawcKxmbGhqONNc44zJ5ZSnhVrE+6yrabXIsutWOgnzUrMvxgq6K7yrm/YTX0eEi5MU8neJgEQgeQiYaj7S2y2CgTQqDOyKQ7DNz1+Mer8bL14tnXyc54OhUyho/2s1GVnt66RnXxz1aR/+oo6viT6TnttNq2/EKvO6UJ06AKKF2SjKB3EEcztwFvB2w1m5rjIc4CTZGl0H3m7bi30d5hN4fyRB9whkibebaTeskkrrUkvbOLbxFLRqGsjGKzz+PFk+44KRnftR89uCz1KgffZLNBPtc9BSnwmY03hKzKMfgqRgbqr5SG+36CeMEMedm2nnb9Sii3nl/ALaLWTzobWzaO9eSpZqp8JbgZbd3Wm/ljv0rnHx9ALKy7fSfJx67PB7HB7ESVlbfELNN4fKjlNIxW0+WMHwVxMMp9h8/B3mA1HiOc8DhFiSCgEBjzafECRJKkk+S0Gi0xXkoKU+43NOYh79ECQF805mR/ok1OaTFMy9z0b1JB/L5imbjzN94WN8eENQt7+Xf/+TEkeURj+eXkChaT5xeBBn/zw3zGBm8xHXfGrTG4BUjbcbVCcfJZ7z3EeHEtfUj8tVpjorNZ9MIS/5SgQkAhIBOwSKDOndEmrzyf5hNpzkk9Zl4TgfEtZ80ug7nWQaI/yrKurFSBopii8EkKYPOg/SlHvUfODttmOT3zgfXz2QjQNAIInebjUhOgH0XpJIOAJavFbC+5Ek8SXm0Y9WgjDv16/fmDFj+vbtq6E0e/ZsXJk1a1b0uPnhmCDMvXXTOFI8HYzXuHHjZs6caU/cz+AaEY4n5uPHjx87dmxpaak3nGPeKp6Yhwfa6tWr+/Tpg+mN+b9y5UoRRvwkLyoq6tWr1+DBg0eOHMna4jbBNhozRIQUq5MRzEtKSjQJp0+fPmLECP6KlfDNmzcX75esGSoC0uYTKrxJIp5ExT1J+JrJKjGPfgQThDl2BqtWreIhKi8vr1+/fvSg+eSYIMy99XTOnDm9e6edz8nTwZBh4Bwpv/TSS+eee65jNdMKOEdo27Zt/E/xxBw4FBQoCbuzssQT8/CgLi4uLiwsrFu37q5dojkOfv311z33rElwgIYbN24EBT8S5hrmfrAKqW0Svd1y3eYTVq6bR2AHFi5XN7SraiTltj6o2zcRllRWJJuRlSDL+REsAvMfQH5M6nVTLVUc9vLNZDr1mWD5SGq+ENi4mGbfROe+b0nEsQIyhU4obEu0gKiNJ1FY27ZXIewi3uXze2np0zTyGxspAYWfPuTFHwQ/3YtVW7Yc9R1PLw6gEV8JibZ0CuF26POAUnlq7+5bVkwmOjzVcpyaMncsLDncIPLzwf/gip6RZdsZafMRGusYV5I2nxgPTsSiyRwpEQMOdhJzibkNAk07UOnatN+RHBnh8okr2T3P7dMbYLAEMhzMJSryqvaAA2It9A5k8cQcE1g9pDU7SzwxDw/rso1KTmokKqgWjvRBCsQGe9RIlFcHGa59qbnyGRre4GY1Zan5ZPXwuuqczJHiCq5AKkvMA4HRFZEEYW6i+VQSUiQnriQIcw/Y2qc3AEGBDAcvE53jgXWqCdIEp/m64Xo8Md+1kxDXnq0lnpiHh7ZyDqlbzWczNWiZ0nzyf1NTw/kquYa5L7BCaZxEbzep+YQyFSRRiYBEQCLgF4GmHal0TRqRqgqqI5Nj+cU14PY/z6e9rVNag1l+PdqNtHx2xafmY2LzCbiTAZHbvYPy6gZES5LJNALsNJ68OlRdJSpK+Waqn9J8iGC/lsuZKHRxrZfEUCup+cR1NkUvV65Z6qNH2MhRYh79KCQI86btqfSnNISwgcY2OnElQZi7xbasWNFqYJ2zKY6az8bFrVRvN88lMd5u2W3zyeJ5bjo1mc2nTl2qEs1woKaxxmRnpTrl9+Z54kuPcR/Q5XBTqfnk8ODrui6txgHNBf54dS2ekl3Ucaie1k930f6r8qwwELGS2kYM40+6K4yLKQVTdiK8tIY+efkdpWTNc53DW0JtPsnC3NUMc3R1AzWY6TBwNuXHt/y4uoEwr/nU3F/q2mJcT7S7j78f9euScaUyWxBs1iKre/ys7lWKiYArpgujqxEIpLINUPxPJms4j1Wr7qZ0dP01XQlrVAF1yPg/m+XddNQcJAwELI1Ijc3HVZwP5+1WXVVr/fEsWBavLZ4xibZhlni7oRt8iRZDO246fOMpZHzgkpJkBAE8ePjj1fFZXFcJUGArMTTZwIv/zH9l15kwfF/sO2Ja06q5DiK3vAIEKu6kdJpPQm0+cUfZh3yOrm6g7Wjz+fHNoDQfm/VH95O3dcn0zjWFz7RmWVmZEhCfhCK+9HnrjdVwiCPsjW+QrViGAwRuVQlnOOC93QLRfILsj6TlBYHs8XZDT7QSB32OKTn8mLDjq2IlpJcpE6s2uWapjwp8XovQvT7MG5p2uCF7FnrbkTj2JqwE7o6M41YhWfO8WUfayoX6JFTzSRbmrmasiM3HXvP57TukQNjPFVNDZZMMBynM2Y3P1pa0Z2hoS41NV97/assh7WvdNY1S+YPBY2vHhVdwWV40eoOxpmlb0+HwKH0Gm3mI8+Fzu1XtbuFf+CxeW/yDIylYICDq7cYUD14DMTW5GC/aV9Ok0qqZysk0HP6nJGqZcZ+E0moc/QhJzCXm9gjovd0qE5kUOIvnuWNKa4yvfW63H9+kfU7zeR+YxPn4wFy3WQ9SP9lVVqeO9a5j/RzCn9cym2iW17aC7eyR6dGjhyCd8KqFOHZGof16uzlpPtx8sBxcH/M8vFHIKcpxsI64BVxU82Fqj6aBmJpcdBf5Jqyhpj7pbDV8wySC6BZ0WT+LEWAPHitHbe2xZNxMaFcE3y/aY2gvhvn7BTOx+Y7YW41Ma1o1N4VInFcWzx9916S3W5wHW0TtYfLbKD/IjnXQKJ+9HEh0jErCw43virVgFApomtesLFv0025m1jZRqNr2plXvj3clEFd5GFE3922DWnht6Lha0sURdt/XQFswtYcVwSN9dnJBPmhVr9mR6RL1JcJfbancToseYV9nha/WBopODhFLoh3CXPPhDTVar8LonpGmBy5MccqhiRZSV6XVOCBgXcT5hIm5CzHYw0sN79H+ap5ohitWIBnbajTZB76hT16+BipMzH0JZto4OzSfZGEuPooirm6Mmo3DW/eRtTtIcdbpNaH2nJy6UnNzDZ3JPnjznrV6RyMehWJeE3vZdMd1fY97j/2e6DH3OLxJdLiPo2DFGdq8vbI5mVp8IMQRtlmKLXVL8X461kzTfMQSW/OubqDfqHWXdC59dJpP60No41cOgmTr2uKIv6zgAwFzzYcPofFBXN1OcYWRYsYf3nHOeEWQKSMi1R5BuByqSatxMDg6UGGPwBpNIB1z7W1fJIJEx0T8qR+FTMma57ojfRIa55MszMVnoUh6A0bNMcmBOFPBmvHEHJoPNgX8GmjozkT17f5UwW6mqqHVaJdNtOpBLbwsV6c3VdOr7Jlrx9IbsCKY5CD9MB9YihwSIzQqBO0N9l2M5zzP3LBEzzmJjlouvN28AcorUbz5iPd/A2UPuQp47ztvsslWEoHAEfD22GO6EP/njQ7/LA+8a34Ixkv58dOTiNvqjvRJaFbriEGLjJ24zccxsXVAMlutG8Yb0DF6x14/8Shv5TaRhs8R4Q9mHMGCzfECIg/BUoILrw4rIzIidPghMB0Owc6yajoKjhK6Ii5U2b+3W14d55RwrQ/ByMoSZwSSaHsIXfPhB0yL89GNogeVUZp6gr8TpNU4CEyNfvY6Ry/20KphJYC5MWTIKogo7V4zhBsZxdB1N2z/cv5RHTYvu5EUwDyIiRAcDd7hrSqZGQ4Sh7nI6OGQ2fwCUl9LOxfU3F3pXM13DW39QZ4xdpdpN75uaeIXBJElhYkmfucaa5aUlFBBY6pWtruOy9EbRAj4mSUGCLzjrhCrKV6Ll99RWvH1XBPAajjEEeYpiEoo3n/Hml40n9+oAXd4aR0nmw9k2OvQ+faSZOXa4gi+rOAPARNXMVOlwniRV1c0nc940b6asaGp+qhTjbRkCXzfvemdYa0Xj+xwMS5XN7SrbCTltj6o2zdxIWvOV7UZWQlyzs+OUAB4bQj1uokQ/I3ywZXUaSDtOyQURpKoKwR+eJVWv0f9HxVqNLU3DZpCLXRxDbVNqyeIaVC2zPKuKhYSJlOVoCu+Opjqt6ChyNRlWXgojiCCGxsCeOxLW9Xm00atFHcQnPqSmN/n/V3RYw+7VhH4qf1p2Fxq4HQw6eIJtL2Yeo+t6eN7l75kcZJV7SCunnHa2xdBDR4HrRv/+57hPp0pmOSKDp8q06dPHzFiBH/FagSbN2+emMF1I2gS7RAmNh9TFcI0FYHmoqahZAwQMg0ZsmloCrjRZU53xZva42ZwZV2JQEwR0L0jDNvRPGJ2MQU9SrH4I31gN4D1QJY4ICDu6gZp7RNbx6E7XmVwsSBUwObThNl8BAve919AhJwHNiWy3AaCMgdezQXCgfO2IbhjIzVMxfl4y+3mGOcD7tLbLcox9cQridvvSL3dPKEqG0WFgLQaR4V0LZ8gMNcFCLGv4XUlYnbBdyQIzIOXyoYi7+22u5zy60fKPRBmicNcpNfiKa1BDaOGsYuyRIW5iwWhvITqNaXqKlcwLFPzfdmEufvJbeBKEufK4WDuAmFnEYOrwWc4ENV8dN5uAnE+jkkOwsE8OJgkpTgiIDWfOI5KZmSSOVKix11iLjF3RCAL4nyyb54j4GrTUmotfHJl9DafGGJevlXRfKpc2HzYzbGeaD8i09wInnMbON52XirEEHMv3RBrw8f5COZ2053nI2LzUX0d7ZIc5BTmYiMTcS0PgfoRS2hkJ1NCZ3wIpAASAYmARMASgdmzZ48ZM2bWrFmo0a9fP3zu27evxCuzCPCDIiIJhmzcuHF9+uDAktwtkydPfuONNxYsWLBy5UoPKJiGEwBVONuMHTvWA0HZxA8CnTt3njlzZlFREYjgf9wRnTp1sieoW75GjhyJKwiSsW+FwWV76/gMtIzz8TNz4tBW2nziMAqxkCGJinssgPMhhMTcB3gemyYOc+wqVq1axXpbXl5ev37yvN0Sh7nj3JozZ07v3mrOCbGCUcPYidUNplYMMd+yZUuLFi127drlrYelpaVNmzbVtZ04ceLo0Z4P8vEmiGWrGGIecA85csXFxYWFNWk56tatKzKsv/7665577qnREGx1xBFHzJ9vmeAtpzAPbzRzjXKu23xCiYjQpf9yTPmF+iLp2hzp5NrkFekvGwsJnQhWsk5sEUDiL5bU6KWTqc/9LpysYtujhAt22oRCbLfFD5A5XT1n83TfmamSDdv8B6hiG/3wCo34yrQjjgnu4NsG3yc4v7GC3AYI8kHiL1ZkVrfopgdOpJ18MF32Yw1Hp9SFrFqbCYVaCj58xR2B0RzteFOUFdOLA6j7xTD6KFku/RWZ280ffiatsyS3W+C4SIISAYmAREAi4B0BLdQnoRkOvPc8pi3nEh3jRjTY6SK1+LiRLbq6yHBQv7mr3G462ZC3ehZRt9TVGOU2iA7EeHDiE7spSmcdkcQVm4haceLnEwmFfLEjs6AzyxJLBGRut1gOixRKEAGZI0UQqACrScwDBFOQVBIx1zQfeZKp4CiHWm3LCmzg+D2cI7d62Lk5Vgq2QgznecVWqt/Mj+YDhJDq4DkinPMTr9wGbOxiiHmwk0qjxqc3UDSffOdhrdyOuwB/WhHVfNCg9SEEy49pyR3MQxrKnCQr43xycthNOy1zpEQ/FyTmEnMRBLQjfXZXUD6/eRBpHIM6WTbPi+e7MvhgADKg+cQQc8Xm00LEOGA/ZeEi9QARoqyuiMHUThMhhpiHBFHZr9SoNmJHSPPZuVn3ssCN5tODtv9s3pXcwTykofRNNomhVlLz8T3skoBEQCIgEQgVgVpvt2RqPqGCEz3xn79wkdxAFU96uykoMM2nymOGA36c+xJtJZoT/dBLjgwBnbdbnbrOw7pzE6cqKTTqEolOhb0OtbT5yBHJNALS2y3TIyD5+0EgN6zGuvOwGWDsog4840VdHdMmgiPAiC8avYHnYnVWt/G6JrP2kw1fmy4bm/vkJdj9TFZL4jyv9XaroDoJtPkkEXObOepe8+FtPqb3I78Q6VYekXWG1eEps7XFqhM2t7njMuj95lW83Vo6u0UJMEBuAyifo4guEKhsAwL/k9W4OOJfW0Gd5/YPDp6LcdBNn0SakFaSOEroAySLpnpvN4E4H182n0Nou/R2C34Yc5aiic0Hpiu+xAcanU0tnkLGBy7XkuSA1RhPCN152IFkenELtSbGoRPbMHmYGJpsus/8V1aHceT7Yt8R05pWzXUQueXlFo2o6ydxnifd5pNEzK3mJbJala49yOWs1Ww+NkuQ7idvS5N287K1xUZM09vcZbdcVofNpwFsPkJh7fakWW6DYUR91Q9BFfEV1Zyj0zy3GuIMjIVPyMo2UqPWtTRE4nz8aD4syQFuPWNxwtxnR2VzRwSyx9sN1iutxKFXTMnhB4Dl0YuVkI7zQ1aIIQK8FsFvNdgjiheYXfG2HXHseCjZ1R25ygpJQaBpRypdowib0DifpOAsIufPX9DevUQq8nVs4nzYvW9ccMJbbWyE1zE1SuW242n1a2w+Vb6IpOc2QKgPch78yT1Fx/VcEH9TOqZtTYfYveDxaKGz+dTJd1Zoy3/zHueDTjcupDKLUJ94QJKzUmSztxtTPHgNxNTkYrxoX02bK1o109nDNJy0bWj615ydc0F2PMs8UoKEJjRaEvPQoLUknETMm7an0p+UHsncbtFPGB3H4vlUiNRi7kqwGQ6E9JMYznPF5uPC2222msPaWB4jupxoJdFMoklEyHa8jmicuwFxUVsIbUYvQ5i7kNBFv22r6rNaC+R282PzgSzVVfTLIhOZMoR5UEBKOhlBwEWGA83MAkFNTS66iyLVNIVKWm8yMvxpTHPAasyeEEZPa4aD9vywMvjwdfyMV60Y0/rZO3ZrXEzF5i/aW41Ma1o198nLDzJRtE3oPIfDW8mPVKcgCogC55FQzE1xaFZE3c5zi1B/ouO4RcZqCXJL1li/lrK6ttgQtFoJbZZBX+LB4FOvmbrOCuySVU59VH+2T4lmED1OdAfRcKLjiR4kuovoJKI7iaAd4Z3oLex4JSilYkVb4QUNO1ZU9XS4ee6Kss1TSaxDkdfSZzgQsPns1fPUdDFPJDpBUPDFEzBvqPHeJtWzaW0RRCNm1eLgF+YWEnPNhzfUaMaWMExaRpquuGhyumrlFiNZP5sQ0HlUh+S95oiYWzEcY2/+v70zgZejqvL/efv+XhZeAknIDkFZAgHCJiQBkXHBHXFPEBQHxXGGAf7z968E/+r8/YvjR0dlGEGMiE4A9flnBCEoBAQMASWshrCFkESyvi1v6+7X/9N18+rVq61vVd26XdX9u59Op1/1vefc+72nuuv0ufeUv0Zrc7OmOFi4FJm4ri+irqJjR4UwBAqezyupTGkdZrQJbnPkB4mdn4CF3R6+Uhcl6LnvqsrLP5HfMSJfM+BYPaoPGzfzKYzfbTd8ZoD2bb6LiK9w2Y35MNGpRHzf0j1E5xF9i2gjUZPx+l+JnjLugPky0R+IbiZaTXQ80dtYcvBYnOTQwnmD8s5P9LkI10PJ4btUC3E/n9nnLJ0oiN2eFTI9eO4W6n6JjvwAzThDpjrqaCaQxstvd8/HuoUmIkTnajcWblvb5jwiqRSRIklQUtUQNR4L+7jGT8yf5aRgSlZKAHP5r2fJMSW9WgKYh0FUuKXPq2n1fFLKPMw8JaZNBObiM8E/jBx4nCM9VN9RSGzNK+dfuZue/hE98hW651N0x9vo5qNozdG07tM/IHrauEXs+4i+S/QX3tNuXBx/gYhXuH2J6ONG6OzwwLpdGqj6PJ8gJwJzFWPSJSOfI/ZjeeGiWaTjeIG7uOXXtONhWsY3cPIoFcI8MDg08CMQYLVbOJBWJ8oaPhLHzTAZfJhweFW2qoCocbgIjy0hT3RXYbwbyWAefUQq7TBuWclgHniUHPPp3ZbKlNY81JQyDzxJxRt4fQQ5z8GivkcR/6S0zAf30u4n6eX/Jl6n9Mcv0d2foN99ivZvpltPLtz4ZfNt1LOVWmbSER+gZd+ijzxKl7xCFz74W6IfEl1FdCHRKURibRP7PGrvWCr5eW7j76TtImetPYZhnVbXKS5uMZYaNglFexhIeIDKtoAPt3SN4wWQ6FF16zra8kt663/4SSqtnUcfY/ollM9qt5jmwtzVY5MfAlyIJjENCmJTREB8c/hvjwn0S6dzvb7MCn6Zbtioxr0Q3PqdGreuFBlMgroqElvXpHOfT4I4lrgrPue+7S3rB5HMp4oYmPzJ619T9mNw4I3C7poXu+gv36MHr6LffpT+6y30o7m09kxafzXxD/Z8G5aOefSmT9Cxl9Cs5fSpF6iuld76Q3rL12jxpTT/ndS5eEL0wDE/vOaNPZ/4EhhYFfp/NUQ3Ha8plp81qwTZOYreb5sEp+cjk9staDd2PEJP/pDe8bOg7VBfM4E0rnYr5IZ2+iEyB62+h1nfedC/mrOhK0SbnyPquEoOOuWxfHb8cHBCNy7j9cm+hev71xECi8oJOnhbfY4al9/PJ3rQhSZflsxD09DTMKXM+Trg0a8SbxP/SArvXJ9S5t4Gmb/euMFIwFL19x53YwwoR6q6Wub92wuOd+HxeiHBeuGZH9uocQqxT865BznxeuF57EWt41tv81ra/jCd/b3C2jYO8ljXSo2Nx4fqDKInjJ0/PkUrXtd+qGUuNc2lqLTtgUIo711rx3XfczEd8T6a/y7/3rjOr/us7d5E66+kD947LvDx64gv/E6+0q4iIPNw6z5sSnt6eswjXV1dK1eutB7xgtDR0VGK2YJOFwIunk9FcYLnU+bTXWrPx/VzNharMyZSs7oyN55EDY8vNH/9TmqYRBdyOiuUEhNIgedjuhNuGd7cP4J4/4bTtxEOT+tMw8OxOTmHB0g2+NSPqG8rnfE1+snRBRtuOsQ5hT5U/5uI7156Z8I9H3VWmehP8hfuoG330zm8LWus3PsZWvBOWvAeNZ4P5zPg5ZG2n3i8PJ+AzOH5BARWvLrI6ly8XpJqpK/HaunFcg2a0piPWrIJkVZqzychGNCNciDAcQZeGmT9HbQcRpXKMaTI83Hhmxu2BHBEJMcI5gzscvFthMMTvZhXrmuOowvWUbNL0Myf6vlElxL5hBVKH/OJTikVEjjgw4sYT1893tn7PktzziuEfXyLVMyH10/efi6t5AR+Ews8n1TYRko6GXuGg5RwQDdLdhe2ikaPvDT6pz+9zAsXiyn7ae3g9KaXuX77VKWRmWf6ad9fibeJP/PjwlLJez9Nv/y7QsjlpiPo7pW06QbiNUV1LTTnXDrtGvrA3fTZHfSxDfTuX9KK79BJV9CiC2nG6WrcHh4UZ3VrMFb7hN0TojzVgSrS43IqxM5dMhzI3qOpCPPMAfr5aS5uj0+zCmGu3lgrWmKlx3wqevIxeBAAgfQQOOGEE3hRwZNPPpmeLqOnOgjs37//1Vdf3bp1q/WZ/8xkMnPmzJk7dy4/my/4z2nT/O5wGlOPL7roomXLlq1atYo7sH79eu5PCEWrV6/mpTXXXHNNiLZoooqAOZWmQJ7WFStW8HaXiCp81k1de+21/OnHBhBRhZLm2OdjxZjG1W6I+Sg5ESAEBEAABOIlwBesuRzfwhGlQgns2rXrscceu/3226+77rrPf/7z559//rHHHtve3r5gwYKLL754zZo17O2wR8EXoDfddNOWLVt6e3uffvrpO++88/vf//6VV155wQUXLF26tCRuD09Yd3f3pEmT+EVtbW02mw03hXzhe8MNN+zcuTNcc7RSQuCNN96YPn3CYsUoc2p2qa2tra+vT0kPIUQngdRt8mE4lR7ziWWfj9XozH0mPgncvN7SvEclYI6UWE6tomnuYtFaOqFJYF660ZdGc3qZ3/f3R2751Wb+1NaZIkzJJMXPPNzGGyWDUy5kO9FWolctz+LPdqK5RBwr4Yd4IZ7b3HqwiWix8p5FEMh3uuFIzXKiRUaigiPDinJNdZCUMyJ+Ow+LTWm7O972+O5NJ1pE8v4r/vNSuc8l11O1aO4+TmvOK32vdaoIyBwZDpSaQlqFIeaT1plT3+/yS2mtnpFqiWCummhxeell3jB5uPjwElkjvczjxMnODOfp+ynRV4kuJjqHaCGHRIybeHLuXr6tJ/8AzheU/0j0a6L9RJwSewPRbUTfIvqcsdf/GA+3h3udKLeH+9NNNMmAWUMUJXApMhyw/5PEUiF2Prjblp4i4pyyM/xAsZTlntNdIcyTaO4H+5TGu2vC80mwQaFrIAACIGASaOiYeKcwoEkBgQzRi0T3Ed1E9GWiTxItMwI1VUQiDPJ7wxM4g+hLRPdwIgCi14keJrqV6BvGTTzPIzqKqDEFY/XroirPh3WkINVByierSPcHdts2ikXxfE4i+nmEGGB5k07F6NK42g2eTypMS0snkSNFC+YJSsAczOUJNHQMyFdOVM0KsHOemueJfkf0H0T/QvQRotOJeA1PC9Hbib5J9ChRA9G5RP+b6CFj6c4rxk/da3gND9GniM4mWmCERJQUXu2WqKLQ8+H7mbJDyNASVyrAzgs3U65tqp+IPrTnw87/dUZgM3ypBObh6aClOwF4PrCMMQKIGuu3BTAHc3kCdS398pUTVbOM7LyXiG81wjtV/p3on4kuIDqZiH8C7yT6ANF3idjl4OTNfE/HbxM9QTRCtIVoHdGNRP+L6BNEZxHNjn92ErvajZfzhcxvYIG22rixaeISHZSRnXta6MBuamZjn1DCzSnfoOkKY+tXpFIJzCMBir1xGle7IcPBrnjtIkUZDuIFISfdkuHAthNR5KIQB215KZwH+Yi1ju1PIUcyuYVPN2xDYoHO3ZPOgz56ZXSZzSPqkpsP1EoSgWduan/of/KF9Txjm6+rtYjuWt+yGozzxHE94mpariB8jLDoSaqWrE+GA15XVmAyUZ95ULwwi6hmHvylI9nAqJFXgJ2fyy3JBtj5scrn5j73XTLf9a+mlk9ppfUYrLqNTvAuJnYal0bukDXVQdAMB84vCGt35E8ZVzleXz3OszLQieZ1qhY9qSOTnihg5wba8PU8P1vK1URTia4OkuHgo0Ts+XBoVLJ4ZjiQbD9WDRkOAgIrz+ouMR924KwlOeP28izT6HEmh+p4T5IUNRbfKNaHkg+soNi9umF2rHCRNNZPIdzWbefBCQNxMHcdsheHYLqCDr5c6yfJzoMxzo1wMOFVo43PCWJ7K9yJ42parr2Vqpls5n8i+i9jQdplhs/DaQN4iRoXXoTDm222GYvQLjKWpfH6NL6IFwvJvmf8Yv1BIt6oEKjouRltola7dY+lN2BQoVdG2SCrTXWg5Ltm0yZP6l5npdTpE8i84q48uJua7DGfoHPKueCWB3F7/MaU7M+WuGcD8sMRcF/txjuWzJIEv0J4Yu7fux7Hw+Go6FbJjhqbv2zZoihe8ZxwF3xFDUAyUlRUzsEKyWYuO4p01Usv89zI5DHPx4ncDIraTNQ1FBn3jNlP0rUrFJ84AQfAzgZ/f/zR8GS+brzm/TZcRNqAfzCSp+0lOtr4c62RRY3L40Qc8/k3oi8Yq9d4/dgkN70idOP6/cQHzYfZVNQ0n92/2AIO0Fk9UavdumPwfHjI4VIdiK8Mny8IyVPGKWfx4sWubZ1fUqU9HSIZ1+AeajrEJiGQ58NrRDmnOe/UUlPS+3muZvyll5IEHyEoBdl9PmJsVg/ENS7kPOhfbfzLYKye6wCEG+Z8K433jg06Q6gPAiAAAgUCo5kp3p5PCERFf0QIIbOETdYbCQPMwn9yoIbTBnARaQM48cDdRCI/HqeN5sIBHC7WmA//yf6PiPlEL/ylZT5cl9WJd8u+dMfj+cSd6iD5J0gJehjC8xnNUu9W2v4w/enrdPs5fLsqDpailA2BMs/tJtwMMUjztThi+kXWYJFMNWdDefcRbo/iMydJUWPxgW59WAdrftz7bOCR/N3On6F/N1zbunbbenDCr30O5q41vZoH06XYXFIrLkl2HgzioaesJDreaBPCMgPp8jn7bHK8ak44SS+8P5D2cJX5h2TePGAW/qLi/NGcSJoLp5bmPznm8zMj5sOvRcyHk60VYFoekn6IV5BHSJMUEm6Ykq0Su9ot3G54r1GvJmotTPABSSzmV0bELwhXOWK1WyDJ8iea5ABjr9a5mOaKs2e88O8LnLeDMv2076+0dR09+xP609do3aX0q3fSTxfTj+bQnRfQE9+mV++m3DBvCgpRlnvlQkjv53kICmiiiIB7zMcaqDH9uTgcO6dMSS1wexQZgEVMwqLGtgXQMa1eK4oxaDeK7r2ZoNHB3NrcrCkOiu9Ua/NguooOtUIqJMzOA1CfdeYqoveONQhqma6KvH5EcDUtLwmuRutv5wFGLV2VIwC8M8e8xyVfKjGuFUZz/7UN1siMtLaDFU0/x9UXcl3tFlRFuPqVsNpNkCkEEOpURenssH1+ZXPOC692EwflnR/5E83LDAL1MJwtTWg151xqnbWR6A4jeyGvFOVPJH7mxIa0ZnHB23l2De19nho6aN7b6Yxr6QP30KXb6WOP0bt/RcddSoedenyoTvCvGHxGu5T0fp6H4pDARvLhiuR0vvg+n4h9da52EzEi68I55xEZpVYhaUQvM0bU8f+4d10tbf6KVmb05L9Ny2zgGE5KCQiL1bmlgXdOc7JjryKcE20BGaHLfKR0EpV0uzue1W7h+qbqC0KVnHCjiL9Vnvpepx2P0gu30xPfoQf+ie78EP3iDPrP2bR22eeM7XCcVVwk/7iZ6CXu0CUv0YXr6R0/o7O+SSdcTgvfS9NPopZDx7ta11qIC6GUFwHJcEWiBi27zyd0p63r36zhI+syORYeNKGCbVldGtGHRhpXwyRFjcNFeHwSXoWDFq4bAXQFZA7nJwBbr6oBmSvQGIMIL8t0WkhR30OHf6KLOef74lvoJO42Lx75D2Kwi3GRiV3tFmg3vFpEkl8QtlPGeYJ4ybHmdrOeia5nZaCh2SQU7WEA4dlB2r+FXvsDPXcLbfgG3XcZdb2bbjmBrj+MfvMe2vhN2rael6jR9BPphM/R29fQxVto1bOPEd1u3IRUJP84noiTrxQv9a00otrz0fXZUnx0qJEeAi7383FdSGY76Pqns2HRaqKCtZrPMjavt6KsfIv990jczyfQySBxPx+vOI/rBZxrGh/n95CPGdguMW01bd9AzuvRQJeVrhepXnuZIuoKNC2onBAC4q414u4lPpZpfcu0WP/6VsN2NS1XAoGMsKgPFgWyeT+f1YabcY1Fllecx8zJ5owCmdkI/ANETsm2I1Y55lvWOqKCtjBUFMJR2v6jcT+fLxoi3mtECfhyWWGRvJ+P5Gep8+vA+Tnv+kXg+nXgegKKg/InmmBl1i/aQxe2Q/upbxv1v16I5IjnwmMbsefTNovaDqfWmYVnft3Kf/LzTJ8Jcr19VvFZ2PEIbfxWnp/DluIqiklW8mtmT4/IjVIoXV1dK1eutB4p1gW8X3oCIT0f7rh1gZkZcnEe9K/mbOiVw82KylYHnk/p7UhVDyyejyqRkAMCZUPA6vmUzaCUDMS8FOOAD+/22aFEKIQoIsCuDu/TWGVI4zsg8f0rC9tC1JXoF8Tq+lJqSf07xhybbeNODrs6NQ2GeyO8GuNZODyNnDAycAnp+ezeROuvzPNz2BJ9ouH5hGVfVu1cPJ+yGl+xwSDmM06Io8Yl3yxYOs/H9QMxPvMQ6nh1hLkplv+MT12x86CS3k+CnYflXULPJ9IJEj9z66UY3xueN/yIO11GL7Zs1EJg8qM0fHWZnCQH7yPitITvNdBdaLg9H4o+MRYJ0S+I1XRHhZ1LnWi5kbEAzvbxSA4HcDiM03rYmGNjDeDMorpmNWMU9m8En22l+Cx0v0R3fyLPz2GLi4qAzOH5hGVfVu3g+UxIlqV+brHaLRDT0nk+gbqJyiBQEgIl9HxKMl55pdZLMU7vxnkO7pRvjJoxE+AMe7z+cLmhhTOPs2vKYR+Fpfg1t0JlOkUN94wHcPrZyTHWp/HzSM9YAMdYonYwhmO4Ou731FXc6ZCez8AbdPu5eX4OW6JPNDyfsOzLql3sGQ7KihYGAwIgAAIgkGwCic1zkGxsMfauO0m53WIcZ2jR7Ay88QS9+Bv6y/fpof9Bd32cbltONy2kW0+m319Oz99KPa9QUyctOJ/O/Ffiu2N9Zht95BE6/zZa/h068Z9o0YdoxukFF0iL2xN6lITcbuHZoaVKApUe81HJMuWyomyXSvnQS9Z9MNePHswrgfnq1at5oq+5xprpQP+4S6kxUXY+b968+++/f+7cuUxk1apVK1as4E3hpaQTj25/5tlsdqtRXnvtNfHCLJ2dnXMmltmzZ/OB9vb2eHpaMqnKzVK5QBk0yHAgQynJdeD5JHl20DcQAAEQAIHABHbu3HniiSfu2BE404F5IYUXDD0iBHPaJk+e/Morr0yaNImPXHLJJaeddtrFF18ceFJT0qCvr890aaxOzq5du4R3I7waa6mtrU3J4KJ2s62tjc/N1tbWqIJK2h6eT0nxK1Be8Z5Plev+VQVkIQIEQAAEQKBUBGx5DqR2CFw/fdWa8Xy1pep5eej9ycoOMtKvF8r1082cEJx8gpPvXWq+5bFdPvkQeIvw1omP14w/s0YKb/GYbXk9I/lDir+HDIHvuDXDMvvx65ygAft8NANPpjrs80nmvJSgV+EzTZags2WiEsz1TySYVwhzvsLmPAeBSlm5PQe2Bxq7+srm1e1IL9WPr9oq4Z1MQ4yRHbZXidYT3UL0NaJPE72NaBFRI9GxRJ8jWmvcOfcIIo5h/QsRpy3rJXqaiNNs/IDoaqIPE53G1/ohdJdjkzaiPrXjwp1M1fKsDGnwfCpjniVGmZwUqBKdLZMqYK5/IsG8QphXep6DFr87UWq1geFeaki65zNA9DzR7wxv+UtEHyc604jYsJ8mEtOtIxomOoXoSiNtIEcGOZ71GNHtRNcRXU70biNV92StZNOnTL3nU/JbcaRvEtBjwmo3rHbDaQACIAACZUhgtZHrSmQ5wGo3zRM8vtqNf5X//eX5sd/mv0g0j+iLJVrtttdjiRp7Pq5L1DhdGopCAsuJruX85hW52q2rq0shSYiKQgCeDzyfg/aTqDvfRbHpFLUFc/2TBeaVw5yXIfGWEpHlQMbzWXUqL2Iql8Kr3Uoa9vnJn4YOotzxCG38Vp6fjfLPRIfxc8yezzbDwxEbb6yPJouHY92Kc4iKacdnS1GKYvfd+Qo9n/TcybSjo6MoH1TQQwCez8EUB7Z7cgt/iA/aHCNRzTzodSdvrmB9y/9P20yblW2t9BgEtIAACIBA2RAw8xyE8Hxu/mT7RT/lXRsHC/9pxWK+ZavGdZwNnXJsR6zSTC3ioE0vH7G2tXbJqydFexjHdI97Pq/8jjb/Is/PRuGtL1P52eH5mN+51s5IfhH/yOHk8B092bH5o7H3xgzmvNn3exlfxHGYgU2muI/tRxV6PgE7jQwHAYGVZ3WXfT6cyNJakjNu7pXZGVsnrW8p7zD7MObD+uns5fZE7EBMYiP2Cs1BAARAIHUEAuU5KCzQ8i3scpgPp0MSHY5wUZwqrAe93J7o2hVLuH76QYEjPVQ/DjZEhoNuQ9BviL5HdAXRB40fHw8lajCOn0u0gaiO6BxjJdX9RDnDEXrIePcbRpDh74jeFHB4+CIOCEyquvp9PlJqUQkEJhBwz3CQt5RYnQrJ2RB+jq2ytZP8WlKUazVbbEdtsEVIc11UxwfNh9kxUdN81rYajyP1KJoJgLlm4KwOzCuKeaA8B9bcbsIJ8XFv/N81IbvKcW3rjMxEcnKSk9tNOsMBZ6Pj7zvOlvZ/jbRp/PoYg+M84/kmoi3GSrkLjT/5RBbL6e4l4pjPl4k+SbSMaK73t23cX8T4bCn62aLe80Fut6LQUcFBQDa3m3A8rB6Ia1zIedC/2vjl/lg9d8/E8HLKb/qc0SRzjGK0ooKegpxXejhbtYA5mOsnoF9jae38M0T/GeeYbW6M04GJU7m3bMsmnxL30Cfmw5t/7v8ix2oWGuM42Xj+NRHnITjaeP0L43m/8WyN+fCfYxElBXRVfRGX1s4VgIhfBN/BtF+tFuR2U8uzMqTJej5MQ9zOWXgg5mtxxPSLrHEYmWrOhvIhJrVL8sxfg5wBH2tkRtIP8fptqcBEozNTGTaMUYIACICAJwHeUi95x3hztZvpvUgGdrx0+8gJJJnlWB/pmGxztdv0E2n22Waf+dVZ1gEcdgpnkb7HOMIxH/7q5JjPN42YD78WMR9R8EWcjnn37eV5xt2NUECgtATcPR+rU2EGW+KIujhlSmqxLXWT95dC4Lb+IBS0uennuPpCrqvdgqpQVR+RelUk5eWAuTwrVTXBXBVJeTmlZc5uD28OkSmh72RqujGBAj7yzk+YfT4TV7uF66EMNM865i72w1cQOz9jhd2eFeYfM06no/fiRM4AABdRSURBVD7CvtACCU3J/yIurZ1LICx9FXZ7+FawKgtWu6mkWSmyXHK7iXiODYDtoOufzoaum3NYsnncVOQ84pwB146Jaj5vFZlJQ7E5WudrrxBN0dCNqOAj0CbB/NP5olKMEeMEARAAgXgIBMrt5prGzT9Dmrmfp2g6OKscaysvr0nemwrRw3hgF6SO53YzdOTNEJD4ynbL7YYv4vimI1GSZU7GmDpcTrndbrjx1pgolb3Y4k6Oq2sh7/n4x3BcnSWvJmXs+Yg0BuJzH55P2Z91GCAIgIBmAlIXW9dP57CPZG7ooqmi5eUwCjOBtTPbtbznw3KcbpWVcyBRESdo/E6m6fF88EUccdIlm0udjJKyAlYrM8/nqisuCwgA1QsEAuzziQ7M3NVjExVirVqIJjL9l9zGIz4fxbNXsQV8uJrtiPhTPFz1mhVkeh69DiL10RkGlQDmQYlFrw/m0RkGlZAW5kVXu1k320RKvOZBUKxJMx/Wm/xI7vMxJXx1RU8cPQww9RHu2ZLSL+K02HmASUx+Vax2S/4cJa+HIWM+het+S5pp56K1woX+WC4Ec9Q+a9v8V7vZ/BwfyYEJO5JlB5aABiAAAiAAAskmIPMz86pTG5M9iDT1LuhqtzSNDX2NRkDmZIymwbN1xcZ8crnc3r17s9lsU1NTc3Oz2MwvrqWrq+0hEPOSm184341panSKdYn5uC42c01FYKYZsPo2toO2bG+ipk9D18Hb8hnYhEgmRYgJqzVLgfk6Jl0QCwIgAAIgEBOBoncyjUlvCLG2bG/izxByYmwycWNPjIoM0fgijpsw5KeawIEDB+666641a9Zs3LiRXaD9Rtm3bx8/9/T09Pb29vf39/X18WuuOWgp7CyleuCunde62q388BW8OLdHGkeKSL3+WQNzMNdPQL/GtNh50dVu+tF5abRlexN/TqicnDuZaqGWhC/itNi5lgnRpQSr3eRIt7W1nXfeeee89ZzTzzjj0MMOmzZt2tSpU0X8p2PSJH5whebmpqrqKj7Y2NjI/xoaGzngMzo6KqchTbVcVrulqfvR+4rVbtEZQgIIgAAIJJuAzAIbrHZTOIdY7aYQZpmJkjkZYxpyxa52+/mdD9+5cW++mmqq8/lcNjsyNDo8kBvNUi5bnR2pbaiZ1lF39GvPdO7eO1RPddVV7c1TNh06s3/WwnedftQpJx0f03SUSmzFez6lAg+9IAACIAACSSJgLnzHC54WVRCSNMPoCwgoIMBLwkwpXV1dK1eutB5RoEBOBGe1ls/tdsMdD//82emtre2jw325zPDwYN9A9w7K9OVzI5QdbmmumdxSdd7j687btfNAbU1VW1PdrOl3t097YuaSpXPyl1/yMbkepaYWPJ/UTFXcHQ1/T6S4e1a+8sFc/9yCOZjrJ6BfI+wczPUT0K+xJHauyvN5/M+Pb5/0UtUwLymrymf4t4bqmkK6AU44UF1FVTVV1fmq/PTp02r3ti1atMjJNpDns+6RZ3/3Umdvpn5gYCiTGR7NDA0P9g8c2JfPDIxmhweGBwcGD3xu8/pVg/sytQ0Hspn8YP9t7dPXnvr+dxyV+/IXVuqf2Vg1Yp9PrHjTJLy0iSLSREpdX8FcHUtZSWAuS0pdPTBXx1JWEpjLklJXD8zVsZSVlGrm27e/vmH6fRsW3PPUMQ++uOSxrcf/eceJT+89eXPPKS/1n/hq73Gv9B778mELGjj3gCwO73qttUN1O9aNbL69ede9U3oemtr3yIzsk/NqX55bv21hyxvHTu4+bmrf3+ZPXr9k4cNL5j+29MiNbzkuu2jaMR3dLXW56NqTJgExn6TNCPoDAiAAAiAAAiAAAiCQRAKqYj6/vfu3d82+dSQ31NzQ3DGlraWxuam2samusam2oa6qPtc/mhkYOW3hGf0PtCw5YUnEmM/AwIH/9+tf7dmz+83HHMfBJc6wzJvcxT53cevIN/72t9H6+taOjpam5tFR9naqOLlBVS43tbPz2GOPM7WLhNc2h9P1oP/MhQ7Wud7M07zVjaQnjJhPEs+rkvQpppvDlmQsaVEK5vpnCszBXD8B/Rph52Cun4B+jam280w2k2kayTZnhmuGD4wcOJAdGMwPDuaHhmg43zzaNqOpdU7zVOrI5xRkV2tsbJ4xe37L5OmnnHb68hVnL1tx9lnLVyw5aelJS09btvzso45ZXF3XOHfGrNmd08485ZSz33LW2W85c/lZy84486w3venN+qfVR6P1pjjOm+hIdhUxH0lQqAYCIAACIAACIAACIFDRBFTFfDZt2rTh5UdrCqGXauPOotWF/wq5RTi5dFVtbc1onlo7WhZNO/qIhUdEjPk80HXTk/fc2DuYmT9jcjabGxzODg5lqLo2kx0dyWSe3z40c96C10YO52NLmp+vz3YP56ur65oOW3DccWddcMrpZ1pjPuxvWCM24rU1IYpZ2YzAmA6qGZyx1rG6r9bojX8oyRk1siVlsTa3iYLnU9EnMAYPAiAAAiAAAiAAAiAgSUCV5yOpzqtaoAwHj9394xm7f97e1tw7kBvO5PoODO/cfaB7IDeSpeEsNbROyjd0rNu5aG/1/FNHbjl6yv59Q/XV9S2th8zsazn+M5dfLe/5WHtrc4r4LZ+MkaKh2YRf+y9d8/d8zOY2/0c4aVjtFtH2yqd5qqPGKZ0GMNc/cWAO5voJ6NcIOwdz/QT0a4SdSzJvmjyr8fCTRlsOrW6awo/G9mmdh82YMmVKS2vbpEmT6yib7dk5NDiwszfX1NIxbdr0KZMnN9YQH6TsoFOF8B+snoxkNyT34RR1e4qqsypyKkXMpyhAVAABEAABEAABEAABEAABUhXz2bBxw59bHqkeqamuqqnOFta8cSbrmuraWs5tzSvfjCVwMw+f2fG3aa6bbQLFfDY/9/Srz9y/d+fWlpYWTl6Q5ZuYZnO9vf3ZXL6hqWlwcCiXy24bmLwnM+mIlh0z2nODGe5BDac6aJg871Of/Qdz1p0rymzZBWy+qG15m3X9m3Plm9BiW03nZXBFV7uJhq4L8+D54DQGARAAARAAARAAARAAgeIEVHk+XXd2fXvuV4arBpoaGhtqG+rz9U3VDZzeraGmsWG0vi5bV5Wnty5+67T7jz1pyUnObgXyfEZGRp58ctPWra8duehIdn7YuxgdHe3p7t6+bduCI4+sqalhj6PgD+RHq2rqhO+RzWaf/POfl5y45JhjjnF6Pl5OhTXAIuOcOOvYJMfh+WC1W3Err5AaiBrrn2gwB3P9BPRrhJ2DuX4C+jXCzsE8EIG62rr6/Y35v9Vkt1eN7q+iwarR4arsEOVG2C2pyo7kh3oy/YNDSuyqvr5+1qyZnZ1T58w+fOGC+UcsXHDkEQtbD5n61Aubt+3e1Ty9s3laZ66lZXcmW93e1jh1Cj+6Bwf29PZMmjQp0KCsPpJ4Haj/gSqH6xi3gucTGl25NZRff1luIy/deMBcP3swB3P9BPRrhJ2DuX4C+jWm2s6zuUzDpIbathqqo1xuNJPLZUazmdEMP3L12bpDaxpm1TU3NXIcRhXYHKsZPSiNU2r/dvv2m49adFlu5KQH1y9Zd+9Z99/3ngf/sPQH3z3h/3zt+K9+5f0/vnH96Oi9jz7qpd0JXwSLRDHf9T9ofdc1/qNq7KacgsZU241yIhAIAiAAAiAAAiAAAiAAAq4EVK12e/qZpx/f8hivMjNuKCoSBhx84tzW1bWFPxpa6pfMWzp/3nxnTwKtdmOH569//euzzz578sknT506laXVVFc/tGfPPe3tA63tIwMDuUwmOzgwtG/v0J49hbsKjQxX56mtoeGE3buvvuiiMrMEeD5lNqHhh6PH2w7fv3JsCeb6ZxXMwVw/Af0aYedgrp+Afo0lsXNVnk9EXIE8n/7+/rVr1z711FPs+XR2drLqupqaXbncC1MP2ZmngeGhfDaTHR4eGRysYhdoaDCfyeQ5QkS0eGjo65d/IWJXk9Ycq92SNiMl6w+if/rRgzmY6yegXyPsHMz1E9CvEXYO5voJSGrkfT4LFy48yihHGGXu/Plto6MzXnpx+b497+jvO39k5Kw9exY888z7m5ounDzlw53TPjnr8NP6B2Y0NEqqSFE1xHxSNFnoKgiAAAiAAAiAAAiAQMkIpDHmw7Aymczw8HBzc3M1Z8w2yuDgIB+sq6vj1xw949dDQ0Otra3iXT4yMDDAad86OjpKxjoexYj5xMM1hVL1pNRIIZgYuwzmMcL1EA3mYK6fgH6NsHMw109Av0bYuTxz9nDYqzHdHm7Y1NTU3t7Oz1waGxvb2tp4IZz4UxzhW52Wn9vDA4fnI282ZV4TkXr9EwzmYK6fgH6NsHMw109Av0bYOZjrJwCNIQjA8wkBDU1AAARAAARAAARAAARAAARSRgCeT8omLL7uImocH1svyWAO5voJ6NcIOwdz/QT0a4Sdg7l+AtAYggAyHISAhiYgAAIgAAIgAAIgAAIVRyBchgNOQq2c1FVXXKZcZiUIhOdTCbOMMYIACIAACIAACIAACEQlENrzgaMSFb2i9ljtpghk+sUgUq9/DsEczPUT0K8Rdg7m+gno1wg7B3P9BMpAY19fn+ZRwPPRDDy56pCXRv/cgDmY6yegXyPsHMz1E9CvEXYO5voJQGMIAvB8QkBDExAAARAAARAAARAAARAAgZQRgOeTsgmLr7uI1MfH1ksymIO5fgL6NcLOwVw/Af0aYedgrp9AGWjUHyyF51MGZqNmCPqNT02/0ywFzPXPHpiDuX4C+jXCzsFcPwH9GmHn+pmXgUZ4PmUwiRgCCIAACIAACIAACIAACIBAEQLwfGAiBwkgUq/fFMAczPUT0K8Rdg7m+gno1wg7B3P9BMpAo/7AHTyfMjAbNUPQb3xq+p1mKWCuf/bAHMz1E9CvEXYO5voJ6NcIO9fPvAw0wvMpg0nEEEAABEAABEAABEAABEAgZQSiu69Bw61V0VWmjDG660GATQfGoNk6wFwzcFYH5mCun4B+jbBzMNdPQL/Gkth5T0+POdKurq6VK1daj3hBuOHGW6+64rJsNpfNjZp1mhrrrfUHh0Y0MGSlehTJjKWutmb3nr2HTJ1iw2LroX+fg44IMR+ZqamIOnB79E8zmIO5fgL6NcLOwVw/Af0aYedgHoIAX+KbD5sjFEIamsgQgOcjQwl1QAAEQAAEQAAEQAAEQCBeAsL/4WfTERKvbUfMOlZ/yb+maOLVMN5RKZLuHKAp2OY3+tCD56NoNtIvJuhCyfSPuPQjAHP9cwDmYK6fgH6NsHMw109Av8ZytXOxfEus+DJfiyPWC3pbsKhoTZYmZJrC9U+Zm8a87aB1mCYBUSdoiMyVCTyfZMx7AnqBSL3+SQBzMNdPQL9G2DmY6yegXyPsHMyLEuBrfLYTs4jLevMxMDgsrEi8EIX/tDXxqiNT0yqQtbBqPsLPVo1WdXG/5j6Pjo7asNhGbRuXk4yo4DV8Jz14PkUNFRVAAARAAARAAARAAARAQDEBdjnMR3TRzU0N5iO6tBJKYCY8EO4AP/NrsychBuhsAs+nhDObLNXlGjVOFuWJvQFz/bMD5mCun4B+jbBzMNdPQL9G2LmNudWPsjoM+qdGUmNV1cFwjUx94QUFdRSdTOD5yNCuiDqI1OufZjAHc/0E9GuEnYO5fgL6NcLOwbw4gYmrx7i+czmZ7aDrnzIH2U9wrgGzHTkwMMTV+DnuVW2e8gsE3Cjk886+WUctIkLWBYGu79pACSbwfIobKmqAAAiAAAiAAAiAAAiAgGYC7AC0NDeaD/5TdMB50KumrcNmNc0Dia7OOkCTgynW9V1XJvB8os9FmUhA1Fj/RII5mOsnoF8j7BzM9RPQrxF2DuZFCdjCG/0HBp3BEOdBPmI+zCiH8yC/5TzopcI8bouKaA7+UN49w4Hohg8K67tmNSsl50HzCDyfooZaKRUQqdc/02AO5voJ6NcIOwdz/QT0a4Sdg3lRAqr8ClXuSmtLU1//gKpehZCjaiCBVMPzKWqoqAACIAACIAACIAACIAACIJB6AvB8Uj+FqgaASL0qkvJywFyelaqaYK6KpLwcMJdnpaommKsiKS8HzOVZqaqZPuZ5l5QGgeIVonJv34EQrZxNVMkJ3RmRpIDXvIWWEKIhPB9VJ2Dq5SBSr38KwRzM9RPQrxF2Dub6CejXCDsH86IE9F7hh3AKtDcZc310KobnU9RQUQEEQAAEQAAEQAAEQAAEQCD1BKrwK0Xq51DRADhqDGNQxFJWDJjLklJXD8zVsZSVBOaypNTVA3N1LGUlgbksKXX1SsK8p6fHHEFXV9fKlSutR7wGd8ONt151xWXPPfecutFDUkgCuNgNCQ7NQAAEQAAEQAAEQAAEKopAFM8nm81WFCuZwfJv7pr3a8HzkZkX1AEBEAABEAABEAABEKh0AlE8n0pnl4zxY59PMuYhAb3Q7HMnYMSl7wKY658DMAdz/QT0a4Sdg7l+Avo1ws71My8DjfB8ymAS1QwBm3zUcAwiBcyD0FJTF8zVcAwiBcyD0FJTF8zVcAwiBcyD0FJTF8zVcKwwKfB8KmzCMVwQAAEQAAEQAAEQAAEQqEgC8HwqctrdBo2osX5TAHMw109Av0bYOZjrJ6BfI+wczPUTgMYQBOD5hIBWnk0QNdY/r2AO5voJ6NcIOwdz/QT0a4Sdg7l+AtAYggA8nxDQ0AQEQAAEQAAEQAAEQAAEQCBlBOD5pGzC4usuIvXxsfWSDOZgrp+Afo2wczDXT0C/Rtg5mIOAfgIhNMLzCQGtPJsgUq9/XsEczPUT0K8Rdg7m+gno1wg7B3NJAuwkW4u1VSX7z4KJjaHrQX/ORRnC85E0VFQDARAAARAAARAAARAAgagE2E82i/VKHf5zVLIS7eH5SECqjCpFveTKwKB1lGCuFbehDMzBXD8B/Rph52Cun4B+jeVh5+ztmAOxvrCGO8RxM1LkRO0MIlnhpAWUFYUYr9UVdA2U2ZhYQVmJWcHC89F/qiZUI35p0D8xYA7m+gno1wg7B3P9BPRrhJ2DuSoC4opfFOslu2uYSCj1CiKp6lIS5DjH6AQlTkMTnSsWeD5JmE30AQRAAARAAARAAARAAAQmEDA96qCutek12SInCecbutvyfOD5JNwG9HUvLcFQfUTi1wTm8TO2awBzMNdPQL9G2DmY6yegX2O52rm4+reudivK1nUlWNFW6argHGNRUK5Y4Pmka95j7K28uxxjJypMNJjrn3AwB3P9BPRrhJ2DuX4C+jWWsZ37LGxzcrYu+ioDJsKfsQ3Ea4z+oKyr3UyB8Hz0n6rQCAIgAAIgAAIgAAIgAAL2ffxMJEosy9rW1YUoA+LWvU/ywzFbwfORh1bmNaOcaWWOJrbhgXlsaD0FgzmY6yegXyPsHMz1E9CvMb12bl2F5YzSWBdxme/6NHGtr386FGqUZOI6cPOg9V1rEAmej8KZSreoMoiQpm4CwFz/lIE5mOsnoF8j7BzM9RPQrzGldu66BEvQM0dk1jGpWltZD5oNRQWrEP0zEkWj12y6MvEBJQiYKGwk+U94PlGmCW1BAARAAARAAARAAARAIHEE0pXVTRs+eD7aUCddUXqjxkkn690/MNc/d2AO5voJ6NcIOwdz/QT0a4Sd62deBhrh+ZTBJKoZQkqjxmoGXyIpYK4fPJiDuX4C+jXCzsFcPwH9GivHzsONNFwr/fOoWSM8H83AoQ4EQAAEQAAEQAAEQAAEQKAEBOD5lAB6MlUiaqx/XsAczPUT0K8Rdg7m+gno1wg7B3P9BKAxBAF4PiGglWcTREX1zyuYg7l+Avo1ws7BXD8B/Rph52CunwA0hiAAzycENDQBARAAARAAARAAARAAARBIGQF4PimbsPi6i0h9fGy9JIM5mOsnoF8j7BzM9RPQrxF2Dub6CUBjCALwfEJAK88miNTrn1cwB3P9BPRrhJ2DuX4C+jXCzsFcPwFoDEHg/wMrrqcFp9eAqAAAAABJRU5ErkJggg==" style="height:447px; width:1107px" /&gt;I have a feeling that it is possible with VTune, but not sure how to achieve it. Any pointers would be helpful.&lt;/P&gt;&lt;P&gt;Thanks.&lt;/P&gt;&lt;P&gt;Uday&lt;/P&gt;</description>
      <pubDate>Mon, 24 Jun 2019 11:03:58 GMT</pubDate>
      <guid>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173214#M18182</guid>
      <dc:creator>K__Uday_Kiran</dc:creator>
      <dc:date>2019-06-24T11:03:58Z</dc:date>
    </item>
    <item>
      <title>Making the attachment proper</title>
      <link>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173215#M18183</link>
      <description>&lt;P&gt;Making the attachment proper this time..&lt;/P&gt;</description>
      <pubDate>Mon, 24 Jun 2019 16:50:32 GMT</pubDate>
      <guid>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173215#M18183</guid>
      <dc:creator>K__Uday_Kiran</dc:creator>
      <dc:date>2019-06-24T16:50:32Z</dc:date>
    </item>
    <item>
      <title>Hi Uday! </title>
      <link>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173216#M18184</link>
      <description>&lt;P&gt;Hi Uday!&amp;nbsp;&lt;/P&gt;&lt;P&gt;You are looking for VTune Amplifier&amp;nbsp;Threading Analysis. Take a look at next help topics:&amp;nbsp;&lt;/P&gt;&lt;P&gt;&lt;A href="https://software.intel.com/en-us/vtune-amplifier-help-threading-analysis" target="_blank"&gt;https://software.intel.com/en-us/vtune-amplifier-help-threading-analysis&lt;/A&gt;&lt;BR /&gt;&lt;A href="https://software.intel.com/en-us/vtune-amplifier-help-threading-efficiency-view" target="_blank"&gt;https://software.intel.com/en-us/vtune-amplifier-help-threading-efficiency-view&lt;/A&gt;&lt;/P&gt;&lt;P&gt;With such analysis you are able to get stacks of every thread of your application with precision of CPU sampling interval (1 ms by default). You will be able to see synchronizations and context switches.&amp;nbsp;&lt;/P&gt;&lt;P&gt;- Pavel&lt;/P&gt;</description>
      <pubDate>Mon, 24 Jun 2019 21:24:05 GMT</pubDate>
      <guid>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173216#M18184</guid>
      <dc:creator>PAVEL_G_Intel</dc:creator>
      <dc:date>2019-06-24T21:24:05Z</dc:date>
    </item>
    <item>
      <title>On screenshot I see you</title>
      <link>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173217#M18185</link>
      <description>&lt;P&gt;On screenshot I see you profiling MPI application. So I recommend to&amp;nbsp;read next topic too:&lt;/P&gt;&lt;P&gt;&lt;A href="https://software.intel.com/en-us/vtune-amplifier-help-mpi-code-analysis" target="_blank"&gt;https://software.intel.com/en-us/vtune-amplifier-help-mpi-code-analysis&lt;/A&gt;&lt;/P&gt;&lt;P&gt;- Pavel&lt;/P&gt;</description>
      <pubDate>Mon, 24 Jun 2019 21:32:26 GMT</pubDate>
      <guid>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173217#M18185</guid>
      <dc:creator>PAVEL_G_Intel</dc:creator>
      <dc:date>2019-06-24T21:32:26Z</dc:date>
    </item>
    <item>
      <title>Hi Pavel,</title>
      <link>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173218#M18186</link>
      <description>&lt;P&gt;Hi Pavel,&lt;/P&gt;&lt;P&gt;Thanks for the response.&lt;/P&gt;&lt;P&gt;I will look into the threading analysis links that you shared.&lt;/P&gt;&lt;P&gt;The snapshot I shared is using the sample trace files that are available in the vampir site (https://vampir.eu/downloads/tracefiles). But, I presume the bars mentioning EM_SOLVE etc are functions - not sure though.&lt;/P&gt;&lt;P&gt;To summarize my objective is to analyse thread-wise behavior at application level over different cores. As I mentioned earlier - "at a given point of time I would like to know which user-level-functions are being executed in each of the threads". This way, I might see a big bar in processor1/thread1 while the other processors/threads show blank. I might end up&amp;nbsp;breaking that big bar (function) into parallelizable chunks.&lt;/P&gt;&lt;P&gt;Thanks.&lt;/P&gt;&lt;P&gt;Uday&lt;/P&gt;</description>
      <pubDate>Tue, 25 Jun 2019 04:29:00 GMT</pubDate>
      <guid>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173218#M18186</guid>
      <dc:creator>K__Uday_Kiran</dc:creator>
      <dc:date>2019-06-25T04:29:00Z</dc:date>
    </item>
    <item>
      <title>Just to make the point</title>
      <link>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173219#M18187</link>
      <description>&lt;P&gt;Just to make the point complete..&lt;/P&gt;&lt;P&gt;Attached are the snapshots from the Amplifier wo/ and w/ sleep which I introduced in the 'matrix' example. When I hover over the initial part of the main thread, I would like to see that it is introduced from 'someDummyFunction'. Could that be possible by manual instrumentation, if not through this automatic analysis?&lt;/P&gt;</description>
      <pubDate>Tue, 25 Jun 2019 05:24:26 GMT</pubDate>
      <guid>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173219#M18187</guid>
      <dc:creator>K__Uday_Kiran</dc:creator>
      <dc:date>2019-06-25T05:24:26Z</dc:date>
    </item>
    <item>
      <title>Hi Uday,</title>
      <link>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173220#M18188</link>
      <description>&lt;P&gt;Hi Uday,&lt;/P&gt;&lt;P&gt;The only way to provide executing function at any moment of time&amp;nbsp;is dynamic/static instrumentation of all the executing functions which usually introduces very high overhead (200-1000%). VTune doesn't do full instrumentation, but instead collecting statistical hotspots via sampling approach. This allows us to remain within 5-10% of collection overhead (depending on analysis options). You can activate "Clocktick Sample" layer on the timeline (Bottom-up view) in order to get function names when sample occurs - hover mouse on green triangles in order to see details in the tooltip.&lt;/P&gt;&lt;P&gt;However, you can statically instrument particular functions using &lt;A href="https://software.intel.com/en-us/vtune-amplifier-help-task-api"&gt;ITT Task API&lt;/A&gt;&amp;nbsp;which would give you exact function boundaries on the timeline.&lt;/P&gt;&lt;P&gt;Threading analysis can dynamically instrument system functions related to threading and synchronization in order to represent waits, locks, transition, context switches.&lt;/P&gt;&lt;P&gt;When you analyze parallel efficiency, the primary metric is "CPU Utilization" which needs to be maximized. You need to decrease thread inactive time in order to increase CPU utilization and thread concurrency. Analyzing locks, transitions or context switches (Threading analysis) should give you enough data for optimization.&lt;/P&gt;</description>
      <pubDate>Tue, 25 Jun 2019 08:36:10 GMT</pubDate>
      <guid>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173220#M18188</guid>
      <dc:creator>Vitaly_S_Intel</dc:creator>
      <dc:date>2019-06-25T08:36:10Z</dc:date>
    </item>
    <item>
      <title>Hi Uday,</title>
      <link>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173221#M18189</link>
      <description>&lt;P&gt;Hi Uday,&lt;/P&gt;&lt;P&gt;If I understand you correctly then you will need to collect Threading sw-based (user-mode) analysis (if it isn't done yet), switch to "Locks and Waits" or "Threading efficiency" viewpoint and on&amp;nbsp;Bottom-up tab set "Sync Object/Function/Call Stack" grouping. In this case you will see your sync objects and wait time spending in them, synchronization objects are instrumented automatically in this analysis type.&lt;/P&gt;&lt;P&gt;Also waits&amp;nbsp;are represented as pale green color on Timeline so you can use filter in for a period of time or filter in some object in grid to highlight what you want. I've attached a screenshot to show what I meant.&lt;/P&gt;&lt;P&gt;To see how such sync objects were executed along different threads you will need to chose a grouping with Thread.&lt;/P&gt;&lt;P&gt;If you wish to mark some specific functions&amp;nbsp;that isn't marked automatically you will need to use ITT API in your code &lt;A href="https://software.intel.com/en-us/vtune-amplifier-help-instrumentation-and-tracing-technology-apis" target="_blank"&gt;https://software.intel.com/en-us/vtune-amplifier-help-instrumentation-and-tracing-technology-apis&lt;/A&gt; .&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Also in your screenshots I see an overtime picture of traced&amp;nbsp;MPI functions. Will it be helpful for you if VTune can trace MPI functions and represent them on timeline like ITT tasks?&lt;/P&gt;&lt;P&gt;BR,&lt;/P&gt;&lt;P&gt;Vladimir&lt;/P&gt;</description>
      <pubDate>Tue, 25 Jun 2019 09:03:16 GMT</pubDate>
      <guid>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173221#M18189</guid>
      <dc:creator>Vladimir_R_Intel</dc:creator>
      <dc:date>2019-06-25T09:03:16Z</dc:date>
    </item>
    <item>
      <title>Hi Vitaly,</title>
      <link>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173222#M18190</link>
      <description>&lt;P&gt;Hi Vitaly,&lt;/P&gt;&lt;P&gt;Thanks for the pointer on ITT Task API. I think this is what I was looking for.&lt;/P&gt;&lt;P&gt;And yes, I agree with you on the overhead with static instrumentation. That is expected I guess, but would that be that too high - especially if I want to instrument selectively, say? Not sure.&lt;/P&gt;&lt;P&gt;Threading analysis, in my opinion, throws some good inferences when we have parallelized the content but not in some optimized manner. This is definitely needed. But, this wouldn't give information of unintentional missing of parallelization which could be caught by seeing the function(s) boundary on the timeline.&lt;/P&gt;&lt;P&gt;Thanks.&lt;/P&gt;&lt;P&gt;Uday&lt;/P&gt;</description>
      <pubDate>Tue, 25 Jun 2019 11:09:45 GMT</pubDate>
      <guid>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173222#M18190</guid>
      <dc:creator>K__Uday_Kiran</dc:creator>
      <dc:date>2019-06-25T11:09:45Z</dc:date>
    </item>
    <item>
      <title>Hi Vladimir,</title>
      <link>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173223#M18191</link>
      <description>&lt;P&gt;Hi Vladimir,&lt;/P&gt;&lt;P&gt;Thanks for the clear instructions. Unfortunately, in the sample collection I had with me I see 'No data to show' under the grouping you mentioned. Probably I missed something while configuring the run.&lt;/P&gt;&lt;P&gt;But I see the same suggestion from you as well - ITT Task API, which is what I am looking for. Though I am not in the state to experiment, I can see that it is what I was intending.&lt;/P&gt;&lt;P&gt;And as mentioned before, the screenshot has MPI stuff because of the sample trace file I downloaded from the vampir homepage.&amp;nbsp;&lt;/P&gt;&lt;P&gt;Thanks.&lt;/P&gt;&lt;P&gt;Uday&lt;/P&gt;</description>
      <pubDate>Tue, 25 Jun 2019 11:19:00 GMT</pubDate>
      <guid>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173223#M18191</guid>
      <dc:creator>K__Uday_Kiran</dc:creator>
      <dc:date>2019-06-25T11:19:00Z</dc:date>
    </item>
    <item>
      <title>Hi,</title>
      <link>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173224#M18192</link>
      <description>&lt;P&gt;Hi,&lt;/P&gt;&lt;P&gt;Sorry&amp;nbsp; for coming back with silly question, but I dont understand what I am missing.&lt;/P&gt;&lt;P&gt;I am working on Windows7 Enterprise and using VS2015. I just added the following line, before main:&lt;/P&gt;&lt;P&gt;"__itt_domain* domain = __itt_domain_create(L"Example.Uday.VTune");"&lt;/P&gt;&lt;P&gt;This is the compilation error I see:&lt;/P&gt;&lt;P&gt;"Initializer is not constant"&lt;/P&gt;&lt;P&gt;When I look into 'ittnotify.h' I see series of macros based on the OS etc. Anyways, I tried looking into where I could have gone wrong but couldn't find anything. It sounds like some silly mistake somewhere, but doesn't catch my eye.&lt;/P&gt;&lt;P&gt;Any help here?&lt;/P&gt;&lt;P&gt;Thanks.&lt;/P&gt;&lt;P&gt;Uday&lt;/P&gt;</description>
      <pubDate>Thu, 27 Jun 2019 09:19:59 GMT</pubDate>
      <guid>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173224#M18192</guid>
      <dc:creator>K__Uday_Kiran</dc:creator>
      <dc:date>2019-06-27T09:19:59Z</dc:date>
    </item>
    <item>
      <title>Hi,</title>
      <link>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173225#M18193</link>
      <description>&lt;P&gt;Hi,&lt;/P&gt;&lt;P&gt;I think the root cause is that you have C code in terms of VS2015 (.c file) and in C language you have to initialize static object with constant expression. So as __itt_domain_create (after macro expressions) is a real call to a function placed in static part of ITT API, it cannot be used in initialization. Thus you have to move initialization later in code&amp;nbsp;or convert your code to C++ for VS.&lt;/P&gt;&lt;P&gt;BR,&lt;/P&gt;&lt;P&gt;Vladimir&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Thu, 27 Jun 2019 16:40:50 GMT</pubDate>
      <guid>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173225#M18193</guid>
      <dc:creator>Vladimir_R_Intel</dc:creator>
      <dc:date>2019-06-27T16:40:50Z</dc:date>
    </item>
    <item>
      <title>Thanks a lot Vladimir. My bad</title>
      <link>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173226#M18194</link>
      <description>&lt;P&gt;Thanks a lot Vladimir. My bad. I knew I missed something, but couldn't think if it.&lt;/P&gt;&lt;P&gt;Anyways, I could finally get the function bar (basically a task from Intel's ITT perspective) which I envisioned. Attached is the image.&lt;/P&gt;&lt;P&gt;Unfortunately, I have one more question. Not sure what was wrong, I am seeing the 'Task Type' in some alien language. I tried Japanese/Korean/Chinese - &amp;gt; English using some online image translators, but no luck. It might sound a bit extreme, but tried it.&lt;/P&gt;&lt;P&gt;Any idea why is that? I have attached the code snippet as well.&lt;/P&gt;&lt;P&gt;Thanks guys for the support. I really appreciate it.&lt;/P&gt;&lt;P&gt;Uday&lt;/P&gt;</description>
      <pubDate>Fri, 28 Jun 2019 04:53:49 GMT</pubDate>
      <guid>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173226#M18194</guid>
      <dc:creator>K__Uday_Kiran</dc:creator>
      <dc:date>2019-06-28T04:53:49Z</dc:date>
    </item>
    <item>
      <title>Hi Uday,</title>
      <link>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173227#M18195</link>
      <description>&lt;P&gt;Hi Uday,&lt;/P&gt;&lt;P&gt;My assumption is the root cause is in encoding mix, i.e. you create domain using a&amp;nbsp;wide character string (UTF-16 __itt_domain_create(&lt;STRONG&gt;L"Example.Uday.VTune"&lt;/STRONG&gt;)) and string_handle is created as single character string (ANSII __itt_string_handle_create(&lt;STRONG&gt;"someDummyFunction"&lt;/STRONG&gt;)). Thus just make it &lt;STRONG&gt;L""&lt;/STRONG&gt;.&lt;/P&gt;&lt;P&gt;BR,&lt;/P&gt;&lt;P&gt;Vladimir&lt;/P&gt;</description>
      <pubDate>Fri, 28 Jun 2019 08:34:00 GMT</pubDate>
      <guid>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173227#M18195</guid>
      <dc:creator>Vladimir_R_Intel</dc:creator>
      <dc:date>2019-06-28T08:34:00Z</dc:date>
    </item>
    <item>
      <title>Spot on.. Thanks Vladimir for</title>
      <link>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173228#M18196</link>
      <description>&lt;P&gt;Spot on.. Thanks Vladimir for your continued support. This is what I wanted - attached the snapshot.&lt;/P&gt;</description>
      <pubDate>Fri, 28 Jun 2019 10:43:19 GMT</pubDate>
      <guid>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173228#M18196</guid>
      <dc:creator>K__Uday_Kiran</dc:creator>
      <dc:date>2019-06-28T10:43:19Z</dc:date>
    </item>
    <item>
      <title>Hi Vladimir,</title>
      <link>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173229#M18197</link>
      <description>&lt;P&gt;Hi Vladimir,&lt;/P&gt;&lt;P&gt;Is it possible to filter on a particular task or a set of tasks? For example, if I have a series of algorithms processing on Data1, Data2, Data3 then I just wanted to see Data1 processing times on the timeline -&amp;nbsp; like, when each of the algorithms are operating on Data1. I did try to play around with the vtune amplifier as well as searched a bit over the forums but couldn't get any lead on it.&lt;/P&gt;&lt;P&gt;Thanks.&lt;/P&gt;&lt;P&gt;Uday&lt;/P&gt;</description>
      <pubDate>Wed, 03 Jul 2019 09:35:21 GMT</pubDate>
      <guid>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173229#M18197</guid>
      <dc:creator>K__Uday_Kiran</dc:creator>
      <dc:date>2019-07-03T09:35:21Z</dc:date>
    </item>
    <item>
      <title>Hi Uday,</title>
      <link>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173230#M18198</link>
      <description>&lt;P&gt;Hi Uday,&lt;/P&gt;&lt;P&gt;yes, you can. You have to switch to a corresponding grouping with Tasks and filter in (context menu is available by click on right button of mouse) interested task, timeline and metrics will be recalculated automatically.&lt;/P&gt;&lt;P&gt;BR,&lt;/P&gt;&lt;P&gt;Vladimir&lt;/P&gt;</description>
      <pubDate>Wed, 03 Jul 2019 09:46:23 GMT</pubDate>
      <guid>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173230#M18198</guid>
      <dc:creator>Vladimir_R_Intel</dc:creator>
      <dc:date>2019-07-03T09:46:23Z</dc:date>
    </item>
    <item>
      <title>Ok, the right click ('filter</title>
      <link>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173231#M18199</link>
      <description>&lt;P&gt;Ok, the right click ('filter in by selection'). Got it. Thanks Vladimir.&lt;/P&gt;</description>
      <pubDate>Wed, 03 Jul 2019 10:25:13 GMT</pubDate>
      <guid>https://community.intel.com/t5/Analyzers/Parallel-behavior-visualization/m-p/1173231#M18199</guid>
      <dc:creator>K__Uday_Kiran</dc:creator>
      <dc:date>2019-07-03T10:25:13Z</dc:date>
    </item>
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