diff --git a/paper-examples/language_dataset_anomalies.ipynb b/paper-examples/language_dataset_anomalies.ipynb
index c79cec6..2319254 100644
--- a/paper-examples/language_dataset_anomalies.ipynb
+++ b/paper-examples/language_dataset_anomalies.ipynb
@@ -23,6 +23,7 @@
     "\n",
     "import Data\n",
     "from signature_mahalanobis_knn.utils import (\n",
+    "    _replace_inf_with_max,\n",
     "    compute_auc_given_dists,\n",
     "    plot_cdf_given_dists,\n",
     ")\n",
@@ -73,7 +74,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def z_score_normalization(\n",
+    "    scores_1: np.array, scores_2: np.array\n",
+    ") -> tuple[np.array, np.array]:\n",
+    "    scores_1, scores_2 = _replace_inf_with_max(scores_1, scores_2)\n",
+    "    mean = np.mean(np.concatenate([scores_1, scores_2]))\n",
+    "    std = np.std(np.concatenate([scores_1, scores_2]))\n",
+    "    return (scores_1 - mean) / std, (scores_2 - mean) / std\n",
+    "\n",
+    "\n",
+    "def compute_mse(scores_1: np.array, scores_2: np.array) -> float:\n",
+    "    scores_1, scores_2 = _replace_inf_with_max(scores_1, scores_2)\n",
+    "    return np.nanmean((scores_1 - scores_2) ** 2)\n",
+    "\n",
+    "\n",
+    "def compute_mae(scores_1: np.array, scores_2: np.array) -> float:\n",
+    "    scores_1, scores_2 = _replace_inf_with_max(scores_1, scores_2)\n",
+    "    return np.nanmean(np.abs(scores_1 - scores_2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,72 +119,150 @@
     "        plot_kwargs = {}\n",
     "\n",
     "    results_language_dataset = {\n",
+    "        \"cumsum\": [],\n",
     "        \"with_prefix\": [],\n",
     "        \"auc\": [],\n",
     "        \"se\": [],\n",
     "        \"total_fit_time\": [],\n",
     "        \"total_compute_score_time\": [],\n",
+    "        \"mse_between_scores\": [],\n",
+    "        \"rmse_between_scores\": [],\n",
+    "        \"mae_between_scores\": [],\n",
+    "        \"mse_between_norm_scores\": [],\n",
+    "        \"rmse_between_norm_scores\": [],\n",
+    "        \"mae_between_norm_scores\": [],\n",
     "    }\n",
     "\n",
     "    fit_times = []\n",
     "    compute_times = []\n",
     "\n",
-    "    for with_prefix in [True, False]:\n",
-    "        # save hyperparameters for results\n",
-    "        results_language_dataset[\"with_prefix\"].append(with_prefix)\n",
+    "    scores = {\"cumsum\": [], \"scores\": [], \"normalised_scores\": []}\n",
     "\n",
-    "        data.load_language_data(with_prefix=with_prefix)\n",
-    "        print(f\"start with_prefix: {with_prefix}\")\n",
+    "    for cumsum in [False, True]:\n",
+    "        scores[\"cumsum\"].append(cumsum)\n",
+    "        anomaly_scores = {}\n",
+    "        for with_prefix in [False, True]:\n",
+    "            # save hyperparameters for results\n",
+    "            results_language_dataset[\"cumsum\"].append(cumsum)\n",
+    "            results_language_dataset[\"with_prefix\"].append(with_prefix)\n",
     "\n",
-    "        if add_depth_to_kwargs:\n",
-    "            anomaly_detection_kwargs[\"depth\"] = 2\n",
+    "            data.load_language_data(cumsum=cumsum, with_prefix=with_prefix)\n",
+    "            print(f\"start cumsum: {cumsum}, with_prefix: {with_prefix}\")\n",
     "\n",
-    "        # compute the inlier and outlier scores\n",
-    "        results = anomaly_detection_method(data, **anomaly_detection_kwargs)\n",
+    "            if add_depth_to_kwargs:\n",
+    "                anomaly_detection_kwargs[\"depth\"] = 2\n",
     "\n",
-    "        # save fit and compute times\n",
-    "        fit_times.append(results[\"fit_time\"])\n",
-    "        compute_times.append(results[\"compute_score_time\"])\n",
+    "            # compute the inlier and outlier scores\n",
+    "            results = anomaly_detection_method(data, **anomaly_detection_kwargs)\n",
     "\n",
-    "        # compute auc for digit\n",
-    "        auc = compute_auc_given_dists(\n",
-    "            results[\"scores_inliers\"], results[\"scores_outliers\"]\n",
-    "        )\n",
+    "            # save fit and compute times\n",
+    "            fit_times.append(results[\"fit_time\"])\n",
+    "            compute_times.append(results[\"compute_score_time\"])\n",
     "\n",
-    "        # plot and compute auc\n",
-    "        auc, se = compute_auc_given_dists(\n",
-    "            results[\"scores_inliers\"],\n",
-    "            results[\"scores_outliers\"],\n",
-    "            bootstrap=True,\n",
-    "            bootstrap_samples=bootstrap_samples,\n",
-    "            random_state=seed,\n",
-    "            plot=plot,\n",
-    "            title=f\"Language Dataset (with prefix={with_prefix})\",\n",
-    "        )\n",
-    "\n",
-    "        # save results\n",
-    "        results_language_dataset[\"auc\"].append(auc)\n",
-    "        results_language_dataset[\"se\"].append(se)\n",
-    "        results_language_dataset[\"total_fit_time\"].append(np.sum(fit_times))\n",
-    "        results_language_dataset[\"total_compute_score_time\"].append(\n",
-    "            np.sum(compute_times)\n",
-    "        )\n",
+    "            # compute auc for digit\n",
+    "            auc = compute_auc_given_dists(\n",
+    "                results[\"scores_inliers\"], results[\"scores_outliers\"]\n",
+    "            )\n",
     "\n",
-    "        if plot:\n",
-    "            # plot CDF\n",
-    "            plot_cdf_given_dists(\n",
+    "            # plot and compute auc\n",
+    "            auc, se = compute_auc_given_dists(\n",
     "                results[\"scores_inliers\"],\n",
     "                results[\"scores_outliers\"],\n",
-    "                bins=10000000,\n",
-    "                **plot_kwargs,\n",
+    "                bootstrap=True,\n",
+    "                bootstrap_samples=bootstrap_samples,\n",
+    "                random_state=seed,\n",
+    "                plot=plot,\n",
+    "                title=f\"Language Dataset (with prefix={with_prefix})\",\n",
+    "            )\n",
+    "\n",
+    "            # save results\n",
+    "            results_language_dataset[\"auc\"].append(auc)\n",
+    "            results_language_dataset[\"se\"].append(se)\n",
+    "            results_language_dataset[\"total_fit_time\"].append(np.sum(fit_times))\n",
+    "            results_language_dataset[\"total_compute_score_time\"].append(\n",
+    "                np.sum(compute_times)\n",
+    "            )\n",
+    "\n",
+    "            # save scores\n",
+    "            anomaly_scores[f\"with_prefix={with_prefix}\"] = np.concatenate(\n",
+    "                [results[\"scores_inliers\"], results[\"scores_outliers\"]]\n",
     "            )\n",
     "\n",
-    "        print(f\"with prefix: {with_prefix} | auc: {auc} | se: {se}\")\n",
+    "            if plot:\n",
+    "                # plot CDF\n",
+    "                plot_cdf_given_dists(\n",
+    "                    results[\"scores_inliers\"],\n",
+    "                    results[\"scores_outliers\"],\n",
+    "                    bins=10000000,\n",
+    "                    **plot_kwargs,\n",
+    "                )\n",
+    "\n",
+    "            print(\n",
+    "                f\"cumsum: {cumsum}, with prefix: {with_prefix} | auc: {auc}, se: {se}\"\n",
+    "            )\n",
+    "\n",
+    "        scores[\"scores\"].append(tuple(anomaly_scores))\n",
+    "        normalised_scores = {}\n",
+    "        (\n",
+    "            normalised_scores[\"with_prefix=True\"],\n",
+    "            normalised_scores[\"with_prefix=False\"],\n",
+    "        ) = z_score_normalization(\n",
+    "            anomaly_scores[\"with_prefix=True\"], anomaly_scores[\"with_prefix=False\"]\n",
+    "        )\n",
+    "        scores[\"normalised_scores\"].append(normalised_scores)\n",
+    "\n",
+    "        # compute MSE, RMSE, MAE between scores\n",
+    "        mse_between_scores = compute_mse(\n",
+    "            anomaly_scores[\"with_prefix=True\"], anomaly_scores[\"with_prefix=False\"]\n",
+    "        )\n",
+    "        rmse_between_scores = np.sqrt(mse_between_scores)\n",
+    "        mae_between_scores = compute_mae(\n",
+    "            anomaly_scores[\"with_prefix=True\"], anomaly_scores[\"with_prefix=False\"]\n",
+    "        )\n",
+    "        print(\n",
+    "            f\"MSE between scores (cumsum={cumsum}) with and without prefix: {mse_between_scores}\"\n",
+    "        )\n",
+    "        print(\n",
+    "            f\"RMSE between scores (cumsum={cumsum}) with and without prefix: {rmse_between_scores}\"\n",
+    "        )\n",
+    "        print(\n",
+    "            f\"MAE between scores (cumsum={cumsum}) with and without prefix: {mae_between_scores}\"\n",
+    "        )\n",
+    "        # save results (twice because we have two with_prefix values to make the dataframe)\n",
+    "        results_language_dataset[\"mse_between_scores\"] += 2 * [mse_between_scores]\n",
+    "        results_language_dataset[\"rmse_between_scores\"] += 2 * [rmse_between_scores]\n",
+    "        results_language_dataset[\"mae_between_scores\"] += 2 * [mae_between_scores]\n",
+    "\n",
+    "        # compute MSE, RMSE, MAE between the normalised scores\n",
+    "        mse_between_scores_with_norm = compute_mse(\n",
+    "            normalised_scores[\"with_prefix=True\"],\n",
+    "            normalised_scores[\"with_prefix=False\"],\n",
+    "        )\n",
+    "        rmse_between_scores_with_norm = np.sqrt(mse_between_scores_with_norm)\n",
+    "        mae_between_scores_with_norm = compute_mae(\n",
+    "            normalised_scores[\"with_prefix=True\"],\n",
+    "            normalised_scores[\"with_prefix=False\"],\n",
+    "        )\n",
+    "        print(\n",
+    "            f\"MSE (normalised) between scores (cumsum={cumsum}) with and without prefix: {mse_between_scores_with_norm}\"\n",
+    "        )\n",
+    "        print(\n",
+    "            f\"RMSE (normalised) between scores (cumsum={cumsum}) with and without prefix: {rmse_between_scores_with_norm}\"\n",
+    "        )\n",
+    "        print(\n",
+    "            f\"MAE (normalised) between scores (cumsum={cumsum}) with and without prefix: {mae_between_scores_with_norm}\"\n",
+    "        )\n",
+    "        # save results (twice because we have two with_prefix values to make the dataframe)\n",
+    "        results_language_dataset[\"mse_between_norm_scores\"] += 2 * [mse_between_scores]\n",
+    "        results_language_dataset[\"rmse_between_norm_scores\"] += 2 * [\n",
+    "            rmse_between_scores\n",
+    "        ]\n",
+    "        results_language_dataset[\"mae_between_norm_scores\"] += 2 * [mae_between_scores]\n",
     "\n",
     "    total_time = time.time() - t0\n",
     "    print(f\"Total experiment time: {total_time} seconds | {total_time / 60} minutes\")\n",
     "\n",
-    "    return results_language_dataset"
+    "    return results_language_dataset, scores"
    ]
   },
   {
@@ -170,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -179,22 +283,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 70615/70615 [00:00<00:00, 275745.29it/s]\n",
+      "100%|██████████| 20000/20000 [00:00<00:00, 238345.23it/s]\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "start with_prefix: True\n",
-      "fit_time: 12929.68161725998\n",
-      "compute_score_time: 133.17037892341614\n",
-      "with prefix: True | auc: 0.889933895\n"
+      "start cumsum: False, with_prefix: False\n",
+      "fit_time: 11909.900458812714\n",
+      "compute_score_time: 131.26868200302124\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x600 with 1 Axes>"
       ]
@@ -204,7 +315,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x600 with 1 Axes>"
       ]
@@ -216,15 +327,29 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "start with_prefix: False\n",
-      "fit_time: 12575.68718290329\n",
-      "compute_score_time: 129.70622277259827\n",
-      "with prefix: False | auc: 0.87747936\n"
+      "cumsum: False, with prefix: False | auc: 0.798655525, se: 0.0030846640070882475\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 70615/70615 [00:00<00:00, 104094.53it/s]\n",
+      "100%|██████████| 20000/20000 [00:00<00:00, 101822.77it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start cumsum: False, with_prefix: True\n",
+      "fit_time: 11778.503013849258\n",
+      "compute_score_time: 139.26858377456665\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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dRKFChbTadG0Xb29vUbp0aa225F/nwcHBqZbPyDpev34t7OzsRL169cSHDx+0lk1+DwghRNu2bbV6bpJl5v0BQJiYmIirV6+mWg9S9KzZ29sLX1/fVMtpSqsmXb8+mzRpIuzs7FL1mmk+x+Q6PvZ3FyJjPWuJiYlarz8hVD35zs7OWq+B5HoLFSqk9Vnxxx9/pOqBr1q1qihevLh48+aNui0sLCxVb2dmfpnrep389ttvAoA4evSouq19+/bCxsZGPH78WN12+/ZtYWZmptWzFh4eLkxNTcWsWbO01nn58mVhZmaWqj2ljGxbe3t7UaNGjUw/h3nz5mn1FmnKyPtl9+7dH63t2LFjAoC6ZzpZ8h4VzXZdvS1puXPnjgAgfvnlF632pKQkkT9/fnWPmVKpFIUKFRLdunUTpqam6tfK/PnzU/XApXzfpbd9AAgLCwtx584dddvff/+ts6aUkl+PLi4uIiYmRt2+bds2AUD89NNP6rbkXvNly5ZprSMznzXJvXO6noPm8xVC9T1tYmIijh49qv6eWbhwodYyae3d0fUvLWn1rOn6TMzM+zej38PJknumIyMj06w1pSwdie/l5YXChQvD1dUVXbt2Rb58+RAYGKjuBXn58iUOHz6M7t27482bN4iKikJUVBRevHgBb29v3L59W3326M6dO1GtWrVUyROA+tfk9u3bUalSJVSsWFG9rqioKDRr1gwAEBoammatPXr0QEJCgtavrgMHDuD169fo0aMHoPrLYufOnWjfvj2EEFqP4e3tjejoaFy4cEFrvT4+Phk6ViP5l0B6BxIm3xYTE6PV3rFjR61f63Xr1kW9evUQFBQEIHPbOdmgQYNSncig+TwSEhLw4sULlC1bFgUKFEj1vDOrf//+Wr2OjRs3BgDcu3cPAHDu3Dm8ePECgwYN0jrotHfv3lo9tWlJ3maZOVATUPUaaWrcuDFevHih9TfQ3C7R0dGIiopC06ZNce/ePURHR2vdv1SpUupeWk0ZWUdISAjevHmDCRMmpDpOIiNnKmX2/dG0aVO4u7t/dL0FChTA6dOn8eTJk48u+zHPnz/H0aNHMWDAAJQoUULrtpTPUQihl141ADA1NVW//pRKJV6+fInExETUrl1b52u7R48eWq+7lK/XJ0+e4PLly+jXr59WL2LTpk1RtWrVLNep+TqJjY1FVFQUPv30UwBQ15mUlISDBw+iY8eOKFasmHr5smXLonXr1lrr27VrF5RKJbp37671mihSpAjKlSuX7mdmRtna2mr1dGTkOXxMRt4vyT2Ce/fuRUJCgs71bN++Hfb29mjRooXW869VqxZsbW2z/PxfvHgBAKk+m0xMTNCgQQMcPXoUAHD9+nW8ePECEyZMgBACJ0+eBKDqbatSpUq2ejW9vLxQpkwZ9fwnn3yC/Pnzq1+jH9OvXz+tz8uuXbuiaNGi6u+VZJaWlujfv79WW3a+i9Mzbdo0VK5cGT4+Phg+fDiaNm2Kr776SmsZb29vhISEZOhfZmX0M1GXrHwPJ79+MjOEVJZ2gy5evBjly5dHdHQ0Vq9ejaNHj2od2H/nzh0IITB58mRMnjxZ5zqePXsGFxcX3L17F126dEn38W7fvo3r16/r7OpPXldaqlWrhooVK2Lr1q0YOHAgANUuUEdHR/UL7Pnz53j9+jWWL1+O5cuXZ+gxSpUqlW7NyZLfFG/evEnzDZpWoCtXrlyqZcuXL49t27YByNx2Tq/uDx8+ICAgAGvWrMHjx4+1hhJJGUoyK+UXc/KL9NWrVwCAf/75B4DqC0eTmZlZmrvnNOXPnx8AMj2UQHp1Ja/zxIkTmDp1Kk6ePIn3799rLR8dHQ17e3v1fFqvh4ys4+7duwCAKlWqZOo5JMvs+yOjr925c+fCx8cHrq6uqFWrFtq0aYN+/fqhdOnSma4x+Yskq88xO9atW4cff/wRN27c0Ppy17Udsvp6TW7L6o+bly9fYvr06diyZUuqv1fye/DZs2f48OFDmo+t6fbt2xBC6PwMAaCXs3Tfvn0LJycn9XxGnsPHZOT90rRpU3Tp0gXTp0/HggUL4OHhgY4dO6JXr17q76Hbt28jOjpaqz5N6X1nZITmZ2Syxo0bY9q0afjw4QOOHTuGokWLombNmqhWrRqOHTuGFi1a4Pjx49k+CS/laxRQvU6TX6Mfk/I1oVAoULZs2VRDMrm4uKQ6vCc738XpsbCwwOrVq1GnTh1YWVlhzZo1qX7EFS1aFEWLFs3S+j8mo5+JumTlezj59ZOZYUOyFNbq1q2rPkOoY8eOaNSoEXr16oWbN2/C1tYWSqUSADBmzBidvQ2A7g+7tCiVSlStWlW9PzslV1fXdO/fo0cPzJo1C1FRUbCzs0NgYCA+//xzdU9Ocr19+vRJdWxbspSnqGf0DKhKlSrh999/x//+9780j1P43//+BwCZTvZZ2c666h4xYgTWrFmDr7/+GvXr14e9vT0UCgV69uypfoysSms4El0fdllRsWJFAMDly5dTHX+Uno/VdffuXTRv3hwVK1bE/Pnz4erqCgsLCwQFBWHBggWptouu7ZrZdWRVZt8fGX3tdu/eHY0bN8bu3btx4MABzJs3D99//z127dqVqifHUG3cuBFffPEFOnbsiLFjx8LJyQmmpqYICAhQh2RN+ny9pvVBnJSUlKqte/fu+OuvvzB27FhUr15d/TnaqlWrLL1OlEolFAoF9u/fr/M5pXdsYUY8evQI0dHRWp8v2X0OGX2/KBQK7NixA6dOncKePXvw559/YsCAAfjxxx9x6tQp9eM6OTlh06ZNOh8rrbDxMcnHhOkKRo0aNUJCQgJOnjyJY8eOqXtlGzdujGPHjuHGjRt4/vy5uj2rcvozNZmuz4nsfhen588//wSg6pW9fft2qgD14cOHDIf+IkWKZOqxdT3XjL5/s/I9nPz6ycyxddk+wSD5g8/T0xOLFi3ChAkT1L+8zc3NtQ6Y16VMmTK4cuXKR5f5+++/0bx58ywNYNejRw9Mnz4dO3fuhLOzM2JiYtCzZ0/17YULF4adnR2SkpI+Wm9mtWvXDgEBAVi/fr3OsJaUlITNmzfDwcEh1QCLt2/fTrX8rVu31D1OmdnO6dmxYwd8fHzw448/qttiY2NTHWCZE4MHlixZEoDq14mnp6e6PTExEeHh4R8dx6l169YwNTXFxo0bM32SQXr27NmDuLg4BAYGav2SzUw3f0bXkbxL48qVK+n+iElr+2f3/ZGeokWLYvjw4Rg+fDiePXuGmjVrYtasWeqwltHHS36tfuy9rm87duxA6dKlsWvXLq1ap06dmqX1ab5eU0rZltwrl/J9lNw7l+zVq1c4dOgQpk+fjilTpqjbU77/nZycYGVllaHHLlOmDIQQKFWqFMqXL/+RZ5V5yeNXJX85ZfQ5AGm/ZjL7nvv000/x6aefYtasWdi8eTN69+6NLVu24Msvv0SZMmVw8OBBNGzY8KM/TjLznilRogSsra1x//79VLfVrVsXFhYWOHbsGI4dO4axY8cCAJo0aYIVK1bg0KFD6nl91ZMVKf8mQgjcuXMnQ2Pm5dRnzf/+9z/MmDED/fv3x6VLl/Dll1/i8uXLWnsvtm7dmmq3bFr0EVwz+v7Nyvfw/fv34ejomKkfDXoZPdbDwwN169bFwoULERsbCycnJ3h4eODXX3/F06dPUy2veamFLl264O+//8bu3btTLZe8wbt3747Hjx9jxYoVqZb58OGD+qzGtFSqVAlVq1bF1q1bsXXrVhQtWlTrDWNqaoouXbpg586dOr9MsnJpiGQNGjSAl5cX1qxZg71796a6fdKkSbh16xbGjRuX6kPl999/19rXfebMGZw+fVr9RZmZ7ZweU1PTVC/uX375JdUviOQx2fQ5snbt2rVRqFAhrFixAomJier2TZs2Zahb39XVFYMGDcKBAwfwyy+/pLpdqVTixx9/xKNHjzJVV/Kv15S7hNesWaP3dbRs2RJ2dnYICAhAbGys1m2a982XL5/OX5bZfX/okpSUlOqxnJycUKxYMcTFxX20ppQKFy6MJk2aYPXq1Xjw4IHWbSlfezdu3Ei1TFbp+hucPn1afQxRZhUrVgxVqlTB+vXr8fbtW3X7kSNHcPnyZa1lS5YsCVNTU/VxTMmWLFny0RoBpLoyhKmpKby8vPD7779rHUd4584d7N+/X2vZzp07w9TUFNOnT0+1XiGE+tirrDh8+DBmzpyJUqVKoXfv3pl6DkDanyMZfb+8evUq1eMk96onvza7d++OpKQkzJw5M9XjJyYmaj12vnz5MvyZZm5ujtq1a+u8aoaVlRXq1KmD3377DQ8ePNDqWfvw4QN+/vlnlClT5qO78nLic1ZT8igDyXbs2IGnT59mqLc8Jz5rEhIS8MUXX6BYsWL46aefsHbtWkRGRqa64kFOHrOmS0bfv1n5Hj5//jzq16+fqXr0Nozw2LFj0a1bN6xduxZDhw7F4sWL0ahRI1StWhWDBg1C6dKlERkZiZMnT+LRo0f4+++/1ffbsWMHunXrhgEDBqBWrVp4+fIlAgMDsWzZMlSrVg19+/bFtm3bMHToUISGhqJhw4ZISkrCjRs3sG3bNvz5559aAzfq0qNHD0yZMgVWVlYYOHBgqgFs58yZg9DQUNSrVw+DBg2Cu7s7Xr58iQsXLuDgwYN4+fJllrfN+vXr0bx5c3z22Wfo1asXGjdujLi4OOzatQthYWHo0aOH+leYprJly6JRo0YYNmwY4uLisHDhQhQqVAjjxo1TL5PR7Zyedu3aYcOGDbC3t4e7uztOnjyJgwcPpjoNvHr16jA1NcX333+P6OhoWFpaolmzZmkeF5IRFhYWmDZtGkaMGIFmzZqhe/fuCA8Px9q1a1GmTJkM/Xr78ccfcffuXXz11VfYtWsX2rVrBwcHBzx48ADbt2/HjRs3tHpSM6Jly5awsLBA+/btMWTIELx9+xYrVqyAk5OTzjdkdtaRP39+LFiwAF9++SXq1KmDXr16wcHBAX///Tfev3+vHim9Vq1a2Lp1K0aPHo06derA1tYW7du318v7I6U3b96gePHi6Nq1K6pVqwZbW1scPHgQZ8+e1eqBTasmXX7++Wc0atQINWvWxODBg1GqVCmEh4dj3759uHTpknq5zA7dsXr1agQHB6dqHzlyJNq1a4ddu3ahU6dOaNu2Le7fv49ly5bB3d1dK2xlxuzZs/HZZ5+hYcOG6N+/P169eoVFixahSpUqWuu0t7dHt27d8Msvv0ChUKBMmTLYu3dvquN68ufPjyZNmmDu3LlISEiAi4sLDhw4oLP3Ztq0aThw4AAaNmyIYcOGISkpSf3YmtuwTJky+O677+Dv768eCsfOzg7379/H7t27MXjwYIwZM+ajz3X//v24ceMGEhMTERkZicOHDyMkJAQlS5ZEYGCg+oSYzDyHWrVqAVD9UO3ZsyfMzc3Rvn37DL9f1q1bhyVLlqBTp04oU6YM3rx5gxUrViB//vxo06YNANUB40OGDEFAQAAuXbqEli1bwtzcHLdv38b27dvx008/oWvXrup6li5diu+++w5ly5aFk5OT+nhmXT777DNMmjQJMTEx6uNbkzVu3Bhz5syBvb29+oQTJycnVKhQATdv3szQVRLS2j76GsC8YMGCaNSoEfr374/IyEgsXLgQZcuWVQ/Bkp6c+Kz57rvvcOnSJRw6dAh2dnb45JNPMGXKFHz77bfo2rWr+m+ak8es6ZLR9y+Que/hZ8+e4X//+1+q4Z8+KsPnjYr0T+dOSkoSZcqUEWXKlFEPDXH37l3Rr18/UaRIEWFubi5cXFxEu3btxI4dO7Tu++LFC+Hn5ydcXFzUg+z5+PhoDaMRHx8vvv/+e1G5cmVhaWkpHBwcRK1atcT06dNFdHS0ermUQ3cku337tvq03uPHj+t8fpGRkcLX11e4uroKc3NzUaRIEdG8eXOtgR+TT+fdvn17ZjadePPmjZg2bZqoXLmysLa2FnZ2dqJhw4Zi7dq1qYYu0Byg88cffxSurq7C0tJSNG7cWPz999+p1p2R7Zze3+7Vq1eif//+wtHRUdja2gpvb29x48YNndtyxYoVonTp0sLU1FTrtOa0hu5IuZ3SGlDw559/FiVLlhSWlpaibt264sSJE6JWrVqiVatWGdi6qiEaVq5cKRo3bizs7e2Fubm5KFmypOjfv7/WsB7JQ3doDrisuX00T5cPDAwUn3zyiXrQze+//16sXr061XLJg+LqktF1JC/boEEDYW1tLfLnzy/q1q0rfvvtN/Xtb9++Fb169RIFChRINUxERt8f+HcASF2gcUp9XFycGDt2rKhWrZqws7MT+fLlE9WqVUs1QGlaNaX1d75y5Yro1KmTKFCggLCyshIVKlQQkydPTlVHZobuSOvfw4cPhVKpFLNnz1a/tmrUqCH27t2bagBbzfdcetsl2ZYtW0TFihWFpaWlqFKliggMDBRdunQRFStW1Fru+fPnokuXLsLGxkY4ODiIIUOGiCtXrqTaNo8ePVJvF3t7e9GtWzfx5MkTnY996NAhUaNGDWFhYSHKlCkjVq5cKb755hthZWWVqvadO3eKRo0aiXz58ol8+fKJihUrCl9fX3Hz5s1MbVsLCwtRpEgR0aJFC/HTTz9pDf2Qlecwc+ZM4eLiIkxMTLTeCxl5v1y4cEF8/vnnokSJEurBbtu1ayfOnTuXqqbly5eLWrVqqT9zq1atKsaNGyeePHmiXiYiIkK0bdtW2NnZZei1FxkZKczMzMSGDRtS3bZv3z4BQLRu3Vqr/csvvxQAUg2yLoTu11da2yet929a33uaNAd09/f3F05OTsLa2lq0bds21XA6aQ27IUTGP2syMnTH+fPnhZmZmRgxYoTWMomJiaJOnTqiWLFi6Q57lVnpDYqrS0bfv0JkPO8sXbpU2NjY6HwPpUfxb7FkQMLDw1GqVCnMmzcvQ79+cyOlUonChQujc+fOOrvciQxN9erVUbhwYb3thsmMjh074urVqzqPESP9GzhwIG7duqU14K2hCwsLg6enJ7Zv367uVSTp1ahRAx4eHpm+DKJhXfGc8qTY2NhUx6CsX78eL1++hIeHhzxFEaUhISFB6/hKQPVF+Pfff0vyev3w4YPW/O3btxEUFMT3ioSmTp2Ks2fP4sSJE3KXQkYkODgYt2/fhr+/f6bvq7dj1oiy6tSpUxg1ahS6deuGQoUK4cKFC1i1ahWqVKmCbt26yV0ekZbHjx/Dy8sLffr0QbFixXDjxg0sW7YMRYoUSTXYck4oXbo0vvjiC5QuXRr//PMPli5dCgsLC61jWSlnlShRItXJQEQf06pVqywfK8uwRrJzc3ODq6srfv75Z7x8+RIFCxZEv379MGfOnDSvuUokFwcHB9SqVQsrV67E8+fPkS9fPrRt2xZz5szJ0LUZs6tVq1b47bffEBERAUtLS9SvXx+zZ89OcwBcIjJ+Rn3M2tGjRzFv3jycP38eT58+xe7du9GxY8d07xMWFobRo0fj6tWrcHV1xbfffpuhM3SIiIiI5GDUx6y9e/cO1apVw+LFizO0/P3799G2bVt4enri0qVL+Prrr/Hll1+qR04mIiIiMjRG3bOmSaFQfLRnbfz48di3b5/WwLc9e/bE69evdY7TRERERCS3PHXM2smTJ1NdDsLb2xtff/11mveJi4vTGrFdqVTi5cuXKFSoUI5fFoSIiCg3EULgzZs3KFasWKrB6SlteSqsRUREwNnZWast+VqhHz580HkNuYCAAEyfPl2qEomIiHK9hw8fonjx4nKXYTTyVFjLCn9/f4wePVo9Hx0djRIlSuDWrVsoWLCgjJXlHQkJCQgNDYWnpyfMzc3lLidP4DaXHre59LjNM+A9oLilAK4BiqsKKK4roLimgCIqY3uWhIXAzwV/xpSIKVrtdnZ2OVFtrpWnwlqRIkUQGRmp1RYZGYn8+fPr7FUDAEtLS1haWqZqL1iwoCSn6ZPqA9XGxgaFChXiB6pEuM2lx20uPW5zDUkA7gK4nOLfHaguOvYxCgClAVTV/jf397mY4v9fUJswYQLmzJnDw4gyKU+Ftfr16yMoKEirLSQkBPXr15epIiIiIolFAvgftEPZNQAf0ruTBkcAn0A7mFUGkOJa83PmzNEarX/mzJkYNmwY5syZk7368yCjDmtv377FnTt31PP379/HpUuXULBgQZQoUQL+/v54/Pgx1q9fDwAYOnQoFi1ahHHjxmHAgAE4fPgwtm3bhn379sn1FIiIiHLGOwBXkbq37HkG728FVQhL0VsGZ6h60tKRkJCAvXv3qudnzZqFiRMn4sWLF5l6CqRi1GHt3Llz8PT0VM8nH1vm4+ODtWvX4unTp3jw4IH69lKlSmHfvn0YNWoUfvrpJxQvXhwrV66Et7e35LUTERHpRSJUuytThrJ7yPguzLJIHcrKADDNWknm5ubYv38/WrVqhfbt22PChAlZWxEBMPKw5uHhkeoC4JrWrl2r8z4XL17MwaqIiIhygAAQAe1A9j+odmHGpXM/TU5IvQvTHYCNvotVnUQQGhrKywbqgVGHNSIiolzpLYArSN1bltG9iDbQvQvTSe+Vqi1btgxdunRB4cKF1W0MavrBsEZERKRvCQDe//t/4r//vwFwG8DfAI5DdUyZ2b+3JQA4B8AFQDwyflyZCYBySB3KSkPSC0pOmzYN06dPx5IlS3D48GE4OjpK9+B5AMMaERFRVjwF8Aiq48XuQtUTtjWb63yczm1FoB3IPgFQCYDukackIYTAtGnTMGPGDADA5cuXsW/fPvj4+MhXVC7EsEZERASoesDuQ9Xr9QKqAPYeqjAUA1Vv1zkAr3PgsU0AmOO/Y8/qIXVvmYF1VgkhMGXKFHz33XfqtgULFjCo5QCGNSIiyl3iodrFGAdV6HoDIByqYSyuACgIYD9gWsYUjV40gtm3Zqr2nNAWqm9acwCWUAUuJYAKAOpCdXC/NSTdZakPQghMnjwZs2bNUrctXLgQI0eOlLGq3IthjYiIjNdtAKugCmXHANzM+F1NnpqgELJwJRobqHrcKv/7eF9BFbYaQzXcRRmoxijLpYQQmDRpEgICAtRtP//8M0aMGCFjVbkbwxoRERmHOwD2AJgNoDiAS/pZrVAIKCwVqm9EDwCloApjzQDkh+qgfxsAdgCK4qMDwuZmQghMnDhR6yoEixYtgq+vr4xV5X4Ma0REZBgSoDpO7CCAfQD+geqaleFQ7drUFPWRdSkAfAbV7se7UIWwOADVAZQHUBxIsEnA/r/2o3X71rw2aAbt2LFDK6gtXrwYw4cPl7GivIFhjYiIpPc3gJ8A/AHgpR7WtwXApwBKIOM9XwmAMM3IEP+UrHPnzujbty82bNiApUuXYujQoXKXlCcwrBERUc5JwH9nUR4B8BdUuzM/1jOWkh1UJwpYApgCwAtAHeTpXZJyMDU1xZo1a+Dj44PmzZvLXU6ewbBGRETZ8xyqXZb7AewFcAaqMcGeQXXmY2aYQXWGpBeAllD1ltnrrVLKJCEEnj59imLFiqnbTE1NGdQkxrBGRERpE1D1iP0N1XFj7wEsAVALqnCWlogMrLsVgCEAvCHrwK6kmxACo0ePxqZNmxAaGorKlSvLXVKexbBGREQqVwCsgapn7B6AJ+ksm15Q0+QO1XUum0M1xtinAOpDdVYlGSwhBL7++mv8/PPPAAAvLy/cunULdnZ2MleWNzGsERHlRUkATgEIBLATqjMms+trqK5J2Rmq4S7IKAkhMHLkSPzyyy8AAIVCgdmzZzOoyYhhjYgotxJQXSYpHKpesz0AdkAV1DLDFKoest4AykI19ljxf6c54kWuIoTAiBEjsHjxYgCqoJZ8QgHJh2GNiMiYCagO8L8CYDeARVCdMWkO1e7HzPoMQFOojiNzg2owWMoTlEol/Pz8sHTpUgCqoLZ27Vr069dP5sqIYY2IyNi8AzABqmCmSxz+uyB4eiwANATQF0AjqHrKOBRGnqRUKuHr64tly5YBUAW1devWoW/fvjJXRgDDGhGRcYiG6tiygZm4Ty2oxiYz/Xe6EVTHlNWC6mLmRFDt+hw+fDh+/fVXAICJiQnWrVuHPn36yFwZJWNYIyIyFALABajGLNsJ1cCx56EKah/zCVTHlBUF0BGqQWSJMkChUKB06dIAVEFtw4YN6NWrl8xVkSaGNSIiOXwAcBRAGIBfAPN35vgMn2VuHb0A/ArAVt/FUV4zbtw4AEDx4sUZ1AwQwxoRkRQEVMFsNlQnA2Rk0NiUKkM1sv+3UI1ZRqRHyYGNDA/DGhFRTngB4AaAqVBdC/OfTNy3PVSXWioIoDFUw2TwwH/Sk6SkJPj6+qJDhw5o06aN3OVQBjCsERFlhwBwGcAhAMFQDS6bmQFm+wLwAhIaJSDochDatGkDc3MOXkY5IykpCQMHDsS6deuwZs0a/PHHH2jVqpXcZdFHMKwREWWWAHAYQIt/pzOqMIByAPwBeALIp3FbAlShjyiHJCUlYcCAAVi/fj0A1XAd7969k7kqygiGNSKijPgHqmPFNmZweVMADQBUBVAHQD8AJjlTGtHHJCUloX///tiwYQMAwMzMDFu3bkXnzp1lrowygmGNiCgtlwEcBzA8A8sOAFAKgAeA2gCscq4sosxISkqCj48PNm3aBEAV1LZt24ZOnTrJXBllFMMaEVGyGKjGOQsAcCADy/eC6uLldXKwJqJsSExMhI+PDzZv3gwAMDc3x/bt2/HZZ5kcJoZkxbBGRHnbK6jO2NwK4FkGlt8AoAd4AXMyeImJiejbty+2bNkCQBXUduzYgQ4dOshcGWUWwxoR5T3xAOZBdQzax1gB2AbV7k1eFYCMyIULF7Bz504AqqC2c+dOtG/fXuaqKCt4uCsR5Q1PAcwHYA/AEmkHNTsAYwAsAfAeqisNtAeDGhmdunXrYvv27bC1tcWuXbsY1IwYe9aIKHd6CtWZmxkdlP0HAEPASzdRrvLZZ5/h/v37cHTkJS+MGXvWiCh3+BPAl1B9qikAFMPHg9rnUIU6AeAbMKiRUUtISEBgYGCqdgY148ewRkTG6y5Uw2UoALQCsArpD1LbEMBCAEn/LrcZQJGcLZFICvHx8ejRowc+++wzLFy4UO5ySM8Y1ojIuARDdRUABYCyAMLTWdYUwBSoLpwuoBozbST4yUe5SnJQ2717NwDA398fDx8+lLkq0ices0ZEhusVgB1Q7eLcmYHlGwCYA6A++OlGeUJ8fDy6d++OP/74AwBgZWWFwMBAuLq6ylwZ6RM/zojIsPwDVQ/YWKiOJ/uY2gBGQHVBdEUO1kVkYOLi4tCtWzfs2bMHgCqo7dmzB15eXjJXRvrGsEZE8hJQDUq7AMDbTNzvfwCqgAGN8qS4uDh07doVe/fuBQBYW1tjz549aN68ucyVUU5gWCMieYRDFdB+zsCy4wG0AVAPqjHSiPKwuLg4dOnSBfv27QOgCmr79u2Dp6enzJVRTmFYIyLpvAfQGsDRDCw7Gapdm+VytCIiozNo0CB1ULOxscG+ffvg4eEhb1GUo3hOFBHlrCQAvlDtrsyHtINaJwBPoNotKgDMAIMakQ7ffPMNChUqBBsbGwQFBTGo5QHsWSMi/XkP1ckBfwB4CeA6gL/TWb4YVENp+AGwyfHqiHKFatWq4dChQ4iOjkaTJk3kLockwLBGRNk3B4B/JpZfDeAL8OQAogyIjY2FhYUFTEz+2xlWrVo1GSsiqXE3KBFljRKq0KXAx4NaAQDTACRCtYuzPxjUiDLg/fv3aN++PQYNGgSlUil3OSQT9qwRUcYJAOehurTTi3SW6wmgHVSD0zqB19wkyoL379+jQ4cOOHToEADA1tYWP/30k8xVkRwY1ogofTcABAGYDdXJAq/TWK4xVFcasJamLKLcLLlH7fDhwwAAOzs79OzZU+aqSC4Ma0SkU505dWDe0fzjC3YAsBaAQw4XRJRHvHv3Du3bt0doaCgAIH/+/Pjzzz/x6aefylwZyYXHrBHRf14B6AGYW5ij2KliaS83GEAgVMet/QEGNSI9effuHdq2basV1A4cOMCglsexZ40or7sPYBGA+eks4wRgHICOAMpIUBNRHvT27Vu0bdsWR4+qBiO0t7fHgQMHULduXZkrI7kxrBHlRaEADkHVO3Y57cVEQQFFhALIwN5QIsq6t2/fok2bNjh27BgAVVALCQlBnTp1ZK6MDAF3gxLlBYkA9gJoCNWQGc0AzEKaQS1pYhL+2PkHEiMSGdSIJPD+/Xu8eKE6xbpAgQI4ePAggxqpMawR5WaXAVSGKnC1B/CXjmWs/r1tIVTHrAlAOU0JmEpVJBE5OTnh8OHDaNiwIQ4ePIjatWvLXRIZEO4GJcptogAshmoQ2vR8DtVlnuqCnwREBsDZ2RnHjh2DQsERo0kbe9aIcou3UO3iLIy0g9qXAHZCNV7aZgANwKBGJIPo6GiMHDkSb9++1WpnUCNd+DFNZOwEgLIA7qWzzFYA3cBLPBEZgOjoaHh7e+P06dO4dOkSgoKCkC9fPrnLIgPGnjUiY5QA4EeoQpoJdAe176C62oAA0B0MakQG4PXr12jZsiVOnz4NALh69SoePHggc1Vk6NizRmRMLgCYAmBfOsvMAzBGmnKIKOOSg9rZs2cBAI6Ojjh06BAqVaokc2Vk6BjWiAxdHIARAFZ8ZDlPqMZOYw8akcF59eoVWrZsiXPnzgFQBbXDhw+jatWqMldGxoC7QYkMVTRUAcwKuoOaJVRndN6HalfnYTCoERmgV69eoUWLFuqgVrhwYYSGhjKoUYaxZ43IkCRCdemnbwG8S2e58wBqSlIREWXDy5cv0aJFC1y4cAHAf+OpVa5cWebKyJiwZ43IEMRAFb7MAYyC7qA2DqoTCwQY1IiMxKxZs9RBzdnZGaGhoQxqlGkMa0Ry+gOqXZf2AC6mscy3AOIBfA/2hRMZmVmzZqFVq1bqoObu7i53SWSE+NFPJIezUF05IC19oBp6o6Q05RBRzrCyssLu3bvx5MkTlC5dWu5yyEixZ41IKgKqy0ApkHZQW/PvchvAoEZkhKKiovDw4UOtNisrKwY1yhb2rBHlNAGgEFQXSU/L/wDwxDAio/b8+XM0b94c7969Q1hYGFxdXeUuiXIJ9qwR5ZQwAJ9A9S7TFdQKALgFVZhjUCMyas+ePUOzZs1w+fJl3Lt3D3369IEQQu6yKJdgzxqRvsVDNQZaWrpAtbvTTppyiChnJQe1q1evAgBcXFywcuVKXpSd9IY9a0T6sg2q49HSCmrtACgB7ACDGlEuERkZCU9PT3VQK168OMLCwlCuXDmZK6PchD1rRNn1HkC+dG6/DdUF14koV4mIiECzZs1w/fp1AICrqytCQ0NRpkwZmSuj3IY9a0RZFQfgU+gOaoUArIXqeDQGNaJc5+nTp/D09FQHtRIlSiAsLIxBjXIEe9aIsuI3AL3SuC0aQH4JayEiSb1+/Rqenp64efMmAKBkyZIIDQ1FqVKlZK6Mciv2rBFlRjxUx6XpCmpjoOpJY1AjytXs7e3RqlUrAKqgFhYWxqBGOYo9a0QZFQmgiI72cVBdCoqI8gSFQoEFCxbA0dERffv2RcmSHMGachbDGlFGdAawW0f7XQAcmJwo1xNCaA3FoVAo8O2338pYEeUl3A1KlJ5hUO32TBnUmkM1DAeDGlGu9+jRI9SpUwenT5+WuxTKoxjWiHR5A1VIW6bjtsUADv57OxHlag8fPoSHhwfOnz+Pli1b4ty5c3KXRHkQd4MSaXoH1RUG/kzj9iTwJw5RHvHgwQN4enri3r17AAAnJycUKaLrwFWinMWvHSIAOA2gGABb6A5qcVCd6cl3DFGe8M8//8DDw0Md1MqVK4ewsDAUL15c5sooL+JXD+VtJ6DanfkpgKc6bl8IVUizkLAmIpJVeHg4PDw8cP/+fQCqoBYaGgoXFxeZK6O8irtBKW9KBFAbwN9p3L4KwADpyiEiw5Ac1P755x8AQPny5REaGopixYrJXBnlZexZo7xnDABz6A5qf0PVk8agRpTn3L9/H02bNlUHtQoVKiAsLIxBjWTHnjXKO34FMDSN2x5DdcwaEeVZp06dwoMHDwAAFStWxOHDh1G0aFGZqyLKBT1rixcvhpubG6ysrFCvXj2cOXMm3eUXLlyIChUqwNraGq6urhg1ahRiY2MlqpZksQ+q49J0BbVpUPWkMagR5Xmff/45VqxYAXd3d4SGhjKokcEw6rC2detWjB49GlOnTsWFCxdQrVo1eHt749mzZzqX37x5MyZMmICpU6fi+vXrWLVqFbZu3YqJEydKXDlJIgGqwWvb6bjNB8AHAFMlrYiIDNyXX36JCxcucIgOMihGHdbmz5+PQYMGoX///nB3d8eyZctgY2OD1atX61z+r7/+QsOGDdGrVy+4ubmhZcuW+Pzzzz/aG0dG6ARUZ3Ae1nHbPQBrAVhJWRARGZrbt2/jr7/+StVuaWkpQzVEaTPaY9bi4+Nx/vx5+Pv7q9tMTEzg5eWFkydP6rxPgwYNsHHjRpw5cwZ169bFvXv3EBQUhL59+6b5OHFxcYiLi1PPx8TEAAASEhKQkJCgp2dD6Unezhnd3oq1CpgN1n5pC2eBxDOJQPJeDf7p0pXZbU7Zx20urVu3bqFFixaIjIxEpUqV0Lt3b7lLyhP4+s4aow1rUVFRSEpKgrOzs1a7s7Mzbty4ofM+vXr1QlRUFBo1agQhBBITEzF06NB0d4MGBARg+vTpqdpDQ0NhY2OTvSdBmRISEpLu7TYRNvAc6QmzOO2X9Z0Od3B1wFXgIlT/KMM+ts1J/7jNc97jx4/x7bff4tWrVwCAGTNmIH/+/DA1NZW5stzv/fv3cpdglIw2rGVFWFgYZs+ejSVLlqBevXq4c+cORo4ciZkzZ2Ly5Mk67+Pv74/Ro0er52NiYuDq6gpPT08UKlRIqtLztISEBISEhKBFixYwNzdPvUA4YF5eRzuAhNMJKFmjJEqiZM4Wmct8dJuT3nGbS+PmzZsYNmyYOqi5ublxHDUJvXjxQu4SjJLRhjVHR0eYmpoiMjJSqz0yMjLNA0MnT56Mvn374ssvvwQAVK1aFe/evcPgwYMxadIkmJikPoTP0tJS5/EL5ubm/ECVWKptngDAC8DRNO7wDDAvzL9RdvB1Lj1u85xz48YNtGjRAhEREQCATz75BGPGjEGxYsW4zSXC7Zw1RnuCgYWFBWrVqoVDhw6p25RKJQ4dOoT69evrvM/79+9TBbLkbm8hRM4VS/p3CaoTCHQFtQNQDcdRWMqCiMiQXb9+HR4eHuqgVr16dfz555/Inz+/zJURfZzR9qwBwOjRo+Hj44PatWujbt26WLhwId69e4f+/fsDAPr16wcXFxcEBAQAANq3b4/58+ejRo0a6t2gkydPRvv27XmsgjE5CKCFjvY90D1MBxHladeuXUOzZs3Ue2Jq1KiBkJAQBjUyGkYd1nr06IHnz59jypQpiIiIQPXq1REcHKw+6eDBgwdaPWnffvstFAoFvv32Wzx+/BiFCxdG+/btMWvWLLmeAmVWTwBbU7TVAnAKRv5qJqKckJCQgPbt26uDWs2aNRESEoKCBQvyzEQyGkb/9ebn5wc/Pz+dt4WFhWnNm5mZYerUqZg6lSOhGhvzt+Ywt0hxrIMtgGMAqstQEBEZBXNzc6xevRpt2rRBpUqVEBISAgcHB7nLIsoUow9rlPspjinQpk+b1Dc8BsC9GET0EU2bNsXBgwdRsWJFBjUySkZ7ggHlAS8A9ADMmuv4TaEEgxoR6RQREZHqpLH69eszqJHRYlgjwzQDgCOAbSnaZ0F1pqdC8oqIyAj8/fffqFKlCiZPnsyz/CnX4G5QMizvoDoWTYfEFYkw+5IvWSLS7dKlS/Dy8sKLFy8wa9YslChRAoMHD5a7LKJsY88aGY7/QWdQS1yZiD92/wHhw1/JRKTbxYsX0bx5c/UI+fXr10fPnj1lropIPxjWyDBMBVBNR3sSIPoJ7vYkojRduHABzZs3x8uXLwEADRo0QHBwMMdRo1yDYY3ktwSqY9Q0LYPq2DS+QokoHefPn4eXl5f6Wp8NGzZkUKNchwcAkbz6AtiYom0VgAEy1EJERuXcuXNo0aIFXr9+DQBo1KgRgoKCYGdnJ29hRHrGsEby+Rmpg9o/AErIUAsRGZVz587By8sL0dHRAIDGjRtj3759DGqUK3EnE8njGoCRKdpOgUGNiDLEwcFBHcyaNGnCHjXK1RjWSHo7AVRO0XYBQD0ZaiEio1SmTBmEhobi888/x759+2Brm8aYP0S5AHeDknQEgM4Afk/RfgFADcmrISIjV7ZsWWzevFnuMohyHHvWSDqNkTqo7QSDGhF91MmTJ+Hj44OEhAS5SyGSHHvWSBrOAJ5pzNcAEAZe35OIPuqvv/6Ct7c33r59i3fv3uG3336Dubm53GURSYY9a5TzhkI7qJWAatcngxoRfcSJEyfUQQ0AoqOjkZiYKHNVRNJiWKOc5Qfg1xRt9+UohIiMzfHjx7WCWosWLRAYGAhra2uZKyOSFneDUs7RdYmoF+BPBCL6qGPHjqF169Z49+4dAMDb2xu7d+9mUKM8iV+bpH+J0B3U7gAoKHEtRGR0jhw5ohXUWrVqhd9//51BjfIs9qyRfr0HkE9HewL4aiOijwoLC0Pbtm3x/v17AEDr1q2xa9cuWFlZyVwZkXz49Un6pSuoKaG7p42ISIMQAjNnzlQHtTZt2mDXrl2wtLSUuTIieTGskX48BVAsRZsbgLtgUCOiDFEoFNi5cydatmwJJycn7Ny5k0GNCAxrpA+tAPypo51nfRJRJhUoUAAhISGwsrJiUCP6F08woKwTUPWapQxq9aE6Ro2I6COOHTuGFy9eaLXZ29szqBFpYFijrBHQ/epZB+AvsM+WiD7qwIEDaNmyJVq0aIGXL1/KXQ6RwWJYo8y7g9SvnNJQnUjQT/pyiMj4/Pnnn+jQoQNiY2Nx8eJFzJs3T+6SiAwWwxplTjMA5XS080QCIsqg4OBgfPbZZ4iLiwMAdO7cGTNmzJC5KiLDxbBGGbcGQGiKtjZQ7RIlIsqAoKAgraDWpUsXbNmyhRdmJ0oHjyyijFkJYFCKtjMA6shQCxEZpX379qFz586Ij48HAHTr1g2bNm1iUCP6CIY1yhjNoGYC4A0AG5lqISKjs3fvXnTu3BkJCapTxXv06IGNGzfCzIxfQ0Qfw92g9HG2KeafgUGNiDLs7NmzWkGtZ8+eDGpEmcCwRukrD+CdxvwnAArJVAsRGaUaNWqgS5cuAIBevXphw4YNDGpEmcB3C6VtOoDbKdouylEIERkzMzMzbNiwAU2aNMGgQYMY1IgyiT1rpNthANM05t2hGkeNrxgiyoDY2FiteTMzMwwbNoxBjSgL+NVLqT0D0DxF2xVwHDUiypCdO3eifPnyuHHjhtylEOUKDGukbR4A5xRtp8GgRkQZsmPHDvTo0QMPHz6Ep6cnHj16JHdJREaPYY3+sxXAuBRt1QDUlaEWIjI627dvR8+ePZGUlAQAaN26NYoWLSpzVUTGj2GNVK4C6JmibSmAS9KXQkTGZ+vWrfj888/VQW3AgAFYuXIlTE1NZa6MyPjxSE9SaZRifh9Ul5IiIvqILVu2oE+fPuqgNnDgQCxfvhwmJuwPINIHvpNINRzHa43538CgRkQZsnnzZvTu3Vsd1L788ksGNSI947uJgJoa06ZIvTuUiEiHTZs2oW/fvlAqlQCAwYMH49dff2VQI9IzvqPyupRneV6VpQoiMkIPHjxQB7UhQ4Zg6dKlDGpEOYDHrOVlP6eYdwNQQYY6iMgo+fv7IykpCU+ePMGiRYsY1IhyCMNaXvUawMgUbfdkqIOIjNq3334LIQQUCg7GSJRT+DMor+qeYv49OPAtEaVr7dq1OHDgQKp2BjWinMWwlhctBhCiMT8PgLVMtRCRUVi9ejUGDBiAzz77DAcPHpS7HKI8hWEtr6kAwC9F2xg5CiEiY7Fy5UoMHDgQQgjExsbq7F0jopzDsJaXTARwK0XbUzkKISJjsWLFCgwaNEg9//XXX+P777+XsSKivIcnGOQVVwAEpGiLBpBfhlqIyCgsX74cQ4YMUc+PHj0aP/zwA49RI5IYe9bygksAqqZoewEGNSJK07Jly7SC2pgxYxjUiGTCsJYX1EgxfxBAQTkKISJjsHTpUgwbNkw9P3bsWMydO5dBjUgmDGu53cIU89MBNJehDiIyCuHh4Rg58r9BGMePH4/vv/+eQY1IRgxrud2oFPNTZKmCiIyEm5sbfvvtN5iamsLf3x8BAQEMakQy4wkGudn/Usy/lKUKIjIyXbp0waVLl1C5cmUGNSIDwJ613EoAqKUxXxCAg0y1EJFB+9//Uv6yA6pUqcKgRmQgGNZyq+UAEjXmf5epDiIyaAsWLEC1atWwePFiuUshojQwrOVGlwAM1ZhvCqCxPKUQkeGaP38+Ro8eDQDw8/PD2bNnZa6IiHRhWMuNUg7VsVWWKojIgP3www/45ptv1PPTpk1DnTp1ZKyIiNLCsJbb3EwxvwCAsxyFEJGhmjt3LsaOHauenzFjBqZOnSpjRUSUHp4NmttUTDH/tRxFEJGh+v777zFhwgT1/HfffYdJkybJWBERfQx71nKTH1PM75ClCiIyUAEBAVpBbdasWQxqREaAPWu5RSKAMSnaushRCBEZovnz52PixInq+ZTBjYgMF3vWcosqKebfyVIFERkoDw8PODioBltMuSuUiAwbe9Zyg23QPrFgFAAbmWohIoNUs2ZNHDx4EMePH8dXX30ldzlElAkMa7nBuBTz82WpgogMjBBC6yoENWvWRM2aNWWsiIiygrtBc4N/NKavy1YFERkIIQSmTp2K4cOHQ6lUyl0OEWUTe9aM3bEU8xVkqYKIDERyUJs5cyYAQKFQYPHixbzOJ5ERY1gzdk00plsA4OcxUZ4lhMDkyZMxa9YsdVvFihUZ1IiMHMOaMbuYYv57WaogIgMghMCkSZMQEBCgbvvll1/g5+cnY1VEpA8Ma8ZsUYr5lNcEJaI8QQiBiRMnYs6cOeq2RYsWwdfXV8aqiEhfGNaM1SMAqzXmd8pVCBHJSQiBCRMmYO7cueq2xYsXY/jw4TJWRUT6xLBmrCqnmG8rSxVEJCMhBMaNG4cffvhB3bZ06VIMHTpUxqqISN9kHbojNjZWzoc3XtEAYjTm9wGwlKkWIpJNTEwM9uzZo55ftmwZgxpRLiR5WFMqlZg5cyZcXFxga2uLe/fuAQAmT56MVatWSV2OcSqQYr6NHEUQkdzs7e0RGhqKihUr4tdff8WQIUPkLomIcoDkYe27777D2rVrMXfuXFhYWKjbq1SpgpUrV0pdjvEJTTG/XpYqiMhAFC1aFBcvXsTgwYPlLoWIcojkYW39+vVYvnw5evfuDVNTU3V7tWrVcOPGDanLMT7NUsz3laUKIpKBEAJLlizB+/fvtdqtrKxkqoiIpCB5WHv8+DHKli2bql2pVCIhIUHqcoxLyjM+r8hSBRHJQAiBkSNHwtfXF+3bt08V2Igo95I8rLm7u+PYsZTXSAJ27NiBGjU4UFi6xmtMl0fqM0KJKFcSQmDEiBH45ZdfAAChoaE4cuSIzFURkVQkH7pjypQp8PHxwePHj6FUKrFr1y7cvHkT69evx969e6Uux3i8BXBXYz513iWiXEipVMLPzw9Lly4FoLrW59q1a9G6dWuZKyMiqUjes/bZZ59hz549OHjwIPLly4cpU6bg+vXr2LNnD1q0aCF1OcZjjcZ0UQBOchVCRFJRKpXw9fXVCmrr1q1Dv379ZK6MiKQky6C4jRs3RkhIiBwPbby+0pj+UrYqiEgiSqUSw4cPx6+//goAMDExwbp169CnTx+ZKyMiqUnes1a6dGm8ePEiVfvr169RunRpqcsxDlNTzPNHNVGuplQqMXToUK2gtmHDBgY1ojxK8p618PBwJCUlpWqPi4vD48ePpS7HOMxIMZ/6ZFoiykUWLVqEFStWAFAFtU2bNqFnz54yV0VEcpGsZy0wMBCBgYEAgD///FM9HxgYiN27d2PmzJlwc3PL9HoXL14MNzc3WFlZoV69ejhz5ky6y79+/Rq+vr4oWrQoLC0tUb58eQQFBWXlKUnjfynm38hSBRFJaNCgQfDy8oKpqSk2b97MoEaUx0nWs9axY0cAqgNkfXx8tG4zNzeHm5sbfvzxx0ytc+vWrRg9ejSWLVuGevXqYeHChfD29sbNmzfh5JT6CPz4+Hi0aNECTk5O2LFjB1xcXPDPP/+gQIECWX1aOSsRQLUUbbZyFEJEUrK2tsYff/yBkydPonnz5nKXQ0QykyysKZVKAECpUqVw9uxZODo6Znud8+fPx6BBg9C/f38AqosY79u3D6tXr8aECRNSLb969Wq8fPkSf/31F8zNzQEgS715kvFMMX9aliqIKIclJSUhKipKq83GxoZBjYgAyHDM2v379/Wynvj4eJw/fx7+/v7qNhMTE3h5eeHkyZM67xMYGIj69evD19cXf/zxBwoXLoxevXph/PjxWpe+0hQXF4e4uDj1fExMDAAgISEhZ6+4EA2YHzdXzwo3gcQaiUAevMhD8nbmFS6kw20unaSkJAwaNAinTp2Cv78/t7mE+DqXHrd11sgydMe7d+9w5MgRPHjwAPHx8Vq3ffXVV2ncS1tUVBSSkpLg7Oys1e7s7JzmNUbv3buHw4cPo3fv3ggKCsKdO3cwfPhwJCQkYOrUlKdcqgQEBGD69Omp2kNDQ2FjY5OhWrPi0xmfwhn/PbfAhYGAAR9aJwUO9yI9bvOclZSUhJ9//ll9NYLp06ejQIECaf54pJzB17l0eJm0rJE8rF28eBFt2rTB+/fv8e7dOxQsWBBRUVGwsbGBk5NThsNaViiVSjg5OWH58uUwNTVFrVq18PjxY8ybNy/NsObv74/Ro0er52NiYuDq6gpPT08UKlQox2o17/hfr1riykS0adMmxx7L0CUkJCAkJAQtWrRQ776mnMVtnvOSkpIwYMAAdVAzMzNDr1690KpVK25zifB1Lj1dQ3fRx0ke1kaNGoX27dtj2bJlsLe3x6lTp2Bubo4+ffpg5MiRGV6Po6MjTE1NERkZqdUeGRmJIkWK6LxP0aJFYW5urvWrtVKlSoiIiEB8fDwsLCxS3cfS0hKWlpap2s3NzXPuzZ2oPWvW30yGEfEMT45uc9KJ2zxnJCYmYuDAgfjtt98AqLbzb7/9BjMzM25zGXCbS4fbOWskjwCXLl3CN998AxMTE5iamiIuLg6urq6YO3cuJk6cmOH1WFhYoFatWjh06JC6TalU4tChQ6hfv77O+zRs2BB37txRn+wAALdu3ULRokV1BjXZDNeYdgCDGlEukpiYiL59+2Lz5s0AVF9eO3bsQIcOHWSujIgMleQxwNzcHCYmqod1cnLCgwcPAAD29vZ4+PBhptY1evRorFixAuvWrcP169cxbNgwvHv3Tn12aL9+/bROQBg2bBhevnyJkSNH4tatW9i3bx9mz54NX19fPT07PVmhMf2tbFUQkZ4lJiaid+/e2LJlCwDV5+HOnTsZ1IgoXZLvBq1RowbOnj2LcuXKoWnTppgyZQqioqKwYcMGVKlSJVPr6tGjB54/f44pU6YgIiIC1atXR3BwsPqkgwcPHqiDIQC4urrizz//xKhRo/DJJ5/AxcUFI0eOxPjx4/X6HLPlYIr5UbJUQUR6lpiYiF69emH79u0AVHsHdu7ciXbt2slcGREZOsnD2uzZs/HmjWoY/lmzZqFfv34YNmwYypUrh1WrVmV6fX5+fvDz89N5W1hYWKq2+vXr49SpU5l+HMm00Jh2AaCQqxAi0idTU1MULlwYgCqo7d69O0+fOEREGSd5WKtdu7Z62snJCcHBwVKXYLj+TjG/RZYqiCgHKBQKLFq0CObm5mjZsiWDGhFlmMEcun7hwgXuDqieYr6RHEUQUU5RKBRYuHAhgxoRZYqkYe3PP//EmDFjMHHiRNy7dw8AcOPGDXTs2BF16tTROkszz1mTYv6BLFUQkZ7Ex8fjiy++wLlz5+QuhYiMnGRhbdWqVWjdujXWrl2L77//Hp9++ik2btyI+vXro0iRIrhy5QqCgvLwEP0pr2HvKksVRKQH8fHx6NatG9atW4cWLVrg/PnzcpdEREZMsrD2008/4fvvv0dUVBS2bduGqKgoLFmyBJcvX8ayZctQqVIlqUoxTFc1pp/KVgURZVNcXBy6du2KwMBAAEBsbCxev34tb1FEZNQkC2t3795Ft27dAACdO3eGmZkZ5s2bh+LFi0tVguF6nmLeWedSRGTgkoPanj17AADW1tbYu3cvmjdvLnNlRGTMJDsb9MOHD+oLnysUClhaWqJo0aJSPbxhW5BinsN1EBmd2NhYdOnSRX04R3JQa9asmcyVEZGxk3TojpUrV8LW1haAaoDItWvXwtHRUWuZnLyQu8EK0JheJFsVRJRFsbGx6Ny5M/bv3w8AsLGxwb59++Dh4SFvYUSUK0gW1kqUKIEVK/67jlKRIkWwYcMGrWUUCkXeC2sixfxAWaogoiyKjY1Fp06d1GNG2tjYICgoCE2bNpW5MiLKLSQLa+Hh4VI9lHE5nGLeSpYqiCiLDh48qA5q+fLlQ1BQEJo0aSJzVUSUmxjMoLh51h6N6YayVUFEWdSuXTssWbIEtra22L9/P4MaEekdw5rcftKY/ka2KogoG4YNG4Y7d+6gcePGcpdCRLkQw5qcXqSY95alCiLKhPfv3+PQoUOp2p2dOeYOEeUMhjU5bU0xbyNLFUSUQe/fv0f79u3h7e2NHTt2yF0OEeURDGtyWqoxncdOgiUyNu/evUO7du1w+PBhJCUlYciQIYiJiZG7LCLKA2QJa3fv3sW3336Lzz//HM+ePQMA7N+/H1evXv3IPXOZaxrTPrJVQUQfkRzUQkNDAQD58+dHUFAQ8ufPL3NlRJQXSB7Wjhw5gqpVq+L06dPYtWsX3r59CwD4+++/MXXqVKnLkZdSY7qGbFUQUTrevn2LNm3aICwsDABgb2+PkJAQ1KtXT97CiCjPkDysTZgwAd999x1CQkJgYWGhbm/WrBlOnToldTnyeZtinpeYIjI4yUHt6NGjAP4LanXr1pW5MiLKSyQPa5cvX0anTp1StTs5OSEqKkrqcuSzSWO6sGxVEFEa3rx5g9atW+PYsWMAgAIFCuDgwYOoU6eOzJURUV4jeVgrUKAAnj59mqr94sWLcHFxkboc+QzVmB4kWxVEpIMQAl26dMHx48cBAA4ODjh48CBq164tc2VElBdJHtZ69uyJ8ePHIyIiAgqFAkqlEidOnMCYMWPQr18/qcuRR8rrgfJMUCKDolAoMH78eFhZWamDWq1ateQui4jyKMmuDZps9uzZ8PX1haurK5KSkuDu7o6kpCT06tUL3377rdTlyOPPFPMcS5PI4DRv3hx79uxBoUKFUKMGzwAiIvlIHtYsLCywYsUKTJ48GVeuXMHbt29Ro0YNlCtXTupS5DNWY7qPbFUQkYa4uDhYWlpqtXl5eclUDRHRfyQPa8ePH0ejRo1QokQJlChRQuqHl18SgCsa8/5yFUJEyaKjo+Ht7Y02bdpgypQpcpdDRKRF8rDWrFkzuLi44PPPP0efPn3g7u4udQny2qkxbQUgjz19IkPz+vVreHt748yZMzh9+jTy5cuHb775Ru6yiIjUJD/B4MmTJ/jmm29w5MgRVKlSBdWrV8e8efPw6NEjqUuRh+b1nzvIVgURAXj16hVatGiBM2fOAAAcHR3RsmVLmasiItImeVhzdHSEn58fTpw4gbt376Jbt25Yt24d3Nzc0KxZM6nLkd5Ljen+slVBlOclB7Vz584BAAoXLozQ0FBUrVpV5sqIiLTJeiH3UqVKYcKECZgzZw6qVq2KI0eOyFmONM5rTFeWrQqiPO3ly5fw8vLC+fOqN6STkxNCQ0NRpUoVmSsjIkpNtrB24sQJDB8+HEWLFkWvXr1QpUoV7Nu3T65ypKN5PVAn2aogyrOSg9qFCxcAAM7OzggNDUXlyvz1RESGSfITDPz9/bFlyxY8efIELVq0wE8//YTPPvsMNjY2UpcivVgA//w77QTAMp1liUjvXrx4AS8vL1y6dAnAf0GtUqVK8hZGRJQOycPa0aNHMXbsWHTv3h2Ojo5SP7y8LmpMN5KtCqI869WrV4iMjAQAFClSBKGhoahYsaLMVRERpU/ysHbixAmpH9JwBGtMF5etCqI8q2zZsggNDUWvXr2wadMmBjUiMgqShLXAwEC0bt0a5ubmCAwMTHfZDh1y8XgWMzSm89A164kMSYUKFXDu3DkoFAq5SyEiyhBJwlrHjh0REREBJycndOzYMc3lFAoFkpKSpChJeik7FDlsB1GOe/bsGRYsWICZM2fCzOy/jzsGNSIyJpKENaVSqXM6T3mQYr6wLFUQ5RnPnj1Ds2bNcPXqVdy/fx8bN27UCmxERMZC8qE71q9fj7i4uFTt8fHxWL9+vdTlSOekxvQy2aogyhMiIyPh6emJq1evAlBdkzgiIkLmqoiIskbysNa/f39ER0enan/z5g3698/F+wavaUzby1YFUa4XEREBT09PXLumetO5urriyJEjKF6cZ/UQkXGSPKwJIXQeL/Lo0SPY2+fiFBOlMc2LtxPliKdPn8LT0xPXr18HAJQoUQJhYWEoU6aMzJUREWWdZAdw1KhRAwqFAgqFAs2bN9c6diQpKQn3799Hq1atpCpHerEa06Vlq4Io10oOajdv3gTwX1ArVaqUzJUREWWPZGEt+SzQS5cuwdvbG7a2turbLCws4Obmhi5dukhVjrSSANzUmLdNa0EiyoonT57A09MTt27dAgCULFkSoaGhDGpElCtIFtamTp0KAHBzc0OPHj1gZWUl1UPL77jGdHW5iiDKvb755ht1UHNzc0NoaCjc3NzkLYqISE8kP4/dx8dH6oeU33yNaWfZqiDKtZYsWYJbt27h1atXCA0NRcmSJeUuiYhIbyQJawULFsStW7fg6OgIBweHdAekfPnypRQlSUvzog3dZauCKNdycHBASEgI3r17B1dXV7nLISLSK0nC2oIFC2BnZ6eezlOjh8dCdc5t8ljA/WSshSiXePToEfLlywcHBwd1W8GCBVGwYEEZqyIiyhmShDXNXZ9ffPGFFA9pOF7jv6AGyLDjmSh3efDgATw9PVGwYEGEhISgQIECcpdERJSjJB9n7cKFC7h8+bJ6/o8//kDHjh0xceJExMfHS11OzgvSmG4nWxVEucI///wDDw8P3Lt3D+fOncOwYcPkLomIKMdJHtaGDBmiPmvr3r176NGjB2xsbLB9+3aMGzdO6nJynq/GtKlsVRAZvfDwcHh4eOD+/fsAgHLlyuGHH36QuSoiopwneVi7desWqlevDgDYvn07mjZtis2bN2Pt2rXYuXOn1OXkvCIa03Nkq4LIqCUHtfDwcABA+fLlERYWBhcXF3kLIyKSgCyXm1IqVQdxHTx4EG3atAGgun5fVFRUenc1PgJAuMZ8RZnqIDJi9+/fR9OmTfHPP/8AACpUqICwsDAUK1ZM5sqIiKQheVirXbs2vvvuO2zYsAFHjhxB27ZtAag+kJ2dc9kgZE81pgvIVQSR8bp37x48PDzw4MEDAEDFihURGhqKokWLylwZEZF0JA9rCxcuxIULF+Dn54dJkyahbNmyAIAdO3agQYMGUpeTs2ZqTDeXrQoio/T48WOtoFapUiUGNSLKkyQfSOKTTz7ROhs02bx582BqmsuOwL+kMd1EriKIjJOTkxPq1q2Lhw8fwt3dHYcPH859ve9ERBkg26hf58+fx/Xr1wEA7u7uqFmzplyl5JxTGtODZauCyCiZm5vjt99+w6RJk/DNN98wqBFRniV5WHv27Bl69OiBI0eOqAezfP36NTw9PbFlyxYULlxY6pJyRniK+Tx03XqirBJCaF3hxNzcHHPnzpWxIiIi+Ul+zNqIESPw9u1bXL16FS9fvsTLly9x5coVxMTE4KuvvpK6nJyzT2M6l+RPopx069YtfPrpp7hz547cpRARGRTJe9aCg4Nx8OBBVKpUSd3m7u6OxYsXo2XLllKXk3NiNKb7ylYFkVG4efMmPD098fTpU3h4eODIkSMoU6aM3GURERkEyXvWlEolzM3NU7Wbm5urx1/LFbZpTOeiDEqkbzdu3ICHhweePlWNdePo6MjrfRIRaZA8rDVr1gwjR47EkydP1G2PHz/GqFGj0Lx5LhrfwlJj2lG2KogM2vXr1+Hh4YGIiAgAQPXq1XHo0CEUKlRI5sqIiAyH5GFt0aJFiImJgZubG8qUKYMyZcqgVKlSiImJwS+//CJ1OTnntcY0r1xAlMq1a9fg6emJyMhIAECNGjUY1IiIdJD8mDVXV1dcuHABhw4dUg/dUalSJXh5eUldSs66qTFtI1sVRAbp6tWraNasGZ49ewYAqFmzJkJCQlCwYEGZKyMiMjyShrWtW7ciMDAQ8fHxaN68OUaMGCHlw0snOsW8QudSRHnSlStX0KxZMzx//hwAUKtWLYSEhMDBwUHmyoiIDJNkYW3p0qXw9fVFuXLlYG1tjV27duHu3buYN2+eVCVI55HcBRAZrv3796uDWu3atXHgwAEGNSKidEh2zNqiRYswdepU3Lx5E5cuXcK6deuwZMkSqR5eWv9oTA+UrQoigzRmzBhMnjwZderUYY8aEVEGSBbW7t27Bx8fH/V8r169kJiYqD5dP1f5oDGdi0YjIdIHhUKB6dOna13FhIiI0iZZWIuLi0O+fPn+e2ATE1hYWODDhw/p3MtI/aExXUu2KogMwqVLlxAaGqrVplAoYG1tLVNFRETGRdITDCZPngwbm/9OjYyPj8esWbNgb2+vbps/f76UJeWMBxrT9mkuRZTrXbx4EV5eXvjw4QOCgoLg4eEhd0lEREZHsrDWpEkT3Lx5U6utQYMGuHfvnnpe8wLORs1UY7qZbFUQyerChQvw8vLCq1evAACzZ89G06ZNc8/7nIhIIpKFtbCwMKkeSn53NaaLyFYFkWzOnz+PFi1aqINaw4YNsXPnTgY1IqIskPwKBnlC8tmgluAWpjzn3LlzWj1qjRo1wv79+2FnZydzZURExolRQt/eaEzHyVYFkSzOnj2LFi1a4PXr1wCAxo0bIygoiEGNiCgbGNb07ZbGdGPZqiCS3JkzZ7SCWpMmTRjUiIj0gGFN3zR71niZQ8ojoqOj0bp1a0RHq6615uHhgaCgINja2spcGRGR8WNY07ftGtM1ZauCSFL29vZYvHgxTExM4Onpib1792qNq0hERFknS1g7duwY+vTpg/r16+Px48cAgA0bNuD48eNylKNfmlvUNM2liHKdnj17Ijg4mEGNiEjPJA9rO3fuhLe3N6ytrXHx4kXExamOwo+Ojsbs2bOlLkf/1mtMt5WtCqIcl3wxdk0tWrTQGviaiIiyT/Kw9t1332HZsmVYsWIFzM3N1e0NGzbEhQsXpC5H/zSvWOAqWxVEOerEiRMoW7Ysli9fLncpRES5nuRh7ebNm2jSpEmqdnt7e/VZZEZNM6wVkq0Kohxz/PhxeHt7IyYmBkOGDMG+ffvkLomIKFeTPKwVKVIEd+7cSdV+/PhxlC5dWupy9O/Kv/8zqFEudOzYMbRq1Qrv3r0DAHh7e6NZM15TjYgoJ0ke1gYNGoSRI0fi9OnTUCgUePLkCTZt2oQxY8Zg2LBhUpeTc5LkLoBIv44cOYLWrVurg1qrVq3w+++/w9raWubKiIhyN8muDZpswoQJUCqVaN68Od6/f48mTZrA0tISY8aMwYgRI6QuR/9sALwH8FrmOoj0KCwsDG3btsX79+8BAK1bt8auXbtgZWUlc2VERLmf5GFNoVBg0qRJGDt2LO7cuYO3b9/C3d099wyemfjv/zVkrYJIb0JDQ9GuXTt1UGvbti127twJS0tLmSsjIsobZBsU18LCAu7u7qhbt262g9rixYvh5uYGKysr1KtXD2fOnMnQ/bZs2QKFQoGOHTtm6/G1JPz7v+QxmEj/QkNDtXrU2rVrx6BGRCQxySOFp6cnFApFmrcfPnw4U+vbunUrRo8ejWXLlqFevXpYuHAhvL29cfPmTTg5OaV5v/DwcIwZMwaNG+vxAp5KAOLfaYY1ygXs7OxgYWGBDx8+oH379ti+fTuDGhGRxCTvWatevTqqVaum/ufu7o74+HhcuHABVatWzfT65s+fj0GDBqF///5wd3fHsmXLYGNjg9WrV6d5n6SkJPTu3RvTp0/X7xmoURrT5mkuRWQ0ateujQMHDqBv377YsWMHgxoRkQwk7/9ZsGCBzvZp06bh7du3mVpXfHw8zp8/D39/f3WbiYkJvLy8cPLkyTTvN2PGDDg5OWHgwIE4duxYuo8RFxenvsoCAMTExAAAEhISkJCQoLWs4i8FzP7dpEqFEkkJPCVUH5K3c8rtTTlHc5vXqFEDq1at0mon/ePrXHrc5tLjts4ag9lZ16dPH9StWxc//PBDhu8TFRWFpKQkODs7a7U7Ozvjxo0bOu9z/PhxrFq1CpcuXcrQYwQEBGD69Omp2kNDQ1NdVqfU/lL4BJ8AAG643sDtoNsZegzKmJCQELlLyPUuXryIS5cu4YsvvoBCoeA2lwG3ufS4zaWTfPwrZY7BhLWTJ0/m+DAAb968Qd++fbFixQo4Ojpm6D7+/v4YPXq0ej4mJgaurq7w9PREoULaI9+a3Pxvr3L51uVRrk05/RSexyUkJCAkJAQtWrTQukQZ6VdwcDDmzJmDuLg4FCtWDC1btkTLli25zSXC17n0uM2l9+LFC7lLMEqSh7XOnTtrzQsh8PTpU5w7dw6TJ0/O1LocHR1hamqKyMhIrfbIyEgUKVIk1fJ3795FeHg42rdvr25TKpUAADMzM9y8eRNlypTRuo+lpaXO43TMzc1Tv7n/+m/SzM6Mx63pmc5tTnoRFBSErl27Ij4+HgAQEREBpVLJbS4DbnPpcZtLh9s5ayQ/wcDe3l7rX8GCBeHh4YGgoCBMnTo1U+uysLBArVq1cOjQIXWbUqnEoUOHUL9+/VTLV6xYEZcvX8alS5fU/zp06ABPT09cunQJrq7ZvPK6Zqbj5abISOzbtw+dOnVSB7Vu3bph48aNMDU1lbkyIiICJO5ZS0pKQv/+/VG1alU4ODjoZZ2jR4+Gj48Pateujbp162LhwoV49+4d+vfvDwDo168fXFxcEBAQACsrK1SpUkXr/gUKFACAVO1Zsk1julb2V0eU0/bs2YMuXbqoD/rt3r07Nm3aBCHER+5JRERSkTSsmZqaomXLlrh+/brewlqPHj3w/PlzTJkyBREREahevTqCg4PVJx08ePAAJiYSdSAWB/Do32mOcEAGLjAwEF27dlUHtZ49e2LDhg0wMzPjGVtERAZE8mPWqlSpgnv37qFUqVJ6W6efnx/8/Px03hYWFpbufdeuXau3OsDvNzISf/zxB7p166YOZZ9//jnWr18PMzODOeeIiIj+Jfkxa9999x3GjBmDvXv34unTp4iJidH6Z9SSz3MoL2sVROlKTEzExIkT1UGtV69eDGpERAZMsk/nGTNm4JtvvkGbNm0AAB06dNC67JQQAgqFAklJRjqQrFJjmof7kAEzMzPDn3/+CQ8PD9SvXx9r167lyQRERAZMsrA2ffp0DB06FKGhoVI9pLTiNKY5Fi4ZuOLFi+Ovv/5CoUKFGNSIiAycZGEt+eyypk2bSvWQ0orXmG4pWxVEOoWFhaFevXqwtrZWtzk5OclYERERZZSkx6xp7vbMdWI1pnkmKBmQ7du3w8vLC506dUJsbOzH70BERAZF0iOKy5cv/9HA9vLlS4mq0TPNS5G+ka0KIi1bt25F7969kZSUhD///BPLly/HV199JXdZRESUCZKGtenTp8Pe3l7Kh5TO3xrTvHoBGYAtW7agd+/e6kuqDRw4MM0hboiIyHBJGtZ69uyZe4+T0TzBwF22KogAAJs3b0bfvn3VQe3LL7/Er7/+Kt0A0UREpDeSfXLn6uPVAOCkxvSnslVBhE2bNmkFtcGDBzOoEREZMck+vXP9tQYdNaYtZKuC8riNGzeiX79+6qA2dOhQLF26lEGNiMiISbYbNPnLI9faqzHtKlsVlIft378f/fr1U/8wGjZsGBYvXpz7e7WJiHI5/tzWl6oa0wVlq4LysCZNmqjHMfT19WVQIyLKJXgxQH15rzGdX7YqKA/Lly8f9u7dizVr1sDX15dBjYgol2DPmr4c15jmMWskkfj4eK35fPnywc/Pj0GNiCgXYVjTF80+Sn5PkgRWrVqFGjVqICIiQu5SiIgoBzGs6UMCgES5i6C8ZMWKFfjyyy9x7do1NGvWDNHR0XKXREREOYRhTR8+aEx7yVYF5RHLly/H4MGD1fNt2rRB/vw8UJKIKLdiWNOHyxrTuXw4OZLXsmXLMGTIEPX8mDFjMG/ePB6jRkSUizGs6cN1jekXslVBudzSpUsxbNgw9fy4ceMwd+5cBjUiolyOYU0f/tGY7idbFZSLLV68GMOHD1fPjx8/HnPmzGFQIyLKAxjW9OGhxrSLbFVQLrVo0SL4+fmp5/39/REQEMCgRkSURzCs6cMmjWln2aqgXEgIgStXrqjnJ06ciFmzZjGoERHlIbyCgT7UB3Ds3+lqchZCuY1CocCSJUuQlJSEIkWKYMaMGQxqRER5DMOaPhzTmLaXrQrKpUxMTLB8+XIAYFAjIsqDuBtUH0poTPO7lLJp8eLFuHjxolabQqFgUCMiyqMY1vQh+fKM/C6lbPrhhx/g5+cHLy8v/P3333KXQ0REBoBhTR/e//t/WVmrICM3b948jB07FgDw8uVLhISEyFwREREZAoY1fYj5938LWasgI/b9999j3Lhx6vmZM2dizJgxMlZERESGgmEtuxI0pq/KVgUZsYCAAEyYMEE9P2vWLHz77bcyVkRERIaEZ4NmV5zGdFXZqiAjNXv2bEyaNEk9nzK4ERERsWctu+I1povLVgUZoe+++04rqM2ZM4dBjYiIUmHPWnY905g2l60KMjJnz57F5MmT1fNz585Vn1xARESkiT1r2fVaY/qmXEWQsalTpw4WLlwIQDVcB4MaERGlhT1r2XVBY7qDbFWQERo5ciSaNGmCGjVqyF0KEREZMPasZZfmMWvP0lyK8jghBK5du5aqnUGNiIg+hmEtuzTDWgvZqiADJoTA5MmTUa1aNfz+++9yl0NEREaGYS27nmpMF5CrCDJUQghMmjQJs2bNQmJiIrp3747w8HC5yyIiIiPCY9ay64HGNK8NShqEEJg4cSLmzJmjbluwYAHc3NzkK4qIiIwOw1p2FdGYLihbFWRghBCYMGEC5s6dq25bvHgxhg8fLmNVRERkjBjWskvzclM2slVBBkQIgXHjxuGHH35Qty1duhRDhw6VsSoiIjJWDGvZlagxza2Z5wkhMHbsWPz444/qtmXLlmHIkCEyVkVERMaM8SK7NMMar2CQ5/n7+2sFtV9//RWDBw+WsSIiIjJ2PBs0u4I0phl98zwPDw9YWloCAFasWMGgRkRE2cZ4kV21ABz4d5rHrOV5rVq1wu+//44nT55gwIABcpdDRES5AMNadiVpTOeTrQqSiRACCoX2mC2tWrWSqRoiIsqNuBs0uzTPBuUxa3mKEAIjRozQGkeNiIhI39izll2xGtMMa3mGUqmEn58fli5dCgAwMTHBuHHjZK6KiIhyI4a17Drz7/8KsJ8yj1AqlfD19cWyZcsAqIJasWLFZK6KiIhyK4Y1fRFyF0BSUCqVGDZsGJYvXw5AFdTWr1+P3r17y1wZERHlVgxrRBmkVCoxdOhQrFixAoAqqG3YsAG9evWSuTIiIsrNGNayQyl3ASQVpVKJIUOGYOXKlQBUQW3Tpk3o2bOnzJUREVFux7CWHZpngjaRrQrKYUqlEoMGDcLq1asBAKampti0aRN69Oghc2VERJQXMKxlB4ftyBOePHmCffv2AVAFtc2bN6N79+4yV0VERHkFz1/MjjiNaYa1XKt48eI4fPgwXFxc8NtvvzGoERGRpNizlh1RGtP/yFYFScDd3R23bt2CjQ2vKUZERNJiz1p2PNGYripbFaRnSUlJ+PXXX5GUlKTVzqBGRERyYFjLDs3doNGyVUF6lJSUBB8fHwwdOhRffPFFqsBGREQkNYa17NAMazwb1OglJiaiX79+2LRpEwBg69atuHDhgsxVERFRXsdj1rLjmca0pWxVkB4kJiaib9++2LJlCwDA3NwcO3bsQJ06dWSujIiI8jqGtew4ojHNPkqjlZiYiD59+mDr1q0AVEFt586daN++vcyVERERMaxlTxGN6YKyVUHZkJCQgN69e2P79u0AAAsLC+zcuRPt2rWTuTIiIiIVhrXsiNWYrixbFZRFCQkJ6NWrF3bs2AFAFdR2796NNm3ayFwZERHRfxjWsuONxjSPWTM6U6ZMUQc1S0tL/P7772jVqpXMVREREWnjkVbZsVdjmlcwMDrffPMNPvnkE1haWuKPP/5gUCMiIoPEnrXsqA7g8L/TzjLWQVni6OiIQ4cO4erVq2jatKnc5RAREenEnrXs0Bwv1Vq2KiiD4uPjERMTo9Xm6OjIoEZERAaNYS07NC83ZSpbFZQBcXFx6Nq1K7y9vVMFNiIiIkPGsJYdtzWmGdYMVnJQ27NnD06dOoXOnTtDCCF3WURERBnCsJYdFTWmuSUNUlxcHLp06YK9e1Vng1hbW2PSpElQKBQyV0ZERJQxjBjZkfDv/w6yVkFpiI2NRefOnbFv3z4AgI2NDYKCguDp6SlzZURERBnHs0GzI/lC7jy5wODExsaiU6dOCA4OBvBfUOPJBEREZGwY1rJKAIj6d5pjrBmU2NhYdOzYEX/++ScAIF++fAgKCkKTJk1kroyIiCjzGNayKg7/XW6K1wU1GB8+fEDHjh1x4MABAKqgtn//fjRu3FjmyoiIiLKGx6xlleZ1QZ1kq4JSMDU1hbW1ar+0ra0tgoODGdSIiMioMaxlVZTGdD7ZqqAULCwssG3bNvTu3RvBwcFo1KiR3CURERFlC3eDZpHijcbQD9wNalAsLCywceNGucsgIiLSC/asZVWixrSdbFXkee/evUPfvn1x7949uUshIiLKEQxrWaUZ1tg/KYt3796hbdu22LhxIzw9PXH//n25SyIiItI7hrWs0gxrvNSU5N6+fYs2bdrgyJEjAIDo6GhERUV95F5ERETGh31CWZWgMc2tKKnkoHbs2DEAgL29PUJCQlCnTh2ZKyMiItI/9qxlkeK2xgkG7FmTzJs3b9C6dWt1UCtQoAAOHjzIoEZERLlWrghrixcvhpubG6ysrFCvXj2cOXMmzWVXrFiBxo0bw8HBAQ4ODvDy8kp3+bQIK/HfTELay5H+JAe148ePAwAcHBxw8OBB1K5dW+bKiIiIco7Rh7WtW7di9OjRmDp1Ki5cuIBq1arB29sbz54907l8WFgYPv/8c4SGhuLkyZNwdXVFy5Yt8fjx40w9riJBo2fNPTvPgDLi/fv3aNeuHU6cOAHgv6BWq1YtmSsjIiLKWUYf1ubPn49Bgwahf//+cHd3x7Jly2BjY4PVq1frXH7Tpk0YPnw4qlevjooVK2LlypVQKpU4dOhQ5h5Y81h2i6zXTxlz7NgxnDx5EgBQsGBBHDp0CDVr1pS5KiIiopxn1IfGx8fH4/z58/D391e3mZiYwMvLS/3F/jHv379HQkICChbUPbJtXFwc4uLi1PMxMTEAAPHgv92giQmJEAki1X1JPxISEtCyZUtYW1tj7dq1CA4ORpUqVZCQwP3POSV523IbS4fbXHrc5tLjts4aow5rUVFRSEpKgrOzs1a7s7Mzbty4kaF1jB8/HsWKFYOXl5fO2wMCAjB9+vRU7f+8+wcF/710wZlbZ/A86Hkmq6fMUCgUaNy4MSpXrownT57gyZMncpeUJ4SEhMhdQp7DbS49bnPpvH//Xu4SjJJRh7XsmjNnDrZs2YKwsDBYWVnpXMbf3x+jR49Wz8fExMDV1RUlipZQt9X1qgtRjz1r+vT69Wvcvn0bderUQUJCAkJCQtCyZUuYm5vLXVqekLzNW7RowW0uEW5z6XGbS+/Fixdyl2CUjDqsOTo6wtTUFJGRkVrtkZGRKFKkSLr3/eGHHzBnzhwcPHgQn3zySZrLWVpawtLSMlW7ScJ/h/uZ2ZgBfJ/rzatXr9CmTRtcv34dwcHBqFevHgDA3NycH6gS4zaXHre59LjNpcPtnDVGfYKBhYUFatWqpXVyQPLJAvXr10/zfnPnzsXMmTMRHByc5WEfTI5qbDq+9vTm1atXaNGiBc6dO4d3797Bx8eHxzgQEVGeZtQ9awAwevRo+Pj4oHbt2qhbty4WLlyId+/eoX///gCAfv36wcXFBQEBAQCA77//HlOmTMHmzZvh5uaGiIgIAICtrS1sbW0z/LiikgCSL0XJC7nrxcuXL9GiRQtcuHABAODk5ITAwED+EiMiojzN6MNajx498Pz5c0yZMgURERGoXr06goOD1ScdPHjwACYm//WCLV26FPHx8ejatavWeqZOnYpp06Zl+HEVIRrjrDlk6ykQVEHNy8sLFy9eBKA6SeTw4cNwd3dnzxoREeVpRh/WAMDPzw9+fn46bwsLC9OaDw8P18+DlgJw69/pfPpZZV714sULeHl54dKlSwBUQS00NBSVKlWStzAiIiIDYNTHrMlKcyDcXBF55REVFYXmzZurg1qRIkUQFhbGoEZERPQvhrWsUv77v7WsVRi1xMREtGzZEn///TcAoGjRoggLC0PFihVlroyIiMhwMKxlVXJY4xbMMjMzM4wePRoKhQJFixZFaGgoKlSoIHdZREREBoU78LKKYU0v+vTpA3Nzc9SoUQPly5eXuxwiIiKDw7CWVUn//s+wlinx8fGwsLDQauvRo4dM1RARERk+Ro2sSr66lKmsVRiVyMhI1KpVC2vXrpW7FCIiIqPBsJZVyWGNWzBDIiIi4OnpiStXrmDAgAHYsmWL3CUREREZBUaNrOIxaxn29OlTeHp64vr16wAAV1dX1K1bV+aqiIiIjAOPWcsqhrUMSQ5qN2/eBACULFkSoaGhKFWqlMyVERERGQeGtSxSPPz3clMMa2l68uQJPD09ceuW6lIPJUuWRFhYGNzc3OQtjIiIyIgwamTXS7kLMEyPHz+Gh4eHOqi5ubkxqBEREWUBw1oWCet/zzCIlbcOQ/T48WN4enri9u3bAIBSpUoxqBEREWURd4NmVfI4a9XlLMIwPX78GBEREQD+C2olSpSQuSoiIiLjxJ61rEr893+Os5ZK3bp1ERwcjOrVq+PIkSMMakRERNnAnrUsUij/PcGAW1CnBg0a4Pz58zAx4e8BIiKi7OA3aXaxZw0PHjxAQEAAhBBa7QxqRERE2cd+oezK41swPDwcnp6eCA8Px4sXLzBv3jwoFAq5yyIiIso12PWRXdfkLkA+4eHh8PDwQHh4OABgz549iI6OlrcoIiKiXIZhLbs+kbsAedy/fx8eHh74559/AAAVKlRAWFgYChQoIG9hREREuQzDWna5yF2A9O7du6cV1CpWrIjQ0FAULVpU5sqIiIhyH4a17IqRuwBp3b17Fx4eHnjw4AEAoFKlSgxqREREOYhhLbss5C5AOnfu3IGHhwcePnwIAHB3d0doaCiKFCkic2VERES5F8NadlWUuwBpCCEwYMAAPHr0CABQuXJlHD58GM7OzjJXRkRElLsxrGVXHulZUygU2LBhA9zc3FClShUGNSIiIonk8VHC9MBc7gKkU7JkSYSFhcHGxgaFCxeWuxwiIqI8gT1r2dVA7gJyzv379xEXF6fVVrJkSQY1IiIiCTGsZVd5uQvIGTdu3ECDBg3QpUuXVIGNiIiIpMOwll0Ochegf9evX4eHhwciIiKwb98+TJo0Se6SiIiI8iyGtezKZcesXbt2DZ6enoiMjAQA1KhRA/7+/jJXRURElHcxrGWHGYBcdM3yq1evagW1mjVr4uDBgyhUqJDMlREREeVdDGvZkSh3Afpz5coVeHp64tmzZwCAWrVq4eDBgyhYsKDMlREREeVtDGuEy5cvo1mzZnj+/DkAoHbt2ggJCYGDQy48II+IiMjIcJy17HCTu4Dsu3HjBpo1a4aoqCgAQJ06dXDgwAEUKFBA3sKIiIgIAHvWsueB3AVkn4uLCypUqAAAqFu3LoMaERGRgWFYy442cheQfXZ2dti/fz/8/PwY1IiIiAwQd4Nmxz25C8gaIQQUiv9OY7Wzs8Mvv/wiY0VERESUFvasZYcRXmrqwoUL8PDwUJ9MQERERIaNYS07jCzvnD9/Hl5eXjh69KjW2Z9ERERkuBjWsqOu3AVk3Llz5+Dl5YVXr14BAAoUKAArKyuZqyIiIqKPYVjLDlO5C8iYs2fPokWLFnj9+jUAoHHjxggKCoKdnZ28hREREdFHMaxlhxFsvTNnzmgFtSZNmjCoERERGREjiBsGzMB71k6fPo0WLVogOjoaANC0aVMEBQXB1tZW5sqIiIgooxjWssOAt96pU6fQsmVLxMTEAAA8PDywb98+5MuXT+bKiIiIKDMMOG4YAQPuWdu0aZM6qDVr1oxBjYiIyEhxUNzsMOCou3DhQrx58waPHj1CYGAgbGxs5C6JiIiIsoBhLTsMuGfN1NQUq1atQnx8PKytreUuh4iIiLLIgPuGjECi3AX85/jx47h8+bJWm6mpKYMaERGRkWNYy44CchegcvToUbRq1QrNmjXDlStX5C6HiIiI9IhhLTsqyl0AcOTIEbRp0wbv3r1DVFQUvv/+e7lLIiIiIj1iWMsOS3kfPiwsTB3UAKB169ZYsWKFvEURERGRXjGsZYe5fA8dGhqKNm3a4P379wCANm3aYNeuXbzeJxERUS7DsJYdMoW1w4cPo23btvjw4QMAoG3btgxqREREuRTDWnbIMMbsoUOHtIJa+/btsXPnTlhayrxPloiIiHIEx1nLjkLSPtzDhw/Rvn17xMbGAgA+++wzbNu2DRYWFtIWQkR5lhACiYmJSEpKkruUbEtISICZmRliY2NzxfMxFObm5jA1NeCBSI0Qw1p2SNwv6erqihkzZmDs2LHo2LEjtm7dyqBGRJKJj4/H06dP1cfKGjshBIoUKYKHDx9CoVDIXU6uoVAoULx4cdja2spdSq7BsJYdMuxEHjNmDMqUKYO2bdsyqBGRZJRKJe7fvw9TU1MUK1YMFhYWRh9wlEol3r59C1tbW5iY8KggfRBC4Pnz53j06BHKlSvHHjY9YVjLDgk+p16+fImCBQtqtXXq1CnnH5iISEN8fDyUSiVcXV1zzbWGlUol4uPjYWVlxbCmR4ULF0Z4eDgSEhIY1vSEr84sEgqR448RFBQENzc37N27N8cfi4goIxhq6GOMvcfVEPFdl1U5vOX27duHTp064c2bN+jSpQvOnj2bsw9IREREBolhLatycMvt3bsXnTp1Qnx8PADVbs8aNWrk3AMSERGRwWJYy6oc6uXds2cPOnfujISEBABAz549sXHjRpiZ8fBCIiKivIhhLatyYMv98ccf6NKlizqoff7559iwYQODGhFRNp08eRKmpqZo27ZtqtvCwsKgUCjw+vXrVLe5ublh4cKFWm3Jl/srVKgQbGxs4O7ujm+++QaPHz/OoeqB2NhY+Pr6olChQrC1tUWXLl0QGRmZ7n3evn0LPz8/FC9eHNbW1nB3d8eyZctSLXfy5Ek0a9YM+fLlQ/78+dGkSRP1wOuAahsoFAqtf3PmzNH7c6S0MaxllZ633O+//45u3bqpg1rv3r2xfv16BjUiIj1YtWoVRowYgaNHj+LJkydZXs+vv/4KLy8vFClSBDt37sS1a9ewbNkyREdH48cff9RjxdpGjRqFPXv2YPv27Thy5AiePHmCzp07p3uf0aNHIzg4GBs3bsT169fx9ddfw8/PD4GBgeplTp48iVatWqFly5Y4c+YMzp49Cz8/v1QnksyYMQNPnz5V/xsxYkSOPE/SjUkgq/QY1nbv3o3u3bsjMTERANCnTx+sXbuWpzwTEenB27dvsXXrVpw7dw4RERFYu3YtJk6cmOn1PHr0CF999RW++uorLFiwQN3u5uaGJk2a6OyZ04fo6GisWrUKmzdvRrNmzQAAa9asQaVKlXDq1Cl8+umnOu/3119/wcfHBx4eHgCAwYMH49dff8WZM2fQoUMHAKoQ+NVXX2HChAnq+1WoUCHVuuzs7FCkSBE9PzPKKPasZZUet5y5ubn6VOe+ffsyqBGR8agNoLjE/2pnrsRt27ahYsWKqFChAvr06YPVq1dDiMwPv7R9+3bEx8dj3LhxOm8vUKBAmvdt3bo1bG1t0/xXuXLlNO97/vx5JCQkwMvLS91WsWJFlChRAidPnkzzfg0aNEBgYCAeP34MIQRCQ0Nx69YttGzZEgDw7NkznD59Gk5OTmjQoAGcnZ3RtGlTHD9+PNW65syZg0KFCqFGjRqYN2+eunOBpMGetSxSxOjvDIN27dph165dCAwMxNKlSxnUiMh4RADIuUO19GLVqlXo06cPAKBVq1aIjo7GkSNH0KRJk0yt5/bt28ifPz+KFi2a6RpWrlypdRxYSubm5mneFhERAQsLi1Rh0NnZGREREWne75dffsHgwYNRvHhxmJmZwcTEBCtWrFA/73v37gEApk2bhh9++AHVq1fH+vXr0bx5c1y5cgXlypUDAHz11VeoWbMmChYsiL/++gv+/v54+vQp5s+fn9GnT9nEsGYg2rVrh3bt2sldBhFR5sixZywTj3nz5k2cOXMGu3fvBgCYmZmhR48eWLVqVabDmhAiywO+uri4ZOl+2fHLL7/g1KlTCAwMRMmSJXH06FH4+vqiWLFi8PLyglKpBAAMGTIE/fv3BwDUqFEDhw4dwurVqxEQEABAdexbsk8++QQWFhYYMmQIAgICYGlpKfnzyosY1rJIlMz6FQy2bduGhw8f4ptvvtFjRUREMjgndwHpW7VqFRITE1GsWDF1mxAClpaW+Pnnn6FQKJA/f34AqmPDUvZevX79Gvb29gCA8uXLIzo6Gk+fPs1071rr1q1x7NixNG8vWbIkrl69qvO2IkWKID4+Hq9fv9aqLzIyMs3jyD58+ICJEydi9+7d6jNgP/nkE1y6dAk//PADvLy81M/B3d1d676VKlXCgwcP0qy1Xr16SExMRHh4uM7j20j/GNayKot7Krds2YI+ffogKSkJCoVC6xcLERHpT2JiItavX48ff/xRfZxWso4dO+K3335Dr169UK5cOZiYmOD8+fMoWbKkepl79+4hOjoa5cuXBwB07doVEyZMwNy5c7VOMEiWMkxpys5u0Fq1asHc3ByHDh1Cly5dAKh6DB88eID69evrvE9CQgISEhJSndVpamqq7lFzc3NDsWLFcPPmTa1lbt26hdatW6dZz6VLl2BiYgInJ6c0lyH9YliT0ObNm9G3b1/1G+XGjRvZ6lYnIqK07d27F69evcLAgQPVvWPJunTpgjVr1qBXr16ws7PDl19+iW+++QZmZmaoWrUqHj58iPHjx+PTTz9FgwYNAACurq5YsGAB/Pz8EBMTg379+sHNzQ2PHj3C+vXrYWtrm+bwHdnZDWpvb4+BAwdi9OjRKFiwIPLnz48RI0agfv36WmeCVqxYEQEBAejUqRPy58+Ppk2bYuzYsbC2tkbJkiVx5MgRrF+/Xn2smUKhwNixYzF16lRUq1YN1atXx7p163Djxg3s2LEDgGpoj9OnT8PT0xN2dnY4efIkRo0ahT59+sDBwSHLz4kySVCmREdHCwDiddnXmbrfxo0bhYmJiQAgAIjBgweLpKSkHKoyd4mPjxe///67iI+Pl7uUPIPbXHqGvs0/fPggrl27Jj58+CB3KRnWrl070aZNG523nT59WgAQx44dE0lJSeLDhw9i6tSpomLFisLa2lqUKlVKDB48WDx//jzVfUNCQoS3t7dwcHAQVlZWomLFimLMmDHiyZMnOfZcPnz4IIYPHy4cHByEjY2N6NSpk3j69KnWMgDEmjVr1PNPnz4VX3zxhShWrJiwsrISFSpUED/++KNQKpVa9wsICBDFixcXNjY2on79+uLYsWPq286fPy/q1asn7O3thZWVlahUqZKYPXu2iI2NTbfWtF4rUVFRAoCIjo7O4pbImxRCZOH85TwsJiYG9vb2eF3hNexv2H/8DgA2btwIHx8fdY/a0KFDsXjx4lTd06RbQkICgoKC0KZNm3R3FZD+cJtLz9C3eWxsLO7fv49SpUrByspK7nL0QqlUIiYmBvnz5+fnsR6l91p58eIFHB0dER0drT5WkD6Or86syuCWW79+Pfr166cOasOGDWNQIyIiogxjYsiqDGy5devW4YsvvlAPvujr68ugRkRERJnC1JBVHzkb9M2bNxg/frw6qPn5+eGXX37hyQRERESUKQxrWSRM0j/Uz87ODiEhIShUqBC++uor9Xg+RERERJnBoTuyKgMxt2rVqrh06RJcXFwY1IgoV+A5afQxfI3oH3vWskrHlgsLC0NSUpJWW/HixRnUiMjoJZ+h+v79e5krIUMXHx8PALzOtR6xZy2rUoS15cuXq6+vtnLlSp5EQES5iqmpKQoUKIBnz54BAGxsbIz+h6hSqUR8fDxiY2P5ma0nSqUSz58/h42NDczMGDH0hVsyqzTe18uWLcOwYcMAAGvWrEHr1q3RrVs3mQojIsoZydehTA5sxk4IgQ8fPsDa2trog6chMTExQYkSJbhN9YhhLav+7d1dsmQJfH191c1jx45F165dZSqKiCjnKBQKFC1aFE5OTkhISJC7nGxLSEjA0aNH0aRJE4MciNhYWVhYsKdSzxjWssoEWLx4Mfz8/NRN48ePR0BAAH9NEFGuZmpqmiuORzI1NUViYiKsrKwY1sig5Yrou3jxYri5ucHKygr16tXDmTNn0l1++/btqFixIqysrFC1alUEBQVl+jGXP1muFdT8/f0Z1IiIiEjvjD6sbd26FaNHj8bUqVNx4cIFVKtWDd7e3mkeU/HXX3/h888/x8CBA3Hx4kV07NgRHTt2xJUrVzL1uOPvjldPT5w4EbNmzWJQIyIiIr0z+rA2f/58DBo0CP3794e7uzuWLVsGGxsbrF69WufyP/30E1q1aoWxY8eiUqVKmDlzJmrWrIlFixZl6fG//fZbfPfddwxqRERElCOM+pi1+Ph4nD9/Hv7+/uo2ExMTeHl54eTJkzrvc/LkSYwePVqrzdvbG7///rvO5ePi4hAXF6eej46OVk+PGTMGI0eOxMuXL7PxLOhjEhIS8P79e7x48YLHlUiE21x63ObS4zaXXvL3JQfOzRyjDmtRUVFISkqCs7OzVruzszNu3Lih8z4RERE6l4+IiNC5fEBAAKZPn67zth9++AE//PBDFionIiLKu168eAF7e3u5yzAaRh3WpODv76/VE/f69WuULFkSDx484AtNIjExMXB1dcXDhw+RP39+ucvJE7jNpcdtLj1uc+lFR0ejRIkSKFiwoNylGBWjDmuOjo4wNTVFZGSkVntkZKR68MaUihQpkqnlLS0tYWlpmard3t6eb26J5c+fn9tcYtzm0uM2lx63ufQ4DlvmGPXWsrCwQK1atXDo0CF1m1KpxKFDh1C/fn2d96lfv77W8gAQEhKS5vJEREREcjLqnjUAGD16NHx8fFC7dm3UrVsXCxcuxLt379C/f38AQL9+/eDi4oKAgAAAwMiRI9G0aVP8+OOPaNu2LbZs2YJz585h+fLlcj4NIiIiIp2MPqz16NEDz58/x5QpUxAREYHq1asjODhYfRLBgwcPtLpbGzRogM2bN+Pbb7/FxIkTUa5cOfz++++oUqVKhh7P0tISU6dO1blrlHIGt7n0uM2lx20uPW5z6XGbZ41C8PxZIiIiIoNl1MesEREREeV2DGtEREREBoxhjYiIiMiAMawRERERGTCGNR0WL14MNzc3WFlZoV69ejhz5ky6y2/fvh0VK1aElZUVqlatiqCgIIkqzT0ys81XrFiBxo0bw8HBAQ4ODvDy8vro34hSy+zrPNmWLVugUCjQsWPHnC0wF8rsNn/9+jV8fX1RtGhRWFpaonz58vx8yaTMbvOFCxeiQoUKsLa2hqurK0aNGoXY2FiJqjV+R48eRfv27VGsWDEoFIo0r7utKSwsDDVr1oSlpSXKli2LtWvX5nidRkeQli1btggLCwuxevVqcfXqVTFo0CBRoEABERkZqXP5EydOCFNTUzF37lxx7do18e233wpzc3Nx+fJliSs3Xpnd5r169RKLFy8WFy9eFNevXxdffPGFsLe3F48ePZK4cuOV2W2e7P79+8LFxUU0btxYfPbZZ9IUm0tkdpvHxcWJ2rVrizZt2ojjx4+L+/fvi7CwMHHp0iWJKzdemd3mmzZtEpaWlmLTpk3i/v374s8//xRFixYVo0aNkrhy4xUUFCQmTZokdu3aJQCI3bt3p7v8vXv3hI2NjRg9erS4du2a+OWXX4SpqakIDg6WpmAjwbCWQt26dYWvr696PikpSRQrVkwEBAToXL579+6ibdu2Wm316tUTQ4YMydE6c5PMbvOUEhMThZ2dnVi3bl1OlZjrZGWbJyYmigYNGoiVK1cKHx8fhrVMyuw2X7p0qShdurSIj4+XqsRcJ7Pb3NfXVzRr1kyrbfTo0aJhw4Y5WmdulZGwNm7cOFG5cmWtth49eghvb+8crMz4cDeohvj4eJw/fx5eXl7qNhMTE3h5eeHkyZM673Py5Emt5QHA29s7zeVJW1a2eUrv379HQkICLwycQVnd5jNmzICTkxMGDhwoRZm5Sla2eWBgIOrXrw9fX184OzujSpUqmD17NpKSkqQq26hlZZs3aNAA58+fV+8qvXfvHoKCgtCmTRtJas6L+B2aMUZ/BQN9ioqKQlJSkvrqB8mcnZ1x48YNnfeJiIjQuXxERESO1ZmbZGWbpzR+/HgUK1Ys1RuedMvKNj9+/DhWrVqFS5cuSVBh7pOVbX7v3j0cPnwYvXv3RlBQEO7cuYPhw4cjISEBU6dOlaJso5aVbd6rVy9ERUWhUaNGEEIgMTERQ4cOxcSJE6UoOU9K6zs0JiYGHz58gLW1tUyVGRb2rJFRmzNnDrZs2YLdu3fDyspK7nJypTdv3qBv375YsWIFHB0d5S4nz1AqlXBycsLy5ctRq1Yt9OjRA5MmTcKyZcvkLi3XCgsLw+z/t3fvQVFVcRzAv+zisoiLDCmzbDwUlI0xCBfQkByCLGBSN1GhZBAFxZEQR9NiSnlEKBrggKNF4whGjDwcC0YSFJOCdSpUFhrBRRDURqQJGhCFeOzpj4Y7rjxyMWHB32fm/nHPPefc3znssL8597H79uHo0aO4evUqTp8+jaKiIsTHx090aOQ5Rytrj5g1axb4fD5aW1s1yltbWyEWi4dtIxaLtapPNI1lzgclJSUhMTERpaWlcHR0fJZhTinaznljYyOam5uxYsUKrkytVgMA9PX1oVKpYGtr+2yDnuTG8jk3NzfHtGnTwOfzuTJ7e3vcu3cPvb29EAgEzzTmyW4sc753714EBQVh06ZNAAAHBwc8ePAAYWFh+OSTTzR+Z5r8P0b6DjU2NqZVtUfQJ+8RAoEAzs7OuHDhAlemVqtx4cIFuLm5DdvGzc1Noz4AnD9/fsT6RNNY5hwADh48iPj4eBQXF8PFxWU8Qp0ytJ3zl156Cb/99huUSiW3rVy5Ep6enlAqlbC0tBzP8CelsXzO3d3d0dDQwCXGAFBfXw9zc3NK1J7AWOb84cOHQxKywWSZ0c9oPxP0HfqEJvoJB12Tk5PDDAwMWGZmJqutrWVhYWHMxMSE3bt3jzHGWFBQEIuKiuLqKxQKpq+vz5KSklhdXR2LiYmhV3doSds5T0xMZAKBgJ06dYq1tLRw2/379ydqCJOOtnP+OHoaVHvazvnt27eZSCRiERERTKVSsTNnzjAzMzP22WefTdQQJh1t5zwmJoaJRCJ28uRJdvPmTXbu3Dlma2vL/P39J2oIk879+/dZVVUVq6qqYgBYSkoKq6qqYrdu3WKMMRYVFcWCgoK4+oOv7ti9ezerq6tjR44coVd3DIOStWEcPnyYWVlZMYFAwBYtWsR+/vln7piHhwcLDg7WqJ+Xl8fs7OyYQCBgCxYsYEVFReMc8eSnzZxbW1szAEO2mJiY8Q98EtP2c/4oStbGRts5v3TpElu8eDEzMDBgNjY2LCEhgfX3949z1JObNnPe19fHYmNjma2tLRMKhczS0pKFh4ezv/76a/wDn6QuXrw47P/nwXkODg5mHh4eQ9o4OTkxgUDAbGxsWEZGxrjHrev0GKO1XUIIIYQQXUX3rBFCCCGE6DBK1gghhBBCdBgla4QQQgghOoySNUIIIYQQHUbJGiGEEEKIDqNkjRBCCCFEh1GyRgghhBCiwyhZI4QQQgjRYZSsEUI0ZGZmwsTEZKLDGDM9PT189913o9bZsGED3nnnnXGJhxBCnhYla4RMQRs2bICent6QraGhYaJDQ2ZmJhcPj8eDhYUFNm7ciD/++ON/6b+lpQW+vr4AgObmZujp6UGpVGrUSU1NRWZm5v9yvpHExsZy4+Tz+bC0tERYWBja29u16ocSS0KI/kQHQAh5Nnx8fJCRkaFRNnv27AmKRpOxsTFUKhXUajWqq6uxceNG3L17FyUlJU/dt1gs/s86M2fOfOrzPIkFCxagtLQUAwMDqKurQ0hICDo6OpCbmzsu5yeETA20skbIFGVgYACxWKyx8fl8pKSkwMHBAUZGRrC0tER4eDi6urpG7Ke6uhqenp4QiUQwNjaGs7MzLl++zB2vqKjA0qVLYWhoCEtLS0RGRuLBgwejxqanpwexWAyJRAJfX19ERkaitLQU3d3dUKvV+PTTT2FhYQEDAwM4OTmhuLiYa9vb24uIiAiYm5tDKBTC2toa+/fv1+h78DLo3LlzAQALFy6Enp4eXn/9dQCaq1VfffUVJBIJ1Gq1RoxyuRwhISHcfkFBAWQyGYRCIWxsbBAXF4f+/v5Rx6mvrw+xWIwXX3wRy5Ytw9q1a3H+/Hnu+MDAAEJDQzF37lwYGhpCKpUiNTWVOx4bG4sTJ06goKCAW6UrKysDANy5cwf+/v4wMTGBqakp5HI5mpubR42HEDI5UbJGyHOGx+MhLS0N165dw4kTJ/DDDz/gww8/HLF+YGAgLCwsUFlZiStXriAqKgrTpk0DADQ2NsLHxwerV69GTU0NcnNzUVFRgYiICK1iMjQ0hFqtRn9/P1JTU5GcnIykpCTU1NTA29sbK1euxI0bNwAAaWlpKCwsRF5eHlQqFbKzszFnzpxh+/31118BAKWlpWhpacHp06eH1Fm7di3a2tpw8eJFrqy9vR3FxcUIDAwEAJSXl2P9+vXYvn07amtrkZ6ejszMTCQkJDzxGJubm1FSUgKBQMCVqdVqWFhYID8/H7W1tYiOjsbHH3+MvLw8AMCuXbvg7+8PHx8ftLS0oKWlBUuWLEFfXx+8vb0hEolQXl4OhUKBGTNmwMfHB729vU8cEyFkkmCEkCknODiY8fl8ZmRkxG1r1qwZtm5+fj574YUXuP2MjAw2c+ZMbl8kErHMzMxh24aGhrKwsDCNsvLycsbj8Vh3d/ewbR7vv76+ntnZ2TEXFxfGGGMSiYQlJCRotHF1dWXh4eGMMca2bdvGvLy8mFqtHrZ/AOzbb79ljDHW1NTEALCqqiqNOsHBwUwul3P7crmchYSEcPvp6elMIpGwgYEBxhhjb7zxBtu3b59GH1lZWczc3HzYGBhjLCYmhvF4PGZkZMSEQiEDwACwlJSUEdswxtj777/PVq9ePWKsg+eWSqUac/D3338zQ0NDVlJSMmr/hJDJh+5ZI2SK8vT0xBdffMHtGxkZAfh3lWn//v24fv06Ojs70d/fj56eHjx8+BDTp08f0s/OnTuxadMmZGVlcZfybG1tAfx7ibSmpgbZ2dlcfcYY1Go1mpqaYG9vP2xsHR0dmDFjBtRqNXp6evDaa6/h2LFj6OzsxN27d+Hu7q5R393dHdXV1QD+vYT55ptvQiqVwsfHB8uXL8dbb731VHMVGBiIzZs34+jRozAwMEB2djbeffdd8Hg8bpwKhUJjJW1gYGDUeQMAqVSKwsJC9PT04JtvvoFSqcS2bds06hw5cgTHjx/H7du30d3djd7eXjg5OY0ab3V1NRoaGiASiTTKe3p60NjYOIYZIIToMkrWCJmijIyMMG/ePI2y5uZmLF++HFu3bkVCQgJMTU1RUVGB0NBQ9Pb2Dpt0xMbGYt26dSgqKsLZs2cRExODnJwcrFq1Cl1dXdiyZQsiIyOHtLOyshoxNpFIhKtXr4LH48Hc3ByGhoYAgM7Ozv8cl0wmQ1NTE86ePYvS0lL4+/tj2bJlOHXq1H+2HcmKFSvAGENRURFcXV1RXl6OQ4cOcce7uroQFxcHPz+/IW2FQuGI/QoEAu5vkJiYiLfffhtxcXGIj48HAOTk5GDXrl1ITk6Gm5sbRCIRPv/8c/zyyy+jxtvV1QVnZ2eNJHmQrjxEQgj5/1CyRshz5MqVK1Cr1UhOTuZWjQbvjxqNnZ0d7OzssGPHDrz33nvIyMjAqlWrIJPJUFtbOyQp/C88Hm/YNsbGxpBIJFAoFPDw8ODKFQoFFi1apFEvICAAAQEBWLNmDXx8fNDe3g5TU1ON/gbvDxsYGBg1HqFQCD8/P2RnZ6OhoQFSqRQymYw7LpPJoFKptB7n4/bs2QMvLy9s3bqVG+eSJUsQHh7O1Xl8ZUwgEAyJXyaTITc3F2ZmZjA2Nn6qmAghuo8eMCDkOTJv3jz09fXh8OHDuHnzJrKysvDll1+OWL+7uxsREREoKyvDrVu3oFAoUFlZyV3e/Oijj3Dp0iVERERAqVTixo0bKCgo0PoBg0ft3r0bBw4cQG5uLlQqFaKioqBUKrF9+3YAQEpKCk6ePInr16+jvr4e+fn5EIvFw77I18zMDIaGhiguLkZrays6OjpGPG9gYCCKiopw/Phx7sGCQdHR0fj6668RFxeHa9euoa6uDjk5OdizZ49WY3Nzc4OjoyP27dsHAJg/fz4uX76MkpIS1NfXY+/evaisrNRoM2fOHNTU1EClUuHPP/9EX18fAgMDMWvWLMjlcpSXl6OpqQllZWWIjIzE77//rlVMhBDdR8kaIc+RV155BSkpKThw4ABefvllZGdna7z24nF8Ph9tbW1Yv3497Ozs4O/vD19fX8TFxQEAHB0d8eOPP6K+vh5Lly7FwoULER0dDYlEMuYYIyMjsXPnTnzwwQdwcHBAcXExCgsLMX/+fAD/XkI9ePAgXFxc4OrqiubmZnz//ffcSuGj9PX1kZaWhvT0dEgkEsjl8hHP6+XlBVNTU6hUKqxbt07jmLe3N86cOYNz587B1dUVr776Kg4dOgRra2utx7djxw4cO3YMd+7cwZYtW+Dn54eAgAAsXrwYbW1tGqtsALB582ZIpVK4uLhg9uzZUCgUmD59On766SdYWVnBz88P9vb2CA0NRU9PD620ETIF6THG2EQHQQghhBBChkcra4QQQgghOoySNUIIIYQQHUbJGiGEEEKIDqNkjRBCCCFEh1GyRgghhBCiwyhZI4QQQgjRYZSsEUIIIYToMErWCCGEEEJ0GCVrhBBCCCE6jJI1QgghhBAdRskaIYQQQogO+wdY5j50FGSrpQAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 600x600 with 1 Axes>"
       ]
@@ -234,7 +359,7 @@
     },
     {
      "data": {
-      "image/png": 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fL36tDwK8g11XCxERkUAMGK6Ufaz4dWBzQHLg6atERERujAHDlUoGDCIioiqCAcNVDNnArfPFy5z/goiIqhAO8hRs1Mo9uHnbAL1GgwfbhGN0tyjbO944plzmAE8iIqpCGDAEO/TndVzLMwAA2tQr4zbVG4eVy4EMGEREVHWwi0SwQotnkejKmmjr+qHi11pfwL+hE6siIiKqXAwYghlMxTN56jRl3BViGTCCWgEarROrIiIiqlwMGIIpWzDKChi/Fb+uHu3EioiIiCofA4ZAsiyj0GQRMEprwbh9Bci/XLwc1NrJlREREVUuBgyBLLIFgDLGYGTzDhIiIqraGDAEKrR4kioAaEubmTPnlHI5sKmTKiIiInINBgyBCks0YZQ6BiP3nMWCBPhFOK8oIiIiF2DAEMhoFTBK+XrzLAKGbzig0TuxKiIiosrHgCGQoWTAKG2QZ96fxa/96jmxIiIiItdgwBCoZAuGtrSAkXu2+LVfpPMKIiIichEGDIFKBgx9aWMwbl0ofl2NAYOIiKoePotEoNAAb/z22gAUGmUUmkwI8LYxtqIwFzDcKF72rVt5BRIREVUSBgyBNBoJgfq7DNjMu6Bc9q3jvIKIiIhchF0kle1WyYAR7po6iIiInIgBo7JZ3qIK8C4SIiKqkhgwKpvlLaoA4McxGEREVPVwDIZAV3Pyse1UBrQaCTqthG6NaiM4wFu5k2UXiXctQOtTuUUSERFVApe3YCxZsgRRUVHw8fFB165dsWfPnjL3X7RoEZo1awZfX19ERERgypQpuH37diVVW7azV/Pw0te/YeqXBzFpdSpOX86x3skyYPhw/AUREVVNLg0YX3zxBRISEpCUlIT9+/ejbdu2iI2NxaVLl2zu//nnn2P69OlISkrC0aNH8dFHH+GLL77Ayy+/XMmV22b1LBJbE23dulj8mneQEBFRFeXSgLFw4UKMHTsWY8aMQcuWLbFs2TL4+flh5cqVNvffuXMnunfvjhEjRiAqKgoDBgzA8OHD79rqUVlMsh0zeSoCBlswiIioanLZGIyCggLs27cPiYmJ5nUajQb9+vXDrl27bB7TrVs3/Oc//8GePXvQpUsXnDlzBuvXr8fIkSNLfZ/8/Hzk5+ebl7OzswEABoMBBoNByGe5c578gkLFetlktHoP3e1M3IkdRq9gmATVQLbd+f5FXWsSg9fFPfG6uCd3ui6O1OCygHHlyhUYjUaEhoYq1oeGhuLYsWM2jxkxYgSuXLmCHj16QJZlFBYW4h//+EeZXSRz587FrFmzrNZv2rQJfn5+FfsQJfy6bz8ArXl5184dOFeteLtGLsAgY555+diZyzj153qhNZBtycnJri6BbOB1cU+8Lu7JHa5LXl7e3Xf6i0fdRbJlyxbMmTMHH374Ibp27YpTp05h0qRJmD17NmbMmGHzmMTERCQkJJiXs7OzERERgQEDBiAwMFBIXQaDAcnJyWjTth1w7JB5fe+ePdEsLKB4x1vnge+LF5u17YGmDR4QUgPZdufa9O/fH/q7zbJKlYbXxT3xurgnd7oud3oB7OGygFG7dm1otVpkZmYq1mdmZiIsLMzmMTNmzMDIkSPxzDPPAACio6ORm5uLZ599Fq+88go0GushJd7e3vD29rZar9frxV+oEu/v413iPXKUF0bnFwLwL3GlcMr1pgrjdXFPvC7uyR2uiyPv77JBnl5eXujYsSNSUlLM60wmE1JSUhATE2PzmLy8PKsQodUWdUnIJQZYuoL149pLfL0FV5XL3rWcXBEREZFruLSLJCEhAaNHj0anTp3QpUsXLFq0CLm5uRgzZgwAYNSoUahbty7mzp0LABg0aBAWLlyI9u3bm7tIZsyYgUGDBpmDhitZBQypxF0k+SUChhcDBhERVU0uDRhxcXG4fPkyZs6ciYyMDLRr1w4bNmwwD/xMT09XtFi8+uqrkCQJr776Ks6fP4/g4GAMGjQIb775pqs+goJVwNCWDBhXlMtswSAioirK5YM84+PjER8fb3Pbli1bFMs6nQ5JSUlISkqqhMocZyw5D8ZdWzBqOLkiIiIi13D5VOFVSckWDJ1VC4ZFwNBXBzQuz3dEREROwd9wArWuE4SX7m8Gk0lGoUlGNa8SX6/lIE92jxARURXGgCFQi/AAtImsWfoO+QwYRESkDuwiqUyKgFHbdXUQERE5GQNGZbLsIuEtqkREVIUxYFQmdpEQEZFKMGBUFpMRKLhWvMyAQUREVRgHeQq0dOsZvL/5NDSSBL1Wg8OzYos3Gq4DsLiNlQGDiIiqMAYMgQqNMgxGGYBsNSeG1SyeHINBRERVGLtIBLKcyVOrucssnmzBICKiKowBQyDLVgsGDCIiUjMGDIHKDBhWj2rnPBhERFR1MWAI5FALBsdgEBFRFcaAIZDlGAxdWQFD6wvofCupKiIiosrHgCGQyaIFQ1PyUe180BkREakIA4ZAhSY7WzDYPUJERFUcA4ZAJosuEk1ZAYMtGEREVMUxYAhUZgsGu0iIiEhFGDAEUozBYBcJERGpGKcKF6iGnxfq1fCFySQjJMC7eIMsK6cKZwsGERFVcQwYAiUObIaZD7e23lCYC5gKipc5yRYREVVx7CKpDJaPaQcAr5quqYOIiKiSMGBUBkO2clkf6Jo6iIiIKgkDRmUovKlcZsAgIqIqjgGjMhhKBowA19RBRERUSTjIU6DP9pxD2pU8aDQSompVw+huUUUbSnaR6BgwiIioamPAEOinY5ew7WTRfBddomoWBwx2kRARkcqwi0Qgo6n4teJZZ+wiISIilWHAEEi2eBaJ1nImz5ItGLpqlVQRERGRazBgCGSUS3lcu2ULhs4fkPi1ExFR1cbfdAJZPIqk9C4Sdo8QEZEKMGAIVGoXieVdJLyDhIiIVIABQyCjqZQukkK2YBARkbowYAhk2UWiHINh0YKhD6q8goiIiFyEAUMgk2KQp8UGRcDgHBhERFT1MWAIZOIYDCIiIgAMGEKZLCbaUnaRXC9+7VW9ssohIiJyGQYMgSxbMKRSu0g4BoOIiKo+PotEoNkPt8QtI2AyyQgO8C5aaTIAxtvFO/EuEiIiUgEGDIHaR1aHXq9XrizMVS7r/CuvICIiIhdhF4mzWQUMP9fUQUREVIkYMJzNKmDwQWdERFT1MWA4W8mAoWXAICKiqo9jMAQ6fTkXGq0WGgkI8vUqGujJFgwiIlIhBgyBxnyyDxdvFN0x8vd7IvHG4Gjlc0gA3kVCRESqwC4SgUy2HnZmvKXcSctBnkREVPUxYAikfBbJXwGjME+5k863EisiIiJyDQYMgYy2AoZVCwYDBhERVX0MGAJZ5Ato73yzJVsw2EVCREQqwIAhkM0uEmPJgMEWDCIiqvoYMAQyWjxNVbI1BkPjDWi0lVsUERGRCzBgCCRbtGCYu0gsx2BwmnAiIlIJBgyBbA/ytGjB4PgLIiJSCQYMgSymwbB9myrHXxARkUowYAhke6Iti4DBLhIiIlIJhwNG79698emnn+LWrVt331llTLbGYBRafE/sIiEiIpVwOGC0b98eL7zwAsLCwjB27Fjs3r3bGXV5pP4tQhDbKhT9W4aiQW3/opWWgzy1Pq4pjIiIqJI5HDAWLVqECxcuYNWqVbh06RJ69eqFli1b4p133kFmZqYzavQYHwxvh3+O7IQVozrhwTbhRStNt4t3YMAgIiKVKNcYDJ1Oh7/97W/47rvv8Oeff2LEiBGYMWMGIiIiMHjwYPz000+i6/Rcxvzi1wwYRESkEhUa5Llnzx4kJSVhwYIFCAkJQWJiImrXro2HHnoIL7zwgqgaPZvRogVD4+26OoiIiCqRztEDLl26hH//+99YtWoVTp48iUGDBuG///0vYmNjzbNXPvnkk7j//vvxzjvvCC/Y45jYgkFEROrjcMCoV68eGjVqhKeeegpPPvkkgoODrfZp06YNOnfuLKRAj2fZgqFlCwYREamDwwEjJSUFPXv2LHOfwMBAbN68udxFeaJ8IzB0+S/QajTQSBKe6tEA97cOU7ZgaNiCQURE6uDwGIykpCRcv37dan12djb69u0roiaPZJSBA+du4Nc/rmHP2SxczvkrWLAFg4iIVMjhgLF161YUFBRYrb99+zZ+/vlnIUV5Ios5tgAAWvNMnhyDQURE6mN3F8lvv/0GoOiJoUeOHEFGRoZ5m9FoxIYNG1C3bl3xFXqIEvkCGglFqUPRRcIWDCIiUge7A0a7du0gSRIkSbLZFeLr64v3339faHGexFQiYWgkCTCVaOlhCwYREamE3QEjLS0NsiyjYcOG2LNnj+LuES8vL4SEhECr1TqlSE9g1YKhkZTjLwC2YBARkWrYHTDq168PADCZTE4rxpOVHIOhkaDsHgHYgkFERKphV8BYu3YtBg4cCL1ej7Vr15a578MPPyykME9TMnZpJBstGLyLhIiIVMKugDF48GBkZGQgJCQEgwcPLnU/SZJgNBpF1eZRrFowNJLyDhKALRhERKQadgUMy24RdpHYZvMuEhPHYBARkTpV6GFnVMzmXSRswSAiIpWyqwVj8eLFdp9w4sSJ5S7Gk1m3YPAuEiIiUi+7Asa7775r18kkSVJvwOBdJERERGZ2BYy0tDRn1+Hxgn2BPYl9oNXqYJRlBProgUu8i4SIiNTJ4aepkm1aCajh5wW9Xl+8smQLBp+mSkREKmFXwEhISMDs2bNRrVo1JCQklLnvwoULhRRWJXAeDCIiUim7AsaBAwdgMBjMr0sj3XmCKBXhXSRERKRSdgWMzZs323xNd8F5MIiISKUqNAbj3LlzAICIiAghxXiyHAPwvwMXoNNpoZEkdGtcCyFswSAiIpVyeKKtwsJCzJgxA0FBQYiKikJUVBSCgoLw6quvmrtR1OjKbeClbw4jYc1BTP4iFacyczgPBhERqZbDAWPChAlYvnw53n77bRw4cAAHDhzA22+/jY8++qhcc2AsWbIEUVFR8PHxQdeuXbFnz54y979+/TrGjx+P8PBweHt7o2nTpli/fr3D7ytayYm2JElS3kUi6QCNeh9nT0RE6uJwF8nnn3+O1atXY+DAgeZ1bdq0QUREBIYPH46lS5fafa4vvvgCCQkJWLZsGbp27YpFixYhNjYWx48fR0hIiNX+BQUF6N+/P0JCQvDVV1+hbt26+OOPP1C9enVHP4ZwNifasmzB4B0kRESkIg4HDG9vb0RFRVmtb9CgAby8vBw618KFCzF27FiMGTMGALBs2TKsW7cOK1euxPTp0632X7lyJbKysrBz507zfBO2anEFq6nCSz5NleMviIhIRRwOGPHx8Zg9ezZWrVoFb++if5Xn5+fjzTffRHx8vN3nKSgowL59+5CYmGhep9Fo0K9fP+zatcvmMWvXrkVMTAzGjx+P7777DsHBwRgxYgSmTZsGrdZ290N+fj7y84t/0WdnZwMADAaDsDEjBoPBqgXDWFgIY2Ee7lQlS94oVPEYFVe5c43VPD7IHfG6uCdeF/fkTtfFkRrsChh/+9vfFMs//vgj6tWrh7Zt2wIADh48iIKCAtx33312v/GVK1dgNBoRGhqqWB8aGopjx47ZPObMmTP46aef8MQTT2D9+vU4deoUxo0bB4PBgKSkJJvHzJ07F7NmzbJav2nTJvj5+dld793IUM4Bsnv3LoR7nUL9v5bz8o340Q3GiqhVcnKyq0sgG3hd3BOvi3tyh+uSl5dn9752BYygoCDF8pAhQxTLlXWbqslkQkhICJYvXw6tVouOHTvi/PnzmD9/fqkBIzExUTH7aHZ2NiIiIjBgwAAEBgYKqctgMODEVz8q1nXv1g0RF1YD6UXLfv418MD9Dwh5P7KfwWBAcnIy+vfvr5zGnVyK18U98bq4J3e6Lnd6AexhV8BYtWpVuYspTe3ataHVapGZmalYn5mZibCwMJvHhIeHQ6/XK7pDWrRogYyMDBQUFNgcA+Lt7W3uyrGk1+uFXqiSXSReej00cnFTkqTzdfl/GGom+nqTGLwu7onXxT25w3Vx5P0dvk1VFC8vL3Ts2BEpKSnmdSaTCSkpKYiJibF5TPfu3XHq1CmYTCbzuhMnTiA8PNzhAaaiWQ3ylCTlXSScA4OIiFSkXDN5fvXVV1izZg3S09NRUFCg2LZ//367z5OQkIDRo0ejU6dO6NKlCxYtWoTc3FzzXSWjRo1C3bp1MXfuXADA888/jw8++ACTJk3ChAkTcPLkScyZM6dc82+IVrIFQ5KgnAeDd5EQEZGKONyCsXjxYowZMwahoaE4cOAAunTpglq1auHMmTOKuTHsERcXh3feeQczZ85Eu3btkJqaig0bNpgHfqanp+PixYvm/SMiIrBx40bs3bsXbdq0wcSJEzFp0iSbt7RWNlOJZasWDM6DQUREKuJwC8aHH36I5cuXY/jw4fj444/x0ksvoWHDhpg5cyaysrIcLiA+Pr7U21u3bNlitS4mJga7d+92+H2czXoeDHAeDCIiUi2HA0Z6ejq6desGAPD19cXNmzcBACNHjsQ999yDDz74QGyFHiLER8aU+xpD0mggy0Btf2/l01Q5BoOIiFTE4YARFhaGrKws1K9fH5GRkdi9ezfatm2LtLQ0yCUHIqhIiC/wQJ+GyhG2bMEgIiKVcngMRt++fbF27VoAwJgxYzBlyhT0798fcXFxePTRR4UX6NF4FwkREamUwy0Yy5cvN98mOn78eNSqVQs7d+7Eww8/jOeee054gR6Nd5EQEZFKORwwNBoNNJriho9hw4Zh2LBhQouqMhRdJGzBICIi9SjXPBjXrl3DRx99hKNHjwIAWrZsiTFjxqBmzZpCi/N4ikGebMEgIiL1cHgMxrZt29CgQQMsXrwY165dw7Vr17B48WI0aNAA27Ztc0aNHuHAFQmtZ/2IFjM2oOXMDTh3NZctGEREpFoOt2CMHz8eQ4cOxdKlS83PBDEajRg3bhzGjx+PQ4cOCS/SExhlIL+weLotCQYoZsfgGAwiIlIRh1swTp06halTpyoeOKbVapGQkIBTp04JLc6TlLxBVysrp1DnXSRERKQmDgeMDh06mMdeWDp69Cjatm0rpChPZDWTp+X4C4AtGEREpCp2dZH89ttv5td3nv9x6tQp3HPPPQCA3bt3Y8mSJZg3b55zqvQEJRKGhi0YRESkYnYFjHbt2kGSJMVMnS+99JLVfiNGjEBcXJy46jyI1cPO5HzlCrZgEBGRitgVMNLS0pxdh8crOUu6xlSiBYN3kRARkYrYFTDq16/v7Do8XskxGFLJMRicB4OIiFSkXBNtnT59GosWLVJMtDVp0iQ0atRIaHGezGoMBlswiIhIRRy+i2Tjxo1o2bIl9uzZgzZt2qBNmzb45Zdf0KpVKyQnJzujRo9gsuoi4V0kRESkXg63YEyfPh1TpkyxumNk+vTpmDZtGvr37y+sOE8mlRyDwbtIiIhIRRxuwTh69Ciefvppq/VPPfUUjhw5IqQoT3TXMRhswSAiIhVxOGAEBwcjNTXVan1qaipCQkJE1OSRvDVAWKA3QgO9ERLgzTEYRESkag53kYwdOxbPPvsszpw5g27dugEAduzYgbfeegsJCQnCC/QUXUJkvPZkb+j1+qIVZz5W7sC7SIiISEUcDhgzZsxAQEAAFixYgMTERABAnTp18Nprr2HixInCC/RYxpITbbEFg4iI1MOhgFFYWIjPP/8cI0aMwJQpU3Dz5k0AQEBAgFOK82hGjsEgIiL1cmgMhk6nwz/+8Q/cvl30yzMgIIDhojSmEi0YvIuEiIhUxOFBnl26dMGBAwecUUvVYtmCIWkBTbnmNCMiIvJIDv/WGzduHKZOnYo///wTHTt2RLVq1RTb27RpI6w4T3LyhoQ31x+DVquFBODlereL0xtbL4iISGUcDhjDhg0DAMWAzjtPWpUkCUajUVx1HiQ9B1h7JN28/PJjFi0YHH9BREQq43DA4JNVbSs50RYsH9fOO0iIiEhlHA4YfLKqbVYzeVqOweAcGEREpDLlGnl4/PhxvP/+++anqbZo0QITJkxAs2bNhBbn0SyfRcIWDCIiUhmH7yL5+uuv0bp1a+zbtw9t27ZF27ZtsX//frRu3Rpff/21M2r0CHKJJgxFCwbHYBARkco43ILx0ksvITExEa+//rpifVJSEl566SUMGTJEWHGexDJfaCQo58HgXSRERKQyDrdgXLx4EaNGjbJa//e//x0XL14UUpQnsmzBkCRJOQ8GWzCIiEhlHA4Yffr0wc8//2y1fvv27ejZs6eQojyRZQuGBLAFg4iIVM3hLpKHH34Y06ZNw759+3DPPfcAAHbv3o0vv/wSs2bNwtq1axX7qodkfqVhCwYREalcuWbyBIAPP/wQH374oc1tAFQ36ZZcsgnDyHkwiIhIvRwOGCaTyRl1eDwO8iQiIirm8BgMsk3ZgCEp58HQeFV6PURERK7ER3wK0iXYhKF9O0Kj0RaNwThmKN7IgEFERCrDgCFIiC/Qp2kw9Hp90YojlgFD75qiiIiIXIRdJM4iM2AQEZF6MWA4i4kBg4iI1KtcAeP06dN49dVXMXz4cFy6dAkA8MMPP+D3338XWpxHsxzkKTFgEBGRujgcMLZu3Yro6Gj88ssv+Oabb5CTkwMAOHjwIJKSkoQX6CmyC4DTl3Nx+nIO/riayxYMIiJSNYcHeU6fPh1vvPEGEhISEBAQYF7ft29ffPDBB0KL8yQpFzSYsXgHAKCGnw4HGltMMsa7SIiISGUcbsE4dOgQHn30Uav1ISEhuHLlipCiPJHlPBh6qcQMpmzBICIilXE4YFSvXt3mU1MPHDiAunXrCinKE1lOFe6lKVRuZMAgIiKVcThgDBs2DNOmTUNGRgYkSYLJZMKOHTvwwgsv2HyMuxrppBLTqTNgEBGRyjgcMObMmYPmzZsjIiICOTk5aNmyJXr16oVu3brh1VdfdUaNHsGyi8RLKtGCwbtIiIhIZRwe5Onl5YUVK1ZgxowZOHz4MHJyctC+fXs0adLEGfV5jLK7SDjIk4iI1MXhgLF9+3b06NEDkZGRiIyMdEZNHqnsQZ4MGEREpC4Od5H07dsXDRo0wMsvv4wjR444oyaPVGYXCcdgEBGRyjgcMC5cuICpU6di69ataN26Ndq1a4f58+fjzz//dEZ9nsMiYeh5FwkREamcwwGjdu3aiI+Px44dO3D69Gk8/vjj+OSTTxAVFYW+ffs6o0aPoOwi4RgMIiJStwo97KxBgwaYPn065s2bh+joaGzdulVUXR6HE20REREVK3fA2LFjB8aNG4fw8HCMGDECrVu3xrp160TW5lFkRRcJAwYREambw3eRJCYmYvXq1bhw4QL69++P9957D4888gj8/PycUZ/HqFdNhn+tYAAS2lbLACyedcZ5MIiISG0cDhjbtm3Diy++iKFDh6J27drOqMkj9QqX8cAD7aHX64GL14HNFhvZgkFERCrjcMDYsWOHM+qoWiwf1Q4wYBARkerYFTDWrl2LgQMHQq/XY+3atWXu+/DDDwspzKPJDBhERKRudgWMwYMHIyMjAyEhIRg8eHCp+0mSBKPRWOp21TAVKJc5BoOIiFTGroBhMplsvqZSmDjRFhERqZvDt6l++umnyM/Pt1pfUFCATz/9VEhRnuiHcxJGfLQXcf/chdW/nFZuZMAgIiKVcThgjBkzBjdu3LBaf/PmTYwZM0ZIUZ4o85aEvWev4Ze0LFy8dlO5kQGDiIhUxuGAIcsyJEmyWv/nn38iKChISFGeyHImT13JmTwlh2/WISIi8mh2/+Zr3749JEmCJEm47777oNMVH2o0GpGWlob777/fKUV6BIuEoQPHYBARkbrZHTDu3D2SmpqK2NhY+Pv7m7d5eXkhKioKQ4YMEV6gp1A8i4RPUyUiIpWzO2AkJSUBAKKiohAXFwcfHx+nFeWJFF0kKNlFwoBBRETq4vDggNGjRzujDo9n+bAzneJx7RKg0VZ6PURERK7kcMAwGo149913sWbNGqSnp6OgQDmpVFZWlrDiPJXWcpCnhgM8iYhIfRy+i2TWrFlYuHAh4uLicOPGDSQkJOBvf/sbNBoNXnvtNSeU6BmUXSQWLRjsHiEiIhVyOGB89tlnWLFiBaZOnQqdTofhw4fjX//6F2bOnIndu3c7o0aPUOptqhzgSUREKuRwwMjIyEB0dDQAwN/f3zzp1kMPPYR169aJrc6DlDoGgwGDiIhUyOGAUa9ePVy8eBEA0KhRI2zatAkAsHfvXnh7e4utzoOU3kXCMRhERKQ+DgeMRx99FCkpKQCACRMmYMaMGWjSpAlGjRqFp556SniBnkjLLhIiIlI5h/95PW/ePPPruLg4REZGYteuXWjSpAkGDRoktDhPMqqJCb3uvRc6nQ5Bv30JpP+1gQGDiIhUqMLt9zExMYiJiRFRi0fz0wEhAd7Q6/WAxuKR9gwYRESkQnYFjLVr19p9wocffrjcxVQZsqH4NW9TJSIiFbIrYNx5DsndSJIEo9F49x2rOpNFwGALBhERqZBdAcNkMt19JyrGgEFERCrHeygFOX5dQuHBi9DptOiVk4cadzYwYBARkQo5HDBef/31MrfPnDmz3MV4so1/anD66CEAwPetbqDGneebcR4MIiJSIYd/+/3vf/9TLBsMBqSlpUGn06FRo0blChhLlizB/PnzkZGRgbZt2+L9999Hly5d7nrc6tWrMXz4cDzyyCP49ttvHX5fZ9GC82AQEZG6ORwwDhw4YLUuOzsbTz75JB599FGHC/jiiy+QkJCAZcuWoWvXrli0aBFiY2Nx/PhxhISElHrc2bNn8cILL6Bnz54Ov6czWI5S0Up82BkREambwzN52hIYGIhZs2ZhxowZDh+7cOFCjB07FmPGjEHLli2xbNky+Pn5YeXKlaUeYzQa8cQTT2DWrFlo2LBhRUp3Ch1bMIiISOWEDRC4ceOG+cFn9iooKMC+ffuQmJhoXqfRaNCvXz/s2rWr1ONef/11hISE4Omnn8bPP/9c5nvk5+cjPz/fvJydnQ2gqGvHYDCUdphDDAaD4mFnWhSf1wQtjILehxx35xqLutYkBq+Le+J1cU/udF0cqcHhgLF48WLFsizLuHjxIv79739j4MCBDp3rypUrMBqNCA0NVawPDQ3FsWPHbB6zfft2fPTRR0hNTbXrPebOnYtZs2ZZrd+0aRP8/PwcqrcsMrTFrwvzAa+i1xcyrmDf+vXC3ofKJzk52dUlkA28Lu6J18U9ucN1ycvLs3tfhwPGu+++q1jWaDQIDg7G6NGjFS0RznDz5k2MHDkSK1asQO3ate06JjExEQkJCebl7OxsREREYMCAAQgMDBRSl8FgwMJDP5mXvS16RerUjUBo1weEvA85zmAwIDk5Gf379y+axp3cAq+Le+J1cU/udF3u9ALYw+GAkZaW5ughpapduza0Wi0yMzMV6zMzMxEWFma1/+nTp3H27FnFQ9XuTAKm0+lw/PhxNGrUSHGMt7e3zcfI6/V6oRfKpOgiKR6DodF5Q8O/qC4n+nqTGLwu7onXxT25w3Vx5P2FDPIsLy8vL3Ts2NH8+HegKDCkpKTYfIBa8+bNcejQIaSmppp/Hn74Ydx7771ITU1FREREZZZfKi0s7iLhIE8iIlIhh1swbt++jffffx+bN2/GpUuXrKYR379/v0PnS0hIwOjRo9GpUyd06dIFixYtQm5uLsaMGQMAGDVqFOrWrYu5c+fCx8cHrVu3VhxfvXp1ALBaX9ksGjCUAYO3qRIRkQo5HDCefvppbNq0CY899hi6dOkCSZIqVEBcXBwuX76MmTNnIiMjA+3atcOGDRvMAz/T09Oh0bi0ocUuyrtI2IJBRETq5nDA+P7777F+/Xp0795dWBHx8fGIj4+3uW3Lli1lHvvxxx8Lq0MUBgwiIlI7hwNG3bp1ERAQ4IxaPFr3MBPC6jcFNBp4XTEW95nwWSRERKRCDvc9LFiwANOmTcMff/zhjHo8VvdQGRP6NkJC/6bQSZzJk4iI1M3hf1536tQJt2/fRsOGDeHn52d1y0pWVpaw4jyWyWKmMwYMIiJSIYcDxvDhw3H+/HnMmTMHoaGhFR7kWeWYjFDcU8KAQUREKuRwwNi5cyd27dqFtm3bOqMezyeXmKedt6kSEZEKOTwGo3nz5rh165YzaqkaTCUChoaDPImISH0cDhjz5s3D1KlTsWXLFly9ehXZ2dmKH7Wa/5sW7WanoPucjcoN7CIhIiIVcvif1/fffz8A4L777lOsl2UZkiTBaDTaOqzKyzcCuQVG+GjzlRvYRUJERCrkcMDYvHmzM+rweHeGdeqkQuUGtmAQEZEKORwwevfu7Yw6PN9fCUMvlWjBYcAgIiIVcjhgbNu2rcztvXr1KncxnowtGERERMUcDhh9+vSxWmc5F4Zax2AUB4wSn59ThRMRkQo5fBfJtWvXFD+XLl3Chg0b0LlzZ2zatMkZNXoUq4DBFgwiIlIhh/95HRQUZLWuf//+8PLyQkJCAvbt2yekME9z53Ht1i0YDBhERKQ+DrdglCY0NBTHjx8XdTqPc6eLRM8xGERERI63YPz222+KZVmWcfHiRcybNw/t2rUTVZfHuRMwtCjZRcIxGEREpD4O//Zr164dJEmCLMuK9ffccw9WrlwprDCPU9ptquwiISIiFXI4YKSlpSmWNRoNgoOD4ePjI6woT8TbVImIiIo5HDDq16/vjDo8XoAe8Pb2Qk3fEsNa2EVCREQqZPcgz59++gktW7a0+UCzGzduoFWrVvj555+FFudJXmprxM5pffBeXGvlBnaREBGRCtkdMBYtWoSxY8ciMDDQaltQUBCee+45LFy4UGhxHsnELhIiIiK7A8bBgwfNT1K1ZcCAAaqdA0PBZFAus4uEiIhUyO6AkZmZCb2+9H+N63Q6XL58WUhRHk0uETDYRUJERCpkd8CoW7cuDh8+XOr23377DeHh4UKK8mhWXSRswSAiIvWxO2A88MADmDFjBm7fvm217datW0hKSsJDDz0ktDhPsi5dg3kbjmP9b+eUG9iCQUREKmT3P69fffVVfPPNN2jatCni4+PRrFkzAMCxY8ewZMkSGI1GvPLKK04r1N39nCHh1vk/kF8zEw/Us9jAFgwiIlIhu3/7hYaGYufOnXj++eeRmJhonslTkiTExsZiyZIlCA0NdVqh7s70159aq5k8tZVeCxERkas59M/r+vXrY/369bh27RpOnToFWZbRpEkT1KhRw1n1eY7SnqbK21SJiEiFytV+X6NGDXTu3Fl0LR6t1KnCJXaREBGR+gh7XLvaFT9N1aTcwC4SIiJSIQYMQWRbXSSSDpAk1xRERETkQgwYgtjsIuEdJEREpFIMGIIUB4wSLRhEREQqxIAhyl8JQytZjMHgHSRERKRSDBiC3GnB0MOii4QtGEREpFIMGILIKBrMqegiYQsGERGpFAOGAHdmNQUYMIiIiIByTrRF1sY2M6Jjx46IPl8duPbXSj7ojIiIVIoBQwBJktC6poz7WoRAn6MrDhi8TZWIiFSKXSSiyZbzYLAFg4iI1IkBQzSTofg1u0iIiEilGDBEkzmTJxEREQOGACaTjEu3gLNXc5F3+3bxBs6DQUREKsWAIcAtgxFvpurQf9EO/Jp2qXgDx2AQEZFKMWAIIFu85rNIiIiIGDCEsJxoSwtOtEVERMSAIYBFvoDe8nHtkrbyiyEiInIDDBgCmCwCBp+mSkRExIAhhAw+i4SIiMgSA4YAihYMcJAnERERA4YIFoMw9LyLhIiIiAFDBOUYDHaREBERMWAIUOo8GJwqnIiIVIoBQwCTbDnI0/I2VQYMIiJSJwYMAWTFIE+L21QZMIiISKX4G1AAb50GLaubEBwSAl8d58EgIiJiC4YANat54bkWJvxrZAf46y2aMzgGg4iIVIoBQzSZYzCIiIgYMERjwCAiImLAEM5kETDYRUJERCrFgCGSbIJiVgy2YBARkUoxYAhw8cZtvP+7BmM+3qPcwBYMIiJSKQYMAfIKjDiVrcHetMvKDZLWNQURERG5GAOGAPJfM21pJZNyA7tIiIhIpRgwBLgzk6fO8lHtALtIiIhItRgwBJBxpwWjRMBgFwkREakUA4YAdx7Xzi4SIiKiIgwYAtzpItGW7CJhCwYREakUA4YAdx7XrivZgsGHnRERkUoxYAjEMRhERERFGDAEKG7BKBkwOAaDiIjUiQFDAFNpYzB4myoREakUA4YAJhMn2iIiIrLEgCFAoam0eTAYMIiISJ34G1CAtvWCMLtjIfp3aAP8YrGBXSRERKRSbMEQwEunQaAXUNuvxF0jbMEgIiKVYsAQSS5ULrMFg4iIVIoBQ6SSAYMtGEREpFIMGCKZ2IJBREQEMGAIcfHGbey7ImFv2mXlBrZgEBGRSvE3oACp567j05NaXMg8jZgoiw2cKpyIiFSKLRgCGO/Mg2H1NFXmNyIiUie3CBhLlixBVFQUfHx80LVrV+zZs6fUfVesWIGePXuiRo0aqFGjBvr161fm/pXhTsCwehYJx2AQEZFKuTxgfPHFF0hISEBSUhL279+Ptm3bIjY2FpcuXbK5/5YtWzB8+HBs3rwZu3btQkREBAYMGIDz589XcuXFDJwqnIiISMHlvwEXLlyIsWPHYsyYMQCAZcuWYd26dVi5ciWmT59utf9nn32mWP7Xv/6Fr7/+GikpKRg1apTV/vn5+cjPzzcvZ2dnAwAMBgMMBoOQz3C7oOg8JbtIDEYZEPQeVD53rrGoa01i8Lq4J14X9+RO18WRGlwaMAoKCrBv3z4kJiaa12k0GvTr1w+7du2y6xx5eXkwGAyoWbOmze1z587FrFmzrNZv2rQJfn5+5Su8hMMXJQBaqy6SlJ+2Il9TQ8h7UMUkJye7ugSygdfFPfG6uCd3uC55eXl27+vSgHHlyhUYjUaEhoYq1oeGhuLYsWN2nWPatGmoU6cO+vXrZ3N7YmIiEhISzMvZ2dnmbpXAwMDyF2/hz22ngbOnrR52dl//AYB3sJD3oPIxGAxITk5G//79odfrXV0O/YXXxT3xurgnd7oud3oB7OHyLpKKmDdvHlavXo0tW7bAx8fH5j7e3t7w9va2Wq/X64VdKJMsAQB0UI7B0Hv5AfxL6hZEXm8Sh9fFPfG6uCd3uC6OvL9LA0bt2rWh1WqRmZmpWJ+ZmYmwsLAyj33nnXcwb948/Pjjj2jTpo0zy7wrg5GPayciIrLk0rtIvLy80LFjR6SkpJjXmUwmpKSkICYmptTj3n77bcyePRsbNmxAp06dKqPUMuXkF00RbnWbKifaIiIilXL5P7ETEhIwevRodOrUCV26dMGiRYuQm5trvqtk1KhRqFu3LubOnQsAeOuttzBz5kx8/vnniIqKQkZGBgDA398f/v7+LvkMuQVFwcKqBYPzYBARkUq5/DdgXFwcLl++jJkzZyIjIwPt2rXDhg0bzAM/09PTodEUN7QsXboUBQUFeOyxxxTnSUpKwmuvvVaZpZsNbB2Ks3+kQ2c1kydbMIiISJ1cHjAAID4+HvHx8Ta3bdmyRbF89uxZ5xfkoO6NauFTHaAxWgzylDRFP0RERCrkFgGjKmgcJKNv7RrAnTt4OMCTiIhUjP/EFqRVDRlt61YrXsHuESIiUjEGDJFMFlOoangPORERqRcDhkiyxSBPBgwiIlIxBgyRLFswOAaDiIhUjAFDJLmw+DUDBhERqRgDhkCSySJgsIuEiIhUjAFDJJldJERERAADhlhswSAiIgLAgCGWZQsGn0NCREQqxoAhkomDPImIiAAGDLFkTrRFREQEMGCIZbKYaIstGEREpGIMGCLJHORJREQEMGCIxZk8iYiIADBgiMUWDCIiIgAMGGKxBYOIiAgAA4ZYihYMBgwiIlIvBgyBJHaREBERAWDAEItdJERERAAYMMRiCwYREREABgyxOFU4ERERAAYMsTjIk4iICAADhliKMRjsIiEiIvViwBCJLRhEREQAGDDEMnGQJxEREcCAIZbM21SJiIgABgyxLMdgsAWDiIhUjAFDJJm3qRIREQEMGOLIMiTZWLzMFgwiIlIxBgxBJBQqVzBgEBGRirEdXxANTMoV7CIhoirOaDTCYDDcfUeqEIPBAJ1Oh9u3b8NoNN79gAry8vKCRlPx9gf+FhSELRhEpBayLCMjIwPXr193dSmqIMsywsLCcO7cOUiS5PT302g0aNCgAby8vCp0HgYMQSSUSJVswSCiKupOuAgJCYGfn1+l/NJTM5PJhJycHPj7+wtpWbjbe124cAEXL15EZGRkha4tfwsKopFLBAy2YBBRFWQ0Gs3holatWq4uRxVMJhMKCgrg4+Pj9IABAMHBwbhw4QIKCwuh15f/dxkHeQrCLhIiUoM7Yy78/PxcXAk5y52ukYqO92DAEERj1UXCgEFEVRe7RaouUdeWAUMQTckWDG3FBscQERF5MgYMQay6SNiCQUTkVvr06YPJkyfbte/Zs2chSRJSU1MBAFu2bIEkSbxzxgEc5CmI9SBPtmAQEbmTb775ptyDFrt164aLFy8iKChIcFVVFwOGIFZdJBzkSUTkVmrWrFnuY728vBAWFlah9y8oKKjw3BKehF0kgvAuEiJSu/PXb2Hv2SyHfq7m5Fudp6DQdNfjzl+/5XB9ll0kUVFRmDNnDp566ikEBAQgMjISy5cvL/VYW10k27dvR8+ePeHr64uIiAhMnDgRubm55u1RUVGYPXs2Ro0ahcDAQDz77LMoKChAfHw8wsPD4ePjg/r162Pu3LkOfxZPwBYMQaxbMNSTUomIAGDN3nN4L+WkQ8e8N6wdHmlXV7Huel4BHl+2q8zjJt3XBFP6N3W4RksLFizA7Nmz8fLLL+Orr77C888/j969e6NZs2Z3Pfb06dO4//778cYbb2DlypW4fPky4uPjER8fj1WrVpn3e+eddzBz5kwkJSUBABYvXoy1a9dizZo1iIyMxLlz53Du3LkKfQ53xYAhCCfaIiLyLA888ADGjRsHAJg2bRreffddbN682a6AMXfuXDzxxBPmFpEmTZpg8eLF6N27N5YuXQofHx8AQN++fTF16lTzcenp6WjSpAl69OgBSZJQv3598R/MTbCLRBDrLhK2YBARubM2bdqYX0uShLCwMFy6dMmuYw8ePIiPP/4Y/v7+5p/Y2FiYTCakpaWZ9+vUqZPiuCeffBKpqalo1qwZJk6ciE2bNon5MG6ILRiCcJAnEand0M4R6NGktkPHNKxdzWpddT8vfPmPmDKPq1Pd16H3saXkHSWSJMFkMpWyt1JOTg6ee+45TJw40WpbZGSk+XW1asrP16FDB6SlpeGHH37Ajz/+iKFDh6Jfv3746quvyvEJ3BsDhiBWM3myBYOIVKZudV/UFfCL30unQeeo8t/xURk6dOiAI0eOoHHjxg4fGxgYiLi4OMTFxeGxxx7D/fffj6ysrArd5eKOGDAEkWS2YBARqcW0adNwzz33ID4+Hs888wyqVauGI0eOIDk5GR988EGpxy1cuBDh4eFo3749NBoNvvzyS4SFhaF69eqVV3wlYcAQxKqLhDN5EhFVWW3atMHWrVvxyiuvoGfPnpBlGY0aNUJcXFyZxwUEBODtt9/GyZMnodVq0blzZ6xfv75SnpJa2RgwBLHqIuGzSIiI3MqWLVvMr8+ePWu1/c604EDRHBayLJuX+/Tpo1gGgM6dO5c5SNPWe4wdOxZjx461u2ZPVvUik4uwBYOIiKgYA4YgvE2ViIioGAOGIJxoi4iIqBgDhiCKFgxJB0iS64ohIiJyMQYMQRRjMNh6QUREKseAIQgDBhERUTEGDEEkyzEYHOBJREQqx4AhCFswiIiIijFgCKIMGGzBICIidWPAEESynMmTk2wREZFADz30EKZMmWJejoqKwqJFi1xXkB04VbggGpldJEREVDn27t1r9Sh4d8OAIYgGhuIFrbfrCiEioiovODjY1SXcFbtIBFEEDA0DBhGpSMEN4NJ21/wU3LC7zA0bNqBHjx6oXr06atWqhYceeginT58GUPRgMkmS8M033+Dee++Fn58f2rZti127dinO8fXXX6NVq1bw9vZGVFQUFixYoNgeFRWFN954A6NGjYK/vz/q16+PtWvX4vLly3jkkUfg7++PNm3a4NdffzUfc/XqVQwfPhx169aFn58foqOj8d///rfMz1Kyi+T69et45plnEBwcjMDAQPTt2xcHDx40bz948CDuvfdeBAQEIDAwEB07dlTU4AxswRBEyxYMIlKr64eAH3u65r37/QyE9LBr19zcXCQkJKBNmzbIycnBzJkz8eijjyqeovrKK6/gnXfeQZMmTfDKK69g+PDhOHXqFHQ6Hfbt24ehQ4fitddeQ1xcHHbu3Ilx48ahVq1aePLJJ83nePfddzFnzhzMmDED7777LkaOHIlu3brhqaeewvz58zFt2jSMGjUKv//+OyRJwu3bt9GxY0dMmzYNgYGBWLduHUaOHIlGjRqhS5cudn22xx9/HL6+vvjhhx8QFBSEf/7zn7jvvvtw4sQJ1KxZE0888QTat2+PpUuXQqvVIjU1FXq9c7vzGTAE0chswSAicmdDhgxRLK9cuRLBwcE4cuQI/P39AQAvvPACHnzwQQDArFmz0KpVK5w6dQrNmzfHwoULcd9992HGjBkAgKZNm+LIkSOYP3++ImA88MADeO655wAAM2fOxNKlS9G5c2c8/vjjAIBp06YhJiYGmZmZCAsLQ926dfHCCy+Yj58wYQI2btyINWvW2BUwtm/fjj179uDSpUvw9i76/fPOO+/g22+/xVdffYVnn30W6enpePHFF9G8eXMAQJMmTcrzFTqEXSSCaFFgseDrukKIiMimkydPYvjw4WjYsCECAwMRFRUFAEhPTzfv06ZNG/Pr8PBwAMClS5cAAEePHkX37t0V5+zevTtOnjwJo9Fo8xyhoaEAgOjoaKt1d85rNBoxe/ZsREdHo2bNmvD398fGjRsVdZXl4MGDyMnJQa1ateDv72/+SUtLM3cBJSQk4JlnnkG/fv0wb94883pnYguGIDr5VvGCPsB1hRARVbbq0UVdFa56bzsNGjQI9evXx4oVK1CnTh2YTCa0bt0aBQXF/0C07DaQ/npopclkcqgkW+co67zz58/He++9h0WLFiE6OhrVqlXD5MmTFXWVJScnB+Hh4diyZYvVturVqwMAXnvtNYwYMQLr1q3DDz/8gKSkJKxevRqPPvqoQ5/NEQwYgmhxu3hB5++6QoiIKptXkN3jIFzl6tWrOH78OFasWIGePYvGi2zfvt2hc7Ro0QI7duxQrNuxYweaNm0KrVZb7tp27NiBRx55BH//+98BFAWPEydOoGXLlnYd36FDB2RkZECn05lbZWxp2rQpmjZtiilTpmD48OFYtWqVUwMGu0gEYQsGEZH7qlGjBmrVqoXly5fj1KlT+Omnn5CQkODQOaZOnYqUlBTMnj0bJ06cwCeffIIPPvhAMX6iPJo0aYLk5GTs3LkTR48exXPPPYfMzEy7j+/Xrx9iYmIwePBgbNq0CWfPnsXOnTvxyiuv4Ndff8WtW7cQHx+PLVu24I8//sCOHTuwd+9etGjRokJ13w0DhgiyDB1bMIiI3JZGo8Hq1auxb98+tG7dGlOmTMH8+fMdOkeHDh2wZs0arF69Gq1bt8bMmTPx+uuvKwZ4lserr76KDh06IDY2Fn369EFYWBgGDx5s9/GSJGH9+vXo1asXxowZg6ZNm2LYsGH4448/EBoaCq1Wi6tXr2LUqFFo2rQphg4dioEDB2LWrFkVqvtu2EUigqkAGsupwhkwiIjcTr9+/XDkyBHFOlmWbb4GisYvlFw3ZMgQq7tRLJ09e9ZqXclzREVFKdbVrFkT3377bZm1f//99wgMDCz1fQICArB48WIsXrzY5vF3m1fDGdiCIULhTeUyu0iIiEjlGDBEKMxRLrMFg4iIVI4BQ4SSLRg6tmAQEZG6MWAIIBXmKlfo2YJBRETqxoAhgoEtGESkLiUHLlLVIeraMmCIwDEYRKQSd2akzMvLc3El5Cx3ZhCtyORhAG9TrbisA9Dun6Bc51XDNbUQETmZVqtF9erVzc/R8PPzM099Tc5hMplQUFCA27dvQ6NxbruAyWTC5cuX4efnB52uYhGBAaOirh+ElH+peFnrA/iEuK4eIiInCwsLA1D8sC5yLlmWcevWLfj6+lZKmNNoNIiMjKzwezFgVFTJ1ora3QCmeSKqwiRJQnh4OEJCQmAwGFxdTpVnMBiwbds29OrVS/HQNGfx8vIS0lLCgFFRXjUhSzrky9XgFdIRmo62Z1EjIqpqtFpthfvp6e60Wi0KCwvh4+NTKQFDFLcY5LlkyRJERUXBx8cHXbt2xZ49e8rc/8svv0Tz5s3h4+OD6OhorF+/vpIqtSG4OwqH5GJjtU9g7L0BqN7KdbUQERG5CZcHjC+++AIJCQlISkrC/v370bZtW8TGxpbat7dz504MHz4cTz/9NA4cOIDBgwdj8ODBOHz4cCVX/hdJwy4RIiKiElweMBYuXIixY8dizJgxaNmyJZYtWwY/Pz+sXLnS5v7vvfce7r//frz44oto0aIFZs+ejQ4dOuCDDz6o5MqJiIioNC4dg1FQUIB9+/YhMTHRvE6j0aBfv37YtWuXzWN27dqFhIQExbrY2NhSn0SXn5+P/Px88/KNGzcAAFlZWcIGJxkMBuTl5eHq1ase1T+mBrw27onXxT3xurgnd7ouN28WTSxpz2RcLg0YV65cgdFoRGhoqGJ9aGgojh07ZvOYjIwMm/tnZGTY3H/u3Lk2n3nfoEGDclZNRESkbjdv3kRQUFCZ+1T5u0gSExMVLR4mkwlZWVmoVauWsPuJs7OzERERgXPnziEwMFDIOUkMXhv3xOvinnhd3JM7XRdZlnHz5k3UqVPnrvu6NGDUrl0bWq0WmZmZivWZmZnmiVxKCgsLc2h/b29veHt7K9ZVr169/EWXITAw0OUXn2zjtXFPvC7uidfFPbnLdblby8UdLh3k6eXlhY4dOyIlJcW8zmQyISUlBTExMTaPiYmJUewPAMnJyaXuT0RERJXP5V0kCQkJGD16NDp16oQuXbpg0aJFyM3NxZgxYwAAo0aNQt26dTF37lwAwKRJk9C7d28sWLAADz74IFavXo1ff/0Vy5cvd+XHICIiIgsuDxhxcXG4fPkyZs6ciYyMDLRr1w4bNmwwD+RMT09XTFnarVs3fP7553j11Vfx8ssvo0mTJvj222/RunVrV30EeHt7IykpyaorhlyP18Y98bq4J14X9+Sp10WSRT34nYiIiOgvLp9oi4iIiKoeBgwiIiISjgGDiIiIhGPAICIiIuEYMARw9HHz5Fxz585F586dERAQgJCQEAwePBjHjx93dVlUwrx58yBJEiZPnuzqUgjA+fPn8fe//x21atWCr68voqOj8euvv7q6LFUzGo2YMWMGGjRoAF9fXzRq1AizZ8+26zkg7oABo4Icfdw8Od/WrVsxfvx47N69G8nJyTAYDBgwYAByc3NdXRr9Ze/evfjnP/+JNm3auLoUAnDt2jV0794der0eP/zwA44cOYIFCxagRo0ari5N1d566y0sXboUH3zwAY4ePYq33noLb7/9Nt5//31Xl2YX3qZaQV27dkXnzp3Nj4s3mUyIiIjAhAkTMH36dBdXRwBw+fJlhISEYOvWrejVq5ery1G9nJwcdOjQAR9++CHeeOMNtGvXDosWLXJ1Wao2ffp07NixAz///LOrSyELDz30EEJDQ/HRRx+Z1w0ZMgS+vr74z3/+48LK7MMWjAq487j5fv36mdfd7XHzVPlu3LgBAKhZs6aLKyEAGD9+PB588EHF3xtyrbVr16JTp054/PHHERISgvbt22PFihWuLkv1unXrhpSUFJw4cQIAcPDgQWzfvh0DBw50cWX2cflMnp6sPI+bp8plMpkwefJkdO/e3aWzvVKR1atXY//+/di7d6+rSyELZ86cwdKlS5GQkICXX34Ze/fuxcSJE+Hl5YXRo0e7ujzVmj59OrKzs9G8eXNotVoYjUa8+eabeOKJJ1xdml0YMKhKGz9+PA4fPozt27e7uhTVO3fuHCZNmoTk5GT4+Pi4uhyyYDKZ0KlTJ8yZMwcA0L59exw+fBjLli1jwHChNWvW4LPPPsPnn3+OVq1aITU1FZMnT0adOnU84rowYFRAeR43T5UnPj4e33//PbZt24Z69eq5uhzV27dvHy5duoQOHTqY1xmNRmzbtg0ffPAB8vPzodVqXViheoWHh6Nly5aKdS1atMDXX3/toooIAF588UVMnz4dw4YNAwBER0fjjz/+wNy5cz0iYHAMRgWU53Hz5HyyLCM+Ph7/+9//8NNPP6FBgwauLokA3HfffTh06BBSU1PNP506dcITTzyB1NRUhgsX6t69u9Wt3CdOnED9+vVdVBEBQF5enuJhnwCg1WphMplcVJFj2IJRQXd73DxVvvHjx+Pzzz/Hd999h4CAAGRkZAAAgoKC4Ovr6+Lq1CsgIMBqHEy1atVQq1Ytjo9xsSlTpqBbt26YM2cOhg4dij179mD58uVYvny5q0tTtUGDBuHNN99EZGQkWrVqhQMHDmDhwoV46qmnXF2afWSqsPfff1+OjIyUvby85C5dusi7d+92dUmqBsDmz6pVq1xdGpXQu3dvedKkSa4ug2RZ/r//+z+5devWsre3t9y8eXN5+fLlri5J9bKzs+VJkybJkZGRso+Pj9ywYUP5lVdekfPz811dml04DwYREREJxzEYREREJBwDBhEREQnHgEFERETCMWAQERGRcAwYREREJBwDBhEREQnHgEFERETCMWAQERGRcAwYRCSELMt49tlnUbNmTUiShNTUVFeXREQuxIBBpAIZGRmYMGECGjZsCG9vb0RERGDQoEGKB/VV1IYNG/Dxxx/j+++/x8WLF/l8ESKV48POiKq4s2fPonv37qhevTrmz5+P6OhoGAwGbNy4EePHj8exY8eEvM/p06cRHh6Obt26lfscsizDaDRCp+P/mog8HVswiKq4cePGQZIk7NmzB0OGDEHTpk3RqlUrJCQkYPfu3QCA9PR0PPLII/D390dgYCCGDh2KzMxM8zlee+01tGvXDv/+978RFRWFoKAgDBs2DDdv3gQAPPnkk5gwYQLS09MhSRKioqIAAPn5+Zg4cSJCQkLg4+ODHj16YO/evebzbtmyBZIk4YcffkDHjh3h7e2N7du3o0+fPpgwYQImT56MGjVqIDQ0FCtWrDA/qTggIACNGzfGDz/8YD6X0WjE008/jQYNGsDX1xfNmjXDe++9p/gunnzySQwePBjvvPMOwsPDUatWLYwfPx4Gg8G8T35+PqZNm4aIiAh4e3ujcePG+Oijj8zbDx8+jIEDB8Lf3x+hoaEYOXIkrly5Iu6CEVURDBhEVVhWVhY2bNiA8ePHo1q1albbq1evDpPJhEceeQRZWVnYunUrkpOTcebMGcTFxSn2PX36NL799lt8//33+P7777F161bMmzcPAPDee+/h9ddfR7169XDx4kVziHjppZfw9ddf45NPPsH+/fvRuHFjxMbGIisrS3Hu6dOnY968eTh69CjatGkDAPjkk09Qu3Zt7NmzBxMmTMDzzz+Pxx9/HN26dcP+/fsxYMAAjBw5Enl5eQAAk8mEevXq4csvv8SRI0cwc+ZMvPzyy1izZo3ivTZv3ozTp09j8+bN+OSTT/Dxxx/j448/Nm8fNWoU/vvf/2Lx4sU4evQo/vnPf8Lf3x8AcP36dfTt2xft27fHr7/+ig0bNiAzMxNDhw6twFUiqqJc+zBXInKmX375RQYgf/PNN6Xus2nTJlmr1crp6enmdb///rsMQN6zZ48sy7KclJQk+/n5ydnZ2eZ9XnzxRblr167m5XfffVeuX7++eTknJ0fW6/XyZ599Zl5XUFAg16lTR3777bdlWZblzZs3ywDkb7/9VlFT79695R49epiXCwsL5WrVqskjR440r7t48aIMQN61a1epn238+PHykCFDzMujR4+W69evLxcWFprXPf7443JcXJwsy7J8/PhxGYCcnJxs83yzZ8+WBwwYoFh37tw5GYB8/PjxUusgUiO2YBBVYbIs33Wfo0ePIiIiAhEREeZ1LVu2RPXq1XH06FHzuqioKAQEBJiXw8PDcenSpVLPe/r0aRgMBnTv3t28Tq/Xo0uXLorzAkCnTp2sjr/TkgEAWq0WtWrVQnR0tHldaGgoAChqWLJkCTp27Ijg4GD4+/tj+fLlSE9PV5y3VatW0Gq1Nj9HamoqtFotevfubfMzHTx4EJs3b4a/v7/5p3nz5ubPS0TFOJKKqApr0qQJJEkSMpBTr9crliVJgslkqvB5AdjsvrH1fpbrJEkCAHMNq1evxgsvvIAFCxYgJiYGAQEBmD9/Pn755Re7P4evr2+Zdebk5GDQoEF46623rLaFh4eXeSyR2rAFg6gKq1mzJmJjY7FkyRLk5uZabb9+/TpatGiBc+fO4dy5c+b1R44cwfXr19GyZctyv3ejRo3g5eWFHTt2mNcZDAbs3bu3QuctzY4dO9CtWzeMGzcO7du3R+PGjR1uVYiOjobJZMLWrVttbu/QoQN+//13REVFoXHjxoofWyGJSM0YMIiquCVLlsBoNKJLly74+uuvcfLkSRw9ehSLFy9GTEwM+vXrh+joaDzxxBPYv38/9uzZg1GjRqF37942uy7sVa1aNTz//PN48cUXsWHDBhw5cgRjx45FXl4enn76aYGfsEiTJk3w66+/YuPGjThx4gRmzJihuGPFHlFRURg9ejSeeuopfPvtt0hLS8OWLVvMA0XHjx+PrKwsDB8+HHv37sXp06exceNGjBkzBkajUfhnIvJkDBhEVVzDhg2xf/9+3HvvvZg6dSpat26N/v37IyUlBUuXLoUkSfjuu+9Qo0YN9OrVC/369UPDhg3xxRdfVPi9582bhyFDhmDkyJHo0KEDTp06hY0bN6JGjRoCPpnSc889h7/97W+Ii4tD165dcfXqVYwbN87h8yxduhSPPfYYxo0bh+bNm2Ps2LHm1p86depgx44dMBqNGDBgAKKjozF58mRUr14dGg3/d0pkSZLtGQVGRERE5ABGbiIiIhKOAYOIiIiEY8AgIiIi4RgwiIiISDgGDCIiIhKOAYOIiIiEY8AgIiIi4RgwiIiISDgGDCIiIhKOAYOIiIiEY8AgIiIi4f4fxJ7Q/AOqpt0AAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 600x600 with 1 Axes>"
       ]
@@ -246,12 +371,80 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Total experiment time: 25880.424757003784 seconds | 431.34041261672974 minutes\n"
+      "cumsum: False, with prefix: True | auc: 0.8645835500000001, se: 0.0025788060394353837\n",
+      "MSE between scores (cumsum=False) with and without prefix: 0.04824604383356553\n",
+      "RMSE between scores (cumsum=False) with and without prefix: 0.21964982092768828\n",
+      "MAE between scores (cumsum=False) with and without prefix: 0.037033117556071235\n",
+      "MSE (normalised) between scores (cumsum=False) with and without prefix: 1.1171786487600954\n",
+      "RMSE (normalised) between scores (cumsum=False) with and without prefix: 1.0569667207438915\n",
+      "MAE (normalised) between scores (cumsum=False) with and without prefix: 0.17820534820763628\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 70615/70615 [00:00<00:00, 122638.28it/s]\n",
+      "100%|██████████| 20000/20000 [00:00<00:00, 157090.63it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start cumsum: True, with_prefix: False\n",
+      "fit_time: 12482.772598981857\n",
+      "compute_score_time: 95.868243932724\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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dc28NKiIiIqpdDBhuYKw0RKLTCq5f5Nx66+vABCDktpoVRUREVIsYMNygcsBwuQejvBQ4/5N1u+EAQKhGSCEiIlIIA4YbVL6LRKdxsZkv/QyYy6zb0YNkqIqIiKj2MGC4gW0Phk4rQKNxsffh0k7pdkQvGaoiIiKqPQwYbmA7yVOnrUYT5220vg5sDPhHyVAVERFR7eGzSNygZXQI7kuKgaHcDJ2r8y9MBuBCpfkXREREHoYBww3uS4rBfUkx1Tv5/GbAdN26HXWnPEURERHVIg6R1DX5m2w2BPZgEBGRR2LAqGsubLG+DmsH+EUoVwsREVE1MWDUJYarwKUd1u2I3srVQkREVAMMGHXJ+c2A2WjdjrlbuVqIiIhqgJM83WDmmt+wN/cydFoN2sSE4NWhbZ078fJ+6XZkH/mLIyIiqgUMGG5w4kIxfvnjKgBA68oiW5f3WV8HNQd0wTJXRkREVDs4ROIGtgtt+bqyDkbBHuvr+h1lrIiIiKh2MWC4gXSpcCeb+PpFoDTXul2vk8xVERER1R4GDDcw2AQMvbMB48ov0u36DBhEROS5GDDcwFguWl47vVT41d+k22HtZKyIiIiodjFguEG1ejCuHrC+1oUBfg3lLYqIiKgWMWC4ge0kT72Pk3eRXM62vq6XBAguPuKdiIioDmHAcAOXJ3maTcAVmx6Meh3kL4qIiKgWMWC4gctDJFd/A0yl1u2wJDdURUREVHsYMNzAaDNE4tQkz4Ld0u0G3WWuiIiIqHYxYLhBtXowbtD6A8G3uaEqIiKi2sOAITNRFGE0WW9T1TvTg1F0zPo6OBHQaN1QGRERUe3hs0jc4PDsu2EwmWEoN8NP50RYKDpufR3UzH2FERER1RIGDJkJggA/nda5YAEA5nKg2CZghHB4hIiIPB+HSJRWnAOYjdbtkJbK1UJERCQTBgylFR2RbrMHg4iIvAADhtIKD0u3GTCIiMgLcA6GzK6WGrHml7PQawXotBrc3jQcMWH+VZ9QaNOD4RcJ6Ou5v0giIiI3Y8CQWV7hdUxfbV32+9+ju9w8YNjeQRLcwo2VERER1R4OkcjM9jkkgBMreZaetr4OTJC/ICIiIgUoHjAWL16MhIQE+Pn5oXv37ti5c+dNj1+wYAFuu+02+Pv7Iy4uDlOnTsX169drqdpbM1QOGJqbPBXVVAaU/G7dDmjkpqqIiIhql6IB49NPP0VaWhoyMjKwd+9eJCUlYdCgQTh//rzD4z/++GO8+OKLyMjIwKFDh/DBBx/g008/xUsvvVTLlVet3GYVTwDwudlS4dfOAGK5dTu0tZuqIiIiql2KBoz58+dj/PjxGDduHFq3bo333nsPAQEBWLZsmcPjt23bhp49e+Lhhx9GQkICBg4ciJEjR96y16M22Q2RaG/Sg1F6VrrtH+uGioiIiGqfYpM8DQYD9uzZg/T0dMs+jUaD/v37Y/v27Q7P6dGjB/7v//4PO3fuRLdu3XDy5EmsXbsWo0aNqvJzysrKUFZWZtkuLCwEABiNRhiNxqpOc8mN6xiNRlw3SK8piOYqP0co/l3yAzDqIwGZalKabZsQ26MytocU28Me20SqrrSHK5+vWMC4ePEiTCYToqKiJPujoqJw+PBhh+c8/PDDuHjxInr16gVRFFFeXo6nnnrqpkMkc+bMwaxZs+z2b9iwAQEBATX7JirJzMzEgQIBgHWZ8O1bt+D3QMfHNzdsQBub7fU/HoBJOCFrTUrLzMxUuoQ6he0hxfaQYnvYY5tIKd0epaWlTh/rUbepZmVl4bXXXsO7776L7t274/jx45g8eTJmz56N6dOnOzwnPT0daWlplu3CwkLExcVh4MCBCAkJkaUuo9GIzMxMDBgwANqjBcCR/Zb37uzbB80iHCcMzZ61wMmK16JvBAbdO1yWeuoC2zbR6XRKl6M4tocU20OK7WGPbSJVV9rjxiiAMxQLGA0aNIBWq0V+fr5kf35+Pho2bOjwnOnTp2PUqFF4/PHHAQDt2rVDSUkJnnjiCbz88svQaOynlPj6+sLX19duv06nk/2HpNPpYBakNQT46qv+nGvWW1SFoCZe+ZfIHe3sydgeUmwPKbaHPbaJlNLt4cpnKzbJU6/Xo3Pnzti4caNln9lsxsaNG5GcnOzwnNLSUrsQodVWDEeIoujolFpXXmmSp8/NJnleO2N9zQmeRETkRRQdIklLS8OYMWPQpUsXdOvWDQsWLEBJSQnGjRsHABg9ejRiY2MxZ84cAMCQIUMwf/58dOzY0TJEMn36dAwZMsQSNJRWbq58m6qTASOAAYOIiLyHogEjJSUFFy5cwIwZM5CXl4cOHTpg3bp1lomfubm5kh6LV155BYIg4JVXXsGZM2cQERGBIUOG4B//+IdS34Idu3UwHAzbAABM14GyS9Zt9mAQEZEXUXySZ2pqKlJTUx2+l5WVJdn28fFBRkYGMjIyaqGy6ukYH4aX72mFcrMIk9mMAH0VPSvXKq2BwR4MIiLyIooHDG/TKjoEraKduDul9Ix0mz0YRETkRRR/FolqVQ4Y7MEgIiIvwoChlGvswSAiIu/FgKEU2x4MXQigC1KuFiIiIpkxYMhMFEXn1uTgGhhEROTFOMlTZvMzj+LtH47DRyMgQK/FLzMHOT6Qa2AQEZEXYw+GzEx/LrRVcZvqTXoyStmDQURE3osBQ2a2oUKrqWIVT1GUroPBHgwiIvIyDBgyK3cmYJRdBMwG6zZ7MIiIyMswYMhM2oNRRfOWXZBu+0W5sSIiIqLax4AhM9uA4VNVD8b1ygEj0o0VERER1T4GDJk5N0RSKWD4RrixIiIiotrHgCEzk9lseV1lwLh+XrrNHgwiIvIyDBgyM1nzxU2GSGwChqAF9GFurYmIiKi2MWDIzKkeDNshEt8IQOCPgYiIvAt/s8nMqTkY1/Otrzk8QkREXogBQ2ZOLbRluGx9ra/v5oqIiIhqH59FIrOIYF8khAeg3CwiKsTP8UGSgFGvdgojIiKqRQwYMnt1aNtbH1R2yfraN9x9xRARESmEQyRKMDBgEBGRd2PAqG2mMqC8xLqtZ8AgIiLvw4BR22yHRwD2YBARkVdiwKhtBgYMIiLyfpzkKbMlWSfwx+VS+GgEtIwOwchu8dIDKvdgcIiEiIi8EAOGzNb9lof9p68AAAa0jrp1wGAPBhEReSEOkcjMfKvHtXOIhIiIVIABQ2a3XCrcboiEK3kSEZH3YcCQ2S0fdmYbMHShgIajVERE5H0YMGR26x6Mi9bXvg1qoSIiIqLax4AhM9s5GFrB0RwMPuiMiIi8HwOGzGx7MHy0DgKG8Yr1tT7M7fUQEREpgQFDZrd8XLvhqvU1AwYREXkpBgyZld9qiMS2B0MX6v6CiIiIFMCAITPJHAyNg+ZlDwYREakAA4bMbjoHQzQDRpuAwR4MIiLyUgwYMrOdg6GpPERiLAJgfZ8Bg4iIvBVXeZLZsrFdYTSZYTKLiK3nL33TtvcC4BAJERF5LQYMmXVrcpO1LQxXpNvswSAiIi/FIZLaZCyUbjNgEBGRl2LAqE12ASNEmTqIiIjcjAGjNpUXSbcZMIiIyEtxDoaMzGYRv/5xFVqNAB+tgMhgX4QF6K0H2PVgBNdugURERLWEAUNG14wmDHlni2V75pDWGNuzifUADpEQEZFKcIhERrZrYACAVlupeY02QyQaHaDxrYWqiIiIah8DhozKKwcMu4W2bHowdCGAo2eVEBEReQEGDBlV7sHwqfw01XKbgOHD4REiIvJeDBgyMomVejAqBwxJDwYneBIRkfdiwJCR3RyMygHDdiVPLhNORERejAFDRnZzMOwCxmXra329WqiIiIhIGQwYMjK7EjB0Ye4viIiISCEMGDK65RCJ7dNU+RwSIiLyYgwYMrILGLa3oYqiNGDoGTCIiMh7MWDIyO4uEq1NwDBdB8xG6zZ7MIiIyIsxYMjIbJZuS3owbHsvAAYMIiLyanwWiYyC/X0wJCkGJrMZJrOIBkE2S4FXDhgcIiEiIi/mcsDo06cPHnvsMTz44IPw9/d3R00eq3H9ALw9sqPjNw3swSAiIvVweYikY8eOeO6559CwYUOMHz8eO3bscEdd3odDJEREpCIuB4wFCxbg7NmzWL58Oc6fP4877rgDrVu3xhtvvIH8/Hx31OgdGDCIiEhFqjXJ08fHB3/961/x9ddf448//sDDDz+M6dOnIy4uDsOGDcMPP/wgd52ej3MwiIhIRWp0F8nOnTuRkZGBN998E5GRkUhPT0eDBg3wl7/8Bc8995xcNXoHzsEgIiIVcXmS5/nz5/Hf//4Xy5cvx7FjxzBkyBB88sknGDRoEIQ/b8scO3Ys7r77brzxxhuyF1yX7f79MuasOwqNIMBHI+CNB5OQ0CCw4k3bJ6kKPoCWE2SJiMh7uRwwGjVqhGbNmuHRRx/F2LFjERERYXdM+/bt0bVrV1kK9CRXS4345Q9rT4XBZLMwRuVVPIVKy4gTERF5EZcDxsaNG9G7d++bHhMSEoJNmzZVuyhPVflpqhrJQls2PRg+IbVUERERkTJcnoORkZGBK1eu2O0vLCxEv3795KjJY5krLRXuY/uws/Ji62tdUC1VREREpAyXA8bmzZthMBjs9l+/fh0//fSTLEV5qso9GJKnqZaXWF/7MGAQEZF3c3qI5JdffgEAiKKIgwcPIi8vz/KeyWTCunXrEBsbK3+FHsR804Bh04PBgEFERF7O6YDRoUMHCIIAQRAcDoX4+/vj7bfflrU4T3PzHgzbgBFYSxUREREpw+mAkZOTA1EU0bRpU+zcuVNy94her0dkZCS0Wq1bivQUledgSCZ5SgJGcC1VREREpAynA0bjxo0BAObKzyQnC1Plx7Xb9mAY2YNBRETq4VTAWLNmDQYPHgydToc1a9bc9Nj77rtPlsI8UeUeDK1tD4ap1PqaAYOIiLycUwFj2LBhyMvLQ2RkJIYNG1blcYIgwGQyyVWbx7EbIrlxj44oVhoiCai9ooiIiBTgVMCwHRbhEEnVTFVN8jSXAWaj9Q0+h4SIiLxcjR52RlKV8oV1kmd5qfQN3qZKRERezqkejEWLFjl9wWeeeabaxXi6R7rF4aHujWE2izCLgK/Pn/nNVDlgcIiEiIi8m1MB46233nLqYoIgqDpg6H000Ol09m9U7sHQMmAQEZF3cypg5OTkuLsO78YeDCIiUhnOwagN7MEgIiKVcaoHIy0tDbNnz0ZgYCDS0tJueuz8+fNlKcyrsAeDiIhUxqmAsW/fPhiNRsvrqgi2C0up0K9nruLYhVIIggC9VoNhHf98+Bt7MIiISGWcChibNm1y+JqkMg+dx5LNFfNV/HQ3CRjswSAiIi9XozkYp0+fxunTp+WqxePZrkFW5TLhAHswiIjI67kcMMrLyzF9+nSEhoYiISEBCQkJCA0NxSuvvGIZRlEr26XCNZJHtbMHg4iI1MXlgDFp0iQsXboUr7/+Ovbt24d9+/bh9ddfxwcffFCtNTAWL16MhIQE+Pn5oXv37ti5c+dNj79y5QomTpyI6Oho+Pr6okWLFli7dq3Ln+sOkoBx0x4M/1qqiIiISBlOP679ho8//hgrV67E4MGDLfvat2+PuLg4jBw5EkuWLHH6Wp9++inS0tLw3nvvoXv37liwYAEGDRqEI0eOIDIy0u54g8GAAQMGIDIyEp9//jliY2Px+++/IywszNVvwy1slwrXVtWDodEDGpebnYiIyKO4/JvO19cXCQkJdvubNGkCvV7v0rXmz5+P8ePHY9y4cQCA9957D99++y2WLVuGF1980e74ZcuWoaCgANu2bbOsmOmoFqXYPuzMNl9IejA4/4KIiFTA5YCRmpqK2bNnY/ny5fD19QUAlJWV4R//+AdSU1Odvo7BYMCePXuQnp5u2afRaNC/f39s377d4Tlr1qxBcnIyJk6ciK+//hoRERF4+OGHMW3aNGi1WofnlJWVoayszLJdWFgIADAajbLNGblxnXKbR9ULNvs1hiLcqE7UBqBcBXNVbnzvap+XcwPbQ4rtIcX2sMc2kaor7eHK5zsVMP76179Ktr///ns0atQISUlJAID9+/fDYDDgrrvucvqDL168CJPJhKioKMn+qKgoHD582OE5J0+exA8//IBHHnkEa9euxfHjxzFhwgQYjUZkZGQ4PGfOnDmYNWuW3f4NGzYgIEDe3oTc3NO4Ma3FYCizzA3pWHYU8X8eU1ImYmMdmTNSGzIzM5UuoU5he0ixPaTYHvbYJlJKt0dpaemtD/qTUwEjNDRUsj18+HDJdlxcnNMfWBNmsxmRkZFYunQptFotOnfujDNnzmDevHlVBoz09HTJ6qOFhYWIi4vDwIEDERISIktdRqMRmZmZiGnUCMg/CwAI8PfHPffcAQDQbv8/4I+KYwNDGuCegffI8rl12Y02GTBggOMHwKkM20OK7SHF9rDHNpGqK+1xYxTAGU4FjOXLl1e7mKo0aNAAWq0W+fn5kv35+flo2LChw3Oio6Oh0+kkwyGtWrVCXl4eDAaDwzkgvr6+lqEcWzqdTvYfku1KplqNYL2+aB2iEXwCVPWXxR3t7MnYHlJsDym2hz22iZTS7eHKZyv2sDO9Xo/OnTtj48aNln1msxkbN25EcnKyw3N69uyJ48ePw2yzotXRo0cRHR3t8gRTdzDZLLQlvU31uvU1b1ElIiIVqNb9kp9//jlWrVqF3NxcGAwGyXt79+51+jppaWkYM2YMunTpgm7dumHBggUoKSmx3FUyevRoxMbGYs6cOQCAp59+Gu+88w4mT56MSZMm4dixY3jttdeqtf6GO9iugyG5TVUSMPxqsSIiIiJluBwwFi1ahJdffhljx47F119/jXHjxuHEiRPYtWsXJk6c6NK1UlJScOHCBcyYMQN5eXno0KED1q1bZ5n4mZubC43G2skSFxeH9evXY+rUqWjfvj1iY2MxefJkTJs2zdVvwy0Gt41Ci4YhMJlFhPjbdCOZrllfM2AQEZEKuBww3n33XSxduhQjR47EihUr8MILL6Bp06aYMWMGCgoKXC4gNTW1yttbs7Ky7PYlJydjx44dLn9ObejbIgID2jgYn7LtwdDYzwchIiLyNi7PwcjNzUWPHj0AAP7+/igqKgIAjBo1Cp988om81XkL24W2fAKVq4OIiKiWuBwwGjZsaOmpiI+Pt/Qm5OTkQLSZg0A2bJcK54POiIhIBVwOGP369cOaNWsAAOPGjcPUqVMxYMAApKSk4P7775e9QK/ApcKJiEhlXJ6DsXTpUsttohMnTkR4eDi2bduG++67D08++aTsBXoF3kVCREQq43LA0Gg0kjs7HnroITz00EOyFuWpJnycjR+OXIBWEJAUF4rPnuoBiCJgtrmVlwGDiIhUoFrrYFy+fBkffPABDh06BABo3bo1xo0bh/r168tanKcxmcWKL4gov/FkVXOZ9CDeRUJERCrg8hyMH3/8EU2aNMGiRYtw+fJlXL58GYsWLUKTJk3w448/uqNGj2G70JZlJU/b4RGAPRhERKQKLvdgTJw4ESNGjMCSJUsszwQxmUyYMGECJk6ciF9//VX2Ij2FNGD8+cJUqQdDyx4MIiLyfi73YBw/fhzPPvus5IFjWq0WaWlpOH78uKzFeRqzzV26lh4Mc6UeDA17MIiIyPu5HDA6depkmXth69ChQ0hKSpKlKE/leIiEPRhERKQ+Tg2R/PLLL5bXzzzzDCZPnozjx4/j9ttvBwDs2LEDixcvxty5c91TpYcw23RhWG60qTwHg5M8iYhIBZwKGB06dIAgCJKVOl944QW74x5++GGkpKTIV52HcTxEwh4MIiJSH6cCRk5Ojrvr8Aq8i4SIiKiCUwGjcePG7q7DK0h7MG7srLwOBgMGERF5v2ottHXixAksWLBAstDW5MmT0axZM1mL8zS2PRhaTVU9GBwiISIi7+fyXSTr169H69atsXPnTrRv3x7t27fHzz//jDZt2iAzM9MdNXoM24AhVHkXCXswiIjI+7ncg/Hiiy9i6tSpdneMvPjii5g2bRoGDBggW3GeJirYD3H1jTCbgQZB+oqdvIuEiIhUyOWAcejQIaxatcpu/6OPPooFCxbIUZPHevfhDtDpdNKddneRsAeDiIi8n8tDJBEREcjOzrbbn52djcjISDlq8i6cg0FERCrkcg/G+PHj8cQTT+DkyZPo0aMHAGDr1q345z//ibS0NNkL9Hi8i4SIiFTI5YAxffp0BAcH480330R6ejoAICYmBjNnzsQzzzwje4Eejz0YRESkQi4FjPLycnz88cd4+OGHMXXqVBQVFQEAgoOD3VKcV7C9i0SjAwSXR6WIiIg8jksBw8fHB0899ZRl/QsGC6m3N53AlWvlEAB0iA/D/R0bSZ+myjtIiIhIJVweIunWrRv27dvH1T0d+N/+c8i5VAoAKCorrwgYtj0YvIOEiIhUwuWAMWHCBDz77LP4448/0LlzZwQGBkreb9++vWzFeRqblcIdP+yMPRhERKQSLgeMhx56CAAkEzpvPGlVEASYTCb5qvMw0oed/fnCdpInezCIiEglXA4YfLJq1Rw+rl0yRMIeDCIiUgeXAwbnXlRNdPQsEskkT/ZgEBGROlTraapHjhzB22+/bbmbpFWrVpg0aRJuu+02WYvzNKKjx7WzB4OIiFTI5UUZvvjiC7Rt2xZ79uxBUlISkpKSsHfvXrRt2xZffPGFO2r0GNI5GA4e1845GEREpBIu92C88MILSE9Px6uvvirZn5GRgRdeeAHDhw+XrThPY9uDcSNf8C4SIiJSI5d7MM6dO4fRo0fb7f/b3/6Gc+fOyVKUp2IPBhERUQWXA0bfvn3x008/2e3fsmULevfuLUtRnsrMHgwiIiIA1Rgiue+++zBt2jTs2bMHt99+OwBgx44d+OyzzzBr1iysWbNGcqyaiGAPBhEREVDNlTwB4N1338W7777r8D0Aqlx0i3eREBERVXA5YJjNZnfU4RXefigJoqCBWQRiw/wrdnIdDCIiUqFqrYNBjnVvUh86nU66kz0YRESkQi5P8iQXmfi4diIiUh8GDHcSReldJJzkSUREKsGA4U5mg3SbQyRERKQSnIMhoyN5RfDR+UAjCAgP1CPct0x6ACd5EhGRSlQrYJw4cQLLly/HiRMnsHDhQkRGRuK7775DfHw82rRpI3eNHuP+93bAaKq4V3XyXYmY2jtMegB7MIiISCVcHiLZvHkz2rVrh59//hlffvkliouLAQD79+9HRkaG7AV6Euk6GIL0DhKAczCIiEg1XA4YL774Iv7+978jMzMTer3esr9fv37YsWOHrMV5Gpt8UbHQlu0dJADvIiEiItVwOWD8+uuvuP/+++32R0ZG4uLFi7IU5akkDzvTCNI7SAD2YBARkWq4HDDCwsIcPjV13759iI2NlaUoTySK0iESAOzBICIi1XI5YDz00EOYNm0a8vLyIAgCzGYztm7diueee87hY9zVonK20AjswSAiIvVyOWC89tpraNmyJeLi4lBcXIzWrVvjjjvuQI8ePfDKK6+4o0aPYB8wYN+DwbtIiIhIJVy+TVWv1+P999/H9OnTceDAARQXF6Njx45ITEx0R32eo1LCEATwLhIiIlItlwPGli1b0KtXL8THxyM+Pt4dNXkkp4ZIOAeDiIhUwuUhkn79+qFJkyZ46aWXcPDgQXfU5JHMdj0YgoMhEvZgEBGROrgcMM6ePYtnn30WmzdvRtu2bdGhQwfMmzcPf/zxhzvq8xgO52CwB4OIiFTK5YDRoEEDpKamYuvWrThx4gQefPBBfPjhh0hISEC/fv3cUaNHqBwwBIA9GEREpFo1ethZkyZN8OKLLyIpKQnTp0/H5s2b5arL42gA3N0mquLWXVFE4/BAB5M82YNBRETqUO2AsXXrVnz00Uf4/PPPcf36dQwdOhRz5syRszaPotcCbz+UBJ1OZ915kAttERGROrkcMNLT07Fy5UqcPXsWAwYMwMKFCzF06FAEBAS4oz7PZtuDIWgBTY06jIiIiDyGy7/xfvzxRzz//PMYMWIEGjRo4I6avIftHAzOvyAiIhVxOWBs3brVHXV4J9u7SDg8QkREKuJUwFizZg0GDx4MnU6HNWvW3PTY++67T5bCvAJ7MIiISKWcChjDhg1DXl4eIiMjMWzYsCqPEwQBJpNJrto8SrEReOBfP0OrEaARBEzs1xx3sgeDiIhUyqmAYTabHb4mq3IzsP+Pq5btgmIDezCIiEi1XF5o6z//+Q/Kysrs9hsMBvznP/+RpShPZLfQVuWHnXENDCIiUhGXA8a4ceNw9epVu/1FRUUYN26cLEV5IocPO7PtweAQCRERqYjLAUMUxYoHeVXyxx9/IDQ0VJaiPJHo6HHttnMwOERCREQq4vRtqh07doQgCBAEAXfddRd8fKynmkwm5OTk4O6773ZLkZ6APRhERERWTgeMG3ePZGdnY9CgQQgKCrK8p9frkZCQgOHDh8teoKdgDwYREZGV0wEjIyMDAJCQkICUlBT4+fEXpq1b9mBwkicREamIyyt5jhkzxh11eDz7gIFKK3kykBERkXq4HDBMJhPeeustrFq1Crm5uTAYDJL3CwoKZCvOk9gPkbAHg4iI1Mvlu0hmzZqF+fPnIyUlBVevXkVaWhr++te/QqPRYObMmW4o0TPYrYMBVFoHgz0YRESkHi4HjI8++gjvv/8+nn32Wfj4+GDkyJH497//jRkzZmDHjh3uqNEj2C+0xbtIiIhIvVwOGHl5eWjXrh0AICgoyLLo1l/+8hd8++238lbnQSoPkdjNwWAPBhERqYjLczAaNWqEc+fOIT4+Hs2aNcOGDRvQqVMn7Nq1C76+6v2/9IYBwK70O6H18YEoigjy1QL72INBRETq5HIPxv3334+NGzcCACZNmoTp06cjMTERo0ePxqOPPip7gZ5CKwBhATrUD9QjPMgXvloRkoET9mAQEZGKuNyDMXfuXMvrlJQUxMfHY/v27UhMTMSQIUNkLc6jmSo9EI53kRARkYq4HDAqS05ORnJyshy1eBfbCZ4AezCIiEhVnAoYa9ascfqC9913X7WL8SrmSj0YnINBREQq4lTAuPEcklsRBAEmk6km9XisIiPwxd4z8PHxgUYA+sQUItz2APZgEBGRijgVMMxms7vr8HgXrwMLvvrNsv313+pJAwZ7MIiISEVcvouEHDNXWgdDK1ae5MkeDCIiUg+XJ3m++uqrN31/xowZ1S7Gk9k97AzSZ7TwLhIiIlITlwPGV199Jdk2Go3IycmBj48PmjVrVq2AsXjxYsybNw95eXlISkrC22+/jW7dut3yvJUrV2LkyJEYOnQoVq9e7fLnyqpyD4a50l0kfJoqERGpiMsBY9++fXb7CgsLMXbsWNx///0uF/Dpp58iLS0N7733Hrp3744FCxZg0KBBOHLkCCIjI6s879SpU3juuefQu3dvlz/THcSKx5tZaNmDQUREKibLHIyQkBDMmjUL06dPd/nc+fPnY/z48Rg3bhxat26N9957DwEBAVi2bFmV55hMJjzyyCOYNWsWmjZtWpPSZVN5Gqym8m2qnINBREQqUuOFtm64evWq5cFnzjIYDNizZw/S09Mt+zQaDfr374/t27dXed6rr76KyMhIPPbYY/jpp59u+hllZWUoK7P+si8sLARQMbRjNBpdqrcqRqPRfhJG+TXpMWYNINPneYIbbStXG3s6tocU20OK7WGPbSJVV9rDlc93OWAsWrRIsi2KIs6dO4f//ve/GDx4sEvXunjxIkwmE6KioiT7o6KicPjwYYfnbNmyBR988AGys7Od+ow5c+Zg1qxZdvs3bNiAgIAAl+q9mcpDJCePHkBTm10/ZG3Ddc0R2T7PU2RmZipdQp3C9pBie0ixPeyxTaSUbo/S0lKnj3U5YLz11luSbY1Gg4iICIwZM0bSE+EORUVFGDVqFN5//300aNDAqXPS09ORlpZm2S4sLERcXBwGDhyIkJAQWeoyGo04tOp7yb7bmjcGTli3+w0YDPg6V7M3MBqNyMzMxIABA6DT6ZQuR3FsDym2hxTbwx7bRKqutMeNUQBnuBwwcnJyXD2lSg0aNIBWq0V+fr5kf35+Pho2bGh3/IkTJ3Dq1CnJQ9VuLALm4+ODI0eOoFmzZpJzfH19HT5GXqfTyfpDqjwHw0eQdiPpfIMBFf4lkbudPR3bQ4rtIcX2sMc2kVK6PVz5bEUX2tLr9ejcubPl8e9ARWDYuHGjwweotWzZEr/++iuys7MtX/fddx/uvPNOZGdnIy4urjbLvymNyLtIiIhIvVzuwbh+/TrefvttbNq0CefPn7dbRnzv3r0uXS8tLQ1jxoxBly5d0K1bNyxYsAAlJSUYN24cAGD06NGIjY3FnDlz4Ofnh7Zt20rODwsLAwC7/bVNtFsHw/YuEgEQZJtPS0REVOe5/Fvvsccew4YNG/DAAw+gW7duEATh1ifdREpKCi5cuIAZM2YgLy8PHTp0wLp16ywTP3Nzc6HR1P0VzSP9RTzbvzkEjQaiCAT42NzdovUDathOREREnsTlgPHNN99g7dq16Nmzp2xFpKamIjU11eF7WVlZNz13xYoVstVRE5H+wD19mlrHp/aWW9/kg86IiEhlXO4aiI2NRXBwsDtq8S5mmzkYWr1ydRARESnA5YDx5ptvYtq0afj999/dUY/3sA0YGgYMIiJSF5eHSLp06YLr16+jadOmCAgIsLtlpaCgQLbiPJokYHCIhIiI1MXlgDFy5EicOXMGr732GqKiomo8ydNrsQeDiIhUzOWAsW3bNmzfvh1JSUnuqMdj7bso4LmZmdAIAgQB2HvnNVgWImfAICIilXE5YLRs2RLXrl279YEqYxIBo0nEjaeeCaLNSp4MGEREpDIuT/KcO3cunn32WWRlZeHSpUsoLCyUfKlV5YepCryLhIiIVMzlHoy7774bAHDXXXdJ9ouiCEEQYDKZ5KnMw9gFDPZgEBGRirkcMDZt2uSOOjxfpYQhcJInERGpmMsBo0+fPu6ow+NVfpqqIDJgEBGRerkcMH788cebvn/HHXdUuxhPVvlhZ4KZQyRERKReLgeMvn372u2zXQtDrXMw7HCIhIiIVMzlu0guX74s+Tp//jzWrVuHrl27YsOGDe6o0SPYT/JkwCAiIvVyuQcjNDTUbt+AAQOg1+uRlpaGPXv2yFKYpzFziISIiMjC5R6MqkRFReHIkSNyXc7zSQKGrurjiIiIvJDLPRi//PKLZFsURZw7dw5z585Fhw4d5KrL41QeIgGHSIiISMVcDhgdOnSAIAgQK902cfvtt2PZsmWyFeZpfDVAbJgfRLFiuITrYBARkZq5HDBycnIk2xqNBhEREfDz85OtKE/ULVLEzLF3WB9f/ykDBhERqZfLAaNx48buqMP7sAeDiIhUzOlJnj/88ANat27t8IFmV69eRZs2bfDTTz/JWpzHMpsA0WY9ED7sjIiIVMbpgLFgwQKMHz8eISEhdu+FhobiySefxPz582UtzmPZPugMYA8GERGpjtMBY//+/ZYnqToycOBA1a6BYcd2eARgwCAiItVxeg5Gfn6+dQKjowv5+ODChQuyFOWJDl8RsPvbw/DRahBgvoLnbd9kwCAiIpVxOmDExsbiwIEDaN68ucP3f/nlF0RHR8tWmKc5XQJ8cygXABDhU4DnW9u8yYW2iIhIZZweIrnnnnswffp0XL9+3e69a9euISMjA3/5y19kLc6T2C4L4qspl77JHgwiIlIZp3swXnnlFXz55Zdo0aIFUlNTcdtttwEADh8+jMWLF8NkMuHll192W6F1ne2yY3qBkzyJiEjdnA4YUVFR2LZtG55++mmkp6dbVvIUBAGDBg3C4sWLERUV5bZC6zrbHgy9xix9k0MkRESkMi4ttNW4cWOsXbsWly9fxvHjxyGKIhITE1GvXj131ecxRAiW13oNezCIiEjdXF7JEwDq1auHrl27yl2LR7MdItEJ7MEgIiJ1k+1x7WonHSKp1IMhMGAQEZG6MGDIRDLJ024OBodIiIhIXRgwZCK5TVWofJsqezCIiEhdGDBkYtuD4cOAQUREKseAIRPpEAkX2iIiInVjwJCJdJKnSfomezCIiEhlqnWbKtlLjjJj1IAuELRaRBf8DuTYvMkeDCIiUhkGDJlE+QO9ExtUPHH2hF+lgOGrWF1ERERK4BCJO5jLpNta9mAQEZG6MGC4g8kg3eYQCRERqQwDhjuYGTCIiEjdOAdDJlcNwLH8Yuj1PggvLob18W8CILCZiYhIXfibTybr/tBgxjvbAAAZjU9iXOifb2j0gCBUfSIREZEX4hCJTGzXwfCBzUJbHB4hIiIVYsBwA8nTVHkHCRERqRADhkzMNj0YOttnkXANDCIiUiEGDJlInkUicIiEiIjUjQFDJrYBQyfYDJHwOSRERKRCDBgy4RAJERGRFQOGTERJwLB5miqHSIiISIUYMGQi7cHgEAkREakbA4ZMzDavdZzkSUREKseAIRMzh0iIiIgsGDBkYhswfDhEQkREKsdnkcikSbCI2OhIiBAQ5isCNzox2INBREQqxIAhk4GNRNxzTwfodDrgOx/g8p9vsAeDiIhUiEMk7mA2WF9zHQwiIlIhBgx3MHMOBhERqRsDhjswYBARkcoxYLiDyIBBRETqxkmeMvnouAb/eX8ntFoNlodcR+CNNwQGDCIiUh8GDJmcKRFw5sIVAIDYzggIf77BHgwiIlIhDpHIRLrQlu1S4QwYRESkPgwYMrF9FokGNgFDYCcRERGpDwOGTGx7MLRgDwYREakbA4ZMbgQMAWZoYfuwMwYMIiJSHwYMmdwIGJInqQIMGEREpEoMGDK5MQdDMsET4MPOiIhIlRgwZHKjB8PHrgeDAYOIiNSHAUMmNwKGXjBK3+AQCRERqRADhkxEyxwMDpEQERExYMjkxsCI3RAJlwonIiIVYsCQiVjVEImWPRhERKQ+XGZSJjM7mdDvrrugKzkM/GjzBnswiIhIhRgwZBKoA8KDfKETBekbnINBREQqxCESuZl5FwkREREDhtzMBuk2ezCIiEiFGDDkxh4MIiIizsGQQ7nJjJ/PCzBmn0Vs2Tkk277JgEFERCrEgCGDsnIzPj6hxccnDqBf8DEkN7F5U2ATExGR+nCIRAbmG4tgwNFKnuzBICIi9WHAkEG52RowtIJZ+iZ7MIiISIUYMGRgtgkY9o9rZw8GERGpT50IGIsXL0ZCQgL8/PzQvXt37Ny5s8pj33//ffTu3Rv16tVDvXr10L9//5seXxuMZtshksqPa2fAICIi9VE8YHz66adIS0tDRkYG9u7di6SkJAwaNAjnz593eHxWVhZGjhyJTZs2Yfv27YiLi8PAgQNx5syZWq7cqrTMGiq0qPywMw6REBGR+ij+22/+/PkYP348xo0bBwB477338O2332LZsmV48cUX7Y7/6KOPJNv//ve/8cUXX2Djxo0YPXq03fFlZWUoKyuzbBcWFgIAjEYjjEaj3fHVUXzdev3KkzyNZgAyfY4nudG2crWxp2N7SLE9pNge9tgmUnWlPVz5fEUDhsFgwJ49e5Cenm7Zp9Fo0L9/f2zfvt2pa5SWlsJoNKJ+/foO358zZw5mzZplt3/Dhg0ICAioXuGV5BQBN5qy8uPaN2RuQrngL8vneKLMzEylS6hT2B5SbA8ptoc9tomU0u1RWlrq9LGKBoyLFy/CZDIhKipKsj8qKgqHDx926hrTpk1DTEwM+vfv7/D99PR0pKWlWbYLCwstwyohISHVL97G9wfzgAO/ALAPGAPvvgfQqi9gGI1GZGZmYsCAAdDpOA+F7SHF9pBie9hjm0jVlfa4MQrgDMWHSGpi7ty5WLlyJbKysuDn5+fwGF9fX/j6+trt1+l0sv2QrpVXfReJTh8AaDy6mWtEznb2BmwPKbaHFNvDHttESun2cOWzFf3N16BBA2i1WuTn50v25+fno2HDhjc994033sDcuXPx/fffo3379u4s85auG629FpV7MCBoa7kaIiIi5Sl6F4ler0fnzp2xceNGyz6z2YyNGzciOTm5yvNef/11zJ49G+vWrUOXLl1qo9Sb6tksHD0iKxbYkkzy1OgAQVCoKiIiIuUo3neflpaGMWPGoEuXLujWrRsWLFiAkpISy10lo0ePRmxsLObMmQMA+Oc//4kZM2bg448/RkJCAvLy8gAAQUFBCAoKUuR7iAnzh9+fHRWJETZDNXxUOxERqZTiASMlJQUXLlzAjBkzkJeXhw4dOmDdunWWiZ+5ubnQaKwdLUuWLIHBYMADDzwguU5GRgZmzpxZm6VLdI00Y9zd3dCjeBNw5M+dAscNiYhInRQPGACQmpqK1NRUh+9lZWVJtk+dOuX+gqohJgC4vWl9aH+xGSLRsgeDiIjUSfGVPL2O2WB9zR4MIiJSKQYMuZltVjnjc0iIiEilGDDkJgkYHCIhIiJ1YsCQm+0QCQMGERGpFAOG3DhEQkRExIAhO/ZgEBERMWDIjj0YREREDBiyYw8GERERA4bsRPZgEBERMWDIzcSFtoiIiBgw5Gbbg8GlwomISKUYMORmO8mTPRhERKRSDBhyk0zyZMAgIiJ1YsCQG29TJSIiYsCQHQMGERERA4bsRM7BICIiYsCQm6nM+lrrq1wdRERECmLAkBtX8iQiImLAkJ0kYLAHg4iI1IkBQ06iKA0YXGiLiIhUigFDTrYTPAEOkRARkWoxYMjJtvcC4BAJERGpFgOGnOwCBnswiIhInRgw5MSAQUREBIABQ14MGERERAAYMORlu8gWwLtIiIhItRgw5GSuFDA0fsrUQUREpDAGDBkJdj0YDBhERKRODBhyMl+XbjNgEBGRSjFgyMnEgEFERAQwYMiLAYOIiAgA4KN0AV6l8hAJJ3kSkRczmUwwGo23PrAajEYjfHx8cP36dZhMJrd8hiepzfbQ6/XQaGre/8CAISf2YBCRCoiiiLy8PFy5csWtn9GwYUOcPn0agiC47XM8RW22h0ajQZMmTaDX12ypBQYMOfEuEiJSgRvhIjIyEgEBAW75hWc2m1FcXIygoCBZ/m/a09VWe5jNZpw9exbnzp1DfHx8jX62DBgyEngXCRF5OZPJZAkX4eHhbvscs9kMg8EAPz8/BgzUbntERETg7NmzKC8vh06nq/Z1+FOTE4dIiMjL3ZhzERAQoHAl5C43hkZqOteDAUNOtgFD0AACO4iIyDtxXoT3kutny4AhJ9shEo0fwL+ARESkUgwYcrKd5MnhESKiOqVv376YMmWKU8eeOnUKgiAgOzsbAJCVlQVBENx654y3YR++nGyHSBgwiIjqlC+//LLakxZ79OiBc+fOITQ0VOaqvBcDhowkd5EwYBAR1Sn169ev9rl6vR4NGzas0ecbDIYary3hSThEIif2YBCRip25cg27ThW49HWpuMzuOoZyM3adKsC+04VVnnfmyjWX67MdIklISMBrr72GRx99FMHBwYiPj8fSpUurPNfREMmWLVvQu3dv+Pv7Iy4uDs888wxKSkos7yckJGD27NkYPXo0QkJC8MQTT8BgMCA1NRXR0dHw8/ND48aNMWfOHJe/F0/AHgw5mSpN8iQiUpFVu05j4cZjLp2z8KEOGNohVrLvSqkBKUt/vul5k+9KxNQBLVyu0dabb76J2bNn46WXXsLnn3+Op59+Gn369MFtt912y3NPnDiBu+++G3//+9+xbNkyXLhwAampqUhNTcXy5cstx73xxhuYMWMGMjIyAACLFi3CmjVrsGrVKsTHx+P06dM4ffp0jb6PuooBQ05mTvIkIvIU99xzDyZMmAAAmDZtGt566y1s2rTJqYAxZ84cPPLII5YekcTERCxatAh9+vTBkiVL4OdX8TugX79+ePbZZy3n5ebmIjExEb169YIgCGjcuLH831gdwSESOXGIhIjIY7Rv397yWhAENGzYEOfPn3fq3P3792PFihUICgqyfA0aNAhmsxk5OTmW47p06SI5b+zYscjOzsZtt92GZ555Bhs2bJDnm6mD2IMhJwYMIlKxEV3j0CuxgUvnNG0QaLcvLECPT5/ojtKSUgQEBjhcGjsmzL/add5Q+Y4SQRBgNpudOre4uBhPPvkknnnmGbv34uPjLa8DA6XfX6dOnZCTk4PvvvsO33//PUaMGIH+/fvj888/r8Z3ULcxYMiJd5EQkYrFhvkjVoZf/HofDbom1EdhoQ9CQkLq5LNIOnXqhIMHD6J58+YunxsSEoKUlBSkpKTggQcewN13342CgoIa3eVSFzFgyEjgJE8iIlWYNm0abr/9dqSmpuLxxx9HYGAgDh48iMzMTLzzzjtVnjd//nxER0ejY8eO0Gg0+Oyzz9CwYUOEhYXVXvG1hAFDTlzJk4hIFdq3b4/Nmzfj5ZdfRu/evSGKIpo1a4aUlJSbnhccHIzXX38dx44dg1arRdeuXbF27do62UtTUwwYcuIQCRFRnZWVlWV5ferUKbv3bywLDlSsYSGKomW7b9++km0A6Nq1600naTr6jPHjx2P8+PFO1+zJvC8yKYmTPImIiAAwYMiLAYOIiAgAA4a8Kj+unYiISKUYMGQiiCYIosm6gz0YRESkYgwYMtHAIN3BgEFERCrGgCETLYyVdjBgEBGRejFgyEQjsgeDiIjoBgYMmbAHg4iIyIoBQyZ2czB4FwkREakYA4ZMtCJ7MIiIyD369euH9PR0y3ZCQgIWLFigXEFO4FLhMrG/i8RXmUKIiMjr7dq1y+5R8HUNA4ZM7OZgcIiEiIjcJCIiQukSbolDJDLhXSREpFqGq8D5LfJ+XdgCbcF24MItjjNcdbrMdevWoVevXggLC0N4eDj+8pe/4MSJEwAqHkwmCAK+/PJL3HnnnQgICEBSUhK2b98uucYXX3yBNm3awNfXFwkJCXjzzTcl7yckJODvf/87Ro8ejaCgIDRu3Bhr1qzBhQsXMHToUAQFBaF9+/bYvXu35ZxLly5h5MiRiI2NRUBAANq1a4dPPvnkpt9L5SGSK1eu4PHHH0dERARCQkLQr18/7N+/3/L+/v37ceeddyI4OBghISHo3LmzpAZ3YA+GTDS8i4SI1OrKr8D3vWW9pAZAsDMH9v8JiOzl1DVLSkqQlpaG9u3bo7i4GDNmzMD9998veYrqyy+/jDfeeAOJiYl4+eWXMXLkSBw/fhw+Pj7Ys2cPRowYgZkzZyIlJQXbtm3DhAkTEB4ejrFjx1qu8dZbb+G1117D9OnT8dZbb2HUqFHo0aMHHn30UcybNw/Tpk3D6NGj8dtvv0EQBFy/fh2dO3fGtGnTEBISgm+//RajRo1Cs2bN0K1bN6e+twcffBD+/v747rvvEBoain/961+46667cPToUdSvXx+PPPIIOnbsiCVLlkCr1SI7Oxs6nc6pa1cXA4ZMtFzJk4ioThs+fLhke9myZYiIiMDBgwcRFBQEAHjuuedw7733AgBmzZqFNm3a4Pjx42jZsiXmz5+Pu+66C9OnTwcAtGjRAgcPHsS8efMkAeOee+7Bk08+CQCYMWMGlixZgq5du+LBBx8EAEybNg3JycnIz89Hw4YNERsbi+eee85y/qRJk7B+/XqsWrXKqYCxZcsW7Ny5E+fPn4evb8X8vzfeeAOrV6/G559/jieeeAK5ubl4/vnn0bJlSwBAYmJidZrQJRwikYmGd5EQEdVpx44dw8iRI9G0aVOEhIQgISEBAJCbm2s5pn379pbX0dHRAIDz588DAA4dOoSePXtKrtmzZ08cO3YMJpPJ4TWioqIAAO3atbPbd+O6JpMJs2fPRrt27VC/fn0EBQVh/fr1krpuZv/+/SguLkZ4eDiCgoIsXzk5OZYhoLS0NDz++OPo378/5s6da9nvTuzBkAl7MIhItcLaVQxVyMgsmlFSUoLAwEBohJv8v3BYu6rfq2TIkCFo3Lgx3n//fcTExMBsNqNt27YwGKz/ftsOGwiCUFGL2exS7Y6ucbPrzps3DwsXLsSCBQvQrl07BAYGYsqUKZK6bqa4uBjR0dHIysqyey8sLAwAMHPmTDz88MP49ttv8d133yEjIwMrV67E/fff79L35goGDJnYzcHgXSREpBb6UKfnQTjNbIbJtxAICQE0Ne9sv3TpEo4cOYL3338fvXtXzBfZsmWLS9do1aoVtm7dKtm3detWtGjRAlqtttq1bd26FUOHDsXf/vY3ABXB4+jRo2jdurVT53fq1Al5eXnw8fGx9Mo40qJFC7Ro0QJTp07FyJEjsXz5crcGDA6RyEQruYtEADTunTxDRETOq1evHsLDw7F06VIcP34cP/zwA9LS0ly6xrPPPouNGzdi9uzZOHr0KD788EO88847kvkT1ZGYmIjMzExs27YNhw4dwpNPPon8/Hynz+/fvz+Sk5MxbNgwbNiwAadOncK2bdvw8ssvY/fu3bh27RpSU1ORlZWF33//HVu3bsWuXbvQqlWrGtV9KwwYMpH0YGj9gD+7wIiISHkajQYrV67Enj170LZtW0ydOhXz5s1z6RqdOnXCqlWrsHLlSrRt2xYzZszAq6++KpngWR2vvPIKOnXqhEGDBqFv375o2LAhhg0b5vT5giBg7dq1uOOOOzBu3Di0aNECDz30EH7//XdERUVBq9Xi0qVLGD16NFq0aIERI0Zg8ODBmDVrVo3qvhUOkchE0oPB+RdERHVO//79cfDgQck+URQdvgYq5i9U3jd8+HC7u1FsnTp1ym5f5WskJCRI9tWvXx+rV6++ae0//PADCgsLq/yc4OBgLFq0CIsWLXJ4/q3W1XAH9mDIxK4Hg4iISMUYMGSiQ6l1wydIuUKIiIjqAAYMmejEIuuGPly5QoiIiOoABgyZ6G0Dhi8DBhERqRsDhkz8xALrhn9D5QohIqoFlScukveQ62fLgCEHswH+tgEjIE65WoiI3OjGipSlpaW3OJI81Y0VRGuyeBjA21TlUXoaAmyWkg1solwtRERupNVqERYWZnmORkBAgGXpazmZzWYYDAZcv34dGhlW8vR0tdUeZrMZFy5cQEBAAHx8ahYRGDBqKvdz6LY8KN0XEKNMLUREtaBhw4ph4Bshwx1EUcS1a9fg7+/vlgDjaWqzPTQaDeLj42v8OQwYNWUut9/nz4BBRN5LEARER0cjMjISRqPx1idUg9FoxI8//og77rhD8qAwtarN9tDr9bL0kjBg1JQuWLod3AIIce/67kREdYFWq63xOP3Nrl1eXg4/Pz8GDHhme9SJga3FixcjISEBfn5+6N69O3bu3HnT4z/77DO0bNkSfn5+aNeuHdauXVtLlToQ1BTmJuNwwmcITEmvAwN+4nNIiIhI9RQPGJ9++inS0tKQkZGBvXv3IikpCYMGDapybG/btm0YOXIkHnvsMezbtw/Dhg3DsGHDcODAgVqu/E+hrWDq8i8c8H0M5hZTAL9IZeogIiKqQxQPGPPnz8f48eMxbtw4tG7dGu+99x4CAgKwbNkyh8cvXLgQd999N55//nm0atUKs2fPRqdOnfDOO+/UcuVERERUFUXnYBgMBuzZswfp6emWfRqNBv3798f27dsdnrN9+3akpaVJ9g0aNKjKJ9GVlZWhrKzMsn316lUAQEFBgWyTk4xGI0pLS3Hp0iWPGRtzN7aJFNtDiu0hxfawxzaRqivtUVRUsWq1M4txKRowLl68CJPJhKioKMn+qKgoHD582OE5eXl5Do/Py8tzePycOXMcPvO+SROuVUFERFQdRUVFCA0NvekxXn8XSXp6uqTHw2w2o6CgAOHh4bLdS1xYWIi4uDicPn0aISEhslzT07FNpNgeUmwPKbaHPbaJVF1pD1EUUVRUhJiYWy/HoGjAaNCgAbRaLfLz8yX78/PzLQu5VNawYUOXjvf19YWvr69kX1hYWPWLvomQkBD+RaiEbSLF9pBie0ixPeyxTaTqQnvcqufiBkUneer1enTu3BkbN2607DObzdi4cSOSk5MdnpOcnCw5HgAyMzOrPJ6IiIhqn+JDJGlpaRgzZgy6dOmCbt26YcGCBSgpKcG4ceMAAKNHj0ZsbCzmzJkDAJg8eTL69OmDN998E/feey9WrlyJ3bt3Y+nSpUp+G0RERGRD8YCRkpKCCxcuYMaMGcjLy0OHDh2wbt06y0TO3NxcyZKlPXr0wMcff4xXXnkFL730EhITE7F69Wq0bdtWqW8Bvr6+yMjIsBuKUTO2iRTbQ4rtIcX2sMc2kfLE9hBEuR78TkRERPQnxRfaIiIiIu/DgEFERESyY8AgIiIi2TFgEBERkewYMGTg6uPmvdmPP/6IIUOGICYmBoIgVPmMGDWYM2cOunbtiuDgYERGRmLYsGE4cuSI0mUpasmSJWjfvr1lsaDk5GR89913SpdVZ8ydOxeCIGDKlClKl6KImTNnQhAEyVfLli2VLktRZ86cwd/+9jeEh4fD398f7dq1w+7du5UuyykMGDXk6uPmvV1JSQmSkpKwePFipUtR3ObNmzFx4kTs2LEDmZmZMBqNGDhwIEpKSpQuTTGNGjXC3LlzsWfPHuzevRv9+vXD0KFD8dtvvyldmuJ27dqFf/3rX2jfvr3SpSiqTZs2OHfunOVry5YtSpekmMuXL6Nnz57Q6XT47rvvcPDgQbz55puoV6+e0qU5R6Qa6datmzhx4kTLtslkEmNiYsQ5c+YoWFXdAED86quvlC6jzjh//rwIQNy8ebPSpdQp9erVE//9738rXYaiioqKxMTERDEzM1Ps06ePOHnyZKVLUkRGRoaYlJSkdBl1xrRp08RevXopXUa1sQejBm48br5///6Wfbd63Dyp19WrVwEA9evXV7iSusFkMmHlypUoKSlR/VL/EydOxL333iv5t0Stjh07hpiYGDRt2hSPPPIIcnNzlS5JMWvWrEGXLl3w4IMPIjIyEh07dsT777+vdFlOY8CogZs9br6qx8eTOpnNZkyZMgU9e/ZUdNXZuuDXX39FUFAQfH198dRTT+Grr75C69atlS5LMStXrsTevXstj0NQs+7du2PFihVYt24dlixZgpycHPTu3RtFRUVKl6aIkydPYsmSJUhMTMT69evx9NNP45lnnsGHH36odGlOUXypcCI1mDhxIg4cOKDq8eQbbrvtNmRnZ+Pq1av4/PPPMWbMGGzevFmVIeP06dOYPHkyMjMz4efnp3Q5ihs8eLDldfv27dG9e3c0btwYq1atwmOPPaZgZcowm83o0qULXnvtNQBAx44dceDAAbz33nsYM2aMwtXdGnswaqA6j5sn9UlNTcU333yDTZs2oVGjRkqXozi9Xo/mzZujc+fOmDNnDpKSkrBw4UKly1LEnj17cP78eXTq1Ak+Pj7w8fHB5s2bsWjRIvj4+MBkMildoqLCwsLQokULHD9+XOlSFBEdHW0XvFu1auUxw0YMGDVQncfNk3qIoojU1FR89dVX+OGHH9CkSROlS6qTzGYzysrKlC5DEXfddRd+/fVXZGdnW766dOmCRx55BNnZ2dBqtUqXqKji4mKcOHEC0dHRSpeiiJ49e9rd2n706FE0btxYoYpcwyGSGrrV4+bVpri4WPJ/Gzk5OcjOzkb9+vURHx+vYGW1b+LEifj444/x9ddfIzg42DIvJzQ0FP7+/gpXp4z09HQMHjwY8fHxKCoqwscff4ysrCysX79e6dIUERwcbDcnJzAwEOHh4aqcq/Pcc89hyJAhaNy4Mc6ePYuMjAxotVqMHDlS6dIUMXXqVPTo0QOvvfYaRowYgZ07d2Lp0qVYunSp0qU5R+nbWLzB22+/LcbHx4t6vV7s1q2buGPHDqVLUsymTZtEAHZfY8aMUbq0WueoHQCIy5cvV7o0xTz66KNi48aNRb1eL0ZERIh33XWXuGHDBqXLqlPUfJtqSkqKGB0dLer1ejE2NlZMSUkRjx8/rnRZivrf//4ntm3bVvT19RVbtmwpLl26VOmSnMbHtRMREZHsOAeDiIiIZMeAQURERLJjwCAiIiLZMWAQERGR7BgwiIiISHYMGERERCQ7BgwiIiKSHQMGERERyY4Bg4hkIYoinnjiCdSvXx+CICA7O1vpkohIQQwYRCqQl5eHSZMmoWnTpvD19UVcXByGDBkieVBfTa1btw4rVqzAN998g3PnzqnyWRpEZMWHnRF5uVOnTqFnz54ICwvDvHnz0K5dOxiNRqxfvx4TJ07E4cOHZfmcG0+97NGjR7WvIYoiTCYTfHz4TxORp2MPBpGXmzBhAgRBwM6dOzF8+HC0aNECbdq0QVpaGnbs2AEAyM3NxdChQxEUFISQkBCMGDEC+fn5lmvMnDkTHTp0wH//+18kJCQgNDQUDz30EIqKigAAY8eOxaRJk5CbmwtBEJCQkAAAKCsrwzPPPIPIyEj4+fmhV69e2LVrl+W6WVlZEAQB3333HTp37gxfX19s2bIFffv2xaRJkzBlyhTUq1cPUVFReP/99y1PKg4ODkbz5s3x3XffWa5lMpnw2GOPoUmTJvD398dtt92GhQsXStpi7NixGDZsGN544w1ER0cjPDwcEydOhNFotBxTVlaGadOmIS4uDr6+vmjevDk++OADy/sHDhzA4MGDERQUhKioKIwaNQoXL16U7wdG5CUYMIi8WEFBAdatW4eJEyciMDDQ7v2wsDCYzWYMHToUBQUF2Lx5MzIzM3Hy5EmkpKRIjj1x4gRWr16Nb775Bt988w02b96MuXPnAgAWLlyIV199FY0aNcK5c+csIeKFF17AF198gQ8//BB79+5F8+bNMWjQIBQUFEiu/eKLL2Lu3Lk4dOgQ2rdvDwD48MMP0aBBA+zcuROTJk3C008/jQcffBA9evTA3r17MXDgQIwaNQqlpaUAALPZjEaNGuGzzz7DwYMHMWPGDLz00ktYtWqV5LM2bdqEEydOYNOmTfjwww+xYsUKrFixwvL+6NGj8cknn2DRokU4dOgQ/vWvfyEoKAgAcOXKFfTr1w8dO3bE7t27sW7dOuTn52PEiBE1+CkReSllH+ZKRO70888/iwDEL7/8sspjNmzYIGq1WjE3N9ey77fffhMBiDt37hRFURQzMjLEgIAAsbCw0HLM888/L3bv3t2y/dZbb4mNGze2bBcXF4s6nU786KOPLPsMBoMYExMjvv7666IoiuKmTZtEAOLq1aslNfXp00fs1auXZbu8vFwMDAwUR40aZdl37tw5EYC4ffv2Kr+3iRMnisOHD7dsjxkzRmzcuLFYXl5u2ffggw+KKSkpoiiK4pEjR0QAYmZmpsPrzZ49Wxw4cKBk3+nTp0UA4pEjR6qsg0iN2INB5MVEUbzlMYcOHUJcXBzi4uIs+1q3bo2wsDAcOnTIsi8hIQHBwcGW7ejoaJw/f77K6544cQJGoxE9e/a07NPpdOjWrZvkugDQpUsXu/Nv9GQAgFarRXh4ONq1a2fZFxUVBQCSGhYvXozOnTsjIiICQUFBWLp0KXJzcyXXbdOmDbRarcPvIzs7G1qtFn369HH4Pe3fvx+bNm1CUFCQ5atly5aW75eIrDiTisiLJSYmQhAEWSZy6nQ6ybYgCDCbzTW+LgCHwzeOPs92nyAIAGCpYeXKlXjuuefw5ptvIjk5GcHBwZg3bx5+/vlnp78Pf3//m9ZZXFyMIUOG4J///Kfde9HR0Tc9l0ht2INB5MXq16+PQYMGYfHixSgpKbF7/8qVK2jVqhVOnz6N06dPW/YfPHgQV65cQevWrav92c2aNYNer8fWrVst+4xGI3bt2lWj61Zl69at6NGjByZMmICOHTuiefPmLvcqtGvXDmazGZs3b3b4fqdOnfDbb78hISEBzZs3l3w5CklEasaAQeTlFi9eDJPJhG7duuGLL77AsWPHcOjQISxatAjJycno378/2rVrh0ceeQR79+7Fzp07MXr0aPTp08fh0IWzAgMD8fTTT+P555/HunXrcPDgQYwfPx6lpaV47LHHZPwOKyQmJmL37t1Yv349jh49iunTp0vuWHFGQkICxowZg0cffRSrV69GTk4OsrKyLBNFJ06ciIKCAowcORK7du3CiRMnsH79eowbNw4mk0n274nIkzFgEHm5pk2bYu/evbjzzjvx7LPPom3bthgwYAA2btyIJUuWQBAEfP3116hXrx7uuOMO9O/fH02bNsWnn35a48+eO3cuhg8fjlGjRqFTp044fvw41q9fj3r16snwnUk9+eST+Otf/4qUlBR0794dly5dwoQJE1y+zpIlS/DAAw9gwoQJaNmyJcaPH2/p/YmJicHWrVthMpkwcOBAtGvXDlOmTEFYWBg0Gv5zSmRLEJ2ZBUZERETkAkZuIiIikh0DBhEREcmOAYOIiIhkx4BBREREsmPAICIiItkxYBAREZHsGDCIiIhIdgwYREREJDsGDCIiIpIdAwYRERHJjgGDiIiIZPf/Bu8gul/LabUAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cumsum: True, with prefix: False | auc: 0.8776404200000001, se: 0.0023891142116481837\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 70615/70615 [00:00<00:00, 73440.07it/s]\n",
+      "100%|██████████| 20000/20000 [00:00<00:00, 73235.38it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start cumsum: True, with_prefix: True\n"
      ]
     }
    ],
    "source": [
-    "results_language_dataset_signature_mahalanobis_knn = language_dataset_experiment(\n",
+    "(\n",
+    "    results_language_dataset_signature_mahalanobis_knn,\n",
+    "    smk_scores,\n",
+    ") = language_dataset_experiment(\n",
     "    data=data,\n",
     "    anomaly_detection_method=compute_signature_mahalanbois_knn_scores,\n",
     "    anomaly_detection_kwargs={\n",
@@ -268,76 +461,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>auc</th>\n",
-       "      <th>se</th>\n",
-       "      <th>total_fit_time</th>\n",
-       "      <th>total_compute_score_time</th>\n",
-       "      <th>with_prefix</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.889934</td>\n",
-       "      <td>0.002244</td>\n",
-       "      <td>12929.681617</td>\n",
-       "      <td>133.170379</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.877479</td>\n",
-       "      <td>0.002386</td>\n",
-       "      <td>25505.368800</td>\n",
-       "      <td>262.876602</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        auc        se  total_fit_time  total_compute_score_time with_prefix\n",
-       "0  0.889934  0.002244    12929.681617                133.170379        True\n",
-       "1  0.877479  0.002386    25505.368800                262.876602       False"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "pd.DataFrame(results_language_dataset_signature_mahalanobis_knn)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -362,27 +495,100 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 70615/70615 [00:00<00:00, 309067.02it/s]\n",
+      "100%|██████████| 20000/20000 [00:00<00:00, 265167.74it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start cumsum: False, with_prefix: False\n",
+      "fit_time: 0.10591506958007812\n",
+      "compute_score_time: 0.07188820838928223\n",
+      "cumsum: False, with prefix: False | auc: 0.48081160500000003, se: 0.004103368581742017\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 70615/70615 [00:00<00:00, 124025.48it/s]\n",
+      "100%|██████████| 20000/20000 [00:00<00:00, 120845.11it/s]\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "start with_prefix: True\n",
-      "fit_time: 0.1220090389251709\n",
-      "compute_score_time: 0.07168197631835938\n",
-      "with prefix: True | auc: 0.7051954349999999 | se: 0.0036900996389149453\n",
-      "start with_prefix: False\n",
-      "fit_time: 0.10636305809020996\n",
-      "compute_score_time: 0.07176589965820312\n",
-      "with prefix: False | auc: 0.7229885650000001 | se: 0.0036416616994392158\n",
-      "Total experiment time: 117.78209710121155 seconds | 1.963034951686859 minutes\n"
+      "start cumsum: False, with_prefix: True\n",
+      "fit_time: 0.10387825965881348\n",
+      "compute_score_time: 0.06962418556213379\n",
+      "cumsum: False, with prefix: True | auc: 0.6342248149999999, se: 0.003884960947243883\n",
+      "MSE between scores (cumsum=False) with and without prefix: 0.003436300899818589\n",
+      "RMSE between scores (cumsum=False) with and without prefix: 0.05861997014515266\n",
+      "MAE between scores (cumsum=False) with and without prefix: 0.05052056571413158\n",
+      "MSE (normalised) between scores (cumsum=False) with and without prefix: 2.5530407537133257\n",
+      "RMSE (normalised) between scores (cumsum=False) with and without prefix: 1.5978237555229067\n",
+      "MAE (normalised) between scores (cumsum=False) with and without prefix: 1.3770556320757623\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 70615/70615 [00:00<00:00, 193389.45it/s]\n",
+      "100%|██████████| 20000/20000 [00:00<00:00, 173391.43it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start cumsum: True, with_prefix: False\n",
+      "fit_time: 0.1050119400024414\n",
+      "compute_score_time: 0.06078672409057617\n",
+      "cumsum: True, with prefix: False | auc: 0.71275548, se: 0.003627706192352872\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 70615/70615 [00:00<00:00, 83713.57it/s]\n",
+      "100%|██████████| 20000/20000 [00:00<00:00, 79831.06it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start cumsum: True, with_prefix: True\n",
+      "fit_time: 0.10580778121948242\n",
+      "compute_score_time: 0.06490182876586914\n",
+      "cumsum: True, with prefix: True | auc: 0.7391926499999999, se: 0.0034993420253571716\n",
+      "MSE between scores (cumsum=True) with and without prefix: 0.0016967020267764222\n",
+      "RMSE between scores (cumsum=True) with and without prefix: 0.041191043040646864\n",
+      "MAE between scores (cumsum=True) with and without prefix: 0.0340457041759254\n",
+      "MSE (normalised) between scores (cumsum=True) with and without prefix: 0.8109855708847232\n",
+      "RMSE (normalised) between scores (cumsum=True) with and without prefix: 0.9005473729264459\n",
+      "MAE (normalised) between scores (cumsum=True) with and without prefix: 0.7443309805193774\n",
+      "Total experiment time: 228.52970695495605 seconds | 3.8088284492492677 minutes\n"
      ]
     }
    ],
    "source": [
-    "results_language_dataset_isolation_forest_moments = language_dataset_experiment(\n",
+    "(\n",
+    "    results_language_dataset_isolation_forest_moments,\n",
+    "    ifm_scores,\n",
+    ") = language_dataset_experiment(\n",
     "    data=data,\n",
     "    anomaly_detection_method=compute_isolation_forest_scores_based_on_moments,\n",
     "    anomaly_detection_kwargs={\"random_state\": seed},\n",
@@ -394,7 +600,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'cumsum': [False, True],\n",
+       " 'scores': [('with_prefix=False', 'with_prefix=True'),\n",
+       "  ('with_prefix=False', 'with_prefix=True')],\n",
+       " 'normalised_scores': [{'with_prefix=True': array([0.13342156, 0.63380013, 0.412591  , ..., 0.32844934, 0.87848177,\n",
+       "          1.73847531]),\n",
+       "   'with_prefix=False': array([-6.46131212e-01, -8.05994725e-01, -7.16190727e-01, ...,\n",
+       "          -7.45588700e-01, -2.74451802e-01, -4.47751595e-04])},\n",
+       "  {'with_prefix=True': array([-0.68918504,  0.02739014, -0.73919443, ..., -0.1830715 ,\n",
+       "           0.26925301,  1.17220621]),\n",
+       "   'with_prefix=False': array([-1.21133284, -0.62809284, -1.2017995 , ..., -0.30176918,\n",
+       "          -0.36429148, -0.78638783])}]}"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ifm_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -418,41 +654,112 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>cumsum</th>\n",
        "      <th>with_prefix</th>\n",
        "      <th>auc</th>\n",
        "      <th>se</th>\n",
        "      <th>total_fit_time</th>\n",
        "      <th>total_compute_score_time</th>\n",
+       "      <th>mse_between_scores</th>\n",
+       "      <th>rmse_between_scores</th>\n",
+       "      <th>mae_between_scores</th>\n",
+       "      <th>mse_between_norm_scores</th>\n",
+       "      <th>rmse_between_norm_scores</th>\n",
+       "      <th>mae_between_norm_scores</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>True</td>\n",
-       "      <td>0.705195</td>\n",
-       "      <td>0.003690</td>\n",
-       "      <td>0.122009</td>\n",
-       "      <td>0.071682</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.480812</td>\n",
+       "      <td>0.004103</td>\n",
+       "      <td>0.105915</td>\n",
+       "      <td>0.071888</td>\n",
+       "      <td>0.003436</td>\n",
+       "      <td>0.058620</td>\n",
+       "      <td>0.050521</td>\n",
+       "      <td>0.003436</td>\n",
+       "      <td>0.058620</td>\n",
+       "      <td>0.050521</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>False</td>\n",
-       "      <td>0.722989</td>\n",
-       "      <td>0.003642</td>\n",
-       "      <td>0.228372</td>\n",
-       "      <td>0.143448</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.634225</td>\n",
+       "      <td>0.003885</td>\n",
+       "      <td>0.209793</td>\n",
+       "      <td>0.141512</td>\n",
+       "      <td>0.003436</td>\n",
+       "      <td>0.058620</td>\n",
+       "      <td>0.050521</td>\n",
+       "      <td>0.003436</td>\n",
+       "      <td>0.058620</td>\n",
+       "      <td>0.050521</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.712755</td>\n",
+       "      <td>0.003628</td>\n",
+       "      <td>0.314805</td>\n",
+       "      <td>0.202299</td>\n",
+       "      <td>0.001697</td>\n",
+       "      <td>0.041191</td>\n",
+       "      <td>0.034046</td>\n",
+       "      <td>0.001697</td>\n",
+       "      <td>0.041191</td>\n",
+       "      <td>0.034046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.739193</td>\n",
+       "      <td>0.003499</td>\n",
+       "      <td>0.420613</td>\n",
+       "      <td>0.267201</td>\n",
+       "      <td>0.001697</td>\n",
+       "      <td>0.041191</td>\n",
+       "      <td>0.034046</td>\n",
+       "      <td>0.001697</td>\n",
+       "      <td>0.041191</td>\n",
+       "      <td>0.034046</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   with_prefix       auc        se  total_fit_time  total_compute_score_time\n",
-       "0         True  0.705195  0.003690        0.122009                  0.071682\n",
-       "1        False  0.722989  0.003642        0.228372                  0.143448"
+       "   cumsum  with_prefix       auc        se  total_fit_time  \\\n",
+       "0   False        False  0.480812  0.004103        0.105915   \n",
+       "1   False         True  0.634225  0.003885        0.209793   \n",
+       "2    True        False  0.712755  0.003628        0.314805   \n",
+       "3    True         True  0.739193  0.003499        0.420613   \n",
+       "\n",
+       "   total_compute_score_time  mse_between_scores  rmse_between_scores  \\\n",
+       "0                  0.071888            0.003436             0.058620   \n",
+       "1                  0.141512            0.003436             0.058620   \n",
+       "2                  0.202299            0.001697             0.041191   \n",
+       "3                  0.267201            0.001697             0.041191   \n",
+       "\n",
+       "   mae_between_scores  mse_between_norm_scores  rmse_between_norm_scores  \\\n",
+       "0            0.050521                 0.003436                  0.058620   \n",
+       "1            0.050521                 0.003436                  0.058620   \n",
+       "2            0.034046                 0.001697                  0.041191   \n",
+       "3            0.034046                 0.001697                  0.041191   \n",
+       "\n",
+       "   mae_between_norm_scores  \n",
+       "0                 0.050521  \n",
+       "1                 0.050521  \n",
+       "2                 0.034046  \n",
+       "3                 0.034046  "
       ]
      },
-     "execution_count": 24,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -463,7 +770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -481,27 +788,94 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 70615/70615 [00:00<00:00, 301502.91it/s]\n",
+      "100%|██████████| 20000/20000 [00:00<00:00, 273485.08it/s]\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "start with_prefix: True\n",
-      "fit_time: 0.20020198822021484\n",
-      "compute_score_time: 0.16024494171142578\n",
-      "with prefix: True | auc: 0.7318700800000001 | se: 0.0035683318883464243\n",
-      "start with_prefix: False\n",
-      "fit_time: 0.12604403495788574\n",
-      "compute_score_time: 0.10890507698059082\n",
-      "with prefix: False | auc: 0.7310202100000001 | se: 0.003581606307013908\n",
-      "Total experiment time: 160.24441504478455 seconds | 2.6707402507464093 minutes\n"
+      "start cumsum: False, with_prefix: False\n",
+      "fit_time: 0.15921783447265625\n",
+      "compute_score_time: 0.12325215339660645\n",
+      "cumsum: False, with prefix: False | auc: 0.643683775, se: 0.003871060373736696\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 70615/70615 [00:00<00:00, 123436.79it/s]\n",
+      "100%|██████████| 20000/20000 [00:00<00:00, 119682.35it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start cumsum: False, with_prefix: True\n",
+      "fit_time: 0.12366199493408203\n",
+      "compute_score_time: 0.10447096824645996\n",
+      "cumsum: False, with prefix: True | auc: 0.6148406450000001, se: 0.003967800265725457\n",
+      "MSE between scores (cumsum=False) with and without prefix: 0.00014941488322926688\n",
+      "RMSE between scores (cumsum=False) with and without prefix: 0.012223538081474892\n",
+      "MAE between scores (cumsum=False) with and without prefix: 0.009520642218273602\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 70615/70615 [00:00<00:00, 190558.19it/s]\n",
+      "100%|██████████| 20000/20000 [00:00<00:00, 167284.36it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start cumsum: True, with_prefix: False\n",
+      "fit_time: 0.1294260025024414\n",
+      "compute_score_time: 0.0986928939819336\n",
+      "cumsum: True, with prefix: False | auc: 0.722898575, se: 0.00357524646388613\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 70615/70615 [00:00<00:00, 84747.59it/s]\n",
+      "100%|██████████| 20000/20000 [00:00<00:00, 81314.11it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start cumsum: True, with_prefix: True\n",
+      "fit_time: 0.12419819831848145\n",
+      "compute_score_time: 0.11759805679321289\n",
+      "cumsum: True, with prefix: True | auc: 0.7214370449999999, se: 0.0035619707497822413\n",
+      "MSE between scores (cumsum=True) with and without prefix: 0.001229020496122409\n",
+      "RMSE between scores (cumsum=True) with and without prefix: 0.03505738860957001\n",
+      "MAE between scores (cumsum=True) with and without prefix: 0.030745399691977444\n",
+      "Total experiment time: 312.391930103302 seconds | 5.2065321683883665 minutes\n"
      ]
     }
    ],
    "source": [
-    "results_language_dataset_isolation_forest_signatures = language_dataset_experiment(\n",
+    "(\n",
+    "    results_language_dataset_isolation_forest_signatures,\n",
+    "    ifs_scores,\n",
+    ") = language_dataset_experiment(\n",
     "    data=data,\n",
     "    anomaly_detection_method=compute_isolation_forest_scores_signatures,\n",
     "    anomaly_detection_kwargs={\"backend\": \"iisignature\", \"random_state\": seed},\n",
@@ -513,7 +887,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'cumsum': [False, True],\n",
+       " 'scores': [{'with_prefix=False': array([0.31781821, 0.32516629, 0.3125105 , ..., 0.32442878, 0.33209533,\n",
+       "          0.33104716]),\n",
+       "   'with_prefix=True': array([0.30953645, 0.3206744 , 0.31051096, ..., 0.33292217, 0.3178306 ,\n",
+       "          0.33336384])},\n",
+       "  {'with_prefix=False': array([0.30489304, 0.32983555, 0.30765616, ..., 0.3281988 , 0.34777753,\n",
+       "          0.34920208]),\n",
+       "   'with_prefix=True': array([0.32966356, 0.37016297, 0.33947163, ..., 0.3879774 , 0.39587632,\n",
+       "          0.39373333])}]}"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ifs_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -537,41 +939,91 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>cumsum</th>\n",
        "      <th>with_prefix</th>\n",
        "      <th>auc</th>\n",
        "      <th>se</th>\n",
        "      <th>total_fit_time</th>\n",
        "      <th>total_compute_score_time</th>\n",
+       "      <th>mse_between_scores</th>\n",
+       "      <th>rmse_between_scores</th>\n",
+       "      <th>mae_between_scores</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>True</td>\n",
-       "      <td>0.73187</td>\n",
-       "      <td>0.003568</td>\n",
-       "      <td>0.200202</td>\n",
-       "      <td>0.160245</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.643684</td>\n",
+       "      <td>0.003871</td>\n",
+       "      <td>0.159218</td>\n",
+       "      <td>0.123252</td>\n",
+       "      <td>0.001229</td>\n",
+       "      <td>0.035057</td>\n",
+       "      <td>0.030745</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>False</td>\n",
-       "      <td>0.73102</td>\n",
-       "      <td>0.003582</td>\n",
-       "      <td>0.326246</td>\n",
-       "      <td>0.269150</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.614841</td>\n",
+       "      <td>0.003968</td>\n",
+       "      <td>0.282880</td>\n",
+       "      <td>0.227723</td>\n",
+       "      <td>0.001229</td>\n",
+       "      <td>0.035057</td>\n",
+       "      <td>0.030745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.722899</td>\n",
+       "      <td>0.003575</td>\n",
+       "      <td>0.412306</td>\n",
+       "      <td>0.326416</td>\n",
+       "      <td>0.001229</td>\n",
+       "      <td>0.035057</td>\n",
+       "      <td>0.030745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.721437</td>\n",
+       "      <td>0.003562</td>\n",
+       "      <td>0.536504</td>\n",
+       "      <td>0.444014</td>\n",
+       "      <td>0.001229</td>\n",
+       "      <td>0.035057</td>\n",
+       "      <td>0.030745</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   with_prefix      auc        se  total_fit_time  total_compute_score_time\n",
-       "0         True  0.73187  0.003568        0.200202                  0.160245\n",
-       "1        False  0.73102  0.003582        0.326246                  0.269150"
+       "   cumsum  with_prefix       auc        se  total_fit_time  \\\n",
+       "0   False        False  0.643684  0.003871        0.159218   \n",
+       "1   False         True  0.614841  0.003968        0.282880   \n",
+       "2    True        False  0.722899  0.003575        0.412306   \n",
+       "3    True         True  0.721437  0.003562        0.536504   \n",
+       "\n",
+       "   total_compute_score_time  mse_between_scores  rmse_between_scores  \\\n",
+       "0                  0.123252            0.001229             0.035057   \n",
+       "1                  0.227723            0.001229             0.035057   \n",
+       "2                  0.326416            0.001229             0.035057   \n",
+       "3                  0.444014            0.001229             0.035057   \n",
+       "\n",
+       "   mae_between_scores  \n",
+       "0            0.030745  \n",
+       "1            0.030745  \n",
+       "2            0.030745  \n",
+       "3            0.030745  "
       ]
      },
-     "execution_count": 27,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -582,7 +1034,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -607,27 +1059,94 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 70615/70615 [00:00<00:00, 304473.41it/s]\n",
+      "100%|██████████| 20000/20000 [00:00<00:00, 271466.32it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start cumsum: False, with_prefix: False\n",
+      "fit_time: 21.977287769317627\n",
+      "compute_score_time: 0.000308990478515625\n",
+      "cumsum: False, with prefix: False | auc: 0.4616964, se: 0.004081516404594727\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 70615/70615 [00:00<00:00, 117732.01it/s]\n",
+      "100%|██████████| 20000/20000 [00:00<00:00, 123815.81it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start cumsum: False, with_prefix: True\n",
+      "fit_time: 23.11544418334961\n",
+      "compute_score_time: 3.695487976074219e-05\n",
+      "cumsum: False, with prefix: True | auc: 0.725796005, se: 0.003553366125326552\n",
+      "MSE between scores (cumsum=False) with and without prefix: 0.010538103328468596\n",
+      "RMSE between scores (cumsum=False) with and without prefix: 0.10265526449465998\n",
+      "MAE between scores (cumsum=False) with and without prefix: 0.06619450836457347\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 70615/70615 [00:00<00:00, 196845.08it/s]\n",
+      "100%|██████████| 20000/20000 [00:00<00:00, 177433.78it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start cumsum: True, with_prefix: False\n",
+      "fit_time: 21.684338092803955\n",
+      "compute_score_time: 0.00022220611572265625\n",
+      "cumsum: True, with prefix: False | auc: 0.769341265, se: 0.0033278189596788873\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 70615/70615 [00:00<00:00, 84776.77it/s]\n",
+      "100%|██████████| 20000/20000 [00:00<00:00, 82397.16it/s]\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "start with_prefix: True\n",
-      "fit_time: 23.063555002212524\n",
-      "compute_score_time: 0.00021028518676757812\n",
-      "with prefix: True | auc: 0.696183475 | se: 0.0037071609347127223\n",
-      "start with_prefix: False\n",
-      "fit_time: 23.183524131774902\n",
-      "compute_score_time: 0.00027823448181152344\n",
-      "with prefix: False | auc: 0.746562 | se: 0.003467639636816925\n",
-      "Total experiment time: 167.48186802864075 seconds | 2.7913644671440125 minutes\n"
+      "start cumsum: True, with_prefix: True\n",
+      "fit_time: 23.27287793159485\n",
+      "compute_score_time: 0.00020599365234375\n",
+      "cumsum: True, with prefix: True | auc: 0.759815645, se: 0.0033655994399676707\n",
+      "MSE between scores (cumsum=True) with and without prefix: 0.005326253971207811\n",
+      "RMSE between scores (cumsum=True) with and without prefix: 0.07298118916000075\n",
+      "MAE between scores (cumsum=True) with and without prefix: 0.05606122924400197\n",
+      "Total experiment time: 324.6962020397186 seconds | 5.411603367328643 minutes\n"
      ]
     }
    ],
    "source": [
-    "results_language_dataset_local_outlier_factor_moments = language_dataset_experiment(\n",
+    "(\n",
+    "    results_language_dataset_local_outlier_factor_moments,\n",
+    "    lofm_scores,\n",
+    ") = language_dataset_experiment(\n",
     "    data=data,\n",
     "    anomaly_detection_method=compute_local_outlier_factor_scores_based_on_moments,\n",
     "    anomaly_detection_kwargs={\"n_neighbors\": n_neighbours},\n",
@@ -639,7 +1158,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'cumsum': [False, True],\n",
+       " 'scores': [{'with_prefix=False': array([1.20986692, 1.06620175, 1.27139665, ..., 1.04026927, 1.12925876,\n",
+       "          1.11252035]),\n",
+       "   'with_prefix=True': array([1.02411849, 1.05819657, 1.02185985, ..., 1.06377009, 1.0853092 ,\n",
+       "          1.07625392])},\n",
+       "  {'with_prefix=False': array([1.01609433, 1.1279413 , 0.97148024, ..., 1.14631549, 1.29521714,\n",
+       "          1.21960133]),\n",
+       "   'with_prefix=True': array([1.05135096, 1.16539665, 0.97552234, ..., 1.13670091, 1.33435137,\n",
+       "          1.34229914])}]}"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lofm_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -663,41 +1210,91 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>cumsum</th>\n",
        "      <th>with_prefix</th>\n",
        "      <th>auc</th>\n",
        "      <th>se</th>\n",
        "      <th>total_fit_time</th>\n",
        "      <th>total_compute_score_time</th>\n",
+       "      <th>mse_between_scores</th>\n",
+       "      <th>rmse_between_scores</th>\n",
+       "      <th>mae_between_scores</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>True</td>\n",
-       "      <td>0.696183</td>\n",
-       "      <td>0.003707</td>\n",
-       "      <td>23.063555</td>\n",
-       "      <td>0.000210</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.461696</td>\n",
+       "      <td>0.004082</td>\n",
+       "      <td>21.977288</td>\n",
+       "      <td>0.000309</td>\n",
+       "      <td>0.005326</td>\n",
+       "      <td>0.072981</td>\n",
+       "      <td>0.056061</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>False</td>\n",
-       "      <td>0.746562</td>\n",
-       "      <td>0.003468</td>\n",
-       "      <td>46.247079</td>\n",
-       "      <td>0.000489</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.725796</td>\n",
+       "      <td>0.003553</td>\n",
+       "      <td>45.092732</td>\n",
+       "      <td>0.000346</td>\n",
+       "      <td>0.005326</td>\n",
+       "      <td>0.072981</td>\n",
+       "      <td>0.056061</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.769341</td>\n",
+       "      <td>0.003328</td>\n",
+       "      <td>66.777070</td>\n",
+       "      <td>0.000568</td>\n",
+       "      <td>0.005326</td>\n",
+       "      <td>0.072981</td>\n",
+       "      <td>0.056061</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.759816</td>\n",
+       "      <td>0.003366</td>\n",
+       "      <td>90.049948</td>\n",
+       "      <td>0.000774</td>\n",
+       "      <td>0.005326</td>\n",
+       "      <td>0.072981</td>\n",
+       "      <td>0.056061</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   with_prefix       auc        se  total_fit_time  total_compute_score_time\n",
-       "0         True  0.696183  0.003707       23.063555                  0.000210\n",
-       "1        False  0.746562  0.003468       46.247079                  0.000489"
+       "   cumsum  with_prefix       auc        se  total_fit_time  \\\n",
+       "0   False        False  0.461696  0.004082       21.977288   \n",
+       "1   False         True  0.725796  0.003553       45.092732   \n",
+       "2    True        False  0.769341  0.003328       66.777070   \n",
+       "3    True         True  0.759816  0.003366       90.049948   \n",
+       "\n",
+       "   total_compute_score_time  mse_between_scores  rmse_between_scores  \\\n",
+       "0                  0.000309            0.005326             0.072981   \n",
+       "1                  0.000346            0.005326             0.072981   \n",
+       "2                  0.000568            0.005326             0.072981   \n",
+       "3                  0.000774            0.005326             0.072981   \n",
+       "\n",
+       "   mae_between_scores  \n",
+       "0            0.056061  \n",
+       "1            0.056061  \n",
+       "2            0.056061  \n",
+       "3            0.056061  "
       ]
      },
-     "execution_count": 30,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -708,7 +1305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -726,27 +1323,94 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 70615/70615 [00:00<00:00, 310553.83it/s]\n",
+      "100%|██████████| 20000/20000 [00:00<00:00, 274292.59it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start cumsum: False, with_prefix: False\n",
+      "fit_time: 42.357036113739014\n",
+      "compute_score_time: 2.9087066650390625e-05\n",
+      "cumsum: False, with prefix: False | auc: 0.740694545, se: 0.0035208531922066005\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 70615/70615 [00:00<00:00, 124500.42it/s]\n",
+      "100%|██████████| 20000/20000 [00:00<00:00, 124513.45it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start cumsum: False, with_prefix: True\n",
+      "fit_time: 39.638593912124634\n",
+      "compute_score_time: 3.600120544433594e-05\n",
+      "cumsum: False, with prefix: True | auc: 0.77737512, se: 0.003251635395765534\n",
+      "MSE between scores (cumsum=False) with and without prefix: 4.90039086672342e+19\n",
+      "RMSE between scores (cumsum=False) with and without prefix: 7000279184.949283\n",
+      "MAE between scores (cumsum=False) with and without prefix: 3394218462.8142953\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 70615/70615 [00:00<00:00, 190309.87it/s]\n",
+      "100%|██████████| 20000/20000 [00:00<00:00, 171146.13it/s]\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "start with_prefix: True\n",
-      "fit_time: 47.30217504501343\n",
-      "compute_score_time: 3.2901763916015625e-05\n",
-      "with prefix: True | auc: 0.7870619050000001 | se: 0.003254721440392075\n",
-      "start with_prefix: False\n",
-      "fit_time: 41.211479902267456\n",
-      "compute_score_time: 0.000213623046875\n",
-      "with prefix: False | auc: 0.790625405 | se: 0.003211425150444873\n",
-      "Total experiment time: 254.18011498451233 seconds | 4.236335249741872 minutes\n"
+      "start cumsum: True, with_prefix: False\n",
+      "fit_time: 40.66683292388916\n",
+      "compute_score_time: 3.0994415283203125e-05\n",
+      "cumsum: True, with prefix: False | auc: 0.7868756699999999, se: 0.003196186581723461\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 70615/70615 [00:00<00:00, 85860.05it/s]\n",
+      "100%|██████████| 20000/20000 [00:00<00:00, 81941.13it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "start cumsum: True, with_prefix: True\n",
+      "fit_time: 43.234313011169434\n",
+      "compute_score_time: 3.1948089599609375e-05\n",
+      "cumsum: True, with prefix: True | auc: 0.775206775, se: 0.0032984200609127838\n",
+      "MSE between scores (cumsum=True) with and without prefix: 0.011479458949545137\n",
+      "RMSE between scores (cumsum=True) with and without prefix: 0.10714223700084452\n",
+      "MAE between scores (cumsum=True) with and without prefix: 0.08431158182023502\n",
+      "Total experiment time: 485.0304901599884 seconds | 8.083841502666473 minutes\n"
      ]
     }
    ],
    "source": [
-    "results_language_dataset_local_outlier_factor_signatures = language_dataset_experiment(\n",
+    "(\n",
+    "    results_language_dataset_local_outlier_factor_signatures,\n",
+    "    lofs_scores,\n",
+    ") = language_dataset_experiment(\n",
     "    data=data,\n",
     "    anomaly_detection_method=compute_local_outlier_factor_scores_signatures,\n",
     "    anomaly_detection_kwargs={\"backend\": \"iisignature\", \"n_neighbors\": n_neighbours},\n",
@@ -758,7 +1422,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'cumsum': [False, True],\n",
+       " 'scores': [{'with_prefix=False': array([9.91719486e-01, 1.02928345e+00, 9.84035562e-01, ...,\n",
+       "          1.41230353e+09, 1.11587116e+00, 1.33388800e+10]),\n",
+       "   'with_prefix=True': array([0.97877268, 0.98635321, 0.98657449, ..., 1.17402375, 1.26703633,\n",
+       "          1.3691457 ])},\n",
+       "  {'with_prefix=False': array([1.22339041, 1.21994408, 1.04862531, ..., 1.27383777, 1.32286783,\n",
+       "          1.29187666]),\n",
+       "   'with_prefix=True': array([1.00820954, 1.1756379 , 1.0497486 , ..., 1.18167617, 1.21942807,\n",
+       "          1.23117207])}]}"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lofs_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -782,38 +1474,88 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>cumsum</th>\n",
        "      <th>with_prefix</th>\n",
        "      <th>auc</th>\n",
        "      <th>se</th>\n",
        "      <th>total_fit_time</th>\n",
        "      <th>total_compute_score_time</th>\n",
+       "      <th>mse_between_scores</th>\n",
+       "      <th>rmse_between_scores</th>\n",
+       "      <th>mae_between_scores</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>True</td>\n",
-       "      <td>0.787062</td>\n",
-       "      <td>0.003255</td>\n",
-       "      <td>47.302175</td>\n",
-       "      <td>0.000033</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.740695</td>\n",
+       "      <td>0.003521</td>\n",
+       "      <td>42.357036</td>\n",
+       "      <td>0.000029</td>\n",
+       "      <td>0.011479</td>\n",
+       "      <td>0.107142</td>\n",
+       "      <td>0.084312</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>False</td>\n",
-       "      <td>0.790625</td>\n",
-       "      <td>0.003211</td>\n",
-       "      <td>88.513655</td>\n",
-       "      <td>0.000247</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.777375</td>\n",
+       "      <td>0.003252</td>\n",
+       "      <td>81.995630</td>\n",
+       "      <td>0.000065</td>\n",
+       "      <td>0.011479</td>\n",
+       "      <td>0.107142</td>\n",
+       "      <td>0.084312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.786876</td>\n",
+       "      <td>0.003196</td>\n",
+       "      <td>122.662463</td>\n",
+       "      <td>0.000096</td>\n",
+       "      <td>0.011479</td>\n",
+       "      <td>0.107142</td>\n",
+       "      <td>0.084312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.775207</td>\n",
+       "      <td>0.003298</td>\n",
+       "      <td>165.896776</td>\n",
+       "      <td>0.000128</td>\n",
+       "      <td>0.011479</td>\n",
+       "      <td>0.107142</td>\n",
+       "      <td>0.084312</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   with_prefix       auc        se  total_fit_time  total_compute_score_time\n",
-       "0         True  0.787062  0.003255       47.302175                  0.000033\n",
-       "1        False  0.790625  0.003211       88.513655                  0.000247"
+       "   cumsum  with_prefix       auc        se  total_fit_time  \\\n",
+       "0   False        False  0.740695  0.003521       42.357036   \n",
+       "1   False         True  0.777375  0.003252       81.995630   \n",
+       "2    True        False  0.786876  0.003196      122.662463   \n",
+       "3    True         True  0.775207  0.003298      165.896776   \n",
+       "\n",
+       "   total_compute_score_time  mse_between_scores  rmse_between_scores  \\\n",
+       "0                  0.000029            0.011479             0.107142   \n",
+       "1                  0.000065            0.011479             0.107142   \n",
+       "2                  0.000096            0.011479             0.107142   \n",
+       "3                  0.000128            0.011479             0.107142   \n",
+       "\n",
+       "   mae_between_scores  \n",
+       "0            0.084312  \n",
+       "1            0.084312  \n",
+       "2            0.084312  \n",
+       "3            0.084312  "
       ]
      },
      "execution_count": 33,
@@ -827,7 +1569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [