{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":59093,"databundleVersionId":7457433,"sourceType":"competition"}],"dockerImageVersionId":30626,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"#
🌩️HMS - EDA and Domain Journey🌍
","metadata":{}},{"cell_type":"markdown","source":"#### \n#
Table of Contents
\n\n
\n \n* [1. Domain info](#1)\n* [2. Inspect train and test csv data](#2)\n* [3. Explore train EEGs](#3)\n* [4. Explore train spectrograms](#4)","metadata":{}},{"cell_type":"markdown","source":"The **goal** of this competition is to detect and classify seizures and other types of harmful brain activity.\n\nThe **task** of this competition is to predict the probability of seizure/LPD/GPD/LRDA/GRDA/other in the electroencephalography (EEG) sample.","metadata":{}},{"cell_type":"markdown","source":"If you have questions or you think I'm wrong somwhere, feel free to leave a comment.\nAnd don't forget to **upvote**😉","metadata":{}},{"cell_type":"markdown","source":"\n# Domain info","metadata":{}},{"cell_type":"markdown","source":"#### What is EEG?\n\n**Electroencephalography**, commonly referred to as EEG, is a non-invasive method used to record electrical activity in the brain. This technique involves placing electrodes on the scalp, which detect tiny electrical charges that result from the activity of brain cells. The signals captured by these electrodes are amplified and recorded, typically resulting in a series of wavy lines that are analyzed by specialists.\n\n![image.png](attachment:73b6d95b-fb29-4ed3-8a35-609a330b9e95.png)","metadata":{},"attachments":{"73b6d95b-fb29-4ed3-8a35-609a330b9e95.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAARUAAAC2CAIAAAB4c45BAAAgAElEQVR4Aex9B3hbx5E/LuWSSy5OHCex47ic4yK523EvsiXbsiSrWYWiKlVIib2TYifFTom9iL333gt6JUCQYAN7ryBIggCI3oH3Fzm5dzxJ9EV3+SeO4/nw4XtYvLdv3+78dmZnZudhkO8ombYhBEGMRqPBYJBIJEqlEkEQtVotl8vFYjH0hE6nMxqNGo0G7RihUIgeIwiiUqkQBJHL5QqFQq/XIwii0WgMBgOCIPANJ0ilUrjKYDDAaQiCoIVwjlqtRhBEJBLBhQqFAkEQ+NZqtQiCmEwmiUSC1mw0GuGnSqUybBKCIMvLy3A5tEQul+t0OolEApWvra1BOTRGq9VCDTqdDm6hUqmgVVqtViaTwWnwL5wgl8vRe6nVaj6fjyCITqdDT0YQZHFxUafToZ2jVCq36X7T1p78DhxjvgPPcN9H+IbxW1lZEQqFJtN/jSXKf4Aik8kEJQaDAYCk1WqNRiOCIEtLS1uhxePxABsikQg4FaqFy0UiEYABeMtkMun1eoVCASwoFovlcvn6+jow4tLSEjwIcLNerzeZTKurq2q1GsAG33AOIF8kEsFPk8kklUrhBLlcrlarAbTr6+tGoxGapFQqoYXA/TAFADLh0QwGAwBGr9cD9kwm09LSkkKhAMjxeDz0vtAJ8FwCgQCgzuPxEARZWVmBn/cdgvsO1j9u4T8dftbX17eOlnGTtkoDHo9nMpl0Oh2wBYIgWq0WrtoKBqVSCWDT6/UzMzNGoxF4C5Cj3ySQdQASQIVGozEajSgSdDodMLdQKASRAm1TqVTo7C4Wi3U6HaAUbfxW8KtUKvhXLpcDB8O1UAhMjCJteXkZQZD19XVgfVTqQvPW19dNJpNWq0VxgjYVvTWPx4NrRSIRiliFQmEwGFQqFTTge/xs5bF/yOP7Tn4o24nFYpFIBCBZXV1FtSaDwSCXyxEEAREBnKRWq2Hmlkqlk5OT0B2AJZT/AGboXyqVCtXuFAoFsCNM9sDWMOVrNBpoBsz3BoNBKpUq/5MARXDmvWMgkUh0Oh3K31AtIBb9Xl1dRcslEglIBpVKZTKZ4DEBS1A5QMhgMMAd9Xo9HMjlcqgEpBMU6jYJQRCYZaBmeEyVSqVWq7+5/+99nH/Qkn86+bOystLZ2Tk4ONjT00Oj0fh8vlwu7+/vRxBkdnYWYKNUKmE2lclkWq0WeHRmZgbGWCwWwwQPU7LBYFAoFDqdTqFQGI1GkDwwzS8sLMBUDchRKpVGoxFda6GsDyCcmpqC+iUSiV6vZzAY8HNpaQkqMZlMIpHIZDIJhUI2m72ysoJiQCAQSKVSvV4vEAhUKpVSqZTJZAsLC1CDXq+HGrRarclkgkaid4e5A55ocXERJoKOjg4SiTQ/P19VVcVisSgUSnNzs0wmI5FIFAoFi8VSqVTokPX1dTabXVFRQSaTEQShUqm1tbVwo/tCCJr0nfn+p8PP6urqM88888gjjzz55JO//vWvk5KSGhoaXnvtNZBCqPoOdgVUHKnVagwGQ6fTgWVBSVtdXZVIJCCI1tbWwCwBkmp5eRnYS61Ww3ICjBAwQ8tkMsASrEmMRiPM30KhEOqvr69/7LHHUF0IuA0W5QiCrK6uPvTQQzk5OVA+MTEB+hIoiiioFAoFmEbgNIA0nAmVIAiiVCpB9KELHq1Wq9Ppdu/eXVdX5+Tk9LOf/ey111574oknDh8+3NHR8etf//r555//7W9/+/bbb4eEhGi1WmtrawwGc+TIEQwGk5KSsrS09Nprr3V0dNwXPKj8/x4/3/Ye2G78+Hz+zp07CwsLEQThcrl8Pr+tre3DDz8UCoU0Gu3MmTNOTk7d3d0ajaazs/PcuXOnT58eGhrKy8v7+c9/7uTkxGazk5OTk5KS4uPj79i+rK2tL126lJmZKRQKpVKpvb29ubl5amoqgiA5OTlJSUmWlpaXLl2anZ1FECQ1NfXkyZMhISHj4+M8Hi89Pf3kyZMZGRkqlSpvkzw8PGxsbEZGRrhc7vPPP5+YmLhr167a2lqlUtnX13d5k9ra2hAEwWAwBAIBQZCEhAQLCwt7e3uhUCgWi4uLi83MzIKDg/38/BAECQsLKy8v9/T0VCqV169f37dvX2ZmJoIgcXFxaWlpZmZmWVlZBALByckpPz8fQRC9Xi+Xy0dGRjAYzODg4JUrVy5dusThcMRisVAo7O7uxmAwRCKRz+ePj4/L5fK8vLx//dd/7e3t1ev1SUlJR48eVavVr776ak5Oznb9/23nmwds3z+d/OHxeE8//fRrr7323HPPvfnmmwwGo7S0dMeOHdPT04888khQUFB2djYGg+np6XnzzTc9PT1TU1N/8IMfcDgcDAaTl5fHYDAwGMzu3bupVOqnn3566NChgoICDAaDxWIDAgJsbGwKCgp+//vfEwgEb2/vRx55pK6u7pVXXgkNDaVQKBgMJjEx8b333vP29o6Ojn733XepVOorr7ySm5sbEhKCwWAaGxvPnz9vYWExNTX1y1/+MioqKjw8/JFHHhkYGHj//fdv3rwZGhr67LPPzszMPPXUU/X19eHh4Y899lhpaenx48cdHR1bW1t/8IMfVFdX792793e/+93IyMgnn3zy9NNPNzQ0+Pv7X7lypaamBoPBcDgcLy+vhx9+GIfDYTCY48ePu7m5YTCYsbExUN7q6+t3794tEoksLCyeffbZy5cvHzt2LDw8fGRk5Ic//OEXX3xx+fLlkydPcjicc+fOmZubK5XK/v5+IpFIoVCkUumlS5d8fHy+x88DIvHbejo6kGBqEwgE77//flpaGhaLra+vFwqFlZWVu3btIpPJP/zhD/ft23fo0KH3338/NDT0d7/73fLysl6vX1hY0Gg0MCW3tbVhMBiDwTA6OorBYGCVcvr06XPnzh04cOCDDz44c+bMyy+/HBoa6u7u7uzsjCDI+fPnozdpz549YLWbm5s7duzYrl27Dh48+Oyzz54+fTo+Pv727dsGg6G6uvqJJ56g0+mPPvqoUqnEYrEvvfTS7OxsaWnpuXPnjhw58sQTT/D5/EcffbSjo+PgwYM+Pj5arba4uPiPf/xjcHCwm5ub0Wjs6enBYDA8Hu+FF14oLy83mUxTU1NXrlw5fvw4BoNhsVh2dnYHDx5EEORf/uVfSktLh4aGfvzjH8tksrm5Ob1eX15e/u677yoUCltbW3Nz86qqqpqaGjKZvLy8jMFg3NzciouLa2pqZDKZg4ODnZ2dXC6vrq4+dOgQBoPB4XChoaEXLlyA3kY7/1vCHVqtFtxZ0DDUsabX65VKJRjl9Xr9+vq6RqMBq4lGo0F1e9QBgD7Od1b+oE+IDiGMaG9v7x/+8IecnByZTNazSS0tLU8++SSJRHr44YcrKyupVGpJSUlrayvoSHQ6/eLFi1wu96GHHqJt0kMPPSSTyZaXl1966aWkpKTJyckjR46EhoYePXo0MDCQRqNlZGTg8XgPDw9LS0uxWOzq6hoaGlpcXPzII48IBIKioqLg4GAHB4djx46NjIwUFRVhsVhbW9vjx48jCBIREXHo0KHu7u6nnnpqdna2u7v73XffpVAoP/rRj1pbW93c3P7whz/w+XwMBtPR0XH9+vULFy6o1ero6Oh9+/aFh4c/++yzAwMD8fHxL7zwwvLy8o4dO6qqqtbW1i5duhQYGNja2vqzn/1scHDQ1tb20qVLQqHw97//fVlZGY1G+8lPfjIxMWEwGEwmExaL/eyzz+RyuYWFxe7du8fGxvh8fm9vb19fHwaDqaqqGhgY4HA4IpGovr4eg8Hg8fg7BnFfX9+f/OQn8/PzXl5eVlZW30L8aLVatVqNuvLAmycWi6enp0dGRhYWFiQSCeoMAP5Rq9WwUgWn2VbXH5zwT4cfkUj03HPP/fCHP8Rs0oULF3A43LPPPjs3N+fo6PjGG29gMJhz584tLi7a29v/9re//fnPf37r1i2TyfTKK68cOnQoMTHx3/7t35RKpVAorKqq+ulPf3pnKf/kk092dXU1NjY++uijBw8efO655+4sD9zd3Z2cnBAEOXTo0K1bt5aWlt59993f/va3GAzmzmzd3d29c+fO3bt3g0BISUn56U9/+sEHH/ziF7+oqqoaGRl57rnnFhYWmpqaPv3004WFhWeeeebjjz/eu3fvU089NTo6+qMf/aitrY3D4Tz55JO/+93vfvOb35SUlKyvr1tYWPz617/+8ssvf/WrX0ml0l/96lewXnJ1dX322We//vrrJ554gkQiJScnnzlzRi6X//KXv8RisWw2+1//9V+NRqNIJBKLxTQa7Uc/+tHq6qqVldXDDz/8y1/+EoPBPPLII1wu95lnnsFgMP/+7//+8MMPX7169Y6hPCAgAIPBPPbYY1999dUf//jH6enpr7/+OiQk5NuGH51Oh0oPoVA4NDTU39/PZDLn5uZmZ2dnZmZGR0fZbDaLxZqfn0cnX41GgzriUMMM+u/GQnTrj+/k8V3yR61WT0xM8Pn8hYWFiYmJnp4eo9GIenWIRCLYkWUymcFgaG1t7e3tBY8Hh8Npb29HEITBYEB4i16vHx0dnZ+fBx+ISqWamZnp7e1dXl5WKpVgghMIBKurq2AXlkql7e3tQ0ND0M8jIyOtra1gpvP39w8PD+dyuY2NjeBUAc1wdXWVRqNJpVKRSEShUBAEGRwclEgkw8PDk5OT4GWqq6sDBaOjo+PGjRttbW3Z2dk7duxYW1sbHh5GuYFOp69t0sLCwtTU1NzcnE6nGx4eXlpakkgk3d3dqIFOJpP95Cc/aWlpmZqa4vF4XC53cnKyr69vcXFxfHz8jvLJ5XJ7enrQOAYsFtvX12c0GmUy2R27yGOPPcZms79t+AHriF6vF4lEXC63t7cX1cpgOCDWRCqVTk9Ps1gs1PipVCrBzQ1G17sw8k+HH1BqFQqFUCiE2Ba5XA7xLyCdIfhFo9GYTCYwN4OXE9yL6By2tLQkl8s1Gs3y8rJMJgM7NXQxlINdG1yZOp1uZWUF9Uvy+XydTgcmY/BOgt0CxgbGGEGQtbU1qFAoFCqVSr1ez+PxIJIA7qjRaMB3tL6+LhQKRSLR0aNHd+7cuXfv3ubmZogkEIlEKysrUqkUDSAAuzbUDG4uaKRUKl1dXQVTYUVFRV5eHgTsgYMLPK3gEAOX0dramlKphG5RKpUwEfD5fA8PDx6P923DD/S2UCjs7++fm5tDLekQ7oi6yMBvsbi4ODY2Njc3p1KpILIEzke9eSiK/unwg0ayyGQydAYCLgQ7AcTR3PFswHwMnhmdTgfxaXq9HjyPADbURwnCHeqBTl9eXgZO1Wg0aL8LBAKwSaCOJgjnEYvFgEyRSLS0tAQgh7gHdCJEw09B3EE0gFqtRiWMWCw2Go131v2rq6toS2AiALZYXl6GtplMJgiPAFii3KDX69E+AZkJj48GyyEIIpFIAIoikQjaCdMQOncYDAZgSihHV+roXf4uByqVamVlZWxsDI0zlMvlIpEI/OOaTQI5Dw+ysrLS1dU1OTmJBhCi0bFb2/9Phx8EQcRiMXAJBMqgESvgT9waKwA9NT8/D6iDMEqYq1DNWKvVApOBwEGDqeFa0J63ihFgQQAeoA44Va/Xo2HOEAaBSjDAKrC+Wq1GWZzP50PNPB4PZXTw/cNdoA0SiQSdbrcG7GzlA3D+QgkIFqgBmAyUH0As2kswj6DwBvxD3KpUKv22yR+j0cjlckdHR3U6HSAcfXyhUMjj8dCADJi2tFrt+Pj4wMAAzERbY6/QC/8Z1z8yhdyEIFr9nyPHtGoNYkI0KrVGpTYZjAbdxn6EDXYxGnQGPRqhrVQqgePBRAMBpignIQgiFAplMplKpYKYNLVaDWIHIqChTmBiwIbJZAKNAv6CmBrQ2UAjgnI0vhs1p4ISKBAIAJlqtRoiG7RaLRhhYe6HoDupVAqnwYLNaDSKxWKIY4CGgbKKrmQAJ3BrdLksl8shZE4mk4FOiAaYQpwb3AKmcPTabxt+pqamOBwOgATdkNLU1HT79u24uLjIyMjbt29TqVSYKCFgFxa009PTqCEbnm7r9wPLH3Q5ftfB1kq/tccmBNEjJh1i0iMmI2JCjJsfgwlBP5slxs0T4DQUQnc91F2Pj/6867Tvf/7tewBkKSpyYc8Ii8VCw9hBmOTn5ycmJs7OzkL4Yn9/v7+/P0RCohOKVqtlMpmwDP7r6G8oo9x18Lfvpv/FHU0IorsvfvQmRL+Jov+OHx2yZZPQf7/fXY+P/vzvZ33/6+/QA/fFT2dnJ7rNUaPR3HEJFBQUDAwMCIVC8AUjCFJbWxsdHQ2GONCZdTpdT08PumbeqjLAg30vf7YIH8OfJdL38ufvwPV/vVveFz9cLlcul8PiUKFQdHV1paamGgyGuLi4mzdvzszMrK+vM5nM6OhosLWAyqrX68fGxlArEWoHQhv7z4gfUN7+S38z/ieENoUPSBKAkP57+YNyyj/OwX3xMzY2JpPJQFUzmUwcDic+Pl4qlcbFxdXW1sJij0KhpKWlwfpToVDAfuG5ubnJyUnQBmEhtLUnvsfPf66CYC20sdl5g1D8GDdyEDwAbe3c74//Lj1wX/yMjIxoNBqI30EQZGFhoba2lsfj5efnU6lUSBERFRVVX18PqR2+x8/9xw6AgaLizyaE/wQPsgU/xk0bA9iR7v3eDlL3v+v3pX/DHrgvfrq7u2HvE+pbW1pauhMp4unpWVFR0dnZCftNwLe2uLj4vf52/xEDVGzl/rsgBH/9GTzIxm7N+9LWGrYe3/+u35f+DXvgvvhpb28HfQxiZAFF/f39iYmJHR0dtbW1TU1NYCeAsI/v7Qf3H7GtvL71GJU8Wwtht/P3+Ll/V35bS++LHxqNptFoYPMCuie/rKyspKQEDAZbzdNSqRTd8f5/tV+j/ARsBCstsKkbDAbwnYFTD3VFA9tB90JYIXh8wXWIbrqAeHLUmYUmXkNd6XcNEGr9QNMsgdqKeo7RrDcQ0AUeTGgbuDjRrDoqlQruDpEpPP5GDOXQ0BCTyayrq2ttbZ2enobpCg2WkcvlECEGmQaMRqNuk7RarUAggJRx8CwajQaadK+5864n+oafqPsCuAF9CrhkqwcdzErfUNXf5S9wp/7l3zB2//fv7R52fn6+u7sbuBeyl2m12rKysrGxseXlZTS0Ar0cYk0gVQa4g9C1E3rOXxR/cBd+QLrB6gqiD4GxQLlcX19HQ/FQrxNstABmWlhYAMihAWDgOB8dHUWnAQiOhMgUNMARQjwVCgUIVkijAfhBrSLoASANogSgUKlUQh8tL/HFQpFQsJFVUCQSSWTSpWU+kUwaGhlWqJQlJSXnz5+PjIwMDg6+cO78jcCgkJCQ4OBgGo2GRr5otdqsrKzKysrBwUE06ZRCoYCoMAgsACUBTTaAIn9r13/z8V3djuIHspRAoADMaJCxYLva7is8/waFaPv/woPtmrTdc21Xvt3tFAoFh8MZGRmBE2Qy2fr6OjrjQ1gjpM6DmUuj0XR0dIyMjMAMC3C6dxz/Z/sb2iB4wju7ShISEuzt7W1tbZOTkycmJiCqf2vVGo0GEoIplcrBwcGtMcvoY0Oci0KhIBKJwJ3AbSi64Ew0wQUaXQ/pabYKMVTaAFQghhJyF6LzCgiEjZaYEINOPzQw2NzcnJqampuf5xfgn5aV2d3fN8dbpNJpcXFx0dHR+bl5cbeir1paOTo6hoWF5efnLy4uCjZpbGyMwWBwOJzS0lIsFjs4OKhQKOBGd1IgrK2t/V/EDto/0BvQ5zAEEOMD0wqchsbLwV/3nbzhnL/999YH+b8cP2jLv+Fe6+vrHA6nr68PDU2C+Xfrjjrodu0mEQiE6elpCDaHHan3Vv7A+Glubr6zIZ7L5bJYLAaDMTAw4OnpKZVKZ2ZmGhoagIM1Gg1AYmBg4OjRowiC8Hg8HA53ZzPz1NRUeXk5TJ9zc3MDAwPe3t4dHR2wrwamcLVaTSAQQCLBtidAv0ajgXhhvV4PW1lQFIGegIo+0NYgMBS2TENta6uC+emZqLDw5MSkoKCgxMTETk5XQPANBxfnzp7umYV5ZgcLi8fRqbTe7h5cS2tuZlZ6alpaWpqjo6Ovr6+fn19UVFRcXFxZWRkej6+srKyoqKitrS0vL8disSsrK6gmCXISQIUG+d/b+99ccpfmptPplEolpOfVarUgmUEaQ94CGPW7vh+U//5a53/zo9377/9v+QOcubS01NfXB3Ox0WhcW1tTKBRSqRTyKIFCLpfL19bWiEQiGnEPeEM16q2Nf2D84HC406dPDw4O0un06enpiYmJwMBANpt95swZDw+PhISElZWVPXv2HDt2zNrauqKi4sc//vHg4OCePXt8fHwqKysPHz585cqVmJiY/v7+8+fPW1paPvbYY5OTk+np6WfOnNm3b19LS0tVVdWJEyfMzMzy8/NBCZRKpSaTCdI+oRPz1qB61Nq4VXxJJJL29vbS0tKwsDBbW9va2loSgXji6NdlRcXdXRx7e3tvb+/E5KTAkOCKupo2EmFqbra5tSU3P6+qonJkaLi3i1NaWDQzNe3m5nbt2rWEhITDhw+fOXMmLCzs9OnTubm5ExMTeXl5WVlZzc3NZDK5oaFhZWUFnAygbUokEthLB4O3td//kmN0IwCcrFarIXJcIBDcyWewsLAwNzc3MjLC4/GWlpYWtqHZvxNNPyBt18zxB6TtbruysjI/Pz87O8vlcvF4PI1Gu3eZbTQaBQJBb2/vne33EPwOSSS1Wi0aC3/XwD0wfshk8qFDhzw9Pe3t7RsaGohEYnR0tKOj482bN8vKyk6ePFlbW+vt7Y0gCIlEmpycjI2NnZ6e3rNnj0aj+eijjzo7O++Ims8//zwxMbGoqOiOSPH09Kyvr//ss8+amppAd4qPjw8ODm5ubp6YmDAajfPz8zQaTSgUQvySRqMByz0s2eF5UGsjOI8FAgGTyczKynJycgoJCYmJiUlOTmYymXzeUlRY+HUXt4SYWBqFWlZRbuNgn5KZnpGXczsrIyk99Zqd7a2Y6Mz0jPLikpK8gpKCwhuBQUwms7y83MXFxW+THB0dbWxsSktL5+bmMjIy8vLyysrKCARCeXl5X18fgiDT09MqlQri3gHPaADVXb3/zT8hIfXWlc/Kysrjjz/+9ttvv/DCCy+99NKrr776xBNPPP/88y+++OKObeiFvxNt05xti1/ehl5/QPrjNrRz587HH3/8xRdffOutt55//vk9e/ZERETU19f39fUNbBKLxSorK/P09NyzZ8/TTz8NqxI0IB0CF+4drwfGT0NDQ0REBFSk1+uHhoZiYmKsrKza2tpYLFZlZWVcXFx5eblAIMDj8XcsHu7u7svLyydOnEAQ5J133oHsmPv3709OTq6vr78TsW9vbx8TE3Ps2LGxsbGioqL29vb5+XkcDgdbmhEEmZ+fv5MF5uWXX3Z3d6+rqwNdTiKRAIOCVIXVF6guGo2mtrY2ISEhMTHR19c3Pj4esu3g8XhWO3NyeBTf3BoVFh4WEmrnYJ9XVEhi0KxdHB2vuy+uLifeTrG8apWfmzc7OdVS15ASl5CXkxsbG9vY2MjhcBoaGuzs7OLj45ubmw0Gw8DAQHh4eGtrK2R+0ul0XV1dRUVFZmZmx48fj4yM7OrqAo0U3UNy7wB8Q4ler4d82XCOTCZbXFx89913IyIiYmNjExISsrOzk5OT8/Pzi4uLM7ehnL8T5T0g5W9DWQ9I2dsQVFNQUJCXl5eenp67SfHx8dnZ2QUFBSUlJcXFxfBXcnJyYmLijh070IUGuqPkXhXugfHDZrNff/31iIgIb29vSGsWERFRUlJy6tSphISE5ORkhUKxe/fusLCwzz77TCwW/+IXv+jp6Tl16pRMJisrK4MkNYGBgVNTU3v27ImOjn7qqafu7MK/evVqQEDA2bNnWSxWQkKCn5/frVu34uPjwX4wOjp65swZJpMZFxf33nvveXl5wcJuY1eMYSNEWqffeDmCVruxe0cilvr5+DvaOxFweCqZUphfEBzgT6OSu1jMucmxzLi4j998/bqDfXlR/t69n9e3NHQPD2SWFGAZlNyyYtfrbhUVZUW5WQ1lpYSa6prC/GAfr+L8nJraipi4aCwJV9NYW1VffSMkuLa2trK8IiosnEYgra+uIVq9UatbXV7p7u7OyMhwcXGJi4vbt29fUFAQiUTaBIARQe772RZBkLgUlT9SqXR+fv4Pf/hDcnJyWVlZZmZmbm5uUlJSZmZmenr6NvDJTP07UcoDUtI2lPiAlLENZWZmZmVlQe7L3NzcrKys+E1KSkoCJSI3Nzc9PT05OTklJSU9PT0tLe2nP/2ps7MzqM2QIfley9D/jJ+7hhdSzs7MzIyMjIyNjS0uLspkMqPR2N7ezuFwpFKpWq3u7OykUql8Pl+v13d3d09NTUEic6lUStokqLOjo6OhoWFqagq2DQ8MDEBuDciVQSKRwPcCe7YSEhIgaejCwkJKSsrhw4fd3d0bm1pEUoVMqVPrEBOCKORaxIh0MTmrC6u4RpyHvYurjb2ng2Ow1/X68pIeBs35yvkQJ+t4b5f0yBukxmo/b/czFqc9gnx8I0Iu2ltftLOqqilPiA4PcLHxvXYxxPpSlLONs4V5bFgAl9uBo7R4hfpEpEbXEZsKKktu3LgR4OVjdfp81q0Eck0TojYiUpVgnldRVtnXP8BksR0cnb19fW6EBDs7O1ZUlOm0Sq1GgZi0Wo3cZFIZDAqJiL+xnWIDVNsSWCDQaW92dnbHjh3p6elZWVkZGRn19fUUCgWHw7W1tWE3CbdJ+E0ibBKOgH+gD1z7l38/UOV/xZPbcNh7P3/uhDYs7p4PAYe/72e7J+VyuWVlZZcuXSooKIBMGFstzOiAPTB+4Ep0jwQ6tADTu97PYTAYIH0JanWVy+WACgRBZDIZaklEd0rCmzwUmwTv4QEekp8hrBsAACAASURBVMlkX375ZU9PD+oC6+rq8vHxO37idEBgKInEVKr0JhMyOTE3PDCek5VfmFO0PL+0trB047pXXnJyRlwMC9ty87pLdVxEU1J0d1M1saq4rb7ydnpibFq8T0SQlaudk7fL+MRQRJCX7amjBWGB2NTYRBfbkphIYk1pfyelsbkiIi70kt1lLB3/6d49udk51x1dLh83P7X3oNln+88fPEasaZwfmRgbmSQQqTR6O3dgKCQs9Kr1tYCAgBMnjonXBRub9xCdQa+SyQQIolGpxCaj5kHxs3Pnzu/xgyPg7wVPG24DPvciB0ruCx4Cbjv44JlMJpVKbWxsZDAY9vb2kJXpXivCA+NHv0molVOn06FhrWAvQj2VsL0ZNsRvxS7ELqCbmUC8qNVqOAdFFApxMF6ZTKbp6ekDBw5APkiwywkFojW+kM3odLRxtr5mX15Ra0CQFjwpPSc/ODyqpraeiCdkpqQQGupZ2DbHCxfSggII6ak14aFFYcHlyXHZidH5+emOHg6h8eEX7a6cu3IWi63PjI8++t47mX4+5TeCKekZ6b7e+JLCtpICenNDamxUZlJ8Y2U5qa3tNz9/KNjbr664PP92Rll2vuXp8x+/9W5dRRW2lcDp6uvr43Z2cmJi4jw9vUJCw8lUSh+3v4/bv7AwZ0KMWr3GhBjlSpnpG4UPWKXB3gi9MTs7i+InMzOzoaEB5A/Muxvcc4/82ZZB/sH/gCe99xuPxd3386D4YTAYdXV1bDZ7cHDwhRdeGB0dhXkcZUs4eGD8QPoFEDtGoxG2wqOWZYh0gN32qE0MBQ8qhcBPApfDHnpoDeo/USqV6PtzAJYCgWBlZQVUf0hzrtPp5FKZWihD9IhSrExKSL546coLr7zy5kcfMnq6s0tLsDSy1TXL8uIiQlPDC489Fu8fSC2tYGbncwqKioJDbrk6ultd4LTjs3MTvQPdzl85ZX76a1xzbQ+ZYLF3f3V0Ei2toPFWUm5waFF0tM/FS1cPHvKzuHL1q6NB1+xczl16d8crpMZWKp54/uy5q5ZWDBr9jNmpxNi4ptpGmUhKxpEqy6vSbqfHRseNjU2IxRJ2J6e1DYcnkPiCNb5gTYsgcq1WZ9pQO7+B7tXfdu7cmZGRkZ2dDfihUql4PP57/KBAui948Fjcg+IHj8cXFBRgsVgajbZ///6FhQXUirB1vB4YP1svhjg3kDBQDi4/cGuiXicUuIArtAZwq8NP9Bh0NqhHq9WCYxSkjUAgWF9ff/XVV8H/NTw8bNTqjFLl6sRspH9ITkZmVU11Z19PSl7WGx+/v+vA3hMWp1Oz0kgkAotETr0VE+3tb/H5/gvvfBJifqm3sra/sbE0Kbo4NZrDaCVgK0qLUlrri8syU/Lj4m5cdXA4dMb+C/MC/9hQO6c9O1+OuGZfEXyzxCuk+Hpwtpt/XmB4dmTMdQdHCokcl5To7O7R0NLa0NBkduw4k0gaYHdSWlovnz6Xm5451Ns/NTpp0BnFItmqQDwwOMbq7CVQWXyRXIsgUpXuQfHz4osvAn6ysrIaGxsBPzjcpu7yzyR/thOf2+Fku/Lt6qmqqiKRSCwWi0AgHDlyZHFxEay7KPfCwf8GPxBYCZwN0WvGTYLVDqAFEhRCGJhUKgVZpFarQapAJgflJgH80GMIxoHGQbUmkwmyz0D9w8PDFy5cgKRn62vC2aGxCN/AAA+vyNAQKpXsd8O3pLokv7KwsLo4Oin6rfffdnS0z7p9uyQru7WyGpGoEd56pKVjmJUtp6YGn5sZ4nA5OcA5LczT3/bskQ9fP/nph7iikuPvf57mGyMdEiDrGyt83tDQtX2H2mJuM25l0CJTu1IKU+09y2IS06Jj9+79PC4lKSYpITYx6dy5C0f2f+V59WovEd9LJIZ5eOTEx/s6uRCbmoX8lQ1NzYTotMjCkrC7b6Srd3RNtpHs50Hx89JLL2VmZubk5AB+aDQagUD4Hj8oDLbDyXbl6IV3HbS0tDAYDCqVOjAw8N5770FaSTTaA0XR/wY/oJGjEgPqQpU0AAAkcEL/gjUMKp1A/QP7LKrUoTmc4ABQCjWg/ntwR/r4+BQXF2+8eVcizc/Oyky9XV1eVllaFBl6Iy46IjQkoLqqtLAgOzEhOj7ulp2t9fvvvWN24uTyIo/URkB0SEliZmZYZE9T40BTbV9NUUXo9US7c/m+jsS0uJbkRHpJhcd5W+WCUr2CBHvfrK5pQEz6QRI1/NzVnuSCuZzavsT8MvegpuR0amNjZxfT2tH2i6/2f7Tr45K8Ij8H5zN7dn38zOOup0/cdHMojI1qLcpbGRtjtrUJ5uZFvOUNC4IJ0aiRhgZcL3cCzIboYNx7cK/+huInOzu7qakJ8IOqcKDGACuA/W07vtmu/C42+h9/blfP/+9yIp7wQJ/t2rPdA1Kp1Obm5pqamomJiQ8//BBy9N07QA+MH41GA8FXqOUNKkXxg2Yqg+UvCg/wpqNIgPdLA9ggSgK0wa2J0dDMnRAeCzIKbrdv377l5WUShfz5/i+iYm+WlhUO9fdYnjudnRg7yKQXJsfZnDlpfeZUqI9X+I2A2ZkpgVj01gfvuXp5aU1IUGBo8s1bgbbXusoKRqvyF2sKZK1VkwWps+UFzLS046+/gy+uR7QIhTSA+ZdfZdfVCNXSlrKyW9ZOieZXq228ss/aDOeUEtKyOttaYm+Fnzp9wszsBBVPjPYPTvQLsN2/J9n1qvPhz60PfGq179Nj775OryzVLS+yWlvHurt163K9UqtXGcpLqskk5uTE3L1DsrXke/xsx/cPBB4inrBdPdvhp7m5mUAggAr35ZdfLi8vr6+vo8yMjtED4we98u9yAA8gEonYbPaRI0cEYlFiflZCQYatu311VWlSZCi2KL81PdX92NeJzk6SgX5adYXVGbMl3pwWMfZOj0kRJK2+xsLVWSBcfeOpx2OuWtBvhrQHevQFezVbXcg58TU98mbImQtXjphrVMjkqmpYrBxSrLOmB0+ZHQu0ss518Cyyco4/bL7WiOspKSEU5SVFBh/Ztyc+PDQlLDzSwT0/IDjq0qlEa/P4q+ZxtmdSXKzCr571Ont8oZOOyMUcXMtUd7dBIltbWCrNL5wYGutidm7Eg2+S0WiEJNpbO/Ze/LzwwgtpaWmZm9TY2Eij0YhEIow0+Nbu+qZRqM31De1UGoVApBCIRCyupaER/W5raibh8G1NzW1NzYQ2LB6La21tbWtrI5FIeDy+ra0NNEMymUwkEqlUKrz5lMPhkEgkOp1Oo9HIRBIRiyPh8EQsjown0MkUOGZQqO1UGhlP6ON0UwjEtqZmFp3BoFBJODxjs0ltTc19nG5OB7ub3UknU3AtrYQ2LI1EJuHwuJZW+FCJJFxLK1zOpNFpJHIHo51OppDxBDKeQMLhKQQivrWttbEJ19KKb22jEkkbJmksjkGjE3B4Ip6Aa8MScHgKiUzEE5iM9vraOhqFSiVTWptbqGQKmXhXb/3XTyKRSCaTGQxGZ2cn4OcugQHD9A+DH6VSCbEwUqkUFlG+vr4FZSVriL6HNxWTkXT5yoWa3CxaSWGirS07Oyvdzu7Eyy9e2//l8d0fe7rZt/cy++fH/dPiPrtywTokYGR2IsDJ9tbFMz7v/6n18vmqQ/u77K15MdFVV62z7Z1PfrQnNCSqntZZ19N3oyj75X0fsbuYH76w4+yrf8qzsks5dS7X8mpnTia7LL8sIcr+9PHa1BRGaTkuMbPYyzfHySr20uFiHxv/458l2J5NcLBI8bANvGLe31bPrK+i1dWwsG1JERGIwYjoDQIef3x0DJIqyWQykOFbJ7nt8JOVlZWZmfmX4Afb3IJtbqGRyGQ8gUmjt1NpVCKpi9VBJZIYFCq+tY1GIjMoVDqZwqTRSQQihUSmUCh0Op1KpVIoFOomMRiM+vr6Oy+6xOPxnZ2d7e3tTU1NAC3gZhIOT8LhoSoKgUjC4Qlt2LamZiIWh29ta2tqZlCoZDwB39oGoIJygEdTXX1bUzO0EFBBJZJQkDTW1pHxBGxzC5NGJ7Rh4UZkPIFGIsPtoFpCG5aMJ7RTaSQCkYgn0ChUEoEIx/BQ7XQGhUQGUBFweAaNzqDR8VjcfyHmvx991/ADYSxbw6tnZ2d/+9STzV0d7jFRLv6+kZHhNblZyR5ulOQkUvQtcnQUKfZmZVhwc1bqhZMHDx75/OlX/uh+68YxN/t9VhcDQoNyoiPbUxIuP/67FnMzrq3NgI0N9pQ5+7o31j+wLj7J4qTZl0eOXfP3tQ0LOH7twsT40MlPPw44cfLmiRMj6RlF1teSLp6rDPKuDA+IsbWsv3WTnJLamZaf7+QafGxv/JWjOW6Xa0LdHfa+62O275bdxcbU6Gg3e3xpXlZU2MtP/P66jXWItxei0473c6sqKtG9SbA83bpIvRc/O3bsSEtLQ/FDp9NJJBKBQCBvQzQSeaC3DxAy0NvX09nVTqV1MlkgH3AtrTQSmd3OZFConUwWEU9gszpYLFZ7ezuLxWKz2Z2bxGKxhoaG2tvbCQRCX19fd3d3R0dHT08PnU7vYnWw6AwaiQzVdrE62O1MOpnSx+kGoJLxBBadMdDbB9Kpi9VBJ1M6GO2cDjZIJDitp7MLLmTRGd3szp7Ori5WB6Cuj9PdwWgf5g6w25ksOoPTweZ0sDuZrJ7Orp7OLngWGoncyWT1dnGYjPZ2OoNGobbTGe10BvykU2kdTBauDctmddCpNAIO39vdM9DPpVGo23Tbhrz9Tskf2NkKNkSRSLS+vm4ymZgczgcHD8sQpKYNl56a5mtnF+1on+3hVuXn1Z2VykxNwCVFs6uKJruo1aWZkTf9YzPiMT/BvLTrg+SkuOU+zuXXXk7cu7fT1o5iZlbw1tsdFy81nTkX8+V+UmzsLIP+7z/4l9///tHQm+E0OolcX1MRe+v8G6+nXDibeeFs563QRi/nGi/HtKvnAw/vDzp8KP7cxd7b2az4pEwny3wfmxxvmwSH85k+doEWx0Ktz+dFBhTFR4U4214+fvjaabPsxHjrC+cvnDQrzc/3cHPv6+uDnfDwrgTADKgH98VPenp6dnZ2VlZWU1MT4AcG+15WoJDI3J7ePk6373Uvy4uXXB2dHG3t7K5ZO9nZuzg4+nhed3Ny9nR18/G8bnfNOsDH18PF1c7G1tra2sLCwsrKyn6THBwcrly54u3tbWNjY2Fh4eDgYG9vb2Nj4+rqetXSytXRycnO3vLiJbtr1q6OTvC5dsXyupu73TVrFwdHZ3sHH8/rTnb29tY2193cHW3tXB2drlhcvO7m7u7s4u7s4ufl7ero5OLg6O7sAv+6OTk72dm7O7s42dk72dn7Xveysbrq7+3j6erm7uxy3c3dwcbWyc7ey90DSuytba5dsXR3dvH28PT39fP39XNycPTx8vb19gn0D/Dz8XV3dfP19rlqaXXN6qr11WsW5y/4eHnTqTRWO/PeToOS7xp+ZDIZ7GO9s+c2KCiouLh4aWkJT6BEJ2VmFlTV1bVFh9+8HR4R7e480tYYZXk239d5BltFSIvuqSlsy06cYZOotcUXjh+KigytraxgNjV5nzx59Le/STt4cMjbp3bfftb588OOTmO+vhxfn+TjX/MaaoeqKpLcXV1OnSkMv0nLyevIyi5xc861vDiUEl1w4WRHqC8t0LP1umODm32xjXXhNRt2dEJDQFDIuZOFYT7FEf7u5oeCr10Is7e6fuWc9ckj/o428cGBdcWFHna2/u4e1pcv21vb7P7kU3OzU0VFRRCgDUZLg8GAxineix+IP0Dxw2AwYHFCuR9RyZS2puah3v6UuAQmhYbojYjOIF0TTY+OL0zNaOVKvVKtkSnkovWxgSGDSqNVqpRyhUQimZ2dhY20d17crVKpYH8RJJ6H94TyeDyFQqGQyXUqtVauXFngyUXrRrVWp1AZVBohf2VlgaeWypXrUiiZm5jasOBr9SqJDDEio9xBjUxhUGl0ChViRHgzc3+OwVVrEZ1BJZGtLS0bVBqTRgeXT42MqaVyo1oLl8xNTCnXpYgRMWl0iFavEEvWlpY3jo0mhUyukMknxsYl4nWJeF2jUsskUt7C4tqqQLouka5LEBPCameGhYSyWR31tXX367aNsu8gfmBhMDU1FRkZWVJScsckHxQYGh+dWVXcGnUjviijwNLMrCQ5Xj47FO50KcL+LKM0pbcuh5qXwKO34lKi54mtjIK86uTUoGt2r/7sof2PPh711WGymwfb2bXyq4OdVywnPNwWg2/UHD9Wf/7szY8+GIyNXimv4N7Oq3INsH3jA99dnzEiwhEyFudu32B9Hu9o2eXv1h3kRXR3KL1kUWZtzboV03E7Nc7ZweyT9z958bkjH73z3nP/YXH0q1P7vnC2uhLg7rY4MTk2MPTpRx+nJN1+5plnnRzdbG3tY2Li0N1aEonkLif3ffGTkZGRk5OTnZ3d3NwM+CGRSPflA+rmurybxfZydafiiRscvPlRS+WL07MbWfONG6+a0MqVgz19G8cmRK/d2OU6PT0tEokgR4rRaFxaWgIDrkqlYrPZsAVrI9+L3gD4WZiaka6JjGqtVq40qrUrCzwBjw+VG9VamVDM5fQgRkTIX1FL5YgR4TA7FGLJRgN0BkSrX55fXJ5fNGl0BpUG0eolAuHcxJRWrgR4GFSage5emVCMaPUmjU4rVw73cTfwZjDplWqjWisRCBenZzfwptNvZCMzIVMTkzqNVqfRbrwkQG8QrKxuvFnDhEAJg0a3uWbN6eyCxd59u+67hh/QZ9Rq9e3bt7FYbEpKyunTp1sbsKZ1pLOl1/WSe3xIjK+La7C3K6e9+ezxXbHBtiXJfgNthbPkqjir0w4fvpVve607JS3L1u3MS2/77j2ccuZiytGTOcfM5qLj0nbtmg70nw3w7XewK/l0V9qbrzMvX64/dCTjw93+z/zJ4dGXEj49MhWflnvyROA7rxnqSkaj/BJ3/Ql35fRUVODErVCyu0vplYtF1tdoyclX9u878NGHhJrqbhoFX1trde78oc+/CLsRPMgdmJycnptfamjG7dq9Nybxtm9Q+OHjp9qw+NnZWUgGJJfL0bfVbqe/QfwBip/29nYKhUIikWChf9c3jUIltGH7u7p93D03EGIwiZZXZULx9Oj49Oi4XqlGtHqQEhxmB6LbAMMGk+l0U1NTsM8KmrG0tATCn8vl+vn5CQQC8MdvsKPeYNLoFqdnQSAY1Rsh8AIeX6dQSQTCTZmAaOXKge5exIigmBnq7dfIFADmxelZnUK1gTedYQNdm/KENzNnUGk2WrgJ+LGBIa1cCQLHpNFNDI2IVwSIEdnA26ZEnZ+c3riXwbjxMZoGuQMalRpeTqPX6pYWebPTMyqFEvDT3cXxdPfo7e5hMtrv6jH053cNP5sbgZR83lJ8XAyhrbW8uKi8uMjP1Sc/Nv/4x8e8rrh/9cHnp776KivpJoNUt+v9578+8G6477Wm/Hh/ixM+R/a1Bgawo25S/W50hkYPxKfS/EKHE27PZubw8wtv7vksed++3sCALk+3HmfHhoMHqnd/Mm5tLfHxm7F1nbD1G3cLp1/zrLWwzjA7dePTj0otLwzHhLd7OHZ6OE+FBk1FhrF9vQsunI03O9FXWnzl0JG40EhCY0t6UkpeTn59faPFxctRMfE93KH+odFWPKm2sSUgOOzz/QcPHTt54OjRzMzM0ZGhtVWBYGV1IxjBhCjlCjQq4V758+KLL0L8QU5OTnNzM+CHTCajA7/1gEahsuiM0cEhX4/roDLNjk9qZArpmmh+clqnUOkUKoVYolOo+jo5iMGkksgMGq1cKhvo567wl00Go1atMeoNUxOT87NziAnp7e6xOH9hhb88MzWtVqoMug2BYFRrZ8cnN+SDwbShjxlMy/OLAh6/rqJquI9rUGkMKs2G/NEZAAzSNdH44LBEIFRL5aLl1erScvGKQMDj65VqEFBy0TpvZk6nUEnXRHLRul6pHujula6JVBIZ4H+gu3d1cQkxmDQyhVGtFS2vTg6PgizSqzU6jZbTwZZJpEqpDDEharlienJKv/miJ+heFp0RFBDY28Xp7GBv7a6tx/+w+PmzjrGRlhrIiBhMJoNItIYgegaFXJSVUVWQX52fT21uzk5I2vH4s7bnr775Hy88hMFYHz9Kqiy6cvLg0b0fVZfkjHE5V81PRri6xNs7p1ra5FhcI7h4Zu/fX3joYNW5sxVXLiWfOFHl6sKMj006fzb5jPlKWUmnn0/4Sy9OuLhMWlsPnTQbOXFa6OQz5+Lf5xFQYWmfbetk8c4HCddsUq2sx1IzOf7BVSfPFh3+ui80tMHVOfa8+RKdanXkWHRgOAVLeetP7+14/U+1OEJqQdFFe3s7Nw8Sjd7U0nzmjPmZs2ZfHzv43odv5ualDfV1k1ubJ7gDeplcI5MhRiOi1xk0G3IAMZoAP5sqyUbB7NwCyB+wvzU1NYGtjMlk0rahluZG/tLirYhwR1ub2JtRnq4uZUWF0+Nj/V1dk8PDfayOdf7y1ODQueMnKK1tc6Nj44ODouXlUS6XgsPNTUz0stljAwMLU1M0AoE/N0fF4yODg2vKymgEwvjg4Pzk5CiXuzQ7m5qQMD06Oj446OnsLF5ZYdPpLnZ2kcHBXe3t44ODI/39bDo9NSEB19Q00tMrWxXcjomd4A5IllfuhAseP/DVaG9fF42+MD4x0tM7PTQ8PzZObGrmTU4NdHYRWlroRCKHyZwaGVmanb0VFlZVUkLB4Rqrq/MzM0e5XAGPN9Lf39XePjM2NjE0NDYwMDk60t/VNTo4MDYwMNTfNzcxwaLTmBTKxMgwh8nkL8y3k8mRoSFDvb0sOo1OvX/HUSgUGo3GZDK7urr27t0LL9VEvf8glv+i/G/oqX+jAwOCGDZYZyt+jBtFekSvCfD2PPv10diQG0lhoZSamm48PuPmzYL42Gh35ysHvsDnpYfZX/v8jVfYJAJiQrQao2hl/erpy4nuQRV+Ecnml/PNzteZn2LaWc/GRfdEhiefOxt17nRZ8I3W5MTAU6fynJ1TTpknffFFn7ML/sBB/Ce7h06Ydx05JQmJ7vMPG88tNn/7ozcf/+PX73564YPPz+94U91AzD96ejYuZSwhOeuiBbc4r6uqPO9WvPmhk+VFFYm309/b83ludXVUauqvn3pi3+GDBw7su3Da7PCXu7/6/KNQf7eaihypaKGfzVwYG22pqkSMeq1EKhMKlevr8Havvwp+iAQcp4t962ZkYUFeeXFRTUX56ZMnaisrjGp1YXZ2gIdnTXFJmJ9/fwf7ZtCN6cGhoOteaXHx4/1c8yNHa4pLRLwlfEMjsamZgScgWl1VYRGpuYXahq0tKU2MupmdnNJcW5sYHX325MnxwcGu9vYbvr43Q0Nz0tKwjY3FubkMEsnd0ZGKx48NDGSnptaUlYX5+XPojDNfH+uk0sRLfGJTc31ZeVVhUWtNbX1ZOaLWICo1tQ1bU1wSExomWxVwORyL06fFKyuI0ahcX09NSKgsLlZLpYnR0daXL1cUFaUmJIQFBl4wNx/u60P0ep1CwSCR8jMzEaNRr1QiBgNiNGanphbn5rLp9JiICL1SWZCVdTM0lE4k9nSyt8MPlboBrO8OfuQyMWLS11WWYWtrqC1Nwe5uN5ycUoMDC8MDamNCGmJCa2NCzn/0tpPZ17EBAXqlto3QnllYpzEhYTfisQUNKU4B3Kxyq6d2lpww7/L27YuMKrG1pSXfRqZnkDUxotUhy4Ki0LCQk6fwPgFTt+IGXdxHbR1YJ05X7foybMebFZaOdru+uOUe0N/eK5heHiazE665+H92hOobhnPxogSGFNjbM3MzWjPT8JU1LtYOWRnZOQWFzSR8PaHtRkTIO+/96b23X3/ikYe++ujdEBeHICfr6qyUtop8Un15a3UlYjCyKdTWmtoN3V2n21D6NxW5B8IPfRuiUkgjw4Ne1z1amhtdHR3Y7YzSwoLUpMTxwcHS/PwhTncHmSKYX1CJxCwiqSwnF9/QeDsm1triIqW1bYjTnZNyuyQ7J9jbJyIg0NHqan1Z+Vhff11pGbUNS2hsKs7KriwuHujuJmOxRTk5xbm5HCbT1tKyoapKJhTWVVTkpqcLeLyCrKz/ePzxpdlZnUJRnpefn5beSaWdOnyESSRVFhSuzs1bnj0XdN0r93ZqUWZWdEhosLdPQ3lFdnJKZGDQdReXbharvLAwPCiIiscP9fY219Z2tbenJSaaNJqqkhKZUNhQVbWysJAUE0NoaXG1t78jZK5duiRaXraysBjs6eHNzHi7uZXk5aUnJR07eNDd0dHJxiY9KUnI57c01DNo9+84wA+LxeJwOF9++eXS0pJhk+6SIt+++IPt5Q9/YRbbVI9o1FI+r6Go0NrMzOrgl82xgay00MZQ147smI6C1Ax/b3pjE2JE3nh/r//NTCWCWF7zoTczG5MKmMl5qeZXPHf+iRMY0XUz/uapc+OteESjnx0a21j76pHpru7U636x5y9VWNkOBIQtR8QOu3h3O/vE7v4q1uySzYFj+FpseWlDZnpRY2m9oGvY7fPDuZdtk46ZU8MjI06eYOXn4PKzY/wDgq77HD9y2MvbPSwi2M7R6q03dni7Wt9wtrY89OVXr71kd/jAjSuXyqKjKhJj6XVVS+PjkuUVpVDUQaONcrlzU5MbJiPt5n70B9HfGNsQHtc2NMi97uk+PTWBb21BjAbEZKwqK7126RK+uRkW3GrxOpNIQgxGg0wuXVlFNNqclNvtBOLyzOzGCXqDWryuWBNODQ4hBuPyzGxjRSWbQl0Yn9CsSzYUTqOxv6urpqyM0NKyurhoUKnWlpaka2u3wsLSEhMRnc6gUgn5fJ1CYVSrFycm68vK+dMz4iX+SE9v0s1bepl8emh4vJ+rWBPSsDgmkTQ7MjrE6UYMxrG+i2MX6AAAIABJREFU/qiQEESr/eiddzhMpv3Vqx00WmF2NqmtDdvYaNJo4O6L09MCHm96dDTQ27u8sNDFzi4nLc3Xw6ODRqPi8TQCoZPBILS0JEZHI0ZjT0dHY3V1gJcXtrGxp5PdTr9/x1GpVDqdjuIHXtW8NTQEgPQPgx+TUdvHYZcV5ukk64LZ6U4C/vxXX/lfPFPlbTmQ6s9J9GHG+XbnJrYkxRArqqRi9cCkcGLVwBxaDQhLa6kk1N8uZmaUZl9xivniONEjpMze67adB6IwIkr95SvWXv435vkCxIQ0F1U4fvV1xmW74dj0paRsYWpB4cnLkQdOLmDbrU9YcLnTRVXYnz/y1KmTFzubSUN1bed2vhpvfrbBzy/S3Kw40A+bnZEeHvn6s8+//cpLX+756PjBPXt3vRHiYe1nczbW2SrRwdJ1/2c3zE9kuLpWRUZW3orG5uf30Bhg0l2YmklNSKIQiLDq/W/rn83XeG2sf+bntlv/tG9DdBqlm9N562Ykp4tt0mkF/CWVTIqYjAqxWCOTbQDAYFQKRatz82qRWLkmRAzGudExrUSK6A1KoUizLjEqlHLBmmB+AcBjUqoKMzJrikuMCqVBrlCIxYjBIBeJAry8mmpqEKNRJhQqxGK1VMrlcKpLSxGjcXl+nj83t8Hruk0Tmd4gWxXcgZDT1WvzY+OIwaiVSLUSqVywZpArNtaB6xKA06YxzWjSaNrJZESnGxsYQIzGwZ6esydPCng8AA9iNIpXVpbn5xGjcXVxEdFqYWE2Oz5uVKujQkISo6M3VGKj0aBSyUUixGAYHxy0s7JanJ4m4bAbAQr3IxptQzB1dHTcCbbYt28fn8+H3TQAG/T7HwY/qyu8qbFhDouBGPRKwUoXkWB57Nhtb7emILuOSAd6iO1kbvRgbkJNiJ/5Rx9KeWsGE0JoHzxibhMeleHnHJDqf3O0pu3Qr5+K/uxEq+ONBo+wysBYRKJXrslvhEc3kekSPdI3MEpsaKtOTL951sp/1/4Cs8t5xyzCdh9kJub01eLeeuGNFhwT295/zcX/6xPne4jt7cUVXz/9bIalZaGDXW2AT9hps6irltVJyeTKqiBHe9dLZ+ICXZmNRYVRXrfsz5QHuxV52aVbX0q5ejHy3NlkB0dybl5temaErz9iQpZmF3EtrW2NrdjmltHhkbv1t78AP8xtiE6jzM5MhYeFFBXmC1eWuT3dYsEqEdsGaxL+9Mxobx9/eoZJJPGnprkd7MEuDqLVsUjk2ZHRldm5gc4u8RJ/cWJysIszMzyytrAI2iaxqVkvk/e0M1cWFob7+mbGxsQrKz0dHSP9/auLi1MjI71stkYmM6hUs+PjQ729d5h+YmhoaXZ2iNM9OzLaRaPPjowy8ATJ8srM8Eg7gTjc3bMwPrE8Mzs/Nt7fwV6emZ0cGJwfGxevrCzNzlLx+Mnh4ZH+fiGfPzM2NjcxIeDxFqam5iYm1paWhvv6Brq7ZULh5PCwkM/vZrHgjtOjo13t7S11dRNDQwtTU1wOR6dQLExNEVpaUhMS2HQ6oa2V1X7/jqPRaAwGA8XP8vLyPzZ+EJOuj8Pu57BNCjlvfJRQU12Xk3Pbx7PM2yb1/H529HVWpGf/7XDyrYAYq/MXP9998dixI/sPffT+p3/4zR8+f+Pdqpik+Ku2ljvezDtuVXbWKeOMXaS59WALDdEh/GXhzIpgUbSemZU31Nkj5I4EHTv3CebnUZ8cvPH2niaPoKUmMq2g2sfWw87Bm949UtVMGh2ZGevouWXnaPfhh4SwYGyADzbIGxvs7/zJhwf+4zmLDz/J8fMNvnLB7tCedK9r3WUpWW4W6bbm6danyzztCtzsbl2+EHDuLLm4pCAuoSA9q6SoFN9CmJuZX1sVN9Q1MhksvdawuckKuct+PTe3rfxhbUM4bOvszJS/n09Pd5d8XWzSaafHx77Y/SmNQECMxrWFRb1MrhKJN8wDGq1CsCZdWdWsS6aHhhGDUbK8At9cdufkwKBkeUW6spqdnCJc5C3PzPKnZwxyxdzEhEYm6+/qEvL5aqkU0eula2srCwujXG6Al9fk8LBcJJodHwf+3pAYGq10ZXVDM1RrFicm1xYWbS9dFvGWpgaHdFIZb3IK0Wh5k1NcdqdcsIZodcr1df7c3NjAgEIsNqrVS7OziF7PpFD6OjsRvR4xGtdXV9dXVwU83uriok6hkAmFKomknUzWyuUyoXBuYsKoVs+MjakkElgLCXg8DpN5MzS0nUweGeB2MO/fcXT6hmLHZrN7enr279+/vLwM1mBU8vyD6W8Ioq+vrlicmTQp5Omx0ddOm0+w2X6Wl33NDrdG+EYc+5wa7jmWEdVxy5sS6d0Q6lEVfWP/6zvfeur37/3xaU9zs+irVhGnTiWdPBP/8ZGc/RfanIJzr3oU+oYLBicRlXZxnnc7OSU2MrIhN7847Gb6VYdb+4+3WHskfHY0dPeB/qyirpKatJDoowdOXL5sTyaz0hPTk/yDzP70RrXPdVpYUPnVC/3RYa3u9qmnToYcOOr9+YHAI0dSrK8kXj3rf2xPtMXB2frs+IsHcxwulHjaJllbRNtdvXpof9nt25kJiaUFJVWVdUx6p1qll0tVC/P8qIjo9XXpXws/fb3d3P7eiPDQ4aGBytISJo0a6OvTTqWQsVghn68UipRC0fTQ8OmjXzdXViE6vXCRp1mX8CanSM0t/OkZRKdHDEbB/AIoWlx2Z5ifP7aufnVuHtHpN9SwuTlEq73h67uysDA/Oent5sabmRHweG4ODmmJibyZmfnJyf/H3XtAt3VY2aKeP2te+fN+XmbevEmmpI3jxL3HlmSrWlaXSEmURLFI7L13EiAJNrD33sDee++dRCd6IwoBkiBAEIUger/fl9dx8l/sWcmszF9J7uJiASEQAu6+55x99tkHgtDWykpFURH4mEZTNgyuOBADNvv25la4n79MtAci5+xvOfQG1aEEs7yikyvM6lPp3t6JTCbicq16veroSHV0ZNJooCBTVlAA2O1mrZa+vb2xtATY7d2trcmxsc21tWUFBQ1VVRVFRa0NDdCPuxwOYLdzGQyFRNLd2lqSl0cjEgkY9J8KfiDVsF6vV6vVFotFIpF8Y0+l0Wgg7EJLGSxnBwAAp6enBoNBqVRCDiFfG4lYHNCQJpjDAODyUYvNbLaaFhdmyosLjg5E2xvrlXnImrzcwYaGuuycspRU/ODgRG52g5+bsLmYVZo8G+++ku53MFS5WJIYe+ec/6X3Aj97v8rPbasop8Hlaf3Fe8P3vRmIMuvketaDp9mePq3I3CgPd6fPLsD9fB69/151YDC9uiHtowvDrr511+5W3rrf6OnD6R6pjku988nlf/reP779szciX/pEPXrUGh+5gEgZDHo57u8+6efOzEyZDw1ciIofDoqqevysyvUZKuhFhffjtPuf1wY9ZXZX1YW4N8YG5gW+CH50z/ep82BvV2ZmZnVVva9fcGlJTWFRufxYPTe/PDQ4tra6pdHoQGPIs+PXbQe7UCiA9G/Q/M/k5CQajd7a+o5LKHRh3dpg0KlNjfWDA31XPv8MnpQo5HExG+uj/f215eVxoWHNVdU58FS9Qlmem9dQVp6blh4XGrY+N5+ekNhUWSVgMOtKyyryC7JhcJloLykyaodCHe3pLcjILM7OqS4q7mhuLs3Pd7p9m0OjTY+OFufmeru7lxcWUvD4iaGh+clJeEJCc23t1MhIdWkpm0otyMiU7grjw8K7mpoBm328r7+jobEkB1lXWra5sMjaJlk1WtWhpLW2rqmyCruy2t7U1NveDtU/Ii734d27+3w+i0JBpKSg6upS4uKGe3sTo6PbGhu7W1vHBwcHurrIONzi9PQ2BrPL4ezz+TaDYXxwkL693dbYmJeZmZ+VVVVSkpaUxCSTVxbmvws/0HoENBrNZDIvX758dHQErcP5+i359Zc/Wv1jNBq1Wu03BlkajUatVhuNxm/cRRQKhdlshmZ4Tk5OoFmX37YVdjgc4G4Sg8VmtIKNbZsdwpXBpNcZtDPT4421VcV5OXlpqSuT41M93S3Fxfj5pdKs4on2waHisrznj0mladT8aCoyeC3hWW/ADflwObYiqczjxkhiwGJmHC4/td3FufjtTyXpZY72keWI5MzLN1x/9vOmqMgs9+fwp49Drl6OunTR5Yc/PGxu7Xr8rO7i9QnXl4Tk1JHAkFoPH8ncKrprODs8/sXtB9y1zeqYqJ6kmJHYYHxmQq/r/XH3h4zEKFFO5n5h+VJY7JB/UM71a/Vez2t8nhV7OmU8vTFVmMocaUcGuMe4P04O81tdmp5ZnC6tqx4Zn0FkFSwvY+vr2+DwbJcnbotL6+UVNZpTw++DHwwGs7W1hcGAQwffeqyvrUgl4qLC/AB/3z0Bf39XADjsh3ui5NjYThTq9EimkR079AYxj8+j0ghr6wwCcXJgMDYkdLCjk0ulzY2OEdY3UNU1w13dni5PlOJDh96QFBlFw+FXZ2bN6lODWi0Rifo6OqZHR9ubmuYnJ1vq68cGBlRHRytzc+WFhVa9vqet7eKnn9oMBsBunx4ahsXE7u9wo4OCe1AtM8MjJxJpXGhYVGBQf1t7ZUFhQnhEblp6Z2PTcFd3XjoiLiKiE4WaHh3VKBRFSCQZh6stLxew2b3t7dK9vQOBALDbaUSicGdnYmgoNTHxRCYDLBbx7i7U/AHpDbudjMNVFhfXV1YuTE1tYzCL09N5mZkCNpu6Tfw98SOTyf5z8QMAALRejk6nf+9733Nycrp69erExAQ03AIZqH/j5jw3N9fc3Pzbgy6KswNENaQ/gBogZyh3gFmzbX1tqaOliUEmbi3MI2HJ+bCU4aamprIqIo5RnFMxg+oM++JyW4ArqzSZmO6LTnFbTX7eF3TnaKD0aLBqMS14Ot5X0VlddP6D+SfuJL8oTiIi951P5kOjkFeuvfjZTz3+7ace//Zj+NWL8M8+jXrjteP6ulHXZxUffkwIDRfn5Ru7euZiksI//qzcNzzzZQBjZgGQSmFPnUtePptIDONV5jKzkibdHlKjQhxNjbLishmfwJbHT4bDQ7Pu3Szzfp7gfDPd83GE8w3iWG9LYfbjG5fwmNUN7Nrq9ubEyhKqa6B3cCqvoKazZ/TRkxcx8XBUS2dIaKT6VG8723IMWphbIJtFu3CX/8Ybb0D+iQ0NDZOTkxBy/n38cHfYKcmJy0sLgM2qOJIeSw4Bh721oYHPYgEGo12nN52ocatrgNFkUZ8CBuPx3n5vS6uQxbZqtGCWZbUd7+0rDsQnEqldpwds9pTomO5mFGCzn0ikZw0rPY/JrCkr60ShdCqV/PDwaH9ff3KCRCCKkEiNQuEwmXhMJgBN2KpOZoZH5PsHRtVJYWZWb0urWX16eiTb3+EKGMzlqWn00jIVi2MQiNpjuVF10tHczKJQCnNyVubmhnt7Abu9t729q6Vlc3nZpNGAxJrVusvhHAqFmLW13IwM6d7eoVCI29jQKpU2g0EplQJ2+4FAsDI3R0Sjj8VinUpFxuFy0tOpBMLy/Nx34QeDwWxubkIrtK5cufKfjp9vhMP9/f3p6ekAALBYLBcXFzabvbm5+ZXHvFgsjomJCQ4O3jtbI8Fms7e3t7u7u6Oiovh8/jfOb98VfybGh0cH+44P9+dHRwZaUV21NbU5OTEBQSmJCPQqvrei+uX5TwYTwvqDXDs8buGzwidi3dr87xe5XOwOcwXWRnYbcqeCnw89vc9LggFNnR3Xbm8Ehe8VlqS+83bzI6eS61d9/+kfGx7eHfN9uRkbae1oW/b1GXV+SIuJ3cvIJMTEieuacHllaXddKoKjAbEsPzgk+sGdxfI8QnX+eJTfZIAHJiqYHhclSIMDfX3j3l69Pj51Hu7FXu7el85FuTg7f/bJudd+du6NXzy8cT3Y+wWXxxKIBSqrbmJlaXhqqQHVF5OQkZpRlF9YSyCx5XLt5hb+1/gBM9j/Az/19fWNZ8fU1BQWi0Wj0ZjvPvA4zNbmel1tNQ6LVivkPDbLYtBTiAQqgWAzGA64PLP6dH+HS1jf0B3L5Xv7JxKp4kB8JBSBEYlGNyhVYh7/SChiErchpptHo5fn5aOXlkVszolEus/n7/F4TDK5E4VamJrSqVTSvb1dDofPYgl3diDyDeLoyDjcoVCokyuMqpPxvn7FgbixorI4O0fM4x/yBUdCEVRlQVwCaQutOpScHskUEonDZMqAwTpRKINafSqXi7jcguxs8e6uUioVcbkHAgGTTGZRKNCvTuVyiUi0tbKiOjo6kcmg6ovPYlnPBpi1SqX6+JiIRudmZHAZjM3VFex3v3j/B36+2cXw69wN/PpHy9+gB9Xr9aOjo++99x4MBvv888/z8vJoNNrf/u3fQvMqvb299fX1KSkpBAIhKysLBoP5+fk1NDQ8efIEAABoBoZNY+0wODtsDpezs7Ozw2az6UwalU7p6+0sLy7YWF4oQ+bQMFsDzU2ed+/e/ezzW5cu56embk+Mt6WlNIUHzmbBal66jqfGt0QFTeUjemAx+c+dRhNCRM3FmJSQxusXyLFxvPTsms8vySuqeJnZTTe+JCYldD926n7slP7266TkhK3wYCOqgZIYuxkcMPLQKeu11/qcHkrrmslF1SWu/qPIMg2VE+/qURwZPlqK7EwKX0SmbGWmcPJzlgP8d5E59uG+lZRY5P0biXeup754XpgUl5kYf+38hYLsvOS4JG8Pn4vnPieRSLsioVAs2sDhBsbnI+IQPUNzQ5PLyal5bJ5YrtK3d/WfqHVWm8N+FoIsZuNZXLbuCnhvvvnmN/5vEH4wGAz2u4/NjTUWk56dlYHHYQyaU4tBrz1RCXY4uxwOKLez2UHJjM1Ow+EBvUFzJAOLeJMZbJWazIDJDHVjjoQisKFpARVG4D8xGI+EIqjcB7Mym+1QKFQfH4Mpk91u0mhOZDJQcWOzmTQau9FoNxr3+XyoXaNXKB16g0Z2rD2WzwyPDLR36BVKxYHYcnr2ZM4oBJ1coRQfAmYL2HQ668+yKBRIwmM3GgGbTby7q1UqDWq1Wat1mEyncvkOnQ7dE/xstfJZLKjboz4+Nmk0wp0di07nMJkcJpNFp9vGYDLhcBGXSyGCEtLvOra2trBYLJvNvnr1KmQ6/etC9DcI+qPhx2q1ms1mpVLZ1tYWEBCwuLiIx+OvXr26sbERExMDbZ4LCAjw9vYODAxcX19vaGjw9PREo9FqtTosLEyn02m12qWlpfu37jndeeB0/4HzAycnJ6f79+/fvnvr5u0bz11dvv8//vu9m9e//1/+5vt/89e/+OEP3G/f9nF+cOWdX1x6/Uf/+5VXnn70Tpaba/TNW3deff36q29GP/Z89vkXX77+jvMbbwZ+/H7YGz8N+Zfvjb94XHTuE5/v//20t5+5q4uRlo6LjmKnpxqbm/qc76W99rOK879aCfDhZqYx4EkUWELFhXPVF86PubrRsvLnEzJ6YxAjyKrWzKLJ5o7M8LDX/+f/7X/9s9b4sClYYpe3Fy4ldTIgYDsfMZYc5n/+XRQiISchkojZjIiMjklMzUSWtrYPV5Q1/uAfftTS3L65vjU7OdHb2zs0tgjPLGtsG5lZwoVFwawAoNbaUG3dJ2o9hB9wCOc3+NmB/Kuazo7p6WkcDofBYHA43HedBFjMFg6Lzs7K2NpcBxx22aHYrNcBDrCnaVCDzVPwxDVbSFtoUMRktQEms1l9StzY1MiOQaiYwY6ndFdoVp/adfoTiZSCwQI2+1dcgulErTqUgA9ypj+Auj3QOapRKOSHh9AZD9jtFh1Ic8sODs5qErBdC9Llv0ajTas75AsceoNBqVIdSgCbXSM75lJpUOtWdnCgPzkB6xybTSGRnMrlZq12n8+H4hKEGa1SCbZWz4QONoPBrNXu0OlWvR7sllosgN0OMo1nmR50fxqRCHHrW2urfxB+/nP1B1AOhsFgPDw8AADA4/H/+q//2t7eXldXJ5FIXn311c3Nzfr6+qCgoNXV1erqamhrkFAodHZ2hvwQQQMag8Vustmttt/mD/RG3chwf0dL0+bK4mh3V2l2ZnxgQFVmZmMuoq8oJcP73lR5xmQpsjg4uCw6OTcqEz1DYpNkkl3D9ioj8WVYdVA4oSR/Iz54LdLXNj5CzM4ZDwq0Dg0oGhsw0VHsVPiSj3fbjS9mPd0Qv3yVkhTHSIezkZnoxJgeV5eu+/cavry1FJO809AFu/VMjmE6fXr9w1ffzIiPH25rbECmf/7jf+yDJ8+kI6ZjU9YRGQtZCZnPrjfAgtvKMzGYlWXM5rmb9zIqml6Gp/zinYvwtOJg/9j3fvlhZmJqXFDIW//2WnVtR3pu1aeX79155FnX0qezguzj+hb+RK23WO02MATZzSYD4AAVtAI+56233oLmF5qamiD8QC4F34UfPA7D43JiY6LwOIyQxwUcdofFLD3Yh1oiYDwxW2xa3fbmlkl1YlSqwHmes1NZJtqzarSAzQ6xCxrZsV2nX5udK0Xm7lCoeoUSMJocegN0grKpVJ1KZdHpLDodYDZ/HX/OmjMgwKxWEZcL2EElAQRL1jYJfIRf4/OAywMDztmPNq3uSCg65AvAH01mk0YDlU/gI9tsp3I5YLdj19c1CgXU/7HodJB4B8SGzQbVYwI2G7Dbzy4QNrvReCwWK6VSs1ZrUKvtRiODRMqEww8EAtzW5nfh5yvbHTQajcPhOBzOtWvXoPGn/1z8WCwWpVK5trb2+PHj4OBgLy+v2dlZgUBQXl6u1+srKys9PT1TU1MjIiJWVlbq6+uHhoZoNJrFYiktLdVqteA+PbsDsDhAsfWv+QM74DA7LEabCU/GzS5MLi/NWnWq0Y7ml/duIIL8mtLi2pP9x5HhA5lRfpc/GS4rk9J41UUtCERdQHh+ceXQLv80Pxnpdf4S8s7tzYQwfFL4SlzkYUszEZm9V9+wmQLDpyFmg0I7H7msBgaT4+NnPD0GHztxstK4uRmV1y8vRYTsFReXXPyi/O7jMlfvNhhysWvk1R/+FLOFnZud3lieX50Y6i0tePTm6xPpGevZ+V3BwX1JwZF3fzVQjijJTKAytnlS6Y/f+wRZ1+4WEPfKK9+rqmgN9o747N1z7/7otbTQyIXBEXg8nICn3b37mEBi2wDAZAeqalGfX75+otLaLFbwOmK3Wo0Qfux8Pve78AMZffzuZwadisdhystK5manAYcdj95Syo4sBv0+nw+mNHqDTasDbPa12TnwfLXZT49kUBwwKFWAzW5UnUAtVKPqxHSi3lpcwq+tL09NQwkYWBGdnDhMJgGbDZ7QZ7kTYLNpFIqj/X2NQgHBSXZwQESjoewOsNrU0iOomoKyQbP6dI8DigDBJ2AwAja7VaOVifYspxqdHHxMh8l0LBZD8jm70WjV6w8EAoNabdHpDGq1Va9XHx/v8XjgXzebHSaTTqWC0jmzVgvlkPLDw2OxGNTLnYUjMg6XEBXFZ7EwG+v4ry1SfveV+/8XP1CH53c/Q6qH3/OzA3wLHYD17MMG/iOz3WYALDrAtHcqmVibHJ7sVsr5gE3ZikyujPTtSQ5ZQMYNJQUslWbGO98yCoVWtUEmNz7xDk8pqnMPjskrrKBsor0ufpZ8+bMmp5vrEX4kWCwlA37U1iLu7Cp1ceuNTGjxDe0JiCAgcqx9Q6NPn067u+6kJfMy4T2P7nPzc0ZfeDU/eV7+3APm8kREIkVGhIUlxq5RCM297ekZ8PGeDvbyvPe5T2q8vJYRGdVuTwdh4Rke97oLEeWZqQsz0xgCyc07OC23JK+wIjAgrLOx1fvRM7ebdxNe+vvcdgp69CTey317ccr76aORvr6hgeH6uubHLq7/9MMfHUukgNVkUqsAM0j7Qt0wgUD41ltv1dfXNzQ01NfXT05O4nA4AoFAIpHw33FQKSQalZyZkc6gU4lYjNPdO8jMjKz0NBqRiF5dZZPIbBKZQSB+/tHHg+0dPCqtra4xLT5pbXahu7mVhiMqxVLC+paAwRYw2Hsc3ljvAGkLW5ZbsL2JCfMNEDDYqOoaJnE7LjQMkvxAxNrc6NjU4JBMtLfH2ZkcGJQIdsloTF46oigre6C945AvCHzxkkulidgcUBBktgx1dvHpDDGPz6czoPmflekZqEs72N3NZ7Gw6+scGu1YLG6srmaQSLsczlBPT0RQEJ/FAmGwtrYyN8ckk6kEAgmLPRAIMGtrLAplG4PRqVQsCmWPxzsWiyEdkFIq5TGZOenpAjZ7fXmJgPv2F45AIGCxWDwez+FwoPrnP5e//l3kQLf8nsiB7vb/wY/VDs4/2m16wKIBLMc29RxhYWC6i0ReAvTiutQwmMuXrdHeraGezLaqtviwrsw088Hhy6fuBAqre2IuIiMnPju3qKz0gEmLuPVliYvzoLf7lI9H33Pn+cjgnsDA5YKStZoWbOtAfSSsISh2JCqZXlg68Ny9+csvai5d0KJqV6OCNxNiKq/fnImJr/cPbE6DSfmsz66cbx7smiGs+0QGvfJXr+SkJNFnptOePG7w89nIRQ7HhvfFB8XfvZj07EE1IjU/NT03E5mUmPrBh58U5OQ3VpaXpqd//LMfZYWGlicm5YZENqSmt2TDOooRIjLmv77yyl+/8spf/1//9Xv/z9+7uXlwGQzAbgVsFsCgA0wGu9litQAsNu+tt975g/AzOzPFYtKLiwpWlhdrKyuIWMzo4MDy/Nz85GRuRgYyNa27GVWKzP1KM5ocGdVaU9tW18gh0zxdnvWg2ioLike6+5IiYzKT4ZnJ8IKM7MWJaaPqdG50YqS7j02i1pdVIhKT8tIRId4+pC204kDs5+5BxeJKcpAJ4RG7TFZ1UfFoT291UTEsJrYH1XLIFxRlZVMw2Bx4anF2juVU01pblw2DB754ub25Ferju8tkETc2c+Cp3c2oxopKIYuN29h7ssHRAAAgAElEQVQI9vU9lcv3+XwGiVRbXu7r6bk6P9/a0LA0M4Pb2OAxmc537mSnpQ12d4t3dyODg4d7e4loNMR0V5eW2o3GsoKCsYEBJplcXVpaXliIRCBK8vLABit35y8OP3YbmMWCUQjEj9VuMzlsOsCiBgwTG9NrxEUKfd16IkTlJmf7PO2ARU4XIQYQiYO5GQgfL4vk6PLHF1aWN6mc3YLKuvq2jvLiogMyKfSLq0WPHizFRmGS41vcnpQ/cXH+8U/uvPp6UUQSaxGnpAqVOGbZi+Dw9z8tvnFnPCiw3umeZqBrNj6829cz49NP1tNSAz56HwVLRM+Mdnej/CICRhcmuge6Hj64vTk+ypieTHZ+UB/gO5YYP5eWgCvPnMlLeP7RL9L9PPtqqx/dun3x0/NPHz385Y/+9eH1Szc+esf18rmWrPTiqLDK+Njy+JiiuBBkXAh+aaYgB9QiDAyOfXnjrn9AyNrSMmAxAka9TaMGUQTOHQDy45O3334XWkUIxR88Hk8gEMhk8rdfRfF4JoNGIW+XlRaHhQbnZWedyI8dFjPgsMeGh8+MjR0JRVBLRyba41FprTW1E/1DgA3ITctQS48PuALog7VNQSSmrM7MU7EEPp21NrvQVtdo1eg35pfWZueoWFxfaxsNhx/u6u5va68pLqHh8HqFsr+tvSgrGzBbtje3Al+8hGqtrwi3+LDwAy4vLT5hon9gbnQsPSERGtdjEIh9rW39be2NFZXdzSgyGvN3/+2/x4aHTw4Pr87PB/n4VBYXr8zNpScntzc1QZNFbi4ucRERJCzWeHqKqqvraG7uaG728fCAJyTQiMSGqqr6yko2ldrf2dnd2lpdWrq+uLg4PT3Y3V2Sl0fB43Fbm3/q+PmuuPRdt4OFssMGFkJn6Tjomg2mcDYdYO6dGtw54DA4eC5tqywtBub1JPXlk6hHd0oiQ5Zamm+/975mV6Q7PD7alXSgOgvyCmMiIr0fPxquLg+59Fnpk4fYjNTd2uqJ5KTn77zLGJuSkJnLYwt3L9+tyi6fbOrjTq5kPn4+lpAkGxyYTo5DF+ZUejyBf/F5l5d7p5dn5Qu3pMd3V4e6F6eGXni6bqFXp0aHxDvsraGBVVTzvVd/tllZ2hkZOhwfNgMPGU707UoJiXG+ceX1n8f5+xRlpH/4y9ee3ryGjA279/GbDWkxlfGh+aHeXbnprVmplekJyMQoP4/nUxOTR3I1g7PX3j1SWFK5sLAEnuhmA2DSAw6rSafVafRHR8dvv/02hJ+GhoapqSk8Hk8kEslkMuE7jvW1FTaLUVSYj8Oi5VLJ2WSrXSk7mhkbAysWm92u0xtVJ4T1DcBmN4FzPirAbNvf4QMWu11nBD3vTVYBgy0RiIyqU4feZNXoS5H5ixPTgA2w64yA0WRUnRiUqrLcvPqycoNS1dXUrFcoTyTS5anp4uwch95gOdVId4Ug0WcyAza7QanaZbL0CmVBRiYiMcmq0aqlR9BwkZjHZ22TTiRSKJdjbZMAu51NpX5x8aJ0by8DBhNxuQa1OjwwkLC1pVEoQIbNbBbv7grYbOneXklenkGtHujqYpLJCVFR6NXVyuLiThRKwGarjo5G+sCpW6tev0On52dlCdjsbRyWCF5/vv34k8jffkOV/37fnRmoWEAIQV4qFofN5jDbHWQOs7G7dQW31j3YPj8zXJoDi/V6Hu/thp8bRxXl5URHVadnRD73OKJzjpg7Yc/dPe7de+sH//jxv/wg4u7NqMufNXt5ribHr2ekh16+Qp2c1QgPASugUJj4u/JHzp4rEytLHYONSfByX9/FwtwKX096D4rUjWqPDaVWFOomB9QLExG3LtcjkrEzo2vT41d+9UFVdvZcZ0ddSsrjd97OeubSHBEkGultCfZcyYzsCHjYEPi00PdZkvvjF7ev3/zVB263r60MtBt2aXGud8rjfPKCntckBPbkpjQi4iszU7qaal+6u9U3oOpbe0vq2uNSkX6hsXn5xUcSKWC3AFbTr/GjPTg4fOeddxobG6H9WVNTUwQCYXt7m0KhfPspQCAwGbSDfVFyUgKFvG01GjQqpc1kFIuEECEGCghsdptWB7Z3DEaQf7MBMtHB/g7/eE98eiSHQKI4kBzviQGjBSpNcasbEoHo61tsdtWhRCdXbC4sgqSZwejQG2TgsgmwgSPm8SH8GJQqUHNtsYKEuMVK3NgErLbJgcHVmVnABsrAD/kCUHB99qYrDsSQuhTkEvR64+kppDaAFAx2o/H+zZtbKytgWXj2cSwWq4+PzVotSBicTRlBum/AZqsqKclOS7PodGCzy2y26HRapXJ9cbGiqIi+vU0nk/6i8OMAzxeLBbCANIL9jMq1gIyczeZY20LPLi+PzU8hi5Ezs+NTo/1V+Vmd9dWzw4Ozw4PPbt7sKi8riUvMCw2/+JOfuJ7/tDIuGub2pD4mtDU6tNnPczYxmpafhc1D+l28tIsjAVagrLR2eHrtSOMoq2rdWsHVZhe0ZyM9Pv6wKtg30+2RErvCmR2Kd7qOL0eKe5tH02K70+JcL7wf/vRBf3UFd2vznf/1D5d+/JNbP/u30gC/cWRWR0IUpbO+Puj5EiJ0MuFltZdzXdjLgmAfv7s3vO/d9L5/Y5+4hhtpD7n/WW6QS1H488Iw1/I4v4SXDwdb6htrq0LDopxc3MoaOl39opJzygMikzxe+jHpDMBhBSxGh9lwhiLgt/HT2Ng4PT39DX6I33GsriyRScSc7MzxsRG1Qi7Z3zuWHBIwaB4TNNMATGY2iXzIF5DRGB6Vdio90slVBqWaTaLScESdXKU6PBIw2LtMjojNFbJ29nf4hPUtjUyxQ6HbtAY+nSVksXVyxcL4hFJ8qDgQG5QqmWiPQ6aQttC7TNaJRKo6lJxIpAvjE2b1qU2rg4Yg8GvrEsGuUnwoYDCNqpMdClUjO4ZGgE6PZBQMlobDa4/lEsGueHdXo1CQcTg2lcokk5VSqXRvj0EiQeNxEpGIx2TyWaw9Hg/qCEPUORmHUx0daRQKFoWiPj5Gr67ymMxjsRjqIEEuKMKdncXZmW3Ct79wfyr8we8Xdb6+lw2wm8CsH+xCf40fswMwg0usdoUHIrF0Ryjs7O+ta6idGB8e6e/ubGlsqKmcmxxFxEQWJsQuNjduoppR8dGLpQVzBZmrZUhSQymmOBP1wqX52f2NuPAVWJLX+Qsa4YGAzvvRj15z9Q4xAsDo5FJ9VWO8j1+Gr3fA5QujOfDlmiIteYM1N+Bz6f2eWP+NQvhkVvx8WXZbenxWwItwp/uTVdWM0XFUfHJDWMRgOnw0K602zHc0D45rLKz3utfi71T47Hax77OU5y4RTx7mxoQneHsAst3WfFiI06XyOJ/8cNeMgIe54R79dQVL06MYDCa3qKJ3bB41ONczg67pHOOKVT39I6enWhA/dovdpDNq1RaTmcvZeffdd5uamqD9WdPT00QicXt7m0qlfvtZQCRurK+KD/bS0+CbG2tCHld7opJLJQfC3W0MxqzVcsgUUPZ2qgEJZYNxn7PDpTJOJDI2ibrL5AAmq3z/0HSiUUuP+XQWYANwqxtBL32sGj2PxlQcSPSKE5BAs9p4NDqXStPJFTq5QshiSwS76KVlwGSWCHaP9/aP9/Z5NLqQxd5lsiBxEB1PEPP4UOZ2wOUZlCrixqb2WA6BbZfJYhCIOrlCuis82t+XHRygV1dFXK76+FinUjlMJiqBcCgUSkQi1dHRPp+vU6m4DIZWqQTVbmYzn8WaGhmx6vUUPF59fKyUSiEVD41IPBaLDWo1enU1LSnpVC7Hbm78peHHCFgMgBkcOoHij9kBmADADDAZXAKRojjVbeKxHi88S0uLVxdmcxCpRYXIDlRdwDPnzkLkVmvDXBESXVW8nJc2lhy2lBU/HufPrs6ZDHvR63afmBKJyUh2eesNzNiETa3DbxHQOMqxUldf0zzQ2vns8sWgLy+3xoetV+XSehvWmoqYU92xTper/J+sFKYul2XNlGbNVBetoGo32lpmyspZg6OzBcWjaRl1Af5DaUnbHbVloe6UnurmYJeOUDdUlG91RGBeSJDP/Xsu164gIkLQE4P9tfnhT2/AfR5mBD4tSw6M9LhrUQh5HDpzh7t7qFwhMpuHFqkHp8n5dfqzVd0AABg1J4DDAjgsZr3GbrPIZce/ix8SifTv4GebiN/hsAryc2emJwGHfXl+DrBapAf7EpHIbjSCTR6T2ag6YZPIUHEC2ACDUk1G42xawx6HB/bibIB8/xAshAzm/R1+Vkoqfm0TrHzO7mnX6a0aLSi0MVv0CiXUNZKJ9vZ3uBX5BaDM56wDS0ZjwNzMbIH+CpStQf2fzOSUAy4PnCQ/6+Q69IbTI5lMtOfQG864DVAERCUQoH6oiMs9lcvFu7t8FgtqAQF2u/r4mEWhmLWgARgkeuAxmZBy1KBWG09PJSIRlNopJBLAYsFvbnaiUPTtbTad9qeCnz8oznzXnW2A3QCY9YDJ7LDYbBbAbAcbikbAYXRsrmHHJ2YaUG0lFeUdHR2p8JRQX+/trfWoYO+mirwoj8cN8JithnLhUPtyTsp2RfZ0cuA8LHgzI2IkyGXM7xEhKWDs+V1sSgT89pfxjx+ZhPv6wyPACojYXB8Xl+k21MN3Xw+9/ElvXCCuKnupCLZUmi5eGUp6fBUV69MS7TNekDZekjlamjdZWbLR0jJZWLrd0r5cVDqVkVXr571Ugpwrzcj1c0a3lUzlxfemhJaH+oQ/uJsZFuHn8uzu5au5MNjS8MBAQ0VddnJKoGtqiHtykGthWoReKdJqVDYAUOpso0vo7umtEHhBZkXbnspoAcDzzWrUAXYzYANLIACwC4XC9957r6qqqqGhoaamZnp6msFgEAgECoVC+o6DRiWLhILyspLYmKiOFlROBmJ6fGx/FzTcOJHJIPKNtU1ydXJen53TgwFEBRjMh3yhdHcPMFm/7sUZLWA4sgEcMq25qnZ5alavOBEw2Ga11q7TO/SGgoxMqPFalJVt0+q+ql7SExKTo6Jloj3QkMRmN52oF8YnZkdGhSw2YDQ1V1WD4garbYdC9XjsAmlVoV6tVaM94PKYxG2j6sRyqhGw2SN9fRwaDSxgLJbWhoa2xkbV0RGLQpkZG7Pq9adyOYdGY1Op0HwEYLGYNBoakag/OYEkRdAMOYdGOxAIoL4qpD9gksl0Mum78AO11IhEIpfLvXbtGqR/g4Zufvvs/aPp3377Qf/D30P4MUBKLJsF1CKc4QcwAeRtWg6y4KWXz3vvvTfU14tMg9++9Pn9S+dn+lARHg98b3/enwcfRMTvj3UsI5MJFRnLWdGDkR5zSX7Tke5jfs6YmBeCzCgyPAJfgoy4+vnd13+x3N421tR47d13pprqenMRo9mwaUTcSJzfTEoQKuCJeLJVvNBXHvK8IdZvvCi9Lze1PSOpNCasPDa6LCJyNL9orqic2dHdGh7WFRMxk5c2WZjckODVAg84WBxwfvMnpVGh5cnJRbCMjAS4j6vXy6fus4ODXo+cthcn28qzg5/fq8xNzkgMlQiZet0JhUZVaM2jS1tja6TkwvoS1MCR3mH6Bj8OM5jVgrIe4D+AHyIBx91hV5SXzs5MdbW1ri8vuTg7Dfb2OEym2vLy1Lj4udGx+rJyMY+fkZi0OD7h6+bZ29KOXVlPi0+CekGzI+M9qLa50QmlWFqQkY1dWZ8bnSjJyZvoH8pHZHU1NTdWVEYHBctEe70tralx8Y0VlcjUNBoO31JTi15azk1LJ22hx3r74LFxpC10WnzCV54KEf4BQ51dPBp9bnSsv629qbKqB9VCRmPoeIJDb9je3Opva28or6BicZPDwznp6ZBziEQkaq6txW9uyg8Pi5DIIiRyY2kpKSamJC8vJS6OQSJBo6nCnZ2c9HTAbj+VyyFfkUw4fLS/f21hIT4yks9itTc1FSGRp3L5ysL8XxR+HIDdCJiNZ/ixQ/ixOACz3WG27+0d+AcE0WgU2eH+nSuXrnzwLiI0KOml23hjMSorJvn5neE82HRB+lRuynIJYrU8YzY3odTjdr7zxVaf+9vZ0cQU/5OqzF1kEj47ZQoev1Ze7HX+Y8/PP90e7iP2ddSG+S4iU3pD3EfC3NlVWes5sZyO8oXi1OXavKooP8TLp5UJUf3lRdjRwaWujsHS8ie/OteSkCIan2yPiuxNjFoqRcyVwPpzQtM9ry82FU/Xl5ckJ/TW1Y/1jLg8cP3VhxfvfHnvozff8X7yJOi5C6os95BDXproaaktYlC2xkd6x0YHh8en6tq6R5cxk1vkeGSZygomrTaH3WzSAQ4zYDMDYCAAdoV7f2j8wWK2DvZFBfm5oyNDESHBePRWO6q5oqR4dny8p61NuivcZbIgvfPG3PxwZxdhfSsHnh7q49/b0o5ZXmupqe9ubi3MzOlubg166cOlMogb6MmB4Y35pbLcgo6G5tbaOg6Z0oNqaampnR4aJm5sQlamPBp9dWa2qrDogMtbGJ+48MGHEGc92NFZU1yyy2Q9e+DE2ib1trRC3aEXT54uTU41V1XXlZblwFOHu7qLsrLz0hHwhITJ4eG6ioqs1FQekzk2MDA2MEDf3s6Ew+1G4+bysv7kZLS/f5fDycvMnBkb83/5cm5iIjk29lAofHDr1oFAQN/ezkpNrS4tbayuToiKSk1MzEpNRaSksCgUNp1GAuvHbzn+LOOPA2QKQNzYHOD8KSgHtjjsVpB/0+i0TDaDgEdLhDts7AZgUPfmI9M9niY+vbkz1UXorsnydG5KCO1GJDJHeyYr8vR0jGR9cjI/GX7z06k4n8VIT2pS0E5GrKimcCE1Zi4TNleQvYWqXW+uhj2+PZef2uz7ZCbeX9RYKGwqlAzUbzfkzxanTlcgO3JSyxJjh+qq6WsryISE3MTEtuJSMX474fEzSm9fd3JCQ6j3WmXWbElCZ6pX1IOP4t1u05anUKVFLAI5B5Eb4BdZX9tZU9YAj0360d/9XaSXe3zgixcPb+Wnx8Pjgnvbage7GrEbS7293RUNTai+kU0arxzVbQRfh9/gx2E3nzmyAnzR3rvv/2H5Gw6L5vN2MhBpy0sLIwP9skOxTn1SXV7m+ujRcG8vn86ASDNI/yYV7B4J9wGLfaizZ2txRXV4dMAV6OQqKpaAXVknbqBlogM+ndVUWdODatMeK4+E+zLRnl2nxyyvtNTU1paUkrbQ2mM5GY1hErdLkbmwmFiIhdve3BIwmHqFkkulDXV2kbbQmOWV2ZHRUmSuRLArE+1tzC/IRHuE9Y35sXE6niBgMA+4vB0KNSkmRrq3d+/GjYGurge3bu3Q6ZCYYLi3l4LHQ1MSezweGYcjYbFpSUntTU2+np51FRXJsbFLMzPQRPfawsLy7GxTTQ3IuU1PtzU2QpN2JDzuLw0/Z/0fG7hr0u5w2EAJAjgBbrdZAdu+WIjBrAy01Wn5DAOTNIBIyXd7hHC5WeTzCNtaFXrjYsIT55xg//K0lJnejvggn6XBTjFxvSkhtC82kFuVJ8hPo8Ej96rzGBXI9dx0QmM5uacF9uTeTFFWTYDbeFLIZk4CuTgVm58kG2tfrchaR1Ust9WjkNl12TkLQ0NXPvkUVVdXW16ZHBn1/MatBx9/utneluPh2hYfslSWOogIRMU/i3H6qCUvYaqziYHbmh2f/Ozc5ZqqlkxEYXJsalV+aeDz51fefTPs+aPM6MCXD2/cvfqrT97+KaoqLyslOh2e+MLH29nVPSg2Gc/kQema3W41m7SggNZudoAChP8IfsgkIoNORaSnkklE8DGsFmj+VLizA7ZTzuZJtcdyBoFoUCjP+jO2Q77wgCuAGkFgz8diB0zWQ74QsAFG1anpRNNcVTveNwh1WqH6x3Siri8rx66sQpJTaJ5Uvn9AxeIg+Snkh6g6lEBFDh1POODyUuPiIZU3qMW2WI2qE6jmAWx2sHN6ttwBGn9YmpkB7Paj/X2IVQvw8oIGWs1aLSSsBttZVisoUbVYduh0EZd7tA/ODqUlJRXm5HyTyKmPj09ksl0Op6yggM9i0Ujb5O3vKBzPJIV/ZvXPWeF0NsB95n8N2EH1vgW8/Fr1Nv0aZmllaWxrbkhB2VqoLKSj6uybCxb03GJJRpm/R4zTbRWDLmYw25tQGZnZ0YmJKfDkrcWZiaaa8GufTSZFsXIR1PQ4Wh6MXp4l6m5kdDaEXP5krDCz0OvZRE4StiwLX5o+kxzCbi5ldtXOVuRO1JSQpscrEZlr4zOv/vNP5JJjFpPLYvOOJDLM4qr7zVsz9bWB1z7Ht1atVKT3w31Gs/wyPC4XJ/g1l+cQNpcTY2OeuLh1dgy89foHX166UZ6TH/3y5cs7X6YGvAhzvf/oyocRXo/hEd5Joe7ZSWFVxTlHMvHhsZLAZJsAwAB6qDhA+9Ez/NgdIH6sAMATHbz73gd/EH+wTcQL+NxcZPbszBTgsJt0WqvRsI3DMkgkUI8Mzf+YzOD5ajQZlSqb1gDYAD6dBRJuRss35BuTSLac6hx6k11ndOhNVCzBqtEDBrNaeqQ9lu9xdiSCXZBDMxgBg1HM40MSBLP6VCk+tGq0xI1NsCV6NqsHGIwb8wvQeLb2WG451ZxIpF/PzxlNgNnyNaEHPbezqQQugyHd27MbjRC9ppRKwW8sFnDl8umpeBek420Gg0GtPpHJILEcOOlgszFIJIlIBLk3yg8PoZEHEhaLRCD2+fyttdW/OPxAkwtn2wccDgdYAwBWI2A1AxbBAXtspJ1HWF5rq5nKgQ3HhDR6PJ5Ji7Vvr5G6WhJdn1iksuG+kc1thm8cLLex3TMytrqheZdBj3F+0BMTsZWWQkhLWogNnIoPZqMqK/3c2lNiigJfZHo8HEiP26zM0S+NKqd6jyZ7RrJhtMnB0caa0dbW2rzSz947PzexxGQKltaxbt6BGDx5T7hfk5cX5fo09tFt5nDLcjl8LCt4LNMv3+fLRO87NQUpE4PtFaUFgf5BZcVVD247f3Hu0kRH54Pzn0Q8vpf84vHLWxfiXz6M93FJj3hRDA+Z7KmFxQYBdoMNJOqBQ+UJyLWBvO9v8AP96j+AHyxmi8NmFhbk4bBowGG3mYxcFvOj997FbWyAjuwKJWAw6uQKKHSciXFOARtISyoOJAal+kQiA2yAXnGiV5ycHsnNau3M8JhRdQqFI4ceRCDkgAXShd/MpRqMHDIlB54KkmwWq1WjBYnss1E8iOAGhyasNsg9C5GYJObxIfNR0N3qTKCwy2SBiLLabAaD/uQEijYahcJmMFh0OiIaDYLhzBseugWaaICG+WQHB9D0BDiseuZOyiSTJSIRZDYCTdfVVVRsLi/TySQKifytx59l/XM29mP/evuAAxz9B+d/QFLBanQYVaeSTlQFe21yoTK3yt25yfX+Rkr0SGRA9t3rG5Wlae7P1fzdm9duTCyuwYuritq6c+qasgqLyVubrhfO1Qb4zCUlYLPS0LkI5eRQgfuTvszUuYY6TF/PdG1VXXzkZn0FsbmS14fiD7a2JUdSR3uGKgoxk6MDTR23L98a7h1nsXZT0nNe+au/QWTlyaTHW3Nzf/fKK6XRwTPl2aM5MSMZwfUhD1IfX7j57j/nJoYgk6MKMuAeT5+2N6Hy0jMbS8vrkMg7v/ow5rlLRoBn4ssnudEBCT7PKtPjSmDhI83lVQUIAFSq6DQmA8gVgMZd4KXDatKC/VM7SB78x+IPlUKikLfzsrNYNGp1eRl1m1iAzKGRtqdGRujb2zq5Qi09omJxbg8fDXd1A0aTfP9QLT02qk4n+ocg8OjkKvn+oeJAAtiA7U1MSU5eS029XnFiOdXx6SwwcNnseekIGg4vYnMSIyLV0iOJYLcoK7u2pJRDpuxQqHadXibaW5yYnOgfWJ+b18kVGUnJ8v0DnVyBX1sP9vJWHUrABu6Z0TYYxIymlekZHo2uPZZDHogUPB4aK4KkBpCkoKO5GZot3aHTxwcHAZttfnIS4uUyYLCKoqKJoaHZ8fGW+npYfLxSKrUZDEM9PScyWUleXkF2toDN3uXu/GXhB7zsntFNVrCDCiYxkA7OYdfrtYDFsDjcdUrHTSNTugPcsCkRkqr8nYKc4UD/Bk8P19d/wVucFxBwCzOTre0tMUlxHi+e+3t55MZF+F65gIoMnkyOXc3J2Kqs6IDBE566L7YP7GxR53un9yhc3iYxw9e/KiJ0oRRJbqtqifZer8ruTQ1DdzbMdnZdP3+1F9V9dCAjEal5yHwuc4eOJ9QV5F//4C2Y1zPY8/v1Mb5I11sFbjeS734WeufqtTd+nh8XWQJLvP7h+1FeXj31denRUZ++/trzOzfTQgMjPVzjfV5kRoSHuz6Le+lRCUtuK8rvrqsGrCazRW87G1r4etGUHbz6AjZwFBd0EQEAnlAE8QeNjY21tbWzs7MsFotEItHpdMq3HVQyhU4mMSjk1oaGxurqi59+GhEUdCAQ4Dc3u1paslJTU+Li0pOTkQgEfXu7IDu7IDv7hatrkI/PSF9fBgyWnpzc19EBT0hAIhBIBIJGJPp4ePCYzPysLL8XLxqqqqJDQ3MzMlITE589fLg4Pd3X0VGERF65cCEqJIRJJleVlFQUFRUhke1NTWlJSY3V1bscDiw+fp/Ph8XH56Sn7/P5/Z2dpfn5USEhSARifHBwaWZmh04n43CFOTlIBKKvo6O+stLLzW11fp5NpRLR6Id3726trFDweMifMSkmJsjHJ8TPr76ycmxgoKKoqLe9nbC1tTg9jV5d3eVw9ng84c5OQ1UVBY8vyM6GJyTER0ai6urKCgqOxWIKkfBd+KFQKEQikUQi8Xi8L774Apo//VPv/4Bpi90EtgttZnDZ7Rl+QDU2WBM57HrN+kgfoJH1JwT3+j2TVucbW2uWQ/3FNRU71RXDiXFRN79Q0UiARevv5lJVmP36j//3pepVkPMAACAASURBVHdeS3n5NODqueH0+KXc1GpfD/bY6JXX3kSExadFpXu7hpbno7JhRZR18vb0co5/ALGjmdBc1hb+ciYzilSTM1eYJtxYL4Znfv+v/gt+eV3E3AFMNjoOv72x9vaP/+XmJ+/B/N2SPR8mPLqe4XY78fb5iGu/SnVzeXbhk2cXPnH+9MPLv3z145/882t//71X/+F/+ro4Bbs983ns7O3k5PXggcet234PHkY8cW3OQnaVlndVVwNGndmkcwBWC2A+s5C0A3bbWYIEStHBUOwA8QPx178/fvDoLdXRUUF2dqi/v3BnB2zAn/kBJMXE9LS17XI4+pMTyP+WhMWuLy6SsNj5ycmHd++ODQxAVmlkHK69qWl6dDQxOloplX7lcJCWlIRdX5+bmIDs2Cl4fEdz8+L09Pzk5OTwcBESid/cPBaLu1paWurrVUdH06Ojns+eQYrV5dlZX09PNpWaCYcP9fQ0VFVxaLTK4uJQf/+W+vqKoqLcjIzK4uLm2tr+zs6GqqoMGGxiaGh8cFC6t1dXUUHB4zeWlhgkUntTk0mjAS247PbN5WX18fFof38GDHYoFBpPT/ksFuQZDwlMOTRaYU5OQXb22sICCYsl43BlBQUMEmlhZvovDj+2s3bh7+DHZDDadKeUpRlAKV4sSB8O81KhKjWoyoPqwsOmSk518eFgx1BaktObP88N8Uvx9ZzvbqnLhrXlp1clhLbCQgcQkdV+jzHNpQ0Z8NXxca3y9Fiq2dhiOD/0HR9ZHe2a4GwQK+ISlqoqxhDJmPLcrjBvdnPFclFOX2bWzuJqZ36J+5e3uiqqClJgGbExP/gf/+3KR+94Ot1wv33x6eUPHn7y2ouLb3qcfy3e+VK8863wezcDb1wNuX8T7uUR6vLA7csrKYHeIc8eRr1wc7t13ePWTacL5+98+GG4y7PIJ64D5VX9VTUdlZUWzYnRoIHwY7GdGe78MfBDJuAFbHZWaurC1BS0oQBaMTLQ1QXWA6BY1wIpyiAvXKjCHuzu1iqVELsF2mQfHoL1hh3c2mDWalMTE6HcCbBYQM9rm+0rm094QkJDVZVFpzNrtfLDw6/a/0kxMTVlZZAgGtyMcKaMtur1G0tL8sPDE5ksPysLzLvsdmg4VKNQMEgkMg7HPXPZhQQ7K3NzO3R6ZXHxzNjYSF8fYLOh6uqKkMivBoEgbEC83NH+Pn5zs66iQqtUapVK3MaG/uQEKo2sej3oW08iQaKEE5mMQSKVFRSIuNx/p/75c40/IF9gN4Hv4q/jj/0s/ug0WsBhERC3NroacfXFozH+3OIscXUeMx8GUNaUAy3c1qr5vPSk+zdKQ/yOMKuc+fGOHBh1rHu0FNEBD50pTpzIicC0l3ZVFewJuBQqvXdogrojWUGzUtNLDnhHOdEpUQ+fCibHe+OjJ5LjN3MzyRXFa7k5IxkZbckpJSFhYffuPz5/zvWLK553b8YHvIz0eu7/7J7rrfNPrn3kfefcjXd+4PTRjzwvven1+fsBl8+luz2Cuz8KunMt7rlzZrBPopfr/fMf3jv3waf/9qN3/+kffvWjf/G+eaM+FV6TDJtqRI3UN7ZXVJjUSoP+FFyADZos6c9C8R8h/lC3iUwyuTQ/XyISfe25YbMJd3ZAI/Yz8T+YIp5JwiCm2G40QkSW/PAQ/NXZfWQHB5ACAGrqtzU2Ctjsr3aW2I1Gi04HnaBdLS1bKytmrdaq1yskEunenlaphOKD+vh4G4Ox6vUnMpn6+Biw20lYrM1gqK+szM3IsOh0IAjPbHqO9vfNWq1CIuEyGJD3FfQgXS0t/Z2dgNkMmcfXlpdL9/ag/T8HAoFOpWJTqRC8wXTXZALnT61W8OpgtUIGVxAaIRNt/OZmflYWl8EgYNBU8rclvme3/Vnmb2CvA0zhflP/gASmA2wGAQ6LiIrPD/NDxQR2h3lh0xP4ZVnssrTFJP/xKK8Wv6eIO1fyXB/UhQcAPCZ9sKso8MVCdUF3RuRkYSKxvQDTmEkYqCrJTeTtsRs72175679pG54+UBpT0grGB6ff+uGP55tQE7l5Nb5es3DYXHIiNi9vMjGhJzICFR48W5jXGBdZHhWKyoAVxIZ63LkS7PrghfMXLx9ddb39yf3Pfnn5rf/lceNd7y/fhz+7CXv4ZVnQi+JAj7A7V9K9n+aFed99/7UnFz/68u3X7nzw9rU3fv7ss3OFkWH1sOSSqKj55pbxJlRnVZX5VAXFH5PdaDTr/lj4YVIpuxxOaX4+fnMTBIPZDJjNezwel8H42tDDDi7GgUY4IXcbwG6HZJqA3Q6d2Uf7oF8PNKwG2O1apfLreHK2AQEwm6EtDNA5qlOpdCoVhE/9yQlk0QatZoD2K1p0uh06XatUbiwtgaiwWHQqFcStQU9Jfni4y+HYjUadSgXJQPd4PCjzBCFht+9yOJDU2nh6atXrlVIp2M46ezIg52a10ohE8D97RtABZrPs4ADaywDdyCSTK4uLZQcHIj7vLxI/5jP8gPM/UPFjc4AFkO5UIaBgu4qzstwfNgd6LqVEk3NhuIyYwaAnNS7Xwz54tSnIvTc5EuZ8W7o2i+9qRrxwma/Jmy5PH86JxjZmc/orqCM1V869ptJJOPvcKlTz1OrmrlQVFBrz1OlpSkDQWlu7zycfFT17PJ4Q3+z1cjwmZiAifDA6dCYjsdrffTQbVhsdlOzqFOt6P+7FwwiPB09vfHr/0ltOl95891//y+evf9/79gf5IU8KvJxbIr27k0K7YOH1sQGVkd7ZPk+fXXjb69q5pxfei3a5F+Z0M+z+LYS3R7qne2lUxFRdw3gTqq++/pv6x2Q3/hHjzw6TIeJyM+FwMg6nU6n4LBabSpUfHrIolGOx+FgsVh0diXd30aurEpFIKZXucjincvnG0tKxWAyN1khEoh06XbizIxGJyDjcUE+P7OCAhMVCIzdUAuFof399cZFDo+3xeEqpVKtU0ohEyPQDwgaHRqPg8euLi4dCof7kRH54uDg9rVEoODTaoVB4IpMJ2Gw2lXogEEB9TyaZDC76FYm4DMYOna5RKFbn56FRHxqRKN3bW5qZ4TIYEGyMp6dcBoOIRu9yONCmhqP9/dX5eenenkIikR8eSkQizNqagM3e4/H2eDzx7u7s+DgiJWWPx+Nz2DQK9VuPP9f8DewVgopjK/jxNX7sEJWrUh6RNhdYKzNzVYV1AZ6T8RGUgsy+l4+G/J/FvP/znnDv9QrkYlVB5IMv5ZStzBCvoer8tpzEpGc3FmuyyO3FI/DAndGmzGhPl4dX9HoZnUXm8HlLy6ufnzufFhNDnpn64qf/muP6qN7PI+XqhZ6wgKrnT1r9Xw4nBPfE+SKf3Sz1e1wY8DTo1vnAuxe971zwuvO5x81Pgl2+cL363pMLv/S8+nbo3U/qYr2mkHH8vpr9sVbuQP1iOaImzAP29Ib7hTdcPvyF24V3PC9+6PzeL5zf+WXAFxeLQwLmaqsHS8tHG5qGUSjAbrFZjY6vliTbjVZQMPrH4Q+2cVjI8JaIRh8KhSaNxqzVCnd2djkcjUKhOjqC0if85uaJTCY/PFRIJEqpFKpAoCAjOzjgMhjo1VWTRjM2MFCSl3cik2mVylO5/EQmM2u1kH2hUiqFhgXMWi1kM606OgJLLJtNKZXOTUxA0QlKFwlbW1A8gWxEOTSaVqmEjEsh02oOjaY+PgbjocVyIpOtzs9DTwaqykhY7NH+vvEUXLd8KpcrJBJIoG3WatXHx+rjYxIWq5RKoYxRo1DQiEStUglB13h6SkSjywoK9ni8bRz2Lww/4NZksN0BWs+ctRDPXKxAORxgtVkN8xN9PNwKIGKX+ngMxYUftDaQMmAVX14cDfffKs0p83/hde1cd1UBIi4oKsCDhp4XkVarUkLrYwM2a3KJtbn8ocaZpvysOG8/1zsVBelFOelP790Z62gdqKtw/ugthJtTQ5hn/pObAwlBGXcvI+5cqfF5Uux5O+ramyV+9+BPL8Off1mX7I/wf/Ti+kc33vrnNN9HAyVpt1//QfS9C7kv7jVFe03mxK4XJh7011g3Rw0bw+iazD54cH3UC89zv3D/9JfJT276XPrwxfn3o+99kePl2p6a2AJP6snPH6lvHG9vB86EFhB+bA5Q8PZH4d/AETEMpre9HWqYQieiSaOB8jfj6SlkaQuxC9A6Ko1CcSwWm7Vam8FwtL9v0mj+X/beM7qt7MwSnd/v/XlrvdVvpuf1LM+0p7tddrfdbrudyi5XViVVqSSVsihRFCXmnAmCIEAQBEGCAUxgJhgBJhBMYE4gQBI558QEZuQc7tPFkavL7ir3eNb0a7fGd92FRYIUSEJ33++c/e1vb4/NBvqVjNHR9cXFpdlZcLMHmx/QrwQWbRGfz221Hu+9nC96uYiKampe7k8CATD0BgWDABJgnuefFoR+P0jgggKBF1wF6IGaNBqAB+APrFcqgSEwMFV0nJ/DW7tAABgm+hwOkLcFwiRfugdHV3fAEY7HZr/IWTFrtRIB/5XDD9w1hcVvX57RLmooBAVt9hPW2tyFUaHfmF8gVVff/cLHnN1rqN8tKuRg0KJO8iff+2t6d0spIhuDL6lpqmomE8eonSPkmoJbV/tzU3fqcJcztDF0JpfWgkm4jU6KyXtyl9HVUl+QWfTgOoOIGsdk9WY/IMV+hP7sF9nv/CDp9dfif/ZXKW+/VvbwV83ZX9Smfp5x/Wc3fvqt97/7Z+/+zZ+99Zf/V13Kg+GyrPwPf1F5+0NWdekWoZSDL1K24Q6GGiKs8TBnQkYhjJQ8w8d8nPDrv3v4k7+Kf/Pv0Q8+Q937rPiLT3CxdxqSn07gK/qw2I6KypXxcSgcCAbg8BZP0P0/wl+3trbOz8+rVCqRSCSXy792ESIRiTmbGwcGQy0eD6zcn8bENBKJzXV1Qbebx2aD2zafw7l65QqTwdDIZFurq2gEQiYQdLa0wJej329QqTQyme309PL4eGl2dl+vHxkY0MrlaQkJezrdYE+PQaV69vgxoApgwITDm8vL4FO9Ujk2NAQFAoLt7XoCoa6qqr+r68WUQcqzZzAh4fcDS4NhCgXkmcJDPtFXWGEyF2dmIj7f/NQUZ3395eYtGCRWVmpksuO9vfHh4UYi0XF+HnS7V5jM+akpj81mOz2Fh9KDQaVYDAAJv2AweHl8DKQJ7LU1j81mMZsbiUSZQMDlsGUS6dcekuihUChMJtM777wD8n/CcCPlt44/rvmfaHwJrDn4JwhFQrCMFAqEoIA/5BygkNVCNuS3axemSq9fDa6uQEurcmyld2ZutqIC/Szu1KxZ2lzENhGq2usLKxBVBIxka/nRm6+TU55PFeZKWmp1w21LtYj+kjRmc5Vmnj5SgzEsMfRztH3mgGywdhGfNpD1RWfytf7c+1MV6ctNiJn6TGLqJ+nXfvjsyndv/OOfv/GX/8fHP/jz1E9/gbz3EfHpTdS1t5N/8p3e+Ht7PY2mtjoDuUrfhjvsJ4bXqdAuY3+siVmZSU66VXL915kf/CTx7R+VfPEh8s5VxK2rVU/vt2UmTxIqqQRCU2nZ7sICFA4E/O5g2OcNReVv/1L9+R/Ej4jHPTKZOpqb+zo7e9radlks9toaj80m1dQM9fY+efCgEo3uam29PD7+/OOP25uammprLyyWj959l06jYUtLa/F4EPienZra3tQ0PT5+ZDINUyi0/v71xUUyiYQqKkp9/jw3PX1rdfWFIvP6J59w1terysufPHjA53B62tomR0aexsQkx8evLy4GXK5yJFK4s4NGILClpdaTk3oCoSArK+np0+2NjQe3bom5XAmPF3PnDq2/v7+rS6dQ0Gm0gqwssIa8sFjIJNLVK1cWpqepfX20/n7g4nD1yhVkQQEYKX3y4EFbYyOgxXFlZZ0tLVA43Egkjg0NMRmMqvLynra2tIQEwF+rZNJXGz+wlxXATwDyOXyXTOb4sVkJ+Z3QgWmotKQrIQGamT9q6zJRBjzbu724isUpen1bU2JJTnVv69PcZDwRt7U0g3wSU3Lz2gwKwW0gCNuqV2qQjPJ87MMb7J4W2cSgZX3WNDtonuo4mmo9nmo8niIdMkj7jEYDvVkz2WRe6dqlVo4R09uKYwipn5fFfYx89FF57NWKR59WPbha+uGvMFd+yULl+icHvNRu50DrUTfhsI8QXKRAglnnYv92c+lwwdPmxFv1Cbdyrr5RcO1d5K1PkLc+JcY/6i3ImcBXTJJI1XkFBoEACnj9Plco4veF4V3Qv7h++x38RG+Xv/sgFUvkYpFRrW4kErNTU6srKsAO4cV6LDk+vq+zE7Bb9rMzJoMBhcPY0tL+rq6g291UW6uWSi8slq3V1fOjI6VYjCkp4ayv89hso1rdTSb3tLWFPJ6t1dVdFkvM5fa2t7PX1oiVlRNUKqakRMrn28/OWhsa2puawP4kOT4eaNJYKyv5mZlHJlMlGj02NETt68tOTd3e2Lj/xRei3d1KNJpYWdlQXT03OTk/NfX973yntaFhfHiYtbJSjkQSsNhZOp1YWVmBQlE6Ok4PDq5euVKcm8tjs60nJ73t7c11dX2dnQ9u3cKUlLBWVvo6Oxuqq9VS6ejg4FBvLwGL3Vha4rHZgz09ZBLp0GiUCPivFH6iKq8ILD0GJSjqBReJquqDkO/cccwTbHocJxcGNXS0JxofS//1r7crKqGFJRae0JOTnfz5tfXZ6dp6Iq6hugCLvP7gZsyju+uz9Jj33mzOSd5qbeB3krjttXN4BLu9QT4+1FmQRS1H8Ic6DhdoR1NdF3Pt7qXu4BrFt06xL3VaZjqMky0b5JK1diSTVDCIed6YdRf75JPc6288f/sHGe//hHD/auvjG4MJ97UtVdDSBDRN9VDJtuGG4wGCZ7oD4k0FN0dFHRXjyARK7pOpynxC7I2SLz5G37tedud67bPHlMJcKga90NGFy8x27O0B/i0UgS2c/xfih7fNMWk04JZ8sr8P57H5/RcWy4thTLfVatJorCcnHptNLhQ6Ly4uj4/tZ2dgC24/Owt5PC8iTcFmSSkW+xzwNLVJo8FjMJMjIy9kOGBeAAqHXxASMXfuDFMol8fHo4ODgGAgk0j5mZlQOLyn0+3r9eBlg263SiJhrawAHRoeg4GCweO9PfDTxVzuzubm2eGhUiwWc7liLlcrl8sEgh9+73sXFguTwbg8Pn6hmchNT99YWnJeXACfUWDV67HZmuvqLiwWPofDXltDIxBiLrcWj+9obgZk4yydDnPc0RUpsbLSqFar5bJXDz/g3htdwn0FPwHI64c89Cma5UALee0m7jZ3bEzQP1D+2cdb1dgIl2PjbGKfPa3Nz4WCgUpkSV5aynf/8lvf//a3KPX4++/9orEgZbqhgkFEjeBLd6kUDq3fuLl6KuDWZafSKkv350eOpnouptod0+2eWfi0z7SfM7qOJztkPVVjxU9rHl7B3no77+Ofxf3iOwm//jvkzXeRN94hxlxreXJzujD5dLgN2pqF1ib94+0+BvmCVuegt0KskdDGqLwLP1byvDcnlt1OGChOrYm/j4+7V/HoDvH5k56CvG5E0UoPpTqvALJaAX7CcIPDH4bH5/4F/uB36s/XLuJlEqlKJj09OKjB4YAFx9nhYdjrBf1QsAUHXUi9UvlyB+/3+51OrVwOejJwzyeqD4CCwZfuatG8RIvZ/LJ9FG3ImDSardXVL0N4zg4PAVEW8nhAoAiYxnnZkAmHOevrPodjenx8Y2nJ73SC6xvmAKLNKIvZDF4KPAOFw+y1NaAngNuvodD1Tz4RbG8DSYTf6TzZ3wftILB3gkIhWG8ajbsjYLFkEgkY98BkQ5STWF9crKuq0iuVOpXyFccP7P4G2/H4nUFbEPJRJwaOj01Q2MOdn1EuL0HHR4jPPyj46FdNiTHSkT4Xb7c5O3OwAnf17/8h9t33CZkZ6bdv/Oy//XnGvU9memqaSpMSb76FzU6cpnRVlxRRGhoGGxsPhDzEwztLjVXqgRYLrcUz3QUtDkCrI9AyLTA35GL0h1cmVG2E6cLEofTHlLTHbUkP2lIe9mTFkeJvNT+/3Zv6SNhS4VmkRlbHoNVRaHEwwux2TTZbx5r8i5Tg+oimjzhRmtid9Wi1sXy+HttdmEZKia9LfNqQlNBbmN+DQKz0UMgYLATHfngCfncYCvgjvv+F+NGrVS9GZUoLC9cWFr4kqYBhDagMILBAsL0NpP6A71JLpQBdYa8XmNrs6XQem811eQm2FlF77mh3NVo94G5plFgDlIBKIvlSeQAT1n7/1upq0O32ORyAIZDy+S/7odEWJ/xqUS4BfjIUAh0h0CoFOgP49f1+oB4KuFxHJhP45yGPx352dmg0GtVqAKeID+4/g18esIu201Mpnw+eARy6lM8H+aeC3Z1XDT+B6Ojyl+u3L/HjCtnt/su55Sm35/LYrF2ZGA3u758JdrmjbYSEm00ZMZi7V9vTEqR9veTnSdOYCmpB8Qy+MuvqlaIHn/dWF3bW5Pzqx/9xfLhufXXq3r07ra2tpSWonLSMwZa2DdowpSTfOEY5GelwjnQExrsgxgA0NwItMKClWQ+11zHcfdjdpCBVcWuxm1VltIJUUtxtcvIDanHyUk3J4UxvgD3pXqFGWOORtaHQfLd/tt061uSc6QhtjB6Ok5mVOf15zwYRKayOOnpVWXteWnteBjk7YxBVSsViFzu7qaQmCE7a8IeC3lDE/z/HH8i+7pBLZUIuPORcg8MBhQugdC+Pj+GJmuglCwUCjvPz7Y0NgB94gRcISPl8x/k5kGCCpESQqKMUi2Pv3w+4XC+v+GjxCbhcQB8AdlMHBsORyQS/DlDQ+P2gIQMXh+hP9NhsZq0WKBugYPDQCJuhgrhFgK7TgwOFSPSymEQ1OKD5C+h1r92ulkpBYAkoj2DNBoQRUDh8aDTu6/Xwl4LBgMt1vLdnMZvdViuI0LKenGhkMmAkAovTXyX+Lep/ANYu0dm5KHkQgQeIA0EosH9sHp0YPjs9FHDWJ3q7IOuFbG2ezxwK7fNRsVfxT282P3tAy023zkxtEarWKnH1jx+MYkqa8xLrixIwuffRxTEy2ZJYK1EdHLz18dXaxlY0CjNNG9GxWI3pibL+tr0hsmOsM8KgQNND0GS/f5TiGuqE6FQ7pf2ku9nSS5bUVtAzE/oSY0ZyE0fyE7fqMcIOwtFkt299DNqdhWTL3sW+ALMrMN95OU6yTpEjrNHLOQqrHjmCSCIl3OF0kxYaqnqLsvsQ+T1F+aMV2Mnaupn2jqnuHshug+d8Qj6YPwj8E38N37xhN/0wPM0eiRgMhi/117+zfvs6+MjkUplKJn0RSV1aWAhTwKHQ1uoqqEJgFtprt8OrmmgiFXxxR6tB0O1WCoSuy0v78Qk8uRkKw217rc5rt6uEcHA87FUdgJ3cPDYbGAgV7uzAwddRaQ9QsqkkkqrycnDhRnw+uBzBfwisZfQ5HICttpjNrsvLuqoq+9mZ22oFFLPFbA55PAcGg+vyMuh2B1wu1+UlKI8gxgcKh2UCwenBAQAb0O+AATvQbAWB2wBaQG4HVptwaY0Cnr221lxXB4uYFPJXDz+hwEv1G+yCECWyo+YZEchls87PTDsvzwTszY35aSjkEbGXN2aHIdeBbHkk74u3m5LudqU+sC6MjuYlTZUVjJUVtGQlFj+8TkZn9dYWZjy+YtkT/vy9d9BNZPrKenpOPmN8gt7btd7fg7r3+U5rlbQNr++qtvTXX1AbbaNNjvEWxzjZTm0/62vZayPKiOgdbMF6ScZSUcpcQeJmea6gFmXorrPSu4PzVGh5LLI8ElocCix0X042nk2QLmfaAhtUiDdtHG3py4rtTo9lVBTSy4tnqsophbnDKORQaekiuZXe3sYcHoZNU4K+SNADQQF/0BOGV63BCBSA/XdgC+wgfOUFYZnz9177bnNzc2dnJ4lEmpmZ0Wg0EolELBZ/0/5n32gwqFTUvr7333yzHInElpY21dbKhUKDSqUUi41qtdtqVYrFP//Rj9YXF712+6FO7z6/4G1sbswvHEaTffdU6rO9fSVfsKfWrM7MthBrZ0fH4NSgXa5aBItuTvb3saWlZq0WTCV4bLYX25uCrKyctDSFSKSRyaR8vkwgGB0c7OvsFGxvnx4cgDaOQiR6cd66dg3od45MJovZrFcqNTKZQiTirK/bTk856+sgqgRscghYLKqoaF+vn5mYGOjuBtF0Uj5/l8WynZ6ClMhDo3F9cfHQaBRzuWat1qTRzNLpYDqDMTp6YDCwVlYqUCi1VCrm814x/ISB/05UvROKwCOo8EYaCkEhTyDocK/MMI8MusUpukLIgSKetfmJ/p6msPv0QseTMgemicUVd9+X9TWMl6Qx8aV9JTm4xNjS5w9xmU/Ls+MyHl01KHZInV2ldS3MdTaRWFeJQPzH//Afnr71enNS7DYJK2wsV5Fxpq7Kwz6CZQCcNcd9pP2uWm0zTlxdslOeu4lMXy1KXilMEleXaJtxJ/1NXkYftDgKrUxAS2PhhaHQap99tvVisulimuxdHYR2GXvjZFphAhNXMI7Knq0qoyLz6biy4TLEXG3NRB1xpKV5a4EJ48fviQQ94YgvGPYF4X4XvPrx+V2w/yg8UxiAgrAd+x+KH87mxtnhIRqBAL4zK0zmg1u3xoeHj/f2yCTSg1u3anC41OfP7WdnBCyW2tf3+Padp/fuK3h8bDGik9So5AvG+wfa6xvqcZUBm52Er5qfoC9OMsb6+vvb2rGIkrbGxqryclxZmUwgmKXT2xobY+7cqScQgOhhfmqKgMUuzc6ODAx0tbYCJR6PzU5LSNhaXdXIZIzRUZAsUoFCjQwMqCQSg0oF2lCTIyOc9fWpsbHqigqwnDOq1X2dnSAbq6q8nIDF5mdm1hMID27dqquqotNoUDgsFwqHentLCwuBjhuspaXTqwAAIABJREFUEusJhKXZ2bWFhaKcHOHODhg02tPpXj3+GkRGwf7XsAX2V/DjOLcF3V728ppomz3S12M7MQecZ+O03umpcev54YlOypnshy6NG+3VbWmxE9jC8oe3Uj+5Upef/eiT95uxyN0VRk7CvQc3PlJp1DKljrvDi/3i9vVf/fK9//6tzoyk3uRYbg1KTESq6kv1jWWmVrSpFWVsKTW0lJla8brGCgURJcQV7qJz2MiMrZJ0FiJNVYc2tVZdDLT46BSISYMWRiEmLcQcgDaHPYudtunW86lWx0JvhMOwTHXNlGcv4ItmcEVrjfgxdDGtrLi3KHeRVL/YQR5qIukkImByHQp6vT5ntFkcBPjx+j2w8Bx+M+DFz/9E/ZEKBedHRwQsVrC9XZCVxedwetvbySSSlM+ndHTs6XQmjSbs9e7r9bN0+szEhEYs6WpqfnDj5tocU7y900FqpPb0tjeQ5umTD29+AYaxe1paJ4epk8PUzsamge5upVhM6ehgMhgD3d1Ls7OVaPRQb69Zq2UyGGQSyahWd7a0/OqnPzVrtX6nkzE6WoPD7bJYz2Njtzc2GqqrVRJJbno6sqCgt70dU1LS19mZl5ExMzExNTZWVV5ekJW1sbRUTyB0k8lLs7N0Gq29qQkIqINuN3dry3V5yd3aOjAYGonEucnJJw8eLM3OZiQlHRgMMXfuSHg8o1pdVV5O7etDFhRgSkoqUChUURGppkYuFJr1ulet/kQj117iJ/wlfsKQ89Lhd7rlPOHSNINBG4ICDtuJeXiga3V9RS6XQj7nVA8Zsh5AeundH31XNzf56d9+N+XaZ1kxjx5fuzZG6VmdneRuzH/xyXuxd2+P9lAwGVkP3n236nl8d1bqUEaitKlKVIWUV5doiQhjXamxvsRQh9ARizQ1xUpiqZyAFOOK+Ji8XVQ2OLllOYrqUj2p4rir3kntCE72R6aHItNDoZk+iDUSWO5xMzusU2T7XHeYPXm5MMAmoUeLU9ZJFVM4xHgFsic/k4ouGa/EcsdpQ61NjlMLFAkEfc5gwON02+CFGlx8ggEo4A54XkZRwEL0kFlv+EPrj0Yhl/B4Hc3NKolkZ3MTvh+HQhNUak5a2vbGBtiHOM7PNTIZyPY4MZmhUJg+NAwyEeCtl9cH0kdgk+tQWCUUTdFGFiYZIGIEcAynBwcVKFR7U5PXbgdTrhazmU6joRGIgMvltlq/5ANODw7WFxdBL3V1fr61oSHi850fHWnlco/NppHJZiYmrCcngL8+3tvraG4+NBpvXL06NTaWHB9v0mh62troNNry3BxMUUTFPnql8vzo6MBgwGMwdBotPzNzgkrNTE5mMhhMBoPW3w9Ldba2+ru6Lo+Pt1ZXN5aWulpbxVyuUip5BfETjOYvwALSqAguOrsARfxht92xrzdOjVB3N1ahkPvUrBkf6RdKZZMzTCgc1vF3RXMMaN/wxn/5T6r52U9/+KP82KfTfdRN5tL26sZAdzdjjDrURV6mDWbfvhXzy9f7iguHC7PX8GhJQ+VyfrKagDRUI81E5GEtfJqJCAOhUEcokuOKJBUFQkwuvyybX5YtxOSKsflSXKGSUKKvx1jaaxyDrcGJXmh6EJqjQswhaG04uEIJLPU5Zjvsc93Q9pR3c1xOqR/Ke75aX96Tk9ienTyCRszWVlLLkbtj1Kn+XjgkKxII+F2BIIyf6J8Pqx0DUMgV8MEadHj8CX4vjHrDd7/7h+1/xHyeUiyuQKHAANyFxQIIg0OjETRwQEAI2H9H0QUDxn1+AazffVabWaWG/AH4MRQ+Ne8FHc6upuaupmaQ2wNUobbTU2pfH9zN9Pu9dvvl8TEIGgGs8Zd0M9jTQ+GwWipVSSQ3rl7d1+vDXq/j/PzAYIDCYVivHSUYhDs7gM2DBcVuN3CrOz86cl1evsggyUxOhgnAYBAYu/kcDoNK9ZKvixruHJlMpwcHIY+HVFODKyuL+Hwn+/tfsucamQxXVnayvy8TCV8p/EDwexD65/iBIRSBvE7X2aFlcpRqkEkgv2NfI52fppsOLBOMOSgQ0gsFh/wdDm3wvb/+dhcKWfjoiWKd3YCpWp1eGB+gtTU2s9dXJigdn/zgteq4h8PFedPoYmVX00Z54Q4mX1eH1lUhjNWI/RrEUS3yqBZ5QCwxExAGQom8olBSni/G5InQuWJMnqyiUIlHqAlIHbHMTKo4aSfa+5r9I10Reh/Mek/3B2a7g4s90Oqgb6nfvTgA8ZgQd25voqs/++kUtmACU9CSHs+oxgyWFU5UV7ColC0mI2r24I0EPf6Ayxdwe0Oe6DvwT/gJROAA4XAE0htMfyh+pELB8d5eLR4/NzlpPzs72d+3mM1AxgKGbU729+1nZ1urq/t6PTwUcGkFcT3AH0ctEkfcHpVQBBJKbJbjiNtzvn+g4AuO9Ab78YlJo9HIZJz1dZ1CYTGb93S6s8NDPodzaDR6bDbgGgUGisCPu7BY9ErlCpMJuLuAy7Wn0ylEIjCos6fTASHCLovltdtlAsHl8TGIjtNFQ3480cBtjUx2aDSeHR4CYataKt3e2AA+b2atNuz1To2Ngb4ta2UFqBMuj4/Pj472dDqL2by9sVGSn3+yvy8Xi15B/ISj+VlRIEW1CBGg54HCwZBl/2COMWFWyiGfXSvaXZuf0mr1rM1tKBRWbm3Kl+awz2Lj3/01LuFZ0tXPRpvJ5dmFDRVVjKGRtdm52nI0Lict97P3aQWpy7iSuZJMYT16uShJgM0xNqCUFdnayjxTVeFedfF+DdJUU2KsRuqqEMrKYgWuSIYtkGEL5BWFKjxCW12qJ5bpiWWm+vL9Rtwxufqso/aiq/6yu+G8p8422uKZ7YTWRyJr1NAqDRIvQ5KV8/nh8dJ0aknGRmv1dDW6H5nbnpe60FrL7GxWcTchvzPsc0Bhj99vh6CA22OPboHgraDbD9cfmIwD/lVG02vf+8PqD2+bs6fTdZPJPDYbtPMlPN6H77yzMD0NmolghGaCSg24XPt6/eXxMQioCrhcFrP5eG/v0GjUKRQamQzkyFM6OrRyOWwGEggYVKqAywU6Kj6Hw221glLjczhm6fS0hASL2WxUqwF1JhcK7WdnzosL8MoBl+vCYnlRQJ7Hxoq5XKNa7bXbYRI8EJDweGAiCAzkaeVyuVDotds9NpteqbSenLzw34H/Fr/feXFhVKstZrNWLrednoKfzlpZ4XM4RyaTXCj0O518DkcmELit1n293mu3W8xm7tZWb3u7WirlbG68avgBalHg4gsuGlhKGYE8Xj8EQXsG4zJzRisWQn6nYmdzkznttlq3lpad+/vnciEm/mFHflpT2vPCm5+i42Ib8wvRKWmtWNwilfrpL3/x1g++N9aAz3r/50Ppj5fLs8WN6JWS5IW8WBbiGbc0QYxJlZenqyuzdYQCQw1CW12sISDUVcXqqhIVHqHAFSlwRcrKYg0BqSeWGesw6qoScGoISPA9yspieWXBWX+di9EGbYxCrAlokw7J1iE5y7E6zmmtHCxOXqzHiGidjRlPaZXI9Z6WHiziSCUMu84iXisU8YR8VgjyuR0X8AQh7P8Q8vo9UfyEQVtZ+4fjZ2t97fzoCFVUJOXzySTSUG8vqaaGz+EszsyAAVLX5SWfw3l09+4Eleq2Wg8MBr/TeWAwjA4OXh4fAzmC6/LyZH/feXGhlcuHenunxsbgyzcUAhxxxOdD5OVZzGbryUliXBwYFEUjEEO9vWIul7Wy4nc6D41GPAZTi8czGQy/0/ns8WM4GSEYVIhEqc+fH+/t6ZVKeMEWDIKuzszEBFhSgtEjg0oFRncODIbzoyPnxQWdRqsnEIC3yb5ePz48HPJ4aP39QFtdkJVFwGLLiourKypGBgbqqqoAPseHhzUyWQUK1dXaChPur9j+J8pVh6O3WrCQg++70QRHyO5wQRBk2T+gdLaxlxagkEfH32bN0H0nR8yBXujyVDo1XHr749XG8pGSjORf/xgbczv3+qf6tSVmB/m91/4q7oO37rz+o+LbH5ffeHsw9d4yJn0VnbqKTOBgkwWVaXxsihCbIqpIFVeky/A5CkKBqqZIQ0Rqa0vF5TkybJ4KX6StLtETS/XEUm11iYaAMDdgVPgieUW+uqp4uzDpsBm3mhErryyw05q9M92BuX4/sx/anIQka5Bg2bk2IRtoYuDyuwsS23Ke96Pz+jB5EsYQ+vmD5VEKe4lhkO26z8xQwA5FXFDEAwXdEBTweZ1np0c226XX74vGWEEKjfq7f/u97u5uCoXS29s7NjYmk8kMBgOXy/2m/o9erVKIRJMjI8BIDQR7qCSSge7uzpaWTz/4oL+rC1dWFnS7m+vqKlCoorzcuEcxc1OM0uKi9pZmIXcXjSxBIYrLUaUKiTgrLdVpvRzu70uMfzpNn0AjS8gkUkl+fsKTJzqFAhBuX3z2WV5Ghl6pZK2svFhHIfLyKB0dbY2Ngz09+3p9I5G4s7nZUF394vcxaTQrTCZIUwSKaSmfD8RvtXg8HoNhMhiDPT3J8fF8Difi88mFws8//li4s2M7PQVjFzF37pBqah7evj3Y0zPY0zM2NLQwPX1gMIh2d8EIILBwmBob425tdZPJBCy2Fo8fHRzElZU5Ly5EPO6rVn+iEIJLDli/BSDYDc0flfEcn57Af62AuzLNONUrtTtb8o0lu0YmmRnzSXcbk+4ZxztXCHkzqLSiKz8hPLxW8+RuLyK78PanKR+9ibjzSUvm0+rYa2OFT5fKU5fQSZO596fz73NwyfzqtE1UHL8yWYBPEeLTJdXZspp8RW2xoqFU3YBSVhVpCAg9sdRYV2aqRxvrygCE9huxKnwRF5GqrS6RV+RLMDnUex9b2qqgpRGINQmfK6OR1XGIxfCsjB0yerfJ+N68ZzRMTkdBwlh1yXQLfq6tuhdXpGTNsmepDErTYDN+uK12eaJfsbN+rJV6z4+hiA80f3xe2I4nDIUEIuG3v/1tCoVSU1NTV1fH4/GUSuX6+rparf4m/LA31o9MJjBmA3w5wAY9MS5uamwM9OPtZ2dyoRD0OkU87jiNmpqYMDI0qFUqFmZnBLs7g5TeMeowoiDfdn52enSILCrkbG4IubtO6yUQm60wmTMTE/1dXdytLTqNtjgzYz8762xpofb1QcHg9Ph4cnw8cBTZ3thAFRVp5fKq8vK5ycmh3l6FSETp6CjOzd1YWqJ0dBRmZ9cTCFNjY3OTk7V4/P0vvjBpNKODg0G3uxyJ5LHZMxMTpwcHdBrtwmIBmjc4iUQs7uvsfB4bq1cq/U4nzFtEpQaAVNDK5c11de1NTYszM2IuV8rn1+BwRybTizSkVw4/8OzC1+DH5vEcHFtWV1cvjg9FbJZZJhCvLspXmD6NaJvSREOkaodJ6t4qSTNisfR52+OPepLv0oqTyckPiE++oJWm1zz6rCH2WlfyrYmCOCYqcQmdNI+KXyyN28AlbhNSdqpTdwkpu4QUfnWGsCZbUlcga0AoSChVY5muHmlsQJkb0XtNGHMj2tiA0tWWaGqKBWXpvNLUlcxHKkLhaSdBXV00ePfKERlvG2q2U9ustLbLkTYXg+JfotmYQ3sTXfNVRdWxnw+WZhCT7vWX5y501pARKbKFkUPByols80LFOVdsm7krwvnRxf6O0dZaLpNxadRAXhcUCkQ80SBUKGg2Gf7iL/6ip6dnYGCARCIxGAwg25FIvl7FJZNI1XKZx2arrqiYHBmBotrqs8NDs1Y7MjAARhIc5+fWkxMemw0GdaBwyH5xTh8dOTAZIwH/xckxFAqadFqTTntmOZIKBZGAP+5RTG9nBxQJ+92w9dTpwQGg+Frq60FA755OZzs97WptzU1PBzwBa2UF6LhP9vf39XrgHNJUWzs2NHRhsYAJ8POjIzBQBHb5HpvNcX4+Pjzsdzor0ejBnp61hQUoEBgbGsKUlHC3tpwXF2BTBGYcFCLRyMAAMCSR8HhA7Woxm22np4CiMKrVsCjp7Ey0u4tGIA6NxleQP4hyBb+FHy8EeSHozG73h0OTk5PHB2bn8ZFkc2V5ZOhYuH3GWezJiptCpToX+4UNecuIx3u92MmcO6T771JzH08gkucr81n1qJmyFOSVf6Ak3aBm3htKvU3Pe7yOS2cR0pYw8avlz7h1GZyq5G1CCq86Q0DMEtXlyxqK5KQSJQmlbSg1kFDGxjJTE9pAQmnrSpTVhfKqfAkuR1VTJCzPNJBQl311h224wfsfjDz6bCXr6WJW3GL207WSFD6hWNaME7XiOY2YtuT7iM/fbM18XPXsVm9Z1kQDuiL5nkPJssrWL0QrJ7yFffa0YX1KszyhWqAr5ifHSdXdlejeahx/cc5tOQDBygqJ8Fe//EV1dTWJRGppaVldXVUoFFKplMfjfVP9UcthCxtcWRm4yIxq9b5eL+ZywW5eIRKZtVopn7+5vAxCRSUCvkYh1yoVcLb7Nufi5FghEYt4cKvkzHIk5vP2DPq6aoJgd4fLYWsU8hdtSoNKJRcKu8nkWTr9yGQ62d8X7uyw19bAC2rl8PcABx/ryQnsLOX3z01O7uv1NThc/KNHh0ajQiTSyuXgF9PIZHKhkL22BhxzTg8OdApFXVXV8tzc+dGR22rlbm3VEwgKETwXqFMoVBIJd2trfXERkOaAhdtYWrKYzRaz+YXg1azVAqGQVi4HqcPstbVyJPL04MCgUb9y9QemaqPhHZEQvPmJgscTfQxAETqdfnxghvze5XHqi06OUyWerkbPYvP2RprXypOUzUWc8udHfRUcXHLdzV9OFsXPoTMYJclzqBRuA7L57rtFb/w1LfXeYOItasrtqfxH8yVx8yWx88jHS2VxLFzCVmXiDj6VR0gX1uRKiPnS2iJ4FVdbrKpFaOqRmnqkqhYhry4UV+YKK7Il+DwJPk/bUHrYgT/swJ/2EtcL4rtvvVf0/f9a+o/fwb/1I+KHv6i9+uu66++QH31OSX1Y8ukbyBtvN2XE9KJSpxrR/bjc+rw4p2rzjDdn4885RUtuyYpXsuERr7uFLKdgyyPjnnA3ZfMM1tjQcFNtf1Mde3FOvMP+f/7s/+7o6Ojr68NgMCwWS6FQsFgspVL5Tfgx6bR6pRIIauDABasVihp0wAQacG+LPn7pDhfyeSPhoFwmsVkv7LZLr8fl93kOD/b2zEYICnvcTpv1AoLCTocNTChF15WhI5MJePEE3W7QbPHa7aC/eWGxAGLNdnoK938CAXBle+32tYWFge5uKBg8MBgAbQAcQ08PDsxabdDtdl1eglE8KZ8PXB2Bm5xeqYSFoVE7RSBahRdsv9GnQsGgWioF5o9Btzvk8cBfhecSfWAeScrnV1dUmLVa3jbnFccPHIYTPb0QZHU75+fnTw73IJ9Hy+PI1pcnG4nDiEx5T52yE8+tyeHhU5zjteLadENnKeXZJ4uYlAVM+lJ59m49cqY4frHkGeL1vyp/70eU+BvMwueTOY+oqV9MZN+dL4ldRMauYZ5ulsdvlSfsVCTzcWnCyiwJPk9alS8h5MtqChW1xco6hKK2WFZTKK7KE1bm8CuytpBJGlLpaV+tiYw97Ko66MSL8Pn88lxZVYmsqmQ+J57w4esJ3/1/E3/w34rf/wnuzgcNz291Fjyj1yKmmzAtiERmNyFg2Alp1v2yBRd35nJr8mJj0sqadm0vevnrB0vT1t0Nt4wP7em8Js2FRmYW7czQBv/ute8QCISBgYHa2tqVlRU+n8/lcn8PfsD8NhqB2FpdBZuBgMt1dnh4YDAA80FY9RwKAYFztPiHI+Egj7tzemKJhIPBAOykZTLqLUcHF+enwYDPbrv0uJ0atdLpsIWCsAQbDMC9RGN01+G2WgEdB/g0r90Ov35UfB3yeKwnJzBcAwG4KxoK2aKOU7bTU+CnBUAu5nJB/xT4XemVSpBkCvo/QH8NhUJhrzfi8x0a4Zh78NfB255QSC2Vvpz2iw6c6hQKACQQ2c3ncIAh66vnfwC7t3xZfwIQHAPqgSA3BF36vBdOO4PBgOuPx3WikW+OjzTkpu90Nuw0lis78frO8r1utGOMaBnAGrtQ9Jx7jML4BUz6Tj1K2UVYRCbvVGZNZT0o+/UPyPc+ns56MlfwbDTt9nDS54zcu+tl8cvIR6vIx+vIODbq2U5ZIh+dLMKki7AZEgJci+T1RUoSQtVYomgoltcXSWsL9GS0tgW13115Olhr6avWk9HmzgpzWwUPkyOqKBTjECsFKa23Pij65d+VvvfTxkefUbLjunPihjGZY9WF3Zg0QsZ93eZEZI9n5zECghlIvADJVyDZGiRei/BXg9xV2/q8l7flEe945AILb8si2vXs647U0q31FRQKlZ+f39PTo1QqQXja1xYfqRQ2x1DLZedHR8TKSqVYDNQ6UCgEOo9fTl97bDYw8AOFgpGA3+W0Wy/Pjy2HoWA0NzIcPLYc7u+Z4E8jIQgKAxS5XY5gwBd0u8E8z8v9eiAA/HuB1ACmv6P1DVY/RK9sMO7K53DAtAL83x0MAsurlwCI2mSDsYgXAXLg+2UCwZfVJuTxgJ4v/Mp+f9DtBjs60M4CP06vVIJ/CP8CoRDQIkDR3w2MP9QTCBqZjLW2+u+s/nyZjhX9ALgB/eYRfA0wtfAAHUxef4kfZyRg9zq7u8jO033Ifb5Oo9RmJ0gn+sS9pH0a2dBLNHRhT6k1kcUu/yxZ24aUNBZvVGZwagqni57NFMSp28tHk64vFDxeRSR33P2w/tobo8m3ZwtihxKv9T+7OlPwgFkcs1j8aLk4dh35lI1M2C1N4qPS+Oh0WXWegligaUDommA5qb4ZpWsqVTch1U3I08HaIwphr6fSO9N5MULa78XvdeEP2mrUteUiHHKrLI+e8ZT88HpzzOe9qY+HC5LaMh5PViNGCUVNeU9wybeO+UyHdCUsXYSEzBBvxrfNcLHo9rUJ2yr9coXu21k9XZ09Wmf6VUKnQnAsYrsNCqtB4bqwnBzuzUyOIwpy52cYOpVcr9PotGqZ9OtHGCQC4b7RgMeW19dU72yxlueZXA6bPjoyMUJbYs5tszZ3tlhb62tdbeTleSZnc2NzY21xgTk6Qp2dmdrd4TAmJ3a22UuL81MMOofNYs7NMCYnOGwWfWJsYX5ubXWZt82ZmhifHBtdmJ3hbXPYG+tLzLmF2Rnm9NTi3Ozq4oJKJl1izoHob6lQoJCIl5hzY9RhLoc9TZ/YWFkWcndHhgZXFuY5mxtTE+Pry0tb62v00RHwDJfDXp5njgwNLjHn1pYW15YW1XJZW3MTa211eZ65sbK8ubqyvrzUSW5lb6xvrCyz1lbZG+uUrs7N1ZWNleVZxuTW+tri3Ow2a3NjZXlmkr6zxRodHsKWoYxa2D/+jx4/wMPgpY0OsBWFH2E5PuQPRYJRw9FgVKIf9Yx3R+BNjx9yO4I2Z8ATgSHkioRtAYcv6KD2tZxpeevDjf1lqfLRZlk/UdGJN/URz8aaXbOdPmane7r1cqz+eLha246SNOTv4NO2sEnbFSk8fBoPn7KLS9G2oBbyYltvvVF3/ee98R9P5N0fzb7Tm/jpSPYdRmHMbNGT2YLYjdIUCaFQWlXILU2XYLMVlTk6YpG5EXVALrd04I66Kg+7Kk8GiOfD9ZejjZfjTeejpFNa/TGt/pTaZOltsg31HPe2i4kVy8j8qYIsak5KV+qzmtg7iM8/SHj7H+tTH5HSH613ES548ydbE86o06Jnc9y9NQmf7GkXe9bOmbPtLFi5y1bBqlXCsst3HCquQyOw6cQXRlnEfrynEq7NjvW01O1rpGoJTyMXS4U8UTSLUCgUSmUKhVItlSl4fOHR0ZFCJl+cX6D09DY2kBrq6lubW7o6OhsbSF97kv7A42tf5I/wSXJLa0NdPQFfVV1FaCe3TTOmRALhnsn8TaUbPK9UKvf29t59992Tk5Pf8n37zSf/+v5vv8HPbyQF8HhcdKIl6Iej+vyhl4a90disQDhk9cOIgeOn4VLs9IUcgZAvErqwnUCQU7w104RKRsZcmSFkbTUXsYhZBkrl0XCtdbLVO98TXOzxznXYJ5vPR+sNnWXyxgIePmUbm8CvTBYR0oRVcIeHj0uV1+ZtYRJ7Y9+v+vTHDXff6Eu+Tsu5R82+O5r3YK40Yb08fRmRNJcTt45IURJLRGUZ8vJMNSHfWF+y34w+bMMedFQcdOKM7eVnQ3XuqXbXdPvFWOPJSIONQQ7MD7gn+uwjfVZa32F/1041jpLwFHfzWtn1T3I/eBv1xaeY+5+P4YqG0FmSsfbDtTHLCs2+PuZaG3Gtjzs3J1ybDCd7ysGZte7MWncXLniLl4LVC8m6TcZ2qHYcWr5DJ3Dvy0/UvDON6FQntu1rmqvRCu7WoUG1p1fLxQKhgKdQwOARiiR8sUyu1qhUGqVSKY8eCoVCpVJptVq9Xm/43+zQ6/VarVapVMpkMnhlK5Mplcpv6pt9Cao/AvxERdQgiRHsbAJwXzAUjTQNBqAgXIKihr3RpNMgnAAaFav4nAGvOwDBwlEoDPvZevRSTktFHgWXJRwhyYZrJL0YeU/Z4Wj9Ob3ZxewKrg6E1/oDy72we85ks7mnXN2KENVk8PApIkKalJgpJaaLq9MVxFxlbb68oYCFTuiIea/0ndeqrv98JPvecNadoczbE/mP50sTFxFJs7lxi0UJO9isHWSyoCxdhstVE4thFrul3NyKNZGx2ha0oqFEXFsoqS/WkDF7vYSjgdqjgXplPXarNH8iKY585zrqnTeSf/yD5J/9OO/KW8lv/Ax770ZbVsJ8PXalpfJkY9LAHDDNUM4Wh6yLg9YlqnV1xL42YWdN2tkz1p3Zy535c+7CBX/ld/ATPNYoOUzIuuc9MTgOdWrhFnNsSC7Y3tOrtUqZUgFfFjK5UiiSiGRKlU6vUKhgRCkUcrkcXDpgXvV3reJe9c/FYrFMJtNoNAapqNIzAAAgAElEQVSDwWg06vV6tVoN2P8v0fLPP/i3xk/02o868cKGbmF4aRaJ4ifijaaa+uFV3EsIgYWc1+6E4D0q5Lq02U5hkRUUDnoujjnz9JKkB7v0bgmjc5eCP1+lnC+2n0w3Xc60uee7g6sDEIsGsYYj6wPBxR4fs/NoAK/vQMkbcsXETHldtoqUpyLlKOtyDtrKpJWZfFyapDZ3A53Y9fgK/rOfVt98vTnmPUrK9dGch2O5MYyCp8uo1GVkylze0zVEIrs0mYfJkBBgUYK6AakhlaoaS7fL01mo5C1MKq8qR1Cdt4J41vXg/fK3/v7Bf/o/n3/7Pyf9zbeefPsv4r7zl5m/+EfEh+8hP/uw4OP38DG3+xFZg8gs/mCLbYepm+1Tj7fvT3UfT3efzPadLgxeLo9aNyZsW9OX2zNfxY9VumVXbts1PLuWfyTdglxH+1J26NxoUQugoGNPJZwbHxLvsnRqmUGjFkcT1VRqrUZrlMoUQqFQIBDwowfvKwcA1f8+j+LoAe4S/xwn3/TMHwF+vsIHRPEDVxcfrPEK/w6EflOI/JGAN+B2QD4PvIALBg4kgllKe9mzh5rlCSm9c6uj4nxtYJtcfDDVAO3S3Cs9/rWByBYN2h6DOCMQazi81h9a6j2jEfd6sermQgUpT9NUoG8tNrQU6poKtbX5hoZiaWUmCxnPwabuVmUOp9wsfus11Ac/IMd+MJoXM5oXQ8t8OJH3ZK4kcaEkaan4+SoyaQudvovL4VflCwgF4ByOv94b83HrzbeIH/208t0fVr77Q8KVHxM//PlA7M3JtPjpnOTBpCdtsfdIMXcId26iPv+47OanhMd36VhEW8ZT+Ujn/jxVy+iRDpL0tBbTaMs+vfNwpvd0fvhibczKmrrcnrnYZp7tzp/zls/Fa1/Fz4WG5z1UWo2SS4PYe2Kw7qldp2a1aIe9Oi/gsmUioUjIl0gkaq1GpdaKxHBrVSgUymQytVqt0+m0Wq1arQbLmK+1HHlVnzSbzQaDAbZMlMsBimQy2b+H+gPwE4JbC+EopQZYaS8UjkIoCErQyyoEr9N8Aa8N8jmgkAeynem21xd7yKMEtHZuZLur7nSZdrLYfzzfuc9ocG30ujcpga3B8BYN4ozC+NmG8RNZHwit9l2ON1gGqwztSE1LkYGMMHeU7rWX7LWWqGtyDXVFiqpsNvLZBvI5uzyFWRRHefZp/e1f13zxy4YH7/Ym36BmPRzOfDCWF8ssTWYiE5ZKk1bRqRvYDFZF5iY2YxOdtlGWOvDkM0b6Aw4mQ9tSfkSptfTVmdsq1Y3lLHTObF7SSOqT7ri7xC8+KX7/jaIP3sLc/BR142r9s4fztZiB4lTFaIegr0E63MTrxMspteqBOh212UTvsMwOnK2MXG4yLjjTX8XPpYRlU3Bsaq5Nw4uc6TXbC5Fz04GM4zrSOo40Fo0k7LpQCHc2lufXluZlUrFOpxOLxXyBSKXWKhQKmUwG1mwiERx2A1b/33THfVWfBylzoAp99fH3/71/NPXn6/DjhcJ+COAHZhHC8Ok5OzNCkAOCnOcm2fxAWx8OsdRC3J8fE3TVnc4NnjP77MsDZ8z2g8kGSDFzymwJblMj26OR7VEYQmxqmDUUjuLHwWg+pdbsdaMN7ci9TtRRN+aou+yoo8xQVyjHZUpxGQpCzg46ZS77wXzBk+2qbHpeDOn+26Uf/rDi+i/I8Z/1Z90fzns0UvhkFp0yj05dLE9fwqQvodPmS5Pnip7P5sdTE26NJNxmpD5cyI3fKEpeK0icS3s08uz2QNwX5Puf1t38EP/Zu/lv/fTpP7wW+/evJb3+o5rYOz05yfTyQmZ1qWigabejWtRXx2ktF3VWSburVAMN+jHywRTlZIl6sTF5wZk+58yd7jDPuEtnotWv4semF0BWo2Z7wXOkdhyoz4zSiP3k2KhyXVh2WGvzMwyRkK/Vank83s4uT6szgNstaLPy+XypVKrVak0m0zemFr6iX5DJZIBEAXcToVDI4/F2d3f/PeAnCJNp0G9BKOKPggew2FHk+CJhXyTigiC7127eXaf3NaDHSOWKmcHD+VF5f5N+oFnXV3c83no52xnmjAQ4w6fzrcFdanCXFtoZDe2MhjkjYfZwaHMwsN4fXO1zzLSejdTu92BMnajDLvRxL/a4F3PchVHiszTVeeqaPBk+i4tJZaESN1BJ65iUueK4gdSb+Buv5771WsnHP25+9tlwQSy18MkMNm22Ip2JzWCWp82hUqaKntFz4yayHo2k3Gfmx6+VpM7nPO1/dK3ho9er3vxRxVv/kPy3/yXjH/8m7cffif+7//r8R39d9MGvah7eID19QMlLHUfnd2TEbTRid9oJYkqdpLeO3YzmkctFHTg5pVY70rLP6D1Zop6v08/ZU1+LH7uG5zaKLzTcgEXtNEud+wrPsf5ILToxKqCwRycXrszPcDbXpBKRED7EKrUWFBzAvIFlm0gkAkD6/ZfOK/ZVwJ2IxWJwf5BKpXK5/Jt0G1/+7f/29cdt88I7njDk94TdLn8wBKAEOX2us4tjq+0sSrf5wgFn0GODIi7XhZ6zOjrSgZ+n1AnpXdKxNuVgk3Go+ZDWejLacsloc8x3edcoAc5wmDcSFo5FRBOQcBIS0CPccWh3JMKhRraGIdawY6b1Yqz+eKDykII9oVSc9uFOutFHHWUHrai95lJDQ7GqJk+Mz+aWp7HRqeuopKncmOmiJ2O5MaRHVwqv/H3ue9+vjnl/IP/RdEU6DRE/lB83ing2XpxAzX08lvt0tiRpMvspI/vJVGYcPfXh8JNbnXc+bPzkzar3f57y/W9l/Oxvit/5MebTN3G3Pqh+cK3+yZ3G+PtNiTELRExn+pOtJhy7qULUTWTVo4QdeFEHTtSBk3RXy/rrNNRW01TP4QLVsj5+xp492Z473V08Fa5ciDcvZVuXyh2rateh59kNfIdR5DBJnPsKx4HacaizWwxHeoXz/EglFQ73915enKnVMDkrEAi+vBR+5wNwM/7T4ze9AzKZTCgUmkwmlUr1zjvvXFxcuFzwBNrvHP/K/Z+oe1vIGwn5ozx01E/H6XY53Q4/HDENb4VCPmvYcwmFXcHLI61wsxGXtzbRruMwlIuDqqme81WadWlon9Z8Nk6+pJNhf/eFHv9GP7xs449BYnpEOBkW0EP8iTBvPLRDC29Tw+zhMGvIOUuOboFg/Jz24c76K097MDB+2sr2WlAGEkJZmy+pyuFh09no1M2y5CXk8yVUwjQifjDjTv2jDzA3fln6+c9RN37Zlxc7Wpo0gUqhFj2jFsZPIpOnkCnjefEjGY/HM2LpmU/oaY9Hnt+jxHzedvODhmtvE794t/7BR82xn5MefV794FPi4xvkpMf9uclTOMRQYcooImMBX7xBRO02YbfqkJxaJJ9cLmjDCjvwEgoR4OdokfZN+LGpuU4D3xnFj8ssce3JXAdK56HGeaR3HBvdl0cXh3ujQ306lXx3h6NSKL8U+ACZz1cfowXqTw/f+A5wOByBQGCxWAwGw09/+tO9vT2v1/s74IEg6F8dP16HD9bhRKBQIOx0Oj0eV3S8NOD3WO0XB56LAyjshEJO94FmYbAdn5ewRe9WrtCMm+PqWYpwsE42WLs/Qb6c7XTMdriYXd7Fbv9aX4hNg6uNcBIST4WE9KBgIsAfD3DHQju0EGc4tDUUZg255tqsE6TjIbylHwbP+QD+rLf8uAuz3442tZXpmkuU9QUSQi6/IoODSWOhU9jY9BVU4kxxPL0gdjgnpuX5NfTNX2a9933MrbfaMx7SMRkTqLThwufUguejhQm03PjhjNjRrKf03OfTeYmMrGcTqbHU+HuUuJsDqQ+oeXFjRYl9WXGNT28RH3/e+OxuR0bcDK6o7skXa3Xls9j8jZrSTQKC21DGIhRwG1HcFjS/DSfurVEPt5imeo4WaccbE2fsWbD/AfXHKmdbVbsAPy6jwGkSA/y4D1WuI63zSO+9OHBfHkHhAGt1kb/D5u6ytRrV4cHewTcce386fu87YDQaTSbT6empQqF444039Hp9KBT6/x0/oP0ZgfzeQMDnj8BOTH63y2o9P4QZbO8l5LdB1n3pMqOvsmSChDNuTJ/sLMimesSj5L35gYO5HvkAUTtc41jo9qz2+lYpcOXZooZ2RiO8SUg0DUlmAsIJv2DczxvzcUcDO1Q/eyiwNRjaHHQvdNgnm06phNOByovBKtsg4bKv4qS7fK+73NiJ0ZFLlY1Fspp8YWXWDhaG0BYmdbHoKT07ZjwnZqIgbjjvETnxenXMlZJrryNvvFn/9POhouf0svShgmeUzMdDOXFjBQmMouT50swVTO46Jm+1LGcJkTFbnErLjRspjh9FJAzmx3ekxbQm3W9Li+3JeU6Kv9MUf5fbVr1WjeSSyjerCiUt2N3aEm4jare57Kv4sSyNHG9M/M7+B+DHruG5jAKXUeA2wvhx78vdhwrPodZlMbhOzWd7Wr/jfHNlgYArJzc1VOKwNdVVdd9wVP/p+L3vQG30aG1tbW5ujomJ0ev1wWDw3wI/EBQMBh0ORygUgKCgx20LeC7gKf+ADQrZ94WsHnxxd1medn4sKN8+3ZhVjnUbp/v3ZwZMk50mRpuF2WVd6XGs9vi2BmFscKJswe5EiD8V5s+GhTN+Pt3DH/PyRr27Iz7OEPxtrIHgxoBnsdPBaD6jVZ8PVV0OEexD1dZ+3AmlYq8Xq+8p17ajlE3F0toCQVU2D5u5XZ6+hni+WBg3lft4MufRRN6T8fwnw3mP+nNimhNvIa+9mfP+TwgPr9KKk6exuRPIdGru82lU1nx53kZVyTYRza3H7taVb1eXsaoQ02Vp46hEatGz/vynlJynPbnxlLzE/oLkmkfXFwmlLBJ2vaZU0IzbqSuVt1bwG1DCVnj9BrNwfbVaGtk83WtZGjnZpJ9z5kD/56v8G8wfGARuE99jFrn3pJ4DhedI6T3SuSwGu8VgPTKG3NawzzUzOe53O44thy6n3fUNh/VPx+99B/b29vb39w8ODnQ63cHBAQTBw7uwC99vH//K6zcIcjgc4XAwFArYHRcety3aO3VB3gvbnnKoCV+ZGbfUXe8QrFm353X0Pg2twzI7fLkwejzTrx4kqakNZ0s9Hg7VxR7w7VL9XJp/d9S/O+7fpft3GIHdaT93ysebcPNGPdwR9y7NzRn0bA34NvsD6/2+5W7XdOvFKPFimGCj1jipRPsg/rQPZ+7HGSgVmi60glwiaygSVecKcDCEVori1xBwq3SxJGG66NlYzuPBrAd9Gff7cp6Qk+/j7n6I+PQN/L2P+nPjZ7F5cxX5U6WZzPK81SoEq7Zsuw7DqUVvEpCrVUUMdBodkzJamtSbH9ucfK869lrlg88q7l2drSw2TvQuViHWCMV8ElbSgpM1lQvqS5U91QpKjaKvTjXcaBjr2J/tO14ePdmkf7X/Y5Vugf6PQ8v1GIUeI4wf377EeyD3Hiq9Fo3nWO89NTtPzZeHJijkGR3qg4K+gAd2Tfjt/+5/+iz8p+P3vgNutzscDvv9frvdDkFQIBA4OTnx+2ETqK8e//r4cdkjECyydrouw2E3BHnUYnZXQwWlASNg0k74q6b/r703f24rO+8+9Rfkl1RN1dSUf0jVVOKqmZryVFLjvJ7x5HVe500mHs+bOG/y2um2u93tXtyLWr1IrW71ZrVa+75LFClK3PedBEhsBEAAxEYQCwkQIIh9vfu+4k7OPRTE5iK3EnUnzivUtx5eHFxcEBfnc59znvOcc03Dkb6W1PDdmnEAMwyWhzs2um5t9NxEDB38wjDr6iedXaSzg/f36xrkvEOcZ5R1j3KuccY1wvqHaf8A7e1nFvtYdzfr7OTtHcJ8h2hpZ6duYUMX8P5zZP95pv8C1XO63n0q230y1XUi0f75yu1PolePLJ/bnAxnP/IK4OejX1s+/rXxo1+Pvf9iz4Fn7r7+D+1vPd996NW2/c9/8fd/+f5f/oejP/lhy2vP9n/wes+hV4c+eGP8k7enjx6c+c3ByU/eGf1w/8D7v7737rOd7z/f/u4vzvzyR+//v//nez/6Dyef/S93Dry4PnI/0deycOmY/8aJ0PWT8dZzKzeOr94+kem7lu6/tt5/Y33oVnb0btHQVTEPVuwjqMcA89+25h9AfvhMkM8tQ36EUlyoJLnKuoKXNA7lsarWECaG+0msCm7ooMqPrCRPX9zzDEBImjfc5jjuG4y/QS8HrKppkirRWoNVBDzktrReOdV++Qv3VA+26i56jVnbSMk6XLMOV4x9ubH27OAdbLoPm+xGJjtq0/fqxvuIuQO3dxELXUJgiPUPc94hdnGYcQ3TCyBhmXQM8r5RxjfALg4yi33MQg/r7Obnu0CA23IfhBCGL6P958iBS/TgRar3XL37VL771HrniVT7sXjLp7FrHy1feD94+qD/5LuOT18zf/Cr6Xefnz74S8MHr0x/9OrwwV92Hnim8+BLt15/9tKL//Xcc3/zm7/74Xv/+bsH/+K7n/6XH9x54xd33/pl57sv9Rx6tfvgK/fffrHttZ/ffOWnV3/1N1df+tuTP/uL1//v/+WZ/+1/OvzX3zOf+1TwzJZn+qznP129fznX37Ladm69/XzyzulS3/XSSEt+tCU3cic71pqfvF8ydgN+5kcf5r+FNvN36ISfSga4jSU+E4L8CMUVoRQXy0m+mi4lQiySV2hEawjW2eliJgUumiKrKpKy26Px9PHIMwDJoShK0zQURZsgbXU+/5z4m6wqoixxAg/C0DTN8/xmo1Cf/SZwvMgLWgOsFSqynCqwmspoAhK0Tdw48WHHxaNpt5Fa8aRMQxnrcN4ymDf3F029xbmu8mxHyXi/bLyPmHr1ZOQ+cn6AcQ6z7hF+cYxfHBO847xvHFjwdJzzjEExrkHa3c+4BllXP+fs4519gr1XnO+WrV2S+Z5gaGUmbhBDV+q956odp4r3T2TvHc+0f7HeejRx65PYlQ+WLxwMnn0vcOZd1xdvOT9/03b0TdMnv5758KXJ918YOfjcwHvPtb/7/M23fn7p1X8488LfnH7u/zv3wt9effkfbr72j5/8zX/84r/95cVf/u2NV39649WfXnrhJ6d++v98/pP/tP97/+uzf/Q//ux//h8++/H/ZTv3EWLsJU2D+eG2dN+NbN/NfP+NwsDN0uDNyvDN+vCt6sit6lR7eaajauyqzfWglkFkfhR1TNSdE2zIRi3bqLCDjLqoFS+96mPiAWYtQKeCzPoSkw5xmTCXB+03oQTab2QhySJ5Aa9oCuNz2dZiS5omqAr/iCbctqrw6KewGaOqKkEQmqYJgrB1f0nSM+UbDVVVmy/BgFWlUoElzSOUy+XmtRy+ER4Q9pNlWa7Vas2DkyTZaDQQBGmWsCwriiJ0HE0Emq9+wxuP3X5rTiBVwOx48ACnSW1wDCvr5Oi3Uy7Ui0WBouh6ZbD9au/NU8buG+tuIxF15RamkjN964bu4txA0dRbmustmbpLps6yGahq7kStvai1F5/vJx2D9MIwaKd5ADCCd0LwTvCL41vhYd2jjGtYh2eQWRjgHQOcvV+c7wWydYnmDs7YykzeIoav1vsuVLvPljtP5++dyN79In3n6NrNT1aufhi+eCh4/qD/7LvO4285vthvPfqm6dPXDEdennz/hbH3nhs8+Py99355853nrr7+swsv/9fzL/3dlVf/2803n73z9vM333z20ot/d+wf/vP7f/WnB37wv7/1H7/zzn/640N/8X9c/+XfzX1+qDB4l7eOksb+wmBLpud6ru96YeBmYeAGJKc6cqs2ehMdu1WfuFM3gMzruqkXtQxitlHcOYEvTKGeaTZko8MOOuKiYm5qxcvE/WwiyCQAPGw6xG4s89kIX1jhiwmhvM5X02QhyaEFwI9MLzotqUT4yfKjadrGxoamaRiGGY3G/fv3Hzly5Ec/+pHNZrNarYcPHz506NCbb745ODjIsmwul/v444//8R//cXR0VNO0arUKbieoKDzPIwgCB1Lgpb1UKsGuOaz3sINRLBZtNtvVq1fX1tY0vQutaVoymaRpWpZlFEV/h/mBOaAwI+dhMEJtNCRRk2WsUmHRuqaoLFqfGui/cfZkyD4Tc0zl/BYk5q76Levm4czsUM0xnjf2FYzdlbneqqmnbulBrL3ofC9m78Pn+/H5fsI+QDmHtvIDyWnCw7pBFwjw4xwCWhignf2cvZ+d7xNsPYKtB/LDG9vZqTvkyDV04FKt93yl+2zh/slc+/GN1s+Ttz5duf7x8qXDwfMHA+fec51823n8Ldvn+82fvW786JWpwy+Ovf/i0KEX7r/7y5Z3nr/xxjPXXvvZtdd+1vLmz9vfeaHz0MvTJw9PfnFw9Ddvj3zy1sTRd+dOH3FeOea/eTrX31oeuotOdFGGfto4gE11lgZaNjovV4fvVEdakJE72NgdfLyVmLxDTrXiM224qQe19OO2YdI+Sjknac8MvWgk/bPc8jzkh17x0Ks+NhFg15aA0iEWOJ8In43y+bhQWhMrab6aBkNAWJHHyppAOK3GbHoVtJwVsPrHk70ksyz79ttvnz592mq1jo6OLi0tXbly5cUXX5ycnJyfn3c4HKVS6Qc/+EFfX18kEvn+97/vcDjK5TJN04qi4DgOHU6tVuN5nqZpDMNomhYEoel24vH41atXr1y5MjAwgGEYqj9YloXfIplMapoWjUZ/J/0PuD9pQ+NlhRUlTpKVzZULGpossTjOIDVw8wSenervvXnu1MLMZCURTvttTDYs5qIFz+za7FDJPom6Zsrmobyht2Torcz11i19qG2AcAySziHKNUQ5N0UvDIPwgO5/mq01zjO2iY1rhNFFOwaBnP2MvY+d72NsvZytm7d2C5Zu3tLBzbYz063U+A1i+Co6cKned6HYcSp/70Sm7Vjq9merNz4JX/5g6cIh//mD7lPvuE6+bf/igOU3b8zpTbjxw78aOfyreweea33ruZY3f377jWfv7P9F+9u/7Dr0cu8HYHi0/4PXpo++N3/uU+/1k0st50J3zi/fORtvu5juuloZakUnO4ipLnK6k5rpYma7kfE2dKKVmGgjp1rpmbuMoZ013qVm79G2AWJ+iHKM0a4JZtHA+mfZoJkN2biwk4k4maibXl0EzicZ5JIhJhXiNpZ15xPVG28JoZKUqhsg+FbNCHiJxUoSh1rmJivFtNYANyR+UvxA5yNJoDd14MABo9GYTqcxDNM07dChQ729vfl83u/3EwRx6tSptra2er1OkiSKoqFQqNFoyLL8T7MnnnnmmR/+8Ic0TX9Hf8zMzKAo+id/8iff+973Tp48qWnaq6+++kd/9Ed//Md/bDabDxw4sLa2tn///u9///t/8Ad/UC6Xx8fHf+/3fu/P//zPL168+LvKj6IvqMsIMiPIsqJ7ILWhioKm3zkutDDfcv6kaai3vhbBUytYKhyzjied0yWvpR60IV5L1TFds40j82OV2YHa3EDd1I9ZQQVqupqmY9l1Y5OZBT1+sDAMYLMPUPYBxj5Az/cztl7a2sNaezhLF+DH3MXP3WNn2qjJFnLsJj5yFRu+Uuo8Xbh/MtN+InXn8/jNT6NXjyxd+jBw4fDimUOuU+85T7xj+3z/7GdvzHz868kPXx47/FLHgZ/fPfALqHvvPN/57gs9h17qO/zKyEevT3z21tzxQ/ZzHy9ePRa4eTLUcibScjZw+Wiy/Twy0opN3ENH2+rDLcREOz/bRUy0QZ8D4eFm2/m5e4ypg7IPkk4AD+uZYr1GPmjilizc8jwfWWCiC/SKB7bcuGSITy2z68v8RoTLRIRcTCiuiuWkWE3JtYxQz7H1nERWGLTIU4hhapRAK0+WH3j5h/2f/fv3v/TSS4cPH3777bdzudzly5f/6q/+6qOPPnrjjTeSyeRzzz1nMpn+abH/F1544Q//8A9PnjzJcZymaVNTUy+//LKmad/61rdmZ2dbWlr27dvH87zdbu/u7n7++efv37+/f/9+TdMuXrx46tSpH/zgB06n87vf/a6maZcuXdq/f/8LL7zQaDQmJiZeeeWV31V+pAZYk4BgeEHUvQ/oD6mawBGlwnB760h7SyUWZLNr5ZAn559Pu2bJNX85YMm7DGW3oeqYLpiHK6YR0jGJWoZxywhhHSZtI7QDLKPBusZZ9+iu7TSIDeMaAWE3iI3upkjHIDnfT8730/P9lK2PtvZRlm5GF2+F/HSwxrv0TCs12UJM3CDHrpe6zxQ7TmXvnVxvPZa49Vnk2kehy0eCFz9YPPu+58yhhZPv2o4fMB3db/jktakjr0x8+MrAoV/1vv9yv64BPXln6MNfD3/w67GP3pj6eP/c5+/Nn/pw8cJvQtdPRm+fXW07l+u6kmw7E7v5xertLzIdFysD12tDIPMVn2iB/FDTbYyhjTXeBb4R8DNMOseohXHaM8X4jEzAxIZs7PI8F3OxMdByY9b0ltv6MpcOc+kwaLZlo3xhRSzFpUpKqm5ISF5E8gJWlOg6jZQYvDoxOsAyuNYAgZ4n5X9gPwTGij777LOxsbFmj+W9997r7u6GTyVJeuedd86fPw+fDgwMPPfcc7CHA8fyURT9/d///a6uLrPZ3NPT09vbu3///q6urp/85CcOh+Ptt9+G/LS1tf34xz/2er3vvvsuwzBHjhy5dOnSn/3Zn5XL5VAo9LOf/ex3lR9ObJCcRHGipGczyLxQyWcSS4EznxxeWbDQmcTGoiXtMmERDx5xo8sLOZeh4J4puWaKzqmSZbxkGq7ODdfnhmj7BDs/wdrHGMc4kHOU1iX6JkXfJAwVwNbaLj7HOQTI0UXY+ikrEGntpSy9pLmHNnfR5i7e2stZujhTJzN7nzK2AYSm71CTLeWe88Wus7n7p9JtX6zeOhq59kno8sfBSx95zx1ePPu+8/Sh+RPvmlHScLUAACAASURBVI8eMHz6xvTHr00eeXX6N29Nf/7OzNFNTX12YPLTt6Y+3m89cdh+8kPX2U98lz5funo8dO3E8vWTkWvHgxc+TrWdqg/dxMdu1wZvFHsuVvqvYKO3sPHb2PhtcFPHqRZi5g5paKWMbeTcPdw2iNpHcOcE4Z4iFg1UYI4KWsiQlVtxMw9abkwqxKSX2Y0ol4nx2ShwPoWEWFyTKmm5lpGQvIQWRLwkMwhVL5JIaWSwRxKZhipK4IbeT6b/AwO4PM/jOP7Tn/70448/vnfv3uXLlyORyLFjx15//fUzZ860tbWNjIxUKpU//dM/vXPnjt1u//a3v/2LX/wChgo8Hs/rr7+uadqRI0dGRkauXLny6aefHjx48PPPP29pafnWt761sLDw13/919PT0z/+8Y+vXbv2ne98JxwOP/vss5qmnThxor29/fjx42/qjwMHDvyu8gOnk8J1DViaWV9dNQwP3jl/Bk+vZgKuUmiBS4eFVAgN2es+C5h+7DdvWEdz8+OE34IsTG9MdFfnBrWoi1uYFpzTvGOKdU6AdWec45RjjHKMyIFpyT8FEWrys4vbcQwS9gEQZoDwWPpISx9p7gH8mABCOj89rLmTneugjfdAZTUAhIoP+Em1fRG//Xn0+qehK58EL33kO/+B5/zhhTPvz5941/T5AeNnbwJ+PnrdfvqD+bMfberUh5bj75uOHZw9+q7j1BHHqSMLpz/ynPs0eOWL2O2z6fYruc5rsRtHk7ePpdtPZTvO5rvOF3sulvsuVwavIqM362O3kPHb6OQtHEQO7hCGu9jsfdQ2ULcPIw4wZw5bnMH9RjxoxpdsOjzezYA1gCfMZWJcJibkVvnCCuCnDHo+EpKF8IhEVWFRol7EkWJ/X6ei8mpDlMDqoU/mIUlSMwG5UqmYTCaj0fhP7mVjY6NYLA4ODno8noGBgZ6eHjhaPzo6OjY2hiBIKpWC/0Emk2FZlqIohmF6enomJibq9booimNjYx6PJxAIFIvFtbW1sbGxcDiM4zhJkuVyOZlMqqrK83yhUIhEIrOzs1NTU88888y/dX4kSYLOGp64ZvIcjF+LoqxIssdub7lyKWC3cOVcJRasRXzIsgsJOdGADfWbMe8sumiseg1Vr6G2aEA9BsJlIBcM9MI045zmXQbeNdMU557h3DOgD6C34rY21aCfgXE5fL4fs/Vhtj4Y5katvZR1UBdwQTQQaMXR1j7YiqPNXYypgzF10HP36bn7wBdNthLjLcjwjXLf5VznhdTd0/GW4yu3joVvHlu69hvvhY+dZw7PnzxoPf6u5fhB64lD86cO+64c9V/93H76A8vxgwvnPvJfOeq/ctR15ojn3MeBC5+FLn8evX4ifvtMsvV86u65zP1zmftnsh1nc53n8j0XCr0Xi32XCv2Xa6O3q2O36xN30Kk7hOEeOXePNneR1l7EPlhzjiELk5gbrFlFBszEko0IzRORBSLqIVd8ZCJApZapdITJrLJZnZzimlRalyppqZrVnU9RwIp4aQPcNagh2cyGgN+jNkRFkUT5ifGzF4WwHn99VtM0OFhEkuSZM2e+/e1v//3f/30wGGyO/Gzb2Ov//JrKHzX+o6pqM2e70WgIggA6gjpAEsdevXC2o+WWxlHleKQUCdSj/npkEV1eQEN2sOqfz4x7ZzEfIKfqNSCeXfkBCG2S455hH58fzLKTn02KGHMvY+mGbTna3EGZAD/03H1yapOfSv+VXNfF9fYza60nV+4cj9z6Yun6Ud/lT13nPnScft9+CpBjP/2B+8InoZsnwrdPeS5+6jx7xHflaPjWyfCtk/5LvwlcPrp85Vjk2vGVm6cSLWfX7pyD/EB4ct3nC70X8wCeq4XBa9Wx29Xx1tpEW33mHmrswOe6CEsPZutDHCP1hXFkYRL1TOO+OTxoJkLzeNiJR9x4zIOv+oi1ALkepjJRJhdncnGhkAQx6/K6WEmL9ZwIGm8gbC2QdZ5CNE0aGe7L5tZFiZUUUQb3rft6H18fOc0jUxQFIxA0TSeTSThktA2b5tOv99vuOPqj+JEkaWvCHM/zHEtrqoqXixePH3POTbO10orbJpQ36qtBJOoFfZ5lF74E+CH8FsI3h3ln696Zmm8G9c6AxolnhnLPMC4gzmWAYt0G2mOgdNGeGcYNoghf0f9AfmjL4EM98EKUBXaHukgzEGHqpHQR0+3ERBs62lIdvFHouZzpOJ+8dy5x98xKy6nwrePBa8cWL33mvvCJ6/zH7gtgfpv/5ucr98+tdpwP3Drmu/pZuOVE/N65+L1z0ZaT0ZaTKy2nVlpOrd45HW89k2w7m2w7m+28kOu6mO++lOu9nO+7Uhi8Vhy8WRi6WRq/U5poq0y112bu12Y7UTMY9kFtQzXnWG1hAnFPod4Zwm8ilmw6PAt4zAMmnCb8eDJEpKNUdpXOrTH5hFBKCaXUNn7AsKl+I1ISq/b1dkqSwPOsKAsqmOz79T6atfxr2lAURZZlURSbl/JmJKPJzNaNr/fb7jj6nvw0Gg3ITzPzRxRFnqbQfPbW2eOppUUVK1dWQ3xhvbjsqce8eHQRXDLDTiLk2OTHa8Z9c4jPUPcbEJ8B8xlA/9gDaGHdDwXhIRbBq4T3sfkhLQOU+cuy9FFAm+EEyA9u7oQIEdPt5ORdbOxOffhWuf9arvfyRtel9Y4LifZzq21nwrdPBa9/Ebh2LHDtGNi4eXy59WSi+1Ky5/Jy68nArWMrd8+kui5BrXdeArp/MXXvQrL9PFS254pOzrV8//XCwI3i8O3SSEthtKU00V6culec7qwYe6qmvrploG4bqdtH686JmnsSWZxGfLPgvOktNzzmwlYXsbgPW1vC15eJjRidizP5JFt4CI9U3RDrOQktilhFTztgZYH2uZ1mk0HTVEHiRVloPKHgwY5q87Bga939OrabqQaCINA0DWdjUBS112c9/M++ka09+QEz3/SciyY/9Xo9Hl3uuH5Ro+pCMb3umxeLKXYjWo8ssmtLRHSRjHio8AK57CSCdjwwT/hBLwgJGJGAAQ0YcL+B8BlIn4FeNDKLxqbPIRYNoKWnC/caKM/j+R+dnyHKDAVAAoEEKGsvYenR1YVaOiFC5Mw9amrTBVWGbxcHbuR7r2d7r613Xkrev7B693z0zpnlO2fDrecibedj7efDd08n+q6k+q6G754OthxfuX8u1Xsl1XslN3yrMHQrP3gz1wfevtF9Jd11Od11Odd3XSfnZmHoVmG0pTDWVhy/m5+4m5+8n5/uyBt6irN9JdNg1TpSmR+t2sfqrsm6ZwpdNKL+Od352Imoi1hx43HvA+cTprKrVCFBF1NccR14HtB4y8i1nFQvQH5EoiozmKbwAz2dWZA5ChZ9FSTxYYLIN1KZvqYPURQFXLt5fusMnN8BfiBC8KTQNL28vGycHEsHFuoxr5BbVUqJ8tJCecmhFRJMPEBt4YdcckB+sIAFDc7Wl4xo0IgFjUTASPqNlG+W8c5SHt3hAHgMmHcW8QFhvsfmhzAPkqam+klTP2HuJcy9ECHID2YF/ECESON9evoeOXkXH2+tj7VWR1pKQ7eLg7cgA8mOy4l7F1fvX453XEl2X0/2Xot2nl8fupEevhnrugBY6r60MQhWPCxPtJUn2ipjreXRO6WRluLgrcLAzfwgUGHoVnH4dmG0pTh+tzBxvzjZUZzuzk915WZ6cobevGmoaBkp2SYq9qmKc7LunoHOBwuYdOfjIGMAHiLux5IB4HwyETIfp4trTGmdLacFvecjVbObzgcvSXhVIOsMXq9X8iPD/bDn09BUThJl0Fv9d/LgOA72JhqNBo7jvwP8CILQjPpHIhGHw7HkdfHFZCloFzJROhVEw25xfRk025ZdVHSRii7SEQ+57CJCC/iSEw840IAFCc0hIQO2NIsvzZLBWSowS/vnaP8cbLBBz4P4Zut+oCfETz9h7of8YJYeKNTcjeguiJrtYGbukzMd+NQ9dOIuMt5WHW2tjrbmB25n+25u9Fxf77qa6rmx3nszM9CSHW5JDlzLT7UWptuSQ9fivZfSwzcKk62lydbqTHtl+m51Cqgy2VabaKuOt1bH24rDt/PDdwojrZvwTHcWZ3ryht7MdPeGoS8zN5A1j+StYwX7dNExU3KC+ErdC5wPHjSTy3Yy6oT8kIkAngoS6TCZWSHzAB6mnOYqG2IlI1Yy2/gRKYQlkHmLMb4akSRBv/mLxoqC1Hgygz//igg2s7lhawj+JzA4vCtC3/C/urP9puojbirL0oh+vzFNk4vZ9PT4yKJzHsy/zq5ImUh1yU6sLIobYSTkrAXtRNhNx7xNF6TzY8eCgB88ZMKXjXhojgjNUUtzdHCODpqYgElvtunOxwcmiundJJ0f9yTrGmcWxuBwKuUYIe3DpH0Ytw1CYdYBzDoAOt+6MPMg8VCAHMwChFv7cGsfZulFrT2YpRexdCOWbtTcTc52UYZOYqYDn76PTrYjE3druopDd7KDAKF03410343MwK38cFthrC071lKd66yZurITd9aHbhQm22qznbXZzur0PajazP36FuVHW7Njrbnxtvzk/cJUR2G6O2/ozRv70jO9aWNfZnYoax4p2CYL9umS01ByGureOTBPzj+Hg8gBaLxRK14qDsLWZHKTHyafYEpJrrLBVzN8JctVs3wtx6MFAS0LeEUk6iKJCSzRcvu6pqkEgcExBk7g1X8X/geSA2lRFAUOq+wKz+ZUmm+QoU1+GnqLWbcyXGRXbQj6XGuBqufN44PIRqKeitXXwkg8gK/6mgIDFKtePcHeS68uUiseegWk3JNRJxFZoMIOOmxlls10yAQUnKMCs6QftOK2igjMEgEjob9Ee/RRIM8U455k3JO0awKKWhjfVbjtIVpNxnDbIDE/RMwPgdF9Wx9q6a9bQJZ33dxdN3Qgho76zP3a9L3qVHtlEizfXp5oy4/czo2ARlcedFru6AJdl/J0F1Rlprsy01019EBh5kEo1DSAmwawuX5srh+Z668Ye8qzQMXZvuJsX2GurzDbn5vrL5iH85bhgnWkOD9Wsk9WnNPVhZmyawbxmRC/GQtaQdwy7CSiLjK2SK562fVlJr3MZCNsfpUrxPkyzLPOMNU8T9SwcpYnaixac9stsZBfa8j93R3rqTWYbQBmyMgSHOP+BuvSf48f9SV+FE1WNLkBcqwBPA2Z0hrMasCZ8NnZQqq+GqTSMTwRxBN+Iv5Q4NZOcR8T9zJxLx13U6suKrYA2iFRJxWZZ8NWbtnChszM0iY/uyIEcaL8RmbRwIEAHRgOYlzTTVHOyV2F20dw+9BOEY5hWIjND6K2AcTaX7f01S19tdlOxNgJZt3M3K9O36tMtVem2suTdx8wc6c43locby1NtJUm2gqT7ZWZ7rKhtzLTWzH2VQ191dn+mrG/OtuPW0Yw6whuGUEtw5h5GDEPoaYhxDwEZjTN9RZN/VB5c3/eNJA3DRSsIwAe22jJPl52TEF+Km4D4jejAQsWnMeWHURkgYy5wfUo7mPXQc4Bk43APGuQbVBJCbUsVS2IDIFWChJLBhZdseUgR+HZ9bW5WUOpmIdVmOM4cJ3WwJ0t/nus1N/gd96TH0VhNI3Lp8Jhj5XIJZh8kt5YodIxYm1JVwCM6yWA6ISfTviZNR9QHHghesWle6EFOmrnIjZ+C0JbKaICoDu0VXRglvEZWS8Qs2hgFg1gPowu0jW1qzDH6K7CnWOwHLWPIPPDddtQzTqIWPsRUzdi6q7PdYE2mLGjYrhfMdwvz9wrTt0tTt0tTbdDlWfu6eqozvZW50CqeBVkiw/WTIOIeahuHiLmx/D5MWJ+DEx6s42iVoATah2pmAfLlk0VLUNQBesQIEf3PGXHVHVhpuI2VN1zVfccGrBhwXkw6Bx2ggDmipda9dPxAMgWzUTY7Io+yQcM+MCANVUtqAJD1Ms0Vhvs7aqV8lpDNkyOra5EKRIHM+b1GaCgeaNpgrjLkkvfYO369/9Re/IjCISmcVPDnUguwVczeHpFQ/JUOgZySYCWqNQSnQRik0EwOyW1xCb93JqfTfjYuIdZddMrLnbFKcTsQsQm7EaR3heCPaKHlgvMsf5ZzjcLZsJ4jSAledFAewFI1CJgaZslFiZx1zixML6rxZ1jqHMUIOQAFCHzw6DvZO3FLP2IpQcx9dZMXbXZ7upcZ9XYVZkFU0Grc531uZ6aqQvYuZ66eahmHUYsYJEG1DpSt41gtlFkHsx1IxYmSccESP20j2OOccI+jtrHavMjFfsItJX50bK9qfGyY6LinC67ZqoeY21xtr5orvss+JIdW3bAlhvs+TCJIJ1cAvDkolxulS8m4GipXMuISJ5GSprMcyQaC/knR4cUgaVxZGJkkCLxhgpokfQHXHiP47evF/Pvv0Z/s99wH+yHPbi5oiRrkqqJqsZLEpXPrM5O9Kl0jSmtU9mEUtngcgl6PbxFIXodTOpi1pf49RBQMsAnA5AiJuFh4y5+xSGuOABFMTsfneciNjZsZcNWOmTeKWbJDGaPBc18wMwETJzfRPvnWB+I14GQNwx8b7V+I+kBdzvcyxLuKZCXuTBRXxhHneOIY4RwjOL2IWJ+BJsfxKxDwCOZB8AcPgvwTtBi1iHUNoDbhrH5YdQ+hgAwxlHHBOaYwJyTuHMSAxNFDaQbpFOQ7hnSNU24pomFKXxhCoyHLoAFDGoLE7WFqeoDQXIqrtmKZ67mBeQg/vl6wI6FFvBlFxnxkLFFetXPJILs2jKTCvPZFQBPfk0opaQyGO1R6nqqDlHTJE7h6Ynh/nh0WWvIs9MTSz4PjPrAJZfgUH1D03jha8/f+War67+5T9sH0y6UBrgJnARvSKoJisbRdH3OMFIvrHFIni6mpGq2vhps1LLsRvTLApNSuHRY3NC1viykghAhLunlEoti3C3GF6QVAJIQdbLReT7iYCI2BqyPYdlmmWUwdYxbtoI5MCELs2RllszMkpUOmqiAGeT277CEz0j4jKR3F4t7DbgH5A1h7mnEPYUsTCKuCWJhEjgr5wTmGAVsAKc0WrcNYY5xvb03jjvHwCIEzjF9z0mwGoFrCnWBg2BucEDcYwSJSD4T5ZulfCbSayS9MBw/SywaEPdU3TMFrHum7p6peqA1Vj3Gimeuumiq+MyI31oP2JGgAwk5ibAb5InGFikdHiYVYtcjYKpCbpUrrPHFpABabiBPVEYLMlYSyLrC05oi9HbcJZCq1pCvXDiL1cqQn6bzgf5Hkn/n49f/5oj58j+0Jz+hkMfnsWoSwaMFMr8mlDdK4cVGLctkVh8IpNPr0mdEZqJCNiJuhIX0Mr8e5FLAEfFrXjHhFdc8YnyRB77Iza44uZiLiTnosIOKgCn+Wy0dnmciTi5sZ5cd7PI8u+xgQjYmZAeYBW1U0LLTkgEzCaDabkm/CffNwRw8sBaUx4C4ZxA3yMEjPcBd4C7gRlAncCyIYww4EPcU4QKuDDiWxRk4wostGrFFI75owr2zhNdM+OYIr5n0m+iAjQ5a6IANIO23kn4T6bMQfhO6aATjOcDO1b1ztU1rBuR4rVWfrRoAbgcJLaDLLmTZRUQXiZiXXPHR8SV2bRnAk15hMsDzcPkUX0rz1YxYy4sIgEfEKjxRExlCU8W56fFKIasp4rVL50WW0hqKJPKCIDTzxGAU+8s/99NnT/gM7MlPb2+7xGMMmtdkCt1YwdZjeDLM59fYbLwpPrvyQFExGxMyUSnzECHYlhOSAYBQwivEF/lVD7fiZsHMShcVduwqMGk54oSCO4BRxWU7GJvfITI0D7L9l2xU0L7NEn4bFrDgPiviM6GLZjDM4jHV3UbcOwsyHhaNqGcarJHrmgQrtS9MAEi8IJMI9xoAJD4jQMJvwrwm1G+GwgIWcMygFQ9aydA8FPwHYCEeBB9X95mgrftMVb8FquK1ln3Wit9WDTpqS05k2YWG3WjYTcT85EqAWg1SayEmFWbWY+DylItzeZCtw5bTXDUr6C03GQOpBixWEWhcU4RI0BsNBVSR6+28x1G4wLMsA/KUm/lWT/l5wqzsdrh9MNZZR2sNTVU0WVD5hiaNTw3Warl6ZQMrrxOldaqQBMmLkJxcks0l2XyCzSe4XILLrQqbioF5kZkon4Eu6EF3aM3P6+ISPg6EFryPFpg39kiB9WhWPGBVJ11M1A0U9tARF73spsIL9LKbXHaykcWd5VTIqWNmIQNWMNPGDzwG4bfgvjlYss0CKpZsYGRmyU6EHETIQS47ocDH7SYi5ACRaN1iyw4woWNTwNugYXc9sohEvVBozEevRZhkFGg9pnueBJtdY7JrfGmDK4PRUr5WEOpFAS3reaI1gUBljtIa0pLXlUklWBLrbG+lsLrWULQGGFtsPvQbn+/2mz8te3JnAPCjaRrNUg1NlTRR0WSCRmbNk7VaDqlm8EqaKK3TxRSTT+q0JMB1MZ/iCmugdZ4HEnNrYj6uawV6oQetOIjQErfm5xNBbs3PxQMgOrfqZ+LevSwT99MJrx4T391S8UUwPL/qgeO2dMxLx7xMBGQ/0GGQw0qHF4mwm436dpZTYTfEACIBRl10oYFNSGD5Vqsz4MBCC1jYCWZnRBaIsBuPLIBki5h7p8XDTmxTLizsQiNA9YhbFyCnHvMiK77aSgCJBdCVIJ2K0ikAD70RZzIJRoeHyae4coat5Lhqnq0VeKTEY1UBrwlkXaBxiSU1RYgu+VLxGE8TvZ33nvLz5Ih4vCPtg/lFqqYIEs8rnKLJ0dWQL7hQr+fRWpaobpDlNMhcLKQgLXxhnSuu84UHKibBjK5CQtIl5lc2vdBGhN+ICOthPrWsx+VCXGqJWwPBbm5tCayJkQjuakFMPAUCuLtaai1IJfVxpzU/mQgQcT+9CsSsBKkVHx0LkCCNyE/GvMxKcGc5FfXiyyBbD192ASRCC+iSEwo+3WbR5QUs7MKibjziwaJuIrqIxzxkzEusLFKbiRcw/eKhJaIeMO8t5gETOqKLaAwIWfEB6cCAuVLxpXp8GYkvo4kwlV6l0qsAno01NpNkcykmn6ILG1w5x1WKbK3EImUeq/I4IhCoSGISS4oMoQpMciUcWw4KDDnc30PjyFP/83gV/wnt/XD8hxc5sSEommw0TWXyayhaxJE8WcvQ1QxbTvOltFhM8cWkWNoQyhti6aHAXOLSulJKysU1ubgmFVal3KYjEjJRYT0MBUBKLXPJ0CMEF8qgNkDqyq6WXg+R6RCVWiLWwQAUmQwyiRCTCLHxEB1fYlaXqNUgvRKkVoNsfPdyIuLDo4tEdFFf4cSDht2Y3g8BhIA1T3bY6CK24oWCWUsw9wKOHe+04NWEr5mfAeYgJIIoUGhTyWV0LYIlo1B0JsFkEmwmyWQBPGxhnSlm6FKmCQ+LVR7CQ+EyR4kMIbFkOhELB30CQ06Pj7Ak9pSfJ0TE4x0G8CPLMmi8KaDxhpD1kfEBikVwvEygBRoBC4uBEFAlI5XAzBOpnBErWSipnAGqpMFCMOW0XE5BiqTCqphfeUDRipAB4jdi/EaMS0cfLSYTo7OxR1gqE6U3ouRGhEoDsakYk4xyeheCXYuA7kQiTCXC/K7layFqNYivBsiVAL4aIGJ+bMWPR31ozAe3d1o9XymIJ4Iw8YJMhqAejCPD0eSmBUjr2twNT4bwZFhXFE/F8FQMRGLSq3h6ldiIExvxTWxyaS6/wRcyfDHLlQtsucBXK1y9yiE1DqsLBMZTuEATIkMqPA1dUGo1ElsOsiRmNk7D+NvT/s/j1f0nsTfgRxTBRHlVU0DjLREx24yiQhNEhQLrWRY4JA+mmlSzSmVDrm4o1bxc25RSzQHVMkoto1YzSmWjUVlXAUUJuRiX86tyflXKxcXsKtRWkCBO2yyIhudW2fyjxORWmNwKnQWY0dkYt7HKra9AsakYxIlJRvn07uVUIkwmlqCIeJCIA5wAS/r2NgvgAQAAEallIrVMroehIL07LUj6BNrEW98hRm7ESAAMEJmJk5kEkV2DYgvrXH4dkiOUcrDZxldLTXh4HGnCI7GUwtMKT8scFQl602urBFK1W+ZUkXvqf54EDo99jH1wRVY4fqpo8rzLtra+wgo4SVZposzhRR4tgMG7Wg4QUsuotYJSbyqv1PNqDahRyzZqWa2afoBQEiAEKErI+YSUi+8ECeK01YJoeDHBlXYXW4xDMYVVcN/cPACJz8T5zBqfWeM2EtxGgk3HoWDhjvIVKhmBItfC5FqYSCxDwac7LZFaBpPY0mEwFWcjAgV84AOAt20wGeA8H2iVzj5QJkFlE1R2jcwlyVySyqeofJrKp7lCpgmPWC4K1RJfrQi1Ko/UeLTO46hAYAJFiTQtsozIUiD4JnGqwHgc1kohWyvlXXYrCP08jb89duV/Am/YR1EEnO4rKbwoc7PmaYpGCaLCkBWWrPBYUUKLErLJiVLNqUhBV/HBRqFRB9LqOR2hja0IKaUkjCvI+YSYBwgJOeCLHmGFUorfQ1wxCcUW1tgCWExDj6EnhSzQVoq4jQQs3FkO4110KrqVInIt3ORq6waRihDpaJMcKhOFAi1M3Q3uZsEqOU2BdQuya3RuUw/ISdGFdaq4AeIEm2024Hn4SpGvloRamauXBbTOo3UBQ0DMgCJkmpQAPDRoqil8Q+JtJiNerxQLGY9zHtySeSc/YGHYp/nXTwCSRxxiX0OTaZYQRKahiRSJdHW1cjTSEGmRqoEl+fCyipUbaKmBFiEnDaKs4gUVL6l4QcHyCpZXkRxQPavWs82GnFLZgNqMLhT1GMMWK+VB4E7KJ8ViSi6kQARCf1XvX4Hu1jYJZRC32CYQzCis7youl4Ris2tgREXvpoOeuv70K9s4nV0Fyw+A5W/i+iIeCTCb7YEgyU2wdfJBrKUpPfSywZY2wEjObuKqeb5WgBLqRa5eBNFqpCThdQmvywQiU6hCYyqDreLWUAAAC7NJREFUA7GUHmqTBJ4NLQWqlVI4HLJZzfo6iZsTH2EizwP7iJ/+6UtP4Azsa2gSL9GCSGuaVC1tjA33CDSqCZRE1mSiphIVFStrOkIaUgQUEWWVLDaIok4RQKiBAkF+diKkxxVgdOFLVo96A3jEIoBnU+WMVM3tqmbQYtsGvwc/fD7F51PbKGKza82Sr7LB5pIQm63kQGa2krPFYaa5cmZXCdXcTvG1HFsrsLUCp5PThEfnpyrhVZmoKWRdpZAGjTZoVOUIjsJVVRZF3uv1VKtln2/R7V7Q7828Kz9P89+eACSPOMQ+WQELvYoSo2nSanRp3mIUGazBkzJVV8h6g6yrRK2Bb1KkYSWNrDTIikqUgYAXAlKwzeZco16A3SHQ0tO1GaODkbotViimm0FwuI9cyQI9CE5s29iGTfOpUEzvpeYgFRzzBWkTueTm+C8cBf4KlsnruReFFFsACTVQTffCl9LQJTa9pVDNgcnVOyTU87tKJ6fMIxUeqQhoFUrEapvOh0AUElUprEHjGkOoLCXzjKJIqiovLDjq9er8vDUaDT/wNrsi9Ihf/+lL/9IzsI8XaBDBVjhNk5zzpuXgoswRCk/qDQZUpRCNQjSiCtXAKxpV1ahqQ6eoQZShVFxv4KFF4KP07pAeUQBxhU0qIBtbbJMruAMM5elZ+s3gxJc2dnVKoHALk9u2m3xCwCBOe8G2azlfSm8Fptl6bNICl/KQqlm5lpNrOX1B0IKI7CLQjdwhsIDBFmYgNg9bbl+GR2OIBkfLEgdba06nHUXrMzNT+Xz2KT//Ug7+ue/fx3KkpskNVdQ0eXpiOJ1cUXi6IdIaRzZYQmMwIBoFImtAdL3BAKl0TaNqDbIKpWKgpwRbeg20pAKQgL4cr3uIRNO9wGieihQ3VS/LyC6S6qXdVIAVdy8LMv+r2WZ1F8obsOSrWwEmoeldF9gAA9nQWwQWYasXZKQIpeCg3bVTIlbZQzWwAIguiUQkEnR4ZAptbsDOD/gtOLLB0ZLIiiK4K5bX68EwZHR0mCCwp/z8c+v/v/R9+zieksFdZgFCI4M9hWxKFRhNETSB0nhK4wiNxYEASAgQuylIkUYBigBIeAVK20IRBOkhG01IkOJWbPT4RAnsjJUVtCJjlZ1WRsoSCrjaasFTEEwH4cFtguFBWNikC4xi7djzESUSkgfM1AEwIAm6DhyLjABP0rQyVlLQEgy0KHhZIUG7d6dkAvQndwoys5UWhcYUGoMUbYVH40iNZziWhPzEYhEcR4eHB+HTvRH6l1aRp+9/xBnYJ4k8y1BgqQmZH+7vKWbTgJ+GpIk0kEADivgHFLG4xgE1WKzBYioDOrWgX0shDb2zBCxea+A1FatC7epMYKGCAk4UtAL3hG9UMBB32mklBPQNtlmdpc0Lf9MDbPoBVK/WaKlZvs1RNMsfvSHCaBhoaAF0RQzgLWIV6GegVXUwGjo2DVoPlMFw2RYLedjFkrhM4gpFKBSh0mRTWwsbDKWxtMYxGs8QeB0Ck8tlcBwdGRlqNJS94XkaP3hE5X8CL+1TFYnUGwCSyA739xQy6+Cm84AfTpOYTYogQjwByOGhSB0hQmVQlcEbNKqQKICHAFbFER2huopVFbSm+5M9rYJXVQxGKRAVR2Drf6cVsdqukkGFBqhss8CV6cF3WN70GDv3fGRJRe+igF6K3vqq6Q2zOrS6M0H0EBmqR8mA32iwhMoROy1wKYzuWLZY4Gp0cprwNBgKag9+WKReFUW+0VDq9SqOo6Ojw/qSIfLeCD2BWvL0EHudgX1aQxMFjucYTVP7ujrzGyk9GURUBeaBKNCW2yJVAFkkDZ5UWUrlQFCowRLg9yZRmcBkApFwVMHqCobKaK1BgPKdViMJlcJ2WpUClewrCuwMr/pfmwVfh0QkQm9Qkfhmjacx4CgYfKdVWFxi8Z22wZMgKsMRIDzzYB+ZwcA5ZOmdkmlSpkmFoRTm4Q4KzzRUudFQKIrgeTadTlmtZk1TYURO1V9qgFlAWz3SXj/90/IncAYAP5IgCjyraero4EAuvQ74USVVYB/ww6gCtUWbXMFELP3np0BcVW+HAAsQApJwFIAEuNpFDZrcKdh6UVjQBPoqVmVwMLy4W3/jiRSCfjyBiSQmk7hEgVaWTIOGlsKQDVCtga/YZmWOknVItllF50fhSVgu6QhJLLmTHFgCyWnC0+CYBscoPKMqkqap5XKR45hSqWCzWTRNBWv2qvJTfp4AEI95CMBPQ5IljtcamtloyK6nwM20FVEVOSCJ3SJalWjwVH9JEVggHvyoQAy1ecmkSJkkZJKQCFwicIUid5VKU7uJVBhSYr+qVVjdIXxlf/UV3drW3SQKFxngCkDuM0NJuruQORrW5h1WT+4UaHlvSTwFJXKkyJEyR+8qmK0DX4JnWNVPuAxuyajG4yssS9M06XDMNxqKIHBP+XnMmv9kdgf86LEDQVPUoHcxk0oCfmSpIYEkq4bMQSlgg1FkTpF5RQYvNSnSPRXbvI6CSyZNNZnZDRKwQ4Ohd0plYWUCWZJfUdBNbaa3bOmvP5ESHWbATLM2N68Xm98aeOmH2rymiIyyhyBXTYQknmoecNuGzNHNEvgRDf2yJQrglsDBoJ9l6UZD8fkWOY55ys+ToeHxj7IP3KEMzD4VNVnJrqfTa0lZEhRZVBVhi3hwP1oFWEUVQGtbkRRZVGRRlgRV1MXzKpjAyikcpzIslEIzze1tGxrH7yIe1MXNWvgVNkALkwP99a9LYL4AI/OczHOKwCsCv/ll4VfexQKnLUu7SBLZpkSBEQVG4GmJo+EZ22lV/XyqPN8QhIYgaKKoiWJD2mxph0JBQQcpkVglSfxp++3xa/6TeccmP6ooNSSZwvD1xJok8rIkgH6qKkKpDeGBxM12giqrDyhqSOB3hT9zQxBUHoDRYLlHS+OFnQJvh+3Gr2glFkYyGjz5dQj08QDGm9hsflNJ1P2z/q3hd99ioX/eaSFU2xASBQYc/AEqWzea5xOSo0kSkCzxHCNJQjqdgkECGIVrNJSn7bcnA8RjHmWTn4aoqoKiKWoqnpAE4FU28+HBWvKb0tcllfTwDljjRVVV/VdTVBmoIckNSdZEoIYgNQRJ40SNE1V+d8F9dtjH5kcPcmwNbzzZbUYVBfjVwLcDy+uDswQEt7dbSVUE4KJ3SJI5KFFiRYkVREYQgReCbm2n1T93E1FNBuRoiqwpMs8xHMeQYKlrEHaTZRHHURjCfho/eMzK/wR21/lpaA1RlUVFa2hrOj+SIG4ZT4BjC+C+JkBbF0lSvoQQrFKwtgGHplPUECS4vc0CzHTevmxhjdG7XrAD9tssDG80RBDnePJW5BqSqOiXBlVHpaHDA6x+1dhpFUWCTdxtVpZFwI8kSDInijxASOAEgZNEXhJBy3CbBZ8rg7MBAm6yBNa2VuSGKsOwAXQ+sAkH83ee+p8nQMPjH2KfpgJnoqmb/kR58NhyKDiGvWtu78NCcPHbTbCZp8mgy/RV7K4HaTxoLsKjfdmCfhpYuO5rsZIqgzOy0wLfCz3wl62+HrK80+rnVdphN0/3Yx5f/830hd62/ExPN/8VzsDm+vGwPaaq6ubvqTzivucPmdnio1R4/dvF6t2kL9d4wNJeenx+9jzUXh/xWOXNE7JtA64b/tXttrf/1qePPnJzkcR/hSrz9CO3nAGwfkjzx4C/Gfxpt+yzbfMpP+AMPbp+73z1twKzbYedR9ha0vzJtv02T59+w2dgc/23JkXNH2nv/2N3fmDayC52t0bdI9pje/mfvcr3ciZ77f+45c0Tsm2jWYO/4sa2t//Wp3sdFr6x+ereP9PTV76JM/CQnyZCsC2394fvzs/WttzW7b3q65Oq90/qOHv+n3vU9L3Pz+6v7HGYPYt3P4oeaAP9rgePvXZ7Wv7NnIEv8bMVob0//vH4gXHwnfZx6/3OI8CSvY6z1/6PW/6gom7/u/f52f2V7e//bc93P8pTfvY6L/9K5f8/y3mfP8pMgrcAAAAASUVORK5CYII="}}},{"cell_type":"markdown","source":"#### How electroencephalography data looks like?\n**Electroencephalography** (EEG) data typically appears as a series of wavy lines, each representing the electrical activity recorded from different electrodes placed on the scalp. These lines, called traces, show the voltage changes over time. The patterns observed in EEG data are influenced by the brain activity of the individual, and they can vary significantly depending on the state of consciousness, activity, or any neurological conditions.","metadata":{}},{"cell_type":"markdown","source":"#### What patterns may be observed in the EEG data?\n- **Wave Patterns**: EEG data is characterized by different types of wave patterns, such as alpha, beta, delta, and theta waves. Each type corresponds to different brain states. For example, alpha waves are often associated with a state of relaxation, while beta waves are linked with active thinking or concentration.\n- **Amplitude and Frequency**: The waves have varying amplitudes (heights) and frequencies (speeds). The amplitude indicates the strength of the signal, and frequency shows how fast the brain waves are oscillating.\n- **Artifacts**: These are non-brain waveforms that can appear in the data due to muscle movements, eye blinks, or electrical interference.\n- **Abnormal Patterns**: In cases of neurological disorders like epilepsy, the EEG may show spikes, sharp waves, or other unusual patterns that indicate abnormal brain activity.","metadata":{}},{"cell_type":"markdown","source":"#### How spectrograms related to EEG?\nSpectrograms are closely related to EEG in the context of analyzing and visualizing the frequency spectrum of brain waves over time. A spectrogram is a visual representation of the spectrum of frequencies of a signal as they vary with time. In the case of EEG data, a spectrogram can provide valuable insights into the frequency content of the brain's electrical activity.","metadata":{}},{"cell_type":"markdown","source":"#### How spectrograms are used in relation to EEG?\n- **Frequency Analysis**: EEG signals consist of brain waves with different frequencies, like alpha, beta, theta, and delta waves. A spectrogram can visually display these frequencies, showing how they change over time during the EEG recording.\n- **Identifying Patterns**: Spectrograms can help in identifying patterns that might not be easily discernible in the raw EEG waveforms. For example, they can be used to detect changes in brain activity during different sleep stages, or to identify oscillatory activity associated with certain neurological disorders, like epilepsy.\n- **Temporal and Frequency Resolution**: A key advantage of spectrograms in EEG analysis is their ability to provide information about both the timing (temporal resolution) and the frequency (frequency resolution) of brain waves. This is crucial for understanding dynamic changes in brain activity.\n- **Data Visualization**: Spectrograms offer a more intuitive way to visualize and interpret complex EEG data. They can transform the EEG's time-domain data into a more accessible frequency-domain representation, which can be easier to analyze and understand, especially in research and clinical diagnostics.","metadata":{}},{"cell_type":"markdown","source":"#### What is LPD/GPD/LRDA/GRDA conditions and how they may be seen in data?\n**Lateralized Periodic Discharges (LPDs)** are typically associated with acute or subacute brain dysfunction, often related to structural brain lesions or acute brain injuries. LPDs are of particular interest in the field of neurology and are often investigated in patients with acute neurological symptoms such as seizures or altered mental status.\n- **Lateralization**: LPDs are lateralized, meaning they predominantly occur on one side (hemisphere) of the brain.\n- **Periodicity**: LPDs are periodic, meaning they display a repeating pattern. This periodicity is a key feature in their identification on an EEG.\n- **Waveform Characteristics**: LPDs typically consist of sharp waveforms or complexes that are clearly distinguishable from the background EEG activity. These sharp waves are usually followed by a slow-wave component.\n- **General Characteristics**: In EEG data, LPDs appear as regular, sharply contoured waveforms that stand out against the background brain activity and repeat at consistent intervals. They are usually unilateral, affecting either the left or right hemisphere, which is an important aspect in their interpretation.\n \n \n**Generalized Periodic Discharges (GPDs)** are EEG patterns often associated with diffuse or generalized brain dysfunction. These discharges can be linked to various neurological conditions, ranging from toxic-metabolic encephalopathies to severe diffuse brain injuries. GPDs are of significant interest in clinical neurophysiology and neurology, particularly in the context of diagnosing and managing patients with altered consciousness or comatose states.\n- **Generalized Distribution**: GPDs are characterized by their distribution across both hemispheres of the brain, rather than being localized to one side.\n- **Periodicity**: Like LPDs, GPDs exhibit a repeating pattern. Their periodic nature is crucial for their identification on EEG and distinguishes them from other generalized EEG abnormalities.\n- **Waveform Characteristics**: GPDs usually consist of repetitive, sharply contoured waveforms that can vary in shape and duration. They are often more synchronized compared to other EEG patterns.\n- **Clinical Implications**: GPDs may be seen in various clinical scenarios, including severe diffuse brain injury, hypoxic-ischemic encephalopathy, and in association with certain drug toxicities or metabolic derangements. Their presence can indicate a severe underlying brain dysfunction.\n\n\n**Lateralized Rhythmic Delta Activity (LRDA)** refers to a particular EEG pattern characterized by rhythmic, slow-wave activity, typically in the delta frequency range, and is usually localized to one hemisphere.\n- **Lateralization**: The key feature of LRDA is its lateralized nature, affecting predominantly one hemisphere of the brain, which can be crucial in localizing a neurological lesion or dysfunction.\n- **Rhythmic Delta Waves**: Unlike the sharp waveforms of LPDs, LRDA is defined by smoother, more rhythmic waveforms, predominantly in the delta frequency range (1-4 Hz).\n- **Clinical Context**: LRDA is often observed in patients with focal brain lesions, such as those caused by stroke, tumors, or inflammation. It may also be seen in the context of focal seizure activity.\n- **Interpretation**: The presence of LRDA in an EEG reading can provide valuable information regarding the location and possibly the nature of brain pathology, aiding in diagnosis and treatment planning.\n\n**Generalized Rhythmic Delta Activity (GRDA)** is an EEG pattern characterized by rhythmic delta activity that is distributed more uniformly across both hemispheres of the brain.\n- **Generalized Distribution**: GRDA differs from LRDA in that it is not lateralized but involves both hemispheres, often symmetrically.\n- **Rhythmic Delta Waves**: This pattern is defined by continuous or quasi-continuous rhythmic delta waves. The activity is slower and more rhythmic compared to GPDs.\n- **Associated Conditions**: GRDA can be seen in various clinical conditions, including encephalopathies of different etiologies (like toxic-metabolic disturbances), and in some cases, during certain sleep stages or in diffuse brain disorders.\n- **Diagnostic Significance**: The presence of GRDA can be indicative of a global brain dysfunction and may warrant further investigation to identify its cause. It is particularly significant in assessing patients with altered levels of consciousness or diffuse neurological impairments.","metadata":{}},{"cell_type":"markdown","source":"**Disclaimer**: I'm not expert in this domain, for more detailed explanation from competition host read this paper https://www.acns.org/UserFiles/file/ACNSStandardizedCriticalCareEEGTerminology_rev2021.pdf","metadata":{}},{"cell_type":"markdown","source":"\n# Inspect train and test csv data","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt","metadata":{"execution":{"iopub.status.busy":"2024-01-11T00:44:20.413207Z","iopub.execute_input":"2024-01-11T00:44:20.413652Z","iopub.status.idle":"2024-01-11T00:44:20.942780Z","shell.execute_reply.started":"2024-01-11T00:44:20.413619Z","shell.execute_reply":"2024-01-11T00:44:20.941528Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = pd.read_csv(\"/kaggle/input/hms-harmful-brain-activity-classification/train.csv\")\ntrain","metadata":{"execution":{"iopub.status.busy":"2024-01-11T00:44:20.944921Z","iopub.execute_input":"2024-01-11T00:44:20.947268Z","iopub.status.idle":"2024-01-11T00:44:21.379677Z","shell.execute_reply.started":"2024-01-11T00:44:20.947219Z","shell.execute_reply":"2024-01-11T00:44:21.378538Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test = pd.read_csv(\"/kaggle/input/hms-harmful-brain-activity-classification/test.csv\")\ntest","metadata":{"execution":{"iopub.status.busy":"2024-01-11T00:44:21.381451Z","iopub.execute_input":"2024-01-11T00:44:21.382187Z","iopub.status.idle":"2024-01-11T00:44:21.404811Z","shell.execute_reply.started":"2024-01-11T00:44:21.382143Z","shell.execute_reply":"2024-01-11T00:44:21.403508Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"There are 106_800 rows in our training data, which represents labeled eeg subsample\n\nEach row has an *id* corresponding to the EEG and spectrogram data, which is the data we'll use to make our predictions.\n\n*lpd_vote*, *gpd_vote*, *lrda_vote*, *grda_vote*, *other_vote* are our target columns, which must be probabilities\n\nEach row has *sub_id* and *offset_seconds* for EEG and spectrogram data. During inference, we'll obtain 50-second-long subsample of EEG and 10-minute-long subsample of the spectrogram to make a prediction. Therefore, target values are applicable for this subsamples, but not for the entire EEG and spectrograms\n\nThe *patient_id* column would be benefitial to make a train/val/test split\n\n*expert_consensus* will be the useful indicator of contraversial samples. When consensus is low, the row is, probably, the row is probably going to be dropped.","metadata":{}},{"cell_type":"markdown","source":"### Inspect classes distribution","metadata":{}},{"cell_type":"code","source":"train[\"expert_consensus\"].value_counts().plot(kind='bar');","metadata":{"execution":{"iopub.status.busy":"2024-01-11T00:44:21.409718Z","iopub.execute_input":"2024-01-11T00:44:21.410446Z","iopub.status.idle":"2024-01-11T00:44:21.810660Z","shell.execute_reply.started":"2024-01-11T00:44:21.410412Z","shell.execute_reply":"2024-01-11T00:44:21.809389Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The little class inbalance is observed here","metadata":{}},{"cell_type":"markdown","source":"#### Inspect number of patients","metadata":{}},{"cell_type":"code","source":"print(f\"Number of patients: {train['patient_id'].nunique()}\")","metadata":{"execution":{"iopub.status.busy":"2024-01-11T00:44:21.812355Z","iopub.execute_input":"2024-01-11T00:44:21.813343Z","iopub.status.idle":"2024-01-11T00:44:21.823082Z","shell.execute_reply.started":"2024-01-11T00:44:21.813305Z","shell.execute_reply":"2024-01-11T00:44:21.821902Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### Inspect offset seconds number","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(10,6))\nplt.hist(train['eeg_label_offset_seconds'], bins='auto', log=True)\nplt.xlabel('Offset Seconds')\nplt.ylabel('Frequency (Log Scale)')\nplt.title('Histogram of Offset Seconds (Log Scale)')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-11T00:44:21.824491Z","iopub.execute_input":"2024-01-11T00:44:21.825889Z","iopub.status.idle":"2024-01-11T00:44:24.663814Z","shell.execute_reply.started":"2024-01-11T00:44:21.825832Z","shell.execute_reply":"2024-01-11T00:44:24.661301Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"As we see from this plot, the most offsets in train data are very low. Most of all, it means, that most of the EEG samples are relatively slow, so there are not so much offsets for labeling here","metadata":{}},{"cell_type":"markdown","source":"In the test we'll have the *spectrogram_id*, which is 10 minutes long, and *eeg_id*, which is 50 seconds long, for making prediction probabilities","metadata":{}},{"cell_type":"code","source":"sample_submission = pd.read_csv(\"/kaggle/input/hms-harmful-brain-activity-classification/sample_submission.csv\")\nsample_submission","metadata":{"execution":{"iopub.status.busy":"2024-01-11T00:44:24.665701Z","iopub.execute_input":"2024-01-11T00:44:24.666543Z","iopub.status.idle":"2024-01-11T00:44:24.690430Z","shell.execute_reply.started":"2024-01-11T00:44:24.666496Z","shell.execute_reply":"2024-01-11T00:44:24.688885Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"For evaluation the Kullback Liebler divergence metric will be used. It measures the difference between the predicted probability and the observed target. More about it here https://www.kaggle.com/code/metric/kullback-leibler-divergence/notebook","metadata":{}},{"cell_type":"markdown","source":"\n# Explore train EEGs","metadata":{}},{"cell_type":"code","source":"print(\"Train eeg files: \")\n! ls /kaggle/input/hms-harmful-brain-activity-classification/train_eegs | wc -l\nprint(\"Size of train eegs\")\n! du -sh /kaggle/input/hms-harmful-brain-activity-classification/train_eegs","metadata":{"execution":{"iopub.status.busy":"2024-01-11T00:44:24.692210Z","iopub.execute_input":"2024-01-11T00:44:24.693128Z","iopub.status.idle":"2024-01-11T00:45:10.307139Z","shell.execute_reply.started":"2024-01-11T00:44:24.693079Z","shell.execute_reply":"2024-01-11T00:45:10.305580Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"So, it's a lot of data. Let's analyze one of this eegs to explore their structure","metadata":{}},{"cell_type":"code","source":"sample_train_eeg = pd.read_parquet(\"/kaggle/input/hms-harmful-brain-activity-classification/train_eegs/1000913311.parquet\")\nsample_train_eeg","metadata":{"execution":{"iopub.status.busy":"2024-01-11T00:45:10.309496Z","iopub.execute_input":"2024-01-11T00:45:10.309921Z","iopub.status.idle":"2024-01-11T00:45:10.594778Z","shell.execute_reply.started":"2024-01-11T00:45:10.309872Z","shell.execute_reply":"2024-01-11T00:45:10.593792Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"There are 10_000 rows and 20 columns. Each column represent value from some specific electrode, which placed on it's [specific location](https://en.wikipedia.org/wiki/10%E2%80%9320_system_%28EEG%29/). Rows represent values from this electrodes over time. The frequency is 200 samples (rows) per second. So, this is a result of 50-second brain activity tracking.\n\nLet's visualize this EGG","metadata":{}},{"cell_type":"code","source":"fig, ax = plt.subplots(20, figsize=(10, 100))\n\n# Generate a line plot for each column in the DataFrame\nfor i, column in enumerate(sample_train_eeg.columns):\n ax[i].plot(sample_train_eeg.index, sample_train_eeg[column], label=column)\n ax[i].grid(True)\n ax[i].set_title(str(column))\n\n# plt.legend()\n# plt.title('Simulated Data Line Chart')\n# plt.xlabel('Index')\n# plt.ylabel('Values')\n# plt.grid(True)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-11T00:45:10.599102Z","iopub.execute_input":"2024-01-11T00:45:10.600509Z","iopub.status.idle":"2024-01-11T00:45:19.353688Z","shell.execute_reply.started":"2024-01-11T00:45:10.600438Z","shell.execute_reply":"2024-01-11T00:45:19.352385Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- Data has a lot of peaks and troughs, indicating a high variation \n- The scale of most of the plots ranging from -200 to 100, excluding EKG chart\n- The line seems to fluctuate above and below a central value, which seems to be around zero value","metadata":{}},{"cell_type":"markdown","source":"\n# Explore train spectrograms","metadata":{}},{"cell_type":"code","source":"sample_train_spectrogram = pd.read_parquet(\"/kaggle/input/hms-harmful-brain-activity-classification/train_spectrograms/1000086677.parquet\")\nsample_train_spectrogram","metadata":{"execution":{"iopub.status.busy":"2024-01-11T00:45:19.355167Z","iopub.execute_input":"2024-01-11T00:45:19.355640Z","iopub.status.idle":"2024-01-11T00:45:19.449157Z","shell.execute_reply.started":"2024-01-11T00:45:19.355601Z","shell.execute_reply":"2024-01-11T00:45:19.448027Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def plot_spectrogram(spectrogram_path):\n sample_spect = pd.read_parquet(spectrogram_path)\n \n split_spect = {\n \"LL\": sample_spect.filter(regex='^LL', axis=1),\n \"RL\": sample_spect.filter(regex='^RL', axis=1),\n \"RP\": sample_spect.filter(regex='^RP', axis=1),\n \"LP\": sample_spect.filter(regex='^LP', axis=1),\n }\n \n fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(15, 12))\n axes = axes.flatten()\n label_interval = 5\n for i, split_name in enumerate(split_spect.keys()):\n ax = axes[i]\n img = ax.imshow(np.log(split_spect[split_name]).T, cmap='viridis', aspect='auto', origin='lower')\n cbar = fig.colorbar(img, ax=ax)\n cbar.set_label('Log(Value)')\n ax.set_title(split_name)\n ax.set_ylabel(\"Frequency (Hz)\")\n ax.set_xlabel(\"Time\")\n\n ax.set_yticks(np.arange(len(split_spect[split_name].columns)))\n ax.set_yticklabels([column_name[3:] for column_name in split_spect[split_name].columns])\n frequencies = [column_name[3:] for column_name in split_spect[split_name].columns]\n ax.set_yticks(np.arange(0, len(split_spect[split_name].columns), label_interval))\n ax.set_yticklabels(frequencies[::label_interval])\n plt.tight_layout()\n plt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-11T00:45:19.450954Z","iopub.execute_input":"2024-01-11T00:45:19.451412Z","iopub.status.idle":"2024-01-11T00:45:19.465011Z","shell.execute_reply.started":"2024-01-11T00:45:19.451370Z","shell.execute_reply":"2024-01-11T00:45:19.464139Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Took this function from notebook https://www.kaggle.com/code/clehmann10/plot-spectrograms","metadata":{}},{"cell_type":"code","source":"plot_spectrogram(\"/kaggle/input/hms-harmful-brain-activity-classification/train_spectrograms/1000189855.parquet\")","metadata":{"execution":{"iopub.status.busy":"2024-01-11T00:45:19.466774Z","iopub.execute_input":"2024-01-11T00:45:19.467393Z","iopub.status.idle":"2024-01-11T00:45:22.860023Z","shell.execute_reply.started":"2024-01-11T00:45:19.467338Z","shell.execute_reply":"2024-01-11T00:45:22.858723Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### The notebook is not finished yet. Stay tuned, leave a comment and upvote","metadata":{}}]}