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    "# Linear Regression Notes\n",
    "\n",
    "**These notes rely on a variety of sources:**\n",
    "- [Chapters 1-2 of Shalizi's book](https://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf)\n",
    "- [Atri's slides from 440](http://www-student.cse.buffalo.edu/~atri/algo-and-society/support/notes/models/index.html)\n",
    "- [Varun's prior notes from this class](https://mlcourse-ub.readthedocs.io/en/latest/docs.html)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "concerned-dress",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from scipy.stats import norm\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.simplefilter(action = \"ignore\", category = FutureWarning)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "local-filing",
   "metadata": {},
   "source": [
    "# A Motivating Example"
   ]
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   "execution_count": 3,
   "id": "accepted-freeze",
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "np.random.seed(seed=25)\n",
    "data = pd.DataFrame([(x, (-.5*(x**2)+12*x+30)/40) \n",
    "                     for x in range(1,24)], \n",
    "                    columns= ['x','y'])\n",
    "data['y'] = data['y'] #+ norm(0,.1).rvs(len(data))\n",
    "missing_data = [False] * len(data)\n",
    "\n",
    "missing_data[2] = True\n",
    "missing_data[15] = True\n",
    "data = data.assign(missing = missing_data)\n",
    "fig = px.scatter(data, x=\"x\",y='y', \n",
    "           labels = {\"x\" : \"Hour of the day\", \n",
    "                     \"y\" : \"Ounces of coffee Kenny<br>consumed\"},\n",
    "           color=\"missing\",\n",
    "          height=600, width=800)\n",
    "fig.update_traces(marker={'size': 15})\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "destroyed-shelter",
   "metadata": {},
   "source": [
    "## Necessary thought exercise #1\n",
    "\n",
    "- What is your best guess for the ounces of coffee I consumed at $x=3$ (3am)? \n",
    "- What about for $x=16$ (4pm)? \n",
    "- **How would you figure that out?**\n",
    "\n",
    "### Why is this exercise necessary?\n",
    "In Machine Learning, we ask the question of \"how would you figure this out\" via a **three step process**:\n",
    "\n",
    "1. We define a **model class** (or synonymously, a **hypotheses class**). We will pick one of those to be our predictive model.\n",
    "2. We define a **loss function**. This defines the \"best\" model, i.e. the one we're going to pick.\n",
    "3. We define an **optimization algorithm**. This is *how we actually find the best model*.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incoming-liver",
   "metadata": {},
   "source": [
    "### Digging deeper... Model Class\n",
    "\n",
    "A **model class**, or **hypothesis class**, sometimes represented as $\\mathcal{H}$, is the set of all possible functions that we could possibly learn, and select for our final model.\n",
    "\n",
    "Some desireable properties of a model class:\n",
    "- **Parsimonious representation**: We would like our model class to have its representation size not depend on the size of the dataset. In addition, models with small representation size can be considered to be the \"right\" model... we'll talk about this later on in class.\n",
    "- **Efficient training**: We have to actually be able to find the best model.\n",
    "- **Efficient prediction**: We don't want to have to wait forever for our model to make predictions!\n",
    "- **Model expressivity**: In other words, for \"real life\" data, how accurately can this model class predict the correct labels?\n",
    "\n",
    "### Digging deeper... Loss function\n",
    "\n",
    "From [Weinberger's ML course](https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote01_MLsetup.html):\n",
    "\n",
    "> We try to find a function $h$ within the hypothesis class that makes the fewest mistakes within our training data... How can we find the best function? For this we need some way to evaluate what it means for one function to be better than another. This is where the loss function (aka risk function) comes in. A loss function evaluates a hypothesis $h \\in \\mathcal{H}$ on our training data and tells us how bad it is. The higher the loss, the worse it is - a loss of zero means it makes perfect predictions. \n",
    "\n",
    "Let's call our loss function (for a given hypothesis) $\\mathcal{L}(h)$.\n",
    "\n",
    "### Digging deeper ... Optimization Algorithm\n",
    "\n",
    "Our goal now is just to find $h^* \\in \\mathcal{H}$ such that:\n",
    "\n",
    "$$h^* = \\mathop{\\mathrm{argmin}}_{h \\in \\mathcal{H}} \\mathcal{L}(h)$$\n",
    "\n",
    "# Motivating Linear Regression\n",
    "\n",
    "One way to motivate/understand (ordinary least squares) linear regression is that it defines a specific model class, with a specific loss function, and has a closed-form approach to optimization. However, I'm going to take a slightly different motivating approach first, and then circle back to this.\n"
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    "## Necessary thought exercise #2\n",
    "\n",
    "OK, now... **what if you could only make 1 guess for both of them?**\n"
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    "### The \"Predict Only One\" Best Answer\n",
    "\n",
    "**This section largely follows [Shalizi, Chapter 1](https://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf)**\n",
    "\n",
    "As we saw above, to say one model is \"better\" than another, we need some notion of \"better\". For reasons that will become clear as we move through this course, a typical notion of \"better\" is the **mean squared error (MSE)**. For a true value $y$ and a predicted value $\\hat{y}$, the squared error of the prediction is: $(y-\\hat{y})^2$. The MSE is the mean of this over all $y$ values that we consider (e.g. all $y$ values in our test set).\n",
    "\n",
    "**Exercises**: \n",
    "- Why do we square this?\n",
    "- What other things could you think to do instead?\n",
    "\n",
    "Now, let's assume we're trying to predict the value of some random variable $Y$, and our goal is to make the best single prediction for it. Let's use MSE as our error metric.  Because $Y$ is a random variable, we cannot specify an exact value for this MSE. Instead, we have to think about the *expectation* of that MSE. Let's keep $\\hat{y}$ as our prediction. Then our goal is to find the best possible value of $\\hat{y}$; let's call (that best possible) value $h^*$ to remind us of the connection to the text above.  Put formally, we want to solve the following optimization problem:\n",
    "\n",
    "$$h^* = \\mathop{\\mathrm{argmin}}_{\\hat{y} \\in \\mathbb{R}} MSE(\\hat{y}) =  \\mathop{\\mathrm{argmin}}_{\\hat{y} \\in \\mathbb{R}} \\mathbb{E}\\left[(Y -\\hat{y} )^2\\right]$$\n",
    "\n",
    "With some fancy manipulations (see Shalizi), we get that\n",
    "\n",
    "$$MSE(\\hat{y}) = (\\mathbb{E}[Y] - \\hat{y})^2 + \\mathrm{Var}[Y]$$\n",
    "\n",
    "Now, we want to find the optimal value for $\\hat{y}$. Notice that the second term doesn't depend on our prediction, so we don't need to worry about it during optimization.  So now, we want to find:\n",
    "\n",
    "$$ h^* = \\mathop{\\mathrm{argmin}}_{\\hat{y} \\in \\mathbb{R}} \\, (\\mathbb{E}[Y] - \\hat{y})^2$$\n",
    "\n",
    "The solution to this is $h^* = \\mathbb{E}[Y]$; that is, the best prediction to make is the expected value of the random variable!\n"
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    "### \"Predict Only One\" When you only have a sample\n",
    "\n",
    "The above tells us what to do when we know $\\mathbb{E}[Y]$. Sadly, to know that, we would need all of the data from the population. What to do? Well, our friend the *law of large numbers* tells us that if we have iid samples $y_1,...y_n$ from the population, then our sample mean---let's call that $\\hat{\\mu}$, converges to $\\mathbb{E}[Y]$! So even those we don't *know* $\\mathbb{E}[Y]$, we have something that converges to it as $n$ increases.\n",
    "\n",
    "So, our \"predict only one\" *optimal answer* is the sample mean. Let's see what that looks like with our data from above!"
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f7075XPfdeZ0uaHraYZ1zuTLqO2yFtcDlylhnLnqaNOpx+0KmH35Zrm3/7tSj991s5ZBKlSxhP4Z7ErhouHtaRO3PW7xUDY6rowSPJ9cWvl34q46oXkX3DxihGzoPUP8h47Qp2xfyJXpcCkXyuDN2GvMQivqoIzTbJaYcEyyTWE/W69G8kBPDOM4Eqw+JCW6RMIjWfcB8HtmfRezHvI7ZBxzvA+ZcgM8DPgfy+hzI2Dfi4LisoOOxAtbL+hfOYzratvZRc1xLCsn5fkHnldbuXqT/T7QCU4/3uiVHG+ZOPRPwyr7wt7njdOsNl9mLzjnjJJnntWpUsZ/3vrutfv7idX0+aagWfDTcvnPvonMb2evMr21+PfVlmfTl5Bf14ZuDdGeHVvY6M5kwvJ+e7NPJzKpM6ZJ2vaaMveC/iWnrlusv/e+ZZG4bHdr/Hn3y9jN20C7zBwuO+K8/WRnDNBPXwbQZs+fLpN6d2+bJb6KtJira4rzG6tT+cvue4E49n1HmF+KVhf3MrwAAEABJREFUL5OkUKTSJTN+BSPB4w5JfaHoE3WEZttGjGMpazwxnsqWTpLLejWXD+M4y1iv5QqlE0XCIFr3gVLJCUpK9LAP8zpmH8hjHwjktZ2U4FGpEgkYFsIwEOdozVPGep8N57FKtLRtHdIpWvoas/0M0bluxJwPRfB4zP4eDSnB41HNapVUrkypw7rrcrlUuWI5mSvVDlvpYMGDA0dp4Etv6c33Z2noqEm6/4kRurJFE1WpVN5BbaEvErfBtHmLlqrvoNF63IrOmohrfrQd2lxi3xp66QVn6bl+d9s/B/v3mo12kW27DioUafe+NLu+1HRfSOoLRZ+oIzTbNmIcd1vjifG03Rqf+T7KbdZjuNLOvWnaar0vkA7iEFn7QcDbY8/+NKWkegPOz77Ovs4+cPg+cDDNq93Wawmbw20wOaid1nF/uI5Toqldc3IUTf2Nyb5axzIRcy4T430x+zspp0C9Okfol9//tm8Z/WXZ37r39ms14IGMq9ty5gzPs7gMppmfcb3rgSF66sHb1faqC/OVN7eArlm/OSuPz+ez51PT0u1HJgggUJQC1I0AAggggAACCCCAAAIIIBBvAnfd1Frm9tRZE57TuBf7qvPNrWW+wz5SHOIumDZ91jz16j9cfbvdqLMaNdD6TdvsZH621WwU8+sUN3cfZGbtZO7jfeO9T+w8u/bs01uTP7N/kODYo2vZ63OdsBABBBBAAAEEEEAAAQQQQAABBGJfgBHGpUDcBdN+/n2lvaEHv/quWra7PyvNmrvIXr5120798dcae95Mbmpzic5ufKKdr2nre/T19z9r2KD7IioiavpJQgABBBBAAAEEEEAAAQQCFSAfAggggIBzgbgLpj3Ws6P9yxG/zR2X4/Gay8+3FR/o2k6LZo60580kKSlRQx7rYv9axecTn9cXk4bqlBPrmVUkBBBAAAEEEEAAgeIVoDUEEEAAAQQQQCDsAnEXTHMqbn6tomb1ynK5zG8FOq2FcggggAAC8SnAqONJ4N/tLs2c7dbY8R4NHpJgJzNvlpl18WTBWBFAAAEEEEAgOgXS0qTFS/ya/olXw19P16g3vfpotk+//u6X3/zqW3QOK2S9JpgWMkoqQiAGBRgSAggggEDAAunp0pyv3Bo2wqMF37m16h+X9u+Xncy8WWbWzbXymLwBV0xGBBBAAAEEEECgGAU2bpaeeC5NI8ema8Ysn378xa9FP/o07WOvXhqVrhdGpGv3nmLsUAQ2FZPBtAh0pksIIIAAAgggEOMCU6Z7ZAfKvHkPNN1aZwJuJm/euViDAAIIIIAAAoEKkC90AuaKszlf+zTg2TSZgFpeNf++3K/+z6TpJyvIlleeolq+as1Gff/TsqKqPuB6CaYFTEVGBBBAAAEEEAingNcnLf/TpbnfuDXpA4/em+TWnLlu/f6HW+G+0uvXpS4t/S3wr4IweZdZ/Q6nZ1qqbDtjaCwnTbGCgZatMTbW4ewbbSMQBwIMEQEEEIg4ga3/SpNneAM6rjJXpk2alq6D1vGE04FcdH1PnXTBrTnSDZ0H5FvdN9//olHjP8w3T3GsJJhWHMq0gQACCCCAAAKFEliz1qXhIz165z2P5nzptgNXJog292u3HVR7dYRHK1cFHswqVGdyKbzg++APqb6ZF77+moDZS8MzApLG0FgutQKCxtYYG+sNG3IZqFiGAAIIIIAAArEoYK5Ke/3tdKUGERwzwbdJU72F4rjvzuv00fins9KLT3QrVH3FVTj4I7/i6hntIIAAAgggECoB6olaAfOdY9M+9Oi1sR5t3ZZ38Gn7DpfefMuj9z/waE8xf4eH+YLeTZvz7lte+KaMKZvX+qJYvmuXS29PcNtByd278+6zsR71eoI+/tStlINF0RPqRAABBBBAAIFIEti0RdYfJoP/ZYGvF/gKdXVa9aoVVfeomlnJ/PDjW5NnK/OqtfP/113Dx02T30T7DgFLOZiqgS+9JZPnzMvv1h33PydzG6jJtmHTNnV/5CVlLp81d5FZHLJEMC1klFSEQOgFqBEBBBCId4EDKS79+rsrYIalVt4dVmAt4AIhyLhuvSug2yEObcrcmmrKHrq8KJ/v2i2t+Cuwwz9zzPrjErf27Qvcvyj7Tt0IIIAAAgggUHQCa9f7HFVujhc2bw0+CJfZ2Nff/SITPMtMO3ftVfWqlfTofR01bexTGnD/bRpmBdNMvswymY9vf/CZPv1yoV4ddJ/GvvigTj2xnrZt36W0dK8dWCtbppTGv/yQ2lzeTL36D9N6K8CWWbawj4EdTQXXCrkRQAABBBBAAIFCC5iDsynT3TLf7RVoZZllgrlFIdC688q3arXzYFNhyubVn7yWG5Mp09zWX3bzynH4cmM/1doGxvXwtSxBAAEEEEBAEMSIwJp1fscj2bTZedmV/6zXdz/+npX2H0hRy+ZnqE6tavpjxRr9s26TKlUoaz8e2sGUlFSVKpms5BJJOqn+0ereqY3OPO0E/fDLcq1et1ltrmhmF6l7VA01PL6uvlrws/08FBOCaaFQpA4EEEAAAQQQCLnAps3S2rXBB6rMLZ8r/y6+Q5wja/kdj70wZYNt1JgYm2DLme+rM9si2HLkRyCyBegdAggggEB2gVIlgz/myixfmLK3tbtcwwbdl5WOqFFFg199V1fd+ohmf7VI23fuUWJigny5/DrSta2aq5aVv83t/XTWFV00dNQk7T9wUOYWT9O3F8dM1sCX3raTqeNACL+7oviONM1ISAgggAACCCCAQIACO3c6P0zZsy/ARkKQrUZ158G0oMsWor87djovvHev8wNs561SEgEEEEAAAQSKS6De0c4/64+s5bzsoeP7d8du+7bPN154UK8M7KH7775B9Y+pfWg2+3nNapVk8n0+8Xk9et/NmjBtjj798ntVrljevmJt3Et99farj2Sl29tfYZcLxcT5UWooWqcOBBBAAIG4EGCQCDgRKFzwx0mLzsqULSudcHzw3zNiypiyzloNvtSuXcGXySyxd2/mHI8IIIAAAgggEIsCdeu45HIFP7Jy1nFQ+XLBl8urhLmCzKwzV5ft259i35r5wy8rzKLD0jtTPtOPv/6pqlUq6IKmp6lsmZIqmVxCpzU81s777LD3tP9Aip3MLZ6ff/ODvTwUE4JpoVCkjlgVYFwIIIAAAmEUqFrF+RVfZcoUb8evvMyvpKTA20xKlK68wvn4Am/p/3NWrvL/88HOFbdnsP0jPwIIIIAAAggUTsAcx7RoFnyI6KrLPYVr+JDS5cqUUq/ObfXoM6/rrCvu1vMjJ9rfmeZyZUT6XK6MR1PsYGqabu4+SKe2uF0Xt+2tc04/SZc0P0Ply5bW6Od669uFv9i/5ml+0fP+J0bIZf1nyuWRglocvFRQ1ZMZAQQQQAABBBBwJlCYWyBrFuLWSye9LV/er4suCPzqtIsu9FkHesUbTCuMSWG2hRNPyiCAAAIIBCpAPgRCJ3D91R7Vr+cKuMLmTd264FznYaU577+gq1qee1h75nbMhZ+M1OeThurDNwdp1oTndOsNl9n5br6upX1rp3nSqd0V+vmL12XqWfDRMA3se4cSPBnBvUYNj9Mnbz+j+TOG6aspL2nhJyPU4vzGplhIkvNRh6R5KkEAAQQQQACBcAscSJE+n+PWm2979MRglx583KfXxnn00Uy3Nm8JX+/MLZBOrogyf7CsXszBNKPUtIlP7dv6VDLZPMs9JVvrOrTzyeTNPUfRLa1RzS9jE2wLZhuYbRFsuVDlN/ug2RdfG+vRoGcS9NxQj8a/47H32f0HQtUK9RS7AA0igAACCEScgIlD9eicoCZn5B8qMscTrS5168brMgJXRTGQ0qWSZb4TraC6TfCsetWKWUG0Q/Obq9SqVCpvHQMFHiQ8tI7cnucvlFsJliGAAAIIIIBAzAj8vcqlYSM8+vpbt1b+7dLuPdIBK0CxZo1LCxe5Ner1BC38IXyHC+c1Dfxqr8yNcu45PpnbKDOfh/KxoLoanOBT185enWMF1uoe7VepUrKTmTfL7uni1fH1gx9TQe0Gsj4xSWpyVvBtX9A8+DKB9CeQPN8vtPbB16x90NoX16x1yfwI1569Lv210mXvs8NHebTa2lcDqYs8CCCAAAIIIFCwQIkS0h03e3T7TR7VPsIlT7bDwAQrdlb3KOsPrz0S9L8rPDLBN8XpP3ecjpthI4AAAvEkwFgROEzA65VmznZr3FseK4CW91/q0tOkjz52a8JEt8JxFZC5guuUhv7D+p/XgqPr+HVJi/AFf0y/zC2fl7f06baOXvW9P91OZt4sK1828LGYukKdWl7skzEKtN7TTvXrrNOL39P84ME7E9z6+FO30tPz7u3u3S698aZHc75yy1f83cy7Y6xBAAEEEEAgygXOOdOt/g8maPiQRD3eJ0FPPpyoEc8n6pHeCTq2bt7HjlE+7IC7TzAtYCoyFr8ALSKAAAIIFJXA/v3SkiWBHwYsW+7WunXhOXC65mqvzNVmBVk0tIJuN7Xzytx6UFDeeF1v/oJsjE49ueCgnrmS7upW3rBQbdzk0vIVge2ffmso5gq2vfvCs3+GBYhGEUAAAQQQKCYBc+xwZC2XalZXER9jKar+BXaUElVDorMIIIAAAgggUJDA1A89Mt+VVlC+7OunzwjP1WnmIO7SS3zq2MGr2kf4ZW4xyOyXufWgunVwd30br9paKalE5hoe8xIwRtde41Wb/3ll7IxhZl5ja4xvvdkrcyWdsc9cV1yP5grIaR8Gd4hqbk02+2dx9ZF2EEAAgSwBZhBAIC4FgjtSiUsiBo0AAggggEBsCezYmfGdU8GOynxX1fLl4bv659h6ft11h1f9Hk7Xvfd41eWudD36ULru6Zyukxv6gx1O3Oc/7RS/bWcMjWX3rhm2xviYuuHzNPuY2deC3UAr/nLJ7NvBlovX/IwbAQQQQAABBJwLEExzbkdJBBBAAAEEolJg507n3d6xK3zBNEl2x81tnFUq+1WzhuL6i29tjBBMzNVnxrJqFX9E3L5RmH1s584QgFAFAggggAACCEipqUr77kulTHpN+4Y8on0vPq6UKW8q/ccFkvmOhTg3IpgW5zsAw0cAgeIQoA0EIktg507n/dm50+W8MCURCECgMPvYzp0BNEAWBBBAAAEEEMhXwLfuH+1+8DbtG9pPKZPHKW3hV0qb/4VS3hujvYMf0N6BveTfuT3fOmJ9JcG0WN/ChRkfZRFAAAEEYlKgShXnw6pd2++8MCURCECgMPtY7doBNEAWBBBAAAEEEDhcwCzx+3Xw0w+sQFon+davNktyTem/LNLuB26xgmxf57o+HhYSTIuHrcwYEUAAAQQQyCZQo5rf8e18Nav7s9XELAKhF3C6jyUkSub239D3iBoRQCCSBegbAgiETsC3eYMOvDNCSkstsFL/rh068NarUsqBAvMemmHP3v066YJb80zvTv3i0CIR95xgWsRtEjqEAAIIIIBA0bA7cfUAABAASURBVAokJklNzvIF3Yj5Xq1aRxBMCxqOAkEJmH3siJrB72dnNvY5DhIH1cHQZKYWBBBAAAEEIkvA59O+V5+UDqYE3C8TfNs/3gqoBVwiI2PpUiX10fins5JZ+kiPm7OeX3lxE7MoohPBtIjePHQOAQQQQACBohG46EKfypULPGBhvvT/f63T5ebIoWg2CLVmCZh9rPWVwQXGypX1q8UFvqw6mEEAAQQQQACB4AR8G9fK++fS4ApZuVO/+FDBXp3mdrtU96iaWalUyWRVr1Ix6/mIN6fLXJ02Yvx03dx9kD754nv1HzJOM+d8b7WY8f+X83/SgwNHZTyxpot/Xq4bOg/QmZffrYefHqNf/1hlLS26/zkkLjpbakYAgUgRoB8IIHCYQIkk6crLAg+mmSvZatY8rBoWIFAkAubqtDMaBR4cu/Jyv5JKFElXqBQBBBBAAIG4EPD+s8LZOP1+eTetc1Y2j1Kr123WwJfe0oq/1+viZqerRrVKWrl6g3bs2ptVYqc1/+fKtfbzNeu36JYeT+vSC8/Uu8Mf1RHVq6hHv5flt/pmZyiCCcG0IkANVZXUgwACCCCAQFEKNDjBp9tv9eZ7hVpCggm6+XT5pYEHNoqyz9QdPwKtW/nU6kqfzHeh5TVqc0XaHbd5ZfblvPKwHAEEEEAAgWgQCHcf050G06yO5/djBdZqR//f2aGVhvbvqluuv1SNTz4u3zo++my+6tSurnNOP0np6V41P+dUbd66Q8v/C7blW9jhSoJpDuEohgACCCCAQCwI1DnKr66dvWp2nk/H1vOrXFmpZLJ01JF+nXWmT53vSNfZDr5fLRZsGEP4Bc463doHb0+398WjrH012do3y5bxq94xfnufvaeL195Xw99TeoBA3AowcAQQiBEBV+kyjkdSmLJ5NVq6lPWhn9fKQ5av2bBFW//dpYEvvW2nZ4ZNUKOGx+nfHbsPyRm6pwTTQmdJTQgggAACCESlQKmS0sUX+dSxg1eP9fXrmSfcMlf7tLrcp+rVonJIdDqGBMw+aPbFO2716uE+6Xqgl1e33OS191kT+HU2VEohgAACCCCAQHaBhONOyv40qHnP0flfORZUZXlkTkzwKDU1Lde1VStVUJPGDfT2q4/kSOee2TDX/KFYSDAtFIrUgQACCCCAQB4Cf//j0rfz3ZoyzaN3Jrj1+Ry3flnqUsrBPArkt5h1CCAQsQIHUmS/ts1rfNQbPk2cnPHaN+8BEdtpOoYAAggggMB/AgnHniiZX5xScP9c5SvKVaFycIUc5D7jtBM0d8ES7dqzT7//+Y+mfPJNVi0Xnnua5sz7STNmz1e612tfkfbe9Dn6a9X6rDyhniGYFmpR6kMAgcMEWIBAPAqYE2sTPBs33qPZn7u15BeXlq9w6+tv3Zo8xaORoz1avcYVjzSMGYGYE1iz1qVhIzz2a9u8xn9dJv2wRPZr37wHTJjo1v4DMTdsBoQAAgggEEsCJZJV4vLrgh5RyetvD7pMIAVcrpzHyZdfeJZ27d6rpq3v0S09Bqt0qRLyeDx2VY1Prq8n+3TSEy+M16ktbleza+7V+PdnKSkpwV5fFBOCaXmrsgYBBBBAAAFHApkn1iZ4llcF23e49MabHs35yi0f3+2fFxPLEYhoAfPa/WKuW6+P82j3npwH/dk7vmy5W8NHEUDPbsI8AgggEGECdMcSKHnTPUpocKo1F9j/SRdfraSW/wsscz65Fs0cqRbnN87KMWJwT91x45VZz81M3aNqauobT+mL94dqwUfDNPKZ3po8ZoBZZac2VzTTwk9G6KspL2n+jGH65O1ndFSt6va6opgQTCsKVepEAAEEEIhbAb9fmvW5O98T60wck/ebb93avCVzCY8IIBBNAlu2St/Oc8u8lgvq9+7dLn32RWB5C6qL9Qj8vwBzCCCAQAgFEhJU5qHnlHR+y/wrdbuVfO2tKtWpZ/75imBtjaqVlPDfFWmHVu9yuVSlUnmVL1v60FUhf+4OeY1UiAACCCCAQBwLfGOdWK9dm/cVKofSeL3SlOkJSrceD13HcwRiViAGBmZes1OmJci8hgMdjrlq1bxHBJqffAgggAACCBS7QHIpler+mEp16ydPnWOl7IGrhER5jm2gsgOGKfmGOyQr+Fbs/YuQBt0R0g+6gQACCCCAQMQLBNLB7xYG/9G6ebO0apUrkOrJgwACESJgXrObrNdusN1x8h4RbBvkRwABBBBAoLACSc0uVdnnxqnCW1+o7LPjVO6Fd1ThbWt+0Bh5jj+5sNVHffngj/ijfsgMAIG4E2DACCBQTAJ79kh79zprbOMmgmnO5CiFQHgEnL5mzXuEea8IT69pFQEEEEAAgSAFEhLkOfpYuWvVkdyEkDL1Ilgis4s8IoAAAgggEB0Cmza7HHd027/OyzpulIIIIOBYoDCv98KUddxhCiKAAAIRLUDnEIguAYJp0bW96C0CCCCAQAQLVKjgvHNlSvudF6YkAggUu0CF8s5fsxUqFHt3abCoBKgXAQQQQCAuBQimxeVmZ9AIIIAAAkUhUKWyX4mJzmquWcP5ibmzFikVzwKMvfACTl+zCdZ7hHmvKHwPqAEBBBBAAAEEwiVAMC1c8rSLAAIIIBCsQMTnd7mk6tWcBcVq1Ij44dFBBBDIJuD0NVvDeo8w7xXZqmIWAQQQQAABBKJMgGBalG0wuhuNAvQZAQTiSaD5+b6gh3tKQ7+qVnEWhAu6MQoggEBIBMxr1rx2g6nMBNGcvEcE0wZ5EUAAAQQQQKDoBfIOphV922Frwefza9v2Xdq1Z1/Y+kDDCCCAAAKxKXB8fb/Oaxp4QM3c7nV1K29sYjAqBGJcwLx2zWs40GE2b+aTeY8IND/5EEAAgWIToCEEEAhKIO6CaQsW/6azr+yi5m16qGnre3TrfYO1dPmqgNBeGP2+TrrgVu3euz+g/GRCAAEEEIhPgUta+HTpJT55CviUPf44nzrd4lViUnw6MWoEol3AvHbNa7jeMf58h2LeC8z7woXNAg+051shK7MEmEEAAQQQQCAcAgUc5oejS0Xbpsvt0mM9O2r+jGGa8/4LKl0qWcPGTiuw0akzv9Fr735cYD4yIIAAAgggYG7lOvccn+7slK6j6/hzBMvMusqV/LrMCrZ1aO9TmTJ4xaEAQ44hAfMa7tjBq8sv9cm8ts1rPHN4Jthm3gPuuj1d55/rU/Z1mXl4RAABBBBAAIHoE4i7YFqTxieqdcumKl+2tKpXrajLLjhLX3/3s9K9XuX1b9GSPzTo5Xc05LEueWVhOQIIIBAHAgwxWIEjjpB95dmjD6brvu5e3X1Huvo9lK4e3bxqagXbgq2P/AggEJkCJkh2ztk++7VtXuN9erj0YC+/zGvfXLlWs2Zk9pteIYAAAggggIAzgbgLph3KNG/xUjU4ro4SPJ5DV9nPV6/brK4PvagXn+im4+rWtpcxiTIBuosAAgiEWcCcaFeq6JcJriUkhLkzNI8AAkUqYF7jR9V2qXIlcSWa+IcAAggggEAxCxRTc3EdTJsxe75M6t25ba7cu3bv010PDFHPu67XuWc2zDVPzUolFYpUqWwJu/4Sie6Q1BeKPlFHaLYtjsXnWL1iSbldCutrqFK5pLC2z/5WfPtbrFqXL5OkUiU87Mch+nyP1f2EceX/XpOc5FHFsnwesJ/kvp9wrJC7y6H7izk5OnRZLD9nbIHtF7HqZPZ3UnQJxG0wbd6ipeo7aLQe73WLzjnjpFy32nc//qZ1G7dq7YYtenbYBL02IeM7014cM1nLVqy2y2zacUChSNv3HLTrS03zhaS+UPSJOkKzbXEsPsctOw/I51dYX0Pbd6eGtX32t+Lb32LVevfeVO0/6GU/DtHne6zuJ4wr//ealFSvdu6Ji88D3iscvFdwrJD/6yfz/cWcHGXO8xiYGU7R62T2d1J0CcRlMG3W3IX2FWdPPXi72l51YZ5b7Nija6nHHdeqYvkyqmClcmVK2XkrlCutpMQEe95vnbiHItmVWROrOoWiPuoQjtbOFI/7gfUyCuu2D3f78bjNGXOI3+/MTmyl2HYNsVmcvt+yj+S9H1kvIVm7RVg/j9g+eW+fcNvY+4e1g4S7H5HePk6Ruw9H+r4Tjf0z+zspugTiLpg2fdY89eo/XH273aizGjXQ+k3b7LT/QIq95cZN+lQ3dx9kz9ezgml33dRamalt6wvt5bfecLnMOvsJEwQQQKC4BGgHAQQQQAABBBBAAAEEEEAg7AJxF0z7+feVNvrgV99Vy3b3Z6VZcxfZy7du26k//lpjzzMJjQC1IIAAAggggAACCCCAAAIIIIBA7AvEywjjLpj2WM+O+m3uuMPSNZefb2/zB7q206KZI+35QyfH1q1ll8u83fPQ9TxHAAEEEEAAAQQQQAABBBCIOgE6jAACCAQlEHfBtKB0yIwAAgggELEC/6x2ad58t6ZM9+jtCW7N/tytJb+4tHdvxHaZjiGAAAIhFgi+OtfuHfJ8/5kSp4xRiWGPKmn8c0r87H25V/wafGWUQAABBBBAIE4FCKbF6YZn2AgggEC0Cvy73aWx4z16402PZpkA2s8u/bnCrW9NYG2aRy8PS9DCRW77i7+jdYwx328GiAACxS/g8ylh7nQl979NJcY9awXQJsmz9HslLJhtBdZGK3loL5V48QG5t28p/r7RIgIIIIAAAlEmQDAtyjYY3UUAgfAJ0HJ4BdLTpTlfuTVshEer/nHl2ZmUg9JHM916bZxHW7bmmY0VCCCAQNwIuFf/qeRnuilp4qtyHdiX57g9y5eoxBO324E2eb155mMFAggggAAC8S5AMC329wBGiAACCMSEgM8n/fKLS+kBnt+tXevSP2v4mIuJjc8gEECgUAImmOZesyKgOlwHU5TwzUdypaUGlJ9MCCCAAAIRJUBnikmAs4xigqYZBBBAAIHCCcya7db2HXlfkZZb7bM+c8vcFprbOpYhgAAC8SDg2rJBiVNGBTVU19aNSvwguDJBNUBmBA4TYAECCCAQXQIE06Jre9FbBBBAIC4FvD7phyXBf2SZCyt+/jW4AFxcAjNoBBBwJhAFpRIWfi5ztVmwXU2Y/6m41TNYNfIjgAACCMSLQPBnJvEiwzgRQAABBCJGYPdul8xtnk46tHMnwbRD3XiOAALxI+D6d7Ozwfq8cu3giyed4VEKAQQQQCDWBQimxfoWZnwIxI4AI4ljgZ07nQ9+717nZSmJAAIIRLuAe/smx0Nw7d7huCwFEUAAAQQQiGUBgmlFvnVpAAEEEECgsAJVKvsdV1GxvPOyjhulIAIIIBAhAr5qRzruib9yNcdlKYgAAgjEpwCjjhcBgmnxsqUZJwIIIBDFAmXLSmXKOBtA9ZrOylEKAQQQiAUB35HHOhqGv1xF+ctXdlSWQlEoQJcRQAABBIISIJgWFBeZEUAAAQTCJVCjurMrzGo6LBeucdIuAggELkDOggV8tesVnCmXHE7L5VKNVPHAAAAQAElEQVQVixBAAAEEEIg5AYJpMbdJGRACCCAQmwLNzvPJFeRvCRx3nF+1azkLwhWhIlUjgAACxSbgO/p4+Y47Obj2PB6lXdo+uDLkRgABBBBAII4ECKbF0cZmqAgUToDSCIRX4Og6frW40BdwJ8qX9+v6a7xBB+ACboCMCCCAQDQIuN06eEc/+SoF/v1nqdfcJV/9U6JhdPQRAQQQQACBsAi4w9JqcTZKWwgggAACMSNgrk5rdaVPCYn5D+nII/26o5NPycn552MtAgggEA8C5vvPUnu/IO+xDfMfbmKSUm/opvQWbfLPx1oEEEAgUgXoFwLFJEAwrZigaQYBBBBAIDQCZ53u0923p6veMX4lJf1/neYW0EoV/WpxgU+33+JV+bLc3vn/OswhgEC8C5gr0w72HKLUqzvJX+0IZb9s11+ilLwNTlfKQ8OVfsHV4l/xC9AiAggggEB0CRBMi67tRW8RQAABBCyBatWkW27y6tG+6ep1r1ed7/DqEWv+vu5eNW/mk5tPN0uJ/xEocgEaiDYBt0fpl7XXgQFvav+LM5TSd5gOPPW2Drw4XQfvHSxfzTrRNiL6iwACCCCAQFgEON0ICzuNIoAAAgiESqBCBb9qHeFXUgG3fv5/e8whgAACCCiphHx16stfuToYCCCAAAIIIBCkAMG0IMHIjkDYBGgYAQQQQAABBBBAAAEEEEAAAQTCLlDkwbSwj5AOIIAAAggggAACCCCAAAIIIIBAkQvQAALxIkAwLV62NONEAAEEEEAAAQQQQACB3ARYhgACCCCAQFACBNOC4iIzAggggAACCCAQKQL0AwEEEEAAAQQQQCAcAgTTwqFOmwgggEA8CzB2BBBAAAEEEEAAAQQQQCCKBQimRfHGo+vFK0BrCCCAAAIIIIAAAggggAACCCAQ+wIFjZBgWkFCrEcAAQQQQAABBBBAAAEEEEAg8gXoIQIIFJMAwbRigqYZBBBAAAEEEEAAAQQQyE2AZQgggAACCESXAMG06Npe9BYBBBBAAAEEIkWAfiCAAAIIIIAAAgjEpQDBtLjc7AwaAQTiWYCxI4AAAggggAACCCCAAAIIOBcgmObcjpLFK0BrCCCAAAIIIIAAAggggAACCCAQ+wIRP0KCaRG/ieggAggggAACCCCAAAIIIIBA5AvQQwQQiBcBgmnxsqUZJwIIIBCgQLpX2rhJWvKLS/MWuLX8T5d27nQFWJpsCCCAAAJRJxChHd5hffaYzyDzWWQ+k8xnk/mMitDu0i0EEEAAgTgSIJgWRxuboSKAAAIFCSz/061nn0/QiNEJmjLNo1mfufXOex4Nfdmj18Z5tGsXQbWCDFlffAK0hAACsSmwa49LY8Z69IL12WM+g8xnkflMMp9Nz1mfUcv+4BQmNrc8o0IAAQSiR4BPoujZVvQUAQRiQyAiR5F6UPrwIxM4cyslJfcurlnj0vBRHnESk7sPSxFAAAEECi/w61KXho3waO3a3P94c8D6jJowya0PP3YrLa3w7VEDAggggAACTgQIpjlRi8syDBoBBGJZYO43bi3+seCPBHMSM+kDt/7dnvtJTiwbMTYEEEAAgaIV2LRZmjLdk+cfdbK3vvgHt76ZX/DnVvYyzCOAAAIIBCpAvoIE+AQqSIj1CCCAQIwLrLH++m++jybQYXq90tTpbvn9gZYgHwIIIIAAAvkLmM+UKdMSZD5j8s/5/2u/+dZtf8fn/y9hLu4FAEAAAQSKSYBgWjFB0wwCCCAQqQLmijRzEhNM/0wA7p/VXJ0WjBl5EUAAgbwEWC6ZzxRzZVowFibwtuB7TzBFyIsAAggggEBIBAimhYSRShBAAIHoFfjnH2d9X7uOYJozuZgpxUAQQACBkAk4/UxZuy5kXaAiBBBAAAEEAhYgmBYwFRkRQCA2BBhFdoE9e6Sdu5wFxdatz14T8wgggAACCDgXcPqZsn27S3v3Om+XkggggAACCDgRIJjmRC0cZWgTAQQQiDCB9HRnQbgIGwbdQQABBBCIAAGnnynmawpMioAh0AUEEEAgdALUFPECBNMifhPRQQQQQKDoBMqWlcqUcVZ/jer8AoEzOUohgAACCBwq4PQzxXyGmc+yQ+vjeXgEaBUBBBCIFwGCafGypRknAgggkIeA0xOYmjUIpuVBymIEEIguAXobAQJOP1OcfoZFwJDpAgIIIIBAFAsQTIvijUfXEUAAgVAING3ikyvIOzarVZVOqE8wLRT+zuugJAIIIBA7AsdbnynVqwU3HvPZdV5TX3CFyI0AAggggEAIBOIymObz+bVt+y7t2rMvIMJ0r1cbt2zXwdS0gPKTCQEE8hFgVcQJHFvPr3PPCfxkxOOR2l6brsSkiBsKHUIAAQQQiFKBJOsz5dpr0mU+YwIdgvnsOqYuf9gJ1It8CCCAAAKhE4i7YNqCxb/p7Cu7qHmbHmra+h7det9gLV2+Kk/RMe98pFNb3K6L2/ZS45Z3qlf/Ydq1O7AgXJ6VsgIBBBCIMIGLLvQp0CsCLmnhU7Ugrx6IsOHSHQQQQACBCBSoUV26sLkvoJ7VPsIv89kVUGYyIYAAAkEKkB2BggTiLpjmcrv0WM+Omj9jmOa8/4JKl0rWsLHT8nSqUL6MXh/aR4s/Ha2pbzylRUv+0NSZ3+SZnxUIIIBANAokeKR77k5X+7Y+lUzOfQRHHulX755emdtCc8/BUgQQQAABBAon0Ow8n3r38Oqoo3K/4izZ+ozq0M6nu+7wynx2Fa61mCvNgBBAAAEEikkg7oJpTRqfqNYtm6p82dKqXrWiLrvgLH393c8yt3Iql3/Xt7pApkzJ5CTVP6a2LmjayM6fS1YWIYAAAlEv0OAEnx7ona4ud6Wrzf+8uvQSnzq086rnvV7deZvXeu/0R/0YGQACCESaAP1BIKdA+fJ+3XGrV72szx7zGWQ+i8xnkvls6mN9Rh1fP7Cr13LWyjMEEEAAAQRCJxB3wbRD6eYtXqoGx9Wx/rLlOXTVYc/T0r2at+hXnXR83cPWsQABBBCIFQHzl/6aNaTTTvHb36V2fH2/KlYgiHbY9mUBAggggECRClSwPnvMZ5D5bjTzmWQ+m8xnVJE2SuUIIIAAAggEIBDXwbQZs+fLpN6d2wZAJT314njt2XtAN1/XMit/xTJJCkUqUzLRrjPR4wpJfaHoE3WEZttGmmMs96eC9Xp0Wa+kYhtjWWsfOSSVK5WoStYyUhIOUboflE5OUIlEN9svSrcf7z2R8d6TlOBWmZIJvI54HeW6D5hjhYqWDenw46jsJtYhnbI/L3DeOg4stmNA2hLW1v4bwv3A7O+k6BKIlmBayFXnLVqqvoNG6/Fet+icM04qsP7h46Zp8kdf6Y0XHlS1KhWy8h9ITVco0sE0r12n1+8PSX2h6BN1hGbb4liMjgfTZa6fKjbzFGtsh6SDqV7tP0jCIHr3gdR0n9K9fvZjXsfsA4XYB9J9fh1M82FYCMNY/hwxx/0HDjl+4Pnhx1Tm5CgolxCdlxXbcST9ze+8N+7Wmf2dFF0CcRlMmzV3oe56YIieevB2tb3qwny3mM86GHpu+HsaO/FTvT+6v04+IectnimpPoUipVknL6YjPp9CUl8o+kQdodm2OBafozlxMa+jYjO3TpRSDkkHrddyihVQI3mt9zJSNO4H5vPIa332RWPf6TOvuUjZB8zxo3ktRUp/6EdxvTYCa8ccrxx6/MBz63jxkGMq+5jukGX5OoXovKzYjiPpr3WsaG13HGwHs7+Toksg7oJp02fNU6/+w9W32406q1EDrd+0zU77D6TYW27cpE91c/dB9ryZPPbcGzLLhva/R+XLlbHzmjJ5/WCBKUNCAAEEEEAAAQSiRoCOIoAAAggggAACCAQlEHfBtJ9/X2kDDX71XbVsd39WmjV3kb1867ad+uOvNfa8mSxa8od50N0PPp+V15Rbv3GbvZwJAggggEB4BGgVAQQQQAABBBBAAAEEEAiHQNwF0x7r2VG/zR13WLrm8vNt/we6ttOimSPteTOZNeG5w/Ka8nVqVzerSQgEK0B+BBBAAAEEEEAAAQQQQAABBBCIYoEAg2lRPEK6jgACCCCAAAIIIIAAAggggEAhBMx3IW7ZtkMHDhwsRC15F/3ltxVavXZD3hmKdU1kNJZyMFVpaelZnUlL92rxkt/12VffWdsh42uaslZG4MxX83/Ujp27I7BndCkUAgTTQqFIHQgggAACCCCAAAIIIBBeAVpHoAgETDDnzfc+0qXXd1WHzg/rqpvuU/eHntU/a0Ib+Jo4/TPNX/RLSEcwcdpsfTX/h5DWWZyV9X3iZb3+zjS7SfOd5R27PqqRYyfr2wU/afeeffbySJ489fwYrduwJZK7SN8KIUAwrRB4FEUAAQQQQAABBAorQHkEEEAAgcgVGDdhhj6Y8YUGPnKPPprwst4ZNUjVqlTUvQ89F/EBnWV//q016zZFLm4BPevd9Sa1ad3CzvXbspXaty9Fo4Y+qgF9u6h6tcr2ciYIhEuAYFq45GkXAQQQiG4Beo8AAggggAACCMS0wM5dezRp+mx1v+sGndW4oUokJdqBtAe63aISJRI1feZce/wff/aNxoyfas+bibkdtFufwdq3/4B5al/JtmDxL3pp9AR7fvW6jfbyQyd/rVqrB594WZdc20X3PTJEGzZtzcpibgM1dV7VoaeefeVN/fHX6qx102d+pVu7PS6zrssDT+u7Rb/YV6T9+PNyffjpVzLlhgx7Kyt/9pm9+/br+eFv22VN+YeefEWjx0+xsyxfuVo9H33ens+cPDLwVf36+1/205SUVI0Y+76u79RHd/R4QtM++VJmmVlp+t7v6eF2vWb90y+8odTUNJlbN1997T27jGnPjHft+twDfh9/9q1+XPK7Nm/5V4NfHqsDKSnq8dCztqNpI3sa9vokq/2M7eH1+nT/Yy/op6XL7SzrN26xDTL7lp+lXSDbxGyrBx5/0d4mZoxfL/gpa60Zu7F6ZFDGOM0YzbizMvw3s2Tpn/b2zH6L8HeLf7W3tbl9+L9sPESZAMG0KNtgdLewApRHAAEEEEAAAQQQQAABBAoWWLdhs53p7NNPth8zJ8klknTu2adp5T9r7UXbd+zW+k0Zec2CtLQ0mUCU1+czT/XHn6v02NMjlODxqPk5jVUyOdlefujEBHnOs+p9tPcdMoG8CVNm2VnWb9yq3o8NVbNzG+ulp+9X9aqVNeDZkfL7/frtj5UywanbbrzKXnflJedp87YdOvH4ejrm6Fo6s9FJurNjG/3v8uZ2XYdOhr/xvn62gk733tVOzzx+r9xulxXEy7g1cf+BFC1d9leOIn+uXKN9VgDOLBxpBdLMFWMP9eyke+68wQ5mfft9RrDplTHvyeeXXhjYW4/2ukMuq950r9fK86UV6PvRvrrsuSfuU4P6dWX8TH2HpvVWEOxfy7Z8ubJq3vR01axexR6LGeOheStWKKtvv8to29j//NufmvP1qzjNDgAAEABJREFUIjvb73/+rbT0dCUnJ2l9PpZ25myTg1bwz9xqWqpkCT3/RC+rD4315JDR+uvvNXaudVZdH836Rmee1kADHrrbqnuLPrECgPbKbJMTjjtaf/+zXt/8Z2NWmasdj6t7pIy3eU4qKoGiq5dgWtHZUjMCCCCAAAIIIIAAAggggECUCvy7Y5fd83JlStuP2ScVy5fT5q07si/Kd37gI910z+1tdd1VF9tXt+WWuU2ri9T60mZqbgWOOnX4n75b/Iudbc7X36tWzWpqfPIJ8qZ7dfbpDbXt351WMG+dDh5MtfOYAN2RtWqoVcvzdbUVOKtauYIqlC9jB6BObVhfxx5zlA79Z64S+2zud7qp7ZW6uPnZdmDrhOPqHpot1+fmKq+PP/9Wl1x4jsqVLqWypUrqDCuolBlMO5ByUCUSE2QCYab9vj1uU6mSyVZ/06xgYgmZq/zqW326tV1rmfW5NvLfQhMEO8oam9kOJu+xVhDqv1VZD6eeVF8mgGaufjNX5jU542R9Ne8HK4jm1bLlq3T6aSfaefOztDNkm/z6+wrb+Z472umUk47TzTe0Up3aNfXFf0E6k7X9tZfqf1dcqEYNj9eVlv2in34zi3MkE3y96rJm+vCTjCvn1qzfpCVWAPPyi8/LkY8n0SXgjq7u0lsEEEAAAQQQQAABBBCIRAH6hECsCVSuWMEeUm5fdr/DCrRVr1rRXh/IpHSp3K9Gy6ts7SOq2Venbdu+Sxs2b7Ov3nr19UkyaeS4yTrphHr2+pNPPE6trADcIwNf1eVtu+nJ51/TRit/XvVmX77dGoN53uC4o81DUGnrv9vt/J/OmW/3yfTrr1Xr5PF47OW3tr9Ky1asUvs7+6pj1376+LNv7OWXX3yufWXd3b0H6uqbeum1t6Yq++2PdiYHk/r16tilVv6zTnPnLZZpv4IVTPx12Qr76r1TLCeTIT9Lsz572vbvDisgWTZH8POkBvW05b+xZ89r5ktZ29gEEc38ocmM21wxZ65qm/nZPJlgX62aVQ/NxvMoEiCYFkUbi64igAACCCCAQMgFqBABBBBAAIFcBY6sVc1evmDRz/Zj5sRclTXPWnbcMRkBHLfbrbT0jFs6M/MU9nH1mo12FWXLlFKlCuXV6OT6enHg/TnSGaedqMTEBPW4q70+GDfE/pGEdes3afzEj+yy9sTvtx9ym9SoVkXmijZz62Nu6xPcntwW28vKly9rP/a6+8YcferX+057+WkN6+vtEQM15oV+anlhE7048l2ttfpmfrzhuQH36Z2RA9Xtzhv04adf66v5i+0yhZkYh7Man6QZs762q6l3dG21aHa2Zs9ZoL9Xr9eJJxxjL8/P0s6QbWLGaG63zR5MXb12oyr+N/ZsWQucNVcWmu31wUdzrMDit2p9afMCy5AhsgUIpkX29qF3CCCAQB4CLEYAAQQQQAABBBAoSgFzi2Lbq1tq2Ovvy9w6aL5Da8vW7Rr88hv27YqtL2tmN2+uEvv1txUyQalNW/7V+x9+bi8PdvLv9p1KS0vXP2s26MNZX9lXL5nbIc8582TNX/SLPv/qe5nvHduxc7cdNDL5Fv64VHO+WaSkxESd1vB4HX3kEUpOLmE3Xb9eHf3+5yqZfpugkL0w28Ttdun8cxpp5LjJmjVnvswPCJgfLMjMcmzd2vasGfuu3Xtkfuggsx5zy6Vpb/jYyTI/uJCW7rW/J27yjC/sMmPGT7WvkKtzZE01+e8758wtqaYN8z1slSqVt8dXunSykktk9NcuWIhJo5NPkLlt9aJmZ9m1NLXcvvh6oY63HEx/zcL8LM367OnE+nXtYOOkabO1Z99+zV/4s32V2xmnnZQ9W8DzrS9tZm9Dc8XcGVYgNOCCZIxIAYJpEblZYrhTDA0BBBBAAAEEEEAAAQQQiBKBW9u31rWtW6jf4BFq1f5edbj7EW3btlMvP/2AMgM0Jph2asPjdGu3x3Rzl0e1Z+8+R6Ob/eX3uqJdd93Z80mZ7zPrfkc7u56GDY5V76436eXR79m3cra9/UGZL7A3V2MlJiTIXPXVukMPXWn1b7fVdrv/tbTLnd+kkcytnKbf/Z8dZS87dHJL+9aqZwXNzI8dmB9AOOmEY1QiMcnOVrJksjq2vdIe+3W39dHiJf99H5jLZa/vc++tKlWqpDp0flhX3NDN/sXMzLGv27BJt3V/XC2v62qXv/2m/9nf25aalmb/Qqi5JfWmzo+q8SkNdJ4V0LMrPGTittqx/reXulwZbdpP8picetJx9ppzzzzFfjTfE1elcgU1PrWB/dxM8rM067MnE0x9uGcnmavn2nTsrcefGakO119hBwEz87lc2fuVfT4jR/bVZzZuaC8037FmApn2k1ifxPD4CKbF8MZlaAgggAACCCCAAAIIIIAAAsEJZM9tAla3tGulWe8P19sjntL0t1/Qq8/21dFHHZGVLTHBoycf6qpJrz+jGe+8JHOr42cfjMgKtpn5k06ol5U/t5mBD3fVh++8oMljn9U7owZpxHMPqVrVSllZL2txrtX2UE18bbA+eHOIxr06wP5RgkannJC1/MO3X9TT/bqrerXKdjnzgwSjhj5q9+u5AT3tZYdOKlUoZ/fX1Pdo7zt18GCaateqnpXt5htaWfW/IHMbqRmjGYv5vi+TwfzIgen3jHdf0rujn9bMSa/qtvZXmVUa0LeLzPIJYwZrgrWu3TWX2svNlX4mn8k/ZfwQPdCtoxL++541O0O2ianjxmsvt5eY7xwz7vaTPCbH1asj0z/zaLK4XC677U4drjZPs1JellkZss2YsU4d/7zesra92bbmBxMyV5uxZ47LLGvetLHGD3/SzNrJ9MUE7+wn1mTpH39ZU+niC862H5lEtwDBtOjefvQeAQQQQAABBBBAAAEjQEIAgSIUMFcSmSCV+UXKvJqpaAWmkpMzrurKK09By83VUOZ7xXLL53K5VKli+awgXWYel8tlLy9ZMvfbJU2/TMAvM3/2x8kffq7OvZ7Sk8+PUZf7B+qvVWt1xcXnZs9i/wpnubKlcyzL/iS5RJJMYO3QoJhZXqVS+exZ7XmTL7f89spimrhcGWaZVxfm16zH41YNK0CZXMhtO2XGF/YvfgbSZn79YV1kCBBMi4ztQC8QQAABBBCIUwGGjQACCCCAAALhErio2Vm6/upLdMqJ9XVT2yvtK94qV6oQru7EbLter0/nnX2a2v93hV7MDjSOBkYwLY42NkNFAIEQCjioyuuVNm2Wflnq0rfz3fpjuVvbd7iUz48sOWiFIggggAACCCBQXALmM9x8li+zPtPNZ7v5jDef9eYzv7j6QDsIFEagWuWKurj52br68uYy37FWulTJwlRH2TwEzNVtl17UVObqxjyysDjKBAimRdkGK2x3KY8AAuER+HuVS0NeTNDwUQmaPMWj2Z+79e5Et158xaNRr3n07/bDv7A0PD2lVQQQQAABBBAIRMAE0UaOSbA/yydYn+nms918xpvP+udfStBfK/lsD8SRPAggUHQC1Fx0AgTTis6WmhFAAAGlpUkzZ7s17i2P9u3LHWTDRpdGjPJoyS8cdOcuxFIEEEAAAQQiS+CnJS4NH+nRxk2592vvXmn8Ox7N+swtrlLL3SifpaxCoECBNes3acnSPwvMRwYEikqAYFpRyVIvAgggYAks+N6tBd8V/FabagXdps/I+6Dcqor/EUAAAQQiWoDOxYuAuY1zmvWZbT67CxrzvAVufb+o4OOAguphPQJ5CUycNltfzf8hr9Uxu3zh4qWaMOXTmB0fA4t8Ad7ZI38b0UMEEIhSAXOw/eVXgb/Nmr9cT5meoHRvlA6YbkenAL1GAAEEEAhYwHxGT5mWENT3nX4x183XOQQsTMZgBZb9+bfWrMvjEslgKyM/AggELBD4Wd4hVX7z/a/atn3XIUt5igACCBSPQDS08tOS4G/t2LxZWrHC8VtzNLDQRwQQQAABBKJWwHxGmz+WBTOAtFRp4SK+yiEYs3jMu/DHpbqjxxO65Nou6vno8+rWZ7DWrs8IkqWkpGrE2Pd1fac+dp5pn3wps8xckfbjz8v14adf2fmHDHsrV7rde/fpuVfH66oOPdX+rof01qSPrT/eZvz19vOvvtfAF17Xy6Mn2OtN2z8tXZ5Vj2knt7azMmSbKaiuF0e8o7nfLs4qsWDxLxr80lj7+T9rNqjLA09r0vTZdh/NWGfNma853yyyx2yev/vBTDtv5mTnzt1Z4+rYtZ8WL/k9c5V++W2FbWLG/Owrb+qPv1Znrev+0LMybb9kjdnMr1630V73/PC3Nez1ifY8EwQKEnB8xjbpwzlq3qaHHhn8mpYuX1VQO6zPEGCKAAJxJPD3P87eYteuiyMkhooAAggggEAUCTj9jF67jmBaFG3mYu+q+f6vRwYO08knHaeXn+6jqy5truUrV+vgQSsSa/VmpBVI+23ZSj3Us5PuufMGTftkrr79/iedeHw9HXN0LZ3Z6CTd2bGN/nd5cyv34f8PGzNRy1f8o4fvu0233NBK70//XB/N+sbOuHPnHjvAlZxcQv37dtZRtWto9JtT7HVmklfbZt2hqaC6VlvBwV17/v9LhHfv3qtVq9fb1aRYY/3r7zX6489/1LvrzbrqsuYywcH3P/xcHdu1Vtfb2mrsux9mBRhNob+tslWrVNTDlsuRtWvq+eEZwcT1G7eq92ND1ezcxnrp6ftVvWplDXh2pPzm53etgn/8uUqPPT1CCR6Pmp/TWCWTk62lsutet2GLPR+iCdXEsICzMz0L5Ik+nfRAl3Za8MNvuqHzALXv+qQ++eJ7paWlW2v5HwEEEIhvAfPDA1scfhavW++KbzxGjwACCCCAQIQKOP2M3rTFpUC+Yy0yhk0vilvgq3k/qEL5supxV3s1qF9X5zU5LasL5sqwjz//VpdceI7KlS6lsqVK6ozTGtjBtKqVK1jlyqhm9So6tWF9HXvMUTr034EDKZrz7SJ1uO4KNTnzFF3W4lxdfnFTzbTqzMzb+NQGussKxjU++QRd17qFTFBrtxX0yq/tzLKHPuZV16H58nre7/47rfGdaPdD1r+enW9Us3Ma6cLzz9AxdWrp9+V/W0sz/jdt3WoF2pqccbJ6d+mgbf/u1PqNWzTn6+9Vq2Y1mfF40706+/SG9rqV//z/X6wHPtJN99zeVtdddbGqWQE5U+NzA3rqiYe6mlkSAgUKuAvMkUeGitaL/dYbLtPs94Zo+NM9Vb5sKT3w5AhddH1PjRz/obZaO3IeRVmMAAIIxL6A3/kQ0/mbhHM8SiKAQPwKMHIEikHA6We0l8/2Ytg60dvE5q3bdboV0MptBFv/3W4v/nTOfL36+iQ7/bVqnTwej728oMm27TvtLMfV+/9AW/16dbRx87/28kMnJUuWtBelpKZa5/Tb7XmnbWevy64oiElSUpKd25/tb8wlSyYr5WCavfzQSaWK5e0rzP5atVYbNm/T9h27bStjNrrKmyQAABAASURBVHLcZJ10Qj3t3LUnq1jpUhlXo2UtsGYSEzwyyZrlfwQKFHAcTMusOePSyFN17+3XqlHD47R95x698sYUXXDtfeo7aLR++X1lZlYeEUAAgbgRSLQ+/ytVchZRq1ndWbm4wY2xgTIcBBBAAIHoEXD6GW2OCZISo2ec9LR4BU447mitzuNHBMqXL2t3ptfdN+rFgfdnpX6977SX25P/bl+05w+ZlC+XUX7d+ozvXzOrza2MlSqWM7P5poDazreGnCtN7CAtwEs0XS5XzsIFPNvy7w4dSEmRGW+lCuXV6OT6WVaZbmecdmIBtbAagcAFChVMSzmYqhmz59u3eV5/V38tX7lWd3e8StPGPqWH7+2ghT8t032Pvxp4b8iJAALRIkA/AxCoWSOATLlkqV3bn8tSFiGAAAIIIIBAuAWq13TWA6fHBM5ao1S0CTQ6+QT71sphr0/UN9/9pMEvj8saQrkypXVaw+M1fOxkbdm2Q2npXuu8e7Umz/jCzmOuMvv9z1U6aAWpsl95Za+0JuXKlravypox+2tt2bpdK1au1hdff69zzzrNWpv//wW1nX/pw9eectJx+u6HX2V+EMH049M5Cw7PFMQScyvqnn37tX3HLr33waeqYAUej693lM4582TNX/SLzA8ipHu92rFzt2bM+lrmRw7yq978SMOrr72XXxbWIZAl4M6aC3LmvelzdP7/7rWvPnN73Hq23936dvor6t6pjY6rW1sd2lxi3wI66KFsEfMg2wh9dmpEAAEEik/gnCY+eQK7Aj+rUxUq+HXCCf6s58wggAACCCCAQOQINDzRp4oVg/ucNscCTa1jgsgZBT2JNIFaNauqR+cbtfSPvzVxyizVPSojaptUIsnuap97b1WpUiXVofPDuuKGbvavVO7Zm/FF/uc3aWQHk1q1v1f9nx1l5z900rvrTdq8Zbs63P2IuvYZrDpH1lT7ay/NyOaS3NmuAnMpY/92yWWvz69tO0P2iVUkv7ouOPd0mX5fe8v96v3YiypVsoTcnv8OlrP1IXuV2eddLpes/zMWuaT1G7aoTcfeuuGOvlbgbKEe6XWHzK2gDRscKzPml0e/p8vbdlPb2x/UB1bwMTExIaNsHlPzfWsbN2/LYy2LEcgp4DiYtvCnP3TZhWdp0qj+mjC8n65s0UQlDrl22VzG2aQxl1LmJOcZAgjEi8CRtfy6sLkv4OGag4O21/pU8vCvcAi4DjIigAACCCCAQNEJWPEMXXeN7/9P6ANoyhwL1LaOCQLImpGFaVwKXNaiqUY895BefbavTmt4gm1QrXIl+9H80MDAh7tqxrsv6d3RT2vmpFd1W/ur7HVH1qqhUUMf1aTXn5H5An174SETk+e1lx7ThDGD9cGbQ/TkQ11VpnQpO5f5Av6n+3W3582kcqUK+uyDETJtmufmMa+2zfrsqaC6TD9MX98ZPUhTxg+R+REAM2ZTxwnH1rHbdbmsKJm1wO122c+Pr1fHepbx/wtP9dZVlzW3n5i2PnznBX349ot6c9gTdn2nNaxvrzOTy1qcq+lvD9XE1zLGPO7VATI/SmDWmfGZ71Az89mTuR3U9Cn7MuYRyEvAcTDtuX5368k+nXTS8UfnVTfLEUAAgbgXOP9cn46pm/EXvoIwmjfziYPtgpRYjwACkSpAvxCIF4Eja/tlPrMDGa85BjDHAoHkJU98C7TpeL8eevIVPT54hHo+OkS3tmut5OSkHCjJJZLsIJe5aCXHCutJxQrlCvzy/CqVysvcumllD/r//NoOtrJqlSsqtzEEW4/JX7JkCR1Ro2qu9blcLlWqWN7xmE39JATyEnAcTPN43Nq8dYemfPK1Xhwz+bBkvk8tr0ZZjgACCMSLgPUZrltv9qpjB6/KlMl91OZ7VO7r7tVFQVzFlntNLHUgQBEEEEAAAQSCFjCf2T26eXVEzdz/YFa6tOzPf3MMYI4Fgm6AAnEn8NgDd6rpWaeqyRmnaNgzfdXh+ivizoABIxBNAo6DabPmLtRF1/dUv2ff0Acff6UPZ8/LkdLS0qPJgb4iEGUCdDfaBI6t51fvHunq2jld17XxquXFPrW/wScTRLv7znTrr2a5H4xH2zjpLwIIIIAAAvEiULmSX53v8Nqf5Tdan+nms918xpvP+vvvSw/4yvR48WKc+QuccdqJan1pM11+8bmqf+z/39qYfynWIoBAuAQcB9PemDBTZzdqoEUzR+mbaa9ozvsv5Ehly5Q6fEwsQQABBOJYwHy/ao3q0ikN/TqvqU8NjvfZQTT+Yh3HOwVDRwABBBCIagHzGV6pol8nWJ/p5rPdfMabz3rzmR/VA6PzCIRCgDoQiGEBx8G0AykHdcZpJ9i/wBHDPgwNAQQQQAABBBBAAAEE4kiAoSKAAAIIIFCQgONgWrNzTtX8RUsLqp/1CCCAAAIIIIAAAkUvQAsIIIAAAgiEXCAlJVWbt/wrny/wryTZvmOXTLm8OpPu9QZVX171sByBcAo4DqYdV7e2flq6QkNGTtQ7Uz4/LKWmpoVzXLSNAAIIIBAVAnQSAQQQQAABBBBAIBIFzC+Ltu7QQzd1eVQ33PGgxoyfmm8312/colu7PW7l7StT7vnhbyst3ZujjAmy3d1roL78dlGO5TxBINoEHAfTvpz3kz3Wse/N1KCX3z4sHTiYaq9ngkBMCjAoBBBAAAEEEEAAAQQQQCCGBY45+kiNGvqoZrz7knp3uUmTps/WHyv+yXPEr742UfXq1taMd17SGy8/rq/m/WClxVn5R4+fYgfZVq/bmLWMGQSiQiCXTjoOpr34RDf9Nndcnql82dK5NMciBBBAAAEEEEAAAQQQQAABBBAoaoHC1n9Lu1Y6pk4tJZdIUpMzT1GVyhX04y/Lcq129959Wrzkd7W58iIlJyfpyFo11PLCs/XNgh+z8rf7X0u9M3qQXU/WQmYQiFIBx8G0KB0v3UYAAQQQQAABBBBAAIHIFaBnCCAQgQLrNmzRtn93WsG12rn2bseOXfbyGtUq249mckSNatpilTHzJpUrV0bVKldUYkKCeUpCIKoFHAfTUg6matbcheo7aLRu6DzgsLR334GohqHzCCCAAAIIIIBA4ALkRAABBBBAIDYF9h9I0ZNDRqthg2N1ZqOTch1k5vl/YlJi1voka37nrt1Zz5lBIJYEHAfTJkz9Qr36D9f6jdt0bN1aanBcnRzJ4/HEkhNjQQABBGJTgFEhgAACCCCAAAIIIJCHgPnBgCeHjJHX69OAPp3l8eQeQihTuqRdQ3pauv1oJuZHCSuUL2dmSQjEnEDur4QAhvne9Dlqc0UzvfXKwxrY9w71v//WHKlkclIAtZAFAWcClEIAAQQQQAABBBBAAAEEECg6gT379uvRQcO0Z88+DX2yl8xtmnm1VrFieXvVxs3b7EczWbdhs6pVrmBmSQgUSiASCzsOplWqWE6VrRSJg6JPCCCAAAIIIIAAAggggAACCIRRIKqbPnDgoHo+PETbtu9Uz643aX/KQW3a8q+2bNuRNa4nn39No8dPsZ+XK1NajU9toCkffylzNdva9Zv0+VcLdf45je31ZpLu9SrtvyvX0tPTs+bNOhIC0SbgOJjW+pKmmjnnex1MTYu2MdNfBBBAAAEEEEAAAQQQyFWAhQgggIC0d/8BrV63Ues3btHdvQfq5i6P2umePk9n8ayzAmabt/6b9bzb7W21YuVqte7QQ53uHWAF0hqpedPTs9Y//eJYXdGuu8zVa0OGvWXPm6BbVgZmEIgiAcfBtF179mrdxq269b7Burffy4cl8yWFUeSQb1dNBH3jlu0EDvNVYiUCCCCAAAJhFKBpBBBAAAEEEAiZQNXKFfTZByMOS++/8WxWG6OGPqp+ve/Men5krRoaP/xJvTv6aU1/+wU90K2jEhP//5c7+/W+47D6TJmsCphBIIoEHAfTzBibNTlVFcqVUVqauVwzZzLrYyGNeecjndridl3ctpcat7xTvfoP067d+2JhaIwBAQQiQIAuIIAAAggggAACCCAQSwImEFeqZHIsDYmxIHCYgONgWpeOV2vE4J55plh58VQoX0avD+2jxZ+O1tQ3ntKiJX9o6sxvDoOMswUMFwEEEEAAAQQQQAABBBBAAAEEYl+AEeYi4DiYZury+/1atWajFiz+TZu3ZnwR4Zr1m7Vt+y6zOibS9a0uUJPGJ6pkcpLqH1NbFzRtpK+/+zlrbEuW/qlQpJ9++VMLf/pDi61gXSjqo47QbBcco8zx14zXEdstyrZbiN5H2e6h2e4//rJci39eHpLPNrZJaLYJjtHnuHjJcpnXEtsu+rYd2yxytpk5Nyrc9oicsTAOtkVB+0BWgIGZqBFwHEzbtz9FHe99Wq06PqQ77n/OCgQtswc9ZOREder5jD0fa5O0dK/mLfpVJx1fN2toDzz+gkKRejz6vG7rOVh3PfBsSOoLRZ+oIzTbFsfic+xtvR5v7zWY15DlwH5XfPtdrFl3f/h5denDZ1GsbVfGU7zvCZ2t11CPR5539nnEezhu7AP2PmDOjXjvKt73LrzD550VYGAmagQcB9M+/uI7/bN2owY/fJcaZgsu3dSmpVau3qAt23ZGDUKgHX3qxfHas/eAbr6uZVaRRicfr1CkxqccrzNPO0FnnHpCSOoLRZ+oIzTbFsfic2xsvR7N6yi85vV5DVvbIbzboPj2uVgc5+nW55GTz6JYtGBMvJac7gPmNWSO7ZyWp1ys73scKwSyj4f/mC7W90PGF8h+WFx5sgIMzESNgONg2oSpn6tDm0vUumVTlS9XOmvAx9atZc9v3pZx26f9JAYmw8dN0+SPvtIbLzyoalUqZI1o9HMPKBRpzJA+GvdiX4216g9FfdQRmu2CY3Q5jhrygMZar6MgtltIXr/Z2xvxTHSZZe8782w7sw+89vyDen3ogyF/bZi6Sexj8bIPmONF81qKl/EyzuBe2xwrBOZlzo3YtwKzwin6nbICDMxEjYDjYJrX61NSUsJhA92xc7e9rFKFsvZjtE98Pr+eG/6exk78VO+P7q+TT6ibY0g796UpFGlfSrpdb5rXH0B9oWkzFP2mDrZFJO0Du63Xo9+vsL6G9hxID2v7kbQ96Et0vj/sP5iu1HQf+7H1fsI+HJ37cCRstzTrNWSO7SKhL/Qh8vZjjhUC2ybm5Ij9NzArnKLfyezveSfWRKKA42DamaedoInTv9SmrduzxrX/wEENHf2+TCCtRtVKWcujeeax597QuEmfamj/e1S+XBmt37TNTulebzQPi74jgAACCCCAAAIIIIAAAkUnQM0IIIBADAs4DqZ163SN0tLT1eL6Xpq3aKleHTtVzdv00Nz5S/REn07yeBxXHVHci5b8Yffn7gefV8t292el9Ru32cuZIIAAAggggAACCMSOACNBAAEEEEAAAQQKEnAc8apYvqw+fusZ9bmnva5s0UTH1DlC17dqrmljn9I5p58kr9dXUNtRsX7WhOf6/GbDAAAQAElEQVT029xxh6U6tatHRf/pJAIIIIBAXAgwSAQQQAABBBBAAAEEECgmgaCCaT//vlKTZszN6lrJ5CTdcv2lerbf3RoxuKcdWKtSqbxu6/mM9u4/kJWPGQQQQCB3AZYigAACCCCAAAIIIIAAAgggEF0C7mC667IyD3h+nMx3iFmzh/2/Zv0Wtbv7Cf1iBd0SPJ7D1sfMAgaCAAIIIIAAAggggAACCCCAAAKxL8AIEchFIKhg2ikn1tN9d16n54a/p+FvTs9R3a9/rNK1dzym7Tv36O1XH1HpUsk51vMEAQQQQAABBBBAAAEEEECgeARoBQEEEECg6ASCCqaZbtzZoZUe73WLho2dqiEjJ8rv9+urBT+r3d0DVLZMSU0a9bgaNTzOZCUhgAACCCCAAAIIIBCMAHkRQAABBBBAAIGIFwg6mGZG1PaqC/Vcvy4a+95M+/vRuj70ghocV0cTRz6uukfVNFlICCCAQLEIWPF8LfzBrYnvu/Xiqx51eyBNw0claPIUj5b+Zm5OL5Zu0EjcCwCAAAIIIIBA5AqYYyJzbGSOkZ58OkEvD/No4mS3Fv3gkjmWitye0zMEEEAgMgUcBdPMUK5ocbZGPtNLi5b8oepVK+rNlx5S1coVzCoSAghEi0CU93PXHpfefNujjz5267dlbm3716XUNGnTZumXpS5N+sCjD6Z6lHowygdK9xFAAAEEEEAAAQcC5hhokvUHRnNMZI6NzDFSmnWsZI6ZfvvdrRkfezT+HY/27nVQOUUQQACBOBYIKpi2bfsuTZ35TVYyz69s0USbt+7QS699kLXc5ElLSy8yVipGAAEElv/p1rARHv29Kv+rz37+1aXhoz3asAEzBBBAAAEEEEAgfgTWrHVpmHUMtNT6A2N+o175tzlWStCyP4I6NcyvStYhEFIBKkMgEgWCesdcs36LHn3m9Rzp4y++s8f1zpTPcizfn8KlIDYMEwQQKBKB7xe5lJISWNXbd7j0w49Bvd0FVjG5EEAAAQQQQACB3AXCvvTnn13aYR0DBdIRc2Xa4h/z/wNlIPWQBwEEEIgXgaDOLk858Rh9PfXlgFK5MqXixZBxIoBAMQss/MGtv1YGd8C3yAqmrVkTXJliHhbNIYAAAhEgQBcQQCAWBFb85ZI59glmLKaMOcYKpgx5EUAAgXgVCCqYluDxqHLFcgEll4uT1njdqRg3AkUtsGhxUG9dWd35dgHvS1kYsTbDeBBAAAEEEEAgS2DRD86OeZweY2U1zAwCCCAQJwLOzkjjBIdhIlDUAtTvTGDXTmflduzkLc+ZHKUQQAABBBBAIJoEnB7zOD3GiiYb+ooAAgiEQsDJmWUo2qUOBBBAwJHAgRTJ6VcycoDoiJxCCCCAAAIIIBBlAk6PecwxljnWirLh0t2iFaB2BBDIRYBgWi4oLEIAgcgVKJkslSnjrH+1a/udFaQUAggggAACCESZQHx31+kxT+XKfpljrfjWY/QIIIBAwQIE0wo2IgcCCESYQI3qzoJiTstF2PDpDgIIxLIAY0MAAQRCIOD0mKdmjRA0ThUIIIBAHAiEJZg2cfocPfnC+IDSgZTUONgMDBEBBIIROOdsXzDZ7bwej9S4kd+eZxJ6AWpEAAEEEEAAgcgRMMc8CdaxT7A9OuuM4I+xgm2D/AgggEAsCBQqmPb9T8vUd9Bote/6pL5a8LPtMWTkRI19b6Y9n9fkn3Wb9fPvK+304ez5es8KrmU+z3w0y2Z/tUherzevaliOQGEFKB+lAscd69cJxwd3sHf+eT5VqUwwLUo3Od1GAAEEEEAAgSAEzDHPedaxTxBFdNqpfh1dh2OlYMzIiwACUSUQ0s46Dqb9tvwfder5jBZaAbW/Vq3X7j377I4dUb2KTEAt5WDeV5Q9eE97TR4zwE5nNTpBHa+/1J7PXGYe7+zQSrWPqKZS3LRvuzJBAIGcAlde5ldSUs5leT2rZAXRmgV5QJlXXSxHAAEEEEAAAQSiQaDZuT6Z70ALpK8lS0qXXcJFDIFYFX8eWkQAgUgUcBxMM1eOtTi/sb6YNFSNGh6bNbZzTj/Rnl+/cav9WNBk2YrVqla5wmHZLr/obP3y+0qt3bDlsHUsQAABBMqX9+u+bumqd0z+f0Ft2NCvrnd45eRWB5QRQAABBBBAwKEAxcIukJAgdbGOgU492Z9vX46p61f3LukqVSrfbKxEAAEEEMgm4DiYNnf+T7rgnNPkcrmyVSeVL5fxM3upaek5luf1xFzJ9v5Hc+Xz5XyT//3Pf+wi/+7YZT8yQQABBA4VML/q2bGDV62v9OqkEzNu40xKlGpUl06xgmhtr/WqbRuvkkocWpLnCCCAQO4CLEUAAQRiScAcA117jXU8ZB0TmWMjc4yUaB0rmdtAT2rgU6srfbrlJq/MMVUsjZuxIIAAAkUt4DiYdvyxR8l8Z9qhHZy/aKm9qHbNqvZjQZMbrrpQq9dt1t0PPq8J076wv3tt+JvTNXTUJNU/prZOObFeQVWwHgEE4ljAxPPPPN2vG67z6b5uXr36XKK6dk7XdVYQreFJOYP0MczE0BBAAAEEEEAAgTwFzDGROTYyx0j9HkrXvfd4dcP1Pp11uk/mWCrPgqxAAAEEEMhVwHEwzQTBPvpsgQa+9JY2bdmuFavWadzET/XgwFH632XnqWyZwK4Tbt2yqR7vdYt+WvqXnnrxLXV96AUNGztV5oq1Z/vdrQTzE3y5dp2F0S/ACBBAAAEEEEAAAQQQQAABBBBAIPYFYmuEjoNplzQ7Q4/3vlXTPp2nlas36PUJn+i5Ee/pyhZN1Oee9kEptb3qQi34aJhmvvOM3hv5uL6c/KImjnpcx9WtHVQ9ZEYAAQQQQAABBBBAAAEEEEAgZAJUhAACCOQi4DiYZupq2/oCzZv+imaMf9oOfs2b/qrM1WTly5Y2qwNOO3bt0Yez5mm6ldwul6pVqaCPv/hO3/34e8B1kBEBBBBAAAEEEEAAAQQyBJgigAACCCCAQNEJFCqYZoJgH32+QB9bySWXKpQvo2CDYBu3bFfLdg+o37NvaOT4D/X36g32aP9YsUYPPDFC6V6v/ZwJAggggAACCMS8AANEAAEEEEAAAQQQQCDiBRwH00IVBJv6ydeqU7u6Zr83ROee2TAL7LILz9L2nXu0cfO/WcuYQQABBCJTgF4hgAACCCCAAAIIIIAAAgjEi4DjYFqogmCTP/5K117ZTLVqVMlhnvlroDt378uxnCchFKAqBBBAAAEEEEAAAQQQQAABBBCIfQFGGFIBx8G0UAXBqletpHUbth42qD//Xmsvq1mtkv3IBAEEEEAAAQQQQAABBBBAIL4EGC0CCCAQiQKOg2mhCoK1OK+xJs2Yq1lzFyk93SvzHWm/Lvtbjw8Zq1NOrKcqlcpHoht9QgABBBBAAAEEEEAgLwGWI4AAAggggEAMCzgOpoUqCHbrDZep+Tmnqlf/Yfr+p2V69JnX1a7LE/J6fXqqT6cYpmdoCCCAAAIIRJoA/UEAAQQQQAABBBBAAIGCBBwH00IVBEvweDTksS6aOOpxDbj/Nj3QpZ1eGdhD08YOVL2jaxXUf9YjgAACEgYIIIAAAggggAACCCCAAAIIFJNAUMG0FavWySTTt1AHwRoeX1fXtWouE6S76NxGKpmcZJqJ6cTgEEAAAQQQQAABBBBAAAEEEEAg9gUYYWwJBBVMe274e5o+a54t8PzISZryydcKRRBszfrNmvThlxo6atJh6UBKqt0eEwQQQAABBBBAAAEEEEAAgWIVoDEEEEAAgVwEggqmlSldStu277Kr+WftRm3etsOeL8zEBOQu7/CgBgx9Ux99vsD+IQLzYwSZKT09vTDVUxYBBBBAAAEEEEAg7gQYMAIIIIAAAgggUHQCQQXTWrc8RzNmz1efJ0dq5eoNmr/oNw1/c3quKeVgakC9HvXWDDVrcqqWfPaa5rz/gmZNeC5HKlumVED1kAkBBBBAAIGoF2AACCCAAAIIIIAAAgggEPECQQXTzjvrFD3Zp5N2792n1es268df/9SEqZ/nmlLTAruirFTJEjqp/tFKTEyIeCw6iAACuQuwFAEEEEAAAQQQQAABBBBAAIF4EQgqmDbn2x9kbvUc+UxvdWp3hf3rm99Me0W5pXIBXlHW4rzT9YVVb7rXW9zmtIcAAggggAACCCCAAAIIIIAAArEvwAgRCKlAUMG0idO/1O9//mN3wHxn2tbtO+35wkxuv/FKrdu4Td0feVn9nn3jsMQPEBRGl7IIIIAAAggggAACCCAQvQL0HAEEEEAgEgWCCqbVO/oILV2+Sj6fP2RjeXfq59p/IEWLf16uZStW68+Va3MkL1eshcyaihBAAAEEEEAAgWIRoBEEEEAAAQQQQCCGBYIKprW+pKkWLP5NZ1/ZRd/9uEzvTvlcN3cflGvatz8lILbpn36rSy84Uws/GaHJYwZo4qjHc6QypUsGVA+ZEEAAAQQQKKwA5RFAAAEEEEAAAQQQQACBggSCCqadcmI9fTT+aXW55Soll0hUxfJlVatmlVyTy+VSIP9KJpfQ0UfWkMsVWP5A6iQPAnEmwHARQAABBBBAAAEEEEAAAQQQQKCYBIIKppk+1T2qpv3jAw91v0mP3tdRgx++K9dkfqXT5M87Zay58uIm+vTLhUpNTctYwBQBBBBAAAEEEEAAAQQQQAABBGJIgKEgEFsCQQfTMod/RYuzdVajE7R63WZ9/s0PmjF7vn5aukLpQX7H2d79KXYdnXo9q3v7vXxYMt+nltkmjwgggAACCCCAAAIIIIBAsQnQEAIIIIAAArkIOA6mpaWl6+Gnx+iKmx5Uj36vqO+g0bqp20BddcvD+vPvdbk0lfsic3dnsyanqmyZUkpL8x6Wci8VmqXBBP5MUG/X7n2haZhaEEAAAQQQQACBIhSgagQQQAABBBBAAIGiE3AcTBvz7seaPmueunW6Rm+/+ohmjH9aA+6/ze7pfY+9EvAVal06Xq0Rg3vmmUqVTLbrDPVkzfotOrXF7dqwaVu+VW/eukPdH3lJzdvcp4tv6G3/2IL51dF8C7ESAQQQQMCJAGUQQAABBBBAAAEEEEAAgYgXcBxM+3TO97qyRROZYFijhsfpmKNq6rpWzfVQ9w72bZur124KaPB+v1/5pYAqCTJT+65P6vIOfQIq9fzIiTqYmqb5H76qBR8Ns38s4aXXJgdUlkzxIsA4EUAAAQQQQAABBBBAAAEEEEAg9gUyRug4mGYCTHVqV8+oJdv0iBpV7Ge79gR2S2SPx15RwwtvyzMFWo/daICTl57orgnD+wWUe8Pmf1W1cgUlJiYoweNR45OPC+o21oAaIRMCCCCAAAIIIIAAAggggAACRSVAvQggEFIBx8G0RlZQadykWVq5eoN9ZZnp1Y5dezRq/IdmVsfXO8p+LGhyfasL9HivWw5LlSqU1flnn6ySJZIKqiLo9dWqVFD1qpUCKtep3eWa9um39g8jdQqiVQAAEABJREFUfDn/J4155yN1veV/WWU9bpdCkdzmy+MkmYdQ1EcdrpBsFxyjz1HWv3BuN7f1rprgcVnBdxIO0bkPmM8j81nE9ovO7cd2i4ztZl5Dbndk9CXa94lY7L/HOlbwWPsHyZXv8bqsfxi58jXCJ3Z8xL+oE7Deyp31ucft19oFzQ8ONLvmXl3T6VGdd3V3ffzFd+rXs6NKlwrsu87OP/sUtb3qwsNSr85t9cMvK+w2wjk5/tijZK7Ac7vc6vPkKO3Zu1+nnXRsVpcql0tSKFK50gl2nYked0jqC0WfqCM02xbH4nOsWDZJLuuVFE7z8qUTValsCRIGUbsPlCmZoORET9T2P0Jef/jF+XtAUoJH5azXEvsjn4e57QPlShXfsVE4j4kK27asf4Wtg/Lsa9GyD1i7O/9HmYDjYFrN6pX1+aTndd+d1+nM0xqoRrXKuvm6lpo0qr/aXX1RoRlOP6W+zC9o/vXP+kLXVZgKej0+TK0uaaoXn+imOe8PtcdqvnMt3eu1q92y86BCkXbuTbPrS033haS+UPSJOkKzbXEsPsdtuw7Kb72Swmm+Y0+a9RpOidJEv7fsxGD3/jQdSPWyD7MvsA8UYh84mObVzn18HvCemvtnyo69qdbrq/iOj7aE6HyluOuxDulwitJtV9z7Siy0Z/Z3UnQJOA6mbdu+S0uW/qVrLj9fQ/t3tX+Ns2+3G7V95x4V9tcufT6/vl241JYsU7qU/RiOyb79KVq6fJVOqHek3XzZMqV0e/sr7CDfqjUb7WVMIkCALiCAAAIIIIAAAggggAACCCCAQOwLRMgIHQfT3pw0S48+85pKJCXmGMr8xUt11wNDlHnlVo6VuTzp9+wbOv9/3XOkky+6TQNfekuXXnCmjqpVLZdShVuUlu5ValrGlWBm3qTMGsdN+lQ3dx9kPzW3qtauWVWTZnwp80MIaWnp+uSL7+zbPuseVdPOwwQBBBBAAAEEEEAAAQQQQACB/ARYhwACsSXgOJi28KdluvbK5jJXa2Unadv6AvvqtPUbt2VfnOd8k9NPtG8LNbeGZqYHurTT1Dee0tD+9+RZrjArmra+R5fd2Meu4oqbHtRF191nz5vJ1m079cdfa8ysncztnUlWwNCUaXpVN/29ZqOeeaSzEjweez0TBBBAAAEEEEAAAQRiVIBhIYAAAggggEAuAo6DaQdSDiopMeGwKs33FZmFZr15LChd2aKJ7rntmhzp1hsuU/1jahdU1PH6RTNH6re547LSN9Neyarrga7tZNZnLmhwXB29/OS9WvjJSH0+8XmNGNxTJzc4JnM1jwgggAACCCAQcQJ0CAEEEEAAAQQQQACBohNwHExrUL+OJkz7QikHU3P0btKHX9rPjzwisNszf/59pYaMnKidu/ba5WZ/tdi+zbJL3xe0Zv1me1kkTMwtn+XLlY6ErtAHBIpN4KclLk36wKNXRyToiacT7MdJUzxa8rP5zcxi60b8NMRIEUAAAQQQQACBOBbIfuzZ5f40jj3jeF9g6AhEuoDjYNpdHVrZt3Oefuld6tV/uJ4ZNkGXtn9Ab02erU7trpAJPgUyeJP/599WqkL5Mtr67071fPxV7d23X8tXrtGgl98OpAryhFmA5mNPYK8V235ngltTP/Ro6W8ubdkqpafJfly61KUp0z2aMNGt/Qdib+yMCAEEEEAAAQQQQKB4BQ6kSIcee5qvuDbHoBx7Fu+2oDUEChJgfYaA42BavaNrafKYATr/7JP1zfe/aPz7s+wfI3j43g7qcee1GbUHMP1z5Vpd3Ox0O+fcBUvsx1HP3q/Xn+9j1furDqTkvPLNzsAEAQSKTODvVS4NG5Wg5Svyf3tYttyt4aM8Wr2Gq9SKbGNQMQIIIIAAAgggEOMC9rHnCE/Ax55/rXR07BnjigwPAQSKWyD/s+UCemO+T2zkM73t7xj7dc5YffjmIHVoc0nQX86f+d1rP/26wv6utGpVKqh61Up263/9s95+ZIIAAsUjsPhHt/btC6yt3btd+n5Rod5GAmuIXAgggAACCMSlAINGIPYFzLHn7j2BBcjMseePSzj2jP29ghEiEPkCIXsncrsDewM8lOSUE+vZt4ZOmjFX02fN00XnNbazrFi1zn6sXKGs/cgEAQSKXuDXpS77ts5gWjK3gS77I2RvJcE0TV4EEIhUAfqFAAIIIIBAAAJOjz1NuQCqJwsCCCBQZAJhPwPufHNr+0cMBjw/TtWrVlTH6y+1Bzv2vZn28xrVKtvPmSCAQNELLPrB2VvCgu+dBdOLfkTBtUBuBBBAAAEEEEAAgeITcHrs6bRc8Y2MlhBAINYFnJ05h1DF/Ornp+88q0/efkafTxyq8mUzfjGzU7vLNfaFvnJ6xVsIuxjpVdE/BEImsHOns6p27nRWjlIIIIAAAggggAAC8Suwc6ezse/c6awcpRCIAQGGECECYQ+mGYekpETVqV09R+DM3P5plpn1JAQQKHoBn0/atdvZFWZ79zorV/SjogUEEEAAAQQQQACB8Avk3gOnx5DmO9a81rFr7rWyFAEEECh6gaCCaS+OmazX3v3Y7tWyFau1Zv1me74wk5SDqZo1d6H6DhqtGzoPOCzt3XegMNVTFgEEAhRwW+8GlSr5A8ydM1uNGs7K5ayFZwgggAACCESYAN1BAIEiFXB6DFm1quSxjl2LtHNUjgACCOQjENRb0C+/r9Sevfvt6oaPm6aPv/jOni/MZMLUL9Sr/3Ct37hNx9atJfMLodmTx+MpTPWURQCBIARq1ggic7asNasTTMvGwSwCYRegAwgggAACCESDgNNjyKNq+aJhePQRAQRiWCCoYNpZjRroy3k/aeXqDTaJz+tTWlp6rsnOEMDkvelz1OaKZnrrlYc1sO8d6n//rTlSyeSkAGohCwIIhELgjNODPzBxuSQn5XLpL4sQQAABBBBAAAEE4kigcWO/zLFkMEM2+U8+2R9MEfIigAACIRcIKpjW6pJztGPXHl11y8OaYwXVhr85XaddckeuadeefQF1tlLFcqpspYAyR2QmOoVA7Agcc7Rfp50a3MFJk7N8qlkzdgwYCQIIIIAAAggggEDxCNQ6wi9zLBlMayb/0XWCO14Npn7yIpC/AGsRyBAIKphWu2ZVfTXlZb07vJ/qH1NbFzQ9TU89eHuuqVRyiYwWCpi2vqSpZs75XgdT0wrIyWoEECgOgcsu8apUqcBaKlfWrxYXBH81W2C1kwsBBBBAAAEEEEAgJAIRXMlFF/o49ozg7UPXEEAgd4GggmmmCrfbpVNPrKfBj3TWQ9076JrLz881JSYmmOwFpl179mrdxq269b7Burffy4el/QdSCqyDDAggEDoBE0jrdne6Ghyff5Ds+ON8uqeLV0mBxc1D10FqQgABBBCIGwEGigACsS9QIkm69x6OPWN/SzNCBGJLIOhgWubwj693pKpWrqCpM7/R4Fff1eNDxmrMOx9p7YYtmVkCfmzW5FRVKFdGaWnew1LAlZARAQRCJlCmjNT+Bp/aXO1Vw4Z+VasqJSTKfmx4kl/XXOVVh/Y+lUwOWZNUhEAsCTAWBBBAAAEEEAhCoFTJw489Ezn2DEKQrAggUNwCbqcNbtu+S+a70x595nW9NXm2Pvnie704ZrIuu7GPZs1dFHC1XTperRGDe+aZSnG2HrAlGREonMDhpc33p7Vt41W3Lul67KF0+7HttV41Oo3vqThciyUIIIAAAggggAAChRHIfuw5Ykgix56FwaQsAggUqYDjYNpLr32g7Tv3aNyLffXj7DFaNHOkZox/2v4etV79h+lASmpQHV+9brM+/+YHzZg9Xz8tXaF0rzew8uRCAAEEEEAAAQQQQAABBBBAAIHYF2CECESIgONg2rxFv6r9/y7SmaedoBJJifZwjjmqprp3amPPr1y93n4saJKWlq6Hnx6jK256UD36vaK+g0brpm4D7ave/vx7XUHFWY8AAggggAACCCCAAAIIRLQAnUMAAQQQiC0Bx8G08mVLa/ee/Ydp5LbssEzZFox592NNnzVP3Tpdo7dffcS+um3A/bfZOe577BWuULMlmCCAAAIIIIAAAsUuQIMIIIAAAggggAACuQg4Dqadf/Ypev+juXYyt2ju2rNPc+b9pOdHTlSlCmV1fL2jcmnu8EWfzvleV7ZoIvPdaY0aHidzddt1rZrbvxRq6l29dtPhhViCAAIIIIBAngKsQAABBBBAAAEEEEAAAQSKTsBxMK3LLf/TuWc2VP8h4+xbNJu2vkfdH3lJGzZv00tP3qvEBE9AvT6YmqY6tasflveIGlXsZSZIZ88wQSDWBRgfAggggAACCCCAAAIIIIAAAghEvIDjYFrJ5CSNfu5+mVszH+91ix7o0s4KonXXrAnPqfHJxwU88EZW3nGTZmnl6g3y+/12uR279mjU+A/t+UCvcLMzM0EAAQQQQAABBBBAAAEEEEAAgSIRoFIEEMgQcBxMyygumVsz2151oW694TJdfP7pKlUyOXNVQI89br/WznfVLQ+r2TX36ppOj+q8q7vr4y++U7+eHVW6VHD12ZUxQQABBBBAAAEEEEAAAQQyBJgigAACCCAQUoFCB9MK25ua1Svr80nP6747r9OZpzVQjWqVdfN1LTVpVH+1u/qiwlZPeQQQQAABBBBAIEoF6DYCCCCAAAIIIIBAJAqEPZi2bfsuLVn6l665/HwN7d9VIwb3VN9uN2r7zj1atmJ1JJrRJwQQQACB/ARYhwACCCCAAAIIIIAAAgjEsEDYg2lvTpqlR595TSWSEnMwz1+8VHc9METpXm+O5TxBoKgEqBcBBBBAAAEEEEAAAQQQQAABBGJfoLAjDHswbeFPy3Ttlc1VtkypHGNp2/oC++q09Ru35VjOEwQQQAABBBBAAAEEEEAAAQTiUIAhI4BAhAg4DqalpaVr15598np99lDSvV4t/OkP/frHKvt5oJMDKQeVlJhwWPaM3/WUzPrDVrIAAQQQQAABBBBAAAEEokSAbiKAAAIIIBBbAo6DaWPe/VgXt+2tvfsPyO/3q0PXp3Rbz8Fqd/cAvT7hk4CVGtSvownTvlDKwdQcZSZ9+KX9/MgjqtmPTBBAAAEEEEAAgWIVoDEEEEAAAQQQQAABBHIRcBxMW7D4N13XqrnKly2t7374XUuXr9KA+2+zf5XznSmf5dJU7ovu6tDKvp3z9EvvUq/+w/XMsAm6tP0DemvybHVqd4VKl0rOvSBLEUAAAQRyFWAhAggggAACCCCAAAIIIIBA0Qk4DqZt2bZD9Y+pbffsp9/+UqmSyfYvct5w9UXavHWHVq/bbK8raFLv6FqaPGaAzj/7ZH3z/S8a//4s+8cIHr63g3rceW1BxVkfOwKMBAEEEEAAAQQQQAABBBBAAAEEYl8g6kfoOJhWrUpFLVuxxr7F89M536tJ4wbyeNzafyDFRjn0tk17YR6TBsfV0chnemvRzJH6dc5YffjmIHVoc4kSPBcHXy8AABAASURBVJ48SrAYAQQQQAABBBBAAAEEEEAAgeIUoC0EEEAgQ8BxMO3qS8+VuZ3zrCu6aOXqDbrxmovtGr9e8LP9WLtmVfsx2Inb7Qq2CPkRQAABBBBAAAEEEEAgLwGWI4AAAggggEBIBRwH0669spn9HWktzm+spx++U+eccZLdsZ9/X6nb2/NdZzYGEwQQQAABBBBwLEBBBBBAAAEEEEAAAQQiUcBxMM3lctk/QDD44bt0SbMzlZbutcc3sO8d6tW5rT3PBAEEEIhDAYaMAAIIIIAAAggggAACCCAQwwKOg2npXq+Gvzld5/+vu8647C6Z700zTnc/+Lzu7feymSVFlQCdRQABBBBAAAEEEEAAAQQQQACB2BdghIUVcBxM+/b7XzVs7FRd0LSRsn8/WpsrmuuLb37Urj37Cts3yiOAAAIIIIAAAggggAACCCCQIcAUAQQQiBABx8G096Z/oRuvaaEn+3RSndrVs4ZzyonH2PMbNm2zH5kggAACCCCAAAIIIBDPAowdAQQQQAABBGJLwO10OH/+vU716x2ZZ/GkpMQ817ECAQQQQAABBCJegA4igAACCCCAAAIIIIBALgKOg2mnNKinjz//Tj6fP0e1kz780n6e/dZPewETBBBAoFgEaAQBBBBAAAEEEEAAAQQQQACBohNwHEzrcsvVWrTkD7Xq2FfLVqzW7K8WqUvfFzTqrRm6787rVIIr04LbauRGAAEEEEAAAQQQQAABBBBAAIHYF2CEUS/gOJh2fL0jNeX1J1X3qJpKOZimOfN+0qYt/2rA/bfp9vZXRgVMutcbVD/T0tK1ftM2paamBVWOzAgggAACCCCAAAIIIIBAtAvQfwQQQACBDAHHwTQTiKpZvbJeerK7Fs0cqaVfjtXUN57SpRecqYNREGxas36LTm1xuwL5oYRVazbq5u6DdNold6hlu/s1ZeY3GXpMEUAAAQQQQAABBCJdgP4hgAACCCCAAAIhFXAcTHv7g890Tquu2rRlu90hl8tlP/Z5apQVeBpoz0fqpH3XJ3V5hz4BdW/z1h1q1fEhVa9aUW+98rAWfzraDhgGVJhMCCCAAAIIOBagIAIIIIAAAggggAACCESigONg2tcLflbbqy7UoT80cHfHq+zvUNu0NSPIFomDfumJ7powvF9AXXtz0qeqVKGsBj9ylxqfXF8lk5NUsXzZgMqSCYG4FGDQCCCAAAIIIIAAAggggAACCMSwgONgmgmW1alV/TCamtUq28v+3b7bfozESbUqFVS9aqUcXcvrybcLf9UR1avo/gEjdEPnAeo/ZJzM2PPKz3IEEEAAAQQQQAABBBBAAAEEEIhcAXqGQGEFHAfTzA8PTJ/17WHtfzLnO3tZjWqBBavszBE8Wbl6g0qXSlaL8xqrU/vLtXT5KnXq+YzMjxGYblevmKxQpIplkkx1Skp0h6S+UPSJOkKzbXEsPseqFZLldimsr6GKZZPC2j77W/Htb7FqXa50kkomediPQ/T5Hqv7CePK/72mhPUaqmAd2+GUv1O8+hTiWCGu3pvNyVG87iOMO/7eO8z+ToouAbfT7l7Xqrn+/Hud7n7weU368EvN+fZHPfnCeA0bO03NmpyqyhXLOa064sp1aHOJWrdsqksvOEvP9btbq9dt1t9rNtr93LbroEKRdu3L+IXQtHRfSOoLRZ+oIzTbFsfic9y++6B8foX1NbRrX2pY22d/K779LVat9+5PVUqal/04RJ/vsbqfBDeu+HtdpqZ6tZvPA95H8ngf4VghsPcEc7LFe01gVjhFv5PZ30nRJeA4mHZh00Z6vNct+uGXFRow9E11f/RlvTd9jpo0bqAn+3SKLoV8etvguDpas35zVg6fz2fPp6al249e68w9FMnntyIAVo3mIRT1UYdfGMSngfUyCuu2N28R7Hsxsu+F6P092vYHa9jis4h9ONr220jrrzmqM6+lSOsX/YmM1zbHCoFth3Af0/F6CWw74RQaJ7O/k6JLwHEwzQzT/ADBt9Nf0fSxA+0v9DfzrwzsoSqVypvVEZvS0r1KTcu8Eswr8zyzs+Mmfaqbuw/KfKorWpytN977ROs3bdOuPfv01uTP7B8kOPboWll5mEEgkgToCwIIIIAAAggggAACCCCAAAIIFJ1AoYJpplslkhJ1bN1aOuXEeoX5lUtTVbGlpq3v0WU39rHbu+KmB3XRdffZ82ayddtO/fHXGjNrp5vaXKKzG5+olu3ulyn39fc/a9ig+1QyOeM7zuxMTBBAAAEEEEAAAQQQQAABBBBAIBAB8iAQ9QKOg2kpB1M1a+5C9R002v6VS/NLl9nT3n0HIhZn0cyR+m3uuKz0zbRXsvr6QNd2MuszFyRZwcIhj3XRgo+G6/OJz+uLSUPtwGHmeh4RQAABBBBAAAEEEEAgHgQYIwIIIIAAAhkCjoNpE6Z+oV79h2v9xm32lWnmu8WyJ4/Hk9FCjEzLlSmlmtUry+VyxciIGAYCCCCAAAIIxIUAg0QAAQQQQAABBBAIqYDjYJr5sYE2VzTTW688rIF971D/+2/NkbgNMqTbicoQQACBuBNgwAgggAACCCCAAAIIIIBAJAo4DqZVqlhOla0UiYOiTwiEUYCmEUAAAQQQQAABBBBAAAEEEEAghgX+C6YFP8LWlzTVzDnf62BqWvCFKYFAvAv4/XJtXifPkm+V+NkkJSyeK/fGfySfN95lGD8CCCCAAAIIIIAAAhEt4PNJW7ZIS5e69O18t37/w61t/7pkHeJHdL//v3PMIYBAYQUcB9N27dmrdRu36tb7Buvefi8flvYfSCls3yiPQEwKuNesUPITd6hk/9tUYtQAJU4Zo6TXB1rL7lTyozfL/dfSmBw3g0IAAQQQQAABBAolQGEEIkBgzVqXhr7k0asjEzRpikezP3frvUluvTzMo2HWMhNki4Bu0gUEEChiAcfBNNOvZk1OVYVyZZSW5j0smfUkBBDIJmD9qSpx1ntKfra73JvWZFvx/7PuHVuV/EJvJX40nqvU/p+FOQQQQCCqBeg8AggggED0C5ir0b6Y69br4zzavSf3H6XbslUa+XqCFv7g5iq16N/kjACBfAUcB9O6dLxaIwb3zDOVKpmcb8OsRCDeBDy/L1bitNclbwG3clqf1Ikfv6WEH76ONyLGG1kC9AYBBBBAAAEEEEDgP4Hffnfpq68LDpKlp0kffezWir9yD7j9Vx0PCCAQ5QKOg2l+v9+KtuedotyF7ketQIR2fN8eJY0fElTnEie+ItfOf4MqQ2YEEEAAAQQQQAABBBAIrcCePVaA7BNPUJVOn+FWampQRciMAAJBC4SvgONgWo/HXlHDC2/LM+3asy98o6JlBCJMIOGHr+TavT2oXrmsAFzCtx8HVYbMCCCAAAIIIIAAAgggEFqBRT+6FexXgu/Z69KSX/M43Q5t96gNAQTCIOD41X19qwv0eK9bDkuVKpTV+WefrJIlksIwHJpEIDIF3KuWOeqY03KOGqMQAggggAACCCCQjwCrEIhXgXXrnN2y6bRcvDozbgSiScBxMO38s09R26suPCz16txWP/yyIpoM6CsCRS7gNCjmXvuX5PMVef9oAAEEEIhhAYaGAAIIIICAYwG/X1q/wVkwbe06x81SEAEEIlzAcTAtr3Gdfkp97T+Qor/+WZ9XFpYjgECgAgf3B5qTfDEnwIAQQAABBBBAAAEEIkEgPT0SekEfEEAgkgRCGkzz+fz6duFSe3xlSpeyH5nEmQDDzVXAd+QxuS4vaKH/6BMkd0hfpgU1yXoEEEAAAQQQQAABBBD4T8DlkqpX8//3LLiHmjWCy09uBKJOII477Pgsvd+zb+j8/3XPkU6+6DYNfOktXXrBmTqqVrU4ZmXoCOQU8NWul3NBgM+8RzcIMCfZEEAAAQQQQAABBBBAIBCBYPPUrO4smFbDYblg+0d+BBAofgHHwbQmp5+odldflCM90KWdpr7xlIb2v6f4R0KLCESwgLdJS6lU2aB66E8qIe/ZFwdVhswIIIAAAgggELMCDAwBBMIkcOaZPiUmBdd4mTJSo1P57uPg1MiNQPQIOA6mXdmiie657Zoc6dYbLlP9Y2pHz+jpKQLFJOAvX1kHO/QMqrW0dt3lO+LooMqQGQEEEIg8AXqEAAIIIIBAdAvUqC5deklwgbHrrvGqbNnoHje9RwCBvAUcB9N+Xfa33p36hQa/+q6eHzlJkz/6SqvWbMy7JdYgEOcC3sbnK/2iawJSSD+judLPuTSgvGQqIgGqRQABBBBAAAEEEEDgP4GzTvfppBMDC6idc7ZPx9R1dmvof83xgAACES4QdDBt1+59uu+xV9WuyxP296O9NXm23njvEz0+ZKxadXxIzwyboHSvN8KHHbvdY2SRLZB6fVel9BsjX83crzjzV6isg/e/qNTbH43sgdA7BBBAAAEEEEAAAQTiTOCG63y6/VavypXLPVBWtYpf3bt6dfmlgQXd4oyP4RaBAFWGTyDoYNqAoW/qs68X697br9WXk1/U0i/H6ucvXtfMd57Rjde00Pj3Z+mNCZ+Eb0S0jECEC5hbN1P6jdaB/mN1sPPjSmtzpxU8e8QOsh0Y+I689U6K8BHQPQQQQAABBBBAAAEEHAtEdcE6R/nV616vunVJV9s2XrW82Kd2bX269x6zzCsTUIvqAdJ5BBAISMAdUK7/Mq1cvUGz5i7UUw/ers43t1a1KhXkcrmU4PHoqFrV9UiPm3XL9Zfqpdc+UFpa+n+leEAAgcMErNeNv3pteU87T2mXtFX6GRfIBNnk9hyWlQUIIIAAAgggEAkC9AEBBBDIEHBbZ9HVqkoNG/p1XlOfTjzBpyqV/da5ccZ6pgggEPsC1ttA4IP8a9U6O3OrS5raj7lNrm3V3F68et1m+5EJAggggAACCIRRgKYRQAABBBBAAAEEEEAgpAJBBdP27U9RqZLJSvDkXax0qWS7g/sPpNiPTBBAAAEnApRBAAEEEEAAAQQQQAABBBBAIBIF8o6K5dLbY4+uJRMkW7ZidS5rMxYtWPybPXNMnSPsxzibMFwEEEAAAQQQQAABBBBAAAEEEIh9AUYYxwJBBdNOOPYo1aldXT0fH6Y//lpzGNu8RUs16OV3dNG5jVSmdMnD1rMAAQQQQAABBBBAAAEEEEAgnAK0jQACCCBQWIGggmlJSYka8lgXbd+5R9fe8Zhu6DxAfQeNVq/+w3XVLQ/rrgeGqFKFsnq8962F7RflEUAAAQQQQAABBBD4fwHmEEAAAQQQQACBCBEIKphm+nxi/aP1+aTn1aXj1TLfjzZv4a9a8tsKHVGjsh68p70+fHOQqlQqL/4hgAACCCCAgIQBAggggAACCCCAAAIIxJZA0ME0M/zyZUurW6dr9MYLD+qbaa9ozvsvaOQzvdXx+ktVIinRZCEhgEB0C9B7BBBAAAEEEEAAAQQQQAABBBDIRcBRMC2XeiJkEd1AAAEEEEAAAQQQQAABBBD7BVfEAAAQAElEQVRAAIHYF2CECIRPgGBa+OxpGQEEEEAAAQQQQAABBOJNgPEigAACCES9AMG0qN+EDAABBBBAAAEEECh6AVpAAAEEEEAAAQQQyBAgmJbhwBQBBBBAIDYFGBUCCCCAAAIIIIAAAgggEFIBgmkh5aQyBEIlQD0IIIAAAggggAACCCCAAAIIIBCJAqENpkXiCOkTAggggAACCCCAAAIIIIAAAgiEVoDaEIhjAYJpcbzxGToCCCCAAAIIIIAAAvEmwHgRQAABBBAorADBtMIKUh4BBBBAAAEEECh6AVpAAAEEEEAAAQQQiBABgmkRsiHoBgIIIBCbAowKAQQQQAABBBBAAAEEEIgtAYJpsbU9GU2oBKgHAQQQQAABBBBAAAEEEEAAAQRiX8DBCAmmOUCjCAIIIIAAAggggAACCCCAAALhFKBtBBAInwDBtPDZ0zICCCCAAAIIIIAAAvEmwHgRQAABBBCIegGCaVG/CRkAAggggAACCBS9AC0ggAACCCCAAAIIIJAhQDAtw4EpAgggEJsCjAoBBBBAAAEEEEAAAQQQQCCkAnEdTEv3ekOKSWWhE6AmBBBAAAEEEEAAAQQQQAABBBCIfYFoHGHcBtPWrN+iU1vcrg2btgW83V4Y/b5OuuBW7d67P+AyZEQAAQQQQAABBBBAAAEEEIg5AQaEAAJxLBCXwbT2XZ/U5R36BLXZp878Rq+9+3FQZciMAAIIIIAAAggggEBkCdAbBBBAAAEEECisQFwG0156orsmDO8XsN2iJX9o0MvvaMhjXQIuQ0YEEEAAAQQQCKEAVSGAAAIIIIAAAgggECECcRlMq1algqpXrRTQJli9brO6PvSiXnyim46rWzugMmRCAAEEMgV4RAABBBBAAAEEEEAAAQQQiC2BuAymBboJd+3ep7seGKKed12vc89smGux8qUTFYpUOjnBrj/B4wpJfYXsU9j7UMFyJSUKg+AMyln7jct6JYXTrWzJBFUok0TCIGr3gVLW51FSgjtq+8/rj/efSNgHEq3XkDm2i4S+0IfIe02ULWUdK1jHLOE8XomGtq1DOo6F2U9ibh/I6zzd7O9hTDTtQMDtoEzcFPnux9+0buNWrd2wRc8Om6DXJmR8Z9qLYyZr2YrVtkNauk+hSOlen12f36+Q1BeKPoWzjoOWK8knDIIzSEvzyXoJhdUtzXotp6Z5RcIgWveBdOv912d9GEVr/+k3r71I2Ad8Pr/MsV0k9IU+RN5rwhxjc4xX8DGeOTnK36ngOiiPUaTtA+b1n1sy+zspugQIpuWzvY49upZ63HGtKpYvowpWKlemlJ27QrnSSkpMsOf3H/QqFOmgFQQwFXqtg69Q1BftdRywXEleYRCkQarXvIzC6paS6gvJe0K0v4bpvzdq94NUK5iW7vVHbf/Z96J334ulbWeO58yxXa5jso5xWB7f+6k5VuAYz1vg8Zo5qMOpYCeMossor/d/s7+ToksgLoNpaenmL1Rp9pYy8ybZT6zJuEmf6ubug6w5qZ4VTLvrptbKTG1bX2gvv/WGy+119hMmCCCAAAIIxIEAQ0QAAQQQQAABBBBAAIEMgbgMpjVtfY8uu7GPLXDFTQ/qouvus+fNZOu2nfrjrzVmloQAAtEvwAgQQAABBBBAAAEEEEAAAQQQCKlAXAbTFs0cqd/mjstK30x7JQv1ga7tZNZnLcg2c2zdWnaZzNs9s60K8SzVIYAAAggggAACCCCAAAIIIIBA7AswwmgUiMtgWjRuKPqMAAIIIIAAAggggAACCESMAB1BAAEE4liAYFocb3yGjgACCCCAAAIIxJsA40UAAQQQQAABBAorQDCtsIKURwABBBBAoOgFaAEBBBBAAAEEEEAAAQQiRIBgWoRsCLqBQGwKMCoEEEAAAQQQQAABBBBAAAEEYkuAYFpu25NlCCCAAAIIIIAAAggggAACCCAQ+wKMEAEHAgTTHKBRBAEEEEAAAQQQQAABBBAIpwBtI4AAAgiET4BgWvjsaRkBBBBAAAEEEIg3AcaLAAIIIIAAAghEvQDBtKjfhAwAAQQQQKDoBWgBAQQQQAABBBBAAAEEEMgQIJiW4cAUgdgUYFQIIIAAAggggAACCCCAAAIIIBBSgYgMpoV0hFSGAAIIIIAAAggggAACCCCAAAIRKUCnEIhGAYJp0bjV6DMCCCCAAAIIIIAAAgiEU4C2EUAAAQTiWIBgWhxvfIaOAAIIIIAAAvEmwHgRQAABBBBAAAEECitAMK2wgpRHAAEEECh6AVpAAAEEEEAAAQQQQAABBCJEgGBahGwIuhEaAb9f2r7DpWXL3fp2vlu/LHVp02bJ6w1N/cHWQn4EEEAAAQQQQAABBBBAIBwC5hzInAuZcyJzbvSHdY5kzpXMOVM4+kObCMSSQG7BtFgaH2OJI4Gt21waOSZBL77i0YSJbs3+3K3JUzwaPipBz7+UoL9WuuJIg6EigAACCCCAAAIIIIBAvAr8vcqlIS8m2OdC5pzInBu9a50jmXOlUa959O/2rHOjeCVi3AgUSoBgWqH4KBwpAt8vdGvEaI82bsq9R3v3SuPf8WjWZ26uUsudiKUIIIAAAggggEAUCdBVBBDITcBcjTZztlvj3vJo377cckgbNrrsc6clvxBQy12IpQgULEAwrWAjckS4wJq1Ln0yy6309II7Om+BWz8sYbcvWIocCCCAAAJFIkClCCCAAAIIFKGAOddZ8F3B5zupqdLU6R6tWUNArQg3B1XHsEDBr7IYHjxDi34B8yEwZZpbwdz3b65O47Lm6N/2jKB4BWgNAQQQQAABBBBAILIFzDmOOdcJtJfmHGrKdLfS+X7pQMnIh0CWAMG0LApmolFg+Z8u+wcH8uh7rovTrL/CLFzEX2ByxWEhAggggAACCCCAAAIIRKWAOccx5zrBdN78IMGyZZwbBWNG3ogVKNaOEUwrVm4aC7XAuvXO3vjXrnNWLtT9pz4EEEAAAQQQQAABBBCIZ4HQjd3pOY7Tc6rQ9ZyaEIg+AYJp0bfN6HE2AacfGJu2uJSalq0iZhFAAAEEEEAAAQQCFyAnAghElECadW6zabOzCwacnlNFFACdQaCYBQimFTM4zUWGgDeAHyuIjJ7SCwQQQACBUApQFwIIIIAAAjEp4JfML3nG5NgYFAIRKEAwLQI3Cl0KXKBmdetTI/DsWTmrVZOSErOeMoNApAvQPwQQQAABBBBAAAEE8hRITJIqVXJ2buT0nCrPzrACgTgQIJgWBxs5fEMs+par13TWxlG1fM4KUgoBBBBAAAEEEEAAAQQQiECBmjWcdcrpOZWz1igVuwLxNTKCafG1vWNutA1P9KlsmeD+AuPxSI0aBVcm5uAYEAIIIIAAAggggAACCEgxZHBOE5/MuU4wQ6pQwa+TT+JCg2DMyIuAESCYZhRIUStQqqR0zdU+uYL4rs1LWvhUu5Y/asdMxxFAAAEEEEAAAQQQQACBQwWOtM5xLmzuO3Rxns/NOVTba30qmZxnFlYggEAeAgTT8oBhcfQIHFvPr3PPCexD45i6fjW1/mITPaOjpwgggEBMCTAYBBBAAAEEEChCgfPP9cmc8wTSRPNmXGQQiBN5EMhNgGBabiosizqBlhf71O3udB1RM/crzkqXlm692WunqBscHY4AAbqAAAIIIIAAAggggEDkC5irzcx5T8cOXpUpk3t/zXer3dfdq4uCuIot95pYikD8ChBMi+VtH2djq1ZN6nyHV+aD4cYbfDIBtuvaeNW1c7ruvy894L/QxBkbw0UAAQQQQAABBBBAAIEYEzB37/TukW6fC5lzInNu1N46RzLnSnffma5KFXO/CCHGGOJrOIy2WAUIphUrN40VtYD5S4z5YDjheJ/Oa+rTKQ39qlFdQX8RZ1H3k/oRQAABBBBAAAEEEEBAwqDoBMyPEZhzIXNOZM6NGljnSOZcyZwzFV2r1IxAfAgQTIuP7cwoEUAAAQQQQAABBEInQE0IIIAAAgggEMcCBNPieOMzdAQQQACBeBNgvAgggAACCCCAAAIIIFBYAYJphRWkPAIIFL0ALSCAAAIIIIAAAggggAACCCAQIQIE04pwQ1A1AggggAACCCCAAAIIIIAAAgjEvgAjjC8Bgmnxtb0ZLQIIIIAAAggggAACCCCQKcAjAggggIADAYJpDtAoggACCCCAAAIIIBBOAdpGAAEEEEAAAQTCJ0AwLXz2tIwAAgggEG8CjBcBBBBAAAEEEEAAAQSiXoBgWtRvQgaAQNEL0AICCCCAAAIIIIAAAggggAACCGQIxHIwLWOETBFAAAEEEEAAAQQQQAABBBBAIJYFGBsCxSpAMK1YuWkMAQQQQAABBBBAAAEEEMgU4BEBBBBAIBoFCKZF41ajzwgggAACCCCAQDgFaBsBBBBAAAEEEIhjgbgOpqV7vQFtepNv45btOpiaFlB+MiGAAAIIRKYAvUIAAQQQQAABBBBAAAEECisQt8G0Neu36NQWt2vDpm35Go555yM738Vte6lxyzvVq/8w7dq9L98yrEQgxAJUhwACCCCAAAIIIIAAAggggAACESJQhMG0CBlhLt1o3/VJXd6hTy5rDl9UoXwZvT60jxZ/OlpT33hKi5b8oakzvzk8I0sQQAABBBBAAAEEEEAAAQQQiEsBBo1AfAnEZTDtpSe6a8LwfgFt6etbXaAmjU9UyeQk1T+mti5o2khff/dzQGXJhAACCCCAAAIIIIAAAhEsQNcQQAABBBBwIBCXwbRqVSqoetVKQXOlpXs1b9GvOun4ukGXpQACCCCAAAIIIBAqAepBAAEEEEAAAQQQCJ9AXAbTnHI/9eJ47dl7QDdf1zKripJJHoUiJSVkbAqP2xWS+kLRJ+oIzbbFsfgck63Xo8t6dYbTvESim9ewtR3y2AbYRIFNovV5xGdR8b1v8VqJTWvzGjLHdmzf2Ny+hd2uHCsEtl9Yh3QcN0TBcUNhXw+Uz3g9mP2dFF0CGRGc6OpzWHo7fNw0Tf7oK73xwoMyV7ZldiK5hEehSEmJHrtKt7VFQlEfdYRmu2Q4UlfUOFgHHLKiaeHsbwnrtRzO9mmb12th9wETAEjwuELy2VbYvlCe/Tla9wG39cfRJOuPK9Haf/pdtK89jhUC8zUnR+yLgVnhFP1OZn8nRYpAYP2wQjeBZYzXXD6fX88Nf09jJ36q90f318kn5LzFc8eeVIUi7T2QZhOnpftDUl8o+kQdodm2OBaf4869qfL7FdbX0O79aWFtn/2t+Pa3WLXel5Kug2k+9uMQfb7H6n7CuPJ/r0lL92nvgXReR7yOct0HOFbI//WT+f5iTo4y53kMwIzXW66vt2jZd8z+ToougbgMppnvPktNywxeeWWeZ262cZM+1c3dB2U+1WPPvSGzbGj/e1S+XBmt37TNTuleb1YeZhBAAAEEEEAAAQQQQCB4AUoggAACCCAQjQJxGUxr2voeXXZjH3t7XXHTg7rouvvseTPZz3SxRAAAEABJREFUum2n/vhrjZm106Ilf9iPdz/4vFq2uz8rrd+4zV7OBAEEEEAAAQTiToABI4AAAggggAACCMSxQFwG0xbNHKnf5o7LSt9MeyVrF3igazuZ9ZkLZk14Litf9jJ1alfPzMIjAgggECUCdBMBBBBAAAEEEEAAAQQQQKCwAnEZTCssGuWLWYDmEEAAAQQQQAABBBBAAAEEEEAg9gWiZIQE06JkQ9FNBBBAAAEEEEAAAQQQQACByBSgVwggEF8CBNPia3szWgQQQAABBBBAAAEEMgV4RAABBBBAAAEHAgTTHKBRBAEEEEAAAQTCKUDbCCCAAAIIIIAAAgiET4BgWvjsaRkBBOJNgPEigAACCCCAAAIIIIAAAghEvQDBtKjfhEU/AFpAAAEEEEAAAQQQQAABBBBAAIHYF2CEgQkQTAvMiVwIIIAAAggggAACCCCAAAKRKUCvEEAAgWIVIJhWrNw0hgACCCCAAAIIIIBApgCPCCCAAAIIIBCNAgTTonGr0WcEEEAAAQTCKUDbCCCAAAIIIIAAAgjEsQDBtDje+AwdgXgTYLwIIIAAAggggAACCCCAAAIIFFaAYFphBYu+PC0ggAACCCCAAAIIIIAAAggggEDsCzDCKBEgmBYlG4puIoAAAggggAACCCCAAAKRKUCvEEAAgfgSIJgWX9ub0SKAAAIIIIAAAghkCvCIAAIIIIAAAgg4ECCY5gCNIggggAACCIRTgLYRQAABBBBAAAEEEEAgfAIE08JnT8sIxJsA40UAAQQQQAABBBBAAAEEEEAg6gUIphW4CcmAAAIIIIAAAggggAACCCCAAAKxL8AIEQhMgGBaYE7kQgABBBBAAAEEEEAAAQQiU4BeIYAAAggUqwDBtGLlpjEEEEAAAQQQQACBTAEeEUAAAQQQQACBaBQgmBaNW40+I4AAAgiEU4C2EUAAAQQQQAABBBBAII4FCKbF8cZn6PEmwHgRQAABBBBAAAEEEEAAAQQQQKCwApEfTCvsCCmPAAIIIIAAAggggAACCCCAAAKRL0APEYgSAYJpUbKh6CYCCCCAAAIIIIAAAghEpgC9QgABBBCILwGCafG1vRktAggggAACCCCQKcAjAggggAACCCCAgAMBgmkO0CiCAAIIIBBOAdpGAAEEEEAAAQQQQAABBMInQDAtfPa0HG8CjBcBBBBAAAEEEEAAAQQQQAABBKJeoMBgWtSPkAEggAACCCCAAAIIIIAAAggggECBAmRAAIHABAimBeZELgQQQAABBBBAAAEEEIhMAXqFAAIIIIBAsQoQTCtWbhpDAAEEEEAAAQQyBXhEAAEEEEAAAQQQiEYBgmnRuNXoMwIIIBBOAdpGAAEEEEAAAQQQQAABBOJYgGBaHG/8eBs640UAAQQQQAABBBBAAAEEEEAAgdgXKOoREkwrauEYqX/XHpd+WuLSp7PdemuCR9NmeDT/O7fWrXfFyAgZBgIIIIAAAggggAACCCAQVoG4bNycU5pzy2kfeuxzTXPOueRnl3Zb56BxCcKgo0KAYFpUbKbwddLrk76d79bLwzyaar25mTe5FStc+vGnjMDa6Nc9ev8Dj/bsCV8faRkBBBBAAAEEEEAgnAK0jQACCAQvYM4hJ03xyJxTmgDaj0tcMuea5pxzynSPXrLOQectcMvvD75uSiBQ1AIE04paOIrrX7PWpeEjPZr9uVtpqXkP5NffXHpleIIWLuKNLm8l1iCAAAIIRJwAHUIAAQQQQACBYhcwwbHvF7rtc8ilS/O+08mcg876zK2RryVow4Zi7yYNIpCvAMG0fHnie+U/q13aui3vN7fsOikHpUU/umWuZMu+nHkEEAi9ADUigAACCCCAAAIIIBCtAuac8Yef3DLnkIGMYeNG6a+/CV0EYkWe4hNgjyw+66hqadNm6cuvgts9Nltlvv42zzJRNX46iwACCCCAAAIIIIAAAgggEHqBOV+6Zc43g6nZnJtu3BRMCfKGWSDmmyfyEfOb2NkAf1riltcbfNklSwK7ki34mimBAAIIIIAAAggggAACCBSlAHUXh8DiH4IPQ5hz0yU/B1+uOMZDG/EpwN4Yn9u9wFHv2FlgllwzmF9cMZft5rqShQgggAACCCCAAAKhF6BGBBBAIEoEDqQo4Ns7Dx3Snr1cuHGoCc/DJ0AwLXz2Ed3yjp3Odg2fT9q9mze5iN64dA4BBBCIEAG6gQACCCCAAALxJbC3EAGxvXvjy4rRRraAs4hJZI+J3oVAoGwZZ78/7LLiaKVLOysbgm5TBQLFIUAbCCCAAAIIIIAAAggg4ECgQjm/zDmjg6IqU8ZJKcogUDQCBNOKxjUCaw2uS7Vr+4Mr8F/uSpX8Skr87wkPCCCAAAIIIIAAAggggAACCPwnkJgkmXPG/54G9VCjurNz1KAaiZnMDKSoBQimFbVwlNZ/ZC1nb1Q1a0TpgOk2AggggAACCCCAAAIIhFeA1uNCwOk5Y80azs5R4wKVQRa7AMG0YiePjgaPqetXzZrB9TUxUTrvHAc/ARpcM+RGAAEEEEAAAQQiSoDOIIAAAggELmDOGc25Y+AlpKPr+GXOUYMpQ14EilKAYFqAun6/X+nm93gDzB/t2TweqX1br0qXDnwkV7f26ogjAs9PTgQQQACBsArQOAIIIIAAAgggUOwC5pyx1eWBX4RhzknbXuuVOUct9s7SIAJ5CBBMywPm0MUffbZALdvdf+jimH5eobxfd9xmBchq5n85bXKydH0br05pmH++mMZicMUoQFMIIIAAAggggAACCCAQzQKNTvOrzf+8BX7ftrkl9M5OXn58IJo3doz2nWBaARt2zfrNurT9A+o7aHQBOQtYHaWrK1fyy7x5NT/fp4oVcgbLkktI9Y/z654uXp1MIC1KtzDdRgABBBBAAAEEEEAAAQSKX+C0U/zq0tmr46xzSnNumb0H5tyz2Xk+3XV7uipVzHkemj1fxM7TsZgXIJhWwCY+okYVvfnyQ3qkx80F5Izd1eZy2hYX+tTzXq8e7Zuuu+7wqncPrx5+MF03tfeqfFne3GJ36zMyBBBAAAEEEEAAgXgRYJwIFLeAuXjjZuuc0pxb9r7Pa59rmnNOc+558UU+bu0s7g1CewELEEwrgCrBiiTVqFpJFcuXyTWn2yWFIrn+q908hqK+oqrD/MXgqFp++yq1omqDehWSfSpeHc1LKaxjtzoQ1vZdYv9xCQOXHBu4XNZObP3PfizHhtgVu13EbSvrJSTzWmJfYF/IdR+wdpBcl7sUcftyOPtpMeHhUlwZVCzvlznXNOec4dz3wtG22d9J0SVAMK2Q26taxZIKRapQtoTdk6REd0jqC0WfqCM02xbH4nOsUqGkfcARTvMKZZN4DYfofTGc27F42i6+10Yw4ylXKkklS3jYj9mP2QcKsQ8kJ3lUoTSfB8G898RTXo4VSgb0/mJOjuJpv2Csge0Xsepk9ndSdAm4o6u7kdfbTdsPKBRpx56D9uAOpvlCUl8o+kQdodm2MecYon2+KFy27Dggn19hfQ1t35Ma1vaLwpU64+u9YNe+VB046GU/juD3Ol6Tkf+aTEn1asdePg/YV3PfVzlWyN3l0P3FnBwduozngdnhFH1OZn8nRZdA3ATTomuz0FsEEEAAAQQQQAABBBBAAAEEEHAiQBkEilqAYFoBwn6/X2lp6UpP99o57Xlvxry9gAkCCCCAAAIIIIAAAgggUHgBakAAAQQQiBIBgmkFbKiV/2zQaZfcob6DRmvz1h32/KPPvF5AKVYjgAACCCCAAALxIsA4EUAAAQQQQACB+BIgmFbA9j62bi39NndcjjT44bsKKMVqBBBAAIGIF6CDCCCAAAIIIIAAAggggIADAYJpDtAogkA4BWgbAQQQQAABBBBAAAEEEEAAAQTCJ1BcwbTwjZCWEUAAAQQQQAABBBBAAAEEEECguARoB4GYFyCYFvObmAEigAACCCCAAAIIIIBAwQLkQAABBBBAIDABgmmBOZELAQQQQAABBBCITAF6hQACCCCAAAIIIFCsAgTTipWbxhBAAAEEMgV4RAABBBBAAAEEEEAAAQSiUYBgWjRuNfocTgHaRgABBBBAAAEEEEAAAQQQQACB2BfIc4QE0/KkYQUCCCCAAAIIIIAAAggggAAC0SZAfxFAoKgFCKYVtTD1I4AAAggggAACCCCAQMEC5EAAAQQQQCBKBAimRcmGopsIIIAAAgggEJkC9AoBBBBAAAEEEEAgvgQIpsXX9ma0CCCAQKYAjwgggAACCCCAAAIIIIAAAg4ECKY5QKNIOAVoGwEEEEAAAQQQQAABBBBAAAEEYl8gckdIMC1ytw09QwABBBBAAAEEEEAAAQQQiDYB+osAAjEvQDAt5jcxA0QAAQQQQAABBBBAoGABciCAAAIIIIBAYAIE0wJzIhcCCCCAAAIIRKYAvUIAAQQQQAABBBBAoFgFCKYVKzeNIYAAApkCPCKAAAIIIIAAAggggAACCESjAMG0aNxq4ewzbSOAAAIIIIAAAggggAACCCCAQOwLMMI8BQim5UnDCgQQQAABBBBAAAEEEEAAgWgToL8IIIBAUQsQTCuk8BGVSyoUqXK5EnZPSiS6Q1JfKPpEHaHZtjgWn2ONSiXldonXUIjel9h3i2/fjSTrCmWSVKqEh9cRryP2gULsA//H3p3A2VT+Dxz/jmHs+1gG6Scq2Ur2op8fWVIJpR+yVCQRxa/QSGRtConIHspPoaQUk2x/IXu2+lFkS7bsDLOY//0+497uzNzhNnPvzLlnPvPqOee5z/Oc5zzP+3Q7zXfOkiMkWArlDcEwDYZW+u8iY8mY86H+coR9xtjjnv7u+u87KbAECKYF1vFitAgggAACCPhLgH4RQAABBBBAAAEEEEDACwGCaV4g0QQBBKwswNgQQAABBBBAAAEEEEAAAQQQSD8BgmnpZ514Tx4+nTp9TqKuRHuooQgBBBBAAAH/CcTGxaXYOeemFGmoQMAlEB8fLzf6HrkakkEAAY8C+jvQ0WOn5Nq1eI/10dExcvzkGdHvmscGFCJgdQHGZzsBgmkWOKSHfj8uzdr3k3+2ekmqN+0qA9+eITGxKf9iY4EhMwQELCVw+uwFqVj/6WTph60/WWqcDAYBKwoc+v2E3N2ws+gvMe7j49zkrkEegRsLLF62Xhq3eSVZo+adwpOdmybO/CJZOwoQsKpAeoyr54D3zO9AjRzfoX+26iWjJ81z7VaDZxNnLZKqjZ+TBq17ywMte8n2n/a56skggAACGSVAMC2j5N32O2zsR1K+XGnZvHSKLJ49Upau3ChLV2xwa0EWAQRuJKD/o6X1kyL+I998HOFKd1cop8UkBBBIQaBt96Hy0FN9PdZybvLIQmFgCKTbKDXo3KTtq9J/xJQU99mr8+Ou85Keo9q1fDDFtlQgkBkF7ixbWhbOGCZbIqfI0L6dZcYn38jOn/cbih93/yoTPlwoH40Plx+XTZMWTYP+9ywAABAASURBVOtJ70Hvp3gFm9mIBQIIIJAOAgTT0gH5Rrs4d+GSrN20Szo80Vhy5giRMqXDHCeJ++Xb1ZtutBl1CCDgQaBUWKjcWqqYK+l3ykMzihCwqED6D+u9IT1l7sSByXbMuSkZCQUIeBQoUTxUZo17TQa81MFjvRYWKZzfdV7Sc1SB/HmEHwQQ+EvgxWdbyh23lZIc2UOk/n33SLEiBWX9lt2mwYrvt0md6hXl3sp3SLZsWc3vTMdPnpE9+w6ZehYIIIBARgkQTMso+ev7PfXnWZMrFVbErHVRumQx+ePEac2SELC+gIVGOGbyPHk9YrrMnh8pGgyw0NAYCgKWFCgaWsDxS0uhZGPj3JSMhAIEPApkDQ6W4kUKScEbBMjmL15tzk16e6deyeaxIwoRQMAIHDxy3DwbTa9W04I/TvwpZW4prlmT9LylmROnEn6H0jwJAQQQyAiBTBtMywhsT/s8f/GyKQ4JyWbWusju+KvM6bPnNUtCAAEvBLI7vj/tWjaUKhXKSqECeWXqnMXy9EsjRR9W68XmNEEAgSQCnJuSgPARgVQKNKlfQx6oXUX0CrYVa7fJ410GCQG1VGKyme0FLl2+Ii+/Md5chVa3ZmUz3/MXLkmO7NlN3rnIlTOHXLwc5fzIGgGvBGiEgK8FCKb5WvRv9pcvTy6zRUxMrFnr4urVaEdAIJ9mSQgg4IVAntw5zS02zz31iPR5/kmZPS5c9u4/Iv/7lVsAvOCjCQLJBDg3JSOhAIFUCfR4pqW80PEx6d7pMXNLdd48OWX5mq2p6ouNMqVAppl01JVo8yy0uLhrMn5YLwkOTvg1NV/e3HI1OjqRw+WoK5InV85EZXxAAAEE0lsg4b9S6b1X9ucSCC1cwOQPHz1h1ro4cPiYhBVNftuN1pEQQODmAkVDC5pGUY7AtMmwQACBvyXAuelvcdE4mQAFngT0eU9FChUQzk2edCjLzAJ6NfQL/cfIufOXzB9E3Z8rGFa0sOjvRk4f5+2dRUMTfodylrNGAAEE0luAYFp6iyfZX37HX1v0oZofLVgm+heZ3w79IV9+u04a/7NGkpZ8RACBlARWr98ukas2ij4nTf9a+d60z0RvAdC35Ka0DeUIJBPIhAUxsXESHRNjZq55TfqBc5MqkBC4uUB8fLzo3QWxju+Stjb5uDjNmts5Z85bKsdOnhb9bi1etl527flNalW9y9SzQAABkctRV6V9j2Fy4tQZGdL3WbkUdUV+P3bK9fzoBnWrmpe1bd35i/kezV4QKfqCAucz1TBEAAEEMkqAYFpGybvtd0Cv9rLb8T9X1Zt2lUc6vuYIpFWXpv+q6daCLAIpC1AjJhjwesQMue/RHlLjoW6yZMUP5hYBDQjggwACKQvod6Zpu76mQbP2/aTBEy+bvC44N6kCCYEbC+w7cFTuadRF+o+YYh6arnl9EY5zK30hTsPWfeSeBztLv+GTpV+PtlKtyh3OatYIZHqBCxcvy76DR0VfPNCq80Bp3OYVk57sOsjY3FOxnHTr2Fw69BxuvkefLlopowd1lyxZgkw9CwQQQCCjBDIqmJZR87XkfsuUDpPIue/IivnvysZvJsnw/l1EbwWw5GAZFAIWFGj0QHVZv3iC+Q7p9+j/Fo6T2tUqWHCkDAkBawlsWjJJdq+a6UprvhjvGiDnJhcFGQRSFChXpqTr++P8Lr0V3tW0L12ymCyfN0b0nLT0v2/L9uXTpWPrJqaOBQIIJAjoVWbO74772nk+CgoKkp7PtpItkVNk2SejZMPXH0jVSrcnbMwykAQYKwK2EyCYZqFDqieT3LlyWGhEDAWBwBHIGhxsLvvX71FQEH+tDJwjx0itLqDfKc5NVj9KjM+qAkFBQVK4YD65pURR0fOU8BNgAgzXKgI5sodIieKhXJFmlQPCOBBAQAim8S8BAggggAACCCBgJwHmggACCCCAAAIIIOBXAYJpfuWlcwQQQAABbwVohwACCCCAAAIIIIAAAggEggDBtEA4SozRygKMDQEEEEAAAQQQQAABBBBAAAEE7C/gmiHBNBcFGQQQQAABBBBAAAEEEEAAAQTsJsB8EEDA1wIE03wtSn8IIIAAAggggAACCCCQdgF6QAABBBBAwKICBNMsemAYFgIIIIAAAggEpgCjRgABBBBAAAEEELC3AME0ex9fZocAAgh4K0A7BBBAAAEEEEAAAQQQQAABLwQIpnmBRBMrCzA2BBBAAAEEEEAAAQQQQAABBBCwv4B1ZkgwzTrHgpEggAACCCCAAAIIIIAAAgjYTYD5IICA7QQIptnukDIhBBBAAAEEEEAAAQTSLkAPCCCAAAIIIOBZgGCaZxdKEUAAAQQQQCAwBRg1AggggAACCCCAAAJ+FSCY5ldeOkcAAQS8FaAdAggggAACCCCAAAIIIIBAIAgQTAuEo2TlMTI2BBBAAAEEEEAAAQQQQAABBBCwvwAzdAkQTHNRkEEAAQQQQAABBBBAAAEEELCbAPNBAAEEfC1AMM3XovSHAAIIIIAAAqkS2PHTPtm8fU+ybc+cuyArvt8q5y5cSlZnpYLtjvG/OWaWNGvfT8ZN/8zroW3b9Yu07T5UTp0+5/U2qW14/OQZs69de35LbRdsl34C7AkBBBBAAAEELCpAMM2iB4ZhIYAAAgggEJgCqR/1tLlfy+jJ85J1sHf/Een5+jg5dOR4sjqrFFyOuiLtHAGxw0dPyL8fayD/uKW4x6F16DlCXo+Ynqju4qUo0UBiTGxconJ/fLgaHW32dfFilD+6p08EEEAAAQQQQCBTCBBMyxSHmUkigMBNBWiAAAIIpEFg3ebdZutRA1+QTq2bSPPG95vPSRfx8fGiKWk5nxFAAAEEEEAAAQQCR4BgWuAcK48jpRABBBBAAIHMKLB7zwF5+uW3pGL9p6VJ21dl/IzPJSYm1kXxypAPZO4Xy12fNTNi3ByZOGuRZiXqSrS0f3G4rNmwQz5ZtEJ6hI+VAW9NM3WeFnoLZt+hk6Rei55S46Fu0m/4ZNdtmT9s/UlGjPvYbPbigPdMv78d+sN8dl9M+fgr0Vs6v1292bTR/esVac42P2zZbeak/eu+dI7OOl0fPHJcejr613odR/jIqXL67AWtSjGdO3/JzEu30fTGOx8manvs5GmzT+1PLRu07i1jpy4Q51Vyetvq4FEzE22jdd36jZY5n3+XqJwPCCCAAAIIIOBfAXq3jgDBNOscC0aCAAIIIIBAphc4fea8eT6aPiPNmbbs2JvI5dDvJ+TJ5wfLiVNnZGDvjlK7WgWZNPtLGTF+jqvdzp/3y9Fjf7o+a2bv/sNy4HqQKzY21gS2uvUbIxM+XCjZQ0IkJCSbNkuWNHjUsdcIWbnuR2nb8kF5ps1DjjFuEy3TuhLFCstdt99qtnv4wdqiKV/e3Oaz+6Jy+dukWJGCclvpMNNG24UWyi/OH739U/vp3bW16PPXhoyZ5axyzPWseRbbWUdw7A3HnLs89YgJBIaPnOJqkzRz7Vq8dH11lHyx9Htp1rCW6HYli4cmahYdHSOFCuSVF59pKWOHvChPPFJfps5ZLDM/XWLalS97i8xfvEr2X3fTwtXrf3Tse6fcW/l2/UhCAAEEbiZAPQIIIGA7AYJptjukTAgBBBBAAIHAFTjyx0nzfDR9RpozabDLfUYfLYg0H+d+8Ia0eayBvPnKMybANe/LlSboZCq9XOjzzVbMf1fGDO4ug/p08riVBvX0qrBRb7wg3Ts9ZtI7A7uJlq1cu01KlyzmCCzdIbly5pC2LRqaVLhgvmR91aleUUoUC5VyZUqaNtq2hFtw67NpQ6Rfj7bSrmVD6du9rehLAk6cOmv6mTVvqel/8tt95NHG95lbSXs4AmBrNuyUPx0BSNMoyWLtpp2mj4gBzxsj3e75Do8maqVjHzO4h3nO233VK0lzR99VK90uW3bsMe0efrCOWc//apVZ6+K/n38n2kYDf/rZvomZIYAAAggggAACngWyeC6mFAEEEEAAAQQCUiDAB13pzjKyffn0RGnaqFcTzep/vx4WbZff7eqvWlXvMm0OHD5m1t4uqle5U7Jly3rD5vsOHjX1Vd2uxHLmfz3wu6nzxSJ/vjyubooWKWjyx0+dMevdew+IvuSgY6+R8sRzg0z68JOEq8eOnzxt2iRd7Nl32BTVcQTxTMbDIjYuTj6YvUj09s6azbqZW2b1VtRLl6+a1nly55SnWjWSBYtXm/3vc8x3w7afpcMTjUw9CwQQQAABBBBAIDMKEEzLjEedOSNgQQGGhAACCKhAluAskjU4OFHK4igTtx+9NTF3rhxuJSI5soeYz3GO4JDJOBbXrl1zLNP+j+5Pe8nudhuoM+++P23jq5Q1yZyjoq5KlQpl5aUuj7vS6y93kEkRfaRUiaIed6tvCdUKT7ecarmmiTO/kPdnLDQBs4Uzhsn6xRNFr2DTOmf6d/P6JpC2ZMVG+fTLVea20AZ1qzmrWSOAAAIIIIAAAplOgGBa2g45WyOAAAIIIIBAOguUuTVM9OoofYmAc9ebtifcllgyLNQUFSqYT86ev2jyzkVcXOqCa7eWKma6cH9ZwI6f9puyUmFFzNrbRa6c2c3LD7xt72ynt4bqFWjV7y4v9WpVcaW6NStLvjy5nM0SrfVZblrwy/6EK9Q0nzSt3bhL7q9RSTq3bSZ33FbK9JUlKChRs7L/KCl65Z++QGHO58ukY+smki1rsPCDAAIIIIBAJhNgugi4BAimuSjIIIAAAggggEAgCDz56L/MMPXtm/qg/nlfrZK5C78zQSF9BphWNri/quhbM1et+1H0bZvhI6fK1p17tepvp4b1qpnnlUVMmCva3+r12+XtiXNNmdb9nQ7rVKsoazbskM2O4J8G55zPRLtZH+0fbyTHT56RPoMnmJcT6K2nXy//QVp3HSxnzyUOGjr7eqDO3Sarb/BUi8hVmxzbTzRlzkXNqnfJtl2/yvI1W02/+lbURZFrndWutd7qqc+z04KWD9XTFQkBBAJGgIEigAACCPhagGCar0XpDwEEEEAAAQR8KhAkQQn9Xb9iSt8iObTvsxK5aqO06z5U3hw9U+4sV1qG9euS0M6xfLhhbalaqZz0CB8rnfu8LRcvR4leYRaUJaGvoKCEtaPpTf/RK7+mjX5VTp89b/rr/tq7Jj/dUaZ12oG33TWuX0Mqly8jnV4aKW0dY9dnkOn2mjz14SzTh/3rLZ37Dx41c27eKVz6Dp0koYXySfbrt7hqH+6peJFC8lZ4V/n5l4PSe9D7jkDaBLmz7C0JTa5Pv02LBlLeYddr4DjT77rNu83z6IKT3GZat1Zls12LpnUd+/zrDaSm0F8L+kUAAQQQQAABBCwqkMWi42JYCCCAAAIIBKQAg069wLihvWTuxIHJOqhZtbzsXjXTBKGcla2aPWBeUrBkToSsXfS+6EsKioYWcFZLieKhMuWdV2TlgrGy7qsJon1/83GERAx43rTRB+trn82cUMPKAAAF6klEQVQa1jKfb7a4u0JZWT5vjHx3PWlen2Hm3O7ZNs1k05JJzo8prks6xjXj3X7y/aLxZlx1qlc0t2zqWDT45dxQg2dapi9acJbVq1VFIue+Y7b79pNRsu3bqTIp4j+SM0fC8+Kc7dzX+vyzLZFTZPHskab98P5djGXteyuYZjqej8aHy3efjpYV8981/p9OHiQzx/Y39c7Fus27TLZNi4ZmzQIBBBBAAAEEEMjMAgTTMvPRZ+4I/CVADgEEEAg4gazBwaK3dRbInyfFsWuAzf2tnyk29KIiKChIwooWMiko6PqlXV5s56lJwfx5JbXj0u00CBbi9kIET/twlunLGcqUDpMbtQ8rVliKFSno3CTZesbcJeYFCJXLl0lWRwECCCCAAAIIIJDZBAI8mJbZDhfzRQABBBBAAAEE0lfg6LFTom80fa7dw+m7Y/aGAAIIIIBAIgE+IGAdAYJp1jkWjAQBBBBAAAEEELCcgN4yq7d+Nqh7r+XGxoAQCAgBBokAAgggYDsBgmm2O6RMCAEEEEAAAQQQSLsAPSCAAAIIIIAAAgh4FiCY5tmFUgQQQACBwBRg1AgggAACCCCAAAIIIICAXwUIpvmVl84R8FaAdggggAACCCCAAAIIIIAAAgggEAgCaQumBcIMGSMCCCCAAAIIIIAAAggggAACCKRNgK0RQMAlQDDNRUEGAQQQQAABBBBAAAEE7CbAfBBAAAEEEPC1AME0X4vSHwIIIIAAAgggkHYBekAAAQQQQAABBBCwqADBNIseGIaFAAIIBKYAo0YAAQQQQAABBBBAAAEE7C1AMM3ex5fZeStAOwQQQAABBBBAAAEEEEAAAQQQsL+AD2ZIMM0HiHSBAAIIIIAAAggggAACCCCAgD8F6BsBBKwjQDDNOseCkSCAAAIIIIAAAgggYDcB5oMAAggggIDtBAim2e6QMiEEEEAAAQQQSLsAPSCAAAIIIIAAAggg4FmAYJpnF0oRQACBwBRg1AgggAACCCCAAAIIIIAAAn4VIJjmV14691aAdggggAACCCCAAAIIIIAAAgggYH8BO8yQYJodjiJzQAABBBBAAAEEEEAAAQQQ8KcAfSOAAAIuAYJpLgoyCCCAAAIIIIAAAgjYTYD5IIAAAggggICvBQim+VqU/hBAAAEEEEAg7QL0gAACCCCAAAIIIICARQUIpln0wDAsBBAITAFGjQACCCCAAAIIIIAAAgggYG8Bgmn2Pr7ezo52CCCAAAIIIIAAAggggAACCCBgfwFm6AMBgmk+QKQLBBBAAAEEEEAAAQQQQAABfwrQNwIIIGAdAYJp1jkWjAQBBBBAAAEEEEDAbgLMBwEEEEAAAQRsJ0AwzXaHlAkhgAACCCCQdgF6QAABBBBAAAEEEEAAAc8CBNM8u1CKAAKBKcCoEUAAAQQQQAABBBBAAAEEEPCrAME0v/J62zntEEAAAQQQQAABBBBAAAEEEEDA/gLM0A4CBNPscBSZAwIIIIAAAggggAACCCDgTwH6RgABBBBwCRBMc1GQQQABBBBAAAEEELCbAPNBAAEEEEAAAQR8LUAwzdei9IcAAggggEDaBegBAQQQQAABBBBAAAEELCpAMM2iB4ZhIRCYAowaAQQQQAABBBBAAAEEEEAAAXsLEEzT40tCAAEEEEAAAQQQQAABBBBAAAH7CzBDBHwgQDDNB4h0gQACCCCAAAIIIIAAAgj4U4C+EUAAAQSsI0AwzTrHgpEggAACCCCAAAJ2E2A+CCCAAAIIIICA7QQIptnukDIhBBBAAIG0C9ADAggggAACCCCAAAIIIOBZgGCaZxdKEQhMAUaNAAIIIIAAAggggAACCCCAAAJ+FbBEMM2vM6RzBBBAAAEEEEAAAQQQQAABBBCwhACDQMAOAgTT7HAUmQMCCCCAAAIIIIAAAgj4U4C+EUAAAQQQcAkQTHNRkEEAAQQQQAABBOwmwHwQQAABBBBAAAEEfC1AMM3XovSHAAIIIJB2AXpAAAEEEEAAAQQQQAABBCwq8P8AAAD//8I229UAAAAGSURBVAMA8KPcRy4PwNAAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute sample mean (ignoring the \"missing data\")\n",
    "sample_mean = data[~data.missing].y.mean()\n",
    "\n",
    "\n",
    "# Plot the sample mean along with the figure\n",
    "fig.add_hline(y=sample_mean, \n",
    "    annotation_text=f\"Our best guess if we only<br>get one number:<br>{sample_mean:.2f}\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "excellent-stocks",
   "metadata": {},
   "source": [
    "## How good of a guess is that?\n",
    "\n",
    "**Exercise (don't peek!)**: How would *you* evaluate how good of a guess this is for the two missing data points?\n",
    "\n",
    "We'll return to how I would do it in a bit."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fixed-plumbing",
   "metadata": {},
   "source": [
    "# The Regression Function\n",
    "\n",
    "In the meantime, let's look at the more interesting case. To this point, we've totally ignored the information that we have on the x-axis; i.e., what attempt number it is for me.  Clearly, there is some valuable information there!\n",
    "\n",
    "Put another way, if instead of just making a prediction based on our samples $y_1,...y_n$, we *also* used information from our $x_1,...,x_n$s, we should be able to make better predictions.  \n",
    "\n",
    "Put yet another way, then, we want our predictions to *be a function of the $X$s!*. Let's call that function $f(X)$. \n",
    "\n",
    "Now we can again ask, what should the function be to minimize our MSE? In other words, what should we set $f$ as to minimize $MSE(f)$?\n",
    "\n",
    "In the notation from above, we want to solve:\n",
    "\n",
    "$$h^* = \\mathop{\\mathrm{argmin}}_{f} MSE(f(X)) =  \\mathop{\\mathrm{argmin}}_{f} \\mathbb{E}\\left[\\left(Y - f(X)\\right)^2\\right]$$\n",
    "\n",
    "With similar math as above (see Shalizi, 1.12-1.15 if you want to do it out yourself), we get that:\n",
    "\n",
    "$$ h^* = \\mathbb{E}[Y | X = x] $$\n",
    "\n",
    "That is, the optimal prediction in terms of MSE for $Y$ when we have information about $X$ is to take the *conditional expected value* at that value of $x$. This equation above is called the **regression function**.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "laden-county",
   "metadata": {},
   "source": [
    "# Now what?\n",
    "\n",
    "Let's come back to our machine learning perspective, now. When we say we want to define a model/hypothesis class, we can now say precisely what we are doing:\n",
    "\n",
    "- We are building a model/hypothesis class *for the regression function*. \n",
    "- The regression function, in turn, makes an assumption that our *loss function is MSE*\n",
    "\n",
    "What model class might we choose that optimizes for our goals above---parsimony, efficient training, efficient prediction, and model expressivity?  The most common answer to this question for a very long time in both statistics and machine learning has been a **linear model**. In a **linear model**, we *choose* to approximate the regression function with a function that is linear in the model parameters. \n",
    "\n",
    "In the case where $x$ is a single variable, then, we approximate the regression function with the function $b_0 + b_1x$. \n",
    "\n",
    "We will sometimes write these parameters as a vector $\\boldsymbol{\\beta} = [b_0, b_1, \\ldots, b_p]^\\top$ for compact notation — you'll see this in the linear algebra section below.\n",
    "\n",
    "**Exercise:** Why don't we just use $b_1x$ as the approximation?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "common-setup",
   "metadata": {},
   "source": [
    "# Returning to our simple example\n",
    "\n",
    "Neat! Let's run a linear regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "married-pension",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YsEATJkyos7/D4ZDVapXdbldkZGSj4wdCTc7WA8petc3tBiTx1ghljUxWRkp8ECMDEEoYSwB35JgNw9cNLR3XUwCNxTgCuPM2v2wVwJgaraKiQps2bdLs2bNdbWazWenp6crNzfXqPUpLS1VZWano6Jo/nlJeXq7y8nLXc4fD0biggRCXkRKvy5JtyttVouIjZYrtcPLjXfx1GkB9MJYAANB4XE8BNBbjCNAwIVVEP3TokKqqqhQXF+fWHhcXpx07dnj1Hn/4wx+UkJCg9PT0Gl+fO3eusrOzGx0r0JyEmU1K69Ep2GEACHGMJQAANB7XUwCNxTgC1F/IrYneGI888oiWLVumFStWKCKi5rsNz549W3a73fXYs2dPgKMEAAAAAAAAADQVITUTvXPnzgoLC1NRUZFbe1FRkWw2W63bPv7443rkkUf0/vvvq2/fvh77WSwWWSwWn8QLAAAAAAAAAAhtITUTPTw8XAMGDNDatWtdbU6nU2vXrlVaWprH7R577DH96U9/Uk5OjgYOHBiIUAEAAAAAAAAAzUBIFdElKTMzUy+88IJeffVVbd++XVOnTtWxY8c0adIkSdKECRPcbjz66KOP6r777tNLL72kbt26qbCwUIWFhTp69GiwDgEAAADwq59++klLliwJdhgAAABAsxByRfQxY8bo8ccf15w5c9S/f39t2bJFOTk5rpuNFhQU6MCBA67+ixYtUkVFha699lrFx8e7Ho8//niwDgEAAADwq4KCAtckEwAAAACNE1Jrop8yffp0TZ8+vcbXNmzY4PZ89+7d/g8IAAAACCCHw1Hr60eOHAlQJAAAAEDzF5JFdAAAAKAli4qKkslk8vi6YRi1vg4AAADAexTRAQAAgBDToUMH/fGPf1RqamqNr3/77be65ZZbAhwVAAAA0DxRRAcAAABCzHnnnSdJGjp0aI2vR0VFyTCMQIYEAAAANFshd2NRAAAAoKW7/vrrFRER4fF1m82mrKysAEYEAAAANF8mgykqtXI4HLJarbLb7YqMjAx2OAAAAGgGyDEbhq8bAAAAfMnb/JKZ6AAAAEAzd84552jPnj3BDgMAAAAISayJDoSoKqehvF0lKj5SptgOERrUPVphZlOwwwKAJoExEnC3e/duVVZWBjsMoFnimgMAtWOcRHNAER0IQTlbDyh71TYdsJe52uKtEcoamayMlPggRgYAwccYCQAIFK45AFA7xkk0FyznAoSYnK0HNPX1zW4XIEkqtJdp6uublbP1QJAiA4DgY4wEAAQK1xwAqB3jJJoTiuhACKlyGspetU013Q34VFv2qm2qcnK/YAAtD2MkACBQuOYAQO0YJ9HcUEQHQkjerpJqf8E9nSHpgL1MebtKAhcUADQRjJEAgEDhmgMAtWOcRHNDER0IIcVHPF+AGtIPAJoTxkgg8BYuXKhu3bopIiJCqampysvLq7X/W2+9pd69eysiIkLnnHOO3nvvvQBFCvgW1xwAqB3jJJobiuhACIntEOHTfgDQnDBGAp4999xziouL8+l7Ll++XJmZmcrKytLmzZvVr18/DR8+XMXFxTX2//TTT3Xddddp8uTJ+vLLLzVq1CiNGjVKW7du9WlcQCBwzQGA2jFOorkxGYbB4kO1cDgcslqtevzxx9WpUye1bdu2zkfr1q1lMpmCHTqaoSqnoYseXadCe1mN64qZJNmsEfr4D5cqzMw5CKBlYYxEKDmVY9rtdkVGRjbqvY4dO6YPPvhABQUFqqiocHvt1ltvbdR71yY1NVXnn3++FixYIElyOp1KTEzUjBkzNGvWrGr9x4wZo2PHjmn16tWutgsuuED9+/fX4sWLvdrnqa/bX9ZuVafoKEW0DlOb1mFqE37y34jT/t+mdZgsrcwy8/MOP+CaAwC1Y5xEqPA2L28VwJhC2p133ul137CwMK+K7Y15tGnTRmFhYX48YjRFYWaTskYma+rrm2WS3C5Epy45WSOTuQABaJEYI9ESffnllxoxYoRKS0t17NgxRUdH69ChQ2rbtq1iY2P9VkSvqKjQpk2bNHv2bFeb2WxWenq6cnNza9wmNzdXmZmZbm3Dhw/XypUrPe6nvLxc5eXlrucOh0OSdP+qbTJb2noVa0Rrs6uoHnFagb1NeNj/ivA/fx5urrEo72n71mEmJtG0MFxzAKB2jJNobiiie+nqq69WZWWlSktLa3wcO3ZMVVVVkqSqqiodOXJER44c8WtM4eHhfi/WR0RE8AtBE5OREq9F489T9qptbjfpsFkjlDUyWRkp8UGMDgCCizESLc3MmTM1cuRILV68WFarVZ999plat26t8ePH67bbbvPbfg8dOqSqqqpqS8TExcVpx44dNW5TWFhYY//CwkKP+5k7d66ys7OrtV/Sq7OqWrXR8YoqHa90qqyy6v/+f/JRccLp6ltW6VRZpVM/qbI+h1gvYWbT/xXdzW6z4xtbpD+9T0Qrs1qFsRpnU8I1BwBqxziJ5oQiupeWLFlS50dtayuy+/JxSkVFhSoqKnT48GG/Hnt9Zsc3tFjPEjj1k5ESr8uSbcrbVaLiI2WK7RChQd2j+QsuAIgxEi3Lli1b9Nxzz8lsNissLEzl5eVKSkrSY489phtvvFHXXHNNsENslNmzZ7vNXnc4HEpMTNSC6wfUmptXOY2ThfX/K66f/v/jlac/d/7v+WlF+LLT/u+2/f9tc+p5ldNw7e9o+QkdLfcYkk+Eh5lPzqz3sHyN55n25v8V41kCx6e45gBA7Rgn0VxQRPeh1q1by2q1ymq1+m0fhmGorKzMrah+/PhxnxfqT//Y7M+L9/7gzyVwTi/uN6clcMLMJqX16BTsMACgSWKMREvRunVrmc0nZyfHxsaqoKBAffr0kdVq1Z49e/y2386dOyssLExFRUVu7UVFRbLZbDVuY7PZ6tVfkiwWiywWS73jCzOb1M7SSu0s/vt1xzAMVVYZOl5ZpfJK96L7/wrzzroL89UK+1Uqq3S6vXZKRZVTFVVOOcpO+O24JJbAqS+uOQBQO8ZJNAcU0UOMyWRSmzZt1KZNG3Xq5L8BqKqqyifF+dregyVwAAAAGufcc8/V559/rrPOOktDhw7VnDlzdOjQIb322mtKSUnx237Dw8M1YMAArV27VqNGjZJ08saia9eu1fTp02vcJi0tTWvXrtXtt9/ualuzZo3S0tL8Fqc/mUwmhbcyKbyVWWrT2m/7MQxD5SecbjPlPRbh/295m1PPyzwU6ZvWEjhh/yvaswQOAABooiiio0ZhYWFq37692rdv79f9tMQlcBqz7A1L4AAAgNM9/PDDrkkIDz30kCZMmKCpU6fqrLPO0l/+8he/7jszM1M33nijBg4cqEGDBmnevHk6duyYJk2aJEmaMGGCzjjjDM2dO1eSdNttt2no0KF64okndOWVV2rZsmX64osv9Pzzz/s1zlBnMpn+r9Acpo5+3E/wlsDx76z6xiyBc+o5S+AAAACK6AiqYC2B44+lcEJ5CZzaCvvNaQkcAACam4EDB7r+Hxsbq5ycnIDte8yYMTp48KDmzJmjwsJC9e/fXzk5Oa6bhxYUFLiWmpGkwYMHa+nSpbr33nt1zz336KyzztLKlSv9OmMe3gvEEjiSVFnlrLHozhI4LWsJHAAAQo3JMAwj2EE0ZQ6HQ1arVXa7vc4bi6Jl89USOLUV+E9fAidQWAIHAADf81WOeemll+pvf/uboqKiqr3/qFGjtG7dukZG2rSQm8Mb9V0Cp+z/+tZWpK9rCZxAOH0JnNML8CyBAwBAw3mbXzITHfARlsBpHH8te8MSOACA5mzDhg2qqKio1l5WVqaPPvooCBEBwdcclsApq3SqrKJKpSG6BM7Pt2MJHABAqKOIDoSYYC+B48tlcEJtCRxvC/ssgQMA8Lf//ve/rv9v27ZNhYWFrudVVVXKycnRGWecEYzQgBaDJXAahyVwAAChJCSL6AsXLtSf//xnFRYWql+/fpo/f74GDRrksf9bb72l++67T7t379ZZZ52lRx99VCNGjAhgxEBoMZlMatOmjdq0aaNOnTr5bT++XgKnpgL/6UvgVFVV6ciRI64bsPkLS+AAAPytf//+MplOFocuvfTSaq+3adNG8+fPD0JkAHytdZhZrcPMioxo7bd9eLMETtkJZ72K9HUtgVNW6VRZpVM/qdJvxxWIJXDatA5TGLPqAaDZC7ki+vLly5WZmanFixcrNTVV8+bN0/Dhw5Wfn6/Y2Nhq/T/99FNdd911mjt3rn71q19p6dKlGjVqlDZv3sxNjIAgYwmcxvHXsjcsgQMATd+uXbtkGIaSkpKUl5enmJgY12vh4eGKjY3lk1EAvBayS+D8rLAf6kvgeFoOhyVwACD4Qu7GoqmpqTr//PO1YMECSZLT6VRiYqJmzJihWbNmVes/ZswYHTt2TKtXr3a1XXDBBerfv78WL15c5/64eREAb9S1BI6vlsE5fQmcQGjIEjgNKexT6AHQ0pBjNgxfNwDeCPQSOIHCEjgA4HvN8saiFRUV2rRpk2bPnu1qM5vNSk9PV25ubo3b5ObmKjMz061t+PDhWrlyZY39y8vL3YpUDoej8YEDaPZCeQmcnxf4m+MSOG3atFFERITMZrNfjwMAguG1117T4sWLtWvXLuXm5qpr16566qmnlJSUpKuvvjrY4QFAwAVrCZyyRhbpm+oSONWWwWEJHAAtUEgV0Q8dOqSqqirFxcW5tcfFxWnHjh01blNYWFhj/9NvvnS6uXPnKjs72zcBA4CPsQRO4/h7+RuWwAEQaIsWLdKcOXN0++2366GHHnL9EbRjx46aN28eRXQA8JNQWgKn7MTpbc1vCRy3Ij9L4ADwk5AqogfC7Nmz3WauOxwOJSYmBjEiAAi81q1by2q1ymq1+m0f3iyB44tlcE7/dNHx48d1/Phx/fjjj347Lm+WwPFFMZ8lcABI0vz58/XCCy9o1KhReuSRR1ztAwcO1J133hnEyAAAvhBmNqmdpZXaWfxbvgn0EjgVVU5VVDnlKPNvsZ4lcAD4SkgV0Tt37qywsDAVFRW5tRcVFclms9W4jc1mq1d/i8Uii8Xim4ABAB6F+hI4pxf5m8sSOD8v7rMEDtD07dq1S+eee261dovFomPHjgUhIgBAKArkEjg1Ft2b6RI4bcLDFNGKJXCA5iCkiujh4eEaMGCA1q5dq1GjRkk6eWPRtWvXavr06TVuk5aWprVr1+r22293ta1Zs0ZpaWkBiBgAEGwsgdM4LIEDNG3du3fXli1b1LVrV7f2nJwc9enTJ0hRAQBQ3elL4ET5cT8NWQKn2jI4bs9ZAgdAiBXRJSkzM1M33nijBg4cqEGDBmnevHk6duyYJk2aJEmaMGGCzjjjDM2dO1eSdNttt2no0KF64okndOWVV2rZsmX64osv9PzzzwfzMAAAzUxTWgKnscvgNJUlcHxdwGcJHDRHmZmZmjZtmsrKymQYhvLy8vTXv/5Vc+fO1Ysvvhjs8AAACLjmsgTOqTbjZK2eJXCAIAu5IvqYMWN08OBBzZkzR4WFherfv79ycnJcNw8tKChw++j54MGDtXTpUt1777265557dNZZZ2nlypVKSUkJ1iEAANAgLIHTOL5YAqeuwj5L4CDQbrrpJrVp00b33nuvSktLdf311yshIUFPP/20xo4dG+zwAABotlgCp+HqWgLHbaY8S+CgiTAZxqm/aaEmDodDVqtVdrtdkZGRwQ4HAIBmI9BL4AQKS+DAG/7IMUtLS3X06FHFxsb65P2aInJzAAB8z5slcH5+k9jGLIETKP5YAufnM/JZAif0eZtfhtxMdKCpqnIayttVouIjZYrtEKFB3aP5qycA1KKpLYHTmKVwgrkEjr+K9m3atFGrVqSKoaK4uFj5+fmSTn5qJSYmJsgRAcFFbg4A3gvlJXDKKp3ur7MEDvyE34wAH8jZekDZq7bpgL3M1RZvjVDWyGRlpMQHMTIAaNkCsQROztYDuv/vX2n/jw4ZleUyTpSrk0WanJag/vFtfTqrPlSWwKlPYZ8lcBrnyJEj+v3vf6+//vWvcjpPfhQ7LCxMY8aM0cKFC/36RyqgqSI3B4Cmyd9L4ORsPaD73/1ahY7/TXCJaR+umy9O0nldOzb7JXDatA6ThSVw/IblXOrAR0ZRl5ytBzT19c36+Q/SqWFn0fjzSNYBoJkK9DWAJXCazxI4vsoxx4wZoy+//FLz589XWlqaJCk3N1e33Xab+vfvr2XLlvkq5CaB3Bx1ITcHgJYp0OO/pyVwTl/2pua17FvmEjiu/zfBJXC8zS8poteBRB21qXIauujRdW6zXE5nkmSzRujjP1zKX/MAoJlprteAhiyB05BlcE5fAicQzGaz3wv19VkCx1c5Zrt27fSvf/1LF110kVv7Rx99pIyMDB07dqzB790UkZujNs11XAYA1K45j/++WAKnrIYivaclcAKlKS2Bw5roQADk7SrxOEhLkiHpgL1MebtKlNbDP8sIAACCo7leAwKxBI50ckmahqw/39AlcJxOp44ePaqjR4/67Zgk75fA8dV68506dapxyRar1aqOHTv6ZB9AqGiu4zIAoHbNefz39xI40slJNOUnnB5mzjf/JXBMJ4579X4U0YFGKD7ieZBuSD8AQOjgGtA4YWFhat++vdq3b++3fRiGocrKyoAU60+pqKhQRUWFDh8+7LfjOt29996rzMxMvfbaa7LZbJKkwsJC3XXXXbrvvvsCEgPQVDAuA0DLxPjfOCaTSRH/V1yO8uN+Tl8Cp6yGIr2vl8Cpcho6Wn5CR8trv7Gss9y75SwpogONENshwqf9AAChg2tA02cymRQeHq7w8HC/3mCzIUvg/PTTT3r00UcbtL9zzz3X7aOp3377rbp06aIuXbpIkgoKCmSxWHTw4EHdcsstPjlGIBQwLgNAy8T4HxrCzCa1s7RSO4t/y9H1XQLnp8N2/WFe3e/b4KgLCgr0ww8/qLS0VDExMTr77LNlsVga+nZASBrUPVrx1ggV2suq3bxC+t+6W4O6Rwc6NACAn3ENwCkNWQLH4XA0uIg+atSoBm0HNHeMywDQMjH+43T1XQLH4XDoD170q1cRfffu3Vq0aJGWLVumvXv36vR7koaHh2vIkCG6+eabNXr0aJnN5vq8NRCSwswmZY1M1tTXN8skuQ3Wp+aHZY1MDrkbVwAA6sY1AMGSlZUV7BCAJolxGQBaJsZ/BILXle5bb71V/fr1065du/Tggw9q27ZtstvtqqioUGFhod577z1ddNFFmjNnjvr27avPP//cn3EDTUZGSrwWjT9PNqv7x4Js1ggtGn+eMlLigxQZAMDfuAYgmJYuXarPPvtMkvTFF19o6dKlQY4ICD7GZQBomRj/4W8m4/Tp5LWYPXu27rzzTq8+opqTk6PS0lJdc801jQ4w2BwOh6xWq+x2uyIjI4MdDpqwKqehvF0lKj5SptgOJz8mxF85AaBl4BqA+vJFjvn9999r0qRJWrdunYYNG6aXX35Z3bt393GkTQu5ObzFuAwALRPjP+rL2/zS6yJ6S0WiDgAAAF9rbI5ZUFAgSVq0aJE2btyoCy64QL/73e8kyXWDUX8qKSnRjBkztGrVKpnNZo0ePVpPP/202rdv77F/VlaW/v3vf6ugoEAxMTEaNWqU/vSnP9Xrpq/k5gAAAPAlb/PLeq2Jvm3bNiUnJ9fa5/XXX9f48ePr87YAAAAA6uHUuuh79+7VRx99pFatWikrK0smk0kvvfSS3/c/btw4HThwQGvWrFFlZaUmTZqkm2++2eOSMvv379f+/fv1+OOPKzk5WT/88IN+97vfaf/+/Xr77bf9Hi8AAADQGPWaid6mTRv96U9/0h133CGTyf2jEEVFRZoyZYrWr1+vI0eO+DzQYGG2CwAAAHzNVzlmRkaGbrvtNj3zzDP65z//6cMIPdu+fbuSk5P1+eefa+DAgZJOLuc4YsQI7d27VwkJCV69z1tvvaXx48fr2LFjatXKu7k95OYAAADwJW/zS69vLCqdnGX+2GOP6eKLL9bOnTvd2pOTk3X48GF9+eWXDY8aAAAAgFeeffZZDRgwQFdccYUGDRqkRYsWBWS/ubm5ioqKchXQJSk9PV1ms1kbN270+n1O/aJSWwG9vLxcDofD7QEAAAAEWr2Wcxk9erSGDBmiW265Rf369dP999+vjz76SGvWrNGDDz6omTNnVpuhDgAAAMD3rrvuOkVEREiS7rnnHpWWlgZkv4WFhYqNjXVra9WqlaKjo1VYWOjVexw6dEh/+tOfdPPNN9fab+7cucrOzm5wrAAAAIAv1GsmuiTFxsZqxYoVuvrqq3X33Xdr3bp12rhxozIzMymgAwAAAAHSsWNHffbZZ5Iki8Wijh07ur3+3HPP1ev9Zs2aJZPJVOtjx44djY7b4XDoyiuvVHJysu6///5a+86ePVt2u9312LNnT6P3DwAAANRXvYvoP/30k66//nqtXLlSs2bNUmxsrK677jpt3rzZH/EBAAAA8CAjI0N33XWXKisrXW2HDh3SyJEjNWvWrHq91x133KHt27fX+khKSpLNZlNxcbHbtidOnFBJSYlsNlut+zhy5IgyMjLUoUMHrVixQq1bt661v8ViUWRkpNsDAAAACLR6FdFXr16t5ORk7dy5U5s2bdLDDz+s//73vxoyZIjS0tJ033336cSJE/6KFQAAAMBp1q9frxUrVuj888/Xtm3b9I9//EMpKSlyOBzasmVLvd4rJiZGvXv3rvURHh6utLQ0HT58WJs2bXJtu27dOjmdTqWmpnp8f4fDocsvv1zh4eF69913XUvRAAAAAE1dvYroo0eP1owZM5Sbm6vevXtLktq1a6dFixZp9erVWrJkidsNhgAAAAD4z+DBg7VlyxalpKTovPPO069//WvNnDlTGzZsUNeuXf2yzz59+igjI0NTpkxRXl6ePvnkE02fPl1jx45VQkKCJGnfvn3q3bu38vLyJP2vgH7s2DH95S9/kcPhUGFhoQoLC1VVVeWXOAEAAABfqdeNRT///HP17du3xtcuu+wyffXVV5o5c6ZPAgMAAABQt2+++UZffPGFzjzzTO3fv1/5+fkqLS1Vu3bt/LbPN954Q9OnT9ewYcNkNps1evRoPfPMM67XKysrXXFI0ubNm7Vx40ZJUs+ePd3ea9euXerWrZvfYgUAAAAay2QYhhHsIJoyh8Mhq9Uqu93OGowAAADwCV/lmI888oiysrJ08803689//rO+++473XDDDXI4HHr99deVlpbmw6iDj9wcAAAAvuRtflnvG4sCAAAAaBqefvpprVy5UvPnz1dERIRSUlKUl5ena665RpdcckmwwwMAAACahXot5wIAAACg6fjqq6/UuXNnt7bWrVvrz3/+s371q18FKSoAAACgeQmpmeglJSUaN26cIiMjFRUVpcmTJ+vo0aO19p8xY4Z69eqlNm3aqEuXLrr11ltlt9sDGDUAAADgHz8voJ9u6NChAYwEAAAAaL5Cqog+btw4ff3111qzZo1Wr16tDz/8UDfffLPH/vv379f+/fv1+OOPa+vWrXrllVeUk5OjyZMnBzBqAAAAAAAAAECoCpkbi27fvl3Jycn6/PPPNXDgQElSTk6ORowYob179yohIcGr93nrrbc0fvx4HTt2TK1a1b2aDTcvAgAAgK+RYzYMXzcAAAD4krf5pddroj/wwAMNCuSSSy7RxRdf3KBtT5ebm6uoqChXAV2S0tPTZTabtXHjRv3617/26n1OfUE8FdDLy8tVXl7ueu5wOBoXOAAAAAAAAAAgZHldRN+1a1eDdtC/f/8GbfdzhYWFio2NdWtr1aqVoqOjVVhY6NV7HDp0SH/6059qXQJm7ty5ys7OblSsAAAAAAAAAIDmwesi+ssvv+yXAGbNmqVHH3201j7bt29v9H4cDoeuvPJKJScn6/777/fYb/bs2crMzHTbLjExsdH7BwAAAPzp1BKHZnNI3fYIAAAAaPK8LqL7yx133KGJEyfW2icpKUk2m03FxcVu7SdOnFBJSYlsNlut2x85ckQZGRnq0KGDVqxYodatW3vsa7FYZLFYvI4fAAAAaAqSk5O1ZcsWJSUlBTsUAAAAoFkJehE9JiZGMTExdfZLS0vT4cOHtWnTJg0YMECStG7dOjmdTqWmpnrczuFwaPjw4bJYLHr33XcVERHhs9gBAACApsIwjGCHAAAAADRLQS+ie6tPnz7KyMjQlClTtHjxYlVWVmr69OkaO3asEhISJEn79u3TsGHDtGTJEg0aNEgOh0OXX365SktL9frrr8vhcLhuFBoTE6OwsLBgHhICoMppKG9XiYqPlCm2Q4QGdY9WmNkU7LAAAGgwrm0AQhFjFwCgueHa1rKETBFdkt544w1Nnz5dw4YNk9ls1ujRo/XMM8+4Xq+srFR+fr5KS0slSZs3b9bGjRslST179nR7r127dqlbt24Bix2Bl7P1gLJXbdMBe5mrLd4aoayRycpIiQ9iZAAANAzXNpxuyZIlbs9PnDihv/3tb4qNjXW1TZgwIdBhAdUwdgEAmhuubS2PyeBzn7VyOByyWq2y2+2KjIwMdjjwUs7WA5r6+mb9/OQ+9ffARePPY1ADAIQUrm3Niy9yzF/+8pduzz/66CMNHDhQbdq0kSSZTCatW7eu0bE2JeTmoYexCwDQ3HBta168zS8bNBP9xhtv1OTJk3XxxRc3OEDAX6qchrJXbas2mEmSoZODWvaqbbos2cbHbAAAIYFrG2qyfv16t+cdOnTQ0qVLubEomgzGLgBAc8O1reUyN2Qju92u9PR0nXXWWXr44Ye1b98+X8cFNFjerhK3j9P8nCHpgL1MebtKAhcUAACNwLUNQChi7AIANDdc21quBhXRV65cqX379mnq1Klavny5unXrpiuuuEJvv/22KisrfR0jUC/FRzwPZg3pBwBAsHFtAxCKGLsAAM0N17aWq0FFdEmKiYlRZmam/vOf/2jjxo3q2bOnbrjhBiUkJGjmzJn69ttvfRkn4LXYDhE+7QcAQLBxbYM37rnnHkVHRwc7DMCFsQsA0NxwbWu5GlxEP+XAgQNas2aN1qxZo7CwMI0YMUJfffWVkpOT9dRTT/kiRqBeBnWPVrw1Qp5WnjLp5B2TB3Xnl0wAQGjg2gZvzJ49W1FRUcEOA3Bh7AIANDdc21quBhXRKysr9c477+hXv/qVunbtqrfeeku333679u/fr1dffVXvv/++3nzzTT3wwAO+jheoU5jZpKyRyZJUbVA79TxrZDI3eAAAhAyubQBCEWMXAKC54drWcjWoiB4fH68pU6aoa9euysvL0xdffKHf/e53ioyMdPX55S9/yUwYBE1GSrwWjT9PNqv7x2ds1ggtGn+eMlLigxQZAAANw7UNQChi7AIANDdc21omk2EYRn03eu211/Sb3/xGERHNf30fh8Mhq9Uqu93u9kcChIYqp6G8XSUqPlKm2A4nP07DXwMBAKGMa1vzQI7ZMHzdQhdjFwCgueHa1jx4m182qIjekpCoAwAAwNfIMRuGrxsAAAB8ydv80uvlXH73u99p7969XvVdvny53njjDW/fGgAAAAAAAACAJqmVtx1jYmJ09tln68ILL9TIkSM1cOBAJSQkKCIiQj/99JO2bdumjz/+WMuWLVNCQoKef/55f8YNAAAAtEjvvvuu132vuuoqP0YCAAAAtAz1Ws6lqKhIL774opYtW6Zt27a5vdahQwelp6frpptuUkZGhs8DDRY+MgoAAABfa0yOaTZ792FSk8mkqqqqhoRXp5KSEs2YMUOrVq2S2WzW6NGj9fTTT6t9+/Z1bmsYhkaMGKGcnBytWLFCo0aN8nq/5OYAAADwJW/zS69noktSXFyc/vjHP+qPf/yjfvrpJxUUFOj48ePq3LmzevToIZOJxfMBAAAAf3I6ncEOQePGjdOBAwe0Zs0aVVZWatKkSbr55pu1dOnSOredN28evzcAAAAgpNSriH66jh07qmPHjr6MBQAAAEATt337duXk5Ojzzz/XwIEDJUnz58/XiBEj9PjjjyshIcHjtlu2bNETTzyhL774QvHx8YEKGQAAAGiUBhfRP/roIz333HP6/vvv9dZbb+mMM87Qa6+9pu7du+uiiy7yZYwAAAAAPDh27Jg++OADFRQUqKKiwu21W2+91ef7y83NVVRUlKuALknp6ekym83auHGjfv3rX9e4XWlpqa6//notXLhQNpvNq32Vl5ervLzc9dzhcDQueAAAAKABGlREf+edd3TDDTdo3Lhx2rx5syuxtdvtevjhh/Xee+/5NEgAAAAA1X355ZcaMWKESktLdezYMUVHR+vQoUNq27atYmNj/VJELywsVGxsrFtbq1atFB0drcLCQo/bzZw5U4MHD9bVV1/t9b7mzp2r7OzsBscKAAAA+IJ3dyX6mQcffFCLFy/WCy+8oNatW7vaL7zwQm3evNlnwQEAAADwbObMmRo5cqR++ukntWnTRp999pl++OEHDRgwQI8//ni93mvWrFkymUy1Pnbs2NGgON99912tW7dO8+bNq9d2s2fPlt1udz327NnToP0DAAAAjdGgmej5+fm6+OKLq7VbrVYdPny4sTEBAAAA8MKWLVv03HPPyWw2KywsTOXl5UpKStJjjz2mG2+8Uddcc43X73XHHXdo4sSJtfZJSkqSzWZTcXGxW/uJEydUUlLicZmWdevWaefOnYqKinJrHz16tIYMGaINGzbUuJ3FYpHFYvH2EAAAAAC/aFAR3Waz6bvvvlO3bt3c2j/++GMlJSX5Ii4AAAAAdWjdurXM5pMfLo2NjVVBQYH69Okjq9Va71nbMTExiomJqbNfWlqaDh8+rE2bNmnAgAGSThbJnU6nUlNTa9xm1qxZuummm9zazjnnHD311FMaOXJkveIEAAAAAq1BRfQpU6botttu00svvSSTyaT9+/crNzdXd955p+677z5fxwgAAACgBueee64+//xznXXWWRo6dKjmzJmjQ4cO6bXXXlNKSopf9tmnTx9lZGRoypQpWrx4sSorKzV9+nSNHTtWCQkJkqR9+/Zp2LBhWrJkiQYNGiSbzVbjLPUuXbqoe/fufokTAAAA8JUGFdFnzZolp9OpYcOGqbS0VBdffLEsFovuvPNOzZgxw9cxAgAAAKjBww8/rCNHjkiSHnroIU2YMEFTp07VWWedpZdeeslv+33jjTc0ffp0DRs2TGazWaNHj9Yzzzzjer2yslL5+fkqLS31WwwAAABAoJgMwzAaunFFRYW+++47HT16VMnJyWrfvr0vY2sSHA6HrFar7Ha7IiMjgx0OAAAAmgFyzIbh6wYAAABf8ja/bNBM9FPCw8OVnJzcmLcAAAAAAAAAAKDJalQRHQAAAEDwdO/eXSaTyePr33//fQCjAQAAAJqnkCqil5SUaMaMGVq1apVr7cWnn37aq2VkDMPQiBEjlJOToxUrVmjUqFH+DxgAAADwo9tvv93teWVlpb788kvl5OTorrvuCk5QAAAAQDMTUkX0cePG6cCBA1qzZo0qKys1adIk3XzzzVq6dGmd286bN6/WWToAAABAqLnttttqbF+4cKG++OKLAEcDAAAANE/mYAfgre3btysnJ0cvvviiUlNTddFFF2n+/PlatmyZ9u/fX+u2W7Zs0RNPPKGXXnopQNECAAAAwXPFFVfonXfeCXYYAAAAQLMQMkX03NxcRUVFaeDAga629PR0mc1mbdy40eN2paWluv7667Vw4ULZbLY691NeXi6Hw+H2AAAAAELJ22+/rejo6GCHAQAAADQLIbOcS2FhoWJjY93aWrVqpejoaBUWFnrcbubMmRo8eLCuvvpqr/Yzd+5cZWdnNypWAAAAIBDOPfdctyULDcNQYWGhDh48qGeffTaIkQEAAADNR9CL6LNmzdKjjz5aa5/t27c36L3fffddrVu3Tl9++aXX28yePVuZmZmu5w6HQ4mJiQ3aPwAAAOBPo0aNcntuNpsVExOjSy65RL179w5OUAAAAEAzE/Qi+h133KGJEyfW2icpKUk2m03FxcVu7SdOnFBJSYnHZVrWrVunnTt3Kioqyq199OjRGjJkiDZs2FBtG4vFIovFUp9DAAAAAIIiKysr2CEAAAAAzV7Qi+gxMTGKiYmps19aWpoOHz6sTZs2acCAAZJOFsmdTqdSU1Nr3GbWrFm66aab3NrOOeccPfXUUxo5cmTjgwcAAAACrD737ImMjPRjJAAAAEDLEPQiurf69OmjjIwMTZkyRYsXL1ZlZaWmT5+usWPHKiEhQZK0b98+DRs2TEuWLNGgQYNks9lqnKXepUsXde/ePdCHAAAAADRaVFSU2zrotamqqvJzNAAAAEDzFzJFdEl64403NH36dA0bNkxms1mjR4/WM88843q9srJS+fn5Ki0tDWKUAAAAgP+sX7/e9f/du3dr1qxZmjhxotLS0iRJubm5evXVVzV37txghQgAAAA0KybDMIxgB9GUORwOWa1W2e12Pg4LAAAAn/BVjjls2DDddNNNuu6669zaly5dqueff77GewCFMnJzAAAA+JK3+aU5gDEBAAAgxFQ5DeXu/FF/37JPuTt/VJWT+RdNSW5urgYOHFitfeDAgcrLywtCRAAAAPAH8vLgCqnlXAAAABA4OVsPKHvVNh2wl7na4q0RyhqZrIyU+CBGhlMSExP1wgsv6LHHHnNrf/HFF5WYmBikqAAAAOBL5OXBRxEdAAAA1eRsPaCpr2/Wz+e3FNrLNPX1zVo0/jwS9ibgqaee0ujRo/XPf/5TqampkqS8vDx9++23euedd4IcHQAAABqLvLxpYDkXAAAAuKlyGspeta1aoi7J1Za9ahsfIW0CRowYoW+++UYjR45USUmJSkpKNHLkSH3zzTcaMWJEsMMDAABAI5CXNx3MRAcAAICbvF0lbh8V/TlD0gF7mfJ2lSitR6fABYYaJSYm6uGHHw52GAAAAPAx8vKmgyI6AAAA3BQf8ZyoN6QffOu///2vUlJSZDab9d///rf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      "text/plain": [
       "<Figure size 1500x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "\n",
    "f = 'y~x'\n",
    "model = ols(formula=f, data=data[~data.missing]).fit()\n",
    "\n",
    "fig = plt.figure(figsize =(15,8))\n",
    "fig = sm.graphics.plot_regress_exog(model, 'x', fig=fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tight-riverside",
   "metadata": {},
   "source": [
    "## Making and evaluating predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "featured-american",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     2.061862\n",
       "15    1.982348\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(data[data.missing])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "viral-personality",
   "metadata": {},
   "source": [
    "**Uh. Not so good. What if we make the task a little less trivial?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "intellectual-progressive",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NnD592vrfk7+/f6mOLSIiIiIV55///CdXr14lJiamWAP9ulatWhEREQHA2bNnWbJkCQ899NBN7zZ0cHDgd7/7XbFZ6Dfz8+8yJenZsydQtNTM7SrtuTZs2ECXLl2oVasWjRo1YtSoUZw7d876/jPPPMOCBQsAii0bIyJSFWgmuojUWNcLyYYNGwIwZ84cBg0axMiRI8nNzWXdunU88cQTfPjhhzzyyCPF9v3iiy946623mDJlCo0aNaJDhw4sWrSISZMm8Zvf/IYhQ4YAcM8995R4/kWLFtGuXTsGDRqEo6MjmzZt4rnnnqOwsJDJkyff1jX9PJe/vz8pKSncd9991qavp6cnH3/8MeHh4aSnp1uL98zMTB588EGSkpKIiIjA29ubtWvXsnXr1pue66effmLAgAE89dRTjBo1isaNG1NYWMigQYOIi4vj2WefpW3btnz//fdYLBaOHTvGxo0bAfjhhx949NFHueeee/jLX/6Ci4sLJ06cYMeOHdbjv/baa0ybNo3HH3+ciIgIsrOz+e677/jmm28YMWJEiZ/Byy+/zMyZM+nbty+TJk3i6NGjLFq0iD179rBjxw6cnJys216+fJn+/fszZMgQnnzySd5++23+8Ic/0L59ewYMGPCrn/eXX37J+vXrmTZtGi4uLixcuJD+/fuze/duAgMD7yjjn/70J9LS0jh79qx1NlLdunV/NZOIiIiIVKxNmzbRokULunfv/qvbfvzxx+Tn5/P000/f0Tl//l2mJNcnmzRo0KBcz7VixQrCwsK49957mTVrFikpKcyZM4cdO3Zw4MAB6tevz4QJEzh//jyfffYZq1atuu08IiI2YYiIVHPLly83AOPzzz83Ll68aJw5c8ZYt26d0bBhQ6NWrVrG2bNnDcMwjKysrGL75ebmGoGBgcaDDz5YbBww7O3tjR9++KHY+MWLFw3AmDFjxg0ZZsyYYfz8r9yfn88wDKNfv35GixYtio316tXL6NWr169eZ0m5wsPDDR8fH+PSpUvFxp966inD3d3dmuM///mPARgbN260bnPt2jWjTZs2BmBs3bq1WCbAWLx4cbFjrlq1yrC3tze2b99ebHzx4sUGYOzYscMwDMOwWCwGYFy8eLHE63nssceMdu3a/eI1X/93Gx8fbxiGYVy4cMFwdnY2Hn74YaOgoMC63fz58w3AWLZs2Q3XsHLlSutYTk6O4e3tbQwdOvQXz2sYRZ83YOzdu9c6dvr0acPV1dX4zW9+UyYZH3nkEaN58+a/mkVEREREbCMtLc0AjMcee6xU25vNZgMwDhw4UKrtS/tdZuvWrdZa8uLFi8b58+eN2NhYo1WrVoadnZ2xe/fuMj/X9e8Hubm5hpeXlxEYGGhcu3bNerwPP/zQAIyXXnrJOjZ58uQbvheJiFQFWs5FRGqMvn374unpiZ+fH0899RR169blvffeo0mTJgDUqlXLuu3ly5dJS0ujZ8+e7N+//4Zj9erVi4CAgDvK87/nS0tL49KlS/Tq1YtTp06RlpZ2W8f8eS7DMHjnnXcIDQ3FMAwuXbpk/adfv36kpaVZry82NpYmTZowaNAg6/6urq6MHz/+pudycXEhLCys2NiGDRto27Ytbdq0KXau67eAXp/VXr9+fQDef/99CgsLb3r8+vXrc/bsWfbs2VPq6//888/Jzc0lMjISe/v//xE3fvx46tWrV2wtSiia2T1q1Cjra2dnZ4KCgjh16lSpzhccHEyXLl2sr5s1a8Zjjz3GJ598QkFBQZlkFBEREZHKKz09HQA3N7dy2f66X/suc93YsWPx9PTE19eX/v37k5aWxqpVq7j33nvL/FzX7d27lwsXLvDcc88Vex7TI488Qps2bVTfiki1oOVcRKTGWLBgAXfddReOjo40btyYu+++u1gT88MPP+Rvf/sbBw8eLLZ+983W6TOZTHecZ8eOHcyYMYNdu3aRlZVV7L20tDTc3d1v+Zg/z3Xx4kWuXLnC0qVLWbp06U33uXDhAlC0HnrLli1vuN5WrVrddL8mTZrg7OxcbOz48eP8+OOP1vXhSzrXsGHDeP311xk3bhwvvPACffr0YciQITz++OPWfyd/+MMf+PzzzwkKCqJVq1Y8/PDDjBgxgh49epR4/adPnwaKHqD0v5ydnWnRooX1/euaNm16w/U2aNCA7777rsRz/K/WrVvfMHbXXXeRlZXFxYsX8fb2vuOMIiIiIlJ51atXD4CMjIxy2f66X/suc91LL71Ez549uXr1Ku+99x7r1q276XZlca7rSqpvAdq0aUNcXNwtnV9EpDJSE11EaoygoCDrU+Z/bvv27QwaNIj777+fhQsX4uPjg5OTE8uXLy/2IMvr/ncW+e04efIkffr0oU2bNsyePRs/Pz+cnZ356KOPsFgsJc7O/jU/z3X9OKNGjWLMmDE33eeX1m2/lXNdP1/79u2ZPXv2Tffx8/Oz7vvVV1+xdetWNm/eTGxsLOvXr+fBBx/k008/xcHBgbZt23L06FE+/PBDYmNjeeedd1i4cCEvvfQSM2fOvK3MP+fg4HDTccMwyuT4IiIiIlK91atXD19fXw4dOlSq7du0aQPA999/T8eOHUt9nl/6LvO/2rdvT9++fQEYPHgwWVlZjB8/npCQEGstXlbnEhGpSdREFxEB3nnnHVxdXfnkk09wcXGxji9fvrzUx7iVJ8tv2rSJnJwcPvjgA5o1a2YdL+khnrfL09MTNzc3CgoKrMV0SZo3b87hw4cxDKPYtZw4caLU52vZsiXffvstffr0+dXPw97enj59+tCnTx9mz57NK6+8wp/+9Ce2bt1qzVqnTh2GDRvGsGHDyM3NZciQIfz9739n+vTpxW4V/d9rADh69CgtWrSwjufm5hIfH/+rn8GtOn78+A1jx44do3bt2iXOxr+VjLfy35SIiIiI2Majjz7K0qVL2bVrF8HBwb+47YABA3BwcGD16tV3/HDR0vjHP/7Be++9x9///ncWL15cLuf43/r2+jKO1x09etT6Pqi+FZGqS2uii4hQNCPZzs6u2DrWCQkJbNy4sdTHqF27NgBXrlwp1fmg+IzntLS0W2ral4aDgwNDhw7lnXfeuensmIsXL1r/3K9fP86dO8cHH3xgHcvOzua1114r9fmefPJJzp07d9N9rl27RmZmJgCpqak3vH99Js71pXR++umnYu87OzsTEBCAYRjk5eXd9Px9+/bF2dmZuXPnFvtsY2JiSEtL45FHHin1tZTGrl27iq2Zf+bMGd5//30efvjhEme530rGOnXq3Pb6+CIiIiJSMX7/+99Tp04dxo0bR0pKyg3vnzx5kjlz5gBFd2aOHz+eTz/9lHnz5t2wbWFhIf/5z384e/ZsmWRr2bIlQ4cOZcWKFSQnJ5fJMX+ua9eueHl5sXjx4mLLYn788cf8+OOPN9S3ULrvTCIilYlmoouIUPTQm9mzZ9O/f39GjBjBhQsXWLBgAa1atSr1+ti1atUiICCA9evXc9ddd+Hh4UFgYCCBgYE3bPvwww/j7OxMaGgoEyZM4OrVq7z22mt4eXmRlJRUptf2j3/8g61bt9KtWzfGjx9PQEAAqamp7N+/n88//9za0J4wYQLz589n+PDhRERE4OPjw5o1a6wzvksza+Tpp5/mrbfeYuLEiWzdupUePXpQUFDAkSNHeOutt/jkk0/o2rUrf/nLX/jqq6945JFHaN68ORcuXGDhwoU0bdqUkJAQ62fk7e1Njx49aNy4MT/++CPz58/nkUceKfFBTJ6enkyfPp2ZM2fSv39/Bg0axNGjR1m4cCH33ntvsYeIloXAwED69evHtGnTcHFxYeHChQC/uNzMrWTs0qUL69evJyoqinvvvZe6desSGhpaptcgIiIiInemZcuWrF27lmHDhtG2bVtGjx5NYGAgubm57Ny5kw0bNvDMM89Yt//Pf/7DyZMnmTZtGu+++y6PPvooDRo0IDExkQ0bNnDkyBGeeuqpMsv3/PPP89ZbbxEdHc0//vGPMjvudU5OTrz66quEhYXRq1cvhg8fTkpKCnPmzMHf3x+z2WzdtkuXLgBMmzaNfv364eDgUKbXKiJSbgwRkWpu+fLlBmDs2bPnF7eLiYkxWrdubbi4uBht2rQxli9fbsyYMcP4+V+VgDF58uSbHmPnzp1Gly5dDGdnZwMwZsyYYRiGcdPjfPDBB8Y999xjuLq6Gv7+/sarr75qLFu2zACM+Ph463a9evUyevXq9avX+Uu5UlJSjMmTJxt+fn6Gk5OT4e3tbfTp08dYunRpse1OnTplPPLII0atWrUMT09P47e//a3xzjvvGIDx9ddfF8vUrl27m54rNzfXePXVV4127doZLi4uRoMGDYwuXboYM2fONNLS0gzDMIwtW7YYjz32mOHr62s4Ozsbvr6+xvDhw41jx45Zj7NkyRLj/vvvNxo2bGi4uLgYLVu2NJ5//nnrMQzj///d/u/nZRiGMX/+fKNNmzaGk5OT0bhxY2PSpEnG5cuXi21T0jWMGTPGaN68+U2v7X9d/7xXr15t/e+mU6dOxtatW4ttdycZr169aowYMcKoX7++AZQql4iIiIjYxrFjx4zx48cb/v7+hrOzs+Hm5mb06NHDmDdvnpGdnV1s2/z8fOP11183evbsabi7uxtOTk5G8+bNjbCwMOPAgQPW7Ur7XWbr1q0GYGzYsOGm7/fu3duoV6+eceXKlRKPcavn+nndu379eqNTp06Gi4uL4eHhYYwcOdI4e/bsDdc9depUw9PT07Czs7vhO5KISGVlZxh6epqIiJQsOjoas9nM2bNnadKkia3jVBp2dnZMnjyZ+fPn2zqKiIiIiIiIiJQjrYkuIiJW165dK/Y6OzubJUuW0Lp1azXQRURERERERKRG0proIiJiNWTIEJo1a0bHjh1JS0tj9erVHDlyhDVr1tg6moiIiIiIiIiITaiJLiIiVv369eP1119nzZo1FBQUEBAQwLp16xg2bJito4mIiIiIiIiI2ITWRBcRERERERERERERKYHWRBcRERERERERERERKYGWc/kVhYWFnD9/Hjc3N+zs7GwdR0RERESqGMMwyMjIwNfXF3t7zWEpS6rVRUREROROlLZWVxP9V5w/fx4/Pz9bxxARERGRKu7MmTM0bdrU1jGqFdXqIiIiIlIWfq1WVxP9V7i5uQFFH2S9evVsnEZERESkYhmGwZYtW1i4cCFbtmyxjnfq1InnnnuO3/zmNzg5OdkwYeWXnp6On5+fta6UsqNaXURERGqyjOw83t1/ljXfJHL+SjYATg529A/0ZnRwc9r6uNs4YeVX2lpdTfRfcf220Hr16qkwFxERkRrj2rVrrFmzBovFwuHDh4Giumjw4MFERUXRo0cPLZ9xi/R5lT3V6iIiIlITnUnNYvmOBN7ae4arOfmAPQ0buDPqvuY8fV9zvOq52jpilfNrtbqa6CIiIiJilZyczMKFC1m0aBGXLl0CoG7duoSHhzNt2jRatGhh44QiIiIiIjWPYRjsO32ZmLh4PvkhmUKjaLyVV13G9jAxpHMTXJ0cbBuyGlMTXURERET49ttvsVgsvPnmm+Tm5gLQvHlzpk2bRnh4OO7uuhVURERERKSi5RUU8tH3SSyLi+fbs2nW8Z6tGxEeYuL+1p7Y2+uOx/KmJrqIiIhIDVVYWMjmzZuxWCxs3brVOh4cHExUVBSDBw/G0VHlooiIiIhIRUvLymPt7kRW7kogKa1ovXNnR3uGdGrC2BATdzXW83Yqkr4ViYiIiNQwmZmZvPHGG8yZM4djx44B4ODgwOOPP47ZbKZbt242TigiIiIiUjOduniV5TsSeHvfWa7lFQDQqK4Lo4ObM7JbMxrWdbFxwppJTXQRERGRGuLs2bPMnz+fpUuXcvnyZQDc3d159tlnmTJlCs2aNbNxQhERERGRmscwDHad+ollcfFsOXIB47/rnbfxdiM8xMSgjr64OGq9c1tSE11ERESkmtuzZw8Wi4UNGzaQn58PQMuWLYmIiCAsLIy6devaOKGIiIiISM2Tk1/Apm+L1js/nJRuHe/TxovwEBPBLRtiZ6f1zisDNdFFREREqqGCggLef/99LBYLcXFx1vFevXphNpt59NFHcXCoObNZCgoNdsenciEjGy83V4JMHjjoAUwiIiIiYgM/Xc1hzTeJrPr6NBczcgCo5eTA412aEtbDnxaeNWuSS1Wo1dVEFxEREalG0tPTWbZsGXPnziU+Ph4AJycnnnrqKSIjI+ncubONE1a82ENJzNx02PpAJgAfd1dmhAbQP9DHhslEREREpCY5npLBsh3xvLv/HDn5hQB413NldPfmjAhqRv3azjZOWPGqSq2uJrqIiIhINZCQkMDcuXOJiYkhPb3oVlAPDw8mTpzI5MmT8fX1tXFC24g9lMSk1fsxfjaenJbNpNX7WTSqc6UqzkVERESkejEMg6+OXyImLp6vjl20jt/T1J3wEBMD2/vg5GBvw4S2U5VqdTXRRURERKoowzDYtWsXFouFd999l8LCotksbdq0ITIykqeffpratWvbOKXtFBQazNx0+IaiHMAA7ICZmw7zUIB3pbtdVERERESqtuy8AjYeOEdMXDzHL1wFwM4OHg5ozLieLejavEGNXu+8qtXqaqKLiIiIVDF5eXm88847WCwWdu/ebR1/6KGHMJvN9OvXD3v7mjmb5X/tjk8tdlvozxlAUlo2u+NTCW7ZsOKCiYiIiEi1dSEjm9W7TrP6m0RSM3MBqOPswJP3+hHW3USzhjV3ksv/qmq1uproIiIiIlXE5cuXee2115g3bx5nz54FwMXFhVGjRhEZGUlgYKCNE1YuFzJKLspvZzsRERERkZIcPp9OTFw8m749T25B0R2iTerXIqyHP0/e60c9VycbJ6xcqlqtria6iIiISCV34sQJ5syZw/Lly8nMzATAy8uL5557jkmTJuHl5XXLxywoNNgdn8qFjGy83FwJMnlUitsky5KXm2uZbiciIiIi8r8KCw2+OHKBmLh4dp36yTreuVl9xvVswcMBjXG8jfXOVavf+nblTU10ERERkUrIMAy+/PJLLBYLmzZtwjCKVgts3749ZrOZ4cOH4+p6ewVl7KEkZm46XOz2SR93V2aEBlSaB/eUhSCTBz7uriSnZd90rUU7wNu96EuJiIiIiEhpZeXm886+syzfkcCpS0WTXBzs7RgQ6E14iIlOzRrc9rFVqxepbLW6mugiIiIilUhubi7r1q0jOjqaAwcOWMcHDhyI2WymT58+d/QAothDSUxavf+GQjU5LZtJq/ezaFTnalOcO9jbMSM0gEmr92MHxa75+ic4IzSg2s3qEREREZHykZR2jTd2nubN3YmkXcsDwM3VkRFBzRjd3Z8m9Wvd0fFVqxepjLW6mugiIiIilcClS5dYsmQJ8+fPJzk5GYBatWoxZswYIiIiaNOmzR2fo6DQYOamwzed6WFQVKzO3HSYhwK8K02xeqf6B/qwaFTnG2bzeFfD2TwiIiIiUj6+PXOFmLh4Pvo+ifzComrav2FtwnqYeLxLU+q43HmLVbV65a7V1UQXERERsaEff/yR6OhoVq5cSXZ2UeHo6+vLlClTePbZZ2nYsOyeRL87PrVYcfpzBpCUls3u+FSCW5bdeW2tf6APDwV4V/t1JUVERESk7BQUGnx2OJmYuHj2JFy2jnczeRAeYqJP28ZlWk+qVq/ctbqa6CIiIiIVzDAMPv/8c2bPnk1sbKx1vHPnzkRFRfHEE0/g7Oxc5uct7ZPtS7tdVeJgb1etvmyIiIiISPnIyM7jrb1nWbEznjOp1wBwcrAj9B5fxoaYCGziXi7nVa1euWt1NdFFREREKkh2djZr1qwhOjqaQ4cOAWBnZ8djjz2G2WymZ8+ed7Te+a8p7ZPtS7udiIiIiEh1cSY1ixU7E3hrzxkycvIBqF/biZHdmjE62J/G9cq3RlatXrmpiS4iIiJSzlJSUli4cCGLFi3i4sWLANStW5exY8cybdo0WrZsWSE5gkwe+Li7kpyWfdO1Fu0oWn8wyORRIXmkevnqq6/417/+xb59+0hKSuK9995j8ODBpdp3x44d9OrVi8DAQA4ePFiuOUVERESuMwyD/YmXiYmLJ/ZQMv9d7pyWnnUYG2JiSKem1HJ2qJAsqtUrNzXRRURERMrJd999h8ViYe3ateTm5gLQrFkzpk2bRnh4OPXr16/QPA72dswIDWDS6v3YQbHi/Pr89xmhAZVu/UGpGjIzM+nQoQNjx45lyJAhpd7vypUrjB49mj59+pCSklKOCUVERESK5BUU8vGhovXOvz1zxTres3UjxoaY6NXaE/sKrolVq1duaqKLiIiIlKHCwkI+/vhjLBYLW7ZssY7fd999mM1mhgwZgqOj7Uqw/oE+LBrVmZmbDhd7cJG3uyszQgPoH+hjs2xStQ0YMIABAwbc8n4TJ05kxIgRODg4sHHjxl/cNicnh5ycHOvr9PT0Wz6fiIiI1FxpWXm8uSeRN3YmWGthZ0d7ftOxCWNDTNzt7WbTfKrVKy810UVERETKQGZmJitXrmTOnDkcPXoUAAcHB4YOHYrZbOa+++6zccL/1z/Qh4cCvNkdn8qFjGy83IpuC9WsFqloy5cv59SpU6xevZq//e1vv7r9rFmzmDlzZgUkExERkeok4VImy3fEs2HfWbJyCwBoVNeZUfc1Z9R9zWlU18XGCf+favXKSU10EREREaCg0LitQvXcuXPMnz+fJUuWcPnyZQDc3d0ZP348U6ZMoXnz5uUd/bY42NsR3LKhrWNIDXb8+HFeeOEFtm/fXuq7M6ZPn05UVJT1dXp6On5+fuUVUURERCqJ26nVDcPg61OpxMTFs+VICsZ/10dp4+3G2BATgzr44upUMeud3yrV6pWPmugiIiJS48UeSrrhlkmfX7llct++fVgsFtavX09+fj4ALVq0ICIigrCwMNzcbHsrqEhlVlBQwIgRI5g5cyZ33XVXqfdzcXHBxaXyzBQTERGR8nertXpufiGbvj1PTFw8h5P+f+m3B9t4ER5ionvLhtjZaVa33Bo10UVERKRGiz2UxKTV+4s9uAcgOS2bSav3s2hUZ2txXlBQwAcffIDFYmH79u3Wbe+//37MZjOhoaE4OFTO2SwilUlGRgZ79+7lwIEDTJkyBSh6noBhGDg6OvLpp5/y4IMP2jiliIiI2Nqt1Oqpmbms/eY0b+w6zcWMomeouDrZM7RzU8J6mGjlVbeC00t1oia6iIiI1FgFhQYzNx2+oSgHMAA7YOamw9znV4c3Vixn7ty5nDp1CgBHR0eeeuopIiMj6dKlS0XGFqny6tWrx/fff19sbOHChXzxxRe8/fbbmEwmGyUTERGRyqK0tbqpUV1W7Ezg3f1nyckvBKBxPRdGB/szIqgZDeo4V2RsqabURBcREZEaa3d8arHbQn8uL+0Ch7/YRNNXPifzagYAHh4eTJgwgcmTJ9OkSZOKiipS6V29epUTJ05YX8fHx3Pw4EE8PDxo1qwZ06dP59y5c6xcuRJ7e3sCAwOL7e/l5YWrq+sN4yIiIlIz/VqtbgBJadn0i/7KOhbYpB7jQlowsL0Pzo72FZBSaooq+V/TggUL8Pf3x9XVlW7durF79+5S7bdu3Trs7OwYPHhw+QYUERGRKuFCxs2L8pxzP3Jx4z84t2Qc6XveI/NqBnfffTeLFi3izJkzvPLKK2qgi/zM3r176dSpE506dQIgKiqKTp068dJLLwGQlJREYmKiLSOKiIhIFVJSrf5zdsDDAY1Z/+x9bJoSwuBOTdRAlzJX5Wair1+/nqioKBYvXky3bt2Ijo6mX79+HD16FC8vrxL3S0hI4He/+x09e/aswLQiIiJSmXm5uVr/bBQWkHV0B+l73ic36ah13LV5R/725z9gDnsSe3sV4yIl6d27N4Zxsxuui6xYseIX93/55Zd5+eWXyzaUiIiIVFn/W6v/EsuwjgzupAkuUr6qXBN99uzZjB8/nrCwMAAWL17M5s2bWbZsGS+88MJN9ykoKGDkyJHMnDmT7du3c+XKlRKPn5OTQ05OjvV1enp6iduKiIhI1RZk8sDTOZ+T2z8gff8mCtIvFr3h4EidgN64d32MZq3bEhn2IPb2drYNKyIiIiJSgwSZPGhU15lLV3NL3MbH3ZXQDr4VmEpqqirVRM/NzWXfvn1Mnz7dOmZvb0/fvn3ZtWtXifv95S9/wcvLi/DwcLZv3/6L55g1axYzZ84ss8wiIiJSOZ08eZI5c+Zw6PUYsq9lAWBf2x23TgNx6zQQxzoNAJgRGoCDGugiIiIiIhWisNBg27ELxMTFl9hAv16dq1aXilKlmuiXLl2ioKCAxo0bFxtv3LgxR44cuek+cXFxxMTEcPDgwVKdY/r06URFRVlfp6en4+fnd9uZRUREpPIwDIOvvvoKi8XCBx98YF16wr9VG+zveYR8Uw/sHJ0B8HZ3ZUZoAP0DfWwZWURERESkRsjKzeed/edYviOeUxczAXCwt6OjnzsJl7L4KfP/G+qq1aWiVakm+q3KyMjg6aef5rXXXqNRo0al2sfFxQUXF5dyTiYiIiIVKTc3l7feeovZs2dz4MAB6/iAAQOIioqiT58+FBqwOz6VCxnZeLm5EmTy0KwWEREREZFylpyWzRu7Elj7TSJp1/IAcHN1ZHhQM8Z096dJ/VoUFBqq1cWmqlQTvVGjRjg4OJCSklJsPCUlBW9v7xu2P3nyJAkJCYSGhlrHCgsLAXB0dOTo0aO0bNmyfEOLiIiIzfz0008sWbKE+fPnk5SUBECtWrUYPXo0ERERtG3b1rqtgx0Et2xoq6giIiIiIjXK92fTiIk7xYffJZFfWHSHaDOP2oT18OeJrn7Udfn/tqWDvZ1qdbGpKtVEd3Z2pkuXLmzZsoXBgwcDRU3xLVu2MGXKlBu2b9OmDd9//32xsRdffJGMjAzmzJmjZVpERESqqSNHjhAdHc3KlSu5du0aAD4+PkyZMoUJEybQsKEKcBERERGRilZQaPDZ4RSWxcWzOyHVOh5k8iA8xETfto01w1wqpSrVRAeIiopizJgxdO3alaCgIKKjo8nMzCQsLAyA0aNH06RJE2bNmoWrqyuBgYHF9q9fvz7ADeMiIiJStRmGweeff47FYuHjjz+2jnfq1Amz2cywYcNwdna2YUIRERERkZrpak4+b+05w4qdCSSmZgHgaG9HaAdfxvYw0b6pu40TivyyKtdEHzZsGBcvXuSll14iOTmZjh07Ehsba33YaGJiIvb29jZOKSIiIhUlOzubtWvXEh0dbb0Dzc7OjkGDBmE2m7n//vuxs9NsFhERERGRinb2chYrdiSwfs8ZMnLyAahf24mR3ZoxOtifxvVcbZxQpHTsDMMwbB2iMktPT8fd3Z20tDTq1atn6zgiIiLyXykpKSxatIhFixZx4cIFAOrUqcPYsWOZNm0arVq1snFCkSKqJ8uPPlsREZHKad/pyyyLi+fjQ0n8d7lzWnjWYWwPE0M7N6WWs4NtA4r8V2nrySo3E11EREQqRkGhwe74VC5kZOPl5kqQyaNSrE/4/fffEx0dzerVq8nNzQXAz8+PqVOnMn78eOvSbSIiIiIi1VVlrNXzCwr5+FAyMXHxHDxzxToe0qoR4SEmet3liX0l+D4hcjvURBcREZEbxB5KYuamwySlZVvHfNxdmREaQP9AnwrPU1hYSGxsLBaLhc8//9w63q1bN8xmM0OGDMHJyanCc4mIiIiIVLTKVqunXctj/Z5E3th5mnNXrgHg7GDPYx19GRtioq2P7haTqk9NdBERESkm9lASk1bv5+frvSWnZTNp9X4WjepcYcV5VlYWK1euJDo6mqNHjwJgb2/P0KFDMZvNBAcHV0gOEREREZHKoDLV6qd/ymT5jgTe2nuGrNwCABrWcWbUfc0ZdV9zPN1cKiSHSEVQE11ERESsCgoNZm46fENRDmAAdsDMTYd5KMC7XG8XPX/+PPPnz2fJkiWkpqYCUK9ePcaNG8fUqVPx9/cvt3OLiIiIiFRGlaFWNwyDb+JTiYmL5/MfU7j+pMW7G7sRHmJiUEdfXJ203rlUP2qii4iIiNXu+NRit4X+nAEkpWWzOz6V4JYNy/z8+/fvx2KxsH79evLy8gAwmUxEREQwduxY3NzcyvycIiIiIiJVgS1r9dz8QjZ/f57Xt8fzw/l063jvuz0ZF9KCHq0aYmen9c6l+lITXURERKwuZJRclN/OdqVRUFDApk2bsFgsfPXVV9bxnj17YjabGTRoEA4Oms0iIiIiIjWbLWr1y5m5rN2dyBs7E7iQkQOAq5M9Qzo3ZWwPf1p5aZKL1AxqoouIiIiVl5trmW73SzIyMli+fDlz587l5MmTADg6OjJs2DDMZjNdunS543OIiIiIiFQXFVmrn7hwlWU74nl3/1my8wr/e1wXxnT3Z0RQMxrUcb7jc4hUJWqii4iIiFWQyQMfd1eS07JvutaiHeDt7kqQyeO2z5GYmMi8efN47bXXSEtLA6BBgwZMmDCBKVOm0KRJk9s+toiIiIhIdVXetbphGMSduERMXDzbjl60jrfzrce4niYeae+Ls6P97YUXqeLURBcRERErB3s7ZoQGMGn1fuygWHF+fYXDGaEBt/Wgoq+//hqLxcI777xDQUEBAHfddReRkZGMHj2aOnXq3HF+EREREZHqqrxq9ey8Aj44eJ5lO+I5kpxRdDw76Nu2MeEhJrqZPLTeudR4aqKLiIhIMf0DfVg0qjMzNx0u9uAib3dXZoQG0D/Qp9THys/P57333mP27Nl8/fXX1vEHH3wQs9nMwIEDsbfXbBYRERERkdIoy1r9YkYOq78+zZpvTnPpai4AtZ0deLKrH89098e/kSa5iFynJrqIiIjcoH+gDw8FeLM7PpULGdl4uRXdFlraWS1paWm8/vrrzJs3j9OnTwPg7OzMiBEjiIyMpEOHDuUZX0RERESk2rrTWv1IcjrL4uLZeOA8uQVF6537ursyprs/TwU1w72WU3nGF6mS1EQXERGRm3KwtyO4ZcNb2ufkyZPMnTuXZcuWcfXqVQA8PT2ZNGkSkyZNwtvbuzyiioiIiIjUKLdaqxcWGnx57CIxcfHEnbhkHe/oV5/wEBP9A71xctAdoiIlURNdRERE7ohhGMTFxTF79mzef/99DKNodcZ27dphNpsZOXIkrq6uNk4pIiIiIlLzXMst4J39Z1m2I55TFzMBsLeDAYE+jA0x0aV5AxsnFKka1EQXERGR25Kbm8uGDRuwWCzs27fPOt6/f3/MZjMPPfSQHkAkIiIiImIDKenZvLEzgbW7E7mSlQeAm4sjw+71Y0x3f/w8ats4oUjVoia6iIiI3JLU1FSWLFnC/PnzOX/+PACurq6MHj2aiIgIAgICbJxQRERERKRmOnQujZi4eD787jx5BUV3iPp51CKsu4kn7/WjrotagSK3Q//niIiISKkcPXqU6Oho3njjDa5duwaAt7c3U6ZMYcKECTRq1MjGCUXElr766iv+9a9/sW/fPpKSknjvvfcYPHhwidu/++67LFq0iIMHD5KTk0O7du14+eWX6devX8WFFhERqQYKCg0+/zGFmLh4dsenWseD/D0YG2LioYDGpX7oqIjcnJroIiIiUiLDMPjiiy+wWCxs3rzZOt6xY0fMZjPDhg3DxcXFhglFpLLIzMykQ4cOjB07liFDhvzq9l999RUPPfQQr7zyCvXr12f58uWEhobyzTff0KlTpwpILCIiUrVdzclnw94zrNiZwOmfsgBwtLfj0Xt8CA9pQfum7jZOKFJ9qIkuIiIiN8jJyWHt2rVER0fz3XffAWBnZ0doaChms5levXppvXMRKWbAgAEMGDCg1NtHR0cXe/3KK6/w/vvvs2nTJjXRRUREfsG5K9d4Y2cCb+5OJCM7HwD3Wk6M6NaMMcH+eLu72jihSPWjJrqIiIhYXbhwgUWLFrFw4UIuXLgAQO3atQkLCyMiIoLWrVvbOKGIVFeFhYVkZGTg4eFR4jY5OTnk5ORYX6enp1dENBERkUphf+JlYuLiiT2UTEFh0XrnLRrVISzExNDOTajtrDafSHnR/10iIiKlVFBosDs+lQsZ2Xi5uRJk8qg2awseOnQIi8XCmjVrrA2qpk2bMnXqVMaPH0+DBg1snFBEqrt///vfXL16lSeffLLEbWbNmsXMmTMrMJWIiFQV1bVWzy8oJPaHZGLi4jmQeMU63r1lQ8JDTDxwtxf21eA6RSo7NdFFRERKIfZQEjM3HSYpLds65uPuyozQAPoH+tgw2e0rLCzkk08+wWKx8Nlnn1nHg4KCMJvNDB06FCcnJxsmFJGaYu3atcycOZP3338fLy+vErebPn06UVFR1tfp6en4+flVREQREanEqmOtnp6dx/rdReudn7tyDQBnB3sGdfRlbA8TAb71bJxQpGZRE11ERORXxB5KYtLq/Rg/G09Oy2bS6v0sGtW5ShXnWVlZrFq1iujoaI4cOQKAvb09Q4YMwWw2ExwcrPXORaTCrFu3jnHjxrFhwwb69u37i9u6uLjoYcYiIlJMdavVT/+UyfIdCWzYe4bM3AIAGtZxZuR9zRl1XzO83LTeuYgtqIkuIiLyCwoKDWZuOnxDUQ5gAHbAzE2HeSjAu9LfLpqUlMSCBQtYvHgxP/30EwBubm6MGzeOqVOnYjKZbJxQRGqaN998k7Fjx7Ju3ToeeeQRW8cREZEqprrU6oZhsCfhMq9vP8VnP6Zg/PeC7mpcl/AQE491bIKrk4NtQ4rUcGqii4iI/ILd8anFbgv9OQNISstmd3wqwS0bVlywW3DgwAEsFgvr1q0jLy8PAJPJxLRp0xg7diz16ulWUBG5c1evXuXEiRPW1/Hx8Rw8eBAPDw+aNWvG9OnTOXfuHCtXrgSKlnAZM2YMc+bMoVu3biQnJwNQq1Yt3N3dbXINIiJStVT1Wj03v5CPvk8iJi6e78+lWcd73eVJeIiJnq0b6Q5RkUrC3tYBbseCBQvw9/fH1dWVbt26sXv37hK3fe211+jZsycNGjSgQYMG9O3b9xe3FxER+V8XMkouym9nu4pSUFDA+++/T+/evencuTOrVq0iLy+PkJAQ3nnnHY4fP05kZKQa6CJSZvbu3UunTp3o1KkTAFFRUXTq1ImXXnoJKLobJjEx0br90qVLyc/PZ/Lkyfj4+Fj/iYiIsEl+ERGpeqpqrX4lK5cFW0/Q859fELn+IN+fS8PF0Z7hQc34zHw/b4wN4v67PNVAF6lEqtxM9PXr1xMVFcXixYvp1q0b0dHR9OvXj6NHj970IUTbtm1j+PDhdO/eHVdXV1599VUefvhhfvjhB5o0aWKDKxARkaqktGsOVpa1Ca9evcqKFSuIjo7m5MmTADg6OvLEE09gNpu59957bZxQRKqr3r17Yxg3u6G+yIoVK4q93rZtW/kGEhGRaq+q1eonL15lWVw87+w/S3ZeIQCebi6MCW7OiG7N8ajjbOOEIlISO+OXKt1KqFu3btx7773Mnz8fgMLCQvz8/Jg6dSovvPDCr+5fUFBAgwYNmD9/PqNHj/7V7dPT03F3dyctLU2z9UREaqCCQoOQV78gOS37pmst2gHe7q7E/eFBm66zeObMGebNm8drr73GlStXAKhfvz4TJkxgypQpNG3a1GbZRGo61ZPlR5+tiEjNVhVqdcMw2HnyJ2Li4vniyAXreIBPPcJDTDzawQcXR613LmIrpa0nq9RM9NzcXPbt28f06dOtY/b29vTt25ddu3aV6hhZWVnk5eXh4eFx0/dzcnLIycmxvk5PT7+z0CIiUqU52NsxIzSASav3YwfFivPrZfiM0ACbFeXffPMNFouFt99+m4KCAgBat25NZGQkY8aMoU6dOjbJJSIiIiJS3ipzrZ6TX8D7B8+zLC6eI8kZRZnsoE+bxoSHmLivhYeWaxGpQqpUE/3SpUsUFBTQuHHjYuONGzfmyJEjpTrGH/7wB3x9fenbt+9N3581axYzZ86846wiIlJ99A/0YdGozszcdLjYg4u83V2ZERpA/0CfCs2Tn5/Pxo0bmT17drFfIj/wwANERUUxcOBA7O2r5GNPRERERERuSWWr1S9dzWH116dZ/fVpLl3NBaC2swNPdGnKMz1MmBppkotIVVSlmuh36h//+Afr1q1j27ZtuLrefD2s6dOnExUVZX2dnp6On59fRUUUEZFKqn+gDw8FeLM7PpULGdl4ubkSZPKo0FktaWlpxMTEMHfuXE6fPg2Ak5MTI0aMIDIyko4dO1ZYFhERERGRyqIy1OpHkzOIiTvFxoPnyc0vWu/cx92VMd39GX5vM9xrO1VYFhEpe1Wqid6oUSMcHBxISUkpNp6SkoK3t/cv7vvvf/+bf/zjH3z++efcc889JW7n4uKCi4tLmeQVEZHqxcHejuCWDSv8vKdOnWLu3LksW7aMjIyiW0EbNWrEpEmTeO655371Z6CIiIiISHVni1q9sNDgy+MXWRYXz/bjl6zjHfzqEx5iYkCgN04OukNUpDqoUk10Z2dnunTpwpYtWxg8eDBQ9GDRLVu2MGXKlBL3++c//8nf//53PvnkE7p27VpBaUVERG6fYRjs2LEDi8XCxo0bKSwsms0SEBBAZGQko0aNolatWjZOKSIiIiJS81zLLeDdA2dZFhfPyYuZANjbQf9Ab8JDTHRu1kDrnYtUM1WqiQ4QFRXFmDFj6Nq1K0FBQURHR5OZmUlYWBgAo0ePpkmTJsyaNQuAV199lZdeeom1a9fi7+9PcnIyAHXr1qVu3bo2uw4REZGbycvLY8OGDVgsFvbu3Wsd79evH2azmYcfflgFuYiIiIiIDVxIz2blrtOs+eY0l7PyAKjr4siwe/14prs/fh61bZxQRMpLlWuiDxs2jIsXL/LSSy+RnJxMx44diY2NtT5sNDExsdjD1BYtWkRubi6PP/54sePMmDGDl19+uSKji4iIlCg1NZWlS5cyf/58zp07B4CrqytPP/00kZGRBAQE2DihiIiIiEjNdOhcGsvi4tn03XnyCgwA/Dxq8Ux3E092bYqbq9Y7F6nu7AzDMGwdojJLT0/H3d2dtLQ06tWrZ+s4IiJSzRw7dow5c+awYsUKsrKyAGjcuDGTJ09m4sSJeHp62jihiNwp1ZPlR5+tiIiUl4JCgy0/phATF8838anW8Xv9GxAeYuKhAO8KfXCpiJSP0taTVW4muoiISFVnGAZbt27FYrHw4YcfWsc7dOiA2Wzmqaee0kOuRURERERsIDMnn7f3nWXZjnhO/1Q0ycXR3o6B7X0IDzHRwa++bQOKiE2oiS4iIlJBcnJyePPNN4mOjubbb78FwM7OjkcffRSz2Uzv3r213rmIVAqXL19m06ZNjB492tZRREREKsT5K9d4Y2cCb+5OJD07H4B6ro6M6NacMd2b4+Ney8YJRcSW1EQXEREpZxcvXmTRokUsXLiQlJQUAGrXrk1YWBgRERG0bt3axglFRIpLTEwkLCxMTXQREan2DiReJiYuno8PJVNQWLTisalRHcb28Gdol6bUdlbrTETURBcRESk3P/zwA9HR0axatYqcnBwAmjRpwtSpUxk/fjweHh42TigiNVV6evovvp+RkVFBSURERCpefkEhnx5O4fXtp9ifeMU6HtyiIeN6mnjgbi/std65iPwPNdFFRETKkGEYfPLJJ1gsFj799FPreNeuXYmKiuLxxx/HycnJhglFRKB+/fq/uHyUYRhaXkpERKqd9Ow83tpzhuU7Ejh35RoATg52DOrQhLEh/rTzdbdxQhGprNREFxERKQPXrl1j1apVREdH8+OPPwJgb2/P4MGDMZvN9OjRQw0pEak03Nzc+NOf/kS3bt1u+v7x48eZMGFCBacSEREpH4k/ZbF8Zzwb9p7lak7ReucedZwZ1a0Zo4Kb4+XmauOEIlLZqYkuIiJyB5KSkli4cCGLFi3ip59+AoqaU+Hh4UybNg2TyWTjhCIiN+rcuTMAvXr1uun79evXxzCMiowkIiJSpgzDYO/py8Rsj+fTw8n8d7lzWnvVZWyIid90aoKrk4NtQ4pIlaEmuoiIyG04ePAgFouFN998k7y8PAD8/f2ZNm0a4eHh1KtXz8YJRURKNmLECK5du1bi+97e3syYMaMCE4mIiJSNvIJCPvo+iZi4eL47m2Ydv/8uT8JDTNzfupHuEBWRW2ZnaIrJL0pPT8fd3Z20tDQ1REREarjCwkI2b97M7Nmz2bZtm3W8e/fuREVF8dhjj+HoqN9Pi0hxqifLjz5bERG57kpWLmt3J7Jy52mS07MBcHG0Z0jnJoztYaJ1YzcbJxSRyqi09aS+6YuIyG0rKDTYHZ/KhYxsvNxcCTJ54FANn2KfmZnJihUrmDNnDsePHwfAwcGBJ554ArPZTFBQkI0TioiUr/bt2/PRRx/h5+dn6ygiIlJKNaVWP3XxKst3JPD2vrNcyysAoFFdF0YHN2dkt2Y0rOti44QiUh2oiS4iIrcl9lASMzcdJikt2zrm4+7KjNAA+gf62DBZ2Tlz5gzz589n6dKlXLlyBShaJ/jZZ59lypQpaiaJSI2RkJBgXbpKREQqv+peqxuGwa6TPxETF8+WIxes42196hEeYiK0gw8ujlrvXETKjproIiJyy2IPJTFp9X5+vh5Yclo2k1bvZ9GozlW6ON+9ezcWi4UNGzZQUFA0m6VVq1ZERkYyZswY6tata+OEIiIiIiI3V51r9Zz8Aj44eJ5lOxL4MSkdADs76NPGi7EhJoJbNNR65yJSLtREFxGRW1JQaDBz0+EbinIAA7ADZm46zEMB3lXqdtGCggI2btyIxWJhx44d1vHevXtjNpt59NFHsbe3t2FCEREREZFfVl1r9Z+u5rD660RWfX2aS1dzAKjl5MATXZsS1sOEqVEdGycUkepOTXQREbklu+NTi90W+nMGkJSWze74VIJbNqy4YLcpPT2dmJgY5s6dS0JCAgBOTk4MHz6cyMhIOnXqZNuAIiIiIiKlVN1q9WMpGSyLi+fdA+fIzS8EwLueK2O6+zM8yI/6tZ1tnFBEago10UVE5JZcyCi5KL+d7WwlPj6euXPnEhMTQ0ZGBgANGzZk4sSJTJ48GR+fqnmLq4iIiIjUXNWhVjcMgy+PXSQmLp7txy9Zx+9p6k54iImB7X1wctAdoiJSsfS3joiI3BIvN9cy3a4iGYbBjh07ePzxx2nVqhXR0dFkZGTQtm1blixZwpkzZ/jb3/6mBrqIyG346quvCA0NxdfXFzs7OzZu3Pir+2zbto3OnTvj4uJCq1atWLFiRbnnFBGpzqpyrZ6dV8DabxJ5yPIVzyzfw/bjl7C3g/7tvNkwMZj3J/fgsY5N1EAXEZvQTHQREbklQSYPfNxdSU7Lvulai3aAt7srQSaPio5Wory8PN5++20sFgt79uyxjj/88MOYzWYefvhhrXcuIvILlixZQuPGjX9xm8zMTDp06MDYsWMZMmTIrx4zPj6eRx55hIkTJ7JmzRq2bNnCuHHj8PHxoV+/fmUVXUSkRqmKtfqF9GxWfX2a1V+f5nJWHgB1XRx5sqsfYT388fOobeOEIiJqoouIyC1ysLdjRmgAk1bvxw6KFefXH000IzSgUjyo6PLlyyxdupT58+dz9uxZAFxcXBg1ahSRkZEEBgbaOKGIiO1lZmby5ZdfkpiYSG5ubrH3pk2bBsCIESN+9TgDBgxgwIABpT7v4sWLMZlM/Oc//wGgbdu2xMXFYbFY1EQXEblNValW/+F8GjFx8Wz69jx5BUVJmzaoxTPd/Rl2rx9urk42Tigi8v/URBcRkVvWP9CHRaM6M3PT4WIPLvJ2d2VGaAD9A227HMrx48eZM2cOy5cvJysrC4DGjRvz3HPPMXHiRLy8vGyaT0Sksjhw4AADBw4kKyuLzMxMPDw8uHTpErVr18bLy8vaRC8Pu3btom/fvsXG+vXrR2RkZIn75OTkkJOTY32dnp5eXvFERKqsylyrFxYafHHkAq/HneLrU6nW8S7NGzAuxMRDAY1x1HItIlIJqYkuIiK3pX+gDw8FeLM7PpULGdl4uRXdFmqrWS2GYbBt2zYsFgsffvghhlE0m+Wee+7BbDYzfPhwXFxcbJJNRKSyMpvNhIaGsnjxYtzd3fn6669xcnJi1KhRRERElOu5k5OTb1gipnHjxqSnp3Pt2jVq1ap1wz6zZs1i5syZ5ZpLRKQ6qGy1elZuPm/vO8vyHQnEX8oEimbND2zvQ3iIiY5+9W2SS0SktNREFxGR2+Zgb0dwy4Y2zZCTk8O6deuIjo7m4MGD1vFHH30Us9nMAw88gJ2d7W9XFRGpjA4ePMiSJUuwt7fHwcGBnJwcWrRowT//+U/GjBlTqrXNK9L06dOJioqyvk5PT8fPz8+GiUREKq/KUKufv3KNN3Yl8OY3iaRn5wNQz9WR4d2aMSbYH9/6N/7CVESkMlITXUREqqRLly6xePFiFixYQHJyMgC1atXimWeeISIigrvvvtvGCUVEKj8nJyfrg5W9vLxITEykbdu2uLu7c+bMmXI9t7e3NykpKcXGUlJSqFev3k1noUPRcy10V5GISOV38MwVYuLi+ej7JAoKi+4Q9W9Ym7EhJoZ2bkodF7WjRKRq0d9aIiJSpRw+fJjo6GhWrVpFdnbRGo++vr5MnTqVZ599Fg8PDxsnFBGpOjp16sSePXto3bo1vXr14qWXXuLSpUusWrWq3B++HBwczEcffVRs7LPPPiM4OLhczysiIuWjoNDg0x+SiYmLZ+/py9bx+1p4EB7Sgj5tvLCvBA80FRG5HWqii4hIpWcYBp9++ikWi4VPPvnEOt61a1fMZjNPPPEETk5ONkwoIlI1vfLKK2RkZADw97//ndGjRzNp0iRat25NTEzMLR3r6tWrnDhxwvo6Pj6egwcP4uHhQbNmzZg+fTrnzp1j5cqVAEycOJH58+fz+9//nrFjx/LFF1/w1ltvsXnz5rK7QBERKXcZ2Xms33OGFTsTOHv5GgBODnaEdvBlbA8TgU3cbZxQROTOqYkuIiKV1rVr11izZg0Wi4XDhw8DYGdnx+DBg4mKiqJHjx5a71xE5A507drV+mcvLy9iY2Nv+1h79+7lgQcesL6+vnb5mDFjWLFiBUlJSSQmJlrfN5lMbN68GbPZzJw5c2jatCmvv/46/fr1u+0MIiJScc6kZrF8RwJv7T3D1Zyi9c4b1HZiZLfmjA5ujlc9VxsnFBEpO3aGYRi2DnGrFixYwL/+9S+Sk5Pp0KED8+bNIygoqMTtN2zYwJ///GcSEhJo3bo1r776KgMHDizVudLT03F3dyctLY169eqV1SWIiMgvSE5OZuHChSxatIhLly4BULduXcLDw5k2bRotWrSwcUIRkdKrzPXkgw8+yLvvvkv9+vWLjaenpzN48GC++OIL2wQrpcr82YqIVEeGYbDv9GVi4uL55Idk/rvcOa286jK2h4nfdGpCLWcH24YUEbkFpa0nq9xM9PXr1xMVFcXixYvp1q0b0dHR9OvXj6NHj+Ll5XXD9jt37mT48OHMmjWLRx99lLVr1zJ48GD2799f7us8iojIrfn222+xWCy8+eab5ObmAtCsWTMiIiIIDw/H3V23goqIlKVt27ZZ/779X9nZ2Wzfvt0GiUREpDLKKyjko++TWBYXz7dn06zjPVs3IjzExP2tPbXeuYhUa1VuJnq3bt249957mT9/PgCFhYX4+fkxdepUXnjhhRu2HzZsGJmZmXz44YfWsfvuu4+OHTuyePHiG7bPyckhJyfH+jo9PR0/Pz/NbhERKSeFhYV89NFHWCyWYjMeg4ODMZvN/OY3v8HRscr9zldExKoyzpb+7rvvAOjYsSNffPFFsYcyFxQUEBsby5IlS0hISLBRwtKpjJ+tiEh1kpaVx9rdiazclUBSWjYAzo72/KZjE8aGmLjb283GCUVE7ky1nImem5vLvn37mD59unXM3t6evn37smvXrpvus2vXLut6jNf169ePjRs33nT7WbNmMXPmzDLLLCIiN5eZmckbb7zBnDlzOHbsGAAODg48/vjjmM1munXrZuOEIiLVV8eOHbGzs8POzo4HH3zwhvdr1arFvHnzbJBMREQqg/hLmSzfEc+GvWe5llcAQKO6zjx9nz8j72tGo7ouNk4oIlKxqlQT/dKlSxQUFNC4ceNi440bN+bIkSM33Sc5Ofmm2ycnJ990++nTpxdrul+fiS4iImXj7NmzzJ8/n6VLl3L58mUA3N3defbZZ5kyZQrNmjWzcUIRkeovPj4ewzBo0aIFu3fvxtPT0/qes7MzXl5eODhoTVsRkZrEMAx2nfqJZXHxbDlygevrFrTxdiM8xMSgjr64OOpng4jUTFWqiV4RXFxccHHRb1RFRMra3r17sVgsvPXWW+Tn5wPQsmVLIiIiCAsLo27dujZOKCJSczRv3hwoWlJLRERqtpz8AjZ9W7Te+eGkdOt4nzZehIeYCG7ZEDs7rXcuIjVblWqiN2rUCAcHB1JSUoqNp6Sk4O3tfdN9vL29b2l7EREpOwUFBbz//vtYLBbi4uKs47169cJsNvPoo49qpqOIiI2tWrWKxYsXEx8fz65du2jevDkWi4UWLVrw2GOP2TqeiIiUk9TMXNZ8fZqVX5/mYkbRs+Fcnex5vEtTwnqYaOmpSS4iItfZ2zrArXB2dqZLly5s2bLFOlZYWMiWLVsIDg6+6T7BwcHFtgf47LPPStxeRETuXHp6OtHR0bRu3ZqhQ4cSFxeHk5MTTz/9NPv27WPbtm089thjaqCLiNjYokWLiIqKYuDAgVy5coWCgqJ1bxs0aEB0dLRtw4mISLk4npLB9He/I3jWFv7z2TEuZuTQuJ4Lv+9/N19P78PfBrdXA11E5Geq1Ex0gKioKMaMGUPXrl0JCgoiOjqazMxMwsLCABg9ejRNmjRh1qxZAERERNCrVy/+85//8Mgjj7Bu3Tr27t3L0qVLbXkZIiLVUkJCAnPnziUmJob09KJbQT08PJg4cSKTJ0/G19fXxglFROR/zZs3j9dee43Bgwfzj3/8wzretWtXfve739kwmYiIlCXDMPjq+CVi4uL56thF6/g9Td0JDzExsL0PTg5Vap6liEiFqnJN9GHDhnHx4kVeeuklkpOT6dixI7GxsdaHhyYmJmJv//9/8Xfv3p21a9fy4osv8sc//pHWrVuzceNGAgMDbXUJIiLVimEY7Nq1C4vFwrvvvmtdX7dNmzZERkby9NNPU7t2bRunFBGRm4mPj6dTp043jLu4uJCZmWmDRCIiUpay8wrYeOAcMXHxHL9wFQA7O3g4oDHjeraga/MGWu9cRKQUqlwTHWDKlClMmTLlpu9t27bthrEnnniCJ554opxTiYjULHl5ebzzzjtYLBZ2795tHX/ooYcwm83069ev2C81RUSk8jGZTBw8eND6oNHrYmNjadu2rY1SiYjInbqQkc3qXadZ/U0iqZm5ANRxduDJe/0I626iWUNNchERuRVVsokuIiK2c/nyZV577TXmz5/PmTNngKIZiyNHjiQyMpL27dvbOKGIiJRWVFQUkydPJjs7G8Mw2L17N2+++SazZs3i9ddft3U8ERG5RYfPpxMTF8+mb8+TW1B0h2iT+rUI6+HPk/f6Uc/VycYJRUSqJjXRRUTKUEGhwe74VC5kZOPl5kqQyQMH++pxe+SJEyeYM2cOy5cvt97i7+XlxXPPPcekSZPw8vKycUIREblV48aNo1atWrz44otkZWUxYsQIfH19mTNnDk899ZSt44mIlKnqWqsXFhpsPXqBmLh4dp78yTreuVl9wkNa0K9dYxy13rmIyB1RE11EpIzEHkpi5qbDJKVlW8d83F2ZERpA/0AfGya7fYZh8OWXX2KxWNi0aROGYQDQvn17zGYzw4cPx9XV1cYpRUTkTowcOZKRI0eSlZXF1atX9UtREamWqmOtnpWbzzv7zrJ8RwKnLhVNcnGwt2NAoDfhISY6NWtg44QiItWHmugiImUg9lASk1bvx/jZeHJaNpNW72fRqM5VqjjPzc1l/fr1WCwWDhw4YB0fOHAgZrOZPn366AFEIiLVyIULFzh69CgAdnZ2eHp62jiRiEjZqW61elLaNd7YeZo3dyeSdi0PADdXR0YENWN0d3+a1K9l44QiItWPmugiIneooNBg5qbDNxTlAAZgB8zcdJiHArwr/e2ily5dYsmSJSxYsICkpCQAatWqxZgxY4iIiKBNmzY2TigiImUpIyOD5557jjfffJPCwqK1cx0cHBg2bBgLFizA3d3dxglFRO5MdarVvzt7hZi4eDZ/l0R+YdEVNW9Ym7Du/jzR1Y86LmrxiIiUF/0NKyJyh3bHpxa7LfTnDCApLZvd8akEt2xYccFuwY8//kh0dDQrV64kO7voWnx9fZkyZQrPPvssDRtWztwiInJnxo0bx4EDB9i8eTPBwcEA7Nq1i4iICCZMmMC6detsnFBE5M5U9Vq9oNDgs8PJxMTFsyfhsnW8m8mD8BATfdo2rvTNfxGR6kBNdBGRO3Qho+Si/Ha2qyiGYfD5558ze/ZsYmNjreOdO3cmKiqKJ554AmdnZxsmFBGR8vbhhx/yySefEBISYh3r168fr732Gv3797dhMhGRslFVa/WM7Dze2nuWFTvjOZN6DQAnBztC7/FlbIiJwCa6U0hEpCKpiS4icoe83Er3YM3SblfesrOzWbNmDdHR0Rw6dAgoWv/2sccew2w207NnT613LiJSQzRs2PCmS7a4u7vToIEeSCciVV9Vq9XPpGaxYmcCb+05Q0ZOPgD1azsxslszRgf707he5cgpIlLTqIkuInKHgkwe+Li7kpyWfdO1Fu0Ab3dXgkweFR2tmJSUFBYuXMiiRYu4ePEiAHXq1CE8PJxp06bRsmVLm+YTEZGK9+KLLxIVFcWqVavw9vYGIDk5meeff54///nPNk4nInLnqkKtbhgG+xMvExMXT+yhZP673DktPeswNsTEkE5NqeXsYLN8IiKiJrqIyB1zsLdjRmgAk1bvxw6KFefX53PPCA2w2VqF3333HRaLhbVr15KbmwtAs2bNmDp1KuPGjaN+/fo2ySUiIrbRqVOnYnccHT9+nGbNmtGsWTMAEhMTcXFx4eLFi0yYMMFWMUVEykRlrtXzCgr5+FDReuffnrliHe/ZuhFjQ0z0au2JvdY7FxGpFG67iZ6YmMjp06fJysrC09OTdu3a4eLiUpbZRESqjP6BPiwa1ZmZmw4Xe3CRt7srM0ID6B/oU6F5CgsL+fjjj7FYLGzZssU6ft9992E2mxkyZAiOjvo9qohITTR48GBbRxARqVCVrVZPy8rjzT2JrNyZwPn/5nF2tGdwx6L1ztt416vQPCIi8uvsDMO42R1NN5WQkMCiRYtYt24dZ8+e5X93dXZ2pmfPnjz77LMMHToUe3v7cglc0dLT03F3dyctLY169fSDTER+WUGhwe74VC5kZOPlVnRbaEXOasnMzGTlypXMmTOHo0ePAuDg4MDQoUMxm83cd999FZZFRESKqJ4sP/psReRW2LpWT7iUyfId8WzYd5as3AIAGtV1ZtR9zRl1X3Ma1dXERBGRilbaerLUTfRp06bxxhtv0K9fP0JDQwkKCsLX15datWqRmprKoUOH2L59O+vWrcPBwYHly5dz7733ltkF2YoKcxGpCs6dO8eCBQtYvHgxly9fBqBevXqMHz+eqVOn0rx5cxsnFBGpuSprPbl27VpatGjBfffdx969ezl27BgjRoywdaxbUlk/WxGR6wzD4OtTqcTExbPlSArXOzBtvN0YG2JiUAdfXJ203rmIiK2Utp4s9b38derU4dSpUzRs2PCG97y8vHjwwQd58MEHmTFjBrGxsZw5c6ZaNNFFRCqzffv2YbFYWL9+Pfn5+QC0aNGCiIgIwsLCcHNzs3FCERGprO677z7CwsL44osv+N3vfsfy5cttHUlEpNrIzS/kw+/OExMXzw/n063jD9ztSXhIC3q0aljs+RQiIlK5lbqJPmvWrFIftH///rcVRkREfl1BQQEffPABFouF7du3W8fvv/9+zGYzoaGhODhoNouIiJQsMTERR0dHunfvzkMPPUT37t1xcHAgMTHR+oBRERG5damZuaz95jQrd53mQkYOAK5O9gzt3JSwHiZaedW1cUIREbkdt/RUucOHDxMQEPCL26xevZpRo0bdUSgREblRRkYGy5YtY+7cuZw6dQoAR0dHnnrqKSIjI+nSpYuNE4qISFUxY8YMAM6ePcv27dtxdHRkxowZ2NnZsWzZsts+7oIFC/jXv/5FcnIyHTp0YN68eQQFBZW4fXR0NIsWLSIxMZFGjRrx+OOPM2vWLFxdXW87g4iILZy4kEFMXALv7j9LTn4hAI3ruTA62J8RQc1oUMfZxglFRORO3FITvUuXLvz1r3/lt7/97Q23HaWkpDB+/Hi2bt2qJrqISBk6ffo08+bN47XXXiM9vehWUA8PDyZMmMDkyZNp0qSJjROKiEhVc33plv79+/PBBx8wd+7cO17OZf369URFRbF48WK6detGdHQ0/fr14+jRo3h5ed2w/dq1a3nhhRdYtmwZ3bt359ixYzzzzDPY2dkxe/bsO8oiIlIRDMMg7sQlXt8ez5fHLlrHA5vUY1xICwa298HZ0d6GCUVEpKzcUhN99erVTJo0iffff58VK1bQsmVL63hERATt2rXjwIED5RJURKSm2bVrFxaLhXfffZeCggIA7r77biIjIxk9ejS1a9e2cUIREanKFi5cSJcuXRgwYABff/01ixYtYtKkSbd9vNmzZzN+/HjCwsIAWLx4MZs3b2bZsmW88MILN2y/c+dOevToYX2Yqb+/P8OHD+ebb74p8Rw5OTnk5ORYX1//5bKISEXKzivg/YPnWBaXwNGUDADs7OChto0JDzERZPLQeuciItXMLTXRhw4dSs+ePZkwYQIdOnTg5ZdfZvv27Xz22Wf87W9/w2w26weFiMgdyM/P55133sFisRRrIvTt2xez2Uz//v2xt9dsFhERuXPDhw+3Lpvyxz/+kaysrNs+Vm5uLvv27WP69OnWMXt7e/r27cuuXbtuuk/37t1ZvXo1u3fvJigoiFOnTvHRRx/x9NNPl3ieWbNmMXPmzNvOKSJyJy5m5LDq69Os+fo0P2XmAlDb2YEnu/oR1sOf5g3r2DihiIiUl1tqogN4eXnx3nvvMXLkSH7/+99Tp04dvvnmG9q3b18e+UREaoQrV67w+uuvM2/ePBITEwFwdnZm5MiRREZGcs8999g4oYiIVDcNGjRg69atPPDAA7i4uODi4lLs/SVLljBhwoRSHevSpUsUFBTQuHHjYuONGzfmyJEjN91nxIgRXLp0iZCQEAzDID8/n4kTJ/LHP/6xxPNMnz6dqKgo6+v09HT8/PxKlVFE5Hb9mJROTFw8Hxw8T25B0XrnTerXYkz35gy7txnutZxsnFBERMrbLU9nvHz5MiNGjGDjxo288MILeHl5MXz4cPbv318e+UREqrWTJ08ybdo0mjZtyvPPP09iYiKenp7MmDGDxMREli1bpga6iIiUm/79+/P888+Tl5dnHbt06RKhoaE3XYKlLG3bto1XXnmFhQsXsn//ft599102b97MX//61xL3cXFxoV69esX+EREpD4WFBl8cSWHk618zYM523t53ltyCQjo1q8/8EZ348vnePHt/SzXQRURqiFuaif7hhx8yfvx4mjVrxr59+2jTpg1/+tOf+N3vfkdwcDC///3vmTFjBo6OtzzBXUSkxjAMg+3btzN79mw++OADDMMAoF27dpjNZkaOHGm9vV5ERKQ8bd26ldGjR/PZZ5+xdu1a4uPjCQ8P5+677+bgwYOlPk6jRo1wcHAgJSWl2HhKSgre3t433efPf/4zTz/9NOPGjQOgffv2ZGZm8uyzz/KnP/1Jy5eJiE1k5ebzzv5zLN8Rz6mLmQA42NvRP9Cb8BATnZs1sHFCERGxhVteE33GjBm88MIL1qK2Tp06LFq0iCFDhjBu3Dg2bdp0SwW3iEhNkZuby1tvvYXFYil2986AAQMwm8307dtXz5UQEZEK1b17dw4ePMjEiRPp3LkzhYWF/PWvf+X3v//9Lf1McnZ2pkuXLmzZsoXBgwcDUFhYyJYtW5gyZcpN98nKyrqhUe7g4ABg/QWziEhFSU7LZuWuBNZ8k0jataK7c9xcHHkqyI8x3f1p2qC2jROKiIgt3VITfc+ePSUuK/DQQw/x/fffYzabyySYiEh18dNPP7FkyRLmz59PUlISALVq1WL06NFERETQtm1bGycUEZGa7NixY+zdu5emTZty/vx5jh49SlZWFnXq3NoD8qKiohgzZgxdu3YlKCiI6OhoMjMzCQsLA2D06NE0adKEWbNmARAaGsrs2bPp1KkT3bp148SJE/z5z38mNDTU2kwXESlv359NIybuFB9+l0R+YdEv8Jp51Cashz9PdPWjrovutBcRkVtsov/aurz16tUjJibmjgL9ktTUVKZOncqmTZuwt7dn6NChzJkzh7p165a4/YwZM/j000+t6wwPHjyYv/71r7i7u5dbThERgCNHjhAdHc3KlSu5du0aAD4+PkyZMoUJEybQsGFDGycUEZGa7h//+AczZszg2Wef5V//+hcnTpzg6aef5p577mH16tUEBweX+ljDhg3j4sWLvPTSSyQnJ9OxY0diY2OtDxtNTEwsNvP8xRdfxM7OjhdffJFz587h6elJaGgof//738v8OkVE/ldBocFnh1NYFhfP7oRU63iQyYPwEBN92zbGwV53iIqIyP+zM6rQvZIDBgwgKSmJJUuWkJeXR1hYGPfeey9r16696faHDh1ixowZPPPMMwQEBHD69GkmTpzIPffcw9tvv12qc6anp+Pu7k5aWpoeXCQiv8owDLZs2YLFYuGjjz6yjnfq1Amz2cywYcNwdna2YUIREalolbme9PHxYdmyZQwYMMA6lpeXxx//+Efmzp1LTk6ODdP9usr82YpI5XM1J5+39pxhxc4EElOzAHC0tyO0gy9je5ho31ST7UREaprS1pNVpon+448/EhAQwJ49e+jatSsAsbGxDBw4kLNnz+Lr61uq42zYsIFRo0aRmZl50weg5uTkFPuykJ6ejp+fnwpzEflF2dnZrF27lujoaL7//nsA7OzsGDRoEGazmfvvv1/rnYuI1FCVudF76dIlGjVqdNP3vvzyS3r16lXBiW5NZf5sRaTyOHs5izd2JrBu9xkycvIBqF/biRFBzRgd7I+3u6uNE4qIiK2Utp6sMot77dq1i/r161sb6AB9+/bF3t6eb775ht/85jelOs71D+RmDXSAWbNmMXPmzDLJLCLVX0pKCosWLWLRokVcuHABKHrg8tixY5k2bRqtWrWycUIREZGSldRAByp9A11E5NfsO32ZZXHxxP6QTMF/1ztv4VmHsT1MDO3clFrOev6CiIiUTpVpoicnJ+Pl5VVszNHREQ8PD5KTk0t1jEuXLvHXv/6VZ599tsRtpk+fTlRUlPX19ZnoIiL/6/vvvyc6OprVq1eTm5sLgJ+fH1OnTmX8+PHUr1/ftgFFRERERGqg/IJCPj6UTExcPAfPXLGO92jVkPAQE73v8sJe652LiMgtsnkT/YUXXuDVV1/9xW1+/PHHOz5Peno6jzzyCAEBAbz88sslbufi4oKLi8sdn09Eqp/CwkJiY2OxWCx8/vnn1vFu3bphNpsZMmQITk5ONkwoIiIiIlIzpV3LY/2eRN7YeZpzV64B4Oxgz2MdfRkbYqKtj5Z8EhGR21fqJvpf/vKX2zpB7969uf/++0t8/7e//S3PPPPMLx6jRYsWeHt7W5dKuC4/P5/U1FS8vb1/cf+MjAz69++Pm5sb7733nppcInJLsrKyWLlyJXPmzOHIkSMA2NvbM3ToUMxmM8HBwTZOKCIiIiJSM53+KZPlOxJ4a+8ZsnILAGhYx5lR9zVn1H3N8XTTJDkREblzpW6ix8fH39YJOnbs+Ivve3p64unp+avHCQ4O5sqVK+zbt48uXboA8MUXX1BYWEi3bt1K3C89PZ1+/frh4uLCBx98gKurHhgiIqVz/vx55s+fz5IlS0hNTQWgXr16jBs3jqlTp+Lv72/bgCIiIiIiNZBhGOyOT+X1uHg+/zEFo2i5c+5qXJfwEBOPdWyCq5PWOxcRkbJT6ib68uXLyzPHr2rbti39+/dn/PjxLF68mLy8PKZMmcJTTz2Fr68vAOfOnaNPnz6sXLmSoKAg0tPTefjhh8nKymL16tWkp6eTnp4OFDXvHRz0Q1VEbrR//34sFgvr168nLy8PAJPJREREBGPHjsXNzc3GCUVERMre2bNn8fX1xd7e3tZRRERuKje/kM3fnycmLp5D59Kt473v9iQ8xERIq0bY2Wm9cxERKXs2XxP9VqxZs4YpU6bQp08f61IKc+fOtb6fl5fH0aNHycrKAooaYd988w0ArVq1Knas+Ph4zSIVEauCggI2bdqExWLhq6++so737NkTs9nMoEGD9Is3ERGp1gICAjh48CAtWrSwdRQRkWIuZ+aydnciK3clkJKeA4Crkz1DOjdlbA9/WnlpkouIiJSvKtVE9/DwYO3atSW+7+/vj3H9Pi6K1mP/39ciUvEKCotutbyQkY2XmytBJg8c7CvP7JCrV6+yfPly5syZw8mTJwFwdHTkySefxGw207VrVxsnFBERqRiqm0Vqnspeq5+4cJVlO+J5d/9ZsvMKAfByc2FMd39GBDWjQR1nGycUEZGaoko10UWkaok9lMTMTYdJSsu2jvm4uzIjNID+gT42TAaJiYnMmzeP1157jbS0NAAaNGjAhAkTmDx5Mk2bNrVpPhERERGR8lRZa3XDMNhx4idi4k6x9ehF63g733qEh5h49B5fnB217JSIiFQsNdFFpFzEHkpi0ur9/HxOW3JaNpNW72fRqM42Kc6//vprLBYL77zzDgUFBQDcddddREZGMnr0aOrUqVPhmURERGxh5cqVxV7n5+fz7rvv4uXlZR0bPXp0RccSkQpQGWv17LwCPjh4nmU74jmSnAGAnR30bduY8BAT3UweWu9cRERsRk10ESlzBYUGMzcdvqEoBzAAO2DmpsM8FOBdIbeL5ufn89577zF79my+/vpr6/iDDz6I2Wxm4MCBeoiaiIjUOMuXLy/2Oi8vj7fffptatWoBYGdnpya6SDVU2Wr1ixk5rP76NGu+Oc2lq7kA1HZ24IkuTQnrYcK/kSa5iIiI7d1WE33MmDGEh4dz//33l3UeEakGdsenFrst9OcMICktm93xqQS3bFhuOdLS0nj99deZN28ep0+fBsDZ2ZkRI0YQGRlJhw4dyu3cIiIild3WrVuLvXZzc2Pt2rV6sKhINVdZavUjyeksi4tn44Hz5BYUrXfu6+7KmO7+PBXUDPdaTuV2bhERkVt1W030tLQ0+vbtS/PmzQkLC2PMmDE0adKkrLOJSBV1IaPkovx2trtVJ0+eZO7cuSxbtoyrV68C4OnpyaRJk5g0aRLe3t7lcl4RERERkcrOlrV6YaHBl8cuEhMXT9yJS9bxjn71CQ8x0T/QGycH3SEqIiKVz2010Tdu3MjFixdZtWoVb7zxBjNmzKBv376Eh4fz2GOP4eSk3xiL1GRebq5lul1pGIZBXFwcs2fP5v3338cwim5QbdeuHWazmZEjR+LqWnbnExERERGpimxRq1/LLeDdA2dZFhfPyYuZANjbQf9Ab8JDWtCleYMyO5eIiEh5uO010T09PYmKiiIqKor9+/ezfPlynn76aerWrcuoUaN47rnnaN26dVlmFZEqIsjkgY+7K8lp2Tdda9EO8HZ3Jcjkccfnys3NZcOGDVgsFvbt22cd79+/P2azmYceekgPIBIRESmFP/7xj3h43PnPZhGp3CqyVk9Jz2blrgTWfJPIlaw8ANxcHBl2rx9juvvj51H7js8hIiJSEe74waJJSUl89tlnfPbZZzg4ODBw4EC+//57AgIC+Oc//4nZbC6LnCJShTjY2zEjNIBJq/djB8WK8+vt7BmhAXf0oKLU1FSWLFnC/PnzOX/+PACurq6MHj2aiIgIAgICbvvYIiIiNdH06dNtHUFEKkBF1OqHzqURExfPh9+dJ6+g6Ax+HrUI627iia5NcXPV3esiIlK12BnX1zy4BXl5eXzwwQcsX76cTz/9lHvuuYdx48YxYsQI6tWrB8B7773H2LFjuXz5cpmHrkjp6em4u7uTlpZmvTYRKZ3YQ0nM3HS42IOLfNxdmREaQP9An9s65tGjR4mOjuaNN97g2rVrAHh7ezN58mQmTpxIo0aNyiS7iIhIWVE9WX702YrcvrKu1QsKDT7/MYWYuHh2x6dax4P8PRgbYuKhgMZ31JgXEREpD6WtJ29rJrqPjw+FhYUMHz6c3bt307Fjxxu2eeCBB6hfv/7tHF5Eqon+gT48FODN7vhULmRk4+VWdFvorRbPhmHwxRdfYLFY2Lx5s3W8Y8eOmM1mhg0bhouLS1nHFxERERGptsqqVs/MyWfD3jMs35nA6Z+yAHC0t+ORe3wIDzFxT9P65ZBeRESkYt1WE91isfDEE0/84kP66tevT3x8/G0HE5HqwcHejuCWDW9r35ycHNauXUt0dDTfffcdAHZ2doSGhmI2m+nVq5fWOxcRERERuU13Uqufu3KNN3Ym8ObuRDKy8wFwr+XEiG7NGB3cHB/3WmUZVURExKZuq4n+9NNPl3UOERGrCxcusHjxYhYsWMCFCxcAqF27NmFhYUREROihxSIiIiIiNrI/8TIxcfHEHkqmoLBoddgWjeoQFmJiaOcm1Ha+40eviYiIVDql/uk2ceJEXnzxRZo2bfqr265fv578/HxGjhx5R+FEpGY5dOgQ0dHRrF69mpycHACaNm3K1KlTGT9+PA0aNLBxQhERERGRmie/oJBPfkjh9bhTHEi8Yh3v3rIh4SEmHrjbC3utdy4iItVYqZvonp6etGvXjh49ehAaGkrXrl3x9fXF1dWVy5cvc/jwYeLi4li3bh2+vr4sXbq0PHOLSDVRWFjIJ598gsVi4bPPPrOOBwUFYTabGTp0KE5OTjZMKCIiUr188MEHpd520KBB5ZhERCq79Ow81u8+w4qdCZy7cg0AZwd7BnX0ZWwPEwG+eqCviIjUDHaGYRil3TglJYXXX3+ddevWcfjw4WLvubm50bdvX8aNG0f//v3LPKitlPYJrSJya7Kysli1ahXR0dEcOXIEAHt7e4YMGYLZbCY4OFjrnYuISLVQ2epJe3v7Um1nZ2dHQUFBOae5M5XtsxWpLhJ/ymLZjng27D1DZm7R3wMedZwZdV9zRt3XDC+3kp+PJiIiUpWUtp68pSb6/7p8+TKJiYlcu3aNRo0a0bJly2rZ8FJhLlK2kpKSWLBgAYsXL+ann34Cin4JN27cOKZOnYrJZLJxQhERkbJVk+rJBQsW8K9//Yvk5GQ6dOjAvHnzCAoKKnH7K1eu8Kc//Yl3332X1NRUmjdvTnR0NAMHDizV+WrSZytS3gzDYE/CZWLiTvHp4RSudwpae9UlPMTE4E5NcHVysG1IERGRMlbaevK2n/jRoEEDrU8sIqV24MABLBYL69atIy8vDwB/f38iIiIYO3asvviKiIhUcevXrycqKorFixfTrVs3oqOj6devH0ePHsXLy+uG7XNzc3nooYfw8vLi7bffpkmTJpw+fZr69etXfHiRGiw3v5CPvk8iJi6e78+lWcd73eVJeIiJnq0bVcsJcyIiIrfitpvo27dvZ8mSJZw6dYoNGzbQpEkTVq1ahclkIiQkpCwzikgVVVBQwIcffojFYuHLL7+0jvfo0QOz2czgwYNxcNBsFhEREVvKzMzkyy+/JDExkdzc3GLvTZs2rdTHmT17NuPHjycsLAyAxYsXs3nzZpYtW8YLL7xww/bLli0jNTWVnTt3Wp9/4u/v/4vnyMnJsT58HIpmDonI7bmSlcuabxJZuSuBlPSi/69cHO0Z0rkpY3v407qxm40TioiIVB631UR/5513ePrppxk5ciT79++3FrJpaWm88sorfPTRR2UaUkSqlqtXr7JixQqio6M5efIkAI6OjjzxxBOYzWbuvfdeGycUERERKLpTbODAgWRlZZGZmYmHhweXLl2idu3aeHl5lbqJnpuby759+5g+fbp1zN7enr59+7Jr166b7vPBBx8QHBzM5MmTef/99/H09GTEiBH84Q9/KPGX7LNmzWLmzJm3fqEiYnXy4lWW74jn7X1nyc4rBMDTzYXR9zVn5H3N8ajjbOOEIiIilc9tNdH/9re/sXjxYkaPHs26deus4z169OBvf/tbmYUTkarlzJkzzJs3j9dee40rV64AUL9+fSZMmMCUKVNo2rSpbQOKiIhIMWazmdDQUBYvXoy7uztff/01Tk5OjBo1ioiIiFIf59KlSxQUFNC4ceNi440bN7Y+QPznTp06xRdffMHIkSP56KOPOHHiBM899xx5eXnMmDHjpvtMnz6dqKgo6+v09HT8/PxKnVOkpjIMg50nfyImLp4vjlywjgf41CM8xMSjHXxwcdQdoiIiIiW5rSb60aNHuf/++28Yd3d3tzbORKTm2L17N7Nnz+btt9+moKAAgNatWxMZGcmYMWOoU6eOjROKiIjIzRw8eJAlS5Zgb2+Pg4MDOTk5tGjRgn/+85+MGTOGIUOGlNu5CwsL8fLyYunSpTg4ONClSxfOnTvHv/71rxKb6C4uLri4uJRbJpHqJie/gPcPnmdZXDxHkjMAsLODPm0aEx5i4r4WHlrvXEREpBRuq4nu7e3NiRMnblizMC4ujhYtWpRFLhGp5PLz89m4cSMWi4WdO3daxx944AHMZjOPPPII9vb2NkwoIiIiv8bJycn689rLy4vExETatm2Lu7s7Z86cKfVxGjVqhIODAykpKcXGU1JS8Pb2vuk+Pj4+ODk5FVu6pW3btiQnJ5Obm4uzs5aUELldl67msObrRFZ9ncClq0XPOqjl5MATXZsS1sOEqZEmuYiIiNyK22qijx8/noiICJYtW4adnR3nz59n165d/O53v+PPf/5zWWcUkUokLS2NmJgY5s6dy+nTp4GiL+AjRowgMjKSjh072jagiIiIlFqnTp3Ys2cPrVu3plevXrz00ktcunSJVatWERgYWOrjODs706VLF7Zs2cLgwYOBopnmW7ZsYcqUKTfdp0ePHqxdu5bCwkJrI//YsWP4+PiogS5ym44mZ7AsLp73Dp4jN79ovXMfd1fGdPdn+L3NcK/tZOOEIiIiVdNtNdFfeOEFCgsL6dOnD1lZWdx///24uLjwu9/9jqlTp5Z1RhGpBE6dOsXcuXNZtmwZGRlFt4I2atSISZMm8dxzz5U4y0xEREQqr1deecX6c/3vf/87o0ePZtKkSbRu3Zply5bd0rGioqIYM2YMXbt2JSgoiOjoaDIzMwkLCwNg9OjRNGnShFmzZgEwadIk5s+fT0REBFOnTuX48eO88sorpX6YqYgUKSw0+PL4RZbFxbP9+CXreIem7oT3bMGAQG+cHHSHqIiIyJ24rSa6nZ0df/rTn3j++ec5ceIEV69eJSAggLp165Z1vmJSU1OZOnUqmzZtwt7enqFDhzJnzpxSndcwDAYOHEhsbCzvvfeedYaMiJTMMAx27NiBxWJh48aNFBYWzWYJCAggMjKSUaNGUatWLRunFBERkdvVtWtX65+9vLyIjY297WMNGzaMixcv8tJLL5GcnEzHjh2JjY21Pmw0MTGx2FJvfn5+fPLJJ5jNZu655x6aNGlCREQEf/jDH27/gkRqkGu5Bbx74CzLdyRw4sJVAOztoF87b8JDTHRp3kDrnYuIiJSR22qiX+fs7ExAQEBZZflVI0eOJCkpic8++4y8vDzCwsJ49tlnWbt27a/uGx0drQJCpJTy8vLYsGEDFouFvXv3Wsf79euH2Wzm4Ycf1v9PIiIicoMpU6aUuHzLtm3bbhgLDg7m66+/LudUItXLhfRsVu46zZpvTnM5Kw+Aui6ODLvXj2e6++PnUdvGCUVERKqfO2qiV6Qff/yR2NhY9uzZY50xM2/ePAYOHMi///1vfH19S9z34MGD/Oc//2Hv3r34+PhUVGSRKic1NZWlS5cyf/58zp07B4CrqytPP/00ERERtGvXzsYJRUREpCyZTKZf/MX4qVOnKjCNiPySQ+fSWBYXz6bvzpNXYADQtEEtwnqYeLJrU9xctd65iIhIeakyTfRdu3ZRv379Yrec9u3bF3t7e7755ht+85vf3HS/rKwsRowYwYIFC0q1ZnNOTg45OTnW1+np6XceXqSSO3bsGHPmzGHFihVkZWUB0LhxYyZPnszEiRPx9PS0cUIREREpD5GRkcVe5+XlceDAAWJjY3n++edtE0pErAoLDbYcuUBM3Cm+PpVqHe/avAHhISYebueNg73uEBURESlvVaaJnpycjJeXV7ExR0dHPDw8SE5OLnE/s9lM9+7deeyxx0p1nlmzZjFz5sw7yipSFRiGwdatW7FYLHz44YfW8Q4dOmA2m3nqqadwcXGxYUIREREpbxERETcdX7BgQbEl3USkYmXm5PP2vrMs3xFPwk9Fk1wc7O14pL0P4SEmOvjVt21AERGRGsbmTfQXXniBV1999Re3+fHHH2/r2B988AFffPEFBw4cKPU+06dPJyoqyvo6PT0dPz+/2zq/SGWUk5PDm2++SXR0NN9++611/NFHHyUqKorevXtrvXMREZEabsCAAUyfPp3ly5fbOopIjXL+yjXe2JnAm7sTSc/OB6CeqyMjujVnTPfm+LjXsnFCERGRmsnmTfTf/va3PPPMM7+4TYsWLfD29ubChQvFxvPz80lNTS1xmZYvvviCkydPUr9+/WLjQ4cOpWfPnjd9uJGLi4tm30q1dPHiRRYvXsyCBQtISUkBoHbt2jzzzDNERERw11132TihiIiIVBZvv/02Hh4eto4hUmMcPHOF17ef4uNDyRQUFq13bmpUh7E9/BnapSm1nW3+1V1ERKRGs/lPYk9Pz1KttxwcHMyVK1fYt28fXbp0AYqa5IWFhXTr1u2m+7zwwguMGzeu2Fj79u2xWCyEhobeeXiRKuCHH34gOjqaVatWWdf7b9KkCVOnTmX8+PH6giwiIlKDderUqdgdaIZhkJyczMWLF1m4cKENk4lUf/kFhXx6OIWYuHj2nb5sHQ9u0ZDwEBMPtvHCXuudi4iIVAo2b6KXVtu2benfvz/jx49n8eLF5OXlMWXKFJ566il8fX0BOHfuHH369GHlypUEBQXh7e1901nqzZo1w2QyVfQliFQYwzD45JNPsFgsfPrpp9bxrl27EhUVxeOPP46Tk5MNE4qIiEhlMHjw4GKv7e3t8fT0pHfv3rRp08Y2oUSqufTsPN7ac4blOxI4d+UaAE4Odgzq0ISxIf6083W3cUIRERH5uSrTRAdYs2YNU6ZMoU+fPtjb2zN06FDmzp1rfT8vL4+jR4+SlZVlw5QitnPt2jVWr15NdHQ0hw8fBoq+DA8ePBiz2UyPHj203rmIiIhYzZgxw9YRRGqMxJ+yWL4zng17z3I1p2i9c486zozs1oyn72uOVz1XGycUERGRklSpJrqHhwdr164t8X1/f38Mw/jFY/za+yJVUVJSEgsXLmTx4sVcunQJADc3N8LDw5k2bZruvBARERGr9PT0Um9br169ckwiUv0ZhsHe05eJ2R7Pp4eT+e9y57T2qsvYEBO/6dQEVycH24YUERGRX1WlmugiUtzBgwexWCy8+eab5OXlAUW/TJo2bRpjx47F3V23goqIiEhx9evXL/WdaQUFBeWcRqR6yiso5KPvk4iJi+e7s2nW8fvv8iQ8xMT9rRvpDlEREZEqRE10kSqmsLCQzZs3M3v2bLZt22Yd7969O1FRUTz22GM4Oup/bREREbm5rVu3Wv+ckJDACy+8wDPPPENwcDAAu3bt4o033mDWrFm2iihSZaVl5bF2dyJv7EwgOT0bAGdHe4Z0asLYEBN3NXazcUIRERG5Heq0iVQRmZmZrFixgjlz5nD8+HEAHBwceOKJJzCbzQQFBdk4oYiIiFQFvXr1sv75L3/5C7Nnz2b48OHWsUGDBtG+fXuWLl3KmDFjbBFRpMo5dfEqy3ck8Pa+s1zLK7qDo1FdF0YHN2dkt2Y0rOti44QiIiJyJ9REF6nkzp49y7x581i6dClXrlwBwN3dnQkTJjBlyhT8/PxsG1BERESqrF27drF48eIbxrt27cq4ceNskEik6jAMg10nfyImLp4tRy5Yx9v61CM8xERoBx9cHLXeuYiISHWgJrpIJbV7924sFgsbNmywrkfaqlUrIiIieOaZZ6hbt66NE4qIiEhV5+fnx2uvvcY///nPYuOvv/66flEvUoKc/AI+OHieZTsS+DHp/x/U26eNF+E9TQS3aKj1zkVERKoZNdFFKpGCggI2btyIxWJhx44d1vHevXtjNpt59NFHsbe3t2FCERERqU4sFgtDhw7l448/plu3bkDRL/KPHz/OO++8Y+N0IpXLT1dzWPNNIit3nebS1RwAajk58HiXpoT18KeFpya5iIiIVFdqootUAunp6cTExDB37lwSEhIAcHJyYvjw4URGRtKpUyfbBhQREZFqaeDAgRw7doxFixZx5MgRAEJDQ5k4caJmoov817GUDJbFxfPegXPk5BcC4F3PlTHd/Rke5Ef92s42TigiIiLlTU10ERtKSEhg7ty5vP7662RkZADQsGFDJk6cyOTJk/Hx8bFxQhEREanu/Pz8eOWVV2wdQ6RSMQyDL49dJCYunu3HL1nH72nqTniIiYHtfXBy0B2iIiIiNYWa6CIVzDAMdu7cicVi4b333qOwsGg2S9u2bYmMjGTUqFHUrl3bxilFRESkuvruu+8IDAzE3t6e77777he3veeeeyoolUjlkJ1XwHsHzrEsLp7jF64CYG8HDwd4E97TRNfmDbTeuYiISA2kJrpIBcnLy+Ptt9/GYrGwZ88e6/jDDz+M2Wzm4Ycf1nrnIiIiUu46duxIcnIyXl5edOzYETs7OwzDuGE7Ozs768PNRaq7CxnZrNp1mjXfJJKamQtAXRdHnuzqR1gPf/w8NMlFRESkJlMTXaScXb58maVLlzJ//nzOnj0LgIuLC6NGjSIyMpLAwEAbJxQREZGaJD4+Hk9PT+ufRWqyH86nERMXz6Zvz5NXUPTLpCb1axHWw58n7/WjnquTjROKiIhIZaAmukg5OX78OHPmzGH58uVkZWUB4OXlxeTJk5k4cSJeXl42TigiIiI1UfPmzW/6Z5GaorDQ4IsjF4iJi2fXqZ+s412aNyA8xPR/7d15XFV1/sfxNxdkcQEXkMVQUCs1TVxRpGVGTLOxbMzUsVQiNVNHLi2j07jNmGTjCG6BmRqWjuZYjlnRz2icJMk1G/dSQdxAjQREWe/9/eF4ZxjFUJYjl9fz8eCP+73fc877cuT0vZ++53v0SDtvObHeOQAA+C8U0YFKZLVatWXLFsXExGjTpk22W6Pvv/9+mc1mDRs2TC4uLganBAAAuCohIUGenp567LHHJEmvvvqq3n77bbVr105//etfKbLDrlwuLNbfdp/Siq/TlHohT5LkaHJQ/w6+iggNVJB/Q2MDAgCAOxZFdKASFBQUaM2aNYqNjdXevXtt7Y899pjMZrN++ctf8gAiAABwx5k9e7bi4uIkSSkpKVq0aJFiY2O1adMmmc1mffjhhwYnBCrubPYVvbstTX/dnq6c/GJJUgNXJ/0muLlG9gyQX0M3gxMCAIA7HUV0oAIuXLig+Ph4LV68WBkZGZIkNzc3jRo1SpMmTdK9995rcEIAAICynTx5Uq1bt5YkbdiwQU899ZTGjBmjXr166eGHHzY2HFBB3528qGXJqfp031kVW67eIRrQpK7CewXqqS53qZ4LX4cBAED5sNAbcBsOHjyoMWPGyN/fX1OnTlVGRob8/PwUHR2tU6dO6a233qKADgAA7nj169fXjz9eXRP6//7v/9SnTx9Jkqurq65cuXLL+1u8eLECAgLk6uqq4OBg7dixo1zbrVmzRg4ODho4cOAtHxP4byUWqz7bd1ZPxW3TE4u/1sbvzqjYYlWPlo21dERXJb30sEaGBFBABwAAt4SRA1BOVqtVmzdv1rx58/T555/b2rt06SKz2azBgwfL2dnZwIQAAAC3pk+fPnr++efVqVMnff/99+rfv78k6cCBAwoICLilfa1du1ZRUVGKj49XcHCwYmNj1bdvXx05cuSmD1RPS0vTyy+/rAceeKAiHwW1XG5+kdbuPKl3t6Xp1E9X/wdQHUcHDbjfT8+FBqp9Mw+DEwIAgJqMIjrwM65cuaJVq1YpNjZWBw4ckCTbTCmz2azQ0FDWOwcAADXS4sWL9Yc//EEnT57U+vXr1aRJE0nS7t27NWzYsFva17x58zR69GiFh4dLkuLj4/XJJ59o+fLlmjx58g23KSkp0fDhwzVz5kxt3bpVFy9erNDnQe1zMuuy3t2WprU7T+pSwdX1zhvVraPhwS00omcLNXV3NTghAACwBxTRgTJkZGTorbfeUlxcnC5cuCDp6i3PERER+u1vf6uWLVsanBAAAKBiGjZsqEWLFl3XPnPmzFvaT2FhoXbv3q0pU6bY2kwmk8LCwpSSklLmdn/84x/VtGlTRUREaOvWrT97nIKCAhUUFNhe5+Tk3FJO2Aer1ardJ37SsuRUfX4gQ/9e7lytm9bXc70C9WSnZnJzdjQ2JAAAsCsU0YH/8d133ykmJkZ//etfVVhYKElq3ry5Jk2apIiICHl4cCsoAACwH1u3btWSJUt0/PhxrVu3Ts2aNdN7772nwMBAhYaGlmsfFy5cUElJiby9vUu1e3t76/DhwzfcJjk5WcuWLdPevXvLnTU6OvqWC/ywH0UlFn22P0PLth7Xd6eybe0P3O2piNBAPXi3l0wm7hAFAACVjyI6IMlisejTTz9VTEyMvvzyS1t7z549ZTab9eSTT8rJiT8XAABgX9avX69nn31Ww4cP1549e2yzvLOzszV79mx9+umnVXLc3NxcPfvss1q6dKk8PT3Lvd2UKVMUFRVle52TkyN/f/+qiIg7SPblIv11Z7oStqXpbHa+JMnZyaQng5rpudBA3evTwOCEAADA3lEVRK2Wl5enhIQEzZ8/X99//70kydHRUU899ZTMZrOCg4MNTggAAFB1Zs2apfj4eI0YMUJr1qyxtffq1UuzZs0q9348PT3l6OiozMzMUu2ZmZny8fG5rv+xY8eUlpamAQMG2NosFoskycnJSUeOHFGrVq2u287FxUUuLi7lzoWaLfVCnlZ8nap1u07pSlGJJMmzvrOe7RGg4T2ay7M+/xYAAED1oIiOWunUqVNatGiR3n77bf3000+SJA8PD40ePVoTJ05U8+bNDU4IAABQ9Y4cOaIHH3zwunYPD49besins7OzunTpoqSkJA0cOFDS1aJ4UlKSJkyYcF3/Nm3aaN++faXa/vCHPyg3N1fz589ndnktZrValXL8Ry1PTlXS4XOy/nu98zY+DRQRGqjHg/zk4sR65wAAoHpRREetsmvXLsXExOiDDz5QcXGxJKlVq1aaNGmSwsPDVb9+fYMTAgAAVB8fHx8dPXpUAQEBpdqTk5Nv+SHqUVFRGjlypLp27aru3bsrNjZWeXl5Cg8PlySNGDFCzZo1U3R0tFxdXdW+fftS2zds2FCSrmtH7VBYbNHH353RsuRUHTz7nwfG/rJNU0WEBiqkVRM5OLDeOQAAMEaNKqJnZWVp4sSJ+vjjj2UymTRo0CDNnz//ZwufKSkpeu2117R9+3Y5OjoqKChIn3/+udzc3KopOYxUUlKiv//974qJiVFycrKt/aGHHpLZbNavfvUrOToymwUAANQ+o0eP1qRJk7R8+XI5ODjozJkzSklJ0csvv6ypU6fe0r6GDBmi8+fPa9q0acrIyFBQUJASExNtDxtNT0+XyWSqio+BGiwrr1Crvjmhld+c0Pncq2vyu9Yx6akudym8V6BaeTHJBQAAGM/Bar12g9yd79FHH9XZs2e1ZMkSFRUVKTw8XN26ddPq1avL3CYlJUX9+vXTlClTNGDAADk5Oem7777TE088Ua71FHNycuTh4aHs7Gy5u7tX5sdBFcvNzdXy5cs1f/58paamSrq6xubQoUNlNpvVuXNngxMCAIDa4E4eT1qtVs2ePVvR0dG6fPmypKvrjr/88sv605/+ZHC6n3cn/25xcz9k5mr516n6cM9pFRRfXQ/f291FI0MCNKxbczWq52xwQgAAUBuUdzxZY4rohw4dUrt27bRz50517dpVkpSYmKj+/fvr1KlT8vPzu+F2PXr0UJ8+fW77SwAD85onLS1NCxcu1DvvvKOcnKu3gjZu3FgvvPCCxo8fX+a/FQAAgKpQE8aThYWFOnr0qC5duqR27dqpfv36unLlyh1/52ZN+N3iP6xWq7b+cEHvJKfqq+/P29o7NPNQRGig+nfwlbMTdysAAIDqU97xZI1ZziUlJUUNGza0FdAlKSwsTCaTSdu3b9eTTz553Tbnzp3T9u3bNXz4cE0MLU8AAC22SURBVIWEhOjYsWNq06aNXn/9dYWGht7wOAUFBSooKLC9vlaExZ3NarUqJSVFMTEx+vDDD2WxXJ3N0qZNG0VGRurZZ59V3bp1DU4JAABwZ3J2dla7du0kXR0Pz5s3T2+++aYyMjIMTgZ7kF9Uog3fntbyr1P1feYlSZKDg/RIO29FhLZUt4BGrHcOAADuaDWmiJ6RkaGmTZuWanNyclLjxo3LHNwfP35ckjRjxgzNnTtXQUFBWrlypXr37q39+/fr7rvvvm6b6OhozZw5s/I/AKpEUVGR1q9fr5iYGO3YscPW3qdPH5nNZvXt25e1NwEAAP5HQUGBZsyYoc2bN8vZ2VmvvvqqBg4cqBUrVui1116To6OjzGaz0TFRw53Lzdf7KSf0/vZ0ZeUVSpLqOTvq6W7+Cg8JVPMmTHIBAAA1g+FF9MmTJ2vOnDk37XPo0KHb2ve12chjx45VeHi4JKlTp05KSkrS8uXLFR0dfd02U6ZMUVRUlO11Tk6O/P39b+v4qDoXL17U0qVLtXDhQp08eVLS1fU7hw8frsjISHXo0MHghAAAAHeuadOmacmSJQoLC9O2bds0ePBghYeH65tvvtG8efM0ePBgHryO23bwTI6Wf52qjXvPqLDk6neyZg3dNCokQEO6+8vdtY7BCQEAAG6N4UX0l156SaNGjbppn5YtW8rHx0fnzp0r1V5cXKysrCz5+PjccDtfX19Jst2aek3btm2Vnp5+w21cXFzK9cBRGOPo0aOaP3++VqxYoby8PElS06ZN9eKLL2rcuHHX3a0AAACA661bt04rV67U448/rv379+v+++9XcXGxvvvuO5bVwG2xWKz6x5FzWpacqm3HfrS1d27eUBGhLdX3Pm85OXKHKAAAqJkML6J7eXnJy8vrZ/v17NlTFy9e1O7du9WlSxdJ0pdffimLxaLg4OAbbhMQECA/Pz8dOXKkVPv333+vRx99tOLhUS2sVqv++c9/KiYmRh9//LGuPQu3Q4cOMpvNGjZsmFxdXQ1OCQAAUHOcOnXKNqZu3769XFxcZDabKaDjll0uLNb63ae04us0Hb9wdZKLo8lBj7b3UURooDo1b2RwQgAAgIozvIheXm3btlW/fv00evRoxcfHq6ioSBMmTNDQoUPl5+cnSTp9+rR69+6tlStXqnv37nJwcNArr7yi6dOnq2PHjgoKClJCQoIOHz6sv/3tbwZ/IvycwsJCrV27VjExMfr2229t7f3795fZbFbv3r35ogcAAHAbSkpK5OzsbHvt5OSk+vXrG5gINU1Gdr4SUtK0enu6sq8USZIauDppWPfmGhkSoGYN3QxOCAAAUHlqTBFdklatWqUJEyaod+/eMplMGjRokBYsWGB7v6ioSEeOHNHly5dtbZGRkcrPz5fZbFZWVpY6duyozZs3q1WrVkZ8BJTDhQsXtGTJEi1evFhnz56VJLm5uWnkyJGaNGmS2rRpY3BCAACAms1qtWrUqFG2ZQzz8/P1wgsvqF69eqX6ffjhh0bEwx3sX6cuallyqj7511kVW67eIdqiSV2FhwToqa7+qu9So75iAgAAlIuD9draGLihnJwceXh4KDs7W+7u7kbHsWuHDh1SbGysVq5cqfz8fElX17WfOHGixowZoyZNmhicEAAA4NbdiePJ8PDwcvVbsWJFFSepmDvxd2uPSixWbT6YoWXJqdqZ9pOtPTiwsSJCA9W7rbccTdwhCgAAap7yjieZJgBDWa1WffHFF5o3b54SExNt7Z07d5bZbNbTTz9d6lZjAAAAVNydXhzHneFSQbE+2HlSK7al6mTWFUmSk8lBAzr6KSI0UO2beRicEAAAoHpQRIch8vPztWrVKsXGxmr//v2SJAcHBz3xxBMym8164IEHWO8cAAAAMMDJrMtK2JamtTtPKregWJLUsG4dDQ9urhE9A+Tt7mpwQgAAgOpFER3VKjMzU2+99Zbi4uJ0/vx5SVK9evUUERGh3/72t6xVDwAAABhk94mftCz5uBL3Z+jfy52rpVc9RYQG6ted7pKbs6OxAQEAAAxCER3VYt++fYqJidGqVatUWFgoSWrevLkmTpyo559/Xg0bNjQ2IAAAAFALFZVY9Nn+DC1PTtXekxdt7aGtPRURGqiH7vGSifXOAQBALUcRHVXGYrHos88+U0xMjJKSkmztPXr0kNls1q9//Ws5OfFPEAAAAKhu2VeKtGZHuhK2pelMdr4kydnJpIFBfnouNFBtfHhQKwAAwDVUMFHp8vLytHLlSs2fP19HjhyRJDk6OmrQoEEym83q0aOHwQkBAACA2intQp5WfJ2qdbtP6XJhiSTJs76znunRQs/0aCHP+i4GJwQAALjzUERHpTl9+rQWL16s+Ph4/fTTT5Ikd3d3jR49WhMnTlSLFi0MTggAAADUPlarVdtTs7QsOVVfHMqU9d/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      "text/plain": [
       "<Figure size 1500x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "\n",
    "f = 'y~x'\n",
    "model = ols(formula=f, data=data[(~data.missing)&(data.x < 12)]).fit()\n",
    "\n",
    "fig = plt.figure(figsize =(15,8))\n",
    "fig = sm.graphics.plot_regress_exog(model, 'x', fig=fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "distinct-visit",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1    1.371978\n",
       " dtype: float64,\n",
       " 1    1.3\n",
       " Name: y, dtype: float64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(data[data.x == 3]), data[data.x == 3].y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "liked-chemistry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['x_sq'] = data['x'].pow(2)\n",
    "f = 'y~x+x_sq'\n",
    "model = ols(formula=f, data=data).fit()\n",
    "\n",
    "fig = plt.figure(figsize =(15,8))\n",
    "fig = sm.graphics.plot_regress_exog(model, 'x', fig=fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ruled-dryer",
   "metadata": {},
   "outputs": [
    {
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Ts+KuG76d/QqtvtTT0tLSpdj8BW3Zsq3+8z/iiAM+vmQ695RD4pKzPp+tG8iXUibTbrr1zjjw2HPi9OP3j10/+oFleqevgE6e8tySMgsXLszmOwYy++CFAAECBAgQWKaAlQQIECBAgAABAo0XGDliWHz8I++L9EcD0tc911lzYmz29jdkHfv7//0rez/ukN2yr3imP8Q4bOiQbFk1L+n3q/39/x6NT+26bfZTb2uvMbGSwBvWaxV33/tQVmbdtVbL3pd+Sd8YTL/PfsYrs+O9m7+10/Sf73zz0kUHZL50ybRf3nR7HH3KRXHCYXvEO9+xUUx5dmo2pe8AJ/HLr7wx9j78jDSbTdtvtXl8/2e/zsqkLO2Prv5tlk1d/3VrZOu9ECBQaAHBESBAgAABAgQIECBAoFACO2333kg5kPTXPffc+UPR0rL4J8E2fsPir1ledd2tkZJhv/3j3Vn+pNrgR48akf1u+vQHCu7824OR/rJn+rroS9Nmdqkq/TXP9O2/X/3mbblpgQAAEABJREFU9jjn4p9lfyjyPzd/S5dyacHBn/pYpB+OSr877ZHHnooHHno8fvKL38ZBx30jrR7QqXTJtPRnXJPwmd/6aWyz2zFLpptuvSstjhemTsu+I5x9qLzstfPWsfkmb8rKbbHjofHHv9wbk844MvurnpXVOf1PtwgQIECAAAECBAgQIECAAIHiC1Qf4RvWWyve8eYNsg0/stW7s/f0kn7y69iDd4vf/vF/Y/dDTosjT/5WpLJp3b/zbWk2m7p+XpyQy1ZWXtIfFJg245X49FFnZr+DrbV1cfrptdt996fXx/Z7HR9fOOM70d6+MC4+8+jo+Gm4lpbOdX7yox+I4w/dPa649pb42Ke/FLsedEqc8c2fRPrDCpUmB/S/xdEMaJONbezko/aJB269vMuUMrOpZ8ceslvcdcMlaTabhg4dEuecfHD86fqL4nc//0bcfOW58dY3rZet80KAAAECBAgQIECAAAEC/RCwCQECDRX48bdOzPIi48eN7tSPfT+5bfzPLy+M63/4tbj7xkvjqycckJXb7O1vzMq9d/O3Zp9XXXlC9rnj5fD9ds5+x1rH5/RXN2/86dezeu64blKcdeJB2XYH7/NfWZGOen7383PjNz87J5tS+Y7kXSr02jpTQm6fT3w47vz1xXHr/zs/fn/1+fH3m7+f/VXPVH4gp9Il0/qLO3b0yFht4opLfvyxv/XYjgABAgQIECBAoHkF9JwAAQIECBRdIP1k2Lprr7bc38hraWmJVM+4MaN6JBs0qCXWWHWlbGpp6fyTaD1t1NLSEiuvOD5WWWl8pARbT+XquVwyrZ666iZAgAABAvkQ0AsCBAgQIECAAAECBGokIJlWI0jVECBQDwF1EiBAgAABAgQIECBAgACBfAlIptVjPNRJgAABAgQIECBAgAABAgQIFF9AhKUUkEwr5bALmgABAgQIEBgogXkvvxJT73440vtAtakdAgQI9CZgPQECBAj0X0Ayrf92tiRAgAABAgQILFNgypnfjPFf2CnW/t6h2fvTX7tgmeWt7FVAAQIECBAgQIBAwwUk0xo+BDpAgAABAsUXEGEZBZ7/472xwRPXRcu//zBVel9/8vXx3C1/LSOHmAkQIECAAAEChRGQTCvMUAqEQB0EVEmAAAEC/RaY949/dLvt/Ece7na5hQQIECBAgAABAs0hUMhkWnPQ6yUBAgQIECDQH4GZT7wQz335q7Ho0F2i7bDd45kzzos5L87oT1V13WbQ+And1t/T8m4LD9DC5Jcck2dyTb7JeYCa1wwBAgQIEOi3gA0J9CawcOGiuOGWv8T0mbN6K9rn9ZJpfaZSkAABAgQIEMiDwLxvnx/rPn9rjFo4Pca2T431nvx1vPydH+Sha536MGHLd8bclpGdlqXPaXmnhTn48NJ3Ls8ck2dyTb7JOQdd0wUCRRUQFwECBAgMkEB7e3scc+rF8fSzU2vWomRazShVRIAAAQIECNRbYGFbe6z58t1dmlnh2b91WdboBSMmjo+XD58Uj2ywSzwxYfPsPX1Oyxvdt9e2v8qUO1+7KHNO3p1X+ESAAAECBAiUVWDRoohnnls8pfmyOqS4JdOSgokAAQIEii0gOgINEhi30ZqxxtEHxcpfPT17T58b1BXNEiBAgAABAgT6LfCvxxfFCV9ZECedsXhK849PrmTX+l1j5w13P+S0+Nb3r4md9vtSbLzlvnHCGZfG7DnzskLX/eaOOO28H8avfnN7HHjsOXH2RT+LRZVs3s9/eUtsv9fx8d6PHR7nXXpVPPvCS1n59PKnux+Ij37qi1ld+xzxtbSoppNkWk05VUagtgJqI0CAAIHOAoMGt8ZTK2zaeWHl08urvqPy6r/+Cjy/xju7bJqck3eXFRYQIECAAAECpRO44hft8eLLr4ad5q+8tv3VBcs59/f/ezRu+8vf4+BPfSy++Lm94ubb/ho33br4J+dffHlG/KySOLvi2lti803eFBu/Yd349S1/iXMuuTIO+/TOcenZx8RjTz4Tky67NuvFk08/Hwccc3a8+Y3rxo8uPDH2/vg22fJavgyqZWX/rssbAQIECBAgQKBuAsMOOjIeW2XLmDVoXMxoXSkeXWv7WOEzn6pbe2WoeMJn9s0ck2dyTb7JuQyxi5EAAQIElkvAxiUQWLgworufQntySu1+Mi0xnnD4HrHN+zeNPXf+UHxs2/fEX/72j7Q4m976pvXiJ9/6Uuy/+/ax/VabxxXX3JyVXXftVbP1W7777XHj7++Mtvb2+PXNf4kJ48fEV084IDZ5ywax9fv+IytTyxfJtFpqqosAAQIECBCou8CYdVaOiV85MVomXR2Dv3VFrPbFo2LEimPr3m6RG0h+yTF5Jtfkm5yLHLPYyi4gfgIECBDoq8CgSuZozOiupUeO6LqsVkvWe90a8bf7HllS3aiRw2PQoJYln5946tm4+96H4qsX/DibfvHr2+IN660V06a/Eukn0979HxtHS8ur5ZdsWKOZCkmNalINAQIECBAgQIBAfQXUToAAAQIECBBogMAW7+yaPtpsk67LatW1Bx56PFabuGKP1U1ceULs9fGt48ffOrHTtNKEcbHB69eMp597scdta7GifpHXonfqIECAAIFCCAiCAAECBAgQIECAAIHmFdhph9bYd/fW2Owdg7IpzadltYzon49Pidlz5sZNt96VfWVz2w90/Z2uHe2lr4Ne+uPr4r5//Cva2xfG5CnPx7nfvjJbvfk7Noq/3f9I9nXPqS9Nj5/84nfZ8lq+SKbVUlNdRRMQDwECBAgQIECAAAECBAgQKL1AayV79J/vGhQH7duaTWk+LaslzDe/+/9is+0+G0efMin7nWm77PD+xdW3RAx6zVc2991129hh6y1it4NPjbdutV9st+dx8fdKYi1tsOHr14rtPrh5HHvaxfH+nY+Iu+59MC3u7WufWZm+vlQ4+lpUOQIECBAgQKDIAlPvfjiePOPC+Mdhx8aUCy6LWU/V98fji2wptvoJpP0y7Z8vnPilePrMCyPtt/VrTc0ECBDIu4D+ESiOwDdP/1zc8atJ8b83XRonHrF3DG5tzYLbt5I4u/TsY7L5jpehQ4fE8YfuHvfe/L245arz4u4bL43Lzz8hW51+t9o5Jx8cf/jFBXHHdZNi0hlHxgO3Xh5vXH/tbH0tXiTTaqGoDgIECBAg0OQC0x54PNb83uGx3uO/jLVe/HNs8OBPY+hZR0TbnPlNHpnuF0kg7Y9pv0z75zov/SXWf+JX2X6b9t8ixVmKWARJgAABAgS6ERg3dlQMHza0mzXdL0oJt4krrxAjhnfdJv3+tHFjRnW/4XIulUxbTkCbEyBAgACBIgi88pubY1As7BTKuLbn4qW7Xv2T5J1WlvSDsBsrkPbHtF8u3Yu036b9d+ll5gkQIECAAIHmEjjjC5+JtVZfpWk6LZnWNEOlowQIEOi3gA0J9Cow7MXJ3ZZZ8Mxz3S63kEAjBHraH3vafxvRR20SIECAAAEC1Qv814ffE+knyarfsjFbSKY1xl2rfRJQiAABAgQGSmDeG7r+taRF0RKjN3nLQHVBOwR6FUj7Y9ovX1uwu/33tWV8JkCAAAECBPIs0Fx9k0xrrvHSWwIECBAgUBeBVXfZOp5YcYsldbdHazz81k/HuA1WW7LMDIFGC6T9Me2Xaf/s6Evab9P+2/HZOwECBAZUQGMECJRSQDKtlMMuaAIECBAg0Flg8IihsfLpX4mXT7s6ph92abzy9WtirYN371zIJwI5EEj7Zdo/J+8/KV485apsv037bw661lRd0FkCBAgQIECg/wKSaf23syUBAgQIECicwKiJ42ON92wUQ8aMyGNs+kQgE0j750qbbhgjKvtrtsALAQIECBAgQGAABSTTBhBbUwQIlFVA3AQIECBAgAABAgQIECBQFAHJtKKMZD3iUCcBAgQIECBAgAABAgQIECBQfAERViUgmVYVl8IECBAgQIAAAQIECBAgkBcB/SBAoBgCx3/12/HIY081TTCSaU0zVDpKgAABAgQIECBQEAFhECBAgACB5hNYuDAWTnkim6IyX8sArv/tn+Llaa/Ussq61iWZVldelRMgQIAAgSIJiIUAAQIECBAgQKCMAu0PPxAzDvtEzDhqz8VTZb790QdrQnHut6/M6jnp69+LTx70lfjZL2+Jfz42JZv/2/2PxNGnTIq9Dvtq3P/QY7H34WdkZTtePnv8N+J///5w9nHRokXx88q22+91fLz3Y4fHeZdeFc++8FK2rtYvkmm1FlUfAQL5E9AjAgQIECBAgAABAgQIEOi3wOzLL4iFU59bsn2an/Ojby35vDwzO26zRbb5Hjt/KI45+JPxns3eHHPmzsuSZ5//ykWx3jqrxwfe846YNXtu/PW+xYmzbIPKywMPPR4zX5ldmYv49S1/iXMuuTIO+/TOcenZx8RjTz4Tky67NltX6xfJtFqL1rA+VREgQIAAAQIECBAgQIAAAQLFF8h1hAvbo/1fXX8Kre3xR2rS7Q3WXTOrZ6P114nN3v7GWGv1VbLP6eW6H3wtDv30TrH/7tunj8ucrrjm5tjm/ZvGumuvmpXb8t1vjxt/f2e0tbdnn2v5IplWS011ESBAgAABAgQIECBAoDwCIiVAoAwCg1qjZcy4LpEOGjm6y7JaLxg5Ylifq3ziqWfj7nsfiq9e8ONs+sWvb4s3rLdWTJte+9/FJpnW52FRkAABAgQIECBAoBgCoiBAgAABAgSqERi6ZdefDBvyng9VU0WvZRcuWrjMMoNbW5e5fuLKE2Kvj28dP/7WiZ2mlSZ0TQQus6I+rJRM6wOSIgQIECBAoL8CU+96KF78whdi0CH/FfM+96l46puXR9u8Bf2rzlYECDSNQDrO0/E+4+C94+W9ts3OA+l80DQB6CgBAgQIEFhKYMRuB8bIz54QQ7b4YDal+bRsqSLLNbv5OzaK9McGFrS1x/QZs7qta6MN1s6W33rHPfHy9Jnx02tujpemzcyWpZf0Fc9Lf3xd3PePf0V7+8KYPOX56PjjBml9LSfJtFpqqosAgW4FLCRQVoH2Be0x7oenxVrT7o7hi2bHCguejg3/8ZN49opfl5VE3ARKI5CO83S8p+M+Hf/pPJDOB+m8UBoEgRIgQIBAcQRaW2PoB3eIUUeemk1pPirLokb/9tx56/jpL34Xb//Q/vGjq38T0dISr/03csTwOHTfj8WhXzw//vO/Do/b77ovK9LSsrjsvrtuGztsvUXsdvCp8dat9ovt9jwu/l5JrGWFavwimdYzqDUECBAgQGC5BGb+8+kY1/bqXz3qqGzYP//aMeudAIGCCgx/6C9dIkvng1lPTu2y3AICBAgQaLiADjRYYKv3bhK3/r8L4g+/uCAO/fTH4i1vXDceuPXySk5tcaKso3uHVJJpd/76krjjV5Ni0hlHZmXe/+63ZauHDh0Sxx+6e9x78/filqvOi7tvvDQuP/+EbF2tXzdiM8kAABAASURBVCTTai2qPgIECBAgQIAAgdILLGod0q3Bwnnzu11uIYH+CdiKAAECxRFobR0U6febtbR0TqC9NsJRI4fHuLGjXrt4yef0u9UmrrxCjBg+dMmyWs9IptVaVH0ECBAgQODfAmPWXz2mD57470+vvs1bf5NXP5gjUEaBEsQ8b723d4kynQ/GbbhGl+UWECBAgAABAs0lIJnWXOOltwQIECDQQIFqm24d0hrT9zkpnhy/acxtGRkvD1k9Ht5oz1h19+2rrUp5AgSaTGDVPXbIjvd03KfjP50H0vmgpfJ/3ZssFN0lQIAAAQIEXiMgmfYaEB8JFFBASAQINFBgpc3eECt+7Wux8KJfxrBv/iDW/Ny+MXhY91//amA3NU2AQI0F0nGejvexF/8oVvjxjdl5YKXK+aDGzaiOAAECBAgQaIBAjpNp9dNYuHBRTH1pekyfOat+jaiZAAECBAgQIECAAAECBAgQ6IOAIgSaS6B0ybQ/3f1AbP6Rg+P9Ox8RW+x4aOx75Jlx/0OP9WnUzrv0qth4y31jxiuz+1ReIQIECBAgQIAAAQIECiwgNAIECBAopUDpkmktg1ri5KP2iTuum5T9qdT0VyAmXXZtr4N/zQ23xXd/+t+9llOAAAECBAgQIJB3Af0jQIAAAQIECBDov0Dpkmnv2uRNseM2W8S4MaMi/anUbbd8Z/zxz/dGW3t79PTvrnsejDO++ZM45+SDeypiOQECBAjUX0ALBAgQIECAAAECBAgQaLhA6ZJprxW//e77Y6MN1onBra2vXZV9fuKp5+KQL5wf5596WGyw7prZMi8EqhNQmgABAgQIECBAgAABAgQIECiKQM/JtKJEuIw4rvvNHZGmzx+0a7elps+YFQcee04cdeAn4j2bvbnbMqOGD45qpqFDFifthrS2VLVdNW0oW92Y8Gp+r3RwNmIcR1eO/+6mkWn5iMEx2sSgCfeBkcMGx6CWMHY1HruRw1qZ1ti0Wc6xI4a2RmvloGqW/urn4AE7VrPzQrpnMEV391M9LRs6ZFCkZ6me1jfinrAQbVb2Q3EMzsUzevjXFAKDmqKXdejk7XfdHyeccWl8+ehPxbs33bjbFv781wfiqWdeiCeffj6+PumK+O4Vi39n2vnfuTr+8cgT2TatFcFqpsq9VLZdS0tUbqxM1dgpa3/paR9IB1VP6+q5vKVy/Hc3peN8UOUgN7VUkjKmptsPKvt12H/tu/aBmu0DLS0t6ZCqWX1Nd06pxF/PPjd33RHd3UdYtmyXdJlKB1VPTvW891N3eIat7ID13g/Cv6YQqOwKTdHPmnbyplvvzH7i7PTj949dP/qBHute/3VrxBEHfDxWGDc6xlemsaNHZmXHjx0VQ4cMzuZnzG6Laqa58xf/brb5bYuq2q6aNpStbkx4Nb9XOhgbMY4zK8d/d9Mrc5Lpgsoxbpoxm0GzGaT9d+HCdI0ydrUcu1fmdjovOD+U6Nwwe15btLU7pmp5PBWlrlfmtkd39xGWtS3TZe6ChbGgbWGPZRpxT6jNtsp1zVSr/SA925jyL1C6ZNovb7o9jj7lojjhsD3ine/YKKY8OzWbZs+Zm43W5VfeGHsffkY2v14lmXbgXjtGx7Trjh/Ilu/7ye0ircs+eCFAgEBpBARKgAABAgQIECBAgAABAqVLpt37f49mo37mt34a2+x2zJLpplvvypa/MHVaPPjPydm8l4IICIMAAQIECBAgQIAAAQIECBAovsAARVi6ZNrJR+0TD9x6eZdpp+3em5Efe8hucdcNl2Tzr31Zf901su06vu752vU+EyBAgAABAgQIECBAgACBagWUJ0CguQRKl0xrruHRWwIECBAgQIAAAQK5FdAxAgQIECBQSgHJtFIOu6AJECBAgECZBcROgAABAgQIECBAoP8Ckmn9t7MlAQIEBlZAawQIECBAgAABAgQIECDQcAHJtIYPQfE7IEICBAgQIECAAAECBAgQIECg+AJliVAyrSwjLU4CBAgQIECAAAECBAgQ6E7AMgIECFQlIJlWFZfCBAgQIJAXgdnPvBxTfv7beOq7v4ipdz6Yl27pBwECBAZQoHZNpfNoOp+m82o6v9auZjURIECAAIHiCUimFW9MRUSAAIHCC7x4z6Mx+rR9Y4Nbvx4b/u/FsfZlh8eUc79T+LgLE6BACBDIlUA6f6bzaDqfpvNqOr++WDnP5qqTOkOAAAECBHIkIJmWo8HQFQIE8i2gd/kRmH/9NTF80exOHXr9I/8vFsyc02mZDwQIECCwbIF03kznz6VLpfNrOs8uvcw8AQIECBAg8KqAZNqrFkWdExcBAgQKJzDy5cldYmqN9pj+wONdlltAgAABAj0LpPNmOn++tkR359nXlvGZAAECBHInoEMDJCCZNkDQmiFAgACB2gnMXmHtLpW1R2uM2/h1XZZbQIAAAQI9C6TzZjp/vrZEd+fZ15bxmUDtBNREgACB5hKQTGuu8dJbAgQIEKgIDN1hp5jbMrIy9+p//9rg4zFkzIhXF5gjQIBAvQUKUH86b6bz59KhpPNrOs8uvcw8AQIECBAg8KqAZNqrFuYIECBAoEkEVnz7evHKSZfHI1seFw//x8Ex+dMXxhpHf6ZJet/4buoBAQIElhZI5890Hk3n03ReTefXdJ5duox5AgQIECBA4FWBQa/OmiNAgECuBXSOQCeBkautEGt8cutY84CdY6V3vrHTOh8IECBAoDqBdB5N59N0Xk3n1+q2VpoAAQIECJRLQDKt7uOtAQIECBAgQIAAAQIECBAgQKD4AiIsi4BkWllGWpwECBAgQIAAAQIECBDoTsAyAgQIEKhKQDKtKi6FCRAgQIAAAQIE8iKgHwQIECBAgACBRghIpjVCXZsECBAgUGYBsRMgQIAAAQIECBAg0MQCkmlNPHi6TmBgBbRGgAABAgQIECBAgAABAgQIFD+ZZowJECBAgAABAgQIECBAgACB4guIkMAACUimDRC0ZggQIECAAAECBAgQINCdgGUECBAg0FwCkmnNNV56S4AAAQIECBDIi4B+ECBAgAABAgRKKSCZVsphFzQBAgTKLCB2AgQIECBAgAABAgQI9F9AMq3/drYkMLACWiNAgAABAgQIECBAgAABAgQaLlD3ZFrDI9QBAgQIECBAgAABAgQIECBAoO4CGiBQFgHJtLKMtDgJECBAgAABAgQIEOhOwDICBAgQIFCVgGRaVVwKEyBAgAABAgTyIqAfBAgQIECAAAECjRCQTGuEujYJECBQZgGxEyBAgAABAgQIECBAoIkFJNOaePB0fWAFtEaAAAECBAgQIECAAAECBAgUX6C3CCXTehOyngABAgQIECBAgAABAgQI5F9ADwkQGCABybQBgtYMAQIECBAgQIAAAQLdCVhGgAABAgSaS0AyrbnGS28JECBAgACBvAjoBwECBAgQIECAQCkFJNNKOeyCJkCgzAJiJ0CAAAECBAgQIECAAIH+C0im9d/OlgMroDUCBAgQIECAAAECBAgQIECg+AK5j1AyLfdDpIMECBAgQIAAAQIECBAgkH8BPSRAoCwCkmllGWlxEiBAgAABAgQIEOhOwDICBAgQIECgKgHJtKq4FCZAgEDxBRbMnBNT73445jw3rfjBirCpBXSeAIFyCqTrU7pOpetVOQVETYAAAQKNFpBMa/QIaJ8AgbIJ5DreJy++IkYft1Os/b1DY8VTPhEvfOnL0TZnfq77rHMECBAgUA6BdD1K16V0fUrXqXS9StetckQvSgIECBDIk4BkWp5GI9d90TkCBIouMP2RZ2LDv18WrdG+JNR1Xrwjnrvm5iWfzRAgQIAAgUYJpOtRui51tJ+uV+m6la5fHcu8EyBAgEAtBNTRm4BkWm9C1hMgQKAkArMfeixaYlGXaFueeLjLMgsIECBAgMBAC3R3PUrXrXT9Gui+aC+nArpFgACBARKQTBsgaM0QIEAg7wKtY0d128WFw0d3u9xCAgQIEKiNgFr6JtDT9ain61ffalWKAAECBAhULyCZVr2ZLQgQIFBIgQmbbRTTB0/sFNvCGBSjt9mq0zIfCPxbwBsBAgQGVCBdj9J1aelG03UrXb+WXmaeAAECBAjUW0Ayrd7C6idAIGcCutOTwOARQ2P+8RfEI2/cI56YsHn8c52PxlP7XxjjN35dT5tYToAAAQIEBkwgXY/SdSldn9J1Kl2v0nUrXb8GrBMaIkCAAAECFQHJtApCU/ynkwQIEBgAgVFrrhhrHPHpWPmrp8fqJxweK2264QC0qgkCBAgQINA3gXRdStendJ1K16t03erblkoRIECgiQR0NfcCpUymLVy4KKa+ND2mz5zVpwFqa2+PZ55/KebNX9Cn8goRIECAAAECBAgQIECgbALiJUCAQFkESpdM+9PdD8TmHzk43r/zEbHFjofGvkeeGfc/9FiP4/2dn1wfb9tq//jQrkfHJtt8Jo4+ZVJMn9G3JFyPlVpBgAABAgQIECCQFwH9IECAAAECBAhUJVC6ZFrLoJY4+ah94o7rJsUtV50Xo0YOj0mXXdsj2vhxo+N75x4Xd994aVzz/dPjrnsejGtuuK3H8lYQIECAAIGBEdAKAQIECBAgQIAAAQKNEChdMu1dm7wpdtxmixg3ZlRMXHmF2HbLd8Yf/3xvpK9yRjf/PrHDlpG2GTF8aGz4+jVjyy3ekZXvpqhFBAj0RUAZAgQIECBAgAABAgQIECDQxAKlS6a9dqxuv/v+2GiDdWJwa+trV3X6nD4saGuP2++6LzZ+w7rpo4kAAQIECBAgQIAAAQIECBAomIBwCPQmUOpk2nW/uSPS9PmDdu3NKVt/+vk/jJmvzIm9d9km+5xeJowZGtVMo4YPTpvFsCGDqtqumjaUrW5MeDW/Vzqo8jSO40YNcXxXeW7M0/iVvS/jRg2N1kEt9uEa78PjRjovlPXYGlMZ+yGD3feVdfyXFXcd7hdKce4eWXmeGj60tRSxLmv/sa75n2F6GsP0bGPKv0Bpk2m333V/nHDGpfHloz8V7950415H6qLLr42rr/9DfP+842OVlcYvKT97XntUM81vW5ht29a+qKrtqmlD2erGhFfze6WDKk/jOHd+85vmyVNfBnZ/mju/LRYuco2q9X43d8HAjmOt+1/f+optM69yTWhvX+i+r8p75jLsc3Mr+0YZ4qx1jAsq59MFlWeqWtervmKfi5tpfNOzjSn/AqVMpt10651x4LHnxOnH7x+7fvQDyxylhQsXxdkX/Swu+/mNcdWlp8Rb3tj5K57pIljNlE78qcH2Sr3VbKdsezBg0NM+kI6pntY1Yvm8BQvtr5UHhEbYD2ibBY0x7b+VXJp9uMbjm1ztn+W8jqX/kVq57XNM1fiYKsLx5LzQv3PCgvZF4Vmq3TmlwOeU9Gxjyr9A6ZJpv7zp9jj6lIvihMP2iHe+Y6OY8uzUbJo9Z242WpdfeWPsffgZ2Xx6Ofns70dadu4ph8a4saP4wCUfAAAQAElEQVSzsmmbtvb2tNpEoOkEdJgAAQIECBAgQIAAAQIECBDov0CzJNP6H+Frtrz3/x7Nlpz5rZ/GNrsds2S66da7suUvTJ0WD/5zcjafXu6658H0Fp89/htLyqbtpjwzNVvuhQABAgQIECBAgAABAgQIEKiZgIoI5F6gdMm0k4/aJx649fIu007bvTcbrGMP2S3uuuGSbD693HTF2V3Kpu3XWXNiWm0iQIAAAQIECBAgQIBAREAgQIAAgbIIlC6ZVpaBFScBAgQIECBAoE8CChEgQIAAAQIECFQlIJlWFZfCBAgQIJAXAf0gQIAAAQIECBAgQIBAIwQk0xqhrs0yC4idAAECBAgQIECAAAECBAgQaGKBPibTmjhCXSdAgAABAgQIECBAgAABAgT6KKAYAQK9CUim9SZkPQECBAgQIECAAAEC+RfQQwIECBAgMEACkmkDBK0ZAgQIECBAgEB3ApYRIECAAAECBAg0l4BkWnONl94SIEAgLwL6QYAAAQIECBAgQIAAgVIKSKaVctjLHLTYCRAgQIAAAQIECBAgQIAAgeIL1C9CybT62aqZAAECBAgQIECAAAECBAhUJ6A0AQK5F5BMy/0Q6SABAgQIECBAgACB/AvoIQECBAgQKIuAZFpZRlqcBAgQIECAQHcClhEgQIAAAQIECBCoSkAyrSouhQkQIJAXAf0gQIAAAQIECBAgQIAAgUYISKY1Qr3MbYqdAAECBAgQIECAAAECBAgQKL5AgSOUTCvw4AqNAAECBAgQIECAAAECBKoTUJoAAQK9CUim9SZkPQECBAgQIECAAIH8C+ghAQIECBAgMEACkmkDBK0ZAgQIECBAoDsBywgQIECAAAECBAg0l4BkWnONl94SIJAXAf0gQIAAAQIECBAgQIAAgVIKSKaVbNiFS4AAAQIECBAgQIAAAQIECBRfQIT1E5BMq5+tmgkQILBEYNHCRTHtoSkx7f7HY2Fb+5LlZggQIECAAIFiCqTrfbrup+t/ug8oZpR1iUqlBAgQyL2AZFruh0gHCRBodoGX7n005h316Vj9/H1j9UmfiUVH7hnP33Zfs4el/wQIECDQScAHAq8KpOt8ut6n6366/qf7gHQ/8GoJcwQIECDQzAKSac08evpOgEBTCLRfeVlMmD9lSV/HtL8YI38xaclnMwQaKqBxAgQIEKi5QLrOp+t9R8XpPiDdD3R89k6AAAECzS0gmdbc46f3BEor0EyBrzj94S7dXWnuYzFv2qwuyy0gQIAAAQIEmltgwYzZka7zr42iu/uB15bxmQABAgSaQ0AybWDHSWsECJRQYPaQCV2injdoRAwbP6rLcgsIECBAgACB5hYYMnZkpOv8a6Po7n7gtWV8JkCgUAKCKbDAoALHJjQCBAjkQuDlN27ZpR9T1vxAl2UWECBAgAABAsUQ6O463939QD6j1SsCBAgQ6E1AMq03IesJECCwnAJrHrBrPPKhL8S/Vt0qHl/lffHwFkfEKkcfspy12pwAAQIEOgn4QCBHAuk6n6736bqfrv/pPiDdD+Soi7pCgAABAsshIJm2HHg2JUCAQF8EWloHxRof/2Cs+uUTYpWvnBRr7r1DDB42pC+bKlMCASESIECAQPEE0nU+Xe/TdT9d/9N9QLofKF6kIiJAgEA5BSTTyjnuoiawvAK2J0CAAAECBAgQIECAAAECpRQoWTKtlGMsaAIECBAgQIAAAQIECBAgUDIB4RKon4BkWv1s1UyAAAECBAgQIECAAIHqBJQmQIAAgdwLSKblfoh0kAABAgQIECCQfwE9JECAAAECBAiURUAyrSwjLU4CBAgQ6E7AMgIECBAgQIAAAQIECFQlIJlWFZfCBPIioB8ECBAgQIAAAQIECBAgQIBAIwQGNpnWiAi1SYAAAQIECBAgQIAAAQIECAysgNYIFFhAMq3Agys0AgQIECBAgAABAgSqE1CaAAECBAj0JiCZ1puQ9QQIECBAgACB/AvoIQECBAgQIECAwAAJSKYNELRmCBAgQKA7AcsIECBAgAABAgQIECDQXAKSac01XnqbFwH9IECAAAECBAgQIECAAAECBIov0E2EkmndoFhEgAABAgQIECBAgAABAgSaWUDfCRCon4BkWv1s1UyAAAECBAgQIECAQHUCShMgQIAAgdwLSKblfoh0kAABAgQIEMi/gB4SIECAAAECBAiURUAyrSwjLU4CBAh0J2AZAQIECBAgQIAAAQIECFQlIJlWFZfCeRHQDwIECBAgQIAAAQIECBAgQKD4AnmMUDItj6OiTwQIECBAgAABAgQIECDQzAL6ToBAgQUk0/owuG3t7fHM8y/FvPkL+lBaEQIECBAgQIAAAQLNKqDfBAgQIECAQG8Ckmm9CH3nJ9fH27baPz6069GxyTafiaNPmRTTZ8zqZSurCRAgQIAAgQEV0BgBAgQIECBAgACBARKQTOsFevy40fG9c4+Lu2+8NK75/ulx1z0PxjU33NbLVlYTIECgbwJKESBAgAABAgQIECBAgEBzCUim9TJen9hhy3jXJm+KEcOHxoavXzO23OId8cc/39vLVoVfLUACBAgQIECAAAECBAgQIECg+AIi7EZAMq0blJ4WLWhrj9vvui82fsO6PRWxnAABAgQIECBAgAABAgQaLqADBAgQqJ+AZFoVtqef/8OY+cqc2HuXbZZstdqEEVHNNG7U0GzbkcNaq9qumjaUrW5MeDW/Vzqo8jSOK40b5viu8tyYp/Ere19WHjc8Bre22IdrvA9PGDuUaY1Nm+VYnTB2WAwbMqjv419Sp2YZz1r2cyXnhX4dF2NHDYlRwwf3a9tajp+6mv8ZIq9jGP41hYBkWh+H6aLLr42rr/9DfP+842OVlcYv2erZl+dENdP02fOzbefMa69qu2raULa6MeHV/F7poMrTOL44fZ7ju8pzY57Gr+x9mTpjbrS1L6p6Hy67W2/xvzRjPtOSnhdenjEv5i1YaPxLOv7LOje86LzQr+Ni5qwFMWtuW7+2XdZ4WNf8zwRFGcP0bGPKv4BkWi9jtHDhojj7op/FZT+/Ma669JR4yxs7f8Vz0aKIaqaolE9NprdqtlO2Omde5fLKjqnKQdWPca/q+O1z/ZUO9blsnvqtL/XZH5rQtbILs6jxuDEt13Wp0zUgDX5l6rSsxvuXupt0/7Jf9O9aU3FL/9nvm3S/d/7rdb9P+7cp/wKSab2M0clnfz8uv/LGOPeUQ2Pc2NEx5dmp2dTW3t7LlvVcrW4CBAgQIECAAAECBAgQIECg+AIizKOAZFovo3LXPQ9mJT57/Ddim92OWTJNeWZqttwLAQIECBAgQIAAAQIECLxGwEcCBAgUWEAyrZfBvemKs+OBWy/vMq2z5sRetrSaAAECBAgQIECg2QT0lwABAgQIECDQm4BkWm9C1hMgQIAAgfwL6CEBAgQIECBAgAABAgMkIJk2QNCaIUCgOwHLCBAgQIAAAQIECBAgQIBAcwlIpvVnvGxDgAABAgQIECBAgAABAgQIFF9AhAS6EZBM6wbFIgIECBAgQIAAAQIECDSzgL4TIECAQP0EJNPqZ6tmAgQIECBAgACB6gSUJkCAAAECBAjkXkAyLfdDpIMECBAgkH8BPSRAgAABAgQIECBAoCwCkmllGWlxEuhOwDICBAgQIECAAAECBAgQIECgKoGmTKZVFaHCBAgQIECAAAECBAgQIECAQFMK6DSBPApIpuVxVPSJAIE+CUz/x1Mx5dxvx32HHJO9p8992lAhAgQIECBAgEB9BZqm9nT/lO6nXjjxS+6nmmbUdJQAgUYLSKY1egS0T4BAvwTmPDctVrjw0Njgkatjral/zt7T57S8XxXaiAABAgQiAgIBAmUSSPdN6f4p3U+t89Jf3E+VafDFSoDAcglIpi0Xn40JEGiUwIu/uT2GL5rdqfn0+aVb7+y0zIeSCAiTAAECBAgQqFog3Tel+6elN0yf033W0svMEyBAgEBnAcm0zh4+ERhQAY31X2DQc5O73XjR0092u9xCAgQIECBAgACBzgI93Tf1dJ/VeWufCBAgUF6B/iTTyqslcgIEciPQusnm3fZl6H9s2u1yCwkQIECAAAECBDoL9HTf1NN9VuetfSqJgDAJEOhGQDKtGxSLCBDIv8DED24S/1x7h1i0aHFf0/sj6+wYq7zvbYsXeCVAgAABAgRKLCD0vgik+6Z0/5Tuo1L59J7ur9J9VvpsIkCAAIHuBSTTunexlACBJhBY/QtHxLSvXRMzj/hO9r7GCZ9rgl7rIgECBJYhYBUBAgQGWCDdP6X7qcn7T8rup9L91QB3QXMECBBoOgHJtKYbMh0mQGBpgWErjI613rNRpPell5sfWAGtESBAgAABAs0rkO6jVtp0Q/dTzTuEek6AwAALDBrg9jRHIE8C+kKAAAECBAgQIECAAAECBAgUX6CmEUqm1ZRTZQQIECBAgAABAgQIECBAoFYC6iFAII8Ckml5HBV9IkCAAAECBAgQINDMAvpOgAABAgQKLCCZVuDBFRoBAgQIECBQnYDSBAgQIECAAAECBHoTkEzrTch6AgQI5F9ADwkQIECAAAECBAgQIEBggAQk0wYIWjPdCVhGgAABAgQIECBAgAABAgQIFF+gWBFKphVrPEVDgAABAgQIECBAgAABArUSUA8BAgS6EZBM6wbFIgIECBAgQIAAAQLNLKDvBAgQIECAQP0EJNPqZ6tmAgQIECBAoDoBpQkQIECAAAECBAjkXkAyLfdDpIMECORfQA8JECBAgAABAgQIECBAoCwCkmllGenu4rSMAAECBAgQIECAAAECBAgQKL6ACGsqIJlWU06VESBAgAABAgQIECBAgECtBNRDgACBPApIpuVxVPSJAAECBAgQIECgmQX0nQABAgQIECiwgGRagQdXaAQIECBAoDoBpQkQIECAAAECBAgQ6E1AMq03IesJEMi/gB4SIECAAAECBAgQIECAAIEBEshFMm3RokXx2ORn4k93PxDPvfByFvrkKc/F1JemZ/NFfREXAQIECBAgQIAAAQIECBAgUHwBERZLoOHJtFmz58Y+n/ta7LDPF+KAY86OO//2j0z4nEt+HvsddVY274UAAQIECBAgQIAAAQIEBlxAgwQIECDQjUDDk2n/ffOf4/Enn4kzv3hgvPkN6y7p4l47bxOPPvF0PD912pJlZggQIECAAAECBAj0LqAEAQIECBAgQKB+Ag1Ppl1xze9iz523jh232SLGjR21JNL1110jm39u6uKvfWYfvBAgQIAAgSILiI0AAQIECBAgQIAAgdwLNDyZ1t6+MIYOHdwF6uVpM7JlE8aPyd69ECCQXwE9I0CAAAECBAgQIECAAAECZRFoeDJts7e/MX7+y9/Hsy+8tMR89px5ce6lV0VKpK268oQly2s8ozoCBAgQIECAAAECBAgQIECg+AIiJFBTgYYn0w7bb6dY0NYWW33i6Lj9rvvjW5dddoPjtAAAEABJREFUE+/f+Yi49Y574tTj9ovW1oZ3sabgKiNAgAABAgQIECBAgEDfBJQiQIAAgTwKNDxTtcK4MfHfPzorjjt09/jIVu+K16+zenxih/fHtZedHh/Y4h15NNMnAgQIECBAgACBZQlYR4AAAQIECBAosEDDk2nJdsTwofGpT3w4vn7SZ+PiM4/KEmsbrLtmWmUiQIAAAQIDJqAhAgQIECBAgAABAgQI9CYwqLcC9V7/6BNPx9/uf6THqa29vd5dUD+BZhfQfwIECBAgQIAAAQIECBAgQGCABBqYTFsc4fmXXhV7HfbVHqdZs+cuLuiVAAECBAgQIECAAAECBAgQaEIBXSZQLIGGJ9O++Lm94peXfbXL9OY3rBvbfXDzGD1yRLHERUOAAAECBAgQIECAQHMI6CUBAgQIEOhGoOHJtNUmrhjrr7tGlyn9lc8bbvlLzJ5Tv59Mq+YrpKkf02fM6obQIgIECBAgQIBAvgT0hgABAgQIECBAoH4CDU+m9RTaOmtOzFb98/Ep2XutXyZPeT7ettX+8fSzU5dZ9XMvvByHn3hBvH/nI+NDn/x87H34GfGPR55Y5jZWEiBAgEC/BGxEgAABAgQIECBAgACB3As0PJn2wovTYvKU5zpNDzz0eHz7R9dleK9fZ/XsvZYvux9yWmy353F9qvIbl/w85s1fEHf86lvxp+snxevWWjUu+O7VfdpWobIIiJMAAQIECBAgQIAAAQIECBAovsDiCBueTDv13B9UElvHd5p2PeiU+M0f7o7jDt09xo0ZtbinNXy94NTD44qLTupTjU8/92KsvOL4GDJkcAxubY1N3rJBPPyvp/q0rUIECBAgQIAAAQIECBAgQKDhAjpAgEBNBRqeTDtsv53je984rtP0s4tPzn4K7FOf+HBNg+2obJWVxsfElSd0fFzm+367bRfX3vg/8bmTvhm/v+Nv8Z2fXB+HfOpjS7YZ3NpSSbL1fRo0qCXbNr1Vu63yfXdmVS6rdFDlaswrB3iu+lPleUrfy3X8dDfeuTumirAPV+64urO2rPjHW2vlmtBSuf0z1v0b60K7VfaNQsdXp3N3ep6q0FX1DMbZ8ddM+0C6DzPlX6Bya9fYTr5hvbXiXf/xpk7TWzZ6feXk2NrYjv279Tesv3ak3982qGVQHHfat2PmK7Pj7Ruv/++1ESuMGVbVNGr44GzboUNaq9qu2naUr25ceDW3Vzqo8jSGY0cNcXxXeW7M0/iVvS9jRw6tXINb7MPLtw938RtdcS37vlXW+MeMHBJDWgd12SfK6iHuV++53C+8alHNfjFqWGsMGzrYMVXj61Q1Y6Bs//bdvrqFf00h0JBk2i23/y1+eNVNfZrS7ytrpOTRX54UO2y9RZx/6mFxy1XnxmZv3yjS71xra2/PuvXCtLlRzTRz9oJsu7nz26varpo2lK1uTHg1v1c6qPI0ji/NnN+kx3fz7wt52g+atS8vzZwXbe2L7MNVXt97G+9przgv9GZU1PVp7Oe3LXRM1fiYKsL+4n6hf/cdM+e0xZx5bY4px1Rh94H0bGPKv0BDkmnX//ZPcdakK/o0zZ03v2GKs2bPjfsfeizeuN5aWR/GjB4Z++++fcyeMzcem/xMtsxLDgR0gQABAgQIECBAgAABAgQIECi+QE4ibEgy7dxTDokHbr28T1M9/gDBgrb2mL9g8U+Ipfk0xb//XX7ljbH34Wdkn0aNHB5rrrZyXHnd72P6zFmxYEFb/PrmP2df+1x37dWyMl4IECBAgAABAgQIECBAgMCyBKwjQKBYAg1JpjWacIsdD41t9zgu68b2ex0fH9zlyGw+vbwwdVo8+M/JaTab0tc7hw4dEmmbLT56WPxr8jNx1okHxeDW1my9FwIECBAgQIAAAQIFFRAWAQIECBAg0I1ALpJpt991f5z/navjqxf8qMs0Z27tv+Z51w2XdPqpuNuuvXAJzbGH7BZpfceCjTZYJ7552ufizl9fEr/7+Tfi4jOPivQHEjrWeydAgAABAgTyJqA/BAgQIECAAAECBOon0PBk2n/f/Oc48Nhz4ie/+F389JqbIyXW7r73oWz+xt/fGe3//kX/9SPoW83pK5/jxo7qW2GlCBAg0B8B2xAgQIAAAQIECBAgQIBA7gUanky76rpb48Nbbha/u/IbGdZ3zzk2rvn+6fGZPXeINVdfJUaPGpEt95JfAT0jQIAAAQIECBAgQIAAAQIEii8gwsUCDU+mPfPci7HFpm+OMaNGZj164aXp2fv2W70r/v5/j8Zjk5/JPnshQIAAAQIECBAgQIAAAQL9ELAJAQIEairQ8GTasKFDYuYrs2PQoJZIv58sfcUzRdjW1pbeYkZlXTbjhQABAgQIECBAgECpBARLgAABAgQI5FGg4cm0tdZYJe7++0OZzQf/c5M499tXxlmTrogTz/xuTBg/JjZ+w+uydV4IEBg4gYVt7THt/sdj2kNTYtHCRQPXsJYIECiGgCgIECBAgEAdBNJ96Uv/eDKm3vNYpPvVOjShSgIECPRJoOHJtMM+vVPsuuMHss4esPv2scPW744fXnVTjB41Mr7+pc/G4NbWbJ0XAgQGRuDZW++JRUfuGatP+kysfv6+Me+oT8e0Bx4fmMYb3IrmCRAgQIAAAQIE8imQ7kfTfekKX98nxp376ex+Nd235rO3ekWAQNEFGp5MGz9uTLzvXW/NnIcOHRJnnXhQ3HfLZfGjC78Y795042y5l2UKWEmgpgLjrv1mjGl/cUmdE+ZPiQVXXLbksxkCBAgQIECAAAECAy2Q7kfTfWlHu+l+dcy1kzo+eidQFgFx5kSg4cm0My74UfzXvifGFdfeHNNnzspY0u9Py2a8ECAwoAJzX5oZK857skub46c/2mWZBQQIECBAgAABAgT6JrD8pbq7H11x3uSYN23xM+Tyt6AGAgQI9F2g4cm0T++2Xay/7hpx+vk/ii12PDS+fM5lcd8//tX3CJQkQKBmAkPHjoy5LYv/su7Slc4bPHrpj+YJECBAgEA5BERJgEBuBLq7H13QMiyGjB6emz7qCAEC5RFoeDJtk7dsGOeecmj88ZpvxgmH7RF33fNg7HbwqbHTfl+Kq6//Q7S1t5dnNERKoMECgwa3xlNrfbBLL15+45ZdlllAgEB+BfSMAAECBAgUTaC7+9En19oq0v1r0WIVDwEC+RdoeDKtg2jFFcbG3rtsE9f/8Mz4/Gd3jYf/9VT2U2qzZs/tKOKdAIEBEFjl6EPi4S2OiMdXeV/8a9Wt4pEPfSHWPGDXAWg5tEGAAAECBAgQIECgW4F0P5ruSx9bbat4YuL7s/vVdN/abWELCRAgUGeB3CTTpr40Pfsrnjvsc0J845IrY+LKK8TRB+0aI0fk/cd26zxCqicwwAKDhw2JNffeIVb5ykmx6pdPiDU+/sFoac3NqWKANTRHgAABAgQIECCQB4F0P5ruS19/xpdi/bO/kt2vpvvWPPRNH8okIFYCiwUa/oT81/seiaNPmRTv3/mIOGvSFbHh69eKS88+Jn77s2/E/rtvH0MGty7uqVcCBAgQIECAAAECBAgQqF7AFgQIECBQU4GGJ9Mu+9mv454H/hlHfmaX+P3V58f5px4W79nszdHqJ2FqOtAqI0CAAAECBAg0m4D+EiBAgAABAgTyKNDwZFr6Kmf6KbTP7LlDrLLS+Dwa6RMBAgQIEKhGQFkCBAgQIECAAAECBAos0PBk2rprr+an0Aq8gwmtmQT0lQABAgQIECBAgAABAgQIEOhNoOHJtN462Ot6BQgQIECAAAECBAgQIECAAIHiC4iQQE4EJNNyMhC6QYAAAQIECBAgQIBAMQVERYAAAQLFEpBMK9Z4ioYAAQIECBAgUCsB9RAgQIAAAQIECHQjIJnWDYpFBAgQINDMAvpOgAABAgQIECBAgACB+glIptXPVs0EqhNQmgABAgQIECBAgAABAgQIEMi9wHIn03IfoQ4SIECAAAECBAgQIECAAAECyy2gAgIEFgtIpi128EqAAAECBAgQIECAQDEFREWAAAECBGoqIJlWU06VESBAgAABAgRqJaAeAgQIECBAgACBPApIpuVxVPSJAAECzSyg7wQIECBAgAABAgQIECiwgGRagQdXaNUJKE2AAAECBAgQIECAAAECBAgUX2B5I5RMW15B2xMgQIAAAQIECBAgQIAAgfoLaIEAgZwISKblZCB0gwABAgQIECBAgEAxBURFgAABAgSKJSCZVqzxFA0BAgQIECBQKwH1ECBAgAABAgQIEOhGQDKtGxSLCBAg0MwC+k6AAAECBAgQIECAAAEC9ROQTKufrZqrE1CaAAECBAgQIECAAAECBAgQKL5A00comdb0QygAAgQIECBAgAABAgQIEKi/gBYIECCwWEAybbGDVwIECBAgQIAAAQLFFBAVAQIECBAgUFMBybSacqqMAAECBAgQqJWAeggQIECAAAECBAjkUUAyLY+jok8ECDSzgL4TIECAAAECBAgQIECAQIEFJNMKPLjVhaY0AQIECBAgQIAAAQIECBAgUHwBES6vgGTa8grangABAgQIECBAgAABAgTqL6AFAgQI5ERAMi0nA6EbBAgQIECAAAECxRQQFQECBAgQIFAsAcm0Yo2naAgQIECAQK0E1EOAAAECBAgQIECAQDcCkmndoFhEgEAzC+g7AQIECBAgQIAAAQIECBCon4BkWv1sq6tZaQIECBAgQIAAAQIECBAgQKD4AiJsegHJtKYfQgEQIECAAAECBAgQIECg/gJaIECAAIHFAqVOprW1ty9W6OPrggVtMeXZqTF//oI+bqEYAQIECBAgQIBAgwU0T4AAAQIECBCoqUBpk2mTpzwfb9tq/3i6khzrTfSxyc/E3oefEW/f+oDYZrdj4hc33NbbJtYTIECAAIHlFLA5AQIECBAgQIAAAQJ5FChlMm33Q06L7fY8rk/j8dwLL8cO+3whJq68Qvzowi/G3TdeGh/ecrM+basQgVIKCJoAAQIECBAgQIAAAQIECBRYoJTJtAtOPTyuuOikTsPa04cfXHljTBg/Js488cDY5C0bxojhQ2OFcWN6Km45AQIECBAgQIAAAQIECBAgkGMBXSOwvAKlTKatstL4mLjyhD7Z/c+d98XqE1eKY75ycXzyoK/EKedcHs++8FKftlWIAAECBAgQIECAAAECNRJQDQECBAjkRKCUybRq7B994ukYNXJ4bPWfm8R+u28X9z/0WOx31FmR/hhBqmfU8MFRzTR0SGvaLIa0tlS1XTVtKFvdmPBqfq90UOVpHEcOa3V8V3luzNP4lb0vIyr776CWsA/XeB9OruXdtwaXen8aPrQ1WisHlfEv937Q3fi7X+jfPjF0yKDKs9SgUp9XutufLOvf/pRHt/CvKQQGNUUvG9zJPXfeOnbcZov48JbvjLNP+mw88dRz8a/Jz2S9GlxJilUztf5bvKWlJarZTlle9oGe94F0MObJpwwQoR4AABAASURBVLXy0JSn/uhLz/vOMm2qPL8Xpa7WQZULlWtUza/RzgvlPQ7T2FcOqZrvU0U555Q5jrRvlDn+/sbeWjmgWiqXqv5ub7vyno+bZezTs40p/wKV01D+O9nIHm60wToxecpzS7qwcOHCbH7+grbsffqsBVHNNGdee7bd/LaFVW1XTRvKVjcmRfMqYzzpoMpT3DPntDm+qzw35mn8yt6XV+YsiIULF9mHa7wPv+K8UNp9atbctmhrd0yV/dzaXfzuF/p3zz5nfnvMX+BZqrt9yrL+7VN5c0vPNqb8C+QlmTagUgva0gl4QdZmmk9T9qHycvmVN8beh59RmVv83/ZbbR7f/9mvY8qzU2P6zFnxo6t/m/1BgvVft8biAl4JECBAgAABAgQIECBAgACBvgooR6DpBUqZTNtix0Nj2z2OywZv+72Ojw/ucmQ2n15emDotHvzn5DSbTXvtvHVsvsmbYpvdjom03R//cm9MOuPI7K96ZgW8ECBAgAABAgQIECBQAgEhEiBAgACBxQKlTKbddcMl8cCtly+Zbrv2wsUalddjD9kt0vrKbPbf0KFD4pyTD44/XX9R/O7n34ibrzw33vqm9bJ1XggQIECAAAECuRfQQQIECBAgQIAAgZoKlDKZ1h/BsaNHxmoTV4yWlpb+bG4bAgQIEKhSQHECBAgQIECAAAECBAjkUUAyLY+jok/NLKDvBAgQIECAAAECBAgQIECAQIEF/p1MK3CEQiNAgAABAgQIECBAgAABAgT+LeCNAIHlFZBMW15B2xMgQIAAAQIECBAgUH8BLRAgQIAAgZwISKblZCB0gwABAgQIECimgKgIECBAgAABAgSKJSCZVqzxFA0BAgRqJaAeAgQIECBAgAABAgQIEOhGQDKtGxSLmllA3wkQIECAAAECBAgQIECAAIHiCzQuQsm0xtlrmQABAgQIECBAgAABAgTKJiBeAgSaXkAyremHUADNJDDnuWkx9e6HY8HMOc3UbX0lQIAAAQIECAQCAgT6LpDu99N9f7r/7/tWShIg0CwCkmnNMlL62dQCbXPmxwtf+nKseMonYu3vHRqjj9spnrz4iqaOSecJECDQJAK6SYAAAQIEBlQg3een+/1035/u/9NzQHoeGNBOaIwAgboKSKbVlVflBBYLPHv1b2OdF+9Y/KHy2hrtseHfL4vpjzxT+eQ/At0JWEaAAAECBAgQINBsAun+Pt3np/v9jr6n54D0PNDx2TsBAs0vIJnW/GOYrwj0pluBwY//o8vyllgUsx96rMtyCwgQIECAAAECBAgQaE6BdH+f7vNf2/vungdeW8ZnAk0nUOIOS6aVePCFPnACbaNX6Lax1rGjul1uIQECBAgQIECAAAEC9RGoZ6093d/39DxQz76omwCB+glIptXPVs0ElgiM3marWBidD7fpgyfGhM02WlLGDAECBAgQIEBgGQJWESDQBALp/j7d5y/d1fQckJ4Hll5mngCB5hbo/HTf3LHoPYHcCozf+HXx1P4Xxj/X+Wg8MWHzeOSNe8T84y+IwSOG5rbPOkaAAIHaCKiFAAECBAiURyDd36f7/HS/n+770/1/eg5IzwPlURApgeILSKYVf4xFmBOBlTbdMFY/4fBY+aunxxpHfDpGrbliTnqmG90KWEiAAAECBAgQIECgHwLpPj/d76f7/nT/n54D+lGNTQgQyLGAZFqOB6c/XbMNAQIECBAgQIAAAQIECBAgUHwBETZOQDKtcfZaJkCAAAECBAgQIECAQNkExEuAAIGmF5BMa/ohFAABAgQIECBAgED9BbRAgAABAgQIEFgsIJm22MErAQIECBAopoCoCBAgQIAAAQIECBCoqYBkWk05VUaAQK0E1EOAAAECBAgQIECAAAECBPIoIJlW21FRGwECBAgQIECAAAECBAgQIFB8ARGWWEAyrcSDL3QCBAgQIECAAAECBMomIF4CBAgQWF4BybTlFbQ9AQIECBAgQIBA/QW0QIAAAQIECBDIiYBkWk4GQjcIECBAoJgCoiJAgAABAgQIECBAoFgCkmnFGk/REKiVgHoIECBAgAABAgQIECBAgACBbgQKlkzrJkKLCBAgQIAAAQIECBAgQIAAgYIJCIdA4wQk0xpnr2UCBAgQIECAAAECBMomIF4CBAgQaHoBybSmH0IBECBAgAABAgTqL6AFAgQIECBAgACBxQKSaYsdvBIgQIBAMQVERYAAAQIECBAgQIAAgZoKSKbVlFNlBGoloB4CBAgQIECAAAECBAgQIEAgjwK1TablMUJ9IkCAAAECBAgQIECAAAECBGoroDYCJRaQTCvx4AudAAECBAgQIECAQNkExEuAAAECBJZXQDJteQVtT4AAAQIECBCov4AWCBAgQIAAAQIEciIgmZaTgdANAgQIFFNAVAQIECBAgAABAgQIECiWgGRascZTNLUSUA8BAgQIECBAgAABAgQIECBQfIF+RCiZ1g80mxAgQIAAAQIECBAgQIAAgUYKaJsAgcYJSKY1zl7LBAgQIECAAAECBMomIF4CBAgQIND0ApJpTT+EAiBAgAABAgTqL6AFAgQIECBAgAABAosFJNMWO3glQIBAMQVERYAAAQIECBAgQIAAAQI1FZBMqymnymoloB4CBAgQIECAAAECBAgQIECg+ALNGKFkWjOOmj4TIECAAAECBAgQIECAQCMFtE2AQIkFJNNKPPhCJ0CAAAECBAgQKJuAeAkQIECAAIHlFSh1Mq2tvX15/WxPgAABAgQIDISANggQIECAAAECBAjkRKC0ybTJU56Pt221fzz97NQ+D8V5l14VG2+5b8x4ZXaft1GQAIFyC4ieAAECBAgQIECAAAECBIolUMpk2u6HnBbb7XlcVSN5zQ23xXd/+t9VbdPEhXWdAAECBAgQIECAAAECBAgQKL6ACPshUMpk2gWnHh5XXHRSn7nuuufBOOObP4lzTj64z9soSIAAAQIECBAgQIAAAQL1ElAvAQIEGidQymTaKiuNj4krT+iT+hNPPReHfOH8OP/Uw2KDddfs0zYKESBAgAABAgQIEOhWwEICBAgQIECg6QVKmUzr66hNnzErDjz2nDjqwE/EezZ7c7ebTRgzNKqZRg0fnNUzbMigqrarpg1lqxsTXs3vlQ6qPI3juFFDHN9VnhvzNH5l78u4UUOjdVBLl3247C7LG/+4kc4Ly2vYrNuPqYz9kMHu+5p1/OrZb/cL/bsHHVl5nho+tNV1yr1WYfeB9Gxjyr+AZNoyxujPf30gnnrmhXjy6efj65OuiO9esfh3pp3/navjH488kW05e157VDPNb1uYbdfWvqiq7appQ9nqxoRX83ulg6qHcWzIcTZ3fvOb5slTXwZ2f5o7vy0WLnKNqvV+N3fBwI5jrfuvvv6P37zKNaG9fWFDrkfGrf/jNhB2cyv7xkC0U7Q2FlTOpwsqz1RFi0s8+T5eB3J80rONKf8CkmnLGKP1X7dGHHHAx2OFcaNjfGUaO3pkVnr82FExdMjgbD5dBKuZ0ok/bdi+cFH0vF27dZWbCz72g77uA+mY6mvZgSg3b8FCx7BjuGn3gbT/VnJpTdv/gTjG+9NGcu3PdrZp/mth+h+plds+x5TrQpd9wHmhf8f3gvZF4Vmqvcv+5HrRzCad+56ebUz5FyhlMm1BW3vMX7AgG500n6bsQ+Xl8itvjL0PP6MyF7FeJZl24F47Rse0644fyJbv+8ntsnXZBy8ECBAgQIAAAQIECBAom4B4CRAgUGKBUibTttjx0Nh2j+OyYd9+r+Pjg7scmc2nlxemTosH/zk5zZoIECBAgAABAgQKJiAcAgQIECBAgMDyCpQymXbXDZfEA7devmS67doLlzgee8hukdYvWbDUzPrrrpFt0/F1z6VWmSVAgAABAvUUUDcBAgQIECBAgAABAjkRKGUyLSf2ukGgBAJCJECAAAECBAgQIECAAAECxRKQTOtuPC0jQIAAAQIECBAgQIAAAQIEii8gQgL9EJBM6weaTQgQIECAAAECBAgQINBIAW0TIECAQOMEJNMaZ69lAgQIECBAgEDZBMRLgAABAgQIEGh6Acm0ph9CARAgQIBA/QW0QIAAAQIECBAgQIAAgcUCkmmLHbwSKKaAqAgQIECAAAECBAgQIECAAIGaCuQymVbTCFVGgAABAgQIECBAgAABAgQI5FJApwg0o4BkWjOOmj4TIECAAAECBAgQINBIAW0TIECAQIkFJNNKPPhCJ0CAAAECBMomIF4CBAgQIECAAIHlFZBMW15B2xMgQIBA/QW0QIAAAQIECBAgQIAAgZwISKblZCB0o5gCoiJAgAABAgQIECBAgAABAgSKJdBdMq1YEYqGAAECBAgQIECAAAECBAgQ6E7AMgIE+iEgmdYPNJsQIECAAAECBAgQINBIAW0TIECAAIHGCUimNc5eywQIECBAgEDZBMRLgAABAgQIECDQ9AKSaU0/hAIgQIBA/QW0QIAAAQIECBAgQIAAAQKLBSTTFjt4LaaAqAgQIECAAAECBAgQIECAAIHiCwxohJJpA8qtMQIECBAgQIAAAQIECBAg0CHgnQCBZhSQTGvGUdNnAgQIECBAgAABAo0U0DYBAgQIECixgGRaiQe/KKG3zZkfL979cLwy+YWihCQOAgQIEKiTgGoJECBAgACBzgLpOSo9T6Xnqs5rfCJAoCcBybSeZCxvCoEpP7sphn1+l1jre4fGKl/bI6Yde1TMeX5aU/RdJwlUIaAoAQIECBAgQIAAgZoKpOem9PyUnqPS81R6rkrPVzVtRGUECiogmVbQgc1HWPXtxYIZs2OtP14UwxbNWdLQ6q/cHy/+5P8t+WyGAAECBAgQIECAAAECBLoKpOem9PzUsSY9V6Xnq/Sc1bHMO4G+C5SrpGRauca7UNHO+NfTMXzR7C4xjXr2H12WWUCAAAECBAgQIECAAIEuAiVe0N1zU3q+Ss9ZJWYROoE+CUim9YlJoTwKDBk7pttuzR+xQrfLLSRAgAABAgQIFEVAHAQIEFhegZ6em3p6zlre9mxPoEgCkmlFGs2SxTL29RPjmVFv6hr1e7bpuswSAgQIEMiDgD4QIECAAAECeRHo5rkpPV+l56y8dFE/CORVQDItryOjX30SGHrC6fHQW/eLyRPeHY+utX089omzYuLWm/VpW4UI9F1ASQIECBAgQIAAAQLFEkjPTen5KT1Hpeep9FyVnq+KFaVoCNRHQDKtPq75qLUEvRix0phY6+DdY6WvnhqrffGomPjBTUoQtRAJECBAgAABAgQIECCw/ALp+Sk9R6XnqfRclZ6vlr9WNTREQKMDKiCZNqDcGiNAgAABAgQIECBAgACBDgHvBAgQaEYBybRmHDV9JkCAAAECBAgQaKSAtgkQIECAAIESC0imlXjwhU6AAAECZRMQLwECBAgQIECAAAECyysgmba8grYnQKD+AlogQIAAAQIECBAgQIAAAQI5EZAlpuaeAAAQAElEQVRMq+NAqJoAAQIECBAgQIAAAQIECBAovoAIyyUgmVau8RYtAQIECBAgQIAAAQIEOgS8EyBAgEA/BCTT+oFmEwIECBAgQIAAgUYKaJsAAQIECBAg0DgBybTG2WuZAAECBMomIF4CBAgQIECAAAECBJpeQDKt6YdQAATqL6AFAgQIECBAgAABAgQIECBAYLFAkZNpiyP0SoAAAQIECBAgQIAAAQIECBRZQGwEBlRAMm1AuTVGgAABAgQIECBAgACBDgHvBAgQINCMApJpzThq+kyAAAECBAgQaKSAtgkQIECAAAECJRaQTCvx4AudAAECZRMQLwECBAgQIECAAAECBJZXQDJteQVtT6D+AlogQIAAAQIECBAgQIAAAQIEciJQx2RaTiLUDQIECBAgQIAAAQIECBAgQKCOAqomUC4BybRyjbdoCRAgQIAAAQIECBDoEPBOgAABAgT6ISCZ1g80mxAgQIAAAQIEGimgbQIECBAgQIAAgcYJlDqZ1tbe3if5VO6Z51+KefMX9Km8QgQIECDQrYCFBAgQIECAAAECBAgQaHqB0ibTJk95Pt621f7x9LNTlzmI3/nJ9Vm5D+16dGyyzWfi6FMmxfQZs5a5jZVFExAPAQIECBAgQIAAAQIECBAgUHyBvkVYymTa7oecFtvteVyfhMaPGx3fO/e4uPvGS+Oa758ed93zYFxzw2192lYhAgQIECBAgAABAgQIECBQdwENECAwoAKlTKZdcOrhccVFJ/UJ+hM7bBnv2uRNMWL40Njw9WvGllu8I/7453v7tK1CBAgQIECAAAECBAj0LGANAQIECBBoRoFSJtNWWWl8TFx5QtXjtaCtPW6/677Y+A3rVr2tDQgQIECAAIHCCAiEAAECBAgQIECgxAKlTKb1d7xPP/+HMfOVObH3LtssqWLVFYZHNdO4UUOybUcMba1qu2raULa6MeHV/F7poMrTOK40blisNmFEDid9Mi697wMrjxseg1tb7L81PoYnjB3KtMamzXI8Txg7LIYNGWT8Szr+y9pPV6ycF/J0/9IsfRk7cnCMHOZZqlnGSz+HV/3cn55tTPkXkEzr4xhddPm1cfX1f4jvn3d8pJ9s69jsuWlzo5pp+uzFfxF07vz2qrarpo3Cla3SWPzV7ZNF8ErHY57ieHH6vHj25TkmBk25D0ydMTfa2hc1Zd/zfNy9NGM+05KeE16eMS/mLVho/Es6/ss6L71UOS/k6f6lWfoyY3ZbzJ7nWapZxks/q382S882pZ6aJHjJtF4GauHCRXH2RT+Ly35+Y1x16Snxljd2/ornokUR1UxRKZ+aTG/VbKdsdc68yuWVHVOVgyo3417pUG76kicXfanqetHIfaiyCzdNXxvpVE3bTMt1Xeq0b6TBr0ydljkfOsekfcB+0a/9oMKW/Ze3Y0p/SnyeT8dzDadsB/eSe4FSJtPS7z6bv2DxT4il+TR1jNTlV94Yex9+RsfHOPns70dadu4ph8a4saNjyrNTs6mtvX1JGTMECBAgQIAAAQIEmlBAlwkQIECAAIF+CJQymbbFjofGtnscl3Ftv9fx8cFdjszm08sLU6fFg/+cnGaz6a57HszeP3v8N2Kb3Y5ZMk15Zmq23AsBAgQIECAw0ALaI0CAAAECBAgQINA4gVIm0+664ZJ44NbLl0y3XXvhkhE49pDdIq3vWHDTFWcvKbf0NuusObGjiHcCBAj0TUApAgQIECBAgAABAgQIEGh6gVIm05p+1AY4AM0RIECAAAECBAgQIECAAAECxRcQYd8EJNP65qQUAQIECBAgQIAAAQIECORTQK8IECAwoAKSaQPKrTECBAgQIECAAAECHQLeCRAgQIAAgWYUkExrxlHTZwIECBAg0EgBbRMgQIAAAQIECBAosYBkWokHX+gEyiYgXgIECBAgQIAAAQIECBAgsLwCkmnLK1j/7bVAgAABAgQIECBAgAABAgQIFF9AhE0iIJnWJAOlmwQIECBAgAABAgQIEMingF4RIECgXAKSaeUab9ESIECAAAECBAh0CHgnQIAAAQIECPRDQDKtH2g2IUCAAAECjRTQNgECBAgQIECAAAECjROQTGucvZYJlE1AvAQIECBAgAABAgQIECBAoOkFJNN6HUIFCBAgQIAAAQIECBAgQIAAgeILiJBA3wQk0/rmpBQBAgQIECBAgAABAgTyKaBXBAgQIDCgApJpA8qtMQIECBAgQIAAgQ4B7wQIECBAgACBZhSQTGvGUdNnAgQIEGikgLYJECBAgAABAgQIECixgGRaiQdf6GUTEC8BAgQIECBAgAABAgQIECCwvAL5T6Ytb4S2J0CAAAECBAgQIECAAAECBPIvoIcEmkRAMq1JBko3CRAgQIAAAQIECBDIp4BeESBAgEC5BCTTyjXeoiVAgAABAgQIdAh4J0CAAAECBAgQ6IeAZFo/0GxCgAABAo0U0DYBAgQIECBAgAABAgQaJyCZ1jh7LZdNQLwECBAgQIAAAQIECBAgQIBA0wv0mkxr+ggFQIAAAQIECBAgQIAAAQIECPQqoAABAn0TkEzrm5NSBAgQIECAAAECBAjkU0CvCBAgQIDAgApIpg0ot8YIECBAgAABAh0C3gkQIECAAAECBJpRQDKtGUdNnwkQINBIAW0TIECAAAECBAgQIECgxAKSaSUe/LKFLl4CBAgQIECAAAECBAgQIECg+AL1jlAyrd7C6idAgAABAgQIECBAgAABAr0LKEGAQJMISKY1yUDpJgECBAgQIECAAIF8CugVAQIECBAol4BkWrnGW7QECBAgQIBAh4B3AgQIECBAgAABAv0QkEzrB5pNCBAg0EgBbRMgQIAAAQIECBAgQIBA4wQk0xpnX7aWxUuAAAECBAgQIECAAAECBAgUX6DwEUqmFX6IBUiAAAECBAgQIECAAAECvQsoQYAAgb4JSKb1zUkpAgQIECBAgAABAvkU0CsCBAgQIEBgQAUk0waUW2MECBAgQIBAh4B3AgQIECBAgAABAs0oIJnWjKOmzwQINFJA2wQIECBAgAABAgQIECBQYgHJtNIMvkAJECBAgAABAgQIECBAgACB4guIsN4Ckmn1Fm7S+p+58c/xzOnnxAtf+nI89Z2rYv6M2U0aiW4TIECAAAECBAgQINAUAjpJoAeB9DyankvT82l6Tk3Pqz0UtZjAgAhIpg0Ic3M18vQ1t8Z6vzwp1ptyU6zz4h2x4V8vjRlfO725gtBbAgQIECBAgMAACWiGAAECBOorkJ5H03Npej5Nz6npeTU9t9a3VbUT6FlAMq1nm9KuGfK/v+8S+9rT7opZT7/YZbkFBAgQINC0AjpOgAABAgQIEMi9QHoOTc+jr+1od8+try3jM4F6CUim1Uu2iesd/cqT3fZ+1j+f6na5hQQGVkBrBAgQIECAAAECBAiURaCn59CenlvL4iLOxgpIpg2UfxO18/Kam3Xp7ZxBo2LFd7+5y3ILCBAgQIAAAQIECBAgQIBAvQTSc2h6Hn1t/d09t762TMM+a7jwAoMKH6EAqxYYt8eu8dzIDZdsN7dldEz54BHROqR1yTIzBAgQIECAAAECBAgUS0A0BPIokJ5D0/Noei7t6F96Xk3PrR2fvRMYaAHJtIEWb4L2Rq2+Yoz5xqR47rifxJOfuSjmn3dlrP7xDzRBz3WRAAECBAgQKKGAkAkQIECg4ALpeTQ9l6bn0/Scmp5X03NrwcMWXo4FJNNyPDiN7tqYdVeJFTfZIAYPG9LormifAAECBRQQEgECBAgQIECAQF8F0nNpej5Nz6l93UY5AvUSkEzro+yiRYuirb29j6UVI1BgAaERIECAAAECBAgQIECAAIESC5Qmmba8Y3z9b/8U2+x2zPJWY3sCBAgQIECAAAECBAgQIECgjgKqJlBvAcm0XoQnT3kuPrz7sXHCGZf2UtJqAgQIECBAgAABAgQI9FvAhgQIECDQJAKSab0M1OqrrhQ/+OYX4sQj9u6lpNUECBAgQIAAgTIKiJkAAQIECBAgUC4BybRexntwa2usuvKEWGHc6G5LDm5tiWqmQYNasnrSWzXbKVudM69yeaWDKldjXjnAc9WfKs9Tpek7lx6vX7k7poowVpU7LsdWua5NHePdWrkmtFRu/zo+ey/nftDtuFf2jW6XF+GcV8cYBlXOpxW6Hq9hTB1jzb4PpPswU/4FKqei/Hcyzz1cYfTQqGYaNWxwFs7QIYOq2q6aNpStbkyazUt/u45vOqjy5DJ21JBYYcwwE4Om3AfGjhyaPaDYh2t7DI+uuDKtrWmzeI4ZOSSGtFbu+5wTm/KcWM/9bEy6X6jyWSJP9zuN6svIoYNj6JDWaFT72u16L86ktibp2caUf4GBSqblX6KfPXxh+ryoZpo5Z0HW0tz5C6varpo2lK1uTHg1v1c6qPI0ji/NnB8vTJtrYtCU+8BLM+dFW/uipux7no+7aa84L+R5fOrZtzT289sq933Oic4rr9kHXk73C1U+S+TpfqdRfXllblvMnd8ejWpfu316djA+y3Fsp2cbU/4FJNPyP0Z6SIAAAQIECBAgQIBA3QU0QIAAAQIE+iYgmdaL06JFi2LBgrZoa2vPSmbz7YvnswVeCBAgQIAAAQKNFNA2AQIECBAgQIDAgApIpvXC/ejjT8fbtz4gTjjj0njuhZez+S+d9b1etrKaAAECBHoTsJ4AAQIECBAgQIAAAQLNKCCZ1suorb/uGvHArZd3ms784oG9bGV1gQWERoAAAQIECBAgQIAAAQIECBRfoMcIJdN6pLGCAAECBAgQIECAAAECBAg0m4D+EiBQbwHJtHoLq58AAQIECBAgQIAAgd4FlCBAgAABAk0iIJnWJAOlmwQIECBAgEA+BfSKAAECBAgQIECgXAKSaeUab9ESIECgQ8A7AQIECBAgQIAAAQIECPRDQDKtH2g2aaSAtgkQIECAAAECBAgQIECAAIHiC+Q3Qsm0/I6NnhEgQIAAAQIECBAgQIBAswnoLwEChReQTCv8EAuQAAECBAgQIECAQO8CShAgQIAAAQJ9E5BM65uTUgQIECBAgEA+BfSKAAECBAgQIECAwIAKSKYNKLfGCBAg0CHgnQABAgQIECBAgAABAgSaUUAyrRlHrZF91jYBAgQIECBAgAABAgQIECBQfAER9iggmdYjjRUECBAgQIAAAQIECBAg0GwC+kuAAIF6C0imLafw6iuOiGqm8aOHZi2OHNZa1XbVtKFsdWPCq/m90kFlHJt/HI1hPsZwlfHDY3Bri2tUldd3+28+9t88jsOKY4fFsCGD+nJMKeO4sw/0YR8YN2pIjBo+mFUfrPJ4TtSnEb3uu+FfUwhIpjXFMOkkAQIECBDIq4B+ESBAgAABAgQIECiXgGRaucZbtAQIdAh4J0CAAAECBAgQIECAAAEC/RCQTOsH2vJssnDhonj2hZeirb29X9XYiAABAgQI1FogXZPS9am7eme+He+WxQAAEABJREFUMjtenj6zu1WWESDQg0A6pnpYZTEBAlUIpGvT1Jemx/SZs3rcKq2fM3d+j+utINDMAvqeXwHJtAEcmz/86d7Y/CMHx1afODrettX+ceV1tw5g65oiUDyBj37qi7Hxlvt2mi66/NriBSoiAnUUSA8gO+93Uvz65j93amX2nLlx+IkXxLt2OCT+878Oj90POS3SA0unQj4QINBFYPKU57P7vKefndpp3c23/bXT9arj+jVv/oJO5XwgUACBmoTwp7sfyJ6d3r/zEbHFjofGvkeeGfc/9NiSuidPeS623+v4SOs33fbAOOnr348Fbe1L1pshQIBAPQUk0+qpu1Td6WHlmFMvjsP22ynuvfl7ccFph8dXvnF5PPXMC0uVMkuAQLUCn9v/4/HrH5+1ZNpjpw9VW4XyBEorcM4lP4/0APLoE093MfjpNTfHw/96Kn5/9fnx5+svitZBg+KC7/6/LuUsIFAcgeWPJCWdt9vzuG4rWhSLYuSI4UuuVx3XrqFDBndb3kICZRdoGdQSJx+1T9xx3aS45arzYtTI4THpslf/p+np5/8o3rj+2nH3jZfG9T/8Wtz4+zvjxlv+UnY28RMgMEACkmkDBH3n3/4R6f/y7/5fH4zBra3xoff+R6yz5sT4w5/uGaAeaIZAMQVWXnFcdiyl4ylN48eNDv8IlEpgOYI9YPePxM1XnRsTV16hSy3poWSXHd4fq6w0PsaMHhl777J1/OLXf4xFixZ1KWsBAQKLBS449fC44qKTFn/o5nX4sCGdrlnputXS0tJNSYsIEHjXJm+KHbfZIsaNGRXpOrXtlu+MP/753uzX5aSvfd5+1/2Va9M2MWL40Fh37dXiY9u+J37zh7vCPwIECAyEgGTaQChX2nhu6svZzdPQoUMqnxb/t946q8ezz7+8+INXAiUTqFW4V13/h/jSWd+Liy6/NtKP+9eqXvUQKINASj6vuvKEGDK460/GPPHUc7H2GhOXMKy1+irZ/IxXZmfvXggQ6CqQks8TK8dU1zWLl7w0bWZ88Wvfia+c+4P475v/nCUFFq/xSoBAbwK3331/bLTBOtkPJkx9cVpWfM3VVs7e00u6Zj3z/Etp1kSAAIG6C0imVUfc79IzZs7KfrR/6QqGDRsa6Rc7L73MPAECfRf48Jabxfve9dZYfdWV4pbb/xYfP+DLEmp951OSQI8C6afP0k9TD69cpzoKDfv3/wyaPXtuxyLvBAhUIZCSbJ/ebbvsJ2jSZseddkmc9a2fplkTAQK9CFz3mzsiTZ8/aNesZMf/2Fn6BxXSs9VL02Zk670QqJGAagj0KCCZ1iNNbVeMHTMq+5rn0rXOmzc/++rM0svMEyDQd4FDP71THLzPf8Uhn/qv7Gs1Y0aPiPQLnvteg5IECHQn0NLSkv0PoKV/MXrH/MiRw7vbxDICBHoReMsb141jPvvJ+MyeO8SXj/5UnHbcfpF+N2Fbu1+Y3gud1VULFGuD9HXOE864NDtu3r3pxllwY0ePzN4XLGjL3tNLeraaMH5smjURIECg7gKSaXUnXtzAxJVWiPSVmaVP+OkXO6+6StffU7N4C68ECFQjMGTI4Fh5wviYU0lSV7OdsgQIdC+QfpfT0l+dfvLp57OCHQ8w2QcvBGopULK6Vl5x8T1gm78+WLKRF241AjfdemcceOw5cfrx+8euH/3Akk1XWnF8Nt9xbUofHn/y2VhtlQlp1kSAAIG6C0im1Z14cQObvf2N2cwV196c/X6M3932v9lf8nz/u9+eLfdCgEB1Aukh//Irb4xnX3gp+zPo1//2T5H+XPrm79iouoqUJrCcAs28efqJmI7/ybOgrS065lNM6WvUV113azw/dVq8MmtO/Ojq38bO278vWlr8svTkYyLQncCCSmJs/oIF2ao0n6bsQ+Ul/RTa//794Zgzd3527br0x9dFumYt/XXqSjH/ESDwb4Ff3nR7HH3KRXHCYXvEOyv3d1OenRppSr+GIP1RgvRTaunalI6pxyY/E7/6zR2xzfs3+/fW3ggQIFBfAcm0+vouqX3kiGFx4VePiLMmXRFv22r/OOKkC+NLR+4dS//SzCWFzRCov0AhWvjhVTfFVp84Ot7+of3j+K9+O44/dPf4j7duWIjYBEFgIASOO+3b8fatD8j+5076Qx5pPj2QpLb32OlD8fp1Vo8P7HJkbP6Rg7NE2+H77ZxWmQgQ6EFgix0PjW33OC5bu/1ex8cHK8dP9qHy8uzzL8Y+nzsjNt32wOzalZLZpx63X2WN/wgQ6E7g3v97NFt85rd+GtvsdsyS6aZbF//FzhM/t1c88NBj2TG1wz5fqCTSNo1tP/DObBsvBAgQqLdAkyXT6s1R3/o/+J53xN9v/n785mfnxD2//W7s/rGt6tug2gkUWCD9xaabrzw3/njNN+PGn3497r35e7HPJz5c4IiFRqD2Aueeckg8cOvlnaZ1114t0r9RI4fHxWceFXdcNyn+8IsL4uff/nKkv1SY1pkIEOhe4K4bLul0PN127YVLCh590K7xvzddml2zbv/lt7Lf9el/qi7hMUOgi8DJR+3T6XjquF7ttN17s7LpenXTFWfHLVedF3f++pL46gkHRPq1H9lKLwUREAaB/ApIpg3w2LS2Doo1Vl3JiX6A3TVXTIGWlpZYcYWxsdbqq8Tg1tbwjwCB2gukr9KsNGFc7StWI4ESCqSvdKZr1vhxo0sYfYlCFuqACkxceYVI/wNoQBvVGAECpReQTCv9LgCAAAECBAgQIBDBgAABAgQIECBAoG8Ckml9c1KKAAECBPIpoFcECBAgQIAAAQIECBAYUAHJtAHl1hiBDgHvBAgQIECAAAECBAgQIECAQDMKVJdMa8YI9ZkAAQIECBAgQIAAAQIECBCoTkBpAgR6FJBM65HGCgIECBAgQIAAAQIEmk1AfwkQIECAQL0FJNPqLax+AgQIECBAgEDvAkoQIECAAAECBAg0iYBkWpMMlG4SIEAgnwJ6RYAAAQIECBAgQIAAgXIJSKaVa7xF2yHgnQABAgQIECBAgAABAgQIECi+QB0ilEyrA6oqCRAgQIAAAQIECBAgQIDA8gjYlgCB/ApIpuV3bPSMAAECBAgQIECAQLMJ6C8BAgQIECi8gGRa4YdYgAQIECBAgEDvAkoQIECAAAECBAgQ6JuAZFrfnJQiQIBAPgX0igABAgQIECBAgAABAgQGVEAybUC5NdYh4J0AAQIECBAgQIAAAQIECBAovkARI5RMK+KoiokAAQIECBAgQIAAAQIElkfAtgQIEOhRQDKtRxorCBAgQIAAAQIECDSbgP4SIECAAAEC9RaQTKu3sPoJECBAgACB3gWUIECAAAECBAgQINAkApJpTTJQukmAQD4F9IoAAQIECBAgQIAAAQIEyiUgmVau8e6I1jsBAgQIECBQR4HJU56LvQ8/IyZddk2nVs6adEUcfMJ5MXvOvE7LfSBAgAABAgQI1ElAtXUQkEyrA6oqCRAgQIAAgXILrL3GxNhis43joh/8Mn77x7szjKuv/0P88KqbYo+dPhQjRwzLlnkhQIAAgZ4ELCdAgEB+BSTT8js2ekaAAAECBAg0scCBe+4Y7938LfHFr303S6h9+ZzL4sjP7JIta+KwdL03AesJECBAgACBwgtIphV+iAVIgAABAgR6F1Ci9gKtrYPizC8eFGNGj4gjT/5WfHjLzeKAPT5S+4bUSIAAAQIECBAgMKACkmkDyq0xAgRqLKA6AgQI5Fpg1KgRsfrElbI+jhs7OlpaWrJ5LwQIECBAgAABAs0rIJnWkLHTKAECBAgQIFAGgfO+fWU89OiT8Zk9d4grf/X7uO43d5QhbDESIECAAAECSwTMFFFAMq2IoyomAgQIECBAoOECN9zyl/jBVTfFGV84IPtdadt9cPM44YxLs+RawzunAwQIEOhNwHoCBAgQ6FFAMq1HGisIECBAgAABAv0TeGzyM3HMqRfHHjttFVu/b9Oski8f/alYc7WV43Nf+mbMnjMvW+al9gJqJECAAAECBAjUW0Ayrd7C6idAgAABAr0LKFEwgXXXXi0euPXyOPGIvZdENmb0yLjpirOzaeSIYUuWmyFAgAABAgQIEGguAcm05hovvSWQMwHdIUCAAAECBAgQIECAAAEC5RIoZzKtXGMsWgIECBAgQIAAAQIECBAgUE4BUROog4BkWh1QVUmAAAECBAgQIECAAIHlEbAtAQIECORXQDItv2OjZwQIECBAgACBZhPQXwIECBAgQIBA4QUk0wo/xAIkQIAAgd4FlCBAgAABAgQIECBAgEDfBCTT+uakFIF8CugVAQIECBAgQIAAAQIECBAgMKACDUmmDWiEGiNAgAABAgQIECBAgAABAgQaIqBRAkUUkEwr4qiKiQABAgQIECBAgACB5RGwLQECBAgQ6FFAMq1HGisIECBAgAABAs0moL8ECBAgQIAAAQL1FpBMq7ew+gkQIECgdwElCBAgQIAAAQIECBAg0CQCkmlNMlC6mU8BvSJAgAABAgQIECBAgAABAgSKL7B0hJJpS2uYJ0CAAAECBAgQIECAAAECxREQCQECdRCQTKsDqioJECBAgAABAgQIEFgeAdsSIECAAIH8Ckim5Xds9IwAAQIECBBoNgH9JUCAAAECBAgQKLyAZFrhh1iABAgQ6F1ACQIECBAgQIAAAQIECBDom4BkWt+clMqngF4RIECAAAECBAgQIECAAAECxRfIVYSSabkaDp0hQIAAAQIECBAgQIAAgeIIiIQAgSIKSKYVcVTFRIAAAQIECBAgQGB5BGxLgAABAgQI9CggmdYjjRUECBAgQIBAswnoLwECBAgQIECAAIF6C0im1VtY/QQIEOhdQAkCBAgQIECAAAECBAgQaBIBybQmGah8dlOvCBAgQIAAAQIECBAgQIAAgeILiHBpAcm0pTXMEyBAgAABAgQIECBAgEBxBERCgACBOghIptUBVZUECBAgQIAAAQIElkfAtgQIECBAgEB+BSTT8js2ekaAAAECBJpNQH8JECBAgAABAgQIFF5AMq3wQyxAAgR6F1CCAAECBAgQIECAAAECBAj0TUAyrW9O+SylVwQIECBAgAABAgQIECBAgEDxBUSYKwHJtFwNh84QIECAAAECBAgQIECgOAIiIUCAQBEFJNOKOKpiIkCAAAECBAgQWB4B2xIgQIAAAQIEehSQTOuRxgoCBAgQINBsAvpLgAABAgQIECBAgEC9BSTT6i2sfgIEehdQggABAgQIECBAgAABAgQINImAZNpyDJRNCRAgQIAAAQIECBAgQIAAgeILiJDA0gKSaUtrmCdAgAABAgQIECBAgEBxBERCgAABAnUQkEyrA6oqCRAgQIAAAQIElkfAtgQIECBAgACB/ApIpuV3bPSMAAECBJpNQH8JECBAgAABAgQIECi8gGRa4YdYgAR6F1CCAAECBAgQIECAAAECBAgQ6JtAM0hbBQkAAAB6SURBVCfT+hahUgQIECBAgAABAgQIECBAgEAzC+g7gVwJSKblajh0hgABAgQIECBAgACB4giIhAABAgSKKCCZVsRRFRMBAgQIECBAYHkEbEuAAAECBAgQINCjgGRajzRWECBAgECzCegvAQIECBAgQIAAAQIE6i3w/wEAAP//m/SyFwAAAAZJREFUAwD6GYMdP+aiAwAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = pd.DataFrame(zip(model.predict(data),data.y,data.x), columns=['pred','true','x'])\n",
    "px.scatter(pd.melt(results,id_vars=\"x\"), \n",
    "           x=\"x\",y=\"value\",color=\"variable\",\n",
    "          height=600,width=700)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9724ea1b",
   "metadata": {},
   "source": [
    "# Linear Regression via Linear Algebra\n",
    "\n",
    "So far we have described linear regression conceptually and used libraries to fit models. But how does it actually work under the hood? The answer is elegant: we can express and solve linear regression entirely with linear algebra.\n",
    "\n",
    "## Setting up the problem\n",
    "\n",
    "Suppose we have $n$ observations and $p$ features. We want to fit a model:\n",
    "\n",
    "$$\\hat{y} = b_0 + b_1 x_1 + b_2 x_2 + \\cdots + b_p x_p$$\n",
    "\n",
    "We can write all $n$ predictions at once as a matrix-vector product. First, build the **design matrix** $\\mathbf{X}$ by prepending a column of 1s (this handles the intercept $b_0$ automatically):\n",
    "\n",
    "$$\\mathbf{X} = \\begin{bmatrix} 1 & x_1^{(1)} & \\cdots & x_p^{(1)} \\\\ 1 & x_1^{(2)} & \\cdots & x_p^{(2)} \\\\ \\vdots & \\vdots & & \\vdots \\\\ 1 & x_1^{(n)} & \\cdots & x_p^{(n)} \\end{bmatrix}, \\quad \\boldsymbol{\\beta} = \\begin{bmatrix} b_0 \\\\ b_1 \\\\ \\vdots \\\\ b_p \\end{bmatrix}, \\quad \\mathbf{y} = \\begin{bmatrix} y^{(1)} \\\\ y^{(2)} \\\\ \\vdots \\\\ y^{(n)} \\end{bmatrix}$$\n",
    "\n",
    "Then all predictions are simply $\\hat{\\mathbf{y}} = \\mathbf{X}\\boldsymbol{\\beta}$.\n",
    "\n",
    "## The loss function in matrix form\n",
    "\n",
    "Our MSE loss over all $n$ points is:\n",
    "\n",
    "$$\\mathcal{L}(\\boldsymbol{\\beta}) = \\frac{1}{n}\\|\\mathbf{y} - \\mathbf{X}\\boldsymbol{\\beta}\\|^2 = \\frac{1}{n}(\\mathbf{y} - \\mathbf{X}\\boldsymbol{\\beta})^\\top(\\mathbf{y} - \\mathbf{X}\\boldsymbol{\\beta})$$\n",
    "\n",
    "## Solving for the optimal $\\boldsymbol{\\beta}$: The Normal Equations\n",
    "\n",
    "To minimize, take the gradient with respect to $\\boldsymbol{\\beta}$ and set it to zero:\n",
    "\n",
    "$$\\nabla_{\\boldsymbol{\\beta}} \\mathcal{L} = -\\frac{2}{n}\\mathbf{X}^\\top(\\mathbf{y} - \\mathbf{X}\\boldsymbol{\\beta}) = \\mathbf{0}$$\n",
    "\n",
    "... the details of the rest of this are not for this class :)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8ac5d91",
   "metadata": {},
   "source": [
    "# Great! But... \n",
    "\n",
    "We've already established that linear models aren't very good. What can we do instead? \n",
    "\n",
    "Essentially, we can replace our approximation of $\\mathbb{E}[Y | X = x]$ with something that is better than a linear model. That is, we can **select a different model class**.\n",
    "\n",
    "What, exactly?\n",
    "\n",
    "Well, there are [many options](https://scikit-learn.org/stable/supervised_learning.html).  But one model class that is very popular these days are neural networks.  This is where we'll head in this class. We'll talk about neural networks generally at first, and then more specifically about the transformer architecture\n",
    "\n",
    "# Predicting with a Neural Network\n",
    "\n",
    "Linear regression has a clean closed-form solution. But what if the true relationship is nonlinear? A **neural network** is a much more flexible model class that can learn arbitrary nonlinear functions — at the cost of no closed-form solution and more data/tuning required.\n",
    "\n",
    "## What is a neural network (briefly, to be extended in slides next class)?\n",
    "\n",
    "A neural network stacks **layers** of linear transformations interleaved with **nonlinear activation functions**:\n",
    "\n",
    "$$\\mathbf{l}^{(1)} = \\sigma\\!\\left(\\mathbf{W}^{(1)}\\mathbf{x} + \\mathbf{b}^{(1)}\\right)$$\n",
    "$$\\mathbf{l}^{(2)} = \\sigma\\!\\left(\\mathbf{W}^{(2)}\\mathbf{l}^{(1)} + \\mathbf{b}^{(2)}\\right)$$\n",
    "$$\\hat{y} = \\mathbf{W}^{(3)}\\mathbf{l}^{(2)} + b^{(3)}$$\n",
    "\n",
    "where $\\sigma$ is an activation function such as ReLU ($\\max(0, z)$) or tanh, and $b^{(3)}$ is a scalar (not a vector) because the output $\\hat{y}$ is a single real number. Without the activations, stacking linear layers would still just give a linear model — the nonlinearities are what give neural networks their expressive power.\n",
    "\n",
    "Training uses **gradient descent**: we compute the gradient of the MSE loss with respect to all weights $\\mathbf{W}$ and biases $\\mathbf{b}$ (via backpropagation) and take small steps downhill.\n",
    "\n",
    "## Fitting a neural network to the coffee toy example\n",
    "\n",
    "Let's use scikit-learn's `MLPRegressor` (Multi-Layer Perceptron) to fit the same toy dataset and compare to OLS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "58b6fbe7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MSE  — OLS:            0.247522\n",
      "Training MSE  — Neural Network: 0.009611\n",
      "\n",
      "Predictions for missing points (x=3, x=16):\n",
      "  x=3  true=1.5375  NN=1.5128  OLS=2.0000\n",
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         ],
         "title": {
          "text": "Ounces of coffee Kenny consumed"
         }
        }
       }
      },
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# ── Recompute OLS beta_hat (so this cell works standalone) ───────────────────\n",
    "X_vals = data['x'].values\n",
    "y_vals = data['y'].values\n",
    "X_mat  = np.column_stack([np.ones(len(X_vals)), X_vals])\n",
    "beta_hat, _, _, _ = np.linalg.lstsq(X_mat, y_vals, rcond=None)\n",
    "\n",
    "# ── Prepare training data (observed points only) ──────────────────────────────\n",
    "train_data     = data[~data.missing]\n",
    "X_train_nn     = train_data[['x']].values   # shape (n_train, 1)\n",
    "y_train_nn     = train_data['y'].values\n",
    "\n",
    "# Neural networks are sensitive to input scale — standardize x\n",
    "scaler         = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train_nn)\n",
    "\n",
    "# ── Define and train the network ──────────────────────────────────────────────\n",
    "# Architecture: 1 input → 20 ReLU neurons → 10 ReLU neurons → 1 output\n",
    "nn_model = MLPRegressor(\n",
    "    hidden_layer_sizes=(20, 10),\n",
    "    activation='relu',\n",
    "    solver='adam',\n",
    "    max_iter=5000,\n",
    "    random_state=42,\n",
    "    learning_rate_init=0.01,\n",
    ")\n",
    "nn_model.fit(X_train_scaled, y_train_nn)\n",
    "\n",
    "# ── Generate predictions ───────────────────────────────────────────────────────\n",
    "x_range        = np.linspace(1, 23, 300).reshape(-1, 1)\n",
    "x_range_scaled = scaler.transform(x_range)\n",
    "\n",
    "y_nn_pred  = nn_model.predict(x_range_scaled)\n",
    "y_ols_pred = np.column_stack([np.ones(len(x_range)), x_range]) @ beta_hat\n",
    "\n",
    "# Training MSE comparison\n",
    "y_train_nn_pred  = nn_model.predict(X_train_scaled)\n",
    "y_train_ols_pred = np.column_stack([np.ones(len(X_train_nn)), X_train_nn]) @ beta_hat\n",
    "\n",
    "print(f\"Training MSE  — OLS:            {mean_squared_error(y_train_nn, y_train_ols_pred):.6f}\")\n",
    "print(f\"Training MSE  — Neural Network: {mean_squared_error(y_train_nn, y_train_nn_pred):.6f}\")\n",
    "\n",
    "# Predictions for the two missing points\n",
    "missing_pts    = data[data.missing][['x']].values\n",
    "missing_scaled = scaler.transform(missing_pts)\n",
    "nn_missing     = nn_model.predict(missing_scaled)\n",
    "true_missing   = data[data.missing]['y'].values\n",
    "print(f\"\\nPredictions for missing points (x=3, x=16):\")\n",
    "for x_val, nn_p, true_p in zip(missing_pts.flatten(), nn_missing, true_missing):\n",
    "    ols_p = float(np.array([1, x_val]) @ beta_hat)\n",
    "    print(f\"  x={x_val:.0f}  true={true_p:.4f}  NN={nn_p:.4f}  OLS={ols_p:.4f}\")\n",
    "\n",
    "# ── Plot ───────────────────────────────────────────────────────────────────────\n",
    "fig_nn = px.scatter(data, x='x', y='y', color='missing',\n",
    "                    labels={\"x\": \"Hour of the day\",\n",
    "                            \"y\": \"Ounces of coffee Kenny consumed\"},\n",
    "                    title=\"OLS vs. Neural Network on toy data\",\n",
    "                    height=550, width=800)\n",
    "fig_nn.update_traces(marker=dict(size=12))\n",
    "fig_nn.add_scatter(x=x_range.flatten(), y=y_ols_pred, mode='lines',\n",
    "                   name='OLS (linear)', line=dict(dash='dash', color='blue'))\n",
    "fig_nn.add_scatter(x=x_range.flatten(), y=y_nn_pred, mode='lines',\n",
    "                   name='Neural Network', line=dict(color='red'))\n",
    "fig_nn.show()"
   ]
  }
 ],
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