diff --git a/.travis.yml b/.travis.yml index b9b28b92..2c2cb433 100644 --- a/.travis.yml +++ b/.travis.yml @@ -4,6 +4,9 @@ language: python python: - 3.6 + - 3.7 + - 3.8 + - 3.9 dist: xenial services: diff --git a/README.md b/README.md index 7f17ae5b..e1044d23 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,5 @@ # Py-FEAT +[![Package versioning](https://img.shields.io/pypi/v/py-feat.svg)](https://pypi.org/project/py-feat/) [![Build Status](https://api.travis-ci.org/cosanlab/feat.svg?branch=master)](https://travis-ci.org/cosanlab/feat/) [![Coverage Status](https://coveralls.io/repos/github/cosanlab/py-feat/badge.svg?branch=master)](https://coveralls.io/github/cosanlab/py-feat?branch=master) Python Facial Expression Analysis Toolbox (FEAT) diff --git a/notebooks/_build/.doctrees/content/analysis.doctree b/notebooks/_build/.doctrees/content/analysis.doctree index cf99995d..174cd34c 100644 Binary files a/notebooks/_build/.doctrees/content/analysis.doctree and 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ff4268ce..40ce9a78 100644 Binary files a/notebooks/_build/.doctrees/environment.pickle and b/notebooks/_build/.doctrees/environment.pickle differ diff --git a/notebooks/_build/html/_images/analysis_17_1.png b/notebooks/_build/html/_images/analysis_17_1.png index 397a92dd..3a5ae5c1 100644 Binary files a/notebooks/_build/html/_images/analysis_17_1.png and b/notebooks/_build/html/_images/analysis_17_1.png differ diff --git a/notebooks/_build/html/_images/dev_plotting_12_1.png b/notebooks/_build/html/_images/dev_plotting_12_1.png new file mode 100644 index 00000000..0cbc1739 Binary files /dev/null and b/notebooks/_build/html/_images/dev_plotting_12_1.png differ diff --git a/notebooks/_build/html/_images/dev_plotting_14_1.png b/notebooks/_build/html/_images/dev_plotting_14_1.png new file mode 100644 index 00000000..714d64af Binary files /dev/null and b/notebooks/_build/html/_images/dev_plotting_14_1.png differ diff --git a/notebooks/_build/html/_images/dev_plotting_15_1.png b/notebooks/_build/html/_images/dev_plotting_15_1.png new file mode 100644 index 00000000..97a89170 Binary files /dev/null and b/notebooks/_build/html/_images/dev_plotting_15_1.png differ diff --git a/notebooks/_build/html/_images/dev_plotting_17_1.png b/notebooks/_build/html/_images/dev_plotting_17_1.png new file mode 100644 index 00000000..2081480c Binary files /dev/null and b/notebooks/_build/html/_images/dev_plotting_17_1.png differ diff --git a/notebooks/_build/html/_images/dev_plotting_19_1.png b/notebooks/_build/html/_images/dev_plotting_19_1.png new file mode 100644 index 00000000..590f64ac Binary files /dev/null and b/notebooks/_build/html/_images/dev_plotting_19_1.png differ diff --git a/notebooks/_build/html/_images/dev_plotting_22_1.png b/notebooks/_build/html/_images/dev_plotting_22_1.png new file mode 100644 index 00000000..df6b6878 Binary files /dev/null and b/notebooks/_build/html/_images/dev_plotting_22_1.png differ diff --git a/notebooks/_build/html/_images/dev_plotting_2_1.png b/notebooks/_build/html/_images/dev_plotting_2_1.png new file mode 100644 index 00000000..9efde8ef Binary files /dev/null and b/notebooks/_build/html/_images/dev_plotting_2_1.png differ diff --git a/notebooks/_build/html/_images/dev_plotting_5_1.png b/notebooks/_build/html/_images/dev_plotting_5_1.png new file mode 100644 index 00000000..1775c07e Binary files /dev/null and b/notebooks/_build/html/_images/dev_plotting_5_1.png differ diff --git a/notebooks/_build/html/_images/dev_plotting_7_1.png b/notebooks/_build/html/_images/dev_plotting_7_1.png new file mode 100644 index 00000000..1a1eea52 Binary files /dev/null and b/notebooks/_build/html/_images/dev_plotting_7_1.png differ diff --git a/notebooks/_build/html/_images/dev_plotting_8_1.png b/notebooks/_build/html/_images/dev_plotting_8_1.png new file mode 100644 index 00000000..2de979c7 Binary files /dev/null and b/notebooks/_build/html/_images/dev_plotting_8_1.png differ diff --git a/notebooks/_build/html/_images/dev_trainAUvisModel_8_0.png b/notebooks/_build/html/_images/dev_trainAUvisModel_8_0.png new file mode 100644 index 00000000..d5c1cc4b Binary files /dev/null and b/notebooks/_build/html/_images/dev_trainAUvisModel_8_0.png differ diff --git a/notebooks/_build/html/_sources/content/analysis.ipynb b/notebooks/_build/html/_sources/content/analysis.ipynb index a0640419..c128328f 100644 --- a/notebooks/_build/html/_sources/content/analysis.ipynb +++ b/notebooks/_build/html/_sources/content/analysis.ipynb @@ -59,11 +59,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2021-03-26T01:35:57.204484Z", - "start_time": "2021-03-26T01:35:56.969949Z" + "end_time": "2021-03-27T16:52:47.049268Z", + "start_time": "2021-03-27T16:52:47.041360Z" } }, "outputs": [ @@ -106,8 +106,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T18:54:19.816980Z", - "start_time": "2021-03-24T18:48:21.412688Z" + "end_time": "2021-03-27T16:52:35.840134Z", + "start_time": "2021-03-27T16:52:33.285107Z" }, "scrolled": true }, @@ -121,11 +121,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2021-03-26T01:36:04.630894Z", - "start_time": "2021-03-26T01:36:02.098727Z" + "end_time": "2021-03-27T16:53:33.106459Z", + "start_time": "2021-03-27T16:53:32.697327Z" } }, "outputs": [], @@ -144,11 +144,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2021-03-26T01:36:04.662326Z", - "start_time": "2021-03-26T01:36:04.632024Z" + "end_time": "2021-03-27T16:53:33.116599Z", + "start_time": "2021-03-27T16:53:33.107762Z" } }, "outputs": [ @@ -240,7 +240,7 @@ "4 5 gn 5 your application has been accepted 005.mp4 goodNews" ] }, - "execution_count": 4, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -264,11 +264,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "metadata": { "ExecuteTime": { - "end_time": "2021-03-26T01:36:04.916876Z", - "start_time": "2021-03-26T01:36:04.663369Z" + "end_time": "2021-03-27T16:53:34.341195Z", + "start_time": "2021-03-27T16:53:34.245364Z" } }, "outputs": [ @@ -411,11 +411,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "metadata": { "ExecuteTime": { - "end_time": "2021-03-26T01:36:05.612696Z", - "start_time": "2021-03-26T01:36:04.917989Z" + "end_time": "2021-03-27T16:53:35.944844Z", + "start_time": "2021-03-27T16:53:35.417200Z" } }, "outputs": [ @@ -643,11 +643,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": { "ExecuteTime": { - "end_time": "2021-03-26T01:37:30.117739Z", - "start_time": "2021-03-26T01:37:29.898669Z" + "end_time": "2021-03-27T16:53:36.933517Z", + "start_time": "2021-03-27T16:53:36.912989Z" } }, "outputs": [ @@ -693,6 +693,11 @@ " 2.660164e-12\n", " \n", " \n", + " mean_AU05\n", + " -11.942339\n", + " 8.022300e-07\n", + " \n", + " \n", " mean_AU06\n", " 3.905366\n", " 3.589972e-03\n", @@ -703,11 +708,21 @@ " 3.864728e-04\n", " \n", " \n", + " mean_AU09\n", + " -22.647961\n", + " 3.024835e-09\n", + " \n", + " \n", " mean_AU10\n", " 10.361594\n", " 2.660301e-06\n", " \n", " \n", + " mean_AU11\n", + " -42.691265\n", + " 1.059418e-11\n", + " \n", + " \n", " mean_AU12\n", " 11.949046\n", " 7.984040e-07\n", @@ -728,6 +743,11 @@ " 6.562759e-01\n", " \n", " \n", + " mean_AU20\n", + " -45.470892\n", + " 6.019391e-12\n", + " \n", + " \n", " mean_AU23\n", " -0.300423\n", " 7.706780e-01\n", @@ -737,6 +757,26 @@ " -4.533345\n", " 1.419377e-03\n", " \n", + " \n", + " mean_AU25\n", + " 3.119502\n", + " 1.232852e-02\n", + " \n", + " \n", + " mean_AU26\n", + " -5.015769\n", + " 7.232461e-04\n", + " \n", + " \n", + " mean_AU28\n", + " -7.169935\n", + " 5.251451e-05\n", + " \n", + " \n", + " mean_AU43\n", + " -78.139016\n", + " 4.660607e-14\n", + " \n", " \n", "\n", "" @@ -746,18 +786,26 @@ "mean_AU01 -2.927201 1.683346e-02\n", "mean_AU02 -5.838201 2.473720e-04\n", "mean_AU04 -49.805912 2.660164e-12\n", + "mean_AU05 -11.942339 8.022300e-07\n", "mean_AU06 3.905366 3.589972e-03\n", "mean_AU07 5.487334 3.864728e-04\n", + "mean_AU09 -22.647961 3.024835e-09\n", "mean_AU10 10.361594 2.660301e-06\n", + "mean_AU11 -42.691265 1.059418e-11\n", "mean_AU12 11.949046 7.984040e-07\n", "mean_AU14 23.155621 2.485336e-09\n", "mean_AU15 -12.587565 5.119202e-07\n", "mean_AU17 -0.460219 6.562759e-01\n", + "mean_AU20 -45.470892 6.019391e-12\n", "mean_AU23 -0.300423 7.706780e-01\n", - "mean_AU24 -4.533345 1.419377e-03" + "mean_AU24 -4.533345 1.419377e-03\n", + "mean_AU25 3.119502 1.232852e-02\n", + "mean_AU26 -5.015769 7.232461e-04\n", + "mean_AU28 -7.169935 5.251451e-05\n", + "mean_AU43 -78.139016 4.660607e-14" ] }, - "execution_count": 11, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -778,11 +826,11 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 14, "metadata": { "ExecuteTime": { - "end_time": "2021-03-26T01:54:46.741635Z", - "start_time": "2021-03-26T01:54:46.568587Z" + "end_time": "2021-03-27T16:53:38.291686Z", + "start_time": "2021-03-27T16:53:38.179248Z" } }, "outputs": [ @@ -795,7 +843,7 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -826,11 +874,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "metadata": { "ExecuteTime": { - "end_time": "2021-03-26T01:33:23.062956Z", - "start_time": "2021-03-26T01:33:23.046675Z" + "end_time": "2021-03-27T16:53:41.207034Z", + "start_time": "2021-03-27T16:53:41.189230Z" } }, "outputs": [ @@ -916,11 +964,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 16, "metadata": { "ExecuteTime": { - "end_time": "2021-03-26T01:33:24.774852Z", - "start_time": "2021-03-26T01:33:24.727406Z" + "end_time": "2021-03-27T16:53:44.423224Z", + "start_time": "2021-03-27T16:53:44.377780Z" } }, "outputs": [ @@ -963,15 +1011,23 @@ " AU01\n", " AU02\n", " AU04\n", + " AU05\n", " AU06\n", " AU07\n", + " AU09\n", " AU10\n", + " AU11\n", " AU12\n", " AU14\n", " AU15\n", " AU17\n", + " AU20\n", " AU23\n", " AU24\n", + " AU25\n", + " AU26\n", + " AU28\n", + " AU43\n", " \n", " \n", " \n", @@ -980,30 +1036,46 @@ " 0.066\n", " 0.052\n", " -0.065\n", + " -0.251\n", " 0.497\n", " 0.291\n", + " 0.105\n", " 0.408\n", + " -0.002\n", " 0.569\n", " 0.022\n", " 0.111\n", " 0.101\n", + " 0.038\n", " -0.029\n", " -0.284\n", + " 0.305\n", + " 0.140\n", + " -0.016\n", + " 0.013\n", " \n", " \n", " t-stats\n", " 8.221\n", " 8.219\n", " -17.153\n", + " -31.413\n", " 43.783\n", " 33.364\n", + " 21.079\n", " 35.625\n", + " -2.671\n", " 46.713\n", " 4.088\n", " 17.515\n", " 15.673\n", + " 7.927\n", " -5.063\n", " -22.483\n", + " 19.415\n", + " 16.822\n", + " -2.293\n", + " 2.422\n", " \n", " \n", " p-values\n", @@ -1015,25 +1087,38 @@ " 0.000\n", " 0.000\n", " 0.000\n", + " 0.008\n", + " 0.000\n", + " 0.000\n", + " 0.000\n", + " 0.000\n", + " 0.000\n", " 0.000\n", " 0.000\n", " 0.000\n", " 0.000\n", + " 0.022\n", + " 0.016\n", " \n", " \n", "\n", "" ], "text/plain": [ - " AU01 AU02 AU04 AU06 AU07 AU10 AU12 AU14 AU15 \\\n", - "betas 0.066 0.052 -0.065 0.497 0.291 0.408 0.569 0.022 0.111 \n", - "t-stats 8.221 8.219 -17.153 43.783 33.364 35.625 46.713 4.088 17.515 \n", - "p-values 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 \n", + " AU01 AU02 AU04 AU05 AU06 AU07 AU09 AU10 AU11 \\\n", + "betas 0.066 0.052 -0.065 -0.251 0.497 0.291 0.105 0.408 -0.002 \n", + "t-stats 8.221 8.219 -17.153 -31.413 43.783 33.364 21.079 35.625 -2.671 \n", + "p-values 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.008 \n", + "\n", + " AU12 AU14 AU15 AU17 AU20 AU23 AU24 AU25 AU26 \\\n", + "betas 0.569 0.022 0.111 0.101 0.038 -0.029 -0.284 0.305 0.140 \n", + "t-stats 46.713 4.088 17.515 15.673 7.927 -5.063 -22.483 19.415 16.822 \n", + "p-values 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 \n", "\n", - " AU17 AU23 AU24 \n", - "betas 0.101 -0.029 -0.284 \n", - "t-stats 15.673 -5.063 -22.483 \n", - "p-values 0.000 0.000 0.000 " + " AU28 AU43 \n", + "betas -0.016 0.013 \n", + "t-stats -2.293 2.422 \n", + "p-values 0.022 0.016 " ] }, "metadata": {}, @@ -1056,17 +1141,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Intersubject correlations\n", - "To compare the similarity of signals over time between subjects or videos, you can use the `isc` method. You can get a sense of how much two signals, such as a certain action unit activity, correlates over time. For example, if you want to see how AU01 activations are similar across the videos, you can do the following which shows that the temporal profile of AU01 activations form two clusters between the goodNews and the badNews conditions. " + "## Intersubject (or intervideo) correlations\n", + "To compare the similarity of signals over time between subjects or videos, you can use the `isc` method. You can get a sense of how much two signals, such as a certain action unit activity, correlates over time. \n", + "\n", + "In this example, we are calculating the ISC over videos. We want to check how similar AU01 activations are across videos so our session is set to the `input` which is the video name. Executing the `isc` method shows that the temporal profile of AU01 activations form two clusters between the goodNews and the badNews conditions. " ] }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 18, "metadata": { "ExecuteTime": { - "end_time": "2021-03-26T02:03:17.392348Z", - "start_time": "2021-03-26T02:03:16.179007Z" + "end_time": "2021-03-27T17:08:20.852347Z", + "start_time": "2021-03-27T17:08:20.586761Z" } }, "outputs": [ @@ -1088,6 +1175,13 @@ "isc = fex.isc(col = \"AU01\")\n", "sns.heatmap(isc.corr(), center=0, vmin=-1, vmax=1, cmap=\"RdBu_r\");" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/notebooks/_build/html/_sources/content/dev_plotting.ipynb b/notebooks/_build/html/_sources/content/dev_plotting.ipynb new file mode 100644 index 00000000..31af4a14 --- /dev/null +++ b/notebooks/_build/html/_sources/content/dev_plotting.ipynb @@ -0,0 +1,754 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotting examples\n", + "*written by Jin Hyun Cheong*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Included in the toolbox are two models for Action Units to landmark visualization. The 'pyfeat_aus_to_landmarks.h5' model was created by using landmarks extracted using Py-FEAT to align each face in the dataset to a neutral face with numpy's least squares function. Then the PLS model was trained using Action Unit labels to predict transformed landmark data. \n", + "\n", + "Draw a standard neutral face. Figsize can be altered but a ratio of 4:5 is recommended. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:00:07.356496Z", + "start_time": "2021-03-27T00:00:07.295746Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Load modules\n", + "%matplotlib inline\n", + "from feat.plotting import plot_face, predict\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plot_face(au=np.zeros(20))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Draw lineface using input vector" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Affectiva vectors should be divided by twenty for use with our 'blue' model. " + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:12:08.615110Z", + "start_time": "2021-03-27T00:12:08.526307Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 236, + "width": 353 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data, AU is ordered as such: \n", + "# AU1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43\n", + "\n", + "# Activate AU1: Inner brow raiser \n", + "au = np.array([5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "# Plot face\n", + "fig, axes = plt.subplots(1,2)\n", + "plot_face(model=None, vectorfield = vectors,\n", + " ax = axes[0], au = np.zeros(20), color='k', linewidth=1, linestyle='-')\n", + "plot_face(model=None, vectorfield = vectors,\n", + " ax = axes[1], au = np.array(au), color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:24:31.270625Z", + "start_time": "2021-03-27T00:24:31.260037Z" + } + }, + "outputs": [], + "source": [ + "%config InlineBackend.figure_format = 'retina'\n", + "intensity = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:24:31.740226Z", + "start_time": "2021-03-27T00:24:31.433797Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'AU43: Eye closer')" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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udqbXikBfxAjctD5B4LcytH2vj3KwvWralLPGg20RDwc9a6CTW5YWdakAyjhYZiDss8qfOnm/j22WydIKAPqf+YD59SMAqjkppzwyWqMdQaYeQ7BvZSHQQT04h/tf5jJXwkFrRwAPI6P14tHMdTMvY1vOIQBlPdxfX3WyzDs2y6xw8PpM82uxmbcDdCJIoAPdbvPvbzoow9IDbE42t5/tfu3w+8+0fEUAxd0s85lNedFutl+W/0HoEzyBPq5UdvE+wbBpBWjz/Dib9ds7WTcE+gJDoFu/ZGkB6s3+wEf+fKCQxC8Pymxhc3y8BOe9NmyPt2ty+J6WzyE5XN9hPWzLhz7JbuJgmVLIiG1Oj1ce1MW2J+wWOIjHNstWd/DcZJv1v3SyXu9Mx6MsPUe9OWZlek8DgM4OlgmGbqhhWa6hg2VqQU/+LNC9DSKcvN89Nsv95Ob/wW0s8vB7yVzmJ06WG2WzzEQHrw+0eb2dm/e0HAtOZfN16/fi5HXLeYURQB8X9fjKps73ufn/uASgTk63Nx9227dQxJZM9XP2SAHwmpty2tksvyaH2966b+dwfYf1AGMLY4v9dfMkJ9vs+UzvO9nBMsWhb5wJgI1wPqJKXZvlsrRQR9br/ake1GmVg9drZipnnbPtbl7e0qPkMpz3YI4EsMG83A3YXENCNyy1tKif52abl8n0d7bXtdmm6ciIgbc5WKYY9PBqlu2RJebmxv7FR/59IB/EfmSMgHQYNtd7yGYPrEz7f+ZHHIAuLta1fc81Odz2p2zKaujB404HZbS3KSPLfTeb5d61We5BB6/n6NjoxWcOhU7oCHSDtQEulg0BUMnF95flOwdQwmafTQTQ2km5i2zKedfFNh3m5vM4Olbfi4yewN/ASe9FADE2y73n4PXJNvUwAeiak/2tMD8KwhxYvaGTKUDGBIUAABFJQUbro0eVUmyB4r0PRWRX5idFJAk6Aw7oVkH3uClnN/RQAI58CX0SB+hAklkP6Aw7AAwSkbOOChGRK9DdOwHdKs1RWRbxAIaIiNHFMt5KN9cvzUHdliGjFV9d6ISWKy+Jk1bYSo/zfr/5z5Ui8q2j5UTkM+gLEgDoqJRqkGkRSw+1EtAtBmxZekOsslnOroWi0pOAWuYcc9XbzVPrROQ7VwuIyCURiXdTznvQvaUA3SLCW5bPdERctNoVEaOIxNo+p5Sqgoyxpf8jIn85WdcA3WozDTrwumqhDrjYHyhfK/TxS+l5Gn5Gxpyc74lIqh+rlBs+FZHdmZ8U3dt4iM1Tr3lbsHnM7/8z/5kEoIeIXHe2vIicybR+eejW2oC+Mfi2k/VmQzd6AIB6yEEMc+B/IrLcwXsaoXt4W7RzsO5b0L2nb0BfMCc7egMR2YSMFphPKRdzlsCDWJQNB6Av7h15D/pCHgD6KaXK+Pi9fcK8zSzj7E8UkVkuFn8bunUjYL+PO/KuiPyT0/qRncISW0zQPXzegW492wz6emQQdK8SQN9oHquUcnhsy+cYW1wrcLHF3Jr8BfOfl6EbMYqDev0I3ajSleeheywYoJMjNx0tJCLHoJNmAPCgUqq2izKvAnjZgzo94OC61JYJ+pra4XZXSrWE7k0CAK+LyD4ndU9ExvYqBfteAGWg72EAuueTUw72/ZysC+iRWSwjIb0pIscdrJcA3ZPOZH7qdVfvg9w5d6H8JaBjv1LqQejeUQLg2Vy+3vsOQD0RWZSL7+HMPg8emzKvZL5vZOmZ+qyjgs2x2dKr+h9kugfno2Ojp/pBN/YAgG9FZKqzBUXEICIXnb3uxCDo+5QAMFJENjsq17yc5f7ga+bRIiwq2fzu8n6lk2P1vwEo6POY183v52jdedC9qQD31z3TRGShm2XIiYKQwLLc+L0I3Z0+M8vBOwQZXUnzHRH5SESU+fFRHr71NBev2Q7zUMdNOdMdncwCgIjEQfdKclaOJQlxRkT+dvM+a2x+b+NiuUXm9/WlP5wl18wm2PzeyelSwDlniQ8H6/7opk4/2PyeeQiDVTa/W4fvMgfGduY/VyPjYB+daXJC2yG/XJ68e8jVvuaQecLIqkqpO5We1Lkh9AXwOfMiTbNRj/Pmnw2UUu4Ss5l1gW6NAuju2k6Zg6TlpMLVvupuf6D8q1DEL2fMwz1MQ8ZwmwtE5Cdny4vIGps42C4v6ugDlhbSjl8U2Qh9UgwAHdxNQutAI2RcPMwWkfOuFnagPTKOWZOcnZib2Q7BkZMYlpnTCx64P8/oYf652Hyzx5U15p/h0MNnOON1LPLAZGcNZszP/2z+MxwZDVQCzf3QN+0A9/EtHYDlfM1VfEuH/RA35BuFJbb0FJF2IvK5iKwQkV0isllEJovIo9A38yzHtP+YG15lwdjiEGOLFmixpRl00gnQ34urYYomuHgNyLi+/jtzAtKBNTa/uzqm/+pme3l6LbzJTcMGy/eTDt1DzikR2Q895CVgX/fr0L2oAKC3eQh4T+VkXSDjs9+Ei6EeReQwMq7DmyulSrsoMzfOXSh/CdjYb/4fsfz/jxORDT4q+i7zozH0van/gx4C9SUAPyulqjtbMUBjv+XeXj2l1H0OXn8AGTHrJwf3V31xbPRUN5vfv8zG+u5YjpOCjGEnszA3arEcRytCn79Y2J67POPNuZC5ka3l/ulvzq7jbKwx/6ymlKrmYjkeq3MgXyewlFL1kNHzZ7qjncp8An/M/Ke7Xg5k76qbVm62440Xd1PWITevW8pyVI5lXPXqSilx9YAeTsOikoOyLHa7qU92bHHz+i5k9DRr5GI5d3NJ2a6bpfVGJrYJv8a2L4jISWTMMWGbjLoLGRdHq6Fbtxqg59uyHePess4lEfHFnDW7PVlIKVVcKfWeUmo3dHfis9Bz5ti2amliXrxsNurxM3SgDAOwXim1XCk1VCnVPFMCzxHb7XPCg/3V0vPN1b6a07nFKAAxfgHQN626m3/fBT1+dEFzUpyMYW7DEjtKQM9b4g3b3rNrvVwX8C6ebENGDGvsYjlvj1muzg+cnmeYL0ormv8c4MHx1rYVZqCdH9i+7ur8wJ9s49tqD7a35eaoq219VHSPfvKRwhRbnPUYsXn9F+ghOgE9bNyruV6pvMPY4l5BjC2229WbuGLH3ELd8h239eAz2rbid/UZfRXrdrspxxKPQgGkeFB/S+MLa93NvT+mm/+8B8BppdR4pVRvVze9c7quUioMep4eANghNvO1OGG5llfI2TajAiwfxP7PoRtFnIfuNe0TIrLf/NgrIn+JyCjohpEroRMg29309vQ5m6SYq0dJJ6tPgR76GHDcC8vynAEZjd9s5fjY6AVLDDnpQSOI7LAc7/7x4HzH2T3Pjcjo1TYUwGGl1Cil1CNKqVJuymyGjJ6yH3iwLW1Hxcrr68xCI18nsJAxVBeQqZtsJpYWWA3M3Sodsc1ee5qZtV3OYe+ifM7dTQWTze/uJp3ztCxH5ZR3s64zRV285vKiN5uuuHrRfIJqaeXgaoggd3WzXdfVZMFAxhA+zt7T0pLxPvMJNZCRmDooesi+BADbzc89CFh7TtyfqYyccvudmHtYHQLwCXRwcpdQcrUPOCQi66CPLXHQ+2MnAGOgL7BvKqWWKKX6OUlm5Zd9lfyvUMcvpdRX0MPXAHp4tY650Cs2ELiMC2a2x3Fvh48rZ/O70yFPXfA4nogeHtdy0y8nMSxzuU7PD0TE1XlGdo+3QICdHyBn+0Beye72djWkFuOb7xXq2OLA98ioxwOuFsxnGFvcKKCxxXb7eBNXMisF99dQzrj6jL6Kde62la+ut4Yio0dxGejhzWZBJ6ROKaW+V0o5G80ju+uWRsZx0t11POD+Wt6C8bRwC9jYr5RqC90jCtDDwLqbDiJHROQW9PCbydCx7AfXawQOEbkB4Dfzn08opSxD6EHpIcZjzH8uFJHLDorIjXtRzljOE7JzjuAJy/Eu28dJc+/vxwDsMD91O3QCdQmA60qp3Uqpj5RSjhJOgXwuUGhl98TF78w3j/vbPLXfwx6Bz8B+2AAL25NcT/+BbbuLu+rCTzlj2U+3w7vWIq4ODr6c+8rCkwt1T3ZSX9ZNnPxusRp6m0ZAt9pZC/v5r2yXa21+7RPoHk6lbV7zBZefW+lx338HUNn81DQAs6Fvfl8BkGq5IFVKrQMQDc9PuuyIyGSl1HwATwDoAN2tujJ0L7RHzI+3lVJdROS0zaqWfdUE3WrD0+8yy7xpNnJjXyU/KuzxSyn1GTLG8j8CPQFtQZ3jzVdxwVfv5av1XS2bV8cs23PYH2A/DJU751y85o/zA1/tA7nJdnt3hv2wHNnF+OZDhT22OCIil5RS16F75Ff1d318iLEl9+SX2JKT78X2My6EnufDU66SVL5KWrvbVpb6X0TWIfJdsTsmmYdg7KuUGgmgF3TjzBbQ18U1oOcrflEp9Q2AYbbDdeVkXdsqeFF3d8sznhZS+SD2W+YROgqgqFKqt4P1bYf4rWWzzBER2eVhHaxE5IpSagP0fZxopVQl8X4OJn8ZB/19FoWeS9KSgBsAPVQt4HxIPZ8cG72U242VcnScFJHjSqkW0A3xuwG4D3rEqWDoBvGNAQxXSg0RkRk2q9rGyfcBzPeiDiedVs79UITkQr5NYEFP8lrR7VJZ9VZKvS5ZJwW1nbStEvRNcXdsM7VOJ7alHLsKfdFZyTxWa6ByORmm+eTC0lU1J/uL7boV4fomku0+esPB63bzYJkDfVvz37aJqdXQYwrfo/QExb6e/8oT7ZEx5MJnIvKui2VdjRHuERGJhR6reQIAKKVqQffGehG6S/Nd0Ak023myLN2bgwAkioNJeYlQiOOXUuoTZEzmfhRAeyctyAoKTyZJtm3h5e13YTukQhUv1838fpUAnHC2oLmXruXY6iie5DXbz148H5wfHHXxuqt9wLangLuRE7ydh8MbttvbGODbu7AqtLHFDcn0syBgbMk9gRxbbLeru33A1evXof8fFIAyPvyM3tQpJ8eHq9DXhOWgh5fKfOzyivnz7wesDSabQ/d0eB56iMnXoIerzzL3dDbWvYGMbe/JsF22x/RA+P+gwBPosd+SdLkdbuZQNWuLjPtRY6GHmc8O28aRNWHfSydgicjfSqm90PebnkNGAssyfOApAH86Wd2nx0Y3rgKohuydI3jiOnQD8hwfJ80NCFaaH1BKFYPex/pCz5daFMAUpdQum6lRbM8FigTYuUChlZ+HELR0kzVCZ6P7uHmMNi9fAhmT29myHVfb1QSstizLCYC9nlacvGbp8llFKXWXX2viWis3rzeFnlcJyNn+YrtuazfL3mvze5ax480tUSzjwj4IPZZtCegbZmtsFt0IPVltOHRvJEsC64ybSXZ9yfa7d3ryYw5Idzh7PbtE5KSIjINuXbfb/HRrpZTtvAI7bH5/2Nd1oAKjUMYvpdSHAN4z/3kcwAP5qDVcdtVSSpVzs4wldsQBOO1qQQe22/x+v9OlnPMmnjSHHlMdCIy5+U4i4yLlISfDugYKd+cHtq9n/n+0ndvT6Xjt5qF9b/eyXt5gfAt8hTK2uKKUqoCM+VBza5gbf2BsyT2BHFtst6s3ccWOeVh7S1mtlFI5bviXjTrl5PhgiUchAB7KQTlZiEi6iGwSkX9lKttRrxGv1zUPmWm59r7bnPRyxXItHxDHVApIjP2O2SZWEpwuFZgsCe+mSqlmSqk2yOil9lOmYXBt5dqx0QHLeUItd3P/ZZNlP6rjwfmOy3uemYlIgogsEZF+yJiTLRh69CWLXchoRMjrngCRLxNY5h34UfOfa0RkmojMdvUA8CkyJsRzNAzdHza/9zPfCHBVh3uQcaNgp4gESivDgmiuze/vOV3K/zoopVwNT2I7EeMfTpdyb4XN70PcLPuCB+9p6WnVEkAX8+87zT2QAAAikoKMyRE7Qne9tV03L9heQLpqZf48crF3qfnCw7bXmW1AXYiMIRxeV0plZzxhKsAKa/xSSr0L4CPznyege175YgiyQKfgYuhb83dhuSBZ6WSIGVf2IaNle2+llLet4P4CYJlA/BmlJ3Z35kWb31c4XSqPmC/eFpj/rADdSjFQPe3s/9K8zQea/0yFHsrXlm0jkRYu3qMLdGvv3LIKgGXegmeVUtlp7Uu5pLDGFg+8iIyh9PJqxIC8wNiSSwI8tuxERq+CJ91cZzzr4jUg4/o6BBk943Oql1IqysXrvroWtr038K6b/SvbRGQbMqYkcHcD1Zt1Lft5KQCPOytDKXU7MhqNbjfPj0NklR9iv4i0ExHl6gE90o7FFJvXhnm2JbLUqQYyRslJgv25dH4wHRnD+j2HjGOnAcDPLtbLk2Oj2QKb39/MhfItx0kFF/HM3ACjl/nPS/A+gWp73mE9VpuPt5ZrsmZKKSaxAkC+TGBBjwlqaa3yiycriEgCgGXmPx9QStXM9Po+ZNyMbwh9YHdIKVUe5mHFzL7ypA45YZ5cTsyPj3L7/QLMTGS0VOplbsXvlFKqqFJqmLtgmwvCAExy1JJKKdUJwGDzn/8gY1/0mojsRsbBtKNS6kVHyyml3oKeBwoA/hARZ92/Lft9KIBXzb+vcrHcEADFMj2XF2yHXxrsaAGlVDSAj3PyJkqpPq4uvpRS4QDamf80waZVq4icQMbEqbUA/OKmLKWU6qGUqu9sGSpwCmP8egvASPOfJ6GTV67miXBWTjubOLjGl3XMZf9WSjXK/KRSqiTsxzD/1tuCzTclLdu2KIA5rlpSK6WqZVr/CnSMBfTNzpFZVtLrPQGgn/nPwwCWe1vXXDISOukDAF+6u7hQSlVRSj3japlc0hDAB05eG4GMG82zHNz0P4CMYSwGKaXKZnod5paPXu8/3jAfh74w/1kcwALz8cQppdSD5lajlPsKVWxRSrV312tEKdULGXP7mAB852Q5xpZMGFsCM7aYe05ZWuZXhB5ey1F9noX7+U++QcZQX2+5q79SqrRS6hU3ZZYD8K1SWSfgyVSnNTkZjklE1iHj2HQPgB9c9ZRTSoUqpQaZe2RanqutlHrA2TrmZVoio+fzCZvns72u2f+gb0QDwFdKqdoO1o8CMBW6VwAAfO3q/ajQKmyxv7ubBuMwnyf/goztMtvRUHqBHPtFJB4ZIw71RUaCZomIOO1N7otjoxdmIuPY9qpSaoCL9wlRSnkyFKCtydA9yAF9vpOlN6D5s02C7k0IAN+IzRxTSqlopVQ9N+/T2eb3zMfqj5Ax/PRUR3XIVJ/blVJPunk/yoFA6hLvDUtLAQPss8zu/Ao9JrEC8DT0TQNbz0F3uywJnbFuC30z+gB05r4cdELgBWRkZ+eKyEw4oZRqAqBJpqctN7SjlFIDM712XEQ2ePqBCgMRMSilekIPY1cCwEdKqcegD2p7ANwyP38H9FimXaETLN/Bfu6I3LYVeqLIrUqpr6DHuy4GPVngy9AnoCYAQyTnk/cNgd5XowB8b07czIAeHqUqdPdxS4uuWNj3xMrsL2SMxW05+DtKTK0G8InNMpZ188py6LGLKwEYrJQqA70PnIP+f+wK3TojFsAZZH8YwVEAxiullkIPo3gYugVdcQD1oHt4NTUvO1tErmZafyiAZtD/948BOKyUGg+9/16HvhFQE3oYjR7Q31cH6P2FCr5CFb+UUi8B+K/5zwToJHlxpVRDZ+8L4GZe9s5y8DmcWZiN1q/HoLf3RqXUl9Bjb6dCD9f6L+hEN6BbG2bueeMREZlobiTxBPRx5bBS6nsA66GPOcWgL0AfM7+eOQHyFvQwE1UA/Mt8Q3QC9BjrZc3lPgO976UDGOhi6Io8JXpi3mehb/JEAFiilJoPYA70tjdAf4a7oG+cPQBgC/TFTl7aCuBDpVRj83ufh97ezwDobl7mKhy0ghcRo1Lqf9AXUeWh96X/QM+5EQndoGIo9PnFUeTuMIIjoYcR7gTda/uQUmoCdKy8DD3McFXonmIxAOpCH5s25mKdSCtUscVc1yeUUsugz0UPQZ+rhUGf/z0J4BGb5T8035TLM4wtjC25ZCT0zcy60L1h6wD4HvrGWznoIcIGQMedls4KEZFYcwJxBfSN3olKqX7Q15OHoHtolIJOQD4APYTSdThJBJtthe5RXMsct/6B3k59oP9nASAZwEvefmgHnoLe5tWgj1P3m+PRNuibnlEAboMeWirGXI+60LEKAKoDWKWU+ge6J8FW6OvHFOhYez/sewfafu6crAsROaaU+gD6u6wIYLtS6gvoBqpp0NeZw831B4AFIuLJ3EFU+OSb2O8j3QH8qpT6Ezru7Yc+LgVBz5fUFvoYVMa8/An4roepR9xc49pyd737A/S9LdvRFcY7WdZWTo+NHhGRdHOyZj2AItBzSD0Jndg6Bj0qUTXo64YnoZObY7woP1bphvozoa931imlvoFOvt6Cjk1Doc97AD2k4ReZinkQwAdKqS0AlkJPA3LJ/FoV6Pu0lsTbTej4Z1uHdUqp96GTuGUB/K2UmgVgEfS5DKCP+Y2he0Lea66vR8lkygYRyVcP6BMxMT9WeLluJPQBV6BbgSsHyzSAPmkTNw8TdAuocDfv+ZEHZdk+JntQzkc52H41bcpZ42SZNebXT3lRVpY6QQcPy+vt3JTl9j2hkwd7PNyO8QCCM63fzub1gT7aH+3KNO8TzuqUCqC/i7Jc7gMOlm8BnbBytR3OAGjiQVm7M9WzqINlQqBvQFuWO+qD7We7X9f0cHsnuPi8l6Bbm7jcn1y9Dh2MPNnHlgIo5qT84gB+87AcA4B7cro/8BH4DxTC+GXzv5ajcsxltbNZZk0OvwtP/89tH00crO+wHravQ9/sj3dR7nx334UHnycU+gaWyc1niHWy/m0e7Ds3ADzkog4eH7OgGx8IAPFgWbflQjdguOrh97jEzb5e00f/77ZlNoG+mHRWp0sAGrsoKwz6Qt3Z+peh55lZg2zGPk+/F3Nd/gd9YerJseZJV/8fvtjWhf2BwhlbJnu4bhKAV93Up53N8jnaJ8HYknl9xhYfxxZzuTWhbxA6q8sR6CSLJ5/xHi/22wMO1h9o83oHAItdrB8PoIOLz2RZ7iMPt0NlZDTEdPdIAVDNyf+9q0cqgJddHDO8WjdTOR9AXwe6KuNXAEWcrJ8r+xcf+eOBfBb73dTH9n9qsovlJnv4vyfQjZ8re/iea3L4XZzyol5uP6dNudtslj8DIMjD+mT72JjN/fCMB+8zzMG6nnzng811dFX2GgClHaz7kQf1EujrsDYu6jAEOmnmSVn/c7Xf5vT/vrA/8mMPLNtxWn/1ZkURSVRKLYHumVITujXRqkzLHDBnzR+HzvC3hG5VEAGdsT4FnWWeKDno+k7eE5HDSqmm0N9LT+jWfhWgM/4J0N/NTgB/AlgkOe/llJ06Pq+UWgF9kGsC3XLtMvR+NlpEDvnwvbYppepCt3zpBuBO6FYysdCtY+YDGC8iSR4Utwq65QAAbHa0juiecOuQ0aI1L4cPtNRhjbnl7tvQF0lVoMcHPg3dEuJbEbniYOQKb9wH3VrjAeiWlRWgjwHp0AnDbQBmiMgSF/WMh24Z3AK6VVNb6FbpxaFPFs9DtxhaDWCeiFxyVhYVKIxfhZCIrFBK3Q3gDeiW2pWhWyDvBjBBfNCqVvSwQi8ppX6Ejj/toFu9hUGflJ+BHtPeYYsw0a3NG0Hvo48DaASgNHRsPQZgCYDvRORmTuuaG0RkoXkIlEHQrcQbQ7eUU9At6o5BJ5AsPWvzWix0S9WXoVui14XurXQKOlb/19W2FZE08xBWL0C3rKwP3av7DPTci1+JyKUcxj6PiJ4H8mWl1LfQF5XtoHt7lIS+wLwE3aN4DYD5oofWpdxVGGPLZ9Dn/K2gewGVhW5xLdAJkf3Qn2Oy6OHsChzGltwXqLFFRE6Ze/QOg+6NdRv0TeQT0L3ExohIgicxQUQ2ma8n+0In7O6G/v8Ohf7/PgF97bMC7od4TIeej3EQ9PVPfeieeueht9Hnko0hpF3U/QKA9kqph6Bj673Q/weR0Dcbz0DPh7ISOh7F2qy+Hvr48QB0j6ma0Nd8xczrHoO+ThvvII7lZF3b+n+slJoD3SPtAehrxRDoewd/A/hZRP70bqtQIVIYY/9b0Pd82gFoDt2DsTz0/008dK/PLQB+EZG/nZSRn0yG/pyA3s4e9VLO4bHRKyKyVen5+gZB35NsDH0+lgB972wvgN+RzSlURPcG/xN6FJeO0PtrEejGJduge03NEdGZokxGA9gEvX/fA33vsAL0OcxN6HPFJQB+Mt+/c1aH8eZjtWUo3PrQ5zIm6B6AR6GP2YtEZEt2Pid5Rjn+nomIiIgoO5RSpwDUALBWRNr5tzbkD0rPV/qh+c9aInLKf7UhooKAsYUCjXmozJ/Nf7YXkTX+qw0RUcFhnobiOehRD2r6sgEAUX4U5O8KEBEREREREREREREVZkqpYtC9pwBgKZNXRExgERERERERERERERH522sAosy/f+fPihAFivw4BxYRERERERERERERUb6llIqAnqMpAnrOpvfNL20VkT/8VjGiAMIEFhERERERERERERFR3moF4K9MzyUBGOKHuhAFJA4hSERERERERERERETkP5cBLARwr4js8XdliAKFEhF/14GIiIiIiIiIiIiIiIjIij2wiIiIiIiIiIiIiIiIKKAwgUVEREREREREREREREQBhQksIiIiIiIiIiIiIiIiCihMYBEREREREREREREREVFACcnNwpVSkpvlExGRb4mI8ncdAhVjGhFR/sKY5hjjGRFR/sJ45hxjGhFR/pKdmMYeWERERERERERERERERBRQcrUHloUIG0QQEQUypdioz1OMaUREgY0xzTOMZ0REgY3xzHOMaUREgS0nMY09sIiIiIiIiIiIiIiIiCigMIFFREREREREREREREREAYUJLCIiIiIiIiIiIiIiIgooTGARERERERERERERERFRQGECi4iIiIiIiIiIiIiIiAIKE1hEREREREREREREREQUUJjAIiIiIiIiIiIiIiIiooDCBBYREREREREREREREREFFCawiIiIiIiIiIiIiIiIKKAwgUVEREREREREREREREQBhQksIiIiIiIiIiIiIiIiCihMYBEREREREREREREREVFAYQKLiIiIiIiIiIiIiIiIAgoTWERERERERERERERERBRQmMAiIiIiIiIiIiIiIiKigMIEFhEREREREREREREREQUUJrCIiIiIiIiIiIiIiIgooDCBRURERERERERERERERAGFCSwiIiIiIiIiIiIiIiIKKExgERERERERERERERERUUBhAouIiIiIiIiIiIiIiIgCChNYREREREREREREREREFFCYwCIiIiIiIiIiIiIiIqKAwgQWERERERERERERERERBRQmsIiIiIiIiIiIiIiIiCigMIFFREREREREREREREREAYUJLCIiIiIiIiIiIiIiIgooTGARERERERERERERERFRQGECi4iIiIiIiIiIiIiIiAIKE1hEREREREREREREREQUUJjAIiIiIiIiIiIiIiIiooDCBBYREREREREREREREREFFCawiIiIiIiIiIiIiIiIKKAwgUVEREREREREREREREQBhQksIiIiIiIiIiIiIiIiCihMYBEREREREREREREREVFAYQKLiIiIiIiIiIiIiIiIAgoTWERERERERERERERERBRQmMAiIiIiIiIiIiIiIiKigMIEFhEREREREREREREREQUUJrCIiIiIiIiIiIiIiIgooDCBRURERERERERERERERAGFCSwiIiIiIiIiIiIiIiIKKExgERERERERERERERERUUBhAouIiIiIiIiIiIiIiIgCChNYREREREREREREREREFFCYwCIiIiIiIiIiIiIiIqKAwgQWERERERERERERERERBRQmsIiIiIiIiIiIiIiIiCigMIFFREREREREREREREREAYUJLCIiIiIiIiIiIiIiIgooTGARERERERERERERERFRQGECi4iIiIiIiIiIiIiIiAIKE1hEREREREREREREREQUUJjAIiIiIiIiIiIiIiIiooDCBBYREREREREREREREREFFCawiIiIiIiIiIiIiIiIKKAwgUVEREREREREREREREQBhQksIiIiIiIiIiIiIiIiCihMYBEREREREREREREREVFAYQKLiIiIiIiIiIiIiIiIAgoTWERERERERERERERERBRQmMAiIiIiIiIiIiIiIiKigMIEFhEREREREREREREREQUUJrCIiIiIiIiIiIiIiIgooDCBRURERERERERERERERAGFCSwiIiIiIiIiIiIiIiIKKExgERERERERERERERERUUBhAouIiIiIiIiIiIiIiIgCChNYREREREREREREREREFFCYwCIiIiIiIiIiIiIiIqKAwgQWERERERERERERERERBRQmsIiIiIiIiIiIiIiIiCigMIFFREREREREREREREREAYUJLCIiIiIiIiIiIiIiIgooTGARERERERERERERERFRQGECi4iIiIiIiIiIiIiIiAIKE1hEREREREREREREREQUUJjAIiIiIiIiIiIiIiIiooDCBBYREREREREREREREREFFCawiIiIiIiIiIiIiIiIKKAwgUVEREREREREREREREQBhQksIiIiIiIiIiIiIiIiCihMYBEREREREREREREREVFAYQKLiIiIiIiIiIiIiIiIAgoTWERERERERERERERERBRQmMAiIiIiIiIiIiIiIiKigMIEFhEREREREREREREREQUUJrCIiIiIiIiIiIiIiIgooDCBRURERERERERERERERAGFCSwiIiIiIiIiIiIiIiIKKExgERERERERERERERERUUBhAouIiIiIiIiIiIiIiIgCChNYREREREREREREREREFFCYwCIiIiIiIiIiIiIiIqKAwgQWERERERERERERERERBRQmsIiIiIiIiIiIiIiIiCigMIFFREREREREREREREREAYUJLCIiIiIiIiIiIiIiIgooTGARERERERERERERERFRQGECi4iIiIiIiIiIiIiIiAIKE1hEREREREREREREREQUUJjAIiIiIiIiIiIiIiIiooDCBBYREREREREREREREREFlBB/V4CIAkNqaioOHjyIlJQUGI1GGI1GmEwmj3/3Zlmj0Yhq1arhscceQ5kyZfz90YmIiIiIiIiIiIgowDCBVYgcO3YMs2fPxqpVq5Ceng4AUEpBKeXV796uFxISgsaNGyM6OhqtW7dGVFRUHn1iciUuLg5///031q9fj/Xr12Pbtm1ITU3N0zoEBwejffv26NmzJ2JiYlChQoU8fX8iIiIiIiIiIiIiCkxKRHKvcKUEAHLzPci1s2fP4pdffsHs2bOxY8cOf1cHwcHBaNasGaKjoxEdHY02bdqgXLly/q5WoXDx4kWsX78eGzZswPr167F3716YTCa7ZSpWrIcaNUogODgYwcHBCAoKcvu7p8tZfj97Nhj79ytUqrQdq1evhtFoBKATnvfddx969uyJHj16oFq1av7YTIWWJfEsIsrPVQlYjGlERPkDY5prjGdEZDKZEBsbixs3biA2NhZ16tRBqVKl/F0tyoTxzD3GNCKi/CEnMY0JrALo6tWr+P333zFr1iysX7/e+nyxYsUQExODoKCeKFWqDHr0EOt3I+LZ794sa/n9hx8SUbLkFhw9uh67du2yJiws6tWrZ01oRUdHo0aNGtadmrJHRHDs2DFrsmr9+vX4559/7JYJDQ1F8+bNER0djXvuicaoUfdi4MDSePHF3KoT8P33wNChwLhxwLPPAtevX8fChQsxZ84c/Pnnn0hLS7Mu37JlSzz++OPo2bMnateunTuVIiteHLnHmBY4TCYTjh49iq1bt+LKlSuoUqUKqlatiqpVq6Jy5coIDw/3dxWpgImPj8epU6dw8uRJnDp1CkePnsS2baeQknISV65cRp06ddCkSRPro2HDhoiIiPB3tQstxjTXGM8CW0pKCrZt24bY2FiULVsW5cqVQ7ly5VC8eHFeI1EWIoKEhARcv34dN27ccPjT0XM3b960OwYopdCoUSO0bdvW+ihfvrwfPxkBjGeeYEwjIgBISQGmTgWKFgWeesrftSFHmMAixMXFYf78+Zg1axZWrlxpTRIVKVIEjz32GPr06YOHH34YERERGD8eePVVYPNmoGnT3K9b377A5cvAypXArVsJ2Lx5szWpsmXLFiQnJ9stX7VqVURHR+O+++5DdHQ0GjRogKCgoNyvaD5mNBqxZ88e63bdsGEDLl++bLdMVFQU7rnnHmuisGXLlihatCgAYPhw4IsvgNmzgSefzJ06GgzA118DI0cChw4BFSvavx4fH4/Fixdjzpw5WLZsmd1+0aRJE/Ts2RM9e/bEnXfemTsVLOR4ceQeY5r/XLhwAVu3brU+tm3bhvj4eKfLV6hQwZrQqlq1KqpVq2b3d5UqVVCkSJE8/AQU6BITE3H69Glrgirzzxs3bnhVXnBwMOrVq2dNaDVt2hRNmjThvI95hDHNNcazwBIXF4eNGzfaDett26jLIiwszC6hVa5cuSx/2z5Kly7Na6h8TERw+PBhrF+/HteuXXOaoLpx4wYMBkO23qNEiRIoU6YMzp6NRHr6EQD2+129evVw//33WxNaVatW9cEnI28wnrnHmBbYEhKAYsX8XQsqaG7cuIFdu3YhNjYWpUpVx6pVNfDTT+Vw5YpC587AsmX+riE5wgRWIZWUlIQlS5Zg1qxZWLp0qXX+opCQEHTs2BF9+vRB165dUbx4cbv1Zs3SSaVnnwXGjwdyuyHfQw8Bq1bpA0jnzvavpaWlYefOnXaJl5s3b9otU6pUKbRp08aaeLn77rsRFhaWu5UOcMnJydi6dat1m/39999ISEiwW6ZcuXJ2PdsaN26MkJCs094tWAB0765//+MPoEOH3K37xYtApUqul0lMTMTy5csxZ84cLF682O6z3XnnndZkVuPGjdkS1Ud4ceQeY1reiI+Px44dO6zJqi1btuD8+fNZlqtatSpatmyJ9PRqWL78AipVOgeRc7hw4UKWnr6OlCtXzmmCq1q1aqhSpQp70BQgKSkpOHPmjNME1ZUrV1yuX6RIEdSsWRO1atVCzZo1ceNGLSxZUhNvvlkLgwaVw7Fjx7B7927s2rULu3fvxuHDh7MM0wvo/dY2odWkSRPUqlWLsczHGNNcYzzzr0uXLlmvfdavX489e/Zk+i4U6tRpiNtvr4pr167h6tWruHr1KhITE716n6CgIJQpU8ajZFedOnUQGRnp2w9KXjOZTNi8eTPmz5+P+fPn49ixYx6tFxUVhTJlyqB06dIe/yxVqpT12nDAAGD16mR8/fVWHDq0FuvWrcPff/+dpaFp7dq1rcms+++/n/ErDzCeuceYFtjefhswGnVD5kJ+G4+y6dKlS9i5c6f1sWvXLpw6dcrBkhEAqqN06Zp4/PEaqFHD/lG5cmUEBwfnce3JFhNYhUhaWhr+/PNPzJo1CwsWLMCtW7cA6J3g/vvvR58+fdCjRw+ULVvWaRn//AMkJwO33543AaRRI2DfPv1z507A1fHCZDLh4MGDdhd1586ds1smIiICrVq1svbSuueee1CsgDfpiI2NtWuZuX379iwtM2vXrm2XsKpbt67bC4oTJ4BmzYC4OP33tm1A8+a59SmyJyUlBStXrsScOXOwYMECuwRn7dq1rcmsli1b8gIqB3hx5B5jmu+lpaVh3759dr2rDh06lGUbFy9eHC1btrQ+WrRogcqVKwPQF0P//jfQrx8wfbrukXrp0iWcO3fO7nH27Fnr7+fPn/eotXKZMmWyJLnatWuHe++9l8ebAGY0GvHXX3/hl19+wcGDB3Hq1ClcuHDB5TqhoaGoUaOGNUGV+WeFChWyfOcizhsBJScnY//+/XZJrT179iApKSnLssWLF0fjxo3tklr169fnUJg5wJjmGuNZ3hER/PPPP3bXNsePH7dbxnZY7wULonHkSBtMn14K/frZl5WcnGxNZl29etUuueXoERsb63E9w8PD0aFDB8TExKBLly6cozgPpaSkYPXq1Zg/fz4WLlxoN4pGyZKl0blzZ9SsWd1lMio3GnempaVhx44dWLduHdauXYsNGzZkaTBZpUoVux5a9erV4/mRjzGeuceYFrji4oCqVYFbt/R9phkzTChR4iqKFi1a4O/hkfdEBGfOnLFLVO3cuRMXL17MsmxERASaNGmCsmXL4syZMzh9+rTb856QkBBUrVrVmtCqWbOmXYKrWrVqvP7KZUxgFXBGoxHr1q3DrFmzMGfOHLthbFq2bIk+ffrgiSeeQJUqVfxYS+cqVtRDCALAlCm6hZc3Tp8+bXfRd+jQIbvXg4OD0aRJE2vipkuXLggNDfVR7f3vxZdfwY8/fO9wjHLLMIvR0dHWm7neOHkSWLcOGDIEKF0a2LgRCOTpptLT0/HXX39hzpw5mD9/vl2L+apVq6JHjx7o2bMn2rRpw5YVXuLFkXuMaTljuYm3ZcsWa7Jq165d1t7DFqGhoWjSpIldwur22293OgyS0QisWAG0agV4OjqbyWTC5cuXnSa4LI/09HSH69esWRtPP90f/fv3R506dbzaDpR7Dhw4gO+/n4rx46fDYLBPWAUHB6NatWpOE1SVK1fO9aG2jEYj/vnnH7uk1u7du3Hp0qUsy4aGhqJ+/frWhFanTp04hK4XGNNcYzzLPUajEfv378e6deusIyVkvvESGRlpHda7bdu2dsN6X70K7NoFdOyY87qkp6fj+vXrLpNcV69excWLl3Hs2BHr/hAUFIT77rsPMTExiImJQY0aNXJeGbITGxuLpUuXYv78+Vi2bJm1USoA1KhRAzExMQC6YcyY+zBkSAh+/NF/dbUwGAzYs2cP1q1bZ31kHl63XLly1t5Zbdu2xV133cVhLHOI8cw9xrTAYTAYcP78eZw+fRqnT5/Gjh2nsHDhacTHn0Zi4mmkpp6BiL72K1OmDGrWrGl3Pm77yE6v4NRUIDQU4GEn8GzcqDs1WPKWJpMJx48ftyapLA9Hw7YXL14cTZs2RbNmzayPO+64I8s9v/j4eOu+l/lx6tSpLNOsZKaUQsWKFa0Jrdtuuw2DBw9GrVq1kJwMHDkCHDyY8ShaVN/j5q1HzzGBVQCJCLZs2YLZs2fj119/tbvwadiwIfr06YMnn3wy4G+cmUxA5cp6/qPWrYGuXXWyJCeuXbuGDRs2WC8Kd+zYYR0uKqpYcdy4cR2hDobKy49MInjjvU/wv//+B83uvhsPtLsf0dHRuPfee1GyZEmfvc/Nm8D580CNGvlnfGKj0YgNGzZgzpw5mDt3rt0QY0WLVsCDD/bA+PEfoGLmybbIIV4cuceY5p3Lly9j27Ztdr2rMg8RCwB33HGHXbKqcePGAdHyyWQy4erVq3YJrmPHjmHGjDm4fj3jeHPvvfdiwIAB6NWrF0qVKuXHGhdOV65cwaxZszB16lTs3LnT+nz16rUwaNAA6xBHVatWdTiMbiC4dOkS9uzZY5fUOnr0qN2xJjQ0FNOnT0evXr38WNP8gzHNNcYz30lNTcX27dutDe02btyIOMvQBmZly5a1a3TWpEmTgGls98ILwE8/ARMnXkJKygLMmzcPq1evtmvA0bRpU2syq0GDBuxhk03nzp3DggULsGDBAvz11192PcGbNGmC7t27o3v37mjUqBGUUpg8GRg0CBg6FBgzxm/VdsoycsratWutvbQy3xwsWbKkNVH75JNPYsWKarh0CXjvPT9VOh9iPHOPMS3vpKamWnu7WJICtkmCc+fOuR3KvXTp0khMTMzSiDGzcuXKuUxwZR7qPTERaNwYuHEDOHUKyDSTCvnRhQsG1K17GA8+uBO1a+tE1e7du7P06gX0OZNtoqpZs2aoVauWTxpDWIaUd5bkcrT/BgWFoVix1xAX928AJe1e+/xz4F//ynG1ChUmsAoIEcG+ffswe/ZszJ49GydPnrS+Vrt2bfTu3Rt9+vRBw4YN/VhL76SmApcuASEhuidWbmSmExMT8df6jZgwcyVE0vDDd5+hSokivn8jPzgXm4w5qy8hLjEIAx4th5qli/q7SgHJZDJh69atmDNnDn7+eQ6uX9f/O3feeSfWrVvnckhN0nhx5B5jmmcOHTqEfv36YdeuXVleq1ChAlq1aoWWLVuiVatWaN68uU+T8Xlh0iQjBg9eg2rVpuLGjTnWOUnCwsLQpUsX9O/fHw8//HChn6sxN6WkpGDhwoWYOnUqli9fbr3QKFGiBJ588kkMGDAg3w/zmJiYiH379mHQoN04fHgVgN+hlMK3336Ll19+2d/VC3iMaa4xnmVfYmKitSHd+vXrsXXrVqSkpNgtU716detN++jo6IAeVi0mBpg/336UjLi4OCxduhTz5s3D0qVL7ebeuu2229C9e3fExMSgdevW7F3jgojg4MGD1vmstm/fbn0tKCgIbdu2Rffu3dGtWzfUrFnTYRlXrgDly+dRhXNIRHDs2DFrMmvt2rU4e/as9fVSpUojPn4xjMZ7sHmz7jlP7jGeuceY5p2jR4GZM4GGDYG77gLq1NH36ywuXLiA3bt3O0xQORrKLbNKlSplGZrN8nf16tURFRUFk8mEK1eu2M1Ha3mcPHkSp0+fzjJlRmYVKlSwS3BVqlQT//53TZhMNbFvX23Urh0YDUUKq5SUFMyfPx8///wzVq9eB4MhJcsyVapUsUtUNW3aFFWrVvXbOZPBYMCFCxes+/2KFSswY8YMAEBwcGkYjR8CeAGAvs5v3lxPw0KeYwKrAPj777/Ru/fzOHt2v/W5SpUq4cknn0SfPn3QokWLgL3wCQQX41MwdVk8DCJo1yoUTaoUR2RYYLa09lRimgE7zsRj7pJ0lCoF9OgQgbsqsRmJO1evCp56ajcOHRqAs2f3o0WLFli1ahXHWHaDF0fuMaa5t2TJEvTp0wcJCQkoUiQKzZs3x733ZvSu8ucJqS/t3q0v+lJTEzFv3jxMnToVK1eutO4bZcqUQe/evTFgwADGbx8REWzcuBFTp07Fr7/+au3hEBISgocffhj9+/dHly5dUKRIwWjAYrFsGTBpkqB27dEYPfodAMAHH3yAjz76iPuVC4xprjGeecdkAq5fB9avX4shQ57A9etX7V6vX7++3Ty01atX91NNvZeUpIcUb9DA8euWuWjnzZuHhQsX4tq1a9bXKlasiG7duiEmJgbt27dnww3oESI2b95sTVrZzncWERGBzp07o3v37nj00UdRxtNxj/OxU6dOYd26dZgyZQpWr16N0NAIdOjwGxYtepRDfHmI8cw9xjTvpKcDDzwAbNig/w4PB+68E6hV6yzOn/8UO3ZMgtHoeK7g4OBg6xxCjpJUvppDyGQy4dKlS04TXGfOnHE61Dugr8X69euHgQMHokmTJjxnzkO7d+/GpEmTMH369Czz11uSVJafFSpU8GNNPbNz5068+eabWLNmDQCgaNG6SEr6HGFh3bF8uUL79v6tX37DBFY+t2HDBnTo8DBSUm4hOLg0Bg9+HH369EF0dDTn8fHQocsJmLowCf8cCEG/wQbULhuBBhWLISifBiqTCA5cSsCanclYtTgEdRsa0f2RELSuUQrBQfnzM+W1Cxcu4L777sPJkyfRvn17LF26tMDd2PQlXhy5x5jmnIhg9OjRePfddyEiqFbtCZw9+zPeey8Sn3zi79rljfPnz2PmzJmYOnUq9u/PaIxyxx13oH///njqqac4j0g2HD9+HNOmTcO0adPseqbffffdGDBgAHr37o3y+aV5eg5NnDgRQ4YMgclkwosvvohvv/2W54lOMKa5xnjmHRGgfPkJuHbtJQAG3HXXXejYsSOio6PRpk2bQtPT32AwYOPGjZg/fz7mzZuH06dPW18rXrw4Hn30UcTExODhhx9GVFSUH2uat1JSUrBq1SrMnz8fCxcutJujt0yZMujatSu6d++Ohx56yDrXWWFjMBjw/PPPY9KkSQgODsakSZMwwNuJsQspxjP3GNO8s3070KKF7TMXAIyEUhMgkoagoCDcf//9qF27tl2SyjJnbCAMyW00GnHx4kWHCa7jx4/jzJkz1mUbNWqEgQMHol+/foXmmiGvxcbGYubMmZg4caLdkO5NmzbF4MGD8eSTT+brcyURweLFizF8+HAcOXIEAFChQjRGjPgCzz/f0s+1y19yFNNEJNceAES/BTmzYcMGiYqKEgBSrFg/ee21VH9XKd8xGE2y5uh1GfzJZXnkmRvy49Ir8seRK3IuNtnfVcu287HJsnT/FXnmwyvyyDM3ZMgnV2XV0asSl5zu76rlK8ePH5eKFSsKAOnWrZukp3P7OWNzvM7VuJCfH4xpjiUlJUnfvn2t+9Cnn34qP/5okqpVRZYt83ft8p7JZJJdu3bJ66+/LuXLl7duFwBy//33y8SJEyUuLs7f1QxoN27ckHHjxsm9995rt/2qVq0q77zzjhw4cMDfVfSb+fPnS3h4uACQJ554QlJSUvxdpYDEmMZ45ivp6ekydOhQ6z71xhtviMFg8He1/M5kMsnOnTvl/fffl4YNG9odq8PDw+Wxxx6TiRMnypUrV/xd1Vxz5coV6dmzp0RGRtp9/lq1asnrr78ua9eu5bWHDZPJJO+++651O40ePdrfVcoXGM8Y03zt5k2Rxx4TAS4L8LoEBRURAKKUkj59+sihQ4f8XcUcMZlMsmPHDnn11VeldOnS1v+hkJAQ6dq1q8ybN09SU3nfNaeMRqOsXr1a+vXrJ0WKFLFu55IlS8orr7wiO3fu9HcVfS4tLU3+97//SdmyZa2ft0+fPnLy5El/Vy3fyElMYw8sP9q4cSM6d+6MW7duoV+/fpgyZQpb0mZDbHI6lmyPxaKFCgk3glG7tqBzTDpKRuTPoQST043YdT4Om3elY9XiUAAKkcVMeOYFIxpXLo5KxdmLyBv79+9H27ZtcfPmTQwYMAA///wzx+t3gK373GNMy+r8+fPo3r07tm/fjsjISEyfPh3du3f3d7UChsFgwJ9//ompU6di/vz51nlSihQpgu7du2PAgAHo0KFDQLRk9Le0tDQsX74cU6dOxaJFi6zj3kdGRuLxxx9H//790a5dO54nAVi3bh26dOmC+Ph4PPjgg5g3bx6Hyc2EMc01xjPPxMbGonfv3lixYgVCQ0Pxww8/YPDgwf6uVkA6fvw45s2bh3nz5mHz5s3WfSsoKAj33XcfYmJiEBMTU6B6IhsMBpQpUwHx8TfQrFkzdO/eHd27d0fDhg05XJULY8eOxbBhwwAAb775JkaPHs1rMxcYz9xjTPPO9evX8dBD/8X+/d/CYEgCAPTo0QMjRoxAw4YN/Vw730pNTcWSJUvw888/Y9myZdZ5c8uWLWs3xCB57vz585g8eTImTZqEEydOWJ9/8MEHMXjwYMTExBT4kY/i4uLw2Wef4euvv0ZqairCw8MxdOhQvPvuu/lufu+8xiEE8yHb5FXfvn0xdepU3pTJptM3kzBvXQJWLw2F0aD/B14daoIxxIDqpfLXUIIiggOXE3D0YjJmTwlBYoLlZF7w3NB0NKgUhbrlIv1ax/xo8+bNeOihh5CYmIjXXnsNY8aM4YVlJrw4co8xzd6WLVvQvXt3XLp0CbVq1cKCBQtw1113+btaASs+Ph6///47pk2bZh1DG9ATEPft2xf9+/cvdGO0iwh27NiBqVOnYtasWda5VZRSeOihhzBgwADExMQgMpJxL7M9e/agU6dOuHz5Mu6++24sXbqUw6LYYExzjfHMvWPHjqFLly44cuQIypYti7lz5yI6Otrf1coXLl68iIULF2LevHlYvXq1dZ6SsLAw3Lhxo8Ac0+fNA/7znz8xd+4d+Wres0Awa9YsPP3000hPT8dTTz2FSZMmITQ01N/VCkiMZ+4xpnkmNjYWX331FcaMGYOEhAQAQJcuXTBixAg0bdrUz7XLfZcuXcKMGTPw888/48CBA9bnGzdubB1isFy5cn6sYeBKS0vD4sWLMXHiRCxfvhwmkwkAULVqVQwaNAiDBg1CrVq1/FzLvHf69Gm89957mD59OgA9bPCHH36IF154gTHNCSaw8pm///4bnTp1YvLKR3afj8PcP1KwY0PGxMEPPgg0aWGAUYD6FaJQpUSEH2vouYvxKThwKQHbtgKH94XAZp5kPP1CGupUDMfd1Ur6rX752cqVK/Hoo48iLS0NH330ET788EN/Vymg8OLIPca0DFOnTsWQIUOQmpqKdu3a4bfffsvX41rntdOnT2P69OmYNm2adRxtAGjYsCEGDBiAvn37okqVKn6sYe5Yt05PHN2wIbBgwXiMGTMGhw4dsr7eoEEDDBgwAP369SuQn9/XTpw4gY4dO+Kff/5B3bp18ccff6BmzZr+rlZAYExzjfHMtdWrV+Pxxx/HzZs30bBhQyxcuLBQ3pjxhdjYWCxduhTffjsPpUsrLFnyq7+r5BO//gr07QsMHw6MGuXv2uRPf/zxB3r06IHExER07twZv//+e4FJbvoS45l7jGmuxcfHY+zYsfjyyy8RFxcHAOjUqRM+/vhjtGxZ+ObvERHs3LkTkydPxsyZM3Hjxg0AQEhICB577DEMHDgQjzzySKFJQKSlAfPnA3fdBdx5p/1rBw8exMSJEzFt2jRcvXoVABAaGopu3bph8ODB6NChA+9lA9i+fTvefPNNrFu3DgBQt25djB49Gt26dStUjVM9wQRWPpI5eTVlyhQOHZQDqQYjNp+KxbRZRlw8pQOMUkDp0sBLLwmuJ+WfoQRT0o3YeT4ON5PTEan00IELFgBRUYDBADS824BqlYLQukYphIVwmIXsmDt3Lp544gmYTCaMHTsWr732mr+rFDB4ceQeY5qeMPftt9/Gl19+CQB46aWXMGbMmEJzgu9rIoJt27Zh2rRpmDVrFq5fvw4AaNPmMSxYsAhlyvi5gj721lvAl18CwcFA48bvYOfOz1G+fHn07dsXAwYMKHQ90Hzh8uXL6Ny5M3bv3o1KlSphxYoV7AkJxjR3GM+c++GHH/Dqq6/CaDSiS5cumDFjBofo9IGnngLuuQd4+WV/18Q31q0DXnsNGDEC6NbN37XJv7Zt24ZHHnkE165dQ8uWLbFkyRI2iMqE8cw9xjTHEhMT8d1332H06NHWJE379u3xySefoE2bNn6uXWBITU3FokWLMHnyZCxbtszas6hcuXLo168fBg0ahEaNGmVZT0TfdywIFi7UcaxWLeDECSAhIQG//PILJk6ciM2bN1uXa9iwIQYPHoynnnqKx2kHRAQLFy7Ev/71Lxw9ehQA0LZtW3z55Zdo3ry5n2sXOJjAyif+/vtvdO7cGQkJCejTpw+mTp3K5FUOXUlIxdpDcTi4LxgRoUHYswdo/4AOKHVqA0UiTLiRbECNAB9KUERw6PItnLyRhJIRIQgNDsr0OpCQbASCBM2qlkDpomFOSiJ3Jk2aZJ2/YMqUKRgwYICfaxQYeHHkXmGPabGxsejTpw+WL1+OkJAQfPfdd3j++ef9Xa0CIy0tDcuWLcO0adMQEdEP06fHYPVqoH17f9fMd5YsAdav1zcwDYaTOHToEDp06MAEaA7FxcWhe/fuWLNmDUqWLIlFixbhvvvu83e1/IoxzbXCHs8cSU9Px7Bhw/D9998DAN5++2385z//YctiHzCZgK5dgaZNgU8+8XdtfMdkApKTAXYaypmjR4+iY8eOOH36NO644w6sWLGiQM2VllOMZ+4xptlLTk7GuHHj8Nlnn+HKlSsAgDZt2uCTTz5B+4J0YeFjFy9exPTp0zF58mQcPHjQ+nzTpk0xcOBA9O3bF6VLl8XIkUD9+kCPHn6srA8dPw7ExADNmu1EcPB3+PXXX5GYmAgAKFasGPr06YPBgwejRYsWbGjogfT0dPz444/46KOPrI1T+/Xrh//85z+MbWACK1/YtGkTOnXqxOSVjx27mogjV2+hXGSo04PprdTAH0rwckIq9l+KhwhQvIjj/cJoEtxMTkfDisVRvVRgfo784quvvsKbb76J4OBgzJkzB93YdJIXRx4ozDHtyJEj6Nq1K44ePYqyZctizpw5aNu2rb+rVaCtXQu0aQPwVIE8kZKSgn79+mHu3LkoUqQIfv31V3Tp0sXf1fIbxjTXCnM8c+TmzZt44oknsGrVKoSFhWHChAls4ESUhy5cuIDOnTtj3759qFKlClasWIEGDRr4u1oBgfHMPcY0LTU1FT/99BNGjhyJCxcuAABatmyJTz75BB06dGDywUMigu3bt1uHGIyNjQWgh86rXfstJCaORJ8+wOjR/q2nr33zzTcYOnQoAN1z6JlnnsHjjz/OoV2zKS4uDiNHjsTYsWORmpqKUqVK49y5syhatKi/q+ZXTGAFONvkVe/evTFt2jQmr3zAJIJtZ2JxIykNZSKd90gSEVxLDNyhBFMMRuw+H4/riWko6yIRBwBXbqWidulI1K/IoUxy6v3338enn36K8PBwLFu2rNC3RuLFkXuFNaYtX74cvXv3RlxcHBo1aoQFCxZwnh2iAGQ0GvHSSy9h/PjxCA4Oxk8//YSBAwf6u1p+wZjmWmGNZ44cPnwYXbt2xbFjx1C+fHnMnz8f99xzj7+rRVToxMbGomvXrli/fj1KliyJxYsXc5gzMJ55orDHNJPJhIkTJ+KTTz7B2bNnAeheQx9//DEeffRRJq5yICUlBYsWLcLPP/+MFStW4JtvvsHLBWUs3EyuX7+OL774As888wzq1q3r7+oUGKdOncK///1vHD9+G8qWHYEBA3Sv9IhC2ieBCawAxuRV7klINWD7mVgIBFHhrrepwRiYQwmKCA5fuYUTN5JQokgIwoJdz211IykdJSJC0ap6yYD5DPmViODVV1/F//73P0RFRWH16tVo0aKFv6vlN7w4cq+wxTQRwVdffYV//etfMJlM6NGjB6ZMmYKoqCh/V42InBARfPDBB/j0008BAKNHj8bw4cP9XKu8x5jmWmGLZ8788ccf6NWrF+Li4tC4cWMsXLgQ1atX93e1iAqt5ORk9O3bF/Pnz2dvYjPGM/cKe0wzGAQNG7bBkSOb0LBhQ4wYMQIxMTFMXPnYhQsXEBUVheLFi/u7KpQPJScL7r1XYfduoHhxoFcvYMAAPeJKkOvbwAUKE1gBavPmzejYsSOTV7nkXGwy9l2MR/EiWeeMciQQhxK8eisV+y7Gw+Ri6EBbt1INUEqhZfWSAdeTLD8ymUzo378/Zs6ciTJlymDdunWoX7++v6vlF7w4cq8wxbSUlBQ8//zzmDp1KgDgww8/xAcffICgwnR2RZSP2Q4D8tZbb2H06NGF6kYGY5prhSmeOSIi+Pbbb/H666/DZDIhJiYGU6dOZQMNogBgMBjw0ksvYcKECQgODsaECRMwaNAgf1fLbxjP3CusMS0+Hpg0CRg7FoiO/huPPHIGvXr14vUaUYA6ehRo1gwwTzEGAKhZE+jfXz8KQ8c3JrACkG3y6sknn8T06dOZvPKx/ZficepGEspFhnl0UybQhhJMM5iw63wcriamuZzDy1aqwYTkdCMaVy6BCsXC86CWBV96ejp69OiBxYsXo0qVKtiwYUOhHB6NF0fuFZaYdvHiRcTExGDLli0oWrQopkyZgscff9zf1SIiL82cORNPP/00DAYDnn76afz000+F5lyUMc21whLPHElLS8Orr76K8ePHAwDee+89jBgxgjf8iAKIiODDDz/EJ598AgAYNWoU3n777ULVEMOC8cy9whbTTp0CvvkG+OknICEBuP12YM8eoEgRf9eMiNyZPl0nqzKLigKmTQO6d8/zKuUpJrACzObNm9GpUyfEx8ejV69emDFjRqG5YZBX0o0mbD4di+R0A0pGhHq8XqAMJSgiOHr1Fv65noxi4cEID/HsotlkTsLVrxCF2mU4maKvJCcno3Pnzli3bh1uu+02rF+/HhUrVsSGDUCJEsBdd/m7hrmPF0fuFYaYtm3bNnTv3h0XLlxA9erVsWDBAjRp0sTf1SKibFqxYgV69OiBpKQktGjxGObN+wVVqhT8yYMZ01wrDPHMkevXr6Nnz55Yu3YtwsPD8fPPP6NPnz7+rhYROfHdd9/htddeg4hg2LBh+PLLLxEUFIQzZ/TNvtKl/V3D3Md45l5hiGkiwKZNwNdfA3PnAiZTxmtr1wJt2/qvbkTknUGDgMmTM/4ODQXWrAHuvddfNco7OYlpbGrmY1u2bGHyKg/EpxiQajB6nPixCAkOQtHQIFxKSMXF+JRcqp1715PScSEuFaFB8OozBCkFBSAhxZB7lSuEIiIisHDhQjRr1gzHjx9Hp06dsHHjTbRrB0RHA0lJ/q4hUe6bMWMGoqOjceHCBURHR2Pbtm1MXhHlc506dcLq1atRtGhpbNu2Dk89ddLfVSLyi4MHD6Jly5ZYu3YtKlWqhHXr1jF5RRTgXnnlFcyaNQuhoaEYM2YMWrR4Aw0aADVqACdO+Lt2RLkvPR2YPRto3VrPlfP77/bJq+efZ/KKKL/57jugXr2Mv41GIDXVf/XJL5jA8qEtW7agY8eOiI+PxxNPPMHkVS6KS0lHqsHkdQILACLDgpFmMOHUjWQkpuV9IijNaMKJ60lITDeimAfzXmUWGqwQn2qAwWhyvzB5rESJEli+fDnuuOMO7N27F2+++Rhq105E06a6RQRRQfbLL7/gqaeeQmpqKp577jmsXLkS5cuX93e1iMgHWrVqhW++2YBixRahU6cG/q4OUZ5bunQpWrdujRMnTuDuu+/Gtm3b0LJlS39Xi4g88OSTT2Lp0qWoWrUq/vvfZ/Daa0DnzkCpUv6uGVHu27cPmDkT2LYt62uVKwOff573dSKinImMBH75BQgPB4YOBWbMANq393etAh+HEMyBpCRgxQqgSxdgxw775NXMmTOZvMolIoKd5+JwKSEV5aLCslWGv4YSFBEcv5aEY9duoVh4SLYScElpRhhFcHfVkl4Nn0ieOXv2LNq0aYOzZ8/i6aefxmTbvr0FGIencK8gx7Tk5GTce+8DaNfuKXz11UuFco4BIio4GNNcK8jxzOLSJWD/fuDYsSsYNqwm0tKS8cQTT2Dy5MkoWrTgD6NJVNCkpqYiPLzwzQHNeOZeQY9pKSlA167An3/aPz9/PtCtm1+qREQ+8OOPQKVK+v+7sOAcWH4yeDAwaRLQv/9WLFjQwa7nVSi7bOSapDQjtp65CYNJUDwbPZgsbqUaYBSgfoUoVCkR4cMaOnc9KQ17L8Qj3WjKdvLJYDQhLtWIRpWK5Vm9C5ujR49i0KBBmDFjBmrWrOnv6uQJXhy5V5BjmghQtqwRN24EY9MmPUwFEVF+xZjmWkGOZxYHDwINGuj5BHr3no4bN/7BBx98wAYaRJSvMJ65V9BjmsGgb3AvW5bx3OOPA7/95r86EVHOiegEdUQhuq3LBJafjBsHvPPObqSl3Y/k5Hg8/vjjmDlzJpNXuexifAr2XohHZFgwwrLRg8lCRHAtMR0lI0LRpEpxRIblbo+5dKMJu8/H4/KtFJSNDMt2ry8RwdXEdNQtG4k7ykf5uJZkISKF6iYHL47cK+gxrWdPYPduYOVKoFYtf9eGiCj7GNNcK+jxDNBzhEybBvTpA4Rlb8AGIiK/YzxzrzDEtIQEYOpUncTauBE4dAioWNHftSIi8g4TWH5082YcOnfuiOrVqzN5lUcOXU7A8etJKB8ZmuMEg2UoweolI9CwUu4OJfjPtUQcvXoLkeHBKBISnKOyriemoUxkOFpUK1GokiyUe3hx5F5hiGkmExDE2TGJKJ9jTHOtMMQzIqKCgPHMvcIU0778Us//9swz/q4JEZH3mMDys/j4eERERDB5lQeMJsGWMzcRn2JA6aK+2d63Ug0wmoA7K0Shasnc6bt5MykNey7EI9VoQikfzFsVn2JAaLBCq+qlUCQ0Z8kwIoAXR54oLDGNiCi/Y0xzjfGMiCh/YDxzrzDFtKtXgbJlAbZhJqL8KCcxje2sfaB48eJMXuWRhFQDktONCM/B0IGZRYYFI91kwqmbyUhMM/isXAuD0YQT15NwK82IEjmYs8tWWLBCmlFwK83ok/KIiIiIiIiIiCgwlSvH5BURFU5MYFG+EpeSjlSDCUV8mMBSSqFkRChik9Pxz7UkmHzccudMbDKuJqaheHiwz4YoDAsOQprBhFupvk+4ERERERERERERERH5GxNYlK/EJqXDJEBwkG+bnYQEKRQNDcLlhFRciEvxWbmxyek4G5sMAD4d6i8oSEEAJrCIiIiIiIiIiIiIqEBiAovyjVSDCXEpBoSH5E6faV8PJWg0CU5cT0RCqu+GDrQVGqQQl2KA0VTwx3omIiIiIiIiIiIiosKFCSzKN+JS0pFi8O38V7Z8PZTg2dhkXL2VhmLhwT7vMQYAocEKaQYTktI5DxYRERERERERERERFSxMYFG+EZdiQJpREB6ce7utr4YSjE9Jx5mbyRAIInw4dKCtsJAgpBlNSOQwgkRERERERERERERUwDCBRfmCiOBmUhqClO4plZtyOpSg0ST453oS4lMNKFEkNBdqqIUGKaSbBLfS2AOLiIiIiIiIiIiIiAoWJrAoX7iVZkRimhFFcmn4QFs5HUrwXFwyriSkIiosd4YOtFBKQUH39iIiIiIiIiIiIiIiKkiYwKJ8IS45HSnpplyb/yqz7A4lmJBqwJmbyTCJICI09+saFqJwK9WINIMp19+LiIiIiIiIiIiIiCivMIFF+UJcSjqMYkJILvZoyszboQSNJsE/1xIRl2JAyYjQXB/qEADCgs3zYGVjqEMiIiIiIiIiIiIiokDFBBYFPIPRhJvJBoQGB+VJUsjC26EEL8Sn4MqtVETm8tCBtiwJLM6DRUREREREREREREQFCRNYFPDiUg1ISc+b+a8y83QowVupBpy6kQyDCSiaB0MHWgQHKZhEvz8RERERERERERERUUHBBBYFvLhkA1INJoQH+2d3dTeUoEkE/1xPQlxKOkpGhORpLzEACA4C4lIMEDc9xIiIiIiIiIiIiIiI8gsmsCigiQhuJqcBAILycP4rW+6GErwQl4IrCakoGhqUp3N0WYQFByEl3YjkdFOevzcRERERERERERERUW5gAquQ27oVOHTIN2WZTMCePUBSkm/KA4DkdBMSUgwI98PwgbacDSWYmGbA6ZvJSDeZEBkW7Je6ZcyD5bthBNPSgP79gZEjfVPejh1ATAzw11++KY+IKDMRYNkyIDHRN+XFxQH79+vYRvnbtm1Aly7AmjW+Ke/HH4EnngBu3vRNeUREmd24AaxerWObL5w5A1y44JuyyL8mTQJ69wbOnct5WUlJwJAhwNtv57wsIiIiotwS4u8KkP/s2JGEVq3+RLFiqfj++yAULRqE4OBgBAVl/enoucyvzZkThBEjSqNXr0r45Rff1DE+JR2pBv8lh2xFhgXjelI6Tt1MRqmIUESEBePE9STEJqejlB+GDrQIDVaITxHcSjWifFTOy0tPT8fLL3+J6dPnQinB3LkKwcEKSikEBQVBKWV92P7t6rWDBxVOn66NtLR30L59lZxXkojIhojgxRc348cfx6NYsY2oVSsCUVFRKFasWLYeUVFR6Ns3GEuXArNm6RtFlH/NnClYvPgKDIZkVKqUitRU+0daWppXz02ZYsStWx3w6KPdMHCg/89PiKjgefRRYPNmwQcfbES5crthMplgMplgNBrtfnrye2KiEZMnm2A0mvDBB7UxfPhLKFq0qL8/Yq7ZuXMnNm3ahKZNm+Lee+/1d3V8RkQwZ84cDBu2CAkJQbh4MRR33RWGsLAwhIaGIizM/ndHz2V+PSioHiZMqAgAeO89oFgxP39IIiqQbt4EtmwB6tcHqlfPWVkJCUByMqAUUK6cb+pH/nHyJDBhAnDqFDB8ONC0ac7KGz0a+OcfvZ8NHeqTKlIAUbk5b45SSgBwbp4Ak5SUhHHjxmHUqM9x7doVn5ffqNFL2Ljxc0RF5TybcvhyAo5dT0L5yFC/JYhsGUyCG0npKGqMQHBqKNKK30KwAqLC/ZsLvnorDdVKRqBR5eI5Kmf//v0YOHAgduzY4aOa2StSJAJvv/0vDB8+HJGRkbnyHpQ9lv8vEfH/P1qAYkwLPDdv3sT06dMxfvx47N+/36dlh4QUhcFQAl269MD06SNRvHjOjq/kHwcOHMDAgc9h+/ZNPi+7du06ePPNNzBw4MACfTM4P2JMc43xLLCZTCY89tgC/Pnn5zAYtvi8/Jo1a+Lbb7/FY4895vOy/S0lBXj55c8xadI7GD58OEaPHu3vKvnE/v378dprr+EvHw9nERYWhtatX8Djj/8fXn21gk/LJt9gPHOPMS2wHT4M3Hmn/n3MmJwnFoYNA8aOBYKDAYPvBiEKSCdPAhcvAgWoLYadnTuBu+/Wv0+eDDz9dM7Ki44GNmzQZW7fnuPqUS7ISUxjD6xCJCkpCT/++CM+//xzXL58GQDQtGlT3HbbbQ5b9RkMBly5cgWJiYkwmUwICgpCqVKlEBwc7LQF4NGjR7F37/do3Hg5Jk+ejOjo6GzX12gS3EhOR2iQCojkFaCHEowICcL7b49F/I3LeOGdZ9G8UQ6bkPhAaLBCfKoBRpMgOBvzcBkMBnzxxRf48MMPkZaWhurVq+Prr79G1apVISIwmUwQEYgIjEYjdu7ciSVLlmDbtm1ISkpCWFgYatWqhU6dOiE6OhpFihSxW8dgMGDatGmYM2cORowYgQkTJmDkyJHo378/goI4kikReU5EsGnTJowfPx6//PILUlL0sK7ly5fH008PQp8+T0IphYSEBLvHtWvXsGXLFuzfvx+J5nEGixUrhtKlSyM9Pd1u2Vu3bsFgSAKQhEWL/ocGDRZg3LhxePTRR/34yXNPbCxw+TJwxx3+ronvpKamYtSoURg5ciTS09MRFRWF0qVLIzw8PMsjODgY165dw/Xr12E0GhEWFoYqVaqgYcOGKFmyZJblExISMGHCBJw48Q9efvllfPDBB3jppZfw8ssvo0IF3gAkouxJTU3FjBkzMHr0aBw5cgQAULp0acTExCAiIiLL6BfXr1/Hvn37cObMGaSmpiIiIgK1a9dG8+bNUalSJbtlgSCkpwPTp0/Enj170KVLF3Tr1g1jx45FjRo1/PvBfejdd4FJk3TPWKPR6Ofa5FxsbCw++OADfP/99zAajShTpgzefvttlC1bFmlpaUhPT0daWhrS0tKQkpKCQ4cOYe/evYiLi4OIIDIyErVr10bZsmWRnp5ut3xiYiK2bNmCdeu+wY4dE3H58jC89dZbKFmypL8/NhEVIHXrAlFRwK1bwObNOU9gFZY85ZUrV9G+fQJOny6Lv/4qjnbt/F0j37M9/Th1KuflWYbWrcJBnwomyw3m3HgAEP0W5E9JSUny9ddfS8WKFcXynTRv3lyWLFkiJpMpy/JXrlyRYcOGSenSpa3LWx5BQUHy8MMPy6pVqxy+1+7du6Vx48YCQJRSMmzYMElMTMxWvW8mpcnKo1dk9bGrsuX0Db89Np+6Luv/uSYrj16RpQcvyaIDF6VCtYYCQD6f/qcsPnBJ/jhyRdYevyqbTl33Sx3/OnZVVh29IrHJaV5v54MHD0rLli2t3/GQIUMkLi7O4bJHjhyR+vXrZ9kvLN83AImKipLZs2c7XH/dunXSvHlz6zrNmjWTNWvWeF1n8j2b43WuxoX8/PBXTNuyZYusWrVKUlJS8vy9A8mNGzdk7Nix0qBBA7tjz0MPPSS//fabpKamOlzPZDLJqFGjpGjRotZjlVJKgoKCrGV069ZNrl+/bl3HaDRKfHy8bNu2TVq0aGFdrm/fvnLlypW8+sh5ZuhQkYcf9nctfGfTpk12ser555+X2NjYLMsZjUYZMWKEREZGWveNoKAgazwLCQmRwYMHS1JSUpZ1DQaD/Pbbb3bxMzw8XJ577jk5fPhwXnxMcoExLTDjGTkWHx8v//3vf6Vy5crWfbd69eoyduxYuXXrVpblr127Jp06dXJ4Pm55dO3a1eFxLz09XcaMGSPFihUTABIRESGjRo1yGkPzm59+EgkL+0oAyNChQ/1dnWwzGo0yYcIEKVu2rPUa/OWXX7Y7V7G1cOFCKVeunN01meV8B4BUrlxZVq5cmWW9PXv2SNeuXa37TalSpeTzzz/P9vU7+R7jGWNaQfDAAyKASM2aOS/r1Vd1WSEhOS8rkL322msCQIoWHSPHjvm7NrnDZBKJitLf56BBOSvLaBQJDdVlvfSSb+pHvpeTmMauDwVYcnIyxowZg9q1a+P111/HpUuX0Lx5cyxevBhbt27FI488kqVn06FDh9CsWTOMGTMGN27cyFKmyWTC8uXL8dBDD+Grr77K8nrjxo2xdetWvP/++wgKCsKYMWPQtGlTbNrk/dA98SkGpBpMKBKSt7upiCDVYEJCigHXEtNwNTEdiWlGBCuFMpFhuL1cFMqU0NvtzspFUblEEYQHByHVILiemI6rt9IQn2JAisEIk+RN85DQYIU0oyAx1fOWhkajEV988QWaNm2KrVu3omrVqlixYgV+/PFHh0Nl7d+/H61atcKhQ4cclifmz5qYmIjevXvj+++/z7JMdHQ0tmzZgqlTp6JKlSrYuXMn2rVrhx49euDYsWMe152oMOnRowcefPBBXL161d9VyXMigg0bNmDAgAGoXLkyhg4digMHDqBChQp45513cPz4cfz55594/PHHERYW5nD9l156Ce+++y6SkpKsz4nonqUWCxYswL333mvdxkFBQShWrBiaN2+OTZs24YsvvkBERARmzpyJ+vXrY/bs2dZjXkHw/vsp+OeflzFs2DB/VyVHbt26haFDh+Lee+/FwYMHUbduXaxZswbjxo1DiRIl7JY1mUwYOHAgPvzwQ2uPPMt+YfluDQYDJk6ciAcffNC6jEVwcDAef/xxbN68GevXr0e3bt2QlpaGCRMmoF69eujWrRvWr19foPYTIvKty5cv49///jeqV6+O4cOH48KFC2jYsCGmTp2K48eP47XXXssy5Pa1a9dw3333YcWKFS7LXrhwIe6//37ExsbaPR8SEoKhQ4fi8OHDePLJJ5GcnIx3330XTZo0wZo1a3z8CfPe4MHA6NH5uwfWli1b0KpVKzz33HO4du0a2rZti507d+K7775D6dKlsyw/ZcoUdOvWDdeuXQOQcU0mGTf3cenSJXTq1Alz5861W7dRo0ZYsGAB/v77b7Rr1w43b97E22+/jdtuuw0//PAD0tLScvnTElFh0Lq1/nnqFHDpkm/KDJBBmnKN5fj73/+G4rbb/FyZXKIUULOm/t3SA2v1auD4ce/LunoVSE/Xv1et6ovaUaBhAqsASk5OxtixY+0SV3fffTcWLVqErVu34tFHH3U4JN/FixfxwAMP4Pz58y7Lt5wMv/nmm5gwYUKW18PCwvDxxx9jy5YtaNCgAY4ePYr77rsPb7/9tnWoJ0/cTE6HSZCtIfG8YRJBisGEeJuEVXK6EcFBCuUiw3FHuSg0rlICLaqXRItqJXF7uSiEBut/nWqliqJplRJoVaMkmlQpjjsrRKFS8XCEBgchNV1wzS6hZcq1hFZIkILBJLiV5tkgwEePHkV0dDSGDx+O1NRUPPPMM9i/fz86duzocHmj0Yju3btbh6NwRUSglMLLL7+MvXv3Znk9KCgI/fv3x9GjR/Hxxx+jaNGimDdvHho0aIA33ngDN2/e9OgzEBUWYWHhAPTQQoXFjRs3MGbMGDRo0ADR0dGYNm0aUlJS0LFjR8yZMwdnz57FqFGjUKdOHZfl/Prrrxg3bpxH73nkyBG88sorWZ4PDg7Gm2++ib1796Jdu3a4du0a+vTpg27durmNl/lFeLgBR49+7zCm5xcrVqxAw4YN8c033yAoKAjvvPMO9uzZg/vvv9/h8hMmTMC0adM8KnvTpk149913Hb6mlMJ9992H+fPn49ChQxgyZAjCw8OxcOFCtG3bFq1bt8Zvv/0GQ0EfpJ+IPHbixAm89NJLqFmzJkaOHInY2Fjcd999WLx4Mfbu3Yv+/fsjNDTU4brDhw/H4cOHPXqfPXv24N///rfD1ypXrozZs2fjjz/+QN26dXHo0CG0b98e/fv3tw41n1+FhOTPBNbly5cxaNAgtG7dGtu3b0eVKlUwa9YsrFmzBo0bN3a4zqlTpzB48GAAruf/sQz337dvX1y5knUO7HvuuQerV6/GihUrcPfdd+PixYt46aWXcOedd2L69On5blsSUWC5556M3zdv1j+9OTVOTwcSEvTvmQ91Ino49IIm3ZyNcdRIM7+7fBkYPhzYtStjGMFjx4AXXwQefBAoW9bzsiz7g2X4QIAJrIKKCawCJDk5Gd988w3q1KmDYcOG2SWutm3bhscee8zlXFJff/01Ll265FVr4f/7v/9zmpS6++67sWPHDrzzzjsAgNGjR+Puu+/Gdg9m00s1mBCXnI7wYN8nr0wiSEk3Ii7FgKu30nAtMR0p6UaEBitUiApHvfJRaFK5BFrVKIXm1UqgbrlIVCwWjsiwENsJ5+zKDA8JRrmocNQuE4lmVUtaE1r1rQkthZR0o10PrVQfJrSUUghSQFyKwe3Fy5gxY9C4cWNs2rQJlStXxpIlSzBx4sQsrdNtLVq0CP/884/H+4ZlOUe99CyKFi2K999/H8eOHcOgQYNgMBjw9ddf47bbbsM333xjDdhEpBNYyckFO4ElIli3bh2eeuopVK5cGa+//joOHTqEihUr4v/+7/9w4sQJrFixAj169HB6Yy9zef/973+9mkPx999/xyknA3DfdtttWL16NcaPH4/ixYtj0aJFqF+/PiZMmJDve9no+VHy380+ALh+/ToGDBiAzp074/Tp02jWrBm2bduGUaNGISIiwuE6JpMJX3zxhVf7xk8//eS2gcUdd9yBH3/8EWfOnMEHH3yAMmXKYOvWrejVqxduv/12fPvtt7h165ZXny/QTJ8+HeHh4Xj22Wf9XRWifGf37t3o06cP6tatix9++AEpKSno2rUrNm7ciPXr1zttZGhx4cIFTJ8+3av3nDRpEq5fv+709Q4dOmDfvn345JNPUKRIEUyfPh133HGHdc6l/Ci/xbT09HR8/fXXuP322zF58mSEhYXh3XffxeHDh9G7d2+X+8TYsWNhNBo9Og8REaSmpuKHH35w+LpSCh07dsS2bdvw+++/o169ejhx4gT69++PJk2aYOHChfn+fIeI/KNVq4zfN2/WvWV69vR8/ZAQoG1bYNSojEQWAKxcqZ//+2/f1TVQWHpgeXLdm99UqADs3Qs0awZYOpSfOweMG6fnr/JmKsa33gLWrXOcwHIwqBjlY0xg5VO7dgGWht+2iauhQ4fi4sWLaNasGRYuXOhR4goAEhISPG6lbuvatWuYOXOm09fDw8MxatQobNy4EXfccQcOHjyI1q1b4/3333c5JEF8SjpSDEaE+2D4QJNJkJxuRFyyTh5dT0xHqlEQHhyESiXCUb9ClO5FVb0UmlUtgdvKRqJ8sXBEhAa73W7OXi9ik9CylN20SgnUqxCFisV0QivZnNC6lpiR0MrJRUFYcBCS0gxINZocvn78+HG0a9cOr7/+OlJSUjBgwADs378fjzzyiNuyv/nmG69u9FnMmDHDOpyFM5UrV8akSZOwY8cOtG/fHjdu3MDQoUPRsGFDLFq0yLpN9u8Hfv/d6yoQ5XtGYxEAwJo1nvdgzU9Onrykk/BBQbj//vsxY8YMpKWloXPnzpg7dy7OnDmD//znP6hVq5ZX5R44cAA7duzw6rhqMpkwdepUp68rpfDcc8/h4MGD6NKlC+Lj4zFkyBA8+OCDOJ6dsQ4ChOVmn+2wioFORDB79mzceeedmDZtGooUKYLRo0djy5YtaNq0qct1N2zYgOPHj3u1byQnJ2P27NkeLVu+fHmMGDECZ86cwffff486derg5MmTeO2111C9enX8+9//xtSpl6wtUPOTlJQUpKWlYds2wcmT/q4NUeC5ehV2/9sigr/++gudOnVC06ZNMXv2bAQFBeHpp5/GgQMHrEPYemL27Nle9+ZMSUnB725OoMPDw/Hee+/hwIEDePjhhxEXF4eXX34ZrVq1wrZt27x6v0CQnxJYK1euROPGjfHGG28gPj4ejz76KPbv34+RI0ciKirK5boigp9++snr93TX21ophZ49e2Lfvn2YNGkSqlevjv3796Nbt25o06aNw6Em89HpAxHloWPHgOXLgYgIwDJwxoIFQPPmOoHhKaX0MIT/93/AlCn6ufR0oEMHYOdO4IEHfF93f7PcLy2IPbAA4PXX9c/MpzUNGnhXTkQEcP/9wHvvZTz355/AvffqhBgVHExg5UObN+tMdfPmKfj222/tEldNmzbFggULsH37dnTp0sXjpMO6deuQYNuUwUNKKcyfP9/tcq1bt8auXbvwxhtvwGQy4dNPP0WLFi2we/duh8vHpRiQZhSEZSOBZTQnrGKT03HlVhquJ6Uj3SgIDwlGlRJFUL9CMeuwf82qlETtMpEoFxWOIh4krCy8uemllEKRUJ3QqlMmEs2q6oRWkyolUK98FMpHZSS0ruYgoRXmZB4sk8mE7777Do0bN8b69etRsWJFLFiwAFOmTEGpUqU8KvvQoUPZSq4ZDAac9PAOV9OmTbFq1SosWLAAdevWxdGjR9G1a1d06NABe/bswaOPAk88ASxZ4nU1iPK11FTdA2vWrILZA+uxx163+zs4OBgbN27EsmXLEBMTk+1WZ5eyOcD6xYsX3S5TpUoVLFiwALNmzULZsmXx119/oVGjRvjyyy/zxQ2zzIKCdKzNL3U/d+4cunbtij59+uDq1ato164d9u3bh+HDhyMkJMTt+hcuXMjW+3o7ZGTRokXx4osv4siRI5gzZw7uuece3Lx5EyNHjsTTT9fAgw/OR3JytqriN5Ze95GRRXDbbUDv3oAHHeuJCo327fVQSTNmXMSzzz6Lu+++Gw888AD++OMPREZGYtiwYThx4gQmT56M+vXre1V2dof283S92rVrY8mSJZg7dy6qVq2KHTt2oFWrVnjppZfy1RDf+SGBderUKfTs2RMdOnTAoUOHcNttt2Hx4sVYvHgx6tat61EZiYmJ2erVe/HiRY+u60JCQjBo0CAcPXoUY8eORbly5bBp0ya0b98eHTt2xPbt2yECfP454KLtDxHlY/HxuuF8dtvpVasGvPACUKpURgP8w4eBM2eAMmW8K+uxxxw/37EjUKRI9uoXyCwjEhXEHliA/t7q1cv6fMOG3pVjGWF3//6M50aNAnbsALwdMOLIEeD0acDSdoedjgMLE1j5kMFwCUFBVXHpUlG89tprdomrHTt2oGvXrl73lsk8wa83PL2giYiIwJdffom1a9eiTp062Lt3L1q0aIGPP/7Ybrg4EcGNpHQEKSDIg89hNAmS0jISVjeTdcIqIjQY1UpGoEFFnbBqXUMnjWqVKYqykWEIN4+PnhPZ6ZVkSWiVjwpHnbKRuLtqCbSsXgpNKmcktELMCa1r5oRWggcJrdDgIKQbTXbzYJ08eRIPPvggXn31VSQlJaFv377Yv38/unbt6lWdc3IB6E1LUaUUunbtiv3792Ps2LEoVaoUVq1ahaZNmyIi4lk0bHjN64BGlN+lpekz8mvXUnD6tJ8rkwsGDvzU7m+j0Yjo6Gh07doVCxYsyPZwos6Gj3OnaNGiHi2nlELv3r1x6NAh9O3bF8nJyXjrrbdwzz33YL/tGXQ+YNsDK5CHBzKZTPjhhx9Qv359LF68GCVKlMCECROwevVq3ObF7MZFsnmVm931jEYjjEZjpn0rDfXqXcx3F9yWufhatgzHXXcBv/wCtGgBPP44kJjo58pRwPv7b+Do0YLdW6NxY31T7qmnKmPixInYtWsXAKBLly44ffo0vv76a1SrVi1bZXsan3KynlIKMTExOHToEIYPH47g4GD88MMPqFevHqZNmxbQMcIikBNYycnJGDFiBO68807MnTsXkZGRGDVqFPbv349HH33Uq7LCw8OzVYfw8HCvrmHDw8Px2muv4cSJE/j0009RvHhx/Pnnn2jRogXatx+JGTPsh24iKmxGjwZmzwYMBhNu3rzpctjW/GTpUt0oo149YPHi7JVRpAjw2We6l03mmUe8TWA98IDubZNZly7Zq1ugK+g9sIKCgGHDsj7vbQ+sJk0cP//kk0D58t6VtXq1Po976CEgJsa7edoC0YYNGzBixI84cOCAv6viEwUygSUCvPsu0K4d8l3LVk88/HBdmEznAegLiEGDBmHz5s3ZSlxZuBuiwJVixYp5tXx0dDT27NmDV155BQaDAR9++KHdDb9D/xhx5IQBRRz0vhIRGEyCxDQjbial40qCTlgZTYKiYSGoUUonrJpV1QmrxpWLo2bpoigTGZat3lzO+PLiTSmFiNBglC8WjtvMCa1W1UuhceUSuKN8FMpFhSM4yL6HVkJq1oRWcJBCcgrw83QDNmwQjBs3DnfddRfWrFmDcuXKYc6cOZgxYwbKeHumAKB69erZ3reqZmMGxbCwMLz22mtYvnw5ypQpAxHBkSMT0bz5cOskj/ldamrBvoFDvtO4sb5B8fnnqahe3c+VyQXDh9fRx3aDAUuXLkWPHj2glMKiRYvQvXt3VK9eHe+88w6OHTvmVbmNGjXKVmyLjo72avmyZctixowZWLRoEapUqYJt27ahWbNm+Oijj5Camoo1a/QN20CmlHI6x6M/bdsGDB0KXLgAHDlyBO3atcNLL72EhIQExMTE4ODBg3j22We9jk8tWrSw3uD0Rps2bbxa/sSJE3jnnXdQrVo19OrVC6tWrUJERAQGDhyITZs2Yfv2F5DN0Oo3lh5YRYsWwfjxsNZ/0CAgMtKPFaN8Ydo0PQTQwIH+rknumTFDtzJ/6qmn7J5ftGgR2rRpg08//dTj0Qkyu//++7O1Xtu2bb1eJyoqCqNHj8auXbsQHR2NK1euYMCAAbjrrnZYuzawb4QEYq/iK1eu4KGHhqJs2er46KOPkJKSgj59+uDw4cN45513spWMCg0NRbNmzbyKgUoptG7d2uv3MhqNWL9+PbZv345Em9YK9eqdxd699kM35VdpaboFfzY7OlIhNnky0KcP0LhxHEqXLo06lrHy8rn77wcuXQJu3QJKl85+OU8+CbRsmfV5b29LRUQADz5o/5xSgJe5f6vjx02YOPEEDIbAiRW2ArkH1rVrBjz00FeYNy8pR+X075913/I2gVWnjuNrkFdf9b4+9esDcXG652Hr1kAAbnqvvPXWbHz00Qt4/fW//F0V3xCRXHtAZ1jEH957T6RSJb+8da6bNWuWWLat5VGxYkV5++235ciRI9kq8/LlyxIWFpalXE8e//3vf7P9WVavXi01a9YUABIWFiajRo2S+zrFyzOfXJYZf12Tzaeuy4YT12T1sauy7NBlWXTgkiw9eElWHr0iG05cl/0X4+TMzSS5mZQm6UZjtuvhrQYNGggA2bt3b66/l8lkksRUg1yKT5GjV2/J1tM35a9jV2X5Yb09lpi3x/p/rsk38y9Ih367pVixh6zfT69eveTKlSs5qsO4ceO83i+UUtKpUyev3ys5OVlmzJgh7dq1syvvzjvvlNWrV+focwSKESNEoqJEPvnE3zXJYHO8ztW4kJ8f/opp3bp1EwAyb968PH9vf7l8+bJ88cUXUq9ePbvjQNu2bWXKlCmSmJjoUTlvvfWWV8es2rVri8FgyHa94+Li5IUXXrCWWbt2AwE2S4kSIiZTtovNE8HBwQJA0tLS/F0Vq06dRIA0eeih/0h4eLgAkAoVKsjvv/+e47Kfeuopr/aNJk2aiMmDLzEtLU1+//136dChg10ZDRo0kG+++UZu3ryZ47r70/vvvy8AZMSIESIi8sorIr/+KpKHp2AeYUwLvHiWni5SrpwIIBIRIfK//+Xp2+c5k8kkaWlpsn37dhk6dKiUL1/e7pjQpk0bGTdunFy/ft2rMps0aSJKKY+PXW3atMnRZzh16pTMmDFDatWqZS03JKSYV/XOazNmzBAA8uSTT/q7KlZVq1a1+27q168vkydPliNHjngUW5yxfFZvHosXL/a4/LNnz8qIESOkevXq1vWDg4MlJiZGli5dmqNzpkCTkCBSpoxIWJi+l7Rtm79rpDGeBWZMszAaRf74Q2TPHpGTJ+MFgERFRfmlLrlh8mQdtxctylk5Gzbocmwfr77qfTnjxtmX0bq1d+tfvHhRRo0aZXdM7NXrU+8rkgfat28vAGTlypX+rkoWr7wyxW4bli5dWr777ju5deuW12X93//Zf6fx8d7Xp3Vr+zJatvS+DBGRK1f0+pGRIjduZK+MQHLPPfq+xGOPBc5Jd05iWoENJIcOXZFWrTbL8ePH/fL+ecHZDb7o6GiZPHmy1wePZ5991usT4MjISImNjc3R54iPj5chQ4ZYy6xQpbk898lm+W3bJVm4/5IsPXRZVh29KhtPXpcDF+Pl7M0kiU1KE4PRf3cDLQmsffv25fl7WxJaF+NT5OiVW7LFktA6dFnuatnW7vt55plnfHKBmZSUJKVKlfL4gtny+PPPPz1+j3379snQoUOldOnS1vUjIiJk4MCBsmHDhhxd3AWauXP10XfOHH/XJAMvjgI3pvXq1UsAyOzZs/P8vf3NZDLJxo0b5ZlnnpHIyEjrflq8eHF54YUXZNu2bS6PDRcuXJBKlSq5PXYppUQpJXPnzvVJvf/66y+pXLmyuXwltWoNl9TUwD6GWRqxJCcn+7sqVt27v273PT322GNyw0dXE4cOHZJixYpJUFCQ230jODhYli9f7rK8kydPyv/93/9JxYoVresWKVJEBgwYUKBi2PDhwwWAfPbZZyKib/gF4kdjTAu8ePbnnyKAvjn844+Bud/kpvT0dFm2bJn069dPihYtat1Hw8LCJCYmRubMmSMpKSluy1mxYoUEBwd7FNdCQ0Nl7dq1HtcxLS1Ntm3bJmPGjJFevXpJlSpVHJQdLPXrP+JRXf1l9uzZAkAef/xxf1fF6r333nf6XZUuXVoefvhh+fjjj+WPP/7w6to6LS1NGjVq5PG1WZs2bcTopsVBenq6LFiwQB577DG7GFm7dm0ZOXKkXLhwIaebIyDFxor15mfJkiLnzvm7RhrjWWDGNEcSExOt538FhdEo0rSpQTZuzHlZPXtm/I8BIh995H0ZZ8/alzFypOvlk5OTZeXKlfLyyy87PS5OmPBH9j5QLrvvvvsEgKxbt87fVcni9dffcxlrqlSpIhMmTJBr1665LevcOZGQEP191qiRvfq88IL9fjF1avbKEREpWzZ7ydVAZLnPPm7cOH9XxYoJLAe+//57ASDPP/+8X94/L7m6wff888/L1q1bPbpxcvjwYSlatKjbmzm2jw8++CDb9b5y5YosWrRI3nvvPXnooYckNDTUWm77R2Pk71M35OCleDkXmyRxyf5NWGVWv3598VcCKzOTySS3UtPlYnyyhJlbqGd+1KlTR/r06SNfffWVbNiwwePeC7YWLlwowcHBHu8fzz//vNv9LiEhQSZOnCitW7e2W7dZs2by/fff5zg5GqhSUkRKl9YtPAIFL44CN6b1799fAMiUKVPy/L0DSXx8vEyYMCHL8aJRo0byzTffOE3WHzlyRGrXrm29oefoeBUWFibTp0/3uk4pKSmyb98++f333+XTTz+V/v37S4sWLaR48eJZ3uPgwYM53QS5KiIiQgBkq+VcbjCZTA6/qxIlSkjz5s2lb9++8tFHH8nMmTNl+/btEhcX5/V7bN68WcqWLety3wgPD5dffvnF4frp6ekyb9486dy5s936d955p4wZMyageyhk16uvvioAZMyYMf6uikuMaYEXz559VqR6dZGtW/P0bQNSfHy8TJ06VTp27Gh3Xl2yZEl57rnnZN26dS6TDL///rsUKVLE5Xl40aJF3fa0uXHjhixdulT+/e9/S7t27ewSa7Z1euSRR+Q///mP/PXXXwETI1z57bffBID06NHD31Wxk5ycLImJibJu3ToZPXq0xMTE2DV6sDyUUtKgQQN59tln5aeffpIDBw643B8uX74szZo1cxrLLM+1adPGZS/gkydPynvvvWfTAAcSGhoqvXr1kpUrV7pNfOV3N26IT25++hrjWWDGNEdSU1Ot/zcFie3xpFatWvLxxx/LX3/9JefOnfOqgdaxYyKhoRn/Z99+m736NGmSUcb+/favmUwmOXDggHz99dfSqVMnl3Hys88+k9TU1OxVIg+0bNlSAMimTZv8XRWndu7cKV26dHF7b/C2226TiRMnypkzZxyW06+f/j4ffTR79fjhh4x9olw5key2xzSZTNK2baoUlH4wgwcPFgAyfvx4f1fFKicxrcAGkp9++kkAyKBBg/zy/v7i7AbfXXfdJWPHjrXLgO/aJfLFFyK2owWtXr1aIiMjXbbqs7z2/PPP253IHjki4qyDQGpqqmzbtk2+/fZb6devn9SpU8dh2dWqVZMePXvKXxv+FmMAN820JLD2Z46YfmYwGOS7776TGTNmyLBhw6RNmzYOL3CDg4OlcePG8txzz8n48eNl9+7dkp6ebldWSorIc8+JfPVVxnNLly6VYsWKubzZB0DefvvtLBc5M2fqrr07dphk27ZtMmTIEGtZgE64vvjii7Jjx4682FR+tXr1aqlWrbm88847/q6KFS+OAjemPffccwJAfvzxxzx/70C1f/9+ef3116VMmTI2N1nCpUWL7vLSSy/Jv/71L/n444/lq6++kh9//FEmTJgggwcPlho1amRJhrz88sty4MCBLO+xZIke7sJkMsnly5dl7dq18uOPP8obb7whjzzyiNSpU8dlQr9UqVJyzz33yKBBg2TixIkBf+MnKipKAEh8dsZtyCWWIT569OghLVu2lBIlSri8OKpQoYJER0fL4MGD5fPPP5d58+bJgQMHJCUlRXbv1i0/16yxf4/z58/L+++/L5UqVbIrKyoqSmJiYmTcuHHy66+/ys8//yzffvutfPbZZ/Lee+/JXXe9LEWKZNzkCw8Pl379+sm6desKTG8rRwKxJZ8jjGmBE88OHjwonTs/LLff/ppcvZonb5mvnD9/Xr788ktp2rRpphhSQwYO/D85dOiQmEwmMRgMkpqaKklJSZKQkCBbt26V5557zu5c2hLXBg0aJCtWrJADBw7I3r17ZdOmXfLxx9tl7tzNMnnyZBkyZIh1NInMj7p168rAgQNl/PjxbhMngWru3LkCQLp16+bvqrhlGaZx9uzZMnToUGnVqpVdo07b77Vjx47ywQcfyLJly+Szz45K167/yN69p+XcuXNy8uRJ+eCDD6wNdmwfDRo0kDFjxji8UZuWliY//vi7lCnTSYCMa7vbb79dvvjiixwPQ5+fXLsmAoh07RpYPUQZzwIrprliMBis90kKirS0NLfJiYiICOnRo4e8/fbb8tNPP8natWvlwoULdufDN2+KrFolMmyYWBMNM2dmr04vvqjXr1lT/69eu3ZNZs+eLc8880yW4VozP7766qt808DMcl6QX+6PGY1GWbduXZZh1B09GjZsKOPGjZP/Z+++w6MqsweOf2cyk0YagUBoISRAICC9BFCkKIq6UkRRFGGFVX+6dl11LWtfe93VXRUEQUVdaVZQ6U16CwmBQBJII4T0Nu39/THMSKSkzeRmJufzPPNkSObOnBlm5r33nvecNykpSVmtNnXTTfb/07/9rX6PvXnz7++rMWOUqqm7bWlpqdq1a5f67LPP1HXXXXdWfD/++GP9AmliZs6cqQA1Z84crUNxasiYprNv7x6nT3Djzsc4n4ULFzJ9+nSmTZvGZ5991uiP3xQkJiYyZ84cFixYwMmTJwHw9fVl0qRJzJo1i6eeGstvv+l55x24997ftztw4AAPP/wwK1aswGaznXW/Xbp04bHHHuMvf/lLtUVju3aF1FT45hvF4MHH2bJli/OyY8cOqqqqqt1PQEAAgwcPJiEhgYSEBIYOHUr79u3d82K4WHx8PElJSezfv59edV1lsJGZzWYSExPZtm0bW7duZdu2bezfv/+shY0DAgIYMGAAQ4YMYfDgwZjNQ5gxI4bAQB2nToGfn/2znJ2dzaeffsonn3xCSkqKc/vg4GBuuOEGbrzxRuLj47HZbFitVmw2GzabjT//2cL69b8QGfkxOTm7nduNGDGC2bNnc/3119OimawAv2jRIm666SamTp3KokWLtA4HwPlZVkrVfiXoZkarMe2ee+7hX//6F++++y731Gc1Ui9WVVXF8uXLef/9OaxZsxL7/lDd+fj4EBQURIsWLQgKCsLHJ4ikpCD0+iqCgw9SVFR4zu30ej0xMTH06NGDuLg4evTo4bzeunXrOi2srrXQ0FCKi4spKCggLCxM63DOSSnFyZMnSUlJISUlhUOHDlW7XllZec7tdDodwcGdKS6OoU0bG507l1FaWkpZ2e8//7iPUludO3fn3nvvYMaMGbSq62rUHmjGjBnOfYCZM2dqHc55yZh2YY05nm3fvp3BgwczcOBAtm/f7vbH82SJiYksXLiQ//73MwoKjrn98Xx9fRk0aBAjRoxgxIgRDBs2jDZt2rj9cd1t+fLlTJgwgWuuuYZvv/1W63DqrLKykl27drF582a2bNnC5s2bOX78eL3vT6fT4ePjg8FgOOtnRUUFJSUlp2/px803T+H222/nkksu8ah9GFc4cQJ69oTERIiM1Dqa38l4VjMtzzueSSmFXq8HwGq1Oq97upycHD777DPmzp3LgQMHar1dUFAQXbt2pWvXrhQWduOXX7oxYUI3Vq3qRklJG1as0DFu3O+3V0phMpmoqqqqdqmsrKz275kzq0hPr6Bz5+1ERKxgx44d5/2/NxqNPPLII0yfPp0ePXo09KVoVL179yYxMZG9e/dy0UUXaR1OnVVWVvLrr7/y8ssvs2HDhhpu3RcYztVXt2PChEjatWtHZKT9Z5s2bTAajRfcuqwMgoJApwMfH3jkEXjuOQtpaWnOY8WDBw+ydetWdu7cWWPsW7ZsYejQobV/sk2U47ht3rx5zJgxQ+twgIaNaV6bwPrqq6+YOnUqU6ZM4euvv270x29KTCYTy5cv5+OPP2blypXO/4/w8M74+AxnyBA9LVvq0Ol06PV6dDr79fLyco4cOUJJSQk2m42AgACio6Np3749er2+2m11Oh1r1ug4ciQNf//fOHEi66w44uLinMmqhIQEevfujcFgaOyXwyUcCazExETi4+O1DqfOysrK2LVrV7WkVmpq6lm30+uN2L9fbGclvBqiVatWzJgxg1mzZnnk69dQc+bMYfbs2dx2223MmTNH63AAOTiqDa3GtEceeYTXX3+dV199lUceeaRRH9tTKAWTJ2eQlbWaqVNLMJvtiYkzL45kxbn+bTKZLnj/oaGh1ZJUjp+xsbH4+fk10rN0r/DwcAoKCjh58qRHJmJsNhvHjx8/K7GVkpLC0aNHaxzD9Hq9M4F5ZjLzjz8d148da0Hr1oN4/vmRGAzN52vzxhtv5Msvv+SLL77gxhtv1Dqc85Ix7cIaczxLTEykd+/exMfHk5iY6PbH8wYpKTamTl2Pv/8CkpL+R1FRkTMJUZ9LZaUPx4/70LatDwMGdGTEiBEMHz6cgQMHes0Ydqbvv/+ea665hvHjx/PDDz9oHY5LHD9efXLokSM5lJdbadHCglJWLBYLVuu5f9akV69exMX9hVmzpnPVVeGN8Gyanvz8fIYMGYavbyRJSeu0DqcaGc9q1lQSWAAGgwGr1YrJZKrxpLunslgs7N69m/Xr1/Pdd9+xatWqetxLMG3bhmK1/p6cqul4rLYCAgK4/vrrufXWWxk9erTHJhLj4uJISUkhOTmZuLg4rcNpsLy8PH7++WfefPNNduzYUadtAwMCCGrRgr79+9OuXbtqCa7IyEiUgquuSqGiIgVIISQkhfLyw7UaAx2mTZvGzJkzGTNmDD4+PnV8dk3TLbfcwmeffcann37K9OnTtQ4HaNiY5pnZg1pw7IzXd0atN/H19WXKlClMmTKFjIwM5s2bx9y5c0lPTwfS+f772t/Xnj17arxNcTG0bNmSoUOHOiurhgwZQni49+wQN4Wdo4Zo0aIFF198MRdffLHzd/n5+Wzbts2Z1Nq6dSsnTpw4a1u9Xo+Pj0+dfjqux8TE8Oc//5mJEyd65QFzbZWXlwMQGBiocSTCE8h4VjOdDpYsiQLqN7PIZDJRVlZ2VpJLr9fTvXt32rZt6/UzkR076q6crNCY9Ho9UVFRREVFcdlll1X7m8lk4ujRoxw9ehRfX99zJqf8/f29/v/YFRxVbs15DBd1ExAQAEBFRYXGkXiO7t317Np1KXApSn0EIN9PdeDp49m5dOzY0Xk8X1eOrhjnS261adOm2b+/KioqOHLkEB06lGsdivBwjgSW1Wr12gSWwWBg0KBBDBo0iAceeAClFCkpKaxfv54NGzawfv16jhw5UsO9lJCbW1LDbWpPp9MxevRobr31Vq677jqCgoJcdt9acST0vOV9FBERwbRp05g2bZrzPfPzzz/zwQcf1FjZV15RQXlFBT///HOtHqu4+Px/a9OmDTfddBMjR45k+PDhRDalklsXc3RU85aEnCSwmpmoqCiefvppnnzySdatW8fx48er9ZS02WwN+nfr1q1JSEigW7duHjvToS68aWe/VatWXHnllVx55ZWAPUlXWVl5ViJKNFxZWRkgCSxRO/7+/gDnbY8mGs7X1xdfX19atmypdSiacezYnqt1sKfz9fUlLi7OK2Yvas3xPeT4XhKiJo4ElmPyjqgbbzrWaCzemMBqCMcxnLecBHUHxzkjX19fjSMRns7x/VOXyg9Pp9PpnPvZs2fPBiAzM9OZzNqwYQN79+51yyTw7t27M2PGDG655RaioqJcfv9aciSwvPF76cz3zF//+lfMZjPbtm0jMTGRzMxMjh8/TmZmJpmZmaSnp1N8oYzUBURERDirzh2V583pGMaxH+Qt53G9NoHl+JC7qgzV2+j1ekaNGqV1GB7L0yuwakOn0zlPOgjXcpzEaS5rfomGkQkZojGc2bNfiPNxfA81p4M/0TBSgSUamySwRF1584li0bgcS2Q09++fDh06MHXqVKZOnQpAYWEhmzZtYv369axfv55t27Y5P3c6nY7AwEACAwNp0aIFgYGBVFVVERQURPv27av9/syfl156KUOHDvXaiR5msxnwngqsCzEajc4k07mUl5eTlZV1VnLrzOsnTpyge/fuDB8+3Jm0io2N9dr3R204voekAquJkxN+ojE05y9DUX9SgSXqwjGeSQWWcCc54SdqQ1oIirqSBJZobDKeibpynEiXsU00VHOswKqNsLAwrrrqKq666irAfp62pKRE2nhfgCTWfxcYGEjXrl3p2rWr1qF4FGkh6CGkAku4U3OowBLuIxVYoi4clQ4yIUO4kze3EBSuIxVYoq58fX3R6XSYzWasVqvXHESLpksSWKKupIWgcBWpwKodPz8/SRjXwFGBJd9Lor68rYWgdzyLc5AKLNEYZKaIqA9HAksqsERtyHgmGoO0EBS1IRVYoq7ObEktVViiMUgCS9SVVDoIV5EKLOEqju+l5tBCULiHt1VgeX0CSyqwhDtIBZZoCGkhKOrCUekgLQSFO8kJP1Ebju8hqcASdSEJLNGY5ASyqCtpIShcRfanhSsopZxjmCSwRH1JBZaHcMyekRnrwp2kAkvUh7QQFHUhFViiMcgBt6gNaSEo6kMSWKIx6fUykVXUjbQQFK7iaCEoCXTREI72gQaDQc45inpzHNdLBVYTJyf8hDtVVUkFlqg/qcASdSEVWKIxyBpYojakhaCoD8f+jiSwRGN4+217wlT2m0RtSQWWcBWZECZc4fcElpEvvwRpACXqQ1oIegjH7BmZeSXcITvb/nPVKpkNIepOKrBEXciEDOFumZlgMskaWKJmUoEl6kMqsERjKS6Gigr795O830RtyRpYwlWkAku4guN4zGw2cOONMGeOxgEJjyQtBD2EnPAT7nT6OJzu3bWNQ3gmRwJLKrBEbTjGM5lJLNxhxQoYNw4OH5YZo6JmsgaWqA9JYInGEhICH3wgleuibqSFoHAVqcASruConDEY9HTpAn36aByQ8EjeVoFl0DoAd3Gc8JMKLOEObdrYZ/h17Ci1vKLupIWgqAvHiWKZkCHcYexYsK8NLAfc4sIsFgtWqxW9Xu+cYSxEbUgCSzQmx/tNEliitqSFoHAVqcASruBIPAQG6jlyRONghMeSCiwPYbSfjcFsNst6DsLlHAspKmlGK+pBWgiKupAKLOFOBgP8+9/gSGDJPpM4H2kfKOrLkVBw7P8I4U6O7yhJmIrakhaCwlWkAku4guN4zFsSD0Ibju8hb6nA8tpPg06nc+6AOBbAE8JVJIElGkIqsERdSAWWcLcRI6B1a1kDS1yYI4kuM9RFXUkFlmhMRqMRHx8frFarVEGIWpEWgsJVpAJLuIIksIQreFsLQa/+NDh2QOSkn3A1SWCJ+lJKOWcgO07oCHEhsqajaAxdusiMUXFhUoEl6ksSWKKxOb6npHpd1Ia0EBSuIhVYwhUkgSVcQVoIehBZB0u4iySwRH05DqT9/f29ZiaEcC85CSMaQ2CgvfWyzBgV53Pm+CVEXUgCSzQ2aSMo6kJaCApXkQos4QqSwBKuIC0EPYhUYAl3kQSWqC9pHyjqSiqwRGNw7DPJpB9xPtJCUNSXJLBEY3O852Tyj6gNaSEoXEUqsIQreFvljNCGtyVCveNZnIec9BPuIgksUV+O9oEtWrTQOBLhKRxjmZyEEe5kNNorsCSBJc5HWgiK+pIElmhsUr0u6kJaCApXkQos4QrelngQ2pAKLA8iLQSFu0gCS9SXVGCJujIajeh0OiwWi8zmE24jFViiJlKBJepLEliisUkLQVEX0kJQuIpUYAlXcCSwvCXxILThbe8jr05gSQtB4S6SwBL15ajAkgSWqC2dTicVxcLtHPtMZrNZ40hEUyUVWKK+JIElGpu0EBR1IS0EhatIBZZwBanAEq7gba0oveNZnIdUYAl3kQSWqC9pISjqw3HCWBJYwl2kAkvUxHEiWBJYoq4kgSUam7QQFHUhLQSFq0gFlnAFSWAJV5AWgh5EKrCEu0gCS9SXtBAU9SEVWMLdJIElaiItBEV9OfZ5JIElGosksERdSAtB4SpSgSVcQRJYwhW87X3kHc/iPKQCS7iLJLBEfUkFlqgPOREj3E0SWKIm0kJQ1JdUYInGJu85URfSQlC4ilRgCVfwtsSD0IZUYHkQmbEu3EUSWKK+pAJL1IeMZ8LdjEYjIAkscX7SQlDUlyOZ4JjEI4S7ycQfURfSQlC4ilRgCVeQBJZwBcf7SBJYHkBaCAp3kQSWqC/HyRtJYIm6cBxQy4kY4S5SgSVq4tiflhN8oq6kGkY0NklgibqQFoLCVaQCS7iCJLCEKzi+h7zlfeQdz+I8pIWgcBdJYIn6clRgSQtBUReOEzFy8k+4i+Okjdls1jgS0VRJBZaoL0lgicYm7zlRFzJBQ7iKVGAJV5AElnAFaSHoQaQCS7iLJLBEfUkFlqiPNm3aAJCdna1xJMJbSQWWqElJSQkASsn4JepGkgmisUkFlqgLqcASriIVWMIVJIElXMHb3kfe8SzOQyqwhLtIAkvUlyOBJRVYoi66dOkCwMaNRzWORHgrSWCJmqSlpQGweHEUTz8NRUXaxiM8hySwRGNzvOckgSVqQxJYwlUcFViSwBIN4W2JB6ENqcDyILLovXAXSWCJ+nK0EJQKLFEXnTtHA/D112maxiG8l9FoBCSBJc7v6FF7Av3WW2N4/nmIjYU33gA5PyxqIgks0dh8fe3nAeQ9J2pDWggKV3GcKJYWgqIhJIElXMHxPpIElgcoLrbPoDl4UE7GCNeSBJaoL2khKOojIsJegZWRcZQVKzQORnglqcASNTly5AgAt97ahcGDIT8fnnwSPvwQZHdIXMgfE1g7dmgZjWgOdu+WTiyi9qQCSzRUWRmcOiUVWMI1JIElGurnn6Giwv495C3vI+94FueRnGzfcV23TiqwhGtJAkvUV1aWvQJLWgiKuoiMjAagc+c0RozQNhbhnSSBJS7EYrGQkZGBTqejS5fOvPoqBAZCv34wezac3i0S4pzOTGBlZsLEiTBlCqSkaBuX8E47d8L338uYJmqvtNT+PvnhB19OnzcWok42boSuXWHPHqnAEg3nSIAWF8sOtqifG26wUlVlnzjmWBfU03l1AmvIEPuOa0KCJLCEayllH0gsFklgibpJT5cKLFF3gwdHA5Cfn0aLFvK9I1zPkcAym80aRyKaomPHjmG1WunQoQP+/v6MGmWvvNq0yZ7IEuJCzkxgLVkCx4/DN9/Ac89BcrLGwQmvUlwMN9wAVqssJSBqz/E+efxxP6ZMAek8Keqqe3dYvhwuuigUgJMnT2ockfBkjgqstDQfFi3SOBjhkWbMyAIsRES09Zr2uF6dwGrf3v6fFB4uO67CtdLS7AmsZcvkRLKom1at7AksqcASddGyZUtCQkIoLS0lPz9f63CEl1EKSkrsCaz8fJmtLs7maB/YpUsX5+9uvlkqr0TtOGZ+VlVV8b//2U/K6HQQHw9xcVpGJrxNejr07g2BgVKBJWrPYrG/T2w2XyZNgtM5dyFqLToaLr4YEhK6ApCamqptQMKjORJYoaE+vP46FBVpHJDwOFOmpAMQExOtbSAu5NUJrFatWgGQk5OjcSTC2xgM9o9OYKAksETdlJWdAiA4OFjjSIQn0el0REdHA5CWlqZpLMJ7mEzw2WcwYwY89JD9ZN+pU3KyT5zNkcCKiYnROBLhiXQ6nTOJtW5dJWFh8P338Pe/SxJUuNZFF8GSJfDGG1KBJWrPkcAaOtSXW27ROBjh0bp2tSewDh8+rHEkwpM5ElgxMXo2b4bQUI0DEh7Hcc6oc+fO2gbiQl6dwOrevTsAKdJgXbhYTIz9o3PZZdIkW9SeUsr5fdStWzeNoxGexlH5IAks4Sq+vvYWcAsWgM1mBECnkwSWONvRo0cBSWCJ+nO0EezRo5zt22H8eI0DEl5Lp4PQUKnAErVnMtkTnW+95SdJddEgsbGxgD2BJeuli/pyrIHl76/HaNQ4GOGR0tPtFViOSdDewKsTWI4TxIcOHdI4EuFtfHzsHx2brPIq6iA7O5uSkhJatWpF69attQ5HeBjHzofjRLIQrvDss45ZffaTfZLAEuciFViioRxrfy5ZUsHp83tCuI1jvQepwBK1YTbb930GDvTVOBLh6SIiIggODqaoqIhTp05pHY7wUI7zjHq9V5+yF27kSGBJBZaH6NixI/7+/uTm5lJcXKx1OMKLOAYSSWCJukg+vVJ5jx49NI5EeCJpISjcoXVrePppcCSwwKxhNKKpOtcaWELURUREBACFhVkaRyKaA19fqcAStWMymTCbzej1eoxS6iAaSKfTSRtB0WCO84w+Pj4aRyI8lbQQ9DB6vd45eEgVlnAlSWCJ+nAksOJkxXJRD44Tx1KBJVztr3+F9u3tJ/uUkpN94mzSQlA0lKMzhrR2F43BkcCSCixRE8cs9U6dOqGT/oHCBSSBJRpKKrBEQ0kLQQ8kbQSFO0gCS9THwYMHAanAEvUjFVjCXXx94Z577Cf7HG10hHAoLi7m5MmT+Pv7ExkZqXU4wkM51iaWYzLRGBwtBKUCS9TEsV8tFcbCVRzrYKWmpmocifBUksASDWGz2aSFoCdyHCzJbD/hSpLAEvUhLQRFQ5yZwJJFgYWrjR0r7ZbEuTmqr7p06SKz00W9SQWWaExSgSVq68wxTghXkAos0VBWqxWQBJaonxMnTlBVVUWrVq0ICgrSOhyX8fpPg1RgCXeQBJaoD0lgiYYIDQ2lZcuWVFRUcOLECa3DEV7G19e+7oOc7BN/JO0DhStIBZZoTFKBJWrLMcZ5U5sloS1JYImGkjWwREN4Y/UVNIMEllRgCXeQBJaoq7KyMjIyMjAajTLDT9SbtBEU7iIn+8T5HDlyBJAElmiYMycVShWxcDdHBZaMaaImUoElXE0SWKKhpIWgaAjHuSJJYHmYM9tVyMGScBVJYIm6csw47tq1KwaDQeNohKdyHFxLAku4miOBJRVY4o8cCSw5uScaolWrVoSFhVFSUkJubq7W4QgvJ2OaqC1ZA0u4Wrt27QgICCAvL4/i4mKtwxEeSBJYoiEcFVjeVlns9Z+Gtm3bEhQURGFhIfn5+VqHI7yEJLBEXUn7QOEKjp0Qx2xRIVxFTvaJ85EWgsIVdDqdtBEUjUYqsERtSQWWcDW9Xu/cZ0pNTdU4GuGJZA0s0RBSgeWh5GBJuIMksERdSQJLuIK0EBTuIgvei/ORFoLCVc7sjCGEO8mYJmqjrKyMEydO4OvrS7t27bQOR3gRaSMoGkLWwBINIRVYHuzMnutCuIIksERdORJYcXFxGkciPJljdqhUYAlXkzWwxLnYbDaZnS5cRiYVisYiY5qojTMXupdKB+FKksASDSEtBEVDnDm2eZNm8WlwHCzJbD/hKpLAEnV18OBBQCqwRMNIBZZwF2khKM4lJyeHqqoqIiIiCAoK0joc4eFkUqFoLFKBJWpDJmgId5EElmgISWCJ+lJKSQtBTyYHS8LVJIEl6sJmszkTWFKBJRrCkcBKT0+X7x/hUkajEQCLxSLvLeEk7QOFK0kLQdFYpAJL1IYksIS7xMbGArIGlqgfWQNL1NepU6coKysjJCSEsLAwrcNxqWbxaZCDJeFqksASdXHs2DEqKiqIjIz0ukFENK6goCBat25NVVUVOTk5WocjvIhOp5MqLHEWRwJLTu4JV3Ackx0+fFj2oYVbGQwGQCZliAtzJLC8bZ0QoT2pwBINIWtgifo6s32gTqfTOBrXahYJrDP7rSulNI5GeANJYIm6kPWvhCtJG0HhLpLAEn/kOLknFVjCFUJDQ2nTpg2VlZVkZmZqHY7wYmdOypAqLHE+jn1pmaQhXK1Tp04YjUYyMzMpLy/XOhzhYaSFoKgvb20fCM0kgRUeHk54eDhlZWUyY124hCSwRF3I+lfClRwH2ZLAEq4mCSzxR9JCULiarE0sGovBIOtgiQuTFoLCXQwGg/N95diXEqK2JIEl6stRgeWNlcXN5tMgB0vClSSBJerCUYElCSzhCo6dEcdBtxCuIoveiz+SFoLC1WRtYtEYzGbo2HEbu3YdJigoSOtwRBMlLQSFOznWwZI2gqKuZA0sUV9SgeUF5GBJuJIksERdSAJLuJJUYAl3kXZL4o+kAku4mqxNLBrDK6/AwYNxREfHyhoi4pwKCwspLCwkMDCQiIgIrcMRXsixDlZqaqrGkQhP4zjPaLPJ+CXqxpsrsAxaB9BYpAJLuJIksERdyBpYwpWkAku4i7QQFGeqrKwkKysLg8FAx44dtQ5HeIkz1yYWwh0SE+G55+zXZflrcT5nrn/lbQvdi6bBkcCSCixRV9dccw0GQwz/+U8nSkogOFjriISncCSwpALLg0kFlnAlSWCJ2iouLiY7Oxt/f3+ioqK0Dkd4AUcCSyqwhKtJAkucyfEdExUVhcHQbOa8CTeTYzLhTlYrzJplbyEIksAS5yfrXwl3kwSWqK/OnTvzl7+M58iR3lxxBRQVaR2R8BTSQtALyMGScCWz2T5La8MGSWCJCzt48CBgn3EsLUyEKzgSWBkZGc7+2EK4giOBVV4uCSwh7QOFe5zZUslisWgcjfA233wDFRW//1sSWOJ8ZP0r4W6yBpZoCL0eBg+GzZvhgQfsEzSEuJCioiIKCwsJCAjwyta4zWY6pSOBdfjwYWw2myyGJxokM9P+/lmyxIZSIF0HxPnI+lfC1QICAmjbti25ublkZWXRqVMnrUMSHi4xERYuhMJCewJr1aoqLr5Y46CE5hwn9ySBJVwpMDCQjh07cvz4cdLT050n+IRwhRtugH79YMECMBgkgSXO78wWgkK4Q3R0NHq9noyMDEwmE76+vlqHJDzMZZfBkCH2BJbMhRY1ObN9oDe2xm02Cazg4GAiIyPJycnh2LFjXllOJxrP8uX/5rHH3ichQSfJK3FBsv6VcIcuXbqQm5tLWlqaJLBEvR0+DI89Br6+8MUXoNfbE1j+/lKBJX6vwJKTe8LVunXrxvHjxzl06JAksITLde8Ozz9vvy4JLHE+0kJQuJufnx9RUVGkpaWRlpbmXANSiNp6+GH7ZAwhasORwPLWyuJmVYbkGDBSUlI0jkR4On9/PW+/rePGG7WORDR1jhaCUoElXEnWwRKu0LUrGI325BWAzWafGRoYKAksIS0EhfvIMZloLDLRUJyPJLBEY5B1sERDSPJK1MWZFVjeqFklsGQdLCFEY5MWgsIdHAksx8G3EPX14ov2JJadvQIrIEASWEJaCAr3kWMyIYSWlFKyBpZoFLIOlhCisTgmN0sCyws4DpZktp8QojFYLBbnyRlpGSBcyTFbVCqwREPFxMCddzr+ZU9g+fqaNItHNA1KKWkhKNxGjsmEEFo6efIk5eXlhIWFERYWpnU4wotJBZYQorFIC0Ev4jiBLLP9hBCNIS0tDZPJRMeOHQkKCtI6HOFFpAJLuNJTT0FwMDgSWGazVGA1d/n5+ZSUlBASEkJ4eLjW4QgvI8dkQggtSftA0VgcCazU1FSNIxFCeDupwPIi0q5CCNGYZP0r4S6yBpZwpYgI+NvfwJHAqqqSBFZzdzj19/WvdLKIjHCxmJgY9Ho96enp8n0jhGh00j5QNBapwBJCNBapwPIisbGx6HQ6jhw5gtls1jocIYSXk/WvhLs4ZtUcO3YMi8WicTTCGzzwAAQGSgJLQKXZys+bdwAQGCTrXwnX8/X1JTo6GpvN5mxVKYQQjcUxAUwqsIS7OdYRPXr0qByzCSHcpry8nLy8PHx9fYmMjNQ6HLdoVgmsgIAAOnXqhNVqlVnrQgi3kwSWcBc/Pz/ad+iA1Wpl/a4DWG1K65CEh2vRAi6+WBJYzV1+uYk9WcVs+W0rAKXlg/jTn0A63whXk84YQgitSAtB0VgCAwNp3749ZrOZY8eOaR2OEMJLOaqvOnXqhF7vnake73xWF+DouS6LBgsh3M2RwIqLi9M4EuGNesT3BmD5z1vZm11EcaXM6hMNM3CgLyAJrObIphRpp8rZn11MXpmJQ/t2AfB/tyfw3XcQHw9PPgllZRoHKryGI4Elx2RCiMYmCSzRmGQdLCGEu3l7+0Bohgksx8HSxo0y208I4T45ObB1q6yBJdynf//+AKz7ZR+HsqvYk1VERkGFVGOJegsIkAqs5qjKYuVAbgkpeaWUm62EGGwcPXgAnU7HzdMGMX48mEzwxRewf7/W0Qpv4ZhU6KjAMpm0jEYI0ZzIGliiMZw8CYcOyTpYQgj3KSuDm26CV1+1t+R2LDXhjZptAuuVVw6xZ4/GwQghvNbatfmYTHlACzp06KB1OMILDRo4AIDcjH2UnTRSWmUh+UQJ+7OLKa2SaixRd7fffjuJiYk88MADWociGklhhZk9WSWkn6rAoNcRHmDkcNJ+LBYzXbvHERwczFNPQUICPPEEDB2qdcTCW/yxheCbb8Ivv4B0eRdCuNM339g4csQ+U72iIlrbYITXUgquvBK6dweDwZ7Akpa5QghXO3AAFi2CNWs2AL9PcvZGzS6BFRtrn+2n16cQHKxxMEIIrxUZaW8f2L17D3Q6ncbRCG80ZNBAAMoK9hHfE8IDjRh99BwrqmRXZjHHCiuwKanGErXXtm1b4uPjad26tdahCDdTSnGssIK9WcWcKK0ixN9AkJ8BnU7Hvl07ARgwaDAAw4bBl1/CbbdpGbHwNn9s6/7tt3DFFTB8OFRWahmZEMKbnTyZjdVqAtrw9dcttA5HeCmdDsLCwGCA+NNt37ds2aJtUEIIrzN4MPz734qwsNUAjB07VuOI3KfZJbB69rQfLEVEHCAmRuNghBBea8eO3wC45JJ+2gYivFaXLl0ICQ2j8NRJTp7IQafT0cLXh/BAIyVVFpJzS9mfXUKZSaqxhBC/M1lsJOWWknyilDKTlVaBRvwMvx8SJO7eAcDQIYOdv4uKavQwhZeLiorCaDSSmZnJ8ePlbNkCNhtkZ9tnrZeUaB2hEMIbGY329oE6XRek4Fy4088/Q2Eh/PnPl2IwGPjtt98oKirSOiwhhJcZPTqZgoIcIiMjvXr5kmaXwIqNjSU0NJTs7CyOHz+udThCCC+1YYO9hPfiiy/WOBLhrXQ6Hb0u6gNA0r69zt8b9DpaBRrw0UNGYQW7M4vJKqpESTWWEM1eUaWZPVnFpBWUo9dBeKABH331KuHkfbsBGDEsQYMIRXNhMBiIOT2b8LPPDmOz2X8/bRr89BPSKUMI4RYWiz2BFRsbTWSkxsEIr6bTQYsWEBISwrBhw7BaraxevVrrsIQQXmbVqlUAjBkzxqu7PzW7BJZer2fo6Qb+UsIrhHAHpZQksESj6He6x3HSvuqLOup0OoL8DIQHGCiqMHMgt4TE3BIqzFYtwhRCaEwpRWaRvWVgbmkVwX4Ggk+3DDxTaXExx44cwujrS7++fTWKVjQXjjaCP/xgbyP49NOwcCH4+2sZlRDCm+XmpgFw+eVdtA1ENCvjxo0DYOXKlRpHIoTwNo4E1ujRozWOxL2aXQILYNiwYYAksIQQ7nHo0CHy8vJo27YtsbGxWocjvNigAQMASEnce86/G3z0tGphRKeD9FMV7MosIrtYqrGEaE7MVhsHT5SSlFtKSZXlrJaBZ0rcY1//qtdFffH19W3MMEUz1K1bNwB27z7E/Pnw7LP2GetCCOEuR4/aK7D69ZMElmg8ksASnuLUqVNahyDqwGazOSs7x4wZo3E07tUsE1gJCfaWKJs3b9Y4EiGEN9q4cSNgr77y5hJeob0hg+wJrEMH9p33NjqdjmA/A2EBBgoqzBzIKSEpt5RKqcYSwuuVVFnYm1XMkVPlALQKNJ7VMvBM+06vfzVw0KBGiU80b44KrGHDDnLrrRoHI4RoFg4cOABA165dNY5ENCcDBw6kZcuWpKamkpqaqnU4oo4sFotXTwA1mUx89dVXjB07lujoaEpkIVKPsWfPHgoKCujcuTNdunj3xIxmmcBytBDcsWMHJpNJ42iEEN7G0T5wxIgRGkcivF2PHj3w8/cnN/MYRYUFF7yt0UdP60AjAEdPlbMrq5jckiqv3hkXorlSSpFdXMmerGKyS6oI8vUhxP/sloF/lLRnFwDDTu8rC+FOF110EQBZWTs1jkS4g1KKbdu2UVVVpXUoQgBgNpvZvXs3AANOdzEQojH4+PgwduxYAH7++WeNoxF19fnnnxMbG8uTTz5JcnKy1uG4TGpqKo899hidOnVi6tSprFq1iqoqCzt27NA6NFFLzWX9K2imCayWLVvSo0cPqqqqnDswQgjhKrL+lWgsBoOBnr3sJwAPnqeN4Jl0Oh3B/vZqrFNlJvbnFJN8opQqi83doQohGonFauPQyTIO5JZQVGEmPNCIv9GnVtsm7bUnsEYMkwSWcL8BAwZgNBrZv38/xcXFWocjXCwjI4MhQ4YQEhLCsGHDeOCBB/jyyy9JT0/3iskzNpuNlJQUPvvsM+677z62bt2qdUiiBomJiVRVVdGtWzfCwsK0Dkc0M9JG0HP98MNPHD16lBdffJGePXsycOBA3nrrLbKzs7UOrc7MZjPffPMN48aNo2vXrrzyyiucOHGC3r1789577zFiRBZVVaO0DlPU0pkJLG9n0DoArSQkJJCcnMyWLVsYMmSI1uGIeqiqgt27oTlNEi4ogJYttY5CXMiJEydISUkhMDCQfv36aR2OaAb69uvH7h3bSNq3lyEjLq3VNkYfPa1bGCmpsnLkVDnFlRZiWgXSuoWv18/cEU1LVRX4+WkdhfcorbKQcrKU3OIqDHodrVsYa/2ZzsvNJj83m+CQUGdrNyHcyd/fn/79+7N161a2bdvmnJ0uPF9FBbz11gnatetJdnYSW7Zsqbb+dGRkJEOHDiUhIYGEhAQGDRpEUFCQhhHXLCcnh61btzov27Zto7Cw0Pn31q0j5LxCE+eoKhg4cKDGkYjm6PLLLwfg119/xWKxYDA029OxHiciYgGPP/4XTpz4jK+//pqdO3eyc+dOHn74YcaOHcstt9zCpEmTCA4O1jrU80pLS+Ojjz5i7ty55OTkAPb9sKlTp3LHHXeQkJCATqdj0ya46SbYsQO8vCOdxzObzaxbtw6A0aNHaxyN+zXLCiyAYcOGAbIOliebOBESEmD+fK0jaRyLF0NkJNxzj9aRiAtxrH+VkJCA0WjUOBrRHAwc0B+oXQXWmXQ6HSH+BkL9DOSVmdiXXUJKXhkmqcZqdOXlsHQpWJvZsmRHjtgPkCortY7E8ymlyC2pYm92MVlFVQT6+hAaUPvkFcD+XfY2bn36D0Cvb7aHCKKRydrE3umtt2D58sEcOXKAgoICVqxYwbPPPstVV11FeHg4OTk5LFu2jMcff5zRo0cTGhpKv379uPPOO5k3bx7JycnYbNrtj5SUlLBmzRpeffVVpkyZQlRUFO3atWPChAm8+OKL/PzzzxQWFhIZGclVV00gPPxFXn/9asrKNAtZ1ML27dsBGCTrPAoNREdH0717d4qLi6Vi04NkZ8PatT5ccsloPv74Y3Jzc/nf//7HxIkT8fHx4eeff2bGjBm0bduWm266ie+//x6z2ax12IB97a4lS5Ywfvx4YmJieOmll8jJySE+Pp533nmHrKws5s2bx7Bhw5zHDG3aQEkJSKfLpm/79u2UlpYSFxdHhw4dtA7H7Zptyt9xsHTmTDDhWTIy7sLHJ49t20YzePAoevbs6dWVA+npJzGZ1pGa2heI1ToccR6OBJa0DxSNZehg+0H4ocR99dre13C6GqvSQmp+OUWVFmJbBRIeWLeT36L+fvwRpkyBl1+GRx/VOprGM2sWrFkDf/4zfPYZSM6kfqw2xdFT5WQUVFBhthIeYMDgU/cXc/9u+8z0wYMHuzpEIc5r2LBhvPvuu3JM5mUefRSmTQN/f/D3D2PcuHHO9llKKQ4fPsyWLVv47bff2LJlC3v27HFe/vvf/wIQGBhI9+7diYuLo0ePHsTFxREXF0f37t1dUq2llCI3N5fU1FRSU1M5cuQIqamp7Nq1iwMHDpzV5jA4OJhBgwYxZMgQ56VDhw5UVOho29Y+hp06BS1aNDg04SaSwBJaGzduHCkpKfz8888MHz5c63BELbRrB3v2gCMn5e/vz3XXXcd1113HqVOn+N///sfChQtZv349ixYtYtGiRbRu3ZobbriBm2++mcGDBzfaxGalFNnZ2ezZs4cNGzbwySefONsc+vn5cf3113PHHXcwYsSI8x7n33UXPPkktG7dKCGLBmhO7QMBdO7sP63T6RTQJHtcW61WwsLCKC0tJTs7m8jISK1DEnWglKJt27bk5eU5f9emTRtGjRrF6NGjGTVqFHFxcR598jUvL49169axZs0a1q5dy7599pPTL774T/7+98c0jk6cT0JCAr/99hsrVqxwHqh7AsdnRSnluR8aN2uqY1plZSVBQUEopVh9IAP/gMB631eVxUZRpYUWvj50DPUnOjwQYz1OhIvaO3z4ME88EUtVlY758yE0VOuIGsexY/DII7BiBRQW2g+Upk7dT2BgIDExMVqH5zHKTVZS8krJKalCr4NQf0O9933uvGkiuzat46v/fcP11012caSNS8a0C2tK41laWhpdunShVatW5OXlefS+u6i/8vJydu7c6Uxq/fLLFgoLj5/39h07dqyW1HJc79ixY7UKUrPZTHp6erUE1ZkJq7LzlEwZjUb69u1bLVkVFxd33urU3FwoLYVYmWPYZJlMJoKDgzGbzRQWFhISEqJ1SLUi41nNmtKYVpNvv/2Wa6+9luHDhzsnvgrvkJaWxhdffMHChQs5cOCA8/dGo5G4uDh69epV7RIbG9ugNpImk4mkpKRqE0D27NnDyZMnq90uLi6OO+64g1tvvZVWrVrV+/FE0zN27FhWrVrF119/zZQpU7QOp1YaMqY12wQW/P6fvWTJEiZOnKh1OKIOHDP31qxZw+rVq1m9erWzj6tDZGRktYRWt27dnB+W0lL7LLnA+p/nbRCTCXx9q//uxIkTzoTVmjVrSExMrPZ3Pz8/hg8fzuzZs5k2bVojRitqq7y8nNDQUGw2GwUFBR5zYARycFQbTXlM69GrNwcPJPLR4p/oM7Bh6y/YlKK40oLVpmjdwo+urQNpGehb84aizgoKCoiMjKRlyxjuv38m06ff0izK/89UXm7i9deXMGfO+2RkrGPWrFl8/PHHWoflEfJKqzh8spz8chNBvj4E+vrU+75sNhuX9YmhrKSY48ePe/z7UMa0C2tK45lSivbt25OTk0NKSgrdunXTOiShIaXgoYcgKwv+859CDh48yMGDB0lOTnZeP3ToECaT6ZzbO6q2wsPDOXr0KBkZGVgv0J+3ZcuWxMbGOi+HDsVy6aXxzJrVD39/f3c9TaGBnTt3MnDgQOLi4khOTtY6nFqT8axmTWlMq0lJSQnh4eEopTh58iRhYWFahyRcTCnFnj17WLhwIYsXL+Xo0dRz3s7Pz48ePXqcldjq0qULPj7V9+lPnDjhTFDt3buXPXv2kJSUdM5WhWFhYfTt25fAwL4MHjyZZ54ZKZODvFBlZSVhYWFUVVWRl5dHaw8pmWvImNZsWwiCvWXFqlWr2LJliySwPIxOp6Nbt25069aNv/zlLyilSElJYfXq1c4EUE5OjrOEF6B9+/aMGjWKAQNG8+KLowgNjeXAAR0BAY0b+/vv2w/MXnjhBFFRa53xnjlLA+ylycOHD2fUqFFceumlDBkyRA6kmritW7disVjo37+/RyWvhOfr268/Bw8kkrRvb4MTWHqdjrAAI5UWK7mlVZSZLES1DCCqZQAG6fHmUklJSYSFhZGbm8zjjz/GE0/8ncsvv5yZM2cyYcIEAmoxQFmt9gkZWh6XFBfD11/bWwLW1vHjx/nwww/56KOPnBNQgoKC5EC+Fqw2RXpBBekF5ZSZrLQMMDS4UvLY0VTKSoppE9nO45NXwrPodDoSEhJYunQpmzdvlgRWM7dvH3z8sb0qNywsjKFDhzJ06NBqt7FaraSlpVVLajmu5+bmsnv3budtdTodnTp1qpakio2NJSYmhtjYWFq2bFntvkePhsxMe+tD4V2kfaBoCoKDgxk+fDjr1q1j9erVTJo0SeuQhIvpdDr69etHdHQ/Fi9+nZCQUpYtSyItLZHERPtl//79HDt2zJmUOlNAQAA9evQgPj6e/Px89uzZ42wD+MfH6datG3379q126dSpEzqdjp9/hltvhXvukXaA3mjz5s1UVVXRt29fj0leNVSzTmDJosHeQ6fTOVtI3HnnnSilSE5OrpbQysrK4vPPP+fzzz8HoLCwNVdf3Zd+/frQp08f+vbtS8+ePV2eJLLZbKSlpZGYmMiBAweYNy+RysrtPPxwUrXbBQQEnJWw8vPzc2kswr1k/SuhlQED+vPV5wtJTtzrsvv0N/jg20JPUYWFlLwyCirMxLZqQVhA4/Twbg6GDx/O8ePHWbFiBfPmzWP58uWsWLGCFStWEBoaytSpU5k5cyYJCQnnnTl3552wZAnccAO89RY09rCRnQ3x8VBWBgkJ0KvX+W+rlOLXX3/l/fffZ/ny5c5Z8b169eKuu+7illtukeR/DSrMVg7llZFdXIkCWrcwondB9nLf6fWvBgyUE3ui8Q0bNsyZwLr11lu1DkdoqE8fyMiwT4w4Hx8fH2ci6uqrr672t8LCQh566CBduxYwaVI00dHRdTq2W7z47C4ZwjtIAks0FZdffjnr1q1j5cqVksDyYoGBcOIE+PoG0bXrYEaNqr7GbHFxMQcOHKiW1EpMTCQrK4tdu3axa9cu522DgoKc5ywdl969e19wPcgBA2DHDkleeavmtv4VSAILgG3btmGxWBrUf1Q0LTqdjp49e9KzZ0/uuusulFIcOHDAmdBatWoNBQUnWb36V1av/tW5nY+PD3FxcfTt25c+fX5PbLVv377aycOqKjAY4MzKXpvNRnp6unMAcgxGSUlJlJeXnxVjQEAAI0aMYNSoUYwaNYrBgwfjK0dMHm3Dhg2AJLBE4xsycCAAhxL3ufR+9TodLQONVJitZBVVUVplIaplIFFhAfjopRWBKxiNRq655hquueYa8vPzWbRoEfPmzWP79u18+OGHfPjhh3Tv3p2ZM2cyffp0OnbsWG37wEDIz7e3pm3s5FV5OcyebV/DCuCzz+Cll86+XWFhIfPnz+f9998nJSUFAIPBwNSpU7nrrru45JJLpLVFLeSXmTh0soz8MhOBvj4EGvUue93279oJwJAhDavgFKI+HMdkW7Zs0TgS0RSEhdkv9ZGbG8bChUNZvBh69Kj79n8oyBJexJHAGnh6n1kIrYwbN46nnnqKlStXah2KcCNfX3s73KIi+MPhGwAhISEkJCQ494EcCgoKOHDgAFu2JLN4cUvmz+9LTEyX867BeD6y3JV3a44JrGa9BhZAt27dOHz4MDt27GDAgAFahyMaiVLKWbK7d+9eZx/ZQ4cOYbPZzrp9eHi4M6HVunVfXn+9FzExJ7nppt/LgM+XqAJo164d8fHxzr62F110EQMHDpSElRexWq2Eh4dTXOyZ64dIf/WaNeUxrbi4mNDQUAxGX9YmHcNgdH2VlNWmKKq0oAMign3p2qoFIf5SjeUu+/fvZ/78+SxcuNDZYk+n03H55ZczY8YMJk6cSGBgIPPmwZ//bJ9hV9/dGKXsl7w8aNu2btuaTPDYY/bqr86d4cgRe0tDgF27dvH+++/z2WefUVFRAUDHjh254447mD17NpGRkfULuJmxKUVGQQVpBeWUVrmmZeAfzbz2cpL27OCnn1ZwxRXjXHrfWpAx7cKa2nhWXl5OSEgISimKioouOKNYiAt5+mn45z/hyy9h8mStoxFNRWVlJSEhIVgsFoqLiz3qO0bGs5o1tTGtJlarlYiICAoKCjh8+DCxsbFahySaIKvVPjnxf/8DWfFGnOnMtfROnTrlUR1MGjKmNfsE1q233sqCBQv497//zV133aV1OEJj5eXlHDhwwJnQcvwsKCio1faRkZHVFmCMj48nPj6e8PBwN0cutLZnz57TvY6jOXr0qNbh1JkcHNWsqY9p0TFdST+ayoIf1tK910Vue5xyk5VSk5UQPwOdwwPoGCrVWO5ksVhYuXIl8+bNY9myZc6F60NCQpg6dSojRszkgw+GsWVL3f8PsrLg6FGYOhViYuDGG6G+u0JLl8LMmfDNN5VkZX3N+++/X62a4rLLLuOuu+7iT3/6k1S810GlxcrhvDKyiiuxKUVYgGtaBp7JbDIxuldnzKYqCgoKvGIdMhnTLqwpjmcDBw5k586drF69mlGjRmkdjvBQNpt9MgbUfUKG8F7btm1jyJAhxMfHk5iYqHU4dSLjWc2a4phWkxtuuIGvv7bvL//f//2f1uGIJio5Gbp3/31yoBAAP/74I1dddRVDhw71uO4FDRnTmv0ZhISEBBYsWMDmzZslgSUIDAxk0KBB1XpjK6XIzMysVqm1c2cS7dq1pk+f+GrJKklUNV/SPlBorU/fvqQfTSVp/163JrACfX3wM+gprDBz8EQpBeVmurZuQZBfs9+lcAuDwcBVV13FVVddxalTp1i0aBHz589n69atfPTRR3z00UdERHTmttvGOFvSRkVF1eq+W7aEkSPtC9ZnZsJtt9U9PqUUSUlJHD++iqFDV3HttaspLy8EIDQ0lD//+c/ceeedxMXF1f3Om7mCcjOHTpaSV2oiwKinha/BLa0WDycnYjZVEdO1m1ckr4RnGjZsGDt37mTz5s2SwBL1ptdL4kqcTda/Ek3NuHHj+Prrr1m5cqUksMR51acVrvB+zbF9IEgCi2HDhgHSc12cn06no2PHjnTs2JGrrrpK63BEE7Vx40ZAElhCO/0H9OfbpYtJ3r+XCVNvdutj+eh1hAcaqTDbOF5USUmVhejwQDqE+ru8OkT8Ljw8nLvuuou77rqLAwcOMH/+fBYsWEB2djqffPIJn3zyCQDR0dHOZNall15KdHT0Oe8vIADeew8cQ1tgYM0xKKU4evQoq1atcl5yc3Or3aZfv37cfffd3HTTTbRo0aIhT7lZsilFZlElR/PLKa6yEOZvwNfgvqmXe3ftAKD/QDmxJ7STkJDAv//9bzkmE0K43I4d9nFO1r8STcXll18O2E9EWywW6U4ghKg1SWA1UxdddBGBgYEcPnyYvLw8IiIitA5JCOGBpAJLaG3I6YPylMS9jfJ4Op2OQF8ffE9XYyXllnCq3EzX1oG08G32uxduFx8fzyuvvMJLL73E7t27WbNmDWvWrGH9+vWkpaUxb9485s2bB0Dnzp2dyaxRo0YRHR3trOQZPx4mTYIlS+wJrXPJyspi9erV/Prrr6xatYr09PRqf4+MjGTs2LGMGTOG0aNH06VLF3c+da9mstg4fLKMzOJKLFZFq0Cj21t0HtizC4CEoUPc+jhCXIhjUuHmzZtRSrml2lAI0TxJBZZoajp37kxcXBwHDx5k69atDB8+XOuQhBAe4NSpU+zatQtfX99m973R7M8wGQwGBg8ezNq1a/ntt9+45pprtA5JCOFhMjIyOHbsGGFhYfTs2VPrcEQzNXiQPYGVmrQfm82GvpGaZRv0OloFGikzWTlWWEHp6WqsdiF+Uo3VCHx8fBg4cCADBw7koYcewmq1smfPHtasWcPatWtZt24d6enpzJ8/n/nz5wMQFRXlTGaNGjWKt97qwooVOmcFVn5+PmvWrHFWWCUnJ1d7zJYtWzJ69GjGjBnDmDFj6NGjh5xsdoGiCjMpeWXklVXhZ9ATHuieloF/lLRnJwAjhiW4/bGEOJ+YmBhat25NXl4eR48eJSYmRuuQhBBeoKKigv3796PX6+nXr5/W4QjhNG7cOA4ePMjKlSub3YloIUT9rF27FqUUw4YNI7A27VO8SLNPYIG9ZcXatWvZvHmzJLCEEHXmqL4aMWJEoyUNhPijNm3a0CayHSdysjmWdoTOMV0b7bF1Oh1Bfgb8DTYKKswcyC2hoNxETKsWBPr6NFocwp7QGjBgAAMGDODBBx/EarWyd+9e1q5dy5o1a1i3bh0ZGRksWLCABQsWANCxY0e6dx/FRx+15uGH17Bnz55qC2EHBQUxcuRIZ8Kqb9++8l3nQkopsoorOZJfTlGFhdAAA35ubBl4prLSEjJSUzAYjfSXE3tCQzqdjmHDhvHtt9+yefNmSWAJIVxiz549WK1Wevfu3exO9ommbdy4cbz33nusXLmSZ555RutwhBAeYPXq1UDzax8IksACZB0sIUTDyPpXoqno07cfv+Rkk7x/b6MmsBwMPnpat7BXY6UXVFBcZaFLeCCRwX5SoaMRHx8f+vfvT//+/bn//vux2Wzs27fPWaG1du1ajh8/zvHjC9m9276Nn58fw4cPZ8yYMYwdO5ZBgwZhNBo1fR7eymw93TKwqBKTVdGqhftbBp4paZ89Wdkzvjf+/v6N9rhCnEtCQgLffvstW7Zs4eab3buWoxCieXCsfyXtA0VTc+mll2IwGPjtt98oLCwkLCxM65CEEE1cc13/CiSBBcDQoUMB2Lp1K1arFR8fmS0uhKg9Wf9KNBX9+vfnlxU/krRvD1dcO1mTGBzVWH4GGwXlZspNJZwqNxHbqgX+RhlftabX6+nbty99+/blvvvuw2azsX//ftasWUNBQQGXXHIJw4YNI+B8C2IJlymutHAor5QTpSaMPjpaNVLLwDPt33V6YfvBgxv1cYU4lzPXwRJCCFeQ9a9EUxUcHMzw4cNZt24dq1atYvJkbY7dhBCeITc3l8TERAIDAxkypPmtXSwJLOyLj0dHR5OWlkZiYiJ9+vTROiQhhIcoLCxk3759+Pr6yoGR0NzggQMASDmwT+NIwHi6GqvUZCXtVAXFVVZiwgNpE+Qr1VhNiF6vp0+fPrLv04iUUuSUVJGaX05hhZkQPx/NkruJp9e/Sjg9mUsILQ0ePBi9Xs+ePXsoLy+Xdl9CiAaTBJZoysaNG8e6detYuXKlJLCEEBfkaB94ySWX4Ovrq3E0jU8WMDhN2ggKIepj8+bNKKUYNGiQtF8SmnMksA4f2FdtDSOt6HQ6gv0MhAUYOFVmYn9OMcknSqmyWLUOTQhNWKw2Dp0s40BuCcUVZsIDjZpWJiafTmCNSGh+s/hE0xMUFMRFF12ExWJxtv0SQoj6Ki8v58CBA/j4+MhEHdEkjRs3DoCVK1dqHIkQoqlrzu0DQRJYTgkJCYC0rBBC1I20DxRNSXR0NKFhYRSdyudETpbW4Tg5qrGUgiOnytmdWUxeaVWTSLIJ0VhKqyzszSkm9WQZNpt9vStDI6539Uf5eSc4kZ1JYIsgevbsqVkcQpzJcUwmkwqFEA21e/dubDYbvXv3ltbIokkaMGAA4eHhHD16lNTUVK3DEUI0YY4E1ujRozWORBuSwDpNKrCEEPWxceNGQBJYomnQ6XT06tMPgOR9e7UN5g90Oh0h/gZC/QzklZnYl11CSl4pJotN69CEcCulFLklVezNKiarqIpAXx9CA4yat9JM3G2vvurTf4Cs/yqaDFkHSwjhKtI+UDR1Pj4+XHbZZYBUYQkhzi89PZ3U1FRCQ0Pp37+/1uFoQhJYp/Xt2xd/f3+Sk5M5deqU1uEIITyAyWTit99+A2D48OEaRyOEXb9+/QBI2t+0ElgOvgZ7NZZV2UjNr2B3VhH5ZSapxhJeyWpTpOaXk5hTQkGFmfAAAwEatgw8077d9hZtgwcP1jgSIX53ZlcMGReEEA3hSGANHDhQ40iEOD9pIyiEqIlj/atLL70Ug8GgcTTakATWab6+vs4dm61bt2ocjRDCE+zcuZPKykp69uxJq1attA5HCAAGnx7LUhKbZgILQK/TEepvJNjPhxOlJvZmF3P4ZBlmq1RjCe9RbrKy7/R722Kz0bqFEYNP09n1TtqzC4BhQ2X9K9F0dO/enZYtW5KTk0NGRobW4QghPJhjLT2pwBJN2eWXXw7Y24OZzWaNoxFCNEXNff0rkARWNbIOlhCiLmT9K9EUDRk0AGjaCSwHv9PVWGarjUMny9idWcypcpPWYQnRYHmlVezOKuJ4USX+Bj1hTaBlIIBNKSrMVvJLKkjaY28hODxhqMZRCfE7nU4n62AJIRqstLSUpKQkjEYjffr00TocIc4rKiqKuLg4iouLZTK9EOIsSilJYCEJrGpkHSwhRF3I+leiKYqLiyMgIJC87EyKCpp+S1y9TkdYgJEgPx9yS+3rBB0+WYZFqrGEB7LaFEfyy9ifU0JBuZmWAQYCfbVrGaiUospio7jSQl6pifwyM2arYt+W9ZQWF9I6oju7dkUhndpEUyLrYAkhGmrXrl0opbjooovw8/PTOhwhLkjaCAohzufQoUNkZmYSERFBr169tA5HM5LAOoNjtt9vv/2GzSYnzoQQ56eUkgos0ST5+PjQo1dvAJKb6DpY5+Jv8KF1CyNVp6ux9mQVU1ghbTSE56gwW9mfU8KhvDJMFhutWhgxatAy0GJTlJks5JebyCszU26yYvDR0S7Ej55tg+nfIZTtKxcDcMvNM5k8Wcf48XDwYKOHKsQ5SQWWEKKhZP0r4UkkgSWEOB9H9dXo0aPR65tvGqf5PvNz6NChA506daKoqIjk5GStwxFCNGHJySmcPHmS8PB2dOnSRetwhKim/wB7G8H1a9dxotREQbmZMpMFs9WGasKlFnqdjpYBRloY9WQXV7Enq4ij+eVYbU03ZiEA8stM7Mkq5nhhBb4GPWEBBvSN1DLQ0RawsMLMiVITRRVmlNIRFuBLt9Yt6NshhKFRLenfIZSYVoH4mMtZvmwZOp2OBx+8hSlTYMUKGDYM1q1rlJCFuKAhQ4ag0+mca40KIURdyfpXwpOMGjUKo9HI1q1bKSgo0DocIUQT8vHHPwDNu30gSALrLI4Zf//852akCEsIcT6ffroagMLCEVRVab+uiRBnmjb1egCWL/yK7b8a0VsNKKWjpMpKXpmZk2UmiistVJitTTI55G/0oVULIxVmGwfzStmTVURxpVRjiabHphRpp8rZl1PMyTITYQEGWvj6uHW9q/O1BQww+hDdMoDe7UIY1CmUoVFhxLUJIjLYnwDj7zF9/fXXVFVVMWbMGDp16sSTT0LLljBuHIwc6bawhai10NBQ4uPjMZvN7Nq1C4CcHI2DEkJ4hOxsOHIE1q61V2BJAks0ZQsWwCefwN69QQwePBybzcYvv/yidVhCiCZi1aoUduz4DvBl6NAJWoejKYPWATQ1Q4YM4+uvv2bhws3ccsssrrhC64iEEE3RqlWfAjB69DX4+2scjBB/MGb0aLp27crhw4eZ98Zm/jb7Kqx6K6VVFkqrrBRXmikzWTFZbZSZzNhsYPDR4eujw9dHj9FH59YT8LXho9cRHmikwmwlq7iK0iorncMD6BgagI9eksZCe5UWK4fzysgqrsSmFK1bGN1WdWWxKaosViotNqxWMPro8DPqCQ/0IyzAl1B/AyH+hlq1LJw/fz4AM2bMAKBPH1i2DKQbrmhKhg0bRmJiIps3b2bYsGG88AJ06ADt2sGtt0Iz7qAihLiAoiLo16+YqqqD6PW+9O7dW+uQhDivb76x74OFh8PAgdcCa/nPf/7D9ddfr3VoogEyMzP55ptv+Prrr8nNzWXYsGGMHDmSkSNH0rVrV5cfZ5eWlrJ161Y2bdrEtm3baNOmDZMmTWLs2LGyBqCH++KLNwBFbOwM+vWL1DocTenc2UpIp9MpoEm3K/qj7du3M3jwYHx925CZeYzWrX21DkkI0cQkJSURHx9PcHAw2dnZtGjRQuuQGsyxE6WUkszAeXjamPbKK6/w2GOPcdVVE/j++6XV/qaUwmRVlJoslFVZKakyU1Rpocpiw2SxYbYpdICvwZ7Q8vXRa5o0stoUhRVm9DodbYP9iG3dgmA/mYMjtFNQbuLQyTLySk0EGPUur7qyna6yqrLYMFkVPjrwM/gQ4OtDeICR0AADof5G/A36Oj3u4cOH6datGy1atCAnJ4egoCCXxdyUyJh2YZ4wns2ZM4fZs2dz/fXX89VXX9GnD+zbB0YjLFwIl14KbdtqHaUQoqlRClq1WktBwShiYwdx+PA2rUNqEBnPauYJY9r5/O9/9qrB224Ds7mQqKgoSkpK2LFjBwNOt4QXniErK4tvvvmGr776io0bN573/diuXTtGjhzJpZdeysiRI4mPj6/TvrxSirS0NDZt2uS87N27F9s5WogFBwdz9dVXM3nyZMaPH++1+/3eKicnh+joaEwmE0lJScTFxWkdUoM1ZEyTsz9/MHDgQHr16kViYiJr137Lddddp3VIQogmZu7cuQDcdNNNXpG8Et5pxowZPPnkk6xY8R3Z2dm0a9fO+TedToefQYefwZdWgQAB2JSi3GSl1HS6SqvKTGnlGVVaSmHQ6/H10WH0sf9srCqt36uxbBwvqqSkykJ0eCAdQv0bbZ0hIcCeWDpeWMnRU+WUVFkI8zfga2h4KYhSCrNVUXk6aQXga9ATYPShXYiRsAAjof4GgvwatrbWggULAJgyZYocxIombdiwYQBs3ryZggLYv9/+e7MZvvoKJjTvLirNwtGjR2nZsiVhYWFahyI8iE4HUVHbKSiA0aOlfaBo2qZMOfNfYdx+++288cYbvPbaa3zxxRdahSVqKTs725m02rBhgzNp5efnx1VXXcV1113PY49154EHNrJx41rWrVtHdnY2X375JV9++SUArVu35pJLLnEmtfr06YOPj4/zMSorK9m5c6czWbV582Zy/tBX2cfHh0GDBjF8+HAGDRrCgw+m0rbtYhIT97Bo0SIWLVqEn58fV1xxBZMmTeJPf/oTrVq1arwXStTLe++9R1VVFZMmTfKK5FVDSQXWObz77rvcd999jBs3jhUrVmgdjhCiCTGbzXTs2JETJ06wZcsWhg4dqnVILiGz+2rmiWPa5MmTWbJkCS+99BKPP/54nbc3WWyUmSyUmqyUVFooqrJQabZittowW+2vg/F020Ffgx4fHW5PalltioIKMwa9jrbB/nRtFUgLqcYSjcBksXHoZClZxVVYrIqWAQb0DahMtLcFtFFlsWJxtAU06An2M9Ay0Eiov7HWbQFrw2azERsbS1paGr/++qtXLwQsY9qFecJ4ZrPZCA8Pp6ioiE8+Ocaf/9wRgD//GT78EAzyte+VrFYry5cv580332TDhg3o9XqGDBnC5Zdfzrhx4xg6dChGo9Flj1dVVcXOnTvZsGEDGzduBOD+++9n1KhRLnsM0fgGD76J7dsX8fHHHzNr1iytw2kQGc9q5gljWm0dO3aMmJgYbDYbhw8fpkuXLlqHJP4gJyfHmbRav359taTV+PHjueGGG7jmmmsIDg4mNxciI+Hdd+Gee+zv0eTkZNautSez1q5dS1ZWVrX7Dw0N5eKLLyYmJobt27ezY8cOTCZTtdu0atWK4cOHM2zYsNNJq0HVJlZ37Wp/3I8+OsIPPyxh8eLFbNq0yfl3Hx8fRo0axaRJk5g4cSIdOnRw4ysm6qOkpISoqCgKCwvZvHkzCQkJWofkEg0Z0ySBdQ6nTp2iQ4cOVFZWcuTIERk0vIxSio0bN/LeewvYt28rQ4b0ZfTo0YwaNYrOnTu75fFSU1PZuHEjGzduJDX1CFdccQUzZtxKW+l94nGWLl3KpEmTiI+PZ//+/ZqvE+QqcnBUM08c03788UeuuuoqYmJiOHToEPoGLhpiU4oKs5XSKitlJgvFlRZKqiyYLDZMVhtWG/j44Gw7aPTRuaVCSilFudlKmclGWICRLuEBtAtp3tVYGzdC//4QGKh1JN6pqMJMSl4ZeWVV+Bn0BNWjZeAf2wLqdeBv0BPga7C3BfQ3EOJvJMBYt7aAtbVu3TouvfRSOnXqRFpaWoO/D5oyGdMuzFPGsyuuuIKVK1cyYcLXLFs2hfvvhzfekPWvvFFZWRnz5s3j7bff5vDhwwAEBQVRWVmJxWJx3i44OJjRo0c7E1rdunWr0/dlQUEBmzdvZsOGDWzYsIFt27ZRWVl51u3GjBnD888/z/Dhwxv+5ESj69y5GxkZh9m1axf9+vXTOpwGkfGsZp4yptXWrbfeyoIFC7jnnnt49913tQ5HALm5uc41rdauXet8r/n6+lZLWoWEhFTbbtcuGDAA2rSBI0fgj817lFIcOXKkWkIrLS2t2m10Oh29evVyJquGDx9e49h32WXw668wbZq97bJOZ68WW7ZsGYsXL2b16tXVxtahQ4cyefJkJk+eTNeuXRv2YgmXeOutt3jwwQe55JJLWLdundbhuIwksNzAMWj8/e9/58UXX9Q6HOEChw4dYsGCBSxcuJCjR4+e8zZdunRh1KhRzktUVFSdH8dkMrFz505nwmrjxo2cOHHirNsZDAauueYaZs+ezRVXXIFBppJ6hGuvvZZvv/2WN954gwcffFDrcFxGDo5q5oljmtVqJSYmhoyMDH755RfGjh3r8scwW22UmayUVtmTWcWVFirM9taDZ1Vp+ejw0buu9aDFpigoN+Nr0BMZ7EdsqxYE+vrUvKGXWbgQpk+HUaNg9erGfeziYliyBGbMaNzHbSxKKTKL7C0DiyothPob8Ktly0BHW8Aqi41Kiw0F+Bn0+Bv0hAXY2wKG+BsIbmBbwNqaNWsWc+fObRb7tjKmXZinjGf/+Mc/eO6552jX7kHuuOMNnn7afhJGeI/s7Gzee+89/vOf/1BQUABAixbRlJXdzyuv3MZdd+lYu3YtK1eu5OeffyYpKana9lFRUYwbN47LL7+csWPHVmuJpJQiIyPDmazasGEDiYmJZ73v4+Pjufjii0lMHMFvv6Xh5/cmZWVFgD2J+txzzzFkyBA3vxLCVQ4dOkT37t0JDg4mPz/fpRV7WpDxrGaeMqbV1t69e+nbty+BgYFkZGRIqzeNnDhxgsWLF/PVV1+xdu1a5xpTvr6+XHnlldxwww386U9/OitpdaYff4R//APi4+GBB6Bv35ofNyMjg/Xr1/POOxn84x8DGDFiaJ1b6c6aBZ9+CjffDM8/D506Vf97QUEB3333HYsXL+ann36qNpHjoosuYtKkSUyePJk+ffp4zYRtT2I2m4mJieH48eN8++23XHPNNVqH5DKSwHKDDRs2cMkllxAZGUlGRobH7/g0VydPnuTLL79kwYIF/Pbbb87ft2/fgbKymwkMHMf99+9l48Y1rF27lqKiomrbx8TEVEtodfrjNz/2ir1NmzY5k1XnmskXERHBiBEj6NFjBK+91hGjcRFm83dYrdbT8bRn5syZ3HbbbcTGxrrhlRCukJ2dTadOndDpdGRmZtKmTRutQ3IZOTiqmaeOac8++yzPPPMMU6dOZdGiRW5/PKUUFWabcy0te1LLjMlqw2RRWJXCR2ev0jIa7Emthpy8V0pRZrJSbrYRHmAkulUg7YL9ms3OtlKK778v5cYbT3HTTZF89JFfoz7+s8/CM8/A88+XMHLkLgwGA61ataJVq1a0bNmyWg93T2Oy2kg9WUZmUSWm0y0DfWpoGfh7W0AbFpvCqK/eFjDkdFtAXxe1Bayt8vJyIiMjKSkpITk52ev7qMuYdmGeMp799NNPjB8/ni5dEjhyZLPW4QgX2rt3L2+++Saff/45ZrMZgISEBB566CF27pzIq68aWLoU/njO5tixY/z888/OS35+vvNvOp2OgQMHMmrUKI4dO8aGDRvIzMystr2vry+DBw/m4osv5uKLL2bYsGHOk8OXXw6//AJz5hRw9OibvP3225SWlgJwzTXX8Nxzz9G/f3/3vSjCJRz7vTNmzGDevHlah9NgMp7VzFPGtLq48sorWbFiBc8//zxPPvmk1uE0G3l5ec6k1Zo1a6olra644gquv/56rr32WkJDQ2t1fxZL/Vse9+kDt90G999f923XrYOoKPulpqr1srIyfvrpJ5YsWcK3335LcXGx828xMTFMnjyZSZMmkZCQ4NXdG5qSBQsWcOuttxIfH8++ffu86nWXBJYbKKXo1asXSUlJLF68mEmTJmkdkqilyspKvvvuOxYsWMAPP/zgLI0NCgpiypQpTJ8+nUsvvfSsk2pWq5U9e/awZs0aVq9ezbp166p9eQPExsYyatQo+vbty969e9m4ceNZMwEBevbsyYgRI5yXrl27Oj+oqang6wtGYw6ffvopc+bMISUlxbntqFGjmD17NpMnTyYgIMDVL49ogFdeeYXHHnuMSZMmsXjxYq3DcSk5OKqZp45pGRkZREdHYzQayczMpHXr1o0eg8Vmo6zKal9Lq8pMcaWFcrMVs0VhstlQqnqVlqEeVVpmq43CCgt+Bj3tQuzVWP5Gz0ueWK1W8vPzOXnyJHl5eeTl5Tmv//Gn43pVVRUA/v7+DB06lJEjRzJy5EiGDRtWrR+6K+Xl5bF+/XpWrlzPqlXrOXx4F0rZqt1Gp9PRsmVLZ0LLcWnduvUF/+3n17hJuHMprrRwKK+UE6UmjD46gv3O3TLQphSm0xVW1doCGn0ID/R1e1vA2vr888+5+eabGTp0KFu2bNEsjsYiY9qFecp4VlRURGRkpLR19xJKKVauXMkbb7zBzz//DIBer2fSpEk8+OCDdW7XZ7PZ2L17t7M6a8OGDWetExIeHu48Hrv44osZOHAg/v7+57w/kwkSE+3teME+EfL111/nvffeo7y8HLCvLfrMM89w0UUX1fHZi8aglKJHjx6kpKSwYsUKxo0bp3VIDSbjWc08ZUyri19//ZXLLruMNm3akJ6eft7vLeEaGzZs4Nlnn2XVqlXOpJXRaKyWtKprFVRDdeoEpaVw6BA01uG7yWRi9erVLF68mKVLl1brJNWuXTseeOABHnjgAeke5UZKKfr06cP+/fv55JNPmDlzptYhuZQksNzk7bff5oEHHuDKK6/kxx9/1DoccQE2m40NGzawYMECvv76a2cllY+PD+PGjWP69OlMmDCBwDosDmK1Wtm9e3e1hFZJSclZt/Pz82PIkCHOg6MzZ/LVhlKKDRs2MGfOHL766isqKioACAsLY9q0acyaNYsBAwbU+v6Ee5x5QPTdd99x9dVXax2SS8nBUc08eUy76qqr+PHHH3nzzTd54IEHtA4HpRSVFhulVRbKTPYqraJKCyaLFZNVYbHZ0Ot+T2j5+ujR11D54rjfUpOVSrON8EBfurQKpG2Qr6aJg6qqKnJyci6YgDrzZ0FBQZ3fYwEBAYSFhZGdnV3t9waDgYEDB3LppZcycuRIRowYcdbBV0YGZGXBhdaFVUqRnp7O+vXrnZfk5ORqt/Hx8aFfv34YDAZnAq6wsLBOz8MhKCjonEmvjh07Eh8fT69evYiOjnbLbDSlFNklVRzJL6ewwkyIn0+1RGhTawtYW46ZvO+//z7/93//p3U4bidj2oV50ng2ffp0Fi5cyJNPPsnzzz+vdTiiHqqqqvjss8948803SUxMBKBFixbcdttt3HfffS7rPlFWVsacOetISdlEnz6duPjii+nRo0eDx4rc3FxeffVV3n//fSorK9HpdNxwww0888wz9OjRwyWxC9fYvn07gwcPpm3bthw/ftwrTrLKeFYzTxrTakspxaBBg9i5cyf//e9/uf3227UOySsVFhby6KOP8uGHHwL2pNW4ceO4/vrrmTBhQqMnrc4UHGxPYN11F/z7343/+FarlU2bNrFkyRIWL15Meno6AP369eOjjz5i0KBBjR9UM+BYw7x9+/YcPXoUX19frUNyKUlguUl+fj4dOnTAZDJx5MgRoqOjtQ5J/MHBgwed61o5vlABBgwYwPTp07npppto27atSx7LYrGwe/du/vrXNfz2WyJjxvTihRdGMGDAAJfNFi8qKmLRokXMmTOHbdu2OX/fv39/Zs2axbRp02jZsqVLHkvUjaOtaLt27cjIyPCKA6IzycFRzTx5TFuyZAmTJ0+mZ8+eJCYmNsn2elaboux028FSk4WiCgvlZgsmq73CRQFGvQ5fgz2hdaEqLUc1lr9RT/sQf2JaBeJncG81lsVi4fDhw+zfv5/9+/eTmJjI/v37OXTokLNdbG2Fh4cTERFB69atiYiIqHb9jz8jIiKckzPy8/PZsGED69atY926dezcudM5ixDsn/O+ffs6K7QuvvgSBg9uQ2YmbNgAw4bZb2ez2UhKSmL9+vWsW7eO9evXc/z48WoxBgQEkJCQwCWXXMIll1xCQkICQUFBZ70mBQUFzoRWfn6+8/LHf5/5u9q8XgEBAfTo0YNevXo5k1rx8fF06dKl3m0LLVYbqfnlHC+qoMpso2WgER+9rlpbQLNN4Xu6LWCQn4FwDdsC1lZWVhadOnXCYDCQnZ1NeHi41iG5nYxpF+ZJ49natWsZNWoU7du3Jz093ev2v7xZfn4+H3zwAf/617/Izc0F7G3T77nnHu644w6XH9Ps3AkDB0LHjnDkCLh6BYCsrCz++c9/8uGHH2IymdDr9UybNo1//OMfsuh9E/Hggw/y1ltvce+99/LOO+9oHY5LyHhWM08a0+pi0aJF3HTTTXTv3p0DBw54dFvupkYpxddff819991HTk4ORqORhITHuP/+B5g8WfvzbRYLXHIJnDgBr7wCEya4fkyrC6UU06b9yKpVd3PiRBp6vZ57772X559//qzjP9Ewo0ePZs2aNbz22ms8/PDDWofjcpLAcqNbbrmFzz77TGb9NSF5eXksWrSIBQsWVEvydOrUiVtuuYVbbrmF+Ph4tz3+O+/AE0/A++/Drbe67WHYt28fc+bMYcGCBZw6dQqwt4eaPHkys2bNYtSoUV7VC7Wpu+222/jkk094/PHHeemll7QOx+Xk4Khmnjymmc1mOnXqRG5uLhs2bGDEiBFah1QjpRRVVnvrwZIqC6Wnq7SqLDbMVnsiQa8D4xlVWmeuT6SUoqTKSpXVRqtAX2JaBRLRouHVWDabjfT0dGeiynFJTk4+q3UR2NsjBQW1w8cngvbtW9O9ewSdOp07EdW6dWvCw8NddoK2uLiYTZs2ORNaW7duda414tCiRQ+s1pG8/PIIzOYTrF+/ng0bNjjHHYewsDAuvvhiZ8Jq4MCBbpkRppQiI6OY77/P55dfTrJ5cz7Fxfncc89JKivTSExM5MCBA2RlZZ1ze39/f3r06FEtqRUfH09sbOwFD/xLqyyk5JWSW1KFz+kEVdUf2gL6G31oFehLiL+B0CbQFrC2XnvtNf72t79x3XXX8b///U/rcBqFjGkX5knjmVKKuLg4Dh065HULWXurlJQU3nrrLebPn+/sLNGnTx8eeughbrzxRrfNJk5Ls1cT9+sHP/3klocA7Otwvfjii8yZMweLxYKPjw8zZszgySeflDaXGrJarXTq1Ins7Gx+++03hgwZonVILiHjWc08aUyrC4vFQrdu3UhLS5NlTVwoPT2du+++m++//x6AESNG8OGHH7JwYTxffgn79kEdGje5hdVqvzSl4pvp0yE5uYxLL32Gt956E5vNRlRUFO+//77XdSfSytatWxk6dCghISEcO3aMkJAQrUNyOUlgudG6deu49NJLvbbqwhNYLPDf/1ZRVbWM1asX8NNPPznXtQoODub6669n+vTpjBw5stESOlVV0FhLdFRVVbF06VLmzJnDL7/84vw8xcTEcNtttzFz5kw6dOjQOME0UyUlJbRr146ysjJSUlLo1q2b1iG5nBwc1czTx7THHnuMV155hZkzZ/LJJ59oHU69WG2KcrOV0ioLpSYrxRVmykxWTFYbJqsNmwKDXudMaBl9dJhtisIKC4FGH9qH+hET3gJfQ81jhVKKrKysatVU+/fv58CBA5SVlZ1zm86dO9O7d2969+5Nr1696N27Nz169MDXN4DrroNly+y3mzULPvig8WfSVVRU8NtvvzkTWps2bXKeXPyj9u3bM3LkSGfCqlevXm4fY5WyzzJ84glwFI75+sLKlXDppdVvW1hYyIEDBzhw4IAzqZWYmEhmZuY579vPz4+4uLhqSa1evXoRExNDfqWN1JNl5JebsNlOr8dm0OPnaAvobyTU30CQn6FaktQTKKW46KKLSExMZNmyZVx77bVah9QoZEy7ME8bz1599VUeffRRJkyYwNKlS7UOR5zh55+hvByuvVaxfv163njjDb799lvne2v8+PE89NBDjBkzplES/jZbzQvWu0paWhrPP/888+fPx2q1YjAYmDVrFk888QSdOnUCGveYsblzrBkUGxvLoUOHPGKCSW3IeFYzTxvT6uLdd9/lvvvuY9iwYWzatEnrcDyaxWLhvffe46mnnqKsrIzQ0FBeffVVZs+ejV6v529/g9deg0cegVdf1TrapmfECNi0yT7uh4fv5Pbbb2fHjh0A3HDDDbzzzjtERkZqHKVnu/766/nf//7Ho48+yssvv6x1OG4hCSw3UkrRs2dPDh48yNKlS5kwYYLWITU799+/mXfemQmkAPZ1Nq688kqmT5/OtddeS0BAgKbxNaa0tDQ++eQTPvnkE44dOwaATqdHp7uezz9/l6lT22gcoXeaM2cOs2fP5pJLLmHdunVah+MWcnBUM08f0w4fPky3bt0ICAggOzub0NBQrUNqMKUUJqui1GQ5XalldlZpmU63e9NhT0hUWezrakUE+RLTqgWtAo3O9/3JkyfPav23f//+867hFBkZ6UxUOZJV8fHxF5wlVV4Oo0fD9u3w7rtw991ueEHqyGQysWPHDtatW8fmzZtp3bq1M2HVpUsXTU7+ZGdDXByUlIBOB4sWwQ031H77oqIikpKSqiW1Dhw44Bwz/0in0zFw+EhemPMVfj72toAtA42E+hsJ8TPUKtnZlO3cuZOBAwfSunVrsrKyMGrZf6QRyZh2YZ42nuXk5NCpUyeUUhw7dox27dppHZIA8vOhQweoqipk5MibWbfuB8A+YWD69Ok88MADbu2K0VQcPnyY5557js8++wybzYavry+3334711zzd6ZPb8fYsfDFF1pH6f0c3TKefvppnn32Wa3DcRkZz2rmaWNaXZSWlhIVFUVBQYHHdNFoinbt2sVf/vIXZ8Ll+uuv55133qm2P/Haa7B5M9x+O1xxhf04RPwuLg7S0+Hhh+GFF35PCD755JOUl5cTFhbGq6++yqxZs6RTVD0cPnyY7t27YzQaOXr0KO3bt9c6JLdo0JimlHLbBVD2h/Bsb7zxhgLUVVddpXUozUpFRYV65JFHlF6vV4Bq0yZOvfPOOyo3N1fr0DRnsVjUTz/9pK6//nql0xkVoIKCItTSpUu1Ds0rDR8+XAFq3rx5WofiNmd8X7t1XPDkizeMaaNHj1aAev/997UOxW2sNpsqqTSr7OIKdSivVO04XqDWHj6pfj54Qn1/IEct25etHnz+Ded7/kKXli1bqksuuUT93//9n/r3v/+t1q5dq06ePFnv2HJzlXrtNaVMJhc+YS9jsyn1wgtKBQQo9dZbrrvfoqIiNX/+fNWhQ4ez/p87de6isooqVGmVWdlsNtc9aBNw7733KkDde++9WofSqGRM877xbNKkSQpQL730ktahiDOMH39QBQbGOcfMp59+WuXk5GgdliaSkpLUjTfeqE6fTFd+fgEKvlG9emkdmferqKhQISEhClDJyclah+NSMp5555hWF0888YQC1IQJE7QOxeOUlpaqhx56yHlOsVOnTurbb789522t1kYOzoPYbEplZiqVmnr2344eParGjx/v/K4aOXKkSkpKavwgPdydd96pADVr1iytQ3GrhoxpUoFVCydPnqRDhw6YzWbS0tKIiorSOiSvt3XrVmbOnElSUtLpct6/8Y9//AN/f3+tQ2tydu9OZ9asP7Nz52oAZs6cydtvv+0V1RVNQVJSEvHx8QQFBZGTk0OLFi20DsktZHZfzbxhTPv888+5+eabGTBggHMGWnNgstgoM9nbDpZUWhjcrQPlpSVn3S4wMJC77rqLcePG0bt3byIjI11ehaSUzOirjX//u2FVajabjaysLFJSUvj8889ZtmwZJ0+ePOt2ISHhHDiw1ytb8ZrNZtq3b8/JkyfZsWMHAwYM0DqkRiNj2oV54nj2448/ctVVVxEbG0tKSorM7m0CVq5cydSpUyksLKRPnz4sX76czp07ax2W5vbv38+TTz7JsmXL0Ov1vPXWh9x77yytw/Jq33zzDVOmTGHgwIFs375d63BcSsazmnnimFYXubm5dO7cGZPJRFJSEnFxcVqH5BF+/PFH/u///o/09HT0ej333Xcfzz33HEFBQW59XLPZTGpqKgcPHiQ5OZnMzEzatm1LVFQUUVFRdOrUiY4dO7psPUilFIWFhRw7doxjx45x/PhxMjMzadmyJT179iQ+Pp5OnTq5tbOGUoqvvvqKe++9lxMnTuDr68sTTzzBo48+ip/00a2R4zNeVVVFUlISPXr00Dokt5EWgo1g2rRpfPHFF15Xkt7UVFVV8eyzz/LKK69gs9mIi4tj/vz5DB06VOvQmjSbzcZ7773HY489RmVlJVFRUcybN4/Ro0drHZrH+9vf/sZrr73G7Nmz+eijj7QOx23k4Khm3jCmVVZW0qFDB06dOtXsTmif6bvvvuNPf/rTef/eokULevfuzZgxY+jTpw/t27enQ4cOtG/fvlm1rW3KlFLk5eVx9OhR0tLSOHr0aLVLeno6ZrP5rO1at25LVVUnSkq2o9MZ2bBhLcOHD9PgGbjf8uXLmTBhAr169WLfvn1esx5IbciYdmGeOJ5ZrVa6dOnCsWPH+PXXXxkzZozWITVbSineffddHnzwQWw2G5MmTeLTTz91+0lBT6KU4vnnn+cf//gHYF/H7ZFHHtE4Ku81efJklixZwptvvskDDzygdTguJeNZzTxxTKurO+64gw8//JC//OUvfPjhh1qH06Tl5uZy//33s2jRIgD69evHRx99xKBBg1z6OAUFBc4k1ZmX1NRULBbLBbfV6XRERkY6E1qO5NaZ/46IiECn01FUVFQtOXWu6+Xl5Rd8vKCgIGcy68xLdHS0SycEnTp1ikcffZSPP/4YgJ49e/Lhhx9y8cUXu+wxvNFTTz3FCy+80CzWepUEViNYs2YNo0ePpkOHDqSlpWEwGLQOyevs2LGDmTNnsn//fnQ6HQ899BDPPfecnCysg6SkJG699VbnzLP777+fl156SV7DejKbzXTq1Inc3Fw2b95MQkKC1iG5jRwc1cxbxrT777+fd955hzvvvJMPPvhA63A0YzabGT/+en79dRkAYWH9sFqPUlJSdMHtWrZsSYcOHZwJrXNdj4iIwMfHpzGehlcrLCw8Z3IqLS2NtLQ0ysrKanU/LVqEcs014+nW7Vlef72MysrhQCV//vN/mDv3Dvc+CQ1dd911LF68uFmeOJUx7cI8dTx75plnePbZZ7npppv4/PPPtQ6nWTKZTNx9993Ok1NPPfUUzzzzjFTEnce//vUv7rnnHsA+Ke7ll19uVpMJGkNhYSFt27bFbDZz/Phxr1s3RMazmnnqmFYXKSkp9OjRA19fX9LS0oiMjNQ6pCbHZrMxd+5cHnnkEQoLCwkMDOS5557jvvvuq/f5W5vNRnp6OsnJyWclq3Jzc8+5jU6no3PnzvTo0YMePXqwalVHrrzyBBkZGWRkZHDs2DEyMzOx2WwXfGw/Pz98fX0pKTm7a8gfBQUF0alTJ2d1l1IdSE4+iZ9fEgcOHODEiRPn3C4gIIC4uLizEluxsbENOue9Zs0a7rjjDlJSUgB7Avbll18mLCys3vfprc5c527jxo0MHz5c65DcShJYjUApRVxcHIcOHWL58uUXnLkt6sZkMvHCCy/w0ksvYbVa6datG5988oksUFlPZrOZl156ieeffx6r1UqPHj1YsGCBy2ecNAfLli1j4sSJ9OzZk8TERK8+4JSDo5p5y5i2f/9+LrroIoKDg8nOzvbatpi1UVlZSY8e15Ke/jPQEVjPhAkBdOz4H1avXsaBA3sBKwA6nR6djhoPNgB8fHxo167deRNcjushISFufX5NXXl5ebUE1R+TVYWFhRfcPiwsjC5dutClSxcCAgJISkpi9+7dZ/wfXca4cQ/z7rvj+MtfdKxffxIYBKTj7z+LgoKP8Pf3zq+8U6dOERkZidVq5dixY153Qq8mMqZdmKeOZ+np6XTp0gWj0UhWVhatWrXSOqRmJS8vj+uuu47169fj7+/PvHnzmDp1qtZhNXmff/45M2bMwGKxMHv2bP7zn//IJBcXmjNnDrNnz2bs2LH88ssvWofjcjKe1cxTx7S6mjRpEkuXLuXvf/87L774otbhNCnJycncfvvtrF+/HoArr7yS999/ny5dutRq+7KyMlJSUs6qpkpJSaGysvKc2wQGBhIXF+dMVDku3bp1c04gP3EC2raF3buhb9/ft7VYLGRlZTkTWmcmtxzXCwoKnI9zZnLqXNdDQkKqnatavRquuQaSkiAqyr4sTlKSPZl15iUrK+ucz81oNFZLbDmqt7p161brloCVlZW89NJLvPzyy5jNZiIjI3nvvfe47rrrvPq8Wl2988473H///YwYMYINGzZoHY7bSQKrkbz++us88sgjXHPNNXz77bdah+MV9uzZw4wZM9izZw86nY777ruPF198kcDAQK1D83jbt29n+vTpJCcn4+Pjw1NPPcXf//53jEaj1qF5jAkTJrB8+XJee+01Hn74Ya3DcSs5OKqZN41pw4YNY8uWLXzyySfMnDlT63A0lZVVRufOV2KxbABigPXo9e256Sa46KIiHnvsC2AO8PuaCt26dWfMmNHExcVRVFREZmYmmZmZZGVlkZmZec61ls4lKCjonImtM38XGRnpsh7pjc1kMpGRkXHO5FRaWtp5Zy46BAYG0qVLF6Kjo52JKsclOjqakJAQfvjhB9544w3WrFlzeisDcCPwEOHh/bj3Xnj5ZaistADjgV+AwTzxxDpeeMF719V8//33ufvuu7niiiv46aeftA6n0cmYdmGePJ5deeWVrFixgrfffpv77rtP63Cajb1793LttdeSnp5Ohw4dWLZsGQMHDtQ6LI/xww8/MGXKFCoqKpg8eTKff/65rAviImPHjmXVqlXMmTOH2267TetwXE7Gs5p58phWF5s2bWLEiBG0bNmSjIwMadsK/Prrr3z11VfMmzcPk8lEmzZteOedd5g6depZSRKlFNnZ2c7k1JkVVRkZGed9jPbt2zuTU2cmrDp27Fhj9fGSJTB5Mjz0ELz+et2e244dJTzxhIUffwyrc8Lnp59g/Hi49lpYtuz8tysqKjpnYis9Pf2ct/fx8aFr165nVWzFxcWdt+tTYmIit99+O5s2bQLs+3H/+te/iI2NrdNz8kZms5muXbuSkZHB0qVLmTBhgtYhuZ0ksBpJXl4eHTp0wGq1kp6eTseOHbUOyWOZzWZefvllnnvuOSwWCzExMXzyySeMHDlS69C8SkVFBX//+995++23ARg0aBALFizw6kUBXSUnJ4eOHTui0+k4fvw4bdu21Tokt5KDo5p505g2d+5cZs2axfDhw9m4caPW4WjunXeKuP/+scAOoCewhYZBBAAAjmRJREFUFohAr4du3eDgQYC9wFxgIZAPgNHoy+TJk5g1axZjx451HsRUVVWRnZ19VmLL8dNxqaioqFV8bdq0OW8Vl+Nnq1atNJnNVlZWxuHDh0lJSSElJYVDhw45k1Q1tccwGo107tz5nMmpLl26OHu//1FlZSULFy7kjTfeIDk5+fRvg4E7gHuBTgB06QJHjzq2ehR4FWiDwbCd48c74c1f6wkJCfz222989tlnTJs2TetwGp2MaRfmyePZN998w5QpU+jduzd79+6VWbyNYOnSpdxyyy2UlZUxZMgQli5dSrt27dzyWCdOnGDdunWsXbuWffv2ERoaSkREBG3atCEiIqLadcfPukzyUEpRVlZGUVFRtUtxcTFFRUUopRg5ciRxcXEuf29t2LCBa665hqKiIsaOHcuSJUsIDg526WM0N5mZmXTq1AlfX19yc3MJDQ3VOiSXk/GsZp48ptXViBEj2LRpk0zi4PeuImfq3Lkz3bp1o0ePHkRHR1NZWVktUXW+dnxGo9G53ZmXuLi4BnXMeOghePNNiIyEY8egLl35vv4apk6FQ4egrrmeZctg4kT79aVLoa55kdLSUpKTk89KbqWmpp7zc6bT6ejSpUu1pFZMTAxHjhxh3bp17Nmzh927d2O12jub6PV6SktLm/1SJ5999hm33HILcXFxHDhwoFm0Y5YEViO68cYb+fLLL3n22Wd5+umntQ7HI+3fv58ZM2awc+dOAP7617/y8ssvN0obK6UU+fn57Nt3hHnzjtKhQxFDh7alffv2tGvXjrZt27qtQslsNpOfn8+WLSdZvz6Ap5/uTGho46yltnr1ambOnElGRgb+/v68/PLL3HPPPc3iC7K+Xn31VR599FEmTpzIkiVLtA7H7eTgqGbeNKaVlpbSvn17SkpK2L9/P7169dI6JE1ZrdC3bz6JiaOA/UB/YBUQBkBgIPy+Nm4VsBx7VdZKwP5+aN8+ir/85c/8+c9/pnPnzjU+plLKWb11ZmLrj9dzcnJq1bbQYPAnOjqKLl06OxcB7ty5s/Nnx44d613JZTabSUtLcyapzrwcP378vNvp9Xo6dux4VmLKcWnfvn2dxqH8/Hw++OAD3nvvPWcvd52uI0rdD8wGfj9x1b69vWXHnj1gs30FTAV8gF+ZPv1SPv20Pq+EZzh48CA9evQgODiYnJycZlnVLmPahXnyeGYymejYsSN5eXlevz6p1pRSvPTSSzz55JMA3HLLLXz00Uf4+7uuejUrK8uZsFq7di1JSUl1vg9HksuR1GrVqhWVlZXOpNQfE1W1GVO7dOnC+PHjGT9+PKNHj3bZcerevXsZN24cubm5DB48mB9++IHWrVu75L6bozfffJOHHnqIyZMn880332gdjlvIeFYzTx7T6mrp0qVMmjSJzp07c/jw4QatU+TpSkpK6pxcCg8Pp2fPnmclqqKjo93yWg4dClu32q//+CNceWXtt336aXj+ebjvPjg9H73WvvwSbrzRfj0uDrZvB1cU7FVUVJCSkuJMaDkSXIcOHcJisdT6foKCWlFcnNesJyEppejXrx979+7l448/ZtasWVqH1CgkgdWIVq1axdixY+nUqRNHjx6V/tV1YLFYePXVV3nmmWcwm81ER0czd+5cRo8e7dLHKS8vr9am6MiRI9Wul5aWnndbnU5HRESEc+2Sdu3aVbvu+Onr2xZf3wpOnTpJXl4eJ0+e5OTJC18vKir6w2P50KVLZ2JjY4mNjSUmJqbadVfPyCsqKuL+++9n3rx5AIwePZp58+YRFRXl0sfxBkopevbsycGDB5vNmndycFQzbxvT7rjjDj788EPuv/9+3nrrLa3D0dzq1TBmTA5wCXAYGAasZPnyIOLjYcAAKC7+41bH8Pefh7//XAoL0wD7Z2ns2LHMmjWLiRMnNvhEn8ViITc395xVXI7rKSmZWCxFF7wfnU5HZGRktaRWVFQUHTtG0apVZ3r3jqK8vPycSaojR46c96DEPukjFl/f7lx3XXdGjOhGbGws0dHRzlnRDZWamspbb73F3LlznVVrbdr0o7T0YcrLbwCqTzzx8YGdO6FTJ+jffz/p6QlAGfA2cB/bt4M3d7564okneOmll7jtttuYM2eO1uFoQsa0C/P08exvf/sbr732GrNmzeLjjz/WOhyvVFFRwW233caiRYvQ6XT885//5G9/+1uDTzZlZGQ4k1Vr167l8OHD1f5uNBoxm1sDevz8TmEw6PH19cVoNDonPFitViwWC1VVVVRUVNT5fezj44PBYECv16PT6bDZbNhsNsxmC0rpAR1gdt7ez8+PSy+9lKuuuorx48fTrVu3Br0OqampXH755Rw9epSePXuycuVK6exSTwMHDmTnzp188803TJ48Wetw3ELGs5p5+phWFzabjZ49e5KSksLnn3/OTTfdpHVImrr//kN89dV1ZGfvA+BPf/oTOTk5pycBnsRqrXLeNjg4mPHjxzNhwgTGjx9Py5Yt3RqbxQIffwz79sGYMfbjkiFDar/9pEn26qngYMjMtP+srUWL7MdCOTnw4Yf237lw7kk1mZmZ/O9//2PRokVs3bq12iQRPz8/2rZtS0xMDNu3b6e0tJQOHWKJiNjIrl1e3AqjFlauXMkVV1xBZGQkaWlpzaatcIPGNKWU2y7YpyUrb2K1WlVsbKwC1Pfff691OB4jMTFRDRo0SDneE3feeacqLi6u131ZLBaVlpamVq1apebMmaOeeOIJNW3aNDVs2DDVtm1b52Oc7xISEqLi4/uq1q0nqWHDblNXX321GjBggIqMjFR6vb7G7et70ev1KiIiQrVv30P5+nZUp3e0znuJiIhQCQkJ6uabb1ZPP/20mjdvnlq/fr3KyspSNput3v8XS5YsUREREc7XYt68eQ26P2+0ceNGBajIyEhlNpu1DqdRnPF97dZxwZMv3jambdu2TQEqPDxcVVZWah1OkzBpklKQriDq9GdijIqMrFCJiUp9/bVScPbl/vuVMpms6tdff1XTpk1Tfn5+zs9Ty5Yt1V//+le1a9cut8b95ZdKXXxxkVq0aL/6/vvv1QcffKAef/xxNW3aNHXxxRerqKgo5ePjU+/xS6fTqc6dO6vLL79c3X333eqdd95RP/74o0pNTVVms1lt2KBUPYf0C9q8ebO67rrrqo2XV155pfrll1+UzWZTx48r1abN2f8nDz6olMmk1MiRBQq6KkAZjTcrsKnhw10fZ1NitVpVp06dFKDWrFmjdTiakTHNu8ez5ORkBagWLVrU+3hCnN/x48edx21BQUFq+fLl9bofm82mDh8+rObOnatmzJihoqOjzxpffHx8VGBgoNuOv9xxiY2NVX/961/V999/r8rKyur12mRmZqrevXsrQEVFRank5OR63U9zlpSUpAAVGhqqKioqtA7HbWQ88/4xra4+/PBDBaj+/fs36/M4NptS3bopNX58vvP7dODAgaqwsFAppdS0aVZ15ZW/qb///Qnn3x0Xg8GgxowZo95++2115MgRjZ/JuY0Zo1RYmFKTJyv1889129ZicU9MDklJSeqll15SgwcPPmtMHzt2rHriifdUaGiGUsp+bHLjjTcqQLVp00a9+OJhBUpt2eLeGJu6sWPHKkC9/PLLWofSqBoypkkFVj288sorPPbYY0yYMIGlS5dqHU6Tc+oU/OtfcNllMHSolTfeeIOnnnoKk8lEp06dmDNnDu3bX05IiH0Wwh8pZW/z56ic+mMFVUZGxgXLUx1rasTExDjbFJ15PTw8/Lyz5iwWC3l5eWRlZZGdnU12drbzuuPn0aPZ5Ofn4OMTSOfOEbRu3dp5iYiIOOf11q1b07Jly2qtkiorK0lLSyM1NdV5OXLkiPNnVVXVOWMECAgIqFaxdWblVnR09HlnvOfm2lsqnThxgjvuuMP5/p04cSKPP/5f/vvfNtxxR91mhnijWbNmMXfuXB599FFefvllrcNpFDK7r2beNqYppRgwYAC7d+/miy++4EZHn4Fm7MgR6NkTLJZD+PmNpKIiB7ia8PDFHDjgywsv2Me3Pxo5Ev73P4iIgIKCAr744gvmzJnjbJUL0L9/f2bNmsW0adPcPuPvXCwWC1lZWWRkZJCenk5GRgYZGRl89106x49n4O+fQVCQH3FxcXTv3r3aJTY2ttF6lFutVr799ltef/115/psRqORm2++mYceeojevXsDkJ5ub8PhXALrtA4dICkJ3njDxrPP/gn4geDgfixZspHLLgvkyy/hhhsa5alowtEpIDo6mtTU1GbbKljGtAvzhvFs5MiRrF+/ng8//JC//OUvWofjcYqK4FzLBW3dupWJEyeSnZ1NTEwMy5cvr3WbYaUUKSkpzuqqNWvWkJWVVe02Op3unO87o9FI79696devH3379uXo0X6Ulvbm1Vd1lJSUUFxcTHFxsfP6uX6eOlXM99+XUFFRwqWX+hMREUJwcDAhISHOy4X+XVERzJgxp3jxxY1s2rSBDRs2VFuz41x8fX0ZPXo0V199NePHj6dr1661eq0ATp06xTXXXMPmzZuJiIjgp59+YsCAAWfd7sQJaNOm1nfbbDz99NM8//zzXl9tLONZzbxhTKuLyspKoqOjyc3N5ZdffmHs2LFah6SJ336DhARo1Qr27s1m5MhLSE1N5ZJLLuGnn37ipZcCefFFePZZezu+I0eOsHz5cpYvX866deuqfbdfdNFFTJgwgWuvvZaBAwc2if3nsjKw2eyt/7TutKeUYtu2bSxdupQlS5acsQax/dzkFVdcwaRJk7jmmmsICgonIcG+DvGpU4r777+fd999l+DgYNauXcsnn/Tnvfdg2jT47DMNn5SGduzYwaBBgwgODiYjI4OwsDCtQ2o0UoHVyHJycpTBYFA+Pj7q+PHjWofT5MyZoxQoFRh4ZbVsfPv27dULL7ygHn30XQXvqaCgf6u33npbvfDCC+q+++5TEyZMUBdddJEKCgqqceZbu3bt1IgRI9Qtt9yinnrqKfXJJ5+oNWvWqPT0dGVx93QDpdSRIzZVVOS++7darerYsWNq7dq1au7cueqJJ55QN954oxoyZIhq1arVBV8bvV6vOnfurMaMGaNmz56tXnrpJTVv3jw1c+bXCv6rLr30WXXXXXepG264QUVGRp6xnZ+CT9VNN7nveXmCkpIS1aJFCwWogwcPah1Ooznj+1rzWXRN9eKNY9q//vUvBagxY8ZoHUqT8eijSo0apdTevftUQID9+7Znz+uVxWJRlZVKDRhgH+Ouvlqp6Gj79T59lDrX5N9du3ape+65R7Vs2dL5GfPz81PTpk1Tv/zyi7JarY3/BP+grEypw4fdP1Ov5jjK1AcffKC6devmfK3CwsLUY489pjIzM8+6/a+/KmUw/P5/0bWr/frXX9v//sgjT50e28LVnj1HlVJKTZlir8zyZrfeeqsC1FNPPaV1KJqSMc37x7NPP/1UAWrIkCFah+JRCguV+vvflbrllrP/tnDhQmcVcWzsKJWXl3fB+7JarWr//v3q3//+t5o4caIKDw+vVQVTy5Yt1ZgxY9QDDzyg5s+fr/bs2aOqqqqc95ucrFRAgL2atq527lQqKKh+Y9qaNfZx5Lvvfv9dSUmJ+uWXX9QzzzyjLrvsMucxwvkuHTp0UHfddZf68ccfVXl5eY2PWVpaqoYOvUIBKjg4WK1evfqs21x+uVL/939KHTtW9+fkrWw2m4qJiVGA+uWXX7QOx61kPGseY1pdvfDCCwpQ48aN0zoUzezZo9Sbbyr1zjtK5ecrdfToUdWhQwcFqPHjx6v3369S7dvbb3PGEKOUUio/P18tXLhQ3XDDDSo4OPis85Z33HGH+uGHH7y6urMmJpNJ/fLLL+ruu+92vq5njuO33nqrWrJkyVmVyJs324/LYmKU+uc//6kA5evrq3799VellFI//KDUP/+p1P799iq65mjq1KkKUA8//LDWoTS6hoxpMpDU05QpUxSgnn/+ea1DaXIyM5UaNmx1rQ5gzncxGo0qMjJS9evXT02YMEH97W9/U59++qnat29frQ4GmrqCgoZtX1hYqLZv367mzp2r7r//fjV+/HgVHx+vwsLCGvS6t2o1SO3d65Kn6LHmzp2rAHXxxRdrHUqjkoOj5jmmFRQUqICAAAWow4cPax1Ok1BUpNRnn9mvb9u2XQUGhihAzZw5U1mtVnX4sFIhIUp9+qn9u3z6dPvJtgupqKhQixYtUpdffnm1dnjR0dHq2WefVenp6W5/Xu5W3wRYbm6u+sc//qFat25d7XV55513VElJyQW3XbhQqdmzlTKblbrnHqWuvNJ+ILR06VLnhI4vvljpvH09Oz15jDMnYBw6dEjrcDQlY5r3j2dlZWUqNDRUAWrPnj1ah+MRbDal/vEPpUApo1GpEyfsv7dareqxxx5zfm58fe9UgYEmlZVVfXur1ap27typnn76aTV06NBatf6LjY1VkydPVs8995xavny5ysjIuGDLK5NJqcGD7TE+9ljdn+Pjj9u3rc8ctP/8x77tqFHnv43ZbFY7duxQ77zzjrr++usv2L7eYDCoQYMGqVdeeUWlpqae9z4nTqxSMNU5yWXp0qXOv2Vl2WMCpW680T3tej3Rli1bFNgntTbG5FUtyXjWPMa0usrPz3fu8+3evVvrcJqMpKQk5zHFtdder0ymmr8fKisr1YoVK9Rdd92lOnbsWO17vEWLFuq6665T8+fPVydPnmyEZ6CtsrIytXjxYjV9+vRqEzAdEzTuvvtu9csvvyhTDTMCy8qUevVV+3k1nU6nvvzyy0Z6Bk1famqq0uv1ymg0qmPNcGaKJLA0sHLlSgWozp07e/1OU31UVVWdtRMfGhqqQkNDVUhIiAoODlbBwcEqKChI+fv71zrBotfrVdu2bVX//v3VVVddpWbNmqWeeuop9f7776slS5ao3377TWVkZNT4hbp2rX2tk61bG+kFOcNzzynl46PUhx+e++9Wq1Xl5OSoXbt2qe+//159/PHH6vnnn1d33XWXmjRpkho6dKiKiopSvr6+tX7dfHx8lK+vrwoMbKFatWqloqKiVHx8vHM9N0B17dq1WX6B/tGIESMUoObOnat1KI1KDo6a75jmqNh4/PHHtQ6lSVq/fr3zJN1f//pXZbPZ1Ndf22d510daWpp65plnVOfOnZ2fO51Op6644gr15ZdfeuR6ZBkZSj388NmzGy8kOTlZ3X777dX2AQYNGqS+/PLLeq09+PPPSh06ZL9fx0zK5tZTfP78+QpQw719oa9akDGteYxnd999twLUPffco3UoHiM/X6mpU5UCpV57Tani4mL1pz/9yXm88O67/1Zz5ijVqZNVXXfdYbVw4UI1depU1aVLF2UwGC6YrOnVq5e67bbb1HvvvafWr1+viurRrmLePKU6dLDH9/TTddu2rEyp8HD7tosX1/mh1SOPKNWqlVKjRyt19GjttrHZbOrIkSPq008/VbNnz1ZdunQ572sUGhqqrrjiCjV//nznhMz0dKX0eqW6d7eoiRPvdP4/zJs3Tyml1Acf2J9PTIxS+/bV/Tl5q3vuuUcB6sH6lOl5GBnPms+YVlf33nuvAtQt5yqpbcZ27NihQkLsExBnzZpVp3XCbDab2rFjh/rHP/6h+vfvf9a5yJEjR6o33njDIyaKVVTUrsIpPz9fzZ8/X02cONE5sdVx6dGjh3r88cfV1q1b6/Q6Ll++3Ln+8nvvvdeAZ+F9HPuuM2fO1DoUTTRkTJM1sOrJZrPRtWtXjh49yo8//siVV16pdUhNjtls5qOPPuLee+/FarVy9dVX8/nnnxMSEnLWbauqqsjJyTlr3ak/Xj9x4kStHz8iIoJ27drRrl072rdvX+36v//djlWr2nH77ZH897/+dXpeFRWwYAFcey1ERtZ+u6qqKnJzc3nwwRy++SaHCROy6dcv+6znmZube8Fe62cKDQ2t9tzOvJz5++Dg4LO2/e233xg/fjwFBQWMHj2aZcuWnfN2zcnBgwfp0aMHQUFBZGdnExQUpHVIjUb6q9fMW8e0DRs2cMkllxAZGUlGRgZGo1HrkJqcX375hauvvhqTycSjjz7KP//5T0DXoH7kNpuNX3/9lblz57J48WJMJhMArVq14pZbbuG2226jT58+Nd7PsmWQmmrv/z56NERF1T+m+khNhfh4sFjg889h6tTz31YpxYYNG3j99ddZvny58/d/+tOfePjhh7nkkkvOu0ZlbRQXFzN06FCSk5OZMmUKX331VYPuz9Ncdtll/Prrr/z3v//l9ttv1zocTcmYdmHeMp7t3r2b/v37ExYWRlZWVqOt1ecNPv8c/u//rqC4eKXzd3q9Hn9/f6qqqi54LOLn50dsbCzDhg1j1KhR9O/fn7i4OAwGg0tiW7cODh2CgAD7Ghm19d//wp132q8/9xw89VTdHre42D6WhYaCj0/dtj3TqVOn2LRpEytWrGDFihWkpqZis9mq3Uan0xEZGYm//0W0aNGea67pRNeunfnuu++caxS/8cYbrFjxIErBokUQHl7/mLyJxWKhQ4cOnDhxgu3btzNw4ECtQ3IrGc9q5i1jWl2lpaXRtWtXdDodR44codO5FphvpjZs2MC4ceOoqKjggQce4I033qjXMUFGRgbffvsty5YtY82aNZjNZuffevbs6Vw3a+jQoedcN+vbb2HXLrjlFoiJadBTqrO//hV+/BHefReuvrr6344fP+5cz2rt2rXVxvwhQ4YwadIkJk6cSI8ePer8uJs2bWLs2LFUVlby5JNP8vzzzzf0qXiNvLw8OnfuTEVFBfv376/1GqPepCFjmiSwGuCf//wnf//735k0aRKLFy/WOpwma9WqVVx//fWcOnWKnj17snz58jotcHsms9lMbm5ujYmu3Nzcsw4UzicoKIiIiIhzXlq3bn3W7559Nog33tAxfTp89JE9KeW45OTknPd6UVFRrZ9n69atL5iQclzqe6D+66+/MmHCBMrKypgwYQKLFi3C379uiTxv9Oijj/Lqq68ya9YsPv74Y63DaVRycFQzbx3TlFLEx8eTnJzMkiVLmDhxotYhNUnLly9n8uTJWK1WXnjhBZ544on/b++uw6LO3jaA3zN0iGCCCbZid69r6+raumvn2l1Yi+2ai11rr93t2t1dGIAgJiAlMDB13j/4Ma8oguR3Zrg/1zUXiMzMDQPzcOY5kWa3HRwcjK1bt2LdunV48OCB7uOVK1dGnz598Ntvv333cNd//wW6dYs93HfRImDEiOTdd1gYcOUKYG4O1K0b+/ZHhYQArVvHvtgIxDbQzp799vPUajX279+PBQsW4ObNmwBiXwDt3r07Ro0alaLB0de0Wi3at2+P/fv3w9XVFdevX89UkxD8/f1RsGBBmJub48OHD5nqMOCEsKYlzpjqWeXKlXHnzh38+++/6NKli9RxDIZarf6hCSvm5ubIkycPKleujF9++QWNGzeGk5OTXk4OuHABuHQJePcOKFoUGDlS6kSxoqOjcePGDWzfvh1nzpzBq1evfniyYpkyPXD37kakUW/QKPz3339o2rQpihcvDk9PT738WUxLrGdJM6aally///47duzYgVGjRmHhwoVSx9Er//33H1q2bAmVSoVp06bhzz//TNXthYWF4cSJEzh06BCOHj0a7/W93Llzo0WLFmjVqhUaNmyoe51u0SJg7Fjg/n2gTJnk3Z+fH/D+PVChAmBhkfy8/frFjhOfPgVcXABPT09d0+rWrVu6zzM1NUW9evXQunVrtGrVCvny5Uv+nf3PkydPUKdOHYSEhKBv375Ys2aN0T9HJ8fUqVMxbdo0tGjRAocPH5Y6jiRSVdNSsmzrRy8w8qW879+/F6ampsLExES8+3qTcIrHy8tLlCpVSgCxB/6l92GrarVavHv3Tty5c0ccPnxYrFmzRkybNk30799f/Prrr6JKlSoib968iW6F8b2LmZmFMDXNK6ytk3felKmpqcibN6+oVKlSgtsfXr9+Xfj5+cU7xDg97Nu3T7f9YLdu3VK0VZMxUiqVun3sr1y5InWcDPfF87Xk20Do68WYa9qCBQsEAPHLL79IHUWvbd++XXeGlYeHR7rcx507d8SgQYN0Z7sAEJaWlqJr167i3LlzQqPRxPv8ixeFAIRwdBQiuUdEXr0qROPGsde3sEjZQbqfPwvRv3/sbQBCeHp++X+fxZIlS+JtqZQtWzYxZcoU8eHDh+TfWSJmzZql26LpxYsXaXrbhiDuMO+OHTtKHUUvsKZlnnq2atUqAUDUS+zgIvqGRqMRdlm/HcuULFlSbN26VXz69EnqiEZLo9GIc+fOie7du4sqVaqIwoULi5w5cyZ4plgJ19JSx9U73bp1EwDEtGnTpI6SIVjPMldNS647d+4IAMLW1laEpPagdSO0e/duIZfL03zsplQqxZkzZ8SwYcOEs7NzvOdtKysr0apVK7Fu3TqxePFH0bdvyu5j+XIhgNjtbVNixAiN6Nv3hnBzcxPFixf/JmObNm3E5s2bRXBwcMru4CuvX7/WnSHWqlUrvs74lYiICJE9e3YBQFy8eFHqOJJJTU3jCqxUateuHfbt24dZs2Zh4sSJUsfRa+Hh4ejSpQuOHDkCExMTeHh4YPDgwZJ25IUQCAsLQ2BgoO4SFBQU799f/19UVJTu+qampsiVKxdy584NR0fHeG+//piDg4Pksw82btyIPn36QKvVYujQofDw8EhwqXNmdPjwYfz6668oUaIEnj59KvljldE4uy9pxlzTAgMDkTdvXmg0Gvj5+aVq5pWxW79+Pfr06QMAWLt2Lfr27Zsu96NQKLB//36sX78eZ86c0X28UKFC6N27N3r27Im8efPi9WugYMHYGX7JnW0uBFC7NnD1auyWSaGhKc97/DjQpw/w22/A2LHvsWzZMqxcuRIhISEAgMKFC2PUqFHo0aMHbGxsUn5HCThx4gSaN28OIQQOHz6MFi1apOnt6zt/f3+ULl0a4eHh2LjxOHr04LbWrGmJM6Z6Fh4eDicnJ0RFReHFixcoWrSo1JEMQqRSjVuvQxGlVEPx6iHWr1iMY8eOAYjdSrBt27YYO3YsqlatKnFS46fVarFnzx5MmzYNT58+BQDkzZcPTX/rhW59B6J2UUeYyPlUBgBRUVHInTs3IiIi8PLlyxTv6mJIWM+SZkw1LSXitpCeM2cO3NzcpI6jdzZs2IDevXsDANatW6d7P60IIfD48WMcPHgQhw4dire6SSaTIU+e/HB2zo/8+fMjX758yJ8//vu5cuVK8DW5CxeAevWA58+BYsW+vV+lUon379/j7du3CV4ePHiBsLAPus/Pli0bWrZsiTZt2qBRo0awtrZOs+/Bp0+fUKdOHXh6eqJ27do4efIkt3X+yrJlyzB06FBUr14dV69ezXSvN8bhCiwJnThxQgAQzs7O38yKpm+p1Wrh5uam67r+8ccf6b7iKK1FRkYKPz8/ERgYaFCPuYeHh+777u7unqxDGDODVq1aCQBi3rx5UkeRBFIxEyKzXIy9pnXs2DFTzWhNjcWLFwsAQiaTiW3btqX7/fn4+IgpU6boZrUBsQcJN2vWTOzcuUfkyRMjIiNTdtt37gghkwmRL1/yrxsaGipu374tdu7cKWbNmiW6dOktnJzq6lb5AhA1atQQe/fuFWq1OmUBk+Dl5SXs7e0z7c+uVqsVjRo1EgBEs2atRI4cWjF4sBCZfWMA1rTMVc969eolAIjx48dLHcVg+IdEiWOeH8XhJx+EV1CEEEKIx48fi169egkzMzPd71DdunXF4cOHDWrMYyg0Go3YtWuXcHV11X2/CxQoIFavXi1eBYSJ454fxennASI40rDGyulpx44dAoCoWrWq1FEyDOtZ5qtpyRX3mqSjo6OIjo6WOo5einstTC6Xi127dqXrfb1580asWrVKNGvWLN6Y6HsXMzMz4ezsLOrUqSM6d+4sxo0bJ5YuXSo2bz4gqlY9IdatWyemT58u+vfvL1q0aCEqVKggcuXK9UO7QOXLl08MHTpUnDlzJt1WREVERIjq1asLAKJ06dJptqLLmCgUCt1KvX379kkdR1KpqWlcgZVKWq0WhQoVgp+fH/777z80btxY6kgGYevWrejTpw9iYmJQt25d7NmzBzlz5pQ6llESQmDq1KmYPn06AMDDwwPDhw+XOJV+ef78ue4ART+/N8ib11HiRBmPs/uSZuw17dSpU2jcuDEKFCgAHx8fmKTmBPNMYPbs2Zg0aRJMTEywd+9etGrVKt3vU6PR4PTp01i3bh0OHDigO0jY3NwSefM6fXNO4tfnJ2bPnj3BGX4DBsSeG/LkSfyPCyEQGBgILy8veHt7x3vr5eWFT58+JZhTJpOhTZs2GD16NGrWrJnm34c4kZGRqFGjBh49eoRff/0V+/fvz3SriletWoWBAwcie/bsePLkCTZtyo3x4wErK+DPP4Hx42PPR8tsWNMSZ2z17OrVq6hVqxZy584Nf3//HzrbKTMTQuDhu3D4h0VDBiC/gxXKOtnp/v/du3dYsmQJVq1apTvjo2TJkhgzZgy6dOkCi5QcxkE6Wq0W+/btw7Rp0/D48WMAQP78+TFp0iT06tUL5ubm8AqKxLOACMgAlMydBYWyp91MeUPWqlUrHDp0CIsXL8awYcOkjpMhWM+SZmw1LTEaDXDzZuw5fzlyxH5MCIHy5cvj4cOH6bLCyFhMnz4d7u7uMDMzw6FDh9C0afrvWBATE4M3b97A399f9/br9783nkqKXC6Ho6Mj8ubNm+Alf/78KFq0aLqu9FGpVGjdujWOHTuGAgUK4OrVq8ibN2+63Z+hGjhwIFatWoXcuUti6dJH+OUXE6ThAjiDkpqaxgZWGpg1axYmT56MihVb4s6dQ1LHMRi3bt1Cq1at8P79exQsWBCHDh1C2bJlpY5lVLRaLUaMGIGlS5dCLpdj3bp16Nmzp9Sx9IpSqUSNGjVw9+5dAL2we/d6tG8vdaqMx8FR0oy9pmm1WhQpUgSvXr1C587LMX78IPApOXETJ07EnDlzYG5ujiNHjqBRo0YZdt9BQUHYunUr1q9fj4cPH/7QdUxNTeHo6PhNkytLFids3ZobAwcGftOkioiI+O7tWVlZoXDhwihSpEi8t66ursiTJ09afakJEkKgc+fO2LFjB4oVK4abN28ia9as6Xqf+sbHxwdly5ZFZGQkdu7ciY4dOyI6GiheHHj3LrYhmdCWI5kBa1rijK2eCSHg6uoKT09P7Nu3D23atAEAREYCabxjqVGIUmpw0z8Eao2ARitga2GKGs4OkH/1Itfnz5+xdu1aeHh4wN/fHwDg5OSEYcOGoX///nBwcJAivsHSarXYv38/pk2bhkePHgEA8uXLp2tcfdkYfPguHP6hCshkQE5bC1TOlzXTbjckRGxN02qDUbiwIzQaDd6+fQtHx8wx4ZD1LGnGVtOSMmQIsHw5kC1b7N98xYsDkZFbsHt3dzg7O+P+/fuZ7m/iHyGEwJgxY7Bo0SJYWVnhv//+Q506daSOBYVC8d0ml0Kh+G6DKnfu3DA1NZUstxACPXv2xObNm5E9e3ZcvnwZJUqUkCyPvtqxYwd+//13mJub48CB6+jZswL++AOYMUPqZNLgFoIS8/R8JwCr/21fs0HqOAbl7du3okqVKgKAsLGxyfTLKdOSUqnUHXJrbm7O7+13jBs3TgAQWbK4CAuLMHHtmtSJpPHF87Xk20Do6yUz1LSdO3f+72fBXDg63pE6jt7TarVi6NChusNwpTqQ9fPnz+LFixfiwoULYvv27WLRokVi7NixomvXrqJBgwaiVKlSwsHB4Ye2mvj6kjVrVlG5cmXRqVMnMWnSJLF+/Xpx8eJF8fbtW0m3ol24cKEAYg+tfvLkiWQ5pKLRaETdunUFANGpU6d4/7djhxBeXhIF0xOsaZmvni1atEgAEM2bN9d9bNQoIdasEeLgQSG4c/b/8w+JEsc9P4rLPkHizIsAcfZloIiI+f7WQkqlUmzZskWUK1dO97tla2srRowYIXx9fTMwuWHSaDRi7969omzZsvG2dVqxYkWC231ptFpx2eeTOPHso+7xCY9On62fDMGYMULY2wthb79aABCNGzeWOlKGYj3LnDUtMYULCxHb2o29lCkjxIYNSlG+fAUBQLRv357HRXyHVqsVffr0EQCEnZ2duHOH492UinsdzdraWly/fl3qOHrpxYsXwtbWVgAQK1asEEII8fChEOm0m6NBSE1N4wqsNFKnzgZcvtwbVlZWuHnzJkqXLi11JIOhUCjQr18/bN26FQAwY8YMTJo0KdPOMksL0dHR6NSpEw4dOgQbGxscPHgQDRo0kDqW3jl79iwaNmwIuVyOS5cuoVq1Gshku0/pcHZf0jJLTevSZSC2bVsFB4fCePXqDmfwJUGr1aJv377YsGEDsmTJgrNnz6Jy5cpSx0pQdHQ0Pnz4gPfv3+Pdu3d4//697vLx40fkyJHjm9VU2bJl07t6fPbsWTRu3BgajQZ79+5F27ZtpY6U4Tw8PDBy5Ejkzp0bT548Qfbs2aWOpFdY0xJnjPUsKCgIefLkgUajga+vL/Lnz49atYCrV2NXYa1cCTRvDvBXBXjwv9U9OW3MoNQIKFQaVMibFTltE98aUAiB06dPY968eTh9+jQAwMTEBJ06dcLYsWNRvnz5DEhvOLRaLQ4ePIhp06bhwYMHAIC8efNi4sSJ6NOnz3e3YoxUqnHzdSiEELA0lSMsWo3STnbIb2+VkfH1xqFDQKtWgJXVT1AoLmLTpk3o3r271LEyDOtZ0oyxpiVmwwagd2/g559jt4pu3Dh2u2gvLy9UrFgRnz9/xrJlyzB48GCpo+oljUaDzp07Y9euXciRIwcuXryIkiVLSh3LoCxatAijR4+GqakpDh8+nCHbMRqa6OhoVK9eHQ8ePEDHjh2xY8cOvRtTS4ErsPREz549BQBRvHhx8fnzZ6njGBStVivmzp0r/vfHh+jYsaOITOmJ9JlcWFiYqFevngAgHBwcOBviOz59+iTy5s0rAIipU6dKHUdy4Ow+1rT/USgUonz58pzBlwxqtVp06tRJABDZsmUTjx49kjqS0Xr27JnIkSOHACAmTJggdRxJPHv2TFhaWgoA4uDBg1LH0UusaZmznnXs2PF/O2JMExqNEDY2QjdDffhwrsISQogopVqc9woUp54HiBt+weLaq0/iyJMPwjc4eeOue/fuiS5duggTExPd71vDhg3F9u3bhaenZ7odFq/v1Gq18PT0FJs2bdL9LQVA5MmTRyxbtkwoFIokbyMwIkacfBYgLnoHiRt+weLwkw/i/pvQDEivn5RKIbJnfy0ACEtLSxEWFiZ1pAzFepZ5a9r3XL4sxM2bCf/frl27dDvw3L59O2ODGZCYmBjRrFkz3fOzj4+P1JEMxr///qt7Xtq8ebPUcfTWgAEDBABRuHDhTFe3EpOamsYVWGkoKioK1apVw+PHj9G5c2f8+++/7LAm05EjR9C5c2d8/vwZFSpUwMGDB5E/f36pYxmMoKAgNGvWDLdv34aTkxNOnjzJ1YAJEEKgQ4cO2Lt3L2rUqIGLFy9Kun+wPuDsvqRlpprGGXzJp1Kp0LZtWxw5cgS5c+fGpUuXULRo0W8+T6kETE2RaVd7/oijR4ElSwBLS8DCIvatiYkCT578hbt350KjiUGTJk1w9OhRmJiYSB03Q6nVatSuXRs3btxAjx49sHHjRqkj6SXWtMQZaz07ffo0GjVqhAIFCuDECR+UKhX7/DBgALBiRewM9czubZgCj95/hq2FCcxNYgtRQIQShbNbo2TuLMm+vdevX8PDwwNr166Nd2aihYUFSpYsiTJlyqB06dK6t/ny5TOa8XFISAgePnyIhw8f4sGDB3j48CEeP34MhUKh+5w8efJgwoQJ6Nu3LywtLX/odl+HKPD4QzgcrMxgIpchPFoNMxMZqhZwgJVZ5qp5cerWnYdLl8ajY8eO2Llzp9RxMhTrWdKMtaal1JAhQ7B8+XIUKlQId+/e5W4a3xEVFYVmzZrh4sWLKFSoEC5fvgwnJyepY+m1AwcOoEOHDlCr1ViwYAFGjx4tdSS99OW5V9evX0eFChWkjqQ3UlPT2MBKY8+ePUPlypURGRmJVatWoX///lJHMjhPnjzBr7/+Ch8fH+TOnRtr1uxHsWI1wPMAv2/bNuD8+Te4eLExnj/3RKFChXDq1CkUKlRI6mh6acOGDejduzeyZMmC+/fv8/sEDo5+RGarabt27UKnTp1gbm6Oq1evolKlSlJH0nvR0dH45ZdfcPbsWTg5OWHgwOkoWrQ7Xrwwx5MnwOPHsQct793LF1ITExAAVKsG+PrGfeQwgOEAXgEAunXrhqVLl2bKAflff/2FCRMmIG/evHj8+DHs7e2ljqSXWNMSZ6z1TKvVokiRInj16hUmTDiBOXOaoGdPYN06ThqI8+X2gXG/J58ilchuY4Eq+bMmq7mk0QD79sVu8Xb8eChat/4HHz9ewKNHj+Dn55fgdbJmzRqvoRX3Nlu2bGny9aUHjUYDLy8vXZMq7u3r168T/PwCBQqgbNmysLJqivv3+6BPH0uMGQP86HyL5wEReBkUiVy25gAApVqLSKUGZfPYwcnux5pgxkSj0aB48bLw9n6KAwcOoFWrVlJHylCsZ0kz1pqWUtHR0ahVqxbu3r2Ldu3aYffu3UYzcSCthYeHo379+rhz5w4KFSqMQYPmY9So1vx+feXBgwdwc3PDiRMnAAADB47BihXzJU6ln16+fImKFSsiIiICK1aswMCBA6WOpFfYwNIz27ZtQ5cuXWBubo5r166hYsWKUkcyOJ8+fUKHDh1w7tw5AOYAVuP+/Z4oV07qZPrJweElQkMbAfBD6dKlcfLkSc4e+Y6XL1+iQoUKiIyMxObNm9GtWzepI+kFDo6Slhlr2uDBg7FixQq4uLjg7t27fLH8B0RERKB58+a4dOnS/z5SAIAbgN4ALFC9OnDtmnT5DMGrV0DsvAIfxDaujgAAypQpg+XLl6NOnToSppPOo0ePUKlSJahUKvz3339o3Lix1JH0Fmta4oy5ns2aNQuTJ09GiRLtUaHCbmzZ8uONA2OnUGlw83UIlBqBrJb/v/NAeLQa5iZyVC/oAHPT5HX6RowAFi8G/v479n3dbYaH48mTJ3j8+DEePXqkexsUFJTg7eTJkwelS5eO19QqVaoUrK2tU/CVJp9arUZ4eDjCwsLg7+8fr1n19aqqOJaWlihTpgzKli2LcuXKoWzZsihbtiwcHBwAxG5e2a0bUL48MGbMj2e57R+KgIgY5LAx/9/tCARGqFAouzVKOSZ/lZyhmzNnDiZOnIi8efPC29v7u2eHGSvWs6QZc01LKW9vb1SsWBHh4eFYunQphgwZInUkvRUUFIT69evj0aNHAICiRStj6dKZaNy4caZvZPn6+mLKlCnYunUrhBCQy7NAq52IdevGoXdvzgz6mkKhQI0aNfDgwQN06tQJ27dvz/Q/Q19jA0sPDRw4EKtWreKy3VRQqVQYNWoUli1bBgBo3LgNxowZiAYNGkDOaZQQQuDGjRtYvnw5duzYBbVaiUqVquHkyWN6PYtRSiqVCrVq1cKtW7fw+++/Y+vWrSwo/8PBUdIyY02Ljo5GzZo1ce/ePbRt2xZ79uzh78wP0Gg02LlzJ2bOnAlPT08AgEyWF0KMw5gx/TB/fuY8iP1HKRQKdOo0D4cPzwEQAzs7O0yfPh2DBw/OtNu9KpVKVK9eHffu3UP//v2xatUqqSPpNda0xBlzPXv79i0KFCgAmcwET58+Q7FiXGUf521YNB69D4+3fSAQ29hSawQq5beHvZVZsm4zNBTYtAkYNizplcVCCAQEBMRraD1+/BiPHz9GVFRUgtexsLCAra0tbG1tkSVLFt37Sf3bxMRE15D6kbffu/84+fPn1zWq4ppVRYsWTXIb28jIuG1wE//exFFptLjmF4IYlQZZv3gsQhUqWJmZonpBe5iZZJ5x8L1791C1alWo1WqcPHkSjRo1kjpShmM9S5ox17TU2L17Nzp27Ahzc3NcuXIFlStXljqS3oqJiUHVqmvx6NEsCPEBAFCnTh3MmjUrU06cCwoKwqxZs7BixQoolUqYmZlh8ODBcHWdiCtXcmLePCBnTqlT6p8BAwZg9erVKFKkCO7cuQM7OzupI+kdNrD0EF/0Sztr1qzBkCFDoFKpAAAuLi7o168fevXqBUdHR4nTZbyoqCjs2LEDy5cvx927dwHEPgl06NAB69atg62trcQJ9dfkyZMxa9YsFChQAA8ePOBqki9wcJS0zFrTvpzBt2TJEgwdOlTqSAZDq9Vi7969mDlzJh4+fAgAsLfPjcmTx2LAgAGwsbGROKH+OXLkCIYNG4ZXr/5/u8B58+Zlynr/JXd3d0yfPh3Ozs54+PAhsmTJfLPwk4M1LXHGXs86d+6M7du3o1y5crh69WqGreLRdw/fh+N1SBRy2pjHG5eqNVqExWhQzskOebJm/BZ1Wq0Wvr6+8RpbDx8+xosXz6HRqDMkg0wmg52d3f8mneZCw4bxV1Vl1OTA8GgVbvuHQi6Twdr8/7te0SoNYtQC5fPaIadt5liBpFAoULlyZTx9+hRDhw7FkiVLpI4kCdazpBl7TUuNuPOwuJtG0oQAAgOjsHHjMsydOxfBwcEAgCZNmmDWrFmZYjv9yMhIeHh4YN68eQgPD4dMJkOXLl0wffp0uLi4SB1Pr23fvh2dO3eGhYUFrl27xnOvvoMNLD315Yt+Hh4eGD58uNSRDNbbt2+xYcMGrF27VrffuKmpKX799Vf88ccfaNSokdGvyvLy8sKqVauwfv16hISEAACyZ8+OPn36YMCAASwoSbh48SLq1asHmUyG8+fPZ8qZNInh4Chpmbmm7dmzBx06dICZmRmuXr3KGXzJpNVqcejQIcyYMUM38SBHjhwYNWoUBg8ezNlZAF69eoXhw4fj8OHDAIDSpUtj+fLlqFu3rsTJpHfnzh1Uq1YNGo0G58+fx08//SR1JL3HmpY4Y69nYWFhqFKlCl6+fIkuXbpgy5YtmX4iYbRKgxuvQ6HUaJDVMv4qKyEEAiNVKJHLFkVy6MfEiv79AVdXgX79ohEREYGIiAh8/vxZ935i/377NgKhoWqUKpVV15RK7K2dnR1sbW0hk8kQGQnkyxe7la0Ur/N++ByNB2/DYfPVKjmtEAiKVKF4ThsUzZk5JiuOHDkSHh4eKFGiBO7cuZNpG9GsZ0kz9pqWGjwPK2XCwsLw999/Y9GiRfj8+TMAoG3btpg+fTpcXV0lTpf2VCoV1q9fj6lTp+LDh9gVaE2bNsWcOXNQvnx5acMZgBcvXqBSpUqIiIjAypUrMWDAAKkj6a1U1TQhRLpdAIjYu8i89u7dKwAIU1NTce3aNanjGDy1Wi2OHz8u2rRpI0xMTETcz5izs7OYOXOmePv2rdQR05RarRaHDx8WTZs21X2tAESVKlXExo0bRVRUlNQRDUJISIgoUKCAACAmTZokdRy99MXzdbrWBUO+ZPaaNmTIEAFAuLi4iJCQEKnjGCStViuOHj0qqlWrpvudc3BwENOmTcu031OFQiGmTZsmLC0tBQCRJUsWsWjRIqFUKqWOphcUCoVwdXUVAMTw4cOljmMwWNNYzx49eiRsbGwEALFkyRKp40jubahCHPf8KC55B4kbfsHfXI48+SDuvQmVOqYQQohDh4QAhFi9OmXXHzhQiJT+ue/pGXvf06al7Pqp5RMUKY48+SCu+3765jE65vlBXPUNFmqNVppwGej06dO611Bu3boldRxJsZ6xpqWWl5eXsLOzEwDE0qVLpY5jUIKCgsTYsWOFlZWVACBkMpno2rWrePnypdTR0oRWqxW7d+8WxYoV0z3XVK5cWZw5c0bqaAYjKipKlCtXTgAQnTp1Elqt8dfo1EhNTWMhyQAjRowQAESBAgVEUFCQ1HGMxrt378SsWbOEs7Oz7pfAxMREtG7dWhw7dkyo1WqpI6ZYUFCQmDt3rnBxcdF9bRYWFqJHjx7i5s2bUsczKFqtVvz222+6xh9fFE0YB0esaUmJjo4WlSpVEgBEmzZt+MdZKmi1WnHy5ElRu3Zt3e+enZ2dmDx5cqb6O+Ho0aOicOHCuu9Bly5dxLt376SOpVfGjRsnAIhixYqJyMhIqeMYDNY01jMhhNi5c6fuRfCLFy9KHUdSD9+FicNP3ifYGLnhFyz+e/ZRXPL5JHlz5MMHIXLmFAIQYtOm5F8/PFwIW1shmjVL2f2fOBF73/b2Qkgxr+Tx+zBx+MmHBB+jcy8DxekXASI40rjHMsHBwSJfvnwCgJgxY4bUcSTHesaalhZ2794tAAhzc/NM3xROiXfv3onBgwcLMzMz3euO/fr1E69fv5Y6WoqdO3dOVK1aVfccU6RIEbFr1y6O8ZOpf//+uu9fWFiY1HH0HhtYei4mJkY327p58+ZCo9FIHcmoaDQa8d9//4l27doJU1NT3S9EgQIFxPTp08WbN2+kjpigz5+F+Lo23Lx5U/To0UNYWFjEW102d+5cERgYKE1QA7d582YBQNjY2BjNTJn0wMERa9qP8Pb21s3g8/DwkDqOwdNqteLcuXOifv36ut9BW1tbMX78ePHx40ep4yVbTMyPfd6rV69Eq1atdF+zq6urOH/+fPqGM0BXr14VcrlcyOVyruJPJtY01rM4Y8aMEQBE7ty59XZMkN4USrW44BUkTj7/mGBj5IZfsDjzIkCcfREoPkerJMup1QrRooUQsSeRCLFjR/JvY8WK2OvmyvXtOOtHrF79//ef0auwtFqtuOobLI57Jvw4XX31SRx5+kH4BBn3ZIbOnTsLAKJ69epCpZLu51FfsJ6xpqUV7qaReq9evRK9evUScrlcN8l8xIgRej9u+3KM9uDBA9GsWTPdc0vu3LnFihUrONE7BbZt26b7Obh7967UcQxCamqacR8apCfMzc2xa9cuZMuWDceOHcP8+fOljmRU5HI5GjdujD179sDf3x9z5sxBoUKF8Pr1a/z5558oUKAAWrVqhaNHj0Kj0SA6GujTBxg1SrrMz58DhQsDtWrF7ku8adMmVK1aFVWrVsWmTZsQExODZs2a4fDhw/Dy8sK4ceOQI0cO6QIbKB8fHwwePBgAsHTpUhQpUkTiRESGrVChQli/fj0AYOzYsbh586bEiQybTCZDvXr1cObMGVy+fBlNmjRBREQE5s6dC2dnZ4waNQrv379P9DaEnmz3/+kTMGsW8L8jvhIUHR2NmTNnomTJkjh48CBsbW2xcOFC3Lt3j+c6fSUqKgo9evSAVqvF2LFjUb16dakjERmkOXPmoH79+vj48SPat2+PmJgYqSNluGCFCgqVBlamJt/9HDMTOVRaLaKUmgxMFt+NG4Cp6f//28IiedcXAli5Mvb9gAAgifKZID8/wMUFcHIClEpArU7+baRUjFoLpVoDM3nCx0KYyGWAAIKilHEv2hudHTt2YNu2bbC2tsbmzZth+uUPBBGlyoIFC1CxYkW8evUKffv2NdrnkfTk7OyM9evX4+nTp+jUqRNiYmLg4eGBQoUKYdKkSbqz6uNI/S3+/BnIkQMoXx7w9PRF9+7dUb58eRw/fhy2traYPn06vLy8MHDgQJiZmSV5e/T/Xrx4gT/++AMA4OHhgQoVKkicKBNISdfrRy/gTIh4jhw5oltueuHCBanjGDWNRiNOnTolOnToEG9VVr58+cTgwe4CeC0AIT59StntHzwoxLp1QqRkl0K1Wi1OnfogZLLbwsZmnMiePXu8s1BGjx4tvLy8UhaMdFQqlahRo4YAINq3b8+l0EkAZ/expiXDsGHDBABRsGBBERwcLHUco3Ljxg3RokWLeNvHDhky5LtbVMycKcTgwUJIfQRkv35CAEJUqZJwbTx27Fi87QI7d+5sdOdWpqW43zFXV1cRHR0tdRyDw5rGevalgIAAkT9/fgFADBw4UOo4GS6p7QNv+AWLa76fxJEnH8SrT9Ku7gkOFmLePCG2bBHi9OnkXffyZSFMTYVuBdWRI8m//9evY8eHoaHJv25qfYqMEaeeB4jzXoHffZxOvwgQZ18GinAJV8qllzdv3gh7e3sBQKxatUrqOHqD9Yw1LS19eR4Wz4dMvfv374uWLVvqfk+zZs0qZsyYIcLDw4UQseMjKU8A8fYWwsoqUFhYjBTm5uYCgDAzMxPDhw8XAQEB0gUzcDz3KuVSU9NksddPHzKZTMQ1ySjWhAkT8Ndff8HJyQn3799Hrly5pI5k9D5+/IhNmzZhzZo18Pb2BgDIZHKULFkPVarkh4ODQ7xLtmzZvvmYubm57vaiogBb29ih0aNHQOnSsR9XKBT48OED3r9//83bL98PCAiARhN/dmPFihUxePBg/Pbbb7C2ts6w740xmzZtGqZOnYq8efPi4cOHyJYtm9SR9JpMFjvbUwiR8LRPYk37QkxMDOrUqYNbt26hVatW2L9/v+5niNLGvXv3MHPmTOzbtw8AYGZmht69e8PNzQ3Ozs66z2veHDh+HJg4MXYFVEpcuRI7W93ZGUjJ5LEnT4AWLQBf39h/L18ODBoU+76vry9GjhyJAwcOAABKlSqF5cuXo169eikLmwmcP38eP//8M0xNTXHjxg1UrFhR6kgGhzUtcZmxnt2+fRu1a9dGTEwM1q9fj169ekkdKUNEqzW46ReKGLUGWa0Sn10dEKGESzYruDraZVC6xAkBJOdPi2fPAI0GmDMHaNkSsLICfv01/fKltbdhCjx8F46sVmYw/c4qLLVGi7BoNUo72SG/vVUGJ0w/Wq0WTZs2xalTp9C8eXMcOXKEf1f+D+tZ0jJjTUuNPXv2oEOHDjAzM8PVq1dRuXJlqSMZvOvXr2Py5Mk4c+YMACBHjhwYMWICVq9ugWPH8qN06ZQ9Xx8+DBQpApQo8WP1UKVS4fXr1/D29oaPjw88PT2xfv1GRESEAwC6dOmCGTNmwMXFJUV5KFb//v2xZs0aFC1aFLdv34adnX783WQIUlPT2MDKYGq1GvXr18elS5fQsGFDnDhxAiYm39/OgdKOVqvF+fPnsWbNGuzbtw8qleqHr2ttbR2vseXv7wCVyhIuLh8RGBjbmAoLC/vh28uePTucnJxQsWJFDBw4ENWqVeMf6Wno6tWrqFOnDoQQOHPmDH7++WepI+k9Do6SxpoW36tXr1ChQgWEhYVh0aJFGDlypNSRjNKjR48wa9Ys7Nq1C0IImJqaonv37pgwYQKKFCmCJk1it0m6dSv52y0BwObNwNixsQ2s//4DGjdOWc7oaGDTJmD+fCAwEHj4MAb//rsAs2bNgkKhgK2tLdzd3TF8+HBuUZGIz58/o2zZsvD19cXUqVPh7u4udSSDxJqWuMxaz9avX48+ffrAwsICly9fzhQv2r0Pj8bDd+GwNjeBhWnipwcERylhb2WOagXsOS6RwMvASDwPjEBOG7NEv/8BEUrkz2qJcnmzZmC69LV06VIMGzYM2bNnx+PHj+Ho6Ch1JL3Bepa0zFrTUmPo0KFYtmwZXFxccPfuXdjb20sdySicO3cOkyZNwrVr1+J9PEeOHMifPz8KFCiA/Pnzf/N+njx5EtwytXZtIHduYM+e/29ghYSEwMfHBz4+PrpGVdz7r1+/hlar/eZ2mjRpgjlz5nCbuzSwbds2dOnSBRYWFrh+/TrKly8vdSSDwgaWgXn37h3Kly+PwMBAvjghkcDAQFy5cgXBwcEICQlJ8PLl/329YiohZmZmcHR0hJOT0zdvv3w/d+7c8VZ0UdoKDw9HuXLl4Ovri3HjxmHu3LlSRzIIHBwljTXtW/v370fbtm1hamqKy5cvo1q1alJHMlqenp6YPXs2tm3bBq1WC7lcjs6dO8PHZxQWLCiOGjVStnpXpQIqVwYePgTOnAHq1//x62o0Grx58wbe3t66i5eXN+7e9ca7d16IifkMAPjtt9+wYMEC5M2bN0UZM5O4GX0VK1bE9evX2exLIda0xGXmejZgwACsXr0a+fPnx507d5AzZ06pI6Wrxx/C4RusSLIpAgDh0WqYmchQrYADLM04wTKj3X8bhrdh0chpm/g4MSxaDXMjepw8PT1RsWJFREdHY+/evWjbtq3UkfQK61nSMnNNS6mYmBjUrFkTd+/eRdu2bbFnzx5OXEgjQggcO3YMS5YswcuXL/HmzZskJ8/L5XLkyZPnm8bWP/9Y4uefX+H9+/9vVH19ztaXZDIZ8uXLh0KFCqFQoUIoXLgw6tatizp16qT1l5kpPX/+HJUrV0ZERARWrVqF/v37Sx3J4LCBZYBOnz6Nxv+b5nzy5Ek0bNhQ4kT0PUIIREREfNPYUigUyJ07t64x5eDgALk88ZmNlP66d++OLVu2oGLFirh27RqbhT+Ig6OksaYlbOTIkfDw8ECBAgVw7949bteZzry8vDB79mxs2bIF6i9Ol7ezs0OePHl0kya+d7Gzs/tmgHrzJlC9OnD+PFC3bvz7UygUePXqVbwmVdzF19cXSqXyu1ldXV2xZMkS1E9OVywT+++//9C0aVOYm5vjzp07KB23RzElG2ta4jJzPYuJiUG9evVw/fp1NGjQACdOnEhw1rMxiFFrcMMvFNFqDeyT2D4QABQqDVQagYr5siKbNf9+zkgarcA1vxBEKdVJPlYxai2ilBqUzWMHJzvLDEqYPpRKJWrUqIG7d++iZ8+e2LBhg9SR9A7rWdIyc01LDW9vb1SsWBHh4eFYsmQJhg4dKnUko6TVavHx40f4+/vj9evX8d7Gvf/hw4cfvj1ra2sULlw4XpMq7n1nZ2dYpGRLDkqSQqFA9erV8fDhQ/z222/Ytm0bm74pwAaWgYo7oydnzpy4f/8+8uTJI3UkIoO2fft2dO7cGVZWVrh37x6KFy8udSSDwcFR0ljTEqZUKlGnTh3cvHkTLVu2xMGDB/nHXAbw9fXFX3/9hRMnTuD9+/eJNpK+ZGVlFa+hFdf0OnvWCTVrmsHU1Cdek+rdu3eJ3p6joyMKFy6sGzzFvV+4cGHkzJmTPws/KDQ0FKVLl8bbt2/x119/Yfz48VJHMmisaYnL7PXs7du3qFixIgICAox6tX5ytg8EALVWIEyhQhknO+QzovOVDEFEjBq3/EMhhICtReINVSEEAiNUKJTdGqUcs2RQwvQxZcoUzJw5EwULFsTDhw95jkgCWM+SltlrWmrwPCz9oFQq8fbt228aXNHR0XBxcYnXqMqVKxfHVxLguVdpgw0sA6XRaNC0aVOcPn0aderUwdmzZ412BiBRevPz80O5cuUQFhaG1atX448//pA6kkHh4ChprGnf5+vriwoVKiA0NBQLFizA6NGjpY6UqQghEBwcjPfv3yd5iYyMTNZtm5qawtnZOcEmVaFChWBjY5NOX1Xm0qNHD2zevBnVq1fH5cuXeT5qKrGmJY71DLh48SLq168PjUaDXbt2oUOHDlJHSnPJ2T4Q+P/GSLFcNiiW0zYDElKcwIgY3HsbBiuzH2s2hihUsDE3RbUC9jAzMcwdQK5fv45atWpBCIHz58+j7tdLwAkA69mPYE1LHZ6HRZQ4nnuVdtjAMmABAQEoX7483r9/Dzc3N8yZM0fqSEQGR6PR4Oeff8alS5fQqlUr7N+/n7NSkomDo6SxpiXu4MGDaN26NUxNTXHx4kXUqFFD6kiUgM+fP3/T1Hr37h3ev3+PmJgYuLi4xFtFlT9/fk6uSWdxvzuWlpa4f/8+Vw+nAda0xLGexVq8eDFGjBgBGxsb3LhxA66urlJHSjMxai1u+IX88PaBcYIilXDMYoGK+ezTLxx9wy8kCo/ff0Z2azPI5Uk/bSlUGig1AuXz2CGnreFtFxUREYEKFSrAy8vLqFdBpgXWs6SxpqVOTEwMatWqhTt37vA8LKKv8NyrtMUGloH7cgbgkSNH8Msvv0gdicigzJo1C5MnT4ajoyMePXqEHDlySB3J4HBwlDTWtKSNHj0aixYtQv78+XHv3j1kz55d6khEei0oKAiurq4ICAiAh4cHhg8fLnUko8CaljjWs1hCCHTr1g1bt25F0aJFcevWLWTNmlXqWGniQ3g0HrwPh/UPruiJE6pQwcrMBDWcHWDKs30zzLOACHgFRSKX7Y+dPaYVAkGRKhTPaYOiBrhabsCAAVi9ejXKli2Lmzdv8syWRLCeJY01LfV8fHxQoUIFnodF9AWee5X2UlPT+FepHqhbty5mzpwJAOjWrRv8/PwkTkRkOG7evAl3d3cAwObNm9m8IpLQnDlzUK1aNfj7+6NHjx7QarVSRyLSa4MHD0ZAQAB++uknvlhAlMFkMhnWrFmDcuXK4eXLl+jevbvR1K1PUUqo1ALmJsl7fcDMRA6VRosopSadktHXhBD4HK1Gch4quUwGEzkQFKWCRmtYL9ofPXoUq1evhrm5Of799182r4j0QKFChbB+/XoAsRMSb9++LXEiIukNHz4cDx8+RNGiRbF69Wo2ryTGBpaeGDduHJo3b46QkBB06tTphw9jJ8rMIiIi0LlzZ2g0GowaNQqNGjWSOhJRpmZubo6dO3fCwcEBR48excKFC6WORKS3du7ciV27dsHGxgYbNmyAnKsdiDKctbU19u3bB3t7exw6dAizZs2SOlKqKdVafIpSwdxUluwXW8xMZFBpBKJUxtHIMwQqjYBCpYFpMs+ysjQ1QZRSjc8x6nRKlvYCAwPRp08fAMDs2bNRpkwZiRMRUZx27dph6NChUKlU6NixI0JDQ6WORCSZrVu3Yu3atbCwsMCuXbtgZ2cndaRMjyNlPSGXy7F582bkz58fN27cwPjx46WORKT3hg8fDm9vb5QtWxazZ8+WOg4RAShYsCA2bdoEAJgwYQKuXr0qcSIi/fPhwwcMGjQIALBw4UK4uLhInIgo8ypUqJBuWxh3d3ccO3ZM6kipEqxQIlqlgZWZSbKvayaXQaUVXIGVgRQqDVRaLcx+4OyrL1mayhGt1iIkSpVOydKWEAJ//PEHPn78iHr16mHkyJFSRyKir8yfPx+VKlXCq1ev0Lt3b27LSJnS8+fPdWddLVmyBOXLl5c2EAFgA0uvZM+eHbt27YKpqSk8PDywb98+qSMR6a09e/Zg/fr1sLS0xLZt27j9BJEeadmyJcaMGQONRoNOnTohKChI6khEekMIgf79+yM4OBiNGzfGH3/8IXUkokyvWbNmmD59OoQQ6NKlC7y9vaWOlGLBkSrEpGD7QCB2W0UZgCil4azqMXRRKg1UGgGzZD5eJnIZIGK3izSEF5k3bdqEAwcOwM7ODhs3buSqYyI99OVqk/3792Pp0qVSRyLKUAqFAh06dEBkZCR+//139OvXT+pI9D/8q0HPVK9eHfPnzwcA9OrVy6AHT0Tpxd/fX/eC34IFC+Dq6ipxIiL62uzZs1G9enW8efOG52ERfWHlypU4dOgQsmbNin/++Yf7qRPpiYkTJ6JVq1YIDQ1FmzZtEBkZKXWkZIvdPlAJixRsHxjHRC5DeIzaIJoixkCh0kCjFTBN5gosALA0k+NzjBqRer5i7tWrVxg2bBgAYOnSpShYsKDEiYjoe748D2vMmDG4deuWxImIMoYQAkOHDsWjR4947pUeYgNLDw0fPhxt2rRBeHg4OnTogOjoaKkjEemNoKAgtG/fHiEhIfjll190WzARkX4xMzPDzp07kS1bNhw7dkw3OYMoM1u3bh0GDx4MAKhdeykCAvKDrxET6Qe5XI5NmzahWLFiePToEfr162dwTZxghQoKlQaWKdg+MI6ZiQxKtRYKnoOVISKVGgggRS+SWZnKEa3SIEShv9sIajQa9OjRA58/f0a7du3QrVs3qSMRURJ4HhZlNmq1GgMGDMC6detgZmaBtWt3I0uWLFLHoi+wgaWHZDIZ1q9fj0KFCuHevXsYNGgQNBr9nlVFlBFevnyJGjVq4ObNm8iVqwCWLl3PGRFEeqxAgQK687AmTZqEy5cvS5yISDrr16/XbUMxf/58VK/eDZUrA8WKAfPnA1ykSCS9rFmzYv/+/bCxscH27duxePFiqSMlS3CkEkqNgEUKtg+ME3cOlkLF8Wd6E0Lgc4w6RauvAMDURA6NAIL1+ByshQsX4tKlS3B0dMSqVas4diPSQx8/Avv2AVevAj4+QFRU7N+qlStXhq+vL3r06AGVSn+fZ4hSIyoqCm3btsWaNWtgaWmJP//cgaZNy6FKFWDSJCAkROqEBLCBpbfs7e2xe/dumJlZYMOGDWjVqhXCw8OljkUkmUuXLqF69erw8vJCnjwVERBwDe7uuaSORURJaNGiBcaOHQuNRoOWLX/D+fO+UkciynAbNmxA3759IYTAvHnzMGbMGEycCDRvDnh5AblzAzwOhEg/lCpVChs3bgQQu33ShQsX4v2/vi7Kit0+UAVzk5RvHwgAZiZyqDRaRLGBle6i1Voo1dpkn3/1JQtTGUIVKkTryeMVEQH4+QGfPgEPHjzA5MmTAcRO4siRI4fE6YgoITlzAsuWAbVqAYULAzY2QK5cFggP3wlr66w4dOgQWrVqZZBb6xIlJjAwEPXr18fhw4fh4OCA06dPY/Lk1li/Hrh9G7h+HXBwkDolAWxg6TUbm4pQqY4DyIajR4+ievXqePnypdSxiDLctm3b0LBhQwQHB6NFixaYO/cCbG3zoGRJqZMR0Y+YNWsWnJ1rITT0LZo0qYn79+9LHYkow2zcuBF9+vSBEAJz587F2LFjAcQ2rLZsARYuBH77TeKQRBRP+/btMX78eGg0GnTs2BFv3rzR/d/Zs8DGjcC1a9LlS0iIQgWFSg1Ls9QN8U3kMmgFEKXn5yoZgyilBiqtgFkKV2ABgKWpid5sIxgaCtSrBxQpAvj5RaNr165QqVQYOHAgmjVrJnU8IvoOpRK4dOn//y2TAS1bAsePF8K5cyeRI0cOHD9+HD///DMCAwOlC0qUhnx8fFCrVi3cuHEDBQsWxJUrV1CrVi0AwO+/A/PmATt3ShySdNjA0mOOjkCzZj+jZctbcHV1haenJ6pWrYpTp05JHY0oQwghMGvWLHTp0gVKpRJDhgzBgQMH0LWrLUJCgAkTpE5IRD/CzMwMW7ceha1tPSiV71G3bl2cOXNG6lhE6W7Tpk3o3bs3hBD466+/MG7cuHj/ny0bMHIkYG4uUUAi+q6ZM2eiYcOGCAgIQPv27RETEwMA8PQEevWKbTxv2SJxyC8ERykRoxGwMEn9EF8uAz7HqNMgFSVGodJArdHCLBWPmbmJDEqNQIgebCNobw80aQKo1cDatZPx+PFjFC1alOegEuk5S0tgzpzY95s3B+7dA/79FyhUCKhatSquXLkCZ2dn3Lp1CzVr1oSPj4+0gYlS6fbt26hRowZevnyJ8uXL49q1ayj51Qz5sWMBLhzWH2xg6bGsWYFjx4BDhwrh2rVr+PXXXxEaGopmzZphyZIlBneoMFFyqFQq9O3bF5MnT4ZMJsPff/+NJUuWwMQk9lBqU1OJAxJRstSsmRVBQSfQqVMnfP78Gc2aNcP27duljkWUbjZv3oxevXpBCIE5c+Zg/PjxCX4ejwMh0k+mpqbYvn07ChQogBs3bmDYsGEAYs8HAWIbz/qyqESpSZvtA+OYmcgQpdRAqeHhfOkpSqWBRhvbMEwpmUwGc1MZghUqqPTg8Ro/HrC3P4fVqxfBxMQEW7ZsgY2NjdSxiCgJZcsCFy4AR48C5crF/79ixYrh2rVrqFChAry8vFCjRg3cvXtXmqBEqXT8+HHUq1cPAQEBaNiwIS5cuAAnJyepY1ES2MAyEFmyZMH+/fsxadIkaDQaDB8+HP369dPNBCQyJmFhYWjevDnWr18PKysr7N27FyNGjOChv0QGzsLCAtu2bcPIkSOhUqnQuXNnLFy4UOpYRGluy5Yt6NmzJ4QQmD17Ntzc3KSOREQpkCNHDuzbtw8WFhZYs2YN/vnnH/j4ANmzx0401JeZuaFRKkQp1bAyTZvhvZlcDpVWCwW3EUxXEUo1ZDKkeoxjaSqHQqVBqB5sI/jmzVPI5d0hhMDkyZNRrVo1qSMR0Q9o3BioW/f7/+/o6Ijz58/rVib/9NNP3B2KDM6GDRvQsmVLREZGomvXrjh69Cjs7OykjkU/gA0sAyKXyzFz5kzs2LEDVlZWWLduHRo0aICPHz9KHY0ozfj5+aFWrVo4ffo0cuXKhfPnz6NNmzZSxyKiNCKXy7Fo0SIsWLAAADBmzBiMGjUKWq30s4aJ0sK///6LHj166LbBncD9bokMWqVKlbBq1SoAwODBg/HixU0cPAgULSpxsC98ilJCqRGwSKsGlokMKo1AlIoNrPSi0QpExmhgZpL6CXoWpnLEqLUIVUi77ePBgwdRrVo1BAe/Qc2aNTFp0iRJ8xBR2rKzs8PRo0fRuXNnREREoHnz5ti6davUsYiSJITAjBkz0Lt3b2g0Gri5uWHz5s0w5z7uBoMNLAPUqVMnXLp0Cfny5cOVK1dQpUoVLt8lo3Dnzh1Ur14dT548QcmSJXHjxg1UrVpV6lhElA5Gjx6Nbdu2wczMDH///Tc6d+7MVcVk8LZu3aprXs2cORMTJ06UOhIRpYGePXti0KBBUCqVeP++JWJizkodSUf1v+0DzeRps30gAJjKZVBr2cBKT1EqDVQakarzr+LIZTKYyGIbmVoJjhnQarWYNm0aWrdujYiICHTq1AknT56EmZlZhmchovRlbm6OLVu2YPTo0VCr1ejatSsWLFjAI05Ib6nVavTv3x9//vknZDIZli1bhjlz5nCHJwPDBpaBqlSpEm7duoUaNWrA398ftWvXxq5du6SORZRihw4dQt26dfHhwwfUr18fV69ehbOzs9SxiCgd/f777zh+/DiyZMmCnTt3omnTpggLC5M6FlGKbNu2Dd27d4dWq8WMGTM485zIyPz9999o1KgRQkNjz0xwd3eHRiN9gydEoYJCqYGVWdoN7WUyGWQAImOk//qMlUKpgVqrhWlqDsD6gqWZCSKVaoRHZ+wqrM+fP6Ndu3aYOnUqZDIZ/vrrL2zfvp3nXhEZMblcjgULFui2gh87dixGjx7NHTVI70RGRqJNmzZYu3YtLC0tsXfvXgwePFjqWJQCbGAZMEdHR5w7dw69evWCQqFAp06dMGXKFBYNMjhLlixB69atERUVhR49euD48eOwt7eXOhYRZYAGDRrg4sWLun3V69Spg7dv30odiyhZtm3bhm7dukGr1WL69OmYPHmy1JGIKI2Zm5vj2LFj+PPPPwEA06dPR4MGDSSvWcGRSsSotWm2fWAcUxMZPseoJVnRkxn8/wqsNGpgmcoRrdYiJAPPwfLy8kL16tVx4MABZM2aFUePHsX48eM5q50okxg1alS8HTW6dOnCHTVIbwQGBqJBgwY4cuQIHBwccPr0aR5PYsDYwDJwFhYWWLduHTw8PHRnZLVt2xafP3+WOhpRkjQaDUaMGIHhw4dDCIHp06djw4YN3IeWKJMpX748rl27huLFi+PRo0eoWbMmPD09pY5F9EO2b9+ua15NnToVU6ZMkToSEaUTU1NTTJs2DadOnYKjoyMuXLiA8uXL4/jx45Lk0W0faJJ22wfGMZPLoNRooeA2gukiSqWBQOz2f2nBRC4DBPApUpkhW3n9999/qFKlCp4+fYqSJUvi1q1baNasWbrfLxHply931NixYweaNWvGHTVIct7e3qhZsyZu3LiBggUL4sqVK6hVq5bUsSgV2MAyAjKZDMOHD9etWjl48CBq1qyJV69eSR2N6LsiIyPRrl07LF68GGZmZtiyZQumTJnCGXtEmZSzszOuXLmCGjVq4PXr16hVqxauXLkidSyiRO3YsQNdu3aFVquFu7s73N3dpY5ERBmgQYMGuH//Pho1aoSgoCA0b94c48ePh0qVcatfACBUoUJUGm8fGMfMRA6VRosoJRtYaU0IgYgYNdLg+Kt4LM3k+ByjRmQ6PmZCCMybNw/NmzdHaGgofv31V1y/fh1FixZNt/skIv325Y4a586dw08//YT3799LHYsyqdu3b6NmzZrw8vLSTZQtWbKk1LEoldjAMiKNGzfGzZs3UaJECTx+/BhVqlTB+fPnpY5F9I0PHz6gXr16OHjwIBwcHHDq1Cl07dpV6lhEJLHs2bPj9OnT+PXXXxESEoKGDRviwIEDUsciStDOnTvRpUsXaLVa/Pnnn5g6darUkYgoA+XOnRsnTpzA7NmzYWJignnz5qFu3brw8/PLsAyfopSI0WjSfPtAIHYFlkojEMUVWGlOqRFQqLQwk6ft42ZpKke0SpNu2whGRUWhS5cuGD9+vG7ixv79+2FnZ5cu90dEhqN8+fK4evUqihUrhgcPHqBGjRp4/vy51LEokzl+/Djq1auHgIAANGrUCBcuXICTk5PUsSgNsIFlZIoWLYrr16+jefPm+PTpExo1aoSVK1dKHYtI58mTJ6hevTpu374NFxcXXL16FT/99JPUsYhIT1hbW2Pv3r3o378/oqOj0a5dO9Yx0ju7du3SNa+mTJnC5hVRJiWXyzFhwgScP38e+fLlw/Xr11G+fPkMmXyh1mjxKVIFU7k8XXYwkMtlEBBcgZUOFCoN1Bptmp1/FcfMRA6NAEKi0r6B5efnh1q1amH79u2wtbXFvn37MHXqVMjTuAlHRIbLxcUFV65cQbVq1XTPGdevX5c6FmUSGzZsQMuWLREZGYlu3brhyJEjnGBhRPjXhhHKmjUrDh06hHHjxkGtVmPQoEEYOHAglEql1NEokztz5gxq1aoFPz8/VKtWDdevX0eJEiWkjkVEesbU1BQrV67E9OnTodVqMWjQIEyePDlDznQgSsru3bvRuXNnaDQaTJ48GdOmTeP2t0SZXO3atXH//n20aNECoaGhaNOmDYYPH56uh9mHKFSIUqXP9oFxTGQyfI5Rs/6msSiVBiqtgKk87WuHhakMoQoVotNw5dz58+dRuXJl3L9/H0WKFMH169fRpk2bNLt9IjIeOXLkwJkzZ/DLL7/g06dPqF+/Po4cOSJ1LDJiQgjMmDEDvXv3hkajwYQJE7Bp0yaYm5tLHY3SEBtYRsrExARz587Fli1bYGFhgVWrVqFx48YIDAyUOhplUhs2bEDTpk0RFhaGdu3a4dy5c8iVK5fUsYhIT8lkMkyZMgXr1q2DiYkJZs2ahd69e2f4+SJEX9qzZw9+//13aDQaTJo0CdOnT2fziogAxG6De+jQISxatAhmZmZYsmSJ7gyG9BAcpUSMWgvLdNg+MI6ZiRwKlRZKDRtYaUmh1ECdTg0sS1MTKFQahKbBNoJCCCxbtgwNGzZEUFAQmjRpgps3b8LV1TUNkhKRsbKxscGBAwfQu3dvKBQKtGrVCv/884/UscgIqdVq9O/fH3/++SdkMhmWL1+O2bNnc3xmhNjAMnJdu3bFxYsX4eTkhAsXLqBq1ap4+PCh1LEoExFCYMqUKejduzfUajXGjBmDXbt2wcrKSupoRGQAevfujYMHD8La2hobN25Eq1atEBERIXUsyoT27t2L3377DRqNBhMnTsSMGTM4OCKieGQyGUaOHInLly/DxcUFd+/eRcWKFbFjx440vZ//3z5Qlq7PQ2YmMqg0Wm4jmMYilRrIgHR57MxNZFBpRarPwYqOjkafPn0wdOhQaDQajBs3DkePHoWDg0MaJSUiY2Zqaop//vkHkydPhlarRb9+/TBjxgyu6KU0ExkZiTZt2mDt2rWwtLTE3r17MWjQIKljUTphAysTqFq1Km7fvo0qVarA19cXNWvWxP79+6WORZlATEwMunbtipkzZ0Iul2PlypWYP38+90onomT55ZdfcO7cOeTIkQPHjx/Hzz//jICAAKljUSayb98+XfNqwoQJmDlzJptXRPRdVatWxd27d9G+fXt8/vwZv//+O/r37w+FQpEmtx+iUCEynbcPBAAzeWwzRJGG29Fldloh8DlGnS6rr4DYppiZiQyfolRQabQpuo13796hXr162LBhA6ysrLBt2zbMnTsXJiYmaZyWiIyZTCbDjBkzsHLlSsjlcvz5558YOHAgNBrWFEqdwMBANGjQAEeOHEG2bNlw5swZbm1r5PgqciaRJ08eXLhwAV27dkVkZCTatm2rO1uEKD18+vQJjRo1wrZt22Bra4sjR45gwIABUsciIgNVtWpVXL16FS4uLrh9+zZq1qwJb29vqWNRJrB//3506tQJarUabm5umDVrFptXRJQke3t77Nq1CytXroSFhQXWrFmDqlWrwtPTM9W3HRylQoxaC4t03D4QAEzkMmi0ApFsYKWZaJUWKo0WZibpV0csTeVQqDQIi1Yn+7rXrl1DpUqVcOPGDRQoUABXrlzB77//ng4piSizGDBgAPbs2QMLCwusXr0a7du3T7MJHZT5eHt7o2bNmrhx4wYKFiyIK1euoGbNmlLHonTGBlYmYmVlhc2bN2P+/PmQyWRwd3dHp06dEBkZKXU0MjJxBeXSpUvIkycPLl26hGbNmkkdi4gMXNGiRXHt2jVUrFgR3t7eqFGjBm7fvi11LDJi+/fvR8eOHaFWqzF+/HjuqU5EySKTyTBgwADcuHEDxYoVw+PHj1G5cmVs2rQpxbep1mrxKUoJU7kM8nR+PpLJZJABiIxJfiOEEhal0kClETBNxwaWhakcMWptsrcRXLduHerVq4cPHz6gXr16uH37NipUqJBOKYkoM2nTpg1Onz4Ne3t7HDhwAI0aNUJwcLDUscjAxE1k9fLyQvny5XHt2jWUKFFC6liUAdjAymRkMhnGjBmDI0eOwM7ODnv27EGtWrXg5+cndTQyEteuXUP16tXx4sULlC1bFjdu3ED58uWljkVERiJ37tw4f/48mjRpgsDAQNSrVw8nTpyQOhYZoQMHDuiaV+PGjcOcOXPYvCKiFClXrhzu3LmDrl27IioqCj179kT37t1TdKZjqEKNSGX6bx8Yx8xEhgilBhotzy1JCwqVBiqtFmbpuKW6XCaDiQz4FKmE9gfOm1EqlRg8eDD69u0LpVKJoUOH4uTJk8iZM2e6ZSSizKd27dq4fPky8uXLhytXrqB27dp4/fq11LHIQBw/fhz16tVDQEAAGjVqhAsXLsDJyUnqWJRB2MDKpJo3b44bN26gaNGiePDgAapUqYLLly9LHYsM3O7du/Hzzz8jKCgITZs2xaVLl5AvXz6pYxGRkcmSJQsOHz6M7t27IzIyEi1btkzVbHairx08eBAdOnSAWq3G2LFj8ddff7F5RUSpYmtri82bN2PDhg2wtrbGli1bULlyZTx48CBZtxMcqcyQ7QPjmJnIoFRrEcVtBNNElFIDrYjdnjE9WZqZIFKpRngS2wgGBASgYcOGWLFiBczNzbF+/XosWbIEZmZm6ZqPiDInV1dXXLt2DaVLl4anpydq1qyJR48eSR2L9Nz69evRsmVLREZGolu3brpFGZR5sIGViZUoUQI3btxA48aNERgYiPr16+Off/6ROhYZICEE5s2bh44dOyImJgb9+/fH4cOHWVCIKN2YmZlh48aNmDBhAtRqNXr27InZs2dD/MBMY6LEHDp0SNe8Gj16NObOncvmFRGlCZlMhp49e+LWrVsoXbo0nj9/jmrVqmHVqlU/VL80WoGgDNo+MI6pXA61VguFkg2stBChVCOde1cAYs/BilFrEZrINoJ37txB5cqVcenSJTg5OeHChQvo1atX+ocjokwtX758uHjxIurUqYO3b9+iTp06uHDhgtSxSA8JITB9+nT06dMHGo0GEyZMwKZNm2Bubi51NMpgbGBlcg4ODjh69ChGjRoFlUqFfv36YdiwYVCpkrdfNmVer169QocOHTB+/HgAwLx587By5UqYmppKnIyIjJ1MJsPs2bOxdOlSyGQyTJo0CUOGDIFGwxfZKGUOHz6M9u3bQ6VSYdSoUbpzQ4mI0lKpUqVw48YN9OvXDzExMRg4cCA6deqEsLCwRK8XqlAhKgO3DwRiV2CpNIIrsNKAWqNFlFIDswzoYJnIZYCI3UYwoebo1q1bUbt2bfj7+6N69eq4c+cOqlevnu65iIiA2NciT548iXbt2iEsLAyNGzfGnj17pI5FeuTdu3f4/fff4e7uDrlcjuXLl/M84kyMDSyCqakpFi5ciPXr18Pc3BxLly7FTz/9hEuXLkkdjfRYaGgoxo4dixIlSmDv3r2wsrLCrl27MHbsWBYUIspQQ4YMwe7du2FhYYEVK1agQ4cOUCgUUsciA3P48GG0a9cOKpUKI0eOxIIFC1jPiCjdWFtbY82aNdi2bRtsbW2xe/duVKxYEbdu3frudT5FKRGdgdsHArHnKQkAkcrEt6KjpEWpNFBptDA1yZjHz8JMjvCY2DPT4qjVaowZMwZdu3ZFdHQ0+vTpg/Pnz/McESLKcJaWlti5cycGDx4MpVKJjh07YsGCBVCrWW8ys5iYGPz1118oVqwYdu7cCXNzS3TtuhdNmw6SOhpJiA0s0unVqxfOnz8PR0dHXLt2DXXr1kWjRo1w7do1qaORHlGpVFi2bBmKFCmCBQsWQKlUokOHbvjtt+fIl6+D1PGIKJNq164dTp06BXt7e+zfvx+NGjVCcHCw1LHIAGg0GsydO1fXvOrSZQTGj1/I5hURZYjff/8dd+/eRYUKFeDj44NatWrBw8Pjm1UzGq1AUKQSpnJk2PaBcUzlwOcYDbfpTSWFSguVRsDMJGMeP0tTOR57avHPltjdVYKDg9G8eXMsXLgQpqamWL58OdauXQsLC4sMyUNEFOfpU2DXLmDzZhMUL74UTZrEbgU/duxYlC9fHsePH2fNyWSEEDh06BBcXV0xYcIEREZGonXr1njy5Ak+fWqNwoWB+vWBwECpk5IU2MCieBwcaiBbNk/UqOEOOzs7nD59GjVr1kSzZs1w8+ZNqeORhIQQOHz4MMqUKYOhQ4fi06dPqFu3Lm7duoVSpTZjw4b8mDZN6pRElJnVqVMHly9fRr58+XDlyhWUKVMbBQq8xuHDUicjfeXr64uff/4Zbm5uUKlUaNduPC5cWARvbzaviCjjFC1aFNeuXcPQoUN1q0Bbt24dbyJGSFTs9oGWpiYZns9MLke0WoMYtTbD79uYRKk0UGsFTDPiECwAkZ9leOIpsP+4CidOPEKVKlVw6tQp5MyZE2fOnMGgQYM4WYOIJKHVAl26AL17A8OGyVCq1ATs3LkPzs7OePLkCZo3b44mTZrgwYMHUkelDODp6YmmTZuiVatW8Pb2RsmSJXHy5Ens378fRYoUwr//AkWLAi4uQM6cUqclKbCBRfE8egQ8fWqPT5+m4tWrV5g0aRJsbW1x4sQJVKtWDS1btsTdu3eljkkZ7N69e2jQoAF+/fVXPH/+HEWKFMH+/ftx/vx5VK5cGR06AO3bA6NHS52UiDI7V1dXXLt2DaVLl8a7d57w96+BLVt4KDDFJ4TAli1bUK5cOVy6dAmOjo44fvw49uz5C56eMlSpInVCIspsLCwssGTJEuzbtw/29vY4dOgQypcvjytXrgAAFi5X4eHTjN0+MI6piQxqnoOValFKNQQybgWdvb0MhQrIkC/vfrRvXwM+Pj6oWLEibt++jbp162ZIBiKihOTKBRQuHNuMOHYMWLQI6NixDZ49e4b58+cja9asOHXqFCpUqIDevXvj7du3UkemdBAaGoqRI0eibNmyOHnyJLJmzQoPDw88ePAAjRo10n2evT1w4ACwerVkUUlisvRckimTyQQALvs0IEIA27cDVarEdrcBICgoCAsXLsSSJUsQFRUFAGjdujWmTp2KcuXKSZiW0tvbt28xefJkbNq0CUIIODg4wN3dHQMHDoS5ubnU8SgNxc2+FEJwGuZ3sKYZltDQULRo0RpXrsQ2rzp16oQFCxYgX758EicjqQUHB2PgwIHYtWsXAKBNmzZYs2YNcuTIIXEySiusaYljPdN/vr6++O2333Djxg2YmJhg+owZeKPqh5IVNKhWLuP/BldrBcIUKpRxskM+e6sMv39jIITAjdehCFUokc06/R9DIQTuXLuCdUsX4u7V2L+FOnfujLVr18La2jrd75/SButZ0ljTDNf+/UCNGoCj47f/9+nTJ8yYMQPLly+HWq2GtbU1xowZg7Fjx8LW1jbjw1Ka0mg02LBhAyZOnIjAwEDIZDL069cPM2fORE4usTJaqalpbGDRDwsICMD8+fOxfPlyKBQKAED79u3h7u6O0qVLS5yO0lJkZCTmz5+P+fPnIyoqCmZmZhgyZAgmT56MbNmySR2P0gEHR0ljTTM8MTExmD9/PmbPng2FQgFra2tMmjQJo0eP5nkPmdTp06fRs2dPvH37Fra2tliyZAl69uzJLZSMDGta4ljPDINKpcKkSZMwf/58AECR0nUxZMJYVK9VM8Ofs4QQCIxQoVguGxTLyRcOUyJarcENvxCoNAJ2lqbpdj9CCFw+ewobli3Ck7uxRwBY29hi8p/ucBs7mvXOwLCeJY01zbi9fPkSbm5u2LdvHwAgd+7cmDFjBnr37g0Tk4zfUpdS78qVKxg2bJhud6/atWtj8eLFqFixosTJKL2xgUUZ6sOHD5g7dy5WrlyJmJgYyGQydOzYEe7u7ihZsqTU8SgVNBoNNm/ejMmTJ+Pdu3cAgLZt22Lu3LkoUqSIxOkoPXFwlDTWNMPl5+eHMWPGYM+ePQCAwoULY/Hixfjll18kTkYZJTo6GhMmTICHhwcAoGbNmtiyZQsKFSokbTBKF6xpiWM9MyzHjx9H127dEfwpCADgUrwU2nfvg1/adoSVtU2G5QiKVMIxiwUq5rPPsPs0JiFRStx5EwYzExmszNL+RVeNRoOzx49g4/JF8Hr6CACQ1cEBgwYPxegRw5A9e/Y0v09Kf6xnSWNNyxwuX76M0aNH4+bN2MZ86dKlMX/+fDRp0oSNeQPx9u1bjBs3Dtu2bQMA5M2bFwsWLECnTp34GGYSbGCRJN69e4c5c+ZgzZo1UCqVkMlk6Ny5M/78808UK1ZM6niUTGfPnsXo0aNx//59AEDlypWxaNEi1KlTR9pglCE4OEoaa5rhO3PmDIYNG4anT58CAH755Rd4eHiwQW/kHjx4gC5duuDJkycwNTWFu7s73NzcYGqafjPgSVqsaYljPTM879+/x0KPJdiw7h9dI8smix2atv0NnXr0QcHCRdM9Q5hCBUszE9Qo6ABTEx6lnVxvw6Lx8F04slqapOn3T61S4dj+3di8cjH8fV4CAHLkyo0RI0dh2OCByJIlS5rdF2U81rOksaZlHkII7Nq1C25ubvD19QUANGrUCPPnz+fxJnosOjoaCxcuxOzZsxEVFQULCwuMHTsWbm5usLHJuIk4JD02sEhS/v7+mD17NtatWweVSgW5XI5u3bphypQpKFy4sNTxKAnPnj3DuHHjcPjwYQBAvnz5MGfOHHTu3BlyOQenmQUHR0ljTTMOKpUKy5cvh7u7O8LDw2Fubo7Ro0dj4sSJ3E/dyGi1WixatAiTJk2CUqlEsWLFsHXrVlSuXFnqaJTOWNMSx3pmuGJiYrBtxy4sW74cd2/d0H28Uu166Ni9L+o0bJJuWypFKjUQQqByfnvYWZqly30Ys5dBkXgeEIGcNmZpMtM8JjoaB3b+i62rl+LjW38AQN78BTBm7FgM6NcXlpaWqb4Pkh7rWdJY0zKfmJgYLF26FDNnzkRYWBhkMhl69uyJGTNmIG/evFLHo/8RQuDgwYMYNWoUXr16BSB2h6cFCxbAxcVF4nQkBTawSC/4+vpi1qxZ2LBhAzQaDUxMTNCzZ09MnjwZzs7OUsejrwQFBWHq1KlYtWoVNBoNbG1tMWHCBIwcORJWVjycObPh4ChprGnG5ePHj3Bzc8PGjRsBcAsDY/P69Wv06NED58+fBwAMHDgQ8+fP5yy/TII1LXGsZ8bh9p27+HvJUuzbtRPR0bHnE+fKkw+tu/RCm9+6IluOtD0EXanRIjJGg3J57eCYhc2R5HrwNgxvwqKR09Y8VbcTFRmBXZvXY8c/KxASFAAAKFSkKNzcJqBn964wM2Nz0ZiwniWNNS3z+vTpE2bMmIHly5dDrVbD2toaY8aMwdixYzkxUWJPnz7F8OHDcfr0aQCAq6srFi9ejAYNGkicjKTEBhbpFR8fH8yYMQObN2+GVquFqakp+vTpg0mTJiF//vxSx8v0vp6tIpfL0bdvX0ybNg2Ojo5SxyOJcHCUNNY043T9+nUMHToUt2/fBgD89NNPWLJkCcqWLStxMkqpbdu2YdCgQQgLC0OuXLmwfv16nneWybCmJY71zLgEBwdj9T/rsGrlKrz29QEAmJqZ4+fmrdCpZ1+UrlA5TSZmCCEQGKlCqdxZUCi7dapvLzPRaAWu+YUgUqmGg1XKGkyhIcHYsXEt9mxYg89hIQCAUmXKYtKkSejUvl26rbwjabGeJY01jV6+fAk3Nzfs27cPAODo6Ijp06ejd+/efG7MYCEhIZg6dSqWL18OjUYDe3t7zJgxAwMGDOD27cQGFumnly9fYsaMGdi6dSu0Wi3Mzc3Rr18/TJgwgct6JSCEwJ49ezB+/Hjd8t0mTZpgwYIFKF26tMTpSGocHCWNNc14abVarF+/HhMmTEBQUBDkcjkGDRqE6dOnw8HBQep49INCQkIwaNAg7NixAwDQqlUrrF27Fjlzpu0qBNJ/rGmJYz0zTlqtFkePn8CSpctx5uRx3eNb1LUc2nfvg6at28HSMnW7LAREKFHQwQplnOzSInKmERmjxk3/UAghYGuRvBfwggI+4t+1K3Dg3/VQREUCACpVrY4pkyfh1xa/cNW4kWM9SxprGsW5fPkyRo8ejZs3bwIASpcujfnz56NJkyZ8rkxnGo0G//zzDyZPnqwbT//xxx+YMWMGcuTIIXU80hNsYJFee/bsGaZPn44dO3ZACAELCwsMGDAAbm5uXPGTQa5fv47Ro0fj6tWrAGKX7y5YsABNmzaVOBnpCw6OksaaZvxCQkLg7u6O5cuXQ6vVIkeOHJg9ezZn7xmAs2fPokePHnjz5g1sbGzg4eGBPn36cLCaSbGmJY71zPj5+PjAY+lybN60AWEhsat1smR1QPMOndGxe2/kK5iysyeCo1SwszRF9YIOkPP59YcFRsTg3tswWJmZwML0x84Yfv/GHxtXLsax3dugjIkGANT5uQHcJ09C/Z/rsb5lEqxnSWNNoy8JIbBr1y64ubnB19cXANCoUSPMnz8f5cqVkzackbp06RKGDRuG+/fvAwDq1q2LJUuW8PtN32ADiwzCkydPMG3aNOzevRsAYGVlhUGDBmHcuHHIlSuXxOmMk6+vLyZMmKCbjZ4rVy5Mnz4dffr04fJdioeDo6SxpmUeDx8+xNChQ3Hx4kUAQKVKlbBs2TJUr15d4mT0tZiYGEyaNAkLFy4EAFSrVg3//vsvihQpInEykhJrWuJYzzIPhUKBLdu2Y/my5Xh4/y6A2N+Pqj81RMcefVGzXgPI5T/WUAGAzzFqmMhkqFrAAdbmnNjxo16HKPD4QzgcrMxgIk/8acnP+yXWL/fAqYO7oVGrAQBNW7SE++TJqF6takbEJT3CepY01jRKSHR0NJYtW6Y7OkMmk6Fnz56YOXMm8uTJI3U8o+Dv749x48bpXm/Mnz8/FixYgA4dOnCSBSWIDSwyKA8fPsTUqVOxf/9+AIC1tTWGDh2KMWPGcGlpGgkLC8OcOXPg4eGBmJgYWFhYYNSoUXBzc4OdHbf8oG9xcJQ01rTMRQiBnTt3YsyYMXj79i0AoGfPnvjrr7+QO3duidMRADx69AhdunTBo0ePYGJigj///BMTJ07kBA1iTUsC61nmdO36Dfy9ZCkO7tsDZUwMAMCpgDPadOmFVp26wN4hW5K3Ea3WIEYlUCFfVuSwMU/vyEbjWUAEvIIikcv2+9+z508eYf3SRbhw4hCEEJDL5WjdrgP+nDwJ5cqWycC0pE9Yz5LGmkaJ+fTpE6ZPn44VK1ZArVbD2toaY8aMwdixY2Frayt1PIOkUCiwYMEC/PXXX4iKioKlpSXGjx+PcePGwdqaZ2TS97GBRQbp7t27mDp1Kg4fPgwAsLW1xbBhwzB69Ghky5b0AIq+pVarsWbNGri7uyMoKAgA0KVLF8yePRsFChSQOB3pMw6OksaaljlFRERg9uzZWLhwIZRKJezs7DB16lQMGTIEZmYpO4idUker1cLDwwMTJkyAUqlEkSJF8O+//6JatWpSRyM9wZqWONazzC0wMBArVq/F2jWr8db/NQDA3MISDVq2RaeefVGyTPnvXlejFQhRqFDGyQ757VN3nlZmcut1KAIjYxJs+j28cxPrli7C9XMnAQCmZmb4vUs3TJ7ohmJFi2Z0VNIzrGdJY02jH/Hy5Uu4ublh3759AABHR0dMnz6dW8UngxAC+/fvx+jRo3XbM7Zv3x4LFixAwYIFpQ1HBoENLDJot27dgru7O44fPw4AsLOzw4gRIzBy5EjY29tLG85ACCFw/PhxjBkzBp6engCA2rVrY+HChahalVtNUNI4OEoaa1rm5uXlhREjRuDo0aMAgFKlSmHJkiVo0KCBxMkyF39/f/Ts2RNnz54FAPzxxx9YuHAhZ1BSPKxpiWM9IyD2wPUDhw5jybLluHj2tO7jJctVQocefdCoRRuYW1h8c72Az0oUyWGNErmzZGRcg6XUaHHdNwQxGg2yWsZOfBFC4OblC1i/dBHu37gMALC0tEKP3n0wyW0c8ufPL2Vk0iOsZ0ljTaPkuHz5MkaPHo2bN28CAEqXLo358+ejSZMm3PYuEY8ePcKIESN0Y7AyZcpg8eLF+PnnnyVORoaEDSwyCtevX4e7uztOnoydfZY1a1aMGDEC9erVQ+HChZE3b95k7dFujNRqNV69egVPT094enri2bNnurdhYWEAgEKFCmHevHlo27YtCzD9MA6OksaaRgBw5MgRjBgxAt7e3gCAdu3aYeHChZx1lgF27NiBgQMHIjQ0FDlz5sS6devQsmVLqWORHmJNSxzrGX3t2fPn8Fi6HNu2bMbn8NgxRdZs2dGiY1d06NYbTvn+v6ESFKlEThsLVClgL1FawxIWrcJt/1CYyGSwMpPj4qkTWL9sIZ49iD2TzDaLHf4YMBDjx4ziudD0DdazpLGmUXIJIbBr1y64ubnpVhI1atQI7u7uKF++PGxsbKQNKLHw8HA8efJEd3n8+DHOnTsHjUaDbNmyYcaMGfjjjz+4bTslGxtYZFQuX74Md3d3XWc/joWFBVxcXFC4cGEUKlQIhQsX1l1cXFxgaWkpUeK0FxkZiefPn+saVHFNqpcvX0KpVCZ4naxZc+PPP8dh8ODBsEhgtiRRYjg4ShprGsWJjo7GokWLMGvWLERFRcHKygpubm4YO3YsrKy4pVJaCw0NxZAhQ7B161YAQIsWLfDPP//wLDL6Lta0xLGe0fdERkZi4+Z/sXzFcng+fgQAkMvlqFG/CTr26ItqdeohPEYNCxMTVHd2gLlJ5p5c+CM+hEfjrn8Irp8+gn9XeuDV86cAAIds2TF42DCMGjYUDg4OEqckfcV6ljTWNEqp6OhoLFu2DDNnztRNCAeA/Pnzo0SJEihRogSKFy+ue5s3b16jmiQeEREBT09PPH78OF7Dyt/f/5vPlcvl6NFjIObPn4bs2bNLkJaMARtYZJTOnTuPtWs3wNf3Jby9vREQEPDdz5XJZMibN2+Cza3ChQvr5ZlaQggEBQUluJrKz8/vu9crUKAASpQogZIlS6JkyZIoUaIENJqSmDo1Jy5eNJ5iShmLg6OksabR1/z9/TF27Fjs3LkTAODi4oK///4bv/76q1ENbqR0/vx5dO/eHf7+/rC2tsbff/+Nfv368ftLiWJNSxzrGSVFCIGLly9j8ZJlOHxwP9QqFQAgn0sRtOzcEw1a/oYGZQvA3opnQX5NCIGAgAD4+vri/n1f7DvqiYf3t+CDvw8AIJejE0aNHo0hAwdk+ln+lDTWs6SxplFqnT37CcOGzYZWexxeXl5Q/a/mfc3W1hbFixeP19QqUaIEihYtqteTGBUKBTw9PeOtqHry5Ilu9dnXLCwsULJkSbi6usLV1RWlS5eGhUVF/PZbXixcCPTsCXAoRinBBhYZpRIlgIgI4No1IH9+4PPnz/Dx8YG3t7fubdzFz88PGo3mu7dlb2//3eZW3rx50/XQRo1GAz8/v29WU3l6eiI4ODjB65iZmaFo0aK6BlXc2+LFiyd4zsetW8DevcCcOSwklDIcHCWNNY2+5/z58xg6dCgeP34MAGjSpAkWL16M4sWLS5zMMB08CJQsGYN//pmCBQsWQAiBqlWrYsuWLShWrJjU8cgAsKYljvWMkuP9+/dYtmo11q1di4/v3wEATM2tUahwFVSpmA958uRB3rx5kSdPnngXY90R4ssG1fcu0dHR31wvv7MLxo8bj769exrt94bSHutZ0ljTKDV8fICSJYG1a4Hu3f//2I5nz57pdkWKexsUFJTgbchkMhQsWPCbFVslSpSAo6Njhk28i4mJwfPnz79ZUeXt7Z3g74eZmRmKFy+O0qVL65pVrq6uKFy4cIKvkXbqBLRoAXTrlhFfDRkjNrDI6ERGAi4uwKdPsQUlqaNFVCoVXr9+/U1jK+4SGRn53euam5snujXhj86kiI6OxosXL75ZTfX8+fMEBzEAkCVLlngrqeLed3FxgZkZZzRSxuHgKGmsaZQYtVqNlStX4s8//0RoaCjMzMwwYsQITJkyBZ8/Z0H9+sDw4cDAgVIn1X/lyz9GWFhX+Po+gFwux+TJkzF58mTWRfphrGmJYz2jlFCr1dizbz/Guy3H61cXkvz87NmzJ9jc+vLfuXPnTteJhCmRVIPKz88PCoUi0dvInj07nJ2dkT27Mzw9ndGqTWX8vbA9zwuhZGM9SxprGqXWuXNAzZpAUnMLPn36pGtmfdnY8vb2/u6Eejs7u2+aWiVKlECRIkVSPJlBpVLhxYsX36yo8vLySjCHiYkJihUrFq9JVbp0aRQpUiRZ46uYmKS/R0SJYQOLjFLctqv58yf+eUkRQiAwMDDBxpaPjw8+fPiQ6PUT2prQ0dERPj4+8VZVvXr16rs/605OTgk2qpycnLgNEukFDo6SxppGPyIwMBATJ07EunXrIISAk5MT5s6dh9Gju2D/fhlq1ZI6of4JDw/H3bt3cfv2bdy5cwd79uyHWh2DwoULY8uWLahRo4bUEcnAsKYljvWMUkOjAQ4f9oWNjRfevXuLd+/e6S5v38b++/3791Cr1Unellwuh6Oj43cbXHHvZ8uWLc3GTHFjw8RWUCXVoMqWLRucnZ2/ubi4uKBgwYLIkiVLmmQlYj1LGmsaSU2pVOpeH/x61VZISEiC15HL5XBxcUmwuZUzZ07IZDKo1Wp4e3t/s6Lq+fPnCdZYuVyOwoUL6xpUcc2qYsWKceUv6QU2sIhSITIy8puVW3H/9vX1/aHBFxA7q6Fw4cLxtvyLe5s1a9Z0/iqIUoeDo6SxplFy3Lp1C0OGDMHNmzcBAHny1MCQIb+iVKmSKFWqFAoVKqR3s84zQnh4OO7du4c7d+7oGlYvXrz45vP69u2Lv//+O8Ftc4mSwpqWONYzSm9arRaBgYEJNre+/HdiZxx/ydzcPMnVXHny5EGWLFnStUHl7OyMggULws7OLi2+TURJYj1LGmsa6au4evR1U+vZs2d49eoVtFptgtezt7fXTZpXKpUJfk6hQoW+WVFVvHhxvT6Li4gNLKJ0olar4e/v/01j68OHDyhYsGC8VVWpWQJMJDUOjpLGmkbJpdVqsWnTJri5uX3zIp2FhQWKFSuGUqVKoVSpUihZMraxVbRoUZibm0uUOG1FRETg3r17ukbV7du38eLFi29+h8zNzVG2bFlUqlQJlStXRo0aNeDq6ipRajIGrGmJYz0jfaFSqfDhw4dvGlxfN7tCQ0N/6PayZMkCtVqdZIPKwcHhm5VTbFCRPmI9SxprGhmimJgYeHl5JbglYXh4uO7zChQo8M2KqpIlS8LGxkbC9EQpwwYWUQb65ZfY7Q1PngQcHaVOQ5Q2ODhKGmsapVRoaCj27t2Lx48f4+nTp/D09IR/3D65XzExMUGRIkW+aWwVL14c1tbWGZz8x0VEROD+/fvxVlY9e/bsm98XMzMzXbMqrmFVunRpo2nakX5gTUsc6xkZmsjISLx//z7BBlfc+69fv4VKFXvu8NcNqq9XUHF3DDIUrGdJY00jYyKEwMePH9G793s8f14Yly/bwclJ6lREaYMNLKIMEh0NZM8OREUBT54ApUpJnYgobXBwlDTWNEpL4eHhunMUnz59qmts+fj4JPgzJpPJ4OzsHK+pFfd+SmeKnzwJrFoFFCkCzJ4N/OjZ8lFRUbh//368lVXPnj37ZhsMU1NTlClTBpUrV47XrOJqZUpvrGmJYz0jY/THHwJr14Zi4EA5Vqxgg4qMA+tZ0ljTyNhotYCLC/D6NXDqFNCwodSJiNIGG1hEGejhQyAsDKhTR+okRGmHg6OksaZRRlAoFHj+/Pk3ja2XL19+90zGvHnzJtjYypEjR6L39fAhUK4c0KYNsG9fwp8TFRWFBw8exFtZ9fTp0wSbVaVLl9Y1qipVqoQyZcrA0tIyRd8HotRgTUsc6xkZo5AQ4Px54KefgGzZpE5DlDZYz5LGmkbG6OlTwNcXaN5c6iREaYcNLCIiShUOjpLGmkZSUiqV8Pb2jtfUevr0KZ49e4aYmJgEr5MzZ84EG1tOTk6QyWTQamO3wl22DOjYMbZ59vDhw3grq54+fQqNRhPvdk1MTODq6hpvZVXZsmXZrCK9wZqWONYzIiLDwHqWNNY0IiLDwAYWERGlCgdHSWNNI32k0Wjg6+v7TWPL09MTERERCV4na9asumbW/fuFULq0Dx48uIPHjx9/06ySy+VwdXWNt7KqXLlysLKyyogvjyhFWNMSx3pGRGQYWM+SxppGRGQY2MAiIqJU4eAoaaxpZEiEEHjz5s03ja2nT58iJCQkwevI5XKULFky3sqqcuXKwdraOoPTE6UOa1riWM+IiAwD61nSWNOIiAwDG1hERJQqHBwljTWNjIEQAgEBAbpmlo+PDwoWLKhrVtnY2EgdkSjVWNMSx3pGRGQYWM+SxppGRGQY2MAiIqJU4eAoaaxpRESGgTUtcaxnRESGgfUsaaxpRESGITU1TZ7maYiIiIiIiIiIiIiIiIhSgQ0sIiIiIiIiIiIiIiIi0itsYBEREREREREREREREZFeYQOLiIiIiIiIiIiIiIiI9AobWERERERERERERERERKRX2MAiIiIiIiIiIiIiIiIivcIGFhEREREREREREREREekVNrCIiIiIiIiIiIiIiIhIr7CBRURERERERERERERERHqFDSwiIiIiIiIiIiIiIiLSK2xgERERERERERERERERkV5hA4uIiIiIiIiIiIiIiIj0ChtYREREREREREREREREpFfYwCIiIiIiIiIiIiIiIiK9wgYWERERERERERERERER6RU2sIiIiIiIiIiIiIiIiEivsIFFREREREREREREREREeoUNLCIiIiIiIiIiIiIiItIrbGARERERERERERERERGRXmEDi4iIiIiIiIiIiIiIiPQKG1hERERERERERERERESkV9jAIiIiIiIiIiIiIiIiIr3CBhYRERERERERERERERHpFTawiIiIiIiIiIiIiIiISK+wgUVERERERERERERERER6hQ0sIiIiIiIiIiIiIiIi0itsYBEREREREREREREREZFeYQOLiIiIiIiIiIiIiIiI9AobWERERERERERERERERKRX2MAiIiIiIiIiIiIiIiIivcIGFhEREREREREREREREekVNrCIiIiIiIiIiIiIiIhIr7CBRURERERERERERERERHqFDSwiIiIiIiIiIiIiIiLSK2xgERERERERERERERERkV5hA4uIiIiIiIiIiIiIiIj0ChtYREREREREREREREREpFfYwCIiIiIiIiIiIiIiIiK9wgYWERERERERERERERER6RU2sIiIiIiIiIiIiIiIiEivsIFFREREREREREREREREeoUNLCIiIiIiIiIiIiIiItIrphlxJzKZLCPuhoiIKN2xphERkTFgPSMiImPBmkZEZLy4AouIiIiIiIiIiIiIiIj0ikwIIXUGIiIiIiIiIiIiIiIiIh2uwCIiIiIiIiIiIiIiIiK9wgYWERERERERERERERER6RU2sIiIiIiIiIiIiIiIiEivsIFFREREREREREREREREeoUNLCIiIiIiIiIiIiIiItIrbGARERERERERERERERGRXmEDi4iIiIiIiIiIiIiIiPQKG1hERERERERERERERESkV9jAIiIiIiIiIiIiIiIiIr3CBhYRERERERERERERERHpFTawiIiIiIiIiIiIiIiISK+wgUVERERERERERERERER6hQ0sIiIiIiIiIiIiIiIi0itsYBEREREREREREREREZFeYQOLiIiIiIiIiIiIiIiI9AobWERERERERERERERERKRX2MAiIiIiIiIiIiIiIiIivcIGFhEREREREREREREREekVNrCIiIiIiIiIiIiIiIhIr7CBRURERERERERERERERHqFDSwiIiIiIiIiIiIiIiLSK2xgERERERERERERERERkV75P7DgzMttxX7wAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 253, + "width": 856 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "f,axes = plt.subplots(1, 4, figsize=(15,4))\n", + "ax = axes[0]\n", + "import seaborn as sns\n", + "sns.set_context(\"paper\", font_scale=1.5)\n", + "# Add data, AU is ordered as such: \n", + "# AU1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43\n", + "\n", + "# Activate AU1: Inner brow raiser \n", + "au = np.array([intensity, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"AU1: Inner brow raiser\")\n", + "\n", + "ax = axes[1]\n", + "au = np.array([0, 0, 0, 0, 0, 0, 0, 0, intensity, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"AU12: Lip corner puller\")\n", + "\n", + "ax = axes[2]\n", + "au = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, intensity, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"AU15: Lip corner depressor\")\n", + "\n", + "ax = axes[3]\n", + "au = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, intensity])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"AU43: Eye closer\")" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:24:24.064595Z", + "start_time": "2021-03-27T00:24:23.762529Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Fear')" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 253, + "width": 856 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "f,axes = plt.subplots(1, 4, figsize=(15,4))\n", + "ax = axes[0]\n", + "import seaborn as sns\n", + "sns.set_context(\"paper\", font_scale=1.5)\n", + "# Add data, AU is ordered as such: \n", + "# AU1, 2, 4, 5, 6, \n", + "# 7, 9, 10, 12, 14, \n", + "# 15, 17, 18, 20, 23, \n", + "# 24, 25, 26, 28, 43\n", + "intensity = 2\n", + "# Activate AU1: Inner brow raiser \n", + "au = np.array([0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " intensity, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " intensity, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"Happiness\")\n", + "\n", + "ax = axes[1]\n", + "au = np.array([intensity, \n", + " 0, \n", + " intensity, \n", + " 0, 0, 0, 0, 0, 0, 0, intensity, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"Sadness\")\n", + "\n", + "ax = axes[2]\n", + "au = np.array([intensity, intensity, 0, intensity, 0, 0, 0, 0, 0, 0, \n", + " 0, 0, 0, 0, 0, 0, 0, intensity, 0, 0])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"Surprise\")\n", + "\n", + "ax = axes[3]\n", + "\n", + "# FEAR\n", + "au = np.array([intensity, intensity, intensity, intensity, 0, \n", + " intensity, 0, 0, 0, 0, \n", + " 0, 0, 0, intensity, 0, \n", + " 0, 0, intensity, 0, 0])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"Fear\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add a vectorfield with arrows from the changed face back to neutral and vice versa " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:00:25.672632Z", + "start_time": "2021-03-27T00:00:25.589946Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face, predict\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "model = load_h5('pyfeat_aus_to_landmarks.h5')\n", + "# Add data activate AU1, and AU12\n", + "au = np.array([2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ])\n", + "\n", + "# Get neutral landmarks\n", + "neutral = predict(np.zeros(len(au)))\n", + "\n", + "# Provide target landmarks and other vector specifications\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "fig, axes = plt.subplots(1,2)\n", + "# Plot face where vectorfield goes from neutral to target, with target as final face\n", + "plot_face(model = model, ax = axes[0], au = np.array(au), \n", + " vectorfield = vectors, color='k', linewidth=1, linestyle='-')\n", + "\n", + "# Plot face where vectorfield goes from neutral to target, with neutral as base face\n", + "plot_face(model = model, ax = axes[1], au = np.zeros(len(au)), \n", + " vectorfield = vectors, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add muscle heatmaps to the plot" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:27.181441Z", + "start_time": "2021-03-26T04:54:27.109016Z" + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "model = load_h5()\n", + "\n", + "au = np.array([2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "\n", + "# Add some muscles\n", + "muscles = {'orb_oris_l': 'yellow', 'orb_oris_u': \"blue\"}\n", + "muscles = {'all': 'heatmap'}\n", + "\n", + "plot_face(model=model, au = np.array(au), \n", + " muscles = muscles, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:35.665640Z", + "start_time": "2021-03-26T04:54:35.587594Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "au = [0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, \n", + " 0.450328, 1.02961, 0.871225, 0, 1.1977, 0.457218, 0, 0, 0, 0]\n", + "au = np.array([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "\n", + "# Add some muscles\n", + "muscles = {'all': 'heatmap'}\n", + "\n", + "# Plot face\n", + "plot_face(model=None, au = np.array(au), muscles = muscles, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make sure muscle array contains 'facet' for a facet heatmap" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:44.068508Z", + "start_time": "2021-03-26T04:54:43.994084Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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FmPjZyWMxHGMxo8esiCEo5NNnZnL6zHSKqpzISudCbr6T2529N0f7itvrx2zSM2FEassF6FKgr3OEKEK0SU9hvZsHP9rbpWVR6/Dy0Mc5qKqKRd/3y6B5XSyKItPGZaIo6lGzaBRFxeXxMX18BiZj5xF7DU4fAgLXnz6eCHP4VU7pjmNaxBAU8tlzszl5ehoFlU7kTuJ+BUFg7PDklnC/UCMrCl5fgJkTstA3rR+bo7T6s3nfLOScKid//TQHpYMbl9sn88BH+2j0BYg09f1skhBcvzdjsxiZOibtqFk0jS4vw9PjSYzrvPODyxvA7vZz/enjSYwO38IFXXFMBHt0hyAInHfCMPwBhS92lpGVYOtwTWQxGYiLtuJwejAfEeTh8zgp2fc9NXlbUWU/os6EqDcg6k1IehOS3ohOb0IyNP82oTOYMEfEEhnXvtaUqqrYHV7Gj0gmJvJwDezOorR6S7OQt5fa22U+KYrCY5/tp6TBQ3Q/481FwK8E1/FSkyWRnhxDRU0jpVUNRNk6LpOkKAqy34uqKiiyjKLIqIrS9Dv4o8hy8HFFBlUlJmVYqwIEzTfbrLS4TsfnDyhU1Lu59tRxjEg5dlu8HHURl5eX4/V6ycrKOqrnFUWBixYMJ6CofL27nKxEW9AZ04as1Fi27ilCIijcqgPf0liyh4TsCUyduQiLLQKvx4XP46G6rICKgt34PC6M1ihMEXG4ZT8+r4eAz4OjppTorKmMXXIJZlt0yzmcHj9x0VZGZia0OndXUVq95cjMJ9uGfK5YMByA/67PZ2dZIzG92ErqjOaPz6uARWr+m8Ck0WnU1Dvw+gIYjwiDVVSVwl3ryV//MgGvA0EQEUSp6beIqioIgoikNyGKUvAxUURVZDzOBhLGzCdz6lKiEtLxBWSsFiOR1s5uFCpF1Q4unD+c6SOGftHE/nDURfzyyy9TWFjIo48+erRPjSSKXLJoBAFZYdP+SrISbK2cIY7GBnI2r2XvFx/gqsghIWsCM088iRlz78YWcTjIUFVV3n3+YXK+/5yAP2g66u016KqLuPqWf5CUFsxFdTocvPfK02x87mbSZ53DiNkrkEQJWZaZMDKlVV6wqqq4FOggVLnv77cp8+mzvVXYjDpsRh2r91cRZdT1W8DNCIBbUbEcsSdmNOiYMDKVLXsKW0RcW5bH/rXPQ8DNuT+7lQlTDteh/uazN/j8zaeDn4cq4He5WXDqj1hy9hUt309RYR7ffP4+W1+7G1N0MtEj57NwxVkdOrPUpr3gkyansnxq2sC80W7YtGkTHo+HxYsXH5XzHclRF7HBYMDrPXrOo7ZIoshlS0YRkFW+P1BJnFll64Y17P9+DWUHf2DCtLmcdPLpxA+7jez0xA6Pse+Hb9ix6XP8Pk/L3/w+D36fh1f+eTs33rcqGNhhs/Gjn95EwdIzeee/j/HNni8Yvuhy4jLGtTKj4XCUVm8zg7pClsGvqAiiwN9ffA+vs56Rc09GVoLRYAMhZIlgHHXb1MTEuAhEQcDlsJOz/jXqDn7L3NN+wtLTzmtV2mfHps/5/I2n8PtaXxPrP34Fk9XGvJMvACAjcxgXXXUjvp9cw5Zv1rFxzbu8cc9rjJy1nBOWn8ew0YercBTXuJiUHcuF84cfte2uNWvW4HQ6jw8RG41GfL6jF1jRETpJ5Mqlo3nwph+Ru/cHAP7y0D/42eXvk5QQy7ofCrj12a+o89cSadZjMeowGyRMegmjXuLbNW/j93o6PHZDTSVVpQUkpmW3/C1r2EhuuP0fbPpqNWtef5za7InMGPUn4hMPRxe1RGn18ppTFPDLKj5VISCrqKiICKgE16yRJh2ZMWZkuZzymkJGxp1NeaOXBo//cPykIKCXBAyiiE4UeiVuUQCfEoylPnKFLYlQd3A9h75+lWGT53HZ/auIim6fqPH5m8+2EzAEb4pfvreKE5af30qIBr2BcdMXcOKSkzljRhIP/+3fvPrXmzBFxDFx4UpGzVhGWlIsVy8fE7IQ2o7w+XwYB6mbx3E3Ezej14l88cl7vP3OO7zx+ms8/tf78NSX89Of/pTCCjuNngDUuymrcwWdTS3Xu0B1bX2nxxVEEZ+3ffijKAqcsGg58cMm07B/DU/84WKmn3IpJ593GXq9AVcXUVqKAgFFxa8q+AMqSptZz2oQSbWayIgxkxFjJjnaTFqMmaRIY4vJ/tdDsRTpndx1TrC2ssvrJ6/KSWGNi7xqJ/k1LqocPpz+4LpcVVVEUcAoiRgkke704JVV9E1rgT3bN/PJqocwG40sv/IO5s3pMG4fgIaais6P6XJSVt2AKhpwegM4PH6cngBV9Q5+e0GwT/SqJ/+K52/3c/U11/PSfx8kf+tavt/41YDnlneHz+cjMnJwnGeDMhMPBREDZGZm8Ksbb+BXN97Anj17eOqpp5g5cybWhGySJy4F8yQiLa0dJ6qqkjB6Lo2VBSiB9haFokKJPxpHcQM2sw6TQYdZL2EySLg8PrJTE7jrz//mu23Xc/UvruOx317IsktvJnbyPCQV3IqCX1YIKCoioArBogMmvUisWU9KpInMODMp0RZSY8ykRZt7lLEly3IrM9Zi1DMhPZoJ6dGHx64o1Dp9TeJ2klvloqjORZ070BLKraoqOknAKEropeCsLQjgVsFTWcEHqx6lLGcrt97xF351zZXc/Pjn+ANyq0qdqgq+gILXL2ONiqOxrrLDMQs6I9sKGxEIZkvpJAFJFEAQ2VdQRW5uLv/5z394/vnnSUtL46mnnuKiiy4iMuLoNw/wer0YDIOTznhcmtMdMX78eB577DEuu+Y3/OaOx8jZ/CHVFU+TMfkk0qcsxRwV9CQLgkDWtOWUbl+N216NKh/ORBJ1BkaddAWNHpU6lwNZUREFmsrOgM/r4+enT6GoqIj6qlKu/vE5/O0f/+KNh29g+iV/YsKSs4k26UiINZMVZyEt2kxKtIn0WCuR/QxSaCvijhBFkfgIE/ERJmYNP7x1E5AVimtdFNa4yK9xcqjS2cokVxSZV+66lIbSXOYtOYWv9u8hOTHoEZ4xJpUPNh3EbDLi8PhxeQO4vDIqwVTLhBln4/zy+XY3RFFnYPjcs4lqEwvtcrsJlGzlP5//lSf+UMJPfvITPvnkEyZOHNzuDT6f7/gR8VAxpztj26EaJs5ZxpIVZ/HOmm+p2fslG5//PVGpo7ClTUDSGxF1etJnnU3Noa3UF+9GDfixxKaSPu1kYtLGoXjs6PUGcNXjrCnBWVuCo6aYhsoirnu+nMS4GMaNG8e4ceO4+Te/RrDEo4vJ5mdnzQxZJcueiLgzdJJIdoKN7AQbR/ZHcHn95Fc7ya9y8nJpLlnZwygvOEB2ZjqZmZmMGTMGhxDJ/mqJuNQsbLEpmCPjsJl0LcsB2/RliJ56cje+jdC0D6zKATKmLmfY3LOC/1cVGisLKN6xhrI9G0jKHM3YOSv47XVXcNq8cf36XAaK42pNPFRnYgiGQW7eW0pSrBVJFBg9egwl8RmMWXwJRbs3UJK3H0Hxo8h+VNmHKvvRx2SgBvz4An4ObXqPgxveRJWDzzFaIomIT8cal0Zc5iQypp1CI1H87bdnsXzq4QZe2w+Ws+NgeUhL0fZHxJ1hMeoZnxbNmORI/vzUGv74kwVYTHq8Xi+5ubns3buXB599H399LsVF3+OqLUMOeLHGpCBZ45EVJfhZBnwYYtKRvQ5UOYCgFynevZ6iHV+gygFU2Y8hIpaMyUuYfNE9LD9hEqqi8M3uMk49YeyQKGx3XJnTQ3km3rCzCL+stHg1U2ItFFU7kUxGsqYsIZA4A0kUepQB4/HJxEeZiGuzPpPrHaz6fB8Lxqe2lBHSSWLIS++GQsTNuH2BYM3mps/FaDQybtw4RGsCE/frkEY4iLToAQG/x4G9upSCggL0ooQo6RF0+uBvSY+oMyBKekRJ1/J3nyyRmRSBXpKClU2NOkClsMJOcVUjGYmDH411XJnTQ3UmLqq08/oXe0iJs7b8LcYaLNUjKwqSKBJlNVDn8GIUuxeDJAm4vYF2haisZiO5RdVs2FfB0qZqlTpJxBnifGZZlkNm7rm9fmRFaRcBt2l3CUZD0KkXkIMOMb3JhjlhOFYhEVMPHHKKoiIJKmaDDofHz6iUyKZIMQFREPh6ewE/Xj4pJO+rNwymOX3UEyCG4kzs9QV45v2tmA06jEdUpJREgbRYCy5vMCsnwqynp3H9kiDg8cm0LdFhMujw+3y8sSGXxqbZVyeJOD2hF3GoZmKXx99qJobgZ/rd3hISoi1EmPUEjkjE8PnlHlcu8csKkZZgjrKqQkLU4SSG1Hgbn2/OY09+1YC9l74ymDPxURfxUJyJ312/n5LqRuKjLe0eS4k1t2TkmAwSOknoNBPqSEQx+LxAm3hoQQgWnc8vrubLXcF6UEER+0Oa+RNKEXuazekjZuK9BdX4mraWIs0G/IHD783tC/Q4oEQFIi0GArKCQS+18tJLkkh8lJln3t92VFNIO2Iw18TH/Uy8J7+KT749REZCxwVYo60G9DqxKRdZINpm7LbkTwtCcD+0LREWI9W1Dbz2VQ51Dm9Q8AElOEOFCEVRQj4TtyRE+AK8tW4vEU0dGyLMulY3KI9P7lGRdkVRkUQBk17C7ZNJi7W0K5YQaTXi8vp4cfXOo17Q4UiOK3N6KM3EdqeXZ97fRnyUudNi5qIgkB5nbTGpbU3pez26YFQ6FKYoitjMBvYeLOHdbwuC625Vxd1J9cuBQJblkHm/XV4/inq49vY7X++jrMZBbGTQ9DUbdC3iU1QVn6x0W8kTgqZ0lMXQZEqrJEV3HMSRFh/B93tL2bCzaEDeT184rszpoTQTe3wBBAHqGj1U17vaVZ1sJiXG3FJLyqgXMerEHprUdCpMo0GPToRVn+ygttGLoih4QiziUM3EDpeP5kXu7rxKPvsul/QjLBuLUWpZAvsDCgL0aFtIVYN+CFlR0Uki0Z0UeBcEgZQ4Gy9+tpPy2v6V7u0rx5U5PZRm4sQYKw9eu4wbzp9NVko0RZV2iirt7cQUZTFg1EtNdboEoq1GAj0QsSSKXda6irKZqa5t4LlP9yIrwULooSKUIq53eJAEkQaHh2c+2EZcdGvLxqCTEAUhOAv3sHRPULgCJoOE2xcgOabr8reSKNDg9PL++px+v5++cNxtMQ2VmRiCTqXJI5KYPCKJqnon3+4pYc2WPCpqnVhNemIjgxdPRpyV3IpGIswiVrMe6t3ddgYUBfD6FRRF7fACFASB+Cgrn3ybw/i0yOCWVIgIpYjtTi+CCC+u3onH6yeuTSKAIARnVK9fbvLYd09AVoiNMAECiqKS0kFpHVVVqW300OjyYTLoOHvBGBZMObrFJpo5riK2DAYDPp+vX60xQ0VCtJUz5o3mlNkj2JNfxdot+ezJr0IUgnvGzetgvSRiNkr4Agr6LgpEC4KAIIAvIGPQSfgDMv6ATEBu6q7YlHpoMhpocHrCVsQNDg+VtU4a3SWMSO24L1SkWU+px9/k1Or6e29ucxph1gcztkSBGNthgbi9fqobgm1mx2TFs2RaFhOHJbaqInK0Oa4itkRRRJIkAoEAev3QrDyo10lMGZnMlJHJVNY52bS7mLVb8/D5/dQ2BJAkAZ2g0uDxIrcI43AycDC4P1jT1S+r1Da4iLAYiLAaSYqLICrSjM1swmI2IEkidU4fDeVluH3haU47XF78skqy1URRpR1RFAhuC6uYjTrMRj02k46ArOLxB7rN81VUFYMkYtSLuLwyiZEmBEGlos6FxxsgwmJk5fwxzBqbSmKMtctjHS2OK3MaDs/GQ1XER5IYY2Xl/DGsmDOCv3+wmwOldmIjDHi8Ab7eVdbitGlpiq0E832VJmdMQBHITolm2qiEDmt6FdU4OWVKKu9WVjQ5iEJDKLeY7C4fI1Kj+b/rTsbl8VNjd1Nrd1NZ56SgooGSKjsudyONDjcerx9DKxE3NRIXaLrxgV+WibIYaXB4cHkCJEXqKa1yMHV0MoumZjE6I+6oJvz3hOPKnIbD62KrdWjcRXvC3hI7BytdZCdHBde3EZCZ7KbW4e0yAd3rl3F4Ax0KOCAriAgsHJ/MB1+INDg7rhYyEIRqi0lVVZxuH9ERppZeU1azgcyk1l0ay2pd3PTsRg6W1GHUifh9fhBAFEREUWhaegQ/I6c3wNyxydjMeqrsHuaMTuSSRSOJtA6OSHrCcTsThwt1Di//W3eIxEhTKwdVRoKNioaum5jpdSJ2l79D51Zlg4cF45OIagooCWXUUahmYq9fxhfoPngjPtKETwaD0UiUtfOL3euXsdlEslKCfZxjoizsKLKzqNbF+CEs4uNqiwmGnoe6KxRF5cWvcwkEFKxtCq0nRJmDWyddbDeJTf1OnG2cVrKiIisqiycG62zpJZHGEJrToZqJXR4//oCC1E0HOL1ORFG6D/Lw+GWykiION20XReIijPz3y0PYQ/j59AdVVY+vYA8Ir5n4qz0V7C6sJzmm/RaHXieSGmvB1Y1XWUVo16OossHN7FHxJESamo4l0egKv5nY7fU3ZRp1v9MgK13vSKhqsFhgUpvtpAizHo9P5uX1eV3eMAeLQCCATqcLaT54V2gzcReU1Lp4c1M+KTHmTi++jAQbcjchmAIqDc7DNy1FVfEH1JZURAgGRITSsRWqmdjt9QMqYjeOJm9T5lJX4apev0K0zYC1g84UKTFmfsirZVNOx/W4BpPBNKVhkEQcFxdHdXX1YJy6x3j9Ms+tPYDZIHVZiC42whSMfe4iKUKvE6lpPOy0qrZ7mZgZTVrs4awpvT60OcWh2mJyewOoKt3u/VY2uDEbdXRVk9cbkMnqJBFFEASSo828uiGf8vq+N1MPBZWVlSQkJHT/xBAxKCLOysoiPz9/ME7dYz7cUkxZnbtdZY62SKJARrwNVxeRSAadRL3D17IN5fYFOGVaWrvnNKf0hYJQmdMurx8FtVvHVkW9G4NO6FTDzTN0YgfLlmZMBgmDJPL82gP4O8gOGywKCwvJzBy8puWDIuLs7OwhLeJ9xfWs3l5KWmzPuuilxVm6XKtJooCsKHh8MnVOHyOSIxiWaGv1HL1Owi8rTebpwBMyx5bbDz2YiQurHF1uxXn8MnERJszdRF3FR5korHbyybaSPo03FBQVFZGRkdH9E0OEJuI22N1+nvviIHERxh7lvAJEW40Y9VK3ecaNbh+Nbj+nTU9vt8Y26CUCATlkoZehmonrHR50ktitiHPLG4m2GJDEjr35Pr9CVoKtg1e2Jy3WwsfbijlYbu/TmAea43YmLigoGIxTd4mqqry2Pg+3N9CrZtSiKJCVGNGlAFUVKhrcpMRYGJMa1e5xvS6YJRVuM7Hd5UUSxS6PrSgqhVVObGY9EWZ9u5tdMI4eEnrYP1gniURbDTy35gAOT2gLDPaE41bEQ3Em3pRTxZbcGlJj25fp6Y6UWEtL+GVH6HUipTVuzpiZ3mFGk14n4pdDl1McKsdWg8MT7MzQxT5xndOLX5bRSSJRZn27kkVun0xCVM86WTQTZTFgd/t545v8Qa3oAcepiNPT0yktLSUQCF3WTm+pqHfz6vo8kqM7307qikiLHqtR36XDJRCQmZTZcZaPQSchh3AmVhQlNDOx04skiV0uPaoaPC3+rEiLvl1BBX9AIaOHpvSRpMZa+PZANVsO1fT6tQNJUVHR8Sdig8FAYmIiJSVDwznhDyj898uDLUnofUEQBLITbZ0WAfAF5GDoYSciN+olFJWQrolDIeJGlze4Ju5iJi6tddKsW7NR16pOltIUAJIY1TNT+khEQSAp2sSLX+dSbQ9d3HlXqKpKQUHB8efYgqFlUn+2vYT8Sgfxkf1rxJXcZIa3Ne8CsoJRryMh0kRFJ3uchqZSua4QrfGCXQ4H/ut2uHzdzsS55Yc902aDrlW12uaqHXpd38ZmNugQEVi17lDItue6oqGhAUEQiIpq7+c4Whz3Ij5YZuejLcWkxlr6XaTAatITbTPg9be+mFxemZHJwSCGijpXh6816qVgiRlHaGYURVEGvAhDQFbwBWREQehSxHkV9pa4c3PTurdZyAFZJT2+f9lsCVFGDpbZWbuzrF/H6QvN6+HBLHBxXIvY6Q3w/BcHibIY0A9QfmpWQgTeI+pIyU1lV9PirBj1IrkVjR2+zqAP1qGyhyh+OhSVVFwef4tp3Fn9K49PpqbR29LtQRQFrEYdAflw2aKEPpjSRyIIAqkxFt79rpCCqqNbKG+wnVpwnIu40e3H7ZNpcPmod/oGxMuZ1BRx1HwspzfAsCQbBp2IzaznYFnHe5tGvQ5JEkKWBBEKEbu9/pZysp3tE1c2uBGPyBWGYEJDQFZxeQOkxln7neCvqioev4zbLx/12XiwnVowiCLOysoa9L3i5Ggz9/x4GlcsGUW01UBRjYvSWle/QvpMBh0JkWbcPjlYHwrIjA96Xi1GHeV17g6PL0oC+hAmQYRMxAhNsdMdX0rlda6Wcr/NRFn0BGQFWVVJj+u7Ke2XFcpqXRTVuNBLIlcuGcl5J2T3+Xh9obCwcFCdWjBIRQFgaMzEEHSMzBoVz8yRcZTWuvhmfxXf7K/E55eJshha+gD1hswEK1V2N7JHJTPB1uLxFoVg7HCV3U1qbOuLVyeJ6HShyykOlTmtEqwp1pl3urDa2W6mtZr0BBQVvRTMFe4NqqrS4PLT6PajkwTmjE7ghDGJZCVYB2VdWlhYyIoVK476eY9k0ESckZFBSUlJSy7mYCMIwXXrBfOsnDEznV0F9azZWUZRjQOdKJIQaeqxB7U58khWVLLbxEirikpFvadDEeslCUeIGquFwjvt9gWakjoEdJ0cO7fc3q6YgsUg4fPLDE+K6LTzRlu8fpnqRi+KopKZYOW8uVlMyIzuNtY61AyFNfGgfQJGo5GEhARKS0sH/UNoy5Gzc0mti41Ns7MsB8MDmyOzBEFAVdRWkVqiGGy5qdeJmPVSUy/dw4iiQGGVg2nD41r9XSeJ6CQRjyfQaZ3q/hAK77TL7UNRVFSx4yym5nDLxKjWW3dmgy7o8JNESmqcNH+ozZ9tK9Rg7VCTXmTZ5BRmjYzvc0BOKBgKa+JBvY01m9SD/SF0htDUh6l5dq5p9BKQVfyygiyrBBQFv6wQkIOldnwBGZ9fwRuQGZ5o5Yfc2nbHtJo6dm7pJDFY6gcVjy+ApYPE+P4QCnO60eVDJzWtiTswp2sd3pYWLEeiqCrThsVxzanjMBkk9E3BIjpRRCcJ6MRg50idKLQkV5gN0pCrcCnLMmVlZaSlpXX/5BAyqCJuziteuHDhYA6jR5gNOtLjev5xeSen8rvnvsMfUFqZ4TaTjoJKR7vZVieJwbKtatBhFA4iDmYwSfjljgvlVda7O0wfrrZ7OG1mBnNGD14i/UBQVlZGXFzcoFb1gEH0TsPQcW6FAqNeYvbohHbhgDpJxC/L1LWpbNk8ywhC503Y+kMoRGx3etHrRFSVDmfJsjpXh9t2sqoyY0Rcu7+HG0NhPQxDQMSDvc0USmaPTugwx1hAoLJN+KVOEltmrVA0VguFiBtaRNyxd/pQeWNTSZ7DuH0BIs0GshM7LsMTTgyF9TAMAREfqzMxwIjkCKwmHZ42PYpVVaWkpnX4ZbOXVg1REkQovNMOtxedJHVaYyuvorGdZ7rG7uXEcUkD7rgbDLSZmGNfxJIocuK4JGramNQWk66dc0sShZY911CIOBTe6UanD71ORBDaB3u4fQHqHIfDLVvGoartPPPhylAI9IBBFnFGRgbFxcXIcs/aXYYjM0YktCtpazXpyWsTQ62TxKY9V3B7B36vePny5URHRw/Y8RRFxeXxtywDhLbdLeo97cItXd4AsTYjGf1MeBgqaDMxYDKZiIuLo7S0dDCHEVIy4q3EWo2tCswbdSINLl+r0jI6SYQmB1FDCNq5PPLII2RlDVzvXo8vEPSmCwIgtOs1VV7fPtyyttHDggnJQ2aPtz/4AwoHDuWRnp4+2EMZXBEDJKdmsHbT9sEeRsgQRYGFE5NbmdTNzcOOzC3WSSJq0+9Q9mQaKFxHOt+E9llMhZWOVplhqqqiqDBlWOzRGmLIKK9zcf+r33PgwAFGjBw92MMZfBEnZY3msVUf8t81OTS6B7/oWSiYOiyuXf0tRVVb5RY3m6XhImK3199q9m07Ex8qb+3UcnoCJEWbSYnpff2yoYKqqmzcV8FfXt3Gd1u2kJCaRWzM4BUDaGbQRbxo4XwaiveyaX8ld760hW2Hqge98NlAkxRtJjXW2uomZdJLHCo/vC5unon1OjGk7VwGCrcn2L4FgnvbR5rIiqJSXONsJeJap5cF48PXlHZ6/Pxn9X6eXb2fGJuB2sK9TJk+a7CHBQwBEa9YuoiKvN2kxpox6EX+9dEenvp0H3WOoT8b9RRBEFg0MZn6I/ox2Uw6co8QsSQ277eKNIawnctA4fYFWuKcVbX1TFzj8CIrSovHurlRWria0ofK7Nz9ylY2H6wiK9GG2aCjInc380+cN9hDA4aAiCePH42ASmV5CTaTnuxEG9vzarjjpS1s2lcxJLvg9YWJWcEql81Whtmoo7zeja+pCojYEi8s4ggDEbs8ftSm70YAjtxhahvI0uj2kx5v7XcFj6NNQFb4aHMhD7z5A4qqkh5va7lZVeTtYsXSRYM8wiCDLmJRFBkzaToHdm8DDqcERpj1PLN6P//4YDdVDYNTyXAgiYswMTIlsmU2FgUBAVq9N50kIooCLvfAVBkJJW2bvx05E5fWujhy+PVOHwvGJx+toQ0IVQ0eHn1nJ29vyict1kr0EQ3OK8pLUOQA0yaNHcQRHmbQRQwwb948inJ2tPqbxagjO9HGgbIG7nhpM1/uLO2y82A4sGB8cqt1sapCed3hWUuvC0Y/VRTlYHd0XFBvqFBQXIoSaFrytPFO55bbW1Iwm29GzZbIUEdVVbYcrOLuV7ZQVO0kK8HWLo/84O5tjJ4wbdD6EbdlSIxi2eIFVObtavd3QRBIibEQF2nihS8P8vDbOyirHdoXd1eMz4xBEISW4umSJFBQdXhdrJdEZEVh6+evMGJ4Nr/61a/Yvn3obL8pisJnn33GBRdcwI0/OY3Kwn3BB9qsiYPhlsEsrHqnjxHJkd12lxwKuLwBVn1xgMc/3kOExUBSJ32piw7sZO4JcwdhhB0zNES8aB71FYW4XB1XKjTpJbITbZTWurjrlS18srVoSLW27CkRZj2TsmJanHbWNuGXhqaZeMWVf2bDhm+Ijo7mzDPPZPr06fzzn/+ktrZ9fvLRoKSkhHvuuYcRI0bwxz/+kZNOOokX3vuK5BFTgaB3unkmdnkD1Dt9GPXBS8vu9jN/fNKgjLs35Fc0cs9r29i4t4LshIh2xRyOpDJvN0sWzj+Ko+uaISFiq8VM2rAxHNyzo9PnCIJAYrSZpGgzb36Tx32vb6OgsuPyr0OZeWMTW6K3rEYdhVXOFuedXh+ciQVBZPSokdx1113k5eXx4IMP8s033zB8+HAuuugiPv3005CHqgYCAd577z1WrlzJpEmTKC4u5o033mDr1q1ce+21ZKS2FmaLw6fejSgGg1mUpkZp4ztpXTMUkGWF1duKuf+NH/D6ZTISbF0mZ3g9HmpLczl58YlHcZRdMyREDDBj1hzy9v3Q7fMMOonsxAjqnT7ufX0b72zKx+sPn9jrsekx6CSRgKygazKfa5pmZoNOwh9QsBh1LWacJEksX76cl156iby8PBYvXsxtt91GdnY2t9xyC+vWrcPjGRjHn9/vZ9OmTdx2221kZWVx//33c/bZZ1NYWMi///1vZsyY0fLcKKuxdbCHeFjEzcuFOoePsWnRRFkGN2m+M2obvfz9/V28uj6X5Bgzsbbui/Yd2r+TpPRhxERHHoUR9owhI+JFC+dTkdt+XdwZ8ZEm0mKtfLSliLtf2UpOSX3oBjeAmAwSM0fGtyoW0Lwlo9cFC8h1VtUjJiaGa6+9lu+//56PPvoIRVH43e9+R3x8PIsXL+aOO+5g7dq1uFw98xt4PB6++uor7rnnHpYvX05sbCzXXHMNbrebTz/9lI0bN3LVVVdhs7VvdhZhNaK27BQfjp0uqHJgaHIEOTx+Thw39ExpVVXZkVfDXS9vIbeikexEGwZdz3pw5e3dztQZs0M8wt4x+GUmmzh16SJ+/+vretWCUyeJZCXYqHd6+b+3drB0SipnzcnusiP9UGDumEQ27a8K/keF4honEzJjMOgl/IHORXwkkyZN4oEHHgCgsbGRDRs2sG7dOm677TZ27NjB1KlTWbRoEYsWLWLevHnYbDacTicbN25k3bp1fPXVV2zZsoXx48ezcOFCbrzxRl599VViY3sWkBFlNbbaBmueiQ+V2bGZgp0PJUFgXEZ07z6cEOPxBXh7UwGfby8mMcqMrZdlkMrzdnHWlZeEaHR9Y8hc7aOGZ2K2RlBckEvW8FG9em201UiEWc+XO8vYeqiGy08axYQmT/BQZERyJCaDiLdp1j1UZodpQXPaF5CxmHpnfkZERLBixYqW+sdHivWee+5hy5atRMclUl9TybRpQXHfcsstzJs3j4iIvlXYMBl0GPS6liZmQa+7QkmNk+QYM3UOLxOzYnotklBSXO3g6c/2UV7nJishotOuFZ2hKAoVubs55aShVRNuyIgYYPyUmRzas63XIoZg2GJmgg27y8dj7+1i3tgkzj9xGJFDcD2m14nMG5vEV7vKiIs0kVfRiKqq6HXBmdhm7t+YrVYry5YtY+GiJcz+7hBvfbGDhuoSohPSOWfxJE6ZPQJDLxp6d4QgCMRHWVqaoouiQG2jF6WpG4TLG+CEsYn9OsdAoSgq63aV8er6XMwGicw+9EIGqCwvAVQmjx/8zKUjGVIinn/iPN757Cvgwj4fI9JiwGbWs/lAFTsKavjJ4lFMHxE/5GblmSPjWbuzFINOpLI+2NFArxPxBRSs5v7PXodKannuox+oqHWSmRKHPiMRf0DmvfU5fLenhCtPn8rw1P55jROirRwsDjb4Fo9IrQzICpIoMDY9ur9vo980OH2s+uIA2/NqSI21YOzHzevg7h8YM3HoBHk0M6RGs/ykhVT2wrnVGaIgkBZvxWzQ8cTHe3n8oz3UNg6thIrsxAiizAY8frklt9ioDzq2+jMTuzx+Xlmzi/v+tx63109WchT6JqeNXieRlRyJ0+Pjvv99zWtrd+PuR1G+5Dhby86AKAqU1LpAValt9DJteNygd2fYU1THXa9sYV9xHdmJtn4JGKDowA5OOOGEARrdwDGkRLxw7kycDdXY6+sG5HjNCRV7Cuu5/aXNbNhTPmQSKkRRYMGEJGrsXhRVpbzOjUGv67M5raoqu/MquePZL1m7JY/MxEhiIjpOOIiNNJOeEMnnm/O44z/r2Jtf1af3kBhtwd+cwCEIHCq3Y2kqDHjC2MHzSvv8Mm9syOWv7+zEoBNJjR2YPk2Vebs5adGCARjhwDKkRGww6Bk2ZhI5TckQA4EgCKTGWYi2GnhuTQ6PvbezVUWNwWTa8HgUVcVkkDhUbseol1AUFbOxd+a03enlPx/+wF9f3YQgQGZSVLc9jnSSSGZSJKqi8PArG/nvx9t73VY1ymZq2VoSBIH8CgdGfbCjw6jUwdlHLat18eCb2/nsh2IyE6xE9NO/0IzH46auPJ/lQyjIo5khJWKA2XPmUrB/4OOFzYZgQkVeRSN3vryFNT+UtHhWB4vUWAtJTc3XcisaMeglJEnEqO+ZGaqqKt/vLeG2Z75g875SspKiiLT2rstglM1EVlIUG3cVcfuzX7Itp6zHGVRRNlOwH1VT6xm7y0ej28/s0Qk93ncdKFRVZcOecu5+dSs1Dk+T93ngLu9De3eSnDmCyIihV+RvyIl4yaKFVOTtDsmxBUEgOcZCQqSJl78+xENv7Qg29BokBEFg4YRk3D452PKkqfeQ0dC9AKobXPzzre/59ztbsBp1pCVE9LmWsygKpCdGYtRL/PPN73nqva3UO7qPAots3itWobrRiyAI+AMKs0Yd3fYsjW4/T326j+fW5BAfYSQhcuDzlnP3/cC0IVLJoy1DTsSnLVtIVcFeAoHQ1dsyNiVUlNe7uPuVrXy0uRDfIIVuTh4WFyz5Kgg0uv0YdCKmLhxCsqyw7ocCbn/mC/blV5GdEoV1gExGm9lAdkoU2w+W8+env+Db3cVd+hAiLMGoLQWVWrsHj0/GZJAYkXz0TOmckgbufmUr23NryE60dfnZ9YeK3F0sWDD0TGkYgiJOTownJiGV/AP7QnoeQRBIjAoWbnt7Uz73vr6tXS3oo0FCpInsxAjqHF7qnX70OqnTPdzS6kYefmUjqz7ZTmykmZT4iAHfOhMEgdT4CCIsBp56fyt/f/M7quo7tlZ0kkikxYTPJ1NY7aLR7eOEMYk97uPcH/wBhXc35fPQW9sRBEiLD12TcUVRqMjbzSlLh1aQRzNDTsQAk6fP4tDegXNudYVeJ5KdGEGj2899r2/jjQ25LQEMR4sFE5Jw+wJUNngw6KV2s4k/IPPxxgPc9dw6SqrsZCdHhWzGacZi0pOdHMXBohpuf+ZLvtyW32FRhoSm6pXNMdMzRobelK6od/PIOzv4YHMh6fHWkCRYBGSFqgY3hVUOtm/fgdlsZuKYkQN+noFgSIp4wfwTKdr/AxX17pZu9KEmLsJEepyVz34o4c6Xt7KveGC2uXrCxKxY4iJMlNa40OukVo6t/LJ67l31NW+u20tSjJXEmNDNOG0RBIHkOBtxUWb+9+kOHnp5I6XVra2VhJigo6es1kVKrJVhSX2LhuoJqqryXU4ld7+ylbI6F1kJtgHrWayqKg63n+JqJ0XVDqrsHsZlRHP18jHE1W/hul9cPeSCPJoZUhFbzfzsJxfw8D23MSZBorBBoarB01SMTQhGZJl0/b6QO+oSeGRCxSPv7GTRhBTOnpuNbQAiqLoiymJg6rA4Nu4uJrZpTezxBfjgmxw+2XSQCKuR7JToTl/v8/nQ6XQhu8hMBh3ZyVGUVjdy53/Wce7CsSydOQy9TiI5NgJvQMHvCXDqzIwB9QgfidPj55Wvc/lmbzkpsZYBCSTxywp1Di8eX9Afkhxj4YxZGYxNjyGzqSyPx+PhzddfYfPmzf0+X6gYkiJOSU7inLPPwnXwKx64+WYaXD6Kqp0cLG1gV2EdRdVOREFAVVVsZj0RZn2v7sgVjV4qG70kRRiJsehbdSqA5oQKA+v3lPNDXg2XLRnFpOzYkM6AJ45LZP3OIhSCIZPPf7ydukY36YmR7d6bx+MmZ/d2Du7ZSlnuLqqKDmA020gdNYXsMdOYMH0O8Qk9K0xXXVXOvh1bKTywg/K83TTWVpA8bAIZo6cxftpcMoeNRBTFoA8hxhIMpFi3l2/3lHDFaVOIizIjKypGvci0EfEh+GSCNbue/GQvDW4/2UkR7QrVA3gDMtUOPw0eP6MSrO2+U2iabT1+Gpw+VDW4lJqUHcuMEfEMT44kpoN84nfeeYdp06aRnZ0dirc2IAi9MVVnzpypHq070vfff8+FF17IwYMH26Umur0BSmqd5Fc42FVYy8FSOwFFQVHAbJSIshi6DLErrHNTZvciCsFyqzEWPQk2AxHG9jN8o9tPld3DnNEJXDR/BFHW0CRUuLwBfv63tZSXVpCVFEVspIkoW7AuVbNoD+3ZRlnuTqpKDpGaOYIT5s3nlJOXcdZpy8krKOaNdz9g9erV7Nr2LWZbDGmjpjJi/HTGT5mF1RaBoiiUlxazf9cWig/uojxvNz6Pk7GTprNw4UJOPXkpE8aO5p0PPuWDjz7m2w3r8Pt9pI+axrDxM5gwbS4xscGOhtUNLpxuP1NGJrH6+zzGjMnmgctnD2jL0kBT1Y23N+UTZTW0qjgJQVE2egNUNPqobypAqKowOTWipRujP6BQ5zw826bFWZk5Mp5xGTFkxFu7vfkvX76cq6++mosvvnjA3ldfEARhi6qqMzt8bKiKGGDu3LnceuutnHnmmV0+T5YVyuuDTog9RXXsKarH6fG3dLCPtOixHiHQwjo3VQ4fJn2wy703oKCoKnpJJNFmIM6qx3hEsIKiqpTVutBLIpcsHsmsUQkDOiurqsoPB8r59T8/p7rewTknjuDAnu0cbBZt8UFSM4Yz98T5rDh5OWeuWEp8XOfJC35/gM/XbeCd9z7iiy/Wkpezi5ikTFwNNSCoTJw6m8WLFnLaKcs4cc70TvO3FUVh6/Y9vP72e3z22Wfs2b6ZyLhk0kdPZ/SkWYwcN4WCSiff7i3lt5cu5JrTJw3YZ1Jt9/D8mv3sK24gPc7ayuPtlxVqXX7K7V58soIkCBh1IgjBm2F2jLnFOWnQiUwZFse04fEMT45odyPoiry8PGbPnk1RUREm0+AW+gtbEb/wwgusWrWKzz77rFevU1WVmkYvxTVOckrq2V1YR3mdm2bdNfoUnH4Fi7H1xSsrKr6AggpEGHUkRRiJNOla8k6d3gCV9S6mZMfxo0UjiY/s/xdb1+jmlTW7+X53MT98u5YD33+Kp7awRbSnLF/GylOXdSna7miwN/LJ518xcng20yaP6/Pa2ePx8v6na3n3vQ/5at1ayovzSc6egN2UxUkrzub8pVM4a/6Yfu1bq6rKtkM1PL8mBxVIim6KClNVXD6ZSoePGqcPFTBIIjpJICArON1+ArKCN6Bw4ugEFoxNYmx6NBnx1m5DUDvj9ttvp6Ghgb/97W99fj8DRdiK2Ov1kpmZybp16xg7tn+Fup0eP8U1TnLLG/lwWwl7iusxSCIKKka9hNmoO2xaqeCTFQKKiigIJNgMxFkNWJoqOJbXuVGBixcMZ97YpD5dJIqisnFXES9+toNtm75g3/o3iIyI4Pe//z0XnXtmv0R7tCgqKef1dz7gv/97iQO7f2D4nJUsPf18fnbWbCYM67214vYGeOObPL7cVUZStBmrUYesqNS7/ZTZvbibMr6MkoDXL+PyBGdbvU4kKzmS7ORIdEY9V56QSXQ/nZGyLJOdnc2HH37I5MmT+3WsgSBsRQxw2223Ybfb+fvf/z5gx9yQW8uOEjuqLFPT4KG4spHSagc+WYGmfkhmow6DrsnclhVUFcx6iaQIAzFmPQFZoazOxZi0aC5dMqpX3f4qah2s+mQ7b7z5Njkb3sRqMXLnnXdx1aUXDNltjO747IsN/PZ3fyDv0AHGLzifn//0Si5cOqnHsdwFlY08/dk+qho8pMdZ8ckKVQ4flQ4fihKsmunzBggoCiAQH2VmeFoUKXFW4iLNLWvxGqePi6en9lvEH3/8MXfccQffffddv44zUIS1iIuLi5k8eTIFBQV9LiXTlg25tewudxBjOfxFq6qK3emjxu6mrNpJUVVjU3dCAUEIJlBIOpGAoiIAsZbg2tnj8eNXVM6dm82SyaldOkoCssLaLXk89K/n2fzZi5iNOv7859v5xZU/DlvxtuX1dz/mj3/8E9XVNZx4xuXc/ftrmTE2tdNZWZYVvthZyusb8rAYdej0EhV2Lw0eP16fjCIrCIKAQSeSnRxJVkoUiTEWzJ3UURsoEZ9//vksX76cX/ziF/06zkAR1iIGuOCCC1i8eDHXXXfdgByvIxF3hNsboKbBTUWdi+LKRqobgimMiqIiiCIGg4hJryPWLOHxBBiVEsnlJ40mo4PyLwXl9fz5oaf56NWn0AsKt9x2O7/86aU9LgoYTiiKwnMvvsGf/3wbvoDKxVfdyF2/vZq4qNbWSp3Dy3/X5vBDXg1Go54Kuw97UzqkThJIjLYwPDWK1HgbMZGmDreW2jIQIq6srGT06NEUFhYSGTk0StOGvYjXrVvHNddcw549ewbEK9xTEbfFH1Coa/RQVR8UdVmNE19AwRdQ0OlETJJAlFnPBfOyOW1GJka9hMfr58F/reKJvz+M4vfwp1tu48ZrrjgmxdsWWZZ57In/cP+9f8ESEcutt9/Jz358FqIosD2vmn98uJeCagf+pkvQatQzIi2KzKRIkmItfQotHQgRP/LII+zcuZPnn3++z8cYaMJexKqqMnnyZB577DGWLl3a7+P1VcRtUVSVBoeXmgY3xVUO8svtNDh9+P0yEzKjKfjoMXZu24yqqvzuD7dw03U/Rd+DXGFVVfF4PLhcLpxOJy6Xq+Wn7f9dLhdut5v4+HiGDRtGdnY2GRkZGAw98xA3NDSQl5fX8lNQUACAxWLp049e3/4z9fn83HLXA/zzkQdIHzaKJZf8ioP+ZPyySkqclXFZMcHZNqJns21X9FfEqqoyYcIEnnzySRYsGDpVPMJexABPPvkkn3zyCW+//Xa/jzVQIm6Lqqq4PAEq6lxs3LqLd/98QctjRqORqKgo4uLiiI2NxWw24/V6OxSo2+3GaDR2KBKr1drubyaTiaqqKvLz88nPz6e0tJTExESys7Nb/fh8PnJzc1uJ1uv1MmzYsJaf7OxsJElqd6Po7OfIm4rT6UQURcxmMzqdDlmW8Xq9+HzBVq2iKKIoCvFjT+QX9/2bsWlRGAc4kaO/Iv7mm2+48sor2bdv35AqrtiViIdk2GVHXHrppdx6660UFBSQlZU12MPpEEEQMBokrBEmfrTiBBzVT7Lh2T/y69/8lmkTRrF9+3b27t3L3r17KSgoYNy4ccybN49FixYxffp0bDYbFosFs9ncL3M7EAhQUlLSIur8/Hy+/vprDAYDw4YNY8aMGS2iTUgYmMCVyspKPvzwQ95//33WrFlDdnY2ixYtYs6cOUyZMoWRI0dy6jk/4oc9h7j0Dw8zOi0aV0DGOMQqCj/77LNcffXVQ0rA3RE2IrZarVx22WU88cQTLZ0Phhpuv4zDG2DB8BgERSVh+HimnfsH/vbXB/jr3//Fvffe2/Jcu93Ol19+yerVq7ntttuoqalh6dKlLF++nGXLlvXrRqXT6cjKyiIrK4tFi0LTzV5VVX744Qc+/PBDPvjgA/bt28fy5ctZuXIlTzzxBElJrQvl/ez637F1y1YmXHAHRouFlRMT+TynhiqHlzirYUiIprGxkbfeeou9e/cO9lB6RdiIGOCXv/wlJ5xwAnfccQdm88CXYOkPDW4/giBw1qRk0qJMfLazgpgoC97hY1lx5Z+56YZfYjGbuOzicwGIjIxk5cqVrFy5EoCioiJWr17N6tWr+dOf/kR0dDTLly9n+fLlLFmyhKioqMF8ewC4XC7WrFnDBx98wIcffojZbOaMM87g3nvvZcGCBZ2uwx949AleeXEVJ//0PoS45GDHRAVWTkxi7YFqDlW7iLcaBjTuui+8+uqrLF68mOTkniWPDBXCanNy5MiRzJo1i1dffXWwh9KCoqpUO3xEm/VcMDWFtKhgKGZhrZPESBNRcdHEpo7i7J/9mWt+dhWvv/txh8fJyMjgqquu4uWXX6a8vJzXXnuN7OxsHn/8cdLT05k3bx533HEH69evx+8PXemithQUFPD4449z2mmnkZyczKOPPsqYMWNYu3YtBw4c4NFHH2Xp0qWdCvi1tz/krj//iYuuvwedNYbkxGgEQaDe5cOgEzl5TAJT0yKpcvrwD3LhwmZTOtwIq5kY4IYbbuC2227j8ssvH3QTzC8r1Ln8TEiJ4MRhMS3pb6qqUlLnIdKsIy4miip7PWMnz8B/+R+5/NIfYX7jbc44ZUmnxxVFkalTpzJ16lR+97vf4Xa72bBhA6tXr+bGG28kNzeXhQsXtszUY8aMGbDPwu12s3Xr1hYzuaysjNNOO40rr7ySl19+uVcWwabNP3Dl5Zdy+hV/JCF9OM5KB5ERZhRVpdbpYzjBHPF5w2KIMutZd7CGSJOuJQPpaLJ7924KCwtb+lmFE2En4lNOOYUbbriBTZs2DWo1fqc3gNuvsGRUHOOSbK1E5PAG8PhkYq0G9AaJrJRYqhscTJ01D7//N1x0wbm898FHLF3Ys/GbzWaWLVvGsmXLePDBB6mqqmLNmjWsXr2ahx56CK/Xy/Dhw8nMzOzwJzb2cC603++nqKiI/Pz8Vl7q5p+6ujrGjh3L6aefzpNPPsns2bP75GTLKyjmtNNOZ+7pVzB11jyKKuxEx0VhMxlw+2TKGw7XuBYEgYkpEdiMEp/urSKgqNiOcmfLZ599liuuuAKdLuwkEX4iFkWR6667jn/+85+DImJVValzBzDpRM6dkkxSRPvY4BqHr0U0ekkkITGGssp6AGbNW4zf5+GsM8/g088+48Q5M9q9vjsSEhK4+OKLufjii4OzfkkJhYWFLT/79+9n9erVLf/3er1kZGTg8XgoKysjOTm51bbSihUryM7OZtiwYaSmpvY7BLTB3sjiZSsYNmUBS1acg6qqBBQFi9mC1SChqiplHRTwz461cO6UFD7cXUG9y0/0AG8BdobX6+WFF15g48aNR+V8A03YiRjgiiuu4O6776a8vPyoOiEURaXG6Sc92siyMQlYOqkPXd0YbM0CYDPqcKoKVrMBjy+AyaBj3uIV+H0+Tj31VL5Y+wUzpk7o85gEQSA9Pb1l3dwRjY2NFBUVYTQaexUI0hf8/gBLTz0bvS2Bs358DRDsD2U2GbBZg4kKJr1ERUPH3SYSbAbOm5LCR3srqXb4iLPqQ75seu+995g4cSIjRowI6XlCRVg5tpqJiYnhwgsv5Omnnz5q5/QGFKqcPqalR3D6hKROBQxQXOvG0JTEbtKLNLgDzJ2YQVW9q+U5i05eyfSlF7F02TJ27c0J6dgjIiIYP348I0aMCKmAAS687OeUlFXy42tvaZnRa+1uRmQmBsuoAHpJwOkL4Omk1neEScfZk5LJjDFT7fC33BBDgcvl4o477uCGG24I2TlCTViKGOD666/niSeeoK4u9FUp7Z4ATm+AFeMSOGFYbLfNqYtqXS0iF4RgFlRmaiyKoraq3LnsjAuYeOKZLF6ylAOH8kP5Fo4KN996D1+sWc1Pfn0vBmNwmaGqKooKosmMWX/4MxEFgXpX5152o05kxbgEJqbYqHL4QtZy5/e//z1Tp07lnHPOCcnxjwZhK+JJkyZx4YUXctlll6EoofmCVVWlxunDpBM5f2oKI+O778MjKyrlDd52Hla3LJCZFEWDs7UZueLcSxk+bTHzFy6hsLhsIId/VHni2Rd44h+PcsmvHyAyKrrl706Pn8QYK7UeGZvp8OpNVVXqnb4ujymJAgtGxDJ/WAy1Lj/ewMB+z80RZo8//viAHvdoE7YiBnjooYeoq6trFQk1UMiKSpXDR1aMmfOmJBPXwwJ59a5gnPCRs7XVoONAhYOlM4bR4Gi/Flx58c9IGzuL2SfM53+vvB2ym1IoKC2v5IKf/Jybbvwl5157JylpGa0er7V7mDI6Fb9MqwqUqhp0AHaHIAhMTY/i1HEJNHoDOAeosH9lZSU/+9nPWLVqFdHR0QNyzMEirEWs1+t5/fXX+fe//83HH3ccRNEXPH6ZGqePOdnRnDIuoVf7ljUOH7RxxFiNOopq3YwbloAkih12Ujj70l8yefF53HDjDYwYO4XnXnxjSIu5wd7IL371B0aOHM3OnEJ+ccfTjBk/pdVzVDW4fIiPi0Cl9brWqBcpre++aVszw+OtnDMpCUUJRsf1B1VVueqqq7j88stDFpZ6NAlrEQOkpKTw6quvcsUVV5CXl9fv4zW4/XgCCmdOSGRmRnSvU+Mq7R7UNk3IJLGp0JtfZebYFKoaXO1eJ4oiC5edwU0PrGLM3NP5zW9+w7Axk3lm1atDSsw+n59b7/4/0rOG89na9fzkd49y6S9vISEppd1zHW4fKXE26r1quzrQJr1EWUPv+kQnR5o4b0oyVoOOWqevz51B/v3vf1NeXs5dd93Vp9cPNcJexADz58/nlltu4dxzz8Xt7lsDcbUpfDLCpOPCqSlkxva8ZtaRFNW6O5y5FaCk1s38yZl4fZ13YJQkHfOXnsav7/8vE+av5Pe//wNZoybw7/+8OKhiVhSFRx//D6mZI3juvy9w9s9v5+qb7ycju/NtmbpGDwunZnGwwtEueMOkl6iy916IUWY950xOIiXSSLXD12vP9b59+7j99tt58cUXQ+6pP1ocEyIGuPHGGxk3bhzXXnttry8Mf1NRttGJVs6ZnExUP6pCFNW425XCBTDrJA5WOhiVHkuk1Yjb27VJKEkS8xav4Ff3PceURedz621/JmP4OP7x5H+R5aPbhvWl198je9RE7r33Xhaddw3X3Pb3dqZzW4KmNAxPi6PO5cekb32pSaKArKg0enq/xjXpJU6bkMS45KDnWu6i/eqR+Hw+LrnkEu655x7GjBnT6/MOVY4ZEQuCwNNPP82WLVt48skne/w6l0+mwR1g4cg4ThoVh6EfDbp8AYU6ly9YyLwNNpOOQ5UORFFgyfTslnpd3SFJEnMXLefGe55l+vKLuevuu0kfNoZHH/9PyMX86dr1jJ92Atdc+wvGn7iSG/7yDNNmz+9RRFejy0d6QgR+hJb+y20RBLrcZuoKnSiwaGQcJ2THUOP04euB5/r2228nPT2dn//8530651DlmBExBHOO33rrLW6//Xa+/fbbbp9f7woGEpw9OYlJKf3v9Vvj8DbtC7c/jkEn4vLJ1Lv8zByb2m7PuDskSWLO/KXceM+zzD71Mu67735Ss0bx8N+forpmYPbKK6pq+HzdN/zjqVXMWbSCc88+i4QR07np/heYv/S0XsVQ1zuCpnRxrbutn68FRVWp62abqStEQWBGRhQnj0nA7gng6mKZ8uWXX7Jq1SqeeeaZQU+cGWjCMuyyK0aNGsUzzzzDBRdcwObNm0lMTOzweQ1uPyPjrSwfGz9gwfY1Dl87p9aRCEBpvYcJaZGMSIuhqt5FTETvukiIosiseYuZMXch275bzz0PPsIff3sDoiQRFZNAbEIiycmppKWnkZGeTnZWJsOyMxk1PJvICBu79x1gX85BDhw8RG5usKZWWWkxdVVlyHKAiNgkDLZ4zAnD+fWDL2C2dL833pZmU3ri8ERe21KG1djx8kQUBCobOw6/7A2jEq1YjRIf7anscC+5rq6Oyy67jGeffZaEhND3Tz7aHHMiBli5ciXfffcdF110EatXr26XmZJoMzBveAxzsmLQDWAielmDp8vEdlEUyKt2MiEtkiXTsnnmg229FvHhY4lMnHEiRXIaw1MiyY7TU1tVQW1NJfU1VewvqmLb7lxc9hqc9lqcDTUEfB5sMYlExiZhi0kkIiaJ5LEnMHFRKkkpaURERiOKIut3lVBr96Az9K3wgt3pJTMpiiibmdI6D4kdJIlAMCS1rK7n20xdkRpl4vypKXxXUN9qOaOqKtdeey1nnXUWp5566oCca6hxTIoY4K677uK0007jlltu4f/+7/9aPTYq0caoEJyzoNqFuYuYaptRx4FyB0yBSSOS0EkiAVnpc6PsyjoXshLsaD95eAKRUdFkj+yfw8bnl6lr9IIQrAudGN17Idc7vZwxbzQVDR5UlU5vbH3ZZuqKaLOek8e2nmlfeOEFdu7cOaT7C/eXY2pNfCSSJPHSSy/x2muv8cYbb4T8fKqqUlrvbokP7giLQaLS7sHtk7GY9MwZn9YqKaK35FfYiTAbcHkCLa07+0ttk3kriQIVdc5ev15VVWgypUvq3O2CPI7EqBOpd/lDVtEjLy+P3/zmN7z44otDrpzTQHLMihggLi6ON998k2uvvTbkxc+cXhm3V27JXuqI5mSIsoagCXnipAz8gb6Jz+nxU+fwYmya+dvGZPeV8jonkihgNugorXHS23iKBqeXYanRxEVZyCl3dHlTa06EaOijh7orAoEAP/nJT/jjH//I1KlTB/z4Q4ljWsQAM2bM4MEHH+Tcc8+lsbExZOepcXgRerC+VhUorg3OviPSYomymnB5en8RV9S5gls3BLdqerpl1eXYVCircWJq6hDp88s0unvnPW5weFkwORNFUcmtcrRKeuj4nCp1IRDxAw88gMlk4qabbhrwYw81jnkRA1x11VUsWLCAK6+8ss+het1R3ejt0jPdjNWoI6fcAQTXiktnDOu1AFUV8svtmJqCSkwGHeV9MH3b0uDyBtfoR9yMauw9H5uqqiAETeng3m37cMv2r4G6AbIimvnuu+/4xz/+wfPPP3/MNKrrimP/HTbxj3/8g8LCQh5++OGQHL+4ztPtBQvBoI+CaldLlNGMsSktiQI9pdHtw+HxYdAFRazXiQOyLq5pczMx6iVKqh09fn29w8vI1FhiIsyU1Xe9Hm5GrxMpqx84ETscDi655BL+9a9/kZ6ePmDHHcocNyI2Go288cYbPPLII6xdu3bAj19Y27VnuhlJFJBVhaomB1JCtJVRGXHUNfZ8q6WsxhlcXzf9v/l3f9fFJdUOTEe8B6Neoq7Ri6+TChxtsTu9LJiSCcChSmePbmomvUhpB/W2+oKiKFx33XXMnz+f888/f0COGQ4cNyIGyMzM5MUXX+SSSy5paRw2ECiKSmWDt0cihuC6uLTu8IW7ZFoWja6erT1VNeiVtrQJUOnvutjrl6l3+jAc4YhqDmyq7UFARrARuMD4YcEtngPl7ZMeOsKkl6i0e/u9zPH5fFx22WUcOnRoQBvShwPHlYgBli5dyq233sr8+fPZunXrgByz3uUnoCjdlu1pxqATOVR5eA07cXgiBp3UI091ncODzy+3T+0z6Kio6/t2VY3dA6i0fQeSKPRovV3v8DA6I5ZomwmHJ0Cdy9cu6aEjdKKAxy/j7uFs3xEOh4OVK1fS2NjI6tWrB6wZfbhw3IkYgvW5HnvsMU455RTefffdfh+vxulrd/F3RYQpWOmjefYxG/XMndizPePSakeHwRN6nYjT4+/zuri81tlh0InZqKOsxonSjdOu0eVj/qSgKV1a7+406aEtzcuCOmffPNRVVVWcdNJJpKWl8eabbx7T+8GdcVyKGOC8887jo48+4rrrruORRx7plzlXaff0aj/VoBNp9Pixuw+n4c2bmIHXL+PpovyMrKgUVjW2M6Whf+tiRVEpr3Ni7qDNqCQKBBQFeydbTaqqUlHnxKCXWkzpohpXp0kPndGXbKaCggLmz5/P8uXLeeaZZ8Ky8PtAcNyKGGDWrFls3LiRVatWcc011/S5x1FRjQtjL0r4NM8+5Q2HnVnDUmK46KQJNDi8FJQ3dCjmGruHgKx2arYLQu+2hJqxu3zIXRwXtf16W1VVauxuCsobGJYSw+8vOZFIazBGel95I9ZeJpVUd1B7rCt27tzJ/Pnzuf7667n33nuPucyk3nBcixiCjczWr19PSUkJp556KvX19b0+RnGtu8s61B0hCAIF1YfNZ1EUOHn2CB68dhkXnjQBu9NHfnlDq+IBxVWN6KTOL1aTQUd5be/XxR2VC2p93NZbTXZn8EYTF2nm5h/N46YL55CVFOzR5AsolNZ5sPaiebhRJ7Vy9HXH119/zbJly3jooYfCul70QHHcixiCxdXfffddJk6cyAknnEBubm6PX+sLKMGytj1w4hyJzahjf3n7CDKLSc/yWcN58Nql/GjpBBxuPwXlDdidXkprHB2a0s30dV1cUu1stbXUFoNewu70UWv3kF9Wj04Sue7cWdzykwWMzYpvNQt2l/TQESaD2Ko3U1e88847nHfeebz44otcfPHFPT7HsYwm4iYkSeKxxx7j+uuv58QTT2TDhg09el1tk1Ort+acxShRVu/ptCKF2ahn6czhPHjtMn68fCIlNQ7sTm+HlTKb6cu62O0NYHe13lpqixyQcbi8VNY7uer0adz90yVMG53SoVC7S3roCKNOCra+6cZ59swzz3Dttdfy0UcfsWzZsl6d41hGE3EbrrvuOp577jnOOeccXnrppW6fX93o7eUlG0QUBBBar4s7wmTQsWT6MKaNzWDC8CQCskq9w4Ovk+2o3q6Laxs73loCkBWFBocHj19mdHYC86YMY96kjC5TJ7tLeuiI5mqgnZWiVVWVe++9l/vuu4+vvvqKmTNn9ur4xzrHpzuvG1asWMHatWs544wzOHDgALfffnunM215gwehVxtMh1EVlZI6N5lxXVfWbHB6ySmuY9KIJCYMT6Sksp59BdXUOzxYjPpWs2jzunh8VlyPxlBW62xT1F0lICvBtbggMCoznuFpwdaoewpru8x/bk56iOxDocHmelsxbYr0y7LMr3/9a77++ms2bNhASkr70rjHO5qIO2HixIls2rSJs846i5ycHJ599llMpvZVOApqXL12ajVjMUrklDdywsiuBbcrrxqV4DpTRCArJZb0xGhKKhvYV1BFg9OLAKgERejyBCiuCuYaG/Q6jHqpxfRVFBWvX8brC+D0+jlUUofJoEOWlSYzWMBi0pOVEsvIjDjMR5TW8ftlCivtDE+J7nCcPU166AhFbb/N5PV6ueyyy6ioqGDdunW9anB+PKGJuAuSk5P58ssvueKKK1i6dCnvvPNOqxpNqqpSVufpcbhlW6xGHblVwUCKrhxB63YUE2VpPUNJkkhmSgzpSdF4/QF8/gBev4zPJ1NY2UB6YjQmnUiN3U1pTSOKQpPQVWIjLaQmRCCKIsW1brKSojAZdJiMeox6qcv1/Z6C2k5F3NOkh46QBIEK++Glhd1u55xzziE6OppPPvmkwxuoRhBNxN1gNpt5+eWXueOOO5g7dy7vv/8+48ePB8Dpk3H6AkSa+/Yx6iURfyDYtC2hkzpUlfUu8ivsZCTYOnxcFAXMRn2rGVOQJMYOS+T8BaOB5tnZjy8gE2kxIjXNlO9tPMS+EjvJcT0LU4yJMPLdvnLOmDu8w8d7mvTQESaDREnTNlNFRQWnnXYas2bN4l//+levqmwej2iOrR4giiJ/+ctfuOOOO1i8eDGff/45ALUOHyK990wfiYJKWRd7pNsPVUIvzxFlNbLjUHXL/wVBwGo2EBNhbhGwqqp8t6+caFvHN4+OsJr0lNU5O3Wc9TTpoSNMepHKBi+HDh3ixBNP5Mwzz+SJJ57QBNwDNBH3gssuu4w33niDSy+9lKeeeiq4LdLPYxokkUNVHScYqKrKl9tLiOlklu4MqylYJMDu6nyrqbbRQ0W9C6up506o4I1E5WBJfbvHepP00BEGSSRn7w4WLlzIzTffzJ133nlcR2H1Bs2c7iULFy7k66+/5owzzmD8+q2MP+OX/TqezajjYEXHiffF1Q4q611kJnZsSndGcy2vwspGJmZ3fAMIClHttVCsRj2bD1QwZ1xrL3Fvkh7a4vV4eP+FJ9j88Us88/RT/OiiC3p9jOMZbSbuA6NGjWLjxo0UHdrPU789l2+/Wt3nZmcmvUit04fD2z5WetuBSiSx444S3SEKAvuLOu8MsflARadF3bsi2mZkd351u0IBfUl6APh+/Vru/fnpeCpz+X7zZk3AfUATcR+JjY3lu6/XcP/99/P5qkd45DeXsG/ntl4fpyUZok2vXllWWLezmLiovnllo6xGth+q6vAxn19md351r9bDzegkEVlRya+wt/p7b5MeykuL+Odt1/DR0/fy6KOP8e0XHzN+dOcdFjU6RxNxPxBFkZ9eegGFB/Zy9eWX8cK91/P4nTdQUpjfuwMJUFjTOgkhr9xOo8vXYXpgT7CadJTVOjusGJJfYUdW1D4XrRcEgV35hx1nvoBCSa27R0kPPp+Xt5//J4/ecC4L5s4k/8Berrj4nD6NQyOIJuIBwGDQc8fvbyA/9yALZk/j7zddwKrH7qK+rqZHr7cadOS0SYb4bn9Zn0UGTTWdRSiotLd7bFd+db+cRjE2I9/vK2/Jwa5oCh3tLulh27dfc9/Pz6Q+fycbN27imb89gM3atz7QGofRRDyAxERF8NSj97Fnz25SIvXc/9NTefu//+q28bnVqKOw1tXSCcHnl9m4p4z4yP4FOHS0LlZVle/3VxDTB1O6GYtRR7XdTWVTgbtg0kPnVFWU8fidN/DOP/7Mfffdx+avVzNlwrHTH3iw0UQcArLTU3nv5f+w7quvcJflcM9VJ/P5u68iBzqu2iGJAqhQaQ9uCR0oqcfrl7vMLOoJUVZDu3VxVYObGru7y5TG7mhu33qgOHiD6CzpIRDw8/6LT/HwL1cyc9JY8g7u4xeXX3xc1II+mmifZgiZNXUiG9d8yAsvvsSedW9z7zUr+X792g492QpQUhuc2TbsLulVpZDOsJr07dbFB4rrUNXeby21xWbS831ORaedHnZu3cR915xF2e6NfLnuK/7370eJiujdVplGz9BEfBRYecoS9m//jr/cdRef/OcBHr35MnJ2b2/1HLNO4mClA5fHz9aDlcT105SG5l5HrdfFm3MqsPUhy6gtUTYD+4tqKalztUp6qK2u5Kl7fsOrD93MbbfexvZv1zF72qR+n0+jczQRHyVEUeSaK35E4cF9XHLxBTx/9zX8++5fU15SCAQ7QxysdLCnsAalH57jdueVBHKazF6PL8DeolqirX1fDzcjiSKKqrLlYBUqKnIgwEevPsf//eIMxg9PJ/fAfn7188s00/kooH3CRxmT0cC9t/6W/EMHmTNlHI/ecC4v/P0vuB31uH0yn20pwtpNE7LeEGUxsD03uC7OK29AVdWW+On+IokC63eXUbBvOw9cdy55mz/n088/59XnHicuRksbPFpoIh4k4mKi+M8/H2LHzp3EGRXuu3oFa197mi37i4mxDVzandWkp6wmuC7elVcTrCjST3xeL7k5e9j/3WpefOxW3n/0Jm769a/Zs3Uj82dPH4BRa/QGLXZ6kBmZncFHb/yPjZu3c+2vfss7bz/B5zGJRCdmEBmfRmxyBklp2aRmDCM2PqHX5mlzRFhBhZ3vc8qJjej5DUJRFCrLSyjKzaGs8BC1ZXnUluZhrykjJjGV4aPGcsZJc7nn1tdJSuhZJRGNgUcT8RDhhJlT+GHD57jdHrbu3MP2nXvYvWcf+/btZ/PH66goKcDv8xCVkE5UQhpRCenEp2SSnJ5NakY2VlvnOcGiJPDd/nLqHV4yEzt+nsPeQEFuDiX5OVSV5FFXlkdteT5Gk4WMYaOZMGEiZ510PnNmTmfuzClakMYQQuhN54OZM2eqmzdvDuFwNLqivLKazT/sYvvO3ezdu5+cAzkU5edSXV6EwWRpEXh0YgaJqZkkpw8jOTUDr6xSUedCJ4mkxJgoLsilOC+HyuLc4OxalovP7SQlcwSjx45n8uRJzJ4xnXmzp5ORljzYb1sDEARhi6qqHVYI1ER8DCDLMvsO5rFt+x527t7Dvv37OXggh9KifBrrqrDFJCFa4lA99TQ2mcLDRo5h8qRJTJ82hXmzZzJp/CgtAX8I05WINXP6GECSJCaMGcmEMSOBla0ec7rcbNu5hx279zNyeDbzZk3VTOFjDE3ExzhWi5n5c2Ywf86MwR6KRojQtpg0NMIcTcQaGmGOJmINjTBHE7GGRpijiVhDI8zRRKyhEeZoItbQCHM0EWtohDmaiDU0whxNxBoaYY4mYg2NMEcTsYZGmKOJWEMjzNFErKER5mgi1tAIczQRa2iEOZqINTTCHE3EGhphjiZiDY0wRxOxhkaYo4lYQyPM0USsoRHmaCLW0AhzNBFraIQ5mog1NMIcTcQaGmGOJmINjTBHE7GGRpjTq9amgiBUAQWhG46GhkYnZKmqmtDRA70SsYaGxtBDM6c1NMIcTcQaGmGOJmINjTBHE7GGRpijiVhDI8zRRKyhEeZoItbQCHM0EWtohDmaiDU0wpz/B0jFPtJSBRPpAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "au = np.array([0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, \n", + " 0.450328, 1.02961, 0.871225, 0, 1.1977, 0.457218, 0, 0, 0, 0])\n", + "\n", + "# Load a model \n", + "model = load_h5()\n", + "\n", + "# Add muscles\n", + "muscles = {'all': 'heatmap', 'facet': 1}\n", + "\n", + "# Plot face\n", + "plot_face(model=model, au = au, muscles = muscles, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add gaze vectors\n", + "Add gaze vectors to indicate where the eyes are looking. \n", + "Gaze vectors are length 4 (lefteye_x, lefteye_y, righteye_x, righteye_y) where the y orientation is positive for looking upwards." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:50.850198Z", + "start_time": "2021-03-26T04:54:50.805184Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "au = np.zeros(20)\n", + "\n", + "# Add some gaze vectors: (lefteye_x, lefteye_y, righteye_x, righteye_y)\n", + "gaze = [-1, 5, 1, 5]\n", + "\n", + "# Plot face\n", + "plot_face(model=None, au = au, gaze = gaze, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Call plot method on Fex instances\n", + "It is possible to call the `plot_aus` method within openface, facet, affdex fex instances" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OpenFace" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:56:59.035420Z", + "start_time": "2021-03-26T04:56:58.928294Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from feat.utils import load_h5, get_resource_path, read_openface\n", + "from feat.tests.utils import get_test_data_path\n", + "from os.path import join\n", + "\n", + "test_file = join(get_test_data_path(),'OpenFace_Test.csv')\n", + "openface = read_openface(test_file)\n", + "openface.plot_aus(12, muscles={'all': \"heatmap\"}, gaze = None)" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "toc": { + "base_numbering": 1, + "nav_menu": { + "height": "171px", + "width": "252px" + }, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": "block", + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/_build/html/_sources/content/dev_trainAUvisModel.ipynb b/notebooks/_build/html/_sources/content/dev_trainAUvisModel.ipynb new file mode 100644 index 00000000..1f7a6b01 --- /dev/null +++ b/notebooks/_build/html/_sources/content/dev_trainAUvisModel.ipynb @@ -0,0 +1,481 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Training AU visualization model\n", + "You will first need to gather the datasets for training. In this tutorial we use the datasets EmotioNet, DISFA Plus, and BP4d. After you download each model you should extract the labels and landmarks from each dataset. Detailed code on how to do that is described at the bottom of this tutorial. Once you have the labels and landmark files for each dataset you can train the AU visualization model with the following. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:28:05.706631Z", + "start_time": "2021-03-27T00:28:03.811433Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EmotioNet: 24587\n", + "DISFA Plus: 57668\n", + "BP4D: 143951\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import pandas as pd, numpy as np, matplotlib.pyplot as plt\n", + "from sklearn.cross_decomposition import PLSRegression\n", + "from sklearn.model_selection import KFold\n", + "from feat.plotting import predict, plot_face\n", + "from feat.utils import registration, neutral\n", + "from natsort import natsorted\n", + "import os, glob \n", + "import pandas as pd, numpy as np\n", + "import seaborn as sns\n", + "sns.set_style(\"white\")\n", + "\n", + "au_cols = [1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43]\n", + "au_cols = [f\"AU{au}\" for au in au_cols]\n", + "\n", + "base_dir = \"/Storage/Projects/feat_benchmark/scripts/jcheong/openface_train\"\n", + "\n", + "labels_emotionet = pd.read_csv(os.path.join(base_dir, \"emotionet_labels.csv\"))\n", + "landmarks_emotionet = pd.read_csv(os.path.join(base_dir, \"emotionet_landmarks.csv\"))\n", + "print(\"EmotioNet: \", len(labels_emotionet))\n", + "\n", + "labels_disfaplus = pd.read_csv(os.path.join(base_dir, \"disfaplus_labels.csv\"))\n", + "landmarks_disfaplus = pd.read_csv(os.path.join(base_dir, \"disfaplus_landmarks.csv\"))\n", + "# Disfa is rescaled to 0 - 1\n", + "disfaplus_aus = [col for col in labels_disfaplus.columns if \"AU\" in col]\n", + "labels_disfaplus[disfaplus_aus] = labels_disfaplus[disfaplus_aus].astype('float')/5\n", + "print(\"DISFA Plus: \", len(labels_disfaplus))\n", + "\n", + "labels_bp4d = pd.read_csv(os.path.join(base_dir, \"bp4d_labels.csv\"))\n", + "landmarks_bp4d = pd.read_csv(os.path.join(base_dir, \"bp4d_landmarks.csv\"))\n", + "bp4d_pruned_idx = labels_bp4d.replace({9: np.nan})[au_cols].dropna(axis=1).index\n", + "print(\"BP4D: \", len(labels_bp4d))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We aggregate the datasets and specify the AUs we want to train. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:28:06.960825Z", + "start_time": "2021-03-27T00:28:05.707732Z" + } + }, + "outputs": [], + "source": [ + "labels = pd.concat([\n", + " labels_emotionet.replace({999: np.nan}), \n", + " labels_disfaplus,\n", + " labels_bp4d.replace({9: np.nan}).iloc[bp4d_pruned_idx,:]\n", + " ]).reset_index(drop=True)\n", + "landmarks = pd.concat([\n", + " landmarks_emotionet, \n", + " landmarks_disfaplus,\n", + " landmarks_bp4d.iloc[bp4d_pruned_idx,:]\n", + " ]).reset_index(drop=True)\n", + "\n", + "landmarks = landmarks.iloc[labels.index]\n", + "\n", + "\n", + "labels = labels[au_cols].fillna(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We train our model using PLSRegression with a minimum of 500 samples for each AU activation. We evaluate the model in a 3-fold split and retrain the model with all the data which is distributed with the package." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:28:08.890994Z", + "start_time": "2021-03-27T00:28:06.961852Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pseudo balancing samples\n", + "Evaluating model with KFold CV\n", + "3-fold accuracy mean 0.13\n", + "N_comp: 20 Rsquare 0.15\n" + ] + } + ], + "source": [ + "min_pos_sample = 500\n", + "\n", + "print('Pseudo balancing samples')\n", + "balY = pd.DataFrame()\n", + "balX = pd.DataFrame()\n", + "for AU in labels[au_cols].columns:\n", + " if np.sum(labels[AU]==1) > min_pos_sample:\n", + " replace = False\n", + " else:\n", + " replace = True\n", + " newSample = labels[labels[AU]>.5].sample(min_pos_sample, replace=replace, random_state=0)\n", + " balX = pd.concat([balX, newSample])\n", + " balY = pd.concat([balY, landmarks.loc[newSample.index]])\n", + "X = balX[au_cols].values\n", + "y = registration(balY.values, neutral)\n", + "\n", + "# Model Accuracy in KFold CV\n", + "print(\"Evaluating model with KFold CV\")\n", + "n_components=len(au_cols)\n", + "kf = KFold(n_splits=3)\n", + "scores = []\n", + "for train_index, test_index in kf.split(X):\n", + " X_train,X_test = X[train_index],X[test_index]\n", + " y_train,y_test = y[train_index],y[test_index]\n", + " clf = PLSRegression(n_components=n_components, max_iter=2000)\n", + " clf.fit(X_train,y_train)\n", + " scores.append(clf.score(X_test,y_test))\n", + "print('3-fold accuracy mean', np.round(np.mean(scores),2))\n", + "\n", + "# Train real model\n", + "clf = PLSRegression(n_components=n_components, max_iter=2000)\n", + "clf.fit(X,y)\n", + "print('N_comp:',n_components,'Rsquare', np.round(clf.score(X,y),2))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:28:12.319990Z", + "start_time": "2021-03-27T00:28:12.316860Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(10000, 20)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We visualize the results of our model. The regression was trained on labels 0-1 so we do not recommend exceeding 1 for the intensity. Setting the intensity to 2 will exaggerate the face and anything beyond that might give you strange faces. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T23:58:58.227979Z", + "start_time": "2021-03-26T23:58:57.624279Z" + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot results for each action unit\n", + "f,axes = plt.subplots(5,4,figsize=(12,18))\n", + "axes = axes.flatten()\n", + "# Exaggerate the intensity of the expression for clearer visualization. \n", + "# We do not recommend exceeding 2. \n", + "intensity = 2\n", + "for aui, auname in enumerate(axes):\n", + " try:\n", + " auname=au_cols[aui]\n", + " au = np.zeros(clf.n_components)\n", + " au[aui] = intensity\n", + " predicted = clf.predict([au]).reshape(2,68)\n", + " plot_face(au=au, model=clf,\n", + " vectorfield={\"reference\": neutral.T, 'target': predicted,\n", + " 'color':'r','alpha':.6},\n", + " ax = axes[aui])\n", + " axes[aui].set(title=auname)\n", + " except:\n", + " pass\n", + " finally:\n", + " ax = axes[aui]\n", + " ax.axes.get_xaxis().set_visible(False)\n", + " ax.axes.get_yaxis().set_visible(False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is how we would export our model into an h5 format which can be loaded using our load_h5 function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# save out trained model\n", + "# import h5py\n", + "# hf = h5py.File('../feat/resources/pyfeat_aus_to_landmarks.h5', 'w')\n", + "# hf.create_dataset('coef', data=clf.coef_)\n", + "# hf.create_dataset('x_mean', data=clf._x_mean)\n", + "# hf.create_dataset('x_std', data=clf._x_std)\n", + "# hf.create_dataset('y_mean', data=clf._y_mean)\n", + "# hf.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:43:45.310255Z", + "start_time": "2021-03-26T04:43:45.307443Z" + } + }, + "source": [ + "## Preprocessing datasets\n", + "Here we provide sample code for how you might preprocess the datasets to be used in this tutorial. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from PIL import Image, ImageOps\n", + "import math, cv2, csv\n", + "from scipy.spatial import ConvexHull\n", + "from skimage.morphology.convex_hull import grid_points_in_poly\n", + "from feat import Detector\n", + "import os, glob, pandas as pd, numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from skimage import data, exposure\n", + "from skimage.feature import hog\n", + "from tqdm import tqdm\n", + "\n", + "def padding(img, expected_size):\n", + " desired_size = expected_size\n", + " delta_width = desired_size - img.size[0]\n", + " delta_height = desired_size - img.size[1]\n", + " pad_width = delta_width // 2\n", + " pad_height = delta_height // 2\n", + " padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)\n", + " return ImageOps.expand(img, padding)\n", + "\n", + "def resize_with_padding(img, expected_size):\n", + " img.thumbnail((expected_size[0], expected_size[1]))\n", + " delta_width = expected_size[0] - img.size[0]\n", + " delta_height = expected_size[1] - img.size[1]\n", + " pad_width = delta_width // 2\n", + " pad_height = delta_height // 2\n", + " padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)\n", + " return ImageOps.expand(img, padding)\n", + "\n", + "def align_face_68pts(img, img_land, box_enlarge, img_size=112):\n", + " \"\"\"\n", + " img: image\n", + " img_land: landmarks 68\n", + " box_enlarge: relative size of face\n", + " img_size = 112\n", + " \n", + " \"\"\"\n", + " leftEye0 = (img_land[2 * 36] + img_land[2 * 37] + img_land[2 * 38] + img_land[2 * 39] + img_land[2 * 40] +\n", + " img_land[2 * 41]) / 6.0\n", + " leftEye1 = (img_land[2 * 36 + 1] + img_land[2 * 37 + 1] + img_land[2 * 38 + 1] + img_land[2 * 39 + 1] +\n", + " img_land[2 * 40 + 1] + img_land[2 * 41 + 1]) / 6.0\n", + " rightEye0 = (img_land[2 * 42] + img_land[2 * 43] + img_land[2 * 44] + img_land[2 * 45] + img_land[2 * 46] +\n", + " img_land[2 * 47]) / 6.0\n", + " rightEye1 = (img_land[2 * 42 + 1] + img_land[2 * 43 + 1] + img_land[2 * 44 + 1] + img_land[2 * 45 + 1] +\n", + " img_land[2 * 46 + 1] + img_land[2 * 47 + 1]) / 6.0\n", + " deltaX = (rightEye0 - leftEye0)\n", + " deltaY = (rightEye1 - leftEye1)\n", + " l = math.sqrt(deltaX * deltaX + deltaY * deltaY)\n", + " sinVal = deltaY / l\n", + " cosVal = deltaX / l\n", + " mat1 = np.mat([[cosVal, sinVal, 0], [-sinVal, cosVal, 0], [0, 0, 1]])\n", + " mat2 = np.mat([[leftEye0, leftEye1, 1], [rightEye0, rightEye1, 1], [img_land[2 * 30], img_land[2 * 30 + 1], 1],\n", + " [img_land[2 * 48], img_land[2 * 48 + 1], 1], [img_land[2 * 54], img_land[2 * 54 + 1], 1]])\n", + " mat2 = (mat1 * mat2.T).T\n", + " cx = float((max(mat2[:, 0]) + min(mat2[:, 0]))) * 0.5\n", + " cy = float((max(mat2[:, 1]) + min(mat2[:, 1]))) * 0.5\n", + " if (float(max(mat2[:, 0]) - min(mat2[:, 0])) > float(max(mat2[:, 1]) - min(mat2[:, 1]))):\n", + " halfSize = 0.5 * box_enlarge * float((max(mat2[:, 0]) - min(mat2[:, 0])))\n", + " else:\n", + " halfSize = 0.5 * box_enlarge * float((max(mat2[:, 1]) - min(mat2[:, 1])))\n", + " scale = (img_size - 1) / 2.0 / halfSize\n", + " mat3 = np.mat([[scale, 0, scale * (halfSize - cx)], [0, scale, scale * (halfSize - cy)], [0, 0, 1]])\n", + " mat = mat3 * mat1\n", + " aligned_img = cv2.warpAffine(img, mat[0:2, :], (img_size, img_size), cv2.INTER_LINEAR, borderValue=(128, 128, 128))\n", + " land_3d = np.ones((int(len(img_land)/2), 3))\n", + " land_3d[:, 0:2] = np.reshape(np.array(img_land), (int(len(img_land)/2), 2))\n", + " mat_land_3d = np.mat(land_3d)\n", + " new_land = np.array((mat * mat_land_3d.T).T)\n", + " new_land = np.array(list(zip(new_land[:,0], new_land[:,1]))).astype(int)\n", + " return aligned_img, new_land\n", + "\n", + "def extract_hog(image, detector):\n", + " im = cv2.imread(image)\n", + " detected_faces = np.array(detector.detect_faces(im)[0])\n", + " if np.any(detected_faces<0):\n", + " orig_size = np.array(im).shape\n", + " if np.where(detected_faces<0)[0][0]==1: \n", + " new_size = (orig_size[0], int(orig_size[1] + 2*abs(detected_faces[detected_faces<0][0])))\n", + " else:\n", + " new_size = (int(orig_size[0] + 2*abs(detected_faces[detected_faces<0][0])), orig_size[1])\n", + " im = resize_with_padding(Image.fromarray(im), new_size)\n", + " im = np.asarray(im)\n", + " detected_faces = np.array(detector.detect_faces(np.array(im))[0])\n", + " detected_faces = detected_faces.astype(int)\n", + " points = detector.detect_landmarks(np.array(im), [detected_faces])[0].astype(int)\n", + "\n", + " aligned_img, points = align_face_68pts(im, points.flatten(), 2.5)\n", + "\n", + " hull = ConvexHull(points)\n", + " mask = grid_points_in_poly(shape=np.array(aligned_img).shape, \n", + " verts= list(zip(points[hull.vertices][:,1], points[hull.vertices][:,0])) # for some reason verts need to be flipped\n", + " )\n", + "\n", + " mask[0:np.min([points[0][1], points[16][1]]), points[0][0]:points[16][0]] = True\n", + " aligned_img[~mask] = 0\n", + " resized_face_np = aligned_img\n", + "\n", + " fd, hog_image = hog(resized_face_np, orientations=8, pixels_per_cell=(8, 8),\n", + " cells_per_block=(2, 2), visualize=True, multichannel=True)\n", + "\n", + " return fd, hog_image, points" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Replace the paths so that it points to your local dataset directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "detector = Detector(face_model = \"retinaface\", landmark_model=\"mobilenet\")\n", + "# Correct path to your downloaded dataset.\n", + "EmotioNet_images = np.sort(glob.glob(\"/Storage/Data/EmotioNet/imgs/*.jpg\"))\n", + "labels = pd.read_csv(\"/Storage/Data/EmotioNet/labels/EmotioNet_FACS_aws_2020_24600.csv\")\n", + "labels = labels.dropna(axis=0)\n", + "for col in labels.columns:\n", + " if \"AU\" in col:\n", + " kwargs = {col.replace(\"'\", '').replace('\"', '').replace(\" \",\"\"): labels[[col]]}\n", + " labels = labels.assign(**kwargs)\n", + " labels = labels.drop(columns = col)\n", + "labels = labels.assign(URL = labels.URL.apply(lambda x: x.split(\"/\")[-1].replace(\"'\", \"\")))\n", + "labels = labels.set_index('URL')\n", + "labels = labels.drop(columns = [\"URL orig\"])\n", + "\n", + "aus_to_train = ['AU1','AU2','AU4','AU5', \"AU6\", \"AU9\",\"AU10\", \"AU12\", \"AU15\",\"AU17\", \n", + " \"AU18\",\"AU20\", \"AU24\", \"AU25\", \"AU26\", \"AU28\", \"AU43\"]\n", + "\n", + "with open('emotionet_labels.csv', \"w\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow([\"URL\"] + aus_to_train)\n", + " \n", + "landmark_cols = [f\"x_{i}\" for i in range(68)] + [f\"y_{i}\" for i in range(68)]\n", + "with open('emotionet_landmarks.csv', \"w\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow(landmark_cols)\n", + " \n", + "for ix, image in enumerate(tqdm(EmotioNet_images)):\n", + " try:\n", + " imageURL = os.path.split(image)[-1]\n", + " label = labels.loc[imageURL][aus_to_train]\n", + " fd, _, points = extract_hog(image, detector=detector)\n", + " with open('emotionet_labels.csv', \"a+\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow([imageURL]+list(label.values))\n", + " with open('emotionet_landmarks.csv', \"a+\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow(points.T.flatten())\n", + " except:\n", + " print(f\"failed {image}\")" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "toc": { + "nav_menu": { + "height": "81px", + "width": "252px" + }, + "navigate_menu": true, + "number_sections": true, + "sideBar": true, + "threshold": 4, + "toc_cell": false, + "toc_section_display": "block", + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/_build/html/_sources/content/intro.md b/notebooks/_build/html/_sources/content/intro.md index 017b2d55..a54bbdc5 100644 --- a/notebooks/_build/html/_sources/content/intro.md +++ b/notebooks/_build/html/_sources/content/intro.md @@ -1,11 +1,11 @@ Py-Feat: Python Facial Expression Analysis Toolbox ============================ - -[![Build Status](https://api.travis-ci.org/cosanlab/feat.svg?branch=master)](https://travis-ci.org/cosanlab/feat/) +[![Package versioning](https://img.shields.io/pypi/v/py-feat.svg)](https://pypi.org/project/py-feat/) +[![Build Status](https://api.travis-ci.org/cosanlab/py-feat.svg?branch=master)](https://travis-ci.org/cosanlab/py-feat/) [![Coverage Status](https://coveralls.io/repos/github/cosanlab/py-feat/badge.svg?branch=master)](https://coveralls.io/github/cosanlab/py-feat?branch=master) -[![GitHub forks](https://img.shields.io/github/forks/cosanlab/py-feat)](https://github.com/cosanlab/feat/network) -[![GitHub stars](https://img.shields.io/github/stars/cosanlab/py-feat)](https://github.com/cosanlab/feat/stargazers) -[![GitHub license](https://img.shields.io/github/license/cosanlab/py-feat)](https://github.com/cosanlab/feat/blob/master/LICENSE) +[![GitHub forks](https://img.shields.io/github/forks/cosanlab/py-feat)](https://github.com/cosanlab/py-feat/network) +[![GitHub stars](https://img.shields.io/github/stars/cosanlab/py-feat)](https://github.com/cosanlab/py-feat/stargazers) +[![GitHub license](https://img.shields.io/github/license/cosanlab/py-feat)](https://github.com/cosanlab/py-feat/blob/master/LICENSE) Py-Feat provides a comprehensive set of tools and models to easily detect facial expressions (Action Units, emotions, facial landmarks) from images and videos, preprocess & analyze facial expression data, and visualize facial expression data. diff --git a/notebooks/_build/html/content/analysis.html b/notebooks/_build/html/content/analysis.html index b38bda15..5ccdec90 100644 --- a/notebooks/_build/html/content/analysis.html +++ b/notebooks/_build/html/content/analysis.html @@ -369,8 +369,8 @@

Py-Feat

  • - - Intersubject correlations + + Intersubject (or intervideo) correlations
  • @@ -894,6 +894,11 @@

    Simple t-test -

    Intersubject correlations¶

    -

    To compare the similarity of signals over time between subjects or videos, you can use the isc method. You can get a sense of how much two signals, such as a certain action unit activity, correlates over time. For example, if you want to see how AU01 activations are similar across the videos, you can do the following which shows that the temporal profile of AU01 activations form two clusters between the goodNews and the badNews conditions.

    +
    +

    Intersubject (or intervideo) correlations¶

    +

    To compare the similarity of signals over time between subjects or videos, you can use the isc method. You can get a sense of how much two signals, such as a certain action unit activity, correlates over time.

    +

    In this example, we are calculating the ISC over videos. We want to check how similar AU01 activations are across videos so our session is set to the input which is the video name. Executing the isc method shows that the temporal profile of AU01 activations form two clusters between the goodNews and the badNews conditions.

    fex.sessions = fex.input()
    diff --git a/notebooks/_build/html/content/contribute.html b/notebooks/_build/html/content/contribute.html
    index a4b628f6..cf8593bf 100644
    --- a/notebooks/_build/html/content/contribute.html
    +++ b/notebooks/_build/html/content/contribute.html
    @@ -130,11 +130,6 @@ 

    Py-Feat

    Analyzing FEX data -
  • - - Two sample independent t-test - -
  • Plotting examples diff --git a/notebooks/_build/html/content/detector.html b/notebooks/_build/html/content/detector.html index c6034a2a..538de0a6 100644 --- a/notebooks/_build/html/content/detector.html +++ b/notebooks/_build/html/content/detector.html @@ -131,11 +131,6 @@

    Py-Feat

    Analyzing FEX data
  • -
  • - - Two sample independent t-test - -
  • Plotting examples diff --git a/notebooks/_build/html/content/dev_plotting.html b/notebooks/_build/html/content/dev_plotting.html new file mode 100644 index 00000000..f6d07665 --- /dev/null +++ b/notebooks/_build/html/content/dev_plotting.html @@ -0,0 +1,813 @@ + + + + + + + + Plotting examples — Py-Feat + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + + + + + +
    + + +
    +
    + +
    + +
    +

    Plotting examples¶

    +

    written by Jin Hyun Cheong

    +

    Included in the toolbox are two models for Action Units to landmark visualization. The ‘pyfeat_aus_to_landmarks.h5’ model was created by using landmarks extracted using Py-FEAT to align each face in the dataset to a neutral face with numpy’s least squares function. Then the PLS model was trained using Action Unit labels to predict transformed landmark data.

    +

    Draw a standard neutral face. Figsize can be altered but a ratio of 4:5 is recommended.

    +
    +
    +
    # Load modules
    +%matplotlib inline
    +from feat.plotting import plot_face, predict
    +import numpy as np
    +import matplotlib.pyplot as plt
    +
    +plot_face(au=np.zeros(20))
    +
    +
    +
    +
    +
    <AxesSubplot:>
    +
    +
    +../_images/dev_plotting_2_1.png +
    +
    +
    +

    Draw lineface using input vector¶

    +

    Affectiva vectors should be divided by twenty for use with our ‘blue’ model.

    +
    +
    +
    from feat.plotting import plot_face
    +import numpy as np
    +import matplotlib.pyplot as plt
    +
    +# Add data, AU is ordered as such: 
    +# AU1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43
    +
    +# Activate AU1: Inner brow raiser 
    +au = np.array([5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
    +
    +vectors = {'target': predict(au),
    +           'reference':  neutral, 'color': 'blue'}
    +
    +# Plot face
    +fig, axes = plt.subplots(1,2)
    +plot_face(model=None, vectorfield = vectors,
    +          ax = axes[0], au = np.zeros(20), color='k', linewidth=1, linestyle='-')
    +plot_face(model=None, vectorfield = vectors,
    +          ax = axes[1], au = np.array(au), color='k', linewidth=1, linestyle='-')
    +
    +
    +
    +
    +
    <AxesSubplot:>
    +
    +
    +../_images/dev_plotting_5_1.png +
    +
    +
    +
    +
    %config InlineBackend.figure_format = 'retina'
    +intensity = 2
    +
    +
    +
    +
    +
    +
    +
    f,axes = plt.subplots(1, 4, figsize=(15,4))
    +ax = axes[0]
    +import seaborn as sns
    +sns.set_context("paper", font_scale=1.5)
    +# Add data, AU is ordered as such: 
    +# AU1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43
    +
    +# Activate AU1: Inner brow raiser 
    +au = np.array([intensity, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
    +vectors = {'target': predict(au),
    +           'reference':  neutral, 'color': 'blue'}
    +
    +plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},
    +          ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')
    +ax.set_title("AU1: Inner brow raiser")
    +
    +ax = axes[1]
    +au = np.array([0, 0, 0, 0, 0, 0, 0, 0, intensity, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
    +vectors = {'target': predict(au),
    +           'reference':  neutral, 'color': 'blue'}
    +
    +plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},
    +          ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')
    +ax.set_title("AU12: Lip corner puller")
    +
    +ax = axes[2]
    +au = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, intensity, 0, 0, 0, 0, 0, 0, 0, 0, 0])
    +vectors = {'target': predict(au),
    +           'reference':  neutral, 'color': 'blue'}
    +plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},
    +          ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')
    +ax.set_title("AU15: Lip corner depressor")
    +
    +ax = axes[3]
    +au = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, intensity])
    +vectors = {'target': predict(au),
    +           'reference':  neutral, 'color': 'blue'}
    +
    +plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},
    +          ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')
    +ax.set_title("AU43: Eye closer")
    +
    +
    +
    +
    +
    Text(0.5, 1.0, 'AU43: Eye closer')
    +
    +
    +../_images/dev_plotting_7_1.png +
    +
    +
    +
    +
    f,axes = plt.subplots(1, 4, figsize=(15,4))
    +ax = axes[0]
    +import seaborn as sns
    +sns.set_context("paper", font_scale=1.5)
    +# Add data, AU is ordered as such: 
    +# AU1, 2, 4, 5, 6, 
    +# 7, 9, 10, 12, 14, 
    +# 15, 17, 18, 20, 23, 
    +# 24, 25, 26, 28, 43
    +intensity = 2
    +# Activate AU1: Inner brow raiser 
    +au = np.array([0, 
    +               0, 
    +               0, 
    +               0, 
    +               intensity, 
    +               0, 
    +               0, 
    +               0, 
    +               intensity, 
    +               0, 
    +               0, 
    +               0, 
    +               0, 
    +               0, 
    +               0, 
    +               0, 
    +               0, 
    +               0, 
    +               0, 
    +               0])
    +vectors = {'target': predict(au),
    +           'reference':  neutral, 'color': 'blue'}
    +
    +plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},
    +          ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')
    +ax.set_title("Happiness")
    +
    +ax = axes[1]
    +au = np.array([intensity, 
    +               0, 
    +               intensity, 
    +               0, 0, 0, 0, 0, 0, 0, intensity, 0, 0, 0, 0, 0, 0, 0, 0, 0])
    +vectors = {'target': predict(au),
    +           'reference':  neutral, 'color': 'blue'}
    +
    +plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},
    +          ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')
    +ax.set_title("Sadness")
    +
    +ax = axes[2]
    +au = np.array([intensity, intensity, 0, intensity, 0, 0, 0, 0, 0, 0, 
    +               0, 0, 0, 0, 0, 0, 0, intensity, 0, 0])
    +vectors = {'target': predict(au),
    +           'reference':  neutral, 'color': 'blue'}
    +plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},
    +          ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')
    +ax.set_title("Surprise")
    +
    +ax = axes[3]
    +
    +# FEAR
    +au = np.array([intensity, intensity, intensity, intensity, 0, 
    +               intensity, 0, 0, 0, 0, 
    +               0, 0, 0, intensity, 0, 
    +               0, 0, intensity, 0, 0])
    +vectors = {'target': predict(au),
    +           'reference':  neutral, 'color': 'blue'}
    +
    +plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},
    +          ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')
    +ax.set_title("Fear")
    +
    +
    +
    +
    +
    Text(0.5, 1.0, 'Fear')
    +
    +
    +../_images/dev_plotting_8_1.png +
    +
    +
    +
    +

    Add a vectorfield with arrows from the changed face back to neutral and vice versa¶

    +
    +
    +
    from feat.plotting import plot_face, predict
    +from feat.utils import load_h5
    +import numpy as np
    +import matplotlib.pyplot as plt
    +
    +model = load_h5('pyfeat_aus_to_landmarks.h5')
    +# Add data activate AU1, and AU12
    +au = np.array([2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ])
    +
    +# Get neutral landmarks
    +neutral = predict(np.zeros(len(au)))
    +
    +# Provide target landmarks and other vector specifications
    +vectors = {'target': predict(au),
    +           'reference':  neutral, 'color': 'blue'}
    +
    +fig, axes = plt.subplots(1,2)
    +# Plot face where vectorfield goes from neutral to target, with target as final face
    +plot_face(model = model, ax = axes[0], au = np.array(au), 
    +            vectorfield = vectors, color='k', linewidth=1, linestyle='-')
    +
    +# Plot face where vectorfield goes from neutral to target, with neutral as base face
    +plot_face(model = model, ax = axes[1], au = np.zeros(len(au)), 
    +            vectorfield = vectors, color='k', linewidth=1, linestyle='-')
    +
    +
    +
    +
    +
    <AxesSubplot:>
    +
    +
    +../_images/dev_plotting_12_1.png +
    +
    +
    +
    +

    Add muscle heatmaps to the plot¶

    +
    +
    +
    from feat.plotting import plot_face
    +from feat.utils import load_h5
    +import numpy as np
    +import matplotlib.pyplot as plt
    +
    +# Add data
    +model = load_h5()
    +
    +au = np.array([2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
    +
    +# Add some muscles
    +muscles = {'orb_oris_l': 'yellow', 'orb_oris_u': "blue"}
    +muscles = {'all': 'heatmap'}
    +
    +plot_face(model=model, au = np.array(au), 
    +          muscles = muscles, color='k', linewidth=1, linestyle='-')
    +
    +
    +
    +
    +
    <AxesSubplot:>
    +
    +
    +../_images/dev_plotting_14_1.png +
    +
    +
    +
    +
    from feat.plotting import plot_face
    +from feat.utils import load_h5
    +import numpy as np
    +import matplotlib.pyplot as plt
    +
    +# Add data
    +au = [0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, 
    +      0.450328, 1.02961, 0.871225, 0, 1.1977,  0.457218, 0, 0, 0, 0]
    +au = np.array([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
    +
    +# Add some muscles
    +muscles = {'all': 'heatmap'}
    +
    +# Plot face
    +plot_face(model=None, au = np.array(au), muscles = muscles, color='k', linewidth=1, linestyle='-')
    +
    +
    +
    +
    +
    <AxesSubplot:>
    +
    +
    +../_images/dev_plotting_15_1.png +
    +
    +
    +
    +

    Make sure muscle array contains ‘facet’ for a facet heatmap¶

    +
    +
    +
    from feat.plotting import plot_face
    +from feat.utils import load_h5
    +import numpy as np
    +import matplotlib.pyplot as plt
    +
    +# Add data
    +au = np.array([0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, 
    +      0.450328, 1.02961, 0.871225, 0, 1.1977,  0.457218, 0, 0, 0, 0])
    +
    +# Load a model 
    +model = load_h5()
    +
    +# Add muscles
    +muscles = {'all': 'heatmap', 'facet': 1}
    +
    +# Plot face
    +plot_face(model=model, au = au, muscles = muscles, color='k', linewidth=1, linestyle='-')
    +
    +
    +
    +
    +
    <AxesSubplot:>
    +
    +
    +../_images/dev_plotting_17_1.png +
    +
    +
    +
    +

    Add gaze vectors¶

    +

    Add gaze vectors to indicate where the eyes are looking.
    +Gaze vectors are length 4 (lefteye_x, lefteye_y, righteye_x, righteye_y) where the y orientation is positive for looking upwards.

    +
    +
    +
    from feat.plotting import plot_face
    +from feat.utils import load_h5
    +import numpy as np
    +import matplotlib.pyplot as plt
    +
    +# Add data
    +au = np.zeros(20)
    +
    +# Add some gaze vectors: (lefteye_x, lefteye_y, righteye_x, righteye_y)
    +gaze = [-1, 5, 1, 5]
    +
    +# Plot face
    +plot_face(model=None, au = au, gaze = gaze, color='k', linewidth=1, linestyle='-')
    +
    +
    +
    +
    +
    <AxesSubplot:>
    +
    +
    +../_images/dev_plotting_19_1.png +
    +
    +
    +
    +

    Call plot method on Fex instances¶

    +

    It is possible to call the plot_aus method within openface, facet, affdex fex instances

    +

    OpenFace

    +
    +
    +
    from feat.plotting import plot_face
    +import numpy as np
    +import matplotlib.pyplot as plt
    +from feat.utils import  load_h5, get_resource_path, read_openface
    +from feat.tests.utils import get_test_data_path
    +from os.path import join
    +
    +test_file = join(get_test_data_path(),'OpenFace_Test.csv')
    +openface = read_openface(test_file)
    +openface.plot_aus(12, muscles={'all': "heatmap"}, gaze = None)
    +
    +
    +
    +
    +
    <AxesSubplot:>
    +
    +
    +../_images/dev_plotting_22_1.png +
    +
    +
    +
    + + + + +
    + + +
    + + +
    + +
    +
    +
    +
    +

    + + By Jin Hyun Cheong
    + + © Copyright 2020.
    +

    +
    +
    +
    + + +
    +
    + + + + + + + + \ No newline at end of file diff --git a/notebooks/_build/html/content/dev_trainAUvisModel.html b/notebooks/_build/html/content/dev_trainAUvisModel.html new file mode 100644 index 00000000..203aa3c5 --- /dev/null +++ b/notebooks/_build/html/content/dev_trainAUvisModel.html @@ -0,0 +1,733 @@ + + + + + + + + Training AU visualization model — Py-Feat + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + + + + + +
    + +
    +
    + +
    + + + + + + + + + + + + + + + + + + +
    + + +
    + +
    + Contents +
    + + +
    +
    +
    +
    +
    + +
    + +
    +

    Training AU visualization model¶

    +

    You will first need to gather the datasets for training. In this tutorial we use the datasets EmotioNet, DISFA Plus, and BP4d. After you download each model you should extract the labels and landmarks from each dataset. Detailed code on how to do that is described at the bottom of this tutorial. Once you have the labels and landmark files for each dataset you can train the AU visualization model with the following.

    +
    +
    +
    %matplotlib inline
    +import pandas as pd, numpy as np, matplotlib.pyplot as plt
    +from sklearn.cross_decomposition import PLSRegression
    +from sklearn.model_selection import KFold
    +from feat.plotting import predict, plot_face
    +from feat.utils import registration, neutral
    +from natsort import natsorted
    +import os, glob 
    +import pandas as pd, numpy as np
    +import seaborn as sns
    +sns.set_style("white")
    +
    +au_cols = [1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43]
    +au_cols = [f"AU{au}" for au in au_cols]
    +
    +base_dir = "/Storage/Projects/feat_benchmark/scripts/jcheong/openface_train"
    +
    +labels_emotionet = pd.read_csv(os.path.join(base_dir, "emotionet_labels.csv"))
    +landmarks_emotionet = pd.read_csv(os.path.join(base_dir, "emotionet_landmarks.csv"))
    +print("EmotioNet: ", len(labels_emotionet))
    +
    +labels_disfaplus = pd.read_csv(os.path.join(base_dir, "disfaplus_labels.csv"))
    +landmarks_disfaplus = pd.read_csv(os.path.join(base_dir, "disfaplus_landmarks.csv"))
    +# Disfa is rescaled to 0 - 1
    +disfaplus_aus = [col for col in labels_disfaplus.columns if "AU" in col]
    +labels_disfaplus[disfaplus_aus] = labels_disfaplus[disfaplus_aus].astype('float')/5
    +print("DISFA Plus: ", len(labels_disfaplus))
    +
    +labels_bp4d = pd.read_csv(os.path.join(base_dir, "bp4d_labels.csv"))
    +landmarks_bp4d = pd.read_csv(os.path.join(base_dir, "bp4d_landmarks.csv"))
    +bp4d_pruned_idx = labels_bp4d.replace({9: np.nan})[au_cols].dropna(axis=1).index
    +print("BP4D: ", len(labels_bp4d))
    +
    +
    +
    +
    +
    EmotioNet:  24587
    +DISFA Plus:  57668
    +BP4D:  143951
    +
    +
    +
    +
    +

    We aggregate the datasets and specify the AUs we want to train.

    +
    +
    +
    labels = pd.concat([
    +                    labels_emotionet.replace({999: np.nan}), 
    +                    labels_disfaplus,
    +                    labels_bp4d.replace({9: np.nan}).iloc[bp4d_pruned_idx,:]
    +                   ]).reset_index(drop=True)
    +landmarks = pd.concat([
    +                       landmarks_emotionet, 
    +                       landmarks_disfaplus,
    +                       landmarks_bp4d.iloc[bp4d_pruned_idx,:]
    +                      ]).reset_index(drop=True)
    +
    +landmarks = landmarks.iloc[labels.index]
    +
    +
    +labels = labels[au_cols].fillna(0)
    +
    +
    +
    +
    +

    We train our model using PLSRegression with a minimum of 500 samples for each AU activation. We evaluate the model in a 3-fold split and retrain the model with all the data which is distributed with the package.

    +
    +
    +
    min_pos_sample = 500
    +
    +print('Pseudo balancing samples')
    +balY = pd.DataFrame()
    +balX = pd.DataFrame()
    +for AU in labels[au_cols].columns:
    +    if np.sum(labels[AU]==1) > min_pos_sample:
    +        replace = False
    +    else:
    +        replace = True
    +    newSample = labels[labels[AU]>.5].sample(min_pos_sample, replace=replace, random_state=0)
    +    balX = pd.concat([balX, newSample])
    +    balY = pd.concat([balY, landmarks.loc[newSample.index]])
    +X = balX[au_cols].values
    +y = registration(balY.values, neutral)
    +
    +# Model Accuracy in KFold CV
    +print("Evaluating model with KFold CV")
    +n_components=len(au_cols)
    +kf = KFold(n_splits=3)
    +scores = []
    +for train_index, test_index in kf.split(X):
    +    X_train,X_test = X[train_index],X[test_index]
    +    y_train,y_test = y[train_index],y[test_index]
    +    clf = PLSRegression(n_components=n_components, max_iter=2000)
    +    clf.fit(X_train,y_train)
    +    scores.append(clf.score(X_test,y_test))
    +print('3-fold accuracy mean', np.round(np.mean(scores),2))
    +
    +# Train real model
    +clf = PLSRegression(n_components=n_components, max_iter=2000)
    +clf.fit(X,y)
    +print('N_comp:',n_components,'Rsquare', np.round(clf.score(X,y),2))
    +
    +
    +
    +
    +
    Pseudo balancing samples
    +Evaluating model with KFold CV
    +3-fold accuracy mean 0.13
    +N_comp: 20 Rsquare 0.15
    +
    +
    +
    +
    +
    +
    +
    X.shape
    +
    +
    +
    +
    +
    (10000, 20)
    +
    +
    +
    +
    +

    We visualize the results of our model. The regression was trained on labels 0-1 so we do not recommend exceeding 1 for the intensity. Setting the intensity to 2 will exaggerate the face and anything beyond that might give you strange faces.

    +
    +
    +
    # Plot results for each action unit
    +f,axes = plt.subplots(5,4,figsize=(12,18))
    +axes = axes.flatten()
    +# Exaggerate the intensity of the expression for clearer visualization. 
    +# We do not recommend exceeding 2. 
    +intensity = 2
    +for aui, auname in enumerate(axes):
    +    try:
    +        auname=au_cols[aui]
    +        au = np.zeros(clf.n_components)
    +        au[aui] = intensity
    +        predicted = clf.predict([au]).reshape(2,68)
    +        plot_face(au=au, model=clf,
    +                  vectorfield={"reference": neutral.T, 'target': predicted,
    +                               'color':'r','alpha':.6},
    +                 ax = axes[aui])
    +        axes[aui].set(title=auname)
    +    except:
    +        pass
    +    finally:
    +        ax = axes[aui]
    +        ax.axes.get_xaxis().set_visible(False)
    +        ax.axes.get_yaxis().set_visible(False)
    +
    +
    +
    +
    +../_images/dev_trainAUvisModel_8_0.png +
    +
    +

    Here is how we would export our model into an h5 format which can be loaded using our load_h5 function.

    +
    +
    +
    # save out trained model
    +# import h5py
    +# hf = h5py.File('../feat/resources/pyfeat_aus_to_landmarks.h5', 'w')
    +# hf.create_dataset('coef', data=clf.coef_)
    +# hf.create_dataset('x_mean', data=clf._x_mean)
    +# hf.create_dataset('x_std', data=clf._x_std)
    +# hf.create_dataset('y_mean', data=clf._y_mean)
    +# hf.close()
    +
    +
    +
    +
    +
    +

    Preprocessing datasets¶

    +

    Here we provide sample code for how you might preprocess the datasets to be used in this tutorial.

    +
    +
    +
    from PIL import Image, ImageOps
    +import math, cv2, csv
    +from scipy.spatial import ConvexHull
    +from skimage.morphology.convex_hull import grid_points_in_poly
    +from feat import Detector
    +import os, glob, pandas as pd, numpy as np
    +import matplotlib.pyplot as plt
    +from skimage import data, exposure
    +from skimage.feature import hog
    +from tqdm import tqdm
    +
    +def padding(img, expected_size):
    +    desired_size = expected_size
    +    delta_width = desired_size - img.size[0]
    +    delta_height = desired_size - img.size[1]
    +    pad_width = delta_width // 2
    +    pad_height = delta_height // 2
    +    padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)
    +    return ImageOps.expand(img, padding)
    +
    +def resize_with_padding(img, expected_size):
    +    img.thumbnail((expected_size[0], expected_size[1]))
    +    delta_width = expected_size[0] - img.size[0]
    +    delta_height = expected_size[1] - img.size[1]
    +    pad_width = delta_width // 2
    +    pad_height = delta_height // 2
    +    padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)
    +    return ImageOps.expand(img, padding)
    +
    +def align_face_68pts(img, img_land, box_enlarge, img_size=112):
    +    """
    +    img: image
    +    img_land: landmarks 68
    +    box_enlarge: relative size of face
    +    img_size = 112
    +    
    +    """
    +    leftEye0 = (img_land[2 * 36] + img_land[2 * 37] + img_land[2 * 38] + img_land[2 * 39] + img_land[2 * 40] +
    +                img_land[2 * 41]) / 6.0
    +    leftEye1 = (img_land[2 * 36 + 1] + img_land[2 * 37 + 1] + img_land[2 * 38 + 1] + img_land[2 * 39 + 1] +
    +                img_land[2 * 40 + 1] + img_land[2 * 41 + 1]) / 6.0
    +    rightEye0 = (img_land[2 * 42] + img_land[2 * 43] + img_land[2 * 44] + img_land[2 * 45] + img_land[2 * 46] +
    +                 img_land[2 * 47]) / 6.0
    +    rightEye1 = (img_land[2 * 42 + 1] + img_land[2 * 43 + 1] + img_land[2 * 44 + 1] + img_land[2 * 45 + 1] +
    +                 img_land[2 * 46 + 1] + img_land[2 * 47 + 1]) / 6.0
    +    deltaX = (rightEye0 - leftEye0)
    +    deltaY = (rightEye1 - leftEye1)
    +    l = math.sqrt(deltaX * deltaX + deltaY * deltaY)
    +    sinVal = deltaY / l
    +    cosVal = deltaX / l
    +    mat1 = np.mat([[cosVal, sinVal, 0], [-sinVal, cosVal, 0], [0, 0, 1]])
    +    mat2 = np.mat([[leftEye0, leftEye1, 1], [rightEye0, rightEye1, 1], [img_land[2 * 30], img_land[2 * 30 + 1], 1],
    +                   [img_land[2 * 48], img_land[2 * 48 + 1], 1], [img_land[2 * 54], img_land[2 * 54 + 1], 1]])
    +    mat2 = (mat1 * mat2.T).T
    +    cx = float((max(mat2[:, 0]) + min(mat2[:, 0]))) * 0.5
    +    cy = float((max(mat2[:, 1]) + min(mat2[:, 1]))) * 0.5
    +    if (float(max(mat2[:, 0]) - min(mat2[:, 0])) > float(max(mat2[:, 1]) - min(mat2[:, 1]))):
    +        halfSize = 0.5 * box_enlarge * float((max(mat2[:, 0]) - min(mat2[:, 0])))
    +    else:
    +        halfSize = 0.5 * box_enlarge * float((max(mat2[:, 1]) - min(mat2[:, 1])))
    +    scale = (img_size - 1) / 2.0 / halfSize
    +    mat3 = np.mat([[scale, 0, scale * (halfSize - cx)], [0, scale, scale * (halfSize - cy)], [0, 0, 1]])
    +    mat = mat3 * mat1
    +    aligned_img = cv2.warpAffine(img, mat[0:2, :], (img_size, img_size), cv2.INTER_LINEAR, borderValue=(128, 128, 128))
    +    land_3d = np.ones((int(len(img_land)/2), 3))
    +    land_3d[:, 0:2] = np.reshape(np.array(img_land), (int(len(img_land)/2), 2))
    +    mat_land_3d = np.mat(land_3d)
    +    new_land = np.array((mat * mat_land_3d.T).T)
    +    new_land = np.array(list(zip(new_land[:,0], new_land[:,1]))).astype(int)
    +    return aligned_img, new_land
    +
    +def extract_hog(image, detector):
    +    im = cv2.imread(image)
    +    detected_faces = np.array(detector.detect_faces(im)[0])
    +    if np.any(detected_faces<0):
    +        orig_size = np.array(im).shape
    +        if np.where(detected_faces<0)[0][0]==1: 
    +            new_size = (orig_size[0], int(orig_size[1] + 2*abs(detected_faces[detected_faces<0][0])))
    +        else:
    +            new_size = (int(orig_size[0] + 2*abs(detected_faces[detected_faces<0][0])), orig_size[1])
    +        im = resize_with_padding(Image.fromarray(im), new_size)
    +        im = np.asarray(im)
    +        detected_faces = np.array(detector.detect_faces(np.array(im))[0])
    +    detected_faces = detected_faces.astype(int)
    +    points = detector.detect_landmarks(np.array(im), [detected_faces])[0].astype(int)
    +
    +    aligned_img, points = align_face_68pts(im, points.flatten(), 2.5)
    +
    +    hull = ConvexHull(points)
    +    mask = grid_points_in_poly(shape=np.array(aligned_img).shape, 
    +                               verts= list(zip(points[hull.vertices][:,1], points[hull.vertices][:,0])) # for some reason verts need to be flipped
    +                               )
    +
    +    mask[0:np.min([points[0][1], points[16][1]]), points[0][0]:points[16][0]] = True
    +    aligned_img[~mask] = 0
    +    resized_face_np = aligned_img
    +
    +    fd, hog_image = hog(resized_face_np, orientations=8, pixels_per_cell=(8, 8),
    +                    cells_per_block=(2, 2), visualize=True, multichannel=True)
    +
    +    return fd, hog_image, points
    +
    +
    +
    +
    +

    Replace the paths so that it points to your local dataset directory.

    +
    +
    +
    detector = Detector(face_model = "retinaface", landmark_model="mobilenet")
    +# Correct path to your downloaded dataset.
    +EmotioNet_images = np.sort(glob.glob("/Storage/Data/EmotioNet/imgs/*.jpg"))
    +labels = pd.read_csv("/Storage/Data/EmotioNet/labels/EmotioNet_FACS_aws_2020_24600.csv")
    +labels = labels.dropna(axis=0)
    +for col in labels.columns:
    +    if "AU" in col:
    +        kwargs = {col.replace("'", '').replace('"', '').replace(" ",""): labels[[col]]}
    +        labels = labels.assign(**kwargs)
    +        labels = labels.drop(columns = col)
    +labels = labels.assign(URL = labels.URL.apply(lambda x: x.split("/")[-1].replace("'", "")))
    +labels = labels.set_index('URL')
    +labels = labels.drop(columns = ["URL orig"])
    +
    +aus_to_train = ['AU1','AU2','AU4','AU5', "AU6", "AU9","AU10", "AU12", "AU15","AU17", 
    +                "AU18","AU20", "AU24", "AU25", "AU26", "AU28", "AU43"]
    +
    +with open('emotionet_labels.csv', "w", newline='') as csvfile:
    +    writer = csv.writer(csvfile, delimiter=',')
    +    writer.writerow(["URL"] + aus_to_train)
    +    
    +landmark_cols = [f"x_{i}" for i in range(68)] + [f"y_{i}" for i in range(68)]
    +with open('emotionet_landmarks.csv', "w", newline='') as csvfile:
    +    writer = csv.writer(csvfile, delimiter=',')
    +    writer.writerow(landmark_cols)
    +    
    +for ix, image in enumerate(tqdm(EmotioNet_images)):
    +    try:
    +        imageURL = os.path.split(image)[-1]
    +        label = labels.loc[imageURL][aus_to_train]
    +        fd, _, points = extract_hog(image, detector=detector)
    +        with open('emotionet_labels.csv', "a+", newline='') as csvfile:
    +            writer = csv.writer(csvfile, delimiter=',')
    +            writer.writerow([imageURL]+list(label.values))
    +        with open('emotionet_landmarks.csv', "a+", newline='') as csvfile:
    +            writer = csv.writer(csvfile, delimiter=',')
    +            writer.writerow(points.T.flatten())
    +    except:
    +        print(f"failed {image}")
    +
    +
    +
    +
    +
    +
    + + + + +
    + + +
    + + +
    + +
    +
    +
    +
    +

    + + By Jin Hyun Cheong
    + + © Copyright 2020.
    +

    +
    +
    +
    + + +
    +
    + + + + + + + + \ No newline at end of file diff --git a/notebooks/_build/html/content/installation.html b/notebooks/_build/html/content/installation.html index dfdd7119..effa3cbe 100644 --- a/notebooks/_build/html/content/installation.html +++ b/notebooks/_build/html/content/installation.html @@ -131,11 +131,6 @@

    Py-Feat

    Analyzing FEX data
  • -
  • - - Two sample independent t-test - -
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    Py-Feat

    Py-Feat: Python Facial Expression Analysis Toolbox¶

    -

    Build Status +

    Package versioning +Build Status Coverage Status -GitHub forks -GitHub stars -GitHub license

    +GitHub forks +GitHub stars +GitHub license

    Py-Feat provides a comprehensive set of tools and models to easily detect facial expressions (Action Units, emotions, facial landmarks) from images and videos, preprocess & analyze facial expression data, and visualize facial expression data.

    Why you should use Py-Feat¶

    diff --git a/notebooks/_build/html/content/modelContribution.html b/notebooks/_build/html/content/modelContribution.html index 5be4b886..6510b1da 100644 --- a/notebooks/_build/html/content/modelContribution.html +++ b/notebooks/_build/html/content/modelContribution.html @@ -131,11 +131,6 @@

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    Analyzing FEX data
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\ No newline at end of file diff --git a/notebooks/_build/jupyter_execute/content/analysis.ipynb b/notebooks/_build/jupyter_execute/content/analysis.ipynb index 32355cb3..d95ba8a5 100644 --- a/notebooks/_build/jupyter_execute/content/analysis.ipynb +++ b/notebooks/_build/jupyter_execute/content/analysis.ipynb @@ -59,11 +59,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2021-03-26T01:35:57.204484Z", - "start_time": "2021-03-26T01:35:56.969949Z" + "end_time": "2021-03-27T16:52:47.049268Z", + "start_time": "2021-03-27T16:52:47.041360Z" } }, "outputs": [ @@ -106,8 +106,8 @@ "execution_count": null, "metadata": { "ExecuteTime": { - "end_time": "2021-03-24T18:54:19.816980Z", - "start_time": "2021-03-24T18:48:21.412688Z" + "end_time": "2021-03-27T16:52:35.840134Z", + "start_time": "2021-03-27T16:52:33.285107Z" }, "scrolled": true }, @@ -121,11 +121,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2021-03-26T01:36:04.630894Z", - "start_time": "2021-03-26T01:36:02.098727Z" + "end_time": "2021-03-27T16:53:33.106459Z", + "start_time": "2021-03-27T16:53:32.697327Z" } }, "outputs": [], @@ -144,11 +144,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2021-03-26T01:36:04.662326Z", - "start_time": "2021-03-26T01:36:04.632024Z" + "end_time": "2021-03-27T16:53:33.116599Z", + "start_time": "2021-03-27T16:53:33.107762Z" } }, "outputs": [ @@ -240,7 +240,7 @@ "4 5 gn 5 your application has been accepted 005.mp4 goodNews" ] }, - "execution_count": 4, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -264,11 +264,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "metadata": { "ExecuteTime": { - "end_time": "2021-03-26T01:36:04.916876Z", - "start_time": "2021-03-26T01:36:04.663369Z" + "end_time": "2021-03-27T16:53:34.341195Z", + "start_time": "2021-03-27T16:53:34.245364Z" } }, "outputs": [ @@ -411,11 +411,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "metadata": { "ExecuteTime": { - "end_time": "2021-03-26T01:36:05.612696Z", - "start_time": "2021-03-26T01:36:04.917989Z" + "end_time": "2021-03-27T16:53:35.944844Z", + "start_time": "2021-03-27T16:53:35.417200Z" } }, "outputs": [ @@ -643,11 +643,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": { "ExecuteTime": { - "end_time": "2021-03-26T01:37:30.117739Z", - "start_time": "2021-03-26T01:37:29.898669Z" + "end_time": "2021-03-27T16:53:36.933517Z", + "start_time": "2021-03-27T16:53:36.912989Z" } }, "outputs": [ @@ -693,6 +693,11 @@ " 2.660164e-12\n", " \n", " \n", + " mean_AU05\n", + " -11.942339\n", + " 8.022300e-07\n", + " \n", + " \n", " mean_AU06\n", " 3.905366\n", " 3.589972e-03\n", @@ -703,11 +708,21 @@ " 3.864728e-04\n", " \n", " \n", + " mean_AU09\n", + " -22.647961\n", + " 3.024835e-09\n", + " \n", + " \n", " mean_AU10\n", " 10.361594\n", " 2.660301e-06\n", " \n", " \n", + " mean_AU11\n", + " -42.691265\n", + " 1.059418e-11\n", + " \n", + " \n", " mean_AU12\n", " 11.949046\n", " 7.984040e-07\n", @@ -728,6 +743,11 @@ " 6.562759e-01\n", " \n", " \n", + " mean_AU20\n", + " -45.470892\n", + " 6.019391e-12\n", + " \n", + " \n", " mean_AU23\n", " -0.300423\n", " 7.706780e-01\n", @@ -737,6 +757,26 @@ " -4.533345\n", " 1.419377e-03\n", " \n", + " \n", + " mean_AU25\n", + " 3.119502\n", + " 1.232852e-02\n", + " \n", + " \n", + " mean_AU26\n", + " -5.015769\n", + " 7.232461e-04\n", + " \n", + " \n", + " mean_AU28\n", + " -7.169935\n", + " 5.251451e-05\n", + " \n", + " \n", + " mean_AU43\n", + " -78.139016\n", + " 4.660607e-14\n", + " \n", " \n", "\n", "
  • " @@ -746,18 +786,26 @@ "mean_AU01 -2.927201 1.683346e-02\n", "mean_AU02 -5.838201 2.473720e-04\n", "mean_AU04 -49.805912 2.660164e-12\n", + "mean_AU05 -11.942339 8.022300e-07\n", "mean_AU06 3.905366 3.589972e-03\n", "mean_AU07 5.487334 3.864728e-04\n", + "mean_AU09 -22.647961 3.024835e-09\n", "mean_AU10 10.361594 2.660301e-06\n", + "mean_AU11 -42.691265 1.059418e-11\n", "mean_AU12 11.949046 7.984040e-07\n", "mean_AU14 23.155621 2.485336e-09\n", "mean_AU15 -12.587565 5.119202e-07\n", "mean_AU17 -0.460219 6.562759e-01\n", + "mean_AU20 -45.470892 6.019391e-12\n", "mean_AU23 -0.300423 7.706780e-01\n", - "mean_AU24 -4.533345 1.419377e-03" + "mean_AU24 -4.533345 1.419377e-03\n", + "mean_AU25 3.119502 1.232852e-02\n", + "mean_AU26 -5.015769 7.232461e-04\n", + "mean_AU28 -7.169935 5.251451e-05\n", + "mean_AU43 -78.139016 4.660607e-14" ] }, - "execution_count": 11, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -778,11 +826,11 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 14, "metadata": { "ExecuteTime": { - "end_time": "2021-03-26T01:54:46.741635Z", - "start_time": "2021-03-26T01:54:46.568587Z" + "end_time": "2021-03-27T16:53:38.291686Z", + "start_time": "2021-03-27T16:53:38.179248Z" } }, "outputs": [ @@ -795,7 +843,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -829,11 +877,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "metadata": { "ExecuteTime": { - "end_time": "2021-03-26T01:33:23.062956Z", - "start_time": "2021-03-26T01:33:23.046675Z" + "end_time": "2021-03-27T16:53:41.207034Z", + "start_time": "2021-03-27T16:53:41.189230Z" } }, "outputs": [ @@ -919,11 +967,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 16, "metadata": { "ExecuteTime": { - "end_time": "2021-03-26T01:33:24.774852Z", - "start_time": "2021-03-26T01:33:24.727406Z" + "end_time": "2021-03-27T16:53:44.423224Z", + "start_time": "2021-03-27T16:53:44.377780Z" } }, "outputs": [ @@ -966,15 +1014,23 @@ " AU01\n", " AU02\n", " AU04\n", + " AU05\n", " AU06\n", " AU07\n", + " AU09\n", " AU10\n", + " AU11\n", " AU12\n", " AU14\n", " AU15\n", " AU17\n", + " AU20\n", " AU23\n", " AU24\n", + " AU25\n", + " AU26\n", + " AU28\n", + " AU43\n", " \n", " \n", " \n", @@ -983,30 +1039,46 @@ " 0.066\n", " 0.052\n", " -0.065\n", + " -0.251\n", " 0.497\n", " 0.291\n", + " 0.105\n", " 0.408\n", + " -0.002\n", " 0.569\n", " 0.022\n", " 0.111\n", " 0.101\n", + " 0.038\n", " -0.029\n", " -0.284\n", + " 0.305\n", + " 0.140\n", + " -0.016\n", + " 0.013\n", " \n", " \n", " t-stats\n", " 8.221\n", " 8.219\n", " -17.153\n", + " -31.413\n", " 43.783\n", " 33.364\n", + " 21.079\n", " 35.625\n", + " -2.671\n", " 46.713\n", " 4.088\n", " 17.515\n", " 15.673\n", + " 7.927\n", " -5.063\n", " -22.483\n", + " 19.415\n", + " 16.822\n", + " -2.293\n", + " 2.422\n", " \n", " \n", " p-values\n", @@ -1018,25 +1090,38 @@ " 0.000\n", " 0.000\n", " 0.000\n", + " 0.008\n", + " 0.000\n", + " 0.000\n", + " 0.000\n", + " 0.000\n", + " 0.000\n", " 0.000\n", " 0.000\n", " 0.000\n", " 0.000\n", + " 0.022\n", + " 0.016\n", " \n", " \n", "\n", "
    " ], "text/plain": [ - " AU01 AU02 AU04 AU06 AU07 AU10 AU12 AU14 AU15 \\\n", - "betas 0.066 0.052 -0.065 0.497 0.291 0.408 0.569 0.022 0.111 \n", - "t-stats 8.221 8.219 -17.153 43.783 33.364 35.625 46.713 4.088 17.515 \n", - "p-values 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 \n", + " AU01 AU02 AU04 AU05 AU06 AU07 AU09 AU10 AU11 \\\n", + "betas 0.066 0.052 -0.065 -0.251 0.497 0.291 0.105 0.408 -0.002 \n", + "t-stats 8.221 8.219 -17.153 -31.413 43.783 33.364 21.079 35.625 -2.671 \n", + "p-values 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.008 \n", + "\n", + " AU12 AU14 AU15 AU17 AU20 AU23 AU24 AU25 AU26 \\\n", + "betas 0.569 0.022 0.111 0.101 0.038 -0.029 -0.284 0.305 0.140 \n", + "t-stats 46.713 4.088 17.515 15.673 7.927 -5.063 -22.483 19.415 16.822 \n", + "p-values 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 \n", "\n", - " AU17 AU23 AU24 \n", - "betas 0.101 -0.029 -0.284 \n", - "t-stats 15.673 -5.063 -22.483 \n", - "p-values 0.000 0.000 0.000 " + " AU28 AU43 \n", + "betas -0.016 0.013 \n", + "t-stats -2.293 2.422 \n", + "p-values 0.022 0.016 " ] }, "metadata": {}, @@ -1059,17 +1144,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Intersubject correlations\n", - "To compare the similarity of signals over time between subjects or videos, you can use the `isc` method. You can get a sense of how much two signals, such as a certain action unit activity, correlates over time. For example, if you want to see how AU01 activations are similar across the videos, you can do the following which shows that the temporal profile of AU01 activations form two clusters between the goodNews and the badNews conditions. " + "## Intersubject (or intervideo) correlations\n", + "To compare the similarity of signals over time between subjects or videos, you can use the `isc` method. You can get a sense of how much two signals, such as a certain action unit activity, correlates over time. \n", + "\n", + "In this example, we are calculating the ISC over videos. We want to check how similar AU01 activations are across videos so our session is set to the `input` which is the video name. Executing the `isc` method shows that the temporal profile of AU01 activations form two clusters between the goodNews and the badNews conditions. " ] }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 18, "metadata": { "ExecuteTime": { - "end_time": "2021-03-26T02:03:17.392348Z", - "start_time": "2021-03-26T02:03:16.179007Z" + "end_time": "2021-03-27T17:08:20.852347Z", + "start_time": "2021-03-27T17:08:20.586761Z" } }, "outputs": [ @@ -1094,6 +1181,13 @@ "isc = fex.isc(col = \"AU01\")\n", "sns.heatmap(isc.corr(), center=0, vmin=-1, vmax=1, cmap=\"RdBu_r\");" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/notebooks/_build/jupyter_execute/content/analysis.py b/notebooks/_build/jupyter_execute/content/analysis.py index 6859867f..6f8f0bb4 100644 --- a/notebooks/_build/jupyter_execute/content/analysis.py +++ b/notebooks/_build/jupyter_execute/content/analysis.py @@ -115,9 +115,12 @@ p.round(3).loc[[0]].rename(index={0:"p-values"})]) display(results) -## Intersubject correlations -To compare the similarity of signals over time between subjects or videos, you can use the `isc` method. You can get a sense of how much two signals, such as a certain action unit activity, correlates over time. For example, if you want to see how AU01 activations are similar across the videos, you can do the following which shows that the temporal profile of AU01 activations form two clusters between the goodNews and the badNews conditions. +## Intersubject (or intervideo) correlations +To compare the similarity of signals over time between subjects or videos, you can use the `isc` method. You can get a sense of how much two signals, such as a certain action unit activity, correlates over time. + +In this example, we are calculating the ISC over videos. We want to check how similar AU01 activations are across videos so our session is set to the `input` which is the video name. Executing the `isc` method shows that the temporal profile of AU01 activations form two clusters between the goodNews and the badNews conditions. fex.sessions = fex.input() isc = fex.isc(col = "AU01") -sns.heatmap(isc.corr(), center=0, vmin=-1, vmax=1, cmap="RdBu_r"); \ No newline at end of file +sns.heatmap(isc.corr(), center=0, vmin=-1, vmax=1, cmap="RdBu_r"); + diff --git a/notebooks/_build/jupyter_execute/content/analysis_17_1.png b/notebooks/_build/jupyter_execute/content/analysis_17_1.png index 397a92dd..3a5ae5c1 100644 Binary files a/notebooks/_build/jupyter_execute/content/analysis_17_1.png and b/notebooks/_build/jupyter_execute/content/analysis_17_1.png differ diff --git a/notebooks/_build/jupyter_execute/content/dev_plotting.ipynb b/notebooks/_build/jupyter_execute/content/dev_plotting.ipynb new file mode 100644 index 00000000..db3e325a --- /dev/null +++ b/notebooks/_build/jupyter_execute/content/dev_plotting.ipynb @@ -0,0 +1,784 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotting examples\n", + "*written by Jin Hyun Cheong*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Included in the toolbox are two models for Action Units to landmark visualization. The 'pyfeat_aus_to_landmarks.h5' model was created by using landmarks extracted using Py-FEAT to align each face in the dataset to a neutral face with numpy's least squares function. Then the PLS model was trained using Action Unit labels to predict transformed landmark data. \n", + "\n", + "Draw a standard neutral face. Figsize can be altered but a ratio of 4:5 is recommended. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:00:07.356496Z", + "start_time": "2021-03-27T00:00:07.295746Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/dev_plotting_2_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Load modules\n", + "%matplotlib inline\n", + "from feat.plotting import plot_face, predict\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plot_face(au=np.zeros(20))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Draw lineface using input vector" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Affectiva vectors should be divided by twenty for use with our 'blue' model. " + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:12:08.615110Z", + "start_time": "2021-03-27T00:12:08.526307Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/dev_plotting_5_1.png" + }, + "image/png": { + "height": 236, + "width": 353 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data, AU is ordered as such: \n", + "# AU1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43\n", + "\n", + "# Activate AU1: Inner brow raiser \n", + "au = np.array([5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "# Plot face\n", + "fig, axes = plt.subplots(1,2)\n", + "plot_face(model=None, vectorfield = vectors,\n", + " ax = axes[0], au = np.zeros(20), color='k', linewidth=1, linestyle='-')\n", + "plot_face(model=None, vectorfield = vectors,\n", + " ax = axes[1], au = np.array(au), color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:24:31.270625Z", + "start_time": "2021-03-27T00:24:31.260037Z" + } + }, + "outputs": [], + "source": [ + "%config InlineBackend.figure_format = 'retina'\n", + "intensity = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:24:31.740226Z", + "start_time": "2021-03-27T00:24:31.433797Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'AU43: Eye closer')" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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udqbXikBfxAjctD5B4LcytH2vj3KwvWralLPGg20RDwc9a6CTW5YWdakAyjhYZiDss8qfOnm/j22WydIKAPqf+YD59SMAqjkppzwyWqMdQaYeQ7BvZSHQQT04h/tf5jJXwkFrRwAPI6P14tHMdTMvY1vOIQBlPdxfX3WyzDs2y6xw8PpM82uxmbcDdCJIoAPdbvPvbzoow9IDbE42t5/tfu3w+8+0fEUAxd0s85lNedFutl+W/0HoEzyBPq5UdvE+wbBpBWjz/Dib9ds7WTcE+gJDoFu/ZGkB6s3+wEf+fKCQxC8Pymxhc3y8BOe9NmyPt2ty+J6WzyE5XN9hPWzLhz7JbuJgmVLIiG1Oj1ce1MW2J+wWOIjHNstWd/DcZJv1v3SyXu9Mx6MsPUe9OWZlek8DgM4OlgmGbqhhWa6hg2VqQU/+LNC9DSKcvN89Nsv95Ob/wW0s8vB7yVzmJ06WG2WzzEQHrw+0eb2dm/e0HAtOZfN16/fi5HXLeYURQB8X9fjKps73ufn/uASgTk63Nx9227dQxJZM9XP2SAHwmpty2tksvyaH2966b+dwfYf1AGMLY4v9dfMkJ9vs+UzvO9nBMsWhb5wJgI1wPqJKXZvlsrRQR9br/ake1GmVg9drZipnnbPtbl7e0qPkMpz3YI4EsMG83A3YXENCNyy1tKif52abl8n0d7bXtdmm6ciIgbc5WKYY9PBqlu2RJebmxv7FR/59IB/EfmSMgHQYNtd7yGYPrEz7f+ZHHIAuLta1fc81Odz2p2zKaujB404HZbS3KSPLfTeb5d61We5BB6/n6NjoxWcOhU7oCHSDtQEulg0BUMnF95flOwdQwmafTQTQ2km5i2zKedfFNh3m5vM4Olbfi4yewN/ASe9FADE2y73n4PXJNvUwAeiak/2tMD8KwhxYvaGTKUDGBIUAABFJQUbro0eVUmyB4r0PRWRX5idFJAk6Aw7oVkH3uClnN/RQAI58CX0SB+hAklkP6Aw7AAwSkbOOChGRK9DdOwHdKs1RWRbxAIaIiNHFMt5KN9cvzUHdliGjFV9d6ISWKy+Jk1bYSo/zfr/5z5Ui8q2j5UTkM+gLEgDoqJRqkGkRSw+1EtAtBmxZekOsslnOroWi0pOAWuYcc9XbzVPrROQ7VwuIyCURiXdTznvQvaUA3SLCW5bPdERctNoVEaOIxNo+p5Sqgoyxpf8jIn85WdcA3WozDTrwumqhDrjYHyhfK/TxS+l5Gn5Gxpyc74lIqh+rlBs+FZHdmZ8U3dt4iM1Tr3lbsHnM7/8z/5kEoIeIXHe2vIicybR+eejW2oC+Mfi2k/VmQzd6AIB6yEEMc+B/IrLcwXsaoXt4W7RzsO5b0L2nb0BfMCc7egMR2YSMFphPKRdzlsCDWJQNB6Av7h15D/pCHgD6KaXK+Pi9fcK8zSzj7E8UkVkuFn8bunUjYL+PO/KuiPyT0/qRncISW0zQPXzegW492wz6emQQdK8SQN9oHquUcnhsy+cYW1wrcLHF3Jr8BfOfl6EbMYqDev0I3ajSleeheywYoJMjNx0tJCLHoJNmAPCgUqq2izKvAnjZgzo94OC61JYJ+pra4XZXSrWE7k0CAK+LyD4ndU9ExvYqBfteAGWg72EAuueTUw72/ZysC+iRWSwjIb0pIscdrJcA3ZPOZH7qdVfvg9w5d6H8JaBjv1LqQejeUQLg2Vy+3vsOQD0RWZSL7+HMPg8emzKvZL5vZOmZ+qyjgs2x2dKr+h9kugfno2Ojp/pBN/YAgG9FZKqzBUXEICIXnb3uxCDo+5QAMFJENjsq17yc5f7ga+bRIiwq2fzu8n6lk2P1vwEo6POY183v52jdedC9qQD31z3TRGShm2XIiYKQwLLc+L0I3Z0+M8vBOwQZXUnzHRH5SESU+fFRHr71NBev2Q7zUMdNOdMdncwCgIjEQfdKclaOJQlxRkT+dvM+a2x+b+NiuUXm9/WlP5wl18wm2PzeyelSwDlniQ8H6/7opk4/2PyeeQiDVTa/W4fvMgfGduY/VyPjYB+daXJC2yG/XJ68e8jVvuaQecLIqkqpO5We1Lkh9AXwOfMiTbNRj/Pmnw2UUu4Ss5l1gW6NAuju2k6Zg6TlpMLVvupuf6D8q1DEL2fMwz1MQ8ZwmwtE5Cdny4vIGps42C4v6ugDlhbSjl8U2Qh9UgwAHdxNQutAI2RcPMwWkfOuFnagPTKOWZOcnZib2Q7BkZMYlpnTCx64P8/oYf652Hyzx5U15p/h0MNnOON1LPLAZGcNZszP/2z+MxwZDVQCzf3QN+0A9/EtHYDlfM1VfEuH/RA35BuFJbb0FJF2IvK5iKwQkV0isllEJovIo9A38yzHtP+YG15lwdjiEGOLFmixpRl00gnQ34urYYomuHgNyLi+/jtzAtKBNTa/uzqm/+pme3l6LbzJTcMGy/eTDt1DzikR2Q895CVgX/fr0L2oAKC3eQh4T+VkXSDjs9+Ei6EeReQwMq7DmyulSrsoMzfOXSh/CdjYb/4fsfz/jxORDT4q+i7zozH0van/gx4C9SUAPyulqjtbMUBjv+XeXj2l1H0OXn8AGTHrJwf3V31xbPRUN5vfv8zG+u5YjpOCjGEnszA3arEcRytCn79Y2J67POPNuZC5ka3l/ulvzq7jbKwx/6ymlKrmYjkeq3MgXyewlFL1kNHzZ7qjncp8An/M/Ke7Xg5k76qbVm62440Xd1PWITevW8pyVI5lXPXqSilx9YAeTsOikoOyLHa7qU92bHHz+i5k9DRr5GI5d3NJ2a6bpfVGJrYJv8a2L4jISWTMMWGbjLoLGRdHq6Fbtxqg59uyHePess4lEfHFnDW7PVlIKVVcKfWeUmo3dHfis9Bz5ti2amliXrxsNurxM3SgDAOwXim1XCk1VCnVPFMCzxHb7XPCg/3V0vPN1b6a07nFKAAxfgHQN626m3/fBT1+dEFzUpyMYW7DEjtKQM9b4g3b3rNrvVwX8C6ebENGDGvsYjlvj1muzg+cnmeYL0ormv8c4MHx1rYVZqCdH9i+7ur8wJ9s49tqD7a35eaoq219VHSPfvKRwhRbnPUYsXn9F+ghOgE9bNyruV6pvMPY4l5BjC2229WbuGLH3ELd8h239eAz2rbid/UZfRXrdrspxxKPQgGkeFB/S+MLa93NvT+mm/+8B8BppdR4pVRvVze9c7quUioMep4eANghNvO1OGG5llfI2TajAiwfxP7PoRtFnIfuNe0TIrLf/NgrIn+JyCjohpEroRMg29309vQ5m6SYq0dJJ6tPgR76GHDcC8vynAEZjd9s5fjY6AVLDDnpQSOI7LAc7/7x4HzH2T3Pjcjo1TYUwGGl1Cil1CNKqVJuymyGjJ6yH3iwLW1Hxcrr68xCI18nsJAxVBeQqZtsJpYWWA3M3Sodsc1ee5qZtV3OYe+ifM7dTQWTze/uJp3ztCxH5ZR3s64zRV285vKiN5uuuHrRfIJqaeXgaoggd3WzXdfVZMFAxhA+zt7T0pLxPvMJNZCRmDooesi+BADbzc89CFh7TtyfqYyccvudmHtYHQLwCXRwcpdQcrUPOCQi66CPLXHQ+2MnAGOgL7BvKqWWKKX6OUlm5Zd9lfyvUMcvpdRX0MPXAHp4tY650Cs2ELiMC2a2x3Fvh48rZ/O70yFPXfA4nogeHtdy0y8nMSxzuU7PD0TE1XlGdo+3QICdHyBn+0Beye72djWkFuOb7xXq2OLA98ioxwOuFsxnGFvcKKCxxXb7eBNXMisF99dQzrj6jL6Kde62la+ut4Yio0dxGejhzWZBJ6ROKaW+V0o5G80ju+uWRsZx0t11POD+Wt6C8bRwC9jYr5RqC90jCtDDwLqbDiJHROQW9PCbydCx7AfXawQOEbkB4Dfzn08opSxD6EHpIcZjzH8uFJHLDorIjXtRzljOE7JzjuAJy/Eu28dJc+/vxwDsMD91O3QCdQmA60qp3Uqpj5RSjhJOgXwuUGhl98TF78w3j/vbPLXfwx6Bz8B+2AAL25NcT/+BbbuLu+rCTzlj2U+3w7vWIq4ODr6c+8rCkwt1T3ZSX9ZNnPxusRp6m0ZAt9pZC/v5r2yXa21+7RPoHk6lbV7zBZefW+lx338HUNn81DQAs6Fvfl8BkGq5IFVKrQMQDc9PuuyIyGSl1HwATwDoAN2tujJ0L7RHzI+3lVJdROS0zaqWfdUE3WrD0+8yy7xpNnJjXyU/KuzxSyn1GTLG8j8CPQFtQZ3jzVdxwVfv5av1XS2bV8cs23PYH2A/DJU751y85o/zA1/tA7nJdnt3hv2wHNnF+OZDhT22OCIil5RS16F75Ff1d318iLEl9+SX2JKT78X2My6EnufDU66SVL5KWrvbVpb6X0TWIfJdsTsmmYdg7KuUGgmgF3TjzBbQ18U1oOcrflEp9Q2AYbbDdeVkXdsqeFF3d8sznhZS+SD2W+YROgqgqFKqt4P1bYf4rWWzzBER2eVhHaxE5IpSagP0fZxopVQl8X4OJn8ZB/19FoWeS9KSgBsAPVQt4HxIPZ8cG72U242VcnScFJHjSqkW0A3xuwG4D3rEqWDoBvGNAQxXSg0RkRk2q9rGyfcBzPeiDiedVs79UITkQr5NYEFP8lrR7VJZ9VZKvS5ZJwW1nbStEvRNcXdsM7VOJ7alHLsKfdFZyTxWa6ByORmm+eTC0lU1J/uL7boV4fomku0+esPB63bzYJkDfVvz37aJqdXQYwrfo/QExb6e/8oT7ZEx5MJnIvKui2VdjRHuERGJhR6reQIAKKVqQffGehG6S/Nd0Ak023myLN2bgwAkioNJeYlQiOOXUuoTZEzmfhRAeyctyAoKTyZJtm3h5e13YTukQhUv1838fpUAnHC2oLmXruXY6iie5DXbz148H5wfHHXxuqt9wLangLuRE7ydh8MbttvbGODbu7AqtLHFDcn0syBgbMk9gRxbbLeru33A1evXof8fFIAyPvyM3tQpJ8eHq9DXhOWgh5fKfOzyivnz7wesDSabQ/d0eB56iMnXoIerzzL3dDbWvYGMbe/JsF22x/RA+P+gwBPosd+SdLkdbuZQNWuLjPtRY6GHmc8O28aRNWHfSydgicjfSqm90PebnkNGAssyfOApAH86Wd2nx0Y3rgKohuydI3jiOnQD8hwfJ80NCFaaH1BKFYPex/pCz5daFMAUpdQum6lRbM8FigTYuUChlZ+HELR0kzVCZ6P7uHmMNi9fAhmT29myHVfb1QSstizLCYC9nlacvGbp8llFKXWXX2viWis3rzeFnlcJyNn+YrtuazfL3mvze5ax480tUSzjwj4IPZZtCegbZmtsFt0IPVltOHRvJEsC64ybSXZ9yfa7d3ryYw5Idzh7PbtE5KSIjINuXbfb/HRrpZTtvAI7bH5/2Nd1oAKjUMYvpdSHAN4z/3kcwAP5qDVcdtVSSpVzs4wldsQBOO1qQQe22/x+v9OlnPMmnjSHHlMdCIy5+U4i4yLlISfDugYKd+cHtq9n/n+0ndvT6Xjt5qF9b/eyXt5gfAt8hTK2uKKUqoCM+VBza5gbf2BsyT2BHFtst6s3ccWOeVh7S1mtlFI5bviXjTrl5PhgiUchAB7KQTlZiEi6iGwSkX9lKttRrxGv1zUPmWm59r7bnPRyxXItHxDHVApIjP2O2SZWEpwuFZgsCe+mSqlmSqk2yOil9lOmYXBt5dqx0QHLeUItd3P/ZZNlP6rjwfmOy3uemYlIgogsEZF+yJiTLRh69CWLXchoRMjrngCRLxNY5h34UfOfa0RkmojMdvUA8CkyJsRzNAzdHza/9zPfCHBVh3uQcaNgp4gESivDgmiuze/vOV3K/zoopVwNT2I7EeMfTpdyb4XN70PcLPuCB+9p6WnVEkAX8+87zT2QAAAikoKMyRE7Qne9tV03L9heQLpqZf48crF3qfnCw7bXmW1AXYiMIRxeV0plZzxhKsAKa/xSSr0L4CPznyege175YgiyQKfgYuhb83dhuSBZ6WSIGVf2IaNle2+llLet4P4CYJlA/BmlJ3Z35kWb31c4XSqPmC/eFpj/rADdSjFQPe3s/9K8zQea/0yFHsrXlm0jkRYu3qMLdGvv3LIKgGXegmeVUtlp7Uu5pLDGFg+8iIyh9PJqxIC8wNiSSwI8tuxERq+CJ91cZzzr4jUg4/o6BBk943Oql1IqysXrvroWtr038K6b/SvbRGQbMqYkcHcD1Zt1Lft5KQCPOytDKXU7MhqNbjfPj0NklR9iv4i0ExHl6gE90o7FFJvXhnm2JbLUqQYyRslJgv25dH4wHRnD+j2HjGOnAcDPLtbLk2Oj2QKb39/MhfItx0kFF/HM3ACjl/nPS/A+gWp73mE9VpuPt5ZrsmZKKSaxAkC+TGBBjwlqaa3yiycriEgCgGXmPx9QStXM9Po+ZNyMbwh9YHdIKVUe5mHFzL7ypA45YZ5cTsyPj3L7/QLMTGS0VOplbsXvlFKqqFJqmLtgmwvCAExy1JJKKdUJwGDzn/8gY1/0mojsRsbBtKNS6kVHyyml3oKeBwoA/hARZ92/Lft9KIBXzb+vcrHcEADFMj2XF2yHXxrsaAGlVDSAj3PyJkqpPq4uvpRS4QDamf80waZVq4icQMbEqbUA/OKmLKWU6qGUqu9sGSpwCmP8egvASPOfJ6GTV67miXBWTjubOLjGl3XMZf9WSjXK/KRSqiTsxzD/1tuCzTclLdu2KIA5rlpSK6WqZVr/CnSMBfTNzpFZVtLrPQGgn/nPwwCWe1vXXDISOukDAF+6u7hQSlVRSj3japlc0hDAB05eG4GMG82zHNz0P4CMYSwGKaXKZnod5paPXu8/3jAfh74w/1kcwALz8cQppdSD5lajlPsKVWxRSrV312tEKdULGXP7mAB852Q5xpZMGFsCM7aYe05ZWuZXhB5ey1F9noX7+U++QcZQX2+5q79SqrRS6hU3ZZYD8K1SWSfgyVSnNTkZjklE1iHj2HQPgB9c9ZRTSoUqpQaZe2RanqutlHrA2TrmZVoio+fzCZvns72u2f+gb0QDwFdKqdoO1o8CMBW6VwAAfO3q/ajQKmyxv7ubBuMwnyf/goztMtvRUHqBHPtFJB4ZIw71RUaCZomIOO1N7otjoxdmIuPY9qpSaoCL9wlRSnkyFKCtydA9yAF9vpOlN6D5s02C7k0IAN+IzRxTSqlopVQ9N+/T2eb3zMfqj5Ax/PRUR3XIVJ/blVJPunk/yoFA6hLvDUtLAQPss8zu/Ao9JrEC8DT0TQNbz0F3uywJnbFuC30z+gB05r4cdELgBWRkZ+eKyEw4oZRqAqBJpqctN7SjlFIDM712XEQ2ePqBCgMRMSilekIPY1cCwEdKqcegD2p7ANwyP38H9FimXaETLN/Bfu6I3LYVeqLIrUqpr6DHuy4GPVngy9AnoCYAQyTnk/cNgd5XowB8b07czIAeHqUqdPdxS4uuWNj3xMrsL2SMxW05+DtKTK0G8InNMpZ188py6LGLKwEYrJQqA70PnIP+f+wK3TojFsAZZH8YwVEAxiullkIPo3gYugVdcQD1oHt4NTUvO1tErmZafyiAZtD/948BOKyUGg+9/16HvhFQE3oYjR7Q31cH6P2FCr5CFb+UUi8B+K/5zwToJHlxpVRDZ+8L4GZe9s5y8DmcWZiN1q/HoLf3RqXUl9Bjb6dCD9f6L+hEN6BbG2bueeMREZlobiTxBPRx5bBS6nsA66GPOcWgL0AfM7+eOQHyFvQwE1UA/Mt8Q3QC9BjrZc3lPgO976UDGOhi6Io8JXpi3mehb/JEAFiilJoPYA70tjdAf4a7oG+cPQBgC/TFTl7aCuBDpVRj83ufh97ezwDobl7mKhy0ghcRo1Lqf9AXUeWh96X/QM+5EQndoGIo9PnFUeTuMIIjoYcR7gTda/uQUmoCdKy8DD3McFXonmIxAOpCH5s25mKdSCtUscVc1yeUUsugz0UPQZ+rhUGf/z0J4BGb5T8035TLM4wtjC25ZCT0zcy60L1h6wD4HvrGWznoIcIGQMedls4KEZFYcwJxBfSN3olKqX7Q15OHoHtolIJOQD4APYTSdThJBJtthe5RXMsct/6B3k59oP9nASAZwEvefmgHnoLe5tWgj1P3m+PRNuibnlEAboMeWirGXI+60LEKAKoDWKWU+ge6J8FW6OvHFOhYez/sewfafu6crAsROaaU+gD6u6wIYLtS6gvoBqpp0NeZw831B4AFIuLJ3EFU+OSb2O8j3QH8qpT6Ezru7Yc+LgVBz5fUFvoYVMa8/An4roepR9xc49pyd737A/S9LdvRFcY7WdZWTo+NHhGRdHOyZj2AItBzSD0Jndg6Bj0qUTXo64YnoZObY7woP1bphvozoa931imlvoFOvt6Cjk1Doc97AD2k4ReZinkQwAdKqS0AlkJPA3LJ/FoV6Pu0lsTbTej4Z1uHdUqp96GTuGUB/K2UmgVgEfS5DKCP+Y2he0Lea66vR8lkygYRyVcP6BMxMT9WeLluJPQBV6BbgSsHyzSAPmkTNw8TdAuocDfv+ZEHZdk+JntQzkc52H41bcpZ42SZNebXT3lRVpY6QQcPy+vt3JTl9j2hkwd7PNyO8QCCM63fzub1gT7aH+3KNO8TzuqUCqC/i7Jc7gMOlm8BnbBytR3OAGjiQVm7M9WzqINlQqBvQFuWO+qD7We7X9f0cHsnuPi8l6Bbm7jcn1y9Dh2MPNnHlgIo5qT84gB+87AcA4B7cro/8BH4DxTC+GXzv5ajcsxltbNZZk0OvwtP/89tH00crO+wHravQ9/sj3dR7nx334UHnycU+gaWyc1niHWy/m0e7Ds3ADzkog4eH7OgGx8IAPFgWbflQjdguOrh97jEzb5e00f/77ZlNoG+mHRWp0sAGrsoKwz6Qt3Z+peh55lZg2zGPk+/F3Nd/gd9YerJseZJV/8fvtjWhf2BwhlbJnu4bhKAV93Up53N8jnaJ8HYknl9xhYfxxZzuTWhbxA6q8sR6CSLJ5/xHi/22wMO1h9o83oHAItdrB8PoIOLz2RZ7iMPt0NlZDTEdPdIAVDNyf+9q0cqgJddHDO8WjdTOR9AXwe6KuNXAEWcrJ8r+xcf+eOBfBb73dTH9n9qsovlJnv4vyfQjZ8re/iea3L4XZzyol5uP6dNudtslj8DIMjD+mT72JjN/fCMB+8zzMG6nnzng811dFX2GgClHaz7kQf1EujrsDYu6jAEOmnmSVn/c7Xf5vT/vrA/8mMPLNtxWn/1ZkURSVRKLYHumVITujXRqkzLHDBnzR+HzvC3hG5VEAGdsT4FnWWeKDno+k7eE5HDSqmm0N9LT+jWfhWgM/4J0N/NTgB/AlgkOe/llJ06Pq+UWgF9kGsC3XLtMvR+NlpEDvnwvbYppepCt3zpBuBO6FYysdCtY+YDGC8iSR4Utwq65QAAbHa0juiecOuQ0aI1L4cPtNRhjbnl7tvQF0lVoMcHPg3dEuJbEbniYOQKb9wH3VrjAeiWlRWgjwHp0AnDbQBmiMgSF/WMh24Z3AK6VVNb6FbpxaFPFs9DtxhaDWCeiFxyVhYVKIxfhZCIrFBK3Q3gDeiW2pWhWyDvBjBBfNCqVvSwQi8ppX6Ejj/toFu9hUGflJ+BHtPeYYsw0a3NG0Hvo48DaASgNHRsPQZgCYDvRORmTuuaG0RkoXkIlEHQrcQbQ7eUU9At6o5BJ5AsPWvzWix0S9WXoVui14XurXQKOlb/19W2FZE08xBWL0C3rKwP3av7DPTci1+JyKUcxj6PiJ4H8mWl1LfQF5XtoHt7lIS+wLwE3aN4DYD5oofWpdxVGGPLZ9Dn/K2gewGVhW5xLdAJkf3Qn2Oy6OHsChzGltwXqLFFRE6Ze/QOg+6NdRv0TeQT0L3ExohIgicxQUQ2ma8n+0In7O6G/v8Ohf7/PgF97bMC7od4TIeej3EQ9PVPfeieeueht9Hnko0hpF3U/QKA9kqph6Bj673Q/weR0Dcbz0DPh7ISOh7F2qy+Hvr48QB0j6ma0Nd8xczrHoO+ThvvII7lZF3b+n+slJoD3SPtAehrxRDoewd/A/hZRP70bqtQIVIYY/9b0Pd82gFoDt2DsTz0/008dK/PLQB+EZG/nZSRn0yG/pyA3s4e9VLO4bHRKyKyVen5+gZB35NsDH0+lgB972wvgN+RzSlURPcG/xN6FJeO0PtrEejGJduge03NEdGZokxGA9gEvX/fA33vsAL0OcxN6HPFJQB+Mt+/c1aH8eZjtWUo3PrQ5zIm6B6AR6GP2YtEZEt2Pid5Rjn+nomIiIgoO5RSpwDUALBWRNr5tzbkD0rPV/qh+c9aInLKf7UhooKAsYUCjXmozJ/Nf7YXkTX+qw0RUcFhnobiOehRD2r6sgEAUX4U5O8KEBEREREREREREREVZkqpYtC9pwBgKZNXRExgERERERERERERERH522sAosy/f+fPihAFivw4BxYRERERERERERERUb6llIqAnqMpAnrOpvfNL20VkT/8VjGiAMIEFhERERERERERERFR3moF4K9MzyUBGOKHuhAFJA4hSERERERERERERETkP5cBLARwr4js8XdliAKFEhF/14GIiIiIiIiIiIiIiIjIij2wiIiIiIiIiIiIiIiIKKAwgUVEREREREREREREREQBhQksIiIiIiIiIiIiIiIiCihMYBEREREREREREREREVFACcnNwpVSkpvlExGRb4mI8ncdAhVjGhFR/sKY5hjjGRFR/sJ45hxjGhFR/pKdmMYeWERERERERERERERERBRQcrUHloUIG0QQEQUypdioz1OMaUREgY0xzTOMZ0REgY3xzHOMaUREgS0nMY09sIiIiIiIiIiIiIiIiCigMIFFREREREREREREREREAYUJLCIiIiIiIiIiIiIiIgooTGARERERERERERERERFRQGECi4iIiIiIiIiIiIiIiAIKE1hEREREREREREREREQUUJjAIiIiIiIiIiIiIiIiooDCBBYREREREREREREREREFFCawiIiIiIiIiIiIiIiIKKAwgUVEREREREREREREREQBhQksIiIiIiIiIiIiIiIiCihMYBEREREREREREREREVFAYQKLiIiIiIiIiIiIiIiIAgoTWERERERERERERERERBRQmMAiIiIiIiIiIiIiIiKigMIEFhEREREREREREREREQUUJrCIiIiIiIiIiIiIiIgooDCBRURERERERERERERERAGFCSwiIiIiIiIiIiIiIiIKKExgERERERERERERERERUUBhAouIiIiIiIiIiIiIiIgCChNYREREREREREREREREFFCYwCIiIiIiIiIiIiIiIqKAwgQWERERERERERERERERBRQmsIiIiIiIiIiIiIiIiCigMIFFREREREREREREREREAYUJLCIiIiIiIiIiIiIiIgooTGARERERERERERERERFRQGECi4iIiIiIiIiIiIiIiAIKE1hEREREREREREREREQUUJjAIiIiIiIiIiIiIiIiooDCBBYREREREREREREREREFFCawiIiIiIiIiIiIiIiIKKAwgUVEREREREREREREREQBhQksIiIiIiIiIiIiIiIiCihMYBEREREREREREREREVFAYQKLiIiIiIiIiIiIiIiIAgoTWERERERERERERERERBRQmMAiIiIiIiIiIiIiIiKigMIEFhEREREREREREREREQUUJrCIiIiIiIiIiIiIiIgooDCBRURERERERERERERERAGFCSwiIiIiIiIiIiIiIiIKKExgERERERERERERERERUUBhAouIiIiIiIiIiIiIiIgCChNYREREREREREREREREFFCYwCIiIiIiIiIiIiIiIqKAwgQWERERERERERERERERBRQmsIiIiIiIiIiIiIiIiCigMIFFREREREREREREREREAYUJLCIiIiIiIiIiIiIiIgooTGARERERERERERERERFRQGECi4iIiIiIiIiIiIiIiAIKE1hEREREREREREREREQUUJjAIiIiIiIiIiIiIiIiooDCBBYREREREREREREREREFFCawiIiIiIiIiIiIiIiIKKAwgUVEREREREREREREREQBhQksIiIiIiIiIiIiIiIiCihMYBEREREREREREREREVFAYQKLiIiIiIiIiIiIiIiIAgoTWERERERERERERERERBRQmMAiIiIiIiIiIiIiIiKigMIEFhEREREREREREREREQUUJrCIiIiIiIiIiIiIiIgooDCBRURERERERERERERERAGFCSwiIiIiIiIiIiIiIiIKKExgERERERERERERERERUUBhAouIiIiIiIiIiIiIiIgCChNYREREREREREREREREFFCYwCIiIiIiIiIiIiIiIqKAwgQWERERERERERERERERBRQmsIiIiIiIiIiIiIiIiCigMIFFREREREREREREREREAYUJLCIiIiIiIiIiIiIiIgooTGARERERERERERERERFRQGECi4iIiIiIiIiIiIiIiAIKE1hEREREREREREREREQUUJjAIiIiIiIiIiIiIiIiooDCBBYREREREREREREREREFFCawiIiIiIiIiIiIiIiIKKAwgUVEREREREREREREREQBhQksIiIiIiIiIiIiIiIiCihMYBEREREREREREREREVFAYQKLiIiIiIiIiIiIiIiIAgoTWERERERERERERERERBRQmMAiIiIiIiIiIiIiIiKigMIEFhEREREREREREREREQUUJrCIiIiIiIiIiIiIiIgooDCBRURERERERERERERERAGFCSwiIiIiIiIiIiIiIiIKKExgERERERERERERERERUUBhAouIiIiIiIiIiIiIiIgCChNYREREREREREREREREFFCYwCIiIiIiIiIiIiIiIqKAwgQWERERERERERERERERBRQmsIiIiIiIiIiIiIiIiCigMIFFREREREREREREREREAYUJLCIiIiIiIiIiIiIiIgooTGARERERERERERERERFRQGECi4iIiIiIiIiIiIiIiAIKE1hEREREREREREREREQUUJjAIiIiIiIiIiIiIiIiooDCBBYREREREREREREREREFlBB/V4CIAkNqaioOHjyIlJQUGI1GGI1GmEwmj3/3Zlmj0Yhq1arhscceQ5kyZfz90YmIiIiIiIiIiIgowDCBVYgcO3YMs2fPxqpVq5Ceng4AUEpBKeXV796uFxISgsaNGyM6OhqtW7dGVFRUHn1iciUuLg5///031q9fj/Xr12Pbtm1ITU3N0zoEBwejffv26NmzJ2JiYlChQoU8fX8iIiIiIiIiIiIiCkxKRHKvcKUEAHLzPci1s2fP4pdffsHs2bOxY8cOf1cHwcHBaNasGaKjoxEdHY02bdqgXLly/q5WoXDx4kWsX78eGzZswPr167F3716YTCa7ZSpWrIcaNUogODgYwcHBCAoKcvu7p8tZfj97Nhj79ytUqrQdq1evhtFoBKATnvfddx969uyJHj16oFq1av7YTIWWJfEsIsrPVQlYjGlERPkDY5prjGdEZDKZEBsbixs3biA2NhZ16tRBqVKl/F0tyoTxzD3GNCKi/CEnMY0JrALo6tWr+P333zFr1iysX7/e+nyxYsUQExODoKCeKFWqDHr0EOt3I+LZ794sa/n9hx8SUbLkFhw9uh67du2yJiws6tWrZ01oRUdHo0aNGtadmrJHRHDs2DFrsmr9+vX4559/7JYJDQ1F8+bNER0djXvuicaoUfdi4MDSePHF3KoT8P33wNChwLhxwLPPAtevX8fChQsxZ84c/Pnnn0hLS7Mu37JlSzz++OPo2bMnateunTuVIiteHLnHmBY4TCYTjh49iq1bt+LKlSuoUqUKqlatiqpVq6Jy5coIDw/3dxWpgImPj8epU6dw8uRJnDp1CkePnsS2baeQknISV65cRp06ddCkSRPro2HDhoiIiPB3tQstxjTXGM8CW0pKCrZt24bY2FiULVsW5cqVQ7ly5VC8eHFeI1EWIoKEhARcv34dN27ccPjT0XM3b960OwYopdCoUSO0bdvW+ihfvrwfPxkBjGeeYEwjIgBISQGmTgWKFgWeesrftSFHmMAixMXFYf78+Zg1axZWrlxpTRIVKVIEjz32GPr06YOHH34YERERGD8eePVVYPNmoGnT3K9b377A5cvAypXArVsJ2Lx5szWpsmXLFiQnJ9stX7VqVURHR+O+++5DdHQ0GjRogKCgoNyvaD5mNBqxZ88e63bdsGEDLl++bLdMVFQU7rnnHmuisGXLlihatCgAYPhw4IsvgNmzgSefzJ06GgzA118DI0cChw4BFSvavx4fH4/Fixdjzpw5WLZsmd1+0aRJE/Ts2RM9e/bEnXfemTsVLOR4ceQeY5r/XLhwAVu3brU+tm3bhvj4eKfLV6hQwZrQqlq1KqpVq2b3d5UqVVCkSJE8/AQU6BITE3H69Glrgirzzxs3bnhVXnBwMOrVq2dNaDVt2hRNmjThvI95hDHNNcazwBIXF4eNGzfaDett26jLIiwszC6hVa5cuSx/2z5Kly7Na6h8TERw+PBhrF+/HteuXXOaoLpx4wYMBkO23qNEiRIoU6YMzp6NRHr6EQD2+129evVw//33WxNaVatW9cEnI28wnrnHmBbYEhKAYsX8XQsqaG7cuIFdu3YhNjYWpUpVx6pVNfDTT+Vw5YpC587AsmX+riE5wgRWIZWUlIQlS5Zg1qxZWLp0qXX+opCQEHTs2BF9+vRB165dUbx4cbv1Zs3SSaVnnwXGjwdyuyHfQw8Bq1bpA0jnzvavpaWlYefOnXaJl5s3b9otU6pUKbRp08aaeLn77rsRFhaWu5UOcMnJydi6dat1m/39999ISEiwW6ZcuXJ2PdsaN26MkJCs094tWAB0765//+MPoEOH3K37xYtApUqul0lMTMTy5csxZ84cLF682O6z3XnnndZkVuPGjdkS1Ud4ceQeY1reiI+Px44dO6zJqi1btuD8+fNZlqtatSpatmyJ9PRqWL78AipVOgeRc7hw4UKWnr6OlCtXzmmCq1q1aqhSpQp70BQgKSkpOHPmjNME1ZUrV1yuX6RIEdSsWRO1atVCzZo1ceNGLSxZUhNvvlkLgwaVw7Fjx7B7927s2rULu3fvxuHDh7MM0wvo/dY2odWkSRPUqlWLsczHGNNcYzzzr0uXLlmvfdavX489e/Zk+i4U6tRpiNtvr4pr167h6tWruHr1KhITE716n6CgIJQpU8ajZFedOnUQGRnp2w9KXjOZTNi8eTPmz5+P+fPn49ixYx6tFxUVhTJlyqB06dIe/yxVqpT12nDAAGD16mR8/fVWHDq0FuvWrcPff/+dpaFp7dq1rcms+++/n/ErDzCeuceYFtjefhswGnVD5kJ+G4+y6dKlS9i5c6f1sWvXLpw6dcrBkhEAqqN06Zp4/PEaqFHD/lG5cmUEBwfnce3JFhNYhUhaWhr+/PNPzJo1CwsWLMCtW7cA6J3g/vvvR58+fdCjRw+ULVvWaRn//AMkJwO33543AaRRI2DfPv1z507A1fHCZDLh4MGDdhd1586ds1smIiICrVq1svbSuueee1CsgDfpiI2NtWuZuX379iwtM2vXrm2XsKpbt67bC4oTJ4BmzYC4OP33tm1A8+a59SmyJyUlBStXrsScOXOwYMECuwRn7dq1rcmsli1b8gIqB3hx5B5jmu+lpaVh3759dr2rDh06lGUbFy9eHC1btrQ+WrRogcqVKwPQF0P//jfQrx8wfbrukXrp0iWcO3fO7nH27Fnr7+fPn/eotXKZMmWyJLnatWuHe++9l8ebAGY0GvHXX3/hl19+wcGDB3Hq1ClcuHDB5TqhoaGoUaOGNUGV+WeFChWyfOcizhsBJScnY//+/XZJrT179iApKSnLssWLF0fjxo3tklr169fnUJg5wJjmGuNZ3hER/PPPP3bXNsePH7dbxnZY7wULonHkSBtMn14K/frZl5WcnGxNZl29etUuueXoERsb63E9w8PD0aFDB8TExKBLly6cozgPpaSkYPXq1Zg/fz4WLlxoN4pGyZKl0blzZ9SsWd1lMio3GnempaVhx44dWLduHdauXYsNGzZkaTBZpUoVux5a9erV4/mRjzGeuceYFrji4oCqVYFbt/R9phkzTChR4iqKFi1a4O/hkfdEBGfOnLFLVO3cuRMXL17MsmxERASaNGmCsmXL4syZMzh9+rTb856QkBBUrVrVmtCqWbOmXYKrWrVqvP7KZUxgFXBGoxHr1q3DrFmzMGfOHLthbFq2bIk+ffrgiSeeQJUqVfxYS+cqVtRDCALAlCm6hZc3Tp8+bXfRd+jQIbvXg4OD0aRJE2vipkuXLggNDfVR7f3vxZdfwY8/fO9wjHLLMIvR0dHWm7neOHkSWLcOGDIEKF0a2LgRCOTpptLT0/HXX39hzpw5mD9/vl2L+apVq6JHjx7o2bMn2rRpw5YVXuLFkXuMaTljuYm3ZcsWa7Jq165d1t7DFqGhoWjSpIldwur22293OgyS0QisWAG0agV4OjqbyWTC5cuXnSa4LI/09HSH69esWRtPP90f/fv3R506dbzaDpR7Dhw4gO+/n4rx46fDYLBPWAUHB6NatWpOE1SVK1fO9aG2jEYj/vnnH7uk1u7du3Hp0qUsy4aGhqJ+/frWhFanTp04hK4XGNNcYzzLPUajEfv378e6deusIyVkvvESGRlpHda7bdu2dsN6X70K7NoFdOyY87qkp6fj+vXrLpNcV69excWLl3Hs2BHr/hAUFIT77rsPMTExiImJQY0aNXJeGbITGxuLpUuXYv78+Vi2bJm1USoA1KhRAzExMQC6YcyY+zBkSAh+/NF/dbUwGAzYs2cP1q1bZ31kHl63XLly1t5Zbdu2xV133cVhLHOI8cw9xrTAYTAYcP78eZw+fRqnT5/Gjh2nsHDhacTHn0Zi4mmkpp6BiL72K1OmDGrWrGl3Pm77yE6v4NRUIDQU4GEn8GzcqDs1WPKWJpMJx48ftyapLA9Hw7YXL14cTZs2RbNmzayPO+64I8s9v/j4eOu+l/lx6tSpLNOsZKaUQsWKFa0Jrdtuuw2DBw9GrVq1kJwMHDkCHDyY8ShaVN/j5q1HzzGBVQCJCLZs2YLZs2fj119/tbvwadiwIfr06YMnn3wy4G+cmUxA5cp6/qPWrYGuXXWyJCeuXbuGDRs2WC8Kd+zYYR0uKqpYcdy4cR2hDobKy49MInjjvU/wv//+B83uvhsPtLsf0dHRuPfee1GyZEmfvc/Nm8D580CNGvlnfGKj0YgNGzZgzpw5mDt3rt0QY0WLVsCDD/bA+PEfoGLmybbIIV4cuceY5p3Lly9j27Ztdr2rMg8RCwB33HGHXbKqcePGAdHyyWQy4erVq3YJrmPHjmHGjDm4fj3jeHPvvfdiwIAB6NWrF0qVKuXHGhdOV65cwaxZszB16lTs3LnT+nz16rUwaNAA6xBHVatWdTiMbiC4dOkS9uzZY5fUOnr0qN2xJjQ0FNOnT0evXr38WNP8gzHNNcYz30lNTcX27dutDe02btyIOMvQBmZly5a1a3TWpEmTgGls98ILwE8/ARMnXkJKygLMmzcPq1evtmvA0bRpU2syq0GDBuxhk03nzp3DggULsGDBAvz11192PcGbNGmC7t27o3v37mjUqBGUUpg8GRg0CBg6FBgzxm/VdsoycsratWutvbQy3xwsWbKkNVH75JNPYsWKarh0CXjvPT9VOh9iPHOPMS3vpKamWnu7WJICtkmCc+fOuR3KvXTp0khMTMzSiDGzcuXKuUxwZR7qPTERaNwYuHEDOHUKyDSTCvnRhQsG1K17GA8+uBO1a+tE1e7du7P06gX0OZNtoqpZs2aoVauWTxpDWIaUd5bkcrT/BgWFoVix1xAX928AJe1e+/xz4F//ynG1ChUmsAoIEcG+ffswe/ZszJ49GydPnrS+Vrt2bfTu3Rt9+vRBw4YN/VhL76SmApcuASEhuidWbmSmExMT8df6jZgwcyVE0vDDd5+hSokivn8jPzgXm4w5qy8hLjEIAx4th5qli/q7SgHJZDJh69atmDNnDn7+eQ6uX9f/O3feeSfWrVvnckhN0nhx5B5jmmcOHTqEfv36YdeuXVleq1ChAlq1aoWWLVuiVatWaN68uU+T8Xlh0iQjBg9eg2rVpuLGjTnWOUnCwsLQpUsX9O/fHw8//HChn6sxN6WkpGDhwoWYOnUqli9fbr3QKFGiBJ588kkMGDAg3w/zmJiYiH379mHQoN04fHgVgN+hlMK3336Ll19+2d/VC3iMaa4xnmVfYmKitSHd+vXrsXXrVqSkpNgtU716detN++jo6IAeVi0mBpg/336UjLi4OCxduhTz5s3D0qVL7ebeuu2229C9e3fExMSgdevW7F3jgojg4MGD1vmstm/fbn0tKCgIbdu2Rffu3dGtWzfUrFnTYRlXrgDly+dRhXNIRHDs2DFrMmvt2rU4e/as9fVSpUojPn4xjMZ7sHmz7jlP7jGeuceY5p2jR4GZM4GGDYG77gLq1NH36ywuXLiA3bt3O0xQORrKLbNKlSplGZrN8nf16tURFRUFk8mEK1eu2M1Ha3mcPHkSp0+fzjJlRmYVKlSwS3BVqlQT//53TZhMNbFvX23Urh0YDUUKq5SUFMyfPx8///wzVq9eB4MhJcsyVapUsUtUNW3aFFWrVvXbOZPBYMCFCxes+/2KFSswY8YMAEBwcGkYjR8CeAGAvs5v3lxPw0KeYwKrAPj777/Ru/fzOHt2v/W5SpUq4cknn0SfPn3QokWLgL3wCQQX41MwdVk8DCJo1yoUTaoUR2RYYLa09lRimgE7zsRj7pJ0lCoF9OgQgbsqsRmJO1evCp56ajcOHRqAs2f3o0WLFli1ahXHWHaDF0fuMaa5t2TJEvTp0wcJCQkoUiQKzZs3x733ZvSu8ucJqS/t3q0v+lJTEzFv3jxMnToVK1eutO4bZcqUQe/evTFgwADGbx8REWzcuBFTp07Fr7/+au3hEBISgocffhj9+/dHly5dUKRIwWjAYrFsGTBpkqB27dEYPfodAMAHH3yAjz76iPuVC4xprjGeecdkAq5fB9avX4shQ57A9etX7V6vX7++3Ty01atX91NNvZeUpIcUb9DA8euWuWjnzZuHhQsX4tq1a9bXKlasiG7duiEmJgbt27dnww3oESI2b95sTVrZzncWERGBzp07o3v37nj00UdRxtNxj/OxU6dOYd26dZgyZQpWr16N0NAIdOjwGxYtepRDfHmI8cw9xjTvpKcDDzwAbNig/w4PB+68E6hV6yzOn/8UO3ZMgtHoeK7g4OBg6xxCjpJUvppDyGQy4dKlS04TXGfOnHE61Dugr8X69euHgQMHokmTJjxnzkO7d+/GpEmTMH369Czz11uSVJafFSpU8GNNPbNz5068+eabWLNmDQCgaNG6SEr6HGFh3bF8uUL79v6tX37DBFY+t2HDBnTo8DBSUm4hOLg0Bg9+HH369EF0dDTn8fHQocsJmLowCf8cCEG/wQbULhuBBhWLISifBiqTCA5cSsCanclYtTgEdRsa0f2RELSuUQrBQfnzM+W1Cxcu4L777sPJkyfRvn17LF26tMDd2PQlXhy5x5jmnIhg9OjRePfddyEiqFbtCZw9+zPeey8Sn3zi79rljfPnz2PmzJmYOnUq9u/PaIxyxx13oH///njqqac4j0g2HD9+HNOmTcO0adPseqbffffdGDBgAHr37o3y+aV5eg5NnDgRQ4YMgclkwosvvohvv/2W54lOMKa5xnjmHRGgfPkJuHbtJQAG3HXXXejYsSOio6PRpk2bQtPT32AwYOPGjZg/fz7mzZuH06dPW18rXrw4Hn30UcTExODhhx9GVFSUH2uat1JSUrBq1SrMnz8fCxcutJujt0yZMujatSu6d++Ohx56yDrXWWFjMBjw/PPPY9KkSQgODsakSZMwwNuJsQspxjP3GNO8s3070KKF7TMXAIyEUhMgkoagoCDcf//9qF27tl2SyjJnbCAMyW00GnHx4kWHCa7jx4/jzJkz1mUbNWqEgQMHol+/foXmmiGvxcbGYubMmZg4caLdkO5NmzbF4MGD8eSTT+brcyURweLFizF8+HAcOXIEAFChQjRGjPgCzz/f0s+1y19yFNNEJNceAES/BTmzYcMGiYqKEgBSrFg/ee21VH9XKd8xGE2y5uh1GfzJZXnkmRvy49Ir8seRK3IuNtnfVcu287HJsnT/FXnmwyvyyDM3ZMgnV2XV0asSl5zu76rlK8ePH5eKFSsKAOnWrZukp3P7OWNzvM7VuJCfH4xpjiUlJUnfvn2t+9Cnn34qP/5okqpVRZYt83ft8p7JZJJdu3bJ66+/LuXLl7duFwBy//33y8SJEyUuLs7f1QxoN27ckHHjxsm9995rt/2qVq0q77zzjhw4cMDfVfSb+fPnS3h4uACQJ554QlJSUvxdpYDEmMZ45ivp6ekydOhQ6z71xhtviMFg8He1/M5kMsnOnTvl/fffl4YNG9odq8PDw+Wxxx6TiRMnypUrV/xd1Vxz5coV6dmzp0RGRtp9/lq1asnrr78ua9eu5bWHDZPJJO+++651O40ePdrfVcoXGM8Y03zt5k2Rxx4TAS4L8LoEBRURAKKUkj59+sihQ4f8XcUcMZlMsmPHDnn11VeldOnS1v+hkJAQ6dq1q8ybN09SU3nfNaeMRqOsXr1a+vXrJ0WKFLFu55IlS8orr7wiO3fu9HcVfS4tLU3+97//SdmyZa2ft0+fPnLy5El/Vy3fyElMYw8sP9q4cSM6d+6MW7duoV+/fpgyZQpb0mZDbHI6lmyPxaKFCgk3glG7tqBzTDpKRuTPoQST043YdT4Om3elY9XiUAAKkcVMeOYFIxpXLo5KxdmLyBv79+9H27ZtcfPmTQwYMAA///wzx+t3gK373GNMy+r8+fPo3r07tm/fjsjISEyfPh3du3f3d7UChsFgwJ9//ompU6di/vz51nlSihQpgu7du2PAgAHo0KFDQLRk9Le0tDQsX74cU6dOxaJFi6zj3kdGRuLxxx9H//790a5dO54nAVi3bh26dOmC+Ph4PPjgg5g3bx6Hyc2EMc01xjPPxMbGonfv3lixYgVCQ0Pxww8/YPDgwf6uVkA6fvw45s2bh3nz5mHz5s3WfSsoKAj33XcfYmJiEBMTU6B6IhsMBpQpUwHx8TfQrFkzdO/eHd27d0fDhg05XJULY8eOxbBhwwAAb775JkaPHs1rMxcYz9xjTPPO9evX8dBD/8X+/d/CYEgCAPTo0QMjRoxAw4YN/Vw730pNTcWSJUvw888/Y9myZdZ5c8uWLWs3xCB57vz585g8eTImTZqEEydOWJ9/8MEHMXjwYMTExBT4kY/i4uLw2Wef4euvv0ZqairCw8MxdOhQvPvuu/lufu+8xiEE8yHb5FXfvn0xdepU3pTJptM3kzBvXQJWLw2F0aD/B14daoIxxIDqpfLXUIIiggOXE3D0YjJmTwlBYoLlZF7w3NB0NKgUhbrlIv1ax/xo8+bNeOihh5CYmIjXXnsNY8aM4YVlJrw4co8xzd6WLVvQvXt3XLp0CbVq1cKCBQtw1113+btaASs+Ph6///47pk2bZh1DG9ATEPft2xf9+/cvdGO0iwh27NiBqVOnYtasWda5VZRSeOihhzBgwADExMQgMpJxL7M9e/agU6dOuHz5Mu6++24sXbqUw6LYYExzjfHMvWPHjqFLly44cuQIypYti7lz5yI6Otrf1coXLl68iIULF2LevHlYvXq1dZ6SsLAw3Lhxo8Ac0+fNA/7znz8xd+4d+Wres0Awa9YsPP3000hPT8dTTz2FSZMmITQ01N/VCkiMZ+4xpnkmNjYWX331FcaMGYOEhAQAQJcuXTBixAg0bdrUz7XLfZcuXcKMGTPw888/48CBA9bnGzdubB1isFy5cn6sYeBKS0vD4sWLMXHiRCxfvhwmkwkAULVqVQwaNAiDBg1CrVq1/FzLvHf69Gm89957mD59OgA9bPCHH36IF154gTHNCSaw8pm///4bnTp1YvLKR3afj8PcP1KwY0PGxMEPPgg0aWGAUYD6FaJQpUSEH2vouYvxKThwKQHbtgKH94XAZp5kPP1CGupUDMfd1Ur6rX752cqVK/Hoo48iLS0NH330ET788EN/Vymg8OLIPca0DFOnTsWQIUOQmpqKdu3a4bfffsvX41rntdOnT2P69OmYNm2adRxtAGjYsCEGDBiAvn37okqVKn6sYe5Yt05PHN2wIbBgwXiMGTMGhw4dsr7eoEEDDBgwAP369SuQn9/XTpw4gY4dO+Kff/5B3bp18ccff6BmzZr+rlZAYExzjfHMtdWrV+Pxxx/HzZs30bBhQyxcuLBQ3pjxhdjYWCxduhTffjsPpUsrLFnyq7+r5BO//gr07QsMHw6MGuXv2uRPf/zxB3r06IHExER07twZv//+e4FJbvoS45l7jGmuxcfHY+zYsfjyyy8RFxcHAOjUqRM+/vhjtGxZ+ObvERHs3LkTkydPxsyZM3Hjxg0AQEhICB577DEMHDgQjzzySKFJQKSlAfPnA3fdBdx5p/1rBw8exMSJEzFt2jRcvXoVABAaGopu3bph8ODB6NChA+9lA9i+fTvefPNNrFu3DgBQt25djB49Gt26dStUjVM9wQRWPpI5eTVlyhQOHZQDqQYjNp+KxbRZRlw8pQOMUkDp0sBLLwmuJ+WfoQRT0o3YeT4ON5PTEan00IELFgBRUYDBADS824BqlYLQukYphIVwmIXsmDt3Lp544gmYTCaMHTsWr732mr+rFDB4ceQeY5qeMPftt9/Gl19+CQB46aWXMGbMmEJzgu9rIoJt27Zh2rRpmDVrFq5fvw4AaNPmMSxYsAhlyvi5gj721lvAl18CwcFA48bvYOfOz1G+fHn07dsXAwYMKHQ90Hzh8uXL6Ny5M3bv3o1KlSphxYoV7AkJxjR3GM+c++GHH/Dqq6/CaDSiS5cumDFjBofo9IGnngLuuQd4+WV/18Q31q0DXnsNGDEC6NbN37XJv7Zt24ZHHnkE165dQ8uWLbFkyRI2iMqE8cw9xjTHEhMT8d1332H06NHWJE379u3xySefoE2bNn6uXWBITU3FokWLMHnyZCxbtszas6hcuXLo168fBg0ahEaNGmVZT0TfdywIFi7UcaxWLeDECSAhIQG//PILJk6ciM2bN1uXa9iwIQYPHoynnnqKx2kHRAQLFy7Ev/71Lxw9ehQA0LZtW3z55Zdo3ry5n2sXOJjAyif+/vtvdO7cGQkJCejTpw+mTp3K5FUOXUlIxdpDcTi4LxgRoUHYswdo/4AOKHVqA0UiTLiRbECNAB9KUERw6PItnLyRhJIRIQgNDsr0OpCQbASCBM2qlkDpomFOSiJ3Jk2aZJ2/YMqUKRgwYICfaxQYeHHkXmGPabGxsejTpw+WL1+OkJAQfPfdd3j++ef9Xa0CIy0tDcuWLcO0adMQEdEP06fHYPVqoH17f9fMd5YsAdav1zcwDYaTOHToEDp06MAEaA7FxcWhe/fuWLNmDUqWLIlFixbhvvvu83e1/IoxzbXCHs8cSU9Px7Bhw/D9998DAN5++2385z//YctiHzCZgK5dgaZNgU8+8XdtfMdkApKTAXYaypmjR4+iY8eOOH36NO644w6sWLGiQM2VllOMZ+4xptlLTk7GuHHj8Nlnn+HKlSsAgDZt2uCTTz5B+4J0YeFjFy9exPTp0zF58mQcPHjQ+nzTpk0xcOBA9O3bF6VLl8XIkUD9+kCPHn6srA8dPw7ExADNmu1EcPB3+PXXX5GYmAgAKFasGPr06YPBgwejRYsWbGjogfT0dPz444/46KOPrI1T+/Xrh//85z+MbWACK1/YtGkTOnXqxOSVjx27mogjV2+hXGSo04PprdTAH0rwckIq9l+KhwhQvIjj/cJoEtxMTkfDisVRvVRgfo784quvvsKbb76J4OBgzJkzB93YdJIXRx4ozDHtyJEj6Nq1K44ePYqyZctizpw5aNu2rb+rVaCtXQu0aQPwVIE8kZKSgn79+mHu3LkoUqQIfv31V3Tp0sXf1fIbxjTXCnM8c+TmzZt44oknsGrVKoSFhWHChAls4ESUhy5cuIDOnTtj3759qFKlClasWIEGDRr4u1oBgfHMPcY0LTU1FT/99BNGjhyJCxcuAABatmyJTz75BB06dGDywUMigu3bt1uHGIyNjQWgh86rXfstJCaORJ8+wOjR/q2nr33zzTcYOnQoAN1z6JlnnsHjjz/OoV2zKS4uDiNHjsTYsWORmpqKUqVK49y5syhatKi/q+ZXTGAFONvkVe/evTFt2jQmr3zAJIJtZ2JxIykNZSKd90gSEVxLDNyhBFMMRuw+H4/riWko6yIRBwBXbqWidulI1K/IoUxy6v3338enn36K8PBwLFu2rNC3RuLFkXuFNaYtX74cvXv3RlxcHBo1aoQFCxZwnh2iAGQ0GvHSSy9h/PjxCA4Oxk8//YSBAwf6u1p+wZjmWmGNZ44cPnwYXbt2xbFjx1C+fHnMnz8f99xzj7+rRVToxMbGomvXrli/fj1KliyJxYsXc5gzMJ55orDHNJPJhIkTJ+KTTz7B2bNnAeheQx9//DEeffRRJq5yICUlBYsWLcLPP/+MFStW4JtvvsHLBWUs3EyuX7+OL774As888wzq1q3r7+oUGKdOncK///1vHD9+G8qWHYEBA3Sv9IhC2ieBCawAxuRV7klINWD7mVgIBFHhrrepwRiYQwmKCA5fuYUTN5JQokgIwoJdz211IykdJSJC0ap6yYD5DPmViODVV1/F//73P0RFRWH16tVo0aKFv6vlN7w4cq+wxTQRwVdffYV//etfMJlM6NGjB6ZMmYKoqCh/V42InBARfPDBB/j0008BAKNHj8bw4cP9XKu8x5jmWmGLZ8788ccf6NWrF+Li4tC4cWMsXLgQ1atX93e1iAqt5ORk9O3bF/Pnz2dvYjPGM/cKe0wzGAQNG7bBkSOb0LBhQ4wYMQIxMTFMXPnYhQsXEBUVheLFi/u7KpQPJScL7r1XYfduoHhxoFcvYMAAPeJKkOvbwAUKE1gBavPmzejYsSOTV7nkXGwy9l2MR/EiWeeMciQQhxK8eisV+y7Gw+Ri6EBbt1INUEqhZfWSAdeTLD8ymUzo378/Zs6ciTJlymDdunWoX7++v6vlF7w4cq8wxbSUlBQ8//zzmDp1KgDgww8/xAcffICgwnR2RZSP2Q4D8tZbb2H06NGF6kYGY5prhSmeOSIi+Pbbb/H666/DZDIhJiYGU6dOZQMNogBgMBjw0ksvYcKECQgODsaECRMwaNAgf1fLbxjP3CusMS0+Hpg0CRg7FoiO/huPPHIGvXr14vUaUYA6ehRo1gwwTzEGAKhZE+jfXz8KQ8c3JrACkG3y6sknn8T06dOZvPKx/ZficepGEspFhnl0UybQhhJMM5iw63wcriamuZzDy1aqwYTkdCMaVy6BCsXC86CWBV96ejp69OiBxYsXo0qVKtiwYUOhHB6NF0fuFZaYdvHiRcTExGDLli0oWrQopkyZgscff9zf1SIiL82cORNPP/00DAYDnn76afz000+F5lyUMc21whLPHElLS8Orr76K8ePHAwDee+89jBgxgjf8iAKIiODDDz/EJ598AgAYNWoU3n777ULVEMOC8cy9whbTTp0CvvkG+OknICEBuP12YM8eoEgRf9eMiNyZPl0nqzKLigKmTQO6d8/zKuUpJrACzObNm9GpUyfEx8ejV69emDFjRqG5YZBX0o0mbD4di+R0A0pGhHq8XqAMJSgiOHr1Fv65noxi4cEID/HsotlkTsLVrxCF2mU4maKvJCcno3Pnzli3bh1uu+02rF+/HhUrVsSGDUCJEsBdd/m7hrmPF0fuFYaYtm3bNnTv3h0XLlxA9erVsWDBAjRp0sTf1SKibFqxYgV69OiBpKQktGjxGObN+wVVqhT8yYMZ01wrDPHMkevXr6Nnz55Yu3YtwsPD8fPPP6NPnz7+rhYROfHdd9/htddeg4hg2LBh+PLLLxEUFIQzZ/TNvtKl/V3D3Md45l5hiGkiwKZNwNdfA3PnAiZTxmtr1wJt2/qvbkTknUGDgMmTM/4ODQXWrAHuvddfNco7OYlpbGrmY1u2bGHyKg/EpxiQajB6nPixCAkOQtHQIFxKSMXF+JRcqp1715PScSEuFaFB8OozBCkFBSAhxZB7lSuEIiIisHDhQjRr1gzHjx9Hp06dsHHjTbRrB0RHA0lJ/q4hUe6bMWMGoqOjceHCBURHR2Pbtm1MXhHlc506dcLq1atRtGhpbNu2Dk89ddLfVSLyi4MHD6Jly5ZYu3YtKlWqhHXr1jF5RRTgXnnlFcyaNQuhoaEYM2YMWrR4Aw0aADVqACdO+Lt2RLkvPR2YPRto3VrPlfP77/bJq+efZ/KKKL/57jugXr2Mv41GIDXVf/XJL5jA8qEtW7agY8eOiI+PxxNPPMHkVS6KS0lHqsHkdQILACLDgpFmMOHUjWQkpuV9IijNaMKJ60lITDeimAfzXmUWGqwQn2qAwWhyvzB5rESJEli+fDnuuOMO7N27F2+++Rhq105E06a6RQRRQfbLL7/gqaeeQmpqKp577jmsXLkS5cuX93e1iMgHWrVqhW++2YBixRahU6cG/q4OUZ5bunQpWrdujRMnTuDuu+/Gtm3b0LJlS39Xi4g88OSTT2Lp0qWoWrUq/vvfZ/Daa0DnzkCpUv6uGVHu27cPmDkT2LYt62uVKwOff573dSKinImMBH75BQgPB4YOBWbMANq393etAh+HEMyBpCRgxQqgSxdgxw775NXMmTOZvMolIoKd5+JwKSEV5aLCslWGv4YSFBEcv5aEY9duoVh4SLYScElpRhhFcHfVkl4Nn0ieOXv2LNq0aYOzZ8/i6aefxmTbvr0FGIencK8gx7Tk5GTce+8DaNfuKXz11UuFco4BIio4GNNcK8jxzOLSJWD/fuDYsSsYNqwm0tKS8cQTT2Dy5MkoWrTgD6NJVNCkpqYiPLzwzQHNeOZeQY9pKSlA167An3/aPz9/PtCtm1+qREQ+8OOPQKVK+v+7sOAcWH4yeDAwaRLQv/9WLFjQwa7nVSi7bOSapDQjtp65CYNJUDwbPZgsbqUaYBSgfoUoVCkR4cMaOnc9KQ17L8Qj3WjKdvLJYDQhLtWIRpWK5Vm9C5ujR49i0KBBmDFjBmrWrOnv6uQJXhy5V5BjmghQtqwRN24EY9MmPUwFEVF+xZjmWkGOZxYHDwINGuj5BHr3no4bN/7BBx98wAYaRJSvMJ65V9BjmsGgb3AvW5bx3OOPA7/95r86EVHOiegEdUQhuq3LBJafjBsHvPPObqSl3Y/k5Hg8/vjjmDlzJpNXuexifAr2XohHZFgwwrLRg8lCRHAtMR0lI0LRpEpxRIblbo+5dKMJu8/H4/KtFJSNDMt2ry8RwdXEdNQtG4k7ykf5uJZkISKF6iYHL47cK+gxrWdPYPduYOVKoFYtf9eGiCj7GNNcK+jxDNBzhEybBvTpA4Rlb8AGIiK/YzxzrzDEtIQEYOpUncTauBE4dAioWNHftSIi8g4TWH5082YcOnfuiOrVqzN5lUcOXU7A8etJKB8ZmuMEg2UoweolI9CwUu4OJfjPtUQcvXoLkeHBKBISnKOyriemoUxkOFpUK1GokiyUe3hx5F5hiGkmExDE2TGJKJ9jTHOtMMQzIqKCgPHMvcIU0778Us//9swz/q4JEZH3mMDys/j4eERERDB5lQeMJsGWMzcRn2JA6aK+2d63Ug0wmoA7K0Shasnc6bt5MykNey7EI9VoQikfzFsVn2JAaLBCq+qlUCQ0Z8kwIoAXR54oLDGNiCi/Y0xzjfGMiCh/YDxzrzDFtKtXgbJlAbZhJqL8KCcxje2sfaB48eJMXuWRhFQDktONCM/B0IGZRYYFI91kwqmbyUhMM/isXAuD0YQT15NwK82IEjmYs8tWWLBCmlFwK83ok/KIiIiIiIiIiCgwlSvH5BURFU5MYFG+EpeSjlSDCUV8mMBSSqFkRChik9Pxz7UkmHzccudMbDKuJqaheHiwz4YoDAsOQprBhFupvk+4ERERERERERERERH5GxNYlK/EJqXDJEBwkG+bnYQEKRQNDcLlhFRciEvxWbmxyek4G5sMAD4d6i8oSEEAJrCIiIiIiIiIiIiIqEBiAovyjVSDCXEpBoSH5E6faV8PJWg0CU5cT0RCqu+GDrQVGqQQl2KA0VTwx3omIiIiIiIiIiIiosKFCSzKN+JS0pFi8O38V7Z8PZTg2dhkXL2VhmLhwT7vMQYAocEKaQYTktI5DxYRERERERERERERFSxMYFG+EZdiQJpREB6ce7utr4YSjE9Jx5mbyRAIInw4dKCtsJAgpBlNSOQwgkRERERERERERERUwDCBRfmCiOBmUhqClO4plZtyOpSg0ST453oS4lMNKFEkNBdqqIUGKaSbBLfS2AOLiIiIiIiIiIiIiAoWJrAoX7iVZkRimhFFcmn4QFs5HUrwXFwyriSkIiosd4YOtFBKQUH39iIiIiIiIiIiIiIiKkiYwKJ8IS45HSnpplyb/yqz7A4lmJBqwJmbyTCJICI09+saFqJwK9WINIMp19+LiIiIiIiIiIiIiCivMIFF+UJcSjqMYkJILvZoyszboQSNJsE/1xIRl2JAyYjQXB/qEADCgs3zYGVjqEMiIiIiIiIiIiIiokDFBBYFPIPRhJvJBoQGB+VJUsjC26EEL8Sn4MqtVETm8tCBtiwJLM6DRUREREREREREREQFCRNYFPDiUg1ISc+b+a8y83QowVupBpy6kQyDCSiaB0MHWgQHKZhEvz8RERERERERERERUUHBBBYFvLhkA1INJoQH+2d3dTeUoEkE/1xPQlxKOkpGhORpLzEACA4C4lIMEDc9xIiIiIiIiIiIiIiI8gsmsCigiQhuJqcBAILycP4rW+6GErwQl4IrCakoGhqUp3N0WYQFByEl3YjkdFOevzcRERERERERERERUW5gAquQ27oVOHTIN2WZTMCePUBSkm/KA4DkdBMSUgwI98PwgbacDSWYmGbA6ZvJSDeZEBkW7Je6ZcyD5bthBNPSgP79gZEjfVPejh1ATAzw11++KY+IKDMRYNkyIDHRN+XFxQH79+vYRvnbtm1Aly7AmjW+Ke/HH4EnngBu3vRNeUREmd24AaxerWObL5w5A1y44JuyyL8mTQJ69wbOnct5WUlJwJAhwNtv57wsIiIiotwS4u8KkP/s2JGEVq3+RLFiqfj++yAULRqE4OBgBAVl/enoucyvzZkThBEjSqNXr0r45Rff1DE+JR2pBv8lh2xFhgXjelI6Tt1MRqmIUESEBePE9STEJqejlB+GDrQIDVaITxHcSjWifFTOy0tPT8fLL3+J6dPnQinB3LkKwcEKSikEBQVBKWV92P7t6rWDBxVOn66NtLR30L59lZxXkojIhojgxRc348cfx6NYsY2oVSsCUVFRKFasWLYeUVFR6Ns3GEuXArNm6RtFlH/NnClYvPgKDIZkVKqUitRU+0daWppXz02ZYsStWx3w6KPdMHCg/89PiKjgefRRYPNmwQcfbES5crthMplgMplgNBrtfnrye2KiEZMnm2A0mvDBB7UxfPhLKFq0qL8/Yq7ZuXMnNm3ahKZNm+Lee+/1d3V8RkQwZ84cDBu2CAkJQbh4MRR33RWGsLAwhIaGIizM/ndHz2V+PSioHiZMqAgAeO89oFgxP39IIiqQbt4EtmwB6tcHqlfPWVkJCUByMqAUUK6cb+pH/nHyJDBhAnDqFDB8ONC0ac7KGz0a+OcfvZ8NHeqTKlIAUbk5b45SSgBwbp4Ak5SUhHHjxmHUqM9x7doVn5ffqNFL2Ljxc0RF5TybcvhyAo5dT0L5yFC/JYhsGUyCG0npKGqMQHBqKNKK30KwAqLC/ZsLvnorDdVKRqBR5eI5Kmf//v0YOHAgduzY4aOa2StSJAJvv/0vDB8+HJGRkbnyHpQ9lv8vEfH/P1qAYkwLPDdv3sT06dMxfvx47N+/36dlh4QUhcFQAl269MD06SNRvHjOjq/kHwcOHMDAgc9h+/ZNPi+7du06ePPNNzBw4MACfTM4P2JMc43xLLCZTCY89tgC/Pnn5zAYtvi8/Jo1a+Lbb7/FY4895vOy/S0lBXj55c8xadI7GD58OEaPHu3vKvnE/v378dprr+EvHw9nERYWhtatX8Djj/8fXn21gk/LJt9gPHOPMS2wHT4M3Hmn/n3MmJwnFoYNA8aOBYKDAYPvBiEKSCdPAhcvAgWoLYadnTuBu+/Wv0+eDDz9dM7Ki44GNmzQZW7fnuPqUS7ISUxjD6xCJCkpCT/++CM+//xzXL58GQDQtGlT3HbbbQ5b9RkMBly5cgWJiYkwmUwICgpCqVKlEBwc7LQF4NGjR7F37/do3Hg5Jk+ejOjo6GzX12gS3EhOR2iQCojkFaCHEowICcL7b49F/I3LeOGdZ9G8UQ6bkPhAaLBCfKoBRpMgOBvzcBkMBnzxxRf48MMPkZaWhurVq+Prr79G1apVISIwmUwQEYgIjEYjdu7ciSVLlmDbtm1ISkpCWFgYatWqhU6dOiE6OhpFihSxW8dgMGDatGmYM2cORowYgQkTJmDkyJHo378/goI4kikReU5EsGnTJowfPx6//PILUlL0sK7ly5fH008PQp8+T0IphYSEBLvHtWvXsGXLFuzfvx+J5nEGixUrhtKlSyM9Pd1u2Vu3bsFgSAKQhEWL/ocGDRZg3LhxePTRR/34yXNPbCxw+TJwxx3+ronvpKamYtSoURg5ciTS09MRFRWF0qVLIzw8PMsjODgY165dw/Xr12E0GhEWFoYqVaqgYcOGKFmyZJblExISMGHCBJw48Q9efvllfPDBB3jppZfw8ssvo0IF3gAkouxJTU3FjBkzMHr0aBw5cgQAULp0acTExCAiIiLL6BfXr1/Hvn37cObMGaSmpiIiIgK1a9dG8+bNUalSJbtlgSCkpwPTp0/Enj170KVLF3Tr1g1jx45FjRo1/PvBfejdd4FJk3TPWKPR6Ofa5FxsbCw++OADfP/99zAajShTpgzefvttlC1bFmlpaUhPT0daWhrS0tKQkpKCQ4cOYe/evYiLi4OIIDIyErVr10bZsmWRnp5ut3xiYiK2bNmCdeu+wY4dE3H58jC89dZbKFmypL8/NhEVIHXrAlFRwK1bwObNOU9gFZY85ZUrV9G+fQJOny6Lv/4qjnbt/F0j37M9/Th1KuflWYbWrcJBnwomyw3m3HgAEP0W5E9JSUny9ddfS8WKFcXynTRv3lyWLFkiJpMpy/JXrlyRYcOGSenSpa3LWx5BQUHy8MMPy6pVqxy+1+7du6Vx48YCQJRSMmzYMElMTMxWvW8mpcnKo1dk9bGrsuX0Db89Np+6Luv/uSYrj16RpQcvyaIDF6VCtYYCQD6f/qcsPnBJ/jhyRdYevyqbTl33Sx3/OnZVVh29IrHJaV5v54MHD0rLli2t3/GQIUMkLi7O4bJHjhyR+vXrZ9kvLN83AImKipLZs2c7XH/dunXSvHlz6zrNmjWTNWvWeF1n8j2b43WuxoX8/PBXTNuyZYusWrVKUlJS8vy9A8mNGzdk7Nix0qBBA7tjz0MPPSS//fabpKamOlzPZDLJqFGjpGjRotZjlVJKgoKCrGV069ZNrl+/bl3HaDRKfHy8bNu2TVq0aGFdrm/fvnLlypW8+sh5ZuhQkYcf9nctfGfTpk12ser555+X2NjYLMsZjUYZMWKEREZGWveNoKAgazwLCQmRwYMHS1JSUpZ1DQaD/Pbbb3bxMzw8XJ577jk5fPhwXnxMcoExLTDjGTkWHx8v//3vf6Vy5crWfbd69eoyduxYuXXrVpblr127Jp06dXJ4Pm55dO3a1eFxLz09XcaMGSPFihUTABIRESGjRo1yGkPzm59+EgkL+0oAyNChQ/1dnWwzGo0yYcIEKVu2rPUa/OWXX7Y7V7G1cOFCKVeunN01meV8B4BUrlxZVq5cmWW9PXv2SNeuXa37TalSpeTzzz/P9vU7+R7jGWNaQfDAAyKASM2aOS/r1Vd1WSEhOS8rkL322msCQIoWHSPHjvm7NrnDZBKJitLf56BBOSvLaBQJDdVlvfSSb+pHvpeTmMauDwVYcnIyxowZg9q1a+P111/HpUuX0Lx5cyxevBhbt27FI488kqVn06FDh9CsWTOMGTMGN27cyFKmyWTC8uXL8dBDD+Grr77K8nrjxo2xdetWvP/++wgKCsKYMWPQtGlTbNrk/dA98SkGpBpMKBKSt7upiCDVYEJCigHXEtNwNTEdiWlGBCuFMpFhuL1cFMqU0NvtzspFUblEEYQHByHVILiemI6rt9IQn2JAisEIk+RN85DQYIU0oyAx1fOWhkajEV988QWaNm2KrVu3omrVqlixYgV+/PFHh0Nl7d+/H61atcKhQ4cclifmz5qYmIjevXvj+++/z7JMdHQ0tmzZgqlTp6JKlSrYuXMn2rVrhx49euDYsWMe152oMOnRowcefPBBXL161d9VyXMigg0bNmDAgAGoXLkyhg4digMHDqBChQp45513cPz4cfz55594/PHHERYW5nD9l156Ce+++y6SkpKsz4nonqUWCxYswL333mvdxkFBQShWrBiaN2+OTZs24YsvvkBERARmzpyJ+vXrY/bs2dZjXkHw/vsp+OeflzFs2DB/VyVHbt26haFDh+Lee+/FwYMHUbduXaxZswbjxo1DiRIl7JY1mUwYOHAgPvzwQ2uPPMt+YfluDQYDJk6ciAcffNC6jEVwcDAef/xxbN68GevXr0e3bt2QlpaGCRMmoF69eujWrRvWr19foPYTIvKty5cv49///jeqV6+O4cOH48KFC2jYsCGmTp2K48eP47XXXssy5Pa1a9dw3333YcWKFS7LXrhwIe6//37ExsbaPR8SEoKhQ4fi8OHDePLJJ5GcnIx3330XTZo0wZo1a3z8CfPe4MHA6NH5uwfWli1b0KpVKzz33HO4du0a2rZti507d+K7775D6dKlsyw/ZcoUdOvWDdeuXQOQcU0mGTf3cenSJXTq1Alz5861W7dRo0ZYsGAB/v77b7Rr1w43b97E22+/jdtuuw0//PAD0tLScvnTElFh0Lq1/nnqFHDpkm/KDJBBmnKN5fj73/+G4rbb/FyZXKIUULOm/t3SA2v1auD4ce/LunoVSE/Xv1et6ovaUaBhAqsASk5OxtixY+0SV3fffTcWLVqErVu34tFHH3U4JN/FixfxwAMP4Pz58y7Lt5wMv/nmm5gwYUKW18PCwvDxxx9jy5YtaNCgAY4ePYr77rsPb7/9tnWoJ0/cTE6HSZCtIfG8YRJBisGEeJuEVXK6EcFBCuUiw3FHuSg0rlICLaqXRItqJXF7uSiEBut/nWqliqJplRJoVaMkmlQpjjsrRKFS8XCEBgchNV1wzS6hZcq1hFZIkILBJLiV5tkgwEePHkV0dDSGDx+O1NRUPPPMM9i/fz86duzocHmj0Yju3btbh6NwRUSglMLLL7+MvXv3Znk9KCgI/fv3x9GjR/Hxxx+jaNGimDdvHho0aIA33ngDN2/e9OgzEBUWYWHhAPTQQoXFjRs3MGbMGDRo0ADR0dGYNm0aUlJS0LFjR8yZMwdnz57FqFGjUKdOHZfl/Prrrxg3bpxH73nkyBG88sorWZ4PDg7Gm2++ib1796Jdu3a4du0a+vTpg27durmNl/lFeLgBR49+7zCm5xcrVqxAw4YN8c033yAoKAjvvPMO9uzZg/vvv9/h8hMmTMC0adM8KnvTpk149913Hb6mlMJ9992H+fPn49ChQxgyZAjCw8OxcOFCtG3bFq1bt8Zvv/0GQ0EfpJ+IPHbixAm89NJLqFmzJkaOHInY2Fjcd999WLx4Mfbu3Yv+/fsjNDTU4brDhw/H4cOHPXqfPXv24N///rfD1ypXrozZs2fjjz/+QN26dXHo0CG0b98e/fv3tw41n1+FhOTPBNbly5cxaNAgtG7dGtu3b0eVKlUwa9YsrFmzBo0bN3a4zqlTpzB48GAAruf/sQz337dvX1y5knUO7HvuuQerV6/GihUrcPfdd+PixYt46aWXcOedd2L69On5blsSUWC5556M3zdv1j+9OTVOTwcSEvTvmQ91Ino49IIm3ZyNcdRIM7+7fBkYPhzYtStjGMFjx4AXXwQefBAoW9bzsiz7g2X4QIAJrIKKCawCJDk5Gd988w3q1KmDYcOG2SWutm3bhscee8zlXFJff/01Ll265FVr4f/7v/9zmpS6++67sWPHDrzzzjsAgNGjR+Puu+/Gdg9m00s1mBCXnI7wYN8nr0wiSEk3Ii7FgKu30nAtMR0p6UaEBitUiApHvfJRaFK5BFrVKIXm1UqgbrlIVCwWjsiwENsJ5+zKDA8JRrmocNQuE4lmVUtaE1r1rQkthZR0o10PrVQfJrSUUghSQFyKwe3Fy5gxY9C4cWNs2rQJlStXxpIlSzBx4sQsrdNtLVq0CP/884/H+4ZlOUe99CyKFi2K999/H8eOHcOgQYNgMBjw9ddf47bbbsM333xjDdhEpBNYyckFO4ElIli3bh2eeuopVK5cGa+//joOHTqEihUr4v/+7/9w4sQJrFixAj169HB6Yy9zef/973+9mkPx999/xyknA3DfdtttWL16NcaPH4/ixYtj0aJFqF+/PiZMmJDve9no+VHy380+ALh+/ToGDBiAzp074/Tp02jWrBm2bduGUaNGISIiwuE6JpMJX3zxhVf7xk8//eS2gcUdd9yBH3/8EWfOnMEHH3yAMmXKYOvWrejVqxduv/12fPvtt7h165ZXny/QTJ8+HeHh4Xj22Wf9XRWifGf37t3o06cP6tatix9++AEpKSno2rUrNm7ciPXr1zttZGhx4cIFTJ8+3av3nDRpEq5fv+709Q4dOmDfvn345JNPUKRIEUyfPh133HGHdc6l/Ci/xbT09HR8/fXXuP322zF58mSEhYXh3XffxeHDh9G7d2+X+8TYsWNhNBo9Og8REaSmpuKHH35w+LpSCh07dsS2bdvw+++/o169ejhx4gT69++PJk2aYOHChfn+fIeI/KNVq4zfN2/WvWV69vR8/ZAQoG1bYNSojEQWAKxcqZ//+2/f1TVQWHpgeXLdm99UqADs3Qs0awZYOpSfOweMG6fnr/JmKsa33gLWrXOcwHIwqBjlY0xg5VO7dgGWht+2iauhQ4fi4sWLaNasGRYuXOhR4goAEhISPG6lbuvatWuYOXOm09fDw8MxatQobNy4EXfccQcOHjyI1q1b4/3333c5JEF8SjpSDEaE+2D4QJNJkJxuRFyyTh5dT0xHqlEQHhyESiXCUb9ClO5FVb0UmlUtgdvKRqJ8sXBEhAa73W7OXi9ik9CylN20SgnUqxCFisV0QivZnNC6lpiR0MrJRUFYcBCS0gxINZocvn78+HG0a9cOr7/+OlJSUjBgwADs378fjzzyiNuyv/nmG69u9FnMmDHDOpyFM5UrV8akSZOwY8cOtG/fHjdu3MDQoUPRsGFDLFq0yLpN9u8Hfv/d6yoQ5XtGYxEAwJo1nvdgzU9Onrykk/BBQbj//vsxY8YMpKWloXPnzpg7dy7OnDmD//znP6hVq5ZX5R44cAA7duzw6rhqMpkwdepUp68rpfDcc8/h4MGD6NKlC+Lj4zFkyBA8+OCDOJ6dsQ4ChOVmn+2wioFORDB79mzceeedmDZtGooUKYLRo0djy5YtaNq0qct1N2zYgOPHj3u1byQnJ2P27NkeLVu+fHmMGDECZ86cwffff486derg5MmTeO2111C9enX8+9//xtSpl6wtUPOTlJQUpKWlYds2wcmT/q4NUeC5ehV2/9sigr/++gudOnVC06ZNMXv2bAQFBeHpp5/GgQMHrEPYemL27Nle9+ZMSUnB725OoMPDw/Hee+/hwIEDePjhhxEXF4eXX34ZrVq1wrZt27x6v0CQnxJYK1euROPGjfHGG28gPj4ejz76KPbv34+RI0ciKirK5boigp9++snr93TX21ophZ49e2Lfvn2YNGkSqlevjv3796Nbt25o06aNw6Em89HpAxHloWPHgOXLgYgIwDJwxoIFQPPmOoHhKaX0MIT/93/AlCn6ufR0oEMHYOdO4IEHfF93f7PcLy2IPbAA4PXX9c/MpzUNGnhXTkQEcP/9wHvvZTz355/AvffqhBgVHExg5UObN+tMdfPmKfj222/tEldNmzbFggULsH37dnTp0sXjpMO6deuQYNuUwUNKKcyfP9/tcq1bt8auXbvwxhtvwGQy4dNPP0WLFi2we/duh8vHpRiQZhSEZSOBZTQnrGKT03HlVhquJ6Uj3SgIDwlGlRJFUL9CMeuwf82qlETtMpEoFxWOIh4krCy8uemllEKRUJ3QqlMmEs2q6oRWkyolUK98FMpHZSS0ruYgoRXmZB4sk8mE7777Do0bN8b69etRsWJFLFiwAFOmTEGpUqU8KvvQoUPZSq4ZDAac9PAOV9OmTbFq1SosWLAAdevWxdGjR9G1a1d06NABe/bswaOPAk88ASxZ4nU1iPK11FTdA2vWrILZA+uxx163+zs4OBgbN27EsmXLEBMTk+1WZ5eyOcD6xYsX3S5TpUoVLFiwALNmzULZsmXx119/oVGjRvjyyy/zxQ2zzIKCdKzNL3U/d+4cunbtij59+uDq1ato164d9u3bh+HDhyMkJMTt+hcuXMjW+3o7ZGTRokXx4osv4siRI5gzZw7uuece3Lx5EyNHjsTTT9fAgw/OR3JytqriN5Ze95GRRXDbbUDv3oAHHeuJCo327fVQSTNmXMSzzz6Lu+++Gw888AD++OMPREZGYtiwYThx4gQmT56M+vXre1V2dof283S92rVrY8mSJZg7dy6qVq2KHTt2oFWrVnjppZfy1RDf+SGBderUKfTs2RMdOnTAoUOHcNttt2Hx4sVYvHgx6tat61EZiYmJ2erVe/HiRY+u60JCQjBo0CAcPXoUY8eORbly5bBp0ya0b98eHTt2xPbt2yECfP454KLtDxHlY/HxuuF8dtvpVasGvPACUKpURgP8w4eBM2eAMmW8K+uxxxw/37EjUKRI9uoXyCwjEhXEHliA/t7q1cv6fMOG3pVjGWF3//6M50aNAnbsALwdMOLIEeD0acDSdoedjgMLE1j5kMFwCUFBVXHpUlG89tprdomrHTt2oGvXrl73lsk8wa83PL2giYiIwJdffom1a9eiTp062Lt3L1q0aIGPP/7Ybrg4EcGNpHQEKSDIg89hNAmS0jISVjeTdcIqIjQY1UpGoEFFnbBqXUMnjWqVKYqykWEIN4+PnhPZ6ZVkSWiVjwpHnbKRuLtqCbSsXgpNKmcktELMCa1r5oRWggcJrdDgIKQbTXbzYJ08eRIPPvggXn31VSQlJaFv377Yv38/unbt6lWdc3IB6E1LUaUUunbtiv3792Ps2LEoVaoUVq1ahaZNmyIi4lk0bHjN64BGlN+lpekz8mvXUnD6tJ8rkwsGDvzU7m+j0Yjo6Gh07doVCxYsyPZwos6Gj3OnaNGiHi2nlELv3r1x6NAh9O3bF8nJyXjrrbdwzz33YL/tGXQ+YNsDK5CHBzKZTPjhhx9Qv359LF68GCVKlMCECROwevVq3ObF7MZFsnmVm931jEYjjEZjpn0rDfXqXcx3F9yWufhatgzHXXcBv/wCtGgBPP44kJjo58pRwPv7b+Do0YLdW6NxY31T7qmnKmPixInYtWsXAKBLly44ffo0vv76a1SrVi1bZXsan3KynlIKMTExOHToEIYPH47g4GD88MMPqFevHqZNmxbQMcIikBNYycnJGDFiBO68807MnTsXkZGRGDVqFPbv349HH33Uq7LCw8OzVYfw8HCvrmHDw8Px2muv4cSJE/j0009RvHhx/Pnnn2jRogXatx+JGTPsh24iKmxGjwZmzwYMBhNu3rzpctjW/GTpUt0oo149YPHi7JVRpAjw2We6l03mmUe8TWA98IDubZNZly7Zq1ugK+g9sIKCgGHDsj7vbQ+sJk0cP//kk0D58t6VtXq1Po976CEgJsa7edoC0YYNGzBixI84cOCAv6viEwUygSUCvPsu0K4d8l3LVk88/HBdmEznAegLiEGDBmHz5s3ZSlxZuBuiwJVixYp5tXx0dDT27NmDV155BQaDAR9++KHdDb9D/xhx5IQBRRz0vhIRGEyCxDQjbial40qCTlgZTYKiYSGoUUonrJpV1QmrxpWLo2bpoigTGZat3lzO+PLiTSmFiNBglC8WjtvMCa1W1UuhceUSuKN8FMpFhSM4yL6HVkJq1oRWcJBCcgrw83QDNmwQjBs3DnfddRfWrFmDcuXKYc6cOZgxYwbKeHumAKB69erZ3reqZmMGxbCwMLz22mtYvnw5ypQpAxHBkSMT0bz5cOskj/ldamrBvoFDvtO4sb5B8fnnqahe3c+VyQXDh9fRx3aDAUuXLkWPHj2glMKiRYvQvXt3VK9eHe+88w6OHTvmVbmNGjXKVmyLjo72avmyZctixowZWLRoEapUqYJt27ahWbNm+Oijj5Camoo1a/QN20CmlHI6x6M/bdsGDB0KXLgAHDlyBO3atcNLL72EhIQExMTE4ODBg3j22We9jk8tWrSw3uD0Rps2bbxa/sSJE3jnnXdQrVo19OrVC6tWrUJERAQGDhyITZs2Yfv2F5DN0Oo3lh5YRYsWwfjxsNZ/0CAgMtKPFaN8Ydo0PQTQwIH+rknumTFDtzJ/6qmn7J5ftGgR2rRpg08//dTj0Qkyu//++7O1Xtu2bb1eJyoqCqNHj8auXbsQHR2NK1euYMCAAbjrrnZYuzawb4QEYq/iK1eu4KGHhqJs2er46KOPkJKSgj59+uDw4cN45513spWMCg0NRbNmzbyKgUoptG7d2uv3MhqNWL9+PbZv345Em9YK9eqdxd699kM35VdpaboFfzY7OlIhNnky0KcP0LhxHEqXLo06lrHy8rn77wcuXQJu3QJKl85+OU8+CbRsmfV5b29LRUQADz5o/5xSgJe5f6vjx02YOPEEDIbAiRW2ArkH1rVrBjz00FeYNy8pR+X075913/I2gVWnjuNrkFdf9b4+9esDcXG652Hr1kAAbnqvvPXWbHz00Qt4/fW//F0V3xCRXHtAZ1jEH957T6RSJb+8da6bNWuWWLat5VGxYkV5++235ciRI9kq8/LlyxIWFpalXE8e//3vf7P9WVavXi01a9YUABIWFiajRo2S+zrFyzOfXJYZf12Tzaeuy4YT12T1sauy7NBlWXTgkiw9eElWHr0iG05cl/0X4+TMzSS5mZQm6UZjtuvhrQYNGggA2bt3b66/l8lkksRUg1yKT5GjV2/J1tM35a9jV2X5Yb09lpi3x/p/rsk38y9Ih367pVixh6zfT69eveTKlSs5qsO4ceO83i+UUtKpUyev3ys5OVlmzJgh7dq1syvvzjvvlNWrV+focwSKESNEoqJEPvnE3zXJYHO8ztW4kJ8f/opp3bp1EwAyb968PH9vf7l8+bJ88cUXUq9ePbvjQNu2bWXKlCmSmJjoUTlvvfWWV8es2rVri8FgyHa94+Li5IUXXrCWWbt2AwE2S4kSIiZTtovNE8HBwQJA0tLS/F0Vq06dRIA0eeih/0h4eLgAkAoVKsjvv/+e47Kfeuopr/aNJk2aiMmDLzEtLU1+//136dChg10ZDRo0kG+++UZu3ryZ47r70/vvvy8AZMSIESIi8sorIr/+KpKHp2AeYUwLvHiWni5SrpwIIBIRIfK//+Xp2+c5k8kkaWlpsn37dhk6dKiUL1/e7pjQpk0bGTdunFy/ft2rMps0aSJKKY+PXW3atMnRZzh16pTMmDFDatWqZS03JKSYV/XOazNmzBAA8uSTT/q7KlZVq1a1+27q168vkydPliNHjngUW5yxfFZvHosXL/a4/LNnz8qIESOkevXq1vWDg4MlJiZGli5dmqNzpkCTkCBSpoxIWJi+l7Rtm79rpDGeBWZMszAaRf74Q2TPHpGTJ+MFgERFRfmlLrlh8mQdtxctylk5Gzbocmwfr77qfTnjxtmX0bq1d+tfvHhRRo0aZXdM7NXrU+8rkgfat28vAGTlypX+rkoWr7wyxW4bli5dWr777ju5deuW12X93//Zf6fx8d7Xp3Vr+zJatvS+DBGRK1f0+pGRIjduZK+MQHLPPfq+xGOPBc5Jd05iWoENJIcOXZFWrTbL8ePH/fL+ecHZDb7o6GiZPHmy1wePZ5991usT4MjISImNjc3R54iPj5chQ4ZYy6xQpbk898lm+W3bJVm4/5IsPXRZVh29KhtPXpcDF+Pl7M0kiU1KE4PRf3cDLQmsffv25fl7WxJaF+NT5OiVW7LFktA6dFnuatnW7vt55plnfHKBmZSUJKVKlfL4gtny+PPPPz1+j3379snQoUOldOnS1vUjIiJk4MCBsmHDhhxd3AWauXP10XfOHH/XJAMvjgI3pvXq1UsAyOzZs/P8vf3NZDLJxo0b5ZlnnpHIyEjrflq8eHF54YUXZNu2bS6PDRcuXJBKlSq5PXYppUQpJXPnzvVJvf/66y+pXLmyuXwltWoNl9TUwD6GWRqxJCcn+7sqVt27v273PT322GNyw0dXE4cOHZJixYpJUFCQ230jODhYli9f7rK8kydPyv/93/9JxYoVresWKVJEBgwYUKBi2PDhwwWAfPbZZyKib/gF4kdjTAu8ePbnnyKAvjn844+Bud/kpvT0dFm2bJn069dPihYtat1Hw8LCJCYmRubMmSMpKSluy1mxYoUEBwd7FNdCQ0Nl7dq1HtcxLS1Ntm3bJmPGjJFevXpJlSpVHJQdLPXrP+JRXf1l9uzZAkAef/xxf1fF6r333nf6XZUuXVoefvhh+fjjj+WPP/7w6to6LS1NGjVq5PG1WZs2bcTopsVBenq6LFiwQB577DG7GFm7dm0ZOXKkXLhwIaebIyDFxor15mfJkiLnzvm7RhrjWWDGNEcSExOt538FhdEo0rSpQTZuzHlZPXtm/I8BIh995H0ZZ8/alzFypOvlk5OTZeXKlfLyyy87PS5OmPBH9j5QLrvvvvsEgKxbt87fVcni9dffcxlrqlSpIhMmTJBr1665LevcOZGQEP191qiRvfq88IL9fjF1avbKEREpWzZ7ydVAZLnPPm7cOH9XxYoJLAe+//57ASDPP/+8X94/L7m6wff888/L1q1bPbpxcvjwYSlatKjbmzm2jw8++CDb9b5y5YosWrRI3nvvPXnooYckNDTUWm77R2Pk71M35OCleDkXmyRxyf5NWGVWv3598VcCKzOTySS3UtPlYnyyhJlbqGd+1KlTR/r06SNfffWVbNiwwePeC7YWLlwowcHBHu8fzz//vNv9LiEhQSZOnCitW7e2W7dZs2by/fff5zg5GqhSUkRKl9YtPAIFL44CN6b1799fAMiUKVPy/L0DSXx8vEyYMCHL8aJRo0byzTffOE3WHzlyRGrXrm29oefoeBUWFibTp0/3uk4pKSmyb98++f333+XTTz+V/v37S4sWLaR48eJZ3uPgwYM53QS5KiIiQgBkq+VcbjCZTA6/qxIlSkjz5s2lb9++8tFHH8nMmTNl+/btEhcX5/V7bN68WcqWLety3wgPD5dffvnF4frp6ekyb9486dy5s936d955p4wZMyageyhk16uvvioAZMyYMf6uikuMaYEXz559VqR6dZGtW/P0bQNSfHy8TJ06VTp27Gh3Xl2yZEl57rnnZN26dS6TDL///rsUKVLE5Xl40aJF3fa0uXHjhixdulT+/e9/S7t27ewSa7Z1euSRR+Q///mP/PXXXwETI1z57bffBID06NHD31Wxk5ycLImJibJu3ToZPXq0xMTE2DV6sDyUUtKgQQN59tln5aeffpIDBw643B8uX74szZo1cxrLLM+1adPGZS/gkydPynvvvWfTAAcSGhoqvXr1kpUrV7pNfOV3N26IT25++hrjWWDGNEdSU1Ot/zcFie3xpFatWvLxxx/LX3/9JefOnfOqgdaxYyKhoRn/Z99+m736NGmSUcb+/favmUwmOXDggHz99dfSqVMnl3Hys88+k9TU1OxVIg+0bNlSAMimTZv8XRWndu7cKV26dHF7b/C2226TiRMnypkzZxyW06+f/j4ffTR79fjhh4x9olw5key2xzSZTNK2baoUlH4wgwcPFgAyfvx4f1fFKicxrcAGkp9++kkAyKBBg/zy/v7i7AbfXXfdJWPHjrXLgO/aJfLFFyK2owWtXr1aIiMjXbbqs7z2/PPP253IHjki4qyDQGpqqmzbtk2+/fZb6devn9SpU8dh2dWqVZMePXvKXxv+FmMAN820JLD2Z46YfmYwGOS7776TGTNmyLBhw6RNmzYOL3CDg4OlcePG8txzz8n48eNl9+7dkp6ebldWSorIc8+JfPVVxnNLly6VYsWKubzZB0DefvvtLBc5M2fqrr07dphk27ZtMmTIEGtZgE64vvjii7Jjx4682FR+tXr1aqlWrbm88847/q6KFS+OAjemPffccwJAfvzxxzx/70C1f/9+ef3116VMmTI2N1nCpUWL7vLSSy/Jv/71L/n444/lq6++kh9//FEmTJgggwcPlho1amRJhrz88sty4MCBLO+xZIke7sJkMsnly5dl7dq18uOPP8obb7whjzzyiNSpU8dlQr9UqVJyzz33yKBBg2TixIkBf+MnKipKAEh8dsZtyCWWIT569OghLVu2lBIlSri8OKpQoYJER0fL4MGD5fPPP5d58+bJgQMHJCUlRXbv1i0/16yxf4/z58/L+++/L5UqVbIrKyoqSmJiYmTcuHHy66+/ys8//yzffvutfPbZZ/Lee+/JXXe9LEWKZNzkCw8Pl379+sm6desKTG8rRwKxJZ8jjGmBE88OHjwonTs/LLff/ppcvZonb5mvnD9/Xr788ktp2rRpphhSQwYO/D85dOiQmEwmMRgMkpqaKklJSZKQkCBbt26V5557zu5c2hLXBg0aJCtWrJADBw7I3r17ZdOmXfLxx9tl7tzNMnnyZBkyZIh1NInMj7p168rAgQNl/PjxbhMngWru3LkCQLp16+bvqrhlGaZx9uzZMnToUGnVqpVdo07b77Vjx47ywQcfyLJly+Szz45K167/yN69p+XcuXNy8uRJ+eCDD6wNdmwfDRo0kDFjxji8UZuWliY//vi7lCnTSYCMa7vbb79dvvjiixwPQ5+fXLsmAoh07RpYPUQZzwIrprliMBis90kKirS0NLfJiYiICOnRo4e8/fbb8tNPP8natWvlwoULdufDN2+KrFolMmyYWBMNM2dmr04vvqjXr1lT/69eu3ZNZs+eLc8880yW4VozP7766qt808DMcl6QX+6PGY1GWbduXZZh1B09GjZsKOPGjZP/Z+++w6MqsweOf2cyk0YagUBoISRAICC9BFCkKIq6UkRRFGGFVX+6dl11LWtfe93VXRUEQUVdaVZQ6U16CwmBQBJII4T0Nu39/THMSKSkzeRmJufzPPNkSObOnBlm5r33nvecNykpSVmtNnXTTfb/07/9rX6PvXnz7++rMWOUqqm7bWlpqdq1a5f67LPP1HXXXXdWfD/++GP9AmliZs6cqQA1Z84crUNxasiYprNv7x6nT3Djzsc4n4ULFzJ9+nSmTZvGZ5991uiP3xQkJiYyZ84cFixYwMmTJwHw9fVl0qRJzJo1i6eeGstvv+l55x24997ftztw4AAPP/wwK1aswGaznXW/Xbp04bHHHuMvf/lLtUVju3aF1FT45hvF4MHH2bJli/OyY8cOqqqqqt1PQEAAgwcPJiEhgYSEBIYOHUr79u3d82K4WHx8PElJSezfv59edV1lsJGZzWYSExPZtm0bW7duZdu2bezfv/+shY0DAgIYMGAAQ4YMYfDgwZjNQ5gxI4bAQB2nToGfn/2znJ2dzaeffsonn3xCSkqKc/vg4GBuuOEGbrzxRuLj47HZbFitVmw2GzabjT//2cL69b8QGfkxOTm7nduNGDGC2bNnc/3119OimawAv2jRIm666SamTp3KokWLtA4HwPlZVkrVfiXoZkarMe2ee+7hX//6F++++y731Gc1Ui9WVVXF8uXLef/9OaxZsxL7/lDd+fj4EBQURIsWLQgKCsLHJ4ikpCD0+iqCgw9SVFR4zu30ej0xMTH06NGDuLg4evTo4bzeunXrOi2srrXQ0FCKi4spKCggLCxM63DOSSnFyZMnSUlJISUlhUOHDlW7XllZec7tdDodwcGdKS6OoU0bG507l1FaWkpZ2e8//7iPUludO3fn3nvvYMaMGbSq62rUHmjGjBnOfYCZM2dqHc55yZh2YY05nm3fvp3BgwczcOBAtm/f7vbH82SJiYksXLiQ//73MwoKjrn98Xx9fRk0aBAjRoxgxIgRDBs2jDZt2rj9cd1t+fLlTJgwgWuuuYZvv/1W63DqrLKykl27drF582a2bNnC5s2bOX78eL3vT6fT4ePjg8FgOOtnRUUFJSUlp2/px803T+H222/nkksu8ah9GFc4cQJ69oTERIiM1Dqa38l4VjMtzzueSSmFXq8HwGq1Oq97upycHD777DPmzp3LgQMHar1dUFAQXbt2pWvXrhQWduOXX7oxYUI3Vq3qRklJG1as0DFu3O+3V0phMpmoqqqqdqmsrKz275kzq0hPr6Bz5+1ERKxgx44d5/2/NxqNPPLII0yfPp0ePXo09KVoVL179yYxMZG9e/dy0UUXaR1OnVVWVvLrr7/y8ssvs2HDhhpu3RcYztVXt2PChEjatWtHZKT9Z5s2bTAajRfcuqwMgoJApwMfH3jkEXjuOQtpaWnOY8WDBw+ydetWdu7cWWPsW7ZsYejQobV/sk2U47ht3rx5zJgxQ+twgIaNaV6bwPrqq6+YOnUqU6ZM4euvv270x29KTCYTy5cv5+OPP2blypXO/4/w8M74+AxnyBA9LVvq0Ol06PV6dDr79fLyco4cOUJJSQk2m42AgACio6Np3749er2+2m11Oh1r1ug4ciQNf//fOHEi66w44uLinMmqhIQEevfujcFgaOyXwyUcCazExETi4+O1DqfOysrK2LVrV7WkVmpq6lm30+uN2L9fbGclvBqiVatWzJgxg1mzZnnk69dQc+bMYfbs2dx2223MmTNH63AAOTiqDa3GtEceeYTXX3+dV199lUceeaRRH9tTKAWTJ2eQlbWaqVNLMJvtiYkzL45kxbn+bTKZLnj/oaGh1ZJUjp+xsbH4+fk10rN0r/DwcAoKCjh58qRHJmJsNhvHjx8/K7GVkpLC0aNHaxzD9Hq9M4F5ZjLzjz8d148da0Hr1oN4/vmRGAzN52vzxhtv5Msvv+SLL77gxhtv1Dqc85Ix7cIaczxLTEykd+/exMfHk5iY6PbH8wYpKTamTl2Pv/8CkpL+R1FRkTMJUZ9LZaUPx4/70LatDwMGdGTEiBEMHz6cgQMHes0Ydqbvv/+ea665hvHjx/PDDz9oHY5LHD9efXLokSM5lJdbadHCglJWLBYLVuu5f9akV69exMX9hVmzpnPVVeGN8Gyanvz8fIYMGYavbyRJSeu0DqcaGc9q1lQSWAAGgwGr1YrJZKrxpLunslgs7N69m/Xr1/Pdd9+xatWqetxLMG3bhmK1/p6cqul4rLYCAgK4/vrrufXWWxk9erTHJhLj4uJISUkhOTmZuLg4rcNpsLy8PH7++WfefPNNduzYUadtAwMCCGrRgr79+9OuXbtqCa7IyEiUgquuSqGiIgVIISQkhfLyw7UaAx2mTZvGzJkzGTNmDD4+PnV8dk3TLbfcwmeffcann37K9OnTtQ4HaNiY5pnZg1pw7IzXd0atN/H19WXKlClMmTKFjIwM5s2bx9y5c0lPTwfS+f772t/Xnj17arxNcTG0bNmSoUOHOiurhgwZQni49+wQN4Wdo4Zo0aIFF198MRdffLHzd/n5+Wzbts2Z1Nq6dSsnTpw4a1u9Xo+Pj0+dfjqux8TE8Oc//5mJEyd65QFzbZWXlwMQGBiocSTCE8h4VjOdDpYsiQLqN7PIZDJRVlZ2VpJLr9fTvXt32rZt6/UzkR076q6crNCY9Ho9UVFRREVFcdlll1X7m8lk4ujRoxw9ehRfX99zJqf8/f29/v/YFRxVbs15DBd1ExAQAEBFRYXGkXiO7t317Np1KXApSn0EIN9PdeDp49m5dOzY0Xk8X1eOrhjnS261adOm2b+/KioqOHLkEB06lGsdivBwjgSW1Wr12gSWwWBg0KBBDBo0iAceeAClFCkpKaxfv54NGzawfv16jhw5UsO9lJCbW1LDbWpPp9MxevRobr31Vq677jqCgoJcdt9acST0vOV9FBERwbRp05g2bZrzPfPzzz/zwQcf1FjZV15RQXlFBT///HOtHqu4+Px/a9OmDTfddBMjR45k+PDhRDalklsXc3RU85aEnCSwmpmoqCiefvppnnzySdatW8fx48er9ZS02WwN+nfr1q1JSEigW7duHjvToS68aWe/VatWXHnllVx55ZWAPUlXWVl5ViJKNFxZWRkgCSxRO/7+/gDnbY8mGs7X1xdfX19atmypdSiacezYnqt1sKfz9fUlLi7OK2Yvas3xPeT4XhKiJo4ElmPyjqgbbzrWaCzemMBqCMcxnLecBHUHxzkjX19fjSMRns7x/VOXyg9Pp9PpnPvZs2fPBiAzM9OZzNqwYQN79+51yyTw7t27M2PGDG655RaioqJcfv9aciSwvPF76cz3zF//+lfMZjPbtm0jMTGRzMxMjh8/TmZmJpmZmaSnp1N8oYzUBURERDirzh2V583pGMaxH+Qt53G9NoHl+JC7qgzV2+j1ekaNGqV1GB7L0yuwakOn0zlPOgjXcpzEaS5rfomGkQkZojGc2bNfiPNxfA81p4M/0TBSgSUamySwRF1584li0bgcS2Q09++fDh06MHXqVKZOnQpAYWEhmzZtYv369axfv55t27Y5P3c6nY7AwEACAwNp0aIFgYGBVFVVERQURPv27av9/syfl156KUOHDvXaiR5msxnwngqsCzEajc4k07mUl5eTlZV1VnLrzOsnTpyge/fuDB8+3Jm0io2N9dr3R204voekAquJkxN+ojE05y9DUX9SgSXqwjGeSQWWcCc54SdqQ1oIirqSBJZobDKeibpynEiXsU00VHOswKqNsLAwrrrqKq666irAfp62pKRE2nhfgCTWfxcYGEjXrl3p2rWr1qF4FGkh6CGkAku4U3OowBLuIxVYoi4clQ4yIUO4kze3EBSuIxVYoq58fX3R6XSYzWasVqvXHESLpksSWKKupIWgcBWpwKodPz8/SRjXwFGBJd9Lor68rYWgdzyLc5AKLNEYZKaIqA9HAksqsERtyHgmGoO0EBS1IRVYoq7ObEktVViiMUgCS9SVVDoIV5EKLOEqju+l5tBCULiHt1VgeX0CSyqwhDtIBZZoCGkhKOrCUekgLQSFO8kJP1Ebju8hqcASdSEJLNGY5ASyqCtpIShcRfanhSsopZxjmCSwRH1JBZaHcMyekRnrwp2kAkvUh7QQFHUhFViiMcgBt6gNaSEo6kMSWKIx6fUykVXUjbQQFK7iaCEoCXTREI72gQaDQc45inpzHNdLBVYTJyf8hDtVVUkFlqg/qcASdSEVWKIxyBpYojakhaCoD8f+jiSwRGN4+217wlT2m0RtSQWWcBWZECZc4fcElpEvvwRpACXqQ1oIegjH7BmZeSXcITvb/nPVKpkNIepOKrBEXciEDOFumZlgMskaWKJmUoEl6kMqsERjKS6Gigr795O830RtyRpYwlWkAku4guN4zGw2cOONMGeOxgEJjyQtBD2EnPAT7nT6OJzu3bWNQ3gmRwJLKrBEbTjGM5lJLNxhxQoYNw4OH5YZo6JmsgaWqA9JYInGEhICH3wgleuibqSFoHAVqcASruConDEY9HTpAn36aByQ8EjeVoFl0DoAd3Gc8JMKLOEObdrYZ/h17Ci1vKLupIWgqAvHiWKZkCHcYexYsK8NLAfc4sIsFgtWqxW9Xu+cYSxEbUgCSzQmx/tNEliitqSFoHAVqcASruBIPAQG6jlyRONghMeSCiwPYbSfjcFsNst6DsLlHAspKmlGK+pBWgiKupAKLOFOBgP8+9/gSGDJPpM4H2kfKOrLkVBw7P8I4U6O7yhJmIrakhaCwlWkAku4guN4zFsSD0Ibju8hb6nA8tpPg06nc+6AOBbAE8JVJIElGkIqsERdSAWWcLcRI6B1a1kDS1yYI4kuM9RFXUkFlmhMRqMRHx8frFarVEGIWpEWgsJVpAJLuIIksIQreFsLQa/+NDh2QOSkn3A1SWCJ+lJKOWcgO07oCHEhsqajaAxdusiMUXFhUoEl6ksSWKKxOb6npHpd1Ia0EBSuIhVYwhUkgSVcQVoIehBZB0u4iySwRH05DqT9/f29ZiaEcC85CSMaQ2CgvfWyzBgV53Pm+CVEXUgCSzQ2aSMo6kJaCApXkQos4QqSwBKuIC0EPYhUYAl3kQSWqC9pHyjqSiqwRGNw7DPJpB9xPtJCUNSXJLBEY3O852Tyj6gNaSEoXEUqsIQreFvljNCGtyVCveNZnIec9BPuIgksUV+O9oEtWrTQOBLhKRxjmZyEEe5kNNorsCSBJc5HWgiK+pIElmhsUr0u6kJaCApXkQos4QrelngQ2pAKLA8iLQSFu0gCS9SXVGCJujIajeh0OiwWi8zmE24jFViiJlKBJepLEliisUkLQVEX0kJQuIpUYAlXcCSwvCXxILThbe8jr05gSQtB4S6SwBL15ajAkgSWqC2dTicVxcLtHPtMZrNZ40hEUyUVWKK+JIElGpu0EBR1IS0EhatIBZZwBanAEq7gba0oveNZnIdUYAl3kQSWqC9pISjqw3HCWBJYwl2kAkvUxHEiWBJYoq4kgSUam7QQFHUhLQSFq0gFlnAFSWAJV5AWgh5EKrCEu0gCS9SXtBAU9SEVWMLdJIElaiItBEV9OfZ5JIElGosksERdSAtB4SpSgSVcQRJYwhW87X3kHc/iPKQCS7iLJLBEfUkFlqgPOREj3E0SWKIm0kJQ1JdUYInGJu85URfSQlC4ilRgCVfwtsSD0IZUYHkQmbEu3EUSWKK+pAJL1IeMZ8LdjEYjIAkscX7SQlDUlyOZ4JjEI4S7ycQfURfSQlC4ilRgCVeQBJZwBcf7SBJYHkBaCAp3kQSWqC/HyRtJYIm6cBxQy4kY4S5SgSVq4tiflhN8oq6kGkY0NklgibqQFoLCVaQCS7iCJLCEKzi+h7zlfeQdz+I8pIWgcBdJYIn6clRgSQtBUReOEzFy8k+4i+Okjdls1jgS0VRJBZaoL0lgicYm7zlRFzJBQ7iKVGAJV5AElnAFaSHoQaQCS7iLJLBEfUkFlqiPNm3aAJCdna1xJMJbSQWWqElJSQkASsn4JepGkgmisUkFlqgLqcASriIVWMIVJIElXMHb3kfe8SzOQyqwhLtIAkvUlyOBJRVYoi66dOkCwMaNRzWORHgrSWCJmqSlpQGweHEUTz8NRUXaxiM8hySwRGNzvOckgSVqQxJYwlUcFViSwBIN4W2JB6ENqcDyILLovXAXSWCJ+nK0EJQKLFEXnTtHA/D112maxiG8l9FoBCSBJc7v6FF7Av3WW2N4/nmIjYU33gA5PyxqIgks0dh8fe3nAeQ9J2pDWggKV3GcKJYWgqIhJIElXMHxPpIElgcoLrbPoDl4UE7GCNeSBJaoL2khKOojIsJegZWRcZQVKzQORnglqcASNTly5AgAt97ahcGDIT8fnnwSPvwQZHdIXMgfE1g7dmgZjWgOdu+WTiyi9qQCSzRUWRmcOiUVWMI1JIElGurnn6Giwv495C3vI+94FueRnGzfcV23TiqwhGtJAkvUV1aWvQJLWgiKuoiMjAagc+c0RozQNhbhnSSBJS7EYrGQkZGBTqejS5fOvPoqBAZCv34wezac3i0S4pzOTGBlZsLEiTBlCqSkaBuX8E47d8L338uYJmqvtNT+PvnhB19OnzcWok42boSuXWHPHqnAEg3nSIAWF8sOtqifG26wUlVlnzjmWBfU03l1AmvIEPuOa0KCJLCEayllH0gsFklgibpJT5cKLFF3gwdHA5Cfn0aLFvK9I1zPkcAym80aRyKaomPHjmG1WunQoQP+/v6MGmWvvNq0yZ7IEuJCzkxgLVkCx4/DN9/Ac89BcrLGwQmvUlwMN9wAVqssJSBqz/E+efxxP6ZMAek8Keqqe3dYvhwuuigUgJMnT2ockfBkjgqstDQfFi3SOBjhkWbMyAIsRES09Zr2uF6dwGrf3v6fFB4uO67CtdLS7AmsZcvkRLKom1at7AksqcASddGyZUtCQkIoLS0lPz9f63CEl1EKSkrsCaz8fJmtLs7maB/YpUsX5+9uvlkqr0TtOGZ+VlVV8b//2U/K6HQQHw9xcVpGJrxNejr07g2BgVKBJWrPYrG/T2w2XyZNgtM5dyFqLToaLr4YEhK6ApCamqptQMKjORJYoaE+vP46FBVpHJDwOFOmpAMQExOtbSAu5NUJrFatWgGQk5OjcSTC2xgM9o9OYKAksETdlJWdAiA4OFjjSIQn0el0REdHA5CWlqZpLMJ7mEzw2WcwYwY89JD9ZN+pU3KyT5zNkcCKiYnROBLhiXQ6nTOJtW5dJWFh8P338Pe/SxJUuNZFF8GSJfDGG1KBJWrPkcAaOtSXW27ROBjh0bp2tSewDh8+rHEkwpM5ElgxMXo2b4bQUI0DEh7Hcc6oc+fO2gbiQl6dwOrevTsAKdJgXbhYTIz9o3PZZdIkW9SeUsr5fdStWzeNoxGexlH5IAks4Sq+vvYWcAsWgM1mBECnkwSWONvRo0cBSWCJ+nO0EezRo5zt22H8eI0DEl5Lp4PQUKnAErVnMtkTnW+95SdJddEgsbGxgD2BJeuli/pyrIHl76/HaNQ4GOGR0tPtFViOSdDewKsTWI4TxIcOHdI4EuFtfHzsHx2brPIq6iA7O5uSkhJatWpF69attQ5HeBjHzofjRLIQrvDss45ZffaTfZLAEuciFViioRxrfy5ZUsHp83tCuI1jvQepwBK1YTbb930GDvTVOBLh6SIiIggODqaoqIhTp05pHY7wUI7zjHq9V5+yF27kSGBJBZaH6NixI/7+/uTm5lJcXKx1OMKLOAYSSWCJukg+vVJ5jx49NI5EeCJpISjcoXVrePppcCSwwKxhNKKpOtcaWELURUREBACFhVkaRyKaA19fqcAStWMymTCbzej1eoxS6iAaSKfTSRtB0WCO84w+Pj4aRyI8lbQQ9DB6vd45eEgVlnAlSWCJ+nAksOJkxXJRD44Tx1KBJVztr3+F9u3tJ/uUkpN94mzSQlA0lKMzhrR2F43BkcCSCixRE8cs9U6dOqGT/oHCBSSBJRpKKrBEQ0kLQQ8kbQSFO0gCS9THwYMHAanAEvUjFVjCXXx94Z577Cf7HG10hHAoLi7m5MmT+Pv7ExkZqXU4wkM51iaWYzLRGBwtBKUCS9TEsV8tFcbCVRzrYKWmpmocifBUksASDWGz2aSFoCdyHCzJbD/hSpLAEvUhLQRFQ5yZwJJFgYWrjR0r7ZbEuTmqr7p06SKz00W9SQWWaExSgSVq68wxTghXkAos0VBWqxWQBJaonxMnTlBVVUWrVq0ICgrSOhyX8fpPg1RgCXeQBJaoD0lgiYYIDQ2lZcuWVFRUcOLECa3DEV7G19e+7oOc7BN/JO0DhStIBZZoTFKBJWrLMcZ5U5sloS1JYImGkjWwREN4Y/UVNIMEllRgCXeQBJaoq7KyMjIyMjAajTLDT9SbtBEU7iIn+8T5HDlyBJAElmiYMycVShWxcDdHBZaMaaImUoElXE0SWKKhpIWgaAjHuSJJYHmYM9tVyMGScBVJYIm6csw47tq1KwaDQeNohKdyHFxLAku4miOBJRVY4o8cCSw5uScaolWrVoSFhVFSUkJubq7W4QgvJ2OaqC1ZA0u4Wrt27QgICCAvL4/i4mKtwxEeSBJYoiEcFVjeVlns9Z+Gtm3bEhQURGFhIfn5+VqHI7yEJLBEXUn7QOEKjp0Qx2xRIVxFTvaJ85EWgsIVdDqdtBEUjUYqsERtSQWWcDW9Xu/cZ0pNTdU4GuGJZA0s0RBSgeWh5GBJuIMksERdSQJLuIK0EBTuIgvei/ORFoLCVc7sjCGEO8mYJmqjrKyMEydO4OvrS7t27bQOR3gRaSMoGkLWwBINIRVYHuzMnutCuIIksERdORJYcXFxGkciPJljdqhUYAlXkzWwxLnYbDaZnS5cRiYVisYiY5qojTMXupdKB+FKksASDSEtBEVDnDm2eZNm8WlwHCzJbD/hKpLAEnV18OBBQCqwRMNIBZZwF2khKM4lJyeHqqoqIiIiCAoK0joc4eFkUqFoLFKBJWpDJmgId5EElmgISWCJ+lJKSQtBTyYHS8LVJIEl6sJmszkTWFKBJRrCkcBKT0+X7x/hUkajEQCLxSLvLeEk7QOFK0kLQdFYpAJL1IYksIS7xMbGArIGlqgfWQNL1NepU6coKysjJCSEsLAwrcNxqWbxaZCDJeFqksASdXHs2DEqKiqIjIz0ukFENK6goCBat25NVVUVOTk5WocjvIhOp5MqLHEWRwJLTu4JV3Ackx0+fFj2oYVbGQwGQCZliAtzJLC8bZ0QoT2pwBINIWtgifo6s32gTqfTOBrXahYJrDP7rSulNI5GeANJYIm6kPWvhCtJG0HhLpLAEn/kOLknFVjCFUJDQ2nTpg2VlZVkZmZqHY7wYmdOypAqLHE+jn1pmaQhXK1Tp04YjUYyMzMpLy/XOhzhYaSFoKgvb20fCM0kgRUeHk54eDhlZWUyY124hCSwRF3I+lfClRwH2ZLAEq4mCSzxR9JCULiarE0sGovBIOtgiQuTFoLCXQwGg/N95diXEqK2JIEl6stRgeWNlcXN5tMgB0vClSSBJerCUYElCSzhCo6dEcdBtxCuIoveiz+SFoLC1WRtYtEYzGbo2HEbu3YdJigoSOtwRBMlLQSFOznWwZI2gqKuZA0sUV9SgeUF5GBJuJIksERdSAJLuJJUYAl3kXZL4o+kAku4mqxNLBrDK6/AwYNxREfHyhoi4pwKCwspLCwkMDCQiIgIrcMRXsixDlZqaqrGkQhP4zjPaLPJ+CXqxpsrsAxaB9BYpAJLuJIksERdyBpYwpWkAku4i7QQFGeqrKwkKysLg8FAx44dtQ5HeIkz1yYWwh0SE+G55+zXZflrcT5nrn/lbQvdi6bBkcCSCixRV9dccw0GQwz/+U8nSkogOFjriISncCSwpALLg0kFlnAlSWCJ2iouLiY7Oxt/f3+ioqK0Dkd4AUcCSyqwhKtJAkucyfEdExUVhcHQbOa8CTeTYzLhTlYrzJplbyEIksAS5yfrXwl3kwSWqK/OnTvzl7+M58iR3lxxBRQVaR2R8BTSQtALyMGScCWz2T5La8MGSWCJCzt48CBgn3EsLUyEKzgSWBkZGc7+2EK4giOBVV4uCSwh7QOFe5zZUslisWgcjfA233wDFRW//1sSWOJ8ZP0r4W6yBpZoCL0eBg+GzZvhgQfsEzSEuJCioiIKCwsJCAjwyta4zWY6pSOBdfjwYWw2myyGJxokM9P+/lmyxIZSIF0HxPnI+lfC1QICAmjbti25ublkZWXRqVMnrUMSHi4xERYuhMJCewJr1aoqLr5Y46CE5hwn9ySBJVwpMDCQjh07cvz4cdLT050n+IRwhRtugH79YMECMBgkgSXO78wWgkK4Q3R0NHq9noyMDEwmE76+vlqHJDzMZZfBkCH2BJbMhRY1ObN9oDe2xm02Cazg4GAiIyPJycnh2LFjXllOJxrP8uX/5rHH3ichQSfJK3FBsv6VcIcuXbqQm5tLWlqaJLBEvR0+DI89Br6+8MUXoNfbE1j+/lKBJX6vwJKTe8LVunXrxvHjxzl06JAksITLde8Ozz9vvy4JLHE+0kJQuJufnx9RUVGkpaWRlpbmXANSiNp6+GH7ZAwhasORwPLWyuJmVYbkGDBSUlI0jkR4On9/PW+/rePGG7WORDR1jhaCUoElXEnWwRKu0LUrGI325BWAzWafGRoYKAksIS0EhfvIMZloLDLRUJyPJLBEY5B1sERDSPJK1MWZFVjeqFklsGQdLCFEY5MWgsIdHAksx8G3EPX14ov2JJadvQIrIEASWEJaCAr3kWMyIYSWlFKyBpZoFLIOlhCisTgmN0sCyws4DpZktp8QojFYLBbnyRlpGSBcyTFbVCqwREPFxMCddzr+ZU9g+fqaNItHNA1KKWkhKNxGjsmEEFo6efIk5eXlhIWFERYWpnU4wotJBZYQorFIC0Ev4jiBLLP9hBCNIS0tDZPJRMeOHQkKCtI6HOFFpAJLuNJTT0FwMDgSWGazVGA1d/n5+ZSUlBASEkJ4eLjW4QgvI8dkQggtSftA0VgcCazU1FSNIxFCeDupwPIi0q5CCNGYZP0r4S6yBpZwpYgI+NvfwJHAqqqSBFZzdzj19/WvdLKIjHCxmJgY9Ho96enp8n0jhGh00j5QNBapwBJCNBapwPIisbGx6HQ6jhw5gtls1jocIYSXk/WvhLs4ZtUcO3YMi8WicTTCGzzwAAQGSgJLQKXZys+bdwAQGCTrXwnX8/X1JTo6GpvN5mxVKYQQjcUxAUwqsIS7OdYRPXr0qByzCSHcpry8nLy8PHx9fYmMjNQ6HLdoVgmsgIAAOnXqhNVqlVnrQgi3kwSWcBc/Pz/ad+iA1Wpl/a4DWG1K65CEh2vRAi6+WBJYzV1+uYk9WcVs+W0rAKXlg/jTn0A63whXk84YQgitSAtB0VgCAwNp3749ZrOZY8eOaR2OEMJLOaqvOnXqhF7vnake73xWF+DouS6LBgsh3M2RwIqLi9M4EuGNesT3BmD5z1vZm11EcaXM6hMNM3CgLyAJrObIphRpp8rZn11MXpmJQ/t2AfB/tyfw3XcQHw9PPgllZRoHKryGI4Elx2RCiMYmCSzRmGQdLCGEu3l7+0Bohgksx8HSxo0y208I4T45ObB1q6yBJdynf//+AKz7ZR+HsqvYk1VERkGFVGOJegsIkAqs5qjKYuVAbgkpeaWUm62EGGwcPXgAnU7HzdMGMX48mEzwxRewf7/W0Qpv4ZhU6KjAMpm0jEYI0ZzIGliiMZw8CYcOyTpYQgj3KSuDm26CV1+1t+R2LDXhjZptAuuVVw6xZ4/GwQghvNbatfmYTHlACzp06KB1OMILDRo4AIDcjH2UnTRSWmUh+UQJ+7OLKa2SaixRd7fffjuJiYk88MADWociGklhhZk9WSWkn6rAoNcRHmDkcNJ+LBYzXbvHERwczFNPQUICPPEEDB2qdcTCW/yxheCbb8Ivv4B0eRdCuNM339g4csQ+U72iIlrbYITXUgquvBK6dweDwZ7Akpa5QghXO3AAFi2CNWs2AL9PcvZGzS6BFRtrn+2n16cQHKxxMEIIrxUZaW8f2L17D3Q6ncbRCG80ZNBAAMoK9hHfE8IDjRh99BwrqmRXZjHHCiuwKanGErXXtm1b4uPjad26tdahCDdTSnGssIK9WcWcKK0ixN9AkJ8BnU7Hvl07ARgwaDAAw4bBl1/CbbdpGbHwNn9s6/7tt3DFFTB8OFRWahmZEMKbnTyZjdVqAtrw9dcttA5HeCmdDsLCwGCA+NNt37ds2aJtUEIIrzN4MPz734qwsNUAjB07VuOI3KfZJbB69rQfLEVEHCAmRuNghBBea8eO3wC45JJ+2gYivFaXLl0ICQ2j8NRJTp7IQafT0cLXh/BAIyVVFpJzS9mfXUKZSaqxhBC/M1lsJOWWknyilDKTlVaBRvwMvx8SJO7eAcDQIYOdv4uKavQwhZeLiorCaDSSmZnJ8ePlbNkCNhtkZ9tnrZeUaB2hEMIbGY329oE6XRek4Fy4088/Q2Eh/PnPl2IwGPjtt98oKirSOiwhhJcZPTqZgoIcIiMjvXr5kmaXwIqNjSU0NJTs7CyOHz+udThCCC+1YYO9hPfiiy/WOBLhrXQ6Hb0u6gNA0r69zt8b9DpaBRrw0UNGYQW7M4vJKqpESTWWEM1eUaWZPVnFpBWUo9dBeKABH331KuHkfbsBGDEsQYMIRXNhMBiIOT2b8LPPDmOz2X8/bRr89BPSKUMI4RYWiz2BFRsbTWSkxsEIr6bTQYsWEBISwrBhw7BaraxevVrrsIQQXmbVqlUAjBkzxqu7PzW7BJZer2fo6Qb+UsIrhHAHpZQksESj6He6x3HSvuqLOup0OoL8DIQHGCiqMHMgt4TE3BIqzFYtwhRCaEwpRWaRvWVgbmkVwX4Ggk+3DDxTaXExx44cwujrS7++fTWKVjQXjjaCP/xgbyP49NOwcCH4+2sZlRDCm+XmpgFw+eVdtA1ENCvjxo0DYOXKlRpHIoTwNo4E1ujRozWOxL2aXQILYNiwYYAksIQQ7nHo0CHy8vJo27YtsbGxWocjvNigAQMASEnce86/G3z0tGphRKeD9FMV7MosIrtYqrGEaE7MVhsHT5SSlFtKSZXlrJaBZ0rcY1//qtdFffH19W3MMEUz1K1bNwB27z7E/Pnw7LP2GetCCOEuR4/aK7D69ZMElmg8ksASnuLUqVNahyDqwGazOSs7x4wZo3E07tUsE1gJCfaWKJs3b9Y4EiGEN9q4cSNgr77y5hJeob0hg+wJrEMH9p33NjqdjmA/A2EBBgoqzBzIKSEpt5RKqcYSwuuVVFnYm1XMkVPlALQKNJ7VMvBM+06vfzVw0KBGiU80b44KrGHDDnLrrRoHI4RoFg4cOABA165dNY5ENCcDBw6kZcuWpKamkpqaqnU4oo4sFotXTwA1mUx89dVXjB07lujoaEpkIVKPsWfPHgoKCujcuTNdunj3xIxmmcBytBDcsWMHJpNJ42iEEN7G0T5wxIgRGkcivF2PHj3w8/cnN/MYRYUFF7yt0UdP60AjAEdPlbMrq5jckiqv3hkXorlSSpFdXMmerGKyS6oI8vUhxP/sloF/lLRnFwDDTu8rC+FOF110EQBZWTs1jkS4g1KKbdu2UVVVpXUoQgBgNpvZvXs3AANOdzEQojH4+PgwduxYAH7++WeNoxF19fnnnxMbG8uTTz5JcnKy1uG4TGpqKo899hidOnVi6tSprFq1iqoqCzt27NA6NFFLzWX9K2imCayWLVvSo0cPqqqqnDswQgjhKrL+lWgsBoOBnr3sJwAPnqeN4Jl0Oh3B/vZqrFNlJvbnFJN8opQqi83doQohGonFauPQyTIO5JZQVGEmPNCIv9GnVtsm7bUnsEYMkwSWcL8BAwZgNBrZv38/xcXFWocjXCwjI4MhQ4YQEhLCsGHDeOCBB/jyyy9JT0/3iskzNpuNlJQUPvvsM+677z62bt2qdUiiBomJiVRVVdGtWzfCwsK0Dkc0M9JG0HP98MNPHD16lBdffJGePXsycOBA3nrrLbKzs7UOrc7MZjPffPMN48aNo2vXrrzyyiucOHGC3r1789577zFiRBZVVaO0DlPU0pkJLG9n0DoArSQkJJCcnMyWLVsYMmSI1uGIeqiqgt27oTlNEi4ogJYttY5CXMiJEydISUkhMDCQfv36aR2OaAb69uvH7h3bSNq3lyEjLq3VNkYfPa1bGCmpsnLkVDnFlRZiWgXSuoWv18/cEU1LVRX4+WkdhfcorbKQcrKU3OIqDHodrVsYa/2ZzsvNJj83m+CQUGdrNyHcyd/fn/79+7N161a2bdvmnJ0uPF9FBbz11gnatetJdnYSW7Zsqbb+dGRkJEOHDiUhIYGEhAQGDRpEUFCQhhHXLCcnh61btzov27Zto7Cw0Pn31q0j5LxCE+eoKhg4cKDGkYjm6PLLLwfg119/xWKxYDA029OxHiciYgGPP/4XTpz4jK+//pqdO3eyc+dOHn74YcaOHcstt9zCpEmTCA4O1jrU80pLS+Ojjz5i7ty55OTkAPb9sKlTp3LHHXeQkJCATqdj0ya46SbYsQO8vCOdxzObzaxbtw6A0aNHaxyN+zXLCiyAYcOGAbIOliebOBESEmD+fK0jaRyLF0NkJNxzj9aRiAtxrH+VkJCA0WjUOBrRHAwc0B+oXQXWmXQ6HSH+BkL9DOSVmdiXXUJKXhkmqcZqdOXlsHQpWJvZsmRHjtgPkCortY7E8ymlyC2pYm92MVlFVQT6+hAaUPvkFcD+XfY2bn36D0Cvb7aHCKKRydrE3umtt2D58sEcOXKAgoICVqxYwbPPPstVV11FeHg4OTk5LFu2jMcff5zRo0cTGhpKv379uPPOO5k3bx7JycnYbNrtj5SUlLBmzRpeffVVpkyZQlRUFO3atWPChAm8+OKL/PzzzxQWFhIZGclVV00gPPxFXn/9asrKNAtZ1ML27dsBGCTrPAoNREdH0717d4qLi6Vi04NkZ8PatT5ccsloPv74Y3Jzc/nf//7HxIkT8fHx4eeff2bGjBm0bduWm266ie+//x6z2ax12IB97a4lS5Ywfvx4YmJieOmll8jJySE+Pp533nmHrKws5s2bx7Bhw5zHDG3aQEkJSKfLpm/79u2UlpYSFxdHhw4dtA7H7Zptyt9xsHTmTDDhWTIy7sLHJ49t20YzePAoevbs6dWVA+npJzGZ1pGa2heI1ToccR6OBJa0DxSNZehg+0H4ocR99dre13C6GqvSQmp+OUWVFmJbBRIeWLeT36L+fvwRpkyBl1+GRx/VOprGM2sWrFkDf/4zfPYZSM6kfqw2xdFT5WQUVFBhthIeYMDgU/cXc/9u+8z0wYMHuzpEIc5r2LBhvPvuu3JM5mUefRSmTQN/f/D3D2PcuHHO9llKKQ4fPsyWLVv47bff2LJlC3v27HFe/vvf/wIQGBhI9+7diYuLo0ePHsTFxREXF0f37t1dUq2llCI3N5fU1FRSU1M5cuQIqamp7Nq1iwMHDpzV5jA4OJhBgwYxZMgQ56VDhw5UVOho29Y+hp06BS1aNDg04SaSwBJaGzduHCkpKfz8888MHz5c63BELbRrB3v2gCMn5e/vz3XXXcd1113HqVOn+N///sfChQtZv349ixYtYtGiRbRu3ZobbriBm2++mcGDBzfaxGalFNnZ2ezZs4cNGzbwySefONsc+vn5cf3113PHHXcwYsSI8x7n33UXPPkktG7dKCGLBmhO7QMBdO7sP63T6RTQJHtcW61WwsLCKC0tJTs7m8jISK1DEnWglKJt27bk5eU5f9emTRtGjRrF6NGjGTVqFHFxcR598jUvL49169axZs0a1q5dy7599pPTL774T/7+98c0jk6cT0JCAr/99hsrVqxwHqh7AsdnRSnluR8aN2uqY1plZSVBQUEopVh9IAP/gMB631eVxUZRpYUWvj50DPUnOjwQYz1OhIvaO3z4ME88EUtVlY758yE0VOuIGsexY/DII7BiBRQW2g+Upk7dT2BgIDExMVqH5zHKTVZS8krJKalCr4NQf0O9933uvGkiuzat46v/fcP11012caSNS8a0C2tK41laWhpdunShVatW5OXlefS+u6i/8vJydu7c6Uxq/fLLFgoLj5/39h07dqyW1HJc79ixY7UKUrPZTHp6erUE1ZkJq7LzlEwZjUb69u1bLVkVFxd33urU3FwoLYVYmWPYZJlMJoKDgzGbzRQWFhISEqJ1SLUi41nNmtKYVpNvv/2Wa6+9luHDhzsnvgrvkJaWxhdffMHChQs5cOCA8/dGo5G4uDh69epV7RIbG9ugNpImk4mkpKRqE0D27NnDyZMnq90uLi6OO+64g1tvvZVWrVrV+/FE0zN27FhWrVrF119/zZQpU7QOp1YaMqY12wQW/P6fvWTJEiZOnKh1OKIOHDP31qxZw+rVq1m9erWzj6tDZGRktYRWt27dnB+W0lL7LLnA+p/nbRCTCXx9q//uxIkTzoTVmjVrSExMrPZ3Pz8/hg8fzuzZs5k2bVojRitqq7y8nNDQUGw2GwUFBR5zYARycFQbTXlM69GrNwcPJPLR4p/oM7Bh6y/YlKK40oLVpmjdwo+urQNpGehb84aizgoKCoiMjKRlyxjuv38m06ff0izK/89UXm7i9deXMGfO+2RkrGPWrFl8/PHHWoflEfJKqzh8spz8chNBvj4E+vrU+75sNhuX9YmhrKSY48ePe/z7UMa0C2tK45lSivbt25OTk0NKSgrdunXTOiShIaXgoYcgKwv+859CDh48yMGDB0lOTnZeP3ToECaT6ZzbO6q2wsPDOXr0KBkZGVgv0J+3ZcuWxMbGOi+HDsVy6aXxzJrVD39/f3c9TaGBnTt3MnDgQOLi4khOTtY6nFqT8axmTWlMq0lJSQnh4eEopTh58iRhYWFahyRcTCnFnj17WLhwIYsXL+Xo0dRz3s7Pz48ePXqcldjq0qULPj7V9+lPnDjhTFDt3buXPXv2kJSUdM5WhWFhYfTt25fAwL4MHjyZZ54ZKZODvFBlZSVhYWFUVVWRl5dHaw8pmWvImNZsWwiCvWXFqlWr2LJliySwPIxOp6Nbt25069aNv/zlLyilSElJYfXq1c4EUE5OjrOEF6B9+/aMGjWKAQNG8+KLowgNjeXAAR0BAY0b+/vv2w/MXnjhBFFRa53xnjlLA+ylycOHD2fUqFFceumlDBkyRA6kmritW7disVjo37+/RyWvhOfr268/Bw8kkrRvb4MTWHqdjrAAI5UWK7mlVZSZLES1DCCqZQAG6fHmUklJSYSFhZGbm8zjjz/GE0/8ncsvv5yZM2cyYcIEAmoxQFmt9gkZWh6XFBfD11/bWwLW1vHjx/nwww/56KOPnBNQgoKC5EC+Fqw2RXpBBekF5ZSZrLQMMDS4UvLY0VTKSoppE9nO45NXwrPodDoSEhJYunQpmzdvlgRWM7dvH3z8sb0qNywsjKFDhzJ06NBqt7FaraSlpVVLajmu5+bmsnv3budtdTodnTp1qpakio2NJSYmhtjYWFq2bFntvkePhsxMe+tD4V2kfaBoCoKDgxk+fDjr1q1j9erVTJo0SeuQhIvpdDr69etHdHQ/Fi9+nZCQUpYtSyItLZHERPtl//79HDt2zJmUOlNAQAA9evQgPj6e/Px89uzZ42wD+MfH6datG3379q126dSpEzqdjp9/hltvhXvukXaA3mjz5s1UVVXRt29fj0leNVSzTmDJosHeQ6fTOVtI3HnnnSilSE5OrpbQysrK4vPPP+fzzz8HoLCwNVdf3Zd+/frQp08f+vbtS8+ePV2eJLLZbKSlpZGYmMiBAweYNy+RysrtPPxwUrXbBQQEnJWw8vPzc2kswr1k/SuhlQED+vPV5wtJTtzrsvv0N/jg20JPUYWFlLwyCirMxLZqQVhA4/Twbg6GDx/O8ePHWbFiBfPmzWP58uWsWLGCFStWEBoaytSpU5k5cyYJCQnnnTl3552wZAnccAO89RY09rCRnQ3x8VBWBgkJ0KvX+W+rlOLXX3/l/fffZ/ny5c5Z8b169eKuu+7illtukeR/DSrMVg7llZFdXIkCWrcwondB9nLf6fWvBgyUE3ui8Q0bNsyZwLr11lu1DkdoqE8fyMiwT4w4Hx8fH2ci6uqrr672t8LCQh566CBduxYwaVI00dHRdTq2W7z47C4ZwjtIAks0FZdffjnr1q1j5cqVksDyYoGBcOIE+PoG0bXrYEaNqr7GbHFxMQcOHKiW1EpMTCQrK4tdu3axa9cu522DgoKc5ywdl969e19wPcgBA2DHDkleeavmtv4VSAILgG3btmGxWBrUf1Q0LTqdjp49e9KzZ0/uuusulFIcOHDAmdBatWoNBQUnWb36V1av/tW5nY+PD3FxcfTt25c+fX5PbLVv377aycOqKjAY4MzKXpvNRnp6unMAcgxGSUlJlJeXnxVjQEAAI0aMYNSoUYwaNYrBgwfjK0dMHm3Dhg2AJLBE4xsycCAAhxL3ufR+9TodLQONVJitZBVVUVplIaplIFFhAfjopRWBKxiNRq655hquueYa8vPzWbRoEfPmzWP79u18+OGHfPjhh3Tv3p2ZM2cyffp0OnbsWG37wEDIz7e3pm3s5FV5OcyebV/DCuCzz+Cll86+XWFhIfPnz+f9998nJSUFAIPBwNSpU7nrrru45JJLpLVFLeSXmTh0soz8MhOBvj4EGvUue93279oJwJAhDavgFKI+HMdkW7Zs0TgS0RSEhdkv9ZGbG8bChUNZvBh69Kj79n8oyBJexJHAGnh6n1kIrYwbN46nnnqKlStXah2KcCNfX3s73KIi+MPhGwAhISEkJCQ494EcCgoKOHDgAFu2JLN4cUvmz+9LTEyX867BeD6y3JV3a44JrGa9BhZAt27dOHz4MDt27GDAgAFahyMaiVLKWbK7d+9eZx/ZQ4cOYbPZzrp9eHi4M6HVunVfXn+9FzExJ7nppt/LgM+XqAJo164d8fHxzr62F110EQMHDpSElRexWq2Eh4dTXOyZ64dIf/WaNeUxrbi4mNDQUAxGX9YmHcNgdH2VlNWmKKq0oAMign3p2qoFIf5SjeUu+/fvZ/78+SxcuNDZYk+n03H55ZczY8YMJk6cSGBgIPPmwZ//bJ9hV9/dGKXsl7w8aNu2btuaTPDYY/bqr86d4cgRe0tDgF27dvH+++/z2WefUVFRAUDHjh254447mD17NpGRkfULuJmxKUVGQQVpBeWUVrmmZeAfzbz2cpL27OCnn1ZwxRXjXHrfWpAx7cKa2nhWXl5OSEgISimKioouOKNYiAt5+mn45z/hyy9h8mStoxFNRWVlJSEhIVgsFoqLiz3qO0bGs5o1tTGtJlarlYiICAoKCjh8+DCxsbFahySaIKvVPjnxf/8DWfFGnOnMtfROnTrlUR1MGjKmNfsE1q233sqCBQv497//zV133aV1OEJj5eXlHDhwwJnQcvwsKCio1faRkZHVFmCMj48nPj6e8PBwN0cutLZnz57TvY6jOXr0qNbh1JkcHNWsqY9p0TFdST+ayoIf1tK910Vue5xyk5VSk5UQPwOdwwPoGCrVWO5ksVhYuXIl8+bNY9myZc6F60NCQpg6dSojRszkgw+GsWVL3f8PsrLg6FGYOhViYuDGG6G+u0JLl8LMmfDNN5VkZX3N+++/X62a4rLLLuOuu+7iT3/6k1S810GlxcrhvDKyiiuxKUVYgGtaBp7JbDIxuldnzKYqCgoKvGIdMhnTLqwpjmcDBw5k586drF69mlGjRmkdjvBQNpt9MgbUfUKG8F7btm1jyJAhxMfHk5iYqHU4dSLjWc2a4phWkxtuuIGvv7bvL//f//2f1uGIJio5Gbp3/31yoBAAP/74I1dddRVDhw71uO4FDRnTmv0ZhISEBBYsWMDmzZslgSUIDAxk0KBB1XpjK6XIzMysVqm1c2cS7dq1pk+f+GrJKklUNV/SPlBorU/fvqQfTSVp/163JrACfX3wM+gprDBz8EQpBeVmurZuQZBfs9+lcAuDwcBVV13FVVddxalTp1i0aBHz589n69atfPTRR3z00UdERHTmttvGOFvSRkVF1eq+W7aEkSPtC9ZnZsJtt9U9PqUUSUlJHD++iqFDV3HttaspLy8EIDQ0lD//+c/ceeedxMXF1f3Om7mCcjOHTpaSV2oiwKinha/BLa0WDycnYjZVEdO1m1ckr4RnGjZsGDt37mTz5s2SwBL1ptdL4kqcTda/Ek3NuHHj+Prrr1m5cqUksMR51acVrvB+zbF9IEgCi2HDhgHSc12cn06no2PHjnTs2JGrrrpK63BEE7Vx40ZAElhCO/0H9OfbpYtJ3r+XCVNvdutj+eh1hAcaqTDbOF5USUmVhejwQDqE+ru8OkT8Ljw8nLvuuou77rqLAwcOMH/+fBYsWEB2djqffPIJn3zyCQDR0dHOZNall15KdHT0Oe8vIADeew8cQ1tgYM0xKKU4evQoq1atcl5yc3Or3aZfv37cfffd3HTTTbRo0aIhT7lZsilFZlElR/PLKa6yEOZvwNfgvqmXe3ftAKD/QDmxJ7STkJDAv//9bzkmE0K43I4d9nFO1r8STcXll18O2E9EWywW6U4ghKg1SWA1UxdddBGBgYEcPnyYvLw8IiIitA5JCOGBpAJLaG3I6YPylMS9jfJ4Op2OQF8ffE9XYyXllnCq3EzX1oG08G32uxduFx8fzyuvvMJLL73E7t27WbNmDWvWrGH9+vWkpaUxb9485s2bB0Dnzp2dyaxRo0YRHR3trOQZPx4mTYIlS+wJrXPJyspi9erV/Prrr6xatYr09PRqf4+MjGTs2LGMGTOG0aNH06VLF3c+da9mstg4fLKMzOJKLFZFq0Cj21t0HtizC4CEoUPc+jhCXIhjUuHmzZtRSrml2lAI0TxJBZZoajp37kxcXBwHDx5k69atDB8+XOuQhBAe4NSpU+zatQtfX99m973R7M8wGQwGBg8ezNq1a/ntt9+45pprtA5JCOFhMjIyOHbsGGFhYfTs2VPrcEQzNXiQPYGVmrQfm82GvpGaZRv0OloFGikzWTlWWEHp6WqsdiF+Uo3VCHx8fBg4cCADBw7koYcewmq1smfPHtasWcPatWtZt24d6enpzJ8/n/nz5wMQFRXlTGaNGjWKt97qwooVOmcFVn5+PmvWrHFWWCUnJ1d7zJYtWzJ69GjGjBnDmDFj6NGjh5xsdoGiCjMpeWXklVXhZ9ATHuieloF/lLRnJwAjhiW4/bGEOJ+YmBhat25NXl4eR48eJSYmRuuQhBBeoKKigv3796PX6+nXr5/W4QjhNG7cOA4ePMjKlSub3YloIUT9rF27FqUUw4YNI7A27VO8SLNPYIG9ZcXatWvZvHmzJLCEEHXmqL4aMWJEoyUNhPijNm3a0CayHSdysjmWdoTOMV0b7bF1Oh1Bfgb8DTYKKswcyC2hoNxETKsWBPr6NFocwp7QGjBgAAMGDODBBx/EarWyd+9e1q5dy5o1a1i3bh0ZGRksWLCABQsWANCxY0e6dx/FRx+15uGH17Bnz55qC2EHBQUxcuRIZ8Kqb9++8l3nQkopsoorOZJfTlGFhdAAA35ubBl4prLSEjJSUzAYjfSXE3tCQzqdjmHDhvHtt9+yefNmSWAJIVxiz549WK1Wevfu3exO9ommbdy4cbz33nusXLmSZ555RutwhBAeYPXq1UDzax8IksACZB0sIUTDyPpXoqno07cfv+Rkk7x/b6MmsBwMPnpat7BXY6UXVFBcZaFLeCCRwX5SoaMRHx8f+vfvT//+/bn//vux2Wzs27fPWaG1du1ajh8/zvHjC9m9276Nn58fw4cPZ8yYMYwdO5ZBgwZhNBo1fR7eymw93TKwqBKTVdGqhftbBp4paZ89Wdkzvjf+/v6N9rhCnEtCQgLffvstW7Zs4eab3buWoxCieXCsfyXtA0VTc+mll2IwGPjtt98oLCwkLCxM65CEEE1cc13/CiSBBcDQoUMB2Lp1K1arFR8fmS0uhKg9Wf9KNBX9+vfnlxU/krRvD1dcO1mTGBzVWH4GGwXlZspNJZwqNxHbqgX+RhlftabX6+nbty99+/blvvvuw2azsX//ftasWUNBQQGXXHIJw4YNI+B8C2IJlymutHAor5QTpSaMPjpaNVLLwDPt33V6YfvBgxv1cYU4lzPXwRJCCFeQ9a9EUxUcHMzw4cNZt24dq1atYvJkbY7dhBCeITc3l8TERAIDAxkypPmtXSwJLOyLj0dHR5OWlkZiYiJ9+vTROiQhhIcoLCxk3759+Pr6yoGR0NzggQMASDmwT+NIwHi6GqvUZCXtVAXFVVZiwgNpE+Qr1VhNiF6vp0+fPrLv04iUUuSUVJGaX05hhZkQPx/NkruJp9e/Sjg9mUsILQ0ePBi9Xs+ePXsoLy+Xdl9CiAaTBJZoysaNG8e6detYuXKlJLCEEBfkaB94ySWX4Ovrq3E0jU8WMDhN2ggKIepj8+bNKKUYNGiQtF8SmnMksA4f2FdtDSOt6HQ6gv0MhAUYOFVmYn9OMcknSqmyWLUOTQhNWKw2Dp0s40BuCcUVZsIDjZpWJiafTmCNSGh+s/hE0xMUFMRFF12ExWJxtv0SQoj6Ki8v58CBA/j4+MhEHdEkjRs3DoCVK1dqHIkQoqlrzu0DQRJYTgkJCYC0rBBC1I20DxRNSXR0NKFhYRSdyudETpbW4Tg5qrGUgiOnytmdWUxeaVWTSLIJ0VhKqyzszSkm9WQZNpt9vStDI6539Uf5eSc4kZ1JYIsgevbsqVkcQpzJcUwmkwqFEA21e/dubDYbvXv3ltbIokkaMGAA4eHhHD16lNTUVK3DEUI0YY4E1ujRozWORBuSwDpNKrCEEPWxceNGQBJYomnQ6XT06tMPgOR9e7UN5g90Oh0h/gZC/QzklZnYl11CSl4pJotN69CEcCulFLklVezNKiarqIpAXx9CA4yat9JM3G2vvurTf4Cs/yqaDFkHSwjhKtI+UDR1Pj4+XHbZZYBUYQkhzi89PZ3U1FRCQ0Pp37+/1uFoQhJYp/Xt2xd/f3+Sk5M5deqU1uEIITyAyWTit99+A2D48OEaRyOEXb9+/QBI2t+0ElgOvgZ7NZZV2UjNr2B3VhH5ZSapxhJeyWpTpOaXk5hTQkGFmfAAAwEatgw8077d9hZtgwcP1jgSIX53ZlcMGReEEA3hSGANHDhQ40iEOD9pIyiEqIlj/atLL70Ug8GgcTTakATWab6+vs4dm61bt2ocjRDCE+zcuZPKykp69uxJq1attA5HCAAGnx7LUhKbZgILQK/TEepvJNjPhxOlJvZmF3P4ZBlmq1RjCe9RbrKy7/R722Kz0bqFEYNP09n1TtqzC4BhQ2X9K9F0dO/enZYtW5KTk0NGRobW4QghPJhjLT2pwBJN2eWXXw7Y24OZzWaNoxFCNEXNff0rkARWNbIOlhCiLmT9K9EUDRk0AGjaCSwHv9PVWGarjUMny9idWcypcpPWYQnRYHmlVezOKuJ4USX+Bj1hTaBlIIBNKSrMVvJLKkjaY28hODxhqMZRCfE7nU4n62AJIRqstLSUpKQkjEYjffr00TocIc4rKiqKuLg4iouLZTK9EOIsSilJYCEJrGpkHSwhRF3I+leiKYqLiyMgIJC87EyKCpp+S1y9TkdYgJEgPx9yS+3rBB0+WYZFqrGEB7LaFEfyy9ifU0JBuZmWAQYCfbVrGaiUospio7jSQl6pifwyM2arYt+W9ZQWF9I6oju7dkUhndpEUyLrYAkhGmrXrl0opbjooovw8/PTOhwhLkjaCAohzufQoUNkZmYSERFBr169tA5HM5LAOoNjtt9vv/2GzSYnzoQQ56eUkgos0ST5+PjQo1dvAJKb6DpY5+Jv8KF1CyNVp6ux9mQVU1ghbTSE56gwW9mfU8KhvDJMFhutWhgxatAy0GJTlJks5JebyCszU26yYvDR0S7Ej55tg+nfIZTtKxcDcMvNM5k8Wcf48XDwYKOHKsQ5SQWWEKKhZP0r4UkkgSWEOB9H9dXo0aPR65tvGqf5PvNz6NChA506daKoqIjk5GStwxFCNGHJySmcPHmS8PB2dOnSRetwhKim/wB7G8H1a9dxotREQbmZMpMFs9WGasKlFnqdjpYBRloY9WQXV7Enq4ij+eVYbU03ZiEA8stM7Mkq5nhhBb4GPWEBBvSN1DLQ0RawsMLMiVITRRVmlNIRFuBLt9Yt6NshhKFRLenfIZSYVoH4mMtZvmwZOp2OBx+8hSlTYMUKGDYM1q1rlJCFuKAhQ4ag0+mca40KIURdyfpXwpOMGjUKo9HI1q1bKSgo0DocIUQT8vHHPwDNu30gSALrLI4Zf//852akCEsIcT6ffroagMLCEVRVab+uiRBnmjb1egCWL/yK7b8a0VsNKKWjpMpKXpmZk2UmiistVJitTTI55G/0oVULIxVmGwfzStmTVURxpVRjiabHphRpp8rZl1PMyTITYQEGWvj6uHW9q/O1BQww+hDdMoDe7UIY1CmUoVFhxLUJIjLYnwDj7zF9/fXXVFVVMWbMGDp16sSTT0LLljBuHIwc6bawhai10NBQ4uPjMZvN7Nq1C4CcHI2DEkJ4hOxsOHIE1q61V2BJAks0ZQsWwCefwN69QQwePBybzcYvv/yidVhCiCZi1aoUduz4DvBl6NAJWoejKYPWATQ1Q4YM4+uvv2bhws3ccsssrrhC64iEEE3RqlWfAjB69DX4+2scjBB/MGb0aLp27crhw4eZ98Zm/jb7Kqx6K6VVFkqrrBRXmikzWTFZbZSZzNhsYPDR4eujw9dHj9FH59YT8LXho9cRHmikwmwlq7iK0iorncMD6BgagI9eksZCe5UWK4fzysgqrsSmFK1bGN1WdWWxKaosViotNqxWMPro8DPqCQ/0IyzAl1B/AyH+hlq1LJw/fz4AM2bMAKBPH1i2DKQbrmhKhg0bRmJiIps3b2bYsGG88AJ06ADt2sGtt0Iz7qAihLiAoiLo16+YqqqD6PW+9O7dW+uQhDivb76x74OFh8PAgdcCa/nPf/7D9ddfr3VoogEyMzP55ptv+Prrr8nNzWXYsGGMHDmSkSNH0rVrV5cfZ5eWlrJ161Y2bdrEtm3baNOmDZMmTWLs2LGyBqCH++KLNwBFbOwM+vWL1DocTenc2UpIp9MpoEm3K/qj7du3M3jwYHx925CZeYzWrX21DkkI0cQkJSURHx9PcHAw2dnZtGjRQuuQGsyxE6WUkszAeXjamPbKK6/w2GOPcdVVE/j++6XV/qaUwmRVlJoslFVZKakyU1Rpocpiw2SxYbYpdICvwZ7Q8vXRa5o0stoUhRVm9DodbYP9iG3dgmA/mYMjtFNQbuLQyTLySk0EGPUur7qyna6yqrLYMFkVPjrwM/gQ4OtDeICR0AADof5G/A36Oj3u4cOH6datGy1atCAnJ4egoCCXxdyUyJh2YZ4wns2ZM4fZs2dz/fXX89VXX9GnD+zbB0YjLFwIl14KbdtqHaUQoqlRClq1WktBwShiYwdx+PA2rUNqEBnPauYJY9r5/O9/9qrB224Ds7mQqKgoSkpK2LFjBwNOt4QXniErK4tvvvmGr776io0bN573/diuXTtGjhzJpZdeysiRI4mPj6/TvrxSirS0NDZt2uS87N27F9s5WogFBwdz9dVXM3nyZMaPH++1+/3eKicnh+joaEwmE0lJScTFxWkdUoM1ZEyTsz9/MHDgQHr16kViYiJr137Lddddp3VIQogmZu7cuQDcdNNNXpG8Et5pxowZPPnkk6xY8R3Z2dm0a9fO+TedToefQYefwZdWgQAB2JSi3GSl1HS6SqvKTGnlGVVaSmHQ6/H10WH0sf9srCqt36uxbBwvqqSkykJ0eCAdQv0bbZ0hIcCeWDpeWMnRU+WUVFkI8zfga2h4KYhSCrNVUXk6aQXga9ATYPShXYiRsAAjof4GgvwatrbWggULAJgyZYocxIombdiwYQBs3ryZggLYv9/+e7MZvvoKJjTvLirNwtGjR2nZsiVhYWFahyI8iE4HUVHbKSiA0aOlfaBo2qZMOfNfYdx+++288cYbvPbaa3zxxRdahSVqKTs725m02rBhgzNp5efnx1VXXcV1113PY49154EHNrJx41rWrVtHdnY2X375JV9++SUArVu35pJLLnEmtfr06YOPj4/zMSorK9m5c6czWbV582Zy/tBX2cfHh0GDBjF8+HAGDRrCgw+m0rbtYhIT97Bo0SIWLVqEn58fV1xxBZMmTeJPf/oTrVq1arwXStTLe++9R1VVFZMmTfKK5FVDSQXWObz77rvcd999jBs3jhUrVmgdjhCiCTGbzXTs2JETJ06wZcsWhg4dqnVILiGz+2rmiWPa5MmTWbJkCS+99BKPP/54nbc3WWyUmSyUmqyUVFooqrJQabZittowW+2vg/F020Ffgx4fHW5PalltioIKMwa9jrbB/nRtFUgLqcYSjcBksXHoZClZxVVYrIqWAQb0DahMtLcFtFFlsWJxtAU06An2M9Ay0Eiov7HWbQFrw2azERsbS1paGr/++qtXLwQsY9qFecJ4ZrPZCA8Pp6ioiE8+Ocaf/9wRgD//GT78EAzyte+VrFYry5cv580332TDhg3o9XqGDBnC5Zdfzrhx4xg6dChGo9Flj1dVVcXOnTvZsGEDGzduBOD+++9n1KhRLnsM0fgGD76J7dsX8fHHHzNr1iytw2kQGc9q5gljWm0dO3aMmJgYbDYbhw8fpkuXLlqHJP4gJyfHmbRav359taTV+PHjueGGG7jmmmsIDg4mNxciI+Hdd+Gee+zv0eTkZNautSez1q5dS1ZWVrX7Dw0N5eKLLyYmJobt27ezY8cOTCZTtdu0atWK4cOHM2zYsNNJq0HVJlZ37Wp/3I8+OsIPPyxh8eLFbNq0yfl3Hx8fRo0axaRJk5g4cSIdOnRw4ysm6qOkpISoqCgKCwvZvHkzCQkJWofkEg0Z0ySBdQ6nTp2iQ4cOVFZWcuTIERk0vIxSio0bN/LeewvYt28rQ4b0ZfTo0YwaNYrOnTu75fFSU1PZuHEjGzduJDX1CFdccQUzZtxKW+l94nGWLl3KpEmTiI+PZ//+/ZqvE+QqcnBUM08c03788UeuuuoqYmJiOHToEPoGLhpiU4oKs5XSKitlJgvFlRZKqiyYLDZMVhtWG/j44Gw7aPTRuaVCSilFudlKmclGWICRLuEBtAtp3tVYGzdC//4QGKh1JN6pqMJMSl4ZeWVV+Bn0BNWjZeAf2wLqdeBv0BPga7C3BfQ3EOJvJMBYt7aAtbVu3TouvfRSOnXqRFpaWoO/D5oyGdMuzFPGsyuuuIKVK1cyYcLXLFs2hfvvhzfekPWvvFFZWRnz5s3j7bff5vDhwwAEBQVRWVmJxWJx3i44OJjRo0c7E1rdunWr0/dlQUEBmzdvZsOGDWzYsIFt27ZRWVl51u3GjBnD888/z/Dhwxv+5ESj69y5GxkZh9m1axf9+vXTOpwGkfGsZp4yptXWrbfeyoIFC7jnnnt49913tQ5HALm5uc41rdauXet8r/n6+lZLWoWEhFTbbtcuGDAA2rSBI0fgj817lFIcOXKkWkIrLS2t2m10Oh29evVyJquGDx9e49h32WXw668wbZq97bJOZ68WW7ZsGYsXL2b16tXVxtahQ4cyefJkJk+eTNeuXRv2YgmXeOutt3jwwQe55JJLWLdundbhuIwksNzAMWj8/e9/58UXX9Q6HOEChw4dYsGCBSxcuJCjR4+e8zZdunRh1KhRzktUVFSdH8dkMrFz505nwmrjxo2cOHHirNsZDAauueYaZs+ezRVXXIFBppJ6hGuvvZZvv/2WN954gwcffFDrcFxGDo5q5oljmtVqJSYmhoyMDH755RfGjh3r8scwW22UmayUVtmTWcWVFirM9taDZ1Vp+ejw0buu9aDFpigoN+Nr0BMZ7EdsqxYE+vrUvKGXWbgQpk+HUaNg9erGfeziYliyBGbMaNzHbSxKKTKL7C0DiyothPob8Ktly0BHW8Aqi41Kiw0F+Bn0+Bv0hAXY2wKG+BsIbmBbwNqaNWsWc+fObRb7tjKmXZinjGf/+Mc/eO6552jX7kHuuOMNnn7afhJGeI/s7Gzee+89/vOf/1BQUABAixbRlJXdzyuv3MZdd+lYu3YtK1eu5OeffyYpKana9lFRUYwbN47LL7+csWPHVmuJpJQiIyPDmazasGEDiYmJZ73v4+Pjufjii0lMHMFvv6Xh5/cmZWVFgD2J+txzzzFkyBA3vxLCVQ4dOkT37t0JDg4mPz/fpRV7WpDxrGaeMqbV1t69e+nbty+BgYFkZGRIqzeNnDhxgsWLF/PVV1+xdu1a5xpTvr6+XHnlldxwww386U9/OitpdaYff4R//APi4+GBB6Bv35ofNyMjg/Xr1/POOxn84x8DGDFiaJ1b6c6aBZ9+CjffDM8/D506Vf97QUEB3333HYsXL+ann36qNpHjoosuYtKkSUyePJk+ffp4zYRtT2I2m4mJieH48eN8++23XHPNNVqH5DKSwHKDDRs2cMkllxAZGUlGRobH7/g0VydPnuTLL79kwYIF/Pbbb87ft2/fgbKymwkMHMf99+9l48Y1rF27lqKiomrbx8TEVEtodfrjNz/2ir1NmzY5k1XnmskXERHBiBEj6NFjBK+91hGjcRFm83dYrdbT8bRn5syZ3HbbbcTGxrrhlRCukJ2dTadOndDpdGRmZtKmTRutQ3IZOTiqmaeOac8++yzPPPMMU6dOZdGiRW5/PKUUFWabcy0te1LLjMlqw2RRWJXCR2ev0jIa7Emthpy8V0pRZrJSbrYRHmAkulUg7YL9ms3OtlKK778v5cYbT3HTTZF89JFfoz7+s8/CM8/A88+XMHLkLgwGA61ataJVq1a0bNmyWg93T2Oy2kg9WUZmUSWm0y0DfWpoGfh7W0AbFpvCqK/eFjDkdFtAXxe1Bayt8vJyIiMjKSkpITk52ev7qMuYdmGeMp799NNPjB8/ni5dEjhyZLPW4QgX2rt3L2+++Saff/45ZrMZgISEBB566CF27pzIq68aWLoU/njO5tixY/z888/OS35+vvNvOp2OgQMHMmrUKI4dO8aGDRvIzMystr2vry+DBw/m4osv5uKLL2bYsGHOk8OXXw6//AJz5hRw9OibvP3225SWlgJwzTXX8Nxzz9G/f3/3vSjCJRz7vTNmzGDevHlah9NgMp7VzFPGtLq48sorWbFiBc8//zxPPvmk1uE0G3l5ec6k1Zo1a6olra644gquv/56rr32WkJDQ2t1fxZL/Vse9+kDt90G999f923XrYOoKPulpqr1srIyfvrpJ5YsWcK3335LcXGx828xMTFMnjyZSZMmkZCQ4NXdG5qSBQsWcOuttxIfH8++ffu86nWXBJYbKKXo1asXSUlJLF68mEmTJmkdkqilyspKvvvuOxYsWMAPP/zgLI0NCgpiypQpTJ8+nUsvvfSsk2pWq5U9e/awZs0aVq9ezbp166p9eQPExsYyatQo+vbty969e9m4ceNZMwEBevbsyYgRI5yXrl27Oj+oqang6wtGYw6ffvopc+bMISUlxbntqFGjmD17NpMnTyYgIMDVL49ogFdeeYXHHnuMSZMmsXjxYq3DcSk5OKqZp45pGRkZREdHYzQayczMpHXr1o0eg8Vmo6zKal9Lq8pMcaWFcrMVs0VhstlQqnqVlqEeVVpmq43CCgt+Bj3tQuzVWP5Gz0ueWK1W8vPzOXnyJHl5eeTl5Tmv//Gn43pVVRUA/v7+DB06lJEjRzJy5EiGDRtWrR+6K+Xl5bF+/XpWrlzPqlXrOXx4F0rZqt1Gp9PRsmVLZ0LLcWnduvUF/+3n17hJuHMprrRwKK+UE6UmjD46gv3O3TLQphSm0xVW1doCGn0ID/R1e1vA2vr888+5+eabGTp0KFu2bNEsjsYiY9qFecp4VlRURGRkpLR19xJKKVauXMkbb7zBzz//DIBer2fSpEk8+OCDdW7XZ7PZ2L17t7M6a8OGDWetExIeHu48Hrv44osZOHAg/v7+57w/kwkSE+3teME+EfL111/nvffeo7y8HLCvLfrMM89w0UUX1fHZi8aglKJHjx6kpKSwYsUKxo0bp3VIDSbjWc08ZUyri19//ZXLLruMNm3akJ6eft7vLeEaGzZs4Nlnn2XVqlXOpJXRaKyWtKprFVRDdeoEpaVw6BA01uG7yWRi9erVLF68mKVLl1brJNWuXTseeOABHnjgAeke5UZKKfr06cP+/fv55JNPmDlzptYhuZQksNzk7bff5oEHHuDKK6/kxx9/1DoccQE2m40NGzawYMECvv76a2cllY+PD+PGjWP69OlMmDCBwDosDmK1Wtm9e3e1hFZJSclZt/Pz82PIkCHOg6MzZ/LVhlKKDRs2MGfOHL766isqKioACAsLY9q0acyaNYsBAwbU+v6Ee5x5QPTdd99x9dVXax2SS8nBUc08eUy76qqr+PHHH3nzzTd54IEHtA4HpRSVFhulVRbKTPYqraJKCyaLFZNVYbHZ0Ot+T2j5+ujR11D54rjfUpOVSrON8EBfurQKpG2Qr6aJg6qqKnJyci6YgDrzZ0FBQZ3fYwEBAYSFhZGdnV3t9waDgYEDB3LppZcycuRIRowYcdbBV0YGZGXBhdaFVUqRnp7O+vXrnZfk5ORqt/Hx8aFfv34YDAZnAq6wsLBOz8MhKCjonEmvjh07Eh8fT69evYiOjnbLbDSlFNklVRzJL6ewwkyIn0+1RGhTawtYW46ZvO+//z7/93//p3U4bidj2oV50ng2ffp0Fi5cyJNPPsnzzz+vdTiiHqqqqvjss8948803SUxMBKBFixbcdttt3HfffS7rPlFWVsacOetISdlEnz6duPjii+nRo0eDx4rc3FxeffVV3n//fSorK9HpdNxwww0888wz9OjRwyWxC9fYvn07gwcPpm3bthw/ftwrTrLKeFYzTxrTakspxaBBg9i5cyf//e9/uf3227UOySsVFhby6KOP8uGHHwL2pNW4ceO4/vrrmTBhQqMnrc4UHGxPYN11F/z7343/+FarlU2bNrFkyRIWL15Meno6AP369eOjjz5i0KBBjR9UM+BYw7x9+/YcPXoUX19frUNyKUlguUl+fj4dOnTAZDJx5MgRoqOjtQ5J/MHBgwed61o5vlABBgwYwPTp07npppto27atSx7LYrGwe/du/vrXNfz2WyJjxvTihRdGMGDAAJfNFi8qKmLRokXMmTOHbdu2OX/fv39/Zs2axbRp02jZsqVLHkvUjaOtaLt27cjIyPCKA6IzycFRzTx5TFuyZAmTJ0+mZ8+eJCYmNsn2elaboux028FSk4WiCgvlZgsmq73CRQFGvQ5fgz2hdaEqLUc1lr9RT/sQf2JaBeJncG81lsVi4fDhw+zfv5/9+/eTmJjI/v37OXTokLNdbG2Fh4cTERFB69atiYiIqHb9jz8jIiKckzPy8/PZsGED69atY926dezcudM5ixDsn/O+ffs6K7QuvvgSBg9uQ2YmbNgAw4bZb2ez2UhKSmL9+vWsW7eO9evXc/z48WoxBgQEkJCQwCWXXMIll1xCQkICQUFBZ70mBQUFzoRWfn6+8/LHf5/5u9q8XgEBAfTo0YNevXo5k1rx8fF06dKl3m0LLVYbqfnlHC+qoMpso2WgER+9rlpbQLNN4Xu6LWCQn4FwDdsC1lZWVhadOnXCYDCQnZ1NeHi41iG5nYxpF+ZJ49natWsZNWoU7du3Jz093ev2v7xZfn4+H3zwAf/617/Izc0F7G3T77nnHu644w6XH9Ps3AkDB0LHjnDkCLh6BYCsrCz++c9/8uGHH2IymdDr9UybNo1//OMfsuh9E/Hggw/y1ltvce+99/LOO+9oHY5LyHhWM08a0+pi0aJF3HTTTXTv3p0DBw54dFvupkYpxddff819991HTk4ORqORhITHuP/+B5g8WfvzbRYLXHIJnDgBr7wCEya4fkyrC6UU06b9yKpVd3PiRBp6vZ57772X559//qzjP9Ewo0ePZs2aNbz22ms8/PDDWofjcpLAcqNbbrmFzz77TGb9NSF5eXksWrSIBQsWVEvydOrUiVtuuYVbbrmF+Ph4tz3+O+/AE0/A++/Drbe67WHYt28fc+bMYcGCBZw6dQqwt4eaPHkys2bNYtSoUV7VC7Wpu+222/jkk094/PHHeemll7QOx+Xk4Khmnjymmc1mOnXqRG5uLhs2bGDEiBFah1QjpRRVVnvrwZIqC6Wnq7SqLDbMVnsiQa8D4xlVWmeuT6SUoqTKSpXVRqtAX2JaBRLRouHVWDabjfT0dGeiynFJTk4+q3UR2NsjBQW1w8cngvbtW9O9ewSdOp07EdW6dWvCw8NddoK2uLiYTZs2ORNaW7duda414tCiRQ+s1pG8/PIIzOYTrF+/ng0bNjjHHYewsDAuvvhiZ8Jq4MCBbpkRppQiI6OY77/P55dfTrJ5cz7Fxfncc89JKivTSExM5MCBA2RlZZ1ze39/f3r06FEtqRUfH09sbOwFD/xLqyyk5JWSW1KFz+kEVdUf2gL6G31oFehLiL+B0CbQFrC2XnvtNf72t79x3XXX8b///U/rcBqFjGkX5knjmVKKuLg4Dh065HULWXurlJQU3nrrLebPn+/sLNGnTx8eeughbrzxRrfNJk5Ls1cT9+sHP/3klocA7Otwvfjii8yZMweLxYKPjw8zZszgySeflDaXGrJarXTq1Ins7Gx+++03hgwZonVILiHjWc08aUyrC4vFQrdu3UhLS5NlTVwoPT2du+++m++//x6AESNG8OGHH7JwYTxffgn79kEdGje5hdVqvzSl4pvp0yE5uYxLL32Gt956E5vNRlRUFO+//77XdSfSytatWxk6dCghISEcO3aMkJAQrUNyOUlgudG6deu49NJLvbbqwhNYLPDf/1ZRVbWM1asX8NNPPznXtQoODub6669n+vTpjBw5stESOlVV0FhLdFRVVbF06VLmzJnDL7/84vw8xcTEcNtttzFz5kw6dOjQOME0UyUlJbRr146ysjJSUlLo1q2b1iG5nBwc1czTx7THHnuMV155hZkzZ/LJJ59oHU69WG2KcrOV0ioLpSYrxRVmykxWTFYbJqsNmwKDXudMaBl9dJhtisIKC4FGH9qH+hET3gJfQ81jhVKKrKysatVU+/fv58CBA5SVlZ1zm86dO9O7d2969+5Nr1696N27Nz169MDXN4DrroNly+y3mzULPvig8WfSVVRU8NtvvzkTWps2bXKeXPyj9u3bM3LkSGfCqlevXm4fY5WyzzJ84glwFI75+sLKlXDppdVvW1hYyIEDBzhw4IAzqZWYmEhmZuY579vPz4+4uLhqSa1evXoRExNDfqWN1JNl5JebsNlOr8dm0OPnaAvobyTU30CQn6FaktQTKKW46KKLSExMZNmyZVx77bVah9QoZEy7ME8bz1599VUeffRRJkyYwNKlS7UOR5zh55+hvByuvVaxfv163njjDb799lvne2v8+PE89NBDjBkzplES/jZbzQvWu0paWhrPP/888+fPx2q1YjAYmDVrFk888QSdOnUCGveYsblzrBkUGxvLoUOHPGKCSW3IeFYzTxvT6uLdd9/lvvvuY9iwYWzatEnrcDyaxWLhvffe46mnnqKsrIzQ0FBeffVVZs+ejV6v529/g9deg0cegVdf1TrapmfECNi0yT7uh4fv5Pbbb2fHjh0A3HDDDbzzzjtERkZqHKVnu/766/nf//7Ho48+yssvv6x1OG4hCSw3UkrRs2dPDh48yNKlS5kwYYLWITU799+/mXfemQmkAPZ1Nq688kqmT5/OtddeS0BAgKbxNaa0tDQ++eQTPvnkE44dOwaATqdHp7uezz9/l6lT22gcoXeaM2cOs2fP5pJLLmHdunVah+MWcnBUM08f0w4fPky3bt0ICAggOzub0NBQrUNqMKUUJqui1GQ5XalldlZpmU63e9NhT0hUWezrakUE+RLTqgWtAo3O9/3JkyfPav23f//+867hFBkZ6UxUOZJV8fHxF5wlVV4Oo0fD9u3w7rtw991ueEHqyGQysWPHDtatW8fmzZtp3bq1M2HVpUsXTU7+ZGdDXByUlIBOB4sWwQ031H77oqIikpKSqiW1Dhw44Bwz/0in0zFw+EhemPMVfj72toAtA42E+hsJ8TPUKtnZlO3cuZOBAwfSunVrsrKyMGrZf6QRyZh2YZ42nuXk5NCpUyeUUhw7dox27dppHZIA8vOhQweoqipk5MibWbfuB8A+YWD69Ok88MADbu2K0VQcPnyY5557js8++wybzYavry+3334711zzd6ZPb8fYsfDFF1pH6f0c3TKefvppnn32Wa3DcRkZz2rmaWNaXZSWlhIVFUVBQYHHdNFoinbt2sVf/vIXZ8Ll+uuv55133qm2P/Haa7B5M9x+O1xxhf04RPwuLg7S0+Hhh+GFF35PCD755JOUl5cTFhbGq6++yqxZs6RTVD0cPnyY7t27YzQaOXr0KO3bt9c6JLdo0JimlHLbBVD2h/Bsb7zxhgLUVVddpXUozUpFRYV65JFHlF6vV4Bq0yZOvfPOOyo3N1fr0DRnsVjUTz/9pK6//nql0xkVoIKCItTSpUu1Ds0rDR8+XAFq3rx5WofiNmd8X7t1XPDkizeMaaNHj1aAev/997UOxW2sNpsqqTSr7OIKdSivVO04XqDWHj6pfj54Qn1/IEct25etHnz+Ded7/kKXli1bqksuuUT93//9n/r3v/+t1q5dq06ePFnv2HJzlXrtNaVMJhc+YS9jsyn1wgtKBQQo9dZbrrvfoqIiNX/+fNWhQ4ez/p87de6isooqVGmVWdlsNtc9aBNw7733KkDde++9WofSqGRM877xbNKkSQpQL730ktahiDOMH39QBQbGOcfMp59+WuXk5GgdliaSkpLUjTfeqE6fTFd+fgEKvlG9emkdmferqKhQISEhClDJyclah+NSMp5555hWF0888YQC1IQJE7QOxeOUlpaqhx56yHlOsVOnTurbb789522t1kYOzoPYbEplZiqVmnr2344eParGjx/v/K4aOXKkSkpKavwgPdydd96pADVr1iytQ3GrhoxpUoFVCydPnqRDhw6YzWbS0tKIiorSOiSvt3XrVmbOnElSUtLpct6/8Y9//AN/f3+tQ2tydu9OZ9asP7Nz52oAZs6cydtvv+0V1RVNQVJSEvHx8QQFBZGTk0OLFi20DsktZHZfzbxhTPv888+5+eabGTBggHMGWnNgstgoM9nbDpZUWhjcrQPlpSVn3S4wMJC77rqLcePG0bt3byIjI11ehaSUzOirjX//u2FVajabjaysLFJSUvj8889ZtmwZJ0+ePOt2ISHhHDiw1ytb8ZrNZtq3b8/JkyfZsWMHAwYM0DqkRiNj2oV54nj2448/ctVVVxEbG0tKSorM7m0CVq5cydSpUyksLKRPnz4sX76czp07ax2W5vbv38+TTz7JsmXL0Ov1vPXWh9x77yytw/Jq33zzDVOmTGHgwIFs375d63BcSsazmnnimFYXubm5dO7cGZPJRFJSEnFxcVqH5BF+/PFH/u///o/09HT0ej333Xcfzz33HEFBQW59XLPZTGpqKgcPHiQ5OZnMzEzatm1LVFQUUVFRdOrUiY4dO7psPUilFIWFhRw7doxjx45x/PhxMjMzadmyJT179iQ+Pp5OnTq5tbOGUoqvvvqKe++9lxMnTuDr68sTTzzBo48+ip/00a2R4zNeVVVFUlISPXr00Dokt5EWgo1g2rRpfPHFF15Xkt7UVFVV8eyzz/LKK69gs9mIi4tj/vz5DB06VOvQmjSbzcZ7773HY489RmVlJVFRUcybN4/Ro0drHZrH+9vf/sZrr73G7Nmz+eijj7QOx23k4Khm3jCmVVZW0qFDB06dOtXsTmif6bvvvuNPf/rTef/eokULevfuzZgxY+jTpw/t27enQ4cOtG/fvlm1rW3KlFLk5eVx9OhR0tLSOHr0aLVLeno6ZrP5rO1at25LVVUnSkq2o9MZ2bBhLcOHD9PgGbjf8uXLmTBhAr169WLfvn1esx5IbciYdmGeOJ5ZrVa6dOnCsWPH+PXXXxkzZozWITVbSineffddHnzwQWw2G5MmTeLTTz91+0lBT6KU4vnnn+cf//gHYF/H7ZFHHtE4Ku81efJklixZwptvvskDDzygdTguJeNZzTxxTKurO+64gw8//JC//OUvfPjhh1qH06Tl5uZy//33s2jRIgD69evHRx99xKBBg1z6OAUFBc4k1ZmX1NRULBbLBbfV6XRERkY6E1qO5NaZ/46IiECn01FUVFQtOXWu6+Xl5Rd8vKCgIGcy68xLdHS0SycEnTp1ikcffZSPP/4YgJ49e/Lhhx9y8cUXu+wxvNFTTz3FCy+80CzWepUEViNYs2YNo0ePpkOHDqSlpWEwGLQOyevs2LGDmTNnsn//fnQ6HQ899BDPPfecnCysg6SkJG699VbnzLP777+fl156SV7DejKbzXTq1Inc3Fw2b95MQkKC1iG5jRwc1cxbxrT777+fd955hzvvvJMPPvhA63A0YzabGT/+en79dRkAYWH9sFqPUlJSdMHtWrZsSYcOHZwJrXNdj4iIwMfHpzGehlcrLCw8Z3IqLS2NtLQ0ysrKanU/LVqEcs014+nW7Vlef72MysrhQCV//vN/mDv3Dvc+CQ1dd911LF68uFmeOJUx7cI8dTx75plnePbZZ7npppv4/PPPtQ6nWTKZTNx9993Ok1NPPfUUzzzzjFTEnce//vUv7rnnHsA+Ke7ll19uVpMJGkNhYSFt27bFbDZz/Phxr1s3RMazmnnqmFYXKSkp9OjRA19fX9LS0oiMjNQ6pCbHZrMxd+5cHnnkEQoLCwkMDOS5557jvvvuq/f5W5vNRnp6OsnJyWclq3Jzc8+5jU6no3PnzvTo0YMePXqwalVHrrzyBBkZGWRkZHDs2DEyMzOx2WwXfGw/Pz98fX0pKTm7a8gfBQUF0alTJ2d1l1IdSE4+iZ9fEgcOHODEiRPn3C4gIIC4uLizEluxsbENOue9Zs0a7rjjDlJSUgB7Avbll18mLCys3vfprc5c527jxo0MHz5c65DcShJYjUApRVxcHIcOHWL58uUXnLkt6sZkMvHCCy/w0ksvYbVa6datG5988oksUFlPZrOZl156ieeffx6r1UqPHj1YsGCBy2ecNAfLli1j4sSJ9OzZk8TERK8+4JSDo5p5y5i2f/9+LrroIoKDg8nOzvbatpi1UVlZSY8e15Ke/jPQEVjPhAkBdOz4H1avXsaBA3sBKwA6nR6djhoPNgB8fHxo167deRNcjushISFufX5NXXl5ebUE1R+TVYWFhRfcPiwsjC5dutClSxcCAgJISkpi9+7dZ/wfXca4cQ/z7rvj+MtfdKxffxIYBKTj7z+LgoKP8Pf3zq+8U6dOERkZidVq5dixY153Qq8mMqZdmKeOZ+np6XTp0gWj0UhWVhatWrXSOqRmJS8vj+uuu47169fj7+/PvHnzmDp1qtZhNXmff/45M2bMwGKxMHv2bP7zn//IJBcXmjNnDrNnz2bs2LH88ssvWofjcjKe1cxTx7S6mjRpEkuXLuXvf/87L774otbhNCnJycncfvvtrF+/HoArr7yS999/ny5dutRq+7KyMlJSUs6qpkpJSaGysvKc2wQGBhIXF+dMVDku3bp1c04gP3EC2raF3buhb9/ft7VYLGRlZTkTWmcmtxzXCwoKnI9zZnLqXNdDQkKqnatavRquuQaSkiAqyr4sTlKSPZl15iUrK+ucz81oNFZLbDmqt7p161brloCVlZW89NJLvPzyy5jNZiIjI3nvvfe47rrrvPq8Wl2988473H///YwYMYINGzZoHY7bSQKrkbz++us88sgjXHPNNXz77bdah+MV9uzZw4wZM9izZw86nY777ruPF198kcDAQK1D83jbt29n+vTpJCcn4+Pjw1NPPcXf//53jEaj1qF5jAkTJrB8+XJee+01Hn74Ya3DcSs5OKqZN41pw4YNY8uWLXzyySfMnDlT63A0lZVVRufOV2KxbABigPXo9e256Sa46KIiHnvsC2AO8PuaCt26dWfMmNHExcVRVFREZmYmmZmZZGVlkZmZec61ls4lKCjonImtM38XGRnpsh7pjc1kMpGRkXHO5FRaWtp5Zy46BAYG0qVLF6Kjo52JKsclOjqakJAQfvjhB9544w3WrFlzeisDcCPwEOHh/bj3Xnj5ZaistADjgV+AwTzxxDpeeMF719V8//33ufvuu7niiiv46aeftA6n0cmYdmGePJ5deeWVrFixgrfffpv77rtP63Cajb1793LttdeSnp5Ohw4dWLZsGQMHDtQ6LI/xww8/MGXKFCoqKpg8eTKff/65rAviImPHjmXVqlXMmTOH2267TetwXE7Gs5p58phWF5s2bWLEiBG0bNmSjIwMadsK/Prrr3z11VfMmzcPk8lEmzZteOedd5g6depZSRKlFNnZ2c7k1JkVVRkZGed9jPbt2zuTU2cmrDp27Fhj9fGSJTB5Mjz0ELz+et2e244dJTzxhIUffwyrc8Lnp59g/Hi49lpYtuz8tysqKjpnYis9Pf2ct/fx8aFr165nVWzFxcWdt+tTYmIit99+O5s2bQLs+3H/+te/iI2NrdNz8kZms5muXbuSkZHB0qVLmTBhgtYhuZ0ksBpJXl4eHTp0wGq1kp6eTseOHbUOyWOZzWZefvllnnvuOSwWCzExMXzyySeMHDlS69C8SkVFBX//+995++23ARg0aBALFizw6kUBXSUnJ4eOHTui0+k4fvw4bdu21Tokt5KDo5p505g2d+5cZs2axfDhw9m4caPW4WjunXeKuP/+scAOoCewhYZBBAAAjmRJREFUFohAr4du3eDgQYC9wFxgIZAPgNHoy+TJk5g1axZjx451HsRUVVWRnZ19VmLL8dNxqaioqFV8bdq0OW8Vl+Nnq1atNJnNVlZWxuHDh0lJSSElJYVDhw45k1Q1tccwGo107tz5nMmpLl26OHu//1FlZSULFy7kjTfeIDk5+fRvg4E7gHuBTgB06QJHjzq2ehR4FWiDwbCd48c74c1f6wkJCfz222989tlnTJs2TetwGp2MaRfmyePZN998w5QpU+jduzd79+6VWbyNYOnSpdxyyy2UlZUxZMgQli5dSrt27dzyWCdOnGDdunWsXbuWffv2ERoaSkREBG3atCEiIqLadcfPukzyUEpRVlZGUVFRtUtxcTFFRUUopRg5ciRxcXEuf29t2LCBa665hqKiIsaOHcuSJUsIDg526WM0N5mZmXTq1AlfX19yc3MJDQ3VOiSXk/GsZp48ptXViBEj2LRpk0zi4PeuImfq3Lkz3bp1o0ePHkRHR1NZWVktUXW+dnxGo9G53ZmXuLi4BnXMeOghePNNiIyEY8egLl35vv4apk6FQ4egrrmeZctg4kT79aVLoa55kdLSUpKTk89KbqWmpp7zc6bT6ejSpUu1pFZMTAxHjhxh3bp17Nmzh927d2O12jub6PV6SktLm/1SJ5999hm33HILcXFxHDhwoFm0Y5YEViO68cYb+fLLL3n22Wd5+umntQ7HI+3fv58ZM2awc+dOAP7617/y8ssvN0obK6UU+fn57Nt3hHnzjtKhQxFDh7alffv2tGvXjrZt27qtQslsNpOfn8+WLSdZvz6Ap5/uTGho46yltnr1ambOnElGRgb+/v68/PLL3HPPPc3iC7K+Xn31VR599FEmTpzIkiVLtA7H7eTgqGbeNKaVlpbSvn17SkpK2L9/P7169dI6JE1ZrdC3bz6JiaOA/UB/YBUQBkBgIPy+Nm4VsBx7VdZKwP5+aN8+ir/85c/8+c9/pnPnzjU+plLKWb11ZmLrj9dzcnJq1bbQYPAnOjqKLl06OxcB7ty5s/Nnx44d613JZTabSUtLcyapzrwcP378vNvp9Xo6dux4VmLKcWnfvn2dxqH8/Hw++OAD3nvvPWcvd52uI0rdD8wGfj9x1b69vWXHnj1gs30FTAV8gF+ZPv1SPv20Pq+EZzh48CA9evQgODiYnJycZlnVLmPahXnyeGYymejYsSN5eXlevz6p1pRSvPTSSzz55JMA3HLLLXz00Uf4+7uuejUrK8uZsFq7di1JSUl1vg9HksuR1GrVqhWVlZXOpNQfE1W1GVO7dOnC+PHjGT9+PKNHj3bZcerevXsZN24cubm5DB48mB9++IHWrVu75L6bozfffJOHHnqIyZMn880332gdjlvIeFYzTx7T6mrp0qVMmjSJzp07c/jw4QatU+TpSkpK6pxcCg8Pp2fPnmclqqKjo93yWg4dClu32q//+CNceWXtt336aXj+ebjvPjg9H73WvvwSbrzRfj0uDrZvB1cU7FVUVJCSkuJMaDkSXIcOHcJisdT6foKCWlFcnNesJyEppejXrx979+7l448/ZtasWVqH1CgkgdWIVq1axdixY+nUqRNHjx6V/tV1YLFYePXVV3nmmWcwm81ER0czd+5cRo8e7dLHKS8vr9am6MiRI9Wul5aWnndbnU5HRESEc+2Sdu3aVbvu+Onr2xZf3wpOnTpJXl4eJ0+e5OTJC18vKir6w2P50KVLZ2JjY4mNjSUmJqbadVfPyCsqKuL+++9n3rx5AIwePZp58+YRFRXl0sfxBkopevbsycGDB5vNmndycFQzbxvT7rjjDj788EPuv/9+3nrrLa3D0dzq1TBmTA5wCXAYGAasZPnyIOLjYcAAKC7+41bH8Pefh7//XAoL0wD7Z2ns2LHMmjWLiRMnNvhEn8ViITc395xVXI7rKSmZWCxFF7wfnU5HZGRktaRWVFQUHTtG0apVZ3r3jqK8vPycSaojR46c96DEPukjFl/f7lx3XXdGjOhGbGws0dHRzlnRDZWamspbb73F3LlznVVrbdr0o7T0YcrLbwCqTzzx8YGdO6FTJ+jffz/p6QlAGfA2cB/bt4M3d7564okneOmll7jtttuYM2eO1uFoQsa0C/P08exvf/sbr732GrNmzeLjjz/WOhyvVFFRwW233caiRYvQ6XT885//5G9/+1uDTzZlZGQ4k1Vr167l8OHD1f5uNBoxm1sDevz8TmEw6PH19cVoNDonPFitViwWC1VVVVRUVNT5fezj44PBYECv16PT6bDZbNhsNsxmC0rpAR1gdt7ez8+PSy+9lKuuuorx48fTrVu3Br0OqampXH755Rw9epSePXuycuVK6exSTwMHDmTnzp188803TJ48Wetw3ELGs5p5+phWFzabjZ49e5KSksLnn3/OTTfdpHVImrr//kN89dV1ZGfvA+BPf/oTOTk5pycBnsRqrXLeNjg4mPHjxzNhwgTGjx9Py5Yt3RqbxQIffwz79sGYMfbjkiFDar/9pEn26qngYMjMtP+srUWL7MdCOTnw4Yf237lw7kk1mZmZ/O9//2PRokVs3bq12iQRPz8/2rZtS0xMDNu3b6e0tJQOHWKJiNjIrl1e3AqjFlauXMkVV1xBZGQkaWlpzaatcIPGNKWU2y7YpyUrb2K1WlVsbKwC1Pfff691OB4jMTFRDRo0SDneE3feeacqLi6u131ZLBaVlpamVq1apebMmaOeeOIJNW3aNDVs2DDVtm1b52Oc7xISEqLi4/uq1q0nqWHDblNXX321GjBggIqMjFR6vb7G7et70ev1KiIiQrVv30P5+nZUp3e0znuJiIhQCQkJ6uabb1ZPP/20mjdvnlq/fr3KyspSNput3v8XS5YsUREREc7XYt68eQ26P2+0ceNGBajIyEhlNpu1DqdRnPF97dZxwZMv3jambdu2TQEqPDxcVVZWah1OkzBpklKQriDq9GdijIqMrFCJiUp9/bVScPbl/vuVMpms6tdff1XTpk1Tfn5+zs9Ty5Yt1V//+le1a9cut8b95ZdKXXxxkVq0aL/6/vvv1QcffKAef/xxNW3aNHXxxRerqKgo5ePjU+/xS6fTqc6dO6vLL79c3X333eqdd95RP/74o0pNTVVms1lt2KBUPYf0C9q8ebO67rrrqo2XV155pfrll1+UzWZTx48r1abN2f8nDz6olMmk1MiRBQq6KkAZjTcrsKnhw10fZ1NitVpVp06dFKDWrFmjdTiakTHNu8ez5ORkBagWLVrU+3hCnN/x48edx21BQUFq+fLl9bofm82mDh8+rObOnatmzJihoqOjzxpffHx8VGBgoNuOv9xxiY2NVX/961/V999/r8rKyur12mRmZqrevXsrQEVFRank5OR63U9zlpSUpAAVGhqqKioqtA7HbWQ88/4xra4+/PBDBaj+/fs36/M4NptS3bopNX58vvP7dODAgaqwsFAppdS0aVZ15ZW/qb///Qnn3x0Xg8GgxowZo95++2115MgRjZ/JuY0Zo1RYmFKTJyv1889129ZicU9MDklJSeqll15SgwcPPmtMHzt2rHriifdUaGiGUsp+bHLjjTcqQLVp00a9+OJhBUpt2eLeGJu6sWPHKkC9/PLLWofSqBoypkkFVj288sorPPbYY0yYMIGlS5dqHU6Tc+oU/OtfcNllMHSolTfeeIOnnnoKk8lEp06dmDNnDu3bX05IiH0Wwh8pZW/z56ic+mMFVUZGxgXLUx1rasTExDjbFJ15PTw8/Lyz5iwWC3l5eWRlZZGdnU12drbzuuPn0aPZ5Ofn4OMTSOfOEbRu3dp5iYiIOOf11q1b07Jly2qtkiorK0lLSyM1NdV5OXLkiPNnVVXVOWMECAgIqFaxdWblVnR09HlnvOfm2lsqnThxgjvuuMP5/p04cSKPP/5f/vvfNtxxR91mhnijWbNmMXfuXB599FFefvllrcNpFDK7r2beNqYppRgwYAC7d+/miy++4EZHn4Fm7MgR6NkTLJZD+PmNpKIiB7ia8PDFHDjgywsv2Me3Pxo5Ev73P4iIgIKCAr744gvmzJnjbJUL0L9/f2bNmsW0adPcPuPvXCwWC1lZWWRkZJCenk5GRgYZGRl89106x49n4O+fQVCQH3FxcXTv3r3aJTY2ttF6lFutVr799ltef/115/psRqORm2++mYceeojevXsDkJ5ub8PhXALrtA4dICkJ3njDxrPP/gn4geDgfixZspHLLgvkyy/hhhsa5alowtEpIDo6mtTU1GbbKljGtAvzhvFs5MiRrF+/ng8//JC//OUvWofjcYqK4FzLBW3dupWJEyeSnZ1NTEwMy5cvr3WbYaUUKSkpzuqqNWvWkJWVVe02Op3unO87o9FI79696devH3379uXo0X6Ulvbm1Vd1lJSUUFxcTHFxsfP6uX6eOlXM99+XUFFRwqWX+hMREUJwcDAhISHOy4X+XVERzJgxp3jxxY1s2rSBDRs2VFuz41x8fX0ZPXo0V199NePHj6dr1661eq0ATp06xTXXXMPmzZuJiIjgp59+YsCAAWfd7sQJaNOm1nfbbDz99NM8//zzXl9tLONZzbxhTKuLyspKoqOjyc3N5ZdffmHs2LFah6SJ336DhARo1Qr27s1m5MhLSE1N5ZJLLuGnn37ipZcCefFFePZZezu+I0eOsHz5cpYvX866deuqfbdfdNFFTJgwgWuvvZaBAwc2if3nsjKw2eyt/7TutKeUYtu2bSxdupQlS5acsQax/dzkFVdcwaRJk7jmmmsICgonIcG+DvGpU4r777+fd999l+DgYNauXcsnn/Tnvfdg2jT47DMNn5SGduzYwaBBgwgODiYjI4OwsDCtQ2o0UoHVyHJycpTBYFA+Pj7q+PHjWofT5MyZoxQoFRh4ZbVsfPv27dULL7ygHn30XQXvqaCgf6u33npbvfDCC+q+++5TEyZMUBdddJEKCgqqceZbu3bt1IgRI9Qtt9yinnrqKfXJJ5+oNWvWqPT0dGVx93QDpdSRIzZVVOS++7darerYsWNq7dq1au7cueqJJ55QN954oxoyZIhq1arVBV8bvV6vOnfurMaMGaNmz56tXnrpJTVv3jw1c+bXCv6rLr30WXXXXXepG264QUVGRp6xnZ+CT9VNN7nveXmCkpIS1aJFCwWogwcPah1Ooznj+1rzWXRN9eKNY9q//vUvBagxY8ZoHUqT8eijSo0apdTevftUQID9+7Znz+uVxWJRlZVKDRhgH+Ouvlqp6Gj79T59lDrX5N9du3ape+65R7Vs2dL5GfPz81PTpk1Tv/zyi7JarY3/BP+grEypw4fdP1Ov5jjK1AcffKC6devmfK3CwsLUY489pjIzM8+6/a+/KmUw/P5/0bWr/frXX9v//sgjT50e28LVnj1HlVJKTZlir8zyZrfeeqsC1FNPPaV1KJqSMc37x7NPP/1UAWrIkCFah+JRCguV+vvflbrllrP/tnDhQmcVcWzsKJWXl3fB+7JarWr//v3q3//+t5o4caIKDw+vVQVTy5Yt1ZgxY9QDDzyg5s+fr/bs2aOqqqqc95ucrFRAgL2atq527lQqKKh+Y9qaNfZx5Lvvfv9dSUmJ+uWXX9QzzzyjLrvsMucxwvkuHTp0UHfddZf68ccfVXl5eY2PWVpaqoYOvUIBKjg4WK1evfqs21x+uVL/939KHTtW9+fkrWw2m4qJiVGA+uWXX7QOx61kPGseY1pdvfDCCwpQ48aN0zoUzezZo9Sbbyr1zjtK5ecrdfToUdWhQwcFqPHjx6v3369S7dvbb3PGEKOUUio/P18tXLhQ3XDDDSo4OPis85Z33HGH+uGHH7y6urMmJpNJ/fLLL+ruu+92vq5njuO33nqrWrJkyVmVyJs324/LYmKU+uc//6kA5evrq3799VellFI//KDUP/+p1P799iq65mjq1KkKUA8//LDWoTS6hoxpMpDU05QpUxSgnn/+ea1DaXIyM5UaNmx1rQ5gzncxGo0qMjJS9evXT02YMEH97W9/U59++qnat29frQ4GmrqCgoZtX1hYqLZv367mzp2r7r//fjV+/HgVHx+vwsLCGvS6t2o1SO3d65Kn6LHmzp2rAHXxxRdrHUqjkoOj5jmmFRQUqICAAAWow4cPax1Ok1BUpNRnn9mvb9u2XQUGhihAzZw5U1mtVnX4sFIhIUp9+qn9u3z6dPvJtgupqKhQixYtUpdffnm1dnjR0dHq2WefVenp6W5/Xu5W3wRYbm6u+sc//qFat25d7XV55513VElJyQW3XbhQqdmzlTKblbrnHqWuvNJ+ILR06VLnhI4vvljpvH09Oz15jDMnYBw6dEjrcDQlY5r3j2dlZWUqNDRUAWrPnj1ah+MRbDal/vEPpUApo1GpEyfsv7dareqxxx5zfm58fe9UgYEmlZVVfXur1ap27typnn76aTV06NBatf6LjY1VkydPVs8995xavny5ysjIuGDLK5NJqcGD7TE+9ljdn+Pjj9u3rc8ctP/8x77tqFHnv43ZbFY7duxQ77zzjrr++usv2L7eYDCoQYMGqVdeeUWlpqae9z4nTqxSMNU5yWXp0qXOv2Vl2WMCpW680T3tej3Rli1bFNgntTbG5FUtyXjWPMa0usrPz3fu8+3evVvrcJqMpKQk5zHFtdder0ymmr8fKisr1YoVK9Rdd92lOnbsWO17vEWLFuq6665T8+fPVydPnmyEZ6CtsrIytXjxYjV9+vRqEzAdEzTuvvtu9csvvyhTDTMCy8qUevVV+3k1nU6nvvzyy0Z6Bk1famqq0uv1ymg0qmPNcGaKJLA0sHLlSgWozp07e/1OU31UVVWdtRMfGhqqQkNDVUhIiAoODlbBwcEqKChI+fv71zrBotfrVdu2bVX//v3VVVddpWbNmqWeeuop9f7776slS5ao3377TWVkZNT4hbp2rX2tk61bG+kFOcNzzynl46PUhx+e++9Wq1Xl5OSoXbt2qe+//159/PHH6vnnn1d33XWXmjRpkho6dKiKiopSvr6+tX7dfHx8lK+vrwoMbKFatWqloqKiVHx8vHM9N0B17dq1WX6B/tGIESMUoObOnat1KI1KDo6a75jmqNh4/PHHtQ6lSVq/fr3zJN1f//pXZbPZ1Ndf22d510daWpp65plnVOfOnZ2fO51Op6644gr15ZdfeuR6ZBkZSj388NmzGy8kOTlZ3X777dX2AQYNGqS+/PLLeq09+PPPSh06ZL9fx0zK5tZTfP78+QpQw719oa9akDGteYxnd999twLUPffco3UoHiM/X6mpU5UCpV57Tani4mL1pz/9yXm88O67/1Zz5ijVqZNVXXfdYbVw4UI1depU1aVLF2UwGC6YrOnVq5e67bbb1HvvvafWr1+viurRrmLePKU6dLDH9/TTddu2rEyp8HD7tosX1/mh1SOPKNWqlVKjRyt19GjttrHZbOrIkSPq008/VbNnz1ZdunQ572sUGhqqrrjiCjV//nznhMz0dKX0eqW6d7eoiRPvdP4/zJs3Tyml1Acf2J9PTIxS+/bV/Tl5q3vuuUcB6sH6lOl5GBnPms+YVlf33nuvAtQt5yqpbcZ27NihQkLsExBnzZpVp3XCbDab2rFjh/rHP/6h+vfvf9a5yJEjR6o33njDIyaKVVTUrsIpPz9fzZ8/X02cONE5sdVx6dGjh3r88cfV1q1b6/Q6Ll++3Ln+8nvvvdeAZ+F9HPuuM2fO1DoUTTRkTJM1sOrJZrPRtWtXjh49yo8//siVV16pdUhNjtls5qOPPuLee+/FarVy9dVX8/nnnxMSEnLWbauqqsjJyTlr3ak/Xj9x4kStHz8iIoJ27drRrl072rdvX+36v//djlWr2nH77ZH897/+dXpeFRWwYAFcey1ERtZ+u6qqKnJzc3nwwRy++SaHCROy6dcv+6znmZube8Fe62cKDQ2t9tzOvJz5++Dg4LO2/e233xg/fjwFBQWMHj2aZcuWnfN2zcnBgwfp0aMHQUFBZGdnExQUpHVIjUb6q9fMW8e0DRs2cMkllxAZGUlGRgZGo1HrkJqcX375hauvvhqTycSjjz7KP//5T0DXoH7kNpuNX3/9lblz57J48WJMJhMArVq14pZbbuG2226jT58+Nd7PsmWQmmrv/z56NERF1T+m+khNhfh4sFjg889h6tTz31YpxYYNG3j99ddZvny58/d/+tOfePjhh7nkkkvOu0ZlbRQXFzN06FCSk5OZMmUKX331VYPuz9Ncdtll/Prrr/z3v//l9ttv1zocTcmYdmHeMp7t3r2b/v37ExYWRlZWVqOt1ecNPv8c/u//rqC4eKXzd3q9Hn9/f6qqqi54LOLn50dsbCzDhg1j1KhR9O/fn7i4OAwGg0tiW7cODh2CgAD7Ghm19d//wp132q8/9xw89VTdHre42D6WhYaCj0/dtj3TqVOn2LRpEytWrGDFihWkpqZis9mq3Uan0xEZGYm//0W0aNGea67pRNeunfnuu++caxS/8cYbrFjxIErBokUQHl7/mLyJxWKhQ4cOnDhxgu3btzNw4ECtQ3IrGc9q5i1jWl2lpaXRtWtXdDodR44codO5FphvpjZs2MC4ceOoqKjggQce4I033qjXMUFGRgbffvsty5YtY82aNZjNZuffevbs6Vw3a+jQoedcN+vbb2HXLrjlFoiJadBTqrO//hV+/BHefReuvrr6344fP+5cz2rt2rXVxvwhQ4YwadIkJk6cSI8ePer8uJs2bWLs2LFUVlby5JNP8vzzzzf0qXiNvLw8OnfuTEVFBfv376/1GqPepCFjmiSwGuCf//wnf//735k0aRKLFy/WOpwma9WqVVx//fWcOnWKnj17snz58jotcHsms9lMbm5ujYmu3Nzcsw4UzicoKIiIiIhzXlq3bn3W7559Nog33tAxfTp89JE9KeW45OTknPd6UVFRrZ9n69atL5iQclzqe6D+66+/MmHCBMrKypgwYQKLFi3C379uiTxv9Oijj/Lqq68ya9YsPv74Y63DaVRycFQzbx3TlFLEx8eTnJzMkiVLmDhxotYhNUnLly9n8uTJWK1WXnjhBZ544on/b++uw6LO3jaA3zN0iGCCCbZid69r6+raumvn2l1Yi+2ai11rr93t2t1dGIAgJiAlMDB13j/4Ma8oguR3Zrg/1zUXiMzMDQPzcOY5kWa3HRwcjK1bt2LdunV48OCB7uOVK1dGnz598Ntvv333cNd//wW6dYs93HfRImDEiOTdd1gYcOUKYG4O1K0b+/ZHhYQArVvHvtgIxDbQzp799vPUajX279+PBQsW4ObNmwBiXwDt3r07Ro0alaLB0de0Wi3at2+P/fv3w9XVFdevX89UkxD8/f1RsGBBmJub48OHD5nqMOCEsKYlzpjqWeXKlXHnzh38+++/6NKli9RxDIZarf6hCSvm5ubIkycPKleujF9++QWNGzeGk5OTXk4OuHABuHQJePcOKFoUGDlS6kSxoqOjcePGDWzfvh1nzpzBq1evfniyYpkyPXD37kakUW/QKPz3339o2rQpihcvDk9PT738WUxLrGdJM6aally///47duzYgVGjRmHhwoVSx9Er//33H1q2bAmVSoVp06bhzz//TNXthYWF4cSJEzh06BCOHj0a7/W93Llzo0WLFmjVqhUaNmyoe51u0SJg7Fjg/n2gTJnk3Z+fH/D+PVChAmBhkfy8/frFjhOfPgVcXABPT09d0+rWrVu6zzM1NUW9evXQunVrtGrVCvny5Uv+nf3PkydPUKdOHYSEhKBv375Ys2aN0T9HJ8fUqVMxbdo0tGjRAocPH5Y6jiRSVdNSsmzrRy8w8qW879+/F6ampsLExES8+3qTcIrHy8tLlCpVSgCxB/6l92GrarVavHv3Tty5c0ccPnxYrFmzRkybNk30799f/Prrr6JKlSoib968iW6F8b2LmZmFMDXNK6ytk3felKmpqcibN6+oVKlSgtsfXr9+Xfj5+cU7xDg97Nu3T7f9YLdu3VK0VZMxUiqVun3sr1y5InWcDPfF87Xk20Do68WYa9qCBQsEAPHLL79IHUWvbd++XXeGlYeHR7rcx507d8SgQYN0Z7sAEJaWlqJr167i3LlzQqPRxPv8ixeFAIRwdBQiuUdEXr0qROPGsde3sEjZQbqfPwvRv3/sbQBCeHp++X+fxZIlS+JtqZQtWzYxZcoU8eHDh+TfWSJmzZql26LpxYsXaXrbhiDuMO+OHTtKHUUvsKZlnnq2atUqAUDUS+zgIvqGRqMRdlm/HcuULFlSbN26VXz69EnqiEZLo9GIc+fOie7du4sqVaqIwoULi5w5cyZ4plgJ19JSx9U73bp1EwDEtGnTpI6SIVjPMldNS647d+4IAMLW1laEpPagdSO0e/duIZfL03zsplQqxZkzZ8SwYcOEs7NzvOdtKysr0apVK7Fu3TqxePFH0bdvyu5j+XIhgNjtbVNixAiN6Nv3hnBzcxPFixf/JmObNm3E5s2bRXBwcMru4CuvX7/WnSHWqlUrvs74lYiICJE9e3YBQFy8eFHqOJJJTU3jCqxUateuHfbt24dZs2Zh4sSJUsfRa+Hh4ejSpQuOHDkCExMTeHh4YPDgwZJ25IUQCAsLQ2BgoO4SFBQU799f/19UVJTu+qampsiVKxdy584NR0fHeG+//piDg4Pksw82btyIPn36QKvVYujQofDw8EhwqXNmdPjwYfz6668oUaIEnj59KvljldE4uy9pxlzTAgMDkTdvXmg0Gvj5+aVq5pWxW79+Pfr06QMAWLt2Lfr27Zsu96NQKLB//36sX78eZ86c0X28UKFC6N27N3r27Im8efPi9WugYMHYGX7JnW0uBFC7NnD1auyWSaGhKc97/DjQpw/w22/A2LHvsWzZMqxcuRIhISEAgMKFC2PUqFHo0aMHbGxsUn5HCThx4gSaN28OIQQOHz6MFi1apOnt6zt/f3+ULl0a4eHh2LjxOHr04LbWrGmJM6Z6Fh4eDicnJ0RFReHFixcoWrSo1JEMQqRSjVuvQxGlVEPx6iHWr1iMY8eOAYjdSrBt27YYO3YsqlatKnFS46fVarFnzx5MmzYNT58+BQDkzZcPTX/rhW59B6J2UUeYyPlUBgBRUVHInTs3IiIi8PLlyxTv6mJIWM+SZkw1LSXitpCeM2cO3NzcpI6jdzZs2IDevXsDANatW6d7P60IIfD48WMcPHgQhw4dire6SSaTIU+e/HB2zo/8+fMjX758yJ8//vu5cuVK8DW5CxeAevWA58+BYsW+vV+lUon379/j7du3CV4ePHiBsLAPus/Pli0bWrZsiTZt2qBRo0awtrZOs+/Bp0+fUKdOHXh6eqJ27do4efIkt3X+yrJlyzB06FBUr14dV69ezXSvN8bhCiwJnThxQgAQzs7O38yKpm+p1Wrh5uam67r+8ccf6b7iKK1FRkYKPz8/ERgYaFCPuYeHh+777u7unqxDGDODVq1aCQBi3rx5UkeRBFIxEyKzXIy9pnXs2DFTzWhNjcWLFwsAQiaTiW3btqX7/fn4+IgpU6boZrUBsQcJN2vWTOzcuUfkyRMjIiNTdtt37gghkwmRL1/yrxsaGipu374tdu7cKWbNmiW6dOktnJzq6lb5AhA1atQQe/fuFWq1OmUBk+Dl5SXs7e0z7c+uVqsVjRo1EgBEs2atRI4cWjF4sBCZfWMA1rTMVc969eolAIjx48dLHcVg+IdEiWOeH8XhJx+EV1CEEEKIx48fi169egkzMzPd71DdunXF4cOHDWrMYyg0Go3YtWuXcHV11X2/CxQoIFavXi1eBYSJ454fxennASI40rDGyulpx44dAoCoWrWq1FEyDOtZ5qtpyRX3mqSjo6OIjo6WOo5einstTC6Xi127dqXrfb1580asWrVKNGvWLN6Y6HsXMzMz4ezsLOrUqSM6d+4sxo0bJ5YuXSo2bz4gqlY9IdatWyemT58u+vfvL1q0aCEqVKggcuXK9UO7QOXLl08MHTpUnDlzJt1WREVERIjq1asLAKJ06dJptqLLmCgUCt1KvX379kkdR1KpqWlcgZVKWq0WhQoVgp+fH/777z80btxY6kgGYevWrejTpw9iYmJQt25d7NmzBzlz5pQ6llESQmDq1KmYPn06AMDDwwPDhw+XOJV+ef78ue4ART+/N8ib11HiRBmPs/uSZuw17dSpU2jcuDEKFCgAHx8fmKTmBPNMYPbs2Zg0aRJMTEywd+9etGrVKt3vU6PR4PTp01i3bh0OHDigO0jY3NwSefM6fXNO4tfnJ2bPnj3BGX4DBsSeG/LkSfyPCyEQGBgILy8veHt7x3vr5eWFT58+JZhTJpOhTZs2GD16NGrWrJnm34c4kZGRqFGjBh49eoRff/0V+/fvz3SriletWoWBAwcie/bsePLkCTZtyo3x4wErK+DPP4Hx42PPR8tsWNMSZ2z17OrVq6hVqxZy584Nf3//HzrbKTMTQuDhu3D4h0VDBiC/gxXKOtnp/v/du3dYsmQJVq1apTvjo2TJkhgzZgy6dOkCi5QcxkE6Wq0W+/btw7Rp0/D48WMAQP78+TFp0iT06tUL5ubm8AqKxLOACMgAlMydBYWyp91MeUPWqlUrHDp0CIsXL8awYcOkjpMhWM+SZmw1LTEaDXDzZuw5fzlyxH5MCIHy5cvj4cOH6bLCyFhMnz4d7u7uMDMzw6FDh9C0afrvWBATE4M3b97A399f9/br9783nkqKXC6Ho6Mj8ubNm+Alf/78KFq0aLqu9FGpVGjdujWOHTuGAgUK4OrVq8ibN2+63Z+hGjhwIFatWoXcuUti6dJH+OUXE6ThAjiDkpqaxgZWGpg1axYmT56MihVb4s6dQ1LHMRi3bt1Cq1at8P79exQsWBCHDh1C2bJlpY5lVLRaLUaMGIGlS5dCLpdj3bp16Nmzp9Sx9IpSqUSNGjVw9+5dAL2we/d6tG8vdaqMx8FR0oy9pmm1WhQpUgSvXr1C587LMX78IPApOXETJ07EnDlzYG5ujiNHjqBRo0YZdt9BQUHYunUr1q9fj4cPH/7QdUxNTeHo6PhNkytLFids3ZobAwcGftOkioiI+O7tWVlZoXDhwihSpEi8t66ursiTJ09afakJEkKgc+fO2LFjB4oVK4abN28ia9as6Xqf+sbHxwdly5ZFZGQkdu7ciY4dOyI6GiheHHj3LrYhmdCWI5kBa1rijK2eCSHg6uoKT09P7Nu3D23atAEAREYCabxjqVGIUmpw0z8Eao2ARitga2GKGs4OkH/1Itfnz5+xdu1aeHh4wN/fHwDg5OSEYcOGoX///nBwcJAivsHSarXYv38/pk2bhkePHgEA8uXLp2tcfdkYfPguHP6hCshkQE5bC1TOlzXTbjckRGxN02qDUbiwIzQaDd6+fQtHx8wx4ZD1LGnGVtOSMmQIsHw5kC1b7N98xYsDkZFbsHt3dzg7O+P+/fuZ7m/iHyGEwJgxY7Bo0SJYWVnhv//+Q506daSOBYVC8d0ml0Kh+G6DKnfu3DA1NZUstxACPXv2xObNm5E9e3ZcvnwZJUqUkCyPvtqxYwd+//13mJub48CB6+jZswL++AOYMUPqZNLgFoIS8/R8JwCr/21fs0HqOAbl7du3okqVKgKAsLGxyfTLKdOSUqnUHXJrbm7O7+13jBs3TgAQWbK4CAuLMHHtmtSJpPHF87Xk20Do6yUz1LSdO3f+72fBXDg63pE6jt7TarVi6NChusNwpTqQ9fPnz+LFixfiwoULYvv27WLRokVi7NixomvXrqJBgwaiVKlSwsHB4Ye2mvj6kjVrVlG5cmXRqVMnMWnSJLF+/Xpx8eJF8fbtW0m3ol24cKEAYg+tfvLkiWQ5pKLRaETdunUFANGpU6d4/7djhxBeXhIF0xOsaZmvni1atEgAEM2bN9d9bNQoIdasEeLgQSG4c/b/8w+JEsc9P4rLPkHizIsAcfZloIiI+f7WQkqlUmzZskWUK1dO97tla2srRowYIXx9fTMwuWHSaDRi7969omzZsvG2dVqxYkWC231ptFpx2eeTOPHso+7xCY9On62fDMGYMULY2wthb79aABCNGzeWOlKGYj3LnDUtMYULCxHb2o29lCkjxIYNSlG+fAUBQLRv357HRXyHVqsVffr0EQCEnZ2duHOH492UinsdzdraWly/fl3qOHrpxYsXwtbWVgAQK1asEEII8fChEOm0m6NBSE1N4wqsNFKnzgZcvtwbVlZWuHnzJkqXLi11JIOhUCjQr18/bN26FQAwY8YMTJo0KdPOMksL0dHR6NSpEw4dOgQbGxscPHgQDRo0kDqW3jl79iwaNmwIuVyOS5cuoVq1Gshku0/pcHZf0jJLTevSZSC2bVsFB4fCePXqDmfwJUGr1aJv377YsGEDsmTJgrNnz6Jy5cpSx0pQdHQ0Pnz4gPfv3+Pdu3d4//697vLx40fkyJHjm9VU2bJl07t6fPbsWTRu3BgajQZ79+5F27ZtpY6U4Tw8PDBy5Ejkzp0bT548Qfbs2aWOpFdY0xJnjPUsKCgIefLkgUajga+vL/Lnz49atYCrV2NXYa1cCTRvDvBXBXjwv9U9OW3MoNQIKFQaVMibFTltE98aUAiB06dPY968eTh9+jQAwMTEBJ06dcLYsWNRvnz5DEhvOLRaLQ4ePIhp06bhwYMHAIC8efNi4sSJ6NOnz3e3YoxUqnHzdSiEELA0lSMsWo3STnbIb2+VkfH1xqFDQKtWgJXVT1AoLmLTpk3o3r271LEyDOtZ0oyxpiVmwwagd2/g559jt4pu3Dh2u2gvLy9UrFgRnz9/xrJlyzB48GCpo+oljUaDzp07Y9euXciRIwcuXryIkiVLSh3LoCxatAijR4+GqakpDh8+nCHbMRqa6OhoVK9eHQ8ePEDHjh2xY8cOvRtTS4ErsPREz549BQBRvHhx8fnzZ6njGBStVivmzp0r/vfHh+jYsaOITOmJ9JlcWFiYqFevngAgHBwcOBviOz59+iTy5s0rAIipU6dKHUdy4Ow+1rT/USgUonz58pzBlwxqtVp06tRJABDZsmUTjx49kjqS0Xr27JnIkSOHACAmTJggdRxJPHv2TFhaWgoA4uDBg1LH0UusaZmznnXs2PF/O2JMExqNEDY2QjdDffhwrsISQogopVqc9woUp54HiBt+weLaq0/iyJMPwjc4eeOue/fuiS5duggTExPd71vDhg3F9u3bhaenZ7odFq/v1Gq18PT0FJs2bdL9LQVA5MmTRyxbtkwoFIokbyMwIkacfBYgLnoHiRt+weLwkw/i/pvQDEivn5RKIbJnfy0ACEtLSxEWFiZ1pAzFepZ5a9r3XL4sxM2bCf/frl27dDvw3L59O2ODGZCYmBjRrFkz3fOzj4+P1JEMxr///qt7Xtq8ebPUcfTWgAEDBABRuHDhTFe3EpOamsYVWGkoKioK1apVw+PHj9G5c2f8+++/7LAm05EjR9C5c2d8/vwZFSpUwMGDB5E/f36pYxmMoKAgNGvWDLdv34aTkxNOnjzJ1YAJEEKgQ4cO2Lt3L2rUqIGLFy9Kun+wPuDsvqRlpprGGXzJp1Kp0LZtWxw5cgS5c+fGpUuXULRo0W8+T6kETE2RaVd7/oijR4ElSwBLS8DCIvatiYkCT578hbt350KjiUGTJk1w9OhRmJiYSB03Q6nVatSuXRs3btxAjx49sHHjRqkj6SXWtMQZaz07ffo0GjVqhAIFCuDECR+UKhX7/DBgALBiRewM9czubZgCj95/hq2FCcxNYgtRQIQShbNbo2TuLMm+vdevX8PDwwNr166Nd2aihYUFSpYsiTJlyqB06dK6t/ny5TOa8XFISAgePnyIhw8f4sGDB3j48CEeP34MhUKh+5w8efJgwoQJ6Nu3LywtLX/odl+HKPD4QzgcrMxgIpchPFoNMxMZqhZwgJVZ5qp5cerWnYdLl8ajY8eO2Llzp9RxMhTrWdKMtaal1JAhQ7B8+XIUKlQId+/e5W4a3xEVFYVmzZrh4sWLKFSoEC5fvgwnJyepY+m1AwcOoEOHDlCr1ViwYAFGjx4tdSS99OW5V9evX0eFChWkjqQ3UlPT2MBKY8+ePUPlypURGRmJVatWoX///lJHMjhPnjzBr7/+Ch8fH+TOnRtr1uxHsWI1wPMAv2/bNuD8+Te4eLExnj/3RKFChXDq1CkUKlRI6mh6acOGDejduzeyZMmC+/fv8/sEDo5+RGarabt27UKnTp1gbm6Oq1evolKlSlJH0nvR0dH45ZdfcPbsWTg5OWHgwOkoWrQ7Xrwwx5MnwOPHsQct793LF1ITExAAVKsG+PrGfeQwgOEAXgEAunXrhqVLl2bKAflff/2FCRMmIG/evHj8+DHs7e2ljqSXWNMSZ6z1TKvVokiRInj16hUmTDiBOXOaoGdPYN06ThqI8+X2gXG/J58ilchuY4Eq+bMmq7mk0QD79sVu8Xb8eChat/4HHz9ewKNHj+Dn55fgdbJmzRqvoRX3Nlu2bGny9aUHjUYDLy8vXZMq7u3r168T/PwCBQqgbNmysLJqivv3+6BPH0uMGQP86HyL5wEReBkUiVy25gAApVqLSKUGZfPYwcnux5pgxkSj0aB48bLw9n6KAwcOoFWrVlJHylCsZ0kz1pqWUtHR0ahVqxbu3r2Ldu3aYffu3UYzcSCthYeHo379+rhz5w4KFSqMQYPmY9So1vx+feXBgwdwc3PDiRMnAAADB47BihXzJU6ln16+fImKFSsiIiICK1aswMCBA6WOpFfYwNIz27ZtQ5cuXWBubo5r166hYsWKUkcyOJ8+fUKHDh1w7tw5AOYAVuP+/Z4oV07qZPrJweElQkMbAfBD6dKlcfLkSc4e+Y6XL1+iQoUKiIyMxObNm9GtWzepI+kFDo6Slhlr2uDBg7FixQq4uLjg7t27fLH8B0RERKB58+a4dOnS/z5SAIAbgN4ALFC9OnDtmnT5DMGrV0DsvAIfxDaujgAAypQpg+XLl6NOnToSppPOo0ePUKlSJahUKvz3339o3Lix1JH0Fmta4oy5ns2aNQuTJ09GiRLtUaHCbmzZ8uONA2OnUGlw83UIlBqBrJb/v/NAeLQa5iZyVC/oAHPT5HX6RowAFi8G/v479n3dbYaH48mTJ3j8+DEePXqkexsUFJTg7eTJkwelS5eO19QqVaoUrK2tU/CVJp9arUZ4eDjCwsLg7+8fr1n19aqqOJaWlihTpgzKli2LcuXKoWzZsihbtiwcHBwAxG5e2a0bUL48MGbMj2e57R+KgIgY5LAx/9/tCARGqFAouzVKOSZ/lZyhmzNnDiZOnIi8efPC29v7u2eHGSvWs6QZc01LKW9vb1SsWBHh4eFYunQphgwZInUkvRUUFIT69evj0aNHAICiRStj6dKZaNy4caZvZPn6+mLKlCnYunUrhBCQy7NAq52IdevGoXdvzgz6mkKhQI0aNfDgwQN06tQJ27dvz/Q/Q19jA0sPDRw4EKtWreKy3VRQqVQYNWoUli1bBgBo3LgNxowZiAYNGkDOaZQQQuDGjRtYvnw5duzYBbVaiUqVquHkyWN6PYtRSiqVCrVq1cKtW7fw+++/Y+vWrSwo/8PBUdIyY02Ljo5GzZo1ce/ePbRt2xZ79uzh78wP0Gg02LlzJ2bOnAlPT08AgEyWF0KMw5gx/TB/fuY8iP1HKRQKdOo0D4cPzwEQAzs7O0yfPh2DBw/OtNu9KpVKVK9eHffu3UP//v2xatUqqSPpNda0xBlzPXv79i0KFCgAmcwET58+Q7FiXGUf521YNB69D4+3fSAQ29hSawQq5beHvZVZsm4zNBTYtAkYNizplcVCCAQEBMRraD1+/BiPHz9GVFRUgtexsLCAra0tbG1tkSVLFt37Sf3bxMRE15D6kbffu/84+fPn1zWq4ppVRYsWTXIb28jIuG1wE//exFFptLjmF4IYlQZZv3gsQhUqWJmZonpBe5iZZJ5x8L1791C1alWo1WqcPHkSjRo1kjpShmM9S5ox17TU2L17Nzp27Ahzc3NcuXIFlStXljqS3oqJiUHVqmvx6NEsCPEBAFCnTh3MmjUrU06cCwoKwqxZs7BixQoolUqYmZlh8ODBcHWdiCtXcmLePCBnTqlT6p8BAwZg9erVKFKkCO7cuQM7OzupI+kdNrD0EF/0Sztr1qzBkCFDoFKpAAAuLi7o168fevXqBUdHR4nTZbyoqCjs2LEDy5cvx927dwHEPgl06NAB69atg62trcQJ9dfkyZMxa9YsFChQAA8ePOBqki9wcJS0zFrTvpzBt2TJEgwdOlTqSAZDq9Vi7969mDlzJh4+fAgAsLfPjcmTx2LAgAGwsbGROKH+OXLkCIYNG4ZXr/5/u8B58+Zlynr/JXd3d0yfPh3Ozs54+PAhsmTJfLPwk4M1LXHGXs86d+6M7du3o1y5crh69WqGreLRdw/fh+N1SBRy2pjHG5eqNVqExWhQzskOebJm/BZ1Wq0Wvr6+8RpbDx8+xosXz6HRqDMkg0wmg52d3f8mneZCw4bxV1Vl1OTA8GgVbvuHQi6Twdr8/7te0SoNYtQC5fPaIadt5liBpFAoULlyZTx9+hRDhw7FkiVLpI4kCdazpBl7TUuNuPOwuJtG0oQAAgOjsHHjMsydOxfBwcEAgCZNmmDWrFmZYjv9yMhIeHh4YN68eQgPD4dMJkOXLl0wffp0uLi4SB1Pr23fvh2dO3eGhYUFrl27xnOvvoMNLD315Yt+Hh4eGD58uNSRDNbbt2+xYcMGrF27VrffuKmpKX799Vf88ccfaNSokdGvyvLy8sKqVauwfv16hISEAACyZ8+OPn36YMCAASwoSbh48SLq1asHmUyG8+fPZ8qZNInh4Chpmbmm7dmzBx06dICZmRmuXr3KGXzJpNVqcejQIcyYMUM38SBHjhwYNWoUBg8ezNlZAF69eoXhw4fj8OHDAIDSpUtj+fLlqFu3rsTJpHfnzh1Uq1YNGo0G58+fx08//SR1JL3HmpY4Y69nYWFhqFKlCl6+fIkuXbpgy5YtmX4iYbRKgxuvQ6HUaJDVMv4qKyEEAiNVKJHLFkVy6MfEiv79AVdXgX79ohEREYGIiAh8/vxZ935i/377NgKhoWqUKpVV15RK7K2dnR1sbW0hk8kQGQnkyxe7la0Ur/N++ByNB2/DYfPVKjmtEAiKVKF4ThsUzZk5JiuOHDkSHh4eKFGiBO7cuZNpG9GsZ0kz9pqWGjwPK2XCwsLw999/Y9GiRfj8+TMAoG3btpg+fTpcXV0lTpf2VCoV1q9fj6lTp+LDh9gVaE2bNsWcOXNQvnx5acMZgBcvXqBSpUqIiIjAypUrMWDAAKkj6a1U1TQhRLpdAIjYu8i89u7dKwAIU1NTce3aNanjGDy1Wi2OHz8u2rRpI0xMTETcz5izs7OYOXOmePv2rdQR05RarRaHDx8WTZs21X2tAESVKlXExo0bRVRUlNQRDUJISIgoUKCAACAmTZokdRy99MXzdbrWBUO+ZPaaNmTIEAFAuLi4iJCQEKnjGCStViuOHj0qqlWrpvudc3BwENOmTcu031OFQiGmTZsmLC0tBQCRJUsWsWjRIqFUKqWOphcUCoVwdXUVAMTw4cOljmMwWNNYzx49eiRsbGwEALFkyRKp40jubahCHPf8KC55B4kbfsHfXI48+SDuvQmVOqYQQohDh4QAhFi9OmXXHzhQiJT+ue/pGXvf06al7Pqp5RMUKY48+SCu+3765jE65vlBXPUNFmqNVppwGej06dO611Bu3boldRxJsZ6xpqWWl5eXsLOzEwDE0qVLpY5jUIKCgsTYsWOFlZWVACBkMpno2rWrePnypdTR0oRWqxW7d+8WxYoV0z3XVK5cWZw5c0bqaAYjKipKlCtXTgAQnTp1Elqt8dfo1EhNTWMhyQAjRowQAESBAgVEUFCQ1HGMxrt378SsWbOEs7Oz7pfAxMREtG7dWhw7dkyo1WqpI6ZYUFCQmDt3rnBxcdF9bRYWFqJHjx7i5s2bUsczKFqtVvz222+6xh9fFE0YB0esaUmJjo4WlSpVEgBEmzZt+MdZKmi1WnHy5ElRu3Zt3e+enZ2dmDx5cqb6O+Ho0aOicOHCuu9Bly5dxLt376SOpVfGjRsnAIhixYqJyMhIqeMYDNY01jMhhNi5c6fuRfCLFy9KHUdSD9+FicNP3ifYGLnhFyz+e/ZRXPL5JHlz5MMHIXLmFAIQYtOm5F8/PFwIW1shmjVL2f2fOBF73/b2Qkgxr+Tx+zBx+MmHBB+jcy8DxekXASI40rjHMsHBwSJfvnwCgJgxY4bUcSTHesaalhZ2794tAAhzc/NM3xROiXfv3onBgwcLMzMz3euO/fr1E69fv5Y6WoqdO3dOVK1aVfccU6RIEbFr1y6O8ZOpf//+uu9fWFiY1HH0HhtYei4mJkY327p58+ZCo9FIHcmoaDQa8d9//4l27doJU1NT3S9EgQIFxPTp08WbN2+kjpigz5+F+Lo23Lx5U/To0UNYWFjEW102d+5cERgYKE1QA7d582YBQNjY2BjNTJn0wMERa9qP8Pb21s3g8/DwkDqOwdNqteLcuXOifv36ut9BW1tbMX78ePHx40ep4yVbTMyPfd6rV69Eq1atdF+zq6urOH/+fPqGM0BXr14VcrlcyOVyruJPJtY01rM4Y8aMEQBE7ty59XZMkN4USrW44BUkTj7/mGBj5IZfsDjzIkCcfREoPkerJMup1QrRooUQsSeRCLFjR/JvY8WK2OvmyvXtOOtHrF79//ef0auwtFqtuOobLI57Jvw4XX31SRx5+kH4BBn3ZIbOnTsLAKJ69epCpZLu51FfsJ6xpqUV7qaReq9evRK9evUScrlcN8l8xIgRej9u+3KM9uDBA9GsWTPdc0vu3LnFihUrONE7BbZt26b7Obh7967UcQxCamqacR8apCfMzc2xa9cuZMuWDceOHcP8+fOljmRU5HI5GjdujD179sDf3x9z5sxBoUKF8Pr1a/z5558oUKAAWrVqhaNHj0Kj0SA6GujTBxg1SrrMz58DhQsDtWrF7ku8adMmVK1aFVWrVsWmTZsQExODZs2a4fDhw/Dy8sK4ceOQI0cO6QIbKB8fHwwePBgAsHTpUhQpUkTiRESGrVChQli/fj0AYOzYsbh586bEiQybTCZDvXr1cObMGVy+fBlNmjRBREQE5s6dC2dnZ4waNQrv379P9DaEnmz3/+kTMGsW8L8jvhIUHR2NmTNnomTJkjh48CBsbW2xcOFC3Lt3j+c6fSUqKgo9evSAVqvF2LFjUb16dakjERmkOXPmoH79+vj48SPat2+PmJgYqSNluGCFCgqVBlamJt/9HDMTOVRaLaKUmgxMFt+NG4Cp6f//28IiedcXAli5Mvb9gAAgifKZID8/wMUFcHIClEpArU7+baRUjFoLpVoDM3nCx0KYyGWAAIKilHEv2hudHTt2YNu2bbC2tsbmzZth+uUPBBGlyoIFC1CxYkW8evUKffv2NdrnkfTk7OyM9evX4+nTp+jUqRNiYmLg4eGBQoUKYdKkSbqz6uNI/S3+/BnIkQMoXx7w9PRF9+7dUb58eRw/fhy2traYPn06vLy8MHDgQJiZmSV5e/T/Xrx4gT/++AMA4OHhgQoVKkicKBNISdfrRy/gTIh4jhw5oltueuHCBanjGDWNRiNOnTolOnToEG9VVr58+cTgwe4CeC0AIT59StntHzwoxLp1QqRkl0K1Wi1OnfogZLLbwsZmnMiePXu8s1BGjx4tvLy8UhaMdFQqlahRo4YAINq3b8+l0EkAZ/expiXDsGHDBABRsGBBERwcLHUco3Ljxg3RokWLeNvHDhky5LtbVMycKcTgwUJIfQRkv35CAEJUqZJwbTx27Fi87QI7d+5sdOdWpqW43zFXV1cRHR0tdRyDw5rGevalgIAAkT9/fgFADBw4UOo4GS6p7QNv+AWLa76fxJEnH8SrT9Ku7gkOFmLePCG2bBHi9OnkXffyZSFMTYVuBdWRI8m//9evY8eHoaHJv25qfYqMEaeeB4jzXoHffZxOvwgQZ18GinAJV8qllzdv3gh7e3sBQKxatUrqOHqD9Yw1LS19eR4Wz4dMvfv374uWLVvqfk+zZs0qZsyYIcLDw4UQseMjKU8A8fYWwsoqUFhYjBTm5uYCgDAzMxPDhw8XAQEB0gUzcDz3KuVSU9NksddPHzKZTMQ1ySjWhAkT8Ndff8HJyQn3799Hrly5pI5k9D5+/IhNmzZhzZo18Pb2BgDIZHKULFkPVarkh4ODQ7xLtmzZvvmYubm57vaiogBb29ih0aNHQOnSsR9XKBT48OED3r9//83bL98PCAiARhN/dmPFihUxePBg/Pbbb7C2ts6w740xmzZtGqZOnYq8efPi4cOHyJYtm9SR9JpMFjvbUwiR8LRPYk37QkxMDOrUqYNbt26hVatW2L9/v+5niNLGvXv3MHPmTOzbtw8AYGZmht69e8PNzQ3Ozs66z2veHDh+HJg4MXYFVEpcuRI7W93ZGUjJ5LEnT4AWLQBf39h/L18ODBoU+76vry9GjhyJAwcOAABKlSqF5cuXo169eikLmwmcP38eP//8M0xNTXHjxg1UrFhR6kgGhzUtcZmxnt2+fRu1a9dGTEwM1q9fj169ekkdKUNEqzW46ReKGLUGWa0Sn10dEKGESzYruDraZVC6xAkBJOdPi2fPAI0GmDMHaNkSsLICfv01/fKltbdhCjx8F46sVmYw/c4qLLVGi7BoNUo72SG/vVUGJ0w/Wq0WTZs2xalTp9C8eXMcOXKEf1f+D+tZ0jJjTUuNPXv2oEOHDjAzM8PVq1dRuXJlqSMZvOvXr2Py5Mk4c+YMACBHjhwYMWICVq9ugWPH8qN06ZQ9Xx8+DBQpApQo8WP1UKVS4fXr1/D29oaPjw88PT2xfv1GRESEAwC6dOmCGTNmwMXFJUV5KFb//v2xZs0aFC1aFLdv34adnX783WQIUlPT2MDKYGq1GvXr18elS5fQsGFDnDhxAiYm39/OgdKOVqvF+fPnsWbNGuzbtw8qleqHr2ttbR2vseXv7wCVyhIuLh8RGBjbmAoLC/vh28uePTucnJxQsWJFDBw4ENWqVeMf6Wno6tWrqFOnDoQQOHPmDH7++WepI+k9Do6SxpoW36tXr1ChQgWEhYVh0aJFGDlypNSRjNKjR48wa9Ys7Nq1C0IImJqaonv37pgwYQKKFCmCJk1it0m6dSv52y0BwObNwNixsQ2s//4DGjdOWc7oaGDTJmD+fCAwEHj4MAb//rsAs2bNgkKhgK2tLdzd3TF8+HBuUZGIz58/o2zZsvD19cXUqVPh7u4udSSDxJqWuMxaz9avX48+ffrAwsICly9fzhQv2r0Pj8bDd+GwNjeBhWnipwcERylhb2WOagXsOS6RwMvASDwPjEBOG7NEv/8BEUrkz2qJcnmzZmC69LV06VIMGzYM2bNnx+PHj+Ho6Ch1JL3Bepa0zFrTUmPo0KFYtmwZXFxccPfuXdjb20sdySicO3cOkyZNwrVr1+J9PEeOHMifPz8KFCiA/Pnzf/N+njx5EtwytXZtIHduYM+e/29ghYSEwMfHBz4+PrpGVdz7r1+/hlar/eZ2mjRpgjlz5nCbuzSwbds2dOnSBRYWFrh+/TrKly8vdSSDwgaWgXn37h3Kly+PwMBAvjghkcDAQFy5cgXBwcEICQlJ8PLl/329YiohZmZmcHR0hJOT0zdvv3w/d+7c8VZ0UdoKDw9HuXLl4Ovri3HjxmHu3LlSRzIIHBwljTXtW/v370fbtm1hamqKy5cvo1q1alJHMlqenp6YPXs2tm3bBq1WC7lcjs6dO8PHZxQWLCiOGjVStnpXpQIqVwYePgTOnAHq1//x62o0Grx58wbe3t66i5eXN+7e9ca7d16IifkMAPjtt9+wYMEC5M2bN0UZM5O4GX0VK1bE9evX2exLIda0xGXmejZgwACsXr0a+fPnx507d5AzZ06pI6Wrxx/C4RusSLIpAgDh0WqYmchQrYADLM04wTKj3X8bhrdh0chpm/g4MSxaDXMjepw8PT1RsWJFREdHY+/evWjbtq3UkfQK61nSMnNNS6mYmBjUrFkTd+/eRdu2bbFnzx5OXEgjQggcO3YMS5YswcuXL/HmzZskJ8/L5XLkyZPnm8bWP/9Y4uefX+H9+/9vVH19ztaXZDIZ8uXLh0KFCqFQoUIoXLgw6tatizp16qT1l5kpPX/+HJUrV0ZERARWrVqF/v37Sx3J4LCBZYBOnz6Nxv+b5nzy5Ek0bNhQ4kT0PUIIREREfNPYUigUyJ07t64x5eDgALk88ZmNlP66d++OLVu2oGLFirh27RqbhT+Ig6OksaYlbOTIkfDw8ECBAgVw7949bteZzry8vDB79mxs2bIF6i9Ol7ezs0OePHl0kya+d7Gzs/tmgHrzJlC9OnD+PFC3bvz7UygUePXqVbwmVdzF19cXSqXyu1ldXV2xZMkS1E9OVywT+++//9C0aVOYm5vjzp07KB23RzElG2ta4jJzPYuJiUG9evVw/fp1NGjQACdOnEhw1rMxiFFrcMMvFNFqDeyT2D4QABQqDVQagYr5siKbNf9+zkgarcA1vxBEKdVJPlYxai2ilBqUzWMHJzvLDEqYPpRKJWrUqIG7d++iZ8+e2LBhg9SR9A7rWdIyc01LDW9vb1SsWBHh4eFYsmQJhg4dKnUko6TVavHx40f4+/vj9evX8d7Gvf/hw4cfvj1ra2sULlw4XpMq7n1nZ2dYpGRLDkqSQqFA9erV8fDhQ/z222/Ytm0bm74pwAaWgYo7oydnzpy4f/8+8uTJI3UkIoO2fft2dO7cGVZWVrh37x6KFy8udSSDwcFR0ljTEqZUKlGnTh3cvHkTLVu2xMGDB/nHXAbw9fXFX3/9hRMnTuD9+/eJNpK+ZGVlFa+hFdf0OnvWCTVrmsHU1Cdek+rdu3eJ3p6joyMKFy6sGzzFvV+4cGHkzJmTPws/KDQ0FKVLl8bbt2/x119/Yfz48VJHMmisaYnL7PXs7du3qFixIgICAox6tX5ytg8EALVWIEyhQhknO+QzovOVDEFEjBq3/EMhhICtReINVSEEAiNUKJTdGqUcs2RQwvQxZcoUzJw5EwULFsTDhw95jkgCWM+SltlrWmrwPCz9oFQq8fbt228aXNHR0XBxcYnXqMqVKxfHVxLguVdpgw0sA6XRaNC0aVOcPn0aderUwdmzZ412BiBRevPz80O5cuUQFhaG1atX448//pA6kkHh4ChprGnf5+vriwoVKiA0NBQLFizA6NGjpY6UqQghEBwcjPfv3yd5iYyMTNZtm5qawtnZOcEmVaFChWBjY5NOX1Xm0qNHD2zevBnVq1fH5cuXeT5qKrGmJY71DLh48SLq168PjUaDXbt2oUOHDlJHSnPJ2T4Q+P/GSLFcNiiW0zYDElKcwIgY3HsbBiuzH2s2hihUsDE3RbUC9jAzMcwdQK5fv45atWpBCIHz58+j7tdLwAkA69mPYE1LHZ6HRZQ4nnuVdtjAMmABAQEoX7483r9/Dzc3N8yZM0fqSEQGR6PR4Oeff8alS5fQqlUr7N+/n7NSkomDo6SxpiXu4MGDaN26NUxNTXHx4kXUqFFD6kiUgM+fP3/T1Hr37h3ev3+PmJgYuLi4xFtFlT9/fk6uSWdxvzuWlpa4f/8+Vw+nAda0xLGexVq8eDFGjBgBGxsb3LhxA66urlJHSjMxai1u+IX88PaBcYIilXDMYoGK+ezTLxx9wy8kCo/ff0Z2azPI5Uk/bSlUGig1AuXz2CGnreFtFxUREYEKFSrAy8vLqFdBpgXWs6SxpqVOTEwMatWqhTt37vA8LKKv8NyrtMUGloH7cgbgkSNH8Msvv0gdicigzJo1C5MnT4ajoyMePXqEHDlySB3J4HBwlDTWtKSNHj0aixYtQv78+XHv3j1kz55d6khEei0oKAiurq4ICAiAh4cHhg8fLnUko8CaljjWs1hCCHTr1g1bt25F0aJFcevWLWTNmlXqWGniQ3g0HrwPh/UPruiJE6pQwcrMBDWcHWDKs30zzLOACHgFRSKX7Y+dPaYVAkGRKhTPaYOiBrhabsCAAVi9ejXKli2Lmzdv8syWRLCeJY01LfV8fHxQoUIFnodF9AWee5X2UlPT+FepHqhbty5mzpwJAOjWrRv8/PwkTkRkOG7evAl3d3cAwObNm9m8IpLQnDlzUK1aNfj7+6NHjx7QarVSRyLSa4MHD0ZAQAB++uknvlhAlMFkMhnWrFmDcuXK4eXLl+jevbvR1K1PUUqo1ALmJsl7fcDMRA6VRosopSadktHXhBD4HK1Gch4quUwGEzkQFKWCRmtYL9ofPXoUq1evhrm5Of799182r4j0QKFChbB+/XoAsRMSb9++LXEiIukNHz4cDx8+RNGiRbF69Wo2ryTGBpaeGDduHJo3b46QkBB06tTphw9jJ8rMIiIi0LlzZ2g0GowaNQqNGjWSOhJRpmZubo6dO3fCwcEBR48excKFC6WORKS3du7ciV27dsHGxgYbNmyAnKsdiDKctbU19u3bB3t7exw6dAizZs2SOlKqKdVafIpSwdxUluwXW8xMZFBpBKJUxtHIMwQqjYBCpYFpMs+ysjQ1QZRSjc8x6nRKlvYCAwPRp08fAMDs2bNRpkwZiRMRUZx27dph6NChUKlU6NixI0JDQ6WORCSZrVu3Yu3atbCwsMCuXbtgZ2cndaRMjyNlPSGXy7F582bkz58fN27cwPjx46WORKT3hg8fDm9vb5QtWxazZ8+WOg4RAShYsCA2bdoEAJgwYQKuXr0qcSIi/fPhwwcMGjQIALBw4UK4uLhInIgo8ypUqJBuWxh3d3ccO3ZM6kipEqxQIlqlgZWZSbKvayaXQaUVXIGVgRQqDVRaLcx+4OyrL1mayhGt1iIkSpVOydKWEAJ//PEHPn78iHr16mHkyJFSRyKir8yfPx+VKlXCq1ev0Lt3b27LSJnS8+fPdWddLVmyBOXLl5c2EAFgA0uvZM+eHbt27YKpqSk8PDywb98+qSMR6a09e/Zg/fr1sLS0xLZt27j9BJEeadmyJcaMGQONRoNOnTohKChI6khEekMIgf79+yM4OBiNGzfGH3/8IXUkokyvWbNmmD59OoQQ6NKlC7y9vaWOlGLBkSrEpGD7QCB2W0UZgCil4azqMXRRKg1UGgGzZD5eJnIZIGK3izSEF5k3bdqEAwcOwM7ODhs3buSqYyI99OVqk/3792Pp0qVSRyLKUAqFAh06dEBkZCR+//139OvXT+pI9D/8q0HPVK9eHfPnzwcA9OrVy6AHT0Tpxd/fX/eC34IFC+Dq6ipxIiL62uzZs1G9enW8efOG52ERfWHlypU4dOgQsmbNin/++Yf7qRPpiYkTJ6JVq1YIDQ1FmzZtEBkZKXWkZIvdPlAJixRsHxjHRC5DeIzaIJoixkCh0kCjFTBN5gosALA0k+NzjBqRer5i7tWrVxg2bBgAYOnSpShYsKDEiYjoe748D2vMmDG4deuWxImIMoYQAkOHDsWjR4947pUeYgNLDw0fPhxt2rRBeHg4OnTogOjoaKkjEemNoKAgtG/fHiEhIfjll190WzARkX4xMzPDzp07kS1bNhw7dkw3OYMoM1u3bh0GDx4MAKhdeykCAvKDrxET6Qe5XI5NmzahWLFiePToEfr162dwTZxghQoKlQaWKdg+MI6ZiQxKtRYKnoOVISKVGgggRS+SWZnKEa3SIEShv9sIajQa9OjRA58/f0a7du3QrVs3qSMRURJ4HhZlNmq1GgMGDMC6detgZmaBtWt3I0uWLFLHoi+wgaWHZDIZ1q9fj0KFCuHevXsYNGgQNBr9nlVFlBFevnyJGjVq4ObNm8iVqwCWLl3PGRFEeqxAgQK687AmTZqEy5cvS5yISDrr16/XbUMxf/58VK/eDZUrA8WKAfPnA1ykSCS9rFmzYv/+/bCxscH27duxePFiqSMlS3CkEkqNgEUKtg+ME3cOlkLF8Wd6E0Lgc4w6RauvAMDURA6NAIL1+ByshQsX4tKlS3B0dMSqVas4diPSQx8/Avv2AVevAj4+QFRU7N+qlStXhq+vL3r06AGVSn+fZ4hSIyoqCm3btsWaNWtgaWmJP//cgaZNy6FKFWDSJCAkROqEBLCBpbfs7e2xe/dumJlZYMOGDWjVqhXCw8OljkUkmUuXLqF69erw8vJCnjwVERBwDe7uuaSORURJaNGiBcaOHQuNRoOWLX/D+fO+UkciynAbNmxA3759IYTAvHnzMGbMGEycCDRvDnh5AblzAzwOhEg/lCpVChs3bgQQu33ShQsX4v2/vi7Kit0+UAVzk5RvHwgAZiZyqDRaRLGBle6i1Voo1dpkn3/1JQtTGUIVKkTryeMVEQH4+QGfPgEPHjzA5MmTAcRO4siRI4fE6YgoITlzAsuWAbVqAYULAzY2QK5cFggP3wlr66w4dOgQWrVqZZBb6xIlJjAwEPXr18fhw4fh4OCA06dPY/Lk1li/Hrh9G7h+HXBwkDolAWxg6TUbm4pQqY4DyIajR4+ievXqePnypdSxiDLctm3b0LBhQwQHB6NFixaYO/cCbG3zoGRJqZMR0Y+YNWsWnJ1rITT0LZo0qYn79+9LHYkow2zcuBF9+vSBEAJz587F2LFjAcQ2rLZsARYuBH77TeKQRBRP+/btMX78eGg0GnTs2BFv3rzR/d/Zs8DGjcC1a9LlS0iIQgWFSg1Ls9QN8U3kMmgFEKXn5yoZgyilBiqtgFkKV2ABgKWpid5sIxgaCtSrBxQpAvj5RaNr165QqVQYOHAgmjVrJnU8IvoOpRK4dOn//y2TAS1bAsePF8K5cyeRI0cOHD9+HD///DMCAwOlC0qUhnx8fFCrVi3cuHEDBQsWxJUrV1CrVi0AwO+/A/PmATt3ShySdNjA0mOOjkCzZj+jZctbcHV1haenJ6pWrYpTp05JHY0oQwghMGvWLHTp0gVKpRJDhgzBgQMH0LWrLUJCgAkTpE5IRD/CzMwMW7ceha1tPSiV71G3bl2cOXNG6lhE6W7Tpk3o3bs3hBD466+/MG7cuHj/ny0bMHIkYG4uUUAi+q6ZM2eiYcOGCAgIQPv27RETEwMA8PQEevWKbTxv2SJxyC8ERykRoxGwMEn9EF8uAz7HqNMgFSVGodJArdHCLBWPmbmJDEqNQIgebCNobw80aQKo1cDatZPx+PFjFC1alOegEuk5S0tgzpzY95s3B+7dA/79FyhUCKhatSquXLkCZ2dn3Lp1CzVr1oSPj4+0gYlS6fbt26hRowZevnyJ8uXL49q1ayj51Qz5sWMBLhzWH2xg6bGsWYFjx4BDhwrh2rVr+PXXXxEaGopmzZphyZIlBneoMFFyqFQq9O3bF5MnT4ZMJsPff/+NJUuWwMQk9lBqU1OJAxJRstSsmRVBQSfQqVMnfP78Gc2aNcP27duljkWUbjZv3oxevXpBCIE5c+Zg/PjxCX4ejwMh0k+mpqbYvn07ChQogBs3bmDYsGEAYs8HAWIbz/qyqESpSZvtA+OYmcgQpdRAqeHhfOkpSqWBRhvbMEwpmUwGc1MZghUqqPTg8Ro/HrC3P4fVqxfBxMQEW7ZsgY2NjdSxiCgJZcsCFy4AR48C5crF/79ixYrh2rVrqFChAry8vFCjRg3cvXtXmqBEqXT8+HHUq1cPAQEBaNiwIS5cuAAnJyepY1ES2MAyEFmyZMH+/fsxadIkaDQaDB8+HP369dPNBCQyJmFhYWjevDnWr18PKysr7N27FyNGjOChv0QGzsLCAtu2bcPIkSOhUqnQuXNnLFy4UOpYRGluy5Yt6NmzJ4QQmD17Ntzc3KSOREQpkCNHDuzbtw8WFhZYs2YN/vnnH/j4ANmzx0401JeZuaFRKkQp1bAyTZvhvZlcDpVWCwW3EUxXEUo1ZDKkeoxjaSqHQqVBqB5sI/jmzVPI5d0hhMDkyZNRrVo1qSMR0Q9o3BioW/f7/+/o6Ijz58/rVib/9NNP3B2KDM6GDRvQsmVLREZGomvXrjh69Cjs7OykjkU/gA0sAyKXyzFz5kzs2LEDVlZWWLduHRo0aICPHz9KHY0ozfj5+aFWrVo4ffo0cuXKhfPnz6NNmzZSxyKiNCKXy7Fo0SIsWLAAADBmzBiMGjUKWq30s4aJ0sK///6LHj166LbBncD9bokMWqVKlbBq1SoAwODBg/HixU0cPAgULSpxsC98ilJCqRGwSKsGlokMKo1AlIoNrPSi0QpExmhgZpL6CXoWpnLEqLUIVUi77ePBgwdRrVo1BAe/Qc2aNTFp0iRJ8xBR2rKzs8PRo0fRuXNnREREoHnz5ti6davUsYiSJITAjBkz0Lt3b2g0Gri5uWHz5s0w5z7uBoMNLAPUqVMnXLp0Cfny5cOVK1dQpUoVLt8lo3Dnzh1Ur14dT548QcmSJXHjxg1UrVpV6lhElA5Gjx6Nbdu2wczMDH///Tc6d+7MVcVk8LZu3aprXs2cORMTJ06UOhIRpYGePXti0KBBUCqVeP++JWJizkodSUf1v+0DzeRps30gAJjKZVBr2cBKT1EqDVQakarzr+LIZTKYyGIbmVoJjhnQarWYNm0aWrdujYiICHTq1AknT56EmZlZhmchovRlbm6OLVu2YPTo0VCr1ejatSsWLFjAI05Ib6nVavTv3x9//vknZDIZli1bhjlz5nCHJwPDBpaBqlSpEm7duoUaNWrA398ftWvXxq5du6SORZRihw4dQt26dfHhwwfUr18fV69ehbOzs9SxiCgd/f777zh+/DiyZMmCnTt3omnTpggLC5M6FlGKbNu2Dd27d4dWq8WMGTM485zIyPz9999o1KgRQkNjz0xwd3eHRiN9gydEoYJCqYGVWdoN7WUyGWQAImOk//qMlUKpgVqrhWlqDsD6gqWZCSKVaoRHZ+wqrM+fP6Ndu3aYOnUqZDIZ/vrrL2zfvp3nXhEZMblcjgULFui2gh87dixGjx7NHTVI70RGRqJNmzZYu3YtLC0tsXfvXgwePFjqWJQCbGAZMEdHR5w7dw69evWCQqFAp06dMGXKFBYNMjhLlixB69atERUVhR49euD48eOwt7eXOhYRZYAGDRrg4sWLun3V69Spg7dv30odiyhZtm3bhm7dukGr1WL69OmYPHmy1JGIKI2Zm5vj2LFj+PPPPwEA06dPR4MGDSSvWcGRSsSotWm2fWAcUxMZPseoJVnRkxn8/wqsNGpgmcoRrdYiJAPPwfLy8kL16tVx4MABZM2aFUePHsX48eM5q50okxg1alS8HTW6dOnCHTVIbwQGBqJBgwY4cuQIHBwccPr0aR5PYsDYwDJwFhYWWLduHTw8PHRnZLVt2xafP3+WOhpRkjQaDUaMGIHhw4dDCIHp06djw4YN3IeWKJMpX748rl27huLFi+PRo0eoWbMmPD09pY5F9EO2b9+ua15NnToVU6ZMkToSEaUTU1NTTJs2DadOnYKjoyMuXLiA8uXL4/jx45Lk0W0faJJ22wfGMZPLoNRooeA2gukiSqWBQOz2f2nBRC4DBPApUpkhW3n9999/qFKlCp4+fYqSJUvi1q1baNasWbrfLxHply931NixYweaNWvGHTVIct7e3qhZsyZu3LiBggUL4sqVK6hVq5bUsSgV2MAyAjKZDMOHD9etWjl48CBq1qyJV69eSR2N6LsiIyPRrl07LF68GGZmZtiyZQumTJnCGXtEmZSzszOuXLmCGjVq4PXr16hVqxauXLkidSyiRO3YsQNdu3aFVquFu7s73N3dpY5ERBmgQYMGuH//Pho1aoSgoCA0b94c48ePh0qVcatfACBUoUJUGm8fGMfMRA6VRosoJRtYaU0IgYgYNdLg+Kt4LM3k+ByjRmQ6PmZCCMybNw/NmzdHaGgofv31V1y/fh1FixZNt/skIv325Y4a586dw08//YT3799LHYsyqdu3b6NmzZrw8vLSTZQtWbKk1LEoldjAMiKNGzfGzZs3UaJECTx+/BhVqlTB+fPnpY5F9I0PHz6gXr16OHjwIBwcHHDq1Cl07dpV6lhEJLHs2bPj9OnT+PXXXxESEoKGDRviwIEDUsciStDOnTvRpUsXaLVa/Pnnn5g6darUkYgoA+XOnRsnTpzA7NmzYWJignnz5qFu3brw8/PLsAyfopSI0WjSfPtAIHYFlkojEMUVWGlOqRFQqLQwk6ft42ZpKke0SpNu2whGRUWhS5cuGD9+vG7ixv79+2FnZ5cu90dEhqN8+fK4evUqihUrhgcPHqBGjRp4/vy51LEokzl+/Djq1auHgIAANGrUCBcuXICTk5PUsSgNsIFlZIoWLYrr16+jefPm+PTpExo1aoSVK1dKHYtI58mTJ6hevTpu374NFxcXXL16FT/99JPUsYhIT1hbW2Pv3r3o378/oqOj0a5dO9Yx0ju7du3SNa+mTJnC5hVRJiWXyzFhwgScP38e+fLlw/Xr11G+fPkMmXyh1mjxKVIFU7k8XXYwkMtlEBBcgZUOFCoN1Bptmp1/FcfMRA6NAEKi0r6B5efnh1q1amH79u2wtbXFvn37MHXqVMjTuAlHRIbLxcUFV65cQbVq1XTPGdevX5c6FmUSGzZsQMuWLREZGYlu3brhyJEjnGBhRPjXhhHKmjUrDh06hHHjxkGtVmPQoEEYOHAglEql1NEokztz5gxq1aoFPz8/VKtWDdevX0eJEiWkjkVEesbU1BQrV67E9OnTodVqMWjQIEyePDlDznQgSsru3bvRuXNnaDQaTJ48GdOmTeP2t0SZXO3atXH//n20aNECoaGhaNOmDYYPH56uh9mHKFSIUqXP9oFxTGQyfI5Rs/6msSiVBiqtgKk87WuHhakMoQoVotNw5dz58+dRuXJl3L9/H0WKFMH169fRpk2bNLt9IjIeOXLkwJkzZ/DLL7/g06dPqF+/Po4cOSJ1LDJiQgjMmDEDvXv3hkajwYQJE7Bp0yaYm5tLHY3SEBtYRsrExARz587Fli1bYGFhgVWrVqFx48YIDAyUOhplUhs2bEDTpk0RFhaGdu3a4dy5c8iVK5fUsYhIT8lkMkyZMgXr1q2DiYkJZs2ahd69e2f4+SJEX9qzZw9+//13aDQaTJo0CdOnT2fziogAxG6De+jQISxatAhmZmZYsmSJ7gyG9BAcpUSMWgvLdNg+MI6ZiRwKlRZKDRtYaUmh1ECdTg0sS1MTKFQahKbBNoJCCCxbtgwNGzZEUFAQmjRpgps3b8LV1TUNkhKRsbKxscGBAwfQu3dvKBQKtGrVCv/884/UscgIqdVq9O/fH3/++SdkMhmWL1+O2bNnc3xmhNjAMnJdu3bFxYsX4eTkhAsXLqBq1ap4+PCh1LEoExFCYMqUKejduzfUajXGjBmDXbt2wcrKSupoRGQAevfujYMHD8La2hobN25Eq1atEBERIXUsyoT27t2L3377DRqNBhMnTsSMGTM4OCKieGQyGUaOHInLly/DxcUFd+/eRcWKFbFjx440vZ//3z5Qlq7PQ2YmMqg0Wm4jmMYilRrIgHR57MxNZFBpRarPwYqOjkafPn0wdOhQaDQajBs3DkePHoWDg0MaJSUiY2Zqaop//vkHkydPhlarRb9+/TBjxgyu6KU0ExkZiTZt2mDt2rWwtLTE3r17MWjQIKljUTphAysTqFq1Km7fvo0qVarA19cXNWvWxP79+6WORZlATEwMunbtipkzZ0Iul2PlypWYP38+90onomT55ZdfcO7cOeTIkQPHjx/Hzz//jICAAKljUSayb98+XfNqwoQJmDlzJptXRPRdVatWxd27d9G+fXt8/vwZv//+O/r37w+FQpEmtx+iUCEynbcPBAAzeWwzRJGG29Fldloh8DlGnS6rr4DYppiZiQyfolRQabQpuo13796hXr162LBhA6ysrLBt2zbMnTsXJiYmaZyWiIyZTCbDjBkzsHLlSsjlcvz5558YOHAgNBrWFEqdwMBANGjQAEeOHEG2bNlw5swZbm1r5PgqciaRJ08eXLhwAV27dkVkZCTatm2rO1uEKD18+vQJjRo1wrZt22Bra4sjR45gwIABUsciIgNVtWpVXL16FS4uLrh9+zZq1qwJb29vqWNRJrB//3506tQJarUabm5umDVrFptXRJQke3t77Nq1CytXroSFhQXWrFmDqlWrwtPTM9W3HRylQoxaC4t03D4QAEzkMmi0ApFsYKWZaJUWKo0WZibpV0csTeVQqDQIi1Yn+7rXrl1DpUqVcOPGDRQoUABXrlzB77//ng4piSizGDBgAPbs2QMLCwusXr0a7du3T7MJHZT5eHt7o2bNmrhx4wYKFiyIK1euoGbNmlLHonTGBlYmYmVlhc2bN2P+/PmQyWRwd3dHp06dEBkZKXU0MjJxBeXSpUvIkycPLl26hGbNmkkdi4gMXNGiRXHt2jVUrFgR3t7eqFGjBm7fvi11LDJi+/fvR8eOHaFWqzF+/HjuqU5EySKTyTBgwADcuHEDxYoVw+PHj1G5cmVs2rQpxbep1mrxKUoJU7kM8nR+PpLJZJABiIxJfiOEEhal0kClETBNxwaWhakcMWptsrcRXLduHerVq4cPHz6gXr16uH37NipUqJBOKYkoM2nTpg1Onz4Ne3t7HDhwAI0aNUJwcLDUscjAxE1k9fLyQvny5XHt2jWUKFFC6liUAdjAymRkMhnGjBmDI0eOwM7ODnv27EGtWrXg5+cndTQyEteuXUP16tXx4sULlC1bFjdu3ED58uWljkVERiJ37tw4f/48mjRpgsDAQNSrVw8nTpyQOhYZoQMHDuiaV+PGjcOcOXPYvCKiFClXrhzu3LmDrl27IioqCj179kT37t1TdKZjqEKNSGX6bx8Yx8xEhgilBhotzy1JCwqVBiqtFmbpuKW6XCaDiQz4FKmE9gfOm1EqlRg8eDD69u0LpVKJoUOH4uTJk8iZM2e6ZSSizKd27dq4fPky8uXLhytXrqB27dp4/fq11LHIQBw/fhz16tVDQEAAGjVqhAsXLsDJyUnqWJRB2MDKpJo3b44bN26gaNGiePDgAapUqYLLly9LHYsM3O7du/Hzzz8jKCgITZs2xaVLl5AvXz6pYxGRkcmSJQsOHz6M7t27IzIyEi1btkzVbHairx08eBAdOnSAWq3G2LFj8ddff7F5RUSpYmtri82bN2PDhg2wtrbGli1bULlyZTx48CBZtxMcqcyQ7QPjmJnIoFRrEcVtBNNElFIDrYjdnjE9WZqZIFKpRngS2wgGBASgYcOGWLFiBczNzbF+/XosWbIEZmZm6ZqPiDInV1dXXLt2DaVLl4anpydq1qyJR48eSR2L9Nz69evRsmVLREZGolu3brpFGZR5sIGViZUoUQI3btxA48aNERgYiPr16+Off/6ROhYZICEE5s2bh44dOyImJgb9+/fH4cOHWVCIKN2YmZlh48aNmDBhAtRqNXr27InZs2dD/MBMY6LEHDp0SNe8Gj16NObOncvmFRGlCZlMhp49e+LWrVsoXbo0nj9/jmrVqmHVqlU/VL80WoGgDNo+MI6pXA61VguFkg2stBChVCOde1cAYs/BilFrEZrINoJ37txB5cqVcenSJTg5OeHChQvo1atX+ocjokwtX758uHjxIurUqYO3b9+iTp06uHDhgtSxSA8JITB9+nT06dMHGo0GEyZMwKZNm2Bubi51NMpgbGBlcg4ODjh69ChGjRoFlUqFfv36YdiwYVCpkrdfNmVer169QocOHTB+/HgAwLx587By5UqYmppKnIyIjJ1MJsPs2bOxdOlSyGQyTJo0CUOGDIFGwxfZKGUOHz6M9u3bQ6VSYdSoUbpzQ4mI0lKpUqVw48YN9OvXDzExMRg4cCA6deqEsLCwRK8XqlAhKgO3DwRiV2CpNIIrsNKAWqNFlFIDswzoYJnIZYCI3UYwoebo1q1bUbt2bfj7+6N69eq4c+cOqlevnu65iIiA2NciT548iXbt2iEsLAyNGzfGnj17pI5FeuTdu3f4/fff4e7uDrlcjuXLl/M84kyMDSyCqakpFi5ciPXr18Pc3BxLly7FTz/9hEuXLkkdjfRYaGgoxo4dixIlSmDv3r2wsrLCrl27MHbsWBYUIspQQ4YMwe7du2FhYYEVK1agQ4cOUCgUUsciA3P48GG0a9cOKpUKI0eOxIIFC1jPiCjdWFtbY82aNdi2bRtsbW2xe/duVKxYEbdu3frudT5FKRGdgdsHArHnKQkAkcrEt6KjpEWpNFBptDA1yZjHz8JMjvCY2DPT4qjVaowZMwZdu3ZFdHQ0+vTpg/Pnz/McESLKcJaWlti5cycGDx4MpVKJjh07YsGCBVCrWW8ys5iYGPz1118oVqwYdu7cCXNzS3TtuhdNmw6SOhpJiA0s0unVqxfOnz8PR0dHXLt2DXXr1kWjRo1w7do1qaORHlGpVFi2bBmKFCmCBQsWQKlUokOHbvjtt+fIl6+D1PGIKJNq164dTp06BXt7e+zfvx+NGjVCcHCw1LHIAGg0GsydO1fXvOrSZQTGj1/I5hURZYjff/8dd+/eRYUKFeDj44NatWrBw8Pjm1UzGq1AUKQSpnJk2PaBcUzlwOcYDbfpTSWFSguVRsDMJGMeP0tTOR57avHPltjdVYKDg9G8eXMsXLgQpqamWL58OdauXQsLC4sMyUNEFOfpU2DXLmDzZhMUL74UTZrEbgU/duxYlC9fHsePH2fNyWSEEDh06BBcXV0xYcIEREZGonXr1njy5Ak+fWqNwoWB+vWBwECpk5IU2MCieBwcaiBbNk/UqOEOOzs7nD59GjVr1kSzZs1w8+ZNqeORhIQQOHz4MMqUKYOhQ4fi06dPqFu3Lm7duoVSpTZjw4b8mDZN6pRElJnVqVMHly9fRr58+XDlyhWUKVMbBQq8xuHDUicjfeXr64uff/4Zbm5uUKlUaNduPC5cWARvbzaviCjjFC1aFNeuXcPQoUN1q0Bbt24dbyJGSFTs9oGWpiYZns9MLke0WoMYtTbD79uYRKk0UGsFTDPiECwAkZ9leOIpsP+4CidOPEKVKlVw6tQp5MyZE2fOnMGgQYM4WYOIJKHVAl26AL17A8OGyVCq1ATs3LkPzs7OePLkCZo3b44mTZrgwYMHUkelDODp6YmmTZuiVatW8Pb2RsmSJXHy5Ens378fRYoUwr//AkWLAi4uQM6cUqclKbCBRfE8egQ8fWqPT5+m4tWrV5g0aRJsbW1x4sQJVKtWDS1btsTdu3eljkkZ7N69e2jQoAF+/fVXPH/+HEWKFMH+/ftx/vx5VK5cGR06AO3bA6NHS52UiDI7V1dXXLt2DaVLl8a7d57w96+BLVt4KDDFJ4TAli1bUK5cOVy6dAmOjo44fvw49uz5C56eMlSpInVCIspsLCwssGTJEuzbtw/29vY4dOgQypcvjytXrgAAFi5X4eHTjN0+MI6piQxqnoOValFKNQQybgWdvb0MhQrIkC/vfrRvXwM+Pj6oWLEibt++jbp162ZIBiKihOTKBRQuHNuMOHYMWLQI6NixDZ49e4b58+cja9asOHXqFCpUqIDevXvj7du3UkemdBAaGoqRI0eibNmyOHnyJLJmzQoPDw88ePAAjRo10n2evT1w4ACwerVkUUlisvRckimTyQQALvs0IEIA27cDVarEdrcBICgoCAsXLsSSJUsQFRUFAGjdujWmTp2KcuXKSZiW0tvbt28xefJkbNq0CUIIODg4wN3dHQMHDoS5ubnU8SgNxc2+FEJwGuZ3sKYZltDQULRo0RpXrsQ2rzp16oQFCxYgX758EicjqQUHB2PgwIHYtWsXAKBNmzZYs2YNcuTIIXEySiusaYljPdN/vr6++O2333Djxg2YmJhg+owZeKPqh5IVNKhWLuP/BldrBcIUKpRxskM+e6sMv39jIITAjdehCFUokc06/R9DIQTuXLuCdUsX4u7V2L+FOnfujLVr18La2jrd75/SButZ0ljTDNf+/UCNGoCj47f/9+nTJ8yYMQPLly+HWq2GtbU1xowZg7Fjx8LW1jbjw1Ka0mg02LBhAyZOnIjAwEDIZDL069cPM2fORE4usTJaqalpbGDRDwsICMD8+fOxfPlyKBQKAED79u3h7u6O0qVLS5yO0lJkZCTmz5+P+fPnIyoqCmZmZhgyZAgmT56MbNmySR2P0gEHR0ljTTM8MTExmD9/PmbPng2FQgFra2tMmjQJo0eP5nkPmdTp06fRs2dPvH37Fra2tliyZAl69uzJLZSMDGta4ljPDINKpcKkSZMwf/58AECR0nUxZMJYVK9VM8Ofs4QQCIxQoVguGxTLyRcOUyJarcENvxCoNAJ2lqbpdj9CCFw+ewobli3Ck7uxRwBY29hi8p/ucBs7mvXOwLCeJY01zbi9fPkSbm5u2LdvHwAgd+7cmDFjBnr37g0Tk4zfUpdS78qVKxg2bJhud6/atWtj8eLFqFixosTJKL2xgUUZ6sOHD5g7dy5WrlyJmJgYyGQydOzYEe7u7ihZsqTU8SgVNBoNNm/ejMmTJ+Pdu3cAgLZt22Lu3LkoUqSIxOkoPXFwlDTWNMPl5+eHMWPGYM+ePQCAwoULY/Hixfjll18kTkYZJTo6GhMmTICHhwcAoGbNmtiyZQsKFSokbTBKF6xpiWM9MyzHjx9H127dEfwpCADgUrwU2nfvg1/adoSVtU2G5QiKVMIxiwUq5rPPsPs0JiFRStx5EwYzExmszNL+RVeNRoOzx49g4/JF8Hr6CACQ1cEBgwYPxegRw5A9e/Y0v09Kf6xnSWNNyxwuX76M0aNH4+bN2MZ86dKlMX/+fDRp0oSNeQPx9u1bjBs3Dtu2bQMA5M2bFwsWLECnTp34GGYSbGCRJN69e4c5c+ZgzZo1UCqVkMlk6Ny5M/78808UK1ZM6niUTGfPnsXo0aNx//59AEDlypWxaNEi1KlTR9pglCE4OEoaa5rhO3PmDIYNG4anT58CAH755Rd4eHiwQW/kHjx4gC5duuDJkycwNTWFu7s73NzcYGqafjPgSVqsaYljPTM879+/x0KPJdiw7h9dI8smix2atv0NnXr0QcHCRdM9Q5hCBUszE9Qo6ABTEx6lnVxvw6Lx8F04slqapOn3T61S4dj+3di8cjH8fV4CAHLkyo0RI0dh2OCByJIlS5rdF2U81rOksaZlHkII7Nq1C25ubvD19QUANGrUCPPnz+fxJnosOjoaCxcuxOzZsxEVFQULCwuMHTsWbm5usLHJuIk4JD02sEhS/v7+mD17NtatWweVSgW5XI5u3bphypQpKFy4sNTxKAnPnj3DuHHjcPjwYQBAvnz5MGfOHHTu3BlyOQenmQUHR0ljTTMOKpUKy5cvh7u7O8LDw2Fubo7Ro0dj4sSJ3E/dyGi1WixatAiTJk2CUqlEsWLFsHXrVlSuXFnqaJTOWNMSx3pmuGJiYrBtxy4sW74cd2/d0H28Uu166Ni9L+o0bJJuWypFKjUQQqByfnvYWZqly30Ys5dBkXgeEIGcNmZpMtM8JjoaB3b+i62rl+LjW38AQN78BTBm7FgM6NcXlpaWqb4Pkh7rWdJY0zKfmJgYLF26FDNnzkRYWBhkMhl69uyJGTNmIG/evFLHo/8RQuDgwYMYNWoUXr16BSB2h6cFCxbAxcVF4nQkBTawSC/4+vpi1qxZ2LBhAzQaDUxMTNCzZ09MnjwZzs7OUsejrwQFBWHq1KlYtWoVNBoNbG1tMWHCBIwcORJWVjycObPh4ChprGnG5ePHj3Bzc8PGjRsBcAsDY/P69Wv06NED58+fBwAMHDgQ8+fP5yy/TII1LXGsZ8bh9p27+HvJUuzbtRPR0bHnE+fKkw+tu/RCm9+6IluOtD0EXanRIjJGg3J57eCYhc2R5HrwNgxvwqKR09Y8VbcTFRmBXZvXY8c/KxASFAAAKFSkKNzcJqBn964wM2Nz0ZiwniWNNS3z+vTpE2bMmIHly5dDrVbD2toaY8aMwdixYzkxUWJPnz7F8OHDcfr0aQCAq6srFi9ejAYNGkicjKTEBhbpFR8fH8yYMQObN2+GVquFqakp+vTpg0mTJiF//vxSx8v0vp6tIpfL0bdvX0ybNg2Ojo5SxyOJcHCUNNY043T9+nUMHToUt2/fBgD89NNPWLJkCcqWLStxMkqpbdu2YdCgQQgLC0OuXLmwfv16nneWybCmJY71zLgEBwdj9T/rsGrlKrz29QEAmJqZ4+fmrdCpZ1+UrlA5TSZmCCEQGKlCqdxZUCi7dapvLzPRaAWu+YUgUqmGg1XKGkyhIcHYsXEt9mxYg89hIQCAUmXKYtKkSejUvl26rbwjabGeJY01jV6+fAk3Nzfs27cPAODo6Ijp06ejd+/efG7MYCEhIZg6dSqWL18OjUYDe3t7zJgxAwMGDOD27cQGFumnly9fYsaMGdi6dSu0Wi3Mzc3Rr18/TJgwgct6JSCEwJ49ezB+/Hjd8t0mTZpgwYIFKF26tMTpSGocHCWNNc14abVarF+/HhMmTEBQUBDkcjkGDRqE6dOnw8HBQep49INCQkIwaNAg7NixAwDQqlUrrF27Fjlzpu0qBNJ/rGmJYz0zTlqtFkePn8CSpctx5uRx3eNb1LUc2nfvg6at28HSMnW7LAREKFHQwQplnOzSInKmERmjxk3/UAghYGuRvBfwggI+4t+1K3Dg3/VQREUCACpVrY4pkyfh1xa/cNW4kWM9SxprGsW5fPkyRo8ejZs3bwIASpcujfnz56NJkyZ8rkxnGo0G//zzDyZPnqwbT//xxx+YMWMGcuTIIXU80hNsYJFee/bsGaZPn44dO3ZACAELCwsMGDAAbm5uXPGTQa5fv47Ro0fj6tWrAGKX7y5YsABNmzaVOBnpCw6OksaaZvxCQkLg7u6O5cuXQ6vVIkeOHJg9ezZn7xmAs2fPokePHnjz5g1sbGzg4eGBPn36cLCaSbGmJY71zPj5+PjAY+lybN60AWEhsat1smR1QPMOndGxe2/kK5iysyeCo1SwszRF9YIOkPP59YcFRsTg3tswWJmZwML0x84Yfv/GHxtXLsax3dugjIkGANT5uQHcJ09C/Z/rsb5lEqxnSWNNoy8JIbBr1y64ubnB19cXANCoUSPMnz8f5cqVkzackbp06RKGDRuG+/fvAwDq1q2LJUuW8PtN32ADiwzCkydPMG3aNOzevRsAYGVlhUGDBmHcuHHIlSuXxOmMk6+vLyZMmKCbjZ4rVy5Mnz4dffr04fJdioeDo6SxpmUeDx8+xNChQ3Hx4kUAQKVKlbBs2TJUr15d4mT0tZiYGEyaNAkLFy4EAFSrVg3//vsvihQpInEykhJrWuJYzzIPhUKBLdu2Y/my5Xh4/y6A2N+Pqj81RMcefVGzXgPI5T/WUAGAzzFqmMhkqFrAAdbmnNjxo16HKPD4QzgcrMxgIk/8acnP+yXWL/fAqYO7oVGrAQBNW7SE++TJqF6takbEJT3CepY01jRKSHR0NJYtW6Y7OkMmk6Fnz56YOXMm8uTJI3U8o+Dv749x48bpXm/Mnz8/FixYgA4dOnCSBSWIDSwyKA8fPsTUqVOxf/9+AIC1tTWGDh2KMWPGcGlpGgkLC8OcOXPg4eGBmJgYWFhYYNSoUXBzc4OdHbf8oG9xcJQ01rTMRQiBnTt3YsyYMXj79i0AoGfPnvjrr7+QO3duidMRADx69AhdunTBo0ePYGJigj///BMTJ07kBA1iTUsC61nmdO36Dfy9ZCkO7tsDZUwMAMCpgDPadOmFVp26wN4hW5K3Ea3WIEYlUCFfVuSwMU/vyEbjWUAEvIIikcv2+9+z508eYf3SRbhw4hCEEJDL5WjdrgP+nDwJ5cqWycC0pE9Yz5LGmkaJ+fTpE6ZPn44VK1ZArVbD2toaY8aMwdixY2Frayt1PIOkUCiwYMEC/PXXX4iKioKlpSXGjx+PcePGwdqaZ2TS97GBRQbp7t27mDp1Kg4fPgwAsLW1xbBhwzB69Ghky5b0AIq+pVarsWbNGri7uyMoKAgA0KVLF8yePRsFChSQOB3pMw6OksaaljlFRERg9uzZWLhwIZRKJezs7DB16lQMGTIEZmYpO4idUker1cLDwwMTJkyAUqlEkSJF8O+//6JatWpSRyM9wZqWONazzC0wMBArVq/F2jWr8db/NQDA3MISDVq2RaeefVGyTPnvXlejFQhRqFDGyQ757VN3nlZmcut1KAIjYxJs+j28cxPrli7C9XMnAQCmZmb4vUs3TJ7ohmJFi2Z0VNIzrGdJY02jH/Hy5Uu4ublh3759AABHR0dMnz6dW8UngxAC+/fvx+jRo3XbM7Zv3x4LFixAwYIFpQ1HBoENLDJot27dgru7O44fPw4AsLOzw4gRIzBy5EjY29tLG85ACCFw/PhxjBkzBp6engCA2rVrY+HChahalVtNUNI4OEoaa1rm5uXlhREjRuDo0aMAgFKlSmHJkiVo0KCBxMkyF39/f/Ts2RNnz54FAPzxxx9YuHAhZ1BSPKxpiWM9IyD2wPUDhw5jybLluHj2tO7jJctVQocefdCoRRuYW1h8c72Az0oUyWGNErmzZGRcg6XUaHHdNwQxGg2yWsZOfBFC4OblC1i/dBHu37gMALC0tEKP3n0wyW0c8ufPL2Vk0iOsZ0ljTaPkuHz5MkaPHo2bN28CAEqXLo358+ejSZMm3PYuEY8ePcKIESN0Y7AyZcpg8eLF+PnnnyVORoaEDSwyCtevX4e7uztOnoydfZY1a1aMGDEC9erVQ+HChZE3b95k7dFujNRqNV69egVPT094enri2bNnurdhYWEAgEKFCmHevHlo27YtCzD9MA6OksaaRgBw5MgRjBgxAt7e3gCAdu3aYeHChZx1lgF27NiBgQMHIjQ0FDlz5sS6devQsmVLqWORHmJNSxzrGX3t2fPn8Fi6HNu2bMbn8NgxRdZs2dGiY1d06NYbTvn+v6ESFKlEThsLVClgL1FawxIWrcJt/1CYyGSwMpPj4qkTWL9sIZ49iD2TzDaLHf4YMBDjx4ziudD0DdazpLGmUXIJIbBr1y64ubnpVhI1atQI7u7uKF++PGxsbKQNKLHw8HA8efJEd3n8+DHOnTsHjUaDbNmyYcaMGfjjjz+4bTslGxtYZFQuX74Md3d3XWc/joWFBVxcXFC4cGEUKlQIhQsX1l1cXFxgaWkpUeK0FxkZiefPn+saVHFNqpcvX0KpVCZ4naxZc+PPP8dh8ODBsEhgtiRRYjg4ShprGsWJjo7GokWLMGvWLERFRcHKygpubm4YO3YsrKy4pVJaCw0NxZAhQ7B161YAQIsWLfDPP//wLDL6Lta0xLGe0fdERkZi4+Z/sXzFcng+fgQAkMvlqFG/CTr26ItqdeohPEYNCxMTVHd2gLlJ5p5c+CM+hEfjrn8Irp8+gn9XeuDV86cAAIds2TF42DCMGjYUDg4OEqckfcV6ljTWNEqp6OhoLFu2DDNnztRNCAeA/Pnzo0SJEihRogSKFy+ue5s3b16jmiQeEREBT09PPH78OF7Dyt/f/5vPlcvl6NFjIObPn4bs2bNLkJaMARtYZJTOnTuPtWs3wNf3Jby9vREQEPDdz5XJZMibN2+Cza3ChQvr5ZlaQggEBQUluJrKz8/vu9crUKAASpQogZIlS6JkyZIoUaIENJqSmDo1Jy5eNJ5iShmLg6OksabR1/z9/TF27Fjs3LkTAODi4oK///4bv/76q1ENbqR0/vx5dO/eHf7+/rC2tsbff/+Nfv368ftLiWJNSxzrGSVFCIGLly9j8ZJlOHxwP9QqFQAgn0sRtOzcEw1a/oYGZQvA3opnQX5NCIGAgAD4+vri/n1f7DvqiYf3t+CDvw8AIJejE0aNHo0hAwdk+ln+lDTWs6SxplFqnT37CcOGzYZWexxeXl5Q/a/mfc3W1hbFixeP19QqUaIEihYtqteTGBUKBTw9PeOtqHry5Ilu9dnXLCwsULJkSbi6usLV1RWlS5eGhUVF/PZbXixcCPTsCXAoRinBBhYZpRIlgIgI4No1IH9+4PPnz/Dx8YG3t7fubdzFz88PGo3mu7dlb2//3eZW3rx50/XQRo1GAz8/v29WU3l6eiI4ODjB65iZmaFo0aK6BlXc2+LFiyd4zsetW8DevcCcOSwklDIcHCWNNY2+5/z58xg6dCgeP34MAGjSpAkWL16M4sWLS5zMMB08CJQsGYN//pmCBQsWQAiBqlWrYsuWLShWrJjU8cgAsKYljvWMkuP9+/dYtmo11q1di4/v3wEATM2tUahwFVSpmA958uRB3rx5kSdPnngXY90R4ssG1fcu0dHR31wvv7MLxo8bj769exrt94bSHutZ0ljTKDV8fICSJYG1a4Hu3f//2I5nz57pdkWKexsUFJTgbchkMhQsWPCbFVslSpSAo6Njhk28i4mJwfPnz79ZUeXt7Z3g74eZmRmKFy+O0qVL65pVrq6uKFy4cIKvkXbqBLRoAXTrlhFfDRkjNrDI6ERGAi4uwKdPsQUlqaNFVCoVXr9+/U1jK+4SGRn53euam5snujXhj86kiI6OxosXL75ZTfX8+fMEBzEAkCVLlngrqeLed3FxgZkZZzRSxuHgKGmsaZQYtVqNlStX4s8//0RoaCjMzMwwYsQITJkyBZ8/Z0H9+sDw4cDAgVIn1X/lyz9GWFhX+Po+gFwux+TJkzF58mTWRfphrGmJYz2jlFCr1dizbz/Guy3H61cXkvz87NmzJ9jc+vLfuXPnTteJhCmRVIPKz88PCoUi0dvInj07nJ2dkT27Mzw9ndGqTWX8vbA9zwuhZGM9SxprGqXWuXNAzZpAUnMLPn36pGtmfdnY8vb2/u6Eejs7u2+aWiVKlECRIkVSPJlBpVLhxYsX36yo8vLySjCHiYkJihUrFq9JVbp0aRQpUiRZ46uYmKS/R0SJYQOLjFLctqv58yf+eUkRQiAwMDDBxpaPjw8+fPiQ6PUT2prQ0dERPj4+8VZVvXr16rs/605OTgk2qpycnLgNEukFDo6SxppGPyIwMBATJ07EunXrIISAk5MT5s6dh9Gju2D/fhlq1ZI6of4JDw/H3bt3cfv2bdy5cwd79uyHWh2DwoULY8uWLahRo4bUEcnAsKYljvWMUkOjAQ4f9oWNjRfevXuLd+/e6S5v38b++/3791Cr1Unellwuh6Oj43cbXHHvZ8uWLc3GTHFjw8RWUCXVoMqWLRucnZ2/ubi4uKBgwYLIkiVLmmQlYj1LGmsaSU2pVOpeH/x61VZISEiC15HL5XBxcUmwuZUzZ07IZDKo1Wp4e3t/s6Lq+fPnCdZYuVyOwoUL6xpUcc2qYsWKceUv6QU2sIhSITIy8puVW3H/9vX1/aHBFxA7q6Fw4cLxtvyLe5s1a9Z0/iqIUoeDo6SxplFy3Lp1C0OGDMHNmzcBAHny1MCQIb+iVKmSKFWqFAoVKqR3s84zQnh4OO7du4c7d+7oGlYvXrz45vP69u2Lv//+O8Ftc4mSwpqWONYzSm9arRaBgYEJNre+/HdiZxx/ydzcPMnVXHny5EGWLFnStUHl7OyMggULws7OLi2+TURJYj1LGmsa6au4evR1U+vZs2d49eoVtFptgtezt7fXTZpXKpUJfk6hQoW+WVFVvHhxvT6Li4gNLKJ0olar4e/v/01j68OHDyhYsGC8VVWpWQJMJDUOjpLGmkbJpdVqsWnTJri5uX3zIp2FhQWKFSuGUqVKoVSpUihZMraxVbRoUZibm0uUOG1FRETg3r17ukbV7du38eLFi29+h8zNzVG2bFlUqlQJlStXRo0aNeDq6ipRajIGrGmJYz0jfaFSqfDhw4dvGlxfN7tCQ0N/6PayZMkCtVqdZIPKwcHhm5VTbFCRPmI9SxprGhmimJgYeHl5JbglYXh4uO7zChQo8M2KqpIlS8LGxkbC9EQpwwYWUQb65ZfY7Q1PngQcHaVOQ5Q2ODhKGmsapVRoaCj27t2Lx48f4+nTp/D09IR/3D65XzExMUGRIkW+aWwVL14c1tbWGZz8x0VEROD+/fvxVlY9e/bsm98XMzMzXbMqrmFVunRpo2nakX5gTUsc6xkZmsjISLx//z7BBlfc+69fv4VKFXvu8NcNqq9XUHF3DDIUrGdJY00jYyKEwMePH9G793s8f14Yly/bwclJ6lREaYMNLKIMEh0NZM8OREUBT54ApUpJnYgobXBwlDTWNEpL4eHhunMUnz59qmts+fj4JPgzJpPJ4OzsHK+pFfd+SmeKnzwJrFoFFCkCzJ4N/OjZ8lFRUbh//368lVXPnj37ZhsMU1NTlClTBpUrV47XrOJqZUpvrGmJYz0jY/THHwJr14Zi4EA5Vqxgg4qMA+tZ0ljTyNhotYCLC/D6NXDqFNCwodSJiNIGG1hEGejhQyAsDKhTR+okRGmHg6OksaZRRlAoFHj+/Pk3ja2XL19+90zGvHnzJtjYypEjR6L39fAhUK4c0KYNsG9fwp8TFRWFBw8exFtZ9fTp0wSbVaVLl9Y1qipVqoQyZcrA0tIyRd8HotRgTUsc6xkZo5AQ4Px54KefgGzZpE5DlDZYz5LGmkbG6OlTwNcXaN5c6iREaYcNLCIiShUOjpLGmkZSUiqV8Pb2jtfUevr0KZ49e4aYmJgEr5MzZ84EG1tOTk6QyWTQamO3wl22DOjYMbZ59vDhw3grq54+fQqNRhPvdk1MTODq6hpvZVXZsmXZrCK9wZqWONYzIiLDwHqWNNY0IiLDwAYWERGlCgdHSWNNI32k0Wjg6+v7TWPL09MTERERCV4na9asumbW/fuFULq0Dx48uIPHjx9/06ySy+VwdXWNt7KqXLlysLKyyogvjyhFWNMSx3pGRGQYWM+SxppGRGQY2MAiIqJU4eAoaaxpZEiEEHjz5s03ja2nT58iJCQkwevI5XKULFky3sqqcuXKwdraOoPTE6UOa1riWM+IiAwD61nSWNOIiAwDG1hERJQqHBwljTWNjIEQAgEBAbpmlo+PDwoWLKhrVtnY2EgdkSjVWNMSx3pGRGQYWM+SxppGRGQY2MAiIqJU4eAoaaxpRESGgTUtcaxnRESGgfUsaaxpRESGITU1TZ7maYiIiIiIiIiIiIiIiIhSgQ0sIiIiIiIiIiIiIiIi0itsYBEREREREREREREREZFeYQOLiIiIiIiIiIiIiIiI9AobWERERERERERERERERKRX2MAiIiIiIiIiIiIiIiIivcIGFhEREREREREREREREekVNrCIiIiIiIiIiIiIiIhIr7CBRURERERERERERERERHqFDSwiIiIiIiIiIiIiIiLSK2xgERERERERERERERERkV5hA4uIiIiIiIiIiIiIiIj0ChtYREREREREREREREREpFfYwCIiIiIiIiIiIiIiIiK9wgYWERERERERERE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+ "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/dev_plotting_7_1.png" + }, + "image/png": { + "height": 253, + "width": 856 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "f,axes = plt.subplots(1, 4, figsize=(15,4))\n", + "ax = axes[0]\n", + "import seaborn as sns\n", + "sns.set_context(\"paper\", font_scale=1.5)\n", + "# Add data, AU is ordered as such: \n", + "# AU1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43\n", + "\n", + "# Activate AU1: Inner brow raiser \n", + "au = np.array([intensity, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"AU1: Inner brow raiser\")\n", + "\n", + "ax = axes[1]\n", + "au = np.array([0, 0, 0, 0, 0, 0, 0, 0, intensity, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"AU12: Lip corner puller\")\n", + "\n", + "ax = axes[2]\n", + "au = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, intensity, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"AU15: Lip corner depressor\")\n", + "\n", + "ax = axes[3]\n", + "au = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, intensity])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"AU43: Eye closer\")" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:24:24.064595Z", + "start_time": "2021-03-27T00:24:23.762529Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Fear')" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/dev_plotting_8_1.png" + }, + "image/png": { + "height": 253, + "width": 856 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "f,axes = plt.subplots(1, 4, figsize=(15,4))\n", + "ax = axes[0]\n", + "import seaborn as sns\n", + "sns.set_context(\"paper\", font_scale=1.5)\n", + "# Add data, AU is ordered as such: \n", + "# AU1, 2, 4, 5, 6, \n", + "# 7, 9, 10, 12, 14, \n", + "# 15, 17, 18, 20, 23, \n", + "# 24, 25, 26, 28, 43\n", + "intensity = 2\n", + "# Activate AU1: Inner brow raiser \n", + "au = np.array([0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " intensity, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " intensity, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"Happiness\")\n", + "\n", + "ax = axes[1]\n", + "au = np.array([intensity, \n", + " 0, \n", + " intensity, \n", + " 0, 0, 0, 0, 0, 0, 0, intensity, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"Sadness\")\n", + "\n", + "ax = axes[2]\n", + "au = np.array([intensity, intensity, 0, intensity, 0, 0, 0, 0, 0, 0, \n", + " 0, 0, 0, 0, 0, 0, 0, intensity, 0, 0])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"Surprise\")\n", + "\n", + "ax = axes[3]\n", + "\n", + "# FEAR\n", + "au = np.array([intensity, intensity, intensity, intensity, 0, \n", + " intensity, 0, 0, 0, 0, \n", + " 0, 0, 0, intensity, 0, \n", + " 0, 0, intensity, 0, 0])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"Fear\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add a vectorfield with arrows from the changed face back to neutral and vice versa " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:00:25.672632Z", + "start_time": "2021-03-27T00:00:25.589946Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/dev_plotting_12_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face, predict\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "model = load_h5('pyfeat_aus_to_landmarks.h5')\n", + "# Add data activate AU1, and AU12\n", + "au = np.array([2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ])\n", + "\n", + "# Get neutral landmarks\n", + "neutral = predict(np.zeros(len(au)))\n", + "\n", + "# Provide target landmarks and other vector specifications\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "fig, axes = plt.subplots(1,2)\n", + "# Plot face where vectorfield goes from neutral to target, with target as final face\n", + "plot_face(model = model, ax = axes[0], au = np.array(au), \n", + " vectorfield = vectors, color='k', linewidth=1, linestyle='-')\n", + "\n", + "# Plot face where vectorfield goes from neutral to target, with neutral as base face\n", + "plot_face(model = model, ax = axes[1], au = np.zeros(len(au)), \n", + " vectorfield = vectors, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add muscle heatmaps to the plot" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:27.181441Z", + "start_time": "2021-03-26T04:54:27.109016Z" + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/dev_plotting_14_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "model = load_h5()\n", + "\n", + "au = np.array([2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "\n", + "# Add some muscles\n", + "muscles = {'orb_oris_l': 'yellow', 'orb_oris_u': \"blue\"}\n", + "muscles = {'all': 'heatmap'}\n", + "\n", + "plot_face(model=model, au = np.array(au), \n", + " muscles = muscles, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:35.665640Z", + "start_time": "2021-03-26T04:54:35.587594Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/dev_plotting_15_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "au = [0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, \n", + " 0.450328, 1.02961, 0.871225, 0, 1.1977, 0.457218, 0, 0, 0, 0]\n", + "au = np.array([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "\n", + "# Add some muscles\n", + "muscles = {'all': 'heatmap'}\n", + "\n", + "# Plot face\n", + "plot_face(model=None, au = np.array(au), muscles = muscles, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make sure muscle array contains 'facet' for a facet heatmap" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:44.068508Z", + "start_time": "2021-03-26T04:54:43.994084Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/dev_plotting_17_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "au = np.array([0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, \n", + " 0.450328, 1.02961, 0.871225, 0, 1.1977, 0.457218, 0, 0, 0, 0])\n", + "\n", + "# Load a model \n", + "model = load_h5()\n", + "\n", + "# Add muscles\n", + "muscles = {'all': 'heatmap', 'facet': 1}\n", + "\n", + "# Plot face\n", + "plot_face(model=model, au = au, muscles = muscles, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add gaze vectors\n", + "Add gaze vectors to indicate where the eyes are looking. \n", + "Gaze vectors are length 4 (lefteye_x, lefteye_y, righteye_x, righteye_y) where the y orientation is positive for looking upwards." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:50.850198Z", + "start_time": "2021-03-26T04:54:50.805184Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/dev_plotting_19_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "au = np.zeros(20)\n", + "\n", + "# Add some gaze vectors: (lefteye_x, lefteye_y, righteye_x, righteye_y)\n", + "gaze = [-1, 5, 1, 5]\n", + "\n", + "# Plot face\n", + "plot_face(model=None, au = au, gaze = gaze, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Call plot method on Fex instances\n", + "It is possible to call the `plot_aus` method within openface, facet, affdex fex instances" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OpenFace" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:56:59.035420Z", + "start_time": "2021-03-26T04:56:58.928294Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/dev_plotting_22_1.png" + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from feat.utils import load_h5, get_resource_path, read_openface\n", + "from feat.tests.utils import get_test_data_path\n", + "from os.path import join\n", + "\n", + "test_file = join(get_test_data_path(),'OpenFace_Test.csv')\n", + "openface = read_openface(test_file)\n", + "openface.plot_aus(12, muscles={'all': \"heatmap\"}, gaze = None)" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "toc": { + "base_numbering": 1, + "nav_menu": { + "height": "171px", + "width": "252px" + }, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": "block", + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/notebooks/_build/jupyter_execute/content/dev_plotting.py b/notebooks/_build/jupyter_execute/content/dev_plotting.py new file mode 100644 index 00000000..913c0125 --- /dev/null +++ b/notebooks/_build/jupyter_execute/content/dev_plotting.py @@ -0,0 +1,276 @@ +# Plotting examples +*written by Jin Hyun Cheong* + +Included in the toolbox are two models for Action Units to landmark visualization. The 'pyfeat_aus_to_landmarks.h5' model was created by using landmarks extracted using Py-FEAT to align each face in the dataset to a neutral face with numpy's least squares function. Then the PLS model was trained using Action Unit labels to predict transformed landmark data. + +Draw a standard neutral face. Figsize can be altered but a ratio of 4:5 is recommended. + +# Load modules +%matplotlib inline +from feat.plotting import plot_face, predict +import numpy as np +import matplotlib.pyplot as plt + +plot_face(au=np.zeros(20)) + +## Draw lineface using input vector + +Affectiva vectors should be divided by twenty for use with our 'blue' model. + +from feat.plotting import plot_face +import numpy as np +import matplotlib.pyplot as plt + +# Add data, AU is ordered as such: +# AU1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43 + +# Activate AU1: Inner brow raiser +au = np.array([5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) + +vectors = {'target': predict(au), + 'reference': neutral, 'color': 'blue'} + +# Plot face +fig, axes = plt.subplots(1,2) +plot_face(model=None, vectorfield = vectors, + ax = axes[0], au = np.zeros(20), color='k', linewidth=1, linestyle='-') +plot_face(model=None, vectorfield = vectors, + ax = axes[1], au = np.array(au), color='k', linewidth=1, linestyle='-') + +%config InlineBackend.figure_format = 'retina' +intensity = 2 + +f,axes = plt.subplots(1, 4, figsize=(15,4)) +ax = axes[0] +import seaborn as sns +sns.set_context("paper", font_scale=1.5) +# Add data, AU is ordered as such: +# AU1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43 + +# Activate AU1: Inner brow raiser +au = np.array([intensity, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) +vectors = {'target': predict(au), + 'reference': neutral, 'color': 'blue'} + +plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'}, + ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-') +ax.set_title("AU1: Inner brow raiser") + +ax = axes[1] +au = np.array([0, 0, 0, 0, 0, 0, 0, 0, intensity, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) +vectors = {'target': predict(au), + 'reference': neutral, 'color': 'blue'} + +plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'}, + ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-') +ax.set_title("AU12: Lip corner puller") + +ax = axes[2] +au = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, intensity, 0, 0, 0, 0, 0, 0, 0, 0, 0]) +vectors = {'target': predict(au), + 'reference': neutral, 'color': 'blue'} +plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'}, + ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-') +ax.set_title("AU15: Lip corner depressor") + +ax = axes[3] +au = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, intensity]) +vectors = {'target': predict(au), + 'reference': neutral, 'color': 'blue'} + +plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'}, + ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-') +ax.set_title("AU43: Eye closer") + +f,axes = plt.subplots(1, 4, figsize=(15,4)) +ax = axes[0] +import seaborn as sns +sns.set_context("paper", font_scale=1.5) +# Add data, AU is ordered as such: +# AU1, 2, 4, 5, 6, +# 7, 9, 10, 12, 14, +# 15, 17, 18, 20, 23, +# 24, 25, 26, 28, 43 +intensity = 2 +# Activate AU1: Inner brow raiser +au = np.array([0, + 0, + 0, + 0, + intensity, + 0, + 0, + 0, + intensity, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0]) +vectors = {'target': predict(au), + 'reference': neutral, 'color': 'blue'} + +plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'}, + ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-') +ax.set_title("Happiness") + +ax = axes[1] +au = np.array([intensity, + 0, + intensity, + 0, 0, 0, 0, 0, 0, 0, intensity, 0, 0, 0, 0, 0, 0, 0, 0, 0]) +vectors = {'target': predict(au), + 'reference': neutral, 'color': 'blue'} + +plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'}, + ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-') +ax.set_title("Sadness") + +ax = axes[2] +au = np.array([intensity, intensity, 0, intensity, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, intensity, 0, 0]) +vectors = {'target': predict(au), + 'reference': neutral, 'color': 'blue'} +plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'}, + ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-') +ax.set_title("Surprise") + +ax = axes[3] + +# FEAR +au = np.array([intensity, intensity, intensity, intensity, 0, + intensity, 0, 0, 0, 0, + 0, 0, 0, intensity, 0, + 0, 0, intensity, 0, 0]) +vectors = {'target': predict(au), + 'reference': neutral, 'color': 'blue'} + +plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'}, + ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-') +ax.set_title("Fear") + + + + + +## Add a vectorfield with arrows from the changed face back to neutral and vice versa + +from feat.plotting import plot_face, predict +from feat.utils import load_h5 +import numpy as np +import matplotlib.pyplot as plt + +model = load_h5('pyfeat_aus_to_landmarks.h5') +# Add data activate AU1, and AU12 +au = np.array([2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]) + +# Get neutral landmarks +neutral = predict(np.zeros(len(au))) + +# Provide target landmarks and other vector specifications +vectors = {'target': predict(au), + 'reference': neutral, 'color': 'blue'} + +fig, axes = plt.subplots(1,2) +# Plot face where vectorfield goes from neutral to target, with target as final face +plot_face(model = model, ax = axes[0], au = np.array(au), + vectorfield = vectors, color='k', linewidth=1, linestyle='-') + +# Plot face where vectorfield goes from neutral to target, with neutral as base face +plot_face(model = model, ax = axes[1], au = np.zeros(len(au)), + vectorfield = vectors, color='k', linewidth=1, linestyle='-') + +## Add muscle heatmaps to the plot + +from feat.plotting import plot_face +from feat.utils import load_h5 +import numpy as np +import matplotlib.pyplot as plt + +# Add data +model = load_h5() + +au = np.array([2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) + +# Add some muscles +muscles = {'orb_oris_l': 'yellow', 'orb_oris_u': "blue"} +muscles = {'all': 'heatmap'} + +plot_face(model=model, au = np.array(au), + muscles = muscles, color='k', linewidth=1, linestyle='-') + +from feat.plotting import plot_face +from feat.utils import load_h5 +import numpy as np +import matplotlib.pyplot as plt + +# Add data +au = [0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, + 0.450328, 1.02961, 0.871225, 0, 1.1977, 0.457218, 0, 0, 0, 0] +au = np.array([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) + +# Add some muscles +muscles = {'all': 'heatmap'} + +# Plot face +plot_face(model=None, au = np.array(au), muscles = muscles, color='k', linewidth=1, linestyle='-') + +## Make sure muscle array contains 'facet' for a facet heatmap + +from feat.plotting import plot_face +from feat.utils import load_h5 +import numpy as np +import matplotlib.pyplot as plt + +# Add data +au = np.array([0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, + 0.450328, 1.02961, 0.871225, 0, 1.1977, 0.457218, 0, 0, 0, 0]) + +# Load a model +model = load_h5() + +# Add muscles +muscles = {'all': 'heatmap', 'facet': 1} + +# Plot face +plot_face(model=model, au = au, muscles = muscles, color='k', linewidth=1, linestyle='-') + +## Add gaze vectors +Add gaze vectors to indicate where the eyes are looking. +Gaze vectors are length 4 (lefteye_x, lefteye_y, righteye_x, righteye_y) where the y orientation is positive for looking upwards. + +from feat.plotting import plot_face +from feat.utils import load_h5 +import numpy as np +import matplotlib.pyplot as plt + +# Add data +au = np.zeros(20) + +# Add some gaze vectors: (lefteye_x, lefteye_y, righteye_x, righteye_y) +gaze = [-1, 5, 1, 5] + +# Plot face +plot_face(model=None, au = au, gaze = gaze, color='k', linewidth=1, linestyle='-') + +## Call plot method on Fex instances +It is possible to call the `plot_aus` method within openface, facet, 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In this tutorial we use the datasets EmotioNet, DISFA Plus, and BP4d. After you download each model you should extract the labels and landmarks from each dataset. Detailed code on how to do that is described at the bottom of this tutorial. Once you have the labels and landmark files for each dataset you can train the AU visualization model with the following. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:28:05.706631Z", + "start_time": "2021-03-27T00:28:03.811433Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EmotioNet: 24587\n", + "DISFA Plus: 57668\n", + "BP4D: 143951\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import pandas as pd, numpy as np, matplotlib.pyplot as plt\n", + "from sklearn.cross_decomposition import PLSRegression\n", + "from sklearn.model_selection import KFold\n", + "from feat.plotting import predict, plot_face\n", + "from feat.utils import registration, neutral\n", + "from natsort import natsorted\n", + "import os, glob \n", + "import pandas as pd, numpy as np\n", + "import seaborn as sns\n", + "sns.set_style(\"white\")\n", + "\n", + "au_cols = [1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43]\n", + "au_cols = [f\"AU{au}\" for au in au_cols]\n", + "\n", + "base_dir = \"/Storage/Projects/feat_benchmark/scripts/jcheong/openface_train\"\n", + "\n", + "labels_emotionet = pd.read_csv(os.path.join(base_dir, \"emotionet_labels.csv\"))\n", + "landmarks_emotionet = pd.read_csv(os.path.join(base_dir, \"emotionet_landmarks.csv\"))\n", + "print(\"EmotioNet: \", len(labels_emotionet))\n", + "\n", + "labels_disfaplus = pd.read_csv(os.path.join(base_dir, \"disfaplus_labels.csv\"))\n", + "landmarks_disfaplus = pd.read_csv(os.path.join(base_dir, \"disfaplus_landmarks.csv\"))\n", + "# Disfa is rescaled to 0 - 1\n", + "disfaplus_aus = [col for col in labels_disfaplus.columns if \"AU\" in col]\n", + "labels_disfaplus[disfaplus_aus] = labels_disfaplus[disfaplus_aus].astype('float')/5\n", + "print(\"DISFA Plus: \", len(labels_disfaplus))\n", + "\n", + "labels_bp4d = pd.read_csv(os.path.join(base_dir, \"bp4d_labels.csv\"))\n", + "landmarks_bp4d = pd.read_csv(os.path.join(base_dir, \"bp4d_landmarks.csv\"))\n", + "bp4d_pruned_idx = labels_bp4d.replace({9: np.nan})[au_cols].dropna(axis=1).index\n", + "print(\"BP4D: \", len(labels_bp4d))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We aggregate the datasets and specify the AUs we want to train. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:28:06.960825Z", + "start_time": "2021-03-27T00:28:05.707732Z" + } + }, + "outputs": [], + "source": [ + "labels = pd.concat([\n", + " labels_emotionet.replace({999: np.nan}), \n", + " labels_disfaplus,\n", + " labels_bp4d.replace({9: np.nan}).iloc[bp4d_pruned_idx,:]\n", + " ]).reset_index(drop=True)\n", + "landmarks = pd.concat([\n", + " landmarks_emotionet, \n", + " landmarks_disfaplus,\n", + " landmarks_bp4d.iloc[bp4d_pruned_idx,:]\n", + " ]).reset_index(drop=True)\n", + "\n", + "landmarks = landmarks.iloc[labels.index]\n", + "\n", + "\n", + "labels = labels[au_cols].fillna(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We train our model using PLSRegression with a minimum of 500 samples for each AU activation. We evaluate the model in a 3-fold split and retrain the model with all the data which is distributed with the package." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:28:08.890994Z", + "start_time": "2021-03-27T00:28:06.961852Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pseudo balancing samples\n", + "Evaluating model with KFold CV\n", + "3-fold accuracy mean 0.13\n", + "N_comp: 20 Rsquare 0.15\n" + ] + } + ], + "source": [ + "min_pos_sample = 500\n", + "\n", + "print('Pseudo balancing samples')\n", + "balY = pd.DataFrame()\n", + "balX = pd.DataFrame()\n", + "for AU in labels[au_cols].columns:\n", + " if np.sum(labels[AU]==1) > min_pos_sample:\n", + " replace = False\n", + " else:\n", + " replace = True\n", + " newSample = labels[labels[AU]>.5].sample(min_pos_sample, replace=replace, random_state=0)\n", + " balX = pd.concat([balX, newSample])\n", + " balY = pd.concat([balY, landmarks.loc[newSample.index]])\n", + "X = balX[au_cols].values\n", + "y = registration(balY.values, neutral)\n", + "\n", + "# Model Accuracy in KFold CV\n", + "print(\"Evaluating model with KFold CV\")\n", + "n_components=len(au_cols)\n", + "kf = KFold(n_splits=3)\n", + "scores = []\n", + "for train_index, test_index in kf.split(X):\n", + " X_train,X_test = X[train_index],X[test_index]\n", + " y_train,y_test = y[train_index],y[test_index]\n", + " clf = PLSRegression(n_components=n_components, max_iter=2000)\n", + " clf.fit(X_train,y_train)\n", + " scores.append(clf.score(X_test,y_test))\n", + "print('3-fold accuracy mean', np.round(np.mean(scores),2))\n", + "\n", + "# Train real model\n", + "clf = PLSRegression(n_components=n_components, max_iter=2000)\n", + "clf.fit(X,y)\n", + "print('N_comp:',n_components,'Rsquare', np.round(clf.score(X,y),2))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:28:12.319990Z", + "start_time": "2021-03-27T00:28:12.316860Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(10000, 20)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We visualize the results of our model. The regression was trained on labels 0-1 so we do not recommend exceeding 1 for the intensity. Setting the intensity to 2 will exaggerate the face and anything beyond that might give you strange faces. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T23:58:58.227979Z", + "start_time": "2021-03-26T23:58:57.624279Z" + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/home/jcheong/packages/feat/notebooks/_build/jupyter_execute/content/dev_trainAUvisModel_8_0.png" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot results for each action unit\n", + "f,axes = plt.subplots(5,4,figsize=(12,18))\n", + "axes = axes.flatten()\n", + "# Exaggerate the intensity of the expression for clearer visualization. \n", + "# We do not recommend exceeding 2. \n", + "intensity = 2\n", + "for aui, auname in enumerate(axes):\n", + " try:\n", + " auname=au_cols[aui]\n", + " au = np.zeros(clf.n_components)\n", + " au[aui] = intensity\n", + " predicted = clf.predict([au]).reshape(2,68)\n", + " plot_face(au=au, model=clf,\n", + " vectorfield={\"reference\": neutral.T, 'target': predicted,\n", + " 'color':'r','alpha':.6},\n", + " ax = axes[aui])\n", + " axes[aui].set(title=auname)\n", + " except:\n", + " pass\n", + " finally:\n", + " ax = axes[aui]\n", + " ax.axes.get_xaxis().set_visible(False)\n", + " ax.axes.get_yaxis().set_visible(False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is how we would export our model into an h5 format which can be loaded using our load_h5 function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# save out trained model\n", + "# import h5py\n", + "# hf = h5py.File('../feat/resources/pyfeat_aus_to_landmarks.h5', 'w')\n", + "# hf.create_dataset('coef', data=clf.coef_)\n", + "# hf.create_dataset('x_mean', data=clf._x_mean)\n", + "# hf.create_dataset('x_std', data=clf._x_std)\n", + "# hf.create_dataset('y_mean', data=clf._y_mean)\n", + "# hf.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:43:45.310255Z", + "start_time": "2021-03-26T04:43:45.307443Z" + } + }, + "source": [ + "## Preprocessing datasets\n", + "Here we provide sample code for how you might preprocess the datasets to be used in this tutorial. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from PIL import Image, ImageOps\n", + "import math, cv2, csv\n", + "from scipy.spatial import ConvexHull\n", + "from skimage.morphology.convex_hull import grid_points_in_poly\n", + "from feat import Detector\n", + "import os, glob, pandas as pd, numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from skimage import data, exposure\n", + "from skimage.feature import hog\n", + "from tqdm import tqdm\n", + "\n", + "def padding(img, expected_size):\n", + " desired_size = expected_size\n", + " delta_width = desired_size - img.size[0]\n", + " delta_height = desired_size - img.size[1]\n", + " pad_width = delta_width // 2\n", + " pad_height = delta_height // 2\n", + " padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)\n", + " return ImageOps.expand(img, padding)\n", + "\n", + "def resize_with_padding(img, expected_size):\n", + " img.thumbnail((expected_size[0], expected_size[1]))\n", + " delta_width = expected_size[0] - img.size[0]\n", + " delta_height = expected_size[1] - img.size[1]\n", + " pad_width = delta_width // 2\n", + " pad_height = delta_height // 2\n", + " padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)\n", + " return ImageOps.expand(img, padding)\n", + "\n", + "def align_face_68pts(img, img_land, box_enlarge, img_size=112):\n", + " \"\"\"\n", + " img: image\n", + " img_land: landmarks 68\n", + " box_enlarge: relative size of face\n", + " img_size = 112\n", + " \n", + " \"\"\"\n", + " leftEye0 = (img_land[2 * 36] + img_land[2 * 37] + img_land[2 * 38] + img_land[2 * 39] + img_land[2 * 40] +\n", + " img_land[2 * 41]) / 6.0\n", + " leftEye1 = (img_land[2 * 36 + 1] + img_land[2 * 37 + 1] + img_land[2 * 38 + 1] + img_land[2 * 39 + 1] +\n", + " img_land[2 * 40 + 1] + img_land[2 * 41 + 1]) / 6.0\n", + " rightEye0 = (img_land[2 * 42] + img_land[2 * 43] + img_land[2 * 44] + img_land[2 * 45] + img_land[2 * 46] +\n", + " img_land[2 * 47]) / 6.0\n", + " rightEye1 = (img_land[2 * 42 + 1] + img_land[2 * 43 + 1] + img_land[2 * 44 + 1] + img_land[2 * 45 + 1] +\n", + " img_land[2 * 46 + 1] + img_land[2 * 47 + 1]) / 6.0\n", + " deltaX = (rightEye0 - leftEye0)\n", + " deltaY = (rightEye1 - leftEye1)\n", + " l = math.sqrt(deltaX * deltaX + deltaY * deltaY)\n", + " sinVal = deltaY / l\n", + " cosVal = deltaX / l\n", + " mat1 = np.mat([[cosVal, sinVal, 0], [-sinVal, cosVal, 0], [0, 0, 1]])\n", + " mat2 = np.mat([[leftEye0, leftEye1, 1], [rightEye0, rightEye1, 1], [img_land[2 * 30], img_land[2 * 30 + 1], 1],\n", + " [img_land[2 * 48], img_land[2 * 48 + 1], 1], [img_land[2 * 54], img_land[2 * 54 + 1], 1]])\n", + " mat2 = (mat1 * mat2.T).T\n", + " cx = float((max(mat2[:, 0]) + min(mat2[:, 0]))) * 0.5\n", + " cy = float((max(mat2[:, 1]) + min(mat2[:, 1]))) * 0.5\n", + " if (float(max(mat2[:, 0]) - min(mat2[:, 0])) > float(max(mat2[:, 1]) - min(mat2[:, 1]))):\n", + " halfSize = 0.5 * box_enlarge * float((max(mat2[:, 0]) - min(mat2[:, 0])))\n", + " else:\n", + " halfSize = 0.5 * box_enlarge * float((max(mat2[:, 1]) - min(mat2[:, 1])))\n", + " scale = (img_size - 1) / 2.0 / halfSize\n", + " mat3 = np.mat([[scale, 0, scale * (halfSize - cx)], [0, scale, scale * (halfSize - cy)], [0, 0, 1]])\n", + " mat = mat3 * mat1\n", + " aligned_img = cv2.warpAffine(img, mat[0:2, :], (img_size, img_size), cv2.INTER_LINEAR, borderValue=(128, 128, 128))\n", + " land_3d = np.ones((int(len(img_land)/2), 3))\n", + " land_3d[:, 0:2] = np.reshape(np.array(img_land), (int(len(img_land)/2), 2))\n", + " mat_land_3d = np.mat(land_3d)\n", + " new_land = np.array((mat * mat_land_3d.T).T)\n", + " new_land = np.array(list(zip(new_land[:,0], new_land[:,1]))).astype(int)\n", + " return aligned_img, new_land\n", + "\n", + "def extract_hog(image, detector):\n", + " im = cv2.imread(image)\n", + " detected_faces = np.array(detector.detect_faces(im)[0])\n", + " if np.any(detected_faces<0):\n", + " orig_size = np.array(im).shape\n", + " if np.where(detected_faces<0)[0][0]==1: \n", + " new_size = (orig_size[0], int(orig_size[1] + 2*abs(detected_faces[detected_faces<0][0])))\n", + " else:\n", + " new_size = (int(orig_size[0] + 2*abs(detected_faces[detected_faces<0][0])), orig_size[1])\n", + " im = resize_with_padding(Image.fromarray(im), new_size)\n", + " im = np.asarray(im)\n", + " detected_faces = np.array(detector.detect_faces(np.array(im))[0])\n", + " detected_faces = detected_faces.astype(int)\n", + " points = detector.detect_landmarks(np.array(im), [detected_faces])[0].astype(int)\n", + "\n", + " aligned_img, points = align_face_68pts(im, points.flatten(), 2.5)\n", + "\n", + " hull = ConvexHull(points)\n", + " mask = grid_points_in_poly(shape=np.array(aligned_img).shape, \n", + " verts= list(zip(points[hull.vertices][:,1], points[hull.vertices][:,0])) # for some reason verts need to be flipped\n", + " )\n", + "\n", + " mask[0:np.min([points[0][1], points[16][1]]), points[0][0]:points[16][0]] = True\n", + " aligned_img[~mask] = 0\n", + " resized_face_np = aligned_img\n", + "\n", + " fd, hog_image = hog(resized_face_np, orientations=8, pixels_per_cell=(8, 8),\n", + " cells_per_block=(2, 2), visualize=True, multichannel=True)\n", + "\n", + " return fd, hog_image, points" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Replace the paths so that it points to your local dataset directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "detector = Detector(face_model = \"retinaface\", landmark_model=\"mobilenet\")\n", + "# Correct path to your downloaded dataset.\n", + "EmotioNet_images = np.sort(glob.glob(\"/Storage/Data/EmotioNet/imgs/*.jpg\"))\n", + "labels = pd.read_csv(\"/Storage/Data/EmotioNet/labels/EmotioNet_FACS_aws_2020_24600.csv\")\n", + "labels = labels.dropna(axis=0)\n", + "for col in labels.columns:\n", + " if \"AU\" in col:\n", + " kwargs = {col.replace(\"'\", '').replace('\"', '').replace(\" \",\"\"): labels[[col]]}\n", + " labels = labels.assign(**kwargs)\n", + " labels = labels.drop(columns = col)\n", + "labels = labels.assign(URL = labels.URL.apply(lambda x: x.split(\"/\")[-1].replace(\"'\", \"\")))\n", + "labels = labels.set_index('URL')\n", + "labels = labels.drop(columns = [\"URL orig\"])\n", + "\n", + "aus_to_train = ['AU1','AU2','AU4','AU5', \"AU6\", \"AU9\",\"AU10\", \"AU12\", \"AU15\",\"AU17\", \n", + " \"AU18\",\"AU20\", \"AU24\", \"AU25\", \"AU26\", \"AU28\", \"AU43\"]\n", + "\n", + "with open('emotionet_labels.csv', \"w\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow([\"URL\"] + aus_to_train)\n", + " \n", + "landmark_cols = [f\"x_{i}\" for i in range(68)] + [f\"y_{i}\" for i in range(68)]\n", + "with open('emotionet_landmarks.csv', \"w\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow(landmark_cols)\n", + " \n", + "for ix, image in enumerate(tqdm(EmotioNet_images)):\n", + " try:\n", + " imageURL = os.path.split(image)[-1]\n", + " label = labels.loc[imageURL][aus_to_train]\n", + " fd, _, points = extract_hog(image, detector=detector)\n", + " with open('emotionet_labels.csv', \"a+\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow([imageURL]+list(label.values))\n", + " with open('emotionet_landmarks.csv', \"a+\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow(points.T.flatten())\n", + " except:\n", + " print(f\"failed {image}\")" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "toc": { + "nav_menu": { + "height": "81px", + "width": "252px" + }, + "navigate_menu": true, + "number_sections": true, + "sideBar": true, + "threshold": 4, + "toc_cell": false, + "toc_section_display": "block", + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/notebooks/_build/jupyter_execute/content/dev_trainAUvisModel.py b/notebooks/_build/jupyter_execute/content/dev_trainAUvisModel.py new file mode 100644 index 00000000..b607ed3d --- /dev/null +++ b/notebooks/_build/jupyter_execute/content/dev_trainAUvisModel.py @@ -0,0 +1,277 @@ +# Training AU visualization model +You will first need to gather the datasets for training. In this tutorial we use the datasets EmotioNet, DISFA Plus, and BP4d. After you download each model you should extract the labels and landmarks from each dataset. Detailed code on how to do that is described at the bottom of this tutorial. Once you have the labels and landmark files for each dataset you can train the AU visualization model with the following. + +%matplotlib inline +import pandas as pd, numpy as np, matplotlib.pyplot as plt +from sklearn.cross_decomposition import PLSRegression +from sklearn.model_selection import KFold +from feat.plotting import predict, plot_face +from feat.utils import registration, neutral +from natsort import natsorted +import os, glob +import pandas as pd, numpy as np +import seaborn as sns +sns.set_style("white") + +au_cols = [1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43] +au_cols = [f"AU{au}" for au in au_cols] + +base_dir = "/Storage/Projects/feat_benchmark/scripts/jcheong/openface_train" + +labels_emotionet = pd.read_csv(os.path.join(base_dir, "emotionet_labels.csv")) +landmarks_emotionet = pd.read_csv(os.path.join(base_dir, "emotionet_landmarks.csv")) +print("EmotioNet: ", len(labels_emotionet)) + +labels_disfaplus = pd.read_csv(os.path.join(base_dir, "disfaplus_labels.csv")) +landmarks_disfaplus = pd.read_csv(os.path.join(base_dir, "disfaplus_landmarks.csv")) +# Disfa is rescaled to 0 - 1 +disfaplus_aus = [col for col in labels_disfaplus.columns if "AU" in col] +labels_disfaplus[disfaplus_aus] = labels_disfaplus[disfaplus_aus].astype('float')/5 +print("DISFA Plus: ", len(labels_disfaplus)) + +labels_bp4d = pd.read_csv(os.path.join(base_dir, "bp4d_labels.csv")) +landmarks_bp4d = pd.read_csv(os.path.join(base_dir, "bp4d_landmarks.csv")) +bp4d_pruned_idx = labels_bp4d.replace({9: np.nan})[au_cols].dropna(axis=1).index +print("BP4D: ", len(labels_bp4d)) + +We aggregate the datasets and specify the AUs we want to train. + +labels = pd.concat([ + labels_emotionet.replace({999: np.nan}), + labels_disfaplus, + labels_bp4d.replace({9: np.nan}).iloc[bp4d_pruned_idx,:] + ]).reset_index(drop=True) +landmarks = pd.concat([ + landmarks_emotionet, + landmarks_disfaplus, + landmarks_bp4d.iloc[bp4d_pruned_idx,:] + ]).reset_index(drop=True) + +landmarks = landmarks.iloc[labels.index] + + +labels = labels[au_cols].fillna(0) + +We train our model using PLSRegression with a minimum of 500 samples for each AU activation. We evaluate the model in a 3-fold split and retrain the model with all the data which is distributed with the package. + +min_pos_sample = 500 + +print('Pseudo balancing samples') +balY = pd.DataFrame() +balX = pd.DataFrame() +for AU in labels[au_cols].columns: + if np.sum(labels[AU]==1) > min_pos_sample: + replace = False + else: + replace = True + newSample = labels[labels[AU]>.5].sample(min_pos_sample, replace=replace, random_state=0) + balX = pd.concat([balX, newSample]) + balY = pd.concat([balY, landmarks.loc[newSample.index]]) +X = balX[au_cols].values +y = registration(balY.values, neutral) + +# Model Accuracy in KFold CV +print("Evaluating model with KFold CV") +n_components=len(au_cols) +kf = KFold(n_splits=3) +scores = [] +for train_index, test_index in kf.split(X): + X_train,X_test = X[train_index],X[test_index] + y_train,y_test = y[train_index],y[test_index] + clf = PLSRegression(n_components=n_components, max_iter=2000) + clf.fit(X_train,y_train) + scores.append(clf.score(X_test,y_test)) +print('3-fold accuracy mean', np.round(np.mean(scores),2)) + +# Train real model +clf = PLSRegression(n_components=n_components, max_iter=2000) +clf.fit(X,y) +print('N_comp:',n_components,'Rsquare', np.round(clf.score(X,y),2)) + +X.shape + +We visualize the results of our model. The regression was trained on labels 0-1 so we do not recommend exceeding 1 for the intensity. Setting the intensity to 2 will exaggerate the face and anything beyond that might give you strange faces. + +# Plot results for each action unit +f,axes = plt.subplots(5,4,figsize=(12,18)) +axes = axes.flatten() +# Exaggerate the intensity of the expression for clearer visualization. +# We do not recommend exceeding 2. +intensity = 2 +for aui, auname in enumerate(axes): + try: + auname=au_cols[aui] + au = np.zeros(clf.n_components) + au[aui] = intensity + predicted = clf.predict([au]).reshape(2,68) + plot_face(au=au, model=clf, + vectorfield={"reference": neutral.T, 'target': predicted, + 'color':'r','alpha':.6}, + ax = axes[aui]) + axes[aui].set(title=auname) + except: + pass + finally: + ax = axes[aui] + ax.axes.get_xaxis().set_visible(False) + ax.axes.get_yaxis().set_visible(False) + +Here is how we would export our model into an h5 format which can be loaded using our load_h5 function. + +# save out trained model +# import h5py +# hf = h5py.File('../feat/resources/pyfeat_aus_to_landmarks.h5', 'w') +# hf.create_dataset('coef', data=clf.coef_) +# hf.create_dataset('x_mean', data=clf._x_mean) +# hf.create_dataset('x_std', data=clf._x_std) +# hf.create_dataset('y_mean', data=clf._y_mean) +# hf.close() + +## Preprocessing datasets +Here we provide sample code for how you might preprocess the datasets to be used in this tutorial. + + + +from PIL import Image, ImageOps +import math, cv2, csv +from scipy.spatial import ConvexHull +from skimage.morphology.convex_hull import grid_points_in_poly +from feat import Detector +import os, glob, pandas as pd, numpy as np +import matplotlib.pyplot as plt +from skimage import data, exposure +from skimage.feature import hog +from tqdm import tqdm + +def padding(img, expected_size): + desired_size = expected_size + delta_width = desired_size - img.size[0] + delta_height = desired_size - img.size[1] + pad_width = delta_width // 2 + pad_height = delta_height // 2 + padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height) + return ImageOps.expand(img, padding) + +def resize_with_padding(img, expected_size): + img.thumbnail((expected_size[0], expected_size[1])) + delta_width = expected_size[0] - img.size[0] + delta_height = expected_size[1] - img.size[1] + pad_width = delta_width // 2 + pad_height = delta_height // 2 + padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height) + return ImageOps.expand(img, padding) + +def align_face_68pts(img, img_land, box_enlarge, img_size=112): + """ + img: image + img_land: landmarks 68 + box_enlarge: relative size of face + img_size = 112 + + """ + leftEye0 = (img_land[2 * 36] + img_land[2 * 37] + img_land[2 * 38] + img_land[2 * 39] + img_land[2 * 40] + + img_land[2 * 41]) / 6.0 + leftEye1 = (img_land[2 * 36 + 1] + img_land[2 * 37 + 1] + img_land[2 * 38 + 1] + img_land[2 * 39 + 1] + + img_land[2 * 40 + 1] + img_land[2 * 41 + 1]) / 6.0 + rightEye0 = (img_land[2 * 42] + img_land[2 * 43] + img_land[2 * 44] + img_land[2 * 45] + img_land[2 * 46] + + img_land[2 * 47]) / 6.0 + rightEye1 = (img_land[2 * 42 + 1] + img_land[2 * 43 + 1] + img_land[2 * 44 + 1] + img_land[2 * 45 + 1] + + img_land[2 * 46 + 1] + img_land[2 * 47 + 1]) / 6.0 + deltaX = (rightEye0 - leftEye0) + deltaY = (rightEye1 - leftEye1) + l = math.sqrt(deltaX * deltaX + deltaY * deltaY) + sinVal = deltaY / l + cosVal = deltaX / l + mat1 = np.mat([[cosVal, sinVal, 0], [-sinVal, cosVal, 0], [0, 0, 1]]) + mat2 = np.mat([[leftEye0, leftEye1, 1], [rightEye0, rightEye1, 1], [img_land[2 * 30], img_land[2 * 30 + 1], 1], + [img_land[2 * 48], img_land[2 * 48 + 1], 1], [img_land[2 * 54], img_land[2 * 54 + 1], 1]]) + mat2 = (mat1 * mat2.T).T + cx = float((max(mat2[:, 0]) + min(mat2[:, 0]))) * 0.5 + cy = float((max(mat2[:, 1]) + min(mat2[:, 1]))) * 0.5 + if (float(max(mat2[:, 0]) - min(mat2[:, 0])) > float(max(mat2[:, 1]) - min(mat2[:, 1]))): + halfSize = 0.5 * box_enlarge * float((max(mat2[:, 0]) - min(mat2[:, 0]))) + else: + halfSize = 0.5 * box_enlarge * float((max(mat2[:, 1]) - min(mat2[:, 1]))) + scale = (img_size - 1) / 2.0 / halfSize + mat3 = np.mat([[scale, 0, scale * (halfSize - cx)], [0, scale, scale * (halfSize - cy)], [0, 0, 1]]) + mat = mat3 * mat1 + aligned_img = cv2.warpAffine(img, mat[0:2, :], (img_size, img_size), cv2.INTER_LINEAR, borderValue=(128, 128, 128)) + land_3d = np.ones((int(len(img_land)/2), 3)) + land_3d[:, 0:2] = np.reshape(np.array(img_land), (int(len(img_land)/2), 2)) + mat_land_3d = np.mat(land_3d) + new_land = np.array((mat * mat_land_3d.T).T) + new_land = np.array(list(zip(new_land[:,0], new_land[:,1]))).astype(int) + return aligned_img, new_land + +def extract_hog(image, detector): + im = cv2.imread(image) + detected_faces = np.array(detector.detect_faces(im)[0]) + if np.any(detected_faces<0): + orig_size = np.array(im).shape + if np.where(detected_faces<0)[0][0]==1: + new_size = (orig_size[0], int(orig_size[1] + 2*abs(detected_faces[detected_faces<0][0]))) + else: + new_size = (int(orig_size[0] + 2*abs(detected_faces[detected_faces<0][0])), orig_size[1]) + im = resize_with_padding(Image.fromarray(im), new_size) + im = np.asarray(im) + detected_faces = np.array(detector.detect_faces(np.array(im))[0]) + detected_faces = detected_faces.astype(int) + points = detector.detect_landmarks(np.array(im), [detected_faces])[0].astype(int) + + aligned_img, points = align_face_68pts(im, points.flatten(), 2.5) + + hull = ConvexHull(points) + mask = grid_points_in_poly(shape=np.array(aligned_img).shape, + verts= list(zip(points[hull.vertices][:,1], points[hull.vertices][:,0])) # for some reason verts need to be flipped + ) + + mask[0:np.min([points[0][1], points[16][1]]), points[0][0]:points[16][0]] = True + aligned_img[~mask] = 0 + resized_face_np = aligned_img + + fd, hog_image = hog(resized_face_np, orientations=8, pixels_per_cell=(8, 8), + cells_per_block=(2, 2), visualize=True, multichannel=True) + + return fd, hog_image, points + +Replace the paths so that it points to your local dataset directory. + +detector = Detector(face_model = "retinaface", landmark_model="mobilenet") +# Correct path to your downloaded dataset. +EmotioNet_images = np.sort(glob.glob("/Storage/Data/EmotioNet/imgs/*.jpg")) +labels = pd.read_csv("/Storage/Data/EmotioNet/labels/EmotioNet_FACS_aws_2020_24600.csv") +labels = labels.dropna(axis=0) +for col in labels.columns: + if "AU" in col: + kwargs = {col.replace("'", '').replace('"', '').replace(" ",""): labels[[col]]} + labels = labels.assign(**kwargs) + labels = labels.drop(columns = col) +labels = labels.assign(URL = labels.URL.apply(lambda x: x.split("/")[-1].replace("'", ""))) +labels = labels.set_index('URL') +labels = labels.drop(columns = ["URL orig"]) + +aus_to_train = ['AU1','AU2','AU4','AU5', "AU6", "AU9","AU10", "AU12", "AU15","AU17", + "AU18","AU20", "AU24", "AU25", "AU26", "AU28", "AU43"] + +with open('emotionet_labels.csv', "w", newline='') as csvfile: + writer = csv.writer(csvfile, delimiter=',') + writer.writerow(["URL"] + aus_to_train) + +landmark_cols = [f"x_{i}" for i in range(68)] + [f"y_{i}" for i in range(68)] +with open('emotionet_landmarks.csv', "w", newline='') as csvfile: + writer = csv.writer(csvfile, delimiter=',') + writer.writerow(landmark_cols) + +for ix, image in enumerate(tqdm(EmotioNet_images)): + try: + imageURL = os.path.split(image)[-1] + label = labels.loc[imageURL][aus_to_train] + fd, _, points = extract_hog(image, detector=detector) + with open('emotionet_labels.csv', "a+", newline='') as csvfile: + writer = csv.writer(csvfile, delimiter=',') + writer.writerow([imageURL]+list(label.values)) + with open('emotionet_landmarks.csv', "a+", newline='') as csvfile: + writer = csv.writer(csvfile, delimiter=',') + writer.writerow(points.T.flatten()) + except: + print(f"failed {image}") \ No newline at end of file diff --git a/notebooks/_build/jupyter_execute/content/dev_trainAUvisModel_8_0.png b/notebooks/_build/jupyter_execute/content/dev_trainAUvisModel_8_0.png new file mode 100644 index 00000000..d5c1cc4b Binary files /dev/null and b/notebooks/_build/jupyter_execute/content/dev_trainAUvisModel_8_0.png differ diff --git a/notebooks/content/dev_plotting.ipynb b/notebooks/content/dev_plotting.ipynb new file mode 100644 index 00000000..31af4a14 --- /dev/null +++ b/notebooks/content/dev_plotting.ipynb @@ -0,0 +1,754 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotting examples\n", + "*written by Jin Hyun Cheong*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Included in the toolbox are two models for Action Units to landmark visualization. The 'pyfeat_aus_to_landmarks.h5' model was created by using landmarks extracted using Py-FEAT to align each face in the dataset to a neutral face with numpy's least squares function. Then the PLS model was trained using Action Unit labels to predict transformed landmark data. \n", + "\n", + "Draw a standard neutral face. Figsize can be altered but a ratio of 4:5 is recommended. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:00:07.356496Z", + "start_time": "2021-03-27T00:00:07.295746Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Load modules\n", + "%matplotlib inline\n", + "from feat.plotting import plot_face, predict\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plot_face(au=np.zeros(20))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Draw lineface using input vector" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Affectiva vectors should be divided by twenty for use with our 'blue' model. " + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:12:08.615110Z", + "start_time": "2021-03-27T00:12:08.526307Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "image/png": { + "height": 236, + "width": 353 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data, AU is ordered as such: \n", + "# AU1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43\n", + "\n", + "# Activate AU1: Inner brow raiser \n", + "au = np.array([5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "# Plot face\n", + "fig, axes = plt.subplots(1,2)\n", + "plot_face(model=None, vectorfield = vectors,\n", + " ax = axes[0], au = np.zeros(20), color='k', linewidth=1, linestyle='-')\n", + "plot_face(model=None, vectorfield = vectors,\n", + " ax = axes[1], au = np.array(au), color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:24:31.270625Z", + "start_time": "2021-03-27T00:24:31.260037Z" + } + }, + "outputs": [], + "source": [ + "%config InlineBackend.figure_format = 'retina'\n", + "intensity = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:24:31.740226Z", + "start_time": "2021-03-27T00:24:31.433797Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'AU43: Eye closer')" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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udqbXikBfxAjctD5B4LcytH2vj3KwvWralLPGg20RDwc9a6CTW5YWdakAyjhYZiDss8qfOnm/j22WydIKAPqf+YD59SMAqjkppzwyWqMdQaYeQ7BvZSHQQT04h/tf5jJXwkFrRwAPI6P14tHMdTMvY1vOIQBlPdxfX3WyzDs2y6xw8PpM82uxmbcDdCJIoAPdbvPvbzoow9IDbE42t5/tfu3w+8+0fEUAxd0s85lNedFutl+W/0HoEzyBPq5UdvE+wbBpBWjz/Dib9ds7WTcE+gJDoFu/ZGkB6s3+wEf+fKCQxC8Pymxhc3y8BOe9NmyPt2ty+J6WzyE5XN9hPWzLhz7JbuJgmVLIiG1Oj1ce1MW2J+wWOIjHNstWd/DcZJv1v3SyXu9Mx6MsPUe9OWZlek8DgM4OlgmGbqhhWa6hg2VqQU/+LNC9DSKcvN89Nsv95Ob/wW0s8vB7yVzmJ06WG2WzzEQHrw+0eb2dm/e0HAtOZfN16/fi5HXLeYURQB8X9fjKps73ufn/uASgTk63Nx9227dQxJZM9XP2SAHwmpty2tksvyaH2966b+dwfYf1AGMLY4v9dfMkJ9vs+UzvO9nBMsWhb5wJgI1wPqJKXZvlsrRQR9br/ake1GmVg9drZipnnbPtbl7e0qPkMpz3YI4EsMG83A3YXENCNyy1tKif52abl8n0d7bXtdmm6ciIgbc5WKYY9PBqlu2RJebmxv7FR/59IB/EfmSMgHQYNtd7yGYPrEz7f+ZHHIAuLta1fc81Odz2p2zKaujB404HZbS3KSPLfTeb5d61We5BB6/n6NjoxWcOhU7oCHSDtQEulg0BUMnF95flOwdQwmafTQTQ2km5i2zKedfFNh3m5vM4Olbfi4yewN/ASe9FADE2y73n4PXJNvUwAeiak/2tMD8KwhxYvaGTKUDGBIUAABFJQUbro0eVUmyB4r0PRWRX5idFJAk6Aw7oVkH3uClnN/RQAI58CX0SB+hAklkP6Aw7AAwSkbOOChGRK9DdOwHdKs1RWRbxAIaIiNHFMt5KN9cvzUHdliGjFV9d6ISWKy+Jk1bYSo/zfr/5z5Ui8q2j5UTkM+gLEgDoqJRqkGkRSw+1EtAtBmxZekOsslnOroWi0pOAWuYcc9XbzVPrROQ7VwuIyCURiXdTznvQvaUA3SLCW5bPdERctNoVEaOIxNo+p5Sqgoyxpf8jIn85WdcA3WozDTrwumqhDrjYHyhfK/TxS+l5Gn5Gxpyc74lIqh+rlBs+FZHdmZ8U3dt4iM1Tr3lbsHnM7/8z/5kEoIeIXHe2vIicybR+eejW2oC+Mfi2k/VmQzd6AIB6yEEMc+B/IrLcwXsaoXt4W7RzsO5b0L2nb0BfMCc7egMR2YSMFphPKRdzlsCDWJQNB6Av7h15D/pCHgD6KaXK+Pi9fcK8zSzj7E8UkVkuFn8bunUjYL+PO/KuiPyT0/qRncISW0zQPXzegW492wz6emQQdK8SQN9oHquUcnhsy+cYW1wrcLHF3Jr8BfOfl6EbMYqDev0I3ajSleeheywYoJMjNx0tJCLHoJNmAPCgUqq2izKvAnjZgzo94OC61JYJ+pra4XZXSrWE7k0CAK+LyD4ndU9ExvYqBfteAGWg72EAuueTUw72/ZysC+iRWSwjIb0pIscdrJcA3ZPOZH7qdVfvg9w5d6H8JaBjv1LqQejeUQLg2Vy+3vsOQD0RWZSL7+HMPg8emzKvZL5vZOmZ+qyjgs2x2dKr+h9kugfno2Ojp/pBN/YAgG9FZKqzBUXEICIXnb3uxCDo+5QAMFJENjsq17yc5f7ga+bRIiwq2fzu8n6lk2P1vwEo6POY183v52jdedC9qQD31z3TRGShm2XIiYKQwLLc+L0I3Z0+M8vBOwQZXUnzHRH5SESU+fFRHr71NBev2Q7zUMdNOdMdncwCgIjEQfdKclaOJQlxRkT+dvM+a2x+b+NiuUXm9/WlP5wl18wm2PzeyelSwDlniQ8H6/7opk4/2PyeeQiDVTa/W4fvMgfGduY/VyPjYB+daXJC2yG/XJ68e8jVvuaQecLIqkqpO5We1Lkh9AXwOfMiTbNRj/Pmnw2UUu4Ss5l1gW6NAuju2k6Zg6TlpMLVvupuf6D8q1DEL2fMwz1MQ8ZwmwtE5Cdny4vIGps42C4v6ugDlhbSjl8U2Qh9UgwAHdxNQutAI2RcPMwWkfOuFnagPTKOWZOcnZib2Q7BkZMYlpnTCx64P8/oYf652Hyzx5U15p/h0MNnOON1LPLAZGcNZszP/2z+MxwZDVQCzf3QN+0A9/EtHYDlfM1VfEuH/RA35BuFJbb0FJF2IvK5iKwQkV0isllEJovIo9A38yzHtP+YG15lwdjiEGOLFmixpRl00gnQ34urYYomuHgNyLi+/jtzAtKBNTa/uzqm/+pme3l6LbzJTcMGy/eTDt1DzikR2Q895CVgX/fr0L2oAKC3eQh4T+VkXSDjs9+Ei6EeReQwMq7DmyulSrsoMzfOXSh/CdjYb/4fsfz/jxORDT4q+i7zozH0van/gx4C9SUAPyulqjtbMUBjv+XeXj2l1H0OXn8AGTHrJwf3V31xbPRUN5vfv8zG+u5YjpOCjGEnszA3arEcRytCn79Y2J67POPNuZC5ka3l/ulvzq7jbKwx/6ymlKrmYjkeq3MgXyewlFL1kNHzZ7qjncp8An/M/Ke7Xg5k76qbVm62440Xd1PWITevW8pyVI5lXPXqSilx9YAeTsOikoOyLHa7qU92bHHz+i5k9DRr5GI5d3NJ2a6bpfVGJrYJv8a2L4jISWTMMWGbjLoLGRdHq6Fbtxqg59uyHePess4lEfHFnDW7PVlIKVVcKfWeUmo3dHfis9Bz5ti2amliXrxsNurxM3SgDAOwXim1XCk1VCnVPFMCzxHb7XPCg/3V0vPN1b6a07nFKAAxfgHQN626m3/fBT1+dEFzUpyMYW7DEjtKQM9b4g3b3rNrvVwX8C6ebENGDGvsYjlvj1muzg+cnmeYL0ormv8c4MHx1rYVZqCdH9i+7ur8wJ9s49tqD7a35eaoq219VHSPfvKRwhRbnPUYsXn9F+ghOgE9bNyruV6pvMPY4l5BjC2229WbuGLH3ELd8h239eAz2rbid/UZfRXrdrspxxKPQgGkeFB/S+MLa93NvT+mm/+8B8BppdR4pVRvVze9c7quUioMep4eANghNvO1OGG5llfI2TajAiwfxP7PoRtFnIfuNe0TIrLf/NgrIn+JyCjohpEroRMg29309vQ5m6SYq0dJJ6tPgR76GHDcC8vynAEZjd9s5fjY6AVLDDnpQSOI7LAc7/7x4HzH2T3Pjcjo1TYUwGGl1Cil1CNKqVJuymyGjJ6yH3iwLW1Hxcrr68xCI18nsJAxVBeQqZtsJpYWWA3M3Sodsc1ee5qZtV3OYe+ifM7dTQWTze/uJp3ztCxH5ZR3s64zRV285vKiN5uuuHrRfIJqaeXgaoggd3WzXdfVZMFAxhA+zt7T0pLxPvMJNZCRmDooesi+BADbzc89CFh7TtyfqYyccvudmHtYHQLwCXRwcpdQcrUPOCQi66CPLXHQ+2MnAGOgL7BvKqWWKKX6OUlm5Zd9lfyvUMcvpdRX0MPXAHp4tY650Cs2ELiMC2a2x3Fvh48rZ/O70yFPXfA4nogeHtdy0y8nMSxzuU7PD0TE1XlGdo+3QICdHyBn+0Beye72djWkFuOb7xXq2OLA98ioxwOuFsxnGFvcKKCxxXb7eBNXMisF99dQzrj6jL6Kde62la+ut4Yio0dxGejhzWZBJ6ROKaW+V0o5G80ju+uWRsZx0t11POD+Wt6C8bRwC9jYr5RqC90jCtDDwLqbDiJHROQW9PCbydCx7AfXawQOEbkB4Dfzn08opSxD6EHpIcZjzH8uFJHLDorIjXtRzljOE7JzjuAJy/Eu28dJc+/vxwDsMD91O3QCdQmA60qp3Uqpj5RSjhJOgXwuUGhl98TF78w3j/vbPLXfwx6Bz8B+2AAL25NcT/+BbbuLu+rCTzlj2U+3w7vWIq4ODr6c+8rCkwt1T3ZSX9ZNnPxusRp6m0ZAt9pZC/v5r2yXa21+7RPoHk6lbV7zBZefW+lx338HUNn81DQAs6Fvfl8BkGq5IFVKrQMQDc9PuuyIyGSl1HwATwDoAN2tujJ0L7RHzI+3lVJdROS0zaqWfdUE3WrD0+8yy7xpNnJjXyU/KuzxSyn1GTLG8j8CPQFtQZ3jzVdxwVfv5av1XS2bV8cs23PYH2A/DJU751y85o/zA1/tA7nJdnt3hv2wHNnF+OZDhT22OCIil5RS16F75Ff1d318iLEl9+SX2JKT78X2My6EnufDU66SVL5KWrvbVpb6X0TWIfJdsTsmmYdg7KuUGgmgF3TjzBbQ18U1oOcrflEp9Q2AYbbDdeVkXdsqeFF3d8sznhZS+SD2W+YROgqgqFKqt4P1bYf4rWWzzBER2eVhHaxE5IpSagP0fZxopVQl8X4OJn8ZB/19FoWeS9KSgBsAPVQt4HxIPZ8cG72U242VcnScFJHjSqkW0A3xuwG4D3rEqWDoBvGNAQxXSg0RkRk2q9rGyfcBzPeiDiedVs79UITkQr5NYEFP8lrR7VJZ9VZKvS5ZJwW1nbStEvRNcXdsM7VOJ7alHLsKfdFZyTxWa6ByORmm+eTC0lU1J/uL7boV4fomku0+esPB63bzYJkDfVvz37aJqdXQYwrfo/QExb6e/8oT7ZEx5MJnIvKui2VdjRHuERGJhR6reQIAKKVqQffGehG6S/Nd0Ak023myLN2bgwAkioNJeYlQiOOXUuoTZEzmfhRAeyctyAoKTyZJtm3h5e13YTukQhUv1838fpUAnHC2oLmXruXY6iie5DXbz148H5wfHHXxuqt9wLangLuRE7ydh8MbttvbGODbu7AqtLHFDcn0syBgbMk9gRxbbLeru33A1evXof8fFIAyPvyM3tQpJ8eHq9DXhOWgh5fKfOzyivnz7wesDSabQ/d0eB56iMnXoIerzzL3dDbWvYGMbe/JsF22x/RA+P+gwBPosd+SdLkdbuZQNWuLjPtRY6GHmc8O28aRNWHfSydgicjfSqm90PebnkNGAssyfOApAH86Wd2nx0Y3rgKohuydI3jiOnQD8hwfJ80NCFaaH1BKFYPex/pCz5daFMAUpdQum6lRbM8FigTYuUChlZ+HELR0kzVCZ6P7uHmMNi9fAhmT29myHVfb1QSstizLCYC9nlacvGbp8llFKXWXX2viWis3rzeFnlcJyNn+YrtuazfL3mvze5ax480tUSzjwj4IPZZtCegbZmtsFt0IPVltOHRvJEsC64ybSXZ9yfa7d3ryYw5Idzh7PbtE5KSIjINuXbfb/HRrpZTtvAI7bH5/2Nd1oAKjUMYvpdSHAN4z/3kcwAP5qDVcdtVSSpVzs4wldsQBOO1qQQe22/x+v9OlnPMmnjSHHlMdCIy5+U4i4yLlISfDugYKd+cHtq9n/n+0ndvT6Xjt5qF9b/eyXt5gfAt8hTK2uKKUqoCM+VBza5gbf2BsyT2BHFtst6s3ccWOeVh7S1mtlFI5bviXjTrl5PhgiUchAB7KQTlZiEi6iGwSkX9lKttRrxGv1zUPmWm59r7bnPRyxXItHxDHVApIjP2O2SZWEpwuFZgsCe+mSqlmSqk2yOil9lOmYXBt5dqx0QHLeUItd3P/ZZNlP6rjwfmOy3uemYlIgogsEZF+yJiTLRh69CWLXchoRMjrngCRLxNY5h34UfOfa0RkmojMdvUA8CkyJsRzNAzdHza/9zPfCHBVh3uQcaNgp4gESivDgmiuze/vOV3K/zoopVwNT2I7EeMfTpdyb4XN70PcLPuCB+9p6WnVEkAX8+87zT2QAAAikoKMyRE7Qne9tV03L9heQLpqZf48crF3qfnCw7bXmW1AXYiMIRxeV0plZzxhKsAKa/xSSr0L4CPznyege175YgiyQKfgYuhb83dhuSBZ6WSIGVf2IaNle2+llLet4P4CYJlA/BmlJ3Z35kWb31c4XSqPmC/eFpj/rADdSjFQPe3s/9K8zQea/0yFHsrXlm0jkRYu3qMLdGvv3LIKgGXegmeVUtlp7Uu5pLDGFg+8iIyh9PJqxIC8wNiSSwI8tuxERq+CJ91cZzzr4jUg4/o6BBk943Oql1IqysXrvroWtr038K6b/SvbRGQbMqYkcHcD1Zt1Lft5KQCPOytDKXU7MhqNbjfPj0NklR9iv4i0ExHl6gE90o7FFJvXhnm2JbLUqQYyRslJgv25dH4wHRnD+j2HjGOnAcDPLtbLk2Oj2QKb39/MhfItx0kFF/HM3ACjl/nPS/A+gWp73mE9VpuPt5ZrsmZKKSaxAkC+TGBBjwlqaa3yiycriEgCgGXmPx9QStXM9Po+ZNyMbwh9YHdIKVUe5mHFzL7ypA45YZ5cTsyPj3L7/QLMTGS0VOplbsXvlFKqqFJqmLtgmwvCAExy1JJKKdUJwGDzn/8gY1/0mojsRsbBtKNS6kVHyyml3oKeBwoA/hARZ92/Lft9KIBXzb+vcrHcEADFMj2XF2yHXxrsaAGlVDSAj3PyJkqpPq4uvpRS4QDamf80waZVq4icQMbEqbUA/OKmLKWU6qGUqu9sGSpwCmP8egvASPOfJ6GTV67miXBWTjubOLjGl3XMZf9WSjXK/KRSqiTsxzD/1tuCzTclLdu2KIA5rlpSK6WqZVr/CnSMBfTNzpFZVtLrPQGgn/nPwwCWe1vXXDISOukDAF+6u7hQSlVRSj3japlc0hDAB05eG4GMG82zHNz0P4CMYSwGKaXKZnod5paPXu8/3jAfh74w/1kcwALz8cQppdSD5lajlPsKVWxRSrV312tEKdULGXP7mAB852Q5xpZMGFsCM7aYe05ZWuZXhB5ey1F9noX7+U++QcZQX2+5q79SqrRS6hU3ZZYD8K1SWSfgyVSnNTkZjklE1iHj2HQPgB9c9ZRTSoUqpQaZe2RanqutlHrA2TrmZVoio+fzCZvns72u2f+gb0QDwFdKqdoO1o8CMBW6VwAAfO3q/ajQKmyxv7ubBuMwnyf/goztMtvRUHqBHPtFJB4ZIw71RUaCZomIOO1N7otjoxdmIuPY9qpSaoCL9wlRSnkyFKCtydA9yAF9vpOlN6D5s02C7k0IAN+IzRxTSqlopVQ9N+/T2eb3zMfqj5Ax/PRUR3XIVJ/blVJPunk/yoFA6hLvDUtLAQPss8zu/Ao9JrEC8DT0TQNbz0F3uywJnbFuC30z+gB05r4cdELgBWRkZ+eKyEw4oZRqAqBJpqctN7SjlFIDM712XEQ2ePqBCgMRMSilekIPY1cCwEdKqcegD2p7ANwyP38H9FimXaETLN/Bfu6I3LYVeqLIrUqpr6DHuy4GPVngy9AnoCYAQyTnk/cNgd5XowB8b07czIAeHqUqdPdxS4uuWNj3xMrsL2SMxW05+DtKTK0G8InNMpZ188py6LGLKwEYrJQqA70PnIP+f+wK3TojFsAZZH8YwVEAxiullkIPo3gYugVdcQD1oHt4NTUvO1tErmZafyiAZtD/948BOKyUGg+9/16HvhFQE3oYjR7Q31cH6P2FCr5CFb+UUi8B+K/5zwToJHlxpVRDZ+8L4GZe9s5y8DmcWZiN1q/HoLf3RqXUl9Bjb6dCD9f6L+hEN6BbG2bueeMREZlobiTxBPRx5bBS6nsA66GPOcWgL0AfM7+eOQHyFvQwE1UA/Mt8Q3QC9BjrZc3lPgO976UDGOhi6Io8JXpi3mehb/JEAFiilJoPYA70tjdAf4a7oG+cPQBgC/TFTl7aCuBDpVRj83ufh97ezwDobl7mKhy0ghcRo1Lqf9AXUeWh96X/QM+5EQndoGIo9PnFUeTuMIIjoYcR7gTda/uQUmoCdKy8DD3McFXonmIxAOpCH5s25mKdSCtUscVc1yeUUsugz0UPQZ+rhUGf/z0J4BGb5T8035TLM4wtjC25ZCT0zcy60L1h6wD4HvrGWznoIcIGQMedls4KEZFYcwJxBfSN3olKqX7Q15OHoHtolIJOQD4APYTSdThJBJtthe5RXMsct/6B3k59oP9nASAZwEvefmgHnoLe5tWgj1P3m+PRNuibnlEAboMeWirGXI+60LEKAKoDWKWU+ge6J8FW6OvHFOhYez/sewfafu6crAsROaaU+gD6u6wIYLtS6gvoBqpp0NeZw831B4AFIuLJ3EFU+OSb2O8j3QH8qpT6Ezru7Yc+LgVBz5fUFvoYVMa8/An4roepR9xc49pyd737A/S9LdvRFcY7WdZWTo+NHhGRdHOyZj2AItBzSD0Jndg6Bj0qUTXo64YnoZObY7woP1bphvozoa931imlvoFOvt6Cjk1Doc97AD2k4ReZinkQwAdKqS0AlkJPA3LJ/FoV6Pu0lsTbTej4Z1uHdUqp96GTuGUB/K2UmgVgEfS5DKCP+Y2he0Lea66vR8lkygYRyVcP6BMxMT9WeLluJPQBV6BbgSsHyzSAPmkTNw8TdAuocDfv+ZEHZdk+JntQzkc52H41bcpZ42SZNebXT3lRVpY6QQcPy+vt3JTl9j2hkwd7PNyO8QCCM63fzub1gT7aH+3KNO8TzuqUCqC/i7Jc7gMOlm8BnbBytR3OAGjiQVm7M9WzqINlQqBvQFuWO+qD7We7X9f0cHsnuPi8l6Bbm7jcn1y9Dh2MPNnHlgIo5qT84gB+87AcA4B7cro/8BH4DxTC+GXzv5ajcsxltbNZZk0OvwtP/89tH00crO+wHravQ9/sj3dR7nx334UHnycU+gaWyc1niHWy/m0e7Ds3ADzkog4eH7OgGx8IAPFgWbflQjdguOrh97jEzb5e00f/77ZlNoG+mHRWp0sAGrsoKwz6Qt3Z+peh55lZg2zGPk+/F3Nd/gd9YerJseZJV/8fvtjWhf2BwhlbJnu4bhKAV93Up53N8jnaJ8HYknl9xhYfxxZzuTWhbxA6q8sR6CSLJ5/xHi/22wMO1h9o83oHAItdrB8PoIOLz2RZ7iMPt0NlZDTEdPdIAVDNyf+9q0cqgJddHDO8WjdTOR9AXwe6KuNXAEWcrJ8r+xcf+eOBfBb73dTH9n9qsovlJnv4vyfQjZ8re/iea3L4XZzyol5uP6dNudtslj8DIMjD+mT72JjN/fCMB+8zzMG6nnzng811dFX2GgClHaz7kQf1EujrsDYu6jAEOmnmSVn/c7Xf5vT/vrA/8mMPLNtxWn/1ZkURSVRKLYHumVITujXRqkzLHDBnzR+HzvC3hG5VEAGdsT4FnWWeKDno+k7eE5HDSqmm0N9LT+jWfhWgM/4J0N/NTgB/AlgkOe/llJ06Pq+UWgF9kGsC3XLtMvR+NlpEDvnwvbYppepCt3zpBuBO6FYysdCtY+YDGC8iSR4Utwq65QAAbHa0juiecOuQ0aI1L4cPtNRhjbnl7tvQF0lVoMcHPg3dEuJbEbniYOQKb9wH3VrjAeiWlRWgjwHp0AnDbQBmiMgSF/WMh24Z3AK6VVNb6FbpxaFPFs9DtxhaDWCeiFxyVhYVKIxfhZCIrFBK3Q3gDeiW2pWhWyDvBjBBfNCqVvSwQi8ppX6Ejj/toFu9hUGflJ+BHtPeYYsw0a3NG0Hvo48DaASgNHRsPQZgCYDvRORmTuuaG0RkoXkIlEHQrcQbQ7eUU9At6o5BJ5AsPWvzWix0S9WXoVui14XurXQKOlb/19W2FZE08xBWL0C3rKwP3av7DPTci1+JyKUcxj6PiJ4H8mWl1LfQF5XtoHt7lIS+wLwE3aN4DYD5oofWpdxVGGPLZ9Dn/K2gewGVhW5xLdAJkf3Qn2Oy6OHsChzGltwXqLFFRE6Ze/QOg+6NdRv0TeQT0L3ExohIgicxQUQ2ma8n+0In7O6G/v8Ohf7/PgF97bMC7od4TIeej3EQ9PVPfeieeueht9Hnko0hpF3U/QKA9kqph6Bj673Q/weR0Dcbz0DPh7ISOh7F2qy+Hvr48QB0j6ma0Nd8xczrHoO+ThvvII7lZF3b+n+slJoD3SPtAehrxRDoewd/A/hZRP70bqtQIVIYY/9b0Pd82gFoDt2DsTz0/008dK/PLQB+EZG/nZSRn0yG/pyA3s4e9VLO4bHRKyKyVen5+gZB35NsDH0+lgB972wvgN+RzSlURPcG/xN6FJeO0PtrEejGJduge03NEdGZokxGA9gEvX/fA33vsAL0OcxN6HPFJQB+Mt+/c1aH8eZjtWUo3PrQ5zIm6B6AR6GP2YtEZEt2Pid5Rjn+nomIiIgoO5RSpwDUALBWRNr5tzbkD0rPV/qh+c9aInLKf7UhooKAsYUCjXmozJ/Nf7YXkTX+qw0RUcFhnobiOehRD2r6sgEAUX4U5O8KEBEREREREREREREVZkqpYtC9pwBgKZNXRExgERERERERERERERH522sAosy/f+fPihAFivw4BxYRERERERERERERUb6llIqAnqMpAnrOpvfNL20VkT/8VjGiAMIEFhERERERERERERFR3moF4K9MzyUBGOKHuhAFJA4hSERERERERERERETkP5cBLARwr4js8XdliAKFEhF/14GIiIiIiIiIiIiIiIjIij2wiIiIiIiIiIiIiIiIKKAwgUVEREREREREREREREQBhQksIiIiIiIiIiIiIiIiCihMYBEREREREREREREREVFACcnNwpVSkpvlExGRb4mI8ncdAhVjGhFR/sKY5hjjGRFR/sJ45hxjGhFR/pKdmMYeWERERERERERERERERBRQcrUHloUIG0QQEQUypdioz1OMaUREgY0xzTOMZ0REgY3xzHOMaUREgS0nMY09sIiIiIiIiIiIiIiIiCigMIFFREREREREREREREREAYUJLCIiIiIiIiIiIiIiIgooTGARERERERERERERERFRQGECi4iIiIiIiIiIiIiIiAIKE1hEREREREREREREREQUUJjAIiIiIiIiIiIiIiIiooDCBBYREREREREREREREREFFCawiIiIiIiIiIiIiIiIKKAwgUVEREREREREREREREQBhQksIiIiIiIiIiIiIiIiCihMYBEREREREREREREREVFAYQKLiIiIiIiIiIiIiIiIAgoTWERERERERERERERERBRQmMAiIiIiIiIiIiIiIiKigMIEFhEREREREREREREREQUUJrCIiIiIiIiIiIiIiIgooDCBRURERERERERERERERAGFCSwiIiIiIiIiIiIiIiIKKExgERERERERERERERERUUBhAouIiIiIiIiIiIiIiIgCChNYREREREREREREREREFFCYwCIiIiIiIiIiIiIiIqKAwgQWERERERERERERERERBRQmsIiIiIiIiIiIiIiIiCigMIFFREREREREREREREREAYUJLCIiIiIiIiIiIiIiIgooTGARERERERERERERERFRQGECi4iIiIiIiIiIiIiIiAIKE1hEREREREREREREREQUUJjAIiIiIiIiIiIiIiIiooDCBBYREREREREREREREREFFCawiIiIiIiIiIiIiIiIKKAwgUVEREREREREREREREQBhQksIiIiIiIiIiIiIiIiCihMYBEREREREREREREREVFAYQKLiIiIiIiIiIiIiIiIAgoTWERERERERERERERERBRQmMAiIiIiIiIiIiIiIiKigMIEFhEREREREREREREREQUUJrCIiIiIiIiIiIiIiIgooDCBRURERERERERERERERAGFCSwiIiIiIiIiIiIiIiIKKExgERERERERERERERERUUBhAouIiIiIiIiIiIiIiIgCChNYREREREREREREREREFFCYwCIiIiIiIiIiIiIiIqKAwgQWERERERERERERERERBRQmsIiIiIiIiIiIiIiIiCigMIFFREREREREREREREREAYUJLCIiIiIiIiIiIiIiIgooTGARERERERERERERERFRQGECi4iIiIiIiIiIiIiIiAIKE1hEREREREREREREREQUUJjAIiIiIiIiIiIiIiIiooDCBBYREREREREREREREREFFCawiIiIiIiIiIiIiIiIKKAwgUVEREREREREREREREQBhQksIiIiIiIiIiIiIiIiCihMYBEREREREREREREREVFAYQKLiIiIiIiIiIiIiIiIAgoTWERERERERERERERERBRQmMAiIiIiIiIiIiIiIiKigMIEFhEREREREREREREREQUUJrCIiIiIiIiIiIiIiIgooDCBRURERERERERERERERAGFCSwiIiIiIiIiIiIiIiIKKExgERERERERERERERERUUBhAouIiIiIiIiIiIiIiIgCChNYREREREREREREREREFFCYwCIiIiIiIiIiIiIiIqKAwgQWERERERERERERERERBRQmsIiIiIiIiIiIiIiIiCigMIFFREREREREREREREREAYUJLCIiIiIiIiIiIiIiIgooTGARERERERERERERERFRQGECi4iIiIiIiIiIiIiIiAIKE1hEREREREREREREREQUUJjAIiIiIiIiIiIiIiIiooDCBBYREREREREREREREREFFCawiIiIiIiIiIiIiIiIKKAwgUVEREREREREREREREQBhQksIiIiIiIiIiIiIiIiCihMYBEREREREREREREREVFAYQKLiIiIiIiIiIiIiIiIAgoTWERERERERERERERERBRQmMAiIiIiIiIiIiIiIiKigMIEFhEREREREREREREREQUUJrCIiIiIiIiIiIiIiIgooDCBRURERERERERERERERAGFCSwiIiIiIiIiIiIiIiIKKExgERERERERERERERERUUBhAouIiIiIiIiIiIiIiIgCChNYREREREREREREREREFFCYwCIiIiIiIiIiIiIiIqKAwgQWERERERERERERERERBRQmsIiIiIiIiIiIiIiIiCigMIFFREREREREREREREREAYUJLCIiIiIiIiIiIiIiIgooTGARERERERERERERERFRQGECi4iIiIiIiIiIiIiIiAIKE1hEREREREREREREREQUUJjAIiIiIiIiIiIiIiIiooDCBBYREREREREREREREREFlBB/V4CIAkNqaioOHjyIlJQUGI1GGI1GmEwmj3/3Zlmj0Yhq1arhscceQ5kyZfz90YmIiIiIiIiIiIgowDCBVYgcO3YMs2fPxqpVq5Ceng4AUEpBKeXV796uFxISgsaNGyM6OhqtW7dGVFRUHn1iciUuLg5///031q9fj/Xr12Pbtm1ITU3N0zoEBwejffv26NmzJ2JiYlChQoU8fX8iIiIiIiIiIiIiCkxKRHKvcKUEAHLzPci1s2fP4pdffsHs2bOxY8cOf1cHwcHBaNasGaKjoxEdHY02bdqgXLly/q5WoXDx4kWsX78eGzZswPr167F3716YTCa7ZSpWrIcaNUogODgYwcHBCAoKcvu7p8tZfj97Nhj79ytUqrQdq1evhtFoBKATnvfddx969uyJHj16oFq1av7YTIWWJfEsIsrPVQlYjGlERPkDY5prjGdEZDKZEBsbixs3biA2NhZ16tRBqVKl/F0tyoTxzD3GNCKi/CEnMY0JrALo6tWr+P333zFr1iysX7/e+nyxYsUQExODoKCeKFWqDHr0EOt3I+LZ794sa/n9hx8SUbLkFhw9uh67du2yJiws6tWrZ01oRUdHo0aNGtadmrJHRHDs2DFrsmr9+vX4559/7JYJDQ1F8+bNER0djXvuicaoUfdi4MDSePHF3KoT8P33wNChwLhxwLPPAtevX8fChQsxZ84c/Pnnn0hLS7Mu37JlSzz++OPo2bMnateunTuVIiteHLnHmBY4TCYTjh49iq1bt+LKlSuoUqUKqlatiqpVq6Jy5coIDw/3dxWpgImPj8epU6dw8uRJnDp1CkePnsS2baeQknISV65cRp06ddCkSRPro2HDhoiIiPB3tQstxjTXGM8CW0pKCrZt24bY2FiULVsW5cqVQ7ly5VC8eHFeI1EWIoKEhARcv34dN27ccPjT0XM3b960OwYopdCoUSO0bdvW+ihfvrwfPxkBjGeeYEwjIgBISQGmTgWKFgWeesrftSFHmMAixMXFYf78+Zg1axZWrlxpTRIVKVIEjz32GPr06YOHH34YERERGD8eePVVYPNmoGnT3K9b377A5cvAypXArVsJ2Lx5szWpsmXLFiQnJ9stX7VqVURHR+O+++5DdHQ0GjRogKCgoNyvaD5mNBqxZ88e63bdsGEDLl++bLdMVFQU7rnnHmuisGXLlihatCgAYPhw4IsvgNmzgSefzJ06GgzA118DI0cChw4BFSvavx4fH4/Fixdjzpw5WLZsmd1+0aRJE/Ts2RM9e/bEnXfemTsVLOR4ceQeY5r/XLhwAVu3brU+tm3bhvj4eKfLV6hQwZrQqlq1KqpVq2b3d5UqVVCkSJE8/AQU6BITE3H69Glrgirzzxs3bnhVXnBwMOrVq2dNaDVt2hRNmjThvI95hDHNNcazwBIXF4eNGzfaDett26jLIiwszC6hVa5cuSx/2z5Kly7Na6h8TERw+PBhrF+/HteuXXOaoLpx4wYMBkO23qNEiRIoU6YMzp6NRHr6EQD2+129evVw//33WxNaVatW9cEnI28wnrnHmBbYEhKAYsX8XQsqaG7cuIFdu3YhNjYWpUpVx6pVNfDTT+Vw5YpC587AsmX+riE5wgRWIZWUlIQlS5Zg1qxZWLp0qXX+opCQEHTs2BF9+vRB165dUbx4cbv1Zs3SSaVnnwXGjwdyuyHfQw8Bq1bpA0jnzvavpaWlYefOnXaJl5s3b9otU6pUKbRp08aaeLn77rsRFhaWu5UOcMnJydi6dat1m/39999ISEiwW6ZcuXJ2PdsaN26MkJCs094tWAB0765//+MPoEOH3K37xYtApUqul0lMTMTy5csxZ84cLF682O6z3XnnndZkVuPGjdkS1Ud4ceQeY1reiI+Px44dO6zJqi1btuD8+fNZlqtatSpatmyJ9PRqWL78AipVOgeRc7hw4UKWnr6OlCtXzmmCq1q1aqhSpQp70BQgKSkpOHPmjNME1ZUrV1yuX6RIEdSsWRO1atVCzZo1ceNGLSxZUhNvvlkLgwaVw7Fjx7B7927s2rULu3fvxuHDh7MM0wvo/dY2odWkSRPUqlWLsczHGNNcYzzzr0uXLlmvfdavX489e/Zk+i4U6tRpiNtvr4pr167h6tWruHr1KhITE716n6CgIJQpU8ajZFedOnUQGRnp2w9KXjOZTNi8eTPmz5+P+fPn49ixYx6tFxUVhTJlyqB06dIe/yxVqpT12nDAAGD16mR8/fVWHDq0FuvWrcPff/+dpaFp7dq1rcms+++/n/ErDzCeuceYFtjefhswGnVD5kJ+G4+y6dKlS9i5c6f1sWvXLpw6dcrBkhEAqqN06Zp4/PEaqFHD/lG5cmUEBwfnce3JFhNYhUhaWhr+/PNPzJo1CwsWLMCtW7cA6J3g/vvvR58+fdCjRw+ULVvWaRn//AMkJwO33543AaRRI2DfPv1z507A1fHCZDLh4MGDdhd1586ds1smIiICrVq1svbSuueee1CsgDfpiI2NtWuZuX379iwtM2vXrm2XsKpbt67bC4oTJ4BmzYC4OP33tm1A8+a59SmyJyUlBStXrsScOXOwYMECuwRn7dq1rcmsli1b8gIqB3hx5B5jmu+lpaVh3759dr2rDh06lGUbFy9eHC1btrQ+WrRogcqVKwPQF0P//jfQrx8wfbrukXrp0iWcO3fO7nH27Fnr7+fPn/eotXKZMmWyJLnatWuHe++9l8ebAGY0GvHXX3/hl19+wcGDB3Hq1ClcuHDB5TqhoaGoUaOGNUGV+WeFChWyfOcizhsBJScnY//+/XZJrT179iApKSnLssWLF0fjxo3tklr169fnUJg5wJjmGuNZ3hER/PPPP3bXNsePH7dbxnZY7wULonHkSBtMn14K/frZl5WcnGxNZl29etUuueXoERsb63E9w8PD0aFDB8TExKBLly6cozgPpaSkYPXq1Zg/fz4WLlxoN4pGyZKl0blzZ9SsWd1lMio3GnempaVhx44dWLduHdauXYsNGzZkaTBZpUoVux5a9erV4/mRjzGeuceYFrji4oCqVYFbt/R9phkzTChR4iqKFi1a4O/hkfdEBGfOnLFLVO3cuRMXL17MsmxERASaNGmCsmXL4syZMzh9+rTb856QkBBUrVrVmtCqWbOmXYKrWrVqvP7KZUxgFXBGoxHr1q3DrFmzMGfOHLthbFq2bIk+ffrgiSeeQJUqVfxYS+cqVtRDCALAlCm6hZc3Tp8+bXfRd+jQIbvXg4OD0aRJE2vipkuXLggNDfVR7f3vxZdfwY8/fO9wjHLLMIvR0dHWm7neOHkSWLcOGDIEKF0a2LgRCOTpptLT0/HXX39hzpw5mD9/vl2L+apVq6JHjx7o2bMn2rRpw5YVXuLFkXuMaTljuYm3ZcsWa7Jq165d1t7DFqGhoWjSpIldwur22293OgyS0QisWAG0agV4OjqbyWTC5cuXnSa4LI/09HSH69esWRtPP90f/fv3R506dbzaDpR7Dhw4gO+/n4rx46fDYLBPWAUHB6NatWpOE1SVK1fO9aG2jEYj/vnnH7uk1u7du3Hp0qUsy4aGhqJ+/frWhFanTp04hK4XGNNcYzzLPUajEfv378e6deusIyVkvvESGRlpHda7bdu2dsN6X70K7NoFdOyY87qkp6fj+vXrLpNcV69excWLl3Hs2BHr/hAUFIT77rsPMTExiImJQY0aNXJeGbITGxuLpUuXYv78+Vi2bJm1USoA1KhRAzExMQC6YcyY+zBkSAh+/NF/dbUwGAzYs2cP1q1bZ31kHl63XLly1t5Zbdu2xV133cVhLHOI8cw9xrTAYTAYcP78eZw+fRqnT5/Gjh2nsHDhacTHn0Zi4mmkpp6BiL72K1OmDGrWrGl3Pm77yE6v4NRUIDQU4GEn8GzcqDs1WPKWJpMJx48ftyapLA9Hw7YXL14cTZs2RbNmzayPO+64I8s9v/j4eOu+l/lx6tSpLNOsZKaUQsWKFa0Jrdtuuw2DBw9GrVq1kJwMHDkCHDyY8ShaVN/j5q1HzzGBVQCJCLZs2YLZs2fj119/tbvwadiwIfr06YMnn3wy4G+cmUxA5cp6/qPWrYGuXXWyJCeuXbuGDRs2WC8Kd+zYYR0uKqpYcdy4cR2hDobKy49MInjjvU/wv//+B83uvhsPtLsf0dHRuPfee1GyZEmfvc/Nm8D580CNGvlnfGKj0YgNGzZgzpw5mDt3rt0QY0WLVsCDD/bA+PEfoGLmybbIIV4cuceY5p3Lly9j27Ztdr2rMg8RCwB33HGHXbKqcePGAdHyyWQy4erVq3YJrmPHjmHGjDm4fj3jeHPvvfdiwIAB6NWrF0qVKuXHGhdOV65cwaxZszB16lTs3LnT+nz16rUwaNAA6xBHVatWdTiMbiC4dOkS9uzZY5fUOnr0qN2xJjQ0FNOnT0evXr38WNP8gzHNNcYz30lNTcX27dutDe02btyIOMvQBmZly5a1a3TWpEmTgGls98ILwE8/ARMnXkJKygLMmzcPq1evtmvA0bRpU2syq0GDBuxhk03nzp3DggULsGDBAvz11192PcGbNGmC7t27o3v37mjUqBGUUpg8GRg0CBg6FBgzxm/VdsoycsratWutvbQy3xwsWbKkNVH75JNPYsWKarh0CXjvPT9VOh9iPHOPMS3vpKamWnu7WJICtkmCc+fOuR3KvXTp0khMTMzSiDGzcuXKuUxwZR7qPTERaNwYuHEDOHUKyDSTCvnRhQsG1K17GA8+uBO1a+tE1e7du7P06gX0OZNtoqpZs2aoVauWTxpDWIaUd5bkcrT/BgWFoVix1xAX928AJe1e+/xz4F//ynG1ChUmsAoIEcG+ffswe/ZszJ49GydPnrS+Vrt2bfTu3Rt9+vRBw4YN/VhL76SmApcuASEhuidWbmSmExMT8df6jZgwcyVE0vDDd5+hSokivn8jPzgXm4w5qy8hLjEIAx4th5qli/q7SgHJZDJh69atmDNnDn7+eQ6uX9f/O3feeSfWrVvnckhN0nhx5B5jmmcOHTqEfv36YdeuXVleq1ChAlq1aoWWLVuiVatWaN68uU+T8Xlh0iQjBg9eg2rVpuLGjTnWOUnCwsLQpUsX9O/fHw8//HChn6sxN6WkpGDhwoWYOnUqli9fbr3QKFGiBJ588kkMGDAg3w/zmJiYiH379mHQoN04fHgVgN+hlMK3336Ll19+2d/VC3iMaa4xnmVfYmKitSHd+vXrsXXrVqSkpNgtU716detN++jo6IAeVi0mBpg/336UjLi4OCxduhTz5s3D0qVL7ebeuu2229C9e3fExMSgdevW7F3jgojg4MGD1vmstm/fbn0tKCgIbdu2Rffu3dGtWzfUrFnTYRlXrgDly+dRhXNIRHDs2DFrMmvt2rU4e/as9fVSpUojPn4xjMZ7sHmz7jlP7jGeuceY5p2jR4GZM4GGDYG77gLq1NH36ywuXLiA3bt3O0xQORrKLbNKlSplGZrN8nf16tURFRUFk8mEK1eu2M1Ha3mcPHkSp0+fzjJlRmYVKlSwS3BVqlQT//53TZhMNbFvX23Urh0YDUUKq5SUFMyfPx8///wzVq9eB4MhJcsyVapUsUtUNW3aFFWrVvXbOZPBYMCFCxes+/2KFSswY8YMAEBwcGkYjR8CeAGAvs5v3lxPw0KeYwKrAPj777/Ru/fzOHt2v/W5SpUq4cknn0SfPn3QokWLgL3wCQQX41MwdVk8DCJo1yoUTaoUR2RYYLa09lRimgE7zsRj7pJ0lCoF9OgQgbsqsRmJO1evCp56ajcOHRqAs2f3o0WLFli1ahXHWHaDF0fuMaa5t2TJEvTp0wcJCQkoUiQKzZs3x733ZvSu8ucJqS/t3q0v+lJTEzFv3jxMnToVK1eutO4bZcqUQe/evTFgwADGbx8REWzcuBFTp07Fr7/+au3hEBISgocffhj9+/dHly5dUKRIwWjAYrFsGTBpkqB27dEYPfodAMAHH3yAjz76iPuVC4xprjGeecdkAq5fB9avX4shQ57A9etX7V6vX7++3Ty01atX91NNvZeUpIcUb9DA8euWuWjnzZuHhQsX4tq1a9bXKlasiG7duiEmJgbt27dnww3oESI2b95sTVrZzncWERGBzp07o3v37nj00UdRxtNxj/OxU6dOYd26dZgyZQpWr16N0NAIdOjwGxYtepRDfHmI8cw9xjTvpKcDDzwAbNig/w4PB+68E6hV6yzOn/8UO3ZMgtHoeK7g4OBg6xxCjpJUvppDyGQy4dKlS04TXGfOnHE61Dugr8X69euHgQMHokmTJjxnzkO7d+/GpEmTMH369Czz11uSVJafFSpU8GNNPbNz5068+eabWLNmDQCgaNG6SEr6HGFh3bF8uUL79v6tX37DBFY+t2HDBnTo8DBSUm4hOLg0Bg9+HH369EF0dDTn8fHQocsJmLowCf8cCEG/wQbULhuBBhWLISifBiqTCA5cSsCanclYtTgEdRsa0f2RELSuUQrBQfnzM+W1Cxcu4L777sPJkyfRvn17LF26tMDd2PQlXhy5x5jmnIhg9OjRePfddyEiqFbtCZw9+zPeey8Sn3zi79rljfPnz2PmzJmYOnUq9u/PaIxyxx13oH///njqqac4j0g2HD9+HNOmTcO0adPseqbffffdGDBgAHr37o3y+aV5eg5NnDgRQ4YMgclkwosvvohvv/2W54lOMKa5xnjmHRGgfPkJuHbtJQAG3HXXXejYsSOio6PRpk2bQtPT32AwYOPGjZg/fz7mzZuH06dPW18rXrw4Hn30UcTExODhhx9GVFSUH2uat1JSUrBq1SrMnz8fCxcutJujt0yZMujatSu6d++Ohx56yDrXWWFjMBjw/PPPY9KkSQgODsakSZMwwNuJsQspxjP3GNO8s3070KKF7TMXAIyEUhMgkoagoCDcf//9qF27tl2SyjJnbCAMyW00GnHx4kWHCa7jx4/jzJkz1mUbNWqEgQMHol+/foXmmiGvxcbGYubMmZg4caLdkO5NmzbF4MGD8eSTT+brcyURweLFizF8+HAcOXIEAFChQjRGjPgCzz/f0s+1y19yFNNEJNceAES/BTmzYcMGiYqKEgBSrFg/ee21VH9XKd8xGE2y5uh1GfzJZXnkmRvy49Ir8seRK3IuNtnfVcu287HJsnT/FXnmwyvyyDM3ZMgnV2XV0asSl5zu76rlK8ePH5eKFSsKAOnWrZukp3P7OWNzvM7VuJCfH4xpjiUlJUnfvn2t+9Cnn34qP/5okqpVRZYt83ft8p7JZJJdu3bJ66+/LuXLl7duFwBy//33y8SJEyUuLs7f1QxoN27ckHHjxsm9995rt/2qVq0q77zzjhw4cMDfVfSb+fPnS3h4uACQJ554QlJSUvxdpYDEmMZ45ivp6ekydOhQ6z71xhtviMFg8He1/M5kMsnOnTvl/fffl4YNG9odq8PDw+Wxxx6TiRMnypUrV/xd1Vxz5coV6dmzp0RGRtp9/lq1asnrr78ua9eu5bWHDZPJJO+++651O40ePdrfVcoXGM8Y03zt5k2Rxx4TAS4L8LoEBRURAKKUkj59+sihQ4f8XcUcMZlMsmPHDnn11VeldOnS1v+hkJAQ6dq1q8ybN09SU3nfNaeMRqOsXr1a+vXrJ0WKFLFu55IlS8orr7wiO3fu9HcVfS4tLU3+97//SdmyZa2ft0+fPnLy5El/Vy3fyElMYw8sP9q4cSM6d+6MW7duoV+/fpgyZQpb0mZDbHI6lmyPxaKFCgk3glG7tqBzTDpKRuTPoQST043YdT4Om3elY9XiUAAKkcVMeOYFIxpXLo5KxdmLyBv79+9H27ZtcfPmTQwYMAA///wzx+t3gK373GNMy+r8+fPo3r07tm/fjsjISEyfPh3du3f3d7UChsFgwJ9//ompU6di/vz51nlSihQpgu7du2PAgAHo0KFDQLRk9Le0tDQsX74cU6dOxaJFi6zj3kdGRuLxxx9H//790a5dO54nAVi3bh26dOmC+Ph4PPjgg5g3bx6Hyc2EMc01xjPPxMbGonfv3lixYgVCQ0Pxww8/YPDgwf6uVkA6fvw45s2bh3nz5mHz5s3WfSsoKAj33XcfYmJiEBMTU6B6IhsMBpQpUwHx8TfQrFkzdO/eHd27d0fDhg05XJULY8eOxbBhwwAAb775JkaPHs1rMxcYz9xjTPPO9evX8dBD/8X+/d/CYEgCAPTo0QMjRoxAw4YN/Vw730pNTcWSJUvw888/Y9myZdZ5c8uWLWs3xCB57vz585g8eTImTZqEEydOWJ9/8MEHMXjwYMTExBT4kY/i4uLw2Wef4euvv0ZqairCw8MxdOhQvPvuu/lufu+8xiEE8yHb5FXfvn0xdepU3pTJptM3kzBvXQJWLw2F0aD/B14daoIxxIDqpfLXUIIiggOXE3D0YjJmTwlBYoLlZF7w3NB0NKgUhbrlIv1ax/xo8+bNeOihh5CYmIjXXnsNY8aM4YVlJrw4co8xzd6WLVvQvXt3XLp0CbVq1cKCBQtw1113+btaASs+Ph6///47pk2bZh1DG9ATEPft2xf9+/cvdGO0iwh27NiBqVOnYtasWda5VZRSeOihhzBgwADExMQgMpJxL7M9e/agU6dOuHz5Mu6++24sXbqUw6LYYExzjfHMvWPHjqFLly44cuQIypYti7lz5yI6Otrf1coXLl68iIULF2LevHlYvXq1dZ6SsLAw3Lhxo8Ac0+fNA/7znz8xd+4d+Wres0Awa9YsPP3000hPT8dTTz2FSZMmITQ01N/VCkiMZ+4xpnkmNjYWX331FcaMGYOEhAQAQJcuXTBixAg0bdrUz7XLfZcuXcKMGTPw888/48CBA9bnGzdubB1isFy5cn6sYeBKS0vD4sWLMXHiRCxfvhwmkwkAULVqVQwaNAiDBg1CrVq1/FzLvHf69Gm89957mD59OgA9bPCHH36IF154gTHNCSaw8pm///4bnTp1YvLKR3afj8PcP1KwY0PGxMEPPgg0aWGAUYD6FaJQpUSEH2vouYvxKThwKQHbtgKH94XAZp5kPP1CGupUDMfd1Ur6rX752cqVK/Hoo48iLS0NH330ET788EN/Vymg8OLIPca0DFOnTsWQIUOQmpqKdu3a4bfffsvX41rntdOnT2P69OmYNm2adRxtAGjYsCEGDBiAvn37okqVKn6sYe5Yt05PHN2wIbBgwXiMGTMGhw4dsr7eoEEDDBgwAP369SuQn9/XTpw4gY4dO+Kff/5B3bp18ccff6BmzZr+rlZAYExzjfHMtdWrV+Pxxx/HzZs30bBhQyxcuLBQ3pjxhdjYWCxduhTffjsPpUsrLFnyq7+r5BO//gr07QsMHw6MGuXv2uRPf/zxB3r06IHExER07twZv//+e4FJbvoS45l7jGmuxcfHY+zYsfjyyy8RFxcHAOjUqRM+/vhjtGxZ+ObvERHs3LkTkydPxsyZM3Hjxg0AQEhICB577DEMHDgQjzzySKFJQKSlAfPnA3fdBdx5p/1rBw8exMSJEzFt2jRcvXoVABAaGopu3bph8ODB6NChA+9lA9i+fTvefPNNrFu3DgBQt25djB49Gt26dStUjVM9wQRWPpI5eTVlyhQOHZQDqQYjNp+KxbRZRlw8pQOMUkDp0sBLLwmuJ+WfoQRT0o3YeT4ON5PTEan00IELFgBRUYDBADS824BqlYLQukYphIVwmIXsmDt3Lp544gmYTCaMHTsWr732mr+rFDB4ceQeY5qeMPftt9/Gl19+CQB46aWXMGbMmEJzgu9rIoJt27Zh2rRpmDVrFq5fvw4AaNPmMSxYsAhlyvi5gj721lvAl18CwcFA48bvYOfOz1G+fHn07dsXAwYMKHQ90Hzh8uXL6Ny5M3bv3o1KlSphxYoV7AkJxjR3GM+c++GHH/Dqq6/CaDSiS5cumDFjBofo9IGnngLuuQd4+WV/18Q31q0DXnsNGDEC6NbN37XJv7Zt24ZHHnkE165dQ8uWLbFkyRI2iMqE8cw9xjTHEhMT8d1332H06NHWJE379u3xySefoE2bNn6uXWBITU3FokWLMHnyZCxbtszas6hcuXLo168fBg0ahEaNGmVZT0TfdywIFi7UcaxWLeDECSAhIQG//PILJk6ciM2bN1uXa9iwIQYPHoynnnqKx2kHRAQLFy7Ev/71Lxw9ehQA0LZtW3z55Zdo3ry5n2sXOJjAyif+/vtvdO7cGQkJCejTpw+mTp3K5FUOXUlIxdpDcTi4LxgRoUHYswdo/4AOKHVqA0UiTLiRbECNAB9KUERw6PItnLyRhJIRIQgNDsr0OpCQbASCBM2qlkDpomFOSiJ3Jk2aZJ2/YMqUKRgwYICfaxQYeHHkXmGPabGxsejTpw+WL1+OkJAQfPfdd3j++ef9Xa0CIy0tDcuWLcO0adMQEdEP06fHYPVqoH17f9fMd5YsAdav1zcwDYaTOHToEDp06MAEaA7FxcWhe/fuWLNmDUqWLIlFixbhvvvu83e1/IoxzbXCHs8cSU9Px7Bhw/D9998DAN5++2385z//YctiHzCZgK5dgaZNgU8+8XdtfMdkApKTAXYaypmjR4+iY8eOOH36NO644w6sWLGiQM2VllOMZ+4xptlLTk7GuHHj8Nlnn+HKlSsAgDZt2uCTTz5B+4J0YeFjFy9exPTp0zF58mQcPHjQ+nzTpk0xcOBA9O3bF6VLl8XIkUD9+kCPHn6srA8dPw7ExADNmu1EcPB3+PXXX5GYmAgAKFasGPr06YPBgwejRYsWbGjogfT0dPz444/46KOPrI1T+/Xrh//85z+MbWACK1/YtGkTOnXqxOSVjx27mogjV2+hXGSo04PprdTAH0rwckIq9l+KhwhQvIjj/cJoEtxMTkfDisVRvVRgfo784quvvsKbb76J4OBgzJkzB93YdJIXRx4ozDHtyJEj6Nq1K44ePYqyZctizpw5aNu2rb+rVaCtXQu0aQPwVIE8kZKSgn79+mHu3LkoUqQIfv31V3Tp0sXf1fIbxjTXCnM8c+TmzZt44oknsGrVKoSFhWHChAls4ESUhy5cuIDOnTtj3759qFKlClasWIEGDRr4u1oBgfHMPcY0LTU1FT/99BNGjhyJCxcuAABatmyJTz75BB06dGDywUMigu3bt1uHGIyNjQWgh86rXfstJCaORJ8+wOjR/q2nr33zzTcYOnQoAN1z6JlnnsHjjz/OoV2zKS4uDiNHjsTYsWORmpqKUqVK49y5syhatKi/q+ZXTGAFONvkVe/evTFt2jQmr3zAJIJtZ2JxIykNZSKd90gSEVxLDNyhBFMMRuw+H4/riWko6yIRBwBXbqWidulI1K/IoUxy6v3338enn36K8PBwLFu2rNC3RuLFkXuFNaYtX74cvXv3RlxcHBo1aoQFCxZwnh2iAGQ0GvHSSy9h/PjxCA4Oxk8//YSBAwf6u1p+wZjmWmGNZ44cPnwYXbt2xbFjx1C+fHnMnz8f99xzj7+rRVToxMbGomvXrli/fj1KliyJxYsXc5gzMJ55orDHNJPJhIkTJ+KTTz7B2bNnAeheQx9//DEeffRRJq5yICUlBYsWLcLPP/+MFStW4JtvvsHLBWUs3EyuX7+OL774As888wzq1q3r7+oUGKdOncK///1vHD9+G8qWHYEBA3Sv9IhC2ieBCawAxuRV7klINWD7mVgIBFHhrrepwRiYQwmKCA5fuYUTN5JQokgIwoJdz211IykdJSJC0ap6yYD5DPmViODVV1/F//73P0RFRWH16tVo0aKFv6vlN7w4cq+wxTQRwVdffYV//etfMJlM6NGjB6ZMmYKoqCh/V42InBARfPDBB/j0008BAKNHj8bw4cP9XKu8x5jmWmGLZ8788ccf6NWrF+Li4tC4cWMsXLgQ1atX93e1iAqt5ORk9O3bF/Pnz2dvYjPGM/cKe0wzGAQNG7bBkSOb0LBhQ4wYMQIxMTFMXPnYhQsXEBUVheLFi/u7KpQPJScL7r1XYfduoHhxoFcvYMAAPeJKkOvbwAUKE1gBavPmzejYsSOTV7nkXGwy9l2MR/EiWeeMciQQhxK8eisV+y7Gw+Ri6EBbt1INUEqhZfWSAdeTLD8ymUzo378/Zs6ciTJlymDdunWoX7++v6vlF7w4cq8wxbSUlBQ8//zzmDp1KgDgww8/xAcffICgwnR2RZSP2Q4D8tZbb2H06NGF6kYGY5prhSmeOSIi+Pbbb/H666/DZDIhJiYGU6dOZQMNogBgMBjw0ksvYcKECQgODsaECRMwaNAgf1fLbxjP3CusMS0+Hpg0CRg7FoiO/huPPHIGvXr14vUaUYA6ehRo1gwwTzEGAKhZE+jfXz8KQ8c3JrACkG3y6sknn8T06dOZvPKx/ZficepGEspFhnl0UybQhhJMM5iw63wcriamuZzDy1aqwYTkdCMaVy6BCsXC86CWBV96ejp69OiBxYsXo0qVKtiwYUOhHB6NF0fuFZaYdvHiRcTExGDLli0oWrQopkyZgscff9zf1SIiL82cORNPP/00DAYDnn76afz000+F5lyUMc21whLPHElLS8Orr76K8ePHAwDee+89jBgxgjf8iAKIiODDDz/EJ598AgAYNWoU3n777ULVEMOC8cy9whbTTp0CvvkG+OknICEBuP12YM8eoEgRf9eMiNyZPl0nqzKLigKmTQO6d8/zKuUpJrACzObNm9GpUyfEx8ejV69emDFjRqG5YZBX0o0mbD4di+R0A0pGhHq8XqAMJSgiOHr1Fv65noxi4cEID/HsotlkTsLVrxCF2mU4maKvJCcno3Pnzli3bh1uu+02rF+/HhUrVsSGDUCJEsBdd/m7hrmPF0fuFYaYtm3bNnTv3h0XLlxA9erVsWDBAjRp0sTf1SKibFqxYgV69OiBpKQktGjxGObN+wVVqhT8yYMZ01wrDPHMkevXr6Nnz55Yu3YtwsPD8fPPP6NPnz7+rhYROfHdd9/htddeg4hg2LBh+PLLLxEUFIQzZ/TNvtKl/V3D3Md45l5hiGkiwKZNwNdfA3PnAiZTxmtr1wJt2/qvbkTknUGDgMmTM/4ODQXWrAHuvddfNco7OYlpbGrmY1u2bGHyKg/EpxiQajB6nPixCAkOQtHQIFxKSMXF+JRcqp1715PScSEuFaFB8OozBCkFBSAhxZB7lSuEIiIisHDhQjRr1gzHjx9Hp06dsHHjTbRrB0RHA0lJ/q4hUe6bMWMGoqOjceHCBURHR2Pbtm1MXhHlc506dcLq1atRtGhpbNu2Dk89ddLfVSLyi4MHD6Jly5ZYu3YtKlWqhHXr1jF5RRTgXnnlFcyaNQuhoaEYM2YMWrR4Aw0aADVqACdO+Lt2RLkvPR2YPRto3VrPlfP77/bJq+efZ/KKKL/57jugXr2Mv41GIDXVf/XJL5jA8qEtW7agY8eOiI+PxxNPPMHkVS6KS0lHqsHkdQILACLDgpFmMOHUjWQkpuV9IijNaMKJ60lITDeimAfzXmUWGqwQn2qAwWhyvzB5rESJEli+fDnuuOMO7N27F2+++Rhq105E06a6RQRRQfbLL7/gqaeeQmpqKp577jmsXLkS5cuX93e1iMgHWrVqhW++2YBixRahU6cG/q4OUZ5bunQpWrdujRMnTuDuu+/Gtm3b0LJlS39Xi4g88OSTT2Lp0qWoWrUq/vvfZ/Daa0DnzkCpUv6uGVHu27cPmDkT2LYt62uVKwOff573dSKinImMBH75BQgPB4YOBWbMANq393etAh+HEMyBpCRgxQqgSxdgxw775NXMmTOZvMolIoKd5+JwKSEV5aLCslWGv4YSFBEcv5aEY9duoVh4SLYScElpRhhFcHfVkl4Nn0ieOXv2LNq0aYOzZ8/i6aefxmTbvr0FGIencK8gx7Tk5GTce+8DaNfuKXz11UuFco4BIio4GNNcK8jxzOLSJWD/fuDYsSsYNqwm0tKS8cQTT2Dy5MkoWrTgD6NJVNCkpqYiPLzwzQHNeOZeQY9pKSlA167An3/aPz9/PtCtm1+qREQ+8OOPQKVK+v+7sOAcWH4yeDAwaRLQv/9WLFjQwa7nVSi7bOSapDQjtp65CYNJUDwbPZgsbqUaYBSgfoUoVCkR4cMaOnc9KQ17L8Qj3WjKdvLJYDQhLtWIRpWK5Vm9C5ujR49i0KBBmDFjBmrWrOnv6uQJXhy5V5BjmghQtqwRN24EY9MmPUwFEVF+xZjmWkGOZxYHDwINGuj5BHr3no4bN/7BBx98wAYaRJSvMJ65V9BjmsGgb3AvW5bx3OOPA7/95r86EVHOiegEdUQhuq3LBJafjBsHvPPObqSl3Y/k5Hg8/vjjmDlzJpNXuexifAr2XohHZFgwwrLRg8lCRHAtMR0lI0LRpEpxRIblbo+5dKMJu8/H4/KtFJSNDMt2ry8RwdXEdNQtG4k7ykf5uJZkISKF6iYHL47cK+gxrWdPYPduYOVKoFYtf9eGiCj7GNNcK+jxDNBzhEybBvTpA4Rlb8AGIiK/YzxzrzDEtIQEYOpUncTauBE4dAioWNHftSIi8g4TWH5082YcOnfuiOrVqzN5lUcOXU7A8etJKB8ZmuMEg2UoweolI9CwUu4OJfjPtUQcvXoLkeHBKBISnKOyriemoUxkOFpUK1GokiyUe3hx5F5hiGkmExDE2TGJKJ9jTHOtMMQzIqKCgPHMvcIU0778Us//9swz/q4JEZH3mMDys/j4eERERDB5lQeMJsGWMzcRn2JA6aK+2d63Ug0wmoA7K0Shasnc6bt5MykNey7EI9VoQikfzFsVn2JAaLBCq+qlUCQ0Z8kwIoAXR54oLDGNiCi/Y0xzjfGMiCh/YDxzrzDFtKtXgbJlAbZhJqL8KCcxje2sfaB48eJMXuWRhFQDktONCM/B0IGZRYYFI91kwqmbyUhMM/isXAuD0YQT15NwK82IEjmYs8tWWLBCmlFwK83ok/KIiIiIiIiIiCgwlSvH5BURFU5MYFG+EpeSjlSDCUV8mMBSSqFkRChik9Pxz7UkmHzccudMbDKuJqaheHiwz4YoDAsOQprBhFupvk+4ERERERERERERERH5GxNYlK/EJqXDJEBwkG+bnYQEKRQNDcLlhFRciEvxWbmxyek4G5sMAD4d6i8oSEEAJrCIiIiIiIiIiIiIqEBiAovyjVSDCXEpBoSH5E6faV8PJWg0CU5cT0RCqu+GDrQVGqQQl2KA0VTwx3omIiIiIiIiIiIiosKFCSzKN+JS0pFi8O38V7Z8PZTg2dhkXL2VhmLhwT7vMQYAocEKaQYTktI5DxYRERERERERERERFSxMYFG+EZdiQJpREB6ce7utr4YSjE9Jx5mbyRAIInw4dKCtsJAgpBlNSOQwgkRERERERERERERUwDCBRfmCiOBmUhqClO4plZtyOpSg0ST453oS4lMNKFEkNBdqqIUGKaSbBLfS2AOLiIiIiIiIiIiIiAoWJrAoX7iVZkRimhFFcmn4QFs5HUrwXFwyriSkIiosd4YOtFBKQUH39iIiIiIiIiIiIiIiKkiYwKJ8IS45HSnpplyb/yqz7A4lmJBqwJmbyTCJICI09+saFqJwK9WINIMp19+LiIiIiIiIiIiIiCivMIFF+UJcSjqMYkJILvZoyszboQSNJsE/1xIRl2JAyYjQXB/qEADCgs3zYGVjqEMiIiIiIiIiIiIiokDFBBYFPIPRhJvJBoQGB+VJUsjC26EEL8Sn4MqtVETm8tCBtiwJLM6DRUREREREREREREQFCRNYFPDiUg1ISc+b+a8y83QowVupBpy6kQyDCSiaB0MHWgQHKZhEvz8RERERERERERERUUHBBBYFvLhkA1INJoQH+2d3dTeUoEkE/1xPQlxKOkpGhORpLzEACA4C4lIMEDc9xIiIiIiIiIiIiIiI8gsmsCigiQhuJqcBAILycP4rW+6GErwQl4IrCakoGhqUp3N0WYQFByEl3YjkdFOevzcRERERERERERERUW5gAquQ27oVOHTIN2WZTMCePUBSkm/KA4DkdBMSUgwI98PwgbacDSWYmGbA6ZvJSDeZEBkW7Je6ZcyD5bthBNPSgP79gZEjfVPejh1ATAzw11++KY+IKDMRYNkyIDHRN+XFxQH79+vYRvnbtm1Aly7AmjW+Ke/HH4EnngBu3vRNeUREmd24AaxerWObL5w5A1y44JuyyL8mTQJ69wbOnct5WUlJwJAhwNtv57wsIiIiotwS4u8KkP/s2JGEVq3+RLFiqfj++yAULRqE4OBgBAVl/enoucyvzZkThBEjSqNXr0r45Rff1DE+JR2pBv8lh2xFhgXjelI6Tt1MRqmIUESEBePE9STEJqejlB+GDrQIDVaITxHcSjWifFTOy0tPT8fLL3+J6dPnQinB3LkKwcEKSikEBQVBKWV92P7t6rWDBxVOn66NtLR30L59lZxXkojIhojgxRc348cfx6NYsY2oVSsCUVFRKFasWLYeUVFR6Ns3GEuXArNm6RtFlH/NnClYvPgKDIZkVKqUitRU+0daWppXz02ZYsStWx3w6KPdMHCg/89PiKjgefRRYPNmwQcfbES5crthMplgMplgNBrtfnrye2KiEZMnm2A0mvDBB7UxfPhLKFq0qL8/Yq7ZuXMnNm3ahKZNm+Lee+/1d3V8RkQwZ84cDBu2CAkJQbh4MRR33RWGsLAwhIaGIizM/ndHz2V+PSioHiZMqAgAeO89oFgxP39IIiqQbt4EtmwB6tcHqlfPWVkJCUByMqAUUK6cb+pH/nHyJDBhAnDqFDB8ONC0ac7KGz0a+OcfvZ8NHeqTKlIAUbk5b45SSgBwbp4Ak5SUhHHjxmHUqM9x7doVn5ffqNFL2Ljxc0RF5TybcvhyAo5dT0L5yFC/JYhsGUyCG0npKGqMQHBqKNKK30KwAqLC/ZsLvnorDdVKRqBR5eI5Kmf//v0YOHAgduzY4aOa2StSJAJvv/0vDB8+HJGRkbnyHpQ9lv8vEfH/P1qAYkwLPDdv3sT06dMxfvx47N+/36dlh4QUhcFQAl269MD06SNRvHjOjq/kHwcOHMDAgc9h+/ZNPi+7du06ePPNNzBw4MACfTM4P2JMc43xLLCZTCY89tgC/Pnn5zAYtvi8/Jo1a+Lbb7/FY4895vOy/S0lBXj55c8xadI7GD58OEaPHu3vKvnE/v378dprr+EvHw9nERYWhtatX8Djj/8fXn21gk/LJt9gPHOPMS2wHT4M3Hmn/n3MmJwnFoYNA8aOBYKDAYPvBiEKSCdPAhcvAgWoLYadnTuBu+/Wv0+eDDz9dM7Ki44GNmzQZW7fnuPqUS7ISUxjD6xCJCkpCT/++CM+//xzXL58GQDQtGlT3HbbbQ5b9RkMBly5cgWJiYkwmUwICgpCqVKlEBwc7LQF4NGjR7F37/do3Hg5Jk+ejOjo6GzX12gS3EhOR2iQCojkFaCHEowICcL7b49F/I3LeOGdZ9G8UQ6bkPhAaLBCfKoBRpMgOBvzcBkMBnzxxRf48MMPkZaWhurVq+Prr79G1apVISIwmUwQEYgIjEYjdu7ciSVLlmDbtm1ISkpCWFgYatWqhU6dOiE6OhpFihSxW8dgMGDatGmYM2cORowYgQkTJmDkyJHo378/goI4kikReU5EsGnTJowfPx6//PILUlL0sK7ly5fH008PQp8+T0IphYSEBLvHtWvXsGXLFuzfvx+J5nEGixUrhtKlSyM9Pd1u2Vu3bsFgSAKQhEWL/ocGDRZg3LhxePTRR/34yXNPbCxw+TJwxx3+ronvpKamYtSoURg5ciTS09MRFRWF0qVLIzw8PMsjODgY165dw/Xr12E0GhEWFoYqVaqgYcOGKFmyZJblExISMGHCBJw48Q9efvllfPDBB3jppZfw8ssvo0IF3gAkouxJTU3FjBkzMHr0aBw5cgQAULp0acTExCAiIiLL6BfXr1/Hvn37cObMGaSmpiIiIgK1a9dG8+bNUalSJbtlgSCkpwPTp0/Enj170KVLF3Tr1g1jx45FjRo1/PvBfejdd4FJk3TPWKPR6Ofa5FxsbCw++OADfP/99zAajShTpgzefvttlC1bFmlpaUhPT0daWhrS0tKQkpKCQ4cOYe/evYiLi4OIIDIyErVr10bZsmWRnp5ut3xiYiK2bNmCdeu+wY4dE3H58jC89dZbKFmypL8/NhEVIHXrAlFRwK1bwObNOU9gFZY85ZUrV9G+fQJOny6Lv/4qjnbt/F0j37M9/Th1KuflWYbWrcJBnwomyw3m3HgAEP0W5E9JSUny9ddfS8WKFcXynTRv3lyWLFkiJpMpy/JXrlyRYcOGSenSpa3LWx5BQUHy8MMPy6pVqxy+1+7du6Vx48YCQJRSMmzYMElMTMxWvW8mpcnKo1dk9bGrsuX0Db89Np+6Luv/uSYrj16RpQcvyaIDF6VCtYYCQD6f/qcsPnBJ/jhyRdYevyqbTl33Sx3/OnZVVh29IrHJaV5v54MHD0rLli2t3/GQIUMkLi7O4bJHjhyR+vXrZ9kvLN83AImKipLZs2c7XH/dunXSvHlz6zrNmjWTNWvWeF1n8j2b43WuxoX8/PBXTNuyZYusWrVKUlJS8vy9A8mNGzdk7Nix0qBBA7tjz0MPPSS//fabpKamOlzPZDLJqFGjpGjRotZjlVJKgoKCrGV069ZNrl+/bl3HaDRKfHy8bNu2TVq0aGFdrm/fvnLlypW8+sh5ZuhQkYcf9nctfGfTpk12ser555+X2NjYLMsZjUYZMWKEREZGWveNoKAgazwLCQmRwYMHS1JSUpZ1DQaD/Pbbb3bxMzw8XJ577jk5fPhwXnxMcoExLTDjGTkWHx8v//3vf6Vy5crWfbd69eoyduxYuXXrVpblr127Jp06dXJ4Pm55dO3a1eFxLz09XcaMGSPFihUTABIRESGjRo1yGkPzm59+EgkL+0oAyNChQ/1dnWwzGo0yYcIEKVu2rPUa/OWXX7Y7V7G1cOFCKVeunN01meV8B4BUrlxZVq5cmWW9PXv2SNeuXa37TalSpeTzzz/P9vU7+R7jGWNaQfDAAyKASM2aOS/r1Vd1WSEhOS8rkL322msCQIoWHSPHjvm7NrnDZBKJitLf56BBOSvLaBQJDdVlvfSSb+pHvpeTmMauDwVYcnIyxowZg9q1a+P111/HpUuX0Lx5cyxevBhbt27FI488kqVn06FDh9CsWTOMGTMGN27cyFKmyWTC8uXL8dBDD+Grr77K8nrjxo2xdetWvP/++wgKCsKYMWPQtGlTbNrk/dA98SkGpBpMKBKSt7upiCDVYEJCigHXEtNwNTEdiWlGBCuFMpFhuL1cFMqU0NvtzspFUblEEYQHByHVILiemI6rt9IQn2JAisEIk+RN85DQYIU0oyAx1fOWhkajEV988QWaNm2KrVu3omrVqlixYgV+/PFHh0Nl7d+/H61atcKhQ4cclifmz5qYmIjevXvj+++/z7JMdHQ0tmzZgqlTp6JKlSrYuXMn2rVrhx49euDYsWMe152oMOnRowcefPBBXL161d9VyXMigg0bNmDAgAGoXLkyhg4digMHDqBChQp45513cPz4cfz55594/PHHERYW5nD9l156Ce+++y6SkpKsz4nonqUWCxYswL333mvdxkFBQShWrBiaN2+OTZs24YsvvkBERARmzpyJ+vXrY/bs2dZjXkHw/vsp+OeflzFs2DB/VyVHbt26haFDh+Lee+/FwYMHUbduXaxZswbjxo1DiRIl7JY1mUwYOHAgPvzwQ2uPPMt+YfluDQYDJk6ciAcffNC6jEVwcDAef/xxbN68GevXr0e3bt2QlpaGCRMmoF69eujWrRvWr19foPYTIvKty5cv49///jeqV6+O4cOH48KFC2jYsCGmTp2K48eP47XXXssy5Pa1a9dw3333YcWKFS7LXrhwIe6//37ExsbaPR8SEoKhQ4fi8OHDePLJJ5GcnIx3330XTZo0wZo1a3z8CfPe4MHA6NH5uwfWli1b0KpVKzz33HO4du0a2rZti507d+K7775D6dKlsyw/ZcoUdOvWDdeuXQOQcU0mGTf3cenSJXTq1Alz5861W7dRo0ZYsGAB/v77b7Rr1w43b97E22+/jdtuuw0//PAD0tLScvnTElFh0Lq1/nnqFHDpkm/KDJBBmnKN5fj73/+G4rbb/FyZXKIUULOm/t3SA2v1auD4ce/LunoVSE/Xv1et6ovaUaBhAqsASk5OxtixY+0SV3fffTcWLVqErVu34tFHH3U4JN/FixfxwAMP4Pz58y7Lt5wMv/nmm5gwYUKW18PCwvDxxx9jy5YtaNCgAY4ePYr77rsPb7/9tnWoJ0/cTE6HSZCtIfG8YRJBisGEeJuEVXK6EcFBCuUiw3FHuSg0rlICLaqXRItqJXF7uSiEBut/nWqliqJplRJoVaMkmlQpjjsrRKFS8XCEBgchNV1wzS6hZcq1hFZIkILBJLiV5tkgwEePHkV0dDSGDx+O1NRUPPPMM9i/fz86duzocHmj0Yju3btbh6NwRUSglMLLL7+MvXv3Znk9KCgI/fv3x9GjR/Hxxx+jaNGimDdvHho0aIA33ngDN2/e9OgzEBUWYWHhAPTQQoXFjRs3MGbMGDRo0ADR0dGYNm0aUlJS0LFjR8yZMwdnz57FqFGjUKdOHZfl/Prrrxg3bpxH73nkyBG88sorWZ4PDg7Gm2++ib1796Jdu3a4du0a+vTpg27durmNl/lFeLgBR49+7zCm5xcrVqxAw4YN8c033yAoKAjvvPMO9uzZg/vvv9/h8hMmTMC0adM8KnvTpk149913Hb6mlMJ9992H+fPn49ChQxgyZAjCw8OxcOFCtG3bFq1bt8Zvv/0GQ0EfpJ+IPHbixAm89NJLqFmzJkaOHInY2Fjcd999WLx4Mfbu3Yv+/fsjNDTU4brDhw/H4cOHPXqfPXv24N///rfD1ypXrozZs2fjjz/+QN26dXHo0CG0b98e/fv3tw41n1+FhOTPBNbly5cxaNAgtG7dGtu3b0eVKlUwa9YsrFmzBo0bN3a4zqlTpzB48GAAruf/sQz337dvX1y5knUO7HvuuQerV6/GihUrcPfdd+PixYt46aWXcOedd2L69On5blsSUWC5556M3zdv1j+9OTVOTwcSEvTvmQ91Ino49IIm3ZyNcdRIM7+7fBkYPhzYtStjGMFjx4AXXwQefBAoW9bzsiz7g2X4QIAJrIKKCawCJDk5Gd988w3q1KmDYcOG2SWutm3bhscee8zlXFJff/01Ll265FVr4f/7v/9zmpS6++67sWPHDrzzzjsAgNGjR+Puu+/Gdg9m00s1mBCXnI7wYN8nr0wiSEk3Ii7FgKu30nAtMR0p6UaEBitUiApHvfJRaFK5BFrVKIXm1UqgbrlIVCwWjsiwENsJ5+zKDA8JRrmocNQuE4lmVUtaE1r1rQkthZR0o10PrVQfJrSUUghSQFyKwe3Fy5gxY9C4cWNs2rQJlStXxpIlSzBx4sQsrdNtLVq0CP/884/H+4ZlOUe99CyKFi2K999/H8eOHcOgQYNgMBjw9ddf47bbbsM333xjDdhEpBNYyckFO4ElIli3bh2eeuopVK5cGa+//joOHTqEihUr4v/+7/9w4sQJrFixAj169HB6Yy9zef/973+9mkPx999/xyknA3DfdtttWL16NcaPH4/ixYtj0aJFqF+/PiZMmJDve9no+VHy380+ALh+/ToGDBiAzp074/Tp02jWrBm2bduGUaNGISIiwuE6JpMJX3zxhVf7xk8//eS2gcUdd9yBH3/8EWfOnMEHH3yAMmXKYOvWrejVqxduv/12fPvtt7h165ZXny/QTJ8+HeHh4Xj22Wf9XRWifGf37t3o06cP6tatix9++AEpKSno2rUrNm7ciPXr1zttZGhx4cIFTJ8+3av3nDRpEq5fv+709Q4dOmDfvn345JNPUKRIEUyfPh133HGHdc6l/Ci/xbT09HR8/fXXuP322zF58mSEhYXh3XffxeHDh9G7d2+X+8TYsWNhNBo9Og8REaSmpuKHH35w+LpSCh07dsS2bdvw+++/o169ejhx4gT69++PJk2aYOHChfn+fIeI/KNVq4zfN2/WvWV69vR8/ZAQoG1bYNSojEQWAKxcqZ//+2/f1TVQWHpgeXLdm99UqADs3Qs0awZYOpSfOweMG6fnr/JmKsa33gLWrXOcwHIwqBjlY0xg5VO7dgGWht+2iauhQ4fi4sWLaNasGRYuXOhR4goAEhISPG6lbuvatWuYOXOm09fDw8MxatQobNy4EXfccQcOHjyI1q1b4/3333c5JEF8SjpSDEaE+2D4QJNJkJxuRFyyTh5dT0xHqlEQHhyESiXCUb9ClO5FVb0UmlUtgdvKRqJ8sXBEhAa73W7OXi9ik9CylN20SgnUqxCFisV0QivZnNC6lpiR0MrJRUFYcBCS0gxINZocvn78+HG0a9cOr7/+OlJSUjBgwADs378fjzzyiNuyv/nmG69u9FnMmDHDOpyFM5UrV8akSZOwY8cOtG/fHjdu3MDQoUPRsGFDLFq0yLpN9u8Hfv/d6yoQ5XtGYxEAwJo1nvdgzU9Onrykk/BBQbj//vsxY8YMpKWloXPnzpg7dy7OnDmD//znP6hVq5ZX5R44cAA7duzw6rhqMpkwdepUp68rpfDcc8/h4MGD6NKlC+Lj4zFkyBA8+OCDOJ6dsQ4ChOVmn+2wioFORDB79mzceeedmDZtGooUKYLRo0djy5YtaNq0qct1N2zYgOPHj3u1byQnJ2P27NkeLVu+fHmMGDECZ86cwffff486derg5MmTeO2111C9enX8+9//xtSpl6wtUPOTlJQUpKWlYds2wcmT/q4NUeC5ehV2/9sigr/++gudOnVC06ZNMXv2bAQFBeHpp5/GgQMHrEPYemL27Nle9+ZMSUnB725OoMPDw/Hee+/hwIEDePjhhxEXF4eXX34ZrVq1wrZt27x6v0CQnxJYK1euROPGjfHGG28gPj4ejz76KPbv34+RI0ciKirK5boigp9++snr93TX21ophZ49e2Lfvn2YNGkSqlevjv3796Nbt25o06aNw6Em89HpAxHloWPHgOXLgYgIwDJwxoIFQPPmOoHhKaX0MIT/93/AlCn6ufR0oEMHYOdO4IEHfF93f7PcLy2IPbAA4PXX9c/MpzUNGnhXTkQEcP/9wHvvZTz355/AvffqhBgVHExg5UObN+tMdfPmKfj222/tEldNmzbFggULsH37dnTp0sXjpMO6deuQYNuUwUNKKcyfP9/tcq1bt8auXbvwxhtvwGQy4dNPP0WLFi2we/duh8vHpRiQZhSEZSOBZTQnrGKT03HlVhquJ6Uj3SgIDwlGlRJFUL9CMeuwf82qlETtMpEoFxWOIh4krCy8uemllEKRUJ3QqlMmEs2q6oRWkyolUK98FMpHZSS0ruYgoRXmZB4sk8mE7777Do0bN8b69etRsWJFLFiwAFOmTEGpUqU8KvvQoUPZSq4ZDAac9PAOV9OmTbFq1SosWLAAdevWxdGjR9G1a1d06NABe/bswaOPAk88ASxZ4nU1iPK11FTdA2vWrILZA+uxx163+zs4OBgbN27EsmXLEBMTk+1WZ5eyOcD6xYsX3S5TpUoVLFiwALNmzULZsmXx119/oVGjRvjyyy/zxQ2zzIKCdKzNL3U/d+4cunbtij59+uDq1ato164d9u3bh+HDhyMkJMTt+hcuXMjW+3o7ZGTRokXx4osv4siRI5gzZw7uuece3Lx5EyNHjsTTT9fAgw/OR3JytqriN5Ze95GRRXDbbUDv3oAHHeuJCo327fVQSTNmXMSzzz6Lu+++Gw888AD++OMPREZGYtiwYThx4gQmT56M+vXre1V2dof283S92rVrY8mSJZg7dy6qVq2KHTt2oFWrVnjppZfy1RDf+SGBderUKfTs2RMdOnTAoUOHcNttt2Hx4sVYvHgx6tat61EZiYmJ2erVe/HiRY+u60JCQjBo0CAcPXoUY8eORbly5bBp0ya0b98eHTt2xPbt2yECfP454KLtDxHlY/HxuuF8dtvpVasGvPACUKpURgP8w4eBM2eAMmW8K+uxxxw/37EjUKRI9uoXyCwjEhXEHliA/t7q1cv6fMOG3pVjGWF3//6M50aNAnbsALwdMOLIEeD0acDSdoedjgMLE1j5kMFwCUFBVXHpUlG89tprdomrHTt2oGvXrl73lsk8wa83PL2giYiIwJdffom1a9eiTp062Lt3L1q0aIGPP/7Ybrg4EcGNpHQEKSDIg89hNAmS0jISVjeTdcIqIjQY1UpGoEFFnbBqXUMnjWqVKYqykWEIN4+PnhPZ6ZVkSWiVjwpHnbKRuLtqCbSsXgpNKmcktELMCa1r5oRWggcJrdDgIKQbTXbzYJ08eRIPPvggXn31VSQlJaFv377Yv38/unbt6lWdc3IB6E1LUaUUunbtiv3792Ps2LEoVaoUVq1ahaZNmyIi4lk0bHjN64BGlN+lpekz8mvXUnD6tJ8rkwsGDvzU7m+j0Yjo6Gh07doVCxYsyPZwos6Gj3OnaNGiHi2nlELv3r1x6NAh9O3bF8nJyXjrrbdwzz33YL/tGXQ+YNsDK5CHBzKZTPjhhx9Qv359LF68GCVKlMCECROwevVq3ObF7MZFsnmVm931jEYjjEZjpn0rDfXqXcx3F9yWufhatgzHXXcBv/wCtGgBPP44kJjo58pRwPv7b+Do0YLdW6NxY31T7qmnKmPixInYtWsXAKBLly44ffo0vv76a1SrVi1bZXsan3KynlIKMTExOHToEIYPH47g4GD88MMPqFevHqZNmxbQMcIikBNYycnJGDFiBO68807MnTsXkZGRGDVqFPbv349HH33Uq7LCw8OzVYfw8HCvrmHDw8Px2muv4cSJE/j0009RvHhx/Pnnn2jRogXatx+JGTPsh24iKmxGjwZmzwYMBhNu3rzpctjW/GTpUt0oo149YPHi7JVRpAjw2We6l03mmUe8TWA98IDubZNZly7Zq1ugK+g9sIKCgGHDsj7vbQ+sJk0cP//kk0D58t6VtXq1Po976CEgJsa7edoC0YYNGzBixI84cOCAv6viEwUygSUCvPsu0K4d8l3LVk88/HBdmEznAegLiEGDBmHz5s3ZSlxZuBuiwJVixYp5tXx0dDT27NmDV155BQaDAR9++KHdDb9D/xhx5IQBRRz0vhIRGEyCxDQjbial40qCTlgZTYKiYSGoUUonrJpV1QmrxpWLo2bpoigTGZat3lzO+PLiTSmFiNBglC8WjtvMCa1W1UuhceUSuKN8FMpFhSM4yL6HVkJq1oRWcJBCcgrw83QDNmwQjBs3DnfddRfWrFmDcuXKYc6cOZgxYwbKeHumAKB69erZ3reqZmMGxbCwMLz22mtYvnw5ypQpAxHBkSMT0bz5cOskj/ldamrBvoFDvtO4sb5B8fnnqahe3c+VyQXDh9fRx3aDAUuXLkWPHj2glMKiRYvQvXt3VK9eHe+88w6OHTvmVbmNGjXKVmyLjo72avmyZctixowZWLRoEapUqYJt27ahWbNm+Oijj5Camoo1a/QN20CmlHI6x6M/bdsGDB0KXLgAHDlyBO3atcNLL72EhIQExMTE4ODBg3j22We9jk8tWrSw3uD0Rps2bbxa/sSJE3jnnXdQrVo19OrVC6tWrUJERAQGDhyITZs2Yfv2F5DN0Oo3lh5YRYsWwfjxsNZ/0CAgMtKPFaN8Ydo0PQTQwIH+rknumTFDtzJ/6qmn7J5ftGgR2rRpg08//dTj0Qkyu//++7O1Xtu2bb1eJyoqCqNHj8auXbsQHR2NK1euYMCAAbjrrnZYuzawb4QEYq/iK1eu4KGHhqJs2er46KOPkJKSgj59+uDw4cN45513spWMCg0NRbNmzbyKgUoptG7d2uv3MhqNWL9+PbZv345Em9YK9eqdxd699kM35VdpaboFfzY7OlIhNnky0KcP0LhxHEqXLo06lrHy8rn77wcuXQJu3QJKl85+OU8+CbRsmfV5b29LRUQADz5o/5xSgJe5f6vjx02YOPEEDIbAiRW2ArkH1rVrBjz00FeYNy8pR+X075913/I2gVWnjuNrkFdf9b4+9esDcXG652Hr1kAAbnqvvPXWbHz00Qt4/fW//F0V3xCRXHtAZ1jEH957T6RSJb+8da6bNWuWWLat5VGxYkV5++235ciRI9kq8/LlyxIWFpalXE8e//3vf7P9WVavXi01a9YUABIWFiajRo2S+zrFyzOfXJYZf12Tzaeuy4YT12T1sauy7NBlWXTgkiw9eElWHr0iG05cl/0X4+TMzSS5mZQm6UZjtuvhrQYNGggA2bt3b66/l8lkksRUg1yKT5GjV2/J1tM35a9jV2X5Yb09lpi3x/p/rsk38y9Ih367pVixh6zfT69eveTKlSs5qsO4ceO83i+UUtKpUyev3ys5OVlmzJgh7dq1syvvzjvvlNWrV+focwSKESNEoqJEPvnE3zXJYHO8ztW4kJ8f/opp3bp1EwAyb968PH9vf7l8+bJ88cUXUq9ePbvjQNu2bWXKlCmSmJjoUTlvvfWWV8es2rVri8FgyHa94+Li5IUXXrCWWbt2AwE2S4kSIiZTtovNE8HBwQJA0tLS/F0Vq06dRIA0eeih/0h4eLgAkAoVKsjvv/+e47Kfeuopr/aNJk2aiMmDLzEtLU1+//136dChg10ZDRo0kG+++UZu3ryZ47r70/vvvy8AZMSIESIi8sorIr/+KpKHp2AeYUwLvHiWni5SrpwIIBIRIfK//+Xp2+c5k8kkaWlpsn37dhk6dKiUL1/e7pjQpk0bGTdunFy/ft2rMps0aSJKKY+PXW3atMnRZzh16pTMmDFDatWqZS03JKSYV/XOazNmzBAA8uSTT/q7KlZVq1a1+27q168vkydPliNHjngUW5yxfFZvHosXL/a4/LNnz8qIESOkevXq1vWDg4MlJiZGli5dmqNzpkCTkCBSpoxIWJi+l7Rtm79rpDGeBWZMszAaRf74Q2TPHpGTJ+MFgERFRfmlLrlh8mQdtxctylk5Gzbocmwfr77qfTnjxtmX0bq1d+tfvHhRRo0aZXdM7NXrU+8rkgfat28vAGTlypX+rkoWr7wyxW4bli5dWr777ju5deuW12X93//Zf6fx8d7Xp3Vr+zJatvS+DBGRK1f0+pGRIjduZK+MQHLPPfq+xGOPBc5Jd05iWoENJIcOXZFWrTbL8ePH/fL+ecHZDb7o6GiZPHmy1wePZ5991usT4MjISImNjc3R54iPj5chQ4ZYy6xQpbk898lm+W3bJVm4/5IsPXRZVh29KhtPXpcDF+Pl7M0kiU1KE4PRf3cDLQmsffv25fl7WxJaF+NT5OiVW7LFktA6dFnuatnW7vt55plnfHKBmZSUJKVKlfL4gtny+PPPPz1+j3379snQoUOldOnS1vUjIiJk4MCBsmHDhhxd3AWauXP10XfOHH/XJAMvjgI3pvXq1UsAyOzZs/P8vf3NZDLJxo0b5ZlnnpHIyEjrflq8eHF54YUXZNu2bS6PDRcuXJBKlSq5PXYppUQpJXPnzvVJvf/66y+pXLmyuXwltWoNl9TUwD6GWRqxJCcn+7sqVt27v273PT322GNyw0dXE4cOHZJixYpJUFCQ230jODhYli9f7rK8kydPyv/93/9JxYoVresWKVJEBgwYUKBi2PDhwwWAfPbZZyKib/gF4kdjTAu8ePbnnyKAvjn844+Bud/kpvT0dFm2bJn069dPihYtat1Hw8LCJCYmRubMmSMpKSluy1mxYoUEBwd7FNdCQ0Nl7dq1HtcxLS1Ntm3bJmPGjJFevXpJlSpVHJQdLPXrP+JRXf1l9uzZAkAef/xxf1fF6r333nf6XZUuXVoefvhh+fjjj+WPP/7w6to6LS1NGjVq5PG1WZs2bcTopsVBenq6LFiwQB577DG7GFm7dm0ZOXKkXLhwIaebIyDFxor15mfJkiLnzvm7RhrjWWDGNEcSExOt538FhdEo0rSpQTZuzHlZPXtm/I8BIh995H0ZZ8/alzFypOvlk5OTZeXKlfLyyy87PS5OmPBH9j5QLrvvvvsEgKxbt87fVcni9dffcxlrqlSpIhMmTJBr1665LevcOZGQEP191qiRvfq88IL9fjF1avbKEREpWzZ7ydVAZLnPPm7cOH9XxYoJLAe+//57ASDPP/+8X94/L7m6wff888/L1q1bPbpxcvjwYSlatKjbmzm2jw8++CDb9b5y5YosWrRI3nvvPXnooYckNDTUWm77R2Pk71M35OCleDkXmyRxyf5NWGVWv3598VcCKzOTySS3UtPlYnyyhJlbqGd+1KlTR/r06SNfffWVbNiwwePeC7YWLlwowcHBHu8fzz//vNv9LiEhQSZOnCitW7e2W7dZs2by/fff5zg5GqhSUkRKl9YtPAIFL44CN6b1799fAMiUKVPy/L0DSXx8vEyYMCHL8aJRo0byzTffOE3WHzlyRGrXrm29oefoeBUWFibTp0/3uk4pKSmyb98++f333+XTTz+V/v37S4sWLaR48eJZ3uPgwYM53QS5KiIiQgBkq+VcbjCZTA6/qxIlSkjz5s2lb9++8tFHH8nMmTNl+/btEhcX5/V7bN68WcqWLety3wgPD5dffvnF4frp6ekyb9486dy5s936d955p4wZMyageyhk16uvvioAZMyYMf6uikuMaYEXz559VqR6dZGtW/P0bQNSfHy8TJ06VTp27Gh3Xl2yZEl57rnnZN26dS6TDL///rsUKVLE5Xl40aJF3fa0uXHjhixdulT+/e9/S7t27ewSa7Z1euSRR+Q///mP/PXXXwETI1z57bffBID06NHD31Wxk5ycLImJibJu3ToZPXq0xMTE2DV6sDyUUtKgQQN59tln5aeffpIDBw643B8uX74szZo1cxrLLM+1adPGZS/gkydPynvvvWfTAAcSGhoqvXr1kpUrV7pNfOV3N26IT25++hrjWWDGNEdSU1Ot/zcFie3xpFatWvLxxx/LX3/9JefOnfOqgdaxYyKhoRn/Z99+m736NGmSUcb+/favmUwmOXDggHz99dfSqVMnl3Hys88+k9TU1OxVIg+0bNlSAMimTZv8XRWndu7cKV26dHF7b/C2226TiRMnypkzZxyW06+f/j4ffTR79fjhh4x9olw5key2xzSZTNK2baoUlH4wgwcPFgAyfvx4f1fFKicxrcAGkp9++kkAyKBBg/zy/v7i7AbfXXfdJWPHjrXLgO/aJfLFFyK2owWtXr1aIiMjXbbqs7z2/PPP253IHjki4qyDQGpqqmzbtk2+/fZb6devn9SpU8dh2dWqVZMePXvKXxv+FmMAN820JLD2Z46YfmYwGOS7776TGTNmyLBhw6RNmzYOL3CDg4OlcePG8txzz8n48eNl9+7dkp6ebldWSorIc8+JfPVVxnNLly6VYsWKubzZB0DefvvtLBc5M2fqrr07dphk27ZtMmTIEGtZgE64vvjii7Jjx4682FR+tXr1aqlWrbm88847/q6KFS+OAjemPffccwJAfvzxxzx/70C1f/9+ef3116VMmTI2N1nCpUWL7vLSSy/Jv/71L/n444/lq6++kh9//FEmTJgggwcPlho1amRJhrz88sty4MCBLO+xZIke7sJkMsnly5dl7dq18uOPP8obb7whjzzyiNSpU8dlQr9UqVJyzz33yKBBg2TixIkBf+MnKipKAEh8dsZtyCWWIT569OghLVu2lBIlSri8OKpQoYJER0fL4MGD5fPPP5d58+bJgQMHJCUlRXbv1i0/16yxf4/z58/L+++/L5UqVbIrKyoqSmJiYmTcuHHy66+/ys8//yzffvutfPbZZ/Lee+/JXXe9LEWKZNzkCw8Pl379+sm6desKTG8rRwKxJZ8jjGmBE88OHjwonTs/LLff/ppcvZonb5mvnD9/Xr788ktp2rRpphhSQwYO/D85dOiQmEwmMRgMkpqaKklJSZKQkCBbt26V5557zu5c2hLXBg0aJCtWrJADBw7I3r17ZdOmXfLxx9tl7tzNMnnyZBkyZIh1NInMj7p168rAgQNl/PjxbhMngWru3LkCQLp16+bvqrhlGaZx9uzZMnToUGnVqpVdo07b77Vjx47ywQcfyLJly+Szz45K167/yN69p+XcuXNy8uRJ+eCDD6wNdmwfDRo0kDFjxji8UZuWliY//vi7lCnTSYCMa7vbb79dvvjiixwPQ5+fXLsmAoh07RpYPUQZzwIrprliMBis90kKirS0NLfJiYiICOnRo4e8/fbb8tNPP8natWvlwoULdufDN2+KrFolMmyYWBMNM2dmr04vvqjXr1lT/69eu3ZNZs+eLc8880yW4VozP7766qt808DMcl6QX+6PGY1GWbduXZZh1B09GjZsKOPGjZP/Z+++w6MqsweOf2cyk0YagUBoISRAICC9BFCkKIq6UkRRFGGFVX+6dl11LWtfe93VXRUEQUVdaVZQ6U16CwmBQBJII4T0Nu39/THMSKSkzeRmJufzPPNkSObOnBlm5r33nvecNykpSVmtNnXTTfb/07/9rX6PvXnz7++rMWOUqqm7bWlpqdq1a5f67LPP1HXXXXdWfD/++GP9AmliZs6cqQA1Z84crUNxasiYprNv7x6nT3Djzsc4n4ULFzJ9+nSmTZvGZ5991uiP3xQkJiYyZ84cFixYwMmTJwHw9fVl0qRJzJo1i6eeGstvv+l55x24997ftztw4AAPP/wwK1aswGaznXW/Xbp04bHHHuMvf/lLtUVju3aF1FT45hvF4MHH2bJli/OyY8cOqqqqqt1PQEAAgwcPJiEhgYSEBIYOHUr79u3d82K4WHx8PElJSezfv59edV1lsJGZzWYSExPZtm0bW7duZdu2bezfv/+shY0DAgIYMGAAQ4YMYfDgwZjNQ5gxI4bAQB2nToGfn/2znJ2dzaeffsonn3xCSkqKc/vg4GBuuOEGbrzxRuLj47HZbFitVmw2GzabjT//2cL69b8QGfkxOTm7nduNGDGC2bNnc/3119OimawAv2jRIm666SamTp3KokWLtA4HwPlZVkrVfiXoZkarMe2ee+7hX//6F++++y731Gc1Ui9WVVXF8uXLef/9OaxZsxL7/lDd+fj4EBQURIsWLQgKCsLHJ4ikpCD0+iqCgw9SVFR4zu30ej0xMTH06NGDuLg4evTo4bzeunXrOi2srrXQ0FCKi4spKCggLCxM63DOSSnFyZMnSUlJISUlhUOHDlW7XllZec7tdDodwcGdKS6OoU0bG507l1FaWkpZ2e8//7iPUludO3fn3nvvYMaMGbSq62rUHmjGjBnOfYCZM2dqHc55yZh2YY05nm3fvp3BgwczcOBAtm/f7vbH82SJiYksXLiQ//73MwoKjrn98Xx9fRk0aBAjRoxgxIgRDBs2jDZt2rj9cd1t+fLlTJgwgWuuuYZvv/1W63DqrLKykl27drF582a2bNnC5s2bOX78eL3vT6fT4ePjg8FgOOtnRUUFJSUlp2/px803T+H222/nkksu8ah9GFc4cQJ69oTERIiM1Dqa38l4VjMtzzueSSmFXq8HwGq1Oq97upycHD777DPmzp3LgQMHar1dUFAQXbt2pWvXrhQWduOXX7oxYUI3Vq3qRklJG1as0DFu3O+3V0phMpmoqqqqdqmsrKz275kzq0hPr6Bz5+1ERKxgx44d5/2/NxqNPPLII0yfPp0ePXo09KVoVL179yYxMZG9e/dy0UUXaR1OnVVWVvLrr7/y8ssvs2HDhhpu3RcYztVXt2PChEjatWtHZKT9Z5s2bTAajRfcuqwMgoJApwMfH3jkEXjuOQtpaWnOY8WDBw+ydetWdu7cWWPsW7ZsYejQobV/sk2U47ht3rx5zJgxQ+twgIaNaV6bwPrqq6+YOnUqU6ZM4euvv270x29KTCYTy5cv5+OPP2blypXO/4/w8M74+AxnyBA9LVvq0Ol06PV6dDr79fLyco4cOUJJSQk2m42AgACio6Np3749er2+2m11Oh1r1ug4ciQNf//fOHEi66w44uLinMmqhIQEevfujcFgaOyXwyUcCazExETi4+O1DqfOysrK2LVrV7WkVmpq6lm30+uN2L9fbGclvBqiVatWzJgxg1mzZnnk69dQc+bMYfbs2dx2223MmTNH63AAOTiqDa3GtEceeYTXX3+dV199lUceeaRRH9tTKAWTJ2eQlbWaqVNLMJvtiYkzL45kxbn+bTKZLnj/oaGh1ZJUjp+xsbH4+fk10rN0r/DwcAoKCjh58qRHJmJsNhvHjx8/K7GVkpLC0aNHaxzD9Hq9M4F5ZjLzjz8d148da0Hr1oN4/vmRGAzN52vzxhtv5Msvv+SLL77gxhtv1Dqc85Ix7cIaczxLTEykd+/exMfHk5iY6PbH8wYpKTamTl2Pv/8CkpL+R1FRkTMJUZ9LZaUPx4/70LatDwMGdGTEiBEMHz6cgQMHes0Ydqbvv/+ea665hvHjx/PDDz9oHY5LHD9efXLokSM5lJdbadHCglJWLBYLVuu5f9akV69exMX9hVmzpnPVVeGN8Gyanvz8fIYMGYavbyRJSeu0DqcaGc9q1lQSWAAGgwGr1YrJZKrxpLunslgs7N69m/Xr1/Pdd9+xatWqetxLMG3bhmK1/p6cqul4rLYCAgK4/vrrufXWWxk9erTHJhLj4uJISUkhOTmZuLg4rcNpsLy8PH7++WfefPNNduzYUadtAwMCCGrRgr79+9OuXbtqCa7IyEiUgquuSqGiIgVIISQkhfLyw7UaAx2mTZvGzJkzGTNmDD4+PnV8dk3TLbfcwmeffcann37K9OnTtQ4HaNiY5pnZg1pw7IzXd0atN/H19WXKlClMmTKFjIwM5s2bx9y5c0lPTwfS+f772t/Xnj17arxNcTG0bNmSoUOHOiurhgwZQni49+wQN4Wdo4Zo0aIFF198MRdffLHzd/n5+Wzbts2Z1Nq6dSsnTpw4a1u9Xo+Pj0+dfjqux8TE8Oc//5mJEyd65QFzbZWXlwMQGBiocSTCE8h4VjOdDpYsiQLqN7PIZDJRVlZ2VpJLr9fTvXt32rZt6/UzkR076q6crNCY9Ho9UVFRREVFcdlll1X7m8lk4ujRoxw9ehRfX99zJqf8/f29/v/YFRxVbs15DBd1ExAQAEBFRYXGkXiO7t317Np1KXApSn0EIN9PdeDp49m5dOzY0Xk8X1eOrhjnS261adOm2b+/KioqOHLkEB06lGsdivBwjgSW1Wr12gSWwWBg0KBBDBo0iAceeAClFCkpKaxfv54NGzawfv16jhw5UsO9lJCbW1LDbWpPp9MxevRobr31Vq677jqCgoJcdt9acST0vOV9FBERwbRp05g2bZrzPfPzzz/zwQcf1FjZV15RQXlFBT///HOtHqu4+Px/a9OmDTfddBMjR45k+PDhRDalklsXc3RU85aEnCSwmpmoqCiefvppnnzySdatW8fx48er9ZS02WwN+nfr1q1JSEigW7duHjvToS68aWe/VatWXHnllVx55ZWAPUlXWVl5ViJKNFxZWRkgCSxRO/7+/gDnbY8mGs7X1xdfX19atmypdSiacezYnqt1sKfz9fUlLi7OK2Yvas3xPeT4XhKiJo4ElmPyjqgbbzrWaCzemMBqCMcxnLecBHUHxzkjX19fjSMRns7x/VOXyg9Pp9PpnPvZs2fPBiAzM9OZzNqwYQN79+51yyTw7t27M2PGDG655RaioqJcfv9aciSwvPF76cz3zF//+lfMZjPbtm0jMTGRzMxMjh8/TmZmJpmZmaSnp1N8oYzUBURERDirzh2V583pGMaxH+Qt53G9NoHl+JC7qgzV2+j1ekaNGqV1GB7L0yuwakOn0zlPOgjXcpzEaS5rfomGkQkZojGc2bNfiPNxfA81p4M/0TBSgSUamySwRF1584li0bgcS2Q09++fDh06MHXqVKZOnQpAYWEhmzZtYv369axfv55t27Y5P3c6nY7AwEACAwNp0aIFgYGBVFVVERQURPv27av9/syfl156KUOHDvXaiR5msxnwngqsCzEajc4k07mUl5eTlZV1VnLrzOsnTpyge/fuDB8+3Jm0io2N9dr3R204voekAquJkxN+ojE05y9DUX9SgSXqwjGeSQWWcCc54SdqQ1oIirqSBJZobDKeibpynEiXsU00VHOswKqNsLAwrrrqKq666irAfp62pKRE2nhfgCTWfxcYGEjXrl3p2rWr1qF4FGkh6CGkAku4U3OowBLuIxVYoi4clQ4yIUO4kze3EBSuIxVYoq58fX3R6XSYzWasVqvXHESLpksSWKKupIWgcBWpwKodPz8/SRjXwFGBJd9Lor68rYWgdzyLc5AKLNEYZKaIqA9HAksqsERtyHgmGoO0EBS1IRVYoq7ObEktVViiMUgCS9SVVDoIV5EKLOEqju+l5tBCULiHt1VgeX0CSyqwhDtIBZZoCGkhKOrCUekgLQSFO8kJP1Ebju8hqcASdSEJLNGY5ASyqCtpIShcRfanhSsopZxjmCSwRH1JBZaHcMyekRnrwp2kAkvUh7QQFHUhFViiMcgBt6gNaSEo6kMSWKIx6fUykVXUjbQQFK7iaCEoCXTREI72gQaDQc45inpzHNdLBVYTJyf8hDtVVUkFlqg/qcASdSEVWKIxyBpYojakhaCoD8f+jiSwRGN4+217wlT2m0RtSQWWcBWZECZc4fcElpEvvwRpACXqQ1oIegjH7BmZeSXcITvb/nPVKpkNIepOKrBEXciEDOFumZlgMskaWKJmUoEl6kMqsERjKS6Gigr795O830RtyRpYwlWkAku4guN4zGw2cOONMGeOxgEJjyQtBD2EnPAT7nT6OJzu3bWNQ3gmRwJLKrBEbTjGM5lJLNxhxQoYNw4OH5YZo6JmsgaWqA9JYInGEhICH3wgleuibqSFoHAVqcASruConDEY9HTpAn36aByQ8EjeVoFl0DoAd3Gc8JMKLOEObdrYZ/h17Ci1vKLupIWgqAvHiWKZkCHcYexYsK8NLAfc4sIsFgtWqxW9Xu+cYSxEbUgCSzQmx/tNEliitqSFoHAVqcASruBIPAQG6jlyRONghMeSCiwPYbSfjcFsNst6DsLlHAspKmlGK+pBWgiKupAKLOFOBgP8+9/gSGDJPpM4H2kfKOrLkVBw7P8I4U6O7yhJmIrakhaCwlWkAku4guN4zFsSD0Ibju8hb6nA8tpPg06nc+6AOBbAE8JVJIElGkIqsERdSAWWcLcRI6B1a1kDS1yYI4kuM9RFXUkFlmhMRqMRHx8frFarVEGIWpEWgsJVpAJLuIIksIQreFsLQa/+NDh2QOSkn3A1SWCJ+lJKOWcgO07oCHEhsqajaAxdusiMUXFhUoEl6ksSWKKxOb6npHpd1Ia0EBSuIhVYwhUkgSVcQVoIehBZB0u4iySwRH05DqT9/f29ZiaEcC85CSMaQ2CgvfWyzBgV53Pm+CVEXUgCSzQ2aSMo6kJaCApXkQos4QqSwBKuIC0EPYhUYAl3kQSWqC9pHyjqSiqwRGNw7DPJpB9xPtJCUNSXJLBEY3O852Tyj6gNaSEoXEUqsIQreFvljNCGtyVCveNZnIec9BPuIgksUV+O9oEtWrTQOBLhKRxjmZyEEe5kNNorsCSBJc5HWgiK+pIElmhsUr0u6kJaCApXkQos4QrelngQ2pAKLA8iLQSFu0gCS9SXVGCJujIajeh0OiwWi8zmE24jFViiJlKBJepLEliisUkLQVEX0kJQuIpUYAlXcCSwvCXxILThbe8jr05gSQtB4S6SwBL15ajAkgSWqC2dTicVxcLtHPtMZrNZ40hEUyUVWKK+JIElGpu0EBR1IS0EhatIBZZwBanAEq7gba0oveNZnIdUYAl3kQSWqC9pISjqw3HCWBJYwl2kAkvUxHEiWBJYoq4kgSUam7QQFHUhLQSFq0gFlnAFSWAJV5AWgh5EKrCEu0gCS9SXtBAU9SEVWMLdJIElaiItBEV9OfZ5JIElGosksERdSAtB4SpSgSVcQRJYwhW87X3kHc/iPKQCS7iLJLBEfUkFlqgPOREj3E0SWKIm0kJQ1JdUYInGJu85URfSQlC4ilRgCVfwtsSD0IZUYHkQmbEu3EUSWKK+pAJL1IeMZ8LdjEYjIAkscX7SQlDUlyOZ4JjEI4S7ycQfURfSQlC4ilRgCVeQBJZwBcf7SBJYHkBaCAp3kQSWqC/HyRtJYIm6cBxQy4kY4S5SgSVq4tiflhN8oq6kGkY0NklgibqQFoLCVaQCS7iCJLCEKzi+h7zlfeQdz+I8pIWgcBdJYIn6clRgSQtBUReOEzFy8k+4i+Okjdls1jgS0VRJBZaoL0lgicYm7zlRFzJBQ7iKVGAJV5AElnAFaSHoQaQCS7iLJLBEfUkFlqiPNm3aAJCdna1xJMJbSQWWqElJSQkASsn4JepGkgmisUkFlqgLqcASriIVWMIVJIElXMHb3kfe8SzOQyqwhLtIAkvUlyOBJRVYoi66dOkCwMaNRzWORHgrSWCJmqSlpQGweHEUTz8NRUXaxiM8hySwRGNzvOckgSVqQxJYwlUcFViSwBIN4W2JB6ENqcDyILLovXAXSWCJ+nK0EJQKLFEXnTtHA/D112maxiG8l9FoBCSBJc7v6FF7Av3WW2N4/nmIjYU33gA5PyxqIgks0dh8fe3nAeQ9J2pDWggKV3GcKJYWgqIhJIElXMHxPpIElgcoLrbPoDl4UE7GCNeSBJaoL2khKOojIsJegZWRcZQVKzQORnglqcASNTly5AgAt97ahcGDIT8fnnwSPvwQZHdIXMgfE1g7dmgZjWgOdu+WTiyi9qQCSzRUWRmcOiUVWMI1JIElGurnn6Giwv495C3vI+94FueRnGzfcV23TiqwhGtJAkvUV1aWvQJLWgiKuoiMjAagc+c0RozQNhbhnSSBJS7EYrGQkZGBTqejS5fOvPoqBAZCv34wezac3i0S4pzOTGBlZsLEiTBlCqSkaBuX8E47d8L338uYJmqvtNT+PvnhB19OnzcWok42boSuXWHPHqnAEg3nSIAWF8sOtqifG26wUlVlnzjmWBfU03l1AmvIEPuOa0KCJLCEayllH0gsFklgibpJT5cKLFF3gwdHA5Cfn0aLFvK9I1zPkcAym80aRyKaomPHjmG1WunQoQP+/v6MGmWvvNq0yZ7IEuJCzkxgLVkCx4/DN9/Ac89BcrLGwQmvUlwMN9wAVqssJSBqz/E+efxxP6ZMAek8Keqqe3dYvhwuuigUgJMnT2ockfBkjgqstDQfFi3SOBjhkWbMyAIsRES09Zr2uF6dwGrf3v6fFB4uO67CtdLS7AmsZcvkRLKom1at7AksqcASddGyZUtCQkIoLS0lPz9f63CEl1EKSkrsCaz8fJmtLs7maB/YpUsX5+9uvlkqr0TtOGZ+VlVV8b//2U/K6HQQHw9xcVpGJrxNejr07g2BgVKBJWrPYrG/T2w2XyZNgtM5dyFqLToaLr4YEhK6ApCamqptQMKjORJYoaE+vP46FBVpHJDwOFOmpAMQExOtbSAu5NUJrFatWgGQk5OjcSTC2xgM9o9OYKAksETdlJWdAiA4OFjjSIQn0el0REdHA5CWlqZpLMJ7mEzw2WcwYwY89JD9ZN+pU3KyT5zNkcCKiYnROBLhiXQ6nTOJtW5dJWFh8P338Pe/SxJUuNZFF8GSJfDGG1KBJWrPkcAaOtSXW27ROBjh0bp2tSewDh8+rHEkwpM5ElgxMXo2b4bQUI0DEh7Hcc6oc+fO2gbiQl6dwOrevTsAKdJgXbhYTIz9o3PZZdIkW9SeUsr5fdStWzeNoxGexlH5IAks4Sq+vvYWcAsWgM1mBECnkwSWONvRo0cBSWCJ+nO0EezRo5zt22H8eI0DEl5Lp4PQUKnAErVnMtkTnW+95SdJddEgsbGxgD2BJeuli/pyrIHl76/HaNQ4GOGR0tPtFViOSdDewKsTWI4TxIcOHdI4EuFtfHzsHx2brPIq6iA7O5uSkhJatWpF69attQ5HeBjHzofjRLIQrvDss45ZffaTfZLAEuciFViioRxrfy5ZUsHp83tCuI1jvQepwBK1YTbb930GDvTVOBLh6SIiIggODqaoqIhTp05pHY7wUI7zjHq9V5+yF27kSGBJBZaH6NixI/7+/uTm5lJcXKx1OMKLOAYSSWCJukg+vVJ5jx49NI5EeCJpISjcoXVrePppcCSwwKxhNKKpOtcaWELURUREBACFhVkaRyKaA19fqcAStWMymTCbzej1eoxS6iAaSKfTSRtB0WCO84w+Pj4aRyI8lbQQ9DB6vd45eEgVlnAlSWCJ+nAksOJkxXJRD44Tx1KBJVztr3+F9u3tJ/uUkpN94mzSQlA0lKMzhrR2F43BkcCSCixRE8cs9U6dOqGT/oHCBSSBJRpKKrBEQ0kLQQ8kbQSFO0gCS9THwYMHAanAEvUjFVjCXXx94Z577Cf7HG10hHAoLi7m5MmT+Pv7ExkZqXU4wkM51iaWYzLRGBwtBKUCS9TEsV8tFcbCVRzrYKWmpmocifBUksASDWGz2aSFoCdyHCzJbD/hSpLAEvUhLQRFQ5yZwJJFgYWrjR0r7ZbEuTmqr7p06SKz00W9SQWWaExSgSVq68wxTghXkAos0VBWqxWQBJaonxMnTlBVVUWrVq0ICgrSOhyX8fpPg1RgCXeQBJaoD0lgiYYIDQ2lZcuWVFRUcOLECa3DEV7G19e+7oOc7BN/JO0DhStIBZZoTFKBJWrLMcZ5U5sloS1JYImGkjWwREN4Y/UVNIMEllRgCXeQBJaoq7KyMjIyMjAajTLDT9SbtBEU7iIn+8T5HDlyBJAElmiYMycVShWxcDdHBZaMaaImUoElXE0SWKKhpIWgaAjHuSJJYHmYM9tVyMGScBVJYIm6csw47tq1KwaDQeNohKdyHFxLAku4miOBJRVY4o8cCSw5uScaolWrVoSFhVFSUkJubq7W4QgvJ2OaqC1ZA0u4Wrt27QgICCAvL4/i4mKtwxEeSBJYoiEcFVjeVlns9Z+Gtm3bEhQURGFhIfn5+VqHI7yEJLBEXUn7QOEKjp0Qx2xRIVxFTvaJ85EWgsIVdDqdtBEUjUYqsERtSQWWcDW9Xu/cZ0pNTdU4GuGJZA0s0RBSgeWh5GBJuIMksERdSQJLuIK0EBTuIgvei/ORFoLCVc7sjCGEO8mYJmqjrKyMEydO4OvrS7t27bQOR3gRaSMoGkLWwBINIRVYHuzMnutCuIIksERdORJYcXFxGkciPJljdqhUYAlXkzWwxLnYbDaZnS5cRiYVisYiY5qojTMXupdKB+FKksASDSEtBEVDnDm2eZNm8WlwHCzJbD/hKpLAEnV18OBBQCqwRMNIBZZwF2khKM4lJyeHqqoqIiIiCAoK0joc4eFkUqFoLFKBJWpDJmgId5EElmgISWCJ+lJKSQtBTyYHS8LVJIEl6sJmszkTWFKBJRrCkcBKT0+X7x/hUkajEQCLxSLvLeEk7QOFK0kLQdFYpAJL1IYksIS7xMbGArIGlqgfWQNL1NepU6coKysjJCSEsLAwrcNxqWbxaZCDJeFqksASdXHs2DEqKiqIjIz0ukFENK6goCBat25NVVUVOTk5WocjvIhOp5MqLHEWRwJLTu4JV3Ackx0+fFj2oYVbGQwGQCZliAtzJLC8bZ0QoT2pwBINIWtgifo6s32gTqfTOBrXahYJrDP7rSulNI5GeANJYIm6kPWvhCtJG0HhLpLAEn/kOLknFVjCFUJDQ2nTpg2VlZVkZmZqHY7wYmdOypAqLHE+jn1pmaQhXK1Tp04YjUYyMzMpLy/XOhzhYaSFoKgvb20fCM0kgRUeHk54eDhlZWUyY124hCSwRF3I+lfClRwH2ZLAEq4mCSzxR9JCULiarE0sGovBIOtgiQuTFoLCXQwGg/N95diXEqK2JIEl6stRgeWNlcXN5tMgB0vClSSBJerCUYElCSzhCo6dEcdBtxCuIoveiz+SFoLC1WRtYtEYzGbo2HEbu3YdJigoSOtwRBMlLQSFOznWwZI2gqKuZA0sUV9SgeUF5GBJuJIksERdSAJLuJJUYAl3kXZL4o+kAku4mqxNLBrDK6/AwYNxREfHyhoi4pwKCwspLCwkMDCQiIgIrcMRXsixDlZqaqrGkQhP4zjPaLPJ+CXqxpsrsAxaB9BYpAJLuJIksERdyBpYwpWkAku4i7QQFGeqrKwkKysLg8FAx44dtQ5HeIkz1yYWwh0SE+G55+zXZflrcT5nrn/lbQvdi6bBkcCSCixRV9dccw0GQwz/+U8nSkogOFjriISncCSwpALLg0kFlnAlSWCJ2iouLiY7Oxt/f3+ioqK0Dkd4AUcCSyqwhKtJAkucyfEdExUVhcHQbOa8CTeTYzLhTlYrzJplbyEIksAS5yfrXwl3kwSWqK/OnTvzl7+M58iR3lxxBRQVaR2R8BTSQtALyMGScCWz2T5La8MGSWCJCzt48CBgn3EsLUyEKzgSWBkZGc7+2EK4giOBVV4uCSwh7QOFe5zZUslisWgcjfA233wDFRW//1sSWOJ8ZP0r4W6yBpZoCL0eBg+GzZvhgQfsEzSEuJCioiIKCwsJCAjwyta4zWY6pSOBdfjwYWw2myyGJxokM9P+/lmyxIZSIF0HxPnI+lfC1QICAmjbti25ublkZWXRqVMnrUMSHi4xERYuhMJCewJr1aoqLr5Y46CE5hwn9ySBJVwpMDCQjh07cvz4cdLT050n+IRwhRtugH79YMECMBgkgSXO78wWgkK4Q3R0NHq9noyMDEwmE76+vlqHJDzMZZfBkCH2BJbMhRY1ObN9oDe2xm02Cazg4GAiIyPJycnh2LFjXllOJxrP8uX/5rHH3ichQSfJK3FBsv6VcIcuXbqQm5tLWlqaJLBEvR0+DI89Br6+8MUXoNfbE1j+/lKBJX6vwJKTe8LVunXrxvHjxzl06JAksITLde8Ozz9vvy4JLHE+0kJQuJufnx9RUVGkpaWRlpbmXANSiNp6+GH7ZAwhasORwPLWyuJmVYbkGDBSUlI0jkR4On9/PW+/rePGG7WORDR1jhaCUoElXEnWwRKu0LUrGI325BWAzWafGRoYKAksIS0EhfvIMZloLDLRUJyPJLBEY5B1sERDSPJK1MWZFVjeqFklsGQdLCFEY5MWgsIdHAksx8G3EPX14ov2JJadvQIrIEASWEJaCAr3kWMyIYSWlFKyBpZoFLIOlhCisTgmN0sCyws4DpZktp8QojFYLBbnyRlpGSBcyTFbVCqwREPFxMCddzr+ZU9g+fqaNItHNA1KKWkhKNxGjsmEEFo6efIk5eXlhIWFERYWpnU4wotJBZYQorFIC0Ev4jiBLLP9hBCNIS0tDZPJRMeOHQkKCtI6HOFFpAJLuNJTT0FwMDgSWGazVGA1d/n5+ZSUlBASEkJ4eLjW4QgvI8dkQggtSftA0VgcCazU1FSNIxFCeDupwPIi0q5CCNGYZP0r4S6yBpZwpYgI+NvfwJHAqqqSBFZzdzj19/WvdLKIjHCxmJgY9Ho96enp8n0jhGh00j5QNBapwBJCNBapwPIisbGx6HQ6jhw5gtls1jocIYSXk/WvhLs4ZtUcO3YMi8WicTTCGzzwAAQGSgJLQKXZys+bdwAQGCTrXwnX8/X1JTo6GpvN5mxVKYQQjcUxAUwqsIS7OdYRPXr0qByzCSHcpry8nLy8PHx9fYmMjNQ6HLdoVgmsgIAAOnXqhNVqlVnrQgi3kwSWcBc/Pz/ad+iA1Wpl/a4DWG1K65CEh2vRAi6+WBJYzV1+uYk9WcVs+W0rAKXlg/jTn0A63whXk84YQgitSAtB0VgCAwNp3749ZrOZY8eOaR2OEMJLOaqvOnXqhF7vnake73xWF+DouS6LBgsh3M2RwIqLi9M4EuGNesT3BmD5z1vZm11EcaXM6hMNM3CgLyAJrObIphRpp8rZn11MXpmJQ/t2AfB/tyfw3XcQHw9PPgllZRoHKryGI4Elx2RCiMYmCSzRmGQdLCGEu3l7+0Bohgksx8HSxo0y208I4T45ObB1q6yBJdynf//+AKz7ZR+HsqvYk1VERkGFVGOJegsIkAqs5qjKYuVAbgkpeaWUm62EGGwcPXgAnU7HzdMGMX48mEzwxRewf7/W0Qpv4ZhU6KjAMpm0jEYI0ZzIGliiMZw8CYcOyTpYQgj3KSuDm26CV1+1t+R2LDXhjZptAuuVVw6xZ4/GwQghvNbatfmYTHlACzp06KB1OMILDRo4AIDcjH2UnTRSWmUh+UQJ+7OLKa2SaixRd7fffjuJiYk88MADWociGklhhZk9WSWkn6rAoNcRHmDkcNJ+LBYzXbvHERwczFNPQUICPPEEDB2qdcTCW/yxheCbb8Ivv4B0eRdCuNM339g4csQ+U72iIlrbYITXUgquvBK6dweDwZ7Akpa5QghXO3AAFi2CNWs2AL9PcvZGzS6BFRtrn+2n16cQHKxxMEIIrxUZaW8f2L17D3Q6ncbRCG80ZNBAAMoK9hHfE8IDjRh99BwrqmRXZjHHCiuwKanGErXXtm1b4uPjad26tdahCDdTSnGssIK9WcWcKK0ixN9AkJ8BnU7Hvl07ARgwaDAAw4bBl1/CbbdpGbHwNn9s6/7tt3DFFTB8OFRWahmZEMKbnTyZjdVqAtrw9dcttA5HeCmdDsLCwGCA+NNt37ds2aJtUEIIrzN4MPz734qwsNUAjB07VuOI3KfZJbB69rQfLEVEHCAmRuNghBBea8eO3wC45JJ+2gYivFaXLl0ICQ2j8NRJTp7IQafT0cLXh/BAIyVVFpJzS9mfXUKZSaqxhBC/M1lsJOWWknyilDKTlVaBRvwMvx8SJO7eAcDQIYOdv4uKavQwhZeLiorCaDSSmZnJ8ePlbNkCNhtkZ9tnrZeUaB2hEMIbGY329oE6XRek4Fy4088/Q2Eh/PnPl2IwGPjtt98oKirSOiwhhJcZPTqZgoIcIiMjvXr5kmaXwIqNjSU0NJTs7CyOHz+udThCCC+1YYO9hPfiiy/WOBLhrXQ6Hb0u6gNA0r69zt8b9DpaBRrw0UNGYQW7M4vJKqpESTWWEM1eUaWZPVnFpBWUo9dBeKABH331KuHkfbsBGDEsQYMIRXNhMBiIOT2b8LPPDmOz2X8/bRr89BPSKUMI4RYWiz2BFRsbTWSkxsEIr6bTQYsWEBISwrBhw7BaraxevVrrsIQQXmbVqlUAjBkzxqu7PzW7BJZer2fo6Qb+UsIrhHAHpZQksESj6He6x3HSvuqLOup0OoL8DIQHGCiqMHMgt4TE3BIqzFYtwhRCaEwpRWaRvWVgbmkVwX4Ggk+3DDxTaXExx44cwujrS7++fTWKVjQXjjaCP/xgbyP49NOwcCH4+2sZlRDCm+XmpgFw+eVdtA1ENCvjxo0DYOXKlRpHIoTwNo4E1ujRozWOxL2aXQILYNiwYYAksIQQ7nHo0CHy8vJo27YtsbGxWocjvNigAQMASEnce86/G3z0tGphRKeD9FMV7MosIrtYqrGEaE7MVhsHT5SSlFtKSZXlrJaBZ0rcY1//qtdFffH19W3MMEUz1K1bNwB27z7E/Pnw7LP2GetCCOEuR4/aK7D69ZMElmg8ksASnuLUqVNahyDqwGazOSs7x4wZo3E07tUsE1gJCfaWKJs3b9Y4EiGEN9q4cSNgr77y5hJeob0hg+wJrEMH9p33NjqdjmA/A2EBBgoqzBzIKSEpt5RKqcYSwuuVVFnYm1XMkVPlALQKNJ7VMvBM+06vfzVw0KBGiU80b44KrGHDDnLrrRoHI4RoFg4cOABA165dNY5ENCcDBw6kZcuWpKamkpqaqnU4oo4sFotXTwA1mUx89dVXjB07lujoaEpkIVKPsWfPHgoKCujcuTNdunj3xIxmmcBytBDcsWMHJpNJ42iEEN7G0T5wxIgRGkcivF2PHj3w8/cnN/MYRYUFF7yt0UdP60AjAEdPlbMrq5jckiqv3hkXorlSSpFdXMmerGKyS6oI8vUhxP/sloF/lLRnFwDDTu8rC+FOF110EQBZWTs1jkS4g1KKbdu2UVVVpXUoQgBgNpvZvXs3AANOdzEQojH4+PgwduxYAH7++WeNoxF19fnnnxMbG8uTTz5JcnKy1uG4TGpqKo899hidOnVi6tSprFq1iqoqCzt27NA6NFFLzWX9K2imCayWLVvSo0cPqqqqnDswQgjhKrL+lWgsBoOBnr3sJwAPnqeN4Jl0Oh3B/vZqrFNlJvbnFJN8opQqi83doQohGonFauPQyTIO5JZQVGEmPNCIv9GnVtsm7bUnsEYMkwSWcL8BAwZgNBrZv38/xcXFWocjXCwjI4MhQ4YQEhLCsGHDeOCBB/jyyy9JT0/3iskzNpuNlJQUPvvsM+677z62bt2qdUiiBomJiVRVVdGtWzfCwsK0Dkc0M9JG0HP98MNPHD16lBdffJGePXsycOBA3nrrLbKzs7UOrc7MZjPffPMN48aNo2vXrrzyyiucOHGC3r1789577zFiRBZVVaO0DlPU0pkJLG9n0DoArSQkJJCcnMyWLVsYMmSI1uGIeqiqgt27oTlNEi4ogJYttY5CXMiJEydISUkhMDCQfv36aR2OaAb69uvH7h3bSNq3lyEjLq3VNkYfPa1bGCmpsnLkVDnFlRZiWgXSuoWv18/cEU1LVRX4+WkdhfcorbKQcrKU3OIqDHodrVsYa/2ZzsvNJj83m+CQUGdrNyHcyd/fn/79+7N161a2bdvmnJ0uPF9FBbz11gnatetJdnYSW7Zsqbb+dGRkJEOHDiUhIYGEhAQGDRpEUFCQhhHXLCcnh61btzov27Zto7Cw0Pn31q0j5LxCE+eoKhg4cKDGkYjm6PLLLwfg119/xWKxYDA029OxHiciYgGPP/4XTpz4jK+//pqdO3eyc+dOHn74YcaOHcstt9zCpEmTCA4O1jrU80pLS+Ojjz5i7ty55OTkAPb9sKlTp3LHHXeQkJCATqdj0ya46SbYsQO8vCOdxzObzaxbtw6A0aNHaxyN+zXLCiyAYcOGAbIOliebOBESEmD+fK0jaRyLF0NkJNxzj9aRiAtxrH+VkJCA0WjUOBrRHAwc0B+oXQXWmXQ6HSH+BkL9DOSVmdiXXUJKXhkmqcZqdOXlsHQpWJvZsmRHjtgPkCortY7E8ymlyC2pYm92MVlFVQT6+hAaUPvkFcD+XfY2bn36D0Cvb7aHCKKRydrE3umtt2D58sEcOXKAgoICVqxYwbPPPstVV11FeHg4OTk5LFu2jMcff5zRo0cTGhpKv379uPPOO5k3bx7JycnYbNrtj5SUlLBmzRpeffVVpkyZQlRUFO3atWPChAm8+OKL/PzzzxQWFhIZGclVV00gPPxFXn/9asrKNAtZ1ML27dsBGCTrPAoNREdH0717d4qLi6Vi04NkZ8PatT5ccsloPv74Y3Jzc/nf//7HxIkT8fHx4eeff2bGjBm0bduWm266ie+//x6z2ax12IB97a4lS5Ywfvx4YmJieOmll8jJySE+Pp533nmHrKws5s2bx7Bhw5zHDG3aQEkJSKfLpm/79u2UlpYSFxdHhw4dtA7H7Zptyt9xsHTmTDDhWTIy7sLHJ49t20YzePAoevbs6dWVA+npJzGZ1pGa2heI1ToccR6OBJa0DxSNZehg+0H4ocR99dre13C6GqvSQmp+OUWVFmJbBRIeWLeT36L+fvwRpkyBl1+GRx/VOprGM2sWrFkDf/4zfPYZSM6kfqw2xdFT5WQUVFBhthIeYMDgU/cXc/9u+8z0wYMHuzpEIc5r2LBhvPvuu3JM5mUefRSmTQN/f/D3D2PcuHHO9llKKQ4fPsyWLVv47bff2LJlC3v27HFe/vvf/wIQGBhI9+7diYuLo0ePHsTFxREXF0f37t1dUq2llCI3N5fU1FRSU1M5cuQIqamp7Nq1iwMHDpzV5jA4OJhBgwYxZMgQ56VDhw5UVOho29Y+hp06BS1aNDg04SaSwBJaGzduHCkpKfz8888MHz5c63BELbRrB3v2gCMn5e/vz3XXXcd1113HqVOn+N///sfChQtZv349ixYtYtGiRbRu3ZobbriBm2++mcGDBzfaxGalFNnZ2ezZs4cNGzbwySefONsc+vn5cf3113PHHXcwYsSI8x7n33UXPPkktG7dKCGLBmhO7QMBdO7sP63T6RTQJHtcW61WwsLCKC0tJTs7m8jISK1DEnWglKJt27bk5eU5f9emTRtGjRrF6NGjGTVqFHFxcR598jUvL49169axZs0a1q5dy7599pPTL774T/7+98c0jk6cT0JCAr/99hsrVqxwHqh7AsdnRSnluR8aN2uqY1plZSVBQUEopVh9IAP/gMB631eVxUZRpYUWvj50DPUnOjwQYz1OhIvaO3z4ME88EUtVlY758yE0VOuIGsexY/DII7BiBRQW2g+Upk7dT2BgIDExMVqH5zHKTVZS8krJKalCr4NQf0O9933uvGkiuzat46v/fcP11012caSNS8a0C2tK41laWhpdunShVatW5OXlefS+u6i/8vJydu7c6Uxq/fLLFgoLj5/39h07dqyW1HJc79ixY7UKUrPZTHp6erUE1ZkJq7LzlEwZjUb69u1bLVkVFxd33urU3FwoLYVYmWPYZJlMJoKDgzGbzRQWFhISEqJ1SLUi41nNmtKYVpNvv/2Wa6+9luHDhzsnvgrvkJaWxhdffMHChQs5cOCA8/dGo5G4uDh69epV7RIbG9ugNpImk4mkpKRqE0D27NnDyZMnq90uLi6OO+64g1tvvZVWrVrV+/FE0zN27FhWrVrF119/zZQpU7QOp1YaMqY12wQW/P6fvWTJEiZOnKh1OKIOHDP31qxZw+rVq1m9erWzj6tDZGRktYRWt27dnB+W0lL7LLnA+p/nbRCTCXx9q//uxIkTzoTVmjVrSExMrPZ3Pz8/hg8fzuzZs5k2bVojRitqq7y8nNDQUGw2GwUFBR5zYARycFQbTXlM69GrNwcPJPLR4p/oM7Bh6y/YlKK40oLVpmjdwo+urQNpGehb84aizgoKCoiMjKRlyxjuv38m06ff0izK/89UXm7i9deXMGfO+2RkrGPWrFl8/PHHWoflEfJKqzh8spz8chNBvj4E+vrU+75sNhuX9YmhrKSY48ePe/z7UMa0C2tK45lSivbt25OTk0NKSgrdunXTOiShIaXgoYcgKwv+859CDh48yMGDB0lOTnZeP3ToECaT6ZzbO6q2wsPDOXr0KBkZGVgv0J+3ZcuWxMbGOi+HDsVy6aXxzJrVD39/f3c9TaGBnTt3MnDgQOLi4khOTtY6nFqT8axmTWlMq0lJSQnh4eEopTh58iRhYWFahyRcTCnFnj17WLhwIYsXL+Xo0dRz3s7Pz48ePXqcldjq0qULPj7V9+lPnDjhTFDt3buXPXv2kJSUdM5WhWFhYfTt25fAwL4MHjyZZ54ZKZODvFBlZSVhYWFUVVWRl5dHaw8pmWvImNZsWwiCvWXFqlWr2LJliySwPIxOp6Nbt25069aNv/zlLyilSElJYfXq1c4EUE5OjrOEF6B9+/aMGjWKAQNG8+KLowgNjeXAAR0BAY0b+/vv2w/MXnjhBFFRa53xnjlLA+ylycOHD2fUqFFceumlDBkyRA6kmritW7disVjo37+/RyWvhOfr268/Bw8kkrRvb4MTWHqdjrAAI5UWK7mlVZSZLES1DCCqZQAG6fHmUklJSYSFhZGbm8zjjz/GE0/8ncsvv5yZM2cyYcIEAmoxQFmt9gkZWh6XFBfD11/bWwLW1vHjx/nwww/56KOPnBNQgoKC5EC+Fqw2RXpBBekF5ZSZrLQMMDS4UvLY0VTKSoppE9nO45NXwrPodDoSEhJYunQpmzdvlgRWM7dvH3z8sb0qNywsjKFDhzJ06NBqt7FaraSlpVVLajmu5+bmsnv3budtdTodnTp1qpakio2NJSYmhtjYWFq2bFntvkePhsxMe+tD4V2kfaBoCoKDgxk+fDjr1q1j9erVTJo0SeuQhIvpdDr69etHdHQ/Fi9+nZCQUpYtSyItLZHERPtl//79HDt2zJmUOlNAQAA9evQgPj6e/Px89uzZ42wD+MfH6datG3379q126dSpEzqdjp9/hltvhXvukXaA3mjz5s1UVVXRt29fj0leNVSzTmDJosHeQ6fTOVtI3HnnnSilSE5OrpbQysrK4vPPP+fzzz8HoLCwNVdf3Zd+/frQp08f+vbtS8+ePV2eJLLZbKSlpZGYmMiBAweYNy+RysrtPPxwUrXbBQQEnJWw8vPzc2kswr1k/SuhlQED+vPV5wtJTtzrsvv0N/jg20JPUYWFlLwyCirMxLZqQVhA4/Twbg6GDx/O8ePHWbFiBfPmzWP58uWsWLGCFStWEBoaytSpU5k5cyYJCQnnnTl3552wZAnccAO89RY09rCRnQ3x8VBWBgkJ0KvX+W+rlOLXX3/l/fffZ/ny5c5Z8b169eKuu+7illtukeR/DSrMVg7llZFdXIkCWrcwondB9nLf6fWvBgyUE3ui8Q0bNsyZwLr11lu1DkdoqE8fyMiwT4w4Hx8fH2ci6uqrr672t8LCQh566CBduxYwaVI00dHRdTq2W7z47C4ZwjtIAks0FZdffjnr1q1j5cqVksDyYoGBcOIE+PoG0bXrYEaNqr7GbHFxMQcOHKiW1EpMTCQrK4tdu3axa9cu522DgoKc5ywdl969e19wPcgBA2DHDkleeavmtv4VSAILgG3btmGxWBrUf1Q0LTqdjp49e9KzZ0/uuusulFIcOHDAmdBatWoNBQUnWb36V1av/tW5nY+PD3FxcfTt25c+fX5PbLVv377aycOqKjAY4MzKXpvNRnp6unMAcgxGSUlJlJeXnxVjQEAAI0aMYNSoUYwaNYrBgwfjK0dMHm3Dhg2AJLBE4xsycCAAhxL3ufR+9TodLQONVJitZBVVUVplIaplIFFhAfjopRWBKxiNRq655hquueYa8vPzWbRoEfPmzWP79u18+OGHfPjhh3Tv3p2ZM2cyffp0OnbsWG37wEDIz7e3pm3s5FV5OcyebV/DCuCzz+Cll86+XWFhIfPnz+f9998nJSUFAIPBwNSpU7nrrru45JJLpLVFLeSXmTh0soz8MhOBvj4EGvUue93279oJwJAhDavgFKI+HMdkW7Zs0TgS0RSEhdkv9ZGbG8bChUNZvBh69Kj79n8oyBJexJHAGnh6n1kIrYwbN46nnnqKlStXah2KcCNfX3s73KIi+MPhGwAhISEkJCQ494EcCgoKOHDgAFu2JLN4cUvmz+9LTEyX867BeD6y3JV3a44JrGa9BhZAt27dOHz4MDt27GDAgAFahyMaiVLKWbK7d+9eZx/ZQ4cOYbPZzrp9eHi4M6HVunVfXn+9FzExJ7nppt/LgM+XqAJo164d8fHxzr62F110EQMHDpSElRexWq2Eh4dTXOyZ64dIf/WaNeUxrbi4mNDQUAxGX9YmHcNgdH2VlNWmKKq0oAMign3p2qoFIf5SjeUu+/fvZ/78+SxcuNDZYk+n03H55ZczY8YMJk6cSGBgIPPmwZ//bJ9hV9/dGKXsl7w8aNu2btuaTPDYY/bqr86d4cgRe0tDgF27dvH+++/z2WefUVFRAUDHjh254447mD17NpGRkfULuJmxKUVGQQVpBeWUVrmmZeAfzbz2cpL27OCnn1ZwxRXjXHrfWpAx7cKa2nhWXl5OSEgISimKioouOKNYiAt5+mn45z/hyy9h8mStoxFNRWVlJSEhIVgsFoqLiz3qO0bGs5o1tTGtJlarlYiICAoKCjh8+DCxsbFahySaIKvVPjnxf/8DWfFGnOnMtfROnTrlUR1MGjKmNfsE1q233sqCBQv497//zV133aV1OEJj5eXlHDhwwJnQcvwsKCio1faRkZHVFmCMj48nPj6e8PBwN0cutLZnz57TvY6jOXr0qNbh1JkcHNWsqY9p0TFdST+ayoIf1tK910Vue5xyk5VSk5UQPwOdwwPoGCrVWO5ksVhYuXIl8+bNY9myZc6F60NCQpg6dSojRszkgw+GsWVL3f8PsrLg6FGYOhViYuDGG6G+u0JLl8LMmfDNN5VkZX3N+++/X62a4rLLLuOuu+7iT3/6k1S810GlxcrhvDKyiiuxKUVYgGtaBp7JbDIxuldnzKYqCgoKvGIdMhnTLqwpjmcDBw5k586drF69mlGjRmkdjvBQNpt9MgbUfUKG8F7btm1jyJAhxMfHk5iYqHU4dSLjWc2a4phWkxtuuIGvv7bvL//f//2f1uGIJio5Gbp3/31yoBAAP/74I1dddRVDhw71uO4FDRnTmv0ZhISEBBYsWMDmzZslgSUIDAxk0KBB1XpjK6XIzMysVqm1c2cS7dq1pk+f+GrJKklUNV/SPlBorU/fvqQfTSVp/163JrACfX3wM+gprDBz8EQpBeVmurZuQZBfs9+lcAuDwcBVV13FVVddxalTp1i0aBHz589n69atfPTRR3z00UdERHTmttvGOFvSRkVF1eq+W7aEkSPtC9ZnZsJtt9U9PqUUSUlJHD++iqFDV3HttaspLy8EIDQ0lD//+c/ceeedxMXF1f3Om7mCcjOHTpaSV2oiwKinha/BLa0WDycnYjZVEdO1m1ckr4RnGjZsGDt37mTz5s2SwBL1ptdL4kqcTda/Ek3NuHHj+Prrr1m5cqUksMR51acVrvB+zbF9IEgCi2HDhgHSc12cn06no2PHjnTs2JGrrrpK63BEE7Vx40ZAElhCO/0H9OfbpYtJ3r+XCVNvdutj+eh1hAcaqTDbOF5USUmVhejwQDqE+ru8OkT8Ljw8nLvuuou77rqLAwcOMH/+fBYsWEB2djqffPIJn3zyCQDR0dHOZNall15KdHT0Oe8vIADeew8cQ1tgYM0xKKU4evQoq1atcl5yc3Or3aZfv37cfffd3HTTTbRo0aIhT7lZsilFZlElR/PLKa6yEOZvwNfgvqmXe3ftAKD/QDmxJ7STkJDAv//9bzkmE0K43I4d9nFO1r8STcXll18O2E9EWywW6U4ghKg1SWA1UxdddBGBgYEcPnyYvLw8IiIitA5JCOGBpAJLaG3I6YPylMS9jfJ4Op2OQF8ffE9XYyXllnCq3EzX1oG08G32uxduFx8fzyuvvMJLL73E7t27WbNmDWvWrGH9+vWkpaUxb9485s2bB0Dnzp2dyaxRo0YRHR3trOQZPx4mTYIlS+wJrXPJyspi9erV/Prrr6xatYr09PRqf4+MjGTs2LGMGTOG0aNH06VLF3c+da9mstg4fLKMzOJKLFZFq0Cj21t0HtizC4CEoUPc+jhCXIhjUuHmzZtRSrml2lAI0TxJBZZoajp37kxcXBwHDx5k69atDB8+XOuQhBAe4NSpU+zatQtfX99m973R7M8wGQwGBg8ezNq1a/ntt9+45pprtA5JCOFhMjIyOHbsGGFhYfTs2VPrcEQzNXiQPYGVmrQfm82GvpGaZRv0OloFGikzWTlWWEHp6WqsdiF+Uo3VCHx8fBg4cCADBw7koYcewmq1smfPHtasWcPatWtZt24d6enpzJ8/n/nz5wMQFRXlTGaNGjWKt97qwooVOmcFVn5+PmvWrHFWWCUnJ1d7zJYtWzJ69GjGjBnDmDFj6NGjh5xsdoGiCjMpeWXklVXhZ9ATHuieloF/lLRnJwAjhiW4/bGEOJ+YmBhat25NXl4eR48eJSYmRuuQhBBeoKKigv3796PX6+nXr5/W4QjhNG7cOA4ePMjKlSub3YloIUT9rF27FqUUw4YNI7A27VO8SLNPYIG9ZcXatWvZvHmzJLCEEHXmqL4aMWJEoyUNhPijNm3a0CayHSdysjmWdoTOMV0b7bF1Oh1Bfgb8DTYKKswcyC2hoNxETKsWBPr6NFocwp7QGjBgAAMGDODBBx/EarWyd+9e1q5dy5o1a1i3bh0ZGRksWLCABQsWANCxY0e6dx/FRx+15uGH17Bnz55qC2EHBQUxcuRIZ8Kqb9++8l3nQkopsoorOZJfTlGFhdAAA35ubBl4prLSEjJSUzAYjfSXE3tCQzqdjmHDhvHtt9+yefNmSWAJIVxiz549WK1Wevfu3exO9ommbdy4cbz33nusXLmSZ555RutwhBAeYPXq1UDzax8IksACZB0sIUTDyPpXoqno07cfv+Rkk7x/b6MmsBwMPnpat7BXY6UXVFBcZaFLeCCRwX5SoaMRHx8f+vfvT//+/bn//vux2Wzs27fPWaG1du1ajh8/zvHjC9m9276Nn58fw4cPZ8yYMYwdO5ZBgwZhNBo1fR7eymw93TKwqBKTVdGqhftbBp4paZ89Wdkzvjf+/v6N9rhCnEtCQgLffvstW7Zs4eab3buWoxCieXCsfyXtA0VTc+mll2IwGPjtt98oLCwkLCxM65CEEE1cc13/CiSBBcDQoUMB2Lp1K1arFR8fmS0uhKg9Wf9KNBX9+vfnlxU/krRvD1dcO1mTGBzVWH4GGwXlZspNJZwqNxHbqgX+RhlftabX6+nbty99+/blvvvuw2azsX//ftasWUNBQQGXXHIJw4YNI+B8C2IJlymutHAor5QTpSaMPjpaNVLLwDPt33V6YfvBgxv1cYU4lzPXwRJCCFeQ9a9EUxUcHMzw4cNZt24dq1atYvJkbY7dhBCeITc3l8TERAIDAxkypPmtXSwJLOyLj0dHR5OWlkZiYiJ9+vTROiQhhIcoLCxk3759+Pr6yoGR0NzggQMASDmwT+NIwHi6GqvUZCXtVAXFVVZiwgNpE+Qr1VhNiF6vp0+fPrLv04iUUuSUVJGaX05hhZkQPx/NkruJp9e/Sjg9mUsILQ0ePBi9Xs+ePXsoLy+Xdl9CiAaTBJZoysaNG8e6detYuXKlJLCEEBfkaB94ySWX4Ovrq3E0jU8WMDhN2ggKIepj8+bNKKUYNGiQtF8SmnMksA4f2FdtDSOt6HQ6gv0MhAUYOFVmYn9OMcknSqmyWLUOTQhNWKw2Dp0s40BuCcUVZsIDjZpWJiafTmCNSGh+s/hE0xMUFMRFF12ExWJxtv0SQoj6Ki8v58CBA/j4+MhEHdEkjRs3DoCVK1dqHIkQoqlrzu0DQRJYTgkJCYC0rBBC1I20DxRNSXR0NKFhYRSdyudETpbW4Tg5qrGUgiOnytmdWUxeaVWTSLIJ0VhKqyzszSkm9WQZNpt9vStDI6539Uf5eSc4kZ1JYIsgevbsqVkcQpzJcUwmkwqFEA21e/dubDYbvXv3ltbIokkaMGAA4eHhHD16lNTUVK3DEUI0YY4E1ujRozWORBuSwDpNKrCEEPWxceNGQBJYomnQ6XT06tMPgOR9e7UN5g90Oh0h/gZC/QzklZnYl11CSl4pJotN69CEcCulFLklVezNKiarqIpAXx9CA4yat9JM3G2vvurTf4Cs/yqaDFkHSwjhKtI+UDR1Pj4+XHbZZYBUYQkhzi89PZ3U1FRCQ0Pp37+/1uFoQhJYp/Xt2xd/f3+Sk5M5deqU1uEIITyAyWTit99+A2D48OEaRyOEXb9+/QBI2t+0ElgOvgZ7NZZV2UjNr2B3VhH5ZSapxhJeyWpTpOaXk5hTQkGFmfAAAwEatgw8077d9hZtgwcP1jgSIX53ZlcMGReEEA3hSGANHDhQ40iEOD9pIyiEqIlj/atLL70Ug8GgcTTakATWab6+vs4dm61bt2ocjRDCE+zcuZPKykp69uxJq1attA5HCAAGnx7LUhKbZgILQK/TEepvJNjPhxOlJvZmF3P4ZBlmq1RjCe9RbrKy7/R722Kz0bqFEYNP09n1TtqzC4BhQ2X9K9F0dO/enZYtW5KTk0NGRobW4QghPJhjLT2pwBJN2eWXXw7Y24OZzWaNoxFCNEXNff0rkARWNbIOlhCiLmT9K9EUDRk0AGjaCSwHv9PVWGarjUMny9idWcypcpPWYQnRYHmlVezOKuJ4USX+Bj1hTaBlIIBNKSrMVvJLKkjaY28hODxhqMZRCfE7nU4n62AJIRqstLSUpKQkjEYjffr00TocIc4rKiqKuLg4iouLZTK9EOIsSilJYCEJrGpkHSwhRF3I+leiKYqLiyMgIJC87EyKCpp+S1y9TkdYgJEgPx9yS+3rBB0+WYZFqrGEB7LaFEfyy9ifU0JBuZmWAQYCfbVrGaiUospio7jSQl6pifwyM2arYt+W9ZQWF9I6oju7dkUhndpEUyLrYAkhGmrXrl0opbjooovw8/PTOhwhLkjaCAohzufQoUNkZmYSERFBr169tA5HM5LAOoNjtt9vv/2GzSYnzoQQ56eUkgos0ST5+PjQo1dvAJKb6DpY5+Jv8KF1CyNVp6ux9mQVU1ghbTSE56gwW9mfU8KhvDJMFhutWhgxatAy0GJTlJks5JebyCszU26yYvDR0S7Ej55tg+nfIZTtKxcDcMvNM5k8Wcf48XDwYKOHKsQ5SQWWEKKhZP0r4UkkgSWEOB9H9dXo0aPR65tvGqf5PvNz6NChA506daKoqIjk5GStwxFCNGHJySmcPHmS8PB2dOnSRetwhKim/wB7G8H1a9dxotREQbmZMpMFs9WGasKlFnqdjpYBRloY9WQXV7Enq4ij+eVYbU03ZiEA8stM7Mkq5nhhBb4GPWEBBvSN1DLQ0RawsMLMiVITRRVmlNIRFuBLt9Yt6NshhKFRLenfIZSYVoH4mMtZvmwZOp2OBx+8hSlTYMUKGDYM1q1rlJCFuKAhQ4ag0+mca40KIURdyfpXwpOMGjUKo9HI1q1bKSgo0DocIUQT8vHHPwDNu30gSALrLI4Zf//852akCEsIcT6ffroagMLCEVRVab+uiRBnmjb1egCWL/yK7b8a0VsNKKWjpMpKXpmZk2UmiistVJitTTI55G/0oVULIxVmGwfzStmTVURxpVRjiabHphRpp8rZl1PMyTITYQEGWvj6uHW9q/O1BQww+hDdMoDe7UIY1CmUoVFhxLUJIjLYnwDj7zF9/fXXVFVVMWbMGDp16sSTT0LLljBuHIwc6bawhai10NBQ4uPjMZvN7Nq1C4CcHI2DEkJ4hOxsOHIE1q61V2BJAks0ZQsWwCefwN69QQwePBybzcYvv/yidVhCiCZi1aoUduz4DvBl6NAJWoejKYPWATQ1Q4YM4+uvv2bhws3ccsssrrhC64iEEE3RqlWfAjB69DX4+2scjBB/MGb0aLp27crhw4eZ98Zm/jb7Kqx6K6VVFkqrrBRXmikzWTFZbZSZzNhsYPDR4eujw9dHj9FH59YT8LXho9cRHmikwmwlq7iK0iorncMD6BgagI9eksZCe5UWK4fzysgqrsSmFK1bGN1WdWWxKaosViotNqxWMPro8DPqCQ/0IyzAl1B/AyH+hlq1LJw/fz4AM2bMAKBPH1i2DKQbrmhKhg0bRmJiIps3b2bYsGG88AJ06ADt2sGtt0Iz7qAihLiAoiLo16+YqqqD6PW+9O7dW+uQhDivb76x74OFh8PAgdcCa/nPf/7D9ddfr3VoogEyMzP55ptv+Prrr8nNzWXYsGGMHDmSkSNH0rVrV5cfZ5eWlrJ161Y2bdrEtm3baNOmDZMmTWLs2LGyBqCH++KLNwBFbOwM+vWL1DocTenc2UpIp9MpoEm3K/qj7du3M3jwYHx925CZeYzWrX21DkkI0cQkJSURHx9PcHAw2dnZtGjRQuuQGsyxE6WUkszAeXjamPbKK6/w2GOPcdVVE/j++6XV/qaUwmRVlJoslFVZKakyU1Rpocpiw2SxYbYpdICvwZ7Q8vXRa5o0stoUhRVm9DodbYP9iG3dgmA/mYMjtFNQbuLQyTLySk0EGPUur7qyna6yqrLYMFkVPjrwM/gQ4OtDeICR0AADof5G/A36Oj3u4cOH6datGy1atCAnJ4egoCCXxdyUyJh2YZ4wns2ZM4fZs2dz/fXX89VXX9GnD+zbB0YjLFwIl14KbdtqHaUQoqlRClq1WktBwShiYwdx+PA2rUNqEBnPauYJY9r5/O9/9qrB224Ds7mQqKgoSkpK2LFjBwNOt4QXniErK4tvvvmGr776io0bN573/diuXTtGjhzJpZdeysiRI4mPj6/TvrxSirS0NDZt2uS87N27F9s5WogFBwdz9dVXM3nyZMaPH++1+/3eKicnh+joaEwmE0lJScTFxWkdUoM1ZEyTsz9/MHDgQHr16kViYiJr137Lddddp3VIQogmZu7cuQDcdNNNXpG8Et5pxowZPPnkk6xY8R3Z2dm0a9fO+TedToefQYefwZdWgQAB2JSi3GSl1HS6SqvKTGnlGVVaSmHQ6/H10WH0sf9srCqt36uxbBwvqqSkykJ0eCAdQv0bbZ0hIcCeWDpeWMnRU+WUVFkI8zfga2h4KYhSCrNVUXk6aQXga9ATYPShXYiRsAAjof4GgvwatrbWggULAJgyZYocxIombdiwYQBs3ryZggLYv9/+e7MZvvoKJjTvLirNwtGjR2nZsiVhYWFahyI8iE4HUVHbKSiA0aOlfaBo2qZMOfNfYdx+++288cYbvPbaa3zxxRdahSVqKTs725m02rBhgzNp5efnx1VXXcV1113PY49154EHNrJx41rWrVtHdnY2X375JV9++SUArVu35pJLLnEmtfr06YOPj4/zMSorK9m5c6czWbV582Zy/tBX2cfHh0GDBjF8+HAGDRrCgw+m0rbtYhIT97Bo0SIWLVqEn58fV1xxBZMmTeJPf/oTrVq1arwXStTLe++9R1VVFZMmTfKK5FVDSQXWObz77rvcd999jBs3jhUrVmgdjhCiCTGbzXTs2JETJ06wZcsWhg4dqnVILiGz+2rmiWPa5MmTWbJkCS+99BKPP/54nbc3WWyUmSyUmqyUVFooqrJQabZittowW+2vg/F020Ffgx4fHW5PalltioIKMwa9jrbB/nRtFUgLqcYSjcBksXHoZClZxVVYrIqWAQb0DahMtLcFtFFlsWJxtAU06An2M9Ay0Eiov7HWbQFrw2azERsbS1paGr/++qtXLwQsY9qFecJ4ZrPZCA8Pp6ioiE8+Ocaf/9wRgD//GT78EAzyte+VrFYry5cv580332TDhg3o9XqGDBnC5Zdfzrhx4xg6dChGo9Flj1dVVcXOnTvZsGEDGzduBOD+++9n1KhRLnsM0fgGD76J7dsX8fHHHzNr1iytw2kQGc9q5gljWm0dO3aMmJgYbDYbhw8fpkuXLlqHJP4gJyfHmbRav359taTV+PHjueGGG7jmmmsIDg4mNxciI+Hdd+Gee+zv0eTkZNautSez1q5dS1ZWVrX7Dw0N5eKLLyYmJobt27ezY8cOTCZTtdu0atWK4cOHM2zYsNNJq0HVJlZ37Wp/3I8+OsIPPyxh8eLFbNq0yfl3Hx8fRo0axaRJk5g4cSIdOnRw4ysm6qOkpISoqCgKCwvZvHkzCQkJWofkEg0Z0ySBdQ6nTp2iQ4cOVFZWcuTIERk0vIxSio0bN/LeewvYt28rQ4b0ZfTo0YwaNYrOnTu75fFSU1PZuHEjGzduJDX1CFdccQUzZtxKW+l94nGWLl3KpEmTiI+PZ//+/ZqvE+QqcnBUM08c03788UeuuuoqYmJiOHToEPoGLhpiU4oKs5XSKitlJgvFlRZKqiyYLDZMVhtWG/j44Gw7aPTRuaVCSilFudlKmclGWICRLuEBtAtp3tVYGzdC//4QGKh1JN6pqMJMSl4ZeWVV+Bn0BNWjZeAf2wLqdeBv0BPga7C3BfQ3EOJvJMBYt7aAtbVu3TouvfRSOnXqRFpaWoO/D5oyGdMuzFPGsyuuuIKVK1cyYcLXLFs2hfvvhzfekPWvvFFZWRnz5s3j7bff5vDhwwAEBQVRWVmJxWJx3i44OJjRo0c7E1rdunWr0/dlQUEBmzdvZsOGDWzYsIFt27ZRWVl51u3GjBnD888/z/Dhwxv+5ESj69y5GxkZh9m1axf9+vXTOpwGkfGsZp4yptXWrbfeyoIFC7jnnnt49913tQ5HALm5uc41rdauXet8r/n6+lZLWoWEhFTbbtcuGDAA2rSBI0fgj817lFIcOXKkWkIrLS2t2m10Oh29evVyJquGDx9e49h32WXw668wbZq97bJOZ68WW7ZsGYsXL2b16tXVxtahQ4cyefJkJk+eTNeuXRv2YgmXeOutt3jwwQe55JJLWLdundbhuIwksNzAMWj8/e9/58UXX9Q6HOEChw4dYsGCBSxcuJCjR4+e8zZdunRh1KhRzktUVFSdH8dkMrFz505nwmrjxo2cOHHirNsZDAauueYaZs+ezRVXXIFBppJ6hGuvvZZvv/2WN954gwcffFDrcFxGDo5q5oljmtVqJSYmhoyMDH755RfGjh3r8scwW22UmayUVtmTWcWVFirM9taDZ1Vp+ejw0buu9aDFpigoN+Nr0BMZ7EdsqxYE+vrUvKGXWbgQpk+HUaNg9erGfeziYliyBGbMaNzHbSxKKTKL7C0DiyothPob8Ktly0BHW8Aqi41Kiw0F+Bn0+Bv0hAXY2wKG+BsIbmBbwNqaNWsWc+fObRb7tjKmXZinjGf/+Mc/eO6552jX7kHuuOMNnn7afhJGeI/s7Gzee+89/vOf/1BQUABAixbRlJXdzyuv3MZdd+lYu3YtK1eu5OeffyYpKana9lFRUYwbN47LL7+csWPHVmuJpJQiIyPDmazasGEDiYmJZ73v4+Pjufjii0lMHMFvv6Xh5/cmZWVFgD2J+txzzzFkyBA3vxLCVQ4dOkT37t0JDg4mPz/fpRV7WpDxrGaeMqbV1t69e+nbty+BgYFkZGRIqzeNnDhxgsWLF/PVV1+xdu1a5xpTvr6+XHnlldxwww386U9/OitpdaYff4R//APi4+GBB6Bv35ofNyMjg/Xr1/POOxn84x8DGDFiaJ1b6c6aBZ9+CjffDM8/D506Vf97QUEB3333HYsXL+ann36qNpHjoosuYtKkSUyePJk+ffp4zYRtT2I2m4mJieH48eN8++23XHPNNVqH5DKSwHKDDRs2cMkllxAZGUlGRobH7/g0VydPnuTLL79kwYIF/Pbbb87ft2/fgbKymwkMHMf99+9l48Y1rF27lqKiomrbx8TEVEtodfrjNz/2ir1NmzY5k1XnmskXERHBiBEj6NFjBK+91hGjcRFm83dYrdbT8bRn5syZ3HbbbcTGxrrhlRCukJ2dTadOndDpdGRmZtKmTRutQ3IZOTiqmaeOac8++yzPPPMMU6dOZdGiRW5/PKUUFWabcy0te1LLjMlqw2RRWJXCR2ev0jIa7Emthpy8V0pRZrJSbrYRHmAkulUg7YL9ms3OtlKK778v5cYbT3HTTZF89JFfoz7+s8/CM8/A88+XMHLkLgwGA61ataJVq1a0bNmyWg93T2Oy2kg9WUZmUSWm0y0DfWpoGfh7W0AbFpvCqK/eFjDkdFtAXxe1Bayt8vJyIiMjKSkpITk52ev7qMuYdmGeMp799NNPjB8/ni5dEjhyZLPW4QgX2rt3L2+++Saff/45ZrMZgISEBB566CF27pzIq68aWLoU/njO5tixY/z888/OS35+vvNvOp2OgQMHMmrUKI4dO8aGDRvIzMystr2vry+DBw/m4osv5uKLL2bYsGHOk8OXXw6//AJz5hRw9OibvP3225SWlgJwzTXX8Nxzz9G/f3/3vSjCJRz7vTNmzGDevHlah9NgMp7VzFPGtLq48sorWbFiBc8//zxPPvmk1uE0G3l5ec6k1Zo1a6olra644gquv/56rr32WkJDQ2t1fxZL/Vse9+kDt90G999f923XrYOoKPulpqr1srIyfvrpJ5YsWcK3335LcXGx828xMTFMnjyZSZMmkZCQ4NXdG5qSBQsWcOuttxIfH8++ffu86nWXBJYbKKXo1asXSUlJLF68mEmTJmkdkqilyspKvvvuOxYsWMAPP/zgLI0NCgpiypQpTJ8+nUsvvfSsk2pWq5U9e/awZs0aVq9ezbp166p9eQPExsYyatQo+vbty969e9m4ceNZMwEBevbsyYgRI5yXrl27Oj+oqang6wtGYw6ffvopc+bMISUlxbntqFGjmD17NpMnTyYgIMDVL49ogFdeeYXHHnuMSZMmsXjxYq3DcSk5OKqZp45pGRkZREdHYzQayczMpHXr1o0eg8Vmo6zKal9Lq8pMcaWFcrMVs0VhstlQqnqVlqEeVVpmq43CCgt+Bj3tQuzVWP5Gz0ueWK1W8vPzOXnyJHl5eeTl5Tmv//Gn43pVVRUA/v7+DB06lJEjRzJy5EiGDRtWrR+6K+Xl5bF+/XpWrlzPqlXrOXx4F0rZqt1Gp9PRsmVLZ0LLcWnduvUF/+3n17hJuHMprrRwKK+UE6UmjD46gv3O3TLQphSm0xVW1doCGn0ID/R1e1vA2vr888+5+eabGTp0KFu2bNEsjsYiY9qFecp4VlRURGRkpLR19xJKKVauXMkbb7zBzz//DIBer2fSpEk8+OCDdW7XZ7PZ2L17t7M6a8OGDWetExIeHu48Hrv44osZOHAg/v7+57w/kwkSE+3teME+EfL111/nvffeo7y8HLCvLfrMM89w0UUX1fHZi8aglKJHjx6kpKSwYsUKxo0bp3VIDSbjWc08ZUyri19//ZXLLruMNm3akJ6eft7vLeEaGzZs4Nlnn2XVqlXOpJXRaKyWtKprFVRDdeoEpaVw6BA01uG7yWRi9erVLF68mKVLl1brJNWuXTseeOABHnjgAeke5UZKKfr06cP+/fv55JNPmDlzptYhuZQksNzk7bff5oEHHuDKK6/kxx9/1DoccQE2m40NGzawYMECvv76a2cllY+PD+PGjWP69OlMmDCBwDosDmK1Wtm9e3e1hFZJSclZt/Pz82PIkCHOg6MzZ/LVhlKKDRs2MGfOHL766isqKioACAsLY9q0acyaNYsBAwbU+v6Ee5x5QPTdd99x9dVXax2SS8nBUc08eUy76qqr+PHHH3nzzTd54IEHtA4HpRSVFhulVRbKTPYqraJKCyaLFZNVYbHZ0Ot+T2j5+ujR11D54rjfUpOVSrON8EBfurQKpG2Qr6aJg6qqKnJyci6YgDrzZ0FBQZ3fYwEBAYSFhZGdnV3t9waDgYEDB3LppZcycuRIRowYcdbBV0YGZGXBhdaFVUqRnp7O+vXrnZfk5ORqt/Hx8aFfv34YDAZnAq6wsLBOz8MhKCjonEmvjh07Eh8fT69evYiOjnbLbDSlFNklVRzJL6ewwkyIn0+1RGhTawtYW46ZvO+//z7/93//p3U4bidj2oV50ng2ffp0Fi5cyJNPPsnzzz+vdTiiHqqqqvjss8948803SUxMBKBFixbcdttt3HfffS7rPlFWVsacOetISdlEnz6duPjii+nRo0eDx4rc3FxeffVV3n//fSorK9HpdNxwww0888wz9OjRwyWxC9fYvn07gwcPpm3bthw/ftwrTrLKeFYzTxrTakspxaBBg9i5cyf//e9/uf3227UOySsVFhby6KOP8uGHHwL2pNW4ceO4/vrrmTBhQqMnrc4UHGxPYN11F/z7343/+FarlU2bNrFkyRIWL15Meno6AP369eOjjz5i0KBBjR9UM+BYw7x9+/YcPXoUX19frUNyKUlguUl+fj4dOnTAZDJx5MgRoqOjtQ5J/MHBgwed61o5vlABBgwYwPTp07npppto27atSx7LYrGwe/du/vrXNfz2WyJjxvTihRdGMGDAAJfNFi8qKmLRokXMmTOHbdu2OX/fv39/Zs2axbRp02jZsqVLHkvUjaOtaLt27cjIyPCKA6IzycFRzTx5TFuyZAmTJ0+mZ8+eJCYmNsn2elaboux028FSk4WiCgvlZgsmq73CRQFGvQ5fgz2hdaEqLUc1lr9RT/sQf2JaBeJncG81lsVi4fDhw+zfv5/9+/eTmJjI/v37OXTokLNdbG2Fh4cTERFB69atiYiIqHb9jz8jIiKckzPy8/PZsGED69atY926dezcudM5ixDsn/O+ffs6K7QuvvgSBg9uQ2YmbNgAw4bZb2ez2UhKSmL9+vWsW7eO9evXc/z48WoxBgQEkJCQwCWXXMIll1xCQkICQUFBZ70mBQUFzoRWfn6+8/LHf5/5u9q8XgEBAfTo0YNevXo5k1rx8fF06dKl3m0LLVYbqfnlHC+qoMpso2WgER+9rlpbQLNN4Xu6LWCQn4FwDdsC1lZWVhadOnXCYDCQnZ1NeHi41iG5nYxpF+ZJ49natWsZNWoU7du3Jz093ev2v7xZfn4+H3zwAf/617/Izc0F7G3T77nnHu644w6XH9Ps3AkDB0LHjnDkCLh6BYCsrCz++c9/8uGHH2IymdDr9UybNo1//OMfsuh9E/Hggw/y1ltvce+99/LOO+9oHY5LyHhWM08a0+pi0aJF3HTTTXTv3p0DBw54dFvupkYpxddff819991HTk4ORqORhITHuP/+B5g8WfvzbRYLXHIJnDgBr7wCEya4fkyrC6UU06b9yKpVd3PiRBp6vZ57772X559//qzjP9Ewo0ePZs2aNbz22ms8/PDDWofjcpLAcqNbbrmFzz77TGb9NSF5eXksWrSIBQsWVEvydOrUiVtuuYVbbrmF+Ph4tz3+O+/AE0/A++/Drbe67WHYt28fc+bMYcGCBZw6dQqwt4eaPHkys2bNYtSoUV7VC7Wpu+222/jkk094/PHHeemll7QOx+Xk4Khmnjymmc1mOnXqRG5uLhs2bGDEiBFah1QjpRRVVnvrwZIqC6Wnq7SqLDbMVnsiQa8D4xlVWmeuT6SUoqTKSpXVRqtAX2JaBRLRouHVWDabjfT0dGeiynFJTk4+q3UR2NsjBQW1w8cngvbtW9O9ewSdOp07EdW6dWvCw8NddoK2uLiYTZs2ORNaW7duda414tCiRQ+s1pG8/PIIzOYTrF+/ng0bNjjHHYewsDAuvvhiZ8Jq4MCBbpkRppQiI6OY77/P55dfTrJ5cz7Fxfncc89JKivTSExM5MCBA2RlZZ1ze39/f3r06FEtqRUfH09sbOwFD/xLqyyk5JWSW1KFz+kEVdUf2gL6G31oFehLiL+B0CbQFrC2XnvtNf72t79x3XXX8b///U/rcBqFjGkX5knjmVKKuLg4Dh065HULWXurlJQU3nrrLebPn+/sLNGnTx8eeughbrzxRrfNJk5Ls1cT9+sHP/3klocA7Otwvfjii8yZMweLxYKPjw8zZszgySeflDaXGrJarXTq1Ins7Gx+++03hgwZonVILiHjWc08aUyrC4vFQrdu3UhLS5NlTVwoPT2du+++m++//x6AESNG8OGHH7JwYTxffgn79kEdGje5hdVqvzSl4pvp0yE5uYxLL32Gt956E5vNRlRUFO+//77XdSfSytatWxk6dCghISEcO3aMkJAQrUNyOUlgudG6deu49NJLvbbqwhNYLPDf/1ZRVbWM1asX8NNPPznXtQoODub6669n+vTpjBw5stESOlVV0FhLdFRVVbF06VLmzJnDL7/84vw8xcTEcNtttzFz5kw6dOjQOME0UyUlJbRr146ysjJSUlLo1q2b1iG5nBwc1czTx7THHnuMV155hZkzZ/LJJ59oHU69WG2KcrOV0ioLpSYrxRVmykxWTFYbJqsNmwKDXudMaBl9dJhtisIKC4FGH9qH+hET3gJfQ81jhVKKrKysatVU+/fv58CBA5SVlZ1zm86dO9O7d2969+5Nr1696N27Nz169MDXN4DrroNly+y3mzULPvig8WfSVVRU8NtvvzkTWps2bXKeXPyj9u3bM3LkSGfCqlevXm4fY5WyzzJ84glwFI75+sLKlXDppdVvW1hYyIEDBzhw4IAzqZWYmEhmZuY579vPz4+4uLhqSa1evXoRExNDfqWN1JNl5JebsNlOr8dm0OPnaAvobyTU30CQn6FaktQTKKW46KKLSExMZNmyZVx77bVah9QoZEy7ME8bz1599VUeffRRJkyYwNKlS7UOR5zh55+hvByuvVaxfv163njjDb799lvne2v8+PE89NBDjBkzplES/jZbzQvWu0paWhrPP/888+fPx2q1YjAYmDVrFk888QSdOnUCGveYsblzrBkUGxvLoUOHPGKCSW3IeFYzTxvT6uLdd9/lvvvuY9iwYWzatEnrcDyaxWLhvffe46mnnqKsrIzQ0FBeffVVZs+ejV6v529/g9deg0cegVdf1TrapmfECNi0yT7uh4fv5Pbbb2fHjh0A3HDDDbzzzjtERkZqHKVnu/766/nf//7Ho48+yssvv6x1OG4hCSw3UkrRs2dPDh48yNKlS5kwYYLWITU799+/mXfemQmkAPZ1Nq688kqmT5/OtddeS0BAgKbxNaa0tDQ++eQTPvnkE44dOwaATqdHp7uezz9/l6lT22gcoXeaM2cOs2fP5pJLLmHdunVah+MWcnBUM08f0w4fPky3bt0ICAggOzub0NBQrUNqMKUUJqui1GQ5XalldlZpmU63e9NhT0hUWezrakUE+RLTqgWtAo3O9/3JkyfPav23f//+867hFBkZ6UxUOZJV8fHxF5wlVV4Oo0fD9u3w7rtw991ueEHqyGQysWPHDtatW8fmzZtp3bq1M2HVpUsXTU7+ZGdDXByUlIBOB4sWwQ031H77oqIikpKSqiW1Dhw44Bwz/0in0zFw+EhemPMVfj72toAtA42E+hsJ8TPUKtnZlO3cuZOBAwfSunVrsrKyMGrZf6QRyZh2YZ42nuXk5NCpUyeUUhw7dox27dppHZIA8vOhQweoqipk5MibWbfuB8A+YWD69Ok88MADbu2K0VQcPnyY5557js8++wybzYavry+3334711zzd6ZPb8fYsfDFF1pH6f0c3TKefvppnn32Wa3DcRkZz2rmaWNaXZSWlhIVFUVBQYHHdNFoinbt2sVf/vIXZ8Ll+uuv55133qm2P/Haa7B5M9x+O1xxhf04RPwuLg7S0+Hhh+GFF35PCD755JOUl5cTFhbGq6++yqxZs6RTVD0cPnyY7t27YzQaOXr0KO3bt9c6JLdo0JimlHLbBVD2h/Bsb7zxhgLUVVddpXUozUpFRYV65JFHlF6vV4Bq0yZOvfPOOyo3N1fr0DRnsVjUTz/9pK6//nql0xkVoIKCItTSpUu1Ds0rDR8+XAFq3rx5WofiNmd8X7t1XPDkizeMaaNHj1aAev/997UOxW2sNpsqqTSr7OIKdSivVO04XqDWHj6pfj54Qn1/IEct25etHnz+Ded7/kKXli1bqksuuUT93//9n/r3v/+t1q5dq06ePFnv2HJzlXrtNaVMJhc+YS9jsyn1wgtKBQQo9dZbrrvfoqIiNX/+fNWhQ4ez/p87de6isooqVGmVWdlsNtc9aBNw7733KkDde++9WofSqGRM877xbNKkSQpQL730ktahiDOMH39QBQbGOcfMp59+WuXk5GgdliaSkpLUjTfeqE6fTFd+fgEKvlG9emkdmferqKhQISEhClDJyclah+NSMp5555hWF0888YQC1IQJE7QOxeOUlpaqhx56yHlOsVOnTurbb789522t1kYOzoPYbEplZiqVmnr2344eParGjx/v/K4aOXKkSkpKavwgPdydd96pADVr1iytQ3GrhoxpUoFVCydPnqRDhw6YzWbS0tKIiorSOiSvt3XrVmbOnElSUtLpct6/8Y9//AN/f3+tQ2tydu9OZ9asP7Nz52oAZs6cydtvv+0V1RVNQVJSEvHx8QQFBZGTk0OLFi20DsktZHZfzbxhTPv888+5+eabGTBggHMGWnNgstgoM9nbDpZUWhjcrQPlpSVn3S4wMJC77rqLcePG0bt3byIjI11ehaSUzOirjX//u2FVajabjaysLFJSUvj8889ZtmwZJ0+ePOt2ISHhHDiw1ytb8ZrNZtq3b8/JkyfZsWMHAwYM0DqkRiNj2oV54nj2448/ctVVVxEbG0tKSorM7m0CVq5cydSpUyksLKRPnz4sX76czp07ax2W5vbv38+TTz7JsmXL0Ov1vPXWh9x77yytw/Jq33zzDVOmTGHgwIFs375d63BcSsazmnnimFYXubm5dO7cGZPJRFJSEnFxcVqH5BF+/PFH/u///o/09HT0ej333Xcfzz33HEFBQW59XLPZTGpqKgcPHiQ5OZnMzEzatm1LVFQUUVFRdOrUiY4dO7psPUilFIWFhRw7doxjx45x/PhxMjMzadmyJT179iQ+Pp5OnTq5tbOGUoqvvvqKe++9lxMnTuDr68sTTzzBo48+ip/00a2R4zNeVVVFUlISPXr00Dokt5EWgo1g2rRpfPHFF15Xkt7UVFVV8eyzz/LKK69gs9mIi4tj/vz5DB06VOvQmjSbzcZ7773HY489RmVlJVFRUcybN4/Ro0drHZrH+9vf/sZrr73G7Nmz+eijj7QOx23k4Khm3jCmVVZW0qFDB06dOtXsTmif6bvvvuNPf/rTef/eokULevfuzZgxY+jTpw/t27enQ4cOtG/fvlm1rW3KlFLk5eVx9OhR0tLSOHr0aLVLeno6ZrP5rO1at25LVVUnSkq2o9MZ2bBhLcOHD9PgGbjf8uXLmTBhAr169WLfvn1esx5IbciYdmGeOJ5ZrVa6dOnCsWPH+PXXXxkzZozWITVbSineffddHnzwQWw2G5MmTeLTTz91+0lBT6KU4vnnn+cf//gHYF/H7ZFHHtE4Ku81efJklixZwptvvskDDzygdTguJeNZzTxxTKurO+64gw8//JC//OUvfPjhh1qH06Tl5uZy//33s2jRIgD69evHRx99xKBBg1z6OAUFBc4k1ZmX1NRULBbLBbfV6XRERkY6E1qO5NaZ/46IiECn01FUVFQtOXWu6+Xl5Rd8vKCgIGcy68xLdHS0SycEnTp1ikcffZSPP/4YgJ49e/Lhhx9y8cUXu+wxvNFTTz3FCy+80CzWepUEViNYs2YNo0ePpkOHDqSlpWEwGLQOyevs2LGDmTNnsn//fnQ6HQ899BDPPfecnCysg6SkJG699VbnzLP777+fl156SV7DejKbzXTq1Inc3Fw2b95MQkKC1iG5jRwc1cxbxrT777+fd955hzvvvJMPPvhA63A0YzabGT/+en79dRkAYWH9sFqPUlJSdMHtWrZsSYcOHZwJrXNdj4iIwMfHpzGehlcrLCw8Z3IqLS2NtLQ0ysrKanU/LVqEcs014+nW7Vlef72MysrhQCV//vN/mDv3Dvc+CQ1dd911LF68uFmeOJUx7cI8dTx75plnePbZZ7npppv4/PPPtQ6nWTKZTNx9993Ok1NPPfUUzzzzjFTEnce//vUv7rnnHsA+Ke7ll19uVpMJGkNhYSFt27bFbDZz/Phxr1s3RMazmnnqmFYXKSkp9OjRA19fX9LS0oiMjNQ6pCbHZrMxd+5cHnnkEQoLCwkMDOS5557jvvvuq/f5W5vNRnp6OsnJyWclq3Jzc8+5jU6no3PnzvTo0YMePXqwalVHrrzyBBkZGWRkZHDs2DEyMzOx2WwXfGw/Pz98fX0pKTm7a8gfBQUF0alTJ2d1l1IdSE4+iZ9fEgcOHODEiRPn3C4gIIC4uLizEluxsbENOue9Zs0a7rjjDlJSUgB7Avbll18mLCys3vfprc5c527jxo0MHz5c65DcShJYjUApRVxcHIcOHWL58uUXnLkt6sZkMvHCCy/w0ksvYbVa6datG5988oksUFlPZrOZl156ieeffx6r1UqPHj1YsGCBy2ecNAfLli1j4sSJ9OzZk8TERK8+4JSDo5p5y5i2f/9+LrroIoKDg8nOzvbatpi1UVlZSY8e15Ke/jPQEVjPhAkBdOz4H1avXsaBA3sBKwA6nR6djhoPNgB8fHxo167deRNcjushISFufX5NXXl5ebUE1R+TVYWFhRfcPiwsjC5dutClSxcCAgJISkpi9+7dZ/wfXca4cQ/z7rvj+MtfdKxffxIYBKTj7z+LgoKP8Pf3zq+8U6dOERkZidVq5dixY153Qq8mMqZdmKeOZ+np6XTp0gWj0UhWVhatWrXSOqRmJS8vj+uuu47169fj7+/PvHnzmDp1qtZhNXmff/45M2bMwGKxMHv2bP7zn//IJBcXmjNnDrNnz2bs2LH88ssvWofjcjKe1cxTx7S6mjRpEkuXLuXvf/87L774otbhNCnJycncfvvtrF+/HoArr7yS999/ny5dutRq+7KyMlJSUs6qpkpJSaGysvKc2wQGBhIXF+dMVDku3bp1c04gP3EC2raF3buhb9/ft7VYLGRlZTkTWmcmtxzXCwoKnI9zZnLqXNdDQkKqnatavRquuQaSkiAqyr4sTlKSPZl15iUrK+ucz81oNFZLbDmqt7p161brloCVlZW89NJLvPzyy5jNZiIjI3nvvfe47rrrvPq8Wl2988473H///YwYMYINGzZoHY7bSQKrkbz++us88sgjXHPNNXz77bdah+MV9uzZw4wZM9izZw86nY777ruPF198kcDAQK1D83jbt29n+vTpJCcn4+Pjw1NPPcXf//53jEaj1qF5jAkTJrB8+XJee+01Hn74Ya3DcSs5OKqZN41pw4YNY8uWLXzyySfMnDlT63A0lZVVRufOV2KxbABigPXo9e256Sa46KIiHnvsC2AO8PuaCt26dWfMmNHExcVRVFREZmYmmZmZZGVlkZmZec61ls4lKCjonImtM38XGRnpsh7pjc1kMpGRkXHO5FRaWtp5Zy46BAYG0qVLF6Kjo52JKsclOjqakJAQfvjhB9544w3WrFlzeisDcCPwEOHh/bj3Xnj5ZaistADjgV+AwTzxxDpeeMF719V8//33ufvuu7niiiv46aeftA6n0cmYdmGePJ5deeWVrFixgrfffpv77rtP63Cajb1793LttdeSnp5Ohw4dWLZsGQMHDtQ6LI/xww8/MGXKFCoqKpg8eTKff/65rAviImPHjmXVqlXMmTOH2267TetwXE7Gs5p58phWF5s2bWLEiBG0bNmSjIwMadsK/Prrr3z11VfMmzcPk8lEmzZteOedd5g6depZSRKlFNnZ2c7k1JkVVRkZGed9jPbt2zuTU2cmrDp27Fhj9fGSJTB5Mjz0ELz+et2e244dJTzxhIUffwyrc8Lnp59g/Hi49lpYtuz8tysqKjpnYis9Pf2ct/fx8aFr165nVWzFxcWdt+tTYmIit99+O5s2bQLs+3H/+te/iI2NrdNz8kZms5muXbuSkZHB0qVLmTBhgtYhuZ0ksBpJXl4eHTp0wGq1kp6eTseOHbUOyWOZzWZefvllnnvuOSwWCzExMXzyySeMHDlS69C8SkVFBX//+995++23ARg0aBALFizw6kUBXSUnJ4eOHTui0+k4fvw4bdu21Tokt5KDo5p505g2d+5cZs2axfDhw9m4caPW4WjunXeKuP/+scAOoCewhYZBBAAAjmRJREFUFohAr4du3eDgQYC9wFxgIZAPgNHoy+TJk5g1axZjx451HsRUVVWRnZ19VmLL8dNxqaioqFV8bdq0OW8Vl+Nnq1atNJnNVlZWxuHDh0lJSSElJYVDhw45k1Q1tccwGo107tz5nMmpLl26OHu//1FlZSULFy7kjTfeIDk5+fRvg4E7gHuBTgB06QJHjzq2ehR4FWiDwbCd48c74c1f6wkJCfz222989tlnTJs2TetwGp2MaRfmyePZN998w5QpU+jduzd79+6VWbyNYOnSpdxyyy2UlZUxZMgQli5dSrt27dzyWCdOnGDdunWsXbuWffv2ERoaSkREBG3atCEiIqLadcfPukzyUEpRVlZGUVFRtUtxcTFFRUUopRg5ciRxcXEuf29t2LCBa665hqKiIsaOHcuSJUsIDg526WM0N5mZmXTq1AlfX19yc3MJDQ3VOiSXk/GsZp48ptXViBEj2LRpk0zi4PeuImfq3Lkz3bp1o0ePHkRHR1NZWVktUXW+dnxGo9G53ZmXuLi4BnXMeOghePNNiIyEY8egLl35vv4apk6FQ4egrrmeZctg4kT79aVLoa55kdLSUpKTk89KbqWmpp7zc6bT6ejSpUu1pFZMTAxHjhxh3bp17Nmzh927d2O12jub6PV6SktLm/1SJ5999hm33HILcXFxHDhwoFm0Y5YEViO68cYb+fLLL3n22Wd5+umntQ7HI+3fv58ZM2awc+dOAP7617/y8ssvN0obK6UU+fn57Nt3hHnzjtKhQxFDh7alffv2tGvXjrZt27qtQslsNpOfn8+WLSdZvz6Ap5/uTGho46yltnr1ambOnElGRgb+/v68/PLL3HPPPc3iC7K+Xn31VR599FEmTpzIkiVLtA7H7eTgqGbeNKaVlpbSvn17SkpK2L9/P7169dI6JE1ZrdC3bz6JiaOA/UB/YBUQBkBgIPy+Nm4VsBx7VdZKwP5+aN8+ir/85c/8+c9/pnPnzjU+plLKWb11ZmLrj9dzcnJq1bbQYPAnOjqKLl06OxcB7ty5s/Nnx44d613JZTabSUtLcyapzrwcP378vNvp9Xo6dux4VmLKcWnfvn2dxqH8/Hw++OAD3nvvPWcvd52uI0rdD8wGfj9x1b69vWXHnj1gs30FTAV8gF+ZPv1SPv20Pq+EZzh48CA9evQgODiYnJycZlnVLmPahXnyeGYymejYsSN5eXlevz6p1pRSvPTSSzz55JMA3HLLLXz00Uf4+7uuejUrK8uZsFq7di1JSUl1vg9HksuR1GrVqhWVlZXOpNQfE1W1GVO7dOnC+PHjGT9+PKNHj3bZcerevXsZN24cubm5DB48mB9++IHWrVu75L6bozfffJOHHnqIyZMn880332gdjlvIeFYzTx7T6mrp0qVMmjSJzp07c/jw4QatU+TpSkpK6pxcCg8Pp2fPnmclqqKjo93yWg4dClu32q//+CNceWXtt336aXj+ebjvPjg9H73WvvwSbrzRfj0uDrZvB1cU7FVUVJCSkuJMaDkSXIcOHcJisdT6foKCWlFcnNesJyEppejXrx979+7l448/ZtasWVqH1CgkgdWIVq1axdixY+nUqRNHjx6V/tV1YLFYePXVV3nmmWcwm81ER0czd+5cRo8e7dLHKS8vr9am6MiRI9Wul5aWnndbnU5HRESEc+2Sdu3aVbvu+Onr2xZf3wpOnTpJXl4eJ0+e5OTJC18vKir6w2P50KVLZ2JjY4mNjSUmJqbadVfPyCsqKuL+++9n3rx5AIwePZp58+YRFRXl0sfxBkopevbsycGDB5vNmndycFQzbxvT7rjjDj788EPuv/9+3nrrLa3D0dzq1TBmTA5wCXAYGAasZPnyIOLjYcAAKC7+41bH8Pefh7//XAoL0wD7Z2ns2LHMmjWLiRMnNvhEn8ViITc395xVXI7rKSmZWCxFF7wfnU5HZGRktaRWVFQUHTtG0apVZ3r3jqK8vPycSaojR46c96DEPukjFl/f7lx3XXdGjOhGbGws0dHRzlnRDZWamspbb73F3LlznVVrbdr0o7T0YcrLbwCqTzzx8YGdO6FTJ+jffz/p6QlAGfA2cB/bt4M3d7564okneOmll7jtttuYM2eO1uFoQsa0C/P08exvf/sbr732GrNmzeLjjz/WOhyvVFFRwW233caiRYvQ6XT885//5G9/+1uDTzZlZGQ4k1Vr167l8OHD1f5uNBoxm1sDevz8TmEw6PH19cVoNDonPFitViwWC1VVVVRUVNT5fezj44PBYECv16PT6bDZbNhsNsxmC0rpAR1gdt7ez8+PSy+9lKuuuorx48fTrVu3Br0OqampXH755Rw9epSePXuycuVK6exSTwMHDmTnzp188803TJ48Wetw3ELGs5p5+phWFzabjZ49e5KSksLnn3/OTTfdpHVImrr//kN89dV1ZGfvA+BPf/oTOTk5pycBnsRqrXLeNjg4mPHjxzNhwgTGjx9Py5Yt3RqbxQIffwz79sGYMfbjkiFDar/9pEn26qngYMjMtP+srUWL7MdCOTnw4Yf237lw7kk1mZmZ/O9//2PRokVs3bq12iQRPz8/2rZtS0xMDNu3b6e0tJQOHWKJiNjIrl1e3AqjFlauXMkVV1xBZGQkaWlpzaatcIPGNKWU2y7YpyUrb2K1WlVsbKwC1Pfff691OB4jMTFRDRo0SDneE3feeacqLi6u131ZLBaVlpamVq1apebMmaOeeOIJNW3aNDVs2DDVtm1b52Oc7xISEqLi4/uq1q0nqWHDblNXX321GjBggIqMjFR6vb7G7et70ev1KiIiQrVv30P5+nZUp3e0znuJiIhQCQkJ6uabb1ZPP/20mjdvnlq/fr3KyspSNput3v8XS5YsUREREc7XYt68eQ26P2+0ceNGBajIyEhlNpu1DqdRnPF97dZxwZMv3jambdu2TQEqPDxcVVZWah1OkzBpklKQriDq9GdijIqMrFCJiUp9/bVScPbl/vuVMpms6tdff1XTpk1Tfn5+zs9Ty5Yt1V//+le1a9cut8b95ZdKXXxxkVq0aL/6/vvv1QcffKAef/xxNW3aNHXxxRerqKgo5ePjU+/xS6fTqc6dO6vLL79c3X333eqdd95RP/74o0pNTVVms1lt2KBUPYf0C9q8ebO67rrrqo2XV155pfrll1+UzWZTx48r1abN2f8nDz6olMmk1MiRBQq6KkAZjTcrsKnhw10fZ1NitVpVp06dFKDWrFmjdTiakTHNu8ez5ORkBagWLVrU+3hCnN/x48edx21BQUFq+fLl9bofm82mDh8+rObOnatmzJihoqOjzxpffHx8VGBgoNuOv9xxiY2NVX/961/V999/r8rKyur12mRmZqrevXsrQEVFRank5OR63U9zlpSUpAAVGhqqKioqtA7HbWQ88/4xra4+/PBDBaj+/fs36/M4NptS3bopNX58vvP7dODAgaqwsFAppdS0aVZ15ZW/qb///Qnn3x0Xg8GgxowZo95++2115MgRjZ/JuY0Zo1RYmFKTJyv1889129ZicU9MDklJSeqll15SgwcPPmtMHzt2rHriifdUaGiGUsp+bHLjjTcqQLVp00a9+OJhBUpt2eLeGJu6sWPHKkC9/PLLWofSqBoypkkFVj288sorPPbYY0yYMIGlS5dqHU6Tc+oU/OtfcNllMHSolTfeeIOnnnoKk8lEp06dmDNnDu3bX05IiH0Wwh8pZW/z56ic+mMFVUZGxgXLUx1rasTExDjbFJ15PTw8/Lyz5iwWC3l5eWRlZZGdnU12drbzuuPn0aPZ5Ofn4OMTSOfOEbRu3dp5iYiIOOf11q1b07Jly2qtkiorK0lLSyM1NdV5OXLkiPNnVVXVOWMECAgIqFaxdWblVnR09HlnvOfm2lsqnThxgjvuuMP5/p04cSKPP/5f/vvfNtxxR91mhnijWbNmMXfuXB599FFefvllrcNpFDK7r2beNqYppRgwYAC7d+/miy++4EZHn4Fm7MgR6NkTLJZD+PmNpKIiB7ia8PDFHDjgywsv2Me3Pxo5Ev73P4iIgIKCAr744gvmzJnjbJUL0L9/f2bNmsW0adPcPuPvXCwWC1lZWWRkZJCenk5GRgYZGRl89106x49n4O+fQVCQH3FxcXTv3r3aJTY2ttF6lFutVr799ltef/115/psRqORm2++mYceeojevXsDkJ5ub8PhXALrtA4dICkJ3njDxrPP/gn4geDgfixZspHLLgvkyy/hhhsa5alowtEpIDo6mtTU1GbbKljGtAvzhvFs5MiRrF+/ng8//JC//OUvWofjcYqK4FzLBW3dupWJEyeSnZ1NTEwMy5cvr3WbYaUUKSkpzuqqNWvWkJWVVe02Op3unO87o9FI79696devH3379uXo0X6Ulvbm1Vd1lJSUUFxcTHFxsfP6uX6eOlXM99+XUFFRwqWX+hMREUJwcDAhISHOy4X+XVERzJgxp3jxxY1s2rSBDRs2VFuz41x8fX0ZPXo0V199NePHj6dr1661eq0ATp06xTXXXMPmzZuJiIjgp59+YsCAAWfd7sQJaNOm1nfbbDz99NM8//zzXl9tLONZzbxhTKuLyspKoqOjyc3N5ZdffmHs2LFah6SJ336DhARo1Qr27s1m5MhLSE1N5ZJLLuGnn37ipZcCefFFePZZezu+I0eOsHz5cpYvX866deuqfbdfdNFFTJgwgWuvvZaBAwc2if3nsjKw2eyt/7TutKeUYtu2bSxdupQlS5acsQax/dzkFVdcwaRJk7jmmmsICgonIcG+DvGpU4r777+fd999l+DgYNauXcsnn/Tnvfdg2jT47DMNn5SGduzYwaBBgwgODiYjI4OwsDCtQ2o0UoHVyHJycpTBYFA+Pj7q+PHjWofT5MyZoxQoFRh4ZbVsfPv27dULL7ygHn30XQXvqaCgf6u33npbvfDCC+q+++5TEyZMUBdddJEKCgqqceZbu3bt1IgRI9Qtt9yinnrqKfXJJ5+oNWvWqPT0dGVx93QDpdSRIzZVVOS++7darerYsWNq7dq1au7cueqJJ55QN954oxoyZIhq1arVBV8bvV6vOnfurMaMGaNmz56tXnrpJTVv3jw1c+bXCv6rLr30WXXXXXepG264QUVGRp6xnZ+CT9VNN7nveXmCkpIS1aJFCwWogwcPah1Ooznj+1rzWXRN9eKNY9q//vUvBagxY8ZoHUqT8eijSo0apdTevftUQID9+7Znz+uVxWJRlZVKDRhgH+Ouvlqp6Gj79T59lDrX5N9du3ape+65R7Vs2dL5GfPz81PTpk1Tv/zyi7JarY3/BP+grEypw4fdP1Ov5jjK1AcffKC6devmfK3CwsLUY489pjIzM8+6/a+/KmUw/P5/0bWr/frXX9v//sgjT50e28LVnj1HlVJKTZlir8zyZrfeeqsC1FNPPaV1KJqSMc37x7NPP/1UAWrIkCFah+JRCguV+vvflbrllrP/tnDhQmcVcWzsKJWXl3fB+7JarWr//v3q3//+t5o4caIKDw+vVQVTy5Yt1ZgxY9QDDzyg5s+fr/bs2aOqqqqc95ucrFRAgL2atq527lQqKKh+Y9qaNfZx5Lvvfv9dSUmJ+uWXX9QzzzyjLrvsMucxwvkuHTp0UHfddZf68ccfVXl5eY2PWVpaqoYOvUIBKjg4WK1evfqs21x+uVL/939KHTtW9+fkrWw2m4qJiVGA+uWXX7QOx61kPGseY1pdvfDCCwpQ48aN0zoUzezZo9Sbbyr1zjtK5ecrdfToUdWhQwcFqPHjx6v3369S7dvbb3PGEKOUUio/P18tXLhQ3XDDDSo4OPis85Z33HGH+uGHH7y6urMmJpNJ/fLLL+ruu+92vq5njuO33nqrWrJkyVmVyJs324/LYmKU+uc//6kA5evrq3799VellFI//KDUP/+p1P799iq65mjq1KkKUA8//LDWoTS6hoxpMpDU05QpUxSgnn/+ea1DaXIyM5UaNmx1rQ5gzncxGo0qMjJS9evXT02YMEH97W9/U59++qnat29frQ4GmrqCgoZtX1hYqLZv367mzp2r7r//fjV+/HgVHx+vwsLCGvS6t2o1SO3d65Kn6LHmzp2rAHXxxRdrHUqjkoOj5jmmFRQUqICAAAWow4cPax1Ok1BUpNRnn9mvb9u2XQUGhihAzZw5U1mtVnX4sFIhIUp9+qn9u3z6dPvJtgupqKhQixYtUpdffnm1dnjR0dHq2WefVenp6W5/Xu5W3wRYbm6u+sc//qFat25d7XV55513VElJyQW3XbhQqdmzlTKblbrnHqWuvNJ+ILR06VLnhI4vvljpvH09Oz15jDMnYBw6dEjrcDQlY5r3j2dlZWUqNDRUAWrPnj1ah+MRbDal/vEPpUApo1GpEyfsv7dareqxxx5zfm58fe9UgYEmlZVVfXur1ap27typnn76aTV06NBatf6LjY1VkydPVs8995xavny5ysjIuGDLK5NJqcGD7TE+9ljdn+Pjj9u3rc8ctP/8x77tqFHnv43ZbFY7duxQ77zzjrr++usv2L7eYDCoQYMGqVdeeUWlpqae9z4nTqxSMNU5yWXp0qXOv2Vl2WMCpW680T3tej3Rli1bFNgntTbG5FUtyXjWPMa0usrPz3fu8+3evVvrcJqMpKQk5zHFtdder0ymmr8fKisr1YoVK9Rdd92lOnbsWO17vEWLFuq6665T8+fPVydPnmyEZ6CtsrIytXjxYjV9+vRqEzAdEzTuvvtu9csvvyhTDTMCy8qUevVV+3k1nU6nvvzyy0Z6Bk1famqq0uv1ymg0qmPNcGaKJLA0sHLlSgWozp07e/1OU31UVVWdtRMfGhqqQkNDVUhIiAoODlbBwcEqKChI+fv71zrBotfrVdu2bVX//v3VVVddpWbNmqWeeuop9f7776slS5ao3377TWVkZNT4hbp2rX2tk61bG+kFOcNzzynl46PUhx+e++9Wq1Xl5OSoXbt2qe+//159/PHH6vnnn1d33XWXmjRpkho6dKiKiopSvr6+tX7dfHx8lK+vrwoMbKFatWqloqKiVHx8vHM9N0B17dq1WX6B/tGIESMUoObOnat1KI1KDo6a75jmqNh4/PHHtQ6lSVq/fr3zJN1f//pXZbPZ1Ndf22d510daWpp65plnVOfOnZ2fO51Op6644gr15ZdfeuR6ZBkZSj388NmzGy8kOTlZ3X777dX2AQYNGqS+/PLLeq09+PPPSh06ZL9fx0zK5tZTfP78+QpQw719oa9akDGteYxnd999twLUPffco3UoHiM/X6mpU5UCpV57Tani4mL1pz/9yXm88O67/1Zz5ijVqZNVXXfdYbVw4UI1depU1aVLF2UwGC6YrOnVq5e67bbb1HvvvafWr1+viurRrmLePKU6dLDH9/TTddu2rEyp8HD7tosX1/mh1SOPKNWqlVKjRyt19GjttrHZbOrIkSPq008/VbNnz1ZdunQ572sUGhqqrrjiCjV//nznhMz0dKX0eqW6d7eoiRPvdP4/zJs3Tyml1Acf2J9PTIxS+/bV/Tl5q3vuuUcB6sH6lOl5GBnPms+YVlf33nuvAtQt5yqpbcZ27NihQkLsExBnzZpVp3XCbDab2rFjh/rHP/6h+vfvf9a5yJEjR6o33njDIyaKVVTUrsIpPz9fzZ8/X02cONE5sdVx6dGjh3r88cfV1q1b6/Q6Ll++3Ln+8nvvvdeAZ+F9HPuuM2fO1DoUTTRkTJM1sOrJZrPRtWtXjh49yo8//siVV16pdUhNjtls5qOPPuLee+/FarVy9dVX8/nnnxMSEnLWbauqqsjJyTlr3ak/Xj9x4kStHz8iIoJ27drRrl072rdvX+36v//djlWr2nH77ZH897/+dXpeFRWwYAFcey1ERtZ+u6qqKnJzc3nwwRy++SaHCROy6dcv+6znmZube8Fe62cKDQ2t9tzOvJz5++Dg4LO2/e233xg/fjwFBQWMHj2aZcuWnfN2zcnBgwfp0aMHQUFBZGdnExQUpHVIjUb6q9fMW8e0DRs2cMkllxAZGUlGRgZGo1HrkJqcX375hauvvhqTycSjjz7KP//5T0DXoH7kNpuNX3/9lblz57J48WJMJhMArVq14pZbbuG2226jT58+Nd7PsmWQmmrv/z56NERF1T+m+khNhfh4sFjg889h6tTz31YpxYYNG3j99ddZvny58/d/+tOfePjhh7nkkkvOu0ZlbRQXFzN06FCSk5OZMmUKX331VYPuz9Ncdtll/Prrr/z3v//l9ttv1zocTcmYdmHeMp7t3r2b/v37ExYWRlZWVqOt1ecNPv8c/u//rqC4eKXzd3q9Hn9/f6qqqi54LOLn50dsbCzDhg1j1KhR9O/fn7i4OAwGg0tiW7cODh2CgAD7Ghm19d//wp132q8/9xw89VTdHre42D6WhYaCj0/dtj3TqVOn2LRpEytWrGDFihWkpqZis9mq3Uan0xEZGYm//0W0aNGea67pRNeunfnuu++caxS/8cYbrFjxIErBokUQHl7/mLyJxWKhQ4cOnDhxgu3btzNw4ECtQ3IrGc9q5i1jWl2lpaXRtWtXdDodR44codO5FphvpjZs2MC4ceOoqKjggQce4I033qjXMUFGRgbffvsty5YtY82aNZjNZuffevbs6Vw3a+jQoedcN+vbb2HXLrjlFoiJadBTqrO//hV+/BHefReuvrr6344fP+5cz2rt2rXVxvwhQ4YwadIkJk6cSI8ePer8uJs2bWLs2LFUVlby5JNP8vzzzzf0qXiNvLw8OnfuTEVFBfv376/1GqPepCFjmiSwGuCf//wnf//735k0aRKLFy/WOpwma9WqVVx//fWcOnWKnj17snz58jotcHsms9lMbm5ujYmu3Nzcsw4UzicoKIiIiIhzXlq3bn3W7559Nog33tAxfTp89JE9KeW45OTknPd6UVFRrZ9n69atL5iQclzqe6D+66+/MmHCBMrKypgwYQKLFi3C379uiTxv9Oijj/Lqq68ya9YsPv74Y63DaVRycFQzbx3TlFLEx8eTnJzMkiVLmDhxotYhNUnLly9n8uTJWK1WXnjhBZ544on/b++uw6LO3jaA3zN0iGCCCbZid69r6+raumvn2l1Yi+2ai11rr93t2t1dGIAgJiAlMDB13j/4Ma8oguR3Zrg/1zUXiMzMDQPzcOY5kWa3HRwcjK1bt2LdunV48OCB7uOVK1dGnz598Ntvv333cNd//wW6dYs93HfRImDEiOTdd1gYcOUKYG4O1K0b+/ZHhYQArVvHvtgIxDbQzp799vPUajX279+PBQsW4ObNmwBiXwDt3r07Ro0alaLB0de0Wi3at2+P/fv3w9XVFdevX89UkxD8/f1RsGBBmJub48OHD5nqMOCEsKYlzpjqWeXKlXHnzh38+++/6NKli9RxDIZarf6hCSvm5ubIkycPKleujF9++QWNGzeGk5OTXk4OuHABuHQJePcOKFoUGDlS6kSxoqOjcePGDWzfvh1nzpzBq1evfniyYpkyPXD37kakUW/QKPz3339o2rQpihcvDk9PT738WUxLrGdJM6aally///47duzYgVGjRmHhwoVSx9Er//33H1q2bAmVSoVp06bhzz//TNXthYWF4cSJEzh06BCOHj0a7/W93Llzo0WLFmjVqhUaNmyoe51u0SJg7Fjg/n2gTJnk3Z+fH/D+PVChAmBhkfy8/frFjhOfPgVcXABPT09d0+rWrVu6zzM1NUW9evXQunVrtGrVCvny5Uv+nf3PkydPUKdOHYSEhKBv375Ys2aN0T9HJ8fUqVMxbdo0tGjRAocPH5Y6jiRSVdNSsmzrRy8w8qW879+/F6ampsLExES8+3qTcIrHy8tLlCpVSgCxB/6l92GrarVavHv3Tty5c0ccPnxYrFmzRkybNk30799f/Prrr6JKlSoib968iW6F8b2LmZmFMDXNK6ytk3felKmpqcibN6+oVKlSgtsfXr9+Xfj5+cU7xDg97Nu3T7f9YLdu3VK0VZMxUiqVun3sr1y5InWcDPfF87Xk20Do68WYa9qCBQsEAPHLL79IHUWvbd++XXeGlYeHR7rcx507d8SgQYN0Z7sAEJaWlqJr167i3LlzQqPRxPv8ixeFAIRwdBQiuUdEXr0qROPGsde3sEjZQbqfPwvRv3/sbQBCeHp++X+fxZIlS+JtqZQtWzYxZcoU8eHDh+TfWSJmzZql26LpxYsXaXrbhiDuMO+OHTtKHUUvsKZlnnq2atUqAUDUS+zgIvqGRqMRdlm/HcuULFlSbN26VXz69EnqiEZLo9GIc+fOie7du4sqVaqIwoULi5w5cyZ4plgJ19JSx9U73bp1EwDEtGnTpI6SIVjPMldNS647d+4IAMLW1laEpPagdSO0e/duIZfL03zsplQqxZkzZ8SwYcOEs7NzvOdtKysr0apVK7Fu3TqxePFH0bdvyu5j+XIhgNjtbVNixAiN6Nv3hnBzcxPFixf/JmObNm3E5s2bRXBwcMru4CuvX7/WnSHWqlUrvs74lYiICJE9e3YBQFy8eFHqOJJJTU3jCqxUateuHfbt24dZs2Zh4sSJUsfRa+Hh4ejSpQuOHDkCExMTeHh4YPDgwZJ25IUQCAsLQ2BgoO4SFBQU799f/19UVJTu+qampsiVKxdy584NR0fHeG+//piDg4Pksw82btyIPn36QKvVYujQofDw8EhwqXNmdPjwYfz6668oUaIEnj59KvljldE4uy9pxlzTAgMDkTdvXmg0Gvj5+aVq5pWxW79+Pfr06QMAWLt2Lfr27Zsu96NQKLB//36sX78eZ86c0X28UKFC6N27N3r27Im8efPi9WugYMHYGX7JnW0uBFC7NnD1auyWSaGhKc97/DjQpw/w22/A2LHvsWzZMqxcuRIhISEAgMKFC2PUqFHo0aMHbGxsUn5HCThx4gSaN28OIQQOHz6MFi1apOnt6zt/f3+ULl0a4eHh2LjxOHr04LbWrGmJM6Z6Fh4eDicnJ0RFReHFixcoWrSo1JEMQqRSjVuvQxGlVEPx6iHWr1iMY8eOAYjdSrBt27YYO3YsqlatKnFS46fVarFnzx5MmzYNT58+BQDkzZcPTX/rhW59B6J2UUeYyPlUBgBRUVHInTs3IiIi8PLlyxTv6mJIWM+SZkw1LSXitpCeM2cO3NzcpI6jdzZs2IDevXsDANatW6d7P60IIfD48WMcPHgQhw4dire6SSaTIU+e/HB2zo/8+fMjX758yJ8//vu5cuVK8DW5CxeAevWA58+BYsW+vV+lUon379/j7du3CV4ePHiBsLAPus/Pli0bWrZsiTZt2qBRo0awtrZOs+/Bp0+fUKdOHXh6eqJ27do4efIkt3X+yrJlyzB06FBUr14dV69ezXSvN8bhCiwJnThxQgAQzs7O38yKpm+p1Wrh5uam67r+8ccf6b7iKK1FRkYKPz8/ERgYaFCPuYeHh+777u7unqxDGDODVq1aCQBi3rx5UkeRBFIxEyKzXIy9pnXs2DFTzWhNjcWLFwsAQiaTiW3btqX7/fn4+IgpU6boZrUBsQcJN2vWTOzcuUfkyRMjIiNTdtt37gghkwmRL1/yrxsaGipu374tdu7cKWbNmiW6dOktnJzq6lb5AhA1atQQe/fuFWq1OmUBk+Dl5SXs7e0z7c+uVqsVjRo1EgBEs2atRI4cWjF4sBCZfWMA1rTMVc969eolAIjx48dLHcVg+IdEiWOeH8XhJx+EV1CEEEKIx48fi169egkzMzPd71DdunXF4cOHDWrMYyg0Go3YtWuXcHV11X2/CxQoIFavXi1eBYSJ454fxennASI40rDGyulpx44dAoCoWrWq1FEyDOtZ5qtpyRX3mqSjo6OIjo6WOo5einstTC6Xi127dqXrfb1580asWrVKNGvWLN6Y6HsXMzMz4ezsLOrUqSM6d+4sxo0bJ5YuXSo2bz4gqlY9IdatWyemT58u+vfvL1q0aCEqVKggcuXK9UO7QOXLl08MHTpUnDlzJt1WREVERIjq1asLAKJ06dJptqLLmCgUCt1KvX379kkdR1KpqWlcgZVKWq0WhQoVgp+fH/777z80btxY6kgGYevWrejTpw9iYmJQt25d7NmzBzlz5pQ6llESQmDq1KmYPn06AMDDwwPDhw+XOJV+ef78ue4ART+/N8ib11HiRBmPs/uSZuw17dSpU2jcuDEKFCgAHx8fmKTmBPNMYPbs2Zg0aRJMTEywd+9etGrVKt3vU6PR4PTp01i3bh0OHDigO0jY3NwSefM6fXNO4tfnJ2bPnj3BGX4DBsSeG/LkSfyPCyEQGBgILy8veHt7x3vr5eWFT58+JZhTJpOhTZs2GD16NGrWrJnm34c4kZGRqFGjBh49eoRff/0V+/fvz3SriletWoWBAwcie/bsePLkCTZtyo3x4wErK+DPP4Hx42PPR8tsWNMSZ2z17OrVq6hVqxZy584Nf3//HzrbKTMTQuDhu3D4h0VDBiC/gxXKOtnp/v/du3dYsmQJVq1apTvjo2TJkhgzZgy6dOkCi5QcxkE6Wq0W+/btw7Rp0/D48WMAQP78+TFp0iT06tUL5ubm8AqKxLOACMgAlMydBYWyp91MeUPWqlUrHDp0CIsXL8awYcOkjpMhWM+SZmw1LTEaDXDzZuw5fzlyxH5MCIHy5cvj4cOH6bLCyFhMnz4d7u7uMDMzw6FDh9C0afrvWBATE4M3b97A399f9/br9783nkqKXC6Ho6Mj8ubNm+Alf/78KFq0aLqu9FGpVGjdujWOHTuGAgUK4OrVq8ibN2+63Z+hGjhwIFatWoXcuUti6dJH+OUXE6ThAjiDkpqaxgZWGpg1axYmT56MihVb4s6dQ1LHMRi3bt1Cq1at8P79exQsWBCHDh1C2bJlpY5lVLRaLUaMGIGlS5dCLpdj3bp16Nmzp9Sx9IpSqUSNGjVw9+5dAL2we/d6tG8vdaqMx8FR0oy9pmm1WhQpUgSvXr1C587LMX78IPApOXETJ07EnDlzYG5ujiNHjqBRo0YZdt9BQUHYunUr1q9fj4cPH/7QdUxNTeHo6PhNkytLFids3ZobAwcGftOkioiI+O7tWVlZoXDhwihSpEi8t66ursiTJ09afakJEkKgc+fO2LFjB4oVK4abN28ia9as6Xqf+sbHxwdly5ZFZGQkdu7ciY4dOyI6GiheHHj3LrYhmdCWI5kBa1rijK2eCSHg6uoKT09P7Nu3D23atAEAREYCabxjqVGIUmpw0z8Eao2ARitga2GKGs4OkH/1Itfnz5+xdu1aeHh4wN/fHwDg5OSEYcOGoX///nBwcJAivsHSarXYv38/pk2bhkePHgEA8uXLp2tcfdkYfPguHP6hCshkQE5bC1TOlzXTbjckRGxN02qDUbiwIzQaDd6+fQtHx8wx4ZD1LGnGVtOSMmQIsHw5kC1b7N98xYsDkZFbsHt3dzg7O+P+/fuZ7m/iHyGEwJgxY7Bo0SJYWVnhv//+Q506daSOBYVC8d0ml0Kh+G6DKnfu3DA1NZUstxACPXv2xObNm5E9e3ZcvnwZJUqUkCyPvtqxYwd+//13mJub48CB6+jZswL++AOYMUPqZNLgFoIS8/R8JwCr/21fs0HqOAbl7du3okqVKgKAsLGxyfTLKdOSUqnUHXJrbm7O7+13jBs3TgAQWbK4CAuLMHHtmtSJpPHF87Xk20Do6yUz1LSdO3f+72fBXDg63pE6jt7TarVi6NChusNwpTqQ9fPnz+LFixfiwoULYvv27WLRokVi7NixomvXrqJBgwaiVKlSwsHB4Ye2mvj6kjVrVlG5cmXRqVMnMWnSJLF+/Xpx8eJF8fbtW0m3ol24cKEAYg+tfvLkiWQ5pKLRaETdunUFANGpU6d4/7djhxBeXhIF0xOsaZmvni1atEgAEM2bN9d9bNQoIdasEeLgQSG4c/b/8w+JEsc9P4rLPkHizIsAcfZloIiI+f7WQkqlUmzZskWUK1dO97tla2srRowYIXx9fTMwuWHSaDRi7969omzZsvG2dVqxYkWC231ptFpx2eeTOPHso+7xCY9On62fDMGYMULY2wthb79aABCNGzeWOlKGYj3LnDUtMYULCxHb2o29lCkjxIYNSlG+fAUBQLRv357HRXyHVqsVffr0EQCEnZ2duHOH492UinsdzdraWly/fl3qOHrpxYsXwtbWVgAQK1asEEII8fChEOm0m6NBSE1N4wqsNFKnzgZcvtwbVlZWuHnzJkqXLi11JIOhUCjQr18/bN26FQAwY8YMTJo0KdPOMksL0dHR6NSpEw4dOgQbGxscPHgQDRo0kDqW3jl79iwaNmwIuVyOS5cuoVq1Gshku0/pcHZf0jJLTevSZSC2bVsFB4fCePXqDmfwJUGr1aJv377YsGEDsmTJgrNnz6Jy5cpSx0pQdHQ0Pnz4gPfv3+Pdu3d4//697vLx40fkyJHjm9VU2bJl07t6fPbsWTRu3BgajQZ79+5F27ZtpY6U4Tw8PDBy5Ejkzp0bT548Qfbs2aWOpFdY0xJnjPUsKCgIefLkgUajga+vL/Lnz49atYCrV2NXYa1cCTRvDvBXBXjwv9U9OW3MoNQIKFQaVMibFTltE98aUAiB06dPY968eTh9+jQAwMTEBJ06dcLYsWNRvnz5DEhvOLRaLQ4ePIhp06bhwYMHAIC8efNi4sSJ6NOnz3e3YoxUqnHzdSiEELA0lSMsWo3STnbIb2+VkfH1xqFDQKtWgJXVT1AoLmLTpk3o3r271LEyDOtZ0oyxpiVmwwagd2/g559jt4pu3Dh2u2gvLy9UrFgRnz9/xrJlyzB48GCpo+oljUaDzp07Y9euXciRIwcuXryIkiVLSh3LoCxatAijR4+GqakpDh8+nCHbMRqa6OhoVK9eHQ8ePEDHjh2xY8cOvRtTS4ErsPREz549BQBRvHhx8fnzZ6njGBStVivmzp0r/vfHh+jYsaOITOmJ9JlcWFiYqFevngAgHBwcOBviOz59+iTy5s0rAIipU6dKHUdy4Ow+1rT/USgUonz58pzBlwxqtVp06tRJABDZsmUTjx49kjqS0Xr27JnIkSOHACAmTJggdRxJPHv2TFhaWgoA4uDBg1LH0UusaZmznnXs2PF/O2JMExqNEDY2QjdDffhwrsISQogopVqc9woUp54HiBt+weLaq0/iyJMPwjc4eeOue/fuiS5duggTExPd71vDhg3F9u3bhaenZ7odFq/v1Gq18PT0FJs2bdL9LQVA5MmTRyxbtkwoFIokbyMwIkacfBYgLnoHiRt+weLwkw/i/pvQDEivn5RKIbJnfy0ACEtLSxEWFiZ1pAzFepZ5a9r3XL4sxM2bCf/frl27dDvw3L59O2ODGZCYmBjRrFkz3fOzj4+P1JEMxr///qt7Xtq8ebPUcfTWgAEDBABRuHDhTFe3EpOamsYVWGkoKioK1apVw+PHj9G5c2f8+++/7LAm05EjR9C5c2d8/vwZFSpUwMGDB5E/f36pYxmMoKAgNGvWDLdv34aTkxNOnjzJ1YAJEEKgQ4cO2Lt3L2rUqIGLFy9Kun+wPuDsvqRlpprGGXzJp1Kp0LZtWxw5cgS5c+fGpUuXULRo0W8+T6kETE2RaVd7/oijR4ElSwBLS8DCIvatiYkCT578hbt350KjiUGTJk1w9OhRmJiYSB03Q6nVatSuXRs3btxAjx49sHHjRqkj6SXWtMQZaz07ffo0GjVqhAIFCuDECR+UKhX7/DBgALBiRewM9czubZgCj95/hq2FCcxNYgtRQIQShbNbo2TuLMm+vdevX8PDwwNr166Nd2aihYUFSpYsiTJlyqB06dK6t/ny5TOa8XFISAgePnyIhw8f4sGDB3j48CEeP34MhUKh+5w8efJgwoQJ6Nu3LywtLX/odl+HKPD4QzgcrMxgIpchPFoNMxMZqhZwgJVZ5qp5cerWnYdLl8ajY8eO2Llzp9RxMhTrWdKMtaal1JAhQ7B8+XIUKlQId+/e5W4a3xEVFYVmzZrh4sWLKFSoEC5fvgwnJyepY+m1AwcOoEOHDlCr1ViwYAFGjx4tdSS99OW5V9evX0eFChWkjqQ3UlPT2MBKY8+ePUPlypURGRmJVatWoX///lJHMjhPnjzBr7/+Ch8fH+TOnRtr1uxHsWI1wPMAv2/bNuD8+Te4eLExnj/3RKFChXDq1CkUKlRI6mh6acOGDejduzeyZMmC+/fv8/sEDo5+RGarabt27UKnTp1gbm6Oq1evolKlSlJH0nvR0dH45ZdfcPbsWTg5OWHgwOkoWrQ7Xrwwx5MnwOPHsQct793LF1ITExAAVKsG+PrGfeQwgOEAXgEAunXrhqVLl2bKAflff/2FCRMmIG/evHj8+DHs7e2ljqSXWNMSZ6z1TKvVokiRInj16hUmTDiBOXOaoGdPYN06ThqI8+X2gXG/J58ilchuY4Eq+bMmq7mk0QD79sVu8Xb8eChat/4HHz9ewKNHj+Dn55fgdbJmzRqvoRX3Nlu2bGny9aUHjUYDLy8vXZMq7u3r168T/PwCBQqgbNmysLJqivv3+6BPH0uMGQP86HyL5wEReBkUiVy25gAApVqLSKUGZfPYwcnux5pgxkSj0aB48bLw9n6KAwcOoFWrVlJHylCsZ0kz1pqWUtHR0ahVqxbu3r2Ldu3aYffu3UYzcSCthYeHo379+rhz5w4KFSqMQYPmY9So1vx+feXBgwdwc3PDiRMnAAADB47BihXzJU6ln16+fImKFSsiIiICK1aswMCBA6WOpFfYwNIz27ZtQ5cuXWBubo5r166hYsWKUkcyOJ8+fUKHDh1w7tw5AOYAVuP+/Z4oV07qZPrJweElQkMbAfBD6dKlcfLkSc4e+Y6XL1+iQoUKiIyMxObNm9GtWzepI+kFDo6Slhlr2uDBg7FixQq4uLjg7t27fLH8B0RERKB58+a4dOnS/z5SAIAbgN4ALFC9OnDtmnT5DMGrV0DsvAIfxDaujgAAypQpg+XLl6NOnToSppPOo0ePUKlSJahUKvz3339o3Lix1JH0Fmta4oy5ns2aNQuTJ09GiRLtUaHCbmzZ8uONA2OnUGlw83UIlBqBrJb/v/NAeLQa5iZyVC/oAHPT5HX6RowAFi8G/v479n3dbYaH48mTJ3j8+DEePXqkexsUFJTg7eTJkwelS5eO19QqVaoUrK2tU/CVJp9arUZ4eDjCwsLg7+8fr1n19aqqOJaWlihTpgzKli2LcuXKoWzZsihbtiwcHBwAxG5e2a0bUL48MGbMj2e57R+KgIgY5LAx/9/tCARGqFAouzVKOSZ/lZyhmzNnDiZOnIi8efPC29v7u2eHGSvWs6QZc01LKW9vb1SsWBHh4eFYunQphgwZInUkvRUUFIT69evj0aNHAICiRStj6dKZaNy4caZvZPn6+mLKlCnYunUrhBCQy7NAq52IdevGoXdvzgz6mkKhQI0aNfDgwQN06tQJ27dvz/Q/Q19jA0sPDRw4EKtWreKy3VRQqVQYNWoUli1bBgBo3LgNxowZiAYNGkDOaZQQQuDGjRtYvnw5duzYBbVaiUqVquHkyWN6PYtRSiqVCrVq1cKtW7fw+++/Y+vWrSwo/8PBUdIyY02Ljo5GzZo1ce/ePbRt2xZ79uzh78wP0Gg02LlzJ2bOnAlPT08AgEyWF0KMw5gx/TB/fuY8iP1HKRQKdOo0D4cPzwEQAzs7O0yfPh2DBw/OtNu9KpVKVK9eHffu3UP//v2xatUqqSPpNda0xBlzPXv79i0KFCgAmcwET58+Q7FiXGUf521YNB69D4+3fSAQ29hSawQq5beHvZVZsm4zNBTYtAkYNizplcVCCAQEBMRraD1+/BiPHz9GVFRUgtexsLCAra0tbG1tkSVLFt37Sf3bxMRE15D6kbffu/84+fPn1zWq4ppVRYsWTXIb28jIuG1wE//exFFptLjmF4IYlQZZv3gsQhUqWJmZonpBe5iZZJ5x8L1791C1alWo1WqcPHkSjRo1kjpShmM9S5ox17TU2L17Nzp27Ahzc3NcuXIFlStXljqS3oqJiUHVqmvx6NEsCPEBAFCnTh3MmjUrU06cCwoKwqxZs7BixQoolUqYmZlh8ODBcHWdiCtXcmLePCBnTqlT6p8BAwZg9erVKFKkCO7cuQM7OzupI+kdNrD0EF/0Sztr1qzBkCFDoFKpAAAuLi7o168fevXqBUdHR4nTZbyoqCjs2LEDy5cvx927dwHEPgl06NAB69atg62trcQJ9dfkyZMxa9YsFChQAA8ePOBqki9wcJS0zFrTvpzBt2TJEgwdOlTqSAZDq9Vi7969mDlzJh4+fAgAsLfPjcmTx2LAgAGwsbGROKH+OXLkCIYNG4ZXr/5/u8B58+Zlynr/JXd3d0yfPh3Ozs54+PAhsmTJfLPwk4M1LXHGXs86d+6M7du3o1y5crh69WqGreLRdw/fh+N1SBRy2pjHG5eqNVqExWhQzskOebJm/BZ1Wq0Wvr6+8RpbDx8+xosXz6HRqDMkg0wmg52d3f8mneZCw4bxV1Vl1OTA8GgVbvuHQi6Twdr8/7te0SoNYtQC5fPaIadt5liBpFAoULlyZTx9+hRDhw7FkiVLpI4kCdazpBl7TUuNuPOwuJtG0oQAAgOjsHHjMsydOxfBwcEAgCZNmmDWrFmZYjv9yMhIeHh4YN68eQgPD4dMJkOXLl0wffp0uLi4SB1Pr23fvh2dO3eGhYUFrl27xnOvvoMNLD315Yt+Hh4eGD58uNSRDNbbt2+xYcMGrF27VrffuKmpKX799Vf88ccfaNSokdGvyvLy8sKqVauwfv16hISEAACyZ8+OPn36YMCAASwoSbh48SLq1asHmUyG8+fPZ8qZNInh4Chpmbmm7dmzBx06dICZmRmuXr3KGXzJpNVqcejQIcyYMUM38SBHjhwYNWoUBg8ezNlZAF69eoXhw4fj8OHDAIDSpUtj+fLlqFu3rsTJpHfnzh1Uq1YNGo0G58+fx08//SR1JL3HmpY4Y69nYWFhqFKlCl6+fIkuXbpgy5YtmX4iYbRKgxuvQ6HUaJDVMv4qKyEEAiNVKJHLFkVy6MfEiv79AVdXgX79ohEREYGIiAh8/vxZ935i/377NgKhoWqUKpVV15RK7K2dnR1sbW0hk8kQGQnkyxe7la0Ur/N++ByNB2/DYfPVKjmtEAiKVKF4ThsUzZk5JiuOHDkSHh4eKFGiBO7cuZNpG9GsZ0kz9pqWGjwPK2XCwsLw999/Y9GiRfj8+TMAoG3btpg+fTpcXV0lTpf2VCoV1q9fj6lTp+LDh9gVaE2bNsWcOXNQvnx5acMZgBcvXqBSpUqIiIjAypUrMWDAAKkj6a1U1TQhRLpdAIjYu8i89u7dKwAIU1NTce3aNanjGDy1Wi2OHz8u2rRpI0xMTETcz5izs7OYOXOmePv2rdQR05RarRaHDx8WTZs21X2tAESVKlXExo0bRVRUlNQRDUJISIgoUKCAACAmTZokdRy99MXzdbrWBUO+ZPaaNmTIEAFAuLi4iJCQEKnjGCStViuOHj0qqlWrpvudc3BwENOmTcu031OFQiGmTZsmLC0tBQCRJUsWsWjRIqFUKqWOphcUCoVwdXUVAMTw4cOljmMwWNNYzx49eiRsbGwEALFkyRKp40jubahCHPf8KC55B4kbfsHfXI48+SDuvQmVOqYQQohDh4QAhFi9OmXXHzhQiJT+ue/pGXvf06al7Pqp5RMUKY48+SCu+3765jE65vlBXPUNFmqNVppwGej06dO611Bu3boldRxJsZ6xpqWWl5eXsLOzEwDE0qVLpY5jUIKCgsTYsWOFlZWVACBkMpno2rWrePnypdTR0oRWqxW7d+8WxYoV0z3XVK5cWZw5c0bqaAYjKipKlCtXTgAQnTp1Elqt8dfo1EhNTWMhyQAjRowQAESBAgVEUFCQ1HGMxrt378SsWbOEs7Oz7pfAxMREtG7dWhw7dkyo1WqpI6ZYUFCQmDt3rnBxcdF9bRYWFqJHjx7i5s2bUsczKFqtVvz222+6xh9fFE0YB0esaUmJjo4WlSpVEgBEmzZt+MdZKmi1WnHy5ElRu3Zt3e+enZ2dmDx5cqb6O+Ho0aOicOHCuu9Bly5dxLt376SOpVfGjRsnAIhixYqJyMhIqeMYDNY01jMhhNi5c6fuRfCLFy9KHUdSD9+FicNP3ifYGLnhFyz+e/ZRXPL5JHlz5MMHIXLmFAIQYtOm5F8/PFwIW1shmjVL2f2fOBF73/b2Qkgxr+Tx+zBx+MmHBB+jcy8DxekXASI40rjHMsHBwSJfvnwCgJgxY4bUcSTHesaalhZ2794tAAhzc/NM3xROiXfv3onBgwcLMzMz3euO/fr1E69fv5Y6WoqdO3dOVK1aVfccU6RIEbFr1y6O8ZOpf//+uu9fWFiY1HH0HhtYei4mJkY327p58+ZCo9FIHcmoaDQa8d9//4l27doJU1NT3S9EgQIFxPTp08WbN2+kjpigz5+F+Lo23Lx5U/To0UNYWFjEW102d+5cERgYKE1QA7d582YBQNjY2BjNTJn0wMERa9qP8Pb21s3g8/DwkDqOwdNqteLcuXOifv36ut9BW1tbMX78ePHx40ep4yVbTMyPfd6rV69Eq1atdF+zq6urOH/+fPqGM0BXr14VcrlcyOVyruJPJtY01rM4Y8aMEQBE7ty59XZMkN4USrW44BUkTj7/mGBj5IZfsDjzIkCcfREoPkerJMup1QrRooUQsSeRCLFjR/JvY8WK2OvmyvXtOOtHrF79//ef0auwtFqtuOobLI57Jvw4XX31SRx5+kH4BBn3ZIbOnTsLAKJ69epCpZLu51FfsJ6xpqUV7qaReq9evRK9evUScrlcN8l8xIgRej9u+3KM9uDBA9GsWTPdc0vu3LnFihUrONE7BbZt26b7Obh7967UcQxCamqacR8apCfMzc2xa9cuZMuWDceOHcP8+fOljmRU5HI5GjdujD179sDf3x9z5sxBoUKF8Pr1a/z5558oUKAAWrVqhaNHj0Kj0SA6GujTBxg1SrrMz58DhQsDtWrF7ku8adMmVK1aFVWrVsWmTZsQExODZs2a4fDhw/Dy8sK4ceOQI0cO6QIbKB8fHwwePBgAsHTpUhQpUkTiRESGrVChQli/fj0AYOzYsbh586bEiQybTCZDvXr1cObMGVy+fBlNmjRBREQE5s6dC2dnZ4waNQrv379P9DaEnmz3/+kTMGsW8L8jvhIUHR2NmTNnomTJkjh48CBsbW2xcOFC3Lt3j+c6fSUqKgo9evSAVqvF2LFjUb16dakjERmkOXPmoH79+vj48SPat2+PmJgYqSNluGCFCgqVBlamJt/9HDMTOVRaLaKUmgxMFt+NG4Cp6f//28IiedcXAli5Mvb9gAAgifKZID8/wMUFcHIClEpArU7+baRUjFoLpVoDM3nCx0KYyGWAAIKilHEv2hudHTt2YNu2bbC2tsbmzZth+uUPBBGlyoIFC1CxYkW8evUKffv2NdrnkfTk7OyM9evX4+nTp+jUqRNiYmLg4eGBQoUKYdKkSbqz6uNI/S3+/BnIkQMoXx7w9PRF9+7dUb58eRw/fhy2traYPn06vLy8MHDgQJiZmSV5e/T/Xrx4gT/++AMA4OHhgQoVKkicKBNISdfrRy/gTIh4jhw5oltueuHCBanjGDWNRiNOnTolOnToEG9VVr58+cTgwe4CeC0AIT59StntHzwoxLp1QqRkl0K1Wi1OnfogZLLbwsZmnMiePXu8s1BGjx4tvLy8UhaMdFQqlahRo4YAINq3b8+l0EkAZ/expiXDsGHDBABRsGBBERwcLHUco3Ljxg3RokWLeNvHDhky5LtbVMycKcTgwUJIfQRkv35CAEJUqZJwbTx27Fi87QI7d+5sdOdWpqW43zFXV1cRHR0tdRyDw5rGevalgIAAkT9/fgFADBw4UOo4GS6p7QNv+AWLa76fxJEnH8SrT9Ku7gkOFmLePCG2bBHi9OnkXffyZSFMTYVuBdWRI8m//9evY8eHoaHJv25qfYqMEaeeB4jzXoHffZxOvwgQZ18GinAJV8qllzdv3gh7e3sBQKxatUrqOHqD9Yw1LS19eR4Wz4dMvfv374uWLVvqfk+zZs0qZsyYIcLDw4UQseMjKU8A8fYWwsoqUFhYjBTm5uYCgDAzMxPDhw8XAQEB0gUzcDz3KuVSU9NksddPHzKZTMQ1ySjWhAkT8Ndff8HJyQn3799Hrly5pI5k9D5+/IhNmzZhzZo18Pb2BgDIZHKULFkPVarkh4ODQ7xLtmzZvvmYubm57vaiogBb29ih0aNHQOnSsR9XKBT48OED3r9//83bL98PCAiARhN/dmPFihUxePBg/Pbbb7C2ts6w740xmzZtGqZOnYq8efPi4cOHyJYtm9SR9JpMFjvbUwiR8LRPYk37QkxMDOrUqYNbt26hVatW2L9/v+5niNLGvXv3MHPmTOzbtw8AYGZmht69e8PNzQ3Ozs66z2veHDh+HJg4MXYFVEpcuRI7W93ZGUjJ5LEnT4AWLQBf39h/L18ODBoU+76vry9GjhyJAwcOAABKlSqF5cuXo169eikLmwmcP38eP//8M0xNTXHjxg1UrFhR6kgGhzUtcZmxnt2+fRu1a9dGTEwM1q9fj169ekkdKUNEqzW46ReKGLUGWa0Sn10dEKGESzYruDraZVC6xAkBJOdPi2fPAI0GmDMHaNkSsLICfv01/fKltbdhCjx8F46sVmYw/c4qLLVGi7BoNUo72SG/vVUGJ0w/Wq0WTZs2xalTp9C8eXMcOXKEf1f+D+tZ0jJjTUuNPXv2oEOHDjAzM8PVq1dRuXJlqSMZvOvXr2Py5Mk4c+YMACBHjhwYMWICVq9ugWPH8qN06ZQ9Xx8+DBQpApQo8WP1UKVS4fXr1/D29oaPjw88PT2xfv1GRESEAwC6dOmCGTNmwMXFJUV5KFb//v2xZs0aFC1aFLdv34adnX783WQIUlPT2MDKYGq1GvXr18elS5fQsGFDnDhxAiYm39/OgdKOVqvF+fPnsWbNGuzbtw8qleqHr2ttbR2vseXv7wCVyhIuLh8RGBjbmAoLC/vh28uePTucnJxQsWJFDBw4ENWqVeMf6Wno6tWrqFOnDoQQOHPmDH7++WepI+k9Do6SxpoW36tXr1ChQgWEhYVh0aJFGDlypNSRjNKjR48wa9Ys7Nq1C0IImJqaonv37pgwYQKKFCmCJk1it0m6dSv52y0BwObNwNixsQ2s//4DGjdOWc7oaGDTJmD+fCAwEHj4MAb//rsAs2bNgkKhgK2tLdzd3TF8+HBuUZGIz58/o2zZsvD19cXUqVPh7u4udSSDxJqWuMxaz9avX48+ffrAwsICly9fzhQv2r0Pj8bDd+GwNjeBhWnipwcERylhb2WOagXsOS6RwMvASDwPjEBOG7NEv/8BEUrkz2qJcnmzZmC69LV06VIMGzYM2bNnx+PHj+Ho6Ch1JL3Bepa0zFrTUmPo0KFYtmwZXFxccPfuXdjb20sdySicO3cOkyZNwrVr1+J9PEeOHMifPz8KFCiA/Pnzf/N+njx5EtwytXZtIHduYM+e/29ghYSEwMfHBz4+PrpGVdz7r1+/hlar/eZ2mjRpgjlz5nCbuzSwbds2dOnSBRYWFrh+/TrKly8vdSSDwgaWgXn37h3Kly+PwMBAvjghkcDAQFy5cgXBwcEICQlJ8PLl/329YiohZmZmcHR0hJOT0zdvv3w/d+7c8VZ0UdoKDw9HuXLl4Ovri3HjxmHu3LlSRzIIHBwljTXtW/v370fbtm1hamqKy5cvo1q1alJHMlqenp6YPXs2tm3bBq1WC7lcjs6dO8PHZxQWLCiOGjVStnpXpQIqVwYePgTOnAHq1//x62o0Grx58wbe3t66i5eXN+7e9ca7d16IifkMAPjtt9+wYMEC5M2bN0UZM5O4GX0VK1bE9evX2exLIda0xGXmejZgwACsXr0a+fPnx507d5AzZ06pI6Wrxx/C4RusSLIpAgDh0WqYmchQrYADLM04wTKj3X8bhrdh0chpm/g4MSxaDXMjepw8PT1RsWJFREdHY+/evWjbtq3UkfQK61nSMnNNS6mYmBjUrFkTd+/eRdu2bbFnzx5OXEgjQggcO3YMS5YswcuXL/HmzZskJ8/L5XLkyZPnm8bWP/9Y4uefX+H9+/9vVH19ztaXZDIZ8uXLh0KFCqFQoUIoXLgw6tatizp16qT1l5kpPX/+HJUrV0ZERARWrVqF/v37Sx3J4LCBZYBOnz6Nxv+b5nzy5Ek0bNhQ4kT0PUIIREREfNPYUigUyJ07t64x5eDgALk88ZmNlP66d++OLVu2oGLFirh27RqbhT+Ig6OksaYlbOTIkfDw8ECBAgVw7949bteZzry8vDB79mxs2bIF6i9Ol7ezs0OePHl0kya+d7Gzs/tmgHrzJlC9OnD+PFC3bvz7UygUePXqVbwmVdzF19cXSqXyu1ldXV2xZMkS1E9OVywT+++//9C0aVOYm5vjzp07KB23RzElG2ta4jJzPYuJiUG9evVw/fp1NGjQACdOnEhw1rMxiFFrcMMvFNFqDeyT2D4QABQqDVQagYr5siKbNf9+zkgarcA1vxBEKdVJPlYxai2ilBqUzWMHJzvLDEqYPpRKJWrUqIG7d++iZ8+e2LBhg9SR9A7rWdIyc01LDW9vb1SsWBHh4eFYsmQJhg4dKnUko6TVavHx40f4+/vj9evX8d7Gvf/hw4cfvj1ra2sULlw4XpMq7n1nZ2dYpGRLDkqSQqFA9erV8fDhQ/z222/Ytm0bm74pwAaWgYo7oydnzpy4f/8+8uTJI3UkIoO2fft2dO7cGVZWVrh37x6KFy8udSSDwcFR0ljTEqZUKlGnTh3cvHkTLVu2xMGDB/nHXAbw9fXFX3/9hRMnTuD9+/eJNpK+ZGVlFa+hFdf0OnvWCTVrmsHU1Cdek+rdu3eJ3p6joyMKFy6sGzzFvV+4cGHkzJmTPws/KDQ0FKVLl8bbt2/x119/Yfz48VJHMmisaYnL7PXs7du3qFixIgICAox6tX5ytg8EALVWIEyhQhknO+QzovOVDEFEjBq3/EMhhICtReINVSEEAiNUKJTdGqUcs2RQwvQxZcoUzJw5EwULFsTDhw95jkgCWM+SltlrWmrwPCz9oFQq8fbt228aXNHR0XBxcYnXqMqVKxfHVxLguVdpgw0sA6XRaNC0aVOcPn0aderUwdmzZ412BiBRevPz80O5cuUQFhaG1atX448//pA6kkHh4ChprGnf5+vriwoVKiA0NBQLFizA6NGjpY6UqQghEBwcjPfv3yd5iYyMTNZtm5qawtnZOcEmVaFChWBjY5NOX1Xm0qNHD2zevBnVq1fH5cuXeT5qKrGmJY71DLh48SLq168PjUaDXbt2oUOHDlJHSnPJ2T4Q+P/GSLFcNiiW0zYDElKcwIgY3HsbBiuzH2s2hihUsDE3RbUC9jAzMcwdQK5fv45atWpBCIHz58+j7tdLwAkA69mPYE1LHZ6HRZQ4nnuVdtjAMmABAQEoX7483r9/Dzc3N8yZM0fqSEQGR6PR4Oeff8alS5fQqlUr7N+/n7NSkomDo6SxpiXu4MGDaN26NUxNTXHx4kXUqFFD6kiUgM+fP3/T1Hr37h3ev3+PmJgYuLi4xFtFlT9/fk6uSWdxvzuWlpa4f/8+Vw+nAda0xLGexVq8eDFGjBgBGxsb3LhxA66urlJHSjMxai1u+IX88PaBcYIilXDMYoGK+ezTLxx9wy8kCo/ff0Z2azPI5Uk/bSlUGig1AuXz2CGnreFtFxUREYEKFSrAy8vLqFdBpgXWs6SxpqVOTEwMatWqhTt37vA8LKKv8NyrtMUGloH7cgbgkSNH8Msvv0gdicigzJo1C5MnT4ajoyMePXqEHDlySB3J4HBwlDTWtKSNHj0aixYtQv78+XHv3j1kz55d6khEei0oKAiurq4ICAiAh4cHhg8fLnUko8CaljjWs1hCCHTr1g1bt25F0aJFcevWLWTNmlXqWGniQ3g0HrwPh/UPruiJE6pQwcrMBDWcHWDKs30zzLOACHgFRSKX7Y+dPaYVAkGRKhTPaYOiBrhabsCAAVi9ejXKli2Lmzdv8syWRLCeJY01LfV8fHxQoUIFnodF9AWee5X2UlPT+FepHqhbty5mzpwJAOjWrRv8/PwkTkRkOG7evAl3d3cAwObNm9m8IpLQnDlzUK1aNfj7+6NHjx7QarVSRyLSa4MHD0ZAQAB++uknvlhAlMFkMhnWrFmDcuXK4eXLl+jevbvR1K1PUUqo1ALmJsl7fcDMRA6VRosopSadktHXhBD4HK1Gch4quUwGEzkQFKWCRmtYL9ofPXoUq1evhrm5Of799182r4j0QKFChbB+/XoAsRMSb9++LXEiIukNHz4cDx8+RNGiRbF69Wo2ryTGBpaeGDduHJo3b46QkBB06tTphw9jJ8rMIiIi0LlzZ2g0GowaNQqNGjWSOhJRpmZubo6dO3fCwcEBR48excKFC6WORKS3du7ciV27dsHGxgYbNmyAnKsdiDKctbU19u3bB3t7exw6dAizZs2SOlKqKdVafIpSwdxUluwXW8xMZFBpBKJUxtHIMwQqjYBCpYFpMs+ysjQ1QZRSjc8x6nRKlvYCAwPRp08fAMDs2bNRpkwZiRMRUZx27dph6NChUKlU6NixI0JDQ6WORCSZrVu3Yu3atbCwsMCuXbtgZ2cndaRMjyNlPSGXy7F582bkz58fN27cwPjx46WORKT3hg8fDm9vb5QtWxazZ8+WOg4RAShYsCA2bdoEAJgwYQKuXr0qcSIi/fPhwwcMGjQIALBw4UK4uLhInIgo8ypUqJBuWxh3d3ccO3ZM6kipEqxQIlqlgZWZSbKvayaXQaUVXIGVgRQqDVRaLcx+4OyrL1mayhGt1iIkSpVOydKWEAJ//PEHPn78iHr16mHkyJFSRyKir8yfPx+VKlXCq1ev0Lt3b27LSJnS8+fPdWddLVmyBOXLl5c2EAFgA0uvZM+eHbt27YKpqSk8PDywb98+qSMR6a09e/Zg/fr1sLS0xLZt27j9BJEeadmyJcaMGQONRoNOnTohKChI6khEekMIgf79+yM4OBiNGzfGH3/8IXUkokyvWbNmmD59OoQQ6NKlC7y9vaWOlGLBkSrEpGD7QCB2W0UZgCil4azqMXRRKg1UGgGzZD5eJnIZIGK3izSEF5k3bdqEAwcOwM7ODhs3buSqYyI99OVqk/3792Pp0qVSRyLKUAqFAh06dEBkZCR+//139OvXT+pI9D/8q0HPVK9eHfPnzwcA9OrVy6AHT0Tpxd/fX/eC34IFC+Dq6ipxIiL62uzZs1G9enW8efOG52ERfWHlypU4dOgQsmbNin/++Yf7qRPpiYkTJ6JVq1YIDQ1FmzZtEBkZKXWkZIvdPlAJixRsHxjHRC5DeIzaIJoixkCh0kCjFTBN5gosALA0k+NzjBqRer5i7tWrVxg2bBgAYOnSpShYsKDEiYjoe748D2vMmDG4deuWxImIMoYQAkOHDsWjR4947pUeYgNLDw0fPhxt2rRBeHg4OnTogOjoaKkjEemNoKAgtG/fHiEhIfjll190WzARkX4xMzPDzp07kS1bNhw7dkw3OYMoM1u3bh0GDx4MAKhdeykCAvKDrxET6Qe5XI5NmzahWLFiePToEfr162dwTZxghQoKlQaWKdg+MI6ZiQxKtRYKnoOVISKVGgggRS+SWZnKEa3SIEShv9sIajQa9OjRA58/f0a7du3QrVs3qSMRURJ4HhZlNmq1GgMGDMC6detgZmaBtWt3I0uWLFLHoi+wgaWHZDIZ1q9fj0KFCuHevXsYNGgQNBr9nlVFlBFevnyJGjVq4ObNm8iVqwCWLl3PGRFEeqxAgQK687AmTZqEy5cvS5yISDrr16/XbUMxf/58VK/eDZUrA8WKAfPnA1ykSCS9rFmzYv/+/bCxscH27duxePFiqSMlS3CkEkqNgEUKtg+ME3cOlkLF8Wd6E0Lgc4w6RauvAMDURA6NAIL1+ByshQsX4tKlS3B0dMSqVas4diPSQx8/Avv2AVevAj4+QFRU7N+qlStXhq+vL3r06AGVSn+fZ4hSIyoqCm3btsWaNWtgaWmJP//cgaZNy6FKFWDSJCAkROqEBLCBpbfs7e2xe/dumJlZYMOGDWjVqhXCw8OljkUkmUuXLqF69erw8vJCnjwVERBwDe7uuaSORURJaNGiBcaOHQuNRoOWLX/D+fO+UkciynAbNmxA3759IYTAvHnzMGbMGEycCDRvDnh5AblzAzwOhEg/lCpVChs3bgQQu33ShQsX4v2/vi7Kit0+UAVzk5RvHwgAZiZyqDRaRLGBle6i1Voo1dpkn3/1JQtTGUIVKkTryeMVEQH4+QGfPgEPHjzA5MmTAcRO4siRI4fE6YgoITlzAsuWAbVqAYULAzY2QK5cFggP3wlr66w4dOgQWrVqZZBb6xIlJjAwEPXr18fhw4fh4OCA06dPY/Lk1li/Hrh9G7h+HXBwkDolAWxg6TUbm4pQqY4DyIajR4+ievXqePnypdSxiDLctm3b0LBhQwQHB6NFixaYO/cCbG3zoGRJqZMR0Y+YNWsWnJ1rITT0LZo0qYn79+9LHYkow2zcuBF9+vSBEAJz587F2LFjAcQ2rLZsARYuBH77TeKQRBRP+/btMX78eGg0GnTs2BFv3rzR/d/Zs8DGjcC1a9LlS0iIQgWFSg1Ls9QN8U3kMmgFEKXn5yoZgyilBiqtgFkKV2ABgKWpid5sIxgaCtSrBxQpAvj5RaNr165QqVQYOHAgmjVrJnU8IvoOpRK4dOn//y2TAS1bAsePF8K5cyeRI0cOHD9+HD///DMCAwOlC0qUhnx8fFCrVi3cuHEDBQsWxJUrV1CrVi0AwO+/A/PmATt3ShySdNjA0mOOjkCzZj+jZctbcHV1haenJ6pWrYpTp05JHY0oQwghMGvWLHTp0gVKpRJDhgzBgQMH0LWrLUJCgAkTpE5IRD/CzMwMW7ceha1tPSiV71G3bl2cOXNG6lhE6W7Tpk3o3bs3hBD466+/MG7cuHj/ny0bMHIkYG4uUUAi+q6ZM2eiYcOGCAgIQPv27RETEwMA8PQEevWKbTxv2SJxyC8ERykRoxGwMEn9EF8uAz7HqNMgFSVGodJArdHCLBWPmbmJDEqNQIgebCNobw80aQKo1cDatZPx+PFjFC1alOegEuk5S0tgzpzY95s3B+7dA/79FyhUCKhatSquXLkCZ2dn3Lp1CzVr1oSPj4+0gYlS6fbt26hRowZevnyJ8uXL49q1ayj51Qz5sWMBLhzWH2xg6bGsWYFjx4BDhwrh2rVr+PXXXxEaGopmzZphyZIlBneoMFFyqFQq9O3bF5MnT4ZMJsPff/+NJUuWwMQk9lBqU1OJAxJRstSsmRVBQSfQqVMnfP78Gc2aNcP27duljkWUbjZv3oxevXpBCIE5c+Zg/PjxCX4ejwMh0k+mpqbYvn07ChQogBs3bmDYsGEAYs8HAWIbz/qyqESpSZvtA+OYmcgQpdRAqeHhfOkpSqWBRhvbMEwpmUwGc1MZghUqqPTg8Ro/HrC3P4fVqxfBxMQEW7ZsgY2NjdSxiCgJZcsCFy4AR48C5crF/79ixYrh2rVrqFChAry8vFCjRg3cvXtXmqBEqXT8+HHUq1cPAQEBaNiwIS5cuAAnJyepY1ES2MAyEFmyZMH+/fsxadIkaDQaDB8+HP369dPNBCQyJmFhYWjevDnWr18PKysr7N27FyNGjOChv0QGzsLCAtu2bcPIkSOhUqnQuXNnLFy4UOpYRGluy5Yt6NmzJ4QQmD17Ntzc3KSOREQpkCNHDuzbtw8WFhZYs2YN/vnnH/j4ANmzx0401JeZuaFRKkQp1bAyTZvhvZlcDpVWCwW3EUxXEUo1ZDKkeoxjaSqHQqVBqB5sI/jmzVPI5d0hhMDkyZNRrVo1qSMR0Q9o3BioW/f7/+/o6Ijz58/rVib/9NNP3B2KDM6GDRvQsmVLREZGomvXrjh69Cjs7OykjkU/gA0sAyKXyzFz5kzs2LEDVlZWWLduHRo0aICPHz9KHY0ozfj5+aFWrVo4ffo0cuXKhfPnz6NNmzZSxyKiNCKXy7Fo0SIsWLAAADBmzBiMGjUKWq30s4aJ0sK///6LHj166LbBncD9bokMWqVKlbBq1SoAwODBg/HixU0cPAgULSpxsC98ilJCqRGwSKsGlokMKo1AlIoNrPSi0QpExmhgZpL6CXoWpnLEqLUIVUi77ePBgwdRrVo1BAe/Qc2aNTFp0iRJ8xBR2rKzs8PRo0fRuXNnREREoHnz5ti6davUsYiSJITAjBkz0Lt3b2g0Gri5uWHz5s0w5z7uBoMNLAPUqVMnXLp0Cfny5cOVK1dQpUoVLt8lo3Dnzh1Ur14dT548QcmSJXHjxg1UrVpV6lhElA5Gjx6Nbdu2wczMDH///Tc6d+7MVcVk8LZu3aprXs2cORMTJ06UOhIRpYGePXti0KBBUCqVeP++JWJizkodSUf1v+0DzeRps30gAJjKZVBr2cBKT1EqDVQakarzr+LIZTKYyGIbmVoJjhnQarWYNm0aWrdujYiICHTq1AknT56EmZlZhmchovRlbm6OLVu2YPTo0VCr1ejatSsWLFjAI05Ib6nVavTv3x9//vknZDIZli1bhjlz5nCHJwPDBpaBqlSpEm7duoUaNWrA398ftWvXxq5du6SORZRihw4dQt26dfHhwwfUr18fV69ehbOzs9SxiCgd/f777zh+/DiyZMmCnTt3omnTpggLC5M6FlGKbNu2Dd27d4dWq8WMGTM485zIyPz9999o1KgRQkNjz0xwd3eHRiN9gydEoYJCqYGVWdoN7WUyGWQAImOk//qMlUKpgVqrhWlqDsD6gqWZCSKVaoRHZ+wqrM+fP6Ndu3aYOnUqZDIZ/vrrL2zfvp3nXhEZMblcjgULFui2gh87dixGjx7NHTVI70RGRqJNmzZYu3YtLC0tsXfvXgwePFjqWJQCbGAZMEdHR5w7dw69evWCQqFAp06dMGXKFBYNMjhLlixB69atERUVhR49euD48eOwt7eXOhYRZYAGDRrg4sWLun3V69Spg7dv30odiyhZtm3bhm7dukGr1WL69OmYPHmy1JGIKI2Zm5vj2LFj+PPPPwEA06dPR4MGDSSvWcGRSsSotWm2fWAcUxMZPseoJVnRkxn8/wqsNGpgmcoRrdYiJAPPwfLy8kL16tVx4MABZM2aFUePHsX48eM5q50okxg1alS8HTW6dOnCHTVIbwQGBqJBgwY4cuQIHBwccPr0aR5PYsDYwDJwFhYWWLduHTw8PHRnZLVt2xafP3+WOhpRkjQaDUaMGIHhw4dDCIHp06djw4YN3IeWKJMpX748rl27huLFi+PRo0eoWbMmPD09pY5F9EO2b9+ua15NnToVU6ZMkToSEaUTU1NTTJs2DadOnYKjoyMuXLiA8uXL4/jx45Lk0W0faJJ22wfGMZPLoNRooeA2gukiSqWBQOz2f2nBRC4DBPApUpkhW3n9999/qFKlCp4+fYqSJUvi1q1baNasWbrfLxHply931NixYweaNWvGHTVIct7e3qhZsyZu3LiBggUL4sqVK6hVq5bUsSgV2MAyAjKZDMOHD9etWjl48CBq1qyJV69eSR2N6LsiIyPRrl07LF68GGZmZtiyZQumTJnCGXtEmZSzszOuXLmCGjVq4PXr16hVqxauXLkidSyiRO3YsQNdu3aFVquFu7s73N3dpY5ERBmgQYMGuH//Pho1aoSgoCA0b94c48ePh0qVcatfACBUoUJUGm8fGMfMRA6VRosoJRtYaU0IgYgYNdLg+Kt4LM3k+ByjRmQ6PmZCCMybNw/NmzdHaGgofv31V1y/fh1FixZNt/skIv325Y4a586dw08//YT3799LHYsyqdu3b6NmzZrw8vLSTZQtWbKk1LEoldjAMiKNGzfGzZs3UaJECTx+/BhVqlTB+fPnpY5F9I0PHz6gXr16OHjwIBwcHHDq1Cl07dpV6lhEJLHs2bPj9OnT+PXXXxESEoKGDRviwIEDUsciStDOnTvRpUsXaLVa/Pnnn5g6darUkYgoA+XOnRsnTpzA7NmzYWJignnz5qFu3brw8/PLsAyfopSI0WjSfPtAIHYFlkojEMUVWGlOqRFQqLQwk6ft42ZpKke0SpNu2whGRUWhS5cuGD9+vG7ixv79+2FnZ5cu90dEhqN8+fK4evUqihUrhgcPHqBGjRp4/vy51LEokzl+/Djq1auHgIAANGrUCBcuXICTk5PUsSgNsIFlZIoWLYrr16+jefPm+PTpExo1aoSVK1dKHYtI58mTJ6hevTpu374NFxcXXL16FT/99JPUsYhIT1hbW2Pv3r3o378/oqOj0a5dO9Yx0ju7du3SNa+mTJnC5hVRJiWXyzFhwgScP38e+fLlw/Xr11G+fPkMmXyh1mjxKVIFU7k8XXYwkMtlEBBcgZUOFCoN1Bptmp1/FcfMRA6NAEKi0r6B5efnh1q1amH79u2wtbXFvn37MHXqVMjTuAlHRIbLxcUFV65cQbVq1XTPGdevX5c6FmUSGzZsQMuWLREZGYlu3brhyJEjnGBhRPjXhhHKmjUrDh06hHHjxkGtVmPQoEEYOHAglEql1NEokztz5gxq1aoFPz8/VKtWDdevX0eJEiWkjkVEesbU1BQrV67E9OnTodVqMWjQIEyePDlDznQgSsru3bvRuXNnaDQaTJ48GdOmTeP2t0SZXO3atXH//n20aNECoaGhaNOmDYYPH56uh9mHKFSIUqXP9oFxTGQyfI5Rs/6msSiVBiqtgKk87WuHhakMoQoVotNw5dz58+dRuXJl3L9/H0WKFMH169fRpk2bNLt9IjIeOXLkwJkzZ/DLL7/g06dPqF+/Po4cOSJ1LDJiQgjMmDEDvXv3hkajwYQJE7Bp0yaYm5tLHY3SEBtYRsrExARz587Fli1bYGFhgVWrVqFx48YIDAyUOhplUhs2bEDTpk0RFhaGdu3a4dy5c8iVK5fUsYhIT8lkMkyZMgXr1q2DiYkJZs2ahd69e2f4+SJEX9qzZw9+//13aDQaTJo0CdOnT2fziogAxG6De+jQISxatAhmZmZYsmSJ7gyG9BAcpUSMWgvLdNg+MI6ZiRwKlRZKDRtYaUmh1ECdTg0sS1MTKFQahKbBNoJCCCxbtgwNGzZEUFAQmjRpgps3b8LV1TUNkhKRsbKxscGBAwfQu3dvKBQKtGrVCv/884/UscgIqdVq9O/fH3/++SdkMhmWL1+O2bNnc3xmhNjAMnJdu3bFxYsX4eTkhAsXLqBq1ap4+PCh1LEoExFCYMqUKejduzfUajXGjBmDXbt2wcrKSupoRGQAevfujYMHD8La2hobN25Eq1atEBERIXUsyoT27t2L3377DRqNBhMnTsSMGTM4OCKieGQyGUaOHInLly/DxcUFd+/eRcWKFbFjx440vZ//3z5Qlq7PQ2YmMqg0Wm4jmMYilRrIgHR57MxNZFBpRarPwYqOjkafPn0wdOhQaDQajBs3DkePHoWDg0MaJSUiY2Zqaop//vkHkydPhlarRb9+/TBjxgyu6KU0ExkZiTZt2mDt2rWwtLTE3r17MWjQIKljUTphAysTqFq1Km7fvo0qVarA19cXNWvWxP79+6WORZlATEwMunbtipkzZ0Iul2PlypWYP38+90onomT55ZdfcO7cOeTIkQPHjx/Hzz//jICAAKljUSayb98+XfNqwoQJmDlzJptXRPRdVatWxd27d9G+fXt8/vwZv//+O/r37w+FQpEmtx+iUCEynbcPBAAzeWwzRJGG29Fldloh8DlGnS6rr4DYppiZiQyfolRQabQpuo13796hXr162LBhA6ysrLBt2zbMnTsXJiYmaZyWiIyZTCbDjBkzsHLlSsjlcvz5558YOHAgNBrWFEqdwMBANGjQAEeOHEG2bNlw5swZbm1r5PgqciaRJ08eXLhwAV27dkVkZCTatm2rO1uEKD18+vQJjRo1wrZt22Bra4sjR45gwIABUsciIgNVtWpVXL16FS4uLrh9+zZq1qwJb29vqWNRJrB//3506tQJarUabm5umDVrFptXRJQke3t77Nq1CytXroSFhQXWrFmDqlWrwtPTM9W3HRylQoxaC4t03D4QAEzkMmi0ApFsYKWZaJUWKo0WZibpV0csTeVQqDQIi1Yn+7rXrl1DpUqVcOPGDRQoUABXrlzB77//ng4piSizGDBgAPbs2QMLCwusXr0a7du3T7MJHZT5eHt7o2bNmrhx4wYKFiyIK1euoGbNmlLHonTGBlYmYmVlhc2bN2P+/PmQyWRwd3dHp06dEBkZKXU0MjJxBeXSpUvIkycPLl26hGbNmkkdi4gMXNGiRXHt2jVUrFgR3t7eqFGjBm7fvi11LDJi+/fvR8eOHaFWqzF+/HjuqU5EySKTyTBgwADcuHEDxYoVw+PHj1G5cmVs2rQpxbep1mrxKUoJU7kM8nR+PpLJZJABiIxJfiOEEhal0kClETBNxwaWhakcMWptsrcRXLduHerVq4cPHz6gXr16uH37NipUqJBOKYkoM2nTpg1Onz4Ne3t7HDhwAI0aNUJwcLDUscjAxE1k9fLyQvny5XHt2jWUKFFC6liUAdjAymRkMhnGjBmDI0eOwM7ODnv27EGtWrXg5+cndTQyEteuXUP16tXx4sULlC1bFjdu3ED58uWljkVERiJ37tw4f/48mjRpgsDAQNSrVw8nTpyQOhYZoQMHDuiaV+PGjcOcOXPYvCKiFClXrhzu3LmDrl27IioqCj179kT37t1TdKZjqEKNSGX6bx8Yx8xEhgilBhotzy1JCwqVBiqtFmbpuKW6XCaDiQz4FKmE9gfOm1EqlRg8eDD69u0LpVKJoUOH4uTJk8iZM2e6ZSSizKd27dq4fPky8uXLhytXrqB27dp4/fq11LHIQBw/fhz16tVDQEAAGjVqhAsXLsDJyUnqWJRB2MDKpJo3b44bN26gaNGiePDgAapUqYLLly9LHYsM3O7du/Hzzz8jKCgITZs2xaVLl5AvXz6pYxGRkcmSJQsOHz6M7t27IzIyEi1btkzVbHairx08eBAdOnSAWq3G2LFj8ddff7F5RUSpYmtri82bN2PDhg2wtrbGli1bULlyZTx48CBZtxMcqcyQ7QPjmJnIoFRrEcVtBNNElFIDrYjdnjE9WZqZIFKpRngS2wgGBASgYcOGWLFiBczNzbF+/XosWbIEZmZm6ZqPiDInV1dXXLt2DaVLl4anpydq1qyJR48eSR2L9Nz69evRsmVLREZGolu3brpFGZR5sIGViZUoUQI3btxA48aNERgYiPr16+Off/6ROhYZICEE5s2bh44dOyImJgb9+/fH4cOHWVCIKN2YmZlh48aNmDBhAtRqNXr27InZs2dD/MBMY6LEHDp0SNe8Gj16NObOncvmFRGlCZlMhp49e+LWrVsoXbo0nj9/jmrVqmHVqlU/VL80WoGgDNo+MI6pXA61VguFkg2stBChVCOde1cAYs/BilFrEZrINoJ37txB5cqVcenSJTg5OeHChQvo1atX+ocjokwtX758uHjxIurUqYO3b9+iTp06uHDhgtSxSA8JITB9+nT06dMHGo0GEyZMwKZNm2Bubi51NMpgbGBlcg4ODjh69ChGjRoFlUqFfv36YdiwYVCpkrdfNmVer169QocOHTB+/HgAwLx587By5UqYmppKnIyIjJ1MJsPs2bOxdOlSyGQyTJo0CUOGDIFGwxfZKGUOHz6M9u3bQ6VSYdSoUbpzQ4mI0lKpUqVw48YN9OvXDzExMRg4cCA6deqEsLCwRK8XqlAhKgO3DwRiV2CpNIIrsNKAWqNFlFIDswzoYJnIZYCI3UYwoebo1q1bUbt2bfj7+6N69eq4c+cOqlevnu65iIiA2NciT548iXbt2iEsLAyNGzfGnj17pI5FeuTdu3f4/fff4e7uDrlcjuXLl/M84kyMDSyCqakpFi5ciPXr18Pc3BxLly7FTz/9hEuXLkkdjfRYaGgoxo4dixIlSmDv3r2wsrLCrl27MHbsWBYUIspQQ4YMwe7du2FhYYEVK1agQ4cOUCgUUsciA3P48GG0a9cOKpUKI0eOxIIFC1jPiCjdWFtbY82aNdi2bRtsbW2xe/duVKxYEbdu3frudT5FKRGdgdsHArHnKQkAkcrEt6KjpEWpNFBptDA1yZjHz8JMjvCY2DPT4qjVaowZMwZdu3ZFdHQ0+vTpg/Pnz/McESLKcJaWlti5cycGDx4MpVKJjh07YsGCBVCrWW8ys5iYGPz1118oVqwYdu7cCXNzS3TtuhdNmw6SOhpJiA0s0unVqxfOnz8PR0dHXLt2DXXr1kWjRo1w7do1qaORHlGpVFi2bBmKFCmCBQsWQKlUokOHbvjtt+fIl6+D1PGIKJNq164dTp06BXt7e+zfvx+NGjVCcHCw1LHIAGg0GsydO1fXvOrSZQTGj1/I5hURZYjff/8dd+/eRYUKFeDj44NatWrBw8Pjm1UzGq1AUKQSpnJk2PaBcUzlwOcYDbfpTSWFSguVRsDMJGMeP0tTOR57avHPltjdVYKDg9G8eXMsXLgQpqamWL58OdauXQsLC4sMyUNEFOfpU2DXLmDzZhMUL74UTZrEbgU/duxYlC9fHsePH2fNyWSEEDh06BBcXV0xYcIEREZGonXr1njy5Ak+fWqNwoWB+vWBwECpk5IU2MCieBwcaiBbNk/UqOEOOzs7nD59GjVr1kSzZs1w8+ZNqeORhIQQOHz4MMqUKYOhQ4fi06dPqFu3Lm7duoVSpTZjw4b8mDZN6pRElJnVqVMHly9fRr58+XDlyhWUKVMbBQq8xuHDUicjfeXr64uff/4Zbm5uUKlUaNduPC5cWARvbzaviCjjFC1aFNeuXcPQoUN1q0Bbt24dbyJGSFTs9oGWpiYZns9MLke0WoMYtTbD79uYRKk0UGsFTDPiECwAkZ9leOIpsP+4CidOPEKVKlVw6tQp5MyZE2fOnMGgQYM4WYOIJKHVAl26AL17A8OGyVCq1ATs3LkPzs7OePLkCZo3b44mTZrgwYMHUkelDODp6YmmTZuiVatW8Pb2RsmSJXHy5Ens378fRYoUwr//AkWLAi4uQM6cUqclKbCBRfE8egQ8fWqPT5+m4tWrV5g0aRJsbW1x4sQJVKtWDS1btsTdu3eljkkZ7N69e2jQoAF+/fVXPH/+HEWKFMH+/ftx/vx5VK5cGR06AO3bA6NHS52UiDI7V1dXXLt2DaVLl8a7d57w96+BLVt4KDDFJ4TAli1bUK5cOVy6dAmOjo44fvw49uz5C56eMlSpInVCIspsLCwssGTJEuzbtw/29vY4dOgQypcvjytXrgAAFi5X4eHTjN0+MI6piQxqnoOValFKNQQybgWdvb0MhQrIkC/vfrRvXwM+Pj6oWLEibt++jbp162ZIBiKihOTKBRQuHNuMOHYMWLQI6NixDZ49e4b58+cja9asOHXqFCpUqIDevXvj7du3UkemdBAaGoqRI0eibNmyOHnyJLJmzQoPDw88ePAAjRo10n2evT1w4ACwerVkUUlisvRckimTyQQALvs0IEIA27cDVarEdrcBICgoCAsXLsSSJUsQFRUFAGjdujWmTp2KcuXKSZiW0tvbt28xefJkbNq0CUIIODg4wN3dHQMHDoS5ubnU8SgNxc2+FEJwGuZ3sKYZltDQULRo0RpXrsQ2rzp16oQFCxYgX758EicjqQUHB2PgwIHYtWsXAKBNmzZYs2YNcuTIIXEySiusaYljPdN/vr6++O2333Djxg2YmJhg+owZeKPqh5IVNKhWLuP/BldrBcIUKpRxskM+e6sMv39jIITAjdehCFUokc06/R9DIQTuXLuCdUsX4u7V2L+FOnfujLVr18La2jrd75/SButZ0ljTDNf+/UCNGoCj47f/9+nTJ8yYMQPLly+HWq2GtbU1xowZg7Fjx8LW1jbjw1Ka0mg02LBhAyZOnIjAwEDIZDL069cPM2fORE4usTJaqalpbGDRDwsICMD8+fOxfPlyKBQKAED79u3h7u6O0qVLS5yO0lJkZCTmz5+P+fPnIyoqCmZmZhgyZAgmT56MbNmySR2P0gEHR0ljTTM8MTExmD9/PmbPng2FQgFra2tMmjQJo0eP5nkPmdTp06fRs2dPvH37Fra2tliyZAl69uzJLZSMDGta4ljPDINKpcKkSZMwf/58AECR0nUxZMJYVK9VM8Ofs4QQCIxQoVguGxTLyRcOUyJarcENvxCoNAJ2lqbpdj9CCFw+ewobli3Ck7uxRwBY29hi8p/ucBs7mvXOwLCeJY01zbi9fPkSbm5u2LdvHwAgd+7cmDFjBnr37g0Tk4zfUpdS78qVKxg2bJhud6/atWtj8eLFqFixosTJKL2xgUUZ6sOHD5g7dy5WrlyJmJgYyGQydOzYEe7u7ihZsqTU8SgVNBoNNm/ejMmTJ+Pdu3cAgLZt22Lu3LkoUqSIxOkoPXFwlDTWNMPl5+eHMWPGYM+ePQCAwoULY/Hixfjll18kTkYZJTo6GhMmTICHhwcAoGbNmtiyZQsKFSokbTBKF6xpiWM9MyzHjx9H127dEfwpCADgUrwU2nfvg1/adoSVtU2G5QiKVMIxiwUq5rPPsPs0JiFRStx5EwYzExmszNL+RVeNRoOzx49g4/JF8Hr6CACQ1cEBgwYPxegRw5A9e/Y0v09Kf6xnSWNNyxwuX76M0aNH4+bN2MZ86dKlMX/+fDRp0oSNeQPx9u1bjBs3Dtu2bQMA5M2bFwsWLECnTp34GGYSbGCRJN69e4c5c+ZgzZo1UCqVkMlk6Ny5M/78808UK1ZM6niUTGfPnsXo0aNx//59AEDlypWxaNEi1KlTR9pglCE4OEoaa5rhO3PmDIYNG4anT58CAH755Rd4eHiwQW/kHjx4gC5duuDJkycwNTWFu7s73NzcYGqafjPgSVqsaYljPTM879+/x0KPJdiw7h9dI8smix2atv0NnXr0QcHCRdM9Q5hCBUszE9Qo6ABTEx6lnVxvw6Lx8F04slqapOn3T61S4dj+3di8cjH8fV4CAHLkyo0RI0dh2OCByJIlS5rdF2U81rOksaZlHkII7Nq1C25ubvD19QUANGrUCPPnz+fxJnosOjoaCxcuxOzZsxEVFQULCwuMHTsWbm5usLHJuIk4JD02sEhS/v7+mD17NtatWweVSgW5XI5u3bphypQpKFy4sNTxKAnPnj3DuHHjcPjwYQBAvnz5MGfOHHTu3BlyOQenmQUHR0ljTTMOKpUKy5cvh7u7O8LDw2Fubo7Ro0dj4sSJ3E/dyGi1WixatAiTJk2CUqlEsWLFsHXrVlSuXFnqaJTOWNMSx3pmuGJiYrBtxy4sW74cd2/d0H28Uu166Ni9L+o0bJJuWypFKjUQQqByfnvYWZqly30Ys5dBkXgeEIGcNmZpMtM8JjoaB3b+i62rl+LjW38AQN78BTBm7FgM6NcXlpaWqb4Pkh7rWdJY0zKfmJgYLF26FDNnzkRYWBhkMhl69uyJGTNmIG/evFLHo/8RQuDgwYMYNWoUXr16BSB2h6cFCxbAxcVF4nQkBTawSC/4+vpi1qxZ2LBhAzQaDUxMTNCzZ09MnjwZzs7OUsejrwQFBWHq1KlYtWoVNBoNbG1tMWHCBIwcORJWVjycObPh4ChprGnG5ePHj3Bzc8PGjRsBcAsDY/P69Wv06NED58+fBwAMHDgQ8+fP5yy/TII1LXGsZ8bh9p27+HvJUuzbtRPR0bHnE+fKkw+tu/RCm9+6IluOtD0EXanRIjJGg3J57eCYhc2R5HrwNgxvwqKR09Y8VbcTFRmBXZvXY8c/KxASFAAAKFSkKNzcJqBn964wM2Nz0ZiwniWNNS3z+vTpE2bMmIHly5dDrVbD2toaY8aMwdixYzkxUWJPnz7F8OHDcfr0aQCAq6srFi9ejAYNGkicjKTEBhbpFR8fH8yYMQObN2+GVquFqakp+vTpg0mTJiF//vxSx8v0vp6tIpfL0bdvX0ybNg2Ojo5SxyOJcHCUNNY043T9+nUMHToUt2/fBgD89NNPWLJkCcqWLStxMkqpbdu2YdCgQQgLC0OuXLmwfv16nneWybCmJY71zLgEBwdj9T/rsGrlKrz29QEAmJqZ4+fmrdCpZ1+UrlA5TSZmCCEQGKlCqdxZUCi7dapvLzPRaAWu+YUgUqmGg1XKGkyhIcHYsXEt9mxYg89hIQCAUmXKYtKkSejUvl26rbwjabGeJY01jV6+fAk3Nzfs27cPAODo6Ijp06ejd+/efG7MYCEhIZg6dSqWL18OjUYDe3t7zJgxAwMGDOD27cQGFumnly9fYsaMGdi6dSu0Wi3Mzc3Rr18/TJgwgct6JSCEwJ49ezB+/Hjd8t0mTZpgwYIFKF26tMTpSGocHCWNNc14abVarF+/HhMmTEBQUBDkcjkGDRqE6dOnw8HBQep49INCQkIwaNAg7NixAwDQqlUrrF27Fjlzpu0qBNJ/rGmJYz0zTlqtFkePn8CSpctx5uRx3eNb1LUc2nfvg6at28HSMnW7LAREKFHQwQplnOzSInKmERmjxk3/UAghYGuRvBfwggI+4t+1K3Dg3/VQREUCACpVrY4pkyfh1xa/cNW4kWM9SxprGsW5fPkyRo8ejZs3bwIASpcujfnz56NJkyZ8rkxnGo0G//zzDyZPnqwbT//xxx+YMWMGcuTIIXU80hNsYJFee/bsGaZPn44dO3ZACAELCwsMGDAAbm5uXPGTQa5fv47Ro0fj6tWrAGKX7y5YsABNmzaVOBnpCw6OksaaZvxCQkLg7u6O5cuXQ6vVIkeOHJg9ezZn7xmAs2fPokePHnjz5g1sbGzg4eGBPn36cLCaSbGmJY71zPj5+PjAY+lybN60AWEhsat1smR1QPMOndGxe2/kK5iysyeCo1SwszRF9YIOkPP59YcFRsTg3tswWJmZwML0x84Yfv/GHxtXLsax3dugjIkGANT5uQHcJ09C/Z/rsb5lEqxnSWNNoy8JIbBr1y64ubnB19cXANCoUSPMnz8f5cqVkzackbp06RKGDRuG+/fvAwDq1q2LJUuW8PtN32ADiwzCkydPMG3aNOzevRsAYGVlhUGDBmHcuHHIlSuXxOmMk6+vLyZMmKCbjZ4rVy5Mnz4dffr04fJdioeDo6SxpmUeDx8+xNChQ3Hx4kUAQKVKlbBs2TJUr15d4mT0tZiYGEyaNAkLFy4EAFSrVg3//vsvihQpInEykhJrWuJYzzIPhUKBLdu2Y/my5Xh4/y6A2N+Pqj81RMcefVGzXgPI5T/WUAGAzzFqmMhkqFrAAdbmnNjxo16HKPD4QzgcrMxgIk/8acnP+yXWL/fAqYO7oVGrAQBNW7SE++TJqF6takbEJT3CepY01jRKSHR0NJYtW6Y7OkMmk6Fnz56YOXMm8uTJI3U8o+Dv749x48bpXm/Mnz8/FixYgA4dOnCSBSWIDSwyKA8fPsTUqVOxf/9+AIC1tTWGDh2KMWPGcGlpGgkLC8OcOXPg4eGBmJgYWFhYYNSoUXBzc4OdHbf8oG9xcJQ01rTMRQiBnTt3YsyYMXj79i0AoGfPnvjrr7+QO3duidMRADx69AhdunTBo0ePYGJigj///BMTJ07kBA1iTUsC61nmdO36Dfy9ZCkO7tsDZUwMAMCpgDPadOmFVp26wN4hW5K3Ea3WIEYlUCFfVuSwMU/vyEbjWUAEvIIikcv2+9+z508eYf3SRbhw4hCEEJDL5WjdrgP+nDwJ5cqWycC0pE9Yz5LGmkaJ+fTpE6ZPn44VK1ZArVbD2toaY8aMwdixY2Frayt1PIOkUCiwYMEC/PXXX4iKioKlpSXGjx+PcePGwdqaZ2TS97GBRQbp7t27mDp1Kg4fPgwAsLW1xbBhwzB69Ghky5b0AIq+pVarsWbNGri7uyMoKAgA0KVLF8yePRsFChSQOB3pMw6OksaaljlFRERg9uzZWLhwIZRKJezs7DB16lQMGTIEZmYpO4idUker1cLDwwMTJkyAUqlEkSJF8O+//6JatWpSRyM9wZqWONazzC0wMBArVq/F2jWr8db/NQDA3MISDVq2RaeefVGyTPnvXlejFQhRqFDGyQ757VN3nlZmcut1KAIjYxJs+j28cxPrli7C9XMnAQCmZmb4vUs3TJ7ohmJFi2Z0VNIzrGdJY02jH/Hy5Uu4ublh3759AABHR0dMnz6dW8UngxAC+/fvx+jRo3XbM7Zv3x4LFixAwYIFpQ1HBoENLDJot27dgru7O44fPw4AsLOzw4gRIzBy5EjY29tLG85ACCFw/PhxjBkzBp6engCA2rVrY+HChahalVtNUNI4OEoaa1rm5uXlhREjRuDo0aMAgFKlSmHJkiVo0KCBxMkyF39/f/Ts2RNnz54FAPzxxx9YuHAhZ1BSPKxpiWM9IyD2wPUDhw5jybLluHj2tO7jJctVQocefdCoRRuYW1h8c72Az0oUyWGNErmzZGRcg6XUaHHdNwQxGg2yWsZOfBFC4OblC1i/dBHu37gMALC0tEKP3n0wyW0c8ufPL2Vk0iOsZ0ljTaPkuHz5MkaPHo2bN28CAEqXLo358+ejSZMm3PYuEY8ePcKIESN0Y7AyZcpg8eLF+PnnnyVORoaEDSwyCtevX4e7uztOnoydfZY1a1aMGDEC9erVQ+HChZE3b95k7dFujNRqNV69egVPT094enri2bNnurdhYWEAgEKFCmHevHlo27YtCzD9MA6OksaaRgBw5MgRjBgxAt7e3gCAdu3aYeHChZx1lgF27NiBgQMHIjQ0FDlz5sS6devQsmVLqWORHmJNSxzrGX3t2fPn8Fi6HNu2bMbn8NgxRdZs2dGiY1d06NYbTvn+v6ESFKlEThsLVClgL1FawxIWrcJt/1CYyGSwMpPj4qkTWL9sIZ49iD2TzDaLHf4YMBDjx4ziudD0DdazpLGmUXIJIbBr1y64ubnpVhI1atQI7u7uKF++PGxsbKQNKLHw8HA8efJEd3n8+DHOnTsHjUaDbNmyYcaMGfjjjz+4bTslGxtYZFQuX74Md3d3XWc/joWFBVxcXFC4cGEUKlQIhQsX1l1cXFxgaWkpUeK0FxkZiefPn+saVHFNqpcvX0KpVCZ4naxZc+PPP8dh8ODBsEhgtiRRYjg4ShprGsWJjo7GokWLMGvWLERFRcHKygpubm4YO3YsrKy4pVJaCw0NxZAhQ7B161YAQIsWLfDPP//wLDL6Lta0xLGe0fdERkZi4+Z/sXzFcng+fgQAkMvlqFG/CTr26ItqdeohPEYNCxMTVHd2gLlJ5p5c+CM+hEfjrn8Irp8+gn9XeuDV86cAAIds2TF42DCMGjYUDg4OEqckfcV6ljTWNEqp6OhoLFu2DDNnztRNCAeA/Pnzo0SJEihRogSKFy+ue5s3b16jmiQeEREBT09PPH78OF7Dyt/f/5vPlcvl6NFjIObPn4bs2bNLkJaMARtYZJTOnTuPtWs3wNf3Jby9vREQEPDdz5XJZMibN2+Cza3ChQvr5ZlaQggEBQUluJrKz8/vu9crUKAASpQogZIlS6JkyZIoUaIENJqSmDo1Jy5eNJ5iShmLg6OksabR1/z9/TF27Fjs3LkTAODi4oK///4bv/76q1ENbqR0/vx5dO/eHf7+/rC2tsbff/+Nfv368ftLiWJNSxzrGSVFCIGLly9j8ZJlOHxwP9QqFQAgn0sRtOzcEw1a/oYGZQvA3opnQX5NCIGAgAD4+vri/n1f7DvqiYf3t+CDvw8AIJejE0aNHo0hAwdk+ln+lDTWs6SxplFqnT37CcOGzYZWexxeXl5Q/a/mfc3W1hbFixeP19QqUaIEihYtqteTGBUKBTw9PeOtqHry5Ilu9dnXLCwsULJkSbi6usLV1RWlS5eGhUVF/PZbXixcCPTsCXAoRinBBhYZpRIlgIgI4No1IH9+4PPnz/Dx8YG3t7fubdzFz88PGo3mu7dlb2//3eZW3rx50/XQRo1GAz8/v29WU3l6eiI4ODjB65iZmaFo0aK6BlXc2+LFiyd4zsetW8DevcCcOSwklDIcHCWNNY2+5/z58xg6dCgeP34MAGjSpAkWL16M4sWLS5zMMB08CJQsGYN//pmCBQsWQAiBqlWrYsuWLShWrJjU8cgAsKYljvWMkuP9+/dYtmo11q1di4/v3wEATM2tUahwFVSpmA958uRB3rx5kSdPnngXY90R4ssG1fcu0dHR31wvv7MLxo8bj769exrt94bSHutZ0ljTKDV8fICSJYG1a4Hu3f//2I5nz57pdkWKexsUFJTgbchkMhQsWPCbFVslSpSAo6Njhk28i4mJwfPnz79ZUeXt7Z3g74eZmRmKFy+O0qVL65pVrq6uKFy4cIKvkXbqBLRoAXTrlhFfDRkjNrDI6ERGAi4uwKdPsQUlqaNFVCoVXr9+/U1jK+4SGRn53euam5snujXhj86kiI6OxosXL75ZTfX8+fMEBzEAkCVLlngrqeLed3FxgZkZZzRSxuHgKGmsaZQYtVqNlStX4s8//0RoaCjMzMwwYsQITJkyBZ8/Z0H9+sDw4cDAgVIn1X/lyz9GWFhX+Po+gFwux+TJkzF58mTWRfphrGmJYz2jlFCr1dizbz/Guy3H61cXkvz87NmzJ9jc+vLfuXPnTteJhCmRVIPKz88PCoUi0dvInj07nJ2dkT27Mzw9ndGqTWX8vbA9zwuhZGM9SxprGqXWuXNAzZpAUnMLPn36pGtmfdnY8vb2/u6Eejs7u2+aWiVKlECRIkVSPJlBpVLhxYsX36yo8vLySjCHiYkJihUrFq9JVbp0aRQpUiRZ46uYmKS/R0SJYQOLjFLctqv58yf+eUkRQiAwMDDBxpaPjw8+fPiQ6PUT2prQ0dERPj4+8VZVvXr16rs/605OTgk2qpycnLgNEukFDo6SxppGPyIwMBATJ07EunXrIISAk5MT5s6dh9Gju2D/fhlq1ZI6of4JDw/H3bt3cfv2bdy5cwd79uyHWh2DwoULY8uWLahRo4bUEcnAsKYljvWMUkOjAQ4f9oWNjRfevXuLd+/e6S5v38b++/3791Cr1Unellwuh6Oj43cbXHHvZ8uWLc3GTHFjw8RWUCXVoMqWLRucnZ2/ubi4uKBgwYLIkiVLmmQlYj1LGmsaSU2pVOpeH/x61VZISEiC15HL5XBxcUmwuZUzZ07IZDKo1Wp4e3t/s6Lq+fPnCdZYuVyOwoUL6xpUcc2qYsWKceUv6QU2sIhSITIy8puVW3H/9vX1/aHBFxA7q6Fw4cLxtvyLe5s1a9Z0/iqIUoeDo6SxplFy3Lp1C0OGDMHNmzcBAHny1MCQIb+iVKmSKFWqFAoVKqR3s84zQnh4OO7du4c7d+7oGlYvXrz45vP69u2Lv//+O8Ftc4mSwpqWONYzSm9arRaBgYEJNre+/HdiZxx/ydzcPMnVXHny5EGWLFnStUHl7OyMggULws7OLi2+TURJYj1LGmsa6au4evR1U+vZs2d49eoVtFptgtezt7fXTZpXKpUJfk6hQoW+WVFVvHhxvT6Li4gNLKJ0olar4e/v/01j68OHDyhYsGC8VVWpWQJMJDUOjpLGmkbJpdVqsWnTJri5uX3zIp2FhQWKFSuGUqVKoVSpUihZMraxVbRoUZibm0uUOG1FRETg3r17ukbV7du38eLFi29+h8zNzVG2bFlUqlQJlStXRo0aNeDq6ipRajIGrGmJYz0jfaFSqfDhw4dvGlxfN7tCQ0N/6PayZMkCtVqdZIPKwcHhm5VTbFCRPmI9SxprGhmimJgYeHl5JbglYXh4uO7zChQo8M2KqpIlS8LGxkbC9EQpwwYWUQb65ZfY7Q1PngQcHaVOQ5Q2ODhKGmsapVRoaCj27t2Lx48f4+nTp/D09IR/3D65XzExMUGRIkW+aWwVL14c1tbWGZz8x0VEROD+/fvxVlY9e/bsm98XMzMzXbMqrmFVunRpo2nakX5gTUsc6xkZmsjISLx//z7BBlfc+69fv4VKFXvu8NcNqq9XUHF3DDIUrGdJY00jYyKEwMePH9G793s8f14Yly/bwclJ6lREaYMNLKIMEh0NZM8OREUBT54ApUpJnYgobXBwlDTWNEpL4eHhunMUnz59qmts+fj4JPgzJpPJ4OzsHK+pFfd+SmeKnzwJrFoFFCkCzJ4N/OjZ8lFRUbh//368lVXPnj37ZhsMU1NTlClTBpUrV47XrOJqZUpvrGmJYz0jY/THHwJr14Zi4EA5Vqxgg4qMA+tZ0ljTyNhotYCLC/D6NXDqFNCwodSJiNIGG1hEGejhQyAsDKhTR+okRGmHg6OksaZRRlAoFHj+/Pk3ja2XL19+90zGvHnzJtjYypEjR6L39fAhUK4c0KYNsG9fwp8TFRWFBw8exFtZ9fTp0wSbVaVLl9Y1qipVqoQyZcrA0tIyRd8HotRgTUsc6xkZo5AQ4Px54KefgGzZpE5DlDZYz5LGmkbG6OlTwNcXaN5c6iREaYcNLCIiShUOjpLGmkZSUiqV8Pb2jtfUevr0KZ49e4aYmJgEr5MzZ84EG1tOTk6QyWTQamO3wl22DOjYMbZ59vDhw3grq54+fQqNRhPvdk1MTODq6hpvZVXZsmXZrCK9wZqWONYzIiLDwHqWNNY0IiLDwAYWERGlCgdHSWNNI32k0Wjg6+v7TWPL09MTERERCV4na9asumbW/fuFULq0Dx48uIPHjx9/06ySy+VwdXWNt7KqXLlysLKyyogvjyhFWNMSx3pGRGQYWM+SxppGRGQY2MAiIqJU4eAoaaxpZEiEEHjz5s03ja2nT58iJCQkwevI5XKULFky3sqqcuXKwdraOoPTE6UOa1riWM+IiAwD61nSWNOIiAwDG1hERJQqHBwljTWNjIEQAgEBAbpmlo+PDwoWLKhrVtnY2EgdkSjVWNMSx3pGRGQYWM+SxppGRGQY2MAiIqJU4eAoaaxpRESGgTUtcaxnRESGgfUsaaxpRESGITU1TZ7maYiIiIiIiIiIiIiIiIhSgQ0sIiIiIiIiIiIiIiIi0itsYBEREREREREREREREZFeYQOLiIiIiIiIiIiIiIiI9AobWERERERERERERERERKRX2MAiIiIiIiIiIiIiIiIivcIGFhEREREREREREREREekVNrCIiIiIiIiIiIiIiIhIr7CBRURERERERERERERERHqFDSwiIiIiIiIiIiIiIiLSK2xgERERERERERERERERkV5hA4uIiIiIiIiIiIiIiIj0ChtYREREREREREREREREpFfYwCIiIiIiIiIiIiIiIiK9wgYWERERERERERERERER6RU2sIiIiIiIiIiIiIiIiEivsIFFREREREREREREREREeoUNLCIiIiIiIiIiIiIiItIrbGARERERERERERERERGRXmEDi4iIiIiIiIiIiIiIiPQKG1hERERERERERERERESkV9jAIiIiIiIiIiIiIiIiIr3CBhYRERERERERERERERHpFTawiIiIiIiIiIiIiIiISK+wgUVERERERERERERERER6hQ0sIiIiIiIiIiIiIiIi0itsYBEREREREREREREREZFeYQOLiIiIiIiIiIiIiIiI9AobWERERERERERERERERKRX2MAiIiIiIiIiIiIiIiIivcIGFhEREREREREREREREekVNrCIiIiIiIiIiIiIiIhIr7CBRURERERERERERERERHqFDSwiIiIiIiIiIiIiIiLSK2xgERERERERERERERERkV5hA4uIiIiIiIiIiIiIiIj0ChtYREREREREREREREREpFfYwCIiIiIiIiIiIiIiIiK9wgYWERERERERERERERER6RU2sIiIiIiIiIiIiIiIiEivsIFFREREREREREREREREeoUNLCIiIiIiIiIiIiIiItIrphlxJzKZLCPuhoiIKN2xphERkTFgPSMiImPBmkZEZLy4AouIiIiIiIiIiIiIiIj0ikwIIXUGIiIiIiIiIiIiIiIiIh2uwCIiIiIiIiIiIiIiIiK9wgYWERERERERERERERER6RU2sIiIiIiIiIiIiIiIiEivsIFFREREREREREREREREeoUNLCIiIiIiIiIiIiIiItIrbGARERERERERERERERGRXmEDi4iIiIiIiIiIiIiIiPQKG1hERERERERERERERESkV9jAIiIiIiIiIiIiIiIiIr3CBhYRERERERERERERERHpFTawiIiIiIiIiIiIiIiISK+wgUVERERERERERERERER6hQ0sIiIiIiIiIiIiIiIi0itsYBEREREREREREREREZFeYQOLiIiIiIiIiIiIiIiI9AobWERERERERERERERERKRX2MAiIiIiIiIiIiIiIiIivcIGFhEREREREREREREREekVNrCIiIiIiIiIiIiIiIhIr7CBRURERERERERERERERHqFDSwiIiIiIiIiIiIiIiLSK2xgERERERERERERERERkV75P7DgzMttxX7wAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "image/png": { + "height": 253, + "width": 856 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "f,axes = plt.subplots(1, 4, figsize=(15,4))\n", + "ax = axes[0]\n", + "import seaborn as sns\n", + "sns.set_context(\"paper\", font_scale=1.5)\n", + "# Add data, AU is ordered as such: \n", + "# AU1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43\n", + "\n", + "# Activate AU1: Inner brow raiser \n", + "au = np.array([intensity, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"AU1: Inner brow raiser\")\n", + "\n", + "ax = axes[1]\n", + "au = np.array([0, 0, 0, 0, 0, 0, 0, 0, intensity, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"AU12: Lip corner puller\")\n", + "\n", + "ax = axes[2]\n", + "au = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, intensity, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"AU15: Lip corner depressor\")\n", + "\n", + "ax = axes[3]\n", + "au = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, intensity])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"AU43: Eye closer\")" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:24:24.064595Z", + "start_time": "2021-03-27T00:24:23.762529Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Fear')" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "image/png": { + "height": 253, + "width": 856 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "f,axes = plt.subplots(1, 4, figsize=(15,4))\n", + "ax = axes[0]\n", + "import seaborn as sns\n", + "sns.set_context(\"paper\", font_scale=1.5)\n", + "# Add data, AU is ordered as such: \n", + "# AU1, 2, 4, 5, 6, \n", + "# 7, 9, 10, 12, 14, \n", + "# 15, 17, 18, 20, 23, \n", + "# 24, 25, 26, 28, 43\n", + "intensity = 2\n", + "# Activate AU1: Inner brow raiser \n", + "au = np.array([0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " intensity, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " intensity, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0, \n", + " 0])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"Happiness\")\n", + "\n", + "ax = axes[1]\n", + "au = np.array([intensity, \n", + " 0, \n", + " intensity, \n", + " 0, 0, 0, 0, 0, 0, 0, intensity, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"Sadness\")\n", + "\n", + "ax = axes[2]\n", + "au = np.array([intensity, intensity, 0, intensity, 0, 0, 0, 0, 0, 0, \n", + " 0, 0, 0, 0, 0, 0, 0, intensity, 0, 0])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"Surprise\")\n", + "\n", + "ax = axes[3]\n", + "\n", + "# FEAR\n", + "au = np.array([intensity, intensity, intensity, intensity, 0, \n", + " intensity, 0, 0, 0, 0, \n", + " 0, 0, 0, intensity, 0, \n", + " 0, 0, intensity, 0, 0])\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "plot_face(model=None, vectorfield = vectors, muscles = {'all': 'heatmap'},\n", + " ax = ax, au = np.array(au), color='k', linewidth=1, linestyle='-')\n", + "ax.set_title(\"Fear\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add a vectorfield with arrows from the changed face back to neutral and vice versa " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:00:25.672632Z", + "start_time": "2021-03-27T00:00:25.589946Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face, predict\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "model = load_h5('pyfeat_aus_to_landmarks.h5')\n", + "# Add data activate AU1, and AU12\n", + "au = np.array([2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ])\n", + "\n", + "# Get neutral landmarks\n", + "neutral = predict(np.zeros(len(au)))\n", + "\n", + "# Provide target landmarks and other vector specifications\n", + "vectors = {'target': predict(au),\n", + " 'reference': neutral, 'color': 'blue'}\n", + "\n", + "fig, axes = plt.subplots(1,2)\n", + "# Plot face where vectorfield goes from neutral to target, with target as final face\n", + "plot_face(model = model, ax = axes[0], au = np.array(au), \n", + " vectorfield = vectors, color='k', linewidth=1, linestyle='-')\n", + "\n", + "# Plot face where vectorfield goes from neutral to target, with neutral as base face\n", + "plot_face(model = model, ax = axes[1], au = np.zeros(len(au)), \n", + " vectorfield = vectors, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add muscle heatmaps to the plot" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:27.181441Z", + "start_time": "2021-03-26T04:54:27.109016Z" + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "model = load_h5()\n", + "\n", + "au = np.array([2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "\n", + "# Add some muscles\n", + "muscles = {'orb_oris_l': 'yellow', 'orb_oris_u': \"blue\"}\n", + "muscles = {'all': 'heatmap'}\n", + "\n", + "plot_face(model=model, au = np.array(au), \n", + " muscles = muscles, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:35.665640Z", + "start_time": "2021-03-26T04:54:35.587594Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "au = [0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, \n", + " 0.450328, 1.02961, 0.871225, 0, 1.1977, 0.457218, 0, 0, 0, 0]\n", + "au = np.array([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n", + "\n", + "# Add some muscles\n", + "muscles = {'all': 'heatmap'}\n", + "\n", + "# Plot face\n", + "plot_face(model=None, au = np.array(au), muscles = muscles, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make sure muscle array contains 'facet' for a facet heatmap" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:44.068508Z", + "start_time": "2021-03-26T04:54:43.994084Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "au = np.array([0.127416, 0.809139, 0, 0.343189, 0.689964, 1.23862, 1.28464, 0.79003, 0.842145, 0.111669, \n", + " 0.450328, 1.02961, 0.871225, 0, 1.1977, 0.457218, 0, 0, 0, 0])\n", + "\n", + "# Load a model \n", + "model = load_h5()\n", + "\n", + "# Add muscles\n", + "muscles = {'all': 'heatmap', 'facet': 1}\n", + "\n", + "# Plot face\n", + "plot_face(model=model, au = au, muscles = muscles, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add gaze vectors\n", + "Add gaze vectors to indicate where the eyes are looking. \n", + "Gaze vectors are length 4 (lefteye_x, lefteye_y, righteye_x, righteye_y) where the y orientation is positive for looking upwards." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:54:50.850198Z", + "start_time": "2021-03-26T04:54:50.805184Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "from feat.utils import load_h5\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Add data\n", + "au = np.zeros(20)\n", + "\n", + "# Add some gaze vectors: (lefteye_x, lefteye_y, righteye_x, righteye_y)\n", + "gaze = [-1, 5, 1, 5]\n", + "\n", + "# Plot face\n", + "plot_face(model=None, au = au, gaze = gaze, color='k', linewidth=1, linestyle='-')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Call plot method on Fex instances\n", + "It is possible to call the `plot_aus` method within openface, facet, affdex fex instances" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OpenFace" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:56:59.035420Z", + "start_time": "2021-03-26T04:56:58.928294Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from feat.plotting import plot_face\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from feat.utils import load_h5, get_resource_path, read_openface\n", + "from feat.tests.utils import get_test_data_path\n", + "from os.path import join\n", + "\n", + "test_file = join(get_test_data_path(),'OpenFace_Test.csv')\n", + "openface = read_openface(test_file)\n", + "openface.plot_aus(12, muscles={'all': \"heatmap\"}, gaze = None)" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "toc": { + "base_numbering": 1, + "nav_menu": { + "height": "171px", + "width": "252px" + }, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": "block", + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/content/dev_trainAUvisModel.ipynb b/notebooks/content/dev_trainAUvisModel.ipynb new file mode 100644 index 00000000..1f7a6b01 --- /dev/null +++ b/notebooks/content/dev_trainAUvisModel.ipynb @@ -0,0 +1,481 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Training AU visualization model\n", + "You will first need to gather the datasets for training. In this tutorial we use the datasets EmotioNet, DISFA Plus, and BP4d. After you download each model you should extract the labels and landmarks from each dataset. Detailed code on how to do that is described at the bottom of this tutorial. Once you have the labels and landmark files for each dataset you can train the AU visualization model with the following. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:28:05.706631Z", + "start_time": "2021-03-27T00:28:03.811433Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EmotioNet: 24587\n", + "DISFA Plus: 57668\n", + "BP4D: 143951\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import pandas as pd, numpy as np, matplotlib.pyplot as plt\n", + "from sklearn.cross_decomposition import PLSRegression\n", + "from sklearn.model_selection import KFold\n", + "from feat.plotting import predict, plot_face\n", + "from feat.utils import registration, neutral\n", + "from natsort import natsorted\n", + "import os, glob \n", + "import pandas as pd, numpy as np\n", + "import seaborn as sns\n", + "sns.set_style(\"white\")\n", + "\n", + "au_cols = [1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 17, 18, 20, 23, 24, 25, 26, 28, 43]\n", + "au_cols = [f\"AU{au}\" for au in au_cols]\n", + "\n", + "base_dir = \"/Storage/Projects/feat_benchmark/scripts/jcheong/openface_train\"\n", + "\n", + "labels_emotionet = pd.read_csv(os.path.join(base_dir, \"emotionet_labels.csv\"))\n", + "landmarks_emotionet = pd.read_csv(os.path.join(base_dir, \"emotionet_landmarks.csv\"))\n", + "print(\"EmotioNet: \", len(labels_emotionet))\n", + "\n", + "labels_disfaplus = pd.read_csv(os.path.join(base_dir, \"disfaplus_labels.csv\"))\n", + "landmarks_disfaplus = pd.read_csv(os.path.join(base_dir, \"disfaplus_landmarks.csv\"))\n", + "# Disfa is rescaled to 0 - 1\n", + "disfaplus_aus = [col for col in labels_disfaplus.columns if \"AU\" in col]\n", + "labels_disfaplus[disfaplus_aus] = labels_disfaplus[disfaplus_aus].astype('float')/5\n", + "print(\"DISFA Plus: \", len(labels_disfaplus))\n", + "\n", + "labels_bp4d = pd.read_csv(os.path.join(base_dir, \"bp4d_labels.csv\"))\n", + "landmarks_bp4d = pd.read_csv(os.path.join(base_dir, \"bp4d_landmarks.csv\"))\n", + "bp4d_pruned_idx = labels_bp4d.replace({9: np.nan})[au_cols].dropna(axis=1).index\n", + "print(\"BP4D: \", len(labels_bp4d))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We aggregate the datasets and specify the AUs we want to train. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:28:06.960825Z", + "start_time": "2021-03-27T00:28:05.707732Z" + } + }, + "outputs": [], + "source": [ + "labels = pd.concat([\n", + " labels_emotionet.replace({999: np.nan}), \n", + " labels_disfaplus,\n", + " labels_bp4d.replace({9: np.nan}).iloc[bp4d_pruned_idx,:]\n", + " ]).reset_index(drop=True)\n", + "landmarks = pd.concat([\n", + " landmarks_emotionet, \n", + " landmarks_disfaplus,\n", + " landmarks_bp4d.iloc[bp4d_pruned_idx,:]\n", + " ]).reset_index(drop=True)\n", + "\n", + "landmarks = landmarks.iloc[labels.index]\n", + "\n", + "\n", + "labels = labels[au_cols].fillna(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We train our model using PLSRegression with a minimum of 500 samples for each AU activation. We evaluate the model in a 3-fold split and retrain the model with all the data which is distributed with the package." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:28:08.890994Z", + "start_time": "2021-03-27T00:28:06.961852Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pseudo balancing samples\n", + "Evaluating model with KFold CV\n", + "3-fold accuracy mean 0.13\n", + "N_comp: 20 Rsquare 0.15\n" + ] + } + ], + "source": [ + "min_pos_sample = 500\n", + "\n", + "print('Pseudo balancing samples')\n", + "balY = pd.DataFrame()\n", + "balX = pd.DataFrame()\n", + "for AU in labels[au_cols].columns:\n", + " if np.sum(labels[AU]==1) > min_pos_sample:\n", + " replace = False\n", + " else:\n", + " replace = True\n", + " newSample = labels[labels[AU]>.5].sample(min_pos_sample, replace=replace, random_state=0)\n", + " balX = pd.concat([balX, newSample])\n", + " balY = pd.concat([balY, landmarks.loc[newSample.index]])\n", + "X = balX[au_cols].values\n", + "y = registration(balY.values, neutral)\n", + "\n", + "# Model Accuracy in KFold CV\n", + "print(\"Evaluating model with KFold CV\")\n", + "n_components=len(au_cols)\n", + "kf = KFold(n_splits=3)\n", + "scores = []\n", + "for train_index, test_index in kf.split(X):\n", + " X_train,X_test = X[train_index],X[test_index]\n", + " y_train,y_test = y[train_index],y[test_index]\n", + " clf = PLSRegression(n_components=n_components, max_iter=2000)\n", + " clf.fit(X_train,y_train)\n", + " scores.append(clf.score(X_test,y_test))\n", + "print('3-fold accuracy mean', np.round(np.mean(scores),2))\n", + "\n", + "# Train real model\n", + "clf = PLSRegression(n_components=n_components, max_iter=2000)\n", + "clf.fit(X,y)\n", + "print('N_comp:',n_components,'Rsquare', np.round(clf.score(X,y),2))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-27T00:28:12.319990Z", + "start_time": "2021-03-27T00:28:12.316860Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(10000, 20)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We visualize the results of our model. The regression was trained on labels 0-1 so we do not recommend exceeding 1 for the intensity. Setting the intensity to 2 will exaggerate the face and anything beyond that might give you strange faces. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T23:58:58.227979Z", + "start_time": "2021-03-26T23:58:57.624279Z" + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot results for each action unit\n", + "f,axes = plt.subplots(5,4,figsize=(12,18))\n", + "axes = axes.flatten()\n", + "# Exaggerate the intensity of the expression for clearer visualization. \n", + "# We do not recommend exceeding 2. \n", + "intensity = 2\n", + "for aui, auname in enumerate(axes):\n", + " try:\n", + " auname=au_cols[aui]\n", + " au = np.zeros(clf.n_components)\n", + " au[aui] = intensity\n", + " predicted = clf.predict([au]).reshape(2,68)\n", + " plot_face(au=au, model=clf,\n", + " vectorfield={\"reference\": neutral.T, 'target': predicted,\n", + " 'color':'r','alpha':.6},\n", + " ax = axes[aui])\n", + " axes[aui].set(title=auname)\n", + " except:\n", + " pass\n", + " finally:\n", + " ax = axes[aui]\n", + " ax.axes.get_xaxis().set_visible(False)\n", + " ax.axes.get_yaxis().set_visible(False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here is how we would export our model into an h5 format which can be loaded using our load_h5 function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# save out trained model\n", + "# import h5py\n", + "# hf = h5py.File('../feat/resources/pyfeat_aus_to_landmarks.h5', 'w')\n", + "# hf.create_dataset('coef', data=clf.coef_)\n", + "# hf.create_dataset('x_mean', data=clf._x_mean)\n", + "# hf.create_dataset('x_std', data=clf._x_std)\n", + "# hf.create_dataset('y_mean', data=clf._y_mean)\n", + "# hf.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2021-03-26T04:43:45.310255Z", + "start_time": "2021-03-26T04:43:45.307443Z" + } + }, + "source": [ + "## Preprocessing datasets\n", + "Here we provide sample code for how you might preprocess the datasets to be used in this tutorial. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from PIL import Image, ImageOps\n", + "import math, cv2, csv\n", + "from scipy.spatial import ConvexHull\n", + "from skimage.morphology.convex_hull import grid_points_in_poly\n", + "from feat import Detector\n", + "import os, glob, pandas as pd, numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from skimage import data, exposure\n", + "from skimage.feature import hog\n", + "from tqdm import tqdm\n", + "\n", + "def padding(img, expected_size):\n", + " desired_size = expected_size\n", + " delta_width = desired_size - img.size[0]\n", + " delta_height = desired_size - img.size[1]\n", + " pad_width = delta_width // 2\n", + " pad_height = delta_height // 2\n", + " padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)\n", + " return ImageOps.expand(img, padding)\n", + "\n", + "def resize_with_padding(img, expected_size):\n", + " img.thumbnail((expected_size[0], expected_size[1]))\n", + " delta_width = expected_size[0] - img.size[0]\n", + " delta_height = expected_size[1] - img.size[1]\n", + " pad_width = delta_width // 2\n", + " pad_height = delta_height // 2\n", + " padding = (pad_width, pad_height, delta_width - pad_width, delta_height - pad_height)\n", + " return ImageOps.expand(img, padding)\n", + "\n", + "def align_face_68pts(img, img_land, box_enlarge, img_size=112):\n", + " \"\"\"\n", + " img: image\n", + " img_land: landmarks 68\n", + " box_enlarge: relative size of face\n", + " img_size = 112\n", + " \n", + " \"\"\"\n", + " leftEye0 = (img_land[2 * 36] + img_land[2 * 37] + img_land[2 * 38] + img_land[2 * 39] + img_land[2 * 40] +\n", + " img_land[2 * 41]) / 6.0\n", + " leftEye1 = (img_land[2 * 36 + 1] + img_land[2 * 37 + 1] + img_land[2 * 38 + 1] + img_land[2 * 39 + 1] +\n", + " img_land[2 * 40 + 1] + img_land[2 * 41 + 1]) / 6.0\n", + " rightEye0 = (img_land[2 * 42] + img_land[2 * 43] + img_land[2 * 44] + img_land[2 * 45] + img_land[2 * 46] +\n", + " img_land[2 * 47]) / 6.0\n", + " rightEye1 = (img_land[2 * 42 + 1] + img_land[2 * 43 + 1] + img_land[2 * 44 + 1] + img_land[2 * 45 + 1] +\n", + " img_land[2 * 46 + 1] + img_land[2 * 47 + 1]) / 6.0\n", + " deltaX = (rightEye0 - leftEye0)\n", + " deltaY = (rightEye1 - leftEye1)\n", + " l = math.sqrt(deltaX * deltaX + deltaY * deltaY)\n", + " sinVal = deltaY / l\n", + " cosVal = deltaX / l\n", + " mat1 = np.mat([[cosVal, sinVal, 0], [-sinVal, cosVal, 0], [0, 0, 1]])\n", + " mat2 = np.mat([[leftEye0, leftEye1, 1], [rightEye0, rightEye1, 1], [img_land[2 * 30], img_land[2 * 30 + 1], 1],\n", + " [img_land[2 * 48], img_land[2 * 48 + 1], 1], [img_land[2 * 54], img_land[2 * 54 + 1], 1]])\n", + " mat2 = (mat1 * mat2.T).T\n", + " cx = float((max(mat2[:, 0]) + min(mat2[:, 0]))) * 0.5\n", + " cy = float((max(mat2[:, 1]) + min(mat2[:, 1]))) * 0.5\n", + " if (float(max(mat2[:, 0]) - min(mat2[:, 0])) > float(max(mat2[:, 1]) - min(mat2[:, 1]))):\n", + " halfSize = 0.5 * box_enlarge * float((max(mat2[:, 0]) - min(mat2[:, 0])))\n", + " else:\n", + " halfSize = 0.5 * box_enlarge * float((max(mat2[:, 1]) - min(mat2[:, 1])))\n", + " scale = (img_size - 1) / 2.0 / halfSize\n", + " mat3 = np.mat([[scale, 0, scale * (halfSize - cx)], [0, scale, scale * (halfSize - cy)], [0, 0, 1]])\n", + " mat = mat3 * mat1\n", + " aligned_img = cv2.warpAffine(img, mat[0:2, :], (img_size, img_size), cv2.INTER_LINEAR, borderValue=(128, 128, 128))\n", + " land_3d = np.ones((int(len(img_land)/2), 3))\n", + " land_3d[:, 0:2] = np.reshape(np.array(img_land), (int(len(img_land)/2), 2))\n", + " mat_land_3d = np.mat(land_3d)\n", + " new_land = np.array((mat * mat_land_3d.T).T)\n", + " new_land = np.array(list(zip(new_land[:,0], new_land[:,1]))).astype(int)\n", + " return aligned_img, new_land\n", + "\n", + "def extract_hog(image, detector):\n", + " im = cv2.imread(image)\n", + " detected_faces = np.array(detector.detect_faces(im)[0])\n", + " if np.any(detected_faces<0):\n", + " orig_size = np.array(im).shape\n", + " if np.where(detected_faces<0)[0][0]==1: \n", + " new_size = (orig_size[0], int(orig_size[1] + 2*abs(detected_faces[detected_faces<0][0])))\n", + " else:\n", + " new_size = (int(orig_size[0] + 2*abs(detected_faces[detected_faces<0][0])), orig_size[1])\n", + " im = resize_with_padding(Image.fromarray(im), new_size)\n", + " im = np.asarray(im)\n", + " detected_faces = np.array(detector.detect_faces(np.array(im))[0])\n", + " detected_faces = detected_faces.astype(int)\n", + " points = detector.detect_landmarks(np.array(im), [detected_faces])[0].astype(int)\n", + "\n", + " aligned_img, points = align_face_68pts(im, points.flatten(), 2.5)\n", + "\n", + " hull = ConvexHull(points)\n", + " mask = grid_points_in_poly(shape=np.array(aligned_img).shape, \n", + " verts= list(zip(points[hull.vertices][:,1], points[hull.vertices][:,0])) # for some reason verts need to be flipped\n", + " )\n", + "\n", + " mask[0:np.min([points[0][1], points[16][1]]), points[0][0]:points[16][0]] = True\n", + " aligned_img[~mask] = 0\n", + " resized_face_np = aligned_img\n", + "\n", + " fd, hog_image = hog(resized_face_np, orientations=8, pixels_per_cell=(8, 8),\n", + " cells_per_block=(2, 2), visualize=True, multichannel=True)\n", + "\n", + " return fd, hog_image, points" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Replace the paths so that it points to your local dataset directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "detector = Detector(face_model = \"retinaface\", landmark_model=\"mobilenet\")\n", + "# Correct path to your downloaded dataset.\n", + "EmotioNet_images = np.sort(glob.glob(\"/Storage/Data/EmotioNet/imgs/*.jpg\"))\n", + "labels = pd.read_csv(\"/Storage/Data/EmotioNet/labels/EmotioNet_FACS_aws_2020_24600.csv\")\n", + "labels = labels.dropna(axis=0)\n", + "for col in labels.columns:\n", + " if \"AU\" in col:\n", + " kwargs = {col.replace(\"'\", '').replace('\"', '').replace(\" \",\"\"): labels[[col]]}\n", + " labels = labels.assign(**kwargs)\n", + " labels = labels.drop(columns = col)\n", + "labels = labels.assign(URL = labels.URL.apply(lambda x: x.split(\"/\")[-1].replace(\"'\", \"\")))\n", + "labels = labels.set_index('URL')\n", + "labels = labels.drop(columns = [\"URL orig\"])\n", + "\n", + "aus_to_train = ['AU1','AU2','AU4','AU5', \"AU6\", \"AU9\",\"AU10\", \"AU12\", \"AU15\",\"AU17\", \n", + " \"AU18\",\"AU20\", \"AU24\", \"AU25\", \"AU26\", \"AU28\", \"AU43\"]\n", + "\n", + "with open('emotionet_labels.csv', \"w\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow([\"URL\"] + aus_to_train)\n", + " \n", + "landmark_cols = [f\"x_{i}\" for i in range(68)] + [f\"y_{i}\" for i in range(68)]\n", + "with open('emotionet_landmarks.csv', \"w\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow(landmark_cols)\n", + " \n", + "for ix, image in enumerate(tqdm(EmotioNet_images)):\n", + " try:\n", + " imageURL = os.path.split(image)[-1]\n", + " label = labels.loc[imageURL][aus_to_train]\n", + " fd, _, points = extract_hog(image, detector=detector)\n", + " with open('emotionet_labels.csv', \"a+\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow([imageURL]+list(label.values))\n", + " with open('emotionet_landmarks.csv', \"a+\", newline='') as csvfile:\n", + " writer = csv.writer(csvfile, delimiter=',')\n", + " writer.writerow(points.T.flatten())\n", + " except:\n", + " print(f\"failed {image}\")" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "toc": { + "nav_menu": { + "height": "81px", + "width": "252px" + }, + "navigate_menu": true, + "number_sections": true, + "sideBar": true, + "threshold": 4, + "toc_cell": false, + "toc_section_display": "block", + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/content/intro.md b/notebooks/content/intro.md index 017b2d55..a54bbdc5 100644 --- a/notebooks/content/intro.md +++ b/notebooks/content/intro.md @@ -1,11 +1,11 @@ Py-Feat: Python Facial Expression Analysis Toolbox ============================ - -[![Build Status](https://api.travis-ci.org/cosanlab/feat.svg?branch=master)](https://travis-ci.org/cosanlab/feat/) +[![Package versioning](https://img.shields.io/pypi/v/py-feat.svg)](https://pypi.org/project/py-feat/) +[![Build Status](https://api.travis-ci.org/cosanlab/py-feat.svg?branch=master)](https://travis-ci.org/cosanlab/py-feat/) [![Coverage Status](https://coveralls.io/repos/github/cosanlab/py-feat/badge.svg?branch=master)](https://coveralls.io/github/cosanlab/py-feat?branch=master) -[![GitHub forks](https://img.shields.io/github/forks/cosanlab/py-feat)](https://github.com/cosanlab/feat/network) -[![GitHub stars](https://img.shields.io/github/stars/cosanlab/py-feat)](https://github.com/cosanlab/feat/stargazers) -[![GitHub license](https://img.shields.io/github/license/cosanlab/py-feat)](https://github.com/cosanlab/feat/blob/master/LICENSE) +[![GitHub forks](https://img.shields.io/github/forks/cosanlab/py-feat)](https://github.com/cosanlab/py-feat/network) +[![GitHub stars](https://img.shields.io/github/stars/cosanlab/py-feat)](https://github.com/cosanlab/py-feat/stargazers) +[![GitHub license](https://img.shields.io/github/license/cosanlab/py-feat)](https://github.com/cosanlab/py-feat/blob/master/LICENSE) Py-Feat provides a comprehensive set of tools and models to easily detect facial expressions (Action Units, emotions, facial landmarks) from images and videos, preprocess & analyze facial expression data, and visualize facial expression data.