{"nbformat":4,"nbformat_minor":5,"cells":[{"cell_type":"markdown","id":"tldr","source":"# Knee MRI in 11 GiB\n\n**TL;DR.** Keep all training studies, store fewer pixels. The default recipe stores **all 4,407 training studies in 11.12 GiB of uint8 pixels**, without keeping roughly 500 GB of DICOM resident: six scan slots, nine slices per slot, 224 × 224 pixels, keeping a central region about 130 × 130 mm when cropping applies. These are training studies, not a count of unique people or the combined train and test sets.\n\n- Independent image probe: **0.660 held-out macro AUC** (95% interval 0.633–0.689), on the same fixed 200 studies and five study folds.\n- 224² → 336²: 2.25× the storage for an observed +0.005 AUC; the gain is uncertain, not a proven flat plateau.\n- Three slices scored lower, but this verifier did not establish a clear quality cliff. Treat the earlier slice-cliff claim as provisional.\n- **This is a storage and geometry result, not a medal.** It makes no leaderboard or foundation-model claim.\n\n**Build your own private cache using the settings below.** A public pixel download is not provided: competition rule **2.4.b.1** prohibits making competition data available to non-participants. Resizing MRI does not provide a stated exception. [Competition rules](https://www.kaggle.com/competitions/rsna-knee-abnormality-detection/rules).\n","metadata":{}},{"cell_type":"markdown","id":"settings-help","source":"## Choose your cache size\n\nThese settings control **your cache**, not the locked 15-point experiment or the figures below. Default: 224² × 9 = **11.12 GiB**. 160² × 9 = **5.67 GiB**, with lower AUC in this probe; 224² × 6 = **7.41 GiB**. Crop changes the anatomy retained, not the array size. The printed estimate excludes small file headers.\n\n`CROP_MM` is the side length **kept** before resizing. `N_SLICES` must be a multiple of three. `WINDOW` selects positions along the ordered stack. Leave `SAVE_CACHE_OUTPUT` off here; enable it only after making your own notebook private. See **Build a private Dataset** below for the final upload step.\n","metadata":{}},{"cell_type":"code","id":"settings","source":"# Edit these values in your own copy.\nIMG = 224\nN_SLICES = 9\nCROP_MM = 130\nWINDOW = (0.35, 0.65)\n\nRUN_VERIFIER = True        # False skips the fixed 15-setting quality experiment.\nSAVE_CACHE_OUTPUT = False  # True ONLY in a PRIVATE notebook; retains MRI outputs.\n\nprint(f\"Pixel storage for 4,407 studies: {4407 * 6 * N_SLICES * IMG**2 / 1024**3:.2f} GiB\")\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","id":"measured","source":"## What we measured\n\nWe changed image resolution, slice count, or physical crop while keeping the study subset, labels, folds, and classifier fixed. A **slot** is one of six combinations of scan plane and the official `Fluid_Sensitive` flag. Nine slices means three groups of three neighboring images.\n\n| What changes | What it means | What stays fixed |\n| --- | --- | --- |\n| Resolution: 128² to 336² | Resize the selected image to that many pixels per side using bilinear interpolation. More pixels give a finer grid, not a higher JPEG quality setting. | Nine slices; requested 130 mm crop |\n| Slices: 3 to 15 per slot | Retain more or fewer images along the scan stack. | 224² pixels; requested 130 mm crop |\n| Crop: 110 / 130 / 160 mm | **Side length of the central square we keep**, not the amount removed. Crop first, then resize. | 224² pixels; nine slices; identical storage |\n\nAll variants use the same eight-bit intensity conversion: clip the bottom/top 1% within each sampled, cropped series, then map to 0–255. We did not sweep bit depth or JPEG compression. Smaller resolution reduces the pixel count; smaller crop changes which anatomy those pixels cover.\n\nThe verifier reads **spatial edge maps and small image grids**, then fits a regularized linear classifier—a model whose weights are constrained to limit overfitting. It uses every retained slice at its cache resolution. This replaces the old model that saw only eight summary numbers per slot; it is a fixed image descriptor, not a pretrained or learned image encoder.\n\n**Protocol:** 200 studies, exactly 40 from each locked `FOLDS_V1` fold; study-level five-fold validation; seed 2026. Targets are the 12 Pilkwang `report_labels_v2.csv` scores, binarized at 0.5. Every study is predicted by a model trained on the other folds. **Macro AUC** is the equal-weight average of the 12 target ranking scores; 0.5 is chance ranking and 1.0 is perfect ranking.\n\nError bars use 800 study resamples. Differences use the **same resampled studies** for both settings. These intervals do not include retraining or alternative-fold uncertainty. We call two settings practically equivalent only if their whole paired interval fits inside **±0.01 AUC**. A sanity check with randomly shuffled labels scored **0.486**. No settings were chosen using leaderboard scores.\n\nEach of the 15 settings is now **built directly from sampled DICOM pixels**. The previous resolution sweep resized already converted eight-bit 336² images; this correction makes the 224² point match the default cache recipe. Within each sampled, cropped series, the lowest and highest 1% of intensities are clipped before scaling to one byte per pixel. Physical order uses image position along the slice stack, with `SliceLocation` and then `InstanceNumber` as fallbacks.\n","metadata":{}},{"cell_type":"code","id":"folds","source":"import base64,gzip\nfrom pathlib import Path\n_ = Path('/kaggle/working/folds.csv').write_bytes(gzip.decompress(base64.b64decode('H4sIAMBnomoC/4zdua42u9Yd5tyA7+TgoKpIFou5E8WCLkCw7EiQAsuB797zGe9WqrmEX/vs5ltcVSw2sxnNf/wf/+9/+f/+w3/7f/7Hf/5v/+f/9Z/+w//xr//7v//X//K//2/3v59/f8/77+vf97/H+13XHP/+/j3P9+/7qn/49niec51zrfG99TdjPPM5z33Wd+/nvf412zHOXOfZs/70eq9n3+fs81x7vesZp8ZZ/7q6Mern67fXr3vfca891ryu9/dc564nHPe/RjvGO+rXjnvXr67HmHc90rXnmGO97/Psd/7raca4R/3+PZ/67XvUu5xZD/N+z/2uc+75rvn86+7G+NaqH1xXDbPrcepH93V9a9zjmtf9jnHad7m/Pc+zai7e59v1W79v1muMb19vzet9X3f7Ls997/rNXum9V41V33Oee99jj2/URF9v+22fu37zWrt+6nvffeb7zJF/qH//1pveo52Pp2Zw1ZzW8ri/69R4Y5+1Vq20e9U819/17/LO+/pqPZ1vzO+7v3qr5Vt/5131eceZ/XOc+/3G+a769fN6v/vcNd7Z7zXrX4x5xveHMeq3XvUF1z713+asDVRD1XKruagFV//Qvsuon3yvUwvsm2vWXNZi90Jj12w+p57wbvdLzUGtrntep9bl/O7agPUa43kfQ7yrFlA7xvpqNuvL1jD1RWuI2iO1D+uL3/W3bz3k267T2vf2aY2yxlNPdftn7/a+Z9/X+GqddmusVvNzfXUIPbVaR/23pzbJs/c3a17n+mrPtM/xznc+tWFrNdXZUZNS66PWhFVSJ0CN/vZ7rh55rpr9/FrHx32/tXBrodd4o06y52m/7a4dttfz1alX32bM96yrvk6dbrULa8Lrf9o1Vm9ea6SmpU7kmttx1dSMWUdQLfvpKDm7HeOrh6+NVR+hjtV6/l07bez9jHmt51nH+ui+S23ub3ubmsV57VOfyHK57/PWYq0jfvbrtG6X+gC12w1TU1ErphZXHdS1UepYrF232+9S2/251qo9Uztt1vW0R51h9UrzHbVOT33j9rt8n0uhTpHa7csTXXUC1vp3vh4fa/Zn0PndDmM/NVKdZrZcfZzsvzrcz1++S33WOrMc3282+qlXqcWf1VvHyvPur3+O9X7bbVuPfTtNlpXy1cG06nyuE77epfsu9eju2bpaxl07ftVfXFXujK/GrL0/+ueow+adX81hTepTV1Nt+nqhup9OLZz6n/WH+XhPLaev9kcFHXPUp77rmjoOxu9+79ous3sXO8aVWzdbrfVaX7X56inqiwsHahvUQuvWWJ1AdZR/V03oU/dA/bUuBzNRL/TV2q3D5G2fw6VY6yFxw/PV39SEHBPsEWrH1IO2Y1gLd/5w3fm1wGrX1TepVVrr9Vhp39OPUZd9XbL1PnWK3Qaqk2fdJzunIizxx92N4TLatcnrM9ak1hU+p3+ZZ6h4pGKqbn3UTNYaex2/dRI/Y1y18ysqqnCq/q6mtW7y9ruIoHxXXzQBTb3RqrXhdK13rPt89GPU+VfL1I6pk8gXqYdZY9py663xvrcfQ+RTP1znec2vAKIiyj1fd8PrTHzbe78u5tr0U/xhMq5dcbO/qatqf3UM1OLt52NUzFBHTa3xOkBnrY/6MhXY5taoE/mrcKL9tqOWR10xdUnbOILj2oOjbjin0ahg4Jr9GLXxXwG2A+cRnlqlNVQFHznUvvauvMWzdSnWZbtrZurGq2DC/n+EMkLUe3Z31D2nC6aCuIoQRz2FVKHmt+6pOg8qFPieP4xRT1uhy3R47FrzdTOMVYfRrpNNOPSX9TG3D1j7pU7wumN9zPrU9UVO3Xi1Ees79fMhMnUv1H5dDsVTx2IlYXUCXD7Y87T3bY1R27buufrbirrrF7ura4pfaU0dRdd9+uc4ta7kLW7Jihi+uvjq0vShau/d9VJtTlhXfV1DFY+KCOtYrSmp5TLEYxUN1Xauu6qd0yXPqVCl3r7umdp5jzTunhW/1LoRCFztOq2zu6agVmVdb1LAOomWh3squK01W5fX1T/H53PW93gdgHUdHPNaJ2CtmRpn1v+036VC0jrVK2ypRT7eHNB1/NV5VlduTVLNUH9+vLmn6jSssKm+6K5gdb7+SOU1r0PhbePk25Vdh2eNIaqsmailUtlP7cRMTi3U7w9j7NpjldC5ImtB1SVXd5OVUsdiJRO1Str18danrA+6rbK62uoAqO8sRpV6V6BdX7f9tvWDp3LQ2p51S9XFW1uuov3auTW1NdpfYn6hWL1AhYVP3SQ1GxXDVUj5CFh3rbna0KP9thWenrpkEslV0DzrTFrOk1p7X8b+dj9Gre26zWo681J1nNex+jwiIJO7a9f17zKElJVK1vo8wv3K8GqxztpHFW/X+XGe9rvUhqgj731cjLVO69BQ3amnqcihtkHise671EGe4LLmz3O8zrUjhaigslawhKB9l7qlK3ARiVVaWju+luilrFIHUf3TI+ztn0MBSnWqEku5z1NzY8EJFOv7WiT9c9QiFcvVt6m0tL5nrdcap47SaS7e3a+PymZv91P9dM3oXT/4ua7dUnZiHdJ9LUdGXJFUXdH1aS8vVsFh5bkVtMrhK/Teqx+jbpXH+191S9W1UDdNJQ11yldeWmFebebRj1FHmJpHxW+nLsa6Hd9a/dbtLbiq6Pvt5+MoY9Wb1+eddQZX6F0JTV0YQv96TfXCZp1WJFhHRcVOpvG57JSa1jpAKsPelkg9VPsuAnx1wkr3a1nUuVMvU7Gq6H1UqFcrt631PbVxay7q19W83NLJWiEOg/o/6YQwun+OOio+58SuEPtdbvp6nopCKnKuS6Pu4DamUzx5xqpgTCJTUUvtubooPxlljVunfn8mP+olnxeqn6+ztRbmfVYFqKte5b5SCO32i0msN5mXyljlCQpbj6RGJKD2V/PzhzFq2sQLx7U9av+OIautO9NA4qN2PqaAciep/VK9nc6A2v/LSE6CthbsDlKMqmjUBfmq/tYBVPnPM1WF6kTa/RgOrn2sk0umWi+yHdC1kaUPyV+671K/9EkuXFFq3fiV99c3eayT5ET1jdp73xh1+CqNr1qwFeOv4Yns/bo0p7J5u2+XzaqgNhIo1En0XnUC1dqtI1qK9/XfRSi6nRpHbaui9DqWtzOgYqtKyOr2/cMYryij4qlLvj/3zN371jFfm6fyu2/3c1rTWau9zqJal+6HS2Us/YZaqXXEnauNP+qR66cVdGrLLRWTz+Zz3dVUCcwqxu3H2GJ033A5W5OYLlHNUAp1SbVj1Cy+281UP1FzWcvkEQTUBVonUMUCtXTbb1uLq24E18BIy+SVWa56iZqdeqqp79GOUZnp1D0R5uoaWP0VY04VmIRk/ZlcYY8w6HU7yjc+X9cVM+tgH5Vhfn94jqXgoe9Sx/L7pOy/hmJZBal1Tbx/mNO67d9aBbM2zr7qE9csbjuwssLKs+uI2m0MU2PUalCYq/yjsqcKmmtJVFLkbBfw1TXcrtN6/zr16mqpMEoj6qqYUvVh1SHwaki9/bvU+Vk79aufrtxQu8KN7aquKa5RVfHb56iU8K7vUJHhq9Cp4F+HkCLzIwWqs6itW45fbLuteZWL9yi11dxU6lIDiADaOHnoqVXwUinH9WYT1+X2GazSShfV3fdf6txRHLhHstM7PbWx3S11u9QGqDjp6ceoR65z7PejyrHvr0RUs+Ov6hDdnFYyXutAQuoWqINPOeVN8HCdpLq7Xeu1MITTdZ7XN6gdVu9TR+tJiFfb51UB6Z/j1+6s16gJrJy0oiF1cYndOSmzv/1z1NTVh6hHUaKve/9xudUE1bm40q3qv0ul9jWLtcPeumBHPXotzLfu4FQu3N1Pe56O59fo/HSTK2B3JtWJUt+0PtC2h2Y/H6KGfMJXWlyLvE7n+qq1yuogVH1bT/8cCtpe/X5UkKSElWNqFz45nOq4786PGqOOLfVg0V0dHbXxRUNqKLX+FCCefox6h9ovR2pcIbvead0PS73KYV033d2/ixxlCXBrl0wTCR1Qp3Tt5nXqC/1hrQ/RcI2zK9quXEV/W81N56XuP2dTv/fHay4f0Vit+6WMaie/ymZ1L6i9tWusvmmtqulSqWNdf6zuWTiHOp3rU5+vr7ENHRfNvcveO9t5pGhYS7Qu7nP0VNo5nQ7Tmtmnlroksw6eYfu5J59KkU/fJ9SwdnIYRIFJapde6uM8qv1SO6d9l1rdte3rC3/SMO2L+WmR1SzXN6tb51p/GKOinWRvu47fbx/Pb61XCDI/oIU2B6pFKkuuP1lXQ10Fj+C2/q6iB0XMChT36Z/jcbveSuoKjHXBuN/qhUSFCo91v3TrdLllVT2uZJS1Wmo+FGR2Pd/rROjf5XU/VMxTX6ZWx0qU90EcPL9Kt9pW9y7bLS2TrAOxVqnosOKRS/sjdeqKY9p30ZWoTKrCQE15zV8dE1XM+mN6WnebvwjmkoMlCKnzcyx7tQ4jsWp9ljrh+zHqGKus41VzNGCdIEuiWdFRAu2a7PYcq2SlpnJV2PIqhFZEo6dbj1ZvU8fBVkPp5lQjfK7KwR79k/rQ9VGlttLLJTOq5+je5ZOv+IqZjEpDxsg6rTdUvQAi6J9Db0OFoi4rPbKKU1+deTXpymqev5zJaj6pnMIK1bEO8PAAg3xZN5+7oXuX2rJ1oN8Viw37RMFdhjpW6rF1Lt9trl5R9SMIrMe/HBt1om5BVT2e/onmcHsWThXUumeDFHsACz7TAwAGWyZMbuvrU7Z+y9W1N7fFLU/f2qnDQVDrv9svFT9WvA0LI5cbmYpHWlTHRx3RcFuzf5cNpDRU51IaWm7wrayujzkdiv1z1BUCKTBV2oBX5lFuuG23qb002tpF5fcv3FmtT+sUjE2Qd6tdSOpqqP671JaYDq6tMr+FvDkXU4N4s9R3P0ZFCa5XFSRB+9kq4mqOin6iivZ+me5HRZwlMq7BKr47FSyfQGIeB9PdzwdMgJQHOLFSmVrhjzJXfR+lg0du2qz1itxcqV9dEfV9K0U2G+OVYQtNZjATVzsGLMERk9W+WQoWI/iCI6J5df3aOa2woxZZhUD1Sy/NbDWgo8f1ABx9lX/077KEc/Xn6xHqA1fMLKZRvatz+VUq6r9LXdKnHl4/UAXy1i5wAhwV2bSU/zCnR6Y/lRs0GdSz0pmrFERdSB+i/bZDdX6INF4r5AExUgwGxqvLUv+x3XNak0pH9VsrPAeEqyhvLwHvXXflufpekoqnEr9Q9yR8F7VD57i4xgTbbN+lNlxtuUojZTufDyyBAVyoC6a+8e4xiipy1vpQL1Gdqul59F6WlV470hnUfdsJwOunPojA+iAz19btLq8TYa0/nGMLUkSWvJUHVQqP/TYD1H0kVW38UTNXLzOlYPVH1KQuNUc7sfLdoYL6hzHqKJUg12KAUpq68nUCgi3UXMv55x/GsMi/fIJraY4JLi+1/8e5WOFM+122nqTGQppqwQa86d3UEak5Vn9tn2NLUuaSKtdvDjxYc6/unMqH6p3e0+/bbeZr14ho1QpkZLX0wfGEelr37btAvNZugwOakLhyXQ3LOotrv8Dn9GfQgaupILWO0Qrv00uu+6GyOBmqUvPT4pNhT74ALV0DI9moF6zzvU5Z8PW+ZjCPi6WSh4odJnyw7/EB6T56zLK6+Ycx6q4HuKo/XOvkgb6qNXaJnjVCaw32YyhgS4Z1x8HVBVTOtrozTEb9+24+lgLYJz4eI9vmg9uE2fikI9O50q2P+rJTLwzwfi2Lu2b502usC6tmpg7qFiOgDf85Ph69Pvu2ZkV3+kr2rZnTYp0WdIQaXyLlyh6Ac2fa25WOVNpd+6+b03WnDlWnt9uxkv1pInS5a6WCKa4e02vDQauqBmtZQG24O8XdtWg1QUf/HGdp4Lq1t6u2Mh+QlnqKGSRtHSvtc8Cd/Q7yXHWifBun1mclAlti1O592N9PV7K+SMKI+qG6JzaogshS9bIfw2GqOggxfwcmvVKMrREeOMzTjwHU/NbZV7OSVh9IyrM07Jf2+rj689SCrk1W/+6F4khqWJH/M3O4BWDSYr9WZTuvto1UAZb4VTB4K85zQdUAdc6336Uyt5rGeuK3locAEaIHTFJqVr8BZqJ9jhfiBLCnTsS3XgEqr6bakVDJRMXLo5/TrTVXJ4+qTs1lXZTaJngF0ola8/3eHw72tKBVCyoIc10ru+nrPtovbb6fo6+m8hIDjSC4oIRzzK4guXe/XyRBdT/VUapqAAxfz2MswPh6xgqd2/Nj/voWQQcfVAlV6gPaA/8NDNHmhPDAtxhGNiasSvekTmRNDGWmP2A3jPHAF1XkVcf4/tyYT/pZAQZKY/p3qf1aU5pGEpRCxZJDOXVA1lxb7bJ/lwoXYFZWcN8pakPpvAk2cafW6J/Dr6vEKehup1oIReqf9WBBH7a14DorQIlry99IFfWBUty+Feru4Mh7LNyCJAL4BFD0MPVEr38SmV1jhrfWvQt2WG3xWvQ33NgBmKpZDS5X879urPY5FJ5G7fE6h+uZwFkeKe/7q1Q9vnC71muzOT+UY3SVcARUG+5A2+sl663ad/lwspTFHxDegLWUYpZLeNXCq0iv3fsfDFB9BKWDiluOgn29zH05mOqj1MHWP8f5PnurUsvArF/NQQBduE1XxWxzMeSUX+0mkFh4kC9L9BUyV0Tx3f36OAgWaQdViq30p0b95VgU4GmF9M+hJY9RtRGAZC71TXQqYXWWM6W/53RpMwHacfuHjnYeDT32sE/6WOroCa7avhCOIeU8I2ifHTYQPl83RpodM2/+hl+RYvkCPXhwlmrltXwxaxTrD5oVeBP2TcIu8Fa4r1CgjU9rDFXpFEvduy+kkVb0Em7WiVT/033bN4hLGeHUMaklPiEO4ZWAyQPo7J9DDeZCS5TsV+idIpk6kW9T0zvbc72WFEJTzeAjOK5VpiKsZnF97gpdtn6M2rmvLV9f9QY7rfhjgcMFEK8C2N77Ws5K2ykSDDHqgXuoKBvWRvYx25rSqyqmI1drIV0tJfoBV5trU2DSnqcv5NkG9KxwEqjtrWnY7qk6H+cbfGo/hus6Ww2rwReCcbxO2FVKb6fFKL64orXLpvbisFmHssGjoPNeOWiffox9oYcOxew5FaMQzYYbx0qv2b7/MAa6CQy97o26H4riAWatTfAIedsxNPjBVo9mfqZkaSSNQAMXbkG/1jW0tyZyuDS6ULVId/DwcnhRXjuGUskr2X8CEa7VqZSMn/SM3FFv/xwV/aj8KtfVdTLThwIYDbf3wMa1/ChszjoA1fvwkzC0TlC5UAofNvJu79uaR0VcC1SdLUW7OhmnlowWv+iwf47ssErMgdmEo8HfK1/o9EE8tef6m1We8NgRPHTqVqIYQ9dYp6+vvwE7HM3vL7HUBqF7Lu2bjbRW/6ofY1ueMztjbKTgYGme9LUq+6jItf22Esh6aNQKzKIZRp5N+GvJoGi3a139tc4hYGLgeRGhCBdXQ9O/DtsWD/O+CeWULZTVFNVzZfu3GP0V27U1A2sAobgu6ImkDlWbQqpq2YBzHv3e3xo1L/waLtKwa+qDpjwTdH3lNf1z1HKW41cgo03oY9eCq1BC27RW7PjLc/gRxAikuydR3YVUXUc1/pjqavsc9QgVRdayHoiRlbB/GtmfoFV5GIus/S4fBrFIG1ij1uWm82CVWi51F1cA34+hoAY6ga5yjHRBxSrVXyFv9PhTX7COoZzjwmzCATequUV2DqJFe45hznwBfLq3H1e3hlj9JIEEuLzV9it11jT23ihV6CE7h3FxVIMczl+7Ti0rmTBgUzB5CKe1eW2+OtpEau13QZMTILyhRL/6A3pymM51vWiMzfY83bbpVPp1hiN4oFyCn6vGICXu9t5HSKj8T5kvfcfaeWkkg2PUfq5zsucT4mUEm13XnMYcOu4iLAAjMHIctPUxx+fjzNOTwliVk0kfXmmQRvlqawYoJndICZemrXT0NiHSXvxIZc1+jBQIwavrUJ0wOhswUOCrk1Shdz8f4wvnBp1H/egBwDwvMY7pIq/XamuwG9YJ5PsDJVQj3Fh4c8EaKIpU7trOqb6YUOjDFhWP3dI5Tf87WJm6J9o1BoDiG8isrwfaoeZhfODrFV4qYvbftvaKzokQBDhAGeOOpoksAr3xtHUpR/J8A+s7inRhedU6HRZtTUm94frDGMo/OJ510z5ODgkuvLBiiCJPW2NDAAoG+P7Si5+BP7wXoI2vtkdf21I0gbp9It9RGd1NPWeKokSGCiNt/hKKqbgFXEsPWa80hcuFso6P0+bI251dDwJpcSUhHFAfYNzpJJmbdk5hiu9gV/cMCVn9F6ZGF7Pec/dcntoXqWHrTT7ETPzTkMwpih7wjaufj0/nlZBBrXj1nwNAX/tN7e/Suvz6+TgJRIFRvhMMrNC2ArocsHUiz34MHYE90vZQx1ipOGLwPP4o6vXux5io0UNJTYuPttP7JjB7NZRrO7dYuB3Q/hWxj6Hn4NjYMLC3gmylmj2eTkSa0tq+Ii9T97SyChStXbvHH3A5u/5sKGo7XdeRE6j2fn2j/cE91IS1Y6g8n3lHMCio8ansdglwnWlKVd0YSn1p5l04Z4CGn4sCZxSiFhCtXWP0Az4Ud0VGvUVkdS2DxFFRN2m/7VcnzktLAAx3RH0ATDEYuYusQW3IdgzCCuhILon0t4AcoMA80tLTfvsxUkKXaoRlAswLanCIvkAvrv4sDHAVRQPUAnl3RY/A3etcHxrb/RgEnY564/kHnAcjJem3DaMu0I5hx3717GogYieHYM2BO/h1TsLjduuj7liR9jVDc9/zjHA3FGRqgQQl3J3rn5D2/YnVODo02nIsJc2sBVJ3aPtdAHPIQuiwLU2xizZahAh+9Ma3vSuBAzQVxKL0oK7AAe8U6gHTnj/Ug76ZLv6JIkPEO96Ai+oj15xoa/UcCSevxljlHZMw0oTmQNPWWNL8rOu8HYOK1IP5jyjxzPQ668HuqCJQD+v7QN+KrFbtW1XGCsLA0CUBJNoC5e41uz5raqsmh2lWO/iJgFZ9XZUY0OW3fw7d/SfyB9YbdBIOvPKU2FdU0X4XLGzBMKS66+m5wb/qi1aWSLNi99phUcnDXZkRdqmXcjQrO6r00yLa/Vko+lo/1Jjm/BATORjHmxpN/aXtNwhOqaDVvQaCXwcBPZAaQgsUc2Wtfj5kL/cDm5cF5eAR5+2wRQ5M7ujHsLhDldGRryuhMqgTTElNzo8g0M4HdsZOEXgEyhNa0aekok4PWNafhTk1Saaph0fsRoysGVWBFCT2089HSB1XULjupJA70U7nfgD28ND6MXLF/4gMYu13W7I/sYYbi2/0e98p5o7fT7RZTkgNj6tWfR49oc2BUgRDbtDTf9RjNeSdPvV1E/mvFqtAO6T2u/rCqGevZVXzAX490uoKoKx7DqRhenSgyCtwCxJ7B1DhU7zQVe2e4yCVQq1UMKbSCUoP3ZtnqXQEXbybU3yAaKFgZ1gp70qR6sDSL0FFv/cB9+GHddZ0csb+iYDtyCvYhl+LQYPeqWDls7/o40X0p9YYbZgI/v2BZwGr8sLOBkgyPgWRQa3iggysWKIOufZdakEhh0E66YWLdGHoUCsRcQi7dXvugA+LmeALa+tKdd/In+mfYvf2eTY6Jbqex1iRlAKmj7iBqjnZuLd/jnwCsbJVdkiQUdcU7NX5Lr9scZ8u602DAPX+EJrQ7cumIUeg0tzPBxxhKKZvElLF7S+Ufhniwmj9+jEcE+Arl3QbpfCJMl3O6MvwbQ3WeXERt0kp20EYhBNYH7j0+kudP2pY9QYTGvja6WHcIUtMkLYLBf4PYyygXFcDvKPkXBlU8RBwCimnXR8u5rpKIKX0bAag8AQouzD83vMHPdiscUXSoNYrCqiEQfIwc4YEFvP2c3qOr+rnARYunJX3cjmAbJ1wJLoxKKV+lHEPBI1J2SKYSBpk1e1Wo4pI6E82USP7DnLMTRFKEuDx6vXHoIsqDxzQRBq3jx4QWsPnGHJC9XWpmgzJ3KR6Jo4IjPcnlUX5QnbU3vsHfMyhhQS4SIdgKmgl1Weuz0vGuF1jP1GbS+EEykKEPWAOaoJv8OfV1y5qgwDn1J6v/IXCDLQEtGDKFmcjCLZjOPvEgrW4n/oeMrOfyAuO0RMmS/tdiGshJQ6cN+ApaQ9CgEC15rjO2fY5lI3WIAoXyi3CvlhCQ1c/Bz26/S7UlyagOJapHkjNaMV20UQeoQpefxhDCkOCTL6xHYwvkSjau+r/ao5XP0Zt1h91Bai57rVDYQULTAHi7vumxgBRVnvZlGEr/5DuDtA+AU19/H6MJUwALzS1dkjqfWrm8gA47v5dPhpjlJ+fN8Kuif/pikA5onu0Md3RVwMyrCN1U1DGzcrSX66nHY3u//Uak3cFyCZhByLZifrHCi5P52Dd3bvQgQL4POAR5/M13id/hz5+42q2fdOgxC7HTeSkZKKbhqgOrq+LBtrF64Tl6A0Px7lbP7o3E6hM4H2Hm9i+i4qeEi6lv/tL6B9Ra71xtM9z9e9CzwpgBa35jRzST+tGbWpDDN5X+11m2lhp+dDbqE/6EURMSKWecLW1LYQgtSlFcFl+3RUK3JGFVVHyif8yxkqf5YO1VEtS9CeTieoYHPbXv8uAbDxkwi+xcV1VJJUVT+5oM7d9sRBxKvbSrFzYCaJELZQnVy5+Q3t+ICdUBBcUTLTTogwh0QVvrv9YSUj/LhCRiBJ2ivI6Np4YxkMJ2a/ra9dpytAQRScauRX0Q9PaPhe8M63adj4WbCH687HQgnI8gX+LejVx2rpUgJ8gKDEXEOpLDwl0v6kXVCi+uz4QENpHF/MODRqb7xNMHAi9vYN8fNs5fXOp7ist9CXer1t/pgWhRGPhtPPh4AQu2IlehjMUEEOLKaCtCkDa+bC6VJXsUwrET+TQojlhJ2qFtu9SL61e4/cqVFwAjw/7gDohyYnS6uzmlNR4LAqC/bbcgD1vunkjSPKz2vnQrUD00t68EasoIarNoxcShp+rnQ/UDJ0jkUJtEndl1L1fMSdg3tefyYrgSHTHvSCI+5Gi6NMIk4kRtPv2yK5VF9Z7vlxJIzLIVB6UdOTZzXfhevDMX0vLSFQmnszLR/BB3bGrXVDrq1uy9i7BzeBQ5bUzCreAYCrU7XPYdfilSREW3jqt2ovIi56YFmy3PgyCNideIFlzQGhD84iMcEAL/RiRjsb/EyE+kYMU4xG9U971Lt18qNsQpgVEA8u5f44FW+m/9jBNsXYMxUIQQLoskbyI5PADTS+xEoB3ax0O3/EjKhZiEv+7HalURXa8V+52Pup6BhNPsR+QF5BDEfLHsgYX+sMYb7pQNak1LXcYTVHKJNOiRlz3VT/GJ647aIkLWpJYzMco4E3vVUG52y84YrX38RtZpWBrBMkuPOVdAO7TjkFRXzAEG5giMlFLSKmblwRCbHsW3qHwoHta3vVlUeaULJWllHfmX8agKhFRm8gRDQUE3J4tI4xc9tvOaa3r8YSH44/L3APi1LCInHt9mHY+tH1I5owIY6WYdMvrdOkVtq4WN+7MQBlTgCF0yWMj+VkA3yrndRa0z7Ew9t6w5v39p8sPs40koHV5Why9MZCYNY7FEe7YFbpmEsPAQfp3CYb10A04zsSRYr8IL4gawIz+LATL1C0lb4MsljXqSK5p2sLoP5zJ9fDjSwu9BtoV+98oMWdG5+an59GuD2J9U4MNlzga7OpT5P0xMGQEf5iPX48wylu+7bL6oelVq5CeVpt7sCqQw8oakNT3Tg+lApkVrsXdY3rxj6HNdi56LPqKFd4fc5MiE0+X3b6LcGlR6b6FEPT7TuDSAAgY6D3W+mbyAlwY3nwdZgBXislW/1KMrI/UvgtgIqmJ3K4rGHD7nhxjzZHcf/RjYHrokGhD0S6v//5z9jralhXn9nvuPLGvkkOF37UiPqYt/QZ6frfYYtTD0IqWWJ+IqwjbOYbgR3f/bbmixqhFsKD3JT8oqxFimzoHtVwI1vTPoVH7xJTrCfjcQf+r0embEpfs7tvooKQxuPDPiP3WfjlSkDsA7NrWfxgDdiMFNjLjev1beXvA9RwGDu1d+USmaks4lEDEtz+RljfKM1Kx0Y9hm7u43xCHqSu8v+6L618Y0uZRzw9P/AVvEIrBzv3E9wg/ecFaX90YU8Dw+yBa8srAKkTXSj0VuLd/jhxhTwogbpObwsqIaETdPWqGbR5lhyH+79AL3CbxLKszgNcaSPkf1tgNCEOxE+dFPfYCygtCF64Ofbb9LnIvcdT6ORcQLiXOQpOB8I7Ion0XRJXw74duodzuBV1SqA467Wt5jXeEDp1CKzLyA7MJES9pphb59/Zz+oQ3P0XZN/IbY5BV1+VHOPdXE2qfgxLTE/WRpP74EdgaRrXYaL+060PuJjAlaEmMKQpiE0cIVEDs3N5RTwQUMG7voIJfhmfKwDOAtJgWts8B+C9Ap/E7I/BA0pqhBEhfxRRXezfQcQYruF375IbDdQd5SinYGdCfQaKeLwyx9PPhnVa4OKScI+Q827W+YpIykP5ONF04nkCU5fsGWNN+29e1AmcdaN1zB4CfwrpgiqXCaJ+jPkS68cKmSDLAFVFhVt6FuNx/GEMpLTruqU4Lb8GfprwdTaG+Wz+G4ms0GIHOSUjjAI4ZTcSLJmN/v2wPoQ0GMwLtBZ3kuKf98YKz9OfH1mOo95DMwkZv9bq4IAy6qNoy/Rgv6qAyx/qJ5J9Ixai7YVdfa/X3fvQk0r2low4kgcgsrwxXjOtGPx+1rbB9HWRRKs+9FfNKNxfudv8uzgm5wg5z16Mk2P+n/4CZ2+451AZ6UC6YK8zmhepANIK9FZHXP4yxI+6S3v6JySOxb2rokQuuFd+PQbhZ7GBmE+szKROEMAt7/8CvNEbsNGAnkHCDaL0Q6E6MKvRB2jnVh2cwNtJOgm5mFbKjQyCvWS33HgK/Jh+1644FVSB+qvbSCELMb+sxhHEbgXOIs6yIhwvVQFLKYaYX0a7TROu6jOL9VyVEV+uoTwVCVjuvH0Ptghrelz4QSN/6UjNbimTy7e5d5Mfr+oEuj5o2NfmKZmYazBwMRrtvCcijZrnuaS87mRlyPIG3xF2mvStJ3OjhghgCbqhEO1Lrub6Id9cR3T4HOqeKunIM6WXtSoU75rNJ/Fue1h17Q+5A5xfqzp/BDyj5DKikDqFuv4w0kStueqEMQR6u7FcAcIBchMv2OcQfKHRhVaf3QvNcbZjmk2Zqu/eBNWK3C1T3zEBp5IKRiXhD6mn37Qg6m2elqOHVsMUWUTzV6NOF/cNz7Kj8DbRIEhwiMHhrOfdl37Z+J1ryqdGpZ1/xmRVNbf16ZixfJe5tT4sy5uPx6S8K6ZTIwtmEOAAGbfU/agyyi6kyaIOt6HZdcTYlDy8xbO/9AZJDQ6Tu2gveC9/tZed5Aw5ZeG2MC9ks7Ao42eZLfdnujQjwuHuOhGss0Og7YmGE7V7y1Dd8TzpMMEbdu7ySl0V0mJSJzPB5cjh6FJ2HVpcW4uPL5opYD3Z1LF8XaiOSNbpWu8Z8jgti+yXtxGW07hTxlOowp9+rjQt/KNWRk3xQGa+9alDefldcjlsc/T2CmbdVrqTbNNwtU9u/rm9Nv7sfA75aXW1G4EmJjci+DYD6cFe8265T6tP1u5Tp+TXNOF77vI5oBZZWZ/N2NTPAgGW12Z8ZiVrE+X2Ir/f+HpwKEqwkjNqcBiTLkv+6HYgnV5jaP4f4b48oOaZDiQWjjnGHB8P1qB8DsrL2GYCzfmm9inospZcI1X6t/+0NavJgRa2Ird9xxRngJ4vlCc5FWzOQtiGJ8U0ggfo4AUfYJ09cz2qp/GUMtmp1Yda1KVSAhf3GT2iPCE7Ln7sDz4ArimU3kR04x/q0OBY0n1aLCwYEoLhcJx7HbE0XdOoAN7Gjk2h1Z1DU8dQFxbhxcKkrxdoFTvwi1dLWTyctBchTRWzPRHmL7zT9cFK1FVu0cxo5WG1WHQpVwndF+FuCdf+wLu181LeoIwT1Vpoav5ClRUZ9vVbpHzhFYbjI93WiAXh3tH9dMioYwZe0NdhwI6Z7G4MvXML4A5qfNX9WMO27GGN/6tmakwRc2c9HN0M3VQOwH0MXR4LwrAieXz8zPO+3iSntvjY+Z5Q+Thxw1NOhP+Qdx+WrwvS0Ma5WA0uLi6T9zJrlGmAuVe2CFeznFLOATVxEmK6fUK3iw/ddsRRv9bUd/wiJKO+Uc5aLRRgV8SDBydXn+w6xz12LlIkHOGAFw/GuGxBC+fTfBRrpi3ZSfF8upyKuA+E9NIOKjPp3URZgMf/C1PErq0PQGmdr98bv+Q9jaF+pgguV6+qcOkKXp+Hj/dz9vR+gFiXIuhjOT+gvdROF95UKXI9Bi7L4Ur9J83Y50NT9iFtKh0avMxEhGQV+AM9Yv/HHoHG5NNZmDLbbdbrDLXHHVICI+bEc8ZBgZAdFNv188Iuh6l9P8jArol1KqbM+lHMNx7IfgxbC5QNRAck8wg3smAPyUeprSuodn+slsJilKR+9YSovkwDO1eceegMy2ZlvA6dEpq7ua5RrCtdf3/OcKNWpHZH9eZLapnkSyborJqn9cxwfF1ksIpK1bKVSI9rlRIzt227PaVAOCtlMs9kL8Ai56oxmdUIGbbQ1A6it946zEblRa9WaIFO5g8/VE2+fAysiDHNK9jedcU7HGN9PxG/e9YcxFlweGWpX94zTqbsKio38X+sbaQwduStF2zdKeaTCEZ6+Hfukr63T6eLvK9xmJeAps3rBBYntaS/tllcALpGynJMsyjlAdER2qQHyJ8ZpHt0YuK6bcqHWg1YU7iw/qtRldKv751AsiADAHcxEhaRp9YG3iYBP6zVQY4AGslgAcVQJfdVyfWYO0sQUWyzt0p7b+gR1OVKIc9No61Dr+pVo23Uqyzmupk2nTyJJVyGAfhDQHY5VP4baoo7NHez2+en1ssDDByAE0s5pJJOCbOb1NCOy/eUWVg7GH2nPdd6dRFTqs0KSbVhv+eR+AkJj9dyvsZ8sBoujKfmIcfMVRuD58Ta+9hxbgDgCKeB39QOYzRFaL6aTKls/H4Hxx+blIS4Jy2FaSdzXqXrAats1ptJxZ8/DwRBGmXHQezRPUCd3u/eDMRX0oMlrQyEmHAy2SQXZMdavD8CgR/s4uYdlquZJ3V+vLtu/H4PVLNMqpm8nOtake0Y4a479VuP/1kZ7wqKG3VD2S8ndw7hlTvyR2+fYIb5VCBZs7vjpjbKOUwFVijn9GAGi3FJ9mwc+iVKewP+LLuFY/btYR1KeAImhWPCSd5LmGPO1XC9cyiDwUL2U158nfNModMvbK6Lp11itUnpZP+QqK6j/CVE+ztSKdlstE5SXN97Vt0psZAmwk6kB4PlgsLc4lEXPMo5toZkpxkYKnZELvubd84FuNlwrMqyOoToFLxYqV7wCVABqE4w/jPFekVs+v4wlin3h9aH2aWP033Y7vnwAvhaAgTC4X3pBUD8KEu1zUMcMjIfDLEMJNW51sp8Ny/lDrL3ivvGmFxf3qNsx6pDG7eGL8bY5oWM4eMABpswuYIsXHCPsT/iK9neUrE8R3FLnYk6QfdsBXCWY4/b5PsocM0OBzBu7D5ttnhmL0y/Oz+1a/9yM8F8TLECjMsKZCPhUVUi5t9+W2Zw8TowtXY/2r6Y4uYo7zmftcxydBqykiJ6fuP3Qq4NqoaWxevxHqGXhIZ/IBqTaAIW/Bc0joivtnEYtAAhnuXcXeZYou+agdpSc3Y+xonHxRpdNNQrS+ET6R/eEBVE/xvvGVV2qodOGKlon2EoTGCzs6++GE8CaJr9OVM1DLSzmSZvcQrKS/vxQdoEq2jF/pNRzRy1vkGogtddqMt3CDLbqwlpKOZ/q9KO3Fd2pJ/yXZr9UVH0j6eN00x15mM7G+WjpGkQXoZtTgn/KJaBak7TLm5KDO5creeXtLRfQzQz8NSzKvVWkNcRsxcX8CCq1vfcdwiSG6/zhT0pqR3xLj1FtlqVb/y5ky/GIGcdcP4EHiFaB3pMWcesHXGMA7zuvKFzemmMVNkeaqn4agXy3df7a29+XlqtcWbbNhIYQNHEHil6zn1PsdFx7opQP3d/0gp5YSIZI22PQ3vB2yJTeO7DXRNk/fQcKpKf3aZYgsN59ibrl5XEkdJEe0KCLOU9bx825CQYEYxjG588hjVAL8JiyUjsf8wlF7I1U6GKg+VGli8khnufd13Ed4Qwx1OfqfkOf08Ctq5PgHLeve/bPwXfhS3YPp1nL7EgJ43JIWf/0tXG62smtY9cWbsWJ8EydB7dD8f76tV5bS6qOOLPSfPZlnp+vKBXRujzb+QC+Je3yhIKHBePBeAwDGSRHa9+FijXMe+SDVKTjh70DLhIGXK2GiIIcMe3Ki+f5XU1Owzt+GFQynz/Uk9+fYaYl8qSEJINDAdyYynXX9JqQt/4V8eYnhVO4JFWditqhHiOw1GNqMKGhEuNkAb6+4rFM+x9QMDWrdk75NC2rLHIsD4yPqFtDajOWmz2/8t3nV+Lk1ZhOuCNNXyvu6rqZ/X7ZP4a2ciVdzk07WBaVSv3RCu7XKcTm2Fka8Yf/fhD0g3PxZhn/YQwBMoUHq1X2ZaW/SA/A7Pr0bV1KOqu+CWcEejKun+Duvn+dYEYK7VmIQLniLCq8DoCOed1CWJIgPn3N4D2cpkOTU0xH8KaRCbNQsxmf9TbG3SK5YKy4ehDVJzB1hyEJOs1bo5uPfUWykObxCnOURTLVT/09shOn9RdT51CPQw8bKTPGkzTgUXRJiOF27xNbvgLYvp5o9ugmq2IAWn9RRb//MMaOE7K7La6qNMBcEsqRIyCM7ruYvgsXkEAV3PzZO7qQajs6VDVd7ZwCRdI7JYqwOQu8KszxhKLX9PyB52lh06Ak1Ac6Fp3+5FRTKCJCbTkSO1zVCjgAb3dqwgsAQsMSxna8d78+nvFT6GIT+4RHJIkKbyxgFPr87bsMCamkhzTt/kXHSuIMdeNoO/rnWPQWLVNIHOZiCUJ0TumcAMG364NmkP+vcqCjFSIs4RnxhHr9194vG2tWBDnFcaz4JPvnio1Nwt/W1+tGF5H9kEDecRchJabQBFUS7Z42ltqUV3WkB6yHiv+Iq97FWAZkql6sXeukvtgAa90QnMDcj1Prp08lJWlr4zuSnoivattg08Ix/frIf9PLbHEGGycKk/ljCOoYUPMHiwkMVhGkf5d/iGUOZBHlE6uex9n0s1urbd2PEYDRkz7u+Zl6TLzXn6PErm/bn2M7qlQ3IFtCOfyXFfleFu/waG2urpKNw8OMXJ9AMkWs66G1UBEua8x+jPg0XpI6StsnwC3Nl9CVSB+39UIUN4cXRYlXx4ZU5tkJRux+cN12rQsZvniqJzKWfOD0vwzCRYa9D++t03AcggRaJJNLVHOHV4kvWWuk33O0B2m2w2yFYcaomLojZvFKt72dU3BoDS1+3jveEfh/ahGhwNd51D8HWNNOqgJ94tKrVGQ9O2IGqog9TxzDC9IRlhgW1gBB984rbvMgcu2+xd9lwXHF+lrcATGRgv9ya719PrfDXEn58syf6XXtGUJELyEAPa6nnw8aJB9h8MFDVEkl5E9qPAEb9JjeOFA8kbnQDD4oefI4YLQrAqBj92No/yKN+TAovDNSUxT+gWxWrwmJOHSTuZP7KK7RlYBxYvbFf9ov6PacCsyZcXvQg6FlwHpKyU9vOWbt7bsAd21mWnYs/o6ZfcKcu+PU0foC3voaT9Dmrpm6dknr/uCbkl5U5bZ2QbvsITyEmXUiHPydbD+15C/q/+18DLQ5IbKsCyw6nlZBgJIzdX12+/b7yaZT16yFDl1MPpywkhICnc2e2xREDeZupI+Ag5zEghF4UK6pu40tv6wEYOnvONx5vIeP9/xkP9fsY23xkos+UO05STrJ7T4Mbb4FWuztfDDhuZ2oAhilSxXUuvjCMgjssz3XAUXPFRdBdhg8IDWGZVHKzLH4aefj3enBA8SxVuRAR6oBaIIcqYnp34WnyG9OyP7uX8/2jnFKne3n7etB2H5fGGN1itWHZCIrWka1rtchZdH2Cj6xQqTDItU1o3kD/lRr/Yvpah+fElHRIBEV3nHipLDFbFUG9TNhaueULt1SeglSirVvnfOMGJjwgrX3Z7I+y4BzrPALQFHhgGxoopeoRX79GaSYFkFZMbvmOvMkF5zERmj2h297ghWtnQqhM6O8N9MTgwZH7rn/MIaSzRs40c9naEZLkufTHXfyHm/JpAFWLBtOpBB/ju+H9rvBBmY7H5RoCI4/RCHrKthgB3ddm64Gajpvz03gGsVo7YKQYHCIpYq8IbHSLq/V1r+LG015HKRn6G6ZEbosg7oD/lY3p/J6ORetHhU7gkhg4AnxREZvz/c4IYosNZMnDEtWZ/aMCzvgoVbHOQ6CAW7QAYDBpcq8RGWRIpHgtNhRTHDIbLm+KcSWsFEYYXMEF0H0Y/B2h7OSwmBLURUIFY+nBDun/Ycx4GZIA4tH49/kmoTE15G13Nr1wU15/NqdinRJ/4+OsDI5hEyPdcoYmwoAwW9yo8i/ZFFl7AoZfU363IzVw3370GZl2opSgOMxlhmt/jrMRUx0T4xROWgS38mhFGALsel2TtVhYueNxX9l38xwCjiN6cz3d4PWTRpAP6k95Uo9O3kqV2xxSXvPRceZiK2GYC32Nz3ksOmelez5ufr5cHxqMZyfRoSqGJw0epD2ydXXg+BUY0CuOH392heEGZFGKF0CuLZjQEUPeBaVbZJlQnj+qnU266l+rd8ruEuKylqmwfS+1H4dK1CgynVfm+8nKh54jAy4qLnzOaC1ff1Tpt4thsRC91ewfnbvCU+DqjsyTuob/XcZXyDOpAtz01F1DY4ECr7Ch6v1jAWgVwK5f6owtdIUlyMulfa6QL7tq0dQMmAFcC1IR9BNVuC56wIBaeeUDJKwlHeVLTZjYhFbHwmjg6h/Dr/KnllgJLUiCDVcJlawF0n49rsselK0tW79Cty/+1cll+EdtiP9GgOco9ExVC+YHLi9AezsuUlbuY35pbH3z8N4Rx7S1c2NJz4bIKV3f36IIpnVMi644x1Bqoe0JfdFsNz+LNwRDVC/IITGnsNtQflqAbTgnLfvEpuk6wkLB60yEg1p2VO7Z3XY39nfz9RYki5LVX/xPBqWDqNZw/5hDNUK14KSnFbfUMANSShomd3vue/+x1EQbOrngXOiQ6LapUq0+3MMzZ/HewTtEYIykGIOzvv0eO1zkA9J+XNHjCTKtg4xWBLkzd36aGICWOi8264dxW8tqeAfIqTMlKGd0/q0sN4fBJgLQtXkfD8RpIso9OzybPA5kPUdZX2M+5/tidzlSZV4tnNKc9QCE5zLkHUakTSo0jNiHLD0/RgaaaIY6o8zd+eM5/rLNYxOQ7dO8bKhaOjSYpz50C7tAb/mZiCd145BKkvjiT9S7TTC0vk8HIJDwWhzoCc9eWYSW3KYnIEJN9o6S84h3mu/C0CB9p6K55UqzkoTmk7Njg/T1c6pnscmH+DSDxGIwOW30qkiDvI87bsQ9gK4Pko5tdcXbXvdXDeGvK69X4K32LCJUnaKhRDTqmuUErRiey2kh+oQ4r/anGwbPseKUf/HvhTPtO8yY4yoLI7nGjs6TnQrlILIn/b75aciRUj2kYCYQ+hXIJb7J5TZz4fL3jlWMTExfm0t8D6aaIcqgHu/W6dLQhfyH+hHBJAn8WAqRtK8u+XPObL0tGlib6tl5ulPFoxAgHJncxY+0V7HsN/qzzqcjlE1SyFyFJn7vQ9lrvca9QHXWzRAGJ3cdfUFDdK+i3vfKgnwnq8q0/nwBLg3TlylfgzZXLS0n4hcB+JHVorEtibC9YfncEc+AHhSQOrHKaAGykJ952q5xPrgkOrbWXNkdBEEeUj1Qo/znu/PD0WfG4WGj/BPcZBYN8hPtFFG6y+GXA9dTKdQe7NOghMHX8gLaFxOBu23DQSnfqOqgzoSU6wfswEtiQbw3c7HyZVGyl8BF0VJh34lOvshutrzQ8tX41Vea30/EWqh34cbWdtntb4av3J2GFkCBmZpiAZureB0XV1/eI7ortcID6wYo68d9T9FhIR299c/h2q4g9lxWMmQxYFg8ah3P+lHdN+WBnPcEnMCgpBEVP77geFDdh79GOleKfcRyoF1YPz4U+o7NKbvp32XCjIsRjyiE8EL/ecv9WUJkUuj23OgezTMckUneYJS+pIrxgCzzoJ+DBjFr54lHGI8WPbf7k8AI4XQ1c7HCDaICprpVTYYAYGpvFttdJxnNwaEN3UY+pQXpxCybrpKSC24wVf/LqDMcaevcEy+7sWAz5xuQDr37Mc4OqWE1nH5XumYaZFRouAxo2jX+k9IEtJ6kLdlUEBnBgBrpKL7fe2crrjHuFGQOm1g0AKks0m9k49Bu8aYGS/EVa6kgy0pcOEdT9E3/s39t1VmBZGIGR/8bWhzTviI3pCsa7+tbAlI1DalHPSE3xOT0RVw3OrfBd4CVz96X3rR1EzV2LFVuQOeP4yhhjIjDk5P2mEQ+U5LNzJHbY+PgR2TOBcCsjytTX2xAdcOUKEV8ocxgAAxXCNrFRycZiEwyaEB/IdzTF+QOiXcPTheHecwecqyoLkQR+18UGysQ/xHWwFavyVl8VukVFF/arbvsqNupek5dIJCm2cAo2D4kR9f/frY5AI/BT9W6qxapvYpKWlZABh4P8aL2rli7fpFeSzezStsovM7T8cfxqDdfuLNs2JH/AYw/qYscrVaak94CHWK1ltMHoFvlGop0auhPhSi+28buY6Ixz5JByNlEJVIlOT19fwXZdqh5qIoTKFTFTZ+zUsZlhxPy/cImE6bVTqY6YyO8YhjykCMwV1px5COqxC6dOFwwmsCsGES8s4/vYsSki7YT/fCI2nII1qgi/Ogbb+LqavDcCRUVTi0eW4QxUCE6wv/YQwaQbyydUkCE0zPE7ImXaLTei5lDI3NkCQ0OB9PpZoMyg332fJfQAAijD+YUggotW1hdXBP4zzU+iSKx5HFIpiD2WRKdU7Qqr8AYk+H/XI7OSd+euGBKYoWSEBDPscRuL0rg9ynoEjJ7ALup8HBRsrMikla7MbzRFHGHRkHGHz5LyTHHemsCJq18+FMf94QyzWm1DvvEy2gCL8ouPVjZE1yXlCLph5EzN6qeaJpOPszKCKyNBdP8jq6OVf0mH68T5S09o5SZzCPUaWSC65YCStnsDsZTI/bNeamRkQccTYc8fHkWDh/Unk6du180LGUSeWGQ3072mwCU2n2wFBsnwNbjEXRocmiXfkEDhPd4Ap364BdfxiDGm+0PQEddZWR4ZhP0qaEoWzHgLmAyHO7LBkIqOA9fnhLZ8hpY8uoDMbM/CTlcLmATFVyh0OD0bP7MUaUFLShuRvHltfhrhSDv3G3GgBP2nIXJWvcnwUizCYlHlkosdQU23WKfhcLXYAJX4d8fH0MVaJ6RWX3fgzeSu8/gXr9hSWxaAyNTYLbew1EzB6yb2iISbm/gKe8YsrStQb7OV0/zq6gW4PxV4kgSFCrd4NttnX+WN2HkrhBHA6M5pXatrw9x+y8++dg/qLPEH/VwReDW1u+DCes0Woysa+K7S63KIltaIkiEI1K8o51SLbfJVAC/T0AJStfSUbH5I4/GJxc/xxUDgXFV1pbV0jejKTkUj/f6XY+oEUntXI7l7Uhw7ed8+gJVfQPez/kJtq8QKtLIEKyg7SR84dCyV/GiKMfDQGNRsSEGXkVN3n0NNoaPf1ZXjMfl9crpi+Mwn5F5TWgBPpzfUfl55UaDnt3IsQMtuabQyeTnn4MvQaCgVrbt+RLZ06NHV2bgsX+wxgTgYYcrLiatC6T4ZofbUfw/j+8i6QN6cUp6ChS4YsyAuuFQdm9Xaf4visRMQUkLbqg4CJQyRkL5rsb4yO6M1O7iPMkuEBEQKNBTIm8zeciRMVbYIfoFkeM+oekDujjQpH+OQhNPMrqtMbjnqIbHj8XwI671T42BlY1yh2k5xUCWWU/0/4PUajVhYNQ/6xouVN4BUQHgQTqMPysmetb7b51hFGgpNAfRScqWZH7Y2alGtmf63Wtir6wkQC8uVLwatR73L7TbjWIYh5ohPoykZS74zoQ4WB+UBTj2n7Dk7oedivzRy5yIGNfLGRrbDWVdr+MQEyBtaDZ1JPrc4JQpAf0wwt081GvfAggIu+9+qR3TFpjPEI1onKPtkY/8DPdiMyslG9WjBNU2LCK1e263ivJVpZkWQshhqNaxOpPSwce5f36+eBRhPQac5LxQ/LG6xRGhtTc6N+FI+CM7Bimp0YphEFdNnDowqmnf5dKILFVdKNhHqCspzgVngVouq9LjTu5HOGfentk94s0nKNYyYw4w9U+xxP78osYy8NVNBpG7BYpM+mZtdqUxAXtzRnrrGQi44kCoaQOVem0en1PPFkjFRYRxbp0iUKDW0H4Y9O2vpEwIs6bHy6RQ1BIYmFqJMgj6dHOR7z76pjwPaJFAMO+Qqp1LpM3at9lxKQdP+J37cdj8U2NHXdCFa9/F57km0EAYtQP7Iy1Dg0P9Xda3smjavrOUMMjJqUdtNJr3/E9Eki331b8tbOk9X9HRORtWZTmER3k/tsShODOBmvF8xFcAxBeewwVtjbCH8bQcqG3M65QXmrH2Pn4mU+6h6efj48mZRTIMB2IKolKA3Hc0SLr9z4B6ZfwIL8TCcOJKAAuzoij3dXm+wqD6iTv+HnHQb89qcXQSNKb+vq9r2ZLpTcC4VrKJwAurqtg6Cqg7bcVCt6xvM7fQK8dcleaHwqB91+e432jXL5wWJTs3nCbyJcdOk+77xWMNxq5K7jCnOO8hCv4JvKElTJbzS5S9i5Hgs06ckBWKfTLw5aaxtPX10VSOhV6TzgmihAIF3W6QtVho7Rx4RAxie8hzl5RnJrKgyJkCIdq20vCh/ppmdTfkKiRWF1ZsneMpfbpz3VUSB19yDGiDjt4S0pG1M1I+rSx5aANdekc67YuwmcKf+8MRegLe71dY+kRyimnXrLQI0QeMvJ413WotP1Kl4Aa2Id/i4ytyV9rMyh4rf/xhzsq8H1ap65IQghwek86/pPkzexr4xpXi4el7iA9lp8W8h16MVOKP+S3g9hpGjZpgiGJbexiquFhNNdQfxjj0ed8TuzIp+wjIvBxZLmic/2HMZxeLpknWFagg4Vejdj/uLbbGj2NcCH+m/wnIyreQcBDQvD6auuWuDrEuR4d7eifMi3apAwDsqXE1T3HRMTSStpydP5EK0kubcb1s6A6/RhUsM+bhmBUPmG+oQNoiHxBcndzWmNolEZ3QBQSZuJE02AzrrTztPixnBIgSVY61gpkILD0Ez8cLdD3D+8iXocVOU/ijueJNq4CcZAhLV77iYisbsGnMaWGoyNMtHirB0OE3X8ZA+tuWpyvvPaKzzBDHFpItZ3atU46/dWE0hy7HgKX2GI0UZXKXvDndk5jVgVPFEImzbs7Z9KIhkVkO9t1imri1IiIwvdrn96+dC02x9hpPZdUw2QbQCsrZELxagxlGDhSAOlxBtOlrecycyWsxJNpy4mN1Npb/pwyrqsx1RPHnzsvDn2I9/Gy7GsGNNNsDQA0V4EaporOmAHjkkTu16nVGLNHZPf7gjjVV+PUpeugjdiOMYkD1hKlLHfo0jH0uqJXnLzoD3mDHJgOJgA7hNQTsc+IfQsoYCbbfG7iU6EVRCwsinmT5g38OqlrUeMfxuBYGZEbhA9+hFEwU/fPNupjhxyaxFg1OFXUIlbD6UCbn8TD3b/LTp+VIsL4MdcJf7AEI+dhtZ33D2Mo49IrEvvA30aQ6XmiHVgzMvv9ItEZ54eqHkwjZTC0v6ImAC3Un6df/o/zA1RSEOMvh/HYJ4G19djimShBm/WQpAN6pkUKalknLZp037/ld8Ai6MRulurFwT9Ttqy8u+6HOp36+UhrjbdgKkLadDTo3zSTVmQK2vmQMk0MuPnzKsFb1TYVnzkK3tmfH77K0j2G7Nk00IZQ+wvWVyI02xwIQKFmtT6CemXIjYoxMeZF5lMb+sNzEAd+f4ZRb9h/QAZBcMnQKs/t58PRDuMAx67ZGUnrn9OHZH+1mrI+H0MLixTSILrOaaDUO+3I3O6nfw4o6fQmwjBTB4mO68kW0EZpa1uIHU82eXgvY8eCRgFl+sY02doeTur7NUbd/Sg4DzWlNWzYWwYOn9zWPtXDUtu6Y7JmNh0empgxoRdndXOKZfKonUYpkG8VnFCke7Bhhf2jn4/z80vhwyFoIcV8URF5YkRBYLod476iqf3jeKT1eI9UX2WmQt1+rYsYFtDEP4UsRnT80+KGFUOovteYrwCQp2FyAcTYZcpJc6ZrcPe52M9hbcGtY50HrbkjLRUbzim26c7ChOeMQHyHaBnE3/gKBw3bo/ew09R8A1J+CBgrFaLPwzkokyu69z0tmj+xOCXzi/XxacCS29A7VXpcbWypaQs0zkQDvflLE8X1e2IEzeiinw86Y+CV749w7hiKCqDv8pFX6/dt2hxsRTSRQkweP48jyFOg0L62BaOhxR/NjLglAILgb/g8fE5PW/sECda3RRkb6+cZsyMKoOrgSJj9Oh1TI+qKcdUihPSlWUCciohwPUhbY9NSdxZWdn9ZG9/5iSBfUQCG9ulzdRBJbWztTv2nU5/ClblE3m/u8BZPt1YE4yPG/Ibn6jicKTF/O/2+rx+DYKk+EHqzBuNOr59kJs05yVA7H5CNVO74WAHysUwklHWSRqiN9N9FiaI2Pm1MGlHYDhiGRAHOFeP13T/H771FXkAXlIrdc7FPYydVf759DqWGKy52TvZUYilk/gA6V1TA22/7flLk9HGoEaXpVwfBzkKF6zirf44TK/M3fMJI/8Fof4ELp+U32hxoITS5Zy/aw+ri98aEEWABCbuu2ufwBQiobp01hIkvDKFogCpB7L7nuXg9XE+kMvFf6cLqgu5odov4z9O/C2TAQfdHkFSWci+wWVb357XY79tkO4oO7usdd1WKeXFAGOH29mfyDsCSM/NQ2eObLSTbfDYQivlGdmehH0qFEoiVYi9V/doyipBw3zRVuu+iVEI3mvrjm/wyho0U+tUsifW3z4HavnMp8FeF4hccVoKoEvrKbtq+x3uFfExKxaJnsfP9DkjD0GrouTx+v5mI8dVCMo+APO0OeNg7de3ZjbGfGMNTp2JBA1Ski1MBgIDAJ+727auudzDcF0oR0V5tcCqZ2jsgNad/F9YV0632UaV7NPlD8HlTs/xwNZrvQnuFY/WOmlOdySnfUGQMEmtF076bD4qUAuSAiS8RQLwYROskhFiXtd8WIhACd8bCJYQ3AiCDkDyZla+/b194se9/ar6HJM1MT8l/BPpdG6cfYz1J8AMrJPWHQQykUM8URP0f1qka20i97uERcolXVdh/KuwfZZN2fVgNMWsDVZ5R9nbIy8bY8Um+229Lim787OUBP5HxH0cpdzH1x/2HdxmVilU8rLzlxLkiua7L4Ivrv/YYEnVJuppUDHICAyw+KBZCXgrNX7/WBy/lbAtKD1BfCN8Hz8Ipsv7A4yM1pmNx7miFPRGFIGjtOILF2H0+Fxaw9iLYpgYOZdfrJBiD/8AFab9trDzeyPQDw7L1Vtf6AjIe2mJtLsZt80Jad2jEMC3SqVfAYNHbHP36mJExipEmphapOzB07aGpc1l3cDsfHLzpKTlNY5G+iGqTZ0qPcPAqaccI8S76X/xjoB9gNyMIRF1q9Vj8NywNWl3ojaFGBnKosTbMzdtquYI5cnrdzqBFGCVooxzxHqmOymf3Y/C/02b4GDepSUFvztjYgV09PcaZx1PQkk/08NF3jpCEIMpJdtPXYF9Cq8xFIJbqDVKapmwNdRkkw+m/y/6JZOQw48O10/u5xUdcOul5tGtM/qYuOMllwBd8mjF1pM/Yn0DGt+8CNhqT+ZmAiig0w1KeKZ4tOiTdu3ww1fGMYs+BelZLMwyn9FMAXdrv4qJ1L+HtzQgP1zq1OnHeiX/3WgRv6Izf84sVokeE+L4pHLhlWEG3c6pESbYE5N2PQjzOEHFBe+D82n6lZjP0KVklp+j1xTWRNqzyLNf1u38XPqD0rWAvPmXgGSMurY84YfZ4us1LPRA2zBtXQlBxpKGecEbrK3Xfdkd4A4ufSAWOJIeAGc7aD6tztfUxb584CCxaXRpxlMrTgjwYIdP378KF28o87A20wvkrErpA7GVecPdjVNjFJLky4yh/fJMkIoY0iTZeKC2eH/sRzHnHF+SKFZ0qLLkXlQMi4O1z3FGOhxASzkh235GOvRQLwGa16zSmJA9V3BOvV2cQQSQwgdC8K1Xtx4jiOpYEGq8SxhvR9TdCaOMPHu1PQDgvqpmeqzMDTRK9eelu0c+8+zGU5hYWYU4uhKKw3gkKscR++/oHRsXKZcC+gv+2qs4Tt6BUyys+ascgH0cGRh+q/hHRYkS+kxrRrbLdr9P41Q0r3FWZ1iuoMmINtsNDl6UdI93b2MLrJuVJCMXdv7SKdXu7Tp+om8cfcEQs9NYlR8vdKjJygO5uQMd2zUXcaWHdCeVqoy1lP2FS69OcN4mBXbTcV5yrLggj3anzc45tn0MseCfaryMUpF9fiZxCHQVE667Ww85lvwEDL6g6BfGd0pxCAoYCQbJ+Pl4Sl8jtdTszJ1JfI9jNw0Cv/ex+nUqk3h/ZBTFJPkcAFH2faK80r/22rHeonms80TGsq41Ct2UKmler7+7fZSXDUG0FJlbwu+DJED8/3Y999WPUZSj4+5QLRvqXO81KqODAJr8/jBFO49BCB5+Lf3dC5KFCxZKynY/9s5yvXc/plPYnCqyWhfow7Ym2V7BjuIAXOjiuobFQdQn5BWPkL1gWnYqott+xaT4p7I/Yegru6LGt/l3ISSZ6stY0QJg3x7netMD8ted6IjnaG5BzgDkM+nS60uBXoerPdX6xChZ3cCSgkghfMZ2mval52L4LJzL9CXlkjEkfgdU/+IPHXdGfhUw7I7RXN6UCCAR/2owILXckn9p3oWRAUXIBrqdUJgSro1pDJy7tbd90p39bd4Cmwwjp8xMK3TmhRyKT9l1O1EYpVuT7/OhVlUl9/LTiRvWH54jPLchZBND0f3TnOYnjBNUmamMYylTjCgZHy5DkRJ1E8oUrbqEyzu45Ip/6Ya9Q6ZsAk3xOlalnCjJ/yKM+p2ZYe3gr0G+BOtHvugN4hJea3RiEgUdMxtLgRC+8XfmAubr7q63jfj99dE/DeBJg4Iuoe4CPDv05+jFm5JcZfaRjAjIGum7ZSz/e9fRjYEARM6lfr6qrqxWSBIedo9rf3vtf3Erw03CHKZfQcaQIt0bEXq6+dvGFnGE11CGEGMBKghbsSq0P+PvtxzgzZeT9S+Bm9IoP6a3ri7NNj5n4sDP5ZrkRWLjwXIWSZE3h8l491lozakQqOEI5U9MXIg3sYOsS7R7/QTjZ/kRYRU/U7hdNrKQA4fO2vZMvOML7B3UMg4XYQ7Cxqfq9b69T8wVhwMQ7ZA3RqurOT24HPvdabTxGM6gy/LrxN+LKhRh+BLibPmyts5qafgwtpJc+LrXTHT9RUFAnrBR5tdrYotP5y8Y+FCUJhGT5i3sIs5HZ4wt1PewTZT3XNoPT+9eIEVSQvG3vBoz/oS3HjIKyXALF8PAn6Tw1yfa71MdzWSo68lpgA8xrGa7tSv3t6ueU3ECFT7TMYl5FZVKhTup8UoRs75ePu7IrjdhfcvR3hOxEbobMyer1UHDdofEHAVKFW3WkX5ZZl/gVDm43hsITuZBNDwb2ctrFdQoAju0o17T3Ph8Kuf1gTkhjJsKy8GignHq7PZdYn3LEcwIU701DX19e8WOO9A5WP8Z8OVCAJW30XSycgzi3oheH/9WtDzcz8P8TEclglOsfZor8HzeLWn/dWld1YTJJRTagJZi0J6LsCn4Ks219PQRgy5H8l4LaI7gCkSGu8IYp0I4x+DZFKSswzZHaFqAOQbY3HnntnHKzSNWBwO8dVXqWMMKqfK960fY5dBbArMhK0A4NKdtIR8irt/b1Y+iuwQOFd3t+oPEvrtoROPlaXWtjVLQ9yMqhwWzXlTOe9I6OtOJquz7w7p/IyQqUWeiB1jlIp03NJrSdU3fqiSvJEuZ/M1Rcg8jwUDXufoxamoQYXY3vTyOBhinHwveEAt+vj0VMYkdix0KvCJGDnOwfGiyVr3ZOV8ArKaZFmiXi/JsI+aOo8fyhfoq2h7OSKfgiBwDwGLWLTRR/nn6MlzNB1EafQBOW1i2aOd/7E/Pqdk5BtOuWkiirSN30XH+6tFO7y6Zr51SGsoIh5kjIQCLTis8oTSUN2z4HY9GF3YkiRcaVup6AHTjmi+R/+122juTJ/ahO9igG08ZnGqIcUedJu9aJpaURdP0ctGDzIrBSwYtKRO+DR91z/nxfKA0Tso7zEkK08vqN0da+C/piZCKwq+BxaAr8w+t3jMxeC+kI01Mr1Ukikacvjby10Unuv/Rvabm4rQXddQCplg2NLNDPNwS01d8NH3t5cD6ddOqNJ0I87m2NJdacfxjDNUvqj0YlN5odWQciCytNiLefU8qnlUxG0gEkTgaFOkswqwLx+jztfIQ+87FrU/L8EkE86R1WTHYQ67pvG1wVexPsFQYZkqD0H0gk0O+o39B82xEn4i+5/hIn1Fq1cOFPHaVsPr/+OQQIIDwj4nDvToBtinELqE2sdoxEbfBfHL4kHZIhS/5E6GGt1iOErVhUV+lKa9kwbb3iLyaCH/GQ7MdAQRaUXS9Z/7rZBHkr7PNBRajF9tCBQD3UwHWa6uKAnoCDckunCfT8YQzlRjjki7UgdSgPohWEqFEz3eUNP5sY9PQTX8Lk2gGAQujPYK7edj7SyBa8Was7boVu/JQi4IWftr8PcELVErI59hG/x/8wR0WIIs6/jJE2gdScHkvEoT4w+B3/bPFuP4Y+KTkU15WjPMbZh5yq2lCN/oc5ZWSIjnNDWriycyy+hL/nT1Wxn9PviT/Q/evFQ5ujzEPqxBL0rH7fKvwI4PwvC19ixXSZ8CMEfKP1FYWrvF/youNmWi1Fn1QN1p3UDma7P4O0e/iCREZWEDUiyIh/A0NK6q59F3bwtV1+EsjnZ5FOCuilfPFDf7bP4WKCJoZv+DmXoDPr2z6YvFfvjT4QmCQq4/sRzBhBaewrHqbIP0+/Pt6EDwDi+yenyODMcWQ+eNP94TwNnnpriqFoi2rRP8hDC7W5x979GFghMgwtR7lXFHuulHOiJdDq0YNERh+3/uj8McV0959ccXRWVD/bb7v1f4I4Xcgnb94i6ET6sAwTR7tfYAlwIZ6UUx4sbzAuLTY1e0507XehH8uQNZUl0hI71ATRA3NuOhj9c9Q/V9zB3XQ8kUrH5L3Dnsd22n+4sz0D4WGjjB2rdML6sS+y6mrt9+8CHv2iYdPb5DkFV3j7UJqq/Nbb+0VIS62j0lMS9hd43XFxK7mTVG7xhbYr3JoMAQ6Y+DsMqVAM7CsS8+1zfDpwn/JLdLoiKUK8SGZKn+4va4wfTjSDWPMQqULtuU6Q8FR2Z6u7IdXRLpohUl7aUcivhMyI21NznP35UaE6ZpdwGQiFJcQmQsb88ORMe/vnUAeKGZ+7WmcMeybS0Bik+/vDPScV1D0JDn7FfZbqnuZ4co86rft3gXSNSdxJpB/XJOW/J//eAdc9B1wiHnBFIIoftHNcmtS2EkJIvrv1oebChHtx41lhCASk7ApPM5d/w9WN4dd9zHdRT+jBx7FAVZrG1ahYoH2OVGxAQMQepFgID8czOXViVY3u/FDLtjSClNQ3QdZQDAFLXUEctHsOPODEshflbGuRHWxagrdHD+fuz0J8iOlcX9FziNkZWUdaeXzTifT3z/HRIZnj517nWslUqDCh56nE9nOqI/nMSFupVS4C8FmgR38+3pPdc/DhiaNGsPRUgGLOU3llvDhrptpzPSh4Qj+v5u2jTxavaMZY+Cd4Eu2cQgZfh7S9taksrk6F/8VDoNLK2d77VpLigJYe1mz4QDe746E7XgFZ62FnDPIJ9X0DIFcgZMBZ+x8rhg9li4Ef8SOuFNsF80W/jHYguKV+di2e3er2RBcPjEbzV/dI77fWrMLuDy1cU96PcasS2v8q/IzORgSILFYHcz1l+y4jMdNJXy9HqigO2fRdMdR7335OgTa2TgGe9veT542HQcXLQbmMft+K8tWQUpTXJkQNfuIl7AnroXb/Lq8bnwKajUoL8Qq/AagjQcTs95zrJWKfKg8kgIRW6Oei9RNV5/67CG25glCYIZvDRRJ3jlZLaE+rH+MQs0QK0HSMSJ91Qk5Eq5xjaTtGpS7zjQjB5Mar5zmeYB1T6KFd034XsCQVzlud/sSIbjDG9I0wp3bb84xs4IyiI/EeNf+PxBaOxq/ZDlPTrTGVlhU4CgpzHcgsclgGMNZZsfZrx2DvGA+6N4X5yEKvWGNRykrv7i9jaKNF8eaBmnqz8Ii5XfEc6OsOIYQtMelODnVRRqk75mMQoMILX9jNKdmxS3PASahZSVXtY43DNpGB2u7fBVWOhsghb2NOlMtQxd4r6o73H95FV8xbwPTRgKRfwiVQuU0Fv+Vnj3hmQYvVDrujCUEWrs6DAGSOU+np58NyortKyowenS40ANQTJycA7HY+XnUo0IBPS1mX8ESpXNRPbqHHJxtjhhcO8DAjMKP2kPslFg5v62+qQCCKjPQQd9bYL3whGax4Wjx/OMeYR0AlMkvkgQXRwkSTKwXWBq3f7tvqrG1SbAJMaqhkxyhwkIV2WreeKSPGnSvWwjlIaiRQAVev9uWcvQaRK/6KNqbSNI/ATzBG4MSdfcVzsH8O9+NO7LGovkOQ7gNi/Llk4Ifa+ZBQut1RTRT29Q3vXz55RUas1UFTFdNJPlF1g7KAO0fXxhoFz+v1LRWwbg4SMsgL/w/dkriJqgbU3777uHCn+XvfsTUUo0IdXfnIqkoEDds1RjKVmepmf/BFhkmJK+K0XDpHi8vBC8+ZedUVNX8EB6UMcSmlihntn2YMduJP+HtWa+pSQOgpBiu10UDu5oNbzaQAf0YEED6IhxA2KE7que3VP8cmmSNgxxb5uCYCCuLx3+792fKBotkRH2OzkL+lf8QPY3LWUGtr9/5z/0CzUaQa3M3ELVIHitlfXKTad4lceCUM6lNKvwpuAaBING+VnT88R2h/6uIjjpWxTL6xIoPzgwtrv8sTfmxqfXRUlMUfbYaPsv4auHTtu4gSHGPxJDpXIBNpM4KiQ7f29SCZD6pb9FAg6f4xSyYvK/5Wse7HOLD/vCujYhBLDlXDiEJ/GGPtvuUFALUvdiBitGNb94UDphRL/7J9DmUPBTlhgsbDHQfd9BrCq3lXv9ajJE/1CIrl/kknSmnoojqs6wO37zJT3M+thkILLxlhRzVM0vLj+vrnUNJmUwI1vl2vOb186TjS3S1fzMZnDp1zjOHzHcO0OohHSh+XGW/HWJwoRr6oHFCR6aMSEbIXYdWrzRueMG85vINaflol4e5tgcfPv2T2Yzzx/UTyuNz1+nNxPALTe2OR+YcxyFFQ7wn+3WWhN7TERMHnzn59vCHAWI+wSiCKJBEf9XrtDJ3Idk7fpOuVT65Up1mEpNUQk3HQklY/mc4g0TS4yGhu0Zl7ZUW2nfr9aLWxMwZZfvptcdNWbZBO0egXXn7nD2Mkm8bpJpcZNWwNdRoxdrL0t51T0jQXm9uaRjToN3IK1DPikJw7qpsPoO8d0xVRTGx9bJsTF4pDMqvNozD+7zsXdG47ut9qH4deVOR6+95JtDVGLgPx4UtF9ou7Id3BSMS2de0HkPe7gqbT8RgYgW/4iXgwLu6nHwMymEEhUXon0JjBcHF10Va/W/yHKyXsFX4P9i4+32WBUO965WetXo4iwQkFsqYxwn/iKsrtRNyHysP6w3NwmBRh8njGjARXDKk6ZX/NxnaduqJgx67gVkNQWJpldd2GkfiHejJoQbpzIw0TdK8bkai+Fv1CAIr2LNRwcPodqtbxXF18yHUr9JZoxrQ1thGLUyZDN5WY6H/ozx3QK2iIr98vSmxhzXFXra0qP2XywzxOkkl2t3+O8J+DAUHE3UIBdDMWbE/cJ9u+BxOMGR7QThH7DYU+ksFiLP6nbYxLTPvbsdEik3vy33WYOPYQQO655sMBouc0uHBASDAooe6C3T188Nnm+0gZFjtaleR6JXyog93qi4xRq7UXwwgoUcVcvD1duYjCPXFeiBdlO6cRo/rSKEgjnsYLLYIHAuLRNGhjKY1SeNcn9jmU/ncA1/hA6hqKMe0aY5wR89+p1aC3NiOqyuMCgKM+UvscPzxvHIGBlccX65kdoTen6tfi+oxBhlGM/fzgqyJC1M1XxqpbeP7wHFxkovCJjAN2STU4MldvMPF3PwYqD6SAexrsiyl4hFiVMYkJtnfU+NkrC8MgBPhYah1Tt47lPNG8dn1E1RYLwS4LEEwlGr7XQQab18a4rO7eJEKxvoFuZmYL/pjmBxpqOx+RQEAM5fh+1HOdPgeqLXdMr4E4qBeyoLi1WupdFEEV+j8uVswb6jbu50PwouG7Ii/x5of9/kNTELe3X6dYmXEVUCRwBhNUSPYOCbc5j7Tf9qWmQmJC1fdGP6bEYY3FWPfZfQ2FM1odXOjm9FPTnzTaiCzsivlHOx8bs0rfU3WQQP8d88TnUj/VoL/751CQxvhYAPzCwhjCyDYp58Vdox+DBEtlTvsg28aLh3jv1Kmso0l7vF0frOHJz0I553RWCIpbqswm/ID2OQ78DlmWJ/qvanOMNrZEE/B6nH6dnqgVOTZFYPDmVB1oZr1xOlI/7dYHn8s44GAXXFPnNlGNk1r/ocLubp3OOBra/FHG/Mn3QFzM+9e8rFOoe5d5SReUfRBnPMhEmR+hiH+RfGtzMT4DMfUERkY1ZTuw1IMSs+Ml9mO4kTBEAcZONAgg31Fwb8xNGPLu2059WsfFHcNnGpN3bHE+4f6PGtB9Fz8oLhTS2jdUa97EaLVkHc69xxCWBjoiymtcMZ70GReuKbYTi9PvD2NEKrWywTQWlV0/SCHkC9O6+978pDPu/BpswNIpqDWBC0SunJpG63cy4IFrlf/q9CAOcRi/LT2xFCB5O8aKb+UbZ0MQqTcgg492YMDf7EO6d4GvWEEDZm2diPrDbbMJWxV9PK1maHzNKmVLiUB5Wpg44rOM/CoMH23+YgY/MGD1U1UxX5bD1s+IitBUG4+9yFQnatqLqYezzNWnIMP3mINQ+xxYGY7SiNASx9YZI6kI1QNduDuOFewaKoH2vIa2+1tcEzeYmTx+dXp9oMS6cB8hOgClVD+CWJC915FwWt/7cJfv6Ca/aZSEW0TmfyWHkRi19dMvlsh8apEBKwgTpoYVtDBQQdnad5HBvi58+dzHqSiyJjEcRSlcf8D2xMIclCVHp260Oj/1De+koNHjpC3x1yUHVsiMM2Xy2GLUOF8UotsxaGtrd9aaUOi/o6vIQDrmCQBdbz9GVCZEC1xzdZXqitJkB6r/IiTcfJcZ5O5hVAk+cxNiupJUXj8gCLGlZk5nCuLeZsTYQnZ6Io0w2W2gKLe1rdoX/3itEumIy9qvSL5ZtCt/zvZu4G22dOhuPCRBFOxXNv2OV6PSTjfGHUYT8fpQKdUh7sAwooA4ct108xGxdTry9a+ZfKm+uu409z8SD/MPz+G9L8APYBZ9ueO6JhIZ1OTaLZeYUp/SXBboHXWteqJ4ClYaMIgOt1p7M7ISLrYfu1KNjhHTE6IzMn19oe7bZg5OQg0mpSuAYAUURSHVjN3yTXH0hQsC7pkSvZtheQX6ORGIGe0YylmMZBRe4qd54IJwxPXq+QN19/6kyDfeEF4E3IdQz6Ckuv2/w5xqtc/hD/LKJfgbhhky0Wb89o2f2XnXB0onD8YKvYJPCQXpz0n4RFYpCJ/uXeZKu0M7e7sf4WqivXMT1463X/su8ABSXEbT+Hba2Q+dXuBTHmF3q9GNqm51qkghe0gQqZiLw2TJ++v7yDUG5Fj84lV0zUxaObA16n5AE91+AUpmzMiBMkAtHWFKIPC0KgCr5WfPKIW5CnDnouIYHKlz6NWRev5wFpIqeil7w0q8wfSvKJnm/kbmvzsc7BT0EE2M/7jARf1VGRY2B4i8x/ODGDjcg86s8ENz6st9u9kfkv/r75cfA+CVr4t2NdktK6VUIXwcR9oxEGUqZPjiuESpivlBBIcpMU8Qzu5dtu6GUywbzCnuPERPJC9LJK699yd3ovCQleZ2Pq7g5QOyfb/Av9sz+XNJqc77y9QPrruJbmk8yk8c3Lr5+CRSPy8NJX3xoDtbTKhkRbSpw8NMGewIKhhudP4IJ0Jt2/chltfqw4C/LfVsrgVSMPJ4ZPa3b0T852sxm6QpdBSpBwRzNeM9BZAn2n1QgrtanzGQXklCO1EPaxtNT8aV3N4Fjd184FdYqxx0TyS1CUW8tt3PP/Fqe8AaWC/u2pfq50+yJ2AMhVzM/NPe2TVGhJMvUpnu2PAsf+yCZP8ig/Y53pjEs7vWBQs8MIakMyKiJqnL1Sk4BYr7jLzUSrVhBiGkHhMibDdGPFmJUn2UfhRUpKl6Yyd+KYSE2jGggPhhjV9l7c3CwPdcke4GiOvmlLTFzd2QMuYAzuOkA+5EHTde2N2ZbIzI/+D6v1ELp+VR6+s7v9C7Jqf7LnoedXEbiZjSEhqzoKMuT6bBF272Ptxu3I2Ygj4SW0BPi1Uy5Q7/Wuzo0iJ80/EFHgdnA8Wb2lQ/hyy+ot0YrmrosUgFck94STCnqFunS9Q7u++ikD5iwCCXAwcmwfqQvaLVm/Jd+xwn1rXyKPoBzKSYWQgQaV0s8Xb3HPzM4rCg60nWRb0ghuThkMV4tVunk3Pei1PExLtyulF7B1JSCYTO5GhrSiwzXVFXBHPUc79gUDbfQ9Hl0/sBL5d+9OTjDJzCcCz5iNyFNfX165TXUYCBwSh6G57AquyBpZ0/eNfC0ljOn+zPwUhtUNZwn5AdtD663onuIKjTSSON6QhJ/pgVmRf55tX1o0CRlF9Tu5SDLE4sM7Uq6y9aBe27HCLO0daWjIbwRoggyt1bbbjNPXCI799BKgG8gqsbiYeuZAMDF7B5F78YUFErC0pqKw8Hd7pzIp/eI4S3K2tk35f2qiguWG+uWol46/Lq9gulP+/DlCep8rXTlXtE78s/P+1dyeuNqdYML+Naao58NH0k8gIOuG4+QBEt6XRf0uGMlXXQrMAhGsnNu8SA783i5P9bv1XNVPTwBJIiRe5yj4h0JhG9+GdCrwpIppgCIMS11cVjMcs2oWhahHJ1hHIUhnc6YzXU7P1XH8slX+sVF+gniz3SFnM9maDuviWDC21Fv/QElAdF5zCdLIvqaa7TrTETyq8kNcInrfXzGww56g5E5m7fRUJb5ya/A5gPLdtkMTtWDLyxu9jy/Un+afXRcVEASnFfTqRb/+0es/k+P5cR2PF0sM8Q5d9qSzhSXPpGOwbYmowQWKxyB/U6bs/OnpEm++zOdeWa2JNp0dloSgVIU6ZZywEiv/u2I0YcKyX1HeYd7NOhgQYB8Tiz2+d4IzuPVC4fpWMksYQlY7mSBlf7HDs8l0ALgAQUREg04XrXX/bda4Zi3GO+Cu8Dwqvs8IGmMRPAU7R7uu+C71sHD37Z97N8IzMZCsxDGQ2apXsOtUkcb9TbW/OaYWxYJJAKTzr/zd3wklxDdcGzgpl8It48zXRIBW7Rbq2vFEqkYbELwYF9f5QATokP77L2uyj4uvCZXifhsDqjOGjvvcF/dvMRkvg4Wt90x1akJeOqrbWrotvqfmG5R210xaxRcBZ0Po08HumQ6F3vlTOB6VzgHjsSGq4llRXG7ZgbLW4cI0IcAzp7qU5zwzv5rsBfKZx3cVClkG9oHbc6lNQBcyudNSmi52n5+7jcdbG5n9yXiLs7LGTxP3nL+jbtWn+BRobQQwN8uLLz/E4nQSOcbbc+eJGw87viGnPAQCGtNIGhnrKf/zBGQPAwvN8P5Q0Ozyt+xQ22oqHuXbaOE41uyyudD8cQ/yfczUhvdBg0F+IV1JRSEKPYSkz1olRG1q+t2p7JPIlWeCNv6L/egePhE3FooLpWz6AifnBxVeObFn1kD9MEBr2WENRd0z2H3tqXZ48G9KM5jbgfxybKKHerzSAmnstxtsP1OixtvljhBXzN+L5dYyowlHEZxHoSwD6YZeB1IIqxW261CP+NCIIw8gCP40rJ3FljOfBbzObLCMDW4NRGefV6PgkMdSSlXbDYrtfIQjCcQg6zqlCiMD1sGDavibHRjuEEjItN/fiJHY3YAZRGKkaVrKsnK6Wpwkx3M6kKfwPsyZ4PU9xB1nyXGmMCWVaoTTT1SBMiXoRv/SadWqMdQxRK1QKkTlg2o0d/VhJmbsmtTzPLTOUt3NefRJV71qTURQ7zPfh6NXN6B8UDaX7jSPoeypUsh05qu72eQV2wRKCgile0qShEkjuPP00spVrdYvVoVe0V/MsTOs52Mp1Ib1O7aXvRQmHuDZz4fOZlLmtIFFiAuiu+PP/rtb5DtyF8WAkQN4hUPyDRoEKe6Ap1Z9C2tQ71DWQ5YvJ3XCWerJl6xvpObzvG1j7Be6FsiWuq1D5+i+ziJXF156k//TyxNgFAXXG/9VEi94/u/Fxd7xUqAnbOAwSuhW269A1rt0AYVZ7WnR+bbgdUsAbFFcMQND7GvjSR61C9W+4KzeaXx+wHC+8C/53oHwAbxc3Bp/npxsCZqehHWuiuen+yqkDswHq6qt0YUtOALpDoFDoJ0Mc4+mXb/Ny9BrNoOlh1zjHXFZVffYIRfODDian1wiKPpdJY0dfiDQQAAvYUv3OrrT5Xl6vDMsGuCi6jraSnhVFQk/wJFjnZde/y/pyeM4sMpAaB78i3Q1zGL7irOUaNX0wqe6ibBVQrghs3ThBk3NdqyynWRJaGYCHFDqt7xZ6DMwQj3dGu0+jqsRIXWH96+hCG6uSYZ3daOt1zuBMZGkVSnlyeOuPPlk/3oM6g1j9KEuxIDy90SK+JBw7OU1mvosSuBovELKgTTJELYXMOpDz+YaCjsXYxzCbkdi/zT0wFHviJrKyqjFoKI6TuXaTFwF4M0sfzgydScqO0qXFKjrgdY0WgbkZlLsR7or0n2IfQtWqKurtS1xmD5corYUq+yS5HxOJytrRnci2M+Lw7dVILwQN8IrPJbJW3xG7HoDAlrbuJZSmtyT1IgKpDKEw/XW5aP6C/H7wGKIqdhsUGRep8Azzon+PDBdB9chs98Xg/aR5eNvAGpGzmNDjCqDBL1rnqBBX3pdlmBCl/8xwU7ElQjkudgDmySqHxhHYKZbzR724MeE+Ce4NpvHIlKhIr8fDhwQ+btQ7YA4YIaupSYyoashPpQtwJnYB+DKQ1ntuDvhykoOvXnXVFT/RpscXkEORAOJEfsQfY1fgwHdwJnZn23tc+XtSokyrTxrRRWOpxULh+7OL2OeAJ+JnGPjvmmxFWAtQbsYRodSaoY3w/+0sYRRyvH0rwZxv76Ri28yGgveIZMX0dpqYEGQn+kxIih9L1PT6CKLxJderQaCazo0CN3Bl8n9s+YfyUXwGVLPKe6VZWUqrkKeXUqWu/i+y8FvnAd6UK96glP9rK+iZkM9p9qz/66hX/0+4gdAsxIdCMbZA2QLdvJfU0InYoqhJKHA+umkxofN/d1Qshcl7um9BfH6xR7ft6lwNwBStIQbP7Lis9NEs8xazgxjTGoL9Afeds++poi0NWKpRJCwfzTbswKps4Rd/TjqGKGzjN4vXhi9o9AaDGQKGv437oNorr0EbB9SJV3kFvilaf4GGa7yIFgxuLpq0KwbdC+vwVRk6QYd1zqDrv+IBSh2QOTJZ1J+2nFE23oX0O2jIquASeODZTgv/J5azY0tXN3z4HLg/1ZAaSQVCYB1DOOIeQVuhibQKb8O7JTEEK9wodjojpE/nNXvec/MJ+fioZ7y8LwhUFGyJEmNizy7OtCEZtYVPTaHBp3gF+RZoIebPLTT/VH2uBijQw3Buf9x32F6tzpfr2ORIgq+EMxwAZMnYFmvSfoJF1RzfGF0sS9a0wzZS2Jo8NcO3gMEerQ/LhA/hlac4pfcwn4EmShIIivindd9HI/tSB5u+KwaHDOkkB5geV7nLTL75mMTe5ncQbPPKNPnd9ovWEfdK+C2S1aqEoiByBsCPkRlCSLxKe3XMkxdhsD0BQRYQEzYAtwYu4Y7R1bdI2pMnDTPjSi+KFGZk4tLrA8rrzFEO91hUxO3TZSmKGnh+FSPE/fZSu13hYcNkkAmzJ7kXArz4xmVgGE1KQbgzkECYyLAGI0sOwgX7H2ps0w2z1ttgD+bzgSJP99UgP58jLaiYC4OxiS2CRJxZBE5plJqlLeTp03DxWlwNpwYGbLQ7maOpKQSGeAY4zxxAXdvPB5iWYrxVbvVvRI/icmhRFgO8P7wLiBV10FA0kuWAGOOYgUIqp62vfRVov3ojJSDxGaTryO+IxCDPRjvGoChJxwcDJD8nqaGZW5i4ArtXe7LkzdKM+PhbcBXTY1S7DskwPpbZuu8ZQAkZoMAGugrOwjVT9/9L/+1ptW7GfcvxPWLYW7K/ipza8icWoeLXfdgIQo8ye8QOe/7Sh8EZwHOpVT3d+SIYXQM6nhoTH4hT+eS5jIxO6bOdjShCgDPf/z9i9JV2KK0kUnhIIXdD8J1b6nJ2vFb+12enT1ZUkm4sIRbgv/9A5H5Be+WPt4DEoPWcs2HYfuoTuo3JI+hNCXCJfT9VYreucENnAKIhbjJ5KCDZnouBNe1n1gwxrYNdemH8dhAAw9KZNAK4WeHl1Tb+0SXQul1fDQqtMKcL6QvxZ6tispZ0aBsxdAB/lGItXRjC2zPiFxTWdX9fXmEJPmD5b7n0CiyMbnKWuD7lQ9KzC8E0QBP46fs+r8Ue4VTLuUjwSoE1BkXF/fL8ugjCioatkVZAU0tDv+85+Q2jRk+iR7D0mpVH5W96ge0w6VtYepiuH3FE6nO3qHtWeUKGgWpCb5HUPeuM8G9c3LGdubL08jyexyFzISLCnmjmvYBp1+DvnAWslV3ILE78A1OTwQHdNWiUxITYUX+O8el8EG55Hw10YUpFlbDQjIP/XCpG42nuIjYJ00I4foEqekw92kXeYRbr83jIzn6vXiaZQelyPHQOMaWMyi8vngw+BRlm37nrTkAbz0L2TJo76Un5fbKMiGgFFuTk+dJE2jlGU9RJgymMEctw5O1VBZwFZ4XfkGCLcrB/FNd2ZAN28/4IxnmDytZqY1kRSlVnxNBbPF2l4U8OjOZsKq5vF1wFHVN6VJBGL8zxrmDpBwtLAqP601ju+3GK2Jk0egQ6WlivbSXypVEb+1KfAhuUxjEswRs+6uhKA98QLc/V0MX0frup6+CrOWIhtQzLYknf0ZsA2g6gveiiNt0LhkJizePDHp71gIelEdW/FIaEdacGDrx4jDlg30VPoWcqbZMiUx5jpy8+IxNUjnLC2iaL4kkdTvHNULOfirWT/olvqYqBMcObQ0cvyvctjhM+z451LyxXojpQOgvBcYFy38hheCyqJWAKSmKBLr+AFpqb7nPUxaBJpyISbJds4hH7bSltkTvbqWRdktUNCMHlxN2HyEiyxgVU466vn1NrhUd8m0lZCG1Vf4aXKnfreV3Uese1iW/rU3YSeb7hSnfccvXju8lnv8Ceh/wzbZauqttagaeF1VJhUv6WbohED2BbSxRnf+M+d5BXZdOUalJ68fYJD0aGlkcPKetafFgj7qq6HaXYsp3BKvjAmddRKg1/hbDhPXVC9LxIFgvrl4ItY4g2BC5lEOo2nvvotXngBLlS1Orq7fypHfFjPCwN/dU0/UofdR1vJd7YdmRnuxdcClF/9Fo4C+y/39XwmzKOwHdsHqUpQaPl8ICaHYs2Ai83U5SZmtDY12e6Sd6FWCpTi1NwYkFlIm/rwQZVjuHwqnxaALAGdNhKl47lH6oUWDb4tso1duRaSNI3IpXT5mOeULysdUHpQnJTqPF42nmi2ry+ZR+vzyRYNqlo4RPlbFDBedgxIuo2IeW1V0ZzVamuWz/rnTgHnBMc9x5qG/JQ211ehnqWkPg824m+kPW2Pg+u4NT170CDjKb+3O/eFeO38vSQtETqH8x+iSG/1d+7U9WEV281x2y65NOCjJqanauTfLs8DB3b61o6kpEAHyi8JT5BXqj/1eUw1pS//elvU550qEADo+iz41f62MZjNXIjGHN2QrCfWQ6pNQ/HzH8V5yATRG8dxNIuWQf1yi7xyguh973JN5mMSMxV5JNxpzz0Kg5X9VATkXf2WOzYJ0T77m6yrKB1PhrT2ruvRimM8aVV66w2T9FIWzWVMI9p9s5q9xslIXAjnICZIv8CIkAclzTsvU3Ue/mB8N+kR6rHLtcIKtZWIWbKYveYnEzbS9PhIzDujpPO+EmM8O/l21TVNuHyn2taU092SlWr2C2VgIlU+61nBk2Rx80uoSenqz9N5qYx2xKDV9eBfDA4fu6vxsLG5e0ofamHy6/J6dJWQby0RnNgnARtvTNWOp3/wVM86aKz2AP6ZbZ1ZP+RegijtClvF2Wwx8LNWhIMWqkm2x91FMoyh96uuR/heQht7ogvuBHOYlomkiONz3dVvyV/P4823gvNtB+HjglyFdlfXMOceJHVzxRhtwphdMcnyWSOd1pzleytpdkXpcAvk2/o6FNgz6aQG0ndVr5Mysnhq7w+MLo1H2ycNzRChr3I9ZZCiu31TwijgEAA4FDTdzjImYae6t6SSdqXyDUU2j7CpRXsh1UfTfpXHeHo21rpL9iwBoWpHKaNIyq5Kt+UYuotCVuRxbSPK8PkZxmHnVqn/MJbwomfnsSNIDnjTa8NqoNH8rPIYvlHSq6eUepxtaNmvfzu+ZJvy+7LSF8w3Opyu+STF5dQCqEgRZZTfly+eUIOdbqNfabK1D9yJae2OVdf05ccipedMHxKa5dUqj7/EtlHOLHxkGaHM4SgLUqzvntEri9YVyGz1W0zwWxDqnajHQXZMlgY5qCill8czQKXVQ6WPGSeHHVFcZxZ9FrXqt+xgEFIANRRkQmHbsqSD2CWviqPI7GYH1RIKKHvKSZGfGyELp2EbLX5Lu6jFKJIoUNt3Hhf8mF3htFZXLEaZ7CPZQiuKpJENL0NgJx45F8PwonhOM81H7yG6OvumpT1/KpqRADQ4xl3NkRur2EIxAstEH7Uipwjh6Bvs8JVW0jFW/A2nUH5x4Zh4yfIjoYS5e8v9HGmy1RechX2W1exsM82UO380cF55HqBcYkybXD8vixRQ8weDPklHveLScgwaojPb0fZOmlPlB07KnfYsJmRxX+7ECJEXrxXlxDCMtn+/8v0Vdlr+Fn3KlD+eVKQe1QxxDNMJz1XFPXcMWneVV/jpgr30ynmzOVElwVT1mHiPFXYB8/+8kxLN5bHTKnLHy29UqjHFh8BabzDVqqErkyKhvp1/dU1Fj1N/7KxkmmwXAS3YAnoX/2VVj6ncomlktiOvHtqG57OgkMci8v5X14Pwli4AuJQLZsXu1cLX7BJTrqt85+QIX0QTI9e2R9480mMijdvKmeqa+tE0CXA9kjSZNe7kLwURj2B1l9fD3tGOxSuj96q5z+tkeElQcba+1XNqt7V01NAgobGx8FfGdjcIai+Z5ebWk3Pf40g/YT8blCl1Hz0tUUh1PUIsGp+jghZInXjO7ZRVIhKfhEFUz9i3K/VpIJUiGbBj8alLlIThUvm+DBreTmw1s/w88TUD1pGBnv+NA18d46W4SPCjhJ7PxSeoS/brm7ZwuY4pWJJyoOiYuqBx8fQQ4ikD7/o7BymrDrpDpcx7uxNnAeGM//FWjEzHIEQeoVxkevQkCBxB3KQf0KiqC0/dEVH1JF5DMj1X1QzIR4p1VIRKtb9tMykQwgrIJKzqlr8uOob046KkqO7LZCkP+4zKnE8iUc9PmBEdhryc4SiEwzFC3ggXQsMim3awO72Z+ps9o5cPj/cOydbkNPO5DDxpGO7qPAxrdC9PQae6NCe0/TD61D5QqZVrEO3/Ct5m3gGlK9G0M62GQgvup7y30gCToKcH/fwgZueljU7AROqtsmt94WzFzBTRIEW2MSYnaich9KP0E55jmHMaM5g6qzdMxn3kNBxDFC33QFRFcvusOdpCETiJJgxXyU3H1v//Y8BR3d8UZriqyYdnzlmh7qwQ2Yt37gkIRpLgC4B6/tyXNnbuRk/c1/mfXp4Hd5bc6isAsqUEYaARuBLawVkfi/sSlweWXeLifHnj+dCkf0C7jSJ7+VsWIn2LIDisLDxKppUnQwQCgmpdN9tHGeOr8oYJwYA1CkeNd5y4rzwPvS1VwwbrDp+GDKz3ILeBXiotbTPgjFlVoz44xiUdVVdZADQZZVlLPfdnsvIngmD3wd0hhWYPkGFZdW9tijW1x0fNIKRRc6BmcF+ApJW/xaxHOcVixuY08/WfyS44B0P/rN7bJwaPOwE0Kv6zimaaY9z3pWvMimHWEulh3EhzukyEZVbPANnAyd6zPLfqt4C53lHxAQ/yN1CLyoL5msRnoaxmjfb56Hgsqku//rx3vvn7Dt7+kqBQ1ZYPXLKpJ3+SLPH5Oa03/XiQjlfl1TjHEGrq3dUyMBFj/+dfdXFYHM0KivOIpWvmrYmx84bsVqJqHEJ2nRtTHuOdIQTGMRp4h51g2kuTQgXNsLq36vGbD8dgUI8s0xtg+BWFDQ1D9YwtsToq3PFFxy2UKLpRejAmh6vy0DhGIH9hO9CxYKZm8LEoBsVLlHtCll9j4/PqWTb450Y4F1DsOx7h9y3PY2QDyQN0HgnxL1GRGgATsvtGVfOoJx+3J7mNuimm++HBw0Grtf2k6r4EQORIfca05QI93B+XNkjq9vJ9oS2XXH0ZyOuHiXp92N7sFrGuK3824cqddgO91ZdiuwO38gpF8bDK/b5CBUHfpBB1EIo+ec9ER3oxvhnleQzuG2LNaRh23liDC/RB5oazwvIkFs+HoSndpoRmhMzLvKAhaJkQXmGsFuehu6kNJR/hyVDoycbWLxI1GAtocW+NXVwUBQun6ULbUsiddw+jReOr+t72QPxl1r6CH22/PheJLBs5zdcuv7f9DlnL05FAZSIMfeQhwIUuh7+oOoaJJa8r0CA8VqAb1CCfpD/MrOp6sHTQaIpZuZNsihmaLN/5hTmU+iC/QxV5NueIUvoEI403+7pztWX7Vt/bVEDAfKcOSRZOD1zaGJaOnBarrPk9EInaPss57weZoN5ycge+Tl+53+/yr0w8oPLsqQQLYyC6PZ27d5X6Dx+hBM6mb0FyqUwMMsf/mM9dvTyGanZ9eddKO98E/L07UaFGbGOVx5gRexMV3GBKM6Ja07mZwJJTXVZzMcp/P8gg7bJB5Us6VcRI7gJZ213O5wAt9bYp1zSQdLcN7A3YXG+fufKdcyNVUkpr6XMSsKnOkwXuczvv8pqeD8F5tLXYUeShuXs8yiiE51HRriqf0/PFZ76TGTcjfE8HVsShQVloQtXeQw/rtYQoKx/KIP1wrW1B2FSo912fh8lTjyyA0oJr5dWBPBeXtjd82+o8JiwXfSDpd8C/eGjiI2BvXpyTai9mJ3b7+8E+GdY/W0EIrLaV5p7VPpsScccCKLaOB1eQrunUK5ZcoGQv3317FIpmiXHqhYtbSicnoIOOP1Oehz5YSymkiWMUBaDMfOYx29b18jm1p4yBgM98pe03Q7N/UoTjQlZ1UCezxC+L3c52BZ7uEYAZqEfDF6ie9aSrRZ01SJwZpu4rqs8gW8I4KY8xUQ5ntHTqddpXMe+gtqm+z2tc/pb1BT2e1Q+64x2Rosyfc2XjIfX6GIMJOW3CFoEBQYvkw2yMDC6rWZIcDu3rRvIlIpECv7W8wLSt7E3V+zIusGPsA2pkqfVLYTQMT3EZ47YsnjEqxOkPXR8DhFmVy+pjU0Ks7oqT19JJQi2LEocF7bYJoemQHIK0tqp9FAFtvADZXl/Q9IYm5ytJxpJEl3LfIN9Ej/LyZSJwHglwEfcFhmYa/e7yPCQW8FC/4jRSUc0gy0PjeZOLWd2X+6yZIsUYRlNkzp3wNTnSI56l1crf4vt2yVdDH6IhBehOYJj+/zmLd5fnoYSaNlNfDpiSAbKn6Q/HzVNx4Ju8lPjEPfTZ2ZHHsMEnrtTX56l6KCOSgJnQajMCnqDnY4H9BHKtl/clMjoCfiIOxv3z+/kh46Vl2jhrbLF+jPYLQ5SQTlL0sJ2fcj0R1g95S8VxDhRGY4litYuOPX+uJbUemgFN+FSH1TVt4nOHqOaV2BaTQ+voFQCAyJS7vB5CypLGY5ZDbNjD8TillLYhANZVPh8GlTc3or/VGCk9SzMH9SG60qr0QSPt0ww4YTxu5zEzkOFrfCJ4qr5R6uDoRaBtk0RuMQBvvsL9Y0h7y/N4rUJ2MKe6xNdzcREI+ALf6M8rndII59TibZyvfEjton3YAgWQZV2dx0Bwf3y34XcisdJHlt56Tk6HucrkahlKCt6ifY+6IHTHszAJF7JS32W97qOys2TFxadMRWfcWViJ0M+WpOrTWTYZKhg+5akr7iKnPdvSSGwgRqr3hfarYakI7oYyQBzdOn0P3lU+FNVvkbXQV1y7XtiEwy/eHrI+QCPzyuJZF5JGiBYOk45WOHs7IwQdlneVGgG9hrjjVbbjyxUHqUZfBwKho676UuPfi3G1BLUoRQRRskhgGfreVDXM+cCfoq33sBCwVWLehQ6Ntv+mWy6fdUuvDvYXqo5+kiIzpCZC0DZKzbdjCJl7YvsH+USRZXM2pH7sCMu+wwjK+jLm0xDOisFmFouWeBkf4vKaEphOiwfxBZBaLJsrFB7DGTPx6nqQJ3xe5AnevMJlWqKLyOKxjMo12XhhZizu2eaanSIDWsLNAfrvcg80cILIaLtn3N5u2YKxBmM8PZLkZvXOaeSrc4fH9Eq6YDK+QOLuBJXVz6nQGU0U9kphKezh0NYiaJ48ZVUen/0Ob2XTwj43OZt9KwjoF4Sv1mP5bQCxHMko88LakirCzJjYNL9xW3UeDICKc85hkYtaYne0UmKyfEif+hj85BRefEUh3cw8+P5pnryr3HsMEg8OFT5VcklTR8GT1ufPlHuVdaHJnmkJN5WBunLm+rptHphuda9+y2bseCN5UxLfcSNICbuS/CZPpXxOg05RG74pd1Xb1xV8Dbi8f1TOtEagrbB6qPK6jOLOkgX1gJez95brBx0OpWTz2ktvo1ZaxrrMXhT6repL4aPvkE6tiDNyNvDzK9KvlXixqm/5E13saGnhnFLox2tNqq0N2/9wHmcf2j5dnpr4Cyfk1d7MjnkJi3d/8t2qsofeA+bXTnkVDzGyIhtJcV/mt618FGAryUAqhztVa4uDv/Rna/w0XnDhpKDFF/PLyJN1fU582QvFMbDro9F4kmVs7yNQhziOtyDI/+L5wB2bWttLoI9h7WPih3GpeUH6WF8PwDWfWIcw2vea6Zz44IL2Y5JVv0VX/c63RfsDHVN9KuaCv5rdY1d6frZwSItzmPDh5pdyAG4fLJkU2EovNZ9orMBkpUVZjZ4Q4WF2n7wAvfwtcUNJS/J1hl+l69WhY5SIM7piZJ5j/BKfKMRZCuysHmJgMU5ePxzF4hjyjJFgUXfsyYCApPztcDd6OrLVb+khHXY0lxEUkqbrI+aQyUIC/KxqS9PvnukitUGIo+rcmTRLyiVelure0nZLk5hNxojWjuwkDcvsswnaqr06cP3zSYFGonjQfoy4OTRbmnetqvln0NwtJdMkMoZDnY/uoZEjVOUu17Gkm5IWoOt+6VxWo70CGEhuY1VrS7z7Kc7jek2iXxJT7EzDVhhVXXhWsZuYaOTD1rTITGG0X57cbDrs8jzAHCYubQP9O7uepA6BxQ0gV3lB1Xkku4bdE/9UcuXzfLgvI9OVdLxyPdXWQrZg1OD0wPtSMOvlduMlPuBWHSMwiOF1kaDlSVdMJcRuRaRY7W9NSGAPbOMMwO06RnBwaac8mkvV3FROgEcCYCFof9JxnUPfHeKY86Pu+jzOVgMoZ+nHJO+a8y2ZdsZRwFXVfYnz5qJk5gYQhPmIWzKC8Mqkd1eexxtZjxlBS1okCTz2l9sbhFj9W7YYcXNrezETz/AqNIUfesmhLKp+i9LH1DZ//B3BsNlgRhs06Q/LXt+M6V6CLSAkdeELw8gXkBwshICqtwVkINqN93VbcLqFiyhIl+tN9Gn13nrOz9dkhfibNZTm8Q7rn/ZUv6m6poppbSOJ97Zfaqfd04vgX90iqe7yPM7l54K8E9GTlRjrXA8kjVmdkfJ66PCFIXvlY2KXGaw8fuHWqK5yVvUKE51A+QrRJQ29D1npHlC13lvWUrE+2/PwIIkDNM5l/xiMHpBbrdQF+6baPL1wisR8+gQ+Lk+GuWyaVV6SY3APzgw6frO5+8ZvZ2B5Y4KtdFs8jYkm3UlpgzF74vKcsSbdoRGX57FJgxelWNKMz5rziuUxL/BKg16W11SH7WF0g+w8NUQPM5hD4gOanb+jOoZpNnR9i+yzcXgQXNhCfbkSreJtybcfCavydDqbJ/HGrKaoBEbtpSdgJSHOVtZue0aflCH/DS4F2nM2d+Ux0gGmzGNnAvtL2MBj3IdQB+tT3Zcn1e15ce1pFXaXzdDZp9vhRqF/l8+6ijR2UdaObnAa1K6f9gZ6/VTZLVKNJZR4picUopktA5gCgP8jA6nyGJKFUAa/hCHIY/tSVsWQZrzW1W+BwFVJCZ/1QAUoIghTu3yPlHjV+0LBwntMVvPGaGb1UfZOvVDi/nId6155yxnP/RMHz9YMmp+wN9uh8jwkywL2PGkHaWUoQagNIZ22n1hd057oWn6vHia01zVuiR3QLfpv9d0/X33jBf5yPaRBe33WdvM54g3+i6v8NnBzsTKTOC5xjaSCI9aGEKNI2cpjULNwSOLbEcCFmtM4Fr6JKj5u8XzMTOXimlVVoZ4KKnXgO4T7U9mU5zHG58uwZAWZHkOATxP7lijOai+WXFZCaaKad/j/3okGUaTNzLbe+hhMgMnCIZliaSSUaAA+CWUhlSmvB/eM5PGehHd23ob2An3F1CvDqHpOg7d5kzEPBWcAK/6aLRFOhSWvfD4oZrFlLIXAtFdKF4+r1LEgp8oahpKYrtjMR9fOnKMlFNfFwUIs/VHUvEF7Zw4kH3770DEmW+rlg45yPVUQjojXafAMgC2uw1JMNfCGH1RdjyQ8hX040+Lb7pBY4Wxl6BVbtQYFyWTPILRtS0vWPOEvoARFnajYx96ty2YUlRNKaQVqHYfPZgvmDyp/y/bDRZPOfE2yECWlEYNP2fyW/mw3UgPW9Gl45s//rwc9JE/cBrqX7A63ReGiG4MAj52eZAq5fDts6VX1cVfcOyY5pEqJ3gYx7HrC5nuME2W9TkA/qdDMHShG7Iqm1po3zrBqV5oa1JCLKkGfreOHfF03TV0N7kBsit/yfvvjJ1Mpu3Rlkc1hzC8Z6JY6+jdSyUTx2GKy0YW0rTfj1bN3r64Hc5sR3Gv3p2XHBicTkHcfKAucoD5GiNagqXfCJv0hWb5vzLSa3tVMCzKUNWohQ43k5hKSBK79calnqak5xzBwjWmn/bCrydXLKFVIYevlb7nH1/YY+TZ531rwYY2/kRHtLIrFGqRsEGtyUTuHrCdgIE2M0Dd0uyqd0msAH3tHaKx0ytyZ2LDi8DK1WOV5gM9yufAnXXB1OPA0DKyAcrp2tX68sXSyRQiKDUQxkr7zWeFh08EsZ/OvRunD+i+KxyzIK7+1TUdoc/5x9b4gls2Rvh71B+aPG9QzZSPTX7vai3kWXdOMf0RYs82zB2gu8eRdelvVMZ4n/V6VPgpaQvXkN+zP2EyZV/2WmHmitfat5e3pwfL3AOY+/VB5HuehWiBS7wz7nafbh/Pc5Z1G1SmOqvPIoED5kIZ287Lb5Wspca7bxVczCw4+7lZ9bTPG+VE4cHGBfl+v9F2ex3mGWHWZzL935O7pEZ01hMeh7V7/lp3mq26BKWOy05hW43NE0Vn1WjiesGleLeGVxGaTTjIl8seVb131zhHOriykT1IkCJOIteQaPyk6y3rsq1cUt3xS/qA7JQPzTej9kqtTnQfEjAyJSbBAujmTgMuoIUQacqXaZydGx6afXVxmJM5vMJNGKsJhziJU/ZYZWg7fPZKKIlHxIGneBldfs9SPEar1KKzXG6s7Q/Z+IjxKWiE4Wvlb2N3cRuZoG5eVeN/oDS22JHblMQabf+YTqjjN9hVQzZNsIGrSp7wvEfJ4sjVM+Wj9WV6vKHPC/C7Pg9LjDQfEPC0+gChP34iNNHiqmZYJh3fu8eKLBoKR5iJh/hAqYRNdHWPFzJDmHGnd1+l3i421tInnVd4XvHcpVMQTN0LkFebYqVzsWknjKv56hvvJW0PdueDqlIPBdBpm8ASMsqbTgwF00b5dyfNmAAwvcyb3rJVMpvcjZryxEtoht8RXLSOLGR3aLr0Jb5C0GlkSW5L+e55uw7rnixCgsKmux/4UX+e6bt8aYxcatJbsQ6GJ8iuL34LFy8RoG6QDrMGNC8n4gD12tlXV/EWaOMhM1Gy9/9CHaY2/2cMTwpa/ZdxJ0vqKUh3H+Od5t4JXot4qz8NDGnS03jRvhgJALi7kN9Vyr/ZzpqUUlzJa9ZPx3ONp4KwhcEN8KX4LWuC5Nz3iM9+W71MvMe7WlzKtq/oO5DA6A36K3v6jL3XOZiXmky1xVHkn/t51+cq0SJ2TE6kRqSn1xIC1Sw5J6gOvDPcMd3SylsynGIRwgGapqXEFdBgTwjX4CpOEszNal2V1PsV3fQw1Q3C2XU+3Jy16hS3yBCFYcot34DxCI9Lg1qaP8Aw+S9ed3rc8DwknM0w3kgP1KR+c/pL9QJLFq5pfnxFoh+fc6glijLBg44Hw8NR8ft91BiLe96mq1Pu9oyRlIcHTOJ+M8jziGUmozlmMF2SInWZ8QRBgvZez6P2EKJVFo39AeqhPajCbbaKYKj+7Ee7d3gr95DdLfPZzBn2i2LK/Kb63H91LaUsxmcH2TrjIKZ0TSzNq/WnIpQbiycvFtzLAhbkx3vLm0xcWx6B68Zncd0IRA+ikQL88I87qnFJ1XzRfwIpxYiS3iirkrGK7CtL5Lb/ZH/MY1NNu+enrk1sFfmN4qbVc6RyB1KTwLOhk7b0bcYIOg8dQ9vsotT1mLCRvN/nryMCTCE5w66VFJir9Kc+DT46M11Q/HWTrx6uuMyo7D2zJM5AjaO+ndJNbdSXwWhFjWX56fFPlMbbocvvz5PlNHDDhDV3qo/cf0754X94Me+N0PSsi2odPN4FLxAb9D3qpLKVt5Y5Aay2RAV01o8cmzOoq+3TbT3kzdOZbxTAJoNK6NNCYNTar32LSAJiOeEF4wbWWN5jFgJXk2X84BhGalLT768Aqau+EFT+RxpX7SpstY14olGyEWMPBka7w3FoCYYrnw3M0A572fqrWQdlSskP6fJbVov/xsEB0W31Ft01dD2ZK+bAys5Rx8f/r2DmGbl4mriyACVgVzMfCmz2adnB5DHqcpF6HdDNmqK6IM5nIIqS06noYsya4k3w0zxl+QHBGyYlnJS2vx9J0/LLRxDcQOr++FNFQimN53+recjLwp8Y8PAMKHonxXJGCYhKU91Zt+tpW3pniEoKGOQxXs9rncSqP8YSe+KkzDbC0PYjyBdfePw9ecV+aLGNydzaAQd9LWrNSV8UQu6v87ORNcTXssJhJFonjhELosHcskoo9CP/SMr2hCnoTPg2z8hj1s5JRkazyPBAl9swcBq89qL/4+u6Pd1P2lBSgEKjXi9XxYKHt8MeH5ql/qHtXHUOvhWBcbdeiFst+MBEM94olpZXH0BEzt0W3DbsVRplPw2Iqy7Zf1b012lAAfWGxnrdbaJLnSzvjMYapj/F8CTT9Tujji2aUFJmeHPu+qqy0J7bQPpN1ff6DyCExR0/40npF/A3F86HH5tN2IZrsMNCBOkVGq9MMc3Z1DNNRY0/MQ9NOXS2tV40pOel73OU1TbHviiIJGXRId/dV0BIOp76aE4rcouIzoUs1aDQ2d3B56O4JXRvV8zHGiqR3vJ/1NyzJ866AXCEUjDIz9smCrKv8aTz53d/oUQ0MXwvqKQvKY8ygNkMLe830zwXgedBdR+GjZqnuywzwOOzyhPIFx2bWcLa3FkilRH2Mh2LW1EVNarqXjdmVBKTUSbv8LfvLiFzBsWYIyxLQYqaVrnOvcj3VF09gPRe0vV2CuR799jBe3jJXw2ozs5nTima8156Cpuorec3kG/UxJvIBU5FEX1OkPrP7UMLwaY5VXo9fNLQAc2R/2ND3Ez0OzWXEvFmdx+sxsHjw/8fBb7+u/Nio8iLOe30M9di0hUOFbsmO0g/Zeeb0R+7q+Xg/idIb/T8F/Eh8E7RtEhX8zOreas7HkHHnQZ/SLFV3in57I6Te8re8sd7DMI2gGQKZmT1e4qV2r3xJkH/6n7e+L4mgfQgt/UUqJK/obPPKb/ZZ7zRwsq0VRHk+tiHU2cVQ6Hqzq2vKuot1g0vzhqP+xrayAp+Q3DjL+7Ltt4Iczq05r4legf+KuBeXdXUe+RPmAp1F3VNFotzlissMeY37q+uh/UsBk8wnY5tbBBsqbTyXvoCVf98x+L3u5GGJB/X33sR07euR+3QW6zq7Dis3W5ERuF/B/CX7Mbwc5qLqGCLmphYl7Vu2Dpj40iAkdajaK7+pbpa0k9gzaECBrpM0msDi6+tOVddUl3PYpjwIWRz3xtHYW2Fuqrf/cAyazXBAVHfqEVGetLmJsxul7yTHQF9isKCuBuEZiaXpIUUY0FXvPiFAtEqko3ACKJcJmj9fjR6cYX1N27du9PjGeAiR3JlzIHpt004JXh6j6c1fyTTH1H6vPB6+Fk+CmK6K9fuosrlOkOy1s4PInkF/KCAIJ+/ymurBvismmPYF8ZnHJKpo844brZfnocSYsb51f79gUyqMmUSG+675/I4RP2H3ouI6eGtZzG08+AMMVatjRIsEKIlLFxt+i8xJdXYnjaLyFdAAiNEkM9J+HgaDRp13Bh8JknjLdQzUjXSV0dW9WUE42mhO42Fmi10eg2bjwizSobQkRXFhEqVZpSAq721A6wO7fYDFsFWMdKf2le26CqI6xgglRz7phslZeWEhH+y0m5H0tatnPbZ0gbeA6VaglwPwiVZpRvN0V7WDNrBYeJpxH//8nOCQHV2MbH/Kb8NMNZfffq/xpWkqlB+bsHNxtbmrZ8xMk8YytF1tGLkUDU8oDE7uul0eIxh5PCVvDmqg3MvzlYNHYYWxjyqP4X0dNLXe2ic90ySt6X6+MZRUx6C8CflIkF24amJK2P9f+7L2lppNqF8UR2wGYMk3dfNI8MqN5mxn1spjmOvRfaAuAzueu3refi0duQPueFV/AFudt0NG4pPQyQxcoWUZ1qZEiap/SpQcLNyppCgfaB7t9C1Cg2lhImmUvyUOoihi9KQ71WSAqCbTM4aF8vvCIduMWs/hkA30CejO74DyYvSs9lFsBGgZifC0b7q/fRDPWsT5yADlMZRzLa4R+3JyUVAHJcV9J2684sOgYCaIcEubTjV6JSzWThGtyYit6o8Ba71zRzuHs6tT+Uh9jbyP9XxWnvfnC3+Knxh85De2JbBZFFyGQ+V3v4V2tvAdrgzEP1IljyN+jk512U9ukUg+UX/KWZU3Bgd37+8J43Vc1Xmgl94BITWonRCgBZ7qDg9tqrMKVNfjCxtgcn1DHXrAV/V0aCUN6EpdH6Be6oPElDzRqwMjtYAMtunULNdkiHTtyUB1z1+byudC4hkJXAKJqWqplpiVDeV+/ev4GyIZd56TkkpRZZM6hk4nw0pL6n0qlxkiq4lWKHzVvYWDIjfjw42FJkIdA8KHLrXne3tVx3Dd9hd0fXYQ+gWkjg8lu6VwVvmEIs1cjfRe2SX8t/1yf/Cgz5UpankM5l1JpPqn56Kclz6jBxUeFJn5VnU9oJusGwacQQlAuPewg/Q932QMXdUxekhuv5fm/VKkSEcc9/buVt+GSE5vakteVysYNb0IW+ID+oVzOuUx5CMGqnuOxCSdfJGEUfp0SuuoahixlSOoTpsV2Z5WFPmT1AbaDueKl8fA1PSQfWGgPShazrPz4ASRMGf5bQiDRQe5qfIFaxDSKXTJD9iSWlVbGsnHaod+2tOG1d1GEBFuGqZpVTs0mxb9KP4XTJXzsqmsSPDhWcySy9/idg5YWXKHh3pq2eLidUGouuXlWgiZFuuydRQ4FX6VxSEZq3YPq1yTF7e9zYMmCHcAagfG06X1iJnbyudjvUnfgR0yMj1vh3YDXhYChdFJlQuYjrh+H2kvwjf9B99/ANfaGWb31Xm8k/uf3OrpidQxMrzAnWJv2Ege1TFQrX3kb8M0p8OFf6r/gDRO7WDUXT1j22KnbUm/dZ7XsEciwT+LuglwK3v0wVuZNiaO5ombzi8Ky14oC1FrcR5c7rh8mrj5YdNmhLUI9tIzd5Xz2ycUFETbmco4pQw/jk4Cwa9Fufgtmjn66yOmmdCYuE/fSB+IFMOm/P9nPakN4fMQiwnSyX5MtETm4wkAKJ51H5iES/tUCyeSLUCn2ZNU/jYN1PIYLlxyeIV4m5UEFgzcm/nrqSRa+Vt4VL0kYDKdq0Ilo+spA4pBqeJLmUt+bPKw7ggd1BDmqKRBfCCj4lt+x2A6sU2f5MSm9HG9SwpFIqpyimyx9xeulgl/9F/xd+r9hbF/1qbyPJIHds5byOmtuNauP28vf1LaS89TXg9D6PN9aeKMvfi35QCR7KKWpDCc1XMaieikyQtZDt7WEOINQVAlU/peM1O83tyK84SgwWb0aVXuTDrudC+P4cYAyLma0Cps/6ccxFWh3W5170K5QikKZXSWCuK6FVKDMIi+v55TdW95qTldFBHK9RFfkfRMqZEwUZVeCvQQuWfGhvepvoyB6IKvL26t5J5Tel1SzXoqyks8SXAC75fWNdC3yudjUma/kdReabRtWlgYDlc7LbzyehDcpo+tTyDIJlEOMLVikiet86iPsdFMsAtG7MyBoX4v0Plo4X9XepjnG3kofvKxix/HyM/ODutbsVqtQfhgLeDyhK2xWGl1a0SOcPbLbHSAKrs+RH7wAJuyZ2QBI9nqkL1vuZ4qjO+Yuy6qsytw7PPJDfhKbsnY5fVgkDWwDXeDa4YQJCpn030UskrHpsCG8/exI9qw4pDo8vDpxbquT/msbyR4OYk3ZRMXY7oxKlaiZQk/5TXdYJxOJHrYxTp7njITe+1Dh6/yCa3IF3k2FY2PjU9DRheW5rM4d9Dq6prupEa9PRNskIwdSL+2CFNz89kozoPgzffIgUQk+zL1SFBBnXEB8MarYzDofztjwhHa1+TZSBbTdqTyL34L8ArVpySf83hB73251Ta6bmwr8z3OMRI217TjKfLDMX54DKAtzVR22VM6/w64bs4l2ZlPekuKIzxS7aW72iPT4ykaKNnw8T3m8iCszUZUN0RS9VvuoJ1aduaqpzuW4ksXkWIAm7JaP3oL9byFvH5W5DAE6QsDBDlX16S9PIY/RaBkDm5A0X23VLldtGmelOKd62hB0hLHCgZSZMgdqMudbIzkfFfHeKRHvDbKF5qjaqhDMCa+2aBxV5kHlFY2S9mqgJYn47ElmcPu9kpGcPV80JkKSNEow/om3GYsNrxMmtNZYMpjPOoHqYJQGeau5BPUdSr1S6Bk+YxpBc2WPDMdLkHaYt/kNyGsheJRXY/uUWyhg8+Q/mNEgfr0gboSyFc9p4Zpfj5bJSn7G3HR22KzNB9/K64kPzUJsSBh7lQEjcB+Rfs1OaHZIxe/5ZMjMv+S1iTHieydBmLZk1y1Pqgn25SjmXzMWOz1kaLl1d6difirjjGptsRfbdbC15B9hDH7RvBDxDSrawr6R8RL4GCRV+8nzGIlItHdrvbZ52eAXAj0YKygLhZDPpMb/yRE+infOa1CLdfgqYyh+8inMi4p7cNe1lL0UNLJQuhGfj5lbo/9X6NeYlore7DnO2TvdBYrotqMLx6rqpbOG4HuKOeVKGr8oWDFfK9aZC8tjo49OcgfdH2nXLenZnRP9itqHtqUa+KhuUDUy9+CsB76o4gDORomJtiKfhxeS70G0eIjqWgeaPQh7tlqEtpRWnNOVPfFN/9UpDM2HMFm8/1g7jfgroOU+kIxIpEC+3O2o+uLBQSm3lci+cZdnkcsu1fSgIfno3+NnDYSAxPhYXFfRmAD3JUPErNmnQ4z+5fkSqKdXemCZVTvhHePkJCAfnXnH931ROC2ypPIZDsVt950n4GW/oUD3OOLQbCf69Ux5H6j9Hj3hZVdGIaE/j3cr11llWQeYIg/gsbJx4oKbuJb4vclIqM8j1NRilVnjHggEa5ffQyzGehWqbmyU2nyexvygA/1ZYrxJtm0Kero2K7qGDI3o07oSaq3queWpNd1y4aojtEw/jTGnuv9fFKm4XrtKenIJ8r70tJ91UEhIc9WY0OQ3DS5a0c5UZ6HJr0wTnvtJ9jeHcWWhNHIjqosznOM9Yz0xKACIem4I3kSznIsGkIDsnhvM7bRsWTkV9j5yGydFXdJD3Bf5TN2dmO2Hue9J3Q676kKJmhKrrMrHqPqGB/oUDXG70ZtOZ/oHQk+BWWc/1f1W0Yw5yHh+3qTBwfo0n1uJGXISruqYyghZ2jlsprsHrr9ru/kK0BhV3tT40QCY0AqHfGWjKM3zCwE1Gm+Xt3bid1xh3+qh2KoDtFwXp4ngl4Ns+q3RKJwpTEWpdBZBUbyZGQGmME+5fdFK4qYv7kuryiMU01esSql/fCGG1jcW9DT245fhS6sEvuHFf+ixdWe3lXv87wtafnMxFiJaSXkeuPTNPhXqZXv3KlNESq8q4bo1LkvbgjBMI+fGPjq3rIULH10V8AujnrrIh998o+4vqprKokwkRGD2NwI6BVIsVg2pS8ZB1W/hZ5aWhQgFBU/NrWGpakQST5vT/Vb3gCYzcNAikmwULVJn5JRfreSwfyEJ2H6fSoqdDraNe05AzOGnEQllucR2+v+5A3JjuOlMRvbkbSRsJfHSIs/SxdPoXXLF8dyjhsD21U+p0D0V6xrNj/aBmJsUfvgWt74i6t7+34saBsYXxjf3J3o2eAQtJaq/Epr+gwW87wkaUCnNqZkZeRrwlPKfZR14kYf2nda+pYfO237KWTDJB2X5yFinS/7Tbi5KBdjnWW4JTRSJVJdU6nK4wtZNlfM2PQJFnaq6Ogxqnnl8CmZKvXEJayQZkChZhDAMNe9rAtRymWrnmpORiuD0Ipdkjr/FKrn8vTymp6VOyb787ZhRPDOS+gSPMu1DvlUXg+btis7SbCclTmS2S2Pk83E+fKU12MZ84ZjSab8zqwhw8fFCqD2Lr8v5yegXN1Ju81MGrlvX8mWIL68quxJzdLo9rOpljtjYGGJVvsmbPFs7oprOjWiZ88+BsjIvHXyK8zQHcy6Kxa0NNDzneWWlf8QtRNaxZOYLgwKyN/yGNvABpPubSFJ7tgjn4B2k0JX7oEEeFnJni/S8/LUL74kCFYbD/zu4jlNcAWSI8d5lj+CD5tdw23Di1b6Cek1pFfCWev7ZS3VWGG6GFnlK7+6Y5BriN3eQhLURFY1tGvzRiz26llnpsAu+zQX9N/CSSMHhtEwlCr3DTNVWHhKCdWDTs3Cw77iGaUbLI+RZdmVxCF4xwccFkxql07vU84s6G4GWs5Ztib3oNnnudPEk9cMXrGcI1v6z08JfxClUjiH3QOuvMHW1K6q7ovs0PtOGu8WUU//qUf19m/LbHGt7ks3OnoSz8T4b8ZPiw9YSSaol1D1PzBdXh9WRWng3Jp7AhCyS4tM7irPQ01sab8YVeRz0dCt1wZc+e0Frs6DXmqSGVhEqS6J6WNCxeHSFyp7W+djC116B/O1YuKNDVA0sC1DQKTlMVgop9yqRz/7ylgt7L1hTnh7/qrnIwH3GuMAtF+wH6g8D6t1PWtkdU3P7w51TSjFY+O+E0pzSl6oTRzV5ynPY6VOzij86/IRopA8BkHik16fhz+9UzVN419ZXBkpRdUfV0ylM6AgTBfJviv+5qQECWNpSUs/z9wojzGIxuUdsI2R08BesCdeHn7fwPIZMyh4Mb6BoC2n50KolFeYCiNj+mpNtkIY9J7X/Um+F4vFvQMlsh3juayesZfo3KuL42Q1h7edO4hrgn7fqfI85N5HrcW/64P7JO5VvzCQibuczfOZqmzNLZOFg3sR2ASJEVT12RtWz1jeMVC3KzR4OEckVYJ2oXwsWOX3VsAhU6TG7yU4e4pZsgJcKXvDM6jOg+vXHNkK6jjBylsMyIzyI6v9HCGxpBc7BShnHFXc4mWq0uLgLDkCUVdcwY8ZYLewboYP/qZD6zLpqp6jhZtsk7y5JbjX503ue0JgHkqoWR7DbTEANoGm3nykxqaPqHzmT6r6DqIhkzb/gBGZw9zx0a7r+zAwntXXQ/kRarrX30CY9OFlQGUuVqNWNZ3viVRyra09uQI4He4doTHe+F1m6T1c3Of2+fu4ZKlFo5N6TdXA6QS3V79FV15YJf//0J+PVEpb/Uryk65odU3TBz/viRLzvsNknhQtOdDO01fpTyUtcVXFEASi7puJnGcpmlJxntKbkIB39htw8BirXJ4RPF6SgRUo5TGYwsg0wi40bkiYJs1gp0wJr6+4L5RIxDjPT1MjQILfEpjsQn16SkaET/15U9ESo3vdLRz01/91PoDmmW9VB63QhhkCXlQC76qk4xm0vm82CHt1PdiGexgVJHxuxRblrTabLcHiT/W9lWUhgOIsG55xVqRw+8Qz66rQ+pTPeo/FgUnkSxYlWCD9EIMVCJZ9dvVbgvdf3y43BLMdN79qVTpgr3sXYU5HRSimhORJw82GsOubN/3dqneRIK1lkHMqIupRN/e8gs+KZelOCmR1b/MBGDIOZD2bZFHC+H66zPHiVTVd7H++mLSssZwltl6LXjPUhKrk5YDJiKXnBhAcOeNtsjXsCc/OlLz6LQDlI6GA6h6qmHMqepkrG8QVn3h1HrpSM+G5asn5ud8lqMXeS/ZQ1R821YITTMERIlT+WrcJ5pgJ4S79HkuIONLOZ480ivZ0gFSPR/l71/5K7U17BaRNsz3sHsmXgsA9dEBe5Xs7sP1sjt8srGcdOY/40g+CnTgV/KifDxWQSV8nXsUPwVTUi9Hze7U1qlxA8a5IHwj9NsqAW7jaBtvqQ8qWUfXYlnkzN855qszzNNoQllJxCpzmfK2u6YpW82IRR+k0UE4XB6ANoZq6ozyG6wZn8vhQ3klyoTxK2c3h1EqdAeV/EGZeNTBHwhzpuVf2mSyF5T4bsdAAPkmRwaCg1MQLeO4Xm+Gsvw1vVIqy2BksJb0Ob/4TST71wnkTyvPodGKNiheeeyS6TcBFpEIOdZXfbD6LF4lFLIm410D/HUMaXqLbKm7P2Rhv7mNkCJ0yhBkT5GTXMwdcdc1PJSK9Sxo6Y882tvEuGwEL6wMUqI4hI0RerlXMS7vs53p2VuRLgjqrPSEJDKmDCkJdfE7LeK8lN1Lijt9X3ReU052kqHwUsMKumYgAJGjImLLHRs1MvArOsEIb85YlnvNLb9KwKp4xxXDnDOM7PY9Y7DgQUQmBVU+cB6W4pv4VuNERH13KGUxcn6gEh1AZVbX2+6VNk/TChNs4LF6pfIXN2vmEi+dUPBz+ota4/aQ+950km2ZD9+Fzy2tK3pBtYKI+eO98PZP0pTwkUix/C6uLt7Zrbp/r8twZr11PJMGnnKjY6ZCHHiNiGp95voj1Aqowe2e33iourY4H3wlHgRVdVKI5krpU0uq7rC7V9bizBQsF2tSHFH1rYNo/8eGcFa1ax95PK8oISHGx7Mi4NkMAenSJ9eirY5CfE4lH3kNMTNAuknx/wW3nxKrnVHylD70oXvpIie308BKtpU8KgCmPYZPwMewf/4uYVpdLCW+4ZU2sjuGXv8m+tbgPxB2N02A4nv5REarrEerfSjqiRtD7SVgTNXSFJ3oWyPoYUwC2j1MTQuHb70j+PZrDq/bgnXUGZIvMO2wW+j7q7/xLn1BnVTX/GzcPCO7yr7OYr3Bu0DMXVa2Mw+qaEvHTvyQEt4ud1s5YUS8lMH608jxmWJADIaZni0yRRkGyshzMMrMt1spvg3CFKN1jpA+TXuEwEwBTnUfEGnwzGABS9e6oYeywyeDYe8tnrGebvQPMe7JqsIzOTKO0NE3Jq/Uj8A8jMF+I1aOxlEnekkBj/lFx4J9EDVgvss9YOiaf5JnPcfREqZXPulpBo80QjSt4B7edQO8RAHkrfdHQ5BgI9OKeFTtLEe1v7MUrgXblGkTz0j5Kb4ya/eP1JCYQrFdGRnVNCat6+FyEmhcHzJAOMGcKQwOR8ruvXuC5CaTbFJdtRNTYtLfV6Cn7yVLNE0dOFD2+Twy58gc1Cdv1HuVvYQASAGGgbHajrtOGeHjHzobqvIvVNfXaeqiClUWAp9OhbzlfXqbVP/AtnTWTZ4/GkWudT4xpi0iSFmyXPj55J3dwUjHkXN66HQTGLY7Bo/qUxzCqjcNCnHpExZJ4Lt7vV19I9HF1b8+/TiCpvcBknsD4N2lhWrmof3d5TVf4NMoue7ChunVCbC9LT1TmfPlbpNbeXwow98wI6/v8+axEYIJVbvWDf8D5ey7El+aHQ4RjZEB9JfS11I+9DMhclmkSkk7ahqyeYXv7AJ5/OEbyUsI+h8vCX/e9AF7HNCGFqo6BDt4tH8yy0F/WRdVMj+oIFL7qF4pW75SrqwfWYfez06wbkUuHolPdW+j66SEl4DdX03M8hb7dYUDK16zPQ8hcS69OT+0VdWpqefc06zQNyu/cufyJ0YC1tLSmCeuRV5hiU/eSpfaGHqm5RnpPyJpkEeXmIpckYSrffYIevtQRygV/GGV+XNLG5NxG5fOROoWHh1s0hHB6ZWu1vqhGf+/1MV7hQoMFGhDb77Es9cSmAH7eb3mMx7PJVvqGZGBHJSIA1CBenrOmVfeFodQ4qvdkrNvOAszOTfJMGLhK7aioOSed+BxGWulA2iGCaIjXoanL+2LijGzld2TaGWrojA5FTbZK/9zGbWt8J94NLg0gHx5xwSntDhOteNYdw+/+jNXpTSkFDB0VJSNGrfo8jLG0031eyB4w/p9AyYj8TenLY5CrzmzxzReknjQ3Q21z7nM4WuVvgYBnsbw+qCZqGHWAtqxzOQ9OVfPz7ypZwKTwCzLR3phdoa9rJY6qf2odBsZeBNE6B6QGLvK2fsiB3CUPxYJz5RuHdacCeUJj5yAVQ8HMUj2niP5JYrf+LKWLYb/gkJ7uDu5meR7Y6z1825fsvdnVSgpCI3rCuC49VltM0/Zw+SBdqgidOpsgjzzuRKm7gKGy7dO2OHVITJ+p/Q3/7mjS3urbsCnVE2tCAby/c8Jh01qGW+CcLo+RWYmsEzhVc3qPy/p0NnY2q2QRyOAkQl5xMS2eKkuHGTd3gHSPVl4P/2LiUmbyq2ick7OuHcKYqzVbnUcPT4JNwzT4iYzeFFbT3TswcDeKZ4yySkKbLX6DlvTno8AiFsJFv676GPIOhkEa9fvCpwVCUxpGen1VWb75sOhn2zBsanifBBA2bT55Unid1TVVXbfwRflVsIQ4DVia42DdPqbVMegHW1JNGjS2K7xB7tFNbhOHWZ8Hn3xksFijd9goQxwE+CZwnoqkPAacAYirsOzA7Id+8ClifuKNXXLhUMLmIJISNxdxtAnqgiG/knt6Llh1HhwWe8UpOkbWM7hOXFkAkMCnynWdmwEWmEzBw30+nWAbPTwT5T++dvHewihQKjYDrJg8RIu6I07N1mhX/Y9t+bnITJM/AwMnKQBfbaSdij5e/RaLEHM55oC9w02MEuUZbOX5/MhGL84jYsLFinenWQG8GVAk2jbOySkzq/tiAUZlY4eYrCuahEoHzPPw+kue9I6TmYouraEM1WypTMQgMsHhi3sblSqZh9HWSvgJA48HT60+Pkz2//8WI707Kvx7RHORCWFPmJW7RQyyRnkeCBkjLbIEQZ2vzP0FuA5QJSK/qzqPNDrspRQtpE7KzPUkNxZ+fV6Vns7HPklRcUUMdjn+kRkn22WuZSDz/88HfQFWlmpWy3Yz4yofyCd12xMGWx3D9ufRSCLiEJspClxsgbixrzQr+kFq5B33Lx1gKGzcGpOhECw3TaunPIaekiUA1dJY0BvDY04wSPZ5/ld9DI01AdVXeg4j8TxEE+5UFrn6PJLC53okBTD2X3qB2LsNYvBQimeMtDphGti+fs9KZKWeFDiDDlHxrdSqpZhoQELY4qyNalUa33jIzo9a1TP2iIt5wlDRDMHZHwk7hCKPDLL6vkAeMJqZNrJ6PBEZcmobNYr400esrofkGbO1bEJGJknqKCXdYB0771P57hMAhdBFspQOLu2YOB0enNDudn0MlmEvaeCF9h6ZC0svGYnoq5gZSRIywl2he74JJH4T1KqtxJF4HtryPNwMVirIkWHWlhy+AXBrm93KvVgP9DkzkzsfKMsFiSQdWfp9tU9LUt4CBT//uiRyGqevd8I5cV5bTvRW3VuaD72PO5MGutMoDUxT3oAZ6NiKY8wU5WAot/UPblMSCx2mQs8FXtV9mVyekbGjHqlenicuGGdBRD3HqI/BmE1+kUUcM3P9Qhc0Qf2q8p0Dte5ProLQGG+gYRYuWs9AxSy6uh66J0/iHbXSRRUJs/DQ0vVfydAtng/SqpWvEjbcOREmeptcpoXLFHP36jykxBOi2IqKs6ENgjE/t+VLO3oqHax9W9IBrEPyfXwRkiaKM0Fkf05xlucR3er5K1+EHXsN+leOd5JaDPH6G2XD0pNVeUHAWrwSgOIr/HHJ6trhjWfwEnqwTCq0L1qAKno68IxVZiyPOz2BTAEYswnSe5mxSX4Kn//t5ToWNJZvUVJW1JNUMIlK0qJToZXPqUunVRmjqU/UVxRCMsiN88D06hnjTUl4CyeSRi4gGcqd5gM32678hNBQuvLpdOiPP0lKJjVG3RJff7495W8RChh3h+bLm4gkq1u3s3ozbH/L89hmWDZ1036DisZirlF0fhBh8C5qfg2fmXxBKjzbXPMgWrRTzEA6SuSsnlP0sjymXDya0eePe0C29ISYDeTwFsfAcdAJU7RrjJ/FQPBC2gAYihC1xb09D+jZvugTkPPmbUXG9uYanCgVZ1Un34mH99UNjvbUQobyiRm7w2KTCVO8L7D1uuBqfr1SbVt+EfXt5ZuFQFVdDwOfiKyIrL1tMpZlYgR+tZJBWd1bktvVwmawdbmj/4DOsmt+0qIvn4+HyN2AIYFcieJJZhQv0CmIhCiO6rc86fwsPXnVzyluxbWlijdYtim7yvOYEap2XIpM2VvayFT465RpyAqjPoaPKpKip4k3IsyI94733SOyenmMNxPbCYmbsR5ceugMpEI91K3qGCByLxt0iM6PniksP3NEYobAUaprKqDh4qRanNC2gBI/zAyjODQRauUxLKFQ2NQjYTSQ58bBfz0BI85ePeuQo/ok8h5jT4xjy6x/x0SfbNLivT0L94zDw1XI42JjRqXAwaVfVn5f7sD13lRQ5uuQ7dZzPfrEz55dVvmcziRQrWUytWPVYke2j/vyRu3+q3uLZQebsQOVeaJ7ZwRKxQvZ0CtOHpMtJ+XI91oUcSKRARrPPyXYhNwuz0PJfu7GRQaT3SBpQYRT3w6Ibry6pt4L2YRsnlYhAHebIB9hFL1+18fgEn8y2jyfgTzg4GxIe7gkNs6zPMYiwv2pLgPJZIV+PhvOTqZ9fT18tCcZ2v3tcU0N+BU2VE0GmFXfwbc9uTNNkYCwYLZOeL5FN+oKVT1HoLBTO1A6h2m7Q6Fn0eDnh6sBr6me05gCdnZgmlAzwdk+nIa4Z/1AN6uuRzaDI7kcShEP+aX7alfZ6fLHU74v7mGgpfwz4FinnBzOJwU8yny5B7otnU1RbToH3rd5LXhgthA405CrPAYZvD1Tkp7v0C9wO7kMuEff0oejn9byw0WKJVDrim9uRJyc5v9VPutvzLK0zfboO5mzo0fOoq/j6zfr80AOWqkpTSqCy5NPpNN+Sfis8l+4OFUZVuYrqU/6HwxXOMaSZOidq/PY2r3eT1KB5Fhw/srCvBKLeVaXQjtqNw8MlUwBF5BEYMc4o/hg33wrDXyOMRMOoDRmADJFtpKwd4s6W5XOQK3wRK+yY5IAUqAYYGaW9cz/fb3leZg+4eElsI4ejyj2mz9OQtRV5Yw4BucyNl3XwhEroT8dEPr50IR4Vx2D4KUHLhcQvA+siTZnI8Y9mmEvj6HjawqdJtBMUnQPuiaghaYpU6wfDc/Se28l5ouAq8Kp0c68lme9momTV97JjwEMA8pl/k2iKBE78EOv/A2OMVBHXnUok2RUnKT4Mpzs785dru5to/ACRElU8jYDNmQnGv8EECXLVTCHvhGJUQKBoqwLRoNlqmn+V957OinQs5VGNuImmTWdNBIQrPL58JTPRw/40CeTZSaC3hdw6orkgfDgHdVvSRRABtLvKYjMxZRkAa3wjk36+OqaKsJa8rsECO+eXVFPDlOcq+eTVe33AfK0bHXXZMtTKmmX2YdYX/mcqz1QPOLc1L7ZnGo74c86kaIbxDGUdTIh3x1lt5lpAP87SR+ary4341f1W6C1X9F5JtEtER1XJifzyhAGq6E8xo6cVh8oS5pJx1e+LHtFmoVd3VsNi/MzaPtstoOxR1rZcrZA8q9Ko3iOcT5kDHMyBqBU3kQDJBVdOBCddPl8LNERLXdAXdW5LTXG+WaVeqqr6rcgv13JIrMP6zFHsKHMNMfNdmb5fMhoMJvXFeLdV5/bmcb4CohS8vocI97fvCVXOJDBqkitDdDNsKr6LSTZn1Jgk6pr5TCrB8HefzE71W9hFAH79TidOhL5r3lhklJOUt/Keh3qWDVlDqPVD3PHDse0qo+I61Q+H4SOoTgF22OTHGSmuveNuASnpjqGhuP5xiVZR3tKms+1MmNHLp5lbrVcZZJm/awBrTTFHX4oVwsKd88uv1F4VPEkGyXJuqa1RuhdsHU6fW95TQXmPrHqzjxUI2sgcR0N602TssvzOGe8iMXuUPF+I8L1WUCeFttTeQxUhxYdmjkrJHPPbOjO3b2AeIprqnOt4EeaiEwqWZx6XMFaR9tSzSvBOqSLPBHQbvqoK18rzQyvzHjLXvCT2k+kBcivpW8ijl3pre0W4G7VQwEwYKscmSXbrwMT5HrIMLkSnF68t/B+gGHiMHaIMI0fyEq/E3xw3r9eHkP/+5w30dQm5LeeQfXLKWlRLZTXNKY9cY1vkg9gaQDWIn0cMU1VuRo5RpLa/MMIpUhj3I4hrXyFoV6dB/qRBlQz0YO4iIlFihbf+ZtCqXrG1PktK3siMKTEYyuJsIVG+nJ5qmMwdVNtGMX7whJGR6Vkc3UHeFndl5CL9VAt6VyJ/euRRdzzSFif9fUYwbYqls+1BVMVMjbucNDU/k+vvi8YcKQXoVwoRgetke9FI8FKe6Z81tVz5zGdwUNxmD1AaGfDkIxl2sVyHXvQrM9iaizAlqXFf67QFe9EejpP2UNJ6hMOG3OEJeTV7zSADoQVnKDKi3YM1iFlDAyUiQPKZ7IF1w62ovI16psQGeiyrXSlr/U1kvqdAAUYkap2CDR0fwOPbZiuqrUue3kBaIyJqnsb/Pp8guRPHmEPqWYmWVNpWPrWeriDQbqxJDhKow2WxURWZ/dS8T4jDaXAb/Ez7Dtd/ggNdHatQed5q+7tEhhtUoJ69sV5aXXvFcJ+xjutPI+dSW9v38gRK1g7uDszwVxzVowZGHvNaD1cnxTaQHgElVzX1jpl56z2Ucw8aJoZXfCHYqwQBBrQUSABIla/ZYMf2z10j0S+0qZSbDVBQ7vqxW/hbH8iAnoyHew6f+G4EyCEq1Y+H+xxSWy8tRtC7Uw8RYL6xk7NWOk/YqCRIkGzZUNpoAvh0++sCE/8YsUxfAcSqkOEL9O7Zx7TMwxB3mhlH8YmSP6exDirBzyMeSV5HjedHc0qjxESt4EjpVhKU5FjRPg78R7nCap+ixrqrKZ3IHUOtVq66rJfksn5Vj5gx0hq96llXs1O6vE7jSobSlEjz5zlefB6aPixijJXuDh6jSjQvpqlH4itS0LqvNK1UC9MHxfDPUqn+XE2q/OY56cLzCYOmHZ1N9vIk4DDSEnK2RpIsHcca9DsKXxrBecy2eUzlKVX/JZ0pb/eTaxRr0nnnUgahHrPR1VLdV2Uf5w/fWCMf8J3b779eiszl3rqajJHPZ1XTHNycTLMVUGHplGeh/pPdNSHs6eo04adAV2/nGSVX7330OIDHEFzt5dM/rYXcCv/77qnRGLZEBrs63WDr5S4I5G4597o9VX9ZHKXMTLEFnhwMREBLLCuii4Q7H2V57FCYO1vJKR3uOXR57wtgO5z/KoeA/7wXUVCsZWD49eAJM9pkbWXHCMCJX3BmYD7b93CrMlkmfGMDb36zoW8GtimFSTjAntTpnESLDr6q3w+Fkq6uNwd11oKVNN9fOpA/85TVr23r7aDplC6r4zM5+fd4VrzvqM+levHWTHtsy+z/YvU4YtaxHiCeQK9r76V3QRLE5fAkvgdo0l4gQ6TVoRltboeEe/w6wk2uSFzRFhZzhNjZwpb9Qz6TpKwBrDxy6KVvsNGidxJeTKr3rgmKX7tdgdSAUHiIxsNKPezCzjHq54xrbQX8VPUiy6jupTR+TFm/z7CxW8xzD71CyNPu8Pe4udZV7indDns4sXzcTZxO2iaEfxQswRlnmYm3pLx+T7leYgUyyjdRh3Ig4SeyrLHzM7xXVxTO1Dzm+5scEMVpfpaaQnpDc9SpzR8QdwYegX+DvrPyJSDqwsVuj4G6GmSLBIdpcZPxa5YZaWnqKuuKfYqittX1+ntpSUtDhi1lx+wmhWMBM77FrXQC8EUcLsZeAeXZDuHre5LC5irabuQwBmGM214aWbYl7O+L+39N9Zv95f1fIc9Ti1N/uBDWh0jbp3ocO+YR1g0FCTIQufHUaFV+jFwqQ8BgBvfGmZhPPcrpOkr3bvqepyXRXBkrr9eg3F4wqafQP9s8nZ5HjF5+nvlEw62+Z0QOGI8I+496mMQv5AUsEUFlkf/PZK4BmClb1b+lvllVrzmp/YKCga5OoL1ktH5jPK+2OY3sxcdqazmp4zI+IMbBfuhvi+7fb1G8YhL7tsMyuPcjgGagdNfrIUsoY38Zn/mSgLjbmMsIRGN4CozZHzcnEtPunM+B6ByLGNsEyaHfOLVMWbGfMSIPYwnTcyRGBdick7UVf4WbZubS4v2fIbo4tOfZF1731HuxUYyjL0exMzivUb4aUIk6UlYmyovD/f000NcTg6xqOjL0wZTP2e8LFU9BqGEXsh+eMMRvOSJJvxIxDibV5Xp12U23PgHLd87tOGzip5KlQA2ycTnU1o96+f9MJ7fAn1Wduq4dJ8j5iUOmhW32L/SwdfJm1SE+MuhS8GIyObz1pXnEVDnCKODPBHdO6FlukIA5DJ0i/OgWNPS3p9Os2ka0n4wkF++3menWr23lMUrIXjXDzVI8piHP1obY4TqPKSDTNNBrdKfhc/vOJuaR4Ar9UV1b0VmfW5z6TyUtTB1zHxUQt2+4Q/HQO6Jj++TtMGwkW5RLgPhzIrP7xivEI8eJcj3gUvOBn7RndKmXtff/Tn+T6k8hYwEBXuLn1K/Z+RY1lLnT+7IYF9KfpknLUmcRpV3Yjbuqq9tky5HhtpTBUGqxSFEYuM+2z1XfTrqBEsxuAJguniOJ6nvUS1rjuxRHkMjadubf8Fx+oeTn8hMq0WFXu3FJoCtbHcvMB8ODuEVa2BYSD369ep6SLwknmnGvUrJ64nnZOpFxJpcPWOCTW29hL6aZuljoO8lF51d8d319bgxsFXZCoYZLCze3nnu1es79IzivZ1xd2lHaVucR0IsL4mvbD0abLzs+hgv4lhkl4AuFKgMCZkLAz1d9W9hJcBbHDOsC9wbr150cJpLOizlMWY8DaEZoIye2jvqok+gzDlbcRW6gRb/jolJIs3RWey7e7KofTpnNfewt6baWPESydTCUCQxupOlqVau9tnKr3CHAneTNW1/+NXw7F5BSFTPKWkkB63u5zOCueBSVDjQ1gz0l+rePskTaRTX0ZGGDbm+xANNTG2M6rdwJpFnMu/ZHPfvIbtNgVbg0uXewxydn9AK5mMVJn3a81coayk0q+vxoWf5kI1wpc8Qaj/0Ct97JOu5+i345nh2JDBfchz1R4hXa3/aw+o5lcoqO+sUGib9GhBmBra540r8UalzpLvT+cmeWLvChtDELxm/zHXnLlfXVHJlwCN0iR2qOB3/5A8IsBPGVB4D3I5EdCeiSWLAUnl0ghZTy7v0EqejvbQoNfZSyekync/+DnTXN7CVx+gfcEPhgLoT9k0ipXjIyOmrjFRJ33ISbdVB/045pXAnxtNOlV57jlatH4Zfc10fAFnFoEGYFKVw+oNULI9BtcZoJaxtJL6Ab44Qa9m0rpJVIWk3Jdm5LrLMHW3QxQkWhVpiUarqj2nCoEvQ4uPrNA+ILA0t2AcdpKR6Pt7I3ylfVqyZz2dqPu+zUQZ23qj6hSYVO3LeKaQRLxdLXaeQUpDzqvRFZ6fRSPm/5G3GU6G8AjSNy+Eiy/VD6zMCskuO4EZV+5Jg7KTmFZhpcT10oZkCyL17wqv5mZ1bxw70zXvu8hiyTPUsuF0J+fnudUFQtPCZzlJZrEHRSGrRNWPtV4ePV4NsEtbsYv3Y9THCP7kixA+PcYXX6aIgC2hWl7/ldf0MJrG65mdv5JRQcisrTs1Z3Fs6CyP5Fr12UlzuhISAfbOP3HuV9+WWnikgyf4JmmZhqdgK2KXnL6i+UesLzbZXTyMc5DueN0QlX7mbnr+4pg3Z16ZB54C4KJMK0TqTslVBMcpjzFTl8a1f9mS2MKRxLzMuk0Nv9THSGYccNbo4Rbf9IFMwTm+iiat+cmTWGvR2Tn5VRN/UbfQwirWn7AetpKNatYzqWMPRDDRBWygYKr2q/hAusDkHBeE89J7U/B7b/iZpXYFWPWOmnAn+5cvqmuqgF1cPlnHZH1XssHMMvfQkadyR5C3NS+W+wh/v/i3nDdS8Ms4ubwmU+/WmEJGOLnj5NhqvzmPYyBrJqUYV5yj0yCL8NFh3rWSIwI7p4wTi/uSxvYhiFUdjJB6m9K2dY8TqwomUuCbNIW6LpJedBchyVj1jmntixUZkPm9wPV2wNzXsFh1W8bYUpR8qHflnxtIUkTWFogi4ZmGqrgdPBGVfJwceyZtdCXk9tziqvjJ7AbCL4RUoC/idvUuVG21tnlVrWnU9SCIFDH7h4SZyN+m2Wtc3B5Gw2gOBaT7sL1ZnBOkW9baW+ynMwu5d5TMG3Wowcc49e6pTh2UkLtl7YG9d9fPx5oJyruxs2dvneJUTAkHRzNar+2I2MPJg5et9jhMBPpeBnkjyLKvrsdmG/LtKbvhlq0gaoYaVQhnK/qkpYRtpv85wT11hS8iTWAts23ItxE+Jw1OqzgdRx7nRFbIJsV8tZ3zvFZgBBdgY6R+nZyi6yVK7TzFU1rjng8jWDK99rqFHlPCMnF8bgU75KWuYl/F1JmXVBoIFJ5CVRL6mcD3Xqbi3qEUICFGfCEpJ+gB1oDlIBPm7/C2PD2tU+PaCQ4nJosgv/aA00W9Xv0Xqo9hpAZyNjYjEJ0UiB96bXlX5WxgzIRCFoXMED8/EhQQmPJLhvHrnKN7h2jXUB2/ClZmH7QMk67m6q/QCyiShlY9ACKuYc1ftvUMmwPyvOKydWOJCHaVL/Cp9UViBVLu9oPBVPfZ+0GQp3JxMjGOT9GtGqQtH0EqtpBF0j2O+96Btfko8dzn+GZODYk2WILMCu4gJ9wEMC9Z1J9T8jmqpPIYAvysSfvY326ezmsVPE5i0dIvqt9j+DOkR4KsYaqAQycADBvCcXpVe6oVx4jkBc0y8CeuoNODzW7YHb5RcJ5zvmHbA1juTQTN5hBbxpXgSUlw9675C4UeSJ+krkaRRgUgxkD5yyt3yGFb01/mbWdz9xQffUY+THgXrVv4W73dPWzvGSCYaK6O/X4WHklY96+l83ondXi0ZrzwCJL2DgpOipFyDwgW+Eah5mDv9fsTzM5d3AppUc2REGXMtfCs2a+Nobq8pFXCL7KkZZq9muLHRKc/tVcxtpvHWOTPpa/yr5TsX3NCVyNkrmw79k9iCRGtrwL3lNU3JROkdRxW9wGd8UVz54BIwl8c4Lzuu1Zwxwmi5jqReUyZ3j0yVFX+OARC6NQhN1JDC3y/nKNhhasdyTogF/Yngs52a0VtcX6SMLBbPWLUnlHp76muidZ0cQxS/RpHL+/bqU6/6GApiLR2lJRF/RiYE0lTxd/JfqmOMQJM+T0RPhm+AMzJXel6D8tsgHKTFDxXbL6+XN0QIwzM+UclV9WAT4/vE17TBTHgReo8h2bTsiWmsOo87Wt50oqI6idsqjvfXlTVgKs9Du8C2nL4Ij/IJ1Qge4UkbZLZy7rHbR04FC8+98NbfIczmpLAnqvfWF57wwxA+sB41jJ7uKbPRP7W4Kv2YV0w52uhyySyoHN6Ama94q7VzyvNIj39bNQwONAvZ1Kd5+B0m4VPVdDtJTSbBtvf7AwcmNj4uRSPmq/pG7QCtzq4Ys+8chBM4Jqc7eylkkLuq+T2cHs/b399sx+5Qoc9FmeFdK5nLY2wDPfoZkmCfFg/pTHiTjB8lavV8JPxCyy/R1yO0nziV+IrRoFv9vsjvGgbgBscUGxE7phMkPkWTp7ymCQ+VP8lNZDnnjowxMjoKZLOqpiOGDmNCsJg94A3T51Z/RGj/sKqlkhVhGWqRaC+zATXpSipDWk5V9mRIEgY2PY67y6Jui/3G5txidCo5NTvucOYhzUFyVltkEbYSFa5MH6uZRVwnkO1yPURFknC8NsZLkHWktOV6SlUl0l03fGVIN0C7TxmifMdoaNVeTPfoNsm7OJwMo0HX4wsODOlNhm51DMbs81QQrlr+ZhCmHONbWzmQyfoYw4Y8VQcvI7HUTABmtrk4hlUdtGMukxN9CRW1DOr1G5WLg9zJpyjPIxG6YLAj+lsMyBX3GW5WeASjPoZOKfNeauWMOVmcrUXnNuUflb/FC4txRxtNZdW+0nspbM6ejm6zek7JxHyc3yS801vS5ZwPFpu479epb8pjeOfDoLyjbXR/wY0SZhHFcv3OMd7AJtvzh9Ab4C+5BcKcEe4sahjVm4kTJa9CJvBQjXUjpifZoGdH9//vHDfB9kWhoUHVB4Id6S7BDtOg3VWORNIQSXDnZ8j0vtuXBtczBVkpQcrzgNtAH2BlZHeP6UsaMMiBTl+l56cQX19zyhiKqjbTlLOjye4OYLHKJnUMeV6YWOeZMJO2fcmWzAxA+lzlRx7E/zrgJC075ELj1x3tFtW22eVTXVM47eXLb8JnVrgVc8bYhsukRlX/wzHYkUBQE49seBm/VA+mIJEHV3UeugOXOSFf5dBwQ1cbNshXDIFyeYp7ix2JyMYadg7xfGaRfxAjuRYV1+mTOHKb6E1fZI9tJ3/m/LIm23Oep7X8LSM9LJLcBwc7wlxSdkOmVMpPK89DAg7rfwaMcmf4vUG65WzpTlezeXvY5R7q5yf0XoifKbavODQRfW51DE7bmfDZdMOMLwTp9fOvgT2hC9TH4JgzuEBWGmGY9RjYW4uMleCuuqZPRJJLmJ4P50sHt60D4ZZeCRqqzkPXgZswueibHoYiHlypCStQPtzVfSE3wWQKOHpltP1GO7UDT/fy9uq9zeCK6hNPlnUkJg8ck2AbDNnK9yUJ6iYOevunzn+yOOpQyQbKR3xU1yMPh1+BukyxgBzGya9/YnRxnvvyPBYdAI2inDMWOHkLmkM2h1fsuNUxpk1sp/VOVmzLZN6FIIMj7x8V7zNJzLLdnoQsstHdb4agF9PbfaUgKc/D18WEXwf4a1aYe/InQnwb9fXyPN7kf21gN4NTe35FPMU0K4kZQHkeSeQzqgnj612tB9vRYMIvYRAVD9YxfKB8kZ5816K1drvIW7Q0ZlXj+pZkDtx+PF0A1enlBXlwp4GNqmMsozk62CuN9WiKxFZmcsrW0+rvnKYAyo2CyPNuXrFSiIXGaE59lccgfEXUbi0XwX5B9KNhkvBVxJnq3Sf32AF/NfG3crW0TiaVnHQKL3B1b8WtGYefFV0P2E+TDXDl7Bjqx1uu63YXQC5UZI8u+VmPDXa4cDff+l1pFGOxipBOaASH47IPgYgAzuuBGvfyPHokBeE4CpDSAokv8eqffQMztFfHgJ3en2FfiM6yW0jOzkiK4nmbyt9iKojWeQoo8VWGR6FjcozQCbyVN8Exwq+fOyErK2op5ccKc9ya3e/qt4hrhFRiKbaD0EGgu3iftLdhL3Z1Tbf2gIWQojaviXU9Je+diuZp5Tu33zRbdMY6+RpxICPOPl/b88Cb8VS/5Sz/k6f5Sv95Jt1PtA1ZDymZdmL1fZGeoU8y6S7OHbYqkqETP33heLusP7xxksS8YM8kiQ/DMReppyl1lc+HHuvLAnxRsgarawkgOwRlYdW+qndOikVy87xdQN9SX81BFme0/CJ5fE9xDI5S33d1P6WXkZ9uxsy3hs+4WpPvuFt9SPZKiiZZ73lLkr6oMUMFWf0WqBALD4HCSIA4ls9OoJ69IRlJdR5PnHvGDZIaT2nHTOAruUVyvWHGlcdY+/naNkQbAo/YNKRAKgpTVFTPOu1IGmpgtCzzUX2aW8BefCzEqoaJAJZq7eNrQy9/pIsv8nklCaU+Bjv5jaDoU+t7ArptbIuc4x++1TvX48XSZBMr7LEEHYOaIPBRnl7lvR3JhxUSH82FgWW4JE+g9G9AJeUxWFzgFDGpg5eieEjvc38IkV7tb4MHh7fofkRL+iZdLtTbE2W//X5xPWZc8yEx2KCuGKJVd3jdIQZX+ZU2GwzaJCBCcexk8Cq4zVu6s2Tc1fVYI51WSRYQZkB7Om40BwQQWtt39XzQVhiv4XSFVAFaMaSDt0A5qPura7rWF+0V9SnrHqwRlrqJ9PnokthX10M9mwGMjqFc37AEQFmvTzFxbld1PbRNN54JCV1+2A64h573UabOaiYe6evdYPQplbZ2nwg43nm9zITKlOtp8EPCtPTGRJQpjr0odyCeE165/C2BW70G6Xc+EEx3hsA7jmLdyKofFJdMv6IT6Fca42H16FqEVne+e6M8xgytIn0LorwFDHP+eNyNnrZZf7OZ9UOpIM2BE5IoitfH3hTebZUNxp95cRE9QbCS9aCZXLQtUC3q3bKXE/8j3hG2vt9+Z4IscmQF1HnvWV6PHdAYlP3IBDo+rzfEcu32JBxXzxg98C2KzDze19UUx/Mmt4xEflS8nGTuedCFLX9xcdryUBwAA3aE56YXxwguXTwzvQlXARKCEXn7glo1yqrr4Rg0PUL1WmDHfPT2tS2mCakOVS1lF3cHdYWtbWXUQpYZqS9ONvhU2nOl8NaMOj9+kldNowbF2WudNxrCd6jOw7Pe/8Uyc5/Ze1hIifoePpRq/WgJV4G03AmPJR3xzboT3X4qgtnK2iGu2/kZwnE272Bxn7Cpzkc/mZjVNypxaIM/O+JvgDw+BR3+paw8x6zYx47BAng+tz0SkL4HJe0QxQuKqndXn4fIpFDx+qe7XD40LznaS7tMkV6dh6dxZ7OO4NG/bAD9k55hLKtm1bcUvmXdbdA9d6gCXmDI25UptbZSdV84j6hhNRqFkqrExCbSPumYnfKw2t+KdpMCjMT4MGoK9Dv1R6JfjE75AavzyCUN8ExBxnsOZnKFnvqyX52bU51HZzEnUXoSONfIE7+pnW0NPXrFqHKMuCklc+gKU0t55AzaOfsDZiuPkTRm45LWwhAaLQxmD4yOxHkVq++cN4sB+g1gamojc8GY6jAapb66y2PsSB50hOEiFIVnOTPiEsglpfyq+kFWr4bofQUBb67NTWi32jNOgWWqrofGMfU/sj9O+mXViPP7Te2sw1XdW7vpZN2f/3Z9m0o3id27BzBzVd5IiGVJI08CdMZnzs4i6JMfW2Avv7cG4HYaVwIin+RW3qEa4bJrsMyyj3uegxGTrL+RoZjTUyBUgqkyf33K98Ur+8qLUpSC69L25LvANX8HAVjdFxty0rHzmYVdfnwuNSGylAZvX/EtHYPfPfGGDGITEfLNl1wE1KDNrfr8jmFm3cMaa7oFAhyia7Hv11O+6/PYplpKpxHBAgVYi7nnPLFik0f5vSVtuPOK6VtSs2Z+HMjJpQvQyv660cSinwkBbLEnw3TS9XrUvdSVH3nocSR/M7Fzof60EADJUgepC39UdYwwjp8v4cdzCiklApfmZyQF5q2P8cbGvOJBAsgciQn4yuwdGnFxbx9lBu26LuVWzJEaYCCzb6UX26veFo13T/I1ou4l8VW30xDpiqUPw7y4L08ALOHIC8AiwUKnHzmil44mozoPs7jn+teqpOp7EgExA3s5i+vbV3VNNWLyi56IrJe1AHZ8JxgHLaqs6TCkE81s6DCBZcPtScuiP6ki3qqn9AS87HuAmDeCZqRb9ehCqTXVQHlNl/hePUqQr3YFo3olX3zZwvNGV7+lpab2M0hrJstnrBIYPqkJVtkvPBuFpJomtzMtYcU+IUazJvGc9qqmewJzuSIR/fq5FNrBKWNWcUtUOc2Zgs/Yq5Iq4v21F0OfuM1Tk4FdnccjBYx7iLhvttCVJHUweZu/tlZp0Az0n3z3tZV5xrck8PjElybEeYerPOARvrafoVbukfac9Tzw9RFD/XmX6t9iaEPV2O1RB9P3+Yqfd5kF1oy7lT0lZoT5zbWeeOgnWZ9K7OJnEQt11fclC/swK/So2Ww8GSA7CiVDq3JFRVhphPOGYH0+ZuItuq09vtb4rBgADDgSdDdGsJqsYSk1Ajnv/mdGq74vz1ksWAEGa/r52PXkSWRLBkRkp77L32ImdxMZ6ijFxbc/94vhiyP3KkMXR1PPJXT8C+Hi1MuabHGR2abZA1THgBzAGwsOX++TWrQHWxMzirSw6npMnlXhSEAx18twCZgZ4+tZnplFqzooKeIOw8P7zKg2RcCkP6NTBRdRrafnPROMARrQSFku9AETO0yEOAXKnqNOOjkzqoSV1JgvYbh09C3J2uV+7vmqFBW2nYZGZVDX+ksJnbbnra6p0XG0s1wmlKce/fONEJ1AMs3IV52HQHICybw2PpxUA/qoqITnZLTuqmNsncXzdnHvG9yqmqlrEPK5pgCbqt+S1JedMZx08q0gEeXgQ3nl323l9wVfyr5Qj4AIdUZ12YKkNpKxJyjOQ7USKs1kmuMWS1HT4IT4T4gYqvU0nHCpQL5tECr0XzcgkhaquJzz1BfPGO26nOo3ynEQIf5OGwAlfCqbspYSkMwdYTZnUgFXdz43HpEprp2EvNrPKRTECEkmYSHaekK9QYkaaj/EHE/1W+5PDReHBDan766N7pYnaet8Hv7yPJ4kEHOGtXezKTzkSYR6MwX8LN+5fA46wRvIQz76moT5TTHz2VGVx8CgvHydrKWcH0jZlGlxwiUJu3o+hJNePZmcD29DIKTNQ8YwjhtaMamxYKBl2DRM5jvC+HkiGOk4vmfkwtVvUXbhLwaN+SbtdbI42Ti8LZi3WR7jpc7mtNVCYW3IMgbVhcdEs1A+H8boBEa5nStYy7AiAr1Q6uHRF9f0MRmjgrPvsaVcsa9PDm0oUYt19VsCOSS7Z+c1KzU1VSkmQf4lyKx6bNBHK5RyQwofG+ixbtw/WYRv+pLytwj+S4dsx6B+j6Du3q+y3In9qp4PN9fn6GyeMq9tKQWjZbXvf/ASqt/CbkvJpl3JZGEFQk/sYWO/X3Zc8Vu+8zfENYXGDnhcDSNYGxFfjOp7C/aTjg24zvOV68pLXbpgRQh8qnubmuFUYG9cf6zNPfs7wEzou3OI8t6OAEpVqI/A6CeZq4b7JnTNTvOtaikicyQHRpPE3SYS9KXpiERGLlzVQ+mGRTHM7MSt+d56wElyl/RqH4jqPCbJp80k5ldC8JYODN+L7TKYQHk9wII7kIHNl+iERLEIA6UhN7ysGMwh24Xx15GU7AV98EkdPCodnnGUz8c0LJmoupRnxDGmLrZz5Aq67xX/lNfe0EMHmjHJhsPAlaBXLnAgZqM8jx0eJxcTwSnLvH23LtO7E5hR9lBiJGJzvdsnlTZzDFlumXKLQSn36tETdmKkpCuRJeJiLhIIKTQBJlTP2AoXL5M4og3P1WcOjl08+d67PEbgPD7UStOQ/aPA3zHCoROUs+hOr3/KXABJP+AmlBTNcypowE9wxlUeg6j7CfGc8e88FiQtY+Uq9Y9FWt3biNesIFvHxcCB4QFJpIVdRwxeHCNGizuJpiPetWxKvbNPRKTM9095jBtwBDj5nPiOCswQOxgvukNE4+K+KCh9Xe5kgPZPnKufupXI/UmTvjwGnMumFN8rnKyIHx6VDQCpgVk1BzLvoYeDKg372OlfIGbxz6844op7SyXFKJOGrdsSJsp5Stitgi/sZV/bSXwwJt/H+RHUGpZA8jgJOire+IhogvLEPBw+KRxVwJgEXdxZDqvf0q4k391nKQPbYI4OG+mK8WJqd1X7KAWX6N+R7pExtCX6SmBQdgKZrVXH8OcVuldPrRs9ncigqcK6Ypouj8FMOIJS8DkKZcqejBvg1R46RWd1PR4qIDQYAVLn8X6iUTBzWF82d+m9d4z5xUBIJ7SItJGtt2gd+TqnkKm+Lz77rLMwMenLqwuDZdULDXRuVusYb7qhJJ1EtpUJkPwkwQHPqbSqa+rDDzJo5bRnmC0teymydhDswVVPaUDpyuEz4Ffhk6CSrmu7Zyi1Sr9HBq0DMc0c+Q0a70t+NUGOd2pVmpoxkfautMB9WnzuRNvc9u+mUho91XnM2E5UcOsOa3tE7/VAoaQTs0udEjqgG3KJez+bGU0P0O+gWGHcyXKr81j5Ho3kCbPQgwAmKNXEnaD0LAjV88FPuZ/I8HdgtBuzIgnc5+RW9PTleWCZ3EmNeD5R0EsJAhSrseHLXT5j9F4rcIxklA6MbwZPQKCWRkjZtxxJzTbqs6gaUwgLYZ+jw1qZxlZ705HuIsv9BVNz9hx3gn0tKfuFm5GpU1zTQJQIJbkCKXGSSpwkb8NKEMPy3vK9IrutrGPJ9eM0EOilAXdL2Kt+ywcxsXIyERvCyBWzmYvKWXBCVRcma+pJOgkePglbBGWGoYI+eT2r/Ry3y50ug9Hpubqa4cJE9Qp9pFzk6n0Jci3LqL+/hxyqo8RU2xiE1/OW54Fr+5Akxuhw1lVvnfaaZ0/kWjkDjrGDGcc87m2fBlUNcSewp4XhWZwHuRhOOUsjfc/10dgoDB+j7FO9XtV9MSGx+TILVw0CZeLvXMkGCmB/3+VvmVxIcgklaXCHWZtJjBMR4Bt4l+exk9d2pw4csY8YQqiyJXRSdlTvnD8skp3B4fwZk3BTObFvxgYyIFrVo59J49FIyfeEO8DmeINUGUxr4FfPGEHeSCApSV23f7gzrBs2mJy4dBfFvSX8PH/XSsaRAgK9MKJvAtZTq559b3U9wDmTBTSEeqxMn9OkywYtAfbV+jElaJBnx/eWlGY1N8ivB5cdp9R8U7udp0IQ3pdqlgaVuJM4WAapZFVLKcMAc7hUQGFUhGb8kdVPXom77MFqKnZZoHa5wYdeEtqxGfSDqHxLXzR5hERE7V+wcbY9E5mlg4dAIy+jPIbnoKe6P2UL9pH9fiL5SHtJSat6PalEKjBLsEgtcgfbVO+uldmGqjoP6+ctUDkJnpINPaKJOdb5u64yQyareWi0ksXDhvOcKHGBEGUvn+1Y9YyNYKEkaQHcYPSbQW0tB3ZxU5hR/hZiQpta3xOdU4X6M5MnA9FvQ1K9LxGuNrQdej7pvwIlQqmJz1ziYnVfGM6GRyI1w3N//UpwSP02kMZyXomEEjzN5CRIHiCsrbxlb+FL5F/1tc2wH006AhT5m7YyRnUjaRsf8ak8xsD8s3JsDVxbuNeIfYRvwhdQMe3PMWhxKCsnq70nM3pjkAWF3Y1vWF3TJURmJpy5xaYg3rzx9T1R2J4Hvz4G+XBLkUyFDpYV6WkcAbbZZgWtOoZycMQsc1NtwNhT50zjw0GWWq4fCfxeKSC8MlMW35OgUY6HWDXKZ+yN0CAa+ES+PvEEsRXvxL7PWutEYI3fxJDAkMkWrn2JMSEmQ5vsKc9DxBIAslyccxEMX+kLMbfY8NUV1TV9ww27v+snseVCGJcTrBBqsbBUz+m7E3d5lsLXWL0BQku+HMnqOC/xU/a2Jq+pufi1EuPwuaw0qGPxJgUrZ0kzMb4CcFgZEkNzxyoKnMdqcB6Y8t23o8ZFaOTWjE3y7K4noUuwJufkynV9vzTOK2KrV3+QqOTsI1Z8H6CIpe5CK5iFBxocxNk3hY4VGZbm0WJUvnObiduO6bUAgP2PHxdm+IGek1Yf43wU2QZHpM5G/QaOrc3kHWcGUxyDGGkm1O9srPlHkkjqM2NHiYt8nrLi3n7SoKbKv91chJvz3ylK5PzAiJb+bK2oHvmG1nwccJsK3s5qpytU1/yL39bQ03Shh2Z0SeXllnp/abLVOnZO2GfkajFDvKEoi8FDZ+gB274Vg5nbxpNKXEldZAcQtbgu2RfdeLbJ1TWF0yZoNjGAHp6Um/0DefRwG0qPxPJhNctDPnmibiA2bNHoJxR4liykZRB2vqozYZf85QSbL5Xg9QlSy4z2UzZZdq+wxkYIdSDbUWECinRXpzwPKWnCCLm77Sl3Cn1ptudLK3dpVJlcthZr/1LMr/x5TIFNWyJw4AYhrvq4dip61zuwULeZ8Rcal8N5WaLKecOKSTfAYV87F9TgwafyyciB2rm6ph7EJ7HdK7q3J2i8K8Yi0ege4/IYKxqh1/slrL3JK8QIBzSJqrfkTBg657StYGavPY0t+ZwXAzkBUuVLYstAPuip62SEyEmDmfISMyaVPPpzDIxPgSTIsErJ8YYdzG7lZ15vxZKHPrFbAPkbL1kdsgKhFJ6inBEkxPI8Hhy08xUBgJMpDsJjfCMV6rkzn6qesTdaGJMrvQ7nYEkmmiStk9VxVfNK1gTdiztkuAiS7TR53p4n1M41yzXoTV+fjua8O8s2TkYgyYYf47Fd5fXY/Omnzk4ss3RyQqtX1tc7iJwJsqrfklRUPZNwXUiSaVdRr2gNLYnPLM9jBiHUswV5E0EOQ+hjI5ELx7zqbUms0MtvGR5Tbs8YTqliIxL+A+fq9YVskquywHvctIPZPEhjLiTy6pq+adElpkW6852pHqIiD6yeMnBt9VtCaddZS0oiiePI994LRxcrw6T6LVLO7s9sZ1oQgtnZbGokxGlo/l8eI4IJmmpPta2ZyOj7+/YLgzrb+OJ9cQwjBR8ln5SIvbRfOoH/uUk2NOUxdkKsbjkaHk8JWMgoO4mDNLVX1bt4bYkVhnolM+Zq+piXTjkWK/G41Xk8JJrw2Aud6gt+VDo/+g/nXyQkqa5psq+k+UaRRxx9/cJLwCF1d0vtxpsQPTFiE7LmIYxLmIa+7ONdWuXcVIq4l2QmpEi9+7xf6ImgQgntZzdQPevB0ZjAN1MU8t438fNMBSRMLODVffkCxcxL5xPvmyEFKw7F9U0cV7FLcwxjCd82Extx9Tue7SStp2tWPusKOgzIhs3AcIVOex6YK67P+Pqrbza8n1OPEXL4YQnnYAZYoezyOla/JWaTO3vZlqGc0IMw1O02bT9m+XyIJLqwCJh69ocOJXSQlH75Yef8ymO0IB00KTyntCxXvEpoYOZcvdQ6OetvcPOmzYdKcopjw5zoaRm0q5mnxNrzpuIJvC21/1CmI6Odo3GMXKVOOpRv4k+9Sw8VxdaXtH5KpIAwqqxW2Ga4rPfzzPZPzS/hr7FZrATrjvIYLTAkHneyGMsR1cGMw1NH+OxRq+uRoCrqxiUrlrFRaoky/tS+V0y0lQ72Zfol/WfHGWm0NQwBLTsH07Wb5TEsog/RFFWxoHd5gnoXK3zWflecbx+T+eavv0gNrjtjTm+JPtWVMKzyWSfObKkm0YgkaVCRcc0T+J7vzHmGq2uq5JiBy3XzAbERdAtQxrZm9LXlWsjWKABKeCebtqGOwI7MhD+OaX0MZbEcmeFzO/Uwr2RIMK33eOHK53S3r+FhsyDIRyoIMwp48hSBOatcjXMMIoGLd38mksfS2DKhUpDwopX1ByzxdvVa8J6MHyKTWtTGMzSiXr23O6fQGVV0QO/71xz2lCeQnGm0+C0yD3SjqT+1cjRe4SB12YhQgkIunvX/GLu7rEtxJFnDUwJJCGn+Eys9xs66DP/WOn2qOyuDYIPQj7vZayyVcRKclxPwMPvuS+gI3xubY7nn30SZLcvlmQ0j2kgVd4w4uPygWZ09+BjlRNGymIMSUBqT6JstmhPIKq/BGCZoOcObYP3so4a26Qo+z2mk+i2N3PpjoLFjUjvNKJ55lNAc8S6K+/AnuOYpx+QTJ9JXJk9XgFBqe6s9v8Ar2I8VQMsTgnL8zdSW0nx1/MproBkElwG/gb5utywBwe8MdaeqfYrJxakK6HgreZ6pcQUVKXHICtWq85z+jWbL8vG6CwiysLGTcGiOGtU8trm6P2gwn0R6nE4eXLxivW5HzPIab2rKTh3JGmpwfYELmOt7LE/1NbyEB3JEHUWRmzh/EixT6xjI1TcneGJ/CjzZXFyvX7oPiewM4aScg0iq368Lb+pj1bKDj/igJx2s/i1IAliFoVQ0TnGuKrlxeDzbUb2cg55oaYmsoGwNeqkaXB8cRl/8QjU+ZH1HOuNAq9IvaHUFwDztAd7aP6c70Cg9/W/3zH6GFuRKTqIK5HlD1XtRDM42Ujs60IuU598vSeqlNSznQoV1Z2EWDe8DJBgchtXZppvLu3qmK4IADcIrkscEhCTbJlE9os6qvdQOkiUU151yrnSzgTQMDCAsvpe1LRup+YYGJ4/sylb1+XTPmqB6quX4sEJFN7ud8jcaZGQxFy3bFLJVciaieXHuExyX3RdS363eLvMDr+0px/pOkrgqqkegGp54DLZRmTwauOV8qmSZEyWZgBPzmQMI9YhK2FFk0lTP9PzhaIobmIOAVPEHQULzFgazWc4fpnURnIiHiPbKqN7SiO09mpJyjG12r9d8jNWHiKmkoy/ty0+23Vv+ltgzz9NQBxefQHQZ2LjiFHlxdVbP2TPBHJd+x8IHTq6Ycw3RdCJby2tglT4oLFEXWuYdI2ybZW70M46Ls6kyK2HQp57f/bXa6vMp+kcLJybk32NspvSEpJBD9esU07Ph3CmKvAmBqe4j52rqnCUUS8oI+bgO+0yl7QzdQpczbXiwKoDBFeYoWkTopgRyFko2umJOZmRmX1JecOjYDkFOp0mUi9zyXK56Ly3TEB6CDdgKvGdkmTxbB3SjVrF+dcHoPDxQuPKIRozaK7EUbka/ofgtHcRgMUdR9gcVYSJqEqg4pNT/yvvw0VFaKCw/mq0rwe0yMFkUZYNW48NLMV2lNybkpKkdqqrc+MPA4eUztZdsIR6P9NZ7zAAQXG96deeZXNX4ELTLE4SWuXdCHCJZDqshWLA9q+eB0hG6XtBpCuxmA59cOFnql+V7GUD8ynN4SvoGE2yOA9AcLU1zX+U13rg0z9EhpDsbmfNHg3aWl5K2bPk8XjuMhcLM2GDW0WRswX9nK1FlUUB5O2IgKmhQKr0SwmB13yMizLOxqK7Bi82F4Ljfvzixx2FXoFaXPn9+WfVbzsf1OhKOGM3MRzusvYuJPkXdeh5Dl/hQGdvA0DGREGDr0N5PSDWq8YEB+Wp/BHGVHFzULT3hnvKDDN3i3SLGq8FClJNYgWbsiMBtIJaz/y7vw5lt6oHT1Crx+6Nvop9TuGtv+W5ZRLR8wpf0vSrHtIQVcSc9XDbVb6FF2FHVCTlMuEf/5AVOY5EaP9VvsZ1tV6aPBEBmqYbFP7uoKzFjVVbJjFBrcYj17GxJjrQ+Qus964Qtaqt+iyTEW6HD9prtIqjzhC+YHTXXyvkD0XI5ttHTGq0JBv74f9Y9lJXit5wz2AipTwUJh/CNNskuhFtajnQreq8Q+ufF6F6bOOhoLz4zSk1c1BjhirO6aod+xWujnZj2S1ypoNaZcwwtfLWHuaPTchwGAABTOl9gz2tqX9ngrnKrXcMxUlZrgE7anZwilkyeVb9p/uEajAmOxD3+DG1LPhi0vBGeaXFu8Nj5S64IviPT8mKFPZrrUTxX/TzSuSbICY5AQ/5supGiSAXZOM7Drd6tgOWzx+aXk8h+PsEZVOVn2FQ0rJgIM/IEfYKLUpSYj54X06Xrd4JpvNW3jzds4VdLd6BTEE+RjnAyvaCn6ptOul1ZoMpxbNYIzk6pGAAOan/IGZnIR5c0EZl5wEwYT3rCYObkIGxC1X3QmWBKc1dEn9ykLutRi9TTR77LcZrEVwNaQWfkJHW2AjD7XUdEQOeqrvGjQUP97xC7me6yMqiYCeup9KfOgpTuZjy6h9e248lJFxx7pyBRvpfnCfhVj85LuvYX9Oxf0/gMa6Yap86BVyI5mxQasOOBEeugej5gvde3fB7LETBtC8Jv+i8KipYQ2oQOlOtLPqudP0vQNpKOKFPv0fCTlbarrBJGpPgClhbUlWYDnIvdKvmHJJa9q99iJ3xHpkWN47ycEobelgONL6BcG6iBRncZrkq+Iq0LJXH7AV9xVQtWDoRfCg0uAbpD41GrwXB/EpB1V/ehSJ80VJ113uad9AKndW0qJutRjTEUecYoyPLEYQIytM/F67BJDl59+5nGYf1piprdiFQh/ko1PzHOVY3e8nSHNP4k38wKK040xvuENbNtFL8FNiwgI12wREWTfw6nOeehnZzl4j5awmKbAO6dhCPiAt0w7B0iY7vG6hpyCgBNrK4qSe0cHsJ6UK9vwW9X87oovZ0cH6vjk3gTy4uqDnELysxd3sd23pJRLd1DdmTIak/aSLenUnma55e0lHG6szmdSRV0XCbwTcGpqFvKLn9HWH+GGx9Ni3zKw042Fl1s9TwaVcQAJADGEsn1JKvss5vpoF69vA9Gl2Q8jbh4kiL5JBlLRcdJaNTXaFF/qR9tu1Ff2UqUBF2qTXNV/wi7WkCiJFEzoqSVScCg64cve6aQ6r10fBjLm2HiA3mjriNiI86J/Lu6D8gwkshL4jNbJpf2+aPyJZwEtFSrb868JeG0JZQC9sdZnb0y6LuYN6tnqgm/xDfoDegl6w8oylLTALKPKptD8Qr38f5gSiEH0AN6z56Fq5XzOka39oh4VR+bpeF6gqajMWJ2fKsaWwPTox6Th2e9p9BRWwpKZEY/UX4vSFColmrpPMkJpdDb8+sUllovv5c5s7U0/YqftMyC7VN+KF4mzaV6HvPDLtkQ+8Liwse9m9TkZ+8Mxl79Fjr+J44kuWv+S06WHaxrwoZL5vAUBKarmGRo1iT1B+ZZTme3do5V1V5bBSvlLM2PgcbP+Iu5Z0OGCKJGX11DSQ2h3OYtWTYghCP+KnhsARHVe1nxRav5Kf+aPTC2CX6IwmaiBqtrbOWOiZdFXyBG6+KWhE8UHg9vW87rVkPxJHyan1Fe9YZa60z3hthdz6eU6+EOt+QMOsMtrnFcZz7Bt8yOk/AAEApQZa0PaFd+r8P+wp0RcFneh/6xphPpl7YPp/sVTatDRZcmWdwH3N/X38OJIMcD8DIxE4QGCVlpWWZyXTWdpJliVPDerM/V3NWZ2fOKeUzwL37bOL97cmlwM91Z4uj0ox2oxrrTY0907mTU0E5H/DoDP8w7PekqP0pXYFrKdjRoyeai0RYspghoy3+Xv+VOVCzWBFN3auN2EI0/mwABTKB6pjfjj9xcRGxWbcWkGJ0FIKmBVmxKIa8Ms7xeN+BM9AYAZBoiSm+8ktXzyDSzUlUmqZHdPSOrWyp4Pebg8j526hT2dhSrKYCGoPpIW0xmV1VfN/u8kazTvSUVUO3wzuCwfQbhqN6LxoCga5zuDv5l8gi7byrAcX9U61zHJ2w6A12vLxv+s6VURwyBSz13lffRkh6RBGFb1GAJpCIPrM2ZALbqmVr4pctG5EkNnJSS/qlizAgyuYox1hLzxPQxTEOqtxgi0FkWXZW3+r2sZPsgomTdpVS4ffOiXlF31emKa8Rfgqg/Y8I/v+zMaU+yrGm4fALlty9nRROGggVxT+3xukcgt4zsAgyr99LfN7uGdC1cxsl4p8acjZaxW11DtACJhT3hJU6d00T2tP0qclhdM+gR3SUoNqdDbDknwYibm0y8e87yGjnWYi2HjQlWaVKl51XSFFFT3oc9ELGGXB0r7hvviI7s1r7Ezqn2Y8ZjAL3Uc0/Ss8YVmg87DXLorHITIrO6dAt6VOgmDVnrlMXa1Hbz5V7bNfgqnxHONlix84Ivlv5RLbesGViNOBj7pw3QD0vV30lbkTdt0PIaItadVM6fE/U034BT7ah4e7C2qzNQ5/djm3nVxx2N3y9oOQGsurJvxVKbWOX4bR/P8cKUtFzl7NIS3rzLejKFVg6mYFdaH6kg9xVvNp/AGXarHB9rZM8OttNJJXX7Ldn7a/tz4VbXIN9h2DknfzFr/eupE9OKOUoHsKo5WhsNRzMfSZAlMvv/F15Bve+8+eo+EhRvyyO2lURHHaenDHBli7Ur/ZhMoZePB4SRIvHW7CPfNkj6nCGrVeNjhW3R7QRtJDU8l5MY17xv0GpefXOLU2TRGhid4Lwi1jxhJTK8/6v89knNR7itDEm3L54o0HZ9ZYE547h6HjwnXJozXaiADZjNE8vDVKevXNzH+OZhLWSWMaJcHMQe9aPS0ChZN7kGbxq8BXSYjUeoNzPsidBLW30fYQIzSZhEuUQ0Ya0sdL49+7FWXKOnhTxCFOBJysGQACJpBZaqao8bC+8M7EfvqKXICEc4AcuVRa8ql8cOducQy7nfQCnZvmMygK7TcH+qPrKWbbZiIZcmIYUj74qXmL6Ior28jxdZK2xhopTPAq/qxqSVftQon4eiNLrdG7W0VR4TJbmew+6Ki7X6LYo5wI8O1BcuttP1eQr2l1bPc/FKuwG5AxPMhbfDqksZsjuEyJ9jMV71NSYy/4gTULjp5P8iiKU4xnarsicn378Niy21Ysj51GwRpxAaUHYxUNW+cBBmXZkOA2SzvBKE2F4uhUja3Ord9g8ZSEfjr0wSJ/3JGXZnF3LmwlHlE84gIS7K7NuZ6uXVkrSMDu84xMly/eEab1wWyQAmD+4hO5mPZ6xCbzUXniniNmuFxMr7s+U/UF6tFOv1VcvxMcJeSjRHlkgQ1skbqdwufO4cPKtxOpzbrAjUybYAOcioD0EYBZpX/pYzHztWL6id/dWCBGtgkN3StYg66mswIe6kw99mNMkzIfeyn+esvstrbJ/9MP1aEJypEkyq1a/6D7BSvdsZ+VpkbMlJl+7DHKG2QyB/lQxmPrEQ/no4KgoQljyNAvKel0mp0tGbj9v1ZZHTaJ/TsiCHdLLDALrtQ6rnoYpGZ9FAVCIOBmUiW79SIuLNLX/LmzLKG0KNcFZgq/M07idalKfXur7z/vkhIAcm/uvZCYnEm19vXaX4vLbqtyT4+0rldgUNlSzOFyuyIV7buVbPVOJughZZwuGpXFbHkEVJGvVVcQNVs3FwffqRrzD0O6VTP9gQiEys9sm4ZegFavEz3eu3hQO5QJmdjt5Vri/E/yoo7D+JsJJBJb7lDRgSCfgp74PaA/LzDm4cYi6tfQoUD1fRq3gvXIB7x5SZshxUhpKhrMbPp4XH1oprvC+do/fRArl4zY0KPDGBnF9U8YMcPEyh7ERSic8+SExj3q378EU/Vd/U4uQsOlhfFKnej+M4siuK1rnsA8kTMFeoVmLZy5PqyRQnU8L+GuWcHBCDvqAKPdJP+oU4IoE6k1OsSoP20ANLZ9qM6/BBIHWCUdlFMJRHed63k4NgVt12BZhzOe/aUWCfSQqsxkfjulUvROta2VCRkVCAXl+JfVRr9qOJrMzGjIBlOa3+QHU92D7b1WpvqfUkES0IWpa+lQ3vSqoXpc/5r67yGpAd6kevBhmDRoqYarvbJ2QEl9eAl4NQWp+MBBHWJK8OEdzCuUz1TFWhVpj4Z0jdeZ93WNukbZnpeznWbcgaVCm91KSLjY1ZhelJGeapWFlIkmTmOfCrcENSD6ohmKjnjfa5qn1KPGA4m5oCui5KBXt+cbzRBr5ljU0t++txptovddVRTmd6xWoh4LN6pkSF7ItLoCfRx/lYkhnCKvDlOczyPrSedrraFEXvG3jyhjXgdcLav8v7ENgWZIVTIUyllr/POfm10CpVjf4JD4Lo5bEYyAPfac+PmOd6iv3VOJ1R9HaY7202PFuFs/mHxscyngiA1dn0eVPoC0qe1TXBxuhqJCRpGs72h2tof4mt8nL3h8pmn/3I+stzrp4H8Aga2/O12Zb+evKskMtXimSrvI/gGJ1vX3KankP36zAx82KYFMr70NVMNpu8B3UC1o2XtKaFvIc/VowPB2EZYDMuRAYxu7tzmplAMQ1ipByn4D+XjTE9roJQGIQApASkEqGup7zGTuDSa1LXdrwigNNhGsbNmeKu6hwl+1PhFBSTLfwl7FlJP4mjhvCiOgM9KdORI4tuote8bObaFxlLvv6WNenwZ8f77S3BQs+80VQPIrkwOV+lt+nJMLKznmHKggHdAcz6HP2Dp/ThxORtM+wlO0wlnK/9Mv2cGM8bLq8R0IZRKc9TiFRESmeB4m/UdFzle9khFvSQKBGJFuzO0njg0qJH3728jzfuSjJRynEVFUAQvfZNeiGCoXgvOjjnN/vbVjyOwzzwqMi8cdBQlhT3wag1Uv1w2BBipYhC2P86QQAzr6q+biToyXHwkjQ+qcna2kSY1uM5L38Lw94OUpdBFMDLPjstDx2Itao8YJifVy+M3EucuuBHNXFnnzvpC0/FGyfO7iu5RLZDLbIPFa0rtfadgnk11rHCGVXFNChR8sAp2/MF5jwlNKMYYzOyOfeSRj/vDaeET1aPCIivrEkTGIbEEvPPx3S2mZg4GmduS4O+uo8WquYt/OlVqKRPFPsaccz1/Wf1bhvAnXMhcjG9Oa5jktyyjYEpGOV9kCSdfcIb+XePNzE97VexDYTzqdYX6+z56NYKzPLrmK5UyMHQ4+CvmG5zfiInfRLdpB4tvizzwfZhg8mvXrwXdecr6Fir0x2PkRiHfUdpwCXY6mtwRQ/3r5xyFstOaNRHkujUZsu+OmTrTGNPZh3BmDo5/HEQhmcemrt+pvakti0We7kgqqmvcgjVULspGar7oMF7v0iQBPPAwTk4sIzHOTbqeWxkJcIvpNnaAduLBlch1rzbu9QnK14gSSZ/dnkU1Eo0dp+kjS6l6nkm+eks1l8VnDf70yvf9KxvuN9XOY+xUG7RIDlt7Cukp5ZEW6h77pyqhpKH6gBmf2x7u+XMP9hq57xpO/aWe0tNUzk6I+rEEajKeCJJv1EV2E7Kbx8V1CGbrsbev8vh/VbdtZOG81S1C+1K1Ytt0YfvCNjpjc8Z4ciDLef19VNoEhPZ5QaKAFxBy95kHrVyTl4qH2ppPVnNSo2ULVn2UdaaeawYp5hFwgpgy97UDl5yi9RNuVjOvFzOhfZhO4a1HUSXM65CTEuLn22q1LLMHTAf6jI+LxEulnK4YdwN/GL1NfhBdUh7AovUg/W47Uq8LNGP1dlDJllP1gtVUwPJPZPyE7+r6t35JVWeZ6hFhsEVcCtHdLTBiqlXTiWaMsV7eelvzqBWClrOpGFMTHa+AXeVebW8xnmzZ+4Q0QR3irIHO/Hqed5ZNu5e/pa1Ivz62mNdpI884R4sWxKL3lFfY1Pl9fSSwsfSQ30MXYCAGM+r38Iy62gJOLSRwxKQLkvu3KDj/9loFePD7Tt7YO7JNwKXJJgKeYIOTIeuvA/e/Rm8p0fzeDQfBG2Iw/oD+3hm4+F0rpIrUwfAGVyBKNAegGS7ug+wAWH1CSi8YjLtJvY+aa+AN0o9v12GatKlhNFSN41SykJOxc6fWGmt36AIzsjUbbDrWIl4F3Qa/JWwvvK32C84zSa9ivaeH3EkppigxBXLsc6Dd1GL0o8hZdvPkU02U+xOO7WYx3yqfs4V2ywTP9XWpwuitMnEWD0PNuzUPWOz12EbsUn5iOywhP6V98G82ACUVePFkqbAZ7S9xkfO2cXzeFI2XdSA1Mnntt6o4QJkoio9u5Pqt9A6fW4cI0RLucVKj4Oeo8eZhqr7sB4wVSudPJEtdTRnhT5nIajM8tvnFrUsEtVyWlBdyVe0U9YLPut2VYN9yXCQMtIsQYmwJ+pJ5FMwvB1Wq3mMyBP/qTvCwJltPFYe4PWJyXuVZYMmd8XYKCdKX99e/wqEsDN+mx3LORkSBySUPZy2GRbGGuOfXtK97reV19De77GrpgWE5oKjYiKCgxVFWY0P3hviWT2x881m7x9cBRBPsNL3KK9BExAepCjB9CpioGGFEy66zwRbvdtAJvhGVHPTl9OWt4FHmrvN7fVveSXIrMS0nKFwvtZz4Phs7HcyG3s9F7KsDenom3UvmWSfgwZRaIP1XlWtT+CtXEcnbJoaERiDkq31T7fNbVBegzApSoBX9kvE9KgMCg6UwuFtFc+UTn3F8UHM0mOKPCMWjkTgosNYuVbKNlM1OCPB68Q+fhL6/FDoGmt3r69BCDhUGB5SCa0scN2PxvTE8lTNp+nEtZ7mOG78GZYPDWa2l2evzAZaXoNKJPWGMSL55HXQQrg/93j4MMV7oXuP7XbC/PDhg7Alm1c950pOUTHGdoBUFFPneBjDikIwO1HyPWJqq96tU5y/FwKaWY6mjSplBx1zUUBWvTUqb6NAqUGyCPVE+1ZOyp6rg70Uz3R9+uiWNh1tju3c8/H6CX2dQarzXJpOXaxfYj7s1LX7k28YeYquTvFedCbOkyfvDslV8oun/CnQzMx1jT5Y2obMkn2yajq/tjkyugFlomqdgxB4AtJFuVUHOY8Fo4Ke/m2ebLmnS1DmOTwOO3VnJpVUOkH121sC1apYrlSE9M2DcYTZwrqQdIBIwW3Lzogvxqk98kemE+D3yIDb31MOO4cgrv/hGob1VC81qemFyQxSSCW3DEKv/C1vTlw7ieBkq1ToNChNbydSuGouXKBHZ7eg9nw/KfVDDqL8ApJdIuTe8hpdSOoTBDM7NV7mecrxesm+bWePVP0WKjgSDfrAJ0Fn6q47lWBlnV7X6ZRrd+hn4QTSw6j0m5q6Ef+6dHkf+D4WBZO5SuGQbIFkGo2ybVH5TLHYNOjjOFV/fWN/F/nTYKrsL6tvriek3jeHR2kPdraG20kKAc0OvDxHrYi+VpJXKHi5mvT60CuWGtV5WeUc5ETO2GAlSB2XzFrJwm4OvvB8NX+4RiJ5LiKHs9wn8f4suTuMuysMvOp5UHU3FARSw8cuTgeHJ/iDHwuRLO8Dk1aZMv7dzgNwdj9dDAsSIC18td6usIqN7vPFXFGuPtlta4/jqRoj1X2IMcQuTWCij5+VN/xk0VZW9FIX7BDJy6gWw+txfx4DaAMxqWnsVvqPkAuStXbdPGLnvxYdO4IiXskWn+V3SwmtLHWHDqHwx7rW0zx5smUv9zAgRjOZ9ewIbxJCwHLFirHjRoxePg8wLN0NnX6nuTeNqZXcsq4TM6r9GF5GIsD4KScNS8535hR2K5mUpSbP6Qv0AmIivRL3pZDCq9kzUkomE6PNhWYSvn9nuXO41dPVPKAbr9cXhQfF3ydyuIvCAfimxYB3nkq3Ro3qGrAQlstEfDDuApA+CV5pwp9bdRZboqq04XkZCAQ0YJktDPKRI24vv9tzgNGFJgbo2eb6RevJftHxTCm0vAbY0BMI2pWuB2SHbR3AxPVlzFS/ZSGdhxIa5bklkyAlBkeL8FXmAjrZ+mbvHQcfegbG/hX/+gj3+y796urR8draC15eswK//s1MOHkyLqrf8mEEEt0ZYhhqRee2wLCA7t6l75XMoIXsIH7LrggU00m9p4oCgHWXv8VhPQxZdzTiCFIeYzFwAjKrFtcAyUBjmF8yAczEGAkKO99h3JbnMZfX6GIrOT0woWzRKehgABjaZuLGymvYEWtFDUGgc8eioQViY/oiIN/VGYgeAU1K+CdHsIBmlhGqFOl6SlRPeY2BtfV++EYs1iidDFvBmIwxpT+KHWspXsgsUYZNkBR++BP1B4RmNZ9K0Hy1Jh9FHCQVhhitsuDLk+RW9efOSiRFi2c1FpodKcl3UA+9atbaUe4b0U2K0krtjhsY8LkHPmAPtXqmDsVQjpDjy/LKyhhpHz7aE3FfdR/yHigmf1ELeF0MMNpIb+DbMg6L9yKLL1oFpSSGL3I2m+4Ln3GGGFP9lhSCgdA0CZSTljqQYI8W0K7ppboPVQtB9RerXKxmTPtYiNyAi+mqOr/shDOHh3QNUeTnuOH07yiWUDnS1uqZxmsHEa5XiVOJTAImhtgVjWHJ7AKcdzC1OU8Zl470DfBqp69OK1j+Fruos7hSBuJlOe6HhnQm9XMXKhnluw0KCR9UjGgPMFSzz5KhySYktdoX7gTNUhnm12tCU1GInKcPEn7Uy/fiqEEDSJtNBIIP0bNB/WzWV+0HovqnDrKFZHeZcQFEfM1Kl6yvXV8DwuUO+HwlQFMwzfnjZ0eAuHvWqWrN5unmFJ2w7W/iGjAmWYTOBqb7X9/6Gm4aSOW+HGPAkJluY++JUXmWtXEC8fO9g8LelDBD2uqL8W8qQiN9qhzvYPUTkMyOgLwhdBZNKPRD5cszp1Xv9uVmMJFC6j6ckcEXigbTyujaweV9sCYYIRnechOSL7hkV041gz+slS9vuyID1/+bfVyklztcRjankmOUzIMc3tg7V8yICOGym97oBVZ5buAAAsTTgwVoGAm1Qd08r4hQRhOi+i07FERHdC0pBHhqJd9MU5LRBC2fB9n63J9I264hQqlHL2UmmfcsYVXvhNjN/HPbRD086oJbGimuk75NdHV+ea3OpARX0klIBG/SDzpYkRqU9bPX10iOUM9QY8T3HKGVelKKBRwX64treJhJwlwrbQd+PCClpCyHF/nvcfraVft37ZT9EVJ6m8JzHJlJBu5lP4qqmTntguzKXoTHO9nsIwhQxf9ibWAtvQMO6ddHudVhkNK3BWA/NK13q55pImM3NpROpQBb89GOV8KFz495q9+StExmLHSMj/1pL3bGHVSd3PT63VIkC41CYdJAQuheHDm3M7+TwyzHWFZHxONEfHFKJaooZu8uUbJVvhNFeHJriLwWrqw2m5IQKCT1Kb1kNT5EHkigGJphjy7KCjcMk/VK+HvFlZTOpJ0+OJu4qVPKprKJpl2Q3DXK9xLy67mOlCKnr/MuCbkIUxFhgY3v8j4mN7J8Iust+Zy2YcutENSAs1bjdJAD5WiZnEaNVAXZO5Hz2g/vU35zVPiu4PhkF0hUbErnojmbqYAXqmswAZztHPIyoZTeMdnq/mZCANR7VOPD/umbzocb2F9y68oZmS1fNmZ5HywjhCRpwTIGUvW2SMdbuOX3qq4RB1HcFWKbyFlkhUjFkbXorLef6rdgU5A4vysd6AswhiGRCN5WhDCuerdOlpon+lGcNGcBH94TMWr4wedjrK5hgy+iVfNYn1UrCIWNMYkGi8mo+i1Q2jZQ+vsPzC6ccrT02K4+obmr74UGBwPAQLcPCiBcMpW6lv8882r1XnR/Eh6TYFW1U/FtSFOU9O3rNxTPI34KaC3zDYKPcifV+cUiqdfWym+On6Fj2GtfLY6cHSS2qflDcI3y2z8j+matttAldc075fSI5uC+wnCvngeirePLmcyhlZxvs5FKuN3Hy69+iwK/nQKyFlLF+Y8tcP1OGhVPzJzV+NjpfSWbrMdybl3JspvsVZS6ck5WNqVKgFxuTnQsDWcmTflh6FnMVY2xT90Am/rzANpZ0vFba+GDzzpYPVPGXbqxoABgZa2Pk4Dy1Y0lRynXSkJJgnxG5JFe4YiuTjOoaam+FQPApPnk8DcjHYfLHIHmOYA0Ch/c4uq9GM44CGEwLZxcdbGYtzR0YWPK+1gptlCOX1rpUuP6yn2sr81X+bNfg7krctAD4OI/vnw1Ge31M0RamdVqM2br1uHPzuZroYQuGyodOjejJ1rex4fL+sGq7B/kYUm40BriZJvVXKjpAtWL2gNMs310d+xzD/t78sCLcUowYYMMFiHbVKTF2S1owSYsjK+oGh/hR71PDlPfeiJsRQkCxALOY1fZLfENBlhkpSV7P5OrWiNPvixYHptdvZeeiQJDjmLb5GU6SNorwLXeQTXGtDk58NC+L7rcc9qnhCcO1L2IVrG8jxUuQ/RI+b/Ev4JJUylQsPfKJ+5bVfDcSW0CPpux8cqTI1AEZqzY+mdOt+ICc9I6K3MHRdTiCDSrULNU78Ue8KIQvJAN6LggOzOPqKEEwlVeI01a+CXiC7igBtVP4zcjRj8TVPVeEENYoYLNASPLAbMBoUq0XITL1TMNCBqzzCPl/Pu8GhJl6FLhwMr3EkmPHjjF5UYsu5M7ScI+YvB5V/lbHHLuaHvvnCWfJCAkhGokIa/SSXNj39Sd0tlV6y0nnESD8zNBf7Ni/7whd9yJONJJlu4JDCMrrKWbIfe0erdWGKLo9GxhM55Px7FJexiLZuXxhsIMDhaThu/DaFfPvhJbwmV4dpzlb7G6qjh+wHOZIEmuPUftnn6B+umoruGu7W6JT5lhtPfvCH5gPO5e7j80f4GgoTd2tP2ogb45PHT68zOZlb9lRjQOePzoGnI2yznAEuMlhNEqf8uZwCYjwbcb5E96DFZlXGVZ8uLqPrQnb5/W++Ywo6u8bQip/Ei/W1kzgPTuke1/Dp7H45UXiYN2hwB2j/I+6DsBMTuiCvW3maPn4G6iXmUmlzuYwamEdgXa42At4Fcyl5isp5XzuhBC4ZN2ZcROMk5x4qLIoY17Z/ntq6DL646I9MxpuGhMKCyS2A+IFdVv2Qk2RT5diW0bmZGc//n440erztmCLkfEzFqnKaVEF6wEQ73ZnJ+La7QEaULTmLGcaR/XWbFY2+spWRXfi5bN9YE9E2AlFY8/2m63X5qQ55L1NTgjfaOBmM3UDUkOdmrVtvxveY0GzaeIzQoILtOtLy6hKyQZpvpe9K61+M4kpjyAr0U3pZCwSVAjWaruI53W5bRP6JUoO/veM7+KhXqjH6/u4w5+2TWu2HrWtzMN8zJE81KziQzYVsS8QPS3mBXu8ERy7wBAZ5Uv5hpWEJgcP+D+FildFOeHb56f5TVA4FWy08GK2kCtjjgOXkqiwqh+S8sXR8aT7QYegildJCdLQFcIrN6LKPEGfNYj0TRYZ6iDikNbO6Pqrb3ef7JftAjJ3lt0XOcjnhS2AqqequbY1G7Xj7J3xrlwEhYpq5xFy371Ln+LI67KjSpfGgf909LwAQb3VHnfXGMGC6UelHpUh+6wUwQz1nld5fPQomz5y8/zUyqHp8VPvIKpinS5vAat9SPGhs2JNcGcHtOGTsr5nVW+h+QNf6nqtU0x9RpXgBOrXd4wK43yPt4QpUWSMCcJrGQnVEhggjHPV3sptSck7cYMEFPhlboDkCHOL9nSXf0W0ZdhKEmbZ+Thh5ZeEqPlk3TB6j7UGxW3d4Tfi1utB7hFdf4dA6ozMnIYTc22SyW8bkAeLWnnTFJ8X+UcpLWg6ZQon7DDIBXHJ0EfWemq81z7tGY0KHdE3vaTeq/aOBGF8EYWv0V87+Tofi2YA5fppqe3kWqISmXGEMsPobh4Zg+V+0VUUkyNSss2BtX+VDFZ0YPLCvTLuTqHdQ1P3kInvOqZ6roi05yHShYcaY1u7Mb5bqndl+tcOsnmL+fiewPsovPCusbj4+OpfovDjyoU8RjyurqpToiaLqc0EWd5DYbGZFftSD+IWMYPCWQPRM5SvdsXy5Xt2McGtoGZsegE0SUHW8AfrmH+i9ABKEwLCFzebJ+hbsNbXYM54kmfdidK4+yH+pMWrnpm3IDlGFtBSQrjpOg7szxY7zcJcsESMFV17Za2pgL4Zd4bXxydCrUdvGZOyan5QmL7SOiEvZ0WBefKG1MOV+3ZWlTjNHiHT3yXtq9STuAwGEJvJpX+h2uoqIVI/wJCaXEHB6AT4wbrZ6oGe6VL2iJJ3kmeJ+WUPfQln1X3sQUHD+XXK5iJHiE/ZN3ZkyWTd5fry4fmx4eQoHEGJlcBnFoUl2wGvdwnq7xcyhz+yNLRiSVXfZ/EsUmxL35Lx1oN6EuZsodvS+2UY6JtInNz8Vu0aB8mk9fSou6Y1GxKb/iMK+rv4r04VZ7dAoVCCnaBOYfsqJCiUY1jVP2W91JOxtS8kk4aTsVO2sgCN3gqBrNrxDfHQpAkCTLtKDE6p4YaYKvWKCmcsfCMAOZkrp5h3r5Gru+IRbB6pnd+gT07T7QtOhYztNGbPoy9VXkNDTqaOoNhprJ8/igO/XLidGKtzi+GIjqDJGNcFbLmJLfeCjrbKyv3/GRrXflX+ngMmTqdbOap7Yb3WY4xFjctHBLWHEIuAQH2JFdujgm0+i0t6Ca/W24VGq6mWNLgTEZL86+8hrjcjn5wPnyKQKZRERLE4AQUZ26vximA6h0CgzDkFq0iz0vDIzOf3GXvFZdHlAaVES6Be3kDA7gopoIh6uVvsTj7XNLtnbdoiyh91HcpGc7bKd+LHkuj6nsSl6jXJ31VcbphcJ1ZuXwevK3ntz/JeyYu/hqY3GItWeNlX11uDscej6wYv3YHvnfWXLRyW5ur4giowz/pM8gzjcQH5JPIGd9a2fusetVv6Vx3bCLnIpT9a4lAeb4EO3X7XWp7cDoEk8Xe4GQZM4skhsaOAjtT5ZzhFwCUJptHzJDqGDbtisWJvKZV3C+s4t++f5BeoH0R2ABMY8SkFlP15kPCG2lIycIRQg62TY1iAe1Sanp5DQ7clvQFn8zXN1koNTMHQrNDdQ1mtwvMwJEsUrwXNJeGxfHdCbHqN4TntKlX6Z3MqexjSkMPpJvBP/9wjff5AuSUSukcWXpHbNsOH3pm5TU6Nzhlz0UQfL64KwpFHl4pobI1qzH2yL4gtKJh1Y0JIZjW+G4h5WFDFWMM24Lmq0eiuOiWHgeHhNqN0GqrmqP9fjo1PXEFT7pS2Pw4W32m/Fk+UzNXf3Pker5g8xCx9WLY2fDEyjHG6RlwtKY4UyGHAhz0NbK5g/GrnoeOFG0BGBNCnbeUU+Jr6G5yh3KdCyKMIvhJBFdLapmJKOqUu+n1Vc+DjUE//I1WilPqrJaoRNzRPDHnWVfXQLRBXxRd/dqS9UxiigZEBoQD5RolJ4ptNuvBHQG4owKh48iD3ZVfLJwfsCBcNzLFMyJivtFO7XEJ1BpFslvv04KJSEnaA9TQiXOosCHrqvvYX2KUoDbtcHOwCuarNZ+TIXZpdR9CJyXyXPIXHjO84vCdlvSOGq7an4Jjknqp96eknlaSLtc1vyjeu6xdKJT2ERMPSumVpIAENOcEkApzK6+hTMBvwp0txoWRYAWrwo0ro6xX1zDTQK+yBe1oHNTGOkY+NSsHZ/VMRyR9arYjrHWSvq5n+nxTE9rEXd6H2e8LKejEhmukOi1ikflBCFx19hjJ7tZhIEtgD5r/8SUT9cryXtVPrde778+zr1hJKrljY1FmclC9qu+FFYscjo9vni9Yx5RJ2qGENl4gzayvwWAlRPgMqkQOKWXimIrBujFb7vIa1CfQqzDZsj1dNsWY80QRPc588JdrRICrmwxx94Kh6KqtSFK8tWJ9gdUVmfDEBAdWxfHdgqobSbRe5d7BNSKs8uX9UgaW7CJuj/nxHJ/6GvbKfKa+VQGnV2z0F9kx6sxTrvs+rvUEh01P+IgJcep4V7AsT1Rc5RhbsK8Sd53JxEhIIZf7oRcMgFexj188SZ1WNSXFaHEHTbgH2cAKlaDUbkBJ9qTRwGLpLH5pq7RgOG8ORld5H/aVDNpZJAcjoIqQeCCuT93ycoyFiQefxr0PjTlCg+Qhw6rCyq/O++QVuO3KUfpzJE6PuiusPGqO7PbqvTBTBzgwggumwaCR5GuUBbHjKSqeKSovzOaOKIbaEsM1GnbRtZFhlr8lvWcNiqQWPO8H+5Y7yznOyV5+c1ZJWO+ZsHjabz6UC5gh7g1q9uq3TKYI7kPi8/SEdMdtHigfUMdbL+8jrevg+AP98LMM01fncdhqrWrvQJJ93Z+/Mia8RAFbLcFZ7ErkrRW/RbcgJVzbQhwlqoch8A3kzudY1mDxRhONTE8rggHHmpOIL2pyK7Z6LkRkCqxzPz9YBp1vyh/jK+iUe+0RAO3ZNd26PivGMTky6NDO8UqSq7wGF3S+c0WL9CmjSuNF9ltAbKprYLGBH6vexpmoOrauMERkQZx9ReUpGsyzlGMOpitxMgseO7vK55OxzvIaZw04G487cnzhyop/VD2I/0OQd3neH9n6ZbdhZVJbVwy+z0s+H1DEIa06ZwNkoEGEOsxkpYfrlmh53zzy8rz/fMD2swlK+xqm4RbE9QaFJsllllqFDDAMFeAuG8ugYXQ6l2aUX1rleCd9w1K3EhmvAKQ0xucQCsEDCnmX90FsNnwdeo6pZbUo85rUIY60KkfTH0iW8dbn5w8A6nQkxDFKelPJ/YKmtJo8Mprt5uy7r9CgOaXDjXjq+6CRfrUobffp4SKSvFuG+0x2UHUNXXyIUQgFdDyABz1Tit5t+ZlVJjkMivGN5tZpSO6QK5ZWrOpbnkr5Xuwme87miwVfRZbPA5Awmb74LNVvkera6dWuJ2T6c5ASKvj1pFMhKz0jz/jQmOC29oQUH3gK0SlfAWaPWf6WpDNJS4JBE3lian3xefVPYjitrvF5u2nzWBhRLR1NA87ji45vspiDEh/O3EhJllzON1AX+/RLb5hgsL5GJ4b2FHSh05ljYvW+yVFVhKpnGnPWw8L3cmnPiARHjpSTH9hxrLwPrEAM7J52skIuYZ2s03mF4FHW+UU9REcbwRcOk4Otsodq0/OVx6v3ohbV+ZkMjp7kgpXsWexvp4b3Kb/9idE7frGeknONq4uCPGqbs26Umrwzd9i+TbgQBTt9D1JrhSZQfJ716lzpLZ5n6avFENohZLUoBhXMWgTG5X14ANztqnsddULqgfju81acAp5e9fc1KowvMjTwkhRDZYzECazUdaahaow5RloXpTXH7tZshkin0rrVkq1qbM+bBEsHMOWxQMfD3GqBd6LelX6gJwFi/jJncz11R1NYAtzy/QX+lc/DsW09F1JQXBEJ735y5hDtZTde/halfn7AjadLQeKE+IAZ3FcMbVVWvP35FZ5NQHfQp7eRFdYlMZxzQ7m+fAnPE7akcxPfvpPY3RXYnGSqfI/zQek2hFpuI0Q777p3SLXimMg66mvMJAvq3KrcPmmRf1dOnbtkqNo2zSgCZNQ7Ui6uvHsMYUs5quNaF+9FX0NxywQK6RAz9J1mGTmevJPqvQT6szn/OvWmKoxK6HbYXfEWr4qHIhHppWf0H8xzAW/Q02TtNFZ7lUFllHIDJOdEEM3n9dQ04cZPTkWV1Ura5ewDn5AgLUR+GyNgWspxGarVWQyhWFpLT/BecsFXsmjO/+lIZet8l9cg1DBtov0R1OHu2J3NO4v32TBW49Tu8QoA9jFA4NCaAyqSukwGy0w1j/GF3WFJcd0Tte0emGEYkT0TaqW5ClH8SlmtJ3pNn0NkO6tUS+m99EjoNxEAxiBqIkOS5QKCPsW6dZipfgs6rtae/b6DKoIfUpyOIRUFPHP1XowIIhCCc3E6NgBB5JM8Wbfqs4f1I/kRdK/qHRLLoL+u8GLU3Cr++huPyXQqJeq5SZ4z8wCqXyJyzjCu9pYz8W5v+pwvCNOCvaKnBR+SGoYJ2cprRDbrHhRMEocB7qiYcke9XdXHZjDS3MfJFWBP9DbIBE1ojq5tlNeQqvzGs/Kd2okFMd1UpHCqeunVMHu0KGBYNbVMEmo3TK8jB+XzbMv7gA7KOYgkNzQ0XhRlzzPf88eUui3Tz81rJh+Ntq7ZtKTtCYt23vM5VlTvhRECcPDOGgexv6MivVs4rHfNYqSSZ454dohB5MFUIwNS/c55Ecuoeh7zo+vMJwmctlQ9fakvY/COybH6LbGIX8KjnoTAvMkrUzRAvKQwKf2mZIBGWbJXGBzVmHrimrwXRby7/F6oq5WxZEjoaM8vTUtJGTNmkOtU11gh3OU81qjrHALte9XZ8LnPeK326/Prn+krOqSyrqyvJRxw5yuZuJw/QrhRKjF5OUB4M0qFitUUciWzyzW2oMSfFiiUCjJFoIXzz28NtvLdbsValG2qZN2Xc1Ag3OCcsl9lKC7vI/TmK7IPEDgp5MIoZoqFk4683MNsRTaHerQyC3VqOmRpOn/OaldVd6Dq61+aO1zVxT7ohxDY6QcRYlRnZIAY5J5YxAwGB7ueNhuCjQJNOT5ebK+Vo/IZ52ejTPF0piVlZda3zXBa/hYMVh7AK+HqNGPf8XYmDoeLq9pbYiYtdVY1/seIuNLst7oQsyHeV3MQM7HCkU6QgW8/1eZ3GiLioK196mtgwJA5wHb1WOC+UCmXUZp+6mskE4xaYWcvYv9veJGPgzRpuVXPVCrbtFEISupspGxf2F4/PBTBY1XLYdQatv0j5KOztYNgSy3DOnPmxVXlelkfRQ/zE2p95lhsUQHPCHAGXL28D+lCAyA3L+I8Wirl51wSv6tR1VZ7uhSeycRMf4RPNDbn42UYpS3xmEb5WxxXeoiuK8YuPZwUQsQu2Wbdb/lbSHdT6ccct0WkLlLpvlR32Xqr3msIHewzAOyODX2nSPdtMlVy74pb7BqcgKFCXfKs5APTbdFb5P8bFSuc0DoZQTy/10oX5oyvrameUx0oSSuvEYaTDaqCpZ4wYb11v8cv9ZYMxNiyfBfW2avFWG0Loymsq+aflvVCbL7bJ3bLrVYZvuJ3WAkovbU/S6+Gf31dOW00hpxAM5CUZ0K5yDLqeazvM5q0r1eG+S2edKapnJlFJbCq5RBv2KvMtEtZ8MFgH4bkxvviBFC+F0coOwW7diQRZ13TNDY1TBxDcDVOR1wa+OlKLl8HVRbwe04jO0KIq3we/lqn2TOJ++cez53UBaIFn866ynl95IgBLiFCqyW8njOa0rBhxO9Su3H2hOv9CPoqULHzGDLMlpgXSVOovn1tJO3ambD5i5pG7EBEFLjyiFnlNdpI/ncUICM5mjScO93lO1W2qsamJHBWNd0eCbZshd3ST0I20s4dpQfv68gRmosoonMW7TtCvFDslybdy98SMbIzIJojrGZgvWx9W/rzrPVBWWZbMuspTwUeiU3xDWLUiLUZ5fgQDhn+of3HnGFGUNRsBc2UpUqPRAD0LGskweqwXEbciC2pg0/oG+U1WjzzktlV1CgmLTC27EGq4j9U45TLha3T2TIGbWwqtGHdFMbk+y2fR5wmxHvnSx+CnjYbjU64UhN9XKkPQvkG+14f7UNe8+O7CQMQY1csVPU8iC3ioMU9k5bMpmS0D2ePlTDbah7T5XNeb9EWaemp/Zure+Z1p/7yPr6FnhsKsT2b3SRQ8W7CkZ2de/U8KFgHGWxn5rPUiuW4U4b5WuurvI8VCMCZyM+yHZAHveRDsXkHrdbqs5gXskPBZS5lLaYxeldiTwZVeZmNrpGk2WqPKzH37ElHWKM0zwqalFPlvvBsdc4/0Bo3f4KXCcHl37M3lQT31vcBMzGIi5F/elz0zUZqP/Hxyw6rnqmzuG8t1p0Q4iY7UcTPS8zm9ZbfrcgZhkCl28nqmaZaXlBTrLeaFr/FpodSk4/nxgtBYG8h313yTsDVqndLM9N5kb9t3cuurWYf+KCvWumy+PYdZhmQd3rGXJpPSzdlJTIyksVq3Zfr5UNNzL1zaixAly7Qintfu768xpKOToU7EuD5yPkSMLSQrp40lsvncdZagiRydeHisSOSolixzlMxjqvncavs+1gvpur96SNvxxezkom2ZP+QRlqQSL6BSL6F5cYrd/IW731X+4+4zBD/Fleh0brP+8g4OdPxHQ1ate7LoLogXOy+uJDA4e5YUOwPvepd7ZOXCOCdBgHWhZOhZC8MjTc90HNK7OV9GOf00FdieJfNTDRc2m3om+dEUV9j9niofR1Q59wOqPL65PI+zkJTXsMLkNag5fvKzjWzvyb55/n8ltUatcLDgi7U0Ub7fr1dmvpAwCSYVWtl9MDoVIjOJuEoz8GQZGM2wvZ5lffxpj/CmrqFNOMHUxuSBdrX2MrU1xCOkoIpPcmt9TITW4/ScuaDp/RqMMie4wGw5XkpD86EoyV6ABJiIDjlM31yysad7w5zelMtKgW2kU1/WDIRsk0/a/xIH11AqTivmIFAfKRb1M/UGMr+b6QVx5mVYRG+Eq3NWRzKa5B6O5WTGesppex2JTRZpV0gZvXNeQjOxngEb3iftrfJ5dICSbmrvIaYJV8NQJXyulCIxxc7IsaftfZLGWxKoBNsGvYONfqKBM1m08akOiPbCl/ZDsqQgg1LCA5Ul7bSEyFl9b2cjwWg+5U+BaimrLbxz7rHraxacmlXDEQkG2eXvROeYD6fodw3b+gqeY6E8p1m4klSoRWuB6umS/5wf5wnUt7HHU2QjbazKOGXzQyrKgufoIpyrAfrfweDQD4m1NQbJrNRMaTArs5zKxBWW+uAS5u6GMt7QmzueJRLX+NyuuDcvWNNFgu8US6tcEtDhHWieB47T4IHaJ6dGRBQ/5J0WhIYnlidi98iSPU26/X09Q2sEYwQKEJiUld5VpdTpGQQ7esV0+nZM2hT0h/sYFlmeR/rDMQ74OU3qyUbGkfefd3ZG52zQzHGtmjDNF2U+LwIT+NMHSvBWLfTXlW33GyednDU+1R9mshIUfJbzc3n2VQ1eliGoP56BFdJfUIgxjXy4Q/W4vIaSbk+GyhcLFWP2VOhJ510PBQSVKwvVAWh0vjQ6BWJWiBRfTEhi8gHatU1Hm81AcsBbwlxYxrXsNSz7OV5jmDsfQNUUGhcXwUGp+b6Qi00Lqrx4aeElAmC+iYuCMyaXJJGL8lW1btNzjZPpdRM5Sw5WiuOE0JsgVCrfKYOxlJoGIgAy81jdKgLZ34l67j8Lf62lSAsz5al8nwzF3NPqqoUMuU10nCVRmE3xeZgrC19fsmxEQyW73bj/QEWh++vx+hbXqFBk5730mex4znWCna69Ri/wvgIyvEcgsh6q/eixv/4169kPeqbhHH7OUfv5KhV15DzZrr4iLhOG5Yb9bU3KOR9VXnRxOUgrrfVzo69Pf7mTtEh0le4VXmujIP4mYmcwtcDliEscl4ltWFQKucxDn19+DeHWyBo7hONDKFaLwt5te7H4hJJUG9p2tgpdklt7BavIKeS/+FI/kOpMEfFxNY+qrPmFJlumXdilmmEDUg5PKa3BeLLi8eMsvWu6h94EucKAh/zasx/+rnc40r2HlE51lPEhf3LMHnTcPBIklnN0XOW//I+3hb16CsFg0Tw3FYgfQ6o6slneqqeB0+pbBLRjFeAvfBhZ9MBPiNA9ux2q/eiadEoaZYwsYd7WPJrFzFmX0MkV/0WRmKYdKBTZRi9GK0l88ZMa6fUoJ0tsphD8QBMmW8+/PN0zh2wzjl1l+c541lZXBIEf9L926pzS3QWUoOsercbK5HMiFAy6gI+D8K68+P2h7b+93ervIZdOh3KpXc+6Sq9M5UZPhIJZv/+LZg4KHDdkdwAXdGz2oLMHNF0+K/yGoqMkUbBv6rImFzPb6Si1DndlfbcywNhcGY5u/5xhaetVKhZSdU/zlbo3+ND9VSKuSOTqhRVoVh2cKWWf6QDUF7DhilHyBVjNKlTalzBhvp4qhp96h4Kvj4M9WPdILyY/VE0mBUqlmtO9DuCwhl706DQ6Z//fDwJR293eR+UWoMhMahx3A0YIk1cKnDxza28DxJ4kYotbsJA+jDxQ1SRaP3u8r1Ev0KA8lhd4R0ANMj8eyZDsNtqrDc7IQUt8T5Q8mOnmHyL+MBmHJUGHvTYMr2D+taIUU6SkTaIp7Klu+9qnPZM/0iKRJIULaxqiGjZk3kwb32Nnd2Y+BnGFaVhvk3gLM9k3hXjX+vmyRnMKMkZuafN/4RvfQUENKrxoaJOVQSVA/vpREQuSHV9wfWdwVK925FNsRnIwaXROqSGiwQq4UvYRfVehvxA7SAvINECVpfzXhUSR065q3qmQyWKk+qK9X8zrzQFB81cnpqzwameh8YoEFvKWcBFRI4r/k5kRTbB8j6Aj+5HiLfNeRsBtCWhz8JJgLlmfY03KiuVeUy2FcYM4WhgREETVuODxDxAXuv0ndQ23tmzHwise2NoVO8WSM6uWFiqh8jlwDkuryBdz1ntLYOpmFJ1un0Y0UnCvBJbsjLH9sq/7xq2hhkeZ1SagV+VOwRQJgsEi3Kc6p5xmSWUbKYSfcW4y7O6vnZGNT5mGmja4FSbKY25/bPhjYcGauGp3u3kDieDuZ39ERrC/gI+1jQgaS/Hx1SvSQ2VrDeD4xZZu9JwuGI5KN/L6zD6KKGuVPfVIJTKg7k7x++SibBi5IFS0YXREP8sFinBJBF4l75X7SgKx8zC9NbYnSZ5e9/pi9SBqZ6HiXw9AKZvMoBJatHfExrUzO6Vpnch6Yo083rSRHfaf4QErTARky9e/hYgF0l1O7Hwth0rzXbn0ogNrnJOVoRKPNLNLposCuuuA26I8E/Jcl0JOUrpEu0v6Q3YXVdScZu40l2vt9QwKqbo9ctouNm81TLjxvF4R30NGzKmrvAp0Aytj/eN8iBD4IzB8nmknH12fz6Y8TGTlzzs6E8ht+q95U7MCbaDc6DpD9wgKYPs2bim1Zp9C/QUqA59mMBqDb8PR00Hoi/y1Nfg7Eq5IpJ+Hw4WLC1bJvxzf+U1YOzfkNsnsaEaDqdVsCojl6nGh4ChS0dqfOJzgXNk+DCfYqRJaa/yPrZktjshxyZ3lFARNtoNds2r1DirqDOtyLFTTdc8OTvb61cQaeGc7Oo+THwxmne5J8uZEsvJ13v2Qmwgb7VW2nMAwEfQ/whrdyjaeS4b1n2V7PQIf3mytV3Ao40vYuefikWZaFTnOTLRcwe2LReFBFREKswsH5H2rKd8HtpxT1Sz4jNjTnhbpMXjic64l+cGJXkTx8ZWeL9cUd1w0skAbk0M9TWc2ehwb4mtCo/WbkncT04kb6VlWUCWCbu2c6AjtcGbJlWsb2Dqucv3MnIaDmbs+QVYsFRefsv5SfSHd3kN1SS8Ukuki5ydmDzyLyZd2kCVLZhotXNMwDFYX9WF/+1RkfUZpj7z1Peh/cWcyuGdRPPslPlvFP2dgMtrvBH0jtRvN06uHVDnlT4bCl2Rct2XIcZqm2QMgyLJCdMs8AZ3PZ8qA3OF69tjISAi03ORR3GHlAAOEJZaMcYIiOkBfDHBDuIOSQsk6tEkvFd1nrsD+Geotr/un6NxBAFGYJuWbHUWS2ELCf5b83HIGPjVq6z4SnC9/C206gAdGugQUSaOREnBGEUqUHlWl6Lz+f5nqLY9cYQt0S8rqStJ2Hr/cA02TOQwGxGfOwLP1DXUpPLDqjHmfBGu3syMeCmAzEg/V3SKtmTlMxUYY2fvWKlmIhjrzEYe6pe0+Fbr/q38DfZ+Rb8rW8DOKOzS0AyvMvd+3TERWXFb1EYabAFd84ifxc7S2cr7YG764KAicwMAS61LavqToNCnei9vMK5RsUQ0pSMPRqZMNqOfrLKNXUOEsYgAq62TLr3CGfdJw40LddT3wYokGCkqK3W61wnsZldnVSwzY40ApF8cR4M+sNHYtTqkx3QmKGsG0k0NBm2xRku8Q0e4kijH4EzxUF6jB9sOjODAbYngmDULki0mP6kap6FJGiBJF7iSj7i/zZ0kaf77txpjmmuYUj610OQS3zaTPsWFrkNT3ceObv28VF11JUsFnGaf18SdBY5WvdsUfNXAz3+CYjcHl/O50eaHnH72IdUzJWNB14ldViDyIIejcEqhi6qtnIP2Ass4P0fQpBgGz4Th7E2Nh/aiHGO4nNIwJIJBMJJOdWhViwMmWpnBrbrQCBKU1mgJfauCVCImbSmkVDzYyEUS6J4wcVLWpIQ5AwldfmyXq/kUxnupQMdyo/zJ18DdI2ZoJva8GqeJjaGx1iNw8FGzQCNRrLrSmb+qXoFcsHlpzU1lS8qAN/YzOJBsIEblBVyMQ+L7oprQwxkM5vRCt3owVsJb7emSfRnuslJ2jy25xy4B1MnIVno0s9zfdLwtDV+ilkSEKQOk6sUuVd1Hyx+VNuf9EMGgOpFzfeJFgJHqGslq9W8/opHtzQISSJNKdAlRSPG9SHiRdHY2xMKVNTDS/jDBp3uqxF6ND8hmWYvNMfCKNVCdm5noCSWzVdxi14CUNAena8mRgz5MYIjYDVV7l/eBQo9XLLxhTNVOJsK29fu+pXyVz8OMpw4j716kXhQK7f1U013frRxj2qzhvpNgaBPAH0G5mZC/omY1n7YIvsmdzKvsMM0xjNP6fMe4aL3Kac41JPKdHTHNK6Rl9trqCMyOAGfVuVLYNKAMs9gkdmCCXW9o0Dvkz7tiZK7kNjwxeWkrUnLEhCbaENFYSl85PoRPaXH0bzG40mM4o4VGRzHH+l1eI+WrpHhrRT/GiQqApUqjST21uobA3+uJtnFwIiuWj+iF/JydMInqvVjYZXlJV1+x0hjrsTw4tWZTUo0PU6imCVAgmCRyYQS2vEpa1E/FuDPjsLkQjlLCcc+Z3N+I0c6HzzxSzuvLL7jjx3mc/DVjNbYeJsE0ZMu9Jaj3GzAMWWOUazgruko6Uaagq+q9qtENjigrJg3oG/NLCz1UaUSsXPVedkKvxg8YhJOFj4s/2KVhIwIVflNIUKqmmT7SfKNsTloI1RHMwhkf1ZrdJXcCqJ8DHZdrsIziOE0g8Aw24cW7DTL5RQskpxvp7l9JGlCJpbooNYpMFoOxwP7NK0FwV4C541fYwcu3+hpO6zpB+FysIzLJZ4qP6KF2z+Xz4I64oxFs2iiAm2oZsQGr9Fy7fKbC4t9QnIEGDW7nCI0HkTgPnPoqryHU8Mw2ji5qYwk19z/xsVN/tuos1rNlOpuMnXdJoIfjkzY2ZQ/NZbVPlk4m2ktxj+aLUVO9YTlBdFvX8xkV35zue/8SAN8gVE3NURU7doBv9XJO7skghR6lzvOGNFDBIO1mcNF25d9HhWDo7mnln+1HS+4B/ZQzdihklaZmRSLhn+gDt7CIlIVnUmgElTNHVuO0fwyz7WA/6aXvJ/4MXfG4wDBDi/fCsHuzhF8mH2fTHf5evyPMM2qqecx9i+OKBU6TgNrZpkRC6adNq/j85181mCVI3LhqBE6Qp8jMBCG0khXvYqkcjZFd6UOBqy2959dv0CBCQy3HGCWtNs5u8dsFKk/n2MKImin7V+8lsR7yTJVunkiOcpGdVN2dAPjqPmYOw/GGC6Qan/JLOUlgIXnNqvaFPevT+jKJbMuYvs+uOfpaLeHk0BTPlNv++9tSLsjL7EFmvgmA/UMPpwOG3cq31IGTyWkFYCGSMhr/t/KrnxlQ+gyXihlwMiEuZ0FsljvAzqvS9a2QKeVMP6nOXbpBm1fc5KaLe87+1d7SxtaH7mRuOXi+Q3s4ADJMNJOqejIj0C0jDp5WqSzh2SOygW3oabsVzxTaT83ipedrsR/vfIRcSSBeuoblb1HnT0X6bNWDzIityLhJRVQZtPwtAqd8LigdZHSUvedIQ8gmlC5jrLyGXQM93I4FxhGKS4rX41Ln6rvqvwgi8/mbKvQr76+C0IO+oaXwD6prNJqzhABb4IibJv0nTb5GkNJKtTYkJuAOAPpNJM0VE9olrqMF5feWvbVhYzjl55hxzrjEQqbSvgNynokWrZ5p139O+5khezC702nGfI/5xANfPY/z9zZt/C8d/vy1EikA2N/wN86QqfiFy25YbWrFdKYOzGx2ZUPVyMhpe8trpBYfWSWZ0h2hDvsLBTqg17zLbw5pbAVG8ORXsANKeF5fVrznVfXmgxRX/3iSAvzKBcw5fQe8dRbjuVp5DaB04gjCE1BKWhAHQq3Ysw5jzlTjQ4HyCR2nxYisUMUMdD414hJzbLXHJcSLWkLK061pIxX9tjdy6L68+/K9vKbQ86F6BZt9JlA26w3oDrdn5W8I+YRqVWT2nY9lWKQePXk5fS+kevVuk7StjUW8yVT8pMyObvSSppJeVddYyiU4bqSVDc2N/NT/Q3geUU9U73YpavFS00aHVuw4Zcy9gbR4w9XzUEK3MSdzeBLNTAyS+F0vndimfB5nKcBbpm8KJY9CW4+OYnr2SDDK9SVJfBYIpTpbfIxxfBObK1WzXtaTA7ixGkjBHb7Uj2myYvIeUKKl3hJpy0qwMc+4ziySDzpkN2wbonk1r4MnCD3QgLrDS2dq0C0L1vHCMF3lfewnZgKGPtltMTuNDgf9+aV3xeyiHYNJjfHlzcllYZjdK0DFz19T7dcfw+tbU5DoHFJnnjGj5+sgP+rnYbd0ljNcg47o5HloXM5vqUB5qeaPZAFLJ6P9kGvzIGU1sW/qPBwgT/lbmo8so0QMXTyA52nKRSbnoi8utRtntxUCPDEMi4mctuCdHgcH8rSrHmNg1hhQHwGfb6QlCiYrhu3JVa77Pnx1gjSBhv9SrJXY91CMzvq1q6znWJca8M9UC7MN4KN98opmrHl3Wad7vs2XZBDlcRaFRJGfSU23gLFnPPU1qBSdCGnHV4JEr4SOCKT9FKXVNTTf1eIS4t1ipOOIcZDTRrCLrmrjj/jsq2XuSBR3KMyipF5okpCzy3HqyLVGxNXkFxIFg74K5Ybw/K2yOZZwojubZIAq1bA7tYjsU+nJtJqqcTryICchMDT++mCSM97/HQN6xZfyDMHTqCScQifB9hX8CO1zT8Ogqp/qvkgDZW3S+lH2m961Jo7Du+p49W6VGBXBJGY6bODjEhnI5H2uyPwqPd1jLdisBD4cADDh4HMnUm9JsZ9Vzuq5RstUAaO4pUUp9acudZ4ris/9lrU+lgTflW0DS7UsyuiUU9u+A6yqtCyPZDUHqAu4AFlCCwr0GLYKWOwcxatv/6wHspkjqr0Y8bgKJ/LmJh6zLyvnMbQL6k5pi+lCmzjazDkESWPU+nWIGsdkq73iAwGa8Eg6CDRYpbpynL4xFTHsnl2dYiWNpE8IhDXhoKUGTb4k1iDZxUxH7+zUw7PmMVJ9uEs9LhdCuAxnGflkeZgw2pT63I8tzCrXqGQUNWNrAk5Ig4jj1MEduYHzq7wPajonuIw2GHmBCSpeqMXQtLvq4Si68s7fH4r6scaFxwYaqvtyvuhyrWR/js4BE899cEbeqS5TTiSFtr6GJGTWxC5PcGqZ2L6w0uL3QJKW14jGHFeJVEhiLdYFuAmpz4e7q8Z6eJrEM2dfFkqvciyW2ZVSEAbRVV7Dl66EtO4PHkhzFEi4PHGbgF2uDVsSKZK+APDzycCFaT+0HO1eg7h8HsnPgSe/oToaRkMcysCu8/2Yc9U4PVOPUksCdQQbKnXpOhLkrYz2ijHjGhyUAYiAvJx3agbQnpLM0/ymcu8QboABr0qvefzGEE2Za6UIebuaP5ycHhR3J3XnU1VU8A3bqfyWUY0PdS12kTvRc/lwdzRGbGR3dspVjpVr7BSDoUsSTBRkhU8nYPq0yIvnQdO8h4Aj9W1ZEIQkM1hKetQrntVRXGNGAHe2qWc7p8q+NPZvqOpbfIE7KZ+HFn/7eHtd/1NFdvKg5GDEKlrNyfZMooqy+3iU6PFolachWuPHH9W7lYCpO3mmiVglCOqDZAx7d6tWVcydpfsLHsu1mtrB+GQ15iJoXOyJaq3EtLBnsuUhNYAYN517LVrtQZGU18Bfout9E9bma80qGx27POxeajYdsE2i5KYKryJfnqCpoPUxzXapHRUHT+1whzR67YAIe/LTkP81C8sa/TT395AtBRw2qDyezZVOrD1Z3Wu0zde+Jc5MHuebuJXMpbgKaW5X18BTsZYRN6p4OAjx3wJma+HcZRZnYJQ5K2AUK7l6mMl4vgAvLehV1oD8UpI1OHin0svLdiTjfklz2aGwGh8eJ0+AEWGTeem1YebmtC1xsNTBzmT4ANM4dUlsehejpabsSqmvVzkjSf9V+iQISC0kqB1rn3CyYMnquXBEobzsPFRR8fIcJL4lmyP4vPPyGs5gXUWp5aCwJA0KCWpouTDCo5xP7SAVcc9ngZFDWRTAyds/zhykdPVuoXklGrbPgJfaHFcfb9MwK771GOMxf0JNplF8UXZMgrfwRSLMM/7KNUrxYqCFMjCbO/bZip2xmoxzcqEzVsprADlxqOpmSxvI2ks5Ppi+pNKVz9QXp9gHX+blID04XuM66SRQdVX3EYal/n4OXzCVsRnJFFWfIdgtxzrD8dC7Xl6NM5XkNM8pZCOd5XKNcn4xNlihHxIpYgU9LU4pAtBZZVD5tzra1wUMA7sXQQpeHgcwNWervxcPY/xO586kuO+//Edylq15UV5jhd7u8ALmKMt7fcE20S44rpbvBaSQOdTuyzvm/bVWSKWRSHHusVwrNx+zMOYP6I8Q4SLcRI/MR4EyxTfHmqaUkzTS2AlIkzGu7t/e9UyrxfMwMIaG40gy+krzyFGCQ8i8iqhcXuNsIsW2tR7sWEvoAqURS0tosP0tryHCq8USbSsqMXLTkYJrW3J7mV1rq6+sTeyJBmlGZIO9RnDfm7C+XKMcFxRMoRySMtujluzUEtrq5DblM71Z9mhQbAB15c4Snp3USpI270O1H9Pgw8dEcbI5luSL4uhJm9YdxMvfgg2MFgC4Z0NI50yEAd4Ljy2Zp/otSRgKKkRGm55hk2Ov1GV6onas9nSClWZc4n4OkhKZQbJXZ6gqpvfyPlpIH6qmcJRPGmo84w08gl7oesr7wNTShnNMv2Dtn1SoVJnHRxGq5rH4ymy43yi91RCwL1DyzghzAjhfX/le/HjahvNnjElU2xTdqTedaZ5V/xbVsBVU2RPweE9KQcc2uihRag8vGbOmuqqJpvwVxRV19CWKl21zVWfTb6uj86GgNcxoGLfArkmBkC54l9eAIjgz6bLrUKJffKIJGkh2CEJlNT6cztWAhqiQBm85psVP2tKQslrXtb94U0ptB2p7MvvEFVrW2OGqPlU/20f1JM78LJDzy417kitIISgbdFd5OEK8FKLiQ57PR8a/20cA3iwkyubVfSTQ5I36/YEwO292xKXlc9MjK7lf2Byor5FfsPNZa4J28YbOB9DrvFfX6F/zW6nQ7D7m12e44xa3n6/6t+lfaxCQWel85FjnCzC1ORzepQ5FqQB+uhkQPWTIoF4sTuySD9xBNU5JeZ2dtl80qXt0GqLzExYArl5+Lz40lGIJA0MPC3LsSXYLJL5yWVW7SCx7wndiOmfCtY9IDpt4UbKFcm2wUGdG1mF78Mi6oy06w3Ii6RhE5TWImpXT7NFH/Ld26WeoP3Yjvp3yt2wSNhvBXxt7Jo4yABFZ6z6Capy+RGbzTkPP0tAFBdpI+ScQd2flr37LSsdFoRTzxvFUp29yGDw2ic6Z5TXeNDgor2YCdams7WF8hKxP2FDF87BN6XEk89uCyqaOTI+DNEkdXJ1f3jRdRMcpzoOenwmEf6/rTC2NiCpfHWyQCZmhQrkzCCV79fi3ziT3Jnd2FNeYHMfe5sy63Z7Ph3+Wck3MbRCW9/Had8kUZSqyLmiCOhRmd0lou8v7UIxSFoq3iJCMIEduOwV96tTV+DjXeL90Q0a+Fw9bk4BTIiHpNrpVby3hKug8TmUj28sX5SEnIpCGXtcdmEXpN8SqNpx+R5jzcFiTvml51s9DoOiT/F6bbGKpkCvsn+84UGa5/1Dk0JuUaCPfYAv0yHpneCRwY1feFfMwzbtO372SLNKMzW+3e7bySojVb9GGRusyoCIS6kn3uXpktkNDtaoXOjLZ20pOYEt+nejwZXX/whQ8a0d5H2gOK0fJuACCuUG53JEXUR7O8j5WDGJzfcRxzCH6swYZ0QLjKHWw+Ohk8xFerZEMXNVGudp3FqhnVjoDG543k0aia31rs0Xrjd2BJCL3rfhekikQHr0ejHCdG5gIme4cJLg0V9UHonP4DmSoTpf8ySFsdUUJGyr90+trsGjgLoY5yocTHks668pe9LjlNZKidX/CVXGRtt0rfrXl+CsjtbjGZN/3GANvDaECNeeOgqx5oqVe+8xg50Gc7/YnUjwvZib5LYLpBHRVGXaOF+gpMn3UwalhHRnOyScGFPv5t6ptrZip7GHkSJy/+UICy64O81/qxxrlb7HXD5uXUe19oudPl1+Kdmdff8tnGguuBG41rU3D2VSB7F5YpgmnyudhuRwQLgj93Wea/Mj2GTedGKt9stItRjBHFWZnz8KrhWFz+gXclGsDwYVkELZkZfLpissJcYUZvDGHi+chO/i8VWjhUPVVQYiNjXcBtOfZ3PU1On1Fj5jYkOfvOqeOGUIuCHqVJ76E3zgMav1wvjCKPjAzfPgrKRd3K6/hFYDBzCDOg99IthDfKszmWXOqcUrzus1/0sD0B59UPOO3oPPnMiyvwcZsSkfrZLhwNqQZoqY749XyUI319aEGbYylE0sEa3F2U7eYV/6wzjlNDpRh6lcfzSMIRormlRS2UddQVjboW2LKneAonX6NJdppxFmlzGqsuw2qYvtaqVzn0e4R9QjBZGhPs76GElgQOb498s2wyx3LYjdq5TcntusjQF60KGEuixn7WMw73cJqvy5BAxaSEyj6TQbRpAPyONm5niWvvA/uCp4xP96Cf6UP48Ts1b42aeU1HNBVr8Xdw/aIAsyzpYLPb6v2MA5gLMdOoY+M6JXZhF2cxoesr2Qz2KlczFoaaVyZULAzR16SYIe8e5bX2ID0Z7oywGkSd/TwdxDwL2VclRGygnNhD5epLmnIdoRSutPBOxit0ve6jVLVuJVNe8pjFE5nX3LWqaD4Sh/OzgqH848pdT6y/cnAzcu2UmhZ5bsFH5IZR8nv2aAaRWtFIwD2Mso5CEARj8FvmrS0unxEziAxqk2j5mtr55kJ46sCVomMlwstSPfQAcrn0TkjLC6631qod3qwjdwyZt63ytFU3kgygZvRFRR2mkRy6JtpE3Bmk/I+nmxBd5zEJChA9CwJeCjWravs7299n6bGqEXgQDuTl0AvfF4w02nJVbAwNcbE1oIRwaVdEY47AEEQ1nvLs6YqEnCt0Sc/yV7A1xNUfjZTog/e8j7O0qaQnuXBKtVCumnMzA9mxS71hTZPhJEq6kTNM8HXxljXqSJaLL2zWwgHPo25EAkhDC+HS3XcHbFu+Vue9+vZQjEqqDv8i6CifN56/GfElNfQm6B5pzSSlLhaEBwLlwUVvbVynD68t1ZWIguVHJyuvKkROIsaUfU8+HBCBMc/Of8NHBy4NDfOFRTirvYODoEjNCfbYmpFOyJJEqT04SNVefNBfcpGGAng1fFDVGqRhDQHkFXm4YSCILJhb3ontGPh8CGqIaL6/3p9jQFruyEIcFkuu83bgkmTg/RWa7/Y7W0v0/ckASFu5vUcKZbvCLiq+WPzhmmA3W/aMBrQzs1Nut9lsLRKU4M4uiIO4sbE9k484KDwOzczOg9LeQ1NMTw3ZT80mB4Lr4mR0K4lk6v6LcFRiGlKSdz551Mpbl2DNzWd8j6UB+nWqK8m1xigMimdPfs6H8xdzKcb84sy+cK60oUCJNBOB2qYaQJX532Vz4QUQXEny7xRTRm+HRXuE3L++3nsKOBoAzxNj/MmoEhJ1OgwvY+nukYmYHqNqdYAhKbzc4Y70pIy9Vlj/v1MhRSdrZAFQR4P3Km2Y3gvHMmmuMr7toXhRCMe0gUTPy7TjgUHU0XuYnGOcg0aRz0jdT8aPe15OymVgxyb3/J5kHqo4oomSI6nI41Dszn+rBM0JMW7bakRnFXhhWOYyfq5o1uCLtSIva/qeZA6AVxCW3Ou8kIr0ckYZp3Nml3dR+Nbjvbi/PaZNN3snh/WhJaicnkfTxxEBD1yMx0jwqO/RD/quJ/PsLwPhiRpd5dd+g5JoaWPG2EuDtYq74MLWh3bqntFlpPNHdSL84SeUPVuW5D4sE5cZhBEO+JTmnjdcTu1aqxbSpT77i9oGfgwkjpbRKLnWsfmGsuJUit6pizGJqbhgAGKmIsdVl0D1Cny3ScpTW94ZFyRl0mxY5NV74VxmF72UnTwofcWJVeaSrQHZb4Y7NrZJjPynAnk+uAl8QTRgSVj753lu+0+tPTCFdT0QGyzMRSxvLTtKqa9a0hhFzzdEoDn9b6gVbfjFDlc5TnbV3bo1qfhUeQi1xf+/pIoaY/N6rcoiKNr9oVLYau7AojVQVEvUKOpxilyGV68pktLxJcWzJwrWU5YPvdVPQ/WDCzW/TkbFIGSVW/kYenUeQVAv1dAGS0KB4Xk8aVrdBJQZ7S7nIOCDLnfaJxzJnzjSbQPkWJre9er38Lc+mT6OVPG0OcUC3K+XVjkxNJUOc3OcKQFSBAOUarr3s7FKjGiZbtW+b3IENWFerkTlOcHbe5wfHFEZo++q9+SUC8D+iwlncL7wTJAlT2vd7doU8prTFG8mTayFbRKyz2RUJH0xvPvV2M96d2WTCvryoES304D4Exm0Nvjqp6pXCCqi29TzItDTszuHa6+ENa3eqbvB9vQwk3Cx3SgOxPgmwqsR9p3eR+anT2R97Ki1F7Ycnyvdncv3k313b5phr0JiT9TOBUXgW6ih+wOCXyr+1hJITwb4tVynlQNoWrXvlnog7vKJ/TB9dibrAqD9iP1OS4+FX9pZ9esnqmTqX364wATR4KjAmsVxXiQbuVaqcsgbM1zmLBwi7BHBxPE6FyC7+Tf17jjqUC027ltZnvDNOR1NBGhvsV7AetowQ0Bw+Ayfecq/cKZjSUO/CiuAVvcNRzP6U9ySoQoeJUjjicqyPoaxBI7do07+tsVirn6DAGH69fXgMbaOa7bkvpecxRrTzx8Zw7a5TU0WeVfp6Bu257vxp5Qzo1uUDVOb0xdGJcrByCd4+v+RdiCblD1FufsMCAQwkak67fp2Y75Ic5RQOSZKJ+pfu8TDTHVBX3UWVw+APJZs+CU33KMwes+KnLsENHBbYmp7QNNRNV+l/dBhNy+uv75Whk8UaaAO51vcQR2eR+pdqR9bX8XoRZLs+R3iixZatVYZ907JzjJAtR5ZAJ7Z0+GxeF4NP9wDWs80dkE3pFmY/tNfEW8kLFbXoMF+tY5mSPtD/jWcBmS1xMTV7FG+dPdNGwrxTRDFPDkK7mISsgXqrPpTbIJ4QaYoZ8MxETZk7yynU10tV8XLCIIbEZIo4QLW2xqFEKbbMBRnfd9ct7nDBeuCQ3oiY+npwUSOfuPcg7y5M1DOydDGDiQ3pdVgiZky2Kp3ssIC/tKpV+sB8sFI2u+f/uqs8srrwGaZmtJgIEUw+CBsZ8tzOWdVWvUPT6WP8anxKfwVBY4G7BZbIpvOcaoxtnLTehXhGze9IpJ4AmJq6oXguM5ZiwN9LsFtnV2ADO+RNPrE31y8VvmHTCEfDDRVwTGSUlUo5pxS7VyPp3M2YQs/U4Oh2oMhRBv95Vgm8p/y8Fo6uo9bWeECrhtTFTFhzPVGzDVGKOMGEGYEpDOiGisCNs2mSP+qrwJLBkJEudwXTMQacY1GmmCqVsZt1W/JVsgjRpHyQQfSaLcoZbqpdy7PL9IEJ3BEGnTkQaqJytGqPSdgcK0WL0XzpVIkTmx0QPEUF1kmG8m1rP+lvsPSIe2P3XVQitcYW22L7FV+a2VY0xZD9tFQ45QQZI3o5NZ1Yesi1H9lq/ay5EklE+BLLnN2bSTpaA1VuPDbAgvIag+NP43FaVkLpsPSDnL+xghlmsAs9nPjwFiGQ7432RZzoWcs/lmaZ4mojV1MVSnxPcvH716L9sK8ODKkFrx/15fzIlILl37UfE/jE+ETCpk8gbdvSv7MCq2AO3lN/TiGnHLqwcR0DN8SDXMad+2e6QGexfXYI/AsWSy591TgqXZ+pI+4SOqcXqGE24h3HciLONOZtRYcaw741aMCNd4LQyc8wl74V8jaDuvN0AE+J/qGvQET3CrAbFF866FDMCpo6v3WL2XlvaTzHn2wbPnoIW1dD7/1c6qnIDtRdBYCU5IisaK1Uz7eCagW2e6ug/wozt8ceud/hxzIuoYekiOQtX+Iyk8Tsi3tKdpIrrvOACJ2YF76m/uLInCAbCTe9yzLzuhpdvqB8f6Vn117fTzDiSCcEfeqVYsqzAfLrOCNaJ6HgPkA7V86DRqBOket7hwlBJW3W+g4f8mYJWp0PDPjEYPjCvAqjT/cB/WFtCvwC1jWMNxBzBM2ZJSoHweyqY6tVFdDIQbBzJhLIlwFUJQzGOarEKbFCw3D63e6TmoUymAAaSqWt2HIyDKuXpaoD92d6hTxKv2SFe5L6QZfe7U5bEvLErnI5PeQEKfMKgqE+NcIxme5394f2Q8gty+Uck4+mK7lN9cBC80/Qz85K9JXkFRAhGyYdzlt8+0FvUbGUj2LHDFXDxvWL1co/U1fGZSniwqavPppafl5t08ciSqZypk7U1ZbMXLhB1G0tGQgGyMVnkNMIPQxU0hnuWEAGFRJhfSJuu9GmOfGRCzwq1cb0BAPSUddUuNsuqM3KJBeYKApDqP1EiZTOTPDb13hnD1TBfs4OewZFh3YneI23BkRIpq+NV9LM4KOmR9fb4VMiU9FM0ss+FV9oHkJgRI9UY7Jwvbp5MAOGGaoi2r8ws8rsbniHruujLYkPs46rxku9Tit6DsXZ/5UCvLrZDoCLDTNKCqX9W50ka9c4rFUbwkob9YkqBV9lXoe9U81j8WvTquvWT/4H3whYBd4XiUPZwebTQMO8dtRDWMSQzOpCiK1WNX1ziPUyuMqVAb5bqT683QC0z5UcGqZ3qP9PGtBQuuWAlmD9cz1pVFyxqsnmsS0AnPA5UDzosCJDtOcYdP+VtWplQHiCdf+5NQc7S/mRjuWWnyXENbgNXDmUN/n7KILTu7XNLHak9nOaOos2GXl2q7DNbzpPSmUbBnfY2eNt+ZSZ9MpDSxXN47j1aS41OOse4UZTnQ+NFJZzvVDrrInXFZeiuvQZiEUhzCglTwpJ2ED5VV9+xXy2sMwXdpNpCzKUFgbw3aR9YLzvryGgjM0Uk0Es+bqsi3ik1AaiwhoxpjHPs9rUaZq1kVgs5JyCg16tkuVvchnkQHTUst4pwPETw4laaW+5lMqvsAELEhDYOR9ooBjlY7X/Hrq6tqn1RNzPozE/PjICNtWeuCkuR2JqjWfapC17iDHeOvsGRykIqk6BiC5TpH8aUu0GOuyENtmmzA51I1QuCqnofjQiJJOC4glM8xTle+MdWlPj3L92I1A4HW4viirFhFIoyzQ/IZv+V96HTA9Cm061oqtfdoa3+EiLKG0vELTYXxSpi1FNZWoKg++wW2Wc1jogXVJ279FuXjzVatajGhWc0qVc9CEznSUxVpxnB/SDhhR3R7Y4uueo0+VbsgGieHWg+hi5JUvHyTkXOX78W675E4+Z9z1wvnLgGBRmHFbl7qttT4aDSjdb8imB5IAFcPlQhPsdzzw7YrGVj4PV1V2DtxP44/WrG93Ev1OHh6CA+DAfhN3qzUFq5Vq2DFcQ6AGdBF6yU5eLeqaaIG1aW4rMv9mJzqL3+CUVTdglQLK0NZnEtC3aG6jy6jcaSl3z8lCAmWfYdqvyJv4fXiVxErQOmoBxOb9znfEfhzrgcFXO1hgo3PxvaMVPX1lpQUW7rOxQEGXc1BI2v0E4vW+gqxdDRvfGS2I8Kgq2dqslrjM5rSBOFnUPn0X5a9jnd9jQdwAzkd+Jc3/Appj/mOKEy2T/FMWwxrd4IaJWsN6c6EdSYxQ/g81uqZth0ozNAce1YaGIpRWlMDJE0zovotPVHR07MNlpvXlOsd9F/kmR5/dR8BF7EAd9xnmlFSdvM7Pwz95ar2QRSszWRMFwlo8qhFCf1ASrAfaWVffUSJwzWWAk42mEb/ptDDxWYfqa6hz0GZyVIp5OsSu6YpP/NzaCaqWrDqguTK8PBuZi38DW1txfXmJisWtGtg5og3eiyyCffQ2Q8A0MloVD7gHRoD6JqO7538WG0TCBMHTuWAVdVynM3tu+x5FvyrN5OPjhT1LML+YTUHBc23VT4D+7JIM/Plnyt/6rdX17BHP2fcnWj2sywhbzSdsZdeW5rBVf6Wc3izxgF3ZdPBt/8wmMcG9ob1UI1TGg9HWyFYZ48utu7M5dTF7K+quWUtJ0XXHAQd3XISlWyBCo27pWhQf7egbncIjP1DMYnfHB/4g+HRybL8LZFWZjeo65O6dmKsjJVoS+6qp8W/0Bma1AwhiIhxWyDZOIwR+1f7IImRSKOC8PC+lF7FTu6IsHwAdz0ny6Ci23+z6yGgiKvZ+Xh8TftSFzyyKir9+EsfIoWEzyM7yzbFud7lfUy+sJx5ItVUq0c0W8krgWeoNPA7khxb2cRvx5hlaXnCLE8L5yl1KGeP4bBxpvWXj4jPfH0Tkfjs70xXrtkrCWJ0kfTqWOFsAgiglEtn7L2zXOfs6eyoxZjb9+yI0AhIObSVHSo+HVPniFDKQfDsyoTMY0Vw4ehkpupVX+NJNNI9wl4eorEeO8z3du4QM/iUv8WHTxKkA/xFaOMHUJSqA6eAX1/jocRjjiKkz2vaoYpcH5T5vPfiGg+ZWBLbsAQSLfxE94i7S3+BjFJeQ4XM3IMwnmRSnSAnKhfr8ZAWY4yyqkcRMHiZuJAUUM0COtSc0WVvjXJGcnfy4nXZH6kSwn2FQWm198orypxFG7BZtVIdxxCNbv0eoe5LPS++OaIRxWjZSuuO791W9f6cSovyqMp+Mq5ytAUf3EF1OnaLTZS9QH3Ry/mUSbypc2bS2UH1UoOg5JHIsp/WvyWNeaWYhvjtLYfUC1+jdYe1Vl0jyTvObkx/grBNi+gVO+lzZ39anuc+A+HC7snZ5RZRYqPNgcIq9Zb+yi2EzBF/cDRdH/nzCeUG9ttccD7fYh6DhiFRtMx+pE+S0xV439khfYqY6j56oHDN58vPI2qNaKvrBOs0WUnLa0g52XHf3uQfadghnocnL9PvKb+XeG/NHmKS70RARE+rcHcWUQKb6hylR5hEc0/0+gTjXxVCf5ok9654n1/QrswULlcichV+G/eW413Wn2q9pSxcmT8Vo/2JzDxJiwesIVWszh5PcknFaQ7Hdl1X0/Cb1BMPFqmu/C1b0VK3UMD8ctzVUOFfPTv4lodUvVvmip4tj7QnXANrCp4TY4CDUMUOO9d4ss0fYUkRwzHOpWt3RT31rIqBKMvrSkT94+ckc9pqF1I2xL8jZrXH9YfAk/olBdd2nZluUW+dG/IJ91IjIFvd3och4QpcnCF73TF+Ac2hglTjw+9NOecNwoglIFxmVSn0dYag6nmEfaL/NEPN4A7QmOcaOXtwsXhVTiK8nlMcMgPKlhI0gzbxRzgN3lB1VhfVqyEFtX6WNA5HsqWU7AAiBUGWa0POTigoiTh/EpOMlndFwGC9eerf4ti1gmUZgEztV0sV8WtydkirnukbdbgfctYqW1yeNyIf6UK8KE8r14YX9ZCA/jY5W6mpJ+AQNMdGgAfVM33tAS9qh6BA7lC1Z+DjmQ2g+svf0rBYiXsktgwrTfAhEKyBsowqPwrSX94UE93WRqN5iAqdQfkJdqPyvIuUxul03unkSXdY1M7bCJMzebzluu8zC/YZsuyhynkN1reF5i70461qnz5baRTqzzooTOM3vpQ2sng/MJLqeQDmWCyxgqkN5y2aZyfFcfIt7Kf8XjDXtn4PaMabZLNzckAhiwD0xcKpxrrZ8xkhv6p6vOnCTgpdda9OXV/uLXeQ1FhMhBOoKEOsRfhXIAsk7cXzyEED8xNzoAcErb58xcRjssVpK9YGYC17MY0ku0BnyvXt/ukveQvKevIUncNYaqs8kmb5IcDGE0FG4lOra9zfzulNa+87uj9BwvGuBiN4Vz0+fVoI/LMHSaGNUlJZO0Dr8KX2H66hBSVXh3KNGE4LgjGbHL5niapqjkpapl05S4SzI08oWzywqwcNqNpbJu5FFysxkb2FWaX5e57x7QyEHF79FvksPRq+6WRH0WeP+SENVkQt1Tf3nXMcoGKUAFREqBXYHl8/Mkr1zc3kkKuyydFJRIq6P/X5iqLfAae6DwVKXK6sKQ4cX5ql4CTeBF7NcqxTjqhtP0AO2Lb3k+arUCrLLgFYeY3/s19VklBlm2Lhm2iwmepF+W6f/1wNDN0RsNL3n6/WEKUQPqO4erePGcwWgtNW+N384KM23URp16j4dInsk0WW7XryvMk5EMTOYEGZrDk1rpF+3ry/8+ybiq6E8zupqTrKb/k86PYTO0cyjicwQtCQEWLR7rUGjcfkUg9ytqYK1Cs9i60iqBNnzlTVNfwIYm/E4MSUrM868kpY3Qq0b7VmzwyqhDaYiM7sZaUNg4iu1WAtz5U5lCsX0L7lf5zTZUBoJTsT9SpTR7WVW9iuLvCMNIIYggcVeWqzZR/ZtlrGaWwNIeN1eIKW2oOz1Sjz5vcX2jtZ3hBMfa4iIISMn03q2Z8mf674LZCaXBF6cVFaKRk8VMt+4PvUOZq/htZMiB9lstZYcorOfjDpQ6p41VjfkRGfeyfUDARlZ2uoLSTjBnmq+i2h+bt95MUEu3d4NZNIa2ZmPLZijPE0AEs4ycoEYr7fwAw5EJpKZnVGfi+AXRllqe6tNNT8cTHr+w5st/JZvJwVZ76xyN3qYrRWzsmP5JGlqFLqk50IacdmbKGEilLbcFnonNnazmdX/pYRN1JMjPktLWl2SOgJgKdZKq/xcMo6dISdHD8f9YSJUKHH6lGMda4GZ6gzmPTWzCZPwpuD8clAqzJk+I3xZBtjFcuWqaSxnIdrYFvzlr1GMkA2zPjuk1IGZc2Ccg4xb6RoFT/I36zGlvju84Ji4Vn2eDaIl+Mmb2TxW2wfoaQQmwOCO+82ys0doR9JRaWn02vosbqbQZN/dTaZJPoCoDpWTOmRiDmK9SZ4ck2kkUwd8YJknBNpsbyPpBy2sxWTGH31kXNYJ4fBiNdKGuV9POkzUr/KjEIa5oNB7DzTCtta6b997YQFXY6YshDKKQ4v9czzX9AWl7rP8JdYcBX21Bpe8Xcz6QUOzbYmVd/DNSC1zOOvYoy9z3lJi+0NOMaKVz6PbA17VF+En4BuPmXzqN3mNSpWuDI+Ss78r7MXc4aCnzrGbad2lfXTN51spagHGwXKoBML6z6cuYXOpdxLvdl24fNCFt5R8l6UR+O3/2BsqX7LlxQpLy4vxJOADtOVRxMCQazWuUTvYGnCnwjUItdUhHiTwsIjfFV7S6kL/k41Rm+TwBj4g65MR0rzcpe/BaY7ZO5GcpJgX+0poa2oIKbn8j4C6JPe+Qs1k4v+fC7J8Qs+rJ6HB6nykegD+6ekJb5fcR44uJe9RshhksRQWSKfk7pmz54+jIp9Wevz90SSi6+X3S1TwLyEJmyKeuypapye3ctn2AU7ZUjEzhFFPkNDPaOmlWNdJIGhIQosLBGFE2uVQ+62ISu91S97pVhGbts7gVwziJcH/4a47GzLqvEBZk+lDSdjmeEE8H5VzYAswPjKazBYYpO/QVmDyrDL+hBHEEdqsNVvwWQ4u4XhSDU5LqBXbWMQjVTxnqo/l4iQHK0BpPn3z817osJKZmb4UlvMEL4DhGCWi5vRQ+Xtsdbl6Fzug86wkOWjl5Xep/A0Q8NWEzHS2b+8j8fh3gKjPm4OCLJzfilwMFUVGxvuWdsYVDrlxUtIqYrDTGABYEOpx42ntKVsOUimVHBkHlBL2lvinldnMeG39nIO/dIG4IJnTwtT73DbuJdzUPicn48amxft85Jo8ak2mmZG5XulJmhhIaHpCwlY4ZmqWsi4mM/Z01TvZX8xhNjeOulZrWaq7LRC5/o150reiSBQSjwSpbFjhbtUkwHEuAGr73ZFTUBNl/gD7ma7MWMfA1zHodzTMYXTrTKbivAWFKCSEa84/vjZi7TyGudhUr3HAphGxf8Ku5csSWEghqJbApwGs/+Nla+ouQY9q+YkP+OIkJ7iCoADIFwi7G89x5UC7vQ50o4CCGfuvEPN0QTZ/2/Wa8qHcIf6bPoRAVWsntkRqp1rD3bJQ6URUiMMG1pFpmFqxDH7b96WFe8YRum2uZDwRzLRPS1nZIFnwIrtXD4ZABz3LiOJta6MQC9doYfAdX+Fy1qYFHCft9sQaeBUMrFeSbZIiNr+WvVjyFW2lRNCzGM9EnYO7UBHuZemVoulfROI3CS8YiEakeUcaNcajxhE7Zqi/aUBdIP0cQWAkqh14zQ37Grn8t2Si3YtBOhPSG+ybRq0YktpvwMsJ0E+gIdGj79w4SEZUguA8LZnTLXPYBojw6sJZCPi9wAoW2Wr/kM2ij6bkBQkNGvgywMks2UXQUHxtHNRONGrBuo9tejV6oD/Jhco4pUhshJMchq7mvK/Sfcg8dczo3hQ+7Zj/GIAJGtOho3Wq97JikKYEHuXAe167JuCAyAMI7RBUstpBxOFEMlV1TgvKnc+SEPLvW/3q1YAc09Q1Wi/9VlX6exrEJBteFDTSEmVn8hZeuu6fkyuiosN0tpMHKyHeKv3Z3LTj6fNPM3ThCp71M4o12XirORWC7HixKnv3GMJP9UMPk/rilDAl25CMlEtXa13sXgz2GYvE1RWcxJrFOfkWBsk17l6EKO2lQYW9rOKqqgcPsibTUh91hdb+52I1TtBHcAo3xh6JmOj+6OU1Bn9JFLDKmhxtSwr8vAjjlqbrs+Szs53w9q+wp54xjDlDXTZ+ervWElLQvv0aEYvEXzhg3AnQLv2YZZ8Bl26RxUSb/kZwr5zjAR11JlntOIh0qEeBWtvYgcr+Tuz6D9VH5StsDXrsCzy8F+YP77UcjHkto/2ziXlwFtyxzyHC2UhFPLz45S4QSfLcwo+qLd3yVoex/9nysYMQ4yNff8r50KlBF8k2ugJStpzeids7Ehv9qgzvtegI1Ab+2TtRummYMG04KbRv8r92o+R/tV0Q4i4jOY0Qv04WyOvXts7vEmcOGZ6H5oMN+mEUs6sLlmjR7u30mFloj7EcOoMRoUP1B8FF05k64/ZhSUA/Aug1A47CS9TIP/cLDkj5d5ecWGKvuOyj6UIOHjYa+qM7iq57cd8EPd66qZku88+5xVCWXlCQ121BmLyfvN6KCoJ4IOYStpBqKiz9z41k1A7pF9rjmNCYLHEHjhCejtGPwZZ0JOQUob3sGnoQcIei96h1YS0r8RqClJlnJZW4NJ35p4/eoimh4kAl36Ool4ILbSu8uPFh6Z2eFp2nGNg4kjRVZUvaFntZCVYGK2rc+DpJJipr5nQuRnCdj65mtMiX/cp1t8BJW1TKPH1F6gsnLJ0PqupechTr0ciwWQ2krLeSZO6o8lJWjoGYl0/LOXzs62tbLqJPyPlj6OGd7PtTw2wVQ3m5yOmgEeIxEidaI5MnNvWDxj7X2pj/kZVnMUQKFKdiu5ae7D7GIpz+WDqdaHbg9NjULFambAF2u+4+dZ+WZejQUu+omVVbIJ28Dn6MWa87fNDWI/0xSNkkeeS8X/tn2YgZU5CO6cEg7iM4hpWlrB1f3Db9XAZ38ibqbZGymUBaWYYh/nBddd3bp9Ihs/HFcc/I07MWlA3V6AxZ723opAh/g1wTfnfCDbNTg8BVQKZWh8mFl7iYGMCQfFPZsgQjMPGgiqu7T9YKy+tow84CjIRcrhXdqyP7vyrxxCb+yS3kpwv56V3yz4mZRXjtV1TVE7R1SsTS/OBFW/VbTFQm76VwRzDjhDBez+w9pXzq62N6BQ3NPlN0+sYNArgErxSfE3Tg47VS+ZPZ1zPRWMhAATi6kfYQUwvcFOB0s/KZXn/A3m0COhmr2DGFwP+3lLt+kPR3d4XzznNZvbtgAi6ymfs0GzO2TG232EWQCwq4HXvFowW7RcOM+lFx+W9LdeDi8dASrWx96gxE9tzHKhAvti7YnZf/gB6PX9xs5IEAA==')))\n","metadata":{"jupyter":{"source_hidden":true},"tags":["hide-input"],"source_hidden":true,"hide_input":true,"collapsed":true,"_kg_hide-input":true},"execution_count":null,"outputs":[]},{"cell_type":"code","id":"implementation","source":"\"\"\"CPU-only image verifier and report-free MRI cache. Geometry attribution: NOTICE.md.\"\"\"\nfrom __future__ import annotations\n\nimport gc\nimport hashlib\nimport json\nimport os\nimport platform\nimport time\nfrom collections import Counter\nfrom concurrent.futures import ThreadPoolExecutor\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pydicom\nimport scipy\nfrom scipy.ndimage import zoom\nfrom sklearn.linear_model import Ridge\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.metrics import roc_auc_score\nfrom threadpoolctl import threadpool_limits\n\nSEED = 2026\nFOLDS_SHA = \"3086df3341333f44adb883292da386857c3230eaa2d501514ddf827a2da11b1a\"\nLABELS_SHA = \"6f704a7bdb2f894cc49445b19ba7c4378c3f548d3449e00361e10044bee40920\"\nTARGETS = [\"ACL\", \"MCL\", \"Medial Meniscus\", \"Lateral Meniscus\", \"Medial OA\",\n           \"Lateral OA\", \"PF OA\", \"Effusion\", \"Synovitis\", \"Baker's\", \"Contusion\", \"Fracture\"]\nSLOTS = [(\"SAG_FLUID\", \"Sagittal\", 1), (\"COR_FLUID\", \"Coronal\", 1),\n         (\"AX_FLUID\", \"Axial\", 1), (\"SAG_STRUCT\", \"Sagittal\", 0),\n         (\"COR_STRUCT\", \"Coronal\", 0), (\"AX_STRUCT\", \"Axial\", 0)]\nDEFAULT = \"res_224x9_c130\"\nGIB = 4407 * 6 * 9 * 224 ** 2 / 1024 ** 3\nTHREADS = min(8, os.cpu_count() or 2)\nSTART = time.time()\n\n\ndef log(message):\n    print(f\"[{time.time()-START:.0f}s] {message}\", flush=True)\n\n\ndef sha256(path):\n    h = hashlib.sha256()\n    with Path(path).open(\"rb\") as f:\n        for block in iter(lambda: f.read(8 << 20), b\"\"):\n            h.update(block)\n    return h.hexdigest()\n\n\ndef save_json(path, obj):\n    Path(path).write_text(json.dumps(obj, indent=2, allow_nan=False) + \"\\n\", encoding=\"utf-8\")\n\n\ndef competition_root():\n    for p in [Path(\"/kaggle/input/rsna-knee-abnormality-detection\"),\n              Path(\"/kaggle/input/competitions/rsna-knee-abnormality-detection\")]:\n        if (p / \"train_series.csv\").exists():\n            return p\n    raise FileNotFoundError(\"Attach the official RSNA knee competition\")\n\n\ndef variants():\n    rows = []\n    def add(vid, family, img, slices, crop, window=(.35, .65)):\n        rows.append(dict(id=vid, family=family, img=img, n_slices=slices,\n                         crop_mm=crop, window=list(window),\n                         full_corpus_gib=4407*6*slices*img**2/1024**3))\n    for img in [128,160,192,224,256,288,336]:\n        add(f\"res_{img}x9_c130\", \"resolution\", img, 9, 130)\n    for n in [3,6,12,15]:\n        add(f\"slc_224x{n}_c130\", \"slices\", 224, n, 130)\n    for crop in [110,160]:\n        add(f\"crp_224x9_c{crop}\", \"crop\", 224, 9, crop)\n    for img in [160,128]:\n        add(f\"tiny_{img}x3_c130\", \"tiny\", img, 3, 130, (.40,.60))\n    return rows\n\n\ndef finite_vector(value, n):\n    try:\n        a = np.asarray(value, dtype=float)\n        return a if a.shape == (n,) and np.isfinite(a).all() else None\n    except (ValueError, TypeError):\n        return None\n\n\ndef order_files(paths, audit):\n    \"\"\"Physical projection, then SliceLocation, then InstanceNumber; fail if unordered.\"\"\"\n    headers = [pydicom.dcmread(p, stop_before_pixels=True, specific_tags=[\n        \"ImagePositionPatient\", \"ImageOrientationPatient\", \"SliceLocation\",\n        \"InstanceNumber\", \"PixelSpacing\"], force=True) for p in paths]\n    normal = None\n    for ds in headers:\n        ori = finite_vector(getattr(ds, \"ImageOrientationPatient\", None), 6)\n        if ori is not None:\n            v = np.cross(ori[:3], ori[3:])\n            if np.linalg.norm(v) > 1e-6:\n                normal = v / np.linalg.norm(v)\n                break\n    positions = [finite_vector(getattr(d, \"ImagePositionPatient\", None), 3) for d in headers]\n    keys = None\n    if normal is not None and all(p is not None for p in positions):\n        keys = [float(p @ normal) for p in positions]\n        audit[\"order_geometry\"] += 1\n    else:\n        for tag in [\"SliceLocation\", \"InstanceNumber\"]:\n            vals = [finite_vector([getattr(d, tag, None)], 1) for d in headers]\n            if all(v is not None for v in vals):\n                keys = [float(v[0]) for v in vals]\n                audit[\"order_\" + tag] += 1\n                break\n    if keys is None:\n        if len(paths) == 1:\n            keys = [0.]\n            audit[\"single_slice_series\"] += 1\n        else:\n            raise ValueError(\"Series lacks all physical-order fallback tags\")\n    idx = sorted(range(len(paths)), key=lambda i: (keys[i], paths[i].name))\n    spacing = finite_vector(getattr(headers[idx[0]], \"PixelSpacing\", None), 2)\n    if spacing is not None and not np.isclose(spacing[0], spacing[1], rtol=.001):\n        audit[\"anisotropic_spacing_series\"] += 1\n    return [paths[i] for i in idx], None if spacing is None else float(spacing[0])\n\n\ndef sample_indices(n, n_slices, window):\n    if n < 1 or n_slices < 3 or n_slices % 3:\n        raise ValueError(\"Require nonempty series and groups of three\")\n    lo, hi = [int(f*(n-1)) for f in window]\n    if hi <= lo:\n        lo, hi = 0, n-1\n    anchors = (np.linspace(lo, hi, n_slices//3).astype(int) if n_slices > 3\n               else np.array([(lo+hi)//2]))\n    indices = []\n    for anchor in anchors:\n        start = int(np.clip(anchor-1, 0, max(0,n-3)))\n        indices.extend(range(start, min(start+3,n)))\n    indices += [indices[-1]] * (n_slices-len(indices))\n    return indices[:n_slices]\n\n\ndef load_series(root, frame, audit, configs):\n    \"\"\"Decode the union of required slices once. Never read reports or free text.\"\"\"\n    records = {}\n    for name, plane, fluid in SLOTS:\n        cand = frame[(frame.Anatomical_Plane == plane) & (frame.Fluid_Sensitive.astype(int) == fluid)]\n        choices = []\n        for row in cand.itertuples(index=False):\n            d = root / \"train_series\" / row.StudyInstanceUID / row.SeriesInstanceUID\n            paths = sorted(d.glob(\"*.dcm\")) if d.is_dir() else []\n            # Keep CSV order for equal counts, matching the original locked probe.\n            choices.append((len(paths), paths, str(row.SeriesInstanceUID)))\n        if not choices:\n            audit[\"absent_slots\"] += 1\n            continue\n        _, paths, sid = max(choices, key=lambda x: x[0])\n        if not paths:\n            raise FileNotFoundError(f\"Metadata series has no DICOM files: {sid}\")\n        paths, spacing = order_files(paths, audit)\n        needed = sorted(set(i for c in configs for i in sample_indices(len(paths), c[\"n_slices\"], c[\"window\"])))\n        pixels = {}\n        for i in needed:\n            ds = pydicom.dcmread(paths[i], force=True)\n            a = ds.pixel_array.astype(np.float32)\n            if a.ndim != 2 or not np.isfinite(a).all():\n                raise ValueError(f\"Unsupported or nonfinite pixels: {paths[i].name}\")\n            a = a * float(getattr(ds, \"RescaleSlope\", 1)) + float(getattr(ds, \"RescaleIntercept\", 0))\n            if str(getattr(ds,\"PhotometricInterpretation\", \"\")).strip() == \"MONOCHROME1\":\n                a = a.max() - a\n                audit[\"mono1_inverted\"] += 1\n            pixels[i] = a\n            audit[\"slices_read\"] += 1\n        records[name] = dict(n=len(paths), pixels=pixels, spacing=spacing, series_uid=sid)\n    return records\n\n\ndef render_slot(rec, cfg, audit):\n    idx = sample_indices(rec[\"n\"], cfg[\"n_slices\"], cfg[\"window\"])\n    vol = np.stack([rec[\"pixels\"][i] for i in idx])\n    px = rec[\"spacing\"]\n    if px is not None and np.isfinite(px) and px > 0:\n        want = int(round(cfg[\"crop_mm\"] / px))\n        h,w = vol.shape[-2:]\n        if 16 < want < min(h,w):\n            half = want//2\n            vol = vol[:,h//2-half:h//2+half,w//2-half:w//2+half]\n            audit[\"crop_applied\"] += 1\n        else:\n            audit[\"crop_skipped_fov\"] += 1\n    else:\n        audit[\"crop_missing_spacing\"] += 1\n    # Same sampled-series percentile scope as the previous probe and credited geometry.\n    low,high = np.percentile(vol,[1,99])\n    vol = np.clip((vol-low)/max(high-low,1e-6),0,1)\n    img = cfg[\"img\"]\n    vol = zoom(vol, (1,img/vol.shape[1],img/vol.shape[2]), order=1,\n               mode=\"nearest\", prefilter=False, grid_mode=True)\n    result = np.clip(np.rint(vol*255),0,255).astype(np.uint8)\n    assert result.shape == (cfg[\"n_slices\"],img,img)\n    return result\n\n\ndef pool_grid(x, grid=8):\n    h,w = x.shape[-2:]\n    assert h%grid == w%grid == 0\n    return x.reshape(*x.shape[:-2],grid,h//grid,grid,w//grid).mean(axis=(-3,-1))\n\n\ndef encode_slot(vol):\n    \"\"\"Unsigned oriented gradients at native resolution; no learned/full-data preprocessing.\"\"\"\n    x = vol.astype(np.float32)/255\n    gy,gx = np.gradient(x, axis=(-2,-1))\n    magnitude = np.hypot(gx,gy)\n    angle = np.mod(np.arctan2(gy,gx),np.pi) * (8/np.pi)\n    low = np.floor(angle).astype(np.int32)%8\n    fraction = angle - np.floor(angle)\n    maps = []\n    for k in range(8):\n        weight = (low==k)*(1-fraction) + ((low+1)%8==k)*fraction\n        maps.append(pool_grid(magnitude*weight))\n    hist = np.stack(maps,axis=1)\n    hist /= np.sqrt((hist**2).sum(axis=1,keepdims=True)+1e-8)\n    hist = np.minimum(hist,.2)\n    hist /= np.sqrt((hist**2).sum(axis=1,keepdims=True)+1e-8)\n    desc = np.concatenate([hist.reshape(len(x),-1),pool_grid(x).reshape(len(x),-1)],axis=1)\n    # Three fixed depth bins give all geometries the same feature dimension.\n    return np.stack([v.mean(axis=0) for v in np.array_split(desc,3)]).ravel().astype(np.float32)\n\n\ndef encode_study(records, cfg, audit):\n    features,mask = [],[]\n    for name,_,_ in SLOTS:\n        if name in records:\n            features.append(encode_slot(render_slot(records[name],cfg,audit)))\n            mask.append(1.)\n        else:\n            features.append(np.zeros(3*9*8*8,np.float32))\n            mask.append(0.)\n    return np.concatenate(features+[np.asarray(mask,np.float32)])\n\n\ndef macro_auc(y,p):\n    values = [roc_auc_score(y[:,j],p[:,j]) for j in range(y.shape[1])\n              if np.unique(y[:,j]).size == 2]\n    return float(np.mean(values)) if values else float(\"nan\")\n\n\ndef oof_predict(x,y,folds):\n    pred = np.full(y.shape,np.nan,np.float64)\n    with threadpool_limits(limits=2):\n        for fold in range(5):\n            tr,va = folds!=fold,folds==fold\n            assert tr.any() and va.any() and not np.any(tr&va)\n            scale = StandardScaler()\n            a = scale.fit_transform(x[tr]).astype(np.float64)\n            b = scale.transform(x[va]).astype(np.float64)\n            model = Ridge(alpha=1000.,solver=\"cholesky\")\n            model.fit(a,y[tr])\n            pred[va] = model.predict(b)\n    assert np.isfinite(pred).all()\n    return pred\n\n\ndef locked_subset(folds_path, labels_path):\n    assert sha256(folds_path)==FOLDS_SHA, \"FOLDS_V1 changed\"\n    assert sha256(labels_path)==LABELS_SHA, \"Pilkwang label version changed\"\n    folds = pd.read_csv(folds_path)\n    labels = pd.read_csv(labels_path,usecols=[\"StudyInstanceUID\"]+TARGETS)\n    assert folds.StudyInstanceUID.is_unique and labels.StudyInstanceUID.is_unique\n    picked = []\n    rng = np.random.default_rng(SEED)\n    for fold in range(5):\n        ids = folds.loc[folds.fold==fold,\"StudyInstanceUID\"].tolist()\n        rng.shuffle(ids)\n        picked.extend(ids[:40])\n    subset = pd.DataFrame({\"StudyInstanceUID\":picked}).merge(folds,validate=\"one_to_one\")\n    subset = subset.merge(labels,how=\"left\",validate=\"one_to_one\")\n    assert subset[TARGETS].notna().all().all(), \"Subset includes missing labels\"\n    scores = subset[TARGETS].to_numpy(float)\n    assert np.isfinite(scores).all()\n    y = (scores>=.5).astype(np.int32)\n    return subset[[\"StudyInstanceUID\",\"fold\"]],y\n\n\ndef paired_statistics(y,predictions):\n    # Multinomial bootstrap weights preserve identical study resamples for every cache.\n    # Weighted rank AUC computes 800 replicates without repeated sklearn calls.\n    rng = np.random.default_rng(SEED+1)\n    weights = rng.multinomial(len(y),np.ones(len(y))/len(y),size=800).astype(float)\n    def draws(pred):\n        out = []\n        for j in range(y.shape[1]):\n            order = np.argsort(pred[:,j],kind=\"stable\")\n            yy = y[order,j]\n            pp = pred[order,j]\n            ww = weights[:,order]\n            pos,neg = ww*yy,ww*(1-yy)\n            cum = np.cumsum(neg,axis=1)-neg\n            # Include half of negative weight tied at the same prediction value.\n            starts = np.r_[0,np.flatnonzero(np.diff(pp))+1]\n            ends = np.r_[starts[1:],len(pp)]\n            for a,b in zip(starts,ends):\n                cum[:,a:b] = (neg[:,:a].sum(axis=1)+.5*neg[:,a:b].sum(axis=1))[:,None]\n            denom = pos.sum(axis=1)*neg.sum(axis=1)\n            out.append(np.divide((pos*cum).sum(axis=1),denom,\n                                 out=np.full(len(weights),np.nan),where=denom>0))\n        return np.nanmean(out,axis=0)\n    boot = {key:draws(p) for key,p in predictions.items()}\n    reference = boot[DEFAULT]\n    result = {}\n    for key,p in predictions.items():\n        delta = boot[key]-reference\n        result[key] = dict(auc=macro_auc(y,p),ci95_lo=float(np.percentile(boot[key],2.5)),\n            ci95_hi=float(np.percentile(boot[key],97.5)),\n            delta_auc=macro_auc(y,p)-macro_auc(y,predictions[DEFAULT]),\n            delta_ci95_lo=float(np.percentile(delta,2.5)),\n            delta_ci95_hi=float(np.percentile(delta,97.5)),\n            paired_p=min(1.,2*min((np.sum(delta<=0)+1)/801,(np.sum(delta>=0)+1)/801)))\n    others = sorted((k for k in result if k!=DEFAULT),key=lambda k:result[k][\"paired_p\"])\n    adjusted = 0.\n    for rank,key in enumerate(others):\n        adjusted = max(adjusted,min(1.,(len(others)-rank)*result[key][\"paired_p\"]))\n        result[key][\"holm_p\"] = adjusted\n    result[DEFAULT][\"holm_p\"] = 1.\n    return result\n\n\ndef verify_curve(root,folds_path,labels_path,out):\n    out = Path(out); out.mkdir(parents=True,exist_ok=True)\n    subset,y = locked_subset(folds_path,labels_path)\n    subset.to_csv(out/\"subset.csv\",index=False)\n    configs = variants()\n    series = pd.read_csv(root/\"train_series.csv\",usecols=[\"StudyInstanceUID\",\"SeriesInstanceUID\",\"Anatomical_Plane\",\"Fluid_Sensitive\"])\n    groups = {uid:g for uid,g in series.groupby(\"StudyInstanceUID\",sort=False)}\n    def one(uid):\n        audit = Counter()\n        records = load_series(root,groups[uid],audit,configs)\n        encoded,per_variant = {},{}\n        for cfg in configs:\n            ca = Counter()\n            encoded[cfg[\"id\"]] = encode_study(records,cfg,ca)\n            per_variant[cfg[\"id\"]] = dict(ca)\n        return encoded,audit,per_variant\n    features = {c[\"id\"]:[] for c in configs}\n    audit = Counter(); crop_audits = {c[\"id\"]:Counter() for c in configs}\n    with ThreadPoolExecutor(max_workers=min(4,THREADS)) as pool:\n        for i,(encoded,counts,per_variant) in enumerate(pool.map(one,subset.StudyInstanceUID)):\n            audit.update(counts)\n            for key in features:\n                features[key].append(encoded[key]); crop_audits[key].update(per_variant[key])\n            if (i+1)%10==0:\n                log(f\"Image descriptors: {i+1}/200 studies\")\n    predictions = {}\n    folds = subset.fold.to_numpy()\n    for cfg in configs:\n        key = cfg[\"id\"]\n        x = np.stack(features[key])\n        predictions[key] = oof_predict(x,y,folds)\n        if key==DEFAULT:\n            perm = np.random.default_rng(SEED+99).permutation(len(y))\n            control = macro_auc(y[perm],oof_predict(x,y[perm],folds))\n        log(f\"{key}: OOF macro AUC {macro_auc(y,predictions[key]):.4f}\")\n    stats = paired_statistics(y,predictions)\n    rows = [dict(c,**stats[c[\"id\"]],**crop_audits[c[\"id\"]]) for c in configs]\n    metrics = pd.DataFrame(rows)\n    metrics.to_csv(out/\"verifier_metrics.csv\",index=False)\n    np.savez_compressed(out/\"verifier_oof.npz\",y=y,folds=folds,**predictions)\n    per_target = []\n    for key,p in predictions.items():\n        for j,target in enumerate(TARGETS):\n            per_target.append(dict(id=key,target=target,auc=roc_auc_score(y[:,j],p[:,j]),positives=int(y[:,j].sum())))\n    pd.DataFrame(per_target).to_csv(out/\"per_target.csv\",index=False)\n    pd.DataFrame([dict(id=key,fold=f,auc=macro_auc(y[folds==f],p[folds==f]))\n                  for key,p in predictions.items() for f in range(5)]).to_csv(out/\"per_fold.csv\",index=False)\n    import sklearn\n    receipt = dict(model=\"Spatial oriented-gradient encoder + Ridge(alpha=1000)\",\n        feature_dim=len(features[DEFAULT][0]),n_subset=len(y),folds_sha256=FOLDS_SHA,\n        labels_sha256=LABELS_SHA,subset_sha256=sha256(out/\"subset.csv\"),seed=SEED,\n        n_bootstrap=800,permutation_control_auc=control,decode_audit=dict(audit),\n        versions=dict(python=platform.python_version(),numpy=np.__version__,scipy=scipy.__version__,\n                      sklearn=sklearn.__version__,pydicom=pydicom.__version__),\n        elapsed_seconds=time.time()-START,default=DEFAULT,rows=rows)\n    save_json(out/\"verifier_receipt.json\",receipt)\n    # Render separately in the public notebook.\n    return receipt\n\n\ndef plot_curve(metrics,out):\n    import matplotlib.pyplot as plt\n    plt.rcParams.update({\"font.size\":11,\"axes.spines.top\":False,\"axes.spines.right\":False})\n    base = metrics[metrics.id==DEFAULT]\n    fig,ax = plt.subplots(figsize=(9,5.8))\n    for family,marker,color,label in [(\"resolution\",\"o\",\"#2364aa\",\"Resolution · 9 slices\"),\n                                     (\"slices\",\"s\",\"#d16b28\",\"Slice count · 224 × 224\")]:\n        d = metrics[metrics.family==family]\n        if family==\"slices\": d = pd.concat([d,base])\n        d = d.sort_values(\"full_corpus_gib\")\n        ax.errorbar(d.full_corpus_gib,d.auc,\n            yerr=np.maximum(0,np.array([d.auc-d.ci95_lo,d.ci95_hi-d.auc])),\n            fmt=marker+\"-\",color=color,capsize=3,label=label)\n        for r in d.itertuples():\n            text = str(r.img) if family==\"resolution\" else f\"{r.n_slices} slices\"\n            ax.annotate(text,(r.full_corpus_gib,r.auc),xytext=(0,9 if family==\"resolution\" else -17),\n                        textcoords=\"offset points\",ha=\"center\",fontsize=8,color=color)\n    tiny = metrics[metrics.family==\"tiny\"]\n    ax.scatter(tiny.full_corpus_gib,tiny.auc,marker=\"^\",color=\".5\",label=\"3-slice extras · narrower window\")\n    ax.axvline(GIB,color=\".3\",ls=\":\",label=\"Primary cache · 11.12 GiB\")\n    ax.set(xlabel=\"Estimated pixel storage for all 4,407 studies (GiB)\",\n           ylabel=\"Held-out macro AUC\",title=\"A · Quality versus storage — crop 130 mm\")\n    ax.grid(alpha=.15); ax.legend(fontsize=9,loc=\"best\")\n    large = metrics[metrics.id=='res_336x9_c130'].iloc[0]\n    fig.text(.08,.035,\n        f\"224 → 336 pixels: +{large.full_corpus_gib-GIB:.2f} GiB for observed {large.delta_auc:+.3f} AUC\\n\"\n        f\"Paired 95% interval: {large.delta_ci95_lo:+.3f} to {large.delta_ci95_hi:+.3f} · gain uncertain; flat plateau not proven\",\n        fontsize=10,color='.25')\n    fig.tight_layout(rect=(0,.13,1,1)); fig.savefig(Path(out)/\"figure_A.png\",dpi=170); plt.show(); plt.close(fig)\n    fig,ax = plt.subplots(figsize=(8,5.6))\n    d = pd.concat([base,metrics[metrics.family==\"crop\"]]).sort_values(\"crop_mm\")\n    ax.errorbar(d.crop_mm,d.auc,yerr=np.maximum(0,np.array([d.auc-d.ci95_lo,d.ci95_hi-d.auc])),\n                fmt=\"o\",color=\"#2364aa\",capsize=5,markersize=8)\n    for r in d.itertuples():\n        ax.annotate(f\"{r.auc:.3f}\",(r.crop_mm,r.auc),xytext=(10,0),textcoords=\"offset points\")\n    ax.set(xticks=[110,130,160],xlim=(100,175),\n           xlabel=\"Requested side length KEPT (mm), then resized to 224 × 224\\n← tighter view / larger anatomy in pixels     wider view / more context →\",\n           ylabel=\"Held-out macro AUC\",title=\"B · What to keep in frame — same 11.12 GiB\")\n    tight = metrics[metrics.id=='crp_224x9_c110'].iloc[0]\n    fig.text(.09,.025,\n        f\"110 mm scores highest; advantage over 130 mm is uncertain.\\n\"\n        f\"Paired difference: {tight.delta_auc:+.3f} [{tight.delta_ci95_lo:+.3f}, {tight.delta_ci95_hi:+.3f}].\\n\"\n        \"Crop is skipped when the source field of view is too small.\",fontsize=10,color='.25')\n    ax.grid(alpha=.15);fig.tight_layout(rect=(0,.18,1,1));fig.savefig(Path(out)/\"figure_B.png\",dpi=170);plt.show();plt.close(fig)\n\n\ndef materialize(root,out,study_limit=None,config=None):\n    \"\"\"One dense uint8 cache in ~323 MiB .npy shards; explicit absent-slot mask.\"\"\"\n    cfg = dict(next(c for c in variants() if c[\"id\"]==DEFAULT) if config is None else config)\n    img, slices = cfg['img'], cfg['n_slices']\n    if not isinstance(img,int) or img < 32 or img > 512:\n        raise ValueError('img must be an integer from 32 to 512')\n    if not isinstance(slices,int) or slices < 3 or slices > 30 or slices % 3:\n        raise ValueError('n_slices must be a multiple of three from 3 to 30')\n    if not 0 < cfg['crop_mm'] <= 300:\n        raise ValueError('crop_mm must be in (0, 300]')\n    if len(cfg['window']) != 2 or not 0 <= cfg['window'][0] < cfg['window'][1] <= 1:\n        raise ValueError('window must satisfy 0 <= start < end <= 1')\n    out = Path(out)\n    if out.exists() and any(out.iterdir()):\n        raise ValueError('Choose an empty cache directory to avoid mixing recipes')\n    out.mkdir(parents=True,exist_ok=True)\n    studies = pd.read_csv(root/\"train.csv\",usecols=[\"StudyInstanceUID\"])\n    assert studies.StudyInstanceUID.is_unique and len(studies)==4407\n    studies = studies.sort_values(\"StudyInstanceUID\").reset_index(drop=True)\n    if study_limit is not None: studies = studies.iloc[:study_limit]\n    series = pd.read_csv(root/\"train_series.csv\",usecols=[\"StudyInstanceUID\",\"SeriesInstanceUID\",\"Anatomical_Plane\",\"Fluid_Sensitive\"])\n    groups = {uid:g for uid,g in series.groupby(\"StudyInstanceUID\",sort=False)}\n    def one(uid):\n        audit = Counter();records = load_series(root,groups[uid],audit,[cfg])\n        pixels = np.zeros((6,slices,img,img),np.uint8)\n        mask = np.zeros(6,np.uint8)\n        for j,(name,_,_) in enumerate(SLOTS):\n            if name in records:\n                pixels[j] = render_slot(records[name],cfg,audit);mask[j]=1\n        if mask.sum()==0: raise ValueError(f\"No usable slots for study {uid}\")\n        return pixels,mask,audit\n    manifest=[];shards=[];audit=Counter();masks=[]\n    for start in range(0,len(studies),128):\n        ids = studies.StudyInstanceUID.iloc[start:start+128].tolist()\n        filename=f\"pixels-{start//128:03d}.npy\"\n        path=out/filename\n        array=np.lib.format.open_memmap(path,mode=\"w+\",dtype=np.uint8,shape=(len(ids),6,slices,img,img))\n        with ThreadPoolExecutor(max_workers=THREADS) as pool:\n            for row,(pixels,mask,counts) in enumerate(pool.map(one,ids)):\n                array[row]=pixels;masks.append(mask);audit.update(counts)\n                manifest.append(dict(StudyInstanceUID=ids[row],shard=filename,row=row))\n        array.flush();del array\n        check=np.load(path,mmap_mode=\"r\",allow_pickle=False)\n        assert check.dtype==np.uint8 and check.shape==(len(ids),6,slices,img,img)\n        del check\n        shards.append(dict(file=filename,n_studies=len(ids),bytes=path.stat().st_size,sha256=sha256(path)))\n        log(f\"Cache: {start+len(ids)}/{len(studies)} studies; {filename} verified\")\n    pd.DataFrame(manifest).to_csv(out/\"studies.csv\",index=False)\n    np.save(out/\"slot_mask.npy\",np.stack(masks),allow_pickle=False)\n    save_json(out/\"AUDIT.json\",dict(audit))\n    aux={p.name:sha256(p) for p in [out/\"studies.csv\",out/\"slot_mask.npy\",out/\"AUDIT.json\"]}\n    spec=dict(img=img,n_slices=slices,group=3,n_group=slices//3,crop_mm=cfg['crop_mm'],window=list(cfg['window']),\n        slot_scheme=[dict(name=n,plane=p,Fluid_Sensitive=f) for n,p,f in SLOTS],\n        dtype=\"uint8\",n_studies=len(studies),shape_per_study=[6,slices,img,img],\n        pixel_bytes=len(studies)*6*slices*img**2,pixel_gib=len(studies)*6*slices*img**2/1024**3,\n        shards=shards,sha256={**{s[\"file\"]:s[\"sha256\"] for s in shards},**aux},\n        ordering=[\"ImagePositionPatient projected on orientation normal\",\"SliceLocation\",\"InstanceNumber\"],\n        unordered_policy=\"fail\",decode_error_policy=\"fail; never substitute broken DICOM with zero\",\n        missing_slot_policy=\"zero-filled with explicit slot_mask.npy; no series for public plane/Fluid_Sensitive slot\",\n        series_choice=\"most DICOM files; CSV order breaks ties\",\n        percentile_scope=\"1st-99th percentile over the sampled cropped volume, independently per series\",\n        crop_policy=\"center crop using row PixelSpacing; skip if crop >= shorter FOV or spacing missing; no padding\",\n        interpolation=\"scipy.ndimage.zoom(order=1, grid_mode=True, mode=nearest); round to uint8\",\n        photometric=\"apply RescaleSlope/Intercept and invert MONOCHROME1\",\n        source=\"rsna-knee-abnormality-detection official training MRI\",\n        data_license=\"Competition rules + RSNA MIRA; private, participating users only\",\n        geometry_license=\"Apache-2.0; Steven Lee; see NOTICE.md\",\n        lossless=False)\n    save_json(out/\"SPEC.json\",spec)\n    assert len(manifest)==len(studies) and len(set(m[\"StudyInstanceUID\"] for m in manifest))==len(studies)\n    return spec\n\n\ndef run_all(folds_path, evidence_dir, cache_dir, cache_config=None):\n    root=competition_root()\n    hits=list(Path(\"/kaggle/input\").glob(\"**/report_labels_v2.csv\"))\n    assert len(hits)==1, \"Attach exactly one Pilkwang report_labels_v2.csv\"\n    receipt=verify_curve(root,Path(folds_path),hits[0],Path(evidence_dir))\n    # Cache default is fixed before measurement. Strong contrary evidence is flagged,\n    # never silently used to tune a recipe or discard a completed experiment.\n    contrary=[r[\"id\"] for r in receipt[\"rows\"] if r[\"delta_ci95_lo\"]>.01 and r[\"holm_p\"]<.05]\n    if contrary: log(\"Review clearly better alternatives before publication: \"+str(contrary))\n    gc.collect()\n    spec=materialize(root,Path(cache_dir),config=cache_config)\n    log(f\"Complete: {spec['n_studies']} studies, {spec['pixel_gib']:.5f} GiB of uint8 pixels\")\n    return receipt,spec\n","metadata":{"jupyter":{"source_hidden":true},"tags":["hide-input"],"source_hidden":true,"hide_input":true,"collapsed":true,"_kg_hide-input":true},"execution_count":null,"outputs":[]},{"cell_type":"code","id":"rebuild","source":"from IPython.utils.capture import capture_output\ncache_config = dict(img=IMG, n_slices=N_SLICES, crop_mm=CROP_MM, window=list(WINDOW))\ncache_dir = Path('/kaggle/working/rsna-knee-cache' if SAVE_CACHE_OUTPUT else '/kaggle/temp/rsna-knee-cache')\nwith capture_output() as build_log:\n    if RUN_VERIFIER:\n        receipt, spec = run_all('/kaggle/working/folds.csv', '/kaggle/working/evidence', cache_dir, cache_config=cache_config)\n    else:\n        spec = materialize(competition_root(), cache_dir, config=cache_config)\n_ = Path('/kaggle/working/build.log').write_text(build_log.stdout, encoding='utf-8')\nprint(f\"Built {spec['n_studies']} studies, {spec['pixel_gib']:.2f} GiB: {cache_dir}\")\n_ = (cache_dir / 'NOTICE.md').write_text(\"# Source and use\\n\\nMRI pixels are derived from the official RSNA Knee Abnormality Detection training\\ndata. They remain subject to the [competition rules](https://www.kaggle.com/competitions/rsna-knee-abnormality-detection/rules)\\nand [RSNA MIRA licence](http://rsna.org/mira-license). This private cache is for\\ncompetition participants who have accepted those terms. Do not redistribute to\\nnon-participants. It is not a substitute for the official train zip or lossless DICOM.\\n\\nGeometry credit: Steven Lee,\\n[RSNA Knee: 500GB to 11GiB CPU Pixel Cache](https://www.kaggle.com/code/stevenleehans/rsna-knee-500gb-to-11gib-cpu-pixel-cache),\\nApache License 2.0. His notebook credits Pilkwang's slot scheme, Karnakbayev's\\nthree-slice grouping, and Will's physical crop. The new pipeline reimplements\\nphysical ordering, slice grouping, millimetre center cropping, percentile scaling,\\nand uint8 caching. Modifications: public train_series flags; native SciPy bilinear\\nresize; explicit absent-slot mask; fail on decoding/order errors; sharded NumPy\\nfiles; spatial gradient verifier with study-level validation. No report parsing or\\nreport lexicon code is included or copied. Apache-2.0 covers geometry code, not\\npermission to redistribute MRI. See LICENSE-APACHE-2.0.txt.\\n\\nNo radiology reports, train.csv, report lexicon, or report-derived labels are\\nincluded in the cache Dataset. Label scores are used only to evaluate the probe.\\n\",encoding='utf-8')\n_ = (cache_dir / 'LICENSE-APACHE-2.0.txt').write_text('\\n                                 Apache License\\n                           Version 2.0, January 2004\\n                        http://www.apache.org/licenses/\\n\\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\\n\\n   1. Definitions.\\n\\n      \"License\" shall mean the terms and conditions for use, reproduction,\\n      and distribution as defined by Sections 1 through 9 of this document.\\n\\n      \"Licensor\" shall mean the copyright owner or entity authorized by\\n      the copyright owner that is granting the License.\\n\\n      \"Legal Entity\" shall mean the union of the acting entity and all\\n      other entities that control, are controlled by, or are under common\\n      control with that entity. For the purposes of this definition,\\n      \"control\" means (i) the power, direct or indirect, to cause the\\n      direction or management of such entity, whether by contract or\\n      otherwise, or (ii) ownership of fifty percent (50%) or more of the\\n      outstanding shares, or (iii) beneficial ownership of such entity.\\n\\n      \"You\" (or \"Your\") shall mean an individual or Legal Entity\\n      exercising permissions granted by this License.\\n\\n      \"Source\" form shall mean the preferred form for making modifications,\\n      including but not limited to software source code, documentation\\n      source, and configuration files.\\n\\n      \"Object\" form shall mean any form resulting from mechanical\\n      transformation or translation of a Source form, including but\\n      not limited to compiled object code, generated documentation,\\n      and conversions to other media types.\\n\\n      \"Work\" shall mean the work of authorship, whether in Source or\\n      Object form, made available under the License, as indicated by a\\n      copyright notice that is included in or attached to the work\\n      (an example is provided in the Appendix below).\\n\\n      \"Derivative Works\" shall mean any work, whether in Source or Object\\n      form, that is based on (or derived from) the Work and for which the\\n      editorial revisions, annotations, elaborations, or other modifications\\n      represent, as a whole, an original work of authorship. For the purposes\\n      of this License, Derivative Works shall not include works that remain\\n      separable from, or merely link (or bind by name) to the interfaces of,\\n      the Work and Derivative Works thereof.\\n\\n      \"Contribution\" shall mean any work of authorship, including\\n      the original version of the Work and any modifications or additions\\n      to that Work or Derivative Works thereof, that is intentionally\\n      submitted to Licensor for inclusion in the Work by the copyright owner\\n      or by an individual or Legal Entity authorized to submit on behalf of\\n      the copyright owner. For the purposes of this definition, \"submitted\"\\n      means any form of electronic, verbal, or written communication sent\\n      to the Licensor or its representatives, including but not limited to\\n      communication on electronic mailing lists, source code control systems,\\n      and issue tracking systems that are managed by, or on behalf of, the\\n      Licensor for the purpose of discussing and improving the Work, but\\n      excluding communication that is conspicuously marked or otherwise\\n      designated in writing by the copyright owner as \"Not a Contribution.\"\\n\\n      \"Contributor\" shall mean Licensor and any individual or Legal Entity\\n      on behalf of whom a Contribution has been received by Licensor and\\n      subsequently incorporated within the Work.\\n\\n   2. Grant of Copyright License. Subject to the terms and conditions of\\n      this License, each Contributor hereby grants to You a perpetual,\\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\\n      copyright license to reproduce, prepare Derivative Works of,\\n      publicly display, publicly perform, sublicense, and distribute the\\n      Work and such Derivative Works in Source or Object form.\\n\\n   3. Grant of Patent License. Subject to the terms and conditions of\\n      this License, each Contributor hereby grants to You a perpetual,\\n      worldwide, non-exclusive, no-charge, royalty-free, irrevocable\\n      (except as stated in this section) patent license to make, have made,\\n      use, offer to sell, sell, import, and otherwise transfer the Work,\\n      where such license applies only to those patent claims licensable\\n      by such Contributor that are necessarily infringed by their\\n      Contribution(s) alone or by combination of their Contribution(s)\\n      with the Work to which such Contribution(s) was submitted. If You\\n      institute patent litigation against any entity (including a\\n      cross-claim or counterclaim in a lawsuit) alleging that the Work\\n      or a Contribution incorporated within the Work constitutes direct\\n      or contributory patent infringement, then any patent licenses\\n      granted to You under this License for that Work shall terminate\\n      as of the date such litigation is filed.\\n\\n   4. Redistribution. You may reproduce and distribute copies of the\\n      Work or Derivative Works thereof in any medium, with or without\\n      modifications, and in Source or Object form, provided that You\\n      meet the following conditions:\\n\\n      (a) You must give any other recipients of the Work or\\n          Derivative Works a copy of this License; and\\n\\n      (b) You must cause any modified files to carry prominent notices\\n          stating that You changed the files; and\\n\\n      (c) You must retain, in the Source form of any Derivative Works\\n          that You distribute, all copyright, patent, trademark, and\\n          attribution notices from the Source form of the Work,\\n          excluding those notices that do not pertain to any part of\\n          the Derivative Works; and\\n\\n      (d) If the Work includes a \"NOTICE\" text file as part of its\\n          distribution, then any Derivative Works that You distribute must\\n          include a readable copy of the attribution notices contained\\n          within such NOTICE file, excluding those notices that do not\\n          pertain to any part of the Derivative Works, in at least one\\n          of the following places: within a NOTICE text file distributed\\n          as part of the Derivative Works; within the Source form or\\n          documentation, if provided along with the Derivative Works; or,\\n          within a display generated by the Derivative Works, if and\\n          wherever such third-party notices normally appear. The contents\\n          of the NOTICE file are for informational purposes only and\\n          do not modify the License. You may add Your own attribution\\n          notices within Derivative Works that You distribute, alongside\\n          or as an addendum to the NOTICE text from the Work, provided\\n          that such additional attribution notices cannot be construed\\n          as modifying the License.\\n\\n      You may add Your own copyright statement to Your modifications and\\n      may provide additional or different license terms and conditions\\n      for use, reproduction, or distribution of Your modifications, or\\n      for any such Derivative Works as a whole, provided Your use,\\n      reproduction, and distribution of the Work otherwise complies with\\n      the conditions stated in this License.\\n\\n   5. Submission of Contributions. Unless You explicitly state otherwise,\\n      any Contribution intentionally submitted for inclusion in the Work\\n      by You to the Licensor shall be under the terms and conditions of\\n      this License, without any additional terms or conditions.\\n      Notwithstanding the above, nothing herein shall supersede or modify\\n      the terms of any separate license agreement you may have executed\\n      with Licensor regarding such Contributions.\\n\\n   6. Trademarks. This License does not grant permission to use the trade\\n      names, trademarks, service marks, or product names of the Licensor,\\n      except as required for reasonable and customary use in describing the\\n      origin of the Work and reproducing the content of the NOTICE file.\\n\\n   7. Disclaimer of Warranty. Unless required by applicable law or\\n      agreed to in writing, Licensor provides the Work (and each\\n      Contributor provides its Contributions) on an \"AS IS\" BASIS,\\n      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\\n      implied, including, without limitation, any warranties or conditions\\n      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A\\n      PARTICULAR PURPOSE. You are solely responsible for determining the\\n      appropriateness of using or redistributing the Work and assume any\\n      risks associated with Your exercise of permissions under this License.\\n\\n   8. Limitation of Liability. In no event and under no legal theory,\\n      whether in tort (including negligence), contract, or otherwise,\\n      unless required by applicable law (such as deliberate and grossly\\n      negligent acts) or agreed to in writing, shall any Contributor be\\n      liable to You for damages, including any direct, indirect, special,\\n      incidental, or consequential damages of any character arising as a\\n      result of this License or out of the use or inability to use the\\n      Work (including but not limited to damages for loss of goodwill,\\n      work stoppage, computer failure or malfunction, or any and all\\n      other commercial damages or losses), even if such Contributor\\n      has been advised of the possibility of such damages.\\n\\n   9. Accepting Warranty or Additional Liability. While redistributing\\n      the Work or Derivative Works thereof, You may choose to offer,\\n      and charge a fee for, acceptance of support, warranty, indemnity,\\n      or other liability obligations and/or rights consistent with this\\n      License. However, in accepting such obligations, You may act only\\n      on Your own behalf and on Your sole responsibility, not on behalf\\n      of any other Contributor, and only if You agree to indemnify,\\n      defend, and hold each Contributor harmless for any liability\\n      incurred by, or claims asserted against, such Contributor by reason\\n      of your accepting any such warranty or additional liability.\\n\\n   END OF TERMS AND CONDITIONS\\n',encoding='utf-8')\n","metadata":{"jupyter":{"source_hidden":true},"tags":["hide-input"],"source_hidden":true,"hide_input":true,"collapsed":true,"_kg_hide-input":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","id":"plots","source":"## Plots\n\n**A** separates resolution from slice count, with crop fixed at 130 mm. Storage is the full 4,407-study pixel payload, not the 200-study probe size or a compressed archive download. The vertical line marks the primary cache. Gray triangles are the original three-slice extras with a narrower sampling window; they are not connected to the controlled families.\n\n**B** compares the **width of the region kept**: 110, 130, or 160 mm, each resized to 224² × 9. Every point uses the same 11.12 GiB. Left means a tighter view; right means a wider view, not automatically better quality. Error bars show uncertainty in AUC, not image sharpness.\n","metadata":{}},{"cell_type":"code","id":"figures","source":"if RUN_VERIFIER:\n    plot_curve(pd.read_csv('/kaggle/working/evidence/verifier_metrics.csv'),Path('/kaggle/working/evidence'))\n","metadata":{"jupyter":{"source_hidden":true},"tags":["hide-input"],"source_hidden":true,"hide_input":true,"collapsed":true,"_kg_hide-input":true,"_kg_hide-output":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","id":"saved-figures","source":"![A: Quality versus storage](attachment:figure_A.png)\n\n![B: Crop comparison at fixed storage](attachment:figure_B.png)\n\nFigures from the completed CPU verifier run. Save & Run regenerates both PNGs.\n","metadata":{},"attachments":{"figure_A.png":{"image/png":"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o4dO5qA8t9//21W+h8/flxWr14tV111lbn/mWeeKTG2AACoGcjot8vSV+np6cWesLS0tFLNyNg/h3rzzTdlwIABBW7T/gDfffedCdpnZWWZrP5XX33V6TELzw7rjPn//d//mSXmeh0XFydffPGFy2PUfxCUlO1f2TNRQGWzTuZZtwv/zO/ASVV9LnhvOM/wHFX9fVCR9N9rLVu2NNtaa7gmv1agPPA7gvKm/41u/XefJubZ/xuwIm3cuNH8N7XS6y1btphSu55GS/oW1qtXL5NZrgF/fR1aY1vLz1UkR+9LZb1XnsSaPFqZn1WgOnDl98Haz7RwLMZTWCwWM0Ydm1aE0R4lnoaMfhGTXW+lGfHFOXz4sLnWmnfusN/fz89PLr30Uof7nXLKKWZGXmmWvzs0o1+X5D344IPm5y+//NJWUxYAAADuGzt2rFlpqRfdBgDUfJq9v3jx4gK36c+emtXvjK+vr7l2tynuihUrTMyiVatWEhgYaHoZDh482CQS2lcrKE5xzXh3794td955p4l9aLBM4yVagkiPr0mPhe3atUtuu+02adu2rQmsaSLiaaedJp988omt1Ic7XD3esWPHTKllDerNmzevyHEmT55sgn6arKnnZeDAgXLttdea+55//nlzn/WiiZz2j3nppZdk8+bNcskll5gSzBpAnDFjhtlHP2c6gXPllVdKp06dzDkKDw83JaQ+/vhjh69Zy5x89NFH5nU0aNDAPEbPr67s0EoSFc3+dWmvCK0+oe+rnl+dIPv000+LPKY0r9OV89e3b1+zj65w0QkvawWMwnG8b7/91nyuIyIixN/f31TY0L4W9n08lU7y6fHOOeecIuPRcet948aNK3KflvvS34OMjIwCty9dutScH+0BqvFBHZeW8P7zzz+LHEPPjx7/mmuuMSW5brzxRvP76OPjI6+99lqx70l5jdtZM177sWlvkFtuucU8Vs9lu3btzO+Ao/I7epsmP3fp0sXWzPvqq682pdKKU1XvV3VDRv+Jxmr6xa0ftsLNce0lJiaaD6/1w+EO/cWtV6+exMbG2v5QONO8eXOJjo42+5aGfmD1S08tWbKkwmfuAQAAAACoKTZs2GDL5rcP+mp2vCdm9RdeAaGBdA2IrlmzRvr3728C0K5asGCBCZDZlwLWHgB6WbhwYbGJi66YO3eumTjXigT2lRP++usvc4mMjJQRI0bY7tPg5+jRowvsr4E4jXXoRe//6quvbJnAJXHneJoU+tNPP8mgQYPk8ssvl1WrVpl4jTVYe99995lYj+6j58UdWrpZqzTY9zexTiRp/8bCTZT1HGkyqF70/vfff7/A/drjUScq7GnZKb1os+aSqleUl02bNpl4lMbP7H+frrvuOjOGW2+91XZ7aV6nK+fPSntj6uRH4YkvdfPNN8sHH3xQZALonXfeMedLPwfW33WN/zVr1sx8PnV8Ovml9u3bZ2J31qC3Pr91RYe+Zg3MDx8+3ASlrbT6xr333ltgrPrdMmvWLJk9e7YJgN91110Ok441Mdi+H0dJE4/lOe7iaOxSJ1P0/FlpbFUTkTWZ+o033iiw/xVXXCHff/99gdemk3zz5883lU4cqar3qzoio1/EvInWD4Q22nFGa95Z6YfYXaeeeqpLqwYSEhLMtc44lob9H5jSzG4DAAAAAFAbOcrmrw5Z/Roc1+C0BrU00KXBag0YaiZ6Sb0I7WlgVYP8EyZMMJmxGpzVQJy+ds3cLUsQTGMhmoGtQXbN4NfgnN6mkyoalNPsc/t4hgZAdX8dgzYV1iCdBnb19t9++80kNX7zzTcFgobFKc3xNLiqwVlN+tTHaua+BjZ1W8/T119/bQv+a8zImrn+2GOPmXiM9aLZzoWzk3v27Gkeo+dD97nwwgvNfZoYqu+nNlPeuXOnGa8GIbVqgyaQasBTJx2sUlJSTOa6vjeaCa/vl05eaLBVX49O9rhKX6d1FYI10dUd+rnr06ePmTTR16XBVQ3SKs3wtufu63T1/FnpxIdWvNi2bZt5r6yTdz/++KM5tmbFa+lr7WGhExP6eezatat53TqxYx9P0wkJHZ/9d4NOWikNDsfHx5uVMIXvs88q17Hec889ZgJJn3/v3r2216w/awzw/vvvd5iArJMAmm3++++/m8+rjs1azaM45THuksycOdOU7ZszZ445nn7+XnzxRXPfu+++W2DSVMuV6++Xfla1VLmeA71fPwP6PWV9fntV9X5VVwT6T9AZXaWzhvol4Ij1C1tnmM866yy3T7Z1ubd+ILVWniP6B0OXxVjr6pWGzoJZkc0PAAAAAEDps/kLZ/VXBxr8XLdunQmklcajjz5qSr9oQE5Lo2hWu8ZErLGT0vjwww9Nb8AhQ4aYlQN6rQFdLcWhqw40WG0faNNsbA3IadBQA3wdOnQwExnWrH8NAFqDh64o7fE0UK2lRTTD/I477pDLLrvMlAt5+umnSx0Y1DIhGrzVMjWFe5vo5IJOPGjgWssn6Xug5U108uX11183+2jA10qD8hrk7NGjh8mc1/dLJ0zatGljxrpo0SKpLBqD0rFpjyN9XVpmZsqUKWYyRLPRNfBf2tfp6vmz0vJML7/8sinRZF9Vw7pK4JFHHjETPloCRmYDOcUAALb3SURBVMs36edRM721fJCuTNDJJyvr+6zBbPvgsJaeee655xzeZ/84pa9J36dp06aZEjyadW59zfqzBr51QkJXiBSm+2lAXUv86OfVVeUx7pLo+dLH6WPq1KljPn8PP/ywOZ9aist+ska/A5Q+t05q6DnQ33/9DPz6668Oq59U1ftVXRHoP0FnV3X2TH/p9Eu88LImnV36+eefzbYus9GZpMJ0Jlr/IOjF0WSB1uDSD6TSOq/Wxr5W+gut47DWvLPWdrO/X2uQFUdnTfUPltIvs8INfwEAAAAAgHvZ/J6e1a/1yTWeoQF+TR7UuIaW2tHKAvbBVS01Y187Xi/22eYaZFWaXa/Z2ZppXV6VAqwVFDQe4soqA2tVhYceesjEYKwNOvWxetHAttIxuqIsx9PMZL1fM4s1k/iCCy4wWfulpRMLxVVx0NiOBulbtmxpgpPW90rfF2U/gaO17XUCRicitDSOBj91QqMq6Osq3KBUz7G1JEvhVQLuvM7Cz1NSFQzN8nbE2rPghhtuKHKf9jewTmbZZ3wPHTq0QMa5fgfo50AnwHQFg05oWO/TeKIGnbUGv9ahL/z5O/PMMx1+/qwrHxx9/nRVhk7cuKs8xl0SnWxx1I/DWjlFJ0itrEF/XR1UmP5+6Zg85f2qrqjRf4LOImstLP3g6MyyNu/QmVCdjdIvns8++8zspx8IDfQ7okF4/YJSb7/9tmk+YU9nVHXp0LnnnmuOqR96/XDrzKY2CdE/olpnzPqHT39Z7OmHTz90OuusH1qdwdRfJv2C0JlRnaXVD6r+EdYlPTpmd5boAQAAoGjgRJcgW/9jwr5uMACg9mTzV5da/ZrdrAEzrV2tSYRaykXjE6+88opLj9e/c1ruR7OPte67JihqxqyW2tGs45EjR5Z6bNbgswbZXGENCjtq6GnP1QbBZTmeZv5r1rGuklBan9/VvgCOaO9GZzR5VOuYFzehVDg5VeNJGsfSUj3Wxr8aGNYMcC0XoxnzrtCJoLJM7GhszRFrySf7c1+a1+nK+StpH11Vou+dBnYd0Sxz+7LaSsvtaDxQg8kaf9PGsfpdoPE9a6kYLTmkFTx0wkV/bwr3H7AGvEvz+XPl9TpSHuMuz/dcz721AbEj+t2g4/CE96u6Igps5/rrrzdfiDobqss+9Itbb9PlXZpNr2+6LpWxNnIoDZ2506VJ+gG1NqfQmUqtraVBfv1F0Ix8rQFXmDYO0X9MaDMVXfqks8c6Pl1CpsvqdNmJfiFrl3WdKSw8UQAAAAD3aOamruzUS0lZngCAmp3N7+lZ/YVpcF7ZVxzQYLd97Xi9WAPDVppYqM1BNQimcQoN+muQTAPdr732WqnHoyU6lJa9cYUmXir9G1x4zPYXa4PNijyeJla+8MILJoCoQUed9LAPLLqruKRMXXGgny+NF2lCqa7S0GCpjk3Lmzii2fBaLkXfL51Q0dUcGmvSwL+WhXZU972qleZ1WrmS1OpsHy37os9h39jWnnUFjPXzYmUt66IJtoVLvVgbWGspbet9hQPHOmGmnx2tyV/c58+aaOzu63WmrOMuT3rudSLDWf8HR98NVfV+VVcE+gvRpTLauVkD6VrPyxpE12UcGkjXWlPO6H1ankcvxdXG1w/Pf//9ZxpQ6MzqlVdeaZ5Ll4Lpc2vNKUe/xDoJoLPH+uHWX/wnn3zSrEDQL2+tEzd58mQzQaHL4bQpCQAAAAAAKJ9s/upWq99as1pXpJWGJht269bNJBha61lr3KG0rMmIegxXJko0iVHp6oTymFgp7fE0MKtxHo3TaOKm1grXhqAaLyqc/a7nrLhM9JJoc2AtV6NZys8++6xprKyrNKwxounTp5d4DA14apKpxoy0j4BO2GjGvycpj9dZWrryQWlSr6OS3Nbntu7nLHCsVTb098Ma57OWitGLBvQLB47186efF403Vqayjrs8WUvzaD8OR9/BjpovV9X7VV1RusdJwF4D7+7SmmOuNoHRVQPjxo0zF3fph1P/oAAAAKBiWeumWrcBALU7m99K9+/atWuV/m3Q5rHaS1CT/7RxrsYKkpOTTWKh1pK3BtOsJYZdMWrUKFMyWEv4aLkXzWDXsheamKjKEnDXLHgtIaRZtIMHDzZJjhp403LEOnGigTytq24N0Gkipq4m0EmG8847z1RC0P01w1czf/UxmrGu5Wkc1fwurDTH06xfjdtoGT+tvKA9D7Re+rJly0zW+UsvvWQC/1bWWuVaD1wD2VqKxJ3PiFaQ0MxvHc///vc/U9pGA+A6saAlmLQcdGGaxX/XXXeZ/go6Nq13r4FLfT3Wz4CnrUApzessL1qSSt8fbcqsnz39/dHPudaCv/vuu81KDY3vaT13e/rea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"}}},{"cell_type":"markdown","id":"interpretation","source":"**How to read A: diminishing returns, not a proven plateau.** The resolution curve still rises: **0.660 → 0.665 AUC**, while storage rises **11.12 → 25.02 GiB**. That is another **13.90 GiB** for an observed **+0.005 AUC**, with a paired 95% interval of **-0.003 to +0.013**. The interval includes zero and a potentially useful gain: neither a reliable improvement nor strict equivalence is established. We keep 224² as a storage compromise. The existing 256² and 288² points already fill the gap; more resolution settings would not fix uncertainty from evaluating only 200 studies.\n\nThree slices scored lower, but this verifier did not establish a clear quality cliff. Treat the earlier slice-cliff claim as provisional. Three slices change AUC by **-0.015**, with a paired 95% interval of **-0.030 to +0.001**. We keep nine slices, but do not present the old “only real quality cliff” claim as established by this model.\n\n160² × 9 uses 5.67 GiB; its paired AUC difference is -0.012 [-0.019, -0.005]. This does not prove equivalence within ±0.01 AUC. Allowing for the fact that we compared 14 alternatives still leaves evidence that 160² scored lower. The earlier claim that 160² is indistinguishable does not hold for this model. 224² remains the default; smaller settings are available for your own storage budget.\n\n**How to read B: more context competes with detail.** Both a 110 mm and a 160 mm square end up as 224 × 224 pixels. When the requested crop applies, a 10 mm structure spans roughly **20 pixels at 110 mm**, versus **14 pixels at 160 mm**. The tighter view spends more pixels on central anatomy but discards more of the periphery. The wider view keeps more context but represents the same structure with fewer pixels. Neither must win. Resizing cannot create detail absent from the original scan.\n\n**110 mm has the highest measured AUC, but is not a confirmed winner.** It scores **0.670**, versus **0.660** at 130 mm: difference **+0.010**, paired 95% interval **-0.008 to +0.026**. A tighter view may suit this edge-based verifier; sampling uncertainty may also explain the difference. This experiment does not identify the cause or establish the best crop for a trained image encoder. We retain the prespecified 130 mm compromise.\n\nThe 160 mm crop changed AUC by -0.018 versus 130 mm (paired 95% interval -0.038 to +0.002). It does not establish a reason to change the primary 130 mm crop. If the source image is too small for the requested square, the crop is skipped and the original field of view is resized instead. This happened at 160 mm for **617 of 977 available series** in the probe. Thus 160 mm is often effectively “no crop,” not a uniform 160 mm view. The millimetre-to-pixel example above applies only when cropping actually occurs.\n","metadata":{}},{"cell_type":"markdown","id":"download","source":"## Build a private Dataset\n\n1. Join the competition and accept its rules and MIRA terms. Make your **own private copy of this notebook**; check its visibility before saving MRI outputs. Keep the official competition input and Pilkwang labels attached. Use CPU and leave internet off.\n2. Edit the settings near the top. For cache generation only, set `RUN_VERIFIER = False`. Set `SAVE_CACHE_OUTPUT = True` **only in your private copy**, then Save & Run. The conversion reads the official mounted DICOM files and writes all 4,407 studies to `/kaggle/working/rsna-knee-cache`. You do not need a separate 500 GB local download.\n3. After the run completes, use Kaggle's Dataset creation flow with your notebook outputs if available, selecting only the `rsna-knee-cache` folder. Alternatively, download that folder and upload its contents as a new Dataset. **Keep the new Dataset Private** and verify that setting. Do not publish the MRI outputs or share them outside the permissions in the competition rules. Creating the Dataset is a separate step; this notebook does not upload it automatically.\n\nThe cache contains NumPy pixel shards, `studies.csv`, `slot_mask.npy`, `SPEC.json`, an audit and licence notices. `SPEC.json` records the chosen geometry, study count, exact byte counts and SHA-256 checksums. No reports, report lexicon, label scores or `train.csv` are included. **uint8** means one byte per pixel; **GiB** means 1,073,741,824 bytes. Shards contain up to 128 studies.\n\nRead one study after building the cache (or replace `cache` with your attached private Dataset's mount path):\n\n```python\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\n\ncache = Path('/kaggle/working/rsna-knee-cache')\nindex = pd.read_csv(cache / 'studies.csv')\ni = 0\nr = index.iloc[i]\nstudy = np.load(cache / r['shard'], mmap_mode='r', allow_pickle=False)[int(r['row'])]\nmask = np.load(cache / 'slot_mask.npy', allow_pickle=False)[i]\nprint(study.shape, study.dtype)  # default: (6, 9, 224, 224), uint8\n```\n\nSlot order: sagittal fluid, coronal fluid, axial fluid, sagittal structural, coronal structural, axial structural. A zero slot with a zero mask means a missing series, not a healthy knee.\n\nWith the published settings, Save & Run rebuilds the measurements and cache, but MRI goes to `/kaggle/temp/rsna-knee-cache` and is **not retained in saved outputs**. Heavy implementation cells are hidden; expand them to inspect the code. Never publish a version saved with MRI outputs.\n","metadata":{}},{"cell_type":"markdown","id":"caveats","source":"## Caveats and credit\n\nThis is a **lossy cache, not lossless DICOM**: it retains selected slices, crops/resizes them, and compresses intensities to eight bits. It cannot recover discarded anatomy or scanner metadata. The full corpus is cached; only 200 studies were used for the quality probe. Weak labels come from report processing and may be wrong. The six scan slots can be absent. Slice count and sampled anatomical coverage change together. A small fixed descriptor can miss benefits that a trained image encoder would find.\n\nThe requested crop uses row pixel spacing and is skipped for short fields of view or missing spacing; the audit counts those cases and anisotropic spacing. We preserve this existing geometry instead of silently changing it. No filename ordering or broken-slice substitution is allowed. These limitations prevent a claim that 11 GiB is universally optimal.\n\nDerived MRI remains governed by the [competition rules](https://www.kaggle.com/competitions/rsna-knee-abnormality-detection/rules) and [RSNA MIRA licence](http://rsna.org/mira-license). The cache is intended for participants who accepted those terms, is not for non-participants, and does not replace obtaining the official training data. No reports, report lexicon, or `train.csv` are shipped.\n\nGeometry credit: [Steven Lee's CPU pixel cache](https://www.kaggle.com/code/stevenleehans/rsna-knee-500gb-to-11gib-cpu-pixel-cache), Apache-2.0, reimplemented and modified here. No report or lexicon code was copied. Labels: [Pilkwang](https://www.kaggle.com/datasets/pilkwang/rsna-knee-llm-labels). The code licence does not override MRI access terms.\n","metadata":{}}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python"},"cache_verifier_receipt_sha256":"914edf1049c022f2d3bf464fa336db2387d833112b89ef50091bf57fe0d2327a"}}