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<!DOCTYPE html>
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>

<title>Glmnet Vignette</title>

<script type="text/javascript">
window.onload = function() {
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</script>

<!-- Styles for R syntax highlighter -->
<style type="text/css">
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     color: rgb(104, 118, 135)
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     color: #990073
   }

   pre .number {
     color: #099;
   }

   pre .comment {
     color: #998;
     font-style: italic
   }

   pre .keyword {
     color: #900;
     font-weight: bold
   }

   pre .identifier {
     color: rgb(0, 0, 0);
   }

   pre .string {
     color: #d14;
   }
</style>

<!-- R syntax highlighter -->
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<body>
<!-- 
%\VignetteEngine{knitr::rmarkdown} 
%\VignetteIndexEntry{An Introduction to Glmnet}
--> 

<p><a id="top"></a></p>

<h1>Glmnet Vignette</h1>

<h3>Trevor Hastie and Junyang Qian</h3>

<h4>Stanford June 26, 2014</h4>

<blockquote>
<p><a href="#intro">Introduction</a></p>

<p><a href="#install">Installation</a></p>

<p><a href="#qs">Quick Start</a></p>

<p><a href="#lin">Linear Regression</a></p>

<p><a href="#log">Logistic Regression</a></p>

<p><a href="#poi">Poisson Models</a></p>

<p><a href="#cox">Cox Models</a></p>

<p><a href="#spa">Sparse Matrices</a></p>

<p><a href="#int">Appendix 1: Internal Parameters</a></p>

<p><a href="#cmp">Appendix 2: Comparison with Otherther Packages</a></p>
</blockquote>

<p><a id="intro"></a></p>

<h2>Introduction</h2>

<p>Glmnet is a package that fits a generalized linear model via penalized maximum likelihood. The regularization path is computed for the lasso or elasticnet penalty at a grid of values for the regularization parameter lambda. The algorithm is extremely fast, and can exploit sparsity in the input matrix <code>x</code>. It fits linear, logistic and multinomial, poisson, and Cox regression models. A variety of predictions can be made from the fitted models. It can also fit multi-response linear regression.</p>

<p>The authors of glmnet are Jerome Friedman, Trevor Hastie, Rob Tibshirani and Noah Simon, and the R package is maintained by Trevor Hastie. The matlab version of glmnet is maintained by Junyang Qian. This vignette describes the usage of glmnet in R.</p>

<p><code>glmnet</code> solves the following problem
\[
\min_{\beta_0,\beta} \frac{1}{N} \sum_{i=1}^{N} w_i l(y_i,\beta_0+\beta^T x_i) + \lambda\left[(1-\alpha)||\beta||_2^2/2 + \alpha ||\beta||_1\right],
\]
over a grid of values of \(\lambda\) covering the entire range. Here \(l(y,\eta)\) is the negative log-likelihood contribution for observation \(i\); e.g. for the Gaussian case it is \(\frac{1}{2}(y-\eta)^2\). The <em>elastic-net</em> penalty is controlled by \(\alpha\), and  bridges the gap between lasso (\(\alpha=1\), the default) and ridge (\(\alpha=0\)). The tuning parameter \(\lambda\) controls the overall strength of the penalty.</p>

<p>It is known that the ridge penalty shrinks the coefficients of correlated predictors towards each other while the lasso tends to pick one of them and discard the others. The elastic-net penalty mixes these two; if predictors are correlated in groups, an \(\alpha=0.5\) tends to select the groups in or out together. This is a higher level parameter, and users might pick a value upfront, else experiment with a few different values. One use of \(\alpha\) is for numerical stability; for example, the elastic net with \(\alpha = 1 - \epsilon\) for some small \(\epsilon > 0\) performs much like the lasso, but removes any degeneracies and wild behavior caused by extreme correlations.</p>

<p>The <code>glmnet</code> algorithms use cyclical coordinate descent, which successively optimizes the objective function over each parameter with others fixed, and cycles repeatedly until convergence. The package also makes use of the strong rules for efficient restriction of the active set. Due to highly efficient updates and techniques such as warm starts and active-set convergence, our algorithms can compute the solution path very fast.</p>

<p>The code can handle sparse input-matrix formats, as well as range constraints on coefficients. The core of <code>glmnet</code> is a set of fortran subroutines, which make for very fast execution. </p>

<p>The package also includes methods for prediction and plotting, and a function that performs K-fold cross-validation. </p>

<p><a id="install"></a></p>

<h2>Installation</h2>

<p>Like many other R packages, the simplest way to obtain <code>glmnet</code> is to install it directly from CRAN. Type the following command in R console:</p>

<pre><code class="r">install.packages(&quot;glmnet&quot;, repos = &quot;http://cran.us.r-project.org&quot;)
</code></pre>

<p>Users may change the <code>repos</code> options depending on their locations and preferences. Other options such as the directories where to install the packages can be altered in the command. For more details, see <code>help(install.packages)</code>.</p>

<p>Here the R package has been downloaded and installed to the default directories.</p>

<p>Alternatively, users can download the package source at <a href="http://cran.r-project.org/package=glmnet">http://cran.r-project.org/package=glmnet</a> and type Unix commands to install it to the desired location.</p>

<p><a href="#top">Back to Top</a></p>

<p><a id="qs"></a></p>

<h2>Quick Start</h2>

<p>The purpose of this section is to give users a general sense of the package, including the components, what they do and some basic usage. We will briefly go over the main functions, see the basic operations and have a look at the outputs. Users may have a better idea after this section what functions are available, which one to choose, or at least where to seek help. More details are given in later sections.</p>

<p>First, we load the <code>glmnet</code> package:</p>

<pre><code class="r">library(glmnet)
</code></pre>

<pre><code>## Loading required package: Matrix
## Loading required package: foreach
## Loaded glmnet 2.0-5
</code></pre>

<p>The default model used in the package is the Guassian linear model or &ldquo;least squares&rdquo;, which we will demonstrate in this section. We load a set of data created beforehand for illustration. Users can either load their own data or use those saved in the workspace.</p>

<pre><code class="r">data(QuickStartExample)
</code></pre>

<p>The command loads an input matrix <code>x</code> and a response vector <code>y</code> from this saved R data archive. </p>

<p>We fit the model using the most basic call to  <code>glmnet</code>.</p>

<pre><code class="r">fit = glmnet(x, y)
</code></pre>

<p>&ldquo;fit&rdquo; is an object of class <code>glmnet</code> that contains all the relevant information of the fitted model for further use. We do not encourage users to extract the components directly. Instead, various methods are provided for the object such as <code>plot</code>, <code>print</code>, <code>coef</code> and <code>predict</code> that enable us to execute those tasks more elegantly.</p>

<p>We can visualize the coefficients by executing the <code>plot</code> function:</p>

<pre><code class="r">plot(fit)
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-5"/> </p>

<p>Each curve corresponds to a variable. It shows the path of its coefficient against the \(\ell_1\)-norm of the whole coefficient vector at as \(\lambda\) varies. The axis above indicates the number of nonzero coefficients at the current \(\lambda\), which is the effective degrees of freedom (<em>df</em>) for the lasso. Users may also wish to annotate the curves; this can be done by setting <code>label = TRUE</code> in the plot command. </p>

<p>A summary of the <code>glmnet</code> path at each step is displayed if we just enter the object name or use the <code>print</code> function:</p>

<pre><code class="r">print(fit)
</code></pre>

<pre><code>## 
## Call:  glmnet(x = x, y = y) 
## 
##       Df    %Dev   Lambda
##  [1,]  0 0.00000 1.631000
##  [2,]  2 0.05528 1.486000
##  [3,]  2 0.14590 1.354000
##  [4,]  2 0.22110 1.234000
##  [5,]  2 0.28360 1.124000
##  [6,]  2 0.33540 1.024000
##  [7,]  4 0.39040 0.933200
##  [8,]  5 0.45600 0.850300
##  [9,]  5 0.51540 0.774700
## [10,]  6 0.57350 0.705900
## [11,]  6 0.62550 0.643200
## [12,]  6 0.66870 0.586100
## [13,]  6 0.70460 0.534000
## [14,]  6 0.73440 0.486600
## [15,]  7 0.76210 0.443300
## [16,]  7 0.78570 0.404000
## [17,]  7 0.80530 0.368100
## [18,]  7 0.82150 0.335400
## [19,]  7 0.83500 0.305600
## [20,]  7 0.84620 0.278400
## [21,]  7 0.85550 0.253700
## [22,]  7 0.86330 0.231200
## [23,]  8 0.87060 0.210600
## [24,]  8 0.87690 0.191900
## [25,]  8 0.88210 0.174900
## [26,]  8 0.88650 0.159300
## [27,]  8 0.89010 0.145200
## [28,]  8 0.89310 0.132300
## [29,]  8 0.89560 0.120500
## [30,]  8 0.89760 0.109800
## [31,]  9 0.89940 0.100100
## [32,]  9 0.90100 0.091170
## [33,]  9 0.90230 0.083070
## [34,]  9 0.90340 0.075690
## [35,] 10 0.90430 0.068970
## [36,] 11 0.90530 0.062840
## [37,] 11 0.90620 0.057260
## [38,] 12 0.90700 0.052170
## [39,] 15 0.90780 0.047540
## [40,] 16 0.90860 0.043310
## [41,] 16 0.90930 0.039470
## [42,] 16 0.90980 0.035960
## [43,] 17 0.91030 0.032770
## [44,] 17 0.91070 0.029850
## [45,] 18 0.91110 0.027200
## [46,] 18 0.91140 0.024790
## [47,] 19 0.91170 0.022580
## [48,] 19 0.91200 0.020580
## [49,] 19 0.91220 0.018750
## [50,] 19 0.91240 0.017080
## [51,] 19 0.91250 0.015570
## [52,] 19 0.91260 0.014180
## [53,] 19 0.91270 0.012920
## [54,] 19 0.91280 0.011780
## [55,] 19 0.91290 0.010730
## [56,] 19 0.91290 0.009776
## [57,] 19 0.91300 0.008908
## [58,] 19 0.91300 0.008116
## [59,] 19 0.91310 0.007395
## [60,] 19 0.91310 0.006738
## [61,] 19 0.91310 0.006140
## [62,] 20 0.91310 0.005594
## [63,] 20 0.91310 0.005097
## [64,] 20 0.91310 0.004644
## [65,] 20 0.91320 0.004232
## [66,] 20 0.91320 0.003856
## [67,] 20 0.91320 0.003513
</code></pre>

<p>It shows from left to right the number of nonzero coefficients (<code>Df</code>), the percent (of null) deviance explained (<code>%dev</code>) and the value of \(\lambda\) (<code>Lambda</code>). Although by default <code>glmnet</code> calls for 100 values of <code>lambda</code> the program stops early if %dev% does not change sufficently from one lambda to the next (typically near the end of the path.)</p>

<p>We can obtain the actual coefficients at one or more \(\lambda\)&#39;s within the range of the sequence:</p>

<pre><code class="r">coef(fit,s=0.1)
</code></pre>

<pre><code>## 21 x 1 sparse Matrix of class &quot;dgCMatrix&quot;
##                        1
## (Intercept)  0.150928072
## V1           1.320597195
## V2           .          
## V3           0.675110234
## V4           .          
## V5          -0.817411518
## V6           0.521436671
## V7           0.004829335
## V8           0.319415917
## V9           .          
## V10          .          
## V11          0.142498519
## V12          .          
## V13          .          
## V14         -1.059978702
## V15          .          
## V16          .          
## V17          .          
## V18          .          
## V19          .          
## V20         -1.021873704
</code></pre>

<p>(why <code>s</code> and not <code>lambda</code>? In case later we want to allow one to specify the model size in other ways.)
Users can also make predictions at specific \(\lambda\)&#39;s with new input data:</p>

<pre><code class="r">nx = matrix(rnorm(10*20),10,20)
predict(fit,newx=nx,s=c(0.1,0.05))
</code></pre>

<pre><code>##                1          2
##  [1,] -5.5155462 -5.6471349
##  [2,]  4.2992190  4.4266457
##  [3,]  1.6747706  1.6740427
##  [4,]  1.2253111  1.2508196
##  [5,]  3.2800685  3.4072855
##  [6,] -1.5786571 -1.7047168
##  [7,] -3.6281723 -3.8167525
##  [8,]  0.9106243  0.8953048
##  [9,]  3.2410747  3.3610994
## [10,]  1.7576098  1.9716290
</code></pre>

<p>The function <code>glmnet</code> returns a sequence of models for the users to choose from. In many cases, users may prefer the software to select one of them. Cross-validation is perhaps the simplest and most widely used method for that task.</p>

<p><code>cv.glmnet</code> is the main function to do cross-validation here, along with various supporting methods such as plotting and prediction. We still act on the sample data loaded before.</p>

<pre><code class="r">cvfit = cv.glmnet(x, y)
</code></pre>

<p><code>cv.glmnet</code> returns a <code>cv.glmnet</code> object, which is &ldquo;cvfit&rdquo; here, a list with all the ingredients of the cross-validation fit. As for <code>glmnet</code>, we do not encourage users to extract the components directly except for viewing the selected values of \(\lambda\). The package provides well-designed functions for potential tasks.</p>

<p>We can plot the object.</p>

<pre><code class="r">plot(cvfit)
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-10"/> </p>

<p>It includes the cross-validation curve (red dotted line), and upper and lower standard deviation curves along the \(\lambda\) sequence (error bars). Two selected \(\lambda\)&#39;s are indicated by the vertical dotted lines (see below).</p>

<p>We can view the selected \(\lambda\)&#39;s and the corresponding coefficients. For example,</p>

<pre><code class="r">cvfit$lambda.min
</code></pre>

<pre><code>## [1] 0.06896889
</code></pre>

<p><code>lambda.min</code> is the value of \(\lambda\) that gives minimum mean cross-validated error. The other \(\lambda\) saved is  <code>lambda.1se</code>, which gives the most regularized model such that error is within one standard error of the minimum. To use that, we only need to replace <code>lambda.min</code> with <code>lambda.1se</code> above.</p>

<pre><code class="r">coef(cvfit, s = &quot;lambda.min&quot;)
</code></pre>

<pre><code>## 21 x 1 sparse Matrix of class &quot;dgCMatrix&quot;
##                        1
## (Intercept)  0.147927056
## V1           1.337393911
## V2           .          
## V3           0.704086481
## V4           .          
## V5          -0.842853150
## V6           0.549403480
## V7           0.032703914
## V8           0.342430521
## V9           .          
## V10          0.001206608
## V11          0.178995989
## V12          .          
## V13          .          
## V14         -1.079993473
## V15          .          
## V16          .          
## V17          .          
## V18          .          
## V19          .          
## V20         -1.061382444
</code></pre>

<p>Note that the coefficients are represented in the sparse matrix format. The reason is that the solutions along the regularization path are often sparse, and hence it is more efficient in time and space to use a sparse format. If you prefer non-sparse format, pipe the output through <code>as.matrix()</code>.</p>

<p>Predictions can be made based on the fitted <code>cv.glmnet</code> object. Let&#39;s see a toy example.</p>

<pre><code class="r">predict(cvfit, newx = x[1:5,], s = &quot;lambda.min&quot;)
</code></pre>

<pre><code>##               1
## [1,] -1.3626977
## [2,]  2.5736774
## [3,]  0.5767335
## [4,]  2.0062604
## [5,]  1.5410061
</code></pre>

<p><code>newx</code> is for the new input matrix and <code>s</code>, as before, is the value(s) of \(\lambda\) at which predictions are made. </p>

<p>That is the end of <code>glmnet</code> 101. With the tools introduced so far, users are able to fit the entire elastic net family, including ridge regression, using squared-error loss. In the package, there are many more options that give users a great deal of flexibility. To learn more, move on to later sections.</p>

<p><a href="#top">Back to Top</a></p>

<p><a id="lin"></a></p>

<h2>Linear Regression</h2>

<p>Linear regression here refers to two families of models. One is <code>gaussian</code>, the Gaussian family, and the other is <code>mgaussian</code>, the multiresponse Gaussian family. We first discuss the ordinary Gaussian and the multiresponse one after that.</p>

<h3>Gaussian Family</h3>

<p><code>gaussian</code> is the default family option in the function <code>glmnet</code>. Suppose we have observations \(x_i \in \mathbb{R}^p\) and the responses \(y_i \in \mathbb{R}, i = 1, \ldots, N\). The objective function for the Gaussian family is 
\[
\min_{(\beta_0, \beta) \in \mathbb{R}^{p+1}}\frac{1}{2N} \sum_{i=1}^N (y_i -\beta_0-x_i^T \beta)^2+\lambda \left[ (1-\alpha)||\beta||_2^2/2 + \alpha||\beta||_1\right],
\]
where \(\lambda \geq 0\) is a complexity parameter and \(0 \leq \alpha \leq 1\) is a compromise between ridge (\(\alpha = 0\)) and lasso (\(\alpha = 1\)). </p>

<p>Coordinate descent is applied to solve the problem. Specifically, suppose we have current estimates \(\tilde{\beta_0}\) and \(\tilde{\beta}_\ell\) \(\forall j\in 1,]\ldots,p\). By computing the gradient at \(\beta_j = \tilde{\beta}_j\) and simple calculus, the update is
\[
\tilde{\beta}_j \leftarrow \frac{S(\frac{1}{N}\sum_{i=1}^N x_{ij}(y_i-\tilde{y}_i^{(j)}),\lambda \alpha)}{1+\lambda(1-\alpha)},
\]
where \(\tilde{y}_i^{(j)} = \tilde{\beta}_0 + \sum_{\ell \neq j} x_{i\ell} \tilde{\beta}_\ell\), and \(S(z, \gamma)\) is the soft-thresholding operator with value \(\text{sign}(z)(|z|-\gamma)_+\).</p>

<p>This formula above applies when the <code>x</code> variables are standardized to have unit variance (the default); it is slightly more complicated when they are not. Note that for &ldquo;family=gaussian&rdquo;, <code>glmnet</code> standardizes \(y\) to have unit variance before computing its lambda sequence (and then unstandardizes the resulting coefficients); if you wish to reproduce/compare results with other software, best to supply a standardized \(y\) first (Using the &ldquo;1/N&rdquo; variance formula).</p>

<p><code>glmnet</code> provides various options for users to customize the fit. We introduce some commonly used options here and they can be specified in the <code>glmnet</code> function.</p>

<ul>
<li><p><code>alpha</code> is for the elastic-net mixing parameter \(\alpha\), with range \(\alpha \in [0,1]\). \(\alpha = 1\) is the lasso (default) and \(\alpha = 0\) is the ridge.</p></li>
<li><p><code>weights</code> is for the observation weights. Default is 1 for each observation. (Note: <code>glmnet</code> rescales the weights to sum to N, the sample size.)</p></li>
<li><p><code>nlambda</code> is the number of \(\lambda\) values in the sequence. Default is 100. </p></li>
<li><p><code>lambda</code> can be provided, but is typically not and the program constructs a sequence. When automatically generated, the \(\lambda\) sequence is determined by <code>lambda.max</code> and <code>lambda.min.ratio</code>. The latter is the ratio of smallest value of the generated  \(\lambda\) sequence (say <code>lambda.min</code>) to <code>lambda.max</code>.  The program then generated <code>nlambda</code> values linear on the log scale from <code>lambda.max</code> down to <code>lambda.min</code>. <code>lambda.max</code> is not given, but easily computed from the input \(x\) and \(y\); it is the smallest value for <code>lambda</code> such that all the coefficients are zero.  For <code>alpha=0</code> (ridge) <code>lambda.max</code> would be \(\infty\); hence for this case we pick a value corresponding to a small value for <code>alpha</code> close to zero.)</p></li>
<li><p><code>standardize</code> is a logical flag for <code>x</code> variable standardization, prior to fitting the model sequence. The coefficients are always returned on the original scale. Default is <code>standardize=TRUE</code>.  </p></li>
</ul>

<p>For more information, type <code>help(glmnet)</code> or simply <code>?glmnet</code>.</p>

<p>As an example, we set \(\alpha = 0.2\) (more like a ridge regression), and give double weights to the latter half of the observations. To avoid too long a display here, we set <code>nlambda</code> to 20. In practice, however, the number of values of \(\lambda\) is recommended to be 100 (default) or more. In most cases, it does not come with extra cost because of the warm-starts used in the algorithm, and for nonlinear models leads to better convergence properties.</p>

<pre><code class="r">fit = glmnet(x, y, alpha = 0.2, weights = c(rep(1,50),rep(2,50)), nlambda = 20)
</code></pre>

<p>We can then print the <code>glmnet</code> object. </p>

<pre><code class="r">print(fit)
</code></pre>

<pre><code>## 
## Call:  glmnet(x = x, y = y, weights = c(rep(1, 50), rep(2, 50)), alpha = 0.2,      nlambda = 20) 
## 
##       Df   %Dev   Lambda
##  [1,]  0 0.0000 7.939000
##  [2,]  4 0.1789 4.889000
##  [3,]  7 0.4445 3.011000
##  [4,]  7 0.6567 1.854000
##  [5,]  8 0.7850 1.142000
##  [6,]  9 0.8539 0.703300
##  [7,] 10 0.8867 0.433100
##  [8,] 11 0.9025 0.266700
##  [9,] 14 0.9101 0.164300
## [10,] 17 0.9138 0.101200
## [11,] 17 0.9154 0.062300
## [12,] 17 0.9160 0.038370
## [13,] 19 0.9163 0.023630
## [14,] 20 0.9164 0.014550
## [15,] 20 0.9164 0.008962
## [16,] 20 0.9165 0.005519
## [17,] 20 0.9165 0.003399
</code></pre>

<p>This displays the call that produced the object <code>fit</code> and a three-column matrix with columns <code>Df</code> (the number of nonzero coefficients), <code>%dev</code> (the percent deviance explained) and <code>Lambda</code> (the corresponding value of \(\lambda\)).</p>

<p>(Note that the <code>digits</code> option can used to specify significant digits in the printout.)</p>

<p>Here the actual number of \(\lambda\)&#39;s here is less than specified in the call. The reason lies in the stopping criteria of the algorithm. According to the default internal settings, the computations stop if either the fractional change in deviance down the path is less than \(10^{-5}\) or the fraction of explained deviance reaches \(0.999\). From the last few lines , we see the fraction of deviance does not change much and therefore the computation ends when meeting the stopping criteria. We can change such internal parameters. For details, see the Appendix section or type <code>help(glmnet.control)</code>.</p>

<p>We can plot the fitted object as in the previous section. There are more options in the <code>plot</code> function. </p>

<p>Users can decide what is on the X-axis. <code>xvar</code> allows three measures: &ldquo;norm&rdquo; for the \(\ell_1\)-norm of the coefficients (default), &ldquo;lambda&rdquo; for the log-lambda value and &ldquo;dev&rdquo; for %deviance explained. </p>

<p>Users can also label the curves with variable sequence numbers simply by setting <code>label = TRUE</code>.</p>

<p>Let&#39;s plot &ldquo;fit&rdquo; against the log-lambda value and with each curve labeled.</p>

<pre><code class="r">plot(fit, xvar = &quot;lambda&quot;, label = TRUE)
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-16"/> </p>

<p>Now when we plot against %deviance we get a very different picture. This is percent deviance explained on the training data. What we see here is that toward the end of the path this value are not changing much, but the coefficients are &ldquo;blowing up&rdquo; a bit. This lets us focus attention on the parts of the fit that matter. This will especially be true for other models, such as logistic regression.</p>

<pre><code class="r">plot(fit, xvar = &quot;dev&quot;, label = TRUE)
</code></pre>

<p><img 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Tfl/vvvl9y5I2P0mgIfMV9PNoQESIAESCC7BIYOHSpVq1aV1q1bZ/cQjtsvMm5THIeVFSIBEiABEggXAtOmTZO1a9fKmDFjwqXKPtWTAu8TJm5EAiRAAiQQiQSwmMyECRMEwXXhnNQmvWtDF316VPgeCZAACZBAxBM4cOCAPPfcc9K/f3+pUKFCxLWXAh9xl5QNIgESIAESyIrAqVOnZODAgXLZZZdJ8+bNs9o8LD+nwIflZWOlSYAESIAEckJg7Nixki9fPrn99ttzchhH78sxeEdfHlaOBEiABEjA3wR+/vln+f333+Wdd97x96EddTwKvKMuBytDAiRAAiQQSAKIlh85cqSxQoUKBfJUIT82BT7kl4AVIAESIAESCAaBdevWSe3atWXx4sUSHx8fjFOG9Bwcgw8pfp6cBEiABEggGASQghbR8r/++qs0btw4GKcM+Tko8CG/BKwACZAACZBAIAmsX79eHnvsMXnkkUekTZs2gTyVo45NgXfU5WBlSIAESIAE/EkAY+5PPPGEPProo9K2bVt/Htrxx+IYvOMvEStIAiRAAiSQHQKrVq2SAQMGyFNPPRWxc90z48IefGZ0+BkJkAAJkEBYEli2bJk8/fTTRuAjNZFNVheGPfisCPFzEiABEiCBsCKAKPkXX3xRXnjhBWnUqFFY1d2flaXA+5Mmj0UCJEACJBBSAgsWLJCXX35ZBg0aJA0aNAhpXUJ9cgp8qK8Az08CJEACJOAXAnPnzpUhQ4bIq6++KnXq1PHLMcP5IBT4cL56rDsJkAAJkIAhgNSzI0aMkKFDh0rNmjVJRQlQ4Pk1IAESIAESCGsCyC3/xhtvyLBhw6RatWph3RZ/Vp4C70+aPBYJkAAJkEBQCcyePVvefvtt03uPi4sL6rmdfjIKvNOvEOtHAiRAAiSQLgH03LEiHBaPqVSpUrrbRPObFPhovvpsOwmQAAmEIYGTJ0/KxIkT5YcffpBRo0ZJhQoVwrAVga8yE90EnjHPQAIkQAIk4CcC//zzj9x9992yefNmM+5Occ8YLHvwGbPhJyRAAiRAAg4hcOrUKZk0aZJMnTpV+vTpIx07dnRIzZxbDQq8c68Na0YCJEACJKAENmzYIK+88oqUKVNGxo8fLyVKlCAXHwhQ4H2AxE1IgARIgASCTwC99o8++ki++OILuf/++6VLly7Br0QYn5ECH8YXj1UnARIggUglkJCQYDLSFS1aVN59910pVapUpDY1YO1ynMAjOvLw4cNSvHjxgDWaByYBEiABEnAmgZSUFJkyZYp8+umncs8998ill17qzIqGQa0cEUWflJQkzzzzjJnHGBsba8ZXChYsKPXr15f33nsvDDCyiiRAAiRAAjklsGXLFhNAt2jRIjO/neKeM6KO6ME/9NBDsmPHDpkxY4ZUrVpVIO6HDh2SVatWSd++fSUxMdGMv+SsqdybBEiABEjAiQRs25bPPvtMJk+eLHfeeadcccUVTqxm2NXJEQL//fffC1YBKleuXCpAjLu0aNHCJDF4/vnnKfCpZPiEBEiABCKHwLZt22Tw4MGSK1cuGTt2bBodiJxWhqYljnDRwxWPlIPplenTp0vp0qXT+4jvkQAJkAAJhDGBL7/8Uh544AHp0KGDSTfr3ckL42Y5puqO6MG/+OKL0qtXL3nttdfMSkBFihSRgwcPyurVqwVBd99++61jgLEiJEACJEACOSOAIVn02jENbsyYMUw1mzOcGe7tCIFv3LixLFmyxLjpMTUCFx+9dsx7bNOmjViWlWED+AEJkAAJkED4EJg2bZpJVnPzzTdLjx49+PsewEvnCIFH+/Llyyft27fPUVPR209OTk73GAjUw+csJEACJEACwSewa9cuGTJkiBw/flxGjx7N1d+CcAkcI/AZtXXt2rVy7NgxQS8/q4I8xYjETK8gIr9KlSpmCkZ6n/M9EiABEiCBwBDADCks63rjjTfKDTfcwF57YDCfdVTHCzwEe9OmTTJu3LizKn/mG7179xZYeqVfv37G9Z/eZ3yPBEiABEjA/wT27NkjQ4cONTFVr7/+uulk+f8sPGJGBBwv8M8++2xGdef7JEACJEACDiUwc+ZME0B33XXXmSDqmBhHTNpyKK3AVMvxAh+YZvOoJEACJEACgSCwb98+GTZsmKD3PnLkSImPjw/EaXhMHwg4QuCHDx+eYXAc2lC7dm256qqrfGgONyEBEiABEggVgR9++EHefPNNufLKK+Wll14yyWtCVReeV8QRAo+pcYiqvO2220ya2jMvDBPdnEmEr0mABEjAOQR27txpgug2btxoeu/Vq1d3TuWiuCaOEPg33nhDsIIQDHd/LCRAAiRAAs4nsHv3bsHspV9//dV4WZ9++mnJndsRsuJ8eEGooWOiHjA/EgvMHDlyJAjN5ilIgARIgASyS8Azvo6FYZB59MMPPzQzmCju2SUamP0cc6tVqFAhs5JQYJrJo5IACZAACeSUwN69e83vNMbau3fvboQdAs/iTAKOEXhn4mGtSIAESIAEEBn/0UcfCVb+xBrtcMtjxU8WZxOgwDv7+rB2JEACJBAyAvv375ePP/5YMKe9W7du8sEHH0ixYsVCVh+e+NwIUODPjRe3JgESIIGIJ4DVPNFj/+677+SSSy6RiRMnSokSJSK+3ZHWQAp8pF1RtocESIAEskkAwv7JJ5/I9OnTpXPnzhT2bHJ0ym4UeKdcCdaDBEiABEJE4PDhwzJlyhT55ptvpGPHjjJhwgQpVapUiGrD0/qLAAXeXyR5HBIgARIIMwKYlowe+9SpU81y3ePHjxcmFguzi5hJdSnwmcDhRyRAAiQQiQQg7Fip8+uvv5Y2bdrIu+++K2XKlInEpkZ1myjwUX352XgSIIFoInD06FH5/PPP5csvv5TWrVvL2LFjpVy5ctGEIKraSoGPqsvNxpIACUQjgePHjxthh7i3bNlS3n77bSlfvnw0ooiqNlPgo+pys7EkQALRRADCjt76p59+Ki1atDDrs1eoUCGaEER1WynwUX352XgSIIFIJJCYmGiEHePszZo1k7feeksqVqwYiU1lmzIhQIHPBA4/IgESIIFwInDixAkTOIcpb02bNpXXX39dKlWqFE5NYF39SIAC70eYPBQJkAAJhIJAUlJSqrA3atRIRo0aJZUrVw5FVXhOBxGgwDvoYrAqJEACJHAuBCDs06ZNM2llGzZsKCNGjJC4uLhzOQS3jWACFPgIvrhsGgmQQGQSSE5ONlnnJk+eLPXq1ZPhw4dLfHx8ZDaWrco2AQp8ttFxRxIgARIILgEI+4wZM8ya7LVq1ZKhQ4dKtWrVglsJni1sCFDgw+ZSsaIkQALRSuDkyZPy7bffyocffijVq1eXV155RWrUqBGtONhuHwlQ4H0Exc1IgARIINgETp06ZZZsnTRpklStWlUGDRok6LmzkIAvBCjwvlDiNiRAAiQQRAIQ9pkzZwqEHdHwL7zwgtSuXTuINeCpIoEABT4SriLbQAIkEBEEIOyzZ8+W999/X8477zx57rnnpG7duhHRNjYi+AQo8MFnzjOSAAmQQBoCKSkpRtg/+OADs/jLgAEDpH79+mm24QsSOFcCFPhzJcbtSYAESMBPBCDsP/74o+mxYx32/v37S4MGDfx0dB4m2glQ4KP9G8D2kwAJBJ2Abdvy008/GWEvXry4PP7444IMdCwk4E8CFHh/0uSxSIAESCATAhhjnzt3rowbN06KFCkiffv2lSZNmmSyBz8igewToMBnnx33JAESIAGfCGzYsMHMY//hhx+kSpUq8vDDD5vFYHzamRs5nsCuXbsE17h58+aOqisF3lGXg5UhARKIFAKHDh0SCDqmux08eFC6desmb7/9tgmii5Q2sh0iiKPAcrzIT7B161bp0aOHY7BQ4B1zKVgREiCBcCcAF/z8+fONqC9atEhatmwp9913H93w4X5hM6l/TEyMPPXUU3LHHXfIk08+mcmWwf+IAh985jwjCZBAhBHYtGmTyTj3/fffS8WKFaVr164mIj5//vwR1tLoa87Bkwfk6Y0Pyls1JqfbeKQRPnbsmOnFDx48WBo3bpzudqF4kwIfCuo8JwmQQNgTOHLkiImE/+6772TPnj3SpUsXeeONN4zAh33j2IBUAokpx0UsK/X1mU9y5cpl1gYoX768tGvX7syPQ/qaAh9S/Dw5CZBAOBHA9LYFCxYYFzxc8RdeeKFxzTZr1kw1IGMRCKc2sq5pCew7uUdK5i6d9k2vV7juWK4XwzMQeycVCryTrgbrQgIk4EgCCJ5CT33WrFlSpkwZ44J/9NFHpVChQo6sLyvlPwI7kv6T0nnKZHlAp4k7KkyBz/KycQMSIIFoJHD8+HHjgkcU/LZt24wLfsSIEWbxl2jkEa1t3pG0TSrEVgrL5lPgw/KysdIkQAKBIrB48WLTW0dCmqZNm0qvXr3koosuEkRLs0QfgZXHlsn1pW8Jy4ZT4MPysrHSJEAC/iSwfft2M64ON3yxYsXMnPWHHnrIZJvz53l4rPAikJSSJBsT/5Fa+cNz4R8KfHh931hbEiABPxFITEyUX375xQg7prl16tRJhgwZIvHx8X46Aw8T7gTWHl8hcfmqS96YvGHZFAp8WF42VpoESCC7BJYtW2Zc8HPmzDELvCDzWIsWLRwXAZ3d9nE//xGYvX+GtCnayX8HDPKRKPBBBs7TkQAJBJ8AcoUjWA5R8Hnz5jUueGSYK1q0aPArwzOGBYFjp47K0qML5N7y/cKivulVkgKfHhW+RwIkEPYEkpKS5NdffzXCvn79eunYsaM8//zzUrNmzbBvGxsQeAJ/HvpVGhZsKgVzhe9USAp84L8nPAMJkEAQCaxcudK44CHu9evXlyuuuEJatWoluXPz5y6IlyHsT/XzwZlyeQnnLByTHaD8xmeHGvchARJwFAGkikUeeETBYzobVm57//33pUSJEo6qJysTHgSm7f1MYq280rxIm/CocAa1pMBnAIZvkwAJOJtAcnKy/P7770bU16xZY/KAP/PMM1KnTh1nV5y1czSBXUk75Ks9H8ng+LccXU9fKkeB94UStyEBEnAMgbVr1xpR/+mnn8wa3Oitv/TSSxIbG+uYOrIi4UtgzPbhcnWpXlI2tnz4NsJdcwp82F9CNoAEIp/A/v37jQsekfAInoOojx8/XkqXzngRkMinwhb6m8DPB2bJ0ZQj0r3Etf4+dEiOR4EPCXaelARIICsCWJ3rjz/+MFHwmLvetm1bwQIvDRo0yGpXfk4C50xg/8l98uGucTKg8qsSY0VGWmIK/Dl/DbgDCZBAIAlgShuC5X744QepVq2aWbkN09swf52FBAJB4KR9UgZveVauKtlTquarcc6nQDzIkiVLzNoFTlpVjgJ/zpeSO5AACfibwMGDB2X27Nmmt3706FEj6mPHjpVy5cr5+1Q8HgmcRWDCjtG6JGxZubxk9qbFvfrqq9KmTRuZPn26XHnllWcdP1RvUOBDRZ7nJYEoJwAX/Lx580xvHb2f1q1bS58+fUz62ChHw+YHkcBvB3+Q5UeXyNCqb2frrLg5XbFihdi2Lddff322jhGonSjwgSLL45IACaRLYOPGjaanjnnrlSpVMgFzmN6WP3/+dLfnmyQQKAKbEzfKezvfkhcqD5f8Mel//+ydIqfaiuRek34tLMuSunXryrPPPmsMvXmnFAq8U64E60ECEUzg8OHDZkwdUfCIiO/SpYu8+eabUqFChQhuNZvmZALHU47L0K3PS++yD0rlfJmsIPiviHVVxi0pUqSIWVYYKxFecMEFGW8Ygk8o8CGAzlOSQDQQSElJkQULFpjeOh6xYts999wjTZo0EfR6WEgglARGbxsijQo109XiOmZajZQZKvBZJLTD7I6TJ086Lh0yBT7TS8sPSYAEzpXA5s2bU13wZcuWNQFzjz/+uBQsWPBcD8XtSSAgBJCKdu/J3dLvvGczPb69VD/eqAL/XKabmQ+duNaBYwV+9+7dUrx4ccfdEWV9mbkFCUQfAUS+I7McprdhadZLLrlERowYIZUrV44+GGyxowksOTJfpu79RIZoKtrcVsYSaO8SSXlBJOZlFfgwnaGZceuCeIluvfVWQZBN7dq1BWko4e748ccfpVChQtKzZ0957bXXJE+ePEGsEU9FAiSQFQFEDS9evNiI+l9//WXGH2+//XZp1qyZWfAlq/35OQkEm8Ccgz9JzzVd5K9G/0ipPGUyPL19SsV9oAr7DWp1M9zM8R84QuAxxQA9ABREIELoJ02aJFghCmKP9557zgcfieNxs4IkEP4Etm3bZlzwCJjDam1IG/vII49I4cKFw79xbEHEEpi5b6p8sWey/HH+GqmUNy7DdtopKu7qubc07i6mZ4abhcUHjhB4b1KzZs2SdevWmR8L/HhgEQmIPAXemxKfk0BwCRw/flx+/vlnI+xbt26Vzp07y7BhwyQuLi64FeHZSCAbBD7YOVYWHflLXo57XcrEZp48yX5NT6Aibz2ajRM5bBfHCPyff/5ppsw0b95c9u7dm9obWL58uTRu3Nhh2FgdEogOAkuXLjWijmVZEf2OIbOLLrpInJSOMzquBFuZHQJJKUnyxn+D5cDJ/fJK3BtSMFehTA+TMk7E1vnuMa+rwOfKdNOw+NARAn/TTTfJN998I4MGDRJkBcqXL598/PHHMnDgQDNXFuPxLCRAAsEhsGPHDiPq8KYh8r1r165y//33S9GiRYNTAZ6FBPxA4PDJQzJ46//pWHtpeb7KsEwD6nC6U/epuP8pkkvNSj/njR9qFdxDOELgH3vsMYGhYHzv0KFD5jl+WDC9BsF2LCRAAoEjAFGfM2eOzJ07V/7991/jgscNd/Xq1QN3Uh6ZBAJEYGfSdnlpc39pXuRiuanMXZmexU5SYR+sm8SquC9QcQ/TiPn0GukIgfeuWMWKFQWGAnc9CwmQQGAIYMYKXO9YkhXZ5ZALHrm0kY2LLvjAMOdRA0/gn+OrdWW4/5OepXtL5+KXZXpC+6AOtz+joq7D8jHD9THCJms5TuDPvBr4ETp27JhP4/DvvPOOfPTRR2cewrzGEpTx8RoWyUICUUoAmbawqAsEHVagQAFp1aqV8ZIhlzYLCYQ7gfmHf5cx20fIwxX6S+NCF2baHHuLivtTKuqdVdx7Z7pp2H7oeIH/7LPPZNOmTTJunEY/ZFGQBhOWXunXr5/ADclCAtFEANNPMUcdgj5//nxzkwtRR26J8847L5pQsK0RTmDGvi9NApvnKg+V+HzVM20tMtQhiY2l4+4xXTLd1KcP33vvPdm5c6fccMMNjupIOk7g0cvAwhTIYoeCFXpYSIAEfCeALJAe1/uqVauM9wuijrnqDJTznSO3DA8CSLg0cecYWXZ0kYmUzyqBjf2djrlPUGF/XgW+kX/auHDhQrnxxhtNNsc777zTPwf1w1EcIfBJSUkmYh7JbRBkhwsG9yFc6gi+6907Qv0nfriAPAQJgMCGDRtMkBx66uhJYGGXq666Sl5++WXJmzeCooZ4uUnAi0BySrKM+u8VOXzqkJnjXiBXxusd2HO01z5Wd8Z4+0gV98peB8rkqZ2cLPYvv0pM507pboVFlbB40rfffistW7ZMd5tQvekIgX/ooYeM+3zGjBlStWpVMzUHkfToffTt21cSExPNNJ1QQeJ5ScBpBE6dOiXLli0zrnf01mNiYuTiiy+WPn36SP369Zkq1mkXjPXxO4F9yXvkdZ3jXjx3CYFbPlcGE9ft5SrsY/T0Gi0f00+Fvek5VkVvmO033hLJQODhccZw18MPPyxYMrZ79+7neILAbe4Igf/+++/N9Jxy5fTWyl3gSkQvZNSoUfL8889T4D1g+Bi1BJBNDuPo6KVjXB2zTeB6RypneLtYSCAaCGAd96/3fCyz9k+TbiWulhtK35Zus+3NKuzv6EfrVdTvUnFPvwOe7r7eb9pr1upNQRPvt9I8h1Yh+dMHH3wg6Kw6qThC4NHjQBpMjGGcWaZPny6lS5c+822+JoGoILBv3z4j6BB19Njxt4LpbPfee6+ULFkyKhiwkSQAAimaJP6HA9/Kp7vfN+u4j6j6rpTIU+osOPY+1xi7/bsK+01qA9VyoHT29G8l5uorzzqP9xvt27cXmNNKDprtv6a8+OKL0qtXLxPZW61aNSlSpIjJaLd69WpB0B3GNlhIIFoIYD11uN1hW7ZsMfkgkPQJnqz8+SMkxVa0XEy20y8EFh+ZJ8gnXzx3SRlQWT1W6UTJ28dU2D9W+1oF/XLtsU/Wx4yH5H2ql71gochevWNoEZ45WRwh8Mg1j/m5yKKVkJBgxuPRa0d6zDZt2pgABp+uBjcigTAkgKBSrKjoiXxH0Cl66YjGbdSoEZPOhOE1ZZX9Q2Bj4noj7PtO7pFby9wrTQufLbT2SRX1qWofqqC3UGF/Tx/P7tifc4Vs1aKUlwdLzP8GixWrae7CsDhC4MEN+eed6OIIw2vKKocBAYj4ggULUt3vuKGFqGP9BaaHDYMLyCoGlAAC6D7ePUGWHFkg15W+VToXu0xirJizzmki4xFAV0WFXVeBs+L0uR+KvWixnGrWUmK+ny5WGKdrdozA++Ga8BAk4GgCWEgJXir01OGxql27tgmSu/3226VMmTKOrjsrRwLBIIAAuql7P5FZunb7JcUvlzeqfyD5Y9IOS9n/am/9B5eJ9tRjkI3ufP/VLuWjKWJ/9oXkWrVUrDq1/XfgEByJAh8C6Dxl9BBAXgeP6x2LuCDPe9u2baV///5cRCl6vgZsaRYEEED344Hv5JPdE+X8gk3lf1XfkZK6Cpyn2Lvcoj5b3zmugo70ssgd7+Ncds9xMnu0Nd4lZYyG3R85KjHjxqib3w9+/sxOGITPKPBBgMxTRBcBBIci6h2GfA6YynbzzTeb9dRz5+afXHR9G9jarAgsOTLfjLMXzV08TQCdfVhF/Rd3Tz1BxVyD1GMe08f6WR3x3D639+4V+70PxJ7zu1g3XCdWz+vF0rwSkVD4axMJV5FtCCkBzPRYvHix6amjt455sRD1J598UurUqRPSuvHkJOBUApsSN8j7O9+WvSd3yy1l7pFmhVsIlm5N+cUt6pov3rpQRb2ntkAfM8hjk+3m2bqImQ13/DQdZ+9+qUbdvy9WhC1NToHP9teDO0YzgSNHjpjxdPTSESyH6Z0Ikhs9erRUqFAhmtGw7SSQKYF9yXtligbQLdKpb9drkpqORS+VXEtySYq63zF3XXRhQ6uT2gC1tMPvmR7X1w9tzQJpfzVVo+4/EquVBtJNeCci3PHptZ8Cnx4VvkcC6RDYtWtX6nj6mjVrzCIuEHWsVMhFXNIBxrdIwItAYkqiCaCbue9rXae9u4xO+VDyfpzPBMylaIypGVe/Vx9d64x57emfp7bml7BnfKfn+0mkdi2JGTVcrCoafh/BhQIfwReXTcs5gfXr16e63rFKG1zv11xzjQmWiw3TubE5p8IjkIDvBJDnAQF0U3a/J82PXCyj/n5fCv5cROSUHgPBcm+oqFf0/XjnsqWt6Z3tn34R+9uZoqswidX1Eol5fYSeL0AnPJfKBWFbCnwQIPMU4UMAi7gsXbo0NUguT548xvWOpVaRJharRrGQAAn4RmCpzmP/ZP37Un9+Exm2aJwU2VVMrA4q6E+rBTA8xV6+wog6AuekSWOJuaWXjuNfEDHBc77RF6HA+0qK20UsASziMm/ePNNTxyIulStXNqI+dOhQqRLhLryIvahsWEgJbD6QIL9+O1vK/FZeHk94UYpfXEIsLJPeTIU9QAHqtq7bYM+a7eqt58ol1qVdJea+u8XSoNdoLRT4aL3yUd7uvTo1BgFyiHpHmtiGDRsaUX/ggQekRIkSUU6HzSeBcyegnng5NPegrJq6TPLNKyAtGrWVuB7VJNfFKrZ5z/14vuxh61rsMvcvSYELftlysdq2kZinnxSrbgDdA75UzCHbUOAdciFYjcATwDoHnqQzSEDTvHlzueyyy+SFF17gIi6Bx88zRCABLMlqLxM5seSE7F6wU9YUXiF5OueWRk81lfwlCgSkxXZiosjiJWL/NV/sP+eKTlvR3noXsf7vGbE05TnLaQIU+NMs+CzCCKTo3f3y5ctTx9Mxvo6o93vuucf02HOpG4+FBEjAdwL2BhX0v922xJYDefbJyhpLZX7VOVLixVJyZb3rpVQe/6ddtrdudQn6X/NEVq7WqXS1xWp+kStgjtNSM7yAmQo8ejnIkZ2od0wTJkwwY5NXX311hgfjByQQagInTpww89LRU0fe93LlypnIdyxJjLnqLCRAAr4R0OyxIv+4xVx76aLCLjqF7Wi9Q7Kw7lyZ0e1zyV0mj3Qo1k0eLPKEFMiVw7VZvapl62JMsmSp2PMWqM0X0deWLtlq1mV/6QX21L1YZfY0Q4H/888/pXPnzrJ27VrBj+PChQuVcZLs00AGLGPJQgJOIYBFXDzj6YiAr1u3rhF1fE+xShsLCZBA1gRsTFvTzjFc7rZmkZOVauU0KK6hzmjrkCwL7v5DvpdvZLNmoGtTtLM8UuwZqZwvPusD+7iFvX27q5cOQf97uUitGmJddKHEvDRQrHj/ncfH6kTEZhkK/IcffijvvvuulC1bVj799FMzdomUnE8//TQFPiIufXg3Yqu67Dzj6Rs3bpQLL7xQOnXqJAMGDJCCBf3XkwhvSqw9CWRMAGlhZZW7h47euYq7VFZB15XZYq7U5/8n8m/utTqH/VuZe+hXqWnVk27FrpQLCreSXH7IG2urnsjfy1TU55meumh2SON279ZF5LkBYhUIzBi+tixqSoYCj14Rej9z5swxbnrMAcZUoiJFNEEBCwmEgMCqVatSRf2Y5pFG0plbb73VZJTjIi4huCA8ZVgRsI9rdVd4Cfp6fa2jVuihm3zvuoiLpZp66ORB+fXgbPlp13eSpHcBHdUFP6Lqu1IiT85XV7M1WZQ9VwV9/gLjgpeqVbWXfoHEPKcBcmG87rpTvwgZCjyii/v27SsITMJ61fhxve2220wPyamNYb0ii0BycrIZGoL7HVa8eHEj6s8884zUqlUrshrL1pCAnwnYR/SAcLdr7xxud0lQ0z8bq5EKem99DkGP1UctWK51seaG/3nLLFl+bLFcqL30u8s/InULqPrnoCDvuyDpjLrdEfWuY7yml251aCfWU4+LVbhwDo7OXbMikKHAX3755Sa/9tGjR6VHjx6yYcMGGTVqlNSsWTOrY/JzEsg2gcOHD6cu4oK4jxo1apjIdyy3ioA5FhIggfQJ2Af1fR07N2PocLn/pwYRRw/9AX2uU8OtM37xtydtk580jewvB76XMrHlpH3RrtKn4lOSPya/7pC9YhLOYAobxtIXLRapVMnVS4egaw54luAROONyI1gxSTC96Nlnn5WuXbvKFVdcYd4777zzTG+qT58+MmPGjODVkGeKeAI7duwwrneMqf/zzz/StGlT01N/7LHHOCQU8VefDcwuAXuPq3eO6Hb00mWvmoq5GUN/XJ/X0Oe5zj46Fn3BmDryw/+XtEXaFb1EBlb5n1TMqwPw2Si2zlyRlau0Djqe/udfrpzvFzQTq3VLsR59JKozyWUDp193OUvgMR3u/vvvNyd5/fXX05yssLpTBg8enOY9viCB7BBYt25d6ng6Zma0bNlSbrjhBiPuXMQlO0S5T6QTsHeokCO6HYIOlztc8OpuNz30K/Q5xtMzWSph7bGVRtT/Ovyb1CvQSK4seZ00KdT8nAPmbA2GQ9Y429gKkQ0b9WaiuliNz5eYR/roHPU6UZfzXek7spwl8Pfdd5/cddddAnHHj26zZs1MxbHIBhODOPIahkWlEMuxZMmS1Ols+fPnN673Rx991Exr4yIuYXEZWckgEjBZ4tAzd4+ji85LR+9cEOXeU5/7sNLpgZP75ZeD3xs3PKresdil8nqZ96VY7uJ46VOxNa0zeueyTMfSVdRFPW5Sv55YDeqbXO9SR5PO6KJMLM4jcJbAo4qISMYPLwsJ5IQAIt2xeAtc7/Pnz5e4uDjjeh8+fLhJmpSTY3NfEog0At5Z4jCWLjoMbgS9iQr67frcxxVOT+mE9kVH5qqoz5JVx/6W5oXbyIPln5BaBer5hMzWBGc25qG7e+micVjSsIHWpaHEYAobeusxAVoxxqcaciNfCaQr8Nj5wIEDgoU3kOoT4/Ke0q1bNxk5cqTnJR9JIA0BBGXOmjXLBMqtXLlSGjVqZET94YcflmLFiqXZli9IIFoJZJQlDoJutVJ7UO0cczRtO7FZftA565jiVjG2smaY6yp9Kw6QfDH5MsSMtdrl33/FXr7S3UtXYdfeuKWCDlGP6XWDWLq6Ikt4EshQ4LFUJubCw1VfqFCh1NZxpa1UFHziRWD//v3y2WefyTfffCMXX3yxCc4cNGiQ5OPiD16U+DQaCUBDZYuOm/+rjzr3HD11uN09WeKsTirmj6n57jXXnV3leMpx+ePgz8YFvzt5p7Qv1kVejntdysem390309bWrHUFxOn0NUxhk5IljaBbrVqIdf89YmlyM5ZzI4Alp5Hx9dVXXxXcNGEq78CBAyVv3rzndiA/b52hwCMPPXrw7du39/MpebhIIoAI+I8//lh+/PFH6dKli1mzgOlhI+kKsy3nQsAkk4GIQ8zVbCST0Rg0KalWXUVcLQYBcc/o8xxMAV91bJkR9fmH/5CGBZvKtaVuksaFNK3rGYutm5XXEOEOdzvG0ddqcvnKOm2toY6f63rp0v8JRrnr5chpwW8glqBG+fbbbwXZNTEbLdQlQ4G/5pprZNKkSXLBBReYTHahrijP7ywC+AJ/9NFHZmwdUyknT55s8iY4q5asDQkEjgCi2k2PHELuFnTZp+/Fu4Qcgh6jGipV9XX2p5WnNmBf8h75+eAsFfaZEqsZahAwd2uZ+6RI7qKp29iHD6eOndsaFCcJm0Rq6pg53O033egKjtMAVxbfCcABozMSJbMRkzvuuEPWr8fdnJglqBcvXmyeh/q/DAX+v//+M3cicLtW1XSCngh6zI3nGHyoL1vozo+MhlinYM2aNXL99debYExExLOQQKQSMDnbtRee6mKHmOO3XNO6enrlVgcV8bv19Xn6aOmjnwoC5uYf/t0EzK07vlJaFWkv/So+K9XzuxLGIPVryrKfTke479zpEnEExD1wr4gmlmGEe84uBvIF9VeblLPDhGTvDAW+e/fuqVPkvGvGMXhvGtHzHFHw6LHv1B+QG2+8UV544QWNxeHUmOj5BkRHS230wFW8PT1y42Lfru9V8uqVX6yvtXeeExd7VjQ3J240i7z8dvAHqZKvmgmYe+K8gZJn206xf1qhoj7VNWVNx35FxdxMWet+qdarmt5g+PEOI6uKRsHnH2kb2/rQzo4dO6Zu1bZtW0f8PmYo8JU0vSAMBeOspUqVMtPnUlvAJxFPAMEiv/76q+mxYzzppptuMjEZMZwiE/HXPtIbaJZG3eweI9ceuUfQ5ZS2HOKtJheqi72XPlbR17kCT+TYqaMy5+CP8uPB7+Sgzl9vX7SLDDv5pJRYuEPF/E/tpY+VFA3aMhHu6KHf1JMR7gG8LJg7dpsa/JNjfTiPt8C3adPGhz0Cv0mGAo8f9FdeecUsFYsf+v/9738yZswYGTduHNfYDvx1CekZsCwwprqhxw6PDRIfNW/ePKR14slJILsEzKIrnl6551GHpkWDxY2Qq5jH9NDX1fR1qeyeJfv7LT+6xATMLd7/h3TYXlUe2KKdqzUaUr/ya9EAKBFNKGNd3EqsPvfr1LnMRoKzXwfumZbAMX05RO0itUfUwtUnkqHAv/POO/LTTz/Jl19+KVdffbV06NBBpk2bJngfa26zRB4BTPXANLdPP/1UquvSjf3795cGDXQ+LAsJhAkBe5tW1C3injFz0bgzI97olddXMb9SHxH4FquPISp7knfJL9unyT8LPpf4f05I94Ricn+CLbnibO2hlxDrCnUKP9tfrCJFQlTD6D0tvi6vqjVU04SBPhXvaXLfffedyQOCGWihnoWWocBjHfjHH39cKlSoYBqI8VYsH4tUthR4n6552Gx06NAhcyP31VdfmVzwQ4YMkWrVtDvDQgIOJWDr+iap09C8XezIpYSeOHrliGDXR6t86BuRoqmat/zzm2xd8ZMcWLtQ8i9bLxfsLiAd614gRZu0FquT3kjXq6vR9gxYDeXV0ogGGaSmyQN9FnfU13uaHDpJb731llnTxbECj/F3iHy7du1Qf1OmTp0q5cs74K/FUyE+5ojAnj175JNPPpGZM2ea64whGM8NXY4OzJ1JwI8E7N16ME+vXB8h7LJLLc4l5BDxmI76GsJeUB9DXGwd4sICLPtWzZOdK+fI8bXLJNfGLZJYqpDE1qwlNeu0lLjLB0vuuprPPXeGfawQtyL6Tr9FmzxKTRMJytVnNB+xjJnde3lPkyvi9ro4YXZRht+ufv36mTnws2fPlu3bt0uLFi0kISFBfvjhhzOazpfhRmDr1q3mjvO3336TSy+9VN5//30z1h5u7WB9I4uArbooCWcEvkHQMVkD4o3euMYuWb31dSV9jNHHEBeTSGa9pnr9Z70cX7NMDqyeL0mbNsiWMidlV1xByV+7oZTtcKdUaXCpFC/k8oaGuMo8fToEvtP3vlS7Ua3DGZ9rP0h663dOO+Y+lYIFCwrW4XB0opuymq4Qc57Rw9u8ebMg7B/mmQ/vU0u5kaMIYK11JKRZunSpIJER3EreaYgdVVlWJqIJ2Ae1eSreZhqax8W+Vd/TDKuWirnplSPCCaLukCUMzDKpKuT2Os0Gp6KevGa1HN2xQbZXzC1rKyRKQmVLit7ZTCrVuUPqF71A2uQ9TxvA4mQCB7RyY9QQVPeKWnohjImJJpuvfpp58UTR33zzzTJ27Fh57LHHMt8hCJ+e1YO/6KKLBHno//zzT3nvvfdSqwAxQMFiM6NGwZHBEi4EIOgQdnhgsOb6008/HfIcyeHCjvXMGQGzqIoKd6qQo0cOF7v+aBrxVgHHgGfMdfoYp2LukNQKtq6tIG4xR+8cz1P0vf3xxSRBvQfLzzssyy86LKWrtZB6RZpKo4JN5Jp8NbQRLOFCYLFW9B21TmrXqGXkENJ116SwD2mFPQIfHx8v8IA7oZwl8IiSj4uLM0FWl1xyyVl1LF68+Fnv8Q1nEsBNGrLOHTlyRHr16iWdO3emB8aZlyoiamWjG4TeuFvETRR7gr5XUs3tYo+5yv1cZ385pdjI/gYxdws6nktyst6AVJfd8YVlXeOTsrBbQVlcbJ8mnSkuDVTMmxdoLLfp8qu5rbN+Qp3SLNYjAwKn9P2Jan+roY+d1W0ZBF5nC2dYsCjbiBEjzKqrJXXhHgSnO6Wc9e08//zzTd0QKIAgu4oVKwom7SMqEEEDt956q1PqznqkQ+CURutieiPmsMfGxprkNFjdjdmt0oHFt7JNIDUPu2phapIY7fSa6WdwsdfUHlE392sHBYbbGn/icbGbR4i5/p2YDHA1a8jezk1k2e01ZHGBDbLi2BIpnaes1Fcxb1Owm9xfsJHkj3FQY7J99aJzR0y8+FHtW7U6akPUfLmaGoJm0hHo5umWokWLmsyeyBWDoU8nlbME3lM5zH9/7bXXTA8Q70HkH330UbMUHiIGWZxFICkpyawdMGXKFBMJ36dPHzPlzVm1ZG3CjUBqHnZPrxyPMKwg7emVd9Ln96ph/NzSRwcUsyyqxg55RNzjZpdixbTLpouvqMVc30P2xxWTZbEbZdnRRbL86AzJo2ME9Qs2lpYF28m95ftJ0dy6PUtYEzistZ/lNmT1QK89Xs3XsmOHrfeAmMqRsdvpsC7ys2/fPrNui6/HDcZ2GQo8Juu//PLLUrOm3oprqV+/vhH8J554Qijwwbg0vp0D0Zpff/21fP7551K3bl0ZOHCg1K5d27eduRUJeBEw09E2aY98nb4JV7uaaO9FKqtwewLfkIGzur6GwDuk2HCn67Q0I+IaAGceNyZoproyYmmvHIIe06qleTymXbblRxerLZHlx76XwwcOSQPtoWPJ1RtK3y5lYzkN2CGXNcfV2KtHmK42Rw3xmi+plVU717Jo0TFZt+7/5N57x2a4K5aIRUI4p5UMBb5KlSomXSnGbT0Feck9c/w87/ExNAQO6MAQVvpDUgVMYYS3BdeMhQSyImDv1C0g5Anux42uR8mnj9q1MWLeXF3sN+lr/UpZufTRIcXGhGRMS1MTj5hv2arT5s47LeZd9DcLi67kzStJKUmyWtdOX35sgSzbPVa2n9gqdQo01HH0xtK5eHeJ04VcWCKLAO5Jp6ktUGuv9j+17PphNEu7rFx5XD3Y6cXX64HdBUPZnnnviRp2j8DmZs2ahXz9lgwFHr30Tp06yYwZM0we8mXLlpmVxNCzZwkdASz8g6mLyEeA64O1ATClkYUEziRg45cuQYVcxdw86nMIu3Gvx6lw436wjnusHK8d1CvXmomZluYRcU8A3O49ehMSZ1zsUr+exFyteWerxosnYQyWV11/fK0sO/SZut0Xy7+Ja6VqvppG0HuXfUBq5q8ruZx0x4KGsviFwAY9ylS1VWq6rp5JWpPTvEdLluxW3VulWelu1yNmXLZt2yaDBg0ynWKstHnllVcaD/jzzz+f8U5B+CRDgccdyV9//WWEBPOnseAIeopcSSwIVyWdU2CKGwLn5s2bJ5dffrlMmjRJhxOze1+azgn4VlgSQA9D/lNL8BJyiDgMXw8VcStOHzWxdswV+qjPrQL66LBi6/jlmdPSRFMoe8bL5cJmZvU0dVNpgpu0E5qwtOqyg+p216A49NbL5ClvBP3qUj2lboHzJW9MXoe1ltXxJ4GVejAIO/4Muqs9qKZhkzkumH30yCNT5JJLOpq1OTI7IDpbv/zyi06+SBZE0k+YMEEuvPDCzHYJymcZCjzOjujAa6+9NigV4UnSJ4BkQ5jDvnr1aunRo4dZD6BAAQf+Qqdffb7rJwKe+eRpXOsJevAtaiXVPELeRIUcgbx4rePNTiy2eqGMm93TO9dHQXpXHS9H8JvVoZ1Y994llnYy0iu7k3ea3jnG0tFLzx9TQMfQm0i7op2lT/knpXDuIuntxvciiAAmbCxS+11NbwNF/TjSWs1fo0kImHvwwQfVxd5f7ulVV04N0GO/rCfIoGDGEmKhsALrpk2bZPTo0Y6YLneWwGeU6MbTLia68ZAI7OPChQvNDAa45DGHHa4efIlYIpuAWad8q7YxwWuMXJ8L3kMQb5wKt4o3ooZirtdHCLmDO6j2li2uoDePix3T0nRs3PTMVdBjuqszFcKeyTKoh04e1ClrS01wHAQ9MeW4EXQExt1c5m4plQdgWCKdQII2cLHaQjXEtDdSu0ztAjV/FoyfP/zww9p7HyDjx58vjTD8Do9YJuXNN99M7eVjoa7x48c7Itg5jcAjd265cuWkUaNGekN90rgYznQDM9FNJlc5hx/Z6m9Ffnj02OHquemmm8wyvRwWySFYB+5u8q5r79sEuiWcFnPjZyynFY5zC3krFfJe+hqR7A6+vzPT0rTncnpamgbBrVcxR2Is9MrVYm64ziXmWQwtJaYkGlc7xBy2O3mHcbUjMK5b8aukcr54BcIS6QTUpyNwv0PU0VvPo9ZU7Ra1WmppB2r0jRyWEydOyPTp083v78iRI2X//ibSRD1iSer/f2+1yEMZHP+PP/6QJUuWCHLQz5o1y3hZkY/ECWnd0wi8J0kKxtwRoY3Ue5ge513y5UOoLYs/CYA7vhhIB4xZCr11ZQPEO7CEPwEzj3yz1/h4grYJvQH1UksFNfTAVa+sdmpx4lpEJc1fpb7nsJI6LQ2udU/PPEEbVa6sEXL0yGMuVocpItkLFcqy9giMW3d8ldvtvkQ2Jv4j1fPXNr30e8v1Nc9jnLCyTJYt4QY5JXBED7BEDYK+XK2SmmqsPK2W/oCNfpDDsnv3bsFS2Qgor1Onjrz77rtm8S2dJS6dMWtS48qX18j4JK1atRLYsGHDpEuXLmZDJ4g7KpLmpwRrvsM1ATc8pmFhlbEze4/I1IMAL5acE8B0CtwxIio+Pj7ejNl4Mgnm/Og8QjAJ2Cf0bKpxaSLWE/Q9+BLPc4t3nPY6OulrfTTv+WvAUA8XqJI6LQ2udc+Y+dZt6lGodHpaWtdLNOlNVTMtzdd6bExc75qLrvPR1xxbLhXyVjKBcdeXvkVq528gsTEOdlf42khu5xMB3OtC0GEJauhSoqeOdGqBjKZYs2aN6cguWLDACDMWiIEHG2XXLk0F8bPI49qf3XSXBu39YN7O9D/kiHFaSSPwqByS28BefPFFPUq3zQAAQABJREFU4x5u3VrvxFn8SgBZj5ApEHeNjRs3lldffTV1/MavJ+LB/E7APq6HhJAneD3i+T417W5YcfqovXKTplUfTXY3f/sS9bD+LLZmQdQ7erWDGrF0yDXHHD1ziDqmpWEamrrYpUF9ibn2atc0tdxn/XRkWqUdSf+5BR1JZhZrIFxR00PvVKyb9K34jBTMlXVPP9MT8MOwIWBrTdeqecbTE/U5eulXqNVTgys+UAWR8ZiJhN/evXv3miBy5I73zGH3nPezd0X6H9a6PCmyTSsUrjOR0/yVYgz+zjvvNKvIIXqe86s9l9s/j3t0YWEMfSCXAPLDIzAD0xFZnEfALJySkI6QqwYatzrEO06FvLvrEe52x6RpRUQ6VkPTRTBk/wGxPeLtfjSv9X3X57rdyVM6Vq5z6jA2XrSIWHHauOYXSswtOvhfufJZ09K0xVmWAyf3pxH0U3LKZIxrUugiub3s/VIiT6ksj8ENIocAHFx/q6GXDmHH1UcvvY9aVbVAFnijf//9dxPfhFlJTXRgvWfPnsatnt4aHXs3atjIGB1Be1H/vtU5tVUd1uH6M51G4DEWjJSnmB6AcQiMwSMHvXfBGDymzwWqILgPPdxICuZDEgTMYcfiPV27djVzJEuV4g9coL5D53JcG4N++ged6lrX3rnxE+L9OBVtj5A30tfx+trlwdMXwSsmgM2Is4qy9rLTCvSZAq7bnNAeeTH9G4Vgq3Bb+Hv1CLj+UsV4PsOjBsFZuohUTstxjWxfedQV6Y40sHtP7jaLtCAw7sqS10vFvBolyBJVBODUgphD1Neo1VSDqGPyR0m1QBaMqyNgGb+56zXYs3nz5nLZZZcZz3RmcWS4sd92s45dt9V8UL1dNVyxQjT3SCBrG7hjpxH4UI3BY6GUgQMHmrF9iCGiyTHXO17HpR977DETdBY4BIE7Mr5YkzUiHhGWyFOM54V9WVg4cFWK2iPbmCybnpDDPxinwq1mXOsXuh6tsvoYoGKrp8y4xH3tYR/XSnqJsgXh9ryuU1titNdtotXd71kazRvoclKnAaw5tsL00rHqWkLiv1Irfz0zjv5AhSekmmaPS693FOh68fihJZCgp4egwxB+0litndojaoEOz4Z2IJ06hH379u2C4WX01H1NGWvrvXHiPSJ/quPrhje0wlqQSAoCP2CA63W4/Z9G4FH59MbgMRcbPc7c5zju5iuMhx56SHAORDFWrVrVTDc4pGOBcKf07dtXEIx2//33+3q4kG+HtL5Yh33jxo1yww03yFNPPSWZ3TWGvMIRVAF7vzZmk/5hqph7j5VLsr5GDzxOH6uo662l69HCHNccFtyQGnd3ui5wTw9bfz0wxo1tjhw1rvB0e9g1dDqZR7zxiB62D5HoOWxClrujjRs0uh3Z4jCGDnGvnDfejKP3LN1bA+PqS56YQI6eZllFbhAEAvrNNeEme92P+HNDTx2vESyHcBOMpwdqKpseOk1BUhksjw1Rh+cXQ5/33XefNGzY8KwA8TQ7nvHCXiVy6nKR2eqST77Xdb+MTf79VwQpGnRyU1gWS/9w9dfp7ILxeGTl+fTTT02PGmvdjhkzxuQ+L51JUoqzj5T1O+ipz507NzWC0XsPpMtFkhdMI8tJ6devn7mJwFS0QBW0Ab10jPkgOQ2mTDhlukSg2hyq49r4RUnwcq3rcwi6KXFuIcejirnpoZcwn/j8n43etbuHDVG2PeLsfrTNGLe6BfB4WP35RQq7XOIqyq4etruXDYE+s4cdJr8W/+nCLGblNRX1Fep2L567pOmhN9CscfUL6NrouZhR0ecvVBhsCCcXxNpj+BM78zkmfuBPCQY3e3Gv57hXDlRE0dGjR2XDhg0quP+aR3Se8BxDuRhGhrBjNc3slJTP9Hdksq4611wXp3laREOlNN2s60hvvSXqTRa5/fbsHNm1D7zQyGmCsf9gl7N68J4KvPPOO+bOCNHecC936NBBpk2bJnh/gJ/9FZhr//PPP8uNN97oOX3qI6aR+fuGIvXgfniCGyHcQWKMHWJ+8803my8c3ZN+gKuHMEuYJriFfONpQTfJplW8rTgx65LHdNBHfW6prqZXbL27N73nVIHWnnQaAdfXHhE/qD91hdTN7R7D1qATsfSHxLjFq+hY8vkNtJd9WsBxe+/k640V1ZJ1Qn6SWrL7ebKd7Hov9bV+npKsoXAndT461kZfIpb+QwrYCwu3ljvLPaQCj591lnAjgB6cfrtTe9oe0T7zMa9uA13DVYbhOSTT+3Wg3eyIA9u8efNZQo7eOby7yBIXHx9v4sPwvFAOvFuIv0kZ7PqNGd9UBV5d8VgSAX/qKOj6/vijLlozyvU6HP/PUOARnIDpAxUqaHiwFozPw10O94e/BR5T8tDjxZKnuGhI9nJQf3yRfx1Bd1hr12kFcQMzZ840wo65kxhCuOCCC5xWzbCpjw3/nvbA7QSvRzxHJ9Ej5LXVBdhVX8epWfrX6S3QWw/qYJnHHa6BaN49bAh2AQ0kM6LsEubUcexK5+nE27oS4xFwbANBP2NBEz1jtkqKJpE/oZPkPcJ6WmRdAmuEN1WA3e+Z124B9hJlz754xBh42n1dIo7zmO10Gwj7SRXy3FYeM688j6bCi1XLo3PMU5/jPffrPLodrGaBenJtqZulfGyg+mPZQsmd0iGg8x9Se9kewfbueeM9/cswCwh6CzWeI+zR8x4eY9WCWXbpZHP0yr1t69atUr58+VQh7969u3numZ/ur/rZa1Xcn9fRsoYiT2lITFmdeKILc+p0udNn0NApHZrWlBX6ExGuJUOBr1SpkolAbNeuXWrbpk6dauCnvuGnJ5gLjkA0uLixahrG49Frh2jC/eKk3tFxXY8aiwpguhuyHj333HPZdg35CV/YHMYMBm3X6p4p5Hid/5hmQjsgVgn9OSqmj/VVrJsdFOu4usC1Z21v1j7IMq8I8nza3zA97OKuqV0egdYfB0HgmbuHnVy0oCQXyS9JMafSiCyEz9OzdQnlARXMXS5xPACxVIFNFd7TPV+XyEJ8vQTYLcIeYfUWWVycVDH1Flbv56kiC4F1Ca4RZYixWj6dM26EGZ/hPbNvbvOZZ3s8pn1++lhh8wVhRdMQ0LkQqeJ9pmh7XmNMvKiad89bNUkQsQ7R1r8O8xjKnErHjh1LI+IeQUdcEnrl6JFj5TUExFXR1QLRmfRnsXGHs9NlNh7VBW/PFFndTeT/9FG957qQV9oz6s+8/sZrJrvOad/P6BXm1GM1OSSCQ5rxRYsWmeA+f7clo/Nn9H6GAo8xa/RIZ8+ebSISkToV4ot1yANRcLHbt28fiEP75ZgQdrjhcZODBXlGjBghcXFxfjl2JB4k5Z/jkrJyv5xcs1fsf1WkVaCt/w5JSp59klJgn5zKq+/n2i8pKfpZ8n45dSpGknarEJ/ILyeO5pUTRfNJYpFYOQ6rnkeOFs0tR9Udd6RIUbWqkqyC7RHoJF34PFnnuXkLq+m9ntDe627tve716rGmK6z4PG0vFwILIc0Xk18KW26BTd03T5pesbeweoQ4twac4TnXHo/Eb3fO25Soh/CIdHo9b3x2Qs27hw0Rhz+1vppH0CHulpoTCsK5EPTmEXDPI7yxEHEYPLToNELY4an1RzHDeCrcRrwh4OoN9H4u2hcQzIpRM7NjFN4nF4t8re73l1/WYQivoXukkJgyxSXuloL1ZTl3eJlvueUWzX63S+CBAAMMaw8ePFj+7//+zx9NzPYxMgyywxGR9QdpVDEm0rZtW2PBDhpbu3at4A4QvfysCuIDIMLpFUxZwxcMQw/ZKWMqXir1di01u2JMy1Nsrz8vz/unH9P+6Z1+X7+AXvt5nuJzzzY4Prbxfn36nKf3P/Nz1zFOn9fzuefx9DFc25jtT29uPvbUzXsfs51nZ0+F9bX3+57tk61csipXYVmttjMmX+pefOJ8ApbmfLfN2rTOr2u41jByGeOHxPMrEJyrE6Nx+8m59GZfu6pJsbYk6+MJPKoTICmPraaueIT2pymZ1BOqriLv+kmMkRT9Jwe2yQ31a8sHX32Z5ijeLyDs6KA++eSTMnHiRNP5Q4A4AgEdGWSHyiOA4aqrrjJ3JTVq1AhJRDhc4bgjGocBkizKPffcI7D0iieKPr3PfHmv06zhsmn2TyKrVkrsvwmyrUY1WdOggRyoW1uKFSosZfTutaz+MJbVoLtCngPqdz1Voo1/2v0Bnnteex71I8v8bXh9hs09n5t93Pubbc3G2OD0NvjYazu9e8M77uK1nff7+tx1Xt0s9X33fnid+h4+dx3KHNf7fc8HZnsdJz+RKB2W/i35V6zUY9tyTG+Rj9erq491JIV5ANzXgw/RSiA2t3qrTqpwRFgJRbuyc87M9llw8ndZotNAb9IsN9YOSxYM3CFDdbjvx33PZXq1MJyMjvDdd99tpnejQ+mEqdEZuugRzXjXXXeZu5ESJUqYqV8Qe/SQ8+aFzyMw5cxMds8++2xgTnSOR61Rv47AULAAh/3XPEn5/U9JVFf9AR032tiqhSxt3VJmaWpP3DBWcVtlfYxTg2stlONgevrglpt7mvPZemdbYuFisRcuUp/YVybno9WsiVjNmprc5laAcisEt7E8GwmQQCQQaGzXkqcTlkhy8b3SsVE3OW/7vzJz3BbZNnxihs2Da37hwoXmc3iRERCI2WdYtC3U5SznhadCWFkHdyFINoMAAiwhizGWIUOGeDbx2yMi0p955hlBYF9sbKxZqg9r62L63Hvvvee38/jrQEjtGdO+neT+P10kY+oXUvGO26TV7j3ywGNPydu39Jbhb78jVy9fIYWU1xI96Ui13mpPq41Rw5yAlWoaBx7xxdIQ1JirrpBcL70gMcoqps/9mgcyt6S8+56kXHGNnOo/QFI+/1JTxW6KeBZsIAmQgLMJYFniB8s/IR/ufEf2n9wncbdUk2uTqsjRm77LsOLo8GJ2WTHt6cMl78nCCs9zqEuGPXiMH2D5O0SKoyAoAhHjGE/wdwnnTHaWzn2XptojVZNH+pgVuAr88afUHDVaamrGBKtVS7UWkqw91q1685Kg8DarLVDD5ccsMPTyvXv8Ggeurv3IK4ZVg/piqUnv28TWGA9ZvER794sl5Qvt3WuwiuF4QVPTwzc51CMPA1tEAiTgYAKV88VLlxJXyrjto+TJSi/I+T2KSPkpGU+BxtosWMNln06iRzD28OHDBct+o2Mc6pKhwLds2dIEpF1xxRWpdUSAWiCSznz//fdnZbIDNETuj9IsA8hkFy6parGsplla8/ZbNZJzp9jqxkcPNdfLg6VK40YSp258q0Vz1wIgSna3WoIaRP9PNQ3glP1qZ4o+bgAiLVzNpGBtc7FYaig21iFYpIL/y29iv/a66JxMsSD2uHlq2EAsP0+fMSflfyRAAiRwBoHrS90qT218QBYd/kviLtbf6xGYt5B+gWf7hRdeMJlfEauGzjCGs+GuD3XJUOCvu+46aaBBZEjej6T9mNe3dOlS89rflQ7nTHaZsbB0EWHr2qtF1NBbtef+JfYfc8V+Q/Mf6nQRS8W+lFppTSbkfX+IKTTo3cMS1H5Tww0A5rR6hN8ztl9a34uUYulKZzC5oruYBVlWrxF7wUJJmTjJlRS6fj1Xzx6iHx8fKc1mO0iABBxGAK76dsUukbmHf5UH6zeXOV0yriDytCCgbr8m17r33ntNUDi0MhSpac+sZYYCX1KT8S5fvlyQWx3zGbHUHhZQwTi5v0s4ZrI7VwborVqdO2nmhE5iY7IlXNPo3ffp68qcpm58487XJC3oqddym+c8tj7Zrgahh/D/7H48ro/e7v04fX2eWqxaOBeTSU4j7y01gTdEc1HLkqUud/6zA0V9Ydq7bybiDtgzqWTDucGsOwmQgKMItCzSTj7d/b4kl0uS2EmZ/6JeeumlRh8RQ4bcMWW1cwcPdKhLpvPgPZVDRD1yrgcyKw9WjDszkx3cHf7KZOeZJhfIxWY8vM710UZPVcft0btH+lWrdSszbm/G9rOIMkegnqe3j0fcAGxTQ88+Ts3T48fzYmqRUmzNdmgvWOSKztebJf2L0t69Ozof7nz9Q2MhARIggZwQGLT5KelU7FJpUaRttg/jqHnwGE8YPXq07NTx45deesk0at68eWZ+H94PVLY5p2eyy/bV9WFHS3vtMLnrDrH/+8/Vs/9IR+NffFlEe6kI0jPj9uoFOLPgnXpu83x2Sp/8p5agBtFH1D6eo3h6+55HdYiH5fQ9S6eiWJdfJqJm3Plr1hqxT/lgssg/6xVKHdPDN6KvwyEsJEACJHCuBNoW7Sy/H/w5RwJ/ruf05/Znuegh6ogIHD9+fOp5kCcYke4Yl8cCK82aqWuUJSAELB2Pt67vIaKGJUvNuP2vc8Qe+Yb67Wu63PgI1NMea0ZF4/oFAymwi702OqDPE9TQy/9bbaraLjXM0fcIvqfHX1jfC5di3PmaRMdSk1tvNnkKjDtfe/gpA/UmVd37aaLzNa8DCwmQAAlkRaBpoebyzvaRcso+FZZpp88S+C+++EJz8U5JnR4HALnVTYx5fnt02hfEnwKf1dfCP59jmpjVtYuImp2UJKLJYuDGT5mkvdRSpUyQHgL1rOrVfTohXPSN3ObZQY8qW9U87v2F7ueIA4DoewQfj7gRsNScXpCnQFqq10MNxcxmQLKdPzXIEQGOupBRqju/0fl05zv9grJ+JBAiAgVzFZIKeSvJP8dXS+0C9UNUi+yfNo3AI4scAupq1qyZ7hGxSACmrLEEn4AZU3aLlv14P82Us0pd+X+4eqgq/iZAT8VeIFiYm+9jwUh1Vbd574Lpex7R/0uff6KG6XvwCngLP56rnDq6mNkMl3UTUcMQlKxd53LnYxjk+UG62oQOkWieAjMlz8ebJUc3mJUjARLwG4HzCzaVVceWhb/Ao6deV/OGY8wd8+DPLHgf7nqW0BIwy+diypia3HePLqW62TVuP+F9VeVNYl2k18gzbo/ebDYKgvRg3oMxmL4H9z4sQe13tS1qRdU8PX0IPm4YMp41qh+GsBh2tWuJpSY393K58zVvPlLpprz4isihQ0bsTXQ+4h90NgkLCZBA9BKoEFtJlmt++nAsaXrwaEDv3r3lzjvvNG56ZONBQQQ9XPcYn58xY4Z5j/85h4Cl+e+tXiqxvXqKrXMxjSt69o9iD3tN873rjYA7m56lbv2cFLjt4dvx9u9ofxirM6aK/i/6/D01eAawCmMdt+XszHqQABXjzkfiITUUe/duE50v83X+/Zh3dL3OEjp+39gE7BnvSADXYQhQE3lYEiCBHBAoH1tRfjowM9MjIIMdpnu/+uqrMmnSJJPmvXbt2nLjjTdmul+gPzxL4DFRH5VFTx1z4ZG5DnnoC+sqYF9//XW6PftAV5LH950A5oNbHne0Tj2U+Qtc4/bjVXaRGQ5j9jA/JYrBmDxS68Iu8qrmVn2+Wg33vRoxIHnVIPYe0Yd3wInFwvj8pV1F1Iw7f52uwbBIU+lO+VQEAXu1NdARPXvNrmdp0CMLCZBAZBMomru4HDyFAcqMC6Zfe1LTrlixQrp27eqIefBnCTya0LdvX5MadvHixSYrT8OGDaVWrVohWS42Y6T8JCsCVj7tc7tTwZqpZMuWm/n2Kc88Z3Y1Qq+9e5MGNiYmq8Od0+dItgPr7N5rmz5C8JeqfayGLx7E3iP4ZfS504px52PmAoQc3pETJ7QB6s5Hdr3Bw0STT7ui8z3j93pzwEICJBBZBPLF5Jfjp45l2qg77rjD9NqxETLAokPcv39/GTlyZKb7BfrDdAUeJ8UKOcjE44RsPIGGEA3HN1PJEICnJg/eL/bGja5xe7iht283LmrMt5cLLxBzY+BnKJhvD+vkPu5/+gjBX66mfWMTne/dwy+n7zmtWHDPa3yDiXHQytk6q8Qsg4vFcsaO00xCxdJG52cz/sFp7WZ9SCCaCRTPXULG1cSvlG8FieEaN25sllb3bY/AbZWhwAfulDyyEwjARW/c9Lfc5BIqTL+b/q3Iq9ozbdTw9Li9uvwDUSroQWEd3QfHOP4qtZVqX6hhbN9b8DEE4LSCmAbPNEbUzdYEOyZYTxcXEgTs1arhis7XHr7JYaA5q1lIgAQik0DHjq5fM3i83377bbMaa6hb6lOq2lBX0h/nd3KqWn+0z1/HsDX+wv5rnogKvnmsUkXFXueUY9xeg/mCVXbqidDDh+jj8ZQaBN9j8AY4uZi8Be7ofCyHq0kk0rrzy5RxcvVZNxIgAT8RcFSqWj+1iYcJUwKIKrfatxNRs9XVZMacsSjOY0/perX5XGKPcXtM0wtgj7Ss8oNpTUzBvHyIPWyaWpKa9xi+0wTf5C3AcIcaiq3j9Ri7F7jz350ggsWH3GP3ossIm2h+syX/IwESIAH/EKCL3j8cI/IoJmEOosXV5JE+Lhe0LoqTMmq0q0fqnn4nEKrY2IAyQPhaW7fhRHvU0LOH4E9XO6bmLfhIyOOkYmG6XZdLRGBa7H//VXe+iv2XX4sMelWkRnVXoh2wrqOJdwJ48+QkLqwLCZBA4AhQ4APHNuKObEGE1MzyrboYkVnuFuPNLw8W0wuFGx9zyjXFbqAL5tVf7Daca68axB6i/53aETVvwQ/e4IKe2Idi6QI4MLnhOlcaYsxwQHT+iFGaWEAjEnBj5YnOL+fEkEMfGslNSIAEQkqAAh9S/OF7cpP+9dqrRdTsI0dci+Jg3B653pEprokmh1EzwWV+noKXHjXkm/MWfMxaheDDZqkdVqutBtHHOH4VNaeEvBnvB8RcDcUkK9LMesad/55mJ8SwCebe63K4Aq4FCpjt+B8JkAAJZEaAAp8ZHX7mEwEL48mdO+mk905i63oGouuzm2jy4ToHFL1RTM8zgq9jzXFxPh0zpxsh9r+V23CsA2oewf9Bnx9U8wTs4TFOzTGCj2RFbp5aLdeURqyMN00HI14eIlJde/+e7Hpw5wfhBgr1YCEBEggvAhT48Lpejq+tlVu/Ut7BZVjyVgVfFi+VlC++Ek2T6BrTh0BB9IMUTV5MyWlooDFAPKTmEfyf9fk+Ne8efpy+9m/qHz1gNgumM8LMEsLJySJw5yO7HpYQ1hwGZnjE3cPHcsMsJEACJAACFHh+DwJKwCx5647Kx4ls7dHbi1Tw0SMd+65IwYJG8K0mjVxCVaRIQOvjOTjO0txteA+C7wna+1WfY0y/lprHpa/y6gjBt/LkcY3PIxjvnrvExg0U3Png+cGHughArGvsHu58jOMrXxYSIIHoJECBj87rHrJWWxow5smVj0qYjHoq+CkzvxcZNkKz31TQnr268iFgDRuIyR4XhNpC8C9yG06HMXsIPmyc2i61mmoewceKebnUQl3MDVTHDpoxSE2LnZDgis7/dqYraVFV7f2r2Jvx/bp1zmkp4VC3jecnARLIGQEKfM74ce8cEkh1P/e4xjXvfs1a49JPmfyxyHMvajdas8GZ8XsN2MN48zmsdZ+TqhXWnS90G46DqHxPD19nsZsV9LwFX+PhnSH4cXFiqQl4Ih5i+QpXPMToMSLbdEUAxEN4ovMrOi17gEJkIQES8BsBCrzfUPJAOSVgxLteXbHUBCl0sbgLxpt1DD8F0flbt7p69XA9o5dfFf3o4JRCehqkrHGlrRE5qs/XqGEcf6KajoRLDTVP4F51fR7qPy4TD4EkOmpy950ud76O3ZvofNxA6c1SanQ+mGqwJAsJkEDkEAj1b1DkkGRL/E7AuOcRPKaGYh9Wxzki9BGwN/Ub9aMfNr17M9aMoL0gzhfHyHZTt6Fux9Q8gj9Jn2tf2Qg+XPoI3oPg51ELZTHu/A7tRWBa7M2bde69jt1jeGTocJ07qGmJ4c5v01qs6qgxCwmQQDgToMCH89WLsrpbhdVx3raNWGoo9u7dJppcMIY/YSKWQHQJvnsMPxgJd0xF9L8Caho1YAzvJap5BP8jfb5FDW58zxg+5DNWLZQFawuY9QWQywDu/BUrXdH5zw8y1bLaXmxYc937UF4lnpsEsk+AAp99dtwzxAQsXX89zWpumzaZ3r390y9iIyMcAvog9jqGL+frCnlBXL41n7JRx7gxYPIIPsbxp6htVsMAg0fw4d4PpeAbd75nOeE7e4u9XlfG+3WOpCBLoQ6VGLFvo4KvaxCwkAAJhAcBCnx4XCfW0gcCFlzManL1lWKnpIisXecav//sC5EXXj6dIAaCj4hyzNkPUjlT8DW6QNaqYQz/M7UEtTg1j+AjgC+vWqgKXPTGTQ+xT0hwif1rr2vGoAPqwkfPXvMG4qaJOfNDdYl4XhLIkkDwfuGyrAo3IAH/ETDZ3RB1ryY33ejK9+5xQY95R0R7+2ZFPASXYfw+yGPOEO+GbkOrIfjr1CD4ejuSKvieoL1a+h72CUWx4tyR+bfdIrZG4puePRjqegSpYo/ef5BmOISCAc9JAuFIgAIfjleNdT5nAibfu2e6ne5tH9U4+CVLXQF7WM1tv2avd39uBD/IU8gg3g3chsYlqf2jBsH/Wm2DWmU19PD1lsUYvALBLpZysXr1FFEzSYvgxh8/0UzBs7C6IHr2uGkKonck2Ax4PhIIFwIU+HC5UqynXwmYDG+tW4mlhmLWa8cUMgTsTZrsmkIGV757DN8qUcKv58/qYBiPx2i3Z8Qbgr9eDYKv8wdkpFolNY9LH6IfbME3SYt0NTyzIh4CHn/7XVImTxF56VXXqoIQe8yCCPBSwtp0FhIggXQIUODTgcK3oo8ABDzNAi9btrgC9ub84VohD5+7o/PN2HOQU8BC8CHmMJRkNQg+gvZmqEHwK6p5C34BfR2sYgIePasL7tsntnIzaw+8OlQsrE0AsW9+UdAyEwar3TwPCTiZAAXeyVeHdQsZAatSJYHJlZeLbdvqL9eociTc+Wqq6aEKxqXdC+aYsXzkiA9iwdk84/M4rU5ySxX8mfr8DbXyat6CX1BfB6OYmyXlZtghV/7vf0rKjO9cc+2RRQ/z7Fu2COqshmC0m+cgAacRoMA77YqwPo4jYCLFa2rKXDXpef3pOeMQfIw//6sj5PU0Kh9j+Dr+LNg2yNHl+EOGmx52tdoptX/V4NL/Xm20Wjk1z00BHoORt84k17msm4iafeSIir16RH782TWNEYF5EHuM3TOLnl4RFhLwLwEKvH958mhRQCDNnPE7dPxel8CVpX+7ksQM+Z+IjkebJVxN0J7Ow9eEMsEuufSEmGoHu0oNgo9APQj+j2pj1MqoeQt+YX0dyAIR9+QtsI8dE/vPuWbc3n79TdeMBky/a61iX7RoIKvBY5NA1BCgwEfNpWZDA0XAJNBp0dwEluEctkbkw50vSKk75VNV11OuZDueDHulSgWqKhkeF4Kv/gdjV+pjitpGNQj+L2pj1Uqq1VfD9D0IfyCn5VkFCojVqaOImp2YKPZf80QQpDdGa1Krpo7Za8bCizUIsnhxrQkLCZBAdghQ4LNDjfuQQCYEIEqW9xKu//1nAvZk3gJJeUsFTHuocOUjaM/09EPgno7R+ldzm46WG8FP0EcI/jQ1BO0h0x6m7kHw8dxSC0Sx8uUTq11bETU7SecLzF/gmmv/zrtayaousYcrPwQ3RoFoL49JAsEiQIEPFmmeJ2oJWFjjXk26X2oYmDSw6N0j8GzwMJ3vpgF9ngj9BvVDMq0Mgg8Rh3VXO6G2Wm25Gnr3e9XQu4fgw8qqBaKYKXXu6YsmP/7CRS6xn/iBi5NG45u0uWUDVYNAtIrHJIHQEKDAh4Y7zxrFBFLTwF7fQ2x138uq1a7x+/c/NNH6Ultd1KaHr/Pwa9cSk5UvyLzgnlf/Qmou/YP6fJnaCrUv1fDD4endY65+ITV/FxPrgKl1aoYTEhMhsc69D+odRlmX0EPwg5yUyN/t5PFIIFAEKPCBIsvjkoAPBEx6V/Ta1eT2W814tCxb7hJ8LJizY4cr57snYC8+3oej+n8ThL1d7DYcfZsaBP9XtbfVyqvBlY9efi01TOPzZzGcMMVOze73sJ5cGUHsH+onghwFnp59CAIa/dlOHosE/EmAAu9PmjwWCeSQAMajBYlh1FBszCPXnqsJ2PvyaxGN2DdT8TyCHyJXNZLqwHQCnInQ/0cf4c7/TG2TGkQePXwIfpyaP4vxaHhWvnukj9jLV6jY/yYpj/fXdXs1eA/j9e00SK8qBhxYSCB6CVDgo/fas+VhQMDMI3cHoKG6ti7wYms6XdG0uinjxovoErjGna9JdwSiF4IpZojQ98zB18S1clwNrnzY62pH1Dxj93hEtL4/C7wfxgPS5wGxV6/RqXfasx/wvCvdsBH7tq4cBv48KY9FAmFAgAIfBheJVSQBDwELY8+XdhWBabE3bjSCnzJrtsiwEeorL386YK9hAzEeAc/OQXrMr+eB/8Hlg3AF6KF3D5usVljNI/gYv8f2/ipYPdCsIHjv3WIj+yDEftArmts32e3G1569LhXMQgLRQIACHw1XmW2MWAKWjsnDpMc1Yqfo7PY1a13j9x9NEXl+kGa60XXdjTtfe/gqbKFY0hU99nZuw4VIUEPv3pNhr4o+h+DDnY+5+vAI+KNYNbTtanKnrmmPGyGM2eMmSDPqGTe+zrUX9P6DnHXQH23jMUjAFwIUeF8ocRsSCAMCZmwaIo4e6i03ueaUIxgNKXVHjxHZulUj4bRX7xm/r4aZ8MEvcXpKWHe1ZLW1aujd60Q42a6G/PmeHj7G+f1RUm+EEMiIhYQg9m+8pe6FvZpQR8fssRgOhjhiYvxxOh6DBBxBgALviMvASpCA/wmYOeXuyHMcHbngRcXexhz8b2aIaACfCdhzJ92x1L0f7IJoe/TcYTeqYbwevXsIvtbQJODxfI4ofUTz57SYhYRu7iWiZm/f7hL7cRNENCERlg82Yg8mufzlS8hpjbk/CWSPAAU+e9y4FwmEHQGzoAvyvauh2Hv2GHe+aNBeynvv69y2PK6lXZtfKIJeft68QW9jIT1jc7fh5DvUIPgL1SaqlVKD4KOHj5H0nNYQNzWWLiBkFhHatcu1pv2kj1xr2mPFOw3SM2vaB3m1QG0aCwnkmAAFPscIeQASCE8CSP1qdblEBKbF3rzZFbD3+Zfy/+2dCbyU8/fHzzM3aS9taI8ipM0W2SI/S6RFqVDqpwhpsYd2hfhFqyuUVsr2pwWVEiqFQiQlWSKF9lS6z/d/Pt+5M+beZqbbbebOM3c+5/U6zTPP8v1+n/dzm/Oc73KODNSJabVPFeecs1V12R5S5yZAkAEP2kTVqH6vCu8+EE4Xgwww+PDusSjOUc2tOOXLi6NzGex8Bu26N4iNP+M1kSGP22A71tiDRwJefHJ7T7wutQnQwKf28+fdk0CQALLe2cx3La71Z8jTpXhm2afiTn/Vv+QMUeXOPjNh3j2Md41MbaGf+1RXq8Lgp6v+pYpZ+fDuYfDLq+ZWnDJlxFEOAhbbton58GP/sMYTT4mg+x6BdZBgSJcpUkjAqwRo4L36ZNguEkggAWu4MmPCoxlmwwYxSJYDjzbUu9fufKdSpYS0FN3zoeF0t+l3GHt06WsrbTS9gLGH4S+mmhtxSpUS55qmIqpm504xHy8WM3e+mCeH+192YOzRnV+0aG6K5zUkEDcCNPBxQ8uCSSD/EHCqVROoXN/6X+8eBv+VGf6x+8yufDt2X7BgQm68lNaK2QX+GQbxCafrFC+eNac9jL3OyDdPj/QvuUOaW+S01/MoJJBoAjTwiX4CrJ8EkozAQd491pijKx/GfsCjauhOC07WS5R3D6RYYge9UjVDda0qPHxtZUzC6dqc9pc1EVE1GkIYOe2tsR+ly+8QcAfGHjnttQeAQgKJIOBZA79lyxY5RvNqFyjg2SYm4nmxThLwHIHgGvNw3r1684irj4l6ifTuseCtVqa21s9w4XQxbh+YoV9Gtw9H8NLjNL5YRNXmtP9kmX/5Xfo4nThwot/YI2yuju1TSCCvCHjCenbo0EH69OkjtWrVkjVr1kjv3r1l/vz5UqxYMWnbtq0MHz5cV/BgxSyFBEjAywTCeveBrvyAd4/ufIzdJzDNK6bG6StHlnC6yI4HD3+KKjrYMX4PPU31cKbS2fgDCJ6jajRErgRy2mMpYtWq/i78Zldzgp5ypcSXgCcM/KpVq2T37t32TocOHWoN/aRJk+QPXacLY499ffv2jS8Jlk4CJBBzAkHvXteaoxvbGjsY/Jeni8C7D4zd168n1jDGvAU5KxB+deNMxRUbVDFZ70jD6TpwTDDbXtXmtMfKhIWa+a7tjeI0b2aX5XG8XkFT4kLAEwY+9M7effdd+e6776S4TlIprXmeBw8ebI08DXwoJW6TQPIRsN59pmeL1pv16/0z86e9omP3g9Vd1rjwmQY/kd492lYtU6/WT/XBYxJO10bGy0wFbH7TKHpTXxG3fQebPMjR4Q1Hf+8oJBBLAp4x8IsXL5YKFSpIw4YNNTz0n9bA40a/+uorqV9fE2VQSIAE8hUB5Gu3OdvbXS9mz55/vXskytFgMrYbXw2+jRGfoJn5AI7BQYzNQ6OF0w106ecknK6NoHd3TzEdbxSjvRlux/+Kc+kl4rRrI8gYSCGBWBDwhIG/4YYb5O2335ZBgwZpeOztUqhQIZk2bZr0799fRo8ebcfjY3GzLIMESMCbBDAjXULD6MK7X7pM3EBWvDrq3SPQDqLqqSOQSCmmlYcLp7tM949XRThdGHu8EJyiGi2cro0miDz2SA6kMQbcLt38a+pvbJew+ALaZEo+IeAYFS/dy8aNG2XHjh1yyimnyNKlS6V27dp2st2RtrFXr16yadMm++JwpGXxehIggbwjEOrdYymaYMY6DL0HvPvsFELD6WLC3nrVwwmna3Quknn9TTEaLthm/YOhT1DWv+z3xu+5I3D33XcLnNgGDRrkroAjuMpzBv4I7iXqpTTwUfHwIAkkDQHz/fd27N7oUjRZu86fAjcwdp9g7z47xNBwujD4CKcL7/5cVUThixQSyOzbJ+atmWIQW6BmDfGph2/TAOs1lOQikEgD74ku+miPC8vm9uj4HMfho1HiMRJIHQLwaK1X276twOO1M/MRaGfyVBHt6rfevU5mS/TYPZ5IpHC6c/UY4udrZH9r7GH0sVY/IEho47RuJUZn2ps574o78FGRCsf7Db2uOKCQQE4IeN7Az5gxQ3788UcZN04DRlBIgARIIISAjf+OiHGqELNunT+q3pRpIv0HidSt829UvQTkuw9pqt0MDae7XfcsUX1ddbQqxvXPU8W4fUCwzM7RNfOm6ZVi5r8v7vARuki/uBr69nZOQuA8fpJAOAL5qot+/PjxMn26rq8NI6tXr5YqVarIokWLwhzlLhIggfxGIOjd67p7O3ZfrJga+zP/HbvHGnWPyBZtx2JVGPydqujCb6RaXTVU7JSpRR+KO0l7K1Rg6O3kRMcJPY3bHiKQyC56zxn4AwcOyE7N2IQwtYcruBYaTu69915B+NuXX9YlOBQSIIGUI2C9exh7VVFP33r3GLtHVL3jkHXeG7JRm/GxKgw+zDa8emhF1VDBS4s19Pp76bS/XhyNiW/X2oeexO2EE0ikgfdEF/3+/fvtkjhEr8MserylFtGxtOrVqwvgdOrUKUcPCXHrI8Wux36Hb7k54siTSCA/EnBq1BCo3NBOzK5dYjSErCCq3sTJmktWvfvAzHx06yfQu4chb5OpmIUPQ68j8FJCNWDssRQPywbTVM3KL9TQTxEzfqIa+rb+wDkJbL82jeIRAp4w8N27d7dL2GbNmiUnaPCLoppXGUvlvvnmG+nZs6fs3btXunXr5hFkbAYJkECyE3Bg0C++SASqYr17rLt/SY29ztK33n1g3X0CvfsTtG3QG1VXq8Kzf1C1giqMPcbtS9arK2mq5ts1fkP/0iSxkfGuvUYcjSlCSV0Cnuiih6e+ZMkSOS7MfySshe/Xr58ghO2RCJfJHQk9XksCqUMg6N2rwTfLtDs/4N2rwbeGX3sDEylIfYsld/DstQ/CrrPHmL3G/JOiqgbpe3WM3mjce6dVC3FaNhe80FASQyDlu+gRzGbBggXSrl27g57AzJkzpVy5cgft5w4SIAESiAeBg7x7XWuPNffuhEl+71695WDM/DBOSTzaFFomltNhoRx0v+oKVRh7bZ3NfHeuOkxn9H1ICmK4c8rL/nj3V18lTpvrhLnpFVIKiSc8+BUrVkj79u1t/PkTdY1riRIlbMhazHzHpLnZs2drlsWqR/RY6MEfET5eTAIkoASsd7/8Uzt2b717XbKWZew+gd79Xm2f9jfYbvy1+okMHvDs627eLL5p0+0yO+c/OhEPiW3oNCmZvJGU9+ARxAZGHt30GzZssOPxc+bMsWlir7zySk6Oy5u/Q9ZCAiRwCALWu298seaWVVUx363N6t0j7W0gql4eJ43BaPsFmbpDPxEbf5bqs+XLy1k97pTzdUndydNfFfPfW8VB3H/MvPdY5D9tLiWGBDzhwYe7HySc+emnn6S8/nHGQujBx4IiyyABEohEwHr3GLNf9qk1+lKypN+7R1Q9zMxPkHeP8LhYX48Jeti+QJfVXfLaG1L+jf/zBwG6QWfeV6umRyjxIJDyHnw8oLJMEiABEshLAta7v6SxCFTFrPnOTtJzdfma6MQ3gXePfPBYdx8jxyUn94cs800zdZN+LtFhhSdv7iBGu+pbvfmW1O19nxQ+7VR/GNyTauakSJ6TJAQSOx00CqSOHTvatLFRTuEhEiABEvAsAefkkwQqSAWrXrPJHLt3J6jBr15NHEx8u+D8PPXsEc6nRab+qFn5Fre7XqbrTPt6s+bIpQ/1lVLaruJIbHM6kt1Skp2AZw18enp6srNl+0mABEjAEnAwGS/Tu0cgL7PwAzEzZ4t5ZpQ4l18mjsaadzSUdl4Kpi1D2xUsKGtbXCsLm10t296dK+c/NkxKlSkjZXTMvuRZSIdDSVYCnjXwyQqU7SYBEiCBaAQQUdPJnKhnfvtNjHrPbq97NRZtRfXq1dBr8B1HjW5eCjrma6alibnqCll15eXy9YKFUnJMuhQuOF57INpLrfMbSfG8bBDrigkBX0xKYSEkQAIkQAKHTcDRDHe+WzqLb/pU8bXR9LALPhD3urbiqmdv1iNQbd6Ko9Wdri8g12hvw3njx0mxDjdIwcnTZMXNt8jLc+fJh64rWI5HSQ4C9OCT4zmxlSRAAvmYgE0So15ymqrRpFhm9jviPvCwiHaV27H6Sy4WR8fM81JgHE5ppAFxVfdpVLwtGu9+m04YHKOx/H06rHCergpAsJ287WvISwLJXxcNfPI/Q94BCZBAPiKAIDROx5vEdNAI9Doxz8VY/dh0f9f9NU39E/fy+H6PPqOBVFKtuOprqaGGfqtOFPyobRt5XttTX4cTEFDndNW0PG4Xq4tOgAY+Oh8eJQESIIGEELDZL3VZXZqq2bpVzJx3xR34qIh68nZS3mWX5nmMeaf2aVLo8SFynIbvbTl5qlyj+m3rVvJ282bynGYAvVxJNVFFTHxK4gnQwCf+GbAFJEACJBCVgHPMMTYVrGg6WLNipZ2B774wXhztPrdd+Hm8rM2pWUPSBvQVnwYjq6Nj9Ke3u0m260z86brErodO1kNEvatUmUUk6mON+0Ea+LgjZgUkQAIkEDsCDgLmqBpNqW3emyfuk8M1qo7xG3osudMIenklWNrn9LlfzKZNUvLZcdKley9p+8iDMkcnD/bRRmA1/TWqSHlLyXsCnEWf98xZIwmQAAkcMQFHk3L5rmspaS+9IL777hZZ/4O4N3S03fjmc+SYyztxNKteWv9HxGlyiRTv1l2u1xn3I7V6DfMj+vohA1Q/V6XkLQF68HnLm7WRAAmQQMwJYGwcavbcIWbufHF1Dbvs2eMfq9d17U5pBKyNv/haNhdTv66+ZAyRgp8slyt63SWXFy0qn2jVr6pOVkXY3AtVj1KlxJcAPfj48mXpJEACJJBnBByd6Oa79hpJe/5Z8fXTZXa/bxa3Q2fJeLifmKWfaE++iXtbnOrVxZc+WjT/t7jIXKcz7zHLfohqF1V48neqvq66U5USPwI08PFjy5JJgARIIGEEEAff17uH+F59WZzzzhV34hRx27QXxMI3miM+noJIfL4ed4qvZ3dx+w3016lBck7RSjVmn/RV/VO1p+qLqr+rUmJPgAY+9kxZIgmQAAl4hoCjqbd9GoI2bcwI8T2hfrQmvnG7dJOM+/uI+fAjMRkZcWur0/Ac8Y0bK+ab1eLe1dtOxkNlFVXhzWN8vpiq9jXI/1TXqlJiR4AGPnYsWRIJkAAJeJqA7T7vfof4ZkyzE+JczQvvtm4n7nPPi/n117i0HeP/aU8M1UA9F4p7253izpsfrKeEbrVRHaWKGff47Ke6XDX+gwlaST4XTrLL5w+Yt0cCJEAC2QmgC925rImIqvnlF/+6+tvv0vVs1f3L7ZDG9qjYToPDjH/ToJ6dgOcu+1Qc7b7HnAHI0ar/Ub1MFcb9LdUpqlhLf7FqQVXK4ROgB3/4zHgFCZAACeQbAk6lSuK7rav4XnvFTtCzEfOQ8Ga0dq1rIJtYinPCCeJ7boyNxocJeOi6DxUkuzlbdZBqN9VVqneoTlfdoUo5PAL04A+PF88mARIggXxJwCa8uehCSVNF4JpgGtsKFXS53RU2xa1zNHztIxPbe6DL58ySpeLq7H5Hw9w6N7YXx5fV3zxZq4FuUp2t2ku1oSqW2VVQpRyaQFaihz6fZ5AACZAACeRzAghc4/tvJztW72vXRsyij/xj9U+PFLNuXUzu3jm3ofh0OZ/58itxe94j5vfwc+mP09o6qz6tWkYVQXOGqWb1/XUH5SACNPAHIeEOEiABEiABEIBXjSV2aUMGie/F59TClhb3oX6Scesd4r49S8zffx8RKDsB78nHNaa+LuNDme8viFhecT3SUhUR8hqojlPtr7pRlRKeAA18eC7cSwIkQAIkEELAKVtWfJpMJu2VKeK7pZOYTz/ze/XD/idm9bchZx7+pu/61uJ78jExEyaJ+9iwqC8OmHB3qSqW1V2k2l/1NdUDqpSsBGjgs/LgNxIgARIggUMQcM46059NbvIEkcqVxB08VDJ0bb1Z+cUhrox82KlRw66ZF53hbyfgfbsm8smZRxrr5xOqG1TvVz30FXpSCgkNfAo9bN4qCZAACcSSgFOqlPjatpG0KS+Jr7MmulHv24bF3Zi7jnNM4rPR926/VdwHHxZX880fKrzuMXpDd6terzpC9QXVIxs40ALyidDA55MHydsgARIggUQSsJPm1KN3NDe9q2vq3VFjxOzalasmOec38k/A06x4bs+7xWzZcshysLwOk+8QIOce1eWqqS408Kn+F8D7JwESIIEYEXAKFBA7nj5RI8wfyPCnr9VoebkJh+uUKSNp/xsmeHFwu94u5oNFh2wlwubcotpddZoqxum3qaaq0MCn6pPnfZMACZBAnAg4JUvaRDO+kcPFLFsubsf/2nXvuakOQwC+x4eI+/x4cZ94KuoEvED5tXQDY/NVVJHcZp5qKgoNfCo+dd4zCZAACeQBAadKFUlT44zMcm7685Jx931i1q8/7Jqdk2r6J+A5jk2UY9Z8d8gyEMXtOtX+qvD98Zm7mQF6YZIKDXySPjg2mwRIgASShQBm3WMdvaNR8ty77xcXS+u2bj2s5tusePf2Fl/X/4r7wEPiTn05R9cjc91A1Uaq/VVnqv6jmgpCA58KT5n3SAIkQAIJJoCgOb5mV4tPZ9xLieK2297Okt+//7Ba5lx4gY1nj8x0GZiAl8N0t5dpLY+rYindo6q5m/6nFyaR0MAn0cNiU0mABEgg2Qkgg5zv1i7WSJt134t7U6eoEezC3a9TrpykbdusaeiOFqPj8jmV0noiltQhxn1fVS0hXwsNfL5+vLw5EiABEvAmAcS7T+v/iPgeflDMK69Khi6ty55dLlrLbY/AoP5iNv6q4/sIXJtzaaenXqXaTzU2kfVzXndenkkDn5e0WRcJkAAJkEAWAlg3n5Y+WpwWzcTtN1DzxT8aMfFMlgv1CzLT+R4bLGaxZqZ79fXsh6N+b6JHb1XFbPv8umaeBj7qnwAPkgAJkAAJ5AUB32VN/OPz1aramfLuuBdytCTOKVbMH8f+lRmH3dVfT2+sj+oE1XdU85vQwOe3J8r7IQESIIEkJWA98g43im/C8yJ/bfUHypk155DhajEm7xs2VMxIjZ6n0e8OR6rpyQNUsVYeM+zzk9DA56enyXshARIggXxAAGlkffffI74nhoiZO8+ffOYQhtupVk18A/v5u/gPM2d9WWU2UHWl6lhVVzU/CA18fniKvAcSIAESyIcEkGEu7emn/IlsnhwuGZqAxvz8c8Q7xXi+T9fKu/f1EbNpU8Tzwh0oojsfUMXq/FGq+cHI08Drg6SQAAmQAAl4l4BNPjNpvDgN6ot7Z09xR4wWs3Nn2AY7jc4Tp5NmttOAOmb79rDnRNpZQA8gtC1S22CtfLILDXyyP0G2nwRIgARSgICTlia+1q3EpxnrIO6NN4v7+pt2O/s/vmuainPZpX5Pfu/e7Iejfj9Kj/ZX3aeKhDXJLDTwyfz02HYSIAESSDECTvHi4rvrDvGNelrMezo+//TIsNHsfDd3EKdmDXH7Dgh7PBq2ND14n+oHqsm8Tp4GPtpT5jESIAESIAFPEnAqVxbfM5pdTsfa3ft1zH337oPa6fTuIXLUUYcV7S5QSAnduE0VIXQyAjuT7JMGPskeGJtLAiRAAiTgJ+BoqFrf0MHiVK8mbrfuYn79NQsaG+2un07My0W0OxSEdfJ4bchaKo4kh9DAJ8dzYitJgARIgATCEHA0hazvjm7itLnOTsAzX36V5awjiXaHgi5UXZqlxIO/fPvtt/LRRx/Jz1Fm+B98Vfz30MDHnzFrIAESIAESiDMB39VXie+RPuL2HyTuO+9mqS0Y7W76q4cd7a6WlnSocfhnn31W9upkvgMHDmSpN9FfaOAT/QRYPwmQAAmQQEwIOPXr+cflJ00V97nns5Rpo9397wkxY9LFrEBIm5zJ8Xra+iinuq4ra9eulcWLF8tff/0V5cy8P0QDn/fMWSMJkAAJkECcCNjJd8+OspnpMh7RbHMhy+ScSpX8gXD+94yYHHrbpbSdXaK0FQZ+4sSJ8sgjj8jUqVOjnJn3h2jg8545ayQBEiABEogjAbuU7qknBJ/uXb3F/PFHsDbnnLNFKlcSM+O14L5oG1gXr1dEFJ/PJ4MHD5YRI0bIf/7zn4jnJeIAAvdQSIAESIAESCBfEUBgHOe+u8VFljmdYe8bPECck0+y9+jrfru4t90pRoPhOGURiT73AgM/fLiG0c3IkDSt00tCD95LT4NtIQESIAESiCkB3/WtxdfrLnEfeEjMog9t2c7xx2v++WvteHysKvOaccd90cDH6umyHBIgARIgAU8ScM4716aTdUeNFffV120bnfZt7Ti9WfmFJ9sci0Z5zsBjmcHWrcjnQyEBEiABEiCB2BBAZjqfTr5zW7cX98UJYtfH39ktYqjb2NSa2FI8YeD3798vffr0kcoaerBgwYJSWnMBFy1aVGrXri3jx49PLCHWTgIkQAIkkC8IIM982tZNNoa9+fAjQZY6KV9OTISkNcl+054w8N27d5evv/5aZs2aJTt27BAsO/hVQw6OGzdOEEBg7Nixyc6Z7ScBEiABEvAAAadUKfEN6i/uU5qs5qefxNdDJ9tNnirGY2vYY4HKEwb+vffek/T0dKlTp44UK1ZMEHqwZMmScu6558ozzzwjb74ZPiVgLACwDBIgARIggdQigCxzzm1dxH24v2iXsTjNrhYz9rl8B8ETBh5d8QsWLAgLd+bMmVKuXLmwx7iTBEiABEiABHJDwHfF5eLUqyvuY+/f3yAAABhpSURBVMPEubG9mC++FPPVqtwU5dlrPLEOfuDAgdK+fXu7lvDEE0+UEiVKyPbt22X16tU2tu/s2bM9C5ANIwESIAESSE4CjuaVNwiE88b/2Rzz7jOjxDdurO1FTs47ytpqTxj4+vXry4oVK2TJkiWyYcMG2aT5feG1d+vWTS688MJ8Azsren4jARIgARJIJAGnQAHxDewrbtfbxdFENcgdL8s/1dB1ZyWyWTGr2xMGHndTqFAhady48UE3tmbNGtmzZ4/gJYBCAiRAAiRAArEkgEh2Ps0Z7w7QvPKtWoj79ixJyycG3hNj8NEe1owZM2TMmDHRTuExEiABEiABEsg1AaduHTsO7y5cJOazFflmRr1nPPhIT+bhhx+OdOig/a+99prMmTPnoP3YsXTpUilfvnzYY9xJAiRAAiSQ2gR8LZuLUQMv1auKmfOuODe0S3ognjfwh0MYXfz16tULe8nrr79uJ+yFPcidJEACJEACKU/A17qlZKRrHvmZOrGbBt5bfw+IgAcNJ1WqVGEI3HBguI8ESIAESMBPQCPbOWPSRTJcMZ9+Js6ZZyQ1GU948E899ZT8888/EUHWqlVLmjdvHvE4D5AACZAACZDAkRJAkDXnupY2dK2dbEcDf6RIxS6NGzVqlHTs2NHGoM9eIgPdZCfC7yRAAiRAAvEg4Fx1hU1GI4uXitm2TRDaNlnFEx78yJEjbfx5xKAfPXp0srJku0mABEiABJKcgFO4sPiuaepPSLPgA5s3PllvyTPL5B5//HGbaGbXrl3JypLtJgESIAESyAcEsB7ebN4sZn74EOrJcoueMfBIMjNlyhSbbCZZ4LGdJEACJEAC+Y+Ao5FUnUsvFaNR7czWrUl7g54x8NkJdu3a1Xr02ffzOwmQAAmQAAnEm4DTqKHopDAxH34c76riVr5nDfzEiRNl7969cbtxFkwCJEACJEACkQg4Dc8R2bdPzPsLI53i+f2eNfCeJ8cGkgAJkAAJ5FsCjmY1lXp1xHyyTIxOAE9G8ayBx5I5JKChkAAJkAAJkEAiCPguvkiD3mSIfL8+EdUfcZ2eNfDp6ek2L/wR3yELIAESIAESIIFcEHDO1W56NfBm9be5uDrxl3jWwCceDVtAAiRAAiSQygScSpVEg7SIWbsuKTHQwCflY2OjSYAESIAE8oTASTVFvv4malV//vmnLF++XIwx8vfff8uiRYtk//79Ua/Ji4M08HlBmXWQAAmQAAkkJQGn9mlifvo5YtsPHDgggwcPlp07d8qIESPkkUcesblVBgwYEPGavDpAA59XpFkPCZAACZBA8hGoXFlky5aI7S5QoIAMHz5cfD6fFClSxKYlv1SD5MDgJ1po4BP9BFg/CZAACZCAZwk4NU8UqXVy1Pa9//77smTJEunSpYuU8lByGk8km4lKjgdJgARIgARIIEEEnPLlxSldOmLtmzVmfY8ePaRkyZJyyimnSNWqVaVt27ZSo0aNiNfk1QF68HlFmvWQAAmQAAkkH4GiRUSiJEErry8AvXr1klNPPVWaN28uF198saDbHp+JFhr4RD8B1k8CJEACJOBdAgi4dojx9M6dO0vZsmV1yXyGvPTSS9KhQwc7oz7RN0UDn+gnwPpJgARIgAQ8SwDd82lvvpqj9u3evdtGYJ0xY4bMnTs3R9fE8ySOwceTLssmARIgARJICQKYOV9C49c/8MAD8sMPP3jCg6eBT4k/Pd4kCZAACZBAPAnAwAekevXqgc2EfrKLPqH4WTkJkAAJkAAJxIcADXx8uLJUEiABEiABEkgoARr4hOJn5SRAAiRAAiQQHwI08PHhylJJgARIgARIIKEEaOATip+VkwAJkAAJkEB8CNDAx4crSyUBEiABEiCBhBKggU8oflZOAiRAAiRAAvEhQAMfH64slQRIgARIgAQSSoAGPqH4WTkJkAAJkAAJxIcADXx8uLJUEiABEiABEkgoAceoJLQFeVT5ypUrpWnTplK/fv1c1Thv3jwpWrRorq7lRVkJIOPS/v37pXDhwlkP8FuuCIAlpGDBgrm6nhdlJfD3339blmlpaVkP8FuuCCABS5MmTXJ1bX64aP369TbxTMWKFfP8dlLGwB8pWeT2Xbhw4ZEWw+uVwKpVqyQ9PV1GjhxJHjEgMGHCBHEcRzp27BiD0lhE9+7d5dZbb5XatWsTRgwI8LczBhBzWQS76HMJjpeRAAmQAAmQgJcJ0MB7+emwbSRAAiRAAiSQSwI08LkEx8tIgARIgARIwMsEaOC9/HTYNhIgARIgARLIJQEa+FyC42UkQAIkQAIk4GUCNPBefjpsGwmQAAmQAAnkkgCXyeUQ3G+//SbHH398Ds/madEI/PPPP7Jjxw4pU6ZMtNN4LIcEdu3aZc8sVqxYDq/gadEI/Pnnn1KiRAk56qijop3GYzkkwN/OHIKKw2k08HGAyiJJgARIgARIINEE2EWf6CfA+kmABEiABEggDgRo4OMAlUWSAAmQAAmQQKIJ0MAn+gmwfhIgARIgARKIAwEa+DhAZZEkQAIkQAIkkGgCNPCJfgKsnwRIgARIgATiQIAGPg5QWSQJkAAJkAAJJJoADXyinwDrJwESIAESIIE4EKCBjwNUFkkCJEACJEACiSZAAx/yBBYuXCjnn3++VK9eXVq0aCFbt24NOfrvZk7P+/eK1NwaOnSo1KlTx/LEdjhBVLt7771XzjzzTKsPPvig7N+/P9ypKb0Pf4tt2rSRmjVryumnny6LFy8+JI+uXbvKrbfeesjzUvGEnPLEeW3btpVTTz1Vzj77bJk8eXIq4jrkPef0N3HOnDnSpEkTqV+/vtxyyy2yZcuWQ5bNE46AgKFYAvqHZjQUrfniiy+MGhjTq1cv06lTp4Po5PS8gy5MsR3Tp083jRo1Mtu2bTMaqtLUrVvXzJ49+yAK48aNM/oyZZmDe7NmzQz2UbISaN26tRk0aJBxXdcsWLDAHHvssWbPnj1ZTwr5NnPmTFO6dGmjRj5kLzcDBHLK87bbbjN9+vSxl/3+++/m5JNPNps3bw4Uw08lkNPfRPy9VqxY0axevdpyu++++0zv3r3JMI4EJI5lJ1XR+mZpLrnkkmCb169fb0qWLBn8HtjI6XmB81P1s3Pnzmbs2LHB23/sscdMly5dgt8DG8uWLTPr1q0LfDUPPPCAufnmm4PfueEnULx4caMx0oM4zjjjDPPee+8Fv4du/PHHH6Zhw4amX79+NPChYEK2c8LzwIED5uijjzbbt2+3L1P79u0LKYGbAQI5/U3UnAmW54YNG+yleGHV3pFAMfyMAwF20Wf2fvz0009ZksmohyT6H1v0P3WW/pGcnpflohT8kp3TcccdJ+oBHUTirLPOkhNPPNHu3717t0ydOlWuvvrqg85L5R3oJsbfoXrkQQzgqZ5k8HvoRrdu3aR///7C5DOhVP7dzinPTZs2ib4IyBNPPCHlypUTfeGX55577t+CuGUJZP+/Hum3s2jRojJy5Ei56KKLpFWrVjJlyhTRl1BSjCMBGvhMuMgghT/AgBQuXNhuardSYJf9zOl5WS5KwS/ZORUpUkRgwCMJxt0x1gmDj//8lH8JZGeJI/j7DGSR+/dMsS9IOHb55ZeH7uZ2CIGc8sT48F9//SU///yz/PLLLzJjxgy5//77D3rpDyk6JTez84z027l3717RXic7J0eHOgTzb1asWJGSzPLqpmngM0mXLVvWpjANgN+5c6cUKlRIjjnmmMAu+5nT87JclIJfsnNCetgKFSqEJQHj3rJlS8nIyLAGKuxJKbwzO0ugCMcTP7Q9e/YUHWoSHYMXHeuUH3/8UZYsWZLC9A6+9ZzyLFWqlOicB9ExeME2epZgmGCkKP8SyM4z0m/n/PnzZeXKlaJzSGTIkCEyfvx46dGjB4aJ/y2MWzElUCCmpSVxYZUqVRIdGwreAbYrV64c/B7YyOl5gfNT9ROcYFwCEomnjnNazx3G/Y033pCCBQsGLuFnJgEYF3hF8CLBFQKeVapUyTzD/6ETGqVGjRqSnp5ud/z6668Cr2nSpEly7rnnZjk3lb/klKdOupW0tDTbTR/ghb/P7L16gWOp+pnT30T0hOjckCCmBg0a2BdVvKxi+IMSBwJxGNdPyiL1h9CUL1/ezJs3z2C7Q4cOdsIXbka76czcuXPtfUU7LylvPE6NxsQbXSJnNm7caH744QejhscsX77c1vbNN9+Yr776ym4PHz7cnqfGyE4iUy/UqAcQp1Ylb7GYtNi9e3ej3Zrm1VdfNbVq1bIrD3BHoTxD73DYsGGcZBcKJGQ7Gk+sUsDMcAhm2wdm0X/99ddGX7SMjjmHlMTNaL+Job+dWH2gTpPB/3WIjscb7RUhwDgS4Cz6ELhY2qUTk+xSjsaNGwcNzQcffGB0DDl4ZqTzgidwwy7nwjJD9ZaMTgizM7oDWO68887gTPmqVauify6LXnXVVYFT+ZlJAC9JtWvXNjrMYXRSol0qF4ATyjOwD5808KE0sm5H44kliLNmzbIXwJjr+nej8QdMmTJlzIQJE7IWxG+WQKTfxOy/nVhZozEv7Atq06ZNzeeff06CcSTgoOw4dAwkbZHoMsYYUvax9+w3lNPzsl+Xat/R/aZLjaym2r3H434x8QszuimxIZBTnpjfgK59dNlTwhM4nN9E/C6UKFEifEHcGzMCNPAxQ8mCSIAESIAESMA7BDiL3jvPgi0hARIgARIggZgRoIGPGUoWRAIkQAIkQALeIUAD751nwZaQAAmQAAmQQMwI0MDHDCULIgESIAESIAHvEKCB986zYEtIgARIgARIIGYEaOBjhpIFkQAJkAAJkIB3CNDAe+dZsCUkQAIkQAIkEDMCNPAxQ8mCSIAESIAESMA7BGjgvfMs2BISIAESIAESiBkBGviYoWRBJEACJEACJOAdAjTw3nkWbAkJkAAJkAAJxIwADXzMULIgEiABEiABEvAOARp47zwLtoQESIAESIAEYkaABj5mKFkQCZAACZAACXiHAA28d54FW0ICJEACJEACMSNAAx8zlCyIBEiABEiABLxDgAbeO8+CLSEBEiABEiCBmBGggY8ZShZEAiRAAiRAAt4hQAPvnWfBluQhgYyMDHEcR4oXLy4lSpQIapMmTWLWin79+sn+/ftteSeccIJ88cUXMSn72WeflQIFCgTbXLZsWWnZsqWsXr061+V/9tlncvLJJ+f6+iO9cNeuXWGfB57Nd999l6vie/bsKYMGDYp6bazv+8MPP5QGDRpErZMHSSCvCNDA5xVp1uNJAl9//bXs2LEjqPPmzYtJO/ECMXDgQHFd15b30UcfyamnnhqTslHIJZdcEmzzjz/+aI3KlVdeKZs3b85VHaeffrosXLgwV9fG8qL169cH7yvwXE466aRYVpGlLK/cd5ZG8QsJxIgADXyMQLKY/EWgcePG8thjj8mxxx4r77zzjnzzzTeCfSVLlpSqVavK8OHDgze8aNEiadSokVSoUEFuv/122bt3r7Rt29Yer1u3rvzxxx/SoUMHgfGCfPDBB9bjLl26tDRv3lw2bdpk9w8bNkyeeuopueiii6RUqVLSrl07+fvvv+2xaP8ULVpUHn74YTnmmGNk2rRp9tQtW7bYOlAO2oA2Qrp16ybTp0+32/jnrbfekq5du8ratWulU6dOdv+ePXvktttuk4oVKwra2Lp1a9m5c6c9dumll8qECROkRo0acvzxx8uYMWPsfvwTjgP2437RBrQFPQ3gcbjy3HPPybXXXivGGHsp+I4bN06++uory+nmm2+W8uXL23PCveREen6h9x2NfySeaMzrr78ueFGoVq2avPHGG4d7azyfBOJGgAY+bmhZcDIQmDp1qjUUMBbQ7du322avW7dO3n//fXnhhRekfv36cuONN8pVV10lv/76qzXu9957r/z111/WOLdq1UpuueUWa2x+/vlnQRc6yoLAuJUpU8Yadxj+H374QZo1a2b1yy+/lMKFC0vHjh3tuTAieKl48MEHbXc+uo9DjbE9Kco/aGegO7tz5872ZeTbb78VdFUHjPfZZ58tkyZNCpYyefJkOeuss+xLCdoGeeaZZ+T777+XFStWyJIlSwTtfOWVV+wx7AezmTNn2nvs1auXZYaXlHAccE/XXHONgBeGEPCCNHToUFtWuH/AbeTIkUGdMmWKPQ0vSLgebccLxqpVqyw3MH355Zftixju9bjjjpMuXbocVHSk5xd4JrggGv9IPPHShhckDAW8/fbbMn/+/IPq5g4SSBSBAomqmPWSgBcIzJ49WwoVKhRsCrq5YYQgMIww6hB4kDCg8CDhqcEwwyBgXB1GJWBAx44daz1UjO1D4LVirD8gb775ptSuXVvgcUJgGGrWrBnsWm/RooVcccUV9thll10mGzZssNs5+QceNQwOXjxwXzCCRYoUsYb3xRdftIYa5eO+4JFjHH/u3Ln2hSRg3FFP+/btbfvgEaMHAe0L9DLgOIx1rVq1rIIVXnrgSYfjAO/2tNNOsy80uPahhx6yBh89FeEEY9hHH3108FDlypXlhhtusM8oPT3dvmgdOHDAvmAULFjQnof7QA8GmAfatm/fvmAZ2Ij0/LKcpF/C8Y/GEy9AuD/0xEDwdzBx4kS7zX9IINEEaOAT/QRYf0IJwIOtUqVK2DbAuAQExvyCCy4QeIn16tUTjLFjfH3NmjUCrzgglSpVEiiOhxOMlzds2DB4CF3d8PBhJCEwqgFB1zuMWU4FvQeo+5dffrEvFRinD5XFixdLnTp17FDDrFmzBAbyvPPOs93woQY+LS1NevToYXsfypUrJ//884/18gNlYdgiIGgjjkfi8Nprr1njn30C38aNG+0QQKCcwCeMI+oMJxgiwUsMXsjQ6xAQGNjACxV44qUGnEMl0vMLPQfb4fhH44kejTPOOCNYzDnnnEMDH6TBjUQToIFP9BNg/Z4lAEMHgQeH7md0Dzdt2tR6mDAi8OYxRv3uu+8G7wFG9tNPPw16rMEDmRuY8f7xxx8Hd//222+2/OrVq9t9od5+8KQcbMDTxgTB0aNHBz1reNWoDwIDF+iZwPg1xorh+QbmCoRWgfF3vCjgehi8m266KTj2jfPCtTESB7z84CXivffeC1aBlxkY6sMVdIFv27bNvlhhG13/ELyc4IUKz+unn34SzCEIfTmL9vyytyHcvaG3AuzC8USvAl6WAhL6ohTYx08SSBQBjsEnijzrTRoCWMIFwRI6eI+YyIaxW3iu8Co///zz4BI1TNRCtz2MDbqaA2P6gZu9/PLLBd3QmL2PHgCMOcMDDRjfwHmH+kTdMFww3Kgf4/gYDsD4PjxzTIaDsUcd6F7HDH70PkBgGPGSsVBnzQe6lkPr+/PPP+X888+3xh3eK7rxUV80icQBzD755BM7no/r0WOCIYjA6oLsZW7dutXeF+4toFhqiBn1mMA4atQoGTFihN3GPgg+MfSBFy6Mz2PCI4ZQAhLt+QXOifYZjScmRC5dutTOfcDfxOHMmYhWJ4+RQCwI0IOPBUWWka8JoAsfBhQzwdGdDmOJbnZMaMMM8yFDhtguY3il6CKGEYPA6METXrlyZZAPupb79u1ru/VRFgx7bmZewzjjenic8NJhaDDbP9DrgIl6119/vWDsHfvuuece2z2PhqBbHcMNMJyBru1gA3UD49i9e/e2xhRGE+PSmG0eTcAkHAcYWuxHfZiVj++YhBhoZ/Yys3fl4zh4YpkhmAfmJzz//PNy//33Cya/YchgwIABdm4BXnIw/yBUoj0/xCfIiUTjOXjwYNtLgZc/vBhRSMArBBz9D+xfd+KVFrEdJOBRArt377YGFd3z2QVj5Tie3RPHPhjU7ILz4d3DSMdT4OHjBSBc13O0evGzAE8+0MUf7dzQY5E4oAsd3euxvt/ly5fbIQT0TsDjx1BBJIn2/CJdk31/JJ7o4YAHH+6FKXsZ/E4CeUWABj6vSLMeEiCBmBMINfAxL5wFkkCSE+AYfJI/QDafBFKZACYnPvroo6mMgPdOAhEJ0IOPiIYHSIAESIAESCB5CdCDT95nx5aTAAmQAAmQQEQCNPAR0fAACZAACZAACSQvARr45H12bDkJkAAJkAAJRCRAAx8RDQ+QAAmQAAmQQPISoIFP3mfHlpMACZAACZBARAI08BHR8AAJkAAJkAAJJC8BGvjkfXZsOQmQAAmQAAlEJEADHxEND5AACZAACZBA8hKggU/eZ8eWkwAJkAAJkEBEAjTwEdHwAAmQAAmQAAkkLwEa+OR9dmw5CZAACZAACUQkQAMfEQ0PkAAJkAAJkEDyEqCBT95nx5aTAAmQAAmQQEQCNPAR0fAACZAACZAACSQvgf8HEWboYiLoz2AAAAAASUVORK5CYII=" alt="plot of chunk unnamed-chunk-17"/> </p>

<p>We can extract the coefficients and make predictions at certain values of \(\lambda\). Two commonly used options are:</p>

<ul>
<li><p><code>s</code> specifies the value(s) of \(\lambda\) at which extraction is made. </p></li>
<li><p><code>exact</code> indicates whether the exact values of coefficients are desired or not. That is, if <code>exact = TRUE</code>, and predictions are to be made at values of s not included in the original fit, these values of s are merged with <code>object$lambda</code>, and the model is refit before predictions are made. If <code>exact=FALSE</code> (default), then the predict function uses linear interpolation to make predictions for values of s that do not coincide with lambdas used in the fitting algorithm. </p></li>
</ul>

<p>A simple example is:</p>

<pre><code class="r">any(fit$lambda == 0.5)
</code></pre>

<pre><code>## [1] FALSE
</code></pre>

<pre><code class="r">coef.exact = coef(fit, s = 0.5, exact = TRUE)
coef.apprx = coef(fit, s = 0.5, exact = FALSE)
cbind2(coef.exact, coef.apprx)
</code></pre>

<pre><code>## 21 x 2 sparse Matrix of class &quot;dgCMatrix&quot;
##                       1            1
## (Intercept)  0.19657091  0.199098747
## V1           1.17495906  1.174650452
## V2           .           .          
## V3           0.52934375  0.531934651
## V4           .           .          
## V5          -0.76126245 -0.760959480
## V6           0.46627405  0.468209413
## V7           0.06148079  0.061926756
## V8           0.38048793  0.380301491
## V9           .           .          
## V10          .           .          
## V11          0.14213614  0.143260991
## V12          .           .          
## V13          .           .          
## V14         -0.91090226 -0.911207368
## V15          .           .          
## V16          .           .          
## V17          .           .          
## V18          .           0.009196628
## V19          .           .          
## V20         -0.86099392 -0.863117051
</code></pre>

<p>The left column is for <code>exact = TRUE</code> and the right for <code>FALSE</code>. We see from the above that 0.01 is not in the sequence and that hence there are some difference, though not much. Linear interpolation is mostly enough if there are no special requirements. </p>

<p>Users can make predictions from the fitted object. In addition to the options in <code>coef</code>,  the primary argument is <code>newx</code>, a matrix of new values for <code>x</code>. The <code>type</code> option allows users to choose the type of prediction:</p>

<ul>
<li><p>&ldquo;link&rdquo; gives the fitted values</p></li>
<li><p>&ldquo;response&rdquo; the sames as &ldquo;link&rdquo; for &ldquo;gaussian&rdquo; family.</p></li>
<li><p>&ldquo;coefficients&rdquo; computes the coefficients at values of <code>s</code></p></li>
<li><p>&ldquo;nonzero&rdquo; retuns a list of the indices of the nonzero coefficients for each value of <code>s</code>.</p></li>
</ul>

<p>For example,</p>

<pre><code class="r">predict(fit, newx = x[1:5,], type = &quot;response&quot;, s = 0.05)
</code></pre>

<pre><code>##               1
## [1,] -0.9802591
## [2,]  2.2992453
## [3,]  0.6010886
## [4,]  2.3572668
## [5,]  1.7520421
</code></pre>

<p>gives the fitted values for the first 5 observations at \(\lambda = 0.05\). If multiple values of <code>s</code> are supplied, a matrix of predictions is produced.</p>

<p>Users can customize K-fold cross-validation. In addition to all the <code>glmnet</code> parameters, <code>cv.glmnet</code> has its special parameters including <code>nfolds</code> (the number of folds), <code>foldid</code> (user-supplied folds), <code>type.measure</code>(the loss used for cross-validation):</p>

<ul>
<li><p>&ldquo;deviance&rdquo; or &ldquo;mse&rdquo; uses squared loss</p></li>
<li><p>&ldquo;mae&rdquo; uses mean absolute error</p></li>
</ul>

<p>As an example,</p>

<pre><code class="r">cvfit = cv.glmnet(x, y, type.measure = &quot;mse&quot;, nfolds = 20)
</code></pre>

<p>does 20-fold cross-validation, based on mean squared error criterion (default though). </p>

<p>Parallel computing is also supported by <code>cv.glmnet</code>. To make it work, users must register parallel beforehand. We give a simple example of comparison here. Unfortunately, the package <code>doMC</code> is not available on Windows platforms (it is on others), so we cannot run the code here, but we make it looks as if we have.</p>

<pre><code class="r">require(doMC)
registerDoMC(cores=2)
X = matrix(rnorm(1e4 * 200), 1e4, 200)
Y = rnorm(1e4)
</code></pre>

<pre><code class="r">system.time(cv.glmnet(X, Y))
</code></pre>

<pre><code>##    user  system elapsed 
##   2.440   0.080   2.518
</code></pre>

<pre><code class="r">system.time(cv.glmnet(X, Y, parallel = TRUE))
</code></pre>

<pre><code>##    user  system elapsed 
##   2.450   0.157   1.567
</code></pre>

<p>As suggested from the above, parallel computing can significantly speed up the computation process especially for large-scale problems.</p>

<p>Functions <code>coef</code> and <code>predict</code> on cv.glmnet object are similar to those for a <code>glmnet</code> object, except that two special strings are also supported by <code>s</code> (the values of \(\lambda\) requested):</p>

<ul>
<li><p>&ldquo;lambda.1se&rdquo;: the largest \(\lambda\) at which the MSE is within one standard error of the minimal MSE.</p></li>
<li><p>&ldquo;lambda.min&rdquo;: the \(\lambda\) at which the minimal MSE is achieved.</p></li>
</ul>

<pre><code class="r">cvfit$lambda.min
</code></pre>

<pre><code>## [1] 0.08307327
</code></pre>

<pre><code class="r">coef(cvfit, s = &quot;lambda.min&quot;)
</code></pre>

<pre><code>## 21 x 1 sparse Matrix of class &quot;dgCMatrix&quot;
##                       1
## (Intercept)  0.14936467
## V1           1.32975267
## V2           .         
## V3           0.69096092
## V4           .         
## V5          -0.83122558
## V6           0.53669611
## V7           0.02005438
## V8           0.33193760
## V9           .         
## V10          .         
## V11          0.16239419
## V12          .         
## V13          .         
## V14         -1.07081121
## V15          .         
## V16          .         
## V17          .         
## V18          .         
## V19          .         
## V20         -1.04340741
</code></pre>

<pre><code class="r">predict(cvfit, newx = x[1:5,], s = &quot;lambda.min&quot;)
</code></pre>

<pre><code>##               1
## [1,] -1.3647490
## [2,]  2.5686013
## [3,]  0.5705879
## [4,]  1.9682289
## [5,]  1.4964211
</code></pre>

<p>Users can control the folds used. Here we use the same folds so we can also select a value for \(\alpha\).</p>

<pre><code class="r">foldid=sample(1:10,size=length(y),replace=TRUE)
cv1=cv.glmnet(x,y,foldid=foldid,alpha=1)
cv.5=cv.glmnet(x,y,foldid=foldid,alpha=.5)
cv0=cv.glmnet(x,y,foldid=foldid,alpha=0)
</code></pre>

<p>There are no built-in plot functions to put them all on the same plot, so we are on our own here:</p>

<pre><code class="r">par(mfrow=c(2,2))
plot(cv1);plot(cv.5);plot(cv0)
plot(log(cv1$lambda),cv1$cvm,pch=19,col=&quot;red&quot;,xlab=&quot;log(Lambda)&quot;,ylab=cv1$name)
points(log(cv.5$lambda),cv.5$cvm,pch=19,col=&quot;grey&quot;)
points(log(cv0$lambda),cv0$cvm,pch=19,col=&quot;blue&quot;)
legend(&quot;topleft&quot;,legend=c(&quot;alpha= 1&quot;,&quot;alpha= .5&quot;,&quot;alpha 0&quot;),pch=19,col=c(&quot;red&quot;,&quot;grey&quot;,&quot;blue&quot;))
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-28"/> </p>

<p>We see that lasso (<code>alpha=1</code>) does about the best here. We also see that the range of lambdas used differs with alpha.</p>

<h4>Coefficient upper and lower bounds</h4>

<p>These are recently added features that enhance the scope of the models. Suppose we want to fit our model, but limit the coefficients to be bigger than -0.7 and less than 0.5. This is easily achieved via the <code>upper.limits</code> and <code>lower.limits</code> arguments:</p>

<pre><code class="r">tfit=glmnet(x,y,lower=-.7,upper=.5)
plot(tfit)
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-29"/> </p>

<p>These are rather arbitrary limits; often we want the coefficients to be positive, so we can set only <code>lower.limit</code> to be 0.
(Note, the lower limit must be no bigger than zero, and the upper limit no smaller than zero.)
These bounds can be a vector, with different values for each coefficient. If given as a scalar, the same number gets recycled for all.</p>

<h4>Penalty factors</h4>

<p>This argument allows users to apply separate penalty factors to each coefficient. Its default is 1 for each parameter, but other values can be specified. In particular, any variable with <code>penalty.factor</code> equal to zero is not penalized at all! Let \(v_j\) denote the penalty factor for \(j\) th variable. The penalty term becomes
\[
\lambda \sum_{j=1}^p \boldsymbol{v_j} P_\alpha(\beta_j) = \lambda \sum_{j=1}^p \boldsymbol{v_j} \left[ (1-\alpha)\frac{1}{2} \beta_j^2 + \alpha |\beta_j| \right].
\]
Note the penalty factors are internally rescaled to sum to nvars. </p>

<p>This is very useful when people have prior knowledge or preference over the variables. In many cases, some variables may be so important that one wants to keep them all the time, which can be achieved by setting corresponding penalty factors to 0:</p>

<pre><code class="r">p.fac = rep(1, 20)
p.fac[c(5, 10, 15)] = 0
pfit = glmnet(x, y, penalty.factor = p.fac)
plot(pfit, label = TRUE)
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-30"/> </p>

<p>We see from the labels that the three variables with 0 penalty factors always stay in the model, while the others follow typical regularization paths and shrunken to 0 eventually. </p>

<p>Some other useful arguments. <code>exclude</code> allows one to block certain variables from being the model at all. Of course, one could simply subset these out of <code>x</code>, but sometimes <code>exclude</code> is more useful, since it returns a full vector of coefficients, just with the excluded ones set to zero. There is also an <code>intercept</code> argument which defaults to <code>TRUE</code>; if <code>FALSE</code> the intercept is forced to be zero.</p>

<h4>Customizing plots</h4>

<p>Sometimes, especially when the number of variables is small, we want to add variable labels to a plot. Since <code>glmnet</code> is intended primarily for wide data, this is not supprted in <code>plot.glmnet</code>. However, it is easy to do, as the following little toy example shows.</p>

<p>We first generate some data, with 10 variables, and for lack of imagination and ease we give them simple character names. 
We then fit a glmnet model, and make the standard plot.</p>

<pre><code class="r">set.seed(101)
x=matrix(rnorm(1000),100,10)
y=rnorm(100)
vn=paste(&quot;var&quot;,1:10)
fit=glmnet(x,y)
plot(fit)
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-31"/> </p>

<p>We wish to label the curves with the variable names. Here s a simple way to do this, using the <code>axis</code> command in R (and a little research into how to customize it). We need to have the positions of the coefficients at the end of the path, and we need to make some space using the <code>par</code> command, so that our labels will fit in.
This requires knowing how long your labels are, but here they are all quite short.</p>

<pre><code class="r">par(mar=c(4.5,4.5,1,4))
plot(fit)
vnat=coef(fit)
vnat=vnat[-1,ncol(vnat)] # remove the intercept, and get the coefficients at the end of the path
axis(4, at=vnat,line=-.5,label=vn,las=1,tick=FALSE, cex.axis=0.5) 
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-32"/> </p>

<p>We have done nothing here to avoid overwriting of labels, in the event that they are close together. This would be a bit more work, but perhaps best left alone, anyway.</p>

<h3>Multiresponse Gaussian Family</h3>

<p>The multiresponse Gaussian family is obtained using <code>family = &quot;mgaussian&quot;</code> option in <code>glmnet</code>. It is very similar to the single-response case above. This is useful when there are a number of (correlated) responses - the so-called &ldquo;multi-task learning&rdquo; problem. Here the sharing involves which variables are selected, since when a variable is selected, a coefficient is fit for each response. Most of the options are the same, so we focus here on the differences with the single response model.</p>

<p>Obviously, as the name suggests, \(y\) is not a vector, but a matrix of quantitative responses in this section. The coefficients at each value of lambda are also a matrix as a result. </p>

<p>Here we solve the following problem:
\[
\min_{(\beta_0, \beta) \in \mathbb{R}^{(p+1)\times K}}\frac{1}{2N} \sum_{i=1}^N ||y_i -\beta_0-\beta^T x_i||^2_F+\lambda \left[ (1-\alpha)||\beta||_F^2/2 + \alpha\sum_{j=1}^p||\beta_j||_2\right].
\]
Here \(\beta_j\) is the jth row of the \(p\times K\) coefficient matrix \(\beta\), and we replace the absolute penalty on each single coefficient by a group-lasso penalty on each coefficient K-vector \(\beta_j\) for a single predictor \(x_j\).</p>

<p>We use a set of data generated beforehand for illustration.</p>

<pre><code class="r">data(MultiGaussianExample)
</code></pre>

<p>We fit the data, with an object &ldquo;mfit&rdquo; returned.</p>

<pre><code class="r">mfit = glmnet(x, y, family = &quot;mgaussian&quot;)
</code></pre>

<p>For multiresponse Gaussian, the options in <code>glmnet</code> are almost the same as the single-response case, such as <code>alpha</code>, <code>weights</code>, <code>nlambda</code>, <code>standardize</code>. A exception to be noticed is that <code>standardize.response</code> is only for <code>mgaussian</code> family. The default value is <code>FALSE</code>. If <code>standardize.response = TRUE</code>, it standardizes the response variables.</p>

<p>To visualize the coefficients, we use the <code>plot</code> function.</p>

<pre><code class="r">plot(mfit, xvar = &quot;lambda&quot;, label = TRUE, type.coef = &quot;2norm&quot;)
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-35"/> </p>

<p>Note that we set <code>type.coef = &quot;2norm&quot;</code>. Under this setting, a single curve is plotted per variable, with value equal to the \(\ell_2\) norm. The default setting is <code>type.coef = &quot;coef&quot;</code>, where a coefficient plot is created for each response (multiple figures). </p>

<p><code>xvar</code> and <code>label</code> are two other options besides ordinary graphical parameters. They are the same as the single-response case.</p>

<p>We can extract the coefficients at requested values of \(\lambda\) by using the function <code>coef</code> and make predictions by <code>predict</code>. The usage is similar and we only provide an example of <code>predict</code> here.</p>

<pre><code class="r">predict(mfit, newx = x[1:5,], s = c(0.1, 0.01))
</code></pre>

<pre><code>## , , 1
## 
##              y1         y2         y3       y4
## [1,] -4.7106263 -1.1634574  0.6027634 3.740989
## [2,]  4.1301735 -3.0507968 -1.2122630 4.970141
## [3,]  3.1595229 -0.5759621  0.2607981 2.053976
## [4,]  0.6459242  2.1205605 -0.2252050 3.146286
## [5,] -1.1791890  0.1056262 -7.3352965 3.248370
## 
## , , 2
## 
##              y1         y2         y3       y4
## [1,] -4.6415158 -1.2290282  0.6118289 3.779521
## [2,]  4.4712843 -3.2529658 -1.2572583 5.266039
## [3,]  3.4735228 -0.6929231  0.4684037 2.055574
## [4,]  0.7353311  2.2965083 -0.2190297 2.989371
## [5,] -1.2759930  0.2892536 -7.8259206 3.205211
</code></pre>

<p>The prediction result is saved in a three-dimensional array with the first two dimensions being the prediction matrix for each response variable and the third indicating the response variables. </p>

<p>We can also do k-fold cross-validation. The options are almost the same as the ordinary Gaussian family and we do not expand here.</p>

<pre><code class="r">cvmfit = cv.glmnet(x, y, family = &quot;mgaussian&quot;)
</code></pre>

<p>We plot the resulting <code>cv.glmnet</code> object &ldquo;cvmfit&rdquo;. </p>

<pre><code class="r">plot(cvmfit)
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-38"/> </p>

<p>To show explicitly the selected optimal values of \(\lambda\), type</p>

<pre><code class="r">cvmfit$lambda.min
</code></pre>

<pre><code>## [1] 0.04731812
</code></pre>

<pre><code class="r">cvmfit$lambda.1se
</code></pre>

<pre><code>## [1] 0.1316655
</code></pre>

<p>As before, the first one is the value at which the minimal mean squared error is achieved and the second is for the most regularized model whose mean squared error is within one standard error of the minimal.</p>

<p>Prediction for <code>cv.glmnet</code> object works almost the same as for <code>glmnet</code> object. We omit the details here.</p>

<p><a href="#top">Back to Top</a>
<a id="log"></a></p>

<h2>Logistic Regression</h2>

<p>Logistic regression  is another widely-used model when the response is categorical. If there are two possible outcomes, we use the binomial distribution, else we use the multinomial.</p>

<h3>Binomial Models</h3>

<p>For the binomial model, suppose the response variable takes value in \(\mathcal{G}=\{1,2\}\). Denote \(y_i = I(g_i=1)\). We model
\[\mbox{Pr}(G=2|X=x)+\frac{e^{\beta_0+\beta^Tx}}{1+e^{\beta_0+\beta^Tx}},\]
which can be written in the following form
\[\log\frac{\mbox{Pr}(G=2|X=x)}{\mbox{Pr}(G=1|X=x)}=\beta_0+\beta^Tx,\]
the so-called &ldquo;logistic&rdquo; or log-odds transformation.</p>

<p>The objective function for the penalized logistic regression uses the negative binomial log-likelihood, and is 
\[
\min_{(\beta_0, \beta) \in \mathbb{R}^{p+1}} -\left[\frac{1}{N} \sum_{i=1}^N y_i \cdot (\beta_0 + x_i^T \beta) - \log (1+e^{(\beta_0+x_i^T \beta)})\right] + \lambda \big[ (1-\alpha)||\beta||_2^2/2 + \alpha||\beta||_1\big].
\]
Logistic regression is often plagued with degeneracies when \(p > N\) and exhibits wild behavior even when \(N\) is close to \(p\);
the elastic-net penalty alleviates these issues, and regularizes and selects variables as well.</p>

<p>Our algorithm uses a quadratic approximation to the log-likelihood,  and then coordinate descent on the resulting penalized weighted least-squares problem. These constitute an outer and inner loop.</p>

<p>For illustration purpose, we load pre-generated input matrix <code>x</code> and the response vector <code>y</code> from the data file.</p>

<pre><code class="r">data(BinomialExample)
</code></pre>

<p>The input matrix \(x\) is the same as other families. For binomial logistic regression, the response variable \(y\) should be either a factor with two levels, or a two-column matrix of counts or proportions.</p>

<p>Other optional arguments of <code>glmnet</code> for binomial regression are almost same as those for Gaussian family. Don&#39;t forget to set <code>family</code> option to &ldquo;binomial&rdquo;. </p>

<pre><code class="r">fit = glmnet(x, y, family = &quot;binomial&quot;)
</code></pre>

<p>Like before, we can print and plot the fitted object, extract the coefficients at specific \(\lambda\)&#39;s and also make predictions. For plotting, the optional arguments such as <code>xvar</code> and <code>label</code> are similar to the Gaussian. We plot against the deviance explained and show the labels.</p>

<pre><code class="r">plot(fit, xvar = &quot;dev&quot;, label = TRUE)
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-42"/> </p>

<p>Prediction is a little different for logistic from Gaussian, mainly in the option <code>type</code>. &ldquo;link&rdquo; and &ldquo;response&rdquo; are never equivalent and &ldquo;class&rdquo; is only available for logistic regression. In summary,</p>

<ul>
<li><p>&ldquo;link&rdquo; gives the linear predictors </p></li>
<li><p>&ldquo;response&rdquo; gives the fitted probabilities</p></li>
<li><p>&ldquo;class&rdquo; produces the class label corresponding to the maximum probability.</p></li>
<li><p>&ldquo;coefficients&rdquo; computes the coefficients at values of <code>s</code></p></li>
<li><p>&ldquo;nonzero&rdquo; retuns a list of the indices of the nonzero coefficients for each value of <code>s</code>.</p></li>
</ul>

<p>For &ldquo;binomial&rdquo; models, results (&ldquo;link&rdquo;, &ldquo;response&rdquo;, &ldquo;coefficients&rdquo;, &ldquo;nonzero&rdquo;) are returned only for the class corresponding to the second level of the factor response.</p>

<p>In the following example, we make prediction of the class labels at \(\lambda = 0.05, 0.01\).</p>

<pre><code class="r">predict(fit, newx = x[1:5,], type = &quot;class&quot;, s = c(0.05, 0.01))
</code></pre>

<pre><code>##      1   2  
## [1,] &quot;0&quot; &quot;0&quot;
## [2,] &quot;1&quot; &quot;1&quot;
## [3,] &quot;1&quot; &quot;1&quot;
## [4,] &quot;0&quot; &quot;0&quot;
## [5,] &quot;1&quot; &quot;1&quot;
</code></pre>

<p>For logistic regression, <code>cv.glmnet</code> has similar arguments and usage as Gaussian. <code>nfolds</code>, <code>weights</code>, <code>lambda</code>, <code>parallel</code> are all available to users. There are some differences in <code>type.measure</code>: &ldquo;deviance&rdquo; and &ldquo;mse&rdquo; do not both mean squared loss and &ldquo;class&rdquo; is enabled. Hence,</p>

<ul>
<li><p>&ldquo;mse&rdquo; uses squared loss.</p></li>
<li><p>&ldquo;deviance&rdquo; uses actual deviance.</p></li>
<li><p>&ldquo;mae&rdquo; uses mean absolute error.</p></li>
<li><p>&ldquo;class&rdquo; gives misclassification error.</p></li>
<li><p>&ldquo;auc&rdquo; (for two-class logistic regression ONLY) gives area under the ROC curve.</p></li>
</ul>

<p>For example,</p>

<pre><code class="r">cvfit = cv.glmnet(x, y, family = &quot;binomial&quot;, type.measure = &quot;class&quot;)
</code></pre>

<p>It uses misclassification error as the criterion for 10-fold cross-validation.</p>

<p>We plot the object and show the optimal values of \(\lambda\).</p>

<pre><code class="r">plot(cvfit)
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-45"/> </p>

<pre><code class="r">cvfit$lambda.min
</code></pre>

<pre><code>## [1] 0.0147554
</code></pre>

<pre><code class="r">cvfit$lambda.1se
</code></pre>

<pre><code>## [1] 0.02578548
</code></pre>

<p><code>coef</code> and <code>predict</code> are simliar to the Gaussian case and we omit the details. We review by some examples.</p>

<pre><code class="r">coef(cvfit, s = &quot;lambda.min&quot;)
</code></pre>

<pre><code>## 31 x 1 sparse Matrix of class &quot;dgCMatrix&quot;
##                       1
## (Intercept)  0.24371065
## V1           0.06897051
## V2           0.66252343
## V3          -0.54275157
## V4          -1.13692797
## V5          -0.19143183
## V6          -0.95852340
## V7           .         
## V8          -0.56528880
## V9           0.77454157
## V10         -1.45079395
## V11         -0.04363484
## V12         -0.06893736
## V13          .         
## V14          .         
## V15          .         
## V16          0.36685293
## V17          .         
## V18         -0.04013948
## V19          .         
## V20          .         
## V21          .         
## V22          0.20882114
## V23          0.34014021
## V24          .         
## V25          0.66310204
## V26         -0.33695819
## V27         -0.10570477
## V28          0.24317817
## V29         -0.22445233
## V30          0.11091002
</code></pre>

<p>As mentioned previously, the results returned here are only for the second level of the factor response. </p>

<pre><code class="r">predict(cvfit, newx = x[1:10,], s = &quot;lambda.min&quot;, type = &quot;class&quot;)
</code></pre>

<pre><code>##       1  
##  [1,] &quot;0&quot;
##  [2,] &quot;1&quot;
##  [3,] &quot;1&quot;
##  [4,] &quot;0&quot;
##  [5,] &quot;1&quot;
##  [6,] &quot;0&quot;
##  [7,] &quot;0&quot;
##  [8,] &quot;0&quot;
##  [9,] &quot;1&quot;
## [10,] &quot;1&quot;
</code></pre>

<p>Like other GLMs, glmnet allows for an &ldquo;offset&rdquo;. This is a fixed vector of N numbers that is added into the linear predictor.
For example, you may have fitted some other logistic regression using other variables (and data), and now you want to see if the present variables can add anything. So you use the predicted logit from the other model as an offset in.</p>

<h3>Multinomial Models</h3>

<p>For the multinomial model, suppose the response variable has \(K\) levels \({\cal G}=\{1,2,\ldots,K\}\). Here we model 
\[\mbox{Pr}(G=k|X=x)=\frac{e^{\beta_{0k}+\beta_k^Tx}}{\sum_{\ell=1}^Ke^{\beta_{0\ell}+\beta_\ell^Tx}}.\]</p>

<p>Let \({Y}\) be the \(N \times K\) indicator response matrix, with elements \(y_{i\ell} = I(g_i=\ell)\). Then the elastic-net penalized negative log-likelihood function becomes
\[
\ell(\{\beta_{0k},\beta_{k}\}_1^K) = -\left[\frac{1}{N} \sum_{i=1}^N \Big(\sum_{k=1}^Ky_{il} (\beta_{0k} + x_i^T \beta_k)- \log \big(\sum_{k=1}^K e^{\beta_{0k}+x_i^T \beta_k}\big)\Big)\right] +\lambda \left[ (1-\alpha)||\beta||_F^2/2 + \alpha\sum_{j=1}^p||\beta_j||_q\right].
\]
Here we really abuse notation! \(\beta\) is a \(p\times K\) matrix of coefficients. \(\beta_k\) refers to the kth column (for outcome category k), and \(\beta_j\) the jth row (vector of K coefficients for variable j).
The last penalty term is \(||\beta_j||_q\), we have two options for q: \(q\in \{1,2\}\).
When q=1, this is a lasso penalty on each of the parameters. When q=2, this is a grouped-lasso penalty on all the K coefficients for a particular variables, which makes them all be zero or nonzero together.</p>

<p>The standard Newton algorithm can be tedious here. Instead, we use a so-called partial Newton algorithm by making a partial quadratic approximation to the log-likelihood, allowing only \((\beta_{0k}, \beta_k)\) to vary for a single class at a time. 
For each value of \(\lambda\), we first cycle over all classes indexed by \(k\), computing each time a partial quadratic approximation about the parameters of the current class. Then the inner procedure is almost the same as for the binomial case.
This is the case for lasso (q=1). When q=2, we use a different approach, which we wont dwell on here.</p>

<p>For the multinomial case, the usage is similar to logistic regression, and we mainly illustrate by examples and address any differences. We load a set of generated data.</p>

<pre><code class="r">data(MultinomialExample)
</code></pre>

<p>The optional arguments in <code>glmnet</code> for multinomial logistic regression are mostly similar to binomial regression except for a few cases. </p>

<p>The response variable can be a <code>nc &gt;= 2</code> level factor, or a <code>nc</code>-column matrix of counts or proportions. 
Internally glmnet will make the rows of this matrix sum to 1, and absorb the total mass into the weight for that observation.</p>

<p><code>offset</code> should be a <code>nobs x nc</code> matrix if there is one. </p>

<p>A special option for multinomial regression is <code>type.multinomial</code>, which allows the usage of a grouped lasso penalty if <code>type.multinomial = &quot;grouped&quot;</code>. This will ensure that the multinomial coefficients for a variable are all in or out together, just like for the multi-response Gaussian.</p>

<pre><code class="r">fit = glmnet(x, y, family = &quot;multinomial&quot;, type.multinomial = &quot;grouped&quot;)
</code></pre>

<p>We plot the resulting object &ldquo;fit&rdquo;.</p>

<pre><code class="r">plot(fit, xvar = &quot;lambda&quot;, label = TRUE, type.coef = &quot;2norm&quot;)
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-51"/> </p>

<p>The options are <code>xvar</code>, <code>label</code> and <code>type.coef</code>, in addition to other ordinary graphical parameters. </p>

<p><code>xvar</code> and <code>label</code> are the same as other families while <code>type.coef</code> is only for multinomial regression and multiresponse Gaussian model. It can produce a figure of coefficients for each response variable if <code>type.coef = &quot;coef&quot;</code> or a figure showing the \(\ell_2\)-norm in one figure if <code>type.coef = &quot;2norm&quot;</code></p>

<p>We can also do cross-validation and plot the returned object.</p>

<pre><code class="r">cvfit=cv.glmnet(x, y, family=&quot;multinomial&quot;, type.multinomial = &quot;grouped&quot;, parallel = TRUE)
</code></pre>

<pre><code>## Warning: executing %dopar% sequentially: no parallel backend registered
</code></pre>

<pre><code class="r">plot(cvfit)
</code></pre>

<p><img 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E3VbW/KF7XsDxs2zLzvvvvyk5QZzWUL5TOyefNmU5mGTDVdx1RdhvZPKkd6dVQ3va4nGfGrPtjM9C383FaimhaqnzupPys9GaUvIrNIrFH08tyoD15TfVTkNgn7+qzKlhVjO4I87qRvwcssFvXxrJ9ZiVLZ40017iCPsWe8LX2rMivGGe8OHCIzGeT/VmbaiKhBduaTTz4Z+IYcngkWj6zeCTnMWraXpWed7Q1ZXBCM92IWyQXlFIISawRHquzc5lVXXaW77ESpypQZZTPSJZJuyqZNm+qpc2ogkp4ql9+iynQgNVhG/+QFqkbV6yile1Cmqsk0GPVVbj788MP5TcpUg/j01DtRBMq2aqr5wHacMi1PjcbW04XUvFo97co+mcedaC1bKJ+Ru+++W3eNqy/0NFvJgzyXF198sZ6uJlPWpKs7v1KzZs006Ui6ffv21dGKMlfz3/WzqgZsmmq2Sb6Sy6psWTHOV6Lq5vQKXuKTDwo1919/tIt54tdff81vMvb96ZVOVoztm3K4I9Pv1Oh0U/X+mf/85z9N1TuQwzuzvixYPAK9E7LOTc7Ppmed8zszvzIY78XMUwpOqCHRBmzex/AJ9YKBjOTNbNCSDDiyprYVBCKpAuluU/azDNFJV6HkQ34FJTJCXkZBpxfpOpR8qHm76U/l+TiayxbKZySrCpD6lGcnv12zWaXhPCeDj2SanHTZB1uyYhyMtOX/LbP/w2CkVVBxiuMcGbCXfhBwQcQfDB5ZvRMKIs8FHUcw3osFncdA8VHBByLDcBIgARIgARKIYAIcZBfBlceskwAJkAAJkEAgAlTwgcgwnARIgARIgAQimAAVfARXHrNOAiRAAiRAAoEIUMEHIsNwEiABEiABEohgAlTwEVx5zDoJkAAJkAAJBCJABR+IDMNJgARIgARIIIIJUMFHcOUx6yRAAiRAAiQQiAAVfCAyDCcBEiABEiCBCCZABR/BlceskwAJkAAJkEAgAlTwgcgwnARIgARIgAQimAAVfARXHrNOAiRAAiRAAoEIUMEHIsNwEiABEiABEohgAlTwEVx5zDoJkAAJkAAJBCJABR+IDMNJgARIgARIIIIJFIrgvOc6659++ilkbV8KCZAACZAACYSCQIUKFdCjR49QJJUhjZhZD/6zzz7DM888g+HDh2eAwAASIAESIAESCAaB559/HlOmTEGLFi2CEX2WccZMC15a7sOGDcNNN92UJRCeJAESIAESIIGCIrBhwwb4/f6Cii5X8dAGnytcvJgESCArAn///TdmzZqV1SWuOrd//34kJia6Kk/MDAkUFAHXK3ifz4fk5OSCKi/jIQESCCIBaamI0owUOX36NA4ePBgp2WU+SSBXBFyh4Ldv3667z8855xxceOGF2Lhxo12IqVOnYujQofYxd0iABNxLQP6HI+n/tXLlyrj00kvdC5Q5I4F8EHCFgp84cSLkH23p0qXo2LEjunTpArFbUEiABEiABEiABPJGwBWD7GbOnInly5cjPj4e48aNQ+PGjXHRRRfhxx9/zFupeBcJkEBYCIgNXmzaffr0CUv6uU1UzAlr1qxB165dc3srrycB1xNwRQteFLq03i254oorcPvtt+uXxIEDB6xgbkmABFxOgDZ4l1cQsxdTBFyh4EeNGoUhQ4Zg/PjxNvyxY8di0KBBGDNmjB3GHRIgAXcToA3e3fXD3MUWAVd00ffu3RubNm3C5s2b09B/+OGHddeZnKOQAAmQAAmQAAnknIArWvCS3WLFiuG8887LkPNu3bph5MiRGcIZQAIk4D4CnAfvvjphjmKXgCta8FnhX79+PeSl0bJly6wu0+cmTZqEDz74INPrpBegQYMGEHMAhQRIIDgEaIMPDlfGSgJ5IeB6X/SPPvootm7ditdffz0v5bPvEVv+7t278eGHH9ph3CEBEiABEiCBYBK46667cPXVV6NVq1bBTCbTuF3fgr///vu5AlymVcdAEiABEiCBYBBYsWIFjh8/bkcts7nKli1rH1esWBH16tWzj9264wobPD3ZufXxYL5IIHcEaIPPHS9e7U4CderUQaNGjfTvvC3bUPvDqWgCww4Tx2yRIK5Q8PRkFwmPCvNIAtkToA0+e0a8wv0ESpQogTJlyqDku1OQcMMtaPTRVJwz6AqUXrtOh8ug8EgQV3TR05NdJDwqzCMJZE+A8+CzZ8QrIoeA+dKrwIkTqRnesxf+9z6A9/zOEVMAV7Tg6ckuYp4XZpQESIAEYoaA0a4NYBip5S1cGMqfekSV3RUKnp7sIuqZYWZJICAB2uADouGJCCTgeeUFoHw5/F2hAoyR18LzzFlvq5FQHFd00dOTXSQ8KswjCWRPgDb47BnxCvcTkAWTkpKSUjP6xsuwR9GrhdFEqlevjhYtWqSed/FfVyh44ZOVJzvxZkchARJwPwHa4N1fR8xh9gSiZXVBV3TRZ4+bV5AACZAACZAACeSGABV8bmjxWhIggSwJ0AafJR6eJIGQEqCCDyluJkYC0U2ANvjorl+WLrIIUMFHVn0xtyTgagK0wbu6epi5GCNABR9jFc7ikgAJkAAJxAYBKvjYqGeWkgRCQoA2+JBgZiIkkCMCVPA5wsSLSIAEckKANvicUOI1JBAaAlTwoeHMVEggJgjQBh8T1cxCRggBKvgIqShmkwRIgARIgARyQ8A1nuxyk2leSwIk4E4CYoMXN599+vRxZwbT5Wr//v1Ys2YNosVzWbri8TAbAnv27MEvv/xiX3X8+HEUKlQIcXFxdljPnj2RkJBgH0fSDhV8JNUW80oCLidAG7zLK4jZS0OgYsWK6N+/vw7zvTIJJ598CkW274D326/h6dUjzbWReMAu+kisNeaZBFxKgDZ4l1YMs5UlAXPJUpi33IG4bdthmCb8w0bCVK37SBcq+EivQeafBEiABEggXwTMgwf1srB2JCmngcNH7MNI3aGCj9SaY75JwIUEOA/ehZXCLGVLwOjcCahSBT5le/eLvb1uXRjnNsj2PrdfQBu822uI+SOBCCJAG3wEVRazChlUt2vXrlQSH76D5DffRuGSJeAZMgjYuFGH16xZE4ULF45IWlTwEVltzDQJuJMAbfDurBfmKnMCprK3nzp1yj759z8Hp46i9/uhTuhwuSZShQo+UmuO+SYBEiABEsgXAfkgbdy4cb7icPPNtMG7uXaYNxKIMAK0wUdYhTG7UU2ACj6qq5eFI4HQEqANPrS8mRoJZEWACj4rOjxHAiSQKwK0wecKFy8mgaASoIIPKl5GTgIkQAIkQALhIUAFHx7uTJUEopIAbfBRWa0sVIQSoIKP0IpjtknAjQRog3djrTBPsUqA0+RiteZZbhIIAgHa4IMAlVEWCIETJ07A5/PZcZ08eRJFixa1j2UVOeexfSKCd6jgI7jymHUSIAESIIGcEZBlgZOSkuyLd+7ciapVq9rHlSpViro58VTwdvVyhwRIIL8EuB58fgny/mARaNOmjY7aTEmB/9+P4PCnn6PUkMHwPj4uWEmGPV4q+LBXATNAAtFDgDb46KnLaC2Jf8iVML/+BiVPn4b57P/gq1kd3ptuiMricpBdVFYrC0UC4SFAG3x4uDPVnBMwt24HlHLXkqz8zX/7Xc5vjrArqeAjrMKYXRIgARIggbwTMAb2h1oeLjUCNbAOF12Y98hcficVvMsriNkjgUgiwHnwkVRbsZlXz78fgHHnrdjTrjWMN16B98aRUQuCNviorVoWjARCT4A2+NAzZ4o5I7Bo0SIcPnw49eLuXbC3SUNUKF8WmDlTh1WpUgUtWrTIWWQRchUVfIRUFLNJApFAgDb4SKil2Mxjhw4d4FzbXT5GPZ6zndiGYUQdGCr4qKtSFogESIAESCA9Aacyl3Nerzf9JVF3fPbzJeqKxgKRAAmEmgBt8KEmzvRIIDABKvjAbHiGBEgglwRog88lMF5OAkEkQAUfRLiMmgRijQBt8LFW4yyvmwnQBu/m2mHeSIAESIAEckVg3759WLx4sX2PmI3E3h4XF2eHde/eHfIxGu1CBR/tNczykUAICdAXfQhhM6lMCZQvXx79+ytnNmdk9erVKFasGGrXrm0FxcyWXfQxU9UsKAkEnwBt8MFnzBRyTsA/Zy5qXjkcVVp1gH/5ipzfGCVXsgUfJRXJYpCAGwjQBu+GWmAehID5x0b4e/dD/Bkc/r7/gLFyCYwKFWIGEFvwMVPVLCgJkAAJxA4Bc7taVKaiQ5mLU5s9e2MHgCopFXxMVTcLSwLBJcB58MHly9hzTsBo3QpQq8WZSrH7ZVGZU2rluMaNch5BFFxJBR8FlcgikIBbCNAG75aaYD6MkiXh3boBh676J5LG3gHvn+thxID3OmfN0wbvpMF9EiCBfBGgDT5f+HhzARA4pVrqhw4dsmPafMuNKFq0KHzHjwPyU1K2bFkUklZ9lEv0lzDKK5DFIwESIAESOEvgxIkT2Lp1qx0gx6dPn04TVqJECSp4mxB3SIAESCAHBDgPPgeQeElQCZRUXfPt2rULahqREjlb8JFSU8wnCUQAAdrgI6CSoiiL4sQmKSnJLtGBAwd097sVIF3x5557rnUYc1sq+JirchaYBIJHgDb44LFlzBkJ1KhRQ3e/W2cSExPTKPQiRYpYp2JySwUfk9XOQpMACZBA5BMQW7pTChcunKYF7zwXi/ucJheLtc4yk0CQCHAefJDAMtosCZhqIJ1v8JXodt1NSGl3PkyZ806hoxs+AyRAAgVHgDb4gmPJmHJOwNfzYpjTvkT8gYPAypXw//eJnN8cxVeyBR/FlcuikUCoCdAGH2riTE8TUPPc4fOnwjh1Glj2K8EoAlTwfAxIgARIgAQimoBx3TCgfLnUMhTywhh7Z0SXp6AyTwVfUCQZDwmQAGiD50MQDgLeoVfD8/5bWD/0Sni/+waeXj3CkQ3XpclR9K6rEmaIBCKXAG3wkVt3kZ5zT+8LsTn5JJp0uSDSi1Jg+aeCLzCUjIgESIA2eD4DwSAgDmwWLVpkRy3uZw3DQEpKiv7JicqVK+Pw4cOYP3++fV25cuXQuHFj+zjWdqjgY63GWV4SIAESiDAC4pGuf//+dq7Xrl0LcWIjSl2UvCWi+OPj461DyLz4WBYq+FiufZadBAqYAH3RFzBQRpclgWLFiqU5L37oKWcJUMGfZcE9EiCBfBKgDT6fAHl7tgTMfftQ5oVXUOTYMZivvggjnZLPNoIYuoAKPoYqm0UlgWAToA0+2IRjO37z5En4KlRHWWV/V0Z4+BIXwLt+FQxHt3xsE0pbek6TS8uDRyRAAiRAAm4lsGQpULECDNOE4T/j2GbtOrfmNuz5ooIPexUwAyQQPQQ4Dz566tKVJalWFRBPdZZs3wFIGCVTAuyizxQLA0mABPJCgDb4vFDjPU4CGzduxNGjR+2g9Gu8l3/lOZS7bQxO16mDcya9BKNiRfta7qQlQAWflgePSIAE8kGANvh8wOOtmoCMhC8qvuXPyKZNm9CwYUPrEEWrV8fmxDl6mlzxevXscO5kJEAFn5EJQ0iABEiABMJEoHz58mlSjouLQ7Vq1dKE7d27N80xDzIn4DoFL04LpHumdOnSmeeYoSRAAq4lwHnwrq2aiM7YkSNH1CqwK+0yHDp0CB6PB3/99Zcd1qpVK0gPEuUsAVco+FOnTuGRRx7Be++9h507d8JUIyQTEhJQu3Zt3HXXXbjuuuvO5ph7JEACriVAG7xrqyYiM+b/agYavvk2iqmFT5t3Od8uw+nTp7Wr2kKFzqowpwc7+8IY3zlLJ4wgbr/9duzevRtff/016qiBE+KdKCkpCeKOcPTo0Tip5j7efPPNYcwhkyYBEsgJAdrgc0KJ1+SEgG/yOzBvuR21k08BX32N4h+/D8/lg3NyK685Q8AV0+S+/fZbvPbaa2jWrJnuYpFFBGSgRceOHfHcc89h2rRprDASIAESIIEYImC+MRkQ5X5GzHfet3a5zSEBVyj4pk2bYt68eZlmecaMGUg/6CLTCxlIAiQQdgKcBx/2KoiaDBidOgBWF7xsaV/Pdd26oot+3LhxuOqqqzBx4kTUrVsXJUqUgAyqWLdunV4paObMmbkuGG8gARIIPQHa4EPPPFJTTE5Oxq5du+zsy0pwIrYt/caRqKpc0Z7YvgPFBvSD5+Xn7Wu5kzMCrlDwLVu2xPLly/HTTz9hy5Yt2h4vrXaxu3fp0kUPpshZcXgVCZBAOAnQBh9O+pGVts/nS+PQZp9aREbE2WN7OnEuEr/7Ls1SsZFVyvDm1hUKXhCIY4Pu3btnoLF+/XpIt598BGQnYstPTEzM9LKFCxdqu36mJxlIAiRAAiQQUgIyU+q8886z09ywYYPeb9CggR3GnfwRcI2CD1SMqVOnYuvWrXj99dcDXWKHi7ejQPMgxf2hdAlRSIAEgkeA8+CDx5Yxk0BuCbhewT/44IM5LlONGjUgv8xEPhRkKh6FBEggeARogw8e21iJefHixbC666XMhw8fxvTp0+3iV1S+59u1a2cfcycwAdcreLHTiHc7cVdIIQEScDcB2uDdXT9uzZ25eTPK33UfCqsBdfW/+BhGBzWCnpJvAq6YJrd9+3YMGzZMd69feOGFkO50S6TlPXToUOuQWxIgARIggSgiYKoZU766jVF8xkwUXfkbfO3Oh7l/fxSVMHxFcYWCl+lxlStXxtKlS7VzGxk5bw24CB8apkwCJJBbApwHn1tivB5r1wFVq5wFUaQIsD51wN3ZQO7lhYAruuhlnrtMk5P5jzInvnHjxrjooovw448/5qVMvIcESCBMBGiDDxP4SE62QX3lsc4xAPovNTe+aZNILpFr8u6KFrwodGm9W3LFFVdA/NP36dMHBw4csIK5JQEScDkB2uBdXkEuzJ5Rtiy8vy7GiTatcXRgf3j//B2GclVOyT8BVyj4UaNGYciQIRg/frxdorFjx2LQoEEYM2aMHcYdEiABEiCB6CNgVK+OXVPewr7xj8GoVSv6ChimErlCwffu3RubNm1C375902B4+OGHIc5rpLueQgIk4H4CtMG7v46Yw9gh4AobvOCWJWKdXo2sKujWrRvkRyEBEnA/Adrg3V9HzGHsEHCNgo8d5CwpCUQvAdrgo7du81OyFStWpHFec+jQIRQvXhzHjh3T0UoDr4iMnldiLToj+/Xr14e4tKXkjQAVfN648S4SIAESIIEcEpCB1NK7Y8ns2bPRuXNn7N27VwcZhgFxaiYi65JY4vV6rV1u80CACj4P0HgLCZBA5gToiz5zLrEearXOLQ4ej0cvCy5Lg1OCR8AVg+yCVzzGTAIkEEoCtMGHkjbTIoGsCVDBZ82HZ0mABHJBgDb4XMDipSQQZAJU8EEGzOhJgARIgATOEvC9/Bpa/+dx+P79yNlA7gWFABV8ULAyUhKITQKcBx+b9Z7TUvsefQLmmLtRYdlymBOege/Z53J6K6/LAwEq+DxA4y0kQAKZE6ANPnMuDD1DYF4icOp06oHamtO+IpogEqCCDyJcRk0CsUaANvhYq/Fclvfi3kBc6nx3NfEdRpPGuYyAl+eGAKfJ5YYWryUBEiABEsgzAc/dY+Dfth0Hv52LMldfAc+D9+c5Lt6YPQG24LNnxCtIgARySIA2+ByCitHLxKGN94WJWPz04/A+9C8Yaj48JXgESDd4bBkzCcQcAdrgY67KWWAXE6CCd3HlMGskEGkEaIOPtBpjfqOZAG3w0Vy7LBsJkAAJuICAaZppciHH6cOk+55SsASo4AuWJ2MjgZgmQF/0MV39AQu/dOlS7Nmzxz5/9OhRfP311/Zx+fLl0b59e/uYOwVDgAq+YDgyFhIgAUWANvjYfgySkpKQmKjmup+RkydP6mfCueRrp06dULZsWesSboNIgAo+iHAZNQnEGgHa4GOtxtOWV1aH69+/vx24ceNGnDp1CrJcLCX0BDjILvTMmSIJkAAJkAAJBJ0AFXzQETMBEogdApwHHzt1zZK6nwAVvPvriDkkgYghQBt8xFQVMxoDBKjgY6CSWUQSCBUB2uBDRdr96fh/mI/KAwahXutO8H/6ufszHIU55CC7KKxUFokESIAEwknA3L4d/u69EXcmE/5Rt8No2gRGw3PDma2YS5st+JirchaYBIJHgDb44LGNqJgPHgKqVzubZfE5v3//2WPuhYQAFXxIMDMREogNArTBx0Y9Z1vKRg2BWjXhj4uDv3Bh4LRaA759u2xv4wUFS4Bd9AXLk7GRQEwToA0+pqvfLryh1nr3fj8bB/7zKFJUaJV774Ihip4SUgI5UvC7d+9GuXLlUKhQji4PaQGYGAmQAAmQgPsIGEpfHB1+jXZ0U7V4cfdlMAZyFLCLXrraHn30UTRr1gwXXnghvvvuOwwcOBD79u2LASwsIgmQQF4I0AafF2q8hwSCQyCggp80aRK+//57fP556vSGHj16oGrVqpBwCgmQAAlkRoA2+MyoMIwEwkMgoIJfsGAB7r77blSpUkXnrLCyn4wePVor/fBklamSAAm4nQBt8G6voeDm77QaTLdjxw77d/DgQRw6dMg+lnPJycnBzQRjtwkENKpXr14douS7detmX/zll1+icuXK9jF3SIAESIAESMAikJKSksaMK8ey7rvTtFuqVCnEqdH1lOATCKjgx4wZg7Zt22LOnDnYtWsXOnbsiC1btmDu3LnBzxVTIAESiEgCXA8+IqutwDIdHx+Pli1bFlh8jCh/BAIq+IoVK2Lt2rX4+OOPsW3bNnTt2lX/vF5v/lLk3SRAAlFLgDb4qK3aDAWTtd6lhW6JHBctWtQ6hOgKUfiU8BEIqOClW2X69OmoX78+Ro4ciZdffhlbt27FsGHDdMWFL8tMmQRIwK0EaIN3a80UfL5krXdn17vY16tVO+u9Trri2ZoveO65iTGggpfR8xMnTsT777+v4+vSpQvGjh2r7SkjRozITRq8lgRIgARIIMoING3aNE2JpEHYvXv3NGE8CC+BgKPoZ82ahcceewwNGjTQOZTKFIX/6aefhjfHTJ0ESMC1BDgP3rVVw4zFIIGACr5mzZqYPXt2GiSJiYkoUaJEmjAekAAJkIBFgDZ4iwS3JBB+AgG76KUbvlevXvj666/RoUMH/Pbbb9izZw+kZU8hARIggcwI0AafGZXoDzOPH0fxP7fCPHYMxjnnRH+BI6SEARW8eK1bvHixnhb3xx9/4Prrr9dT5Tyy7B+FBEiABEiABBQBUzmz8bVsj9Z//w3fnXfDu2U9DNUDTAk/gYAKXrJWsmRJDBo0KPy5ZA5IgAQiggDnwUdENRVoJv1D1aDr7dsRb6ZG67/nAXg/mVKgaTCyvBEIqOAPHz6MW265BatWrdKrAVnR9+nTB//73/+sQ25JgARIwCZAG7yNInZ2KlZQzfizxTXX/X72gHthJRBQwU+YMAFHjhzB888/D7GrWVKmTBlrl1sSIAESSEOANvg0OGLiwPPAvfC99S6SS5ZAnFL03g/eiYlyR0IhAyr4nTt36hY85zVGQjUyjyRAAiQQHgJGvXrw7t+JlS+/io7DrqH9PTzVkGmqAUfMXXbZZXjvvfewd+/eTG9kIAmQAAmkJ8B58OmJxMaxUbYsDrRoRuXusuoOqOD/+usvzJw5U68eJ+5qGzZsqH+yZCyFBEiABDIjQBt8ZlQYRgLhIRCwi75fv35o06ZNhlwlJCRkCGMACZAACQgB2uD5HJCAewgEVPCyCtD48eMhc+B9Ph/ky1xWCxKnNx988IF7SsCckAAJkAAJhJyALDZz6NAhO92jR49iyZIl9nHx4sV1r68dwJ2QEwjYRS9+58WedsMNN+gVgsaNG6fd1D7wwAMhzyQTJAESiAwCtMFHRj0VRC7LlSuH6tWr2z/p8XUeV6igps9RwkogYAt+06ZNuOuuu9C+fXu89tprGDx4MOrWrYtnnnkGb731VlgzzcRJgATcSYA2eHfWSzByJcvBOqVSpUrOQ+67gEDAFry4qt22bZu2qZ06dQoHDhyAzIGXMAoJkAAJZEaANvjMqDCMBMJDIGALfuTIkdr3fD01x3HAgAGQQXei6IcMGRKenDJVEiABEiCBkBMwTVOPw7ISll4aEee6JF6vF4ZhWJdw6xICARV848aNsX79ekjFdezYEa+++iqkS+byyy93SdaZDRIgAbcRoC96t9VI/vNzXK0Ut2DBAjuiEydOICUlBTKIzpK2bdtCbPIUdxEIqOAlm06bym233eaunDM3JEACriNAG7zrqiTfGRKzi6xBYsmff/4JUfpNmza1grh1KYEMCl4G1Ykf+kWLFmU6mE4q+rnnnnNpcZgtEiCBcBKgDT6c9Jk2CaQlkEHBT5o0CbVq1dIj5nv37p32anVUunTpDGEMIAESIAESIAEScBeBDAq+efPmOofvv/++ngc/dOjQNF317so+c0MCJOAmArTBu6k2mJdYJxBwmtwFF1yADRs2aDtL//798fnnn6dZFz7WwbH8JEACGQnQBp+RSTSFmMo/Stnrb0advgPh//mXaCpaVJYloIJv1qwZXn/9dezYsQPSin/nnXdQp04dvPTSS1EJgoUiARLIPwHa4PPP0K0xmEeOwFevCRK+/wFFtu+A/6J+MDdvdmt2mS9FIKCCt+iIH3qZ/y7bQoUK6Z91jlsSIAESIIEYIbBVOTmrXu1sYePiYG6igj8LxH17ARX8smXLcM0112g/9J999hluvPFGyOICN910k/tKwRyRAAm4ggB90buiGoKTifr1oFp4MJVDG1NS2LsPRru2wUmLsRYIgQyD7KxYf/nlF7Rq1QrPPvssuGiARYVbEiCBrAjQBp8Vncg+Z6gVRr0rfkHSVcOQrObGl5/wOIySJSO7UFGe+4AK/uabb7aLvnv3bu2lSLroKSRAAiQQiABt8IHIREe4UaIEDr4wUTu6qVCjRnQUKopLEbCLXr7EH330UchguwsvvBDfffcdBg4ciH379kUxDhaNBEiABEiABKKDQEAFLw5vvv/+ez09Torao0cPyApzEk4hARIggcwI0AafGRWGkUB4CARU8LK4wN13340qVaronBUuXBijR4/WSj88WWWqJEACbidAG7zba4j5iyUCAY3q1atX1ysIdevWzebx5ZdfonLlyvZxMHfkRSGtAbHpUUiABCKDAG3wkVFPucnlsWPHMG/ePPuW5ORkPW1aFp2xpEOHDihfvrx1yK1LCARU8GPGjIEsAThnzhzs2rVLLxm7ZcsWzJ07t8CznpSUpLv+ExMTMXbsWBw9ehSyHr08SFdccYUeyU9FX+DYGSEJkAAJZEtA3r3izZQSeQQCdtFXrFgRa9euxahRo3Drrbfiscce017tmjRpUuClfPLJJyHT8vr27Ys777wT9913H7766ivIB4U42Zk6dWqBp8kISYAECp4AbfAFz5QxkkBeCWTagl+1ahXuv/9+rFixAnv27NHObvbu3YsGDRro/bwmFug+6foXBV+sWDGd3v79+3WPgVwv+ZBW/XXXXRfodoaTAAm4hABt8C6pCGaDBBSBDC146SaXNeFFmb/wwgtYsmSJXv9dFH2nTp0gyregpVGjRtoUcET5Op4/fz7Ei54lv/32m3a4Yx1zSwIk4F4CtMG7t26Ys9gjkKEF//jjj+M///kP7rnnHptGixYtMGDAAAwePBhvv/22Hl1vnyyAnbvuugsjRozAZrVwwR133KFt8KL0ZenaH3/8ET/88EMBpMIoSIAESIAEsiKwc+dOSEPLkgMHDqBs2bLWIeKVN7vatWvbx9xxN4EMCl6WiJ04cWKmue7evTt+/fXXTM/lJ7Bjx47a3n/w4EH9MMngutmzZ+Pw4cN466239EOVn/h5LwmQQGgIiA1eegH79OkTmgTzmYr0SK5ZswZdu3bNZ0zRcbtMh45Ti8hYIgOsranSEibnKZFDIIOClxHszi82Z1EkXM4HQwy1gIGVrjxg0mMgsn79ej1drmXLltkmKy51ZaxAZiIe+OTDgUICJBA8ArTBB49tKGKWdUeca4/IQOu6deuGImmmEQQCGRS8pCHT1pxfcVa6x48ft3ZDtpUR9Fu3btVr02eXqIwXCDSNT77Sy5Qpk10UPE8CJJAPArTB5wMebyWBAiaQqYKXAXaBZMiQIYFOBSX8wQcfzHG8Mlcz0HxNmdcvLXwKCZAACZAACcQCgQwK/o8//oBp6tV+My1/kSJFMg1nIAmQAAnQBs9ngATcQyCDgi9durR7cseckAAJRBQB2uAjqrqyzKy5ZClqTZsOf0IxeHr2yPJannQngQwKPhzZfOaZZ3D69OmASTds2FAvVRvwAp4gARJwBQHa4F1RDfnOhPnzL/B16oaGak0Q/1czYT71BLzXDs13vIwgtARcoeDFJe2LL76I4cOHa2926RFwEYP0RHhMAiRAAsEj4H/yKUApd0OSUFMJzdcnA1TwwQMepJhdoeDFY5507cnvpZdeClJRGS0JkECwCdAGH2zCIYq/ZQtg5myoxUCUv1Ol5o8cDlHCTKYgCWRQ8F26dNEOZgIl0rt3bzz99NOBTuc5fPz48bjpppsgSxNKNx+FBEgg8gjQBh95dZZZjj333QPf9Jk4qbyLxndoD8/H72d2GcNcTiCDghflnZU9vFy5ckEpkij1KVOmBCVuRkoCJBAaArTBh4ZzsFMx1GypQksWYt706QGnHgc7D4w//wQyKPh27dplGeuJEyeyPM+TJEACJEACJEAC4SeQYTU5K0vio1kWl5EFX5o2bYrGjRujTp06GDlypHUJtyRAAiSQhgDXg0+DgwckEFYCARW8LDgj/6w33HCDXgN+3LhxKFGiBB544IGwZpiJkwAJuJcAbfDurRvmLPYIBFTwmzZtgizjeu2110KWEJTWvKzsJnPWKSRAAiSQGQHa4DOjwjASCA+BDDZ4KxtVq1bFtm3b0LNnTzVT4hRkXWBZrEXCKCRAAiRAAtFHQJYDlwadJYcOHcJ0NdDOklKlSuGCCy6wDrl1OYGACl5s7bJOe7169fTSrf369dOKPtSLzbicH7NHAiTgIMB58A4YEbjbqlUryI8SHQQCKngZVCdrsXu9Xq3oX331VcjX2+WXXx4dJWcpSIAECpwAbfAFjpQRkkCeCQRU8BJjpUqV7Ihvu+02e587JEACJJAZAdrgM6PivjDpaZk3b56dMTHD+nw+xMfH22Ft27ZFhQoV7GPuRB6BDAq+ffv2mDBhAhYtWqQH1aUvUp8+ffDcc8+lD+YxCZAACZBAhBBISEiAvMstkbFVhw8fRrNmzawgGIb2RG8fcyfyCGRQ8JMmTUKtWrVQt25diFva9MLlZNMT4TEJkIBFgDZ4i4T7tx7P2UlUsi8K3Rnm/hIwh9kRyKDgxbGNSMmSJVG5cmWsXLkSycnJdjwpKSn2PndIgARIwEmANngnDe6TQHgJZFDwVnYWLlyIQYMGoXjx4ihatKgVrFv1nAtv4+AOCZCAgwBt8A4Y3CWBMBMIqODfe+89PPnkk9rRTZjzyORJgARIgARIgARySeCsESbdjTVr1oQ4OaCQAAmQQE4JiA1+1qxZOb087NfJmhuJiYlhzwczQALBIBCwBS9ualu2bIk5c+agSZMmdtpio7/mmmvsY+6QAAmQgEWANniLRORs/ct+RbnrbkDFTZvhe/E5eK8bFjmZZ06zJBBQwb/00ks4efIkzj333DQ2+MKFC2cZIU+SAAnELgHa4COr7k3Vg+Fv0wlFzmTbvOtemM3Pg9GqZWQVhLnNlEBABb9ixQo8/fTTuPTSSzO9kYEkQAIkQAIRTuCgMsPWrAFsPbPGiJoqZ+7dC86Aj/B6PZP9gDb4AQMG4Ouvv4Z0uVFIgARIICcEaIPPCSUXXVO3DoxG58IfXxR+5ZYcR4/B6NbVRRlkVvJDIKCC37dvHz755BNIl1v9+vXRsGFD/Rs9enR+0uO9JEACUUyANvjIqlxDKXXP118i6b57sGfsnfDu3grDMS06skrD3KYnELCL/pJLLkHr1q21+8K9qsumRo0a2hYvS8ZSSIAESCAzArTBZ0bF3WGG8mL397VD9ayp6ny/u7uycpm7gAq+SpUqeOihh/D222/rdeDFT/HAgQPxwQcf5DIJXk4CJEACJEACJBBqAgG76F977TVs3LgRa9euxYEDB/DHH3/ANE2MHz8+1HlkeiRAAhFCgDb4CKkoZjMmCARU8IsXL8Y999yDRo0aaRB16tTRLXo6hYiJ54KFJIE8EaANPk/YeBMJBIVAQAXfqVMnLFiwIE2icly+fPk0YTwgARIgAYsAbfAWCW5JIPwEAtrghwwZgvPOO0+7cTz//POxbNkyyNx4tuDDX2nMAQmQAAnklMCWLVsgLnktEZNrqVKl7DBxXlakSBGcOHHCukRva9eujRIlSqQJ40FkEQio4MuWLYtVq1ZhypQp2Lx5M2RU/fvvv4/q1atHVgmZWxIggZAR4HrwIUOd44REmcfFxdnXi8IX9+NWWKFChfQMKVkKPCEhwb6OXkttFBG7E1DBS4lEyd9xxx0RWzhmnARIILQEaIMPLe+cpCYKXn6WiGKvVq2a/llh3EYngYAKXqbF3XLLLboVf+rUKbv0ffr0wf/+9z/7mDskQAIkYBGgDd4iwS0JhJ9AQAU/YcIEHDlyBM8//7z2ZmdllY5uLBLckgAJkAAJkIB7CQRU8Dt37tQt+O7du7s398wZCZCAqwjQBu+q6mBmYpxAwGlyl112Gd577z2Im1oKCZAACeSEAG3wOaHEa0ggNAQCKvi//voLM2fOROXKlbnYTGjqgqmQQMQToA0+4quQBYgiAgG76Pv164c2bdpkKCpt8BmQMIAESIAEXE/A9Pngv2o4uv6QiJSkofDu3AyDi8u4vt7yk8GACl7mu3POe37Q8l4SiD0CtMG7t879d4yF+cU0JJxO0Zn033w7vB9PcW+GmbN8E8ig4Nu3bw8ZQb9o0SK89dZbGRKQaXLPPfdchnAGkAAJkABt8C5+Bvw+4Ixyl1yaS5a5OLPMWkEQyKDgJ02ahFq1aqFixYqQEfROb0bJyck4ffp0QaTLOEiABKKQAG3w7q1Uz3XD4Zs2Hf49e+GJj4fxwP+5N7PMWYEQyDDITlaPE09Hr7zyCg4dOqRdGopbQ/lt27ZNt+4LJGVGQgIkQAIkEDICRru28P6UiLU33wDPl1Phvf66kKXNhMJDIIOCnzx5MuLV1504uOnbt6/el2P53XjjjdonfXiyylRJgATcToDrwbu7hgzVO7v94gvh6dXT3Rll7gqEQAYFP2rUKN0N/8wzz+Cnn37S+9ItLwsRJCUlaec3BZIyIyEBEog6ArTBR12VskARTCCDDV7KIqsLjR07NoKLxayTAAmEgwBt8OGgzjRJIHMCGRR8ly5dIAvNBJLevXvj6aefDnSa4SRAAiRAAmEgIL2sshSsJbJImPS+FitWzApCpUqV7H3uRD+BDApelHdWI+XLlSsX/VRYQhIggTwR4Dz4PGErsJucK39KQ03MqjVq1LDjFxMKJXYIZFDw7dq106UXG7z4ok8v0oKXefIUEiABEkhPgDb49ERCdyym1caNG9sJyoJhe/bsSRNmn+ROTBDIoOCtUl966aWwlL1pmhDf9OLgRkbWU0iABEggMwK0wWdGJbxhK1aswPbt2+1MHDhwANOnT7ePS5Qoga5du9rH3IkeAgEVfJ06dSA/p8ixdOF369bNGcx9EiABEiABlxJo0aIF5EeJPQIZpsllheDPP//EkSNHsrqE50iABGKYAOfBx3Dls+iuIxCwBS8t9XfffdfO8IkTJ3Q3z4cffmiHcYcESIAEnARog3fS4D4JhJdAQAU/aNAgdOzY0c6dDOCQLvry5cvbYdwhARIgAScB2uCdNLhPAuElkEHB79+/H/IVLnMn69evnyZ3MthOuuhLliyZJpwHJEACJEAC7iJQ5If5KLFrF8zmzWF4ve7KHHMTEgIZbPANGzbUK8nJanKZ/W644YaQZIyJkAAJRB4B2uDdUWe+a65FyVtHo/rDj8JXsz7MkyfdkTHmIqQEMij44cOHo3bt2rjqqqv0VIqT6sGQFr31+/jjj0OaQSZGAiQQOQRogw9/XZnKm505fSY8qrfV+/ffgHqHm9/ODX/GmIOQE8ig4MXBzR9//AFR9J9//rleJvbmm2/G/PnzIV30hmGEPJNMkARIIDII0AbvgnpKSABKFD+bkaSjQPFzzh5zL2YIZFDwUnKvsteIxzpZOnbt2rXauc2kSZO0R6RXX301ZuCwoCRAAiQQaQSMChXgmfiUzvapShVh3DoKnu7dIq0YzG8BEMhUwTvjPXToEHbs2IFdarCG+DXOyk+98z7ukwAJxB4B2uDdUeeewZdh729LsGnya/CeUfbuyBlzEUoCGUbRS+J79+7FZ599hqlTp2LNmjX4xz/+gX/961/ag5207ikkQAIkkBkB2uAzo1LwYfIhlZycbEd8/PjxNKvGyXvaLFOGDTKbUGzuZFDwAwYMwKJFi9C/f3/ce++96NmzJwoXLhybdFhqEiCBXBGgDT5XuPJ8sfSoys8SWSa2Vq1a1iESlB1eZkFRYptABgUvyl265WUlucxWkxs8eDA++uij2KbG0pMACZBAGAnUrVsX8rNE3tnnn3++dai3spocJbYJZFDwq1at0lPiAmGJj48PdIrhJEACMU5Auo4TExPRp0+fiCAhjr3EDMnV1CKiupjJXBLIoOArV66cyyh4OQmQAAmkEqANnk8CCbiHQLaj6N2TVeaEBEjA7QRog3d7DTF/sUQgQws+lgrPspIACZBAtBCQUfViHrFEVgCVMDFDWNJc+aXn4DuLRvRvqeCjv45ZQhIIGQHa4EOGOkNCRYoUSTOWwHIvLiuBWsIZURaJ2NierfnYKC9LSQIkEEQCtMEHEW42UYsb8bi4uGyu4ulYIkAbfCzVNstKAkEmQBt8kAFnEr3vlUlof9+/kdJnAMxTpzK5gkGxSoAKPlZrnuUmARKIeAK+t96BeftolFn7OzD3e/jvGBPxZWIBCo6AaxW8LFNLv/cFV9GMiQRCQYC+6ENB2ZHGz0sAnz81ICUF5tx5jpPcjXUCrlDw27Ztw7Bhw7B06VLs27cPI0eORKVKlVCqVCmMGDECp9jtFOvPKcsfIQRogw9tRRkX94byJZ6aqBpkZzRtHNoMMDVXE3DFILuHHnoINWrU0GvPP/HEE0hRX6KrV6/WUzzuu+8+/Pe//9U/V5Nk5kiABEAbfME+BNK4mT17th2p9GpKWLFixVLDvAY6Pjsepye/gwqXDYTn/nvta7lDAq5Q8PPnz8fvv/8OmebxxRdfYNq0aahWrZquHVHuo0aNYk2RAAmQQMwRkHeiLPxlye7du7F9+3a0bdvWCtLb6TWrp7kuzUkexCwBV3TRN2jQAO+++66uhG7dumHmzJl2hcyYMQP169e3j7lDAiTgXgK0wbu3bpiz2CPgihb8Sy+9hH79+uHNN99EvXr1cPfdd2Py5MnweDxISkqCtPApJEAC7idAG7z764g5jB0CrlDwsuzh2rVrMWfOHKxfv17b40uXLq1b7pdccgmcnphip2pYUhKIPAK0wUdenTHH0UvAFQpe8IoXpt69e+tf9OJmyUiABEiABEggNARco+ADFVda9GLXa9myZaBL7PBJkybhgw8+sI+dOxs3bkTt2rWdQdwnARIoYAL0RV/AQBkdCeSDgOsV/NSpU7F161a8/vrr2RbzxhtvhPwykzFjxkBGoFJIgASCR4A2+OCxZcwkkFsCrlfwDz74YG7LxOtJgATCRIA2+NCAl2nFO3bssBM7dOgQ5s6dax/LPPmOHTvax9yJTQKuV/CxWS0sNQmQAAmkJeBfuw4lrx6O0hs2wnj4X6hx2832BeL8RubMWyIzkCgk4AoF/8wzz2Tpd75hw4YYOHAga4sESMDlBGiDD04FmUePwt+kJc44pQWemID4Du1gdLlAJ5iQkBCchBlrRBNwhYLfsmULXnzxRQwfPvysC0YH1vLlyzuOuEsCJOBWArTB571m1qxZg82bN9sRHD58GCVLltQzjOIOHETHShURv3tP6nm/H+a+/TDsq7lDAhkJuELBv/DCC5AXg/zE6Q2FBEggMgnQBp/3emvSpIlej8OKQTx6ytRhyw+Ib+a38M2cBZw4CU/SUeiFZqyLuSWBTAi4xlAzfvx47bXu2LFjmWSTQSRAAiQQ2wS8n0zB3/9+ADvvvBXe3VthWAvOxDYWlj4LAq5R8PLlP2XKFL0aVRb55SkSIAEXE6Av+uBWzsnh12D/lZfDqFgxuAkx9qgg4BoFHxU0WQgSiHECtMHH+APA4ruKgCtsyctdkgAAPt9JREFU8K4iciYzpmmmyZYciztdp6Q/dp7jPgnEIgHa4GOx1llmtxKggg9QMwsWLNBjAqzT+/fv1yNajx8/roPEkURcXJyeeyqK3vogaNasGVJSUuCctiLXxcfHW1FxSwIkQAIkQAJBJ0AFHwBxly5d0pyZPn06Lr74YlgKXk7KCnhly5bFvn37cPLkSX29eJjatm0bZGrfwYMHdZil3EXROz8G2rZtC5/Ph6JFi+rr5I9c4zy2T3CHBCKAAOfBR0AlMYsxQ4AKPhdVXbhwYZQqVcq+QxSxdEmee+65dpjsyMdAr169IN6lLFm+fDmqVq2q3UsmJyfr4FWrVmHnzp36Y0DWvReR+OQjQOJ2eqZq3749vF6vvoZ/SMCtBGiDd2vNMF+xSIAKPki1LsrYarlLEvJxIK3zzp072ylKV/63336Lbt26wTk98LfffkO1atUgvQHWR4J8NMhHQIkSJbQJQCKRDwbpQZCPAWuurIRXqVJFNhQSCDkB2uCzRy4fQdu3b7cvlPeA9AAKO0vK/7oCTV98Gfj2O5jPPgWDrmctNNzmggAVfC5gBetSUfzys0Q+DMSDVb9+/awgrejnzZuHVq1aaROAdUK8X4k5QLxeyYtDRI6PKteWpUuXti7TSl8+CCgkQALhJ3DkyBE7E/Jxf+DAAdSsWVOHJSz9FUVGjoJ8ppsLFmHfocPY/3936XPykS8Ly4h50BL5v5b3BYUE0hOggk9PxOXHorzlZ4l8+Tdq1Ei36i0FLyYA6f6XVv0W5QZYZNeuXVi4cKHuVbC6+mVBChlXQCGBgiJAG3z2JOX/TgbjWiJjeJxhvsnvwJrDY5w+jeKJC3D0oQf05aLIpQHgbBBwYRmLJLfpCVDBpycSoccVKlSwc37ixAndbd+8eXOcd955dviPP/6oXyw//fSTHtwnJ6ZNm6YHDkrXvyXywVCvXj3rkFsSyDEB2uBzjCrwhR06AK+9AZxUY3XUx0BhtZBM3bp1A1/PMyQQgAAVfAAw0RAsX/bOr3vZl9b7JZdcYhdPugeXLFmC6tWrY926dTpctosXL9Y2Qau1L9u+ffva93GHBDIjQBt8ZlRyF+a9Ygh8mzbj6Guv45zeveB9YWLuIuDVJHCGABU8HwVNQFrszla72PtlGt8PP/yg5/jLdL4vvvgC0gXrbO3LAhl16tQhRRIggQIk4P3X/2FRy/P0YjOGMrVRSCAvBPjk5IVaDN3jHOgnA4NWrlyJisoP9oYNGzQFGeS3aNEiFC9e3O4tkBkDtO3H0EPiKCpt8A4Y3CWBMBOggg9zBURi8jLv3zn3f+7cuejUqRPExm+JLHUpYwFE8Vsitn0xBVCilwBt8NFbtyxZ5BGggo+8OnNljsU5j9P7n0zbW79+PWrXrq2nAEmmZXrPn3/+qZ0FWWMDZKR/w4YNXVkmZir3BGiDT8tMTFvW7BY5I3PenT4r5P+GQgLBIkAFHyyyMRiv08WueOGTF5m04OUlZ8mmTZtQuXJlO0wG78lAP3kJOl981v3WfdySQCQSkOd98+bNdtZlSpw4p7I+cGW6W9OmTe3z3CGBgiRABV+QNBlXBgLibMfpcEds9+KlT+bkWyJhouTlxWdJ48aN6ZHPghFBW9rg01ZWgwYNID9LZs+eje7du9tuqE313B+/537UX7Yc5puvwjiPyt5ixW3+CVDB558hY8glAWm1i69+S6RVIy2dMmXKYOvWrTpYBu/JR4A49rC6McXBx/nnn2/dxq0LCdAGn/NKMVWvla/2uYhTnuqKql4uX5tO8K5cAqNh2rUtch4jrySBtASo4NPy4FEYCYi9vkaNGnYO5syZg44dO+rBehIoil5cekqXv9OTlyh+58I8dgTcCTkB2uBzgXzvXqBoPAzfgdSb1OwT89flVPC5QMhLsyZABZ81H54NIQGZXic/S6SlL8eyEp8l0gUsS/Y63fXKID2nJz/rWm5JwNUEVI8VypeFuWMH9FA79Vz/mHQEx2fN0tnev3+/XozK2YPVTS1MRSGBnBKggs8pKV4XFgIy8M45On/37t16JS5R8HulBaRE/O0vXboU5cqVs/MoA/6c/r7tE9wJKgHa4HOO11ADUb1zZyG5Sy8cT1ALTI17CJ169bQjkJ4q+ci1xFL01jG3JJAdASr47AjxvCsJyIA86Zq3RJbftFbjkjDniHzrGm6DT4A2+NwxNlQr/ui82XoMSgfxQe8QPsMOGNzNEwEq+Dxh403hJiDT75xOdMQGLwPyxG5vyalTp/S84wS1WIclrVu35uh8C0YQtrFqgzdNEzNmzLCJZrbGu3PhJ/tC7pBAEAlQwQcRLqMOLQHplu/fv7+d6M6dO7Fnzx60aNFC+9O3TpxWS3Ba85AlTLo+ncfWddySQE4JyDPkfPYOHjyoF2/q3LlzmijE+ROFBEJFgAo+VKSZTtgIrF69GjvUQCZLxHbvHJQni+dw+p1FJ39b2uDzx493k0BBEqCCL0iajMuVBGSwnXPA3fTp0/ViOEePHrXzKy0uGdTknG5XrFgx2vJtQjnboQ0+c07She8fchU6/bgQnj17YW7dAMMxJTTzuxhKAvkjQAWfP368O0IJiH1eVsazRDzpJScnp/GmJ0vhOr3rWddyG5hArNrgAxNJPeN/6lmYX81AvDIPifiGjUShH86OF0m9in9JoGAJUMEXLE/GFiEExF7vnH63bds2vRhOvXr1IMrfEunal5a8JTJtSbr0KSSQKwLyTKWkKnd935q1ubqdF5NAXghQweeFGu+JWgIyMM+aXy+FlDn2tWrVsssrI/ebN29uH3MnLYFYsMGLAxr5WSIrJ8pzYc1Zl236nh/P1VfA9+//wFekMDxxcfDcf691O7ckEDQCVPBBQ8uII5FA+sVBZIlbWete1ra35MiRI3pUvtPrnvQIWC9467pY3MaCDV5GzDvreteuXRkUfPq6N5QbZu/hPVj9+JNo1K0rttSrC/zxh75MxoLIc/bHmWMJrFixInuK0kPkca4JUMHnGhlviDUCJ0+exM8//2wXW+z1Ms+5VKlSdphMxZPFcmJdYsEGL61zZwtdFkiqU6dOGsdLMmgzvRjKT8OeHt3QsGtX+FTPkCXyoSjxOZdVlg8lCgnklwAVfH4J8v6oJyCOcnr06GGX888//9T+8LmOt42EO7kgID4XZP0ECgkEmwAVfDaETTXq1f/vR9Dx82nwrVoH7wOptjNTdauVmjELcZUqATeOtGMxVVdunFrxjBL9BMT/vXTPWiIr3TlbdtKiT+/oxLo2WrexYIPPSd0V+noWyv3+O8yWLWE4PCnm5F5eQwIFRYAKPhuSvn8MBuZ+j1JK0ZvjHoOvahV4rrwcvmp1UVWNjPXIms4zv4F32lS9KpSvV190Uuubpzz2FLyrlsFQtlnzt1Wo8fCjOEelZU79AIY1KnvTZpRetQamah3KdZTIItCmTZs0GZb59U5vZmlOxshBLNjgs6tK34gbEf/pF6itTDu+x9V7YO92GGpxJAoJhJoAFXx2xLdtB87MXVUTpYE5c2GWKqn8mwJe9Q8sYq5YCXPzZvg6dwd274FW1ceOwz/xBXiGXQVf87YoK9eprjlfl57wLl4Ac/6PwOVXoZl8IDz4H3h3b4Wh7HB+9eXfQI22LabsvOZPiTpMJ7L2d5RertJR9jt+DGgirvyTlJSExMREO28yt16cnMggPEtk0J6zpW+FR8M22mzwc+fOtatFxl1ID4VzmqQMykwv5udfwlA9fOoVAcSrD3zVADCGD01/GY9JIOgEqOCzQWz07wtzgxrtKkpelm686EIYouAdyzhi6zaoYbRAjepawesoZd6rGhVrLloM9UYA1IvfkIEzB9Tgm7/+gv+Ka4CDhxAnF3s98L88CZ6B/eHvdylklrWpfr4eF8O7ZCFMpfSNETehqXwMPPTf1I8B1f3rnz0H5z48DgnqYvM7dc050kegZO8+xG/foRULl5hMRRKqv/Lyd7biN27cqOfVN27cOFRZYDoFSEBcGMsHmoh8vK1ZswYdO3a0U5ABchs2bLCP9U5lZbZTMy20nFLvDQ6+TMuHRyEjQAWfDWrPo/+B/0gSdi9dhkp33ArvNVfpO4xbboLv6YnwV66MIm++qrvgPHeNgf+fVyNFtdYKqQ8Cz3/+DRw6rBxcpOh75DVhyMeAugcNzwV+XJSaupp2A/XFb65aDeVVBWoEV+rXv3wMKLuuX3X5GYePoIhcrT4s/G+8DU+/PvBf3B8yjlv3DPQZgEILvod/8c8odOnlaKFajr7xz8K74pdUM8GmTajy4ScotPw34M7bUtPl35ARECUg86UtkXnUzvXr5cMgGgZeRZsN3tnzIg6QZAlX5zLFVn06t56P3oO/RTucVP/n8f8cDE//S5ynuU8CISNABZ8NakMpVO/Lz+PXdPZV738fwcpLB+j5qlWrVtWxeC4fBKNubSz/eCra3HYzjOqqRa9+nhlf4Lgo6RbNUey1F2GopU09E5+Cv21nnCin1jU/p3jqx4CYA6Tlr8RU6RqmavGr+bD6Y2DxLzpcLX0GKNOAuWQpEKdUfvKp1J6BLVv1eX+33jCUctc9A2rAn//t9yD58nW/CDV37ARUK9+nlI33pedhqrm3hUeOQhv1UeB74P/gVXm2pIjqBYD6sJHrKfknIIvbyHK2lqRfvz5OOT+JBqENHvA0b4bDB/7KdDW5aKhjliFyCFDBF3BdGa1bYfdfO9MsJOHp3hW/f/K+0vXVcY560Yt42rSGf/smrPzsc3S+8XoY8fHAuQ3gXbkEB264GXEqnnMeHwdD2e09L6ieAvkYKF8O8RXKw3Pf3YC4ukztOUz9GJDxASL16wKr1TkRtXiK8qAB/7TpwK7d6oNB3SA9BbNT7Yq+mg3gUccqZZj/9y/469aBp89F8F0xFM0S56OoGk/gVz0A8sIS8axchZLLlsNUNmRDtWQoOSfgnDMvd8miNjLKXkbei8hce5lPLeJU9mKrdzrU0Re4+E+02eDzi1rmw4tjJEukh0PqWXoCRMRhTg0uOmPh4baACfAtXcBAcxVdpYpIUgpZK/czNxqNGmLj00+gbt26KH5mtL18DKRs24iVM2ei84jrYCi7H1q2gGfebBy+5Q7EtWuLhGcn6Bg8DylFffnVOKVai0WqVYVHdcebc7+HKYO81MA9LWr0vpayZfRgIL2vWvvm7+vh370b5qxvEJ+UutKa/7ob4PlVtfBffg1F//0IGihzg++x8fBuUathqda9qXy1V5zzPYwT6gODXZGpXHP4VwbgSUveEkvZOwfgicKMJAVvlSXStuLbwOlJTuztsgaB5bFO5q63bds2Q7H83/+AhhOehafGpzBfewnS4+eU08pUd1yZ3CwRs4x4RZT4RCxFb53nlgQKkgAVfEHSDGZcFSvgeK2aqcr9TDqeTh2x7qX/oVGjRih2pivdM2QQkn+qjnVqJHerUcosoObgGgP6wbx1FE4pJe1p2hhFPn5fx2B0vQB+1W1vyBgB1bPgUQMK/dK6P5E6O0BftGGj3phj79Vd/+rTInVk8Kefa4XuO78HGvy1C171kvM99Ti8d4+FqQYDFv7kM1RWq7WZanCZUbq0joN/0hIQ5e1UGtZgrcxGZqe9071HkWqDl8F0VapUscEuWLAArVq1shcaksGqTnfFcqHMnvFfOQyV9qrlXwsXgvie877xqh2H7IjLWflRSCAcBKjgw0E92Gk2Pw+HjqlR+w6br/fJR7Gkb2+9UErcmXDPm6/htDIN7FULrFT/130w1EpqHnXOd/sYpKgPA696aXnuuyc1t7VrAaqFr6WQUvOqi9n/yutQTVB4/am2AvPNtwGl4P2XDkFR1aqvJvOAv5kD74+qha/GKZjKrFBDKX7s3gtw2lAqy0z+Ll68GPuULwVLpJvX6QZXFEa7du2s067aRqoNXlrqVmtdgMq+mFGc5pL0Cl6mtKqViTR/43SKmskyz1V1wcyQABV8DD8D0p2Y8tQT2LRkCWq0T1UY4pDDe/wg1k54BnVVWLGLe2tCnuefhb/3JTipWvrxrZV3riv/CfNd1RNQVFnwlV1Ry8bUrn+ZGmiobkg1HFBPGzTnJQKdO8LXtBXqSZiaJ+xTgwK9Dz+YumiLWie73B8bYapV2pzmCrk0FqVDhw5pih1JDnRiyQYvg2a16UsGvaoud0OmxFFIwEUEqOBdVBluyYoo2QNdOqOOw+boubAnjq1Zjg2//ILWQ69Rg/kNeNSUQb9qkScvXIRCysNfYTVbQIt0Se4/465XvfygZgr4J7+rT2mlr6b8mR9NBZSC9w+6AsWU86AENRvA99k0eH9Szn2UndJULdhyv6iZApUqA61augVN2PIhA7VWKpOHJdIVLnXgnLIlXcqiYClZE5DV26y57dLjINPfnNPhnK32rGLyXNIHppoNc+yJCSja5XwUeeWFrC7nORIIOQEq+JAjj9wETaXE/65T2x5IpKcQfj0Ni5S3L/G5XkRmAijxfvUpUhqch5NVKiNBTb3zXKxG5u9RXc4J0to/kQpAOfsxxXapWveG8vpnt/bnfAf07gUZ4d+0kBqw9J/H4f/iE+UEaIC+z6NG9sfJdELHx0dqhNH9VwZ8OdehFwc6MviuZs2adsGdyt4ODPFOJNjg5UPJWrlN/BHIcc+ePW1StWvXtvez2/GOugFL6tXWzz97n7KjxfOhJkAFH2riMZCeoZbOPKr8b69bt85ebMUz7Gr4f0hE8hdforCaFVBIBvopGyfUSH7lASaVinT1lykN/7jH9Vz/Imc8iPkfe1IreL/yL1By9N0ooT4IfB98DM+nH+lWbAwg1aOtnfPoRZmLjViWrrVsw2K3l3XFSzsGNUqLvpIsiBQiiQQbvAyos0Q+lMQ7XVflAtopm5RjKKfIwFGv6nWqqvxPmGolOEOZqigk4HYCVPBur6EoyZ90J3vfeh0LrhiM7t27a2c/UjTPpx/C37I9TqopffEjr4Pnot7w/aY8+qkBfrBsmsq2b6qWll/Nz7cmIZk/zFfT+WbD6HsxfI/8F+dPfgcp9/wL3l+V/V8NEIwVkWlY0sVsiSimZs1S/RZImPhPD6VEmg1ePn4uuOCCbBH51boRcTNno4YMHP12Lrzz58KoVcu+T7r8rVkQEihT48RzoTNMnB2l94dgR8AdEggCASr4IEBllDkn4FEDlQ6o1r4oJmtwmUd16/teehUnla20qIzmnzMzdS0A8fUv3fMiovzlZavcBZtqXn5xUWQeA/4bb4H3/bdhKqUX/94UVFCD+cyn6sOQtQKiUGo5lIwUTzjKNDtp2VuyV5lC5APLOZ++uOLhHDVuXRtN2/QfP+J3QBhYc9BzU1YzcYEeOKrvUb4i/N/9AO/Ia/Wh9Ud6LyyRdMqrAavOMOsctyQQKgJU8KEizXRyTEBsmd4/12PJm5NxwaUD7RX1DLVMr/+Z55ByTjEULlUKxj/6w1Rd9pavf6jpeubCn3Q6vjYdUVzN4T9HtXB9qrXvXfiDjkcUf8JOZf+XQWpR2tIX5b5q1Sqbt0yzE4UjSt2S1q1bIyEI5XeTDX63UsRO5zXyoSPmC+tDRz56xN4uNnjpVcpSatY4O3C0RAkYyi+FUySuaFhLwFkm7kc+ASr4yK/DqCyBvDBPqEF6yoG7XT7vk4/hUIf2OLBlCxrcfJMe7Gdc9g+YL76Seo3y1mcob3qmuHzdu1875jHkzL79aundn4Ge3eFr1xnt9uyF7+Y74d28DkYuBlTZGXH5jnQDO7udV69erR22yJoJzhaleGsTO74l0qK3lJ8Vltutm2zw4hpafpbMnz9fD1R0jmUQG7xwyE68X3yClLqNcUJ5nyx2wwi12FPf7G7heRIIOwEq+LBXATOQGwIpHdohqWplGGcWZ/F07wZjwXf489EnUfPywfCOGK4X0dEL8VgRq2V5pcXlv/VOYN16xIuPfiV+5dDHO2Oavr64uv8cmYv/7pupiwRZ90bRdsWKFWn8ou/atUstbKg+os6I7Ddt2tQ6zNM2Gmzwpno+EmTxJvERf2baoSwc9fe+Hbq136VLlzyx4U0kEGoCVPChJs70CpyAcX5nrLn1RtTp31/HLa5xPdM/h6/9BTippvYVU8v2Gm3bAO99kLoAz5kcmGpRHlO68CvX0l35hrKh+hq1gPeP1TCUstPd+coNb7R056f3ficOdLp166bWH0pdd0CwSLe22KidPtKlRyAvduszmEOy+eabbyA2dxHpRZAyOVvqOXX/K4Pl/P0uRZNfV6Co6tL3qyWYPed3CkkZmAgJFDQBKviCJsr4XEHA0+w87Nq4Bnv27NE+xSVTnjG3w/fCyzilWmVF1IA8j/gN37gJUKv0GbKUroiaq28uVw5llJ1VPhDaqpe8b9Qd8G77Iypb9qII165dm1p29VcWvBFHL06HOW3atEnjCMa+OJOdcNngL774Yjs3Mm1w4cKF6NWrlx0mO/IspBdrHrxlg/e/8RbMBQtR9MwCMf5b71CrKCqHSxQSiEACVPARWGnMct4IiL3dq9bp/m3i82ivXO0ajRvBlNarsvfbouz1RqNz4R95k/K9/zsSfKkjo/133gXv55/o1vw5b72LomranvnUkzDOrPhn3x9hO+Lj3jkvXAacyUA0UZLW/PrfFQdR/M5V7uS+zJY5DbYNXvLxl3KSZIl4+BMnQM4eB1mJMaci0wjT2OBlNkaKw+WstfKiI0KZAudcIU6W+pUPBcsDnvR2yKpxFBIINwEq+HDXANMPKQFDKaZ97Vpr5S4Jy/Q5WQznZLfeSFbzlEs8Ox7yIaCGnKvu/FTlLteZq9ZAbLO+hs1QSnnTU67Q4PvoU3i3qmVzHaPT5dpoEOnedo6yl9Ho0s1tDdKTc+INTgZDOiXYNvj0swF2qOWKJa/OGQLp8+TMX/r99PPgPVf9E757H8Bp1YtRSA069ExQTpfSiTgT2qkWaLJEPi5k2V9r2qEcU8FbdLgNJwEq+HDSZ9quIGCowVT7fvhWe4Gz3MF67hkLn+qu1d35Kpeeya+lrqanptkZZ2y9MpDPVJ7N0LUL/Lfdia5qEZ2UCg/D+8vCiF80J733OxmgJ4pr2bJldp1J9376FdekW9zp192+OA870s1uOfGRDwtprTu99Imb3hLKlCKzA5yr7Vk9DzlJ0li1GiWVvd1U3u0MNZVQVmCUXp5Vz7+IBmo55mLql16qVasG+VFIwO0EqODdXkPMX1gIGA3qw6tGTa967gW0u+pK1W3fEKYagKba/Gfzo1ryhvKE5r9+FMwpHyFBFL+ac+6/+z54X3pOX1dk7e9qlbFkoHHjs/dF6J6Msu/Xr5+d+19//VWvdS7e2izHOjNmzMBPP/2kp+lZXvRkeVvx4mZNwbMWepGPCLlGPhS2ytRGJdK9Ld3fll99axqfzDGXZXTbOtYgkHMyEyCv4lOmlqSx92C/qh/fuCfg3b4RhsxxV/EeVismmmocB4UEIpkAFXwk1x7zHlQCsqrd3nZttHKXhESZe774GCm9+uJUjWpImPBEale/eI2zWvUpPjXnfrHOl++20ajwyafwKIXlU6177wepK+oFNdNhiNwaoCZJi6JftGgRZCqZpbQlXD4CpCtdnO5Yo92l9S3OZ0T5W2HyMSBd7GLvd7p1la5/6UFwDv6TePMjpup1SVEOj5KgTDFxfvjV+gayeAyFBKKFABV8tNQkyxESAp6OHbBz9TLdyrTmjBtDr4b56ZmlcovGwbh+BMzVymY/+W0UOqGWy1Uiq+aZP/8Co307+L+fh7pq4RJ/QjF4evYISb5DlYgoYFH40mXu7DaXlro4nXF2/UvYt99+i4suuihN9qSlLgpeXL1aYnXVW8cFslWD8SqrLvqBssJBocJqGUS1pZBAFBHwRFFZWBQSCAsBz4B+8Hw3C5svHQDPR+/De4sagS+i5o/bIqP1lfi/nA5/nwFo8MEn8F85DLJCHiU8BDxnzCgnK5SH0akDPNdfF56MMFUSCBIBtuCDBJbRxhYBT4/uWH/8GBpYznaaNoFx2UA1UG8y/KorunCvHrr17uve++wqeWpKnl/ZgT3/HAJz7TpUvH00Cm/ZBnPGF7ZZINIoyjx4GYjX/wwHt+TfN+EZtFALGMXv3QdTXBSr8QSeCzpj20+JmPXFFxj1+GMZZgRI3r///ntYYwbEjCAj6L/77ju7WPXr1890uqB9AXdIIIwEqODDCJ9JRzcB74v/w87evXBaDeKqO3iQLqyhbPrmwkXKZq/mW0uXsOqmlqVwfU1aotgZHL4LesC7aplWQpFGSEa7p5lX7oIC+L+ZDXPCs0hQc+hFfAMGo9CShXo/pVxZ7CuWoNc10AHp/lgrHEqwlE3MCtbAPwlz7ssxhQTcRIAK3k21wbxEHYFTjRvaU72kcJ5xD8M3ey5OqHnUCapV73nnTWDZr4ByqQu1yp2WInHAn1tgKgcuvp4Xo5vyi55yaoTypqdGeauBam4WywbvpjyaO9VIezXFzhbxM39G0s+Dt8KtrdMXgBXGLQlECgHa4COlppjPqCAgi+QUWvELfpj8KrwfvqenZEFNwUPyKZiqhPJTc7+A5s3gU7Z6/Loc8ftVy/PYcfgfeEgzEIc7xeb/iHj1o2RCQLkaLrNkGUzlYU7E06Nrak+J2jfj1YqDnTvocP4hgWgnQAUf7TXM8rmegKEG43nX/Iq/e3ZD0tVXwLtzs3aBK+Gyxr0W1T1srvxN74qP/Ir3PYjyt46G7+L+riqfZYMPV6Z8b7+LQpcMRMMnnoavRHmYalqeIS6Kd23BjqFX4ZRyL+z57GM7e5YvejuAOyQQRQTYRR+gMhMTE9PYEmX+7ueff2775xbXmDL4Rnxjr1u3znbhKYNukpOTtetKK+qCnLtrxcltdBEw1FzwPS8/rwtVtorqrldi3D0G5sxvkCKtfnXsfXYCzB+V7Vj5Ry90JHUNcz0db/kK3eL3Pz0Rbad8CN/qdfDef6+OI9R/wm2DN28fC0PNxS8iBS/khf/dKfCOvl37MNh+/bUo07x5Gnu72NTdNmYg1HXG9KKXABV8gLrt2lV166UTUdzidcsScYkpzjfEH7Y1T/fw4cPaQcfmzZvtRTHEg5f47RZ/1dLCkZegiDj4kA+EWbNm2feLD2txFiILaljeweRa57rdckyJfgKe7l1h/P4bVk56Ha2uuxaGGplvqi57NbLrbOHVcrZqtRXlKne0nndfTrr6f98Anxo85r1hpL6usLI5e9VIfoiP/SBLKG3wpvJ+V0l5ECws5VKuhbWohYKguue1ODmlhmT4a9ngN27cqD/UrQvk/3zu3Lm2f3nxM9+3b1/rNLckEBEEXKvgZYUmUZri7cotIqtFWStGOfOUG+Ur5bKm3UgcMvVGlL7lclNcdYonL/kgkOlG0sIQEf/eovA/++wzSBwi4pdb9q2RvVa8jRo10vdb18m18iFCiTwCxrkN8Fe3LmitlLuI0aoljFHXw/fEU/Crj8HC/7oX4lbXXKoG6inlrkX93+DLGYBS8L5b70SNz75AIWXH9z35KLyqVyAaxFSD5ny1zkUNWexGBiOu+x3eyZPgeeUF+Nt0wgn18ZzQsjk8d9yao+LWq1cP8qOQQDQRcO1bXxTZvHnz8MYbb0QT7wwLcYjPbfmIcXr4sgrcqlUra9feyseAsxdBjuWjQ5b5tD4GfvnlF32NeAmzlrUUk4Lsy0eTfESIyIeEeGOT+yQeSyyf4dYxt+4i4P3PQ9jYo5ueolVbedYTMfpelGqjF+WueozQri3MRT/BfPNtFFY9TyKmWibXVM54UKsW/OOfRruPPoFv/cYCVfryHAVlHrzyDFhGFoVRPWuG+tg1p3+tvnqV18CTqmzqw9f8Yb4uo6d1KySr2Qa/qbnqnYZeA+PMs65PZvLHssE73e1mchmDSCAiCbhCwYvdWv7RnCKKSBSPKPqBAwfirbfecp6O2X2ZtpPZ1J0qZ+y2TjBiUnC24mVf5u0uWbLEXupz+fLlumdAHHpYHwPiA1xW7pJzlrKXLkpZLpTiDgK+8uXgc3RBe/79APxqmt0B5Qe/7Mjr4B1zB8xEpfRKqh6wvftSMy3mJdXK94+4EeZHU1FW/Y+Z/1LT9qQ7/9phMJOSUEr5zI9XPUNQduu8SDBs8P5PPoNx061oonq1fP8eB+/urYDKszZViIIX+XOL3ug/6tyJalXT2NrPnky7J+8Y2uDTMuFR9BBwhYIX5T1ixAhcc801GD58uKY7bdo0vSrV+PHj/7+9MwGPqtjy+Okk8NhEAVEgYQmILEEhyDYOQhRkQBAEhKd8KOigyDoKKuiDJyp+PAU/fbyAsqgjDuADR0ZWlcUQiTwQFWSRRVZFIGyCspP01L+g2k6nQ3fITfe9N//6vs7te2913VO/6vSpOudUlbLAmSVA3AM+EjUJdClgsw+kdu3a5Xo8Rl7+Pn+zo9dXauMQmP4x2sce2Mg3T638ZawFWF8ceRFfYBb9gIWAgYW5EBfqBY/qgMVOe0vWLFjwxypyLf9dRJnvs04p6wwC9Tq215vjeL/fqFbT8zPnL1oi3l4PSFZCTakMM39xFTOi1s6PmTdHj4AR2Be/PE28yqLkUVuzXikV1AePjXpqPj9ayqjJA95F/6dnE2Qr5e5RsS068iA2RrKVVSJ2xNPiHfakXFCBhZJUT/70z/+5klh53jM++Dwz8AYJOJiALRR8S7UX87p162Tw4MEybNgwmTJliiDYDD8W2POZqfAJ5GUZ6NatW66HHzp0SO8CZm4gFgCKf/PmzfoSOgNwPcAKkJaWZrIJLDVMkSMApR/35QrZ9857UqJMaany55764Z727cS7+YdLO+DBnK/M/N4v0hCoITG/qZ3xMLLHlLwdP0q2CurLfmyAJKlrWX+fJLFqDr9HzdH3bt8h8WP/JqVU5887833xKMsSknfbdqmw/nvx3qWW5r3cMfeqz17zr7USB0tCx0uBargmz42WpouXSNaBTIn9z77i3blTsm5PkfIoR3USs+76D4nNSBNB4NzqNeqqSjC5X441iH3hL/KvtimCmJMSanObcNOnn36qLVjIj1gXfE8XLVIm/8upTp069McbGDw6moAtFDwIwg89Y8YMmTNnjt5qsnnz5r4IVkcTdqHw2NITr8BUS+3OZZJZ1nPfvn2yZ88efRmdOMQPYNaACQjEqD8pKUnHBvhbEIIFM5qyecwfgdPNm/qULT4ZM/ZFyT5wUI6oFfSu7/+YxA4eIN6v1yEo44+C9+7T5n29uI6yAKhFdXXKnjJdrcb3V8mqc4soI/klRdyilcR+o/z9X2bI793+LIfOn5Wsv74ssb/sFvVFkawGjaW6CvKLUy6ArFEjJXbMaMlq3VZEBQaWUyZy76ChKmBQlaYsQaKWjRX1PA9mmhw8JHL4sGBTmOzGLeSM2l2uZBW1Ze9zz1yW5uoO/hYsuAY3bdokKSkpvsLQQWUiATcQsI2CNzB79uwpt99+uwwYMEAaqjmrTM4hgEC+wIRtP5OTk3NcxqgpPV35hy+nbdu26ViBjIwM377gCQkJes0BmP7h/0fCTAATIHj5ozxcBQFtzv/v6bLWz5zvaar2vf/rXyRrhHpVqyp/mv62njvuadxIvGZpV8zEwII7GasxhUPUziuXFLFS3KI6ctkPPSLZJ36VE9hf3RMn2WrE7/m35nplvmJqJT4kr9pcx4s91w9l6tXl9EU1Isd2ujGPPKyG1OqzyKeUrEdZitRcUolRG8Nc3L9b1i9eLC379lFFF+xny/87ZEbw/te0APxDAi4gULD/lEICgB/3BerHBwk//jD/BiqJYI/GvNUvv/wy2C3tz7fTlLugQhaRi1DYwaKWMff/sBqxmQTTP3bvMteg4OFKwDXjDkBefF+YCk4g9r8Gy6aUO/T0y2rVqukCY95Olax58+W0cpmVbnqbxKS+KbJ12yXzvnkkRvsI8qx7s5RR8/J7Y7wPU7qy1uiI/mLwnl9S8LLvJ73JjufuNmre/vs+Je9R6/LD9B/z2QL5Tfnc45S/v1TqG38EyqkR/umqCQVW7kZkc8QU165du5pTHknAVQRsqeD9Cc+dO1f2qgUtpk2b5n856HuYiE2gV2AGdBLyuheYl+fRIYCZAIGzATDCQvubhNkVCLpEYJ+JfsYRpv9vv1VzwS8nrBtgggrNNR7zT8CjzOJx2Wdkpd9oXxokScz8/5UzasTuVe9LT39LPCrmIjb175JVv5GcUZ2BkuXLScyEv2EBBz2Kv5C+SrkJSknca+MEZcLsnoXOm4p+v3H4kxLT6ZJvPqbVHfLDe1MF/8ulYSUoQIKbyLiCUAzO8X3CyyRjHTLnPJKAmwjYXsGPGjUqbN6JiYlqsa7EoPmxkAxGfkzOIoAf4Jo1a+YSGisGmml9uAllDsWPFcmQYHJFmyOPifjHdS5mAgoFT1hlb9v8j3QsRpnLkfWeenXl3M87ZdLYV2TEm2r0rSL3kWJVNPyWf86RG5XSvqHJbfqaNrPPmiHfqbUaCmuFOHQMsYy0SegEwg1klDp87U2bNtXWoGArV5rP8UgCTiVgewXvVLCUu3AJYK4+XibFKyWDERpG7iZhlUAod3TsECmNBJcPfuixFoBJMPsHix8w93kMn4BXdbQysWPbZeVuPnm2ZqJkq7npkUxX6vAbOdAJxD4TTCTgRgJU8G5s1SJaJ4zag0X3w5ePfQNMQuAfpvGZ6H6Y/eHnRx5/k24wy4Epg8fgBAo6Dz54qYV3lT74wmPLkqNPwBYK/vXXX/dFTwdDUrduXb2aXbB7vEYCoQjAfO/vj8fiJv5me3zerJyI1f4Qr4EEEy/eY9RvEjoGTPYjgFUbTecM7+Fn91/FEQGa3I/Bfu1GiQqXgC0UPEZSqampehW7YKvWVVRBOUwkYCUB/NhjFb7ABJMtTPgmIZocpn8T0AeFj3OM9rGyn0lYmIlJdIeoUNaiDwF39erVvkEC2gdt6D+jBtYY/3UaTHGYB48ZGfTBGyI8uomALRT8P/6hdoBS/5R4TZo0yU18WReHEYDJNnB3QIwEt2zZomuCUSLO8V3FNsE4IiFwC6NG/8V6TDCXzlBE/oCH6QxFssr+C9Xk57mI06APPj/EmNdJBGyh4AEMa873799f/0ByHXMnfYXcLysC94Lt7IcV0MwcfURrQ8H7B+8hCLCoBe/RB+/+/wfW0DkEbKPg8cMwc+ZM55CjpEWeALbaDUwYva5cuVJfxmje+IZ3qnXWTcJKjUxXRwDuvI0bN/o+DFN8ZmZmDv96p06d9OZIvkx8QwJFlIBtFHwR5c9qu4wAVku89957c9Vq7dq1gk16kLBDH8zCZrVGXEOcSfl8bJiCz9gxIUahMH3wNWrUELysSvTBW0WS5diRABW8HVuFMrmOQLNmzXLVCaN9bMCDBMV48OBB/R6+fZOaNGli3jriaKUPHjxMjAN85Yh9wHLGJsHqV9Dlp+mDNzR5dCMBKng3tirr5AgCUFDYNdEkM3UPCt5E8m/fvl2/91+G13/Kn/msXY5W+uBheocCRkJwIxYkwiwHkzDdsaAKnvPgDU0e3UiACt6Nrco6OYIAFuYJNi0USs1/lT4oNmyfC4WHhJX5MPrHVD/TKcB1K03XKC9SCSNz/9kHmIKIpYj99yXA4kTcxjVSLcLnuIUAFbxbWpL1cA2BwDn1WIYXSs+Yq1HRYsWK6fX2oQjNKBdR/DD1Y9RvEkbUkUxX44NHZ8XsIQBZsehQ4M6CsHQUxrRD+uAj+e3gsyJNgAo+0sT5PBK4CgJYQa927dq5PrlP7cMO5W8SFu/BuZm+h5E/zrGTojH7I2+dOnXMRyw9hvLBp6en++SF2R0K9rvvvvPJUL16dYnkLAP64H3o+caFBKjgXdiorFLRIWD2bfevMUze/iZ+jKox+vXvDMDcD1P/8uXLfZYBrOMPKwDM/hhFmwRrQbjJ+OBRvv9OblDkR48e1a4Gs20zAg9hdvdfQhZui0gm+uAjSZvPijQBKvhIE+fzSKCQCWBhHoyEAxM6A1DgJmGOPkb1Zl45gvvQOYDCXbNmjc+/D5cBOgMLFy70WQGgyFEWLAWrVq3yuQmwsA9M7lg61rgUEhISpFWrVrqT4a/MEVtAv7ppDR5JwHoCVPDWM2WJJGBLAv5b6UJABPhh7n2wzgBG8EZBI6/x8586dQqn2v+PkT4Utr/Sxugca8B36NBB57P7H/rg7d5ClK8gBKjgC0KPnyUBlxIwZnRTPdM5CLX0LqLhoTSdkuiDd0pLUc6rIRBZh9fVSMjPkAAJOIYATPcPPfSQY+SlD94xTUVBr4IAFfxVQONHSIAESIAESMDuBKjg7d5ClI8EHEQAEftLlixxjMRwJ5jNgRwjNAUlgTAJUMGHCYrZSIAEQhNAYB598KE5MQcJRIIAFXwkKPMZJFBECNAHX0QamtV0BAEqeEc0E4UkARIgARIggfwRoILPHy/mJgESuAIB+uCvAIe3SCDCBKjgIwycjyMBNxOgD97Nrcu6OY0AFbzTWozykoCNCdAHb+PGoWhFjgAVfJFrclaYBEiABEigKBCggi8Krcw6kkCECNAHHyHQfAwJhEHAo/Zk9oaRz/FZ1q9fLx07dpTk5GTH1yWvCmDBjsA1xPPK6/Tr2PQEm6UUheSkuuLnBLvM+W9Ak582inRdCypvfuoWmDfSdQ18fiTPi1JdsVFT69atfXh37dolS5culfj4eN+1SL0pMgo+UkCj+ZyUlBRJS0uLpggRezbrGjHUEX0Q2zWiuCP2MLZrxFDneBBN9Dlw8IQESIAESIAE3EGACt4d7chakAAJkAAJkEAOAlTwOXDwhARIgARIgATcQYAK3h3tyFqQAAmQAAmQQA4CVPA5cPCEBEiABEiABNxBgAreHe3IWpAACZAACZBADgKcJpcDh7NPDhw4IJUrV3Z2JcKUnnUNE5TDsrFdHdZgYYrLdg0TlMXZqOAtBsriSIAESIAESMAOBGiit0MrUAYSIAESIAESsJgAFbzFQFkcCZAACZAACdiBABW8HVqBMpAACZAACZCAxQSo4C0GyuJIgARIgARIwA4EqODt0AqUgQRIgARIgAQsJkAFbzFQFkcCJEACJEACdiBABW+HVqAMJEACJEACJGAxASp4i4GyOBIgARIgARLwer2SlZUVVRBU8FHFb93D3333XalatWqu1/79+617iI1KWrNmjTRp0kSSkpKkU6dO8sMPP9hIOmtFefHFF6VatWq+V+fOna19gA1L+/zzz6V8+fI2lMw6kT788ENp2bKl1K9fXx588EE5ceKEdYXbrKTZs2fLXXfdJQ0bNpTevXu7+v8V6LOzs6Vnz54yfvz46LaE6mUwuYDAhQsXvKdOnfK9hg4d6u3WrZsLapa7CmfPnvXWrFnTu3r1an1T/Xh4u3fvnjujS66oH0bvwoULfW175swZl9QseDWOHTvmveWWW7zXXXdd8AwuuLpz505vpUqVvJmZmbo2/fr18w4bNswFNctdBbVMrffGG2/0Hjx4UN9UgxFvu3btcmd0yZV169Z5VcfNW65cOe+4ceOiWiuO4KPbv7Ls6XFxcVKqVCn9Wr9+vXzyyScybdo0y8q3U0GLFy+Wm266SVq0aKFHPQ888IB89NFHdhLRUlk2bNig67pjxw65ePGilChRwtLy7VbYkCFD5KmnnhKPx2M30SyTJzExUTZt2iQVK1bUZaJdo23OtaxyAQVhNDtnzhxRSl7fwSj+q6++CsjlntP3339f1ABLW2WiXSsq+Gi3QCE8f9CgQfLSSy+51sS5d+9eXbdWrVrpH8hatWrJ5s2bC4Fk9Iv86aef5OTJk9K6dWvp2LGjdsGsWLEi+oIVkgRz587VHZg2bdoU0hPsUSw6LxUqVJCtW7dKjx49BC6n4cOH20M4i6WoUqWK4H/VpKlTp+rvsjl323HixIm6Te1QLyp4O7SChTJs3LhRlPlP7r//fgtLtVdRhw8f1iOCJ554Qo4ePSrt27eXV1991V5CWiSNMsdLnz59ZNWqVYKODZSAMvtZVLq9ilEmXN0xnTBhgr0EK0Rp0L4333yzKLeTfPbZZ4X4JHsUPX36dFmwYIEUpTaOJnkq+GjSL8CzH3vsMSlevLh+4b1JM2fO1MEdMNe7JQXWVflmdXBdr1695JprrpERI0bI/Pnz5fz5846vcmBd8eMPVwvqHBsbKwMHDpT09HRBJ8fpKbCusDwh6AydmWXLlun2VLEHcu7cOadXVQLraiqUnJwsr7zyim7j5557TpTD1txy7DGvuk6ZMkVGjRql2zYhIcGx9fMXPK+6+ueJ5ntuFxtN+gV49r59++TIkSO6BJj6qlevrt/XrVtXUlNTpW3btgUo3V4fDawr/HeoY0ZGhhYUe03XqFFD++Od7p8OrOvx48cFMRV9+/bVdcWsCLQxFLzb6jpy5EhtpUBFodQRe9CsWTOBknd6RH1gu+J/VwVjSf/+/XW74jsMU7YKMBQVnKWvOfVPYF3x2wS/NNp36dKl0qBBA6dWLZfcwepqMqHDiplNqHe0EhV8tMgXwnNVJL2ULFlSoARMQEshPCbqRf7222+CICUE20EBYASEaVUrV66MumxWC4C2rFevng7Iio+P1yMg+G3nzZtn9aNsVR5+OBs1aqQVnq0Es0gYKHTTrlDso0eP1h0ZdGrclnbv3i1qVoQsWrRIH039nN5pM/XI62gHBR+Xl3C87jwCu3btkrJly7pauaNVYJbHvP8uXbpI6dKlBTMIlixZ4rwGC0NiKPUxY8ZoiwxGtRjduXnGQBhIXJGlcuXKOt4Ac8OR0JmZNWuWK+oWWInJkyeLmsIrKSkpOW7hmptciTkqZ5MTjuBt0hAUI/8E4K+EqdNMNcp/Cc75BOoK8y3cMUzuIYB2hUUKHXMmErCaABW81URZHgmQAAmQAAnYgACj6G3QCBSBBEiABEiABKwmQAVvNVGWRwIkQAIkQAI2IEAFb4NGoAgkQAIkQAIkYDUBKniribI8EiABEiABErABASp4GzQCRSABEiABEiABqwlQwVtNlOWRAAmQAAmQgA0IUMHboBEoAgmQAAmQAAlYTYAK3mqiLI8ESIAESIAEbECACt4GjUARSIAESIAESMBqAlTwVhNleSRAAiRAAiRgAwJU8DZoBIpAAiRAAiRAAlYToIK3mijLIwESIAESIAEbEKCCt0EjUAQSIAESIAESsJoAFbzVRFkeCZAACZAACdiAABW8DRqBIpAACZAACZCA1QSo4K0myvJIgARIgARIwAYEqOBt0AgUgQRIgARIgASsJkAFbzVRlkcCNiVw7NgxOX/+vE2lCy7WuXPn5Pjx48Fv8ioJkMAVCVDBXxEPb5JA9Ak8+eST8vLLLxdIECjKPn36iMfjkQkTJki3bt0KVF5eH167dq3Ur18/r9s5rg8ZMkTGjRuX41rgidfrlYcfflguXrwYeIvnJEACIQhQwYcAxNsk4AYCUOqPP/64FCtWzFHVKVGihPTq1UtSU1MdJTeFJQE7EKCCt0MrUAYSCJPA/v375emnn5YqVarIrbfeKitWrPB98uOPP5ZGjRpJQkKCvPbaa9K2bVt97/Dhw/L2229Lhw4dfHnzerNlyxa588475dprr5Xq1avLG2+8obNu3LhRWwAGDhwoFSpUkPbt28uuXbskJSVFqlat6suHzHADPPLII1KuXDm5++67BTIjYTQ+bNgwqVy5stxxxx3y888/6+v4k9dzce++++6T8ePHy4kTJ3DKRAIkECYBKvgwQTEbCdiBAEazR48ela+//loGDx4sXbp0kV9++UV27twp/fv3lxdeeEGWLl0qc+fO1dcg87p16yQ5OVni4uJCVqF3795yzz336DKh3J955hmB7/7s2bPywQcfSI0aNeTbb7+VQ4cOSYsWLWT06NGyYMECGTt2rMANgARZ0AnYvHmz7oj069dPX588ebKkp6fLF198IYMGDZLFixfr6/iT13Nxr2TJklKnTh35/vvvccpEAiQQJoHQ//FhFsRsJEAChUsAI14oyFmzZkl8fLw2ub/11lsyb948PTqGEu/atasWYsCAAT6//bZt23T+cKSbOnWq7gxgtA1lDuUKCwAS3j/77LP6fZs2bWT79u2CI9L111/vU8BwA4wZM0bKlCkjI0eO1D7533//XWBh6Nu3r9StW1e/Jk6cqD+LP3k9t3z58joP6ot6YOTPRAIkEB4BKvjwODEXCUSdwN69e7U5HMrOJIyiMYI/c+aMNG7c2FyWpk2b+t7v2bNHbrjhBt/5ld5AmUOJbt26VZv7s7KyJDs7W38EbgGTSpUqpZW0OS9evLggb0xMjCQlJWnljnv16tWT0qVL+6wMt912m/mItgCYkys9F3kg/+7du012HkmABMIgQBN9GJCYhQTsQKBixYpaUf76668+cTZt2iSJiYnSrFkzWb9+ve/6hg0bfO/hS0cnIFSCKb579+4yfPhwnX/58uXaMoDRPFJsbGyoIvR9/2ehc3Hq1CmpVKmSVKtWTfvaTSHw4SOFei7yHDhwQMcE4D0TCZBAeASo4MPjxFwkEHUCtWrV0sp8+vTpWvFCoX/zzTd6JNyuXTtZvXq1DrqDKf+dd97xyYtgvB07dvjO8QaBcFCs5oVOA8zoSAjOQ/T67Nmzte/9woUL+nq4fzIzMyUjI0PL+N5770nLli2lbNmy2pw/Z84crfBhjYC7ASmc50L+hg0bhisC85EACSgCNNHza0ACDiGAEfSMGTP0tDH4rxH4NmnSJGnQoIGuwZtvvimPPvqoNpUjYt6MpKHgYXLHSBzz4JEWLVqkA+H0ifoDEzgC5zBXHooUQXKYzw4XAHzt8MeHm/A5RPqjPAT2IUYA6fnnn5cePXpI7dq19Xnz5s31ESP7vJ4LWTAH/scff/TVU3+If0iABEIS8Kh/+kv2t5BZmYEESMAuBDBKhsneKGz4p2HyNkFvGCkjaj0tLU2LjOA7TF3r3LlzyCrApI5y4WcvSIJfHTIGJkx3QwBeoMk/r+fCkoCIe0TxM5EACYRPgAo+fFbMSQK2JXD69Gk9lQxT0hDtjnnvGOV36tRJy2ym0S1btsy2dchLMAT9Qcljfj8TCZBA+ATogw+fFXOSgG0JYLSNufEIuMN7zE03yh1Cw38P8/3JkydtW4dggh05ckSGDh1K5R4MDq+RQAgCHMGHAMTbJEACJEACJOBEAhzBO7HVKDMJkAAJkAAJhCBABR8CEG+TAAmQAAmQgBMJUME7sdUoMwmQAAmQAAmEIEAFHwIQb5MACZAACZCAEwlQwTux1SgzCZAACZAACYQgQAUfAhBvkwAJkAAJkIATCVDBO7HVKDMJkAAJkAAJhCBABR8CEG+TAAmQAAmQgBMJUME7sdUoMwmQAAmQAAmEIEAFHwIQb5MACZAACZCAEwlQwTux1SgzCZAACZAACYQgQAUfAhBvkwAJkAAJkIATCVDBO7HVKDMJkAAJkAAJhCBABR8CEG+TAAmQAAmQgBMJ/D8k4PtLLVi+6QAAAABJRU5ErkJggg==" alt="plot of chunk unnamed-chunk-52"/> </p>

<p>Note that although <code>type.multinomial</code> is not a typical argument in <code>cv.glmnet</code>, in fact any argument that can be passed to <code>glmnet</code> is valid in the argument list of <code>cv.glmnet</code>. We also use parallel computing to accelerate the calculation.</p>

<p>Users may wish to predict at the optimally selected \(\lambda\):</p>

<pre><code class="r">predict(cvfit, newx = x[1:10,], s = &quot;lambda.min&quot;, type = &quot;class&quot;)
</code></pre>

<pre><code>##       1  
##  [1,] &quot;3&quot;
##  [2,] &quot;2&quot;
##  [3,] &quot;2&quot;
##  [4,] &quot;1&quot;
##  [5,] &quot;1&quot;
##  [6,] &quot;3&quot;
##  [7,] &quot;3&quot;
##  [8,] &quot;1&quot;
##  [9,] &quot;1&quot;
## [10,] &quot;2&quot;
</code></pre>

<p><a href="#top">Back to Top</a></p>

<p><a id="poi"></a></p>

<h2>Poisson Models</h2>

<p>Poisson regression is used to model count data under the assumption of Poisson error, or otherwise non-negative data where the mean and variance are proportional. Like the Gaussian and binomial model, the Poisson is a member of the exponential family of distributions. We usually model its positive mean on the log scale:   \(\log \mu(x) = \beta_0+\beta' x\).<br/>
The log-likelihood for observations \(\{x_i,y_i\}_1^N\) is given my
\[
l(\beta|X, Y) = \sum_{i=1}^N (y_i (\beta_0+\beta' x_i) - e^{\beta_0+\beta^Tx_i}.
\]
As before, we optimize the penalized log-lielihood:
 \[
\min_{\beta_0,\beta} -\frac1N l(\beta|X, Y)  + \lambda \left((1-\alpha) \sum_{i=1}^N \beta_i^2/2) +\alpha \sum_{i=1}^N |\beta_i|\right).
\]</p>

<p>Glmnet uses an outer Newton loop, and an inner weighted least-squares loop (as in logistic regression) to optimize this criterion. </p>

<p>First, we load a pre-generated set of Poisson data.</p>

<pre><code class="r">data(PoissonExample)
</code></pre>

<p>We apply the function <code>glmnet</code> with the <code>&quot;poisson&quot;</code> option.</p>

<pre><code class="r">fit = glmnet(x, y, family = &quot;poisson&quot;)
</code></pre>

<p>The optional input arguments of <code>glmnet</code> for <code>&quot;poisson&quot;</code> family are similar to those for others. </p>

<p><code>offset</code> is a useful argument particularly in Poisson models.</p>

<p>When dealing with rate data in Poisson models, the counts collected are often based on different  exposures, such as length of time observed, area and years. A poisson rate \(\mu(x)\) is relative to a unit exposure time, so if an observation \(y_i\) was exposed for \(E_i\) units of time, then the expected count would be \(E_i\mu(x)\), and the log mean would be \(\log(E_i)+\log(\mu(x)\). In a case like this, we would supply an <em>offset</em> \(\log(E_i)\) for each observation.
Hence <code>offset</code> is a vector of length <code>nobs</code> that is included in the linear predictor.   Other families can also use options, typically for different reasons.</p>

<p>(Warning: if <code>offset</code> is supplied in <code>glmnet</code>, offsets must also also be supplied to <code>predict</code> to make reasonable predictions.)</p>

<p>Again, we plot the coefficients to have a first sense of the result.</p>

<pre><code class="r">plot(fit)
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-56"/> </p>

<p>Like before, we can extract the coefficients and make predictions at certain \(\lambda\)&#39;s by using <code>coef</code> and <code>predict</code> respectively. The optional input arguments are similar to those for other families. In function <code>predict</code>, the option <code>type</code>, which is the type of prediction required, has its own specialties for Poisson family. That is,</p>

<ul>
<li>&ldquo;link&rdquo; (default) gives the linear predictors like others</li>
<li>&ldquo;response&rdquo; gives the fitted mean</li>
<li>&ldquo;coefficients&rdquo; computes the coefficients at the requested values for <code>s</code>, which can also be realized by <code>coef</code> function</li>
<li>&ldquo;nonzero&rdquo; returns a a list of the indices of the nonzero coefficients for each value of <code>s</code>.</li>
</ul>

<p>For example, we can do as follows.</p>

<pre><code class="r">coef(fit, s = 1)
</code></pre>

<pre><code>## 21 x 1 sparse Matrix of class &quot;dgCMatrix&quot;
##                       1
## (Intercept)  0.61123371
## V1           0.45819758
## V2          -0.77060709
## V3           1.34015128
## V4           0.04350500
## V5          -0.20325967
## V6           .         
## V7           .         
## V8           .         
## V9           .         
## V10          .         
## V11          .         
## V12          0.01816309
## V13          .         
## V14          .         
## V15          .         
## V16          .         
## V17          .         
## V18          .         
## V19          .         
## V20          .
</code></pre>

<pre><code class="r">predict(fit, newx = x[1:5,], type = &quot;response&quot;, s = c(0.1,1))
</code></pre>

<pre><code>##               1          2
## [1,]  2.4944232  4.4263365
## [2,] 10.3513120 11.0586174
## [3,]  0.1179704  0.1781626
## [4,]  0.9713412  1.6828778
## [5,]  1.1133472  1.9934537
</code></pre>

<p>We may also use cross-validation to find the optimal \(\lambda\)&#39;s and thus make inferences.</p>

<pre><code class="r">cvfit = cv.glmnet(x, y, family = &quot;poisson&quot;)
</code></pre>

<p>Options are almost the same as the Gaussian family except that for <code>type.measure</code>,</p>

<ul>
<li>&ldquo;deviance&rdquo; (default) gives the deviance</li>
<li>&ldquo;mse&rdquo; stands for mean squared error</li>
<li>&ldquo;mae&rdquo; is for mean absolute error.</li>
</ul>

<p>We can plot the <code>cv.glmnet</code> object.</p>

<pre><code class="r">plot(cvfit)
</code></pre>

<p><img 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yi/j4Vv1vTALe/CApWFjeBDDz0kp1BxtytPk2MD4G+w4tm2SpZRo0bJ/zGeEip66TTRY+VvdQrdH24Db9X/8Ouvvy658O8Pv5CtWLGiUD1LO+H/Q9EILfYJ5HfMit+HaDHwDlZQoL0A0Zb+8OHDJPzxIa22eOuVA1O4yz5UgbuDxRt8qIoLSjnh0FVQKqJgpjz4ShhYv7qyS6qGFc+2FbLwTyEPahVT5koS05ZxVvwPMxfRUxPy3zxbAldMaBh4xRQCcUAABEAABEDACgIYZGcFReQBAiAAAiAAAooRgIFXTCEQBwRAAARAAASsIAADbwVF5AECIAACIAACihGAgVdMIRAHBEAABEAABKwgAANvBUXkAQIgAAIgAAKKEYCBV0whEAcEQAAEQAAErCAAA28FReQBAiAAAiAAAooRgIFXTCEQBwRAAARAAASsIAADbwVF5AECIAACIAACihGAgVdMIRAHBEAABEAABKwgAANvBUXkAQIgAAIgAAKKEYCBV0whEAcEQAAEQAAErCAAA28FReQBAiAAAiAAAooRgIFXTCEQBwRAAARAAASsIBBjRSZ2yeOLL76Q+1fbRV7ICQIgAAIgYG8CVapUoa5du4alElGzH/yXX35Jo0aNon79+oUFNAoFARAAARCIPgJvvPEGTZgwgVq1ahXyykdNC76goID69u1L9913X8gho0AQAAEQAIHoJLB27VryeDxhqTx88GHBXrjQ77//ng4fPlw4EmfKE9izZw8tXLhQeTkhIAiAQHQSgIFXQO///PMPxgYooAd/RTh+/DgdPHjQ32S4HwRAAARCQiBquuhDQtNkITfddJPJlEgWTgJnnXUW8QcBBEAABFQkgBa8ilqBTCAAAiAAAiAQIAEY+AABWpEcPngrKIY+D/jgQ88cJYIACJSdAAx82VkF7U744IOGNqgZwwcfVLzIHARAIEAC8MEHCNCK5PDBW0Ex9HnABx965igRBECg7ATQgi87K9wJAiAAAiAAArYhAAOvgKrgg1dACSZEgA/eBDQkAQEQCBkBGPiQofZdEHzwvtmofAU+eJW1A9lAAATgg1fgGYAPXgElmBABPngT0JAEBEAgZARg4EOGGgWBAAiAAAjYgcDy5cspJyfHEHX//v2Unp5unFetWpXq169vnKt6AAOvgGbYB9+uXTtKSkpSQBqIUFYC7INft24dderUqaxJcB8IgIANCNStW7fQ8uHz5s2jJk2aGJLHxcUZxyofwMAroB344BVQggkR4IM3AQ1JQMAGBIo2tsqVK0dpaWk2kLywiDDwhXmE5Qw++LBgD7hQ+OADRogMQAAEgkgAo+iDCBdZgwAIgAAIgEC4CMDAh4u8V7mYB+8Fw0aHmAdvI2VBVBCIQgIw8AooHT54BZRgQgT44E1AQxIQAIGQEYAPPmSofRcEH7xvNipfgQ9eZe1ANhAAARh4PAMgAAIgAAJRSWDZsmW0fft2o+5F57unpKRQ586djet2O4CBV0BjmAevgBJMiIB58CagIQkIKESgdevWxB89TJs2jXr27Kmf2v4bPngFVAgfvAJKMCECfPAmoCEJCIBAyAgo24LPzs6m1NRUiolRVkTLlAQfvGUoQ5oRfPAhxY3CQAAE/CSgRAu+b9++tHr1ain6mjVr6Morr6SaNWtSRkYG9e/fn06cOOFntXA7CIAACIAACEQ3ASUM/IoVK4yF/UeMGEGNGzemnTt30qJFi2jz5s3EcZEcMA/entrFPHh76g1Sg0C0EFDCwHvDnjVrFg0dOlSu+9uwYUN6/vnniRf6j+QAH7w9tQsfvD31BqlBIFoIKOPg5tZ6tWrVqH379sRTFSpWrCh1kJWVVWiUYyQqBj54e2oVPnh76g1Sg0C0EFDCwPfp04d4esLw4cPp0KFDFB8fT5MmTZIt+TFjxtDcuXOjRR+oJwiAAAiAAAhYQkAJAz9o0CDiD4cdO3bQ4cOH5fFll11GgwcPpgoVKsjz0v7MmDGDfvjhhxJv++2334hbXPfff3+J18MZiXnw4aRvvmzMgzfPDilBAASCT0AJA+9dzerVqxN/3G43NW3atMzGnfPgBQsyMzO9szOOd+3aRceOHTPOVTqAD14lbZRdFvjgy84Kd4IACISegBIGnqfBvfLKK7Ru3To5LW7t2rXymw1fr169ZHd9XFxcqXTYh8+fkkLVqlVp9+7dJV0Kexx88GFXgSkB4IM3hQ2JQAAEQkRAiVH0Q4YMkSPl2QizsXvuuefoyy+/lAa/oKCApk6dGiIcKAYEQAAEQAAEIoOAEi34mTNn0pIlSygpKYkSEhJo7969dMEFF0jCPE3u6aefloY/MpAXrwV88MWZ2CEGPng7aAkygkD0ElDCwNetW1euZNe2bVu6++67C+3uw9Pk6tevH9Eagg/enuqFD96eeoPUIBAtBJQw8AMHDqSrr76a3n77bfmt+9GffPJJ+uCDD2jOnDkRrQ/44O2pXvjg7ak3SA0C0UJACR98t27diNeg5xa8d+Bt+zZu3EjNmzf3jsYxCIAACIAACIBAKQSUMPAsI/vfi05x69ChAyUmJpZSBftfxlr09tQh1qK3p94gNQhECwFlDHy0AC+pnvDBl0RF/Tj44NXXESQEgWgmoIQPPpoVwHWHD96eTwB88PbUG6QGgWghgBZ8tGga9QQBEAABEIgqAjDwCqgbPngFlGBCBPjgTUBDEhAAgZARQBd9yFD7Lgg+eN9sVL4CH7zK2oFsIHCaAL+Mb9u2zYg4ePCg3JLc5XLJOKfTSeecc45xPVIOYOAV0CR88AoowYQI8MGbgIYkIBAGAuXLly+0T0l2djalp6cbs7QcDkcYpAp+kTDwwWeMEkAABEAABMJIgLcc9952nDc2q1KlipyeHUaxgl40fPBBR1x6AfDBl85IxTvgg1dRK5AJBEBAJwADr5MI4zd88GGEH0DR8MEHAA9JQQAEgk4AXfRBR1x6AfDBl85IxTvgg1dRK5AJBEBAJ4AWvE4C3yAAAiAAAiAQQQRg4BVQJnzwCijBhAjwwZuAhiQgAAIhIwADHzLUvguCD943G5WvwAevsnYgGwiAAHzwCjwD8MEroAQTIsAHbwIakoAACISMAFrwIUONgkAABEAABEAgdARg4EPH2mdJ8MH7RKP0BfjglVYPhAOBqCcAA6/AIwAfvAJKMCECfPAmoCEJCIBAyAjABx8y1L4Lgg/eNxuVr8AHr7J2IBsIgABa8HgGQAAEQAAEQCACCcDAK6BU+OAVUIIJEeCDNwENSUAABEJGAAY+ZKh9FwQfvG82Kl+BD15l7UA2EAAB+OAVeAbgg1dACSZEgA/eBDQkAQEQCBkBtOBDhhoFgQAIgAAIgEDoCMDAh461z5Lgg/eJRukL8MErrR4IBwJRTwAGXoFHAD54BZRgQgT44E1AQxIQAIGQEYAPPmSofRcEH7xvNipfgQ9eZe1ANhAAAbTg8QyAAAiAAAiAQAQSgIFXQKnwwSugBBMiwAdvAhqSgAAIhIwADHzIUPsuCD5432xUvgIfvMragWwgAALwwSvwDMAHr4ASTIgAH7wJaEgCAiAQMgJowYcMNQoCARAAARAAgdARgIEPHWufJcEH7xON0hfgg1daPRAOBKKeAAy8Ao8AfPAKKMGECPDBm4CGJCAAAiEjoJwPvqCggI4cOUKpqakhgxDuguCDD7cGzJUPH7w5bkgFAiAQGgJKtODz8/PpySefpJo1a1JsbCylpaVR+fLlqXnz5vS///0vNCRQCgiAAAiAAAhEEAElDPxDDz1Ef//9N82YMYMOHz5MHo+Hdu7cSe+++y69/fbbNHbs2AhCXrwq8MEXZ2KHGPjg7aAlyAgC0UtACQM/e/ZsGjduHLVs2ZIqVKhADoeDkpOTqUOHDvT666/T1KlTI1pD8MHbU73wwdtTb5AaBKKFgBI+eO6K//HHH+mWW24pxn369OlUuXLlYvGRFAEfvD21CR+8PfUGqSOXAPcAr1mzxqhgTk4OuVwuio+PN+LY3kRLUMLADxs2jHr37k2jR4+mevXqUVJSEh06dIhWrVpFPOhu5syZ0aIP1BMEQAAEQMAkATbkPJZLDxs3bqSEhATKzMzUo6hcuXLGcaQfKGHgW7duTcuWLaNffvmFNm/eTLt375at9gceeIC6dOkiu+zLoogpU6bQt99+W+KtnHeVKlVKvBbuSPbBt2vXTr7YhFsWlF92AuyDX7duHXXq1KnsiXAnCIBA0AjwIO2MjAwj/3379skB295xxsUoOFDCwDNnfvO66KKLDORut5u4e4X98WUNF1xwgfTjl3T/Cy+8IAfwlXQt3HHwwYdbA+bKhw/eHDekAgEQCA0BJQz8iRMn6JVXXpGtof79+9PatWuJv9nw9erViyZNmkRxcXGlEklPTyf+lBRSUlIoLy+vpEthj4MPPuwqMCUAfPCmsCERCIBAiAgoMYp+yJAhNG/ePKpatSqxsXvuuefoyy+/lAafffCRPoo+RLpGMSAAAiAAAlFEQIkWPA+iW7JkifRB84CIvXv3Ene3c3j++efp6aefloY/UvUCH7w9NQsfvD31BqlBIFoIKGHg69atS6tXr6a2bdvS3XffTdu3bzf4Z2VlUf369Y3zSDyAD96eWoUP3p56g9Qg4A8BTbiKK27aQppwJTtsNgJfCQM/cOBAuvrqq+WqdfxdrVo1yZ+Xr/3ggw9ozpw5/ujDdvfCB287lUmB4YO3p94gNQiUlYC2dh25L7iE2ojl1N2PPUWuXZvJYaN9UpTwwXfr1k0uTsAteO/Qs2dP4nmM0bQwgXf9cQwCIAACIBA+Au7uPYh276H4fw4QFZwgz8ujwyeMiZKVMPAsNy9u470YAcfxUrWJiYl8GNEBa9HbU71Yi96eeoPUIFBmAnXqnL7V7SHavOX0uQ2OlDHwNmAVNBHhgw8a2qBmDB98UPEicxAIOwHn889KGY6LvVEoqSI5R78Udpn8EUAJH7w/AkfivfDB21Or8MHbU2+QGgTKSsDZsQM5Nq6iPz+ZSB0euI8clSqVNakS96EFr4QaIAQIgAAIgICKBByim35/q5a2M+7MEgZegScKPngFlGBCBPjgTUBDEhAAgZARgIEPGWrfBcEH75uNylfgg1dZO5ANBEAAPngFngH44BVQggkR4IM3AQ1JQAAEQkYALfiQoUZBIAACIAACqhJwf/gxnTtsBLkfepQ0j5gSFwEBBl4BJcIHr4ASTIgAH7wJaEgCAgoScL/3P9LueYCqLPmDtHHvkeeZ5xSU0n+RYOD9Z2Z5CvjgLUcakgzhgw8JZhQCAsEn8P1csVKd+2Q5Ys15bfq3wS8zBCXABx8CyKUVAR98aYTUvA4fvJp6gVQg4DeBbpcQff0N0fF8IrGhjKP2WX5noWICGHgVtQKZQAAEQAAEQkbAddft5N6xgw5MmESp119LzmEnV7ALmQBBKghd9EEC60+28MH7Q0ude+GDV0cXkAQEAiXgeuYp+uWVEeR6YRg5XK5As1MiPQy8AmqAD14BJZgQAT54E9CQBARAIGQE0EUfMtS+C4IP3jcbla/AB6+ydiAbCIAAWvB4BkAABEAABEAgAgnAwCugVPjgFVCCCRHggzcBDUlAAARCRgBd9CFD7bsg+OB9s1H5CnzwKmsHskUyAY9Yae7w4cNGFd1uNxUUFFBcXJwRl5iYaBxH6wEMvAKahw9eASWYEAE+eBPQkAQELCDAxjwrK8vIKScnh3Jzc6ly5cpGXOPGjY3jaD2AgY9WzaPeIAACIGBTArGxsdS5c2dD+p07dxJ/2rRpY8TxAbvRojnAB6+A9uGDV0AJJkSAD94ENCQBARAIGQEY+JCh9l0QfPC+2ah8BT54lbUD2UAABNBFr8AzAB+8AkowIQJ88CagIQkIgEDICKAFHzLUKAgEQAAEQAAEQkcABj50rH2WBB+8TzRKX4APXmn1QDgQiHoCMPAKPALwwSugBBMiwAdvAhqSgECYCWhiv/cKq9eSY9PmMEsS/OLhgw8+41JLgA++VERK3gAfvJJqgVAg4JOAJhbI8XTvQY1X/E1x2fvIM20KOXtc4fN+u19AC97uGoT8IAACIAACZSKgif3etd+XSOPOCTxDnihTOrveBAOvgObgg1dACSZEgA/eBDQkAYEwEtDEkrYkWvFG2LTJOIzEAxh4BbQKH7wCSjAhAnzwJqAhCQiEkYDzxuuJUlLoRHISaelp5Hh3bBilCX7R8MEHn3GpJcAHXyoiJW+AD15JtUAoEPBJwCE2oHFtXU9/jR1HDdq1pYrnFV7a1mdCm15AC96mioPYIAACIAAC/hNwuFx0pEUz0ho19D+xzVLAwCugMPjgFVCCCRHggzcBDUlAAARCRgAGPmSofRcEH7xvNipfgQ9eZe1ANhAAAWV98NnZ2ZSamkoxMcqKaNnTAx+8ZShDmhF88CHFjcJAAAT8JKBEC75v3760evVqKfqaNWvoyiuvpJo1a1JGRgb179+fToiVhxBAAARAAARAAATKTkAJA79ixQrKycmRUo8YMYIaN25MO3fupEWLFtHmzZuJ4yI5wAdvT+3CB29PvUFqEIgWAkoYeG/Ys2bNoqFDh1JaWho1bNiQnn/+eZo3b573LRF3DB+8PVUKH7w99QapQSBaCCjj4ObWerVq1ah9+/a0f/9+qlixotRBVlYWtW7dOqL1AR+8PdULH7w99QapQSBaCChh4Pv06UPTpk2j4cOH06FDhyg+Pp4mTZokW/JjxoyhuXPnRos+UE8QAAEQAAEQsISAEgZ+0KBBxB8OO3bsoMOHD8vjyy67jAYPHkwVKlSQ56X9mThxIk2dOrXE25YtW0bVq1cv8Vq4I9kH365dO0pKSgq3KCjfDwLsg1+3bh116tTJj1S4FQRAwB8Cf//9tzHQWtM0OnjwoJxhpedRtWpV/RDfRQgoYeC9ZWIjrBti7q53i80B2NcZFxfnfVuJx1dffTVdeumlJV57+umn6cCBAyVeC3ckfPDh1oC58uGDN8cNqUDAHwIpYu14tgMceEbVli1bqF69ekYWCQkJlJuba5zj4DQBJQz8tm3b6KmnnqIpU6ZQhw4daOzYsVS/fn0p5eeffy7jJ0+efFpqH0fly5cn/pQUEsUaxHrPQEnXwxkHH3w46ZsvGz548+yQEgTKSkBv8PH9/FK9YcMG4v897wAD703j9LESo+hHjx5NmZmZtGTJEmngu3TpQmvXrj0tJY5AAARAAARAAAT8IqBEC37mzJnEPnLuahk2bBg1bdqUunfvTgsXLvSrMna9GT54e2oOPnh76g1Sg0C0EFCiBc8GnVvverj55pvpoYceossvv1xOmdPjI/UbPnh7ahY+eHvqDVKDQLQQUMLA33///XTDDTfQyJEjDe4DBw6k6667jh599FEjLlIP2AfPC/sg2IsA+wF79OhhL6EhLQiAQNQQUKKLvlu3bnLgxMaNGwuBf/bZZ+mCCy6Q1wpdwAkIgAAIgAAIgMAZCSjRgmcJefR7ixYtigl74YUX0l133VUsPpIisBa9PbWJtejtqTdIDQLRQkAZAx8twEuqJ3zwJVFRPw4+ePV1BAlBIJoJKNFFH80K4LpjHrw9nwDMg7en3iB1dBDQsrMp/Z33Ke5EAWmvvUKOMiyWFmlkYOAjTaOoDwiAAAhEOQHt2DFyZ9SiSszB4SD3vPnk+nMJOWJjo4pMmbrod+/eTQUFBVEFJpSVhQ8+lLStKws+eOtYIicQsJKA9utiovQ0cng85OBlbnOPEa2JvsXTfBp4jwDDe7G3bNlSru/OO7r16tWLskW3B4K1BOCDt5ZnqHKDDz5UpFEOCPhHwCFWRhWt0tOJtm4jyoi+TWl8Gvh33nmHfvjhB7kOPFPq2rWr3ASG4xGsJYB58NbyDFVumAcfKtIoBwT8I+Bo3Iicn3xIBcnJdLzNOeRctpgclSv7l0kE3O3TwC9YsEBu1VqtWjVZzXLlytGAAQOk0Y+AeqMKIAACIAACEUzAecVltH7hXNo7eQI5W50dwTX1XTWfBr5mzZrERt47fP3113JTGO84HAdOAD74wBmGIwf44MNBHWWCAAiUlYDPUfS8ROx5551HbHx27dold3nbvHkzzZkzp6x5474yEoAPvoygFLsNPnjFFAJxQAAEChHwaeCrVq1KK1eupM8++4y2bt0ql4zlZWNdLlehDHASOAHMgw+cYThywDz4cFBHmSAAAmUl4NPAa5pG06ZNowYNGsilYv/73//Sli1bqG/fvjDyZaWL+0AABEAABMpEYMWKFXTw4EHj3v3791N6erpxzhty1atXzzjHQekEfPrgp0yZQqNHj6aMjAyZS5cuXWjSpEn00UcflZ4r7vCLAHzwfuFS5mb44JVRBQSJAAK1a9emZs2aGR+3mL/ufc49Zgj+EfBp4L/99lt64YUXqGHDhjLH5s2bS4P/xRdf+FcC7i6VAHzwpSJS8gb44JVUC4SyKYEKFSpQamqq8eGZW97nfB3BPwI+DXytWrVo1qxZhXKbP38+JSUlFYrDSeAEMA8+cIbhyAHz4MNBHWWCAAiUlYBPH/ydd95Jl1xyCc2YMYPat29Pf/31F3GXJLfsEUAABEAABEAABNQm4LMFX716dfr111/lcrWNGjWiN954Q46mL2nPdrWrqL508MGrr6OSJIQPviQqiAMBEFCFgM8WPAuYLJb5u+6661SRNWLlgA/enqqFD96eeoPUIBAtBHwaeJ6u8OCDD1JWVhbl5+cbPC6//HJ67bXXjHMcBE4A8+ADZxiOHDAPPhzUUSYIgEBZCfg08C+99BIdOnRIds17j17kuYgIIAACIAACIAACahPwaeB37NghW/AXXXSR2jWIAOnYB9+uXTvMULCZLtkHv27dOurUqZPNJIe4IAAC0UDA5yC7a6+9lsaPH0979+6NBg5hrSN88GHFb7pw+OBNo0NCEACBEBDwaeB37txJM2fOlLvH8XK1jRs3lh/eMhbBWgKYB28tz1DlhnnwoSKNckDgzARcq9dQYtaKM98UhVd9dtH36NGD2rRpUwxJYmJisThEgAAIgAAIgEA4CLhHvkIpo16j5Lw8Knj/I3LNn0OOADdFKygoII/HY1SHj70HmzudToqJ8Wk+jXThPvApYUJCAo0cOVL6GHlNYK5gngDIi95MnDgx3HJHVPnwwdtTnfDB21NvkDpyCGi7d5P29FByCYMsw6rVpM0WBv7y7gFV8rfffqPDhw8beRw7dox++OEH47xGjRrEy7erHnwaeN5oJjc3l+655x6aPn063X333XLRmyeffFL1OtlOPvjgbacyKTB88PbUG6SOMAKZYkO0bdtPVur4cSKxE2qgoWPHjoFmoUR6nz74DRs20KBBg+j2228nHlF//fXX0//+9z8aNWqUEoJHkhDwwdtTm/DB21NvkDpyCDjEbqeOR/qT5nDQiUpia9lzzyHnFZdFTgUDrIlPA89L1W7dupV4Djz7HnhvXp4Dz3EIIAACIAACIKACAdegAfTP3G9p08svkmve9yqIpIwMPrvo77rrLurQoQPVr1+frrrqKuJBd2zob7jhBmWEjxRB4IO3pybhg7en3iB1eAkcOHCgkH+bV03lXUp54BoHlxggxz5uf4K7UUPKrViBHKIlj3CagE8D37RpU1qzZo2EzYb+7bffppSUFLrxxhtPp8aRJQTgg7cEY8gzgQ8+5MhRYAQQ4MHavEqqHtjO8FRsNuwceB94BGsI+DTwnH2G8G/ooX///vohvi0mgLXoLQYaouywFn2IQKOYiCKQmZkp11fRK8VrrvCIdBh2nYh138UMPC+ZyuvQL1q0SA6qK1oUbzbz+uuvF43GOQiAAAiAAAiAgEIEihn4d955h2rXrk316tWjbt26FRM1NTW1WBwiAiMAH3xg/MKVGj74cJFHuSAAAmUhUMzAn3322TLdJ598IufB33bbbYW66suSKe7xjwB88P7xUuVu+OBV0QTkAAEQKImAz2lynTt3prVr10rfSM+ePWnKlCmFluorKTMr4niJQB5lGU0B8+DtqW3Mg7en3iA1CEQLAZ8GvmXLlvTuu+/S9u3biVvxH330EdWtW5fGjBljORuefscr5NWsWZNiY2PlfPvy5cvLlwteXAcBBEAABEAABKwm8Ndff9G3335rfLg31fucx6LZORTroi9aGV6Hng0wf/Pi+sFYYP+hhx6i3WJN4RkzZsiXCDbuvA7wypUriXev42kVDzzwQFHRIuYcPnh7qhI+eHvqDVKDgE6gWbNmxFPC9cB2Tp+ux3F2n1fvswW/dOlSuvXWW+WCA19++SXde++9tH79errvvvt0FpZ9z549m8aNG0fca8Ar5zHU5ORkudAOj9ifOnWqZWWpmBF88CpqpXSZ4IMvnRHuAAGVCbAx1xuu/B0XF1fo3NvYq1wPX7L5bMHzbjrnnHMOvfrqq1SlShVf6S2J5zmQP/74I91yyy3F8uONbipXrlwsPpIiMA/entrEPHh76g1Sg0C0EPBp4L27xLn7vFKlSkHpnmfQw4YNo969exPvYMfT83jZQl7paNWqVcSD7mbOnBkt+kA9QQAEQAAEfBH4bjZlzv+JtDZtyCH2RkE4MwGfBp73f3/xxRdp8uTJYvc9jV555RUaO3asHHhndYu6devWtGzZMvrll19o8+bN0h/PZfBLRpcuXcrsB/nggw+kvCVVmV8WatWqVdKlsMfBBx92FZgSAD54U9iQCARMEXAPe4GcL4+mRmJvdvfoN8m1dgU5GtQ3lVe0JPJp4HnBG97gnqfHXXPNNdS1a1f65ptviOOfeuopy/nEx8fTRRddVCxfHvTArXj2jZQW+vXrR3369CnxtsGDB1N2dnaJ18IdCR98uDVgrnz44M1xQyoQMENAe2MMOY4eJX07Gc9nn5Pr6SfMZBU1aXwOsluwYAGxUaxWrZqEwesE84h2NvpWh23btlHfvn3lALtLL71UDubTy/j888/lND39/EzfPCCCXwRK+vAAClVHRGIe/Jm0qu41zINXVzeQLAIJtGxxulK8IU3FiqfPcVQiAZ8Gnueks5H3Dl9//XWhTQK8rwVyzL533oBgyZIlcuQ8d8vzIjsIIAACIAACIMAEXOM/kCCOZVQlx229yfUINkAr7cnw2UX/6KOP0nnnnUfsH961a5c0vOwfnzNnTml5+n2dB9GxDz4hIUEOuON5id27d6eFCxf6nZcdE8AHb0etEcEHb0+9QWp7EnBUr07uvEP0y/z5Je6TYs9aBVdqnwa+atWqcqGZTz/9lLgL/YILLpCfYMwLZIPOrXdeHpfDzTffTLyFIO9cF4x598FF6n/u8MH7z0yFFPDBq6AFyAACIOCLQIkGPisri5544glavny5bKXUqFGD9u7dSw0bNpQL3/jKzGz8/fffTzfccANxr8Hjjz8usxk4cCAdOXJExvXq1cts1rZIh3nwtlBTMSExD74YEkSAgEGAG2m8FKwecnJy5FLk3vu+X3zxxfplfAeBQDEDP190f3DLmY3uXXfdRXXq1KGtW7fShx9+SB07dqQ//vhDzom3UhbelnbDhg20cePGQtk+++yzsteAryGAAAiAAAjYh0BGRkYhW8Gro/LYLu+F07yNvX1qZh9Jiw2y47nvzz33nFzBjqfHtWrViq666io5Xa5t27bS0Aejerz+fIsWXqMkTxVy4YUXyheNYJSpSp7sg+e19xHsRYB98NEyTsRemoG0KhBwOp2yxc4biPFHXxZWP+dvhOASKNaC59HrPKq9pMDz1LkFj2AtAfjgreUZqtzggw8VaZQDAoERyM3NJXYR6IE3MOPfXf4f5sBTqHm11kgLxQw8+73T09NLrCfH83UEawnAB28tz1DlBh98qEijHBAIjAAvfb5lyxYjE16dlbdC13dHjRoDzwS4u7ikleO834AMUjgAARAAARAAAYUJ8Dor/Im2UKwFzwB4tLyvwKPdEawlgHnw1vIMVW6YBx8q0igHBEDADIFiBn7dunVycxlfmWFghC8y5uPhgzfPLpwp4YMPJ32UHckEPLNmU4MJn5KnfAVydi2+R0kk193KuhUz8KmpqVbmj7zKQAA++DJAUvAW+OAVVApEsj0Bz6TJ5Ln9bqqfn0+ez74k+vJTcl4b2WuhBEtpxabJBasg5AsCIAACIAACpRHwvDmGSBh3PWj/+1g/xLefBGDg/QQWjNsxDz4YVIOfJ+bBB58xSog+Ao72bUkMbz9Zcf52wUyZfQpAziw5C9PBB28hzBBmBR98CGGjqKgh4HxxOFHjRnSsciVy3HITOSdPjJq6W13RYj54vYCDBw/Sgw8+SLwufb5XdwkvY/vaa6/pt+HbAgLwwVsAMQxZwAcfBugoMuIJOOLjKSZrKc2bNo169uwZ8fUNZgV9GviXXnqJeHGAN954gypUqGDIkJaWZhzjAARAAARAAARAQE0CPg38jh07ZAuel6dFCC4BzIMPLt9g5Y558MEii3xBAASsIODTB3/ttdfS+PHj5TaxVhSEPHwTgA/eNxuVr8AHr7J2IBsIgIDPFjzv5Ttz5kz6/PPPqW7dunInIMZ12WWXwQdv8XMDH7zFQEOUHXzwIQKNYkAABEwR8Gnge/ToQW3atCmWKXzwxZAgAgRAAASilkBBQQHxoGw98KBs3szFez+T5ORk/TK+Q0jAp4GvWbMm8Wf//v1y150GDRpQYmJiCEWLnqLgg7enruGDt6feILW1BNig8xLnemBj73a7C+1K2qxZM/0yvkNIwKeBZwXdfffd9OGHHxK32llpvXr1ookTJxZ6MwuhrBFbFHzw9lQtfPD21BuktpYAN/w6dOhgZLphwwa5z3rTpk2NOByEh4BPAz9u3Dhav349rVy5kpo0aUIbN26kwYMH08iRI+mZZ54Jj7QRWip88PZULHzw9tQbpFaHgHbsGCWtEa3/Ro2J6tQOWLDs7Gz69ddfjXyOifydTietWLHCiOOZYd5Tv40LEXjg08AzpCFDhkjjzvXmgXZs2AcNGhSBGFAlEAABEACBUBLQ8vLI3eRsapmTQ44BQ8jzy3xytm8XkAiVK1fG4jheBH1Ok+vYsSMtWLDA61aS5wwQwVoCWIveWp6hyg1r0YeKNMqJRAKe50cQbd9B8fv2y+p5Hn0sEqsZ1jr5bMHfcMMN1KJFC5o/fz516tSJli5dSsuXL5fnYZU4AguHD96eSoUP3p56g9SKEEhKInJ4ybJ2rdcJDq0g4LMFn56eLtehv/XWW8nj8dCVV14p/RitWrWyolzk4UWAffCYfugFxCaH7IPn6aQIIAAC/hNw3nsXUblYOp6SQlpaKjaV8R9hqSl8GnhOmSd8JA888AANHz5cjpxfsmRJqRniBhAAARAAARAojYBDGHbXwT301zP/Jlok/O8XY1n00pj5e92ngV+0aBE1bNiQ2M/IA+s++ugj+r//+z96//33/S0D95dCAD74UgApehk+eEUVA7FsQ8ARG0uHmogR9HXr2EZmOwnq08B/8skn9N5771HVqlVp8uTJ9PHHHxPHffHFF3aqny1khQ/eFmoqJiR88MWQIAIEQEAhAj4H2fFWsTxinkfSV6lShZo3b06LFy+mJB4YgWApAcyDtxRnyDLDPPiQoUZBIAACJgj4NPA8qG7AgAFyycHbb79dLnjTr18/euqpp0wUgyQgAAIgAAIgAAKhJODTwPfu3ZsqVaokl6i9/vrr5Up2Y8eOJewPb716sBa99UxDkSPWog8FZZQBAiBgloBPA88Z8gYB3D2fI1YamjFjBnGXJIL1BOCDt55pKHKEDz4UlFEGCICAWQI+DTyPor/00ktpzZo1NGzYMOIpcrxrEBuju+4S8xcRLCMAH7xlKEOaEXzwIcWNwkAABPwk4NPAFx1Fv3DhQuJ9f5944gkYeD8h43YQAAEQAAHrCHBj03tTmdzcXNI/eim8SVq0L63u08BjFL3+mAT/Gz744DMORgnwwQeDKvJUmQBvG64H3lKcDW1CQoIeVejYiAzCQUxMDLVs2dLImWXhBmhcXJwRV758eeM4Wg98GniMog/dIwEffOhYW1kSfPBW0kRediCQlZVliMnPP/92ZWZmGnH16tUzjr0PPJM+o8ZDn6f43XtIW76YHHUCW9iGt4BNESvhIZyZgE8DH+5R9Lyvb2pqKvGbWqQH+ODtqWH44O2pN0htnkDnzp2NxNyaZ4PvHccXN2zYYNzDB9rvS8jTux8lnop1X3oluf5aQo5EPabQ7TixkIDPley4jG7dutGNN95I/LZUv379oE2R69u3L61evVpWiwf1ce9BzZo1KSMjg/r3708nTpywsMrICgRAAARAIFQEtK3biJK9FkjLySXR9A9V8VFdTjED365dO7kl7IgRI+Ra9LwevffnkUcesRzYihUr5FQ8zpjLbdy4Me3cuZN4JP/mzZtlnOWFKpQh1qJXSBl+iIK16P2AhVujloCjY3siYdQ1QUATa89Teho5atSIWh6hrHix/u933nmHateuTexL4RY8d8Ps3btXzoGPj4+X3ebBFHDWrFm0VuwLXLFiRbmF6vPPP08DBw6kZ555JpjFhjVv+ODDit904fDBm0aHhFFEwCF89K7tG2jXI4MosVkTSn5scBTVPrxVLWbgzz77bClRhQoV5O5xH374oTS0bOh79epFEydODIrE3FqvVq0atW/fnvbv3y8NPBfEPp7WrVsHpUxVMoUPXhVN+CcHfPD+8cLd0UvAITYt2z7wYdlQTPEa6R69REJT82Jd9Hqx48aNo/Xr18s16Nngrlu3jjRNo5EjR+q3WPbdp08fmjZtGvHLxcyZM+Vce8586NChsvXOa+AjgAAIgAAIgAAIlJ1AsRa8npQXERgyZAjxYgEc6tatK7vJeW94qwPnqee7Y8cOOnz4sCzisssuo8GDBxP3JpQlvPvuuz57GPgFpU6AUzPKIoOZezAP3gy18KfBPPjw6wASgAAI+Cbg08B37NhRbhV71VVXGal569hgrwxUvXp14g8H7q73J9xzzz3En5LCo48+Srt37y7pUtjj4IMPuwpMCQAfvClsSAQCIBAiAj4N/A033EAtWrSQI+o7depES5cupeXLl8vzEMkWNcXAB29PVcMHb0+9QWp7EZgzZw7l5eVJoT0ejxz4PX36dKMSPIWbZ14hFCfg08Cnp6fLAW4TJkyQW8Xy3HRen57np1sdRo0adca57qw8HuCHAAIgAAIgEF0ELr74Yjn+S681jwVzOBz6aaFjIxIHkkCJBp53jvv999+pe/fu9PDDDwcdFc91f+utt4gH05W0fnCw3QJBr2ApBcAHXwogRS/DB6+oYiBWRBFgY+5t0COqckGuTDED//HHH9N9990np6bx/HM28MEYOe9drzfffJO464U/Y8aM8b4UFcfwwdtTzfDB21NvkBoEooVAsWlybGy5K57npS9evJi4+5x/yIId+CWCR88fPXo02EUplz/74NPS0pSTCwKdmQD74Hv06HHmm3AVBEAABMJEoJiB52lqvFwtB96Oj7vHt20TawkHOfBUOPb3l3VKXJDFQfYgAAIgAAJ+EtDE2imV7+tPta+8Ruwa96efqXG71QSKGXje37dcuXJGObyjmz6C0YjEgaUEsBa9pThDlhnWog8ZahQUYgJsB7jnVv9wz6p+zN+893rRoB06RO4GzSlx7o8Uu3kLuS/qTtrWrUVvw3kICRTzwXPZ3FUed2o5QbfbTUeOHJFTE/harNgsIBHb/DEKywJ88JahDGlG/EPHSzgjgECkEeBp0fy7r4ddu3YV2vfde70S/R7asJGohljDZPuOk1HChmgiziFcWQjhIVCigefd47wDL3qjB54fP3nyZP0U3xYQwDx4CyCGIQvMgw8DdBQZEgIdOnQoVA4vJc6bj3mHYi+3jYTdEAOlNZ7CxlPZ9uwhxzmRvY+INw8Vj4sZeH3NeV/CcgseAQRAAARAAAS8CTjKlyfXij/o8M23UX5aKlUa+QI5kpO9bznjMS+Pnp2dbdzDe6Dwi4UeqooNa9q2bauf4rsMBIoZePa5I4SWAObBh5a3VaVhHrxVJJFPpBBwCPux77+vS399ZT+75v1dmjxSmAWzHsUG2QWzMORdMgH44EvmonosfPCqawjygUB0EyjWgo9uHOGpPXzw4eEeaKnwwQdKEOlBAASCSQAt+GDSRd4gAAIgAAIgECYCMPBhAu9dLObBe9OwzzHmwdtHV5AUBKKRAAy8AlqHD14BJZgQAT54E9CQBARAIGQE4IMPGWrfBcEH75uNylfgg1dZO5ANBEAALXg8AyAAAiAAAiAQgQTQgldAqZgHr4ASTIiAefAmoCFJRBDQxEp1MVOmUsaff5HWogU5UlLKXC/+v+FFbPTAS+KuXr2aXC6XjIqJiaGiq6nq9+LbPwIw8P7xCsrd8MEHBWvQM4UPPuiIUYCiBDx9+lHcN9OpVt5xcn8zg1wL5pKjRo0yScsGXN/rhBPUrVuXEhISyMFL3IrA1xGsIQCS1nAMKBf44APCF7bE8MGHDT0KDjMBbdYccuTkkjTJO3eS9r0w8Hf0K5NU6enpxB+E4BOAgQ8+Y5QAAiAAAkoS2LdvH/FWsHrgDWRSvLrbuaWdmZmpXz79Xb0a0T//nDwvcBNVqnT6Go6UIYBBdgqoAvPgFVCCCREwD94ENCRRisCxY8fk9uC8RTh/Vq1aVeg8JyenRHldX30m44+J7WEdTz1Ozp5XlngfIsNLAC348PKXpcMHr4ASTIgAH7wJaEiiFIGaNWsSf/SwZcsWatmypX7q89tRrx4dPbCbsrKyqHPnzj7vw4XwEoCBDy9/WTp88AoowYQI8MGbgIYkIAACISOALvqQoUZBIAACIAACIBA6AjDwoWPtsyT44H2iUfoCfPBKqwfCgUDUE4CBV+ARgA9eASWYEAE+eBPQkAQEQCBkBOCDDxlq3wXBB++bjcpX4INXWTuQDQRAAAYezwAIgAAIgIDlBHja3e7du418uaeSA8froZ4Yja8vUavH4ds6Auiit46l6ZzggzeNLqwJ4YMPK34UbgMCvGa9/qlcubJcklY/52+E4BJACz64fMuUO3zwZcKk3E3wwSunEghkMQHN7SbPsBep3RdfknvTVnI9/K8yl5CUlET8QQgfARj48LE3SoYP3kBhqwP44G2lLghrgoDnuptJ+3YWpeXnkzbkCXJXSidX75tN5IQk4SCALvpwUEeZIAACIGADAtradUTCuMvA3zNn2UBqiKgTgIHXSYTxGz74MMIPoGj44AOAh6S2IOC4vBtRuXInZeX92i/sYgu5IeRJAuiiV+BJgA9eASWYEAE+eBPQkCQsBDZs2EArV640yj506BCVL1/e2Hud92Lv0aOHcV0/cL40gjzZ+yj7ryyq3P8Bct19h34J3zYgAAOvgJLgg1dACSZEgA/eBDQkCQsBno7GHz3Mnz+fWrduXeogOIdotbs+/oB+nzaNevbsqSfHt00IoIveJoqCmCAAAiAAAiDgDwEYeH9oBele+OCDBDbI2cIHH2TAyB4EQCAgAjDwAeGzJjF88NZwDHUu8MGHmjjKAwEQ8IeAcj74goICOnLkCKWmpvpTD1vfCx+8PdUHH7w99QaprSXAv9eLFi0yMs3JySGPx0Pbtm0z4s477zxKS0szznEQGgJKGPh8Mb9y6NChNH78eNqxY4dc2jAxMZHq1KlDgwYNojvuwMjN0DwOKAUEQAAE/CNQoUIFuuiii4xEbNx5GVrvNeZjYpQwNYaM0XKgBPWHHnpIbkowY8YMqlu3rpy+wRsS8LSOAQMGUF5eHj3wwAMRqxP2wbdr167UEa0RC8CmFWMf/Lp166hTp042rQHEBoHACfAUu9jY2MAzQg6WE1DCBz979mwaN24ctWzZkvhtkB+Y5ORk6tChA73++us0depUyyuuUobwwaukjbLLAh982VnhTvUJuF8ZTR0GP0nuq64lTbhKEexPQAkD37x5c/rxxx9LpDl9+nTiXYgiObAPHv4p+2mYffAlLQ5iv5pA4mgn4H5pFGlP/h+liKVpte++J8+Qf0c7koiovxJd9MOGDaPevXvT6NGj5WIMvAMRr7S0atUq4kF3M2fOjAjYqAQIgAAIKElg2Z9EJ0612k+cIO37H5QUE0L5R0AJA88rKi1btox++eUX2rx5s/THc6ud/e5dunSRXfZlqdY777xDEydOLPHW9evXy0F7JV4McyR88GFWgMni4YM3CQ7J1CNw1ZVEX0wh0aIiiosjx/kd1JMREvlNQAkDz1LHx8cXGonpd01EgnvvvVd+Skr76KOPyheHkq6FOw4++HBrwFz5SwNVUAAANbFJREFU8MGb44ZUwSOQnZ1Nv/76q1FAbm4uOZ1O+fuqR3qPeNfjXLfcRO6847Rn7DiqKo6dj/TXL+HbxgSUMfA2Zhiw6JgHHzDCsGSAefBhwY5Cz0CAez6914xfsWKFnJXEU45LC647+tLSSqmF0peWBtfVJqCEgR81ahSdEH4fX6Fx48bUq1cvX5cRDwIgAAIgEAICvIiNHrh3gH+3veN4ulw5fXtZ/UZ8h42AEgae/e5vvfUW9evXT75tFqUR6aPo4YMvqnF7nMMHbw89QUrrCCxevNjIjAdAs5H3jmvYsCHVqFHDuAcH4SWghIF/88035dKGvALSmDFjwkskDKXDBx8G6BYUCR+8BRCRha0IdO3a1VbyRruwSsyDZyWMHDmSePW6o0ePRp1OMA/enirHPHh76g1Sg0C0EFCiBc+weQW7CRMmRAt31BMEQAAEwkYgdm820aHDJNbHDpsMKDj4BJRpwQe/quqWgP3g1dXNmSTDfvBnooNrqhJw33oHtXh4ECWeVZ88fyxTVUzIZQEBGHgLIAaaBXzwgRIMT3r44MPDHaWaJ+D5dDJp02dS/O49MhPP7feYzwwplSegTBe98qSCKCDmwQcRbhCzxjz4IMJF1kEhoB0+QpR37HTeGzedPsZRxBGAgY84laJCIAACkU6Ap6ht2LDBqGZ+fj5xj1LFihWNuMzMTONYP3Befw25//UIFSQmkKtcLDkffUi/hO8IJIAuegWUCh+8AkowIQJ88CagIYllBHhbbf3D89H3799vnOvxRQtzpKWR63A2rX9sEB2f8CG5nn266C04jyACaMEroEz44BVQggkR4IM3AQ1JLCEQExNDvKiMHnbu3Ekul6tQnH6t6LcjIYH2X9iZzhKbfPkKvPHXkSOiO/9UOHDgAM2ePVs/perVq1OzZs2McxyoSQAGXgG9wAevgBJMiAAfvAloSGILAueee65cfEwXlpek9V6Cll8mENQnAAOvvo4gIQiAAAiElACvKe8deLdPBPsRgA9eAZ3BB6+AEkyIAB+8CWhIAgIgEDICMPAhQ+27IPjgfbNR+Qp88CprB7Jp+/ZR2rsfUNKrb5AmRtkjRB8BdNEroHP44BVQggkR4IM3AQ1JQkJAy8sjd0YtqqxpRE4nuef8QK4/fydHka73kAiDQsJGAC34sKFHwSAAAiAQHALaol+I0lLJIXbodIg585Qr9nFfvSY4hSFXZQmgBa+AarAfvAJKMCEC9oM3AQ1J/CbAU+DcbrdMp4kWOU9fS/LaJMb7WM/cUa0aERt2PWzdTpRRVT/Dd5QQgIFXQNHwwSugBBMiwAdvAhqS+E3A+/eBV7DbuHFjofnuJU1ZczRuRM6P3qf82+4kd726lPDeWHJUqeJ32UhgbwIw8AroDz54BZRgQgT44E1AQxK/CTRv3txIwy+VbPBbtWplxPEBt/KLBmfPK2n9wrlUvnx5qlOnTtHL8nz9+vWUnS22jj0VDh8+TLzIjR54G+8WLVrop/i2GQEYeJspDOKCAAiAgFUEqlatSikpKUZ2GRkZhdaz5xXzEOxLANpTQHfwwSugBBMiwAdvAhqSKEWAN6fx3qCmUqVKSskHYQIjgFH0gfGzJLW3j82SDJFJSAjABx8SzCikDARi/l5FiX9mleFO3BJNBNCCV0Db8MEroAQTIsAHbwIaklhOwD38RUp+fQwl5R+ngnc+INeieeTAWvGWc7ZjhmjB21FrkBkEQAAEBAFt927SXvgPucRWsTFHjpLYJJ602XPABgQkAbTgFXgQ4INXQAkmRIAP3gQ0JPFJYPPmzXT0qDDSpwLv756enq6fSl85D4IrFnj627btJ6OP5RE5it2BiCglgBa8AoqHD14BJZgQAT54E9CQxCcB3rEtMTHR+PALpPd5XFxcsbQOYfAdgx8lzeGg/MpigFyHduS8rHux+xARnQTQgldA7/DBK6AEEyLAB28CGpL4JFC0df73339T3bp1C93PL5VFg+vhf9G+Vi1p35Yt1Oy2W4teluf8srB9+6lWvojZJzaiWbFiBekvDQ7xgnDOOeeUmBaR9iUAA29f3UFyEAABEJAE3PXr0bHEBJ80eLGbzMxM4zpPjUtISKCSVsEzbsKB7QnAwCugQvjgFVCCCRHggzcBDUnCQoBXpOMPQnQRgA9eAX3DB6+AEkyIAB+8CWhIYpqAtncvOS/qRp1uvJXcN99KmtgpDgEEzkQALfgz0QnRNfjgQwTa4mLgg7cYKLI7IwF33cbkyMmlWHGX9s0M8nw8gVy333bGNLgY3QRg4KNb/6g9CIBAiAmsXLlSTFffYJTKG7ywT5wHunFg3/gll1xiXDcOatY8vaf7sWNizvtG4xIOQKAkAjDwJVEJcRx88CEGblFx8MFbBDLKsmnatCnxRw/fffcdXXzxxVSuXDk9qsRvx229yTPqNdIOHCSn2Bfeed9dJd6HSBDQCcDA6yTC+A0ffBjhB1A0fPABwENSvwm4nnycChrUp3U/LaBmj/QnR40axfI4cuRIod6B3WKlu9jYWDp06JBxb+PGjYnn3CNEPgEYeAV0DB+8AkowIQJ88CagIUlgBK7qQTuTK1Lz+vVLzId7AbxXv+OFcngqHBt5PWBqnE4i8r9h4CNfx6ghCICAzQi4P51MLd77gNwHDpGrb8mL15RUJW6Z12RfPQIICAKYJqfAY8A+eB5og2AvAuyDX7hwob2EhrTKE3C//hZp/e6mGnPnkXbnfeT+aLzyMkNANQmgBa+AXuCDV0AJJkSAD94EtChKcvDgwULLw7J/nJeG9e4ub9KkSTEi2uQvifLzT8a73USTJhP1u63YfYgAgdIIwMCXRigE1+GDDwHkIBQBH3wQoEZQljExMcRLxOph586d0sB7x+lT4/R7+NtxYWfSfl9CdOIEiaH1RLxbHAIImCAAA28CGpKAAAiAQGkEii4Pm52dTdWqVaOim8oUzcf53DPk/mM5Hc3Kooo3Xk/Ol/9T9BYqKCigtWvXGvE8Sp57DHiOvR5qiFH2SUlJ+im+o5AAfPAKKB0+eAWUYEIE+OBNQEOSQgQ0sWBN0pp1p/dzF1cdouUf8+03tGDsG+R69WVyiFHwRQO3/Lm7X/+kpKTIlwf9nL+dTvy8F+UWbefKtuD5bTc1NZW4myvSA3zw9tQwfPD21JsqUmt5eeRufDa1zM0hx4Ah5Fk0j5wd2pdJPJ7qVq9evTLdi5uil4ASr3h9+/al1atXSy2sWbOGrrzySjnVg7uy+vfvL1xRwhcVwYF98GlpaRFcw8isGvvge/ToEZmVQ62CTsDz/AiiHTsoft9+WZZn4GNBLxMFRBcBJZrHK1asoJycHEl+xIgRxCstjR8/nvbt20cDBw4kjnvmmWeiSzOoLQiAgC0IHBPd7H///bchK597xE5v3oPpGjVqZFw3Dtg/fnL5+ZNRa0VXPQIIWEhACQPvXZ9Zs2bJwSO8+QK3ap9//nlp5Mti4N955x2aOHGid3bG8fr166lOnTrGuUoHWIteJW2UXRasRV92VpF8J68eV716daOKO0SrnHsdvePYJ140OO+9i9zPvUDHK5SjWNHl7vpsQtFbcA4CARFQxsAvWrRIDhJp37497d+/X+6uxDXLEiNJW7duXaZK3nvvvcSfksKjjz5KvC6zigE+eBW1UrpM8MGXziga7uBxQpmZmUZVc3NziZ8N7zi+mLBug9gkRhwI1yMHhxgY5zqwm/4a819qc8UV5GzUUMYX/TN79myZH8dzz8DRo0dp2rRpxm0NGjSQvZ5GBA5A4BQBJQx8nz595AM7fPhwuSkCL7c4adIkGjp0KI0ZM4bmzp0b0QrDPHh7qhfz4O2pt3BI7b78Kqr3xzKK3ZtN7g/GkeuOflIMB28E06QxUV3fvYvdunULh8goMwIIKGHgBw0aRPzhwN1b+rKtl112GQ0ePJh4PikCCIAACNiRgGfGt6TNX0CxwjfPQXt2OGk9ryRHpUp2rA5kthEBJQy8Ny/2W+m+K+6uj4YAH7w9tQwfvD31FojUf/31l5GcF5vhwcHJyclGHC9kUyyI+yhe+OBPGXjau5fESjXFbkMECFhNQDkDb3UF7ZAffPB20FJxGeGDL84k0mO8jTkbdx7Xw64aPXivM6/HOS7vLv3uBWJdeacYkOd66EFynPLD6/fwNz9Pbl57/lTgY/bn64Hnvpc0WE+/jm8QKEoABr4okTCcwwcfBugWFAkfvAUQbZZFrVq1DIl5aVgeEOwdp4kBcHnvf0gVRLzGq9CJtejZz+76exmtffsdqiJm8lS6rGSfOi8zyy/7euCR+L/88ot+SpVEl/7ZZ59tnOMABEojAANfGiFcBwEQAIEyENBEi9tdpzGlCMPvEKPd3VO+JtemNeQQY4h4admj57amNK/WftEsyzpbqGg6nIOALwJKrGTnS7hoicda9PbUNNait6feyiI1b97CrWn9s3XrVuOY4/SFuQrllbWCKMZFTuFfZwMvd4Jb+kehW3ACAqEkgBZ8KGn7KIt/MHjADoK9CMAHby99+SPtpk2bChlxPvdeKKtq1aqyy7xQnlUqi4nqPNH9VNgl1t2oXsKgO/06vkEgyARg4IMMuCzZwwdfFkrq3QMfvHo6sUqiVq1aFcqKX8LPP/98I04T/vHcl1+lur8sJk1sy+oQvnWHGEHvnDqZCrr1oOO1z6Lyo4UPvn59I41+wL72DRs26KdyWvC3334ru/E5MiEhgS655BLjOg5AwCwBGHiz5JAOBEAgagm4L7iEYsWe7VV55Pv0meRak0WOhg3kbnDbli+WI+KbNm1aIh+O93WtxASIBAGTBGDgTYKzMhnmwVtJM3R5YR586FgHqyTevZL1qAduqXvv7Mh7YpQ4+G3jJnII4y5DuRjSFv4sDbyeD75BQAUCMPAKaAE+eAWUYEIE+OBNQFMsSc2aNYn96Xr48ccfqWXLlvop8Trz2vbtlLpyFWkXX0yOxMST186qSbRHLFjD4YQYP1PCUrN5Yr93nkqnBx6Yx//r3qFKlSrkdGKsszcTHFtHAAbeOpamc4IP3jS6sCaEDz6s+C0pPFEYbP7ogQ16itgERg/asuXkvuQKaqWJaW//foZcuzbLRWpc07+iE83OoWMpyVRh8AByXniBnsT4ZoPOo+/1wBvFHDhwQG4Wo8fx3HYYeJ0Gvq0mAANvNVHkBwIgoByBbdu2FRrYxtPgeI8LXh2OAxvZLl26FJPbfd1NJJrdFM9XxFx2z+g3yTXyBXKIlnfuuhVyt8vOnTsXS8cR6enp8lPiRUSCQAgIwMCHAHJpRcAHXxohNa/DB6+mXkqSirvhvVvmixcvpiZNmhjbUnMabdVqqj7nR9KqVSfHueeczEZsxUqbtpw8doivXbtOHuMvCNiAAAy8AkqCD14BJZgQAT54E9CCnIS7wXmJVz3weu68ihy30PV14vm7nFgTnlvwPIiOgyZ87O6WbaipuM/9xn/J+flEcl5/Lble+Y+MP5aeRgkinfPN0XrWxjfvfjl//nzjnH3vmqYV6jHo0KFD8XnzRgocgEBwCMDAB4erX7nCB+8XLmVuhg9eGVUYgrCPe+nSpcY5G1/uhi8v1oTXg/d8dj3O8/SzJHZ6oRjx4eB57U1p4B0tmpNr9xZaNn4Cdb7/XrnsrJ5G/05KSqKePXvqp/gGAWUIwMArowoIAgIgECgB9nt363Z6M5cVK1ZI415HLESjB/e9D9K502dQ/K495FnxBzmbifnqwpDT9G/FiPhTrf+92frt5BDd+4caNZDGvehub9xS947jngLuMUAAARUIwMAroAX44BVQggkR4IM3Ac2iJPn5+TR37lwjN+6W5zjvlnrR1ej4Zs/kL0j7/EuKP3hIpvX07kvOP5eQ899DyD3hU8oTPQDxdeuSa9Y0I2/vg1mzZhmnvLz0MbHHu3ccL2BTu3Zt4x4cgEA4CcDAh5P+qbLhg1dACSZEgA/eBDSLkrAf/dJLLzVy2yUGv/HnnHNODY7jK19Pp4r/foritm4jz6zp5LywC2kHxLx0rz3W9QF0DrE8bMz6lfTjtGln7G6/4oorjDJxAAKqE4CBV0BD8MEroAQTIsAHbwKaiSR//fWXbJ1zUh5Ex1PcUlNTjZwyMzOln527x3keOwft75Xkvv7mk9PbxLnnltvI8cev5LzmKnLf35/ccXHkjBOD5u6/R95f9M8ff/xRaLMZLnPBggXGbTwqv2HDhsY5DkBARQIw8CpqBTKBAAgYBNiY6n5u7orfvXs3NWvWzLheUUxji3mgP6Vv2EjuYc+Sa9AA0vaKVeYyxAp1u08tQyt85fTPAXIIf7sr5x9aM2Ik1RIbylS47hojH++DBmJ6nF4mx9erV6/Qgjj6iHzvNDgGAdUIwMAroBH44BVQggkR4IM3Ae0MSXjVN+991vfv319ooRie1pYhutK3HDpM1cTObRw0sRSsu9NFRq7a8/8hrX1bcpzXhsQatOQReZBT7NEu0lKTxvI+Xm523yVdqWbr1nIqW3b26QF1POr+119/NfLjMps3b15oDr1xEQcgoDgBGHgFFAQfvAJKMCECfPAmoJ0hCXev8/x0PezcuZMyMjL0U4oVLXRn37vo3KNHqeCV18n1+88k+uuJap1FtOXUkrBiSVkte5806K5ff6I9/zeUyqWlUeVHHyaH6MIvGipXrmzMhedrvDY8G3U9eMujx+EbBOxCAAZeAU3BB6+AEkyIAB+8CWgiyV7Rfe696Qof8ypz7EPnwIa+Xt5xOvj7UqrXpg05xKh2DgWt2pFo4lMcn4jR6553PyDng/eRo8255N4nWupigRnn0RxydD85+M4RH08H+t0qR9Yf2bGDeB92PbBPncvVffY8ta1Hjx7SwOv34BsE7E4ABt7uGoT8IGAzAmzI9TXgWXQe/c5btOpxCX9lkfvWO6k5zzHnlrpoiTvatT25Y1vWipO1FVPiaOcuOefc9cUkOjRqNB0S/vaawv9+VExfoyNH5H28qhyXx7vGceucA0+l018mZAT+gECEEoCBV0Cx8MEroAQTIsAHf2ZobLh5oRk9HBVd63Fi9Lp3t/eFq9ZS2pixlCSMbszP84inqxX07idb6PqPk2fkK+SaMpmcjw8iz6130IkK5amc2KLV+dhAPWvKu+Um2i+69FNE/n96rWSXK6bEsTHnXgM9tG3bttCAOT0e3yAQaQT0/6FIq5et6gMfvK3UZQgLH/xJFLzgi/e+57zgDK/wxt3fvPALh6QCN+3+79uUXLMGJd19p4xzfPY5OQYMpmRxryaMsOf2e8j12SfkOLslaUuXiRF0YuS70yEG0p1clMbZ5xY6Uac2rfp+DrW4925asnatzIf/HBEtdn6B4HL1xW545Dv3DCCAQLQSgIFXQPPwwSugBBMiwAd/Ehob9HXr1hkEHQsWkke0sB1dL5RxjuPH6dxLe1AtXsI1IZ5cvy0h18cfkHv1GmmQ+SaHmN+u/XJy9LrzpRfJPX4iHRNrvCecVYNcUz+X+cg/Ype3fUePkEuMkK/J+Z0KPH2OP957u3sf6/fhGwSiiQAMfDRpG3UFgQAJTBMrvemholhMxiMGt+V2bE/aqdHvF4x9j+IW/y4HvLnE1quuud+R9tXX5BGG3XHkqFzrXVvwM2li0Jvzsm7kfulVXr2GNF50RrTcOTjYHy/mqv86/hO6pE9v+n7ePOLeEg680A1PpZsxY4Y85z88Z71x45NT4IxIHIAACBAMvAIPAXzwCijBhAiR7IPn7U95TjiHSqK73Ll2HR29oDNRtUwZ10oY74zxk8gtWs0x735Iru0biDZuIvfCRcYANxItdPpZnKemELm8fmo2byExF40cF3Qh5/czadezz1GVK6+gPy++kP6ZM0fmz392uZw096efpA89QfjmK1WqRCWtL28kwAEIgEAhAl7/dYXicRJCAvDBhxC2hUXZyQfP3eg8olwGsSXqCdH6LifmgK888I8cxc7x9Wd8R5XmLyC3MKRHHhcD2MQ2q41+/Inqvv0eaSKtQxy7pnxGjg7tqeCm28RAuLyTLQRhfLUvv5LxJAbA6SPYSWzcIkbVkbNzJ9L6P0AFo14j7ayadGDUSNqu+88rlqcN/7qPateuLbdr5W51XiWOV6rjLnfvAXn6KHtZB/wBARAolQAMfKmIgn8DfPDBZxyMElTywWuihc1zxI/Wr0ebNm2S1Y0R08aS3/sfxYg53+sH9Kc9Yu44D1w7/6XXKF4sGpMoVnmLe/U/lNq6FdX47Auq/fEE0YUuppht2UaXL1hErlEvUcGjj8sR7dLbLfLzfDieXMLA01licZk1pwa5iW5zEnPOHY0akkMYcs+zw6kgPY3c115NKzxuEkvDEV3ZnXafe7ZcVIaN9olTvQPctc5z4HlxGX2bVTbk3GLnDwIIgIB5AjDw5tkhJQgETEDjnc3EvG/KzCBHrVpGfp4f55G2ROxV3qUTbayULtdFd+TkUvrQ4RS77E86dPMNtPDcVvL+hrPnUoMp35BTtNKPNqhHa54YTDGixd3z9vtIjEMXTm0HnbtqDe35fgYlfDuLUletOukPF5davPcRxfw9gNzvfEAaG3cOouWszT7ZVe5ocw5pGzeKFwMRXy6GNDEynkeqO8a+SVrX7pRXtQqVE0vAzk4Sq7/NnEmO1sKP/vrLpIleAk+d2lQg1o3nwC1yXl6WR9bri8twPHe7Y046k0AAAesJwMCXganGXY27xA9VwwbkED9QetD4x0tM4XE0bqRHyW9NrKnNA4cc3O3oFbR168UqG4nkOLWONl/iH8Lvx4+ndl27UjK3ik4FjVtbvPxm3TrkEF2WetDE6lvEq3Y1qG+0ePiatn272AbzGDmEjN5BlhnjIkedOka0xi0ubn2lJJND7MSlB+6GpfXClyq6UR1iBLMeZJliURES9SxUfy6T69+8mX6r/NY4b1EvR9MmRjwbBVq2nEQTjhxCdj3Ieq74W24M4hCLkehB27fvpIwib0dysh5N2nrBcG82kVi9rBCXP8S0KtGCdYjuYD1wPbWfFpBDtBgd53fUo0kTLVptnlg8RbQyHZ3OPx2/chVpv/0uNySRa5mfuuIRg8S05X+KnciuJkers2Usy73rqWdo3eLfqPNbr5OnRfOTd4u6a4P/LWU//tF7tE9MC+OQ9N9xFC9aybFi69I1779N26plkFMMHDv/7gflwLEEsRHKT8/9H+2sW4uqC593x7fGkUtMP8tNTqId995Ju85tTVc8NIjihe4dgmXsG/+l818ZQXlCnlofiqllrFMRKm/bQdclCZ93pXLkqVKZHMxK3B8jjPxZYnU4T8Wk04acE2zazH+JeohtUL/4Shp3EgPejoqW9U6x8ptzwENUZ/q3lJco5qd3aEdzOonW++TJ0kgnfjiO3EJufhb5OebQpEkTOuvii+Wxt+HmVrneQpcX8QcEQCDoBE5bq6AXZc8CtA0byN3lEtkK4t2oXLs2S4PjmfU9ee645+QPotg20jX/e2n83K+MJu21t8TOVeKH+IH7RDfnSFlxt9iuUlskuirFD7xz8gRy3nCd+KE9Qe6OF9K+zRvp+L5/xBaXy6VR1LKzyd2+y8m8t20n195t5BD+Uo8wkJ6e10rjyYbS9dcS4uU4PWJKkeeJp6Xvk5fpdH42Qf6Yuv/1CGnfiNHGYsERx3+eJ6dY5YsNrcyDjaqQhcQoZ94nW0wiJs854sdbyCTj164QS4TWIfmCIFpqYiFvUad/yLNVdAULg+uY8S3RAw8LWQrkC8GJH2aJFl45cg17kZwffyLWCD9MHlHHY6+9LOufcPX15Fi3gZxClrxXX6bcPjfJkdYpnbqSJgyZS7xAHZw2RRisluQSdU6/8hrSYkV+e/bSrp/m0Inq1ShRDNhKe2SImDPtIEf+Cdo0Z4YwWBWpyth3qMLEyUL2fHKLl5O1/3uH1zulevf1p3K8frlYvvTw5d1ozeABIl0+nS2Mqit7H8WIudObHn6ANlzencqLbun2/xpAHmEI2bAuHTKAtp53LjWcNpMaf/YluUQ6t6jb908OoexGDajn4/9HuWLxlIOeAnKf24FmPPcU5YotTG8URlh2Z4tauy6/irL+M5zKizp0Fs+FQzaniWo/PZRyPnqXMqZOo1ixP7njlHHsKHR1TLSyE18fK2VgcHGC4/mr15Lrpf9QwRPPEgm5OfDLQeaebHLWq0tuwYYEMxnyj9Mxng/eqCHFCYOuy8I6/X3vHtJqn0WtBJuCChVky3ld7xtpxcSJ8nlpfM8dlL7gZzokpqKtufxS8ohFanhOebk/fxPu9ANyi9Y2J0uhJPECyOu4I4AACKhLwDVUBHXFs06yWbNmyYUwrr/++jJlyqOIs7KyqNpV11M58QPNA4d4ik6WWCpzVn4etbihjzSK3Go+IX50F+7aQX+IH8SG//4/crB/URiJPNEinJWbQ1umTaezPhJGXfxIcjj6w3z6KiGO8sUKXVV+WkjNRddrgvgp3jtnLn1ZMZEyxIIfiRs2yXzYJqwV045mODzU5JobRR4HRQY55BZl/LZxAy3cvo2aCkMmBzYdz6d88UP+o3i5WL1gAdURrTyn2G2LW3DHliylacLCZH8ygTK/nk6u/aInQIRDc3+gz9NTqOIjgylpxUpyiNYthx0Lf6avE2Kp3s19KZZbgafqv1IMzpojjFqLW/qRg1vMov4FQqbf92XT32vWUMMXRpLz8BFyiBeF/C1baKHo280WC5Oc9dU35Dq1YEn+H8toXmYVin1rLFUS3dAukTeHXLEC2U/161DdhwdT4o6d5BT5czggfMp/1KxOLYRhZqPsFK1nbjHuLjhB24QFazz0BXIJJk5Rf48wajvTU2XZNT//imIEJzagTvFytle8PKQIA1Z57jxyiTw4JIoXi5zbb6MGo9+khE2byXmqJZwu8koWrdfaw0dQzClWfH/t+vWpyeCBlCBkTxX1bEhO+WLXWKyk1qLteaTN/I5IxHNwieleDe+/l2qLlxDiLu9TU71c4kWowsMPUvkduyhGcNZb31rOUdog/NYxYvR5wsrVsqWuiZbvPpH+p+qZFCd0W0G8sHALnsOsSy+i38QgOYfoeam8chXliRevo8LoTju/Ha3es4dyzzuX0v7MooNNG1PWC0Ppn/g4yhXPZbnHBtFx0TOSL9ZpjxcvYbWEa6B27drU8OabKFm8lFbtdRU1FV3q3K3Oe57zYjHcvc7f+kdfTEYKgj8gAAI+CcyePZtatmxJmV69pT5vtvhCjMX5RUx28aJlzKN4j2dkUMKpVhP/ECeJgUk8SOio2Gu6ghh0xMEpDEicRyMxzIjyRCsugbvRRXAJn2a8MEBx3GqOjRXnonUsQpxYqCNe/HC7ysUaP+4cnyy6wbncfJGHSCYDf6eJcjg+R/hpkzeLbnsRWJaKwrgmClmOVa5kyMiyJIhWaIzYIrOgfAVhVIWBF6GcMHzlRZk8bMnpPtmdy/EVRN145LJbdAXrhoPjU4QsHJ8ryix/ysDJ+ov8ePCTjBfGkYNTDKSKE+XFx5SjfGFk4k/dHyOMZKxwD8SIKVIeIadLnHOIFy8ETiGLW+RvVFTEVzi1tnhO1cqUxu6MUyFRvGBxz0OOmKKVsl64EE4F9kmfEHWRZbLbQgRu2R4XnE+I8tzcAyBc3BzYSP8jXg64294jpl+J/ggZYrdul1uGxqWlUJJo2TqFAWTzmS/maf/888/UUZRZQ7hKnKxDIfMasRzqss8/p+4VK1C6uE/qSaSZIV5mDhbk09XixSFe8GeWDmFkJ61hQ+2hG8QLCPuvPeKzX7Si5//5J5WrkUFXCTkLxMhzl7h/ufCdZ4t8dl3fi7qIFz9N9BrkiwFwO559kiqJso8+P5TyxY+EW7yEecQLRbvzO8g6JF57LcU8NpgqiBeiCmIA3G3iXiMMfJTY2XLaEXPqiuhKRwABEIhwAuKHMyrCgAEDtJtvvrnMdRVTisT4p1zt2Ow52glhoo9Xqqbll0/T/lm5ShP7VGtHX3r1dHxqVS17507Rs56tHbv6Oi0/IUU7nl5Ny+vQRRNzpbU9W7Zox6vX0fKTKmnH0zK1o/f9S9u1a5e2+88/ZR4zyqdo+5LStX/GvauJLTK1vd+fKjM1Q8tPTNX2zJsv1gXZof3z3gcnyxR5sCw7Vq8Wrvft2uEHH9LyY8qLMjO1vHpNtG2ivG2bN2vH2nXS8iukSVmOXneTtm3bNm37qlVCjsra8ZSq2nGR/4EXR4ohA1u1HT8vOpm3yCO/Yrq25+tvtC0inz1Tpsr4vFP13/b7Ehn/z0ujTsVX145XrqFt2bBBxh+9+VYtPz5ZxFXXcrp2l3lvFdeONWulHU+uIjkefOL/ZPy2rBWSVV4lkUdahrb3s89lHjsX/nwy74yztHxR122//KptFvUxZMmspR0/q762ac0aTYwY1/a/+B/j/pyL/r+9c4GNqkrj+DeliEoFocDyfrNAKWALiKhAmyJaIYoYdQEBMYY3LE81GMCAiQaIVZ6GFVFIKNREowiRKJS4KCgUARUQtRRFWJBHYcHS0vbu+R+4I2VbZ+wwM+fe+z9J2/s495zv+313+s35zquvGhOWq3/+O2S4Vaj4XVLl5M+YpcvIgyx3p+j6Chu1tE6t36CZH8s9bBW2TrAK/9bUKux6t3Uu74jaajzfOq/sUQTbNf+7VTR6vFWk3gs15cy6rN6Bo+0SrexOXawSJbedStUzxf8YahX/c4pVqmxjp9KCAqvkXyt03tKSEvuyVarKKt3xpVWq9GMiARJwH4EpU6ZYOTk5UVFMNTRU08EDafLkyfIfNSguMzPzL2uLfnhLtRx9qjXlu7ojFQrBgCwMQPP16ik+tEavJoyA1oPMUlPEd7U1hZCytUn106uWX5mBYGoU9do5c6VverrEq4U/7ISVviw1WtqXmCC+5s3ty2Lt3Sfq24HeXcunWvp2slSo10LIHHWqaIGdSrdk6xAyZLQT+v4tJaNPjWD2JSfZl9Wa3/lXBpm1UAP72rT+47oaRe3X/5p+V0uF2rX+agAbNgmxk7XjS91FUWYAG16zf29To81UpODqQDXkhyyiug9Ejca2twXV1xF5OHBQ9MDGeLSVryT1LUUEXSYdE8Wn5ljbSY8VUH3PsNG1SQ/4qxpbpmzcV98krgz4U2Hnyib1xUj27duntxmtbBl8jgRIwN0Epk6dKkOGDJHk5OSIK0oHH3HkrJAESIAESMArBKLp4O2uSK+wpp4kQAIkQAIk4AkCdPAGmBlr0dvrfhsgDkUIkgDWot+2TXU7MJEACZCAgQSMc/DYWxpzbr2UuBa9M63tpLXonUmYUpMACYRCwAgHj40wZsyYIU3USmbYaAJzbTHPNjExUVauXBmKfo54FmvRQ2cmZxHAWvT9+/d3ltCUlgRIwDMEjJgHP2HCBD3CHXs8t2zZUjt3hKz3q6Uy1fQ2vQvWmDFjPGMUKkoCJEACJEACoRIwwsFjpZ/t27dLfbWojJ1qqgVTevToIa+//rrMnj1bgnHwy5cvlzVq2c3yEnbYatu2bXm3on6N+8FH3QSVEsDN+8FXCggfIgESMIqAEQ4eofjs7GwZNGjQ/8H56KOPgl7zeuTIkYKf8tK6deuM7dtnH3x5FjP/GvvgzbcRJSQBLxMwwsHPmTNHBg8eLBkZGdKqVSu9kcU5tRzngQMH1JLuxWoXyo2uthH3g3emeU3aD96ZBCk1CZBAOAkY4eCTkpLk66+/1mF6tSSp7o/HTlUIy/fq1YvbTIbzDWDZJEACJEACriRghIMHWWymkpqa6krIgZRiH3wgQmbeZx+8mXahVCRAAlcIeGap2j179ki/fv0E0QLTEroj4uLUzm/X7gL2F4REHz70wy5vXksFave2ampN+piYyM/4xPbB2MqhsnYLxVaoF7pjxz+vJbURlN7RMRrco836otpvwotb9WK8C2ZYoVvMaUltfiVoxDVq1CjionvGwUecbAQrxFiFRYsWydKlSyNYqxlVjRgxQs+ywH7mXkpq50IZN26cZGVleUltrev06dMF41a6du3qKd3xpS4tLU22bNniKb2h7CuvvKIbZ/fff7/ndA9F4cg3e0KRls+SAAmQAAmQAAkERYAOPihMzEQCJEACJEACziJAB+8se1FaEiABEiABEgiKAB18UJiYiQRIgARIgAScRYAO3ln2orQkQAIkQAIkEBQBOvigMDETCZAACZAACTiLAKfJOcte5Up7+fJlwVz6OnXqlHvfzRcxXaxWrVoSG2vMmk0RwY05+KdOnZJ69epFpD6TKsG6D1g3AltLey0dP35cGjRo4DW1JT8/X6934cW1PkIxNh18KPT4LAmQAAmQAAkYSoAhekMNQ7FIgARIgARIIBQCdPCh0OOzJEACJEACJGAoATp4Qw1DsUiABEiABEggFAJ08KHQ47MkQAIkQAIkYCgBOnhDDUOxSIAESIAESCAUAnTwodDjsyRAAiRAAiRgKAE6eEMNQ7FIgARIgARIIBQCdPCh0OOzJGAAgeLiYsFe4UzuJwA7l5SUuF9RanhDCNDB3xCM0S0EH/jJkydLUlKSJCQkSEZGRnQFimDt+/fvl0GDBknnzp0lLS1N1q1bF8Hao1/VL7/8Is2aNZPc3NzoCxNmCc6ePSuPP/64tGnTRjp27ChffPFFmGs0q3isXgj958+fb5ZgYZTG65/vUNHSwYdK0IDnlyxZIj///LPk5OTIzp07ZcGCBbJjxw4DJAu/CJMmTZL09HTZu3evrF69WiZOnCgnTpwIf8UG1LBixQpJTU0VLNfrhTRq1Cjp1KmTHDp0SBYtWiQDBw6UgoICL6iuP9u9e/eWzZs3e0JfW0kvf75tBqH8pYMPhZ4hz+If39tvvy0xMTH6p6ioyBNhPLRoxo4dq1vwMEXDhg3ltttuk927dxtimfCJARtnZWXJxo0b5fbbbw9fRQaV/PHHH2t7+3w+SUlJkcaNG8u2bdsMkjB8orzzzjv6yyuiVV5JXv583ygbe2uHjhtFzbByqlWrpjdiWLt2rW699+vXT3r06GGYlDdeHHyhGTBggL9gtG4QxvWC7thoZdOmTX7d3X4AuxYWFkrt2rX9qtavX19OnjzpP3fzwcKFC7V6W7dudbOaZXTz8ue7DIgQTtiCDwGeaY+iZYM++F27dsmBAwdMEy+s8iBsO3ToUFm8eLFnWrRhBWpY4adPn5bq1auXkQo7i124cKHMNZ64kwA/35WzKx185bhF9Sm0YtCCww/ClnZ64oknZNWqVXLPPfdoR2dfd8tftFhtvbFFrJ0OHjyoQ7azZs3yh+vte275W5HN3aJfID2wFfL58+fLZMM5umWY3E3AC5/vcFmQIfpwkQ1judnZ2f4+9tatW8tbb70lHTp0kO7du+tacfzhhx+GUYLoFI3Quz14sEqVKloIjB7v06ePvPDCCzJ69OjoCBaBWq+3eQSqNKoKjDNAi/3o0aO67x3C5eXlSdOmTY2Sk8LcWAJe+XzfWGp/lEYH/wcLxxxhSti1CQOu0HrdsGGDDlmiFX9t3/S1eZ18XKNGDUlOTi6jAsLyQ4YM0S33M2fO6HtxcXG6pV8mo8NPrre5w9WplPiYIjZv3jx59dVX5YMPPtADStElxeReAl75fIfLggzRh4tsBMsdNmyYIITZvn176datm54PPm3atAhKEJ2qMCUQc6HxTz8+Pt7/k5mZGR2BWGtYCcycOVMQycC8/+eee06WLVsmVatWDWudLDx6BPj5Dp29T62MxCWwQudoRAkXL17Uo+ljYxmYMcIgFCIsBDDvv27dumEpm4WSgJsI0MG7yZrUhQRIgARIgASuEmCInq8CCZAACZAACbiQAB28C41KlUiABEiABEiADp7vAAmQAAmQAAm4kAAdvAuNSpVIgARIgARIgA6e7wAJkAAJkAAJuJAAHbwLjUqVSIAESIAESIAOnu8ACZAACZAACbiQAB28C41KlUiABEiABEiADp7vAAmQAAmQAAm4kAAdvAuNSpVIgARIgARIgA6e7wAJkAAJkAAJuJAAHbwLjUqVSIAESIAESIAOnu8ACZAACZAACbiQAB28C41KlUiABEiABEiADp7vAAmQAAmQAAm4kAAdvAuNSpVIgARIgARIgA6e7wAJkAAJkAAJuJAAHbwLjUqVSKA8AmfOnJGioqLybhl7rbCwUM6ePWusfBSMBEwmQAdvsnUoGwkoApMmTZK5c+eGxAKOcvjw4eLz+WTBggUycODAkMqr6OGvvvpKEhISKrpd5vqECRPk5ZdfLnPt+hPLsmTYsGFSXFx8/S2ekwAJBCBABx8AEG+TgBsIwKmPHDlSqlat6ih1br75Zhk8eLAsXrzYUXJTWBIwgQAdvAlWoAwkECSBX3/9VaZNmyYNGzaUTp06yZYtW/xPvvfee3LHHXdI48aNZd68edKnTx9977fffpM33nhD0tPT/XkrOti/f7+kpqZKzZo1pVmzZpKRkaGzfvPNNzoCMHbsWImPj5cHHnhAcnNzJSUlRZo0aeLPh8zoBhgxYoTUqlVL7rvvPoHMSGiNT5kyRRo0aCA9e/aUo0eP6uv4VVG9uDdgwACZP3++nDt3DqdMJEACQRKggw8SFLORgAkE0Jo9ffq07Ny5U8aPHy8PP/ywHDt2TH766ScZNWqUzJ49Wz755BN599139TXIvGvXLklKSpLY2NiAKjz55JPy4IMP6jLh3KdPny7ou7906ZKsXr1amjdvLrt375YTJ07IXXfdJTNnzpT169fLSy+9JOgGQIIs+BLw3Xff6S8izzzzjL6+dOlS+eyzzyQ7O1vGjRsnGzdu1Nfxq6J6ce+WW26Rtm3byr59+3DKRAIkECSBwJ/4IAtiNhIggfASQIsXDnLNmjXSqFEjHXJftmyZvP/++7p1DCf+yCOPaCHGjBnj77f//vvvdf5gpFu+fLn+MoDWNpw5nCsiAEg4fvbZZ/VxWlqaHDp0SPAXqU6dOn4HjG6AF198UeLi4uT555/XffIXLlwQRBieeuopadeunf5ZuHChfha/Kqq3du3aOg/0hR5o+TORAAkER4AOPjhOzEUCUSdw5MgRHQ6Hs7MTWtFowRcUFEhycrJ9Wbp16+Y/zsvLk3r16vnP/+wAzhxO9ODBgzrcX1JSIqWlpfoRdAvY6dZbb9VO2j6/6aabBHljYmKkQ4cO2rnjXvv27aV69er+KEOXLl3sR3QEwD75s3qRB/IfPnzYzs6/JEACQRBgiD4ISMxCAiYQqFu3rnaU+fn5fnG+/fZbadGihdx5552yZ88e//W9e/f6j9GXji8BgRJC8Y8++qhMnTpV59+8ebOODKA1j1SlSpVARej719aFLxcXL16U+vXrS9OmTXVfu10I+vCRAtWLPMePH9djAnDMRAIkEBwBOvjgODEXCUSdQKtWrbQzf/PNN7XjhUPPycnRLeG+ffvK9u3b9aA7hPJXrFjhlxeD8X744Qf/OQ4wEA6O1f7BlwaE0ZEwOA+j1zMzM3Xf++XLl/X1YH+dPHlSPv/8cy3jypUr5d5775UaNWrocH5WVpZ2+IhGoLsBKZh6IX/nzp2DFYH5SIAEFAGG6PkakIBDCKAFvWrVKj1tDP3XGPi2ZMkSSUxM1Bq89tpr8vTTT+tQOUbM2y1pOHiE3NESxzx4pA0bNuiBcPpE/UIIHAPnMFcejhSD5DCfHV0A6GtHf3ywCc9hpD/Kw8A+jBFAmjFjhjz22GPSpk0bfd69e3f9Fy37iuqFLJgD/+OPP/r11A/xFwmQQEACPvWhvxJ/C5iVGUiABEwhgFYyQva2w0b/NELe9qA3tJQxan3r1q1aZAy+w9S1hx56KKAKCKmjXPSzh5LQrw4Zr0+Y7oYBeNeH/CuqF5EEjLjHKH4mEiCB4AnQwQfPijlJwFgCv//+u55KhilpGO2Oee9o5ffv31/LbE+j+/TTT43VoSLBMOgPTh7z+5lIgASCJ8A++OBZMScJGEsArW3MjceAOxxjbrrt3CE0+u8Rvj9//ryxOpQn2KlTp2TixIl07uXB4TUSCECALfgAgHibBEiABEiABJxIgC14J1qNMpMACZAACZBAAAJ08AEA8TYJkAAJkAAJOJEAHbwTrUaZSYAESIAESCAAATr4AIB4mwRIgARIgAScSIAO3olWo8wkQAIkQAIkEIAAHXwAQLxNAiRAAiRAAk4kQAfvRKtRZhIgARIgARIIQIAOPgAg3iYBEiABEiABJxKgg3ei1SgzCZAACZAACQQgQAcfABBvkwAJkAAJkIATCdDBO9FqlJkESIAESIAEAhCggw8AiLdJgARIgARIwIkE6OCdaDXKTAIkQAIkQAIBCNDBBwDE2yRAAiRAAiTgRAL/A+zUxoKybEdrAAAAAElFTkSuQmCC" alt="plot of chunk unnamed-chunk-59"/> </p>

<p>We can also show the optimal \(\lambda\)&#39;s and the corresponding coefficients.</p>

<pre><code class="r">opt.lam = c(cvfit$lambda.min, cvfit$lambda.1se)
coef(cvfit, s = opt.lam)
</code></pre>

<pre><code>## 21 x 2 sparse Matrix of class &quot;dgCMatrix&quot;
##                        1            2
## (Intercept)  0.031262578  0.185696196
## V1           0.619053001  0.575373801
## V2          -0.984550161 -0.932121975
## V3           1.525234026  1.470567302
## V4           0.231590890  0.196923579
## V5          -0.336659414 -0.304694503
## V6           0.001025945  .          
## V7          -0.012829626  .          
## V8           .            .          
## V9           .            .          
## V10          0.015983078  .          
## V11          .            .          
## V12          0.030866986  0.025850171
## V13         -0.027970571  .          
## V14          0.032750270  .          
## V15         -0.005932709  .          
## V16          0.017505507  .          
## V17          .            .          
## V18          0.004026211  .          
## V19         -0.033579041  .          
## V20          0.012048941  0.009929548
</code></pre>

<p>The <code>predict</code> method is similar and we do not repeat it here.</p>

<p><a href="#top">Back to Top</a></p>

<p><a id="cox"></a></p>

<h2>Cox Models</h2>

<p>The Cox proportional hazards model is commonly used for the study of the relationship beteween predictor variables and survival time. In the usual survival analysis framework, we have data of the form \((y_1, x_1, \delta_1), \ldots, (y_n, x_n, \delta_n)\) where \(y_i\), the observed time, is a time of failure if \(\delta_i\) is 1 or right-censoring if \(\delta_i\) is 0. We also let \(t_1 < t_2 < \ldots < t_m\) be the increasing list of unique failure times, and \(j(i)\) denote the index of the observation failing at time \(t_i\).</p>

<p>The Cox model assumes a semi-parametric form for the hazard
\[
h_i(t) = h_0(t) e^{x_i^T \beta},
\]
where \(h_i(t)\) is the hazard for patient \(i\) at time \(t\), \(h_0(t)\) is a shared baseline hazard, and \(\beta\) is a fixed, length \(p\) vector. In the classic setting \(n \geq p\), inference is made via the partial likelihood
\[
L(\beta) = \prod_{i=1}^m \frac{e^{x_{j(i)}^T \beta}}{\sum_{j \in R_i} e^{x_j^T \beta}},
\]
where \(R_i\) is the set of indices \(j\) with \(y_j \geq t_i\) (those at risk at time \(t_i\)).</p>

<p>Note there is no intercept in the Cox mode (its built into the baseline hazard, and like it, would cancel in the partial likelihood.)</p>

<p>We penalize the negative log of the partial likelihood, just like the other models, with an elastic-net penalty.  </p>

<p>We use a pre-generated set of sample data and response. Users can load their own data and follow a similar procedure. In this case \(x\) must be an \(n\times p\) matrix of covariate values - each row corresponds to a patient and each column a covariate. \(y\) is an \(n \times 2\)  matrix, with a column &ldquo;time&rdquo; of failure/censoring times, and &ldquo;status&rdquo; a 0/1 indicator, with 1 meaning the time is a failure time, and zero a censoring time.</p>

<pre><code class="r">data(CoxExample)
y[1:5,]
</code></pre>

<pre><code>##            time status
## [1,] 1.76877757      1
## [2,] 0.54528404      1
## [3,] 0.04485918      0
## [4,] 0.85032298      0
## [5,] 0.61488426      1
</code></pre>

<p>The <code>Surv</code> function in the package <code>survival</code> can create such a matrix. Note, however, that the <code>coxph</code> and related linear models can handle interval and other fors of censoring, while glmnet can only handle right censoring in its present form.  </p>

<p>We apply the <code>glmnet</code> function to compute the solution path under default settings.</p>

<pre><code class="r">fit = glmnet(x, y, family = &quot;cox&quot;)
</code></pre>

<p>All the standard options are available such as <code>alpha</code>, <code>weights</code>, <code>nlambda</code> and <code>standardize</code>. Their usage is similar as in the Gaussian case and we omit the details here. Users can also refer to the help file <code>help(glmnet)</code>.</p>

<p>We can plot the coefficients.</p>

<pre><code class="r">plot(fit)
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-63"/> </p>

<p>As before, we can extract the coefficients at certain values of \(\lambda\).</p>

<pre><code class="r">coef(fit, s = 0.05)
</code></pre>

<pre><code>## 30 x 1 sparse Matrix of class &quot;dgCMatrix&quot;
##               1
## V1   0.37693638
## V2  -0.09547797
## V3  -0.13595972
## V4   0.09814146
## V5  -0.11437545
## V6  -0.38898545
## V7   0.24291400
## V8   0.03647596
## V9   0.34739813
## V10  0.03865115
## V11  .         
## V12  .         
## V13  .         
## V14  .         
## V15  .         
## V16  .         
## V17  .         
## V18  .         
## V19  .         
## V20  .         
## V21  .         
## V22  .         
## V23  .         
## V24  .         
## V25  .         
## V26  .         
## V27  .         
## V28  .         
## V29  .         
## V30  .
</code></pre>

<p>Since the Cox Model is not commonly used for prediction, we do not give an illustrative example on prediction. If needed, users can refer to the help file by typing <code>help(predict.glmnet)</code>.</p>

<p>Also, the function <code>cv.glmnet</code> can be used to compute \(k\)-fold cross-validation for the Cox model. The usage is similar to that for other families except for two main differences. </p>

<p>One is that <code>type.measure</code> only supports &ldquo;deviance&rdquo;(also default), which gives the partial-likelihood. </p>

<p>The other is in the option <code>grouped</code>. <code>grouped = TRUE</code> obtains the CV partial likelihood for the Kth fold by subtraction; by subtracting the log partial likelihood evaluated on the full dataset from that evaluated on the on the (K-1)/K dataset. This makes more efficient use of risk sets. With <code>grouped=FALSE</code> the log partial likelihood is computed only on the Kth fold, which is only reasonable if each fold has a large number of observations.</p>

<pre><code class="r">cvfit = cv.glmnet(x, y, family = &quot;cox&quot;)
</code></pre>

<p>Once fit, we can view the optimal \(\lambda\) value and a cross validated error plot to help evaluate our model. </p>

<pre><code class="r">plot(cvfit)
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-66"/> </p>

<p>As previously, the left vertical line in our plot shows us where the CV-error curve hits its minimum. The right vertical line shows us the most regularized model with CV-error within 1 standard deviation of the minimum. We also extract such optimal \(\lambda\)&#39;s.</p>

<pre><code class="r">cvfit$lambda.min
</code></pre>

<pre><code>## [1] 0.01594373
</code></pre>

<pre><code class="r">cvfit$lambda.1se
</code></pre>

<pre><code>## [1] 0.04868986
</code></pre>

<p>We can check the active covariates in our model and see their coefficients.</p>

<pre><code class="r">coef.min = coef(cvfit, s = &quot;lambda.min&quot;)
active.min = which(coef.min != 0)
index.min = coef.min[active.min]
</code></pre>

<pre><code class="r">index.min
</code></pre>

<pre><code>##  [1]  0.491297030 -0.174600537 -0.218648771  0.175112406 -0.186673099
##  [6] -0.490250398  0.335197365  0.091586519  0.450169329  0.115921793
## [11]  0.017594799 -0.018364649 -0.002805776 -0.001422613 -0.023429230
## [16]  0.001687662 -0.008236046
</code></pre>

<pre><code class="r">coef.min
</code></pre>

<pre><code>## 30 x 1 sparse Matrix of class &quot;dgCMatrix&quot;
##                1
## V1   0.491297030
## V2  -0.174600537
## V3  -0.218648771
## V4   0.175112406
## V5  -0.186673099
## V6  -0.490250398
## V7   0.335197365
## V8   0.091586519
## V9   0.450169329
## V10  0.115921793
## V11  .          
## V12  .          
## V13  0.017594799
## V14  .          
## V15  .          
## V16  .          
## V17 -0.018364649
## V18  .          
## V19  .          
## V20  .          
## V21 -0.002805776
## V22 -0.001422613
## V23  .          
## V24  .          
## V25 -0.023429230
## V26  .          
## V27  0.001687662
## V28  .          
## V29  .          
## V30 -0.008236046
</code></pre>

<p><a href="#top">Back to Top</a></p>

<p><a id="spa"></a></p>

<h2>Sparse Matrices</h2>

<p>Our package supports sparse input matrices, which allow efficient storage and operations of large matrices but with only a few nonzero entries. It is available for all families except for the <code>cox</code> family. The usage of sparse matrices (inherit from class <code>&quot;sparseMatrix&quot;</code> as in package <code>Matrix</code>) in <code>glmnet</code> is the same as if a regular matrix is provided.</p>

<p>We load a set of sample data created beforehand.</p>

<pre><code class="r">data(SparseExample)
</code></pre>

<p>It loads <code>x</code>, a 100*20 sparse input matrix and <code>y</code>, the response vector.</p>

<pre><code class="r">class(x)
</code></pre>

<pre><code>## [1] &quot;dgCMatrix&quot;
## attr(,&quot;package&quot;)
## [1] &quot;Matrix&quot;
</code></pre>

<p>Users can create a sparse matrix with the function <code>sparseMatrix</code> by providing the locations and values of the nonzero entries. Alternatively, <code>Matrix</code> function can also be used to contruct a sparse matrix by setting <code>sparse = TRUE</code>, but this defeats the purpose somewhat.</p>

<p>We can fit the model the same way as before.</p>

<pre><code class="r">fit = glmnet(x, y)
</code></pre>

<p>We also do the cross-validation and plot the resulting object.</p>

<pre><code class="r">cvfit = cv.glmnet(x, y)
plot(cvfit)
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-73"/> </p>

<p>The usage of other functions are similar and we do not expand here. </p>

<p>Note that sparse matrices can also be used for <code>newx</code>, the new input matrix in the <code>predict</code> function. For example,</p>

<pre><code class="r">i = sample(1:5, size = 25, replace = TRUE)
j = sample(1:20, size = 25, replace = TRUE)
x = rnorm(25)
nx = sparseMatrix(i = i, j = j, x = x, dims = c(5, 20))
predict(cvfit, newx = nx, s = &quot;lambda.min&quot;)
</code></pre>

<pre><code>##               1
## [1,]  0.3825586
## [2,] -0.2172428
## [3,] -1.6621635
## [4,] -0.4174700
## [5,] -1.3940989
</code></pre>

<p><a href="#top">Back to Top</a></p>

<p><a id="int"></a></p>

<h2>Appendix 1: Internal Parameters</h2>

<p>Our package has a set of internal parameters which control some aspects of the computation of the path. The <em>factory default</em> settings are expected to serve in most cases, and users do not need to make changes unless there are special requirements.</p>

<p>There are several parameters that users can change:</p>

<p><code>fdev</code> - minimum fractional change in deviance for stopping path; factory default = 1.0e-5</p>

<p><code>devmax</code> - maximum fraction of explained deviance for stopping path; factory default = 0.999</p>

<ul>
<li><p><code>eps</code> - minimum value of lambda.min.ratio (see glmnet); factory default= 1.0e-6</p></li>
<li><p><code>big</code> - large floating point number; factory default = 9.9e35. Inf in definition of upper.limit is set to big</p></li>
<li><p><code>mnlam</code> - minimum number of path points (lambda values) allowed; factory default = 5</p></li>
<li><p><code>pmin</code> - minimum null probability for any class; factory default = 1.0e-5</p></li>
<li><p><code>exmx</code> - maximum allowed exponent; factory default = 250.0</p></li>
<li><p><code>prec</code> - convergence threshold for multi-response bounds adjustment solution; factory default = 1.0e-10</p></li>
<li><p><code>mxit</code> - maximum iterations for multiresponse bounds adjustment solution; factory default = 100</p></li>
<li><p><code>factory</code> - If <code>TRUE</code>, reset all the parameters to the factory default; default is <code>FALSE</code></p></li>
</ul>

<p>We illustrate the usage by an example. Note that any changes made hold for the duration of the R session, or unless they are changed by the user with a subsequent call to <code>glmnet.control</code>.</p>

<pre><code class="r">data(QuickStartExample)
fit = glmnet(x, y)
print(fit)
</code></pre>

<pre><code>## 
## Call:  glmnet(x = x, y = y) 
## 
##       Df    %Dev   Lambda
##  [1,]  0 0.00000 1.631000
##  [2,]  2 0.05528 1.486000
##  [3,]  2 0.14590 1.354000
##  [4,]  2 0.22110 1.234000
##  [5,]  2 0.28360 1.124000
##  [6,]  2 0.33540 1.024000
##  [7,]  4 0.39040 0.933200
##  [8,]  5 0.45600 0.850300
##  [9,]  5 0.51540 0.774700
## [10,]  6 0.57350 0.705900
## [11,]  6 0.62550 0.643200
## [12,]  6 0.66870 0.586100
## [13,]  6 0.70460 0.534000
## [14,]  6 0.73440 0.486600
## [15,]  7 0.76210 0.443300
## [16,]  7 0.78570 0.404000
## [17,]  7 0.80530 0.368100
## [18,]  7 0.82150 0.335400
## [19,]  7 0.83500 0.305600
## [20,]  7 0.84620 0.278400
## [21,]  7 0.85550 0.253700
## [22,]  7 0.86330 0.231200
## [23,]  8 0.87060 0.210600
## [24,]  8 0.87690 0.191900
## [25,]  8 0.88210 0.174900
## [26,]  8 0.88650 0.159300
## [27,]  8 0.89010 0.145200
## [28,]  8 0.89310 0.132300
## [29,]  8 0.89560 0.120500
## [30,]  8 0.89760 0.109800
## [31,]  9 0.89940 0.100100
## [32,]  9 0.90100 0.091170
## [33,]  9 0.90230 0.083070
## [34,]  9 0.90340 0.075690
## [35,] 10 0.90430 0.068970
## [36,] 11 0.90530 0.062840
## [37,] 11 0.90620 0.057260
## [38,] 12 0.90700 0.052170
## [39,] 15 0.90780 0.047540
## [40,] 16 0.90860 0.043310
## [41,] 16 0.90930 0.039470
## [42,] 16 0.90980 0.035960
## [43,] 17 0.91030 0.032770
## [44,] 17 0.91070 0.029850
## [45,] 18 0.91110 0.027200
## [46,] 18 0.91140 0.024790
## [47,] 19 0.91170 0.022580
## [48,] 19 0.91200 0.020580
## [49,] 19 0.91220 0.018750
## [50,] 19 0.91240 0.017080
## [51,] 19 0.91250 0.015570
## [52,] 19 0.91260 0.014180
## [53,] 19 0.91270 0.012920
## [54,] 19 0.91280 0.011780
## [55,] 19 0.91290 0.010730
## [56,] 19 0.91290 0.009776
## [57,] 19 0.91300 0.008908
## [58,] 19 0.91300 0.008116
## [59,] 19 0.91310 0.007395
## [60,] 19 0.91310 0.006738
## [61,] 19 0.91310 0.006140
## [62,] 20 0.91310 0.005594
## [63,] 20 0.91310 0.005097
## [64,] 20 0.91310 0.004644
## [65,] 20 0.91320 0.004232
## [66,] 20 0.91320 0.003856
## [67,] 20 0.91320 0.003513
</code></pre>

<p>We can change the minimum fractional change in deviance for stopping path and compare the results. </p>

<pre><code class="r">glmnet.control(fdev = 0)
fit = glmnet(x, y)
print(fit)
</code></pre>

<pre><code>## 
## Call:  glmnet(x = x, y = y) 
## 
##        Df    %Dev    Lambda
##   [1,]  0 0.00000 1.6310000
##   [2,]  2 0.05528 1.4860000
##   [3,]  2 0.14590 1.3540000
##   [4,]  2 0.22110 1.2340000
##   [5,]  2 0.28360 1.1240000
##   [6,]  2 0.33540 1.0240000
##   [7,]  4 0.39040 0.9332000
##   [8,]  5 0.45600 0.8503000
##   [9,]  5 0.51540 0.7747000
##  [10,]  6 0.57350 0.7059000
##  [11,]  6 0.62550 0.6432000
##  [12,]  6 0.66870 0.5861000
##  [13,]  6 0.70460 0.5340000
##  [14,]  6 0.73440 0.4866000
##  [15,]  7 0.76210 0.4433000
##  [16,]  7 0.78570 0.4040000
##  [17,]  7 0.80530 0.3681000
##  [18,]  7 0.82150 0.3354000
##  [19,]  7 0.83500 0.3056000
##  [20,]  7 0.84620 0.2784000
##  [21,]  7 0.85550 0.2537000
##  [22,]  7 0.86330 0.2312000
##  [23,]  8 0.87060 0.2106000
##  [24,]  8 0.87690 0.1919000
##  [25,]  8 0.88210 0.1749000
##  [26,]  8 0.88650 0.1593000
##  [27,]  8 0.89010 0.1452000
##  [28,]  8 0.89310 0.1323000
##  [29,]  8 0.89560 0.1205000
##  [30,]  8 0.89760 0.1098000
##  [31,]  9 0.89940 0.1001000
##  [32,]  9 0.90100 0.0911700
##  [33,]  9 0.90230 0.0830700
##  [34,]  9 0.90340 0.0756900
##  [35,] 10 0.90430 0.0689700
##  [36,] 11 0.90530 0.0628400
##  [37,] 11 0.90620 0.0572600
##  [38,] 12 0.90700 0.0521700
##  [39,] 15 0.90780 0.0475400
##  [40,] 16 0.90860 0.0433100
##  [41,] 16 0.90930 0.0394700
##  [42,] 16 0.90980 0.0359600
##  [43,] 17 0.91030 0.0327700
##  [44,] 17 0.91070 0.0298500
##  [45,] 18 0.91110 0.0272000
##  [46,] 18 0.91140 0.0247900
##  [47,] 19 0.91170 0.0225800
##  [48,] 19 0.91200 0.0205800
##  [49,] 19 0.91220 0.0187500
##  [50,] 19 0.91240 0.0170800
##  [51,] 19 0.91250 0.0155700
##  [52,] 19 0.91260 0.0141800
##  [53,] 19 0.91270 0.0129200
##  [54,] 19 0.91280 0.0117800
##  [55,] 19 0.91290 0.0107300
##  [56,] 19 0.91290 0.0097760
##  [57,] 19 0.91300 0.0089080
##  [58,] 19 0.91300 0.0081160
##  [59,] 19 0.91310 0.0073950
##  [60,] 19 0.91310 0.0067380
##  [61,] 19 0.91310 0.0061400
##  [62,] 20 0.91310 0.0055940
##  [63,] 20 0.91310 0.0050970
##  [64,] 20 0.91310 0.0046440
##  [65,] 20 0.91320 0.0042320
##  [66,] 20 0.91320 0.0038560
##  [67,] 20 0.91320 0.0035130
##  [68,] 20 0.91320 0.0032010
##  [69,] 20 0.91320 0.0029170
##  [70,] 20 0.91320 0.0026580
##  [71,] 20 0.91320 0.0024220
##  [72,] 20 0.91320 0.0022060
##  [73,] 20 0.91320 0.0020100
##  [74,] 20 0.91320 0.0018320
##  [75,] 20 0.91320 0.0016690
##  [76,] 20 0.91320 0.0015210
##  [77,] 20 0.91320 0.0013860
##  [78,] 20 0.91320 0.0012630
##  [79,] 20 0.91320 0.0011500
##  [80,] 20 0.91320 0.0010480
##  [81,] 20 0.91320 0.0009551
##  [82,] 20 0.91320 0.0008703
##  [83,] 20 0.91320 0.0007930
##  [84,] 20 0.91320 0.0007225
##  [85,] 20 0.91320 0.0006583
##  [86,] 20 0.91320 0.0005999
##  [87,] 20 0.91320 0.0005466
##  [88,] 20 0.91320 0.0004980
##  [89,] 20 0.91320 0.0004538
##  [90,] 20 0.91320 0.0004135
##  [91,] 20 0.91320 0.0003767
##  [92,] 20 0.91320 0.0003433
##  [93,] 20 0.91320 0.0003128
##  [94,] 20 0.91320 0.0002850
##  [95,] 20 0.91320 0.0002597
##  [96,] 20 0.91320 0.0002366
##  [97,] 20 0.91320 0.0002156
##  [98,] 20 0.91320 0.0001964
##  [99,] 20 0.91320 0.0001790
## [100,] 20 0.91320 0.0001631
</code></pre>

<p>We set <code>fdev = 0</code> to continue all along the path, even without much change. The length of the sequence becomes 100, which is the default of <code>nlambda</code>. </p>

<p>Users can also reset to the default settings.</p>

<pre><code class="r">glmnet.control(factory = TRUE)
</code></pre>

<p>The current settings are obtained as follows.</p>

<pre><code class="r">glmnet.control()
</code></pre>

<pre><code>## $fdev
## [1] 1e-05
## 
## $eps
## [1] 1e-06
## 
## $big
## [1] 9.9e+35
## 
## $mnlam
## [1] 5
## 
## $devmax
## [1] 0.999
## 
## $pmin
## [1] 1e-09
## 
## $exmx
## [1] 250
## 
## $prec
## [1] 1e-10
## 
## $mxit
## [1] 100
</code></pre>

<p><a href="#top">Back to Top</a></p>

<p><a id="cmp"></a></p>

<h2>Appendix 2: Comparison with Other Packages</h2>

<p>Some people may want to use <code>glmnet</code> to solve the Lasso or elastic-net problem at a single \(\lambda\). We compare here the solution by <code>glmnet</code> with other packages (such as CVX), and also as an illustration of parameter settings in this situation.</p>

<p><strong>Warning</strong>: Though such problems can be solved by <code>glmnet</code>, it is <strong>not recommended</strong> and is not the spirit of the package. <code>glmnet</code> fits the <strong>entire</strong> solution path for Lasso or elastic-net problems efficiently with various techniques such as warm start. Those advantages will disappear if the \(\lambda\) sequence is forced to be only one value.</p>

<p>Nevertheless, we still illustrate with a typical example in linear model in the following for the purpose of comparison. Given \(X, Y\) and \(\lambda_0 > 0\), we want to find \(\beta\) such that
\[
\min_{\beta} ||Y - X\beta||_2^2 + \lambda_0 ||\beta||_1,
\]
where, say, \(\lambda_0 = 8\).</p>

<p>We first solve using <code>glmnet</code>. Notice that there is no intercept term in the objective function, and the columns of \(X\) are not necessarily standardized. Corresponding parameters have to be set to make it work correctly. In addition, there is a \(1/(2n)\) factor before the quadratic term by default, we need to adjust \(\lambda\) accordingly. For the purpose of comparison, the <code>thresh</code> option is specified to be 1e-20. However, this is not necessary in many practical applications.</p>

<pre><code class="r">fit = glmnet(x, y, intercept = F, standardize = F, lambda = 8/(2*dim(x)[1]), thresh = 1e-20)
</code></pre>

<p>We then extract the coefficients (with no intercept).</p>

<pre><code class="r">beta_glmnet = as.matrix(predict(fit, type = &quot;coefficients&quot;)[-1,])
</code></pre>

<p>In linear model as here this approach worked because we were using squared error loss, but with any nonlinear family, it will probably fail. The reason is we are not using step length optimization, and so rely on very good warm starts to put us in the quadratic region of the loss function.</p>

<p>Alternatively, a more stable and <strong>strongly recommended</strong> way to perform this task is to first fit the entire Lasso or elastic-net path without specifying <code>lambda</code>, but then provide the requested \(\lambda_0\) to <code>predict</code> function to extract the corresponding coefficients. In fact, if \(\lambda_0\) is not in the \(\lambda\) sequence generated by <code>glmnet</code>, the path will be refitted along a new \(\lambda\) sequence that includes the requested value \(\lambda_0\) and the old sequence, and the coefficients will be returned at \(\lambda_0\) based on the new fit. Remember to set <code>exact = TRUE</code> in <code>predict</code> function to get the exact solution. Otherwise, it will be approximated by linear interpolation.</p>

<pre><code class="r">fit = glmnet(x, y, intercept = F, standardize = F, thresh = 1e-20)
beta_glmnet = as.matrix(predict(fit, s = 8/(2*dim(x)[1]), type = &quot;coefficients&quot;, exact = T)[-1,])
</code></pre>

<p>We also use CVX, a general convex optimization solver, to solve this specific Lasso problem. Users could also call CVX from R using the <code>CVXfromR</code> package and solve the problem as follows.</p>

<pre><code class="r">library(CVXfromR)
setup.dir = &quot;change/this/to/your/cvx/directory&quot;
n = dim(x)[1]; p = dim(x)[2]
cvxcode = paste(&quot;variables beta(p)&quot;,
                &quot;minimize(square_pos(norm(y - x * beta, 2)) + lambda * norm(beta, 1))&quot;,
                sep = &quot;;&quot;)
Lasso = CallCVX(cvxcode, const.var = list(p = p, x = x, y = y, lambda = 8), opt.var.names = &quot;beta&quot;, setup.dir = setup.dir, matlab.call = &quot;change/this/to/path/to/matlab&quot;)
beta_CVX = Lasso$beta
</code></pre>

<p>For convenience here, the results were saved in <code>CVXResult.RData</code>, and we simply load in the results. </p>

<pre><code class="r">data(CVXResults)
</code></pre>

<p>In addition, we use <code>lars</code> to solve the same problem. </p>

<pre><code class="r">require(lars)
</code></pre>

<pre><code class="r">fit_lars = lars(x, y, type = &quot;lasso&quot;, intercept = F, normalize = F)
beta_lars = predict(fit_lars, s = 8/2, type = &quot;coefficients&quot;, mode = &quot;lambda&quot;)$coefficients
</code></pre>

<p>The results are listed below up to 6 decimal digits (due to convergence thresholds).</p>

<pre><code class="r">cmp = round(cbind(beta_glmnet, beta_lars, beta_CVX), digits = 6)
colnames(cmp) = c(&quot;beta_glmnet&quot;, &quot;beta_lars&quot;, &quot;beta_CVX&quot;)
cmp
</code></pre>

<pre><code>##     beta_glmnet beta_lars  beta_CVX
## V1     1.389118  1.389118  1.389118
## V2     0.007991  0.007991  0.007991
## V3     0.731234  0.731234  0.731234
## V4     0.031119  0.031119  0.031119
## V5    -0.866793 -0.866793 -0.866793
## V6     0.564867  0.564867  0.564867
## V7     0.069678  0.069678  0.069678
## V8     0.358346  0.358346  0.358346
## V9     0.000000  0.000000  0.000000
## V10    0.070565  0.070565  0.070565
## V11    0.173464  0.173464  0.173464
## V12   -0.027472 -0.027472 -0.027472
## V13   -0.017960 -0.017960 -0.017960
## V14   -1.138053 -1.138053 -1.138053
## V15   -0.070990 -0.070990 -0.070990
## V16    0.000000  0.000000  0.000000
## V17    0.000000  0.000000  0.000000
## V18    0.000000  0.000000  0.000000
## V19    0.000000  0.000000  0.000000
## V20   -1.097528 -1.097528 -1.097528
</code></pre>

<p><a href="#top">Back to Top</a></p>

<h2>References</h2>

<p>Jerome Friedman, Trevor Hastie and Rob Tibshirani. (2008). <br>
<a href="http://www.jstatsoft.org/v33/i01/">Regularization Paths for Generalized Linear Models via Coordinate Descent</a><br>
<em>Journal of Statistical Software</em>, Vol. 33(1), 1-22 Feb 2010.</p>

<p>Noah Simon, Jerome Friedman, Trevor Hastie and Rob Tibshirani. (2011).<br>
<a href="http://www.jstatsoft.org/v39/i05/">Regularization Paths for Cox&rsquo;s Proportional Hazards Model via Coordinate Descent</a><br>
<em>Journal of Statistical Software</em>, Vol. 39(5) 1-13.</p>

<p>Robert Tibshirani, Jacob Bien, Jerome Friedman, Trevor Hastie, Noah Simon, Jonathan Taylor, Ryan J. Tibshirani. (2010).<br>
<a href="http://www-stat.stanford.edu/~tibs/ftp/strong.pdf">Strong Rules for Discarding Predictors in Lasso-type Problems</a><br>
<em>Journal of the Royal Statistical Society: Series B (Statistical Methodology)</em>, 74(2), 245-266.</p>

<p> Noah Simon, Jerome Friedman and Trevor Hastie (2013). <br>
<a href="http://www.stanford.edu/~hastie/Papers/multi_response.pdf">A Blockwise Descent Algorithm for Group-penalized Multiresponse and Multinomial Regression </a><br>
<i>(in arXiv, submitted) </i></p>

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