From 9ccb6d859805ac517d63bfff1d9d50cf085b78fc Mon Sep 17 00:00:00 2001 From: Dmitri Soshnikov Date: Tue, 17 Aug 2021 13:51:20 +0300 Subject: [PATCH] Continue work on prob and stat --- .../04-stats-and-probability/README.md | 40 +++++++++++++++++- .../images/boxplot_explanation.png | Bin 0 -> 55216 bytes .../images/height-boxplot.png | Bin 0 -> 17998 bytes .../images/weight-boxplot.png | Bin 0 -> 3609 bytes .../images/weight-height-relationship.png | Bin 0 -> 8416 bytes .../04-stats-and-probability/notebook.ipynb | 39 +++++++++++++++-- 6 files changed, 75 insertions(+), 4 deletions(-) create mode 100644 1-Introduction/04-stats-and-probability/images/boxplot_explanation.png create mode 100644 1-Introduction/04-stats-and-probability/images/height-boxplot.png create mode 100644 1-Introduction/04-stats-and-probability/images/weight-boxplot.png create mode 100644 1-Introduction/04-stats-and-probability/images/weight-height-relationship.png diff --git a/1-Introduction/04-stats-and-probability/README.md b/1-Introduction/04-stats-and-probability/README.md index 9f673ecc..56b4d8da 100644 --- a/1-Introduction/04-stats-and-probability/README.md +++ b/1-Introduction/04-stats-and-probability/README.md @@ -40,6 +40,22 @@ Suppose we draw a sequence of n samples of a random variable X: x1, x To identify how far the values are spread, we can compute the variance σ2 = ∑(xi - μ)2/n, where μ is the mean of the sequence. The value σ is called **standard deviation**, and σ2 is called a **variance**. +## Mode, Median and Quartiles + +Sometimes, mean does not adequately represent the "typical" value for data. For example, when there are a few extreme values that are completely out of range, they can affect the mean. Another good indication is a **median**, a value such that half of data points are lower than it, and another half - higher. + +To help us understand the distribution of data, it is helpful to talk about **quartiles**: + +* First quartile, or Q1, is a value, such that 25% of the data fall below it +* Third quartile, or Q3, is a value that 75% of the data fall below it + +Graphically we can represent relationship between median and quartiles in a diagram called the **box plot**: + +![Box Plot](images/boxplot_expanation.png) + +Here we also computer **inter-quartile range** IQR=Q3-Q1, and so-called **outliers** - values, that lie outside the boundaries [Q1-1.5*IQR,Q3+1.5*IQR]. + +For finite distribution that contains small number of possible values, a good "typical" value is the one that appears the most frequently, which is called **mode**. It is often applied to categorical data, such as colors. Consider a situation when we have two groups of people - some that strongly prefer red, and others who prefer blue. If we code colors by numbers, the mean value for a favourite color would be somewhere in the orange-green spectrum, which does not indicate the actual preference on neither group. However, the mode would be either one of the colors, or both colors, if the number of people voting for them is equal (in this case we call the sample **multimodal**). ## Real-world Data When we analyze data from real life, they often are not random variables as such, in a sense that we do not perform experiments with unknown result. For example, consider a team of baseball players, and their body data, such as height, weight and age. Those numbers are not exactly random, but we can still apply the same mathematical concepts. For example, a sequence of people's weights can be considered to be a sequence of values drawn from some random variable. Below is the sequence of weights of actual baseball players from [Major League Baseball](http://mlb.mlb.com/index.jsp), taken from [this dataset](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights) (for your convenience, only first 20 values are shown): @@ -69,6 +85,7 @@ samples = np.random.normal(mean,std,1000) If we plot the histogram of the generated samples we will see the picture very similar to the one shown above. And if we increase the number of samples and the number of bins, we can generate a picture of a normal distribution that is more close to ideal: ![Normal Distribution with mean=0 and std.dev=1](images/normal-histogram.png) + *Normal Distribution with mean=0 and std.dev=1* @@ -76,7 +93,7 @@ If we plot the histogram of the generated samples we will see the picture very s One of the reasons why normal distribution is so important is so-called **central limit theorem**. Suppose we have a large sample of independent N values X1, ..., XN, sampled from any distribution with mean μ and variance σ2. Then, for sufficiently large N (in other words, when N→∞), the mean ΣiXi would be normally distributed, with mean μ and variance σ2/N. -> Another way to interpret central limit theorem is to say that regardless of distribution, when you compute the mean of any random variable values you end up with normal distribution. +> Another way to interpret central limit theorem is to say that regardless of distribution, when you compute the mean of a sum of any random variable values you end up with normal distribution. From central limit theorem it also follows that, when N→∞, the probability of the sample mean to be equal to μ becomes 1. This is known as **the law of large numbers**. @@ -90,6 +107,27 @@ One of the things Data Science does is finding relations between data. We say th The absolute value of covariance does not tell us much on how large the correlation is, because it depends on the magnitude of actual values. To normalize it, we can divide covariance by standard deviation of both variables, to get **correlation**. The good thing is that correlation is always in the range of [-1,1], where 1 indicates strong positive correlation between values, -1 - strong negative correlation, and 0 - no correlation at all (variables are independent). +**Example**: We can compute correlation between weights and heights of baseball players from the dataset mentioned above: +```python +print(np.corrcoef(weights,heights)) +``` +As a result, we get **correlation matrix** like this one: +``` +array([[1. , 0.52959196], + [0.52959196, 1. ]]) +``` + +> Correlation matrix C can be computed for any number of input sequences S1, ..., Sn. The value of Cij is the correlation between Si and Sj, and diagonal elements are always 1 (which is also self-correlation of Si). + +In our case, the value 0.53 indicates that there is some correlation between weight and height of a person. We can also make the scatter plot of one value against the other to see the relationship visually: + +![Relationship between weight and height](images/weight-height-relationship.png) + +> More examples of correlation and covariance can be found in [accompanying notebook](notebook.ipynb). + + + + ## 🚀 Challenge diff --git a/1-Introduction/04-stats-and-probability/images/boxplot_explanation.png b/1-Introduction/04-stats-and-probability/images/boxplot_explanation.png new file mode 100644 index 0000000000000000000000000000000000000000..e58ddce7cdf83b41bf9afa312c74f04d765793f1 GIT binary patch literal 55216 zcmdSARa6~K6E?bW_u%dh!8aj5aCi6M!8h*iPH+oOaJP-SOM(a3xVyXkyx%(i)ww%& zXV&!U?&_|Zr>d*hbj@@}Dl1B%Arm13001-@X>nBm00#7tlo4S*D2D02vmY6(rHFzE z08kf?@?s44QHOF?l@bM1PmvscRFFW@I?ez9;lh6jieP%|^rHi*vb?&)`}_Oz^Yh8& z&DGuO+uM6ZeZ|`c>HY14>Fw?9;q`sv;P&?R?(XvV{q-p$BjaRy<^248cX|5o=xA~4 z$kok#b$aOK?(+I*TU+COVrlE`>27|0E-pT9Zm4a%qh@n!D>x*i|L<}`bHmCC1PYFR 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a/1-Introduction/04-stats-and-probability/notebook.ipynb +++ b/1-Introduction/04-stats-and-probability/notebook.ipynb @@ -289,6 +289,37 @@ ], "metadata": {} }, + { + "cell_type": "markdown", + "source": [ + "In addition to mean, it makes sense to look at median value and quartiles. They can be visualized using **box plot**:" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 148, + "source": [ + "plt.figure(figsize=(10,2))\r\n", + "plt.boxplot(df['Height'],vert=False)\r\n", + "plt.grid(color='gray',linestyle='dotted')\r\n", + "plt.show()" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "

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+ }, + "metadata": {} + } + ], + "metadata": {} + }, { "cell_type": "markdown", "source": [ @@ -734,9 +765,11 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 117, "source": [ "plt.scatter(df['Height'],df['Weight'])\r\n", + "plt.xlabel('Height')\r\n", + "plt.ylabel('Weight')\r\n", "plt.show()" ], "outputs": [ @@ -746,8 +779,8 @@ "text/plain": [ "
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tLCJqPO5oTKQea2MRNVJTqDvUFPqoysjkNki5uRVmrjmIc5dL0a5FKBaN64VmIZ47zZZW2LBw41GcyS9Bh+gwPDs6CaFB/h5rrykw+2fC7P1zhSM74MgO1dUUfqU3hT6qNPmTfcg4mlfn9mFJMUif0Mfn22sKzP6ZMGP/RL+/meyAyQ45c9Qdqv3BcPz2McME0KbQR5VcJR4OshMQ1e05mHlUwOyfCbP2j5exiG6AXt0hx264w5JiffYk3xT6qFJpha3BxAMAMo7mobTCJuUSk+r2HMw4KuBg9s+E2fsngkvPia7TFOoONYU+qrRwo+tSETcS523tAeavsm72z4TZ+yeCyQ7RdZpC3aGm0EeVzuSXSI3ztvaaQpV1s38mzN4/EUx2iK7TFOoONYU+qtQhOkxqnLe11xRGBcz+mTB7/0Qw2SG6TlOoO9QU+qjSs6OTpMZ5W3tNYVTA7J8Js/dPBJMdous0hbpDTaGPKoUG+WNYUkyDMcOSYqRNFlbdXlMYFTD7Z8Ls/RPBZIeolqZQd6gp9FGl9Al9XCYgnlgGrrK9pjIqYPbPhNn7p4f77ID77FD9zLyniIPZ+6i6f6p3NFbVnmM1FlB/7S8zfVnyM+FbuKmgG5jsEJmPmfeFMQKfT/JGTHbcwGSHSI2KKjtW7D6DswUlaB8Vhkf6dUBQgPyr6WbdLbY2Vc+ngxGjAmYbiSC5mOy4gckOkeelbTyK9B3ZuH47Fj8LMHlAIuZLWjkEVH85pry2pcHl0m2sIdg5706f/tJU9XwaiaNJpEf0+5sTlIm8hM2uYffpfKzPuoDdp/N9epO22tI2HsUH252/mAHArgEfbM9GmsTdfvX2hQE8ty+MqtdQ5fNpFLPv2kxqsTYWkRcw8y/Yiio70ndkNxiTviMbs4d3kXIJ5sJlsZ2Dq+OiG92eg6rXUPXzaQTWciLZfPOTQGQiZv8Fu2L3mTojELXZteo4Gb75MVdqnAiVr6Hq59MITWHXZlKLyQ6RgZpC3aHs/GtS4/SUVtqkxulR/RqeLRAbuRKN80ZNYddmUovJDpGBmsIvWNGLDLIuRiS2DJcap0f1a9g+SqzmlWicN2oKuzaTWkx2iAzUFH7B9kpoITVOj9lrRz3SrwP0pqn4WarjfFVT2bWZ1GGyQ2SgpvALtk3zUKlxesxeOyoowA+TByQ2GDN5QKLPTk4GWMuJ5PPdTwORCTSFX7COPjZEdh/NXjtq/ugkPDEwsc4Ij58FeGKgOfbZaeq1nEgubioIbipIxmoKdYeM6qPZa0eprsVlBO6gTA3hDspuYLJDRjPzPjsOZu+j6v6Z/fkkEsFkxw1MdsgbGPELVnVtJdXtqR75+LWoHPct2YmCa5WICg/EuikpaBUZLL0dV7W/gOrRJE+NJKl+/Yxo04g+0o3ziWQnLS0Na9euxU8//YTQ0FD0798fr732Gjp37lxv/BNPPIEPP/wQixYtwowZM2puLy8vx5w5c/Dpp5+itLQUd911F5YsWYL4+Hih42CyQ02R6tpKqkciJn+yDxlH8+rcLnvOjkOfP2fg1+KKOre3ahaEfc8Pk9aOUbW/jKjFpbrNplBvzGx8ojZWZmYmpk6dij179iAjIwNVVVUYPnw4rl2ru7nY559/jh9++AFxcXF17psxYwbWrVuH1atXY+fOnSguLsY999wDm03OpmFEZqO6tpLqXaJdJToAkHE0D5M/2Se1PVeJDgD8WlyBPn/OkNaWEbW/jKjFpbrNplBvrCkzNNnZtGkTJk6ciK5du6JHjx5YtmwZzp07h/379zvFXbhwAampqfjb3/6GwMBAp/sKCwvx0Ucf4c0338TQoUPRq1cvrFy5EocPH8bmzZtVdofIJ4jWVqqosktpT/UOw6UVNpeJjkPG0TyUVsj5MVRQXOEy0XH4tbgCBToxoi4K1v4SjdOj+v1iRJtG9JHU8qoLkYWFhQCAqKh/L9G02+145JFHMHfuXHTt2rXOv9m/fz8qKysxfPjwmtvi4uKQnJyMXbt21dtOeXk5ioqKnP6ImgrVtZVU7zC8UPAXuGicngc/rP88c6NxerLOX5Eap8eIWlyq22wK9caaOq9JdjRNw6xZs5CSkoLk5OSa21977TUEBATg6aefrvff5ebmIigoCC1aOO++2rp1a+Tm1l/oLy0tDVarteYvISFBXkeIvJzq2kqqdxg+ky923KJxev55tVxqnD61BTiMqMWlus2mUG+sqfOaZCc1NRWHDh3Cp59+WnPb/v378c4772D58uWwWNz74Gqa5vLfzJ8/H4WFhTV/v/zyS6OOnciXqK6tpHqH4Q7RYsctGqcnIjhAapwe1f0zohaX6jabQr2xps4rkp1p06Zhw4YN2Lp1q9MKqh07diAvLw/t2rVDQEAAAgICcPbsWcyePRsdOnQAAMTGxqKiogKXL192esy8vDy0bt263vaCg4MRGRnp9EfUVKiuraR6h2HVtbGmDOkoNU6P6tfPiFpcTaGPpJahyY6maUhNTcXatWuxZcsWJCY613t55JFHcOjQIWRlZdX8xcXFYe7cufjmm28AAL1790ZgYCAyMv692iEnJwdHjhxB//79lfaHyBeorq2kus5RUIAfgnWOPTjAT1r/EltGSI3To/r1M6IWV1PoI6klZ1z1Bk2dOhWrVq3C+vXrERERUTPHxmq1IjQ0FNHR0YiOjnb6N4GBgYiNja3Zi8dqtWLSpEmYPXs2oqOjERUVhTlz5qBbt24YOnSo8j4R+QLHniGq9hRx1Dmqvc9OrAf22dmbXYBynVUz5VV27M0uQL+O0Q3GieibGIXmYYG4UlLpMqZFWKD02liAutdPdXtGtNmrXQsArldkVd9PvsrQTQVdzalZtmwZJk6cWO99HTp0wIwZM5w2FSwrK8PcuXOxatUqp00FRScec1NBaqpU7xarYpfo9VkXMH11lm7cOw/2xL092za6PZtdQ+8/ZzSY7DQPC8T+54dJ72tT2F1YRZt6GzVaUJ2Yy96okRrPJ3ZQ9hZMdojMY/fpfDyUvkc37tPJv5UysqO6PZKPr6HvEv3+NvQyFhE1LSp+pTsmROcWlrmsHRUrcUK06qX111Nd+8usdaOMfA1JDSY7RKREfXWHFmw8Jn3+hWNC9JMrD9R7vwa5E6JVL613qF0SY8dJYMWecx6r/aXq9TOCUa8hqeP7KTkReT3VdYcOnrvcqPvd0TcxCgE6iVOAn0XqBGXVtb/MXjdK9fYIpB6THSLyKLPXOSqtsKFKp9ZAlV2TVotLde2vplA3SvX2CKQekx0i8iiz1zmaueag1Dg9qmt/NZW6UY7tEWKtzpeqYq0hWDr+NqnbI5B6nLNDRB51Jv+a1Dg9quscnbtcKjVOT/YlsedJNE5PU6obNTK5DYYlxXp8ewRSj8kOEZmK6jpH7VqE4njuVaE4GUIDxVZbicbpaWp1o/z9LFxebkK8jEXUhNnsGnafzsf6rAvYfTofNr3rFTegZ4LYzrOicXpU1zlaNK6X1Dg9I7rGSo3Tw7pRZAYc2SFqojYdyalTvqGNB8o31J4D0dg4PY46Rx9sdz2pVmado2YhAegeH4lD54tcxnSPj0SzEDmn27YtxEZQROP0qH4+iTyB706iJmjTkRw8tfJAne3xcwvL8NTKA9h0JEdeY6KDRRIHlfTqGMmuc7QhdQC6x9e/e2v3+EhsSB0grS3HMumGyF4mPX90Ep4YmFhnhMfPAjwx0Pf32SHz48gOURNjs2t4+Yuj9eYWGqqX2r78xVEMS4qVMjHz0rVyqXF6HP1zRXb/HDakDkBBcQUe/HAX8q5WICYiCKsf74+oZkHS2gD+vUz6qX9tmnj96+jJZdLzRydh9vAuptxBuSlRUZ/OGzHZIWpi9mYXuCx4CFR/eeYUlkmrCq56d1rV/XOovcPwldJK/GZBhs9Xkb9eUIAfJg24ySOPTZ6n6tK1N2KyQ+QlVP3iUl0HqCnUqnLsMFybY4dhAB5JePrd1BKPLd+Li4VliLOG4OOJfWENC5TazvUKSyqVtgeYfyRCVf8cl65rfwYdl67NvpcQkx0iL6DyF1fL8GCpcXrMXqtKdIfh2cO7SL3kM2bxDqdJ0TmFZejxp2+lzxFyGPTGFpzN//deQY722keHInPundLbA8w/EqGqf6ovXXsjXmwlMpjSycJA3f3wGxsn4IXPjzTqfneIjhDJGkkyYofh2onO9Q6dL8KYxTuktQXUTXSudza/FIPe2CK1PcCAz4ViKvvnzqVds2KyQ2QgvV9cGqp/ccnc/+ZSseCEYcE4PQXFFfi1uKLBmF+LK1CgE+NOezLj9BzLuSI1Tk9xWVWDy9yB6oSnuKxKSnuFJZUuEx2Hs/mlKCyplNIeoP+5AOR/LlRS3T8jLu16GyY7RAbS+8UFyP/Fpfoyz4Mf7pIap+e+JTulxunZePifUuP0qK7F9djyvVLjRJh9JEJ1/1R/5r0Rkx0iA+UWif2SEo0T4Zgw7OoqlQVy92nJuyo2giIap6fgmtgIg2icHtFq37KqgquuxXVRJxl3N06E2UcijFokoOoz742Y7BAZqEDwUpFonAjHhGGg7rQcT+zTEhMhts+MaJyeqHCx1UGicXrCgsVqUInG6RGtsSWrFlec4M7WonEizD4Sobp/qj/z3ojJDpGBosLFvuBF40Q59mmpXaIh1hoifQnq6sf7S43Ts25KitQ4PXOGd5Eap0d1La6PJ/aVGifC7CMRRvRP5WfeG7m99Nzf3x85OTmIiYlxuj0/Px8xMTGw2WzSDo7I7GKtYr++RePcMTK5DYYlxXp8jw/RmlCyakdFNQuCBQ1Xn7D8K06GjjHNpMbpUV2Lq1lIAPwsaHDFmZ9F3usHOO8SXfu1NMNIhFH9U/WZ90Zuj+xoWv3v+PLycgQFyf31SWR2RtQ5up6/nwX9Okbj3p5t0a9jtEdOeqJLrmUtzd6bXaBbZkv7V5wUBtT+UlmLa292gdDSetmThc0+EmFU/1R85r2RcCr+7rvvAgAsFgv++te/olmzf/9Ksdls2L59O7p0kTNMS1QfM+6kev0vPFe7C/vyL1gAOJNfIjVOj+rJn6prfzlsSB2A4rIqzFxzEOcul6Jdi1AsGtdL6ggLYOxkYbOPRJi9f95E+FOxaNEiANUjO++//z78/f892S4oKAgdOnTA+++/L/8IiWDunVRd1TkyS/9UD32onvxp5GTaZiEBSH+0j/THvZ7Rk4UdIxFmZfb+eQvhZCc7u3o79CFDhmDt2rVo0aKFxw6K6HpG1XRROZI0MrkN7uzSWnlFaRUjAz3jm2MFzgnFydA3MQrNwwJxpYFN7lqEBUq7NKi69tf1SitsWLjxKM7kl6BDdBieHZ2E0CA5q74cjOwfkSxun9W2bt3qieMgqpdRNV1UjyTV195fd2Z7dGSndsmB47lXkfzSN9LnfMS1CJMaJ4PMfXdV1/5ymPzJPmQczav5/x0ngRV7zmFYUgzSJ8gb7TGqf0Qyuf2z0Waz4aOPPsLDDz+MoUOH4s4773T6I5LJiJ1UVdfkMaIGkMraSn0ToxCg80UY4GeRNjKwN7ugwVEdALhSUumzu+8CdROd62UczcPkT/YpPiIi7+Z2sjN9+nRMnz4dNpsNycnJ6NGjh9MfkUyqJ0eqrlVlRA0g1bWVSitsqNI5/iq7htIKOdtWHP9nodQ4PY7XsCEyX8PSCpvLRMch42ietOdTr3+O0VVfrVNFTYPbl7FWr16Nzz77DKNHj/bE8RA5UT050p1aVTImFbozciVrEqM7tZVkTH5V3d7rXx8XjpvY/6ZGt6f6PbNwY8OJ1fVxr4zt1uj2jHiPEsnm9shOUFAQbr75Zk8cC1EdqncaVV2ryohlvaprK6lur7RSrAaVaJwe1e8Zsy/lJ/IEt5Od2bNn45133nG5uSCRTKpruqiuVWXEsl7VtZVUtye6gE3WQjfV75kO0WITuUXj9Bi99JxIBqHLWPfff7/T/2/ZsgVff/01unbtisBA52J6a9eulXd0RHC9D02sB1ZHqa5VJbJMurnEZdJAdc2k5Je+EYrzxfZ6JjTHvrNXhOJkUP2eeXZ0Elbs0V/K/+zoJCntGfEeJZJNKNmxWq1O/3/fffd55GCIXFG106iRtapckb2g14jaSirb69ImUijZ6dKm/nIL7moZHiw1Tk9okD+GJcU0OEl5WFKM9P12GsJF5+TthM4uy5Yt8/RxEOlSsdOoY45QQxMyZc4RElkmfflfy6Rl9n1D6gCXy89l77MDAFMG3+xynxbH/bKoHvn46Z9XheMGdG4lpc3/uC2+wWTnP26Ll9IOYNx7lEgmz27PSuRjHHOEGpoQLXOOkJGTPzekDsCRl0Zg2K0x6BwbgWG3xuDISyOkJzqqly6LvjayXsOzBdekxulR/XxygjKZgdvjxr169YLFUvckYbFYEBISgptvvhkTJ07EkCFDpBwgkWojk9vg8YGJSN+R7VTt2c8CTB6QKHWOkNGTP1XUVlK9dNmdKuuTBjR+6bloyiTrUo/q59Po9yiRDG6P7IwcORI///wzwsPDMWTIEAwePBjNmjXD6dOn0adPH+Tk5GDo0KFYv369J46XmjCbXcPu0/lYn3UBu0/ne2wTs01HcvDhdudEBwDsGvDh9mypOxqrXlpf269F5Uh59TskvbAJKa9+h1+L5FbmBtSPDJwtEFtyLRqnp1eCWJ1A0Tg9qp9Po9+jqqk6z5Babic7ly5dwuzZs7Fjxw68+eabeOutt7B9+3bMmTMH165dw7fffovnn38er7zyiu5jpaWloU+fPoiIiEBMTAzGjh2L48f/vSFYZWUl5s2bh27duiE8PBxxcXGYMGECLl686PQ45eXlmDZtGlq2bInw8HCMGTMG58+fd7dr5MU2HclBymtb8FD6HkxfnYWH0vcg5bUt0kspNLSjMSB/B2XHZbOG2vNU3aHuL32DPgs34/yVMpRU2nD+Shn6LNyM7gIrp9yhemSgfZTYkmvROD2trWLHLRqnR/XzaeR7VDVV5xlSz+1k57PPPsNDDz1U5/YHH3wQn332GQDgoYceckpaXMnMzMTUqVOxZ88eZGRkoKqqCsOHD8e1a9XXtktKSnDgwAG88MILOHDgANauXYsTJ05gzJgxTo8zY8YMrFu3DqtXr8bOnTtRXFyMe+65BzabnO3SyVgqa0e5sxuuLGlfH2vU/Tei+0vfoMhFOYiisiqpCY/oL35ZIwOP9OsgNU6XaN4raYBA9fMJAH8/0PCPR737fYERNepIHbfn7ISEhGDXrl11dlHetWsXQkKqf0nY7XYEB+svs9y0aZPT/y9btgwxMTHYv38/Bg4cCKvVioyMDKeY9957D3379sW5c+fQrl07FBYW4qOPPsKKFSswdOhQAMDKlSuRkJCAzZs3Y8SIEe52kbyI6qrnFy6LXdqojmv8fIjCkkqczW945+Cz+aUoLKmENSywwThRvxaVu0x0HIrKqvBrUTlaRTZ+ufS+U2KJ4b5TBfhtp8Y/p6dyi4XjkuIbv/z8eJ5gLa68QtxxS8tGt5d7RXDH5itlaBvV+C0S3KnFpXK5u0yqzzOkntsjO9OmTcOTTz6J6dOnY+XKlfjb3/6G6dOn46mnnsLTTz8NAPjmm2/Qq5f7G4QVFlafNKKiXP8iKSwshMViQfPmzQEA+/fvR2VlJYYPH14TExcXh+TkZOzatavexygvL0dRUZHTH3kn1VXPv/kxV2qcnseW75UaJ+K+JTulxul58OM9UuP03CNYsV00Ts9rG8VqcYnG6Rn1bqbUOD3u1OLyVarPM6Se2yM7zz//PBITE7F48WKsWLECANC5c2ekp6fj4YcfBgA8+eSTeOqpp9x6XE3TMGvWLKSkpCA5ObnemLKyMjzzzDN4+OGHERlZ/YssNzcXQUFBaNHCefJf69atkZtb/xdSWloaXn75ZbeOj+qy2TWPb/KnejJmSaXYpU/ROD0XdS6ZuRsnIv9ahdQ4byNa8UpOZSygwiZ2fUo0Ts+1crH3nmicnuxLYqOdonHeiMvrze+Gtiz9/e9/j9///vcu7w8NdX/oNDU1FYcOHcLOnfX/mqysrMSDDz4Iu92OJUuW6D6epmn1LpEHgPnz52PWrFk1/19UVISEhAS3j7kp23Qkp075hjYeKN+gejLmTS3D8f2pfKE4GeJ0NjC8Pk6W8CB/oSKY4T56SUK1IH8LygUSmSB/OT8EwoP9UVSmn8iEB8t5/UIDxS4AiMZ5Iy6vNz+veHdOmzYNGzZswNatWxEfX3fnz8rKSjzwwAPIzs5GRkZGzagOAMTGxqKiogKXL192+jd5eXlo3bp1ve0FBwcjMjLS6Y/EqZzIp3rZq+iuurJ23/14Yl+pcSJmDOskNU5Pt1ixHz+icXp+111sl2LROD3zRnaRGqfn66cHSY3TMzyp/vPojcZ5o6a2vL4pEkp2oqKicOnSJQBAixYtEBUV5fLPHZqmITU1FWvXrsWWLVuQmJhYJ8aR6Jw8eRKbN29GdLTzBMbevXsjMDDQaSJzTk4Ojhw5gv79+7t1PKRPbyIf4Jml2YCaqueOukMNkVl3yBoWiPbRDX/Jt48OlTY5GQASo5tJjdPTs4NYUiEap/s4CWKTgEXj9NwaZ9UPciNOT9uoUN1RoiB/i5TJyQAQHyU2iika541Un2dIPaHLWIsWLUJERAQA4O2335bW+NSpU7Fq1SqsX78eERERNXNsrFYrQkNDUVVVhf/1v/4XDhw4gC+//BI2m60mJioqCkFBQbBarZg0aRJmz56N6OhoREVFYc6cOejWrVvN6iySR/XurYDaqucAkD6hDyZ/sq/eFSjDkmKQPkHujsOZc+/EoDe21Lsqq310KDLn3im1PdVb/qquVfVIvw7488Zj0BrIty0WeUvPVddTA4ATC0aj03Mb650HFORvwYkFo6W1ZUT/jKD6PENqCSU7jz76aL3/3VhLly4FAAwePNjp9mXLlmHixIk4f/48NmzYAADo2bOnU8zWrVtr/t2iRYsQEBCABx54AKWlpbjrrruwfPly+PtzzoFsRk3kU1X13CF9Qh+UVtiwcONRnMkvQYfoMDw7OsljS2sz596JwpJKPLZ8Ly4WliHOGoKPJ/aVOqLjcKlYbJdk0Tg9qqt0+/tZEBroj5IK1/NawgL9pb13HKMCT608UO+Ip+x6ag4nFozGhYJSjHo3E9fKbQgP9sfXTw+SNqLjYFT/jKD6PEPqWDStod8/9Tt9+jSWLVuG06dP45133kFMTAw2bdqEhIQEdO3a1RPH6VFFRUWwWq0oLCzk/B0du0/n46F0/SXCn07+LSsgeymjXkNVo2VG9U/VpH2jmL1/5JtEv7/dXo2VmZmJUaNG4Y477sD27duxYMECxMTE4NChQ/jrX/+K//f//l+jDpy8m2NIO7ewzOWvvFgTDGkb4deicty3ZCcKrlUiKjwQ66akSNnUrzajXsP0CX2Qe6UM97y3HUVlVYgMCcCX0wYitrncFS5Gjj72u6mlktE5h4oqO1bsPoOzBSVoHxWGR/p1QFCAZ9adGDXqoWKLCzI/t0d2+vXrh//8z//ErFmzEBERgX/84x+46aabsG/fPowdOxYXLlzw1LF6DEd23ONYjQU474DvOP0sHX8bf+m5yVX5hsiQABx6Sf4u4JuO5ODJf72G9XnfA6+hqnlJO47/ikeW6W/CuOIPfTGgs5xJ0QAwZvEOHDpfd4PS7vGR2JA6QFo7DmkbjyJ9h3PBWj8LMHlAIuZLmv9kNI4mkR7R72+3fwIcPnwY9913X53bW7Vqhfx8/f1JyPc5JvLF1tr7JdYawkTnBqisU+Uw5W+uEx2R+93lKtEBqsthDHpji7S2fvrnValxIlwlOgBw6HwRxkjardkhbeNRfLDdOdEBALsGfLA9G2k+vJuxA2tVkUxuX8Zq3rw5cnJy6iwTP3jwINq2bSvtwMi7cSKfHKrrVAFAdt61Ol+Stdm16rjEmMYvJ1Zd/+unHLFaVaJxeorLqlwmOg6HzhehuKwKzUJuaB9XJxVVdqTvyG4wJn1HNmYP7+KxS1qexlpVJJvbn4SHH34Y8+bNQ25uLiwWC+x2O77//nvMmTMHEyZM8MQxkpfy97OgX8do3NuzLfp1jOZJ5waorlMFACPfEauZJBqnR3X9ry8Pif3iF43TM3PNQalxelbsPiOUrK7YfUZKe0ZgrSqSTTjZOXXqFABgwYIFaN++Pdq2bYvi4mIkJSVh4MCB6N+/P55//nmPHSiRGRVcq5QaJ0KktIE7cXpU1/+qFNzQUjROz7kCsZpQonF6zgo+jmicN2KtKpJNeEy1U6dOaNu2LYYMGYK77roLf/rTn3DgwAHY7Xb06tULt9xyiyePk8iUosIDUXJFv85RVLi8FT3BgrWcgiXVclJd/6uZYO2oZpJqRzULFjuNisbpaR8VJjXOG7FWFckmPLKTmZmJJ554AhcvXkRqaipuueUWzJ07F9988w1++OEHn1yFRWS0dVNSpMaJ2DRdrGaSaJwe1fW/VNeOeqCPWBFh0Tg9j/TrAL0rxn4Sd4g2AmtVkWzCyc6AAQPw/PPPY/Pmzbhy5Qq2bt2KP/zhD8jOzsbjjz+Odu3aoXPnzp48ViLTiWoWJDVORGJMuNCXpYzJyYD6+l/Ka0c1FxtBEY3TExTgh8kD6tYRvN7kAYk+OzkZYK0qku+GPg2BgYEYOHAg5s6di/nz52PKlClo1qxZzbweIhIjOsFS9kRMkS9LmeaPurVR97vr3Yd6Nep+tyiuNQYA80cn4YmBiXWSVj8L8MRAc+yzwy0uSCa3LiKXlZVh165d2Lp1K7Zt24Z9+/YhMTERgwYNwtKlSzFokJxhYaKmwoiJmKqXLjuWEbsiexmx6vb+KTixWjRO1PzRSZg9vIuyHZSNwC0uSBbhZGfQoEHYt28fOnbsiIEDB2LatGkYNGgQWrdu7cnjIzI1IyZiurN0edKAmxrdnjvLiGXUqlLd3sFfLgvH3d87vtHtXS8owE/Ka+TNHFtcEDWGcLKza9cutGnTBkOGDMHgwYMxcOBAtGzZ0pPHRuREdY2c4rIqzFxzEOcul6Jdi1AsGtdLyqZw1+ubGIXmYYG4UuJ6aXnzsECpEzFVL11WPXqluj3RBexyFro7U/EevV5hSaXS2l9Esgh/Kq5cuYIdO3Zg27ZteO211/DQQw+hU6dOGDRoEAYPHoxBgwahVSt5dWaIrqe6Rk7t7f+P515F8kvfeKzOUUNkp3PxzcUm5orG6YkKE5yELRinR/VomVFLwVW/R2uX/MgpLEOPP30rvbYZkScIX9wNDw/HyJEj8eqrr+KHH37ApUuX8PrrryMsLAyvv/464uPjkZyc7MljJR02u4bdp/OxPusCdp/Oh03SpmlGt6e6Ro7KOkd7swsaHNUBgMsllVInKGuCYwyicXp+ym24lIK7cXpER8FkjZZ1iRUrHiwaJ0J1LS6Vtc2IPOGGxzvDw8MRFRWFqKgotGjRAgEBATh27JjMYyM3qB75UNWe6ho5quscncwTq890Mq9Q2ryF4zlXpMbpOXTuVzfiOja6vR9OiBUk/uFEPvp3afyl+LP5gpcF80sgY7xF9XtUdW0zIk8QHtmx2+3Yu3cvXn/9dYwaNQrNmzdH//79sWTJEsTGxuL//J//g59//tmTx0ouqB75UNme6ho5qusc/eWbE1LjRGz6USz5EI3T88URseRDNE7Pw8t/kBqn5+3Nx6XG6VH9HlVd24zIE4TT/ubNm+PatWto06YNBg8ejLfeegtDhgxBx46N/yVGN071yIfq9lRPNlU9ebe00i41ToTq2lFmd61cvzSFO3F6zl1ueJTF3Tg9qmub1aZ6YQKZk3Cy88Ybb2DIkCHo1KmTJ4+H3GT2Zb2qJ5tGCNYvEo3TYw0JwCWBIp9WiStsVLdpgdhKJF/9+opuFoTzV/S/6KMl7YLdrkUojudeFYqTQXVts+upvjxP5iV8GeuJJ55gouOFzL6sV3WNnHGC9YtE4/R8OW2g1DhvbHODYF0v0Tg9wzuLJdmicXpU1zdbNE5s92fROD2qa5s5qL48T+Zmnq02myjVIx+q21NdI6dtC7HlwaJxelpFBkuNEyE6iVTWZNNu7axS4/T0vklsCwzROD2tIoMRqTMKFhkSIO01FN0hWdZOys1CAuCv81D+fpC6v4/e5XKg+nK5p1ecknkw2fFxqkc+jKhGrLRGjuId4oyojbVwo+tSCjcSJ+KJgQ3X2tK73x0WwddGNE7EoZdGuEx4IkMCcOilEdLaWrH7jNQ4PXuzC2DTmTJms8t9j6pemEDm57mtNkkJx8jHUysP1Jkb4YmRD9XtOaiqkXPpWrnUOD1G1MY6I7hUWjROj+paXOeuiB23aJyoQy+NwK9F5bhvyU4UXKtEVHgg1k1JkToqB5h/B2yj2iRzY7JjAo6Rj9oT+WI9NJFPdXsOKmrkmP2yIAB0iA7DjpNicTKorsVlQBHyGq0ig7Hzmbs88Mj/pnrHZiPeo0a0SebGZMckVFcHNqIacUWV3eMVnh2X6XILy+q9UmVBdVIn87JgeJA/rlW4XpYcHuwv9bLgs6OTsGLPOaE4GVSPRPRKaCHUv14JLaS0d70LBaUY9W4mrpXbEB7sj6+fHoS2UXJWRTk80q8DFmw81mAC6WepjpNB9WfCqDbJ3JjsmIjq6sAq20vbeBTpO7KdTvALNh7D5AGJmC/pSxlQf5nOZtdQ0kCiAwAl5TbY7JrP7i2iuhZXG8HHEY0T1em5jaiw/fsdU1Rmwx2vb0GQvwUnFoyW1k5QgB8mD0jEB9tdXxqcPCBR2g8BIy5dO9p8cuWBeu/XPNAmmRsnKJPXS9t4FB9sz67zS9auAR9sz0aaxIm0gNoJ0St2n9Gd66xB3mRTQP0EZdW1uFTXxgLqJjrXq7Bp6PTcRmltGUHpIoF/+fuB8426n+h6HNkhr6Z6cquDqst0Ry5clhon4qeLV6TG6Tl2UezYReP0XCgQ2zn4QkEp2rVs/LyWCwWlLhMdhwqbhgsFpVIuaZn9MwEApRU2ZBzNazAm42geSitsCA3yl94+mQ9HdsiruTO5VTbHZbp7e7ZFv47RHjmpf3koV2qciEMX9HffdSdOzxeHGv7ScjdOz8h3MqXG6Rn1rtjjiMbpMftnAjBmewQyNyY75NVUT25VrUqw5JVonAi7Jna5SDROj86gh9txelTXG1NdG8vsnwlA/fYIZH5MdsirqV5mq1qQv9gvY9E4EYF62+G6Gaf7OIIPIxqnJ1TwgUTj9IQHi11GEY3TY/bPBCC+7YGs7RHI/JjskFd7pF8H6I2Uy1xmez2bXcPu0/lYn3UBu0/ne2Rr+j+OFqs3JxonYnS31lLj9MwbeavUOD2bpg+SGqfn66fFHkc0Ts8j/TrAovOZsHjoM6GK6LYHsrZHIPPjBGXyaqqX2TqoqrbcJaa51DgRt7ZpDkC/iGJ1XON1bStW80o0Tk+7lmEI8Gv40l+AH6RMTgaA2OZiG9uJxunx97MgNNC/wS0LQgP9fXpZdmiQP4YlxTQ4SXlYUgwnJ5MwjuyQ15s/OglPDEysM8LjZ6muqSRznx1AcbVlA7b7faRfB92Hs0DupnTBOslocICf1KXgpxbeDVdNBvhV3y+L6vpme7ML9PdmqrD5fN2o9Al9MCwppt77hiXFIH1CH8VHRL6MIzvkE+aPTsLs4V08voOyXrVlC6qrLQ9LipXyyznvqliNLdE4USJ7+8hSUWVHuc4M6/IqOyqq7FJ/qZ9aeDfOXSrByHcyUVppR2igHzZNHyRtRMdBdR2nplQ3Kn1CH5RW2LBw41GcyS9Bh+gwPDs6iSM65DYmO+QzggL8pNROaog71ZZl7B5dUCyWxIjGiXCnaraM59udZcSvjO3W6Pau165lGI6+MkrqY9bWFOqpGSk0yF/6+4KaHiY7dMNsdk1pbSwVVP9qjgoPkhonQvXSZSOXEe85kY8HP95T8/+rH/stfttJbokTI+qpGVU3yohRFjOeZ0g9Q+fspKWloU+fPoiIiEBMTAzGjh2L48ePO8VomoaXXnoJcXFxCA0NxeDBg/Hjjz86xZSXl2PatGlo2bIlwsPDMWbMGJw/z63EPWnTkRykvLYFD6XvwfTVWXgofQ9SXtsidz6LAVo2C5YapycmUvBXumCcCNVLl9sL7hosGieqwzNfOSU6APDgx3vQ4ZmvpLbjqOPk6tKf7DpOqttzmPzJPtz6vzdhxZ5z2HHyElbsOYdb//cmTP5kn9R2rmfW8wypZ2iyk5mZialTp2LPnj3IyMhAVVUVhg8fjmvXrtXEvP7663jrrbewePFi7Nu3D7GxsRg2bBiuXv337q4zZszAunXrsHr1auzcuRPFxcW45557YLPJ2cSLnCmdwKua6GQVWZNaVLcH8YnHsiYoD7s1VmqcCL2ERnbCM3NNVqPud9fTnx5s1P3umvzJPpcrozKO5nkk4TH1eYaUMzTZ2bRpEyZOnIiuXbuiR48eWLZsGc6dO4f9+/cDqB7Vefvtt/Hcc8/h/vvvR3JyMv77v/8bJSUlWLVqFQCgsLAQH330Ed58800MHToUvXr1wsqVK3H48GFs3rzZyO6Zkt4EXqB6Aq8n9qRR4WRekdQ4PUdy8qXGidh5RKwsg2icnu9P/yo1Ts+eE2LPlWicntwrZbq7MZdW2pF7Rc6lT3dqccngTp0qWcx+niH1vGrpeWFhIQAgKqr6WnN2djZyc3MxfPjwmpjg4GAMGjQIu3btAgDs378flZWVTjFxcXFITk6uiamtvLwcRUVFTn8kxp0JvL7o9U3H9YPciNN9nK9PSY0T8djq/VLj9Hy444zUOD21L101Nk7PPe9tlxqnR3UtLiPqVJn9PEPqeU2yo2kaZs2ahZSUFCQnJwMAcnOrix+2bu28k2vr1q1r7svNzUVQUBBatGjhMqa2tLQ0WK3Wmr+EhATZ3TEtI5e9qtjRWG+JtLtxelTXjSL5isqqpMbpUV2Ly4gJ5k1peT2p4TWrsVJTU3Ho0CHs3Lmzzn2WWnuja5pW57baGoqZP38+Zs2aVfP/RUVFTHgEGbXsVdWOxuHB/igq0/+SkFXnKNAPEKlHKatuFMkXGRKAS9cqheJkUP0e7RAdhh0nxeJkaWrL68nzvOIUOm3aNGzYsAFbt25FfHx8ze2xsdUTFmuP0OTl5dWM9sTGxqKiogKXL192GVNbcHAwIiMjnf5IjGPZq6tU04LqJETmsleVExVV1zlS3R4AfPxgb6lxeiYPjpMapyf1zkSpcXq+nDZQapwe1e8ZI+pUGXGeIXMzNNnRNA2pqalYu3YttmzZgsRE55NPYmIiYmNjkZGRUXNbRUUFMjMz0b9/fwBA7969ERgY6BSTk5ODI0eO1MSQPI5lr0DdCgaO/5e57FX1REXVdY4SY8KlxokY1F2swKdonJ47b2knNU7PHR3Fjls0Tk+rSLFtCETj9LSNCkWQf8OfryB/C9pKWsrvqFPVENl1qlSfZ8j8DE12pk6dipUrV2LVqlWIiIhAbm4ucnNzUVpavYrAYrFgxowZWLhwIdatW4cjR45g4sSJCAsLw8MPPwwAsFqtmDRpEmbPno3vvvsOBw8exPjx49GtWzcMHTrUyO6Z1sjkNlg6/jbEWp2/8GOtIVg6/japl5VUT1Q0os6RyvaMaLNvYhSahwU2GNM8LFD6pnsNkTkqYMRreGLBaJcJT5C/BScWjJbWFmBMnSqV5xkyP0Pn7CxduhQAMHjwYKfbly1bhokTJwIA/vjHP6K0tBRTpkzB5cuXcfvtt+Pbb79FRERETfyiRYsQEBCABx54AKWlpbjrrruwfPly+Ps3rfopKncaHZncBsOSYj3enuqJihcvi02yrI5r/E68vwjuUvxLQYmU8hQAkFsotiRZNE4Gme8ax6jAUysPuNxhWOaogFGTaU8sGI0LBaUY9W4mrpXbEB7sj6+fHiRtRKc2I+pUqTrPkPkZmuxomv6lB4vFgpdeegkvvfSSy5iQkBC89957eO+99yQenW9RNYH3ev5+FmlfwK6onqiYdf6KcNx//Kbxk9rX7DsnHPdAHzmT6AuuVUiN07M3uwBXShqewHu5pFJavTHg36MCKj4TRk6mbRsVikMvjZT+uK4YUadKxXmGzM8rJihT45h5p1H1ExVFfzHK+WV5tVxsObJonIgowVIXonF6jBr5GJncBqsm/RbB/7rcE+xvwapJv5We/HMyLZH3Y7Lj48y+06jqOkCiy2dlLbNVXacKAGIEkxjROD2q64053PzsVxjy1jaU/2uTonKbhiFvbcPNz/p2bSwich+THR/XFHYaTfv6WKPud4fqulGLxvWSGidE7eCVIfW/bn72K7ja97HKDukJj8r36PVUbLRJZAZes6kg3Riz7zRaWFKJs/kNT5Q9m1+KwpJKWHVW/Ii2JxonYymxaD2h0gobmknalO5s/jX9oH/F3XFzy0a3d/rXq/pB/4q745bGt3fuUonLRMehyl4d165l40fMVL9HHYyYp0fkqziy4+PMvtPoY8v3So3Tc9+Sujt4NybO29oDgCVbxepsicbp+cu3YnXEROP0jHxHrCaUaJwe1e9RwNzz9Ig8gcmOjzP75MiLDVyiu5E4PQUC2/67E6f/OGpXRgHA1TKxYxeN06NXEdzdOG9rT/V71Ozz9Ig8gcmOjzP7TqNxOpvDuRunJypc7DKDaJyeMME9SkTjRESEiB27aJweq+DlN9E4PaGChcRE4/Sofo82hXl6RLIx2TEBM+80+vHEvlLj9KybkiI1Ts/0YbdIjRPx1JCOUuP0qK4dtWm6WE0o0Tg9qt+jZp+nR+QJnKBsEmbdadQaFoj20aENTgBtHx0qbeKn6OPIau/mVmJFaEXjRNzUMkI/yI04PbHNQxAa6NfgZaPQQD9p9cbatQxDgB8anKQc4Acpk5MBoFlIAPz9AFsD7fn7QdoEc7PP0yPyBI7smIhjp9F7e7ZFv47RPp/oOIzsGtuo+92xYvcZqXF6+iZG6V6iCgvylzrnSnWtKgA49sool5eNQgP9cOyVUdLaAoDFD9/WqPvdsTe7oMFEB6hOhGTWGjPzPD0iT2CyQ16tosqO9B3ZDcak78hGhd5aY0E/XxJbli0ap8dm11Ba2fDy89JKm/LJpp5Ik4+9Mgp7nrkLLcMDEeRvQcvwQOx55i7piY5jAq8rFsidwJtbJHa5SDROj9nn6RF5ApMd8mordp+B3neSXZM30pIn+IUkGqdnxe4z0CsRp0nsH+BerSrZYpuH4H9eGI4TC0bjf14YLu3S1fVUT+AtKC6XGifCzPP0iDyBc3bohqmosn5WsCq4aJyemAjBUgqCcXpU9w8wdoLr/p8v4z8+3FXz/39/vD9639RCahuq+xcVHiQ1TtTI5DZIubkVZq45iHOXS9GuRSgWjeslbW5QfVR85mtTXWmdzInJDt0QVbu3qq4ddVOrZlLj9BhRG6tluGCtKsE4UR2eqVuiwZH4nHn1bmntqK7FFWsNlRonavIn+5BxNK/m/4/nXkXyS99gWFIM0if0kdoWYMyOzbX7uOMksGLPOY/1kcyLl7HIbSp3b1Vdq8rs7QFQXxsL9Sc67tzvFsW1uPomRuk+VZZ/xclSOwm4XsbRPEz+ZJ+0tgBjdmxW3UcyNyY75BbVu7e6U6tKhqPni6TG6cm9Iji5VTBOxI7TYl9MonF69v98WWqcnh9zxR5HNE5PYUmlbt6kQd57tLTC5jIJcMg4midcd02PETs2q+4jmR+THXKL6smfqmtHjX3/e6lxelTXcQKA97eekxqn5/o5OjLi9Ly28YTUOD0PCh63aJyehRtdrzS7kTg9RuzYrLqPZH5Mdsgtqid/5gvWhBKN8zaq6zg1BTbBAQbROD15V8Xee6Jxes7ki01WF43TY8SEdtV9JPNjskNuUb17a7jgqgvROG+juo5TUyD6VMl6SmMixFZZicbp6RAtNlldNE6PETs2q+4jmR/PoOQW1bu3zhjaSWqcns+fvENqnB7VdZwA4K5bm0uN0/P3x/tLjdPz9dNiz5VonJ7VgsctGqfn2dFJUuP0GLFjs+o+kvkx2SG3qN69tWOMWH0m0Tg9PTs0lxqnR3RTPZmb7/0mIUZqnJ5u7axS4/QkxoRLjdMT1SwIrZo1PGrTqlkQonRiRIUG+WNYUsOvzbCkGGl70RixY7PqPpL5Mdkht6ncvVWkjlMLyXWc9PZ8kbknjOpaXADwP+fEJpKKxulR3UfRibIyJ9S+Mja5Ufe7K31CH5fJgCf2oDFix2bVfSRz46aCdEO8qcq6J6pGnXn1biW7/Wbni9XYEo0Tcf6KWNkC0Tg9qneJVj2hVrQW17CkWKmfj/QJfZTuLmzEZ151H8m8mOzQDXNUWfckkTpOV/5Vx0nmsTh2i71e6poD0neLNWB/P7RrEYrjuVeF4mRQvUu06gm17izNlv15CQ3yxytju0l9zIao+MzXprqPZE68jEU3zGbXsPt0PtZnXcDu0/keqcxtxLJXlbvF9koQGykSjROxaFwvqXF6HunXQWiHYVm7RKueUGtkrTEVn0EiM+DIDt0QVXVyVNc5Et0tVtYlidaRYqMLonEimoUEoHt8JA41sAt09/hIaQUl/f0sCAvyx7UGdrsNC/aXdjnEMaH2yZUH6r1fg9wJtUYszQaMqVVF5Ks4skNuU1onR3GdI71LEoDk3WKNuI4F4Kechi9j6d3vjr3ZBQ0mOgBwrdwmdcJw2tfHGnW/O0Qm0TeXPIneiFpVRL6MyQ65RXWdnHOXxSbmisbpyf61WGqcnv1n86XGibhQUIoKne2DK2waLhSUSmnv5D/FEifROD2FJZU4m9/wsZ/NL5VWq0qEzFzViFpVRL6OyQ65RXWdnGU7s6XG6Vmy7ZTUOD1vZYg9jmiciFHvitXZEo3T85dvf5Iap+ex5XulxukRmUR/+V+T6GW1p7pWFZGvY7JDblE9GfNqWZXUOP3HEfu1Lxrnja6Vi1WKFo3TU1ohWP9LME7PRZ3LkO7G6VH9mTByQjSRr2KyQ25RPRkzrrnY8mfROD0RIQ3PvXA3zhsFB4h97EXj9FhDxSY6i8bpibOKvfdE4/So/kwYNSGayJcx2SG3qF7W+/HEvlLj9Dw1pKPUOD13J7eSGifij6M6S43T8+W0gVLj9Kh+z6j+TBhRq4rI1zHZIbeorpNjDQtE++iGR23aR4fCqrMaRtRNLcVqbInG6bmtfUupcSJuaRUpNU5PbPMQ3artoYF+0up/qX7PqP5MGFGrisjXMdkht6muk5M5906XX17to0OROfdOaW05fjU3ROav5kf6dYDed5KfRd6GewAMWe5+7JVRLhOe0EA/HHtllLzGoPY9A6j/TBhRq4rIl1k0TWvy6xOLiopgtVpRWFiIyEg5v2abAptdU1onp7CkEo8t34uLhWWIs4bg44l9pf06v55jD5P6PhgWQPqXSdrGo/hgu+vVZE8MTMT80UnS2lt38AJmrsnSjVs0rifu69VWWrsAkHulDPe8tx1FZVWIDAnAl9MGSq3oXpuq94yD6s+E6vaIvI3o9zeTHTDZobpU706btvEo0ndk4/qtUfwswOQBchMdAPhox8945Sv9TfVeuPtWTBpwk9S2iYhkEv3+ZrkIDzHiF5fqNovLqjBzzUGcu1yKdi1CsWhcL2klBupzoaAUo97NxLVyG8KD/fH104PQNkrOKqzaRia3QeeYSIx8JxPlNg3B/hasmvRbJMaEe6S9+aOTcEe7lpiwcl/Nbct/3wcDk2OktxUVHiQ1zh2HzxVizJKd0FA9SrZhSgq6tbNKb8dB9XuUiLyToSM727dvxxtvvIH9+/cjJycH69atw9ixY2vuLy4uxjPPPIPPP/8c+fn56NChA55++mk89dRTNTHl5eWYM2cOPv30U5SWluKuu+7CkiVLEB8fL3wcskd2jKhZo7rNMYt31FtbqXt8JDakDpDeXqfnNta762+QvwUnFoyW3t7Nz36Fqnq2fQnwA04tvFt6ex2e+crlfWdeldve9ycv4fcf/aAb97dJt+OOW+RNjFbZR0D9e5SI1BP9/jZ0gvK1a9fQo0cPLF68uN77Z86ciU2bNmHlypU4duwYZs6ciWnTpmH9+vU1MTNmzMC6deuwevVq7Ny5E8XFxbjnnntgs8nZEM1dRtSsUd2mqy8RADh0vghjFu+Q2p6rRAeoLmvQ6bmNUttzlegAQJW9+n6ZGkoCRO53mwETlFX3UfV7lIi8m6HJzqhRo/DnP/8Z999/f7337969G48++igGDx6MDh064PHHH0ePHj3wP//zPwCAwsJCfPTRR3jzzTcxdOhQ9OrVCytXrsThw4exefNmlV0BYEzNGtVtFpdVNVgtG6j+MimWtKOx6jpO5y6VuEx0HKrs1XEy7PrpktQ4EfvO/io1Ts/hc4VS4/Sofo8Skffz6qXnKSkp2LBhAy5cuABN07B161acOHECI0aMAADs378flZWVGD58eM2/iYuLQ3JyMnbt2uXyccvLy1FUVOT0J4MRNWtUtzlzzUGpcXpU13Ea+Y7Y44jG6Xl4uf7lJHfiRLyd8bPUOD1jluyUGqdH9XuUiLyfVyc77777LpKSkhAfH4+goCCMHDkSS5YsQUpKCgAgNzcXQUFBaNGihdO/a926NXJzc10+blpaGqxWa81fQkKClOM1omaN6jbPXRYbQRGN06O8jlOlYB0nwThCvaOOjYnTo/o9SkTez+uTnT179mDDhg3Yv38/3nzzTUyZMkX3EpWmabBYXE84mD9/PgoLC2v+fvnlFynHa0TNGtVttmshtvpJNE5PeLC/1Dg9ejv9uhtH6qcIqX6PEpH389ozdmlpKZ599lm89dZb+N3vfofu3bsjNTUV48aNw1/+8hcAQGxsLCoqKnD58mWnf5uXl4fWrVu7fOzg4GBERkY6/clgRM0a1W0uGtdLapyer58eJDVOz6bpYo8jGqdn1cTbpcaJ+Pvj/aXG6dkwJUVqnB7V71Ei8n5em+xUVlaisrISfn7Oh+jv7w+7vfoSQu/evREYGIiMjIya+3NycnDkyBH07y/nRO0OI2rWqG6zWUgAusc3nBx2j4+UtpdJ26hQBPk3fOxB/hZp++2IPo6s9vp3EVvaLRonomeH5lLj9IjuoyNrvx3V71Ei8n6GJjvFxcXIyspCVlYWACA7OxtZWVk4d+4cIiMjMWjQIMydOxfbtm1DdnY2li9fjk8++QT33XcfAMBqtWLSpEmYPXs2vvvuOxw8eBDjx49Ht27dMHToUEP6ZETNGtVtbkgd4PLLxBN7mJxYMNplwiN7nx3RidwyJ5m/P/62Rt3vrqbQR9XvUSLyboZuKrht2zYMGTKkzu2PPvooli9fjtzcXMyfPx/ffvstCgoK0L59ezz++OOYOXNmzZycsrIyzJ07F6tWrXLaVNCdSceeKBfRFHZQLiiuwIMf7kLe1QrERARh9eP9EdVM/q67DuculWDkO5korbQjNNAPm6YPQruWYVLbWJ91AdNXZ+nGvfNgT9zbs/F1o2x2DSmvbXG5os6C6qR157w7pb2WqmtjGdFHB+6gTGRuPlEuYvDgwWgo14qNjcWyZcsafIyQkBC89957eO+992QfXqP4+1nQr2O0adusXcvpSmklfrMgwyO1nIB/7xBd8q9VUCWVdoxL3y19h2jVE77d2TpA1mtbUFwuNU6PEX10aBYSgPRH+0h9TCLyPV47Z4e8l6NKd+19Cu0a8MH2bKRtPCq1PVc7ROd4YIdo1RO+jdiuQHVtLCP66FBaYcMLnx/GIx/9gBc+P4zSCs/urG6za9h9Oh/rsy5g9+l8qRuIEtGN43guuaWiyo70HdkNxqTvyMbs4V0QFND4XLqhHaKB6lGBl784imFJsVIugTgmfD+58oDL9mRO+G7ZLFhqnIhYq9jkatE4PUZsyQAAkz/Zh4yjeTX/v+MksGLPOQxLikH6BPmjPUbUxCMiMRzZIbes2H2mzohObXatOk4GvUsggPxdqZdsO9Wo+92iesc9VI9e6SWiwQF+0kav+iZGoXlYYIMxLcICpW7JUDvRuV7G0TxM/mRfvffdKCNq4hGROCY75JbTl4qlxum5eEVsl1vROD2q6yqdyLsiNU5ERZUdFToFwMoFYmSSebGntMLmMtFxyDiaJ+2SlhE18YjIPUx2yC2/FolNWhWN05P1y2X9IDfi9Kiuq/TGppNS40QsFJxTJRqnZ292Aa6UVDYYc6WkUtronBH9U10Tj4jcw2SH3NI6UmxehWict1FdV6lccPRENE7EmXyxiu2icXpUT1A2e/+IyH1MdsgtiS3Dpcbp6RAt9jiicXrMXvsLADpEi+1NJBqnR/UEZbP3j4jcx2SH3PJIvw7QW4jkZ6mO88X2zF77CwCeFdwHSTROj+rl/GbvHxG5j8kOuSUowA+TByQ2GDN5QKKUZedGtGf22l8AEBrkj2FJMQ3GDEuKQWiQnNEk1fXbzN4/InIfkx1yW692LRp1v7vmj07CEwMT64zw+FmAJwbK37HZzLW/HNIn9HGZEHhiHxrV9dvM3j8ico+htbG8hSdqY5mVkXWOKqrsWLH7DM4WlKB9VBge6ddB2ohOfVTXVbpQUIpR72biWrkN4cH++PrpQVJHdOpTWmHDwo1HcSa/BB2iw/Ds6CRpIx71UV2/zez9I2rqRL+/meyAyY47dp/Ox0Ppe3TjPp38W+W1wYiIqGnxiUKg5HuMXGZbWFKJx5bvxcXCMsRZQ/DxxL6w6uzM60vtqajqXpvqyvWq2zP7aCBHkojEcGQHHNlxh1EjO4Pe2IKz+XX3tmkfHYrMuXdKa8eo9m5+9ivUt5VOgB9wauHd0tsDgD5/zsCvxRV1bm/VLAj7nh/m8+2lbTyK9B3OBWv9LNUT2mXP8wKAMYt31Lv7tifmeQGsxUUEiH9/c4IyuUWkzlFzyXWOXCUeAHA2vxSD3tgirS0j2nOV6ABAlb36ftlcJR4A8GtxBfr8OcOn20vbeBQfbM+uU8fNrgEfbM9GmqTdkx1cJTpAdXmRMYt3SG2PtbiI3MNkh6STOYheWFLpMvFwOJtfikKdcgTe2t65SyUuEx2HKnt1nCwFxRUuEw+HX4srUKAT463tVVTZkb4ju8GY9B3Z0mp/qa6nxlpcRO5jskNuEalzdFlinaPHlu+VGudt7Y18J1NqnIgHP9wlNc7b2lux+0ydEZ3a7Fp1nAyq66mxFheR+5jskFtUT1C+2MBJ/UbivK290kqx0QXROBF5V8VGUETjvK29swVio2CicXpU11NjLS4i9zHZIbeorgMUZxV7HNE4b2svNFDsIygaJyImQmz1k2ict7XXPkpsBZtonB7V9dRYi4vIfUx2TMRm17D7dD7WZ13A7tP5Hrlmr3qC8scT+0qN87b2Nk0Xq3klGidi9eP9pcZ5W3tmr6fGWlxE7mOyYxKbjuQg5bUteCh9D6avzsJD6XuQ8toWQ1ZlyJygbA0LRPvohn8Rt48Olbb/jer22rUMg962LwF+kLrfTlSzILTS2dumVbMgafvfqG7P7PXUWIuLyH1MdkxA5TJU1ROUASBz7p0uExBP7Hszf9StjbrfXYsfvq1R99+Ifc8Pc5mAeGLfG9Xtmb2eGmtxEbmHmwrCtzcVVF2ran3WBUxfnaUb986DPXFvz7aNbu96KnY0Vv18GllrDOAOyrJxB2UitVguoolwZxmqjB2NjZwcaQ0LxN+n3CH9ca+n+vlU3V5tUc2C8O2swdIf11vaCwrww6QBNylrr1lIANIflVtRvSH+fhbWoCMSwGTHQ1T94lK9DNUxOTK3sKzeTc0cIxGemBypooK16uezqS0jVj3SQkQEMNnxCJU1a1SPtDgmRz658kC992vwzOTIyZ/sQ8bRvJr/33ESWLHnHIYlxSB9grxf0qqfz6a0jLi+WlULNh7zWK0qIiIH/qSSTHXNmqawDLV2onO9jKN5mPzJPmlt9U2MQpjOaFF4kL+059Po10/FdgWA+lpVRETXY7IjkRE1a1QvQ3X00RUL5PaxtMLmMtFxyDiah9IKm5T2bHYNpZUNP1ZJpU1a/4xcRqxquwLVtaqIiGpjsiORUTVrVC5DVd3HhYK/+EXj9KzYfQZ66xM1iXWVAGOWEascgVRdq4qIqDbO2ZHIyMmmI5PbYFhSrMcnRavu45l8sfpFonF6VNdVclD1+gH6I5CO0blhSbFS2jfqOSUicmCyI5HRk01VLENV3ccO0WHYcVIsTgbVdZWup2oZserl7kY+p0REAC9jSWX0ZFMVVPfxWcFVOqJxelTXVTKC6tG5pvCcEpF3Y7IjUVOoWaO6j0EBfgjW2YclOMBP2l4tqusqGUH16FxTeE6JyLvx7CJZU6hZo3pCdLnOKp3yKrvUSd+q6yqpZsQIpNmfUyLybqyNBc/UxmoKNWtU9NHIWlxm3u3XsRoLgNNEZcer56nE3MzPKRGpx9pYBmsKNWvMOCH6eqrrKqnkGJ2rvdN3rId2+nYw83NKRN6LyY6JqB5NUvEr3ey1uIxsb2RyG6Tc3EpplW6O7BCREQy9jLV9+3a88cYb2L9/P3JycrBu3TqMHTvWKebYsWOYN28eMjMzYbfb0bVrV3z22Wdo164dAKC8vBxz5szBp59+itLSUtx1111YsmQJ4uPjhY/DE5exVFNZjwuov86RnwUeqXO06UiOy1pcAPC+By65uCpRIbsWl1HtGdGmyvcMETUNot/fhv6kunbtGnr06IHFixfXe//p06eRkpKCLl26YNu2bfjHP/6BF154ASEh/75kMWPGDKxbtw6rV6/Gzp07UVxcjHvuuQc2m5zyAb5AdT0us9c5UlmLy4j2jGjT7O8ZIvJuXjNB2WKx1BnZefDBBxEYGIgVK1bU+28KCwvRqlUrrFixAuPGjQMAXLx4EQkJCdi4cSNGjBgh1LYvj+zY7BpSXtvicpM4x2WenfPulHJJq6LKji4vfN3g9v9+FuCnV0ZJuTyhun+lFTbc+r836cYd+9NIKZeYVLdnRJuq3zNE1HT4xMhOQ+x2O7766it06tQJI0aMQExMDG6//XZ8/vnnNTH79+9HZWUlhg8fXnNbXFwckpOTsWvXLpePXV5ejqKiIqc/X6W6VpXqOkdmr8Wluj0j2mRtLCIymtcmO3l5eSguLsarr76KkSNH4ttvv8V9992H+++/H5mZmQCA3NxcBAUFoUWLFk7/tnXr1sjNzXX52GlpabBarTV/CQkJHu2LJ6neDVd1nSOz1+JS3Z4RbbI2FhEZzWuTHbu9eiO5e++9FzNnzkTPnj3xzDPP4J577sH777/f4L/VNA0Wi+tLGvPnz0dhYWHN3y+//CL12FVSvTRbdZ0jI2pxyYzztvaMaJO1sYjIaF6b7LRs2RIBAQFISnJepXHrrbfi3LlzAIDY2FhUVFTg8uXLTjF5eXlo3bq1y8cODg5GZGSk05+vUr0bruo6R2avxaW6PSPaZG0sIjKa1yY7QUFB6NOnD44fP+50+4kTJ9C+fXsAQO/evREYGIiMjIya+3NycnDkyBH0799f6fEaxYhaVSrrHKnuX2iQP4YlxTQYMywpRtpkYdXtGdEma2MRkdEMPbsUFxcjKysLWVlZAIDs7GxkZWXVjNzMnTsXa9asQXp6Ok6dOoXFixfjiy++wJQpUwAAVqsVkyZNwuzZs/Hdd9/h4MGDGD9+PLp164ahQ4ca1S3lVNfjUl3nSHX/0if0cZkMeGIPGtXtGdEma2MRkZEMXXq+bds2DBkypM7tjz76KJYvXw4A+Pjjj5GWlobz58+jc+fOePnll3HvvffWxJaVlWHu3LlYtWqV06aC7kw69uWl59cz4w7K11PdP7PvoGxEm2Z/zxCRWqLf316zz46RzJLsENG/qd5VnIjUYyHQJsjsv2JZV4lEOXYVr/1LzrGruKequhORd2KyYxJm/xVbX12lBRuPsa4S1WGza3j5i6P1Fo7VUD2x/eUvjmJYUqypfgwQkWv8WWwCqmtjqca6SuQO1btuE5H3Y7Lj4/R+xQLVv2Jtevv1e6mKKjvSd2Q3GJO+IxsVVXZFR0TeTvWu20Tk/Zjs+Diz/4plXSVyl+pdt4nI+zHZ8XFm/xXLukrkLtW7bhOR92Oy4+PM/iuWdZXIXap33SYi78dkx8eZ/Vcs6yrRjVC96zYReTcuPfdxjl+xT608AAvgNFHZDL9iHXWVPtjuepIy6ypRfUYmt8GwpFhT7z1FRGK4gzLMsYNyU9xnx88C7rNDRNSEsVyEG8yQ7ADcQZmIiJoWlotogvz9LOjXMdrow/CYoAA/TBpwk9GHQUREPobJjomYfWRHNbNXkSciaiqY7JiE2efsqKb6+WTtLyIiz+HPRhMwe20s1VQ/n6z9RUTkWUx2fJzZa2Oppvr5ZO0vIiLPY7Lj48xeG0s11c8na38REXkekx0fZ/baWKqpfj5Z+4uIyPOY7Pg4s9fGUk3188naX0REnsdkx8eZvTaWaqqfT9b+IiLyPCY7Po4VnuVS/Xw6an81hLW/iIgah2dQE2CFZ7lUP5/zRyfhiYGJdUZ4/CzAEwO5zw4RUWOxNhZYG4vqxx2UiYi8GwuBusEsyQ4REVFTIvr9zZ+NREREZGpMdoiIiMjUmOwQERGRqTHZISIiIlNjskNERESmxmSHiIiITI3JDhEREZkakx0iIiIyNSY7REREZGoBRh+AN3BsIl1UVGTwkRAREZEox/e2XjEIJjsArl69CgBISEgw+EiIiIjIXVevXoXVanV5P2tjAbDb7bh48SIiIiJgsfh24cyioiIkJCTgl19+MWWdL/bP95m9j+yf7zN7H83UP03TcPXqVcTFxcHPz/XMHI7sAPDz80N8fLzRhyFVZGSkz7+JG8L++T6z95H9831m76NZ+tfQiI4DJygTERGRqTHZISIiIlNjsmMywcHBePHFFxEcHGz0oXgE++f7zN5H9s/3mb2PZu9ffThBmYiIiEyNIztERERkakx2iIiIyNSY7BAREZGpMdkhIiIiU2Oy46MuXLiA8ePHIzo6GmFhYejZsyf2799fc39xcTFSU1MRHx+P0NBQ3HrrrVi6dKmBRyyuQ4cOsFgsdf6mTp0KoHrHzJdeeglxcXEIDQ3F4MGD8eOPPxp81O5pqI+VlZWYN28eunXrhvDwcMTFxWHChAm4ePGi0YctTO81vN4TTzwBi8WCt99+W/2B3iCR/h07dgxjxoyB1WpFREQEfvvb3+LcuXMGHrV79Proy+cYAKiqqsLzzz+PxMREhIaG4qabbsKf/vQn2O32mhhfPtfo9c8M5xm3aORzCgoKtPbt22sTJ07UfvjhBy07O1vbvHmzdurUqZqY//qv/9I6duyobd26VcvOztY++OADzd/fX/v8888NPHIxeXl5Wk5OTs1fRkaGBkDbunWrpmma9uqrr2oRERHa3//+d+3w4cPauHHjtDZt2mhFRUXGHrgbGurjlStXtKFDh2pr1qzRfvrpJ2337t3a7bffrvXu3dvowxam9xo6rFu3TuvRo4cWFxenLVq0yJBjvRF6/Tt16pQWFRWlzZ07Vztw4IB2+vRp7csvv9T++c9/GnvgbtDroy+fYzRN0/785z9r0dHR2pdffqllZ2dr//f//l+tWbNm2ttvv10T48vnGr3+meE84w4mOz5o3rx5WkpKSoMxXbt21f70pz853Xbbbbdpzz//vCcPzSOmT5+udezYUbPb7ZrdbtdiY2O1V199teb+srIyzWq1au+//76BR9k41/exPnv37tUAaGfPnlV8ZHLU17/z589rbdu21Y4cOaK1b9/ep5Kd2mr3b9y4cdr48eMNPiq5avfR188xd999t/bYY4853Xb//ffXvG6+fq7R6199fP080xBexvJBGzZswG9+8xv853/+J2JiYtCrVy+kp6c7xaSkpGDDhg24cOECNE3D1q1bceLECYwYMcKgo74xFRUVWLlyJR577DFYLBZkZ2cjNzcXw4cPr4kJDg7GoEGDsGvXLgOP9MbV7mN9CgsLYbFY0Lx5c7UHJ0F9/bPb7XjkkUcwd+5cdO3a1eAjbJza/bPb7fjqq6/QqVMnjBgxAjExMbj99tvx+eefG32oN6y+19DXzzEpKSn47rvvcOLECQDAP/7xD+zcuROjR48GAJ8/1+j1rz6+fJ7RZXS2Re4LDg7WgoODtfnz52sHDhzQ3n//fS0kJET77//+75qY8vJybcKECRoALSAgQAsKCtI++eQTA4/6xqxZs0bz9/fXLly4oGmapn3//fcagJr/d5g8ebI2fPhwIw6x0Wr3sbbS0lKtd+/e2u9//3vFRyZHff1buHChNmzYsJpRAl8e2andv5ycHA2AFhYWpr311lvawYMHtbS0NM1isWjbtm0z+GhvTH2voa+fY+x2u/bMM89oFotFCwgI0CwWi7Zw4cKa+339XKPXv9p8/Tyjh1XPfZDdbsdvfvMbLFy4EADQq1cv/Pjjj1i6dCkmTJgAAHj33XexZ88ebNiwAe3bt8f27dsxZcoUtGnTBkOHDjXy8N3y0UcfYdSoUYiLi3O6vfYIiKZpLkdFvJ2rPgLVkwgffPBB2O12LFmyxICja7za/du/fz/eeecdHDhwwGdfs+vV7p9jAui9996LmTNnAgB69uyJXbt24f3338egQYMMO9YbVd971NfPMWvWrMHKlSuxatUqdO3aFVlZWZgxYwbi4uLw6KOP1sT56rlGtH+AOc4zuozOtsh97dq10yZNmuR025IlS7S4uDhN0zStpKRECwwM1L788kunmEmTJmkjRoxQdpyNdebMGc3Pz89pwuPp06c1ANqBAwecYseMGaNNmDBB9SE2Wn19dKioqNDGjh2rde/eXbt06ZIBR9d49fVv0aJFmsVi0fz9/Wv+AGh+fn5a+/btjTvYG1Bf/8rLy7WAgADtlVdecYr94x//qPXv31/1ITZafX00wzkmPj5eW7x4sdNtr7zyita5c2dN03z/XKPXPwcznGdEcM6OD7rjjjtw/Phxp9tOnDiB9u3bA6jO0isrK+Hn5/zy+vv7Oy2r9HbLli1DTEwM7r777prbEhMTERsbi4yMjJrbKioqkJmZif79+xtxmI1SXx+B6tfwgQcewMmTJ7F582ZER0cbdISNU1//HnnkERw6dAhZWVk1f3FxcZg7dy6++eYbA4/WffX1LygoCH369GnwM+pL6uujGc4xJSUlDR6/r59r9PoHmOc8I8TobIvct3fvXi0gIEBbsGCBdvLkSe1vf/ubFhYWpq1cubImZtCgQVrXrl21rVu3aj///LO2bNkyLSQkRFuyZImBRy7OZrNp7dq10+bNm1fnvldffVWzWq3a2rVrtcOHD2sPPfSQzywHvZ6rPlZWVmpjxozR4uPjtaysLKflv+Xl5QYdrfsaeg1r88U5Ow31b+3atVpgYKD24YcfaidPntTee+89zd/fX9uxY4cBR3rjGuqjr59jHn30Ua1t27Y1S7PXrl2rtWzZUvvjH/9YE+PL5xq9/pnlPCOKyY6P+uKLL7Tk5GQtODhY69Kli/bhhx863Z+Tk6NNnDhRi4uL00JCQrTOnTtrb775psulzd7mm2++0QBox48fr3Of3W7XXnzxRS02NlYLDg7WBg4cqB0+fNiAo2wcV33Mzs7WANT7V3ufGm/W0GtYmy8mO3r9++ijj7Sbb75ZCwkJ0Xr06OEz+89cr6E++vo5pqioSJs+fbrWrl07LSQkRLvpppu05557zumL3pfPNXr9M8t5RpRF0zRN+XASERERkSKcs0NERESmxmSHiIiITI3JDhEREZkakx0iIiIyNSY7REREZGpMdoiIiMjUmOwQERGRqTHZISIiIlNjskNEptOhQwe8/fbbwvFnzpyBxWJBVlaWx46JiIzDZIeIvMbEiRMxduzYOrdv27YNFosFV65cEXqcffv24fHHH5d6bMuXL0fz5s2lPiYRqRFg9AEQEcnWqlUrow+BiLwIR3aIyOfs2rULAwcORGhoKBISEvD000/j2rVrNffXvoz1008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