diff --git a/Chapter05/circles_modeling_EMPTY.py b/Chapter05/circles_modeling_EMPTY.py
index 92c422ef8461a6ba5dc2ac4bb45827480a928134..56eba86116fbba59d1eab461eaf4b6ca17d0cb9d 100755
--- a/Chapter05/circles_modeling_EMPTY.py
+++ b/Chapter05/circles_modeling_EMPTY.py
@@ -30,7 +30,7 @@ def segmentation_histogram(ax, D, S, edges=None):
         ax.plot(centers, np.histogram(D[S==k].ravel(), edges)[0])
         
 
-path = '../../data/week5/'
+path = '../data/week5/'
 D = skimage.io.imread(path + 'noisy_circles.png').astype(float)
 
 # Ground-truth segmentation.
diff --git a/Chapter05/circles_modelling_EMPTY.ipynb b/Chapter05/circles_modelling_EMPTY.ipynb
index 9346443168a26d5b9e557c025ef529f26fc4795e..670d44e550036abb519c2e3abf6535cf5591d90b 100644
--- a/Chapter05/circles_modelling_EMPTY.ipynb
+++ b/Chapter05/circles_modelling_EMPTY.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -84,11 +84,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "path = '../../data/week5/'\n",
+    "path = '../data/week5/'\n",
     "D = skimage.io.imread(path + 'noisy_circles.png').astype(float)\n",
     "\n",
     "# Ground-truth segmentation.\n",
@@ -109,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -196,6 +196,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
diff --git a/Chapter05/gender_labeling.ipynb b/Chapter05/gender_labeling.ipynb
index 3eaef205ac5d4724502186c91e0506037c0067ac..1cf0ac58ba225a16248466a6e09110a023d333be 100644
--- a/Chapter05/gender_labeling.ipynb
+++ b/Chapter05/gender_labeling.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -60,15 +60,15 @@
     "d = np.array([179, 174, 182, 162, 175, 165]) # heights (data term)\n",
     "\n",
     "mu = [181, 165] # means of two classes, used for computation of likelihood\n",
-    "w_s = (d - mu[0])**2 # source weight\n",
-    "w_t = (d - mu[1])**2 # sink weights\n",
+    "w_s = (d - mu[0]) ** 2 # source weight\n",
+    "w_t = (d - mu[1]) ** 2 # sink weights\n",
     "\n",
     "fig, ax = plt.subplots()\n",
     "ax.plot(d, '-og', lw=0.2, label='data')\n",
     "ax.plot([-1, len(d)], [mu[0]]*2, 'b', label='mean M height')\n",
     "ax.plot([-1, len(d)], [mu[1]]*2, 'r', label='mean F height')\n",
     "ax.plot([-1, len(d)], [0.5*(mu[0] + mu[1])]*2, '--k', label='M-F threshold')\n",
-    "ax.legend(bbox_to_anchor=(1, 1))\n",
+    "ax.legend(bbox_to_anchor = (1, 1))\n",
     "plt.show()"
    ]
   },
@@ -82,12 +82,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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ouPVqMID5ALYBkL//2geNjUB1tZh81NmtkTg5XQER2TJeqSGjdZ5w+RfoKGq2f/91h8ZGi0Wiu8jIyMD06dNFx8CAAQNw48YN0TGIyEaxqCGjBQTcuSYJQAuAVgBzdWtdXCyXibrX0NAAZ2fnfjHvUmxsLAoKCkTHICIbxdtPZLSpUwFX19tvQSkBnNZ9LUmAszPg5ycmH+lLS0vD3Llz797QAtzc3NDwwz8cIiKTEv+rG1kdpRIYP77j6x+6aHgC8IQkdQywNmrU7dtIlJaWFigUCjg48PcXIrJ9/KSjXklL24aPPwaeeQa4fPmH9UFBElauPInly99EUFCQuIAEAEhNTcW9994rOoYejipMRObCKzXUa4sXAxcuAGlpwI4dHX+WlgJ//OM9OHr0KFpaWkRHtGttbW1ob2+HSqUSHUWPi4sLb0ERkVmwqKE+USqBGTOAhx7q+FOp7Fh///33Y+/evSKj2b309HTMnDlTdIxOYmJi2FmYiMyCRQ2ZhaOjIyZOnIiMjAzRUeySVqtFQ0MD3NzcREfpZNCgQbh+/broGERkg1jUkNkMGTIEzs7OKCkpER3F7hw8eBDTpk0THYOIyKJY1JBZJSYmoqCggH0oLEiWZdy8eRMDBgwQHYWIyKJY1JDZLVy4EHv37uVTLxZy9OhRTJw4UXQMg5RKJSe3JCKTY1FDZqdQKDB79mx89dVXoqPYhatXr8LX11d0DIOioqJw5swZ0TGIyMawqCGL8PHxgZ+fH06ePCk6ik3Ly8tDQkKC6Bh3FRwcjEuXLomOQUQ2hkUNWUxcXBwuXrzICQ3N6PLly1Yx6CFnCycic2BRQxaVnJyMAwcOsH+NGRQUFGDUqFGiY/QYCxsiMjUWNWRRkiRh/vz5+PTTT0VHsTnnz59HRESE6BhGYXFLRKbEooYszsPDAyNGjEBeXp7oKDbj3LlzGD58uOgYRhk6dCguXLggOgYR2RAWNSREZGQk1Go1qqqqREexCadOnUJ0dLToGEYZPnw4zp49KzoGEdkQFjUkzL333ovMzEyOV9JHFy9eREhIiOgYRlMqldBqtaJjEJENYVFDQi1atIgTX/ZRfn4+4uPjRccgIhKORQ0JpVKpkJCQgMOHD4uOYpWqqqrg5+cnOgYRUb9gdFGTmZmJBQsWIDAwEJIkYc+ePXrbJUnqcnn11VcBAOnp6d22OXbsWLfnnTFjRqf2q1atMjY+9UOhoaGQJImdRnshOzsbiYmJomP0mq+vL6qrq0XHICIbYXRRU19fj7i4OGzcuLHL7ZWVlXrLli1bIEkSlixZAgCYNGlSpzaPPfYYwsLCMHbsWIPnfvzxx/X2W79+vbHxqZ+aNGkSjh8/jqamJtFRrMb169fh7e1t1eO9jBo1CoWFhaJjEJGNcDB2h+TkZCQnJ3e73d/fX+/1J598gpkzZyI8PBwA4OTkpNemtbUVn3zyCZ5++um7fji7urp2Oj7ZjoULF+Ljjz/GT37yE9FRrMKhQ4ewYMEC0TH6RKVSoaWlRXQMIrIRZu1TU11djf3792PFihXdttm7dy9qamrw6KOP3vV477//Pnx8fDBq1CisXbsWDQ0N3bZtbm6GRqPRW6h/c3BwwPTp05Gamio6Sr9XW1sLNzc3q75KQ0RkakZfqTHG9u3b4eHhgcWLF3fbZvPmzZg7d+5d56t5+OGHERoaisDAQJw4cQK/+93vUFRUhI8//rjL9uvWrcOf/vSnPuUny/Pz80N5eTnOnDmDqKgo0XH6rYyMDINXTImI7JFZi5otW7Zg6dKlcHZ27nL75cuXceDAAfz73/++67FWrlyp+zomJgYBAQGYNWsWSkpKuhwafu3atUhJSdG91mg0CA4O7sW7IEsbM2YM9u3bhyFDhsDDw0N0nH6nqakJjo6OUCqVoqOYhIeHBzQaDTw9PUVHISIrZ7bbTwcPHkRRUREee+yxbtts3boVgwYNwsKFC40+/q0nPoqLi7vcrlKp4OnpqbeQ9fjRj36ETz/9lHMDdeGbb77BvffeKzqGycTGxqKgoEB0DCKyAWYrajZv3oyEhATExcV1uV2WZWzduhU///nP4ejoaPTx8/PzAQABAQF9iUn9lCRJSE5Oxueffy46Sr/S2toKWZZ79X+mv/L09ERtba3oGERkA4wuaurq6pCfn68rKkpLS5Gfn4+ysjJdG41Gg127dhm8SvPNN9+gtLS0yzbl5eWIiopCTk4OAKCkpAQvvfQS8vLycOHCBezduxc///nPMW3aNMTGxhr7FshKDBgwAEOHDtX9WyPbu0pDRGRKRhc1ubm5iI+P1w3LnpKSgvj4eLzwwgu6Njt37oQsy3jooYe6Pc7mzZsxadKkLjuDtra2oqioSPd0k5OTE77++mvMmTMHUVFR+PWvf40lS5Zg3759xsYnKxMdHY0rV67g6tWroqMI197ejpaWFri4uIiOYnK8zUhEpiDJdvJpotFo4OXlBbVazf41VkaWZfzrX//Cgw8+aDOdY3vjm2++wbhx42yy83RqaiqmTJkClUolOgoR9TPG/Pzm3E/U70mShIULF9r1lTlZllFXV2eTBQ3AkYWJyDRY1JBVcHV1RUxMDLKzs0VHEeLQoUOYMmWK6Bhm4+fnhytXroiOQURWjkUNWY2IiAi0tLTg8uXLoqNYlCzLqKmpgbe3t+goRET9GosasipTp05FVlaWXc0XlJOTY9UzcRMRWQqLGrI6ixYtwieffCI6hsVUVVXZxXhMCoUC7e3tomMQkRVjUUNWx9HREZMmTUJGRoboKGb37bffdjuApa0ZPnw4zp07JzoGEVkxFjVklYYMGQIXF5dup8mwFWVlZRg6dKjoGBYRFhaG0tJS0TGIyIqxqCGrNX78eBQWFqK+vl50FLM4ffo0Ro4cKTqGxUiSJDoCEVk5FjVk1W6NX2OLY0iePXsWI0aMEB3DoljYEFFfsKghq6ZQKHDffffhyy+/FB3FpEpKShAeHi46hhC2WKASkWWwqCGrN2jQIAQEBNjUiLQnT55ETEyM6BgWFxQUZHfjEBGR6bCoIZsQGxuLS5cu4caNG6Kj9Nnly5cRGBgoOoYQUVFROHPmjOgYRGSlWNSQzUhKSsKBAweg1WpFR+mTvLw8jB07VnQMIRwcHNDW1iY6BhFZKRY1ZDMkScKPfvQj7N+/X3SUXrt69SoGDx4sOgYRkVViUUM2xd3dHZGRkcjNzRUdpVeOHDmCiRMnio5BRGSVWNSQzRkxYgQ0Gg0qKytFRzHKzZs34eXlZfePNXt7e6OmpkZ0DCKyQixqyCbde++9OHjwoFX1z8jMzMS0adNExxAuJiYGBQUFomMQkRViUUM2y5omvqyvr4eLiwsUCv6XdHV1RWNjo+gYRGSF+AlKNkulUmHs2LE4dOiQ6Ch3lZaWhpkzZ4qOQURk1VjUkE0LDQ2FQqHo1xMlNjc3Q6lUwsHBQXQUIiKrxqKGbN6kSZPw7bffoqmpSXSULqWlpeHee+8VHaNfcXNzs9mJSonIfFjUkF3or/1r2tra0NbWBpVKJTpKv8LOwkTUGyxqyC4olUrMnDkTqampoqPoYV+arg0cOBA3b94UHYOIrAyLGrIbvr6+GDhwYL+ZW0ir1aKxsRFubm6ioxAR2QQWNWRXxowZg+LiYmg0GtFRkJmZienTp4uOQURkM1jUkN2ZP38+PvvsM8iyLCyDLMtQq9Xw8vISlqG/c3BwQGtrq+gYRGRFWNSQ3ZEkCcnJyfjss8+EZTh69CgmTZok7PzWYOTIkTh16pToGERkRVjUkF3y8vJCWFgY8vPzhZyfs3HfXWBgICoqKkTHICIrwqKG7FZ0dDSuXr2Kq1evWvS8ubm5SEhIsOg5rZG9T+xJRMZjUUN27b777sM333yD9vZ2i52zvLwcQUFBFjsfkTktX74ckiR1WoqLi0VHIzvEoobs3sKFC7Fv3z6LnOvEiROIiYmxyLlsgSRJ0Gq1omPQXSTNnYvKjz5C5d//3vHn5csICwsTHYvskNFFTWZmJhYsWIDAwEBIkoQ9e/bobe+qYpckCa+++qquzdChQzttf+WVVwyet6mpCatXr8agQYPg7u6OJUuWoLq62tj4RJ24uLggNjYWWVlZZj9XaWkpwsPDzX4eWxEeHo7z58+LjkGGXLwIVUYG/Jcsgf+TT3b8OWEClP1wBG+yfUYXNfX19YiLi8PGjRu73F5ZWam3bNmyBZIkYcmSJXrt/uu//kuv3dNPP23wvGvWrMG+ffuwa9cuZGRkoKKiAosXLzY2PlGXwsPD0draikuXLpntHEVFRRgxYoTZjm+Lhg0bhpKSEtExqDsffwykpwN3zqtWXg78+Mcd24ksyOhpgZOTk5GcnNztdn9/f73Xn3zyCWbOnNnpt1MPD49ObbujVquxefNm7NixQzfx39atWzFy5EhkZWVhwoQJRr4Los6mTp2KDz/8EH5+fnBycjL58c+cOYNFixaZ/Li2TKFQCB1PiAxobweeeQYA8CkA99s2JcsydkkS8OyzwKJFgFIpIiHZIbP2qamursb+/fuxYsWKTtteeeUVDBo0CPHx8Xj11VfR1tbW7XHy8vLQ2tqK2bNn69ZFRUUhJCQER48e7XKf5uZmaDQavYXobsw18eWFCxcQGhpq8uMSCXPwIHD5MgBgJoD825a/AYAsA5cudbQjshCzFjXbt2+Hh4dHp9tEv/rVr7Bz506kpaXhl7/8Jf785z/jt7/9bbfHqaqqgpOTEwYMGKC33s/PD1VVVV3us27dOnh5eemW4ODgPr8fsn2Ojo6YPHky0tPTTXrc7777DqNHjzbpMYmEqqzUfekGYNhtS0A37YjMzejbT8bYsmULli5dCmdnZ731KSkpuq9jY2Ph5OSEX/7yl1i3bh1UKpVJzr127Vq982g0GhY21COBgYEoLy/HuXPnMHz48D4fr7Kysse3Wqkzf39/VFZWIiAg4O6NyXJ6+vfBvzeyILNdqTl48CCKiorw2GOP3bVtYmIi2tracOHChS63+/v7o6WlBTdv3tRbX11d3e0PC5VKBU9PT72FqKfGjRuHU6dOob6+vs/HysnJwfjx402Qyj5FR0dzuoT+aOpUwNB4S5IEBAd3tCOyELMVNZs3b0ZCQgLi4uLu2jY/Px8KhQK+vr5dbk9ISICjoyNSU1N164qKilBWVoaJEyeaLDPR7RYsWIB9+/b1qaNqTU0NvL29OTpuHzg5OaGlpUV0DLqTUgn89a9db7v17/3119lJmCzK6KKmrq4O+fn5ujlzSktLkZ+fj7KyMl0bjUaDXbt2dXmV5ujRo3j99dfx3Xff4fz583j//fexZs0aPPLIIxg4cCCAjhFXo6KikJOTA6Bjnp4VK1YgJSUFaWlpyMvLw6OPPoqJEyfyyScyG4VCgTlz5uDAgQO9PsahQ4cwZcoUE6Yi6kcWL8a2jz7CBz4+eqvloCDgww8BDrtBFmZ0n5rc3FzMnDlT9/pWv5Vly5Zh27ZtAICdO3dClmU89NBDnfZXqVTYuXMn/vM//xPNzc0ICwvDmjVr9Pq/tLa2oqioCA0NDbp1r732GhQKBZYsWYLm5mbMnTsXf//7342NT2QUb29vBAYGoqCgwOiRgGtra+Hu7s6rNGTbFi/GNw4OmO/pCVRW4opSifLwcMSPHSs6GdkhSbaTQSA0Gg28vLygVqvZv4aM9vnnnyMxMRHe3t493mffvn2YN28elLz83mdHjhxBdHR0pycgSbyrV6/i/PnzSExM1K3bt28fFixYIDAV2RJjfn5z7ieiHkhKSsKBAwd6PA9RY2MjnJycWNCYSGxsLE6cOCE6BnUhNzcXY++4KuPs7IzGxkZBiciesagh6gFJknQdh3siLS1NN/o19Z27u7tJnkQj09NqtZ2K92nTpuEgB90jAVjUEPWQu7s7oqOjcezYMYPtWltbAXQM5Edky1pbW+Hg0Llrpkql4hNrJASLGiIjDB8+HHV1daioqOi2TWpqKq/SkF3Izs7W60tzu9DQ0G7HHiMyFxY1REaaOXMmHn74YUiShFWrVulta29vxxtvvAEXFxcsX75cTEAbxX4a/Y9are6283ZMTAwKCgosG4jsHosaol4IDg6Gj48Pdu7cibq6RqSnAx98ALz6ajYOHz6CkJAQ0RFtTkxMDAoLC0XHICNIktTjzvVEpmDWuZ+IbJVSqcSYMWPw3XelCA39GNevL/1+ywU4OoZgxIgwoflskY+PD3Jzc0XHoO8VFxcjIiLCYJtJkybhyJEjHICSLIZXaoh66cYNF1RXr8L161tvW7sFra2P4tgx4OJFYdGIzK6oqAiRkZEG23h7e+PGjRsWSkTEooaoV2QZ6Ogu8B8ADgG4+P1yGMAjAICcHKC9XVRCIvPryWjZAwcOZGFDFsOihqgXqquBpiYAGAxgPoBtALZ+/3XHPDgNDQCH6jAthUKBtrY20THs3q0pQHri1i0oIktgUUPUC/oP4fwCHUXN9u+//kFlpcUi2YXIyEgUFRWJjmH3srKyejyZsEKhYGdhshgWNUS94OJy+6skAC0AWgHM1WsXEGC5TPYgJCQEZWVlomPYvZaWFqhUqh63j4mJ4TQXZBEsaoh6wc8PcHYGOroUKAGcBnDq+687uLoCU6eKyWerOOO5eFqt1ui/h6FDh+Iie86TBbCoIeoFSQJiYn74GvD8frn1Ghg/HuB8lmRrvv32W8THxxu9n5OTE5qbm82QiOgHLGqIemHbtm3IydmDDz8EhgzR3xYUBHz00R6kpW0Tks0eyLIsOoLdqqqqQkAv7qtykkuyBA6+R9QHixcDixZ1POVUWdnRh2bqVF6hMafQ0FBcvHgRQ4cOFR2FjODi4oKmjkcGicyGRQ1RHymVwIwZolPYj8jISKSmprKoEaCqqgr+/v693n/IkCG4dOkSgoODTZiK6Ae8/UREVkWpVPIRYUF625/mltGjRyM/P990gYjuwKKGiIh6RKvVQqHo/Y8NSZI4ySWZFYsaIiK6q+bmZjg5OfX5OBMmTEBWVpYJEhF1xqKGiKyOj48Prl69KjqGXcnOzu7xKMKG+Pj4oKamxgSJiDpjUUNEVmfUqFEoLCwUHcOu1NbWwsPDwyTH8vLyglqtNsmxiG7HooaIrI6zszMfD7YgU48LNHnyZBw+fNikxyQCWNQQEdFdnD17FpGRkSY7nlKpRHt7u8mOR3QLixoiIjKouLgYw4YNM+kxo6OjcfLkSZMek4iD75lIfX19t9uUSiWcnZ171FahUMDltimgjWnb0NDQ7WViSZLg6uraq7aNjY0GH8F0c3PrVdumpiaDv60Z09bV1VU3yV5zczPa2tpM0tbFxUX3CGtLSwtaW1tN0tbZ2RnK74cdNqZta2srWlpaum2rUqng4OBgdNu2tjaD8/I4OTnB0dHR6Lbt7e0GbxM5Ojrqnqgxpq1Wq4VSqURVVVWX/TzubNvY2NjtcR0cHHQzTsuyjIaGBpO0Neb/fX//jGhqatI7jyk+I/z9/fHZZ591GkSRnxEdrPkzQijZTqjVahmArFarzXJ8AN0u8+bN02vr6urabdvp06frtfXx8em27dixY/XahoaGdts2Ojpar210dHS3bUNDQ/Xajh07ttu2Pj4+em2nT5/ebVtXV1e9tvPmzTP4fbvdj3/8Y4Nt6+rqdG2XLVtmsO2VK1d0bZ988kmDbUtLS3Vtf/Ob3xhsW1hYqGv74osvGmybk5Oja7t+/XqDbdPS0nRt33zzTYNtP/30U13brVu3Gmz773//W9f23//+t8G2W7du1bX99NNPDbZ98803dW3T0tIMtl2/fr2ubU5OjsG2L774oq5tYWGhwba/+c1vdG1LS0sNtn3yySd1ba9cuWKw7bJly3Rt6+rqDLb98Y9/rPdv2FBbfkZ0LPyM+GGx5s8IUzPm5zdvPxEREZFNkGTZPqa71Wg0uscIPT09TX58a760fLe2vP3ES8v98fZTY2MjvvzyS8yZM6dHbbvD208duvuM+OKLL5CUlGSwbV8+Iz7//HMkJyd32ZafEdb5GWFqxvz8ZlFDRFarqx+4ZDparRYHDhzQKzpMLTc3F4GBgQgMDDTbOci6GfPzm7efiMhqOTo6GvxNk/omLy8PCQkJZj1HQkIC8vLyzHoOsh9GFzWZmZlYsGABAgMDIUkS9uzZo7f91oRldy6vvvoqAODChQtYsWIFwsLC4OLigoiICLz44ot3/WCaMWNGp2OuWrXK2PhEZEPuuecePhZsRleuXIGvr69Zz3HrNo+d3DQgMzO6qKmvr0dcXBw2btzY5fbKykq9ZcuWLZAkCUuWLAEAnDlzBlqtFv/4xz9w8uRJvPbaa9i0aRN+//vf3/Xcjz/+uN6x169fb2x8IrIh/v7+qK6uFh2D+mj8+PHIyckRHYNsgNHj1CQnJxu8v+rv76/3+pNPPsHMmTMRHh4OAEhKStK7Bx4eHo6ioiK89dZb2LBhg8Fzu7q6djo+ERGZXnl5ucX6ufj5+bGoIZMwa5+a6upq7N+/HytWrDDYTq1Ww9vb+67He//99+Hj44NRo0Zh7dq1Bp88aG5uhkaj0VuIiKhnvvvuO4wePdpi5/Pw8EBtba3Fzke2yaxFzfbt2+Hh4YHFixd326a4uBhvvPEGfvnLXxo81sMPP4z33nsPaWlpWLt2Lf75z3/ikUce6bb9unXr4OXlpVuCg4N7/T6IqP+SJMng48TUO7Is6/q7WMKUKVNw6NAhi52PbJNZp0nYsmULli5dqjf+wu3Ky8uRlJSEBx98EI8//rjBY61cuVL3dUxMDAICAjBr1iyUlJQgIiKiU/u1a9ciJSVF91qj0bCwIbJBw4YNw7lz50w64aK9a2pq6vZz21wcHBwMjgdD1BNmu1Jz8OBBFBUV4bHHHutye0VFBWbOnIlJkybh7bffNvr4iYmJADqu9HRFpVLB09NTbyEi2xMWFobS0lLRMWxKVlaW7jPWkkaMGIGioiKLn5dsh9mKms2bNyMhIQFxcXGdtpWXl2PGjBlISEjA1q1bdSMsGiM/Px8AEBAQ0NeoRGTFFAoFHwc2sfr6eri7u1v8vJGRkTh79qzFz0u2w+hqoq6uDvn5+bqiorS0FPn5+SgrK9O10Wg02LVrV5dXaW4VNCEhIdiwYQOuXr2KqqoqVFVV6bWJiorS9YYvKSnBSy+9hLy8PFy4cAF79+7Fz3/+c0ybNg2xsbHGvgUiIuqG6ALRwcHB4JQARIYY3acmNzcXM2fO1L2+1W9l2bJl2LZtGwBg586dkGUZDz30UKf9v/rqKxQXF6O4uBhBQUF62279Z2ptbUVRUZHu6SYnJyd8/fXXeP3111FfX4/g4GAsWbIEf/zjH42NT0Q2ytIdW23V6dOnER0dLez8U6dOxaFDh/R+zhD1FOd+IiKrV1BQgIEDB3b6RYmMt3//fsyfP19ohn379mHBggVCM1D/wbmfiMiuREVF4cyZM6JjkIn4+vrqdUkg6ikWNURk9RwdHfk4sAncuHEDAwcOFB0D48ePx7Fjx0THICvEooaIiAAAOTk5GD9+vOgYnOSSeo1FDRERAQDa2trg4GDWMVl7bOzYscjLyxMdg6wMixoisgkDBw7E9evXRcewWu3t7VAqlaJj6AQEBKCyslJ0DLIyLGqIyCbExMSgoKBAdAyrdezYMYwdO1Z0DD1ubm6or68XHYOsCIsaIrIJrq6uurGtyHg1NTXw8fERHUPP1KlTcfDgQdExyIqwqCEion7J0dGRowuTUVjUEBHZubKyMoSEhIiO0aWIiIhuJy4muhOLGiKyGS4uLrwF1QuFhYUYNWqU6Bhdio6OxunTp0XHICvBooaIbEZsbCw7C/dCf583S6lUor29XXQMsgIsaojIZnh7e/OxbiM1NDTAxcVFdAyDpkyZgkOHDomOQVaARQ0RkR3LysrChAkTRMcwyNPTExqNRnQMsgIsaojIpvTn2yj9UWNjI1xdXUXHuCsfHx9cvXpVdAzq51jUEJFNcXBw4GPAPWRNcytNmDAB2dnZomNQP8eihohsSlRUFM6cOSM6hlUoLCxETEyM6Bg9IkkSZFm2qkKMLI9FDRHZlCFDhqC8vFx0DKvQn8en6Up8fDy+/fZb0TGoH2NRQ0Q2hX1qbFdQUBALVjKIRQ0RkR26du0aBg0aJDqG0VxcXNDY2Cg6BvVTLGqIyCZptVrREfq13NxcjBs3TnQMo02dOhWZmZmiY1A/xaKGiGxOWFgYSktLRcfo19rb26FUKkXHMJpKpUJLS4voGNRPsaghIpszfPhwToJoQFtbGxwcHETH6LWhQ4eyaKUusaghIpujUCh4+8mAnJwcjB8/XnSMXouJiUFhYaHoGNQPsaghIrIzN27cwMCBA0XH6BOFQsFJLqkTFjVERGR1Jk+ejKNHj4qOQf0Mixoiskn+/v6oqqoSHaPfKS0tRVhYmOgYfTZgwADcuHFDdAzqZ1jUEJFNuueee3Dy5EnRMfqdU6dOYeTIkaJjmMTAgQNx/fp10TGoH2FRQ0Q2ycnJiRNbdsNWRl2eNGkSjhw5IjoG9SMsaoiI7ERdXR3c3NxExzAZhULBCS5JD4saIiI7kZ2djQkTJoiOYVKxsbH47rvvRMegfoJFDRHZLA8PD6jVatEx+o2mpiY4OzuLjmFSoaGhKCsrEx2D+gmji5rMzEwsWLAAgYGBkCQJe/bs0dsuSVKXy6uvvqprc/36dSxduhSenp4YMGAAVqxYgbq6OoPnbWpqwurVqzFo0CC4u7tjyZIlqK6uNjY+EdmR2NhYFBQUiI7RL8iybDN9ae6kUqnQ1NQkOgb1A0YXNfX19YiLi8PGjRu73F5ZWam3bNmyBZIkYcmSJbo2S5cuxcmTJ/HVV1/h008/RWZmJlauXGnwvGvWrMG+ffuwa9cuZGRkoKKiAosXLzY2PhHZEQ8PD9TW1oqO0S989913iIuLEx3DLKZNm8ZJLgkAYPTkH8nJyUhOTu52u7+/v97rTz75BDNnzkR4eDgA4PTp0/jiiy9w7NgxjB07FgDwxhtvYN68ediwYQMCAwM7HVOtVmPz5s3YsWMH7r33XgDA1q1bMXLkSGRlZdncPWIiIlMrLy/H6NGjRccwC2dnZzQ3N4uOQf2AWfvUVFdXY//+/VixYoVu3dGjRzFgwABdQQMAs2fPhkKhQHZ2dpfHycvLQ2trK2bPnq1bFxUVhZCQkG5HlGxuboZGo9FbiIjINgUHB7NvDZm3qNm+fTs8PDz0bhNVVVXB19dXr52DgwO8vb27Hf2zqqoKTk5OGDBggN56Pz+/bvdZt24dvLy8dEtwcHDf3gwRWSX2t+j4BfPOz11bExcXx6egyLxFzZYtW7B06VIhve3Xrl0LtVqtWy5dumTxDEQk3qhRo+x+Rufjx48jISFBdAyzuvVQCmdnt29mK2oOHjyIoqIiPPbYY3rr/f39ceXKFb11bW1tuH79eqf+OLfv09LSgps3b+qtr66u7nYflUoFT09PvYWI7I+vry+uXr0qOoZQWq0WCoXtj+AxceJEZGVliY5BApntX/nmzZuRkJDQqbf9xIkTcfPmTeTl5enWffPNN9BqtUhMTOzyWAkJCXB0dERqaqpuXVFREcrKyjBx4kTzvAEishm2+ihzT7S0tMDR0VF0DIsYNGgQampqRMcggYwuaurq6pCfn4/8/HwAHTO+5ufn63XQ0mg02LVrV6erNAAwcuRIJCUl4fHHH0dOTg4OHz6Mp556Cj/72c90Tz6Vl5cjKioKOTk5AAAvLy+sWLECKSkpSEtLQ15eHh599FFMnDiRTz4RERmQnZ3d7S+MtsjLy6vTVX2yH0YXNbm5uYiPj0d8fDwAICUlBfHx8XjhhRd0bXbu3AlZlvHQQw91eYz3338fUVFRmDVrFubNm4cpU6bg7bff1m1vbW1FUVERGhoadOtee+01/OhHP8KSJUswbdo0+Pv74+OPPzY2PhHZIYVCgfb2dtExhNBoNPDy8hIdw2ImT56Mw4cPi45BgkiyncwGdus/tlqtZv8aIjtz4cIFNDY2YuTIkaKjWNz+/fsxf/580TEsat++fViwYIHoGGQixvz8tv2eY0Rk90JDQ3Hx4kXRMSzu3LlzGD58uOgYFhcdHY2TJ0+KjkECsKghIptnrx2Fz549ixEjRoiOYXERERE4f/686BgkAIsaIiKyOU5OTmhpaREdgyyMRQ0R2Q076UIIoKMfgoeHh+gYwkydOpWTXNohFjVEZBeCg4PtamTx7Oxsux7ywtXVFY2NjaJjkIWxqCEiuxAZGYkzZ86IjmExLS0tcHJyEh1DqMDAQJSXl4uOQRbEooaI7IKDg4PdjFWj1WrttnP07caMGYPjx4+LjkEWxKKGiMjGHD9+HGPGjBEdQ7hbk1zaU18qe8eihojIxhia7NfeJCYmIjs7W3QMshAWNURkN3x8fHDt2jXRMciCBg8ebPeztNsTFjVEZDdiYmJQWFgoOoZZVVZWIiAgQHSMfsXDwwMajUZ0DLIAFjVEZDecnZ3R1NQkOoZZffvttxg9erToGP3KlClTcOjQIdExyAJY1BAR2RBZlqFQ8KP9dvb05Ju94798IiIb0dzcDJVKJTpGv2Rv4xTZKxY1RGRX3NzcUFdXJzqGWWRlZSExMVF0jH5pxIgROHfunOgYZGYsaojIrsTExKCgoEB0DLOoq6uz6/me7sbBwQGtra2iY5AZsaghIrsyYMAAqNVq0TFMjgPM3d3UqVNx8OBB0THIjFjUEJHdscUCoKioCFFRUaJj9Gvu7u6or68XHYPMiEUNEZENKCkpQUREhOgY/Z6fnx+qqqpExyAzYVFDRHbH0dERLS0tomOQAOPGjcOxY8dExyAzYVFDRHYnOjoap06dEh3DZG7evAkvLy/RMazCrdnLbfEWJLGoISI7FBAQgMrKStExTCYnJ4ePchth3LhxyM3NFR2DzIBFDRHZHUmSdL+x24LW1lY4OjqKjmE1/P392a/GRrGoISKyYu3t7ZwWoRdseRBGe8b/CURklyRJglarFR2jz/Ly8jB27FjRMawOx6yxTSxqiMguRUREoKSkRHSMPrt69SoGDx4sOobVcXR0RFtbm+gYZGIsaojILoWHh+P8+fOiY5BAw4YN43xQNoZFDRHZJYVCYfWP9V6+fBlBQUGiY1itkSNHcuZuG8OihojISp04cQKxsbGiY1g1pVLJ21A2hEUNEdktWZat+mqNLMs29Wi6CFOmTMGhQ4dExyATYVFDRHYrMDDQagfha2xshLOzs+gYVs/T0xO1tbWiY5CJGF3UZGZmYsGCBQgMDIQkSdizZ0+nNqdPn8bChQvh5eUFNzc3jBs3DmVlZQCACxcu6Aa+unPZtWtXt+ddvnx5p/ZJSUnGxici0omOjsbJkydFx+iVrKwsTJgwQXQMm+Dj44OrV6+KjkEmYHRRU19fj7i4OGzcuLHL7SUlJZgyZQqioqKQnp6OEydO4Pnnn9f9RhEcHIzKykq95U9/+hPc3d2RnJxs8NxJSUl6+33wwQfGxici0rHmx3obGhrg5uYmOoZNmDBhArKzs0XHIBNwMHaH5ORkg8XHH/7wB8ybNw/r16/XrYuIiNB9rVQq4e/vr7fP7t278ZOf/ATu7u4Gz61SqTrtS0Rkb6y5H1B/JEmSrn8V+yhZN5P2qdFqtdi/fz9GjBiBuXPnwtfXF4mJiV3eorolLy8P+fn5WLFixV2Pn56eDl9fX0RGRuKJJ55ATU1Nt22bm5uh0Wj0FiIiW3Dy5Encc889omPYlDFjxuD48eOiY1AfmbSouXLlCurq6vDKK68gKSkJX375JR544AEsXrwYGRkZXe6zefNmjBw5EpMmTTJ47KSkJLz77rtITU3FX/7yF2RkZCA5ORnt7e1dtl+3bh28vLx0S3BwcJ/fHxHZnoEDB+LGjRuiYxjl4sWLGDp0qOgYNmXIkCGoqKgQHYP6yOjbT4bcmkdl0aJFWLNmDQBg9OjROHLkCDZt2oTp06frtW9sbMSOHTvw/PPP3/XYP/vZz3Rfx8TEIDY2FhEREUhPT8esWbM6tV+7di1SUlJ0rzUaDQsbIuokJiYGeXl5mDZtmugoJJiLiwsaGhrg6uoqOgr1kkmv1Pj4+MDBwQHR0dF660eOHKl7+ul2H374IRoaGvDzn//c6HOFh4fDx8cHxcXFXW5XqVTw9PTUW4iI7uTm5oaGhgbRMXrs+vXr8Pb2Fh3DJk2bNo2TXFo5kxY1Tk5OGDduHIqKivTWnz17FqGhoZ3ab968GQsXLuzVZGyXL19GTU0NAgICep2XiMjaHDt2DOPGjRMdwyY5OTmhpaVFdAzqA6OLmrq6OuTn5yM/Px8AUFpaivz8fN2VmOeeew7/+te/8M4776C4uBhvvvkm9u3bhyeffFLvOMXFxcjMzMRjjz3W5XmioqKwe/du3Tmfe+45ZGVl4cKFC0hNTcWiRYswbNgwzJ0719i3QERktdra2uDgYNKeA3SbsLAwTnRqxYwuanJzcxEfH4/4+HgAQEpKCuLj4/HCCy8AAB544AFs2rQJ69evR0xMDP7v//4PH330EaZMmaJ3nC1btiAoKAhz5szp8jxFRUVQq9UAOh4DP3HiBBYuXIgRI0ZgxYoVSEhIwMGDB6FSqYx9C0REepydndHY2Cg6xl21tbVBqVSKjmHTRo0aZbUDMhIgyXYy4IFGo4GXlxfUajX71xCRnpqaGpSUlGD8+PGioxh09OhRREZGsk+Nme3fvx9JSUksIPsJY35+c+4nIrJ7gwYNwvXr10XHuCt2EraMyZMn4/Dhw6JjUC+wqCEiAkfppR8MGDBA1/2BrAuLGiIiK3Dx4sUunyIl8/D29jY4aj31TyxqiIjQ8UBCf57csrCwkFMjWNDEiRNx9OhR0THISCxqiIjQMUjomTNnRMcwiJMtWo5CoeAtSSvEooaICEBQUBAuX74sOkaX6uvrOXS/AHFxcbox2cg6sKghIkL/vgqSnZ2NCRMmiI5hd0JCQnDp0iXRMcgILGqIiPq5xsZGuLi4iI5hl1QqFZqamkTHoB5iUUNEdJv+1o9CluV+fRXJ1k2bNg2ZmZmiY1APsaghIvre0KFDceHCBdEx9Jw4cQIxMTGiY9gtZ2dnNDc3i45BPcSihojoe8OHD8e5c+dEx9Bz+fJlBAcHi45h10JCQnDx4kXRMagHWNQQEX1PqVRCq9WKjkH9TFxcHE6cOCE6BvUAixoion7q6tWr8PHxER2D0PF0HAve/o9FDRFRP5WXl4exY8eKjkEAJk2ahCNHjoiOQXfBooaI6DaDBw9GdXW16BgAAK1WC6VSKToGoWMuqBs3boiOQXfBooaI6DajRo1CYWGh6BhobW2Fg4OD6Bh0Gy8vLxY2/RyLGiKi26hUKrS0tIiOgZycHIwfP150DLrN5MmTeQuqn2NRQ0TUD6nVagwYMEB0DLoNn47r/1jUEBHdgSP4Unf6y+1J6hqLGiKiO3h4eECj0Qg7f0lJCcLDw4Wdn7oXFhaG0tJS0TGoGyxqiIjuEBMTg4KCAmHnLyoqQmRkpLDzk2FOTk6cOqGfYlFDRHQHT09P1NbWCs3AW2D917Rp03Dw4EHRMagLLGqIiPqRuro6uLm5iY5BBri4uKCxsVF0DOoCixoion4kKysLEyZMEB2D7mLIkCG4fPmy6Bh0BxY1RERdENVvoqWlBSqVyuLnJePEx8fj22+/FR2D7sCihoioC/fccw9Onjxp0XNqtVr2pbESt/6eOG5N/8KihoioC35+frhy5YpFz5mfn4/Ro0db9JzUexMmTEB2drboGHQbFjVERN2QZdmi56uqqkJAQIBFz0m9N3jwYFy7dk10DLoNixoiIqJeEj1QI+ljUUNE1A2FQoH29naLnKu6uhq+vr4WOReZzpQpU7B48WJIktRpKS4uFh3P7rCoISLqxogRI3Du3DmLnOv48eMYM2aMRc5FpuPg4ABZq0XS+PGo/PvfUfnRR6i8fBmVlZUICwsTHc/uGF3UZGZmYsGCBQgMDIQkSdizZ0+nNqdPn8bChQvh5eUFNzc3jBs3DmVlZbrtM2bM6FTRrlq1yuB5ZVnGCy+8gICAALi4uGD27NkW+7AhIvs0dOhQXLhwwSLnkmUZCgV/z7Q6H3+MIUePQpWTA/8nn4T/kiXwnzAB/keOQKlUik5nd4z+H1RfX4+4uDhs3Lixy+0lJSWYMmUKoqKikJ6ejhMnTuD555+Hs7OzXrvHH38clZWVumX9+vUGz7t+/Xr87W9/w6ZNm5CdnQ03NzfMnTsXTU1Nxr4FIqIesdTj1S0tLXB0dLTIuciEPv4Y+PGPobjz51B5OfDjH3dsJ4tyMHaH5ORkJCcnd7v9D3/4A+bNm6dXpERERHRq5+rqCn9//x6dU5ZlvP766/jjH/+IRYsWAQDeffdd+Pn5Yc+ePfjZz35m5LsgIuo/srOzkZiYKDoGGaO9HXjmGeD7J+Q+BeB+a5ssIxnArmefBRYtAnjFxmJMeq1Tq9Vi//79GDFiBObOnQtfX18kJiZ2eYvq/fffh4+PD0aNGoW1a9eioaGh2+OWlpaiqqoKs2fP1q3z8vJCYmIijh492uU+zc3N0Gg0egsRUW+Y+9Hu2tpaeHp6mvUcZGIHDwK3TZMwE0D+bcvfAODSpY52ZDEmLWquXLmCuro6vPLKK0hKSsKXX36JBx54AIsXL0ZGRoau3cMPP4z33nsPaWlpWLt2Lf75z3/ikUce6fa4VVVVADoGw7qdn5+fbtud1q1bBy8vL90SHBxsgndIRPYmKCiIc/xQZ5WVei/dAAy7bQnoph2Zl9G3nwy5NVz0okWLsGbNGgDA6NGjceTIEWzatAnTp08HAKxcuVK3T0xMDAICAjBr1iyUlJR0eauqN9auXYuUlBTda41Gw8KGiIwWFRWFtLQ0s31+nD17FsOHDzfLscmMejpIIgdTtCiTXqnx8fGBg4MDoqOj9daPHDlS7+mnO926l9zdM/23+t5UV1frra+uru62X45KpYKnp6feQkRkLAcHB7OOVVNcXMyixhpNnQoEBQHddSaXJCA4uKMdWYxJixonJyeMGzcORUVFeuvPnj2L0NDQbvfLz88HgG6HBw8LC4O/vz9SU1N16zQaDbKzszFx4sS+ByciEsTSUzGQiSiVwF//2vW2W4XO66+zk7CFGX37qa6uTu+KSmlpKfLz8+Ht7Y2QkBA899xz+OlPf4pp06Zh5syZ+OKLL7Bv3z6kp6cD6Hjke8eOHZg3bx4GDRqEEydOYM2aNZg2bRpiY2N1x42KisK6devwwAMPQJIkPPvss/jv//5vDB8+HGFhYXj++ecRGBiI+++/v8/fBCIiEdRqNa8iW7PFi4EPP8S2Z57R6zSMoKCOgmbxYmHR7JZspLS0NBlAp2XZsmW6Nps3b5aHDRsmOzs7y3FxcfKePXt028rKyuRp06bJ3t7eskqlkocNGyY/99xzslqt1jsPAHnr1q2611qtVn7++edlPz8/WaVSybNmzZKLiop6nFutVssAOp2HiOhusrKy5GvXrpn8uAcOHJCbm5tNflyysLY2WU5Lk+UdOzr+bGsTncimGPPzW5Jl+7j2qdFo4OXlxd+MiMhoDQ0NyMnJwYwZM0x63P3792P+/PkmPSaRrTHm5zfH5CYiugtXV1c0Njaa9JharZbTIhCZGP9HEREJkJeXxwksiUyMRQ0RUQ+Yeh6oK1eudBpQlIj6hkUNEVEPuLq6or6+XnQMIjKARQ0RUQ/ExMSgoKDAJMcqLy9HYGCgSY5FRD9gUUNE1AMDBw7EzZs3TXKs7777DqNHjzbJsYjoByxqiIgsTJZlk/fRISIWNUREFtXU1ASVSiU6BpFNYlFDRNRDDg4OaG1t7dMxsrKyMGHCBBMlIqLbsaghIuqhkSNH4vTp0306Rn19Pdzd3U2UiIhux6KGiKiHAgMDUVFR0ev97WRWGiJhWNQQEfWQJEl9KkxOnz6NkSNHmjAREd2ORQ0RkYWUlpYiPDxcdAwim8WihojICJIkQavVio5BRF1gUUNEZISIiAicP3/e6P1u3LiBAQMGmD4QEemwqCEiMkJERARKSkqM3i8nJwfjx483QyIiuoVFDRGRERQKRa86C7e1tcHR0dEMiYjoFhY1RERm1t7eDqVSKToGkc1jUUNEZGbHjh3D2LFjRccgsnksaoiIjOTv74/Kysoet6+pqYGPj48ZExERwKKGiMho0dHROHXqlOgYRHQHFjVEREZycnLq8cSWZWVlCA4ONnMiIgJY1BARmVVBQQFiYmJExyCyCyxqiIjMTJIk0RGI7AKLGiKiXvD09IRarTbYpqGhAS4uLhZKREQsaoiIeiE2NhYnTpww2CY7OxsTJkywUCIiYlFDRNQL7u7uqKurM9imoaEBrq6uFkpERCxqiIh6yVBfmd5MpUBEfcOihojIDAoLC/nUE5GFsaghIuollUqFxsbGLreVlZUhJCTEwomI7BuLGiKiXoqJiUFhYaHoGET0PaOLmszMTCxYsACBgYGQJAl79uzp1Ob06dNYuHAhvLy84ObmhnHjxqGsrAwAcP36dTz99NOIjIyEi4sLQkJC8Ktf/equj0YuX74ckiTpLUlJScbGJyIyGR8fH9TU1HRaX1NTg0GDBglIRGTfjC5q6uvrERcXh40bN3a5vaSkBFOmTEFUVBTS09Nx4sQJPP/883B2dgYAVFRUoKKiAhs2bEBhYSG2bduGL774AitWrLjruZOSklBZWalbPvjgA2PjExGZ3bFjxzBu3DjRMYjsjoOxOyQnJyM5Obnb7X/4wx8wb948rF+/XrcuIiJC9/WoUaPw0Ucf6W17+eWX8cgjj6CtrQ0ODt1HUqlU8Pf3NzYyEZFFtbe3Q6lUio5BZHdM2qdGq9Vi//79GDFiBObOnQtfX18kJiZ2eYvqdmq1Gp6engYLGgBIT0+Hr68vIiMj8cQTT3R52feW5uZmaDQavYWIyNQUCgXa29t1r+/2yxkRmY9Ji5orV66grq4Or7zyCpKSkvDll1/igQcewOLFi5GRkdHlPteuXcNLL72ElStXGjx2UlIS3n33XaSmpuIvf/kLMjIykJycrPdhcrt169bBy8tLt3CWXCIyh8jISBQVFele5+TkYPz48QITEdkvSe7DCFGSJGH37t24//77AXT0lxkyZAgeeugh7NixQ9du4cKFcHNz69QHRqPR4L777oO3tzf27t0LR0fHHp/7/PnziIiIwNdff41Zs2Z12t7c3Izm5ma9cwUHB+uuChERmYIsyzhw4IDuwYXPPvsM8+bNE5yKyHZoNBp4eXn16Oe3Sa/U+Pj4wMHBAdHR0XrrR44cqXv66Zba2lokJSXBw8MDu3fvNqqgAYDw8HD4+PiguLi4y+0qlQqenp56CxGRqXEGbqL+w6Q3fp2cnDBu3Di9S7EAcPbsWYSGhupeazQazJ07FyqVCnv37tU9GWWMy5cvo6amBgEBAX3OTUTUW8uXL8f27ds7rT937hyGDRsmIBGR/TL6Sk1dXR3y8/ORn58PACgtLUV+fr7uSsxzzz2Hf/3rX3jnnXdQXFyMN998E/v27cOTTz4JoKOgmTNnDurr67F582ZoNBpUVVWhqqpKr39MVFQUdu/erTvnc889h6ysLFy4cAGpqalYtGgRhg0bhrlz5/b1e0BE1HsXL+I+R0dUAj8sAQEI+/ZbwcGI7JBspLS0NBlAp2XZsmW6Nps3b5aHDRsmOzs7y3FxcfKePXvuuj8AubS0VNcOgLx161ZZlmW5oaFBnjNnjjx48GDZ0dFRDg0NlR9//HG5qqqqx7nVarUMQFar1ca+ZSKirn30kbwMkBcBsnz7Ikkdy0cfiU5IZPWM+fndp47C1sSYjkZERHfV3g4MHYrlly/jJoA9d26XJCAoCCgtBThmDVGvCesoTERkNw4eBC5fBgB8CsD9tuVBoOOazaVLHe2IyCI4QhQRUW9UVuq+nAngrds2uXXTjojMi0UNEVFv3PbkpRuAbp9z4hOaRBbD209ERL0xdWpHn5nuSBIQHNzRjogsgkUNEVFvKJXAX//a9bZbA/K9/jo7CRNZEIsaIqLeWrwYmDEDuHMA0aAg4MMPO7YTkcWwTw0RUR9sS0vreLz74MGOTsEBAR23nHiFhsjiWNQQEfWVUtlxxYaIhOLtJyIiIrIJLGqIiIjIJrCoISIiIpvAooaIiIhsAosaIiIisgksaoiIiMgmsKghIiIim8CihoiIiGwCixoiIiKyCXYzorAsywAAjUYjOAkRERH11K2f27d+jhtiN0VNbW0tACA4OFhwEiIiIjJWbW0tvLy8DLaR5J6UPjZAq9WioqICHh4ekCTJpMfWaDQIDg7GpUuX4OnpadJjWwN7f/8Avwf2/v4Bfg/4/u37/QPm+x7Isoza2loEBgZCoTDca8ZurtQoFAoEBQWZ9Ryenp52+48Z4PsH+D2w9/cP8HvA92/f7x8wz/fgbldobmFHYSIiIrIJLGqIiIjIJrCoMQGVSoUXX3wRKpVKdBQh7P39A/we2Pv7B/g94Pu37/cP9I/vgd10FCYiIiLbxis1REREZBNY1BAREZFNYFFDRERENoFFDREREdkEFjUm9vLLL2PSpElwdXXFgAEDRMexiI0bN2Lo0KFwdnZGYmIicnJyREeymMzMTCxYsACBgYGQJAl79uwRHcmi1q1bh3HjxsHDwwO+vr64//77UVRUJDqWxbz11luIjY3VDTY2ceJEfP7556JjCfPKK69AkiQ8++yzoqNYzH/+539CkiS9JSoqSnQsiyovL8cjjzyCQYMGwcXFBTExMcjNzRWShUWNibW0tODBBx/EE088ITqKRfzrX/9CSkoKXnzxRRw/fhxxcXGYO3curly5IjqaRdTX1yMuLg4bN24UHUWIjIwMrF69GllZWfjqq6/Q2tqKOXPmoL6+XnQ0iwgKCsIrr7yCvLw85Obm4t5778WiRYtw8uRJ0dEs7tixY/jHP/6B2NhY0VEs7p577kFlZaVuOXTokOhIFnPjxg1MnjwZjo6O+Pzzz3Hq1Cn8z//8DwYOHCgmkExmsXXrVtnLy0t0DLMbP368vHr1at3r9vZ2OTAwUF63bp3AVGIAkHfv3i06hlBXrlyRAcgZGRmiowgzcOBA+f/+7/9Ex7Co2tpaefjw4fJXX30lT58+XX7mmWdER7KYF198UY6LixMdQ5jf/e538pQpU0TH0OGVGuq1lpYW5OXlYfbs2bp1CoUCs2fPxtGjRwUmI1HUajUAwNvbW3ASy2tvb8fOnTtRX1+PiRMnio5jUatXr8b8+fP1Pgvsyblz5xAYGIjw8HAsXboUZWVloiNZzN69ezF27Fg8+OCD8PX1RXx8PN555x1heVjUUK9du3YN7e3t8PPz01vv5+eHqqoqQalIFK1Wi2effRaTJ0/GqFGjRMexmIKCAri7u0OlUmHVqlXYvXs3oqOjRceymJ07d+L48eNYt26d6ChCJCYmYtu2bfjiiy/w1ltvobS0FFOnTkVtba3oaBZx/vx5vPXWWxg+fDgOHDiAJ554Ar/61a+wfft2IXnsZpbuvvh//+//4S9/+YvBNqdPn7a7zmFEt1u9ejUKCwvtqj8BAERGRiI/Px9qtRoffvghli1bhoyMDLsobC5duoRnnnkGX331FZydnUXHESI5OVn3dWxsLBITExEaGop///vfWLFihcBklqHVajF27Fj8+c9/BgDEx8ejsLAQmzZtwrJlyyyeh0VND/z617/G8uXLDbYJDw+3TJh+xMfHB0qlEtXV1Xrrq6ur4e/vLygVifDUU0/h008/RWZmJoKCgkTHsSgnJycMGzYMAJCQkIBjx47hr3/9K/7xj38ITmZ+eXl5uHLlCsaMGaNb197ejszMTLz55ptobm6GUqkUmNDyBgwYgBEjRqC4uFh0FIsICAjoVMCPHDkSH330kZA8LGp6YPDgwRg8eLDoGP2Ok5MTEhISkJqaivvvvx9AR9WempqKp556Smw4sghZlvH0009j9+7dSE9PR1hYmOhIwmm1WjQ3N4uOYRGzZs1CQUGB3rpHH30UUVFR+N3vfmd3BQ0A1NXVoaSkBP/xH/8hOopFTJ48udMwDmfPnkVoaKiQPCxqTKysrAzXr19HWVkZ2tvbkZ+fDwAYNmwY3N3dxYYzg5SUFCxbtgxjx47F+PHj8frrr6O+vh6PPvqo6GgWUVdXp/cbWWlpKfLz8+Ht7Y2QkBCBySxj9erV2LFjBz755BN4eHjo+lJ5eXnBxcVFcDrzW7t2LZKTkxESEoLa2lrs2LED6enpOHDggOhoFuHh4dGp/5SbmxsGDRpkN/2qfvOb32DBggUIDQ1FRUUFXnzxRSiVSjz00EOio1nEmjVrMGnSJPz5z3/GT37yE+Tk5ODtt9/G22+/LSaQ6MevbM2yZctkAJ2WtLQ00dHM5o033pBDQkJkJycnefz48XJWVpboSBaTlpbW5d/3smXLREeziK7eOwB569atoqNZxC9+8Qs5NDRUdnJykgcPHizPmjVL/vLLL0XHEsreHun+6U9/KgcEBMhOTk7ykCFD5J/+9KdycXGx6FgWtW/fPnnUqFGySqWSo6Ki5LfffltYFkmWZdnypRQRERGRafGRbiIiIrIJLGqIiIjIJrCoISIiIpvAooaIiIhsAosaIiIisgksaoiIiMgmsKghIiIim8CihoiIiGwCixoiIiKyCSxqiIiIyCawqCEiIiKbwKKGiIiIbML/B8UDX6lnKA87AAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -112,7 +112,7 @@
     "\n",
     "# Set the capacities of the terminal edges.\n",
     "for i in range(len(d)):\n",
-    "    g.add_tedge(nodes[i], (d[i]-mu[1])**2, (d[i]-mu[0])**2)\n",
+    "    g.add_tedge(nodes[i], (d[i] - mu[1]) ** 2, (d[i] - mu[0]) ** 2)\n",
     "\n",
     "# Run the max flow algorithm.\n",
     "flow = g.maxflow()\n",
@@ -120,16 +120,15 @@
     "# Get the result as integer labels.\n",
     "labeling = [g.get_segment(n) for n in nodes]\n",
     "\n",
-    "# Get the results as either 'M or 'F.\n",
-    "labeling_str = ['MF'[l] for l in labeling]\n",
-    "\n",
     "# Visualize\n",
     "fig, ax = plt.subplots()\n",
     "ax.plot([-1, len(d)], [0.5*(mu[0] + mu[1])]*2, '--k')\n",
     "ax.plot(d, '-k', lw=0.2)\n",
     "for i, di in enumerate(d):\n",
-    "    ax.plot(i, di, 'o'+'br'[labeling[i]])\n",
-    "    ax.text(i, di, labeling_str[i], ha='center', va='center')\n",
+    "    color = 'b' if labeling[i] == 0 else 'r'\n",
+    "    letter = 'M' if labeling[i] == 0 else 'F'\n",
+    "    ax.plot(i, di, 'o', color=color, alpha=0.5, markersize=15)\n",
+    "    ax.text(i, di, letter, ha='center', va='center')\n",
     "plt.show()"
    ]
   },