From 55fa9b5d64fc9f1cc43f672b19516463cf485810 Mon Sep 17 00:00:00 2001 From: Claudio Maggioni Date: Sun, 29 Nov 2020 22:17:17 +0100 Subject: [PATCH] Done c++ ant colony opt with 2.5opt = 7.5 --- .gitignore | 141 ++++ AI_cup_2020_description.pdf | Bin 0 -> 80241 bytes AI_cup_2020_studentname.xls | Bin 0 -> 29184 bytes Lectures/Student_lecture 1.ipynb | 645 ----------------- Lectures/Student_lecture 2.ipynb | 898 ------------------------ Lectures/Student_lecture 3.ipynb | 255 ------- Lectures/Student_lecture 4.ipynb | 37 - aco.cc | 427 +++++++++++ Lectures/__init__.py => c_prob/.gitkeep | 0 run.py | 48 +- src/TSP_solver.py | 22 +- src/ant_colony.py | 55 ++ src/constructive_algorithms.py | 2 - src/two_dot_five_opt.py | 5 +- src/two_opt.py | 3 +- 15 files changed, 670 insertions(+), 1868 deletions(-) create mode 100644 AI_cup_2020_description.pdf create mode 100644 AI_cup_2020_studentname.xls delete mode 100644 Lectures/Student_lecture 1.ipynb delete mode 100644 Lectures/Student_lecture 2.ipynb delete mode 100644 Lectures/Student_lecture 3.ipynb delete mode 100644 Lectures/Student_lecture 4.ipynb create mode 100644 aco.cc rename Lectures/__init__.py => c_prob/.gitkeep (100%) create mode 100644 src/ant_colony.py diff --git a/.gitignore b/.gitignore index 92425bf..dbab12d 100644 --- a/.gitignore +++ b/.gitignore @@ -24,3 +24,144 @@ /Complete notebooks/ /Lectures/.ipynb_checkpoints/ /.ipynb_checkpoints/Student_lecture 1-checkpoint.ipynb +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +c_prob/* +!c_prob/.gitkeep diff --git a/AI_cup_2020_description.pdf b/AI_cup_2020_description.pdf new file mode 100644 index 0000000000000000000000000000000000000000..0fed3bbf52b4da3ee66f8ad7883e0469a98d46d3 GIT binary patch literal 80241 zcmagE19W8Tvo{>ub~>Jk(XnmYwrx-BWMUf=JDDUC+nU(c#F*&Iod3DsJ$Jq5eeYh~ ztM`6b_*Lzyr6UL 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aNInbb7+d9evXk5yc7GyKOxdjzHbLsuLquk9NWF4qJCTE2azRqlUroqKiyMvL44YbbmDSpEkkJyfrX88iHqR2Q1mHO58CzjTA7avWUJaWlubOWFKNMYaioiIAjh49Smho6GX9fSrATUB2djaZmZn07t2blJQUwsLC6NGjh92xRKSa0KCAGkfA1beL+xljGDBgAMYYJkyYwPjx45k/fz4DBw7kySef5PTp03zyySeXtQ8VYC93/Phx7r77bubPn4+fnx8zZ85kw4YNdscSkVomD+zCMyt31fhcc2NuKGvstm7dSmhoKAUFBSQkJBAZGcn777/PvHnzuPvuu3n33Xd58MEH2bhx4yXvQ9eAvcjqzDxe/GAP+wtLCA0KYFK/a1n07AQGDhzI448/zq5du+jfvz9XXHEFALm5uYSGhpKRkcFVV11lc3oRqf0anjywS6NsKGuMzvf/fvr06QQGBvK73/2OwsJCjDFYlkWbNm2cp6TPRdeAm4CqDsqqfz3nHjnBg+PG0Tc6gscffxyAbt261fjgfl1NBiJin6ExYSq4Nqj9/plTcISnln8O9Cbh+iA2bNjAc889R2hoKJs3byY+Pp709PTLno2uAuwlao/kO5n3NUW70kg/eA0OhwOAWbNmcccdd9gVUUTEI5010vREIdkrX2D0//gQ0a4F9913H4MGDSIwMJCJEydSXl5OixYteOONNy5rvzoF7SWumbKOun6TtTsoRUSkpoZ8/zzfKWgN4vASGsknInJp7Hr/VAH2Et42kk9ExF3sev/UNWAvUdW4oQ5KEZGLY9f7p64Bi4iINBBdAxYREfEwKsAiIiI2UAEWERGxgQqwiIiIDVSARUREbKACLCIiYgMVYBERERuoAIuIiNhABVhERMQGKsAi51FaWkqvXr3o0aMHXbt2Zdq0aQBkZWXRp08fHA4HcXFxZGRkNMj+9+zZg8PhcP7XunVr5s+fz3vvvUfXrl3x8fGhoafLRURE0K1bN+fPCjBq1ChnpoiICOeSlyJSf5oFLXIezZs3Jz09ncDAQMrKyrjlllu4/fbbee6555g2bRq3334769ev56mnnuKjjz5y+f67dOlCVlYWABUVFYSFhTFs2DBOnDjBypUrmTBhgsv3WZdNmzbRvn175+133nnH+fUTTzxBmzZt3JJDxJuoAIuchzGGwMBAAMrKyigrK8MYgzGGoqIiAI4ePUpoaGiDZ0lLS+O6667j6quvbvB91ZdlWbz77rukp6fbHUWk0VEBFrmAiooKYmNj+e677/j1r39N7969mT9/PgMHDuTJJ5/k9OnTfPLJJw2eY8WKFdx7770Nvp/ajDEMGDAAYwwTJkxg/Pjxzvs+/vhjOnbsSOfOnd2eS6Sxq9c1YGNMkDHmfWPMN8aYfxhjbjLGtDPGfGiM2Vv5Z9uGDitiB19fX7KyssjNzSUjI4Pdu3fz6quvMm/ePHJycpg3bx4PPvhgg2Y4deoUKSkp3HPPPQ26n7ps3bqVHTt28Pe//51XXnmFLVu2OO9bvny5Lf8oEPEG9T0CXgCkWpY1whjjD1wB/DeQZllWkjFmCjAFeLqBcorUad68ebz55psYY+jWrRtLliyhRYsWl/V3rs7Mq3Nd0KCgIOLj40lNTWXp0qUsWLAAgHvuuYdx48a54sc55/7NT9vo2bMnHTt2dNl+Lmb/Q0MhODiYYcOGkZGRQd++fSkvL2flypVs3769wTOJeKMLHgEbY1oDfYG3ACzLOmVZViFwF7C08mFLgaENFVKkLnl5ebz88sts27aN3bt3U1FRwYoVKy7r71ydmcczK3eRV1iCBfy0/wBP/eVTVmfmUVJSwsaNG4mMjCQ0NJTNmzcDkJ6e7rJTsLX3n1dYwjMrdzH3j4vdcqRZe/85BUd4avnnrM7Mo7i4mA0bNhAdHQ3g/H8RHh7e4LlEvFF9joCvBQ4CS4wxPYDtwESgo2VZ+QCWZeUbY4IbLqZI3crLyykpKaFZs2acOHHispuhXvxgDyVlFc7bFccPk/3OPEYvs7jmyisYOXIkQ4YMISgoiIkTJ1JeXk6LFi144403LvdHqXP/AMUnivnu402sffdPzm2rVq3it7/9LQcPHmTw4ME4HA4++OADl++/4kQh2StfYPT/+BDRrgX33XcfgwYNAuy7Ji3iLYxlWed/gDFxwGfAzZZlfW6MWQAUAb+1LCuo2uOOWJZ11nVgY8x4YDxAp06dYn/88UdX5pcmbsGCBUydOpWAgAAGDBjAn//858v6+66Zso66XhEG2Jc0+LL+bu1fpOkxxmy3LCuurvvq04SVC+RalvV55e33gZ7Az8aYkModhAAFdX2zZVlvWJYVZ1lWXIcOHS4+vcg5HDlyhDVr1rBv3z72799PcXExy5Ytu6y/MzQo4KK2u1pT379IU3LBAmxZ1gEgxxjTpXJTf+BrIAUYU7ltDLCmQRKKVLM6M4+bk9K5Zso6+vxmPj6tg+nQoQPNmjVj+PDhl/1xoMkDuxDQzLfGtoBmvkwe2OUc3+FaTX3/Ik1Jfbugfwv8ubID+gdgLGeK97vGmAeBnwD3fz5CmpSqBqGqa5RFvm34eOunvPPJd4y86TrS0tKcoxIv1dCYMIA6u6DdoanvX6QpueA1YFeKi4uzGnpurXivm5PSySssqbGt8OM/c2rv/3JtcGtiYmJ48803ad68uU0JRURqOt81YE3CkkZjf63iCxD076Mx/z6a3WoQEpFGRqshSaOhBiER8SYqwNJoqEFIRLyJCrA0GkNjwpg9vBthQQEYICwogNnDu6lBSLxWXWsxT548mcjISLp3786wYcMoLCy0OeUZiYmJBAcHOyelATz77LN0794dh8PBgAED2L9/v40JPY+asEREPFRERATbtm2rsRbzhg0b6NevH35+fjz99Jnx+3PmzLErotOWLVsIDAzk/vvvZ/fu3QAUFRXRunVrAF5++WW+/vprXnvtNTtjut3lDuIQEREPMWDAAPz8zvTP9unTh9zcXJsTndG3b1/atWtXY1tV8QUoLi7GGOPuWB5NXdAiIh7qfGsxAyxevJhRo0bZlK5+pk6dyp/+9CfatGnDpk2b7I7jUXQELCLioc63FvPMmTPx8/Nj9OjRNia8sJkzZ5KTk8Po0aNZuHCh3XE8io6ARUQ8RH3XYl66dClr164lLS3N1tO6tfOO6XbFOR973333MXjwYGbMmOHGhJ5NR8DV1NXFN2rUKBwOBw6Hg4iICBwOh235KioqiImJYciQIQDs3LmTm266iW7duvGLX/yCoqIi27KJyOWp71rMqampzJkzh5SUFK644twFz9158wpLmJO6h6LScudj9u7d6/w6JSWFyMhIG5J6Lh0BV/PAAw/wm9/8hvvvv9+57Z133nF+/cQTT9CmTRs7ogFnlt6LiopyFtpx48Yxd+5cbr31VhYvXsyLL77I7373O9vyicilq+9azP/6r//KyZMnSUhIAM40YtnRWVw778GUZE7+tIvTJUWEh4czY8YM1q9fz549e/Dx8eHqq69uch3QF6KPIdWSnZ3NkCFDnG30VSzLolOnTqSnp9O5c2e358rNzWXMmDFMnTqVl156ibVr19K6dWuOHj2KMYacnBwGDhzI119/7fZsInL5GttazI0tr130MSQX+Pjjj+nYsaMtxRfgscceIzk5GR+ff/7KoqOjSUlJAeC9994jJyfHlmwicvka26jVxpbXE6kA19Py5cu59957bdn32rVrCQ4OJjY2tsb2xYsX88orrxAbG8uxY8fw9/e3JZ+IXL7GNmq1seX1RE3+GnB9uvjKy8tZuXIl27dvtyVX2Wd/ofQfm1i/fj2lpaUUFRXxq1/9imXLlrFhwwYAvv32W9atW+e2fCLiWo1tLebGltcTNelrwLUXeAfwLT5E2d9n89N33zi3paamMnv2bDZv3mxbroBmvswe3o2go3uZO3cua9eupaCggODgYE6fPs0DDzxAfHw8iYmJbskoIiIXpmvA51BXF9+PSyaRu+87wsPDeeuttwBYsWKFW08/184FUFJWwYsf7Kmxbfny5Vx//fVERkYSGhrK2LFj3ZZRREQuT5M+AvbULj5PzSUiIhdHR8Dn4KldfJ6aS0REXKdJF2BP7eLz1FwiIuI6TboL2lO7+Dw1l4iIuE6TvgYsIiLSkHQNWERExMOoAIuIiNhABVhERMQGKsDicqWlpfTq1YsePXrQtWtXpk2bBpxZMKJr1674+PigXgARaeqadBe0NIzmzZuTnp5OYGAgZWVl3HLLLdx+++1ER0ezcuVKJkyYYHdEERHbqQCLyxljCAwMBKCsrIyysjKMMURFRdmcTETEc+gUtDSIiooKHA4HwcHBJCQk0Lt3b7sjiYh4FBVgaRC+vr5kZWWRm5tLRkYGu3fvtjuSiIhH0SlocZnaaytXTe+Kj48nNTWV6OhouyOKiHgMFWBxieprGFecOEpO6XGeWXmKk6UlbNy4kaefftruiCIiHkWnoMUlqq9hXHH8MAeW/zffv/4IY4clkJCQwJAhQ1i1ahXh4eF8+umnDB48mIEDB9qcWkTEPpoFLS6hNYxFRM6mWdDS4LSGsYjIxVEBFpfQGsYiIhdHBVhcYmhMGLOHdyMsKAADhAUFMHt4N61hfJkSExMJDg6u0UGukZ4i3kFd0OIyQ2PCVHBd7IEHHuA3v/kN999/v3ObRnqKeAcVYBEP1rdvX7Kzs2ts00hPEe+gU9AiIiI2UAEWERGxgU5Bi3iY2iM9x3S7wu5IItIAdAQsch6lpaX06tWLHj160LVrV6ZNmwbAzp07uemmm+jWrRu/+MUvKCoqcsn+qkZ65hWWYAF5hSXMSd1DUWm5S/5+uTiFhYWMGDGCyMhIoqKi+PTTT92273M996rMnTsXYwyHDh1yWyZxLRVgkfNo3rw56enp7Ny5k6ysLFJTU/nss88YN24cSUlJ7Nq1i2HDhvHiiy+6ZH/VR3oCHExJ5sclk8jd9x3h4eG89dZbGunpRhMnTmTQoEF888037Ny5060NcOd67gHk5OTw4Ycf0qlTJ7flEdfTKWiR8zDGEBgYCEBZWRllZWUYY9izZw99+/YFICEhgYEDB/K73/3usve3v7Ckxu0Odz51Jgc1R3oOGzbssvcl51dUVMSWLVt4++23AfD398ff399t+z/Xcw9g0qRJJCcnc9ddd7ktj7iejoBFLqCiogKHw0FwcDAJCQn07t2b6OhoUlJSgDODMXJyclyyL4309Bw//PADHTp0YOzYscTExDBu3DiKi4vdmqGu515KSgphYWH06NHDrVnE9epVgI0x2caYXcaYLGPMtspt7YwxHxpj9lb+2bZho4q4x+rMPG5OSueaKeu4OSmdv315gKysLHJzc8nIyGD37t0sXryYV155hdjYWI4dO+ayIyON9LRX9d/9mDc/ZfuOHTzyyCNkZmbSsmVLkpKS3Lb/up57X375JTNnzuT5559v0BziHhdzBHybZVmOaqs6TAHSLMvqDKRV3hZp1Opqgnpm5S5WZ+YRFBREfHw8qampREZGsmHDBrZv3869997Ldddd55L9a6SnfWr/7g/TCp/AK8n3DwdgxIgR7Nixw237r+u5t2bNGvbt20ePHj2IiIggNzeXnj17cuDAgQbLJQ3nck5B3wUsrfx6KTD08uOI2Kt2E1TFiaMUHzt6ZntJCRs3biQyMpKCggIATp8+zQsvvMDDDz/ssgxDY8LYOqUf+5IGs3VKPxVfN6n9u/cNbItvq/Y8v2wjAGlpadxwww1u239dz72YmBgKCgrIzs4mOzub8PBwduzYwVVXXdVguaTh1LcJywI2GGMs4HXLst4AOlqWlQ9gWVa+MSa4rm80xowHxgPq2BOPV7sJquL4YQ6tm8cB6zQ3LmvJyJEjGTJkCAsWLOCVV14BYPjw4YwdO9aOuOJCtX/3AO3+42G++ssLdP/w91x77bUsWbLEbfs/13NPvIexrLqWUa/1IGNCLcvaX1lkPwR+C6RYlhVU7TFHLMs673XguLg4S6u3iCe7OSmdvDreiMOCAtg6pZ8NicRd7P7d271/aRjGmO3VLt3WUK9T0JZl7a/8swBYBfQCfjbGhFTuIAQocE1cEfuoCarpsvt3b80fXZ8AACAASURBVPf+xf0uWICNMS2NMa2qvgYGALuBFGBM5cPGAGsaKqSIu6gJqumy+3dv9/7F/S54CtoYcy1njnrhzDXjv1iWNdMYcyXwLtAJ+Am4x7Ksw+f7u3QK2jtVVFQQFxdHWFgYa9eu5dlnn2XNmjX4+PgQHBzM22+/TWhoqN0xRaQBJSYmsnbtWoKDg9m9ezcAWVlZPPzww5SWluLn58cf//hHevXqZXNS9zrfKeh6XQN2FRVg7/TSSy+xbds2ioqKWLt2LUVFRbRu3RqAl19+ma+//prXXnvN5pQi0pC2bNlCYGAg999/v7MADxgwgEmTJnH77bezfv16kpOT+eijj+wN6maXfQ1Y5Fxyc3NZt24d48aNc26rKr4AxcXFzvF5IuK9+vbtS7t27WpsM8Y4Fyo5evSozoTVolnQclkee+wxkpOTOXbsWI3tU6dO5U9/+hNt2rRh06ZNNqUTETvNnz+fgQMH8uSTT3L69Gk++eQTuyN5FB0By0WpPiovasxMin1aEhsbe9bjZs6cSU5ODqNHj2bhwoU2JBWRhlZ7dOaGr2pO5Hr11VeZN28eOTk5zJs3jwcffNCmpJ5J14Cl3qpG5VVN6zmy+W1OfLWJtoEB+Jwuo6ioiOHDh7Ns2TLn9/z4448MHjzYeU1IRLxD7fcDAN/iQ5T9fTY/ffcNAG3atKGwsBBjDJZl0aZNG5etnd1Y6BqwuETtUXltb32AsP9aSudHl7JixQr69evHsmXL2Lt3r/MxKSkpREZG2hFXRBpQ7fcDgJPlFRw6ftJ5OzQ0lM2bNwOQnp5O586d3ZrR0+kasNRbXaP6/rm9pfP2lClT2LNnDz4+Plx99dXqgBbxQrXfDw6mJHPyp11UlBQRHh7OjBkzWLRoERMnTqS8vJwWLVrwxhtv2JTWM6kAS72FBgXUOSovNCiA+Ph44uPjAfjrX//q5mQi4m613w863PkUcPbozO3bt7s9W2OhU9BSbxqVJyJV9H5w+XQELPVWNRLvxQ/2sL+whNCgACYP7KJReSJNkN4PLp+6oEXkouXk5HD//fdz4MABfHx8GD9+PBMnTmT69OksWrSIDh06ADBr1izuuOMOm9OK2Od8XdA6AhaRi+bn58fvf/97evbsybFjx4iNjSUhIQGASZMm8eSTT9qcUMTzqQCLyEULCQkhJCQEgFatWhEVFUVeXp7NqUQaFzVhichlyc7OJjMzk969ewOwcOFCunfvTmJiIkeOHLE5nYjnUgEWkXqpPXZwdWYex48f5+6772b+/Pm0bt2aRx55hO+//56srCxCQkJ44okn7I4t4rFUgOWS5eTkcNtttxEVFUXXrl1ZsGCB3ZGkgVSNHcwrLMEC8gpLmPJeJv+eMITRo0czfPhwADp27Iivry8+Pj489NBDZGRk2Bu8EduzZw8Oh8P5X+vWrZk/f77dscSFdA1YLtm5GnFuuOEGu6OJi9UeO2hZFrkpL9G6zZU8/vjjzu35+fnOa8OrVq0iOjra7Vm9RZcuXcjKygKgoqKCsLAwhg0bZnMqcSUVYLlk52rEUQH2PrXHDp7M+5rirzZxqkMEDocDOPORo+XLl5OVlYUxhoiICF5//XU74nqdtLQ0rrvuOq6++mq7o4gLqQCLS9RuxBHvUnvsYIvwrlz99Nqzxg7qM78NY8WKFdx77712xxAX0zVguWy1G3HE+2jsoH1OnTpFSkoK99xzj91RxMV0BCwXZXVmXo3Rc5P6XcuiZyfUaMQR76Oxg+5R+/U1eWAXzE/b6NmzJx07drQ7nriYRlFKvdVegNuyLAr/Pp++0RH87S9v2pyucUhMTGTt2rUEBweze/du5/Y//OEPLFy4ED8/PwYPHkxycrKNKcUOdS1wH9DMl/YZr5L4y6GMHTvWxnRyqc43ilKnoKXeanfCnsz7mqJdaaRvSnd+VGL9+vU2JvR8DzzwAKmpqTW2bdq0iTVr1vDll1/y1VdfaYxjE1XXAvfFJ4r57ONNOrvkpXQKWuqtdidsVSOOAbKSBtsTqpHp27cv2dnZNba9+uqrTJkyhebNmwMQHBxsQzKxW+3XF4BPsxaEP7qcNm3a2JBIGpqOgKXeQoMCLmq71M+3337Lxx9/TO/evbn11lv54osv7I4kNtDrq+lRAZZ6UyfsxatrfGNt5eXlHDlyhM8++4wXX3yRkSNH4s7eDPEMen01PV5bgM81JvHZZ5+le/fuOBwOBgwYwP79+21O2ngMjQlj9vBuhAUFYICwoABmD++mTthzqGt84zMrd7HhqwM1HhceHs7w4cMxxtCrVy98fHw4dOiQPaHFNnp9NT1e2wWdn59Pfn5+jTGJq1evJjw83PlZ1Zdffpmvv/6a1157zS2ZpGm5OSm9xvCKKldaRzm5bpazC/q1115j//79PP/883z77bf079+fn376CWOMuyOLiIudrwvaa5uw6jMmsbi4WG9y0mDqaqo5mJJM7k+7MCePER4ezowZM0hMTCQxMZHo6Gj8/f1ZunSpnpciTYDXFuDqao9JnDp1Kn/6059o06YNmzZtsjmdeKva4xsBOtz51FnjGwGWLVvmzmgi4gG89hpwlbrGJM6cOZOcnBxGjx7NwoULbU4o3kpNNSJyPl53BFx9lNtVrZpRunYW/3mOMYn33XcfgwcPZsaMGTYkFW+n8Y0icj5edQRcvev0tGWxa3kSP54O4trbRjkfs3fvXufXKSkpREZG2hFVmoihMWFsndKPfUmD2Tqln4rvRSosLGTEiBFERkYSFRXFp59+CpwZ3dmlSxe6du3KU089ZUu2iooKYmJiGDJkCADTp08nLCxMU+Gk3rzqCLj6KLfq65WOHnwrnYMDmTVrFm+99RZ79uzBx8eHq6++Wh3QIh5s4sSJDBo0iPfff59Tp05x4sSJGqM7mzdvTkFBgS3ZFixYQFRUFEVFRc5tkyZN0ihRqTevKsD761ivFKgxKlHrlYo0DkVFRWzZsoW3334bAH9/f/z9/T1idGdubi7r1q1j6tSpvPTSS27fv3gHrzoFrVFuIt7jhx9+oEOHDowdO5aYmBjGjRtHcXGxR4zufOyxx0hOTsbHp+Zb6MKFC+nevTuJiYkcOXLE7bmkcfGqAqyuU5HGr2p85x3zPuKL7du5od/dZGZm0rJlS5KSkmwb3VmVq+OIafxvbhk5PlfVuP+RRx7h+++/Jysri5CQEJ544okGzySNm1edglbXqUjjVn1NXN9W7fENbM+Svc24PjOPESNGkJSUdM7RnR06dHBLrtK8ryn+aiv33NaTQD+LkyeO86tf/arGZ7kfeughZ3OWuE9ERAStWrXC19cXPz8/tm3bxrPPPsuaNWvw8fEhODiYt99+m9DQULujAl48ilJEGp/a4zsP/Pkprhz0KBHXdSahdAvFxcVcd911bh/dea6xooGH9xCxP521a9eSn5/vnL43b948Pv/8c1asWNFgmeRsERERbNu2jfbt2zu3FRUV2Tp+uEmOohSRxqf2+M52//Ewh9bO5WBFOR3+rQdLliyhZcuWbh/dWddYUYBDx08SUfn1U089RVZWFsYYIiIieP311xs0k9RPVfEFzxs/rCNgEfEY5zrSrGt8pzt5ai6p6ZprrqFt27YYY5gwYQLjx48Hzh4/3JCXK2o73xGwVzVhiUjj5qmNlJ6aS2raunUrO3bs4O9//zuvvPIKW7ZsATx3/LAKsIh4DE9dE9dTczV1VZ3p10xZx81J6WT8fOaMbnBwMMOGDSMjI6PG4++77z7++te/2hG1TroGLCLntGfPHkaN+uco1x9++IHnn3+ewsJCFi1a5DyVN2vWLJcNuRkaE+YRha20tJS+ffty8uRJysvLGTFiBFtnzDjTVfuXNUxf4cMfPayrtimp3pkOkFNwhKeWfw70JuH6IDZs2MBzzz3H3r176dy5M+B544d1DVhE6qWiooKwsDA+//xzlixZQmBgoFePXbQsi+LiYgIDAykrK+OWW25hwYIF3HDDDbZ21coZta/LlxUe4ODKF2jm60NEuxbcd999TJ06lbvvvvus8cNhYe77B566oEXksqWlpXHddddx9dVX2x3FLYwxBAYGAlBWVkZZWRnGGI/uqm1KanemNwu6itDEhRjgq8rRw4BHnXKuTdeARaReVqxYwb333uu83RTGLlZUVOBwOAgODiYhIYHevXsDZ7pq/+Vf/oU///nPPP/88zanbJq8YfRwvQuwMcbXGJNpjFlbebudMeZDY8zeyj/bNlxMEXGX2o0tqzPzOHXqFCkpKdxzzz2A945drP2z/+3LA2RlZZGbm0tGRga7d+8GPLertinxhs70izkCngj8o9rtKUCaZVmdgbTK2yKNVkREBN26dcPhcBAXd+aSzeHDh0lISKBz584kJCR47ZFelepraltAXmEJz6zcxbRXltGzZ086duwIQMeOHfH19cXHx4eHHnrorG7TxuhcP/vqzDyCgoKIj48nNTW1xvd4WldtU+INnen1KsDGmHBgMPBmtc13AUsrv14KDHVtNBH327RpE1lZWVQ1CyYlJdG/f3/27t1L//79SUpKsjlhw6q+pnaVkrIKFr39PzVOP+fn5zu/XrVqFdHR0W7L2FBq/+wVJ45SfOzome0lJWzcuJHIyEj27t3rfIynddU2NUNjwtg6pR/7kgazdUq/RlV8of5NWPOBp4BW1bZ1tCwrH8CyrHxjTJ2LchpjxgPjATp16nQZUUXcb82aNXz00UcAjBkzhvj4eObMmWNvqAZU18jF02WlHPl2O8OHr3Zu88axi7V/9orjhzm0bh4HrNPcuKwlI0eOZMiQIXV21Ypcigt+DMkYMwS4w7Ks/zLGxANPWpY1xBhTaFlWULXHHbEs67zXgfUxJPFkdY2xCwoKorCw0PmYtm3bevVp6KY8crEp/+zScC53FOXNwJ3GmGxgBdDPGLMM+NkYE1K5gxCgwEV5RWxxrjF2TYk3NLZcqqb8s4s9LliALct6xrKscMuyIoBfAumWZf0KSAHGVD5sDLCmwVKKNID6jLHr2LGj83pnfn4+wcF1XmnxGt7Q2HKpmvLPLva4qElYtU5BXwm8C3QCfgLusSzr8Pm+vymegq6oqCAuLo6wsDDWrl1LVlYWDz/8MKWlpfj5+fHHP/6RXr162R2zyak9xu70qVJa+BmS7z0zxi4hIYHnnnuOtLQ0rrzySqZMmUJSUhKHDx8mOTnZ5vTiTepaRP69995j+vTp/OMf/yAjI8PZlS+Nj8smYVmW9RHwUeXX/wf0v9xw3m7BggVERUVRVFQEnGlemTZtGrfffjvr16/nqaeecjb5iPuc3fFaSPbKFxj9P/8cYzdo0CBuvPFGRo4cyVtvvUWnTp147733bEwt3mrTpk01FpGPjo5m5cqVTJgwwcZU0tA0irIB5ebmsm7dOqZOncpLL70EnBlvV1WMjx49qiHuNqnvGLsrr7yStLQ0N6eTpi4qKsruCOIGKsAN6LHHHiM5OZljx445t82fP5+BAwfy5JNPcvr0aT755BMbEzZdoUEBdXa8NqYxduIdjDEMGDDgrEXkxftpFrQLVW/qiRozk2KflsTGxtZ4zKuvvsq8efPIyclh3rx5PPjggzalbdrU8Sp2qv5e0f7eOTz31t+adPd9U6UC7CK1x9jt/zaLjanrCA79F375y1+Snp7Or371K5YuXcrw4cMBuOeee7xihF9jpI7X+tmzZw8Oh8P5X+vWrZk/f77z/rlz52KM4dChQzambFxqv1ccPN2SZ1bu4pO8sjoXkRfvpVPQLlK7qaftrQ/Q9tYHCAsKYGYfH+bOncuyZcuIiopi8+bNxMfHk56e7lwoWtzPUxZ+92RdunQhKysL+Od6wMOGDQMgJyeHDz/8UBPuLlL194rTp0rBOk0JV5D0tyx8KheRl6ZBBdhF6hrh98/tLZ23Fy1axMSJEykvL6dFixa88cYbbkoocnlqrwc8adIkkpOTueuuu2xO1rhUf6+oOFHIwZUvnNl++jTTJo1n0KBBrFq1it/+9rccPHiQwYMH43A4+OCDD+yKLA1EBdhFztfUEx8fT3x8PAC33HIL27dvd3M6kctXfT3glJQUwsLC6NGjh82pGp/q7xVV3fdw5jLI1MqRl8OGDXOeaRDvpWvALqKmHvFm1dcDPnHiBDNnztRC9JdI7xVSRUfALlJ1LfHFD/awv7CE0KAAJg/somuM0uiszsw763lsftrmXA94165d7Nu3z3n0m5ubS8+ePcnIyOCqq66yOb3n03uFVLmoUZSXqymOovRmiYmJrF27luDgYHbv3g3A9OnTWbRoER06dABg1qxZ3HHHHXbGlItQe0QnnDk6a5/xKom/HMrYsWPP+p6IiAi2bdtWY5KTiJxxuashidTpgQceIDU19aztkyZNIisri6ysLBXfRqZ2Nz9A8YliPvt4k/PjcyLiGjoFLZesb9++ZGdn2x1DXKiubn6fZi0If3Q5bdq0qfN79BwQuTQ6AhaXW7hwId27dycxMdGrF6/3RucaxakRnSKupwIsF6X2GrobvjpQ4/5HHnmE77//nqysLEJCQnjiiSdsSiqXQh26Iu6jAiz1VnuEXl5hCXNS91BUWu58TMeOHfH19cXHx4eHHnpIY/UaGU8a0VlaWkqvXr3o0aMHXbt2Zdq0aW7PIPWXmJhIcHAw0dHRzm2TJ08mMjKS7t27M2zYMAoLC21M6HlUgKXe6mrQOVlewaHjJ5238/PznV+vWrWqxotRGoehMWFsndKPfUmD2Tqln20fj2nevDnp6ens3LmTrKwsUlNT+eyzz2zJIhdWV1NmQkICu3fv5ssvv+T6669n9uzZNqXzTGrCknqr3aBzMCWZkz/toqKkiPDwcGbMmMFHH31EVlYWxhgiIiJ4/fXXbUorjZ0xhsDAQADKysooKyvDGGNzKjmXupoyBwwY4Py6T58+vP/++25O5dlUgKXeao/b7HDnU8CZ05RbK0foaXlFcaWKigpiY2P57rvv+PWvf03v3r3tjiSXaPHixYwaNcruGB5Fp6Cl3tSgI+7m6+tLVlYWubm5ZGRkOAe+SOMyc+ZM/Pz8GD16tN1RPIqOgKXeNEJP3KGuUZhDY8KIj48nNTVVfQUepPbvaky3K856zNKlS1m7di1paWm6hFCLCrBcFK2he3kiIiJo1aoVvr6++Pn5odGsNVUfhVlx4ig5pcd5ZuUpTpaWsHHjRp5++mm7I7pNTk4O999/PwcOHMDHx4fx48czceJEAP7whz+wcOFC/Pz8GDx4MMnJyW7PV3ts6ZlPReRQVu1TEampqcyZM4fNmzdzxRVnF+emTgVYxM02bdqkucnnUL3TvuL4YQ6tmwfWacYugin/NZYhQ4bYnNB9/Pz8+P3vf0/Pnj05duwYsbGxJCQk8PPPP7NmzRq+/PJLmjdvTkFBgS35an8qoqop83S1pszZs2dz8uRJEhISgDONWK+99poteT2RCrCIeIzqnfb+wdcQOvZlAAzw3HODbUplj5CQEEJCQgBo1aoVUVFR5OXlsWjRIqZMmULz5s0BCA4OtiVf7U9FVDVlGmBf0pnflZoyz09NWCJuZIxhwIABxMbG8sYbb9gdx+NoFGbdsrOzyczMpHfv3nz77bd8/PHH9O7dm1tvvZUvvvjClkz6XV0+HQGLNKDaTSrPvv4+iQNiKSgoICEhgcjISPr27Wt3TI8xeWCXOpdDbCqd9nU1oP1H5zbcfffdzJ8/n9atW1NeXs6RI0f47LPP+OKLLxg5ciQ//PCD2xucmvrvyhV0BCzSQOoa3fnixwdZnZlHcHAww4YNc/uozsLCQkaMGEFkZCRRUVF8+umnjBo1CofDgcPhICIiAofD4dZM1XnSKEx3q+v5MuW9TP49YQijR492LgcZHh7O8OHDMcbQq1cvfHx8OHTokNvzNuXflavoCFikgdRuUjl9qpTik6d58YM9JFwfxIYNG3juuefcmmnixIkMGjSI999/n1OnTnHixAneeecd5/1PPPHEOZcddJem2mlf+/liWRa5KS/Rus2VPP74487tQ4cOJT09nfj4eL799ltOnTplW1NfU/1duYoKsEgDqd2kUnGikIMrX+AA0Ot/ruC+++5j0KBBbstTVFTEli1bePvttwHw9/fH39/feb9lWbz77rukp6e7LZP8U+3ny8m8ryn+ahOnOvzzrMSsWbNITEwkMTGR6Oho/P39Wbp0qT5f20ipAIs0kNqjO5sFXUVo4sIaozvd6YcffqBDhw6MHTuWnTt3Ehsby4IFC2jZsiUAH3/8MR07dqRz585uzyZnP19ahHfl6qfX1vl8WbZsmbvjSQPQNWCRBuJpozvLy8vZsWMHjzzyCJmZmbRs2ZKkpCTn/cuXL+fee++1JZt43vNFGp6OgEUaiCeM7qzeVdvet4R2wSHOBQ1GjBjhLMDl5eWsXLmS7du3uy2b1OQJzxdxLxVgkQZkZ5NK7VGBBysCOO7XhldWbeHXw/qSlpbGDTfcAMDGjRuJjIwkPDzclqxyhpqamhYVYBEvVburFiCo/wSe/u1DvD6tOddeey1LliwBYMWKFTr9LOJmKsAiXqp2Vy2Af8dr6fCrl/gyqeZYx6rOaBFxHzVhiXgpjQoU8WwqwCJeSl21Ip5Np6BFvJS6akU8mwqwiBdTV62I59IpaBERERuoAIuIiNhABVhERMQGKsAiIiI2UAGWeqlrIffJkycTGRlJ9+7dGTZsGIWFhXbHFHG5iooKYmJiGDJkSI3tc+fOxRjDoUOHbEomjZ0KsNRL1ULu33zzDTt37iQqKoqEhAR2797Nl19+yfXXX8/s2bPtjinicgsWLCAqKqrGtpycHD788EM6depkUyrxBirAckFVC7k/+OCDwJmF3IOCghgwYAB+fmc+ydanTx9yc3PtjCnicrm5uaxbt45x48bV2D5p0iSSk5MxxtiUTLyBCrBcUPWF3GNiYhg3bhzFxcU1HrN48WJuv/12mxKKNIzHHnuM5ORkfHz++VaZkpJCWFgYPXr0sDGZeIMLFmBjTAtjTIYxZqcx5itjzIzK7e2MMR8aY/ZW/tm24eOKu6zOzOPmpHSumbKOMW9+yvbzLOQ+c+ZM/Pz8GD16tI2JRS5f9ed91JiZFPu0JDY21nn/iRMnmDlzJs8//7yNKcVb1GcS1kmgn2VZx40xzYD/Ncb8HRgOpFmWlWSMmQJMAZ5uwKziJrXXkT1MK3wCryTf/8xasdUXcl+6dClr164lLS3N7afjSktL6du3LydPnqS8vJwRI0YwY8YMJk+ezN/+9jf8/f257rrrWLJkCUFBQW7NJo1P7ef9/m+z+O6rTQSH/gs+p8soKiriP//zP9m3b5/z6Dc3N5eePXuSkZHBVVddZWd8aYQueARsnXG88mazyv8s4C5gaeX2pcDQBkkobld7HVnfwLb4tmrP88s2AjgXck9NTWXOnDmkpKRwxRVXuD1n8+bNSU9PZ+fOnWRlZZGamspnn32m5jC5JLWf921vfYCw/1pK50eXsmLFCvr168df//pXCgoKyM7OJjs7m/DwcHbs2KHiK5ekXteAjTG+xpgsoAD40LKsz4GOlmXlA1T+GXyO7x1vjNlmjNl28OBBV+WWBlTXOrLt/uNhvvrLC3Tv3p2srCz++7//m9/85jccO3aMhIQEHA4HDz/8sFtzGmMIDAwEoKysjLKyMowxag6TS1LX8/5820UuV70WY7AsqwJwGGOCgFXGmOj67sCyrDeANwDi4uKsS0opbhUaFEBerTcd/47XEjfxdbZO6efc9t1337k72lkqKiqIjY3lu+++49e//jW9e/eucf/ixYsZNWqUTemkManreV+1PT4+nvj4+LPuy87Obvhg4rUuqgvasqxC4CNgEPCzMSYEoPLPApenE1s0pnVkfX19ycrKIjc3l4yMDHbv3u28T81hcjEa0/NevEN9uqA7VB75YowJAP4D+AZIAcZUPmwMsKahQop7DY0JY/bwboQFBWCAsKAAZg/v5hHL2lXvUr05KZ3VmXkABAUFER8fT2pqKvDP5rA///nP+qym1IsnP+/FOxnLOv9ZYWNMd840WflypmC/a1nW88aYK4F3gU7AT8A9lmUdPt/fFRcXZ23bts0lwT1FTk4O999/PwcOHMDHx4fx48czceJEdeI2gNpdqhUnjhLQ3J/k+25iYGQ7BgwYwNNPP42fnx+PP/44mzdvpkOHDjanFpGmzBiz3bKsuDrvu1ABdiVvLMD5+fnk5+fTs2dPjh07RmxsLKtXryY3N5d+/frh5+fH00+f+XTWnDlzbE7buN2clF7jGt2pgn0cWjePZj4W11x5BSNHjuS5557jX//1Xzl58iRXXnklcKYR67XXXrMrtog0YecrwPVqwpJzCwkJISQkBIBWrVoRFRVFXl4eAwYMcD6mT58+vP/++3ZF9Bq1u1H9g68hdOzLGGB30mDndk9oDhMRuRCNonSh7OxsMjMz6+zE1ZjGyxcaFHBR20VEPJkK8CWoqxHo+PHj3H333cyfP5/WrVs7H6tOXNdRl6qIeBOdgr5ItRuB8gpLmPJeJgGb5vKfo0czfPhw52PtHNPojaq6UV/8YA/7C0sIDQpg8sAu6lIV8UIRERG0atUKX19f/Pz8qOof+sMf/sDChQvx8/Nj8ODBJCcn25z00qkAX6Ta4+osyyI35SVat7mSxx9/3Lm9akzj5s2bbRnT6K2GxoSp4Io0EZs2baJ9+/Y1bq9Zs4Yvv/yS5s2bU1DQuMdPqABfpNqNQCfzvqb4q02c6hCBw+EAYNasWTz66KOcPHmShIQEQJ24IiKX69VXX2XKlCk0b94cgODgOicgNxr6GNJFqv1RmCphQQE1xjSKiMilu+aaa2jbti3GGCZMmMD48eNxOBzcddddpKam0qJFC+bOncuNN95od9Tz0seQXGjywC41rgGDGoFERFxt69atcDd8mgAAIABJREFUhIaGUlBQQEJCApGRkZSXl3PkyBE+++wzvvjiC0aOHMkPP/zQaHtsVIAvkhqBRERcb3Vm3tnvq6FnTjMPGzaMjIwMwsPDGT58OMYYevXqhY+PD4cOHWq0E+/0MaRLMDQmjK1T+rEvaTBbp/RT8RVbJCYmEhwcTHR0vRcns1VOTg633XYbUVFRdO3alQULFgCwc+dObrrpJrp168YvfvELioqKbE566c71M2ZlZdGnTx8cDgdxcXFkZGTYnNSzVH26JK+wBAvIKTjCU8s/Z3VmHsXFxWzYsIHo6GiGDh1Keno6AN9++y2nTp2q0aTV6FiW5bb/YmNjLRFxjc2bN1vbt2+3unbtaneUetm/f7+1fft2y7Isq6ioyOrcubP11VdfWXFxcdZHH31kWZZlvfXWW9b/+3//z86Yl+VcP2NCQoK1fv16y7Isa926ddatt95qY0rP82+z06yrn17r/C90wptWsw4R1hVXXWvdcMMN1gsvvGBZlmWdPHnSGj16tNW1a1crJibGSktLszn5hQHbrHPURJ2CFmmk+vbt26jWoz3X2NY9e/bQt29fABISEhg4cCC/+93v7Ix6yc71MxpjnEf2R48eJTQ01M6YHqf2p0uaBV1FaOJCDPBVtTGz/v7+LFu2zM3pGo4KsIi4XfWxrdHR0aSkpHDXXXfx3nvvkZOTY3c8l6j+M86fP5+BAwfy5JNPcvr0aT755BO743mU0KCAOj9d4u1jZnUNWKSRONdayJ6sPmNbFy9ezCuvvEJsbCzHjh3D39/f7tgXpT4/46uvvsq8efPIyclh3rx5PPjgg3bH9ij/v717D4uyzv8//vwgoZYpmcKOmJJGYmKiuGqXZax9J7Msg0ozK9PI9Le1VmbReu2m5QHLStvo+I1k19I0T6yVaeChq21lPeBqFlnWikRqXyE8Jof79wcwC4Q6GjP3HF6P6+KCuWeU93vGud/e9/2e9ydYx8wGxOeAGxpZNmXKFN544w1Xd9yMGTO4/vrrG/13i3hD/RGoULWDerhfOC8+nsKOHTtsjK5hDcXcLMSqGtt62011JsfV+Oqrr7jzzjv9pknJ3RxbtWpFSUkJxhgsy6JVq1Z+3WzmCQ12QQdAg2tQfA64/sgygIcffphHH33UpohEGk/9EagAx8oqeG3DbpsiOj13x7bu37+fiIgIKisrmTZtGuPGjbMj3LPibo7t2rVj/fr1JCYmkpOTQ0xMjB3h+rRgHDMbMAVYJJDVb1IBOJD1DHv3bMf8fIj27dszdepUnzq16e7Y1l27dpGeng5AcnIyo0eP9nqsZ8vdHN944w0mTJhAeXk5zZo14/XXX7cjXPExAXEKuqGRZVOmTGHevHm0bNmS3r1789xzz3HBBRc0+u8W8QZ/HIHqjzGfqWDIUX6dU52CDogmrE8//ZQtW7bw4Ycfkp6ezoYNGxg/fjzffPMNeXl5OBwOJk6caHeYImfNH5tU/DHmMxUMOYrn+GUBrt91mLuv6ii+9siyyMhImjRpQkhICPfdd5/fNHXIr5efn098fLzrq2XLlsyZM4dJkyYRGxvL5ZdfTlJSEiUlJXaH6rabe0YxM7k7UeHNMVQdYc1M7u7T18z8MeYzFQw5uutUk9lmz56NMYYff/zRhsh8l9+dgq7fdVh54jjNQg3PjOiL89JwnE4nf/7zn+nRo4frA/EvvPACGzduZOHChb86B/EvFRUVREVFsXHjRvLz8xk4cCChoaE8/vjjAMyaNcvmCEUCw4YNG2jRogV33313na78goICUlJS+PLLL9m8ebN/j448CwHVBV2/67DiaAnfLZ3GyL+FEN26GXfccQfXXXcdd911F3l5eRhjiI6O5rXXXrMxarFLdnY2nTt3pmPHjnTs2NG1vV+/frz33ns2RiYSWE42me3hhx/mmWeeYejQod4Pysf5XQF2d2TZ3/72Ny9HJr5o4cKFjBgx4hfbMzIyGD58uA0RiQSPrKwsoqKi6NGjh92h+CS/K8DBOrJMztyJEyfIyspi5syZdbZPnz6d0NBQRo4caVNkIoHv6NGjTJ8+ndWrV9sdis/yuwI8aVCXBicCqeswuDU0Rcfs2USvXr2IjIx0PS4zM5OVK1eSnZ3tt4t4i/iK+u+7Ud3Pdd33zTff8O2337qOfvfu3UuvXr3Izc3lN7/5jV0h+xS/K8A13YWBOLJMzk79xrzCkmM8sXQ7bXIzGFPr9POqVauYNWsW69ev59xzzz3ZXycibmjofTdrVQFlx8sB6N69O/v373c9Pjo6mk2bNgVdE9ap+F0BhuAcWSYn19CYxiNHj/D1J2tZueivrm0PPPAAP//8M06nE6hqxHr11Ve9GqtIoKj/vjuQ9Qw/79lO5bFSn5zM5ov8sgCL1NbQmMaQc5rR/g8LaNWqlWvb119/7c2wRAJa/fdd25seA8AA39ZqiK3hT2tXe4tfDuIQqe1kDXhqzBPxHL3vfj0VYPF7Ggco4n163/16KsDiEatWraJLly5ccsklpKWlefR3aRygiPfpfffr+d0oSvF9FRUVXHrppaxZs4b27dvz29/+lgULFnDZZZfZHZqIiFcF/GpI4ltyc3O55JJL6NSpE2FhYdx+++2sWLHC7rBERHyKCrA0usLCQi666CLX7fbt21NYWGhjRCIivkcFWBpdQ5c1NHVKRKQufQ5YGk3NWLrdn/+H4xu3MWRrITf3jGLv3r20a9fO7vBERHyKCrA0itpj6cIcl/Lj/gImvrmGsruvZuHChbzzzjt2hyhCRUUFvXv3JioqipUrVzJ8+HDy8/MBKCkpITw8nLy8PJujlGChAiyNovZYOhPShNbOcex5ZzJ3LbD40yO/p1u3bjZHKAJz586la9eulJaWAvDuu++67ps4cWKdyWkinqYCLI2i/li65p1/S1Tn32KAyZN/OZZOxNv27t3L+++/z+TJk3n++efr3GdZFosWLSInJ8em6CQYqQlLGoXG0omve+ihh3jmmWcICfnlbu+TTz4hMjKSmJgYGyKTYKUCLI1CY+nEl61cuZKIiAgSEhIavH/BggWMqLV0pYg36BS0NIpAXqe5pKSElJQUduzYgTGGjIwM9u7dy5QpU/jiiy/Izc2ld+8GB934vfz8fIYPH+66vXv3bp566in+7//+jxUrVhASEkJERATz5s3zyU73ms78z1dkcmznOt5b/ndCKssoLS3lzjvvZP78+ZSXl7N06VI2b95sd7i/UL9pTAKLRlGKnMaoUaO46qqrSElJ4cSJExw9epSioiJCQkK4//77mT17dsAW4NoqKiqIiopi48aNXHDBBbRs2RKAF198kZ07d/rc2sr1F4yHqrMyIzsc4p8rMl0FbdWqVcycOZP169fbFepJPf/882zatInS0lIVYD+lUZQiZ6m0tJQNGza4FhYPCwsjPDycrl270qVLcJ1ez87OpnPnznTs2NFVfAGOHDnik4NW6i8YD3CsrIKF/yqos23hwoU+efq5pmksJSXF7lDEQ3QKWuQUdu/eTdu2bRk9ejTbtm0jISGBuXPnct5559kdmtfVL1STJ0/mr3/9K61atWLt2rU2Rtaw+p35NY607sLK1x9x3Z43b56XIjozNU1jhw4dsjsU8ZDTHgEbYy4yxqw1xnxhjPncGDOhentrY8waY8yu6u8XeD5cEe8qLy9ny5YtjB8/nq1bt3Leeed5fHlFX3TixAmysrK47bbbXNumT59OQUEBI0eO5KWXXrIxuob5c2f+6ZrGJDC4cwRcDky0LGuLMeZ8YLMxZg1wD5BtWVaaMSYVSAUe91yoIt5R07jzfckx2jQ5RusIB3379gXg1ltvDfgCXDv/mmY6s2cTvXr1IjIy8hePv+OOO7jhhhuYOnWqDdGe3KRBXRq8BuzLnfnuNI1J4DhtAbYsqwgoqv75kDHmCyAKGAokVj8sE1iHCrAt6ndKTpo0ib///e+EhYXRuXNn3nrrLcLDw+0O0y/Ub9w5UNGcw6GtSF+2gd8nDSA7Ozug1zWun39hyTGeWLqdNrkZjKl1+nnXrl2uz8xmZWURGxtrS7yn4m+d+bWf+/Cr7yH86nvqNI2p+AaeM+qCNsZEAxuAOGCPZVnhte4rtizrlKeh1QXtGfU7JVevXs3AgQMJDQ3l8cer/k80a9Ysm6P0D/3Tciisd+3wxL7dlK55iU6tm9KpUyfeeust1q1bx4MPPsiBAwcIDw8nPj6ejz76yKaoG09D+VeWHafoldH8WFTgGtV4yy23kJ+fT0hICB07duTVV18lKso3C5u/aOi5B2hxMJ/o73PUBe2nTtUF7XYTljGmBbAEeMiyrFJ3ux6NMWOBsQAdOnRw99eJmxoar3fttde67u/Xrx/vvfeeXeH5nYYad8IiO9H2zuf5d9p/R2omJSWRlJTkzdC8oqH8Q85pRvs/LKgzJ3nJkiXeDCsouNs0JoHDrY8hGWPOoar4vm1Z1tLqzfuMMY7q+x3A/ob+rGVZr1uW1duyrN5t27ZtjJilllON1wPIyMhg8ODBXo7Kf/lz405jCPb87aTnPvi40wVtgDeBLyzLqj3BPAsYVf3zKGBF44cnp3K6Tsnp06cTGhrKyJEjvRyZ/wr2kZrBnr+d9NwHH3dOQfcH7gK2G2NqFsr8I5AGLDLG3AvsAW47yZ+XRuZOp2RmZtWkn+zsbJ8ckuCr/K1xp7EFe/520nMffDSK0s+4M15v1apVPPLII6xfvx6d9hcRsY9GUQYQd8brPfDAAxw6dAin00l8fDzjxo3zdpgiInIaGkXpZ9zplPz666+9GZKIiJwFHQH7GXVKiogEBhVgP6NOSRGRwKAC7GEVFRX07NmTIUOGAHDw4EGcTicxMTE4nU6Ki4vP6O+7uWcUM5O7ExXeHANEhTdnZnJ3dUqKiPgZFWAPmzt3Ll27dnXdTktL45prrmHXrl1cc801ZzXY/+aeUXyaOpBv027g09SBKr4iIn5IBdiDGlpQe8WKFYwaVTW/ZNSoUSxfvtyu8ERExEYqwB7U0JjIffv24XA4AHA4HOzf3+AETxERCXAqwB6iBbVFRORU9DngRlR7IfOyf77D8S/W8sEHH3D8+HHXmMjIyEiKiopwOBwUFRURERFhd9jiwwoKCrj77rv54YcfCAkJYezYsUyYMIHhw4eTn58PQElJCeHh4eTl5Z3mbxMRX6IC3Ejqj4gM7TeStlfdzczk7oT/tIvZs2czf/58Jk2aRGZmJqmpqWRmZjJ06FCbIxdfFhoaynPPPUevXr04dOgQCQkJOJ1O3n33XddjJk6cWGepQBHxDzoF3UhONiLy2Y/y62xLTU1lzZo1xMTEsGbNGlJTU70ZpvgZh8NBr169ADj//PPp2rUrhYWFrvsty2LRokWMGDHCrhBF5CzpCLiRnGxE5Pclx0hMvIHExEQALrzwQrKzs70YmQSK7777jq1bt9K3b1/Xtk8++YTIyEhiYmJsjExEzoaOgBuJRkSKJx0+fJhbbrmFOXPm0LJlS9f2BQsW6OhXxE/pCLiRTBrUpcFlAjUiUs5U7Wa+duHNeXhgJ9740/2MHDmS5ORk1+PKy8tZunQpmzdvtjFaETlbOgJuJBoRKY2hppmvsOQYFrC3+Cj3pqQQduFFPPLII3Ue+/HHHxMbG0v79u3tCTZI1B8nC/CXv/yFLl260K1bNx577DEbo/OM48eP06dPH3r06EG3bt148sknXfcFeu7epCPgRnRzzygVXPlV6jfz/Vy4k9Lt2eQcuJj4+HgAZsyYwfXXX8/ChQt1+tkLasbJlpaWArB27VpWrFjBv//9b5o2bRqQw3SaNm1KTk4OLVq0oKysjCuvvJLBgwdz7NixgM/dm1SARXxI/Wa+Zu270fHxlRggL+2GOvfNmzfPe4EFqZpxspMnT+b5558H4JVXXiE1NZWmTZsCBORn+Y0xtGjRAoCysjLKysowxgRF7t6kU9AiPkTNfL6loXGyX331FZ988gl9+/bl6quv5l//+peNEXpORUUF8fHxRERE4HQ66du3b9Dk7i0qwCI+ROs9+46TjZMtLy+nuLiYf/7znzz77LMMGzYMy7JsitJzmjRpQl5eHnv37iU3N5cdO3YETe7eogLsR+o3gyxevJhu3boREhLCpk2bbI5OGoOa+ey1fGsh/dNyuDj1fcbNfodFS5YRHR3N7bffTk5ODnfeeSft27cnOTkZYwx9+vQhJCSEH3/80e7QG0Xt/Pun5bB8ayHh4eEkJiayatUqn8h9zJgxREREEBcX59q2bds2rrjiCrp3786NN97oul7v61SA/Uj9tYXj4uJYunQpAwYMsDEqaWxa79ke9TvQQ/uNpO3Yt5iz7FMWLlzIwIEDmT9/PjfffDM5OTlA1enoEydO0KZNG3uDbwS18y8/+hMFPxzgiaXbefezr10d976Q+z333MOqVavqbEtJSSEtLY3t27eTlJTEs88+69WYzpYKsJ9oaG3hrl270qWLTk2KNAZ3x8mOGTOG3bt3ExcXx+23305mZibGGG+G6hG18684fJAfFvyRb14bz+gkJ06nkyFDhvhE7gMGDKB169Z1tuXn57sORJxOJ0uWLPFqTGdLXdB+oqYZ5NChQ3aHIhKQ3B0nGxYWxvz5870YmXfUzj8s4mLajX4RAAP8+c9VHfi+mntcXBxZWVkMHTqUxYsXU1BQYHdIbtERsB/Q2sIinhfsHej+nH9GRgbp6ekkJCRw6NAhwsLC7A7JLToC9lHurC3si/8TFfFXwT5O1pfzrz+edVT3c+vcHxsby+rVq4Gqa9Pvv/++HWGeMR0B+yB3m0GCXUFBAb/73e/o2rUr3bp1Y+7cuYC6w/3NyV7HgwcP4nQ6iYmJwel0Ulxc7NE4gr0D3Vfzr78/LCw5xqxV+ZQeL3c9pmYiV2VlJdOmTWPcuHE2RXtmjDc/w9W7d29LO8TT65+WQ2ED16OiwpszvV8Is2fPZuXKlSxbtowHH3yQAwcOEB4eTnx8PB999JENEdujqKiIoqKiOovVL1++HGMMISEh3H///cyePZvevXvbHaqcwslex3nz5tG6dWtSU1NJS0ujuLiYWbNm2R2ueFn9/eGBrGf4ec92Ko+V0s7xG6ZOncrhw4dJT08HIDk5mZkzZ/pMY5wxZrNlWQ3uhHQK2ge52wySlJREUlKSFyPzLQ6HA4fDAdRdrN7pdNocmZyJk72OK1asYN26dQCMGjWKxMREFeAgVH9/2PamqgUgDPBtrfGsEyZM8GZYjUKnoH2QPzdD2KWhxerF/9R+Hfft2+cqzA6HQ4P/g1Qg7w9VgH2QxhGemZMtVi/+Ra+jNCSQ94c6Be2Dapoeanf9TRrUxfZmCF/g7mL14tvceR0jIyMpKirC4XBQVFSklXeCVCDvD1WAfZTWFv6lmm7Imo9J1CxWPyAu+heL1Yvvcvd1vOmmm8jMzCQ1NZXMzEyGDh1qV8his0DdH6oLWvxG/W7I43s/Z9/bj3Puby4mJrLqlOWMGTP4+eefg7o73Ne5+zr27duXYcOGsWfPHjp06MDixYt/MYJQxNedqgtaBVj8xsWp79PQv9b63ZDi2/Q6SjA5VQFWE5b4jUDuhgwmeh1FqqgAi98I5G7IYKLXUaSKmrDEbwRyN2Qw0esoUkXXgEVERDxE14BFRER8jAqwiIiIDVSARUREbKACLCJik+PHj9OnTx969OhBt27dePLJJwGYMmUKUVFRxMfHEx8fzwcffGBzpOIJ6oIWEbFJ06ZNycnJoUWLFpSVlXHllVcyePBgAB5++GEeffRRmyMUT9IRsIiITYwxtGjRAoCysjLKysp8ZiF58TwVYBERG1VUVBAfH09ERAROp9O1pvVLL73E5ZdfzpgxYyguLrY5SvGE0xZgY0yGMWa/MWZHrW2tjTFrjDG7qr9f4NkwRUQCU5MmTcjLy2Pv3r3k5uayY8cOxo8fzzfffENeXh4Oh4OJEyfaHaZ4gDtHwPOA6+ptSwWyLcuKAbKrb4uIiBuWby2kf1oOF6e+T/+0HJZvLSQ8PJzExERWrVpFZGQkTZo0ISQkhPvuu4/c3Fy7QxYPOG0BtixrA3Cw3uahQGb1z5nAzY0cl4hIQKpZD7mw5BjlR3+i4IcDPLF0O+9+9jUff/wxsbGxFBUVuR6/bNky4uLibIxYPOVsu6AjLcsqArAsq8gYE9GIMYmIBKxnP8rnWFkFABWHD/Lj+y+AVcnoNyD1/41myJAh3HXXXeTl5WGMITo6mtdee83mqMUT3JoFbYyJBlZalhVXfbvEsqzwWvcXW5bV4HVgY8xYYCxAhw4dEv7zn/80QtgiIv5J6yEHF0/Mgt5njHFU/+UOYP/JHmhZ1uuWZfW2LKt327Ztz/LXiYgEBq2HLDXOtgBnAaOqfx4FrGiccEREApvWQ5Ya7nwMaQHwGdDFGLPXGHMvkAY4jTG7AGf1bREROY2be0YxM7k7UeHNMUBUeHNmJnfXeshuKikp4dZbbyU2NpauXbvy2WefsW3bNq644gq6d+/OjTfeSGlpqd1hukXrAYuIiN8YNWoUV111FSkpKZw4cYKjR4/idDqZPXs2V199NRkZGXz77bc8/fTTdocKnPoasAqwiIj4hdLSUnr06MHu3bvrjOxs2bIlP/30E8YYCgoKGDRoEDt37rQx0v/yRBOWiIiIV+3evZu2bdsyevRoevbsSUpKCkeOHCEuLo6srCwAFi9eTEFBgc2RukcFWERE/EJ5eTlbtmxh/PjxbN26lfPOO4+0tDQyMjJIT08nISGBQ4cOERYWZneoblEBFhERn1YzujNp3peEnt+GorD2ANx6661s2bKF2NhYVq9ezebNmxkxYgSdO3e2OWL3qACLiIjPqj26s0mLC6DFhTzyxocs31pIdnY2l112Gfv3V42iqKysZNq0aYwbN87mqN2jAiwiIj6r9uhOgNb/M47CZbMYef1V5OXl8cc//pEFCxZw6aWXEhsbS7t27Rg9erSNEbtPXdAiIuKz/H10p7qgRUTELwXy6E4VYBER8VmBPLpTBVjETzU0ku/gwYM4nU5iYmJwOp0UFxfbHabIrxLIozt1DVjETzU0km/GjBm0bt2a1NRU0tLSKC4uZtasWXaHKhK0NIpSJMCcbCRfly5dWLduHQ6Hg6KiIhITE8nPz7cxUpHgpiYskQBzspF8+/btw+FwAOBwOFyfjxQR36MCLOKHTjaST0T8R6jdAYiIe5ZvLeTZj/L5vuQYbZoco3WEg759+wJVI/nS0tKIjIykqKjIdQo6IiLC5qhF5GR0BOwnGup4/dOf/sTll19OfHw81157Ld9//73dYYqH1B7HZwEHKppzOLQV6cs2ALhG8t10001kZmYCkJmZydChQ22MWnxdfn4+8fHxrq+WLVsyZ84cddN7iZqw/ERDHa8hISG0bNkSgBdffJGdO3fy6quv2hypeEL/tBwKS47V2XZi325K17xEp9ZN6dSpE2+99RaVlZUMGzaMPXv20KFDBxYvXkzr1q1tilr8SUVFBVFRUWzcuJH09HR10zeSUzVh6RS0HygtLWXDhg3MmzcPgLCwsF8st3XkyJE63bASWL6vV3wBwiI70fbO5/l3vXF82dnZ3gpLAkh2djadO3emY8eOrFixgnXr1gFV//lPTExUAfYAnYL2AyfreAWYPHkyF110EW+//TZPPfWUzZGKpwTyOD7xDQsXLmTEiBEA6qb3EhVgP3Cqjtfp06dTUFDAyJEjeemll2yOVDwlkMfxif1OnDhBVlYWt912m92hBBWdgvZR7nS81nbHHXdwww03MHXqVDvCFQ+rGbtX82+iXXhzJg3qEhDj+HxZSUkJKSkp7NixA2MMGRkZNG/enHHjxnH8+HFCQ0N5+eWX6dOnj92huq32vqXm35HZs4levXoRGRkJ4NVu+ujoaM4//3yaNGlCaGgomzZtYtu2bYwbN47Dhw8THR3N22+/7ep3CSQqwD6opuO1Zg3M2h2vv08a4Op43bVrFzExMQBkZWURGxtrZ9jiYTf3jFLB9bIJEyZw3XXX8d5777maH4cNG8aTTz7J4MGD+eCDD3jsscdc10t9Xf19S2HJMZ5Yup02uRmMqT79DLi66VNTU73STb927VratGnjup2SksLs2bO5+uqrycjI4Nlnn+Xpp5/2aAx2UAH2QfUXoAYIv+Z+Hn/wPl578r8drykpKeTn5xMSEkLHjh3VAS3SiE7W/GiMobS0FICffvqJdu3a2RjlmWlo33Lk6BG+/mQtKxf91bUtNTWVYcOG8eabb7q66b0pPz+fAQMGAOB0Ohk0aJAKsHiHux2vS5Ys8WZYIkGldvPjtm3bSEhIYO7cucyZM4dBgwbx6KOPUllZyT/+8Q+7Q3VbQ/uWkHOa0f4PC2jVqpVr24UXXui1bnpjDNdeey3GGO6//37Gjh1LXFwcWVlZDB06lMWLF1NQUOCVWLxNTVg+SB2vIvY7WfPjK6+8wgsvvEBBQQEvvPAC9957r92hus0X9y2ffvopW7Zs4cMPPyQ9PZ0NGzaQkZFBeno6CQkJHDp06BcfuwwUKsA+SB2vIvZYvrWQ/mk5XJz6PuOXffeL5sctW7aQmZlJcnIyALfddhu5ubl2hnxGfGHfUvs57p+WQ+6+qmFQERERJCUlkZubS2xsLKtXr2bz5s2MGDGCzp07ey0+b1IB9kGBvAC1iK9yd9xnu3btWL9+PQA5OTmuRkh/YPe+pf5zXLC/mMcWbGT51kKOHDnC6tWriYuLc33uuLKykmnTpjFu3DivxOdtGkUpIoL74z4///xzJkyYQHl5Oc2aNePll18mISHBpqj9S/3nuKzkBw4sncY5TUKIbt2MO+64g8mTJzN37lzS09MBSE5OZubMmX476e9UoyhVgEVEgItT36ehvaEBvq037lPOTjA+x6cqwDoFLSKCbzYoBRo9x3WpAIuI4BsNSoFOz3Hp3bKVAAAEYklEQVRd+hywiAga9+kNeo7r0jVgERERD9E1YBERER+jAiwiImIDFWAREREbqACLiIjYQAVYRETEBirAIiIiNlABFhERsYEKsIiIiA1UgEVERGygAiwiImIDFWAREREbqACLiIjYQAVYRETEBirAIiIiNlABFhERsYFX1wM2xhwA/uO1X9h42gA/2h2ETZR78Arm/IM5dwju/Bs7946WZbVt6A6vFmB/ZYzZdLIFlQOdcg/O3CG48w/m3CG48/dm7joFLSIiYgMVYBERERuoALvndbsDsJFyD17BnH8w5w7Bnb/Xctc1YBERERvoCFhERMQGKsC1GGMyjDH7jTE7am1rbYxZY4zZVf39Ajtj9BRjzEXGmLXGmC+MMZ8bYyZUbw+W/JsZY3KNMduq859avT0o8gcwxjQxxmw1xqysvh1MuX9njNlujMkzxmyq3hYU+Rtjwo0x7xljvqx+/18RDLkbY7pUv941X6XGmIe8mbsKcF3zgOvqbUsFsi3LigGyq28HonJgomVZXYF+wO+NMZcRPPn/DAy0LKsHEA9cZ4zpR/DkDzAB+KLW7WDKHeB3lmXF1/oISrDkPxdYZVlWLNCDqn8DAZ+7ZVn51a93PJAAHAWW4c3cLcvSV60vIBrYUet2PuCo/tkB5Nsdo5eehxWAMxjzB84FtgB9gyV/oH31zmYgsLJ6W1DkXp3fd0CbetsCPn+gJfAt1f1AwZR7vXyvBT71du46Aj69SMuyigCqv0fYHI/HGWOigZ7ARoIo/+pTsHnAfmCNZVnBlP8c4DGgsta2YMkdwAJWG2M2G2PGVm8Lhvw7AQeAt6ovP/yvMeY8giP32m4HFlT/7LXcVYClDmNMC2AJ8JBlWaV2x+NNlmVVWFWno9oDfYwxcXbH5A3GmCHAfsuyNtsdi436W5bVCxhM1eWXAXYH5CWhQC/gFcuyegJHCMDTzadijAkDbgIWe/t3qwCf3j5jjAOg+vt+m+PxGGPMOVQV37cty1pavTlo8q9hWVYJsI6qfoBgyL8/cJMx5jtgITDQGDOf4MgdAMuyvq/+vp+q64B9CI789wJ7q8/2ALxHVUEOhtxrDAa2WJa1r/q213JXAT69LGBU9c+jqLo2GnCMMQZ4E/jCsqzna90VLPm3NcaEV//cHPgf4EuCIH/Lsp6wLKu9ZVnRVJ2Ky7Es606CIHcAY8x5xpjza36m6nrgDoIgf8uyfgAKjDFdqjddA+wkCHKvZQT/Pf0MXsxdgzhqMcYsABKpWg1jH/AksBxYBHQA9gC3WZZ10K4YPcUYcyXwCbCd/14H/CNV14GDIf/LgUygCVX/MV1kWdZTxpgLCYL8axhjEoFHLcsaEiy5G2M6UXXUC1WnZN+xLGt6EOUfD/wvEAbsBkZT/R4g8HM/FygAOlmW9VP1Nq+97irAIiIiNtApaBERERuoAIuIiNhABVhERMQGKsAiIiI2UAEWERGxgQqwiIiIDVSARUREbKACLCIiYoP/D65xxVrK/HJTAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "p_inst.plot_data(show_numbers=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "-------------\n", - "### Random solver \n", - "Now we will code the random solver and test it with a class called `SolverTSP` that takes the solvers and the problem instance and act as a framework to compute the solution and gives us some additional information.\n", - "We will also need to code the `evaluate_solution` method of the the `SolverTSP` class" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "import random\n", - "def random_method(instance_): # TODO\n", - " n = int(instance_.nPoints)\n", - " solution = np.random.choice(np.arange(n), size=n, replace=False)\n", - " return solution\n", - "\n", - "def marco_random_method(instance_):\n", - " solution = list(range(instance_.npoints))\n", - " for i in range(len(solution)):\n", - " rand = random.randint(i, len(solution))\n", - " solution[i], solution[rand] = solution[rand], solution[i]\n", - " return solution\n", - "\n", - "def numpy_random_method(insta_):\n", - " return np.random.permutation(np.arange(insta_.nPoints)) \n", - "available_methods = {\"random\": random_method,\n", - " \"marco\":marco_random_method,\n", - " \"numpy\":numpy_random_method}" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "from time import time as t\n", - "\n", - "class SolverTSP:\n", - " def __init__(self, algorithm_name, problem_instance):\n", - " self.duration = np.inf\n", - " self.found_length = np.inf\n", - " self.algorithm_name = algorithm_name\n", - " self.name_method = \"initialized with \" + algorithm_name\n", - " self.solved = False\n", - " self.problem_instance = problem_instance\n", - " self.solution = None\n", - "\n", - " def compute_solution(self, verbose=True, return_value=True):\n", - " self.solved = False\n", - " if verbose:\n", - " print(f\"### solving with {self.algorithm_name} ####\")\n", - " start_time = t()\n", - " self.solution = available_methods[self.algorithm_name](self.problem_instance)\n", - " assert self.check_if_solution_is_valid(self.solution), \"Error the solution is not valid\"\n", - " end_time = t()\n", - " self.duration = np.around(end_time - start_time, 3)\n", - " if verbose:\n", - " print(f\"### solved ####\")\n", - " self.solved = True\n", - " self.evaluate_solution()\n", - " self._gap()\n", - " if return_value:\n", - " return self.solution\n", - "\n", - " def plot_solution(self):\n", - " assert self.solved, \"You can't plot the solution, you need to compute it first!\"\n", - " plt.figure(figsize=(8, 8))\n", - " self._gap()\n", - " plt.title(f\"{self.problem_instance.name} solved with {self.name_method} solver, gap {self.gap}\")\n", - " ordered_points = self.problem_instance.points[self.solution]\n", - " plt.plot(ordered_points[:, 1], ordered_points[:, 2], 'b-')\n", - " plt.show()\n", - "\n", - " def check_if_solution_is_valid(self, solution):\n", - " rights_values = np.sum([self.check_validation(i, solution) for i in np.arange(self.problem_instance.nPoints)])\n", - " if rights_values == self.problem_instance.nPoints:\n", - " return True\n", - " else:\n", - " return False \n", - " \n", - " def check_validation(self, node , solution):\n", - " if np.sum(solution == node) == 1:\n", - " return 1\n", - " else:\n", - " return 0\n", - "\n", - " def evaluate_solution(self, return_value=False):\n", - " total_length = 0\n", - " from_node_id = self.solution[0] # starting_node\n", - " # TODO\n", - " for node_id in self.solution[1:]:\n", - " edge_distance = self.problem_instance.dist_matrix[from_node_id, node_id]\n", - " total_length += edge_distance\n", - " from_node_id = node_id\n", - " self.found_length = total_length\n", - " if return_value:\n", - " return total_length\n", - "\n", - " def _gap(self):\n", - " self.evaluate_solution(return_value=False)\n", - " self.gap = np.round(\n", - " ((self.found_length - self.problem_instance.best_sol) / self.problem_instance.best_sol) * 100, 2)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "----------------------------\n", - "Now we will test our code" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### solving with random ####\n", - "### solved ####\n", - "the total length for the solution found is 2522.0\n", - "while the optimal length is 538.0\n", - "the gap is 368.77%\n", - "the solution is found in 0.0 seconds\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "'\\n### solving with random ####\\n### solved ####\\nthe total length for the solution found is 2424.0\\nwhile the optimal length is 538.0\\nthe gap is 350.56%\\nthe solution is found in 0.0 seconds\\n'" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "# here I'm repeating this two lines just to remind you which problem we are using\n", - "example_problem = \"../problems/eil76.tsp\"\n", - "p_inst = ProblemInstance(example_problem)\n", - "available_methods = {\"random\": random_method,\n", - " \"marco\":marco_random_method,\n", - " \"numpy\":numpy_random_method}\n", - "solver_name=\"random\"\n", - "# TODO\n", - "# 1. create an instance of SolverTSP\n", - "solver = SolverTSP(solver_name, p_inst)\n", - "# 2. compute a solution\n", - "solver.compute_solution()\n", - "# 3. print the information as for the output\n", - "# print(\"the total length for the solution found is\" + solver.solution)\n", - "print(f\"the total length for the solution found is {solver.found_length}\",\n", - " f\"while the optimal length is {solver.problem_instance.best_sol}\",\n", - " f\"the gap is {solver.gap}%\",\n", - " f\"the solution is found in {solver.duration} seconds\", sep=\"\\n\")\n", - "# 4. plot the solution\n", - "solver.plot_solution()\n", - "# this is the output expected and after that the solution's plot\n", - "\"\"\"\n", - "### solving with random ####\n", - "### solved ####\n", - "the total length for the solution found is 2424.0\n", - "while the optimal length is 538.0\n", - "the gap is 350.56%\n", - "the solution is found in 0.0 seconds\n", - "\"\"\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "--------------------\n", - "Finally since our example problem has an optimal solution we can plot it" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "solver = SolverTSP(\"optimal\", p_inst)\n", - "solver.solved = True\n", - "solver.solution = np.concatenate([p_inst.optimal_tour, [p_inst.optimal_tour[0]]])\n", - "solver.plot_solution()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "PyCharm (AI2020BsC)", - "language": "python", - "name": "pycharm-61970693" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/Lectures/Student_lecture 2.ipynb b/Lectures/Student_lecture 2.ipynb deleted file mode 100644 index 7953606..0000000 --- a/Lectures/Student_lecture 2.ipynb +++ /dev/null @@ -1,898 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "collapsed": true, - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Second Lab\n", - "\n", - "What we are going to do today:\n", - "- Recap of the previous Lab\n", - "- Define the nearest neighbor method and use it\n", - "- Define the Best Nearest Neighbors and use it \n", - "- Define the Multi Fragment and use it\n", - "- Finally compare these Constructive Methods" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This cell below is simply importing some useful stuff for later" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import glob\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "\n", - "from time import time as t" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Recap\n", - "### The problems\n", - "As we saw last time, we have 12 problems and two have an optimal solution" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "- ch130.tsp\n", - "- d198.tsp\n", - "- eil76.tsp\n", - "- fl1577.tsp\n", - "- kroA100.tsp\n", - "- lin318.tsp\n", - "- pcb442.tsp\n", - "- pr439.tsp\n", - "- rat783.tsp\n", - "- u1060.tsp\n" - ] - } - ], - "source": [ - "problems = glob.glob('../problems/*.tsp')\n", - "for prob in problems:\n", - " if prob in [\"../problems/eil76.tsp\", \"../problems/kroA100.tsp\"]:\n", - " print(f\"- {prob[12:]} with opt\", sep='\\t')\n", - " else:\n", - " print(f\"- {prob[12:]}\", sep='\\t')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### ProblemInstance Class\n", - "Last time we implemented an ProblemInstance class that we can use to load a problem. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from src.io_tsp import ProblemInstance" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "#############################\n", - "name: eil76\n", - "nPoints: 76\n", - "best_sol: 538.0\n", - "exist optimal: True\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 0. 15. 23. ... 18. 23. 25.]\n", - " [15. 0. 24. ... 7. 12. 15.]\n", - " [23. 24. 0. ... 31. 21. 20.]\n", - " ...\n", - " [18. 7. 31. ... 0. 17. 20.]\n", - " [23. 12. 21. ... 17. 0. 3.]\n", - " [25. 15. 20. ... 20. 3. 0.]]\n" - ] - } - ], - "source": [ - "example_problem = \"../problems/eil76.tsp\"\n", - "p_inst = ProblemInstance(example_problem)\n", - "p_inst.print_info()\n", - "p_inst.plot_data()\n", - "print(p_inst.dist_matrix)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TSPSolver Class\n", - "Even here, instead of reimplementing the class everytime, we can import the one I prepared before.\n", - "This implementation is ready to receive a lot of different types of problems" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from src.TSP_solver import TSPSolver\n", - "from src.constructive_algorithms import random_method" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The class implements the following methods:\n", - "..." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### solving with ['random'] ####\n" - ] - }, - { - "data": { - "image/png": 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NRhMFImqEVbp0aVmlSpVIHlKj0Wg0mqixfv36U1LKMu7WRVQAV6lSBevWrYvkITUajUajiRpCCI8hZbUKWqPRaDSaKKAFsEaj0Wg0UUALYI1Go9FoooAWwBqNRqPRRAEtgDUajUajiQJaAGs0Go1GEwW0ANZoNBqNJgpoAazRaDQaTRTQAlij0Wg0miigBbBGo9FoNFFAC2CNRqPRaKKAFsAajUaj0UQBLYA1Go1Go4kCWgBrNBqNRhMFtADWaDQajSYKaAGs0Wg0Gk0U0AJYowkQuz3aNdBoNJcCWgBrNAFyww3ACy9EuxYajSbe0QJYowmQggWBiROBbduiXRONRhPPaAGs0QRIcjKQnQ0MHgxIGe3aaDSaeEULYI0mQJKT+b1sGfDll9Gti0ajiV+0ANZoAqRhQ6BMGf4eORI4dy669dFoNPGJFsAaTYAkJAAdOvD38ePA009Htz4ajSY+0QJYowkCpYZu2BD4v/8D/voruvXRaDTxhxbAGk0QtG3LkXCTJlRHDxyo/YM1Gk1gaAGs0QRBiRLALbcA//sfMHUq8OefwMyZ0a6VRqOJJ7QA1miCpEMHqp5vuw1o1QoYOxY4eTLatdJoNPGCFsAaTZCoeeAFCzgPfP488MQT0a2TRqOJH7QA1miC5IYbgCuvBH75BbjuOuDxx4GPPwZ+/z3aNdNoNPGAFsAaTZAIwVHwwoWMjDV+PFC5MjBoEP9rNBqNN7QA1mhCIDmZgThWrWKM6DffBLZs4bdGo9F4QwtgjSYEbr8dSEoCfv6Z/++6C+jYEXjmGeDQoejWTaPRxDZaAGs0IVC0KHDrrYYABoA33qBP8IgR0auXRqOJfbQA1mhCJDkZ2LwZOHiQ/6++mvPBs2fTQlqj0WjcoQWwRhMiyh3pl1+MZaNGATVqAEOGABkZ0amXRqOJbbQA1mhC5LrraP1sVkPny0ff4N27gcmTo1c3jUYTu2gBrNGEiBCMirV4MZCZaSxv3Rp44AHgpZeAXbuiVz+NRhObaAGs0VhAcjJw4ULuIBxTpwJ581IVLWV06qbRaGITLYA1Ggu47TYKWrMaGmCkrBdeAH79FZg7Nzp101z62GzRroEmGLQA1mgsoFAhoGVLZ0MsxaBBQL16wPDhQFpaxKumucT55BOgSBFg9epo10QTKFoAazQWkZwMbN8O7NnjvDwpCXjnHeDwYWDixOjUTXPpISUwYQLw0EPA9dcDdepEu0aaQNECWKOxCHfuSIqbbwYeeQR47TWGqtRoQiEzE+jVC3j+eaBRI05xFC4c7VppAkULYI3GIqpXB665Jvc8sOKll4DixYGBA7VBliZ4zp4F2rUDPv8caNCAyUCKF492rTTBoAWwRmMhycnA0qVAenrudaVKAa+8AvzxB/Dpp5Gvmyb+2bsXuOUWYMUKoH59YNEioESJaNdKEyxaAGs0FpKczMhXy5e7X//QQ2xAR48GzpyJZM008c6aNZzK2L6duagXLQJKlox2rTShoAWwRmMhLVoABQp4VkMnJNAg68wZ4KmnIls3Tfwydy6t7E+coMHV4sXUqGjiGy2ANRoLyZ+fEbB+/tnzPG/dusCwYcC77wJ//hnZ+mniCylpuHfffdSs1KkDLFkClC4d7ZpprMCnABZC1BBCbDR9UoUQjwkhSgohFgkhdjq+9UyERgOGpdyzB9ixw/M2zz7LIB0DB+ogChr35OQAQ4cCI0dSENeqReFbpky0a6axCp8CWEr5r5SynpSyHoCGAC4C+A7AWABLpJTVASxx/NdoLns6dOC3JzU0wDzCr70G/PUXMGNGZOqliR/Onwc6d2ZCD4AJP5YuBa64Irr10lhLoCro1gB2Syn3A+gE4BPH8k8A3G1lxWIFnUpOEyhXX80G050/sJn77wfatOFc8LFjkambxj+ysqJ37CNHgObNgZ9+4v+aNSl8y5aNXp004SFQAdwNwJeO32WllEcBwPF9yfXNUlNp4n/vvcDx49GujSaeSE6mq8j58563EQKYPp0uS6NHR65uGs9ICfTvz9Ch0WDzZlo6b9jA/9deS+Fbrlx06qMJL34LYCFEXgB3Afg2kAMIIfoLIdYJIdadPHky0PpFlSJFGFhh7lzOv3z+uQ6goPGP5GSOopYu9b7dtdcCY8bw2fLkuqSJHNOmAe+/D7RtG/ljL14MNGsGHDzI/9WrA8uW0VZAc2kSyAi4A4C/pJRqLHhcCHElADi+T7jbSUr5npSykZSyUZk4sx4QggYQAJCdDfTsCdx1F2P6ajTeaNaMoQG9zQMrnnySautBg6Kr+rzc+flnaiKqVwcmTYrssT/8kLYDqan8X60ahW/58pGthyayBCKAH4ChfgaAeQB6O373BvCDVZWKJbp3p/rn5ptpNLNkCVC7Nl8YPRrWeCJvXs7venNHUhQoQFX0tm18xjSR559/gG7d2On+5BOgYMHIHFdKYPx4oG9fGuYJAVStSuFboUJk6qCJHn4JYCFEQQBtAJgzmr4MoI0QYqdj3cvWVy/65MtHV4BFi4Dbbwc2beL8UN++QPv2wP790a6hJlZJTqY6cetW/7bt3JnZkvQzFVlOnwY6dmSqyFGjgCZNInPczEygRw/gxRcZ0zkzE6hShcL3qqsiUwdNdPFLAEspL0opS0kpz5mWnZZStpZSVnd8X7KB9QYMYI942jTOCS9dSveAlSvpGP/OO4DdHu1aamINf9yRzLz+Or8feyw89dHkJjubQS727KHleqTSRZ4+TQ3JrFm0ht+7l8E1li0DKlWKTB000UdHwvKDkiWBPn1oKHP0KMMJDhrEtHJNmvB369bA7t3RrqkmlqhQgTF7/RXAlSoBzzwDfP+94YKiCR9SUru1fDmQmEjVc/784T/u7t2MB75mDTB2LKe1ihSh8K1cOfzH18QOWgD7yYgRjEwzfbqxrEoV5uGcOZMBFerWBd54Q0c20hgkJzP70blzvrcFOPqtVYuC4eLF8NbtcufttxkOFKAl+o03hv+Yq1bRnuTUKc73v/8+UKgQhe/VV4f/+JrYQgtgP6lWjXN077wDXLhgLBeC88FbtzJY+mOP0Yn+33+jVlVNDNGhAztkixb5t33evBQM+/Yxf7AmPCxeDAwfzt/XXw9MmBD+Y86eDdx2G1CsGAXvM89wxL1sGQ2vNJcfWgAHwOOPMxn2Rx/lXlexItWGn35Ka9YbbmDu15ycyNdTEzs0acIG1181NMCMSj17ApMn645cONi5k/OuNhuQlETVc7584TuelMCrr/KY9esDH3xAu5K8ean+rlYtfMfWxDZaAAdAkyb8vPaaezWzEGw4//mHqscxYzjXs2VL5OuqiQ2SkoB27RiWMhC3tSlTaPg3eLB2d7OSlBRaPKek8P/48RSK4SInh/dw9GgK4OnTgS5d+FwsW0ajTs3lixbAATJqFC0mf/Di9VyuHDBnDvD117RubNAAeOEFWlxqLj+SkxnreeNG//cpW5bBIJYs4XOkCZ2cHPr6Kq1C/frAuHHhO15aGtCpE6etxowBnnuOz0JCAj0prr02fMfWxAdaAAfI3XdzvubVV71vJwR7uv/8w1jSTz8NNG5sxHjVXD60b8/vQNTQAPDoo0DDhjQA9NeIS+OZ0aNpNAkAefJQ9ZwnT3iOdfgwbUF+/ZXZrh5+mJ4SAIVvzZrhOa4mvtACOEASE9kgrloF/O9/vrcvUwb48kvgu+84CmrcmMI4MzP8ddXEBmXLAo0aBS6AExM5ejp+nAY7muCZOZN+1iq047PP0vgqHGzaREvnXbuAH3+k4VWrVhyBL11Kf2ONBtACOCj69GGWpKlT/d/n7rtpKf3gg1RHN2wI/Pln+OqoiS2Sk4HVqxmAIRBuvJEGO2+9FZgKW2Pw22/01a9Xj65dN94IPPFEeI7166+MAy4l3c+qV6fwVYk5atUKz3E18YkWwEFQqBAwcCBHtYEE3yhZEvj4Y2D+fKoUmzRhQ5CeHraqamKE5GRGS1u4MPB9X3wRKFWKz5yOuBYYe/dyCqhqVXaa09P5DiYlWX+s998H7riD/ryrVzMZR6tWzCm+dCmj5mk0ZrQADpIhQzh/pMIHBkJyMi2j+/altWu9egxrqbl0adSIoQYDVUMDFByvvspG/cMPra/bpUpaGrOX2Ww0vlq2DHj+eetHoXY7M1r178/wkr//ToPLVq044l6yJHzqbk2cI6WM2Kdhw4byUuKhh6QsWFDK06eDL2PRIimrVJFSCCmHD5fy/Hnr6qeJLXr0kLJ0aSlzcgLf126X8tZbpSxZUsqTJ62v26VGTo6UHTtKmZgo5aefSlmihJRNmgR37b2Rni5l165SAlL27y9ldraUe/dKWbkyj7lhg7XH08QfANZJDzJRj4BDYNQo9nBnzAi+jNtvBzZvpq/gG28wnOWyZdbVURM7dOjAEITr1gW+rxCMkJWayvjBGu+MH08DqNdfpxtXRgZVz4mJ1h3j1Cm+v19/zaApM2YAR45w5HvuHKNt1atn3fE0lx5aAIdAnToMsvDWW6FZNRcuzDJWrKCP4G23cb4vLc26umqiT7t2FKTBqKEBPm8jRjCSkj8W+Jcrn38OvPwy3bgKFaLNxUsvWet3u3MnbTjWraMAfuIJ4NAhhqM9e5bCt0ED646nuUTxNDQOx+dSU0FLSRUyIOWHH1pT3oULUo4cSZV0pUpSLlhgTbma2KBJEykbNQp+/7Q0KStWlLJuXao7Nc6sWiVlvnxStmwp5Z49UhYtKmXz5lLabNYd448/pCxVip+VK7ns4EEpq1Xj8f7807pjaeIfaBV0+GjdmmrjadOsCRlYsCDdm/73P/5u357GWip0nia+SU7mqOn48eD2L1yYUxWbNjln5rKa8+fjy+LaZgN27KC7X4UKwLff0n3LZmPs9gSLWrpvvuE7X7IkjeJuuYVBN1q1Ak6coJV7JLIqaS4NtAAOESE4F7xlS3AuJp64+WZGzXrySUbsqV1b54i9FEhO5reKyBQMnTtzPvnpp9n4W83FiwwW8fzz1pcdLl56CahRg3WfN48uggsXMiGKFZmGpOQ8b9eutGhftYpxnI8e5ZTRsWO8pzfdFPqxNJcPWgBbQLdujLDjKzxloOTPz3jAa9bQD7RjR6BHj8CDOWhih3r1GCs82HlggJ2+6dMZWWnUKOvqppg5k/OZJ05YX3Y4sNvZGQGYjaxQIWDkSI5UBwwIvfycHJYzdizf9cWL+T4eO8aR75EjwIIFnBPWaAJBC2ALyJsXGDaML+bff1tffsOGVFs++ywNPmrVYrIHTfyRkMDR66+/hpaqsmpVJhL4+mv/cw37Q1aW9R3JcGMeqTdtyikbIWisFqrqOTUVuPNO4L33qI364gt2jI8fp/A9dIiZrpo2De04mssTLYAton9/9rwDCU8ZCHnzMh7w+vXMPXzffUxvFi+jFI1BcjLn9FevDq2c0aMZ6nDwYOtii8+aBRw8aE1ZkWD2bHZMFZMnM+rUtGlA5cqhlX3oEHDrrexYv/8+tVEJCXznbrsNOHCAmoxmzUI7jubyRQtgiyhRAujXj4kXwjEvp6hblyrpSZM411WrFhtNnTM2fmjThv6ooaihAY7Epk+nS8yUKaHXy2aj+069elSxxjobNgC9elH1qyJNTZ1Kd6++fUMre+NGzufu3Us3pn79uPzkSQpftbx589COo7m80QLYQoYP53zUW2+F9zhJSVSHbdzIEdCDD9L688iR8B5XYw3FinHUFKoABoC2bZn28sUXmac6FL7/nrlyx46lCjeWOXaMYSZLl6bB1fnzxrqZM0Or/y+/cOSbkMCECu3acfmpU5xX3r2bBpEtW4Z0Ch756CPgs8/CU7YmxvDknxSOz6XoB+xKly5SFismZWpqZI6XkyPl1KlS5s8vZfHiUn70EcMWamKbyZPpP37oUOhlHTokZeHCUiYnB3/v7XYpGzaU8ppr+EyVLi3loEGh1y0cpKdLefPNDAOrQj1SByTlvfeGVvaMGQxfWa+elIcPG8tPnZLyhhv4ni1aFNoxfFGtGs/l9dfDexxNZID2A44co0YxDF2kguYnJtLic9MmquH69OEc44EDkTm+Jjg6dOD3L7+EXlaFCsDEiRxR//BDcGUsXkz7gieesDZco9VISREJSn0AACAASURBVHuL1as5SqxXD9i+3Vh/993BlWu3A2PG0Nq5XTumMFS5g8+cYcjJ7dt5fW+/PfTz8FaPgwdpT/LYY8Brr4XvWJoYwJNkDsfnchgBSylls2ZMsBDpSEU2m5RvvSVloUJSFinC3rweDccmdjsjWnXubE152dmMjnXVVcEl9GjVSsry5aXMyOD/WB0BK83B88/zf06OlLVrGyPgt98OvMyLF6W8/37uP3Cg83t75oyUDRpImTevlL/8Ys05eOPoUdbjtdekvO8+/p4yJfzH1YQP6BFwZHn8cWDfPmDu3MgeNyGBaRI3bwYaN2Zv/vbbQ58b1FiPENRULFpE159QSUpisoaDBwMPoLFmDROAjBoF5MsXel3CxY8/Gr64Tz3FZdOmAVu3GtucOxdYmSdPcl7322/pfvV//2fkCk5JocHcli2cZ27f3prz8IayQK9alcaVXbrQ2v2VV8J/bE0U8CSZw/G5XEbANpuU1atLeeONkR+BXrjAj90u5XvvcSRcsKCUb75pbTxcTeh8/z1HOEuXWlfmww9LmZQk5dat/u/TqRPTHKalGctibQS8eTPnuRs14ohVSp5jvnzGnCkg5ZNP+l/mv/9y3/z5pfz2W+d1KSl8f/PkkfLHH607D1/Mns3zUHPb2dlSduvGZZMmRa4elytZWWw/rQR6BBxZEhKYtWbtWlpRRpIuXegn/PrrQO/eHB20aMFAIS1aMF6uJjZo3RrIk8caa2jF5MlA0aLAoEH+uaZt2cJ5zaFDGWc6Fjl5klHgihShpXaBAgxi0rs3l02aZGybmupfmb//Tvelc+c4+r/vPucy2rWjl8GcOQzEESnUCLhSJX4nJXGuu3t3Bl554YXI1eVyY+1aepX06RPBg3qSzOH4XC4jYCnZiypViqOLSGGzcZRQvDh7zNWqsUdtt0v58cdcnj8/55SsTkyuCY7bb5eyVi1ry3zvPd7/zz7zvW2PHrQZOHXKeXmsjIAzM5nNKH9+KdesMZa/8ALP8ZtvpJw71xgB9+zpu8xZszine+21Uu7a5bwuNZUZq5KSqKGINCNGUGPlqjnLyeG5AVI++2zk63UpY7fTdiZPHtpBrF1rbfnwMgLWAjiMPP000wr++29kjrdjB+/oBx/QYEQZpzRtKuXq1VIeOcIOASBl48aBqSk14WHaNN6PvXutK9Nmk/Kmm6S84gopz571vN2ePXS5GTEi97pYEMB2u5T9+vH6zJplLP/7bzaWXbvy/wcfcJtixaS86y7v5b34Ire99VYpT592Xp+aynclMVHKOXOsPx9/uO8+KWvWdL8uJ0fK3r1Z/wkTtIGlFaSm8jkC6Np25Ij1x9ACOEocO8Y5qgEDInO8r77iHf3rL/7PzuZoqGxZLu/WjQ39l19ydJ43LxukrKzI1E+Tm+3bZdDWu9746y8pExKkHDzY8zaDBlGQHTyYe10sCOA33uC1eeopY1lmJn10r7hCypMnuWzqVG53/fXMA+yOrCwp+/bldt27G9beirQ0ei8kJuaeD44kjRtL2aaN5/U5OVL26cPzGD9eC+FQ2LSJWhCAz4brM2EVWgBHkX79qD5TjUU4GTOGDWpmpvPy1FS+rAUKsEPwxBNS7tzJoCGAlPXrS7lxY/jrp8mN3S5l1apS3nmn9WUPG0YNzLp1udcdO8bnsm9f9/tGWwD/+is7EHff7Ww8+MwzfGbN6uHx43meycl0GXIlJYVCzZPQOn+eau7ERCm//josp+M35cp5vicKm83QDDz5pBbCwfDxx2wPk5Kk/L//C+811AI4ivzzD6/yxInhP1bbthwdeOLgQSl79WJjVbq0lNOns8EpW5YP4oQJuYW3JvwMGcLGID3d2nJTUtig33hj7jn/sWMp4HbscL9vNAXw9u1UJ9et62yZvX49n9MePZy3HzxYyhIlOLKtVs153f79Utapw/0++CD3sS5c4Kg5IYGaoWiSkSH9nuO12aTs35/bjxmjhbC/XLxoaEJKl5Zy+fLwH1ML4Chzxx1UmVndwJqx2/lAPfyw723Xr2ejA0hZowbDV/bowf916lhvhKDxzvz5vPYLFlhf9qxZLPudd4xlZ8/SPa1LF8/7RUsAnzlDF74yZaTct89YnpHBZ/PKK7mNme7dqUUYMID7Kdav5/ZFi7oPH3nhgpS33Ubh+8UX4TmfQNi9m/fqww/9295mY+AQQMrHH9dC2Bc7djCcqNL6mZ+vcOJNAGs3pAgwahRTmH3+efiOcfgwg8XXr+972wYNmLJt3jz+79OH+0+cyLB7N93EgAcZGeGrr8agZUtmNrLSHUnRrRuz9zz5pJG68u23gbQ03uNYIieHbnT79jHwhTmd4MSJdJmaOZOZx8ykpADFi9P9SrkhqUxFSUnAypW5w0empwOdOtEF6ZNP6OYTbZQL0lVX+bd9QgIDhwwezCAio0bprGiemDOHedX//ht44AG6h4aartISPEnmcHwu1xGw3c4eV82a4QuGMW8ee3Z//BHYfllZVEWXLk3VdKdOUrZrZ4yOV64MT301znTowEQI4WDbNtoG9OrFUV+ZMlK2b+99n2iMgIcO5XP30UfOy9es4SjVk3anSRMpW7c2XJOmTeP2DRq4t2pNT+d0jRCcC4wVPv2U9Q/Ua8Ju53w/IOXw4XokbCYzk9cE4DPxyiuRvz7QKujo88UXvNo//RSe8p97jg2Kec4sEFJSaJyVNy/nIxs35ryaEHRTsTo6jMaZt97i8+FpTjZUxo1j+Srm8YoV3rePtACeMYP1GjXKeXl6OjuuV13FZ9QdNWvSfUdZTQM0anP3LqSns/MhhP+q3kihXKRUpK9AsNulfOwx7j90qBbCUnL+/+abeU2KFw/PFI8/aAEcA2RlMfh+q1bhKb9TJ45YQ2XvXiP0XcGCNF5RQT0iYbBwuaLm/954IzzlX7ggZYUKPIY/IVIjKYCXLeNz1qFDbmOxxx9nnRcu9Lx/uXJSPvigIXzdlSMl55GTk7nNzJmWnoIlPPoor3uw2O3swAC8d5dz6NlffmF4VYCBbsLVsfUHLYBjhClTeMXXr7e+7EqVKDitYvVqBiVQjZr6DBoUuVzHlxs1alD9Hy5Udp0WLXxvGykBvHs3G8rrrss9wl25kiPVRx/1XobrM+ru/crI4KgYoG98LOLJjSoQ7HYpR4/meQ4YcPkJ4Zwcwy0N4MAk2u2VNwGsjbAiyCOPMHbt1KnWlnv6NPP/+mOA5S833cR4ubNnA9WqGcvffpt5hxctsu5YGpKcDCxfDly4YH3ZdruRNWjtWsPgJ5qkpjLGM0CDwGLFjHUXLwIPPURDmSlTPJexcaPxe+hQo1wzWVk07vrpJ+Cdd/gexiIHDvhvgOUJIRgPfOxYYMYMZkSz262pX6xz/DjQti3jZUsJPPssM9IVKRLtmnnBk2QOx+dyHwFLKeXIkXT4P3DAujIXL2Zvz52rhRVkZtKwpUQJ55FGv36e5+U0gbNoEa9rOLLvqHjJL73EOf577vG+fbhHwDk5dM9LTJRyyZLc65XhjLdMUcuXG89inz4MOAJI+cMPxjZZWQzmATDgQixTtCjnb63AbmcEMRXl6VIfCa9YQZczgPHwv/su2jUygFZBxw7797PRcTU2CYVXXuGddA2obzWnTxuGHupTokT4DMsuNzIymBhh4EBry7XbOe9btSrDk06axHs3f77nfcItgJWa1F0ITiVYhwzxvP/nn9OyWz2HX35pxEJXSSiystjRAGjkFsukpLCer7xiXZl2O4PrAFI+9NClmYDFZpPy5ZfZpipblS1bol0rZ7QAjjEeeIC93XPnrCvvqqusKcsfdu6U8t57nQXxgw/mDm6vCZxOnaSsXNlaK1alIZkxg/8zM2k5XLWqZ4vbcArgjz+W/9kTuJKWJuXVV7MhPX8+93q7Xcrnn+f+LVtSW6CCmBw/zt/Tp1P4qjnv118Pz3lYyebNrOtXX1lf9rPPsuxevS4tIXz6tDGvD9C1zDVISyygBXCMoVRlU6daU17Nmt6zwISL339n6EuzIJ47N/L1uJR4911eRyszVbVuTUthcyS2JUt4nAkT3O8TLgG8ciVd3W67zX0SkIEDaUDz+++512VlGYkIevZkR0IJ4DVreH4q7KuKcz5tmvXnEA5+/pn1/d//wlP+xIksv0ePS0MI//knO6qq3Rk9OnbPSwvgGKRlS45aQ81ElJbGBitaOUJtNiPcofrUrSvliRPRqU+8c+AAr+GUKdaU9+efnsvr3p3C0J2LRjgE8P79DMlarZp7bYmaAx85Mve6s2fZkQCYkEFpCFTwih07uExZv1p5DSOB8oN2l5nKKpSfcffunIqIR+x2ajjy5uW55M8fG2FEvaEFcAyieu7mPKfBsHKlzGV4Eg3S0w3VoPq8+aYOCBAMdepY5y/euTPn6d25Yhw5wqmQtm1z3yerBXBaGuPwFi3KBCWunDtHV7oaNXKrxfftY27rpKTckavefJPP2okTHAGpZ2/yZOvqHgmeeorzmOEexb30Eq9Pt27xJ4RTU40YBQAHMOFw6bSakAUwgOIAZgPYDmAbgCYASgJYBGCn47uEr3K0ADaw2djYNGgQmpCaPp130Uqr6lA4ccJI9KA+e/ZEu1bxxRNPUNiEaiOwdSuv/9NPe95GCbBvvnFebqUAttloDJWQQFWrO/r14/pVq5yXr11L9XmxYu6tpZ97jvXPyKBaWj1z8UbPnuyARAJltHn//fGTC3zzZraX6v42b845/3jACgH8CYB+jt95HQL5FQBjHcvGApjsqxwtgJ157z3egWXLgi+jb182lrE20tyyxVkIt2t36btCWIWyAg51Pr1XL0Yz85aLOjubccrLl3ceJVspgJ9+Wnqdj/3lF64fM8Z5+bx5rH/lyp7nxB97jNv07m08ax07WlPvSNKihZTNmkXueFOn8lrde2/sC2GVu1fd38GDY7/OZkISwACKAtgLQLgs/xfAlY7fVwL411dZWgA7k57OwPihJGOvX5/JxmMVNbdldheJB+x2RmmKRqchK4uq2n79gi9j3z6OoocP973t6tWcOzXPvSo/71D56iuW9fDD7juJZ88yRGbt2hzFKt58kyPiRo2kPHrUc/m9ehnP1nPPcWTUvHno9Y40VavSmyGSvPYar1vnzrGZB9ycuxeg29n770e7VoETqgCuB+BPAB8D2ABgJoBCAFJctjvrqywtgHOjXATczYv5IjOTD+UTT1hfLyvJzqaq3SyIoxmb1R/+9z/Ws3x5+qMuWxZZK8v775eyVKngA50MGcJnw9+pif79OQf599/8r+5TKDms166lkUyzZs7C1Uzv3jyuykGdk2P4mnfq5N4VSWGzGfVU1twdO9IyP56w2XivXDUAkUAlsOjUKbaEsDl3L8BpiHjNzBaqAG4EIAfATY7/bwB43l8BDKA/gHUA1lWK1CRHHHHiBBupRx4JfN8NG+JrVKl8HdWnU6fY9R3OyGBieMBw8i9ThvdpwYLwq8DWreOoNJiALceP85nq08f/fU6dotr5llucBVuXLsFpAQ4fZuelcmXPc3U//MBjjB/P/xcuGFGrhg/33uGx2dhpUPVUo+sHH+RoMp44epTnMH16dI6v7EjuvNNzRymSzJ4tZZEixr1t3FjKQ4eiXavgCVUAlwOwz/T/VgDztQraOh59VMp8+QI3KvjgA97BQPOHRhObjW4kZkE8ZUpsvPiuLFzI+j3xhJTffksLzMKFuax4cao/f/ghtFGiNx5+mGrk7dsD22/cOArvQPf78EOem/o2+1gGwsWLjLxVqJAxonbl1CmOaurW5cjr2DHuI4TvjFB2O/2FVf3MPvADB4aWUSgarFnD85g3L3p1ePtt1iE5OXzPsy8yM3NH2uvdO3r1sQorjLB+B1DD8ftZAFMcH7MR1iu+ytEC2D3bt7Ph8RQUwRNDhlAgxKNx0/79HCGZX7Zvv409Y7IuXdg52rWL/9PTKXR79aIQBngPunVj/b2pTAPl6FGOBO64w/99UlI4f3zffYEfz2ZjBqxSpYx7MnhwYKMzu51+pkJI+f33nrd74AF2LjZs4PRLlSo0tPG2jypf1emJJ5jis3dvY/3YsVTnxtpz5I3Zs3k+GzdGtx4qCEz79pEXegcOGLl7ldbp9dfj6z56wgoBXM+hRt4E4HsAJQCUArDE4Ya0BEBJX+VoAeyZu+5izz2QxPdNm/ITr9jtuUdbN99Mo6BY4dAhCtj27XM3BpmZVEc/8gjV0wCFSOfOjFVsRaIK5TLyyy/+ba/8PIP1j/z7b0PlDlAN3LEjDaL88TVXcaYnTfK8jRI4Eycy2ULx4lKWLcugId6w25msAGCeYLud9+axx3IfP55GTdOmsc6xMB3z/vvsPLVt6zlMqdX88otzp69UKfcuZ/GKDsQRB6xYwbvxzjv+bW+zUcXnLWB9vHDoEJOomwVx166e/Ye//DKyIQZVAzlnjudtsrNpqDVkiJGVJW9eqvQ+/DD4RBkZGVJecw3Djfqad754kZGm2rYN7liKkSON+yAlR/U33sjOhTch+f333Kd7d88jlxMn2Flp2JBTKHnyMGH63r3e62S3GxmSRozg/6ws+Z/1s0LNZ8aLj6iUPJ9ChWJntPfBBxTCt98e2IAgUFxz9wKckrjU4gZoARwHqIw1117rn0p5+3b533zdpYDdzlGjCjGn1FCjR9NVxcw11/Cl3bw5MnXLzmbDULEiIzr5wmajxebIkUa82sRENmjvvMP5zkCYN49l+Eoq8H//x+1C8SuXkv7A6h6oaEnHjjFJwhVX0D3Llb//phBp3NjzyMlup2o8Tx5aeQOMCe16f93tpzoFw4cbgurECS57801j288+k3FhZW/m3nvZwYolPv6Y79htt4VHCB87xrLNne7777d2CidW0AI4TlA+k/6o+r78kttu2BD+ekWSY8dyZ1oqVYrp5LKymInJnfFNuPnjDxmUQZLdTovmJ59k5wpgw3brrRSo/rgJ2e309S5WzHOM7exszqPefLM1Iyl1jc3Cbft2KUuWZEQi84j++HF2NCpUYHhLT6jn22xg48v1xW43UhcOHep8bir94KefGsuUZfW6dYGcbXS58cbQtRbh4NNPOfXQsqW1gtGcu1e9D5MmxY4GwGq0AI4TsrPZkN16q+9tn3iCI4lY8t2zkm+/NeZV1adGDQoiwMiK88cfkauTskoONt+o3c5R+zPPMN6zOq+bbuJcr7uRpWLLFo6iBwxwv16N/KyypFV1K1rUWaj+/juN0po14zxrRgZ/FyjgXegdPcrGXJU7caLvBtdup28swKhcrtsr6+EffzSWLVvGZUuXBnzKUaNcOQaciEU+/5z3rXlz/7Q/3rDZGKPbbGNQtOiln09cC+A4QkWn8WWQ0qYNo2Bdypw8yflEsxBWn99+o+FOs2aR6zmfPMkRYPPm1hzz33/Z82/Y0DivevWY1MJdYJahQ9kYurr22GyMJFWnjnUW8ao+efPyHphRI9kuXZjoHZDy6689l2W3OwdVMI9Yve0zbhy3HzDA/XktWJC7E7Z+PZd9951/5xltMjJkrnnsWGPWLD53t97qPqmHP5w5Q2M+8ztcs2bgrnLxiBbAcURqKlWNXbt63sZup1o2VnvNVvPDD84qK9fP/PmRq4uK3+2PEAmEvXsZn/eWW4zzqlWLcZQ3buQ9P32aHYBWrZw7AMr46fPPrauPqsOECfx2tUpV1tlqG28oy2V/56ftdhrnALQw99SpUNMw5jjRu3Zx2Sef+D5OLLB7N+v70UfRrol3vv6aI9emTQNPErJ2LadHzO/snXda4yUQD2gBHGeMHs0epyfLUJUzNlqRc6LBmTM0glIvcMuWzirNSL3MNhtVxldcwTqFg0OHOOdtPsdq1TjtoJIOKItsu531ufpqa9PLqet68SIjS9Wo4TzdoRLIA6yrJ5RhGOC/6l4Faunb1/uI/p13uN3hw8YyZZjlrU6xhFKZL14c7Zr45ttvKYSbNPFPCNvtvP9583K6TD3LTz0Vn7ELgkUL4Djj4EHONZr9G80oQ5N4jY0aLMoStmRJvsyuxlrvvhuZHKfr1/P4Viesd8fx4xx1t2vHZ8J8vosWseEGGMnIStQxpDSErfLt/ecfzt1dfz0tWT35CKvplEDm6idOlP/N8ftqpJXPr9nqWql0X3zRv+NFm08/ZX3jxWp7zhw+hzff7L3Ta87dW7Ik7QYKFaIQv9zQAjgO6dGDQQbcuWg88wwtBy9Fk31v1KrFue9z5xi+U40MzUKpdm0KjHDPCw8dynugkghEgjNnqFotWND5nAEasoRjBKy45x4aWq1fz2tetiyjmZ0/z4xFZh/hnBwphw0zynj5Zf+O+cIL3L5XL/8SX4wezYbdlXz5Yj9BiUKdc6SCXljBd99xRNu4sfv2SeXuTUhgPPU8eaih2bQp8nWNBbQAjkNUooXJk3Ovu+uu2PMbDDf79/N6TJ1qLFuyhC+2augbNzYEcps2nuMQW0FKCoVQo0aRzZKkaN06txAuVYqW2vPnh24d7yqADxwwfLTz5mW2KMWxY5zju+IKNr533WXsf+ut/qkbVQSvHj38v56PPEILYlfKlPFsLR5rPPoo6xtv/PADBWujRs5TMZ98ws5YqVLUkAB8VoMNRHMpoAVwnNK6NX0rXRvTq66KfO7QaKPi1LomZk9Lczby+eYbqj5LlGAPvG9f776pofDFFzIs6l9/UMZGAAXg3LnMBFS0KJcVLUph9t13wY2uXAWweZm7ONPbtuXuEBQq5F9Uo8mTuX337oF1Zu67z31HtFq13JbbsUqHDkzVGY/8+CM7Yw0acB6+Xz9DC1WzJn+PGBGZaaFYRgvgOEXNvZktbk+e5LJXXolevaJB587seHhSLU+ZYjT8/fvTgG3ECPbSCxWim4fVKnu7nRbJxYtHPvShWeCZY2dnZFAd3acP596UILz/froP+etG4iqAzcZUlSvnjo60ZUtuAexPWNVXX+W23boF3lC3bk2DIFcaNAgsgUU0qV2bKRgVhw4x2tesWdGrUyDMn+98z5OTaSyZL1/8WKKHGy2A4xS7nfOeN9xgCB6VIi8erCatIiuLWYH69/e+Xbt2RkNQsSI7MLt2caQEMPvShx9aqzL+5x8KeXNGnkig/G8TE2kF7U7Nm5VFQ60BA6guB9gw3nUXG0dvISDNAnjJEh7nzjsNq91x44xtFy+m61y5ckaIScD3dVYxtrt0CW6U1KABG3xXWrb0L5hNLFC0KDU4Z85w3jp/fl6TZ5+Nds38Y84c43537071c8WKkbWNiHW0AI5jVM5fJXCVui4WMqdECpWoYu5c79tt3Uq1c9Om7LgAFIxnztAK96abuOyGG6ztwDz5JMtdscK6Mr2xfz8tUYcOpf+oq5bEHTk5rN+wYYY7V548zPL0/vu5Q1yqRnXnTqrza9c2XE969+a+//zD4yclcf3evRR+al9vhlBvvMFt7r3Xd5IJT1St6l7VfNddvMexTkoKr0Hp0kZqS4BzwoH62kYad7l7Ab57R49Gu3axhRbAcUxGBkcv7dvzf7duUlaqFN06RZqxY9nI++Pr26cP56V27KC/YWIiR2bff08twpdfGgkS7rjDfcSpQLlwgWXWrh28MAmEYcN4Pfbv58i3USMGKvE3VKDNRrX16NEUYgA7Lq1a0bf88GGjQa1ZkwY15jCZx49zxKu2uf123ps33+T/mTPpogVQde3KW29xXefOoV2vEiWYG9iVHj1oFBbLZGc7W4onJ3PUC0g5Y0a0a+edAweo+gdoi2Ke+rlUQ+OGghbAcY5yVdiyheb9nTpFu0aRpV49hn/0h/37qWZ96CH+/+svIwxit26cQ09PpyahaFEK6IEDQ5/DVdGopkwJrRxfnDhBNZ86PylpkQywwxEodjst7sePNwxnzOnhgNzRq5SvLUCVqUqSUaAAjYrsdgoYlUfYHJ9azSV36hRaY22zsZ7jx+deN3gw579jEbud+ZBVYg7lppWRQeOx2rVj22hJ5e4tXNjoMADsvGncowWwD7Ky/MtKE2lycqTct48m/AUKcK5MiNiOG2s1R47wKX3pJf/3GTmSDb+KvJSZyQAPefJQvff112wIT5xgY52YyDnmSZOC98e02zlHWqhQeJ8llT912zbn5Q8+yI5HqLlUt241gmGoT6NGvP47dlCd36KFsa5sWU6HNG1KNeqhQ0ZZyke4YEH6CM+YwX06dgx9pHT2LMsyu6UpnnySGoJYy66zZAkzHwFSXncdtVoAr9nUqfy9YEG0a+menByGRRWCMcenTOE7o56DffuiXcPYRQtgH7zyCh+sJ5+MjArRX5SBw/jxNKRRD7tVGW/igY8/5jkHknbx5EmObl01BZs2USAADCyh5qq2bzd8VytVYkzlYELl7dnDEeG99wa+rz+cO0fV7z335F538CAFnRXHVnYHADUFjRs7C2RlhLVunfNo2d08tPIRVtvccQdHe6Gydy/L++CD3OuUT3GsBLf46y+mG1TGgcoQcNw4dhSUSl9NM8Uax44Zfue9e7OdFIJJRFq04BRPrHV2YgktgH3QuTNHRwBDrIU6irCK8+cN9Wn16kYjdvBgtGsWObp2De4Ff/55XitzwAgpqd57+WWOFkuWZBo/VfbSpbSsBThS+e23wOurpgt+/jnwfX2hDPA8WZiqkWsoqfh+/914F8xuSN98k1sI16hh/K5Y0fM9Uvl8Aet8sv/6S3o0zHv7ba6LtjHQrl3O4RhffdW5U9CjB20Hhg6lFsbVxz0W+O032hfkz0/DuXvu4fn06MFzueYaZzcqTW60APbBNddw5PDVVxw5FS3qPb1aJNm7l3Mu5obvcglBmZNDQxvzfKe/pKVRPeopdeC2bYYhyZ13GqpTm40uOhUqyP8MhQKJ05uRwfm9atU412wV6ensiNx+u+dtLl7kCL5u3eBcrfbupYre3NmTHsQ6OAAAIABJREFUkpqY/PlpsLV9O4Xo2287WzwD9LtetcpZe/DRRxwtFSrEbVQe4VBZskS6nZ+WkhoMgOkeo8HRozRCS0ri1NG4ce5dvlq04PVWdgixhDl37zXXsKNTpw6ndqZO5TulYhL4G2r0ckULYC+cP+88r7pnD0fBACO7xIKwW7rUOYm1u+ADlyKrVvF8v/oquP2nT/c+Gs3JYdSsAgXY6Zo50xDWFy5wNFuoEBvSYcP8D6e3aBGPa6Uvp8r845oW0BU1Ug3UkjYtjaEDixWjkFXP2tSpfD9uvjm3q9LLL+ceFQPsvAwdyihkAMOCpqcbeYS7dg09G87s2Sxr48bc6+bN864pCBcpKZwuKliQ7+uAAd5H/CqMatGikQ/k4g1z7t777mMHrEQJfhYuNLb76Sdus3x59OoaD2gB7IU1a2QuVVZWljHPUbNmeGMK+4s50hMg5ZAhsTVfHQ6eeYY97mDjyGZmctR2ww3eG/ydOw3DojZtnA1Kjh6le0VCAo2MXn3VvznMbt2o5t65M7i6m8nOZmPduLFvVbzdzlF/qVL+p0u02ThfnpAg5a+/cpn5Wbv33tzzqZs3093rvvt4zBYtuP+0aVRJmvfv2ZPlZmUZeYRDTZYwc6b0aPyj/MYjFawmPZ3nrTRVXbr41prYbMb1cRfvPVqo3L1JSVK+/jo7YAkJHP3u2uW87dNPc10sDFJiGS2AvfD++7wKrg+XlHyBy5VjQzp9enQNDdavzz3SuPXW6M9zhZPGjTnyCgUVr/mLL7xvZ7PRRaZQIbpYvP22s9DevNmwWq1alSNNb8/D4cO0Em3XLvTnRp3D99/7t/2GDew8ekpn6cpTT7H8N97g/7Q04xkbPTp35yUriwY4ZcoYo+KtW9lo9+lj1BegcVvhwvxdogSNeCpV4n93PsL+ojqk7gJWqEQmvgK3hEpODlXs6nzatKFhmj8cPGhcIyunKoLFbucznzcvQ74uW8Z5XmWw6M7HvE0bughqvKMFsBeGDWOj62mEdOIEneRVYxKtrB6qx6+C7QNUnZYvn9vQ6FLg5ElrXK5sNo6Aq1b1z/Vl7142LADnOF07Zr/+amR5adLE+7VX+XBnzw6t/nXqMLJXIGrb/v0pEH0FGlHC8pFH2AgfPixl/frGM+YOZezlel5jxhj7tWxpjIwuXmTnoWdP5wAeyqo2mBHUuHFU87rr3OzezbI//jjwcv3Bbmc2oNq1eZyGDTntEAj9+3PfXr3CU8dASEszAmp06MAOTMOG/P/88+6fO5uNbVG8ZJ2KJloAe6FlS4Yo9IbNRhVTnjyc34rGnMfgwRxRmXvOL79MwZInD3uvl5IrwKxZPMc1a0IvSyW1mD7dv+3tdnZ4ihZlJ+e115yNmnJyuL5cOfmfytGd5Xx2Ng2iKlTwPwmCK2o+01eoSVeOH2f9vbm2rFlD7U7z5uycbNrE0Y8ymHIngDdsoGB3l41LhcUEPMeZzsxkMAdzBCU1yvriC/9DMA4aRJWvO06dch7RW8nvv9PnGaCx2jffBD6fnZpqnHcg7nXhYMsWTrMlJNDmYcUKppUsUoSdDE9s3RreTs6lhBbAHrDb+RL36+ff9uvW0SIwIUHKCRMiG7HmlltoQSqlYeQDcKSoRugPPRQ7vo+h0rMn740ViRPUvGjZsv6Ha5SSnR11bW+5JXfwi7Q0PgcFClB19/jjuQXPypXc//HHg6t3kyZ0VQlmvl8Fd5g/P/e6Q4foXlKlCp+hhQspsMuXN1x8XAVwZiY7FOXK5dYEzZ7tbCjoLkCGuzq4TqvkzUur9I8+8h7v/IEHaGnujsxMY/RmFZs2sV4Ar9uMGcHbYCiVP+D/PH04+PRTGoyVLUtDzxkz2JmvXt235kT5im/fHpm6xjNaAHtAxbx9803/90lNpdoIoEDcvz989VPk5HBUMnSoseyKK4yRg81GgyWAfqzxHpXGZuP5WZnzWIVrDLRRttvZUJUowdHiyy/n7ngdOsTOjxDsNLz5pnPj3LcvhdPmzYEde/nywEburmRm0iXq2mud1e8XLjAgSeHCrNPMmRzVXn+9EcXLnQB++mkucx0ZzZ3L/W+5he/HHXewbH/81bdt47WtXp2j/REjjDnVpCROB8yYwWAQZtq35zl4In9+zl+Hyr59fN+FoPp80qTcqRgDYf9+I+NRoULR0Vqlp3PKAWDHdN8+QyXeoYP3LFmKRx7hfQvVmv1yQAtgDyxYwCvgzpfQF599xkamRInwG3uo3K8ffWQsMwfM//xzLps3j41EqVKBz0nFEsrgLFC1qy86deIo7+TJwPc9epQ+wQAbfnfCdMMGKW+7Tf6nnvzuO8NfsmRJGs0F0uC2a8eOSChaDZWvVY1I7Xa6AQlBQapGY+3aOat/XQXw2rXsRLjOWX7/PQVlkybG/rt3U8jcf79/dfztN458lY+w3c7jjR1LjRNArVPz5uzcHDrEaaM2bTyXecUVvtNXeuPkSRqx5c3Ljtfjj1tj//Hgg7w2jRoxHGWk2bWLhlMAr++hQ4ZKfexY/zVO118fu5G7Yg0tgD2gXCKCfbF27jSMFQYODJ/6V82Huvo8Pvig0VAq68sdO2gckpDA0Vo8zgu/+CLPyXXUEypbtvC6jBwZ3P52OwO0lC5NVd3EibnVkHY7/SNVYoMWLXhvlLW9v0nKVSckkBjYnujQwfA1VRHCJk405mH79ct9HmYBnJFBI7Dy5Z1HR/Pm8To0bpw7U5U6jr+xjT35CNvtdAOcMMEwejJ/PEWtq16drmCBkpbGa1OkCJ+Vhx+2Lra3cnl86ilGWmvb1ppy/WXOHD4HJUpI+eOPjM9doQKnUALxtU9NZQcuXnIWRxstgD3QsycblVDIzJRy1CheyTp1jAQAVjJ6NHviro3kpk1GQ3TVVYYzf1oaGzKAPpzBGgBFi2bNqEoPBw89xBFNKFMHJ04YIQZvuIFzpq5kZdEwrkwZbte9Oxu7MmX8m/e7/342lv6kYPTFtm0cpSo/1Tvu4DVWAt5dJ80sgJV18y+/GOt/+onC98Yb3assMzIoBK+5xn83G398hLdvNzpo6lO/Pg2IzHP0DRty/t5fMjOZJlFN7dx9t7WhIe12jjTLluX7WLas/7YnoZKVRdU+wPu1dy87gvny0b4gUEMwFYXM/DxoPKMFsAduuIGqNyv4+Wc2rgUKSPnuu9aOPFu39iyQVJD3hASqONVcn91OtWNiIlVdrgZEscrZs6zzuHHhKX/fPnZmHn449LK++44GSYmJHNW4C9CRkkLVXr58hsDo0cN7udu3G8lBrMIcNrJyZdbH26hHbbtqFZ8ts7D4+Wdew4YNvc8XLlzIMvx1JbPbqUkCvPsI2+0U/l260B9YRa4DOEqeMIFaiqZNfR/TZqP1tcqL3KIFz9lqVISy99830jlOnGj9cVwx5+4dMoTz10oYt2yZO7qZP6gOkDcjOY2BFsBuyMpiI2KFoYbi6FHG6gUYIcgKC0e7nfOHnnrLqpFr107+pwo3s3QpOwZFilBgxDoqxODvv4fvGCNGUKhYMcI5fZq+rADVtJ7cpvbtc54yePhhz1b0Dz/MeUKrwhMeO+acOq5kSd/XV21bowaNotT87oIFFN4NGvj3fHftyu3dBbpxR3Y2rY1d8wibOX+edTPHID54kPPDKiKXqv+YMVS1unaI7XaO4NR8aN267FiEY8omPZ2RzK6/nnOsu3bJXDYd4WDBAiN371dfcapNZTUaNix4K+6OHflcaPxDC2A3bNkiw2LoY7OxYUhKYsO1cmVo5e3f731EYLez8ahTh4YigJTvvee8zYEDRh7SceOsce0JF/360ZAsnC5eJ09SIHXubF2Z8+czI1BCAjt1nuwBFi92Fm7z5zs3+gcPcnQ3eLA19crIMEZA6vPii773M2+vDPoWLqQwrVfP/9GPigjWvr3/wu38eY6uVR5hV5T7kqd418ePG3VPSjJG/SNGSPnHHxzhKo3A1VcHn37SX5RqXV3HZcv431dc72Bxzd27fTvn0a++moOODz8Mvmy7nR363r0tq+4ljxbAbvjyS+nWsMkqVq/mA5+YyPmpYIXe99+znt4iLqmcufPncyScJw8bGjNm14O2bcMT0ctuZ2Qcf0Mmutu/QoXw5dM1o6I5WaluTEkxrvG11+a+BwpzqEaAWhP1HD72GJ+ZvXtDr4/dbrjMAbQcrlCBHUNfBoNqH6VRWbyYo/K6dQN/dlREsDlz/N/n6FH6KF9xRW5Dq82bWZ63jGVDhtDY6PRpjjSVD6/507lzaC5F/nDiBOfy77zTWPbJJzx+IFm2/OX4cefcvRcuSPntt+zMXHll6M+7ijL2zjuWVPeyQAtgN6hQdlYkB/dESophrNOqlZHyLhAmTPAd8Dwzk8ZkbdpQLXjNNTTycOeH+d577AVXqeLeeChUmjdnzzsQ32qFaljff9/6ermSlsbGvUUL69WOixbx+gpBVZ/rvbPb6a5UsCDneUuW5LadOvH8rQpPqBLTA5wvTU83Rl/e5h+VihfgdVqyhLYN118fnAuXighWsWJggVD++YdCtEYN5xH3b7+xbubMPK489ZQRqvLQIWpWXAUwwLnivn2pjvYnVGmgDBzIephtMFTOaKu9Jsy5e2fO5KheuZndfLM1uZhV5zHaEbziCS2A3XDnnTTYCDd2O1U+BQtyPubHHwPbv2NH//wFVWP7999UrxcuTLWzOwvUNWvYGObPb70K/uxZ+jgC7gP5e0MF2PcngIMVvPWWDJs1Z1oaR2FKzbl0qfP6bduoqejZk52mkSOdhWWoGWaUhkdZFZvvw7338nn0dJ2HDjX2Xb6cwrd27eAMdhQqIligNhcrVjj7CEvpX7rByZO5zeDBfM7z5GFnSM2rX7jAEfkDDxjz48WK8X58/701wlG5vQ0Z4ry8f3+qca3CbnfO3btxIzv/atTft691A42hQ/nsRDIKYLyjBbAbKlcOzk8wWLZto9W1MoDw94WoWJEuLL44c4aRddToSamue/VyP8I7ftyYBxsyxNre/5kzNNIB2MD5e66tW3POKlJkZnKkWq9e+OYAV6wwgkkMGOAc7GLcOEPImeMDAxzJfPBBcFMXv/5qlPPaa7nX79nDudwHH8y9bulS53oULEjjMisMwvr25ZxsoBHBVGdC+QgrFa6nVI8XLvD9VufQs6dnf2EpKdh//JEuaiVKcJ9ChdgR+vrrwEbtZjp0oFB31Rq0b885bis4c4ZJYgB2rFJSOOdbowavtdVZ3Bo3ptZI4z9aALuQksIznzQpssdNT6fwBdjo+4qjeuIEt50yxb/yhw1jT1+pup99lvu//rr77bOzDR/mpk2tUVEpTp82LExbtfId3i4tzYinHEk++4x1/PLL8B3jwgWOcIWgv7YKTqEERa1afBYBaidWrjRca264IbCoZmYB6k2Vr1STZiPB1FR2SFSHAWBAEasCogQbEUxKY0Q7ZgyfZyD3XHR2Nl0Ay5c36v/NN4EdJyuLqu1HHzV8gvPnp1/wZ5/575etouy9+mrudbVrW2MAuG6dc+5eu512IEWLUrVuddKY9HS2L2PGWFvupY4WwC788QfPPFB1sFXMm0d1dKFCNBDx1BipkYy/1pJ79lDlpV4Qm40NR2Ki9+TkX33FkU65cp4Nh4Lh1Clj1F+njnfVslIrhssy1BM2G+cnq1ULzxygmVWrjAhZffpw9KLOG+C8sMJu532pUoXrkpN9u039+KNRlqslvCtpaRRUjRoZo/9HH2UnQVntAtZ2yqQMPCKYwuwjXLYsv5V2wG6nodG113J5kyZUu6sOTbDk5FCIDR1K4zWAAqhDB86xepoPz86mkK1Wzb32p0gRdpaDxTV376pVXDZpEu9f/frhiQev4qnHgztjLKEFsAtvv80zj0QiBU8cOmSogB94wH0atpdf5vpAHN7vv1/K4sUNtVlqKhuDkiW9q+E2b+bIx2q11cmTRv7cihU9qx8HDWKHJJxGcZ746SfW7+23w3+s9HQaXiUmUs1sFsDu3EPS0ykQixXjPgMGuB+RvvuuUc7kyf7VRY3+P/rI6Ow1a+bsM2w1NhtH9/5GBDOjfITNdVuyxHCxq1WLUy92u2GoZVVMdJuNgu7xxzmnD/B+3HYbXQTNHZUZM7jendW30r65Gxn7gzl3b/v2fL/On+d7r9qScFl2T5sWnk7ZpY4WwC4MHMgGLdpxknNyGDM3MZEvtWtvvWtXqigDYfVq3lVzLtSdOymU69b1btxz9qzRwPXqZZ2V5okTHAErQxdXgyS7neffsaM1xwsUu51q0XLlQjd+8pd164yOifp4U0uePMm5+qQkCsgXX+T9sdkYaUuV4Wrw4w0lDAsU4PMBcFRlVkGHgw0bqKlxDRrjD2YL7ZIl+X3VVey8mOfLN270LARDxW5nrO5x4zjXCnDk2awZo34BntXsKnysNxcqT7jm7rXZ2KmuW9fQXISzTevSJfD2SKMFcC6aNjVy68YCf/xB38ykJI5elEqwenWqkAOlaVOqLs2Wir/+yhf3/vu9v6Q2GxsRpcryNmoOhOPHOUJRjbx5zvXff7ncW/jBcKOsdP0JUmEVmZmGSlp93OXuNbN9u+GupOJMq88ddwRuTKYSBKhPtWqcKginAJZSyuHD+Yy5C7ThjZ07nes7ZIh7S/+9ez1rFazEbqdgfO45CkJVL5UMxdVQTGWnCtQfV+XuveIKY5pmyRJOZRUvHpm4zJUqcVCgCQwtgE3Y7TRSCKb3HU7OnKEVI0B/3h07+DuYeLHffSfdGqAoNx9/hMxPP/HFLlmSwtsKjh2jS5VqpKZM4f144w3+373bmuMEy1138dkIR5ASd6goZkp9qq6LP/N3KmSn+RNM0g0lENTHWz5gKzl3jir4hg39s/Q+epTTFCqylRJyNWu6n6I5fZrbeDJADAdK6AOGKx7AezxxIufw33mHy/yNCeCau/fwYT43r79OzVmtWuEJ6OGKSn/qzqpe4x0tgE3s28ezjsVILnY7549Uwu5gDcVycqhGbNzYebRrt9P1RAgKWF/s3Ek1qRA08LBCvXX0qPOob9gwRua69trQyw6VzZt5rpGyxFZzzx9/TG2FiiMOMOCBp+u9Y4ezmlh97r6b2gR/OXPGef/69Y114RbAUhruRd40HykpUo4fz9GfmgMvVYrnqnyEmzfPbTuQlRV8BzZYunalOl8ZG+7bx3nTpk35XJmv9bp1vt+nXbt4TwAaVmZnUyCr2OOdOkUu09ncucGN3DVaADuhLEWttPa1GhWnWhlVBGOd+3//x/1dg+5fvEgf3aJF/cuQdP68YfTRubN7Y7FAOXLEsFhVn0ceCb1cK+jdmz6yVuWA9UbTplTrZWVRdazC/Jk/rurVP/4w1I5FitAqd8ECzgkWLswR4rBh/o3izXPQ/ftLJ6OlSAhgu52+38WK5TYsS09nNi+VQrFrV2OkV6ECrcilNIR4t2651e8FC9LNLhIoC+EJE9yvP3LEeCfVp2pVdvZWr85d97lzeV1U7l4pOWpu3Jj7PvNMeONXu/LEE3zW/E0tqTHQAtiESqVlRZ7VcKLy+Sp1lqegA564cMEYKbiyfz/nD2vU8O862O1UPSUmcp9//gmsLu44fJhz3OYGKRbSm+3dy1FV377hO8bFi4ZFKZDb8tj8MfPVV+wcVK/O+V6A7jCKY8foSpSQwMZ7yhTPVuUqFjbAeX6Vsad2bY60IiGApeScdp48RorGnBxaZVeqJP+bjlm3znmfggXpV60w+wibKVcuMh07u51xtq+80nfQjubNqQF6/31aMefJw7pXrMiO0+LFjAeu3nsVE3zlSrpfFS5M4Rxpmjen8NcEjhbAJrp1iw9LPpWrePZsjnYKF2bWlkAYP56qL3dzRCtWcLR0xx3+R1tavpxGIIULW2NdqrLaqE/NmtYkIQiV4cMpxKzIoXz0KEeoL79MTcJ11zmny0tM5Eh40CD67q5ZY4QV/fZblmG3Gy5pzZoZvx97zP0xt2yhrypAofr1187qThUPGnA+xzlzuGz69MgJYCmNoCAjRrADoISPO9/1zEyuf/55Y5nZR9jsSnbttZExGlKjcH8Mvq6+2jkC2dmzNLBShnXmz08/UTvy3nsU1NWq8d5GmuxsqtZD8V2+nAlZAAPYB2AzgI2qMAAlASwCsNPxXcJXObEggGvVip67i79kZFA4jh3L//v3s5EG6B7kb2i8Y8c4mvNkcKZUYuPG+V+3gwfZ2wdYv1BTGxYq5NzolCsXniQRgXDiBDsZ99zj/z7Z2TSymTWL6rq2bY2AEepTqRKfvY4d+f/hh92rEbOzGUWsQgXO0yojnG7daBCXlMTOma94vAsXGmrmJk2oJjVPb7h2oux2Ri1T7j2REsDm0JlVqtB40NP8qEo1+NZbzsvNeYSVyvbGGznKDCcXL/K+1q/vWyVss1GQqvfazK+/Ott+uL4XADtz0eCvv3j8WbOic/x4xyoBXNpl2SsAxjp+jwUw2Vc50RbAGRkccQQicKLB+vW8M2ZfwexsI8dn9ercxh/69mXv1V3UHrvdyBITSMi+jAyqOgEaDgWTIUdKw2p0xAgjuAFA4afCNUYLFcbTXSSlc+c4t/7WW7x+jRo5N5558lCAPvQQVffLljmr17t2pcrZW3hONaeoPuPG0SinVClOA/gK7anIyaGaulw55/I85XP9+2/nEXo42bQpd5pA88jWHdu3czt32iBzHuG1azm/fMst4am7QoUQdfVtd8eRI7lH6Tk5nDcWgqN/pZFQxqLmT9GijAs/Z0740yiaUYGLrHJJvNwIlwD+F8CVjt9XAvjXVznRFsAbNvCMv/oqqtXwiQrX527ed9kyjozy5GHj7suScutWluXJGlQlbC9YMPDcyB98wDnJypX97xCYUS4Z27axwTEH0E9K4lxgtFAjrXLlGF3puedohFa1qnOjWKoUG/qRI6lK/Ptv70ZzO3Y4hwv1hNkXd/hwWrvWqUOjnGDcTtauda738OGeI1EpdW64BPC+fdTkCMG56kmTKFA6deJz6M0Na9Uq1suTv7Q5j3C9esEl90hLY2fHF8eOsbPYqZN/5aogOWqEfvy4Yfneq5cRBOavvxhcJH9+djQyM6X8+Wd2ppVRWsGCdFucNcsaw0hv9OrF6xntwEXxihUCeC+AvwCsB9DfsSzFZZuzHvbtD2AdgHWVKlWK3Fm74dNPeca+YupGm0GDOELypNI6edJQYyYn+04Tl5zMF8iTBeORI4wLXKVK4KPZP/9kY5EvH91pAqFTJx5Tvdh79lCdlyePEQj/+ecj8+JnZLAToXyS3X1K/z971xkeRdWFbxohCQkJvffeQghI71KU3mtApIP0KoIgiKIo9opSpAiCAgriJ0VBBKRI772GQAgJ6dndme/H6/HemZ3ZnS3ZBMn7PPMkuzvlTrunveecAihksnAh4nO3bzs+tmHDcK1suROPHVM2FGjYEJaij49zpRXPn1dawPXrQ/jly4fzVSsMDx7wdd157e/fh+DPlQvXYNo0pWfg+nV4a2wVn9m+HePav19/HeojTN4UR3D2LDxM5crZX3fECCiKRlO/NmzAmE6cgAelWDFch6VL+XWOiQFBsmRJa/KZLMMTtnMnlCS6p/7+mA9WrHC8vKcRVKqEHPkcOAd3COBi//wtxBg7wRhrZlQAi0tWW8BTp+Llz8jI0mHYRcOGKGVnC5IEF6i/P9iXtpoY7NqFOy0yZtX46y/sq1Urx3t93r+P7RjDxGAkbSo9HZPjqFHK769cweQTEsK7KY0Y4d7+o/fvQ5AtXgz2LZXJVC+iW5kIU61ayfKHHzqXpnT7NpQLW0Vgfv4Z16VkSbhov/qKH//jjx0/5oULeD5oH1Qn+tgxWO6MQeBs2qQUtrT+li2OH1ONxER4EIKDYf0PHarfmIMIaHr572vX4nd7TPw9e/g5GK0v/tNPGGPu3PZDICdP4lwmTDC2b1lGWhWFE3x8QKoSG9tLEngHuXIZI1uZzRDkEyfieSHPUdu2qA3ujhaSVNDE053j/ktwKwuaMTaPMTb1SXRBt2uHST07w2yGe8ko4/D4cbCHvbxQ5F9LuZAknHfVqraJIitWyP+6Jx2FyQSLhiy2O3dsr09t8zZvtv7t8mW42QsU4L1OO3Z0vE6z2Qzrb906XJvnnlNaljRhiZ+rVVMWGyCvydSpmDjFSl6Rkci/PXPGmKU4eTImXr1Y2mef4feICH796PiMOZ6mdemS8nwbNFCS5iQJljydU7NmvMk9bVOhgvMNMtLToSSSN6NbN/uCk8pzlimjHeck4qARQhIVK6E+wnoQOwkFBNjuHEbrt2kDK9uRe0IFNBiDoFWnAK5erVSSHIEkQYmePh2CnTEoCC1a4B4YrbylBnkcjMS4c6ANlwQwYyyIMRYs/L+fMdaeMbZYRcJ6296+sloAFyuG5tzZGWfP4q444s5NSoJVQZOsVioPvdz2ag1PmOD48UV89x0YnIULoyONHiixX6+Sz8WLuF8FC6IdnLc38hD1tPrERORKfvopLOb69aHIiIK2Vi3c/3ffRXy5b19YO97eSBE6edJ6v2YzmMQVKnDl5vx5pAJRz16yIqdPh2tUa7KPjcV1oXxXERYLV146dOAs9wMH4JUgVrLaW2ALly8jtzR/fgiz3Ln1+0+bTBD+VFt6wAClUvL228aPS+ezZg2Plzdv7lgFJVLOZs+2/u31141btWJbRb2Ye3IyngPGcH+M9NCl8p2OlLk8coSPRYu7cfs20g0bNnQ9s0CSoJjPmaMscdqwIbowOZLqN3cu3g9PVdz6L8JVAVzuH7fzCcbYGcbYK/98n58xtuufNKRdjLF89vaVlQI4NhZna7S5fVZhzRr53ziRo1i3Dq7bvHmtWc0ZGZiQxZ6zWjCZsI6/v/O9VE+fhkDy9UWMUcs6rFULKS+2QO7TwoVhFQQEQLv/7Te4CxcfBYhMAAAgAElEQVQskOWePSEcxVJ/YWHQ/CdOhKA9doxP2BcvIv3H1xcKwNCh9klNVD1Nq3zpnTsQ+m3bcmu6aFEIy19+4e54YlWrXYspKbyV3Jgx3NV+8ybOu1w5PLsTJ+IcjdwTcuPnzw+rmzEU/rCHhAR4Cvz9+bVs1gxuWSMWpyTBYqLQQXg4XOrOxJEHDIArVq00TJ2K58AIqEUjeVHU7SZv3OClHoODjVXHy8iAhV6xorFQi9i7lzG8m1rrtG+P88qMus7nzkFxofvCGKrhvfGG/fh1u3Z4V3PgPHIKcci8+EBWp7fYw9SpmACdjVNfvcrzdIcNU7rxyCKwl2f74AGspuLFnc89jI/nE9+AAcpxUAEOe6629HQoFXqkKMYgkHv0AMv7xx8xqWpN+KdOwcr19oY1+NJLxvtBSxLysIsUsZ3+8egRFKiePXkeZ968/DqolZ/792GVeHnBKqdxJyVBMAQHc8IgNS+oU8e2hXTtGohs+fLBvZw3L/gEjpQtvHHD+joPGmR7m4MHeX/rsmXhcXGlVGJ0NMb+7LPK+zl0KK6DEVCBDEp3EnOE9+7lFn9IiHELnVzgWqETNRITkTbEGASZj492Za4vv8Q66tzmzMCVK5gHaI5gDByIuXNxncRrbbHAKh8xIvPH9V9GjgCWObs1uzeTbtUKsUVXkJGBZH8vL8T3yJqOj8ekLlbi0cOJE3DhNmrkfAzQYoGV6uUFa4i6HX39tbWVHxsLstiSJZjsw8N5mT5xKV6c/2+kMMChQ7zKUJ48cBNrNbS3hz/+kB0io6SkYLJ/8UXl+Dt0ALFq3z5Yt7lzo9oZwWKBAPfysg4XkEDRI2Ndvw7FKSwMjO527XAPjaTUqEHjbdqU///aa9bKzblziGcyBoH24YfO1S7XAlXkEtMGu3eHW9UIyFV88KAyR3j4cP5shYbyuLc9PHoEXkKLFsbS/6jq2YIFeB4Ys04HvHYNz2WrVp6t7SzL8LJ88AG8HORBqlgRc8fhwzwcltktHf/ryBHAMqzB/Pmzdy6bJGHydFf92h07YLX5+2MykyQUvfDxMcbi/e47PCHDh7t23X7+GecVFoZJkVxhs2bBMilRQimkihaFS27mTAidc+egnRcsiLjwvn0ofuHtDTejGpKEWF6bNvK/Lul581yvNd2xI6wyR/aTlgZXsrc33MhirjNjaHIukrLIVf3OO9rnRc0L1J6JGzdgeYaGIt5IueTOMKdlmY9PkmR55Ur+uXVrxBdv3cI75e0NATJvnvvjhGYzLP6iRXmua8uW8EYYwb59GDO107x5U3nt8+d3rOra1KkQVPa2WbXKunfv5cuyFbfCYoEwDw421oIyMxEdjRDLs89ifhCv01dfeV45+C8hRwDLIPDYizlmNaj6jTpW5QpiYnhd4C5dMHn4+BhvuTdrlvNjSkqC9fH557DG1Nasjw+q/wwYALfYr7/aTp04dQoWSPHisJ6ff17+l6wjSVh+/pmX7SxUCG5udwmGkycxAU+bZnwbEoQkBCjGTxY5/R8ezusgv/CCvsKjbl4gyxCG5cpBMB86hOcoOBjPu7MTJ42LoO7kQ8uECfbz0F3BX3/hmlPd64gIeBGM4ORJjHHDBoyxeXPl2I0QrgiXLyOOS12YtJCayrtKNW2qzAQgYpmYLkheua+/Nj4OTyA2FlaveK2KFZPlsWNxHu5MCXwa8NQLYIvFsdSerMKmTbgj7u65abEgxujnB2uzWDHEvYxU0LFYMOH5+iK3UguShMlm2za4aHv3RrlEkRgVEqJsUs6Yc67gkydhuZQsCQIJldIMCeHN7UuWRDwtJcXx/dtDVBTcxnp5rCLMZrj0IiNxjagTV7Nm3Iq+fBn3RmRsFysGT8XevdrxXmpe8NtviKeXL4/zp7Z2rVpBuLvS2EItgBMTtQXw3LmOp4c5ilGjYGkfOwYXuxaTXAuk0I4fb+15oPtgNLzSsyfukV56nVbvXhHkRaDqdufP4znq0CH7euXCwxErXrMGrv+AAJxDgQJ470SSYQ708dQL4EuX5H9dKdkZc+ZgosmsOq9HjigbuRtNL4mPh0AtWBCuTpMJ8dcpU+CyKlBAObGVLYtqRvPmQam4do1PMiQ8KN7kTHeX48dBMipWDPE18djvvZe5k8K1a1Bkhg2zv+769RjTt9/yNLEBA6wn/bt3oRj5+4Ot+vzznDVbsCC23bqVVzJLToYgCg3F3+BgrrRR3V4t17wjoOtpMmFfYj5xt26I5/fuLf8bMvj6a9fTZ/QQF4fr0LAhFI1x44xvpxa6xYpB+FFBD60+wmpQ/P+117R/p969oaEgAmqBntPUVFzTBg0QGsmunJTERMxFc+bw75KS4E3o25d7b0JDwdmwVQjoacdTL4B/+AFn6mxajafQsaNxgomzePwYVhxNSEYLrJ8/j8mvTh1uqYtL+fLIjbVn1datCxfx3r2IjQYFOdYIQpYhwMR6xcHBPN4bEZH5k9r48fbbFVLxk6JFecWpOXOsrZ3UVJ6zLNbifvwYArxvX94rOE8epCytXYsYMZ3/n39imytXcD3btnXdqqJ9V6qEvw0b4p716QPLjRjk+/fzfOhatRBGyAxQkRjG9Jvei7BYePiElhIllPXVqa2jrbrcFgu6KhUvbm3pZ2TwNK+6dW2/S8OHIyQiy7za17ff2j+PrAJljejVDUhNBb+AFMWAgBzXtB6eegE8bx7coZntKnMVxYsbYyi7A2S9MGa8wffWrbiO/ftj4n33XQgEap7OGKzDunWR5rNqFSY8sc4tY7zjzZ07mNgZQ1zV3gucnIziB8SEJrJImTIQCNu3QwCVLm2/4pIriImBMOzZU3+dn3/m18TXV5tJKklwp9q7B2lpOLcRI6xbHJJiabHApRoS4lyZTBFUupQxKISbN/N7eOMGBLDYZ1eSoCxQR6vnnnN/31pJ4iVDX3nF9rqPH8MDI16j0qWtBaQkwb3NmD7HYdUq/P7NN8rvb93iXIMxY+y7stu3x3tx8iTekZ49s6/rWZa5khAba/3bsWOYA3x88GwPHKhdxCYHwFMvgHv0gOs1O4OEkxb7NTNgsSjbzo0ebSxmSpWI1AVN7t6FZTxzJpidYj/T/PnhViVrSrSS0tMxgTGG2KUWoSchAbFlytts1gykJklCukTevCAh3bwJN3vhwnDv/fGHa9fIFubOxVgOHdL+nVx0tkobkgX2+uvGj0st7fQWV1ptHj0K61ncn5Zb+dVX8Zu60llaGp6LvHnxbI0c6VycXw9btnCFRg+XL4PM5uPDK1wxps8yNpkQhxVzhAnJybCaIyOVbupff0XYJSjIeI/catXg4apdG5ZwZhLX3IEuXRAiIkgS3jnq3pQnD6x/V5W9pwFPvQCuWNGx5upZgV9+wd3wZM1VqhQUGYm/NWva7xQlSdDevb1tFzUxm6EVf/klYpjE8KWlcmXUxv30U0z8S5ciBlqyJM/LjI2F2zZvXmzTvr22UD10CJZf+fIgJV29CmHv76/MsXUnEhIwCbdubf3byy/z8zx1Snv7LVvgTejb17glFBOD6xgYCAavJPFCD+JSvTqsxCNHjO370iVYtIwhtk5NAxjTXj8pCYIpIkJbQD94ADe9ry8m6oUL3UOIE8s5anVD2rGDp7vNncuVoIYNbe83MVHZR5hAcVsiH5rN2K+6d689SJKS8b5pk7HtsgqSBCU2Kgpu9lWrQMiieP+iRcb7UefgKRfAycl4YebO9fihHQK5fDKjnZgeUlJgVXbsCJdpwYKw2L74wvbEnZgIYR0aqt2zWAsWC+JFRYtiQu7cmRfpJ0uRWJa0kBXdvbt2azYRBw8iVlqxIlzbDx7wKlMffGD8mjiC99+3tujFdB29AhgnT2JCrlvXuGB68ADXPCBAqaRRgQdS3j74AB4I8m6ULAnSklb6SHQ0vA++vhA+r7zCGwTYEsCyzElMtoiNFy5wV3CJEnDjupJPunMnH1d4OD8fScK9oLS2r77C+VSpguO2a2d/39HRcFMXLgyi3d27eP5IcRd790ZFORbOevSIj9teRbHsgGvX5H95HdRlqVo1hFGcLcrzNOOpFsCHDuEsv//e44d2CL17I5bpaVDhh3PnMOnQJNOrl20t9+pVWEvVqhnLs6WG8KtX8+8kCS/7t98iz1Nsm0dLmTLI5f39d/uT3v79EGyVKuFcUlK4AJg61f3FBNLSMGlHRkIYECGHMbjitXD/Ps6pWDH7HaMIsbEgOOXObe3OXryYH1NkrD54gDrYXbrwtor58sHrsHIlxhoYCOE7erQ1cc2eAKbynIUKWXf1UeP337mXJTLSsfxbEdRPl1zg77+PezBkCD536YL3PHduCOJ79/A827OACWfPQqmsUgWC188PCqbYu/fLLx2P3dIcxFj2txzv3MHzSeNt3hzcj5xCHM7jqRbAVPYwM4qcuxMVKiC9w9O4fx8TFtV7tVjgYvL1hXAhhq0Wdu6E1dG1q/0XdP58WKNasa/z51F8gpoZ1KxpLYgZw7HCwxFbXLYM7nL1cfftg+VSpQqsGrMZBQQYg7vX3Rq8WCWKlsBAbU9GejoKNOTOrR87VuPhQ8QN/f2tGcZnzuD7rl1R5zpXLu3nPCkJgomaPojLq69qV/ayJ4BlGV4JLy9jRV0sFrgyyaLq0sV4I3sC1Uy+eRPhCCJX0Xls3YrrUasWf8569HAss0DsIzx2LBQc6t3rSNUsEdTuUS+NKTvgzBkoMmL5Vy03fw4cx1MtgCdMwISYWTmK7kBCAu4EsYM9jREjMHGJVagOHgSr1ccHJCG96/feexj7vHm2j9GoEdI5RBw/Dsuf+rBOmKAscLFxIyxab2/UcJ4zByQhigkzhtjvs8+iGtZPP2Hi3bsXQrhqVVhBkgQrmjG4Z91phYikKCoYoVUpS5J4LrDR9JOHDxFn9fe3jrebTLie+fPjHO/exbXQSkEym2ENE1vd2xupQyKbvHVrFC+h629EAMsyn7SNKrgpKSDUBQdD4Ro3Dta6EdA9TEzkdbEZg2X8449QQCIilMzdIUPghjYKSbJWUrp1s2/l64FyiBkz7vHwFKhka4cO8r9hoLFj4dVo2jSrR/ffwVMtgFu1sp74sxv27sWd2Lo1a45//jyOr46Tx8dzJmnLltoTiCTxRuN65JK4OGVS/8GDstypE7YJDoa7Vq8E5dmzIGz5+PA+qhYLXObLl0PoRUQo69eWK6ds2kBCZc0aCIsaNdzD3jx3jqfe0OLvr52HTPFieyk0hLg4uGtz5UJ8Xg2qqrV+Pf/uww/xHeVVSxLIXkSAq1tX6cK2WGCJv/wyPAZ0DvXqGRfA0dFQkjp1MnZehHv3uMKSNy+KwlChET28/DKE9qpVypaJFMeuV8/a8zBhAhQTo/jxR+v76Wy6UGIi74nMWPZx45rNeEboPhcsCOv8wQN4iHLlcqzcag5s46kVwJIEpurQoR49rMOgmrBZqSF36oRrpSYFSRLc+IGBsLbUqRqyjImzXj1MxFr5n9TU4fXXeVGKfPngljZCOktI4LHcfv20Y8FJSVBk3n4bbkdRABOhZPx4XrqyeHHXchd//x1s20KFkIdLgjgqynrdX36BoDHiqpdlWOj16kFZ0FLKKJe0d2/l9yYTlJFixSC0GzXCmCpWxD2wJ0jOnQMZUGxVV7kyClVQmUstkGXqTKvPM2d4Te8yZdD5SG+cw4fzcbVoAcWKPtepo22lvvoqPCxGrntGBt9f4cKc+avVB9oIxozBsUuVyhp+hxrJySieQUpBhQo4N/GdP3gQv2V3zsyThKdWAEdHy/+SNbIzBg/GRJ6Vifm//45r9fnn2r+fO8cnpAkTrGOpt25h0qpQQSlUKaWBJrYiRZDrnJjo2PgsFrguvbwQIzbSYu/2bR7/1Vtat0b6iiMuxlWrIACrVuXFHagalLqT1blzsPBq1TJ2zvHxEIB+ftplDTMyIGQLFdJ23VLzB8ZAavv8c+d6S9M+2rThsflixUDYOnZMuW5aGhScqlWd72O9Ywev5d2ggTX3QGQSjx2L46xezb/Tq2ZFFcPs1T1X17p+8MB2jrA9/Por9jN5MvLWmzVzbHt34v59KCL58/Pr+/332mEl8tRkN3f5k4ynVgDTS+DJ3FpnUKsWSCVZCUmCi7JSJX1rITUVMTvGIATUJJo//4TgaNcOE+R333GhzRhSdOy5Ge3hl19gPYeG6pfJU2PHDhCfatSAC/azz7glTouXF8g6Q4YgDev4ceu0HUmC1U4WGCkacXHK2rj0/cOHUEgKFjTWbi4hAZOjr69+w3dirasrZ127ButbbIDhSlMP0QUdFwdhRxa1VkiHimS4kvJlNoNcR2z4Xr2gaJ07x4u40JhWrMC5tmiBdyd3bu1SkETcstU848wZKIZarmK9HGFbiI9H3LlKFViXZcp4rsKdiIsX4eYnFnznzohJ21L0+/YFUS4H7sNTK4BJ+zVK8sgKpKZiwn355aweCSe2bNlie70ff4Q2HRSEOKz4QlMzAPXianMAEVevghns5YXYlRH34v/+h3heRARn/T56xHsTh4TA2hEbSwQFIQ1j+nS4RqmlYlSUsuEDCeU1azCmGTOggLRuDYVk3z7743v8GALO11e/LOXff+N3cTK/fx8eiVy5cH7TpiF1Jn9+7M9d7QiPH4fVXbAgCqeoIUmwlkNDXX/fkpKgaIgdooid27YtrHwvL5DvkpMhXIOCtDsLrVuH7fQKzFDvXjrOiRPW66hzhO3hhRdgNf/1F5QKPz/Pvt8HDiCNyssLz8WwYcaLhpQpA8UnB+7DUyuABw+GZpudQfmxjjYkyAyYTIhXGXGX3b7N+6v26wdB8NlnyhzCrl05WcjdLq3kZN5UomNHY8zm7dsxIUVGcis1LY1XgRo3DhPm5csQpuPGoY+0Wpno0QPpKX/8AWGTPz/GIMuoi5s7N5izjEFBsYfERFlu0gREMr3KXWlpcL0XLQoFIjERykdwMCb7oUOVVh6l32nVoDYCUQDv2wc3esmSIOzp4fRpnMOYMc4dU4QkcW+L1tK+vdKbQsq22nOwfTu+V6fUiL17yfq11eFKzBG2xVsgTwCR7e7cwWd39vjWgsWCYzdpguOFhaEsaXS08X1QyO7ddzNvnE8jnloBHBEBjTk7g1xkRmKansCSJRiPkTxVsxnWnjgx1q+P+FKTJkhroPhnZkCSQCrx9YWrV6/so4ht2yCE69blQttiQWtFxmA5iKSU69cR3yS3e9++1sxnxmBJr1jBJ3zGsE97SEqCwuPjY1sJozaOP/yAdCGqItatm3bjCYsFFnCBAtp5vvZA5/C//8FCrFiRd0CyhZdegkLgCsEtORlKHWNQjvbv5wViaFGHMjIyEGIoVUpJ0vvzT6wvEsTE3r3Tp4OAGBRkX1jt2YNnR6+PcGwsrORatbiHhEhNmZXhkJoKjwCx2EuXRhzXUY6FLEN5YcyYxyYHxvFUCmCTCS45I5NgVmL0aLg/s0uKQkICxiN2u9HCo0dgNat7Ab/1Fs4lJgYWA2OIq2Ym9u2DFRMYCJejPfz0E9yCzzyjJF+9/z7cdo0bYzI9fBj7zZvXmkcQEwNrlc6bWgaKS69eEPhaHWVkGYKCykbaGvehQxiXvz9nsDZvbj/Ge+IEBPvIkfaviRqi6zc83HhThYcPEaNv2dI5UuHNm2A0e3mBdEf7ENsvMoY8VbWSSDm3YhWyU6dkhYdp0ybeu3fLFt52z2hDDFt9hPv0wfUS20pSBoCWa9sVxMXBu0QEx4gIjM2VloAzZ0KZdUfd7hxwPJUC+OxZnN3KlR47pFNo0CBrGZJamDYNE7cecejUKV4M4/nnYWXExcF6pDhddDTyfmnCdJYdaxR37/L2cFOm2J+ItmzBZNmggZIhu2GDMse0dGn9+CG5ebdvhzdAnUMqLhUqwD390UcQ7PHxEFLe3rY76qSmKvcTHo4UI6PCbdIkCLODB42tT6DjNW7seOGSjz/m1roj2LcPln1wsJJ1THXSGYPb/fPPuQegf3+lZU4V1eie3bwp/+sCJi9HZCR4BBYLBFfJko4JHepiJQp6ijUvXKhclxQHR67h3Ln62QjXryPmT3XS27UDsdAdGRQtWsAzlAP34qkUwOvX4+ycLR/nCZjNvAJUdsKtW5jEJk3S/71kSazz4Yf85ZckTBy5c2OCFF21Y8dm/rjT0+ECZQyTiV5xD8KmTTiHRo2U9azFXsnbt2tvazaDmRsRgfNOSACLOl8+kKCoXvHixZiwu3bVrnVdsCAm7+vXrSdRcl/SsmaN456ShASkD+l1LtIChSEYc66HtsmE4h9lyxpnvS9dCoWoQgWlS/2117jFyxhCNrKM85o1C8+avz+EYUICuAhhYfAQSBIUHfEajh7Nx7RiBb+ujkDsI/zZZ1A28+WDR0Wt+E2YAIXCqICUJIxfXdjk77/hlqcevFFR7rWqTSYI9Zdect8+cwA8lQL4lVfwsLqa9pKZOHMm+1rpAwYgtUZPc4+NBfGI4qbieqdOKdsPjh+Pv7Y657gTK1diYi5RAkxUW/j+ezwnTZpgsp44EWOtXBnu9Tx5tAtMUGOA776DYOvQARMjuaqpXeGzz/JtJAmpW6JAoBQRxuBO7NIFHXPIfU+LyLp2FKSMfvSR7fUkSem1YMz5Y+7Yge3ffNP2ehkZPFe7XTtOcJIklBdlDNfj77/x/4YNyu1v3IBngZSZTz/lFviqVbzNp1rQJiVBMXnmGefCP2KOsJcX7qMW07hbN8dqUZPF3qkTrsEvv/CUueBgWPGZ0YP3+HEcQ2yWkgP34KkUwJ07ozBAdgYVEnCFsJJZoAnv7bf115EkuNh8fWHtiDG5o0f5xBcRASKTn5/nCrz//TcY2blywbqyBRKmtEycCKF65w5cvj4+SjazJCFOWakS1ps2jVtDIqhO9o4d+JyaCiHj5cX3l5GBpgaffIKyqVru6z59cA4nTzpX01ySoAiEhOgTjSwWzjqmmtWuCGBZhjIRFKTPgH/wAG54xtDQgc5Nkji5b+hQjI2aJKi7QREOH0YohzFlzjAt6jx7yqd2hXAkFu/Qy/OtW9exHP+tW/k+qTBJsWJ4D52tR20E1Bs8u5BB/0t4KgVwmTL2iURZjSlT4D7L7Pios2jVCiUb7VlfBw6AfernByKTJHHh8+67SmuuSBGkMHkCsbGIRzOGClV6nZDu3VNO1qLbNSEB+a2MId+XrBKy6MmNqeViT03FdalbF/8/9xzW/fpr5XoPH0KI586NaxgVxasW+fvDJUljy5MHQuvll8FaNZpmcuEClBEtQWEy8ZSuKVOUDQlcwaVLOObgwda/nTyJd9TfH32CCZLE2zqOGsWtU2Lo2uoLLUlYT+09CA2V5Rdf5Ovdvg3CnrqUp6O4cYMfo2BB7RzhQoWsq6PpISGB33fG4EVavtw174dRDBkCj01WVuP7r+KpE8DUXcgoszGr0LJl9iY9bNsm/+vKs4eHD+F1YAzxznr1kBohy4hvEkGKMbjkPBUaMJsRK2QM7ka1++7MGQiCwEDE2Ly9cV+Sk/k66elwg5Igb9wYislvv0HAtG6tr0SRgKZFtMaTk+GiDQ2FVRwVhUl8zx58JqEuSahq9M03+C4ykpeHZAxEsd69Ebv980/9a0suXZHRnZoKS5XeF5qA3SGAZZlbsmIoYONGWMbFiim/F3N/x41TCoPly/H9lSu2j0dELrUFLPYEHjwY902rcpZRWCzwKgQF4T3RyhEmAp29Lmd37iAdKiSEj9fHx7PCsGpVnsueA/fiqRPAlPunVUs3u0CS8NJSH97sCIsFwrJ2bWOTgSQpCTyNG/PfTCZlfDE83LMTzA8/IIZWsCAXQLt3g81duDAvM7hmDYRw69ZKZqwkcUHOGO5boUIgDdnKszWb+TbUN9dkgsuvWDF837EjJ9RQB51y5Wzncqak4Dl/910IX2o1yBis6Lp1QahZtQrCW5KwTdmyEBTp6SCekdtbHR92lwB+/BhejwYNcC2InNaggbJrlMXCiU2TJ1s/G/Rc6RXBoHCI2Lv32jWlEH71Ve7Knj7dtfP65BPsh9jKWjnCly5hnRUrtPdx+jRY235+eOZE8p+j3aVcAdXZzu4Gy5OKp04Af/45zsxI2bisAk0OznZa8RS++kq2GXvTAsXXyAUtTqaUd8kYhIwnhfC5cxA+Pj5QAHx94eZTp1utWgULtE0b6/QUtWVlpMSfGGPesIHHKBs2RAcnEWPH4tjq743g7l0wu2fOhBVPqSqMwbX53HO8Bd3w4fAI+PgoXcDq83QHyHql5YUXlFa6xcLjzjNnaj8TtroaPXrEu2Wpe/eKDRvExZniJIRLl+AxUfdeVucI79olW3kcJAnvAHWACgyEonTlCp41b2/PC+D//c/xdzwHxvHUCeAxYxyj/mcFvv8eV9/R/ExPIzUVlt5zzxnfhljP7dvjb+fOygnv/n0+EQYG4rOnQOEJWvTi0VTsv107LiyoqIO4XLpk/5jqJu/VqiEPWf187tyJ3ydOdO0cCWYzYq1ffgkBV726slkDxUc//RSkOdGN7k4BTJag2s1NYyT3/pw5+u/suHHwVqhx9CgUOV9fa2VPlpXXfsgQ/n/NmhA8jsJsBmM+b17tBg+UszxzJg8/XL4Mr8f69fBMUMx4/nxlkZZDh/j4PCmAX3sNz4W9jlE5cA5PnQBu2hS5ndkZs2fD+ngSqs4sWIAnRavXrxYqVoTAliR0x/Hzg4tUrNykFoS7dmXO2EWIsVxaatSAi1YLy5ZhYnruObgVBwzg2/XtC9JKgQK2laijRzmJixYtJnNCAq5RpUrK+LO7QekmtIjx0oAACBcqWMGY60rsrl3IkaX9zZ7NfzOZeMnJ+fNt72fgQGVPXco59/dHPF7dvlBEw4ZK5efbb3mOevv2xp9rWeaFNbS8BjSukSOxDvWkfucdfryKFTFurfeevE0hIWGNQqAAACAASURBVJ4VwM89h/cgB5mDp0oASxK001GjMv1QLqFDB1gkTwJiYzE5i0xSPVy+jKfqww/5d4cOYQLy9UVhCnIjXryoFAazZmUeIzwujqe8LFiA5+TXX+GazZtXv98rTYpVq/JxDh3KiVHly+PaqPkGly7xJg/58sE6o+21yjoOHw73Y2amaZ0+jWIg+fLxqmU//YRwyLp1sLwbNlRWAitWDG7dt95Cz2ijhTkkCc+Ajw+E3qVLqFrl74/jZWTIcs+eOIa9XGFZxvsSEYH/ExO5MtS2rX0PyqRJ/HyoK1FaGgRjaCiu+4gR9sttnjmD8XftalsxMZm4q5+Whg3BQ7CVRjZuHFju4eGeE8BU+MNWI4ocuIanSgDfvo2zGjbMuZxJT6FYMWj1TwrGjAHJxF7aC5FT1Fblo0d80u/YkbvexOYFNFG5O3Z/7RoEqJ+fNaP72jXk9DKGOKNWjJFyJBlDBTAxLSQmBpOttzcsm+hoVFvy9YV7/ZVXeEzyrbewD9GSk2We1uQqMcgWDh2C4C1aFK709HRckzJlrC3u9HR+vgMGgGhGn729ISBGjICH4MwZ62uWlsZjul268Cpjt27hmnTuzGO2RjvvNG4MBerMGYybWlEaecepOhpjUHpFJS82FiETX18Iv9df1/ZAZGTAfVyggG1BffEit4BpMdojuVkzPP8REZ4TwFQYxlNFcp5GPFUCODWVxx7r18+eRS4o7/RJavt16RImPdGFqIWOHfXJVZIEtm2uXBBk5DYkwRQeDvdb3rzua8/4119ws4aFwYLTQkoKiEGMgRyjZtqKRUUaNbLOy0xK4qUSGYPVN3q0kuUryxBUtA4Vp3j0CK7KzEzN2r0bwqVsWWUaz+7dspVbmKCOAT94gHSbOXNgdVItcHKZPvsslI2lS+FmpZiuWjhTVye1l8QeqlXDNoGBiJ9ScRMjoONRwZTFi63XuXCBt5AsUQIuZnHs1PNZXYmLsH8/tqemGcOH8+Ma6SNMWREjR3pWAK9cKTsUXsqB43iqBLAs42FevRraKjW7z06xVrL6fvstq0fiGLp2hRWl54ZMS8MEaa8f7JEjENI+Prx7Ur9+mLw+/hiKE2OYxFyJh27aBPdw2bL22cqSBDKSnx/GRmlBKSl8IqWWeN26cSsqNRWKlCiQGjfWd6VTqk2TJvj8wgu4DkbaPzqDH3+EQKheXbsi1YABUIjUfX7tkbAsFlzTFStwTtTeT1z69UNhloMH8WykpPAe0nqxcC2IDSmaNHGskMv583zbGzegIAYF6Zdz3LOHE6Xq1ME7+vffmEf697e+Bps38xz3sDAoGPfu4XnKkwfKSmgorHZbfYSpqMenn3pWAI8eDcJqdvYWPul46gQwITaWWzYVKmQfmv0bb2BMjnaZyWrs24dxf/KJ9u/E4tWLp4qIj0e7PsZAArlxAxNP3rzQxqmAQ9WqzhWdf+89CPT69e03ZRDx559w0wYEICeYqlcFBuL3Dz/E565dYe2VLCn/G4s8coRbSm3aKBs8EEQGOOW2UvN2d2PVKgj3evX0WyJGR3MLVvRa2BPAaojpPgMGyHKPHrAk1UJZXPQ6/oi4coWHCBhznCPQqRPf9tQpFN/InRvj04PFgvOhe0sLMflTU8Esr1xZ/jek8MEHyrxtyq199114XnLlgvKhV42NOmn9+adnBXBEBHLec5B5eGoFMGHXLh7HGjxYfzLyFHr1glX2pEGSINDKl9fWmKdOxURjtBm4JEGY58oFN+yaNfBaVKkCVvCvv6KIg78/LGMjjFyzmVdT6t7dOQs6OlrpUmaM93iVJGXN5ogIawb3smUQfLVrW7uhZVnZXF5s3u5OUEOCli21FQERH32Eddev598ZFcBiLezmza0JUbdvczen1tKkCUhxO3ZY1zqm3r0BAVjX0UIRpBAS+Y5CHq+/js8//2x7e9H7QYrFxIm8B2+dOmBUa7W+PHEC61AoZc0a+V+vgBbPgMb0+LHnBHBSEp7TzFIAcwA89QJYlvEyzZoFV1KBArAOsipPuHx5CIcnEdRgXKvXa/XqzmnTR4/imvj4IH7v7Y0JyGKB9Uox/a5dbRdQSEriFs+UKc51uSHQedJy7x6KYzRqpPy+d2/tCfiXX+DqLF1a2V5PlrkngTH3k18kCT1pGQPZyUhc2WzGpF+0KM8FNSKAHz3iHoLRo7Wt08ePIWS9vSGETCZZPnbMmqjEGDwWVasiX3fYMC7k9u+XbXpe9M4pPBzXn4q/UGvJtDSke5UrZzs0tX8/xk1NEcTFXk/mn37CemKKGuUIExNbRK9eGI8se04AU1UwIx6rHDiPHAEs4ORJlMEjN6G92rLuBvUnfVLLvplMsN7FMpOyzNuoaRFcjCAhgaft0ELkIIsFrjw/P7g19+yx3j46GjWSvb1h/bmC48eVVaTEpWhRsKIzMnCujCE2qOUROHoU1lJYmCz/8Qf/noqw0DPoLkgSvBCMgWHviLv24EEIQOoBbU8Anz8PIebrq+9Kjo/Hu+bjo02qGz4c2x88CG/HggVINypQAMceNQoKxNmz+Lx2rfHz+fprbLNuHW/7uW4d/53aJc6dq719crLyvou1t8nt/O23+kL400+xnugBEXOE1desUiVwC2TZcwKYyI+eLITzNCJHAKtgNmOSDg6Ge+uttzzXkYi0zm3bPHO8zMAHH+AcxMIaS5fiO1fYlJKE0pxiHurGjfz3w4cRSvD2xsRJlufp0yhiERTkujYfE4N9aRX0p7itOOkuWsQFnpYQvnoVsUJ/f5zL/ftg8Yr7dAc3wWzmVuPYsc5Z/yNHQliKxTq0sG0b4sYFC+qXzIyLA5nJz0/bWyLLuNYhIWCei5AkZVUmsoDJgrUHqj/dsCH2desWtv/yS+V6ffvivogpc5JknRo3dSr2QeQsxnCdGENIRqul4cyZOHf1fTCZcL7e3nwOSE6G8kPKgKcEcPfu8DzlIHORI4B1cOsWz0cMD888JqoIatOnFRt8UpCYCGZnz578OyLduMOtf+yYsrkAxV9lGZMrNWBv2hQs3JAQWKZHj7p23LQ0ZdcmWqZNQ4ERcoW/+KLStUukukGDtIVwbCxc11QG0s8P8Ug/P3yuV8+165aezglts2c7v6+HD2F9im52EZIEZdXLC/FtdQ1tQmwshEiuXPYbolBlKVsKKXXlEhU+W6CuT7Q+VV175x3lenfuQAlv1w7XcOVKlKgU7z3Fpale8qJFuEb168PKLloU3/fsqeylO2CAPs8jMRGu9aAgEPf++ktWhHU8IYAlCWPX62OcA/chRwDbwQ8/oDCGt7csT5hgn7TiCgYNglvyScfMmbheV67AexAS4t5qOo8fK1NW1GlE33yjnChv3HDteJLEhZi4iOkqZjOf3OvWVR6TynW+8IK29akm9Ijdf9SWviNITuaKgVrAOINly5TjFMffvz++691bPxXt/n0IMX9/+yQnWYbgq1gRXgI9MhoxrNWpUlq4eRMs5379+HcWC5SGOXOs1yfWOi2lS/O/IoGvWTMomGlpvMbz0qW4Dq+9Bpa8nx86OcXFQTls1kx/nNHROEaRIjw3msJhnhDAFDJSd8HKgfuRI4ANID4e+ateXkg/yCxiQs2ajjU2yK64cwcTzrhxcEMyhtimOyFJvKKS6KqVJC4IaRkzxvlc7/R0XqtXXMSYoYjNm6FwFCigdB9TF6gXX7QWwvfuKRvF9+2rLL5fubI2mcsWHj2Cxe7t7T4yl8Wi9ALIMjxFkZF4N954Q9/Cjo5GwYyAAMcKZWzdKv/r3tcCsbSNpJMNHAjhr7bOQ0KgXBNu34ZnIzCQn+t330GJ9PZW1pam55sqWkkSlMOwMD6mu3fxrHp54XtSVGzhzBnlM0HPjCcEMJEMqQ1nDjIPOQLYAfz5J9i8jMEicqerODUVsaNZs9y3z6zE4MFwo40ahfNSp5G4CxMn8klq9mxewH/oUFggkyfjc82amNSMgvI9RaG7fz+s2woVbBcnOH8ewsbbW5bffpsLJep3O3w4n1AlCSUZ/f1BKCLyS/PmvMITWVRGERMDN7Cfn351JmdBKTSM4X0oXBiuWlvu5Dt3oEQEBjpeYEaSYMXnzatNCCLvgr10LVJotFjGJUqAXX3qFJ5b6sHbpw8X8HQvZsxQbtuuHeLdokV89iz2MWiQct0TJ5AJQNfv++9thwR+/52vSznCnhDAkyfDU5AZKXCewuPH8Ea0aQOPXHZFjgB2EOnpYCn7+2NS+OIL11JaCDRBuHvCzCqIE3XTppl7LLGVHGOo3ytObNu2YZIMCADZxtakJ0lwj4aHK/eZlAQ2rhZhRwuPH/OGAr164bMkcZfiyJF4blatwmeRIb5mDY8B01K8uDEr/sYNsGYDApDulBkQx1Whgm3F5uZNrJMnj3N9jGUZAs3XF9dMjcmTeSEUPUgSLPdChazb6qnbQQYGwnNz9Spfh+qUi4JQlmEhMqbdMILus9jvV5Z5PXpamjTR55eIY6McYU8I4EaNrDMZngSYzXjmBwzg+eGMQanKrsgRwE7iwgVZbtGCv0TqfE5HQUX9PZ36lJmgHEm9dA53gYrG01KkiPXEd/cutz569dKuNHbgAI8tU/nIggV5PLdlS/AB9CoWqSFJsIC9vWFBXbiA715+Gfvu2hXHadTI2qLetUvJ+GYM+7KF8+cRIsmbV5t96w5kZCjH9OCB/rrXr8N9HxLieieniRPhwj12TPn9kCFQTmxhwwZrxclkQhghMpKfy4IF2oV4KI4eGKhUtrt1g5tYq1duSgpydytXVj4vBw5gX1u2IN2IGPX9+1u7xq9flxWx55dfznwBnJ6O527KlMw7hrtx4gTY6ER6o7rZVMBk06asHqE+3CKAGWM+jLFjjLGt/3zOxxjbwRi79M/fMHv7eNIEsCxjMl22DHGdXLkgaIxOzmqMGoWJM6sKgGQGqC2cGF9zNw4etE7doTZy6o44FguYqr6+mNRIKJw7x4vtFyqEeGPjxrintM7Bg/jdGTLTzp2ICYeEIEYsSehuROO9cEF7u5MnlecVFqZfovTvv3EdCha0FlLuQmwsrxxFi17ThCtXcI1DQ8HkdRVxcWgP2by58h3p1s12v9q0NCgBNWviWUhKwpgprl+pEv7WqqW9/caNSiG4fDm+P30an7XIWwRKWVqwgH+3fj2+o0Ywjx/DWs6dG4JvxgwertmyRf439CEWJ8lMAfykeOKio5H/T54qX18Ul9m4kWchjBuH62q0TWZWwF0CeDJjbK0ggN9mjM385/+ZjLG37O3jSRTAhJgYzgKtUsU5V9szz2By+S+hb19ck+rVM0ex2LgRL1j58hBix47B9VS7Ni/c0aqVdZvEAwdQLEEUJMHBENiJiajcxBhSTwhdukAAGi2lqcaNGzxXdPZs3ktYy2Uugio10aIVw/zjDwj3kiX1hbmrOHkSQku0ytu2xXHVXIiLFxFXzZfP9fQvEZ99Zi0cWrTgzSu0QAVRVq/Gdc+XD58bNYJlZLGAEFWlivW2MTFQnCIjYRk2aoTPDx9CuQwKsl+6tndvXLNLl/CZUqvUnIibN2U5Kgq/FSiAyl7EGUhM5DnCjIFTkVmgeua3bmXeMZxFcjLCM1QRjzGk6X30kTU/QJKQrti5c9aM1ShcFsCMsRKMsV2MsVaCAL7AGCv6z/9FGWMX7O3nSRbAhO3b+cQ+fLjtDiciTCYIkokTM3d8noTZzCc7xpAr6S5IErRfLy8UVBBfvnXrcDzqSRsQAKKQyEh++BC/i4KNmjpQz2Kx/y5ZO6660lNTwYKmY9asCe8AY6gypSeEn3lGOVZR4G3fjnOsVMn1dCs9/PADhE3RotwTwBgEba5cyk5A585hvQIFlDna7oDZDEu1dGkeD7fVoF5sbuHvj+ela1cli1mWwW4uWlT5nSTBus6VixeQOXECwq91awiAqVPtj/nOHSgpbdtin+PHQ9nTw5EjyhQ7xvhzkZjIvztyxP6xnUH//vZd+p6ExYJw0gsv4LoxBsE6a5btLmZ//411v/7ac2N1Bu4QwBsZY5GMsRaCAI5XrfNIZ9sRjLEjjLEjpUqV8txZZyKSkvBi+vhg4l+/3r71RxP8N994ZoyeAE3UK1Zgcmvb1j37NZmQVsQYSE5axKSZM/H7Z5/h2lKT9mnTEBfKmxefBw2CCzEwEAJj8mTct44dla7rqChj1o4RqEk/x45xITxlivazsmmTchvGML7vvgNZq3Ztx7o6GYXFwtOnnnkGwuTtt/kY2reHAsQYYtanT+OZL1QIjOLMAPUppnKtpUpZs41lGUJWvF4jRuh7ByZPxv0VQQx4ddyd7pVaEbIFYlKvWwehXr267fUlCaEKOk6rVhAosszjnEWK2O8j7AzKlcsetejPnoW3h7pOBQdDef3tN2Ok17lzoSRl91KaLglgxlhHxtin//zvsAAWl/+CBSzi7785waNjR9vWCTFhM2vSygrMmwchFxvLq0E50zpQRGIi6gGThar3IprNyKf29UU4gGps0xIZyWNwsoyXXazvLBKLrl6FUKY6yK6CyHaDBsHSCAjA/X/pJX5eaiFsMsGl26aNtSBu0iRzWlcmJnL27+DBcP9NmqQ8trokZ0AABIS9/squokcPKE23bytzeC0WKCvqphj37tneHykZpHTdvo3YtRY5jipTMWZ8vGYznrkiRcAIb9/e/jZJSThGwYKIfXt54T4UKoSQi5E+wo4iJkZb6fAU7t+HC5xCNd7eeI/XrnW8c1l4uO3QRHaBqwL4TcbYbcbYdcbYPcZYCmNs9dPqglbDZAKhJzAQE/x772nnj06aBBe0o8UWsjMaNEBJPlmGyzcoyLV0gDt3wAD19oZlaw+PHqGKEgkGteD49Ve+bny8sthGRAS3lsaMgZXpjpjYtWtIx2ndGsLi3j1URGIMhJHhw/H/zJnWQvi11/Cb2C2JMf3m8a7g6lW4x7298cympfF4/vjx/NipqcpYNmNw52Z2L+urV+FSppzvGTOg2BChisJAvr7GLCAqARsXx/OOAwN53FaEeP62um+pceQIj1uOGGF/ffIgbdqE6zltGtzhdOytW+33EXYU1HfY2XQxZ5Caiph+p068qUXt2pg31dwNo7h6FftxR/W3zIbb0pBUFvBiFQnrbXvb/xcFMOH6dd6arW5da5ZqixYgE/xXEBurLCAvyxAwfn4QpI7i5Em4ovLkMVbCUJYR882Th09YFAo4cwYuQC8vkHLS0jDh+vqi8MGWLYhdBwWBMZ07N4p6uAqLBQzi4GBluklGBrcsGzcG2YsxMGNFIXznDi/yLy6VKytzVl3F7t2wuMLCoKQkJPD0rTffVLrQZRmsWUrZoiVPHlilmeEiJcyaZX0tIiPh5v35Z3w22n2LuiNdv45UJca0u2bFxECZI8/WG284NmZSsIwQg8hTIt5bEizkgqbe03p9hB3FrFl4xpzpk+0IJAmK5IgRvNpX0aJQMkTPlLN4/33sU0uBym7ILAGc/x9i1qV//uazt/1/WQDLMh66detgffn44GFLTsb3lLf2X8G33+LpEQvkX7kCC8DRqjS//gqhVayYsfSao0e5q7ZkSe5GHTiQC7TkZCUZijFljuitW0oijDsILxQH1KtotXYtJvciRZBWwxhYsASLhY+nc2c8M0FBEJSFC7vONpYkjNHHB67NS5dgoUdE4DtKv5FlPo4DB+ACLlsWVlNgIGKyAwdCofH2BgvYHWlIIq5etS6+sns3zsFsxvUrW9a4ZUh5wlu2KD0UalCN8wsX8IwVLepYtagjR/h47Xm7xozBc6/2hEREwC2tdrNrseMdRatWaASRWbh8GUp5uXIYc2AgnpX//c92ZTlH0aKF/Th7dkFOIQ4PIy6Ot4YrWxbJ+Izp9019EjF4MKxI9UvVsycEh9FUnq++wkReq5Z9F/ClSzz1KF8+sKQpH5CK6r/7rnIbKqTCmHXVqIcPlRPcsGGIYTszUVy6hMmmfXvbhLwTJxDfEy3defNgJVOXJ8aQHkXpLJ98AlZwnjzOV75KS+PPZKdOsHovXsREGRho7XWgcQQHY7zkBqcymj/+iPs1bRoENMWqN21ybaI9cgT32NtbWSlM7LxFlqMjeaxU4czHB+ekxdeIi8Nvffrg8y+/YJsVK4wfZ9cuPub337e9bpMm2tWoqBCHJOEcSZgxBh6BszCbcX5jxji/Dy3ExWFuI4XBywsKzsqVmdPYJjYW9/GVV9y/78xAjgDOIvz+O9yHYkznvwCLBRZZ377Wv1EVICpcb2sf5GJs10670hAhOhp5u76+EBazZ1vnWFossIS9vXns948/MImXLIkcUMZwTLJMiDimXgIDIbhnzIBAsceEtVgwmebNayyOHBfH8z3Vy+uvI67dsCGUixIlODu5dm1rS9UI7t3jk+Mrr2C8hw9z8s/Bg9bb0HgqVQJhiZCejopfZcpwN+bjx4ixUiGLihXRkN6om5NKg1IBkJAQCPbbt3kbQMZwnIQEeJiaNHEs71xMrVq2THsdisETkVCSYGnXrGn8WMuX8+uWJ4/+8yBJOE8tYaiuhJWWxhUfxkDscyZ2euoUtndHJkZ6OljcPXrwuHW1agjpZHZ+8cqVOJ4n2se6AzkCOAuRmqqc2Jcvf/IrYVH+nZ5l0KgRLH89SygtjZNrRoyA9aeF+HgIjMBACN8xY2xPPImJmDDDwmCJFCyIiTAuDsKALMCmTWGxUnWt0FBYw2Iv2FKllBZYyZIob/nOO4htialRS5Zwq9UoLBa46kThW6IEfnv3XXw+fpzHLn/4AcKHXO/z5xt7jo4cwX4DAhAjl2UItaAgCEytFn87d/IxaV1vKhyitkCo9GO9evg9f36kgOkxlNPT8QyRO754ccR0RWWMqkqRC5ZKfDo6+YppXlrXLTERXhV1vjG1ZxQJfbZAnphz58At6NFDez2K9X7xhfVveqUoqWwlLQsWOBbLXboU2128aHwbEZKEUMNLL+HeMoZ3aMIEhEc8Na9164ZnxR0xcU8gRwBnMZ5/HpYZtXlr1cr5lyA7gCxHPWH4ww+yroswNhYCkDFo9VovbWoqhBAV+ejb1zjZ4vJlLjh9fa3zQlevVqYjNWuGe2OxYCyffYaJs1AhxAv374eA7dOHW3i078hITl6qXNnxCSE2VjmhMgYL4uFDjGHkSAi1KlWwmExQVgYNwrrDh9uOM65di/2UKsVj66tXY+zh4dpkue3bsQ2NRw8DB+I6awlwSZLlPXsQx/byApt56FDe0CE+HmkwxYvjGDVrQnnRirVS+EbsMDRwoP64tGAy8VzTefO016FqWmpvQFoavD3t2hk71rBhvN83vSdani/KAdbyPtiqBX33rvJ5KV4cSoyRZ2/oULxTjgrK69dleeFC7s3z90fcf+tWfeU5s5CSAoXc3W70zESOAM5iFCmCQg8WCyaUkBA8xAsXev4BdgeaNcMkoQezGXHD+vWVL/ulS3BP+vtr99o1m2Fx0GTZtq3jxCOLhQvY4GDtiYlccSRIGVNaXadO8dZ006YpBcO9exDML79sXc0oLAwx4LlzIchspbDcvo1j+PtDwJOLnDEIp8GD4cJ8/JgrNFTxR+y41KGDdR1csxnuc7L2qYAHxZRbtNBuHfnTT3An1q5tXwBHR8Pl3rq17Qn9wgXUQBeFOi2tWuE62dp+0SJutdF2jqZliaEGrfzXlBS8o88+q709tUM0ksPfrh3PdkhPB9lNdNcTXnsNyolWDWN7zRjOnOGs9AoV8Dciwro5iRrVq8MYMIKEBDxvIoeiaVNY0ZmdgmYLlEblzqp7mY0cAexBxMfDzbx2LSY0Kps4diwm75QUWB7Uxq5GDSWTOLsjPh5Cyx4j8+OPcX7UsWf/flSiyp/fuosPVQUioVevHlzIzoDiypQnOn++9ToUQyILjDHr0oXJybw4fr16sKzVoLjcrFkgkw0bBmvOy4vvt2JFKF8ffwx3cEYG2OJly0LAUu/cxES4K2k7ihF/+imuT/36cCWLru/PP4f1XrcuF7Lx8XzbUaMgBCwW3jO5Vy9t5vCmTbBoIyOV5DRboHv87be21zt5UjvmvXq1fQV0xgyMa/9+vp0jz8bJk9i+Z09cq9mz9c9Dr49xbCxc+EOG2D9e1arKKlN79mDfYmZAWhq8EoxpV3kz0g3pt99wXs2aQWml/XXqpF0kJT4ez6XW+0AwmRCL79ePK0wVK2Ibd6bBuYKhQ2HAPEl9jHMEsAdB3U1sLT4+PDdOXNq0QT3UceNg4SxaBAbsqlUQULt2IfZ1/jyE+OPHno+DfP89xrpnj+31kpLg7urWDeUU/f2hratd73v3coJQpUpwWzsbS1qzBvsZMQL7IFbxli18HYsFk2StWlhH7AO7bZv1PjdswL0KDsb+CadPw1rs3t16vI8fwxp54w3k/BYurP0cTJsGNi5tL0lKsg25ZyWJx13VhQd+/BHCoVw5uAQrV4aCRIVM0tN5E5GXXtKOy2/YgG3q1+fWjREBbDYjpaVoUe0evLt28TZ/QUEocHHuHKwosvhLlIBVqmWRyzLuZcGCKPoSGor/a9Y0VtAmPR3WfKFCqHwWGooxiMjIgPBq1Mj2czd6NO63LQ6CJEGpEuu9p6fzlp2NGuF6idwCLQ+P0XaE9Lz36weF8c038Zz6+EDhFwuU7NiBddWxbElCeGLyZP6choXhfA8cyF58FbMZ91+L/JmdkSOAPYwzZzABEtFIvQQF4WUcOFC79GBoqHZBBq3Fy4vn0FapAmutVStM/FFRiJXMmAF394cfwjr//nu8iAcOQJDcvImJ10j6yPDh0ECNuM7JTcoY4t9i+ceTJ3nJyWLFkKPrSpWwv/6CkG/enGvHKSmwDoODeS9nIuOsXYvPlJ4iCkX1uV2/zuP3L7yAaxUZCYveSH1mScI+yApVL0WKoIHAm29C0JLLmRbyGLRrB6VGLaxEhq+oyerWpAAAIABJREFUHD1+zJ+vN97QnkzXrsWz1rixUogaEcCyjOvu5cWFjskEi7hOHWxfuDCY3Wp3vMUChYGYz3nyoFiJul9u7958LF9/zRXATz+1P7Y5c5QKWKlS1pXaiGSlpXyJuHgR52kr9YXKPFarBu9JZKSyshWdJ8Wztepby7Jj/YDJvU4eqZgYvPM+PnhPFy0Cp2LBAoyfnh2q+U3EQz8/KMs//OC+qlvuxh9/YKxa4avsjBwBnIUgt+Ly5XBhlS/PX8bgYFgIb7yBNA6yCrp1Q4wwORlu60uXoCn//jvc2mvWwP24eDEKOUycCNdM797YX+PG0LrLlIHLVz0J2FoCAzFpVqiASbR5c9S57tdP2V3ovffgdl2/HnG8ffuQvnH1Klx2KSkQKrQ+5eteuwbFwMsLisaiRa5X5bl1C0KsbFnr5vE3b8ICqlgRbOhnnoG1SMJe7I06ahT+b9jQOk/UZMKELrqXHclD3bkTile5crhG6elIBfr4YyhiVFKTMbhKxQpfjEEpIPa5KAQkSdk8gQTOvXu4f7bSlr75Bsdq1sw6b9uoAJZlft1efJG7/itXhlJF990Wjh6Fle7jg6VPH85yJsJeeDgURElCXDJ/ftsx9kOHsC9R4NaogXeLYDbjukdEGLP0unSBApSUhPtx/DjegdGjOfNby9vVqBF3EX/+OXeni54ZEY4IYEni76XIqD57Fu8tY5w8WKoUvGlt2vCSmQ0aQJlxRxOSzMbUqVAUbKUsZkfkCOAsRLly1qkIt2/D8hg1isc9GeOEIFoWL3afizktDcLp6lUIyj/+QLxn/Xq4BJcsATFkyhS80P364QUmwhWRPZxdxB6ztAweDAVi8WJMHhQ337MHwoYqNaWk6E+QycmwNPLk4S3l1KB8YFJExIIoFy7gu1Wr8Hn9eihG+fLBvasGlcBjDP8bmbg3b8axa9SwnVMcG4t7MncurF318yCSo44fx3UZMACfe/WCciMKgoAA/dzzr7+GMtGqlTYRyKgAvnePd65iDMJm82bnntubNzHJUmEPqqHNmDLue/w4BIjanUxISUGYoUQJJWGoUSNYnwTiZ2zcaHtcJhM8NmJlNS1SmbiMGQNvC1mcFguUiXz5OKlLbe0THBHANL7nn4ewV1vy5HoWlzJloExmVl/pzIAkYQ4yykbPTsgRwFkE6tCzcKHt9e7fh+tn4kTuuhOXDh3wYunFyTwFYtFevQpr8sYNCL0DB8BK3LgR7kYad7582ueTJw/czmorz9ZCcfNSpcDmbNBA6b4vW1Y/bn74sNL9KxJfyG0o1gW+dAmTIGNQSMglnZ4OS8zXl8etO3Sw3Qzgm28w9vr1HSvsL8uYtElI2FoKFgSJLDWVk34Yg3DVUhAotadtW20SkCzbF8DnzyMcQT14af2vvnLsHLWQkIA0NFFp+/xz5VhHjcJ1pdQmEVOmYBs1U/a55xCSkGVc25o1IahFZcFsBtt5xQrEzBs2tG70oV7y5YMCRM+MnpJ1+jRXqvLm1VfeHBXAsgwPRp068LIcPYpjzZihJBrS0r27NqkwO+PMGYzdSJOW7IYcAZxF+P13XGGjzQUI8fEQuFovT0QEEt+//97zfTCffdZ2/dXjxzHm4GBYjx9+yItdMKZd59liQazy9m0QdA4dgtDctAnC65NPEBudNQvktMGDMYGoY+ehodytZmShuLnYIalrVx43nzhRuf6SJbwv7htvQPmYPx9CoGhR7RSQDz/E+q1bGy/NqYX69Y2fl3oZNEjJGKV61R062HYP0/Zqr8K+fXDFUn7vyJGwpKgaWL581mEAZzF0qPJcChSAxyQmBs9+aCiUCFGQ7d2LsY0ebb2/Pn1A9JNlTpZ8+WU8Z+PHI3QTGMiPFxjIG8RrLQsX4nkl7sTMmfC02LL+qZCILeXGGQEsy3j/1Eprhw7YF2Nwfc+fDyHt54dn3FGlMKuwcCHOwZlGL1mNHAGcRaAKSc623JJlTGaUskSL6P6qVg3WwNq1ypKB7kZSEtyoU6Zo/759O7dsp0/ngq1FC/7yuMM6ImzcyAWMyCKmuPnFi8q4uXrybNECE3yvXvw7MW4uMlUdWWrVss4PHjcO7n29uPm1a3A/66VWrF2rfaxq1fC7mNamt0ydyrt1delin2hD23l5gXX61ltcAcmXDy5MNQHt5ElM+sOGuXJngVOnuGU9axbuIwkSf39Y3xR7/uknbJOYiJBPuXLWCg+5gBmDAqu+PoGBEMA9esBzEBEBgU+/V68OgbVlC77X6kPbvz+ee1ugHsCM6d9vRwRwSgqepw4drImbVCRl7FgIXVIU7t7FPfL2BuN5yZLsn9ZTrx74G08icgRwFiEqCuQgd2DHDl6UfcAAWJhvvolJVdTSy5UDU3fZMriZ3JVGQEJsxw7r3774gr/8ZPGGh/MiC5KEz9WquWc8f/+NCZPqJdvD2bMYE5VFLFECrmzyIBQpggldDYqbb93Kr2/hwnBvi3FzEgRaS7589l2Y4pIrFyb4smUhzJs0UVaBUi9RUZxJnDcvco3v3AEz+osvtL0C5cvDk/DZZ7iWWuzzAgUgqMPClNtOnqwdMyaQ+3f/fkO3Uhft2vFnSmw5eO4cOArqGGxaGqxeLy+44S9cAFlx8mTEkrUs2Vy54GF56y24m0U+RqFCeM9WrLBWbEmxVneAatoUypctXLnCj6EXmrIngC0WnOOwYTxeXrw4XM6nT/Mc4RYtcF0iI/GMqHHiBDwI9Exs3Ji90o4It29jjI62hswuyBHAWYQaNYxXnjGC5GS4uXx8IOjWrOHt2Y4ehYXVrRuv08oYLNK+fcF0PH3aeVLX2LEQeqLlZLHwikuiArBmjfVxVq3C746649WIjkalrBIljHsWBg/G2Mk1evgwrKgWLRDfrVwZDHI9kNuQiGjPPKPdB3f1an4dihZVXgOTSRk337+fx82XL0fzitdfx/UcPRrM6C5dMHGK3XAcWfTyzfWWokXhURCJZqREFC+OSd3XF8qKVjchWUY4oXhxEMacTSujXr8UBtDynNy/j7KSWudBQokxCOr69fH8kleGBHuDBtzTkTs3hNHixXDl2npPEhJwDPUzU7q0/TKZlGJWqhSOeeWK9Tp6AvjCBRQSIaZ5UBDu186d1imElCPcvTvO11bhnO3bYeEzBoXP3a0lXcWnn2JsWvH+JwE5AjgLkJKSeS2zTpyAEGAMloK6So3FwnOR+/aFEKYJKX9+xDqXLIHQNto6rnx5sKIJqanKHM1ChUBk0nNlZWRgYm7VyrlzpmM2aABh+vffxra5fh1CY8IE5ffffINxjxuHCbptW+3tDx6EFfnii/j8/fewNENDEacmpKUpK1kxBkHlaorVqVNKATxnDixQIvzQ0rmzdtxcT9hSuU9Xl/r1edx85kxYdcRebt6c55ufOWMs39xkAjGqQgWeIvb99/hNkuDVWb8e+dqtWmmz6xnD8338OJ67q1d5C0NxiYhAuGTHDmOeFBFTp+K5IEXMbMZzNmuW7e3mzoWVfvEiT0PU6gdMAjg2FvezQQOM2dsbz+qqVbY9EbKsLMGpl/JEMJmQNkbFOPr21VYyswLt2iFdLDta50aQI4CzAH/9hatrL8XBWZjNIPnkyQOBtHixvsVBE9eyZXBPixN6cDDc2G++CSatlgC9dAnrElM4IYEXpggOBrHDCMmI8lW1yFj2IEm8CYEj+bcvvQQrR6t+8KRJ/DpoxZdSUpCbXbKkkoF+5QrYtIxBsMfFcVfekiWY9MkzULUqYqPOYNMm3N+iRaEIPP88/o+Lg6WiFihRUVzgSxK33IcMwfNy7RqsfX9//lxS4wR13NqZxZG4uV6+Of1eoYJ9y79sWWVqEAl98bNeDryrwuXmTQhcKkBCblJ7Pb+7deNEsA8+wDbffadch1zh3brxa1qjBt4fR3geksTPd8ECY9s8fgwrOyAAz8n06VmbfREfj2swdWrWjcFV5AjgLAClemR2DdWbNzk5JSICMUAjuHVLOxc5d264PefOBRs5OZmzeSl1Yc8erDdxomNM7EePIFAc7WYjy1x4v/aa8W1iYjBOvRq+JhOY3XTualA8U6sVXVqaNZmHmiUQfv0VQsbfH1aMUQ3eYuG9aakXsCwryWQ+PpzRTIuXF1y/V69iwmIM8VLRnRoby1Oo2rXjxfZDQjDZ0gRfoACs2vv3cdzZs8E8F9276mXoUBB8HjzgFcZKloRLed06/XzzDh2UnaYyYylfXhk3Vuebx8TYzjfXQv/+eJ7j43lxDXsVtcqVA/FPlvH8USnP+Hh4CkaP5mMsXBhK4rFjzlt/VAxHK0fYFm7d4gpvgQJQvrOicQyl4anrxz9JyBHAWYCRI+Gm9ITbRJJg0RQtChfVpEmOp72oc5GJvCNaNT//7Lo2PHEiLAdHmnb/9BOES+/ejl3PWbOwnVbLPILYElBMcdi3D9uOGqW/7b17ykmeXKXqddq1k/+1aOylfYhNGaKilK5RsSMQMX8p15zWpy45jCHuqb5e6ekITYjjfvtt6+pCJIDVsFhAalu2DM94eLi2wJs/n7Oud+7k20sSlMYffkB4pl07JWeBlmHDeA75jh3KuPmGDXB3U7tKvcVoOVf1NmFhynzztm1xT154AelKlG8u9pemxh0bN+I5Sky0vvYJCVjn9df5d999pzw+KQm+vq6VZiWUKAEFXcwRdgRHj3IlrXJlkD896Qru2xfhLaOhsuyIHAGcBahXDw+uJ/HoEWfkli7tmMarRnw8BK7ayvP2xss8cSImUUdzka9dwyQ3bZqx9U+dgpVRp45j8dSEBAijnj3tr0sNAxo0gGWblAQXaJkycMlp4fp1xKUCAxFfpJj8Sy9Zp/hYLChi4ueHCXHvXu19Xr0K5rO3NwpRiBPd0aOYiOg+iEoFdXdSL6+/zq3f+HiwfYkPUL06J2j16WM9Zj0BrIWkJFiTZDE5IuzCw+FGJou9Tx++33ffxXfx8bCq162DlU1lHWlp2BCCjxpZqGsFx8fDC6IW8Fu38rj5xx/zuPlLLynzzevXh5eoZEk8U0bzzalOe/HiynaTektQEAh5jIFkaTRurodbt7CvDz6AZ6JUKTD+9Spw6UGSIHipH3DLlo4LcmeQng6Py9ChmX+szESOAPYwTCZospMmZc3x9+1D7JExaJD37jm/L3IlbtgAl/TcuXgBXclF7tMHL5a9mq4PHiDOV6SIYxazLPM+skYmCrF614svgpjFmH57unPnIEhDQ3kbw/R0HlOOjNSuNHT4MFyh3t5g8IqT6u7dsARDQ2X5l1+U2+3YASWkVCnkw4qxR1nGBEmTI2OY8KkRSN26IINRGk7r1tg/pYeRa795c8SWCY4IYMKdO5ioO3e2L2wKFYJbe+dOPAe9ekGZoecnLY3zDOrU4TnBefNCMJJVJqammM1wwZcsaa2sRUVZj6FgQdwHR5VIMd/8vfeU+6Q67W+/DcLcxIl4prp1c0w50Voobl6xojJu3r8/LPCpU+F5oHzzPn2w3ZIlIG5u24bPFSoo77VRZGRAUSlQAPdj0CDH30tH8L//Ybzk7XlSkSOAPQxq+E71hbMCaWl4GXPlwqS+dKlzKUiTJiGGqZ7Q0tIgfJzJRSZ265Il+sdNT8cE4+8PApIjSEnBRKXHbFaD+sGKzSbGjdNe9+hRTECFCoFlq8aWLXBhhoRok8USEnj95mbNYOF8/DGswapVrds1rl0Ly7lGDS6cevfGPaV7YjYrrUJfX15vmJbatfWVkTVrcIzq1TlZzZ4Ajo6GBTlvHoRA0aL8WN7e2BcpgbQ0a4Z7PniwvkVYtSoUCJHd3LQpnuUDB6Dc3r0L93P9+tZuWirFOW8e/+7KFVxfUko+/RTKFXXjyp0b995WqEIPFgtXftRV4iQJ13zCBGVFuLFj8Q7Qe5GWxp9Bqv7l66uMm8+bx+Pmffti7FSnvXx5PI+O5Jszhme0bFl4IZo0wXvcuze8A5MmoerYO+/wOu1bt+L6/vYbD5MEBECR0vMUuYLRo6F06JVLfVKQI4A9DEpx0WsO4EmcP8/TQpo1027WbQtVqxoTZCYTJpslS0D80MpF/uwzuNUkCWMpVUo7ziVJsNoYQ26to/jkE9mmBasG5e8ePszHrOW+37sXk1apUtaCUsT167x85Jgx2ikuK1cq4+sdO1p7BKjgQ7NmyqYC1Bt42TJcPxLoWgs1cMiTxzYjf/dunFuxYrCWRAEcE4PrMX8+rFuxRKqXF56RqCi4Ovfts06P+fZbvn7x4hCA6el4Fm2NnaxUEZKEa5U7t77A7N0bgoGUiREjoIgSm/+tt/i6Z8/ymtaMIV76+++OxTkpxSkgAJ9v3YIHhsiNuXJxgdWwofY+JAnx8OBgKI/OlKKUZWW+ORVRoXzzZctwj0TS5YABuKctW8JbUrkylClH6rQzBkHujpg1wWLBs9K9u/v2mVXIEcAexsSJmCDc+UC6AosFLqnQUEwGr71mrOfn9euyXUvV1jFPn8Zk27ev0kIqUIATZKZNs45vEevaVvEAPZhMiN02aGB8EiV2cWSkcoxirGzbNtzTypW1U5rUSE/nLOqICEz+Iu7dU8Z0R4/mgtpiwXVhDBOQWoBLEoReRAR3M1JfV/WybRsEAikEM2boP5cnT1rn1ZYooRS2Vapg0n7vPSgkRsl+6paJWstXX0ERWL2aN7FnTLkf6t/7/vv6x7p+HfeqXz94DXLlQohEkvDcaeXqxsTA4qPyk5GRsPqMMH9TUvhYW7fm7vJGjeCOjovDPQ0K0vesyDK8RaIi4AoyMmyHwShH2FbessUCpZDqtP/1F0IGWnFzZ+YIWyAv2cqV7t1vViBHAHsYzZtnz7ql9+5BGJKr748/bK9Pmj01s3cF6lxkquZDrjDKRabqRl26OOcyp4pb9goPiKBG34yhdOGFC4g11q4NN++6dXAJ1qnjeLzwp5/gLg0O5uSgo0cRpwwIwERGnZpq1oT1OXAgF8p65BvqTCUu5Ar18cGESQ3ht2+HwkVM3WefRXw9NhbW0cKFEPRqchNjEGLvvgur0NE+rBYLznXRIl6HWW9RC7roaP4beRtu3MCz0ry5/Wfj1Vexbb16uB6UDhgWBpKVHlJSIDQrVcL2pUrh/LXO3WwGR0JdgGXuXGuFi6xve/XQKXTg62t7PXs4cgT7Wb9e+3e9PsLZBbNm4b49CX2K7SFHAHsQFgsmCVvpK1mNbdt43uXIkUr3poiuXTEBZVbawezZGEO1atbxwrp1MZHt3m2c/WyxIA5Xo4ZjwnvfPn5cijdt28YtGS8vCBBnU7Bu3OCNDMLCsL+SJZXVvLZtU8bw5s/Xv+43biivVZMmUDgsFggrX1/EGR8+hJXs7w/hv2MHCDxaArBCBShnixfDwqHvFy1y7P7fvIl86D59lM0MatXivYonTsQ+xS5PlStjjOJ9Iy9J2bKI+z77LFyjRnLrk5K4NSnmnZcuDfKQPVgsuKYUvgkJAcnp5k14CqZNU1aYo0WrprgsI0WNMVh2tpCWxvflShU1iinbYjzb6iOc1aheXbt+9ZOIHAHsQVy+nH21ShGJibC8vL3BMt6wQTnRpqfDahs5MvPGkJKCSbpTJ7jpKGbVpQsEBwlAPz+482bOtJ2LTC3mHI0bUxUrdRcfMc/U1ZKS4sTKmHVhgZgYZWy1d29rxejcOW4di4v6epCXY+RI240cGEPsVksBE0l1Y8fqW+KPH4P5PG6ckolNdaVXrVLW7I6Kwv387Te4ZLt0gXCiWsQ1auCzJOG5DA9Xtgh05L0iL8v06fy7WrWgWDqCw4c561pcOnfGe0Ps4kqV4PbV8pK8+ireNSOEItq/vbKWtjBwIK6fPeVJ3Uc4O4C8BbbCDE8ScgSwB7FhgzFNN7vgyBFeV7hTJx7fpF7GYr3jzMDcuThO6dKYmMUc2UePMLnNmAELkpqZq3ORHzzgFpWjZBAq/M8Y77ojSRD24mTrbBMJkwkFJIhxW6IEhFuePIgxyjJYuhUqYPLevBmueB8fCJD9++EiV6f2TJ/O3Yzdu2PsffrwhhG0lC3LuyUxxolY9++jljJjcHWrS5AWKAAvDsWiu3WD8DCZwEaePx+WN92TgACEEZYsQRaA3sR/7x4vFuLry93LZjOuB7l+6ZmcNo1zAhgzxl2QZTw7pEQULsxdyE2aGLeskpMxpuee0879bdkSzOCvv8ZnEsQiA5vQpQvi50ZA5+7n53wDggoVjCsaruQIZwYovJJdalG7ihwB7EFQ7MLR4u5ZCZMJD31gIATDBx9g4vP1dTzu5yhiYviEZi8+lpSkn4tMS9OmxuvlxsXBjUis0Nmz4XqkYiYjRsDCCw+H0Lhwwf4+JQmW6kcfQWhS6UY/PxDSZBlKDuW41quHdcLCeE6xLON/8bzEmsY1aiA2q3Ynly4NYfzGG5xQRqlS9+9ju4AAzg43mbiAbdBAed1EFrRW/1wvL4QJXn4ZYQKjglGWeexRq1WnyQTijZZ7lzHkvBoJL1AsdelSrrDIMlyutqYhiwXXZ8gQLsBLlsR5Ehfi0SOQykSPBWN4Vzp0AHNbbemWLWu74xbh5k1Yo5064Zlo1szxENCDBxjPokXGtzl9Gs941arO5Qi7E02a4J37ryBHAHsQzz0HMs2TiKtXeVUoxjABZDa+/JIfz1GCU1oaXLli1xdaypfHJLp8OSxMrUls0CAoS0eO4FxHjsQEzxisbtrm2jUIpKpVtRWS6Gi4vQcPVk7KZctC2Hz3HW+FSMjI4DFR0dOQkoJ0LZE1rrWULAmrlCxedYrRo0c4J7EdZkwMXL2BgfBwEL77DpN+4cLwQMTFKc9BfexFi5wnx0gSd+f6++uHE4iBLy5U0nPKFNvHSExEGlyHDvg8ZAgUoIsXueKixrlzUJ6JiJYnD7bbvVtf4GdkKFtQFirEvQ1ffsnXoxKUev1/RXTtCiXp2jWuPCxfbn87EdS/WrzHRrB7t7KPcFYgJgbK3dy5WXP8zECOAPYgChc2RvLIrpAkJcN2xozMS4Snqk4Uq9Ny3RkBuWIXLsT/lIssxnCpOhTlIm/ejO/nzME+ihTh6775pvUxdu+GsO7UCRPq1q1wgdeowbfLlw8Vnb74QrvPq4hvv3WsexAtL72ESYqQnIz0MrGEI4FKM+7Zw7+7dw+KRFAQd/dr1YempUsXEHouXICyExaGZ9xo0w81KOUrKgoTrbpNJOHMGeU41ArJO+/oH4NKWO7fj8/R0RConTtDySpUCN8/eABPBZUR9faGArpmjWMxf+IQPP+8coyUp0ws+61bbe+HnkmyXC0WeEry53dM4ZkzB+dir12hFkih6N8/a9r/kTvfmY5p2RU5AthDuHsXV/S997J6JK6B6tGSG7N8eTBo3YmrVzGxVKkCK6hjR1iZzgj7Hj3gPlNbp7ZykWl56y1e2IIxCCI1MjLgElY3DfD3BzN30SIQWIy4RpOTlX2UtRY/Pygjq1fDIqPKVpQbO2mS0kKZMAHbqEuOpqTAlduwoXIyFVN8wsIgjLXGodW16tw5uLqDgpDe5AgyMkDUqlQJ/48eDUGhNdkSB4Ex3qloyRIls1orxzU1FfdZ3XeaSpMS275zZx6/Dg+H0L5717HzIVStimdQlqE4iGz2zp15rratso2JifBq1KihTMk6eRLjpH7URtCmDVLonMXChRivKyQwZ9GpE56vJ7X3rxZyBLCHQCQM0eJ4EtG7NyYxSYLlR7HGqChrV6ozSEiAKzQsjJNwSAg6yh4/dw6W1Cuv2F+XcpFtCT/qi3z8OMg/nTvzWCCxshlDfNSespCSAsLSxx8j91m0ltVLWBi3xGrUwBjq1IFg3bwZ+0tNhQXMGIg6FJM+d07WdXGSi3/5chCKhgxRFtegfW3aBEXIYlGWsdSy5O/exTY+PsjrNgpqn/jjj/gcF4d4aYMG1soLuednzFB+n5TEWzUyBne6WBKULPldu/h39ByL55w/P9KKTpwwPn4tSBKUEVEZyMjg3g1RaVu3Tp8gSLngIg+AMH06ftNr4iHCHWmQYiU6T2ZzJCWB1zF+vOeO6QnkCGAPgYr6ZzZxKTNhMkEYiD10U1NBUPL1xYSycqXzGqrZDGvXx8e6TV1kJCwkR3J4hwyBxWE0frxxI+7R/PkgHYnpNnpLpUo459hYXItnnsGke+oU329qKioFffIJrJVatZTt8CjFSlzy5oWAEdsg/vKLch2t/MzNm+HuDgqS5RUrcO1atoS1rE4XMpk4s5jG0asXBPOff0K5Cg6GoiCC1g8N1WaAP37MXa+vvWb/eYiLw5hbtVKuS52cli7l3x09yo+vV/KTOv3Q0rMnBHHp0rwK2uXL8CSUL2997V1pUCLi4UPsT10Jiip/iTnmjGF8772nrJ187BielREjtI+RlITtqlWzZqurQa77FStcOSs8N+3bezZH+IcfrJWn/wJyBLCH0L07XvYnGdRYXKuCzqlTvKBE69bW1X6MYMYMbP/JJ9a/rV2rtJDs4cYNKAW2yvuJiImBCzMyUln3Wb34+sKyCg3VzkWmWtOM4Z7Xrs3dmYzhGO3bwyrftAlWlrj/EiXg8tQqYH/vnjJ2PWSIdjzy1i1eJGLAAB470+occ/QoBMKhQ9YC+vZtELlCQqBAEOgcwsNxDRYssFaMMjJAPGMMFpOt9K/Jk7EfdQMLSQJzPV8+eFfS0pSeAluK1Z07nGUuLlWqcJa5lxeE/ooVuN60jrvSBI8fx/60SHB58oB3EBiIZ3TzZjB8SfmaNg3P8DPPIC5ti31MsXMtfoIIeg6caSyhxuPH8HR4Kkd40CAoiEbKfz5JyBHAHkLZssb6z2ZnUMEAvcbxFgtiqiEhcBe9+abxF4aaVIwerf17RgbiYM2bG9vf+PEQfDdu2F83I0NJtBKXNm34/2ohc/8+hA/1ztVbRoxAAYkbN7iF9+gRrwdNy6pV+tfr7l0Ij8Ab/AyJAAAgAElEQVRAlDicPRsCpHp17XxQsxnWp7c3Z+8S89cR3LqFDlZ580IxkWWehpSczBsmdO5szVqWJF7R7PnntWtDX7oEBUavr+upU7C0hg7l+dcNGuCvvWfr3Dl9L8aIEdZ1u8m93rixsWtjDz/+iP2Jygth4kQ+lq+/5t//9RfCPGJu8dSp9o/VvTu8PbYqgQ0fDiHmTBlXLdy545kcYZMJSlhUVOYdI6uQI4A9AErdEPuTPomoV0+/Y4uI27cxITAGd6u9loEHDiCXtWVL25MqMVhJEOjh/n1MRoMHa/8uSRBaH3wAYofWBP3RRzxfm2oHHzkCd+jIkchzFfNvw8JgBderp+woQ0v16lAu3n6bV6OiZexY227aW7fgDg4KUnIIfv0V1lFgoL5bce9eKC50LCOlGtW4cQMKZGgob7lIecCShHi4ry/GqNXl6/PPIVDq1rV273bvjvOyRXKaOpWPf9gwCK88efTXlyRYsdS7WVyaNQNBztfXWgjv2sXXO3nS+PXRA3lDxGpfhGvX+LG0WOPqXO9WreDu13tObt3CNXn+ef11ataE58KdoBzhatX0y9a6CuKA2OrY9aQiRwB7AETycJQZmp1w/z4srvnzjW+zaRNSfLy8MBlquVVv3kTqSvny9tMpEhJgXffta3s9sg7FRhF37sDKHjRIWchBzfItXx5W2fHjsExGj7aexPPmhZt9+nTkyerlEhNZpXp1/dzdYcP0t5dlCL9y5WDJqUtUyjIEF+XOvvCCdnrJw4cQfnRMZ2Kc168jJYzi1ep+wHv34j4GBeGaqPHjj1CKypXjBDHqz7tgge1jiwVZ4uJwniVLWq934waUXOon7O8Pr5PozcjIwLMwdiws71y5QF67e1cZemjd2nW27YwZOIaexWnLld67N8Z/6BDY+JRDXq0aitJoFfOhFpVagurxY7wTzqbz2UJm5whPnIhrYbS71pOEHAHsAZDl5i5yR1ZgzRrZqfhYQgImOC8vxDfFTkRJSYgjhYQY76o0dSpcknour4QEWGpt2mDSHz9eaZEWKIDUj6VLYQ2q8zNLl1ZW0RLjiG+9BeFsdGLOyFDuW6s6Fy3qXGRJwvjKlIHAt+VFMJtRnMDLC+eqZYVKEj9WWBg6HTmKa9e4O1vLTXvnDucBTJ1qHff96y9c//z5YeFFRuKZsJdXO348H/v77yP/mAraJCSAbd2yJY/JN20KItmjR7zwBHVciori9+/GDShJvr64N+QNIRc3McydRf/+UDj0QGEPdWri9u34XlR209OhQIaH47dChaC4iEqryQTOQbFi1souWfe//OLaOemBcoQHDHBvmpAk4R1wJnzyJCBHAHsAAwfCAnqSERWFydPZ+NGBA5xA06MH3NQ9esA16Ugt5Zs3MWGq8zwzMlDUQCzOT0KvbVu4fg8fhmtxxQpt9yRjiDFPngzS18WLOF9iRxtNS8nIgMJCDe9pmT8fFlf16nAZUi7yJ59AKdCzkr/8Ur/hgYidO2GFBgRAKKknQprYaZk+3T5zVo0rV/j2WtcjPR3WMblN1dbdpUtK5rG95hjkPRo3Du7T4GBegatfP55XW6ECrq/oYpckCNMyZXBP5s/Huur0pSv/Z+86w6OouvDZkBBqgBAIoUuVXkR67yBFOiJFQHqTooJ8gAVRQayo2EABGwqogAICIkqVDqH3DoEU0pPdme/H6+HO7s7szuzOpsC+zzNPsrszd/o97T3nnBOEMfaKRERAeHpj0TVtCqtQC9x1rEwZoawkJuL8KldW37ck4T536oRtc+eGl4YZ4bt3qxcx4fxdX5aS5H3oSfvTCyayKZnwDxL8AjgDUK1a9tbgbDbkZA4Y4N04aWlwESobu3OTAyMYMADxrh07YBF16YLPSuHy4ovIsf3sM1hQTZo4C2flsm+ftnKxeTPWcZfDHR8Pa4atxEcfhbtw1y6xn/r1tSdBSYKA4txO5RISAmv9zTfBRtcSnDduiEYKgwbZu+1sNkzujz8uai7Xr+++MpcjlN4EZbqVEkuX4j6XKuXsNVHGP9UY74y4OAinihUhmFgRUlryY8bgeqhZXWz1cZ1tSRK1vNW66Rw86Hzdp0/XcUE0UKaMNnEoNlZcfyKRWTBjBj5zTW5XOHYMaW05c0LoPvkklFAuYqJkJ3ftCqHuSyhzhJXlNr3BK6/g3LKz99AV/ALYx0hMxMvwv/9l9pF4Di7nuHy5OeOxpsyuTL1dXa5eRW4ot6fjpVw5+64+bMXw/3nywBqZNAnnoCxlWLOmeyuHY4NK97kSN25g4mQ2dLNmcH/bbM7dk7SIYYzISLgmixSBtX75MqzpUaPs+yLnzg1B+/LLzn2RmQFtsUAJUBKKuOrTsWPozlWwIKxK7r6kB2Fh8CoUL47jVHN5yzKemzJlICCUzTS4WAbHk2fMUBegzz6L33v2hMvZUTi6u2+tW8OSVcZLrVYIKosFxS+UkCR4V7p2FSRCIsSL1fgLrmC1IlSiZQ1u346x166FR6B+fSgzgYHunxFH3LiB+YVT1Lj1Y506OA5Jwn0yOq4nUOYIe9olTIk6dUBufFDhF8A+xu7duJKrVmX2kXgOLiKirDPsKfbtg/Bo1AiVdEJDQeCYNcuZWBIXJ/rJKoWPcile3DnVpFEjbPP11xAOSvetzWa/vp72hNyDdNky++9PnEB6DFsgvXrZx2qVubAjR4q0I60KQkeOYKIsVkxbKbl1C8/SxIlwcWv1RY6Lg2AuVgxu+C++wER8+7YgHskyYumNG2OMoUP1EV2YBX3qFARc0aLaMfyoKJTk5Gtw/jwUot69ce1HjZLvW+ts1SckiMYXvDRoAGv56lV9Api9DgsXOv+WlASFLGdOXCMlCheGwJVl0T6Urf358/XXgeZiIIsXq//Olb+uXoWnhvfDOc+eICEB10jZdrJiReHGVSul6guYlSN86RKOe/58844tq8EvgH0MLn+XnftXNm3quk2bXly/DrJR6dLCpXT7tmgkX64chP3s2RAKan1W1Zbx40XlJVepCunp9k3o9cRV+RiJkG4jSbBemLCTKxdcfo6FRxISRJzu5ZexndUK6yAoyJnRfPAgJv/ixY0VSmCi0QsvIN6p7Iv82GP2guzppyFgBwyAS5uFbXq6YI5Xruy+2L0yDenkSQj58HAoJGqwWp17KLPbW5KEghcQYG95EqEghfJ6KIltc+dqH2OXLrieWk0HoqPhSQkJsS8A8sgj9nWuORzAZSPDw+G+dtdSlIvWaFmBI0ZgTEnCMfI5GSnfqQWrVVSOUi7uGj6YCTNyhLnXs55Wn9kVfgHsY4wcCVdbdi0gHhPj2pWmF0lJcLMp6/NKEtxu775rXy1KbQkOxvZjxyI96OBBaPd16sCqrVEDDGCtOG5KClr08XhG8mFTU7FN7dqwxHhCnjNHPYUkKgrHGhDgbAFFR8NCCQ8XBfi55WGpUp5VEFMiIQEx69mzQQDS6ousFqfbuhUKQM6cyJHWemaVAliWYf2Gh8MadjVZzpwpjoFjnJGRzsKZFzXriZUhVn7U7iPHct2lN12+DBZ2RIRQkGvVQlERRlwczq1RI8RXOdRRogRiy1qx+O+/x3paMfL69TGWLNunWWmV1/QEp0/bX0+LBZ4Gx4pjvoK3OcKtW8Pz9SDDL4B9jHr1xIuWHcFuOLUcVL2QJFEx6cMPQdDhcoCulkKFwH48eFC9QAf3ROVi9Y4uYkZ8vH1FKy23oBqSkjDR8rbly8PNp+WKvHAB9ZWDg0UfX0dERoI0Vq8eOvsUKACmridFMtxB2RdZTRg/8wzux/nzuE9RUbAeifBXzR3qKID5nIoWhQBXEyLKXr/KOtj8uXNnEIr4O61cbxYq8+bBxdmli/M6ffrAstUz6R87hjh45co412bNnJnLS5Zgn998g89bt4rnt0wZuPcdn88FC/C7Wu13qxVhmOeew+dBg8R5O15Xb6FsTjFxouBGtG0LVryvDQNPc4Sjo/FczJjhu2PLCvALYB8iLQ0T8ZQpmX0knmP4cAgIPbFSNcTG2he+UFvCwuAp+OwzWD179ojCEZ07a7uwkpMx6fNEqCako6NFXicRBLGeSScqCq5jZYu7ggVdu60PHYLLrWBBWEuuwP1d2fWup2SmGbh+3b6etHIpWRLu6U8+AVs4KAj3zjFOqiaAZRnWXlgYrENHS54tQrWF3dFKSzBfPvUOP3v24Pe1a4WQU5LjuAOWkYn777+hnDRoAGW5bl373202uPNLlBAubUlCTi13ZipfHgogPx8TJkAJUMOpU9hm6VLB1J45E8pQ7tzG+vu6A7OtiaBMRkejRCy/k9WqQcHwRQENhic5wsuXYxt3VfSyO7wSwESUi4j2EtFhIookolf++z6UiP4gojP//S3kbqwHUQAfOYKr6C7XMatCkjDp6K1hzV1mJk92LXAffxxW5b592i++1YpYW968cFsvXKiuBDDBR00g3LgBljO7t0NCnOv/OuLsWYzF+aVduiD96NFHXV+HrVsxfsmS2qxgJbgKFBHIWRkJm83e7bt8uchF1qqJ/cQT4l5pCWBZxjNfuDCuw9mzEKLKHNvwcMRVjxyBFWuxID/8zBmwk4OD0WHn0Ufx/48/2o/PHaH++QcKV7VqUL5YMA4ZYqwDFmPNGsE5KFPG+XfuXDRrlv33kgSiIOd8P/oolI1u3XBeali5Euvu3IkwSvnyEI48X7iKbRsFx6KJ7DMxUlNBUuQ+0sWKYb9mCn8ljOYI9+6N0IBZdauzKrwVwBYiyvff/0FEtIeIGhLRfCKa/t/304noLXdjPYgC+KuvcBX1ptlkNfCEoEwhYUgSGJxr1mDidOdO3rTJPXFFDZcuYfIngmXiGBdkS9kxR/nCBcRa8+YVhCll0XtH7NmDlz4gADHQYcPs71vDhhD2ali5EttUqeJewMsyrJ48eeD2bNgQ+/zjD/fbmQ1lhbBPPsE95VzkL7+EMCtSxP4+cgpYnTra8c9Vq9SfgcmTnT0IGzfaW+ScF373Lty8Fot9pSi2pPnecDrPjBm45zlyCNeuUTBhkkjdUnvqKVjKah4ZLtjimCKnNs7MmThOVoKUVcnat4cwNMsi5fKUbdvCo+FIlJMkvJsdOmC9PHnAAj971pz9K/ejN0c4ORnv7ahR5h5DVoRpLmgiykNEB4ioARGdIqKI/76PIKJT7rZ/EAXwpEnQxvWybbMauG/p1atgNf76K8g9LBC1lunTUSWpTBm4upQ9bT2BJEHIhYdj4po6FRaPsjdszpyi6P3x47DcCxaEWzB3bvUi9TYbXJncuq9AARy7WmOAjh1huTviww8hJJo00e4SpcTGjZjEq1cHE/zePUzaoaHGC2J4C05P4aVfP/WY5eXL6vdcmYv800/wUnApSselfXtt9+Pff4v1Zs8WVk9SkmBFT5mC71lIKp+pIUNEnDFnTjyvnkLNWmScOgVlqW9f7e2tVtE6k5XGdevsz71rV3hlcuaEUFdi40b5vnvaDPTti/fw1i28Dy1bat+Ho0eRihYUhGe6Rw8UuzELenOEuXxodq6drxdeC2AiykFEh4gogS1dIop1WCdGY9uRRLSPiPaVLl064846g9C8OeJK2Q03boiXgMi+RGJAgH1P1pIlkceojGGmpiJ1KVcu83qryjLiV1zBqUwZ5POGhKBQhsWCSXPfPrhAw8NB3mrcGBOPcsJOSYGFx7nFpUrBUnBVbKFfP7gLGZIkyy+9hO27dYOwcIf16+FWrVXL3kV65gyOsUaNjC8436gRPAVvvIGJsUIFWT5wQH3ds2dFzJMIjS3UhG1gILwm27aJ77Qat0sSrC+LReQjd+kiCFRWq6gF3a8fmOdE9tdbGTvWalyvF5wSRQS3/OXLEPpPPCFIbDVruh4jKQnrVaokymY2bAhLU5JEd6oCBZw7JUkSnoMaNcwhSJUujesmy8g/J4Lr2RWuX8ezzYVSGjZEKMAMQ0JPjvCIEXi3fRmXziow0wIuSER/ElF1vQJYuTxoFrDNBuGg1d82q+DWLWijr74KQcJdV5TLoEFIS/nnHxFr4ybgjnm3kiTYrN9955tj3r7dnkl78yYK9PPkX6YMhNrbb+M7ruAVEwNBwwpFrVqIz+vpWTxqFAhfsoz1n3kGY7hrNs/45RdYFnXrqlvKGzdCuendO2NT1r7+GuexeTMs0RIlYJl99JH6cXBKlnIJDoawadcOCqdWSplafjELBd7fokXYvkIFUcFLkgThihdHcJlRd6lH7vD+++rHXq4cPFqbNrkXDEyyWrYMz8pnnwmhq6zopVUYY+lS2ck17QmuXcM47MK32aBwhYXp89YkJMDDU64cxnnkEeTmeqskusoRttmgPLvyMjxIMJUFTURziGia3wUtqieZVRPVDNy+DbfO3LmI2yr7xHLZwoED8cI+/zy+d2TAMqxWvERt2th//+67sqYLz0xw8Q4iaOrMhmaX+YkTEAzdu8M6nzxZ1Itu105YI3rx4osQTAkJooPS7Nn6xvjpJwiV+vVdp8awkDGThOMOyclwf/fqhc9RUeL8evcGi1aWEV997TUIWkfhxAVKGJyLrCbIChSAUvrdd1Do8uZFfFJJtvnnH0zOefLYK3FK167S4xIVJazT8HBxzHoRG4sQh5ZF/+mnxp4VPvdt28R3KSn2Fa+ItFP7UlJw/u3bGzsPR3Axjl27xHeHD0N5ffZZ/eNYrYjrs4eiYEGEarwJLWnlCDNpzEhp1OwMb0lYRYio4H//5yaiv4moCxEtcCBhzXc31oMmgJnp6K55vK9w5w406HnzEEfjziu8VKoE4tLChWDkOrpfx4zB5OiqWw6767j4woYNsOJ69PAte/HKFViT48Yh3us4YaamiiL3bdtiwsmRA2kQ7qo8aWHePPm+1RwQoL+s3/ffY9+NGrkXDJwvbbHAw5BRmDoVx8gTqs0m4v9E9o0uWrTA36efhueByTs9ethbVZxeM28eBAp7I9SWtm1BWORcZFmGG5RzbadMEV4GTgsrXlwUlOAqXsuWid7T7nDqFJ79Vq2ExR4aKsqUbt6Md6hyZQgKZT1td+C8YbWYvuO5d+qkPkcwa9jIfh3xwgtQGh0t9mnTXCsArrBzp+hiFhSE+LveLmGOUMsRfuEF3A9PCndkR3grgGsS0UEiOkJEx4ho9n/fFyaiLf+lIW0holB3Yz1oAnjGDDxInjB/jSI6GizaN96A1VK2rP1LXrEiChu8/TYqEOkRBGXL2lcEUsONGzjHqVNhcRYoAAHl6zjmc89BYFy4gKIaWhM7C4/Jk73Ps1V2KFq9Wt82y5djomrWTH8x/6QkuKnz59cu7Wg2uLjFK69A0K1fj7ih8joWKyaqRSnTkGw2WO6BgfCobN8Oi6l2bSh9yud/506cV7ly9iVBlSxozkVevBgCdtw4fN+yJcIlbdpgjBIl8Hf1ajx3bMGPHYtr7hjHTk2FUJ08Ge8D7696dVhz//yD4+b2h1yp6+JFhCxKlND/DHEjDEfBx4xtIngJ3nxTnHv37vaC7O5deACeeUbfPtWgxUFJSID3qnp1feEXNZw7B0WHXf/t2kHhNxo+4XxfzhGuXBljPSzwF+LwETp2dE/W8AQxMbAu3noLcRKOz/BSvjy+nz8f63miSZ48ibG4jZsr9OmDyaZkSbiBPa37qhdRUXjpBw2CwkEEgkxSkiiAz0u3buZo0ocPizH19iVduhTXpVUr7XrEWrh0Cek/lSpljCUgScKy5JrHoaEQfuvXCwZ0z544HrU84L178ewFBAgFUI0DsGOHuJaPP45922xg4H70EZ5dZS6yYxoUEVzkV67YkwFZ4EZH4zls0AAW+ldfQSllyzY4GFbnokXq9dm585eyuMfhw+BzVKmiL3Y6fLhz/+/UVJH21bCh+D4uDvyLAgXwW58+IsVq3DhYiGqsfHdITwdL3bEvMOOXX7C/t94yPrYSd+/Cy8G8iho18OwbIVCxJ61XL/xdtMi7Y8pO8AtgH0CSMAl42/4rNhYa+YIFsEiUXU6IQIro0wea9ObN5jXbfu89jK+nNCIXRiAyN2VBC7NnY1+c2/vUU0KLVxbqDwiAABwzxnhMUIk//8Tky+Pqqczz2WfCKtDbPccR27fDquzc2XdpbFevYgJW5q4GBqJKlzL0IEnwngQGClavWiGOuDhBhiNSz4lOTxe/Fyqk3udVkmCVf/EF4rKOHh0WHMp84zfewHYHDzrHqSMiQJb75Rf3ypBW56s//4Q7t3Fj94z39u0RAlGCQxjsUndEdDRc6fny4bl9+mnwNSwWMJKN4sAB7MtVLLV7dwhpMxrFpKRA2WGlKCIC56xHYZEk0XpS67l5UOEXwD4Asw/Vmn5rIS4OpI2FCyFUHCeRMmWgIc6bBwKRryrWyDKsd73Nu9lFSOS742HcuycsBSKUS1QKJ657u2oV3ODPPQdBXLy4frexEj/+KApsfPcdxt6wwfU2H30k34/teRt+4BrUZtbDjY8H87ltW9HKsFEjsF1z50ZMVwu7dsF1yddfzd04a5b4PTQUwlwJFkRjx8KTUbWqvjaXly5pM5RdLUbdopzSpGaFrVyJa9atm2vmu2PVtLNnQRJjReerr7S3jYpCuCN3bnumv1EvCj87rpToS5dEPW2zmPeShGvO3cny5EG3MneFPZTKsxl9hLML/ALYB+AcWrVatrKMSXD7duSePv00hB1PhkSY5Hr0ABFjwwbP+4N6gqQkTBZarisluCgCTxSekjH0gl3OLJSUk8bBg7DQHCti7d2LuDQRmN/cgcgdFi3CPWnUCFr8sWMY44cftLdhz0HXrubkMCqrB7narztYrVDaBg0SMbtHHoE3Qdk44eWX8ZuryfLuXXEPnnzS3uty+TIER79+IDnVqYP1xo+HMnL4MFyqnGLy559Yv3p1faUjbTZ9Qrd4cQhJIv1lVBnJydhu3jz137lF3siR6kJLknCNJ08Wnzt0gAucq1LpIQLevAkFUnleRizDwYPhhXMnWJkc54mC6g6HD4siKQEBMCB27lRf9/p1cZ7e9hHOTvALYB/g1Vdx9eLioLn+8w8m50GDYE0phW3JknAFvfYaXE5Ga9iajd9/x3G5q0Kzdatwkd6+jdiaL3OeY2LENXOMW6WmIt5erJi6yystDTHx3LkxES5apO3WlSTRNq9rV+FC5kbwn36qvh2nEPXs6Zo5bhSpqXB7Kts46sXRo7CmuPB+gQIQ6H//rT4xX70KZer5512Py3HiwEC4h/fswfeDBuE5YJdmSoqoC861ncPD7RXKLVug8NWooa1oWq2YuJXeFuXy0kv2jGy1ZfJklE3V4znKmRPELC3MmIExX37Z+TdWUDj3lktnvv8+jjMw0Jhyxs8dX+8JE/TFhCtVck+ilGW8GzVrYh7SSxQ0imvXcM0KFpTve1xWrbJ/BzkffONGkPm86SOcneAXwD5Ajx54WapVs28qz5r5K6+A3KIW/8psTJqECdFVnOvsWbgXq1YVpQsHD0b8yhcvsbKjS58+zr//73/47ddfXY9z7pxwjTVo4JzikZ4uiogMH27vZoyPx/fz5zuPyykjfft6zip1hRs3wMItU8a9N+TmTVhabH0GBsLFuHKlPpd4z54QsK7WZRLW7t04pqAgeB6I1AXX+vXi/nXr5iz8//gDz1ytWkJAxsbC6h80yL4jFZEIzxQoAMEeEABLTpIwsefKhevFFqfSS0MEi3vsWAhHtVzWsDDXyqQkiQYTjgoZ9yL+6ScojcWKoZOS1Qoym1aDBlfglMawMJxH7tzIPNBS1lkJ0LLiHbFrF4wCttp9hfh4eBCYR1C+PEIfCQkI2ZQrh2vrbR/h7AS/APYBRozAi9elC0rnrV3rGZMxM1C5MmLAWoiLgxUfGmrvqty1C0+MkV67enD7tnAhq8Ud//0Xk5JewpskofpVWBiE04wZUDYSE0Uf3FmznPcjSdiPkhAjScJtO2CA5y0b9WDvXliQrVo5C/mkJMSoO3cWgqZePVhdeuKrSmzahO25epgalCzo6Gj7ZhxqVgu3EOSlXz9nYhzXQCbC/Vbm5g4cCGHJKUJcH/qXX6DwMXu2b19M8v/8g+2KFhXlQt94A5b/66/DUlbmNleoAMWLc5HLlUNoyBXS0iA0AgLsGdO//oox9+4VKVH79uG3UqXcj6uG9HR4Gpo0wTs3eDDGzZsXz6+j14e9WFpFdNQwapR6+pYvYLWCX8FtQvleK+tib9niWR/h7Aa/APbjPs6fx13XIo9ZrZjkAwOdX25JwsRZq5Z5hI4rV2Dh8ETpGKdKToaWXKKEcU35zh1RTpJr3losrlOvODVHlu1d1UOGZEzDDe6uNXEi4qHbtsFSZ5Z2yZKwQL3pvmWzQSA1bqy9jmMa0o8/intUurR95aWkJNzDUqUgrOfNg5JQtix4EJs3I9bpyPAfNUrk5jK4uAcRXNZc7EWSkAkQEACv0+nTyKEuWxZCymKBwFU2aUhPh/K2cCGscn4GlMvixSj0ovU8x8cjlSpXLpEBwNWufv4Z+504Ed+zVarmQdED5hcwC//ECeT2Wyy4/3PmCKVmzhxcCyPeKE7fql8/Y5vHKNPSiPBOHj2K3zhHeODAjC3PmpHwC2A/7oOZkydPqv/OFXS0rFwmZZnRRPv0adFwgQhWt2N1rRdflHXFq12BqxYRYV+uYoRsGUmSKMzx7LMZ27O0Uyf7CStfPkxaW7aYdxxMzNEi1SkFcEoKrku1ahC8ZctCQWOX8NSpGIvbLd66BeGqPIegIJzXRx/hGcqZExa8o1KlTDtSyzH+4w+4z0NCYIneuCFc8Wx5a8FmQ0jCsVwkEXKRe/WCR+HgQXsBdfs2lIdChSCsX3wRwq9GDSiGHKL580+M5Y5FrwXOAHAMwRw9KjwChQrBwm/SxLMaBCtWYBw9+f9mgsMXo0cLkmCHDvDGvPYaPvu6tG1mwS+A/biPbt0Qn+6ODG8AACAASURBVFHTNrlA/Pjx2tvfuweB4G3+86FDIOuEhYkX0DF1Y9cuTHRGato64sgRxOVz5sSkFRiIfS5frn4N6tSBB4DZqWPGZIzwjYqCYODymrxMnGg8PUUP7tyBu3v0aPXflQKYhTU3DoiJEQKBU8YaN8Z9bNBAEBB5omVSjjJEs3YthPLjj9u7qr/4AusHB2tbaRcuoJIYEVjesbH25KxNm9yff9eu8ORo5SIXKIB47ltv4Tk8eRLPa6lSorUlERQGBqdQOXY/MoIXXsAzr5ZadOCACKHwYjQHXZJQaUytS5OvkJaG/Q0dis9370KJCA/HOdSsKf7XWwQnO8EvgP2QZRls27x51ckn//wDIdW2rfs45+jRcMnpScBXw44dYEuWLAk3W9OmcGsq455JSSDilC6t3r9WD/76Cy9+8eKCjHXkCIQEEchajrV8uQ4yEchqvnSLpaRgAu/eHcKIJ6O338Zxli2LY/cVt8AVqY4FcFQUrmGnTva/JySIOs7KpX59ZAgcOIBrJ0mYVHPnxpjKloW//orzbthQ3GMuX/n++66PPSlJhBc6d4bV3b+/OA53SsvAgbDqHXHpEqzEkSPBleDx8uSxL6dJ5JxbO2yY6KblKa5cgZLoKkWQwxRE4KG8/76xfPRTp/CuO6bz+Qp//CHfd9krkZIC75SySEyOHA9ej2C/APZDlmURX1MSSmQZpJqiRVE/V0+lLW7yzmkYRrBpEyazChWwX66d+8EH9utxasvmzcb3IctgqAYHIzbpSBqyWmFt5s8PwfDWWxD+yhzUadN8I3wlCek2o0eLmGSxYnDjOqYgHT6Ma9WokW9IKkyqU2s6wQJ43DhMipGREE4ffwyBx52JlMvUqdreguPHoVwQ4d7y+fz8MwROo0ZQBHgsPWlekoTjCQqCMD10SChXRK7rlY8dC1e2O9y8ifj3hAn2REEiXKMZM+ByvncPTOi2bd2P6Q4DB0Ix0uI8sKfqs89AYGJuwCef6E+P457LerwF3mLcOLxnWta6JOEatm2LYwoJMTfNL7PhF8B+yLKM3M+gIPuJKT4eE0uBAsYaAzRqBAvBiJD66Sfsv2ZNkZ7VqRPib8qXc/t2uDHVSiHqwccfY/uGDV3He69cEWUVuUE6T65mC99z55CaxkSk3LlhgWzY4NrjwOkpzz5r/jFJEhoq1KzpPHZYmL2rlYUnEYRdlSry/XBFbCyKYbA1qpVGlZws8nzr1hUdtlavdu4vbAQ7dqAsYu7csvzNN2KM0FDtNMDp0/EsGr2mn39uf5zKfH9efv7Zuyp2nOakVcN51Ci8r6zsbNki2giWLSvLX37p3ouVnAyFu0IF3zaTkSQoB08+qW/9Q4fggXiQ4BfAfsiyDAHTurX4bLMhnzkgwDhxhJu8602DWLIE+2nUSFjZPNEoe+MmJCB3sFw54x2XJEnkC3fpoj8+pmT48qRqBmJiYKU0bSrGbdUKFowRtzqn2Xz0kTnHpQQXR2CGb0wMcnMdhUrLlnCNnziBdUqWhBDmyVuScHw5c+I3V23w1qyB9Z83L54jWYZypoy/GsWNG+hIRSSUASLwHVjQK8HlMo0IH6WFzorUkCGwIpX75EWZi2w0jNC6NQheapZgrVrOfYQlCa7bevXE8S1f7prtzK7h2bONHZsRcOOLpUt9t4+sDr8A9uN+tR1ligQLK09cyUlJmES55KArvPuufD/mqozN9esHN7DS1TZ+PNb96y9jx5OeLoq9DxumP183Pd0+dsiLo5teL9LSQDDq0wcucCK4wefN87xdos0GQlBgoPHr4g5cfITIvm8uL4895uwKHTIEbum9e53H278fClSOHMjL1XJJX74sLOyBA0WDe70xXDWkpYG05ngvCxd2Lo/I9byN5FAry0b+9pvIGHj9dVFHfM8eeHDmzsXznjevvdBW5iK7sr65sIljrnZ8PBRZLaEpSXh22V1epQoUKq37MGAAlCatrAhvMWsWjjcjS+1mNfgFsB/32aVMRvr2W3wePtxz1+aUKZiwtdiUkiQ6G/XqZR/HPHMGL+aLL4rvOEatp0a1EomJonPS//6n/3zS0kSBB3b39ekjJsy+ffUxRSUJ+aYTJ4rWemFhiBv++685ruPYWLj8ixTxvu9xaiqsH8fc3IgIxDT/+kt852gh/vwzvp81S3v8uDhcOyIUfNGq5mS1osiJspKcUhnwtMvU8uX2ikREBGLWShIQ558q62S7wv79OE6O2x87BqH29NPyfRd9UJCzxZqeDkXl7bedc5G5aMenn8KzoHxObDYobrVr23/PqU7umhnYbPDscHvEmjXheXB8Fm/ehMehdWvfcB5q1ICi9TDDL4D9kHv3hktLkjAh5MoFl503ZAfuKfz6686/2WwQQERIP3C0SEeMgIXIAu7ePeQEV6xobOK9cwdubYvFWI/RlBQR/33nHfE95ww/+yyOr0ABuJHVLIjLl2HZcjw0Z04I8F9/9U25ypMnQVCpU8e4cLp1C27AXr2c++aOGSPbeUe41WKRIvZjREWBrFe7tvvnRpJACgoOtmehq4GtUeUSEACh4KkQZqIgL/XrY0zOf+VeuVzByhWsVrh2w8NFOIBDCKmpaEnJQs4dlLnIffuK9Bu+3spc5MWL8f2WLWJ7blaiN8ZstSIuXrGifN+jsX69vbDl3H5XldE8wblzzu/Xwwi/AH7IkZ4OQTJ8OFzREREQdmY0hWjdGmMpY03p6UhxIQLj1VF4Xb0KYaVMhxo1CkLUSL/hS5cg/HLmhLavF8nJogH9hx/a/7Z6Nb4/eBCxQ2aZNmsGJu+9exBkrVsLAk7TprBizOrV7Arr1mG/zzzj2mKRJKQCOebmFi+OFBvHvrlNm8J1HBsLIUvknK7Wpw+sPFfClPe9fTtcy+yG12pwIcsIRXCcmYXRwoU45rZt3ffm1YKyq1PNmkJQTp8uLEk9HIYPP8S6334LpdIxRn3vHp6PmTONH6NjX+QyZcQx87UjAmM9LQ1KY8WKxveTno7nlms0N2wIL4gk4f1s0AAKgJnPMNfpdkz1e9jgF8APOf75B3d62TJo8vnyuZ9E9YIJTOvW4XNysqgb/Oqr6kJiyhTECLnYANcInjZN/36PHoVFHxKCyVQvkpJE0Qa1al/sBucxOY/V0UIrXx7uU3c9UH0B7sfrWLQgMRHW98iRuDZEEGKOublqYPbwY4+Jc1Sy0LnjzxtvaB/X3buI97NHICQEY7jq8HTqFI7xxRdxbLzv8HAIcIsFsVRPmbo3bogxa9USVcY493TNGtfbX7sGj0G7dji+7t3hVvUlLl6ENcptKnlRFjbZts0zxSQtDcpQqVIYp3lzhBwOHcI7OWqUeefRvLnvr1V2gF8AP+SYORPut44dMaF5SjBSQ1qaaEoRHy8KKTjm9TLu3AExhQvWx8aCNfvoo/on2b/+QiGPiAhj/YkTE3F8FgtSNdSwfz+O/+efMfbUqdiPowA2mwxlBFYrBEJwMI7TMTc3Xz64Mpcu1d+NKyVFnNvTT9tXwrp+HSk9DRs6hxLY2n36aWGxNWwIV74eItXQoTjumzdxf3j/HLtka7xjR8+FMLtyiUDIat1afHb0gDiid28cHytaXCkto3D7No6zYEHR/5iXnDnhuXjpJZGLrBcpKTh3frbbthWpTFr9fI0gKgpzjiuuwMMCvwB+yKG0alxZMJ6CGxZERECL5tQSNXBnIS7GPnQoXlTuN+sOq1djoq9c2Vgv0fh4VLkKCIAnQAvsLeAlMBBWz6pVmLR+/12ULBwxImPczkpYrXDTM+NbaZFPmgS3oqdxfR5rxw4hgCUJylXu3PbpPHfuwMXIjTRCQpDja6Sf8cWLuL7czEDZjzkxEZa88hw7d/asIInVipKXRPAMBATYx17V2hXKsmAiK9PkwsK0y3f6CiNH4pnnUpebN4Np//zz8G5wd6yAAHi4pkzRn4uclAR3P5MHefGWw8DFQvTE2B90PLACOCM7emRX3Lplb9n4gum4e7fYhyuXXnw8LCluIr5uHbZRtv9zhU8+wSTToIGxtIa4OJRNzJEDcTxHJCbCBevY7H3RIvX9JCTAXc4T+Q8/+LZkZUwMXMADB8KCI8K5sMVZqZL39aqV93D2bCGAmZT2/vs4x7/+8tzadcS4cYgpX7mCz0ePYswffhDrrFwp6k0TQRnwRAjv2ydi50qmOxHcsY7dpRIToWhVqSIUGrbQ1UiHvsSJE+JYc+d2Fo7x8chF/t//4PZVxo715iLHxwuCFy9GvEuO6N4dnq0HtcORETyQAvj77/FCBQbiBY2IQEpFzZpgxbZti4n+qadAPpo4EeSL116D5r54MeIsq1bBfbN9O9yPJ06A3Xr3Llxe2f0B4k41OXL4puLN+fOwvvRozkzK2LUL1zciAjEidxOqMp3piSeMTfYxMRASgYGYzBk2Gwg4zzwj+saWLi26L73yivux9+8X3oUuXbxPD2JIEhjPb78NYhJbOIULi765nJvLVpE3ng1JwjtTrBjIRBERohlB/vy4R2+/7WztejNB37gBQaFstMFlSR3LI168KNyjRAgjeGLljxsnevcuWGAvbAoWtA8r8HOg/O7UKXxnNltYD7gJQ7167tdNTtbORa5YEfPh11+jqYXj/BYXZ39d+vYF+dAIEhOhKHBbz4cdD6QAvn4dmnjOnOJhCQmBm6pNG0woNWtCKPOE4lhkQM8SEIAJOjwcDMLq1eH2adUKL0XfvnCjjhuHTiYvv4yX++OPkXD/449wZW3bhvSfyEg8+Ldv40H1ZaedmzfFeSj7pJqFyEiwagsVEsQgLTZySgrcfy1b4vPAgbgf+/e73kd6uiCjDB1qzDV29y4mrKAgYZkfP45cVyah5M+P4gjbtol7kTcv3Hh6kJ4OxSJPHmz33nueeWY4N3fSJHuFpkYNHO+OHerjShKewYAAY83ZleDKV198IZrNqy0NG8K1aEZ3JvYgnDkjvuN9//uv8/rp6SLUQYR30aibNCZG9MNV1v1WxlRXrgRBMTBQdO9hcOWobduMn6+32LBBzHFGkZZmLBf54kXxe758uE8DB9rfK1fgXHFuT/mww5UAtuD3jEG9evXkffv2mTrm7dtES5YQLV5MdOkSUUQE0YgRWEqWdF4/PZ0oMRFLUpLrv56sk5pq/Bzy5MGSN6/4q/xf66+r3wIDiZ54gujYMaKqVYkiI72/1krs20fUsSNRUBDRpk3YR/nyWLZscV7/iy9wTzZuxHXq0YNozhyil1/W3kdyMlH//kS//kr00ktEc+cSWSz6ju/OHaJ27YiOHyf69FOi+HiiZctw3DlyELVvTzR4MFG3brhmSpQsSdShA9GXX+q+HHTxItHYsUS//05Urx7R558T1a7teptbt7D+unW4hvHxRMHBRK1bE3XpgvtXpoz7fcfHE9WvTxQdTXTgAFGJEvqPOyWFqEoVopAQbHv3LlF4uP0648fj3tWsqX9cV7h7F+fVvTvRN9+I75ctIxoyhOjMGaIKFdS33bqVqE0b8Tk1lShnTv37XrGCaNAgPBMLFhAVK4Zr4DgthYURnTxJVLiw+G7pUqJhw4jOnyd65BH9+zQDu3cTNWqE/202ooAAz8eSJMwL27eL5dYt/FakCFHz5kR79hBdvUr02We4H4sWEaWl4f7MmkVUtqz2+MOGEa1eTRQVhfnhYYfFYtkvy3I91d+yuwBm2GyYzD7+mGjDBjyg3btjUmzdWv/E7S2sVggObwW51jrJyZ4dV2ioecL9338xIYeGEu3cSVS5MvYxbx7RzJmYuPg7ItybKlWI8ufHvalenah4caK9e7Vf0Ohooq5diXbtIvrgAwgBvbh9m6hZM6LTpzG+LOO+1KmDyfeppzDxaqFaNaJHHyVatUr/Pomwn5UriSZOhJCZOhVKBgt4WSY6dAgCd906XEdZxrXo0gVL69a4xkZx/DiEcO3aRH/+qX/imz+f6MUXMameO0f000+YaBkJCZ4djyvMmUP06qtER4/iWWC8/z7Rc89BeVIKPkfcuQNBwbhxw/X9VEKWiVq1IjpyhChfPigVP/1ENG4cFHlG4cJ4jpSC7pVXsKSkGBP6ZoCvDRHR2rV4VsyCLEPIKgXypUvi95YtierWxbu+dy8U2OHD8a47GjlWK+5Fhw72ytXDDFcCONu6oF3h3Dm4g5mwUrky8hMzmrHqC9hscAHevg1XdmQk3EvbtsHVvXKlfecaIrhYx41DvLNvX8T2WrWCK656dbjzwsPhblLr7uJuCQqCi19ZUrBxY8Thu3cXrn/OTWV38qefIp62ejVcbH//DZf0H3/A1cbxNr1xeElybqxQvDjYokbynhs3RhjDU0RHC5ZyeDjqWzvm5jZoAD7CwYPm8Qy4vKhe9/nx4/bXqkABURWLF7MRF4d4a48ezr9xizw9LnxJEmx0ImNtKyMjxTPZrJkY79VX7c+9b1973sTw4QhnZQb690eMvmRJEcbxJS5edH4WHJfgYHBrlOVauYypkm/xsIMexBiwHiQnI+WkYUOcae7ceIncxR2zM9avhyDs3RuTfP36xraXJFy3O3dARjtxAtdr+3ZZHjRIvHyvv470hddeA7lt4kRcW/69SRPE4ZUt/jxdAgIQqw0PR5ek6tVxbq1bIw6vTLNSxrY2bfIsHtu5M8b0FBcvoryisnACERiqRnJzPQG3+/vpJ/XfJQnK2oAB4rhCQ8FXSExEOUrlMXtahUoLb76JcdXSUyZNMh7j5FrMRHgO9TbhYJKVUslQjqW8Z6y4t2uH5y4zULYs2NtMHsuoOWzECJAAN26Ecjt+PBR8paKeO7cgLU6Zgli6kZzkBx0PrQBW4sABPEw8KTZoACagL3thZjQiI0Wt4CtXXHdNMYqPPsJL17Kl61Z6W7fi+nIu8O+/4/Obb8IjUa8eJvWYGORfnj4NK3DHDsF0JsKL/t57EPQzZ6JxwMiRmCR79BB5nWrL7797d65PPQXynl5YrcgfnjHDXuEoXx45o02a2As6XzLrU1KgdOXPb5+3GxUFhalyZftrpbSmjh3D5Nmzp/jdzDZyiYkgQXXooP774MFgohuF8rlp0EBffnhCgtgmLU0QrGbNgoKk7IVcsSIY7pUrQ7HNaHA1r4ULUbgmf34oUBmBu3eRI9ywoT1h9O5dkOamTcPzVqMG3oNy5VBtzA8BvwBWICYGqRs8EYWG4iHKjJKCZuLOHUz44eGwXJnZ6m1VG0kSvVO7dnVvEUkSrm2jRvjcvDncZl26wGXlmG/JWLMGFYcqVYJrXQ2pqWBY9uwp2O9Vq0K4cy6pGRgzBnmwrqCVm8t9c0+etBe0kZFCELdurb8Ljye4dAnHVL06uuY89ZS4Xo0bQwlo3hyuYC7WkJYGq79IEeSO8zkZ9aC4AqdMbd+u/nvXrigX6Qn4GSWCgNLyAChRvLh835tToYJ9c/q0NNQx5zHZ4tPr3jcTzCrmOumTJ+NZu3w5Y/a/bBn276qetyyLPG536z1s8AtgFUgSrLXevUWeZceO0OqyW4GPtDTEdIODkWMry4j3Firk3blIEuKnRLA89aZ9cP9f7nJTvz7+KnsRK7F4Maz1+vWdC19IEqpkjRsnhELRorCI9+/3jTU5fTri2sqxJQnu+AULnHNzBw2CwuPYN9cRNhvONSQE9+r1133TNSkqSjQe4NjuhAkiDs5eiYULxTavvILvVq3C57Awsb0Z7s7UVChiHHNVQ9Om3sU35861t+5HjnTdTWnGDPv11dJmOK7OS5cunh+fp+DnkZXfixfx/Bmpne4NJAn3pWBB1+GT117DNXJV8ONhhF8Au8HVq8jfZY24dGlMjr6M1ZkJJktwiUVJAmGjXz/Px7RaRf7t2LHG8pXv3hV1iZms0aiRszIgSYJ406mTfX7pxYuYUNlTERyM81m3zjdCSwmOU8bEeJab6w7XrkHxI4KVykqTN5AkNJBQWru8vPeeWC89HV6D8uVFAZT9+0FKGjhQrBcWBjdn7tx4DrwFN7TYuFF7nerV1clZRsCKBC/Vqomyp47g++wYC3bE4cP262V0IY6WLZ0LcPTrB0XOVTjITJw4ASVA+Yw44rHH4Kr2wx5+AawTaWlwXXFDgaAgTGjbt2fdilhsZSob23MvVE/jd6mpoqH6Sy95du516ogJK1cu+3ikLENwca3fIUNw7ePi0CShRQuxbfPmKBARG+vZuRjFzZvCYlcqEJ07o7iKWdWuZBlNMUqWhHtz3DjPJtOoKLi8K1XCsRYsCELc0aO4xu3bQyAz6Yl7v7Klm5ICIVW8uH2WAJeiHDYMvAlvrn96OgR+vXqun6USJZyLX3gCVupKl4ZLPVcunLfjvh37ELtSDr7+2n7d2bMzZk5IT0eBl/Hj7b/fuxfHkZG9drnYjhrj/PJl/OaLWvPZHX4B7AFOnIDlw3Voq1fHBJyV2H2bN8MV1bWrvSXGmr0nrqDERNGybcECz4+tXDkxWb37rv1vSUmiZeHzz4O53b+/sJorVoQ7SysWbCa4b+6rrzoL3mbNEJIwo/KTFu7dg8C0WCCA3LXH42N2tHYbN4aQcHS5RkWBEV62LK5nkSJQalh4MBv4t9/st2MB/O+/+N1d1yBX4FaH7s4tTx5zYqyShLrIRHjO2B3fsye8M4z27fH9+PF45pQxYEcsWoR1hw0Tz0fnzr4PV7EyvWKF82/NmqF/sF7mt7dISoIiVbGi83Xi63PiRMYcS3aCXwB7gYQEuM/YosuXDxOTllsro3D6NGK81as7KwUtWnhGZomNRRzOYpHlzz7z/NjOnrUXZI7sSSYjBQaKrjShobiuu3f73rJISID1OWKECDsoc3O5KL0Zbdn0Ys8ekb/do4d66VBX1q4r7N4Nbw7fD7aGd+xA7F3NxaxsR1ivHtzWntwXmw3bVqvmOoyRmopje+014/tQgySJGO/o0bL81lt43kqVQr55VJS4Hnv2iJ7Ur76qPt4LL8ATYrPh2eFt8+RxHWf2Fp9+iv2okUSZnPX9977bvyP4OjnWSm/XDuEiP5zhF8AmQJIwkQ0eLLqNNGsmy99953n7N08RE4OHvXBh0dSeEReHiWb6dGNj3roFJSMoyL4bjSdwbCPHgkzZcYdd/D16wDLy9TXk3NxOncT9y58fsVjH3NydO/G7t+lMRpGWBu9FrlyI7330ESysrVvhIWBrt0kTWLtGcnS5KQfHOhMSYPGVLavu1VEK4C+/xHae9EBevRrbfvON6/W4a9eiRcb3oQVJEhb+uHEQtOXK2ReMUbpU+/bFs6Em7Pr3h/XHOH3afozbt807biWGDsW9UFN+bDZYo48/nrEhsv79cZ2YyR8TgznnhRcy7hiyE/wC2GRERYHRy27WokWRq2pmfFAL6enIowwMVJ8Q16zBMRkpGH/5MgR67tzeC53r18Wk9OabmOwCAuyrYBHBna+nX6mn4Nzc6dPhJeD9li8PBvXmzdpCnytEffed747PFc6eleXate2vF1u7x455NqayBd/mzWBFE8GVrQalAE5IQCimf39j+5QkEHPKl3fvJj15UtZ0tXoDSQJbmAjXLzYWcXfltV29GutevQoPV+fOzgKtSRNkGigRH29PeDPSC1kvqlRxzbz++GPsWyu1yxe4fh0KYtu2uE7MFM9Ij1F2gl8A+wg2GwRW165wYQYEoNvIhg2+63LEuYmff67++6hRsOz0WpSnToGsEhIC15y3UE70AwfaT3REvi1Rp5abGxiIiVMtN1cL165h28WLfXesauDUuP797V3GRLBgPS0as2OHGKNKFTHmpEna2ygFsCxDeAUFGcsM4A4+X3zhft1du7Du+vX6x9cLSRLvzdixoq0iLz17inUXLrQXyoxSpeD9Uhu7Vy8xlpkM6ZgYjDl3rvY6iYl41rt3N2+/esAEtm+/hecgPNy3nd2yM/wCOANw4QJiTkWKCEvr7bfNtfK++ML1xClJEKZPPqlvvIMHYb0XKQIikreIjraf2JSN1Imc3eXeQpmb26KFZ7m5auDG62++ae7xauH2bZxDxYrC2p00CdZuVBQmfianaVmsWrDZQC6LiIAlu2ePuB+umM2OApi9AvPm6d93s2YQXHqUQc5N9pUVJUm4pnzu69eLUAMTsZKTEQaoUQPHzeS79HQ8W//7n/b4ixeLscxI25JlEW91V+f6f/+DAeDL4i6OsFrh+i5Y0NxzfhDhF8AZiJQUaIVNm+LqBgcjzWbPHu/iNNu3wwJp317bnceTpJ5KNP/8AwFZqhQsQzPAOZilS0P4ffCBiLcVKmROnColBTWeJ060z82tWdO73FwlJAmW84wZ3h+vq31s3Yp8TrZ2mzZFLrdabPePP0TIY9gwezavK7B7kFPSuEkEEVzxWnAUwLKMfNQyZfRdXy7K/8EHxo7TaPN3IzhzRpz7889D2CoVxFq1oND9/Tc+c2ofp9i4e6+YMc5hKW+fw1degWB1l5524wZc4WPGeLc/o9i/316h8UMdfgGcSTh8GAzMfPlwpR97DIQWo6zJCxcwIVaq5Nqie+cd7MddLdwNGxDvrVTJvLh1QgIszy5dIFxYGHfsKOJUW7Z4NvbNm7K8ZAlchXwtfZWbywgL882Edvs2+ANs7RYqBMtMq0SnEomJiGnnyAGvxbffulZqkpKgYNWpA0t4/Xrsc8YMEQPWCgmoCWAub6pnsm3fHkJIL1GMXZrKzjpmQpJwTPnzg/hHhGuZKxeE8bp1OOc8eeBpeuYZKGGRkcKFr4cfoWRXEzlXdjOCTp3AX9CDYcPwTvuSV6EGPk8jnJOHDX4BnMmIi8MEU62afN/F+NxzzsUp1HDvHlxiBQu6X79dO8T4XOHHH2Fx1aplbqWv996T75NBRo/G/4MHw8pITkaakd5C9pIE7fqVV+xzc0uUQIzb17m5sgyGsFkF7yUJyoejtbt8uWfdhg4fFtelY0dt1/7rr2OdrVthMUdE4FlKKFoaDgAAIABJREFUSYFbuGFDKDRqHhA1AZyailifu3KMXCTCiAufy0hydS6zwRb2hx/ifvAzSgTWviwj9t+6Nb5r0wbemxYtxLZ6lCRZdras9+wxfrySBOXs2Wf1rX/sGPZlVhqXHthsQiGuVSvj8pGzG/wCOItAkuCa699f9CNt2xaED7WH12YDqStHDrhdXSEhAW4oV4UMvvgCk0qTJp7FRrXAdX7r1xfWxYsv2ltnU6finLWKg7jLzTWzb64ePPYYLGxvcOsWrN0KFYS1+9xz+idyV7Ba4d7Nlw9W24IF9s/QjRv4jck5Awbg+h88KNa5fBmCtlo1Z4VGTQDLMtj+FotrL8uTT+JcjVT2mjYNFpwvEBMDxaFePeEWttlEqVUisa7VCsWF+QQc3iAyVoRHkqAc8RgffWTsmE+dknUT2BgdO+I8M6rDG6cV8jufkVW5shP8AjgL4sYNaP2lSgnr7pVX7AUUFxLQE0dj96KWoGZ2Z4cO5luPnCfKi7L2MIMnFCWj88IF5H127Oicm/vVVxBgmYU2bVBdyihsNmdrt1kzz61dd7h8GSx8IriaucjGiBEQuKdPw+tBpF5kYtMmCNSBA+0VHC0BfOkSlLiXXlI/Hu6IM2eOsfPwZbP70aNxzI5NJWw28cy+/LL9bzt3infTUUgbAXshiPBc681O4NKXRtLOuKXil196dqxGweGQ6GhZfuIJlMzMqA5N2Ql+AZyFkZ6OijZcFi8wEKk8TJYZOVKf5TdhAiwIR+1XWZavTx/zC15YrXjxiCBwXOXOtmwp3yfAKHNzK1Rwn5ub0ejZE5ahXvjS2nUHSUIN84gICJq2beX7JKubNyFM69XTbmLB8fqPPxbfaQlgWYbAL1pU/V4NGADLWy9JjNGzJypmmQVJgkDikqdahDN+JtUUlJgYcT+JPO/ywxWriBC7v3bN/TZjxiA10EhqjyTBWq9WLWO8RVWqwGUvy1Cmc+f2vpnGgwi/AM4mOH3avmIREVKZ9BTCr1DB2WVqsyG9gggWhi/q1r76qjhWtXSJ6GgI5aeftj+vVq1gleuJg2cGhg2DV8IVbDacc9++9tbuihUZ5wZUIiYGMXK+xitWQAC56sMsyziPTp0Qwti7F9+5EsC//YbxHUsgnjkDBeD5540fe+vWCI14i7g4eIw41zcsDNa6lvfhySch+IcMwfqvv27/uyTZP7eONbP1guPivLjLua9TB4qUUbDl7OsqbuzRUnrnuAb9r7/6dt/ZDV4JYCIqRUR/EtEJIookokn/fR9KRH8Q0Zn//hZyN5ZfALvHlSsif5bTTvLkgSWsjN8pwekVyoL5aWmiEMbUqb7RiJkdSiRcn5KEVBK13Nz+/fG/GROtrzF5Miw5Ndy6hdrCjtauL1No9GLdOvuJnj0O7nDnDtLHSpfG/64EsNWKEpYtWth/P3w4hL0nTOY6dbzrtXvsGKxGJgU1aICULneK0ODBIrVq0CBs69jRh/kavEye7BlZzLFG+gcfqL+XCQl4b2bONL6P1FR4QjwR3kYwfz7OQZmBkJYG67t0ad+TJLMTvBXAEURU97//8xPRaSKqSkTziWj6f99PJ6K33I3lF8CukZgoy3XrYhLh2M+//8Ia405BjRsjnqicWLgTyZkz+JycDPIWx1x9IXyVBemnTRO5ucouSDVrwvrYuVNY31w0ICM6HXkDdssysUnN2m3ePPOsXTWkpcHyq1RJlHYkgivziy/cPwd798IK7thRNMfQAjesYMv68mVcl3HjPDv2smVd95pVQ3o6XO/sRg4ORvrQv//qH2PCBChQsoxndMAAjDV/Pr7joiycwcAKZd26nhW+uH1b9LgmgtB3TEvkHOq1a42PL8solkLkm9KYjMaNoTQ5gnOo/XWhBUx1QRPRL0TUjohOEVGELIT0KXfb+gWwNiQJMVqLRf3Fu3sXLEPOHw0LA9P4/HlYDlwo/t49kUphZmF7Jbi5Oi9cDzdXLve5ue5IPFkFnFZ14oS9tRsaCgsoK1i7jmBF7OefQbbLkwfkvObN8X2LFu6LrnDONpFrAXzrFgTuhAn4PGECLEVPc7ILFBBjucPNm2DGc33xMmVwjzzJuZ05E0KVlZP0dOGpWbhQKDLLl0PQFyqETmGFCkFRXrbM+D4TE/HOKhXVc+fE72+9he89bfBw9y7uvVrpTDNw8ybmKceOSIzhw/EsHDnim/1nN5gmgImoLBFdJqIQIop1+C3G3fZ+AawNtrhY89aCzQZrs0cP+64ujzyCF7Z+fUwoZtaklWVMUPv2OdcoJkLcce1a/QVGunZFukRWIVyp4auvcG58jbOateuI6Gi4+Vu1EmURmVRls8ECLlgQytKrr2pfe0kS8XpXAliW0Y84JARKYK5c8NR4ApsNE/rs2drrSBJCHgMGiGewfXvEG73hNrCwU7pM09Ph6SACu5cIVunx4xAszzwDi79ZM/w2cKDxPuHp6fbx+rx5Rdy2Z0+8H97kRI8fj+ukh/BlFKyAa1nYd+7gWWzc2F8fWpZNEsBElI+I9hNRz/8+6xLARDSSiPYR0b7SpUtn2ElnJ3CayODBxtzFly/LcqNGzgLRSO6gKyQkwJp69lnRt9dxiY83Pi6nTHnb9tCXiIwEy3Py5OzRZHzqVAixVaswmbdr5/ws3bghrLuqVVGOVA0JCUjDWrLE9T63bxfemIAAz2sRcw3xd991/i0xEc8z9+MuUACVw8wi77Gy4shwTktD2hA/5xwymT4dn7dvhxB9+WWce4UKggehF5Jkn6bEJLBhw/B/8eIgYXryjp09i+fBaFtSPXjiCYQMXM1VS5fiHLSaxjxM8FoAE1EQEW0koimK7/wuaBNw4ADo+40aeWZdObKm2SX89NOwGIzGf9Vyc5VW75AhcC0Rabug3IFJPI7t3fzwDGfP4h4NGQJLPSTEdT7m+vVw2xIhR9bToiySJIqmGG1VqMS5cxiD61XLMs5p6lS4eolQwWvxYs+EkStwlSs1JUtZ0YqVg4QEkIyqVxdpXX/9hUI0QUEQmEatPmYu8/Lkk4htt2qFz4UKwTtgtMxkz57weph5zeLjMS+4qiMuy3g2WrTAsWdmPn9WgLckLAsRLSOi9xy+X+BAwprvbiy/ALbHjRt4cUuV8rwGLr+0oaEoeRcZCfdT/vz4vlYtFJHXegnT00GcePFFQTQhgkY/eTImgoYN8R1Xunn6ac9yPZVgokh2sC6zOnr1gtX7/PO4pl995X6b+HhUTQsIAGv2p588I+sVLYp9elP8Yd8+jLF6NdJ8OneG9RYYCFfw9u2+y2tl1rhWuUhlS0126XNe79tvi/Xu3EHVMSIor0aFzqZNgsFNBCJdZCSqTfG4efJA8F25om9MzlLQ2xBDD376CWPq6cp1/DiUEl/ForMLvBXATYlIJqIjRHTov6UzERUmoi3/pSFtIaJQd2P5BbBAcjIEW548nrcC5JeBCBWIlIiPh+DlMnohIRDMkZH2ubmhofid++Yqc3OvXoXloSywcf48YsxTp3p+7rIMIkdQkHtN2g/XYDfwgAGIw3btakxY7dsn3LvduhmrZHTvnmAFDx1q/NgZK1faW4DFiqGSli/il45g1u4ff6j/3rYtrg9XG/v0U1xfrvykFIaShJKTwcE4B60xtXDwIJQhvg758uEdl2VkRQwejOsdFAQ3tZ4uZg0bIjPBrBoAAwdiztBb93nmTJzL1q3m7D87wl+II4tBkkSPV37BjGLrVvGiukpXkCTE+urWtZ/kOJ42eLB639zjx+Fqy5fPfiIZMwYubjMmx3794CLzRYnGhwE2G2pWFysGl2hoqGeelPR0WHN58uB+f/CBvgmbc0Hr1EEYJTra2H4PHABjVvk8fv99xpLzDh92/R5WrozshJQUQcj6/HO4zXPlUm8wcuQI+AMWCzxLWhXI1HDxIrZVvqcvvCAE3sWLUKRz5cL4vXu7jj0zv8TTeUaJtDS4lI1YtElJUAAqV/Zdo42sDr8AzmLgiUutNq8epKRgsuQXtG5daOijRiEu+/nncOfNnw+m6iOPOAtfIrgPZ892dmnt3InJvGhR+/q5N25Auzer+faff8q6XaZ+OGPZMlw/rvrkLant/HnRQKB+fQgnLSQlgZjXrh0EqRaJyhGpqYi7Nm4s33erWiz4PzPqCF+4gH2rEc4kCYoFNzhJSYF7nF3ur72G/zdscN42MVE0e2jQQLtjlRru3hUMa17atLFPs7p1C6l8XLSnXTso5Y7eD6sV73+jRvr3r4UtW+T7oQIj+P13bJeRnZqyEvwCOAth7VpMOP36eRfX+uYb8XJ26oRYr5qQVS7Fi4OYMWYMqlEpf2vZEnGoX3/FpFOhAogwSrzwAmKGXPDDW0gShEeDBuaM9zAhMRF5sEFBcEv262fOuJIEAVmkCMZ98UV1DwXnHHMf2AYNYOVoPdNXr8ryrFmCTV+hAjgF0dEiFchsgpUeMANbrYHInTvOvyUnQ0mxWEAKq1gR56JFoFy5EkIyJMR1nXRHJCcLFnZICO5z6dLO1m5sLEpA8nVt0AAxaiUR7P338dvOnfr3r4aJE2F5e1Llqm9fKO9mzR3ZCX4BnEVw9ChcfI89pj9nVgtcLWfmTFi9jz9uL1Br1UJc7q23kIoweDDiWVWrwu3rTljXrg0S1oIFEPZr1uB7b8oFqoELXngaB39YoazBHR5ufiP2u3dFOkz58vZhiNRUEAebNBECl/Omt2wR60kSvBy9e0OYWyx4fn7/3V5AzJgBDkJGtptkpKfLmt4otuwdLb7kZFicFosoX+kqI+DCBZEuOGyYfgFmsyHlighWbNGiEGJq1npSEkhi7O2qVg0ekrQ0KDYFC+rvx60GSYIC0LWrZ9tfuwZiaPv2mXOfMxN+AZwFEBWFlyMiAtaAp+DcXKWwtFhAtpg7F8nxeh7wxERYuNu3QzPnjkZ6lvz5Ye20agVSxgsvQJCuXIl48/nz+lOqoqOhVY8a5fk1edhw7Zp9CMKXxe+3bhXV1wYNwnPM7SeVjQmSkhAf7N0bE/7HHwtWfWgoGNpabtjRo5FLnFnIk0edVMjlVtVKWyYlQaFl93lwsLPHSIm0NLiMLRa8O1p13dXArUSrVBFcjtGj1WPl6ekoGMPdxsqUgbdi4kR4r4y4wpVgZcQbtvsHH2AMxyYeDzr8AjiTkZqKnLjgYO10B1e4cAGNFpS5ubx42zfXakX9XiKwolNTMTmMHCkmeWUx+rfeglbepw8soEcecT4mXkJDMRG0b4/qQS+9BJbomjW4DleuYGJ65hkoAEYauD/MGDpUXGNv2Md6kZyMGt6BgSIvt25dZ0WPSUpKbsKSJe5Jdv36QchnFooVU+c1fPghzkPr/UpMFGVfifB+ulN+t2yBEp4zp3YzBjV8/z22qVQJ14sISreWMm+zIdzFsXZehgzRtz9HzJkD5cHbuYZJg57mnmdH+AVwJkKSBBnjm2/0beMuN5fjv/PmeXdsyjjTtGnOBQRiY4XWysvUqc5xHEmCC/TIERBSliyBNT52LIoK1K+PfGdOWXG03pWfR4zAy/7pp5hADhxAypK/pB1w4IC4ZqVK6WtVaRaOHrW/V2fPYlL9+WfRg5iXXbv0C5cOHfCMZBYqV1aPoT//PJRLV+eRkGDfU3jVKvf7u31bKCvduukPH/z5J+LJERFQiPLmhVv6r7+0t5Ek/M7kOraeb97Ut09GrVqy3LSpsW3UsG8fLHFPm3ZkR/gFcCaCBZi75gPR0SC/DBggrAy13FxZFvE2I24sR8TGwionwvha4HgfEYQ1W8MdOsBFZyS/0GrFi3/gAITrp59C2D77rJgcihZ1Fsp8LUqWxET95JMQ7nPnQthv2ADhf+fOgx1fkiRRHYnIeJ6pt7DZ7BVC5VKyJMoo1qyJ58XIc1G/Pp6nzMLjj0NAOaJ/fyi97pCQYG9p6iGTSRLCNjlzgkynp7CFLCMfuGRJhIHefx8WcY4cGMvds8/lIYkQ9hk7Vl9XMmaKKwuPeIOJE/GOc+/pBx1+AZxJ2LgR2l737s4WHPfNnT8f5QPZOgwLA2Fq5Upt66Z/f7hxPBU2165hogwKcm+V80u7fr3Y9pVXRAnCUqUgCI1q1I749FOMt2MH3NJXrsBNvWYNYlgvvQRXdfv2IudVTRAEB8Mt3qQJ3OSTJsFtvnw53H8nTsDVnR0FtbIFZGZYEMw9UDYB4WXHDqyzahU+//KL/nErVjSPxe0J2rSBAHVE48b6y6XGx4vrwp3J9GD/fpy/xQKrVk+BCy6QExiId4Nbjw4Y4J7c2aaNfN8VzQz6gQOdC/kowURJsxjMcXGw4uvU0V/QIzvDL4AzASdPwl1Us6bQiFNSIJQnTLDPza1Vy7lvrhasVggfT2M5J06IAhubNrle12ZDmlCtWs4CKy0Nky2/0EFBUAw8LRsYHw+tftAg/dskJ4NU8s8/UFjeew+EsIEDMXFWrixKcjouefNi4mvRAhPXtGlIi/n+e5zD2bPeM9XNRGqqIEOVL5+xDc+Tk4XXhQhpauPGoaramjWw4CwWWDbR0VDO1CxKLYSFwS2aWejZE0qdI0qVMvae3bsnrtHcufq3i4/HfoigOF686H6b2FgRf547Fzm2FgvmG1dksN9+wzbLlkGQT5kiCJhdu6qnKrVsCc+HmeDqZ2rpXw8a/AI4gxEdDddQkSKo5frll2gfyA96rlyIAX3yifH+qbt2YQwjOYXKbQsXhptXT+cWtmbcsRZPnkRJSS4KUK0ayFZGSVVjx8KCNTul5t49uPD//BMW/4IFiKX364eCB+XL456oCeqCBZG61bYtPBMzZoCcs2oVruelSxlTuYmtEIsF/ICMwIUL4CEULmyvLDre17g4VGeyWOAerVsX/yt73GpBkmDJzZjhk1PQhWeegbBVIj0dFu2sWcbG4sYSRPAYGMGKFVAYCxbUV7kqNRXKIxFy+9etQ/iqYEF7hroSkoTnWalU37mDrk7sVWrRAmEd5nbkyGF+/25JQv2CfPm8ywrJDvAL4AxEejpyBImQQK+MkY0ejZfEG8tqzhxMDEaF1Lp1sFzKl3etITMkSZbr1UMMTG88LyHBvnVcvnyYGPQ25uaygGbFmoxAkqA4HTsGz8BXX4HkNn48LKSGDeE5UDLClUuRIpjUOnVCecVZs6Bg/fILlJ3r1z2vx3v3ruAFTJtm7nk7wmaDl6ZbNzxnAQE4fyJYuq7KCe7aJdJfiPR5M+Ljsa67Pti+xMSJUB6VuHwZx/XZZ8bHW7BAXAOjKWJnz+K9I0JqnjsGuc0GJYkJXUeP4jm0WJDbrEZe/OILrL95s/33CQmoZlaiBH7nCntEvonXcjnPXr3MHzsrwS+AMxDMUPYkN1cPGjTAuEawZAm02Lp19cdq//jD8wlIkmD5DxkiUpSaNgXJzJ212LgxXK1ZlfVss4HFevAg4uKff46Y+KhRmKweewzxLbU4aUAA3LP16mGyHD0ak+QXX8BiOXQIYzs+K889h+2rVPGsZaUexMbCyq5USb5Phps5E4Lon39k3e7CtDTR6YoIRCFX99IbQWcWZs3C+6q87nzOamUm3YFDN3wN1q0ztn1qquhsVa2a6/gsY9EiMedcuoSUQnYrO3JJkpNxfzt10t7/l1+KZ4EIz7kvPD3cD9noNcpO8AvgDERMDCrn+KIH5p07eMleflnf+sqG323bwhWrF61bQ1h4W0D9zh1YBOXKiYn9pZe041zLl6tr59kN6ekgrP37L6zgjz8GyWbYMMRHa9ZE7FPNmg4KQgGFRo1AtuHvJ0yAYhQZiefMDKXuyBEoDxweadgQrlDlfe/UCRa+Ec/NRx+J427SBMesBvZ6/Pijd+fhDdhiVbKXv/sO32kdtztw4QoiMJ21XMKusGED3pdcueBNcXe/V6/GuhUrwpL+4AN4bCpWdBbiXEnN1fklJNg/lyVKgCdhZsnQ1FQolmXLZi3OhZnwC+AHBNw8fPdu9+tarXCfMjvSiPa6e7dsuivYZsOEonRtdu3qXJYwORmxqAfdLcVISYEysnMn4n4ffIDSoYMGCYKb1sIhhebNQYCbMgX37NtvUaP59Gl1slZaGkgwzZvL9zkJQ4eq8wK4V6/RnHObDcdGhPsZFARL09GC55KqmalwMQNfGYvk+tRGlFZHTJwo7lVwsGfW9I0bIqTVq5f7jlM7dohGKnv3glAYHg4FS9msIyoKz8/w4dpj/fqr8AJs3CjynUNDEQozi6vBz8D06eaMl9XgF8APCAYPBiHGXSwxORkpOESYlI26c7t3R8zRm8nHFS5ehBXMzdzLl4cVwi/01KlwmWdEP9isDG45WacOLN4zZzBZffcdcrenTkW3qxYtYOUoy1Mql5AQuEQd29wRgRy4b5+2p6NnT8RHPSn4wZblli2isX3lyqKBgyyL1CY9pEBfga3d48fFd+PGgczkDWJjkS5Yrhw8Hrlyuc88UIPNhhh5YCDIYu5IeCdPIssiTx6ESa5dE7Wop00TqT+jR8M61wpLDR+OZ0epvO/aJdKe8uYFmdEMEtXQoTi/Y8e8HyurwS+AHwDYbNBkn3rK9XqxsUJT9cSCPXYM286Z49FhGkJqKiY/br0WHAwlg93QD2v7MlmGklW7NiZIvSQ2ScL9P34cFuWyZbL8xhvqvaDVlsKF4fLu0AETYo8e+L5GDVhTV68ay9u8cwf3dOxYfN64UaTfPfssrDnOM9fDmPYVODVH6Vnq1g1C01swJ+S110COypXLc2t/714I84AAuJBdKeI3boCPkCMHOAapqbgPREjRu30bmQEWizrT22pF2KF/f/Xxjx6FlyZHDng3hg+3LxZkFFFRsKybNs26/A9P4RfADwD278fd+vpr7XW4wEZgIISYJxg0CJqt2alA7nDkCBjT+fLZCwVfWeFZHdzr9803Pds+IQHEJm5TWbAgrJXTp0V6yeHDCAFwb9sxY+D9ePxxwYR1XCwWWHV166Kz0ciR4CR89hmINI6lQwcNQmoNxw0TE5GrnSMHPCANGmBcd65VX4IJVxs3iu9q10aqoLfg6mUFCyLeWqMGXL9bt3o2XlycSD1q2dK19RkfL0pQzpmDY1m6FEpRqVIQ6N26QfFyjL/yNXGX7njhArwFuXLh2ejTx/POZtzkw5uGD1kRfgH8AIDJVFruopMnQdzJm9d+IjGCCxcwMU6e7OlReo9790BYUk76kybh/B4mrFgB68No6tKZM7h/3HKyVi0IR6OFO86exfZPPw3lb+1a9L+dPRvWa+fOEFIcRnBc2F2q/Pz66xAAGzdCQWRiHpHnXXrMwJEjshMRrHBhKCRm4PhxWIlDhsDyrFYN7mGlK94IJAlpcnnzwmp0VXUsLU007xg2DJ/37cNckTMnPE5EIHkpMW0ajllv6OHmTeRyc+plhw44PyNEQZsN3rDQUFjEDwr8AvgBQLNmsDrUsHs3JowiRdRbp+nFuHF46a5c8XwMs5CWJibnoCD8bdMGBTAehvJ1RmC1wvpkaycwEML77789Z0qPGAFL6fp19+umpiKdaPduMHGVpUOZQKR3GT8e8c4VK2Alnjzpey/IpUv2lldiIj6/8YZ5+5gxA2P+9RcyJKpWhRDevt3zMU+eFDn3EyZop6hJEixgIjwj8fEQcMoGGmXKCK+FJCH/35P63LGxuG6smDVqBDKXXrfysWN4fp95xvi+syr8AjibIzZWuxrN+vV4kcuV865W682bcCO5YkVmNDg/c/duWE9sUZUoAbfnw07SunsXRCeOq0ZEICdZj9B0hStXoPSYZQEuXizfJ2OdOwfF4IcfUPRBr2DOlw95qS1bwgX7/PPY/ocfMN65c+6LVmghJgb7eOcdfD55Ep9XrDDn/GUZQr1MGVi/aWl43x59FFasN5XNUlLgIWJvx4kT2ut+/jnmkccew/6tVjCP+Rp/9BHWi4zE548/9vy4kpIwXtmyGKt6dXg99CjPfEyeegiyGvwCOJvjp59wpxy15aVL8ULVqQPShTeYPh3Czhsihdm4fBmEEy5TmJ4Od1uHDrgeOXKgQ9PWrdmzuYKn2L8f7kQun9m8OdKK0tLMGX/SJFghejrl6MG9e9p1vrt0wfMrSTiH8HCcU9eucHuvWAGL+LnnZLlvX5B0ypXT7kFdqBCEXLt2cPkqS4fu3o1nyvE6Wa3YlvPrN21Sf9+8BTfTWLAAn69fh1KRL59oZuEp1q5FXnmePLDktd4HVtgfeUSEdX74QVy/P/8U4S4z2M3p6RC83EWrbFkIZlfKUmIi1qtSJWPKvPoafgGczfHss0gFYe1RkuDmYbest43sY2MRu+nb1/tjNRvdusGd5fginjmDOBXXr330UVRdelAbfaekQBhxOkmePCigoZchrRe3boEkZLYLcMwY9TrfTZqg6AsjOhrELp6sf/9dfTwuHXr0KGLKS5dCcIwbB/Z2gwbwmGiVDi1aFDHszp1FO8y8eeEunTULn81SQJTo2hX7uXwZn69dQwpZ/vxI8fEG166JdpX9+2vHb/fuRbiqcGHRfEHZErRIERDxzITNhmvLz294OOYwrWNcvx7rvf66uceRGfAL4GwMSUIdaS5MYbOJBP+nnjJHQ+TSgZ6yF30JThHRagiRlARCSv36QjCNGOFdr+SshMuXURKSY2qVKqEkpK8UjRkz4Akxm/TGRCe2/hhVqyLX2BHbt4tyjgMGeF5ZzmbDtlw69LPPYOmOHAnru25dsLodBXTOnOZ5FJQ4f965/vHVq4i5hoSgBac3sFohtHLkgJWrVbTn7FkI/ly50NEqLs7+/I0WXtELSYJrmb1YISF45tTub69eOL7MTFEzA34BnI3Bebmffw4rqG9ffH7uOXPy5ZKSMLkbaR+XkbBaYQm1bOl+3X//hWs2d275PgFk2TLf1U/2FSQJbvWePTGRWizwBGza5NscyehoWGK+8oQ0aYKiK8pziIjQ5h2kpEBY5sxskZQJAAAgAElEQVQJ1/KSJb4LNVSogDrke/eiOIjZ7mcl5s7F86m07q9cwbUpUMA7IiVjxw7EnAMDkcqm9tzcvg1PQUAAiHNTp+L/JUu8L0GrB/v3I23JYoGgHTfO3utw9Src8x07Zu8Qk18AZ2NwNaFjx4R7acEC8x7IRYvk++zMrAp2t7simCgRHQ2CDvfPLVwYuadZXZO+dw/3o2pVcdwvvugbV6gauD7woUO+GX/FCoyvTJPLndt9h6fjx0WxlpYtfcNTaNDAM9avJ0hJgSejfHl75fDyZVitBQuaUxksJgYcCSIwntXIeYmJorLVU09BAE+Z4v2+jeDUKShhQUFQOAcNEhWxuA3nypUZe0xmwi+AszHatIGFWqsWtNlly8wbOy0NWnLjxllbw7x5Ey/npEnGtrPZ0LxAaUl27oyUHU9bA/oCx49D+8+fH29kvXpwq2ek5R4fj3h6ly6+20dKCohCPXqIz3rjfDYb3McFCiCWPHeuuQSddu3gMckocLcxx8YqFy/C41OokDkhIUnCdcudG7FdtaYQ6ekoS8nu5+Bgz0qPeosrV5DDzo1BunVDQZC6deEp8ZbrklnwC+Bsivh4uN+YIKJFSPEUX3+NsdeuNXdcX6BfP1gGnnZMuXIFRSQ43lemDCxrX3St0oP0dOTMtm4t3485DhrkfQzQU7z9No7DWyKQO3AVrKtXwdxXpr/owfXrIgxTrZr37GFGr17wPGQk+veHsHNMH7xwAb2nQ0PN80ZERopezVOmOCsvkmTfRnL2bHP26wnu3EHeMvfAZmb8hAmZd0zewC+AsynWrsUdCgszvyG2zQaaf40aWdv6ZWzbhmuxdKl343AnIHbn58yJak87dmTMdbh1yz6nuXRpTHy3b/t+31pIToZi0qaN7/d17hw8EXPmwPInQvcmo1i7FtfQYgHD2luLbehQkB0zEteuwevRoYPzs3f+PM6vcGGUDDUDSUmiHvRjj6EsqSO4BCqRdsvQjEJ8PJqOFC+O47FYfBce8SX8AjibYscOxLzUXhRvsWaN55NfZkCSwIqtX9+8MSMjoVVz+byaNVE0wsx+p7KMY9+1Cx2B2KPRti3uQVao6sWlPz2tT2wUHTtiUuU2dJ56duLjQUYMCMB4q1d7fkzPPYfnIKPBMc6ffnL+7exZKAVhYc79fL3B6tWwLvPlUw9pTZgghHBW6E6UkoKGEo0bwyWd3eAXwH7YQZIgyMqVyxoCQC94stq/39xx4+PRE5YbF+TPj5KInjZjZyQlgVH62GNi3AkT9JPJMgLMA2jUKOM8IVyQgnNPvXV7790r7t2TT3pWSnX2bFhYGd2JJz0dx16ihHrJzTNn8FuRIuYKw0uXUNSECKEP5b5tNiGACxR4cCpSZRb8AtgPO2zZgju/eHFmH4kxREeDTDJypG/GlyQUJlBaqi1aoFKQEcLP+fMolchFQqpVg5WZFTs7cTvAdesybp9WK9yrXOPbjJzjtDRUzMqdG4rOokXGiHYcA88Mos/Ondi3Fhv89GmQkIoW9V4pVCI9HaGAgACkYSmZ1598IoRwzpzaefh+uIdfAPthh7ZtEfPLbvmxsoxYXd68vp8ob99G/iTXsi1WDBWStKwrm02WN2wAi9hiEWUy//wz68bYrVakw9SunfHHyClPROYS4c6dk+X27TFuw4b6K4V9/jm2yaxGJMOH45nRcjWfPIlnMDzcfA/Ktm1wdQcFIeZqs4HsWLgwrGROAVu40Nz9PizwC2A/7mPvXtz1+fMz+0g8w549YjLICPe51YoKSk88AcEaEAA3JxfFiIlBznGFCvJ9xqYrQZ2VwDWAlW34MgrXr4sykWbX+5Uk5ByHhWEfL73kvlEDX4vMinlyQ/pmzbSVoRMn8HwVK2Z+pbI7d9ALmkiWO3WCUsQlOQ8dQsEMMwsAPUzwC2A/7qNHD6TzZEV3qB5IEvICmRVZpAiY3G3bwnU8bRrcicuXI9fy6FFMbmZMGufPozBGWJiw3nhp3BiEtuxSPF6SEHt89NHMm1D79AEZyFe4cwc1rYmgIG3Zor3u779jPa6NnBlgK/zrr7XXiYyEKzoiwnxypiQhJSw4GEL+m2/w/+jReEaeew7H17t39vSeZRZcCWALfs8Y1KtXT963b1+G7c8Pe5w4QVS1KtGsWUSvvprZR+M5zp8n2riR6OZNolu38Fe5pKY6bxMYSFS0KFGxYljCw8X/jp9DQogsFucx0tOJVq8meucdor177X8bOpRo7FiievV8c85mY906oq5dib7+mmjw4Mw5huhookuXiOrU8e1+tm4lGjWK6OxZomeeIXr7baLChe3X2bWLqHFjog0biDp08O3xaEGSiJo0ITp3jujUKaJChdTXi4wkatWKKGdOom3biCpUMPc4jhwh6t+f6ORJqJeBgUQ3bhCFhRG9+y7RlClEzZoR/fwzUWiouft+EGGxWPbLsqw+M2hJZl8sfgs4czFkCJoVREVl9pH4DpKEnNCTJxHb+uEHdEmaMQPx486dYUEXL46Ym6MlS4S6tGXKoDxh9+7oYOO4zrhxcDMfPAhSWJ48+P7xx0Fs8rQ3bUZAknBuZcv6puFAVkRSElzRgYHwYKxYYe/q5ZrrmV3y8OBBhDnc9WI+cgTnUbIk0pXMRkICmprw8z50qPjthx9AzKpSJfNzhbMDyO+C9uPSJUw+Rss5Psiw2aCMHD0Kd/Xy5XBfT5uG4hxaPWeVS/78qDldo4bzb5UqwS195UrWck1v3ozj++STzD6SjMeRI1A+iEDW4vrgly/ju88/z9zjk2W8oxaL++I7hw6BKFWqFMIjvoCyV7DSNb5tG0JZEREPTucxX8GVAPa7oB8STJxItHgx3FulSmX20WRdJCQQffMN0UcfER09SlSwINHw4UQjRhDlz+/s8lZzgcfFqY8dEkJUsqS6C1z5XVgYUY4cvjvH1q3h4jx3jihXLt/tJ6vCZsO7MGMGkdVK9PLLuMdhYUQLF8LFmpm4d4/o0UeJihcn2rPH9bNw6BBRmzZ4NrdtIypb1vzj+fJLomefxf/DhxO9/z5R3rxwhXfqRBQbS7RqFVG7dubv+0GA3wX9kIObrCvdSH7Y49QpWB4FCkDbr10b1Xc8qT2dnAzX3Jo1aKzgaBmXK4eF3daOS0AASDC1aqFM4ZAhqKH8zjuwqLduBRnn7l3j6UOcc/rOO8bP60HDlSuC+VuzJv7OmZPZRwV8+y2OR0+d7P37QWYrW9Y3LmFJQi47P5+PPipKQl69imtndqOYBwnkt4AfbsycSfTGGyBhVa6c2UeTdWCzEf32G9GiRUSbNhEFBRH17k00fjxRo0bqRCxPYLUSrV1L9PHHRJs3g9TSqxdIW3Xq2FvRrizs9HTnsYOC3JPKeMmXj6hLF1hVFy/CivGDaM0a3PPr1/E5Ph7XKjMhy0Rt2xLt3w9vRXi46/X378f6hQoR/fWX+V6u5ctB1ps2DR6i6GiQ2caNg8XeqxfRli1E8+YRTZ9u3rvzIMCVBewXwA847t0jKl0a7qEff8zso8kauHsXbrVPPoEgKlGCaPRouNmKFfPtvk+fhvtz6VK47qpVIxozhmjQILiotSDLRDEx7l3gt24R3b4NRq0r9OihLbDDwx8+13RcHMINRBBen3xC9MQTmXtMJ08S1axJ9NRTYKu7w7//4j0vXBhCuGRJ844lLY3okUeIqlQh+u47sMl/+42oWzeiJUvgAh82DMJ59GiiDz+EoumH3wX9UOPNN2Wf1E/Ojvj3X+SFMrmqZUsUwc8MJnBioix/+aXIac6bF/mWZnS+sVrRQ/nQIVneuBHkmbfeEi7EunXReo9LZaotBQrIcuXKKMXZr58sT5yIrk1LlqCn7P796OaTnWqJu0PVqmDHs7u17//bO/PwKKqsjb+XyCK7LGKUTUVARAFBBEEGUVl8IowL66jIiAhGPgSGERUXFlEWd0BARAZlERhkVRQRFBcYI+KAEASFCUEhBGQLBJL0+f54u+jEBNLpru7q7jq/58mT7uquu1R13XPv2W43bpnoJE89xbb4m495wwZuKlGnDtXDdmKNJT/8QLX0q68ye9ZllzHjW06OyPDh/M6ddwa+dWisAVVBu5NTpzhrbdSIcbNu5PRpYMECOlVt3Ei16wMPUP3bsKHTraO4++47rrjmzwcyMxkL+uijVOuVLGlPPdu3c7X91FPAmDG+42fOcMXsjwr8+PH85RpD56XzOZVZf5UqAcWK2dOfUNCyJVdyK1YAEyYAo0dTEzBhAp2PnGj7yZOM3S9Thg5XxYsXfs6GDUD79kB8PLB2LZ257OCPP6gduPtuYPZsHtu0iTHDu3YBI0YAzz4LTJsGDBwING9O00vVqvbUH62oCtqlvPUWB/K1a4G2bZ1uTXhJSaGqd8YM4OBB2r4TEyl8K1RwunUFc+gQMGsW79svv3Dg6tuXSSRq1Qqu7N69gUWLmPiiSpXAyjh50j8V+P79nEj8mbi4/KrucwnrcyVDCSUdOlAVvWED3//8M6/9unVMPDFtGlWw4Wb5cqp6x48Hhg3z75xvvmF/LruMz398vD1tGTSIvgyW6QbgxGzgQKrJW7UC5s6lYO7Zk2rwVauAK6+0p/5oRAWwC8nOBurW5cD2zTfucIoQoSPI5MnAsmU81rkzBe+tt0bPNfB4gNWrKYiXL2e/EhJoK+7Qoegrsd27gauuYijaK6+Eps25EaHvQWEr6v37ufrOzs5fRsmS519N535furQ97e7WDdi6Fdi2LW9fZs0Chg4FMjKoQRg+3D7NhL906UIHvuRk/x2svvoK6NiR31+3rnBHLn/YvZuZt4YNA156Ke9nlv33ggvoY3HJJcy2FhcHrFwJ3HBD8PVHIyqAXcicOcB99wFLl1IIxTLHjlElNnkyB6gqVRi3278/HdCimZQUYPp04O23KayuuIL96tPH/5XsgAF0lNm92z51pF14PPSo9UcFnp5OgfhnypUr3Au8WjX+lShx7rb07cvVWmpq/s/S0oDBg7m6q1+f9+Tmm+27DoWxZw9V0Z06MebWX778kufUrs2V8MUXB9+Wrl05Gdi7N7+3+K5dXPkmJfF32r8/Hf4OHKApyGnHNicIygkLwEwAaQC25jpWCcBqADu9/y8qrBxRJ6ywkZNDR5KGDWN755KffhJ59FGRsmXp+NG8OWMRYzFR/OnTIvPm+baGK1lS5IEHuJn9+WKBU1OZNvCRR8LX1lCRlcVdlDZtoiPYzJkiL77I+O3u3ekwVr8+MzSdy7msUiU6W91yi0jPniKDB9NBbdYsHgfowHauvYQ//ti3RWW/ftwNK1yMHct6V64s2nnr1jHm/JpruM1msHz7Ldvx+usFf376NLPJWXthr1kj0rQpU79GQqaxcINgnLCMMW0AnAAwW0Qaeo+NB3BYRF4yxgz3CuAnCpsJ6Ao4PCxbRpXV++8Df/ub062xl+xsruonT+aMvmRJOoEkJrpHxbV1K9XTs2czc1eTJrT19+yZP7Z3yBDgjTeAnTvpkOcWMjO56vInc9nJk/nPL1aMNviCVtXlytG/4PvvuaJ+7z2uCkNt4jhzhmFJWVn8DVx4of/nrl3L1WedOtycIlA/AItWrbhBw86d587UtWoVfQ+OHaPj35o1wMcf01Hr+eejxyQULEGroI0xtQGsyCWAdwBoKyK/G2PiAawTkUJTPKgADj0i3NXlwAE6kcRKLN6BA1TDTp0K7NtHp6QBA3wpBN3I8eOcZE2ZwgG5QgXGZ/bvTzVpejqv0733+hdH6lZOnKAgfuIJ7nb1wguMIChIYBeUDMWiWjVOAs9nsy5bNjjBs2YNE2489xyFWFHPTUigb8jnn+ffEaooLF5ML/2FC/n7Ohf799PxcfVqLgri4nhunz50avPHqzvaCToOGEBt5FVBH/nT53/4U46qoEPP2rVU/UyZ4nRLgsfjYerEXr0Yb2gl0F+69NwqQjfi8YisX0+VqnWd2rWjOhbgRu5K4cyYwev1v/8V/LnHI3L4sMi2bUwHOneuyIQJVPHnVnNXrcp0ogWpwEuXZhrSli1FevSgSr2o9OxJE0Qg+wF/+inPbdyYqUwDJTtb5MorRVq0KPy7OTki48czXWWNGiK3385r0bGjyPHjgbchWkCwuyEFI4AB9AOQBCCpZs2a4eqza2nfXqRatei2g2ZkcDBs0oS/0PLlaefbscPplkU++/eLvPCCL6e1ld/Y7qQMsciCBbxeW7YU/dzdu0U6deL5zZqJJCVxN6ORIymkjMkvjK+6KjAh+ttv3IWrQ4ei5wIXEVm1ikK4SRNOKALlzTfZj6+/9u/7Gzdy8hEXJ3L55XI2KYzTyU5CTSgE8A4A8d7X8QB2+FOOroBDS1IS7+hLLzndksDYtUtk6FAmlge4xd/Uqe6YJdvNqFG8htWq8X9cnMg999AhJpBB2w188knRBIpFdjZXxXPmcIVX0OYaDRpwi8uJE7l6DkbwidABChBZuDCw8z/6iCv3pk0DdyQ7cYLP6t13+3/O0aNcwee+PrVrc//uWCUUAngCgOHe18MBjPenHBXAoeWee7jyOXrU6Zb4T04OB4M77uAqIS5OpGtXkS++UEERKCdOcJ/YO+7g+1276JVqpZ6sX58DeDg9eKMBy7v3o4/O/Z3MTKbhnDGDHvgtW+bd1apECa7ycguYDz+0v61ZWVQjX3aZyLFjgZWxYgVNFjfcIHLkSGBlPPkkn9tdu/w/x+MReffdvNetUqWiT3yihaAEMIB5AH4HkAUgFcBDACoDWAOGIa0BUKmwckQFcEjZvp0PwtNPO90S/zh8WOTll2lHArj93rPPqqrUDl55hdf0m2/yHj95knmhrQ3pS5cW6duXYT0Kw9oAkfnz+f7YMdrW33iDW3k2auSzsQNUA998M/Nkz5rFPN6584qvWydSty6/e9999oQA5caaMAwdGngZy5axTzfeGNjEfd8+nv/YY0U/NzmZkwjrepYqJbJ4cdHLiXSCXgHb9acCOHT06cM9f+1+yO3mhx846F94IX99rVtzwDt92umWxQaZmdxQ4JZbzv+9pCSRhx7y3YcWLSico9l3IBjS0hhXbAmDunXz2m2rVqXNdfhwkQ8+ENm5078Y+1OnOLEsXpyrvFmz7NXsPPwwtUb//W/gZSxZQgepli0DW0337s3JXCBOXZmZ9O+wrrMxIpMmFb2cSEYFcIyTksIHaOBAp1tSMKdP02O0VSvfyuvhh32beiv2MXUqr/Fnn/n3/cOHRV57zbdSq1xZZNgwkV9+CW07ncLjoZfzkiUUjHfeKVK9el6VMSDy17/Sjr58ObUywQrNn37y/f7btaMAt4P0dN6z1q2Da+PixRxDWrUquhD+8Uf2a+zYwOtfvpz9sK7/E0/EThIhFcAxzqBBfHjOFT7hFKmpIs8843MEqlOH6tFgHVCUgsnKonfpjTcWfTD2eCi0776bKypj6NW7bFn0hnxlZ9M0M3cuJxW33pp3C8ZixUSuvpphbhMmiKxezePPPBOa9uTkcIJUvjzVrWPH2rMVphU+NWtWcOUsWsR737p10R0fb79dJD4+OE1Waiq3CLXuT69eXCFHOyqAY5i0NK4oe/d2uiXE46Ht6957fQN5QgJT+MXKjDZSmT2bT/SyZcGVk5rK0KX4eJZXqxaFxYEDdrQyNOR2jkpMFLnpJu6xnNs5qmlTmj8mT6Z9/MSJ/OVUqMAJbSjZt4/PB8B0sd9+G1x5OTlUH1etGlxsrwhDseLiRNq0Kfj6nItVq+yZBGRni4wZwzYANKUE6iAWKagAjmFGjKCQ27bN2XYcPy7y1lu+zcwrVYptVWakkZND7+brrrPPxnjmDMNcbrmF97R4ca5KvvrKWQ/148fZhjffpO9D48Z5naPKluUqbuBAettu3uz/yqxGDZYZDpYupfrbGDoxBRO9sHkzV/T9+wffrvnzWVbbtozJ9wePh8/+tdfa89v4+muRmjXlbDji3r3Bl+kUKoBjlKNHmXi+KHF4dpOczIGufHk5G1g/cyY9bpXwsXAhr/8HH4Sm/G3b6O1r3edrr+WEK9AQGH85eJDZm8aNY+aogpyj2renzXD+fCa2CEbTcs014X2ejh3jdTWGIUVLlgRe1uOPs5yNG4Nv15w5FMLt2vkvhN95h/fk00+Dr1+Epqp77mGZ1asHliAlElABHKOMH887+N134a03O5sDhZVSrnhxJhkobGceJTR4PMxqVK9e6O21J06ITJ/uCx8pV44q361bgyvX46Ez4ZIlVH937pw/qUXNmnSOGjmSava9e+3/vbVsKXLbbfaW6Q8bN1J7AXACEEg43tGjNBtcf709v4P33qNAv+02/ybUmZn09+jQIfi6LTwekWnTaDOvUCE606qqAI5BTp1i7Gw4B4u0NG7/VquWb1Y6ZgzTHyrOsXIl78e774avTitP9/33+3Iht2njX0hZTg41J/Pm0Uxx2215PWCNoTq9Z09OMj/7jN6+4aBjR25r6QRnzjCLXalS1DRMmVL01fz8+byGdoXy/OtfvB/t2/sXojZ6tASczvN8bN0q0qUL7fzRhgrgGMQKN1mzJvR1bdzIvWdLlpSzYRT//je9bhVn8Xi4aqtVyx6P2kBIS6OK2MrvW60afRNSUiiMN22ievKxx/I7RxUvzhXbQw9RaHz9tbOpR7t1oybBSXbt4qQE4PUqinbB4+G5FSrYl2P53XcphDt2LFwIp6cztjxcdvRoQAVwjJGVxXR3zZuHTuV76hQ9Gm+4Qc46tiQmMp5RiRw+/5z3Z/Jkp1tCFejTT+dVG+f+K1uWcaYDB9JP4IcfIi8BS9++VOM6jcdDr/bKlTlJGTHC/yQpyck857777GuPZd+9447CQ4MGDKBWJNY3WfAXFcAxxty5ErIcs3v20KHFUgnWr8+VSTTll3YTt95KU0S4M1ilpzNu1nKOqlev4B1/cv9NmBB8mEyoGTqUK/RI4eBBap8A7p60dq1/51kToXXr7GvL9OksMyHh/EL455+jKy1uqFEBHEN4PPRAbdDAvrjanBx6LnbuTM/HYsVE7rpLd86JdDZs4BM8cWLo6rCco5YuFXn+edrh/uwcVaMGjz//PL+XkuL73WRmirz/vi8LVKlSIg8+yK36IpGRI9nOSEs+snq1b5OHv/+98IlMRgZ3GWrQwF4tg2X66tz5/OV26cJQRH89qGMZFcAxxPLlvGuzZwdf1pEj3BXHSkNYtSpnrSkpwZethJ477+QgZ5fNNCeHey7Pmyfyz3/Sy71KFZ+gNYYr3R496By1ejVXaP6yebPII4/4bMDNmlEVHUmD9Kuvsm2RmK0tI4O5qOPi+KzOnXv+CbI1VowbZ287pkyRs+k6z+V38OWX/M6UKfbWHY2oAI4RPB46ZQTrcLNlCwP2rYGwRQuuUmIh7ZtbsPLvjhoV2PmnT9MGO3MmnaNataKNNrdzVJMmXG29+aa9zlFHjrDMBg1Y10UXiQweTOHvNNaGDHv2ON2Sc7N5M/0/ADpG/frrub/bpQsz5dmdpnbSJDkbMlXQWOTx0H/kqqs0A54K4Bjhiy8k4BCDM2eYZu4vf/GpAvv04a44SvTRvTtjcP1ZqZ04QQE6aRK9jZs08YUOAZyI3XQTBfE779BrORzOUVba0m7dmMsc4Kr7ww+d87BftIjtCGZ3oXCQnc1tEsuWpYCdOLHga7ZnD72S77rL/ja8/jqv1b33FiyErZCoYJKLxAIqgGOEjh1FLr64aFmmfv+ddq1LL+Xdvvxyqg/DFVep2E9yMtXBw4fn/+zQIcbNjh/PONr69fM6R1WuzDCVYcOoak5Ojgx752+/MYbU2pmoenWu7n/7Lbzt+PRT1r9+fXjrDZSUFJoiAE6sCppQv/giP1+xwv76rb2nu3XLPwHIyqK27uab7a83mlABHANs2sS75c+WXx4PB5AePXw5cjt2pE0oEgZbJTgefJCrmk2bmBFq5EiqGq3cubmdozp3ZmapJUuohox0p7qsLK6ArSxrF1zAwX3duvC0feNG1rtyZejrsguPhyv3+Hg6UA4ZktdccPo0J2KXXx6aFLETJ/Ka9eiRXwhbAjpSne7CgQrgGKBrV2bHOd/OIBkZIm+/LdKoEe9shQq0rf38c/jaqYSW1FSfuja3c1TdulRLjxvHVVxRnKMilR07+PutWJH9bNCAtuNQ7o6zfTvrmjcvdHWEij/+oG8HwJVn7kmEFS8eqq0WrbS4vXrlneQfPcpxq3v30NQbDagAjnJ27OAg++STBX++cydnvdZAdd11jNkrynZiSnSQmspMZH360Ab41Veh3xDBaTIy6BzVtKmctVk/8gidkexm3z7WMXWq/WWHi/Xruc+xtSq1UsX26kXbf6gm5Jaq+/778wrhf/yDntuR7NgWSlQARzkPPUSnqdz7sWZn06bTqZNPVde9Ox++SFczKkqg/Oc/VMGXKsXffatW9nrwHz/OcsePt6c8p8jMpA29RAlOzGfM4OSifHnmdQ7VGDFmDK9f794+IZySwvFp8ODQ1BnpqACOYvbupR03MZHvDx1iRiEr7258PBMghNtZRVGc5NAhkZdfFqlTR87GsA8fLrJ7d3Dlejy0o44YYUszHSc5mZtkAIyASEzk6wULQlfnqFGso08fXwhSr1702g+l+SBSUQEcxQweTPXN4sWMybRm/m3acO9XpxLwK0okkJMj8skndEIrVoymmoQE2j8DdTisWJF79MYKOTlcAVesyLEEYFREKE0Xzz3Hevr2Zf3ffy9n05G6DRXAUUp6OmP8rDCS0qVp+4r0GEVFcYKUFK5cq1WTsyF348YV3SGtZk2qUGON33+nTdhy3hsyJHR1eTx0+AJE+vWjEG7bluFlbls0nE8AF4MSsXzyCXDyJFCnDvDaa8C+fcDUqcC11zrdMkWJPGrUAEaPBlJSgPnz+f6JJ4Dq1YEHHgA2bKDoKYwKFYBjx2UCj+QAAAZQSURBVELf3nBzySXAvHnAypVAzZocS/y5HoFgDDByJPDUU8D06UBiIjBkCJCaCixcGJo6oxEjoboDBdCsWTNJSkoKW33RzpkzwJYtQJMmQDGdKilKkdm6lYJm9mzg+HGgcWPg0UeBXr2AMmUKPqd1a6BUKeCzz8Lb1nCSkUFhWK9eaOsRAZ58Ehg3DhgwAPj8c173pCQKaTdgjPleRJoV9JkO6xFMiRJA06YqfBUlUBo2BCZNovborbeAnBygXz/g0kuBQYOA5OT855QvDxw9Gv62hpMyZUIvfAEK2RdfBIYN4/U/dAjYtAn44ovQ1x0N6NCuKErMU64c0L8/8OOPwPr1QEICBcLVVwPt2gGLFgFZWfxurKqgncIYroCHDAHS03ns5ZedbVOkoAJYURTXYAxVzHPmUAU7dizwyy9A165ArVrAc88Bp06pALYbY4CJE4HHH+f7FSsK1j64DRXAiqK4kosvpn3y11+B5ctpHx49Gli6NPZV0E5gDPDKK8D//R/fT5rkbHsigQucboCiKIqTxMVRJZ2QwNXwtGl0gFTsxxhGdFSpAlSq5HRrnEe9oBVFURQlRKgXtKIoiqJEGCqAFUVRFMUBVAAriqIoigOoAFYURVEUB1ABrCiKoigOoAJYURRFURxABbCiKIqiOIAKYEVRFEVxABXAiqIoiuIAKoAVRVEUxQFUACuKoiiKA6gAVhRFURQHUAGsKIqiKA6gAlhRFEVRHEAFsKIoiqI4gApgRVEURXEAFcCKoiiK4gAqgBVFURTFAYyIhK8yYw4C+F/YKrSPKgDSnW6EQ2jf3Yub++/mvgPu7r/dfa8lIlUL+iCsAjhaMcYkiUgzp9vhBNp3d/YdcHf/3dx3wN39D2ffVQWtKIqiKA6gAlhRFEVRHEAFsH9Md7oBDqJ9dy9u7r+b+w64u/9h67vagBVFURTFAXQFrCiKoigOoAI4F8aYmcaYNGPM1lzHKhljVhtjdnr/X+RkG0OFMaaGMWatMWa7MeYnY8wg73G39L+UMeY/xpgfvf0f6T3uiv4DgDEmzhjzgzFmhfe9m/q+xxizxRiz2RiT5D3miv4bYyoaYxYZY5K9z39LN/TdGFPPe7+tv2PGmMfD2XcVwHmZBaDjn44NB7BGRK4CsMb7PhbJBjBURK4G0AJAojGmAdzT/9MA2olIIwCNAXQ0xrSAe/oPAIMAbM/13k19B4BbRKRxrhAUt/T/dQCrRKQ+gEbgbyDm+y4iO7z3uzGApgBOAvgQ4ey7iOhfrj8AtQFszfV+B4B47+t4ADucbmOYrsNSALe7sf8ASgPYBOBGt/QfQHXvYNMOwArvMVf03du/PQCq/OlYzPcfQHkAu+H1B3JT3//U3/YAvg5333UFXDjVROR3APD+v9jh9oQcY0xtAE0AbISL+u9VwW4GkAZgtYi4qf+vAfgnAE+uY27pOwAIgE+NMd8bY/p5j7mh/1cAOAjgXa/5YYYxpgzc0ffc9AAwz/s6bH1XAazkwRhTFsC/ATwuIsecbk84EZEcoTqqOoDmxpiGTrcpHBhjEgCkicj3TrfFQVqJyPUAOoHmlzZONyhMXADgegBviUgTABmIQXXz+TDGlADQGcDCcNetArhwDhhj4gHA+z/N4faEDGNMcVD4zhGRxd7Drum/hYgcAbAO9AdwQ/9bAehsjNkDYD6AdsaY9+GOvgMAROQ37/800A7YHO7ofyqAVK+2BwAWgQLZDX236ARgk4gc8L4PW99VABfOMgC9va97g7bRmMMYYwC8A2C7iLyS6yO39L+qMaai9/WFAG4DkAwX9F9EnhSR6iJSG1TFfS4i98EFfQcAY0wZY0w56zVoD9wKF/RfRPYD2GuMqec9dCuAbXBB33PREz71MxDGvmsijlwYY+YBaAvuhnEAwHMAlgBYAKAmgBQAXUXksFNtDBXGmNYA1gPYAp8d8CnQDuyG/l8H4F8A4sCJ6QIRGWWMqQwX9N/CGNMWwD9EJMEtfTfGXAGuegGqZOeKyAsu6n9jADMAlADwK4A+8D4DiP2+lwawF8AVInLUeyxs910FsKIoiqI4gKqgFUVRFMUBVAAriqIoigOoAFYURVEUB1ABrCiKoigOoAJYURRFURxABbCiKIqiOIAKYEVRFEVxABXAiqIoiuIA/w8+t6nghXrg9QAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "best solution with len 2665.0 \n", - "with a total time of 0.0 seconds\n" - ] - } - ], - "source": [ - "available_solvers = {\"random\": random_method}\n", - "\n", - "solver = TSPSolver(\"random\", p_inst, available_solvers)\n", - "start = t()\n", - "solver.compute_solution(return_value=False, verbose=True)\n", - "end = t()\n", - "\n", - "solver.plot_solution()\n", - "print(f\"best solution with len {solver.found_length} \\nwith a total time of {np.round(end - start, 5)} seconds\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Let's analyse the performances of the random Method\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "best solution 2160.0 on 1000 trials,\n", - "with a total time of 0.59 seconds\n" - ] - } - ], - "source": [ - "number_of_trials = 1000\n", - "found_lens = []\n", - "start = t()\n", - "for _ in range(number_of_trials):\n", - " solver.compute_solution(return_value=False, verbose=False)\n", - " found_lens.append(solver.found_length)\n", - "end = t()\n", - "fig=plt.figure(figsize=(8,6), dpi= 100, facecolor='w', edgecolor='k')\n", - "plt.ylabel(\"Solution Length\")\n", - "plt.xlabel(\"Tries\")\n", - "plt.plot(found_lens, \"*\",label=\"Random\")\n", - "plt.plot(np.ones(number_of_trials)*p_inst.best_sol, \"r-\",label=\"Optimal\")\n", - "plt.legend(loc=\"center right\")\n", - "plt.show()\n", - "\n", - "print(f\"best solution {min(found_lens)} on {number_of_trials} trials,\\nwith a total time of {np.round(end - start, 2)} seconds\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "-------------\n", - "## Constructive Algorithm\n", - "### Nearest Neighbor Method" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def alessio_marco_nn(instance_, starting_node=0): \n", - " # TODO\n", - " tour=[starting_node]\n", - " for i in range(instance_.nPoints):\n", - " next_node = np.argsort(instance_.dist_matrix[i])[0]\n", - " index=0\n", - " while next_node in tour:\n", - " index+=1\n", - " if index" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "best solution with len 680.0 \n", - "with a total time of 0.0378 seconds\n" - ] - } - ], - "source": [ - "available_solvers = {\"random\": random_method,\n", - " \"amnn\":alessio_marco_nn,\n", - " \"nn\":nn}\n", - "\n", - "solver = TSPSolver(\"nn\", p_inst, available_solvers)\n", - "start = t()\n", - "solver.compute_solution(return_value=False, verbose=True)\n", - "end = t()\n", - "\n", - "solver.plot_solution()\n", - "print(f\"best solution with len {solver.found_length} \\nwith a total time of {np.round(end - start, 5)} seconds\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "A25UdIeTJOp8" - }, - "source": [ - "### Best Nearest Neighbors" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "i_cC5eA-XT0B" - }, - "outputs": [], - "source": [ - "def best_nn(instance):\n", - " # TODO\n", - " \n", - " # [...]\n", - " return solution\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 534 - }, - "id": "lRAGk5zEXW0U", - "outputId": "493f692e-ef0c-4a94-dfc9-6726e9a5ac43" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### solving with ['best_nn'] ####\n" - ] - }, - { - "data": { - "image/png": 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/JSQAa9YAM2fy7/33gfHjqaibNKEybt+eSjt3bmsfT1GUEEVMEINZDRs2NMuXLw/a+ylKchw5Asyf73cpr17NmG7evEDTplS2bdvyfr586d/vuXPAokV+hbx8Ofd76aXcn08h16wJiGTXp1MUJZQQkRXGmIbJblMFrLidQ4eocH0u5ZgYKsZLLgGaN6fCbdOGVusllwTufY8c4XvOmEGFvGULny9TJmn8uFy5wL2noiihhSpgxVMcOOC3bufOBdat4/P58/sVbtu2QKNGtHqDxfbtwKxZfgv50CE+X6uWXyG3aQMUKhQ8mRRFyV5UASuuZu9ev8KdNw/YsIHPFygAtGzpV7gNGgB58lgV9R8SEoDoaL8ynj8fOHsWyJmTlrgvoatJE40fK0o4owpYcRV//+23bufNA/78k88XLEiF27Ytle4114SP8jp3Dvjjj6Tx44QExo/btPHHj2vV0vixooQTqoCVsGbnzqQWri+WWrgw0KqV38KtX58ZyG7g6FFgzhy/Qt68mc9fdpnfXd2uncaPFSXUUQWshBXbt/ut23nzgG3b+HzRokDr1v6kqXr16LL1Ajt2MH48YwZvDx7k8zVr+pVx27YaP1aUUEMVsBKyGANs3Zo0aWrnTm4rXpwK1+dSvuoqIIc2T0VCAjO5fdbxvHlJ48c+hdykSejEvBXFq6gCVkIGY+hCTmzh7t7NbSVL+q3btm0Z71SFmzbnzyeNHy9bRiVdoAC/S19CV+3aGj9WlGCjClixhjHApk1Jk6b27uW20qX91m2bNtqgIlAcO5Y0fuxLUitdOmn8uHx5u3IqihdQBawEDWOA9euTJk3t389tZcv6rds2bYAaNVThBoOdO/3KeOZMf/z4yiuTxo8LF7YqpqK4ElXASraRkMBGFz7rdv58/wW+fHm/sm3bFqhaVRWubRISgLVrqYhnzODvdeYM48eNG/sVctOmGj9WlECgClgJGL4GEj6X8vz5bLkIAJUqJXUpV6miCjfUOX8eWLzYbx0vXZo0fuxTyHXq6G+pKJlBFbCSaeLjOazA506eP58xRgC4/HK/sm3TBqhc2aqoSgA4dowLK59C3rSJz5cuzb7V7dvztkIFq2IqStigClhJN3FxHMfncykvWMD5uABQrVpSC1cvwu5n166k8eMDB/j8FVckjR8XKWJVTEUJWVQBKykSG8uB8z6XclQUcPIkt11xhT9+27q1dl3yOsYwfuyb7jRvHuPHOXIkjR83a6bxY0XxoQpY+YcLF1gn6nMpR0UBp09zW61aSV3Kl11mV1YltLlwwR8/njHDHz/Onz9p/PiqqzR+rHgXVcAe5vx5Xhh9LuVFi9g1CWBijc+l3Lo1UKqUTUmVcOf48aTx440b+XypUknrjzV0oXgJVcAe4tw5WiU+l/LixXxOBKhb1+9SbtUKKFHCtrSKm9m1K+n8Y189eI0afmV87bUaPw4EFy6o2z9UUQXsYs6cYRtCn0t58WKejCLA1Vf73cmtWgHFitmWVvEqxrBePHH8+PRpxo8bNUoaP86b17a0oc/FIzm3bOFty5a2JVMuRhWwizh9mm5k34m3dCkTqXLk4Pxbn0u5ZUu1LJTQ5cIFYMkSv0JeupQlb/nzMxySOH6s/cCTjuScOxf46y8+X7gwkC8fsG8flXDVqlbFVJJBFXAYc/IkE6V8J9+yZSwVypkTaNjQb+G2bKmj6JTw5fhxHt++hK7E8ePrr/cr5IoV7coZLLZvT2rhpjaSs2VL5nqsXGlTYiUlVAGHEcePAwsX+hXuihW0DHLlYqmH78Rr3hwoWNC2tIqSPezenTR+vG8fn69enYq4fXt6e4oWtSpmQMjKSM5du7goGTkSGDbMhvRKWqgCDmGOHWOzC9+Jt2oVSzly5+Y8V1/SVLNmbA+oKF7DFz/2KeO5c/3x44YN/dZx8+bhET9O70jONm04QjI1F/y4ccDjj7NjWY0aQRFfySCqgEOII0fYztF34q1ezRMyb142wPedeE2bMh6mKEpSLlxgzNgXP16yhF6ifPmSxo/r1g2N+HFaIzkTTwjL6EjOli0ZplqzJjskVwKBKmCLHDpEhes78WJieEJecgmtWt+J16QJn1MUJWOcOMFzy6eQN2zg8yVLJo0fV6oUHHmMoQyJLVxfCVaZMkknhGVlJOfff3Pi2PDhwHPPBUh4JeCkpoBzBVsYt3PgQNJYzrp1fD5/frrIhg/nideoUXi4yxQl1ClUCLj5Zv4BVEyJ48eTJvH5atUYO/bVHwcqfuwbyZl4QljikZy+ftlt2lCGQHUF++473nbtGpj9KcEnTQtYRK4AMDnRU5cDeB7AZ87zlQFsB9DNGHM0tX250QLeuzfp8Hnf6rtAAbqHfC7lhg21UF5Rgo0xwPr1SePHp07RNd2ggT+hKyPx48QjOX0Twg4f5raKFZNauNk5krN1a4a01q7Nnv0rgSFgLmgRyQngbwBNADwC4IgxZpSIDAFQ1BjzTGr/7wYFfHEB/J9/8vmCBalwfSffNdcwkUpRlNAhNpYxY59CXrzYHz9u1crvrq5Xzx8/jo9njNV33i9YABx1TI0qVZJOCAvWSM69ezkc5YUX+KeELoFUwB0AvGCMaSEimwC0NcbsFZEyAOYaY65I7f/DUQHv2UN3VnIF8K1a+U+8q69mqZCiKOHDiRO0YH3x4/XrU399tWpJk6Zs9bWeMAF49FFav7Vr25FBSR+BVMAfA1hpjHlbRI4ZY4ok2nbUGJNqVCXcFPDx42wEcOECu0olrserV4/NMBRFcQ9//02lmtxl8eabgZ49GT+23db12muZ2JXWgkGxT2oKON1J+iKSB8AtAKZk8M37ishyEVl+0JeZECYULgw8/zxdUaVKMYHq8cfpXlblqyju44MPqHwHD6b3a906YPx4Kt85c4A77uAQk0aN2Phi9mwOOwkm+/fTar/jjuC+rxJ40m0Bi0hnAI8YYzo4jz3hggbofr7rLiZajBsH9Oun800VxW18+ilw//1A797A//7373M8Npb1x4njx3FxLB/0xY/bt08aP84O3nsPeOghJoJddVX2vY8SGALighaRSQB+M8Z84jweA+BwoiSsYsaYwantI1wVMMCygvvuA379lWn/H35IC1lRlPBn1iygY0eGmCIj05dAefKkv3/1zJn+ksPixZPWH1epElhZ27VjC8qNG9UQCAeyrIBFJD+AXQAuN8Ycd54rDuAbABUB7ATQ1RhzJLX9hLMCBlh+8N//0vVUsSIweTJdUYqihC/r1gEtWrBmNyoq8wvrvXupyH0JXXv28PmqVZPOPy5ePPOyHjwIXHYZMHQoMGJE5vejBA/thBVg/vgD6N6dJ9zo0cCgQboSVZRwZN8+dqHzjUcM1LQlY2ih+qY7zZ1Li1mEOSS+hiAtWmSsA96HHwJ9+7JnfP36gZFVyV5UAWcDR44ADzwAfP89EzQ++SRrK1tFUYLL6dOsaNiwgUlNDRpk33vFxnKUqM9d/ccf/vhxy5Z+hVy/furx4w4dOJrwzz910R8uqALOJowB3n4beOopZklPmsQVraIooU18PHDbbcDPP/sX0cHk5EkqfZ9C9nWzKl4cuO46f0JX4vjx4cMc3vD008CrrwZXXiXzaC/obEIEGDCAbezuvJOr6ZdfBp55JjSmsCiKkjxPPAH8+CMX0MFWvgA75914I/8Af/zYp5CnOMWel1/ujx9v28aFg/Z+dg9qAQeIEycYm5k8mW6izz+nVawoSmgxfjzzNp54Ahg71rY0/8Y3vtCnjOfM4fXFx+DBtI5btGALTSW0URd0kDCG9YOPPcbOWV99xaxHRVFCg++/p+v51luBqVPDw1MVFwf8/rvfWs6Vi8/lzfvv+LE2CAo9AtIJS0kbEeDBB1msX6QIawFffJFuI0VR7LJsGRvqNGoEfPFFeChfgAr3wAHeX7qUgyB++QV4+GE+P2QIp62VKkX39AcfAFu32pVZSR9qAWcTp06xWfrEiSzu//JLoGxZ21IpijfZtg1o2pRjQv/4g8lM4cRNNzFRa9u2f2c/79vnjx/PmMF+1gATuHzx4+uuYwtNJfioC9oiEydypZo/P+PCHTvalkhRvMXRo4yX7t1L5XvllbYlyhjHjtG6fewxNgJKDWNYouRTxr74sQgntvkUcsuWGj8OFuqCtkjPnsCKFUCZMkCnTnQXxcbalkpRvMGFC8DttwNbtjD+G27KFwB++onXjPQMXxABrrgCeOQRft7Dh7noGD6cmddvvMEk0aJFqYhHjQKWL9cwmS3UAg4SZ89yktL77wPNmgFffw1UqmRbKkVxL8YAvXoBn31G79M999iWKHPccguwejWwY0fWm2+cOgUsWOC3kGNi+HzRonRT+xK6Lr9cG30ECnVBhxCTJzNRK1cuds/q3Nm2RIriTl56iUmQw4cDzz1nW5rMceIEULIkw1hvvBH4/e/bx5GKPoW8ezefr1zZ3wxE48dZQxVwiPHXX2zcsWIFMHAg+0nnzWtbKkVxD599xvBPr17Axx+HrzX35Ze03KOi2PAnOzEG2LzZP0xizhzg+HFuSxw/btVK48cZQRVwCHL+POPB48axOfvkyUC1aralUpTwZ84c4IYbqCimTwfy5LEtUebp0oXlUzt3Br9sKi6ORoLPOl60iLHovHmZ1OZTyNdco/XHqaEKOIT54QcOAY+L46STO++0LZGihC/r19NSLFeOVmORIrYlyjwnT9L93K8fu3fZ5vRpf/x45kxgzRo+74sf+xRy1arh63HIDrQXdAjTuTNHi/XowRGHs2fTKlYXj6JkjH37gIgInjuRkeGtfAE22zh/Pn3Zz8GgQAGWUfpKKffvTxo//vZbPl+pUtL4ccmS9mQOddQCDhFiY5koMno0cNVVwDffhGfJhOIOTpxg3Wy4EB/PeO+6dcC8eewMFe7ccQfdvrt3h37XLl/82Gcdz57tjx/Xr+9XyC1bsieCl1AXdBjx66/AvfcCZ84A774L3HefbYkUL3LllRwIEE6IsPb1lltsS5J1Tp+m5di7Nyc2hRtxccDKlf6ErqgoGhl58iSNHzdo4P74sSrgMGPPHvasnTePmZxvvw1ceqltqRQvUbIkLZfevW1Lkn6qV3eH5QtwHGG3bsDcuRxzGu6cPg0sXOh3V/vix0WKJI0fV6vmvvixKuAwJD6es4WHD2dnm8mTgbp1bUuleIWSJakAJkywLYk36dYNmD+ffZ3daCEeOJA0frxzJ5+vWNHfDOS669wx0lVbUYYhOXOyicDMmewF26QJp5wEcb2kKIoFzpxhAtZtt7lT+QJUrN27c3zr9u3sX/3OO/RgfPstk1JLl6YX5qmngN9+4/fiNlQBhzjXXUd3TevWLEfo0SPpcG5FUdzF9OlUNqGS/ZzdiDB88NBDVL6HDgFLlgAjRwLFigFvvcXM66JFOV995EiOZXRD/2pVwGFAqVI8KV99lUPEr7mGBfKKoriPqVMZAmjd2rYkdsiZE2jcGBg2jG7qo0dpAT/2GL2B//kPPYIlStBL8M47tKDD0TuoCjhMyJGDnbPmzeOEl2bNgDffDM+DTlGUlNm0iQool3ZpAMCypQ4dgDFj2DPhwAFg0iR6CFau5OSnK65g/+oHHuCgmwMHbEudPlQBhxktWvAg7NiRfaS7dAGOHLEtlaIogSTU635tUrIkOwZ++CGwbRvrj999F2jUCJg2jRUkpUsD9eoxfvzrr8zCDkX0Zw5DihdnC8s33mDHn6uv5sxPRVEULyHC0qX+/em6P3iQ8eFXXqGL+q23OIe9aFGgbVtgxAjGl+PibEtOVAGHKSLAoEEscM+Zk43nR48GEhJsS6YoimKHnDlpCQ8dCsya5Y8fDxrE5NXnngOaNqVy7tKFZXY248caZQhzGjWiS7pPH8aI58zhKDY31M8piqJkBV/8uEMHPj54kNfIGTP49/33fL5CBX8zkIiI4PURVwvYBRQuzN7R777LuuEBA2xLpCiKEnr4Gsz44sdbtgDvvcekt4kTgbvv5vzlYKEK2CWIAF270pWiQxwURVFSR4SjE1u2ZO1xQgIV8SuvBE8GVcAu4vffeRBFRNiWRFEUJbQ5dQp4+ml224qOBt5/n8mswWz5qzFgFxEZyeQCt/NZi1gAACAASURBVDSkVxRFCTTGsOPW449z1OMDDwCjRvHaGWzUAnYJ8fGsd+vY0b39YxVFUbLC5s28RnbtynLOqCj2o7ahfAFVwK5h+XLGMdT9rCiKkpSzZ1mCVKcOsHgxMH48r5nNm9uVS13QLiEykt1zfOn2iqIoCvDTT+wjvX07s5zHjAHKlLEtFVEL2CVMn84C8+LFbUuiKIpin+3bgc6dgVtuAfLl42CHL74IHeULqAJ2Bfv3A8uWqftZURTl/HmOLKxVi30RRo8GVq/mKMNQQ13QLuC333jbqZNdORRFUWwyYwbw6KNsL3n77eyXX6GCbalSRi1gFxAZCVx2GevZFEVRvMbff3NCUocOrAiZPp3DGUJZ+QKqgMOeuDhawJ066QgzRVG8RWwsMHYsu//9+CPw0kvA2rUsNQoH1AUd5ixeDBw7pvFfRVG8xfz5wCOPUOHeeCPw5pvA5ZfblipjqM0U5kRGsvFG+/a2JVEURcl+9u8H7rsPaNOGIwa//56lRuGmfAFVwGFPZCSbiRcubFsSRVGU7CM+nvN7r7gCmDSJM3/Xr2epkYht6TKHKuAw5u+/gTVr1P2sKIq7WbKEk4oefZS97qOjObWoQAHbkmUNVcBhzPTpvFUFrCiKGzl8GOjXD2jWDNi7l5bvjBnuGbmqCjiMmT6dafa1a9uWRFEUJXAkJAAffUR380cfAYMGARs3stQoXN3NyaEKOEy5cIErwU6d3HVAKoribVavZl5Lnz5AzZrAqlXA668DhQrZlizwpEsBi0gREZkqIhtFZIOINBORYiIyQ0Q2O7dFs1tYxU9UFHDypLqfFUVxB8ePc2hCgwbAli3Ap5+y1Oiqq2xLln2k1wIeD+BXY8yVAOoB2ABgCIBZxpjqAGY5j5UgERkJ5M4NXH+9bUkURVEyjzHAl1/S3fz220D//sCmTUDPnu737qXZiENECgFoDaAXABhjLgC4ICKdAbR1XjYRwFwAz2SHkMq/iYxkHdyll9qWxJ2cPs2LQHy8bUnsEBtrWwLFC6xfz2Yac+cCjRoBP//MLGevkJ5OWJcDOAjgExGpB2AFgIEAShtj9gKAMWaviJTKPjGVxGzfzgO3Tx/bkriDhAQmeCxZws5iS5YAMTF83suEe4mHErqcOgUMH85hCQULAu+9x+tZzpy2JQsu6VHAuQBcA2CAMWaJiIxHBtzNItIXQF8AqFixYqaEVJKi5UdZ4+BBKlmfwl26lB11ADY0adwYePZZDrfIm9eurLYQYSKMogQSY4DvvmNW8+7dQO/ewKhRQMmStiWzQ3oU8G4Au40xS5zHU0EFvF9EyjjWbxkAB5L7Z2PMBwA+AICGDRuaAMjseSIj2XatRg3bkoQ+588zqzKxdbt1K7flzMkEj7vuApo0AZo25XeqQy0UJfBs3gwMGMDhMXXrApMnA82b25bKLmkqYGPMPhHZJSJXGGM2AbgewHrnryeAUc7tD9kqqQIAOHcOmDULeOAB9ycoZBRjgG3bklq3q1axZAsAypWjku3fn7fXXKNuVkXJbs6eBV59FRg9mh6lceMY982lo4DSPQ1pAIAvRSQPgK0A7gczqL8RkQcA7ATQNXtEVBIzbx4PaHU/s2xh2bKk1u3Bg9yWPz+TOQYOpLJt0oQKWFGU4PHzzywt2raNnqb//hcoU8a2VKFDuhSwMWY1gORy07QIJshMnw5ccgnQtq1tSezx0UcszN+wgVYvwIL9G2/0K9s6dXSFrSi22LGDi98ffuC5OXs2cO21tqUKPfQSFWZERvJAzpfPtiR2mDUL6NuXxfrDh1PZNmoEFCliWzJFUc6fB8aOBUaMYIhs1Cjg8ceBPHlsSxaaqAIOIzZv5t9jj9mWxA67dwM9erAR+5w5Gr9VlFBi5kzGdv/8E7jtNpYYaeFL6mi+Zxjh5fKjCxeAbt0Y//72W1W+ihIq/P030L070L49G9dMn85zVJVv2qgCDiMiI9mu7fLLbUsSfAYPBv74g/Fft4wiU5RwJjaWuRhXXgl8/z3w4ovA2rVAx462JQsf1AUdJpw+zXZtDz9sW5Lg8803wPjxTOro1s22NIqiLFhAd3NMDD1yb74JVK1qW6rwQy3gMGHOHCY4eM39vHEja56bNQNee822NIribQ4cAHr1Alq3ZhngtGksNVLlmzlUAYcJkZGMe7ZqZVuS4HH6NHDHHSy7+uYbzaRUFFvExwPvvMMQ2FdfAUOGsB/9rbdqQ6CsoC7oMMAYKuB27bzTm9gYoF8/nuS//w6UL29bIkXxJkuXMvS1YgVw3XXAhAmahxEo1AIOAzZuZGF7p062JQke773HGaHDh3PhoShKcDlyhIvgpk2BPXuAr79mqZEq38ChCjgMiIzkrVcU8LJlnJYSEQEMG2ZbGkXxFgkJwMcf09380Uc8FzduZKmRupsDi7qgw4DISLZW9EJd3eHDjPuWKQN8/rlOJlK8yaJFdjw/q1fzHPRRsiQQHc3GGqlRvjyVtp6vGUMVcIhz4gRT/h9/3LYk2U9CAnDvvcC+fUBUFFCsmG2JFCX43HUX62rPnQveex4/zhrexDRrRoWalhx79rBF7Jgx3p3rm1lUAYc4s2ax4N0L5UcjR7KLzrvvcpKRoniRp57iXzAwhrHdJ5+ke7l/f56HRYumfx8TJgCPPpp9MroZVcAhTmQkUKiQ+wdX//478MILwD33MPFDUZTsZf16Ks45c7jg/fFHDjZRgod67EMYX/lRhw5A7ty2pck+du2i261WLWY/a6KHomQfp04BzzwD1KvHmO+773Ketirf4KMWMNhhatkyFpuHErt2Mb7iZvfzhQtA16681SELipJ9GMPOVYMG8dpy//3A6NEat7WJKmCwoXiolrvkyuXu5uZPPQUsWQJMmcKyB0VRAs+WLcCAAcCvvwJ16zLu26KFbakUVcCg9Vu5MtPoQ43SpVmS40YmTQLeeosr8jvusC2NoriPs2eBUaNo6ebJA4wbxyEKufTKHxLozwBO9GjYELj2WtuSeIcNG4A+fZhcpkMWFCXwREbS6t26FejRA/jvf4GyZW1LpSTG80lYp08Df/0FXHWVbUm8w6lTwO23A/nzc8iCmxPMFCXY7NgBdOkC3Hgjrd5ZszhAQZVv6OF5C3jdOiYn1K1rWxJvYAzQty+waRMwYwZQrpxtiRTFHVy4AIwdC7z8MisJRo1iAx+dIha6eF4BR0fzVi3g4PDOO0wAGTmSk1UURck6s2YxtrtpE63fceO80bo23PG8CzomhqUvVarYlsT9LFnCFflNN3GeqKIoWWPPHsZ327UD4uIY9/3uO1W+4YLnFXB0NAcdaBPx7OXQIdb7lisHfPaZft+KkhXi4oA33uBowGnTgBdfZC9nr0xMcwuedkEbQws4rUkfStaIjwfuvhvYv59TXjLSZ1ZRlKQsXAg8/DCvXZ06sZSvalXbUimZwdN2yN69HL2l8d/s5eWX2ev5rbeABg1sS6Mo4cmBA+xe1aoVcOwYXc2//KLKN5zxtAKOieGtZkBnH7/+CgwfDtx3H/Dgg7alUZTwIz6e/ZqvuAL48kvmT2zYwGQr7Zse3njaBa0Z0NnLzp10PdepwwuIXiwUJWMsW0Z38/LlbBQ0YQJQs6ZtqZRA4XkLuFw5HfyeHZw/z6Sr2Fhg6lQ23VAUJX0cOQI89BDQpAnw999spDFrlipft+F5C1it3+zhySeBpUs54ahGDdvSKEp4kJAATJwIDB4MHD0KDBwIvPQSZ4Ir7sOzFnBsLOMoGv8NPF99RVfZE09ohrmipJc1a4DWrYHevbloXbGCpUaqfN2LZxXwn3+ydZtawIFl/XomW7VsyVZ4iqKkzokTbFDToAE7WX38MbBgAVCvnm3JlOzGsy5ozYAOPHFxHCt46aXA5Mk6ZEFRUsMYjuR88klg3z6gXz+2aNWcFO/gaQWcKxc7ySiBYc0auvU//VQnryhKamzYADz6KDB7Ni3fH34AGjWyLZUSbDzrgo6OpvLVSSGBIyqKtzpkQVGS5/Rp1vHWqwesXMnhJEuWqPL1Kp62gJs3ty2Fu4iKAipU4J+iKH6MAb7/Hhg0iPXxvXoBo0cDpUrZlkyxiSct4OPHObRaE7AChzFUwC1a2JZEUUKLv/4CbryRFQGFCzPB6pNPVPkqHlXAa9fyVhOwAsfOnWwYoApYUci5c5xSVLs2Byi8/jrdzi1b2pZMCRU86YLWFpSBxxf/VQWsKMD06cCAAbR+u3cHxo7VxETl33jSAo6JoStIY5WBIyqK5Ue6qFG8zM6ddDVHRLDKYuZM4OuvVfkqyeNJBexrQanDAQLHwoVA06a86CiK17hwgY1natYEfvsNePVVXmeuv962ZEoo4zkFbAwtYI3/Bo7jx/mdamxL8SJz5rCsaOhQoEMHdoMbMkRLHJW08ZwC3rmTrd/UVRo4Fi/mwkbjv4qX2LsXuOsu1r1fuAD88gswbRpQqZJtyZRwwXMOQ21BGXiiooAcOTg6TckcFy6wD/Datf6/YsWAMWOAEiVsS6ckJi4OePtt4Pnn+bu98ALwzDNAvny2JVPCDc8pYF8GdJ06duVwE1FRdMEVLGhbktAnIQHYts2vZGNieLtpEy/sAOPoNWoAW7Ywnvj55xpLDBWiooCHH+Z1pGNH4K23gGrVbEulhCueU8AxMUDlyjriK1DExbGV3v3325YktDCGDfYTK9m1a4F164AzZ/yvq1KFi8FbbmFYpE4dKt+8edlbu3t3oH17zocdPlzjirY4eJBW7iefsHri22+BLl00kVPJGulSwCKyHcBJAPEA4owxDUWkGIDJACoD2A6gmzHmaPaIGTh8GdBKYFizhv1tvRz/PXaMijWxol27Fjh82P+a0qWpXB980K9oa9VK3WtQrx5nwj7+ONsWzprFkha1uIJHfDzw4YfAsGHAyZNUws89BxQoYFsyxQ1kxAK+1hhzKNHjIQBmGWNGicgQ5/EzAZUuwJw/T1dfly62JXEPXmrAcfYssHFjUkUbEwPs3u1/TcGCVK633eZXtHXqACVLZu498+cH3n+f2bUPPghcfTXjj/fdp9ZXdrN8OfDQQ7y99lpgwgSWGSlKoMiKC7ozgLbO/YkA5iLEFfCGDVzRqgUcONw6gMEY4OefaYH6FO6WLYzhAnQF16wJtGmTVNFWrJg9ivH224HGjYF77mEj/99+A959lw1llMCyeDHQtat/YdW9O9CjB3//LVuCI0PjxvSaKO4mvQrYAPhdRAyA940xHwAobYzZCwDGmL0ikmxrcRHpC6AvAFSsWDEAImcezYAOLMawAUebNrYlCTyLFzMuK0KXb506vBD7FG316sFvOlKhAufHvvoqewwvXgx89RUboCiBo1mzpI8nTeJfMLnzzuC/pxJ80nsJaWGM2eMo2RkisjG9b+Ao6w8AoGHDhiYTMgaM6Ggmt1SvblMK97BjB7Bnjzvdz40asYn+mTM8bi65xLZEJGdO4D//YVb0XXex+clLL7HxQ86ctqVzB2vWAMuWAfXr23n/u+9OmqinuJd0KWBjzB7n9oCITAPQGMB+ESnjWL9lABzIRjkDQkwME1+0XWJgcHP8N1cuYPx4oF07YNw4KrhQolkzYPVqoH9/KuQZM1iu5LZQgA3q1rXrJcuf3957K8ElzU5YIlJARAr67gPoAGAtgB8B9HRe1hPAD9klZKCIidH4byCJimLSkVu/0+uvB269FRgxgpZ+qFG4MF3Qn37KRKF69YDvvrMtlaIo6SU9rShLA1goImsALAXwizHmVwCjALQXkc0A2juPQ5bDh3kR1fhv4IiKYvzRza7P//4XiI1lGUooIgL07AmsWgVUrcpkrX791IWpKOFAmgrYGLPVGFPP+attjBnpPH/YGHO9Maa6c3sk+8XNPL4ELLdaa8HGN4DBje7nxFStyjrciROBpUttS5My1atzQTR4MPDBB0DDhoxlKooSunhmGIOvBaVawIHBSwMYnn0WuOwyYOBAfuZQJU8eNuyYMQM4epSlLG++GdoyK4qX8YwCjolhU3utrQsMXhrAULAgS398ZT+hTrt2XHB26MBFw003AQdCPkVSUbyHZxSwrwWldg8KDFFRLNPwygCG++6jW3fwYODUKdvSpE3JksCPP3JYwKxZ9Pz8/rttqRRFSYwnFHBCAjsZqfs5MMTG0hr0gvvZR44cdOfu2UM3bzggAjz6KGPXxYsDN9wAPPUUR+gpimIfTyjgrVuZFaoJWIFhzRp+n15SwABrb+++mzN6t2+3LU36qVuXjSX69wfGjuXn+PNP21IpiuIJBawtKAOLL6GtYUO7cthg1CiWXT39tG1JMkb+/OwdPW0aFw/XXMPRepqgpWQVXz/yyEi7coQjnlDA0dF0x9WubVsSdxAfz9tQac8YTMqXZ1esqVOBefNsS5Nxbr2VHoxGjYDevdnf+tgx21Ip4cyddwJt27L+fMkS29KEF55QwDExbKivLd6UQPDUU5x6NHCgfzESTpQvD8ycCbzyCgfL16vnbyuqKBkld24uSMuV4wIv8XhOJXU8oYB9GdCKEgjy5WOHrDVrgI8+si1N5siZExg6lIo3Z06gdWsOdYiLsy2ZEo4UL86s+9OnqYS1E1v6cL0CPnOGMzw1/qsEkjvuoNJ69tnwduE2acKhDj16cMThtdcCO3falkoJR2rXZp38ypUMb2h+Qdq4XgGvW8cDQS1gJZCIcErS4cPA8OG2pckahQoBX3zBaUqrV9MlPXWqbamUcOSmm9i0ZvJkYORI29KEPq5XwJoBrWQXV18N9OnDZhebNtmWJuvccw8VcI0aQNeu/GynT9uWSgk3Bg/msfTcc8y6V1LG9Qo4OprJV5dfblsSxY2MGMHj64knbEsSGKpWBRYuZKb3xx8DDRpw0pKipBcR4MMP2Yv83nv9ZYvKv3G9Ao6JAerUYScjRQk0pUoBL7zAGki31EHmzk034syZwMmTHDn5xhvsKKco6eGSS4DvvweKFAFuuUV7kaeEq9WSMZoBrWQ/jz5Kt+0TT7irzeN11zHTu2NHfrYbbwT277ctlRIulClDJbx/P5MW3XRuBApXK+D9+4FDhzT+q2QvefIAr7/OOPCECbalCSwlSvAiOmECMHcuz6Vff7UtlRIuNGwIfPopsGAB8PDDmhl9Ma5WwL4ELLWAlewmIoKW4ksvAQcP2pYmsIjw4rlsGV3uEREcTKEo6eHOO1mu99FHetxcjKsVsC/4rwpYyW5EaAWfPs3sTzdSpw5bDXbuzC5ggwaFZycwJfgMH84GHU88oWMxE+NqBRwTwzhEiRK2JVG8QM2ajAd/8AHLedxI/vysER40CBg/Hrj9di1VUtImRw7WmdeuTYtYp3ERVyvg6GiN/yrB5fnn2ZZv0CD3xrty5mRW9JtvAj/9xEb8+/bZlkoJdS69lO0qc+UCbr45vDvIBQrXKuC4OGD9enU/K8GlaFHg5Zc5Kenbb21Lk70MGMBGC+vXs1Rp/XrbEimhTuXKPC+2beMkLq/3HnetAt68GTh/Xi1gJfg8+CCPu6eeAs6etS1N9nLLLcD8+TzXmjcHZs+2LVF4s3u3+2tmW7cG3nkH+O03ds3yMq5VwJoBrdgiZ072id6xAxg71rY02U+DBsDixRxzeMMNwMSJtiUKPxISgPffB2rVAo4eBR54wLZE2UufPsBjjzGU8fHHtqWxh2sVcHQ0L4Q1a9qWRPEi117LBKVXXwX+/tu2NNlPpUpsYdmmDdCrFycruTUGHmg2b2bTk/792b4xJoaZ5m5n7FigfXt+7oULbUtjB9cq4JgY4IorgLx5bUuieJUxY1imM2SIbUmCQ5EibMfZqxfroXv21O5HqREXx7nSdesya/5//wNmzPBO3/pcuTg1qXJl4Lbb6DHyGq5VwNqCUrFNlSrAk09y1N8ff9iWJjjkyUOX4ssvs+zkhhvoUlWSEh0NNGsGPP00v6P16+l2FrEtWXApWpSZ0Rcu0Oo/dcq2RMHFlQr4xAlg+3ZNwFLsM3Qoa9EHDvTOMAMR4D//4cJj0SImZ23bZluq0OD8eZaqNWhAi2/yZGaSly1rWzJ7XHklMGkSvZY9e3rnPAFcqoDXruWtWsCKbfLlY/OBZcuAPXtsSxNc7r6bXY/272eZ0tKltiWyy+LFwDXX0DvQowewYQPQrZv3rN7k6NiR7vjvvmP4wiu4UgH7MqDVAlZsYgx7KM+cyYST8uVtSxR82rShFVygABt2eHFA++nTwOOP0xNw8iTj5J99xoYtip9Bg4D772fbym++sS1NcHClAo6OBgoVAipWtC2J4mWefZZtKYcOZQ9cr3LllbT+6tZlZvi4cd7JkJ41i564ceOAhx6id65TJ9tShSYiwLvvcqHSqxewcqVtibIfVyrgmBge9OraUWwxdixLkPr2BUaOtC2NfUqVAubMAbp0oTX42GPuHuRw7BhrXdu1Y7bvvHkc6ViokG3JQpu8eemGLlGCSVlub3HqOgVsjGZAK3b55BN2weralR1/dCFI8uUDpkxhZvjbb1MZu3GQw/ffs6HGp58CzzwDrFnD7k9K+ihdmpnRR47wGDl3zrZE2YfrFPC+fcDx40CxYrYlUbzI99/T8mnfnmU4OXPalii0yJGDyTYTJgC//MIY8d69tqUKDPv3M6mqSxda/EuWAKNGceGhZIz69RknX7wY6NfPvSEL1yng0qUZQ3j1Vc5ndesPp4Qec+eywXyjRnSjaROYlHn4YVo5GzcyQ3rdOtsSZR5juNiqVQv44QeGHJYtY6mRknluv50Z0Z995t6Wrq5TwDlyMOv0ttvo6nr0UZ24oWQ/K1ZwMEHVqrTsLr3UtkShz403cpBDbCwXzTNn2pYo4+zcyc9x333svLd6NTBsGJA7t23J3MFzzzGUM3gwzyu34ToFDNDl88037DLzzjvArbd6r8OKEjw2bWIdY7FirHvV8pL0c801dNVWqsTs4E8+sS1R+khI4LWldm0uIt58E1iwQHvPBxoRxtLr12fttNtGXrpSAQO0hF97jSfJ9OlMgvBaIwQl+9m1i/FeEfbxLVfOtkThR4UKbMZ/7bVA7960ekI5dLRpE2PXjzzCdpJr13I2ssb7s4f8+enaz5+fXqbDh21LFDhcq4B9PPQQ8NNPwJ9/Mtbka9KhKFnl0CGgQwcm/f32G1C9um2JwpdChehi7NMHGDECuOee0CtTMoaL+nr1qHQ/+YS/e+XKtiVzPxUqsInLrl1MdIuNtS1RYHC9AgaAiAi6h+LjgRYtaKkoSlY4eZLH1fbtXOBdfbVticKbEyeAr7/mQHqAvYFDrX/0V1+xrCgigm0ke/XSErNg0qwZG9vMns1acjfgCQUM8AK5eDFXqxERwEcf2ZZICVfOn2epycqVzDXQGs/McfYsMHUqs11LlWIj/o0bOb5x9WqgWjXbEvo5dYqJQA0bUubLLrMtkTfp2ZPJtRMmAO+9Z1uarJPLtgDBxBdr6taNrq5t29gYXVexSnqJj+eQgVmzgIkTgZtvti1ReBEbSw/U11+zZvrUKZYO9uvHEq6mTUPzfBw9mjkkU6Ywv0Sxx+jRTMYaMIBtTtu2tS1R5vGUAgYYa/rpJ9YhjhwJbN3KWI7WbCppYQzQvz/w7bfAG2+w9ERJm/h4hoC+/prW45EjnAPbvTv/2rYN7QSm7duBMWOAu+5iuZRil5w5eSw1a0bvybJlwOWX25Yqc3hOAQOs0fvgA9ZsDh3KuNO0aVo+oqTO0KHA//7HWbeDBtmWJrQxhhfGr7+mm37PHk5E6tyZSveGG4A8eWxLmT6efpoX/dGjbUui+ChcmI1cGjdmZvSiReHZZ9uTChigm2vIEKBKFcYVmjVjuVLVqrYlU0KRMWN4Ae7fn+PSlORZu5ZKd9Ikepfy5GHORffuwE03UQmHE/Pm0WofPtyb4yRDmWrVgMmTWYnwxBNcHIcbnlXAPu68k7WbnTsz/vTjj1TGiuLj44+ZgHPnnRwiEIoxSpv89RcV7qRJVMA5cgDXX09PQZcuQJEitiXMHPHxwMCBHGv61FO2pVEuJiGBChgI39nvnlfAANCyJTOkIyLYDODzz9n+TFGmTQMefJAu088+C+1YZTDZs4cXv0mTgKVL+VyLFlygdO3KrOZw56OPOMlo8mQdqBBqGMORlh99xMYtjz1mW6LMke58PhHJKSKrRORn53ExEZkhIpud26LZJ2b2U7068McfbKDerRtdjqHcjUfJfmbPpuu0cWMmXoVLzDK7OHwYeP99LlLLl6fbLzaWzSl27GCFwSOPuEP5HjsGPPss0KqVLsZDDWPokZowgSVJL71kW6LMk5GE+oEANiR6PATALGNMdQCznMdhTYkSLC/p1o0/8MMP6yAHr7J8OcMSNWqwQ1O4xS4DxcmTwBdfcODAZZcxBr53L/DCC6zZXbmSSUoVK9qWNLAMH84Fx/jxGnIINV56iSMtH36YhlI4/z7pckGLSHkANwIYCeAJ5+nOANo69ycCmAvgmcCKF3wuuYRJJFWqMOlmxw66oAoWtC2ZEiw2buRggJIl2WrQi7Ol58xhH/Wff+ZA9IoVafH26MFWjOF80UuLjRuBt94CHnhAO5yFGqNHUwHffz9/o3A/DtMbAx4HYDCAxGqotDFmLwAYY/aKSLKOJxHpC6AvAFQMk2VyjhwcpF21KntJt2pFK0gb7bufnTs5XCFnTk42KlvWtkR2uPtudvzq04du+GbNvNOAYtEier4iI9kroG9fLsYUu7z5JitXuncHPvzQHcdjmh9BRG4CcMAYsyIzb2CM+cAY09AY07BkmB3FDz5IxfvXX0CTJkzIUNzLwYMsaTh5kpZvKLVCDDaxsbzQvfUWk6vccLFLL7168byvU4eZ3BUq0BrW898eH37IjPQuXdyVDJme06oFgFtEZDuASQCuE5EvAOwXkTIA4NweyDYpLXLDDUwuAZgt/dtvduVRsoeT0j9m3wAAIABJREFUJ+l23rGDbtd69WxLpNgiRw5WRPz2G1se3n8/s73r12fXrmnTQm9Sk5v54gu2Ku3UieHB3LltSxQ40lTAxpihxpjyxpjKALoDmG2MuQfAjwB6Oi/rCeCHbJPSMvXqcWh4tWpMRvngA9sSKYHk3DkmXK1Zw6YLLVvalkgJFWrWBN59l93yxoxhW8rbbuO1YOxYZksr2ceUKWyUdO21rERwW8vgrDiWRgFoLyKbAbR3HruWcuWA+fPpouzXj7GIhATbUilZJS6OiUVz5gCffsoFlqJcTNGibMaxZQsVga85R7lyzMbduNG2hO7j55/Zf7tZM+CHH9xZi50hBWyMmWuMucm5f9gYc70xprpzeyR7RAwdChZkp6z+/ZmN16MHrSclfHnlFU7l+e9/mXikKKmRKxct4HnzWILVrRubQdSsCXTsyHa2ujDPOjNmcNBC/fqMx196qW2JsgcPpVYEhly5WJ7x2mtsMt+uHXDokG2plMxy1VUsZViwQGu+lYxx9dWcpLZrF8eaRkczdlyzJjuCnTxpW8LwZP58hoSuvJJx+MKFbUuUfagCzgQibD7wzTds2NCsGbB5s22plMzQpQszfX/4gVnvar0oGaVUKWZLb98OfPkle18PGODvFrZ1q20Jw4fFixkGqlSJVrDba/BVAWeBrl3ZrvDYMSrhqCjbEimZ4ZFHWNz/6adcWGkLUiUz5MnDmOWSJWxrGxHBxV21asCttzLPQI+tlFm5km780qXZkdANLU3TQhVwFmnenKu2YsU4AcY3nUMJL557jlbL66+zCYuiZIWmTVkys307MGwYF+fXXceKiv/9Dzh71raEocW6dUxwLVyYytcrDXBUAQeAqlW54m3UiM0LxoyxLZGSUUSAceOYiDVsmJaaKYGhXDlgxAh2WPvoIx5nDz7I5h7PPstuY17nzz9pvOTJQ+VbqZJtiYKHKuAAUbw4YxY1ajAhQ11N4UeOHEyqufFGZrpPmWJbIsUt5MsH9O4NrF5NV3SrVszA9/pCb9s2Kt+EBCpfr3WfUwUcQDZs4GpuwIDwbxLuVXLnZnJdixa0hmfMsC2R4iZE/N20fJO2vMru3VS+p0/zPKtZ07ZEwUcVcAAZNoyx4MGDbUuiZIX8+YGffgJq1WKW9JIltiVS3EhEBDB3LnDmjG1Jgs++fVS+hw6x1MirrV9VAQeIuXOBX38Fhg51d92aVyhShL/nZZfxQrlunW2JFLcREcEY8Jw5tiUJLocOceLY7t2cONWokW2J7KEKOAAYw9aU5cuzpEVxB5ddRtdY3rzM0Ny+3bZEipto3ZrelshI25IEj2PHOOBm82Z6mbzed10VcAD4/nu6KV980Z39Sr1MlSqcC3zmDFft+/fblij7iYujZaZ5DNlL3rzspBcZ6Y2kTd/EsZgY4LvvWJbldVQBZ5G4OJYTXHklp3Yo7qNOHV4k9+xho4Djx21LlL289x4vlh062JbE/URE0LPi9mEOZ84AN98MLFvGXgkREbYlCg1UAWeRzz5j9vPIkewTrbiTZs24al+3DrjlFvc2Ujh8GHj+eSbI3HyzbWncT6dOvHWzG/r8eSYzzp8PfP457ytEFXAWOHcOeOEFoHFjPai8wA038AKyYAFw551AbKxtiQLPiy/Swh83Tl3QwaBiRb+HxY3ExnJi1O+/swNYjx62JQotVAFngQkTmMk3apRerLzCnXfyd//pJ+CBB9w1vGHtWg6ff+ghKgUlOEREcFF34oRtSQJLXBxr6X/8kdOheve2LVHooQo4kxw/zk42HToA115rWxolmDz0ELudff458OST7kigMQYYNAgoVIiDKZTgERFBS3HWLNuSBI6EBCrcKVM4a1urQ5JHo5aZZMwY4MgR4NVXbUsSfHwrdS9nfD/7LOOl48YBJUrwcTjz449UAG+9xbaqSvBo3pwLn8hId4SyjOEi9fPPgeHDuUhVkkcVcCbYtw944w26I6+5xrY0wWf1ajaZd/usztQQAcaOpRL+z3/4XTz0kG2pMsf585xbW6sWe2ArwSV3bnrSfOVI4RzOMgZ4/HH2uB4yhOeGkjLqgs4EI0YAFy7QDelFVq4Err7athT2yZGDE25uvpkutnAdRTluHIfGjxunmfy26NSJZW7R0bYlyTzG0BM0fjwwcCBDdOG8mAgGqoAzyF9/Ae+/D/TpA1Svblua4HPmDGsWvWj5J0fu3FS8rVoB997LvrbhxL59XFDecgsbjSh26NiRt+GcDT1yJENyffvSQ6jKN21UAWeQ55/nRfe552xLYoeYGCZYqAXsJ18+xlBr1wZuu42zocOFYcPogh471rYk3qZsWZ5T4aqAx47lNfHee5lJr8o3fajDKQOsXg189RUHLpQta1saO6xaxVtVwEkpXJjDG1q25Dzh+fNDv5Rn2TLOP3766eydwxofz6qBw4eZuJieW2OA2bPZCtQrRETQgjx6FCha1LY06WfGDOCpp4CuXYGPP2ZoRkkfqoAzwLBhPDG8PG5w5Up+BxUr2pYk9ChdmhejFi2YVBMVFboKxBjG6UqVSn+ijDFUpOlVor7bY8dSLtUS4eSp4sWZyFayJLuMrVvnjvKujBARQTfu778zwTNcKFmSt02bag5BRtGvK53MmwdMnw689hovGF5l1SrGf9XFlDyVK/MC2ro1Y6oLF3KqUihhDLNU//iDyWNLliSvPC9+7uhRWrMpUbgwlahPmVatmvTxxbfFivFcypkz6X7uuAM4dSp0Fy/ZRZMm/E4iI8NLAdevzxyIt97iou7i31NJGTFBXGY2bNjQLF++PGjvFyiMYa3erl0co+XV+tfYWKBgQWDAANZBKymzZAn7KVerxlnR2bFoM4ZJcakpzeRu05rodOmlqSvO5G6LFg2M9ZOQQIuqc2e6M73GXXexHnvv3vBy5X77LRdO06YBt95qW5rQQkRWGGMaJrdNLeB08MMPwOLFwIcfelf5Asx+Pn9e47/poUkTXoxuvJFlSr/9xtmvKXHuXMZdu0eO8PdIiXz5kirJWrV4+8EH3N67N7OfEyvTokU5Js8W0dH8XF7tLhcRAXz9NUM9DZO9ZIcmnTszLDV+vCrgjKAKOA3i4xn7rVED6NXLtjR2WbmSt6qA00f79sCXX9KdeNNNVMopuXrPnEl5P3nyJFWk1ar53ZWpuXeTWyzu2MEJXrfeyhrmUGPOHN56VQHfcAPDO5GR4aWAc+ViOOOZZ7iIqlvXtkThgSrgNPj8c44bnDJFEwxWraIVV6OGbUnCh65dmYT0yCNsuJ9YSVauzHh6Wi7e/PkDF3MfPJj7Gj06MPsLNLNn8/gqX962JMHn5ElWWBiTdpggFOnTh9O03nyTk4+UtNEYcCqcO8eLQenSwNKlmnjUpg3jwIsW2ZYk/LhwgfXjNo+h+fP5G774IsdohhpxcVxw3HUX8N57tqUJLgsXAvfdRw/FM8/wN8qTx7ZUGad/f2DiRObLlChhW5rQILUYcBiF+YPPu+/yQNJxg0yOWbVK3c+ZJU8eu8dQfDwzVCtUYN1vKLJyJa3A666zLUnwOH+eCrd1ax4f8+ezhWM4Kl8AeOwxGi6+PAMldVQBp8Dx46zJa9+e2axeZ+tWXhy1BWV48sknbCTz2mupJ4PZZPZs3rZta1WMoBETAzRuzN+kTx/+Pi1a2JYqa9SqxWvmO+/QW6akjirgFPBNuvHiuMHk0A5Y4cvx40wkbNEitOtLZ89m97BSpWxLkr3Ex3NGbsOGjPX+9BMtxoIFbUsWGAYOBP7+m6VJSuqoAk6G/fuB118HunUDGjSwLU1osGoVk9Bq17YtiZJRxowBDh1iiUiohlLOn2cc1O3u5+3b+RmffpolajExzJB3E506MVN//HjbkoQ+qoCTYcQIxjG8Om4wOVaupPK1WSOqZBxjgC++4LSdUF5MLl3KFpRuVcDGAJ9+yvKcVat4/9tv/W0c3USOHGzWs3gxf1clZVQBX8TWrf5xg1puQ4zxt6BUwosVK5hZ27WrbUlSZ/ZsWuetW9uWJPAcPMgpWfffzxBOdDTQs2foeiMCQa9edKm/+aZtSUIbVcAX8fzzdLU+/7xtSUKHvXuBAwc0/huO+OrXO3e2LUnqzJ7NBV44TQFKDz//zLh2ZCTjvnPmsP7b7RQqxE5r33zD64eSPKqAE7FmDccNDhzo3XGDyaEdsMITY4CpU5nFX6yYbWlS5swZuivd5H4+eRJ48EG2IS1TBli+HHjyyfDq75xVBgxgbfe779qWJHTx0OGQNsOGcaKLl8cNJseqVXSX1atnWxIlI6xaxZBKqLufFy1ioxK3KOCoKE4I+ugj1vguWQJcdZVtqYJP1apMMHvvPebUKP9GFbDD/Pl0Ew0Z4j43WFZZtQqoXt09ZRJeYepUjoYLB/dzrlxAy5a2JckaFy6wlWTr1vQ+zJ/PJj5eTlwcOJAx8EmTbEsSmmgrSvBkadGCySqbN4duowJbVKnC5v96EoUPxjCJsHJlYMYM29KkTrNmdM1GRdmWJPOsXQvccw/DWA88ALzxhi5YAR6HdetygbVypbsTz1JCW1GmwU8/cTj5Cy+o8r2YI0dYu6jx3/AiOhrYsiX03c8nTgDLloWv+zkhgU17GjRgstEPP3AQgSpfIsL2lKtXs85bSYrnFXDicYO9e9uWJvRYvZq3qoDDiylTaFWG+mzWBQt4Dobj+MEdO7hweOopNp+IieF8ZSUpd9/NJEBtzPFvPK+Av/gCWLeOzTe8Pm4wObQFZfhhDBVw27ah39ZxzhzGSJs1sy1J+jGGE3/q1qVb9eOPgWnTQv+7tkX+/MwInzaNixbFj6cV8LlzrPdt0AC44w7b0oQmq1ZxNqsbO/a4lXXrgD//DI9jevZsoHlzIF8+25Kkj0OH+L326sWqgDVr2GDDi7HNjPDII/yOJkywLUlo4WkF/N57wM6dOm4wNVauVOs33JgyhcfzbbfZliR1Dh9miCNc3M+//MKmGj//zAlGc+YwQVFJmwoVeDx++CFw+rRtaUIHzyrgEyc4brBdO/4p/+bMGWDTJlXA4cbUqSyFKV3atiSpM28e3bmhnoB16hTQrx9rWkuVYtLY00+zxEtJPwMHAseOAZ9/bluS0CFNBSwil4jIUhFZIyLrROQl5/liIjJDRDY7t2FVPTt2LN1Jr7xiW5LQJTqaWZ7aAzp8WL+ef6Ge/QzQgixQAGjUyLYkKbNoEZtqfPghG/QsW8bYr5JxmjdnuO/NN7nwUtJnAZ8HcJ0xph6A+gA6ikhTAEMAzDLGVAcwy3kcFhw4QAV8xx2hffLbRltQhh9Tp4aH+xlg/LdlSyBPHtuS/JsLF4BnnwVatWKW9rx5wOjR3m6qkVVEaAVv2ADMnGlbmtAgTQVsyCnnYW7nzwDoDGCi8/xEACFe8ODHN25wxAjbkoQ2q1bx4rhhA1saxsfblkhJiylTqNTKlLEtSers20dLPRTdzzt2AE2b0jvWqxcTrVq1si2VO+jWjaERLUki6Sq8EZGcAFYAqAZggjFmiYiUNsbsBQBjzF4RSTYJX0T6AugLABUrVgyM1Flg2zYmX/XuDVxxhW1pQpu8eWkJdOzIx7lzA5dfzraUF/9VqOCtRvOhyMaN7MgUDhe3uXN5G4oK+Ndfufh8+WXgP/+xLY27yJsX6N8feOkldh2sXt22RHbJUCtKESkCYBqAAQAWGmOKJNp21BiTahw4FFpR3nsv3XRbtgDlylkVJeQxhpbK5s3//tuyhQPUfeTNy+brFyvmatX4Patyzn5GjACeew7YvTv0j+2+fTmq7vDh0EtmOnMGuPJKoHhxTjEKNfnCnX37gIoVqYi9MC84tVaUGWo9YYw5JiJzAXQEsF9EyjjWbxkAB7IuavYSHQ18+SUzGEP9AhUKiNCVWabMvwelJyQAe/b8Wylv3kwL4vx5/2vz5UteOVevzn1rCVhgmDqViS7hcGzPng20aROayi1/fmDMGKB7d+CTT4A+fWxL5C4uu8z/3b78MifQeZU0LWARKQkg1lG++QD8DmA0gDYADhtjRonIEADFjDGpDvKzbQHfdBMbvm/dqhOPspOEBFphyVnOW7fSre2jQAFayckp51KlVDmnl82b2U71jTeAQYNsS5M6O3cClSoB48YxKScUMYaLzk2b+N16WUlkByv+396Zx9lYt3/887Vnyb6MJUv2FMOQPUuiyJI8KFFUtCHK1lOk5aGEtictFCmUkKWSkJAlIfu+ZKfpyZpl5ly/Pz5zfjOEmTHnnO+9XO/Xa15nnBnnvr7nzH1f9/daPtevQEyMO/5e00pad8BRAMYn5IHTAfhCRGYbY5YB+MIY0w3A7wAc3fiweDEb6f/zH3W+4SZdOoaYbriBw+CTEh/PC/Cljvm334AZMzjAO0iOHJcPaZcpA+TLp845KVOn8rFtW7t2pISFC/noxPxvEGOYS4+J4S5txAjbFnmLatU4ge7tt4GnnnJmJCQS+GIcoQirGHftYphUJx45k7g4Tl4KhrKTfu3Zc3EV9j33AF99ZctS51G1KvPwy5bZtiR5unTh7O0jR5xfG/Dww9R93rBBizZDzZdfsir666+9PcTiajtgXzjgWbP4AY8ZQ0UbxX2cP08nvH078P77wNy5wPHjQJYsti2zz86djAyMGAH07WvbmqsjwvBzzZoswnI6R44w4lK/PiUoldARF0cpz3LlvN0X7Ot5wMFxg2XK6LhBN5MpE3OczZvzczx/nnkkJTES4IbhCzt3Avv2OTv8nJSCBTmwZc4cFhcqoSNDBg5pmD+fEQY/4nkH/Nln/HBffpl9rIr7qV2bj0uX2rXDKXz5JRXdihe3bUnyLFjAR7cMYAA4UL5MGeDpp4ELF2xb4y0eeYRdEn5oR7ocnnbA587x7rVqVXfsDpSUUaAAL4jqgBmWX7XKHdrPAAuwChdmNMMtZMoEjBxJoZP//te2Nd4ib16gUycOaIiNtW1N5PG0Ax4zhrJyw4aFttgjPh6YMoX6sIod6talUL7fRd2D1c9uuMEU4Q64USP3VbA3bw7ccQcweDBw7Jhta7xFz56UBv7wQ9uWRB7POuCTJxl2btQodOMGAwFe8G6+mY3kDRtyh60ayZGnTh1Os9q2zbYldpk6lS0dbphLu2kTB6G4KfwcxBj2rJ46xXNeCR2VKrFd8d13/Rfi96wDDo4bHDYs7XfbImybiIlJDPVNnkyh9pdeApo25YVFiRx16vDRz2Ho338HVqxwx+4XSMz/uqUA61IqVmTR0AcfUFVPCR29elG8Z/p025ZEFk864OC4wbZt0z5u8Mcf2UPcvDmHSY8fD6xfD7RvD4wbx6+lSzkzdPHikJivpIBy5Zg/WrLEtiX2cFP1M8D8b8mSQIkSti25doYMoZBP796a/gglzZtTrtZvxViedMCvvMJBAa+8cu2vsXIl0KQJw2W7dwPvvccijM6dL1ZteeghYPlyIHt2/u5rr+mJGQmMYTW0n3fAX37JG7/SpW1bkjzx8byZdWP4OSm5czPqtXCh/3Zr4SRdOuDJJ3k++6m90HMOeM8eOsuHHro25Zp164DWrYFbbwXWruVOescOTu640uDwypVZidqmDdC/P/////6XpmUoKaBOHeaA/VgUs38/Va/cUv382288J9wafk7KI48wb9m3L4uHlNDw0EPcyLhhnGao8JwDHjyYO9TBg1P3/7ZtAzp25I7ixx95l7trF9CnD/vUkuP666ns8+abwLffsvXJT3dyNgjmgX/+2a4dNnBj+Blw/w4YoIDE6NG82R850rY13uH664EaNVhfc+aMbWsig6cc8Pr17Cd76imgaNGU/Z+9e4Fu3VhgMXMmMGAAHe+//81hAKnBGJbUL17MkFvt2tyNa0g6PMTEMCrhxzB0sBrfLf20CxYwIlW4sG1LQkPjxox4vfoqx3IqaePcOfYDL1jAnXBKNj1ewFMO+LnneBc1YEDyv3v4MB112bLAxInMP+zaxRMqT5602XHrrcCaNTxJH38cuP9+ti8ooSVLFrbg+M0BHzzINbsl/HzhAvDTT94IPydlxAiubeBA25a4mz//ZL3N559zWt2YMe7rE79WPOOAlyzh0IX+/a/uQGNj6aBLleLutEsX5nhHj6bua6jIm5fi7a+8QtGO6tWBjRtD9/oKqVOH+Xc/5eKmTWNUxS3h519/5Q2o1xxwqVLMA0+YwHYwJfXs3MlI4YoVwKRJvDb7xfkCHnHAIvzgChViCPhynDgBvPgiT5rXXuM4uy1b2NNXrFh47EqXjoMgfviBBSg1ajBEroSOunX9N5hh6lTgppuAChVsW5Iygv2/DRpYNSMsDBwIREWxjzUQsG2Nu1i+nFOxjh3jQIYOHWxbFHk84YDnzGFIbvBgIFu2i3925gzw+ut0vEOGMCy8bh3DzpFq32jYkCHp6tXZxtS9u792bOHEb4MZDh9mONctu1+ADviWW4B8+WxbEnpy5GDYdMUKhlCVlPHVV7wu5sxJR1y3rm2L7OB6Bxwfz7vQ0qVZTBXk/HlKm5UuDfTrx4KdX35h+K5SpcjbGRXFnfDAgdx1167N8IuSNvLnZx7fL4IcwVqC3bvdU9y3axd37F4lqDO/fr1dO9yACHPn7dqxU2TZMg5W8Suud8Cff37xuMG4OODjj3lRfvJJOuBFizjLM+ayI5EjR4YMLPKaPZstDNWqaTN/KKhTxz+DGUqXZoX+hAnA2LG2rUkZ+fKx0MaLzJnDqt2GDZniUq5MXBylPJ99lhGc+fN5A+1nXO2Ag+MGo6P5gU6Zwt1t16486b/7js63fn3bll5M8+YMSZcrx1x0377+EyEPJXXqsLhu61bblkSGwYNZNfrkk8Dq1batSZ6CBYEjR2xbEXoWL+Z1p0oVYMYMVuUrl+fUKaBVKxa+9u/PXl99v1zugD/4gDvJunW5m+zQgbvMadMYbm7a1LkVdcWL8wR+6ik28zdoQHUjJfX4bTBD+vSM/OTPTwfgdNU1Lzrg334D7r6b5/G337L9Ubk8Bw5QT3/uXOD990M/HtbNuPZtOHmSalUA8PbbvMOaOJEnRps2znW8ScmUieLjU6awMCw6Gvj+e9tWuY/gYAa/OGCAEZ4vv+RNW5cuzq7ALViQA1KcbGNq2LGDN/c5cvB89XsY9WqsW8dK5x07mHp79FHbFjkL1zrgSZNYvl60KHfCmzdT8CLpoAS38K9/sY0mKgpo1owhRp0xnHL8OpihZk1qlc+axdY6p1KgAP+enb5TTwkHDzL8HxcHzJsH3HCDbYucy/ffMzopwiLJZs1sW+Q8XOuAW7Tgjnf7doqjZ8xo26K0UbYsy/G7dAGGDuUfq84YTjl+Hczw5JNMvTz3XKLestMICty4PQz955/c+f7xB+tLype3bZFz+egj4K67OH5y+XIOrFH+iWsdcOHC3PF6KZGfNSsruMeO5R1jdLR/2mvSil8HMxgDfPghb+A6dHCmLrEXHPDp07zp37YN+Ppr+x0VTiUQoPjQI48wUrB4ccp1+f2Iax2wl+nalXeNWbOyOOv11/3RYpMW/DyYIXt2ChucPs10htMq6gsU4KNbIzrnzwNt21JsY/Jk70lqhoqzZ4H77qMwyaOPMjWixWlXRx2wQ6lcmXnhNm0oJKIzhq9Olix0wn6NGFSsyJ3w0qUpG0YSSdy8A46Pp3rd3Ll8f9u0sW2RM4mNBW6/nQWlw4dzoEKGDLatcj7qgB1M0hnD33zDVis/aR6nhrg4oEQJvj9+lfns2JFCByNHJs4LdgJ58rA40m0OWIQ59ilTWOTWtatti5zJihVArVocijJlCjcMbuhCcQLqgB1O0hnDcXE6YzgpgQB3vE8+CRQpwt7YXLko0OJX3niDQz8eeoj5SieQLh3D0G5zwC+8wJ1c//5Ub1IuZtkyFovWrAkcP07N73/9y7ZV7kIdsEuoWfPiGcOdOvlzxrAI1Z/69eOOt149Fq3ddhsFWPbupcC7X8mcmf3BmTJRpOPMGdsWkQIF3JUDHj2a8rYPP8ycppLIkiUssKpdmxGn4cMTxwoqqUMdsIsIzhh++WUWg9SoAWzaZNuqyLBlC6dZlS/PUPyoUZywM3EiL+xffMH8nJeq4q+VG24APvuMGumPPeaMaImb1LAmTACefpoysX4aDp8cixZxA1CvHgU2RoygEmG/fiwEVFKPOmCXkS4dez5/+IF9idWr0wl5kb17mXuLjubs26FDGWr+4AOO5Zs9m61oOXLYttR5NG1KQZcJE1g8ZBu3OOBZs5jrbdyYKQ03CvuEEhH2lzdowK9Nm1hjsHs3NewvHf+qpA6tU3MpwRnDHToADzzAHPGbb7p/B3jkCEOokyYl9vTeeitDgu3asf9bSRnPP8883VNPMWpQrZo9W4IOWMS5O8qffmIOs2pVTinLnNm2RfYQ4bSioUN5bYmK4vXlkUeA666zbZ130B2wi4mK4kkyYIC7Zwz/73/AuHHMKxUuTIdx8iRHN+7cyZ7oXr3U+aaWdOkYHSlYkPlgmyMBCxRgdbpT6xbWrOFwhZIl2XHg16iKSKKEZJMmnOX8zjt87NlTnW+oUQfscjJkYJHIrFmJM4ZnzLBtVfKcPs08dqtWQKFCQLduDGsNGsTc5bp1wMCBQKlSti11N/nyAVOnciJN5872BiI4uRd4+3ZW8+bKReeTL59tiyKPCKc61arF9MW+fcB//8sb4CeecH9kzamoA/YILVqwOrhsWRYjPfOM8xSRzp0DZs5kv2qBAnxctYon+MqVvBC+9BJw0022LfUWNWqwaG3OHHsVvU51wAcOcKcX3Pn5TTZRhLUUt95K7ebDhzkycPt2FvD5OQwfCTQH7CFKlGC+5pln2A+6fDl3mTYvKnFxwI8/Mqc7bRrw11+s5n7gATrgevV0NmgkePxxqmS98AJb2ho3juzxneiAY2OBO+5gaH7hQo619AsijJoNHcpWopIlWazVyDK8AAAgAElEQVTXuTNb2JTIoA7YY2TOzPnIdeuyhzE6mtWcTZpEzoZAgM5/0iS2Bx09ypxa69Z0urff7v7pVW7DGNYJrF3Lz2DNGlaURwqn6UGfOgU0b84Q63ff2S1QiySBAIdJDB3Kv4Ubb2T9RadOek7aQPceHqV9e4Z3CxViTmfIkPDPGN67l3nbkiU5nejDD7nDnTqVO58JE4A779QT3RbBoQ1nzkR+aENwaL0TdsDnzrHH95dfKJ3YoIFti8JPIMDzMDqaaz99Ghg/nv31Dz2k56Qt1AF7mHLlqNPauTPw4ovhmTEswvaNtm1ZMPX668zhTpjAY02dyp9p9aQzqFCBymE//0wBhUiRMSNTD7YdcHw80x/z5vF9aNXKrj3hJj6eUajKldnGd+4c8Omn7Oft3FkHJthGHbDHCc4Y/uij0M4YPnuWr1u1KmUgf/yRerm7d7ON44EHdBSZU2nfnq1eo0ez5zpS2JajFGEu/MsvWSPx4IP2bAk38fFMAd18Mz/v+HimojZuZLhZHa8zUAfsA4xhm0/SGcMjRlybROHBgxR4uOEGKgZduMDc4r59wLBhQLFiITdfCQMjRrAYq2tXYOvWyBzTthrWv//Nv9VBg4A+fezZEU7i4ihDWqkSZ/OmS8dCzPXrmfv3u7KX01AH7CMqV2ZeuHVr7lbbtGFVckpYuZKyj8WLA6+8wov3Dz/wxH7kETp2xT1kysTQZJYsTBGcPh3+Y9p0wCNHUtile3dqqXuNuDimfSpWTCyo+vJL9tO3b6+O16moA/YZOXPyxBw9mn2hVateecbwhQsMY9WqxT7BWbM4+m/7dvbzNm7sXFlBJXmKFWNYctMmoEeP8A9tsOWAP/mEusXt2gHvvuutv9kLF5gKKl8e6NKF2szTprHC+d57tcXP6ejH40OMobRj0hnDY8YkXoCPHeMut0QJhrH++AN46y2KFowaxdYFxRs0acICvYkTKcAQTgoUAE6cYP1ApDh4kO14DRuy+MgrO8ELF1hEVq4c0wg5c1IBb/VqRrbU8boD/Zh8TM2aPGEbNaLqzS23cLhDsWLMl1WqRJWcrVtZtONXfVyv89xzbA/r1YutOeEiKMYRyUKsfPmAPHmA3Lm9oep0/jzz2GXK8MYib15GplatYkW3l3b3fiBZB2yMKWaMWWiM2WyM2WiM6ZXwfB5jzDxjzPaEx9zhN1cJNblz80QGqME8ZQrDzRs3AnPnUqxA76a9Tbp03B0WKsSwZWxseI5jQw0rUyZW5M+cyciOWzl3DnjvPaB0aeaxCxVit8HKlZShVcfrTlJyaY0D0FdEKgCoCeAJY0xFAAMAzBeRMgDmJ/xbcQl//cVWjNKledG94Qb2CWfMyLvp1attW6hEkrx52bN9+DAdVjiGNtiSo+zalamWTz+N7HFDwdmzzFuXLs0WqqJFqdy1bBmjFup43U2yDlhEDonI6oTvTwLYDKAIgFYAxif82ngArcNlpBI6tmzh8IOiRakZXawYL7w7d3Iayp49QEwML8Ldu0c2X6fYpXp1Fud9+y1rAEKNLTnKm25iVGfs2PAXmoWKv/9m3cWNN7LwsUQJiocsXUplO3W83iBVwUVjTAkA0QBWACgoIocAOmkABUJtnBIaAgFeVJs1oxLSRx+xInT16kQVq2BjfuHCnDHcv7+7ZwynlWPHODnIzWHLa6FHD7abDR5McZVQYnMgQ7durPZeuTLyx04t06dTVa5XL+Z6FyzgeXr77ep4vUaKHbAxJjuArwD0FpETqfh/jxpjVhljVh3z29XMMqdOMXxVsSJHjf32G0XY9+1j60J09OX/X4YMFNVw24zhUCHCvPigQYmFaH5h61Z+iQA7doT2ta+7joV8Nhxw+/bsVR83LvLHTi2zZzMV0L49pzQ1bKiO17OISLJfADICmAugT5LntgKISvg+CsDW5F6nWrVqooSfXbtE+vQRyZlTBBCpUUPks89Ezp1L/Wvt3i0SE8PX6dtX5Pz5kJvrOD77jOvt2VOkcmV+//DDIidO2LYsfMTHi7z5pkiWLCL58olMmxae49x4o0iHDuF57eTo0kUkRw6RU6fsHD+l/P033yNA5JFH/HHOeRkAq+RKvvVKP5BER2sATAAw+pLnXwcwIOH7AQBeS+611AGHj0BAZMECkVatRIwRyZCBJ/GyZWl/7bNnRZ54gn8tdeqI7NuX9td0KocPi+TJI1KzpkhcHNfevz/f01KlRJYssW1h6Nm3T+T22/n5Nm8ucuhQ+I5Vu7ZIo0bhe/2rsWgR1/jJJ3aOnxri40UGDaK9TZuKHD9u2yLlWkmrA64LQACsA7A24esuAHnB6uftCY95knstdcCh58wZkY8+ErnlFn6aefPyxN2/P/THmjxZJHt27pC+/z70r+8E2rYVyZxZZNOmi59fvFikZEmRdOlEBgy4tmiCE/n8c5FcuUSyZRP54APeyIWTNm1EbropvMe4EoGASOnSIvXr2zn+tfDRRyLp04vcfLO3b3y9TJoccCi/1AGHjv376Wjz5uWneMstImPH0iGHk82beQE1RmTIEO4SvcIXX/C9/M9/Lv/zEydEunXj71SpIrJ+fWTtCyWxsSLt23MttWuL7NgRmeP26MEbOFu8+irXvG2bPRtSy9y5DJ0XLiyyZo1ta5TUog7YIwQCIj//zAtnhgx0gq1biyxcGP6dS1JOnRLp3Jl/PU2aiBw9Grljh4ujR0Xy52e++8KFq//u11/zdzNlEnnjDYYL3cR33/FiniEDHVIkb6JeeIF/t8m9x+HiwAFGMQYOtHP8a2XdOpFixRiBmjPHtjVKalAH7HLOnROZOFGkenV+Yjlzsshq1y57NgUCIh9+yHBtkSLuz4126CCSMWPKd7VHjjDfDog0aCCyZ0947QsFp08n5vIrVhRZvTryNrz7Lo8fzjxzcrRoIRIVZe8m4Fo5cEAkOpo3EO+9Z9saJaWoA3YpR46IvPiiSKFC/KTKleMF7ORJ25YlsmYNK1vTpxcZMSKyO/FQMX0639+hQ1P3/wIBkXHjGB68/nqR8eOdu/4VK0TKluU6+/Rhpa0Npk6lDWvX2jm+CCu8AZHZs+3ZcK2cPMlCOUDk2WfdF33xI+qAXcbq1WyZyJSJn1CzZiLffuvck+2vv0TuuYe2tm4t8r//2bYo5cTG8ganSpVrb/fYtUukXj2u/557RI4dC62NaeH8eZHBg3mDVKwYK+Vtsngx36e5c+3ZcP68SIECLAhzIxcuiDz+ON/He+8Nf92HkjbUAbuACxe4OwheyLNl40m2ebNty1JGICAyahTziqVKifz6q22LUkbnzrQ5rcUtcXEir73Gm6aCBZ2xu9qyJbGHu3Nn3ijZZutW2vPpp3bt6NuXn/uRI3btuFYCAdYfGMOWOS/UYXiVqzlgnXNjmT//BF57jZqv995Llao33gD276eKVfnyti1MGcYAvXtTMu/8eUpYvv++s7V358wBJkwABg4EqlRJ22ulTw88+yzH+RUowAk13btTjSzSBALAO+9Q6Wz3bmp9jx/PmbG2sSlHmRQ3D2gAeL716cPPdu1ajhbdutW2VUqquZJnDseX7oAT2bhRpHt3keuuk/8v5Jk+3RttPceOUTwAELn/fmflrIP89ReLxypVCn1P79mzIv36cXdy440iS5eG9vWvxv79rEwHRO66S+TgwcgdOyUEAizce/ZZ25Zw51ihgnPz9ill2TJW5efOTbERxVlAd8DOIBCgzusdd3BCyyefAPfdR43mhQuB1q25k3I7+fJxVulLLwGTJgE1alAI30n07Uu93Y8/5szYUJI5MzB8OIcZxMcD9epx6P3586E9zqVMnkzt6qVLgTFj+LcWFRXeY6YWYxghsL0DBjigYfNmYMUK25akjZo1geXL+b42aQJ8/rlti5QUcyXPHI4vv+6Ajx+nzm7p0tyZFCki8sorzirWCRfz57PgJWtWtlI5gblz+TkMGBD+Yx0/LtK1K48XHS2yYUPojxEbm6gdXLOmyPbtoT9GKImJYWGhbY4f59/lww/btiQ0xMaK3HYb/w5eftn9O3uvAC3CssOOHSK9erFNBRCpVYtyjn4TVz9wILG4rHt3ey0wIlSzuuEGhh4jaceMGQwTZs4sMnJk6Crav/8+UVTj5Zfd0dvavDlvRpzAgw9S3MLpAxpSytmzTPsAvPHz27XGiagDjiCBgMi8eWz2N4biDvffL7JypW3L7HLhAocaACJVq4rs3GnHjh49KGQQiiEVqeXIEZGWLfkeNGwosnfvtb/W6dMiTz7J16pQwT1V5yJ0DIUL27aC/PQT38OPP7ZtSegIBESef57ruv12Z1S/+xl1wBHk66/5rubIwZPAaUUwtpk5k+L/OXOy6CySzJ8v/z9W0RaBAAX2s2eneMeECakPFa5cSVEWQKR3b/f1gQ4YwB27E/raAwGRMmUYofEaH3/M97lSpbTd7Clp42oOWIuwQkydOkBMDNtP8ud3XhGMbe6+G1i9GihTBmjTBnjmGeDChfAf99Qp4OGHgdKlgaFDw3+8K2EMi39++w245Ragc2egXTvgjz+S/78XLgAvvgjUqgWcPg388AMwahQH3buJggXZAvTXX7Yt4efRtSuweDGwbZtta0LLgw8C330H/P47cOutwK+/2rZIuRR1wCEmb15Wv7ZsCfTsCTz9NCthlURKlgSWLAEef5w9zw0bAgcOhPeYgwYBe/YA48YBWbOG91gpoVQp/p0MGwbMnAncfDPw7bdX/v1t24C6dYEhQ4COHYH164HGjSNlbWhxSi9wkC5d2H3w8ce2LQk9jRsDP//MSv/69VkZrziIK22Nw/HlhxB0kLg4hgcBivZ7pcgj1Hz+OVW/wjljOJjne+qp8Lx+Wlm7lmHCYJFa0r7pQEDknXfYL54nD0cmup1gKmDhQtuWJOLWAQ0p5dAhkWrVWP/wzju2rfEX0BywPd58k8VY1auLHD5s2xpnEs4Zw6dPs/2rZEln3wT9/bfIM8/wPShdmkViBw4kCpo0a8Z/e4H167mmyZNtW5JIcCDHrFm2LQkfp06J3H031/n0094Q/XEDV3PAGoIOMz17AjNmABs3smHeaYIUTqB8eYohdOrEEOuddwLHjoXmtV94AdixAxg7FsiWLTSvGQ6yZAFef52CLBcusJagQgXmJt97j8ImhQvbtjI0BEPQR4/atSMpzZtTyGLsWNuWhI9s2YDp03lNGjWKtQdnzti2yt+oA44ALVsCixYBf/9NjeSFC21b5DyyZaNe8QcfUE86OpqKTmlh+XJeaHr0YJ7ZDdx2G7BuHQuDqlcH1qyh/cbYtix05M3LnKtTcsAAkDEjC+Jmz3aWXaEmfXrgzTeB0aO5MWjY0NvrdTrqgCNETAx3eUWKAE2bcgiAcjHGAI88Aixbxh3hbbexSEuuYaDD2bPAQw8BRYty2IWbuP564MMPWeVctqxta0JPunTsEHDahd/tAxpSQ69ewLRpLOarWZOSnErkUQccQYoX566uXj1WXr74orOnBdkiOpotEy1bsk2pbdvUt6y8+CKwZQsdWY4c4bFTuXacogedlAoV2OI1dqw/zsvWrRmZO3OGkbkff7Rtkf9QBxxhcuViu8mDDzLf+eCD4RfpdyM5cwJffQWMHAnMmgVUq8ZwbEpYtYr51G7dOPhCcR4FCzorBxykWzfeuC1fbtuSyFC9OiNzUVE8VyZOtG2Rv1AHbIFMmdiPOnQoQ9HNmgH/+59tq5yHMeyjXrSINym1ajFHfLXdyblzDD0XKsTwteJMChZ03g4YAP71L9YjeLkY61JKlGBkrm5d4IEHeF3STUFkUAdsCWOA559nvmnJEla97tlj2ypnUrs2d78NGnDIfefOVIK6HK+8AmzYQEfthAH0yuUJhqCdFurNkYNO+PPP/aUclTs3VbM6dwYGD6ZYzY03cnPw1FPAW28xcrdjB/PkSmgwEsEzICYmRlatWhWx47mFRYsoy5gxI8OtNWrYtsiZBAJ0sIMHM183dSofg6xdy5DaffexolpxLq+9BvTvD5w8CWTPbtuaizl4kDd9p0/z5rhcOdsWRQ4RVkevXg1s3574dfJk4u9kyEA1uzJl/vl1ww3emGkeSowxv4pIzGV/pg7YGWzZAtx1F4fEf/45CySUyzN/Pp3s6dPc6d53H3tna9Tg+7dxI5Anj20rlasxfjzrH3bs4E7LaWzfzqjUddfRCRcrZtsie4gwX5/UIQe/duy4OBqVKRNlVi/nnIsWZQW837iaA84QaWOUy1O+PAs/WrYE7rmH/au9etm2ypk0bsyQdIcOwP33U6wib17ugGfMUOfrBpLqQTvRAZcpA8ydy7THHXfwbyxfPttW2cEYfl4FCzJPnBQR4NChyzvnefPYDhgkSxZ+1kGHXLp04veFC/vTOasDdhAFCgALFlARqndvYNcuVgFrSOefFC7M9+q55xL7fG+9FWjVyq5dSsooUICPTizEChIdzZRQ06aMTs2fry1tl2IMz8XChdm3n5RAgENWduy42DFv28Z88rlzib973XUXO+SkX4UKeUuIJikagnYg8fFAv350vu3aAV98Ydsi5xIXx9x5kBkz1Am7gQMHGJIcM4aFdU5m1izWaDRoAMyZA2TObNsi9xMfD+zff/md865dF48ozZ6dzrlcOeDll/m9m9AQtMtIn54tNBcuAG+/DRw/rhW9V2LECD4OH84bldatKd7x6qsXO2bFWbhhBxzk7rvZNtilC1MeU6ZoVCqtpE9PYaLixYHbb7/4Z3FxnGGc1Clv2cL3vUYNoE8fOzaHA3XADqZkSdsWOJvNm1kR3bYtIwY9e/LkHDGC+fTJkyn9qTiPjBmZq3eDAwbYnvPnn+xL79GDxX9eDYvaJkMGFnKVKsXwPwCcOOHNTYgP096KF4iPp3ZvjhzAu+/yuSxZgP/+F/jsMxZpRUdTT1lxJk6Uo7wavXsD//438NFHwMCBtq1RvIA6YMWVvPkmd7lvvZVYURvkvvuAX36h4P8dd1AXOj7ejp3KlXGqHOXVGDqUO+Dhwyl3qihpQR2w4jq2b2f1c8uWQMeOl/+dChWAlSsTZwzfdVfoZgwrocGpcpRXwxjgnXeA9u2Z9hg3zrZFiptRB6y4ijlzWLSRJQsraK+Wh0s6Y3jRotDMGFZCw969lAz9+2/blqSe9Omp4X7HHRyfOX26bYsUt6IOWHEFhw5Ro7dFC7YlfPcdJ7gkx6Uzhhs0YHuX0zSI/cTs2bwZ2r+fu0k3kikT5+nWqEFBmAULbFukuBF1wIqjCQS4061QAZg5k32Aa9ZQdCM1BGcM33030Lfvtc0YVtJGXBwwYAA/g+LFE2c+u5Vs2RiRKVOGvecqcaCkFnXAimPZuBGoVw947DGgalVg/XrmfjNlurbXC84YfuON1M8YVtLGgQNAo0YsXurenREJtwkqXI48eYDvv6dM5Z13sl9VUVKKOmDFcZw9y3aP6Ghg61bgk08oA1imTNpf2xj2Ci9aRCm8lMwYVtLGvHn8LFev5sD3MWOYDvAKhQtzjenSAU2aUERCUVKCOmDFUSxYANx8M8cOduzIHUWXLqEXPQjOGL7tNu7IunS58oxh5dqIj6dQStOm7Pn95RcqSXmR0qU5vOHECRZnacW9khLUASuO4I8/OJ6ucWPuRufNYwVzOCfQ5M8PfPMN+4QnTmReefPm8B3PTxw5Qsc7dCjwwAPAihUXz272IlWqsMBs716Go5PO0FWUy6EOWLGKCPDppxzH+NlnwKBBzPVeqg8bLtKnB154gXm8o0eB6tWBSZMic2yv8tNPiS1fY8cyhZAtm22rIkO9esCXX3I0ZqtWF4/jU5RLUQesWGPHDubMOncGypZlSPiVVziaLNLcfnuifOV99wGPP37xuDQleQIBYNgwoGFDtoqtWEG5UL9pJrdowZuOhQuZRomLs22R4lTUASsR5/x5Tiu6+WbmBd97D1iyBKhUya5dRYowB/3ss7SpTh1g9267NrmF2Fi2Fw0cyBGaq1YBt9xi2yp7dOpEudQZM1hjoEV+yuVQB6xElJ9/ZvvPc89xp7B5M7V10znkLzFjRuC114CvvwZ27mT708yZtq1yNsuXJw6+ePddhvCvv962Vfbp2ZPpjXHjgP79bVujOBGHXPYUr3P8OMO6devy+5kzmSsrXNi2ZZenZUu2zZQqxVxev34XDwlXuKsbNYp5zwwZeHP1+OP+CzlfjSFDgCee4OCG4cNtW6M4DXXASlgRAaZOZQXs++8DvXoBmzYxXOl0SpZkIVGPHryANmpEQQmFKmJt27KnunlzqlpVq2bbKudhDCd2dexIFbAPP7RtkeIkknXAxphxxpijxpgNSZ7LY4yZZ4zZnvCYO7xmKm7k99+5k2zXDihUiNOJRo1igY5byJKF+WCdMZzIr78yND9rFlXFpk8HcusV4IqkS8eirGbNeDP31Ve2LVKcQkp2wJ8AaHbJcwMAzBeRMgDmJ/xbUQBQgGH0aKBiRRY1vfEGna+bd0iXzhgeOpRVv35ChCpWtWszHL9oEXfAGnJOnkyZ6Hhr1uTfkt9v4hSSrAMWkZ8A/HnJ060AjE/4fjyA1iG2S3EpwUEJTz9NlamNG3mRzpDBtmVpJzhj+P77qfB0553+UTw6eZLrfuwxhuLXrKEjVlJO1qwU6ihXDmjdmn9Lir+51hxwQRE5BAAJjwVCZ5LiRk6d4pShmBiOmZsyhRebEiVsWxZasmXjLNj330+cMfzzz7atCi/r1/NznTKF7WNz5oRXoczL5M5NycoCBXgDp7rR/ibsRVjGmEeNMauMMauO+WW74DO++YY9vCNHcvbuli2c3evV0KQxwKOP0vFmzsydvldnDH/8MSMaJ05wIMbAgc5pGXMrhQrxxu34cR2J6Xeu9VQ6YoyJAoCEx6NX+kUR+UBEYkQkJn/+/Nd4OMWJHD4MtG/PKths2YDFi5kjzJXLtmWRoWpVFiS1aOG9GcNnzgAPPUQlq1q1KK3YoIFtq7zB228D06YxmuBnsRLl2h3wTABdEr7vAuDr0JijuIFAgCP8ypenYMVLLzEnWLeubcsiT65cvJh6acbwli3c9Y4fDzz/PHWyCxa0bZU3+Pln3qy1akXFNcXfpKQNaRKAZQDKGWP2G2O6ARgGoIkxZjuAJgn/VnzApk1A/fqU16taFVi3jrN7M2WybZk9gjOGf/zR/TOGJ03iQIrDh4Fvv2W1d/r0tq3yBkePMjVTvDjbkryaolFSTrK1qSLS8Qo/ahxiWxQHc/YsByUMHw7kyMHcYDjm9LqZOnW4++3UiTcoS5awh9gNk4DOnmXl+pgxXMfkyUDRorat8g7x8Ww/io0Fli3zT5pGuTpaTqEky8KFzFW9/DLQoQNDlA8+qM73crhxxvDOnWwpGjOGkpsLF6rzDTVDhrCI7d13OTdYUQB1wMpViI1lIU6jRsz7zpvHFhytpbs6wRnDc+c6f8bwtGlMJezZQ33u4cM5kEIJHd98w5vXrl35pShB1AEr/0AE+PRTFllNnMjWk/XrOTNXSTlNmjAkXaWK82YMnz/PkHPbthSGWL3aHfrcbmPPHqYkqlQB3nnHtjWK01AHrFzEzp2UWuzcGShdmhfmV18FrrvOtmXupEgRhnSfeYb54Lp17c8Y/v13FtKNHg089RRz1V4TTHEC585RBz0Q4EASPYeUS1EHrACgtu9//kNBjZUrmatauhS4+WbblrmfjBk5TWnGDGD79sRBBjaYM4ciEJs2AV98wUk9fq5gDye9ewOrVrGd68YbbVujOBF1wAqWLaNTGDSIohqbNzNcqopHoaVVK0YUSpbklKj+/YG4uMgcOy6OqYQWLYBixSgg0q5dZI7tRyZOTCxqa9XKtjXux6uDT/QS62OOH+ew8Dp1+P3MmQyVFS5s2zLvUqoUxRi6dwdee40FbgcPhveYBw8CjRsDw4ZRQnPZMqBMmfAe089s2MD3+bbb2LqnpJ2JE/lYsaJdO0KNOmAfIsLRaBUq8C69Z09OLdIinMiQJQvf94kTuRONjmaLSjj44QcWAK1axcK699/XXGQ4OXGChW05c7KX2gtTwGwTG8uugsaNgaZNbVsTWtQB+4x9+xgSu/deyguuWMFinBw5bFvmP+6/nzOG8+ZlxfRLL4Uu1BYfz17kO+5g29gvv7AaVwkfImwz2rmTk6MKFbJtkTcYPJgRutGjvac9oA7YJ8THA2++yV3v/PnAiBG8KMfE2LbM31SsyKK3++7jXf5ddwF//JG21zx6FGjWjOIPnTrx9b0WunMio0czsjRsGKvMlbSzYQO7Bx57jAWiXkMdsA9YswaoWZNVmfXrM9zct6+Gx5xC9uwMD48Zw5al6Gjmaa+FxYsZcl6yBPjwQ1bgukEK0+0sXcqCqzZteG4paUeE16ycORnN8SLqgD3M6dPsP61enaHnyZPZhqI9n87DGBZmLVvGtqX69YFRo1I+0CEQoIpVw4Z06MuXAw8/7L2QnRMJDlkoUYIa6fqeh4aZMxmtGzqUaRovog7Yo3z7LXDTTRyT160bW4vat9eLg9OpWpWtSi1acMLSvfcy/3U1YmPZ1jRgAHDPPSy4qlw5Mvb6nfh4oGNH4M8/2UGQM6dti7zBuXP8+69YEejRw7Y14UMdsMc4fJgDE+66C8ialSHJ998Hcue2bZmSUoIzhkeM4LzlatWAtWsv/7srVtBpf/89pQ6nTAGuvz6y9vqZF14AFixgnlJvekLH6NHArl189HKqTB2wRwgEOIO2QgVg+nSGbdasofSh4j6MYS5x0SKOCqxZkzndYEhahEV19epRMGXpUvZ0a4QjcsyeTZnWhx/mdDAlNBw6xOEVLVuyO8DLqAP2AJs3s+m/e3cW4KxfDzz/PJA5s23LlLQSnDFcvz7FHbp0obDGvfeyQOXOOxmyrl7dtqX+Yvdu4IEHWDD39tu2rfEWgwYxBFsOrg0AAAhLSURBVP3GG7YtCT/qgF3M2bMMgVWuTG3fjz9mOKxsWduWKaEkf37m9IcMoXhHsWIMTY8YQX1pTS9ElrNneQMEMO+bJYtde7zEL78An3zCSV2lS9u2Jvx4OLrubX78kTvebdvY6zlypM7p9TLp01OQoHZt7gyef567YyXy9OrFqMPMmZQWVUKDCN/bggWB556zbU1kUAfsMmJjgWef5W63VCkW33g9T6Ik0qSJft42GT+etRYDBqh0a6iZNIlteGPH+qeQUEPQLkGE4cfy5SnaMGAAc716MVaUyLBuHVtiGjakbKgSOk6fppBJtWr+KmjTHbAL2LmTDnfePFbDfvCBzulVlEhy/DiHLOTOzZ2al1tjbDB8OHDgANvo/DQGVf+MXEDNmpxg8+67zPumT2/bIkXxD8EhC7t3Uyq0YEHbFqWNM2fY6vP33yx0sl1Etncv8PrrFDTxW12DOmAHE1TVuftu4K23gCJF7NqjKH5k5MhEYZR69Wxbc3lEWB9y+DCd66FDl//+8GGOTAySPj1nQ1eqxKhapUr8uvHGyN3o9+vH/vXhwyNzPCehDtjBdOrEqtfy5W1boij+ZPFioH9/Snz26RP5458/T6eZ1IFezsEeOQJcuPDP/589OxAVxdGIVaokfh8VRZ2ATZs4cWjtWk5yCgq9ZMlCGcigQw465yJFQiv28tNPwBdfsMWuWLHQva5bMJJStfcQEBMTI6tWrYrY8RRFUa6Vw4cp85k9O/tTQ6XzLMJd6JV2qEmfi4395/83hi2HSZ1p8PHS57JnT7ldZ85Q1Gf9ejrlDRv4/cGDib+TK1eiU07qmPPkSf37EB/PcaixscCWLZTO9SLGmF9F5LKDX3UHrCiKcglxccxJ/vUXMHduypxvfDxw7FjyIeBg/vVSMmdOdJxly1Ld7nIONn9+TswKNVmzsgq5WrWLn//zT44wDTrm9es5We2vvxJ/Jyrqn2HsihWvPgpz3DjuvCdP9q7zTQ7dASuKolzCwIHAsGHs+23XLmVO9ehRarJfSu7cl9+hXrpbzZXLPVreItwZX7pb3rSJSmEA11Kq1D8dc9my3G2XKQOUK8cwtFvWfS1cbQesDlhRFCUJCxYAjRvz++uvv7hoKUj69HSaVwsBR0WxYtp2lXEkiY/nFKNLHfP27fwZwN17vny8cVm1imF+L6MhaEVRlBSSLh3FNvLlu/KuNV8+f/WrppRgVXWZMixcC3L2LLB168WOuXZt7zvf5NAdsKIoiqKEiavtgPUeTlEURVEsoA5YURRFUSygDlhRFEVRLKAOWFEURVEsoA5YURRFUSygDlhRFEVRLKAOWFEURVEsoA5YURRFUSygDlhRFEVRLKAOWFEURVEsoA5YURRFUSygDlhRFEVRLKAOWFEURVEsoA5YURRFUSygDlhRFEVRLKAOWFEURVEsoA5YURRFUSygDlhRFEVRLGBEJHIHM+YYgL0RO2DoyAfgD9tGWELX7l/8vH4/rx3w9/pDvfbiIpL/cj+IqAN2K8aYVSISY9sOG+ja/bl2wN/r9/PaAX+vP5Jr1xC0oiiKolhAHbCiKIqiWEAdcMr4wLYBFtG1+xc/r9/Pawf8vf6IrV1zwIqiKIpiAd0BK4qiKIoF1AEnwRgzzhhz1BizIclzeYwx84wx2xMec9u0MVwYY4oZYxYaYzYbYzYaY3olPO+X9Wcxxqw0xvyWsP4XE573xfoBwBiT3hizxhgzO+Hfflr7HmPMemPMWmPMqoTnfLF+Y0wuY8xUY8yWhPO/lh/Wbowpl/B5B79OGGN6R3Lt6oAv5hMAzS55bgCA+SJSBsD8hH97kTgAfUWkAoCaAJ4wxlSEf9Z/DkAjEakMoAqAZsaYmvDP+gGgF4DNSf7tp7UDQEMRqZKkBcUv638TwHciUh5AZfBvwPNrF5GtCZ93FQDVAJwBMB2RXLuI6FeSLwAlAGxI8u+tAKISvo8CsNW2jRF6H74G0MSP6weQFcBqALf6Zf0AiiZcbBoBmJ3wnC/WnrC+PQDyXfKc59cP4HoAu5FQD+SntV+y3jsALI302nUHnDwFReQQACQ8FrBsT9gxxpQAEA1gBXy0/oQQ7FoARwHMExE/rX80gH4AAkme88vaAUAAfG+M+dUY82jCc35YfykAxwB8nJB++MgYkw3+WHtSOgCYlPB9xNauDli5CGNMdgBfAegtIids2xNJRCReGI4qCqCGMaaSbZsigTGmBYCjIvKrbVssUkdEqgK4E0y/1LdtUITIAKAqgPdEJBrAaXgw3Hw1jDGZALQE8GWkj60OOHmOGGOiACDh8ahle8KGMSYj6Hw/E5FpCU/7Zv1BROQvAD+C9QB+WH8dAC2NMXsATAbQyBgzEf5YOwBARA4mPB4F84A14I/17wewPyHaAwBTQYfsh7UHuRPAahE5kvDviK1dHXDyzATQJeH7LmBu1HMYYwyAsQA2i8jIJD/yy/rzG2NyJXx/HYDbAWyBD9YvIgNFpKiIlABDcQtEpBN8sHYAMMZkM8bkCH4P5gM3wAfrF5HDAPYZY8olPNUYwCb4YO1J6IjE8DMQwbWrEEcSjDGTADQAp2EcATAYwAwAXwC4AcDvANqJyJ+2bAwXxpi6ABYDWI/EPOAgMA/sh/XfAmA8gPTgjekXIjLUGJMXPlh/EGNMAwDPiEgLv6zdGFMK3PUCDMl+LiKv+Gj9VQB8BCATgF0AHkLCOQDvrz0rgH0ASonI8YTnIva5qwNWFEVRFAtoCFpRFEVRLKAOWFEURVEsoA5YURRFUSygDlhRFEVRLKAOWFEURVEsoA5YURRFUSygDlhRFEVRLKAOWFEURVEs8H9ya4Owqc2PTAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "best solution with len 601.0 \n", - "with a total time of 0.05389 seconds\n" - ] - } - ], - "source": [ - "available_solvers = {\"random\": random_method,\n", - " \"nn\": nn,\n", - " \"best_nn\": best_nn}\n", - "\n", - "solver = TSPSolver(\"best_nn\", p_inst, available_solvers)\n", - "start = t()\n", - "solver.compute_solution(return_value=False, verbose=True)\n", - "end = t()\n", - "\n", - "solver.plot_solution()\n", - "print(f\"best solution with len {solver.found_length} \\nwith a total time of {np.round(end - start, 5)} seconds\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "uXtOOk25XXpR" - }, - "source": [ - "### Multi Fragment" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "id": "1oo-Zv7vXdTq" - }, - "outputs": [], - "source": [ - "def check_if_edge_available(n1, n2, sol):\n", - " return (len(sol[str(n1)]) < 2) and (len(sol[str(n2)]) < 2)\n", - "\n", - "def check_if_not_close(edge_to_append, sol):\n", - " n1, n2 = edge_to_append\n", - " from_city = n2\n", - " if len(sol[str(from_city)]) == 0:\n", - " return True\n", - " partial_tour = [from_city]\n", - " iter_num = 0\n", - " while True:\n", - " if len(sol[str(from_city)]) == 1:\n", - " if from_city == n1:\n", - " return_value = False\n", - " break\n", - " elif iter_num > 1:\n", - " return_value = True\n", - " break\n", - " else:\n", - " from_city = sol[str(from_city)][0]\n", - " partial_tour.append(from_city)\n", - " iter_num += 1\n", - " else:\n", - " for node_connected in sol[str(from_city)]:\n", - " if node_connected not in partial_tour:\n", - " from_city = node_connected\n", - " partial_tour.append(node_connected)\n", - " iter_num +=1\n", - " return return_value\n", - "\n", - "def create_solution(start_sol, sol):\n", - " assert len(start_sol)==2, \"too many cities with just one link\"\n", - " n1, n2 = start_sol\n", - " from_city = n2\n", - " sol_list = [n1, n2]\n", - " iter_num = 0\n", - " while iter_num <300:\n", - " for node_connected in sol[str(from_city)]:\n", - " iter_num += 1\n", - " if node_connected not in sol_list:\n", - " from_city = node_connected\n", - " sol_list.append(node_connected)\n", - " sol_list.append(n1)\n", - " return sol_list\n", - "\n", - "def mf(instance):\n", - " mat = np.copy(instance.dist_matrix)\n", - " mat = np.triu(mat)\n", - " mat[mat == 0] = 100000\n", - " # TODO\n", - " \n", - " #[...]\n", - " return create_solution(start_list, solution)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 516 - }, - "id": "-iTyocncXg0d", - "outputId": "9a48f544-d4ef-4f57-f786-789006ca5432" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### solving with ['multi_fragment'] ####\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "best solution with len 600.0 \n", - "with a total time of 0.00597 seconds\n" - ] - } - ], - "source": [ - "available_solvers = {\"random\": random_method,\n", - " \"nn\":nn,\n", - " \"best_nn\":best_nn,\n", - " \"multi_fragment\": mf\n", - " }\n", - "\n", - "solver = TSPSolver(\"multi_fragment\", p_inst, available_solvers)\n", - "start = t()\n", - "solver.compute_solution(return_value=False, verbose=True)\n", - "end = t()\n", - "\n", - "solver.plot_solution()\n", - "print(f\"best solution with len {solver.found_length} \\nwith a total time of {np.round(end - start, 5)} seconds\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "lp9RXLX9Ye1M" - }, - "source": [ - "### Comparison Constructive methods" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 239 - }, - "id": "jZ4R8UZgYed7", - "outputId": "28475799-f823-42e9-91ad-b9c3ed029bdd", - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 4/4 [00:01<00:00, 2.94it/s, using multi_fragment for d198] \n" - ] - } - ], - "source": [ - "from time import time as t\n", - "import pandas as pd\n", - "from tqdm import tqdm\n", - "\n", - "available_solvers = {\"random\": random_method,\n", - " \"nn\":nn,\n", - " \"best_nn\":best_nn,\n", - " \"multi_fragment\": mf\n", - " }\n", - "\n", - "show_plots = False\n", - "verbose = False\n", - "problems = [\"../problems/eil76.tsp\", \"../problems/kroA100.tsp\", \"../problems/ch130.tsp\", \"../problems/d198.tsp\"]\n", - "methods = available_solvers.keys()\n", - "results = []\n", - "index = []\n", - "with tqdm(problems) as tq:\n", - " for filename in tq:\n", - " p_inst = ProblemInstance(filename)\n", - " if verbose:\n", - " print(\"\\n\\n#############################\")\n", - " p_inst.print_info()\n", - " if show_plots:\n", - " p_inst.plot_data()\n", - "\n", - " for method in methods:\n", - " tq.set_postfix_str(f\"using {method} for {p_inst.name}\")\n", - " solver = TSPSolver(method, p_inst, available_solvers)\n", - " start = t()\n", - " solver.compute_solution(return_value=False, verbose=verbose)\n", - " end = t()\n", - " if verbose: \n", - " print(f\"the total length for the solution found is {solver.found_length}\",\n", - " f\"while the optimal length is {p_inst.best_sol}\",\n", - " f\"the gap is {solver.gap} %\",\n", - " f\"the solution is found in {np.round(end - start, 5)} seconds\",sep=\"\\n\")\n", - " index.append((filename[12:], method))\n", - " results.append([solver.found_length, p_inst.best_sol, solver.gap, end - start])\n", - "\n", - " if show_plots:\n", - " solver.plot_solution()\n", - "\n", - " if p_inst.exist_opt and show_plots:\n", - " solver = TSPSolver(\"optimal\", p_inst)\n", - " solver.solved = True\n", - " solver.solution = np.concatenate([p_inst.optimal_tour, [p_inst.optimal_tour[0]]])\n", - " solver.plot_solution()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "id": "v8iJZ1QtY6WM" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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tour lengthoptimal solutiongaptime to solve
problemmethod
eil76.tsprandom2616.0538.0386.250.000970
nn680.0538.026.390.000997
best_nn601.0538.011.710.049906
multi_fragment600.0538.011.520.005983
kroA100.tsprandom163891.021282.0670.090.001030
nn27807.021282.030.660.001961
best_nn24815.021282.016.600.101760
multi_fragment24287.021282.014.120.007005
ch130.tsprandom44209.06110.0623.550.001029
nn7578.06110.024.030.002980
best_nn6880.06110.012.600.206479
multi_fragment7849.06110.028.460.017952
d198.tsprandom188837.015780.01096.690.001047
nn18975.015780.020.250.005039
best_nn17565.015780.011.310.694035
multi_fragment17111.015780.08.430.065004
\n", - "
" - ], - "text/plain": [ - " tour length optimal solution gap \\\n", - "problem method \n", - "eil76.tsp random 2616.0 538.0 386.25 \n", - " nn 680.0 538.0 26.39 \n", - " best_nn 601.0 538.0 11.71 \n", - " multi_fragment 600.0 538.0 11.52 \n", - "kroA100.tsp random 163891.0 21282.0 670.09 \n", - " nn 27807.0 21282.0 30.66 \n", - " best_nn 24815.0 21282.0 16.60 \n", - " multi_fragment 24287.0 21282.0 14.12 \n", - "ch130.tsp random 44209.0 6110.0 623.55 \n", - " nn 7578.0 6110.0 24.03 \n", - " best_nn 6880.0 6110.0 12.60 \n", - " multi_fragment 7849.0 6110.0 28.46 \n", - "d198.tsp random 188837.0 15780.0 1096.69 \n", - " nn 18975.0 15780.0 20.25 \n", - " best_nn 17565.0 15780.0 11.31 \n", - " multi_fragment 17111.0 15780.0 8.43 \n", - "\n", - " time to solve \n", - "problem method \n", - "eil76.tsp random 0.000970 \n", - " nn 0.000997 \n", - " best_nn 0.049906 \n", - " multi_fragment 0.005983 \n", - "kroA100.tsp random 0.001030 \n", - " nn 0.001961 \n", - " best_nn 0.101760 \n", - " multi_fragment 0.007005 \n", - "ch130.tsp random 0.001029 \n", - " nn 0.002980 \n", - " best_nn 0.206479 \n", - " multi_fragment 0.017952 \n", - "d198.tsp random 0.001047 \n", - " nn 0.005039 \n", - " best_nn 0.694035 \n", - " multi_fragment 0.065004 " - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "index = pd.MultiIndex.from_tuples(index, names=['problem', 'method'])\n", - "\n", - "df = pd.DataFrame(results, index=index, columns=[\"tour length\", \"optimal solution\", \"gap\", \"time to solve\"])\n", - "df" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "PyCharm (AI2020BsC)", - "language": "python", - "name": "pycharm-61970693" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/Lectures/Student_lecture 3.ipynb b/Lectures/Student_lecture 3.ipynb deleted file mode 100644 index 73426c5..0000000 --- a/Lectures/Student_lecture 3.ipynb +++ /dev/null @@ -1,255 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "collapsed": true, - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Third Lab\n", - "\n", - "What we are going to do today:\n", - "- Introduce two optimizers for local search methods\n", - "- Use the optimizer with the methods defined before" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This cell below is simply importing some useful stuff for later" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import glob\n", - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "from time import time as t" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimizers\n", - "### 2opt\n", - "As we saw last time, we have 12 problems and two have an optimal solution" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "from src.utils import compute_length\n", - "\n", - "\n", - "def step2opt(solution, matrix_dist, distance):\n", - " seq_length = len(solution) - 1\n", - " tsp_sequence = np.array(solution)\n", - " uncrosses = 0\n", - " # TODO\n", - " for i in range(1, seq_length - 1):\n", - " \n", - " # END TODO\n", - " return tsp_sequence, distance, uncrosses\n", - "\n", - "\n", - "def swap2opt(tsp_sequence, i, j):\n", - " # TODO\n", - " \n", - " # END TODO\n", - " return new_tsp_sequence\n", - "\n", - "\n", - "def gain(i, j, tsp_sequence, matrix_dist):\n", - " old_link_len = (matrix_dist[tsp_sequence[i], tsp_sequence[i - 1]] + matrix_dist[\n", - " tsp_sequence[j], tsp_sequence[j + 1]])\n", - " changed_links_len = (matrix_dist[tsp_sequence[j], tsp_sequence[i - 1]] + matrix_dist[\n", - " tsp_sequence[i], tsp_sequence[j + 1]])\n", - " return - old_link_len + changed_links_len\n", - "\n", - "\n", - "def loop2opt(solution, instance, max_num_of_uncrosses=10000):\n", - " matrix_dist = instance.dist_matrix\n", - " new_len = compute_length(solution, matrix_dist)\n", - " new_tsp_sequence = np.copy(np.array(solution))\n", - " uncross = 0\n", - " # TODO\n", - " \n", - " # END TODO" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Let's test it" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "#############################\n", - "name: eil76\n", - "nPoints: 76\n", - "best_sol: 538.0\n", - "exist optimal: True\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from src.io_tsp import ProblemInstance\n", - "example_problem = \"../problems/eil76.tsp\"\n", - "p_inst = ProblemInstance(example_problem)\n", - "p_inst.print_info()\n", - "p_inst.plot_data()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from src.TSP_solver import TSPSolver\n", - "from src.constructive_algorithms import (\n", - " random_method,\n", - " nearest_neighbor,\n", - " best_nearest_neighbor,\n", - " multi_fragment_mf\n", - ")\n", - "\n", - "available_solvers = {\n", - " \"random\": random_method,\n", - " \"nn\":nearest_neighbor,\n", - " \"best_nn\":best_nearest_neighbor,\n", - " \"multi_fragment\": multi_fragment_mf\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### solving with ['nn'] ####\n", - "Error the solution of nn for problem eil76 is not valid\n", - "best solution with len 680.0 \n", - "with a total time of 0.0 seconds\n", - "solution found has a 26.39 % gap\n", - "0.08463191986083984\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 7.43 % gap\n", - "with 23 number of uncrossed edges\n" - ] - } - ], - "source": [ - "solver = TSPSolver(\"nn\", p_inst, available_solvers)\n", - "start = t()\n", - "solver.compute_solution(return_value=False, verbose=True)\n", - "end = t()\n", - "\n", - "solver.plot_solution()\n", - "print(f\"best solution with len {solver.found_length} \\nwith a total time of {np.round(end - start, 5)} seconds\")\n", - "print(f\"solution found has a {solver.gap} % gap\")\n", - "\n", - "start = t()\n", - "solution, new_length, uncross = loop2opt(solver.solution, p_inst)\n", - "end = t()\n", - "print(end - start)\n", - "solver.method = \"nn folowed by 2 opt\"\n", - "\n", - "assert solver.pass_and_check_if_solution_is_valid(solution), \"Solution non valid\"\n", - "solver.solved = True\n", - "solver.solution = solution\n", - "solver.plot_solution()\n", - "solver._gap()\n", - "print(f\" {solver.gap} % gap\")\n", - "print(f\"with {uncross} number of uncrossed edges\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 2.5opt\n", - "We will see it in the next Lecture" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "PyCharm (AI2020BsC)", - "language": "python", - "name": "pycharm-61970693" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/Lectures/Student_lecture 4.ipynb b/Lectures/Student_lecture 4.ipynb deleted file mode 100644 index 9a71243..0000000 --- a/Lectures/Student_lecture 4.ipynb +++ /dev/null @@ -1,37 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "collapsed": true, - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "## Fourth Lecture" - ] - } - ], - "metadata": { - "kernelspec": { - "name": "pycharm-61970693", - "language": "python", - "display_name": "PyCharm (AI2020BsC)" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} \ No newline at end of file diff --git a/aco.cc b/aco.cc new file mode 100644 index 0000000..207d701 --- /dev/null +++ b/aco.cc @@ -0,0 +1,427 @@ +// vim: set ts=2 sw=2 et tw=80: + +// compile with +// g++ -lpthread --std=c++11 -o c_prob/aco aco.cc + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +using namespace std; +typedef unsigned int uint; + +ostream& operator<< (ostream& out, vector a) { + out << "["; + for (int i = 0; i < a.size() - 1; i++) { + out << a[i] << ","; + } + out << a[a.size() - 1] << "]"; + return out; +} + +const uint MAX_NODES = 1577; + +// alpha >= 0 +double alpha; +// beta >= 1 +double beta; +double pheromone_evaporation_coeff; +double pheromone_constant; +uint n_iterations; + +uint n_ants; + +uint n_nodes; + +double dist_matrix[MAX_NODES][MAX_NODES]; +double pheromone_map[MAX_NODES][MAX_NODES]; +double ant_updated_pheromone_map[MAX_NODES][MAX_NODES]; + +double sh_dist; +vector sh_route; +bool first_pass; +uint start; + +uint select_random(const set &s) { + auto n = rand() % s.size(); // not _really_ random + auto it = begin(s); + advance(it, n); // 'advance' the iterator n times + return *it; +} + +struct ant { + uint other; + uint idx; + pthread_t t_handle; + uint location; + double distance_travelled; + set possible_locations; + vector route; + bool tour_complete = false; + + void init() { + this->other = 0; + + this->distance_travelled = 0.0; + + this->route.clear(); + + this->possible_locations.clear(); + for (size_t i = 0; i < n_nodes; i++) { + this->possible_locations.insert(i); + } + + + this->location = start; + + this->update_route(start); + + this->tour_complete = false; + }; + + void update_route(uint new_loc) { + this->route.push_back(new_loc); + this->possible_locations.erase(new_loc); + }; + + uint pick_path() { + if (first_pass) { + return select_random(this->possible_locations); + } else { + double attractiveness[n_nodes]; + double sum_total = 0.0; + vector idxs; + + for (uint loc : this->possible_locations) { + idxs.push_back(loc); + double pheromone_amount = pheromone_map[this->location][loc]; + double distance = dist_matrix[this->location][loc]; + if (distance == 0) { distance = 0.000000001; }; + + attractiveness[loc] = pow(pheromone_amount, alpha) * + pow(1 / distance, beta); + //cerr << "ant " << idx << " attr[loc]=" << attractiveness[loc] << endl; + sum_total += attractiveness[loc]; + if (isnan(sum_total)) { cerr << "nanalert " << attractiveness[loc] << + " " << pheromone_amount << " " << distance << endl; } + } + + // it is possible to have small values for pheromone amount / distance, + // such that with rounding errors this is equal to zero + if (sum_total == 0.0) { + // increment all zero's, such that they are the smallest non-zero + // values supported by the system + // source: http://stackoverflow.com/a/10426033/5343977 + for (uint loc : idxs) { + attractiveness[loc] = nextafter(attractiveness[loc], + std::numeric_limits::infinity()); + } + sum_total = nextafter(sum_total, std::numeric_limits::infinity()); + } + + double toss = (double) rand() / (double) RAND_MAX; + + // cerr << "ant " << idx << " sum_total is " << sum_total << " toss is " << toss << endl; + + double cumulative = 0.0; + for (uint loc : idxs) { + double weight = (attractiveness[loc] / sum_total); + //cerr << "ant " << idx << " w: " << weight + cumulative << endl; + if (toss <= (weight + cumulative)) { + return loc; + } + cumulative += weight; + } + + cerr << "cum " << cumulative << " n " << idxs.size() << " toss " << toss << endl; + sleep(1); + + other++; + // cerr << "ant " << idx << " change statement" << endl; + return idxs[idxs.size() - 1]; + } + }; + + void traverse(uint next) { + this->update_route(next); + this->distance_travelled += dist_matrix[this->location][next]; + this->location = next; + //cerr << "traversing " << next << " dist: " << this->distance_travelled << endl; + } + + void run() { + // cerr << "started ant - # can pick: " << this->possible_locations.size() << endl; + while (!this->possible_locations.empty()) { + uint next = this->pick_path(); + this->traverse(next); + } + this->distance_travelled += dist_matrix[this->route[this->route.size() - 1]] + [this->route[0]]; + + // cerr << "stopped ant" << endl; + this->tour_complete = true; + } +}; + +void *ant_thread (void *ant_ptr) { + ant& ant = *((struct ant *) ant_ptr); + ant.run(); + return NULL; +} + +ant* ants; + +void init_ants() { + for (size_t i = 0; i < n_ants; i++) { + ants[i].init(); + ants[i].idx = i; + } +} + +pthread_mutex_t mut; + +void init_aco() { + pthread_mutex_init(&mut, NULL); + start = 0; + first_pass = true; + memset(pheromone_map, 0, sizeof(pheromone_map)); + memset(ant_updated_pheromone_map, 0, sizeof(ant_updated_pheromone_map)); + init_ants(); + sh_dist = -1.0; +} + +void populate_ant_updated_pheromone_map(ant& ant) { + for (size_t i = 0; i < ant.route.size(); i++) { + size_t j = (i + 1) % n_nodes; + double current_ph = ant_updated_pheromone_map[ant.route[i]][ant.route[j]]; + double new_ph = pheromone_constant / ant.distance_travelled; + + ant_updated_pheromone_map[ant.route[i]][ant.route[j]] = + ant_updated_pheromone_map[ant.route[j]][ant.route[i]] = + current_ph + new_ph; + } +} + +void update_pheromone_map() { + for (size_t i = 0; i < n_nodes; i++) { + for (size_t j = 0; j < n_nodes; j++) { + pheromone_map[i][j] = (1 - pheromone_evaporation_coeff) * + pheromone_map[i][j] + ant_updated_pheromone_map[i][j]; + } + } +} + + +void mainloop() { + for (size_t i = 0; i < n_iterations; i++) { + srand(i); + //cerr << "starting ants" << endl; + for (uint j = 0; j < n_ants; j++) { + pthread_create(&(ants[j].t_handle), NULL, ant_thread, &(ants[j])); + } + // cerr << "joining ants" << endl; + for (uint j = 0; j < n_ants; j++) { + pthread_join(ants[j].t_handle, NULL); + } + //cerr << "summing ants" << endl; + uint os = 0; + for (uint j = 0; j < n_ants; j++) { + os += ants[j].other; + populate_ant_updated_pheromone_map(ants[j]); + + if (sh_dist < 0 || ants[j].distance_travelled < sh_dist) { + sh_dist = ants[j].distance_travelled; + sh_route = ants[j].route; + + cerr << "new short distance is " << sh_dist << " ant " << j << endl; + } + } + + update_pheromone_map(); + + first_pass = false; + + init_ants(); + + memset(ant_updated_pheromone_map, 0, sizeof(ant_updated_pheromone_map)); + + /*double real = 0; + for (uint k = 0; k < n_nodes; k++) { + real += dist_matrix[sh_route[k]][sh_route[(k + 1) % n_nodes]]; + }*/ + + cerr << "iteration " << i << ": dist " << sh_dist << endl;// << " os " << os << " rdist " << real << endl; + /* + uint nnz = 0; + double sum = 0, min = 1.0/0.0, max = -1.0/0.0; + for (int i = 0; i < n_nodes; i++) { + for (int j = 0; j < n_nodes - 1; j++) { + if (pheromone_map[i][j] != 0.0) nnz++; + sum += pheromone_map[i][j]; + if (min > pheromone_map[i][j]) min = pheromone_map[i][j]; + if (max < pheromone_map[i][j]) max = pheromone_map[i][j]; + } + } + cerr << "phmap: avg " << sum / (double) (n_nodes * n_nodes) << " min " << min << " max " << max << " nnz " << nnz << endl;*/ + } +} + +uint two_opt() { + uint swaps = 0; + for (int i = 0; i < n_nodes - 1; i++) { + for (int j = i+2; j < n_nodes; j++) { + size_t ip = i == 0 ? (n_nodes - 1) : (i - 1); + size_t ei = sh_route[i], ej = sh_route[j], eii = sh_route[ip], + ejj = sh_route[j]; + double new_dist = sh_dist - dist_matrix[eii][ei] - dist_matrix[ejj][ej] + + dist_matrix[ei][ej] + dist_matrix[ejj][eii]; + if (new_dist < sh_dist) { + //cerr << "2opt " << i << " " << j << ": " << new_dist << endl; + sh_dist = new_dist; + reverse(sh_route.begin() + i, sh_route.begin() + j); + swaps++; + } + } + } + return swaps; +} + +uint two_five_opt() { + uint swaps = 0; + for (int i = 0; i < n_nodes - 2; i++) { + for (int j = i+2; j < n_nodes; j++) { + size_t ip = i == 0 ? (n_nodes - 1) : (i - 1); + size_t x1 = sh_route[ip]; + size_t x2 = sh_route[i]; + size_t x3 = sh_route[i+1]; + + size_t y3 = sh_route[j-2]; + size_t y1 = sh_route[j-1]; + size_t y2 = sh_route[j]; + + double var_a = sh_dist - dist_matrix[x2][x1] - dist_matrix[y2][y1] + + dist_matrix[x1][y1] + dist_matrix[y2][x2]; + + double var_b = sh_dist - dist_matrix[x2][x1] - dist_matrix[y2][y1] + - dist_matrix[x2][x3] + dist_matrix[x2][y2] + + dist_matrix[y1][x2] + dist_matrix[x1][x3]; + + double var_c = sh_dist - dist_matrix[x2][x1] - dist_matrix[y2][y1] + - dist_matrix[y1][y3] + dist_matrix[x1][y1] + + dist_matrix[y1][x2] + dist_matrix[y3][y2]; + + if (var_a < sh_dist && var_a < var_b && var_a < var_c) { + //cerr << "25opt(a) " << i << " " << j << ": " << var_a << endl; + sh_dist = var_a; + reverse(sh_route.begin() + i, sh_route.begin() + j); + swaps++; + } else if (var_b < sh_dist && var_b < var_a && var_b < var_c) { + //cerr << "25opt(b) " << i << " " << j << ": " << var_b << endl; + //cerr << vector(sh_route.begin() + i - 1, sh_route.begin() + j + 1) << endl; + for (int k = i; k < j - 1; k++) { + sh_route[k] = sh_route[k + 1]; + } + sh_route[j - 1] = x2; + //cerr << vector(sh_route.begin() + i - 1, sh_route.begin() + j + 1) << endl; + + sh_dist = var_b; + swaps++; + } else if (var_c < sh_dist && var_c < var_a && var_c < var_b) { + //cerr << "25opt(c) " << i << " " << j << ": " << var_c << endl; + //cerr << vector(sh_route.begin() + i - 1, sh_route.begin() + j + 1) << endl; + for (int k = j - 2; k >= i; k--) { + sh_route[k + 1] = sh_route[k]; + } + sh_route[i] = y1; + //cerr << vector(sh_route.begin() + i - 1, sh_route.begin() + j + 1) << endl; + + sh_dist = var_c; + swaps++; + } + } + } + return swaps; +} + +#define buffersize 100000 + +int main(int argc, char** argv) { + if (argc < 8) { + cerr << argv[0] << " [n_nodes] [n_iter] [n_ants] [alpha] [beta] [evap] [weight]" << endl; + return 1; + } + + n_nodes = atoi(argv[1]); + n_iterations = atoi(argv[2]); + n_ants = atoi(argv[3]); + sscanf(argv[4], "%lf", &alpha); + sscanf(argv[5], "%lf", &beta); + sscanf(argv[6], "%lf", &pheromone_evaporation_coeff); + sscanf(argv[7], "%lf", &pheromone_constant); + ant ants_arr[n_ants]; + ants = ants_arr; + + cerr << n_nodes << endl; + char * pch; + int row = 0, column = 0; + + char buffer[buffersize]; + for (size_t i = 0; i < n_nodes; i++) { + fgets(buffer, buffersize, stdin); + pch = strtok(buffer, " "); + column = 0; + + while (pch != NULL) { + sscanf(pch, "%lf", &(dist_matrix[row][column])); + pch = strtok(NULL, " "); + column++; + } + row++; + } + + cerr << "reading done" << endl; + + + + /*uint nnz = 0; + for (int i = 0; i < n_nodes; i++) { + for (int j = 0; j < n_nodes - 1; j++) { + if (dist_matrix[i][j] != 0.0) nnz++; + } + } + cerr << "dist: nnz " << nnz << endl;*/ + + init_aco(); + // scan and parse + mainloop(); + + //for (uint k = 0; k < n_nodes; k++) { + // cerr << "p: " << sh_route[k] << ": " << dist_matrix[sh_route[k]][sh_route[(k + 1) % n_nodes]] << endl; + //} + + cerr << "pre-optimization length: " << sh_dist << endl; + + uint i = 0; + uint j = 0; + do { + //for (i = 0; i < 50; i++) { + //cerr << "2opt round " << i << endl; + //if(two_opt() == 0) break; + //} + for (j = 0; j < 50; j++) { + //cerr << "25opt round " << j << endl; + if(two_five_opt() == 0) break; + } + cerr << "optimizing length: " << sh_dist << endl; + } while (i > 0 || j > 0); + + cerr << "optimized length: " << sh_dist << endl; + cout << sh_route << endl; +} diff --git a/Lectures/__init__.py b/c_prob/.gitkeep similarity index 100% rename from Lectures/__init__.py rename to c_prob/.gitkeep diff --git a/run.py b/run.py index 298dbcc..4240688 100644 --- a/run.py +++ b/run.py @@ -1,13 +1,17 @@ import glob -import pandas as pd +#import pandas as pd from src.io_tsp import ProblemInstance from src.TSP_solver import TSPSolver, available_improvers, available_solvers import numpy as np def use_solver_to_compute_solution(solver, improve, index, results, name, verbose, show_plots): - solver.bind(improve) + #solver.bind(improve) + # solver.bind("2-opt") + # solver.bind("2.5-opt") solver.compute_solution(return_value=False, verbose=verbose) + # solver.pop() + # solver.pop() if verbose: print(f"the total length for the solution found is {solver.found_length}", @@ -23,8 +27,10 @@ def use_solver_to_compute_solution(solver, improve, index, results, name, verbos def run(show_plots=False, verbose=False): - # problems = glob.glob('./problems/*.tsp') - problems = ["./problems/eil76.tsp"] + problems = glob.glob('./problems/*.tsp') + + problems = ["./problems/fl1577.tsp"] + solvers_names = available_solvers.keys() improvers_names = available_improvers.keys() results = [] @@ -36,19 +42,22 @@ def run(show_plots=False, verbose=False): if show_plots: prob_instance.plot_data() + for solver_name in solvers_names: - for improve in improvers_names: - solver = TSPSolver(solver_name, prob_instance) - use_solver_to_compute_solution(solver, improve, index, results, problem_path, verbose, show_plots) - for improve2 in [j for j in improvers_names if j not in [improve]]: - use_solver_to_compute_solution(solver, improve2, index, results, problem_path, verbose, show_plots) - - for improve3 in [j for j in improvers_names if j not in [improve, improve2]]: - use_solver_to_compute_solution(solver, improve3, index, results, problem_path, verbose, - show_plots) - solver.pop() - - solver.pop() + solver = TSPSolver(solver_name, prob_instance) + use_solver_to_compute_solution(solver, None, index, results, problem_path, verbose, show_plots) + # for improve in improvers_names: + # solver = TSPSolver(solver_name, prob_instance) + # use_solver_to_compute_solution(solver, improve, index, results, problem_path, verbose, show_plots) + # for improve2 in [j for j in improvers_names if j not in [improve]]: + # use_solver_to_compute_solution(solver, improve2, index, results, problem_path, verbose, show_plots) + # + # for improve3 in [j for j in improvers_names if j not in [improve, improve2]]: + # use_solver_to_compute_solution(solver, improve3, index, results, problem_path, verbose, + # show_plots) + # solver.pop() + # + # solver.pop() if prob_instance.exist_opt and show_plots: solver = TSPSolver("optimal", prob_instance) @@ -56,11 +65,12 @@ def run(show_plots=False, verbose=False): solver.solution = np.concatenate([prob_instance.optimal_tour, [prob_instance.optimal_tour[0]]]) solver.plot_solution() - index = pd.MultiIndex.from_tuples(index, names=['problem', 'method']) + #index = pd.MultiIndex.from_tuples(index, names=['problem', 'method']) - return pd.DataFrame(results, index=index, columns=["tour length", "optimal solution", "gap", "time to solve"]) + return None + #return pd.DataFrame(results, index=index, columns=["tour length", "optimal solution", "gap", "time to solve"]) if __name__ == '__main__': - df = run(show_plots=False, verbose=True) + df = run(show_plots=True, verbose=True) df.to_csv("./results.csv") diff --git a/src/TSP_solver.py b/src/TSP_solver.py index b46085a..0e5b13f 100644 --- a/src/TSP_solver.py +++ b/src/TSP_solver.py @@ -6,17 +6,23 @@ from src.two_opt import loop2opt from src.two_dot_five_opt import loop2dot5opt from src.simulated_annealing import sa from src.constructive_algorithms import random_method, nearest_neighbor, best_nearest_neighbor, multi_fragment_mf +from src.ant_colony import ant_colony_opt -available_solvers = {"random": random_method, - "nearest_neighbors": nearest_neighbor, - "best_nn": best_nearest_neighbor, - "multi_fragment": multi_fragment_mf - } - +# available_solvers = {"random": random_method, +# "nearest_neighbors": nearest_neighbor, +# "best_nn": best_nearest_neighbor, +# "multi_fragment": multi_fragment_mf +# } available_improvers = {"2-opt": loop2opt, "2.5-opt": loop2dot5opt, "simulated_annealing": sa} +# available_solvers = {} +# for i in range(1, 10): +# for j in range(1, 10): +# available_solvers["aco_" + str(i) + "_" + str(j)] = ant_colony_opt(i/10, j) + +available_solvers = {"aco": ant_colony_opt} class TSPSolver: def __init__(self, algorithm_name, problem_instance, passed_avail_solvers=None, passed_avail_improvers=None): @@ -52,14 +58,14 @@ class TSPSolver: print(f"### solving with {self.algorithms} ####") start_time = t() self.solution = self.available_solvers[self.algorithms[0]](self.problem_instance) - if self.check_if_solution_is_valid(): + if not self.check_if_solution_is_valid(): print(f"Error the solution of {self.algorithm_name} for problem {self.problem_instance.name} is not valid") if return_value: return False for i in range(1, len(self.algorithms)): improver = self.algorithms[i] self.solution = self.available_improvers[improver](self.solution, self.problem_instance) - if self.check_if_solution_is_valid(): + if not self.check_if_solution_is_valid(): print( f"Error the solution of {self.algorithm_name} with {improver} for problem {self.problem_instance.name} is not valid") if return_value: diff --git a/src/ant_colony.py b/src/ant_colony.py new file mode 100644 index 0000000..bac47c9 --- /dev/null +++ b/src/ant_colony.py @@ -0,0 +1,55 @@ +from src.io_tsp import ProblemInstance +import os +# OOT = out-of-time + +# Run #0 unknown + +# Run #1 +# alpha = 0.5, beta = 7, evap = 0.5, weight = instance.best_sol +# if instance.nPoints > 1000: ants = 550 loops = 15 +# elif instance.nPoints > 195: ants = 1000 loops = 55 +# else: ants = 2500 loops = 150 +# pr439=112263 pcb442=53699 d198=16199 fl1577= ch130=6332 u1060=247703 +# kroA100=21737 eil76=554 rat783= lin318=45580 + +# Run #2 +# alpha, beta, evap, weight = (0.9, 8, 0.4, 100_000) +# ants, loops = (900, 6) if instance.nPoints > 1100 \ +# else (600, 15) if instance.nPoints > 1000 \ +# else (750, 30) if instance.nPoints > 700 \ +# else (975, 40) if instance.nPoints > 500 \ +# else (1000, 50) if instance.nPoints > 300 \ +# else (1100, 70) if instance.nPoints > 195 else (2700, 140) +# pr439=111322 pcb442=52176 d198=16177 ch130=6269 u1060=246085 +# kroA100=21665 eil76=550 lin318=43675 +# Separate run: fl1577=24238 (132s) rat783=9389 (174s) + +dir = "/Users/maggicl/Git/AI2020BsC/c_prob/" +def ant_colony_opt(instance): + fname = dir + instance.name + ".txt" + + # write .mat file for C++ + if not os.path.exists(fname): + with open(fname, "w") as f: + for i in range(instance.nPoints): + print(" ".join(map(str, instance.dist_matrix[i])), file=f) + + alpha, beta, evap, weight = (0.9, 8, 0.4, 100_000) + ants, loops = (1000,7) if instance.nPoints > 1100 \ + else (600,15) if instance.nPoints > 1000 \ + else (750,30) if instance.nPoints > 700 \ + else (975,40) if instance.nPoints > 500 \ + else (1000,50) if instance.nPoints > 300 \ + else (1100,70) if instance.nPoints > 195 else (2700, 140) + + # Call C++ program + cmd = dir + "aco " + str(instance.nPoints) + " " + str(loops) + " " + str(ants) + \ + " " + str(alpha) + " " + str(beta) + " " + str(evap) + " " + str(weight) +" < " + fname + " &2>/dev/null" + print(cmd) + solution = eval(os.popen(cmd).read()) + solution.append(solution[0]) + return solution + +if __name__ == "__main__": + ant_colony_opt(ProblemInstance("../problems/eil76.tsp")) + diff --git a/src/constructive_algorithms.py b/src/constructive_algorithms.py index df0738a..529e39f 100644 --- a/src/constructive_algorithms.py +++ b/src/constructive_algorithms.py @@ -2,13 +2,11 @@ import numpy as np from src.utils import compute_length - def random_method(instance_): n = int(instance_.nPoints) solution = np.random.choice(np.arange(n), size=n, replace=False) return np.concatenate([solution, [solution[0]]]) - def nearest_neighbor(instance_, starting_node=0): dist_matrix = np.copy(instance_.dist_matrix) n = int(instance_.nPoints) diff --git a/src/two_dot_five_opt.py b/src/two_dot_five_opt.py index 0caf6d0..c454525 100644 --- a/src/two_dot_five_opt.py +++ b/src/two_dot_five_opt.py @@ -27,7 +27,8 @@ def step2dot5opt(solution, matrix_dist, distance): uncrosses += 1 tsp_sequence = sequences[best_method] distance = best_len - # print(distance, best_method, [twoOpt_len, first_shift_len, second_shift_len]) + print(i, j, best_len) + #print(distance, best_method, [twoOpt_len, first_shift_len, second_shift_len]) return tsp_sequence, distance, uncrosses @@ -62,7 +63,7 @@ def shift_gain2(i, j, tsp_sequence, matrix_dist): return - old_link_len + changed_links_len -def loop2dot5opt(solution, instance, max_num_of_changes=10000): +def loop2dot5opt(solution, instance, max_num_of_changes=2500): matrix_dist = instance.dist_matrix actual_len = compute_length(solution, matrix_dist) new_tsp_sequence = np.copy(np.array(solution)) diff --git a/src/two_opt.py b/src/two_opt.py index 1268423..cf01ae9 100644 --- a/src/two_opt.py +++ b/src/two_opt.py @@ -2,7 +2,6 @@ import numpy as np from src.utils import compute_length - def step2opt(solution, matrix_dist, distance): seq_length = len(solution) - 1 tsp_sequence = np.array(solution) @@ -26,7 +25,7 @@ def swap2opt(tsp_sequence, i, j): def gain(i, j, tsp_sequence, matrix_dist): old_link_len = (matrix_dist[tsp_sequence[i], tsp_sequence[i - 1]] + matrix_dist[ - tsp_sequence[j], tsp_sequence[j + 1]]) + tsp_sequence[j], tsp_sequence[j + 1]])x changed_links_len = (matrix_dist[tsp_sequence[j], tsp_sequence[i - 1]] + matrix_dist[ tsp_sequence[i], tsp_sequence[j + 1]]) return - old_link_len + changed_links_len