\n",
"\n",
"__Given date:__ June 13\n",
"\n",
"__Due date:__ June 21\n",
"\n",
"__Total: 25pts__\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 1 Logistic regression (15pts)\n",
"\n",
"##### Question 1.1 Logistic regression (5pts)\n",
"\n",
"As we saw during the lectures, one approach at learning a (binary) linear discriminant is to combine the sigmoid activation function with the linear discriminant $\\beta_0 + \\mathbf{\\beta}^T \\mathbf{x}$. We then assume that the probability of having a particular target ($0$ vs $1$) follows a Bernoulli with parameter $\\sigma(\\tilde{\\mathbf{\\beta}}^T\\tilde{\\mathbf{x}})$. i.e. we have \n",
"\n",
"$$\\left\\{\\begin{array}{l}\n",
"P(t = 1|x) = \\sigma(\\mathbf{\\beta}^T\\mathbf{x})\\\\\n",
"P(t = 0|x) = 1-\\sigma(\\mathbf{\\beta}^T\\mathbf{x})\\end{array}\\right.$$\n",
"\n",
"The total density can read from the product of each of the independent densities as \n",
"\n",
"$$P(\\left\\{t_i\\right\\}_{i=1}^N) = \\prod_{i=1}^N \\sigma(\\mathbf{\\beta}^T\\mathbf{x})^{t^{(i)}}(1-\\sigma(\\mathbf{\\beta}^T\\mathbf{x}))^{1-t^{(i)}}$$\n",
"\n",
"we can then take the log and compute the derivatives of the resulting expression with respect to each weight $\\beta_j$. Implement this approach below. Recall that the derivative of the sigmoid $\\sigma(\\boldsymbol x)$ has a _simple expression_. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Step 1 define the sigmoid activation and its derivative\n",
"\n",
"\n",
"def sigmoid(x):\n",
"\n",
" '''the function should return the sigmoid and its derivative at all the \n",
" entries of x '''\n",
" \n",
" \n",
" return sig, deriv_sig\n",
"\n",
"\n",
"def solve_logisticRegression(xi, ti, beta0, maxIter, eta):\n",
" \n",
" '''The function should return the vector of weights for a \n",
" logistic regression classifier learned through gradient descent \n",
" iterations applied to the log likelihood function'''\n",
" \n",
" \n",
" \n",
" return beta\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Question 1.2 Logistic regression and Fisher scoring (5pts)\n",
"\n",
"An interesting aspect of the MLE estimator in logistic regression (as opposed to other objective functions) is that the Hessian is positive definite. We can thus improve the iterations by using a second order method (such as Newton's method) where the simpler gradient iterations $ \\mathbf{\\boldsymbol \\beta}^{k+1}\\leftarrow \\mathbf{\\boldsymbol \\beta}^k - \\eta\\nabla \\ell(\\mathbf{\\boldsymbol \\beta}^k)$ are replaced by \n",
"\n",
"$$\\mathbf{\\boldsymbol \\beta}^{k+1}\\leftarrow \\mathbf{\\boldsymbol \\beta}^k - \\eta H^{-1}({{\\boldsymbol \\beta}^k})\\nabla \\ell(\\mathbf{\\boldsymbol \\beta}^k)$$\n",
"\n",
"(see e.g. [here](https://statacumen.com/teach/SC1/SC1_11_LogisticRegression.pdf) for more details) Start by completing the function 'HessianMLE' below which should return the Hessian of the negative log likelihood. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def HessianMLE(beta, xi, ti):\n",
" \n",
" '''Function should return the Hessian (see https://en.wikipedia.org/wiki/Hessian_matrix) \n",
" of the log likelihood at a particular value of the weights beta'''\n",
" \n",
" \n",
" return HessianMatrix\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then complete the function 'Fisher_scoring' which should learn a logistic regression classifier based on the second order Fisher iterations. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def Fisher_scoring(beta0, xi, ti, maxIter, eta):\n",
" \n",
" '''Function should compute the logistic regression classifier by relying on Fisher scoring\n",
" iterates should start at beta0 and be applied with a learning eta'''\n",
"\n",
" while numIter"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import scipy.io\n",
"class1 = scipy.io.loadmat('class1HW1_LR.mat')['class1']\n",
"class2 = scipy.io.loadmat('class2HW1_LR.mat')['class2']\n",
"\n",
"targets_class1 = np.ones(np.shape(class1)[0])\n",
"targets_class0 = np.zeros(np.shape(class0)[0])\n",
"\n",
"plt.scatter(class1[:,0], class1[:,1], c = 'r')\n",
"plt.scatter(class2[:,0], class2[:,1], c = 'b')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 2. Convolutional nets and autonomous driving (10pts)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this second question, we will use [the Keras API](https://keras.io/) to build and train a convolutional neural network to discriminate between four types of road signs. To simplify we will consider 4 different signs: \n",
"\n",
"- A '30 km/h' sign (folder 1)\n",
"- A 'Stop' sign \n",
"- A 'Go straight' sign\n",
"- A 'Keep left' sign \n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An example of each sign is given below."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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Q/e5ffofDw4ann3uK4sQKa6uPIMeODYn3d4RrbIVnuAPFOpp1lAGTTN8PSJ0In2pwdRm4QQX0Cuh90KH60UbNtfde5r037rFz0yPDknevvMPu/IBqcciinLGoSspqBg8pdDOWPO1xoQtLAUfn/S6y/UkoNs1lRhCGHxB6QC7QVx9NgXqCwG3iK11vYuy6n+tyu12EnoRvOr8n7C+OrPnldx91rPVGDLIMpZpwHu/pDXucWFlh1OtRZJrCOkxTkRVnsWaXwbknufj4ec6VC/Iso6HG2h4XVp4i53WUKehtnOTk1lnGzTZf/6N95pOSe7sz9g5mVJUDZdlcXaesAjgLeiPcZGOF3cM5tdU4cdjaMpstQMGtLGM4MKyvDdmZfIf/3btvoRYLnrhwjsOqol7MkymA98Ks9EwmMw4OSzKtWCnylHfFoD9i6i1reUExGMZJjoRQolc/RDQ8UOh2TSXtQzxcZHEh4lcfUW042ZJKOCqUIn/rPc4HotnVDba2eGcpFxOcbbCNpS5DnKivp/imDNSCq4MAVjrcmDY08woV44V1dKqZPEMZQ5aFQGelNU1TkmFBBc0kJnzWx8QLlRQLUdGiOlfcnYuwpIMJqkAZHnV885//Y773w2/w87/+L3Pi136bdRTrq0kp3O+BHBVw9xN3LVMuS848OYFsfLuJBoRtmhCX6z1GBLEWsQ2+qXG2wVuHWIc0DmU9UpfopmK+e49sc43xufNceO5TSyrmQ+RvK9TLOTevvMl0Z5ud29vsLBaUZcloNGBt1Ge42ac3WCXLP7LL4b6ja0YfR5dd9Jn+3TX1u1TEcaGWhiFsoOOyTB/JvHzwUCyFd9Y5flIYjqUD8Dj1dJw5TDBCR85WVPj+cQWSeOWHHsUKvTxHYVEEB+F+7dCZIdPB0yJag2uQ4WOo3pSxGbJz9XVeefcuv/Tf+CI9mVFUitX158gGX6e/dpZLJ09hspyD/QlX7hyyP6torAbxMfJCcWdvRkocbVlKAShY1NL+QZIF60Fqx6S2TCc1t+8c8v76PudPrXHiwtOcdgvu6LeZliV1dG7axlKVNYXWFP0R6mCBw5IVGU1jEfE45ZFejlEh8iFQPHIkcuUnjQ9d1cFRFhw9Xo6alyKypBXER15JOj+PLlPvhcY6vPeU85JqtsA2FeVkB1uXuKbB1iWIoL1FJ/XiPYjgVXehJdjWsnHoLAOtyfKcejQIqNcYTKYBT6Y13gTvsY/QL8X1AS3Hszx6UiBpLgKrfb+444cZ5f4NesWC2b27fP/77zL+8mUOKshMgzYNWWbQRqONptCanoEFwfG2ohTjY8dL+F1EMLYBJ5i9A+z+nLoSqqrBNw7fWKqyoipLbFVj6xpbVfi6wtYVtl6E51E3VGVNU5XYusY1ZaSLGk48eZHTT3+GvozYeu78/QWvLMOZ0mKc3t1lpZhzdmuVu9t3WX1mAFNDnp2kyHJGwyGj4SCEBj2iYjuOQrs/u8i2K5DVsdf9BHcSlIaQyBJWF5QNeAN1HSyHxQLqxoForHORGhDyXNMrwvGKAmoHRR4okUItlYEjoN+u8O/uqOOKBCJIOoZw4YPz8CjR17q/Tq9I/LtCnGevbAI69x6vFZnWKGpk9BjT/T/k/Zf+lNtX32Wv3KJcKJ44tcrYGPZPXuSpT3+eyvSZzjx37txkd1LROImWsGPpUsxwPv2e5sLHm5PlfcdFJ+m9uPbEQ1N7du8dcHgwY3NzzLlTq5x6/Fl6wxWuvn+NynmU92gxOO8R5Tm1tcrh4ZxpAyiLeM+8qVC+aZVrsIwFHZPGHjQ+lF4QBUfFdzfDbDk+KIIS36twzoO1WOtoqhrnHc1iTrOY42yNrUpcU+GtRayNVIKP9EIUuu1kpmvrbJc0yQ6UGESDawziDWQGJEN8CP43Sh1BCJKOlR4SS466i3lbokTkCDp+lFGIhtJz+703aaaOH7z8z+j3JIQiGTCZaRXJyBjqw7tcnd7j8os/y7/x13+bUQwMPbLJBMpaMywL3vrLP+Wtv/w6o4WlWlhcXaMbi24s1lpsE+fbNuAsKkR5I65BeYuWcN8mWjBGBcVnCsOdvRswLZmVlp3yWZ55/tOoTIUVFdeMuJJFNWUwHHD33h1OnDzNwb13qea3ofZ87guf4/orL3Py1El6+Yhhr8+gP2TYH9IbjMh7g4ee2+PC1XTeO74luvQDLNHrcaSb0Kg59r4D9ufw+q2au86yfegwGeztKuYzh5eCsiyDo0dbxgPD6orgEYYrMFc5F8/2+dRQcaaAkV4K9S4CFpZs+/FrTvAgrYX0t+zI3++P2H/aUfbWyQcGVBBq4h37izmiNFrnaBUiAjQaRluUi5qbd/bYmzty3WBnM06ev8z+9Rv88evXGI3XuH3tDje3Z9SN4H2YmyQsj/rdg1W9FKi6gzAlmKsphlRguVWXVKcI2Mpy984+B4dzzp5e5cLJx/n0cJVbt64zdw3ONuxO55xdXycXT17ZoE21ISc6KU22PAkSrkWrRwsZS+ZgAIUhgF5rhTYaFcl6EHREtUk2e0JWkxeDcwpXVlA11GXJ7PAA2zTU0wn1fBpSQesSvIvo2LcnPu7NT5un+wiC1Iky2AuIw3tFbesQHmOSc0hhtMHoQFG46Bm2zoXoCRG6DDQojshW6egeEZR/dKmryGhKYe/ODcrpIf1RH608WufhThM9pDSFQI993tp5h7e3d/lv/uyvcHJ1fNTkFMFUFUPX45U/+D7f+r3/BzJ9EzWrURiMUhgkPPR4M0GweJR4NCby9YR5RIXQP9IaiBu/9Gifcee1HzJrPFXdMLQZj332qfCBPJpbmWeYAcw4dWoATHnsMxeBs5ykBqn57OeeAZ+B7oFOuDElQ388SPd+DqfjrtAkjI5/tkshpCtDYOph0sDO3Zp5BTd2FD+61vDupGFSOxonKNGEZNUQP2p9gyghUw6DR5TgtcINPJfPG+6cFJ5cUzx5tqDQkGcwMDDg6B6AFKUQ7+PYH7tC9bijj2PvP8w4zAYU/SXS9d5zMJ/ivUfnGoPgtQkLpp9Rzfa4sr9gKI7p/B7/1T/6v/H+TDjc3eX2jT3efP8e2/tznNdhD0p3F8bN3eFr2iQUkkAOtoaIoDsBd6rzHVEKwaNEtXJLvKKc1Vy5us1ktsqlM+s8/sSAnd1bTJ1nPpvSO73Bvd0alw0oChf2g8roaU+e56SVo7QO1vhHUGcPFrptcYnOg1SgW4F7dMOnFNJ4lyAaEYWzDi+Wuiopp1NsXdPMDmnmk4D5naVNqUhcjGpvh6TBl3EP0p58mX5HOFZUPC6l3xgH1qG1xvT6IRBdq1g7Apz37f0dDc4JiLa9v/TsY9D5o2GFMLwIUpUsJoeIc8ymYDQxHE/Hawv+7J4yTHev8/b+TX7l87/OwX6FXx0vN5VAXSmKusdbv/9PePn3/0u2JrcoJ3O0JAO4JWKAuPHivIW5961z06e/tZOTsFT4qRcVhZ+w//oPwTVcrRcsFvtcfv55slEPhhpUj+VT7KrQyFaq+NLHsWOo9PEoRnA3uvg4hZCcUl26oGu+d680B3qd7xoP0zm8NIWXbte89+MJdamZ1znzJqfyPZyEfaBjapiIhFBGvfRLioC4IExc7Xh/3rB/pebbQ8fjj+eMB5rTJwpevJBzKVet+mnpDBEcAfW61pw+er8cu5fuvx+FLd/3isEghHiBIOKZz+c0jcfnghQG5wRX1+TKst63FHlBYYWyFt67dovbh4479ya8d/0eBzMbGcR4dUdMNx/3XlxH7X0undmKJU0oKSpDljIi7eVUGUZ1VZGAWM/O3QPqquGJCyfYOvc4J7ZGPHlmi0U5Yzqbs7q6xrnNFawY5os5rprTz/OAeCONZoz5OOiFxKv4+FIfIIqXi7n7X3DOUlULtNI0TY1zFlsusPNDnG0QW6ElhZLdHzW2SFPHs6ikoY9+/jicP/JX8RFFe6SpEGdAG5TJUUqFCYjFNpykDLROhsmR46pYf+HRBS4EgW9tg/PCfD4PmzSuuSOOOwyubHDVlC0GTL79Cm8/8SNevPgrrVxUFRSV8OYfvc0rf/Zn+IMf08wqjOgW0aZfvHhUG5vUeZ4SamkIBiJvLc7SxmKTMqLCVdl6gZaG7R//AGlqlPX01YCLn38OKYG+Qanhh87DEWRz5OFVDz23iU7oRIs/EPV1hW76fkK2huDMKi28d9Xx3vUF31kMeOmWpTzsYcSAMoT09QQfkpIO7Jjy4eZMO+fBMR3CywzSwF6j2Z5Z3tsXxkPF2mhKXa2insoYO9gqQtxxohvS/SRH2wey5jiqVLoRHI+CdL3OGA8MGdAoUDjq+QzB4MRhJBaw8pDphgtbkBN40ul8zp1Dz42be7x7Y59ZFSKSfMJO4qPgYgluJGyKtIZbsKBCHL4IaFFxXaYRCcLkMO/c+9G4pWDCeiccHsx523nsxS1WNgpk7QTPPXGBzxlFPlpla32F6WSf3Xv3WBv02LI133vlbVxZByd8tIb9oyRHSORTU5KC+GSEHdWmYbI6QlKBsw3lbAoiVLNDmnqBb2pspBS0hHhGICY3qOU5iQeME6uMQik56sCS5eclOtrSUEibMSTikZi4Ls7iUOisQOcx7jbTmCygSu98RwAcE7txowSk++BJ/ahDFNT1grqu0OiAHLSKhYVUNPmFpi4RW1OYhq3M8j//V/46X/xbv7Kc/AakFt77s3/B29/8Q/y9t5BJSY5pTdAWJbQrXGL6dbxXIWaJeVJeqrjAg6PUMkClxcrht5519H3J/puvYpynbiwTO+XSc8+RrwzIx/l9dWpX0L7/3uu8994rPHn50zx26RmWIu/h8VgSlGmdHhc4XUTb5Wa75EY3MmHq4Htvef74pQOuHQoTn7GoM6DABjUVkxkkCtoQU26MJzMe7zV4gxaFTzGM3rcUklcZiAEpkAbmE2F+WPF7i31ePRjytZMDvryqOH0iZi4qRRa3SKYC19vdk0nYJl4YjgraR1nBj2WeQc+EKA0VzuqqOUYJqADSNIbGWbRu+PRJxVQ8hzPL7Ynn2o1d3r6+t4w2QIUrkrRGwzE1gd4Kc+oJEUMOrUpyBb3NE2z1FJODXaalp3QFHt0iT+gYwe2/w9MP8lzR1mwA8MJiUvLe1R1QhuHaNk9depynTm2g8px+ZsjRDNfH1KXj4rnTnB4N2S1LxAXB7cUvT/YTxocLXdUVPorkHW8/c+ymWnbBe5xtQARna1xdI7ZBvI2OsaXuSRxNwJJRWmohyWJliAJXR+4knVChfIqaiP+ReBS1dL6l6mDSCpBotvvwWd0xftPpO1z8/WbmI2WefNgwCry3VFVDhgkoKM6oF4XGM3UNM6l4aqXH0ysZf/vf+Ff54t/668uLq0GscPulbd77/r9gcvPPqO4d0GMUaALxrTDRBHStCOvNtTep2sgUUarNJU0bSkRwSTArUBIjpgXAg/fkOHbffo3KOZxryBrh8Rc+g5EMvcKRaA8R4d237zA92OXe3g5f/5P/nMuXRpw5twVcZikuHl7oyrGfx+W+6rySMEqkRk6IJFACexYODXz/dc8//84+V/cHTDGIaLToFNrdxsX6GD+kteKk0VwoHOsrGbu7Ffdczp6CRhQuKtiwLtMr7C+NCsaZG3Jn13PvZZBzMF2teOaJgsvnFUMVYoGTUE3P+Lgi6b66e/VRlu9qL2drlHcSQDy+LJlXlnEOKEW/6JFnGVmmeHpzkz9/3/PGvuX9Wwe8c3Ofsgn853LvdmPfdXx2gfeGtIcbTDbnxFqPUZ4zPrvBqXHG0DzG9p1bvPX+HfbnOU2HUEn5BJIWL8sUaoUPCFpoQZwXz2JacuX9HbJc0zPC4lNnKCeHlGim+3tkpmBWTWE+paorXGNjiUlp98iDxgNXtReJHu3gLBPxoXCHxO4MEou1RE0UctIj32srmmkw691ihtRViCDwYUkIy3AzlfLBjUHnscOE0aA1Knry6Qpd4iQJEYFHhOrCdYl3iK0CyvVd9Kqj2RPSF8OFFuDzgHBUNA87hHjLXavOA3zUVRuH+KQkwDY1EiupKXFo5cl0idML/s5/93/Es67g737tS/C1LwKjpcBthLuvvcY7L/0xe+/+gPmtA1bIwvPygSxoTWyJyiIBLUnCMGpopZZ8V4z0SHZNqkCclFrCEpqAtIy1FH7B/luvgqu52pRMF1OeeP7zjOo1+ieK1oEBjivvfIsbV67w+pX3uLW7jxP4l377Yngej+hAg6Om9k+Ks+0Kp4R+W4ELMIV3SvjH14UffW+bxUGOpRecsBJL+mkfcvclIFAflZTJGp4ZNvzty0OGn9ZUryr+wVsHfNcWWFvg4z5I1coEFQRMS08olMop0MjC8cqVBddHNa/slny1WeVrlxQDloL0fhTJcaHbnYNHQbqZsgzXRkuAJVDOLful59RYk5mcPNPkeYa4EVvDx3nn1j2u3T3gvZu7LKpYFMZLR0BFuZDAk+r8bJVRydZqwcA4PBlnNtYY6EDT9FbGXL7gefv9bfYWefAtJYHdHj+dKdRyablfldBv/LwX5pM5V6/t0M+g3t1lbZSxPhownUzpD/uMM4NznuY4tamOnuu+8/egP6YJSDxm60mUZaTCciEHs8rosClxFluVgZ6oFohNEYeGFJ6VJjVm/6FyQ94PxWxUlgXuVasodEFps0yraVfPkl6QxgWKwFmsOMTZzsQvtaqIC4ibGP4mgWowWYZWCkcS1NLOn1IfNpU//XAiaC/oqkIrhVEwFc+UhmfXhjy7bvjlf/V3+J2/92+xztnOgwFpwgPae2ePW6/+JXd//I+YvHeLnh9GT3kQta0jRbrPKqDVQAnFja6Wx9bxhsP0SjsBStLyDXUBdDIFY7wz3lLg2Hv3Deqmop7X5HXOYy98BvwJ+ifTopzx1S9fYvrkBuqf7nPzrbcp1p9kff0iH1cJ9ySMzLF/dwXPcXM8UQoKuL0P792Gb94WvvHmhGqyRiYGEYUywSLQKiH/6NjyQYmNDWwVJZ+7POTxL2iysUJ+xvAL2Qh5Z8rVasD7pccRU+vbq17SOCGIRwd3hjeUtsc9q7HOsPjhBPwqX3kSVoEhSw47CWDXubfuuB/V8NOOTBvWirUYixvqPDSLmr1aQly8MeQmp9Aed++A3cNT7B9c571rO0zmNiocWiqhdYQli1cCgJPEWSJoaegPe+RGcbB3F91f4cZeyWOnNzjYvcmoP2aMcGZjxryuKF0iwGh/xkrRtAU/j2lgFR9mKmNwuD/j/Zs5RbaBznJOjEErz+bqKrqaskDTy01bI4NosXyYoPhw+00FNsA2MV3XWbyr8b5TRxIwOlXCjYvbe3DuvnwrQvTmBuSq8ywIvTxHZeF3ZTJQOlIMnfCP4/ExSpYIyii0ErxS6KJAuQxsg9C0O6yrmALI87EeAy1/GXFGeFjiPzCJXaT3KMOLw+DY6CmcKzEmY2YMf/vv/Xt8KS/4t37mefjyZ4CV9jsiBA63Ee7ceJe9d77L1Zf+gu3Xb3HS5zgJwjylASsimo2rIcQReLQEIZpKBbbcinTnlBYBR6gcPqMIYYLtMaMuVkJPFIV3lNffx3nNdeco6wPOfebz9N0WW2dGKLXKaOMJhjLhl1/MufXyAbUfo1R+ZNE+qpLrxkB0Ca021bbzSlyuAqjhrV34vXcXvHO1xpY5RooQPxrjvUWHpJW2eL8SrBfyvGajWPArT435yhdyzDhq65Hii1/KebJY5f/5zpRZ1mN3YrFkQNbGmYoI4iJECA8P5WIlP5VxuFAsdhTlD/Y5lHV+/aml0E3CNiVVdNF+VxY86rzmvQFnV05QaEOqn+lqy8RqekWfcT8nyzOM0fzgO3/Bf/H9V3nrxjY706Z1mIULiub+MuZpiTwleXoDzaaUY6Wn8eUMxGLLGTfe+zHj4jJnT2xiJwdc399lMMoY5QsqJ224Y6Aa1XJO2zmIiG+JRAhALF5K49m5d8hoVNAvwOiatR5INeFgViFoipg1qUThW6nwCEg3MvZYK8xnHu+hLhe4eoq3C8SHlDijcjKdhUXuJWSvWYermzabbOl7DMtAZxmmMChjyAdDtMnAZG1UQYC+adI619R6RKKkUEG7AigThL72HlX0gsasSqhKxDmkqRG3rEIvEFJfnUeZDKN1NM9UW4ksALkUykL7s1tx7GGHFofOM9ZWDSd7fV54/DJnvvxVfve/99/nBL0PfF4EnA2PdX5vSnntNd78F/8JN77/NmO/esRptgwHSz9kubgIgtYnj3BaZPFzid/VWkUaIigjJ4R6rdBWVvJC5MQjQnAOpQUjnv3r79C3JbZZoJ1wXr7IVIaMz4JSq7C5wp0f3aV/+tO40tBXgqtBiigwP4Kp9pNG+pbt/LsrdNLTyziKcA8s3Kngu1fhx+/Mqer1oLzi3GVGk2VQR84mhFVClisKFKtFzZeeGvGLX8zZSAKXsFz7A0X+OcPP5SOK9xve0jlXJ47Kx4SHGOGgs8DpJjrHiY+pxQZvoS4H3NuHv/zRPuf1OluXl9efkH0X6R5H9Y86ir7hxOYqRZQPIDR2wX4zpeidQRU9tCnY377Hv3j5PX58Y59bezOsX6JP2nUUV2ycx+BH8J1NH+GDNPiyphaP1wW9oo+2c66+9WPs+YucHng2VjYxAv1sBsfXjiSLLyLornhUsdgVEYEoQrQJnqZuuHPngEGhWRmsMM4V17YPUMowNjkmW6afJKruw8aDay+ksCEPtol1E2xAuuJtSzUoFfO9pYOAYuRDKii8vJxEKQQOV2UZOssxeQ5KI5FLOFqTP35vqSLbyYkSsL1eCFSB1tF0cQ5lA5UQKI4jdxhNCUfL70QP6lJJQId5jz/U0Ut7yKGcI8syKuv59JOf5r/zO3+Dzb/5N7nfYxEBnGCtZ2//Fmy/zY///J/x1jff4oIaBu49OT3jc9eJGYiteMLfpE2gaectvk90bfjIHRkU3tOaTwHwLo/jJDj8AiBZOli1QE8pCueY3byG8Z4bTUW1mLD13BcYlOe4eGkDgHNPn+b56jle+ouXePmP/zHP/vxT3JtppDGcP32agON++qFZVu/qZpF1EV+KjyhYCuEDA7//Fvzpy/eomgHWhfVlCF/2ItS1IJp2nSkNipqCQ754acyvvVhwakWRHV8jCnRP8cLzmsd7BX/0foMqhPfuHWAZ4HUel2FQYkqFpCcVLZAUTaSBsoQ7+/CjNyZcHKzwxNml4O060ZJz7X6I92GHGq/TP3GCPFfoMiVqKEzeZ+XESYp+D+Ucb3z3h7x+44D3bx8wr7uCf6kOkvtM6URjdlVjsqRCYaXesEcmiqkXts5eYJwL08NDJgthdXyCYZFx7+42tUtlxUPlCYkJEarl2JL1mta1opcbvDhsE9VV0AgoFOW8Zu+wYlGuYNZ6KJ1hcGRAv9CthfFR+Fz4EKGbFxlKQV3N2b1zC+cchzs3mB8cUM/3UcqFmqDJTJDQ7gLnYzqvi4Z41CpahXoIWpH1+2T9fiiKnhWgs2Pa6SdojCR423tTbRuSUAsiuY08Sgk6zyjUAPGOBsHrJlAKztFN/gi0R4isUCYPmWuAeL1EutH0/mhT++HDe6HxFtE5veIEm3/jX7vvkSPFhPMg0znF7hV+8PX/K6//8bdZ4UQ0yXxo8hjNszgDyahqvcCQ0phVi2C1UuBttK7Clk3CN8R2hHhIFdFJi/GTklPBglFKtwKicRZBkWnF/M51er7mnrM469lqLHdswemnR2xt9fnK155nUChe+NXLCFc5xSaaNUKliYcTunAU0XZDxtLrfpXC3n0Zvv3tO9TNFiHFlHYpJvNUmUTbhCNaoJIFz14e8msv9jm3qtr6uR+4JgVZoVh9Cr5QFAzvwkamefuuY8/lLHwoTJS+7B1tlEOegfOGLA9roSwL3psP+PYe5CvwxHh5j4kqsZ15SIc9Dmd+2pFtbFKMM/qZDum+onjimaf53a98icH5c6A9h1ff5gfvXOfa3X32JnVoGZRwbmfv0kYWRXssUXyKNgY3qHqDdYLzgjMaL468N2awZlgrRhiBK9feZVF5ZlaioNVLzliE5bTGGVCCyQ0bmyMe21rjcG/C1du7WLe0DkUE18DO7pxbmwvW1zO2xgUrxqAbx2g4QMdjJdn0YZFNDxS6vV74c13O2L51HWcbDrZvMp8eIK5G49GZDptWQiiWi/n8KhWsaSdZBfO/l6O1JhsMyPujsMmN4Uh0cXq1gnfpUV+aBcEUCFo8xJ868dEUE4z2cYEbTK9ohawzGdKE+g+tHFLxeK4BCWnOmMAvuuhlTkA7bDZ5pEWbRl03GA0eS68Q5A/+DH77l5f3mOJqJWTYTaZ7DKbbfO+/+if84Pe/xQWG7ZxJ/IwgaNWJU5QgLL0k7lWWDrAoeL0PYUoaASXYTgx0KI2po+m6TNFsnRMt+u8Whk+fEXIRMi/M79zGOOFeXeLrOXW14NBtcenSaYbDPl/7lZ8FZigukDHgaPLrw42usDm2klq+dxkhDrwGb3/rLuWsAK+OLMHEJqX0cUOI5lE0OD1l64kBX/rKkCc2FOMPu+woeD91CS5uwIU1w0CXvHRrRukGIL3QcTt9PsaFexccnpUNG9c6zQ9uTylP5Hzmif6R+04bO+X0daMaUpTDQw9pqKo5lUvgR6PWTrB1chNpSvA11199jTeub3Nre0btErBJtEF6xR2lkrV1THi1FIPCUbC7f0jPhBKvO65m5Ykn2No8yWz/Hm+/e5W5UxQYrCtaajBNSFCey30rGgajHhfObnLmxIhCwyjXHE7mbE/KaEn7FkRU84q7u3MunBxx+ewKF1d7zKZT1oYDMq2obIjfT3vsQeNDMtIiPnAWLxXehl5EKcMrQXDiDZHCylKhmjRiMLjSOlAKxqCUaZMiJEL+NmJAYMn+HIUMx28neI1DbU3rHU5cQF46IF1l8tiXSsVWQx7vo5DX3UDmJOzjw4kenSXRkAQ9HZrh0UaWF4SydQ2ytcLuySe4/S3hrrpN39zlqy9+Dq8CdrfTOYPZHt/9vf+UV//gD1mTE7GsnI2cY0oeWd6PxIt0SXDEBZ/iI8N3UjB0skmStRDRhl5m+iwDyDSp71xKq3QQ89uj9RBjVUU8VsCIZ7F9C+MW3LM1lYKN4lOcPrnC1JesjlZZGY3gA7XTHm50zWuO/Q5LoZtogzfeEf70O9u8MtukdsmJGv7m6aSES/BZKA1iNDOxXD5X8Ls/M+SLW4r+T0C4H7g+BcpAbwOeGWnW1lc4890Zr9xQvOMNB65DE2UK58B6jzG6Pb6hR+Yybl455J+f6mGehQtKsd655xTV0E2o9sf+/dMOsSXK5yGDEkB5yoNtdmYNa8M+9f6E77/2Pu9vzziYWSQxX6KWiFNFciFaSUEoJ3O/KzsCveW9wTKgKIRxH7KiT9ZbYbJzh2t3b+PVkNxo9qYNjdctqm4Ft6Rzakyh2TyxymNn1tkYZZEegv4w4+ypVaalpWxckG1tZI7nYG/Gnb0VSgvDlVXGmWI4KEJZBBI8XmYk/qTx4JCxeg6AVxVKLxDnMNKQqeiEUaGln/ICPtTBVbYJ1EK76VUQskZjioJiMEZlGU5nVDHf2tVN21cohablmQ4hKWqZhRbnIJjO8ebqumFRVnjvsS6kGyslGBOQbj8fMCiGaKXJ8h553qOpy1Ak3btQMzZVIHMOvEJphzahPZBRGmWCmeJdJ2P/Y4C6XixaabxX/Cd/8Ef8Z3/4bWy2wdrZp3n63Kf46osv4ETYOZgxntV86x/+Pq/+/j/mvEthcuHlIwmg1LJXatqwKWlZiQ8PW0X0S1jM6Jj4gCFl2wkuKL4UF60J3Qi8xfnY/A8TCgUpKHo5vV4fUxhMYUBn5CbHNzXe1pTzGcxnFN4w396lr4bcff11zjxzicW8Yl4U5HbByjJC9mMdXUSb8FUSuml/v3TjgL9wfe76SMuoZNVASh6RlscC6y1eLxicVnzlK6v84mnF6Z/20lWIgBz1FU+eh8t6xJlvT9i+scdhM8SJQdDYJioxo8kyhYvx6Aroo7BTzT/67ja7+Tp/56mc9Xgd6R49y+w8eHShq7J1ehunWD0x5tRoldWzq/zMFy8zu3ebG3PNnSvv8MMb+yxE0+9r5nUsz5gsLHwbWdB1zSRFl+AOCvqDPis9xcFBSeV6zBc1ojUnx0PKw30W8wlG9Zh7zWxiKV3RCr1W8MbJVloxGPc5d3aDUxtDBrmKVmGIoMDAxuaYE/sLbu9MQgJQCzgU5aJm52DG7Zmlf/ZxTjb79HoDsjxDe99aRvqRkK6r48Qo8Do8aHEhRZVlEHdAuALOgXeoiJBT7F2oVaiDw6zoo02Gk8BdeS/Ypit0Q9CEVnkbC6paeCnxdKqNcWycp6wrvHNYW+Fcg1IeHekF5TWZKjAmC73ssyzUHsgM4gjRFa2XIaBd5ZeOp+BEiuZRgo4ds+WRhnhsLOyemQKjptCvmNRP8eLP/V0qEdR8yuhwynd//5/yvX/4+6xbCa27JRArXglehSadYQEHEZoMEY/GK8FpRWk0jRIaFZw8mdJRIRYUtaNoKmaNo1GKYlTQG47onTrN6NRJ7GyKmx9Sbd9msTMhEzB4mtGA0aXLXHzuM4zPnCDvKbLeCLuoseWCvfevsH/tCvM7N1ncmaCcYbG7BxtnsI2FyjI/3Gft9CYhNO7Ro0IgPNJusoBw1KxWnZ/iYaDXqBZTvFdt1AAQHYkENGPAuZA4UxrYOqH43S+v8fMXAj5/mOXQjcxR5xTPfmmVv+YP+d5txRuNYe4DpSBKoY3gQnUbtA51HKwTxK/CouH2W1MONjZg6+g5uvRKQr+PVHvB5aw8/SX+l//Bf0CWbzEY9chCQDGuKXnr7Rvc2FlwZ7+kTIoqiVKVzO/lvk58bro+UaE29sbWKhdPrVHu73OwvwA0XvWZzR11ecA9fQhorHc00kMoSNFRwbWRaDTIeoYTJ9d47NQq42FGpuPaiNenTbCGi57m9NYKe4cl8yaUmW0Thpzj8GDK3d0pKss5Md7i8XPnOHP2KvnejLpuqOuK0j5C7YUsdoJNmWDOeXTWQBPMbx15WOWDoErwKsmmRCtok2GKEBLmUDgRysayaALK9E3diZWNCMw7nDNkxlAURThXywcJ1jqs8zS2jpSHJ9eKXOcEgzDSDF6o6hpjHHmeoTABwWYFSjlwHpf0fgIyIiE6Q6WsOBPM7uXaaJfMIw0VvN/5oM8w7+PrBWLG/OJvfoWf+XlhbwYnypJ3/vyPeekf/d85bStUI5Eu8KEimVEMhisMhqs0zRyxc+rFjGYxpShGYDJOnj7D6PQpbK+P7fXI19bAWoZFj6Lo0zQNq2XJy1//QxZ1xclLL7B2+ikuffoST3zl81wvS8xizvbVt5hefZvJlSvsXLlCzxUsRmdYe+GrPP7rv85BbtB5RuM9mQj2YMrZx56neO0ltl97Cb94H78/oyeeRVPTVDWqcQxFQXM8suTRhmdJH6TY1eNOM0XIyjt8W7j9xoR6ESwric7VgCM82miMkriRBaVKehuK5z6zxs+dhmePnzuhNXX0fLC01NL51TFJfeYx+Jtuld639tjeNdh6SC2hl5Jtgh2jVViajQ0Wpwio0mMWgvJHY5LTz5Qm3K3F8LBD56A3t/j06hrShDrMYgpU5lnceJ/d/VAkfH/7MMTLJuQpqf9zEoa6tTTSXIk2DMd9zp9d5/TmCoU0XL1dBaDUzqbB+Yx5EuiqiBRbsERFpUpiIZFlPO5z/uwGJzdG9DKFVg7pZMVK9G2IE8p5yfb+tG12GZ9aS5nO5g0724f8+MdvcersBnf39hn3+8hYmM0WTL2lcY8gdPv9QZiuyMNa61hUgraxbY5rIKJfbIfnPVIeIdAKeb+P1xm1aJyDyaJkMpuFaIE2/Iy2zk2VG7JM0yt6wcTvZKiJ95RlSdk0SFPjmxKFMOj16BUZzgtNbHZpnaOqp2RZRtbLIQuFhrPeIKQIW4tTdYfbDQJfbBVajuhUDyIa7irFpX4cI0yUbxylq8lMga0N03tvMT98m0V/g/3tu7z0z/8Y6n3EepCAXOs8o8k1lz7/GS59/itUpdArPHvvv8HetXfJ9u4w257w2V/4ZT77C7+EHY2xoxWszrDa0M9z+lkfXTdQl7z1T/8JeaEZ9cdsXHqOT734N3jixafB7XLJOypr2Th9lrura9zNchaTPdiuEcbkxToiPXr5GHc4RR8e4MVRUODZ4MSnv8hiusfB9iH1dI6xDudCQXsRjRMoq4evKHa/0Q0T64ZLJZspIb2bd+BPfrTDjydjKp/j2kicsCRTim9jI9eohZWB52vnR/z8Bpw6nrEscKsSbmVw2sB5pY4IOY/w3kxY9OGc0pw44smL4wl40W/Adw741jb80EHlo9CP12djZwVi8hBOs70nvLTt6Z9QnDeKIUfD4hQfVAIPNRZTpJrgakumYt1cmYNW3Lpylav3SvYOa5yDbrZLisoE3YZwdRN4TJGzubXGY2fXWRvmaBHKuWdaOlz7xGKYmSy/m9AsHbGMFkyRcfLkKhdOrbMy0DFJQuPFBFpBKVBZoA4rz/bOlOt39jicVTETUyGi0S3HbLClZ3K44L0b26hbN/i9P/0mt6eLUAwsOdL8UlzfbzxQ6KbOo0HYGbRZol7aWFbVOp8+4DyLP5MDzauAcl1EEC52idAS3TJRoSgVQmKwnsxksQvwsqSiAM6HjdsmX6iQFZeZDKU8TiQWbvEhUsErvISwKg1obVCEa+suxPYMsWh7onWOoJXwfD8W9tF7j28qahpMngdP6MEBi8M59sQa+/fu8MSpU/zwlR9hMHgFWdajGq3wld/6TV74rb/G3azHSZMzOzhg9enPMPrxS9z+8fdA36NZP8c7TcGpM0+zGzW7MjkznVNXC8auYffdN7F+SuM8w5OPc/qZp3niK8/w3v779Mt7vP+DV1l57DKsjXn8+S8wm04YXXuPxd5tQuvqUA+5Wuyz++YPOHj9x1y9cZ1LT7/AxuXnGZ5dZf3Jp5lu77F7+y7eSuDUbdNSLFWdgps+ntHt/pDG/YTuTgWvlZr3LaFQimpBzbEhiPJkmeP0OvzcpuMX17MQZJGepYCv4c27nm/4kl8+P+BcdnSheA8/uD7hjUXNb18+yagPJmAB6FzvmSfgt+8OODx0vLoQGpURKyLFDyq0Dg0ZFWCU5mAG//S1A9TKmN+6kDNS9y9x2T3PQ43JPq6cBX96noU9kmdIabl+Y5srtycczGP7npaOk3TZLW8rCZ0BGM3mqQ2eurDOoIix+kpwjaOqbWdfLuviJr9RELzh4MFBqRmvjTh/dpOtlYLM6EA3ohMbipOYBu+Exazixu1d7u7GSIuWOpUocONZlOCcZzKt2dmfcnkDjA3H8Mk0+ggW8AOFrs5iGEoSshLj8lTivWINA9+JaJDl5CoTnFA6y1BZhndCWZWBx2wqMrForRn2h2SxHW2Q3Z66qbDW0jgbKvk4R1YUmCIIT2stTd2EEm9Fn0wrBoMhg0Ef5wVtbYjpWwST2wO2sTS6IteGPMshJmiEFszEEpCEvHefUEFw5IXIMb8kBz8m+eCdo6kDleG8xeGYHhxiawHn0Vq4u30vLFAUtVK4fMCXfutv8oW/8dso7TmnFdV8iqGPjFd57IsjGlvTVO+Q2YKNQ8f1b77CXVex8cQFNh5/Cr83Z//tN7l17R2q66/gdt+lcZ5nXvxlnv7FX6Au7zCe3uSHf/r73Hz1KmvPvsjm889zerzK6sVL7Ly+RZXdw7kaLR7thLo64N473+e9P/tnFKMx7x9O6a2dYv2pM8h4BbI8LHYIYYU2NCa1IuQfDwY7MpKwgaMorz2Lh8MDx/5MUUmwapylDX7zXQmlFMYI50eGz570nL+Y0YYJxDEVuO6EH257rlULpmt95NhnlFJsMOTeyzf4o8kGbz1m+NQKXN5QDEyn1E8NrBeosUNVDWKzpRlI2CfGgHYJ+mjQYxwZkznUDQHexsvPWKYFd+flYYZfLBAriFZ4b4EcYzR2OmVnZxejQmuusHtCcZmA0qPd0UH/bbq6MYzGQ4oslouMqLFclFi3fBBd19jyCBHxKsj7OadOrXLu1DqjnmpRqkrFxRNhL+Cqhu3tQ67fnXC4aNqaEB2escM000azzRcNB5M5zcaYwmRdj1N7nQ8aD85Iy2IqavLgeY1WOqaYCl4cIjbwsbHdThsLpxVkMUQsM5BlONdQVlUInLcVBkuhc1YGfXq9PqljgXOOg4mjamoaZ6nrGmccYjSKvBW6tmnIi5xe0SMzhv5gyGAwwHoBGxGUdaDK4HxzFtuAyQuU6YWIBW3wJguIM/UIE1DBgU/Ew2EhtLt2aTI9yhAf0jvF29hpSNN4TzVf4JqAAo1RnDp9mlu8iohgteb8p57m+V/9Ja5ry2lf8dLv/x6379zg1NmnufD8l1FrQ04+8yUWh5bXvvPnXP/Gn3DTCqdf+CJ5kcN4yMZ0j8mr3+KNP/8zNpoplZsxGJ9Dr21xV3lOVPu8+xd/xmtf/zNOFWu889J3qVbGPPnEJWR1leGJDfZVuAdpGrRArjLWdY+zFOzcPWB8ZovF7j5lXbE+HpD1i6XOcg5XltFE1qgWUXx8QzqvDwhcARawf3vOZOFpohvduxBRorVuPfzRckWrmtNDzVeeHnD+9AfPs1PDH75e8/Uf7jM+l9HrqQ+sEq3gsQ3FuCn45usTfjhd5WdPgFzUPLGlGOWRd+0BqzBxi4hn8livRGF9iK3Gh6iaLFJgTQO7+wsWpcaYZVJJQvbCMmrhUWba2YbMFLEcbY0ygljFbDbl7sGCaVUHTjROzNFZODojITpJyPKMQT8LwjHxqd4zW5Q0bczjUvAFJEwEv6Eq4erGCo+fXWdzpQh1MdI+jXHsIWDCgxMmk5Ibt3bZ2Z+HGFuilyaZOF3zttV1AWPXtWU6L5k3vaD5REWqhI6g/snjQ9OA4zlJTjFiKxk6EB9SCmjKLkkIUbcxm+kQRhO8lcaAysmyPKJN3d7vUiuG73kJBVqWgfnHrpMlmyFphtJpO56K1AUDUp63autLLLPqPKkliJL7nU1IsRuPOtri8CI45xAV5mCxKKkbixNhaBTTvd2Q1uoFW2Sc+dTTqF5OoYX3f/Rdvvt7/zlrmyvMrlxnY3WLCye/TK3XGGQ55fQudu8AMxyx+9ZrnPzUZznYu82FsaNZ3MNIiW8cQp+it4qoHqGr6QJbL+iT0eztk6seRVnSzzLUYIWsyEIcrgt8vvMerzNE5WjdR3mLLx3eCpVt0FrIA5gEFXvTRSVnjELrhL8evawjHI1aSCvgOMplIgztkExnpPoKWRacxz4++6XAFjJtWd0YsH7K4TrX6YCDBXz/9YYfvXyIa9bxVUU1BznWW1MEFlaRjTbQO6vcuVrx0mHGwbThi5dznj9vOFVA4YAhFCs99O6CvtEYrbAqChkdLjKs4WBsKjIoaw7v1ZSzYShB1rnvNMOJ533oETlURajHIkqhG0+9KDlY1HigyDNK23QQYJKWSZgt948oRdErGBQxIUSFZ+EaR7lwS4ujtaIVKWVdaU0x6HHm9BrnT64x6hmMsrjY6SZ0TArf0x6ayrG9fcC12/tM5wHspIuUNoeTKKF9e/0plVjE45ylrBpKS9tpvBukcSTO+D7jQ+Z+ycuGl6BUSm5IBc1DhfwkzNJcahVKJSoTogUcIdB+WBi8V+jY2M5oE2I8TVj4mhQnm8cGjeB8A1gy6S21p7TGf4eU93iJDyk6PUK4Twps9xGQx467RHohy0O8LuG7SsIDShWUaAW1RK3XFod75JGCxb0XtLY0tsTaBY1tsB4y02eY92JVfo3N+syzAl0U1IsJ1155mTNes/POTU6cGjK5ept39Pd49txJFod71NOSvmj8omYTQ1ZaTp46z9jvM+4Ng8MpwoCQFhz4Ka01vSxnoDRjUczLhhWBnjHMlaLoBQcnzuGqCucszmjGq+vsKFC+QtkSbyt0lpPRJ9c5mTaIREda0yAiGKUDvfQx0wtp3PeoAtWBZbFjsY0ihWAfL2KXlrQS8E64Oyl5b6/PqdFS6M4W8L3XG775ozl7h0Xo2Dur6OWjD5xbKdhY0ZxcG9LbBmrNlXsNO1PH7sxT+x4vPmY4UUA+hNpXIA6jhFwfU/US1o/1sSCPgLLhdT9M0LUlHmX96szgfBVCF7WmyHI0QjWbMVtUeDJOjYbMy0NqgeT/CfMoy8yzOCECFP2cPAtO+bAcNbZpWJR1dGotk31SqJnJMlbXV7lwdp0TawWZUUtFQEr9jXdqhcPDOTdu7XH3IFAW0tbR1cvjp4cUF0OAkf7oenBQ10LtNXmRR8GcKsMtweFPGg8Wup3A4HTAgAp1e2Hpf8kul/j59LlQdHyJdHOjEU3khjVaG4yOXWhjHppXhDTIyAGJeLxXHG2T08kV69TM9RLzrZJCjYg25V8nqZ2SLpROCQCdEBGJ9xRU3LKADxzZwY8qIlRCVzHCw7k5m6tFEFZNE6vb9SAPplymDUoyiqygqSpKW+KMZkKGNmPKvTkHb1xheHCH11+Z8fY3v0XfCgoDotEqJy+GnDh1Htmu0f1RSHAgOGK0CE1dhdtWKlBCEFugGDICny8p4UVrpAkNCL116J7hznTGtlEcDHMOXcVT/ZwiK1AlZE1IhHGisDakizvnEZ1FpPDxCd0utXA8MQIAB3sHJfsLjaePVhqXUE5SriqGLxJ5XoHDQcH0XMEBsAmwgGtv1Pz5yzOuHA6ofbAAbGVpSpDV488cenPH+u091jjNHXKU71GWnndvV1RuwZ4b8PNPGC4KDIoeGkvpQJQPCUXEYk46ruG42IPO1EwOLNOpg7WgGLrJEEm1PQqnq/A05ZyFFVyeMxRNv9/HuxA3vL834eZuiUOjVWwBheootVTXNkSJaGMYDXtksd4JOoR/lWWIeU27W8UNLUrRGxacObPJua0VCkNo+56OGR90QKYOW3nubR9w484Bk0WDiO6E1ak2+zQkCKWnFIvlpM8oFWkED95jm9DxOS9SlYugSBKIetD4SFaGKNVqBaVDCq9SscByAoNLI2JJR+jYBDI64rQ2mCwPgiwJ5RjRgPcxIiGk9VprQ6aYUSFxwCi0Tpeb0G2C/MR2Ga2cvO9SaZM4jgRLHqUY0vS3ZV8kVXdY0i0fYj185KG0CmmdDvJej/HKGJ01ZHjENihUQPxFPygH5zE4JvuHNDVkeY/VE6c4/eQlbr7yCgOZcvjWNzl4o8b5ip5PCyUotKauKXoFlW0Y5jnkof6uRpHpgFptYxFlEN3DFP3geFGCwuLrGk2GJ3D1WZYhCxfqJjtBK0+x3mPruc9w7dU3+dlf+jWe/MqLOJWjbI9779/G1w1eFThrEetxjiB0P6YgvA/M8bHf078bBfec424t1G6JotJI68ioWCCIIFDuVJYbDXy2gKqEW281/ODlkqv7GROJMRNxPefZ0fOnU2wWisu54UekzMAQ1TCpMt68bdmuSnTTx40N9yYO6w2ND84xz/IUbRYuhPWrg6VY1YpJLZS0vrT2c8vAq4cfrim5++Z3+V//p7/PoR+xvjpibW2NLB9wZxbLp6YCNlHYJqtRJGYlpDhaQGc5g35OFoFcimQqFzHmVSmQcOU6y1jbWOGxcxusj3M0kaZSGV4cSmUBofoQdzs5nHHj9gG7hyW1gxQxsRSuCUW3wWtHZkepTgEblWSL0HihFkXWC40PvPiPrMk+gtBNUlwT+pYZtMnQ2oS5S+Zx/HS7eHXKdopFyVWofWByvfxc1NiN8zgJHs+qboJHv2mC995k5HmPLDOBb4sEt4pRFBCrYYkcEbzLLZaWWpw56VYBiuhdhyiLxKKEbtlCKvmtxAUqQpmlhvwYBK/4oPGVDpr1zNlzbG0OuP7+JIRTqVCXPyt68dI9xtb4/UP2btxl/NRp3MVLTPZ3OTMyLG7coj6c0EwcxmahB0MbKyT4pqGpSnws/hP49AzB4rwPYXyNJVehsHYS9kYJ4hts01DVDoY6ZPTlhsrXVM7TCDjlWXlsC2WGfPbC83z2l36BgVEUusdr3/kx29duYyJnjoCzoWGj0oZQe6M1rh55dJ9+8tqnjDQFNAJ7vR53c8VCumb70i8BIQMtS9am1njJKecw8HD37Ybff3nODw9y9mWAS3ye0m2jwvsNI3CmlzPSKgbERMLKa2yVcff2jH9azXlzLePmNKMSgilvlhacQrVWr0oaQsKxZlZx08FF4CTLimOOpUh5FBVnTE6mx7zzzm3e2q2iOa9YO/ckn3rycpzvFNgl8aRxcwktNZgszjw39HvBmaojDreNZbGIFAagjKY37HH6zCZnN0cMC4MxGi+KLKJ9F6k/RGjKhrt39rixPaGsJFY5U0cuhURBtHK2wyVB/FDXwXZsbaAYFHlLOyYo/2FL+KNxukd+qiPvdD/5gYZsx3ZQq11kafonlOq9xzmHcza03On0NlMd512ahi7y7NqSraVwv/UuHHHGHeVwuncqyy8kzdu571Zx3+cUP81IHLaOXTKGwzM8+6kvsH/rG3gbWsKjPNoEpadwGFszvXmN/evXmPUUZx9/AVk7hdq/RXnzFvNbtzm4+h77V6+gpyXVrMTEEoXKWZpqERuGBq5bG42T2EHCe1zT4OoaUPhEvXgJTl/rqKoSRjk6D9xlI0JpaxwezICVC8+zdmHAOXocYFhTOW/80e/x7nf/BLU4IBfTJtR424R+doQgdLEOlX88jjQ4Kni76hdCZ4VdJ+z5UDmslQ1dJkktQYRAQFElTO7AKwvH5MeWH27nXPfFMhRLiE4gj7P3XyNKK1Z6ObkB7YKDtI3vFxDpc3PXcnM7HlTlgR6IGl9HDOG9xI683esONY4bv6y3cBy/dTPWHmaIZBSjEeNe7DlIjDrykcZI+0wdF+0RYLSCKUi/ol/Qy0IXDi2CU+CsZb6woa5IrtnYWuP86VVWRxmZViFpRSl0G+sb2gZ56zjYnXL95jY7kyrWQ9ZLpN3eQypmFTGuCscIil+1skR1F4/odnGoGDa7MujF4qfx2aoYWfKA8eDohY45DzqY2pLMhIgYP+B56IiqZR+YeKMEOypedLrlxtY01tFUFdViEbz6bnmOREXo9uVJU6aisEgrdtl1NkhFTWQPuprsSAhJe7fLX1up6lt6gXRP6ZyPtGzTKSNtIyHFOsu3+NZLd6jzLaa1Cwg+A7LQrF4DuVj2b17n1is/5ELeY86ICxcvwNoa+vFPM7l9m+mlS9z84be48YNXcVWNjvVFrXNY27RIV6UeVxLEv/dCXVXYuoEsONNEBVRonYDz1FUNqiDV3VXeY2yD2BqtNuhZRdFYmG6jppZXf/gmb33zD9Hl+0hZoX2BV4G2Uc6GIkmicDagmyIvHjxnH3EkZKs6P488MQ06D/6FZUWxwBt203hFaDv+agzqQHjnO1NulJ56YjiwBY0QinBroj8iUDYu9UU/vlQyheoXNL4BydoNLtHUEjROclTywOsgqIXQzTm0pQo3mYxjH93wGtDO0+zWqJlCdxx+XZvvkVavzjDDIesDk7Bj2MuxxY6JiUcimpQtFjz6S6HVXpNSDIcFvcyETNB4vLqqmFtPf3XAuTObnNwYUGTB90A8lyLQCOE5CfW85NbtPW7enTCrXMfipYuwOoLzKLwKmXJdOqFzrXHSlFKYTGG0oDPFYBSpP+/aBIkPA2MfEjJ2tDyIOi6YSOX9VLtCJd1N53MiqmMCdaF7eM9ZS9M0NHVFU87jg0qOtHC3QehGZNiK3HSOWLWoc7eqvbol99V+PIVAdENQlgc78uODMLo1MvhwQ+LBI2jF2HfOO77z3T9F91bZOHWGWbPAeRs6amQZwQPsyUVBM+fmy9+mme6xc/lT9N4/yfj8SfTGFufOP4U2GRezjHrScOXw+xTOBqMtxjd7FwzNqqrRhILbmhDoXlczmrpEZTlK6eBFhhCdUNc0dUNdN2gd2xt5i6orxDY0dkY2vcdb33uJW9/7Hvm9KbPtGbmf4H2J8YYUKK/F4asFygfu3jrHdDplc/jwRcsfNI7ns2gH/dKy4jS5iu1y4t+6e27ZlUkh5NQzz72FQpTEdFId42fDOoqhtJgspyx/8vrQwxyduaDROkushaQJYqvlVlGQyoGgPBE1LkEO0VTWWjMYhPbncPTwx+/xYcb+fIrXwolRgVbzVnqJbRA8xoRMrpBjGLuDpwiEbpsr8ShtGPQzMhX2U8pdaATWT61x8sRa6Lmml2n4qqVSgqO9aYTJ3oRrN3fYm5Sx9oFqpzL5kMKv0k5ooEBMK8WS4mvDxpKDXmjrzPRzzRPnTzBcHTMeDdnYWKXQmjLWDk9O/AeNBwrd2sYDeR/iMK2jqatYzSs1puzcDMlEO6bN2imI2jwJXxXezYwJYVxFjnJ9xIfOqi4uPBuvQ5uM1JLoCK6W8ACPLqWlk6IVjtKJLBYV678ui1UfoQy6CRAdOdw+RElxyg8/NAQnlYYiN2hTkWUH+KYHYhEcaI3OczAmcNYI0szALbj9+pSdm1fJ19dYv3QZtX6K+pkXuPDpx9EibD79aa6//SZU+yDBdKvKCusFpzXjzRNYbUITQ/EYcRjlsVWF62c4bUKvOAl/r6LQdXWgJ8QYRHwoCl9XiJtTHV7nje/+MXLnNvrujEyi+SVZnEMfqQyHryukqVEiWCesrG880nx2RxfZ3m8PDDI45WvOuJwrCDOWnF+3bnKbyRStASehtmsSbrEWTcsRagmo04lhOv8AsAsjV/ihULoakWy5jySWjxQhdT1I5JZ4iQJXLymyjgRNuEEpwYmDvkBPt2s22WvHhe/DjHJWUqiczfEIxUEr3Jxr8N7F2FUJsfWJM2ltNYXCxYp4ClMUDAY9jA5HSd1KVtdWWF1XGKNiQ1BQsfixioVyxDsWswW3bu9ya3tGVce1FRFr3KzxqqMUard0NwQt8UfSfk4r3yoyhYrlA4Taet7f3efS6oDxaMhpu0qmOuAT+VBH+wOF7mRRgoCLm8o7SzWf0tQlNLFASQylSOs0rABIGdKqe9OK2ComboTY72lQ5PTzDF/k+H4f54TZoqSsQ+WpxWIRSutpQ6/oxUUpUaAuaYhQC9YnbEwI8lcYFauESfB0eq9woV5ekNWdtqldhAxEBK3bzgvLVmSPJnCByJcGxJJlOVmeY7KCfj5Cxy6xSgumyNFZXCii0CIYceBK1M5t9O49rl19j43LT/L2fI/8zG/y5OkzyOYm2eoIf28vOB1dRVnusainuEFGjcdqReWDh9hZS1PVIfbW+8DpGo1IqMpUVQtsU4IfIJF7A0E5j6sdipx6UnF+7RRvvPI2mxL6ysTaTyE7UZb1fWksYsGonNpXTOcl6zHu8VHHcWb4OL2QNbDVKE4pKKLQa1dqBxcs4VJatKlCljpCYSQAJTGkzjnNbH7/VaIANVZkgxxV+ZhWH8PAVGxM6UPCA3DE66WgzQZO17l0AAeUNhxkrPdC/4371Z+43/z8NKNQwswa7HCDfLSHKgpWhyucXN9ipdDUvaxN3gjoO9mlwjJzK1xY1s/pF7HiVxSAihBGlm4yMxJZySWNIdaxtzPh/Ru77M2q5X5u6YNonUuylJOSSvG0yyqJyVKPYp8QKRG/33IQKZ4+dOzIi5zRoM9KPYoNVGO0r4D/ENnwQKHbWBuFboOtQwlFa+tQs7Ztd5OkPO1/H7hp1FGqRBEKZENasBpnPGXdoHVILXbet9lb3XO0Hsju/xKCbhMZEvVBJM87ZHr3pxy9k+4VShLFnS+kffhoQxEy/AyYHirrY73C5CMWpUNhcGIxRY7S4d4dGqcUCyWUCobiGHqH8cLOmz/i3Pk1pnv3aNY2GG1uQKYQCYX9vKvoZxapZqjBCg0x1lMCl+acoy4rtHM476i1whqNKKERz7wug8NMPJnWZFqBOJzzNLUnIyPXfeq5w9BvLYtQqS04Rlu+1HuU9YgFVIYTjS56LLfLo41UvLsrGI+MAk6vFZwtIJ81KJ+D0TEQH5YelOS7EMQnpBQ4BIHYLl3So4yRKCBesb+3QGT0wbPn4AeeWTlFqz5eq9aHAbS1okWBFmIYpScrTES1qo0dVwkEaE18ihTGckILqywdh90MvVTi8WFHOZ8xL4UXf+Y3ePJ5y4SSXq9gVWtefecaP5iU5AYaF2Nr0zNvTfxIZynFoJ/TzyJHHRYLoiSWeg20oheJzsMwL+Vswc2b+9zePlx2eGjt6SDkZbmpowb18Vjx/djqq43E1RpERVAo7UVLkiXoMOda0csz+rkmV4phfxSKbFG12W0ftn4fKHQPdncAQaxDGhsQj6vaAjcpxTd0dEiLlTixPlSfUiwrqbcyrXNZaplSq4xCKYP2nl5ehGaMzlHbOqDZjpdXGxW8+qiQ8hdzvZeIpkNASLi+rhHQbilPPG6CNt0RlIDCtIL5COh5xBH3cijGU9fUXsjynMPpAWU1x3uLGAmlNXXgDwXDcOMU2coWTz/3FP7ONQ7efhMzO2DFNxzevMrj+FBrWOeofBikQbBRqeYzjFNAH2+KGNsZurmKQFNW1NMFbm0Vejkut/RHDc10j/FagUfjXEah12nK4BBrmgbbWDLvKYymUJCKa4VicWF+jV42w1TiMa5BXIN3NR5L5RpGFB9btp/ig8KmfW4WdK+PWQUOapSX0K/OK7SJpUSiIDAanDv6xFOvUhW1f0KbaQV5r6h0n21CXZxhOne8KLOmWVkfo+82HStrmX4e6NqOkNImCqvOPXVO2BpfShgMYNQ3rWBdGs3L+09dkh9m/Gd/8i+Q0jKxntw27E0mNJJRaEXpFUV/QD8zoV522iyp8AzBJ4MEqmQ4KMh13IsqdBT3ouJaV6ErDaEyoW88+zuHXL29y2TWhCxCYvH+VJ6wM2etayn+9OlaWqgt7eeU+OjU9hExx88IIaS0/WDou6g02HKBUYoiy5ZNYQnp/Q8aD5z3/d274Zo9KBeWRGZsaProfdSwKZo1jDawS0L5Pq2gTRHu4pi0AlsBFqmAyOH08lCOsVY1dR2KGPtUKF0kVM7PFKGUr8eQOn+mda3aViDLTqCyvA5JCk2WQjdNdDqKUgRMkMUH1CmB8nFIXdL5PY3zoeqWrVFiqKo5VkKRHzE6FoXR1GjWNi9w8Qu/xBNf+wzvfeOPuHfnNoeLQzJvOXNuC6sEUQatc+pG0Oi2rGW/KFBOcXjY4E0Pp0K7HhFQorDzEpmVuFrI+jknn7nItdlNTj19gWotJxuO0S7nxnvb2NJhxIO1uMYGp6ACq5coYgkcE78YNKPHUztLWS3IfUOmPbimY+S3i+ThppWlkElFzI8crQB9SrMYzLHUaJXjI5KVlCxB5Fijg8yndaRUKFaU3QdDSwQcwI39KbdkDcOxnsYqcOTT+RTotW+2qaupcli0DNCKIofQaNsFp7IJFERo2BbXtghKOU6dhM0Tqnu6dj50nAsL9Hm4MV04mrqmRKFtQxUVwtxZrDfovMegX7C3KDvzEmc0hW8pjclzBoNe7IEWQ+GUtF1bQrR8EISLwzm3b+9yZ39K2USaDzp0Ah1hCkst2EnQwC85+lToPH1MxaiklgqJlnFCZilm18DqSo+eK3n79dfwmWNRV1TWtqDvaObsB8eDe6TFbg7EzKZ0f+ngS+F0n++KgA/ao02jTQsyTk67EGLMXdDey/lLyQ5ptFyqCmac1hrnk9m6jPVVKHykF7yLwppg6pi4gZZccBB6pM+k88RJTpp6eR3plw6secThbBNiZ53GWY1WGVU9pbQNmBGmNwAdBGejFMWJdS79zBeYjBXjJ8+zce8c13ff5vFzl7m+q3lm8zG8ypnPQw0H40M3YJxw7+4u+Z3r6I1VnKrxWi9DXXyNm++zc+06585foj88xalnvsapJ55lvzogW/kUZrBBM7mNlDeZH97FiEIai7cO53K8WqMYb+EjamiBBcu1k+a1tpamrsikJjOWppqBjGMzwbTCHl27JdWpjr2pNhQrjw1ZuenZmwSO+iiPm66Vds0lnW2ihZDWq/cxjDGLm1YUDFe4B2zc55qM0aytraBn9dLqSncc/5FSoxPSlmQie2lpjOQESo6hJtccNJ5ZCVujcC5h6URL53oUemE0yHHGo6xDmyGDXoPoAu8bykXNQSWsrPTIDkoar2kdg7RBlwierAiZaMsHk36JqFICsLOVY39/wt6iwkaKMAlm1fl8sFYT2LrfPvUdilAdyZkitYiPCDucO0ZFpThuJeR5xrCX0cymvLt9yJWmYT86lpfP4cG22oOLmEvqkWYQk8XLVojXoRWKkjZxQelULjnehQ9ebaJA0xK6mdauwXtP09TUTY3RJtbTzYLw1QbvhdI65k3whob7DsLS6LDQ8iwDghPIOgfOMp/N8dbFawp1LmflgtpWaAX9fk6RZ2RGR/vRIU2Fb6pAl0hcxEqDUYhWIY5TgVfLKhNJHD/yUKqtTuSdQ1xDYwzzekFjPJVtaEST9QehHrGEpAI7PWTnxlXOPfYMB2fW0Y9v8dTaV5nvD/nCl3+Hjc2LZNNDhvMF1e4OgxhtobxC1cLk7k2ee2yFertioMMidmJBFOXeXebXbnBz/T3OPfsEm09toLIaYYbISbavTyj8Ltdf/xfU022MXkEaRzZbcMoX3Lk1R5fBueZJFpnqNEIMFTYaEQ6txWchfbPoQ23n7Ez2Ga+sBQGGpt8NP/xpppalIPuJW0DDojkAqVAUQXHHb3rvQ7fbKD8lPi+tpaX8vPNhbRrdmp4pEkZrzfvbu9yRE5y/j+Lw3jObHqIIJUaT4ddivFikKVBsQqpclWXB/A4mcUS3AAhO1WQrmvULY3odaN2tvZDQ7qNQOJN6gSZDFwbtFeicXqYRp1ktDOag5mBlQD+fYKto80gSjPE6tKLo9+jlATyFmOT02VCDFwLwynqas4+d4MS5Tcp5w3RacjhdMJ1XlFWDtb61SCDx3LplB8LsBNpg6QRXCb9xxNOzRJRLaoTg4Cx6io3VHqOeRjkbCv8IobBXPHfLJT9gPLiIOTZhv3bheR/M0NBJNl6dUkeTEgAkJDgoIi8TTXnng2e8airKckFmQgEXrYLQ9tGMs97TtM6zIHRTWIzWxDq44f3UWaJumjjhhlgmLPCNronV4wvyzITwFJ9aa7jQY018KGqyjL0JvZNU0F5HDQY5wpQ87PCxQhs4EIvCsbA1py5f4tbeDnm/wIrDIugstEXR4pnt3OXe9Xexbw547JkXeHL1HIv5PYb9k/R7W8ymO4ybOe986xtUe4cMY/ZNhkPVB6yYFbbGz3Jl7woD06fyES15T7W/y/TqG9zMFIfzO2w9+SQyytgcrbB/+zrl9jZvvvIdbr5xgzOxtnGzmGKm+7z2z/6AsavYfv9dtAsWjVZ6OdcqoAUtFRbL2pktfDYANWZnbw853EXu3WXeBLfB3s4O//pv/8uPNMdHkDZH0R7AxTNrXFrbYzqH2of40ESbpT2XwsVFYkeT9vuRg/VCxABLn4PWDDZOsKcU8/tclzGajfU1zGGJEDoZCDqavSRTD1DkJjrWRBBPdGAGAOITh+Y84x48c2HI02tgHO3u7tZbSEroUZbu6soGthG8yshzhdaePp5JU1OVoAthOFCsjgpm9SJwna0wisJJaYb9jL4JFoPvZo0pUCqL6ybsPWUMhVb0VjPWVgeckfXQFKGsmcyiEJ5WVGVNHXsvomJ0UppTWNIcRzbwUkguC6B9oJoMFkM+GDAoDAN60M+oy5p+Zlo+XyXL/gHjwckREA0BH7CKLN8NlxO8qCpFbMf8eem0ugmtyy3iQhk/rVLBCxV7mHkWZYmtXag8FmN267rC2QatpBWUxqQHExYtKGyW08t7beM652P8V8rQUcIgotteUZBnReA4nQufcb6TaRcnuY1AT9nj0oajLW1NeLSlCym2WCsY9EIb5yf6Oaf27vGLj1/k5KBPEY1DHaMIMhEWO3fZffNV8t6Ye80Kp548S7G6SmEMvi4RO+P7//j/xbvf+gb9yA8GbsuytTHg1Pg0P/qTd+mXBeXuBJHAbXk8hXgO3n8TVx1gd+5gr93GjIfse8vNKy+zf+ctDu8eMpZ+NKQEO5/y7T/8A1zmkPIQKUtMrP/hohRKK6ZRMKdm/YmTDM6eYvPkeTK9wmvvvse3X/5T7t56le3bewEBIg8tdLvCNv1+P6HzqccVX727xu1dy/bcYcUsTVAVTND0yMUHCksb3SZXJfmcOMm0YrzAO7du81R9hvmxlj3h0XvqchYSfVrWQlpZm8WQSO98qCkQK4p1EXYKTwvhTw7tDzgpCy6NNxlG/qBFzixphoR2H3YU/R5Z0YAKtbCdaygbR2YG6F4P4yom5ZTNjRHbByWlh2WNWYn0EXgX6ivofiholZoY6FhlTCm99A0QMhdTGrFWYIwnL3qMxn3OnFrDeViUNdN5yexwweHBgsNZjfeOpcDXSMxopS1mFGNcVJfMSrQIpCicIjOsDAr6RthaWSXLoJlXXF0ZUFQVqFAEKjcPnt0HdwOOE+UIoVsimpC2mJIKYqEOHVryiHeItQTuRIFTob5tU+NC61qyPItexFABH99gy2ja6+AgEFKRa0cvzxj2BmTGkOcGiTEyucmixg/kl3OOpi6pmzoIfBpA0esZRoOCzGQM+yOKooerLc2iQly4XpW467RElSJUSNMRhcR6wUk4twXWH41iaCl7rRn1hmjb529+/kv8O7/4y2R/999k+2AKh/tkGRhTobQj9xrjG7bffAWpG6q9HW69t86lx85z+9q7bAwLfvz973D9+99mzUaNH72xjYOrb1/lnvs6i9sz2L9OdbAfCuMQ9FkPwXiBO3eZ3d1jal7DpXRoO6fnFpxWoSpYCsfy80My5uQ0eIkuq6D28VGQhM68loUSVi88x+jSC5x//uc4cfIE777/CndufJvbd97m+ltvsjVYYzQcMuw/fHaa3Od31Xml0UxhtVxwrtfj3YWn9iZdehBSsWiNUsna0q0pG2nHFhF5H/0FKlAjp0+eCXug24o4DmM06ytD9J0geHVHiLfLUAeLMsuihanBOVrHU4vEFNRKOL2R87nLG5zvQdGhKrpz0UW9DwsZxqMVFA7rVZQJOU0RIma8rSiMsFrnrK0NWB0W1JMQby9tOy8FznP31g4H2/sMBjmjUY/xqMfKaEivl8V+h6BVSpiIJEBsFKnjQwqFyoMgz7RmZZQzHvSo+z2uec+0bII1QGySoAJVEPhf3YIniQJWYiUFRcSNiYIwivFqn1EfbDVn1xuyXJGhOX/5CU4+7hn2emR5Tj/Pf8LMhfGhSHc5kp6UDyxoneiFNsCYVjuohBK9a/8etHt4Bc3uIwXhQ+3W+ICCla8wWgd6oMPvpfMZrTEmVJx33Vp30UzUSpEbgzEGo1PhnIA0kgNNtXfSufPWkSZt9EM3UPcDxX0eYoSqdaEz6TzT1NmYM7/42+R/928hE8vhdMGK8wwz4dSFE1y9cp1MFeBrqBruvPlDdm5cIRtv8o7LwE+p59vU8xl9kWU/sui8FKWZ3brH7VtfpxChr1JcRkAiKaY5CBsXlK2r0Cq0OhIvaJW87dJ6aQUPygc0E1zBgQqJYeIehQUsjnxzTHH6KR7/7O/yxHPP8/p7r/DW+69w7ea3Obh7jcuXnmBr/SRW5Vjdu9+0feTRFbZwf3Q3WIEvPjnmtat36U8z5m4lfFKFQkBtZItSZPlSOAbaK0TamDyuK1mGNIqH7Xt7HOxtYFbuv1iMsuTxT6H9TrAERQRlFM6rUP2tidPqwrET1aVQNM5ijEYKy2OX+zz/uGJDLe//OCz4oEH904/ZbBGKkaOD0tGh64lSmiLvo4seJ0TYm1ZsnRhxON+jctEyjnSdFoU4T+2FurIcHsxBQWYy+v2c4ShnYzxiOOpR9PPQO42o5Yg704cY2iSSFRbXOPZ3D7l2e5f9aYXzSSZJe/NJaUq3qm5y3kogNLrOXJQhLzQbqwUrA8PQ9FBKk+c5vTzn4vpKW5rA48kexZGWBKjqChmJ5hMeF4sRK6ViNwlQNm46lp93dYWIQ+dF6BCgNcOihx4Hh4WtaryNRWvi5jcm9Kkq8ox+fxAEqzFRa7XECybTDPoFIkKRa5zrtygcQiBzLw9EtxLwTY2vK3w1D0jX2yU/Fy9dGRNatEa4E1iAR0O19xuNrcA1DPsZs8zwqZ/7KntbA0Qp7EpG3w5gb4K2GWcufA55IeP6q6+iaiG3Qg9BH+6iJwcIGV5qcmkYpcuNsCkIvnD9I4RRfII6FqP33qElKChB4Xxy7ITsNy0SapbGQwanZpzPWN0pgduw06UNwA/OtICK+6sXUCcu8eSLv8ATn32Ct2/+iDeuf5e3brzH9q7l/NZjrK5t4r3n69/6CwanH3voue2KuSWT+MG/AXAeXnjxFO9+Y4c39hR3XOBQjYrxyyk8TC3vPUVBKi0xjpaEj+IUaFZHG8wnimrOsZixYEKv9DP6uiQj9jnTKlRza7PRpC1fqqAV+C3wUaH7daEtFzYHPL0CquRIh+J0//f7/WFHZR250kCD84J4jfc1iKOSmAZtGwaFYXN9hZ3dOfcOK1IIFzEFPwACaTWJIDTOUTeOybTi3r0ZWkPRy1gd9hiNBoxGBYNBQZHp0PFLoihwnnK+4PadPW7uzFjUR70wqkWytMBiWR6AVhl0acOAdgVlNOubY05tjDh7aoPNYYHo2ApIQJzDOkvjQqKQepQqY8vThyuTKBRbwRtbp4uGtjiNSpGA6WsSS/iFnEbdG6C0ZpAX5HkP5xyVmmNtE5FCyD7pFwV5nmFMRhHrujpJ9RiC2aGAzGhyEypT+SKP3GuiPgLKzXWGSOhA7J3FNxW+XiDetToSBV7HBxJrARM3HIlV6MxL0q+PMnYXM86u9jm1NuTcM09x7twmLz57EUTYuT1l+/YVxj1YffJ5GjXmhOlhdcXd194L11nHhBWxCFW4k2gXHXEUKGKd0jB5GkGUYPABbS8N1YC4okWhI8J3EguGSES3omPCS6AdQnufcKpYBRJRYRWICuvGD3Kq9cd44ef+Nk986bNc2b/GW7dfAbnBpY0xfnIRU2ScWFth794tzq30uXbl7Uea366g/TBz+tNPwZev9dmb1ty1GkUvRA0gAUnGe0sHbAmmeFAXq10l/7JCMT2cUU5GDPvpapZDK1gZCD08OqafpVDKZND5Tvya6sansWS5lPJkesLZvvCFsyfY+BCBKzwanwtQL+bMrce60MU6IH8Xa7Q4xBTkWLJMgxbW1kccLiqqOllVnYIy8aISlGtnVlQMAQ3O8Nm0QqlDlFL0+jmjYY/xqGA86jHKc2bTBTfv7LM/LbEu7c80V92agKpdC8kPlFqPBUEm7WQlKrM/LjizOWJt3GNQGKxtaHzImvMemrrEuuDPcpEqetB4cJxuMvXbCaGF5KFalF8iHB0EXSC6k0pOMxq8ruIczjZRUIdi6GhFlmURZQVYGcqnBZJeGUMbUcByTuK8xGmMP1stpUDpQJJLCM8R8fjY8juVNlwK0sAJpQ4XqRBOdOpz/55Hjx409sJXn2H2/hv8vf/W3+Vf+Tf/Pdo6/wKnT4149+Ub/OU7r7Ny6hJf++yX6YlwRhfkap2rL/+APgpX+bh2dbyq6Nzs0CLJWmnnKZI4KUSuq/FT/v8SCyR7OQndKGzjXOtkWEVLLdFNaaWIUuTFGovhSZ7/pV/l6Z99jqvT17l2+GM2H9/kua1f551v/IDZxDHXmjNbpxgDrzY/4KR+MDf2UYa6z+/3Q7y+hie3ejxxQvHurQVzV5DlClwIc3IRweNbP3Ar1ZMhFKwAAR1or5XBCJk3VJMC1o5el1ah6E4PwUQIptP6VartTJ0Uh7chRFMI7oYgtDylsYw2FV96YZOnThztFHF8Hj4GRgyAsmkQ60Kn7RgjXzeWXIdCQGIX1OLBK8Y9jT65SlVW3Nie48JEkuKLgRYktAVjVEd8pA/FEDDvYTGvWcxr7t3zaK3JjcF5H8qPJsXVXm1sNNs+n2SJu2gIRjWk6MTlhucoCrI849SJMesjTaEc88ODUAQqpQUrAq+cZeQmA5OFsJIHjAcKXRddLA6Lj7nzSkJ0gJcQ26kUoalkwvp5SJGTGDuLSMivB5w0VGoKxpD3h8HLpxXFMCVJSgvxUyufYF3FkGp11BGwNJ1Zmn7opYNPAjfpXSiW3Szm+Ca0E/GxGIcnxEAqZTB5aC9kjaFhudl8eogq4kX1qBg3jF/+6jO88K//Ip97+mf44fd/zKKE7d0527tXyPWMTCteeecNuDWjrFb4wrNP0rOGM8pg1YLrr7yJFpDSBkpGLbU4LJWFJPTUEcJp4bekQUK3qn0K8TPLqknd+/bJk46KUSkdcy2dX8Eiz2h6m3zh1/91nv3Fr3KlvMPbszd59jnNp598lsUO9E9sMdxfYDCsrG/SU5qiGJKb5mOY5Y8mbPojePqxjAs3Djk/gWsHjomL1k5S0FHxBJQEolVcP8lUDgpH67BWfe1ws5KV4QdFoTFwcl0ovI2bV7eCNjlwiBaYRoU2SnFveAkx5OgJo3XL5188wXOX2+a/9wUDaV0cVzwPM2aTRYgAUKFQTy/LMZlGt5EAHmtDreLxOEMvHGdPrzNb1OxO6iUQkORHSABIL7s4pK7c0XLtri0V93bozOtbQQ4pwqPjLIt7NmmwtAO6YrmFF5JohQgAjWb9xJjTm0NWhxmDXJOZDHRGHtMSnPf42Asyj73+jpeSPT4eKHR9qsIjS1TXXu6REKpEPUS0GIgOJGqeVLpOvMfbUCwnK4pW84TgYt1OSrsg0sSx1PotLdS5zqWgUelrJCQaYhtdQLi2wTVNUJcdp1j7SlW1lOqUwZOj54jCKRTPebRx671drrz6Dv/QvoTyQwwGpzKK0YDRaMgv/MLnefXd71Hpa+xOFuxX9/j1z30WK3MuFhk6W+X6D18iV9AsalBZCKuJG/U4Sk8LMTAIS0G7DIzrhpeFzW8k/T3d/3I+JFkgopezlECzUmhTUOUDXviNX+Wzv/4VrlXvcr1+h+FTFzn/1FMMMPRPKNbPn2awvYexsLa2RomjKMyHht487PiJz+0kfPkzq+Tzff6i8vxw4XCqh41KSVgmLaRFmJzIyy4npAkIwnJRs5jygbQ0JZDXwoYS+kotU37TfiIkguLBaEGZLLavF1QmTKRhuOb53a9s8WtPw+l02vhKLqJ0OaHkUccqfOjZgy999jMs6pK9wx3ms3nMkg1dG0SFCmNZ5uP60eTG4jxcPLtB3WwzLV2cr1TkPPGtnT2pafdoWGbL2l3BGov1tv3xYuUSreYY7qHSPET6Ri3j/ZM4P1qBK+4Joxiv9Tl/asTaEMa9EH7ivUMDjQ+0GjFDWJsM5z2NDQr1QeNDugGHPxsVCl2LSBufiAeJRUC8SCCQRch0EKACOOdbk4xknlmPaMGVNbXMQ1xuHsr+YXTM7omTmLR950eYl25+9hJhtcWBxIfGh15wTY1vqiDwG0sqN9eK7Yi2MQZRGaKCs64tWiGd46cTtpktjzaqWzN8MYCiIBto8l6OzjPyYkw/X2N93OO5yxv8V//k2/gFNG7G+qjHC5cvQ9bn3Oc0ylhuvvIaZAo3bxB0Ssc/oqSStl8K4WRZhN9bP64s/5p+6X4/KNj411gaKn0vKSkrsMgMZdbj87/5O3zmN36V6/6Qd5v3eOzxCZeeeIwRF4EDYJ9ydsj6eEDdwNrKiB4NRWFiXdaPdxxX1umVBNJjl+Axv87sm3ts33LsS499FxSSj1aPb9dZiDLQUYl5OmJDwNUOVzcMj/GsEJbdxgqMlKC97ziIo9AUAaPwTlBetfMenL0LVlYavvC5TX7uCfj0fe4xXceRTFe6AOXhx7/0az/LweSAV3duUdQVusm5vbvDle1tVnSOredMZxWNc+A9vV7G6tjjZUjdbPDujV3K2rfO3uU1hSeRrIYj0UJiIiLuRBu1EVMeJNCQybkVxSwqxeop6VjJ4YypFGY7L0mpKRiM+1w8s86JsWGQ0SLqcErbAkYXq8wZ3aC1prHHEOF9xocI3WAWGa3JU5FhH07inMP6oLGcE/A+1DUwefD6EoKfl913Y4qcDXEv1pe42obGiAPQWYbOc0y/IDm2SBPRMYraxdQKgxATCUQvjoQ6AGWF9w5bl7i6bIV/UGop6oIQdZFloA3O5KBMKNwSC6e3x+4K/Y+FXICnNtdZfeIUd+uahbVMa0fZOPqDEYNihRPrp+jTY4WcK++8TVX0eendd3jqi88xND2MPs3j/V8hK1Z4//t/SU9r6nmFeAUSOG1FKpsny3kjbezOJKc6t8mYUwlhRAybqIoWJS8dHmmVKQLtoDTMjeJzf/03+exv/ia35R63ZBd57ALnL36FU2y1MwmGE2sblDMBlbM6WsW4mqLIMObjmec00trxnZcFam/J/YKhGoZ09ifhF9hg7Ru7/MndBT+SDHxGSL75oLAOEni5KpwP0Z46y7GNp1zwgeoyXsA6Q291CJVqEZOzSZYEARtqbgSQoWkosfRWLX/9uXV+8yKcPXaPig9mnIVZXiLgR81Im1ZTevqQz55/HJ0PyLzlWe+ZNQsKZdGu4HA2497kgHv3dpjuL5hWFZP9fQoDtXNcvblH3SylU1h7SZwGIRnkrorhaZIWXwz5Wgo3JbpzBB2iZaKpFhK7UkirdIKQ4rFi6nECKaKgP+7x2NlNTqxmrPQNgzycX6uUopwoJR9eSmNdmONQGOnB8/fgNOCIkzMNWazavqxtu9yEqbTjcjFGTlWZYBYoHx2v0t5vSNPzCMH0T9SEOIe0Oz5xut1Hk86bHHtqeVgXMtECj+tC3YbY5BKR1rF2BAaGIqhI8sJy1CQPT/V+s/PoAqG/uUY+1Fx79228gqvX7vL8819gZTBkY3yC9fWTbG2eJVcZfYG9a9c488u/yuW1HtIrKLPTuOIUj7+YobOKa9//IR6LPawwkhEyU4NGdxFWBM7LH6k1qyXFfn4wBttE5ZTKAaXpaymjyO2GOF9hoRUTrfncX/t1Pvcbv8GOMuzKDpvjm5w68RRrfKpz9CAiDIbheA3TH9Mbr+LcApNp8uzhy7J0zWwd37AWqhqm0xADvT85YG+6x+7kfU6tblPtbHL2iV/jyeeGnH8SzqsNDr+5z8Hdkp16nf3YDtxHx8ySalrSCjH7lLb4tRMGHcCe/AQNMNcGOxzSqKQMo1kcFaCLQUBaKRxCIxO2tjTPPTfiZy/CC2OBXndBh3H0X8v3ksH5qGOQj2kyi0iGlx6VLsnqCmMbmkwxXBlxYWOVdbfFs09eoK5B6x7zg12+++3vgboCCO/fOqRqllosXJsHHRSciFuaWW2lL2gbxCWVp1QrNrsreMkFSwvQujIoUKRBdWolIapqZcDFc5ucXlGM+9DPdLREwvmbxoVn4Rzia6zzQehbwTUNZd1Q19UD5++BQnd1ZQwKepmil4c037KqaGxDVZbUVY2IbfO/fZozBVplmHwY24xXsb5BQMQg0cx3iBIs8xASlmXoKg8pjyYL5MgRASwth6CiIBWJVeVF8I3H25CIEcLUAp+7tAc7GWXGBHPNFGByRBSNFQTLUZn7QYOsVQqPOEZbp+mNc9Y2zvPWm++wMjhJoVfYWFljcy0Ehp85dYJBppnXgnY1Z4c554cjmtF5tvM5u/UdPOtc/OKvovUK177/LTI1oTycgZigmZMnPPKRCeF2rQZ1xMkWxG9ybuhoUqdp1EmgdOdDBK0aSuP57K//Di/8xt9gL/PsqbvsssKm/iJPFRePTWVIfjASUjl9f0x/vIprJpjMxMr+DzemhzvsLeY0Ct54+UdU05qDg4bpVHGw49jdmXBv5y67+zfZP3yVs6f3uHNzlZOfnvLb/+bP8ztfPs/GJc0vm3XOfnvKP7k240doKp9jY8nBlnaRo5s9za23HmkyZA+Ye+Ssxjk4qOHarvCNNz3f33NMJAvxrs51jhVrEjgPpqHMaobnCn77xTG/eEpxqp+mL533w9fj/VDww4zNzRV25x5VV2R+gc6EufH0VMbMg1EePa+QheMgcyANfW1wwyEv/MznYu1bT2YMV2/uMa9CN7WgyGIrniWMXdpRieNNakXR3pEklZU2b6c6WHhOiUBb0hLpo0qBGMNobcTFsxtsjT1GSuzCMF/U+NgMN7QjCtFQpW2omoayqWkaF8uneCrnPjRx6oFCdzDohbi4PGPQyyM6naPqChcb0osQKnBJYGMaCaZVjon8rg+5z+Kh5UkTQggC2MVUG20N2oaGiCovgtmvVEpqby1ahYQYGhHEBQ+miOBqFxZ6KM1E1+wNmyNFtKdjakRnhBbXgnWOpUPk4xGsDxqjrdP0hgblr5NnGxS9If3hFsPVDVSh6Y37nNjaoJ8bjALvG2o7//+0dyY/kl1Xev+dO70XU46VlcUqVolkiZQ4iC1RJJuipLYGS21YhtvwzgY8LAx4Y8Pe+S/odQMGrJ0aRi/sjTduu7Vxy7IbliBL6pYoCaIsUhyKLNacc0bEG+69Xtz7IiJrJFlFrvIAiYqK6Q3x3rnnfOc734H+AN0aHtoscHGfrSuWqNY593yBMoFLv3gFbSLV1gSLSZxa6arwXeSQMK6O3hXTQc+gheR0ZRbdpvOY+br5Eu7oZSDsiTCWlme+8iK/981vMinXqdRV9OGbnC+f4PTgCzfxmYQYHbtXtwhSYK3CjZZw1iGDAUXhkurch7SLr/41e6Fma3rAD7/7X3nvtctc3GnYGUNsVZbnCARaYhzz2o1dlHje/el3uNhcogz/mq+/rDh5TthQQ7bNPtXliuu14erE08Y0sDPmtbzzZrNdzmmwr/u8dgGq6we4T424tjem1n1+e7HmrT3DpbFj4mUWODD/By1gJVLKHo+eLXjmpRFfOAOf1hHpOHofgHV7uwj4w9j23lZyMqFFxQbbgKdHr9djra2YjGuC0QxNuo/2ReOM0PeWndWH+Oof9Cmd4fvmDXqF5Y2LN9g7aFK7NJKYEVmyNV2LHZ4bUaK7y5cO4Em6t4uLjySO7ULb8RGJ1tlnSde8NaysjXjk1BIrA8WocGhVoCT5g6puqHxMsgUxXfdOOwpbsGIMMfcVdGyLo20Zt9pdna5zFhCMNWhjUnFJJ1pVzHSRjiKRUtC0rCRZbDUj5EeV+5Nm1IMceS0ePMyYBlEi0jZ08vzSbUA64ngkSTPGHM1mUN7ntt4Z4yFmJy3ZaeeavtaJT5e1M+eq7/lvXoKm4w7Od1I4MrTyPmx08ixWtayvbvLuuzdwrkfZH6JcibgCrw3rJ9YZlCX6wBPQvP3uNRCXVMdCzer6OQI7XAuXaVWfR174Glr3eeeVn2DUDtMb21gUQmoQiZmmoxYWpEWOb3K2KdWaxRCx61abnYQMy6TikZKKA2k4/wdf41Nf/Tsc9EpCecjl6zVrzcM8f/ocnL756CMiDcsnl5jsbRHFoI3FOYvxSQSbe16+dzZz7TJ9K/i25o+efIrf3Jjyl9cvMGkaWtLATavSiCIfBYlLSAys+EscvPK/+Q/fPsHv/HP8yy8/zYkN4UtfGHHuzZqfb1l+vdvw23f3GXtHGw2Int3UHfQiWjBoxJf8t19VlI2n2K7YnTRYN+XwMFCjmbbdkpc8diraN8QwRdOy6gK/d67kq18Y8ugZYbSQizw4N/rBbP+wYlAW9MyAvWpMYfuslA4vAYxjdbSMUzCdjFlqAiOjEROxsaInBlne4MsvPocthnzvr39Nb+B4++I2V6/v08xiJbnp0DrH2l23cx56Bx8svj/BO4oEYTIL9LocLwIooegVnNhY4vSJPqMeDJ1QGEUIHqNN8i1OMDF13WqB2HpESfJyovBtlZHK5C/sPYKFuzrd/iCpIBuT1HO8D0iViNFRmQwjzw8lRa1prlp6LTlebXQaDunbtAKF3Ovo48KJktzhkSNoqWcn/uZiWkIK5nTqeUdVxhdhplG6+PuJ1YhKkEIwJRGVJuP6TkFt3sg5J6ikLXQ/WfL5HVp4f7a2eY5YHzAcrmBtiSt66c+VaKWxWrO6sky/VyIcIhiu7kzZj5pCNE5ZhIKNUxojW2xfVoS4zOnPvgRGuPjLn2F1w+TaASaAFaGbMjvjzQASc6qshBiaxBeFGU96ES30XeNvvqbHCGNqHn3+CT790suopUeJPc90+3WW95f45BPfhMfudAYaYEzRcwRvKYxFGY2UBcaZ+0o09g8rDtqKGssJvcHpM49g33wHLR7fXT9RkOjn2DeCA0Zc5uKrf8Z/+vYPeGz8j/kHL7zAic0+KwPH0i58sjb88Mfw9uUJ237IbhuoZT7SKWRoIAISLdfGHmSA7AkwQNWA7yh6cw6qDwGjoNA1D50QNlcsZ0bwpWf6PHoyj/yR7qq+M959M277oN3y8mCN7fElXLHMYFCyX0/RjScoj20tB9evYsQwJQno79U1+/GQtq3YnXhO6xFmGtl86HH+8OUhP//Nb+j1DKtrAy6/t832XkUboeNIzwOhFLR1nWLpMp7f+5HUaDLjkysIoQvW5toiiGCsZmVlwOb6gJ6D5b6w1C9wGmIIOK0TO0HAGptolkqSnkiWKJCQs0VdIiESJSnQabl7LeLuKmO5kKHyjK4U4KU8P0pHtVjk8DJLz1OhJvF3dRYFFwLR58sxdBHjgi1gOYvjc44sYjFvZ7FEOFvwu0LRbWKAzKckj74hd5XE4DlSODuyT0cj3jkSOseX7sdMf0DZL+gPRhibpgGXZZlWWWcYH+yztrLC0nCAkgnBe67t7rHVRtatUEjGvumxuvkERbzGxXCVqelz7vNfRtuCi7/8CdZc4+DSVWzUKVWj6zFMzsGqVGgLMV00Ion2NSf+d79A4iYG1fFVa6ZSc+bZ3+exF76CWT+HWurx5pUb6Kslf/vpJ5nVzW5rGkgwklMaTUwZlVhsYe+rkPbTN95lUo3xXmPrwP7WDSZFgbiGOK1REgGDkhKrLEYr+qVl1HecWFvji+vrLJ16mJMxMq4O6EsfPRTO9eA0mvN2yIXfVbyyF/n11pS3tyqqYAhR4aMiep3Ormi0NsxkT7sgQjxqQUcZabCmoq+nPHlmxFc/t8Jjq5qBg5WeZFqDzjDGvd1oFxJ8FEzndnuXZmeft/bfYUJFbUBqw43pIevFCutRszRcI8iEQW8ZVQ5RynBytc8mmrVC4yWCWB5rNjn/xMP87Jev8KNX3mB1WHJ9a8ylq3vsH07xbbouc/M6nQNIt3sXCqUgoEtuk28VRDxInNHxREAXluWlARvrA5aHmp4JLJWOQc/lxi8FVudsL8GkibWQnLrKzjhGsHmVjSE3ScSANRql7sPpJs8lOY3sOr+S8+o4bh1hq3NyXYU0xEhL0jbQeXy6KJUvwAh1DU2ToQafTmgOIG+7Ut/kYzvu00w8fSEazpkviKC0TrKTInitiErSRN3Wp3g2dGvkEWr27IHMoISuh2URbri/GKLs9ZF6iitKolIo5wgiudsvsHe4z8nCsTQaoWWLECL7h2OmTUuYyccJUCDi6J8STrHD1pWGICNOfeZ5ovK896ufITLlyuU9ymgpY9KZiMrQRGiiIEZlibwAomkRWmKmNyQtCmV0nqah07h4O+WRxzdZPfsivZPPsbq+wqW3XsVfEZ757LfgmXudHw1YjHH4Jg8xVRptHM5qivvg6f7Nz389K6ZSN/jqkDbA2vIyy6cKip4gSxs8fO5Fnjz/FCc31lhatTinKEqHdY6y16fo9bBFAqNFwJkETfdPa06slpxt4fxlzS9eCxxODDtbE3p6gJ94DsRyzafJyQRmkygygYRVDWVs0H1BbMtTT6/w8IbnRN/x0JJmxaT26mpSYYz9QB70owQdrnhPsbTM+dWH8LGhsSBNm/pWtaJQgaXhOlFabPCoBpbVALEKaWuaBowdUPsJSpVsrKzwmSc/zcbpc7zx27f4xauvc2JtxPbuhCtb+xzsT5hWSaKxm+idjm/ejj4ruOe6RZSWYU/wXlMHhSsLlldK1lYGLJeKwipKCz3nclSb+PpG6fT9KuswSMhOXqMIiE5BZ0rYI23wBB+JKr1Dok+UzbvY3Z2u6kDp9N+YHa4sOF4lc9pRlK61NB14m4WITXZ+Wim0MsltqTGBCUSfw2J/RJv0iM2qlnOTHG3PHO1NTjep8wnaWrQrCCLZkUgaGe49MWZuXf58uGUreVuzH/nos7cuDx/MlDVIUKnhRMU0i0WlzhYfPbWPFOWQ0XAJpRTRRw4OD7h87RqnPnELSIpIwejUEwxliwtXttin4PSzvw9aGJuWR88/zfYbl9jQmmgM4hy+KNGjZdq6pbAW7RzaFUhREq0mOgOuQLkCsQbrLEpboraYImJL0O4Uzi3x+u8uUF/e5u8+/wX43Pu57QXQFMWAxk8psrARRAp3L4G8u9vJpRU2NjY4fephTq+f4nB3m73Y8olnnmBtqeDt3/yYV/emfPalr/CZx59Mx64Xml7uuV5E3NBzGmFtYHjuzDAhZvsONVbEy56fXL3BpZVV3n3nLcR7JEacMajCsrl5krOh5umHh8gmID0GA0thm1T4jFkzVgmuZ2dqY+/Xm36UTvfc2XPsHlxi3UX8tGSiFG1ZQStE5fB+jNQ1vV6JDzXBRWQ65tpWRJWegVGsupLD3Yts7U8o7SpFW7MxGrH89COs9DU//OUbWF2ztLRO2wT2xlN29mvqaUtVNdTeQ6wZ9AuqKlB32LgkGdfSKVYGirJfUBSOfq/AWSgtDArDsOzTlbwKnfi3Sktyqhk2EAGUIDFNtJaoElxBSDq/ITl5nUXqjdKoyEyR7052d2nHW9KYuPD84l+cVw/jgjuKXbWsQ16FmGlAIfMdIWuRdtFpt6XZBGFhrnnb7Vg6I3PqiJrvV14MUp1MUM6hnUOiJMGdEBea0uaYcudW48JGFo/wyDlYaAi4H3PWItGgVKRwmsIajEqK1b0ijaAvXZ/l0fJsmKbUFdOrVyhucbrziJdNy3qcEC7VRL3K2edeYO3xTSb7nqf/YEhfacRYlLW0SmiVQSmDUpY2SsI8tQENbWzxaBqfVnUnUIjBeyGEJtOce+y8d4H2+h7PvfgN+Pzm7Q73VosK6prmcEoUgxkM6BJJ68xC7vHB7d/88R9jjMEoi91WVDs7NCeWKU8VlKrhQO2x9M67LG+uUvTdhxhHHiBWhLbFxR69QZFEfwYqQdVnha/7NXa0pmnOoHKNIQ0+UGhjWMFQWpXuwqChncJ0F6Ii6hWwKUDRN7VDR1JTxxYwBAY37dlH6XABDC3OKQ58i40VsVU4W9D6MX5cExxQRlRsmI73mDSB6mBCczBhb1rz2+vb1MrSxOvs7hzgVB/vhXFbUdUVbYgsDRw9N2LvcMp40lLagtGgwGjBk9gjoW4T1S7OA0FtwFqN0worEKLHaKFXGoqioOeS8iCdUJPWGYPNabYIxsisvTiITfKvMyiDOX2SNkF0swibFGjeTyGts85lJu+f8ZGbnNI8QJhjsjFGJCTBlMRkUDR5kx6Dz6I2LveVC2DIQjO+JYQUHUhs545cFvZK8iEog6BQ2qCUxhihV+q0ctkSsT0a79nf3aOqq1kr8O2j1yMbuYPNq6j3Y0qBcw5jDIVzWS0/9bBX0xrfesQ5loZDdC5qqWbKW794hW+88Lk7fq+IZXTqEYZc5vXLV9jVhrB8BpZLxpQcxgStNG1L6wNtiDQ+UFUVjReq1tP6QN1U+LaiqSuaZkpEWFKKIRWGCYWOaB0grrISV/jG5595/w4X0oLsLKVWTCOMQ6Cnk3CKdb1ZQe/D2NrJk+lBhFi2HO7U/N/Xf8NTZ59nnZa2EA5UzTj6D1kSFcCiVSLGU9eMx2NsUWJ7BUhLXyapEa2XruyUU81F4FVoCHVDqCPGlTCZpJRxMkGGfXjl57C6jH/zdWRlxHvVhLoac+3qDf5qe5fll/6QP/r807c43Y/aLl98i/d23kHHktXRMofNPtO6opxO2N9qmIwrtndvsNNMuLq3z7SBiMNJYjFZpfHKzzJidI22BSpWRB8IYc4oKgtHYR2Qrkvva6ZtRD/0KZ597Bw3Ll3i4u9eYzw+SFCnUVgXaEPL8nCUMFYrlDoxeCRL0wby4NEYaQmZkpkkNGOmk4YQElabi55RCSqGPE0kpve0gajTGKfgwz3FbuBeTjdjpgtxKtBFlHCrc+omFeRQsgsKZ5YYDWml7igXQuiqgd2qAYQGxCfwa0YBm+E3i1+pQBL9S4xDlEZbhSsN2ijE9sD2iE1D4DDpnsajamVHsdxFjHjhhZuB5QdgQRRtBG0SPpumYAhN2yJoYpv4M8PRKBczPVNfE3sFLXeqX+cbXDSc2mRTT+HqDruHDXs7uzT+kCZMafwUH6BE6AG/fPstXrlwiVhFQlUzmdaMp2OqpqJqDlhb6bG+eoJvvPxlvvLys/SGE3pDR1EAxXngzIc4Aym6EKUQH9EhzGbfOXt/UyOOnBFRDB4+wZOffJgJ4AmsDg0PlTDy9YccRy4pq1AQM/+455YW0rUJHF7ETyaYso/0V0FKmtbyo7/c4cXne1y98BN2rlzPmHPN+HDMeHzI4cGE6rDG7+6wX4/ZmezRFkJTKDY3V9ncOE1/WrH9v37Gm/ppBp9Nrvz+hTDfn23fuES9f0iYbrFz4QqvXbvK3vYN9iYV4zoQY5ppqKyltH3KwqCI1G1NyK0lPt+DdYjZkSWoUaLQeE8IOklZhpagBGP79G1AK0fQjoc/9yz/4u99k/bGNf7jt/893//VFaK2LBcD+q7Ah5Ze2aNnU/1CE3JfVQIVjFIoJUTfJlxW6czLVbS5j0vlWWxonSaOB7K/STICPnO9aaHxzbz9+x6FzntGup0A8KytkZucU45Bu0LUkZLUzPnGhU/NC1MhV3RbSduxxqXIVARjigQxhDalXTG1usW8lMzZC6kYgyiUcShtESNELbk1TqGNQgeFzsP9pCvadfsvi5KI6dEtp232hvcbDd/bona0jUe0JSLpQlUZXxKVBYVKhsNllE6LzcS3XNrdpWEeM93JRCxLG59gVJzgYGmf0aeXoD9KxTIFkiU5r/3V9/jT//yn/LZWXHntbQYxBW8+BtoYsb2SarfHN7/0df7pP/vnsPUurJckhQ3PXFTwg1qGhbRFhxandH4m4GyagvtAzArblfDdH7/KF7/1LB44sbTE2bLHoG0/AHYcydLs8/8f7CdYYTJJVMjXX4PBgOvjq1y88jsuXbvMpetX2R7XTMYt1eQJJgdn+e9/9ipl+yZrruD0aMSZzVOce+opTjzxJGId6vwnYWcHTm4QnU7bFY9IQMQSfWTyg9f5859+j1NPfp2N4uNzun/+P/4Pk2nFJAaWbI82zylDClzmuTbeZx2OkPBXmHXythqUMmg0A8tM39oL+LrBe9A6onUSqzFa4WZc/0BLxPpDrAJbWAZFXja1xhYlRWFoapDoUWISWSprY8zZOArfJm3tEMHTYEwaWqlE0MoSc8QdaZGYBP+R1G8gUSFKMC5pgetW5dZgfzvvccTuEekuVOlnfvNWUKF7KWbaUXq8oAwye5AA6O513zWXZ+1MZXuocpQgAp2cYfAVodpLuIrWSYUsRnzXeRYUMSZ6jnJ9tClQOhK1JyiwxmCsxseA1klztnO0kPC1mXjyLQsDs+ObPZAUOT0IMo7RmhAN1hQJXtAaJI+Xj4IPGigYDJcwJv3IIQT29vYW9upOP3BKfxGLLPcZLW8ceWX2OEY2Tm1QSMXWmxcoVZmglxBQeTJINZ4SB6usb55JxdT1Dz9GZ3H/YgSaQNQG7SOuW1AJOJcikQewGUAIhWXSrGeXKfRtwQoK0/p7Lp/BR376Py8zPDHFHr4OhwdcOThg92Af2dmh2t1nZ2+fejxGTQ7ZDQ3XqekvFyyt9nnozAbnz57jkUce5eSZb/GD70/4W9/qHV2+O0pj95wIrK8vvOEmM9C89BTTd77LpJoQi9tImX1Etj9tidEiITBpUwouIpmhlBX6lMUZhct4dIwQrc10qhTBegISVNbfTk5ZG4UYnRYXcoHeJ80XG8HrRFv01TSdGq2xvRKtNS0KlEZrRavSvR26jbeeSdsSFRRK4aUlxpR3a6Mh6tQ3kJkBM9GrrgOOxMJChNg0VIm4iyhNVAGlNYZAzFDn3ez91w8WMuuOo3vk5YXg8WbBmBTsLkwumBWvOkHojkyeyPuiknNNgHQK/ck0DqV0+lxCudM3hIwz588iSU6sI1HLTX/x5uZoYVYcu12zWbzprYvHcD+WRr9XxNDgnE2pSgx5dQ00PgCW4XCEzarJIQT293dhBjDc345EEoZVKs2aMdRBp4GMbSoMGEDloYNb+we0PLiISkTAWvBgfISqSeVlhKIo7/n5D2IRTxP2aPyZJLhvV3EsYdp7fTASfvT/+OFf/AUXDi7w0OQynxyNGD3xOM+98Dxqd5eT5x+HooC1NdjdgY11YukQWpAuG+iCFM3X/v7wNkXqD2gSiSoyLSaMqz1uGYz2Edq0ahObRiKaiHMGEZ3rJMlBemmQVjFpsyxsvn+1ThGjJv3+SicstM1KhV4llTB8JDRN0q2ViHcRk4d/Kkk4egwgxmD7fZToxPoInugV3geqaooRg1VJOZDoaRqfF4esUax0WohDQPmUQUetERpigDZrvLShRSmNNRotQhv9DPFMPbgJSw2hvT8R81k7QJ7fNB/7zELwOx+Fkd5887eklCzGNoXk2aslokF2dNJFmAElASWpzU5McsheUiODkkQ7ixHQiqgioY3pB8tArWRYIQjzQZMkJ6lEo5Qh0mbqRzcRIu9qkKOBeXecCzBy2vsHU0jzeJSNtKHOUHXMP+QcMwJDORyhrJpNsJjs7yZifS5O3q9FpdC5OSN4kwS1QxJfSadV0QbAugfQh3ezCRSWWLdQzN25yyL39/fF80cPbfT5d//wyfxEAeYsp19+HxG7COaLn+bffvFm1do72MqJmzb+UZkw6A34V//on3zkW7rZYttSS4ooJ21D6RRCO6vfNG2LNTZdLzl9jxLRqfUPkdzuLG2uG6nUCaYtUSmatiEohS5K+jrNHAsR6tan6DRCO/G0bcQZjSuLFIzlmYZ5sDh4jwSN9y2197S+wWiF0aDRVFXDxEdaFbFKoWK6x0LdgMTEGdcaHxLryVqNtTb5nzYplAXfIj4mqEJIcOb9sRcWUuxFeJZbcd2uBfcWpxUz1JAOJzkWiTNHONvSrFUvTald5NomepnCiuQJtukzaWSKJ9Imz5hhny74lbwbqe6XI+Es5Tjb9NEcbwFSWUBTOOKDOfrMhzexFh0dtuwjyuJciZI0Mw4CxjlAZadr0iFKZHpw8MA6jYSkZWycQ2uDEUPbJEFmiWmljTHig+ewrhZHjj4gi+B9wpl9SDqiRMrCvq9K8LF9/LZzOCZqg9UKCZ7ppErBkCictSlb8x7vJzhtU/diEikk+IDgqRGiUUkowCfxfSMRKwEvaQS6FiGGVHBPEoqpEO9FODiccth6nBF6zuAU1G1MnZWkApj3LeNpQETPrqWmEeqmRQi0beojIHoayNFV0l1QWqMUaBtwxlDYAhGYVh6UTg09oSUFbgGFThlqG6j99K7nT24/dDHZd77zXxa6cQUfAvuTMVXbcLi3y/blS7RNlTo1OsUwclttSGI0IkLZd1hnUKbAuAGIYnK4z3SyD3SpBgz6I5ZHa2ijEefAaNqmZny4R/Ce0nRdSpIrhxHfeuqqRVC4wRBT9FAS0KpFJNIrCvplSVW3vHdli739caqINhUdZNExNPBHoZEIt7Rdpoh5Hqn/ybf/5Ng1HNuxHdv7trs63WM7tmM7tmN7sPZR6GEc27Ed27Ed2x3s2Oke27Ed27F9jHbsdI/t2I7t2D5GO3a6x3Zsx3ZsH6MdO91jO7ZjO7aP0Y6d7rEd27Ed28do/x8rNUw0HrdpPgAAAABJRU5ErkJggg==\n",
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