diff --git a/Classification/1-Data/README.md b/Classification/1-Data/README.md
index 42f119fd..24dc19af 100644
--- a/Classification/1-Data/README.md
+++ b/Classification/1-Data/README.md
@@ -1,7 +1,7 @@
# Introduction to Classification
[![Introduction to Classification](https://img.youtube.com/vi/eg8DJYwdMyg/0.jpg)](https://youtu.be/eg8DJYwdMyg "Introduction to Classification")
-> MIT's John Guttag introduces Classification
+> 🎥 Click the image above for a video: MIT's John Guttag introduces Classification
## [Pre-lecture quiz](link-to-quiz-app)
diff --git a/Clustering/1-Visualize/README.md b/Clustering/1-Visualize/README.md
index a665e9b5..779deab0 100644
--- a/Clustering/1-Visualize/README.md
+++ b/Clustering/1-Visualize/README.md
@@ -9,7 +9,7 @@ Clustering is a type of [Unsupervised Learning](https://wikipedia.org/wiki/Unsup
### Introduction
[![Introduction to ML](https://img.youtube.com/vi/esmzYhuFnds/0.jpg)](https://youtu.be/esmzYhuFnds "Introduction to Clustering")
-> MIT's John Guttag introduces Clustering
+🎥 Click the image above for a video: MIT's John Guttag introduces Clustering
[Clustering](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124) is very useful for data exploration. Let's see if it can help discover trends and patterns in the way Nigerian audiences consume music.
@@ -309,6 +309,4 @@ Before you apply clustering algorithms, as we have learned, it's a good idea to
[This helpful article](https://www.freecodecamp.org/news/8-clustering-algorithms-in-machine-learning-that-all-data-scientists-should-know/) walks you through the different ways that various clustering algorithms behave, given different data shapes.
-In the next lesson, you will make use of the most popular clustering method, K-Means. Take a look at Stanford's K-Means Simulator [here](https://stanford.edu/class/engr108/visualizations/kmeans/kmeans.html). You can use this tool to visualize sample data points and determine its centroids. With fresh data, click 'update' to see how long it takes to find convergence. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped?
-
**Assignment**: [Research other visualizations for clustering](assignment.md)
diff --git a/Clustering/2-K-Means/README.md b/Clustering/2-K-Means/README.md
index 56c26987..b7668077 100644
--- a/Clustering/2-K-Means/README.md
+++ b/Clustering/2-K-Means/README.md
@@ -1,6 +1,4 @@
-# [Lesson Topic]
-
-Add a sketchnote if possible/appropriate
+# K-Means Clustering
![Embed a video here if available](video-url)
https://www.youtube.com/watch?v=hDmNF9JG3lo
@@ -12,46 +10,24 @@ Describe what we will learn
### Introduction
-Describe what will be covered
-
-> Notes
-
### Prerequisite
-
-What steps should have been covered before this lesson?
-
### Preparation
Preparatory steps to start this lesson
----
-
-[Step through content in blocks]
-## [Topic 1]
-
-### Task:
-
-Work together to progressively enhance your codebase to build the project with shared code:
-
-```html
-code blocks
-```
✅ Knowledge Check - use this moment to stretch students' knowledge with open questions
-## [Topic 2]
-
-## [Topic 3]
## 🚀Challenge
-Add a challenge for students to work on collaboratively in class to enhance the project
-Optional: add a screenshot of the completed lesson's UI if appropriate
## [Post-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/28/)
## Review & Self Study
+Take a look at Stanford's K-Means Simulator [here](https://stanford.edu/class/engr108/visualizations/kmeans/kmeans.html). You can use this tool to visualize sample data points and determine its centroids. With fresh data, click 'update' to see how long it takes to find convergence. You can edit the data's randomness, numbers of clusters and numbers of centroids. Does this help you get an idea of how the data can be grouped?
+
**Assignment**: [Assignment Name](assignment.md)
diff --git a/Clustering/2-K-Means/solution/notebook.ipynb b/Clustering/2-K-Means/solution/notebook.ipynb
index 483198dc..0c68e7cd 100644
--- a/Clustering/2-K-Means/solution/notebook.ipynb
+++ b/Clustering/2-K-Means/solution/notebook.ipynb
@@ -35,7 +35,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 81,
"metadata": {},
"outputs": [
{
@@ -43,12 +43,12 @@
"name": "stdout",
"text": [
"Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
- "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+ "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
"Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+ "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
"Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
- "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
- "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
"Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+ "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
"Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
@@ -66,7 +66,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 82,
"metadata": {},
"outputs": [
{
@@ -104,7 +104,7 @@
"text/html": "
\n\n
\n \n
\n
\n
name
\n
album
\n
artist
\n
artist_top_genre
\n
release_date
\n
length
\n
popularity
\n
danceability
\n
acousticness
\n
energy
\n
instrumentalness
\n
liveness
\n
loudness
\n
speechiness
\n
tempo
\n
time_signature
\n
\n \n \n
\n
0
\n
Sparky
\n
Mandy & The Jungle
\n
Cruel Santino
\n
alternative r&b
\n
2019
\n
144000
\n
48
\n
0.666
\n
0.8510
\n
0.420
\n
0.534000
\n
0.1100
\n
-6.699
\n
0.0829
\n
133.015
\n
5
\n
\n
\n
1
\n
shuga rush
\n
EVERYTHING YOU HEARD IS TRUE
\n
Odunsi (The Engine)
\n
afropop
\n
2020
\n
89488
\n
30
\n
0.710
\n
0.0822
\n
0.683
\n
0.000169
\n
0.1010
\n
-5.640
\n
0.3600
\n
129.993
\n
3
\n
\n
\n
2
\n
LITT!
\n
LITT!
\n
AYLØ
\n
indie r&b
\n
2018
\n
207758
\n
40
\n
0.836
\n
0.2720
\n
0.564
\n
0.000537
\n
0.1100
\n
-7.127
\n
0.0424
\n
130.005
\n
4
\n
\n
\n
3
\n
Confident / Feeling Cool
\n
Enjoy Your Life
\n
Lady Donli
\n
nigerian pop
\n
2019
\n
175135
\n
14
\n
0.894
\n
0.7980
\n
0.611
\n
0.000187
\n
0.0964
\n
-4.961
\n
0.1130
\n
111.087
\n
4
\n
\n
\n
4
\n
wanted you
\n
rare.
\n
Odunsi (The Engine)
\n
afropop
\n
2018
\n
152049
\n
25
\n
0.702
\n
0.1160
\n
0.833
\n
0.910000
\n
0.3480
\n
-6.044
\n
0.0447
\n
105.115
\n
4
\n
\n \n
\n
"
},
"metadata": {},
- "execution_count": 5
+ "execution_count": 82
}
],
"source": [
@@ -120,7 +120,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 83,
"metadata": {},
"outputs": [
{
@@ -131,13 +131,13 @@
]
},
"metadata": {},
- "execution_count": 6
+ "execution_count": 83
},
{
"output_type": "display_data",
"data": {
"text/plain": "",
- "image/svg+xml": "\n\n\n\n",
+ "image/svg+xml": "\n\n\n\n",
"image/png": "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\n"
},
"metadata": {
@@ -157,15 +157,67 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 84,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "ipykernel_launcher:12: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " artist_top_genre popularity danceability acousticness loudness \\\n",
+ "1 1 30 0.710 0.0822 -5.640 \n",
+ "3 2 14 0.894 0.7980 -4.961 \n",
+ "4 1 25 0.702 0.1160 -6.044 \n",
+ "5 2 26 0.803 0.1270 -10.034 \n",
+ "6 2 29 0.818 0.4520 -9.840 \n",
+ ".. ... ... ... ... ... \n",
+ "514 0 20 0.838 0.0358 -3.723 \n",
+ "515 0 14 0.786 0.1950 -4.232 \n",
+ "519 1 2 0.879 0.2240 -4.602 \n",
+ "522 0 26 0.863 0.0366 -3.130 \n",
+ "525 0 10 0.735 0.6320 -2.582 \n",
+ "\n",
+ " energy \n",
+ "1 0.683 \n",
+ "3 0.611 \n",
+ "4 0.833 \n",
+ "5 0.525 \n",
+ "6 0.587 \n",
+ ".. ... \n",
+ "514 0.931 \n",
+ "515 0.806 \n",
+ "519 0.916 \n",
+ "522 0.896 \n",
+ "525 0.918 \n",
+ "\n",
+ "[286 rows x 6 columns]\n"
+ ]
+ }
+ ],
"source": [
"from sklearn.cluster import KMeans\n",
"wcss = []\n",
"\n",
- "X = df[['popularity','danceability']].values\n",
+ "X = df[['artist_top_genre','popularity','danceability','acousticness','loudness','energy']]\n",
+ "\n",
+ "y = df['artist_top_genre']\n",
+ "\n",
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "\n",
+ "le = LabelEncoder()\n",
+ "\n",
+ "X['artist_top_genre'] = le.fit_transform(X['artist_top_genre'])\n",
"\n",
+ "# X = le.transform(X)\n",
+ "\n",
+ "y = le.transform(y)\n",
+ "\n",
+ "print(X)\n",
"\n",
"for i in range(1, 11):\n",
" kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
@@ -176,7 +228,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 85,
"metadata": {},
"outputs": [
{
@@ -190,8 +242,8 @@
"output_type": "display_data",
"data": {
"text/plain": "",
- "image/svg+xml": "\n\n\n\n",
- "image/png": "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\n"
+ "image/svg+xml": "\n\n\n\n",
+ "image/png": "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\n"
},
"metadata": {
"needs_background": "light"
@@ -209,22 +261,15 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 88,
"metadata": {},
"outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- " popularity danceability\n1 30 0.710\n3 14 0.894\n4 25 0.702\n5 26 0.803\n6 29 0.818\n.. ... ...\n514 20 0.838\n515 14 0.786\n519 2 0.879\n522 26 0.863\n525 10 0.735\n\n[286 rows x 2 columns]\n"
- ]
- },
{
"output_type": "display_data",
"data": {
"text/plain": "",
- "image/svg+xml": "\n\n\n\n",
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEKCAYAAAACS67iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOydd3gU1feH3zvb0wmhhN6RIjUUQYoKShF7QUVsiL33jn7tXUFRrKgIUhURpSMdQaT33kNCerbv3N8fk2yy2dlkExIRfvs+j49hdubO3c3mzLnnnvM5QkpJhAgRIkQ481FO9wQiRIgQIULlEDHoESJEiHCWEDHoESJEiHCWEDHoESJEiHCWEDHoESJEiHCWEDHoESJEiHCWUKZBF0J8LYQ4IYTYHOJ1IYT4WAixWwixUQjRqfKnGSFChAgRyiIcD/1bYEAprw8Emhf8NxIYe+rTihAhQoQI5aVMgy6lXAJklHLK5cB3UmMVkCCESK6sCUaIECFChPAwVsIYdYFDxf59uODYsdIuSkpKko0aNaqE20eIECHC/x/+/vvvdCllDb3XKsOgh40QYiRaWIYGDRqwdu3af/P2ESJEiHDGI4Q4EOq1yshyOQLUL/bvegXHgpBSjpNSpkgpU2rU0H3ARIgQIUKEClIZBn0mMLwg26U7kC2lLDXcEiFChAgRKp8yQy5CiIlAXyBJCHEYeAkwAUgpPwNmA4OA3YAduK2qJhshQoQIEUJTpkGXUt5QxusSuK/SZhQhQoQIESpEpFI0QoQIEc4SIgY9QoQIEc4SIgY9QoQIEc4SIgY9QoQIEc4S/tXCoggRIvz/4lB2NnP37gbgkqbNqBcXf5pndHYTMegRIkSoEr5Zv463ly+hsA39uyuW8lTP3tzaISLIWlVEQi4RIkSodA5mZ/H28iW4fD7cBf+5fD7eWr6EQ9nZp3t6Zy0Rgx4hQoRKZ86eXahSBh1XpWTOnl2nYUb/P4iEXCJEKCD9aAa/fjqHPRv206JLM4bcfTHVakZivhVBx5YXvUYpL0Y4JSIeeoQzBiklqqpWydh7Nx7gjtYPM+W9maz+bR0/vTmD21s9xOGdR6vkfmc7FzdthiKCzYsiFC5u0vw0zOj/BxGDXkGk9yDSswMpfad7Kmc9+dn5vH3rGAbZbmSAeSiPXfASh3boCnpWmI/uGYc9x4HH5QXA7fSQn2Xn04e/rdT7/H+hUUI1HuneA4vBiElRMCkKFoORR7r3oGFCQrnGUqVkx8l09mdlVtFszx6ELG1tVIWkpKTIM1EPXXoPI7PuBe9+EAbAjEh4C2Hpe5pndnYipeT+bs+wb+MBPG7N2AohiI6P4tudHxOfFHfK9/D5fAy03IBUg/8WTBYjsx0TT/ke/1/Zl5XJnN1azPySZs1pnFCtXNcvP3SAR+bMxu7xoEpJ/bh4xg6+jCbVEqtiumcEQoi/pZQpeq9FPPRyIKWKzBwO3p2AE2Q+yExk5oNI7/7TPT2k9xBq7hjUnNeRruWcrod1ZbJt9S4ObjvsN+agGXm3y8MfXy+slHsoioLRpL+dZLFZKuUelYHeJuN/ncYJ1bg7pSt3p3QttzE/mpvDyF9/Jt1ux+7x4PR62Z1xkhumTcbji6yM9YgY9PLgXgNqJlAyjutF2iedjhn5UR2zkemDIX8s2L9FZt2HzLq3SkJC+dn57F6/j9zMvEofuySHd+jHsN0ON3s3hmzcUi6EEPQb1huTxRRw3Gw1MeCOCyvlHqfC9G1b6PH15zQb/T7nffUZU7ZsOt1T+leYsnUz3hJ7JhKwezwsPVg5v/uzjUiWS3lQ09DfoPeCr3JjuuVBqnbIfgZwFjtoB/dKcM0D64BKuY+qqox74nt+HTsHo9mI1+3lomG9eejTOzEYDZVyj0Ky0rJZN38T2ek5qDqhEEuUmeadmlTa/e754BaO7jnO9r92YTAa8Hl8tOvbhtv+N7TS7lERft6+lRcWzcfh1VYoqfn5jPpTW5lc2+bc0zm1KudYbi4enU1wVaqk2fNPw4z++0QMenkwdwS8Oi/YEJZe//ZsivCs0eL5Je2etCMdMxGVZNCnvT+LWZ/Pw+304HZ6AFg4YSlxiTGMeHNYpdwD4OfRsxn35A8YzQYEAq/bi8GkGVkARRFYoixcctsFlXZPW4yNdxeOYt/mgxzecZSGberT4Jy6uucWhrKEEJV2/1C8v3K535gX4vB6+WD1irPeoPeo34BZu3Zg93gCjksJKcl1TtOs/ttEQi7lQBjqgu1qwFbsqAUMtcF22emaFqU+l4W50u4y9f1fcdldAcdcDjczP51TafH6PRv28+XTE/C4PDhyndhzHag+FQHYYq2YLEa6De7MJ3+9SWy1mEq5Z3Eat21Ar6u76xrz4/tP8OzA1xhgHsog2428MewjcjJyK30OxTmapz/+8by8s2KPpDQGNGtBw/gELIai1Z/NaGJg8xY0Tax+Gmf23yXioZcTEfcSmDsh7T+Amg/WAYjo2xDCevomZe6C7rNZ2BC2ayrtNqFi5s58J6pPrZSwy5xvF+FxeYKOm61mHv/mPnpd1e2U71ER7LkOHuj2DDknc1FViepTWTJ1FXs3HuDz9e+iKFXjG9WLi+OgTql8ndjYf2WFUBKX10uu202izYZSxfc3GwxMufYGxm9Yx8wd27Eajdx0bnuuatWmSu97JhMx6OVECAG2yxCn1SMPRAgzVBuLzBypHZA+QIJtKJh7Vtp9WnRuypYVO4KO1z+nbqXF0B15Tt2YuZQSZ75T54p/hwU/LMFpdwXMzev2kro/jQ2Lt9DxwqoJfzzZoxePz/sDZ7Gwi9Vo5IkelRvik1Ky5ugRFuzdQ7TZzOUtWwXki7u8Xl5Zsojp27YggXiLlVF9LmBg85aVOo+SRJlM3JPSjXtSTs+D/EwjLLdCCDFACLFDCLFbCPG0zusNhRALhBAbhRCLhRD1Kn+qEUpDmLsgaixDxL2CiHsakTQLJe6ZSvXi7vngVqxRFhRFG1MIgSXKzP2j76i0e/S6qjvW6OBUQZ/XR+f+7SrtPuVl3+aDOPNdQcd9PpVD26uumnRQ85a8238ADeMTMAhBg/h43rroEi5v2arS7iGl5LG5v3P7L9P54p+1fLJmFQMmjGf6ti3+c55dOI8Z27b6xbbS7Pk8Nu8PVh8+VGnziHDqlOmhCyEMwCdAf+AwsEYIMVNKubXYae8C30kpxwshLgTeAG6uigmfjUgpkfaJYP8K1CwwpyBin0AYm5VrHKFEg21IFc0SWnZpxujVbzDhtWnsXreXhm3qc9NzV1dqtknKJe3pfHF7/p67AWe+CyHAbDNzy8vXk1i7fHnMFcHn9ZGbmUdstZiAVUfT9o2wRluCjLrBoNCwddX6L4Oat2RQFXrCSw7sZ+7e3di9WqhLyyxReX7hfPo1aYqU8NuuHbhL5H47vV4+WbOKbvXqV9ncIpSPcEIuXYHdUsq9AEKIScDlQHGD3hp4tODnRcDPlTnJ04GUMqR3K72HkM5fQc1DWC8AU8opecIy921w/AjSoR1wLUa6/4LqMxHG/9YfS6M29Xnux4erbHxFUbjhmSvZvW4fLkc6oBnTfsN6V2i80n6PS6etYvK7M8lOyyHlkvZExdr4dexcPG4vZquJYS9ey9UPD0YIwYU3ns/4UZNxOz2oPi2VzmQ2Urd5Mu36tK7Ym/2P8OvO7UGZJABGRWHZwQM0S6yOSVGCDDqgG9+PcPoIx6DXBYqvqw4DJQNaG4CrgI+AK4FYIUR1KeXJ4icJIUYCIwEaNGhQ0TlXKdK9BpnzKni3I0UcRN+KiL4bbaECqn0m5DwH+NAKiiaAuStSiQffATB3R0TdgjAkhXc/NRvsPwDFPT8J0onM/wIR/0plv8VSyUrLZvmMv/C4vHS7tBPJjWud8pjrF21mxsezyUrLoecVXbn0rv5ExRZlCmWlZfPzx7NZt2AzibUTNO+8WDbNjjV7eOyCUXy15YOwH5x7Nuxn9P1fsXXlDqxRFgaOuIg73rgJc0Hx0Pf/m8JPb/+Cq8DjnvX5vIDSf4/Lw/gXJhEVa2XQiH7YYmyMWf0Gnzz4NWv++AeD0UDfoT25+93hp2VzsjIxGQwI9EssjIpCg/h43SpVgxB0qJ1c5fOLED5larkIIa4BBkgpRxT8+2agm5Ty/mLn1AHGAI2BJcDVQFspZVaocStDy0Wq2Uj7VPCsB2MLRNT1CEPNio/n2Yo8OZSAAh1sEHUNStwLSDUXeaJnidcLKfyTMIOIRiT9jDCU/WWX7g3IzNtB6qSnGVuiJP0a3tylF5n/NdgnaEVFll6I2McRhvDzdZdMXclbt4xBEcKvanjjc1dz03NXhz0GaBkhf8/biBBwaPsRJrw23Z/uaLaZqVm/Op+ufQtbjI2TxzK5u+MT5GfbdbNbCrHFWvnfzKdp36fsDIfUA2ncee6jOPKKfk9mm4mUizvw8ownyc/O57rkkbid7jLHqlE/iR8PjA3jXZ+5rDl6mFt/nhaU7x5lMrFmxD3YTCY+XbOaT9as8p8jAJvJxC/X3xRJIfyXKU3LJRwP/QhQfN1fr+CYHynlUTQPHSFEDHB1aca8MpC+o8i0y4A8QAXXXGT+F1D9J4SpYhtGMm8MgZ4ygAPsk5ExD4N7BQhjiGrRwoNukD5k3oeI+LfKvqkhGWTwZhsIMDQOf+7ZT4JzXtH8nb8hXcuhxu8IpWwho5yMXN66ZQxuR6CRm/j6dLoN6kSzjuHNZcnUlbx96xgMRgNSShy5gQ8/t8PNiUMn+W3cfK55dAgTXp1KbkYePm/pEgVSlRzfdyIsgz7j49/8qolF9/Wwds56ju1L5eSRDEwWY1gGPfN4+RT+1i/azI+vT+f4vhO07tGSYS9cQ73m/20vtkudetzesTNfrvsbgVa4JaVk7KDLsJm0Fc09KV2pExvHp2tWkW630ym5Dg91O49NJ1L5acsmmlZL5NIW5xBtrry6hwjlJxyDvgZoLoRojGbIhwI3Fj9BCJEEZEgpVeAZ4OvKnmhJZNYzQE7xI4ATmfkQoubcig3q3YmutRYm8B0l/CxPH7iWhXWmMNREWi4A12ICHyYWRMxd/n9J93pkzivg3QoiBqKGI2LuRQgj0nsInHOB4gZKgsxB2ichYu4tcx6rZ63DYAhOevK4PCz4cWlYBj39yEndh0JJ3A43K39dyzWPDmHN7+vLNOagxcLDfajsWrcPrye4otdkMXF4x1HqNk8OEPsqjfqt9KtF9Vg4aRnvjxiLy669/9QDaaz45S9Gr3qDhq3+24lfj513Pte2bsvi/fuIMpm4uGlz4iwWjuTm4PJ6aRifwJHcbNIddnLcLg7nZHPHzBnYvR7sHg82o4l3Vy5j2nU30iA+tDxuvtvNvL27yXQ6Oa9efc5JqvEvvsuznzItlJTSK4S4H5gDGICvpZRbhBCvAGullDOBvsAbQgiJFnK5rwrnrOFZrX9c3Y+ULoSogEqesSX4DhFk1KUHDHXB2IBgYa4QKOFrPouEd5A5/wPHL9r4htqIuFEIk+aNSu9uZMYtQMGmqcyB/C+Raioi/jWkZxugF67wgfNPCMOg+3yqbuWhlIRlcAH+nLxSV4K2JEJAtdra5xNbPYbj+0+Uer7FZqZd7zY0bd8orHk079SYrSt2Bhl1j8tDvZZ1SG5ci5YpTdm2ehfeUgy7xWbmrneGh3VPn8/Hpw994zfmAKpPxZnn4tvnJ/HStMfDGud00iA+geHtOwJaT9Abp/3EnswMFCEQQuBVVf/G6M6MgO0xHF4PLp+XZxbMZcJV1+mO/8+xo9zyyzRUKfGqKooQXNq8JW/1u+SM34f4rxBWHrqUcraUsoWUsqmU8rWCYy8WGHOklFOllM0LzhkhpW4MoZIJbVgrensRcx9QsuLTBlHXI5QYhLAhEsYUnGMFTGjRxJIfow2ibgv/vsKKEv8aotY6RM2ViKQFCEtRVofM+5zgUJATHDORagbIPELEgfRj8zp0G9TRn71RHLPNTJ9re4Q1hjPfFZbxN9vMXHH/QACueXRIUN65waRQvW4i8UlxJNWrztCnr+Dln58Iaw4AVz00GJO1hHKizUyXAR39m7yjZjxB+z6tMVlM2GKsxFSL5vqnLqdFSlNiqkXTukcLXvvtWTr3bx/WPTNTswNi9oVIKdm8fFvYcy9kd8ZJVh0+RI7rX/hTKoFXVbl+6k9sP5mOy+fD4fVi93h0s1yKo0rJX0cO657nU1VGzvqFPLfbP5bT62X2rp38vvu/32P0WG4uzy+czwXjv+L6qZNYsG/P6Z6SLmdupaiIKTBkJTEgRHTFhjS1hsSv/FkuiHiIvg0RPbLY8LVBSQQ1Hc2QWzTv3XdEC81IN0TdgLCVbyMRCio+9bRXvNvQfYAJE3gPIZQEJIr+OUp4G1bVaiVwz4e3MfaRb/F5fag+FbPVTP/hvUk/fJIZH8+mdY+WtExpGnKMLgM7MPHNGUF6L0JopfsGkwGfV+Wud2+mbc9zALhgaE/2bz7ItA9mYbKY8Lq9NOvYmFdmPkVcYmxYcy9JzQY1+HDp/xjzwFdsWaFluQwa2Y/bXr3Bf05cYixvznmBzNQscjPzqdus9ilVu0bHR4VcnVQrR/58mj2fETNnsCvjpD9V8MGu53FPl3+vUnLpwf3kud0V0l8XCF1JgI2pxwOqXQuxez1M3rKJQc1bVGiu/wapeXkMnvgdeW43XlXlQHYWm0+k8th553N7x86ne3oBnLkGPeYhyH2DQCMmwHaDP8UwHKTvJNIxGbw7wHguIuoalCT9NHopfVroQ00nwCP2HYGE0VqYx9QirE3IcmE8B7y7CTLY0g3G+mh71kYCY+gAFrAODvs2l47sT8cL27Jo0nI8Tg+N2zVk9P1fsmDCUrxuH4pBoVO/c3lp6uO6xq9F56Y069SYLcu2BxzvcEFb7n7/VnIz8mjRpSm26KJVkBCC21+7kWseG8LeDQdIqptIvRanrqQXHR9FUt1EYhKiiIrVfjaagudcrVYC1WqVryWaHrZoKxfeeD6LJi0P2EOwRFu48Zkr/f8+lpvLOyuW8ueBfUSZzAxr14E7OnbGWKAFc8+sX9iWdgKvlP5cqjFrVtEyqQYXNq68Aq7SOJGXhyrL37vVKAR9GjX2v5fieKVKqKiKR/1vN6v47O+/yC8w5oU4vF7eW7mMG9q2828c/xc4Yw26iBquhRvyv0ILe6hguxIR90zYY0jPLmTGUM0w4gIWIvPHQfVpCKPOJpZ7lZYSGBTe8IBrCSL+hQq/n9IQ0XchnfPwx9ABsILtUv/DQ8bcC3mfFzvHCsYGiKgrQo4rpWTbqp2snr2OqFgbFwztSd1myQx7/hqklNze+mFyT+YGdHBfN38Tv42bz2X3XhI03rF9qexaG7wU3bJiB7GJMTRp11B3HqkH0pjw2jQ2/rmFGvWTuOHpK+nUr+Jl/pmpWdyT8iT5mfmoqiTnZB7fPD+JA1sP8+i4uys8blk8+MkIPC4PS6etxmg2IFXJsBevpe/1mp5OltPBZZN+IMvpwCclmU4nH61ewba0E3w4YDCHc7LZkpaGt4Rn7PB6+fKftZVu0LOcDranp5McExug29IxuU6oAF4AArAYjahSYlIUqtuieP3C/rrntq+VrOu524zG/7zY1opDB3V12Q2Kwu7MDM6teeq1GpXFmWvQhUDEPoKMvkvzkA21EEr5+kvKnBdKxJ+dIN3I3DcQ1T4JvkDNAl3PxQeOn1AdE8DYDBH7LMISXtw5HISpOSSOL8hy2QIiFqJuLoj5aygx9yJN7ZH270HNAesliKjrQqpASil557ZPWDp1FU6HC6PJyHcvT+HJb+6jz3U9OLY3lbRD6ZRcdbvsLmZ/qW/Ql05dpSusBbBs+mqufHBQ0PFj+1K5p/OTOPOc+LwqR3YdZ9uqXdz38W0MvP2icnxKRfzyyR848wKFtFx2F/N/WMLwUdeRVEd7CG5ZsYMZo2eTcSyT7pemMHhkP6LjokodW0otfdJoNlKjXmA4y2w188wPD3Hfx7lkHMsiuUnNgBZ2EzdtJN/jxlfsQ3V6vczZs4tD2dlku5yYDAouHYc1w273/7wh9Tiv/LmQjanHiTVbGN6+A/d3PU/XMw71Ht5esZRv16/DbDDgUVU61Erms0svJ85ioUX1JC5q3ISF+/b6884tBgMxZgte1UeOy0WrpBo817svPlVr4NwoIYE+DfW9c9CUEz+6ZDD3zp6JKiUun48ok4mU5LqVqktTFdSOiWVXiU1gAI/PR42o0r8v/zZnnEGX0gPSCSJGM+pKFCjNKzCOVytICvJFVHAt1b/G1IlAL7k4Bcts705k5t2Q+C3C3CnwejUPhFmLlZcTYe6ASJpe+jmWnghLeOqKa/5Yz9Jpq/wVmYXZHu/c/gldBnbE6/GFzDzwevSXyF6PTzeOrKoy5DU/vDIVR64zYEPWZXfx+WPf0f/mPiF7fUop2bpyJ+lHMmiR0iSgonXT0m36ErwWE/s2HiCpTiK/fTGPsY+Mx+1wIaVWjTrrs7mM/fstouP192C2rtzBGzd9ROaJbKQqqX9OXV6Y/Ch1mwXmmcclxurG/9ccPaIbRzYZDGxNP0Hfho2DHqCgGcOLmmh7F3szM7hx2mQcBborWS4n49at5XheHm/2C37I6jFj+1a+2/APLp8PV8EG5rpjR3l87u+MG6Kt6D68ZDCTtmxiwqYNuLxehrQ4hxGdUojRyTM/v4H+yqskfRo1ZuHwO/hlxzZOOuz0atCIHvUbVLkM76lyV+curD16OKDwyqwY6Fq3PrVjKrbPU1WcMQ0upPSg5ryGTO2MPNENmdZH66PpmI2a+x7SPh0pQxlbPRS0LEwdQhhc4dtLeM9AJzLvo6K5u/9BTRuEPNEFmdoRNethpFq1jRHKYsGEpbrqgQajgXXzN1K/ZR1iE4MbSJhtZvrfrK+r0uPyLhh04tSKIuhxuW5hGxsWb9HNrvH5VI7v009nPHkskzvaPMzTA17l/RFjGdHmEd6941N/dWuDc+qi6OTUe91eajWqidPu4rNHx+Oyu/wG1O1wk34kg18+naN7z8zULJ6+5FWO70/DZXfjdnrYu/EAj/Z+UTfnXY+miYmYdDxYn6p1s7cYjYzqcwE2o5FCE2cxGEi02RjRUfv8Plv7F25f4P2cXi8/79jGyWJefGl8sW5tUFWoW/Wx5OB+sp1a5N6gKNx0bntm3zicBcNv5+HuPXSNeXmpFRPDyM5deOb8PpzfoOF/3piD1jnpxd4XEmM2E20yYTYY6FG/AaMHXnq6pxbEGeOhy5yXwTETf9m9ehyyH0FiBlxIoiDvHUicHJaglRAK0joAnH8QmMNtKehKpIOaBcICMow/YK+WiiW9h5CZtxYJb+ED5zykZwfSUB/wIGxXgnVwuTZzTxWDMfSzXDEoCCF4buIjPDPgVXxeH26nB1uMlQat6nLFAwN1r2vUpj7XPDaEae/96i/cMZmN3PDsVQFe7OFdx5j+wSz2bzkUMg/c5/ERl6Tv/bx2wwcc2XU84EGw+KcVtOrWnMEj+3PVw4OZ9/2SgGwbk8VIyy7NsEZb+Ov3f3RXDB6Xh6VTV3LjM1cFvTb3u8VBKZlSlTjyXfz1+z/0uKyL7lyLM7xdR37ctDEgHmtSFJpXr07rGppkxdWt29K4WiJf/fM3x/Ny6dOwMcPbdyDBqmnfbEk7ERCyKcRiMHAgO4vqYYQACo12SRQhyHO7ibdakVKyaP8+Jm7eiNPr4bKWrbiiZStMhn/vO1qZ7M3MYPmhg8SaLfRv0rTcFa3Xtz2XK1u1Zn9WJtVsNmpEVSyTrqo5Iwy6VHPBMYPg4hlJUX62HVQnMudZROL3YY0r4kYhvfvBtwcQWmMIc0dE7CP6F5g7h2fMAYzaElnavy/YdC2OR7unT9tAlO514JwFCZ9XaYGFVLUeo3jWcPNjNrYsExzdK0ucI/0bkm16tOS7PWNYMGEpaYczaNerFd0u7YShlD/q214ZSq+rurF06iqEEPS57jwan1u0JN+yYgdPX/I/PC4PPq+q+2AxWU10G9RJN2yRmZrF9tW7g7x6l93Fz2N+Z/DI/tRvWZfXZj3DeyPGknZYi32269uGEwfSua3lg0gpQz5I7Ln6xu7EwXR/H9XieFweTh7JCPl5FKd+fDzjr7iap+bP4XCOplLYt1Fj3ioRKumUXIdOIXpmtkqqwc6T6UFG3eXzUT8+Pqx5nN+gITO2bw0aI8ZsJjlW+8xfX/onEzdv9Evqrjt2jOnbtvDDlddiqKLuTFWBlJJXlixi0uZNgMSoKLy4eD5fX3YVKXXCrwIGLfTVonp4onunizPCoKOeQL85c9CJ4F6LlO6w4tRCiYXqU8GzEXz7NYGvUnRghKE2MuoWsH9PUSzdiKa8WPyPw4qIeVD70bOx4PXScIBrtZZFYzmvzHkXIj07kM55CGEE60CEMXQsU6qZyJNXgS8DcFCrhplxCyXP3NCU7euiC7RX4IUpj2GNKtrIS6gRz9UPl29p2axDY5p10C/T/+CuzwNCPT6vZpgVg4IlyozX7aPLJR148lv9YmNnvgvFoP/QK17Y075vG8bvGk3OyVwkcEerh8jNyNONURcn1AO1dkP9EnWv21sueYCUOnWZf/NtZDmdWIxGosqZ8nZ3Sld+370zIGRiNRoZ2KyF32uUUjJp80bGrv2LfI+bvg0b83zvvlSzad77w917sGDfHvLdHtyqD0UILAYDr1/YH0UIDmVn88Om9f74OmiVoJtOpLJo/176NSmfTv/pZPGBfUzZshlXQZiq8D2N/PVnVo+4+4xdcYTijDDoEgMhKyFPESEEmNsD4VUEitjHwNxZk82VeWAZBDIf7N+CzARDE0TccwhzwRI8yDsPhQPpWooI06Crue9D/reAB4mAvE+QsU+hRA/TPV/mjgHfCYpWOW5MZnhjci6/TRmJNcbG+Vd1rXAxTzg47S4ObT+i+5rJauKjZa+SUCuBajVDe5q1G9cktloMLnugV2w0Gzm/RL9RIQTxSXEsmLAUj8tbpjEHaHyuvqxz5okc3eOKQeHEAU23XUrJr2Pn8OMbM8hKzaZh63rc9d4tdLoosD2dEIJqNpvecGXSLLE6P1x5Lfiea90AACAASURBVC/9uZAtJ1KJNpsZdm4HHulelFV13++/8kex6ssZO7bx+55drLz9LuKtVurExjHp6ut5buE8tqWnUd0WxRM9e/kN9crDB1GEQklHxO7xsHDfmWXQJ2/Z5F9lFMerqvx97Cjdz7LmHGeEQUeG20vSAOaeFcoiCRchBFgv0BpbFCf2Hv1mCkq4sTYzKOFVFErP1gJjXvxz8ULuW0hrP4ShdvBFrjno6b2YDOlc+UAXhKHqc2lN5tBfN4OiBIRmQiGE4Mnx9/PCZW/h83jxenxYoizE14jVjX0DpB/J0A2XBA8Ol96ln0ett3EL2nsqjMdPenMGP74+3b8C2bvxAC9e9iZvznmetudXXmpex+Q6zBw6TPf7djgnO8CYF+L0evnfkkW8e/FAUvPyuHH6FPLcLlw+H05vDk/M+4Nok5m+jRoTb7XqblYaFYXECj6IThch5QqElnZ4tnGGBMPKeu4YQUSDUhMR/5r/qJQe1PzvUdOvQE2/HDV/PDJsj7n86C7XzX2AcB4wmkek5n6EdK3QFcoqRDr/ILgqFECAa1HRee5/UDPuQk0boK0idFG1jd5/iwqUk6uqyrzv/uSB857lznaPsnXlDj5e8SqXPzCQnld0ZcSbN/HFxveJq66/ujinWzNMlrJ9F6PRwK51+3Rf63V1dyxRwb9HVZV0HdQRj9vDxDdmBGUOuRxuvnlhUuAxr5d1x46y82R6qb/nQranp/Hg77Po9/033D/7V7aladk/et+3GdtD68Ys3q+9t49WryDb6fCHH3xS4vR6eXrBHFQp6duwMUZF36Bf07ptmfP9L3HFOa2JMgaHtVQp6VK3fDH0M4EzwkMXxnpaWEFX2rY+IuYGMDQES1+E0H55Ukpk5l3gXovfk819D+lcAInj/zV1NxF1HdL+HagnKfKQzYDQtNURgE8rWMr7FHAi7VFg6gDVvvC/n0CUguuC7uY/Lp0LkFmPoG0ayxDnG8DUCVEOZchwyEzNYvVv60AIul/aiYQaWgjF7fKEDJz5fCpH9xwnPik2KA/8w7vGsWjSMr+x/HH3dBZPWsEna9/ydyAqjXa9W9OySzO2r96FqxRpX6/Hx7Lpqxn6VHB1bcsuTbFGWwPUFAEatq5LUp1E0o+cDOnFH9h62P/zzB3beG7hPIQQ+FSVOrFxfHXZlQGSs5tOpDJ+/TqO5eXSNDGRqVs241ZVVCnZn5XJov17+faKq+lSJ7iauZpVv5AMIN/j5sLvvuJ4Xl5QNSpAjsvFsdxc6sbF8f2V12ryuB4PQmgG8J1+l9Aooer7ulYmg5q14Jft21h15BB2jweTomBQFN7tPwCrjqE/0zkjDDpqDoQy6DIH6VwExtYIc2cQBToqnrXgWUdgWMIJ3o2lbj5K6QbnbKRrJRjqIKKuLVfXn5IIJQ6SfkbmfQbO+aDEIqJvQVqGILwbtEKprMeA9GKTsIN7HdI+BRF9Y/CY1sFad6KgzVYVLP20h1nOqBLvvfCzU6CwelSpiUh4r8LvTY/fv17AmPu/8ueBj77vCx754m763dQbW7SVus2TObzjaNB1HreHuzo8js/r4/yruvPYl3djsVk4svsYCyYsCQiZuJ0eju8/wZ+TV9D/5j5lzkkIweu/P8fPH8/mj68X4nK4yTiWqZu6GF9D38tfOXOtrs77oe1H2bvxAPVa1kHoeLUA9Vtq359taSd4esHcgOKifVmZDJsxhcW3jEARgl93bOepBXNw+3yoUrLq8KGAb70qJQ6vl5cXL2TWjcHSvte2bsuoPxfqCmu5fD72Z4XuO6OqkmizZuTa1qzFittHsj71GC6vj07JyVVmAFUpcXg8RJlMle5oGRSFL4ZcwfJDB1m8fy8JVhtXntOaunHlqyqvDBweD7/s2Maao0donJDAdW3OpWZ0cK3HqXBmGHRpR/Nq9WLp2eBZA541SMf3yMSfUMztwf23fuxdOsDzt65Bl2oe8uR1oB7131Pmfw3VPgt7s1IPoSQi4p6FuGeLjgGYU8CzFYleQYgTHNNBz6CbmiNj7oe80WiGWtH+H/syyCykayuooVLpohBxL4BSB8xdEKLyom7H959gzP1fBcWrP7jzMzpc0JakOok8PHYkz136Bh6X1mxZMSioPhXVq+L0ah748hma1v2zEx5i64qdBUJggWM68138PXdDSIOek5HLtPdnseKXNcQmxnDVw4O57onLue6JywG4s92jHNx2JMCrtkZbuPJBfTGz9Ys368rjAmxZvp0m7RpyzWNDmPLurwH57xabmVtevh6A7zeuD4rbqlKS6XCw7thR2tWqzfOL5gUY/FArmm3paboxdIvRyEeXDOahOb+VSy3RpCicV7+BP98dNGPYObkoLJHjcvHbrh2k5uXRObkOPU+xMEhKybh1axi75i/yPR4SrFae6HE+17U5t+yLy4EQgvMbNAy7orUqyHDYuXzSBDKdDuweDxaDgc//XsMPV11H+1o6e14V5Mww6IYGoMSAWtbmqAoZt0Ptv0FJQtMsL2EshbXgtSKkdIAvDemYVtDgovAPUvPIZPbjUGNppRq/IkoZs5Q/FiVmpFYY5VoIGJHGtpDzHDL3cMGYITYBDTW0QqYqYMmUlSG1XL59fhLndGtOhwvbMnrV60x5dyYHthwi/UgGGccDvUa308Oy6avJzczTGmHobdCZDdSor58TnJ+dzz2dnyTzeJa/Fd2udXvZ/eg+bn15KACvzXqGpwe8StqhkxiMBjwuDzc9fzUpF+tnOyXVTcRsNQU9rAxGg79Zx/CXrsOZ5+SXMX/gcXuJSYzm/o/voMMFWtw5NT9PtyhICEGGw87Ok+mE0R8EAJsxtDc7uEVLzm/QkImbN3IoJ4uJmzeFHCfWbMarqpyTVIMPLtEvGAPYfCKVa6dM9MfdFSFokVidGdffhMVYMTPyxbq1fLx6pT8F86TDzst/LsSsGOhUpw4JVhtxln9xf6cK+XDVClLz8/yKjYWyC4/Nnc38m2+vtPucEQZdCAXi30Jm3oeWj15aTnouqpqPsA6A3Nd1XBwFrJpIlJQ+ZO7bYJ8IQimo5tQL6+SBb5+/WOhUkdKFzBuneeD4QrhhNoTt2lLHEcYGYLxV21hL7w++w5TeUckG0XdWfOJl4PX4kDpxZLfTw8KJS1k8eTlSlQy84yKe+OY+hBAMa6zfTclgMpCdlkPHi9oSHW/Dme8M0IkxGA0MulNfvGvW5/PIOpET0FfUme9iyjszuerBwcRVj6Vmgxp8teVDdv+zj+z0XFp2aUpstdDL3/7D+/Lja4FaOkJo6ZZdB2maPf8s3Myvn83DV/AZuO1uPnvkW9r1bk2NetW5sHFTVh0+FFR27/H56JhcB6fHiy9M2dqy8tdjzGY61k6mZlR0SIMugNEDh1A7JqbUghkpJcNmTAnIS1elZPvJdN5evoQX+lwY1pxLjjl27eqgz8Lh9fL4/D+wGox4pcolTZvz5kUX/6ckaivCnD27A+R3Czmck0O63U5SJYl8nSFZLiAsvRBJv0DUjWAp4wsknVqHocTvQakH2EDYQKmDSBzvV2WUeZ+AfRKayqKeLG7heGpR3PkUkVIiM26D/C9APaJJGOChoI862jPWBpYeYNNPwwvCs75Ao13PGChaMxCsEH17hRpvhMt5l6XoarkAeFxevwbKnG8XsWrW3wC06XmOru6KoghqNaqBwWDgvUUv06hNfSw2M7YYKwk14nhp2hMBglzFWfPHet14t9FsZEcxeV8hBM07NSHl4valGnOApDqJvDrrGRKTE7DGWLFEmanbog7vLX4Zs8WElJL3RnyKy+7yh3HcTg+5mXmMf+knAK5u1Zp6cfFYDUV+lM1o4s5OXagRFU39+HiaJ1bHEEYY46TDHjJDZufJdHp+M447f/2ZUUsWhhyjXa3a9G7YqMzqxx3p6SE7J03eurnMuerh8nnJdetvUKtSYvdqXY3m7tnFk/P/qNA9/ktYQjRPkVJi1vn+V5SwPHQhxADgIzQ1qy+llG+WeL0BMB5IKDjnaSnl7EqbZeF9jI0Rcc8DoB5vSSgDLBRtY0uY2kCNBf4SewxN/ctUKSXYvyG0emIhChibIAyVlOLk+Vtr9BywH+ABojQ9d4MW28bUPvwNIjWDkM9mUzdE3FNgaIBQKncDppDDO4+yYfEWouJsITcGi+PMdzH7i/mcNySF4aOuZdWstTjznP5wjTXKwh2v34ipYIOuTtPajNvwHkd2H8Nld9OwTb1S5QdqNEhCKCJI+VH1qVSrpWXc+Hw+Jr35Mz+Pnk1+toM2PVpwzwe3hdRsB2jfpw0TD33Oga2HMZmN1G2e7P8dZRzPIjM1O+gan1flr9nrtPdlNDH9uhv5YdMG/ti9kziLheHtO3JR46KV37hLr+CWn6dyOCcHRdG0VfSIMZt1vx+qlNz68zRO5IdKU9WIMhoZd+nlpZ5TyAm7XmcwDVcFc7ktBiNJUVFlztPl8zFv7x4yHY4KF2O5vF6mbdvCnD27SLTaGNa+Q8DewL/BDW3aMXrNqoD9EYMQpNSpS5ylcpxFCMOgC00x6hOgP3AYWCOEmCml3FrstOeByVLKsUKI1sBsoFGlzVIPy0Bw6TwzjG0DCouEEGDUq2zzFHjlIRBRaKmFsYiE0ac83aLbbgqhB2MHYUXEVCAkYuoQoiLVBtaLtdZ6VYCUko/uHce875YghKYDE1YBD+ByavOt2yyZT9a8xfcvT2bT0m0k1a3ODc9cyXlDgtUZS8rUhuLKBwaydMrKgBRFxaCQ3KSWv9H0R3ePY+HEZf40xPWLtvDw+c/z+fp3SW4SushKURQatw2uJrVGW0K2oIuOL1pOR5vN3NW5C3d11hfzqhUTw+833cLWtBOk2+1M3LyBuXuDm4b0qK9f0bru2FFy3cHetACaVUukfnw8Peo35KZz24cd++5QKzlUjhmN4iuW8iqE4MkevXhh0fygsEtJTIrCSYe9Qgbd5fVyzZSJ7M3MwOH1IoC5e3fzRI9e3NqhU5nXVxYjOqWw7vgxlh86oDXdBpKionn/4uAeAadCOL/RrsBuKeVeACHEJOByoLhBl0BhHlA8EJyXVsmI+FeQJ7eD7yBaTN0ISjVEtU/Du16YNbVD38HgF40tEVG3gaEGmHtUrgqioU5R79EAbGGpROohDNWR0XeC/etiqo4WMNSusg1QgKXTVrHgh6W64Y3SsEZbuOjGXv5/12uezDM/PBTyfCkliyYuY+bYOTjzXfS9vieX3z8goJVdcVp0bspjX93LR/eMQ1UlPq+PJuc24KXpTyCEIPNENvN/WBqkme52epj8zi88NHak7rilER0XRcolHVg7Z32A8JclysIVOo09SkMIQZuCLjij/lyge86m1FTd43lut27miQSS4+L48rIww3jFiLNaGdy8JbN27QicJ4TsUBQOV7VqQ5TJzPurlnEkJweL0UiO06kbOKwfF57wWEmmbdviN+agfQ4Or5e3li/lqlZt/rVNV5PBwBdDrmBbehqbT6RSNzaO7vXqV7p8cDgGvS5wqNi/DwMlO9aOAuYKIR4AooF+egMJIUYCIwEaNND3MMJFy+/+DdxLwbMdjI3AcmG5yv5F3PPIzAcpCn8IwIKIG6XltFcFlgu0qlbpIDDmbUSKWLD/BOYUhM4GrJQ+LeYu4vxhpUKU2IeQpnMLOhZlg/USELHIk4ORvjQwNkXEPn1K6Zcl+W3cfF1NdT0MRgM+rw9rjJVW3Ztz0U29yr6ogI/uHceCH4r02w/tOMKiicsYvfqNkIVFFwztSa+ru7F/8yFiqkVTu1FN/2uHdxzFbDUFGXSf1xcQYy8vT357H89f+gZ7NhzAaDLgdnnoN6wXQ+6+uMJjHsrR15A5mperm7bYKbkObp2NaZvRxMBmFW/E/MElg6gbG8e3G9bh8vmoExPL0LbteH7RfPZnZ5EcE8uj3XsypOU55Rp3QLPmDGimNag5kJXFkEnfY/d4/CmXNqORR887v8KZNHP27NJdAZgNCuuOHaVvI30RuaqiVVINWiXpC71VBpWV5XID8K2U8j0hxHnA90KItlIGbtlLKccB4wBSUlJOWW1LCANY+mr/hUC6NyCdv4KUCNulCHPHoustfZEJH0POa6CmahuoCS9XnTGnYGUQ/y5k3kWAYqOQkPMi2kemIm2DEXGv+1MlVcfvkPNyQZhIRVouRMS/gSimFSOsFyKs2oaxmv8d5L5VdA/vNq1yNvGrIuGwUyQcz9xkNlKzYQ0uuKEnOSdz6TqwE10GdEAJU4L12N5U5o3/M7CwyOHh6J7jLJmykn7D9JttABhNRpp1DP6DTW5aSzc0pBgUmoQQ5wqH2GoxfLT8NfZvOUTq/hM07dCIpLrVy76wFGrHxHA0N7gZSq3oaN0YepzFwrPn9+aN5Uv8xUk2o4nm1atzxSm0ejMoCk+d35sne/bCJyXLDx7gntkz/THhA9lZPL1gDi6ft8LyAA0TEpg5dBgfrlrB6iOHqR0Tw70p3ejftOJiYIlWm264SJXyrEmJLE44Bv0IWlv5QuoVHCvOHcAAACnlSqE1skwC9FvO/EuoOe+C/Tu0fHKJdExFRt2AEvc0ANK7D7KfAOkCnFrWSdZDyOrT9QWuKgEp3ZB1P4Gbsd6C3qbFcP4O5h5gu0zTS89+ioCNVNdCZNYjiMRxOvfwQd7HBG/4OpG57yGqTwq6piJceFMvdv2zL6CQBrRsErPVhKIo9L2+B3e8cRMxCRVrCLB5+XZdzXRnvou1c9aXatBDkVQnkfMuS2HVrL8DHkomi4lrnwhvo7A0GrWpT6M2laPi90j3nrxYIs5sMxp5qFvonrU3t+9Iu1q1mbBpA5lOJ5c0bcaQFudU2MstjhACoxC8uXxJUDs9h9fLOyuWcXWrNhWu+DyQlcWG1OOk2fPJdbtYd/woFzRuEna/1JIMa9+BuXt3B3x+Aoi3WOlQO7w9mTOJcH7Da4DmQojGaIZ8KFCyfPEgcBHwrRCiFVpFT1plTrS8SO/uAmNePJvEAfYfkbYrEaaWyOznQeZQ9Px2gOpG5ryOqPZx1czLObvgnmWd6EDaJyJslyHzx1FU7FSIG9wrkb7jmk67dBekYU4s8OJDbDR5d5/iOyhiwO0XsPDHpexZvx9HnhOT2YhiVHjhp0fpNjj8Vc7WVTsZ9/h37Fq3l/gacVz3xOXEJETz5+QV2HMdqL7gxZzRZAxZWARaWGbso+PZuHgLtlgrQ+4dwI3PXOnvUfrUdw/wxZPf8/tXC3E73DQ6twEPfjKChq2C9VFKw2l3sfq3dThyHXTq346apcypIlzdqg1en8r7q5aRbrdT3RbFw917MLRtu1Kva187mfZVaLD2Z2XqHj/psOPyeSskE/DPsaMBXr/d42H8hn9Iy8+ne736WIxGLmjUpFyt8Don1+WJHr14a/lSTAYFKSXxFivjr7j6jGh/V15EOGpvQohBwIdoKYlfSylfE0K8AqyVUs4syGz5AohBs45PSinnljZmSkqKXLt2bbkmK30nCqo5jyLMXbXO9iFi5jL/S2Tu+wQbNoPWfCL6TmRqW/Rzt60otTeWa27homY9As7fwjvZ2BYlaTpq+qXg3Rn8uohBJH6LMLVDzRgJ7iWUXlhUNGZl4XZ5+G7UZFb+upaEGnHc9upQ2vYMf2m/e/0+Hj7/hQAvXygCxaDgK9Ra0VkzW6IsfLHxPd2MlPQjJxnR9lHsOQ5/rrbFZqbHFV15dkLg5quU2qap0WQkLyufed8tZt/mQzTv2JiLhvUmKjZ0ZsXmZdt4bvAbSCRSlfi8KkOfvoLhL10X9vsvD26fD5Oi/GvCcqVx4fiv2J8drAsTb7GybuS9FZrjbb9M588D+mqXUUaTlkkl4dPBl9G7YaNyjZ3jcrHu2FHiLBY61E4+o425EOJvKaVuk96w1mAFOeWzSxx7sdjPW4Hw2s1XEOn+G5lxK0Xhk2mQOxqSpofIry5QNAxCFMjFCrTcbR0DqKtweGpIz2ZwLQdfuAsXK9gu0340dQHvXoIeTtIDhiaonv3g/pOym4BYEbGBBm3Phv2s+f0frDFW+lx7HtVqhZ+G5nZ5eLLfy+zZcABnnpOjZgNPX/wqz//0KN0vDc9D/27UZNyOwNWHVCU+tVh+c4FYpNliwmA0YDQbeeq7B0KmF874+HfcDndA4Y3L4WbZjNWcOJhGzQZFm1JCCIwmI0d2H+PB857F5XDjsrtZFG3h+1emMOavN3W9brfLw/ND3sSeGxjWmvzOTJq0b0RSnWrUP6cu0XGVUwEIWgu0UOzLymT+3t0oQmFAs+bUja1a8anG1arpGvTk2JgKP3D2ZoZu5Ve8ScU9v/3C6hH3lMtTj7NY/vUN0NPBGVH6r1VX3kNg2MEL6n5k9gua0JaxEZi6FBUOmbuhr2fiRZq6owgD0nIxuOaVOM8MtmD51MD5OMC1GFQHWHroxtulZwt4tiCVeuD8RYuJ+ytCQ1HwgBFRWhFUlKY7ImJGFmzs5uF/AAmtjF8oMUjHFEIb8wJJXaUWxD4N5p5I31GkiGfMAz8yd/xivG4fBpOBL5/6gWcnPhxWw2OAOd8sYvc/+/y53F63Dy8+3rz5Y6akfukvDCqNPev3hyWRbo22ctNzV5FycQcan9ugQLBLn+1/7fI3qS6O2WLiwNbDAQa9kA/vHkduZr4/l9yZ78Lt9DD24W95adrjQeevX7hZt1LTZXfxyjXvYoux4nX7uPaJIdwy6voq9ao/XbOaMX+twidVhBC8u2Ipz5zfh5rRMeS6XfSo16DS1QXXHdPPTN518iReVa1QzLt1jRoczsku0y1RhGDx/r1c2qJ8GTX/HzgzDLp3PxBC9tP1G9K1UBPWMNSFxO81dUPPuoLWdSUr2QwIz1owt0bEj0Jm7NY0UKTUbJ+xJUTdqknyGmpqsrzF/hilew0yszBPWUKODxlzD0qMpkkipbtAh31dwTkqhauKMhE1tQ5Hlj4QNQJpn4b0bgfjOZA4AfK/BPdKUKojou8Ea6EyYCl58qIOouYchDCj2n9CnugO0o2qqjRtmoDPUwufV/F3tH/jpo+YfPzLkDnexVk4YWmQPrj2GUh2rt1Lmx4tyxyjfss6nDiYXuZ5iiJocE493ayVkjQ+twFbVuwoCtkU4HF5qNOs6OErpWTHmt1knshhw+ItupWlf/2+Dj1KpjwWR6oSe47muU97fxZ1myWHJfNbEXaeTGf0X6v8PTMLGfXnQq2xgwCfqnJbh0482bP8G8ih8OjokoD2LS+PymNxHuzWgyUH9pdZaCSpeIXq2c4ZYdBRywpTOLTfsncfMvtFRLUxSN9J9Jsz+5C+k5rfqiRA9V81+V3vPqShBTj/gPSBSGHWrjfUh2pfIww1NVGtzLuCu//kfY40n4cwd9R0z91rCd7EDAOZDr7jYD8C9vFoYSMHhVo0ovpUhPGdoMuEtT8y9zV0Hxq2wVqqpHOBlp5ZsEmsKHDBFWn4PF5GP1OUkaEYFP6Zv4kel5ftpZt1OviAZtDM1vDCVsNevJbNy7aX2nhCm69CyoAOAcfSj2aQcSyT+i3rYIspinVf9dBg5nyzKMCgm60m2vdt4682Pb7/BE9d/D8yj2eBCJYJKKRwE7UkHS5oo6unXhJnvovJ7/xSZQb9j9278Kr68ygephi/YT3n1W9ArwaNKuW+FzZqwu+7dwaoRwqgc3KdUkNDpdEqqQY/XnUdry/7k42pqUSbjOS63UEPD5+q0ruS3sfZxhkhziVMzSg9VFGIV0vnk54CGdwQFKsOFUIgzF0RUdcj1CPgmAS4tfCGdIB3DzLrQe1k14oQAzqRjqnaj47JVMiYF86/YDztYVQYn3VoOuc5r+heJQzJYL2CIE9dJPilBGTeJ5TUk7dGSfpfn4nFFmgQwtkoB7h0ZH+s0cG5vDHVosPypAHa9jyHF6Y8Rp2mtVAUgS3GSvchnTFbTUTF2YiKtRGfFMubc573FxHZcx08d+nrDG92P09c9DLX1hrBpLdm+Mes07Q27yx4iWYdG6EoApPFRL9hvXlx6uP+9/f8pW9wfG8qjjwnjtyS6Z0aJosxZAFUdHw07fqEJ6mQdSJY46WykGE2T3d4PfxUioxueXm2Vx+q26KwFWSzWI1G4iyWUitH3V4vw2dMpdnH79Pk4/dIGfcpi/YFFnK1r53MT9cMZdt9D7HmznsZ1Lylv4WcIgRWo5EnevSiRnTF0mDPds4ID10oiUhTV/CsDuNsrTAHtZQU+BAev7SPJzh326fFwn3HKWrnFnRlUcm9DE/LRGsKXVNrwiGzCalfXjRpcC8L+aqIfwNpag/547UVhPUiRMz9CKWgZFo9pnudVCGumo80h/Yw8Hp8bFyylfWLt9D76u60Pf8c3fiv1+Ol55Vd6begD3O/XYRQFAwGBaPZwP9mPl2umHG3QZ3oNqgTbqcbo9mIoig48hxsXLINi83Mub1aBcTM3751DP8s2ITH5cVTUCA04dVp1G2WTK+ruwNwTtfmjP37HdwuD0aTIaCQ6cDWw6TuT9PVblcMCiaLNocGretx59vDdOecfjSDjYu3lPneFEXQvm/V9eEc0KwFn/+9Bl8ZYQoI9NhPldoxsSwYfjs/79jGptTjNK9enatbtQlokBE01wnjAzZSM5wO7vj1Z6ZdewMdk4O7ggkheP/igaw8fIg/du/EajRyZas2VVppeaZzRhh0AFHtU2Tm/VprORSKmiQX/6MUYGqHEBaksRW4l+sPZgyRVqeG8KSEQfPYzeeFENaKQhRorGPtB44ZBKdLFvb7NBe8nzEIS2+kZxfy5DWUbdChtFi5EIrWrk6nwxEApvYFDaQDjZjXo5CZbsFsNeLzqaheHz+P/h2pqvzx1QL6Xt+TR7+422+g5//wJ18+PYGMY5nE14jn5peu5fP177JxyTbiqsfQdVCnsPp86mG2FoVwbDE2ug0KFk/KycjlbJVtrQAAIABJREFUr9n/BGidgxba+Ontn/0G3T+mzlzys+0oOsVKAPVaJHPVQ4Np2KY+bXq0DPlg2rpiB0azsVQxMoPJgDXKwm2vDg15zqnSsnoS96Z05ZM1f6EWFGbrxbejjCYuK7aJKKVk5eFDrD9+jDqxcVzStFm5NcejzWZuOrc9nKvfFKQ4W9NSdbNiAF5cPJ9fb9Da6aXl5/PVP2tZfuggdWLjuLNTCj3qNwgpRhYhkDPHoCuxiOrjkb6jWnchEQeZN4Oai+ZVW0GYEfGvaxfE3Fcgj6sTX3TNRk2domXFxD2FKFRjtPbTPNwg42oBQ2OEMCDjni+IRXu0sUWUVtFZoNEuYh5FupaBmlVsXkaIG4XwHdG0yW2DEUpB71NdJUg9TGDTb48WDiLmEa1PasAKxIxD3sPNLyaj+lQmvD4Nb4mmEIt/Wk7/4X1o17s1i39azod3f+HPGc86kc24J75n5NvDuOzeARWeW3nIzcjDYFTw6ES1SnY+CkWzjo10vXOzzczFt/Rl8MiyBacSasbrhqYURVCzYQ2sURbO7d2K65+8gloNq9ajvL/reQxq3pK5e3ZjVBQMiuCdFcvwqipeVSXKZKJj7WR/VojT6+HmGVPZlp6Gy+vFajTyvyWLmHzN9TRNPDWpglAsO3gg5GuFfU5T8/IYPPE7cv+PvfMOj6J62/B9ZvumN3pogqAB6YIVEFEQEBVB7AVFP3vF3nvv5Qc2VBARLDQFEUFUUFBQeu8tEELq9jnfH5MsWXY22XQS5r4uLt3ZKWeT7Dtn3vO+z+Px4FNVVu/P5LftW3n2rP5c0L56FEPrG3UmoBcjTE0082ZAps5GuqaB/18wtUU4hwUd7BUlBjVlChy8HmRW0dFFtenFKRfvr8isvyF1BsLUBBFzA9I1E9RstHyzAlgRCc8EFRcV5yVIS1ek6xuQ+Qh7f7CeHtRcEaYUSPsRWTgd/Mu0G4Hz4sMB/AikzCGyJrsmFoZQwNQSEfdwxX9wSnKRymPJa6k0aNWdyx7qyY8fz8NsNoUEdNCC+tMjXsNi0xpvjmzz9xR6+OzJrysd0LP3HWLjsi2kNk2mVcdQTfJAIMD09+cw/f05eAo9QUegkI9nUuh6dundk8XYHDZGv3QF79z2UUgXalxyLEP+79yoztHh9PbEJcXiLvCELKhabBaenvZAlbX+67ErL5e3/lzEb9u3keJ0Mrprdwa1bcdN3U8O7tO7RSumrF5FttvF2a2Oo0/LVpiK0k5j/17KyszMYGVMgc9Hoc/H7T/OZKaO8XRV0KVRZKP1RrGa0Nw7SxaT6/EEnX2KlRGfWDCPQW3bYangYuuxRJ0L6CURSgwi5lI0bbBwFEsGNFykdZiqWZA1nNAFSwnSjSz4CBH/KEJJgtQZyMLJWr7a1AThvAphCS2/E5a2CMv9h88i3UjXXAjsBktHsPZCiRkBRNEx6J5byptmzVzafBxYuleqllnmPqMjOeBHHroNGvyJ2WqOeP6yFvVyD+Ti9/kjVoSUOi4p+d8945n2/hwsNjMBv0rz9k157oeHSEzT8v8vXPEWi6b/HbyZmMymkO5Rk9mEI87OlY+VbtlXkj++X4KimFBLlPvlH8xn98a9pS7oqqoaNLh4ae5jPDb0RfZtO4DJpCAUwd3jbipXMFelZPr6tXy58j/8gQAXtj+R4RkdI1aK7MvPZ8jEz8nzeghIyZ78PMb8NJvv164h2+PGLASXZJzE0PYnMOY0/QXdqWtWhZU5SmBT9kH2FxRUy4Jjj6bNSHE4yHKFT14eOaMPAAu3bdW1afOrKttyDtGmmp4e6hN1OqCXhVTztPZ/94yixUq9BU0/uGejBnZq+XfnpSixo9D0xqK4hn8LMutSNBs7j9aFam4HyeMR0djWmUrRDhExweYiQFNiVA9oLf9KOTsQPfq62shDyMBueg3uxhs3/a985ywiuXFShYI5wNwvfmXmuLn4PL5gbffm/7bx7KVv8PLcx9m+dhd/TFsaIqIV8Aew2i00bdsYqUpO6pPBJWOGRq2jsm/bfv5bsBq/LzSoeT0+vnr5ex6eeKfucf8uWMVzl75BYb7mb5qWnsITU+8FIXDluTiuc8uomqlKct9PP/Ljxg24ihYs1xzYz4wN65hw0Qjd9vRx/yyhwOcNKRd0B/z8vHVz8PWKzEzmbd3M2wMH616ztMqYaKtmKsLsy69hxJRJbC7SgbEqJh4+sze9izo4U5xOtueGTx78qlrqYqvBYeptQJcygDw4EvzbOLyAGgH1gLZg6PkDWfAppExBmCNbkYVc59DdILMJ3ixkIfhWI/PHIeJuizA2L7LgM3BNQVs8taC7KBpz/eFj3HORuU8ULdxKpH0AIv6pcgT2UqogZAGxiU15ZNLdPDPyNRRFazSKxn3I5rRy3XMRFmKj4Js3ZoRpqgf8AVb9vo7szBzW/bVR13PU6/bR6qQWPPj57eW+Zub2A1hslrDPJ1XJrg361UAHdh/kkcHPh4x11/rd3NP3CSZu/6BCC8FrD+znh43rQ1QLXX4/KzL3sWDbFvq2bB12zOJdOyM29Rw+h495WzaxMnMfHRqEyyNc0O5Exv2zJKQ5RwCtEpNoEFM9NoUAyU4nc6+6Dq/fT6HfFxakb+jag3vmzAppLLIoCr2apleZiXJ9p07UoVcIz69aCqSsYA4c1nPxgMxD5j0f1SVkIAv8Gwif+XvApS+AJaXUOk3z34LA5hI18UfMxqz9UWK1jlTp/Ve7caiZ2rnxgns2Mie8JT0iokGkNxBFTwm9Bnfj/b9f4vSLetLtnE6YIxg+l6THgC6VaprJO6jvKamYBAU5haQ2S0YvE2SxmUu1iiuNFhnNdDs9zVYTJ52pXwH102fzg+bPxUgJPrcv6BlaXv7ctUN3YbXQ5+P3CIuI6fHx0XVkqCp/7tqp+95N3U/m+JRUYoqqWpwWC/E2G28MqPiie3mwms26M+4Bbdpyc4+e2M1m4qxWbCYT3Ro35c0aGld9oM7P0KX0aybJSmKo8qJ/7RELgCUpDlR6HXaqJqIV3dXL/55vOXiXEWYSLZzgHAVKKtjPQTEdzhfKgv8R3qzkAc+vyMA+hCmKwBb/EOTcRehnFmAfHJzl//LV77xy3XsItFxxIKCimJSwQFaS3775k3H3f8Hol64seww6NDu+Mfu2hfcFBHwBmhzXkCbHNSQxLQFPoTdkHCazifOu71eha8YnxzH01oFMe292MC+vKAJ7jJ2L7x6ie8yBXQd1n1gC/oDWbVrE3C9+ZcIzU8nafZDjOrfkhpeu5MRe+k5ByQ4nZkUJa2O3KgrL9+5h9Izv6N2iFRe1PzFYUnhD1x5RtcdbTCaSI6QpnBYL315yOQu2beHfvXtpEhfHoLbtiCmH2FV1cUuPXlx1UhfWHzxAA2cs6QkVs547VqnTM3S14DNkZk/k/n7IzB6oea8RNEkyNdcErI5EOCHmZkh4Gy3VoYPecXq7mVLB3IrwLlYbOCIYJfj+RTe9IgtBFqLEXBoSzIGiWbzODUJYNamAkqeRMvgzkJ5FqAevRM3srUn2Oq9Fq/RRtH+2gYgE7WkkNyuPV659D6/Li8flxefxF1VvSMwWk25HaDFTX58R8b2yiFRqKIGcA3koisIrvzxB+55tsNjM2BxWGjRP5blZD1dKe/yGF6/gljevpfkJTUlskEDvS07lvaUvRnQY6tynA45Y/TWRjNO0csBv3prJGzeNZef63bjy3az8bS1jzn6KdUv0Nej7tz4uWHlSEq+qsnzvHuZu3sRzC+czZNLn5Hm0G0/Xxk146ewBJNkdOMwWLIqim2s3CcE5pTj9KELQt2Vr7ux1KiMyOh4VwbyYOJuNbo2bGsG8AtTZGbpa+C3kvUqw5E8CBeORwoKIvQ3s/SHveUK9OxVt4bLgQ61ZiACECXjZwBl9tYRIeA158DI002dXkVJiS0TMjcF9pFqALJwEntmlm09EupFYukWWzy3yHpXShcx9oaipyYM0tYTALoIpJ09x4Lei/Txsmn66fyNYTuTPmf/oOgNJCefdcDbD7hrM1W311wTUgIrH5cHmKL+llyvPrbvdYjVTkFNIUoMEGqSn8uZvz5K97xAel5eGLdI0s+d9h8jel0PTto10r73p3638M3cFcUkxnHpBD/6atYwfP56Hqqqcc3Vfzrm6DwNHRTfLP3VoD9LbN2Xrqh3BBVp7jI1eg7vR+qQW+H1+Pnt8sm5Z58ePfMmLsx8NO6fdbGHChcMZPeM7cjweBFq6RXL4L9bl97MrN5dP//2H207W/GAHHd+OAW3asjM3lwS7jTX793PrDzPwFlWuxFptfDB4aLUH6R05OYz7Zwn/7dvL8SmpjO7Wo8xKlN93bOOTZf9w0FVIv9bHceVJXeqlFVxtUWcDOgXvEl6/7YKCj5Ext2jpl5TJyJyHwFssGWBBS124Skx4tVpzhFULkLZTELGRHeiPRFjaQtov4P4BGdiNsJwEtjODdetSurRO0MBOytR40e1CLZbPnVEkClb8VXdAzLVBLXiZfXOoKFjgSKOA4g/sOfxf6UEeuheRNgtVVSNWOJitZpoc16jU9EvJLs/y0HNwV2b+76cwoSuT2cQnD0/EEedgwHVn0bZrK9Ys3oC70INE8sFd41ny43IsNjNqQOXqJy8JpkuklLw66j3mT/6DgF/FbDHx+o3/04ybi9ImG/7ezMKpi3hm+oOHJZelZO1fG9m5fjctM9Jp27V1yHheW/Ak37/zIz9PWIjZambITefQ/2pt/eBQZg5+HclegM3/bo34+TMaNOS3a0ezen8m67MO8Ogvcyk8Ip3iCQSYtWF9MKCD5vHZIlHruTglvTl/XX8TK/dnYhaCE9IaVLuBw7qsA1w8+Us8AT9+VWXV/kxmbljHpxcMo0cT/cqtD/9ZyuuLfw+mi9YcOMDXq1cyfeSVxBlBvUqouwE9EEGrRbrRgpZDaxZK/lRTSfSt0zpLwxahVLB0RsRcAea2iKIZb3kQSiw4h+suVsnCb4oWZ6MQ7BLhwVJKiTA1hZSpWgmm9y+tSSjmeoTjQm0f/ybw/h3dNY4ksB0Z2M/J53VFvXlc2NtWu5U+l2jeJX0uOZV5E8P1ZLr2Pymkhn3jsi0s+PoPhKLQZ8SptD4pcsXQ5Q8PY+HUP8nPzsfr9mkVLVLidfv4dcpihBBF15RYbBaklLgLPCiKIOBXg4ub4x//isatG3LaBSfz27d/seDrRSV02rUA4i1xM3IXePhvwWr+W7CaTn0yKMgp4P5znmHb6h0IIVBVSbsex/HszIewO7VgY3PYGHHfUEbo+I7Gp8RF/IyNWpW+xiGEIKNBQ2Ks1oh+U2UFPJOi0Klh9fjg6vHswvkU+EqUkkqJy+/n0V/m8uPl14Ttn+fx8Oqi30Pq3z0BP5n5BUxc8S83lmiKKi8Lt23l3SV/sisvl+5NmnJ7z1NolZhU4fPVZepuDt0SSWvbgTx4FWr2TUWt7iCEDYGHyFooPoR9QIWCeZl4fiFyJ2hJHAjb4cd/tfA71Mwzkfvaaf/1/ouS9BZKw8UoabMQjiHgmoKadTny0J1EpbeuiwRhIqlBAre+Mwqr3aIJZJkUbA4rg2/qzwk92wIwZvytdOnXMWTJoH3Ptlzz1EimvDadOePnM3bM59x5+iNMful7vnrxO24/5SEmPDMl4tWTGiYybsWrXP7IMDr1yaBz3w6YbZZgoJZSFtWo+ynMdeHKcwft3kriLvAw6QVNcXHOp7+ElULq4S7wsHz+SgDeveMTNv+7FXeBB1e+G0+hhzWLNzB2zGdsWbGNg3v1PTSLsdqtnH/LudicoYHX5rRy1ePRpfBaJibRMiExbHbtMFu4+qQuUZ2jpli6+0ifeI2NBw/i0VmwXZm5D6tO+emRNfTl5etVK7hp5vf8tXsnu/JymbF+Led/+UWp7kf1mahm6EKIAcCbaBHxQynlC0e8/zrQt+ilE2ggpYzey6wCiLgxyIOjCK0WEYCnaOERpHcRMvYOlJjrwJwBUq+qxQa2srU7KowpjYhWd8UIB1hPR7pmIgs+AhEP7ukEZ9zqXsh9FBVQnEORUkVm31BULRPNzaK08aUHZQkGXtePzn07sGDyInweH6ec3502nQ93TZpMJl766TGy9x1i5/o9NGyZxocPfMF9/Z4k4A9gMilH6JpLPC4vE5//lt6XnEaztvqmxfHJcVz20DAue2gYr456L6SJqDxk7dGCbrFZR1lYHVYSUuKRUvLLpN/DUiY+j4/p781h7ucL8Xv9dD6rAw9PvIOYhNBOSleBG5/bx6jnL8dsNfPdWz/g9fhITI1n9KtX0WNA9MF47JALuGzqZA4UFiDRbmgjO3TkvLb6lTK1RZzVFlI/X4xFMem26Cc5HPh19HMEkOqoWI25X1V57rcFIRU/2pOCjzcW/8FbERqr6jNlBnShJYPfBfoDO4ElQohpRT6iAEgp7yqx/21AtU8nhLUHJI9H5r8GvnUg7KBmEVJBIl2Q9zrSMRyhxCHjHoS85zgsg2sHU+OQbswqH6fzck0fJuTGo2jlidazQARANILCcQQFv/RckXFD/utIx/maiqRvOaUHc4H26xWaOJh0o3tTUXOQUg1q0TRu1ZCR95duwZfUMJGkhonMm7iQRdOWBhcCIxXSqQGVRdOWMvwe/ZLAksQkxpRZKqmHYlLo3FeTqe145oks+XF5mcdIVTJ7/C9MfWNGxPw3ENRLXz5vBc9e9ibPzXwI0CqDXr7uPZbO1q7VuFUD7v34Zq5+8hLc+W6c8c5yyzUEVEmBz4snEECiTQWyCyt5064GrunUhXeWLA4JpjaTiREZGbr5+3YpqaTHx7Mp+2BIl6vdbObaLuGqmtGwNz8Pr45zkSolSyI8QdR3okm5nAxslFJullJ6gUlAhJo8QBNW+bIqBlcWwtoFJflzlIZ/ac5CeuWAwgI+TdhfiRmJSB4P9vPAcjLE3Y1IiWQyXUVjtHSA+Ce16hcRCzi0KpjkiSiJTyHinwDXpxw2tYCI6RN1N3Jfe2T2TUXVMmFXA2yAGaynIlKnIRosQqR8q+Xd9ZAFpWrHe1we5n35G1Nfn8GaPzeENML88NG8qFIbiiKialICOPfavlisFVjakZLh92qm2tvXRv4y25w2HHF2bE4rQsDGf7awd0sp2vkl8Hn8LJ+3kqw92Ugpuf+cp1n64zL8Xj9+r58d63Zz/7nPkLXrIDEJMRXS3rn46y/JdruDfwEq8N36NXz+X9k3qJpkdLcenN/uBKwmE3FWGzaTib4tW/PQ6X109xdC8PHQi2ibnILDbCbWasVhNvPQGX0iLqKWRYLNHnJzKEnD2Or7Th/NRPPNaQqUtP/ZCfTU21EI0QJoBcyr/NDKiZKK/sw2AMrh7I+wdkFYazYfqTgvRDoGgm8ViDht8bX4y+5fS6npmDAkkbXTJcTdhxJzhGKeEocUiYCed6dadKMJZ8vK7dzb93F8Xj8+jx+zxUSnPhk8+e0YTGYTgXL4Op4xTPdPJoxWHZrzf29cw3t3fIrZasLvC0SVgjFZTPw6ZRGtOjRnfwSPUme8g2ueuoQWGc15dMjzuo1CxU8HQuisn6NV/BzKzOHAzix2rt8dVp3j9/qZ9v5srn9eM8YoyC1k9ie/sGLhGtLbNWHwTedErJ9fvT+TLJfejRreXbKYqzpV/O9WSsmyvXvYnpNDRloD2qZUTujKpCg83+8c7j7lNLZkZ5Men0DjuMgLwwBN4uKZdfnVbMjK4pDHRUZaQ5zl1GAvSZzNxnltj+eHDetDmrMcZjM3R7HImu/1okpZr8omq7rKZSQwRUrdZDVCiNHAaIDmzatWsF7EXIX0/EJ4aqNJZEOLKJHSW5SXt4KlYzA9Ua7xCTtYu+m84Sylo7UiRLg5xFwHuU9zpB46tj66TyhSSp66+BVys/KD2/xeP8t/WcmM/81h6C0DOeeqPmxYuhl3Yfgs3ea0BTtOb3//hogNO3oMuqE/vYefyvJfVrJm8Xqmvz8HV75+vXoxPo+fnycs5OonLqH7OZ1Y8+eGsBuB3+un76Wns3Pdbiw2fXOK5EaJdDzjBPZt38+6vzaF5eOllKS3a8Ki6X+HuCCVvMaOtbsBOLg3m5u730/+oUI8hR4sVjPfvjWLDq8MYbE9nzirlWs6d+XC9poR+caDWWHnKybHU1FbQ8h2ubji26/ZlnMIgZZnPjW9Oe+dd36F/T+LSXPGkOYsnzpjZW8mJXnurP6oquTHTRuwKAoguOeU0zjnuLYRj9mVl8s9s3/gn73a7ykjrQGvnjOQ1kkRnmLrENEE9F1AST3QZkXb9BgJ3BLpRFLKscBYgO7du1eprJuwdivKkb9Q5DAUAHMzRNK4ysnOuucic8YUvwIRA0n/Q1gyqmTcEhuRK1QsHG5+isbRyIaI0JwkHMO08sbCL4pq7r1g7YpIeEF3/92b9rJ/Z3iA8RR6+fzJr5k38TdaZKTTtlsrNizbijvfjdVuQVEU7vn4ZgoOFSCE4JTzu5PUsPzr47GJMZx+YU8yTm3Hd2//ENUxPo+fqa/PoEGLNOKSY8jdr+Iryo3bnFa6nn0Sz13+Ju58T7CksSRCwAm9juehiXeyf2cWN3a+l8JcVzCo2502rn/+Mqx2K8d1bolfZ/HV5rDS4XStc/STR77kUGZu8Hif14/P62fREzPZ/qDm8vPoL3P5b99enujTj66Nm0b8bE3LmP2Wxv0/z2bjwawQUa8/tm/n/SV/ckevUyt83qMBu9nCGwMGkeN2k+UqpGlcPDZz5LDmDQS4ePKXHCgsCKZr/tu3l4u//pJfr7mB2KOoY7YiRBPQlwBthRCt0AL5SCBMXk8I0R5IAhZV6QjLgRJzKdIxFPyrNINkS+S7dCSkbx0y92Xw/QMiAeReQjpJZQEy62po+DtCVP5RTahZSByAzqO2qRVK2gzUQ/cVVb1EkZqx61fsCCEQ8fcjY0drgmJKI4Q58lOSlnbQvxHmHMgj50Ae65ZsxGKzcN1zl3NwbzZJaQn0vex0khpUXct2UsNErn3mUj59bBI+tw9VlQhFhJhKaJ9Pa+756MEJmG1mYhJiGDDqLJbNW0l8ciwIwbKfVwRz/kIRCCFC1gSsDisXFy3cpjVL4X/LX+HL57/hn7krSG2azCVjhgYrVpq1bUyvwd34c8bfwcoek1nBGe8Idp8umv63bsWN+YAbJd+HGmvB5ffz1aoV3NjtZJrFx5OR1oBV+0Nz+gJ49MyzKvTzc/t9zN+6JUxn3B3wM2nVijof0ItJsNtJsJctVz1vy2byvaHywxLw+gPMXL+WSzpEZ5JytFJmQJdS+oUQtwKz0aaKH0spVwkhngKWSimnFe06Epgko7WMryaE4gRrjwodK/2bkVnDCLbLy/wIe+aDZz7Yo3O3KRVzG/QDtQVspwMgYm9Eun8gVDmyONjatGgmVUh4PqIzUvAoJQmsZecXmx3fhPiUuFIXPQN+lYDfw5xP5/H+3y+Xec6KcvHdQ2h8XCO+fP4b3AUeTj6vCz+Nn4/H5cXvDaAGAqgBGaxU8Xn9eAq97Nm8j0/WvMn6vzdxd+/HQ9ryZdGNwWw2YbZZMFtM3Pbu9SFCWmnNUrj93RsijuuhCXfw9avTmf7BbNwFHnoN6sa1z15KbKKWgrA7bUSyBpElZBYsJhP/Ze6lcVwcEy8aodVVFyklWkwmHj2jD31aRjbdKA1fKdVCemWHdZFcj5vv161lZ24OXRs3oV+r4zDrpMMAduQeCkoklKTQ72NbTulGLnWBqHLoUspZwKwjtj12xOsnqm5Y0SEDe5AFn2j5bfPxiJhrEeZwDemoz5f7NNHK7UrfSkQVBHShxCBj74CCt0rk0s2aiUVMkcmGbo5dAetpCPtZ2v72s8sM5uUalxA8POkuHjj3aVS/ekR9eSiblm/T6tDN1WMRNuez+bz5f+NQ/QECfpW9WzLpPeIUep7Xlazd2Yx//CsKckKfcNSAyrKfV+J1e1k+b2WYmQVoQf2CuwZy3vVn0+S4RuUev8lsYuT9F0Qs8xx80zl88fTXIekdaYLC4xOQ9sPXUqWkYZEOeZzNxoSLRpBVWMght4vmCYmVsl6Ls9k4LimZdVmhC8UmITirVcW/K0cLq/dncunUyfjVAC6/nxiLheYJiUy+eKSulk1GWkMsJlOYpnyMxaKrHV/XqFOdolJKpHcJ0jUT1fM78sAgKJwAvmXgmoI8cCHS+3fFL1CeY6NUZIwGJXYUIuE1sHQHUwtwjNRKDk2aubDMH0t4Dj0A3sVg649wjqjSYF7Mib2O54vN73H9C1dwyZih2COoDRZ3llYUd6GHGf/7iWcvfZ0PH/yCvVsPpxzysvN586ZxeF1e/L4AUko8hR5+/XoRsUmxXHj7eQgl0hqJRFUl8SlxuqWQVruFhs0bkN6uabXcjIbfM4STz+uK1WHFEefAFmPD39BJ5uWHO5JNQtAkNi6sbT/F6eS45JSog3mex8PsTRuYu3kjLl/o38pL/QcQY7FgKzqXw2wm2eFkzKn6FnV1iTt+nEme1xOshy/w+diUfZAPlv6lu/8pzdJpm5wS/FmA1gzVKDaO/q2roVO8hqkzWi4ysBd58ErN4FkKtGqNknfZAOBC5jyGSJtZzaMxISydqvSMwt4PYQ9V/pOBvZrjkn89kRZOZdYlSCUGHJcgnCMRomp/pfEpcVxw20AAFny9iL061SaOOHuFF57zsvO5pccDZO87hLvAg9lq4vu3f+Tp6Q/QuW8Hls7+F5NFCS1eQmvbnz/pN7r260jLjHRW/rY27NyJDRKwO22cMawn7931Sdj7ikmhz8jqyyGbzCYem3wPO9btYuOyrTRsmcbehibunzsHl9+HX1Xp0KAh7w4cUqmF++nr13L/3NmYhaJ5oEvJuwOHBK3dOjZoyM9XXcfEFf+xMTuL7o2bMuyEjDoviLUvP5+dOpZ13kCA79fPcJ/nAAAgAElEQVSt4Z5TTw97TwjBhItG8Nafi/h27WpUqTLo+Pbc3evUemFCXXcC+qHbixQLy6h9DmxCSnd0fp5HYu2mdWGWiYo0d4rKOaYiSOlF5tyvGUgLaym5fC+oO7X7Wv7LSO9viKQPqmlU6Fa9AOQeyKtwymXSC9+xf2dWMP/t9wbwewO8ePXbTNz2ga6kL2hfTFNRs9L+Hfp157kH8wn4A8QkxPDwxDt58uJX8Hm065gtJu79+OagEXV1kt6uKenttAqWE4HFo25kW84hYizWSjfA7MrNZcxPs8NMn2+eNY3frxsddAZqEBPLnfVkAbQYRRER68P0dOaLcVosPHD6mTxw+pnVM7BapE6kXGRgH/hWU2YwB7R7VMWaFUT8o2idlmWhILwLKnSNaJB5b4D7Z4ot8aIS3pIu8C5CFnXFVgfmCAFbMSkVTrksnLpYt+0+72A+ezbvo/u5nVED4Z/f6rBy9hWadG3JWvmSqH4Vd6GHgD/A+3ePJ1BigVBVVcbe9zleHSu6qkZVVfKy84MVLyZFoXVScjCYu/0+ft6yidmbNgSNLKJl+vo1qFJv4VMwe5O+sUZ9Ic0Zw/HJKWFSA3azmeEndqilUdUudSKgazok0QzVBo4Lglrk5UWYWyNSpoD1zMNt+roEkIEdEd6rAlyTCMsxRIMMgLdi/pZRESEtUBnp7SPVCYtRVYnNacMZ5+DRr+7C5rBij7FhtVuw2i0Mu2sQGadqipvte+qXp6Y0TcIZ5+CvH5ZxcG82agmFRjUgyTuYz+/f6udaq4qfPl/AyKajGdH4Bi5IvoaPH5kY0mH7+45t9Bj3AXfNnsV9c36k54cf8P26NVGfP9/rCytJBAhIlUJf9d+sapu3Bg4m2e4gxmLFophwWix0adSYUV10mviOAepGysWUTqmu9SKuqFHmFET8w5W6lLC0QyR/CIB68AaIOBOvxnuhrk5LFAgLmCKZQVeeSIJZakCiBtQKpVyG3jKAD+4ZH1JSqJgU2nRuSUpjTdO656BuTNzxAb9/+xfuAg89BnZm8fS/Gd74evKy8mjcuhFWuwWf1x+sT7c5rdz61iiEEGxbvVNXPsCV72brquq7MS+e8Tdv/t/YEF32b96YhQxIRj1/ObkeDzdO/55Cf2jgfXDuHLo0akzzhLIbsvq2asUny/8O8xhVhKB3i5ZV9lmOVlomJrHw2hv4ectm9uTn0alhI7o1blKpNYm6TN2YoasHiJhuURogEt9GpM5CSR4bsVOyQphPJNwvFMCCMEc2bag0lo4VOEigtfJXrAElGjJO09egb92pRYWrRM67oR9nXNwLq92CI9aOI85Oo5ZpPPLV3SH7xSfHMXBUPy68/Tx+Gr+ATx/7ikP7cgj4VXau11q4O/ftQOPWDeh+bidemP0opwzpDkDz9k2xOsJL2ByxdlqcELk7s7J89sRXYR2pnkIP373zAz6vj7mbN+r+eQWkyndro5uld23UhAFtjg9qogg0/fTLO3Sq9lb2XI+bj5Yt5Y4fZ/LuksUcKKzgRKSS2Mxmzmt7PKO6dKN7k6bHbDCHOjJDl/7dRFYgPISwVc9ij3BejCz8mDAnIOEAW59quSaAiH8cefCKIp9SP9qvyQzmDhBYp4mN2c4B10yQOYAEUxNE0jtV0r0aiZtfv4Y7Tn8Ur9tLwBdAMSlY7RZuf/f6Cp9TURTu//RWrnhkGOv+2khK02Q6nnGCrk4KaCWOU9+YGebd6XX7sFjNfLbx3bBjeg7qSmJaAl6X73Ae26wQkxjD6cN6VXjsZbF3237d7WpAJT+7QBOH0tEI96sq+d7oculCCF7pP4Ahx7fn+3VrMCuCi9pncEp61WolHcmuvFwumDSBAp8Xt9+PzWRi7N9L+Xr4SI5Pqbh5t0HlqBMBHaU0HYvq014Q5nRkwmuQO4agkqNwIJLGVmvgFJYOkDJNM7vwrQbLiYiYUWGt+jJuTJF3qLnUNv6qolXHFoz99xWmvDaddUs20fqk5gy/53yaHd+k0udu2qYxTdvoG2CU5MCugygR6s63rNiuu91kNvHmH8/yzm0f8cf3S5CqpOfgrtz29iistoqr/ZXFcZ1asnzeyrDtVoeV+NQ4etta8fxv4Sk9h8XC2a3bRH0dIQR9WraqcDdpRXhu4XyyXS7UoomWJxDAGwjw0M9zmDIiTBnEoIaoEwFdmFsgiQEKwt8s0a0ppR88PyHdv4CSgnAOr1TnKIDi6I+0/6m5AwkbWE6qkNpieRHm5oiEJ0vfRwio5OcrL41aNuDWt0bV6DVLktIkKWIuv0VGuK62K9+F2WomqUECj351d1C7pSYey6979jLu6/dESNrF5rRx7TOXYjKZaJGYyLWduzL+32W4/X4kWkld35at6dGk+lJBVcH8rVuCwbwYCSzftxdvIFBpFUeDilE3ArowIxOehZwxHG7NV4AERJxmliSlF3nwKk1fXBYCZmThBGTC8yiOQZW8vhVs0el5G1Qvjhg7598ygGnvzQ5Ju9icVq58fETw9ao/1vH66A/YuX43iqLQ+5JTuf3d63HEVuEaSxmc0LMtL/30GB8+MIFN/24ltWkyVz0+gt4jDqcIx5x2Jr1btGLK6pV4VZXz27Wnb8vWR30e2Goyhy3Egtb5qudYZFAziNrS0urevbtcunRpuY6Rvv+QBR+DfxfYTkE4r0aYNG1lteCrInu5I3RPhBPRYHHFGo0Mys2h/Tm8f9enWjmgEJx5cS9uevVq4lNKl3/1+/zs27afhNT4oLhVJFRVZdIL3zLl1enkZRfQ/MRm3PLGtXQ9W1PK27lhD//X9b4QYTGLzUKH09vz0k+PRTqtQTl4buECPv9vWYixhEVRGNDmeN4cULkJlEHpCCH+llJ2132vLgX00lCzrgLf4vA3RCwi8T2ErfoWvww0fF4f151wJ/t3ZAUXH80WE41aNeDDla9HrISZOe4nxo35gkCR+NbpF/Xk7nE3YY9Qo14SKWXYbPbtWz9k5ti5YdK1NoeVD5a9XCU5/2Mdt9/H9dO+Y9ne3ShC69hslZjEFxcOj0rG1qDilBbQ60TKJSqUSM7hskqFtKSU4J6BLPwC1AKwn6upPFajL2ld4Y/vl5KzPzckkPp9AbL2ZPPnrH849fxwWeO/fljG+3d9GpJn/v3bP5FS8vDEO8u8pl5qYuuqHfo65FYzezbvMwJ6FWA3W/jiouGsytzHuqwDtEpMonOjxkd9qqi+U28CunCORHoXhUvNitgK1nXrI3OfAtc3BFM7BduQ7pmQ+l2dT+tIKfnrh2X88OHPeNxe+l12Bn1HnlZmjXkgECBz2wHWL92kaxXnKfSybdVO3YA+8blvwmq1vW4fv3/7F3nZ+cQllf9GeeIpx7Nm8fqgbksxPo+Plh2qvxroWCKjQUMy6oHsbH2h3gR0rL3BcTkUfq5Z0BU12mgWdFVTlSIDu8A1hdC6dA8E9oBrOjiHV8l1aov/3TuemWPnBnPPKxeuYe4Xv/LcrIci1oUv/OZP3vy/sbgLPPi9/qDJcklsTivN2unPig9EEPwyWUzk7M+tUEC/4LbzmPHBT5rcbonO0TMuPoW0ZlXnZ2lgcLRRNzpFi5BqPrJwMjL/baRnIbKEKJEQAiV+DCJtNiL+CUTCa4gGvyEs7atuAN5l6N8DXUjPr1V3nVpg96a9TH9/TshCorvAw6o/1rF09r+6x6xbuokXr3qLnP25eIpEsI4M5iazifjkOE4Zoq+tkXF6e11hL0URNGyZVqHPktI4iXf+ep5Th/bAGecgpUkyVzxyMfd++H8VOp+BQV2hzszQpW+1pocu/YALhBPM7SD5s5AmH2FqAg59B5lKo6QF+4tCMYOpbudll/28AqEzC3fnu1k8YyknD+wS9t6U16bjdYULQCmKAKHdZHsN7sbt712P2aL/p3bV48NZPH0p7gJP8GZgc9oY9fzlWKwVb/pp2qYxT0y9r8LHGxjURaIK6EKIAcCbaJ6iH0opw6zihRAjgCfQwt2/UsoqaxeTUiIP3VkkJVu8sRB8a5AFnyBib6qqS5WOtQeIxKI8fcmZqBnhvLRmxlBNxCbFopjCF7TMFlPEksO9m/ehVyXliLXz5Hdj6HjmiRFTNcU0bdOY95a+yGdPfM2KhatJa5bCpQ9eRK/Bx6ZanoFBZSgzoAtNi/ZdoD+wE1gihJgmpVxdYp+2wIPAaVLKbCFE1Ur+BXZpeeow3OD6DmoooAuhQPJnyEM3a05CQgGsiIQXEeaWNTKGSKiqCvkvg+srkD6wdIKEl1DM0T059BzUVTf4mswmzr2mr+4xnfpmsGn5VnxH6Jn7vH5ad2pZZjAvpmmbxjz4xe1R7WtgYBCZaL5xJwMbpZSbpZReYBIw9Ih9bgDelVJmA0gpM6lKhCB0RlwCXXH/6kOY01FSpyNSZyCSJyEa/IGw6we8GuXgJVD4UZG7kQd8f8GBs1H9+gJRR2J32nj+x0dISI3HGefAGe/AHmNjzPhbadxav4ph2J2DccQ7QlyF7DE2ht01uEKLmQYGBpUjmpRLU6CkaPRO4Mg++OMBhBC/o6VlnpBS/lglIwRQSpllmmqnDK0mxLCiRfWtBb/ewqUfcp+G5LeiOs8JPdvy1e6xrF60Hp/HR8Zp7bA5Ijf3JDVM5IN/Xubzp75m6ezlJKTGcfHd53PWZeFejgYGBtVPVS2KmoG2QB+gGfCrEKKjlPJQyZ2EEKOB0QDNm5cjIAZ2oa9LDqj6CnvHFO5Zkd/z/VmuU5nMJjqecULU+6c1S+HusTW0hmFgYFAq0aRcdgHpJV43K9pWkp3ANCmlT0q5BViPFuBDkFKOlVJ2l1J2T0srR0maEKUMtU5VXlYPpT2lKBU3OZBS8s2bMxmZfiODnJdx15mPsm7ppjKPyzmQy0+fLeCnzxeQezCvzP0Njg6klOzJyyPb5Sp7Z4OjkjK1XIQQZrQA3Q8tkC8BLpNSriqxzwDgUinl1UKIVGAZ0FlKqd81Qvm1XNT9AyCw+Yitdoi9FSV2dNTnqY+oqgqZHdC16Ut4E8UxsELnHXf/53z/bqiqod1p4+0/n6dlRrruMXM+m8+bN40NdpeqAZV7Pr6ZvpecVqExlAcpJT6PD4vNYrSgl5Mlu3dy75wfySzIR5XQtXFj3hwwiAYxxlrI0UZpWi5lTm+llH7gVmA2sAaYLKVcJYR4SghxftFus4EsIcRq4BfgvtKCeUUQiW+CSACc2rCFEywdETHXVOVl6iSKokDyBOCIfLfjmgoH88I8F9+9/aOOM5CXCc9M0T0mc/t+3rxpLF63D1e+G1e+G4/LyyvXvkfWnuwKjSNafp64kEvTb2RI7BVclHotX738vW5JpUE4u/Jyuea7b9iRm4MnEMCnBli6exeXTZ1s/AzrGFHl0KWUs4BZR2x7rMT/S+Duon/VgrC0g7QF4PkRAvvA0gWsPY2ZWBGKtQs0WoHqWQxqJtjORokoWFY2e7dkYraa8B4hzaKqkg3/HPmkpLFg8qJgq30IAhZOXcwFt1bs5lIWf3y/hNdHfxDUhMnPLuCLp74GKblkTDU1mdUjvlzxL341tFosICX7CvJZsnsXJzcNNw4xODqpM52iAEJxguOi2h7GUY1SRTLBaekpYeJWoC1nND9B/wvu9fjw66gcBvwBfO7wjtKq4pNHvwwT+HIXePjy+W+5+J4hmAz3nFLZmnMIn6pvwr4nv2bXQLyBALM3bWDxzu00jUvg4hMzjLRPOTBWFA10iUuKpf+VZ2Jzhnq2Wh1WLn94mO4xPc/rCjqP6Kpf5eRBXatlnAB7t+rX2nsKPbh11B8NQunVNB2HOXxu51clHWtQSTHf62XopC948Oc5fLlyBW//tYizPvuYJbt31tgY6jpGQD9KkNKHdP+CLJyC9G+t7eEAcNu71zP0lgHYY2wIRdDs+MY8+e0Y2vXQNzDO2pONoiO1azIrZO2uvhx68/b6fQoxCU4ccYe18PMPFTBv4kLmfvEruVlG9U0xF7Y/kWSHE0uJzl6H2Uz/1sfROqniVVLlZdw/S9h6KJtCn/Y05wkEKPT5uPPHWUYuP0rqVMqlviL9G4uEx9xFna8q0nE+Iv6ZWl0jMFvM3PDilYx6/nL8vgBWW+liWev+2kjAF/7orqqS9Us20rVf1enSl2TU81fw2Pkv4HEdYcb87KVB+YFfpyzixavfwVSk7BjwB7jjg9Gcc1WfahlTXSLGauX7kZfz9l+Lmb1pAw6zhStP6syVJ3Wu0XHMWL8uxNKumENuF1tzDtEqMalGx1MXMQJ6LSOlRGb/H6gHCZFxdM0Aay9wDKm1sRWjKApWW9kPc2npKdhjbCESvKBZv6Wlp1bX8OjaryNPfjeGcWO+YMe6XaQ2S+HqJ0Zw1mVnAJC97xAvXv0OXldonv3Nm8bSqXcGDVtUTKa3PpHscPJ477N4vPdZtTYGa4S1DlXKiO8ZhGKkXGqbwCYIZBKuyetCFk6sjRFVmN4jTsVsNVPyoUIIgcVu4fSLTq7Wa3fr34kPlr3MzMKJjF//djCYAyyc+qdun7GqShZM/qNax2UQPYOPb6e7PcXhpGlcfA2Ppm5iBPTaRnqKVBv13qtbC3rOOAevLXiKFhnNsdgsWGwWWp/Ugtd/fbpUTZjqxuv2as1XR6D6A3jcXp0jDnNofw5//bCMjcu2GHncamZ3Xp7ujTfX68Gnk4oxCMdIudQ25naAXm7aDvbBNT2aStOqQ3PG/fcqWXuyEQKSG9V+3rPnoK588uiksO0Wu4VTBus23CGl5OOHJ/LNGzOx2CwE/AEatWrIC7MfIaVx7X+m+sjv27eFe8egpVy25RyiTbJhH1gWxgy9lhHCjEh8BXBwOLA7wXwcIubyWhxZ5UhpnHRUBHOA9HZNGXbnIGxOrVpHCIHdaWPgqH606dJK95iFUxfz3ds/4HX7KMgpxF3gYfuanTw57OUaHv2xQ4pTvxHOr6ok2h267xmEYszQjwKE7UxInYl0TQF1H8J6OtjPQYiKW7BVF+v/3sT4xyez5b9tpLdvwpWPj6DDaVXo21pNXPfsZZxyfg9+nvArqio5a+RpZJQy7qlvzAxb3FUDKpuWbyVz+34aNK+5hdQCr5dp69aw5sB+2qWmMbTdCcRarWUfWMe4oWsP7pkzC5f/cEObRVHo1Syd1AjB3iAUI6AfJQhzM0TcnbU9jFJZ+ftaHjj3GbwuD1LC/p1ZrPp9HY9PvZceA8I9R482TujZlhN6homA6pKfna+73WQxkX+okAY1JIe/Oy+XC76aQIHXh8vvw2m28Mbi3/n2kstpFp9QM4OoIQa0acvm7F68s2QxFkXBG1Dp0qgxb547qLaHVmc45gO6lBJ8yyCwFczHIywdauaa3j+Qrm+AAMI+BGx9NYu7o5gP7hkfJtblcXl5945P+HTd0R/Qy8OpQ3uwZ9O+MHs9k9lEixNrTtvkiQXzOOhyoRYtyBb6fbgDfh79ZS6fDNXv2K3L3NyjJ1d16sL6rAM0iImpdzet6uaYDuhSzUUevEoL5toWpLkjInkcQlRfzk7mvQCuSUVm0yA988HWFxJeO6rFxjb/u1V3++6Newj4A0HJ3PrA8HvPZ97E3zi0Pxevy4uiaOWXd35wY41+zgVbtwSDeTGqlPy2fRtSyqP676WixFqtdG0cnReuQSjHdkDPfQL8G4ASwlG+f5F5ryPiH6qea/o3Q+FEoMRMVxaCZx74/garftXF0UB8ajxZuw6GbXfEOVBMR/fTRXmJT45j7L+vMHPsXJbOXk6D5qlccPt5tOmsv4haXZgVBZ9OyaUpSgPuuo7H72fyqhV8v34tdrOZyzp0YmCbtvXyRlYVHLMBXUoV3LMJCeYAeMD1DVRTQMfzG+FNRIB0IT3zEUdxQB95/wV8+MCEkLSLzWnjojsH18svWExCDCPuG8qI+470RK85BrVtx/fr1oaoIVoU5ZgIan5V5bJvJrP2wP7gQunyPXtYtHM7T/c9u5ZHd3RybNzmdVGBCM0KsvRmk0ohYtC/j1pAHN0yoUNvGcDwe4dgc9pwxNqxOawMHn02VzxatbncgpwCPnpoAle1vZUbOt7N9+/9SOAYbSx55Mw+tE1OJsZiwWYyE2Ox0DopmSd696vtoVU7czZtZF3WgZCql0K/jymrV7L1UOXF3qSU9a5ZrEwLuuqivBZ01YGadSn4/iF0xqxo5hBJ71TLNaWai9x/RjB/fhg7Iu0HhKlptVy3KvG4POzfeZCUJkk4YuxVem6v28uNXe5j39b9+Dza05PNaeOUId14+Mu7qvRadQUpJX/u2sn6rAO0SU6hV7N0lHo+Owd48Oc5fLVqRdh2h9nC4737MiKjYmJvWYWFPD7/Z+Zs3oiUkt4tWvF037NpHBdX2SHXCJWyoKvPiISnQcQBxUHJASKx2vLnAEKJRyS+p83Gi//hgIQX60QwB7A5bDRr27jKgznAL5N+58DOrGAwB03XfNG0pWxfe6Q3eWR8Xh9zv/iVpy5+hTdvHsumCAu6dQEhBL2apXNVpy6cmt78mAjmAKnOUEnfYkxCkOyoWNFCQFUZPuVL5mzeiF9VCUjJ/G1buHDyBNz+6jNhqSmO2Rw6gDC3gbQ5yMIp4F8Plg4IxzCEUr13amE7DRosAu9ikAHNSk+JqdZr1hWW/7IyrKEHQCiCNYvX07x92Tc9r8fHPb0fY+uqHbgLPCgmhZ/GL+DWd0Yx4NraUxM0KB8jTuzIR8v+DlsUtphMnNmiYovT87dtYX9BQYjlniol+V4vP2zYwIUnnFipMdc2Uc3QhRADhBDrhBAbhRAP6Lx/jRBivxBiedG/66t+qNWDUJJRYkejJL6CEnNNtQfz4HWFDWHrjbCfZQTzEjRq1QCLLXyeIRRBatPozBZ+Gj+fLSt3BG8MakDF4/Lyzm0f4yqoW4JnxzLpCQm8PXAw8VYbsVYrTouFpnHxTLhoeIXldDdnH9TVXC/0+Vh/8EBlh1zrlDlDF0KYgHeB/sBOYIkQYpqUcvURu34lpby1GsZY7UjpA5kHIgHt4xrUFuddfzZTXp0e4meqKIK45Fg6nxVd09eCrxeFNUCB5py0+o91dOvfqcrGa1C99Gt1HEtu+D9WZO7DbjZzQmpapap72ianYjOZwkyxYywW2qXUfV38aGboJwMbpZSbpZReYBJQe3VcVYiUEjX/HWTmycjMM5GZJ6MWjK/tYR3TpDVL4dmZD5GWnoLNacVis9C2W2tem/9U1GbPsYn6TzxSyhBLOoO6gcVkomvjJpyY1qDSpZpnNG9B49g4zCVy8yYhiLfZGdgmOlmIo5locuhNgR0lXu8EeursN0wIcSawHrhLSrnjyB2EEKOB0QDNm9eQGEYpyIJxkD8OKKo4kV7Iew1VxKI4a7etWvq3Iws+Av9qMGcgYq5DmGv/Z1YTnHTmiUzY+j57t2RisVtIbVI+X8vBN53Dkh+W4T5ilh6bEEP7k/X9UA2ODUyKwuThI3n61/n8sHE9qpT0a3Ucj/fui03HKLuuUWbZohDiYmCAlPL6otdXAj1LpleEEClAvpTSI4S4EbhESlnq6lNtly1KKZGZPUDmhr9paoaSNq/mB1WE9K1CHry8qB7eD5hBWBHJExCWjFobV13ii6e/ZuLz32KxaF9Sm9PGS3Mfo2VGei2PzMCgcpRWthjNLWkXUPJb0KxoWxApZVaJlx8CL5V3kDVPUd5cj0BmzQ7lCGTuk5ocQBA/SD8y9ylEyle1Nq6aoiC3kK9fnc6Cr/7A5rAy5P/OYeD1/YKGz9FwxaPDGTS6P//9uoa4pBg69cmotAaLq8DN1NemM+/L3zFbTAwafTaDbzynXmnYGNRtognoS4C2QohWaIF8JHBZyR2EEI2llHuKXp4PrKnSUVYRUnqReW+B60uQBWgfX6f21HxcTQ8tFN9/Ebb/W7PjqAW8bi+3n/IQe7Zk4nNrv5v37x7PfwvX8ODnt5frXPGpcbTskE5sYkylg67f5+euMx5lx9pdeIvGNe7+CSybt5Inpt5XqXMbGFQVZQZ0KaVfCHErMBswAR9LKVcJIZ4ClkoppwG3CyHOR8sPHASuqcYxVxiZ8wC45wLFpWvhokdgR8SFVWbWLMIJUkePW9RMeaOrwI0aUImJr3lTgQWTF5G5/UAwmIPWWPTbN3+y45FdpLeLrvnqt2//5PUb/4fX7SPgD9D+5LY8OvlukhpUTI719++WsHvj3mAwLx7X0tnL2bh8S42LdhkY6BHVM6yUcpaU8ngp5XFSymeLtj1WFMyRUj4opcyQUnaSUvaVUq6tzkFXBBnYC+6fOBzMi1FAJIGIB0tXRPJHCNsptTHEwzhGcrh7tRg7OC6t1stm7cnmwQHPcGHyNQxLvY6bu49hy4pt1XrNI/ln3grdxiJFEaxetD6qc2xcvoUXrnyL3AN5uPPd+Nw+Vi9ax0MDn63wuP77dRWufJ0adglrohyXgUF1c+y0/vu3gdCz7VLB1Byl4VKUlEkIa48aH9qRiLg7wX4WYCuSJrCBvR8irnwph/IQCAS4+8xHWTZvBQFfgIA/wIZ/tnBX78fIPRhhraEaaNgiDYs1/MFRURRSoqx2+e6tWSEzfICAL8COdbvZ/F/FblBp6alY7eGWgCazKepxGRhUN8dOQDe3ABk+8wMzWI6udl8hrCiJbyDSfkIkvotI+wkl8XWE7g2palj280qyM3MI+EPTUH6Pnznj51fbdY/kvOv7YbKE5rsVRRCT6KRLv+gai/Zu3Y+qhldvmS0msnaH67lHwzlX9Q7LwwshsDlt9BjYuULnNDCoao6ZgC5MjcDen7BUhrAiYq6rlTGVhTA1Qth6aWOvZvZs3ofqD19T8Li87Fy3R+eI6qFBeipPT3uAlCZJ2GNsWO0WWndqyavzn4y6sahr/5Ow2sNvfj6Pj7bdWldoXMmNknjuh4dJS0/F7rRhdVhp2SGd1xY8icV69Jl5G9WbhHEAAA5ASURBVByb1KlKeim94PkFAvvA2gVhKZ98pkh4Eam8WWT/VgCWkxDxjyHMLappxHWHNl1aIZTwLjx7jI0TetVsB13nvh2YuP0Ddm/ci9VuoUHz8rVkD7npHKa9N5uc/bn4izxB7TE2zr9lAIlpFfeo7HBaeyZsfY9dG/disZpp2KLut4ob1C/qjB669G9BHrwMpBukD4QJLCcjkt5DCGOGVFmklNzX70nWLF4frOQwWUykNknmo9WvY3PYanmE5ePQ/hwmvfgdi75fSlxyDMPuGkKfS06t9y4/BvWf0hqL6kxAVw8M0SRuQ8wo7BB3N0rMNVU9vGMSr9vLF89MYfYnv+D3BjjtwpO57tlLKzWrNTAwqFrqfECXgT3I/ecQYqxcjKkNStqsqh2cgYGBwVFK3Xcskj4g0qOyP8J2AwMDg2OLuhHQTelgStV5wwb2ITU+HAMDA4OjkToR0IUQiITXi1rfi8oOhRPMrRExo2p1bAb1lzV/buC2Ux5igG0kwxuNYtKL36KqenIRBgZHB3WmbFFYO0Haz0jXNAjsRli7ga0fQtSZj2BQh9iycjv39Xsy6Hx0KDOXL56eysE9h7j5jWtreXQGBvrUiRl6MUJJRom5BiX+IYT9XCOYG1QbE5+ditftDdnmKfQwc+xPFOQU1NKoDAxKx4iIBrVO7sE8Znwwh2U/r6RRyzQuunMQrTrWbrPXxmVbkXryAVYze7ZkGuqKBkclRkA3qFWy9x3ipq5jyM/Ox+v2oZgUfpn0Ow9PuotThuhWZtUILTuks2vDHo4s6/V7/UaHqMFRS51KuRjUPyY++w25B3KD3alqQMXj8vLaDR/U6gLk5Q8Pw+oI7UC2Oa30v6o3cUmxtTQqA4PSMQK6Qa2yeMbf+H2BsO3uAje7N+2rhRFptOnSimdnPkSrjpoxtzPewbC7hnDbO9fX2pgMDMrCSLkY1CqxSU7YGr494FeJSah5x6SSdOqdwdh/X0VV1XL5mRoY1BbGX6lBrXLRnYOxx4QKf5ktJjJOa1dhu7iqxgjmBnWFqP5ShRADhBDrhBAbhRARDTeFEMOEEFIIUXurWQZ1irOvOJNBo/tjsVuISXBgc9podVILHv7yztoemoFBnaNMcS4hhAlYD/QHdgJLgEullKuP2C8OmAlYgVullKUqb5VXbdGgfpO97xAbl20htWlyrZcsGhgczVRWnOtkYKOUcrOU0gtMAobq7Pc08CLhLswGBmWS1DCRHgO6GMHcwKASRBPQmwI7SrzeWbQtiBCiK5AupZxZhWMzMDAwMCgHlV7tEUIowGvAPVHsO1oIsVQIsXT//v2VvbSBgYGBQQmiCei7gPQSr5sVbSsmDugAzBdCbAV6AdP0FkallGOllN2llN3T0oxuOwMDA4OqJJqAvgRoK4RoJYSwAiOBacVvSilzpJSpUsqWUsqWwGLg/LIWRQ0MDAwMqpYyA7qU0g/cCswG1gCTpZSrhBBPCSHOr+4BGhgYGBhER1SdolLKWcCsI7Y9FmHfPpUfloGBgYFBeTFa4AwMDAzqCUZANzAwMKgnGAHdwMDAoJ5gBHQDAwODeoIR0A0MDAzqCUZANzAwMKgnGAHdwMDAoJ5gBHQDAwODeoIR0A0MDAzqCUZANzAwMKgnGAHdwMDAoJ5gBHQDAwODeoIR0A0MDAzqCUZANzAwMKgnGAHdwMDAoJ5gBHQDAwODeoIR0A0MDAzqCUZANzAwMKgnRBXQhRADhBDrhBAbhRAP6Lx/kxBihRBiuRDiNyHEiVU/VAMDAwOD0igzoAshTMC7wEDgROBSnYA9UUrZUUrZGXgJeK3KR2pgYGBgUCrRzNBPBjZKKTdLKb3AJGBoyR2klLklXsYA8v/bu98Yuao6jOPfZ9tuKViBSkOAYlukUAjSFiv/RMUiWpCgL4j8MVgUQ0IgKQWjJRiM6AvlhYARJJR/JhIwFNGmEgELKqChlEKhUEpBlrQItBhqoZSW3f58cc/KdVjY2Tjde+71+SSTuffcMzPPzt397Z0zM+d2LqKZmbVjZBt99gHWltbXAUe0dpJ0HnAh0A3M6kg6MzNrW8feFI2IqyPiY8B3ge8N1EfSOZKWSVq2YcOGTj20mZnRXkF/Cdi3tD4htb2f24CvDLQhIq6LiJkRMXP8+PHtpzQzs0G1U9AfAaZImiypGzgNWFTuIGlKafVLwJrORTQzs3YMOoYeEb2SzgfuBkYAN0bEU5IuA5ZFxCLgfEmfB94BXgfm7MjQZmb2Xu28KUpE3AXc1dJ2aWl5bodzmZnZEPmbomZmDeGCbmbWEC7oZmYN0dYYulknRQRrlv+dfzz3CpMPncjEgyZUHcmsEVzQbVi9uXEz87/4I158ei1dXV309fYxfdYhfP+ObzOqe1TV8cxqzUMuNqyuOvc6nl/Rw9ubt/LWG1vYumUbj923klt+uLDqaGa154Juw6b3nV4evHMpvdt6/6t925Zt/H7BkopSmTWHC7oNm77ePrb3bR9w29YtW4c5jVnzuKDbsBk9ZjT7TZv4nvauLvHJ2TMqSGTWLC7oNqwuWnAuY8aOYdTo4g3Q7jHdjB03lnMuP7PiZGb150+52LDaf8Zkblx1JYuvvYeelWs56MgpnPCt4/jwuLFVRzOrPRd0G3Z77D2Osy47reoYZo3jIRczs4ZwQTczawgXdDOzhnBBNzNrCBd0M7OGcEE3M2sIF3Qzs4ZwQTczawgXdDOzhlBEVPPA0gbgxTa77wG8tgPjdEpdckJ9sjpnZ9UlJ9Qn63DnnBgR4wfaUFlBHwpJyyJiZtU5BlOXnFCfrM7ZWXXJCfXJmlNOD7mYmTWEC7qZWUPUpaBfV3WANtUlJ9Qnq3N2Vl1yQn2yZpOzFmPoZmY2uLocoZuZ2SCyL+iSZktaLek5SfOrztNP0o2S1ktaWWobJ+leSWvS9e5VZkyZ9pV0v6SnJT0laW6OWSXtJGmppBUp5w9S+2RJD6f9/2tJ3VXm7CdphKTHJC1O67nm7JH0pKTHJS1LbVnt+5RpN0kLJT0jaZWko3LLKenA9Dz2XzZJuiCnnFkXdEkjgKuBE4CDgdMlHVxtqv+4GZjd0jYfWBIRU4Alab1qvcBFEXEwcCRwXnoOc8u6FZgVEdOA6cBsSUcCPwGuiIj9gdeBsyvMWDYXWFVazzUnwOciYnrpo3W57XuAq4A/RMRUYBrFc5tVzohYnZ7H6cAngLeAO8kpZ0RkewGOAu4urV8MXFx1rlKeScDK0vpqYK+0vBewuuqMA2T+HXB8zlmBnYHlwBEUX9gYOdDvQ4X5JlD84c4CFgPKMWfK0gPs0dKW1b4HdgVeIL2nl2vOlmxfAB7KLWfWR+jAPsDa0vq61JarPSPi5bT8CrBnlWFaSZoEzAAeJsOsaRjjcWA9cC/wPLAxInpTl1z2/5XAd4Dtaf0j5JkTIIB7JD0q6ZzUltu+nwxsAG5Kw1jXS9qF/HKWnQbcmpazyZl7Qa+tKP5dZ/MRIkkfAu4ALoiITeVtuWSNiL4oXs5OAA4HplYc6T0knQSsj4hHq87SpmMi4jCKYcvzJH2mvDGTfT8SOAz4RUTMADbTMmyRSU4A0vsjJwO3t26rOmfuBf0lYN/S+oTUlqtXJe0FkK7XV5wHAEmjKIr5LRHxm9ScZVaAiNgI3E8xdLGbpJFpUw77/1PAyZJ6gNsohl2uIr+cAETES+l6PcV47+Hkt+/XAesi4uG0vpCiwOeWs98JwPKIeDWtZ5Mz94L+CDAlfYKgm+JlzqKKM32QRcCctDyHYry6UpIE3ACsioifljZllVXSeEm7peUxFOP8qygK+ympW+U5I+LiiJgQEZMofh/vi4ivkVlOAEm7SBrbv0wx7ruSzPZ9RLwCrJV0YGo6DniazHKWnM67wy2QU86q31xo482HE4FnKcZTL6k6TynXrcDLwDsURxhnU4ylLgHWAH8ExmWQ8xiKl4BPAI+ny4m5ZQUOBR5LOVcCl6b2/YClwHMUL3FHV/2cljIfCyzONWfKtCJdnur/+8lt36dM04Flaf//Ftg905y7AP8Edi21ZZPT3xQ1M2uI3IdczMysTS7oZmYN4YJuZtYQLuhmZg3hgm5m1hAu6GYtJE0qz6I5hNv9tXT7MzqfzOyDuaCb/Y/6vyEaEUenpkmAC7oNOxd0q510BPyMpFvS3NkLJe0s6bg0udOTab760al/j6TLU/tSSfun9pslnVK63zff57EekLQ8XY5O7cem9kUU32os3/7HwKfTnNnzJP1F0vTSfT4oadoOe4Ls/5YLutXVgcA1EXEQsAm4kGKO+lMj4uMUEz6dW+r/r9T+c4rZEtu1Hjg+igmuTgV+Vtp2GDA3Ig5ouc184IEo5s6+gmLqhbMAJB0A7BQRK4aQwawtLuhWV2sj4qG0/CuK+T9eiIhnU9svgfLMgreWro8awuOMAhZIepLiK/3lE6wsjYgX2riP24GT0iRp36T4x2PWcSMH72KWpdY5KzZSzKnRTv/+5V7SQY2kLmCg08bNA16lOItOF/B2advmtoJGvCXpXuDLwFcpznZj1nE+Qre6+qik/iPtMygmdprUPz4OnAn8udT/1NL139JyD+8W15MpjsZb7Qq8HBHb032OaCPbG8DYlrbrKYZrHomI19u4D7Mhc0G3ulpNccKGVRQz810BfAO4PQ2PbAeuLfXfXdITFOcCnZfaFgCflbSCYhhmoCPua4A5qc/U9+nT6gmgT8UJr+cBRHFCjE3ATUP7Mc3a59kWrXbSqfQWR8QhbfbvAWZGxGs7MNZgGfYG/gRMTUf7Zh3nI3SzHUzS1ynO43qJi7ntSD5CNzNrCB+hm5k1hAu6mVlDuKCbmTWEC7qZWUO4oJuZNYQLuplZQ/wbuVkLcWecZ1gAAAAASUVORK5CYII=\n"
+ "image/svg+xml": "\n\n\n\n",
+ "image/png": "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\n"
},
"metadata": {
"needs_background": "light"
@@ -232,18 +277,47 @@
}
],
"source": [
- "selection = df[['popularity','danceability']]\n",
- "print(selection)\n",
"\n",
"from sklearn.cluster import KMeans\n",
- "kmeans = KMeans(n_clusters = 3)\n",
- "kmeans.fit(selection)\n",
- "labels = kmeans.predict(selection)\n",
+ "kmeans = KMeans(n_clusters = 2)\n",
+ "kmeans.fit(X)\n",
+ "labels = kmeans.predict(X)\n",
"plt.scatter(df['popularity'],df['danceability'],c = labels)\n",
"plt.xlabel('danceability')\n",
"plt.xlabel('popularity')\n",
"plt.show()\n"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 89,
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Result: 143 out of 286 samples were correctly labeled.\nAccuracy score: 0.50\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "labels = kmeans.labels_\n",
+ "\n",
+ "correct_labels = sum(y == labels)\n",
+ "\n",
+ "print(\"Result: %d out of %d samples were correctly labeled.\" % (correct_labels, y.size))\n",
+ "\n",
+ "print('Accuracy score: {0:0.2f}'. format(correct_labels/float(y.size)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
]
}
\ No newline at end of file
diff --git a/Introduction/1-intro-to-ML/README.md b/Introduction/1-intro-to-ML/README.md
index 7129d6f3..a6821ee4 100644
--- a/Introduction/1-intro-to-ML/README.md
+++ b/Introduction/1-intro-to-ML/README.md
@@ -2,14 +2,14 @@
[![ML, AI, Deep Learning - What's the difference?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, Deep Learning - What's the difference?")
-> Click this image to watch a video discussing the difference between Machine Learning, AI, and Deep Learning.
+> 🎥 Click the image above for a video discussing the difference between Machine Learning, AI, and Deep Learning.
## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
### Introduction
Describe what will be covered
[![Introduction to ML](https://img.youtube.com/vi/h0e2HAPTGF4/0.jpg)](https://youtu.be/h0e2HAPTGF4 "Introduction to ML")
-> MIT's John Guttag introduces Machine Learning
+> 🎥 Click the image above for a video: MIT's John Guttag introduces Machine Learning
### Preparation
Before embarking on this curriculum, you need to have your computer set up and ready to run notebooks locally. Learn more about how to do this in this [set of videos](https://www.youtube.com/playlist?list=PLlrxD0HtieHhS8VzuMCfQD4uJ9yne1mE6)
diff --git a/Introduction/2-history-of-ML/README.md b/Introduction/2-history-of-ML/README.md
index c5e2d27c..f36a4525 100644
--- a/Introduction/2-history-of-ML/README.md
+++ b/Introduction/2-history-of-ML/README.md
@@ -47,7 +47,7 @@ Eliza, an early 'chatterbot', could converse w/ people and act as a primitive 't
"Blocks world" was an example of a micro world where blocks can be stacked and sorted and experiments in teaching machines to make decisions could be tested. Advances built with libraries such as [SHRDLU](https://wikipedia.org/wiki/SHRDLU) helped propel language processing forward.
[![blocks world with SHRDLU](https://img.youtube.com/vi/QAJz4YKUwqw/0.jpg)](https://www.youtube.com/watch?v=QAJz4YKUwqw "blocks world with SHRDLU")
-> Blocks world with SHRDLU
+> 🎥 Click the image above for a video: Blocks world with SHRDLU
## 1974 - 1980: "AI Winter"
@@ -80,7 +80,7 @@ This epoch saw a new era for ML and AI to be able to solve some of the problems
Today, Machine Learning and AI touch almost every part of our lives. This era calls for careful understanding of the risks and potentials effects of these algorithms on human lives. As Microsoft's Brad Smith has stated, "Information technology raises issues that go to the heart of fundamental human-rights protections like privacy and freedom of expression. These issues heighten responsibility for tech companies that create these products. In our view, they also call for thoughtful government regulation and for the development of norms around acceptable uses."[source](https://www.technologyreview.com/2019/12/18/102365/the-future-of-ais-impact-on-society/). It remains to be seen what the future holds but it is important to understand these computer systems and the software and algorithms that they run. We hope that this curriculum will help you to gain a better understanding so that you can decide for yourself.
[![The history of Deep Learning](https://img.youtube.com/vi/mTtDfKgLm54/0.jpg)](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of Deep Learning")
-> Yann LeCun discusses the history of Deep Learning in this lecture
+> 🎥 Click the image above for a video: Yann LeCun discusses the history of Deep Learning in this lecture
## 🚀Challenge
Dig into one of these historical moments and learn more about the people behind them. There are fascinating characters, and no scientific discovery was ever created in a cultural vacuum. What do you discover?
diff --git a/Introduction/3-fairness/README.md b/Introduction/3-fairness/README.md
index b69c62d3..f7beccf8 100644
--- a/Introduction/3-fairness/README.md
+++ b/Introduction/3-fairness/README.md
@@ -24,7 +24,7 @@ As a prerequisite, please take the "Responsible AI Principles" Learn Path and wa
Learn more about Responsible AI by following this [Learning Path](https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)
[![Microsoft's Approach to Responsible AI](https://img.youtube.com/vi/dnC8-uUZXSc/0.jpg)](https://youtu.be/dnC8-uUZXSc "Microsoft's Approach to Responsible AI")
-> Video: Microsoft's Approach to Responsible AI
+> 🎥 Click the image above for a video: Microsoft's Approach to Responsible AI
## Unfairness in data and algorithms
@@ -71,7 +71,7 @@ Stereotypical gender view was found in machine translation. When translating “
An image labeling technology infamously mislabeled images of dark-skinned people as gorillas. Mislabeling is harmful not just because the system made a mistake because it specifically applied a label that has a long history of being purposefully used to denigrate Black people.
[![AI: Ain't I a Woman?](https://img.youtube.com/vi/QxuyfWoVV98/0.jpg)](https://www.youtube.com/watch?v=QxuyfWoVV98 "AI, Ain't I a Woman?")
-> Video: AI, Ain't I a Woman - a performance showing the harm caused by racist denigration by AI
+> 🎥 Click the image above for a video: AI, Ain't I a Woman - a performance showing the harm caused by racist denigration by AI
### Over- or under- representation
Skewed image search results can be a good example of this harm. When searching images of professions with an equal or higher percentage of men than women, such as engineering, or CEO, watch for results that are more heavily skewed towards a given gender.
diff --git a/README.md b/README.md
index 6f6bda32..adadad70 100644
--- a/README.md
+++ b/README.md
@@ -41,7 +41,7 @@ Travel with us around the world as we apply these classic techniques to data fro
> Future space for Promo Video
[![Promo video](screenshot.png)](https://youtube.com/watch?v=R1wrdtmBSII "Promo video")
-> Click the image above for a video about the project and the folks who created it!
+> 🎥 Click the image above for a video about the project and the folks who created it!
## Pedagogy
diff --git a/Regression/1-Tools/README.md b/Regression/1-Tools/README.md
index f8cc59d5..be2fd391 100644
--- a/Regression/1-Tools/README.md
+++ b/Regression/1-Tools/README.md
@@ -22,7 +22,7 @@ In this lesson, you will learn:
[![Using Python with Visual Studio Code](https://img.youtube.com/vi/7EXd4_ttIuw/0.jpg)](https://youtu.be/7EXd4_ttIuw "Using Python with Visual Studio Code")
-> Click this image to watch a video on using Python within VS Code.
+> 🎥 Click the image above for a video: using Python within VS Code.
1. Ensure that [Python](https://www.python.org/downloads/) is installed on your computer. You will use Python for many data science and machine learning tasks. Most computer systems already include a Python installation. There are useful [Python Coding Packs](https://code.visualstudio.com/learn/educators/installers?WT.mc_id=academic-15963-cxa) available as well to ease the setup for some users. Some usages of Python, however, require one version of the software, whereas others require a different version. For this reason, it's useful to work within a virtual environment.