diff --git a/analysis_and_scripts/Analysis_07MAY2019_new.ipynb b/analysis_and_scripts/Analysis_07MAY2019_new.ipynb
index eb0de96a6c4154b5e6e1f5c589c3621c35506a77..1f93c496b8111c9e39eae9ccbba829310c681adb 100644
--- a/analysis_and_scripts/Analysis_07MAY2019_new.ipynb
+++ b/analysis_and_scripts/Analysis_07MAY2019_new.ipynb
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -230,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -313,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -414,7 +414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -442,33 +442,33 @@
     "    return f_values[:, model.classes_ == class_value].flatten()\n",
     "\n",
     "\n",
-    "# def cdf(x_0, model, class_value):\n",
-    "#     '''\n",
-    "#     Cumulative distribution function as described above.\n",
+    "def cdf(x_0, model, class_value):\n",
+    "    '''\n",
+    "    Cumulative distribution function as described above.\n",
     "\n",
-    "#     '''\n",
-    "#     prediction = lambda x: getProbabilityForClass(\n",
-    "#         np.array([x]).reshape(-1, 1), model, class_value)\n",
+    "    '''\n",
+    "    prediction = lambda x: getProbabilityForClass(\n",
+    "        np.array([x]).reshape(-1, 1), model, class_value)\n",
     "\n",
-    "#     prediction_x_0 = prediction(x_0)\n",
+    "    prediction_x_0 = prediction(x_0)\n",
     "\n",
-    "#     x_values = np.linspace(-10, 10, 40000)\n",
+    "    x_values = np.linspace(-10, 10, 40000)\n",
     "\n",
-    "#     x_preds = prediction(x_values)\n",
+    "    x_preds = prediction(x_values)\n",
     "\n",
-    "#     y_values = scs.norm.pdf(x_values)\n",
+    "    y_values = scs.norm.pdf(x_values)\n",
     "\n",
-    "#     results = np.zeros(x_0.shape[0])\n",
+    "    results = np.zeros(x_0.shape[0])\n",
     "\n",
-    "#     for i in range(x_0.shape[0]):\n",
+    "    for i in range(x_0.shape[0]):\n",
     "\n",
-    "#         y_copy = y_values.copy()\n",
+    "        y_copy = y_values.copy()\n",
     "\n",
-    "#         y_copy[x_preds > prediction_x_0[i]] = 0\n",
+    "        y_copy[x_preds > prediction_x_0[i]] = 0\n",
     "\n",
-    "#         results[i] = si.simps(y_copy, x=x_values)\n",
+    "        results[i] = si.simps(y_copy, x=x_values)\n",
     "\n",
-    "#     return results\n",
+    "    return results\n",
     "\n",
     "\n",
     "def bailIndicator(r, y_model, x_train, x_test):\n",
@@ -573,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -615,774 +615,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 73,
    "metadata": {
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[1] 0 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[2] 0 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[3] 0 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4 "
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-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[4] 0 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[5] 0 "
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-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2 "
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-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4 "
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-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[6] 0 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[7] 0 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 "
-     ]
-    },
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-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[8] 0 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4 "
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/rikulain/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:107: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
-      "  If increasing the limit yields no improvement it is advised to analyze \n",
-      "  the integrand in order to determine the difficulties.  If the position of a \n",
-      "  local difficulty can be determined (singularity, discontinuity) one will \n",
-      "  probably gain from splitting up the interval and calling the integrator \n",
-      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n"
+      "[1] 0 1 2 3 4 [2] 0 1 2 3 4 [3] 0 1 2 3 4 [4] 0 1 2 3 4 [5] 0 1 2 3 4 [6] 0 1 2 3 4 [7] 0 1 2 3 4 [8] 0 1 2 3 4 "
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Y5aHrYzP3yJng1f4yxpzFao2uk8NDSJfG6jK5KfmNqy217U3ErlB4mLcFq4ttdrScR2MdB0OxWlTWAxhj9mHdNEfY00Hj3UiraZV7djqHdc/j0q3NHq68alYStsvoMFDL7l7orpX96akLs6dXfTjCfnSfYYxJMsbstH+YGmAHO64vjuM4u6/X3lwDM3PMOmRoHzhk5XxMZz8qle20wqVU3vgDKGW3XgDWu7mwRvDLdMuz/Y/uINBKkr1vSkTKYP36mFEfYXWzel5EGrnPFOv9KbemXCzD/rA/3f8xPwt4eoeN4x+6p4rlJqwhrIeIyO3uM0XER9J5n5mbpVjdYvrbUxJWN5vkaZaxnwlw5/iVNi5Z3EARqSEiN6S3YhEpCAzC6sJ0tzFmiPuENWDBWaCP/POum7ftfE4UkbJuaRayh47GGHMUq8JWF/D0zq3k+3e6neZ09zTtuGVFxNNzQLeL6zvdCgAv2V+T37SlVaYejw8RaYY1Wl1WfYzVhekhEbnNfaZbRWUaVkXjLfHwDjQRqS1evp8uDY4RE2+2n11Lno+XUlnmDB4qFMnyLMC79nMv7nm+Ubx4P50t2v58FquyviHZvA1YN+RV8b51K61yz07f25+DHAF25XQC1iiVWfU+EIg1RL+T3YWtK9YgIps9LDdKXN9JVwV4EKsr46eOMLvV3J3L9cV+zu1jrFdfPOQeWaz3VbVyD/dCutdAMnfMOqS7D9Lg9fno7X5UKidol0Kl8sZbWN1uYkRkCdY/sHZYN0y7gJuykPZUe9osIsuwbibuwHo4+MaMJGSMiRORPlj98b8TkbVYz9D4YPWbb4X1zFXXLOQXrHck/R/WO5XaYlXybsF6CPwzwL3itAfruYP+InLZjp9kjJlkjDEi0g/rl/dVdneYHVj7OBSrG9RfWN1J0mWM+VtEVmE9o5UERNsVleRuALaKyA57Xcewuqf1xKosvZks7m1Yz+99iVXmaelop7PcJHtnlFv+rojIYqz3ovUB5hhjvheR57GevdgnIh9h3RBVsNc5AesYBHgEa1+8KiK9sI4TP6yKbh3smxFjzHYRGYY1/PLPIvIZVuW+BNYACS2w3tmT/GF8sJ7H+8I+zo9jtSI1wHqvUvIKV6plilWJ3g4MsG/gvsdq9boDa1h7r1+S6ol9nN+N1WVxo4istPNTEqu14Begtx39LawyHAy0EZH1WMdTeayKaz2s49bT4Ane5idBRN4GngC22/u6BNb7qqLx3CKzHquys1RE1mDd/G40xmwyxqwQkVexzrED9nl8BKtVoibWoBp9sUadS8/3WKPWlcLqVnY62bxo4K5kf3tjE9bN/NMiUhxrmPmzxpi3vVzeW+8CzwAvi0hDrO1vjjWww09Y+yErXsZ6JuhxEWmAdR5VxNqvccD9HroyJmKdE7tE5GOsCtvdWC1F9xpjYu14DbDKNQbr/Xknsa7l3bG6KyffVw9jDY7xtl3p2oLVDbASVm+B4qQccCZN3lwDM3nMZmQfpCYj52NG9qNS2Ssnhj7USafrceKfYeHTfceTHf9urJvzS1j/cN7DugFKa4jiNIeFTzbvGeA3rJuun7GeJXK8t8nTsPAH0slrKFYLx29YN0dnsIYSfwNo5MW2OobpXpdGnJuxKiHn7OlzOyzFttvxm9v76qI9332flcJ6Ie9erBue8/bfs7GeSctI2d7JP8P7D/YwvzjWu2a+tsvyClarzDKgsVtcR7mlui+SxV2KF68UwBpt0ABfu4V3whqU5SzWKIK/Y/0SX9MtXgl7X/1i5/001q/xwzysqxnwIf/8on0ca+S1UaQy3DfWi0x32Hn4E3gNz+/rSbVMsYa8fh+rMnsJqztkP0/nQVrHWzrzamKN/HjM3rY/sSr8nd3iCdZzY9H2sXoZq7KyGuuGN8W2eViXx+M62Xw/+5g6YKd/AGvAkgBP+ccaJn0K1vmc4L5P7DhdsM6rU/b2HbW34Wk8DBmfRt5X4+HVC1ijwTnOk1APy6UY1tsO74HVKh1nzz+QbF6q16e05qUS/xasH4ji7GN8Phm45qY3D6ub7ETgV3v/nsI6V+p4SOMbu5yCsK6jf9rl/APQyy1uJazzcwtWJeEy1rV4Dp5f51EY64XaO7DOpQtY5/Yi3K4l3u5D0rkGZvKY9XofpHP8eHU+ZnQ/6qRTdk6Od8gopZRS2UZEhmC9D2ygMSbNZ96UUkqpfzN9hksppZRSSimlcohWuJRSSimllFIqh2iFSymllFJKKaVyiD7DpZRSSimllFI5RFu4lFJKKaWUUiqH6Hu4PAgJCTFhYWF5nQ0ALl68SFBQUF5nQ7nRcsl/tEzyJy2X/EfLJH/Scsl/tEzyp/xULt9///0pY0yp9OJphcuDsLAwtm3bltfZACA6OpqIiIi8zoZyo+WS/2iZ5E9aLvmPlkn+pOWS/2iZ5E/5qVxExJsXxmuXQqWUUkoppZTKKVrhUkoppZRSSqkcohUupZRSSimllMohWuFSSimllFJKqRyiFS6llFJKKaWUyiFa4VJKKaWUUkqpHKLDwmfS+fPnOXHiBPHx8Tm6nqJFi7J3794cXYfKOC0X7/j4+FC6dGmKFCmS11lRSimllMoTWuHKhPPnz/PXX39RoUIFAgICEJEcW1dsbCzBwcE5lr7KHC2X9BljiIuL4+jRowBa6VJKKaXUdUm7FGbCiRMnqFChAoGBgTla2VLqWiYiBAYGUqFCBU6cOJHX2VFKKaWUyhNa4cqE+Ph4AgIC8jobSl0TAgICcrzrrVJKKaVUfqUVrkzSli2lvKPnilJKKaWuZ1rhyici5kYQMTcir7OhlFJKKaWUyka5XuESkRtEZJmI/C0i50XkIxGp5MVyoSKyXEQOiUiciJwSkWgR6ewhrkllqp8zW6WUUkoppZRSKeVqhUtEAoGvgBrAvcBAoBqwXkSC0lm8MHAKGAXcDjwAXAA+E5E7PcSfCzR1m37O+lb8u4hIulNYWFheZxOA//znP6nmsUmTJjmyzn379iEiREVF5Uj6AMuWLePNN99MEb569WpEhM2bN+fYupVSSimlVM7K7WHhHwRuBMKNMQcARGQn8AvwMPB6agsaY3ZjVbKcRGQV8DswGPjIbZGjxhi9U01HTEyMy/eePXtSr149IiMjnWF+fn65nKvUFSxYkG+++SZF+LU8RPuyZcvYtm0bTz75pEt406ZNiYmJoXbt2nmUM6WUUkoplVW5XeG6A9jsqGwBGGN+F5Fvge6kUeHyxBiTICJ/AzoEWia5twz5+fkREhLidYvRlStXcr1CllOtWflN0aJFr5ttVUoppZT6t8rtZ7hqAT95CN8N3ORNAiJSQEQKiUhZEfkvUB34n4eoQ0XkiohcEpGvRKRF5rOtAPr160fVqlXZuHEjTZo0ISAggNGjR3P58mVEhIkTJ7rET6073rp164iIiKBw4cIULlyYLl26sHfv3mzJ4/vvv4+I8PPPKXuPtm7d2qUCM2XKFJo0aULx4sUpXrw4zZo1Y+3atemuo0mTJvTs2TNFeNmyZXnkkUec348dO8aDDz5ItWrVCAwMpFKlSgwaNIjjx4874/Tr148lS5bw66+/OrtH1qhRA/DcpTApKYlJkyZRrVo1fH19qVChAsOGDePixYvOOI7yePHFF5k8eTKhoaEEBwfTtm1b9u/fn+72KaWUUkqp7JPbFa4SwFkP4WeA4l6mMQmrResYMALoZ4z50i3OAuBRoB3wEFAS+EpEIjKRZ5XMqVOnGDhwIIMGDeLzzz+nd+/eGVr+o48+omPHjoSEhLBo0SLmz5/PyZMnadmyJceOHfMqjYSEhBRTUlISAL169SIoKIgFCxa4LHP48GE2btzIwIEDnWGHDh3i4Ycf5sMPP2Tx4sXUrl2bTp06sX79+gxtU2pOnTpFcHAwr7zyCqtXr2bixIns2rWLli1bOt9L9eKLL9KuXTsqVqxITEwMMTExLFmyJNU0n332WZ577jm6du3KypUrGT58OLNmzeKOO+7AGOMSd/bs2Xz11Ve89dZbzJ49m59//pmePXs695VSSimllMp5ud2lEMB4CMvIi3qmAlFAWWAQsEhEehtjVjpXYMzAZPG/FpHlWC1rLwLNPSUqIg9hVc4oU6YM0dHRqWagaNGixMbGuoQ9t/45dp3clYHNcLXzxE4AWrzr2hBnjPH6PUZ1StXhldavZDoPjvXFx8en2D6wXvj8999/M2fOHNq1a+cMd8S9cuWKy3KOVpe4uDhiY2NJSkriySefpG3btsyZM8cZr2HDhtSrV49XX32VsWPHppq3q1evkpiYiI+PT4p5TzzxBC+99BIAXbt2Zf78+Tz77LPOfffee+9RsGBBunTp4szj+PHjncsnJSVx66238tNPP/HWW2/RqFEjj9sAkJiY6LLdqe27sLAwl+2pU6cONWvWpGHDhqxYsYIOHTpQpkwZihQpgo+PD7Vq1XLZp5cuXXLmITY2lr/++otp06Zx//33M27cOMB6zsvf358nnniC5cuX07ZtWy5fvgxYLxxeuHAhBQsWBOD8+fM89NBDfPvtt9Svn7sDdl6+fDnNcyo7XLhwIcfXoTJOyyX/0TLJn7Rc8h8tk/znqafqk5hYh2nTovM6KxmS2xWus1itXO6K47nlKwVjzBHgiP11pYhEA68BK9NYJtYeYOOBNOK8A7wD0KhRIxMREZFqHvbu3ZtikAZfX1/njW1mOCoG7mkkJiZ6na6vr2+WB48QEXx8fDym4+PjQ2BgYIrudI4KkJ+fn8tyQUHWwJMBAQEEBweza9cujh49yqRJkwgICHDGq1ChAo0bN2bz5s1p5t+xjz2N2le2bFnnsvfffz9Llixh586dNG9u1a+XLl3K7bff7jLi4nfffUdkZCTff/89J0+edIbXq1fPmZb7NsA/ZeSeV/d9Z4xh2rRpzJo1i99//92l29/hw4ed8Xx8fChQoECK9AIDA515CA4O5osvviAhIYHBgwe7xH3ggQd48skn2bp1Kz169HCWR+fOnSlWrJgz3i233ALA6dOnc32QEX9/fxo0aJCj64iOjiat81blDS2X/EfLJH/Scsl/tEzyn2LF4Ny5c9dcueR2hWs31nNc7m4C9mQyzW3AU17EEzy3rmWLqZ2mZml5x0uPo++LdgmPjY3NVyPwlS1bNtPLnjhxAoD+/fvTv3//FPOrV6/uVTqO1qfUtGvXjvLlyzN//nyaN2/ODz/8wO7du11am3777TfatWtHw4YNmT59OhUrVqRQoUI899xzHD16NANblbrXXnuN5557jhEjRtC2bVuKFStGXFwcrVq1crZCZcSZM2cAKFeunEt4QEAARYoUcc53KFHC9bcNx+AmmVm3UkoppZTKnNyucK0AXhORG40xvwGISBjQDPhPRhMTkQJYXQR/TSdeEaAL8F1G16Fceere6OPjQ8GCBbl69apL+OnTp12+lyxZEoDJkyfTsmXLFOn4+/tnSx4LFCjAPffcw7vvvsubb77JggULKF68OF27dnXGWbVqFRcuXODDDz8kJCTEGX7hwoV00/f393c+g+WQlJTEuXPnXMKioqK4/fbbXQYTycrgII4K1PHjx6lSpYozPC4ujvPnzzv3r1JKKaWUyj9yu8I1C3gcWC4io7BanMYDh4G3HZFEJBSrEjXOGDPODovE6o74LXAc6xmuB4BbgHuSLfssEA6sB/4EQoFn7fgpm1VUlhUsWJAKFSrw00+uA1CuWrXK5XudOnUoX748e/fu5emnn87RPA0aNIjXXnuN5cuXs3jxYvr27esyfL3j+ahChf45BX766Se2bdtGtWrV0kw7NDSUtWvXunT3XLduHVeuXHGJd+nSpRTPmyV/ds3Bz8+PuLi4dLfptttuo1ChQkRFRdGsWTNn+KJFizDG0KpVq3TTUEoppZRSuStXK1zGmIsi0gaYAszH6ub3JfCUMSZ504IABXEdRfEHrK6D/YCiWJWuH4EWxphvk8XbD/S0p6LAeaxK2gPGmC05sV3KGt789ddf55VXXqFRo0asX7+epUuXusQpWLAgb731Fn369OHSpUv06tWLkiVLcvz4cb799luqV6/O448/nu66PD3D5ePjw8033+z8XqdOHerVq8fTTz/N8eO8mv9rAAAgAElEQVTHXUYnBOjQoQPPP/88AwYMYNiwYRw5coQxY8ZQqVIlr7b1/fffZ8iQIfTv358DBw7w5ptvOp/3cujUqRPTpk1j0qRJNGzYkDVr1vDJJ5+kSO+mm27i/fff591336Vu3boEBga6DKDhULZsWZ544gmmTp2Kv78/HTp0YOfOnYwePZo2bdq4DGSilFJKKaXyh1wfpdAY8wfQK504B3EbudAYswKrS2J66X8KfJqFLKpMGDNmDLGxsUyZMoVLly7RrVs35s6d6xy0wqFnz56sX7+el19+mQceeIC4uDjKlStH06ZNGTBgQLrrSUxMpGnTpinCS5YsyalTp1zCBg4cyLPPPsuNN97o0iIE0KBBA+bNm8e4cePo1q0b1apVY8qUKSxdupQdO3akmYfOnTszadIkZs6cSVRUFI0aNWLx4sV07NjRJd748eO5cOECr776KleuXKFNmzasWrWK8PBwl3hDhw5l27ZtPPPMM/z999+Eh4ezb98+j+t+7bXXKFu2LLNmzeKNN94gJCSEIUOG8PLLL3s9mqVSSimllMo94v7uHmWNUrht27ZU5+/du5eaNWtm6zqvlUEzlEXLJWNy4pxxp6NJ5U9aLvmPlkn+pOWS/2iZ5D8REdYohTt2FEs3bm4Qke+NMWmP5kbuv/hYKaWUUkoppa4befHiY+WBe8uWUkoppZRS6tqnLVxKKaWUUkoplUO0wqWUUkoppZRSOUQrXEoppZRSSql87dw52LsX4uOvvVGZtcKllFJKKaWUyrcSE+Gee+DkSbhy5dqrvuigGUoppZRSSql8a9Qo+PxzqFYNAgMT8zo7GXbtVRGVUkoppZRS14UlS2DiRHjkEShfPq9zkzla4covIuxJKaWUUkopxfbtMHgwNG8Ob7yR17nJPK1wKaWUUkoppfKVkyehRw8oWRKWLQNf37zOUeZphUsxd+5cRIQDBw5kOa2IiAiaN2+eDbmy3HfffYSFhWVbepGRkYjk/ug2kZGRfPXVV7m+XqWUUkqpa018PPTuDSdOwCefQJkyeZ2jrNEKl1K5YOzYsVrhUkoppZTywvDhsHEjvPsu3HxzXucm67TCpZRSSimllMoXZs+G//0Pnn3WGgr+30ArXMorW7dupXfv3lSsWJGAgADCw8N5/vnniYuL8xh/+fLl1K5dGz8/P2rUqMEHH3yQIs6PP/7IHXfcQfHixQkICKBZs2Z8/fXX6ebl0qVLPPfcc1SuXBlfX18qV67MSy+9RFJSkku87du306JFC/z9/alQoQLjx4/HGOPV9sbHxzNq1CjCwsLw9fUlLCyMUaNGER8f74wTHR2NiBAdHe2yrKOL5sGDBwGcXRhfeuklRAQRITIy0hl/w4YNtG/fnqJFixIUFES9evV49913M5SXgwcPIiLMnDmTkSNHUrZsWYKDgxkwYACXLl3iwIEDdOzYkcKFC1O1alXmzZuXYpu9KY+tW7fSvn17SpYsSWBgIDfeeCOPPvqoV/tUKaWUUiotmzbBo49Chw7WyIT/FvoeLuWVP/74g/r163PfffcRHBzM7t27GTduHL/99htRUVEucQ8cOMCTTz5JZGQkpUuXZsaMGfTr149SpUrRunVrAH744QdatGhBgwYNmDVrFoGBgcycOZN27dqxadMmbk6l/TghIYGOHTuyZ88e/vvf/1KnTh02b97M+PHjOXPmDJMnTwbg1KlTtGnThrJlyzJv3jz8/Px49dVX+eOPP7za3nvvvZcPPviA559/nubNmxMTE8OLL77Ib7/9xqJFizK072JiYmjatCn33XcfDz/8MAAVK1YErIppr169aNasGW+//TYhISHs3r2bQ4cOZSovEyZMICIignnz5rFnzx5GjBhBgQIF2L59Ow8++CDPPvssM2bMYPDgwTRq1IhatWp5XR4XLlygY8eO3HLLLcydO5fg4GAOHjzIpk2bMrQ/lFJKKaXcHTkCd94JoaEQFQUFC+Z1jrKRMUYnt+nmm282admzZ0/KwGHGmFZZmIrak1t4fPN479MYlma2UzVnzhwDmF9++cWr+ElJSSY+Pt7Mnz/fiIg5deqUc16rVq0MYGJiYpxhCQkJJjw83DRv3twZ1qZNG1OjRg1z5coVl3g1atQw3bt3d4bde++9JjQ01Pn9/fffN4DZsGGDS55efPFF4+PjY/766y9jjDHPP/+88fHxMYcOHXLGuXDhgilZsqSxDvvU7dq1ywBmzJgxLuHjx483gPnxxx/N+fPnzfr16w1g1q9f7xLPsT9///13ZxhgXnjhBZd4SUlJJjQ01Nx8880mMTEx03kxxpjff//dAKZ169Yu8Xr27GkAM3/+fGfYmTNnTMGCBU1kZKQzzJvy2Lp1q8s6M8LjOZPN3MtB5Q9aLvmPlkn+pOWS/2iZ5J64OGMaNzamcGFjdu9OPV6rVsbUq3c21/KVHmCb8aJuoV0KlVfOnz/Pc889R5UqVfDz88PHx4eBAwdijOGXX35xiXvDDTfQpEkT5/eCBQvSp08ftmzZQlJSEnFxcWzYsIE+ffpQoEABEhISSEhIwBhDu3bt2LhxY6r5WL16NaGhodx2223O5RISEujQoQPx8fFs3rwZsFqVmjRpQqVKlZzLBgUF0a1bt3S31bH+AQMGuIQ7vm/YsCHdNLyxf/9+Dh06xJAhQyhQwPOpmNG8dO7c2eV7jRo1AOjYsaMzrHjx4pQuXZrDhw8DeF0e1apVo1ixYjz88MMsWLDAubxSSimlVGYZAw8/DFu3woIFcNNNeZ2j7KddCrPL1CwuH2F/RrsGx8XGERwcnMXEs27w4MGsW7eOcePGUb9+fYKCgtiyZQuPPfYYly9fdolbxsPYnWXKlOHq1aucPHmShIQEEhMTGT9+POPHj/e4vqSkJI+VkBMnTnDo0CF8fHw8Lnf69GkAjh07Ru3atT3mIz1nzpwBoFy5ci7hZcuWdZmfVY68OroXZkdeihcv7vLd135phadwR7mdOXPGq/IoWrQo69evZ/z48Tz66KPExsZSq1Ytxo4dS69evdLcVqWUUkopT954A95/H8aOhe7d8zo3OUMrXCpdly9fZvny5URGRjJs2DBn+K5duzzG/+uvvzyG+fr6UqpUKeLi4ihQoACPPfYYgwYN8phGai0+JUuWpHLlyh4H4QCc7+wqV65cqvlIT4kSJQA4fvw4VapUcYYfP37cmQcAf39/AK5eveqyvKMilZ6QkBAAjh49muW8ZEWxYsW8Lo/69evz4YcfkpCQwLZt25gwYQJ9+/blxx9/9FjBVUoppZRKzbp18Mwz1rNbo0bldW5yjla4VLquXLlCYmJiilaluXPneox/+PBhNm/e7OxWmJiYyNKlS7nlllsoUKAAQUFBtGjRgh9//JGGDRumWrnypFOnTnz44YcULlzY2V3Ok6ZNm/Lqq69y+PBhbrjhBgAuXrzIp59+mu46WrVqBUBUVBQvvPCCM3zhwoUAtGzZEoDQ0FAAfvrpJzp06OCM99lnn6VI09fXN8WIjtWrVycsLIzZs2fz0EMPeXwhs7d5yYrMlEehQoVo0qQJ48ePZ8WKFezdu1crXEoppZTy2m+/wV13WV0I580Db24Ho6MhOnoH/3QNuzZohUs5rV692tlVzaFo0aK0b9+eJk2aMHnyZMqVK0dISAjvvfdeqi0zZcqU4a677mLs2LGUKlWKGTNm8PPPPzNjxgxnnNdff52WLVvSsWNHHnjgAcqVK8epU6f44YcfSExMZGIqY4H279+fOXPm0LZtW5555hnq1avH1atX+fXXX1mxYgWffPIJgYGBDB8+nOnTp9OhQwciIyOdoxQGBASkux9q1arF3XffTWRkJAkJCdx2223ExMQwfvx47r77burWrUtsbCzlypWjVatWTJgwgZCQEEqXLs2CBQv49ddfU6R50003sWrVKjp16kTx4sUpX7485cuXZ+rUqdx55520adOGRx55hFKlSrF3715OnDjB2LFjvcpLdvCmPFauXMk777xDjx49qFy5MhcvXuTNN98kODiYpk2bZks+lFJKKfXvd+HCP90Hly+HwoXzNj85zpuRNa63KVOjFGZVK3tyc/78+exflxvHqHqeplq1ahljrFHwOnXqZAoXLmxKlSplHnvsMbNy5coUo/S1atXKNGvWzCxfvtzUqlXL+Pr6murVq5uoqKgU692zZ4+56667TKlSpYyvr6+pUKGC6datm1m1apUzjvsohcYYExcXZ8aMGWPCw8ONr6+vKV68uGnUqJEZM2aMiY+Pd8b7/vvvTfPmzY2fn58pX768GTdunBk9enS6oxQaY8zVq1fNCy+8YCpVqmQKFSpkKlWqZF544QVz9epVY8w/5XL48GHTtWtXU7RoUVOmTBkzcuRIM2vWrBSjFH7zzTemYcOGxs/PL8Wog19++aWJiIgwQUFBJigoyNStW9e89957XufFUT6AmTVrlst2jBkzxgAu+8UYY0JDQ03//v0zVB779u0zffv2NWFhYcbPz8+EhISYzp07m82bN6e7P3WUwuuXlkv+o2WSP2m55D9aJjkjMdGYO+80pkABY774IuPL56dywctRCsWKq5Jr1KiR2bZtW6rz9+7dS82aNbN3pRH2Z7RrcGxsbL4YNEO50nLJmBw5Z9xER0cTERGRo+tQGaflkv9omeRPWi75j5ZJzhg/HkaPhtdfh+HDM7hwRATnzp2j2I4dOZK3jBKR740xjdKLp8PCK6WUUkoppXLc8uVWZWvQIHjqqYwvn3TlcvqR8iGtcOUX0aRo3VJKKaWUUurfYM8eGDAAGjeGt98GD2OFpengG2OJ3/odP17cmzMZzEE6aIZSSimllFIqx5w9aw2SUbgwfPwx2G/W8Y4x/DxsANWnLeLrSnA68NprL9IKl1JKKaWUUipHJCZCv35w6JA1rHuFChlY+OpV9vVsSY3PvmNF05I0DqxCnVNXciqrOebaqyIqpZRSSimlrgkjR8LatTB9Otx2m/fLJZ09wy+3VKXGZ9+xsHc4bdcfpFxC+q/3yY+0wqWUUkoppZTKdgsXwquvwmOPwZAh3i8X98tejtYNI3TXYeY/055+S3YT5FcYoqPZMXVqzmU4h2iFSymllFJKKZWtvv/eqmS1agVTpni/3OmNa7jUqB7BJ2P59M3HGPjaWgoWKJhzGc0FWuFSSimllFJKZZu//oIePaB0aVi6FHx8vFvu8ILpBLTvzIUCCXy/7E16PfZWzmY0l+igGfmF48V60dF5mQullFJKKaUy7epV6N0bTp+Gb7+FUqW8W+6XscO4ceyb7KxQCFasoG2Dzjmb0VykFS6llFJKKaVUthg2DL75BqKioEEDLxZITGTPfV24acEavqxbmKqffUdohZtyPJ+5SbsUKubOnYuIcODAgRTzEhISEBEiIyNzP2P/Yjm9T6Ojo4mMjCQpKckl/ODBg4gIc+fOzbF1K6WUUur69PbbMHMm/Oc/cNdd6cc3Fy+yt3Vtblqwho/aV+TmTQf/dZUt0AqXUv9K0dHRjB07NkWFq1y5csTExNClS5c8yplSSiml/o2++QYefxw6d4YXX0w//tVjR/itQRjhX+9j4f2N6fr5rxQLKpnzGc0D2qVQqeuIn58fTZo0yetsKKWUUupf5PBh6NULbrwRFi2CgukMKvj3ju+42CGCcmcvs2x8P+55YREikjuZzQPawqUyLDIy0uNJcd999xEWFub87ui+NnPmTEaOHEnZsmUJDg5mwIABXLp0iQMHDtCxY0cKFy5M1apVmTdvnkt6Bw4cYODAgVSuXJmAgABuvPFGhg4dytmzZ1Ost2LFimzfvp0WLVoQGBhItWrVmDlzplfbc+rUKYYOHUqFChXw8/OjRo0avPPOO875W7ZsQUT49NNPUyw7dOhQSpUqRXx8PABRUVG0adOGUqVKUbhwYRo0aJBiuzxx33cOERERRDgGVAEuX77M8OHDqV27NoULF6Zs2bJ069aNffv2OeNERkYyduxYAHx8fBARZ3ml1qVwwYIF1KtXD39/f0JCQhg4cCDHjh1ziRMWFsaAAQOIioqiZs2aBAUF0ahRI7755pt0t08ppZRS/05xcdaIhJcvw/LlUKxY2vGPrVqCaXYbhS5dZsOcMfQdtfhfXdkCrXCpZBITE0lISHCZEhMTs5zuhAkT+PPPP5k3bx7jxo1jyZIlPPLII/Ts2ZMuXbrw8ccfU7duXQYPHszu3budy/35559UrFiRqVOnsmbNGkaPHs2XX37J7bffnmId58+f55577mHAgAEsX76cxo0bM3ToUNavX59m3s6fP0+zZs1YtWoVkZGRrFq1im7dujF06FCmTZsGwC233EJ4eDjz5893Wfbq1at88MEH9OvXDx97vNPffvuN3r17s3DhQj755BO6devGkCFDvK78pefKlSvExsYyatQoVq1axYwZM7h8+TJNmjTh+PHjAAwZMoQHHngAgG+++YaYmBhiYmJSTfOdd95h4MCB1KxZk48++oiJEyeyZs0aWrVqxYULF1zifv3110yePJnx48ezZMkSEhMT6dq1K+fOncuW7VNKKaXUtcMYePBB2L7deslxjRppx//1rfGU6N6PE0Hw+2eL6DwgMlfymde0S2F2eeop2LEj88s7lk3WmgEQkJiYfrusQ/36kIW3b9dI7yzJpCpVqjhbeTp27MjXX3/N/PnzmT9/PgMGDACgUaNGrFixgmXLllGrVi0AWrZsScuWLZ3p3HbbbVStWpUWLVqwfft2GiQb+iY2Npbp06fTunVr57Jr165l8eLFzjBP3njjDQ4dOsSuXbuoVq0aAO3atePcuXOMHTuWoUOHUqhQIQYOHMiLL77I33//TdGiRQH47LPPOHPmDAMHDnSm9/zzzzv/TkpKIiIigmPHjjFjxgweeeSRLO1HgKJFizJ79mzn98TERDp27EiZMmVYvHgxw4cPp2LFilSsWBGAW2+9lUKFUj/NExMT+e9//0tERARRUVHO8Bo1atCiRQvee+89nnzySWf4+fPn2bFjB8WLFwegbNmyNG7cmM8++4x77rkny9unlFJKqWvH5MlWReull6Br1zQiGsOe4QO46Y1FbK7iT4nP13NrtevnEQdt4VJOH3/8MVu3bnWZNm/enOV0O3d2fY+Co2LXsWNHZ1jx4sUpXbo0hw8fdoZdvXqVl19+mRo1ahAQEICPjw8tWrQAYP/+/S5pBgYGulSs/Pz8qFatGn/88UeaeVu9ejW33norlStXdmnZ69ixI6dPn2bPnj0ADBgwgCtXrrB06VLnsvPnzyc8PJxbbrnFGfbLL79w9913U6FCBXx8fPDx8WH27Nkp8psVH3zwAbfeeivFihWjUKFCBAUFceHChUytY//+/Zw4cYL+/fu7hDdv3pzQ0FA2bNjgEt60aVNnZQugTp06AOnuZ6WUUkr9u6xZA889B336wMiRqcczV6/yU/em3PTGIlbfWpIbt/xM9euosgXawpV9stCyBKT64uO42FiCg4OzlraXateuTdWqVV3CEhISspxu8ht0AF9f31TDL1++7Pw+cuRIpk2bxujRo7ntttsIDg7myJEj3HnnnS7xPKUFVqXLPZ67EydOcODAAWeXQHenT58GIDQ0lJYtWzJ//nyGDBnCuXPnWLVqFf/973+dcS9cuED79u0JDAxk4sSJVKlSBV9fX2bMmMF7772XZj689emnn3LXXXdx7733MmbMGEJCQihQoAC33357utvqyZkzZwBr9EJ3ZcuWdc53KFGihMt3Pz8/gEytWymllFLXpgMHoF8/qF0b5syB1B7BSjx3lp/b1KP29sMsu7MGXRZ/T4BvYO5mNh/QCpfKMH9/f8BqgXJUnuCfykl2iYqKYtCgQYwaNcoZ5v5MUVaVLFmS0qVL88Ybb3icHx4e7vx74MCBPPjggxw6dIjly5dz9epVl5ahmJgYDh06xNdff03z5s2d4d5UWv39/bl69WqK8NOnT1Oy5D9DpEZFRVG1alWXQS/i4+NTVIy85ahAOZ7/Su748eM0atQoU+kqpZRS6t8pNha6d7eeePnkEwgK8hzv4q/7ONH6FqoejeWDpzvS+7XPKCDXZ+e663OrVZaEhoYC8NNPPznDzp07x6ZNm7J1PZcuXUrR8jRnzpxsXUenTp3Yt28flSpVolGjRimm5K2Lffr0wd/fn4ULFxIVFUXLli1dRha8dOkSgEuez549y/Lly9PNR2hoKH/99RenTp1yhv36668pugleunQpxTNZ8+fPTzG4iaPlKS4uLs31hoeHU6ZMGZfntwA2bdrEoUOHaNWqVbp5V0oppdT1ISkJBg6E/fth6VKoXNlzvJPffsHFRnUpcSKW1W88Qd/Jq6/byhbkQYVLRG4QkWUi8reInBeRj0SkkhfLhYrIchE5JCJxInJKRKJFpLOHuP4i8qqIHLPjxohIS0/pqozr3LkzRYsW5cEHH2TlypV8+OGHzuHds1OnTp2YN28e06dPZ+3atTzyyCPZXqkbPnw4pUuXpkWLFsycOZP169ezcuVKXnvtNbp37+4St0iRItxxxx3873//Y/PmzS6DZYA1qEeRIkV47LHHWLVqFR988AGtWrUiJCQk3Xz06dMHEaF///6sWbOGhQsX0r179xTLOiqIw4cP58svv2TSpEmMHj2aYm5jsN50k/WW9smTJ/Pdd9+xbds2j+stWLAg48aNY926dQwYMIDVq1fz7rvvcuedd1KtWjUGDx6cbt6VUkopdX0YO9Ya+n3KFEhtTLLfF00noG1HrpgEdi77H90efzN3M5kP5WqFS0QCga+AGsC9wECgGrBeRFJpkHQqDJwCRgG3Aw8AF4DPROROt7jvAg8Co4GuwDFgjYjUz6ZNua4VK1aMlStXUqBAAfr27cvIkSN54okn0hwNMDOmTZvGHXfcwQsvvMBdd91FbGwsixcvztZ1FC1alE2bNnH77bfzyiuv0LFjR+6//36WL1/ucXsGDhzIn3/+iZ+fH71793aZV6pUKT7++GMSExPp3bs3I0eOZMiQIc6RGNNStWpVli1bxtGjR+nRoweTJk3i9ddfp3r16i7xHnzwQV544QWWLFlCt27dWLVqFZ9++qlz5ESHrl278uijjzJ9+nSaNm1K48aNU133Qw89xPz589m1axfdu3dnxIgRtG/fng0bNmR7JVoppZRS16aPPoJx42DwYHj8cc9x9rw4jBsGPsbvIYU4t341Lbo+mruZzKfEGJN7KxMZBrwOhBtjDthhlYFfgBHGmNczmF4h4HdghzGmmx1WD9gB3G+MmZMs3m5gvzHmjvTSbdSokUmtRQBg79691KxZMyNZTV8qg2bE5uKgGcp7Wi4ZkyPnjJvo6GiXl0Sr/EHLJf/RMsmftFzyHy2Tf+zaBU2bWoNkbNgA9pML/0hKYuf9Xag7bzUbawdTZc1WKpQP95hWVuWnchGR740x6T7wnttdCu8ANjsqWwDGmN+Bb4HuqS6VCmNMAvA3EO+2jnhgiVu8KKCjiLgfIkoppZRSSikPzpyBHj2gSBGrlcu9spUUd4ldrWtRd95qVra5gfqbD+ZYZetaldujFNYCPI0gsBvo400CIlIAq6IYgtVtsDowzG0dvxtjLnlYhy9Q1f47f3Fr2VJKKaWUUiovJSTAXXfBkSOwcSOUL+86//LxIxyKaECd/af48L5buWPWRnwK+XpO7DqW2y1cJYCzHsLPAClfpOTZJKwWrGPACKCfMeZLL9fhmK+UUkoppZRKw4gRsG4dzJwJt97qOu/szi2crFeN0AOnWD6uP3e+F6OVrVTkxXu4PD00lsrr0jyaitU9sCwwCFgkIr2NMSuTpZXhdYjIQ8BDAGXKlCE6jRanokWLEhsbm4EsZ15iYmKurUt5T8slYy5fvpzmOZUdLly4kOPrUBmn5ZL/aJnkT1ou+c/1XiZr1pRhypSa3HnnESpXPuDSGevqtvU0GvMiASSxYMxgqrYYxIYNG3IlX9diueR2hessnluYiuO5VSoFY8wR4Ij9daWIRAOvAY4K1xnA0zDzxZPN95TuO8A7YA2akdbDeHv37s21ARN0cIb8ScslY/z9/WnQoEGOriM/PUSr/qHlkv9omeRPWi75z/VcJlu3/jP0e1RURXx8Kjrn7Z8+ntCR4/izaAHOfbyEIS365mrersVyye0uhbuxnrFydxOwJ5NpbsN6Liv5OirbQ9C7r+MqcACllFJKKaVUCsePQ8+eUK4cfPAB+PjYM4zhx2f6E/7YaHZX8oeYGBrmcmXrWpXbFa4VQBMRudERICJhQDN7XobYA2g0B351W4cPyQbhsIeFvwtYa4y5kpmMK6WUUkop9W925Qr06gVnz1ovOA4JscJNfDzbezal3uuL+LJxCGFbD3BjtVvyNrPXkNzuUjgLeBxYLiKjsJ61Gg8cBt52RBKRUKxK1DhjzDg7LBKrO+K3wHGsZ7geAG4B7nEsa4zZISJLgKki4oP1nq6hQGWgfw5vn1JKKaWUUtccY6wXGm/aZLVs1a1rhcf/fZZ97erRYNthVvSoSYcl2/D3de9IptKSqy1cxpiLQBvgZ2A+sBCrQtTGGHMhWVQBCrrl7wegNjANWIs1WuFloIUxJsptVYOBOcCLwCrgBqCTMeaH7N6m7BIR8c+7j5VSSimllMpNM2bA7NnwwgvQx+4nFvv7fg7VC6Xm94dZ/lRnun70k1a2MiHXRyk0xvwB9EonzkHcRhU0xqzAy26Hxpg44Gl7UkoppZRSSqViwwYYNgy6doVx46yw45u+gC5dKHMpnq/efIruj0/J20xew3L7GS6Vj8XExNC3b1/Kly+Pr68vJUuWpH379sybN4/ExMQcWWd0dDSRkZEkJSXlSPrpmTp1Kh999FGK8MjISEQy8raCnBMREXHNjcajlFJKqWvDoUPQuzdUrQoLFkCBAnAgagZBbTqSlJTA7g9n0EErW1miFchY2bMAACAASURBVC4FWBWPZs2acebMGV555RXWrVvHe++9R/Xq1Rk6dCgrV65MP5FMiI6OZuzYsfmuwjVkyBBiYmLyIEdKKaWUUrnj0iVrRML4eGuQjKJFYeeEpwjt/yhHShQiNnotTbo+ktfZvOblxYuPVT6zceNGnn76aR5//HHefPNNl3ndu3fn6aef5uLFi3mUu3/Ex8dTqFChXGl5qlixIhUrVkw/olJKKaXUNcgYeOAB2LEDVq6E6tUM2+6/nUZzVrO5ZjBhX2yjbIXqeZ3NfwVt4VJMnDiREiVKMGnSJI/zq1SpQl3HUDXAli1baNeuHYULFyYoKIi2bduyZcsWl2Xuu+8+KlasyPbt22nRogWBgYFUq1aNmTNnOuNERkYyduxYAHx8fBARZ2Xq4MGDiAjTp09nxIgRlC9fHj8/P86dO8fJkyd5+OGHqV69OoGBgdxwww3cc889HD16NEXef/zxR3r27EnJkiUJCAggPDycCRMmABAWFsahQ4dYuHChc9333XefM2/uFbvz58/z+OOPU758eUJCQggPD2fKlCkYY5xxoqOjERFWrFjB448/TkhICKVKlWLAgAGcO3fO2yJJ1/79++nZsyfFihUjICCAJk2asHr16hTxFi9eTI0aNfD396dOnTqsWLFCuygqpZRSikmTICoKJkyATm3i+KHtTTSas5q1ETdQe+shrWxlI23hus4lJiYSHR1Njx498Pf3Tzf+zp07adWqFTfddBNz585FRJg4cSKtWrVi8+bN1KtXzxn3/Pnz3HPPPTz11FOMHj2aOXPmMHToUMLDw2ndujVDhgzhyJEjvPvuu3zzzTcULFgwxfpeeuklGjduzDvvvENiYiL+/v788ccf+Pv7M2HCBEqVKsWff/7J5MmTadasGfv27XNux5YtW4iIiKBq1apMmTKFihUr8ssvv7Bz504APv74Y26//Xbq1atHZGQkAKVKlfK43UlJSXTp0oUffviBcePGUaVKFdavX8/TTz/NyZMnefnll13iDxs2jK5du7Jo0SL279/PiBEjKFiwIPPmzfOqXNLy559/0rx5c4KDg3nrrbcoWrQo//vf/+jSpQsrV66kc+fOAHzxxRf079+fO+64g8mTJ3Pq1CmeeuopLl++TPXqehFVSimlrleffQYjR0K/fvD4vUfY27ABDfee4tNBTbj9va8pWFCrCNlJ92Y2eeopq0k2sxzLujc8JCYG4KEe4lH9+jB1asbWe+rUKeLi4ggNDfUq/rhx4/Dz8+PLL7+kWLFiALRv356wsDDGjh3r8jxUbGws06dPp3Xr1gC0bNmStWvXsnjxYlq3bu3Sbe/WW2+lUKGUh2OZMmX4+OOPXVqbwsPDeeONN5zfExMTadasGZUqVeLzzz+nZ8+eADz77LOULFmSzZs3ExhoDWHapk0b53INGjTg/9m777Aqjq+B49+lKvauMWLvgCGiYjT23o3RaFTA3htqYsUSu2KJXWPsRo0ay8+CFXvvBTv2XkGKAnfePxZ5pagXBS7o+TwPj7B3dvYsm5vnHmbmjLW1NRkzZsTZ2fmD971p0yb27dvH/PnzcXNzw9/fnwYNGhAQEICnpyfu7u5kfLs7YPi9Tp06FYBq1apx6dIl/vrrr4gk9XNMnDiR58+fc/DgQfLlywdArVq1KFKkCAMHDoxIuIYMGUKRIkUi/f7s7e0pXry4JFxCCCHEV+ryZfj1V/1z41j3Yzx2/JF8j4PZPLQFdTwWJZqiYV8SmVIoYmXPnj3UqVMnItkCSJ06NfXq1WP37t2R2trY2EQkWwDW1tbkz5+fW7duGX29Bg0axPjGnzlzJsWKFSNlypRYWFhga2sL6FPtAAIDA9m/fz/NmzePSLY+x549ezAzM6NZs2aRjrdo0YI3b95EK7BRu3btSD/b29vz+vVrHj58GCexODs7RyRbAObm5jRr1oxTp07h5+dHWFgYx44do1GjRpF+f99//z25c+f+7BiEEEIIkfT4+UH9+mBpCX/23IBNpVKkfhnM4fl/UHPIYkm24omMcMWR2I4sRfV2ZMvbO/Jxf/8gUqVK9Xmdf8DbtU03b940qv2zZ8/Ili1btONZs2bl+fPnkY6lS5cuWjtra2uCg4ONji+ma02dOpXu3bvj7u7O+PHjSZcuHQaDAWdn54i+nz9/jsFgiLPCF8+ePSN9+vRYW1tHOp41a9aI19+VPn36SD+/PS829/6hWBwdHaMdz5o1K0opnj9/TlBQECEhIWTOnDlauyxZsnx2DEIIIYRIWgwGaN4crl6FOd0XUbyNK49Sm/Hyv5WUK9fY1OF90STh+spZWFhQoUIFtm3bxuvXr6MlFFGlT5+eBw8eRDv+4MGDaElGXIjpLy3Lly+ncuXKeHp6Rhzz9fWN1CZdunSYmZnFWEjjU6RPn55nz57x5s0brKysIo6//V1kyJAhTq5jbCzvewaappE+fXpsbGywtLTk0aNH0do9fPgwYkRQCCGEEF8HDw+9GuFvlefjOqk1Z3MmI63XHhwKlDB1aF88mVIo6NevH0+fPqVv374xvu7r6xtRaKJ8+fJs3LgRf3//iNf9/f3ZsGED5cuXj/W13yZ4QUFBRp8TGBiIpaVlpGPz58+P9LONjQ1ly5ZlyZIlH+zb2traqGuXL18eg8HAv//+G+n40qVLsbKy+ugasLj0tkDJjRs3Io6FhYWxYsUKHB0dSZUqFebm5jg5ObF69epIVRSPHz8eLTkVQgghxJft339h5Eiol2sdY3a0Zv/3GbE9dpWckmwlCEm4BOXKlWPixIlMmzaNqlWrsnTpUvbu3cv69evp0aMHdnZ2ER/SBw8eTFBQEJUrV2b16tWsWbOGKlWqEBgYiIeHR6yvXaRIEQA8PT05fPgwx44d++g5NWrUwMvLi1GjRrF9+3YGDBjA8uXLo7WbMGECT58+pXTp0ixevJhdu3Yxb948unXrFun6e/fu5X//+x/Hjh2LlMS8q2bNmpQtW5aOHTsyefJkdu7cSa9evfjrr7/o3bt3pIIZxnJzc/ukudK9evUibdq0VK1alWXLlvG///2PunXrcvnyZUaOHBnRbtiwYZw/f56GDRuyadMmFi1aROPGjcmaNStmZpHf+hYWFrRp0ybWsQghhBAicTt9GtzcFA4pjrHyRhO21CtCqQO3SJchu6lD+2pIwiUA6NmzJ/v27SNt2rT06dOHSpUq4ebmho+PD7Nnz6Zu3boAODg44O3tTerUqXF1daVly5akTJmS3bt3RyoJb6w6derQuXNnZsyYQenSpSlR4uN/afHw8KBDhw5MmjSJhg0bcubMGby8vKK1K1GiBPv37ydHjhx069aNWrVqMX78+EjrukaPHk3BggVp0qQJJUqUiCgPH5WZmRkbN27E1dWVsWPH0rhxYzZu3MjEiRMjJTmxERAQ8Enrqb755hv27dtH0aJF6dSpEz///DPPnj1j48aN1KhRI6Ld2+TZx8eHhg0bMnbsWDw9PcmaNStp0qSJ1GdYWBhhYWGfdB9CCCGESJyePIF6dd6Q6vU9NgbUZWf3KtRYew4rq+SmDu2ror073UjonJyc1IdGWnx8fChcuHCcXvP9RTP847Vohvg0cfFcsmfPTo8ePfjtt9/iKKqPu3PnDvny5WPgwIEMHjw4wa4bH++ZqLy9vWVD50RInkviI88kcZLnkvgk9WcSEgIVSj/j+HEbtpr/SNjEH6nYfaKpw/psiem5aJp2XCnl9LF2UjRDCBO4cuUKwcHBdO7cOd6uERQUhLu7O1WqVCFjxoxcv36dcePGYWNjQ9u2bePtukIIIYQwvdb1z3DguAN/WruSYmVbitfrYOqQvlqScCUSUUe2xJctf/78PH36NF6vYW5uzoMHD+jatStPnz4lRYoU/Pjjj/z7778xltsXQgghxJdhyM/LWLL5V9xsJlNzT0vyFa9i6pC+apJwCfGFsrKy4r///jN1GEIIIYRIKEoxt4E7Y9aPoaTNLkZdqE22nPlNHdVXTxIuIYQQQgghkriw4CA2V6vOkL3LyZDsAat8viebbZqPnyjinSRcQgghhBBCJGEBj+5yvkIpRvis4rlFWg4ftiaHrbmpwxLhJOESQgghhBAiiXp0/ih+Vcox48F0DuPM6hXg4GDqqMS7ZB8uIYQQQgghkqCrW1dAaWfWPGnLQlrj4QE//WTqqERUknAJIYQQQgiRxJya+wfZ6jRll6ESAwxTqF8fhgwxdVQiJjKlMJF4u4Gbt9SHF0IIIYQQH3CwXwtKjlvK9myF6BS4mYI5zVi0CMxkKCVRkoRLCCGEEEKIJECFhbG/aRnKrjrMLoec9Ak7gwq0YN06SJ3a1NGJ95E8WEQ4ePAgTZo04ZtvvsHKyooMGTJQtWpVFi5cSFhYmKnD+6AbN26gaRoLFiwwdSjvtWDBAjRN48aNG7E+V9M0hg4dGucxCSGEECJpeO33nKNlclF21WF21CrC9PzXuOBjyfLlkC+fqaMTHyIJlwBg8uTJlClThmfPnjF27Fi2b9/O33//TYECBejUqRP/+9//TB2iEEIIIcRX6dmNi1x1zInT4Tvs6FqbQz+cZfVqc8aOherVTR2d+BiZUijYs2cP7u7udO3alT///DPSa/Xr18fd3Z2AgAATRSeEEEII8fW6eXgrWu065HkZwoFJ7gTl82RwPWjeHHr3NnV0whgywiUYM2YM6dOnZ9y4cTG+njdvXhzCN3R4/PgxHTp0oECBAtjY2JAjRw5+/fVX7t69G+kcNzc3cuXKFa2vChUqRBQIAXj16hXdunXD1tYWa2trsmTJQpUqVbh48WJEm2nTplG6dGnSp09P2rRpcXZ2ZuPGjZ90r0OHDkXTNC5evEj16tVJkSIFtra2zJ8/H4DFixdTqFAhUqZMScWKFbl27Vqk80NCQhg0aBB2dnZYWVmRK1cuBg0aREhISKR2169fp3bt2tjY2JApUyZ69OjB69evY4xp7ty5FCtWjGTJkpExY0batGnDs2fPPun+YvLkyRM6depE9uzZsba2plChQsyZMydSm7fTHffs2UODBg1ImTIlGTJkoEuXLgQFBcVZLEIIIYQw3tlVM0hdsQbJg0O5/O8sMtbw5NdfwdER5s4FTTN1hMIYMsL1lQsLC8Pb25sGDRqQLFmyj7Z/9uwZyZIlY/To0WTKlIl79+7h6elJmTJluHjxolF9vKtXr16sX7+eUaNGkT9/fp4+fcr+/ft58eJFRJsbN27Qtm1bcuXKRWhoKBs2bKBOnTps2rSJmjVrxvqeARo3bky7du3o06cPM2bMoHXr1ly5cgVvb2/GjBlDSEgIPXr04Ndff+Xw4cMR57m6urJy5Up69+5N5cqVOXjwICNGjOD69essW7YMgDdv3lC1alWCgoKYPn06mTNnZvbs2axZsyZaHP369cPT05Pu3bszfvx47t69y6BBgzh37hwHDhzA3Pzzdon38/OjTJkyBAUFMXToUHLnzo2XlxedOnXi9evXdOvWLVL7Fi1a0KRJEzp37syRI0cYPnw4AQEBiXptnBBCCPElOjS+B479/+RuBkvMNm0mZ97KlCoFyZPD2rX6vyJpkIQrjvTs2ZNTp0598vlvz3139Af0hMjYD93fffcdkydPjtV1nzx5QlBQEDlz5jSqfcGCBZkyZUqk+MqUKYOtrS2bN2+mYcOGsbr+wYMHad68OW3atIk4FrWPCRMmRHxvMBioXLkyly9fZtasWZ+ccPXt2xcXFxcAnJyc2LBhA7Nnz8bX15fU4WV+7t+/T48ePbh58yY5c+bk3Llz/PPPPwwZMoTevXuTKlUqqlWrhrm5OYMHD6Zfv344ODiwcOFCrl+/zsGDB3F2dgagZs2a2NvbR4rhxo0bjB8/niFDhuDh4RFxvECBApQtW5YNGzbQoEGDT7q/t6ZMmcLNmzc5e/Ys+fPnB6BKlSq8ePGCYcOG0alTJyws/v9/A7Vq1Yr4fVerVg1N0/Dw8GDAgAEUKFDgs2IRQgghxMcpg4E9nWtTfvYWThVIje2O46TJlo+6dcHXF3buhBw5TB2liA2ZUihibebMmRQrVoyUKVNiYWGBra0tAJcuXYp1XyVKlGDBggWMGjWKY8eOxVgN8fjx49SpU4csWbJgYWGBpaUl27Zt+6TrvfVuopYuXToyZ86Ms7NzRLIFUKhQIQBu374N6GvdQB8Fetfbn3fv3g3oSWSOHDkiki0AMzMzmjRpEum8bdu2YTAYaN68OaGhoRFfpUqVInXq1BHX+xxbtmyhVKlS5M6dO9I1qlevztOnT7lw4UKk9lFjbNq0KQaDgSNHjnx2LEIIIYT4sNDXQeyrWZTys7ewv6wthU7cIv23+Rg0CDZvhqlToWxZU0cpYktGuOJIbEeWonrfxsf+/v6kSpXqs/r+kAwZMpA8eXJu3rxpVPupU6fSvXt33N3dGT9+POnSpcNgMODs7ExwcHCsrz916lSyZs3K33//zcCBA0mfPj0uLi6MHDkSGxsbbt++TeXKlSlSpAhTp07F1tYWCwsLBg8ejI+PT6yv91a6dOki/WxlZRXjMSDivt6uq8qWLRsGgyGiXdasWSO9fv/+fbJkyRLtmlGPPXr0CIB876nl+vTpU+Nu5gMePXrE1atXsbS0NOoaUWN8+3PUNXpCCCGEiFt+j+9wpfJ3/Hj2Kbt+/YHyi3ZjZm7BihUwZgx06KB/iaRHEq6vnIWFBRUqVGDbtm28fv0aa2vrD7Zfvnw5lStXxtPTM+KYr69vtHbJkiXjzZs30Y4/ffqUDBkyRPycMmVKRo8ezejRo7l58yarVq2iX79+WFlZMXbsWLZs2cLLly9ZuXIl3377bcR5gYGBn3K7nyV9+vQAPHjwgMyZM0ccf/DgAUDEfWXLlo3z589HO//hw4eRfn7bfuvWrdGSvXdf/xwZMmQgc+bMkaaBvqtgwYLRYixatGiknwGyZ8/+2bEIIYQQImb3fY7iX7Ucxe4Hs3eQCxX/WAjAyZPQqpU+qhWlkLRIQmRKoaBfv348ffqUvn37xvi6r68vZ86cAfREJ+poydsKf+/KmTMnDx8+5MmTJxHHrl279sFpgDlz5qR3797Y29tz7ty5iOsBka55+fJl9u/fb+TdxZ3y5csDetL5rqVLlwJQrlw5AEqXLs3t27c5dOhQRBuDwcDKlSsjnVe1alXMzMy4desWTk5O0b5y58792THXqFGDixcvYmtrG+M1oo6eRo1x+fLlmJmZUbJkyc+ORQghhBDRXdq+ApydyfokmNPzRvFjeLL1+DE0aAAZMsCqVRA+8UYkQTLCJShXrhwTJ07E3d0dHx8f3NzcsLW15fnz5+zYsYO//vqLZcuW4eDgQI0aNRg7diyjRo2iZMmS7Ny5k1WrVkXrs3HjxgwePJjmzZvj7u7OkydPGD16NBkzZozUrnTp0tSrVw97e3tSpkzJ7t27OX36NK6uroBe4MHCwgIXFxd69+7N/fv3GTJkCLa2tpGm9SWEokWL0qxZM4YOHUpAQAAVK1bk4MGD/PHHHzRr1iyidL6rqytjxozhp59+YtSoUWTOnJlZs2bh5+cXqb+8efPy+++/07VrVy5dukT58uVJliwZt2/fZtu2bbRt25aKFSvGGMuNGzfInTs3Q4YMYejQoe+NuVevXqxYsYIff/yRXr16UbBgQQICArh48SJ79+5l3bp1kdpv2rSJvn37Uq1aNY4cOcKwYcNwcXGRghlCCCFEPDj613AKdRmCv405DzevonjFRgCEhMDPP8OjR7BvH8SwUkEkIZJwCUCvsliyZEkmTZpEnz59ePLkCalSpcLJyYnZs2dTt25dADw8PHjx4gWTJk0iODiY8uXL4+XlRZ48eSL1ly9fPlatWsWgQYNo0KABBQoUYOLEiYwaNSpSu3LlyrFy5UrGjBlDaGgoefLkYdKkSXTv3h3Qk5ylS5fi4eFBvXr1yJs3L2PGjGHLli3R1rslhIULF5InTx4WLVrE+PHj+eabb/j9998ZMmRIRBsrKyu2bdtG165d6dy5MylSpODXX3+ldu3adOzYMVJ/o0aNonDhwkyfPp3p06ejaRo5cuSgcuXKEVUFY/J2I+q368feJ02aNBw4cIDhw4czduxY7t69S9q0aSlYsCCNGjWK1n7JkiV4enoyc+ZMrKysaNeuXaQqkUIIIYSIG94DmlN27DKuZk9O2m17yV+weMRrvXrBnj2wZAkUL/6BTkSSoCmlTB1DouPk5KSOHTv23td9fHwoXLhwnF7TVEUzxKcx9XOZM2cOAwcO5ObNm9jY2Hx2fwsWLKBVq1ZcuXLlvUU8Pkd8vGei8vb2jratgjA9eS6JjzyTxEmeS+ITX8/EEBaK969lqLTyCMccMlFo52lSZsgW8fpff0G7dtCnD4wfH+eXT/IS03tF07TjSimnj7WTES4hkqDdu3fTq1evOEm2hBBCCJEwgvyfc6K6A5UO3mFfjSI4rzuOhVWyiNcPHIDOnaFaNb0yofgySMKVSJhiepxIut4W6hBCCCFE0vD4pg/3qpSizFV/9nauQ9mp69DM/r9+3Z078NNPkDMnLF8O5uYmDFbEKalSKITAzc0NpVS8TCcUQgghvnbXjnjxqoQDBW/4c8TTnR+nb4iUbAUH68lWQACsXQsx7BYjkjAZ4RJCCCGEECKenFwzgxwuXTFTcH3lbEo2bB/pdaX0DY2PHtWTrXe2wxRfCBnhEkIIIYQQIh7s8exO4V+6EJDcgoDd2ykSJdkCmDIFFi2CYcOgfn0TBCninYxwfSKlFJqmmToMIRI9qYQqhBDia6MMBrZ3qUnVWVs5ly813+44Rlrb6Nu9bN8OvXvr0wkHDTJBoCJBSML1CSwtLQkKCpIKcUIYISgoCEtLS1OHIYQQQiSIN68D2dfge6puucThH3Li6HUaq5RporW7dg2aNIEiRWDhQjCTeWdfLHm0nyBz5szcvXuXwMBA+eu9EO+hlCIwMJC7d++SOXNmU4cjhBBCxLsXT+5wopQtlbZcYv8vP1Byz7UYk61Xr6BBA/37tWshZcoEDlQkqAQf4dI0LQcwCagKaMB2oKdS6tZHznMC2gPlAFvgCbAXGKSU8o3S9gaQM4ZuGiql1n7uPaROnRqAe/fuERIS8rndfVBwcDDJkiX7eEORoOS5GMfS0pIsWbJEvGeEEEKIL9Vtn8P4V69AiTvBHBrgQpmRC2NsZzCAqytcuABeXpA3bwIHKhJcgiZcmqbZADuB14AroIARwC5N0xyUUgEfOL0pUBT4EzgPZAcGA8c0TftOKXU7SnsvYGiUY5c++ybCpU6dOkE+RHp7e+Po6Bjv1xGxI89FCCGEEG+d2/4PaRu3wDbQwPl5o3Fu1e+9bUeOhDVrYOJEqFIlAYMUJpPQI1ztgDxAQaXUVQBN084AV4AOwMQPnDtWKfX43QOapu0HfMP79YjS/olS6lBcBS6EEEIIIURUB/4eTtHOQwi2NufxplU4VG703rbr1oGHB7i4QM+eCRikMKmEXsNVDzj0NtkCCJ8OuB/4YCHMqMlW+LGbwGP00S4hhBBCCCESzLaBzSjRbgiPMybH7NBhcn8g2Tp/Hlq0gBIlYPZskGLXX4+ETriKAudiOH4eKBLbzjRNKwxkBnxieLmupmmBmqa91jTtkKZpDWLbvxBCCCGEEFGFhYXi1awkVUct50LRTHxz6hqZChd/b/vnz/UiGSlS6NMJZRn41yWhpxSmB57HcPwZkC42HWmaZgHMQh/hmhfl5Q3AUfTphlmArsB/mqa1VEotiW3QQgghhBBCAAT4P+NoTQeq77/LkWpFKb7+GObW78+gwsKgaVO4eRO8veHbbxMuVpE4aAlZ1lzTtDeAp1Kqf5TjI4HflVJGJ4Caps0C2gC1lVJbP9LWHDgEZFVK5XhPm/boVRDJkiVL8eXLlxsbSrx69eoVKaVWaKIjzyXxkWeSOMlzSXzkmSRO8lwSn5ieif/TW2T+rSOlrgex8WdnUnQe9dG5gbNm5WHFClv69LlE7dr34zPkr0Jieq9UrFjxuFLK6WPtEnqE6zn6KFdU6Yh55CtGmqaNRk+OXD+WbAEopcI0TfsXGKtpWjalVLT/2pVSc4A5AE5OTqpChQrGhhOvvL29SSyxiP8nzyXxkWeSOMlzSXzkmSRO8lwSn6jP5PJRL8x+aYPt01BOjnOndl/Pj/axdCmsWAGdO8P48QWBgvEX8FciKb5XEnoN13n0dVxRFQEuGNOBpmkDgX5AD6XU4lhc++2fH2SnYiGEEEIIYbQj/00jbaWaZPQP48bKOTgakWwdPw5t20K5cjB5cgIEKRKthE641gPOmqbleXtA07RcQJnw1z5I07Tu6Pt2DVRKTTX2ouHrvRoDt5RSD2IZsxBCCCGE+ErtmNgNu1+6EWJtQZD3dgr81O6j5zx8qBfJyJwZVq0CS8sECFQkWgk9pXAuegGLdZqmDUIfbfoDuA3MfttI07ScwDVguFJqePixpsBkYAuwU9M053f69VNKXQhv1wy9xPym8H6zAF2A4kCzeL07IYQQQgjxRVBKsbFLdWrO2MrlPKnJvus4qWzzffS8N2/g55/h6VPYvx8yZUqAYEWilqAJl1IqQNO0SsAkYDH6NL8dQE+l1Kt3mmqAOZFH4GqEH68R/vWu3UCF8O990UvFj0dfLxaIXrGwhlLKKy7vRwghhBBCfHmOFE7NS+0VDXwUJ0vlxG7baSxTpTHq3B49YN8+WL4cHB3jOVCRJCT0CBdKqVvA+3eF09vc4P/XXL095ga4GdH/IaDSJwcohBBCCCG+WteObycswJ8Gt+Fo4zI4LfNGszDuI/Ps2TBrFvz+O/zyS/zGKZKOhF7DJYQQQgghRKJjCHnDnh4NyFq6KnYPwauABSVW7jM62dq3D7p2hZo1YeTIeA5WJCkJPsIlhBBCCCFEYnJv72b8XH+hnK8/Rxwzkyvlt5TysC2JqQAAIABJREFUCzP6/Nu3oVEjyJMHli0Dc/N4DFYkOTLCJYQQQgghvkoqMJDTreuQuUIt0j96xa7RHShx7D6Z9xznlJG13IOC9IqEwcGwbh2kTRvPQYskR0a4hBBCCCHEV+fZ5jUEtXah2IMANpXNit2izVTM/V2s+lAK2rWDkydh/XooVCieghVJmoxwCSGEEEKIr8fz5/j+XIX0tRrx+nUAq6d0pMaeu9jGMtkC8PSEpUvhjz+gTp14iFV8ESThEkIIIYQQXz6leLVsAS/yfEOONTtYWD0rb04ep1H3mZhpsf9I7OWlVyNs3BgGDIiHeMUXQ6YUCiGEEEKIL9vduzxya0zm7Qc5mQ2OjG1P6zbTsDS3/KTurl6Fpk3Bzg7mzwdN+/g54uslI1xCCCGEEOLLZDDwetoUggrkIZX3QSY0yIzh0CE6tJ/9ycmWvz/Ur69XIly7FlKkiOOYxRdHRriEEEIIIcSXx8eHl65NSXP0DNtzw1GPNvRqMY1kFsk+uUuDAVq2hEuXYNs2yJ07DuMVXyxJuIQQQgghxJfjzRtCR4+EkSMJswijT7MM1Bu1mv65yhvdRYUK8OLFd5w6Ffn4sGF66fcpU6BixbgNW3y5JOESQgghhBBfhoMHCWrVkuSXrvGPHRzr25yhv8wklXWqz+56zRoYPhxatYJu3eIgVvHVkDVcQgghhBAiafP3x9CtK6pMGZ7cv4ZL63SkWbMRT5clcZJsnT0LLi5QqhTMnClFMkTsyAiXEEIIIYRIujZuJKRDO8zv3WdqCTjR9ScmNZpDBpsMcdL9s2fQoAGkTq2Pcllbx0m3IpaCg4MpWrQoKVKk4MyZM6YOJ1ZkhEsIIYQQQiQ9jx6hmjWFOnW4EvKAmp1SkWXecha0XB1nyVZoKPzyC9y5oydb33wTJ92KWDp16hQlSpTg+vXr+Pv7mzqcWJMRLiGEEEIIkXQoBYsWEdarJ2H+LxlRAU63qs6Chn+TLVW2OL3Ub7/B9u3w99/g7BynXQsjhIaGMnbsWIYOHUqmTJmws7PD3Nzc1GHFmiRcQgghhBAiabh2DdWxI9r27RzJaU431+R0aDGZYd+3RYvjhVXPnlkyaRJ0764XyhAJ6/Lly7i4uHD48GGaNm3K9OnTSZ8+Pd7e3qYOLdYk4RJCCCGEEIlbaChMnozy8CCIEHrXhgsNS7Pyp4XkSZcnzi/38iXcuWNDxYowYUKcdy8+wGAwMHPmTPr27UuyZMn4559/aNq0qanD+iyyhksIIYQQQiReJ0/q5QH79mVL7jDsu5iRb8AEdrbyjvNk68kT6NoVTp0CS0sDK1eCpWWcXkJ8wJ07d6hRowZdu3alQoUKnDt3LsknWyAjXEIIIYQQIjEKDIRhw1CenrxMZUnbxuBbuSjrGy6maOaicXqp169h6lQYMQJevdKLY6RL94qMGdPE6XVEzJRSLF26lK5duxIaGsrs2bNp165dnE8TNRUZ4RJCCCGEEInLjh3g4ADjxrHcyZp8Hd9QtJMHh9oejtNkSylYvRqKFIG+faFsWX3Prfz5wcJCxdl1xPs9fvyYxo0b07JlS+zt7Tl9+jTt27f/YpItkBEuIYQQQgiRWDx7Bn36wPz5PPomDb+4woMStmxusIgS2UvE6aWOHgV3d9i3D+ztYetWqFo1Ti8hPmL9+vW0a9eOFy9eMG7cONzd3ZNkFcKPkREuIYQQQghhWkrBypVQuDBq0SJmVUlLrlYvcWzWixPtT8RpsnXnDrRsCSVLwuXLMGeOvkxMkq2E4+fnR5s2bahfvz7ZsmXj2LFj9O3b94tMtkBGuIQQQgghhCndvg1dusCGDdwpkJV6Pxl4VjANmxr8R4VcFeLsMq9ewbhxetVBgwH694d+/SB16ji7hDDC7t27cXV15fbt2wwYMIAhQ4ZgZWVl6rDilYxwCSGEEEKIhGcwwPTpUKQIhu3bmNAoG7l+ecD3NVtzptOZOEu2wsJg/nwoUAD++APq14dLl2DUKEm2ElJwcDC9e/emYsWKWFlZsW/fPkaOHPnFJ1sgI1xCCCGEECKhnT8P7drBwYNcL5GfGmV98fvGwNp6G6hToE6cXWbXLn2d1qlT4OysF8goXTrOuhdGOn78OC1btsTHx4cuXbowduxYUqRIYeqwEoyMcAkhhBBCiITx+jUMHQqOjoRd9GF4m3zkrXWFYqUbcK7zuThLti5fhgYNoFIlvQ7HP//AgQOSbCW0kJAQhg8fjrOzM35+fnh5eTFt2rSvKtkCGeESQgghhBAJYf9+fVTLx4fLNUpQ9ftz+KV+wtJaS2lm1yxOyoA/e6ZPG5w2DZInh9GjoUcP/fvY8PYGb+9TQIXPjulrdfHiRVq2bMmxY8do3rw5U6dOJV26dKYOyyRkhEsIIYQQQsQfPz+9KEbZsoS+8mNgb0cKOh+lcOFynOt0jl/tf/3sZCskBKZMgXz54M8/oXVruHJFL4oR22RLfB6DwcCUKVNwdHTE19eXf//9lyVLlny1yRbICJcQQgghhIgv69dD586oe/e4+Gs1qhQ4xEvLF8yqNov2xT9/c1ulYMMGfdPiy5f10u6envq+WiLh3bx5k1atWrFr1y7q1KnD3LlzyZo1q6nDMjkZ4RJCCCGEEHHrwQNo0gTq1yc0bWr6/1GeIgW2kjuHPac7nqaDU4fPTrZOnYLKlfWqg2ZmsHEjeHlJsmUKSikWLFiAvb09R48eZd68eaxfv16SrXAywiWEEEIIIeKGUnoN9t69ISiICz2bUzXrVp6EXGNclXG4l3bH3OzzNre9fx8GDdIvkz69Xlm+XTuwtIyjexCx8ujRI9q3b8+6desoV64cCxYsIHfu3KYOK1GRhEsIIYQQQny+q1ehfXvYtYvQsj8wqGlmxj5ZimNaR7wa7sQus91ndR8YqE8XHDsW3rzRc7qBAyFt2jiKX8Taf//9R4cOHfDz88PT05OePXtiZiYT6KIy+jeiaZqDpmkrNU17oGnaG03Tvg8/PkLTtGrxF6IQQgghhEi0QkL0LMjeHo4f5+LIXuRtcIsJTzcw6MdBHGp76LOSLYMBliyBggXBwwNq1AAfHxg/XpItU3n58iWurq789NNP5MiRg+PHj+Pu7i7J1nsY9VvRNO0H4DBQDFgDvDsWbAZ0jPvQhBBCCCFEonb8OJQsCf36EVqjOoOn/0zhkEkkt07BgTYH+KPSH1iZW31y9/v26RsWt2wJWbPCnj2wahXkzRuH9yBiZceOHdjb27N06VI8PDw4dOgQRYsWNXVYiZqxaehYYAdQGOgOvLvK8RhQPI7jEkIIIYQQiVVAAPTpoydbDx9yec5oilb2YcS1v+lRqgcnOpygZPaSn9z99evQuDH8+CPcuweLFsHhw/rPwjQCAwPp0aMHVapUwcbGhgMHDjBs2DAsZfHcRxm7hqs40EgpZdCil5R5AmSJ27CEEEIIIUSitG0bdOgAvr6EtWvD6DppGXJqIN+m/pYdLjuolLvSJ3f98iWMHKnvqWVhAcOH62u1bGziMH4Ra0eOHKFly5ZcvnyZHj16MGrUKGzkoRjN2BGu18D7to3LCryMm3CEEEIIIUSi9PQpuLpCtWpgacn1tfNxKn6cwSc9cSnmwpmOZz452QoNhRkz9I2LJ0yA5s31jYsHD5Zky5RCQkLw8PDghx9+ICgoiB07djB58mRJtmLJ2BGufUB3TdPWvnNMhf/bGtgVp1EJIYQQQojEQSlYvhx69IDnzzEM6M+kyikYcKADaZOlZV3TddQrWO+Tu9+8WR/F8vGBChVg4kRwdIy78MWnOX/+PC4uLpw4cQJXV1emTJlCmjRpTB1WkmTsCJcHUAI4CfRHT7ZaaJq2DSgDDIuf8IQQQgghhMncugV16sCvv0Lu3NzetY5yeffQZ+8g6hSow7lO5z452Tp3Tq84WKuWXuhw7VrYuVOSLVMLCwvD09OT4sWLc/v2bdasWcOCBQsk2foMRo1wKaVOappWAZgADEUvmtETOABUVEr5xFeAQgghhBAigYWF6TsKDxgAgJo0iTmlrei9owkWZhYsbriY5vbNib60/+MePoQhQ2DuXEidGiZNgs6dwerTixmKOOLr64ubmxt79uyhfv36zJkzh8yZM5s6rCTP6I2PlVJHgfKaptkAGYHnSin/eItMCCGEEEIkvHPnoG1bvSxgzZo8GDcEt1ND8NriRdU8Vfm7/t98m/rbWHcbHAyTJ8OoURAUBF276vtqZcgQD/cgYkUpxd9//x2xcfGCBQtwcXH5pIRaRGfsPlxzNE3LBaCUClRK3XqbbGmaZqtp2pz4C1EIIYQQQsS74GC9SoWjI1y7hlqyhKWjf6XwxhrsvbWXGbVm4NXCK9bJllKwYgUUKgT9+0PFinpON2WKJFuJwYMHD6hbty5t27alZMmSnD17FldXV0m24pCxa7jaAu8bT8wEtImbcIQQQgghRILbuxe++w5GjIBmzXh6fB9NrNbSYm1LimQqwumOp+lUolOsP4QfPgxlykDTppA2LezYAevWQcGC8XQfIlZWrVqFnZ0dO3bsYMqUKWzbtg1bW1tTh/XFMXpK4QdkAYLioB8hhBBCCJGQXr6E33+H2bMhVy7w8mJDzte0W1me58HPGVN5DH1+6IO5mXmsur15Ux/N+ucfyJoV5s3TK8qbx64bEU+eP39Ot27dWLp0KU5OTixevJhChQqZOqwv1nsTLk3T6gP13zk0WNO0x1GaJQfKAyeMvaCmaTmASUBV9OIb24GeSqlbHznPCWgPlANs0Tdc3gsMUkr5RmlrBvwOdEDfJ+wSMFwptdrYOIUQQgghvmhr10KXLvDgAbi74zewD732DuLv5X9TLEsxtrXchn0W+1h16e8PY8bopd1Bn6H422+QMmU8xC8+ydatW2ndujUPHz5k2LBh9O/fH0tLS1OH9UX70AhXHvSkCPQy8CWBN1HavAaOoSc3HxVecGNn+Hmu4f2OAHZpmuaglAr4wOlNgaLAn8B5IDswGDimadp3Sqnb77T9A+gDDASOh5/7r6ZpdZRSm4yJVQghhBDii3TvHnTrBmvWQLFisG4d3hlf4baoNLf9bjOg7ACGVBiClbnxZQPDwmD+fBg0SK9C2KKFXhwjR454vA8RKwEBAfz222/MmDGDwoULs27dOooXL27qsL4K7024lFKT0Eei0DTtNlBHKXX6M6/XDj2RK6iUuhre9xngCvpo1MQPnDtWKRVphE3TtP2Ab3i/HuHHMqMnW2OUUhPCm+7SNC0fMAaQhEsIIYQQXx+DQZ/b17evXiBj9GiCundmwJ4hTN44mfzp87Ov1T5K5ygdq263bwd3dzh7Vl+vtX49lCwZT/cgPsnBgwdxcXHh2rVruLu7M2LECJInT27qsL4aRhXNUErliINkC6AecOhtshXety+wn8jTF2OKIep0RpRSN4HH6KNdb1UHrIAlUZovAew1Tcv9aaELIYQQQiRRly9DpUrQvr1ehfDsWY62rMz380sx+fBkupboyskOJ2OVbF28CHXrQtWq8OoV/PuvXntDkq3E482bNwwYMICyZcsSEhLCrl278PT0lGQrgRlbpRAATdNSa5r2vaZpP0T9MrKLosC5GI6fB4rEJpbweAqjV098d+PlouhTFq9GaX4+/N9YX0cIIYQQIkkKCdHn9jk4wOnT8NdfhGzzwuPOYkrPK82rN6/Y1nIbU2tNJYVVCqO6fPJEn5FoZwd79sC4cXDhAvz8M0gl8cTjzJkzlCxZktGjR9OqVSvOnDlD+fLlTR3WV0lTSn28kaZZA3OBZrwnSVNKfbTujKZpb4CJSql+UY6PAPoppYyumqhpmgWwAyiMPkXxefjxOUA9pVTWKO3zoU9ddFFKLY6hv/boRTnIkiVL8eXLlxsbSrx69eoVKWWlaaIjzyXxkWeSOMlzSXzkmSRO8fFcUvn4UHDCBFJev86j8uW52r07l6xfMvriaK68ukK1LNXolq8bKS2Mu+6bNxpr12Zn0aJcBAWZU6fOPVq1ukHatCFxGndikVTfK2FhYaxcuZL58+eTMmVK+vTpww8/GDs2kvglpudSsWLF40opp482VEp99Au9CMV99EIXBqAr+rqp/cBl9PVdxvTzBhgdw/GRQKgxfbxzziwgBKgW5fhc4H4M7fOjF+lo+bG+ixcvrhKLXbt2mToEEQN5LomPPJPESZ5L4iPPJHGK0+fi769Uz55KmZkp9c03Sq1dq0LDQtX4/eOV9R/WKtO4TOo/n/+M7s5gUGr1aqXy5lUKlKpZU6lz5+Iu3MQqKb5Xrl69qsqUKaMA9dNPP6lHjx6ZOqQ4l5ieC3BMGZG3GDulsDEwnP9fF3VAKTVXKVUGfYpgZSP7eQ6kj+F4uvDXjKJp2mj00ajWSqmtUV5+BqTTou/Ml+6d14UQQgghvjxbtuhz/SZPhg4d4MIFrpezp+LCivTd1pda+WtxrvM5GhRqYFR3x49DhQrQqBEkS6Z3v2kTFC0av7chYkcpxezZsylWrBjnzp1j8eLFrFq1ikyZMpk6NIHxa7hsgfNKqTD0UaV3J/n+hT7V0Bjn0ddYRVUEuGBMB5qmDQT6AT1UDFMDw69hDeSN4RoYex0hhBBCiCTjyRNo2RJq1oTkyWHvXtT06cy5ugKHmQ6cfniahQ0WsrrJajKnyPzR7u7e1TcqdnICHx+YNQtOnYLq1RPgXkSs3Lt3j1q1atGxY0dKly7N2bNnadGiBdHHHoSpGJtwPQXeTpa8Azi881o69A2QjbEecNY0Lc/bA5qm5QLKhL/2QZqmdUfft2ugUmrqe5ptQZ+62DzK8RbAORVlk2QhhBBCiCRLKViyBAoVghUrwMMDTp3iXrE81F5Wmw7/60DpHKU51+kcLsVcPvohPCAAhg6F/Plh+XL4/Xe4ckUfLLMweqW9SCjLly/Hzs6O3bt3M23aNLy8vMghm58lOsa+dQ4DxdD3sFoD/BG+iXEo8Bv6Wi5jzEVf/7VO07RB6Guq/gBuA7PfNtI0LSdwDRiulBoefqwpMBk9odqpaZrzO/36KaUuACilHmmaNgnor2maP3AC+AWoxEdKzwshhBBCJBk3bkDHjuDlBc7OMHcu2Nmx/NxyOm/sTHBoMNNqTqNTiU6YaR/+G7vBAIsXw4AB+r7Iv/wCo0dDbtlMJ1F6+vQpXbp0YcWKFTg7O7Nw4UIKFChg6rDEexibcI0DcoZ/PwIoAIxGHyE7BnQ2phOlVICmaZXQN1ReDGjolQZ7KqVevdNUA8yJPAJXI/x4jfCvd+0GKrzz80DgFdADyApcApoopTYYE6cQQgghRKIVFgZ//gmDBoGZGUydCp068fT1Czqv+oWV51fi/K0zCxsspECGj38I371b37j4xAl9D61//4UvqKjdF2fz5s20adOGx48fM3LkSH777TcsZPgxUTPq6SiljgBHwr9/CdTXNC05kEyFl2M3llLqFtDoI21uoCdX7x5zA9yMvEYYemI4IjaxCSGEEEIkamfOQNu2cPQo1K4NM2dCjhxsurKJNuvb8DTwKaMqjaJvmb5YmH34Y97Vq/Dbb/Dff5AjByxbpo9smcVql1aRUF69ekXv3r2ZM2cOdnZ2bNy4EUdHR1OHJYzw0beUpmlWmqYd0TSt6rvHlVJBsU22hBBCCCHEJwgK0uf7FS+uTyVcvhw2bMA/c1rarW9H7WW1yWSTiaPtjtL/x/4fTLaeP9dHtIoUgW3bYORIuHQJmjWTZCux2rdvH8WKFWPu3Ln07duXo0ePSrKVhHx0hEsp9UbTtAJAWALEI4QQQggh3rV7N7Rrp1evcHODCRMgQwZ239iN2zo3br28Rb8y/RhaYSjWFtbv7SYkRB8QGzZMT7ratIE//oCsWRPuVkTsvH79Gg8PD8aPH0+uXLnYvXs3P/74o6nDErFk7N8xtgNV4jMQIYQQQgjxjhcvoH17fSOssDB9OGr+fILTpKC3V28qLqyIuWbO3lZ7GV1l9HuTLaVgwwZ9e64ePcDREU6e1GtsSLKVeJ06dQonJyfGjRtHu3btOH36tCRbSZSxK+wmAss0TTMD1gL30SsMRghfmyWEEEIIIT6HUrBmDXTtCo8eQd++eq12GxuO3TuGy38u+DzxobNTZ8ZVHUcKqxTv7er0aejdG3bsgIIF9cSrdm2QLZoSr9DQUMaNG8fQoUPJmDEjGzdupFatWqYOS3wGYxOufeH//gb0fU8b888PRwghhBDiK3b3rp5orV2rD0Vt3Ajff09IWAgjvYcyYs8IsqbMilcLL6rlrfbebh480IsY/v03pEunFzXs2BEsLRPwXkSsXblyBRcXFw4dOkSTJk2YMWMGGTJkMHVY4jMZm3C1J8qIlhBCCCGEiCMGA8yape80/OYNjBsHvXqBhQUXHl/A5T8Xjt8/TguHFvxZ40/SJU8XYzdBQTBxor6H1ps3eheDBulJl0i8DAYDM2fOpG/fviRLlox//vmHpk2bmjosEUeMLQv/V3wHIoQQQgjxVSpZkrIXLkBAAFSuDLNnQ968GJSByQcnMmDHAFJZp2J1k9X8VPinGLswGPTChf36we3b0LAhjB0L+fMn8L2IWLtz5w6tW7dm27ZtVK9enXnz5pE9e3ZThyXikOySJoQQQghhCiEhMH48HDuGmZkZzJ8Prq6gadx4cQO3tW7svrmb+gXrM7vObLKkzBJjN/v362XejxyB77+HxYuhfPkEvhcRa0opli1bRpcuXQgJCWHWrFm0b98eTRbYfXFktwUhhBBCiIR26hSUKgUDB7LLogr1rTeCmxsKmHdiHvYz7Tn54CTz68/nv1/+izHZ8vXVNyouWxbu3IEFC/T9kCXZSvyePHlC48aNadGiBUWLFuX06dN06NBBkq0vlCRcQgghhBAJ5fVrfVFViRJw7x6sXs2wH7ZyO38p7vvfp+4/dWm7oS0ls5fkbKezuH3nFu1D+MuX+lKvQoX0qoNDhsDly/rgmGxcnPht2LABOzs7NmzYwNixY9mzZw/58uUzdVgiHsmUQiGEEEKIhHD4MLRuDRcu6NnRxImQPj38CS+y/4fdzD4EhgQypcYUupbsipkWOXsKDYW//gIPD3j8WO9i5EiQ5T5Jg5+fH+7u7sybNw8HBwe2bt2Kg4ODqcMSCUASLiGEEEKI+BQYCIMHw+TJena0aRPUrAnA44DHHMjTkpCcXpRMX5KFDRZSKGOhaF1s2aLvp3XhApQrB5s3Q/HiCX0j4lPt3r0bV1dXbt++Tf/+/RkyZAjW1jFvVC2+PLEeeNY0Lbmmadk1TZNkTQghhBDiQ7y9wcFBH83q0AHOnYOaNTEoA/NOzKPgtIKEfLsDy1Md2d96f7Rk6/x5PTerWVOfjbhmjd6lJFtJQ3BwML1796ZixYpYWFiwd+9eRo0aJcnWV8bohEvTtJqaph0B/IGbgEP48dmapslGAUIIIYQQb/n5QadOULGi/vOuXTBjBqROzflH5ym/oDxtN7TFPos9TsfOUMR3NBZm//+37EeP9NMdHODgQfD01Ee3GjYEqauQNBw/fpzixYszceJEOnXqxOnTp/nhhx9MHZYwAaMSLk3T6gL/Q0+2BkU57zbgFueRCSGEEEIkRZs3g50dzJmj12s/cwYqVCAwJJABOwbw3ezv8Hnsw9/1/sbb1ZsUgYUjTg0O1vc8zp8f5s6FLl3g6lW9GysrE96TMFpISAjDhw/H2dmZFy9esGXLFqZPn06KFClMHZowEWOnBQ4FFimlWoVPJRz1zmtngY5xHZgQQgghRJLy7JmeGS1cCIUL6xtkOTsDsOXqFjpv7IzvC1/cvnNjfNXxZLTJGHGqUvDvv3r1QV9fqFNH36KrUPTlXCIRu3jxIi4uLhw9epTmzZszdepU0qVLZ+qwhIkZO6WwCLAs/HsV5bXnQEaEEEIIIb5Wa9ZAkSKwZIle9v3kSXB25r7/fZquakrNpTWxMrdil+su5tefHynZ8vODq1dT0qQJpEoF27bp5d4l2Uo6DAYDf/75J46Ojly7do2VK1eyZMkSSbYEYPwIlz+Q4T2v5QQex004QgghhBBJyMOH0K2bPjzl6KiXE/zuO8IMYcw+OoP+O/rzOvQ1wysM57cyv2Ft8f/FEt5uyXXyJFhYmDF3LrRqBebmJrwfEWu3bt3Czc2NXbt2Ubt2bebOnUu2bNlMHZZIRIxNuHYA/TRN2wQEhB9TmqZZAV0Ar/gITgghhBAiUVIKli2D7t3h1SsYNQr69AFLS049OEWH/3XgyN0jVMlThRm1ZpA/Q/5Ip587B82b68u7smWDDBn8aNs2rYluRnwKpRSLFi2ie/fuGAwG5s6dS5s2baJtVC2EsVMKBwDZgYvALPRphX2Bk0Au9DVeQgghhBBfvjt3oG5daNECChSAU6egf39eqdf09uqN0xwnbry4wZKGS9jaYmukZMtggClTwMkJ7t/Xpw4WKCCjWknNo0ePaNiwIW5ubhQrVowzZ87Qtm1bSbZEjIxKuJRSvoATsA2oG364KnACKKWUuhM/4QkhhBBCJBJK6aUDixaFnTth0iTYtw8KF2bdxXUUmV6EiYcm0saxDRe7XKS5Q/NIH8Dv3YMaNaBnT6haFc6e1YtjiKRl7dq12NnZsXnzZiZMmMCuXbvInTu3qcMSiZhRUwo1TUsB3FNKucZzPEIIIYQQic/169CunZ5oVayoJ15583L75W26b+nO2otrsctsx/Kfl/NDjuh7La1eDe3bQ1AQzJqlfy+DIUnLy5cv6dGjBwsXLsTR0ZFdu3ZRtGhRU4clkoCPjnBpmmYJvARqxn84QgghhBCJSFiYPgfQ3h6OHoXZs2H7dkJz52TSwUkUmVEEr6tejK0ylhPtT0RLtvz89EIYP/8MefLoBTI6dJBkK6nZuXMn9vb2LFmyhMGDB3Po0CFJtoTRPjrCpZQK0TTtERCaAPEIIYQQQiQOFy9CmzZw4ADUqqUPTeWbCu1FAAAgAElEQVTIwdG7R+nwvw6cfHCSWvlrMa3mNHKniz6lbP9+aNkSbt7UqxF6eIClpQnuQ3yywMBA+vfvz59//kmBAgXYv38/pUqVMnVYIokxtkrhMqAVsCkeYxFCCCGEML3QUJgwAYYOBRsbWLQIWrTg5Ws/Bm7qyoyjM8iWKhv/Nv6XRoUbRSuUEBICw4frhQtz5oQ9e6BMGdPcivg0FSpU4N69e5iZmXHp0iW6devGmDFjsLGxMXVoIgkyNuG6DPyiadpBYB1wnygbICulFsVxbEIIIYQQCev0aWjdGk6cgEaNYNo0VJYsrLqwih5bevDg1QO6luzKiEojSG2dOtrply/rxQuPHgU3N302YurozUQiFhAQwPXr17l9+zbffvst27dvp3LlyqYOSyRhxiZcs8L/zQ7ENI6qAEm4hBBCCJE0vX4NI0fC6NGQPj2sWgWNGuH73Jcuy2qz+epmHLM6sq7pOkpkLxHt9LcFDHv1AmtrfR/kn382wX2IT6aUYtWqVbi7u3Pnzh3SpUvH2bNnSZtW9kcTn8fYhCv/x5sIIYQQQiRBR47oo1rnz+uLriZNIiRtajz3jWH47uGYm5kzqfokupbsioVZ9I9Ojx5B27b6nlpVqsCCBZA9u/GX9/YGb+9TQIU4uiERWxcuXKBbt27s3LmTYsWKkTFjRpRSkmyJOGHsPlzXPvYV34EKIYQQQsSpwEDo2xdKl4aXL2HjRli0iP0BF3Gc7Uj/Hf2pka8GPl186OncM8Zka+NGvYDh1q0weTJ4ecUu2RKm5efnR+/evSlWrBgnTpxg+vTpHDt2jDRp0pg6NPEFMXaESwghhBDiy7Fnj16B8OpVvU77uHE8swyl34b2zD0xF9s0tqxvup66BevGeHpgIPTpAzNngoMD7NgBdnYJfA/ikymlWLp0KX379uXhw4e0adOGUaNGkSlTJgC8vb3x9vY2bZDii2HsxsdXiFIkIyqlVIE4iUgIIYQQIr74+0O/fjBjhr4x1s6dqAoVWHp2Ke5e7jwLekbv0r3/j737jq/p/uM4/jpZEnvv3dpKEKtW7D0Sito7qpRqa4/YRVGN0cRKjRolEaM2QVGrYteovWdC9rjf3x8nEfyMhJvcm+TzfDw8cO6953zO43hE3vl+v58vro6upLVJ+8ZTHDumN8a4cEEPXRMn6uu2RNLg5+fHgAED+Ouvv6hYsSI+Pj5UqlTJ1GWJZCyuI1yH+f/AlQWoAjwD9hmzKCGEEEIIo9u2Dfr0gZs3YdAgmDiRi6G36besPruu7qJynsrs6LyDsjnLvvHjUVEwdSqMHQs5cuijWnXqJPI9iA/29OlTRo8ezfz588mcOTMLFy6ke/fuWFjEaYWNEB8sToFLKdXpTcc1TcsMbAU2G7MoIYQQQgijefoUBg/Wu1kULw4HDhBWsTxTD0xl8v7J2FrZMq/JPPpU6IOlheUbT3H1qt5P48ABaNtW3wM5U6bEvQ3xYQwGA4sXL2b48OE8efKEfv36MX78eDLJAxSJ5KMivVLqCTANGGuccoQQQgghjGj9eihZEpYtgxEj4MQJfHOFUfbXsoz1HYtTCSf+7f8vX1X86o1hSyl93+OyZeH0af00q1ZJ2Eoqjh49SpUqVejduzfFixfnn3/+wc3NTcKWSFTGGEMNBvIb4TxCCCGEEMbx8CG0bw9OTvr8vyNHeDhiEF23ulD7t9pEGCLY2nErK1uvJGfanG88xZMn0K4ddO0K9vb6nsidOoGmJfK9iHh7+PAhvXv3pnLlyty8eZNly5axb98+ypZ983RRIRLSBwcuTdMsNE0rDYwBzhuvJCGEEEKID6QU/P47lCgB3t4wcSKGI4dZxAmKzy3OytMrGVF9BGe+OkPDTxu+9TQ7d+rt3r299b2Q9+yBggUT7zbEh4mMjGTu3LkULVoUT09PBg8ezIULF+jUqROaJGVhInHtUhjB/zfNsAA0IBBoauS6hBBCCCHi5/Zt+OorfQfiypVh8WLOZYO+K+qz/8Z+quevjnszd0pmK/nWU4SG6jMPZ83Sl3tt3AjlyyfiPYgPduDAAb7++mtOnjxJnTp1cHNzo2TJtz9rIRJLXLsUTuX/A1cocB3YrJR6atSqhBBCCCHiSilYvBi++w7Cw2HGDEL69WHigSlMXzeddKnSsbD5QrqX646F9vbJPadOQceOcOYMfP01TJsGqVMn4n2ID3L37l2GDh3KsmXLyJcvH3/88QetW7eWES1hNuLapXBUQhcihBBCCBFvV6/qrd537oRatWDhQrbxH/08ynLl6RW6lO3CT/V/IluabG89hcEAP/8Mw4frzTA2b4YmTRLxHsQHiYiIwM3NDVdXV8LCwhgxYgQjRowgTZo0pi5NiFfEdYRLCCGEEMJ8GAwwd66ekjQN5s/nXocWfLvjO1adWUXRLEXZ3WU3tQvVfudpbt3Sm2Ls3g0tW8KCBZDt7dlMmIldu3YxYMAAzp8/T+PGjZk9ezZFihQxdVlCvNFbA5emaR7xOI9SSrkYoR4hhBBCiHe7cAF69YK//oJGjTD8Oh/3B1sYPq8kIZEhjHMcx9BqQ0llleqdp1mzBlxc9FmICxZAz57SgdDc3bx5k++++44//viDQoUKsWHDBpo1aybTB4VZe9cIVxP+f93W28T1fUIIIYQQHyYyEmbMgLFjwc4OPD052aAsLpvbc/j2YeoUqsP8pvMpmqXoO08TEAADBuh7alWqBMuXgwyOmLewsDBmzJjBpEmTMBgMjB8/nu+//x47OztTlybEe701cCml8iZmIUIIIYQQb3X6NHTvDsePg5MTQbOmMfbCr/y8oCeZ7TKzzGkZHT/r+N6Rjv37oXNnuHlTz20jR4K1dSLdg/ggW7Zs4ZtvvuHy5cs4OTkxc+ZMCkqPfpGEGGPj43jRNC2fpmlrNU0L0DTtmaZpXpqmxWnjZE3TJmuatl3TtMeapilN07q95X2+0a+//muQUW9GCCGEEAkrPBxcXaFCBbhxA9asYePkbpT0rsuMQzPoUa4H//b/l05l3r3PUni4Hq4cHcHSUp+N6OoqYcucXblyhZYtW9KkSRMsLCzYunUrXl5eErZEkvOuNVy5gQdKqcjoP7+TUurO+96jaVpqYDcQBnRFn4o4EdijaVoZpVTQe04xAPADNgFd3vPeU8Dr68quva9GIYQQQpiJo0ehRw+9T3vHjtyZMIT+R1zxXu1NqWyl+Kv7X1TLX+29p/n3X+jUSR8c69lT32MrXbpEqF98kODgYKZOncrUqVOxsrJi6tSpDBo0CBsbG1OXJsQHedcarptAVeAIcIv3r9OyjMP1egOFgWJKqcsAmqadAi6hh6OZ7/l8BqWUQdO0T3l/4HqulPo7DjUJIYQQwpyEhOjz/WbMgFy5iPJZj1v2q4xeVY0oQxRT6k5hcNXB2Fi++xtwpeDXX/XtuVKnBi8vcHJKpHsQ8aaUwsfHh0GDBnH9+nW+/PJLpk+fTp48eUxdmhAf5V2Bqw/w30t/NkZjjBbA3zFhC0ApdVXTtANAS94TuJRSBiPUIIQQQghztX+/Pgx16RL07s2JwR3ouXcwJ06coPGnjZnbZC6FMhV672nu39cHx/78Exo2hCVLIFeuRKhffJALFy4wcOBAtm3bRunSpdmzZw+Ojo6mLksIo3hX04xFL/15oZGuVwrwecPxs8AXRrpGjHKapgUAqYHzwOyX70kIIYQQZiQwUN9Ta84cKFSIoD99GM5O5q6pS/Y02VnTZg1tSraJU/vvDRv0rvHPn8Mvv0D//tLu3VwFBgYyceJEZs6ciZ2dHT///DP9+vXDWhbXiWQksTc+zgw8fcPxJ0AmI15nH7ACuAhkRJ9+uFDTtFxKqYlGvI4QQgghPtaOHdC7N9y4gfrmG9Z3rkj/fV9x9/ld+lXsx6Q6k8hgm+G9pwkKgsGDwcMD7O31du+lSiVC/SLelFKsXr2a77//ntu3b9OtWzd+/PFHcuTIYerShDA6Tam4zRTUNC0r0A4oBti+9nKcNj7WNC0cmKGUGv7a8UnAUKVUnAJg9BquS0B3pZRnHD/jDTQCsimlAt/weh/0qZPkyJGjwqpVq+Jy2gQXGBhI2rRpTV2GeI08F/Mjz8Q8yXMxP+b0TKwCA/lk3jxybdlCcL58HBzYk1E2Wzj85DCfpv2UwUUGUyJ9iTid6/z5dEyeXILbt+1o1+4m3btfxcYm6WwTak7PJaFdvXqV2bNnc/LkSYoUKcLAgQMpZYbJOCU9k6TEnJ5L7dq1jyulHN73vrgGnKLAIfSgZYs+SpURva18APA8jnU9RR/lel0m3jzyZUwrgVbAZ+j38gqllAfgAeDg4KDMZd6wr6+vzGE2Q/JczI88E/Mkz8X8mM0z2bAB+vaFBw+IGvID8xtmYPShSViEWTCzwUwGVB6AlcX7v02JjIQpU2DcOMidG3bvBkfH/ECcdpwxG2bzXBJQQEAAY8eOZc6cOWTIkIFff/2VXr16YWkZl75riS8lPJOkKCk+l7juwzUd+AfIBmhAAyAN0Bc9bDWN43nOoq/jel1J4Fwcz/GhYmZvJ50fdwkhhBDJzcOH8OWX0LIlZMvGSR8PyhXcyvf7R9Hw04ac//o831b9Nk5h67//oGZNGDMG2rWDU6f0fbaEeTEYDPz2228ULVqUX375hV69enHx4kVcXFzMNmwJYUxxXcNVEegHhEb/3UIpFQZ4aJqWGfgZqBuH82wAftI0rbBS6gqApmkFgWrAsHjU/SE6ACHA6QS+jhBCCCFepxSsXg0DBkBAACGjh/Od/QPmH+tJvvT5WN9uPS2Lt4zzqTw94Ztv9E2MV6yADh0StnzxYf755x/69+/PoUOHqFKlCn/++ScVKlQwdVlCJKq4jnClBx5Ht2V/BmR96bUjQOU4nmcB+ubDPpqmtdQ0rQV618KbgHvMmzRNK6BpWqSmaWNe/rCmabU0TWuDvhYLwEHTtDbRx2LeU0PTtM2apvXUNK2upmnOmqb5oLekHxeHzZWFEEIIYUx37ugbYH35JapQITatHE/B9IvwOOPJ4CqDOff1uTiHrcePoU0bveV7hQr6qJaELfPz+PFjvvrqKxwcHPjvv/9YsmQJBw4ckLAlUqS4jnBdA2LaxlwAWgNbo//eGPCPy0mUUkGaptUBZgHL0Kf57QIGvdbIQkPfSPn1QDgOqPXS37+O/hXzGYC70Z8bjx4MI4BTQAel1Mq41CmEEEIII1BK3wBr8GAIC+Oh61A6FTjK9jPDqZSnEts6bcM+p32cT7d9O3TrBo8ewbRp+mllRpp5iYqKYuHChYwYMYKAgAC++eYbXF1dyZgxo6lLE8Jk4hq4dgL1gLXoYel3TdM+ByKB0sCUuF5QKXUDPbC96z3XiA1QLx93jMP5L6OHQCGEEEKYyvXreqv3HTswVK/G/L4OfHf1Z1LdS8WcxnPo69AXS4u4paWQEBg2TN9Tq2RJfTNj+7jnNJFI/v77b/r378/x48epWbMmbm5ulClTxtRlCWFycQ1cwwA7AKXUKk3TwtBbxKdGnwr4a8KUJ4QQQogkxWCA+fNh6FDQNC6OH0jLDFv49/Js2pZqy6yGs8idLnecT+fnBx07wrlz+pqtH38EO7sErF/E24MHDxg2bBhLliwhd+7c/P7777Rv3z5Om1QLkRLEKXAppUKJbZiBUsob8E6oooQQQgiRBF28CL16wf79hNd1ZHjbLMy8O5tCqhBbOm6h0aeN3n+OaAYDzJgBI0dCliywdSs0bJiAtYt4i4yMZN68eYwZM4bg4GCGDBnCqFGjSJcunalLE8KsvDVwRa+1OvKmTYKFEEIIIV6IjIRZs2DMGJStLX+N7U4ru/U8u/+c4dWHM6rmKFJbp47z6W7ehC5dwNdX77Xh4QFZs773YyIR7du3j/79+3P69Gnq16/PL7/8QvHixU1dlhBm6V1dCneg748FgKZpFpqm7dM0rUjClyWEEEKIJOHMGfj8cxgyhOeOn9N6TDFqaksoka0kJ1xOMLnu5HiFrVWr4LPP4NgxWLwY1q2TsGVO7ty5Q8eOHalVqxYBAQF4eXmxbds2CVtCvMO7AtfrE281oDog48RCCCFEShceDuPHQ/nyqKtXWTXKiSxV9+EbfpEFzRewr/s+SmcvHefT+ftDp076nsglS+prt7p3B1kGZB7Cw8OZPn06xYoVY926dYwePZrz58/j5OQka7WEeI+4Ns0QQgghhNAdP66nodOnudvckZZVrnE0wpvOn3XmpwY/kT1N9nidbu9efQrh7dt6hhs+HKzkOxSzsWPHDgYMGMCFCxdo3rw5s2bN4pNPPjF1WUIkGXHd+FgIIYQQKV1Mf/bKlYl6+ICfhtYgdwVf/NNZs6vLLpY6LY1X2AoP109XuzbY2MCBAzB6tIQtc3H9+nVat25NgwYNiIyMZNOmTWzYsEHClhDx9L4vaXk0TSsc/WfLl47930bHSqkrRq1MCCGEEObjwAHo0QMuXuR8y89pVPYM96wPM7b6WIZVH4atlW28TnfunN7u3c9P365r5kxImzaBahfxEhoayvTp05kyRd9mddKkSQwePBhb2/g9YyGE7n2Ba+0bjq1/y3tlr3chhBAiuQkM1Huzu7kRnicX339bHLcMB6ldsDbbm86nWNZi8TqdUjB3Lvzwgx6w1q+Hli0TqHYRb5s2bWLgwIFcuXKFNm3aMGPGDPLnz2/qsoRI0t4VuLonWhVCCCGEMD87d+rDT9eu8VfLcjQtdRKbjOEsbbCUTmU6xbtZwt27+iDZ1q3QuLHehTBnzgSqXcTL5cuXGTRoEJs3b6Z48eLs2LGDevXqmbosIZKFtwYupdRviVmIEEIIIcxEQAB8/z0sXEhggdx065+ddVlP0LNcT6bWm0qW1FnifUpvbz27BQXpI1xffSUdCM1BUFAQU6ZMYfr06djY2PDTTz8xYMAAbGxsTF2aEMmGLEsVQgghRKyNG6FvX9S9e6xvUZQOn12kcO6S7Gu6lhoFasT7dIGBMGgQLFoE5cvD8uVQokQC1C3iRSmFl5cX3377LTdv3qRTp05MmzaNXLlymbo0IZId6VIohBBCCHj0SO9i0aIFj2wVjn1T0aHSDcY0nMwJlxMfFLb+/hvs7fWpg8OHw6FDErbMwfnz52nQoAFt2rQhU6ZM7Nu3j2XLlknYEiKBSOASQgghUjKlYM0aKFkSw5rV/NosF7k73MWuak3O9jvL8BrDsbGM3/SyyEhwdYXq1fU/790Lkyfrrd+F6Tx//pwffviBMmXKcOzYMdzc3Dh+/Dg1asQ/TAsh4k6mFAohhBAphaMj9v7+ei920LtY9OsH69dzvUh2Wnxh4EFhxbKGq2hbqm28m2IAXL4MnTrB4cPQuTO4uUGGDEa+DxEvSil+//13fvjhB+7evUvPnj2ZPHky2bPHb4NqIcSHkREuIYQQIqVRCjw9USVLEvXnZiY2Tcen7R9QvclXnP/6PO1Kt4t32FIKFi7UpxBeuACrVsHSpRK2TO3kyZPUqlWLTp06kSdPHg4fPszChQslbAmRiGSESwghhEhBtPBwvSf7tm2cK5YJp/oRpC5Vkr+auVM5b+UPOuejR3oHwvXroU4d+O03yJvXyIWLePH392f06NHMmzePTJkysWDBAnr06IGFhfysXYjEJoFLCCGESAlCQuDmTdJdu0b4tf8Y1swaj8qhjKvzEwOrDMTK4sO+Jdi6Fbp3hydPYMYMvSOhfE9vOgaDAU9PT4YNG8bjx4/p27cvEyZMIHPmzKYuTYgUSwKXEEIIkZyFhek92SdOhLt32V8AurYKp2yVFpxr7Eb+DPk/6LQhITBkCMyZA6VLw7ZtUKaMkWsX8XLs2DG+/vprjhw5wueff862bdsoV66cqcsSIsWTwCWEEEIkR5GR+iKq8ePh+nVOF81A/4bwbzYN9x5etCre6oNPfeKE3kH+/Hn49lu9A6GtrRFrF/Hy6NEjRowY8WJt1tKlS+nUqdMHNT0RQhifDPoLIYQQyYnBAL//DiVLQs+eXLD0p0EnqNvTmpaRhfHbWfqDw1ZUFEydCpUrQ0AA7NgBM2dK2DKVqKgo5s+fT9GiRVm8eDHffvstFy9epHPnzhK2hDAjEriEEEKI5EAp8PaGsmWhY0euhd6nZXuo2lujjssUrgy6yuCb+bAzfNh//dev6w0xhg2Dli3h9GmoV8/I9yDi7ODBgzg4ONCvXz/s7e05efIkM2bMIH369KYuTQjxGplSKIQQQiRlSumdK0aPhuPHuZMrHd+2gW3lFIOrjWNZlUGkTxX9TbivL36+vjjG8xIrVujbdSmldyDs3BlkAMU07t27x9ChQ1m6dCl58+Zl9erVfPHFFzKiJYQZk8AlhBBCJFV79sCoUXDwIA+zp2FoK4115aPo//kI5n/+HZntPq4z3dOn8PXXsHIlVKsGy5ZBoUJGql3ES0REBHPmzGHs2LGEhoYyfPhwRowYQdq0aU1dmhDiPSRwCSGEEEnN33/rQWvXLp5mTs3IZhorHKLoU/U7LlcbQrY02d74MUdH8Pe3x8/v/ZfYswe6doW7d/UGh0OHgpV812ASe/bsYcCAAZw9e5ZGjRoxe/ZsihYtauqyhBBxJF86hRBCiKTixAl96uDmzTzPYItrIwsWVoqgW5UBXKgxnJxpc370JcLC9Ev89BMUKQIHD0LFikaoXcTbrVu3+P7771m9ejUFCxbEx8eH5s2by/RBIZIYCVxCCCGEuTt3DsaOhbVrCU6biin1LHGrHMGXVfpwtuZI8qbPa5TLnD2rt3s/eRL69tVDV5o0Rjm1iIewsDBmzZrFhAkTMBgMuLq6MmTIEOzs7ExdmhDiA0jgEkIIIczV5cswbhxqxQrCba2YUduKGZUjaFmlKydqjqZQJuMsqDIY9A2MhwyB9Olh40Zo1swopxbxtHXrVr755hsuXbpEq1atmDlzJoVk4ZwQSZoELiGEEMLc3LgBEyagliwhwkpjXnUrJlWJoGGVjvxdayxFshQx2qXu3IHu3WH7dj1kLVwIOXIY7fQiDhwdHbl//z7FihXDx8eHIkWKsGXLFho1amTq0oQQRiCBSwghhDAXd+/ClCkod3eiVBSLKlni+nk4NSp/wV5HV0pmK2nUy3l5Qe/eEBICv/4KffpIu/fE9ujRI65du8aNGze4efMmP/74I4MGDSJVqlSmLk0IYSQSuIQQQghTe/QIpk1DzZmDCg9jWQVrRn8eRfnKzdjqOI6yOcsa9XLPn8PAgbBkCTg4wPLlUKyYUS8h3kEpxd69e3F3d8fLy4vw8HAyZszI6dOnyZvXOOvxhBDmQwKXEEIIYSr+/jBzJmrWLAgKYm05G4ZXM1C0ch28ao/HIbeD0S8ZFGSJvT1cu6Z3lh8zBqytjX4Z8QaPHz/mt99+w8PDgwsXLpAxY0b69u3LwYMHiYiIkLAlRDIlgUsIIYRIbIGB4OaGmj4d7elTNpWxZWh1Ra7K1VhaewKf5/vcqJdTCo4fhwsX4N69tBQqBPv26ZsZi4SllGL//v24u7uzdu1awsPDqVq1Kp6ennzxxRekTp0aAF9fX9MWKoRIMBK4hBBCiMQSGgrz56OmTEF7+JDdJW354QtIU8WBebUn4FjQ0aiXCwiAFStgwQLw8wMLC8iSJRw/v1SkT2/US4nXPHnyhKVLl+Lh4cH58+fJkCEDffr0oU+fPnz22WemLk+IpEcBVcA+xB5OmbqY+JHAJYQQQiS08HBYvBg1cSLa7dscLGrH9y3AUKUMP9aeQP3C9Y22ma1ScOgQeHjAmjV6Qwx7e5g7F37/HQIDQ0ifXhoyJASlFAcOHMDDw4M1a9YQFhZG5cqVWbx4MW3btiWNbGomRPzcBXYDu6J/3QCrT5JefEl6FQshhBBJRWQkLF+OGjcO7do1ThSy5buuEFC1OONrj6dpkaZGC1qPH8OyZfpo1rlzkDYtdOqkdx6sUEHvPrhmjVEuJV7z9OlTli1bhoeHB2fPniVdunT06NEDFxcXypY1bsMTIZI1f8AXPVztBs5FH88E1AZsIMomyjS1fQQJXEIIIYSxGQywZg1q7Fi0ixc5n8+W7zrCzaqFGV9nAq2Kt8JCs/joyygFe/fqIWvdOggLg0qV9L+3b6+HLpEwlFIcOnQIDw8PVq9eTWhoKBUrVmThwoW0b99eRrOEiIsQ4ACxI1jHAQOQGqgBdAPqAmUBS8ARlL8yRaUfRQKXEEIIYSxKwYYNqNGj0U6f5r/ctvzQDs5Vy4er4zjalmqLpYXlR1/mwQPw9NQ3Kb50CTJk0PfT6t0bypT5+NsQb+fv78/y5cvx8PDg9OnTpE2blq5du+Li4kK5cuVMXZ4Q5i0SOEpswDoIhKMnkirAaPSAVRmwecPnfcHP1w9HHBOjWqORwCWEEEJ8LKVg+3bUqFFox45xM7stQ1vDseq5GOU4lj/KdMTK4uP+yzUYYOdOfW2Wj48+W7F6db21e5s2EN3sTiQApRRHjhzB3d2dVatWERISQoUKFfDw8KB9+/akS5fO1CUKYZ4MwBliA9Y+4DmgAfbAAPSAVQNIxiPyEriEEEKIj7Fvn5569u/nfpZUjGgBe2pkZUTtMSy174a15cdtcnX7tr5B8aJF+t5ZWbLAN99Ar15QooRxbkG8WUBAACtWrMDd3Z1Tp06RJk0aOnXqhIuLCxUqVDB1eUKYHwVcIXYN1m7gYfRrRYCO6AGrNpDFFAWahgQuIYQQ4kMcPgyjR8OOHTzKaMOYJvBnzUz8UHsU88v3IpXVh3cCjIyErVv10azNm/XRrTp1YMoUcHKCVNJkMMEopTh27Bju7u6sXLmS4OBg7O3tmT9/Ph06dCC99NMX4lX3eLWT4PXo47mBRugBqw6QzyTVmQUJXEIIIUR8+PnBmDGwcSMB6awZ3wDW1kzPwDrDOe/wFXbWdh986uvX9ZGsxYv1ka0cOWDIEOjZEz791HsA7SIAACAASURBVIj3IP7P8+fPWbFiBR4eHpw4cYLUqVPz5Zdf4uLigoODg9G6SQqR5PkDe4kNWK93EhyCHrCKoU8dFBK4hBBCiDg5fx5cXWHNGoJSWzG5Dix3TMtXtYdwtlJ/0tp82AKEiAjYuFEfzdq+XT/WsCH88gs0bw7WHzcj8RW+vuDr6wdJbMF5Qjp+/Dju7u78/vvvBAUFUaZMGebOnUvHjh3JkCGDqcsTwvTe10mwK/oolj16J0HxfyRwCSGEEO9y5QqMG4davpxQGwt+qgkLHe3oWed7TlcZRPpUHzbF7PJlvcugpyfcvw958ugzFHv0gAIFjHsL4lWBgYGsXLkSd3d3jh8/jp2dHe3bt8fFxYVKlSrJaJZI2V7uJLgbvZNgGHpqqAyMIraToExvjpNED1yapuUDZgH10QcadwKDlFI34vDZyYADUAHIDHRXSnm+5b29ge+AQsA1YJZS6lcj3IIQQoiU4OZNmDgRtXgxERYKt8oG5tS2o2PdofhV/Y5MdpnifcqwMPDy0vfJ2rMHLC2haVO9nXujRmAlPwZNUCdOnMDd3Z0VK1YQGBhI6dKlcXNzo1OnTmTMmNHU5QlhGjGdBGPWYe1F7yQI+qhVf1JEJ8GElKhf2jVNS43+OMPQByAVMBHYo2laGaVU0HtOMQDwAzYBXd5xnd6AOzAFPdDVBeZpmqYppeZ/9I0IIYRIvu7fhylTUPPnE2WIxL28YqZjKlrXG8zRakPImjprvE95/rwespYuhcePoWBBmDgRuneH3LmNfwsiVlBQEKtWrcLd3Z2jR49ia2tLu3btcHFxoUqVKjKaJVKmmE6CMaNYb+ok6AjE/8udeIPE/llab6AwUEwpdRlA07RTwCXABZj5ns9nUEoZNE37lLcELk3TrIBJwDKl1Mjow3s0TcsNTNA0baFSKsII9yKEECI5efwYpk/H4PYLKjSU3+xhSm0rmtT9igM1hpMzbc54nS44GP74Qw9aBw7oa7FatoQ+faBuXbCwSKD7EACcPHkSd3d3li9fzvPnzylZsiSzZ8+mc+fOZMoU/9FJIZK0lzsJ7kaf+wWxnQTroIesFNxJMCElduBqAfwdE7YAlFJXNU07ALTkPYFLKWWIwzWqAtmA5a8dXwZ0B6oDe+JTtBBCiGQsIABmzcIwcwYEBrLmMwsmOFpSs34v9tQcSd70eeN1upMn9ZC1fLl+6iJFYNo06NoVsmdPoHsQAAQHB7N69Wrc3d05fPgwqVKlom3btvTp04dq1arJaJZIOQIAX2JD1tno4xnROwl+jx6wpJNgokjswFUK8HnD8bPAF0a8BuizUV+/BkBJJHAJIYQICoI5czBMm4rFk6dsKGnBWEcLHBp0Y3Ot0RTMWDDOpwoMhFWr9E6DR4/q+2S1bq2vzapVC+T7/IR1+vTpF6NZAQEBFC9enFmzZtGlSxcyZ85s6vKESHgh6M0tYqYJHkNfm2WHvvaqC9JJ0IQSO3BlBp6+4fgT9O79xroGb7jOk9deF0IIkRKFhoK7O4bJk7B48JAdRS0Z2QZKNOrA2ppjKJKlSJxOoxQcO6aPZq1cqYeuUqXg55+hc2eQ7/MTVkhICGvWrMHd3Z1Dhw5hY2NDmzZtcHFxoUaNGjKaJZK3SPRQFROw3tRJsA5QBekkaAY0pVTiXUzTwoEZSqnhrx2fBAxVSsUpAEav4brEG7oUapo2Er0Rh61SKuyl41ZABDBGKTXhDefsA/QByJEjR4VVq1bF59YSTGBgIGnTSksYcyPPxfzIMzFP5vRctMhIcm7ZQv5lS7F7+Ih9hSwYXtuATQVHuhXoRoE0cevFHhhoyc6dOdi0KTf//ZeWVKmiqF37Ac2a3aVkyWdmP5plTs/kQ1y9epVNmzaxfft2AgMDyZcvH82aNaNhw4ZJet+spP5ckiOzeiYK0lxNQ6Z/MpHxn4xkPJkRq2D92+bnnz7Hv5w/T8s/JaBMAFGpo0xcbMIyp+dSu3bt40oph/e9L7FHuJ7y5hGmTLx55OtDvDySdfel45lfe/0VSikPwAPAwcFBOTo6Gqmcj+Pr64u51CJiyXMxP/JMzJNZPJeoKFixAoOrKxZXr3I0vyVDu0D6xs2Z5ziOsjnLvvcUSsHBg/po1po1EBIC5crBvHnQoYMlGTLkAnIl/L0YgVk8k3gKCQlh7dq1uLu7c+DAAaytrWndujUuLi7UqlUrWYxmJcXnktyZ/JnEdBLcHf3rQfTxT4HO6FMEa0O6rOlIRzrypZCOFyZ/Lh8gsQPXWWLXWL2sJHDOiNcg+jovB66S0b8b6zpCCCHMmcEAa9diGDMGiwsXOJvbimEdQDWuz7Q6E3DI/d4fSvL4sd7KfeFCOHcO0qaFLl30tVkVKiTCPaRw58+fx8PDg99++42nT59SpEgRpk+fTteuXcmWLZupyxPCuO6hdxmImSZ4Lfp4LqABesCqA+Q3RXHiYyR24NoA/KRpWmGl1BUATdMKAtWAYUa6xiHgEfouAjtfOt4JfXTrgJGuI4QQwhwpBZs2YRg1CotTp7iUw4oRbeFZ45qMqzuBz/N9/t6P+/rqo1nr1kF4OFSurIeudu300CUSTmhoKOvWrcPd3Z39+/djbW2Nk5MTLi4uODo6YiH99EVyEYC+yXBMwHpTJ8E6QHGkk2ASl9iBawH6ftU+mqaNQt/4eAJwE32jYgA0TSsA/AeMV0qNf+l4LfSW7zGboThomhYIoJRaG/17hKZpo9E3Or6NHrrqAD2AAUqp8IS9RSGEECahFOzciWHkSCyOHuVGFitGOsOtJpUZV3cijgUd3/nx+/fB01MPVpcvQ8aM+p5ZvXtDmTKJcgcp2oULF16MZj1+/JhPPvmEqVOn0q1bN7JLP32RHLyvk2DMNMFySCfBZCZRA5dSKkjTtDrALPR9sTT0f3KDlFKBL71VQ/+n9vqPscYBtV76+9fRv2I+E3OdXzVNU8B3wA/ADaC/UmqeEW9HCCGEudi/HzVqJNq+/dzLaMmY5nCuiT2u9SdRv3D9t67xMRhgxw59NMvHByIjoUYNGDMG2rQBO7tEvo8UJiwsDC8vLzw8PPD19cXKyopWrVrh4uJCnTp1ZDRLJG0vdxLcjT7HKqaTYCVgJHrAkk6CyV5ij3ChlLoBtH7Pe67xhsFTpZRjPK7jzkujZkIIIZKho0dRo0ehbdvOw/SWjG8MR5uUZnSDiSwo0vStQev2bVi8GBYtguvXIUsWGDgQevWC4sUT+R5SoEuXLuHh4YGnpyePHj2iUKFCTJ48me7du5MzZ873n0AIc6TQpwXGjGDtBZ5Fv1YWfYigLvpoVjpTFChMJdEDlxBCCPHRTp1CjRmD5uODfxpLJteHPU2KMqLhRNyKO70xaEVGwpYt+mjW5s366FbdujB1KrRqpW9WLBJOeHg43t7eeHh4sHv3biwtLWnZsiUuLi7Uq1dPRrOEeXEEe3978HvP+64SG7Be7yT4JXrAckRfECNSLAlcQgghko4LF1Bjx6KtXk2grQXTasPmxgX5odEEfizVFkuL/1/4cO2aPpK1eDHcuQM5c8LQodCzJ3zySeLfQkpz+fJlFixYwJIlS3j48CEFChRg4sSJ9OjRg1y5kkYrfSFeuI8erGIC1tXo4zmB+ugBqy7SSVC8QgKXEEII83f1Kmr8eFi6lFArmFUd1jbJx8DG4zhSpiNWFq/+dxYRARs26KNZ27frxxo1gjlzoFkzsLY2wT2kIOHh4fj4+ODh4cHOnTuxtLSkefPmuLi4UL9+fSwtpSOASCJiOgnGhKwz0cczoo9cDUYPWNJJULyDBC4hhBDm69YtmDQJw8IFRGBgbiXFssa56dfUlcP23bC2fDU5Xbqkdxn09IQHDyBvXr0BRo8ekF9+4pzgrly58mI06/79++TLl4/x48fTo0cP8uTJY+ryhHi/MPROglch7aO0kAWIQu8kWB19k6E6QHmkk6CIMwlcQgghEoSjI/j72+P3vjUQb/LgAfz4I4Z5czFERuBRTrGwcXZ6NhvD3+V7kcoqdsFVaCh4e+ujWXv2gKWlPorVu7c+qiWDKQkrIiKCjRs34u7uzvbt27GwsKBZs2a4uLjQsGFDGc0S5s2Avk5rF/pGQvvR27cDpAaGo49gVUU6CYoPJoFLCCGE+XjyBH76iaifZ0FoKEvLwrzGWejQfCQHHPpiZx3bp/3cOT1kLV2qf6xQIZg0Cbp1g9y5TXcLKcW1a9dYsGABixcv5t69e+TNmxdXV1d69uxJ3rx5TV2eEG93BT1c7USfKvg4+ngpoA9QD/gRAgMDyTgho2lqFMmKBC4hhBCm9+wZzJ5N1PRpWD4PZE1p+LlRBpxbDse3Un/S2KQBIDgY/vhDD1oHDuhrsVq10kez6tYFaXSXsCIjI9m0aRPu7u5s27YNTdNo3LgxLi4uNG7cGCsr+bZCmKGH6MFqJ/pIVkyjizxAM/SAVRd4uYfLT4lZoEju5CujEEII0wkOhrlzifxxMlZP/NlYDKY1TEMj5yHsqDKI9KnSA+Dnp4esFSsgIACKFoXp06FLF8ie3cT3kAJcv36dRYsWsWjRIu7cuUPu3LkZPXo0PXv2JL8sjhPmJgh9amDMNMGYac0ZgNrAd+ghqyjS6EIkCglcQgghEl9YGHh4EDlxPFYPHrHzE5jS3o4arQezuep3ZLLLxPPnsGCpHrSOHtX3yWrTBvr0gRo14C17GgsjiYyM5M8//8Td3Z0tW7YA0KhRI+bNm0fTpk1lNEuYj0jgGLHTBA8CEYANUA2YhB6wyhP373x9wc/XD0ccjV2tSIHkq6UQQoiE4edH2shIwCH2WEQEeHoSOc4Vq9t3OFgAxveyoXzbgayrNoQsdlk5elQPWStXQlAQlC4Ns2dDp06QObPJ7ibFuHnzJosWLWLhwoXcvn2bXLlyMXLkSHr16kWBAgVMXZ4QoIB/iQ1YvsAz9NGqcsAg9IBVHb3xhRAmJoFLCCFEwouKgpUriRwzCqur1/knj4ZrN2uKtv2K5TWGYxuZkxVLwMMDTp2C1KmhXTt9NKtyZRnNSmhRUVFs2bIFd3d3/vzzT5RSNGjQADc3N5o1a4a1bFwmTO02+hTBmGmCd6KPFwbaowes2kBWk1QnxDtJ4BJCCJFgNGWAtWuJHDUCqwuXOJtTw7WDJbm+7I17jZFcP52XoV/rjTBCQqB8eZg/Hzp0gPTpTV198nf79u0Xo1k3b94kR44cDBs2jF69elGoUCFTlydSspgNh2NGsc5HH8+K3uAiptGF/DMVSYAELiGEEMZnMFA1Yi9dw+bAF5e5nE1jbFsL0rXvypiyrvj65KfhEDh/HtKl05tf9O4NFSqYuvDkLyoq6sXarE2bNmEwGKhfvz6zZs2iRYsWMpolTCMM+JvYgHWU2A2HawE90ENWGUC6kYokRgKXEEII47l3DxYvJsJ9PlOCb3Elo0ZXRzB82YFmNlP5c3UeqrSH8HCoUgUWLYK2bSFtWlMXnrwppfjnn39wdnbm7t27REREkD17doYMGUKvXr345JNPTF2iSGkMwCliW7XvA4IBS6Ai+obD9YAqyIbDIsmTwCWEEOLjGAywZw9R8+ei+WzAIjKK/QXBvTXsCmtJ19Lu+IzIzvL/IFMm6NsXevWCzz4zdeHJW1RUFAcOHMDLywtvb29u3LgBQNq0aVmxYgUtW7bExsbGxFWKFOUasSNYu4BH0cdLAD3RA1Yt9PbtQiQjEriEEEJ8mIcPwdOTsPlupLp6k4DUGksqKjbVzEuezFPY4VoP/5DszNxkQc2a4OoKrVuDnZ2pC0++wsLC2LVrF97e3vj4+PDw4UNSpUpFgwYNcHV1ZeHChQQFBfHFF1+YulSREjwC9hAbsq5EH88NNCZ2HVYek1QnRKKRwCWEECLulIJ9+wif54altw+WEZEcyQ8L21gS5NiTtJeHcHVVYXyva1hrkeSyfsDu0zkpVszUhSdfgYGBbNmyBS8vLzZv3szz589Jly4dTZs2xdnZmUaNGpEuXToAunfvjq+vr2kLFslXMPAXsSNYJ9BbuKcHHIlt114c2XBYpCgSuIQQQrzfkycYfvuNkLk/k+a/GwTbwtJysKd+SbJl/JEHexqy/RsbDAaoVw+mTYM5vc9iiIqgWLGcpq4+2Xn8+DEbNmzA29ub7du3ExYWRtasWWnXrh1OTk7UrVuXVKlk4YtIYJHAcWJbtR8AwgFr4HNgPHrAckC+4xQpmvzzF0II8WZKwaFDBLnNxMbLB+vwSE7ngaVt7Ait+w2WFwZy1DMXt29DzpwwdCj07Akx/RfmzSvLM39/095DMnLr1i3Wr1+Pl5cX+/btIyoqivz589O3b1+cnZ2pVq0alpaWpi5TJGcKuEjsFME96O3bAeyBAegBqwaQxhQFCmGeJHAJIYR4VUAAEb95EjRnJhkv3SDKBhaUgePNqpA+/VQuba3Grn76N/aNGoGbGzRrBtJN3PguXrz4ounFkSNHAChRogRDhw7F2dmZ8uXLo8mu0CIh3SV2BGsXcCv6eEHgC/SAVQfIZorihEgaJHAJIYTQR7OOHePJzz+SZt0GUoVF8l8uWP1FRsLrDyf0bB82z8/I/fuQNy+MHg09ekCBAqYuPHlRSuHn5/ciZJ09exYABwcHJk+ejJOTE8WLFzdxlSJZe4a+4XBMyDobfTwzsRsO1wMKm6Q6IZIkCVxCCJGSPX9O8NLFBLnNINuFm6SyhuVlNM61aoh12h85trEse/poWFpC06bQp48+qiUz14wnKiqKgwcP4u3tjZeXF9evX8fCwoKaNWsye/ZsWrVqRf78+U1dpkiuwoHDxE4TPIy+4bAtUBPogh6w7JENh4X4QBK4hBAiBVInTnB3hiuZ1v1J6tBILmcH93a5CG8wlkd+XVjzsx2PH0PBgjBxInTvDrlzm7rq5CM8PJzdu3fj5eWFj48PDx48wMbGhvr16zNmzBiaN29OtmwyR0skAANwhtiAtQ8IQg9TDsBQ9IBVFT10CSE+mgQuIYRIKYKDefqbOyFuM8l9/haZrMCrjBVXW39BZJop7F5XmP09NaysoFUr6N1b7zhoIT/VNorAwEC2bt2Kt7c3mzZt4tmzZ6RNm5amTZvi5ORE48aNSZ8+vanLFMnRdWLXYO0CHkQfLwZ0Qw9YjkBGE9QmRAoggUsIIZK5iJMnuDFtJDnW7yBTcCR3s8LaLz8htMEkrh1zZuV0a/z94dNPYepU6NoVcuQwddXJw5MnT9i4cSNeXl5s376d0NBQsmbNSps2bXB2dqZu3brY2sowgjCyJ7y64fDl6OM5gQboa7HqAvlMUp0QKY4ELiGESI5CQ7m9eDbh836h0Nk75LWEzWVsud22C4F2k9iwKid/dwcbG2jdWh/NcnQEaXj38W7fvs369evx9vbG19eXqKgo8ubNS58+fXBycqJ69epYWcl/v8KIQtD3wIoJWP+gt3BPiz5y1R99FKsksuGwECYgX/GFECIZCTz9D9emDiPf+j3kCYrkYhbw7FiKwPrTOHWwEasmW/D8OZQoATNnQufOkDWrqatO+i5duvSi6cXhw4cBKFasGEOGDMHZ2ZkKFSpI+3ZhPFHooSpmmuBfQBj6d3VVAVf0gFURfRNiIYRJSeASQogkToWGcnHhVAzuv1LizD2KWcDOsum4174HD63HsXp5Bv5ZAba20Lat3mnw889lNOtjKKU4efLki/btZ86cAaBChQpMmjQJJycnSpQoYeIqRbKhgEvEtmrfDcTsKV4G+Bp9imBN9FEtIYRZkcAlhBBJ1MNTf3Nl6nA+3bCfYoFRXMuksbaTA8/rzWT/3uqsHqcRHAxlysCcOdCxI2RMxEXxvr7g6+uHPqcp6YuKiuLQoUMvRrKuXbuGhYUFNWrU4Oeff6ZVq1YUkI3JhLHcJzZg7QRuRh/PDzgTu+GwrLcUwuxJ4BJCiCQkMiwEP/fxWC1chP3ph2TS4KB9Zh582ZdrlqNY6mnH6eWQJg106KCvzapYUUazPlR4eDh79ux50b79/v372NjYUK9ePUaNGkWLFi2kfbv4eI5g/9gefiR2muDp6NcyoQerEegh6xNkHZYQSYwELiGESAKu+O3h2vSRlNp4GIfnBm5ltGBr52o8rTOHLbvt+WMMhIaCgwO4u8OXX0K6dKauOmkKCgp6pX17QEAAadKkedG+vUmTJtK+XXwcBVwDjgBHgROQ4VkGaAakAmoAHdGnCZYDZKNxIZI0CVxCCGGmgoIDOOwxltSLl1Lp9FMKaHDcPgf/fDmAM2oIi5dY8+8ySJ9e35i4d28oV87UVSdNT548YdOmTXh5ebFt2zZCQ0PJnDkzzs7OODs7U69ePWnfLj7cQ/RgdeSl3x9Fv5YKsIGw7GHY/m4LnwN2pilTCJEwJHAJIYQZUUrhd2wTt2a6Uv7PE9R5prif3pIDXeryoNYc1u4ojtcoCA+HqlVh8WK9EUaaNKauPOm5c+cOPj4+eHl5sWfPHqKiosiTJw+9evXC2dmZGjVqSPt2EX9B6B0EjxAbsK5Gv6aht2ZvDlSK/lUaaACh/qHY1pVQL0RyJP+TCCGEGXj47B5/LRxDBs9V1DrznLLAWfvcXG7/PQcjBrBoiRX/LYVMmaBvX300q3RpU1ed9Fy+fBlvb2+8vb05dOgQAEWLFuWHH37AyckJBwcHLCwsTFylSDIigLPEhqsj0X83RL9eAL01+1fo4ao88Kapvr7g5+uHYzJpMCOEeJUELiGEMJEoQxR7D63intsUPt92Fid/eJLOihOdm3CjmhsrthVmw0iIjISaNWHcOHB2BjuZbhRnSilOnTr1orPg6dN6J4Ly5cszYcIEnJ2dKVGihOyRJd5PAVd4NVz9A4RGv54ZPVS1iv69ItJBUAgBSOASQohEd+XxZfYuGkP2Fd40OBOKtQEu2ufjSIeRbAvqySJPK64v1TckHjgQevWC4sVNXXXSYTAY+Pvvv1/skXXlyhU0TaN69erMmjWLVq1aUbBgQVOXKczdfWLXW8VMDXwS/Zod+mhVzMhVRaAw0j1QCPFGEriEECIRhESEsPmAJ4/n/0TdHVfo/hQC0lpzqVNL/q08G8+tBdg8DAwGqFcPpk2Dli0hVSpTV540REREsGfPHry9vVm/fj337t3D2tqaevXqMWzYMFq0aEGOHDLcIN7iOXCcVwPWjejXLNDXWTkTG65KAdaJX6YQImmSwCWEEAlEKcU/d46zb+kE8q3aQoszEdgY4EaZAvgNc8XLvxOLl1pxeynkzAlDh0LPnvDJJ6auPGkIDg5m27ZteHt7s3HjRvz9/UmTJg2NGzfG2dmZJk2akCFDBlOXKcxNOPoeVy93DDyHPmUQoBBQFRiIHrDKAdKURgjxESRwCSGEkT0Ofszavzx4vmAOzfbc4dvHEJTGhludvuCfcj+xcGt+tg/T39uoEbi5QbNmYC0/MX8vf3//F+3bt27dSkhICJkzZ6ZVq1Y4OTlRv3597GSRm4hhAC7z6rTAE0BY9OtZ0UPVF8SOXmVN/DKFEMmbBC4hhDACgzKw67+d7F81laJrfel6xoBtFNz7rBCnBk/g98dt8Vxhzf2lkDcvjB4NPXpAgQKmrtz83b17Fx8fH7y9vdm9ezeRkZHkyZOHHj164OzsTM2aNaV9u9Dd5dVwdRTwj34tNeAA9Ce2JXsBZN2VECLByf9QQgjxEa77X2flX/MJXuLBF389ZfwDCLWz5nGH9uwvNZkF2wuweyRYWkLTptCnjz6qZWlp6srN25UrV150Fjx06BBKKYoUKcJ3332Hk5MTFStWlPbtKd0z4BivTg28Ff2aJVAGaEtsuCqBfNcjhDAJ+dIjhBDxFBoZyvrz3hxY9zPlNhzhm9OQOhKelCrM6X4T8XzUmt9W2vB4mT6CNXEidO8OuXObunLzpZTizJkzLzoLnjx5EoBy5coxbtw4nJ2dKVmypLRvT6nCgFO8Onr1L7Hrrj4FaqJPCawE2KOPaAkhhBmQwCWEEHF08t5Jlh+YT+SKZXQ5FEz7exBua41/uy9ZV9SVBTsLsd8VrKz0DoN9+ugdB2Ug5s0MBgOHDx9+MZL133//oWka1apVY+bMmbRq1YpChQqZukyR2AzARV7d7+okerML0Pe2qgR0QA9YDkCWxC9TCCHiSgKXEEK8g3+oP7+f/p2/fNyotfVfxpyGdOHwvERhTvaYyKLHzixbnQr/FfDppzB1KnTtCtKB/M0iIiLYu3cvXl5erF+/nrt372JtbU3dunUZMmQILVu2lPbtKc1tXg1Xx9CnCwKkRQ9Ug4htapEPWXclhEhSEj1waZqWD5gF1Ef/krkTGKSUuvHOD+qftQUmAJ2AjIAfMFQpte+1911DXwr7Oiel1PqPugEhRLJnUAZ8r/my4pAH1n+so8eRSPrdgYhU1jxz7sjiwqNYsLswf0/WsLGB1q2hd29wdASZ8fb/goOD2b59+4v27U+fPiV16tSvtG/PmDGjqcsUicGf2HVXMb/uRr9mBZQFOhK77qoY+nosIYRIwhI1cGmalhrYjT4buyv67OuJwB5N08oopYLec4pFQFPgB+AK8DWwTdO0qkopv9feuw1wfe3YhY+7AyFEcnbr2S08/Tw58Kc7TXffYuYpyBAGIUULc/y7CSx43Jrf16Xi+XMoUQJmzoTOnSGrtJH+P/fv38fR0ZG7d+8SERFBcHAwmTJlonnz5jg7O9OgQQNp357chaL/WPTlzYQvvvR6UaAuseGqLGCbyDUKIUQiSOwRrt5AYaCYUuoygKZpp4BLgAsw820f1DStLPqM7R5KqSXRx/YCZ4HxQIvXPvJIKfW30e9ACJGshEeFs+HCBpYfXkDGjTvofUwx6iZE2VgT0LIz7gWG47H7E/6ZoWFrC23b6qNZ1arJaNbLQkJC+Ouvv9i+fTs7dux40fTCysqKPn364OTkRK1atbCWzcaSpyj0JhYvdww88OWk8gAAIABJREFUCURGv54LPVR1jf7dAX2eihBCpACJHbhaAH/HhC0ApdRVTdMOAC15R+CK/mwEsPqlz0ZqmrYKGKZpWiqlVNhbPy2EEC858+AMi08s5tDOJbT9yx/PkxoZQxRhhQtx+JuJeDx2ZpW3LcHBUKYMzJkDHTuCzHzTGQwGTp8+/SJg7d+/n9DQUKytralevTqTJ0+mfv36PHv2jDp16pi6XGFMCrjJqx0DjwGB0a+nR19r9T2xo1d5Er9MIYQwF4kduEoBPm84fhZ9n/f3ffaqUir4DZ+1QW8Ke/al4801TQtGn/19AvhR1m8JkbI9C3vGqjOrWHpkAfl2/a+9O4+Psrz3///6BEIgYV/CkrATtgRQFgUU2QxFcd+tW9WqtdXTfuvxVE/P6de69LTn9LTn2+V3qq2251i72dXWquASEFkUFCVhC4tA2EICgbAEksz1++OamUwmC4EsM0nez8fjfky457rvue65mOUz13V9rjV8Ya3xvU8dgcSOHF50Bz9Mf4yfLs1g/Q+MlBS49VafaXDaNPVmAezdu5clS5awePFi3nzzTQoLCwHIzMzkwQcfJDs7m0suuYSUlJTwMTk5OTGqrTSZQ1T1WoVuDwTv64RPwf45qlKyjwaUmVNEJKylA67ewOFa9h8CejXi2ND9IX/FfyzswCeQfQj4k5nd4Zz75VnVWERaNecc7+56lxc+eoG17/6WO1aV8conHeh9DCqGDeHdB57hp4eu4+W/dqGsDKZMgWefhVtuge7dY1372Dp+/DjLli0L92Ll5fnftFJTU8nOziY7O5tLL72UtDR1X7QZJ/E/UUb2XoXGpBgwFlhIVXA1EUhq+WqKiLQm5pw7c6mmejCz08B/Oucej9r/DD7bYJ0BoJktAbo652ZE7c8GFgOXOOferePYDsAqYIBzbnAdZe4H7gfo37//lN/85jcNv7BmdOzYMbp27RrrakgUtUv8iW6T4lPFvHHgDZbs+TtTPtrDl9YmMHd7gEBCApsvuJzn+36Vlz+5gF27UkhOruDSSw9wxRX7yMg4Vs+jtG2BQID8/HzWrFnD2rVryc3Npby8nE6dOjFx4kSmTp3KlClTGDFiBAkNXFxMr5X4E26TSkjZmUK3jd3ovqk73TZ1o+v2rljAd+eW9SujdGwpR8cdpXRMKaWjS6nsWhnj2rddeq3EH7VJfIqndpk7d+5a59zUM5Vr6R6uw1TviQrpRe29V5EOAUPqODZ0f62cc5Vm9jLwHTMb6JzbV0uZ54DnAKZOnermzJlzhuq0jJycHOKlLlJF7RJf5syBkpISPlibwqv5r/L8R8+z8f2/c8+aAGs+SaTPUagYnM7bd/verD++lszp0zBjBjzxBNx0U0dSUtJojxNNdu3aFR4m+NZbb1FcXAzApEmT+MpXvkJ2djYXX3zxOWcU1GslTpThk1rcCmXFZXQe2xnWAqFB+j3xvVY3E17vqvPAznSmM/3oF5s6tzN6rcQftUl8ao3t0tIBVx5+Lla08cCGBhx7rZklR83jGo9ff35r7YeFhWZgtFyXnog0u4ALULr3NxRlvM6w777BtI8K+T/rkpizJYBZAgfm38K3B/0zP1s+hm0/N3r1gi98wWcazMqKde1bXmlpKTk5OeFhgps3+9UyBg0axBVXXBEeJqjFh1upcnzq9TwgN+J2KxDwRZIsyZf7PFVJLUaieVciIs2kpQOuV4DvmtkI59x2ADMbBlwEPNaAY7+JT67xP8FjO+J/j1tcX4bCYLkbgV3Ouf2NvAYRibHSU6W8uf1NXs1/ldc3/Y1+8w7w+Y3GQ/+eRN9DUDmwL0tu/RbPHb6BV5YkU1EBl1zie7Ouvx7a0/JPFRUVrFmzhiVLlrBkyRJWrlxJRUUFycnJzJ49my984QtkZ2czfvx4TJlBWo9K/GqUkUFVHn61yfJgmQQgA8gCbsH/3PkfcOT0EXquVLpNEZGW0tIB10/xCSz+Ymb/gu9tegqfYPbZUCEzGwpsA550zj0J4JxbZ2a/Bf7LzBLxCTEeBIbj16UPHXsrPsX834Pn7Y9fIHkKcGtzX6CINI9th7bxav6rLP3wT1SsXM60Tyu4Y28HflRgdD7lf7zfN+d6nhr4dZ5fMZadvzL69oUvfxk+/3kYOzbWV9Bytm/fHh4m+Pbbb1NSUoKZMXnyZB599FGys7OZOXMmSUnKdhD3AsAuavZYbcQPEwwZjg+srgjeZuITXEQvJHwTrMtZxxzmNG+9RUQkrEUDLufccTObB3wfeBE/zO8t4CvOuciZ6oZP5x49wOFu4BngafyI84+Bhc65DyPK7ABSgf/Azxc7gc+ztNA590aTX5SINIvyynKWf7qMNW//kpKc1xix8QDZu+Efivz9rkMCgUmT2DnzSh75zVRWVU6jcFl/AgGYPx/+/d/h6quhPcQUJSUlvP322+FerG3btgEwePBgrr/+erKzs5k/fz59+/aNcU2lTg7Yhw+mIgOrDVStbwWQjg+m5gVvs4BxQHzMHxcRkVq0dA8XzrldwPVnKPMpVXOuIvefBL4a3Oo6dhX+o0hEWpmi/Tv48JWfcPitV+m1bjPTdlUwN/gr/snuyVROv5DNmTezLGEOSwtGsvS9jhQEf25JtNN87TG4914YOTJml9AiysvLWb16dbgX6/333ycQCNC1a1fmzp0bTnYxevRoDROMRwepORQwFyiJKJOKD6buDt5m4WcsaySgiEir0+IBl4gIAM7htm9n52u/pujNv9J9bS4j95xggYOAwd4hvTh8xTR2ZN3DqsAclm5KZdkyY/9if3j//jB7tt9+/nM4ffoE3/pWp9heUzNxzpGfnx8OsN555x1KS0tJSEhg2rRpfP3rXyc7O5vp06eTmJgY6+pKSAk1g6o8oDCiTC+q5liFhgJmghIDioi0HQq4RKRlnDwJa9dy+t2lFL/9V1I++ITuR04yDOjdCTaM7E7B7fMoHXsfO+xKln2Qwrs5UPR7f3h6uh8qeMklPsgaPRpCnTe/+x2Ul9fxuK1UcXExb731VjjI2rVrFwDDhw/ns5/9LNnZ2cybN49evc60Zrw0u2P4oX/RwdWeiDJd8YHUlVQNBcwEBlLLeA4REWlLFHCJSPPYswdWrIAVKyh7N4fEj9fToaKSTkBpb3hnREdKzp/K8VEPcKzyRj5c3YPlf4WSF/3hw4bBokVVvVjDh1cFWG3R6dOnWbFiRTjAWrt2Lc45evTowbx583j88cfJzs5mZFsfLxnPQmtZRc+z+jSiTGf80L95VAVVWfhVJNvw/18REambAi4Rabzycvj443CA5VaswHbvBqAs0Vg9yLHyQtg+diinh3yRDqXXUbB+BCtfTqC01J8iIwNuuMEHV5dcAkNqW+a8DXHOsXHjxnCAtXTpUo4fP06HDh2YPn06TzzxBNnZ2UybNo2OHfVW3aKi17IKBVcRa1mRCIwBpgP3UjXPajg+5ZOIiEiQPsVF5OwdPAgrV/oAa+VK+OADP2QQKOqbzLJB5eRkwuq0ZFyf++h7+CZKNp/Hul8lh4oxfjzcfntVgDVwYAyvp4UUFhby5ptvhrMJ7tnjx5yNHj2az33ucyxYsIA5c+bQvXv3GNe0najEL0ASPRRwC/WvZZUV3KfpciIi0gAKuESkfpWVsGFDuPeKFStg61YAAokd2ZcxkOUX9+SPvcp4N7ULJ09fxtCSOynfPoNt7/Tl1CnDDCZOhPvu88HVJZdAvyZMCpCTAzk56yDO1hYqKytj+fLl4V6sdevWAdC7d2/mz5/PggULyM7OZujQoTGuaRsXWssqOnlFXWtZXUVVYDWGmmtZiYiInAUFXCJSXUkJrF5d1YO1ahWhcX8utR8HJo5kxay+vJSynVc7n+DUvvEMKL6ZjrnzKdySTmVFAkcTYPJkeOghH1zNmgXtIbeDc47169eHA6xly5ZRVlZGYmIiM2fO5JlnniE7O5vJkyfToYPGnTU5B+yl9syAxyPKpeODqch5VlrLSkREmokCLpH2zDnIz6/quVq5EvLy/P6EBJg4kdKbrmb1kA78uvtOXjq4gVM7+tIx/1K67buc8k9HQmUCRR1h2jS47RE/RPCii6C9jIrbt29feIjgkiVLOHDgAADjx4/ngQceYMGCBVxyySV07apv800qtJZVdGAVuZZVf3wwdS9VPVZay0pERFqYAi6R9uT4cT/fKnL+VXGxv69nT5gxg8CNN7BhVE9e7rqTlzevYeOa4/D32XQqeJTTe8eBSyChkyPrQmP2rT7AmjEDUlJie2kt5cSJEyxbtizci5WbmwtAv379uPTSS1mwYAGXXnop6enpMa5pG3EYH0hF91odjCijtaxERCSOKeASaaucg127qgKrFStg3To/Jwtg3Di4+mqYOZOS88fxWocdvLzmPRa/vZPjf+sLO++Bwu8B0LlLgJkzjNn3G7Nnw4UXGp3bybyWQCDAunXrWLx4MUuWLGH58uWcPn2apKQkZs2axR133EF2djaTJk0iISEh1tVtvUJrWUX3WNW2llXkHKssYABKuS4iInFLAZdIW3HqFHz0UfXhgXv3+vtSUuDCC+Hxx2HGDNyFF7Khcj8vLV/KH1/fy+b/DsCnl0DxbQB0Ti7nootg/lzfgzV1agKdOsXw2lrY7t27w0ME33zzTYqKigCYOHEiDz/8MAsWLODiiy8mOTk5xjVthU7i17KK7rH6NKJMaC2r+VRfJFhrWYmISCukgEuktdq/v3rv1dq1PugCv0rw3Lkwc6Yf7zdhAiddBb95dxW//OtuVn/zbY5vmQIlXwQgKeUkU2eUcWV2gLlzEpg8OZH2tPRTaWkpS5cuDfdibdq0CYABAwZw2WWXhYcJDhgwIMY1bUVOAfnQ7+1+8DZV8622UX0tq7H4taw+T1VwpbWsRESkDWlHX6lEWrGKCli/vnqAtWOHvy8pCaZOhYcfrgqwBgzAOXj3o/389A9beOfh5ezNHYk7MgeATl2Pcv60Yq5eUMJVn+nJxIld6NChS+yur4VVVlaydu3acIC1YsUKKioq6NKlC7Nnz+a+++4jOzubrKwszNSlUq8ifI9V9LYDCEAmmVVrWU0EbqWqx0prWYmISDuggEskHh065AOrUHD1/vs+4QXAoEE+sHr4YR9cnX8+JCXhHKzPreR/n9/Ja29+wpYPB1JxdAAwgISuRYw8bxcL5pdyzzUjOX9idxIS2kkawaAdO3aEhwm+9dZbHD58GIDJkyfzyCOPsGDBAmbOnEnn9jI57WxU4AOoyIBqc/C2OKJcZ2A0MAW4DfgdHA0cpfvH3bWWlYiItFsKuERiLRCATZuq914Fh7TRoQOcdx7cc09V79WQIWBGIACffAJv/OgEf3x9Nx+/34NTR7sDI6B7Af3Gr2PWrAD3XDuSyy4cSUJC35heZks7cuQI77zzTrgXa2twseb09HSuueYaFixYwPz58+nXlCswt3ZHqQqkIrd8oDyiXH/8UMAb8AsDjw1uQ6g+FPCb8GHOh8zpPKfZqy4iIhKvFHCJtLTSUt9jFUpusWqVX2wYoE8fH1jdeae/nTo1nG+9osInGcx52fH3N0tZtSKRk6WdgWToeYBOGa8yY3opNy8ayB2zL6Z3cnbsrjEGKisrWbFiRTjAWr16NZWVlaSkpDB37txwsosxY8a072GCAaCA2ocB7oso1xEYiQ+krqQqqBqDT8MuIiIiDaKAS+RszZnDeSUlPvo5E+dg+/bqvVfr1/teLTPIzISbbqrqvcrI8PuB8nJYswaWLoV3cgIsXx7gxPGOgEHvfZCxlMETd3Bldg9umzWLC9NuokNC+8g0cPToUfLy8sjNzWX9+vW8+OKLHD16lEAggJkxbdo0HnvsMRYsWMD06dPp1J5SLIacBLZQcwjgZuBERLkewDjgM1QFVWOBEWh+lYiISBNQwCXSlE6e9NkCI1OzFxb6+7p3h+nT4ZprfIB14YXQo0f40LIyeP9dH2AtWwbvrQhw8oRf18lSN+PGvUPiiFXMnm1cf8EMLs+4nCE9hsTiKltMWVkZmzZtIjc3Nxxc5ebmsmvXrnCZlJQUEhIS6NWrFz/5yU+YN28evXv3jmGtW5ADCqm9t2pn8H7wqdSH4QOp2VQPrFJRqnUREZFmpIBLpDEKCqp6rlas8OtglQcnu2RkwGWXVfVejR/v52QFnTgBK9+qCrBWrXKcOmVgAbqkbeVk1uswbClpE7Zz1fkzuGL0FcwddjddEtteNsHKykq2bdsWDqhCW35+PpXBhZoTExMZN24cF198MVlZWeFt6NChJCQkkJOTw5w5c2J7Ic2lHJ9OvbbA6khEuWT8kL8ZwN1UBVUZQNv7byMiItIqKOASaQjn4MAByM2FggKSi4t98ordu/39XbrAtGnwyCM+wJo+HaKSMZSWwnvv+eBq6VL44AMfm1mCo/fwT0m48HUY9Bo2dAVTxoxjUcYiFmU8QVZq20lN7pyjoKCgRo/Vxo0bKSsrA8DMGDlyJFlZWdx4443hwCojI4PExDY+xu0wNYcAbsIHWxUR5QbhA6nbqJpXNRZIx6dgFxERkbihgEsk2tGjkJfn51rl5lbdFhWFi3RMTISFC33P1cyZMGkSRAUDJSWwfLkPrpYuhQ8/hMpK6NjRkTZmHwMXLKWg10sE0pcR6NmRa0Yt5IrRt/CZkT+nT3Kflr7qJldcXFyjxyo3N5cjR6q6ZNLS0sjKymLevHlkZWUxYcIExo0bR3Jycgxr3swqgV3U3ltVGFEuEd8zlQlcT/WkFe0ro7+IiEirpoBL2q9Tp2Dz5pqB1c6dVWW6doWsLLj2Wn87YQLXXO0oCnRn+a+nVjtdURG8+25VgPXxx75jrFMnx5hJJUy56QMKev2KvT1fZmenE2T2y+TR0VewKONrzBg8g44JrfPleOzYMTZs2FCtxyo3N5f9+/eHy/Tq1YusrCxuu+22cI9VZmZm255rdYzqSStC2xbgVES53vikFZGZAMfi51y1zv8SIiIiEkEf59L2BQI+U2BkUJWb64Ot4PwgEhNh7Fi46CJ44AGYMMEHWEOGQEL1MVolCesgUMGBA1XB1dKlvlMM/OjCKRec4uoH8ihO/TMfdfgJ691BkjokMW/4PL4++j+4PONyhvUc1rLPQyOdPn2azZs3V+utWr9+PTt27AiX6dKlC5mZmSxcuJAJEyaEg6uBAwe2mWGR1ThgL7UPA9wdUS4Bn/VvLFXZAEPDANvX8mgiIiLtjgIuaTucg/37qwdW69fDhg0+Q0XIiBE+oLr22qrAavToGkMCI4Wmb+XmwuYTgzlSkcyAAf6+rl1h5kWO2VfspSztDT7u+DOWF64EIK1bGreNvo5FGYuYN3weKZ1SmvMZaBKBQIAdO3bUGA64efNmKir8RKKOHTsyZswYLrjgAu65557wcMBhw4bRoUMbTE1/CthK7cMAj0WU64YPpKIzAY4CklqwviIiIhI3FHBJ63TkSO3zrIqLq8oMGOCDqQce8LdZWT5TYNeudZ42evpWaDt4sKpMYmIfOnct56nHy+g0YhUbE1/i9R1/Y/Gx/dghY3r6dJ6Z9wyLMhYxsf/EuO3Zcc6xb9++GgksNmzYwImIAHX48OFkZWVx9dVXh3usRo8eTVJSG4wgiqg9qNqBXzA4ZDA+kIrMBDgWGIhSrIuIiEg1Crgkvp06BRs31hwOGLEOE926+WDq+uvD86zIzKyRJbCu04a29eurn7ZrV3+aq6+GzEzHgBFFWP9c/vlHayjs9hpPViynfFM5PZJ6sHDUQhZlLGLhqIX0S6n7cWPl8OHDNZJX5ObmcujQoXCZAQMGkJWVxf333x8eDjh+/Hi61hOgtkoV+AAqegjgJiAiXicJ31s1BfgsVUHVaKCNPSUiIiLSfBRwSXyorKw5z2r9esjPrz7Patw4mDWrKrAKzbOqoxepshK2baseVOXm1n7aiy/2gdXgUSUkDNjAwcS1bCjKJe9gHi8X5nHko2B2vVGQdHQMX77wy1wx+gpmDp5JYof4SFd+4sQJNm7cWGM44J49e8JlunfvzoQJE6qlXM/KyqJv3zY2megoNQOqTUA+fl2rkFR8IBWZCXAsMARog6MjRUREpGUp4JKW5Rzs21f7PKuTJ30Zs6p5VjfcUBVYZWTUOc/KOdizp2ZgtWEDBJd3wgxGjvSnuuEGGDa6lMSBmzmSsoZNh9aTezCXNwrzKN5YDBv9MX269CErNYvbJtxGZmomWalZZPbLZP3762O6yG55eTn5+fk1hgNu27YN5xwASUlJjB8/nnnz5lVLYJGenh63wxzPigMO4ZNT7Ia0xWnwO6p6rvZGlO2An0c1lurZAMcAvVqy0iIiItLeKOCS5lNSUvs8q4hhbAwc6COgBx+sPs8qpe7kEocO1RxhmJvrHy5k0CB/qi9+EUaOPUHSwHxO9vyQ/NJ15B3M46eFuRzYfgC2+/I9knqQmZrJ9eOuJzM1k8x+PrhKTUmNaXASCATYtWtXjR6rTZs2cfr0aQASEhIYPXo05513Hrfffns4gcXIkSNbdwKLY4SDKXbj167aHbVF5ELJIAN64FOsZ1O9t2oE0Knlqi4iIiISooBLGq+srOY8q/XroaCgqkz37j4CuvHG6vOs6hnGdvy476GK7rXat6+qTM+e/nS33goZY8tITtvB6T4fsvP0R+QW5vK7g3kU7C0I93akJKaQmZrJ5RmXh3urMlMzSeuWFtPAyjlHYWFhjR6rvLw8jh2rSoM3ZMgQJkyYwGWXXRbusRo7diydO3eOWd3PyWmggPqDqcNRxxgwAJ+wYgJwefDv4Pbenve46NqLlLRCRERE4ooCLmm4yAlR0fOsAsEUbp06+QlRc+ZUn2c1eHCd86zKy2HLlpqB1fbtfqggQOfOvuNrwQIYM66cboN3Utl3HXttDRuK8nitMJf/Pvxp+Et6546dGdd3HHOHzQ33VmWmZjKkxxASLKHWerSUo0eP1khesX79eoqKisJl+vbty4QJE7j77rurJbDo0aNHDGveQJXAfurvndpfy3G98fOmhgIXUxVMDQneDqLeXqrynHIFWyIiIhJ3FHBJTc7B3r21z7OKnhA1YQLcdFP1eVYda/9vFQjAzp01hwNu2uSDLoAOHfySWJMnw223V9BzyB7ov56iTu+z4dB6VhTm8eLhbQTyA5APiQmJjO07lunp0/n8+Z8Pz7Ma3nM4HRJiO5yurKyMTZs21RgOuCsiFWLXrl3JysrimmuuCfdYTZgwgdTU1BjWvB4On8mvvmBqDz4TYKQUqgKnidQMptKDZURERETaGAVc7d3hwzW7lnJz/f6Q0ISoL32pqtdq3DhITq71lM5BYWHNU+blQcToOIYO9adbeFmAfsP3Y6l5lHRdzZYjn5BbmMufDuVTsbsCdkMH60BGnwwmDZjEZyd8NjwccFTvUTHPEFhRUcG2bdtqDAfMz88nEOz569SpE2PHjmXWrFnVMgMOGTKEhITY9rhVU0r9w/x2AyejjkmkKoCaRc1gajDQE/U+iYiISLukgKu9OHmy9nlWEenC6dHDR0A331x9nlWfPnWe9ujRmmtZ5eZCxOg4+vb1p7r77gD9RxTRsf8mjvVczbYTH5F3MI8lRZs4feA0HADDGNl7JJn9Mrlu3HXh4YCj+4wmqWPsFtp1zlFUVERBQQG7d+/m1Vdf5YUXXggvFHzq1CkAzIxRo0aRlZXFTTfdFB4OOGrUKBLryLDYYk5R/7ypXcCRqGMS8Iv5DgYmAVdQPZAajE+rHkcxo4iIiEg8UcDV1lRWwtatNQOrrVur5lklJfkeqnnzqs+zSk+vc55VWZkf+lff+sNVCwU70kYdptPALZzqs4ZPy98n72AePzu4kZNHToa/1A/rOYzMfpksHLkwPMdqbN+xJCfW3nPWXCoqKti3bx979uyhoKCAgoKCGn/v2bMnnBUwJD09naysLObPnx/usRo3bhzJdfT8NatKYB/1B1OFtRzXFx80DQcuoWYwNQjfgyUiIiIi50QBV2sVWngqOuX6hg0Q7G3BDEaN8gHVLbdUBVajRtU5z6q2vBh1LxTsGDLqKF3StnO671r22iryitfz24MbOHbqGHwKfApp3dLISs1iztQ54TlW4/qOo1tSt2Z/mk6ePBkOnmoLqPbs2cP+/fvDQ/9COnfuTHp6OmlpacycOTP8d+j2wIEDXHnllc1ef8DPmyqi/mBqLz7oitSNqsDpPGoGU+lADGJDERERkfZEAVdrEFp4KjoKilx4Ki3NB1Pz51efZ9WlS62njI7XQltdCwVfdtVxug7eQUXfdRxMeo+Nh9fz94N5lJSV+J6VfdA/pT+ZqZncc9494bWsMlMz6dm5Z5M/Jc45jhw5Um+vVEFBAYci1/wK6tGjB+np6aSnpzNhwoTw35EBVe/evetNE5+Tk9N0F3OU+oOpAqAs6pgkfMA0GJhDzWBqCH5NKhERERGJKQVc8WzcOC7ZuhUqIlK+9ejhg6lbb60+z6p37zpPU1xc+zyrIxHzdUJ5Me65/yQ9Bu8i0G89h7utYPPRD1l+MI8/nyjy2emKoXeX3mSlZnFr1q3V1rLqm1z3mlpnIxAIcPDgwTqDqNDfx48fr3Fs//79SUtLY9iwYVx88cXVgqjQbdeuXZukng1SRtW8qdqCqd34gCtSAn4o32BgCnANNYOpfigJhYiIiEgroIArnnXqREW3bnR6/PGq4YBpaXXOs4pcKDiy56q2hYJvuPk0PYcUkNB/A0d7rGBb2Qd8UpjL4mP74ThwHLondSezXybXjr02HFRlpWbRP6X/OS8SXF5ezr59++rtldq7dy/loTzxQR07dmTQoEGkp6czadIkFi1aFA6iQoHUoEGD6NSpnoWamloFVfOm6gqmDtZyXD984JQBzKNmVr+B6JUpIiIi0kboa108+/hjVuTkMGfOnGq7QwsFRw8HrG2h4HmXltNn6H4ZzGv5AAAWWUlEQVQ6DtjE8V6r+DSwgg0H81h+dLcPGPZAcmGyT14xaiFZ/bLCwwHTu6efVWB1/PjxcIKJugKqAwcO4EKVDOrSpUs4cJo1a1aNIX7p6emkpqY2b/r0U/hFkw8DhyJu6/h7+vbpvscvEHWe7lQFTlOpfb2pzs13GSIiIiISXxRwxbHZs6GwcDLf+U714YCbN9dcKHjS+ZVkX1dI0sAtnOz1AXsTl5NX9Am/KvkUh4MiSDqcxLh+45g9bHY43Xpmv0yG9hxKgtUdzDjnOHz4cL3JJwoKCiiJnFMW1KtXr3DgdP7559folUpPT6dnz57n3GNWvaL44XkNCJhq7Ks5OrGK4deR6g308lvJ5BIGTBtQc72p7o2/DBERERFpOxRwxbGCAti+vTtXX+3/PXQojM8McMGcYpLTtnGq71oOJC1j05F1/PnQVgIuAEch8VgiY/qO4YK0C7jn/HvCwdWIXiPokNCh2mNUVlZyYP+BMyafOHmy+mq3Zkb//v1JT09n5MiRzJ49u0avVFpa2rmlSD9F/cFRffuie5widcEHTL2D23D8HKneEfsj7w/93YMa60xtytnEgDkDzv7aRERERKRdUcAVx7r2L6R38jaue3AjxV2XsfnYBywp3kJFoAJOQYe9HRjVexQTUidwS+Yt4bWsMnpnkNghkdOnT7N3714KCgr48JMPeaXglRoB1b59+6iITMoBJCYmhudLTZ48mSuvvLJGr9TAgQPrX8g3gO9t2k/DA6bQ3yfqeVKMmsHRCGoGSdFBVC98wCUiIiIi0oIUcMWxE7dcxKHDW3n+oDGiYgSZqZlcM+YaMlMzGZEyguQTyRw8EMzmt3IP7xS8w4sFL4YDqsLCmivdpqSkhAOnuXPn1sjgl56eTr9+/armS5VRPSAqArZw5iF7JdTf25RM9YBoJDCNugOm0L7u1OhtEhERERGJVy0ecJnZYOD7QDa+v+JN4CvOuV0NOLYz8BRwO35WzTrga865ZVHlEoCvAQ8AA4DNwJPOuT804aU0uxcufoFnn3+WwT0HU/hxIQUFBfy54M/8eM+PORKZ0z2od+/e4Z6oKVOmVPVKDUwjvUc6aV3S6FHRAzts1YOk7cAaau+BOlnjYaokUNV71BvoA4yi7p6myN4mJY4QERERkXagRQMuM0sG3sbP0rkLn+bgaeAdM5vonKsvdQHA88Ai4FF8mPAl4A0zm+GcWxdR7ingH4GvA2uBW4CXzewK59zfm/KamtO9t9xLfn4+ZsbAgQNJG5jGmKFjmH/efNK6pZGenE5aYhrpHdJJq0yjS2mXqkBpHf6ZDvU2uXoeKJnqAVEG9QdMoVv1NomIiIiI1Kule7juw8+4GeOc2wpgZp8A+fjeqO/VdaCZTQI+C9zjnPt5cN9SIA94ErgquC8VH2x92zn33eDh75jZKODbQKsJuFb/02oKv1nIiMoRJB5KhL31FA71NoUCoz74wKm+gCl0m9R81yAiIiIi0p61dMB1FbAqFGwBOOd2mNl7wNXUE3AFjy0HfhtxbIWZ/QZ4zMySnHOngM8AnYBfRh3/S+AFMxvunNvRNJfTvHql9aJieAWJoxPPPEyvG+ptEhERERGJMy0dcGUCf6llfx5wYwOO3eGci85hl4cPsEYF/87ED1ncWks5gPFAqwi4uAzyuuTVWPhYRERERERah5buE+mNn2UU7RC+n+Zcjw3dH7otcc5Fz1qKLiciIiIiItKsYpEWvrb0DdaA46yBxza0XPU7ze4H7gfo378/OTk5DahS8zt27Fjc1EWqqF3ij9okPqld4o/aJD6pXeKP2iQ+tcZ2aemA6zC19zD1ovbeq0iHgCF1HBu6P3Tby8wsqpcrulw1zrnngOcApk6d6uJlGF9OTo6GFMYhtUv8UZvEJ7VL/FGbxCe1S/xRm8Sn1tguLT2kMDTHKtp4YEMDjh0eTC0ffexpquZs5eHz7o2spRwNeBwREREREZEm0dIB1yvAdDMbEdphZsOAi4L3nenYRCKSa5hZR+BmYHEwQyHA6/gA7Lao428HcltLhkIREREREWn9WnpI4U+Bh4C/mNm/4OdaPQXsBp4NFTKzocA24Enn3JMAzrl1ZvZb4L/MLBGfafBBYDgRwZVzrtDMvg88bmalwIf4oGwePvW8iIiIiIhIi2jRgMs5d9zM5gHfB17EJ7J4C/iKc+5YRFEDOlCzB+5u4BngaaAn8DGw0Dn3YVS5rwPHgC8DA4DNwE3Oub827RWJiIiIiIjUrcWzFDrndgHXn6HMp9SSVdA5dxL4anCr7/hKfFD29DlXVEREREREpJFaeg6XiIiIiIhIu6GAS0REREREpJko4BIREREREWkmCrhERERERESaiQIuERERERGRZqKAS0REREREpJko4BIREREREWkmCrhERERERESaiQIuERERERGRZqKAS0REREREpJmYcy7WdYg7ZnYQ2BnregT1BYpiXQmpQe0Sf9Qm8UntEn/UJvFJ7RJ/1CbxKZ7aZahzrt+ZCinginNmtsY5NzXW9ZDq1C7xR20Sn9Qu8UdtEp/ULvFHbRKfWmO7aEihiIiIiIhIM1HAJSIiIiIi0kwUcMW/52JdAamV2iX+qE3ik9ol/qhN4pPaJf6oTeJTq2sXzeESERERERFpJurhEhERERERaSYKuGLEzAab2e/N7IiZHTWzP5rZkAYe+y0zW2xmxWbmzOxzzVzdduFc28TMpprZc2a2ycxOmNkuM3vJzIa3RL3buka0y1Az+4uZ7TSzk2ZWZGY5ZnZZS9S7LWvM+1fUeR4Pvoctb456tieN/ExxdWznNXe927rGvlbMbJyZvRx8/zppZpvN7MvNWee2rhGfKU/U81opa4m6t2WNfA8bYmb/E/z+dcLMtpjZ02aW0tz1bigNKYwBM0sGPgZOAf8COOBpIBmY6Jw7fobjS4F1wHbgTuBu59wvmrPObV1j2sTMvgvMAF4C8oA04F+BVOA859zu5q1929XIdskEvgrkAAVAd+A+YBFwvXPuj81a+Taqse9fEecZAXwCHAfynXMXN0+N274m+ExxwC+AZ6Pu+sQ5d6LJK9xONEG7TAXexr+HvQAcATKArs657zVfzduuRn6mpAPpUbtTgNeBPznnbmqWSrcDjWyXFOAjIBF4AtgFTAO+CbzinLu5WSvfUM45bS28AV8GKoFREfuGAxXAVxtwfELwdhT+P+XnYn1NrX1rTJsA/WrZNxQIAE/G+tpa89bY10ot5+sI7Ab+Gutra61bU7UJ8Ab+C34OsDzW19Watyb4THHA07G+jra2NfJzJQH/A96fYn0dbWlrhs+UO4Kvn0WxvrbWvDXytbIg2AYLovZ/O3h8cqyvzzmnIYUxchWwyjm3NbTDObcDeA+4+kwHO+cCzVi39uqc28Q5d7CWfTuBg/jeLjl3jXqtRHPOVeB/JS5vshq2P41uEzP7LDAZeLxZatj+NOnrRJpMY9plDjAeUE9W02rq18pdwAH8D0hy7hrTLp2Ct0ej9pfgf7iwpqpkYyjgio1MILeW/Xn4N1hpeU3aJmY2Dj+kcGMj69XeNbpdzCzBzDqa2QAz+1dgNPDjJqxje9OoNjGzXsD3gX9yzh1q4rq1V03x/vWgmZ0Kzn9428xmNV312q3GtEtoiG1nM1tlZuVmVmhmPzCzLk1ay/alyT7rg0MM5wIvBX/Mk3PXmHZ5E8gHvmNm482sq5nNw/ea/cQ1cJh7c1PAFRu9gcO17D8E9GrhuojXZG1iZh2Bn+B7uJ5vfNXataZol3/H92jtA/4JuMU591bTVK9damyb/AewBT9nSJpGY9vkl8AXgUuB+4E+wNtmNqepKthONaZdBgVvfwssBrLx72WfB37VVBVsh5ry+9cd+O/R/9PYSsm5t4tzrgz/A0VoGG4p8BbwN+Chpq3muesY6wq0Y7VlK4mLbs92rKna5EfATPyY7treQOTsNLZd/gv4DTAAn2TmV2Z2g3Pub01RuXbqnNok2GtyJzDZBQfZS5M559eJc+6OiH++a2Z/wf/a/DRVPS1ybs61XUI/iP/SOfeN4N85ZtYB+LaZjXfObWiSGrY/TfVZfyfwkXPuk0bWR7xz/VzpjP9hIhUfBO8CLgC+gZ/D9WAT1vGcKeCKjcP4aD5aL2qP8KX5NUmbmNm/4X8hvss5t7iJ6taeNbpdnHMF+CyFAH8zsxzgu/hfv+TsNaZNnsX3+haYWc/gvo5Ah+C/TzrnTjVZTduPJv1Mcc6VmtmrwL2NrVg715h2KQ7eLonavxifDOA8QAHX2Wuqz/oLgLHAV5qoXu1dY9rlXvycx1HOuW3BfcvM7AjwnJn9xDn3cZPV9BxpSGFs5OHHq0Ybj95AY6XRbWJmXwceA77snHuxCevWnjXHa2UNPsOnnJvGtMk44Av4D9DQdhEwPfh3XPwS2Qo1x+vEqP0XZ2m4xrRLXvA2ug1Cv/greda5aarXyl343hMN72wajWmXCcDhiGAr5P3g7bhG1q1JKOCKjVeA6cF1aAAws2H4Lx6vxKhO7V2j2sTM/gE//ObrzrkfNlMd26Mmfa2YWQJ+iFT0G7M0XGPaZG4t28f44Wtzgd83fXXbhaZ+nXTHr1e3uonq1141pl1ew69JtDBq/2eCt2uaportTqNfK2bWCbgF+HttWYrlnDSmXfYDvcws+ofUC4O3e5qojo0T67z07XHDL5S3FViPT3d5Ff5Lx3b8goahckPxv6B8I+r42cAN+MmADj9n6AbghlhfW2vdGtMm+DfeAP4DcnrUNj7W19aat0a2yxPAD4Cbg6+Zm/HDcQL4xBkxv77WuDX2/auW8+Wgdbhi1ibAPwI/BT6LH5ZzV/A8p4FZsb621rw1wWf9/w3u/xY+ocljwEngF7G+tta6NcX7F3Bd8LvXdbG+nrayNfI9bBg+JfyW4PvXXODR4L41BNeujfWmOVwx4Jw7HkxZ+X3gRfwQgbeArzjnjkUUNaADNXsiv4n/AhnypeAWOkbOUiPbZGFw/0Jq/hq5FP8lRs5BI9vlQ/z4+luAHvhfwT7Gf4l8rwWq3yY1wfuXNLFGtslm4Nrg1gP/JeU94F7n3PvIOWuC18qT+IxrX8QHxvvwWT6fauaqt1lN9P51Fz57nuYBN5HGtItz7lMzm47/kfVpoC+wG3gOeMbFydq1FowORUREREREpInpl0cREREREZFmooBLRERERESkmSjgEhERERERaSYKuERERERERJqJAi4REREREZFmooBLRERERESkmSjgEhGRs2JmPzMzZ2bfi3VdzoaZPRFc66VdM7NhwediRKzrIiLSHijgEhGRBjOzLsCNwX/eZmYdY1mfs/R/gXYfcAHD8M+FAi4RkRaggEtERM7GtUB34O9AKrAwttURADNLinUdRESkdgq4RETkbNwFHAY+B5wE7qytkJlNMrM/mVmxmZ00s81m9nhUmWvN7D0zO2ZmR83sfTO7KuL+jmb2uJltMrNTZrbXzP7TzDpHlBkWHN74RTP7npkVmtkJM/ubmQ2LKOeCf349WN6Z2RPB+6aZ2e/NrCCirt8K9uZF1jfHzJab2aVm9mHwcXLN7JpzvP7rzGxV8DwlZvaymQ05UwNE1ONKM/vIzE4BXwze95CZrTSzQ8FzrjKzRRHHzgHeCf5zScRzMSeizH1m9rGZlZlZkZk9b2a9z1QvERGpXWsaCiIiIjFkZoOAS4HnnHMHzezPwHVm1ss5dzii3AVADrAV+D9AAZABTIwo8zDwA+DP+CDuGDAZP9wt5JfAlcB3gBXAOOCpYJnro6r3OLAOuBvf8/YtYLGZZTrnyoEZwErgF8CzwWMKgrdDgsf+AigFMoFv4Ifc3RL1OCOB/wf8G1AEPAL83szGOue2nsX1fwH4b+DnwJNAN+AJYKmZTXTOlVK/0fjn7ylgO3AouH8Y8DPgU/xn/JXA38zscufca8CHwJeAHwP/AHwQPG5DsF7fDl7TD4BHgTTgaSDLzGY65yrPUC8REYnmnNOmTZs2bdrOuAFfAxwwI/jvzwT//YWocsuA3UByHefpjg9s/ljPY80KnvvOqP23BfefF/z3sOC/NwAJEeUuCu6/N2KfA54+wzUaPlC5HQgAfSLuywHKgYyIfalAJfDPZ3H9XYEjwAtR+4cBp4GvnKGOOcG6nXeGcgnBa1kM/CVi/5zgc3FpLY9fCXwjan/oubwm1v8HtWnTpq01bhpSKCIiDXUnkO+cWxn895vAXiKGFZpZMv4L+kvOuRN1nGcmPuh4rp7HWogPPv4QHFrYMZigY3Hw/kuiyv/eORcI/cM59x6+Z2nGmS7KzLqb2XfMbBtwCh9UvYgPvjKiiuc75/IjHqcQKMT3kjX0+mfgg86Xoq6tANhUy7XV5lPn3LparmVKcDjlAaAieC3ZwJgGnDMbH6RF12s1cLSB9RIRkSgaUigiImdkZtOA8cB3zKxnxF1/BB4ys9HOuS1AL/yX9oJaThPSJ3hbX5lUoBN+qGF95wg5UEuZA/ghcWfyc/xQyW/ghxYeBy7AD7vrHFX2EDWdiijXkOtPDd6+Wcf9h+vYH2lf9A4zGwy8he/texjYhQ+6nsIPxzyTUL221nF/9HMuIiINoIBLREQa4q7g7deCW7Q7gX/BBwsB6g90ioK3aUBuHWWKgTL80MLa7I36d/9ayvTHB1B1CibguBp4wjn3/yL2T6jvuHo05PqLg7efA/Jquf9M87fAD/GLthDoAdzknAsHfMFet4YI1WsBtQd9xbXsExGRM1DAJSIi9TKzTvjkEauBx2op8n3gDjP7V+fcCTNbDtxuZk86507WUn4FvufqfuCNOh72dXxg18M591YDqnmDmT0RGlZoZhcB6fhEGSGngS5RxyUBHfBD7yJ9rgGPWcNZXH8pMMo59z/n8jh1CAVW4Wsxs9H4IY6RPW6ngrfRz8USfLA4xDm3pAnrJSLSringEhGRM7kCP5zsEedcTvSdZvYsPuPeHHzK8X8ElgIrzew/8V/2R+CTPDzsnCsNpkj/oZn9AXgJH4CcB5Q5537onMsxs1/jMwB+D3gfHwwMAy4HvhYcwhjSDfhzsC798FkE84H/jSizAVhkZq/je3D2Ouf2mtkq4BEz24fvfbuHhg1FrMuZrv+omT0K/NjM+gGv4ZNopAGzgRzn3K/O4XHfxA8h/N/g4w4EvokfWhg5Z3tLsNw9ZnYIH4Btds5tM7PvAD8yszHBaygDBuPnd/3MOfcOIiJyVpQ0Q0REzuQufED0ch33/xq/JtddAM65D/C9KruBH+IXSX6UiF4W59yPgBvxvVAvAX8AbgB2RJz3dnyq9BuAvwC/Bx7CB1LRc7b+DT/36BfA/4dPf/4Z51PChzyEn5/1V3w69PuD+28F1uLnbP0C2A98uc5n4wwaeP3PAlfhk1m8iA+6von/IbTeYZD1PG4ePovjUOAV4J/wPZLLosoV45+LSfig6gNgSvC+f8Y/L5cAv8M/71/DB6j5iIjIWTPnahsGLiIiEv+CixvvAO5zzv0strURERGpST1cIiIiIiIizUQBl4iIiIiISDPRkEIREREREZFmoh4uERERERGRZqKAS0REREREpJko4BIREREREWkmCrhERERERESaiQIuERERERGRZqKAS0REREREpJn8/+0BUOqVQoHkAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 1008x576 with 1 Axes>"
       ]
@@ -1396,20 +643,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[0.005328   0.002272   0.01023348 0.00691889 0.00364671]\n",
-      " [0.02048    0.008424   0.02575853 0.02238238 0.01329275]\n",
-      " [0.046816   0.018624   0.04589419 0.0443047  0.02873453]\n",
-      " [0.081288   0.032552   0.08197224 0.07967856 0.05297712]\n",
-      " [0.127056   0.04776    0.12811773 0.11500238 0.08143095]\n",
-      " [0.182616   0.066528   0.18657189 0.17785139 0.11732526]\n",
-      " [0.2438     0.0902     0.24372187 0.22823061 0.17043125]\n",
-      " [0.321096   0.11352    0.32128957 0.33853595 0.23178933]]\n",
+      "[[0.012528   0.0044     0.00634626 0.01358639 0.00729657]\n",
+      " [0.03768    0.014152   0.01844604 0.04208963 0.02187568]\n",
+      " [0.070736   0.025296   0.03592383 0.05184688 0.03967156]\n",
+      " [0.111064   0.03748    0.0743903  0.09538709 0.06074402]\n",
+      " [0.157216   0.050992   0.11727442 0.15333915 0.08939233]\n",
+      " [0.210904   0.066472   0.16957435 0.19105199 0.12422705]\n",
+      " [0.270336   0.088472   0.23676908 0.25936859 0.16822351]\n",
+      " [0.338856   0.104784   0.3172705  0.33473775 0.2099604 ]]\n",
       "\n",
       "Mean absolute errors:\n",
-      "0.081075\n",
-      "0.0021349209648225077\n",
-      "0.007180198058091217\n",
-      "0.04110651320827825\n"
+      "0.10215900000000001\n",
+      "0.029165652071375857\n",
+      "0.009856070579233042\n",
+      "0.06099110801160007\n"
      ]
     }
    ],
@@ -1434,7 +681,7 @@
     "        print(i, end=\" \")\n",
     "\n",
     "        # Create data\n",
-    "        train_labeled, train, test_labeled, test, df = dataWithUnobservables()\n",
+    "        train_labeled, train, test_labeled, test, df = dataWithUnobservables(beta_Z=1.5)\n",
     "\n",
     "        # Fit model and calculate predictions\n",
     "        logreg, predictions = fitLogisticRegression(\n",
@@ -1497,30 +744,28 @@
     "                                     'acceptanceRate_R', r / 10)\n",
     "\n",
     "        # Causal model - empirical performance\n",
-    "        #       \n",
-    "#         recidivated = test_labeled.dropna().result_Y == 0\n",
     "\n",
-    "#         released = bailIndicator(r * 10, logreg, train.X,\n",
-    "#                                  test_labeled.dropna().X)\n",
+    "        released = bailIndicator(r * 10, logreg, train.X, test.X)\n",
+    "\n",
+    "        #released = cdf(test.X, logreg, 0) < r / 10\n",
+    "\n",
+    "        f_rate_caus[i] = np.mean(test.B_prob_0_logreg * released)\n",
     "\n",
-    "#         f_rate_caus[i] = np.sum(recidivated\n",
-    "#                                 & released) / test_labeled.dropna().shape[0]\n",
-    "        \n",
-    "        percentiles = estimatePercentiles(train_labeled.X, logreg, N_sample=train_labeled.shape[0])\n",
+    "        #percentiles = estimatePercentiles(train_labeled.X, logreg, N_sample=train_labeled.shape[0])\n",
     "\n",
-    "        def releaseProbability(x):\n",
-    "            return calcReleaseProbabilities(r*10, train_labeled.X, x, logreg, percentileMatrix=percentiles)\n",
+    "        #def releaseProbability(x):\n",
+    "        #    return calcReleaseProbabilities(r*10, train_labeled.X, x, logreg, percentileMatrix=percentiles)\n",
     "\n",
-    "        def integraali(x):\n",
-    "            p_y0 = logreg.predict_proba(x.reshape(-1, 1))[:, 0]\n",
+    "        #def integraali(x):\n",
+    "        #    p_y0 = logreg.predict_proba(x.reshape(-1, 1))[:, 0]\n",
     "\n",
-    "            p_t1 = releaseProbability(x)\n",
+    "        #    p_t1 = releaseProbability(x)\n",
     "\n",
-    "            p_x = scs.norm.pdf(x)\n",
+    "        #    p_x = scs.norm.pdf(x)\n",
     "\n",
-    "            return p_y0 * p_t1 * p_x\n",
+    "        #    return p_y0 * p_t1 * p_x\n",
     "\n",
-    "        f_rate_caus[i] = si.quad(lambda x: integraali(np.ones((1, 1))*x), -10, 10)[0]\n",
+    "        #f_rate_caus[i] = si.quad(lambda x: integraali(np.ones((1, 1))*x), -10, 10)[0]\n",
     "\n",
     "    failure_rates[r - 1, 0] = np.mean(f_rate_true)\n",
     "    failure_rates[r - 1, 1] = np.mean(f_rate_label)\n",
@@ -1585,7 +830,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 74,
    "metadata": {
     "scrolled": false
    },
@@ -1599,7 +844,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1008x576 with 1 Axes>"
       ]
@@ -1613,20 +858,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[0.015496   0.005712   0.02127184 0.01525861 0.03124425]\n",
-      " [0.041512   0.01536    0.0420505  0.03878719 0.07901708]\n",
-      " [0.074816   0.026368   0.07148129 0.07542401 0.13066169]\n",
-      " [0.115816   0.040512   0.11178727 0.12729332 0.18676773]\n",
-      " [0.16204    0.052912   0.16180877 0.15743246 0.24149321]\n",
-      " [0.215856   0.064248   0.213719   0.20494553 0.29062086]\n",
-      " [0.275936   0.08228    0.27835584 0.27637565 0.32751767]\n",
-      " [0.34184    0.095128   0.34104192 0.35407834 0.35207248]]\n",
+      "[[0.014736   0.005968   0.02018619 0.01108025 0.01550995]\n",
+      " [0.041928   0.015832   0.04068303 0.04537702 0.04053749]\n",
+      " [0.074152   0.027184   0.07695947 0.07481968 0.07537307]\n",
+      " [0.115096   0.041808   0.11923079 0.11972134 0.11565205]\n",
+      " [0.16168    0.050184   0.15907669 0.17287885 0.16348179]\n",
+      " [0.215112   0.0646     0.21469533 0.20447695 0.21589226]\n",
+      " [0.275176   0.079216   0.27179524 0.27532854 0.27470643]\n",
+      " [0.33916    0.093392   0.34402456 0.327037   0.34278324]]\n",
       "\n",
       "Mean absolute errors:\n",
-      "0.10759899999999999\n",
-      "0.002407990604993744\n",
-      "0.00540544050753119\n",
-      "0.04951037047570979\n"
+      "0.107357\n",
+      "0.0031128409389901916\n",
+      "0.005813402284733342\n",
+      "0.0013270544948680205\n"
      ]
     }
    ],
@@ -1713,35 +958,33 @@
     "                                       r / 10)\n",
     "        #### Causal model\n",
     "\n",
-    "        recidivated = s_test_labeled.dropna().result_Y == 0\n",
+    "        released = bailIndicator(r * 10, s_logreg, s_train.X, s_test.X)\n",
     "\n",
-    "        released_for_bail = bailIndicator(r * 10, s_logreg, s_train.X,\n",
-    "                                          s_test_labeled.dropna().X)\n",
+    "        #released = cdf(s_test.X, s_logreg, 0) < r/10\n",
     "\n",
-    "        s_f_rate_caus[i] = np.sum(\n",
-    "            recidivated & released_for_bail) / s_test_labeled.dropna().shape[0]\n",
+    "        s_f_rate_caus[i] = np.mean(s_test.B_prob_0_logreg * released)\n",
     "\n",
     "        ########################\n",
-    "#         percentiles = estimatePercentiles(s_train_labeled.X, s_logreg)\n",
+    "        #percentiles = estimatePercentiles(s_train_labeled.X, s_logreg)\n",
     "\n",
-    "#         def releaseProbability(x):\n",
-    "#             return calcReleaseProbabilities(r * 10,\n",
-    "#                                             s_train_labeled.X,\n",
-    "#                                             x,\n",
-    "#                                             s_logreg,\n",
-    "#                                             percentileMatrix=percentiles)\n",
+    "        #def releaseProbability(x):\n",
+    "        #    return calcReleaseProbabilities(r * 10,\n",
+    "        #                                     s_train_labeled.X,\n",
+    "        #                                     x,\n",
+    "        #                                     s_logreg,\n",
+    "        #                                     percentileMatrix=percentiles)\n",
     "\n",
-    "#         def integraali(x):\n",
-    "#             p_y0 = s_logreg.predict_proba(x.reshape(-1, 1))[:, 0]\n",
+    "        #def integrand(x):\n",
+    "        #    p_y0 = s_logreg.predict_proba(x.reshape(-1, 1))[:, 0]\n",
     "\n",
-    "#             p_t1 = releaseProbability(x)\n",
+    "        #    p_t1 = releaseProbability(x)\n",
     "\n",
-    "#             p_x = scs.norm.pdf(x)\n",
+    "        #    p_x = scs.norm.pdf(x)\n",
     "\n",
-    "#             return p_y0 * p_t1 * p_x\n",
+    "        #    return p_y0 * p_t1 * p_x\n",
     "\n",
-    "#         s_f_rate_caus[i] = si.quad(lambda x: integraali(np.ones((1, 1)) * x),\n",
-    "#                                    -10, 10)[0]\n",
+    "        #s_f_rate_caus[i] = si.quad(lambda x: integrand(np.ones((1, 1)) * x),\n",
+    "        #                           -10, 10)[0]\n",
     "\n",
     "    f_rates[r - 1, 0] = np.mean(s_f_rate_true)\n",
     "    f_rates[r - 1, 1] = np.mean(s_f_rate_labeled)\n",
diff --git a/analysis_and_scripts/derivation_validation.ipynb b/analysis_and_scripts/derivation_validation.ipynb
index 4a3d8cb9b26e5a3c8abb26fddc90e60ca864bc50..a7e46fccb436910996e4edbe5057b69b1fea5121 100644
--- a/analysis_and_scripts/derivation_validation.ipynb
+++ b/analysis_and_scripts/derivation_validation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 80,
    "metadata": {
     "scrolled": false
    },
@@ -76,31 +76,31 @@
      "output_type": "stream",
      "text": [
       "0.0 % 10.0 % 20.0 % 30.0 % 40.0 % 50.0 % 60.0 % 70.0 % 80.0 % 90.0 % \n",
-      "Analytical: -0.029650894465306284\n",
-      "Estimated:  -0.03198900115570701\n",
-      "Difference: 0.002338106690400729\n",
+      "Analytical: 0.03709585053394618\n",
+      "Estimated:  0.035425483336914504\n",
+      "Difference: 0.0016703671970316747\n",
       "\n",
       "Values for P(y=0|do(r=1)) and P(y=0|do(r=0))\n",
       "\n",
-      "Analytical: 0.04081959604639732 0.0704704905117036\n",
-      "Estimated:  0.04128497107677725 0.07327397223248426\n",
+      "Analytical: 0.14973849934787756 0.11264264881393138\n",
+      "Estimated:  0.15289661989960157 0.11747113656268707\n",
       "\n",
-      "Average difference: -0.0005318273855650264\n"
+      "Average difference: 0.0003938032875558321\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.lines.Line2D at 0x1a22675898>"
+       "<matplotlib.lines.Line2D at 0x1a1d351438>"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -128,9 +128,9 @@
     "\n",
     "N = 10000\n",
     "a = 1\n",
-    "b = 2\n",
-    "c = 3\n",
-    "d = 4\n",
+    "b = 1\n",
+    "c = 1\n",
+    "d = 1\n",
     "\n",
     "\n",
     "def generateData(N=N, a=a, b=b, c=c, d=d):\n",
@@ -143,7 +143,7 @@
     "    y = npr.binomial(n=1, p=sigmoid(c * t + d * x), size=N)\n",
     "\n",
     "    # If decision is negative, we set Y to positive.\n",
-    "    #y[t == 0] = 1\n",
+    "    y[t == 0] = 1\n",
     "\n",
     "    return r, x, t, y\n",
     "\n",
@@ -162,7 +162,7 @@
     "        lambda x: (1 - sigmoid(c * 0 + d * x)) * (1 - sigmoid(a * r + b * x)) *\n",
     "        1, 0, 1)\n",
     "\n",
-    "    return t_1[0]   + t_0[0]\n",
+    "    return t_1[0] #+ t_0[0]\n",
     "\n",
     "# Calculate the difference between the analytic value and an estimate obtained \n",
     "# from synthetic data.\n",
@@ -185,9 +185,8 @@
     "        # Integrate P(Y=0|T=1, X=x) * P(T=1|R=r, X=x) * f(x) from 0 to 1\n",
     "        return si.quad(\n",
     "            lambda x: lr_y.predict_proba(np.array([[1, x]]))[0, 0] * lr_t.\n",
-    "            predict_proba(np.array([[r, x]]))[0, 1] + lr_y.predict_proba(np.array([[0, x]]))[0, 0] * lr_t.\n",
-    "            predict_proba(np.array([[r, x]]))[0, 0], 0, 1)\n",
-    "\n",
+    "            predict_proba(np.array([[r, x]]))[0, 1], 0, 1)\n",
+    "    \n",
     "    r0 = causal_effect_of_R_on_Y(0)\n",
     "    r1 = causal_effect_of_R_on_Y(1)\n",
     "\n",
@@ -229,8 +228,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 81,
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -238,30 +239,30 @@
      "text": [
       "0.0 % 10.0 % 20.0 % 30.0 % 40.0 % 50.0 % 60.0 % 70.0 % 80.0 % 90.0 % \n",
       "Analytical: 0.02047548901524536\n",
-      "Estimated:  0.022226804645399806\n",
+      "Estimated:  0.019144988392514814\n",
       "\n",
       "Values for P(y=0|do(r=1)) and P(y=0|do(r=0))\n",
       "\n",
       "Analytical: 0.10799301700601992 0.08751752799077456\n",
-      "Estimated:  0.10237954151330758 0.08015273686790778\n",
+      "Estimated:  0.10764148848083047 0.08849650008831565\n",
       "\n",
-      "Average difference: 0.0012704228248003452\n",
-      "Std of differences: 0.0017943288120614053\n"
+      "Average difference: 0.0012700827692348328\n",
+      "Std of differences: 0.0017989932120400834\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.lines.Line2D at 0x1a22b58828>"
+       "<matplotlib.lines.Line2D at 0x109619a90>"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
diff --git a/paper/sl.pdf b/paper/sl.pdf
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