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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
<meta name="author" content="Joseph Rickert" />
<title>12 - CARET</title>
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xaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%2FkB08JF83SNM9ZhZf5I9%2B7Rz8J%2FI3IIfrMb5LHcjByu%2BUvGUKxQ%2FkcaLYdZ40f8JWOkw%2BA4iEBtlYSmddP1HFw%2FdKtShd1YQrncW61ZeqsIAcmYleEIEKAhx1ry8ND6aFh%2F8V9VHtiPdBPGObFcRVTzQgcz40RO7WEpW0ba3k2z3i6zBvlL9xO4ka%2F1EVnLTSa%2Buo%2BmkNzrwWDyPcz4lWOAxAp2%2BTrKORXoPGvm6d5aKpzt50u4573rAOfhP1CUXfpbjl36l4XWpysmXBCr6PbGqqsTnSRvReJFj4Xl9rvYfHFko%2FqPrb9YYEpV1CsKxTiSrXhisap%2F%2BC%2B6uu%2F8CZVz1iWlY7fGdnKxd2eM7WS2lcof2ANfjrBHRsoomvoE%2BuiF%2FpXpsCFnJYVZIVk7UsM85PYeLXYofPYf9H42YsMdtUq%2FMTd79Ho4bf2S7Gq9PVRxDC6lKG1VpVBV9cvFXGr6oVycSFZ%2FauXWquCnKooTpFvcfy0YiWVX%2BI%2FD8UOPXvp%2BE5%2Bom3JHt0IXP6MxS5U%2FAqovYd3l62%2BltAxkkq6q7T7LR3xT%2Bcz8gICtekEZU28Hq3d9JkEXhcfclufMmmZusgqh%2B5ww%2B7k3Vwcd9XbHh1JbiELt3Fu67QYbvmRs%2FCg5by%2Fr3TnRlZXhu7ijl5o6qlZ%2BrQrpkGp3PTVTZFKpwyqWabnH%2Fce5T6T%2Ba%2BNhiV5b7vOiQWG1XNvGNzngqewJWX%2BO7%2BPupIVbDas6n8HPju1ZMXmrvkcJ%2FhQ1Ya89RaqlV9P%2Bu0DacBdfrcx5rlPXMqW5%2B%2FVv8cfLHuP4t%2F6ZgDdhPFR8DGRmRoMk6R6Yc9MgU65TdN%2BKH32y1gyJruJn9x3%2BQ4%2B1zUvkZZYoyqq52HfJR1T3OUca8Ss9VrVhgoR1nngEaUXl%2BEwnEj%2B5Lg7fIsNx%2FsEWwXdT%2BI%2FD8UOO75zqXdKad2WS2XFS9L3XHH%2FH3m9r4nePnO%2F7Ys%2Fk6qErLYy0lJ6agZeeN%2FwwccYGT1QoEPwJqvCIveWiGhaw0jqw4olppFhqa8b%2FC%2BdTSMfJ8SlX%2Fp%2B918A8%2Bor5fxP%2FNTuvM3TtoeHe%2FThlVC9NKslFVNwNc6blq86mEHcyuYlSiOqOvmLPKAkLhPzbxCP6j62%2B9f0qwwjHOiiWI80kKVVktH0mD79lpNHuEPZ9yx6Q3iKpAKUiIS%2BdKK4ZmEpg0z1gZ%2FjhD12TDPydq2A6DMfTF%2FCCSlRZO0fLXPJsLM9o5%2FJ%2BS1UU8jHffrIaAVo2H93qXRX3g%2FV5fZeaqtAxwpeeqH3Trtr4z%2Boo5qyzjqus%2FuvixZ5JszVn3T9bAuDz2jM54qngC1XyGupJhFtiO%2BapVZZeFqvBG9bLT64cJ8zEW2juBk3sMRUbTMA9ggQX%2FxuNExVKRpvwrklXWFA5ZaUTl2d%2FRNOseP4pZYB18QoGHvnMyyNx5aXCbSf9x47s%2BV9Vv%2FQ2nBFacueFU%2BfxH298aemcj%2BAollosRz0QwI9ygM56qmBNbdKZpA5nYorobKAtV1yzEXc4xMjWQ2Xzu%2BE7FzK0BZnfYN3ezIOaa7cYL%2BL91Kw6%2BwooX2cJdqES1Yl3fwkumIJzZeGJX6gvVmzcdfOOV0JL6h1jjlZuVAa78e1Xd1scYiWP5mRhHlc9%2F9PFYhwhrwqz%2BfBOLKP8AaClJVcxhKRb%2FbHAgM5D5bBD67tR1UFX8XUSS8JcpqS8QN340V4kWuHQHNRnlvBhKfbH7Vr8H6fkKWPUwq6eGeuYgy1ZNqcPGOz6%2BR9SZ1V7of6CvfPi4Z0Y6zuSjamFa6bv2XhUpNzRDI2ruGXdyLNC1U%2FVs9fxHH4912DsxkOnMds9tPgd%2FPNEpvw99qvKrPL3r8FZaC3Vl26BE558ratV5h5brrxqrSjZWe2oL%2FbGSS62BPrSGWgP31eFR1ZYP7%2F23Jv5tWjqelgG%2BMe9VoU1C5X6i5fIfCh7GYRjtwIM65C%2FhnJc6S%2F9XnldPHdKgJoWtn6q6WQa0qq4yZuwG%2BY%2FxN2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYseVnpcgqGjNm7EYTNd1MEGK0kaUITo7BMiNUZj1D6RBgIRmWfxAO%2FFpHr1Vl6qOHh2WCxTJWwxWpzzycMW1rU6TJ5QMexTRpz5cqLpp7Tvw4WJlhBGp%2BBo7p79qA4m5Bmngs%2F5fXJWZFOsN86VmYRFb3agsKnuuswqLabGc2wig6q2t%2BjKX3TfZN4tc1P6Y0CV1XT7c%2Buvjor4tXpcgXo5daH5QVaUf8fJhRy0f8ShZltOdLW1lj1bW5cG1qRVxmL77HjjgmvE9R%2FzPNIBmL4pz2%2FS4zGXS56nQyJ70tVuA43%2FnhhXCDWLVTsHLHWSNKIav78VHwuOp%2FIIMipCx1YtdARq2zGvoUS986vnXcXu35aXmpqlsfXTwuQ3eviwCdg68rVx9cjSm%2F72K8qp0Ka69yLaEXBOWD7i5NogZ0pBfagNRNqJFxxsqEFfWPwLL%2B09uwHqe3jaYjJJ1eWCSe0SKcjaeJvbvxVmCVVIo0mRsZbeHSfvd3JVC1cDG3mnyFKyZVeNRZBYmKOKvFKsJysjg6o%2BzmrAAX9OBrN1HQg9KAVKq66wPik8r6uPCT1iQBH4jbAtCO9c7Gmb8KgVM%2BfqdTH%2Fyu9bWEasxz4a06Nd5xLnewJl9aLgT0WD2Vqon8Ot6IYncG62CUwRrSIF%2FgCGtkgnsnosow2zrYZMcO3HPU%2FiPwqxhxq40cvmkhMjI00%2FaV%2FG5xiVySOYQtkapLVRdU5HNTVb3sG3VWD28VlUA7vFWus3rbPl76oT28l06y2%2FaVj6ru%2BnC8vD4Cb%2FWFUMG%2FT4XH2hcKkKBASfsT6vpA4Buj1ydHk5BqNakbb68qVa4%2BFe7kXqsqkWWbd4sS5D7Nq8qnh9fhq6ASUbCGlDUMzYSvyueGPCTnI541tkrS2bvxqGrS9jZl7inw218N%2Fe75%2B2RdsdMygrCWQjrAh6pe8igqsjo4SsrB0VkV1VPprIau8tLvS92Xk%2BgMXVU0X8ZyaqQIe9z14TWS14fjYceRL%2B1585dWnQL%2FP5PsR790%2F%2BRHvwQRrA%2FU9YliQHhQVZ%2FkNwJPM6d8qumNqgkP%2F0mw8lE1zh7dUfyzR3fECSoZKDuzd6IZZrcwKtdQ8FVVjzwQWaTGlIj%2F8bZ2hrK8%2FmKhznTC9jV7MiHrLH2pir3s0qaWj5Q6KQe3zmoyT2uZzqoVE5InEAzWijBJLnFcKMQlb2pRH8sWpuLEltWH42E86rbr1I2LlGV43EFndIv7J6NbZDvXuNsHHvmrqvpgcshjZLvsG56mlmoIoIpQuUZV3SSUF1VlSr1JNtlU%2FLPJJlp9qqruehS%2F3vUolXr3DHZmdah6zyC%2Fm3sGfdu%2F353R5d2ebHiTzlX1GloX7%2Bisiscu11nlsp9izM7nFX8lu4aOEJeoD253wLdAkNfHqb%2B4Uzne1nzFunMl2j7LFl%2F1byOn%2FJ452FykRVV%2B%2FwUv3dqB93QSKHKdW71RtRSqWnUDmQP7T287sJ%2FL6B3bIU1yVbtd284wVNPqYwVwRMWR1T8ALqz12mMbzupQde2xzeesGEaAsoCZ%2B7I7D5y8HqqKTF%2B5qaqrs4r5U97TYn24Ui%2BMSl%2FL5w4rGd%2FUaKUyJBf1YbWjaZALrVXVx6m%2FuFM5Pr%2BN0IR9NxOqNnLKB6m1enX53u354H7lm700rf1vxKgKMUqcrYAU0Qq1jF6xzJjjwH6jGB6jaR4AQ964gTXA%2FzUUPFDv2r6dcu8vxEe%2BHv9Z62vgq7WUlzWibUsKgIsTMuWnqp7OqjUswt%2FeP%2FExJx8CD9Nqw5NRlPrAcBdU67668PbGCiq8837RST9I1ded8qspOrT6urVcqfn5%2B%2Bj4So%2BqOjPn0bQXVUcVbylY8MQuzMOLvLHqKgJ%2Faotc974YP33f44mOxUd%2BQXtZI8ZXERIvM6rq6ay2vyHKvOOP%2BD3f70P2ZwTFdQkq1P71dV%2F18N6uK6tRZeuDNcJ3rzr4ys9V6Ybqy0upij9VnFeLGzFqXMfGUzUvHTyrAXo3UF7W8PeqPBMsU6QUvxVqiFVVOPzcEKrq6Kx2LIoyh1%2FG74dfFt%2F7adrru4qu7qseXp%2BqpdYHE1IDGTUeXnak9k5sPkfXuV36Z4X%2B9Xf2vHMO%2BW4LelR1Tz2KPy8Pk9Pb%2FXrGsmM%2B2v4Gzp8YYrYjN9a7Y2eS5mu%2F%2Ft6bVJ1VlwJqkPepKi1UPRVd3fro43V1fa%2BjPt2o2k7QrQ3Z%2BA4dnVsNXeKUBzolL1%2BbDK7rU%2Fd6XTZELtRtroeDrMTsRDdVxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjBeb6o%2By0Pt5KEzRo61BQha4rq6dBmzsnnUdfrlhLodptTKuFmDVJLn0sd0ZGdafebSRTli2lRY0tO8OVCkMzSS0VYAfPQuvfUp3VeDG%2BiNKTVLFK3ZU7aM6KnDijC0TqWfiqSo2CGxdvO7TnwP7e2RBZwpo1jB9Nss3n5Eu3rMmwTX%2BdtTuaqr4lULqUjgBkOUvuaJT4gG7ntEQYW3qvxWdWaOgsrn8SpSdDAxm6CnAhXk3WKLNXSzKqWKU%2BVVGCxCXi9rBegxCbcDgJy73WXiOQzpbYhBUUKy5Al9TxZ%2BpD6nolyZQaBC1QcgsKloK4S79Y%2F4gKvIWCpm7TwRa3Pr271z2L1ZTe0ajwxWVTFnZSl9cVo5JMl3qq8r3WNSU5VVFTIKKhAlyMV5HVq9nKS9X2J9xomXApF8O0Ckq3lHKYaKHfDZ7unU0wykYMos4oZh1fpFI18QcvyS8vpwJH7%2BQrGPOqi%2FX%2Bo7EOtngJHDEi6L%2F%2Bs6hlU%2FD6CzWFkAqt1jpU9fZmf77wPSuKNxTJURXJp6cCXIyXk7WyVEWioaCnc%2FzgvKzvcsbf3lmuczj6G5UcJoZr7ez5%2B7b%2FraojcFPVijWdBE3gs1SqYquqRNyKW05noTVR0lxzxxrtADVQQkBb8QA4yaiRVjGqFKqipK%2BsJvamNFdzeR5bmse%2BCp930lWAvfEyslaWqrbu7DcFwcI3lIBq78TU0NTQ3olkXn5ZZrftQ6mrX4LAaDthLszH6iaGTa4KaZ0gHqcWFMJVlqq6Gha6AapXcHpzA2A%2B9y%2FWIqTUJ0%2FySZ324drbsueLHBt5BYmKm4AEFqw6G89CemT1w%2FuTtfJUjRf0n3HCo%2B%2BdPdC5iq1iBzqFgrAi%2FL26%2BW2rL74B0S0vUNwd91fBRFHzx7TNkOL%2FNcnu%2Fh83n6q6Ghb6qhf6AW3lAmD0F0j9pR0lQZ3a81a10iHJSLzUm9e%2FpRIXtfdACCJRo7YAeuPFHN6LfOvfklTWB7%2F%2BLe%2BzKk9V%2FV66%2B9KmNvy6qa37EiHI7otDd4QCa9F8aKImBUhkNY5MRVngeRKRjifZxqM3n6olvSrTPEMnQK1sAIyEG5liIXcATLtXVxooBCX40ttDEN%2FmjzQUr4MXg4Hm%2B6O5M1y7%2BhSTD4SzpPvQeOGt9Pq3vM8qjaq9s6e3UUa80qgahTzx6W2nt4UfjhK6g9DfgxxnP9%2BJzB7D%2B6ikuB8SXrEPKUSKfYZ731WOqtaYHXyNUQitm8%2FVPUMnQK18AIwZdXcATLvXglGwRa89kT%2F%2BeCvWeKUNIr7b%2Fpdn%2Fd3kUxHVC%2B9P1NKoauvk14gNLtTBZnFqW%2F3om1ngo8BHzaT9Q8Ps4eeEk20%2BB3vo%2FD0prRSwxlqvJdmdv6dQCbuMB%2FoqR9Xm3F3rB8CVyAAvlwC4OANMFwHUzQB3gbokPzh%2F%2FPFNuSlcxK%2BrEeSjELUYLyOqN1WHZmQ3Jza0EBtcEGgx7wp2AkmSGGaURUlymJjHPZB%2FvTH4d%2FbcXPJXS9YenlZaBcRIEBNL%2FJxn%2FlqJg82hnSQGbaTkeLeLydIgJQWcyw1%2F3Rlgan3UVHVUHIGB9lFKWq%2FoKkg%2BKlHdeGtMRlS%2FEMAf7955hpNV1RTFfwKhLYYpkcO0DmK%2BrmlBqNnzfHFgwb9GrQUbJ8GfH9QrCZjh5zReUbZ5C7R4i85IKZIe7iPEdF6tlT9AXV4ZYEHVUupTiqpxGahqZ55COsVyPGjMhsoZUhVuEYUC1cp61BaU31DOpsspEafyWxUrlW4L6tJN2VgB%2Fkghh6a0uX8w5o3nSQ%2F3gYmUcgacyw2v73NLz6HVpxRV41Iy8MtUCVj9ZwDGtNqzRf0TY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8ZujsFqucCyqMe0eRbGjEmI2nhR%2FHG6FDdmTYPuyzRFFsxGn7Euw%2FqXMzQ8LucGaZThkjub4WXQkmmasrIxYyU5%2Bbrm7ksJ6coR7obNoDIUgX%2FNSo1DlOxuxtV32QT8D%2FgMRcKk%2BWOQRvmKgrSGoRNgjuA1djarJcu%2FXfgzuKpUq%2Fx57MRgjX6dmqhhorKyW1sW20ofqz5LDw%2F327dsPDLvLTS%2FWYrnS%2BJoZ1GwrvZM08R4nFand95Qtux%2B0ckDH00N9c6mvpCPkbgwbCAzmsaF1mHFiklUiBnIgGh3SqwTbbyorOjBqK2ZFHhJTaTVuaW4fJ2rVTd4y4ZTvbN%2BMp0C35UdP4prB1cxaw%2Bl%2FL0TQzNd2eM7Wa2VblPeAZe60pFBF%2B0fZbpYyll6%2BDgsJ4J4iTwNEms3SxV4kZ2D3sJd1vkks6V4viSOdhYF67RnmFQjd%2FuTO2%2FwH8n98sJACwjIeqFfVtDa923hbnyiHUjWte%2FLaB3l2FpnLV5UQW6rry2HDCmD4JavQB40nVsq1Dl4S%2BPFxovn1sGysmoFPsUaISwHAaxWdMs6Zfmw1A%2BXwnESbjglb2gUssrpU4QoMuhcUYfLXulhaWfp4fEa7bhGl7gzgLN2s1RVRBlWuGySUcjqhaesV6Vj3e0ZJZJV4OWqZY5xqTSf0p3Cui%2FtvlW%2BEA2lJIZf5p9RFSHC5LQ%2BvLX4EcnVdNd86SDXLMibAkc8FnTfQ%2FelwVvUeFRYbWZ87AO3PKnET9qhr01U2QJ8KHc6zNzjX1jaFQik0%2F5qN3ewtLN08bzlEyC7CkFY382mqnDZJIkaXnhaq1Kx7vakkdXB62qseJTuFObWYPAzFD0R4l6xD%2FEcf2xssSvLl2W7H1HiG9k4HGc4VguHkSv%2BOU3L7wHrL6OqwAeejwCWb%2BgxNHNunWIUmwyhHut0WNncjRfbc4r9yfzXdkXAbCtAXIySZbgcLOUst2PRruI8pyiGwmn1SFZZqnKXpVHDC08VN6Nh3e1JqZE%2BUSVk1SNqVdWGs3w7DEiv2FtiyMLB7rnOrMcjysrG4aEZHKvzAixX6XM3TlQZMQR%2B3dTpbRAmx7uyeycgXK2W45%2B%2BtwuSYu1CS0oSvm845SW3IQ%2BYhYAklapRl5uoz2o6qYcvplJYKSRTeaoW3rN%2FDOSNp%2BsQUrCFRFVnX0T7g%2Bi7psZKz1zR%2FeoStapq306uqNaWU1U7frc%2FtvdPibxomHNz%2FRdk4fXGo32TDjbOKFQNPBbN1V%2BeNMnv3BJiNfj11BbWIKNeTguq9vhOjCU4UWVqRrw3dG%2FnpO5NedvTqepgKWedW6eHL6bS0MzH99x8qrrvwT8G8sbTqUrB5uV13Eenuv3jTO%2FlnZWOF94vfz2jQ1TcoGhkymlmSLrU%2BGPHnoH55tniRzT2jCy87vjz4fWOsmCSRNWew3sneP2jykdfcC81%2BOKJQG0gK9ckalbUSKi8ijPVYQ8ihZAqxakcLOUsoftIv0rSta8PyLiFVF5ReaqibyZJw4kXnkpVGlZ3Vx%2Bn%2FcNM52VNuPh%2BA4%2Fx1zN0oqK9kxCPHLK7QanjNvTO2sFyzN13yaTKNpyNstW%2F7b8g0PKdXDBIwEwlTKoaWDXqkmOoocbnk0CgSEjDgwRai7p8Tlbcyys3V02rwx57%2FK2mqB4XY2ln6eKF4Cq8zuqkyLhVmqrcN2nDiReeGqvQsXqKiPpk9SAqzAw%2FFa9ndOTHWPWR7Tju9cydT6mwWM0I7rA67%2FRdMvzJjV3ZcF6wuCsr8sc%2BwfgTuEGU6NtW2ptF7Xvi5uELkwK0REIuyCaoHi%2FF0s7Sw%2BM1Np%2BDqUHjcnhZI9yVFvd54Sn0o2NLURR0yKrzsqbgfq0AzvC2%2F63q9YzHxSEg7Jk7tkN9HsqD7p0YyHRme%2BY2n4MkTqf8HFZzagv%2BuQF3dXCYWil6xYH9fPotlHcP7Gcrbh7eTVZqxi8fZCtVj72wlLMEyn7Nr8Tb6sUdsmmNl%2FM6X2l4mqMLd7XqaHGfg3I%2Bqemngy3NeKvrvKwput%2Fdt9p50MZSBD0hIOyW08gdBsOsOgX4DopKL2zMFMpJa4fUDsNWQLlu5d0VNxfvNDc14%2Bc4q1r12AtL0UrmKP6cVXiaevGNMXedKV7qoFyf1KOeBvZ6yKrzsqaoY4IGqKkyZszYjeh2qiuHNmbMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmLEym65O7%2FcNb59Dlp1cnvWvuA8FoEYxnda02A2oVVr3SlbMmlyuNM3p9MYZTaf3%2B4YXZ3EZMXp7JrOdWZ36VAp%2FoyzwEUhYfkStC3XBXMGdZ%2FT3fXj%2BPr0rWbHAAuhkTt7Mrobvb5ETbA24WrRYK0al01sani9qw%2F8rU34yL2pW7vK5OPNKG7tyXq3b7%2Bges9SJXQMZSn1Kxe%2BdoOkq2488o%2B0yGtSwJqNK3aPrpWoUBU%2B19k1A4tGvBCMw4BPgpU%2FfW4n6U4nK97ewh4ZY00JeBg11evllhR6QXKfXwbudXYXvnf1kE67f%2FGQTLrClly%2BEoNR4WEUKomYndtHrT7tftKaTHYtIiiR76CUQO5VrGOd0jzfegd9tvIPFkXzy%2BgidZHthoQbeirVeo9QfhcebFqLajkWnhjUcyoum0UJyXVe3%2Bqw9qF%2B5mtHDU0G8E7uGZij4MAggIB6F2W8eVdc1xxdxfwssH%2BsfX1zXbP8CdXqdy6p1eh28%2B5Dju7K49g7Xb%2BKaPRxZaeX3zn58D6eICg8SMCusPVaM1fK19pTyhQCoSpfYqguz3S%2ByBpvejQ%2B91HpN1tBc9xh6QxTlTMP%2FscNbVfUROhd8awUqXjgitpBcV9maXA0uqO9YVGpYgTULzlNrvUYZi3VcHZQx54MgrZ6XcSeO9m2%2FEsSjL%2FEHUb%2BdlFXYlaMqq0HJvUROWteuP1%2BoGlsUI4wNy400%2Fjq9Dl4canzCFRY1nUwQykf5rYEMEnx8AKmtwsO4wkJXezZbdVFGqb%2B4A7UuMYq0JHFGyA7tObTHAodJSGcpXPeY1Q7N4J41sOofPkPDfyPHw3X2WAHcWqHpJBGfIyo8yAYZHgPfkKtzUsl2uTFUagQ%2BSri6bRgRPiqnq%2BPGI4miKK7pCsnpW0amaMRz16n1NfUc1SpqUcq8liaahugm1%2F0mUOqI47vniptMrtNbGt5RlOGqMSr81BCrhyCvmjukGp%2B0FY9YfdNJlc6wwFsFUYQ%2F3nEQqFG9Wg1I6B7z%2B%2BT33ZmV1Qfx1p5WtopFc0KSBLxD1Fo5vulKVEu2ywurogaX23R1gZ2KGXChq8u3uuoLL6lP7%2ByxHbQsLmuhEFVkiXODT0hNZx0RND0tKe8r2L%2Fo%2FZNoMsF%2FuU6vg3f3L3J8V9ZRbj23DkdJVfkBO5OYJ6oSjweEaiys1Bl28O5m8cez%2Fs8G7Zlwv2WPrKj6JBP14LrHVgxHVBxZ8XNCWh%2FET4cxcOdqOgT8mJuocvyxHY4ibqlUVVNDZ5ycGnILx3Zlp4akQezbmLEYmcKYRugSQyvVliOLW0qWuPJUZf0ndjkt1DMHCUTubajTu9R1%2FXV6Hbz7kOMTtpg%2Fzt0wu5UglG%2FL%2F%2FcJotLq4w5o1XgR8Kt1iVn10AwmkxC37qcdi7tflDU01z1mtdDAMDeH%2F2vXnFXVB%2FAwh7ddEEQ61PjmguSHHM9qsWR30C8XHiue2lCooUdVVvPkU47nPPmUXDKoHXWmu2F0tGMa1V4IelncUrLEhW1D1CvU0je0Arz%2BvCOL4wYsfPqBOr3FaRaZTq%2BDd%2Fe6KnwUXnPg3G3lfJRYPu6WktCqj1793TGBXJcYI4HB0825lAkmmKSPJad7LDLAuFWIqj6ID92OLghjMAGPNYF62COqCm%2FrZ4X2TuCj108rqalRyuyTrRCS7yCCrhCVA6IG8xHFLM7My5fFLSVLXPm00u5bU19g%2FXmKqyub%2BmL3rblfOYrrNJ3e0vAio1Wp8iuH586OYXCSfbJJrbjo6B5b800sQqqPLt5OdBHxnODHd6oFSJcE2wRqlOa8rIUH%2FKxFiQy675z6EoWeTNLNEt%2BADDBEWCipa4%2Fatae2uJQLhU5vF1Wn93uGF2dxdXoKUuged2vVp1J4JxDWdRpa4Fua8x7Z3jMnn6V63Xl5srjXkyWuPFWFjmhOW7mm6K71dHq%2Ff3gx4yBjl2P9q%2FVdRgvdr9VCqCJdAWFbSha3mKz6RNW9W2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzVgaDZXN7TCsYM1ZICw1dWVC6QQmPPvtzn72m%2F6DGlfrg%2FDNqWUZc2dpzuOL3PW0xqhwm1PqMVtkZlcJBHsm8P2tcK13GNnkOD%2B%2FvvpO%2BHbsxisPqZ3TdT0lXFzf2Ye9sfDEIq%2BytySDoqfXOxj4kXWUMnTfI2tidv1evSQi8FGU%2F3qbRAUwjleD%2FPh18x%2F%2B98wrtj9KtsQi0Dl3a0gpwsU01Ta0CVY1kSfqyoDYx7f3UoPtlHse8f1m9R%2FDw%2Fu67aO1PoG6H1DPn1a1SOl4QFddrXydZdXVxI%2Bzh53D1IwoQ4kq%2Fh5%2BLqB0y1nilmfHrbD7X%2F09qqq4F2cz1D9hjWZ2y6aaDrJ0hldqhK%2FBzWi%2F87eze1wtEkX3P4TKeoAZBHINZDW2ZlK4AiHASrmMPI8Y8J3i7z1NLeJafYOWmKldbLGV8kY824rdC%2F0gp47aka2qVyNlwmbtEvlUoMnF6eIeoOenV0skqdHFhmS8s6hEKDBIdWlu%2Frwkwzz797NOop9acFxXzf4yBhXhOKWbvxIP7w0Bx1aiHzdH%2BRlXVnb%2BXdxwYKLcXuGE7y4scE%2FAPPtJ8VIYXY3wC1HcHMqDG9xK1ZWlUXSrmIV9clVcFBBkPR3sKjw2n6F2BrF7Q2wWdBd2F38laNczU7bh0fJGPNs5vWUjIzsjr339h6b0Ov6zb%2Fv5PQL897auEuMQLbXleYWvkvxO6uPFF%2FC6%2BqNLFdfSL7nrgrgccXSPZpbmgaFd2%2FCjr7jrULJUd4zYeyfVBk%2BvfiUrnwo3vRmGcFuLeXAsI4oJ3qfhX1%2FSmZXg%2BdqE85%2Bs%2FfP2HttQmaV8WvnGGeusMz%2FCUyV0LZbJSX8Q3CKkRfCYgYxoqj2sV1oA0ZtTxyCxKIKtVl%2FjcGV%2FEaJP4XDEWQaSE7u7oGvmVv%2Fm1pfe6dVy3%2FS2JTFzPnF7n6n4S1FC5aHLDySp0cWEwr4NmVOriOg%2FfTVX5xTsWcdQ%2BtWV0i3W5TdnPWcPWQRE%2BhhiOgOGr%2FikuFCz5oDvJpu9DAtpiHXfbakUHKfjUV7DivsUfzw2FxkbTq0D%2BczSNomUUojZe5LVROW9JfTTIeDz7k9Uu1UI%2FopZC1cLfq%2BsjiJokkRXlSHjsBi54OSyVzGEhrlIIVOhwZxRkNbr%2FiaX3%2BnhCt%2F1lio6P%2FAKyLkw3A0ClKu%2BeChVBc2R1dHQDjwUeU%2BviVlVh4IsHUlV8lo9JCXgYR7YH34MkVK78w%2Bv98a2frJvCG%2BudFSNfnPlJRvdNwoz2X1j1QGb1b%2FfZwlGwSUTN%2Bn9Jsr5JP3z3JY6%2F96Uk23Icf%2BqPFwmlnrkZe5yfifTMRdTbTsS43Bt33pXzsnEYHXHpP9UD7eniRJ3YvX2tfQdvlS9g06OqQ1SxaQmdrPE8Uf1Se1x5WcfpxweK7xRGwZryURVKa2j7yvuc7a9eP1VzOtlFh%2F18HV3c1vxWA%2FIAFSlafEjdanNX9uHnAguRfOlIJ3981O5vkYL78j1kfIPfvGoggyPi%2BVTn%2Fw7aJZ9P4cgJVwj64SGgTiMex3fMMVtpf7xIKL3wU1a9%2Bdzmc6z6hZ%2FaWz3UyYiKgakY4zefs%2BeVMf8HyLO%2Bhf9UXUEgR9Tmj5vssv0zwJWlqkNUGNe7%2BWxSvd2VQ1Y5Uf0cXFYj3Jug8E4H3tOPavzLhzG1L%2B5zjmwAolKVz8kLJV5zMZOujm5V1WrGD6So%2BCy9eH3%2FhWaXtH9X9nxKdUvYGHf%2Bnq1wlO390OdTvbNtLPA8nII3Fg881gaP3%2F8KiIcEyLuv%2FxCdhdVbB8NSPCaUgKJw%2FQf3P7gf7wbJJ9vvLLDQmQWJ745czTsO7O%2FMBhbKNapiSCTkM8cjuBFRkzQDXOy44hn4X4HvpwcZ1z7IL6d5d%2BNPug2nckQNOakf7wSXF1lVRF3q4JgJzonD%2BqSiQHPK5ehd2cG%2FKydVYUx92%2FsMaIOacsxVsRXd3%2BcnN7o6urpzVXxrGylI%2BoBAp%2FThiPFo306%2BcR2eI3%2Fom8%2B1wTiM73mty63gWLLHz8WxI%2Fa2E3f92Qq0MSGV7Z2JxhdSJzfi52bIdePXkxu7sjCK%2Bb6%2F3XAWpCFXiLrDllcrTm3ZcLZco6qgmi2HaW9EJM8Ao%2BPyZ%2FrZoMuFJZ0l77zDkPhtyu1WgF2UzLGcmXLhdyqyylrefb%2F5eZsrE0y7Ck6%2B5G%2FndalqBdp9znh0h%2FSVk6KDKWxTn%2B90dXH1qNp4sfvS6W2gTgd5S8xVqgWgT25EYoNoZn3ra7w%2BzjYa3g%2BddcBmVO8l%2FpD4w8B7h%2FYUpiA88ZCuuAeS%2BtsObWoDvKRGzR9jwm1VvhvDr6vsCKH5Y9%2FyO7B3FYrtOJ6CQGRHuUZVQbX8Jo%2BpgiDJ5xrq0cs9bvBx1TVdaVC8iAj5fad6Duo6uWvivLaR3S28xMqTuwnOmmwqJ1Wbj3rjYXirV4W11A5G1mRaurh6VO2%2BBE5Sk3Mrkogjq%2Fn4HiB3A87AemdpOrewTUIHhMBx%2BL%2BedI36TdAN%2FGwjkKhe1idHmd%2BjjCpe2nTbe42c2NU9p3JF%2FbnqUpO6bp0OUfkkYWRKbMQwMiWfrlTail6KhNTdATzTfmenP9ZP82YqVdde88Y%2F%2FJw6rL1OooqelK4rm2%2B2W%2BBQvlXSc5J851GTu1InXedW8xotTClIDZ1Lv%2BSQng1tWcvVbrVcsb80fVmp61bTRL0LzmjJzfhS8Km66jtnogXVLan7JxCFM2HXoeZNqAxErWijfQcftDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxoz9%2F2u4KsiaNO1gTOYkgWLZCarynNZV6uyy6zRcFwRkdJzX1iee%2F64%2BhRBIyIR81S6W3CtZKy%2BHR53neZrO8zLFT0NXdhn%2BH9PyuTN2Gx0sqydraWHlz8rYyAyt9KVXs7%2BJjBQLTzbfr0X0PUh2BQokTLBsuXhJvoED6LqNFxsvhph9BYoOXl3o2vjRBNN4jJlcMxzU6Azmya41r%2BcgsAakBv9R0VQdJkfnOUHUeV6O%2BLav%2Bv9p5JV9T9zzWzXescZ3caU0LHMMSkrX1kkubXlF1F7rEyWIzRaWJ1YI8UJ%2BvWSB16%2BJzg594yoW%2Fmr3rSqHCX6ZyHUDwY9U5a5rhvWhGVxmhkvQrMwqtq5ZXZvA853ZX3YnSRLZ1suo5rrhFOt%2F9umRqTbSWAaaCJdv%2F%2Bcnn2omdQbN7Mmnbv9nGAX66FQFXZ1v7QXyhNqM%2FsY5lA6rqfNcKr53VkjTlr98EMwJxT6M%2FjryNUUQRvgnSgQNv4nLHP3JnWDutsQDqSHr8kGmr5jYw%2BquIMkKxNnnZX7Ax187qszwETXn17HPiqka%2B4zguOmVi21fJv5P97%2FGGatWadrF3es8FX0iq%2BmdjbrwsGhXOdZYsTb20EsYH1DGow1HhS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2Fy8vL6%2Bvqfn5%2Ft7e339%2Ff29vbo6Ojz8%2FP6%2Bvr19fX19fWmpqbLy8v6%2Bvr4%2BPjT09Pr6%2Bv6%2Bvrr6%2Buqqqrz8%2FPt7e2ioqLPz8%2Fa2trW1taioqLr6%2Bvi4uL5%2BflVVVXNzc3%2F%2F%2F%2FW1tbj4%2BPh4eHq6ur8%2FPz%2F%2F%2F%2F29vb7%2B%2Fvz8%2FP09PTMzMz%2F%2F%2F%2F%2F%2F%2F%2F5%2Bfn19fX%2F%2F%2F%2Fy8vL9%2Ff0AAADZ2dn8%2FPz7%2B%2Fv8%2FPzp6em%2Fv7%2F7%2B%2Fvq6urp6en%2B%2Fv7%2F%2F%2F%2F4ck%2FmAAAA8nRSTlMAGgDUzwIP8SMQ759fCgUvqfDGFeIYA78fbxNTt98%2FhsV%2FBhdD4Q1rRI%2Bvwo3ATxJTD18IoKWasozTETbQ4D40IX5hC6dAMR7RXydvEsRuotKLkZCATYahkzOxQlFqmbZwJiUhFWy1wyJYcXI7gB2XIEFbgjxgiWFtfTSFMy8wSYgEqFBDTSE2KCpnSyZZUaZHRFAsDuWBYJJ7AVZQpC0Z6njBKWjdN4dlMV30iN8bV7%2BzJJeHMRiDYsR6U9yVYxdP2c1dj8CKFZZVFjtaaTxOI9cMKQk4NnBW4PKUOmiNI%2FkwWoQYUdQOSk6GvkUURFSM3n71h14AAB4tSURBVHhe7J2HfyPHmaa%2FYicCDTQCQRAkQWgABpMcDSkOw3CGM5o8Gk2QRjlZOVjBsizbcs5pndb22r7d23ybc7zbdDnnnHPO%2Bd6%2F4FjdIGu6vmp280CtbF%2B%2Fkkn%2Fnip%2BaPSDDqA%2BFOm7J3ncIoDi0NAQkY3d2IaJSws2NNZZnCouduhAsrgf7o4BYy59O4fvn3ROJQKoREkBGKqYytAJCCEQWh220I81TPFIozLaNiCMH4PJr47WIooL1C1isUUsFSwpkMqPAMARDUIFjLcYpj5tGbmqw%2BP6bhz49jZwbZ9S9k8Kd20QQLDdzFbdIz%2FJJ0OFyJFaI6mOcZ6HGOzAGxdi0tN8JL063CmJwi9FviX34m4FUrlxl0PgaBgIbrXJJfVp08yTrbq2tt99bINtCj9p%2F2TiZMMigCzYGa0WxwBgrEjxCBUici56obyLjsF%2B%2Fez8KGLwUdxVJuq9HZcpljX16lgjlaeg8hTpmUVNqubcUjzNKnBbGIBbJS6pT8nMo5ilAms3kxsAbElvsP0DqP2Ttt9KsEYIoBELpWxWDyHMocSDdUiCYMYDvJmAl5sUE%2BcDgiaiLMd6FrTJUmskFaTyEah8JPZsiru8WGL8ouLpx2rfqshkVQix%2Bz%2FOoyRIte64GalXsb5%2FJFX7J4UfxsVI3EUcMyrVrbqAtbVVB1x96q39f4Yo0goYpBJ6EmhWa31nx3WrUmskFaTyJah8iVSofNrqY2umHOeNsyIQ457ksUTnlP0dq4NfVyuLrpTKLlHy0sUp1UASq%2F2Txr2ASAiiwJvNYrVzBLjUbJ4BjlS0qbdE%2F%2FStUgAExAMyWG2jI2EFDd3qujNsWcPOesxquY6d1OOSmiMbId4YCTT%2BBEq0xDjRKACM8mO1HwF2mYm%2BDnRdrRRht5hUmRPHAeD4CdL3jwBEBQ3OheC84VE%2FXi2L1Q9fB14kOgFc%2F%2BzeVgmgJKuVTnzsPSh2iFoncY82uVrw14aH1%2FxCNVbszO4heYa0vDPk30uc%2F%2BOxv2LgBAAGdjSM8R548OvqoxHiUl0bYWyX7R8h1P5J22%2BHUIlAxXSl5FYDeZS67hHgTO%2F%2F2aoPbWy12r9JqFVifFsqsLYGbGtlJyq%2BV2TuBRrA3WTgs%2FAY70S30519XFebg19X3520%2BbakctBO2j%2BZ%2BNt23qsdwduyWCWnjjB1h%2FZvdWkqhPxKVhi3gMamh2Ilhn304xeIaTVJpVlsTp9FLRt3F9DPgvtmX1czbf4FSeXghcT9k4WXLYxtg8oYrLJRKZNTC7XWa7R%2Fq1RDCDfq7RltpDwixPTcjIdii1R87MYnptUktdKYn6Pz89ZSJj6l6k8NcA%2Bc9bqavvmF6jbdHqwW0vYP5%2Fq9dLGo7qVTrY5OXKnXr0yM7m3V%2BFzIAs4S0crEckCmBDOedY1UCmI3%2BtN0LjUucan0yDOycjD8PZk4VJDCB3%2B%2FqmmtSqlc68g2dUYKlLJ%2FUrgzMl73Gu3xESfFqorzwO0OsXiI4kmrCe%2FSRoQ4T3slOK2ea0qa001Tgci0E2TiQkVk5tHooO9XnYJDWcP3TzovT4xOL5dJixDqXz29gHhGRZTRIRogzT2l5mk6b8F%2BG5KhtyB5%2Fj%2B2mie3mie3mlvNk1vNk1vNk1vNk1vNrZbouy65VR8%2BsST3UJbGpopjJYZdoNsFXGOptDrpfAn9LGWvU5xaLJFvmr9Ycu3kzstYuiHrUuaUFsfGFg%2FyQOFa%2BHaCIAeHMNTnPgA%2FwSrnrg3UPPD%2B1R8AHnwQ%2BIEUq7xONv4w%2Brk7ex2vBhRhng9ks%2BoyGAXU6XYrROTzXnTg4PrRW3EAIQQI8tueVn3I%2BGarnNuoz4%2BOztdZ%2F%2Bpl4KWXgMspVnmdbHwWIgxm91XHA7JxiJ1A64jHvJg0WS0CmBqzWf3NLSG2NmGTnopCOm8l8RZKpNsDSbG0l1UfUXyjVcZLqE8EoGCirj1cC%2F20Eq3yOgjrML6wwHgLFoWxUGHzicx1iB4CQJy7Iee7S4biAw4QUM9k1XhodzE%2B1yRqzo2zk3YXkPZaZODoEJl48avVU5pVQQjFJltVUgHfZJX9N%2FDDuJ3Cerdr%2FasvA5icBPByolVeB6yO5B2go%2FOXdzpJLuAVVseuGOv0%2F2M%2Bce6EnO0uIRO3elPbciars9UyhSlXZ%2FkJvoVSCS3OaeNRCTi%2F59j7UGFc0J7XVSWVad2hpv5VEO9fPQXgwx8GcMr4CRybTHWg6ijungJOuRo%2Fhp%2BgMD%2BBw4Y6Xb3OrOSG1BTPdKxCJZNVfJn6%2BbKhTnMcGG9yTv%2B5Zrb6CQT4LLtQEAkBIRKtFgR2IgpZrDa8aEzvX60AQBAAQIVi6bdzEa9jA7aso3FnGph2NA58ncJ8HTBsz5%2FQ69QkD1OM9TmNju7yYsqxmtFqI46fo36eg8HSzwM%2Fb7L3fR6RkYPwfTdzQVBtOklWuT1ulfevCiH0%2FtV7sZt7zd1ovM5F4KKqozgBpHMA6BB1AIDNb0yuGOvIVPAk8YT1BztW3fq5rXMb9fZjcexEEwEHpjPw%2BLjxDPwH2xHgvIIPMy5EBqtCiBSr6f2rswAaQjQAzCbsFVYn2NwMVB3FCWD1AeC9RO8FADb%2FmZ6az7fzcf15%2BWr7BzhWnYnV5irr1gPtWCX9swSbQHOrXN5qck61ByTgfPY9DzUC%2Fs6GICfsbVXCFKtp%2FatL%2FY9pHQKAJUOZyUnwOnNzYR3GhWAcKmDzHbU9GfpsvfcpPsh11ZhNZXVTG5qbt6hJ8l%2FOT%2FeITPyjX6l9kG8mqe0cwGp6%2F%2BrdAHCd6Lr%2BewKopNbh3Gx1kDoET%2FHBrqvGzCmrc6QlGFFA480k3jxdZpsJEoCgeE8lhfdQQ2JocjKj1fT%2B1RoAvJ%2Fo%2FQBQS7fK63CeZjW9TsOrM14dHW%2Br%2Ban6vF3qZbJKyurBpGnQQpicNDY0E4aAXnQC73%2BNh3XZsv5V1ow6RzQnv4%2FyMjJZ6nDO64jMdaZHJxgvUHnZND%2Bh%2FuguHdXmU0KEUF8PPpES0esZG5pJDAkxdDPOk%2FdC5Mmt5smt5smt5smt5lbz5Fbz5Fbz5Fbz5FZzq%2BYGw13usyHXNjcHKKrHZwuvpKWLinmDsECGlAwdND5R2vIg39VWq0atfV5Y14fs2ryIktVqjbUepmUW9zI2CUyes9AlnvJZtEfiaAEL%2B7OabBqJQ619rSnT4jwKiCHaRaD08MdFvVDnWhVXWpm9jFYrEf5AwoYQz1INNQZ7QG91GJfJkH%2BGBzSyghWyJoUAhJi0SIXRAaw292W1eSBWE4uI3YAIGAOYVsWl1sGsvhLhY3FahN6SqXLs0xZKxud5QurmeQee0xGIRnrRD%2FVGFE6iJKIQj0gdYsjI1RCzKhErnWrVj3Hy0Y3OwCDaqx2Ya92%2FVeBYhK8DbLplAbcC7MT2vh%2FEMZPVyhraZAjgMGRe9S2RQiWJg519D%2BoMnPi4e1n1oReZJXLHKtJqNUFrNaZ1EKuzIswstzp%2B6dK44Vm%2B3Ah%2BCmiZ9%2FsDxFNBnX6%2FrTYVHuwMvJBmdcFs1YdmtYp7yLVRdEFUSNBaiGsdwKqKxq0vAF%2BwuNVTn%2B68buH7uQqxdRYnXWL5XXxtYKtChfHEgcHPwKEeSixPJO3BZHVdt1oQc64NwEZM3zqpxPn6%2Bpth9fiLwIvHmdUOwswaVDTPb%2BB%2BYnkYP3dwx2rmu6XWQZyBhdgogHj5XYTChhCm%2BoWqZtXttn4EYW7Wpy%2Beqbi%2FXshk1dqy9mMVH9ja%2BgCY1YcsIcQW0DGp%2BGkcJpYbeGlfVktAaXCrzYM5A6%2FQ3lZpJWE7C9UYr0wBP4kwSh%2BTKrmSmsWqNdwctrhV0Q%2B3ipMnway6eF7usjYe4lZbpRpeIBbge%2FZlVZnI0nyXOjD4PTCHu8hk26tvh6gQ5ypKH5MacSWVW63G9VnDTriYLnNRhEyLdWSaWzLX8AU5%2Fkn98zpXwW6X1Mj%2F0NkCFhKOym0emVgYbKXag74HNtchGGyPTmyH2VbZ1adLVbykpGpW41xKJalV34qd30KwjkxjS2YX4WLXFfY%2BFmEakz3SYpt%2BH7lSXWFHJVud%2BvfXanNAqzxpj1vQpSpeLmQ7VUmpUivrTw9EmDJlyty25SD6oVHD404zqTQiMaOF3R%2FS%2BIboZ6Mw4PrDB3UGFpRchxTkSX3cwePQd0ZW2P8ZNHkvRJ7cap7cap7cam41T241T241T241T241t%2Bq2aOAs0rdVcquuXawYFrpdTF6%2Fl6eDJUqOu0SDxvdpH8mtus8eR9HUnQ3ftK4vANslPSdxisOlKcjZ8uc6jI9Ryzy%2FWKEuq%2BUn9KOHW5VA%2BVASn%2BrQAcR2wy%2FJvAWwoYjyuD4vFDnwTdi33ZhV1z55ybJYx0B9sw2UDOvrRqK0dAHcZ16C2xp2T1ywntO4d3aCcJXPH696M4EPm0s1aQWkISRQPpTEgcUWGQKAkLknBtIRlFbGm6yUokyeHDJyGLT6QKRVTcPJS8P6Wq%2F1ibnlNg4b1tcFwHR3jOunn8KmEGLkhG3fMeJofPR8yQcWXX1%2BuTAa%2BMAFTWpBAKLAtALSEBIoH0riAIrdwa1KEVA2OAfMQyZ5csjM14llHX2tahqOX2PLSr0%2F6kkwrK9r6ts%2BFcKaq1eZix7h%2BAnG%2B4uZr%2Bl8oUK%2B3p3hLiJURVhkjyANgQzUOJTEIWN3BrUqRSgbBs5KKcrlySHOE1tXIq1qGl8V1MPh0KJlWF%2FX0AVYQjuk9xuvb7CGTzB%2BP6w6UL1D4wsoLrLttrEbW7cRjpGR8iFXcaMl3x3UKmxlw8BZKUWZPF6IS%2BVauVSVNjDWHQMu8PWBI6u1%2BEFc%2FaTBNQE7Um3Gf3RrZMIbLzgaf6cHKTV%2Bgr%2BgrF4w2iAj5UNrGmeWpga2GmXNzHkpjbKXsc22v5HUutIAsDbEpao8gCg%2FxtcHJsn19XV%2F7kGE4VafkvVtMF5oEp0ul3QezHhSKvTXYfSpJ%2FY6BSylSKd86A7FjZaqzwxsNXqEOxI4K2WmI2InI3z7fTLGDx93KHxLY5ZKvQ3IbDYN6wMDgLa%2Brnf5i16C1Y%2BMb9cHGJdp%2BpwHMxsFAkjTGg3qUgkYlo7MlA85ihssWZMFZ1Cr1rA6TgycWzVTFcP2%2B4lSh50hoqfkWxopleddFoD6nGl9YAD6%2BrptrB2SuE6xNNClubLjdtFgfDtmHqworrRG72x0qQSEokzUOJTEw7daI71B75akTyXVwFkpTrlVrjVZ6hDRZZy8ZJZKzkUPjTNkWh%2FYsJL%2BxzzIeLfH8Z3YyZ0D8eS2ZSAUlUD5UBIH2qfPD%2F7ORvpUUg2clTJTocK33%2FzOpi91iFqwfvCaQ%2BYEM43H1FjKerzN01UPqJ4O4nj1XAMyjXOrA%2FHktmUgFJVE%2BZBELueNyeVmQk8mZW7dJ2nI5VIljwaZP0Z5uFZzU34kdYiubY2cdygpwbRylLoeb7MwKkSB7ZjVaSEzvToYT25bFkK1IXPKh0LkcD7dowNIVNkx8ugLO%2FY4TddqbsqPpA6R06TvvORxEnDeCzFo8l6IPLnVPLnVPLnV3Gqe3Gqe3Gqe3Gqe3Gpu1dzf%2B5Zx1ShxcPUHT2uqktR9uN%2F4ST9UWThIq65taI95S7gaAsyddTWPzc%2FCB85JnHT3ZdVuUULel2C1UsTCYK8at0XAUMsNrdqm9pi9uQd4%2B5kPq569Prk%2BgOISkaEPeXS%2BDns%2FfJzXJ2oplMnGAoC1fVlV28%2FkGVX529x750BW5YcvgEox7EYrAYAQrL835ICJW09Xq09b5vkNGPi5kYl5jbPHVVmrjQMfqpWI9SE%2FQvTIRB0lnQdEgZlXarw%2BLRVBaTZ4o3opu9XOVQALCVK9%2BaqxkjcTDGT12RqKQBG1Z4eIDgMAEevvlVyGc2%2FYKRScYc8033pmHIxbq1crgZWxPrm4qwyc%2F1CN9D7kCnwfldtxTOPRRxc4j3biuOLqyKOEmGy0apCptTJa7RYRxnTc3yvlFYxWpdRBrDZnPIQvjuYQUQ0QgkgIredTchnOo5PRGufWZH3Y%2BVmPceDUZ12Ac1ld5zt%2FBF1G60MOqkA1CLxZjdPVlo01zkOpM%2BU4b9kpa5oxGyf7%2FGQ2qz5UCyrLyoaSp1V6KKVtKfvirkNEDWCH1khlL74u8Trnl3oTTqXIOXBnbw0mTgSdQ6bXg4zeh7wePrZX0%2FjVsF%2BS8UhqoPEppFhlNkaEAKA3cJJtvi3oYCfWY4a73BUu1Q%2Bri8JBWj0UjbP%2BXsllOPfCRtc7PJ3L%2B8RKMXsd8%2BOKKzgngMmqJ4TWh1xBtSq%2FHtL4j1uyC4vxUiRV449ZKVa5DfNBecmDjHcpjh9FYyM81VSHg5S7XHZXPJBVMe9J2JgXQ0Rvi8ZZf2%2FIARO3Xd93bc5hd4rmOkIYuSASOienWisBf7J2F%2Bl9yMF8oTAfHNHrHHGGq8MOMY5Qqs6D4SqApHUDlY1Uq803IPNGM46xOb3S60F9JIHd5Wa7K%2B5vjcj8B%2Bdbxei9SbE1FPb3yrD%2B3r14ESgmzR%2BUE93xaQC1u8q8D1neA4%2FxOmO%2FUHAqBo67AmKcnMK4B0qIspFqlVY3AGysanRzTgJrppx2l8vvige7W7pmeTPAjGddG%2Br394L19751nJzCFeDpsrEPucjmZ%2BdK%2BIxFhjAbKVZpxYK1osO56MGDlLtcdlc8sFVn%2BHQABKdl735Cf%2B9bwtUQwOBB1g%2BGiYfZSLVKFxuXyBwn5S6X3RUPbpWcJgkx1JS9%2Bwn9vW8lZ82xB1%2BfiU4ZEOYNCqablDXqLlfXGuz1M3kvRJ7vZKt5cqu51Ty51Ty51Ty51Ty51Ty51dxqntyqe5W%2BrZP3A3dhd4g6NrpkSqc7BibV%2FgjtM4twDXRsjIyxYXMI9dUcn7Lk1VdfVd%2FexFSAA18nOXs%2F8GGrhoUF1KzDxOyVbAAvMKv34RNkSGdxbGyxY%2Bb%2F%2Bs84xFKCefnoFoCUngEebT3LoppfjM265ZZb1Lc3Ma94IgbcItsUxlPWSWZWk%2Fty8fyMBXgzzzN5lSkAaH%2BNDdTwx2jM1aC7iDCLrpE%2F0XLZ86kBNZd4mvuwiv5f%2FAwt2sb5yGrV9d3kA8b3GfUN9Yus0YdvTzrn6ySbrZYAWEJYAEqs6tGjkDG8iBrjj8Mj1oh1N71g8xZ6dTLg%2FHvvk1w75Ot13JexOUCoMNvFViQVFzJYnZubU9%2F44svmAyb6ptNCFWHM%2FVvvT9p%2BkdFqtE4y36DIqi%2BtHo6a%2FGp6X64AgNtuAwBBsawB3tnpf6e%2F6Ih%2BE8DCC9p1%2BAjaaABAFUdM%2FE%2F9Zcm1C8%2FHPw5UiMUF4GY8VoWY96zXZfuIQLWQwSoA9S198WX3BqRAH8AN7TBadAv8L36%2Fhp28lO1YbTDbKuaO1cgq%2FKFIJ1yXrwGrrLLzrHX661PsRbfUbQKsTBfWu%2FHRNoAfs9Dl%2FI87KxZ7HWwCm8o1270Zr6vB6T8ddRkqqZmtmhdf5qsJTmktOoVJQCw7hl3%2F09jJV7JZ%2FWAJKsUUq%2FrfgG4AwFNP8fV%2Bj27nttvkV%2F1Qsi4egcwXY%2FzyjxKssFqLVB7Edae%2B9gBQb17Hgzfz49v8d%2FAXiUKuUkLjV4FfbaBkasYDipUYhdj9h7T8QhHe2%2F820TrtzyqX2kjSyhYX7QHmXf%2Bz6KfRzGS1UT4FlXtSra6ryevJ%2Fbp0224ols96zxchU9c3bwrX7wSA10llro7umXcA9TNd1Odi3Jf8E%2BTHOLk1fMZpt53PoObqUtsA2kpryrFakVJPFivqHjijVSa1Ol2VWolpZVKJkqwGHqI8SZmsfrADFXzRZJWvnyyDaoH166afgXuHEMY7wzfvhVUA6N2Mz1i4f0KIiadgndH4kY9b6HU1fh%2FaPXr8ceq1tcWwi965ZQDL57xilmM1%2BszJb9b023sPO9Hu9uqA36n4QDum7gLkbipUgQvEtTKpBCTcqI6KMN4ns1ktPwqVapNb5Vqj62q1wPqB0626dv%2Fm52mHLeiOBypyRHvqbUwtLEyhPezEeRXAn22hGuMdeB%2BLtvpjHjqx%2BqdXZfXK6ul20rHK%2B2%2FDjxTFhIwKwLoiZEbJ2Nb%2BOFvnVH3TtUYbzy35ScveVvCJbFZbUMGXs7yzKURWC0OsHzjdqn18a1rMzwvWDP2xBsZ7D7GVyclZnnzyHe94cnLZ0Xhh8vfwR1%2FU1s4%2BiSj880rLTYrelzeXsxyr0lpAFIyyNd1hXuS6XEeYepmvc6q%2B6c%2BBVYLSKqVyTc9ls3ofVKxeyjrJrBeC9c2mWD0%2B3CQKAmJpPrNVJlisDlFveWJiuUfE%2BGv4B804ryCWCsXSmBfzjf3%2Fbiku1Y2k8pzZ8ABv4wwNFBGlwF4HTYFtDuFks1qDyvNp6yRzq6pvlm%2Fe6ip7uzTs7NFlTGKkTNmzIgKNBCIWbXg6oGA6bclrngJbG9gYZ2VUiNEVh96sCCmdwYQnMCpUmJtC4YB7IRz67kneC5Ent5ont5ont5ont5pbzZNbzZNbzZNbzZNbXZhaiIPcagtRii5ljQsYJy%2B4rnn3dlGkNzH4KFomHIbz0liR9T8fKF%2BcmlqUnMcdA7qUKXt0xNqAbZgeWu3%2FwFfRz4%2BQKa2rLUOTAYoV0%2FK0tg1qceFu7SzMi58i4Ul2i6a9VUzYIWiimdnqm7%2Fu8amv3XPna5LzHEFjck6C9P7kvZrRhICBhVZFNPYw%2BnmYWNzSGH7lGjR6yrhW47OwJbfxLOm53%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNqY7RK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<h1 class="title">12 - CARET</h1>
<h4 class="author"><em>Joseph Rickert</em></h4>
<h4 class="date"><em>Wednesday, September 03, 2014</em></h4>
</div>
<div id="introduction-to-the-caret-package" class="section level2">
<h2>INTRODUCTION TO THE CARET PACKAGE</h2>
<p>caret is the most feature rich package for doing data mining in R. This script explores caret’s capabilities using data included in the package that was described in the paper: Hill et al “Impact of image segmentation on high-content screening data quality for SK-BR-3 cells” BMC fioinformatics (2007) vol 8 (1) pp. 340</p>
<p>The analysis presented here is based on examples presented by Max Kuhn, caret’s author, at Use-R 2012.</p>
<div id="background" class="section level3">
<h3>Background</h3>
<p>“Well-segmented”" cells are cells for which location and size may be accurrately detremined through optical measurements. Cells that are not Well-segmented (WS) are said to be “Poorly-segmented”" (PS).</p>
</div>
<div id="problem" class="section level3">
<h3>Problem</h3>
<p>Given a set of optical measurements can we predict which cells will be PS? This is a classic classification problem</p>
<pre class="r"><code>library(caret)</code></pre>
<pre><code>## Loading required package: cluster
## Loading required package: foreach
## Loading required package: plyr
## Loading required package: reshape2</code></pre>
<pre class="r"><code>library(rpart) # CART algorithm for decision trees
library(partykit) # Plotting trees</code></pre>
<pre><code>## Loading required package: grid</code></pre>
<pre class="r"><code>library(gbm) # Boosting algorithms</code></pre>
<pre><code>## Loading required package: survival
## Loading required package: splines
## Loading required package: parallel
## Loaded gbm 2.1</code></pre>
<pre class="r"><code>library(doParallel) # parallel processing</code></pre>
<pre><code>## Loading required package: iterators</code></pre>
<pre class="r"><code>library(pROC) # plot the ROC curve</code></pre>
<pre><code>## Type 'citation("pROC")' for a citation.
##
## Attaching package: 'pROC'
##
## The following objects are masked from 'package:stats':
##
## cov, smooth, var</code></pre>
<pre class="r"><code>library(corrplot) # plot correlations</code></pre>
</div>
<div id="get-the-data" class="section level3">
<h3>Get the Data</h3>
<p>Load the data and construct indices to divie it into training and test data sets.</p>
<pre class="r"><code>data(segmentationData) # Load the segmentation data set
dim(segmentationData)</code></pre>
<pre><code>## [1] 2019 61</code></pre>
<pre class="r"><code>#
trainIndex <- createDataPartition(segmentationData$Case,p=.5,list=FALSE)
trainData <- segmentationData[trainIndex,]
dim(trainData)</code></pre>
<pre><code>## [1] 1010 61</code></pre>
<pre class="r"><code>#
testData <- segmentationData[-trainIndex,]
dim(testData)</code></pre>
<pre><code>## [1] 1009 61</code></pre>
</div>
</div>
<div id="rpart-tree-model" class="section level2">
<h2>rpart Tree Model</h2>
<p>We build a basic tree model with rpart.</p>
<pre class="r"><code>tree.mod <- rpart(Class ~ .,data=trainData,control=rpart.control(maxdepth=2))
tree.mod</code></pre>
<pre><code>## n= 1010
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 1010 352 PS (0.65149 0.34851)
## 2) TotalIntenCh2< 4.709e+04 500 46 PS (0.90800 0.09200)
## 4) ShapeP2ACh1>=1.236 489 37 PS (0.92434 0.07566) *
## 5) ShapeP2ACh1< 1.236 11 2 WS (0.18182 0.81818) *
## 3) TotalIntenCh2>=4.709e+04 510 204 WS (0.40000 0.60000)
## 6) FiberWidthCh1< 11.88 249 105 PS (0.57831 0.42169) *
## 7) FiberWidthCh1>=11.88 261 60 WS (0.22989 0.77011) *</code></pre>
<p>Visualize the tree</p>
<pre class="r"><code>tree.mod.p <- as.party(tree.mod) # make the tree.mod object into a party object
plot(tree.mod.p)</code></pre>
<p><img src="data:image/png;base64,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" title="plot of chunk unnamed-chunk-4" alt="plot of chunk unnamed-chunk-4" width="672" /></p>
</div>
<div id="generalized-boosted-regression-model" class="section level2">
<h2>Generalized Boosted Regression Model</h2>
<p>We build a gbm model. Note that the gbm function does not allow factor “class” variables</p>
<pre class="r"><code>gbmTrain <- trainData
gbmTrain$Class <- ifelse(gbmTrain$Class=="PS",1,0)
gbm.mod <- gbm(formula = Class~., # use all variables
distribution = "bernoulli", # for a classification problem
data = gbmTrain,
n.trees = 2000, # 2000 boosting iterations
interaction.depth = 7, # 7 splits for each tree
shrinkage = 0.01, # the learning rate parameter
verbose = FALSE) # Do not print the details
summary(gbm.mod) # Plot the relative inference of the variables in the model</code></pre>
<p><img 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" title="plot of chunk unnamed-chunk-5" alt="plot of chunk unnamed-chunk-5" width="672" /></p>
<pre><code>## var rel.inf
## TotalIntenCh2 TotalIntenCh2 19.44495
## FiberWidthCh1 FiberWidthCh1 7.78984
## Cell Cell 6.17669
## ShapeP2ACh1 ShapeP2ACh1 4.89881
## AvgIntenCh1 AvgIntenCh1 4.10429
## AvgIntenCh2 AvgIntenCh2 3.25978
## TotalIntenCh1 TotalIntenCh1 2.24729
## ConvexHullPerimRatioCh1 ConvexHullPerimRatioCh1 2.13171
## KurtIntenCh1 KurtIntenCh1 2.02573
## YCentroid YCentroid 2.02033
## IntenCoocContrastCh4 IntenCoocContrastCh4 1.86772
## EqEllipseLWRCh1 EqEllipseLWRCh1 1.83765
## IntenCoocContrastCh3 IntenCoocContrastCh3 1.70114
## EntropyIntenCh1 EntropyIntenCh1 1.67822
## VarIntenCh4 VarIntenCh4 1.65046
## AngleCh1 AngleCh1 1.57610
## SkewIntenCh1 SkewIntenCh1 1.56331
## EqEllipseProlateVolCh1 EqEllipseProlateVolCh1 1.55825
## DiffIntenDensityCh3 DiffIntenDensityCh3 1.54925
## EntropyIntenCh4 EntropyIntenCh4 1.46053
## ConvexHullAreaRatioCh1 ConvexHullAreaRatioCh1 1.37338
## TotalIntenCh4 TotalIntenCh4 1.35224
## TotalIntenCh3 TotalIntenCh3 1.32869
## DiffIntenDensityCh1 DiffIntenDensityCh1 1.30484
## KurtIntenCh3 KurtIntenCh3 1.20596
## ShapeBFRCh1 ShapeBFRCh1 1.16178
## NeighborMinDistCh1 NeighborMinDistCh1 1.11756
## NeighborAvgDistCh1 NeighborAvgDistCh1 1.06628
## DiffIntenDensityCh4 DiffIntenDensityCh4 1.06418
## XCentroid XCentroid 1.00611
## KurtIntenCh4 KurtIntenCh4 0.99424
## WidthCh1 WidthCh1 0.98154
## FiberAlign2Ch3 FiberAlign2Ch3 0.95967
## EntropyIntenCh3 EntropyIntenCh3 0.93270
## SkewIntenCh4 SkewIntenCh4 0.91561
## VarIntenCh1 VarIntenCh1 0.84271
## NeighborVarDistCh1 NeighborVarDistCh1 0.81813
## LengthCh1 LengthCh1 0.79031
## VarIntenCh3 VarIntenCh3 0.77514
## ShapeLWRCh1 ShapeLWRCh1 0.75709
## FiberAlign2Ch4 FiberAlign2Ch4 0.70186
## AvgIntenCh4 AvgIntenCh4 0.69974
## FiberLengthCh1 FiberLengthCh1 0.67362
## AreaCh1 AreaCh1 0.65601
## EqEllipseOblateVolCh1 EqEllipseOblateVolCh1 0.64621
## IntenCoocEntropyCh3 IntenCoocEntropyCh3 0.64544
## IntenCoocEntropyCh4 IntenCoocEntropyCh4 0.64062
## AvgIntenCh3 AvgIntenCh3 0.59443
## SkewIntenCh3 SkewIntenCh3 0.59158
## IntenCoocASMCh3 IntenCoocASMCh3 0.58938
## IntenCoocMaxCh3 IntenCoocMaxCh3 0.55506
## PerimCh1 PerimCh1 0.44941
## IntenCoocASMCh4 IntenCoocASMCh4 0.41162
## IntenCoocMaxCh4 IntenCoocMaxCh4 0.39386
## SpotFiberCountCh4 SpotFiberCountCh4 0.31606
## SpotFiberCountCh3 SpotFiberCountCh3 0.08726
## Case Case 0.05762
## EqCircDiamCh1 EqCircDiamCh1 0.00000
## EqSphereAreaCh1 EqSphereAreaCh1 0.00000
## EqSphereVolCh1 EqSphereVolCh1 0.00000</code></pre>
<p>This is an interesting model, but how do you select the best values for the for the three tuning parameters?<br /> * - n.trees<br /> * - interaction.depth<br /> * - shrinkage</p>
<p>Algorithm for training the model:<br /> * for each resampled data set do<br /> * hold out some samples<br /> * for each combination of the three tuning parameters<br /> * do<br /> * Fit the model on the resampled data set<br /> * Predict the values of class on the hold out samples<br /> * end<br /> * Calculate AUC: the area under the ROC for each sample<br /> * Select the combination of tuning parmeters that yields the best AUC</p>
<p>caret provides the “train” function to do all of this</p>
<p>The trainControl function to set the training method<br /> Note the default method of picking the best model is accuracy and Cohen’s Kappa</p>
<pre class="r"><code>ctrl <- trainControl(method="repeatedcv", # use repeated 10fold cross validation
repeats=5, # do 5 repititions of 10-fold cv
summaryFunction=twoClassSummary, # Use AUC to pick the best model
classProbs=TRUE)</code></pre>
<p>Use the expand.grid to specify the search space Note that the default search grid selects 3 values of each tuning parameter</p>
<pre class="r"><code>#grid <- expand.grid(.interaction.depth = seq(1,7,by=2), # look at tree depths from 1 to 7
# .n.trees=seq(100,1000,by=50), # let iterations go from 100 to 1,000
# .shrinkage=c(0.01,0.1)) # Try 2 values of the learning rate parameter
grid <- expand.grid(.interaction.depth = seq(1,4,by=2), # look at tree depths from 1 to 4
.n.trees=seq(10,100,by=10), # let iterations go from 10 to 100
.shrinkage=c(0.01,0.1)) # Try 2 values of the learning rate parameter
#
set.seed(1)
names(trainData)</code></pre>
<pre><code>## [1] "Cell" "Case"
## [3] "Class" "AngleCh1"
## [5] "AreaCh1" "AvgIntenCh1"
## [7] "AvgIntenCh2" "AvgIntenCh3"
## [9] "AvgIntenCh4" "ConvexHullAreaRatioCh1"
## [11] "ConvexHullPerimRatioCh1" "DiffIntenDensityCh1"
## [13] "DiffIntenDensityCh3" "DiffIntenDensityCh4"
## [15] "EntropyIntenCh1" "EntropyIntenCh3"
## [17] "EntropyIntenCh4" "EqCircDiamCh1"
## [19] "EqEllipseLWRCh1" "EqEllipseOblateVolCh1"
## [21] "EqEllipseProlateVolCh1" "EqSphereAreaCh1"
## [23] "EqSphereVolCh1" "FiberAlign2Ch3"
## [25] "FiberAlign2Ch4" "FiberLengthCh1"
## [27] "FiberWidthCh1" "IntenCoocASMCh3"
## [29] "IntenCoocASMCh4" "IntenCoocContrastCh3"
## [31] "IntenCoocContrastCh4" "IntenCoocEntropyCh3"
## [33] "IntenCoocEntropyCh4" "IntenCoocMaxCh3"
## [35] "IntenCoocMaxCh4" "KurtIntenCh1"
## [37] "KurtIntenCh3" "KurtIntenCh4"
## [39] "LengthCh1" "NeighborAvgDistCh1"
## [41] "NeighborMinDistCh1" "NeighborVarDistCh1"
## [43] "PerimCh1" "ShapeBFRCh1"
## [45] "ShapeLWRCh1" "ShapeP2ACh1"
## [47] "SkewIntenCh1" "SkewIntenCh3"
## [49] "SkewIntenCh4" "SpotFiberCountCh3"
## [51] "SpotFiberCountCh4" "TotalIntenCh1"
## [53] "TotalIntenCh2" "TotalIntenCh3"
## [55] "TotalIntenCh4" "VarIntenCh1"
## [57] "VarIntenCh3" "VarIntenCh4"
## [59] "WidthCh1" "XCentroid"
## [61] "YCentroid"</code></pre>
<pre class="r"><code>trainX <-trainData[,4:61]
registerDoParallel(4) # Registrer a parallel backend for train
getDoParWorkers()</code></pre>
<pre><code>## [1] 4</code></pre>
<pre class="r"><code>system.time(gbm.tune <- train(x=trainX,y=trainData$Class,
method = "gbm",
metric = "ROC",
trControl = ctrl,
tuneGrid=grid,
verbose=FALSE))</code></pre>
<pre><code>## user system elapsed
## 1.06 0.04 29.37</code></pre>
<div id="tuning-results" class="section level3">
<h3>Tuning Results</h3>
<p>ROC was the performance criterion used to select the optimal model. The final values used for the model were:<br /> * interaction.depth = 7 * n.trees = 500<br /> * shrinkage = 0.01</p>
<pre class="r"><code>gbm.tune$bestTune</code></pre>
<pre><code>## .interaction.depth .n.trees .shrinkage
## 34 3 70 0.1</code></pre>
<pre class="r"><code>plot(gbm.tune) # Plot the performance of the training models</code></pre>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAMAAADDuCPrAAAAw1BMVEUAAAAAAC4AADoAAFIAAGYAMy4AM3MAOjoAOmYAOpAAXFIAXHMAXJEAZpAAZrYAgP86AAA6MwA6OgA6Ojo6OpA6gHM6gJE6gK86kLY6kNtmAABmAGZmOgBmOpBmZgBmo8xmtv+QMwCQOgCQXACQkNuQxcyQ2/+2XAC2ZgC2Zma2xXO225C25cy2/7a2///bgC7bkDrbo1Lbtmbb5czb///m5ub/AP//o1L/tmb/xXP/25D/5ZH/5a//5cz//7b//9v////BVVoOAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nO2da4PkNnaeEVmJrWQ93YmdbHp8q0wSqZx1pIqj6a20vaP6/78qvBO8FUESODgAn+eDpru6iu8h6uAV7jQPAADYhYkdAABAqpjYAQAApIqJHQAAQKqY2AEAAKSKiR0AAECqmNgBAACkiokdAABAqpjYAQAApIqJHQAAQKqY2AEAAKSKiR0AAECqmNgBAACkiokdAABAqpjYAQAApIqJHQAAQKqY2AEAAKSKiR0AAECqmNgBAACkiokdAABAqpjYAQAApIqJHQAAQKqY2AEAAKSKiR0AAECqmNgBAACkiokdAABAqpjYAQAApIqJHQAAQKqY2AEAAKSKiR0AAECqmNgBAACkiokdAABAqpjYAQAApIqJHQAAQKqY2AEAAKSKiR0AAECqmNgBAACkiokdAABAqpjYAQAApIqJHQAAQKqY2AEAAKSKiR0AAECqmNgBAACkiokdAABAqpjYAQAApIqJHQBkxLfP5odfF//4P7xIVFf57Yv5/ueNn7yZno0f7kS/+3GjKGSOiR0AZMQzA72aNw8KzVWOGqjZZIW9KAYKQ0zsACAjnhjo3fgw0PYqxw3UmMsOUQwUhpjYAUBGiBnoHm7WZ69b2qAYKCxiYgcAGZGMgT4+Xs2T4dp5UQwUJpjYAUBGpGOg5ZVcO/EYKCxiYgcAGdEaaD1EWQ061oZ6H4w73gdjkNfiPfeuS30dT/Hc565ijYEOLjYRthgaaCn06TFziaJt+qm8kfbPA9EirvvsxeGkmNgBQEbYBvqHV8s0bQP9aP/QWGBhoP/cmmY/0dNY1Mf8VToDrZyuv9hE2GJkoPf2M6N4SgNtXqpsfGSg161TUJA1JnYAkBGWgQ4XDFkG2vlV61hX8+fVS5+GE+VvzQVnr9IaaP/3+veJsMXIQIuPVm8Yx1P8/pdfrCsMDfQ/DWOHs2NiBwAZMTTQy6O2xMq2rIHEvmVXdZKvXXuztMPON6uXrvVlyt8/ja5SOdi1ufytudiMcMfIQMu3XmbiqQy1vHgXoB360sXhpJjYAUBG2AbaNNFuIxe6d73ztgl47V65d8OShYuVn2/f0v0wMtDibU0zs/lpRrhj3kAn8fTz8+3VbQNdujicFBM7AMgI20AbsypcqHqpdaFr37NuXrrOtOaKCzWNwManbt0oqG2glo/V9jgj3DFvoJN4elNuI7MNdOnicFJM7AAgIywDbU1oZKD2QqdqvruyqcvwMlVfuTHI4Z9GBmp9tL7YjHDHrIFO47E+1/j3zDImDBRqTOwAICOcDNSm/NN1smipm6W5jieChgZqL8ysdbYZaPHWaTyNrTeXwEDhKSZ2AJAR6wZqzXnPGKj9VzcD7SbDa+kNBloPeU7jGRpoeQkMFBYxsQOAjHAy0LHz9C7ZNge//7kZAw3ZAr23djmKhxYobMDEDgAywqkLP15B2bvkrZvlbt62YQy0Hq/cYKD1TqRpPIyBwgZM7AAgI9YNtJn7tukM1PrU3Yxm4aeNwdEs/LWdhXc00Lvp1j0N42lWUDXXvDwwUHiCiR0AZMS6gVqtzH4Z08RAy778cJ3ndEXR0jpQNwMtxz4/zcbT/aWzUgwUFjGxA4CMeG6g1b/VOOel/MvVtAvprXWXlWPduo2YzU6k7i/tVeZ2IpV/cDTQap1Ut0R/GE81rdTMHXV7AJbuCs6OiR0AZMQTA62nuy/2QqXJ0nr7T/WL1iqjt4d1lSd74Z8Z6OT6j2k8xef+fHK8iOkOE1m4OJwUEzsAyIgnBlpvJB+uZKobhNZUe3vUkfmv7dBk55D1e9urPDmNydFA+6mjUTzlaOt98B5LFAOFISZ2AJARTwy0tqF6cLG2sn76p1+rVJvZmz19dB/YXXOVJ+eBOhno3F/s6aoqjkv7914UA4UhJnYAAKqw1oECrGFiBwCgCgwUNmBiBwCgCgwUNmBiBwCgCgwUNmBiBwCgCgwUNmBiBwCgCgwUNmBiBwAAkComdgAAAKliYgcAAJAqJnYAAACpYmIHAACQKiZ2AAAAqWJiBwAAkComdgAAAKliYgcAAJAqJnYAAACpYmIHAACQKiZ2ACDFt8/jJ7Kfle7ce4CDmNgBgBDWUzDOTf2II2MmD6gH2IyJHQAIcTUYaEnvnzgoHMbEDgBEqGwDA33UT5Are+/l8zx5MBwcxMQOACSoR/02GehLTbCQItE/WrNw0P5xoCu814QLCxLFxA4AwlM9PP0vt42Bvrzk6aDWeck35z78+zsOCrOY2AFAeMpe62XbJFLrnPk5aM/V1UBb58RBYYyJHQCE51Y2urYa6PiH7Cja5Y4F0vkmBgojTOwAQIhNBmrZZq4OenOehbdsEweFISZ2ACBEUAN9CcGeu3TkumUV01YDfQ/BrtuE4JjYAYAQWwzUNi8XIwvin1oM1DYvFyML4p8YqFJM7ABACLrwPe1ierd1oHThYRETOwAQAgMdUS7ucnoCPAYKi5jYAYAQzMKPcV5Jzyw8LGFiBwBCsA50ws3xSKZ31oHCAiZ2ACDExtOYJKZyYnN3PdOOqRxYwMQOAITYepxd/v7pbqDshYcFTOwAQAjOA62wu+3OezkBFjCxAwAhMNCKj9euGKwfAfZhYgcAQmCgFeUS0LocbjzWAw5jYgcAQmCgNfXRqDVOy0ABljGxAwAhMNCG6nRU/BO8YGIHAEJgoB11I5TnecBxTOwAAABSxcQOAAAgVUzsAAAAUsXEDgAAIFVM7AAAAFLFxA4AACBVTOwAAABSxcQOAAAgVUzsAAAAUsXEDgAAIFVM7AAAAFLFxA4AACBVTOwAAABSxcQOAAAgVUzsAAAAUsXEDgAAIFVM7AAAAFLFxA4AACBVTOwAAABSxcQOAAAgVUzsAAAAUsXEDgAAIFVM7AAAAFLFxA4AACBVTOwAAABSxcQOAAAgVUzsAAAAUsXEDgAAIFVM7AAAAFLFxA4AACBVTOwAAABSxcQOAAAgVUzsAAAAUsXEDgAAIFVM7AAAAFLFxA4AACBVTOwAAJRxMwWf2t/ug9/C6LWXD6z18Sp4Y3epUrybN3HNHhP4+gBp8e2zqfjux+rXa/3b9z8HEyxsranlgbWGlw8r9tsXqVIsvq/WQCVvsMUEvTpAYhQ1v6z0hav98Oujao2+9b8FEmwMNLDWVfLGhpcPKFb+/+5NWNPGhLw4QGrcm0ZTUfMuffOm/i0It7afGVirvWx9g4HFhpcPKFaNSrzJag4wIS8OkBrXpsVSNAw/lW7T9QcDDaZ9+/z939fXDqx162/sLbjYx2v9v6HQYkU3/S/+tjHQoUr4b67GhLw4QGpc+wHJT72dFvYTaCztai6NZFit2sos2aA3NjTQYGJF8/Ktu7GhSvhvrsYEvDZAslRdwLoZWtL27H1zLwRqAw2sVdzPpf8t9I0NOtDhxD7++tfu/wxDlfDfXIMJeG2AZKnqXd9sa5tUnvn2ubhsZ6AhtcqLVlPjla8Ev7F6Cqfw0R9+DSxmGailEvwGW0zAawOkSl31+yUygarhte7hfno8Qmvdzfd/eO1WFgW/sXoRphG4s9Yqhyrhb7DBBLw2QKIU9a8cObOrYYjJ3Hs1UDc10ABa98Y6C78p7iz0jQ3WgYYVmzNQcwl+gy0m4LUB0qSoflWzJXA75rcv1UWFWqDNZEqlE7qB1hRgaaMXWqAAp6JcnH0pfwg8knZrp98FxkDv1nrzH34NPUR46xcRhRZjDBRAFUWnr6lz9lyu/8UwH6/1Naez8AEW3vQ92cZAQ4pNzCyg2Nws/Pc/B77BHhPw2gAJ0vvnYG2h/x2BN9NTXD2olrWM6SYi9mbJBhWbWwca/AZ7TMBrA6RH4Z99kyXofpaRgYbdO9M3yQQ26vRiVQc6qFhnoOxEAohOM//e/xZ+R/VVZC98u6C8booGFrPGQK05/yBinYGyFx4gOrfhxhWJM33aNlJYrXr9UvmPwGFF7TKGaz11FVKsH2/lNCaAyLSngXZn8QqcKmltvw+p1d6ayHGZH69NIbbjk8HErE3+nAcKEJeu4neHmYc/17wfpZM4JH7h+Hbv3AbuFU7MPiWFE+kBANLBxA4AACBVTOwAAABSxcQOAAAgVUzsAAAAUsXEDgAAIFVM7AAAAFLFxA4AACBVTOwAAABSxcQOAAAgVUzsAAAAUsXEDgAAIFVM7AAAAFLFxA4AACBVTOwAZDEmTy3EUpfOt0zzFasEhfUiQ5VATKd0vmWar1glKKwXGaoEYjql8y3TfMUqQWG9yFAlENMpnW+Z5itWCQrrRYYqgZhO6XzLNF+xSlBYLzJUCcR0SudbpvmKVYLCepGhSiCmUzrfMs1XrBIU1osMVQIxndL5lmm+YpWgsF5kqBKI6ZTOt0zzFasEhfUiQ5VATKd0vmWar1glKKwXGaoEYjql8y3TfMUqQWG9yFAlENMpnW+Z5itWCQrrRYYqgZhO6XzLNF+xSlBYLzJUCcR0SudbpvmKVYLCepGhSiCmUzrfMs1XrBIU1nPFAAAEwK9Reb2aL2IXMQDkil+r8no1XxjzJwBHyBZwBwMFGEC2gDsYKMAAsgXcwUABBpAt4A4GCjCAbAF3MFCAAWQLuIOBAgwgW8AdDBRgANkC7mCgyvnX/2b+zf988sc/+6e5P/zLZ/Pv/m+IcIoLF/ze/U//8nk+Qr2QLd54ki31n+14Vt6sFAxUObqqxE/N7ouZkOb/tBihXsgWXzzJlmk8K2/WCgaqHFVV4pdu/9pEduFPP828VTlkiyeeZEvFIDfW3qwVDFQ5T6vEImGqxP97NdVl/1ik+e9c/lQEn1qFyNhAFxHPlpJhbqy8WS8YqHI0VYmf2uQu0n0U1OyfylqBgUqSSLb8aZIbz9+sGAxUOYqqRHHRNuF/GY32z/2pmhT4D4yBSpJGtkxz4+mbVYOB6uOP1VBQ05Gpq0T1UpvkPxU//bEabm9Gtep/frHe01WJsqP0u/ZjwzH6Kon/7J+s2lML/0P/92FlLBoHv+t/HFS5uT+V8fwDk0ihSTBbprnx9M2qwUC1UfdtuhUdVZVoZyjrbC2qxP8x4yrxv17t97R53teIfpC+Sc/2hf/cVolOuMnqSZWw2gZjX5z70y+lMgYaliSzZZobT9+sGgxUGfVquD67yyrxH80gWX8y//a1bnX0VaKnSuOmSszViCZV/2i9UFWJvio2MnNVom1vjLuKi39KrDqUkC2hs6V/2TLQlTdrBQNVxi9NRpbZ3GZ1nV2/tNn8U9cwGFQJ+z11lehrRFnRft/+UH62/Lf8aNfbK99bJe7yPOhPfZYXPw+yfPFPGGhQ0syWBis31t+sFQxUGV0qWenepNkvTa7+1HWsFt9TV4luarPM8+anoulQvvUXY3XFmvUj/YjYfAbbmT0x0IU/YaBBSTNbGgYGuvZmrWCgyvjFDMfQy3Rvhofa4fWfulesKjF8T1UlfpprHDTznf3/8htBK23/uDAPOszyvsnw7E8YaFDSzJY+2HkDnXuzVjBQZfyxH9CqsIaErCrxD90fmyoxek9ZJf77zBLMdvWytWyk/oS9ksWaEh1AC1QdaWZLf3laoGOr8no1X6RUJbo9wc3/12erRPvKsyphzHB4qp8HqKtEWwPqH+3ZCDNq1vSRYaDaSDJb/jSIx+3NWsFA1dFPgZaV4kCV+Pef+7aJNWtafsRabFdXCfvvS1WCWXiFpJgt/cvMwo+tyuvVfJFWlbAWp/z+SJX4XdmM6FfpNbWh7o5N2hQuy5f/uLxYb/FPGGhw0suW6cvrb9YKBqqUKonrxSX7qkQ91t8v7Osrx8Ko1mrabtyJZEeYEmRL6Gz50yAetzdrBQPVhZVUdZYeqhJlV6tdX928oW5nlNMDg3lV64VFNu6FH99PKpAtobNlKfpnb9YKBqqMftWJhyrRrjrp39Auie7Wv7RrpftWx+LClM2nMf0JAw1MotkyiMftzVrBQJXRbe2oB6YOVol2zV97dm015VC+t60a1XRAN5FQtSp+WjwVfPN5oBhoYBLNlkE8bm/WCgaqjW5hillI9y1Vov0fur2Xud+DN5hFtV9o9/GNq8bk2PD+PQsnimOgYUk0W+x4Ft6cCBioNv61O+uhSraDVaLfOtLwX9rhq+XzdfrzPCdti/GDa6z38EykGKSaLVY8C29OBAxUH80qkm6T86Eq0R4MUSf8762Nzs0Jj9YEaF1L2t9mdzn/y/DRifZ7Rn8aRJgSZItEtvxpkhuz+aMeDPTsLG3FOy1kyxPIlhEY6Dnpd0j/lN7/9cNCtkwgWxbBQM9JN3m6PIt6VsiWCWTLIhjoObF3M9OkGEC2TCBbFsFAT8ofqRELkC1TyJYlTmKgAAAh8GtVXq/mi9hFDAC54teqvF7NF55vsufr10AXjqx1ajGyBTF3MQz0CFSJDMXIFsTcxTDQI1AlMhQjWxBzF8NAj0CVyFCMbEHMXQwDPQJVIkMxsgUxdzEM9AhUiQzFyBbE3MUw0CNQJTIUI1sQcxfDQI9AlchQjGxBzF0MAz0CVSJDMbIFMXcxDPQIVIkMxcgWxNzFMNAjUCUyFCNbEHMXw0CPQJXIUIxsQcxdDAM9AlUiQzGyBTF3MQz0CFSJDMXIFsTcxTDQI1AlMhQjWxBzF8NAj0CVyFCMbEHMXQwDPQJVIkMxsgUxdzHFBnovj3v+9PTFa/HL9z+vR+UzLBuqRIZiZAti7mJ6DfRaH5g/8MffvtQvfvdj+dtH86zAsclOo/IY1gCqRIZiZAti7mJqDfRmzFvlkT/8uvDit8/GXKo26dtaVP7CGkKVyFCMbEHMXUyrgRbuWNliYZaXhRdvTUP0vtqLp0og5i5GtiDmLqbVQDtXvFo99I/X2jKLnvxb+Z/6L8UPl5WovIU1giqRoRjZgpi7mFYDvbZd95vVvhwYaNscHXrsfFTewhpBlchQjGxBzF1MqYF2zcuiKVqbZsmgC1/8cqlfvg7GSeei8hXWGKpEhmJkC2LuYnoNtGletq3OinoSqbDOwjG7txQ/TAzUjPgK4AjZAu6MneagAx78eEffPx8YaL0MtFm41Hbui9cwUPAG2QLuJGCg1hTRYB1o8Zfy39JT6cIj5k2MbEHMXUxpF36+BVq8Wv5S2ujl0TVHf/hnDBQxf2JkC2LuYkoNdH4M9Navbaoss1xKX7zvhoEi5k+MbEHMXUyvgXaz8N0ypoWZJZYxIeZTjGwREXuvkRHbS7IGaq8D7ZqXfb++X8L0sH11MSpvYY2gSmQoRrZIiL2/73RQZXem1UDndiL1zdKqBXprfhs2R2ej8hbWCKpEhmJki4BY65zbHVTZnWk10Nm98NYYaPFD67Gr6+ipEohtECNbBMQ638RAh5fzdqW505iaWfjypDt7Pf3aVniqBGIbxMiW8GKWbW51UGV3ptZAB+eBtpuN2hNAu9ZpxWU1Ko9hDaBKZChGtoQXq13zvf8xpNgBUjZQ+/D5frfmrTPVR7Oufq3//qBKILZFjGwJLfY+JKzYIZI2UH9QJRBzFyNbtuFug+9PcJbLO1v8Xs0XVAnE3MXIlk04WODULQdjoNtcNO9s8Xs1X1AlEHMXI1u2UNpeKTZrf8vNzPEsvLuN5p0tfq/mC6oEYu5iZMsWSr+rxIb2uNY/n10H6uCiu4ZN94OBVlAlEHMXO2+27HCn6t21WNUzdx7ZXH7Ls49v6ux7AAOtOG+VQGy72GmzZY871Qa6wTjHamuxjJuoy+MFIcBAK05bJRDbIXbWbJntVT958xybRZ1FmhdmxgvcL7Q9Agy04qxVArE9YmfNlrXdlQue2VlTqFbhSKcbL9hxhc3aGGjFWasEYnvETpotM7sr1yzzMR4DDccRt97UtB6CgVactEogtkvspNnSGtOqZ04/tqdXvYONkQ1CHP7gDAZacdIqgdgusXNmy9wcuuNckNi8zsKM1YqlHji4BAOtOWeVQGyf2DmzZW//WHJl0dJ4gbOlYqD7OGeVQGyf2PmyZWyBW5eCCvnnhvECl5apCxhoxfmqBGL7xc6VLb21JHDG8a7xgungrvtnMdCKc1UJxI6JnShbBo4y/te3mA/2tCQPHFyCgdacqEogdljsJNkyNZLdo5mCN7Ynwv0Hl2CgNSepEoh5ETtBtiz4x97RTGVf4JjZprWLi7qUBwZ6BD1VAjFvYrlny9525i4x/+zbpLpwy09t1KmZKmeg1fM3jNszODyTe5VAzKdYztni3zyfiIXhwDEpz/86fsu704yVkIF++2xshD005yqBmG+xDLLlWQc9wIIjZV/gPiY26rZmSsRAR/YpbqEZVIlMsjQFsfSzZcYqg5lnibIv8ADvIxtd3+UvYaDXoWU2fflPXoWfRzUblgcw0AzFks+Wcd8zqHmWKPsCDzNujMY10Orp7ZfRi+XTib/70av0E5KvEtJapxZLPlvsvme4fruFsi/QC2oM9L7glKWvXrxqL5N8lZDWOrVY6tnS9z0lzPPxUPcF+kFJF/7b50WbvJvvf/YqvkjqVUJc69RiqWfL8MwiCUVlX6AftBjo3z2ZLfpHDFSn1qnFEs+WqraL9Nw7lH2BnlA0Cx+bxKuEvNapxVLPlmbks/lRAmVfoCdUrQONS+pVQlzr1GKpZ0vV8lzve3pE2RfoC6dmPAZ6BAw0Q7HUs+W9bTpJ+ae2L9AbLsMgYgZarWbqkJo+qkm9SohrnVos7Wxp6vx639Mjyr5AUTEpA70NNyJhoKq1Ti2WdLZU7ik7haTuCxQVEzLQu8FAE9I6tVjC2dJPHkn6p7YvUFRMyECv0p45IOEqEUfr1GLpZotlmsrKNF8xGQP97UtM/0y4SkTSOrVYqtkyaHMqK9N8xWQM9Ntn8+ZVZxupVoloWqcWSzRbhn12ZWWapthLzdP3YKBHwEAzFEsyW8ZDnsrKNEmxlxcHBxXrwmOgCWmdWizFbJlMGSkr0xTFSucsxZ47qNAk0i3Cgzx6UqwSUbVOLZZetszMuCsr0xTFSt+sxDQYaNw+fHpVIrLWqcWSy5a5FUvKyjRBsco2a7FnDiq1kP7jNWIbNLkqEVvr1GKJZcv8gk9lZZqgmCoDnTwUiYX0qrVOLZZWtiwsmFdWppHFXKbT5z7QfAYDXQjrMBhohmIpZcvifiNlZRpXzGk6ffBGDHQY1UJYh8FAMxRLKFuW92sqK9OoYq1zLjnojG2+KOvCRyahKqFD69RiyWTLs+3uyso0qljnf0MjnPfN4cfUzMLHJZkqoUXr1GKpZMvT40KUlWlMMcv/6pblU9u0P6doHWhcUqkSarROLZZGtqyctqSsTGOKteOYjr5pf1DNTqSKZiA0wqkiaVQJRVqnFksiW9ZOq1NWphHFJq3OLR9d/4CYgVrzSOIWmkSV0KR1arEEsmX9sE9lZRpRzHU6faeYlIEO5uG/+9Gr6CoJVAldWqcW058tDoclKyvTWGKjVme6Bvrbl8I3L9WP5eH0wpuS9FcJZVqnFtOeLU5nzSsr0xhinXnu9089Bnq3TLM004tX1TW0Vwl1WqcWU54tbs/qUFam4mLDjvvzdaCHxMQe6WENfBbd+U9eVddQXiX0aZ1aTHW2uD7qSFmZyoqNJ392zB85i0U5D1T6cDvVVUKj1qnFNGeL86PilJWpnNisV+71TzUGWrQ5L9avd7Zy6tY6tZjebNnwpE1lZSoktruluUesBgM9AgaaoZjabNnypGJlZSoh5ts8n4p10IU/AgaaoZiqbOmf8L7tQe/KyjS0mPem5zOxAUwiHQEDzVBMU7a8t2z0T21l6o0nI5ze3fOhyEBZxpSU1qnFFGVLa5rvG+1TXZn6YjKfHtA8S9QYKAvpk9I6tZiibGldc7N/aitTT7wMD0gK7J4PRQY63MopvRleUZVIQ+vUYnqyxW5/YqCP4RGdwc2zRI+BcphISlqnFtOTLbVrNkOggbWOICXWHRIfvunZoMhAOc4uIa1Ti6nJls4/Hw8MtKQ2UDH3fCgz0HioqRKpaJ1aTE+2lK753tpoYK0jCIn1R3q+7DgXZBcYaIWeKpGI1qnF9GRLvXqp/TGs1hEExF5s93zsOVhpFxhohZ4qkYjWqcUUZcv7Xv/UVqZHGHlnLXYaAy0HPi881jgxrVOLKcoWax1ocK0DhBIbNzsdH5TpEQy0jmoSlicw0AzFFGXLe78TKbjWAXaJPZ8Kmnhn+6q9DjQ8GGgd1SQsT2CgGYrpyZb9/qmtTKfM2OP4L8/+tifMHcQ3UBXoqRKJaJ1aTE227DHOvVqH2CHWd8qHDcw1h5T1Twy0Rk2VSEXr1GJasuWIf2or0wnj5xQ5eOd+sf1goBVaqkQyWqcW05ItR/xTW5mOmXvehmPDUtmdSR2oPHiS8ZXzQHVrnVpMSbYc8k9tZTpmeBjIpk65sjuLZKBMIqnWOrWYjmw51IFXV6YjSr/c4Z37xI6g00C/fcZAdWudWkxFthz0T21lOubAniJldxbcQG9mDrrwqrVOLaYhW476p7YyHTBseGKgg8tNXpksAa14m340IBqqRFJapxZTkC2H/VNbmfaMO+6blyQpu7PwXfh7dP/UUCXS0jq1WPxsOe6f2sq0offO2XWgfsV8oMFAS0aTSNLErxKJaZ1aLH62HPdPbWVaMm557lwTr+zOMNAjYKAZikXPFg/+qa1MZ3YQ7d1TpOzOFC+kr/r+4+cfD1+cfctMVD7DssFAMxSLnS0eOvDKytTr/ktVdxbLQL/9zfoypuvMsSPVwz0L6vbs8LdnUTmGtRkMNEOxyNnixT/1lKn30z/U3FmNmIEO55LW14Heqqmmj9fBiqfhi7NvmY1qOaxjYKAZisXNFj/+qaRMQ5ydpOPOOqQM9Gq2Gei3z/VUfWGPl4UXZ98yH9ViWAfBQDMUi5otnvxTQ5kGOjhJwZ3ZCBloYXIDLmsXurcee7XGOD9eu7772/i3p1EthXUUDDRDscgGKqfljRmxcMfOxb6zEUIGeq3mem6VGeaZwqsAACAASURBVF4d/LM/b+RmtVYxUMTCi8XMFl/+KVmm/ibYncg7WxauVnhc6YdNs/K23oMvPtA0PO/WHBFdeMTCi0XMFl8deMkynW4tCuiej9yzZeFqjdc1bcbCHS8r1+lblW07s6KeNiquZk0itb8N4xjxFcCReNlS+Gcs6d0UXmn927hn3JBEmeyy3G6aA+Oaf7kwuUv/T9OVf0bbuhwZaDuX/2nut0EcGCjsJFq2pOifXzu37FqiUcMRR9RA23blx+vawiPbQK3mKutAEQsuFitb/PXf17W80T2qPXTPvSPvbFm4Wtv0bKbUP17XBkHnW6DFq+UvpXFexr89jWohrMNgoBmKxTNQOS1v1AYq5p6P3LNl4Wpt0/NWD1feV2eR5sdAb/3apuI6w9+eRrUQ1mEw0AzFImWLV/+UKtP2ZKXSPTFQDyxdrTG7Zkr9trp3yJ6F78x26KoL80yzUS2FdRQMNEOxONnitQMv2gLde7T8TvLOlqWrfbxW1llvu7w7HABirQPtzLbv15dDAsPfnke1FNZRMNAMxaJki2f/FDTQatqo/lFEMe9sWbxauZXzrd/ReVm70NxOpL5Z2rRArd+eR7UY1kEw0AzFYmSLb/8UNdBaTMg/M8+Wxav1Ez8LC49GzK6St0Y9ix+Gvz2NajGsg2CgGYpFyBbv/ilUps3oZzMLL6GYe7Y8udq9biY2TdFV5o5aaubdy2u8jX97GtWTsA6BgWYoJp8t/v1TpkybuSPJOfjcs8Xj1ezzQJutoP2ZJG3r1PrtWVQewxqAgWYoFsNA5bT80U8eSfpn5tni82rWcfOtgTZPSe567MPflqPyGZYNBpqhmHi2BPBPgTK1TFPZFygqpthA/YGBIuYuJp0tATrw4ct00OZU9gWKimGgR8BAMxQTzpYg/hm6TId9dmVfoKhYaANtpt3HrHe7vUY1CcsTGGiGYrLZEsY/w5bpeMhT2RcoKoaBHgEDzVBMNFsC+WfQMp1MGSn7AkXFMNAjYKAZigkbqJyWH2Zm3JV9gaJiUmOg9+7UudJSL15FV8FAEXMXk8yWUP4ZrkznViwp+wJFxeT2wveLNe/rJ3j6BQNFzF1MMFtCdeCDlen8gk9lX6ComNhD5T4t/hYeDBQxdzG5bAnnn4HKdGHBvLIvUFRM9kDlhvXzQP2CgSLmLiaWLQH9M0iZLu43UvYFiophoEfAQBMTe695+h6pbAnpnyHKdHm/Zq7ZosdA+6PnKlaPkPcMBopYxfu7g4MKZUtQ//Rfps+2u2eaLU5iYmOgg1PpGAPVrZWpWOlZpdhz7xIz0EAyM1rHeXpcSJ7Z4iYmNwvfdtvLH5mF162VqVjpWZWYAgMN65+ey3TltKU8s8VNTGod6G2wjv7iVXQVDBSxR+NZtdgz+xLJlrAdeM9lunZaXZbZ4igmdpjIvbdP4fYnBopYhSIDDe2fPst0/bDPLLPFUUzwNKZmV6fsBHwFBopYZZrdBFJkAw3unx7L1OGw5ByzxVWM4+yOgIGmIzaYgY9roOH901uZOp01n2G2OIthoEfAQNMQs8xTQxc+vH/6KlO3Z3Vkli2bxDDQI2Cg+sUs81QyCy/gn37K1PVRRxlly2YxiePsLtND7diJpForF7HRwnkd60AFOvB+ytT5UXGZZMsuMQz0CBioXrG5XUcadiKJ+KeHMt3wpM0MsmW3GAZ6BAxUp9iiUUbcC//u5N6+OFymW55UnHi2HBJjDPQIGKhCsec+JV4lGmT9c1+Z9k943/ag95Sz5agYBnoEDDSW2PMW5hOTimSgbsOv/thTpi8tG/1Tf7YEFMNAj4CBRhLbPcAZz0Arac0t0NY0Xzbap/5sCSmGgR4BA40j1tqQPb/u2D+OY6DNLtL3ajeU/8tP2WWg7b8b/VN7tgQV46mcR8BA44h1JlSv7twyuBjTQLvFqOHZXqZ2+xMDdRbDQI+AgUYR6z1o+8xMFANt94++P7QbaDMEGlrsAMrEMNAjYKBRxDo/2jGtHbEFur4N3x+by9SefMdA3cUYAz0CBhpB7H2Id7FQBvr+vroN3x97WqBd5x0DdRfDQI+AgR7H3QffZ6wzEQOtz9Jb3Ybvj50G2v4YWuwAysQw0CNgoIdxaElOjNN6czoGWvin4nWg9tTRVv/MNTWdxOIY6Le/YQxUs5ac2LMF5mPjnLHN7XYUfSdSiItP2fgFWv33x+ZJ+ExT000sxiM9mETSriVpoI3YwB7Xhjkn60DdiW2gIa49w6Yv0Nq/uWMRU66p6SYmZaBXZuET0hITGz6maNU4rc/t9aO4O5HE2KA1aHvu8c88U9NRTPCxxjYXr6prYKBaxboF5lsn1ne35zDQIfssc6eYD5SJCRlo0QD9VD7b+FP988Wr6CoYqE6x9b66f6Ju5fR/4SUctXzYZ5ap6SwmY6C/fTE//FqOg1Zd95v0kzkxUBGxze3HgXGqmZ4OshNJpYH6sc8UUjOcmIyBfvts3h5lR756JHxhpxevqmtgoBJiW4Yu23c6PqrdIxG3cirLFl/2mUBqBhQTM9BL/0/TlZcDAxUQezYzvtxXd3vOm0diGGhzb6qyxZ996k/NkGKiBlo0PcuGaNESLTv0cmCgAmJzazPXBznfhQ8axkArfNqn/tQMKSZqoI9r3fT8eGUZk2qtHWLDVqX77JDkBFJJBANt701Ntvi1T/WpGVRMbBKpanreqrmkdjJJDAw0vNjohKTNE0qbQ9yJvIG+azNQz/apPjWDigktY2om3u+mmkVqfFQMDDS42LjVGVTsCDEM1FnaH8ta3u1T3RcoKia3kL60zuKfwjrvhkkk3VqbxQ4tSFJ2Z56zpS8NDdniu/f+VCwIysQkt3K+9Ts6L15V18BAA4pNW50YqI0mAw1in+q+QFExKQP97Uvlms0B9bINUAw0lJhtnvtPSFJ2Z36zxSqN2NkSyD7VfYGiYnLH2d2r4c+2KSoKBhpAbNzwfLYO9LCYP4SrxLsaAw1mn+q+QFExDlQ+wlkNdHauaPeCJE139vBuoFuk/THWCmif6r5AUbHwD5UT7q7PgYH6E3u+KH7XgiQld9biM1sGpREvW4Lap7ovUFRM4qmcdd89IhjoNlYc0veqzYyrhAoDDWyf6r5AUTGhxxrHbYZioJvw20FfJ98qMSwtufscPaEjrH2q+wJFxcKPgTYrl2I2QzHQLbxPtqcH3m6ZbZV4j2Sg9rM5wtunui9QVExiEqlphcZrhmKgWxgckBSo1z4g2yoxKjSp+ywts9Ta+4SOzSj7AkXFhGbh71GboRjoBvojOiXM8/HIt0qMy03OQBstIf/U9gWKisktY7rGa4ZioBsYPaZIQDHXKhHJQCvPLLSk7FPdFygqJrkOtHmynHwzFAN1532AiGSmVWJSeqIGKtZ/f6j7AkXFhBfS36I0QzFQV4bWiYEeYPq/H5n77B/x3vwcHmVfoKiY+E6kclM8z4VXqDXwzloMAz3AtPDEWqBd2xMDDS4WZSvnFQPVpTUwT4WPKRIV85MtM4UnaaC1FgYaXEzeQK+0QDVpTcc7p+tAA5NllYhloE3rs5mFFxB8qPsCRcWEDfTOGKgirYXJItkppDyrxFzhCdxnt4K+WQcaXLBC2RcoKiZpoO2C+otXSQfOa6CLTvh0ol3WP3OsErOlF/w+7d1HcnPw6r5AUTHxdaCyT0OqOa2BrrQwly1SWZaKivkx0F3SR7AtU9Y/tX2BomKyO5HkG58VZzXQ1iJ7q3Rd4aksS0XFPGTLfPGGvM+xYyor03zFJPfCx2h8VpzXQO0ftiyPV5alomIJGui0vamsTPMVEzuNSfw5HhYnNdDBxPq2cU1lWSoqdjxbFoo51H3OddeVlWm+YkLngcouWxpzagPdtS9TWZaKih3OlqWiDnOf86Odyso0XzERA43Y+Kw4p4EW1Xj3rnZlWSoq5sFA90pvZ2mySFmZ5isW3kDjNj4rTmmgh3a1K8tSUbGj2bJY1v7vc3muXVmZ5ivGUzmPoNRA53YXBRM7jjKxZAz02VIlZWWar5isgd7iHKl8JgO1zXM4Cx9AzA/KxA5my3JZ+73P5ys9lZVpvmIY6BGUGei44TldB+pRzB/KxI5ly5Oy9nmfawvllZVpvmIY6BEUGejsbNHuXe3KslRU7KiBHpB2ZX2fkbIyzVcMAz2CDgN9MtW+d1e7siwVFTuULc/K2td9umzTVFam+YphoEeQ+zJXHHKPST5DWZaKiik3ULdd7srKNF8xDPQIYl+m3w76OsqyVFTsSLY8/S483KfzISHKyjRfMQz0CFJf5vvkjOOA5lmiLEtFxVQZ6OBcpQ1nLCkr03zFMNAjyBloo1UvTgprniXKslRU7EC2PP9KdtynfbLnphPqlJVpvmIY6BGEvsz+OW8S5vl4qMtSUbH92bLytWy/z9Yxtx+QrKxM8xVTvBPpPvfwj/7F+vGeTgfl5WGgQt5ZoSxLRcWOGOhB6TGdZW4+H1lZmeYrptdAm2PwBlvpW9Ms27HnMdD3ARKK2rJUVGx3tng/pXow9rnto8rKNF8xaQP9eHU8WPlWHeJUvN1+98KLa+MCSRvo0Dox0OBi2gx01/M5lJVpvmJiBvrtc2l6pX8al4HQb5/rQ/CKD1yev1j8snZcXqoGOvDOWgsDDS62N1tWv5qt99n75/YnFCsr03zFpAz0XnXG2273+hF39/Y9V2sY9OO1tt7iMr1nXtdbtCka6MA8B7PwEijLUlGxndmyPrqyqwX60troto8qK9N8xYQMtGx5FuZX/FP44s3hiOXOFm+W284Z6N3hSXWJGeh0vHO6DjQwyrJUVGy3gR6XHmH13TFQpWJCBnqrp84b67xOJtfHFBbZvOVudfhnuvDFG9eHVBMy0IXJItkpJHVZKiq2L1scvppdy5j2+ae2Ms1XTMZAG5sr/qnc8L7ah+/bmG2rs6I24MJHO9NcaICaEV/10Djh4h9m/7b8KfDNvmwJ8d2U/tn+4P/q4IOx0yw4oCsLHy8s71L/UznnuoG2bc2RgbbPl+8bsAsjoHoNdN4ln5oniLIrW0L5Z4v/q4MXRA303ljfNgO1mpj2OtDpX5ejWgjrMNu7E20vfGZXu+9FMEc4s9iebHEaXNl4n83s+441TNu1jnFmMZkufGOg16a/fVsdt5xvgRavtkvoL/UrN6cHJqsyUPuHLcvjlSVOvmL7DNSLtMUu29ypdZQzi4mNgb71Q6CFDzpMIs0Y6K1f21Q7cD/X9DyqhbAOs/nLHEysb5sXUpY4+YrtyBa3L3HLfR70T21lmq+Y0Cz8tbTOW70JqVzSdFm5jj0L37Uxp67atGxXo1oK6yg7DXTXvkxliZOvmAYDPWif6so0XzHBdaAll3qP+/rKI2sdaPfmvl/fGuf6YGod1VJYR9n6ZQ79M6zWIc4stj1bHL9K9/s87J/ayjRfMamdSLfu0I+by0ak2Z1IfbO0bYGuD6bWUS2GdZA9LdC9u9qVJU6+YpuzxfX/hc73edw/tZVpvmJie+HvppuCvzhcaHbbuzUGWv+wviK/jmo5rGPsMNCu6YmBKhXbYaC+pCuODn9u0fLDmcXUHmc3d/BSMwtfjgJU7jrYE/8sKn9hDdlloO1PobWOcGaxrdnieRrQi39qK9N8xdQa6OA80HbDZjuU2vim4xySHgO1258YqFKxuAbqxT7VlWm+YmrPAx2cSN/veL/ZhzmlZqDvzRDorl3tyhInX7GN2eL+Tbrcpyf/1Fam+YppPQ/UKzoMtDuabt+pIMoSJ1+xbdmy4at0uE9f/qmtTPMV03oeqFdUGOjRs5SUJU6+YlsN1J+0n+FPNy2fnFlM63mgXtFgoIePolOWOPmKbcqWLd/qmrRH/9RWpvmKKT0P1C/xDdTDUZ7KEidfsVgG6tE+1ZVpvmJKzwP1S3QD9XEUsrLEyVdsS7Z43Izr1T+1lWm+YqKnMbmfB+qXyAbq5yR5ZYmTr9iGbPF4moFf/9RWpvmKiRqo+3mgfolroJ6exKEscfIV22SgnqR9Dn+uafnnzGJKzwP1S1QD9fUgI2WJk6+Ye7Zs/GaXpb37p7YyzVdM6XmgfolooP4eBKcscfIVkzdQ7/aprkzzFVN6Hqhf4hmoxwdpKkucfMWcs8XXcTAB/FNbmeYrpvU8UK/EMlCvzyFWljj5irlmi68TXUP4p7YyzVdM63mgXolkoH6f464scfIVczdQH9L+hz+XtUJxZjGt54F6JY6BerVPdYmTr5hjtvg5DiaQf2or03zF9B5n55EYBuq3+flcyz9nFpM00ED2qa5M8xXDQI+wXL7e/VNb4uQrtpYte88jnJEO5p/ayjRfMUED/fY5xklMJfIG6t0+1SVOvmIr2bL7QNepdDj/1Fam+YoJngdqWsQtVNpA/Tc/l7XCcGax59nSPRLwcBc+1PDnnFZYziwmZaCWf2Z/oHIQ/9SWOPmKrRlo89+jBhrUP7WVab5iQgZanaR8qX68Z74ONIx9qkucfMWeZov1SNVjy5iC2qe6Ms1XTMhA75ZplmZ68aq6hqSBhvJPbYmTr5iDgTbPZjkgHdg/tZVpvmJiWzmtgc+c98KHsk91iZOv2LNssRqghww0tH9qK9N8xSQPE+nI9jSmYM3PGa2gnFnMpQs/+nGjdNjhz6GWBGcWEz3OriXX80BD+qe2xMlXLLiBCvintjLNVwwDPcKgfIPap7rEyVfMZRb+sd0/X2oEuu8lyso0XzG68Eewyzewf2pLnHzFnNaBbh7sfrHY9MF9KCvTfMWYRDqCVb6B7VNd4uQrFmQnUmmbpbSQf2or03zFWMZ0hK58Qzc/H+oSJ18xx73w22RL2yykpfxTW5nmK8ZC+v30NUnAP7UlTr5iIbKlss2vX8V68NrKNF+xKFs58zhQue/LCdinusTJVyycgTZzSP4vP0VZmeYrxmEieyldsyzffSfzbEdZ4uQrFiBbatOsG58YaFZiHGe3l9I1WwMNcPkJyhInX7FQLdCX1kb9X36KsjLNV4wDlXdSuebXrzuPhtyBssTJVyycgX5tfwyPsjLNV0xqGVOEdmdPOANthkD9X36KssTJVyxIf6WcPKqkmYXPS0xsIb3wxPuAAFWiO1b3sWNP3y6UJU6+YkENlGVMmYmJbeWUXTo/JFQLtDud3P/lpyhLnHzFghmo3CImdWWarxgt0J3Ug59f6x/9X36KssTJVyysgQa49hzKyjRfMbmdSBevQpsItYzpvSpfGf/Uljj5igVcSO//wksoK9N8xaRm4T9eI7ZBQ7VA22VM/q8+g7LEyVcsyDpQDDRTMbEx0CHJH2f3buH94rMoS5x8xfxnS9Nzx0AzFMNA91Dbpqh/akucfMW8Z0s78omBZiiGge6gs02qRIZiAQzUVdofyso0XzF2Im2nb3VSJTIU822g3cw72ZKhGAa6GavXTpXIUMxzlXjBQHMWw0C3Yo96UiUyFPNbJayln2RLhmLSBhplU7zHmxzOGlElMhTzbaAbpP2hrEzzFRMx0N++fOp+Ej+O/uHzJkez7lSJDMW8Vgl76xHZkqGYhIHeetP8eDXGfPejV811vN3keNESVSJDMZ9V4gUDzVxMwECvA8+8JvxIj8miT6pEhmIeq8Rw7zvZkqFYeAO9jRyzXBMqfDSTn5ucWTRPlUhMzOVED68Gav9GtmQoFtxAS798G7xSdOOFO/FebnJu0xFVIi0xpzOR/FWJkQ7ZkqFYcAO9Tdubt7GlhsbHTc7u2aRKJCVWOmcp9txBvVWJsQzZkqFYcAO9TpubRaNUdibew03O73mnSiQlVhpaJSZioBObJlsyFAttoHNHKRevpbYXfuHMEKpESmLWmXLPHNRTlZg2c8mWDMVCG+jswzyuiRno4plLVImUxKQNdPwK2ZKhGAa6zvKZdVSJdMQGE0jhDXRGgWzJUEzAQFPvwj858pMqEUvM/QlDL2PqF5c/4KVKzMVGtmQoJjAGOjHLtCaRnh2ZTJWIJOawHmlqnIJd+NnIyJYMxSSWMV1GL92lV9IfuMnnR85TJeKItf4061NzLc72L1Kz8LMCZEuGYsENdPo0ufJAkYtX1TX23+TKIzuoEnHEOn8a2uOScVrvkFkHOn99siVDseAGWtrlcNn8TfxApt03ufbEI6pEFDHLn6qe+apz9u8W2Ym0IEC2ZCgWfi98eQCT3WMvTxO5eBVdZe9Nrj4xjioRRaydCHI2TuuT6+88XCWWBMiWDMUETmMqTxNp25z10+VkN3Luvsn1J25SJWKI7TDOLWJHq8RiRGRLhmIS54HeR4/kvHiVdGDXTbo8sZgqISq2cT3STrHjBrpb2h86v8AMxUROpB881Vj+QPpdN+n0xHeqxHGcmpGLjU59BrocEdmSoZjUM5FutXuKH0ZfseMmXeyTKuGBlY74vG/OzsK7EbpKPPl/AdmSoRhP5ZzFzT+pEodZXFn0dJyzfWHrAOgjeJV4FhHZkqEYBjqDU/e9hCpxlOnadqcZon0TSCXBDfSItD/yzBaFYhjoFGf/pEocZbC7csvc+l7/DFwlnkZEtmQohoFOcLZPqsRhagM9sCppM0GrxPPoyZYMxTDQMRv8kypxkE2NTj+ErBIrt0C2ZCiGgY7Y4p9UiUMMnVPGP0NWibX/BZAtGYphoAPchz8rqBI7GXhnOwYaVrIhqIEelfZHVtmiWQwDfdS2WTnnRv+kSuxhYJ5uJ8x5JFyVWL0DsiVDMQy088/3rfZJldjK3JJ4lxPmPBKsSqzfAdmSoRgG2o16bvdPqsQGFuaKRGeQHuGqhMMdkC0ZimGgj/3+SZUYsOiET+fZZf0zoIF6kPaH/mzJRAwDtfyTFugRsZUWpo4tjqGqhMv/AciWDMUw0No1mzmkjRemSvS0FtlbpesCT2V3tqdKODWhyZYMxQQeazyHnscadw3QBwZ6SGx4QtKW1fHK7mxHlXAbgiBbMhRTbKDVOczjx3cOXywfF+LwhE+XLvzoRzeoEh2DifVt45rK7mx7lXC8VbIlQzG9BnqdeWf5hDrrWNG5t8xGNQnLAgP1IjZ+TlFQsQOEMVBP0v5QVqb5ikmNgd471yst9bJ+pVv16KTRQ5GHL16rS06fmzyNajGsks42mYXfLzZ8OmZgsSMEqBKu90u2ZCgmZKCFy/VPkrs7HExf2Oxb88HLwovtn9Yv57wOdC2qMVSJhoF1nsxAnf+HQbZkKCZkoNfRk41Xxy3vbcfcfu/Ha22VRU/+rWyO1i3P+renUS2FVbNzG9KDKlExs7sonNhhvFcJnWO9yso0XzEZAy2ajhfr1/v6sOW17ZffrPcODHTdN/uoFsJq2eufVAnLPPc/p0jZnW02UH/S/lBWpvmKKTXQwh0/te/tO+iDLvzomk+jWgjrMKeuEqOW53QdqE8xb/iuEhvu9tTZkquYjIH2flhxXZ326ZuXbauzop5EKqyz+Hz5h2pWfm44YDzr/xW80pnnymspsilb0r9dOMRkgdEGt5wzroXXr/bM+2197Wbb1hwZaL0MtP540Yz9w6u9qGkQBwYajEWjzMM/NxloBrcLhxAy0HLFe9NtL39cnYW3DdSyXnsd6L35t3htbUCALrw3sX0LlXaKhcBvp0zteldlZZqvmNQ60NvApS9r15lvgRavNpZZXOHeWnL/3sWoFsM6SKZVYsEkQ5hnSbpVYltZZJot5xYTO0zk3tvn+irQhTHQW7+26Ydf76Z9y21tSBUD3cTcevida+SdSLZKbCyOPLPl5GKCpzE1uzqdzhGxZ+G7Dwxdte/bY6BeeRkfEh/SPEtSrRJbSyTLbDm7mNbj7Kx1oJ079n31cglTv4wJA/XK4DFFoc2zJNEqoXrLqrIyzVdMq4HO7UTqm6XNEqbmt9WNTRjoBvoHZUqY5+ORbJVQvd5VWZnmKybchf/ux8dtbQ1o++bpXnhrDLT4oV1iv76iHgPdQG2gUuZZkliV2F00OWbL6cXEDLQeAS0N1OkwptnTmJpZ+HJV6Vu3fqn459hpTAfIsEq8DBCRTKtK7C+cDLMFMSkDbWaQCv+7uixjegwP+2xN8uO1mch/s6558DzQI+RWJUbWiYFO6YoGA0XsIWag5dLNH36tlyRdHWfircPn+1bmbeCY985Mn0e1FNZRMqoSQ++sxTDQKf2OfwwUMTEDvTcd8qoHfnMxPZ9goE+ZaXd2s/ASpFQlOv9kEgmxErHzQMsWZGOgDqOWfsFAl5gb7pysAw1NSlWi68BjoIiViJ3GVLY5211FN0VP5TyE+irxbLpjcTZEdgopqSphNUAxUMQewueBtgbqcKCyV05roKsW+dRat+vtI6UqMWipe5f2h7IyzVcMAz2C8irx0s942K85NDCVZamoGAaKmLtYlC78+oHKfjmvgQ5+2LC+U1mWioo5zcLvmWJTni2I7RETm0Qq25yNgRbt0dWHynnlpAa6ud15ROwAysSc1oHumWLTnS2I7RKTW8b0qTXQ+jhPSU5toFu9c6fYAZSJue5ECiDtD2Vlmq+Y1E6kavV8ZaDlUnjZHvxJDXRwmJLig4O0iTnuhQ8h7Q9lZZqvmPBWTse9l545q4EOF8cHFTuCMrFzZgti+8TEDhNpH2cUwT/PWCVGDU8M1F3shNmC2G4xwePsGgsVt8/TVYnOPPfPGCvLUlGxk2ULYofEtB6o7JUzVYlhx33vjLGyLBUVO1O2IHZUDAM9grIqMZkw2j1jrCxLRcXOki2I+RCT2ok0eBInC+kDaM16ZQorbrSJnSJbEPMkFslA2crpVWv30sQ9Yt5RJpZ9tiDmUSyGgX77jIF61PLuns/EQqBMLO9sQcyvWHADvZk56MJ70gp0cJKyLBUVyzhbEPMuFtxAByvoOziRfiNPBjhDHDunLEtFxTLIFnVlmq9Y+C78Pbp/ZlAlptPpAd3zoS5LRcXSzxZhrVOLRZlEkib5KvEyfMpGWPMsUZalomLJZ4u0umixiAAAHcJJREFU1qnFMNAjyBloo1VtLwrtng91WSoqlny2SGudWoyF9EcQ+jK7Jw2LmOfjoS5LRcVSzxZxrVOLxTHQb3/DMqYN1AYq5p4PdVkqKpZ6tohrnVpMzECHc0msA91A8wzI4fkgYVGWpaJiiWeLvNapxQQPVMZA93LgYM+dKMtSUbHUs0Vc69RiQgb68To00ItX1TVSrxKVfdZaGGhwsdSzRVzr1GJiD5Urn4l0qx4md5X2z9SrRN36bGfhRVCWpaJiiWeLvNapxcQea1xu3myeB3+TPlQ56SrR9t+tdaDhUZalomJJZ0sMrVOLSa0DtZ8LX9jpxavqGglXiWbwc/fBnjtRlqWiYglnSxytU4uJGeil/6fpysuRbJXoPVPWP7VlqahYstkSS+vUYqIGWjQ9q13wH6+cxuTCwDKVJU6+YqlmSzStU4uJGujjWjc9P15ZxrTOqMWpLHHyFUszWyJqnVpMbBKpanre6oNA76wDXWXSY1eWOPmKpZgtUbVOLSa0jKmZeL+bahbpxoHKK8wMeCpLnHzF0suWyFqnFpNbSF9aZ/FPYZ13wyTSU2bni5QlTr5iqWVLdK1Ti0lu5Xzrd3RevKqukViVmJ9uV5Y4+Yolli3xtU4tJmWgv32pXLN5wIdsAzStKrG0WklZ4uQrllS2aNA6tZjccXb3+kzlq/wTPVKqEsurPZUlTr5iCWWLDq1Ti3Gg8hF8f5nPFssrS5x8xZLJFi1apxbDQI/g98t8vtdIWeLkK5ZItujROrWYoIGW459FN/4mu4SpJI0qsbJVU1ni5CuWRrYo0jq1mJiB1tNHpYGKn2aXRJVY3emuLHHyFUshW1RpnVpMykCb6ffCQK/iq5gSqBIOJ4UoS5x8xfRnizKtU4sJGWi5iumHX+vj7K7ST/RQXyWcDlpSljj5imnPFnVapxYTMtB7tXapOQ/0Jr2QSXeVcDynTlni5CumO1sUap1aTOyRHuXUUX+gMnvhO1yP+VSWOPmKqc4WjVqnFhM9jakxUPFneiiuEu6nJCtLnHzFFGeLTq1Ti4meB9oa6KmPs7PPlt9yyryyxMlXTFW2pKB1ajEM9Ag7vkzr6UbbHtKhLHHyFdOULUlonVosShf+et4x0P3POFKWOPmKKcqWNLROLSY2iVS2ORsDLdqjpz0PtPPMzY+IU5Y4+YopypY0tE4tJreM6VNroM3JdoLoqRKtae54xKayxMlXTE+2JKJ1ajHBA5W//7ky0Jsxwj14RVWids1dzyhWljj5iunJlkS0Ti0mvJWzRngjkp4q0fun1ZkPpHWIM4upyZZUtE4tJnaYSNlxj+SfiqpEM/ne/hhU6whnFtOTLYlonVpM8Di7xkLF7VNTlShcs5+HD6x1hDOL6cmWRLROLcaBykfYtYxpn39qS5x8xRRlSxpapxbDQI9waB1ocK0DnFlMUbakoXVqMQz0CPtaoHvm4NUlTr5iirIlDa1Tiwka6MdrNQYq/0QPRVViv39qS5x8xfRkSyJapxYTM9B7vFVMeqrEHuPcq3WIM4upyZZUtE4tJmWgN2sZqKl3xMuhpUoc8U9tiZOvmJZsSUbr1GJCBlp235uGp/WjFEqqxCH/1JY4+YopyZZ0tE4tJnaYiLX9/ayP9Djkn9oSJ18xJdmSjtapxcTOA7Ut85zH2R3zT22Jk6+YjmxJSOvUYlIGOhj2POWBysc68OoSJ18xFdmSktapxTDQI2z4Mo/6p7bEyVdMQ7YkpXVqMbEx0E+Lv4VHQZU47J/aEidfMQXZkpbWqcXkZuH7QdD7+Q5UPu6f2hInX7H42ZKY1qnFpNaBFg7a9OLLk0FlJ+HjVwkP/qktcfIVi54tqWmdWkxsFn4OqZHQ6FXCg39qS5x8xaJnS2papxbDQI/g+GX68E9tiZOvWOxsSU7r1GIY6BHcvkwfHXh1iZOvGAaKmLsYx9kdwenL9OOf2hInXzEMFDF3MQz0CC5fpif/1JY4+YphoIi5i2GgR3D4Mn35p7bEyVcMA0XMXUzQQMuB0PK58PInKkesEt78U1vi5CuGgSLmLiZmoPU8Ummg0svoIxuonJZHziyGgSLmLiZloM08fGGgVyPuoPGqhD//1JY4+YphoIi5iwkZaPlM+B9+/XgtdyNdHdcvVQ8BGW+at19sHjS//pilaFXCXwdeXeLkK4aBIuYuJmSg92r7Zm2gjgcqX2fWiraWWe8K7VaXajVQn/6pLXHyFcNAEXMXEzuNqTS5xkALG1yfSLo1ljt46/DFj1fH+ahIVcKrf2pLnHzFMFDE3MVkDLSwzLLN2Rho4YOrffj2DPvCLC9LL95dT8WLUyX8+qe2xMlXDANFzF1MbCvn5dEbqMOByt1b7LND2883fnxzPdUpSpXw7J/aEidfMQwUMXcxrQbaPTbJbq2ODPTq+njkSAYqp+WdM4thoIi5i0Xpwq8/VK74QNPwvFtPkR924b99/v5/f56ZqJ+JaiGswzwpX9/+qS1x8hXDQBFzFxObRCrbkY2BFj64ZnqN4z56062oJ5GKz9dTUsuHOo3PffoqTdGBF9cEL0TIFkiWyRFzO1zTNq6F1+9VQ7E2w3It0mXlOv1zkAcGWi8DbRqd92b90lx7NraB4p/pgoGCO0IGWq+er8zw5rBwc2CgltsO1oG2o6PFi5fpFQZRLYZ1kKUWvu8JpGdaQTizGF14xNzFhLdyLva5p2+faYEWr841YVcf8ildJUL4p7bEyVcMA0XMXUzsMJFu36XTRs75MdBbv7ZpuLx+pUUrXCWC+Ke2xMlXDANFzF1M8Di7xkKd9sHbs/DdBxZmlhQaqJxWIM4shoEi5i6m9UBlax1o5459v75ZV9qgzEDD+Ke2xMlXDANFzF1Mq4HO7UTqm6VlC7Rvj17XdiSJVokwHXh1iZOvGAaKmLtYJAP9x1174a0x0OKHtuH57fPajiTJKhHKP7UlTr5iGChi7mJRDNThMJHZ05iaWfhyTVT9t7JB2rdLl6NyC2s70/IN5p/aEidfMQwUMXcxCQMtV35aPljuIHKYSbLPA20PwOs2H1Wt02ZV/fpeTrkqEc4/tSVOvmIYKGLuYuENdGh6zVy8y1S8dfh8f4Lozf50fa3LelQzYXlhXL4B/VNb4uQrhoEi5i4W3ECtFfSXR2OADucp+41qGpYfpgYaSGhGKyhnFsNAEXMXC26gt9o564ciuTc//UY1DcsPo/IN6Z/aEidfMQwUMXex0Abab7u8mu9+vLp1uX0jVCVCduDVJU6+YhgoYu5iAgba9Nc/Xs1fucz4BECmSoT1T22Jk68YBoqYu1hoA+3P/qwGQ13PkPeLSJUI7J/aEidfMQwUMXcxWQMVnjxqkagSof1TW+LkK4aBIuYuJmqgcdqfIlUiuH9qS5x8xTBQxNzFRA00UgNUxkADScxoCXBmMQwUMXcxDPQIXfmG909tiZOvGAaKmLsYBnqEtnzDd+DVJU6+YhgoYu5iGOgRmvKV8E9tiZOvGAaKmLsYBnqEunxF/FNb4uQrhoEi5i4mYKBzyO7mDFQlXjqCXH6EssTJVwwDRcxdDAPdjax/akucfMUwUMTcxTDQvZTGWZYvBpqZGAaKmLuY1mcieSWQgVblSws0NzEMFDF3MQx0J5Vtfv1a2idjoFmJYaCIuYthoDupDbRqfmKgWYlhoIi5i2Gg+6hNs+6+Y6BZiWGgiLmLBZ9E+rsnaz9XH27siWAt0NZG/V9+irLEyVcMA0XMXUxgFv6y8N672Fx8KAN9efna/hgeZYmTrxgGipi7WPAu/H3hFOXyYZ1vXrWXCTULXxsos/B5iWGgiLmLCT3W+DJ68SZ6On2odaAvrAPNUAwDRcxdTGISqXqUXL8Rvn40p+TDkQLvRApx8SnKEidfMQwUMXcxkVn4me1IoueKhDXQENeeQVni5CuGgSLmLia0jGlkocLHMoUyUKpEjmIYKGLuYnLrQJuee4wny4XcyimGssTJVwwDRcxdjIX0e8FAMxXDQBFzF8NAd9Lshfd/4SWUJU6+YhgoYu5iGOhOMNBcxTBQxNzFMNB91JPvVIkMxTBQxNzFMNB9YKDZimGgiLmLYaC7aFZ/UiUyFMNAEXMXw0B3gYHmK4aBIuYuhoHuod1+RJXIUAwDRcxdDAPdAwaasRgGipi7GAa6g27/O1UiQzEMFDF3MQx0BxhozmIYKGLuYhjodvoDmKgSGYphoIi5i0kb6FXqMR42GChi7mIYKGLuYiIG+tuXT91P6Z/GZJ0ASpXIUAwDRcxdTMJAb71pVs/3kHuWRwMGipi7GAaKmLuYgIFeB55Z/ibdjfd6k/YR9FSJDMUwUMTcxcIb6G3kmOXh9ILPQyrBQBFzF8NAEXMXC26gpV8OH19cdOOFO/E+b3LwDCSqRIZiGChi7mLBDfQ2bW/e5J4IX+PxJl8w0NzFMFDE3MWCG+h12twsGqWyM/FeDdT+jSqRoRgGipi7WGgD/e3L1CyL12Snkfzd5OghxlSJDMUwUMTcxUIbaNHanM4YXTHQ7ShLnHzFMFDE3MUw0C2M/JMqkaMYBoqYu5iAgWbUhcdATyCGgSLmLiYwBjoxy2Qnkcb+SZXIUQwDRcxdTGIZ02X00l16JT0Gipi7GAaKmLtYcAP9eB03N8sDRS5eVdfwdJMT/6RK5CiGgSLmLhbcQEu7HC6bv4kfyISBIuYuhoEi5i4Wfi98eQCT3WMvTxO5eBVdxc9NTv2TKpGjGAaKmLuYwGlM5WkibZuz3BkvvZETA0VsixgGipi7mMR5oHcz5OJV0gEvNznjn1SJHMUwUMTcxUROpK/bnQ3yB9JjoIhtEMNAEXMXk3om0q12T/HD6Ct83OScf1IlchTDQBFzF+OpnI5goGcRw0ARcxfDQN2Y9U+qRI5iGChi7mJSBlo9TS7hLjwGehoxDBQxdzEZA71ak0jCz0MqOX6T8/5JlchRDANFzF1MwkAHk/AxWqEYKGLuYhgoYu5iAgZqPwu+8tL0ngu/4J9UiRzFMFDE3MXCG2jpmdaJdqNfRcBAEXMXw0ARcxcTeS78YO389HSR4By9ySX/pErkKIaBIuYuJvFc+FGXvejSCzdBMVDE3MUwUMTcxYIb6H3a3pw5YzksB29y0T+pEjmKYaCIuYvFeC580QRN6UT6Fwz0VGIYKGLuYpGeC5/SM5GW/ZMqkaMYBoqYuxiPNV7jiX9SJXIUw0ARcxfDQNfAQE8mhoEi5i6Gga7wzD+pEjmKYaCIuYsxBroCBno2MQwUMXcxZuGf89Q/qRI5imGgiLmLBTfQW9rrQDHQ04lhoIi5i0nsRBoNeKa0E+m5f1IlchTDQBFzF1O8F/4+d3ro9MXb+ngABoqYuxgGipi7WJTTmJymkJpDmAeN1dJ8R0eKugyo7r7JFf+kSuQohoEi5i4mdR5o44Pu54HWY6fFZ223nb5YWioGiphHMQwUMXcxiRPpm+chtTgNgBZO+9Z89vLsxZvLQ0L23uSaf1IlchTDQBFzF5N/JpLbWaD31mevlj9+vNaN16LZ2Vzl2+fv/x4DRcynGAaKmLuY1qdyXtte+s1qsU4N9Gou12AGuuqfVIkcxTBQxNzFlD4XvrDIxhbvlulOuvD34l0YKGJexTBQxNzF9Bpo08ZsW50V9SRS4aN18/Tb5+JvswZqRnzdwcvLnk9B6uzLFjgnY6c56ICuH//HlYmktq05MtB6GWg3bXQt34SBglcwUHAnioHeVmfibQO1puGH60DvVUM0VBd+vQNPpyxLMbrwiLmLSXThy6VG1nLOcj5pi4EOxkDLX0obvZT/VH/BQBHzKoaBIuYuFt5Au1WgtSPWjcg1A50fA731a5sKP272cAYyUBf/pErkKIaBIuYuJnGYSMvl0bRG1/dy2rPwndkOXfXjtf4DBoqYVzEMFDF3MYnj7Or+dmmbbs3PEmsdaOe2fb+++OlyswZuVwx5x006+SdVIkcxDBQxdzGBE+mbSaDyZOVr2xBdZW4nUt8sLVugGChiQcQwUMTcxeQe6fHxav7KOGxcr5ndC2+Ngfat2CBdeDf/pErkKIaBIuYuJvdQOfeDmCrmTmNqZuHLrfX9jnoMFDGvYhgoYu5isga65Vly9nmgbTt2NKPfvM+/gTr6J1UiRzEMFDF3MVEDdW9/lliHz/cDAbfJLFQAA33BQE8shoEi5i4maqCyDzPu2W6gjm+kSmQohoEi5i6GgU5x9k+qRI5iGChi7mIY6BQM9NRiGChi7mIY6AR3/6RK5CiGgSLmLoaBTsBAzy2GgSLmLiZgoHM4PVjOX1STsJ6wwT+pEjmKYaCIuYthoGMw0JOLYaCIuYthoCO2+CdVIkcxDBQxdzGlz0TyCwaKmLsYBoqYuxgGOmSTf1IlchTDQBFzF8NAh2CgpxfDQBFzF8NAB2zzT6pEjmIYKGLuYhjoAAwUMQwUMXcxDNRmo39SJXIUw0ARcxfDQG0wUMQwUMQ2iGGgFlv9kyqRoxgGipi7GAZqgYEihoEitkUMA+3Z7J9UiRzFMFDE3MUw0B4MFLEHBorYFjEMtGO7f1IlchTDQBFzF8NAH9VD5Co2X5gqkaEYBoqYuxgG2vsnBorYAwNFbIsYBto+xHiPg1IlMhTDQBFzF8NAH61/MomEWAkGipi7GAba2Gb5D+tAEcNAEdsihoF2DVAMFLESDBQxd7HTG6jVAMVAEXtgoIhtETu9gdquiYEihoEitkUMA8VAERuAgSLmLoaB9rbJLDxiDwwUsS1iGGi7DvTBOlDESjBQxNzFMFB2IiE2AANFzF0MA310Drr9wlSJDMUwUMTcxTDQI1AlMhQjWxBzF8NAj0CVyFCMbEHMXQwDPQJVIkMxsgUxdzEM9AhUiQzFyBbE3MUw0CNQJTIUI1sQcxfDQI9AlchQjGxBzF0MAz0CVSJDMbIFMXcxDPQIVIkMxcgWxNzFMNAjUCUyFCNbEHMXw0CPQJXIUIxsQcxdDAM9AlUiQzGyBTF3MQz0CFSJDMXIFsTcxTDQI1AlMhQjWxBzF8NAj0CVyFCMbEHMXQwDPQJVIkMxsgUxdzEM9AhUiQzFyBbE3MUw0CNQJTIUI1sQcxfDQI9AlchQjGxBzF0MAz0CVSJDMbIFMXcxDPQIVIkMxcgWxNzFMNAjUCUyFCNbEHMXw0CPQJXIUIxsQcxdDAM9AlUiQzGyBTF3MQz0CFSJDMXIFsTcxTDQI1AlMhQjWxBzF8NAj0CVyFCMbEHMXQwDPQJVIkMxsgUxdzEM9AhUiQzFyBbE3MVOYqAAACHwa1Ver+aL2EUMALni16q8Xk09wXp7kbUQS1063zLNV6wSFNaLDFUCMZ3S+ZZpvmKVoLBeZKgSiOmUzrdM8xWrBIX1IkOVQEyndL5lmq9YJSisFxmqBGI6pfMt03zFKkFhvchQJRDTKZ1vmeYrVgkK60WGKoGYTul8yzRfsUpQWC8yVAnEdErnW6b5ilWCwnqRoUogplM63zLNV6wSFNaLDFUCMZ3S+ZZpvmKVoLBeZKgSiOmUzrdM8xWrBIX1IkOVQEyndL5lmq9YJSisFxmqBGI6pfMt03zFKkFhvchQJRDTKZ1vmeYrVgkK60WGKoGYTul8yzRfsUpQWA8AIBtM7AAAAFLFxA4AACBVTOwAAABSxcQOAAAgVUzsAAAAUsXEDgAAIFVM7AAAAFLFxA4AACBVTOwAAABSxcQOAAAgVUzsAAAAUsXEDgAAIFVM7AAAAFLFxA4AACBVTOwAAABSxcQOQISbKfjU/nYf/BZGr718YK2PV8Ebu0uV4t28iWsC7MHEDkCAb59NxXc/Vr9e69++/zmYYGFrTS0PrDW8fFix375IlWLxfbUGKnmDANsxsQMIT1Hzy0pfuNoPvz6q1uhb/1sgwcZAA2tdJW9sePmAYuX/796ENQH2YWIHEJ5702gqat6lb97UvwXh1vYzA2u1l61vMLDY8PIBxapRiTdZTYCdmNgBhOfatFiKhuGn0m26/mCgwbRvn7//+/ragbVu/Y29BRf7eK3/NxRarOim/8XfNgY6VAn/zQFsxcQOIDzXfkDyU2+nhf0EGku7mksjGVartjJLNuiNDQ00mFjRvHzrbmyoEv6bA9iKiR2AHFUXsG6GlrQ9e9/cC4HaQANrFfdz6X8LfWODDnQ4sY+//rX7P8NQJfw3B7AZEzsAOap61zfb2iaVZ759Li7bGWhIrfKi1dR45SvBb6yewil89IdfA4tZBmqpBL9BgO2Y2AGIUVf9folMoGp4rXu4nx6P0Fp38/0fXruVRcFvrF6EaQTurLXKoUr4GwTYjIkdgBRF/StHzuxqGGIy914N1E0NNIDWvbHOwm+KOwt9Y4N1oGHF5gzUXILfIMB2TOwAhCiqX9VsCdyO+e1LdVGhFmgzmVLphG6gNQVY2uiFFihAjYkdgAzl4uxL+UPgkbRbO/0uMAZ6t9ab//Br6CHCW7+IKLQYY6CQDCZ2ACIUnb6mztlzuf4Xw3y81teczsIHWHjT92QbAw0pNjGzgGJzs/Df/xz4BgH2YGIHIEHvn4O1hf53BN5MT3H1oFrWMqabiNibJRtUbG4daPAbBNiDiR2AAIV/9k2WoPtZRgYadu9M3yQT2KjTi1Ud6KBinYGyEwm0Y2IHEJ5m/r3/LfyO6qvIXvh2QXndFA0sZo2BWnP+QcQ6A2UvPGjHxA4gPLfhxhWJM33aNlJYrXr9UvmPwGFF7TKGaz11FVKsH2/lNCZQjokdQHDa00C7s3gFTpW0tt+H1GpvTeS4zI/XphDb8clgYtYmf84DBd2Y2AEEp6v43WHm4c8170fpJA6JXzi+3Tu3gXuFE7NPSeFEelCNiR0AAECqmNgBAACkiokdAABAqpjYAQAApIqJHQAAQKqY2AEAAKSKiR0AAECqmNgBAACkiokdAABAqpjYAQAApIqJHQAAQKqY2AEAAKSKiR0AAECqmNgBAACkiokdAABAqpjYAQAApIqJHQAAQKqY2AEAAKSKiR0AAECqmNgBAACkiokdAABAqpjYAQAApIqJHQAAQKqY2AEAAKSKiR0AAECqmNgBAACkiokdAABAqpjYAQAApIqJHQAAQKqY2AEAAKSKiR0AAECqmNgBAACkiokdAMAcv30xb+W/d/P9z4+bGfMWOz6AEhM7AIA5vn02l/Lfm/nhVwwUtGJiBwAwx8frdz+W/17NpwcGCloxsQMAmKNqefY9+YqiVVr05wH0YGIHADBH1fLsG6IVGChow8QOAGCGwRxSCwYK2jCxAwAYUnjnwoDn0ECvRSf/Xvy1bqLeq3de+suMX6h//yRwA3AiTOwAAIZsMNB/No2Bfrw2b23/Pn6h+53pJ/CJiR0AwBB3A/3z16ZR2dtj84bxC8UnOy7ytwTZYmIHADDD3BzSxEBNPVNfWW71vnvTSZ+8cGuMtPxD/RkAH5jYAQBMKaxyOoc0NdDGC+/dT8U7SuOcvHBtG56FgzIPBf4wsQMAmNK2PG+D9uLEQN/an7p26r16cfLCjYYnBMHEDgBgyr3rcdtzPhMDvbQvd+748Vp02ScvNFPwDH+Cb0zsAACmNC3PdkN8w9hAm2amPUNUjYtOXqgHTJmEB++Y2AEADHGfhW8M1Jpyr/1y8sLD3k6PhYI/TOwAAIbsMdDhAOfkhfbTOCh4xsQOAGDIZgOd7PBc3vJZmSjTSeANEzsAgAnzc0hLBlo67sV+39wL7QfZTw8+MbEDAJhwnZ1DWjLQbpn8w1q1NHyhX/KEgYJPTOwAAMb0RzHZ+5CWDbTqmF/q16oXJy+0O5Kqv3CgCHjDxA4AYMzgcR7D12cNtF3m2Q+YTl649r/TAAV/mNgBAIwZPM7DYtFAp4ctjV/oZ6aGjVqAQ5jYAQCMuc3PIT0x0Had56flF5pVTJdAMcM5MbEDAABIFRM7AACAVDGxAwAASBUTOwAAgFQxsQMAAEgVEzsAAIBUMbEDAABIFRM7AACAVDGxAwAASBUTOwAAgFQxsQMAAEgVEzsAAIBUMbEDAABIFRM7AACAVDGxAwAASBUTOwAAgFQxsQMAAEgVEzsAAIBUMbEDAABIFRM7AACAVDGxAwAASBUTOwAAgFQxsQMAAEgVEzsAAIBUMbEDAABIFRM7AACAVDGxAwAASBUTOwAAgFQxsQMAAEgVEzsAAIBUMbEDAABIFRM7AACAVPn/hpA/9zAIYdcAAAAASUVORK5CYII=" title="plot of chunk unnamed-chunk-8" alt="plot of chunk unnamed-chunk-8" width="672" /></p>
<pre class="r"><code>res <- gbm.tune$results
names(res) <- c("depth","trees", "shrinkage","ROC", "Sens","Spec", "sdROC", "sdSens", "seSpec")
res</code></pre>
<pre><code>## depth trees shrinkage ROC Sens Spec sdROC sdSens seSpec
## 1 1 10 0.01 0.8363 1.0000 0.0000 0.04606 0.00000 0.00000
## 2 1 10 0.10 0.8587 0.9122 0.5027 0.03991 0.03882 0.08624
## 3 1 20 0.01 0.8470 1.0000 0.0000 0.04221 0.00000 0.00000
## 4 1 20 0.10 0.8659 0.8821 0.6118 0.03770 0.04008 0.07889
## 5 1 30 0.01 0.8534 1.0000 0.0000 0.04060 0.00000 0.00000
## 6 1 30 0.10 0.8705 0.8739 0.6583 0.03825 0.04224 0.08228
## 7 1 40 0.01 0.8565 1.0000 0.0000 0.04014 0.00000 0.00000
## 8 1 40 0.10 0.8731 0.8687 0.6765 0.03623 0.04220 0.08292
## 9 1 50 0.01 0.8581 1.0000 0.0000 0.04060 0.00000 0.00000
## 10 1 50 0.10 0.8760 0.8739 0.6925 0.03579 0.04259 0.07943
## 11 1 60 0.01 0.8592 0.9830 0.1591 0.04022 0.01884 0.13027
## 12 1 60 0.10 0.8797 0.8736 0.6947 0.03545 0.04180 0.07609
## 13 1 70 0.01 0.8606 0.9560 0.3563 0.04000 0.02546 0.08134
## 14 1 70 0.10 0.8815 0.8748 0.7067 0.03392 0.04236 0.07460
## 15 1 80 0.01 0.8619 0.9368 0.4136 0.04046 0.03588 0.08414
## 16 1 80 0.10 0.8826 0.8763 0.7084 0.03331 0.04118 0.07666
## 17 1 90 0.01 0.8626 0.9219 0.4585 0.03979 0.03590 0.08450
## 18 1 90 0.10 0.8842 0.8757 0.7146 0.03304 0.04115 0.07292
## 19 1 100 0.01 0.8633 0.9119 0.4881 0.03921 0.03705 0.08231
## 20 1 100 0.10 0.8848 0.8775 0.7124 0.03266 0.04119 0.07465
## 21 3 10 0.01 0.8610 1.0000 0.0000 0.04178 0.00000 0.00000
## 22 3 10 0.10 0.8668 0.8967 0.5891 0.03950 0.04340 0.08192
## 23 3 20 0.01 0.8655 1.0000 0.0000 0.04096 0.00000 0.00000
## 24 3 20 0.10 0.8762 0.8772 0.6772 0.03654 0.04067 0.07015
## 25 3 30 0.01 0.8695 1.0000 0.0000 0.04001 0.00000 0.00000
## 26 3 30 0.10 0.8827 0.8723 0.7084 0.03485 0.04193 0.06700
## 27 3 40 0.01 0.8707 0.9890 0.1654 0.04049 0.01571 0.06871
## 28 3 40 0.10 0.8863 0.8714 0.7181 0.03441 0.03886 0.06515
## 29 3 50 0.01 0.8720 0.9641 0.3375 0.04017 0.02468 0.06947
## 30 3 50 0.10 0.8879 0.8696 0.7176 0.03415 0.04250 0.06822
## 31 3 60 0.01 0.8733 0.9444 0.4244 0.03999 0.03050 0.07764
## 32 3 60 0.10 0.8883 0.8729 0.7170 0.03424 0.04183 0.06829
## 33 3 70 0.01 0.8746 0.9259 0.4857 0.03903 0.03427 0.07852
## 34 3 70 0.10 0.8889 0.8763 0.7141 0.03344 0.04326 0.06679
## 35 3 80 0.01 0.8753 0.9168 0.5220 0.03863 0.03645 0.07386
## 36 3 80 0.10 0.8880 0.8717 0.7182 0.03279 0.04454 0.07412
## 37 3 90 0.01 0.8759 0.9076 0.5589 0.03860 0.03686 0.07883
## 38 3 90 0.10 0.8877 0.8732 0.7198 0.03306 0.04419 0.06947
## 39 3 100 0.01 0.8774 0.9000 0.5844 0.03796 0.03751 0.08008
## 40 3 100 0.10 0.8867 0.8708 0.7199 0.03308 0.04310 0.06271</code></pre>
</div>
<div id="gbm-model-predictions-and-performance" class="section level3">
<h3>GBM Model Predictions and Performance</h3>
<p>Make predictions using the test data set</p>
<pre class="r"><code>testX <- testData[,4:61]
gbm.pred <- predict(gbm.tune,testX)
head(gbm.pred)</code></pre>
<pre><code>## [1] WS PS WS PS WS PS
## Levels: PS WS</code></pre>
<p>Look at the confusion matrix</p>
<pre class="r"><code>confusionMatrix(gbm.pred,testData$Class) </code></pre>
<pre><code>## Loading required package: class</code></pre>
<pre><code>## Confusion Matrix and Statistics
##
## Reference
## Prediction PS WS
## PS 563 96
## WS 79 271
##
## Accuracy : 0.827
## 95% CI : (0.802, 0.849)
## No Information Rate : 0.636
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.622
## Mcnemar's Test P-Value : 0.226
##
## Sensitivity : 0.877
## Specificity : 0.738
## Pos Pred Value : 0.854
## Neg Pred Value : 0.774
## Prevalence : 0.636
## Detection Rate : 0.558
## Detection Prevalence : 0.653
##
## 'Positive' Class : PS
## </code></pre>
<p>Draw the ROC curve</p>
<pre class="r"><code>gbm.probs <- predict(gbm.tune,testX,type="prob")
head(gbm.probs)</code></pre>
<pre><code>## PS WS
## 1 0.25311 0.7469
## 2 0.50939 0.4906
## 3 0.09218 0.9078
## 4 0.76133 0.2387
## 5 0.07473 0.9253
## 6 0.83736 0.1626</code></pre>
<pre class="r"><code>gbm.ROC <- roc(predictor=gbm.probs$PS,
response=testData$Class,
levels=rev(levels(testData$Class)))
gbm.ROC</code></pre>
<pre><code>##
## Call:
## roc.default(response = testData$Class, predictor = gbm.probs$PS, levels = rev(levels(testData$Class)))
##
## Data: gbm.probs$PS in 367 controls (testData$Class WS) < 642 cases (testData$Class PS).
## Area under the curve: 0.9</code></pre>
<pre class="r"><code>plot(gbm.ROC)</code></pre>
<p><img src="data:image/png;base64,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" title="plot of chunk unnamed-chunk-11" alt="plot of chunk unnamed-chunk-11" width="672" /></p>
<pre><code>##
## Call:
## roc.default(response = testData$Class, predictor = gbm.probs$PS, levels = rev(levels(testData$Class)))
##
## Data: gbm.probs$PS in 367 controls (testData$Class WS) < 642 cases (testData$Class PS).
## Area under the curve: 0.9</code></pre>
<p>Plot the propability of poor segmentation</p>
<pre class="r"><code>histogram(~gbm.probs$PS|testData$Class,xlab="Probability of Poor Segmentation")</code></pre>
<p><img src="data:image/png;base64,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" title="plot of chunk unnamed-chunk-12" alt="plot of chunk unnamed-chunk-12" width="672" /></p>
</div>
</div>
<div id="support-vector-machine-model" class="section level2">
<h2>SUPPORT VECTOR MACHINE MODEL</h2>
<p>We follow steps similar to those above to build a SVM mocel</p>
<pre class="r"><code>set.seed(1)
registerDoParallel(4,cores=4)
getDoParWorkers()</code></pre>
<pre><code>## [1] 4</code></pre>
<pre class="r"><code>system.time(
svm.tune <- train(x=trainX,
y= trainData$Class,
method = "svmRadial",
tuneLength = 9, # 9 values of the cost function
preProc = c("center","scale"),
metric="ROC",
trControl=ctrl) # same as for gbm above
) </code></pre>
<pre><code>## Warning: closing unused connection 8 (<-Joseph-Rickert-PC:11402)
## Warning: closing unused connection 7 (<-Joseph-Rickert-PC:11402)
## Warning: closing unused connection 6 (<-Joseph-Rickert-PC:11402)
## Warning: closing unused connection 5 (<-Joseph-Rickert-PC:11402)</code></pre>
<pre><code>## user system elapsed
## 3.14 0.10 224.89</code></pre>
<pre class="r"><code>svm.tune</code></pre>
<pre><code>## 1010 samples
## 58 predictors
## 2 classes: 'PS', 'WS'
##
## Pre-processing: centered, scaled
## Resampling: Cross-Validation (10 fold, repeated 5 times)
##
## Summary of sample sizes: 910, 908, 909, 909, 909, 909, ...
##
## Resampling results across tuning parameters:
##
## C ROC Sens Spec ROC SD Sens SD Spec SD
## 0.2 0.9 0.9 0.6 0.03 0.04 0.07
## 0.5 0.9 0.9 0.7 0.03 0.04 0.07
## 1 0.9 0.9 0.7 0.03 0.03 0.07
## 2 0.9 0.9 0.7 0.03 0.04 0.08
## 4 0.9 0.9 0.7 0.03 0.04 0.08
## 8 0.9 0.9 0.7 0.03 0.04 0.07
## 20 0.9 0.9 0.6 0.03 0.04 0.06
## 30 0.8 0.9 0.6 0.03 0.04 0.06
## 60 0.8 0.9 0.6 0.03 0.04 0.07
##
## Tuning parameter 'sigma' was held constant at a value of 0.02102
## ROC was used to select the optimal model using the largest value.
## The final values used for the model were C = 1 and sigma = 0.02.</code></pre>
<pre class="r"><code># Plot the SVM results
plot(svm.tune,
metric="ROC",
scales=list(x=list(log=2)))</code></pre>
<p><img src="data:image/png;base64,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" title="plot of chunk unnamed-chunk-13" alt="plot of chunk unnamed-chunk-13" width="672" /></p>
<pre class="r"><code>#---------------------------------------------------
# SVM Predictions
svm.pred <- predict(svm.tune,testX)
head(svm.pred)</code></pre>
<pre><code>## [1] PS WS WS PS WS PS
## Levels: PS WS</code></pre>
<pre class="r"><code>confusionMatrix(svm.pred,testData$Class)</code></pre>
<pre><code>## Confusion Matrix and Statistics
##
## Reference
## Prediction PS WS
## PS 572 109
## WS 70 258
##
## Accuracy : 0.823
## 95% CI : (0.798, 0.846)
## No Information Rate : 0.636
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.608
## Mcnemar's Test P-Value : 0.00451
##
## Sensitivity : 0.891
## Specificity : 0.703
## Pos Pred Value : 0.840
## Neg Pred Value : 0.787
## Prevalence : 0.636
## Detection Rate : 0.567
## Detection Prevalence : 0.675
##
## 'Positive' Class : PS
## </code></pre>
</div>
<div id="comparing-models" class="section level2">
<h2>Comparing Models</h2>
<p>Having set the seed to 1 before running gbm.tune and svm.tune we have generated paired samples (See Hothorn at al, “The design and analysis of benchmark experiments-Journal of Computational and Graphical Statistics (2005) vol 14 (3) pp 675-699) and are in a position to compare models using a resampling technique.</p>
<p>The resamples function in caret collates the resampling results from the two models</p>
<pre class="r"><code>rValues <- resamples(list(svm=svm.tune,gbm=gbm.tune))
rValues$values</code></pre>
<pre><code>## Resample svm~ROC svm~Sens svm~Spec gbm~ROC gbm~Sens gbm~Spec
## 1 Fold01.Rep1 0.9209 0.8769 0.8571 0.8826 0.8000 0.8286
## 2 Fold01.Rep2 0.8784 0.8182 0.7714 0.9043 0.8030 0.8286
## 3 Fold01.Rep3 0.8961 0.8939 0.7143 0.8649 0.8030 0.7429
## 4 Fold01.Rep4 0.9352 0.9545 0.6944 0.9146 0.9091 0.6944
## 5 Fold01.Rep5 0.9134 0.9242 0.6000 0.9390 0.9394 0.6857
## 6 Fold02.Rep1 0.8641 0.8636 0.6944 0.8864 0.9091 0.6667
## 7 Fold02.Rep2 0.9446 0.9091 0.7714 0.9307 0.9091 0.8000
## 8 Fold02.Rep3 0.9009 0.8788 0.7143 0.9004 0.9091 0.7143
## 9 Fold02.Rep4 0.9061 0.9394 0.7143 0.9121 0.8788 0.6571
## 10 Fold02.Rep5 0.9208 0.8939 0.8000 0.9312 0.8939 0.8000
## 11 Fold03.Rep1 0.8701 0.8636 0.6857 0.8887 0.8333 0.7429
## 12 Fold03.Rep2 0.8212 0.8030 0.7143 0.8104 0.8030 0.7143
## 13 Fold03.Rep3 0.9011 0.8923 0.8286 0.9073 0.8769 0.7429
## 14 Fold03.Rep4 0.8826 0.8462 0.6571 0.8954 0.8462 0.7429
## 15 Fold03.Rep5 0.8623 0.8788 0.7143 0.8364 0.8636 0.6571
## 16 Fold04.Rep1 0.8468 0.8939 0.5429 0.8541 0.8788 0.5714
## 17 Fold04.Rep2 0.9125 0.9545 0.6389 0.9200 0.9242 0.7500
## 18 Fold04.Rep3 0.8667 0.8485 0.7143 0.8818 0.8636 0.6857
## 19 Fold04.Rep4 0.8753 0.8939 0.7429 0.9104 0.9091 0.7429
## 20 Fold04.Rep5 0.8996 0.8788 0.7143 0.9251 0.8333 0.8000
## 21 Fold05.Rep1 0.9035 0.9091 0.7714 0.9177 0.8939 0.7429
## 22 Fold05.Rep2 0.9156 0.9385 0.7429 0.8725 0.8923 0.7143
## 23 Fold05.Rep3 0.9100 0.9091 0.6857 0.9134 0.9242 0.7429
## 24 Fold05.Rep4 0.8857 0.8788 0.8286 0.8550 0.8636 0.7429
## 25 Fold05.Rep5 0.8515 0.8485 0.6286 0.8667 0.8636 0.7143
## 26 Fold06.Rep1 0.9013 0.8939 0.8000 0.8896 0.8030 0.7429
## 27 Fold06.Rep2 0.8870 0.8939 0.7429 0.8883 0.9091 0.7143
## 28 Fold06.Rep3 0.8607 0.8769 0.6857 0.8835 0.9231 0.6857
## 29 Fold06.Rep4 0.8571 0.8333 0.6286 0.8723 0.8485 0.8000
## 30 Fold06.Rep5 0.9402 0.9385 0.8333 0.9543 0.9692 0.7500
## 31 Fold07.Rep1 0.8939 0.9242 0.6286 0.8623 0.8485 0.6286
## 32 Fold07.Rep2 0.8193 0.8615 0.6286 0.8444 0.8154 0.6857
## 33 Fold07.Rep3 0.8515 0.8636 0.6286 0.8420 0.8939 0.6286
## 34 Fold07.Rep4 0.8939 0.9242 0.6857 0.8918 0.9394 0.6286
## 35 Fold07.Rep5 0.9077 0.9077 0.7222 0.9068 0.8923 0.7500
## 36 Fold08.Rep1 0.8983 0.8788 0.7429 0.9113 0.8939 0.7714
## 37 Fold08.Rep2 0.8714 0.9091 0.5714 0.9082 0.8788 0.7429
## 38 Fold08.Rep3 0.8577 0.8788 0.5556 0.8628 0.8030 0.6667
## 39 Fold08.Rep4 0.8914 0.8485 0.7222 0.9196 0.8636 0.8611
## 40 Fold08.Rep5 0.9095 0.9091 0.6857 0.9026 0.8485 0.6857
## 41 Fold09.Rep1 0.9104 0.9091 0.7714 0.9165 0.8939 0.7429
## 42 Fold09.Rep2 0.9402 0.9091 0.6944 0.9331 0.9091 0.7222
## 43 Fold09.Rep3 0.8883 0.8939 0.6857 0.8879 0.9242 0.5714
## 44 Fold09.Rep4 0.8654 0.9091 0.6000 0.8866 0.9394 0.6000
## 45 Fold09.Rep5 0.8095 0.8788 0.6857 0.8165 0.8939 0.6571
## 46 Fold10.Rep1 0.8509 0.8615 0.6111 0.8833 0.8923 0.6389
## 47 Fold10.Rep2 0.8403 0.8485 0.7429 0.8524 0.8788 0.5714
## 48 Fold10.Rep3 0.9457 0.9091 0.7500 0.9335 0.8939 0.7500
## 49 Fold10.Rep4 0.8484 0.8462 0.6857 0.8607 0.8308 0.7143
## 50 Fold10.Rep5 0.8372 0.8182 0.6000 0.8160 0.8030 0.7714</code></pre>
<pre class="r"><code>summary(rValues)</code></pre>
<pre><code>##
## Call:
## summary.resamples(object = rValues)
##
## Models: svm, gbm
## Number of resamples: 50
##
## ROC
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## svm 0.81 0.861 0.890 0.885 0.909 0.946 0
## gbm 0.81 0.865 0.889 0.889 0.913 0.954 0
##
## Sens
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## svm 0.803 0.864 0.893 0.886 0.909 0.955 0
## gbm 0.800 0.848 0.886 0.876 0.909 0.969 0
##
## Spec
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## svm 0.543 0.643 0.704 0.701 0.743 0.857 0
## gbm 0.571 0.671 0.718 0.714 0.748 0.861 0</code></pre>
<pre class="r"><code>xyplot(rValues,metric="ROC") # scatter plot</code></pre>
<p><img src="data:image/png;base64,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" title="plot of chunk unnamed-chunk-14" alt="plot of chunk unnamed-chunk-14" width="672" /></p>
<pre class="r"><code>bwplot(rValues,metric="ROC") # boxplot</code></pre>
<p><img src="data:image/png;base64,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" title="plot of chunk unnamed-chunk-14" alt="plot of chunk unnamed-chunk-14" width="672" /></p>
<pre class="r"><code>parallelplot(rValues,metric="ROC") # parallel plot</code></pre>
<p><img 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" title="plot of chunk unnamed-chunk-14" alt="plot of chunk unnamed-chunk-14" width="672" /></p>
<pre class="r"><code>dotplot(rValues,metric="ROC") # dotplot</code></pre>
<p><img src="data:image/png;base64,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" title="plot of chunk unnamed-chunk-14" alt="plot of chunk unnamed-chunk-14" width="672" /></p>
<pre class="r"><code>splom(rValues,metric="ROC")</code></pre>
<p><img 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Nav/TwfYec8mZw/H/B7cC38RdAVRSVEqkOx/t3H4REf5znWnf+5u1S7kje6oqiESHQo1h//9py02V2Cvr2Zyc9Uu5I5uqKohEhuKAZvOi5dgnr+SWcJaig/0+vKJdAVRSVEakMxPGqzKUCdN1IN5WdyXbkGuqKohEhsKDzHjla/CeoksBPHlvIzta5cBF1RVEIkNhT9teNrVYD6vj9OWMrP1LpyEXRFUQmR1lB0Dvv071rwr7vRObzTirS6chV0RVEJkdZQDAN07bVIb73k3etaom3S6spV0BVFJURSQ9E9FSlo5dg/lLTX1ezbJNWVy6ArikqItIai+0lKIXveC75OzoC0unIVdEVRCZHWUPhPRVqRo/3T6QlQLEZXFJUQiQ2F5wNBghO0+QwROfxOgGIaXVFUQiQ2FP1jPquPAQ124X8SoFiGrigqIVIbit6Kc8NnitTHkNrz5wlQTKMrikqI5IbCn59rruh016xNflo7FTS5rlwCXVFUQqQ9FCHHgNw1bJGf9Y68sQRNuyu5oiuKSoikh8LzEXWL/pWTn2542knQpLuSLbqiqIRIeyg2noVU7b93Lu+M9YttlHZXckVXFJUQaQ9FlNM4DZ4LuqArz1vhW+/Oe3Hfl78mH4JgaW8rUVEJkfhQhFyJ2X+/M8kALaOyk5aFX+/1nd/GH4INEt9WYqISIvGhCPgskP4Ro7A3Uvc125XH7fZRJebXv/W+399vt89q3fkx9hBskfi2EhOVEKkPxeoD6MPITXAFWmRlFZJFPH7qnY/bH3+Wfz6rRaf3Idgi9W0lIiohkh+KtScgDXMzwQB9yo75vX2P858fze3ixqf/Idgk+W0lHiohrjYU8v3Fr5koPddcV+6yX/5o3+KUJWcTmb6HYJOrbSsTqIS42lA41x+538nZ+fN8M13RxWaxzqx3219VgDb76mV2eh+CTa62rUygEuJiQ9G9frO919yVSD3dHxfp2Cw2f71rOuqdxY0qQIcPwSYX21amUAlxtaEoQlI+P2T46cxn/VIDMwHa7q276Sj76s9bEaD+h2CLq20rE6iEuNpQGP38up6Zrrjp2B5jL26XUVmePt8NUA7Dx3G1bWUClRCXG4oU8nNNgDrLy2e9Wv36b1agu7jctjKOSojLDcXPn9aOGHnMH0TypmN5Kn3xkwfvge7ictvKOCoh8hmKpe9jmjti5LHiKLznHKXyNKaZhyBAPtvKZlRCZDMUy3PRfn6uOQ90eKFmvficfAhCZLOtbEclRC5DYe9czi1CrkQqkrK+Xe+zcyVSdLlsKxFQCZHLUNi7mmiLoGvhJTLrtSfXwkeXy7YSAZUQmQyFwevZtwj9NKbib8UOfJ2YfBpTbJlsKzFQCZHJUCwIUP36zQSs+zzQ6tKjV/t5oJ/DhyCCTLaVGKiEyGMoFnym58+8AtT9uHkJ0Gr16Sw4+UT6uPLYVqKgEiKToZhdgSaVn7l0JTN0RVEJkclQTAVoec5SWvmZS1cyQ1cUlRC5DMX4UfgmP5M6tpRLV/JCVxSVELkMxeh5oG/N5y8ldYJoLl3JC11RVEJkMxRjVyKVd3Q/PzkB2XQlK3RFUQmRz1CM5+fwpnX5dCUndEVRCZH7UPgC1P7F8Ll3JU10RVEJkflQ+E4QTeDjmDLvSqLoiqISIq2hWJ98wxVoCh87klZXroKuKCohkhqKhWtH90G+AO3+3aKkunIZdEVRCZHSUCxcO761qtOX9G73j95NY1LqynXQFUUlREpDsWztWMar5ufPQeoSoAhEVxSVEAkNxcLoq/OzvPGz+v6j7n7/go8dMSChrlwIXVFUQqQwFMO3Piei7+1NfizXb3b/MStQBKIrikoI80PhvKPp3jnxeCc/PQ8kQBGIrigqIawPRZuey1agugCt15+eB3IUHmHoiqISwvhQyMrzrRuGkytQWYC+xgKU80ARgq4oKiGMD0Xn4M+itaOTmv4A5UokhKErikoI20PRT8Mla0d/5vYfYTo/jXflquiKohLC9lB0j577Dif5/41nrz8ptrsy59d7nt/ElHZXoqISwsBQ+EOxPXYkf195JWfvuFNSDHRlAwI0e1RCnD8U/lgcnL20Yj3pPfMpKed3ZQsCNHtUQpw+FP43Npu/dS4hWhiHzcfPR8rPc4L49K5sQoBmj0qI04fCf2i9+9mdr/adzVlxv3/zpKXs6V3ZhADNHpUQZw+F/+TO/ofHL0+x6PnZ+fMoZ3dlGwI0e1RCnD0UMwG6NkEjf//7WVctnd2VtX5/v91uX/4q/vj6dxOg1V3f9MfF/Y/ijj/+LP9e3rp9nvkLB0mtKzuiEuLkofBf4N69V6NzPkH3ys+jEzSxTbUKxMJ/aQO0+M9NA7MM0P/3o37MRx2tbbqmI7Gu7IlKiLOHYm4F2jn6PpdikfOTAF3meWs1AfqfmpCsE7QM0P8pd/yfH/LY1NagaXVlV1RCnD0UCwJ0eYztl58E6LhyRfnlryZHmwCtbzybdWazh9/cqG6Vj0ltCZpUV/ZFJcTpQzF9FL53+ufBb0WyAl3iUadlHYoSoHU4FrfKJWiZm/VDnjfnVn0jHUl1ZV9UQpw+FJPngfbPXiJADbrrzvhDA7R+77P62UcdoPVDilvNj4pb5Uo0JUl1ZV9UQpw/FDNXIp36EcgchZ/nJGGRnE2AyuLyWa1F24f4biUjpa7sjEoIA0PhP0WpvffUAOU80DnNuUvOTec80PomAZobKiFSGIoTP0OeK5FmOetNb4AW9xCguaESIoWhmF8HRj783n1troWfxAr0gqiESGIo5taBsc//PF0SXWnwHugFUQmRxlBcLD8T6Urtnx+eo/CSjfURegI0N1RCZDAU+eVnWl3R80DldM/2PNCnnj9PgGaFSoj0hyLD/EyrK3Ilkl6AVN0oE/TRXolEgGaFSojkhyLH/EysK1PXwlchSYDmhkqI5Icix/xMrSvjn8ZUZyQBmhsqIRgKi1LrSvNpIc35S+3ngX7ojwnQrFAJwVBYlGhXcv0o+kaiXdkDlRAMhUVJdaX9MJG7rjmzlFRX9kUlBEMxdM7VR66kunKXD57XG5lKqiv7ohLC3FCcn14nXf/uMteVKc3xouYLOzKWVFf2RSWEtaFY8/1xu/0GnT/PYK0r057XyM/EurIrKiGMDcXy9Nrt/M8TP/tJGevKrHua3xK3Umpd2RGVEMaG4rTvjxv+BmcmqLGuoEJXFJUQtoZicXrtd/0RAYoRdEVRCWFrKJam1yH5SYCig64oKiFMDcXS9Nrz+ndWoBhBVxSVELaGYmF67Xn9OwGKEXRFUQlhaygspBdH4eFHVxSVEMaGwkB6cR4o/OiKohLC2FBYSC+uRIIXXVFUQlgbCgPpZeBqUmtdQYmuKCohzA3F+ellgLmu4EVXHFRCmBiKxaGZ5cfPe5joCnroiqISwsJQLN5tz/L7j3wsdAV9dEVRCWFgKBYfOLpMflroCgboiqISwsBQLD11aVN+pvXWqoGuYICuKCohzh+KQ65/t3Bwf4Xzu4IhuqKohDh/KBZfvrktPzt/Wnd+VzBEVxSVEKcPxfIPEInxIgQogtEVRSXE+UNxwPXvFi6xX+X8rmCIrigqIc4fin667fBmJQGKCOiKohLCwFB096/jH+7pPB8BilB0RVEJYWAoOkd44h/u6SYyAYpQdEVRCWFhKNyIi364p3xaJ5kJUISiK4pKCBND4cnP9ubGy4/0afvPb5mJrqCHrigqIU4diuG7ncMA3Xj9pqw76wTlPFCEoyuKSogzh2J4vGh4UujW69/bt1S5Egnb0BVFJcSJQ+E7XtS/GSk/uRYe29EVRSXEqQHavzG4uf3zl5I7BbTCpmoRXVFUQpw3FBPrTrkR4fPrCFDEQlcUlRD2AtTZr4/x+Z+pXQZfYVO1iK4oKiFOG4qxDxGJfbgntQ9iqrCpWkRXFJUQ1lag8Q/3JHYAvsKmahFdUVRC2AvQHV4otfxkUzWJrigqIYwdhUeFTdUiuqKohDB2HigqbKoW0RVFJYStK5HEZb5+cwSbqkV0RVEJYexa+Np1vr94BJuqRXRFUQlhcSgu9P3FIyx2BXRFUQlhcCiu9P3FIwx2BXSlRSXEmacx7bD/nsmBKTZVi+iKohLixCuR/Am67f3PTE6NYlO1iK4oKiHOGorRpWKc/Ew8QdlULaIrikqI8wK0fyPqsxKgiI6uKCohDhyKkS/HjHrZu/92cthULaIrikqI44ai86bnTktFVqDYD11RVEIcNhSdNz33WioSoNgPXVFUQhwYoO6NvZKOo/Doet4K3ybu/OfHrfb175mnoiuKSoijhqKXmPG//12ejPNA4bjX4fjlL+c+icw//iz/9vs7AboalRAnBehwqRjp+neuRILjcbt9vF6/3jvp2L3z1/tscjboiqIS4qChGLzp2V8qRvv8kBzyk001kmJ1+VH+WYTl59idz8EO/hi6oqiEOGsF2lsqXv7zl7rYVON4yr773UnJX+/1vnuxJ18G6aOO0wXoiqIS4rQAfZGf49hU47jLrvvDeRe0F6D3+m8L0BVFJcRJR+G7yM8eNtUoiohsFp7PWxuT3V3439+//Ou750C9B11RVEKccx5oD/nZw6YaRbPGfLWrzkp9EKnI0XJ5WsSo50B97dZ31C9uHpUQJ12JhElsqlHIWrMXoPVpoM2i89mcv3T3nMZEgI6hEuKka+ExiU01CjdAncPwnfNA5d3R4s5P33M46IqiEoKhsIiuROFfgRb3ln8pY9SNzPvs26B0RVEJwVBYRFei8L8H+mjPbeqeXj93Qj1dUVRCMBQW0ZUo3KPweoxo5MgSAboGlRDnDgWH3/3YVONwzgPVdGz364tbzj48AboClRCbhmLrYSHO/xzBphqH70qkdllarkDb9eh99ookuqKohNgyFFtPTCI/x7CpxuG9Ft55D7S4IQvP399nr0iiK4pKiA1DsfWj48jPUWyqkfg+jak5Cl9+0l39s3JB2q5Lx9EVRSXEpgDt31iF/BzHphqL+3mgRUg2H2DXnBdfrU6bs+oXXMtJVxSVEOFDsfFD5cnPCWyq0TgfPi8BWi1M22s36/Pq586if9EVB5UQJwZo6AtfAJuqRXRFUQkRPBQxvxeOizx72FQtoiuKSojTVqCdZyJBu9hULaIrikoIAwGayRfBxcSmahFdUVRCnHcUPvrzZIRN1SK6oqiEOPE8UH0a381rY1O1iK4oKiGOvxKpf/idAB1iU7WIrigqIQ6/Fr5//mfMo/nZYFO1iK4oKiGOHorh+fOsQIfYVC2iK4pKiIOHwnP9EQE6xKZqEV1RVEIcOxTe6zc5Cj/ApmoRXVFUQhw6FP7r3zkPdIBN1SK6oqiEODhAvXdzJVIfm6pFdEVRCWFiKMby87K5aqIr6KErikoIy0Nx3ZWp5a5cF11RVEIYHooLvzdquCsXRlcUlRCGh+LCR+cNd+XC6IqiEsLuUFz5/FC7XbkyuqKohDhmKEI+fp4AhS10RVEJcchQhHz/0aWvkWdTtYiuKCohjhiKsO+PYwUKW+iKohLigKEI/P5NAhS20BVFJcT+QxH8/cUchYcpdEVRCbH7UIR//zvngcIUuqKohDggQIP/KVciwRK6oqiEMD0UV81P2125LLqiqIRgKCyiKxbRFUUlBENhEV2xiK4oKiEYCovoikV0RVEJwVBYRFcsoiuKSoj9hmLD4ffLY1O1iK4oKiF2G4rw8z/BpmoSXVFUQuw1FOTnFmyqFtEVRSXETkNBfm7CpmoRXVFUQuwzFOTnNmyqFtEVRSXELkNBfm7EpmoRXVFUQuwUoDs86ZWwqVpEVxSVEAyFRXTFIrqiqIRgKCyiKxbRFUUlBENhEV2xiK4oKiEYCovoikV0RVEJwVBYRFcsoiuKSoi4Q8Hh9zjYVC2iK4pKiKhDwfmfkbCpWkRXFJUQMYeC/IyFTdUiuqKohIg4FORnNGyqFtEVRSVEvKEgP+NhU7WIrigqIaINBfkZEZuqRXRFUQkRMUAjPRHYVG2iK4pKCIbCIrpiEV1RVEIwFBbRFYvoiqISgqGwiK5YRFcUlRAMhUV0xSK6oqiEYCgsoisW0RVFJcT2oeDwe3xsqhbRFUUlxOah4PzPHbCpWkRXFJUQW4eC/NwDm6pFdEVRCbFxKMjPXbCpWkRXFJUQC4firda/m/zcB5uqRXRFUQmxbCje3rwJujQ/R+IXY9hULaIrimVbMEUAACAASURBVEqIRUMh4dcPwXX5SYIuxqZqEV1RVEIsDND+jTXG4hej2FQtoiuKSoglQ+EEX0gGbovfS2JTtYiuKCoh9g/QjfF7SWyqFtEVRSXEgqFwY48APQSbqkV0RVEJsfsKdGP8XhObqkV0RVEJERaga07/ZAW6HpuqRXRFUQkRdBR+1fnzBOh6bKoW0RVFJUTIeaArrz/iKPxqbKoW0RVFJUTAlUhrr9/kPNDV2FQtoiuKSoj118Kvv/6dK5HWYlO1iK4oKiFWD0U/P5dkI/m5EpuqRXRFUQkREKCdv7K63MOt8K1337Nz59P3kFDeJ3Pv/OfHrfb170gvmSICVFEJsXEoeH9zF1VYffnLuUci7I8/q7/eb8OHBPM9Wff1fn8nQAlQB5UQmwO0fwMRFF359d5Jq8ft9vHSO7t/28j7ZN07f71fOjkbBKiiEmLbUHCO5z7KrhTh9al3FEvAj/LP+s7u3zbyPlnvzme0dwtSRoAqKiEIUIuqrtyd1Pr1Xu9LF3vWH2WeNbvb9wjB5n2y7usV69GPza+TPgJUUQmxaSi4zn0nVVcezruS3UC7y972I8K7oN4n6wXovXnr9doIUEUlxPKh8J3+yQp0H1VXnrc2tjq71EWqNWtF9yGB/E/Wf8vgy7++RzzqnygCVFEJsXgovOfPE6D7qLoiq8BKfVCnyLViudgsC/sPCTPyZO7rlTEa86h/qghQRSXE0qEYuf6Io/C7GAZofVpmvQiU5WGUAB17Muf1ytvVbv6d05hQoRJi4VCMXb/JeaC7aALUOSzunpfpZt7mw/AjT9Y5D1TeHS3u3H7UP10EqKISYtlQjF//zpVIexisQIuYK/9SxtrnESvQzuu1Yhz1TxcBqqiEWDQUU58fQn7uYBCgj/Zco69/H/AeaOf12gc/Lr0PT4AqKiEWBujuvwdczVF4PWbTTTn3wPnmwzreJxuLaAIUFSohGAqLmvNANa3a/ezi1qd76ub2QPM9We/1FAGKCpUQDIVF/SuR2mVitSLc/Uqk7uu169H7pa9IYltRVEIwFBYNroV33pMsbux/LXz39WTh+fv7pa9IYltRVEIwFBYNPo2pOSpefvJcfW36zp/G1H294mflgrRdl14T24qiEoKhsKi97KcIreYD5ZqLgep96N0+D9T/es1Z9dfOT7aVFpUQ00PB4fdztGElgVYtFJ3E3OsT6Uderz6v/spn0b8IUAeVEJNDsf774xAFm6pFdEVRCTE1FOTnWdhULaIrikqIiaEgP0/DphrN7BfmLX87hK4oKiHGh4L8PA+baizzX5i3/IAcXVFUQowOBfl5IjbVSBZ8Yd7yU8LoiqISYiJAD/094GJTjWPBF+atuCiBrigqIRgKi+hKHAu+MG/FZbF0RVEJwVBYRFfiWPCFeSu+oI+uKCohGAqL6EoUC74wb80X9NEVRSUEQ2ERXYliwRfmTX449a3vmF87AVRCMBQW0ZUoFnxh3uTXoxCgY6iEGA4Fh9/Px6YaxYIvzFvzBX10RVEJMRgKzv80gE01igVfmLfmC/roiqISoj8U5KcFbKpRLPjCvDVf0EdXFJUQvaEgP03wbKpHb705pMWCL8xb8wV9OZQkEiohukNBftpAgMax4AvzVnxBXxYliYNKiM5QkJ9GEKBxzH9hHlciBaESohegp/0ecBGgcSz4wjyuhQ9BJQRDYREBGsn8F+bxaUwhqIRgKCwiQGOZ/8I8Pg80AJUQDIVFBGg081+YxyfSr0clBENhEQFqESVRVEIwFBYRoBZREkUlRD0UHH63hQC1iJIoKiGqoeD8T2MIUIsoiaISohwK8tMaAtQiSqKohCiGgvw0hwC1iJIoKiFuN/LTHgLUIkqiqIQYfOg2gBFnb61mUAlx9kgC6Th7azWDSogLDkUCO8MX7EoC6IqiEuKCQ0GAIghdUVRCXHAoCFAEoSuKSogLDgUBitXKU1XoiqIS4oJDQYBirZ8EaAeVEBccCgIUK9XnStMVRSXEBYeCAMU6zbUmdEVRCXHBoSBAsYpcq0dXFJUQFxwKAhRr6LXOdEVRCXHBoSBAsYZ+VgRdUVRCXHAoCFAEoSuKSogLDgUBiiB0RVEJccGhIEARhK4oKiEuOBQEKILQFUUlxAWHggBFELqiqIS44FAQoFik/1UNdEVRCcFQWERXDBh81w1dUVRCMBQW0ZXzDb8rjK4oKiEYCovoyuk837VIVxSVEAyFRXTlbL7vqqUrikoIhsIiunIy73d90xVFJQRDYRFdOZkvP+lKi0oIhsIiumIRXVFUQjAUFtEVi+iKohKCobCIrlhEVxSVEAyFRXTFIrqiqIRgKCyiKxbRFUUlBENhEV05i/fwe4OuKCohGAqL6MpJvOd/CrqiqIRgKCyiK+eYzE+60qISgqGwiK6cYjo/6UqLSgiGwiK6coaZ/KQrLSohMh+K563wrXfnvbjvy1+TD9nt9f75cat9/XviaTLvik1z+UlXWlRC5D0U9zqs2rQs/Hqv7/w2/pAdX+/3dwLUqLn8pCstKiGyHorH7fZRJZiTVmWCfVZLxY+xh+z5er/el7xQ1l1JFl1RVELkPBRFdjWhVUZY43H748/yz2e1TvQ+ZMfXK/5Y8m5Bzl1JF11RVELkPBRP2Ze+t6n1z4/mdnHj0/+QPV+vyNOPBc+Uc1fSRVcUlRA5D8VddqUf7buSskpsUs73kD1f73Wv16Mzcu5KuuiKohIi46HQxV+xNNTYKgKt2b0u4877kB1fr/jrl399nz/qn3FXEkZXFJUQGQ9FEWjN4u/Xuwaa3lncqAJ0+JAdX08Pyc+sdzPuikGzh98bdEVRCZHxULR7z246yu71szyTyP+Q/V6v+W9nb98v467YM3v+p6ArikqIjIfCDbT2sHhxu0y3560foJsPwy94PY3T5pDSmIy7Ys7i/KQrLSohFgxFpKtrDjeyvHw2v+u/j1mBdl6vffD0UX821cMsz0+60qISYn4oYl1dc7ixNzirX/ejWAwe8R5o9/XaOx+TxWJTPcqK/KQrLSohZoci2tU1h3OPinuO2ZRrwJmHRH+9FgFqwpr8pCstKiHmhiLe1TXHc87LHKZVvV6cfMgOr6cIUBPW5CddaVEJMTcU8a6uOZ73MqNHc7vezd79SqTu67Upep+sGZuqRXRFUQkxNxTxrq45nnf1LClX/w/b/1r47uvJwvP398masalaRFcUlRAzQxHx6poTjLx/W/ytPHHgc+whO75e8bOyTG1Z/dhULaIrikqIW1/3xxGvrjmDewZB/cu2ZxB8Dh9ywOs1JzXN/L8Nm6pFdEVRCTEToBGvrjmFcw6rBFp92mr7q+71ifQjr1efNDvzfgGbqkV0RVEJMTMUEa+uwXJsqntbdfi9QVcUlRArAnTj1TVYjk11Z6vO/xR0RVEJMX8QKdbVNViOTXVfQflJV1pUQqw4Cr/x6hosx6Ya7q029ZCw/KQrLSohVpwHuvHqGizHphrs7W02QQPzk660qIQIuRIp7OoaLMemGsJNzokEDc1PutKiEiLoWvigq2uwHJtqgO7acypAA1+ArigqIUI/jWn91TVYjk11vSo5i//MJ2gouqKohFj3eaBbrq7Bcmyq61WB2YSo/j0quqKohFj3ifRbrq7Bcmyqq2l+dv87JrqiqIRgKCyiK6s5qUmA7o5KCIbCIrqylrvfToDujkoIhsIiurKak6BOinYEH35v0BVFJQRDYRFdWc05/7MO0UGABp//KeiKohKCobCIrqzXJqj/SqTN+UlXWlRCMBQW0ZX13uStz1X5ueDKeUVXFJUQDIVFdCXAW0fvh9P5uSxB6YqiEoKhsIiuhHCysB+J4/n58j3cj64oKiEYCovoSiANwkGArnq4H11RVEIwFBbRlTBODi6JxJUPpyuKSgiGwiK6EoYAPQiVEAyFRXQliJuCCxJx5cPpSotKCIbCIroShhXoQaiEYCgsoithCNCDUAnBUFhEVwINDqtPX37EUfhAVEIwFBbRlUD9Eztnrt/kPNBAVEIwFBbRlb6lFwx1Ly2avf6dK5HCUAnBUFhEV3qWB92q/ORa+EBUQjAUFtGVrnW72mL75y910RVFJQRDYRFd6Vp3sKcROz/pSotKCIbCIrrSsfJ0o0bs/KQrLSohGAqL6EpHWIBGR1cUlRAMhUV0xbX2ksu90BVFJQRDYRFd6WAFag2VEAyFRXSlgwC1hkoIhsIiutIVdBQ+OrqiqIRgKCyiK13rzgONfvi9QVcUlRAMhUV0pWfNJZfRz/8UdEVRCcFQWERX+gzkJ11pUQnBUFhEV4Ltl590pUUlBENhEV0JtWN+0pUWlRAMhUV0JdCe+UlXWlRCMBQW0ZVAe+YnXWlRCcFQWERXLKIrikoIhsIiumIRXVFUQjAUFtEVi+iKohKCobCIrlhEVxSVEAyFRXTFIrqiqIRgKCyiK2vtevi9QVcUlRAMhUV0ZaVdz/8UdEVRCcFQWERX1jkkP+lKi0oIhsIiurLKMflJV1pUQjAUFtGVNQ7KT7rSohKCobCIrqxwVH7SlRaVEAyFRXRlhaPyk660qIRgKCyiKxbRFUUlBENhEV2xiK4oKiEYCovoikV0RVEJwVBYlE9Xln+XkX35dGUzKiEYCouy6cqab9OM9mp7PX02XdmOSgiGwqJcurLu+9zX6h9+3zutc+lKBFRCMBQW5dIVzbI9Qq1//ue+af3KpysRUAnBUFiUSVecJIsfaoPz53dN61ImXYmBSgiGwqJMurJngI7n524JmklXYqASgqGwKI+uuDkWO9OG128SoAeiEoKhsCiTruyXaZP5SYDujkoIhsKiTLqyZ4Ae+GIik67EQCUEQ2FRLl3Z/biO77UI0P1RCcFQWJRLV3Y/s6j7Yv0bkeXSlQiohGAoLMqmK0deicR5oMehEoKhsCjFrviT8oQrObkSaX9UQjAUFiXYlWOvep/8HfZ6+gS7shcqIRgKi9LrylHvdh728fMe6XVlN1RCMBQWpdeVg463H/b9Rz7pdWU3VEIwFBYl15X9zyGqnJqf6XVlP1RCMBQWJdeVYwJ0dX7GfVM0ua7sh0oIhsKi1LpywHWUr/D8jPUbpdaVHVEJwVBYlFxXjliBan4uTcXYB7aS68p+qIRgKCxKrivHBKi8wMIEjX1gK7mu7IdKCIbCovS6ctxV74vXldFDPb2u7IZKCIbCovS6ctxV74ujmgDdD5UQDIVFCXblqCuRFsdi/ANbCXZlL1RCMBQWpdiV/fOz/wozL8YKdD9UQjAUFtGVjreu9u6Zf+W7uQFdUVRCMBQWXbQrI4vYnz+dHzgPWPwmKEfhY6MSgqGw6JpdGXkbtTn/s/nBqgDlPNCdUAnBUFh0ya6M5J3m56u3BF1yIihXIu2DSgiGwqJLdsW/spTrj8o7O0vQhdcicS38HqiEYCgsumJXOsd8NPiK/HzTn7ZL0FM+uvmKXRlBJQRDYdEVu+IJ0DfNT2cFeuzXhLiu2JURVEIwFBZdsCvd/JQ/i/135x3PxW9+7uOCXRlDJQRDYdEVu+Ke3+msOnW96RxFOuX3u2ZXRlAJwVBYdMWuOMeQ3MNJg1Poz9h5r12xKyOohGAoLLpkV9p1p7sWbYLTdyXS0S7ZFT8qIRgKiy7ZleGlRq9heJ6Yn9fsih+VEAyFRdfsyjAinUw9Mzkb1+yKF5UQDIVFF+1K5/zP5g7nh2f9WuKiXfGhEoKhsOjaXanP/3z1jhd1Dsmf4tpd6aASgqGw6NpdKU//bPKztwI9933Qa3elg0oIhsKii3flp3yAXe8zPZ0z7M/4tS7eFReVEAyFRdfuSpufvU8YOe6b67yu3ZUOKiEYCosu3RX9/veSu+g8+4jSpbvSRSUEQ2FRvl1Z8B6mm5+dtz0JUDOohFgwFM9b4Vvvzntx35e/Jh+CYNluqqNHgXp3dy/dHOQnAXoyKiHmh6KMyk5aFn6913d+G38INsh1Ux09CtQLVn/OsgI1g0qI2aF43G4fVWJ+/Vvv+/39dvus1p0fYw/BFrluqmNHgXrBOpKzBKgZVELMDUWRlVVIFvH4qXc+bn/8Wf75rBad3odgi0w31dEE7AXreM4Obxx4WmimXQlBJcTcUDxlx/zevsf5z4/mdnHj0/8QbJLppjoWoIPT5f0PG65Mjz2xPtOuhKASYm4o7rJf/mjf4pQlZxOZvodgkzw31dGjQM1fysPv00fbh2+Vdv7cWZ5dCUIlxMxQ6GKzWGfWu+2vKkCbffUyO70PwSaZbqojyaj5+dP59mL50ejR+df4rv5OMu1KCCohbn3dHxfp2Cw2f71rOuqdxY0qQIcPwSaZbqqTu/By/fvI18uteLq9ZNqVEFRCzARou7fupqPsqz9vRYD6H4Itct1UR5aM5d9+ygfY+b4dyZ+gBOhpqISYGQo3Hdtj7MXtMirL0+e7Acph+Dhy3VTHAlE+f2n0KLwvIA8/sT7XrgSgEmJFgDrLy2e9Wv36b1agu8h2Ux3ZJW/zc+w80JkEJUCPRSXE/EEkbzqWp9IXP3nwHuguEthUA88fGvln8vlLo6cnEaCmUAmx4ii85xyl8jSmmYcggP1NNfYZmJNH218jCclR+LNQCbHiPNDhhZr14nPyIQhhflM95gzMuSUm54GehUqIkCuRiqSsb9f77FyJFJ35TfWYtd/sPjpXIp2ESoiga+ElMuu1J9fCR2d9Uz3q3cfZnOZa+HNQCRH6aUzF34od+Dox+TSm2ExvqiNXAwU/w/QDO3+ey3RXjkUlxLrPA60uPXq1nwf6OXwIIrC8qQ6uR9/0DN2Pn597sXNZ7srBqIRY94n0EqDV6tNZcPKJ9HEZ3lTfmmuF2sVh0DM0f/6cCdBj99FnGO7K0aiEYCgsMtyVKsycCy5DAlRvzOanKYa7cjQqIRgKi+x2xYlNjdKgZyillZ+Gu3I4KiEYCovsdsXZ/a5DdEOABubnaXv1drtyOCohGAqLzHal/10aG/MzJAXPO65ktivHoxKCobDIble2fxuR/pvy+veQf37amU12u3I4KiEYCovsdmX7KfQbn+Hg699ddrtyOCohGAqLDHdle4BtPAffd/MYhrtyNCohGAqLDHdl+y70pmcgQE2gEoKhsMhyV7YfxNnwDId/Cr3LclcORiUEQ2GR6a5sSL/mH25IYFagJlAJwVBYlGVXdOG55fR5AtQEKiEYCoty7Iq+9bnt+iOOwltAJQRDYVGOXak/gGTz9e+cB2oBlRAMhUUZdsXJz23Rx5VIBlAJwVBYlGFXmsyr8nNb9nEt/PmohGAoLLLUlTh51Xz4SLX/buTjPVez1JWTUQnBUFhkqCux9pidD6C38gHJaxnqytmohGAoLLLTlWjHbJwnIECTRyUEQ2GRna5EO2vozXkmAjR1VEIwFBaZ6UrE89adtSwBmjoqIRgKi8x0JW6AyrupBGjqqIRgKCyy0pWon92Ren6a6YoBVEIwFBaZ6Uq0FejPn57jUaed0RnGTFfORyUEQ2GRma7ECtDm/M9uYp53TVEYM105H5UQDIVFdroS5yh8c/37ID87f5pnpyunoxIiwaF43krfypu/v9++/NXcXd0u/uvr369H8fM//izvLG/dPk/7VUPZ6UqUlPN/fsiJn6sUxk5XTkclRHJD8ev91vh4VQEp8fgs7ykD9P/9kJ8Xf2vDNiWGuhJhP3smP5NJUENdORuVEKkNhWRis7Is4lTS8V4uOssA/Z/Nj//4Pz+cRybFUld2yk8CNGVUQqQ2FMWSs9pp/6cIx2Jnvfij3ld/1TvvVb6WD6iDtrxVLllTW4Km1pVJs/lJgCaHSojUhuIuy8kiOst4fNS78s0efJWbRY7Wf29v1TfSkVpXQrACTRiVEKkNxaOXhvXC89XswVcB+ik/aNemeqQpEal1JQQBmjAqIVIbimf/Tc06OMuU/PZyw9J3KxmpdSWI/yi84VNDL9GVZaiESG4o7u5B+JfsusvheAI0Hd4zpCyfXH+JrixDJUR6Q/G4dSK03odv3hElQFPiCUvTJ9dfoyuLUAmR4lDoqUxVglb78HI6EwFqyOzXbw4Xm6ZPrs+jK1FQCZHqUFQhWh0+KrLzQ0+oJ0DtCPj+YtsHlrLoShxUQiQ2FLKr/mpzsbirvPioPhZPgJoR8v3vBGgiqIRIbSjuevRIc/Fx++N/vzf3EqBWbMxPAtQ0KiFSG4qn8zkizQVG1dXxg3M+CdBTheTncAVq64h8+l2JhkqI5IZCT2O6aSze9aIjAtSIsPzsB6ixc5qS70o8VEIkNxT/6CeENIvOelXa368nQE8Vlp+9o/DWzmlKvivxUAmR4FA0ZzF9unf0jywRoGnqZKa1c5ou25UhKiEYCouu2xVnr93cIfnrdmWASgiGwqILd6V915MAtYtKCIbCIrpi8ZwmuqKohGAoLKIrL1agllEJwVBYlG5XQg+/exCgdlEJwVBYlGxXAs//9HDPALWRn+l2JT4qIRgKi1LtSvT8lOuRIj3rNql2ZQdUQjAUFiXalaj5+eJKJLuohGAoLEqzK/P5uTgP2/Pp7eRnol3ZBZUQDIVFSXZlcX7OJ6K540eVJLuyDyohGAqLkuzKkvzs/Dn1SN/NsyXZlX1QCcFQWJRnVxZf227vHPpKnl0JQiUEQ2FRll1ZsaxkBWoclRAMhUVZdoUAzQeVEAyFRTl2ZdV+ubVPsqvk2JVAVEIwFBZl2ZU1y0prn6VcybIrYaiEYCgsSqsr44ffO2ctrdovN3YOfSWtruyKSgiGwqKkujJ+/mcvBVftl9vLz7S6si8qIRgKi1LqymR+tn86gWgsFxdLqSs7oxKCobAooa5MXH/krjjfOg763SJLqCt7oxKCobAona4syc96+Sl/JpufCXVld1RCMBQWJdOVqevfewFaR2ey6flKqCv7oxKCobAoma4sy09nBz7lBE2mK/ujEoKhsCiLrnRXoPInAZoDKiEYCouy6EoblW/O4SQCNAdUQjAUFh3QlQOO57ix+RreTA7biqISgqGwaP+uHHFGkbvj3r8vRWwrikoIhsKi3btyzLXmnkNHCecn20qLSgiGwqIDArR/Y6VlXx83OHk+5QUo20qLSgiGwqK9u7L58zZXfP/mW0/Iy9nAtqKohGAoLLIeoJfMT7aVFpUQDIVFO3dl63cOrcvPV3v+JwGaCSohGAqLbK9AV+Rn7wvek85PtpUWlRAMhUWmAzQgP1P/GJEa24qiEoKhsMj0UfgV+Wn02+ECsa0oKiEYCosyOQ/U6he8B2JbUVRCMBQW2b4SacW/ZAWaJyohGAqLTF8L789e//MRoHmiEoKhsMhyV/x7/2Mr2iyu4WxY7srBqIRgKCyy3BVvJI6+p5rFNZwNy105GJUQDIVFNrtSHX7375SPLzSzOAO0ZrMrp6ASgqGwyGRXfo4H6NRbndnkp82unINKCIbCIotdGeTnwgDNh8WunIRKCIbCIoNdkeuP5vbbCdAroBKCobDIXlf0+s3Vu/D5sNeV01AJwVBYZK4rzvXvvnUnAXoxVEIwFBaZ64pz/bv3xKScTvccZa4r56ESgqGwyHRXfCcm5XS65yjTXTkWlRAMhUW2uzJxJWfO+Wm8K4eiEoKhsOjgrgRmX+ef5Z+fbCstKiEYCouO7Urg6vEKi84OthVFJQRDEc3zVvg2cec/P261r3/PPNWhXQl8/3LpP8snZdlWFJUQDEUs9zocv/zl3CeR+cef5d9+fzcaoP0bjvGPn1944D2jdSrbiqISgqGI5HG7fbxev9476di989f7bHI2juzK5Dmc499/tPDUz5wOz7OtKCohGIo4itXlR/lnEZafY3c+Bzv4Y6wE6MT3xy0O0AUPSgTbiqISgqGI4yn77ncnJX+91/vuxZ58GaSPOk4XOLArU9exL8vPqWzM6hIlthVFJQRDEcdddt0fzrugvQC9139bwMYKdPL7i5dFIwGaJyohGIooiohsFp7PWxuT3V3439+//Ou750C9h4kA9eVne0BoUTTm9TFNbCuKSgiGIopmjflqV52V+iBSkaPl8rSIUc+B+tqt76hf/DX+NuV4fnYTdNFboARoVqiEYCiikLVmL0Dr00CbReezOX/p7jmN6dwAXXqgvPPIRf+MAM0TlRAMRRRugDqH4Tvngcq7o8Wdn77ncNi5Eql3uebLubHoDE+OwmeJSgiGIgr/CrS4t/xLGaNuZN5n3wY1cy18JyT7y8klZ8hzHmiWqIRgKKLwvwf6aM9t6p5eP3dCvZWudOMvaH+cK5FyRCUEQxGFexRejxGNHFlKKkCdG4GH1LPJTzNdMYBKCIYiDuc8UE3Hdr++uOXsw1sPUD383ltyZnVEKADbiqISgqGIw3clUrssLVeg7Xr0PntF0qld+UmA+rGtKCohGIo4vNfCO++BFjdk4fn7++wVSWd2xZufr+6boJfMT7aVFpUQDEUkvk9jao7Cl590V/+sXJC269JxJ3blZ+f747o3czqkHoBtRVEJwVDE4n4eaBGSzQfYNefFV6vT5qz6BddynteVzvWbg332jA6pB2BbUVRCMBTROB8+LwFaLUzbazfr8+rnzqJ/ndiV3vXvg332K+cn20qLSgiGwqITA7Tz14vvs/ewrSgqIRgKi8x05dr77D1munI+KiEYCovsdIX8bNnpyumohGAoLKIrFtEVRSUEQ2ERXbGIrigqIRgKi2x0hd33LhtdMYFKCIbCouO7MvPx83ixrTiohGAoLDq8K57vP+IUpj62FUUlBENh0dFd8X9/XP/G1bGtKCohGAqLDu7KZH6SoIJtRVEJwVBYdGxXvN//ToAOsK0oKiEYCouCuhJ6yGcuPwnQBtuKohKCobAopCvBB819+ckK1INtRVEJwVBYFNCV2AfNCdABthVFJQRDYVFQgPZvbMRR+D62FUUlBENh0fquxF8wch5oH9uKohKCobDIQoByJVIf24qiEoKhfvUPWgAADZhJREFUsGh1V3Y5aE5+drGtKCohGAqLjlmB+g+/YwzbiqISgqGw6JAA9Z7/iXFsK4pKCIbCoiOOwpOfa7GtKCohGAqLDjgPlPxcjW1FUQnBUFi0/5VI5Od6bCuKSgiGwqLdr4UnPwOwrSgqIRgKi3bvCvkZgG1FUQnBUFhEVyyiK4pKCIbCIrpiEV1RVEIwFBbRFYvoiqISgqGwiK5YRFcUlRAMhUV0xSK6oqiEYCgs2q8rHH4Px7aiqIRgKCzarSuc/7kB24qiEoKhsGivrpCfW7CtKCohGAqLduoK+bkJ24qiEoKhsGifrpCf27CtKCohGAqLdukK+bkR24qiEoKhsGinAN3hSa+EbUVRCcFQWERXLKIrikoIhsIiumIRXVFUQjAUFtEVi+iKohKCobCIrlhEVxSVEAyFRXTFIrqiqIRgKCyK2xUOv8fBtqKohGAoLIraFc7/jIRtRVEJwVBYFLMr5GcsbCuKSgiGwqKIXSE/o2FbUVRCMBQWxesK+RkP24qiEoKhsGhxV+a+C578jIhtRVEJwVBYtLQrb28zCUp+RsS2oqiEYCgsWtgVSc6pNSiiYVtRVEIwFBYtDtD+DeyIbUVRCcFQWLSsK05sxkjQufdTL49tRVEJwVBYdEaAzr6fenlsK4pKCIbCokVdcaNue+zxfuosthVFJQRDYdH2Fejqw++8nzqLbUVRCcFQWLQ5QFef/xn5/dQssa0oKiEYCou2HoVff/48ATqPbUVRCcFQWLTxPNBN+UmAjmFbUVRCMBQWbbsSKeT6TVag89hWFJUQDIVFm66FD7r+nQCdx7aiqIRgKCza1JWw6985Cj+LbUVRCcFQWHRCVzgPdBbbiqISgqGw6IyucCXSHLYVRSUEQ2HRKV0hP2ewrSgqIRgKi+iKRXRFUQnBUFhEVyyiK4pKCIbCorCu8PHz+2JbUVRCMBQWBXWF7z/aGduKohKCobAopCvk597YVhSVEAyFRQFdIT93x7aiqIRgKCxa3xXyc39sK4pKCIbCotVdIT8PwLaiqIS4Fb717nt27nz6HhLK+2Tunf/8uNW+/h3pJVMUEKC7/B5wEaCKSogqrL785dwjEfbHn9Vf77fhQ4L5nqz7er+/E6ChmyqXEu2LAFVUQhRD8eu9k1aP2+3jpXd2/7aR98m6d/56v3RyNoI2VS5m3xkBqqiEKIeiCK9PvaNYAn6Uf9Z3dv+2kffJenc+o71bkLKQTZWPU9obAaqohKiG4u6k1q/3el+62LP+KPOs2d2+Rwg275N1X69Yj35sfp30hQVo/wbiIkAVlRDVUDycdyW7gXaXve1HhHdBvU/WC9B789brtQVsqnyk/O4IUEUlRDUUz1sbW51d6iLVmrWi+5BA/ifrv2Xw5V/fIx71T9TyTVUPvxOguyNAFZUQ1VDIKrBSH9Qpcq1YLjbLwv5Dwow8mft6ZYzGPOqfqsWbqp7/yddq7o8AVVRCDAO0Pi2zXgTK8jBKgI49mfN65e1qN//OaUxLOOfPswLdHQGqqIRoAtQ5LO6el+lm3ubD8CNP1jkPVN4dLe7cftQ/XQs3Vff6IwJ0dwSoohJisAItYq78Sxlrn0esQDuv14px1D9dyzbV7vWbHIXfGwGqqIQYBOijPdfo698HvAfaeb32wY9L78Mv2lR7179zHujeCFBFJURzFF6P2XRTzj1wvvmwjvfJxiKaAJ3Vv/6dK5F2RoAqKiGa80A1rdr97OLWp3vq5vZA8z1Z7/UUAboe+bkvAlRRCdG/EqldJlYrwt2vROq+XrsevV/6iiQ2VYvoiqISYnAtvPOeZHFj/2vhu68nC8/f3y99RRKbqkV0RVEJMfg0puaoePnJc/W16Tt/GlP39YqflQvSdl16TWyqFtEVRSVEe9lPEVrNB8o1FwPV+9C7fR6o//Was+qvnZ9sqibRFUUlRBtWEmjVQtFJzL0+kX7k9erz6q98Fv1rblPl4+fPQYAqKiEYCosmu8L3H52EbUVRCcFQWDTVFfLzLGwrikoIhiKa2S/MW/52yERXyM/TsK0oKiEYiljmvzBv+QG58a6Qn+dhW1FUQjAUkSz4wrzlp4SNdoX8PBHbiqISgqGIY8EX5q24KGEiQCP8rgjDtqKohGAo4ljwhXkrLoulKxbRFUUlBEMRx4IvzFvxBX10xSK6oqiEYCiiWPCFeWu+oI+uWERXFJUQDEUUC74wb/LDqW99x/zaWIOuKCohGIooFnxh3uTXoxCgCaArikoIhiKKBV+Yt+YL+oZd4fD7+dhWFJUQDEUUC74wb80X9A26wvmfBrCtKCohGIooFnxh3pov6Ot3hfy0gG1FUQnhGYqj5ySHuVzwhXlrvqCvVxLy04QcBjUSKiEI0DgWfGHeii/o65aE/LQhi0GNg0oIAjSO+S/MC74Sifw0IotBjYNKCAI0jgVfmBd6LTz5aUQWgxoHlRAEaCTzX5gX49OYcCK6oqiEIEBjmf/CvBifB4rz0BVFJQQBGs38F+bF+ER6nIauKCohCFCLKIlFdEVRCUGAWkRJLKIrikoIAtSiuiQcfreFQVVUQhCgFlUl4fxPYxhURSUEAWpRWRLy0xoGVVEJQYBaVJSE/DSHQVVUQhCgFt1u5Kc9DKqiEmLwUegARpy9tZpBJcTZIwmk4+yt1QwqAQCBCFAACESAAkAgAhQAAhGgABCIAAWAQAQoAAQiQAEgEAEKAIEIUAAIRIACQCACFAACEaAAEIgABYBABCgABCJAASAQAQoAgQhQzPv1Xn0M+de/V/yb3/+rf2OBh/up5x/1ff/8uH35a+Y1nH/+x5/uPffinm9zv6GYeCXAiwDFnGebacvz5S75pzeW6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