├── edl ├── __init__.py ├── layer.py └── loss.py ├── README.md └── cubic.ipynb /edl/__init__.py: -------------------------------------------------------------------------------- 1 | from edl.layer import Dirichlet, NormalInvGamma 2 | from edl.loss import evidential_classification, evidential_regression, URERN_loss -------------------------------------------------------------------------------- /edl/layer.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import nn 3 | import torch.nn.functional as F 4 | 5 | 6 | class NormalInvGamma(nn.Module): 7 | def __init__(self, in_features, out_units, initial_bias=0.0): 8 | super().__init__() 9 | self.dense = nn.Linear(in_features, out_units * 4) 10 | self.out_units = out_units 11 | 12 | nn.init.constant_(self.dense.bias, initial_bias) 13 | 14 | 15 | def evidence(self, x): 16 | return F.softplus(x) 17 | 18 | def forward(self, x): 19 | out = self.dense(x) 20 | mu, logv, logalpha, logbeta = torch.split(out, self.out_units, dim=-1) 21 | v = self.evidence(logv) 22 | alpha = self.evidence(logalpha) + 1 23 | beta = self.evidence(logbeta) 24 | return mu, v, alpha, beta 25 | 26 | 27 | class Dirichlet(nn.Module): 28 | def __init__(self, in_features, out_units): 29 | super().__init__() 30 | self.dense = nn.Linear(in_features, out_units) 31 | self.out_units = out_units 32 | 33 | def evidence(self, x): 34 | return F.softplus(x) 35 | 36 | def forward(self, x): 37 | out = self.dense(x) 38 | alpha = self.evidence(out) + 1 39 | return alpha -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # UR-ERN 2 | This repository contains the source code associated with our paper titled "Uncertainty Regularized Evidential Regression" which has been accepted at AAAI 2024. 3 | 4 | Paper link: [Uncertainty Regularized Evidential Regression](https://doi.org/10.1609/aaai.v38i15.29583) 5 | 6 | ## Acknowledgments 7 | 8 | We would like to thank [teddykoker](https://github.com/teddykoker) for their PyTorch implementation of Evidential Regression Networks (ERN) in the [evidential-learning-pytorch](https://github.com/teddykoker/evidential-learning-pytorch) repository. 9 | 10 | ## See Also 11 | 12 | - [aamini/evidential-deep-learning](https://github.com/aamini/evidential-deep-learning), original code for [Deep Evidential Regression](https://arxiv.org/abs/1910.02600) in Tensorflow/Keras 13 | 14 | ## Citing This Work 15 | 16 | If you find this work useful in your research, please consider citing: 17 | 18 | ``` 19 | @inproceedings{ye2024uncertainty, 20 | title={Uncertainty regularized evidential regression}, 21 | author={Ye, Kai and Chen, Tiejin and Wei, Hua and Zhan, Liang}, 22 | booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, 23 | volume={38}, 24 | number={15}, 25 | pages={16460--16468}, 26 | year={2024} 27 | } 28 | ``` 29 | -------------------------------------------------------------------------------- /edl/loss.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn.functional as F 3 | 4 | # Normal Inverse Gamma Negative Log-Likelihood 5 | # from https://arxiv.org/abs/1910.02600: 6 | # > we denote the loss, L^NLL_i as the negative logarithm of model 7 | # > evidence ... 8 | def nig_nll(gamma, v, alpha, beta, y): 9 | two_beta_lambda = 2 * beta * (1 + v) 10 | t1 = 0.5 * (torch.pi / v).log() 11 | t2 = alpha * two_beta_lambda.log() 12 | t3 = (alpha + 0.5) * (v * (y - gamma) ** 2 + two_beta_lambda).log() 13 | t4 = alpha.lgamma() 14 | t5 = (alpha + 0.5).lgamma() 15 | nll = t1 - t2 + t3 + t4 - t5 16 | return nll.mean() 17 | 18 | 19 | # Normal Inverse Gamma regularization 20 | # from https://arxiv.org/abs/1910.02600: 21 | # > we formulate a novel evidence regularizer, L^R_i 22 | # > scaled on the error of the i-th prediction 23 | def nig_reg(gamma, v, alpha, _beta, y): 24 | reg = (y - gamma).abs() * (2 * v + alpha) 25 | return reg.mean() 26 | 27 | def L_U(y, gamma, alpha): 28 | return -torch.abs(y - gamma) * torch.log(torch.exp(alpha - 1) - 1) 29 | 30 | 31 | # KL divergence of predicted parameters from uniform Dirichlet distribution 32 | # from https://arxiv.org/abs/1806.01768 33 | # code based on: 34 | # https://bariskurt.com/kullback-leibler-divergence-between-two-dirichlet-and-beta-distributions/ 35 | def dirichlet_reg(alpha, y): 36 | # dirichlet parameters after removal of non-misleading evidence (from the label) 37 | alpha = y + (1 - y) * alpha 38 | 39 | # uniform dirichlet distribution 40 | beta = torch.ones_like(alpha) 41 | 42 | sum_alpha = alpha.sum(-1) 43 | sum_beta = beta.sum(-1) 44 | 45 | t1 = sum_alpha.lgamma() - sum_beta.lgamma() 46 | t2 = (alpha.lgamma() - beta.lgamma()).sum(-1) 47 | t3 = alpha - beta 48 | t4 = alpha.digamma() - sum_alpha.digamma().unsqueeze(-1) 49 | 50 | kl = t1 - t2 + (t3 * t4).sum(-1) 51 | return kl.mean() 52 | 53 | 54 | # Eq. (5) from https://arxiv.org/abs/1806.01768: 55 | # Sum of squares loss 56 | def dirichlet_mse(alpha, y): 57 | sum_alpha = alpha.sum(-1, keepdims=True) 58 | p = alpha / sum_alpha 59 | t1 = (y - p).pow(2).sum(-1) 60 | t2 = ((p * (1 - p)) / (sum_alpha + 1)).sum(-1) 61 | mse = t1 + t2 62 | return mse.mean() 63 | 64 | 65 | def evidential_classification(alpha, y, lamb=1.0): 66 | num_classes = alpha.shape[-1] 67 | y = F.one_hot(y, num_classes) 68 | return dirichlet_mse(alpha, y) + lamb * dirichlet_reg(alpha, y) 69 | 70 | 71 | def evidential_regression(dist_params, y, lamb=1.0): 72 | return nig_nll(*dist_params, y) + lamb * nig_reg(*dist_params, y) 73 | 74 | 75 | def URERN_loss(dist_params, y, lambda_1=1.0, lambda_2=1.0): 76 | gamma, v, alpha, beta = dist_params 77 | 78 | # Calculate L^ERN (your existing evidential regression loss) 79 | L_ERN = nig_nll(gamma, v, alpha, beta, y) + lambda_1 * nig_reg(gamma, v, alpha, beta, y) 80 | 81 | # Calculate L^U 82 | L_U_term = L_U(y, gamma, alpha) 83 | 84 | # Combine the losses according to equation (19) 85 | loss = L_ERN + lambda_2 * L_U_term.mean() 86 | 87 | return loss -------------------------------------------------------------------------------- /cubic.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import torch\n", 10 | "from torch import nn\n", 11 | "from torch.utils.data import DataLoader, TensorDataset\n", 12 | "import matplotlib.pyplot as plt\n", 13 | "\n", 14 | "from edl import NormalInvGamma, evidential_regression, URERN_loss" 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "# ERN within HUA" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 3, 27 | "metadata": {}, 28 | "outputs": [ 29 | { 30 | "data": { 31 | "image/png": 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", 32 | "text/plain": [ 33 | "
" 34 | ] 35 | }, 36 | "metadata": {}, 37 | "output_type": "display_data" 38 | } 39 | ], 40 | "source": [ 41 | "torch.manual_seed(0)\n", 42 | "\n", 43 | "x_train = torch.linspace(-4, 4, 1000).unsqueeze(-1)\n", 44 | "sigma = torch.normal(torch.zeros_like(x_train), 3 * torch.ones_like(x_train))\n", 45 | "y_train = x_train**3 + sigma\n", 46 | "\n", 47 | "x_test = torch.linspace(-7, 7, 1000).unsqueeze(-1)\n", 48 | "y_test = x_test**3\n", 49 | "\n", 50 | "model = nn.Sequential(\n", 51 | " nn.Linear(1, 64),\n", 52 | " nn.ReLU(),\n", 53 | " nn.Linear(64, 64),\n", 54 | " nn.ReLU(),\n", 55 | " NormalInvGamma(64, 1, initial_bias=-14.0),\n", 56 | ")\n", 57 | "\n", 58 | "optimizer = torch.optim.Adam(model.parameters(), lr=5e-4)\n", 59 | "\n", 60 | "for _ in range(500):\n", 61 | " for x, y in DataLoader(TensorDataset(x_train, y_train), batch_size=100, shuffle=True):\n", 62 | " pred = model(x)\n", 63 | " loss = evidential_regression(pred, y, lamb=1e-2)\n", 64 | " optimizer.zero_grad()\n", 65 | " loss.backward()\n", 66 | " optimizer.step()\n", 67 | "\n", 68 | "with torch.no_grad():\n", 69 | " pred = model(x_test)\n", 70 | "\n", 71 | "mu, v, alpha, beta = (d.squeeze() for d in pred)\n", 72 | "x_test = x_test.squeeze()\n", 73 | "var = torch.sqrt(beta / (v * (alpha - 1)))\n", 74 | "\n", 75 | "# plot code modified from\n", 76 | "# https://github.com/aamini/evidential-deep-learning/blob/main/hello_world.py#L48\n", 77 | "plt.figure(figsize=(5, 3), dpi=200)\n", 78 | "plt.scatter(x_train, y_train, s=1.0, c=\"tab:blue\", label=\"Train\")\n", 79 | "plt.plot(x_test, y_test, c=\"k\", label=\"True\")\n", 80 | "plt.plot(x_test, mu, c=\"tab:blue\", ls=\"--\", label=\"Pred\")\n", 81 | "for std in range(4):\n", 82 | " plt.fill_between(\n", 83 | " x_test,\n", 84 | " (mu - std * var),\n", 85 | " (mu + std * var),\n", 86 | " alpha=0.2,\n", 87 | " facecolor=\"tab:blue\",\n", 88 | " label=\"Unc.\" if std == 0 else None,\n", 89 | " )\n", 90 | "plt.gca().set_ylim(-150, 150)\n", 91 | "plt.gca().set_xlim(-7, 7)\n", 92 | "plt.legend(loc=\"upper left\")\n", 93 | "plt.show()\n", 94 | "\n", 95 | "torch.cuda.empty_cache() " 96 | ] 97 | }, 98 | { 99 | "cell_type": "markdown", 100 | "metadata": {}, 101 | "source": [ 102 | "# UR-ERN within HUA" 103 | ] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "execution_count": 4, 108 | "metadata": {}, 109 | "outputs": [ 110 | { 111 | "data": { 112 | "image/png": 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oY0RSUNHkTxkKo7KCGk8Qapo8qCIWGsPR7n1APbHAAafFgAKHKaNBF71eQI7N2OdTUy1GEbl2U79MSU2FwZGIiIiIiLrl/vvv13w9ceJELF68uN+fV1FU7G3yp6x8KquxvRql1LVyYqHRmzo0qqqaNhQWOjMrPGMxisi2maDrw1l9eh2QazPDbh74GMepqkRERERElLH169fjww8/1By7+eab+30PcFVVsa85gFCKwKccCI3p1iuqUNHsi6RsIyorWLlxP/5b7elVX50WPXLt5j4LjYIAZNsMGOm2DUpoBDjiSERERERE3dB5tLGwsBA/+tGP+v15azwheEPJt9VQoaKuLYRQJP3U02Z/BIFI8uHIsCRj5df7Ud0aREWjHyaDiAn5jm71URCBHJsRFkPfxSybSYcce99Pd+0ujjgSEREREVFGdu3ahRUrVmiOXX/99TCb+37fwI4afWE0+VJXWq/3huFPs5cjADQFwin3ewxEJLy8vgrVrUEAgArgnc212NuU+bZger2AfIepz0KjQSegKMuMIpdl0EMjwOBIRJ1UVFRAEAQIgoDly5cPdneIiIhoCHnooYc0Fc/tdjuuuuqqfn3OtlAUNa2pt9Vo8ofhDSYfiWzXHAgjEEoeGttCUby4rhL1Xu3WHnqdkPFWGxajiPw+2p+xfVpqqdsKm3HoTBAdOj0hOoxVVFRgzJgxvW5HTVdTmoiIiKgfNTQ04Omnn9YcW7p0KbKysvrtOYMRGfuaUm+74QlG0OKPpm2jJRCGP0VobPZH8MpXVfCGtcHTatTh3OklyHOYuuyj06KHy9I3W20MRrXUTDE4EhERERFRlx577DGEQgdH/gwGA2644YZ+e76utt3whSQ0eFNPXwWA1kAYvhShsa4thFc3VCMY1Z53mvU4d0YJsrvYd1EQAbfVCGsfjAqKIpBnN8FhNvS6rf7C4Eg0AEaMGIHNmzenPH/qqadi//79KC4uxnvvvTeAPUs0evRojmwSERGRht/vx2OPPaY5dtFFF6GkpKRfnk/uYtuNQFRCnTf19FUgNhrpTREaq1oCWPn1fkRkbTGdHJsRS6aP6DLA6XVCnxWscVj0yLX37bYd/YHBkWgAGAwGTJs2Le35TK4jIiIiGgzLli1Dc3Oz5tjNN9/cL8/V1bYbYUlGjSeUciQSiIXGthTrHnc3+vDmphrIiraBAqcJS44pgcWYPgyaDSLcfRD0DH1cTKe/HRq9JCIiIiKiQRGNRvGHP/xBc2zx4sU44ogj+uX5qlqC8KXYdiMiy6huDUJNs+uGJ5Q6NG6rbcO739SiU2ZEabYFZx09AkZ9+mI4TrMeri6msHZFEIAsqwFumxEChvYoY0esqko0xC1YsACCIGDBggUAgJ07d+Kaa67BhAkTYLVaIQgCKioq4tfX1NTgz3/+M8477zxMmDABNpsNJpMJI0aMwFlnnYUXX3xRUw2ts66qqt59993x8wAQCoXwwAMPYMaMGXA4HHA4HDj22GPx2GOPQZLSVzgjIiKioe/ll1/Gvn37NMduu+22fnmu+rYQWgPJi91IioL9rUGkeRsDbziKtkDy9x8bq1rxzn8TQ+O4PBvOOSZ9aBQEwG039jo0mgwiSrItyLGZDqnQCHDEkeiQ8vrrr+Oiiy6C3598TyFZllFSUpI0GO7fvx9vvPEG3njjDSxbtgyvvvoq7HZ7r/pTV1eHU089FRs3btQcX7t2LdauXYv3338fK1euhCjyMyoiIqJDkaqquP/++zXH5s6di3nz5vX5c7X4I6hrCyc9J6sq9rcGIaXZqtEXjqI1RYXVBm8YH26rTzg+tciJU6YUQBRThzidDsi1m3q1nlEQgBy7EVl9VH11MPDdHNEhYt++ffjhD38Iq9WK++67D5999hm++OILPProo/EA2F7U5lvf+hYeeOABvPvuu1i/fj3Kysrw9NNPY+7cuQCADz74AD/72c963aclS5Zg69atuO666/DBBx9g/fr1eP755zFlyhQAwJtvvom//vWvvX4eIiIiGhzvvPNOwgfEt956a3zmUV/xhSVUtwaTnlNUFTWeICJS6kWNvoiUdluOPIcJJ03I1RybXpqFU6emD41Gg4iCXu7PaDHqMNJtPaRDI8ARR6JDxp49e1BcXIw1a9Zg5MiR8ePHHXdc/LFOp8P27dsxfvz4hPvnz5+Pyy67DL/61a/wv//7v/jHP/6BO++8ExMmTOhxn9pHFdun0QLAjBkzcOqpp2Lq1Kmoq6vDn//8ZyxdurTHz0FERESDQ1VV3HvvvZpjkyZNwplnntmnzxOKytibYtsNFSpqPSGEIqnnp/ojElp86bflAIBZo9wIRRV8WdGMuWNzMGeMO20Atpp1cFt7vg5REGMjla4B3mJDJwrIsvb9c3LEkegQct9992lCY2eCICQNjR3dddddyM3NhaqqeOONN3rVn2uvvVYTGtu53W5cdtllAIBNmzbB4/H06nmIiIho4JWVlWHNmjWaY7fffnufLkGJyrG9GlOtW6xvCyMQST0/1R+R0JxBaGw3b1wOzptRgrljc1KGRkEAsm0G5Fh7vg7RatRhlNs6YKFREACnRY+ROVZMKXKgyGXp8+fgiCMNWfXeEMrrfRifb0e+wzzY3Rl0RqMR559/frfuURQFtbW18Hq9iEYPTt8oKSlBY2NjwtST7rroootSnps5c2b88Z49e3DMMcf06rmIiIhoYHUebRw9ejR+8IMf9Fn7yoG9GqMppqA2+kLwpqiuChwYafRnHhqB2IfsI93WlOdFMbYW0azvWUwSRCDPboJzgAKjySAi22pEttUAva5/xwQZHGlIeuHLfbhz5X8hKSr0ooB7zp6GC49NPdI2HEyYMAFmc9cBWlVVPPfcc1i2bBn+85//IBhMvl4AABobG3vVp8mTJ6c853a744+9Xm+vnoeIiIgG1hdffIEPP/xQc+y2226L7z3dW6qqYm9zAMEUU1CbA2G0pqiOCgCBA6Gx8/TWUFTG25trMHdcDoq7Oepm0AvIsRthEHu2ntFq1CHfaYK+n4sCCgLgssS287CZBi7OMTjSkFPvDcVDIwBIioo7V/4X35qSP6xHHrOzs7u8JhQKYcmSJfjXv/6VUZvpQmUmrNZ0n9gd/EdTltOUQCMiIqIhp/NoY1FRES699NI+a7+6NfVejZ5QFM2+1IVuAhEJzUlCoz8s4ZUNVWj0RVDbFsIFs0qRazdl1B+LUUS2zQRdD4r+DNQoo0EvwG0zwm019vvoYjJc40hDTnm9Lx4a20mKivJ63yD1aGjQZVDN6957742Hxvnz5+Oll15CeXk5fD4fZFmGqqpQVRUnnngigINVWImIiIjabdy4EW+99Zbm2M0335zRzKdM1LeFUlZA9YUkNKTYkgNIHRo9wSheWFeJxgPrHcOSgle+qkJroOuprE6zHrl2c49Co8Wow8hsa7+GRptJh5E5VkwqcCDfYR6U0AhwxJGGoPH5duhFQRMe9aKA8fm923PwcKeqKp566ikAwAknnICPPvoo5eL1lpaWgewaERERHUJ++9vfar7Oycnpswrp6fZq9Eck1HlDKe8NRpOHxiZfGCs2VMEf7jTDSQBkJfWH5LEiOEbYjN2PRIIA5NqNcPXTFhvt01HzHCaYDT3fCqQvccSRhpx8hxn3nD0N+gN76rSvcRzO01Qz0dzcjNraWgDA9773vZSh0efzYfv27QPZNSIiIjpEbNu2DS+//LLm2A033ACbzdbrtr2haMq9GkOShFpPKOmWHEAsNDb5EkNjrSeEF9dXJoRGl8WAC2eNRE6KqaqiCOQ5exYazUYRpW5Lv4RGUYztOTmp0IFSt3XIhEaAI440RF147Eh8a0o+q6p2gyQdXCcQCARSXrds2TJNhVUiIiKidvfdd59mKYvT6cQ111zT63aDERn7mgNJg2FYkrG/NXVoDEnJQ2NVSwCvfV2NqKw9kWs3Ysn0EthTFI4x6gXk2k3QdbOITfs2HW5rZusmu0Ovi/XJbTNCJ/ZsC5D+xhFHGrLyHWYcPy6XoTFDeXl5yMrKAgC88MILiEQS5/SvXbsWd9555wD3jIiIiA4FFRUVePbZZzXHfvazn8XfX/RUREq9V2NUVrC/NZhyH8eQJKHRmxgaK5r8eHVDYmgscpnxvZmlKUOj1ahDnsPc7dBo1Asoybb0eWg06kWMyLZgcqEDeQ7TkA2NAIMj0WFDFMX4vopff/01TjzxRLzwwgtYt24dPvzwQ9x000046aSTYDabMXHixEHuLREREQ01v/vd7zSV0C0WC2688cZetSkrKiqa/JDkxOFEWVGw3xOEnCI0hlOExp31Xqz8ujqhmOIotxXnzShJOb3TadEjx26C2M0iOFlWPUrdVpj0fTdt1GyITXedWGCH22aE0IPCPAONU1WJDiP33nsvPvvsM3z99df48ssv8f3vf19z3u1245VXXsFdd92FHTt2DFIviYiIaKjZu3cvnn76ac2xpUuXIi8vr8dtKgdCYziamAxlVcV+TxBRKfn81LAsoSHJ9NSttW1495vahOPj8+xYdGRh0j0UBQFw24ywdnM9o14H5DvNsBr6LjKZDSLyHWa4rP27dUd/YHAkOoy4XC589tlneOihh/DSSy9h586d0Ov1KC0txXe/+11cf/31KCkpGexuEhER0RDz29/+VlMvwWw249Zbb+1Vm1UtQQQ6VzoFoKgqajxBhKPJQ2NEltHgjUDtlDc3V3vwwda6hOsnFzpw2tRCiEmmeep0QK7dBGMG25p15DDrkevo2b6OyRzKgbGdoHIjt8NCVVUVSktLAQCVlZUZh4OdO3dCkiTo9XpMmDChP7tI1C382SQiIhoYe/fuxfjx4zXB8frrr8cjjzzS4zb3twbR5Eust6BCRU1rCIFIYqAEYqGx3htOCI17m/x4ZUN1wvVHjnDh5Mn5SaefGvUicu3Gbq1nFEQg326Co4/2ZTQZROQ7TMiy9s+2Han0NBukwxFHIiIiIqJhLNlo42233dbj9hq84aShEQDq28JpQ2NDktAIAKVuKyYVOLC9zhs/NqM0C/Mn5iVdH2g16pBtM3ZrPaPZKKLAYYZB1/syMAa9gHyHGdlWwyGxfjETDI5ERERERMNURUVFwtrGq666CkVFRT1qrzUQQa0nlPRcvS8Eb0hKeq49NKaqrioKAk47ohBRWcHuRj+OG+PG8WNzkoYyp0XfrT0WBQHIthqQbTNCQO9Cnk4UkO80IecQKXjTHQyORERERETDVF+ubfSFJVS1BJOea/KH0RboWWhspxMFLD6yCOUNPkwudCacj+2zaIStG0VwDDoBBS4TzPrexSJBAPIcpgP7Qx5egbEdgyMRERER0TBUUVGBv/3tb5pjPR1tDEZk7G3yJ1Q7BYCWQAQt/mjS+6JKZqGxnV4nJg2Nogjk2o0wdSMA9kUBHEEAsqwGFDj7ZorrUMbgSEREREQ0DPXVaGNEUlDR5E8a/jyhaMr1jlFFRn2bNjTKiorPdjVi5shs2EyZRRW9XkCu3QiDmFnl1PbRQWcvC+A4zHoUuswp94083DA4EhERERENM8lGG6+++upujzZKciw0SnLiUKMvJKHRG056X7KRRklR8PbmGuxq8KOiyY/zZ5bC0kUoMxlE5NgzHzU0GQQUOM3d3p6jI7NBRKHL3GeVVw8Vh/d4KhERERERJbj33nt7PdqoKCoqmgIIRxOHGv0RCXXeUNKpq5KioMEbhtyhuGpUVvD61/uxq8EPAGj0RfDqhiqEpeQVWAHAatYhrxtTTbOsepRkW3scGnWigBHZFkwocAy70AhwxJGIiIiIaFgpLy9POtpYWFiYcRuqqmJfcwDBJFtrBKMSaj2Zh8awJGPl1/tR3aotrNPki6DJF0FxliWhHadVD5c5s8qpoggUOM3dKprTkSAAOXYj8h3mw7bwTSYYHImIiIiIhpG7774bcofkZrFYuj3aWN0aTLq1RliSsT9FaJQPhMaO01qDURmvbqhCXZt2SqtRJ+LsY4oTQqMgAG6bEdYMQ6DFKKLAaYZe7NlES4dZj6IsM0z64bGOMR0GRyIiIiKiYeK///0vnn/+ec2xa6+9tlujjXVtoaRVUsOSjOrWINQkRXJkRUG9Txsa/WEJr2yoQmOn4jlmg4glx5Sg0GXWHBdFINdhhEnXdYRp35vRbTNl+Kq0jHoRRVnmXhfQOZwwOBIRERERDRO//OUvoXYYDnQ6nd0abWzyhVHflljwJiorqPEEk1ZWjYdG6eDzekNRrPiqCi0BbQC1GnU4d0YJ8uzawNedyqk6ESh0mWExdD/qCAKQ7zQhz26C0IttOg5HDI5ERERERMPAl19+iZUrV2qO3XTTTcjJycnofk8wiv2toYTjsqJgvyeIZHVskoXG1kAEK76qQlunqa52kx7nzyhBtk27dtGoF5FrN0KXwXRTi1GHQqcpo2s7c1r0KHJZYNSzfmgyDI5ERERERMPAnXfeqfk6NzcXN954Y0b3+sMSKpsDCcdlVUV1axBRKXFRo6woaOgUGpv9Ebz8VSX8YW3KdFkMOG9GCVwW7dRQi1GE22aCmMHon9vWs6mpRr2I4qzht71GdzE4EhEREREd5srKyvDBBx9ojv385z+Hw+Ho8t5QVEZFkz+h4I2iqtjfGkQkTWjsGCgbvGGs+KoKwag2NLqtRpw7Y0RCcLOZdci2GiEgfWjsadVUQQDyHLFpqeIwrpaaKQZHIiIiIqLDmKqquOOOOzTHiouL8dOf/rTLeyOSgj2N/oS1iypU1HhCSfdwlBUFjZ1CY31bCC9/VYWwpL0+z27CuTNGJFRJdVr0cFm63m7DZBBR6DTDoOve9FK7WY9iVkvtFgZHIiIiIqLD2DvvvIPPP/9cc+yXv/wlLJbE/RE7kmQFFU1+TSVUIBYaaz2hpHs4yqqKRl84YRTSYTHAYdYj3KGCaqHTjCXTR8BsOBjeBAHIthkzGj10WvWxIjZdjEh2pBMFFGeZkWXNbA9IOogrP4mIiIiIDlOKoiSsbRw7dix+/OMfd3GfioqmQNIRxfq2cMIaReBAaPSGkk5dtRh0OG9GCbKtsemoJVkWnDejJCE05ti7Do3tlU/z7eZuhcZsmwGTCh0MjT3EEUciIiIiosPUSy+9hK+//lpz7O6774bRmDo8qaqKfc2BpCOK9b4QvJ2qoQLtoTFxpLEjq1GP82aU4Is9zVgwMU8zvVQUY+sNjbr0U0cNOgGFru5NMTUZRBRnWWA3Mfr0Br97RERERESHoUgkkrC2cerUqfjBD36Q9r6qlmDScNjkC6MtkC40JtnEsROH2YDvTCnQHNPrBOQ6ut6j0WrUocBlhi7D/RXbi9/kO7gnY184ZKeq1tfX46233sJdd92F008/Hbm5uRAEAYIg4NJLL+12e++++y6WLFmCkpISmEwmlJSUYMmSJXj33XczbiMQCOCBBx7AscceC7fbDbvdjilTpuDmm2/Gvn37ut0nIiIiIqKeeuKJJ7B7927NsXvvvRe6NKN6NZ4gWgPRhOPNgTBakhxPFhr3NPohK6lHHjsy6kXkO0xdhsZsmwHFWZaMQ6PFqMP4fDsKnGaGxj5yyI44FhQUdH1RBlRVxVVXXYUnn3xSc7y6uhqvvfYaXnvtNVx55ZV44okn0v7Q7dq1C9/97nexfft2zfFt27Zh27ZteOqpp/D8889j0aJFfdJvOrRUVFRgzJgxvW5H7VwHm4iIiCgJj8eD//3f/9UcO/7443HWWWelvKfBG0ajN5JwvDUYQbMvMTQq8UI4B0PjV/taULajARPz7Vg0rSjtNhdmg4gce/o9GgUBKHCYYTdnFlsEIbY1R67dyMDYxw7ZEceOSktLccopp/To3jvvvDMeGqdPn45//vOf+PLLL/HPf/4T06dPBwA8+eST+OUvf5myDZ/Ph8WLF8dD4xVXXIEPP/wQn3/+Oe69917Y7XZ4PB6cf/752LRpU4/6SURERESUqfvvvx9NTU2aYw888EDKMNXij6DWE0o43haKJg2TiqqiwRdGpEPxnP/saULZjgYAwI56H97fWpfyQ2+rSYdcR/rQqNcBJdmWjENj+yhjHqem9otDdsTxrrvuwuzZszF79mwUFBT0aESnvLwc999/PwBg1qxZ+Pjjj+NliWfPno0zzzwT8+fPx7p16/D73/8el112GcaNG5fQzoMPPoht27YBiP0lveWWW+Ln5s6di4ULF+Kkk05CIBDADTfcgI8++qinL5sOUSNGjMDmzZtTnj/11FOxf/9+FBcX47333hvAnhEREdHhprq6Gg8//LDm2Nlnn43jjz8+6fVtoSiqW4MJx30hCQ3ecMLxzqFRVVV8tqsJX1Y0a67bUtOGifl2jM2za45nskejySCiyGWGXux6nEsQgHyHiYGxnx2ywfHXv/51r9t4+OGHIUmxBb6PPvpowl42VqsVjz76KObOnQtJkvDII4/g0Ucf1VwTjUbxxz/+EQAwZcoU3HTTTQnPM3fuXFx++eX4y1/+glWrVmH9+vWYOXNmr/tPhw6DwYBp06alPZ/JdURERERdufvuuxEMHgyCOp0Ov/vd75Je6w9L2NcUQOeBQX9EQp03lHA8WWgs29GADZWtCW2fMD43ITRm2wywmwxp++8w65HvzGx/RpNBRGm2FRZj5lVWqWcOi6mqPaGqKl5//XUAwOTJkzFnzpyk182ZMweTJk0CAKxcuTJhuL2srAytra0AgEsuuQRiik9FOhbsefXVV3vZeyIiIiKiRFu2bMHTTz+tOXb55Zdj8uTJCdeGojIqmvwJ4TAQlVDrSR4aGzuERkVV8cHWuqShceHEPBw72h3/un2Pxq5CY47dGCtok0FozLEbMT7PztA4QIZtcNyzZw+qq6sBAPPnz097bfv5qqoqVFRUaM598sknCdclM2vWLNhsNgDAp59+2pMu0zC1YMECCIKABQsWAAB27tyJa665BhMmTIDVaoUgCPGfy+XLl8erC3f+We2ooqIift3y5cvTPv8HH3yAH/7whxgzZgwsFgucTieOPvpo3HrrraipqembF0lERER94uc//zkU5eC6Q6vVirvvvjvhuoikYE+jH0qnHTRCkoSaNKExfCA0yoqKf/23Fv/d36a5TgBwypQCTB+ZffCYCOQ5jLAaU092FESgKMuMbGv6KawAYNALGJNnQ3GWJW3xHepbh+xU1d7aunVr/HGyT2A66nh+69atmrWUmbaj1+sxbtw4bNq0SXMPUXe8/vrruOiii+D3+/v9ufx+P370ox/htdde0xwPhULYtGkTNm3ahMcffxz//Oc/sXjx4n7vDxEREaX3ySef4M0339Qcu+mmm1BUVKQ5JskKKpr8kGRtOgxLMqpbQ1A7hcnOoVGSFby9uQa7GrXvR0QBOP2IIkwqdMSP6XRArt0EY5otQAw6AYUuM0z6rkcOs6wHtuVgYBxwwzY4VlZWxh+XlJSkvba0tDTpfR2/ttlsyMrK6rKdTZs2oaGhAeFwGCaTKeP+VlVVpT3PkZ/D3759+/DDH/4QVqsVv/zlL3HiiSdCp9Nh7dq1sNvtXTfQDbIs44wzzsCqVasgCAIuvPBCLFmyBGPGjEE0GsWXX36JP/zhD9i3bx/OPfdcfP7551y3S0RENIgURcHNN9+sOZaXl6cp2gjERgormvzxENguFhqDXYbGqKzgjY37sbc5oLlOJwhYfFQRxnVY06jXC8izm9IWuLEYRRQ6zdB1UQRHEGIVVrMyGJGk/jFsg6PX640/7upNd/sUUyC29UaydjJ54965ne4Ex47hdTAoipJQ0vlwlpOTk3K96mDZs2cPiouLsWbNGowcOTJ+/Ljjjuvz53rkkUewatUqGAwGvP766zj99NM15+fMmYMf/ehHOPHEE/HNN9/ghhtu0EzbJiIiooHVvqVcR3fddRccjoOjf6qqYm+TH8GINh1GZQU1nmDCtNXOoTEsyVj59f6ECqx6UcBZRxdjVM7B97pGg4hcuwm6NFVOHRY98h1dF8ExG0SUuq0wG7iWcTAN2+AYCh3cp8Zo7KIccIeA17FCVcd2umqjq3aGuqamJuTn5w92NwZMfX098vLyBrsbCe677z5NaOwP0WgUf/jDHwAA11xzTUJobJednY0HHngAixYtwqeffory8nKMHz++X/tGREREiQKBAH7+859rjk2YMAFXXnll/GtVVVHZHIQ/LGuukxQF+1uDkLSHE0JjMCrj1Q1VqGvTbs9h1Ik4+5hilGRb48csRhFuW+o9GtsL5WR1sSUHEKvCWuziWsahYNgGR7PZHH8ciSRuatpROHzwL0jnLTva2+mqja7a6UrnKbKd1dTU4Nhjj+1Wm3RoMRqNOP/88/v9eb788sv41Ofvfe97aa896aST4o/XrFnD4EhERDQI/vCHPyQsa3rwwQc1AxvVrUF4glHNNbKioLo1iGintY4qtKERAAIRKeF+s0HEkuklKHQefF9tNevgthpTjiIKIlDoNMOWplAOwKmpQ9GwDY4dh+07Tz/trGMhks5TUtvb6aqNrtrpSlfrMOnwN2HCBM0HHv1l3bp18cdz587N+L7a2tr+6A4RERGlUV1djfvuu09z7Fvf+hbOOOOM+Ne1nhBa/J1Co6pivyeIqJQkNHrDCWsgc2wmnDu9BC+vr0JEVmA16nDujBLk2Q/OqHNa9HClGUXMtAiOySBiJKemDjlDaxHXAOoYxLoqPNNxtK/zWsP2dvx+f3w/x67aycvL69b6RiIgNjV0INTX1/fovkAg0PVFRERE1KfuuOMOzf/Boiji4YcfhnBgmmiDN4wGr3Z6qaKq2N8aRDiaPDSGOoXGdgVOM86ZPgLZVgMumFkaD42CEJtSmi40mo0iSrItXYbGLKsB4/PsDI1D0LAdcZw6dWr88bZt29Je2/H8lClTEtp55ZVX4tfNmTMnaRuSJGHXrl1J2zgU5OTk9DhQHIpycnIGuwsJdGnKWPclWT64yKGsrCzj78VwWgNLREQ0FKxfvx7PPPOM5tjll1+Oo446CgDQ4o+g1hPSnFdUFTWeYMKIYlehsd2ILAsumTM6vuZQEAC3Lf0ejQ6zHvnO9EVwBAEocpmRY+fgylA1bIPjmDFjUFxcjP3792P16tVpr/34448BACNGjMDo0aM150444YT449WrV6cMjuvWrYtPVZ03b14vej44RFEcksViSKtjJVilc2m0DtLtA9kxKBqNRkybNq1vOkdERER9RlVV3HjjjZpjDocDv/nNbwAAnmA0ofqpChU1nlBCVdXOodEflmAzpY4J8dAoAnl2I0z61Ne67Qa4renDoFEfm5pqMXKUcSgbtlNVBUHAWWedBSA2UvjFF18kve6LL76IjzieddZZ8WH/dgsWLIDL5QIAPPPMM1BVNaENAFi+fHn88TnnnNPb7hMl1XHtbktLS8rrtm/fnvLc9OnT44/ff//9vukYERER9alXX301YSusO+64AwUFBfCFJVQ2B9DxbakKFbWeEIIRbfnUzqGxxhPE8jUV+LKiOe3ziyKQ7zClDI2CABS6zF2GRodZj/H5dobGQ8CwDY4AcMMNN0B/4If92muvTdgiIxgM4tprrwUA6PV63HDDDQltGI1GXHfddQCArVu34sEHH0y4Zs2aNVi2bBkAYP78+Zg9e3ZfvgyiuDFjxsQfdyxy09nzzz+f8twJJ5wAt9sNAHjiiSfQ1tbWdx0kIiKiXguFQrj11ls1x0aPHo3rr78ewYiMvU1+dB7LqG8LJ2zF0Tk0VrYEsOKrKoQlBZ+WN2LDvuQfQuv1AgqcZhhTLKPR62IVUe1pRi0FAShwmTA61wYdt9o4JByywfHTTz/F8uXL479WrFgRP1deXq4513G0r6OJEyfi5ptvBhB7kz1v3jy8+OKLWLduHV588UXMmzcv/ub7lltuwYQJE5K2c8stt2DixIkAgFtvvRVLly7FqlWr8MUXX+B3v/sdTjnlFEiSBIvFgkceeaTvvglEnUybNi0e+h577DHNFjDt/vnPf8bX5SZjNpvjfy9qa2tx4YUXpp3a6vV68dhjj/Wy50RERJSphx56CLt379Ycu//++wGdAXsa/ei8WqXOG4I3JGmOdQ6Nexr9eHVDtWZrjlU7GrCrQbtzgFEvIt9ugl5MHiNMBgEl2da0RXB0ooDRuTbkO/q/Wjz1HUFNNbdyiLv00ksTFgOnk+plKoqCK664Ak8//XTKey+//HI8+eSTmvVjnZWXl2PRokXYuXNn0vNOpxPPPfccFi9enHGfu6Oqqipe8bWysjLj7Tt27twJSZKg1+tTBmPqf6NHj8bevXsxatQoVFRUaM4tWLAAq1evxvz581FWVtZlW7fffnu8LPfxxx+PW2+9FSNHjkRtbS1efvllPPPMMzjuuOOwZs0aAMDf/vY3XHrppZo2ZFnGqaeeig8//BAAMHLkSFx11VWYO3cusrKy4PV6sX37dpSVlWHlypUwm81obGzs9fehI/5sEhERJdq3bx8mT56smSl3wgkn4N8flWFPkz9he40GXwieQPrQuLPei7c310Dp9HZ5dI4VZxxVDIMu9h7YbBCRYzdBFJKPENpNsSI4qc4DgMWow6gca7xN6h89zQbpDNviOO1EUcSyZctw7rnn4sknn8TatWvR2NiI3NxczJ49G0uXLsXpp5/eZTvjx4/Hhg0b8Kc//Qkvv/wyysvLEYlEUFpaikWLFuH666/HqFGjBuAV0XD3y1/+EmVlZfjiiy/w+eef4+yzz9acnz9/Ph577DEceeSRKdvQ6XR48803cdVVV+Hvf/879u3bh1/84hcpr2dFVSIiooFx8803a0KjIAh46OFHsLc5kBAam3zhpKGxyXcwNG6tbcO739QmTG0dn2fHoiML4yOLVpMObpsxZWXUbKuhy4qoOXYjilzmhJohdGg4ZINjuimoPbFo0SIsWrSoV23YbDbceuutCXPOiQaS1WrFRx99hIcffhgvvPACysvLYTAYMGnSJFxyySW46qqrNHuTpmKxWPDMM8/guuuuw7Jly/Dxxx+jqqoKfr8fdrsdo0ePxsyZM3H66af320g6ERERHfTRRx/h5Zdf1hxbunQpskdOTKiU2uwPoyUQ1RxrD43t126u9uCDrXUJzzO50IHTphbGq6c6zXq4rMn3aBQEIM9hgtNsSNlvQYitecxK0QYdGg7ZqaqkxamqdLjhzyYREdFB0WgUxxxzDLZs2RI/lpOTg/c//woGq0tzbUsggiZfRHOsc2j8al8LynY0JDzPkSNc+Pbk/PioYJbNAIcpeSgUxdjeixZD6rEoo17EqBwrzAZWTR1InKpKRERERDQM/elPf9KERgD4n9vvSgiNrcGuQ+N/9jThs11NCc8xozQL8yfmQRAECAKQbTPCZkweFwx6AUWu1JVVgdhWG6VuK6umHiYYHImIiIiIhrDa2lr86le/0hybdvQxWHTeDzXHPKEoGr2pQ6OqqvhsV1PSPRqPG+PG8WNz4qExx25MOZJoMYoodFmgS7NWscBpQr6TVVMPJwyORERERERD2M9//vOEfZVv+/X90HUY7fOGomho027D1Tk0lu1owIbK1oT2Txifi2NHx7bzEkUg12GESZc8JjjMscqpqYrkiCIw0m2FI82aRzo0MTgSEREREQ1Ra9asSdiC7szzvo+jZx4b/9oXklDvTQyNHbfcqPOG8XVVa0L7CyflYXppNgBApwNy7aaU00/ddgPc1tSVU80GESNz0u/hSIcubqBCRERERDQESZKEq6++WnPMZnfghtvvjn/tj0io84Y022l0Do0AUOg049SphfGvBQCnTC2Ih0aDXkCBI/maRUGI3Z8uNGZZDRiXZ2doPIxxxJGIiIiIaAj6v//7P2zcuFFz7Oobb0NufgGAWGis9XQdGttNLXJCkhV8tL0epx9RhEmFDgCA0SAi125KumZRFIHiLDPM+uSxQRCAQpcZuV3s4UiHPgZHIiIiIqIhprKyEnfddZfm2ITJU/H9y5YCAALR7oXGdkeVZGFUjg0uS2wNosUowm0zQUwSGruqnKoTBYzMscJuYqQYDvinTEREREQ0xFx33XXw+/2aY3fd9wgMBgOCUQk1ralDo6qq8X0Yk2kPjVazDm6rMWmhm64qp1qMIka6bTDqufJtuOCfNBERERHREPLGG29g5cqVmmPnXXQpjp55LMKSjP1pRhqDURkvra/CnkZt6OzMadYjx5q8OqrDokdxVurQmGU1YGyunaFxmOGfNhERERHREOH3+3HttddqjmXn5OL6236FsCSjqjUItcNM1I6h0R+W8PL6SlS3BvHGpv2obAkkfQ6X1QCX1Zj0nNtuQIHDnDRQCgJQlGVGqdsKUUw9okmHJwZHIiIiIqIh4te//jX27dunOXbLXffC7HCiOk1o9IaieGl9JRp9EQCArKhY+XU1ajzB+PWCALjtRjiT7LHYVeVUnShgdK6NRXCGMa5xJCIiIiIaAjZv3oyHHnpIc+zYeSfh22eci/2tQSidQmODN4xwVEFrIIIVX1WhLSRp7jXpdTAf2B5DEIAcuxEWQ+Lbf1EEilzmpOcArmekGAZHIiIiIqJBpigKrrxyKWRZjh8zGI249X/vR40nBLlDaFRUFY2+WGhs9kfw8leV8IdlTXsuiwHnzSiBy2KAKAK5DiNMusS3/l1VTs2yGjAiy8KpqcTgSEREREQ02B597E/44os1mmOXXnU9jDklkDpkwo6hscEbxoqvqhCMakOj22rEuTNGwGE2QKcDcu2mpMHQbBRR5DRDJyaOJAoCUOA0I8/BqakUw+BIRERERDSIdu3eg1/cfrvmWOnosTjtoqtShsYaTxCvbqhGWNLu2ZhnN+HcGSNgNeqh1wvIs5ugTxIMHWY98p3Jq6qKIjDSbYUjyVpIGr4YHImIiIiIBklUknHZT65EIKDdPuOqO+6DoD842tcxNFa2BLDy62pEZVVzT6HTjCXTR8Bs0MGoF5FrNyYdTcy2GpCTosiN2SBiZI4VJn3yqas0fDE4EhERERENAklW8MgTy/DJqn9rjp967kWYMuP4+NcdQ+OeRj/e2LQfsqINjSVZFpx9zAgY9SJMBhG5dhPETvswCgKQ6zDBlWIk0WnRozSbW21QcgyOREREREQDTFZUrNu6B/f+8uea4+68Qvzw2jviX3cMjTvrvXh7cw06ZUaMzrHijKOKYdCJsBp1cNuNCVNQBTE2ImkzJn/7n+80ocBp7psXR4clBkciIiIiogEkKyr2NPpw9+23wNPaojl35e2/hc3hBJBYCOetzTVQO4XG8Xl2LDqyEHpRhM2sS7oPo14HFLksSaefCgJQmm2Fy8r1jJQeN2Mhoj4hCAIEQcDdd9892F0hIiIasmKh0Y933nwT7731mubcvFPOxLHzTwGgDY0AkGs3YnpJlub6KYUOLD6yCHpRhNOiTxoajXoBJdnJ1ywa9ALG59sZGikjDI5EA6ysrCwesjr/slgsKC0txeLFi/HUU08hFAoNdneJiIioj7SHxvrGZtx7x82ac3ZXFi6/5X8BxEJjQ4fQCMQ+oJ0/MQ/TimOjkUeOcOG0IwohigKybQa4LMaE57MYdRiRbU1aVdVq0mF8nh1mA4vgUGYYHImGkFAohKqqKrz99tu44oorcMwxx2DHjh2D3S0iIiLqJUVRUdHkRzAi44Ff/wL1dTWa8z++6W5kuXPjoTESVRLaEAQB355SgNOPKMS3J+dDFAXk2I2wmxJHDB0WPYqzzNAJiYVusm0GjM21Qa9jFKDM8aeFaBBdffXV2Lx5c/zXf/7zH/zlL3/BlClTAADbt2/HaaedhmAwOMg9JSIiop5SFBV7mvwIhGWUffAvrHzpOc356ccvwPxF50JOExrbiYKAKUXOeGi0Jil247YZUOAwJxbIEYCiLDNKsq0QkgRKonQYHIkGUX5+PqZNmxb/deyxx+LKK6/E+vXrceyxxwIA9uzZg2XLlg1yT4mIiKgn2kcaA2EZrS3N+PVt12vOW2x2XPWL30MFYmsaIzL+s6cJnmA0ZZuiCOQ5jbAYtKFREIACpwluW+JaR1EERuVYkZti/0airjA4Eg1BFosF9957b/zrf/3rX4PYGyIiIuqJ9tDoD8sAgN/eeQuaGuo11/z4pruRU1gcW9MYkbF6RwM+29WEl9dXoi1JeNTpgDyHCSZdp9AoxkYTHUn2aDQZRIzPtyc9R5QpBkeiIWrOnDnxx3v37gUAVFRUxAvpLF++HADw6quvYtGiRSguLoZer8eCBQsS2qqqqsLtt9+OGTNmIDs7G2azGSNHjsQFF1yAVatWZdSf5557DgsWLEB2djbsdjumTZuGX/3qV2htbe3tSyUiIjrsKIqKvc2BeGh8/62VePeNVzTXzDzhZCw443vx0Fi2owFfVbYCANpCEl7qFB71egH5DjOMOm1BG70OKMmywGpInLZqN+sxLs+etKoqUXdwH0eiIUqvP/jXU5blhPOqquLiiy/GP/7xj7TtLFu2DNdee23COsnKykpUVlbipZdewuWXX44nnnhC85ztJEnChRdeiFde0f5n98033+Cbb77Bc889hw8++KA7L42IiOiw1h4afSEJANDUUI97fvE/mmvsTheuuvP3aPRH4qFxw4HQ2M4bklDbFoLTYoBRLyLXboSuU4VUk0FAkcuStHJqrsOIQqeZ6xmpTzA4Eg1RmzZtij8uLi5OOP/II49g06ZNOPHEE3H11Vdj4sSJaG1tRUVFRfyap59+Gj/5yU8AANOmTcPSpUsxffp0WK3W+NrJd955B8uWLYPL5cIf/vCHhOe58cYb46Fx0qRJuPXWW3HUUUfB4/Hg5Zdfxl//+ldccMEFffzqiYiIDk2qqmJfh9Coqip+fdv1aG1p1lz3k1vvgWTOShkaBQCnTSvExAIHTAYRuXYTxE4B0GLUodCVWDlVEIDiLAvctsQtOoh6isGRuq3JF+7xvTaTPuV+Qc3+CFRV7VG7FqMuaVUxAGgNRCArmbWbM4QWjP/2t7+NP042/XTTpk24+OKLsXz58qSfJFZWVuLaa68FAFxyySV46qmnNCOK06dPx5IlS3DHHXfgt7/9LR555BEsXboUEydO1DzHn//8ZwDAjBkzsHr1atjt9vj5k08+GccffzwuueSSXr9eIiKiQ52qqtjbFID3QGgEgDdWvICyD7S1CuZ8axEmzTsN4YiMVTsa8HWK0Dil0AmrUQe33ZhQIdVh1iPfaUo4rhMFjMqxwmbi23zqW/yJom6bec+/e3zv/551BC6eOzrpuW8/tBrN/kiP2r3+5Am48TsTk547/4k12Fnvy6idivu+26Pn7yvBYBBff/01fve73+HNN98EADidTlx11VUJ12ZlZeGxxx5LOf3kj3/8IwKBAIqLi1NOQwWAX//613jmmWdQXV2Nv//977jnnnvi55544gkoSqwk+JNPPqkJje0uvvhivPDCCyzgQ0REw1qy0Fi5by9+d9etmuuc2Tk497pfIRxVsGp7A76uatWcFwCcPq0QkwudsJl1cFsTP9R22wxJK6eaDSJG5li5npH6BYvjEA2iX//61/FiN4IgwGq14vjjj9eExldeeQV5eXkJ955xxhlwOBwp23799dfj15nN5pTX6fV6zJ07FwCwZs0azbl//zv2IcGRRx6JmTNnpmzjxz/+ccpzREREh7v26akdQ2MkGsUt1/wEAZ9Xc+33/+c3MFiz8NH2+rSh0WnWJ4RGQQDyU2y34TDrMZZFcKgfMTgSDUGlpaW49tprsXnzZnz7299Oes1RRx2V8n6Px4Py8nIAwF/+8hdNOE32a8WKFQCA2traeBuhUCjexuzZs9P2t33PSSIiouGmPTS2BQ+GRkVV8X8PPYAtG9Zqrp1z6jmYctzJ+Gh7PTZWeTTnBAFYNK0IkwudyLIZ4LIaE84XusxwJtlSI9dhxKgcK3Qii+BQ/+FUVaJBdPXVV+OnP/1p/Guz2YycnBxkZ2d3eW+6a+rr61OeSycQCMQft7a2xtec5ufnp72voKCgR89HRER0KEsVGld98jmefVxbcC63qBRnX3VnF6HRgWybEbZOdRt0YqzYTefRRBbBoYHE4Ejdtv7O5CNgmUi3UPvf/zO/V8VxUnn5qrkZF8cZaPn5+Zg2bVqP7tXpUr/mjtt33HDDDbj88sszatNoPPgfT8c/C5bxJiIi0lJVFZXNQVQ0+VHZHESp24JsqxG7a5rw+9t+CqXD/8WiqMNFtz2Iz6sCSUPjd6cVYVKhA26bMaHYn0EvoNhlgUGnnSioEwWMzLHCziI4NED4k0bd1l+VR/vr07Is6/D7FC4nJyf+OBAI9CicdhzRrKurS3ttV+eJiIgOJ+2h8aV1lXi8rByyGlv/9YM5I/HF8ntRW7VXc/0pP/wpSicfjS+/qtIc7yo0mo0iipzmJHs3ihjFIjg0wLjGkegwlJeXhxEjRgCIFbjpyUiu2WzGhAkTAABr165Ne21X54mIiA4X7aFxT5MvHhoBQAHw1+XP48PXX9RcP3rqdJzyg6th0IlYMr0Exa5YwbquQqPdpMeILEtCaLSZdBjHIjg0CBgciQ5TZ555JgBg9+7d8eI33dVemGfz5s3YsGFDyuuefvrpHrVPRER0KFFVFVUtQXiCUVQ2B+OhEQCktgY0vveY5nqT1YYf/fwB6HSxUGjUx8JjSZYlbWh0WfUodJkT9mjMthkwJtfGIjg0KBgciQ5Tt9xyC0ym2LTiq666CuvWrUt7/TvvvINNmzZpji1dujS+vvHKK6+E3+9PuO+5557DO++800e9JiIiGpraQ2NrIAoAKHVb4m+kVVlC4xsPQAlp940+/5pfIbdopOaYUS/i/JklmFToQI49MTTm2I3Isyduo1XoMqMk28q6AzRoGByJDlNjxozBE088AQBobm7GvHnz8JOf/AQrV67EV199hS+//BKvvvoqfv7zn2P8+PH47ne/i3379mnaOProo/Gzn/0MALBu3TrMmjULy5cvx/r16/HRRx/h6quvxsUXX4xZs2YN+OsjIiIaKJ1DIwBkW434wZyREAG0fvoswtVbOtwh4JiFZ2DWt89K2p4oCsixG2ExaENjntOE7CTbcIzKtSLP0T81JogyxeI4RIexSy+9FBaLBVdeeSXa2tqwbNkyLFu2LOm1oijCZrMlHH/ooYewf/9+vPrqq9i2bRsuu+wyzfkxY8bgpZdewtixY/vlNRAREQ22zqFRhYpaTwgzR7rRvO1LPP5FxyUhAorPuQ222d+CpKgw6LQjhIKApKEx32lK2KPRoBcwym1LWz2eaKBwxJHoMHfBBRegoqIC9913HxYsWID8/HwYDAZYrVaMHTsWZ5xxBh566CFUVFRg4cKFCfcbDAa88sor+Mc//oETTzwRLpcLVqsVU6ZMwS9+8QusX78eY8aMGYRXRkRE1P8qmwOa0NjkD+HtTfvxeXkj3lizGX/53//pcLWA3EXXwzDxBFR5Inhj435EZeXg2SShURCAgiSh0WIUMS7PztBIQ4ag9nTjPBpSqqqqUFpaCgCorKxESUlJRvft3LkTkiRBr9fHK2gSDQX82SQiosHWOTS+998aPFa2CwCgKjLqXrgD4cr/HjgrIOf062A/6juaNsbm2nDW0cVJp6cKApDvMMHRKTQ6LXqUZlshsggO9VBPs0E6nKpKRERERNRJspHGPx0IjQDg+fT5LkOjKABHjnClDI0FDjPsZu3b8VyHEUUuS9+/IKJeYnAkIiIiIuqgc2hUoWJTlQft0/SCezbAs+al2BeCiJzTr4X9yMTQeMZRxRifb884NBa6zCyCQ0MW1zgSERERER1Q2RzA7kYfNla1ojkQjhfCcVoMEABIbfVofPMBAOqB0Hhd90OjMzE0FmUxNNLQxhFHIiIiIiLEQuOLayvxeFk5ZDU2wnLRnJGYMdINl9mI06e4sez2G6EE2w6ExuthP/JkTRtCh9CY6zDCrE8SGk3at+DFWWbk2BkaaWhjcCQiIiKiYa99pLE9NAKAAuAfX+yDzayHAAHvPXUfIjU7Y6Fx0Q2wT/tWQjunTC1IGRoLXWbYjNq33yOyLXDbjJ2bIRpyGByJiIiIaFhrX9NY2RyMh8aOnijbDe/G99H86RtpQ+MRxU5MK3ZlHBpLsi3IZmikQwSDIxERERENWx0L4ZS6LRARG2nsKFyzE80fPJ42NALA1CJnRqFREGKhMcvK0EiHDhbHISIiIqJhR1XVhOqp2VYjLpozSnOdHPCgYeVvATmK7IU/ThkaAWB8vj2j0FiabWVopEMOgyMRERERDSuqqqKqJZiw5UatJ4QZI7Nx8ykTIQBQFRmNbz4Iua0BAODd8A4kb1PSNpdML0a+wxz/WhBilVKThUaX1dA/L4yoHzE4EhEREdGwkS40+sMyAGBElhXnzyxBy6qnEarYEL9OatmPllVPQ1W1CyGvO3k8TpyQH/+6PTRaO23DUepmaKRDF9c4EhEREdGwkElobD+29v1X4F33uuZ+nd0N97d+AkEQ4seWTC/GmBx7/OtUoXFkjhVOM0MjHboYHImIiIjosBdb0xiEJ9h1aPxo9ccoe/p32gZ0euSedTt09uz4oetOHp8QGouzzLAwNNJhaNhPVRUEIaNfCxYs6LKtd999F0uWLEFJSQlMJhNKSkqwZMkSvPvuu/3/QoiIiIgoqe6Exu07d+Ovv7oW1knHa9rIOfUamEumaI5FO+zdkSo0jmJopMMERxz7gKqquOqqq/Dkk09qjldXV+O1117Da6+9hiuvvBJPPPGEZmoDEREREfWvTEKjJxTB7gYfmlo8eO6XV8C58CewTTkJnrwxaP34GThmnw37kd/WtCsAKHCaYo9FoNiVGBpH59pgN/HtNh0e+JN8wNVXX42f/vSnKc/bbLaU5+688854aJw+fTpuvfVWjBs3Drt27cL999+PDRs24Mknn0ReXh7uueeePu87ERERESVSVRX7mgNoC0oHj3UKjR9tr8ObG2ugqgoaX/89rDPOhm3KSQAA19zzoc8qgHXSvIS2z59VApfZCEEERmSZE7bhYGikww1/mg/Iz8/HtGnTun1feXk57r//fgDArFmz8PHHH8NisQAAZs+ejTPPPBPz58/HunXr8Pvf/x6XXXYZxo0b16d9JyIiIiKtTELjqgOhEQBaP30O1sknwDb5RE071sknJswYu/mUiRiRZU0ZGsfk2mBjaKTDzLBf49hbDz/8MCQp9g/So48+Gg+N7axWKx599FEAgCRJeOSRRwa6i0RERETDiqqq2NuUGBprWg+GxurWAN44EBq9G9+HMW9MQmhUVSXpMiN/RE4aGkWRoZEOXwyOvaCqKl5/PVamefLkyZgzZ07S6+bMmYNJkyYBAFauXJmw9w8RERER9Y320OgNSWgOhLGxqhVN/hBqWkMIRGKhcc3uRjz4/g4AQHD3eohmG2yTT9C2oygQhMS3yiJi6xkZGmm44U92L+zZswfV1dUAgPnz56e9dv78+di+fTuqqqpQUVGBMWPGDEQXiYiIiIYNRVGxtzkAX0jCe9/U4vGycshqLOydN6sEc8fmwhOK4OV1VQCASP1uqHIUtk5rGFVFhiDqkj7HGccUYUqREyb9wfPtodFq5FtrOnxxxPGAl19+GZMmTYLFYoHD4cCECRNwySWXYNWqVSnv2bp1a/zx5MmT07bf8XzH+4iIiIio9zqGxuZAOB4aAUABsGJdVbx6qgpAamuE5G2CdYJ2xpgqSylDIwAcU5rF0EjDEn/CD9iyZYvm6/LycpSXl+Pvf/87zj77bCxfvhwul0tzTWVlZfxxSUlJ2vZLS0uT3pepqqqqtOdramq63SYNjksvvRTPPPMMgNio9ejRo7u8Z/To0di7dy9GjRqFioqK/u0gERHRIUZRVFQ0+ePrFyubg5A7rQxSAJRtq0fZjkYo4QCizVWwjputuUaVIoAu9dtjEcDYPPvBr0VgbK4dFmPqoEl0uBj2wdFqteLMM8/EySefjMmTJ8Nut6OhoQGrV6/GE088gaamJqxcuRJnnXUWPvjgAxgMBzdw9Xq98cd2uz1Z83Edt/Pw+Xzd7mfH4DnYNld5BrsL/ebIElfXFxEREdGQ0Tk0AkBJtjnptWU7GqHKUUQaKmAZfYy2HSkCAULSdY1AbN/Gny4cD7c1tnejThQwJtfG0EjDxrAPjtXV1cjKyko4/p3vfAfXXnstTj/9dGzYsAGrV6/G448/juuuuy5+TSgUij82Go1pn8dkMsUfB4PB3neciIiIaJiTD4TGQIfQqKgq6rzhpNerqoJIfQXMJVM1x5VoGKoiQTQl37f7jKMKcd6sUoZGGtaGfXBMFhrbFRQUYMWKFZgyZQoikQgeffRRTXA0mw9+mhWJRNI+Tzh88B+wzlt2ZKKr6a01NTU49thju90uERER0aFIVlTsafQjeKBSanMgjH3NARj1IioaAwnXq6qKtrWvwzlzsea4Eg1BCfmhd+Qk3HPhrBKcPLUAhc6D7910ooCxeTaYDQyNNLwM++DYlbFjx+I73/kO3n77bZSXl2P//v0oLi4GADgcjvh1XU0/9fv98cddTWtNpqs1lERERETDRefQ+N43tfjzqnIoiK1DXHx0EUTE1jW286x5EZ5PnkV432bknXM7BJ0BSiQIqa0BxtyRSZ8ny2ZkaCQ6gFVVMzB16sHpDO3bbwDaMNdV8ZqOI4ZDab0iHdrKysogCAIEQUBZWRkA4KWXXsLJJ5+MvLw8WCwWTJo0Cbfeeiuam5szavOdd97BD3/4Q4wdOxY2mw0ulwtHHHEELrzwQrzyyiucak1ERINKkhXsafRpRhrbQyMQC4tvbazByVPy4/d4N7wDzyfPAgCCu75Ew8rfQQ56EW3clzI0AsBfP96N5kBs1hhDIw13HHHMgKqqSY93DJTbtm1L20bH81OmTOmbjhF1IMsyLrroIjz//POa4zt27MADDzyA1157DZ988gkKCwuT3t/U1IQLLrgAH374YcK5LVu2YMuWLXjxxRfxt7/9DZdeeml/vAQiIqK0YqHRj1D04FjivuaAZmQRiIXHhgPrHP3bPkXz+49rzgfL1yK0dyNsk09I+3yyGqvQmmc3MzTSsMfgmIGOW3W0T1MFgDFjxqC4uBj79+/H6tWr07bx8ccfAwBGjBiR0fYLRN1111134fPPP8fZZ5+Niy++GKNGjUJdXR3+9Kc/xada33jjjfjnP/+ZcG8gEMDChQuxefNmAMDMmTNx5ZVXYtq0aTCZTKisrMTHH3+MF198caBfFhEREeq9IWyrbYNRJ8JmPFjhXlZVGHQCBACdP+b/usqD4O71aHzrwYSz7u8s7TI0AoBOAMbkWhkaicDg2KXdu3fjgw8+ABBb7zhixIj4OUEQcNZZZ+Hxxx/Htm3b8MUXX2DOnDkJbXzxxRfxEcezzjoLgiAMTOdpWPn8889xzz334I477tAcP+2003Daaafh/fffx4oVK/B///d/yMvL01xzxx13xEPjz372Mzz66KOan9OZM2fi7LPPxn333YeWlpb+fzFEREQHvPDlPtzx2mbIaizIXb1gPE49ohCyqmJ/axBf7W1NCI0AIAc88G3+NyBLmuOued+HY8biJHfEiAKgHHiua741HjNHuRkaiTDM1zi++eabkCQp5fm6ujqcd955iEajAGJvqDu74YYboNfH8ve1116bsP4rGAzi2muvBQDo9XrccMMNfdR7Iq2ZM2fiF7/4RcJxQRDwP//zPwAASZKwZs0azfmWlhY8+eSTAIAZM2bgj3/8Y8oPN4xGIwoKCvq450RERMnVe0Px0AjEpo7+aVU5yhu82N8aRL03hBXrEutMyEEvdFYXchffBMuEufHj9unfhWveD+Jfz5+YG38zLAK4ZuF4/O2y2bjn7Gn4++XH4uoF4xkaiQ4Y1iOO1157LaLRKM4991zMnTsXo0ePhsViQWNjI8rKyvDEE0+gqakJAHDCCSckDY4TJ07EzTffjPvuuw/r1q3DvHnzcNttt2HcuHHYtWsXfv/732PDhg0AgFtuuQUTJkwY0NdIw8cPfvCDlIFv5syZ8ce7d+/WnFu1ahUCgVjZ8uuuuw46Hf+DJCKioWFrTVs8NLZTAfzPixtx/qwS5NhNCesb5ZAPOkus8r2g0yPvrNvQ8Pp9EA1muL99peb/yqnFLnx7agFkWcWYPFt8n8Z8hxljcjk9laijYR0cAWD//v149NFH8eijj6a85txzz8VTTz0Fk8mU9Py9996L+vp6PP3009iwYQMuvPDChGsuv/xy3HPPPX3Wb6LOJk+enPKc2+2OP/Z6vZpz7R9sAMBJJ53U9x0jIiLqgVBUhkEnQicgaXhcsa4KVy4YqzmuhP3QmbXbnqlSBIbckcg64SII4sEgKAIozjJjSqETJv3B43qdwNBIlMSwDo7PPPMMVq9ejTVr1mD37t1obGxEW1sb7HY7SktLcfzxx+OSSy7B3Llz07YjiiKWLVuGc889F08++STWrl2LxsZG5ObmYvbs2Vi6dClOP/30AXpVNNT1ZI1re2XfdPdardaU50Tx4Kx0WZY15xobG+OPi4qKut03IiKivhaKytjT6IfDZMDVC8bjT6vKE9YxKgB21R3cR1sJByCabNprwgG0fv4Csk+6WBMaAeB7s0oYGom6YVgHx/nz52P+/Pl91t6iRYuwaNGiPmuvp6Jy50kbNJRYLAc3Em6fItoVv98PALDZbF1cSUREdGjzhyVUNPmhHHg7c+oRhRiXb8ONL25MuLbBGwLQHhq1H6Aq4QBaP30O2fMvhaDTvuUVAJw8tYChkagbhnVxnMOVLxQd7C5QGh2njdbW1nZ5fTgcRmtra8K9fSU3Nzf+uKamps/bJyIiypQnGMWexoOhEQAkRcHaPc1Jr/+6qg1K2J8kNPrRsvoZZJ10MQS9IeE+FUBdWzj+NUMjUdcYHA9DnlDqSrE0+I466qj446+++qrL6zdu3BifXtrx3r4yY8aM+OP2/UaJiIgGWrM/gsrmANQOc1IlRcGWmja88GVl0nuUkD/J9FQ/WlY9jewFl0E0JK9PoROAUndsBhBDI1FmGBwPQ8GIzOmqQ9hJJ50U38Lln//8Z3z9YirPPvts/PHJJ5/c5/1ZuHBhfArso48+mrAGkoiIqD+pqooaTxDVLUFNaIzKCqpbgthZ602onArEqqeK5iShsWw5sr/1E4hGM4DYtNSO2veCdFtNDI1E3cDgeJjyBDlddagqLCzEeeedByA24njfffelvPajjz7CE088AQAYNWoUzjjjjD7vT1ZWFpYuXQoAWL9+PW644YaUYTYajaK+vj7h+OjRoyEIQo8K/xAR0fAlyQoqmgJo9EY0xyOyjP2tQXy8swHPrNmbcJ8c9CZUT1VCPrSs/juyF14O0XiwnsAlc0fhmoXj8OD5R+Kes6fh6ctm49QjChkaibppWBfHOZy1BqLItSefnkGD76GHHsJHH32E+vp6/OIXv0BZWRl++MMfYuLEidDr9aiqqsKbb76JZ555BpIkQRRF/O1vf4uPVPa13/zmN/jggw+wefNmPPbYY1izZg2WLl2KI488EkajEVVVVfj000/x/PPP45577sGll17aL/0gIqLhIxSVsbcpgIikHU8MS7HQ2ByI4OV1VQnVVOWABzqrS3NMCfnQ8smzyF7444TpqeMK7KyeStQHGBwPU8GIjIikwKjnoPJQVFRUhI8//hjnnHMOtm7divfffx/vv/9+0muzsrLw7LPPYuHChf3WH6vVio8++gjnnnsuPv74Y6xfvx5XXnllvz0fERENb+X1Xny5pxkjsi1wW2NBrzkQRnm9D0adCLvJgLJt9Qmh0ffNKkieOmQdf3DPbCXkQ+tn/4R74Y8h6I0Jz7Wj1otjSrLjXzM0EvUMg+NhrDUYQb7D3OftHlni6voi6tKkSZOwadMmvPjii1i5ciXWrl2LhoYGSJIEt9uNI444AqeddhquuOIKuFz9/z3Pzc3F6tWr8dprr+H555/HF198gYaGBlitVowYMQJHH300vve97+G0007r974QEdHhSVVVPPnxbtz/7jbI6sH1hgDw51XlUBBbkzhnnBtrdmkrqbatewMtHz7Z3hCy5n0fcsgHz5qXkL3gUgi6xOqpAPDcF/vw7akFXNNI1EuC2lVlDjokVFVVobS0FADw/pffoLBoBMwGERMKHGnv27lzJyRJgl6vx4QJEwaiq0QZ4c8mEdHhRZIVfF3VigueWAO5w7tPEbHtMVK9IVVVFZ5Pn4Pn8xc0x51zzocg6uA6/sKEfRo7u+fsaZg5KpuhkYaNjtmgsrISJSUlvW6T8xgPY6GoglCUFTKJiIhocIWiMnY1+LGtxqsJjQCgIE1oVGQ0f/B4QmgEAFWKwHXCDzShcWqhPeE6AMi2GhgaiXqJU1UPc55glP9IEhER0YCq94ZQXu/D+Hw7jDoRVQe22ih1W6AToAmP7fW4O4dHJRpG66qn4dvwTkL7WSddHBtx7FTNe0utL2l/bCY93w8R9RKD42GuNRBFgbPv1zkSERERJfPCl/tw58r/QlLU+BrGU48oBAC4rSZcvWB8fD2jCOC8WbEpdB0rqMr+VkjeBrjmXYjQvk2INlUeOCPAfepP4Tjm9JTPLwKafR91AjC12NnHr5Jo+GFwPMxFJAWBiASrkX/URERE1L/qvaF4aARiI4uPl5Vj9pjsePXUWaOzcdeZU1HXFkaB0wSXOVYJdVyeHSs3VGHTlu1QQj6YiicBAAou/C1qX7gdUkstcs+4GbbJJ6R8fhHAJfNG4++fV8SL79x7zpH9UiyQaLhhmhgGWgJRBkciIiLqd+X1vnhobCerQGVzEC6LEbWeEIIROR4W69rC8IUkNPoi2N3gw4b1ayEazPHQCAA6ezbyzrkDE+1RjDryWKze0Zjy+S+ZNxpLppfg5Cn5kGQVU4udDI1EfYRpYhjwBKIodpkT1gEQERER9aUcmzFhDaNOAAqcJlQ1B9DoD6OuLYyqlgDe3lijmVIa2PkFDDmlMLhHaNqUQz4Igg6zj5+LCQV2fLKjUXMfEFsnedm80Thnegl0ooBZo9xc00jUxxgchwFZUeENS3Cak+9vRERERNQbiqKiujWIiKTi6gXj8XhZeXyq6GXzRsMflvH5rkasWFeVEPpURUbb+jdhm3wC9I5czTk52AaogMFdDIMOMOpFnDerJN6OAOCsY4pxzowRcFtN0IkCxuaxeipRf2BwHCY8gSiDIxEREfW5sCSjsjmAYCQWCU89ohCzx2SjsjkASVHjI4zJQqMS9sP71TtwzFwM0WjRnJP9LRAMVojG2NrI57+sihfTufusIyDJCsbl2+NrJ0UR3HKDqB8xOA4TnmAUxYoKncjpqkRERNQ32kJRVDYHoHRKhE6zAdtqvHj+P/sSwmK7aHM1QlVb4Jp7fsI5ydsEnS0LgqgNgQqAFeuqcPKUfBS7rPHjogiMzbXDYmRoJOov4mB3gAaGqgJtwehgd4OIiIgOE3VtIextTAyNwaiEb/Z70obG4N6NkP2tcBz1nYRzsq8FekdOQmhspwBo8EbiX7ePNDI0EvUvBsdhpCUQ6foiIiIiojQkWcGeRj/q28JoDoSxsaoVzYEwAKA1GMH+1hCqW0NJQ6OqKvBt/jcM7hEwlx6R2LavGTp7dtrn1wlAqTs2rbU9NLJ6PFH/49+yYcQflhGRFBj1Bz8v0Ol0kCQJkiRBlmXodPy0jgafoiiQZRkA+DNJRDSEtIWiqG4JQpJVvPdNbbwIjgjg3JkjkOswo8BpQnVLIOFeOehFy4dPIee0ayDotXUXVDkKJRyE3u7usg8nTcyD22qCIACjcxgaiQYK/6YNM62BCPKdB/czslqtCIcPfErY2oqcnJzB6hpRnM/ng6rGarlbLJYuriYiov4WlRXUtIbgObDspTkQjodGIDZ99OX11QBilU7VTveHa3agYeV9kNvqYXAXw3X8BfFzcqANgsEEndWZ8LwLJuZi9Y5GTXsf72jAj08Ygxkjs2Ez8a0s0UDhVNVhpiWgXeeYlZUVf1xfX4/6+nqEQqH4m3aigaQoCtra2lBbWxs/5nA4BrFHRETDm6qqaPSFsaPOGw+NAFDZHNTs1ai5p9P93q/eQu2zt0JuqwcAtH76HIJ7vgIASJ46iBYHRIMpaVtlnUIjENsjMiIpDI1EA4x/44aZiKTAH5bi/9iazWa4XC54PB4AQFNTE5qamiAIAqcI0oCTZVnzoYXFYoHNZhvEHhERDV/eUBQ1nhDC0cTViiXZZohAyuI3ACCHfGh+9zEEtn+qPaEqaP73k8g/724Ysgu73S+dABxZ4ur2fUTUOwyOw1BLIKL5lK6oqAhGoxENDQ3xY6qqQpKkwegeEYBYaBw5ciQEgVvIEBENpFBURq0nBG8o+fsAWVEQiio4b1ZJ0r0ZASBU+V94Pn8JoYqvEs6ZSo5A7pm3Qu/IfHlMe0jVCcC95xyJfIe5q1uIqI8xOA5DnmAUxS4V4oE9HQVBQG5uLpxOJ3w+H/x+PyKRCJTO9bWJ+plOp4PFYoHD4YDNZmNoJCIaQLKioq4thGZ/BKlWrPjCEhq8IcgKMHdsLoJRGW9trIlPJ1VlCZ7/rIAxfxzylvwCtf+4BdGGPfH7ncedh6yTfpRyq41kdALw8IXHwGrU4aiSLIZGokHC4DgMKUqsKlqW1ag5bjQa4Xa74XZ3XdGMiIiIDg+qqqLJH0F9WxiykjwxRmUFjb4w/GE5fqy6NaAJjdGWGrR++hyyTrgIhuwiAEDeOb9A7TM3AIKInEU3wDrhuIz61F5gRycAP10wHvMn5sNlMXR1GxH1IwbHYarZH0kIjkRERDR8RGUFzf4ImnyRlIFRhYqWQAQt/ihagxHUtYVhM+qwrqIZZTsaY9eoCnxfv4twbTlyTrsGouHgiKAhuwh55/0Kelc+9I7clH0RAFwwuwQl2RYcWZIFIFaAZ6TbgqNLshkaiYYABsdhyh+WEZZkmPQsgENERDScBCISmnwReILRpFNSmwNhVDYHkWM3QJaBqKxize5GvLyuKqHCqeSpR9N7j8Ey7ljknn5dQltKNATTiMkQhNSF/EUAPzhuJM6bWQKdePA6t9WEUrcFLitDI9FQwOA4jLUGoihwMjgSEREd7hRFhScYRZM/jGBEQXMgjK372wAAU4qdcFtj22G8900t/ryqHApige68WSWYWuzES+uqNO2pqgrfxnfR9uVK5Hz3BphHTEl8zkgIojH5esQFE3Mxa7Qb/oiMSQV2jM2zQ4B2XfuIbAtnRxENIQyOw1hLIIICJxeYExERHa4iUmw6arP/4HTU976pxZ9WlcdHDwUAP1s4HjNGZWmOKwBWrKvCebNKNG1Knjo0vfsYVDmKwh/cB509O+F5VVlKGRovnTsKR5dmQxSBAqcZNqP27aggACUMjURDDoPjMBaVVLSFonCaOQWEiIjocOINRdHsj6AtqN1SozkQxp87hEMgVoTmT6vKMXecO2EqqgJge21sZFKVJbStex2ez56H/ejTkL3wxwnVUVVFgSCKEHTJ32KKAEbn2WAyiCh0mmHQaaewiiIwKscGu4lvUYmGGv6tHOZa/BEGRyIiosNAVFbQ4o+gORBBVEpe7KayOZh030UVwOe7mpPes7GqDeHqbWh67zFInjrknH49bFNOSmxDjYXGVATEpr6OdFuRazclTE3V6wSMybXBbOAyGqKhiMFxmPOGJERlJeETPyIiIhr6VFVFW1BCSyACX1hKuf9iu1K3Jb7VRSaUkA8tH/8dvg3/giFvFIou+SMM7uKk16YrgAMAN586EdNLs+FI8oG1xShipNsGo57vR4iGKgbHYU5VY2sduZkuERHRoSMUlePbZKTaSqMjWVGwtzmAnfU+LD66CG9urNGcP7rEhY1VnvjXqiLDt/lDtH78dyiB1gMHVeizCrrdVxHAhceW4rgxOUmrubvtRhS7zBAEIfFmIhoyGBwJLf4ogyMREdEQJysqWgMRtAQiCEaSTThNFIxKaAtK+GBLLV5eVxWvlnrm0UVwHyg+k+sw4aH3d8TvCVV9g5Z/P4lI3S5NW9HGvfBueAfOmWdk9NxXLRgLAQLG5dkwId8BsVMwFEWgJMvK7TaIDhEMjoSIpMAbiiadOkJERESDR1VVeMMSWv1RtIWS77vYmayq8IWi8ASjiEgqPKFIPDQCsYI3b26swSVzR2F0ng21nhAUAFJbA1rKliOwdXXSdgWDCVBjfUo3OigAOH9WCSYXOJFrN8JlSayOajaIGJlj5X7SRIcQBkcCEBt1ZHAkIiIaGtqnorYGopDkzFYkBqISvEFJs9axujWAz3Y1JhTEUQEsX7MXAoA5I8xo+fjv8K59HaLVieyTr0TLqqcB5WBFVsv44+D+9lLoXfld9uOmUyZiVI4VhS4zzPrEt5rZNgOKXRaIIqemEh1KGBwJANAWirJIDhER0SCKygpaA1G0BiIIRTObihqVY7OGYsXutAFz+ed7NOsWO1OlCNq+ehsvrXkJqhyFc855cB67BKLBBNnbiLYvX4UhZySyT74CljHTu+xL+0jj+HwHCp0m6DpVWOX+jESHNgZHAnCgSI4/gnwn1zoSERENlKisoC0Ym1bqD8sZ3dPkD2FnnQ9OqwFGMflUz3/9d3/K0KgqMvzflKH102che5tgm3Yysk76EfR2d/wa17zvQ+8eAfu0k1PuydjZTadMxLQRLuTYTAnnTAYRI91WbrVBdAhjcKS45kAEeQ4Tq5oRERH1o2BEhjcUW7OYaZGb5kAYu+p92FDZgrc21kJFrMjNebNKMHdsbvw6TyiCigY/3t9Sn9CGqsgIbPsEns9fhBIJwn7UKbAf9R3onXkJ14pGCxxHn5pR3wQAF8wqwazRbtiMiW8ts6wGjMji1FSiQx2DI8VFJRVtIQkuC9c6EhER9RVFiRW4aZ9SmumaRSA2IvnWpv1Y/llFwjpFBcCKdVWYWuwEAJRtq8fqHY0JezSqigz/ljJ4vlgBQ1YRshZcBsvYmRBSjFZ2x9xxbpx5dDEmFTgTlrsIAlDkMiPHnjgCSUSHHgZH0mj2RxgciYiIekGSFQSjMgIRGf6whEBEjheraQ6EUdkcRKnbArc1eaBSVBW+sARvSEJtWzBpaIxfC+CltfuwpcaXcE6VJfi/WQX/ltUwjz4aBRfcA70jp8ev64yji/D2xpp4X848ughnHjMC+Q4TBGhHEw16AaPcNliMnJpKdLhgcCQNX0hCKCpzDQIREVEGwpKMUFRBKCojGJERkmREpeQjiu99U4vHy8ohq4BOAK5eMB6zx2THg6RJr4MvJGG/J4jd9QeDYFeTWTuHRiUcgG/je2hb/wbc37kaBRfe09uXCQCYOSobM0dlo64tjCKXGWPz7HAlqcjuMOtR6rZCx6mpRIcVBkdK0OyPoDjLMtjdICIiGjIikoKQJCMcVTRhMZN9FYHYSGN7aAQAWQUeW1UOYRXi6xUXH12EtmAUZTsae9RHqa0e3nVvwrvxPaiRQOyYJ3GtY7uu9mPsrK4tjIn5DuTYjChyWZLuwVjgNLHQHtFhisGRErQEIihwmvlJIRENW/XeEMrrfRifb0e+g2+Ch4uorCAiHfglKwhHFUTkWEjMNCCmUtkcRLKlje2HFABvbKzpUdvhul1o+88rCGz7FFC145P+zR/AOXOx9jkPBMbuhEYRsVDosOiRazdB1+lenSig1G3hntBEhzEGR0qgKLHwmMvF7EQ0DL3w5T7cufK/kBQVelHAPWdPw4XHjhzsblEfUBQVETkWCiOSogmKYan34TCZ5kAYFY1+6EURIrqedpopJRJCuHYnRKMFxtxRCFV8nRAaASBStwvRlv3QO/IAnb7bgbHd948rxbRiV9JRRotRh5FuK4x67gVNdDhjcKSkmv0MjkQ0/NR7Q/HQCACSouLOlf/Ft6bk98vI41Ad2Ryq/UpHVVVEZRWSoiAqxQJitMOviKRCVvohGXbSHAhjT6MfeQ4T1u5uxt/X7IWC2IjdjFFZ+GpvKxTEtrDoSW+irTVQ/K0w5I2GZeSR8eO2qQvgXf9GhysFWCfOhWP2OTBkF2fc/rhcG3Y3+jV9EwCcckRh0tDothtR7DJzKy+iYYDBkZIKRxW0haJwcsoJEQ0x/Rlqyut98dDYTlJUlNf7kj6XrMSCiqwcCC2yAklRoaqxrQgEABAAAQLa31eraizkvPZVNe5/b1u8UMptp03GuTNLYNCLMOpEGHTioCwZGEojrrKixn+1f5+lA19HZQVSe1CU1S63uMikmmmm2tsqyTbDbjIgLMXWPX60tR7P/2df0mCoAPhqbyv+55SJ8EdkFDhN2LK/DSvWVXU5CqmEA4g2V0Nny4YhqwjIKkq4xn7kt+Fd/wYEgwn2I78Dx6yzYMhOvK4ruxr9CcdUxKbadvy+CQIwIsuCbJux289BRIcmBkdKqckXYXAkoiGlv0PN+Hw79KKgCY86ASh0mtHkC8dGsSQVEVlGWFKg9HDeYXMgHA+NQKxQyu/f3YZJRY6EN+dGfSxE6kUBRn3sd71OhEEnQC/Gfu+r0Z7+HnHVjArKB8Nf9EDglhUFshILjIqq9snU0eZAGK99VY03Nu6H0qGa6alHFHarneiBKa7vf1Mb3x5DBHDerBLMHZsLTygSD41A8tFEBYA/ImNivgOeUATZNgOuWjAOH22rw7ZabWVUVZYQadwLQRBhyBsFU9GEtP3TufKRffKVsB2xEDqLo1uvrSs6ASh1HyyaZ9SLGJVjZQV2omGGwZFS4tYcRDSU9CbUKIqKqKIcGO0DVKhQVECWY8fbR7IkWcF1J4/HH/+9U7Nlgj8swx+W++y1JCuUIquJozqqGpsBEo6mT6iiiHi41Isi9DoBOjH2SxRiO+yJB4ZA20dCRUE48PjgaOjWmrakI64767xpv8ftI61R+eDoa3xU8MD3tf1YOs2BMLbubwMATCl29npk8L1vavHnVeWaET1ZBf60qhwTCuwYm2vXXC+rB/oqqYgocnzKa0RWoCqAJxTR7KmoAHh5XRWsB/6fzORzBJNewL/+ux/vb0msdqqqCqINe6FEgjDmjYKpYFzatlRFRqRuFwABxoKxcM46M4MeAN+bVdLlSKcoQBO03VYTBAFw24wsoEc0TDE4UlqNvjBKsq2D3Q0iooymkUYkBcGIDH9EQlg6uL6tOyODCycV4OjSrD6b1phMqdsCnQBNeOw8qtMdigKEFQVhAEDvAm6yfkmKis1VHgBA58HNvhwVXPn1/vgxAcBl80ZjbJ4dDrMe3pDUrT+P9u0vkv3RqwBueOFrXHr8aMwbnxtfD5nu58QTiuCrfS0J7akAlq/Zi0xj1CP/Ltfer8gIV29FYPtn0Fmz4Dr+gi7biLbsR7SlFsbckTAVTczwmWNEAFOLnTivi/B466mTYDcb4t9zm0mH4iwLP0wmGsYYHCmt1kAUhU4Feh0rpRHR4Eo1jTTLasDeJj8CEbnLdW6ZcltNfbYOLlnYcVtNuHrB+ITN4PsjpHZHJv3KJCh2Zz1hslFBIBbInv6sQnOsq2mmKtT4usfttd6k2190bP+ZzyswKtcKl1m7Ts8TiqCuLYwCpwkusxFrdjd2OULXnZ88VYogVPlfBMv/g8D2zyH7WwAA+pySlMFRCfsR3r8dgk4P04ip3Sp40659aq3LbMTx43Jx7Bg3qlsC+OOH5VA6fVjQPuJr0AsoclrgsnLpCtFwx+BIaakq0OSP7etIRDSYnGYDbj99Mn77zlZNqJFloC0oDXh/OocjWY2t0VNUFf/eUoe/frwnvg5u6YKxOGXqwbCjqsCCSXk4utSFyuYASt3WIVPJ+tQjCjF7THbK4Nf+ulONAr73TW1C8EwV9NKNCiYjq8CfV5VjarEDDrMhNkVWBiRVgSTH1keqaiz47W8Jdtmegtim9h2DY8eQKAA4qsSJjVVtGfYwNamtEeHqrVAVGW1rX0O0blfiNU1VCO/fBlPxZACxqauR2nIoIT+MheNgGTOjR899c4eCPAVOMxxmA+wmPXSCgEkFTkRlJPyZ5dhMyHeYkGs3QeS0VCICgyNloMkXQR7/4yCiAaYoKrxhCd5QFN6QBElWceyYHDx92ex+nUbaWfu6N6nDOsiPttbj2S8ObrNw/qwSzBmbCwCobg3gyY/3aDZ2/0vZbhQ4zZqA0nFUyxeS4QsFIAiATgT0OhF6QYReB+hEEQZRhE6H+PpFIeOJkT2TasS1Yyhs1zEctgfBjkV/Hi8rx+wx2XBbTVDUA1VS1Vjg29bFqGAyCoDN1W2YmJ+8AEwmo4Pt2je1b/+zsBl1eHldVfzPTgV6HBqVSBDhqi2INldD0BlgKp0K25QTAQCypxatSYIjAPg2/Rs6ew6izfuhd+UnnYp6dBdhtr2iq4DYz+aoHCscZgMcZj18YQkVjX7N35/OHxaMzbWj0GWGgbONiKgDBkfqkqyoaAlEkDNEPg0nosNXWJLhDUnwhiT4w1LSaZE9mUaq4uC2Du1iISYWDBUl9ktSVciyGj/WvrVGO08ogi372/DSuqqD7QB4cV0VSt1W7GsOaM51vKZ9ZMsTiqBsWz1W72iMv7mfPzEXCybnAweua58i2Vl7wCnOMkMUBDR4QyjJskKnE1DrCaHUbUGOzQy9Llb0RicmRszmQASVLQGUZluRbTVqXp/aYcJl5+99SyCSdEpp+yjgpEI7qlqSF/1ZW9GC8Xn2hO9lTUuw2/sZdg57Bc7Yz0J78Ms0NALA4qOLULatHmU7GrvRg+SUaBjhmu2QmvdDVSTo7TkwFU+GZezMhGutk05A6yfPao4Z8kbDOmkerJPmQe/Mg96Zl/K5RubYsKmqLeX37ZK5o2Az6TEmz4ZStxVWQ+ztXrrRYLfVhBFZFhRnWWA18u0hESXivwx9bN++ffi///s/vP3229i3bx9MJhPGjx+P733ve/jpT38Kq/XQLDTT6GNwJKK+p6oq/BE5PqrYVfXQdJoDYexp8CPPaYLFoDuw4XtspLCn22Z0tGZ3Y9JQ2O7B93ekPNcedtbsbtSMaAGx0FS2ozEhvJx5dBEWTirQPH+XlTBxcHuIVK+hvY2uru1sR7035XMrAD7YWoeJ+Q6I0FYXFQGY9SK213kzWjM4a1QWphW70BSI4O2NNQltnTerBOv3tuCtjTXdCpzJvLGxpsf3St4mRJuroIR8EEQddI5cmEdMgTDyqC7vNeSUwJA7CoLRDOv442CdeDwMOSXx81MK7djaaXuOdnPHufF2mtcuApg1xo2SbCt0HSoZpRsNLnJZkGc3cU9GIkqLwbEPvf3227jooovg8XjixwKBANauXYu1a9fiqaeewjvvvIOxY8cOYi97JiIp8ASiXBxPRL0WlZUDo4pR+MJSj0NdVFZQ2xbEnkY/dtX7sXJDdTwQLT66CCOyrQkjd52LniST7BpPKJI2NHbluHFu+EISVnQKjem8cSAcfGtSATyhSGYbxQNYsa4KU4udCa+7osGvCa0KEH9Nna/vfB8A5DpMCaFQ09+vayCiBjNGZeGrva3xP4sZo7Lw0Ps7NH82b3UKhB19tbcVxVkWlGRb8T8H1ubZjLr4Gr2v9rbgzV4Evp5Qwn5E6vcgUrsL4f3bEK7eBtuUk5C98LJutyUHvZADrSj4we+gszgTzi84MPr86c5G/HurdssOAcCkfAfW7GpO2rYI4KcLx2OU25ZwLtUWMLKiYmJB3+77SESHJwbHPrJx40Z873vfQyAQgN1ux+23346FCxciGAzihRdewF//+lds374d3/3ud7F27VrY7fauGx1iGnwhBkci6rb2UUXfgbAYynBUsWPxGZfZgGBUQTgqIyQpCEsyPitPPmql4OBIUsdRta5G2zpPIQWAU6bmY1y+A19XtvTqe7BmV3PKN/vpvLWxBjNHZaOuLZzx9MvOBV+6GqlsD4/f6/T9SDbCOqtDKEz13Ov3tuKmDoGvPTS2n+9qlK/zNR1HXj2hSK9GCbuiqiqUoAeyvxVqNAKIIprefhjRxr0J10bqypO0kJwcaIMqRaCzZ0NncUBnSR3UfGEJdW1hnDAhFxajLh6S29crjs6zJR3VvfW0SWn3vky2BYxeFDBthCvj10FEwxuDYx+54YYbEAgEoNfr8f7772Pu3Lnxc9/61rcwYcIE3Hrrrdi2bRseeugh3HXXXYPY254JRhR4Q1E4zAyPRJReRFLgC/d8VPG9/9bgz2W7EkJexyImmY7AvbyuCiPdVs31nUfmUk1DfX9LPZBko/aBogKobQuh0GlOO9rXUfu0WCBWqKfz1NhUXjrw/QCQsI6z3bq9rfEKnTajDjvqvXjja22QUwGsq2jGWceUpJ3emqk3NtagLRjFrNFu7G0K9LK1GFWOQg56oUbDgCBANFogmh0QRBE6axZ01qyDFwvJC8REUhS3URUFcqAVUGSIVhdEvRE6a+LIYirr9rZi3d7W+GhtxzWgAoARLit+cuIYPPVJrGpv+1rFeeNTr4m0mnQoznLhN2dPw12vfwNJUaEXBdxz9rT4HqhERF1hcOwDa9euRVlZGQDg8ssv14TGdjfddBP+9re/YevWrXjkkUdw++23w2A49AJYgzfM4EhESYWiMloDUbSFoj1aqxiRZdS0BrG52pNQlXTFuioEo3LCmrdMqAD+vaUu6chkZXMAQbvcq2movTHSbcG+5vTbRjR4w2jyRXDSxFzNaGgqi48ugstsxEfb67o9pfO5L/ZhZ33ytXXt9h3YPqSuLYQWfyTpNWU7GjE6z46KhvRtZapsRyMmFjpgMmRW5VNVFci+ZkieesjBNpjyx0IwWaEzx2b7CDoD9HZ3Rm3pswsRbdiTcFwJ+SB56gBBd3CdozMXotGSUdvp1jECsZ/PdXtbD74mxD4EWXRUEc44egTmTchNW13YqBeRZTUgy2qASa8DAPzguFH49tQClNf7MD7fztBIRN3C4NgHVq5cGX982WXJ1zuIooiLL74Yt99+O1paWlBWVobvfOc7A9TDvuMPywhEJFZcIyIAsXWGnmAUrYEIgpHuRTpZVRGMyAhEJQTDMj7e2ZByFFEBerWu7esqT9LjYUnB619X97jdnjhxQg5cZgOKsy2wGw2o94Xw7Bf7Ul7/yvpY/wQAJ07IhdOsx1uba1Ne/8bGGnxT7cGuxu6PznUVGgFga23y0cjOln9WkfR4rs2ARn+0u13D+ooWrK9oghxsgxzwQIkEAVkCIEDQG+Db/CGiDXtigdHXdOAcoLPnoORnz3T7+doZsooRBABRB0NOKUxFE2EsngxT8STonHkQBBFwZlZgaGyuFadOK4qvn/3Xf/fHRrUzFPuwIxivLNw5MOpEAS6rAVkWA2ym5P9P5zvMDIxE1CN8998HPvnkEwCAzWbDzJmJZbfbzZ8/P/74008/PSSDIwDUt4UxOpc/OkTDlaKoaAtF0RKIorLZj30Z7qmoQkVYkrG/NYQ9jT5kW41wdig+051tFPpKusDWXz7Z2QQA+PaUfBw1Igt5NhMmFzqwrdab9j4VwMc7G/HDOSNx0oRcfLwz9RYSPQmNmdrcw30NVTkKRYqiti0ANRKEEg1CjYQ6/B6GqXgioKpQVSU+PzNWGFTAmnoRoskC0eqC0Zad0L5nzYsIV29NOC77mqFEQxANmYUl2d8KyVMHJeyHqigwlR6BoqnzYcgphaDv3YybPY0BTdGl06cVw2TQZVwhVifE1ip2JAiA02xAls0Ah0kPQeCey0TUP/juvw9s3Rr7j2r8+PHQ61N/SydPnpxwz6HIG5IQjMiwGHWD3RUiGiCqqsIXltAaiMITjEJV0+8J1y4iy7FRxYiMQFTGh1vr4m+SO65d7E7xl0NVqGpLbGqjLEFVZLy6PoptBVbYzQZsyz0p4XpVjkKJBKFEglDDQSiRWOD6y7ZP4TTIaGttO3AsBCjygZvU2F6M7RsmqiriCezA49hvSuz4gY0kBZ0Rgt4I6I0Q9bHH0Zb9UCOB2H3xdgHBYILruHMh6I0QDCYIBjNEfdfbOAg6A3Q6Axr+9UcEtn+W9JqRt7wOQezZ/y367OIUZ1RIrbUw5o2OH5G8TZBa9sdCpRSO7XWpN0Nny4IhuwjGoon9EsBUaAsXAbGqucePy4EnIKHWE8Q/vtgb/zt10sQ8fLyjQfN3rP0DGptJhyyrES6LATqRYZGI+h+DYy+FQiE0NsY+9S0pKUl7bXZ2Nmw2G/x+PyorK7v1PFVV6acF1dQMbGnyem8Io3ISy30T0eElEDkYFqUO5RibA2H8uawcSqc94WaOyoJZr4c/KiEYkSDJB9ta1WnNXXvhmqnFThQ402/1MJC8mz6A1FINJeSHEvYf+N0H0WRFwfd+0+N2275YgeCuLzXHPgMg6E0YeVNicBR0BugshqRbNsgA2o82f7QM3rWvJX1O53HnQbQ4IJqsEE222C9z7HfBeOCYMflIXN1LdyG056vEfhlMyD39urSvNR3RlPr/DiUSjK9D7C5DVlHiQZ0BelcBAjvWIFy1BRD1EE1W6F35MBaMgzjyyB49V29UtQQwMd8Bk0GAzaSH3aSHUXcwLC+ckq9Zu3jpvNHxrwudFmRbDciyGmHUZ7bek4iorzA49pLXe3BqUSZbbLQHR5+vewUDSktLu923/tQWlBCKyjAbOOpIdLgJSzI8gdhU1Nq2YNICHK99VR0Pje1kFVi9owEWoz7p/onJtlFQAaz8qhoLJudhzrgcfL6rqVd9V1UVSjgAnbnnH2wFvlmF0L5NCccFoyXJ1ZlTpXDyE2LvAoASaE15zjXv+xAN6acQp5Iq4KnRMFRF7vHIoJjmz0aNBIAMg6OqyJB9zZD9rVDCfggmK7K/vRQ6uxt6uxt6VyFEmyu2BjFDXRWs6Qtvb6zBWceMQEGKdYad1y7m2k0Yl2dHttWYct0iEdFA4L9AvRQKheKPjcaup+qYTLH/DILB9FX0DgX1bWGMzLEOdjeIqA8cLHITRTASGyZMNRV1d6MPK7/en7SdZz7f2z77EfMn5mLWaDf8ETntPohfV3k0xWtKssyoag2lvL5dYOd/EGnYA6m1FnJbIyRvA+S2RohWF0qufrpbr78jMcUee2okCFWKQpXCkANtUNqLtATbYuvhpChUOQpVikBVJAiiHoLBCEFvgqA3QvKkKIKiqghVbYFoskFnd6fd4y8Z2d+a9LigN/Y4NAKAaEr977sSDnS7n0As7Ak6Y7x/gsEMwWiBaDBDMJgR3PM1RIs9FkpFPUSdAdAbYt/D+PfSBJ3ZBkHUQe/Mg96ZehuK7tpa68PRJU5s7OE6zo5EACdNzEXZDu1aVAVArSeUMji2s5l0cNuMcJoNEDkVlYiGAAbHXjKbD/7DH4kkL0veUTgc+8TZYuneJ9ddTW2tqanBscce2602e8sTjHLUkegQJisq2oJRtAaj8Iel+LI4IDYVtT00ArHRxD+vKkdrIIxn/5P63yO1w+9lOxoT3jRnoqo1BFVVoUoRCDoDhBQjct4NbyedSim3NUCJhFJOwVSlaGwKaiQINRqKrxNUQj4oIW/qgAdg38PnHVxPmJQAwWAERD0EUQdBp4891sUeG/LHxAKlqAN0B34XRHg+fzG21hACoDNAZ3VCNDsgWpwQD2wYL5o7Tjm1QjBZIQgijIXjobNlHagic+CXAIim7k/5VBUFqhyBKkVgHnn0wVFHQYw1DQEQBIT2boQgigeK2LRXsdHF/qyE9tctQhAPvH69HqLBAsFghPO4JXCd8IMej1j2t+6Exo57LLZ//fNFkyHJCsbk2mA26PDJzkZ0mOWdtMBNO4NeQLbViGxORSWiIYjBsZccjoOfuGYy/dTv9wPIbFprR12tnxwsdW1c60h0KFFVFW0hCZ4D+y2qavLrKpuDmje7QGyk5Lk0obGn5HAAatgPQWeAaHVCEEQIggChi9EyQ+6opMERUOFdtxJKNIxocxUUvwdK2HcgGPqgSp0+5NMZYsHMZIdotiEnNxf6ktFokY3x9YCxtYCW2AiZITbqJRpNB0bC2gvEmGJhcICrWmbPv6TP2hJEEYJoBgxm2KaeBNvUxLWXh4NxuVbsaQx0uab2lKn5OKokC9vrvEm3g+n4V0QEcPWCcTh+rHZrjqsXjE8Yue84FVUUD1RFtRq4TzIRDWkMjr1kNpuRm5uLxsbGLgvYtLS0xIPjUFuz2FNtQVZYJRrq2iuieoKxIjdKmnfLzYEwKpuDMOqFpMVqMtkyIB0lHECkbhfCNTsRba5G7unXQmeyAmmmRSZtJxqCmGbNYesnz0Fnz4bOkQe9IweG/DHQ2bKgs7qgs2VBtLY/zoZgtGjC3rkzR+DV9dXI6fGrHD4mFdjhsOixrqK1120tPqoQYUnBB93Y17AnBAA/On40AKCiwY9n1uxN+XM9Lt+BEVlWjMiy4tjRbmyrbUv48EQEcOtpkzCl2Jl0S5pTjyjE7DHZmrXC7VtouKyxLTQ4FZWIDgUMjn1gypQp+OSTT1BeXg5JklJuybFt2zbNPYeLurYQRudy1JFoqAlLMpr9EbQGtBVRU3l70348+fFuKIi9uZ5QYMfOOl9864zFRxfhrY01GVc+VVUVsq8Z4f3bEdqzHuGqrYg2VaJj/HTNOReGlNsotLejINqwF+GqbxCq2oLI/u2QPA3QufJgHj0d+uwi6J350DtzoXPmQe/IwxlzpiLHZe32Po1HjnDCZtT3OiD31OzRWTDpdPi0l0WCOjPrRYSkzGvWfv/YEthNeuyo82F1iunGN58yESOyrNhR7+11cBQBzBrtBgB8uKU+45+xU6bmw2zU4Y2vM6ssLgA4f1ZJvHDT0aVGnBFIXrhJQCwY5ztMMBlEGHX2Ax8waIOjAsBuNqTdx9RtNSHHZoLDrIfLYuC6RSI6JDE49oETTjgBn3zyCfx+P9avX4/jjjsu6XWrV6+OP543b95Ada/feUMS/GGJ1d6IhohAREKjN5J2Kmq7sCSjqiWAzdUePP1phWaN4o662PT7BRNzsWByPgCgLRhNuW5RVWP75SnBNggGE/SuAugdOfA3V8O38b3kz1+1JSE4qnIU4ZqdCFd9g0j9HijhIPTOXBiyi2GbOh9Z874Pvasw7WbsrREV46zdm/Y3f2IuFk7OR0WDP2HtWm+dMjUfEUnpcs3nrNE5aPKF+/z5O4ZGp1mPtpCU9vqddT58tbc1ZYBbMDEXI7Jio8Q92Url6BIXNld5oODgfp7tYe68WSVYsa4qZXvthZcWTM6Hy2xEdWsAAmoSvl/Jvoc3HQi7gggYdSKMOhFTipxJg+NZxxRjXJ62AFCp2wKdgIzXLAoCYDPFwiL3WySiQx3f6feBs88+G7/73e8AAH/729+SBkdFUfD3v/8dAJCVlYWFCxcOaB/7W21bCOPyerb3FhH1DU8wikZfGIFw8uIt7dNQC5wmGHQifGEJH+9oSPsmHYgVuQlEJHyZZFRJDngQqd8DVZFgcJfAkF0EZGv30zOVTk3ZdrjqG1gnHo/w/u2xDdnDAYgGEww5pbBP+zZEW1bCmsFMtkz4YlcTHKbuTaFfvaMRH+9o7JfRxn9vqcddZ07FrNFu/OH9HUmfQwRgM+rwl3VV/Tri2RaSugym6/a2pjwnAPEPEgDAZTbivFkleDmDfl86dxRG59nw/+3deXxU1d0G8OfeubOvmclOAiEJYa9YWUREQcV9wyq2Wq0L7lqrdalWK7buVq361rqhaN8ir1pcsVVrEVFBcEVkky2QELLvs97l/WPIkMlM9kkmy/P9fPw4OffOvSeZJMyTc87vOE0G1PuDKG8IxGzdMjM/FROyHShvCMBq0KE5qCDDER7Na3v+mp1VeL3NfVuCqIDwPqEt4fSSI/MwI98NvShC16rgkl4SYsKgCGD+T0fE9N9tMXa4ZlEUAYtBgsWgg9UowaLXcWSRiIYMBscEmD59OmbPno3Vq1dj8eLF+NWvfoWZM2dGnfPII49g8+bNAIDrr78eev3QWgDvDSho8Ifg4MJ+on7X4A9hf70fgVD78W/F9/vw7KqdUSM8E7IdXXqzDyASGjVVQaBsK+Ta/RBNVhizx8GcN6XD5xoziwCdHlBC4QZRB0NaHgyZY2DIGgO5bj9MIydDGH1oVz7dLu2zpwFwWeJvkXTlnHzsq/PFnd7Y0dfi6KLUuNM2W0JYy3ReCIi5topw6ClKt+OcOCNqLa9Jc1Dp1shdTx1dlIpPtlX16F6nHZIVFfSAcNiz6HVYsqY47nNapogekpsSaXOaDDHX6exY271B4/3R4/6fTcKYdAf0OhEn/yQr7j6krXUWBttqvWYxP82K3BRLJCiyyjgRDWUMjgny+OOPY9asWfD5fDj++ONx++23Y+7cufD5fFi2bBmeffZZAEBRURF++9vfJrm3faOiIcDgSNSP/CEFZfV+NLUz7VBRVRTXePF9SR0Wt5qGqgJ4/csSzChwdyk0qqEA/Hs3QmmqgWRzw5g7EaYR7Y8itiU3VsI2+Tjo3SNgHDEehvTREKTO973tDRHAhGwHpo5yRY2eTR3lwth0BzIdJrz7bdfXawKA06LH8RPS8eGmikhQXDA1BxNHOFHe4EeG0wSnSY8Gfyjm2iIQGTVrb0StZRSuo2mfx09Ix386WAPYEmI7GlFsGTGcMy490odHP9jW5a/Fu9+VwWHWY+64dEhCeOsPTQUOGemCuKY46joCwiN9o1NtcJrDbzkEQYAohP8vQIBOECI7idR5gyir8yHLaYbDrIemHfx8REGAIIaDnV4notYbiNvnkBI+DoRDYUdrD1vEK2DTligCZr0OFoOEkR4LZuancssMIhpWBE3rbAUMddU777yDX/7yl2hoiL8HVFFREVasWIHCwsKE37ukpCRSqfWDdT8gMyt2ik1/yEu1sJw4UR9TVQ0VjQFUNQXirmEMyArqfCF8+MP+yFS9bt8j4IX3x7Xw/rgG/l1fwzblZLiPubRLz5UbqxGq2gNoKnTODOjd2RCE7r/BXjA1B69+2XG16nhaRrdmHtgWobTOi20VjShKtyMnxQJBAEQBWLOjGsvW7Y0UA+rKP4YigAsOH4mCDDtGeSzthpL3f9gfGcESAVx+VD7mjk+HqobXgqoAoGnQwv8LFxLSNKgq8PHWCiz9Yk9kdPjUQ7IwIsUSCZct0zPjjVi2BFJfQG539G9Bq69Ni398sTsqYI9OtaD4wHYV8b42OgF44eJpMZ9/68+7ZeTuhImZkeMt06XjhbPOnttWjTeAS15cH7PeMF6/ukM4EEwNkgijJMKs18Fs0HE0kYgGldbZYO/evQnZ2o8jjgl02mmnYcOGDXj88cexYsUKlJSUwGAwoLCwEOeccw6uvfZaWCzdKzk/2JQ3BBgcifpQU0BGaa0PwTgVMpuDMuq8IfiCCur9QbzazbVyaigA384v4d20Cr6dX0btd+j7cS3QQXAM1e2H6m2AaHFCcqZDsidmM4uORt8EAPmpFuyo8kbaxmfacdlR+Ui1GcNr2XThwiVHFqZCFMMjXC3yPDacMCkzEmTW76qNBJeWs9p+/VQA//vFnk7DSVdGsNpz7rSRmDcxA3trfMhJMcFu0kNRNSgKIGsqTp6UhRn5bpTW+mA26NDol5FuN8FmlKBpiBSMiefaYwowKdsFvSRAEgXoRRGNgRC+brOmcU+1F4/9fAoa/TKa/CE88O+tUccVLbzXZ9vPq6PPu6NgWOMNRI61XP9vH2/HtNEpCZti2pogAEYpHA4NUrhIjlGvCxfM4SgiEVFcDI4JNmrUKDz66KN49NFHk92VpPAFFTT6QwyPRAkmKyrK6v2o84ZijjUHZVQ3BRCUNdT7g9hd2YzPd1R3KTRqqoJg+U6oQS/0nlzUf/5/CFXsjL1/XRmClcUwpI2KPE9uqAA0FZI9HXpXJuBqf3SoJyx6XbsVNkUAC6blYNn66BHJLfsb4bEZkNLO+sa2Wk9lbBt6AGDVtkq88OnuqOe0F5o6unZ3xTxXB0AfDlgldeH+FaU7Yp6nahpUTUO9LxhzDAAyHWaMcEVXAN1Z1Rzz9VW0cMXsQ3JcqPEGulVJNN7n3Vkw3FvjQ9sdY7ryde4soOslAUZJFwmJRkmEUdIxHBIR9QCDIyVcRSNHHYkSqbY5iLJ6PxQ1+p11QFZQ1RSELxiuorpmZ1WXp3bKzXVQvfWQnOkwZo2JtFuKDkd9nOAIQUSgdDMEnQTBaIVkdUHvyoo9r4sOG+XCaYdkY9O+hnb7PCbTjgy7CbPHpOL9H/ZjxYb9kfVuF83KQ7o9NlBoADbva8CswrQe9att6Dm6KA0vfba7y6GpL3VlKqcoCBAFAaPTrF0Oe51tMdGbkb0WnQXD7m5z0ZrbYkSGwwSTpINRL0b+b5R03P6CiCiBGBwp4bwBjjoSJUJAVlBa60Nzm+01VE1DbXMQdb4Q6nzBSIGT1zoJjaoSguprgs7igGR1AVZXzDmWwsNR/+nS8AeiBPPoQ2EZeyTMBVOhszgT8nmdfkgW5o7NAHCwUMyKDfuw/kDlVgHA1XMK8JMR4f7VeAP41/f7o/aYfOnz3bhqTkFC+tORRISmROjuVM7u9Lsr5544KROzCj3YV+/HmHQbsl3myBRPAJBVDYqiQVZVKKoW/ljVEFJUqBowJsMWNxiOzbTDbpLgMEu48fixePSDrZE+/PrYMch0mKFq2oF1qeGiOqIgwKjXwSSJMOnDaw8ZEImI+h6DI/UJrnUk6jlN01DZGEBF48HiNy1FRVJtBsiKhpCi4b9by/Hud7Ebn8dcTw5BkPQQdXqItpQOzzVk5MM8bjbMow6BZews6Mz2Ds9vraMCM8dPSEdBuh0ZDiNcFgNMkg6mA6NCeakWHDbS3W7hlPZGq2xGKeaeAoDx2bFTOCPHBURCSOswEq7w2ariJ4TIuefNGImTJmdiT40XBWk2ZDhM0InhaqAt2wG2LVKkalokTCmaBvlAqFJVhIvgaFq4SI52YHqpigNt8fvdk6mc3Vln2XJuWb0fhek2ZDvNkfV/xgMBse1+mt1RCBvunT8Zd7y5EbKqQRIF3HPmJEzLc0fOuWZuIc6ZmoPtFU0oTLch3W7q8f2IiCjxGBypT/iCCup9ITjNDI9E3dEckLGvzgf/gT0Za7wBvPF1Kd76dl9kC4izp+bAH1Lwznex+xC2aCmYLQgCBKnjn0NNUxGqLIYa8EJKyUb6Gbf2qO/zJqQj22lGtTeIFd8d3I7irEOzccoh2eEphAfWmMXT3prA9qYxjs924Jq5hXhq5fZIBdIbjy/CmHR7uCqmToROFy4CczDo9Sz8ZLvMmJidmBHXjrSM0rWEzpCqQlY0TBrh6NFUznhfU50oHJjK2RIMW14XR6/CYWd+Pn0kjhmf3mEwTLebGBiJiAYobscxRLQuubtizQbk5oxMco8Ak17EmIyuj1YQDWeyomJ/gx+1zQeL37z/w/5IKGqto5E9NeBFqLoExuyiDu+nhgII7N0Ixd8IQ9roSNGb3hIBnH/4KEwfnYLKxgDy06zwWHsfBNqu77v1xHFYMC0XBklEnS+I3VXeIT9KtWzdnqgRuz+eMRFnH5Z7cHSz7X8HRjUl8UBRGH04TEs6FoYhIhrquB0HdUlds4zcZHcCgD+kos4bhKuL1Q2Jhqua5iD2tyl+U+MN4K8rt8cNiPHaFG89Gr98G41fvwvBYMGIK5+HIOpizvHtWA+5vgL61JGwFE6HIPX85zNegFUBLP2iGMdNSEeex9az6wrhPzy1rF8z63W4YV4RLjxiVNzRKotBQrZzaG91BHRtxI6IiKivMDgOQc1BGb6QDLM++S9veUMATrO+T6c/EQ1WvqCC0jpfpCpqC1XT8MoXe7q0nYbcUIGGdW+g6bsPoMmBcGOgGb6dX8FSOB1qyA/vltXwbvkM+rRRsE05CbbJXds2oyUYigAm5zixoaQ+8vGFM0fh8AIPvthVjRc/i95ovqvbVQDhkGg2hMOh+UBQNOnjr6fjNEZ+DYiIKHmSnyyoT1Q1BZGbkvyXNyirqG4OItXWvxUIiQayeNNSWzQHZeyoaML7P5R3eA1NVdD03fuo+c8zgKrEHG9Ytxz+3d8gUPYjbJOPQ+oZv4No6HrgEAD89vgiNAcVZDlNyLCbEFQUVDcHMTrVGgmFc8bq8PLnxV1aeyeKCAdEQ+uQGH+9IxEREQ0syU8W1CcCIXXAbIlR0RBAisXAculEiD8tFQBkVUVVYxBNARkldb721zCGAhD1RgiiDuaCaRBWLoYWJziKJhssY2Yi5djLezTiP6vQg4nZDpgNEiwGHcI1R4E8T/R57W3lkGozRoVEs0HXblEcIiIiGvgYHIew6uYArEYJYpKniSpqeGuBTCenV9Hw5Q2Gq6X6gtGlbjRoqPOGUNMcjGzFkOEwQgSiiuKogWYIehNE/cHRe8mRBseMn7Xad1EH64Q5cM44G/rU3q10npLrQqqtaz+zJ0zMxMwCNyobA5iQ7cRIt4UjiUREREMMg+MQJitArTcIjzX500SrmgJwWw0wSKzmR8NLR9NSvSEZ2yua8OP+RgBAXpoVTpMBNqMeJ0/OxLvf74fiCx9rbz9FvScXgmSE7ZDj4Zg+H5IjPSH9thk7/udBFAGrQYLNJMFmlBgUiYiIhjgGxyGuzhuCw6SHPsnl1zUNKG/wI9c99CsfErWobgqgvCEQd1pqdVMQb31XGrMX47Q8FyZkOVHf2IxQTSn07hFxrx2q24/aj1+E3p2DEVe9AJ2la3sMigCOKPDg8x3Vkb0P4233MT7bEd12oIiN3SjBajwwfZVFr4iIiIYNBschTtOA6qbggJgmWucNwW2VYe1kJINosOt0Wqo3iP9uLo8JjQCwbmc1Pl2zHoa0UXFDo6aE0LD+TahBPzwnXAOd2RFzTnsunjUKc8amw20xosYbwN4aH3LdZqzfVRvZL1IEcPXcQrgtRhj1ImzG8Kii1SBxnTIREdEwxnfww0BTQEZzUIbVkPyXu6zeh8L0+FPuiAY7WVFRVu9HnTd2WqovJKOyMYCgrKHeH8TbcUJjoOxHiGY7jFlj4l7fV/wdAvu2wv6TedBZU7rVN1FAJDQC4aI2LY9PmJiJaaNTsK/Oh3FZDuS5rbCZpKTPVCAiIqKBI/lJgvpFVVMAFvfByojJ4guqqGkOwm3t+abjRAONpmmobg5iS1kDdld7kes2R0KZrKrYUdmEHZXNyHAY4TQZUN4QiHq+3FAJua4cppGT4l5fbqqBb/s6mPOnwjzqkG73TwBw9ZzCmH0VBQGwGHSwmSQUpttgNnCdIhEREcXH4DhMhGQNtd5glzbk7mv76/1wmvWc9kZDQnMgPC31rW/3RW9JcXQBZhR4sOK7fXj1y5LINNCzp+agujkIANDkEBrWLUf9uuXIuuDPMdfWVAW+HeshuTJhn3Jil/skCsBFR+Qh3R7+eR+f7Yj87Jv0YqSgjdUgQeTPIREREXUBg+MwUtscgt2Y/EI5iqphf4MfI1yxG4QTDSYVjX6U1wdQ4w1EQiMAKBrw1Mc7YDLoIqERCBehefXLEgCAb+dXqPnP05Brw1NWaz74GzJ+cV/k2sHK3VADPljGHN5hH2aMTsGXu2uhaOFgevqUbMz/6YhIUBRFwG7Uw26SOP2UiIiIeozBcRjRtPCU1Sxn8gNbbXMQbouBU+NoUFJVDaV1vshaxr01vkhojJwDYFtFY0zFUqW5FjX/eRbeLauj2v17NqD5h49hLpiGYOVuGEeMgyB2/vNx3oxRuHpuYaTQjdtihCCEt9NIsRrgMEmsfkpERES9xuA4zDQHlAFRKEfTgNI6HwrTbUntB1F3hRQVxdVelNZ5I2EtJ8UUs62FCKAo3Q4gPKKoaRoCJT+gbtUSBEq3xLmygEDFTpjyp8KUO7FLfZk7Ng35qeGfIbfFCIMkIsWiR4rVwJFFIiIiSigGx2GostEPs9sKMcmjEL6ggqqmAFJtyV93SdQV3qCM4movVmwoi0xNFQAcMy4NpxyShRXflR1cy3hYDjaU1AEAQg2V0HyNMOVOgmvOxSj/x+8AHByiNGSPhXveVTBmFnZ4fwHAnLGpyHCYMLMgNRIaLUYd0u1G2E36vvi0iYiIiBgchyNZAWqaA0i1JX9vx/IGPxwmPQwSR0doYKttDqK0zofq5uj1jBqAj7ZUQgBw2iFZGJFiQYpFj8+3V2Hl1goEy7bDkDEagiMNAGDKmQjr5GPR/P1/IJrsSJl7MayTj4MgdP4z8JefT4mERQAw6kVkOExwmhkYiYiIqG8xOA5T9T4ZdpMCo5TcNYaqCuyr8yEv1ZrUfhB1pKzeh6rGcCXUeOsZgXCAfPu7MpwyKRONNgM++mY7NE2FMbso5tyUOReH/3/0RdBZXV3uR6NfBhAueJPhMMFjNXD9IhEREfULBsdhStPCo325bkvS93Zs9Muo8wbhsnBvRxpYFFXD3hpvJLABwAhX7HrG1t79vgxKQyUkZ3rc43JjFZTGGqSe/JuYY0cXpWLVtqq4z9MJQK7bDJdFj0yniWsYiYiIqF/xnccwFpQ11B7YTy7Z9tX5ISvtvRUn6n/+kIIdlU1RoTGkqPAGFRw3IX4oVIM+QAnFDY2aIsO382uIBkvcUUgAmDTCiZ9Py4n5U45OAK4/dgymjnIj121haCQiIqJ+xxHHYa7WG4LVKCV9yqpyYHuDUR5OWaXka/SHsKfGC7XV3zL8sox9dX6oKmCKs42MGvRBNMTf6iZQugUQBJjzf9ruPUUAh41KQZrNhJMmZ2FvjQ92k4TmoIzDRqZgbKajt58WERERUY8xOA5zmhbexDwnJflTVht8nLJKyVfZGEB5gx9aq3WMjf4QKhoDqPMFUd4QQLbrYEDUVBWCKMYNjUpzHbw/fgHr+CMhGtv/o4gI4Oq5hUg7ULDKbTHCbTEixapHpsMEiSOMRERElGQMjoRASEOtNwi3JfnbYpTW+WAxSKyySv1OPTDqXecNRbXXNAdQ0xzCmp1VeP3LEqgIb4uRajOgssEPQYz9XtU0Fc0b/wvR7IB9ygmRdgEHN+E4osCNEyZlQhQEjHRbon7+zAYdRrjMMMcZ2SQiIiJKBr47JwBAbXMIAVlJdjegqkBJrTfZ3aBhJqSo2FnVHBUaNWgob/CjpjmEen8wEhrDx4DiL/+L5h9WxlwrsH87ale+CHP+VFgKp0faF0zNwamHZEXG9dfuqEFlQxBTclIioVEnChiRYkZhuo2hkYiIiAYUjjgSgIFVZbU5oKCyMYA0e/JHQGno8wZlFFd7IbfaY0NRVZQ1+OEPqqj3B/H1ntqDoVEJoeaj59H0zQpAECGa7bAUTocaaEbdp0shuTKRMveSmG0yRrotePSDbZERRxXA3z7ejmmjUzgtlYiIiAY8BkeKCMoaqpsCSD2wziqZyhv8sBkljrpQn9pW3oj1u2uQkxJen7i3xocMhxH+kAJZQdT0VCC8lUblm/cjuG9ruEFTUfXWg3DO+gW8P34Bz/FXwpBREPdexTXNMVt4KAf+YDN1lBtWI38dExER0cDFdyoUpc4rw2KUYdEn91tD04A9NV4UptugE7nBOSWWpml4dtVOPPT+FigaImPsGsLz98+emoMJ2Q689mVJZITQv+d7VL71IFRvXfS15ABCVcXIWHA3RKMl7v0EAEcWpmH5V6VoNbAJnQAcXZTG0EhEREQDHudEUYyKBj+U1iUlkyQoqyit9SW7GzTEKKqGr/bURkIjEA6MraeQvv5lCd7/YX+4XdMQrNyNqvcejwmNgmSE56Tr4TnlxnZDIwBcNCsP+ak2XDWnELoDKVUnAPfOn4wMR/wtPIiIiIgGEv6Zm2LISjg8ZjmT/4a23hdCVVMAqTaud6Te84cUFFd7saWsMWrkry0VwJodNVBDfiiN1TCk5SHjnLuw/x+3QvU1AAAkVybS5t8OQ3p+u9cRAFw8Kw/zD80BAJzykyyceWg2KhoDKEy3Id2e/GnhRERERF3B4EhxNQcU1PuCcJqTv6fi/no/LAYdLAZ+u1LP1ftCKKn1QlWBXLcZOgEdhke5qQaaHITePQIAoPfkIv3sP6B82e9hyp0Mz2k3QWeyxX2uCOD0KdmY/9MRkYqpbpsBmQ4TdKKAwnR7oj89IiIioj7Fd+LUrqqmIEx6HYxScgvUaBpQXB1e76hnxUnqgYoGP8obApGP3RYjLj8qH8+s2hlTsAYAgpXF0JntkFyZUe2SOweuuZfAPuUkCELs9+K5U3MwOceFXLc5EhhNehEjUsz8wwcRERENanwnQ+3SNGB/gx85KRbohOQWqJEVDXtqvMhPtcZsc0DUHlXVsLfWiwafHNXuC8kYl+XAH06fgPKGAEpqvXjnu7LwseINMGYUQDRZo54jN1YhVFsGx6GnxNznhuMKMWXkwf0YAUAQgAyHCak2A79niYiIaNDj8A11KCRrqGw1UpNM3oCC0joWy6H4Khr9+HxHFSoa/QCAffVe/PObEuyubo46r8Efwr46P2q9QZQ3BGA16PDOd2XQNA3NW1bDNGJcTGgM1eyDJgdhHjk55r5nTsnCMeMyo0Kj3SShKMOONLuRoZGIiIiGBI44UqeaAjLqfEG4BsB6x9rmEIxSAGl2Fsuhg5at24M73twIWdUgiQJO/UkW3vluHxQtXL30qjmFOGFiJqqbAqj1hvDfreV497uySCVVTQmhedMnsE6aGzMFNVixGzq7Bzpz7LpEAcD8n+ZEPpZ0ArKdZjgt+j78bImIiIj6H4MjdUl1UxBGSYQ5yfs7AuFiOQZJhNPMN+cUHmlsCY0AIKsa3vx2X+S4ogFPrdyOPI8FOlHEyq3lkWmpAKAGvPBuXwfb5GNjrh0s3wF96igIutjvexHA1XMLIyONKVY9spxm7jtKREREQ1LyUwANCpoWDmy5bgskMfkznPfWeKFPs7LgCGF7RVMkNLZHBbCxtB4A8Har0Cg3ViNQtg22iXNinhOs2AV9en7cqaa/O3Esxmc74LYYYZDCxW9sRn4vEhER0dDFdzrUZYoKlNX7McJlhpjkdVuaBuyu8qIg3Zr0qq+UXIXpNkii0Gl4fGlNMVqfEaougdxYDWvRzJhzgzUlMKSPjnudi2flYVZhGgDAYtQhz2PlKCMRERENeckfOqJBJRBSUdk4MIrlKKqG3VVehJR4GyrQcJFuN+GOU8ZDdyC76QRg7ti0yC+3lkjXOjT6SzbBt+srmPMOibqWpqoI1e3HZafMwuVHjcZph2RF/ZI8cVIG5owNh0arUYfRDI1EREQ0THDEkbqt0S9DLwWiqkgmS1BWsbuqGaNTrZC4x+OwVO8NYWqeGy9cPA2b9zUAAPLTbDhmXDr21fvhC8hYsqY4cr5vx3pUvnk/IAgwZo+FMXscAECTg1D8TRhXmA9V05DtMmN8phOHjUrB2p3V+OCHcvx7Yzk+/KEcN8wrwtVzCiEyNBIREdEwweBIPVLTFIJB1MFmSv63kD+kYne1F6NTOfoz3NQ0B1FaG96iZf2uWvzt4+1QtPAo49FFqZgzLh3AwT9wNG9ejap3/wyoCgCg4vU/IvP8h6CzuaFpKiSbGzuqvNhRtQcAsGBqDmaPScWHP5SjZSasogGPfbgNC6blIt1u6s9Pl4iIiChpOERDPVbe6Idfljs/sR/4ggp2VzdD6WSdGw0dFY3+SGis8QYioREIT0v9eFsV7n57E77dU4ejx6SiacMHqHrn4UhoBADV14D6L14HBB10JlvMPV77sgSltb7IdVsoWrgoDxEREdFwweBIPaZpwL46P4KK0vnJ/cAbYHgcLsrqfSivP7jWdm9NbLgDwgHyzW/34et/vYLqfz0BaNHrYa2TjoHnxOsgGuJPu265pK7NQLYkCihMjw2aREREREMVgyP1iqoCZXV+yOrAKFDjDSjYVcXwOFRpmoaSWi+qGoNR7ZLuYBGctud7d36JStMICFJ0OLT/9BR4Tv4NBLH9qryiAIzPduDG48dGwqMkCrjnzEmcpkpERETDSvIXqNGgF1I0lNX7kO2yQJfkbTqA8LTVnZVNyEu1Qs+COUOGpmnYU+NFgy96evRb35bghU93o+2fCjRNg3/3t7DkTwUApJ5xKyqX3wNoKhyHnwPXURfG3aOxhQDg6jmFyE+14agxaThnag62VzShMN3G0EhERETDDoMjJUQgpGF/vR/ZLhOEuGM//csfUrGzshl5qRbu8zgEKKqG4upmNAcU1HgD2FvjQ67bjEa/jBc+3Y22492apsG38ytYCqZG2iyF0+E58Voo3gY4Dz877n1+OtKFb/bUQUM4OFoMOuS6LQDC234wMBIREdFwxeBICeMLKqhoDCBjgLy5DsoHwqPHCrOB4XGwkhUVu6ub4QuqeP+H/ZEiOCKAo4pS44fG7V/AMubwmGvpU0fBlj223Xu1hEYAUAE8+uE2nD01h4GRiIiIhj3O46OEavTJqPUGOz+xn8iKhh2VTaj3hZLdFeqBoKxiZ1U4NLatnKoCWLWtKmp8W9M0eLd9Hjc0+ks3w9hBaAQQM91VVjVWTyUiIiICgyP1geqmIJr8A2ObDiBc/XVPtRcVjf5kd4W6wR9SsKOyCYFQeEwxXuVUDcCYjHB1U03T4N2yGpaimTHXCuzbCtOI8d3uA6unEhEREYUxOFKfKG/0wxsaOOERAMrrA9hb44XKiqsDnjcoY2dlM+RWSTHXbY77C2tbeRM0TUPzpo9hGTsLghB9VmD/9piRxotmjsKtJ46NuZ4AQCeGxzBZPZWIiIjoIK5xpD6haUBZvR85LvOAKk5T5w3BH1Iw0sOiOQNVoz+E4movtDb5XlU1nD01B69/WRKzrrF58yewjpsds7VGsGInjJmFMfewm/WYMdqDq+cWRqa/6gTg3vmTccz4dFZPJSIiImqDwZH6jKYC++p8GJFihkE3cEKaP6Rie0UTRrjMcFkMye4OtVLnDaKk1hcTGiua/GjwypiZn4o6bxAfbKqIHGve+hmsRUdA0EX/OgtWFsOQnh9zDxHAoSNd0OtEnDAxE9NGp6DBF8KMfE8kKDIwEhEREUUb9lNV58yZA0EQuvRfV/zwww+48sorUVhYCLPZjLS0NBx11FF45plnIMsDa+pmf1AOhMeQ0naMKLlUNbxmjlNXB46qpvA2G21DY3ljODQCQL0/OjR6t6+DOf8wCJI+6jmh6r0wpI2Ke58r5xQgzXYwGI7NcOC0Q0YwLBIRERF1gCOOCbR48WJcc801CAQCkTa/34/Vq1dj9erVWLJkCd599114PJ4k9rL/yUo4PGa7zNDrBtbfKuq8IXiDCnJSzLAa+eOQLOUNflQ0BKLaNGioaAigsVWhpfJW5/h2fQNT7iSI+ujAF6raC31qbtz7CABm5LsjH6c7jMhwMDASERERdYbvlA+YOnUqXnzxxR4///3338fll18OVVWRkZGB3//+95gxYwZqamrw3HPPYfny5Vi7di3OOussrFy5EqI4sAJUXwsp2oANjy37PXpsBmQ6TBDFro0uU+9pmobSOh9qm6O3S9GgYX+9H80BJdJW7w+irM4LIDzSWP/ZK0g/Z1HU84JVe2BIHdn+/RAeaXZbjAyNRERERN3A4HiA1WrFpEmTevRcWZZx7bXXQlVVOBwOfPbZZygoKIgcP/HEE3HNNdfgqaeewieffIL//d//xYUXXpiorg8aAzk8AuFtRBr8IWS7zHCY9J0/gXpF0zTsqfGiwRc9hVvVNJTV++ELHgyNa3ZW4dUvSwAA/j3fo/LN+wElhPJXbkfGufdAZ0vpNDQC4QI4uW4zMhxGpDM0EhEREXXZwHv3Pgi98cYb2L59OwDgtttuiwqNLR5++GGkpKREHg9XIUVDaZ0XAVnp/OQkCMkaiqu8KK5uHrB9HAoUVcOuquZ2QqMvKjSW1nkjoTFYsQsV//wToIRHKENVxdj/yu8QKN0CvSf+9NQWogBcNacQ47McDI1ERERE3cTgmABvvvlm5PFFF10U9xyLxYIFCxYAADZu3Igff/yxH3o2MMkKUFrnG9DBrMEn48fyJpTV+6CweE5ChRQVOyuboqahAoByYNrq/gY/tlU0ot4fxJqdVfjzB9sAAHJDBSpeuwta0Bv1PGP2eBiyi9otYCUCOHNKNl68eBouPGIUi+AQERER9QCnqibA6tWrAQBjx45FZmZmu+cdffTReOaZZwAAn376KcaMGdMv/RuIVBUoqfMhy2mCRT8wvw01DahqDKK2OYRUuwGpViPXP/ZSQFawu8qLoBxdZVdRVeyr9+HjrZV47csStI3qiq8RFa/eBaWpJqrdXDQTnpOugyDE/xvY704ci/HZDrgtRmQ6TUizGxP56RARERENGxxxPGDLli2YNm0a7HY7TCYTcnJycMYZZ+Dll19GKBRq93lNTU0oKQlPoxs3blyH92h9fPPmzYnp+CCmqUBZnR8N/va/vgOBomoorw9ga3kjKhsD3L6jh3xBBTsqmmNCo6yqKK3zYWdlM16NExrVUABayA/zmMOj2o05E5B66k0QxPh7hJ45JRuzCtMYGomIiIgSYGAO9SRBeXk5ysvLIx+XlpaitLQUb7/9Nh588EG8/vrrGD9+fMzzSkpKoB3YeC4nJ6fDe+TmHlyDtXfv3m71ryWctqesrKxb1xsoNA2oaAggpKjwWAf2G3tZCVf6rGwMwGMzwG01DMgiPwNRoz+E4mpvzB6NIUXFvjofPvmxMrKOsTVNVaB66yE505Fy9K8g6I2oX/2/0HtGIu1nf4Coj/89IwKY/9MRAMDQSERERJQAwz44iqKIY489FieffDIOOeQQeDweNDY24uuvv8YzzzyDzZs3Y9OmTZg7dy7WrVuHkSOjqzY2NjZGHttstg7vZbVaI4+bmpq61c/WoXMoqm0OISRrSHcYIbazVm2gUNTw/oKVjQE4zXq4rQbuAdmB2uYgSut8MaExqCjYV+dDdXMQr8cLjZoGpakGkjM90uY64ueApsE2+TjoTOGfNxHAKYdkYcV3ZVARrpx61ZxCuC1GZDiMDI1ERERECTDs3+0uX74cLpcrpn327Nm4+uqrcdlll+Gll15CeXk5fvOb32D58uVR5/n9/shjg8HQ4b2MxoNvYH0+X+86PgQ1BWQEaxVkOk0w6OJPPxxINA2o84ZQ5w3BqBeRYjHAZdFzFPIARdVQ2RgO2G0F5HBoVFSgvCEANd7zm2shOdKi2kJ1+2EZd2SkXQRw9tQczMxPxQkTM1HnDSHXbYbbYkSKVc/qqUREREQJMiiCoyzL0Ot7v6/eiy++GFP1NF5obKHX6/H888/jiy++wJYtW/DGG2+gtLQUI0aMiJxjMh18YxoMBju8fyBw8A202WzuVt87m9paVlaG6dOnd+uaA1FQ1rC31ocMuwm2QTSKFwip2F/vx/56P6xGHZxmPRzm4RUiZUVFc1CBL6igOSjDF1RiRhmBcGgsrfNBPZAWMxxGiEBUeFS8DZBs7ujrN1RA8dbDlD020jY5x4mZ+alwW/VwW40YdeApNpOEEa7u/YwRERERUfsGzzvzJJEkCZdeeiluvvlmAMCqVatw3nnnRY7b7fbI486mnzY3N0cedzatta3O1k8OJZoK7K/3w2mRkGozQsDAnrraVnNAQXNAwb46P8wGHRwmCTaTBIth6Py4qaoGX0gJ/xdU4A0qMUVv4vGFZOyr90NrdarTZMBPclz4tqQufO2AFzqLI+p5cmM1gpV7YCmYGtX+XUk96n0BFKYf/Hky6UWMdFva3Z6DiIiIiLpvULyTlSQpIVVIs7KyevS8CRMmRB6XlpZGHWsd6DorYNN61HCor1lMhHpveNQqw2GCURr4U1fj8R0YgStvCEAUAZsxHCAtBh3Met2g2N4jICsIyCr8IQX+oAq/rCAQ6jwktuUNySir88eMQpY3+vBdS2iUgxCNlqjjSnMdfDvWIX3qKfDFCafFNV4cNsoDAJB0AkZ5rNANgq8rERER0WAyKIIj0PlWF31Jizff7gCbzYbc3Fzs3bsXW7Zs6fA6rY/Hq9BKsYKyhpJaH9xWA1IsHa8hHehUFWjwyWjwyQAAQQCMkgiTXgeTXgejXoRREmHQif06WqZpGkKKhpCiIiirCCoqAiEVQUWBP6TGnW7aXc1BGfvrY0OjNyhjY2k9NIQrqIpS9Gus+BrR8NU7cM3+ZdzQCABTclMAhL+eeR4rDNLwmR5MRERE1F8GTXBMpk2bNkUeZ2dnxxw/8sgj8corr2Dr1q3Yv38/MjMz415n1apVkcezZs1KfEeHKE0DqpuCaArISLcbB+3oY1uaBvhDKvwhFcDBvSwFAdDrROh1AvQ6EZJOgCSKkEQBoihAJwoQBUSqzwoCItN5NWiRcKZqGlQtXKRGVTUomgZF1SCrGmRFRUjRIKsqZEVLSDhsT6M/hIrGQNQ96v1BbN3fgB/2NWBDSQM0TY3Zj1ENNKN21RJ45l0ZCdJFGTZsKz84JXzu2DTkp4anqea6LTAbhsb3BhEREdFAw+DYCVmW8cILL0Q+Puqoo2LOOfPMM/HKK68AAJYsWYLf/e53Med4vV68+uqrAMJTX4uKivqox0NXIKSipNYHl0WPFIthwG/b0VOahvDInwwASrK70yu13iCqm6KLRq3ZWRWzZ6MgRI8SqkEfqv/9JNzHXw1BFy6MJQA4b8bIA1N//Th0ZEokNGa5THCae19Ai4iIiIjiG9ZzulauXIm6urp2j4dCISxcuDAyxfS0006LuzZx/vz5KCgoAADcf//92LFjR8w5N998M2prayOPqWc0Lbzn456aZjQF5GR3h+LwyzKqmvzYXd0UExrr/cGY0NiWGgqg6p2H4Zp9AXTmg0VyTj0kCy6zAdPy3PjZT3MjodFtMyDVxr0aiYiIiPrSsB5xfOmll3D66afj9NNPx5w5czB27Fg4HA40NTXhq6++wjPPPBMpypOeno7HH3887nX0ej2eeOIJnHbaaWhoaMCsWbNwxx13YPr06aitrcVzzz2Hf/7znwDC01ovuOCCfvschypZCVdeNRtEpNqGzvTVwcoXktEUkNEckCF3MEi6t8Yb9bFcXw6dPTUyTVVTQqh88344Z5wFvfvgtjczC9w4dlwGMp3RhZLsJgnZTu7VSERERNTXhnVwBMJbaCxduhRLly5t95zJkydj2bJlGD16dLvnnHzyyXj66adx7bXXory8HNddd13MOdOnT8cbb7wB3SDY3H6w8AVV7K3xwWaUkGLVM0D2E1XT4D2wX2NzQI7sydgRb1CG1KraqeKtx/5XbocxowCpp98MCCIq33oQlsLpMI38SeQ8AcAJEzORZjfC2mpLE7OB224QERER9ZdhHRxvvfVWTJkyBWvWrMGmTZtQWVmJmpoaGI1GZGRkYOrUqTj77LMxf/78LoW9yy67DDNnzsQTTzyBjz76CPv27YPVasX48eNx/vnnY+HChZCkYf0l7zNNgfCIl90kwWVhgOwLIUWFNyijORDev7E7BXW8QRk1zUFYDXocNz4dH/5Qhqq3H4RSXw5vfTkql98LwWCGzuKEbcpJkecJAM6ZmoPRHiscpoNrGFu23RgM25kQERERDQWC1tFeEzRolJSURNZfPvveOqRmxFZ/HU4sBh1cVj0segb1ntKgwRdS4AuERxaDcs9+VbSExpbfNOuLa/DW0/ej8cu3os4zZo9Dxi/uhyAdDIg3HV+E8dkOpNsOTkcVBKAgzcYKqkRERETtaJ0N9u7dG7X3fE/xXTUNSd6gAm9QgUES4DTrYTPpoeOUxk4F5PBoorcHo4rxNAVl1DUH0RgIYVdlE3ZXe/HNf9+JCY06mxup82+PhEYRwNlTczAm3Y60VoVvBAEY6eG2G0RERET9jcGRhrSgrKGyMYiqpiBsRgl2s8RRyFaCigJfSIU/qMAblKF0Ya1iZzRo8AUVNAVkBEIqNpTW4T+bKwAAoboy1Lz/P9FP0ElIm/97nDRtHAoybBAgIMNhRLrdhEynKbJHJQBkOk1RU1aJiIiIqH/wHTQNC5oGNPplNPplSDrAZpRgNUowD6MQqUFDUFbhCynwB1X4QokJii0UVUXTgWI5yoHKqk3BUCQ0AoBkT4Np9E/h+3FtpM1z/DUwZo/Fh5sr8NHmCpw9NQcTsxzIdpqi9ur0cNsNIiIioqQZPu+aiQ6QFaDOK6POK0MUAatBgtUgwWwQoROHztamQUVBIKQiICvwyyoCIbXXU0/jCSgymvwKfMHYqa0bSxuiPhZ0EtLOvA1lS65HqHI37D89BbafzIscVwG8/mUJjp+YGfVa2E0Ssl3mxHeeiIiIiLqEwZGGNVU9OBIJAEa9AJNeB7MkwagXodcN/CCpaBpCioKArCEoKwjIKgKyCi2Bo4ltqVq4cE6TX0ZQjn+jnZVNWLuzOqa94cu3EKrcDUPWWKQcszD22gjv0ZlhDxfEadl2g4iIiIiSh8GRqJVASEMgJKMe4SApioBR0sEkidBLIgySAL1O1++FdhRNg6yoCCkaQoqKkKoiJKsIympCp5t2Jqgo8AZkNAWVdoNpSFHx7d46fLajCmqbEcjGr1egbuULEE12pJ15KwRd7HpFnQDkusOji9x2g4iIiGhgYHAk6oCqAr5geBpma4IIGHQCJFGETidAJwiQRAGiIEAUBYgCIuvz4mXMlimdqqZB1QBNBRRNhaJpUFUNigrIqgpZ1SCrWp+OHnambbGb9iiqhu9L6/HFrmo0t/l6AUDT9x+i5sOnAQhIPe0mSI70mHMEAFfNKYTbYoQgAHke66AY9SUiIiIa6hgciXpAU4GAqiGA2IA0VIRUBV6/gqagDLWD4KqqGjbtb8DandVoODDlt62m7z9E9b+eBKDBecQvYM4/LO5518wtwAkTMwFw2w0iIiKigYTBkYgiNGjwBhU0dzK6CACapmFreSPW7KxGrTfU7nkN699E7X8XA9BgGjUFzlk/j3ueAGDaaDcAIMvFbTeIiIiIBhIGxyHIFxq6o2DUN4JKOCx6g0qHo4utldX78d7G/e0e9+36GnWr/xfBsm0AAJ3Ng9TTb4Ygxo4iigJw9YEpqm5uu0FEREQ04DA4DkF1zSGYGv1IsRogDaHtJSixFE2DLyijOaC0Wxm1I9kuM0a6LdhT441qz7RJ2P5/96J64+qDjYKI1DNuhc7ijLnOJUfm4eiiNLgtRthMErKdpm73hYiIiIj6FlPFEOUPqdhf70e9PwgNfbB5Hw1avpCM6uYAyup8qG0O9Sg0tphV4Ik8zrAbMX9KNpr/9XB0aATgmv1LmHImxDxfJyASGk368LYbQj9XrCUiIiKiznHEcQjTNKDBG55+6DLrYdbz5R6ugooCX6DzQjdtlTf4samsAXOK0uIGuiynGYeNTEG2y4TCNBs+ffsf2Pj5R1HnmEb9BI4ZPwMApNsMqGwKQkM4NLZUUG3ZdkPHbTeIiIiIBiQmiWFAljVUNQZh0stwWvQw6FipcjhQVBXeUHjfxaDcvVHnqqYAPt9Rje2VTQCA3BQLCtNtcc89uigNAFC6YzPefOaBqGOi2QHPKb+NrGusaAoCCBfC+dUReThhYiYEARjlscAgcQIEERER0UDF4DiM+EMqAg0BWIw6OE166Lj+cchRNQ2+ULjQTVBWI/tFdlWtN4g1O6uxZX9jVPtnO6qQn2aN7E3ZVsDnxUv33gA5FIxqTz3lBkh2T8z5GoCXPt+No8emYUpOCiwG/ioiIiIiGsj4bm2Y0TSg2a/AG1BgN0mwmfTQcU3ZoKZBgy+ohP8LKd0OiwDQ4A9h7c5q/FDWEPf51c1BbCtvxLhMR9znv/H0fSjfuzOqbe7PLsLOgmnt3lPRgKZAeBSciIiIiAY2BsdhStOABp+MxoAMu5EBcrDRoMEfUuBtCYs9rG/THJCxbncNNpTUQ2kncRolEdPy3ChIiz9V9fvPP8Ka916NassfNxlX3nQnXv2mDF8W18V9nk4ApuW5e9ZxIiIiIupXDI7DnKa2CpAGCTaTxCmsA5SqhcOiL9S7sAgAvqCCL4tr8M3eOshq/MBo0In46UgXDhuVAqMUf12sv7EGr/7ljqg2k9mCG+//K/QGI86fkYcTJ/tRWuuDL6jgtS9LoGjh0HjPmZOQbufWG0RERESDAYMjATgQIP3hAGk26mA3SiyiMwAomoZAqHfTUFsLyAq+3lOHr4prEVTiJ09JFDAl14Vpo9wwG+J/DwgCYDPp8L/33ImG2uqoY5fcdDeyR+YDAIx6EdNGeTAjLzyafdLkLOyv92N2USqynZbefTJERERE1G8YHCmKpgFevwKvX4FRL8JmlGA26CCA01j7i6yqkWmoPSlw0x5N07Bs/V5UNwfjHhcF4CcjXJg+2g2bsf1fDUa9iBSrHh+/9X9Yv+rDqGPTjz4Bx57xcwCAzSghzWGMKqiTajNixmgPTHr+UYKIiIhoMGFwpHYFQioCoSBEEbAZJFhMOuhFvuFPNA0agrICf0iFL6Qg1M2tM7pKEAT8JMeJlVsr27QDE7McOHy0Bw5z+4VqRBFwWQywGiTs37sbLz6yKOq4052KK+94EKJOQJrNCIcp+lqCAIx0WxgaiYiIiAYhBkfqlHpgGmuDX4ZBL8Jq0MGs13EtZC8oqgqfrCIQVOCTe7desTsmj3Diy+JaNPplAMDYDDuOyPcgxWro8HkWow4uc3gLF0WW8fgfroff540655o7/4yMjHRkOIxxpzlnOU2wm1hBlYiIiGgwYnCkbgmGVARDKuqEEAySCAtDZJe0rFUMyAr8sgq5j0YVNU3D1vJGZDnNcMYZPZREEYeP9mBnVROOKEhFms3Y4fUknYAUqx4m6eCvijde/hu2bvgq6rzjzzof8046CW6rIe60Zo/NAE8n9yIiIiKigYvBkXpE01qmsqqoRQgGSYBJr4PZoGNRHYRHFAOyiqCsHPh/3wTFFpqmYUdlMz7bUYXq5iAmZDlw4sTMuOdOynZg8ghnh9cTBMBulGA366PWKO7a+gP+7+lHos7NzB2F2+6+Fx5r/GBoM0nIcrJ6KhEREdFgxuBICRGUNQRlGQ0+GZIkwCyJ4RApDY/COkFFQVDREJDDBW36akSxLU3TUFzjxWc7qlDeEIi0by5rwLS8lLhhTuhkv06DJCDFaoj5A4AcCuHJRTdAUeRImyiKeOCJZ+FxueJey6QXMdJt6fSeRERERDSwMThSwsmyhkZZQaNfgSACJkmESa+DQRKHxGhkUFEQUjSE5HBYDCpqv61RbK2k1ovPdlSjtM4Xc0wDsGZHNU79SXaXrycIgN0swWHSxw37y1/8H+zetimqbeG1N+KnU2fEvZ7ZoMMojwU6kaGRiIiIaLBjcKQ+pamAL6jCFwwnK1EMbyxvkA78pxMH7PrIkKpAVrRwSFTDo4ghJXHbY/TU/no/PttZheJqb7vneKwGjMt0dPmaBr2IFIu+3WC/a9smvPb841FtY8ZNwBXX3xL3/BSrHiNcZo40EhEREQ0RDI7Ur1QV8Ksq/KGDQ3SiCOh1IiSdAEkUIYkCJJ0AnSD0WajUoEFRNSiaCkUBZFWDoqqQFQ2ypkFRtKQHxLaqmgL4bEcVdlQ2t3uOy6LHEfkejM2wdym0CSLgMuthM7Zf7VQOhfA/baao6nQ6/OnRp6A3RFdjFYRw9VQWwiEiIiIaWhgcKelUFQioKgIhAFCijglCOFjqBBGCgPC0RwHQodVavbb5SAv/p0ELX00DVFWDqgHqgcCYjKmlPVXrDeLzHdXYWt7Y7jl2k4SZoz2YkOWA2MWpoUa9CLfVAKmTcL58yV+xa+sPUW2XXH0DJkyeEtWmEwWM8lhgNfLXChEREdFQw3d4NKBpGqAogIJBlPQSaOO+eny4ubzd0U+rQYfpo92YPMLZaQBsIYiAy2KAzdD5j//uHzfh9TZTVAuKxuOK62+OajMbRIx0W2GQBua0YyIiIiLqHQZHogEsx2WGgPAgamsmvYhpo9yYkuuCXtf1sGY6MMrYlSnAciiEv/3xJshyKNKm0+lwz6NPwWA8OBXVadYjJ8Xc5ZFOIiIiIhp8GByJBjCXxYCJ2U58X1oPIFxY6LBRKfjpSBeMUtcr1AoikGIxwNqFUUYgPEX4P68+hx83bYhqv+jK6zHxkEMjH6c7jMhwcI9GIiIioqGOwZEoyQKygprmILKc5rjHDx/txo8VjZg8wompI90wG7q3pYlRL8LTxVFGANBLAgJVJVjy10ei2guKxuGqG24FEA6WuSkWOC3tF9UhIiIioqGDwZEoSUKKim/21mH97hpIooBLZo2OO+3UbtLjsiPzuzUlFejeWsYWNqOEVLsBl19xI4KBQKRdFEX88c9/hcFohKQLF8GxdOO6RERERDS48Z0fUT+TFRUbSuuxbncNvMFwFdkAgO9K6jB1lDvuc7obGrtaMbWFIABuqwEpFgOWL3sZX679LOr4+ZdcicmHHgaTXsQoD4vgEBEREQ03DI5E/URRNfxQVo8vdtagMSDHHF+3uwY/GeHqVSgTBMBp0cPewb6MbYkikOk0waKXUF1ZgUfuuTPqeHZOLq656XbYTRJy3ZbwlihERERENKwwOBL1MVXTsGV/I9bsrEa9L9TuebkpFgQVtcfB0SCJSLHqYdB1fQ2kUS8iy2mKjEw+eNfv0FhfH3XO7+97FDkZbmQ7TQf3ziQiIiKiYYXBkaiPaJqG7ZVN+HxHNaqbg+2el+exYFZBao+rkwoCYDdJcJj1END1YGc3S0i3GyPP+eSj9/Hvd5ZHnXPSGWfjnDNPQ5rdGO8SRERERDRMMDgSJZimadhd7cVnO6pQ0Rho97ycFDNmFaRihCt+NdWukCQBbqseRl3Xf5QFAUi1GeA0GyJt3uYm3Pv730ad53Sl4Im//IWhkYiIiIgYHIkSyRdU8NZ3pdhX72/3nEyHCbMKPBjptvRq6qfVpIPLbIDYjWvoDqxnNOujf/T/5+F7UVZaEtX24EMPozBvRI/7R0RERERDB4MjUQKZ9CJkVYt7LNVmwKyCVOSnWnsVGEUxXAG1bfjrjFEvINNhjqnQuun7b7H0xWei2ubMnYvLF17S4z4SERER0dDC4EiUQIIg4IgCD978dl+kLcWixxH5qSjKsPW6uIzpwDYbui5us9HCbpKQZjfGjE6qqop7bv8tVFU9eA+TCc89+ywL4RARERFRBIMjUQ80+ENwmOJveTHaY0W204SmgIyZ+R6Mz3RA7OUWFj3ZZqOF26aH2xJ/neLyV17Gxm+/imq74447UFhY2KN+EhEREdHQxOBI1A31vhDW7qzGprIGnH1YDnJTLDHnCIKAUyZnwWKQErLnoV4S4LYaurXNRrgfQIbDBJsx/o95bU01Hn/g7qi2oqIi3HTTTT3uKxERERENTQyORF3QFJDxxa5qfF9aj5YljJ/vqMaCw8xxp3Ta2xmN7K6eFMABAEkHZDnNMErth82/3L8I9XW1UW1//etfYTSyiioRERERRWNwJOqANyhj/e5afFtSB6VN0ZvSOh92V3sxOtWa8PuKIpBiMcBi6P6PqFEvIstpgtTBOshvv/wCbyz7e1Tbueeei+OOO67b9yMiIiKioY/BkSgOf0jBV3tq8fWeWoSU+FVS9ToBjf5Qwu9t0IvwWA0dBr/22IwS0h2xRXBak2U5Zs9Gm82GRx55pNv3IyIiIqLhgcGRqJWgrOKbvbX4srgWAVmNe45OFHBIjhPT89w9GhHsiMMswWHWQ0D310amWPTw2DqfZrrspeewddPGqLY//vGPGDGCezYSERERUXwMjkQAZEXFhtJ6rNtdA29QiXuOKACTsp2YMdqdsDWMkWuLgMdmgEnq/o+kIIT3iHSaDZ2eW1WxH089cl9U2+TJk3Hdddd1+75ERERENHwwONKwt6msAZ9ur0JTQG73nPGZdszM98Bl6TycdZfxwNTU7u7NCIRDY6bTBGsXRj4FAXj64T+iqbExqv2pp56C1IPASkRERETDB98t0rBX7wu1GxrHpNtwRL6nS1NAe8Jhlro0UhiPTgSyXR1XTm0hCEDZjxvw6rKlUe0XXXQRjjzyyB7dn4iIiIiGDwZHGvZ+OtKFb/bWwh86uKZxdKoVs/I9SHeY+uSeogi4rQaY9T37EdRLArKdZuh1nY9SCgKQm2LCJbdEF8RxOp148MEHe3R/IiIiIhpeGBxpWNA0DYqmxa1UapR0mDbKjdXbq5CbYsasglRku8x91heDXkRqD6emAuGprdlOU5eeLwhAXqoVry39O9avXx917K677kJ6enqP+kBEREREwwuDIw15e2q8+GxHFbKcJswpih+UpuS6kOEwYaTb0qd9sZt0cFoMPaqaCgAWgw6ZTlOH2220aAmNasCL2267LerYuHHjcO211/aoD0REREQ0/DA40pC1r96Hz7ZXYW+tDwBQ0RjAYSNT4lZE1evEPg2Nggi4LYZebd9hN4X3aOxK6GwJjTajhJvv+BPKy8ujjj/22GPQ6xNbGZaIiIiIhq6ezZUbAJqamvDJJ5/gz3/+MxYsWIDRo0dDEAQIgoC8vLxuX++HH37AlVdeicLCQpjNZqSlpeGoo47CM888A1luv9pmW8uWLcMJJ5yArKwsmEwm5OXl4YILLsDatWu73SfqmYpGP974thTL1u+NhEYAUFQNX+yq6ff+SJKAdLuxV6HRZZGQ4TB1OzRu27YNjz/+eNTxU089FSeeeGKP+0JEREREw8+gHXE87bTT8PHHHyfkWosXL8Y111yDQCAQafP7/Vi9ejVWr16NJUuW4N1334XH42n3Gn6/H+eccw7efffdqPbi4mIUFxdj6dKlWLRoEe68886E9JliVTcHsGZHNbZVNLV7zt5aL2RVjbvWsS9YDDqkWA1dmlraHrdND7ela1VdW4dGALjxxhsRCoUix/V6PR599NEe94WIiIiIhqdBO+KoaVrkcUpKCubNmwebzdbt67z//vu4/PLLEQgEkJGRgSeeeAJffPEF/vWvf+Gss84CAKxduxZnnXUWVFVt9zqXXnppJDTOnTsXb775JtatW4fFixejoKAAqqriD3/4A55//vlu95E6VucN4t8/7MfLa4rbDY02o4TjxqXjwsPz+iU0CgLgsEjw2Iy9Co1pDmO3QuMojyUSGv/1r39hxYoVUefccMMNGDNmTI/7Q0RERETDk6C1TmCDyLPPPgubzYbp06ejsLAQAJCXl4fi4mKMGjUKu3fv7vQasixj/Pjx2L59OxwOB77++msUFBREnXPNNdfgqaeeAgC89NJLuPDCC2Ous2rVKsyZMwdAeCT0jTfegE53cG+9qqoqHHbYYdizZw9SUlKwc+dOuFyunn3i7SgpKUFubi4A4O6ln8CVlpnQ6w9Ejf4QvthVg4376qG2811s1uswY7QbPxnhhNSFrSsSQRABTy+22gDCITDDboLN1LVrCAIw0mOB48D6zVAohMmTJ2Pr1q2RczIyMrBt2zY4HI4e94uIiIiIBr7W2WDv3r3Iycnp9TUH7Yjj5ZdfjvPOOy8SGnvijTfewPbt2wEAt912W0xoBICHH34YKSkpkcfxPPTQQwAAnU6Hp556Kio0AkBqampkv7za2losXry4x30mwBuU8fG2Crzw+W5sKI0fGo2SiFkFHlw6azR+OjKl30KjXhKQ4TD2OjRmOrsXGnNTDoZGAHjmmWeiQiMAPPDAAwyNRERERNQjgzY4JsKbb74ZeXzRRRfFPcdisWDBggUAgI0bN+LHH3+MOt7U1ISPPvoIADBv3rx20/xZZ50VedO+fPnyXvZ8eGsKyPh6Tx2UOIlRrxMwY7Qbl84ajRmjPTBI/fctbjaISLeboBd1nZ/cDkEAslwmWLtRSGeEywyn5WBorK+vx6JFi6LOmTp1atzRciIiIiKirhjWwXH16tUAgLFjxyIzs/2pnUcffXTk8aeffhp1bN26dZGiOq3Pa8tgMODwww+PPKd1wRLqnnS7CUXp0etZdaKAw0am4NJZozGrIBUmfc/DW084zBJSbV3bX7E9ggiMSDHB0o3RymyXCSlWQ1Tbfffdh+rq6qi2Rx99FGI/FQQiIiIioqFn2L6TbGpqQklJCYDwZugdaX188+bNUcdaf9zV68iyHDNySd0zs8ADAYAoAIfkOHHJEXk4uiitV1te9IQgAB6bAU6zofOTOyCKQI7LDJPU9f5nOI3w2KIL5+zevTtm+4358+dj9uzZveofEREREQ1vg3Y7jt4qKSmJVGbtbLFoy8JSILy4tLXWH3f3OhMmTOhWfztSVlbW5WsNdIqqYeO+emwrb8TPDs2BKMaO4nmsRhw3PgMj3RY4zcnZyF6nA1JtRhh0vRvdFMXwdFOj1PXrpNoNSLebYtpvv/32qG1lJEnCAw880Kv+EREREREN2+DY2NgYedzZNh5WqzXyuKkperuHRF2nM61D51Clqho272/A2l01qPeFp/Ju2t+ASdnOuOdPHhG/vT8YJBGpNgN0vZz+2ZPQmGLVI8tpjmlft24dXnnllai2q6++GkVFRb3qIxERERHRsA2Ofr8/8thg6HiaodF4cDqgz+frk+sMZ5qmYWt5I9bsrEatN3rt59qd1RiXae+XvRe7ymLQIcVq6NV6RgDQiUB2N0OjwyxhhCs2NGqaht/+9rdRbU6nE3feeWev+khEREREBPRxcJRlGXp976cRvvjii+1WPe0pk+ngNL9gMNjhua2n/pnN0W/aE3WdzrSdIttWWVkZpk+f3q1rJpumadhe2YTPd1Sjujn+167BL2NXVTPGpNv7uXfxOcxSr9czAuGRxu6GRotRh9wUC4Q4gfWNN96IKdx0xx13IDU1tdd9JSIiIiIatiOOdvvBINLZtNHm5ubI47bTURN1nc4kYtPOgULTNOyqbsbnO6pR0Rho97xspwlHFKRipNvSj72LTxCAFKuhW9tktKcn01NNehF5Hmvc9Z7BYBC33nprVFteXh6uvfbaXveViIiIiAjo4+AoSVJMFdKeyMrKSkBvorUOYp0Vnmk92td2rWHb60ydOrVH1xkONE3DnhovPt9ZjbJ6f7vnpduNOKLAg9Eea9zRtf4miECqzdCtiqft6Ulo1EsCRnms0MUJjQDwzDPPYPv27VFtDzzwQNRoOBERERFRb/T5iGNnW1Qki81mQ25uLvbu3YstW7Z0eG7r4+PHj4861royalevI0kSCgsLu9vlQa2k1ovPd1SjpK79tZ0eqwFHFHhQmGYbEIERSFzlVODAPo3dDI2iCOR5rDBI8dd4NjY24k9/+lNU24wZM7BgwYJe9ZWIiIiIqLWBU3EkCY488kgAwNatW7F///52z1u1alXk8axZs6KOTZs2LVIUp/V5bQWDQaxduzbmOcOBqmn4YHN5u6ExxaLHyZMyceHhozAm3T5gQqNBEpBhNyUwNJq6FRoFARjlscKkb/85jz32GCorK6PaHn744QHzNSQiIiKioWFYB8czzzwz8njJkiVxz/F6vXj11VcBhEcX225tYLfbceyxxwIA/vOf/7Q77XX58uVoaGgAEN6QfTgRBQEz8z0x7U6zHidOyMSvDs/DuEzHgAo7Rr2INLup19ttAOEAmO00dXuq6wiXGTZj+8+prKzEww8/HNV26qmnYvbs2T3qJxERERFRe4Z1cJw/fz4KCgoAAPfffz927NgRc87NN9+M2trayON4brrpJgDhKrLXXHMNFEWJOl5VVRUpXuJyubBw4cKEfQ6DxdgMOzzW8Cir3SjhuPHpuGhmHiZkO+IWfEkmi1GHNLux19ttAOHQmOk0wazvXmjMcBiRYu14VPq+++6LKsgkCALuu+++HvWTiIiIiKgjg7aq6vbt22O2H2h5E93U1BQzgnjiiSciMzMzqk2v1+OJJ57AaaedhoaGBsyaNQt33HEHpk+fjtraWjz33HP45z//CSA8rfWCCy6I25djjjkGP//5z7Fs2TK8/fbbmDdvHn7zm98gOzsb33//Pe69917s2bMHQLhoSUpKSiK+BANKbXMQn++sxrS8FKTbY4uyiIKA2WNSUe8LYfII54Dal7E1h0mC05KYacSCAGQ4TN2uxOqy6JHu6LiwTXFxMZ566qmotl/+8peYPHlyt/tJRERERNQZQdM0Ldmd6IklS5bg4osv7vL5K1euxJw5c+Iee+6553Dttde2uw/j9OnTsWLFig73xPP5fDj77LPx3nvvxT0uiiLuvPNOLFq0qMt97o6SkpJIpda7l34CV1pmJ89IjHpfCGt3VmNTWQM0APmpVpw5ZUS/3DvRXFY97Mbe7zvaIt1hhMPUvetZjDrkp3ZeTfZXv/oVXn755cjHBoMBW7duRV5eXk+6SkRERERDSOtssHfv3oRs7TdoRxwT6bLLLsPMmTPxxBNP4KOPPsK+fftgtVoxfvx4nH/++Vi4cCGkTtanmc1mrFixAkuXLsWSJUvw3Xffoa6uDhkZGZg9ezauvfZazJw5s58+o77X6A/hi1012LivHmqrPz3srGpGWb0PWU5z8jrXTYnco7FFqt3Q7dBokESMcls6DY0bN27E3//+96i2q666iqGRiIiIiPrMoB1xpGj9NeLYFJCxbncNvi+ph9LOt05hmg2nH5LdJ/dPNEEAPDZDt9cgdiTFqofHauzWc0QRKEizdVhBtcXpp5+Od955J/KxzWbDjh07kJ6e3u2+EhEREdHQwxFHShpvUMb64lp8t7cOsho/MEqigJ+OTMFhowbHGk5BBNJsBhi7We20I3az1O3QKAjASLelS6Hx008/jQqNQLg4E0MjEREREfUlBkfqkD+k4KviWny9txYhJX5g1IkCpuS4MC0vBZYETvfsS6IIpNmNCdmjsYXVqEO6vXuhEQgX0LF3YVqrpmm4/fbbo9rS0tJw4403dvueRERERETdMTje5VO/C8gKvtlTh6/21CIgq3HPEQVg8ggnZoz2dLjf4ECj04VDo15MXGg0GURkOEwQ0L0tPFwWPdK6GDY//PBDrF69OqrtjjvugN1u79Y9iYiIiIi6a/C826d+tW5XDdYX18Y9JgjAxCwHDh/tgcOcuCqk/UHSCUizGxO6HYheEpDlNHd730ezQYeclK4VEdI0DXfeeWdU26hRo3DFFVd0655ERERERD3B4Ehx/XRkCr5ps55RADA+y4HDR7vhStBeh/1JkgSk24zQJTA06kQg22mGrpuhUdIJGOXpvIJqixUrVmDdunVRbXfeeSeMxu5PjSUiIiIi6i4GR4rLapRwaK4rMuo4NsOOmfkeuK2DLzAC4VHBtASHRkEAsl1m6HXdu2ZLMZyuPk/TNPzhD3+IaisoKMCFF17YrfsSEREREfUUg+MwpaoaNpU1QFY1TMl1xT1n6ig3Gvwypue5u7wObyAySAJS+yA0ZjpNMErdXyeZ6TTB2o01oW+++Sa++eabqLa77roLev3gmiZMRERERIMXg+Mwo2oatuxvxNqd1ajzhWDQiRibaYc5zlYQZoMOp0zOSkIvE8cgiUi1G7s9lbQzqTYDrD2oIOuy6JFq63oIV1U1ZrRx7NixOO+887p9byIiIiKinmJwHCY0TcOPFU34fGc1apqDkfagouKr4locWZiaxN71jb4KjU6LBKe5+1N2TXoRI1xdK4bT4rXXXsPGjRuj2hYtWgRdArcRISIiIiLqDIPjEKdpGnZWNePzHdWobArEPeebvbU4bFRK3FHHwaqvQqPFoEOazdTt54kiMNJjgSh2vT+KomDRokVRbZMmTcKCBQu6fX8iIiIiot5gcByiNE1DcY0Xn+2oQnlD/MAIABkOI2YVpMIkJW79X7L1VWg0SAIynN0PjQCQk2Lp9nrIpUuXYsuWLVFtd999N8QErtUkIiIiIuoKBschaF+dD//evRf76v3tnpNmM+KIAg/yU61d3hJiMDBIQp+ERlEEsnqw7QYApNoNcHZzv8tQKIS77747qu3QQw/F/Pnzu31/IiIiIqLeYnAcgt79vgySI/6aRY/VgJn5HoxJtw2pwAiEt9xItZsSHhoFAchymrq97QYQLjCU6ej+KOXLL7+MHTt2RLXdfffdQ+41IyIiIqLBgcFxmHCZ9ZiZ78HYTDvEIRg+Ivs09sHnlmo3wqzv/o+KKIb3a+xu2AsGg/jTn/4U1TZ9+nSceuqp3e4DEREREVEiMDgOcQ6ThMPzPZiQ6ehWYZbBRNIdCI19sPbPYZHgNPVsv8ScFAsMPVg7+ve//x3FxcVRbX/84x852khEREREScPgOETZjBJmjHZjUrYTuiEaGAFApwPS7H0TGs0GEWnd2HOxNY+t++saAUCWZdx3331RbUcccQSOP/74HvWDiIiIiCgRGByHoJn5bsycnAepB2vyBhNRDIdGqQ9Co6QDMh0mCOh+6DYbRGT1sPrqK6+8gp07d0a13XnnnRxtJCIiIqKkGtrJYpiaPMI15EOjcCA06sXE7z0ZLoZj7tEopiCEp6j2JOgpioJ77703qm3q1Kk44YQTun0tIiIiIqJEGtrpgoYkQQDS7AYYdIkPjUC4GE5391xske0yw6Tv2XNff/11bN26NaqNo41ERERENBAwONKgIgjh9YNGXd/Msrabe14Mx2GW4LYaevRcVVVxzz33RLUdcsghOO2003p0PSIiIiKiRGJwpEElxWro0dYYXWHUC0i396wYjqQTMMJl7vG933rrLWzcuDGq7Y477uBoIxERERENCAyONGi4rHpYDX0TGgURyHSYe1QMBwByUsw9XleqaVrMvo0TJkzAWWed1aPrERERERElGoMjDQoOkwS7sWdTSLsi02GCvofBz2MzwN7D6a0A8N577+Gbb76Javv9738PsQ+qxRIRERER9QTfmdKAZzHq4LT0bO1gV6RYej6SadSLyHT0bOsNIP5o45gxY3Duuef2+JpERERERInG4EgDmlEv9rjgTFeYDCLctp5dXxCA3BQLRLHn6xD/85//4Isvvohqu/3226Hro4qxREREREQ9weBIA5ZeEuCxGXu87rAzoghk2E09vn6a3QizoXcBr+1oY15eHs4///xeXZOIiIiIKNEYHGlA0umANJsRuj6sKprRi3WNZoPY4wqsLVavXo3Vq1dHtd12223Q6/tuLScRERERUU8wONKAI4hAqs0IXR8Wh3FapB6vaxQEICfF0uutMh544IGoj3NycvCrX/2qV9ckIiIiIuoLDI40oAgC4LEaYOjDNX5GvYBUW89HC9PtRpj0vevfhg0b8N5770W13XzzzTAaezeKSURERETUFxgcaUBxWfQw6/tmr0YgHEzTe7Gu0WwQkdbLKaoA8NBDD0V97PF4cOmll/b6ukREREREfYHBkQYMm0kHWx/u1QgAqXYjjFLPRgsFARjh6v0U1d27d2PZsmVRbb/+9a9htVp7dV0iIiIior7C4EgDgkkvwtWHezUCgNWog9PU82CaiCqqAPDII49AUZTIxxaLBddcc02vr0tERERE1FcYHCnpJEmAuw+33QAAnYheVUE16ntfRRUAKisrsXjx4qi2yy+/HB6Pp9fXJiIiIiLqKwyOlFSiCKTaDH267QYApDtMvarSOsJl7vUUVQB48skn4fP5Ih9LkoQbb7yx19clIiIiIupLDI6UNIIAeGwG6MW+q6AKAI5ebL0BAG6bAVZj7wv2NDU14X/+53+i2s4//3zk5ub2+tpERERERH2JwZGSxmnRwyT1XQVVANDrBKRaez7FVNIJyHSYEtKX5557DrW1tVFtt9xyS0KuTURERETUlxgcKSksRh3sfVxBVRCADKcRYi+mmGY7zdCJvZ+iGgwG8cgjj0S1nX766ZgwYUKvr01ERERE1NcYHKnfGSQRbmvfVlAFAJe5dyOadpMEpyUx4Xbp0qUoLS2Narv11lsTcm0iIiIior7G4Ej9ShTD6xr7soIqABgkAW5bz8OpIADZLnNC+qKqKh566KGottmzZ+OII45IyPWJiIiIiPoagyP1m5ZiOFIvqpt29T4ZDlOvwmm6wwiDlJh+vvPOO9i8eXNUG0cbiYiIiGgwYXCkfuPo5dTRrnJZ9DBKPa/UatSLSLP1fs/GFm3XNk6aNAknn3xywq5PRERERNTXGBypX1gMOjhMfVsMBzgwRbWX6yezE7RnIwCsX78eq1evjmq75ZZbEnZ9IiIiIqL+wOBIfU6SBLj6oRiOIISnmPZmiqrLooctAXs2tnj00UejPs7Ozsa5556bsOsTEREREfUHBkfqU4IAeKwG6PphhM1plno1FVYUgUxnYvZsBIA9e/bgtddei2q77rrrYDD0fYgmIiIiIkokBkfqUylWAwy6nq837Cq9ToDb2rt1iel2E/S6xP1IPPnkk1AUJfKxxWLBFVdckbDrExERERH1FwZH6jMWkw5WQ98XwwGANIcRYi9GNY16Eam92L6jrYaGBjz77LNRbZdccglSUlISdg8iIiIiov7C4Eh9Qi8JSDH3z5RMu0mCRd+7gJrlNCW0YM0LL7yAhoaGyMeCIOA3v/lNwq5PRERERNSfGBwp4QQBcFsNvRoB7CpRRK9HCu0mCfYEVnyVZRmPP/54VNuZZ56JgoKChN2DiIiIiKg/MThSwvXXukYASLUZoRN7/m0sCECWK3EFcQDgjTfewO7du6PabrzxxoTeg4iIiIioPzE4UkJZjP23rtFsEHu9N6THZoBRSmzIbbsFx/Tp0zFr1qyE3oOIiIiIqD8xOFLCSDoBLkv/rGsUhPBoY2/oRAHp9sSONq5ZswZr166NarvxxhsTun6SiIiIiKi/MThSQghCePSuP/ZrBACXRd/rkcIMhxE6MbH9feSRR6I+HjlyJH72s58l9B5ERERERP2NwZESwmXR99u6Rr1OQEovRzaNehFua2JHR3fu3Ik33ngjqu3666+HJPXP1F0iIiIior7C4Ei9ZjHqYDMmrippZ1LtvduzEQAyE7z9BgA88cQTUFU18rHdbsell16a0HsQERERESUDgyP1iiT1fvSvO6wJKL5jNep6XVSnrcbGRrzwwgtRbZdddhmcTmdC70NERERElAyDNjg2NTXhk08+wZ///GcsWLAAo0ePhiAIEAQBeXl5XbrG7t27I8/p7L+LLrqoS9dctmwZTjjhBGRlZcFkMiEvLw8XXHBBTMGUoUAQAE8/7dfYcr/eFsQBgCynOQG9ifbSSy+hsbEx8rEoirjuuusSfh8iIiIiomQYtIuvTjvtNHz88cfJ7kaE3+/HOeecg3fffTeqvbi4GMXFxVi6dCkWLVqEO++8M0k9TLz+XNcIACkWPfS63v2tw2XRw2xIbJ9VVcWTTz4Z1Xb66ad3+Q8YREREREQD3aANjpqmRR6npKRg6tSpWLNmDZqamnp0vXvuuQdnnHFGu8dTUlI6fP6ll14aCY1z587F9ddfj+zsbHz//fe47777sGPHDvzhD39AVlYWFi5c2KM+DiQWQ/+ua5R06PVWH4IAZDgSu/0GAHz44YfYtm1bVNuvf/3rhN+HiIiIiChZBm1wPO+883D55Zdj+vTpKCwsBADk5eX1ODiOGDECkyZN6tFzV61ahaVLlwIIj4S+8cYb0B0YiZs2bRpOP/10HHbYYdizZw9uueUWnH322XC5XD2610Cg0wGuBFck7UyqzdTrKbEemwEGKfGzs5944omojydNmoQ5c+Yk/D5ERERERMkyaNc4Xn755TjvvPMioTGZHnroIQCATqfDU089FQmNLVJTU/Hggw8CAGpra7F48eJ+72Oi9Pd+jQBgNoiwGXv3Nw5RBNISsD6yrR9//BHvvfdeVNt1112X8IqtRERERETJNGiD40DR1NSEjz76CAAwb9485OTkxD3vrLPOgsPhAAAsX7683/qXaHaTBKOu/waqE1UQJ81mhNTL9ZHx/PWvf436OCUlBeeff37C70NERERElEwMjr20bt06BAIBAMDRRx/d7nkGgwGHH3545DmhUKhf+pdIRr0Ip7l/p6jazRKMUu+K2ehEISHhs614W3AsXLgQVqs14fciIiIiIkomBscDnnzySYwePRpGoxFOpxMTJ07ElVdeia+//rrD523evDnyeNy4cR2e23JclmX8+OOPve90PxJEwN3P6xoFEfAkYI/IdIcRopj4qaPxtuC4+uqrE34fIiIiIqJkG7TFcRKtdUAMBoPYtGkTNm3ahGeeeQZXXHEFHn/8cRiNsaNWe/fujTxub5pqi9zc3KjnTZgwocv9Kykp6fB4637UV1d0+bpd5bLqURfov603ACDFakBlqHeVW/U6AS7NhpK6xAZHVVXx2GOPRbXNmzcPkiR1+loREREREfWlsrKyyGNZlhNyzWEfHF0uF+bPn485c+ZgzJgxMJlMKCsrwwcffIDFixejqakJzzzzDBobG/GPf/wj5vmtR5xsNluH92o9hbG71V9bh87OPHrd2d26NiXG+++/363XiYiIiIior1VWViZkf/FhHRyzs7NRWloKi8US1X7ooYfi5JNPxjXXXIPjjjsOe/bswdKlS3Huuefi9NNPjzrX7/dHHhsMHU+rbD1i6fP5EvAZEBERERER9b0+DY6yLEOv7/0m8S+++CIuuuii3neoDYPB0GHYGzNmDP7xj39g9uzZAMLrINsGR5Pp4IbywWCww/u1FNEBALPZ3K2+tp6KGo/f78eWLVuQkZGBtLQ0SNLQ+ZtAWVkZpk+fDiBcWCgrKyvJPaKu4ms3OPF1G7z42g1efO0GJ75ug9dQf+1kWUZlZSUAYPLkyQm55tBJF33kyCOPxMSJE/HDDz/g008/haqqEMWDNYXsdnvkcWfTT5ubmyOPO5vW2lZn6ycBDIg9LftaVlZWl74WNPDwtRuc+LoNXnztBi++doMTX7fBa6i+domYntpanwZHSZKiqo72VLL/AjBhwgT88MMP8Pv9qK6uRlpaWuRY62+ykpISTJ06td3rtB415Fo4IiIiIiIaLPp8xLGzLSoGA03T2j3WujLqli1bOrxOy3FJkobF6CAREREREQ0N3MexCzZt2gQgXNzG4/FEHZs2bVpkneSqVavavUYwGMTatWtjnkNERERERDTQMTh24tNPP40ExyOPPDJqfSMQXuN47LHHAgD+85//tLuH3/Lly9HQ0AAAmD9/fh/2mIiIiIiIKLGGdXB88803O5yGun37dpx//vmRj6+++uq45910000AwtWLrrnmGiiKEnW8qqoKt956K4DwvpELFy7sbdeJiIiIiIj6zaCtqrp9+3Z8+umnUW0tVU2bmpqwZMmSqGMnnngiMjMzo9rmz5+PwsJCnHXWWZg+fTpycnJgNBqxb98+fPDBB3j++ecjlVAXLFiAs846K25fjjnmGPz85z/HsmXL8Pbbb2PevHn4zW9+g+zsbHz//fe49957sWfPHgDAAw88gJSUlER8CYiIiIiIiPrFoA2On376KS6++OK4x6qrq2OOrVy5MiY4AuEA+tBDD3V4r6uuugqPPfZYh+e88MILaGhowHvvvYeVK1di5cqVUcdFUcSdd96JK664osPrEBERERERDTSDNjgmwttvv401a9bgiy++QHFxMaqqqtDc3AyHw4H8/HzMnj0bl1xyCSZNmtTptcxmM1asWIGlS5diyZIl+O6771BXV4eMjAzMnj0b1157LWbOnNkPn9XQk5OT0+GUYhq4+NoNTnzdBi++doMXX7vBia/b4MXXrvsEjV8xIiIiIiIi6sCwLo5DREREREREnWNwJCIiIiIiog4xOBIREREREVGHGByJiIiIiIioQwyORERERERE1CEGRyIiIiIiIuoQgyMRERERERF1iMGRiIiIiIiIOsTgSENGKBTCkiVLcMopp2DkyJEwGo1ITU3F5MmTsXDhQrz22mvJ7iJ1wb/+9S8IghD5b9GiRcnuErWxZ88e/O1vf8O5556LsWPHwmq1wmQyIScnB2eccQZeeeUVyLKc7G4OO3v27MFNN92E8ePHw2q1wu12Y/r06fjzn/8Mr9eb7O5RG19//TXuu+8+nHTSScjNzYXRaITNZkNRUREuuugirF69OtldpG665ZZbov79+vjjj5PdJWpHVVUVHnroIcyaNQuZmZkwGo3Izs7GjBkzcPPNN2PNmjXJ7uKAJGiapiW7E0S9tWHDBpx//vnYuHFju+c4nU7U1dX1X6eo25qbmzFx4kQUFxdH2u666y6GxwHkD3/4A+655x509k/H1KlT8c9//hMjR47sp54NbytWrMD555+P+vr6uMfHjh2L9957D/n5+f3cM4rn6KOPxieffNLpeRdccAGef/55GAyGfugV9cZ3332HqVOnRv3RbOXKlZgzZ07yOkVxvfbaa7jqqqtQXV3d7jlnnHEG3nzzzf7r1CAhJbsDRL21YcMGzJ07FzU1NTAYDLj44otx0kknIScnB3V1dSguLsZHH33Ev94OAnfeeSeKi4uRnp6OioqKZHeH4ti3bx80TYPVasX8+fNx7LHHYsyYMTCZTNi8eTOeeOIJrF+/Hl9++SWOO+44fP3117DZbMnu9pD23XffYcGCBfB6vbDZbLjtttswd+5c+Hw+LFu2DM899xy2bt2KU045BevXr+frMQCUlpYCALKzs3HOOedg9uzZGDlyJBRFwZo1a/DII4+gtLQUf//73yHLMpYuXZrkHlNHVFXFZZddBlmW+e/XAPfyyy/j4osvhqqqSE9Px1VXXYUjjzwSbrcb+/fvx44dO/DOO+9Ar9cnu6sDk0Y0iPl8Pq2oqEgDoGVlZWnff/99u+cGAoF+7Bl111dffaXpdDrNaDRqzz77rAZAA6Ddddddye4atXLLLbdoDz74oNbQ0BD3uCzL2oIFCyKv3x//+Md+7uHwM2fOHA2AJkmS9vnnn8ccf+ihhyKvx913352EHlJbp5xyivZ///d/mizLcY9XVlZG/m0DoH3yySf93EPqjscee0wDoI0bN0677bbbIq/bypUrk901amXTpk2a0WjUAGizZ8/W6urq2j2X7xnj4xpHGtT+/Oc/Y9u2bQCApUuXYtKkSe2ey6k+A5eiKLjsssugKApuv/12jBkzJtldonY8+OCDuOWWW2C32+Me1+l0eOqppyI/b6+//np/dm/YWb9+fWQd1aWXXoqZM2fGnPPb3/4W48ePBwD85S9/QSgU6s8uUhzvvvsuFixYAJ1OF/d4amoqHnnkkcjH/DkauPbu3Ys777wTAPC3v/2N7zUGsOuuuw6BQACpqalYvnw5nE5nu+fydYyPwZEGLUVR8PTTTwMA5syZw3UEg9hjjz2Gr7/+GkVFRbj11luT3R3qJY/Hg5/85CcAgB07diS5N0Nb6zU4F198cdxzRFHEhRdeCACora1lwY5BovW/afw5GriuvvpqNDU14Ve/+hXfhwxgW7ZswUcffQQAuPbaa5GamprkHg1ODI40aH3++eeRdSLnnHNOpN3r9WL79u0oKyuDqqrJ6h510e7du3HXXXcBAJ566ikYjcYk94gSIRAIAAiHFuo7LWu3rVYrDjvssHbPO/rooyOPP/300z7vF/VeMBiMPObP0cD06quv4t1334Xb7cbDDz+c7O5QB1pX1m/9nrG2thY//vhjh4Vy6CD+JqJBa+3atZHHM2fOxLp163DCCSfAbrdjzJgxyM7ORlpaGhYuXBhVpZMGlquuugperxfnn38+jj322GR3hxKgoqICmzdvBgCMGzcuyb0Z2lq+zoWFhZCk9uvdtX4dWp5DA9uqVasij/lzNPDU1dXh+uuvBxCewp+WlpbkHlFHWt4zOp1OjB8/Hv/4xz9wyCGHwO12o6ioCKmpqcjPz8fdd9+NpqamJPd24GJwpEFr06ZNkcdr167FrFmz8MEHH0SNMtbU1GDx4sWYMmVKl0qfU/9aunQp/v3vf8PlckWt56HB7eGHH46UpF+wYEGSezN0+f1+VFVVAQBycnI6PDclJQVWqxVAeE0WDWyqquKBBx6IfMyfo4Hnlltuwf79+3HEEUfg0ksvTXZ3qBMt7xnz8vJw3XXX4Ze//CU2bNgQdc6uXbuwaNEizJw5E/v27UtGNwc8BkcatGpqaiKPb7jhBiiKgltuuQU7duxAIBDA9u3bcdNNN0EQBNTV1eGss86KTG2l5KupqcENN9wAALj//vuRkZGR5B5RInzxxRf4y1/+AiAcZq6++urkdmgIa2xsjDzuyhYbLcGRf00f+B577DGsW7cOADB//nxMnTo1yT2i1j799FM8//zzkCQJTz/9NARBSHaXqBMt7xm3bNmCv/71r3C5XHj66adRUVEBv9+P9evX46STTgIAbNy4Eeeccw6XO8XB4EiDVnNzc+RxIBDAQw89hAcffBD5+fkwGAwoKCjAww8/jHvvvRcAUF1djfvvvz9Z3aU2brrpJlRUVGDGjBm4/PLLk90dSoDy8nKcffbZkGUZgiDgpZdegsViSXa3hiy/3x953JUKgC3rh30+X5/1iXpv1apV+N3vfgcASE9Px9/+9rck94haCwaDuPzyy6FpGm644QZMnjw52V2iLmh5zxgIBKDT6fCvf/0LV1xxBdLS0mA0GjF16lS8++67kfD4+eefY/ny5cns8oDE4Eh9ruVNZG//W7JkSdR1TSZT5HFOTk5k9Kqtm2++GVlZWQCAZcuWQdO0Pvtch5q+eu0+/vhjvPjii9DpdHj66adZ+KEP9NVr157GxkaccsopKCkpAQDcd999OOaYY/rwM6TWvwNbF1JpT0vBIrPZ3Gd9ot754YcfMH/+fMiyDKPRiFdffZWzMQaY++67D5s3b8bIkSMjhd1o4Gv9+/Kcc87B4YcfHnOOKIpRRY5eeeWVfunbYMJ3azRotd5Hbt68ee3uhyVJUuQNbHV1NXbt2tUv/aP4AoEArrjiCgDAr3/9a0yZMiW5HaJe8/v9OOOMM/DVV18BAG688cbIiAn1nda/A7sy/bTlL+5dmdZK/W/Xrl04/vjjUVtbC51Oh1deeSWqGi4l35YtWyIzl5588snI9G8a+Fr/vmwZVYxn4sSJGDFiBIDwPrkUrf0SbEQJIklSQqr4tYwatsjNzY087qwwROtzKyoqkJ+f3+v+DAd98dotX74c27ZtgyRJmDBhApYtWxZzfuvCRxs3boycM2PGDIwePbrX/RkO+urnri1ZlrFgwQKsXLkSALBw4UIWOuonJpMJqampqKqqioz0tqe2tjYSHFv/PqSBYd++fTjuuOOwb98+CIKAF154AfPnz092t6iNxx57DMFgEPn5+fB6vXH//dq4cWPk8X//+1/s378fAHDaaacxaCZRbm5u5LXoynvG0tJSVFRU9EfXBhUGR+oXfVFKfOLEiZHHiqJ0eG7r4x2VrKdYiX7tWqbLybKMyy67rNPz//nPf+Kf//wnAODFF19kcOyGvi7hr6oqLrjgArzzzjsAgHPPPRfPPPNMn96Too0fPx6rV6/G9u3bIctyu7/ftmzZEvUcGjiqqqowb9487Ny5E0B4JOvCCy9Mcq8onpZ/v3bu3Ilf/OIXnZ7/pz/9KfJ4165dDI5JNHHixMgIYlffM/L9YixOVaVB66ijjoo83rFjR4fntj7eMgWBiHrniiuuiPzF/dRTT8Xf//53rlftZ0ceeSSA8DTUlqnC8bTeE3DWrFl93i/qmvr6epxwwgmRWRYPPPAArrnmmiT3imjo6c57xpY/4vD9Yiz+C0+D1ujRo3HooYcCAN5//314vd645zU2NuLDDz8EABQUFHQ69Y761kUXXQRN0zr8r2XaIwDcddddkfaLLrooeR2nKDfeeCOef/55AMCxxx6L119/HXq9Psm9Gn7OPPPMyOMXX3wx7jmqquLll18GALhcLsydO7c/ukad8Hq9OOWUU/D1118DAH7/+9/j1ltvTXKvqCNLlizp9N+v1gVzVq5cGWnPy8tLXscJp59+euTfqI6qpa5atQrV1dUAgNmzZ/dL3wYTBkca1FoKcNTV1eG3v/1t3HNuuOGGyH5nV155Zb/1jWioWrRoER577DEAwBFHHIG33norstUD9a/p06dH3twsXrwYa9asiTnnkUceiax3vf766xnwB4BgMIj58+fjs88+AxB+Xe65554k94po6PJ4PFi4cCEA4MMPP4y7PrWxsRG/+c1vIh+3FPKjgzh5lwa1BQsW4KWXXsJ7772Hp59+Gnv37sXll1+O3Nxc7NmzB08//TT+/e9/AwAOPfRQXHvttUnuMdHg9uSTT+Luu+8GEJ7G89BDD3VaqXjs2LEMK33o8ccfx6xZs+Dz+XD88cfj9ttvx9y5c+Hz+bBs2TI8++yzAICioqJ2/8BG/esXv/gFPvjgAwDAMcccg0svvTSqqEpbBoMBRUVF/dU9oiHp7rvvxooVK7Bnzx5ccMEF+Oyzz3DWWWfB4XDg+++/x4MPPhhZD37VVVdh2rRpSe7xwCNo3NSOBrmmpib87Gc/i/wjHM+0adPw9ttvIzMzsx97Rj318ccfR6bT3XXXXVi0aFFyO0QRc+bMiVov1xW7du3iNK0+9s477+CXv/wlGhoa4h4vKirCihUrUFhY2M89o3gEQejW+aNGjcLu3bv7pjOUUIsWLYr8cW3lypWYM2dOcjtEUTZv3ozTTz8d27dvb/ecSy65BE8//TT/4BkHp6rSoGez2fD+++/jlVdewYknnojMzEzo9XqkpaVh3rx5eOGFF7BmzRqGRiIask477TRs2LABN9xwA4qKimCxWOByuTB16lQ8+OCD+OabbxgaiWjYGz9+PL799ls8/PDDmDFjBtxuNwwGA3JycnDuuefiv//9LxYvXszQ2A6OOBIREREREVGHOOJIREREREREHWJwJCIiIiIiog4xOBIREREREVGHGByJiIiIiIioQwyORERERERE1CEGRyIiIiIiIuoQgyMRERERERF1iMGRiIiIiIiIOsTgSERERERERB1icCQiIiIiIqIOMTgSERERERFRhxgciYiIiIiIqEMMjkRERERERNQhBkciIiIiIiLqEIMjERERERERdYjBkYiIiIiIiDrE4EhEREREREQdYnAkIiIiIiKiDjE4EhERERERUYcYHImIiIiIiKhDDI5ERERERETUIQZHIiIiIiIi6hCDIxEREREREXWIwZGIiIiIiIg6xOBIREREREREHfp/AnwqhbosRNQAAAAASUVORK5CYII=", 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" 115 | ] 116 | }, 117 | "metadata": {}, 118 | "output_type": "display_data" 119 | } 120 | ], 121 | "source": [ 122 | "torch.manual_seed(0)\n", 123 | "\n", 124 | "x_train = torch.linspace(-4, 4, 1000).unsqueeze(-1)\n", 125 | "sigma = torch.normal(torch.zeros_like(x_train), 3 * torch.ones_like(x_train))\n", 126 | "y_train = x_train**3 + sigma\n", 127 | "\n", 128 | "x_test = torch.linspace(-7, 7, 1000).unsqueeze(-1)\n", 129 | "y_test = x_test**3\n", 130 | "\n", 131 | "model = nn.Sequential(\n", 132 | " nn.Linear(1, 64),\n", 133 | " nn.ReLU(),\n", 134 | " nn.Linear(64, 64),\n", 135 | " nn.ReLU(),\n", 136 | " NormalInvGamma(64, 1, initial_bias=-14.0),\n", 137 | ")\n", 138 | "\n", 139 | "optimizer = torch.optim.Adam(model.parameters(), lr=5e-4)\n", 140 | "\n", 141 | "for _ in range(500):\n", 142 | " for x, y in DataLoader(TensorDataset(x_train, y_train), batch_size=100, shuffle=True):\n", 143 | " pred = model(x)\n", 144 | " # loss = evidential_regression(pred, y, lamb=1e-2)\n", 145 | " loss = URERN_loss(pred, y, lambda_1=1e-2, lambda_2=1e-2)\n", 146 | "\n", 147 | " optimizer.zero_grad()\n", 148 | " loss.backward()\n", 149 | " optimizer.step()\n", 150 | "\n", 151 | "with torch.no_grad():\n", 152 | " pred = model(x_test)\n", 153 | "\n", 154 | "mu, v, alpha, beta = (d.squeeze() for d in pred)\n", 155 | "x_test = x_test.squeeze()\n", 156 | "var = torch.sqrt(beta / (v * (alpha - 1)))\n", 157 | "\n", 158 | "# plot code modified from\n", 159 | "# https://github.com/aamini/evidential-deep-learning/blob/main/hello_world.py#L48\n", 160 | "plt.figure(figsize=(5, 3), dpi=200)\n", 161 | "plt.scatter(x_train, y_train, s=1.0, c=\"tab:blue\", label=\"Train\")\n", 162 | "plt.plot(x_test, y_test, c=\"k\", label=\"True\")\n", 163 | "plt.plot(x_test, mu, c=\"tab:blue\", ls=\"--\", label=\"Pred\")\n", 164 | "for std in range(4):\n", 165 | " plt.fill_between(\n", 166 | " x_test,\n", 167 | " (mu - std * var),\n", 168 | " (mu + std * var),\n", 169 | " alpha=0.2,\n", 170 | " facecolor=\"tab:blue\",\n", 171 | " label=\"Unc.\" if std == 0 else None,\n", 172 | " )\n", 173 | "plt.gca().set_ylim(-150, 150)\n", 174 | "plt.gca().set_xlim(-7, 7)\n", 175 | "plt.legend(loc=\"upper left\")\n", 176 | "plt.show()\n", 177 | "\n", 178 | "torch.cuda.empty_cache() " 179 | ] 180 | } 181 | ], 182 | "metadata": { 183 | "kernelspec": { 184 | "display_name": "URERN", 185 | "language": "python", 186 | "name": "python3" 187 | }, 188 | "language_info": { 189 | "codemirror_mode": { 190 | "name": "ipython", 191 | "version": 3 192 | }, 193 | "file_extension": ".py", 194 | "mimetype": "text/x-python", 195 | "name": "python", 196 | "nbconvert_exporter": "python", 197 | "pygments_lexer": "ipython3", 198 | "version": "3.9.19" 199 | } 200 | }, 201 | "nbformat": 4, 202 | "nbformat_minor": 2 203 | } 204 | --------------------------------------------------------------------------------