diff --git a/notebooks/lectures/Why_Hedge_II/notebook.ipynb b/notebooks/lectures/Why_Hedge_II/notebook.ipynb
index daf9fe39..912838bd 100644
--- a/notebooks/lectures/Why_Hedge_II/notebook.ipynb
+++ b/notebooks/lectures/Why_Hedge_II/notebook.ipynb
@@ -21,7 +21,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the first lecture on [Why You Should Hedge Beta and Sector Exposure], we covered the information coefficient (IC) and effective breadth, providing yet more reasons to make as many independent bets as possible. Here we expand upon the concepts detailed there by decomposing portfolios of varying numbers of securities to further explore the effects of systematic risk."
+    "In the first lecture on [Why You Should Hedge Beta and Sector Exposure](quantopian.com/lectures/why-hedge-systematic-risk-part-i), we covered the information coefficient (IC) and effective breadth, providing yet more reasons to make as many independent bets as possible. Here we expand upon the concepts detailed there by decomposing portfolios of varying numbers of securities to further explore the effects of systematic risk."
    ]
   },
   {
@@ -56,16 +56,18 @@
    "metadata": {},
    "source": [
     "<div class=\"alert alert-warning\">\n",
-    "<b>TL;DR:</b> beta and sector exposure are **common factors**, i.e., they are among a handful of risk characteristics that are shared among all stocks. Risk exposure to common factors does not diversify away in a portfolio of algos. An allocator will not be able to make a large allocation to you if your algo presents common factor risk. The combination of many algos with modest common factor risk can lead to overwhelming common factor risk at the portfolio level. Allocators do not like this. If you want to get a large capital allocation, you must have low beta and sector exposure consistently over time.\n",
+    "<b>TL;DR:</b> Beta and sector exposure are **common factors**, i.e., they are among a handful of risk characteristics that are shared among all stocks. Risk exposure to common factors does not diversify away in a portfolio of algos. An allocator will not be able to make a large allocation to you if your algo presents common factor risk. The combination of many algos with modest common factor risk can lead to overwhelming common factor risk at the portfolio level. Allocators do not like this. If you want to get a large capital allocation, you must have low beta and sector exposure consistently over time.\n",
     "</div>\n",
     "\n",
     "# Foundations\n",
+    "\n",
     "### Single Stock Risk Decomposition\n",
+    "\n",
     "To build intuition, let's posit a single factor model:\n",
     "\n",
     "$$r_i = \\alpha_i + \\beta_i r_m + \\epsilon_i$$\n",
     "\n",
-    "where $r_m$ is the market return. This should look familiar since it is the CAPM, which posits that the returns to a stock can be attributable to its beta-weighted exposure to the market and a return which is idiosyncratic to that stock. Two important assumptions here are that the $\\epsilon_i$s are uncorrelated to the market and each other across stocks. See the [Lecture on Beta Hedging](https://www.quantopian.com/lectures/beta-hedging) for more background.\n",
+    "where $\\alpha_i$ is the intercept, $\\epsilon_i$ is the error, and $r_m$ is the market return. This is the [Capital Asset Pricing Model (CAPM)](https://www.quantopian.com/lectures/the-capital-asset-pricing-model-and-arbitrage-pricing-theory), which posits that the returns to a stock can be attributable to its beta-weighted exposure to the market and a return which is idiosyncratic to that stock. Two important assumptions here are that the $\\epsilon_i$s are uncorrelated to the market and each other across stocks. See the [Lecture on Beta Hedging](https://www.quantopian.com/lectures/beta-hedging) for more background.\n",
     "\n",
     "In this case, the \"risk\", as measured by the variance, for an individual stock is:\n",
     "\n",
@@ -360,11 +362,7 @@
    },
    "outputs": [],
    "source": [
-    "def portfolio_risk_decomposition(budget=0.2,\n",
-    "                                 correl=1.0,\n",
-    "                                 algo_count=2,\n",
-    "                                 algo_total_risk=0.04):\n",
-    "\n",
+    "def portfolio_risk_decomposition(budget=0.2, correl=1.0, algo_count=2, algo_total_risk=0.04):\n",
     "    N = algo_count\n",
     "    \n",
     "    algo_common_risk = budget*(algo_total_risk**2)\n",
@@ -377,7 +375,7 @@
     "    idio_risk = algo_idio_risk*w\n",
     "    total_risk = common_risk + idio_risk\n",
     "    \n",
-    "    return total_risk, common_risk/total_risk\n"
+    "    return total_risk, common_risk/total_risk"
    ]
   },
   {
@@ -413,7 +411,7 @@
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kJDBr1ixlOjk5mfHjx19SGefLycnh2Wef5f777ycxMZGJEydisVj+VJn1pV27\ndowaNYqkpCRGjBjBhAkTlIfAPv3007Vul5SUxPHjxy+4fNiwYcr7On/+/DrXafny5Ze0fqXKhwnX\nFwlFQgghhBCN3Hvvvac8PBNg3rx5JCUl8dFHHxEdHc3atWvZu3cvXbp0Yd68eSxduhSA3bt3Exsb\nW2N31qmpqaxZs4aPP/6YZcuWMXv2bN577z3A/eDPygd71pcjR44QExPDrl27POZf6MGxdTFp0iT6\n9u3L2rVrWb16NbGxsbz22mt/qsz6YjKZWLp0KcuWLWPFihW0atWKJUuWAPDvf//7T5X9+uuvs2zZ\nMlatWsXGjRvJzs6+ElWu1YIFC+q1fHlOkRBCCCFEI/b7779z8uRJevToocxLTk5Wvuj36tWLDz/8\nkH79+hEcHExwcDAFBQU4nU6WLl1a63/9LRYLZWVllJaWotPpaNasGcuWLQPcLQ2vvvoqW7duxWKx\ncPLkSc6cOcOUKVO46667WLBgAZs3byYqKory8nIeffRRj7Lnzp3LgQMHcDgcPPTQQwwcOLDa/jdt\n2kR8fDxffvklycnJdO3a1WP5+fsYPXo0bdu25aWXXqKwsBCHw8HUqVNp27atx7myWCwkJCQo80aP\nHq10Db1582aWLFmCt7c37dq1Y8qUKcyfP5+8vDxSUlI4e/YszzzzDJ988glpaWksWLCAtLQ0li5d\nilqt5ujRozzxxBPs2bOHo0ePMmnSJHr37l1ruTWduwvp2LGj8oDU22+/ne+++47169ezfPlyNBoN\nsbGxvPLKK8r6VquV0aNHM2vWLFq2bOlRltPpVN5nHx8f9Ho9ycnJfPTRR/zrX//y2Me3337LzJkz\nCQ0NJTg4WDnnL7zwAunp6cTFxbFlyxZ27drF8ePHmT59Ol5eXhgMBmbNmsXq1av55ZdfGD9+vFL2\nlSahSAghhBDiGrDs4Cd8d+bAFS3z9qjOJHUaesF1Zs+ezbRp0/j000+VecXFxfj4+AAQFBREZmYm\n4eHh7Nmzh1OnThEZGcknn3zCwIEDef/998nKymLkyJEeASI2NpYOHTrQu3dvevTowd13302/fv1Q\nq9UerTXp6eksWLCAPXv2sGrVKjp27MiKFSv4/PPPsVgsxMfHM3r0aGX9H374gbS0NJYtW0ZZWRn3\n3Xcfffv2RaPRKOu4XC62bdvG3/72N8AdkKqGooKCghr3sWTJEjp16sRjjz3G4cOHmTlzphLkwB2K\nYmNjPc6umNZcAAAgAElEQVSfSqVCp9Nhs9l455132LBhA76+vjz55JPs27dP2d/ChQuZO3cun332\nGQsXLmTevHns2LGD2NhYjh07xtatW0lOTmbixIns2LGDAwcOsHz5cu64445ay6167lauXFktFFV9\nHKnL5eLzzz+nXbt2Sr0BPvzwQz744APCwsJYt24dpaWlgDv0vPTSS4wfP75aIAKYMmUKvr6+nDx5\nkjFjxqDX6z3KrTr+9ttvM2fOHFq3bs3YsWOJiopiz5492O12Vq5cya5du5QWrBkzZjB9+nSio6NZ\nsWIFK1as4IknnmDhwoX1FohALp8TQgghhGi01q9fz2233UaTJk1qXcflcqFSqbj11lvJzMxkxowZ\nPPDAA2zfvp2YmBjUajXTpk1j3rx51bZ94403+Oijj2jbti0LFy5Uwk3VL+uVD9kMDw/HYrGQkpJC\nmzZt0Gg0BAUF0bFjR48yf/zxRw4dOsSoUaMYM2YMAJmZmR7rJCcnExUVhdlsplevXuzYsQOHw6Es\nr20fhw8fVsJT+/btSUlJ8ShXpVIpLSTnO3XqFM2aNVMeJNq1a1eOHj0KoJQfGhqqBMfg4GDlXqTY\n2Fi8vb0JCQmhWbNmaLVaZfmFyq167qxWa7U6Wa1W5Z6iO++8E6PRyEMPPeSxzr333stTTz3FkiVL\nuPvuu9FqtYD7Hq6IiAi6d+9e4/HOmjWLZcuWsXPnTqU1qDapqam0bt0agNtuuw2AEydO0LlzZwB6\n9OiBWq0G4NChQ0ydOpWkpCQ2bNhATk4O4PkzUx+kpUgIIYQQ4hqQ1GnoRVt1rrTdu3dz9uxZPv/8\nc9LT09FqtYSFhWEwGCgrK0Oj0ZCRkUFoaCiA0mnB/PnzGTNmDGlpaTRp0gStVktRUVG18svKymjR\nogUtWrRg5MiRDBgwgHPnznmsU/llGP744nuh+340Gg1Dhw5l7Nixta6zadMmUlJSGDJkCC6Xi5KS\nEr755hslWNS2Dy8vz/aC8wNQixYtagx/R44cwcvLy2N9u92u7K/qMdZ0vBdaXtdyawoNfn5+yv1f\ns2fPJiwsrNoxjh07lsGDB7N161YeeeQRpWXM39+fvXv3UlBQgNlsrlZ2JR8fH3r06MH+/fvp1q2b\nx7Ly8nLA87xWrWfV+ZXvh06nU+pcVW0/E5lF2TiLXZQ57JQ6yigtL+Ny7hqTliIhhBBCiEZq7ty5\nrFmzhlWrVjFs2DCeeuop7rjjDu644w6lt69t27Z5XJaVkZFBSkoKXbt2JTg4mLS0NEpKSpQWhkpr\n1qxh8uTJypfgwsJCXC4XQUFBF6xTZGQkv/32Gw6Hg9zcXI4cOeKxvGPHjuzYsQOXy0VpaSkzZszw\nWG6329m5cycbNmxg3bp1rF+/nldeeYVNmzZddB8dOnTgu+++A+DgwYNK60al5s2bExERwYoVK5R5\nixcvZunSpTRr1ozTp08rvbslJyfTvn37Cx5rXcTExFx2uVUDyFNPPcXy5cuVDhEql82dO5fg4GAe\neeQROnXqRFpaGgCjRo3iscceY/r06Rfdz6FDh2jevDlGo1FptTt27JgSlMPCwjh16hQul0u59C86\nOpqff/4ZgK+//lppyYuNjeWrr74C3PdoVb4ftbXQ/XP3u0z6fCZTv3yT6bvmMfvr/9Tp3JxPWoqE\nEEIIIYSHcePG8eKLL7Jq1SqaNGnCkCFDlGX/+c9/GDduHOC+FGrx4sU8/PDDPPnkkx5lDB06lJMn\nT5KYmIher1c6LtBoNBdsCQoKCuLee+9l2LBhtGjRgg4dOni0KMTFxdGtWzeGDx8OwIgRIzy2/+qr\nr+jSpQsmk0mZFx8fz9y5cxk8ePAF95GUlMTkyZN5+OGHcblcvPrqq9Xq9/bbb/Paa6+xZs0a9Ho9\nsbGxTJ8+HY1Gw6RJkxgzZgxqtZouXbrQuXNn9u7dW9fTXiOdTsfEiRMvq9yq59loNPL444/z+uuv\n89ZbbynLDAYDw4cPx2QyERUVRdu2bZVlQ4YMYcuWLezcuZNevXp5lD158mR0Oh12u53Y2FgGDhyI\ny+VCr9fz4IMPEhcXp/QwOGHCBMaNG0dkZCTh4eEA9OzZk7Vr1/LQQw/RtWtXpffDKVOmMG3aND74\n4AN8fX2ZM2cOAG3btiUxMZHVq1d71OOOqC641KD11qBRa9CofaB6o+XFz5Wrvi/Qu4L279+vXDsp\nRGMnnwch3OSzIK5XlT2WVb2kS7itW7eOQYMGoVarGTRoEIsWLaqx2+9rfR+idgUFBezbt49+/fqR\nkZHBo48+qvSMV1e1fYYu5++CtBQJIYQQQohrSlZWFsOGDUOr1TJ48OB6CStXYx+idgaDgS1btrBo\n0SJcLhdTpkxp0PpIS5EQ1yn5PAjhJp8Fcb2SliIh/pwr2VIkHS0IIYQQQgghGjW5fE4IIYQQooFU\nPihTCHHpSktLq/V6eLkkFAkhhBBCNIAr9WWu0pEjR2jXrt2fLsfhdFBcXkpxeQkl9hKK7SWUVE6X\nl7qn7SUUl5dSUl5KSXkJJfZSih2llFYsL3WUXda+vb280Xpr0ak1aL21+Pr44quMa9F5a9Gq3eNa\ntRZf74pl3hplvq9ai9Zbg9pLffEdiuuaVquVUCSEEEIIcT1TqVRX/H4irVZLqaMMW1kxNnsxRXYb\nRWXF2CqGRXYbNnsxtrJiiux/zLfZ/3iVOeyXvF+1lxq9j8798tUR6BOA3keHzsfXY6hXpnXovH3R\n+/ii8/GtmNbio/a5oudDiLqSUCSEEEIIcQ0pdzqwldmw2m0UlVWGmSL3sMxGkb1y6F5uqwg7BTYL\nZSc+xOGq+SGXtfH28lbCSpAuAL3GHVL0Nb58MWj0HtM6H5372TBCXMckFAkhhBBCXGEOp+OP8FJm\nw1pWhLVi6J6umG+3UVSxrDLwlJZf2n1GGrUPBh89erUvUaYmGDTuwGLw0aOvMu6e7x4aqgQdjbem\nns6CENcPCUVCCCGEELUod5RjtVeEmtIiJdxYlPHKsGPFWmpT1i22l9R5HypUSgtME7/QigCjx+Cj\ncw81+j/maXRVxt3rVF5yJt3TC3H5JBQJIYQQ4obncrmw2YuxVIQbS5kVS2kRllJrlXlFf4SbMvd0\nySW02mi9tRg1ekL1QRg0eowaQ8VQr0xXHa9cpvfR4aWSp6QI0ZAkFAkhhBDiuuJyuSi2l1BYZsVS\naqWw1HNYGXSqBh5rWVGd77XRemvx0xgIN4a4g4zWoAQao8aAnzJPj5/GqAQd6SRAiOuXhCIhhBBC\nNCiH04Gl1EpBqYXCUiuFpRYKS6xYyqwV05Vhxx10CsusOJyOi5arQuUOMloDYcYQjFp3oPHTGiuG\n7rBj0hrdYUfrDjgSboRofCQUCSGEEOKKcjgdWMqKKCyxUFhqcYedkvNDj3u8oNRCUZmtTuXqfXT4\naY20METjpzVi0hjx0xowaf3c09qKwFMRegw+ery85LI0IcTFSSgSQgghxEWVOewUlBRSUOIOOQUl\nlorpQs/pUguW0iJcuC5YngoVRq2BAF8zMeZITL5+mLRGTFr30Ozrh5/GiKlKyPFWy9cWIUT9kN8u\nQgghRCNV7ignv9QddPJLCskvLnAPq7wqg05delMz+Ogw+foRaQrHpPXDrPXzCDvminGz1g+jxiCt\nOEKIa4aEIiGEEOIG4nK5KCqzkVdSQJ4ScgrILy6sFnisZUUXLEulUmHS+hGqD8Lk64fZ14R/RdDx\n9zVh0vrhXzHfpDXKvThCiOuWhCIhhBDiOuB0ObGWFlWEnULyivPJqwg7uSX55BcXVkwXYHeWX7As\no8aAv6+JZv5N8fc1ucNO5Uv3x7ifxiitOUKIRkFCkRBCCNHASuwl5Bbnk1tcUDH845Vnyye3xN3i\nc6Ee19QqL/x9zUT7RxLgayZA5365A45ZCTxmrZ+06AghxHkkFAkhhBD1xOlyUlhqJceWR44tzx1y\nagg+F7pfR+2lJsDXTIuA6PPCjpnAimGAzoSf1igPABVCiMskoUgIIYS4DC6Xi8JSCzm2fHKK85Tg\nk2PLI6c4nxxbLrnFBZRf4FI2g0ZPsD6QQJ2/50tfOW6WsCOEEFeBhCIhhBCiBiX2ErJteWTZcsgu\nyiPblku2LVcJPbm2vFrv3VGhUu7ZCdT7E6wLqAg6AX+EHl8zGm/NVT4qIYQQNZFQJIQQotFxupwU\nlljIqgg6WUXuYXbl0JZ3wZ7Z/H1NRJsjCdK7w06wPoAgfQBBOvcwwNcsz9QRQojriPzGFkIIccNx\nupzklxSSac0hqyiHzKLsimEOWRWtPbVd1qZVawg2BNIyMIYQfSDBhkCC9ZUvd0uPBB4hhLixyG91\nIYQQ1x2Xy4Wl1EpmUQ5HLb9z5mi2O/BUBKDsotxaL20z+5qI8Y8kRB9EsD7AI/SEGAIxagyoVKqr\nfERCCCEakoQiIYQQ16RyRzmZthwyrFlkWLMrXllkWLPItOVSWl76x8oZf4z6aQxEmyMJMQYRanC/\nQgxBhBqCCdEHyn08QgghqpFQJIQQosHYyopJt2aRUeQOPunWLDIrwk92cR4ul6vaNjofX8KNIRWB\nJ5iS3CK6xMYp4Ufn49sARyKEEOJ6JqFICCFEvbKVFXPOmsk5SwbnLJmcs2aRbskkw5qFpZbODAJ1\n/sQGtyTMEEKYMZgwYwjhRvf4+Ze37d+/ny6RHa/W4QghhLgBSSgSQgjxp5WUl5JuySLdmkmaJYN0\nS5YShApLrdXW9/byJtQQRKug5oQZgj2CT6ghSC5xE0IIcVVJKBJCCFEnTqeTbFsuZwvTSbOkk1aY\nURF8Msktzq+2vpfKi1BDEC0DYwg3hhLhV/EyhhKsD8TLSx5IKoQQ4togoUgIIYSH0vIyzlkySLWk\nk1qYQWphOmmF6aRZM7E77NXWD9YH0iGsDRHGMML9/gg/ofog6bpaCCHEdUH+WgkhRCNVWGLhbGE6\nqYXppFrcwSfVkkF2US4uPDs40Ko1NDWFE2mKINIvjEhTOE38wgg3hsilbkIIIa57EoqEEOIGZy0r\n4mzBOc4UnONMQRpnCtM4W3COglJLtXUDfM3cHHoTkX7hRJrcryamMAJ1/nip5HI3IYQQNyYJRUII\ncYOw2Ys5W3COs4XnOF3gDj5nCtPIKy6otm6oIYguQR1oaopQwk+kXzh6ja4Bai6EEEI0LAlFQghx\nnSl3OkgrTOd0QSqn8lM5U5DKmYJzZNtyq60bpA8gLqIdTU0RRJmbEGVuQqQpHF9vbQPUXAghhLg2\nSSgSQohrmLW0iFP5Z0nJP0tKfiop+Wc5U3iOcme5x3oBvmY6hrWlqTmCqIoA1NQcgd5HWn6EEEKI\ni5FQJIQQ1wCn08k5a6ZH+EnJTyWnOM9jPR8vb6LNTYjxb0qMfyTN/JsSbY7EqDU0UM2FEEKI65+E\nIiGEuMrKnQ7OFqTxe95pfs89ze95pzldkErZed1dB+jMxEW0UwJQjH9TIoyhqL3UDVRzIYQQ4sYk\noUgIIepRuaOcM4Xn+D03xR2C8k5zOj8Ve5XL39ReapqaIpSWn5iKl0lrbMCaCyGEEI2HhCIhhLhC\n7A47Z6q1AKV53P+j9lITbW5Ci4AYWgRE0yIwmmhzE3zUPg1YcyGEEKJxk1AkhBCXweVycc6ayW/Z\nJ/kt9yTHc06RUpCKw+lQ1vH28ibGHEnzwGh3AAqIJsocIQFICCGEqCcOh/OytpNQJIQQdWAtLeJ4\n7il+zTnJ8ZyT/JZ7iqIym7Lc28ubZv5NlfDTIjCGKFME3mr5NSuEEEJcrpLScgqLyigoKqXAWkZh\nURmFVcYLrKUe86zFdv6/EU0veT/y11oIIc5T7nRwOv8sv+Wc4rccd0vQOUumxzphxhDiwtvRKqgZ\nrYNaEOMfKS1AQgghxAW4XC5KyhwUWEvdr6IyCq2l5Fv/CDf51lIKK5YVWMsoszsuWq6XCkwGLQEm\nX5o1MV1W3SQUCSEavcISC8eyT3As+wS/5Zzk97zT2Kv0BKfz8aVjWNuKANScVoHNMPn6NWCNhRBC\niGuDvdxBvsUdavIrw06VoFM1ANU15Gi8vTAZtUSFGTEbtJiMGkwGDWaDFnPFuEkZ12LU+eDlpVK2\n379//yUfh4QiIUSj4nK5yCrK4WjWcY5mH+eXrBOkWtKV5SqVihhzJK2CmnNTYDNuCm5OE78wvFRe\nDVhrIYQQ4upwuVxYi+3kW0rdLyXkuC9PK7C651eGnaKS8ouWqfH2wuynJTrMiNmoxWzUYjJo8DdW\nBBujFv+KeWajFl+NGpVKddFyryQJRUKIG5rT6eR0QRrHso9zrCII5RUXKMt9vbXcEt6WNsGtiA1u\nSavAGHx9fBuwxkIIIcSV5XC6KCwq9Qg6lcEmr8p05TyH03XB8ry8VJgNGkIC9NxUEXLMfpUhR6uE\nncoA1BAh51JJKBJC3FDKHHZO5J7iWNYJjmUf55fs37HZi5XlZq0f3ZrG0TakFbHBrYjxj5SHoQoh\nhLjuOJ0uLLYy8i2l5FlKKoYVIcdSUjF0vwqLSrlIzsFXo8Zs1NIqyh9/oxZ/v4pXxfgfYaf65Wo3\nAglFQojrWrnTwYncUxzO+IXDmb/wa/bvHg9GDTeG0LVpJ2KDW9E2pBXhxpBr/r9VQgghGq/i0nLy\nCt2hJrew5I/AU1gRfqzu8XxrKc6LJB29rzf+Ri2RoUbPcFMRdgKqBB9fbeOOBY376IUQ1x2ny8np\n/DQOZx7j54xfOJr1GyXlpQCoUBHjH0nbkJsqWoJa4q8zN3CNhRBCNHYOp4tCa2XIcbfk5FaEnLzz\nhiVlF+6IQKtRE+CnpU10gNKaE+DnWzH0nNb6yJUQdSWhSAhxTXO5XKRbs/g54xiHM3/hSOavWEqt\nyvImfmG0D21D+7A2tAttjZ/W2IC1FUII0Zg4HE7yK8NOoXtY9ZVX6A4/F2vV8VKB2ailSYiRgIpQ\nE2ByDwNNFYGnYlrXyFt06oucVSHENSfXls/hzF+UIJRjy1OWBekC6NHsdjqExdIutDVB+oAGrKkQ\nQogbkdPposBaSk5FuMkpKCG3oHrgKbBe+F4djbcXgWZf2kQHEGiqGnS0+FcEngA/LSajFvUNdo/O\n9UZCkRCiwZU7yjmWfYIfzx3m4LkjnCk8pyzz0xi4Paoz7UPb0CEsVu4JEkIIcdlcLhdFxXZyagg6\nOQXF7vGCEnItF27Z0WrUBJp8aRJiVMJOkMmXAJMvgX6+BJrd4wZfb/mbdZ2o91A0a9YsfvrpJ1Qq\nFVOmTKFDhw7KsuXLl7Nx40bUajXt27dn8uTJ9V0dIcQ1IseWx4/njvDjucP8nHFMuS9Io/YhLqId\nHcJiaR/ahmj/SHlGkBBCiItyOJzkWUrJKSgmp6CE7IJicgtKlPGcivELPTzUW+3ZshNkdrfmBJrd\n4wF+7qFOK2HnRlOvoej7778nJSWFlStXcuLECV5++WVWrlwJgNVqZdGiRXz55ZeoVCrGjBnDoUOH\n6NixY31WSQjRQMqdDn7JPsGP545w8NwRThekKsvCjSHERbQnLqIdN4fchMZb04A1FUIIca0pszuU\ncJOd737lFpSQU1hCdr478ORbSi54KZu/UUtUmLEi7Oj+CDxVwo/JoJGw00jVayj69ttv6dOnDwAt\nW7aksLCQoqIiDAYDGo0GrVaL1WpFp9NRUlKC2Sy9RAlxI8m15fPjucP8mH6EnzOOUWwvAcBH7UOn\n8JuVIBTuF9rANRVCCNFQSu0OcpSw476MLSu/mJz8P0JQYVFZrdt7q70IMvsS2yxQCTuVw+CKYYDJ\nFx9vuepA1K5eQ1F2djbt27dXpgMCAsjOzlZC0bhx4+jTpw++vr4MHjyYmJiY+qyOEKKeuVwuTuad\nJjn1IPtTfyalSmtQmCGYHjG3E9ekHTeHtEYrrUFCCHHDK3c4yS0oISvfHXSy8mxk5/8RerLyi7HY\nag88Gh81If6+NG9iIsisI8RfR5C/Z+CR1h1xJVzVjhZcrj/aNK1WK++99x6ff/45BoOBhx9+mF9/\n/ZXWrVtfzSoJIf4kp9PJsezjJJ89SHLqT2TbcgHw8fLmlvCbiYtoR6eIdkQYQ+WPlhBC3EBcLheF\nRWVk5RWTlW+rCD3FSujJzi8mr7D2S9q0GjXBZh0tIk0E++vcIce/IviYfQnx12HQ+cjfDnFV1Gso\nCg0NJTs7W5nOzMwkJCQEgN9//52oqCjlkrkuXbpw+PDhi4ai/fv311+FhbjONNTnodzlIMWWyq9F\npzhedBqbw31ZnNZLw83GlrQ2NqO5vikaLx+wwDnLWc5xtkHqKhoH+dsghNuV/CyUO1xYih3kF5VT\nUOSgoMhBvu2P8QJbOeW19FngpQI/vZqmwRrMejUmgzdmvdr9Mqgx6b3RaVRVAo8LsLlfNsi1Qe65\nmssWoj5cViiy2+34+PhcdL0777yT+fPnk5iYyJEjRwgLC0Ov1wMQGRnJ77//TllZGRqNhsOHD3P3\n3XdftMwuXbpcTpWFuOHs37//qn4eiu0l/HjuMMlnD3Lg3GGltzizr4k+zW6jW9NOtAtpjbdaevoX\nV9fV/iwIca261M+CrcROVl4xmXk2MvPcl7ZVTmflu7undtXSymMyaGgWYSYkQE9wRetOsL+OkAD3\nuL+frzx3RzSYy/nnwEW/vUydOpXp06crSf7kyZM8//zzfPrppxctPC4ujnbt2vHAAw+gVquZNm0a\n69atw8/Pjz59+jBmzBiSkpLw9vYmLi6OW2+99ZIPQAhRfwpLLPyQdojkswc5lHGMcmc54L4/qE/L\nu+ga2YnWQc3x8pKbV4UQ4lpjK7GTmVdMZq6NzDwbGRXDzFwbGbm138vj5aUi2OzLzc2DCA3QERKg\ndw/99e7QE6DDVyP/ABM3lov+RIeFhTF+/HjmzJnDhg0beO+993jllVfqvIPnnnvOY7pNmzbKeGJi\nIomJiZdQXSFEfSsqs7Hv7I98nfI9R7J+Ve4FjDFHclvTTnSN7ESMf6Rc4y2EEA2spLScjFyb8vr5\nWD7bfk5Wgo/FZq9xO423FyEBem6K8ickQEdYoJ6QAD0h/jpCA/QEmqWVRzQ+Fw1F48aN49NPP6V/\n//7ExMSwatUq5b4gIcSNocxh50Daz3yd8j0Hzh1WWoRuCmpOt6ZxdG3aiXCjfO6FEOJqKnc4ycor\nJiO36I/wk2MjvWK6wFpTS48VjbcXoYF6booOICxAT2igvmKoIzRQj79RK//YEuI8tYaitWvXekx3\n6tSJrKwsdu/eDcD9999fvzUTQtQrp9PJkaxf2ZOSzL6zPyrPEIoyRdA9pit3xtxGqCGogWsphBA3\nLpfLRYG1jPScIverIvS4A1AR2fnFNfbc5q1WERqgp0UTM2FBBsIC9YQF6snNOE2Pv3TGbJQuqoW4\nVLWGovNvUNJqtTRt2lSZL6FIiOtP5XOE9qR8z97TP5BXUgBAkC6Avi3v5q6Y24g2y6VxQghxpZQ7\nnGTm2UjPdrfwnMt2t/K4h0UUl1bvvk2lgkCT+2GkYYF6wqsEn7BAQ62Xt+3fn46/n/ZqHJYQN5xa\nQ9GsWbOUcZfLhUqloqysjJycHCIiIq5K5YQQV0a6JZOvT3/P1ynfk2bJAMCg0dOnRXe6x3QlNqQl\nXirpLEEIIS6HrcROeo476JyrbPXJKeJcjo3sPFuNrT2+GjXhQQYigg1K8AkPcgef0AA9Gh/11T8Q\nIRqxi95T9P7776PVannggQe47777MBgMdO/enWeeeeZq1E8IcZmsZUV8nfI9X53ax/HcUwD4qH24\nI6oLd8Xcxi3hN+OjvnjX+kIIIaCo2O4OPdlFpOVY3cMsdwjKt5TWuE2gSUubmEAigg1K6IkIco/L\nJW5CXFsuGop27tzJxx9/zPr16+nVqxcTJ05k1KhRV6NuQohL5HK5+CX7BNtPfM23Zw9gd9hRqVTc\nEt6WO6Nvo2vTTuh9dA1dTSGEuCZZbWWkZReRVhF+zmVXhJ/sIgqLqndq4KWC0EA9ca1DiAh2t/pU\nhp6wIL10Wy3EdeSin1Zvb29UKhVfffWVEoacTme9V0wIUXeWUiu7T+3jy9+/JrUwHYBwYwi9W3Sn\nR7Nu+OvMDVxDIYS4NpSUlSutPKlZVlKz3MEnNctac/DxUhEW6O6+ujL4NAk2EhFsIDRAj4+3XHos\nxI3goqHIz8+PsWPHkp6eTlxcHDt37pTmXiGuAS6Xi/8/81e2n/ia787+SLmzHG8vb/4SfSt9WnTn\n5tCb5D4hIUSjVO5wkpFrIzXLSlqWVQlAaVlWsgtKqq1fGXxaRwfQJNhAk2ADERXBJyRAh7dafpcK\ncaO7aCiaM2cOe/fupXPnzgBoNBreeOONeq+YEKJmhSUWdp36js2nvyT3hLv3uCZ+YfRp2Z27m92O\nSWts4BoKIcTVUWAt5WymlbOZ7hafs5kWUjOtpOfacNbQu0Gwv46OrYKJDDHSJMRIkxADkSFGwgL1\nEnyEaORqDUW7d++mR48ebN68GYAdO3Yoy86dOyddcgtxFTldTo5UtAolpx7E4XSgVqm5K6YrvVt0\np21IK2nBFULckModTtJzitzB57wAZLHZq63vp9fQOsqfyFCjO/wEu8NPRLBB7vERQtSq1t8Ov/zy\nCz169Kj2vKJKEoqEqH/W0iK2//41X/7+DRnWLACamiLo07I7pnwt3bve2cA1FEKIK8NWYudsppXT\n6RbOZlqUFqD0nCIc57X6eHmpiAjSc3PzICJDjDQNNSohyGyU5/QIIS5draFo7NixgOfziio5HNUf\nNCaEuHLSrVn83y9fsuvkt5Q6yvBR+9Cj2e30admd1kEtUKlUtf7DQgghrmUF1lJOZ1g4m2HhTKaV\nM+kWzmRayKnhXh+jzoebovxpGupHZGhF+AkxEh5kkA4OhBBXVK2hKCMjg9dff53jx48TFxfH5MmT\n0el0HDt2jJdeeon169dfzXoKccNzd6f9O5t+2c73qT/hwkWQPoDEmwZxT4u/YNDoG7qKQghRJy6X\ni/PCkhMAACAASURBVOz8Es5kuAPPmYzKlxWLrXoPb8H+OuJahxAV5qe8moYaMRnkWT5CiKuj1lD0\n6quv0qNHD5544gk+++wzXn/9dQIDA9m4cSMvv/zy1ayjEDc0h9NBcupBNh3bzm8VD1ltERDNvW36\ncHtUZ7y95KnmQohrk8vlIs9SSsq5Qk5nWDidbiElvZAzGRZsJeUe63qpIDzIwM3NA2kaaiQ63I+m\noe7wo/eVB0kLIRpWraHIarXy4IMPAhAbG0u3bt0YNGgQn332GQaD4apVUIgbVbG9hJ0n9/J/v+4g\nqygHFSpubdKRe9v0kY4ThBDXnAJrqRJ6qg6txZ6dHai9VDQJMRLXxo/oKi0/TYINaHzknzxCiGtT\nraHIy8vzWt3WrVszderUeq+QEDe6HFseW37byfYTX2OzF+Oj9qFvy7sY2KY3TfzCGrp6QohGrri0\nnJT0Qk6lFSotQCnphRRYPS9781JBRLCBDq2CiQ73IybMxP9j777Do6oT7oGfKQnpjfRGQhrpCYFA\nQocAUgRRejEorliwd1BefcEfLPvqLq6guCKuiCIWBFS6UoQQSIA0EggJIb2TPpl6f38E47ISJgIz\nN+V8nodnuTPJ5OAy4Z58m7erNdydrLjeh4i6nU7vTcmfWhPdmSvXirDn4iEkFaZAK+hg28cas8Pu\nxQT/kTxbiIiMTqvVobS6GQVlbeWn4PqvitqWGz5OIgFcHCwQ5O2Afm5toz/erjbwdLbiyA8R9Rgd\nlqK8vDy8/PLLHV6vW7fOsMmIeoisykv4NusnZFZeBNC2pfbUoHEY3i8WpjLOoyciwxIEAXWNyvbS\nU1DW0D71Ta3R3fCxtlamiAxwRD83G/i62cDb1QbeLtYw68PzfYioZ+vwu9yLL754w3VcXJzBwxD1\nJBer8/BVxp72MhTuEoSpQQmIcg3lyCsRGYRGq0NJZRPyS+txpbQBV0rqcaWs/g9T30zlUni7WsPH\nzab9Vz83G9hbm4mUnIhIXB2WohkzZhgzB1GPcbmmADsy9+B8+QUAQKRrCGaHTUVAX1+RkxFRT9Ks\nUKOgrAH5JfW4Utr26+pNRn9cHCwwINShrfy426Cfqw3cHS0hk3HdDxHRbzgeTnSXFFwrwleZe5Ba\nmgEACHMOwuywqRjg5C9yMiLqzgRBQNU1xe+jP6X1yC+p/8PaHxO5FP1creHrbov+HrbwdbeFj5sN\nLM05TZeISB+WIqI7VFhXgh1ZP+B08XkAwABHP8wJn4ZQ50CRkxFRd6PTCSivacbl4jrkFdcjr6Tt\nf/9722sbS1NEBTq1FSB3G/h62MLTyYqjP0REt6nTpUgQBAiC0H7931t2E/U2pQ3l2JH1I5IKUyFA\ngL+DD+aE34sIl2CuGSIivbRaHYqrmm4oP/kl9VAobzz01M3REpGBTvC7Pvrj624DBxszfp8hIrqL\n9Jaijz/+GB9++CGam5sBtJUjiUSC7Oxsg4cj6orKm6rwTdaPOH71NARBgK+dF2aH34uBbmG8SSGi\nm9JodSgsb0RecR3ySuqRV1yH/NIGqNTa9o+RSgAPZ2v4edrCz8MOfp626O9uy+lvRERGoLcUffvt\nt9i9ezfc3d2NkYeoy6pqrsG3WT/hSMEp6AQdvGzdMSfsXgz2iGQZIqJ2Wp2AkspG5BbVIbeoDpeL\n6pBfWn/DBggyqQTertbt5cfPww6+7jbc+pqISCR6v/v269ePhYh6tRa1At9k/YS9ub9Aq9PCw9oV\ns8KmYKjXQEglnEZK1JsJgoCK2hbkFtbhUtG162uB6qBQ/j4CJJNK4ONuA39PO/h7tpWgfq42PPiU\niKgL0VuKgoKC8MILLyA2NhYy2e/fwGfOnGnQYERi0wk6HCtIxrb071Hf2gAny76YE3YvhnsP5po6\nol6qpl5xwwhQblEdGlt+PwNIIgE8na0R4GWHQC87+HvZwdfdlgWIiKiL01uKKisrYWpqivPnz9/w\nOEsR9WSXawqw5exXyK0tgKnMBHPC7sW9QQkwlZuKHY2IjKRVpUFecT0uXq1FztVruFR4DTX1rTd8\njGtfC0QGOCLAyx4B3nbw87CFhRnXABERdTd6S9GaNWsAAHV1dZBIJLC1tTV4KCKx1LU24Mv0Xfjl\nykkAQLxXDBZG3Q9HCweRkxGRIel0Akqrm3Dx6jVcLLyGi1evoaCsATrd77uu2ln3wZBQVwR42SHA\nyx7+XnawseQPSoiIegK9pejs2bN4+eWX0dzcDEEQYGdnh7/97W8IDw83Rj4io9DotNifewQ7sn6A\nQt0Kb1sPPDxwNkJ41hBRj9TYosKl6+Xn4vVRoP88C8hELkWglx2C+jkgqJ89grzt4WRvzk1ViIh6\nKL2l6J133sHGjRsRGNh2c3jhwgW8/fbb2LZtm8HDERlDenk2tpzbgZKGcliaWuDhgXMw3m8EZFKu\nASDqCXQ6AUUVjcguqEV2QS0uXq1FSVXzDR/j1tcSg4JdEOhtj6B+9vB1t4WJnGsHiYh6C72lSCqV\nthciAAgJCblhwwWi7qqyuQafnf8Gp4vPQwIJxvuNwJzwabDpYyV2NCK6A61KDXKL6nChoAbZV9rW\nAzX/xyiQhZkcUYFOCLpegAK97WFr1UfExEREJLZOlaIDBw4gPj4eAHDs2DGWIurWlBoVduXsx66c\ng1Br1Rjg6IeHBs6Br72X2NGI6DY0tGjxa1oJsq/U4kJBLa6U1EP7H2uB3PpaYkioK4J9HBDs4wAv\nF2tIpZwGR0REv9Nbit566y2sWrUKK1asgEQiQVRUFN566y1jZCO6qwRBQHLxOXx2/ltUt9TC3twW\niyLvxzDvwVwnQNRN6HQCrpY34MKVWmRfqUV2QQ0qrykAlAEA5DIJ/L3sEOzjgBBfBwzwcYC9tZm4\noYmIqMvTW4p8fHywefNmY2QhMphaRR02nfkc58qyIJfKcV/wRNwffA/MTHizRNSVabQ65BXXISu/\nBpn5NbhwpfaGqXDWFqYI9DBDXFR/BPs4wN/LDn14JhAREf1JekvRyZMn8cUXX6CxsRGC8Pt0hM8+\n+8ygwYjuBkEQcKLwDDaf/QrNqhZEuATjkZi5cLV2FjsaEd2ESq3FpcJr7SUop6AWrSpt+/NufS0x\nNMwVob59McDHAZ7OVjh79ixiYgJETE1ERN2d3lL05ptv4vHHH4erq6sx8hDdNQ2tjfhX6pdILj6H\nPvI++EvMfCT4DedUOaIuRKHUIKegFpn5NcjKr8HFq9eg0eran/d2tUZo/74I698Xof37oq+tuYhp\niYiop+rU9LkZM2YYIwvRXXOmJA0fndmGemUjgp388UTsg3CxchI7FlGv19KqxoUrtUi/XI2s/Gpc\nLq5vPyBVKgF8PWzbS1CIb1/uCkdEREahtxTNmjULK1asQHR0NOTy3z/8vvvuM2gwotvRrGrBlnM7\ncKwgGSZSORZFPoApgWMhlfK8ESIxKNVa5FypRdrlKqRfrkZuUV17CZLLJAjytkfo9VGgYB8HWJqb\niJyYiIh6I72laNOmTTA3N4dKpWp/TCKRsBRRl5Neno0PTm9FjeIa+tt7Y9mQxfC0dRM7FlGvotbo\ncKnwGtIvVyPjcjWyC2rbp8NJpRIEetkhIsAJEX6OCPKxh5mp3n+GiIiIDE7vv0YmJibYunWrMbIQ\n3ZZWjRKfp32HA5ePQSaRYnbYVNwXfA/kUu5ARWRoWp2AvOK69hKUdaUGyusbI0gkQH8PW0T4OyHC\n3xEhvg6wMONIEBERdT16S9HYsWNx6tQpDBw48Ibpc5yORF1BTlUeNpz+NyqaquBl44YnhyxGfwdv\nsWMR9ViCIKCkqgnnLlYhLbcKmXnVaG7VtD/v7WqNCD9HRAQ4IszPEdYWpiKmJSIi6hy9pWjjxo1Q\nKBQ3PCaRSJCdnW2wUET6qLRq7Mjcgz05hwAA0wZMwJywqTCR8afQRHdbQ7MKablVOHexEucuVaG6\n7vd/E9z6WmJ4lAci/B0R7ucIexue/UVERN2P3lJ07tw5Y+Qg6rT82qt4P/nfKG4og4uVE5YNSUSQ\no5/YsYh6DLVGh5yrtTh3sRLnL1XhcnEdfjumztrCBMMj3REd5IyoACc4O1iIG5aIiOgu0FuKKioq\nsH///j8c3rps2TKDBiP6bzpBh13ZB7Ajcw+0gg4T/UdhQeQMmMm5ZS/RnRAEAcWVTTh/qQrnLlUi\n43J1+4GpMqkEIb59ER3khOhAZ/h52kEm5VlfRETUs+gtRX/5y18QGhoKFxcXY+QhuimFuhUbkv+N\n0yXn0dfcHo/HLkKEa7DYsYi6rWaFGucvVSE1p+IPU+I8nKzaSlCQM8L69+XmCERE1OPpLUV2dnZY\ns2aNMbIQ3VRpYwX+9uuHKGkoR6hzIJ6LewQ2ZtZixyLqVgRBQGF5I1KyK5CSU4HsK7XQXj8v6Lcp\ncVGBzogO5JQ4IiLqffSWonHjxmH37t2Ijo6GTPb7Fsfu7u4GDUYEAGdLM/DeqS1oUSswOXAsFkXe\nDxm32ibqlFalBumXq9uLUNW1ttEgiQQI8LLDoAEuiAl24ZQ4IiLq9fSWotzcXOzZswd2dnbtj0kk\nEhw5csSQuaiX0wk67LywDzsyf4BcJseyIYsx0meI2LGIurzS6iakZFcgNbsSGXnVUGvaDk61NDfB\niCgPDAp2wcAgZ9hZcy0eERHRb/SWorS0NJw5cwampjxrgoxDoW7FhtP/xuni83C0cMCLw5by7CGi\nDqg1WmTm1bSNBmVXoLS6uf05X3cbDAp2QcwAFwzoZw+ZjOfLERER3YzeUhQWFgalUslSREZR1liJ\nv/36IYobyrh+iKgDTS0qnMmuQHJmOc5erIBC2bZTnJmpDEPDXNuLkKOduchJiYiIuodObck9duxY\n+Pn53bCmaNu2bQYNRr3P2dJMvHfqE64fIrqJitoWJGeWITmrHJn5NdBd3yTBra8lJgxxxeBgF4T0\nd4CJnO8ZIiKiP0tvKXrssceMkYN6MUEQsDN7H77K2MP1Q0TXCYKAvOJ6nMoqQ3JmOQrKGtqfC/S2\nw5BQNwwJc4W3izUkEm6SQEREdCf0lqLY2FikpKQgIyMDEokEkZGRiI6ONkY26gX+c/1QXwt7vDTs\nMa4fol5LrdEhI68ayZllOJ1Vjur6VgCAXCZFzABnDAlzQ2yIC/raclocERHR3aS3FK1fvx4nTpxA\nTEwMAGD16tWYMGECli5davBw1LNx/RAR0NKqRkp2BU5lliM1pwItrRoAgJW5CUbHeGJoqBuig5x4\ngCoREZEB6S1FycnJ2L59O6TStl2LNBoNFi5cyFJEd+SG9UMBY7Aw6gHIuX6IeolmhRpnLpTjRHop\nzuZUQnV922xnBwskDPbGkDBXhPj2hZy7xRERERmF3lKk0+naCxEAyOVyzl+n2yYIAnblHMCX6bu4\nfoh6laYWFZKz2orQuYtV0GjbipCXizWGR7ojLtwNPm42/P5KREQkgk5tyf3YY48hPj4eAHDy5EmE\nh4cbPBj1PIIgYFv6TuzOOXh9/dBS9HfoJ3YsIoNpaFYhObMMJ9JLkZZbBY22bcc4HzcbDIt0R3y4\nG7xdbUROSURERHpL0fLly7F3716kpaVBIpFg2rRpmDRpkjGyUQ+iE3T4JPUrHMg7Bg9rV7wx+hk4\nWNiJHYvorqtvUuJUZhl+TStF+uXq9q2z+3vYYliEO4ZFusPDyUrklERERPSfblmKioqK4OXlhSlT\npmDKlClQKBSoqKjg9A76U7Q6LT44sxXHCpLRz84Tr496CrZm/Ok49Rz1TUqcTC/Fr2mlyMyrxvUe\nBH8vu7YiFOEON0dLcUMSERFRhzosRUlJSXjppZewd+9eWFu37QhWVFSEJ554Av/4xz8QFhZmtJDU\nfWm0Grx3agtOFZ9FgIMPXhu1DFamvDmk7k+h1CA5qxxHzxbj3MVKaK83oSBv+7apcRHucHGwEDkl\nERERdUaHpej999/HJ5980l6IACAwMBAffPAB/vrXv+Ljjz82SkDqvlQaFd45+S+cK8tEiFMAXhnx\nBMxNzMSORXTb1Bodzl2qxNGzxUjOKodSpQUA+HnaYlS0J4ZFusPZnkWIiIiou+mwFAmCgMDAwD88\nHhAQAKVSadBQ1P21qlvx118/QFblJUS5huCFYUvRR24qdiyiP02nE5BdUIsjZ4txIq0EjS1qAIBb\nX0uMHOiBUdGe8HLh+VpERETdWYelqKWlpcNPqqurM0gY6hmaVS1Yc2wDLtXkI9YjCs/EPQwTGQ+e\npO5DEAQUlDXg6NliHD1Xguo6BQDAzroPpo3oj1EDPRHgZcf1lURERD1Eh6UoICAAX375JebNm3fD\n4//6178QGRlp8GDUPTW0NmL10fdQUFeMEf1i8UTsg5DxUFbqJsprmnH0XDGOni1BUUUjAMDCTI6E\nwd4YNdAD4f5OkElZhIiIiHqaDkvRyy+/jCeffBK7du1CWFgYdDodzp49CysrK2zatMmYGambqFXU\nYdWR9ShpKEdC/+F4ZNA8SCVS/Z9IJCKFUoMTaSU4dKYIWfk1AAATuRTxEW4YFe2JQcEuMDVhsSci\nIurJOixFTk5O2LFjB5KSkpCbmwuZTIZJkyZh8ODBxsxH3URlcw1W/fIPVDRXY0rgODwY9QCnFlGX\npdMJyLpSg0OnC3EyvRSt1zdMiPB3xJgYT8SFu8PSnFM+iYiIegu9h7fGxcUhLi7OGFmomyptrMCq\nX9ajRnENM0MnY1boVBYi6pIqalvwc0oRDp8pREVt27pJFwcL3D/YG2MHeXELbSIiol5KbykiupXC\nuhKsOvoe6lsbsCBiBqYHTxA7EtENWlUanEwvw+EzhUi/XA0A6GMqw9hBXkiI9Uaob19IuU6IiIio\nV2Mpott2uaYAbx/7J5pVLVgycC4mBowSOxIRgLbd47ILanHodCF+TSuFQqkBAIT274uEwV6Ij3CH\nhRmnxxEREVGbTpWilJQUZGRkQCKRIDIyEtHR0YbORV1cdlUu1h7biFatEk/EPojRvpxiSeKrqVfg\n8Jm26XGl1c0AAEc7c0wb0R9jB3vB3dFK5IRERETUFektRevXr8eJEycQExMDAFi9ejUmTJiApUuX\nGjwcdU35tVfx/46+D41Og2fjliDOK0bsSNSL6XQCzudWYV9SAZKzyqHTCTCVSzF6oCfGDfZChL8T\np8cRERHRLektRcnJydi+fTuk0ratlTUaDRYuXMhS1EtVNddg7fGNUGnVeH7YXzDEk6OGJI76JiUO\nnS7EvlMFKK9p2zTB190Gk+J8MDLak7vHERERUafpLUU6na69EAGAXC7nzmK9VItKgbXHNqCutQGL\no2exEJHRCYKArPwa7E0qwMn0Mmi0OpiayJAw2Bv3xPVDoLc9vz8RERHRn6a3FIWGhuKxxx5DfHw8\nAODkyZMICwszeDDqWjRaDd45uQlFDWWYFDAGkwPHih2JepGmFhV+TinCvlMFKKpoAgB4uVjhnjgf\njI3xgpWFqcgJiYiIqDvTW4pWrFiBvXv3Ii0tDRKJBNOmTcPkyZONkY26CEEQ8FHqF8iouIhB7hFI\njJopdiTqBQRBwKXCa9ibVIDj50qg0uggl0kxMtoDk+J8ENq/L0eFiIiI6K7QW4reffddvPjii5gy\nZUr7YytWrMDbb79t0GDUdezM3ocjV5LQ394bT8c9fMN0SqK7TaHU4EhqEfYlXUV+aT0AwK2vJe6J\n64dxg71ha9VH5IRERETU03RYig4ePIgDBw4gKSkJlZWV7Y9rNBqcOXPGKOFIfMcLTmN7xm44WTjg\n1RFPwEzOG1IyjPKaZvx44goOJl9Fc6sGUqkE8RFuuGeoDyIDuIMcERERGU6HpWjEiBFwcHBAZmYm\n4uJ+P4NGIpFg2bJlRglH4rpQmYsPzmyFhYk5Xh35JOzMbcWORD2MIAi4cKUWu47lITmzDDoBsLfu\ng+mj/DFhiDf62pqLHZGIiIh6gQ5LkZmZGWJiYvD999+jTx+ODvQ2pQ3l+NuJDyEIOrww7FF42bqL\nHYl6ELVGi+PnS7DrWD7yS9qmyPl52mLaCD+MiHKHiVwmckIiIiLqTfSuKWIh6n0aWhux5tgGNKta\n8ETsgwh3GSB2JOoh6hqV2JtUgJ9OXkFdoxJSCRAf4YZpI/wQ4uvAjROIiIhIFHpL0Z1as2ZN+851\ny5cvR3h4ePtz5eXleP7556HRaBASEoI333zT0HFID5VGhb/++gEqmqvxQMhkjPaN0/9JRHrkl9Rj\n9/E8HD1bAo1WB0szOWaM9seUYb5wcbAQOx4RERH1cp0qRdeuXUNxcTHCw8P/cJjrrZw5cwZXr17F\n9u3bkZeXhxUrVmD79u3tz69duxZLlizBuHHjsGrVKpSXl8PV1fX2/iR0x3SCDv9M/hS5NVcwol8s\nZodNFTsSdWNanYDTWeXYczwfGXnVAAAPJ0vcO7w/xg72hnkfg/9MhoiIiKhT9N6V/PDDD3jvvfdg\namqKH374AatWrUJISAhmzZql98WTkpKQkJAAAPDz80NDQwOam5thaWkJQRCQmpqKv//97wCAN954\n4w7/KHSntqXtRHLxOQQ7BeCxwQs5lYluS6tKg4PJhdh1LA8VtS0AgKhAJ0wf6YeBQc7cRY6IiIi6\nHL2laMuWLdi1axceffRRAMArr7yCRYsWdaoUVVdXIywsrP3a3t4e1dXVsLS0RG1tLSwsLPD222/j\nwoULGDRoEJ5//vk7+KPQnThw+Sj2XDwEd2sXvDRsKUxkJmJHom6mqUWFH09cwe7j+WhoVsFULsXE\nof1w74j+6OdqI3Y8IiIiog7pLUXW1tYwN/99W1wzMzOYmNzeDbMgCDf8vrKyEosXL4a7uzseffRR\nHD16FKNGjbrla6Smpt7W16aO5TUX4duyA7CQmeFeh1G4mJkjdiTqpK7wfmhUaJGU04iU3GaoNALM\nTCQYGWaNIYFWsDTTorokF9UlYqeknq4rvBeIugK+F4huj95SZG9vj507d0KpVCIrKws//fQTHBwc\nOvXizs7OqK6ubr+urKyEk5NT++t6eHjA09MTABAXF4fLly/rLUUxMTGd+trUOVeuFeEfP2+FXCbH\nijFPI6Cvr9iRqJNSU1NFfT+UVjfhu18u4/CZUmi0OjjY9MH0kf64J64fLMw40kjGI/Z7gair4HuB\nqM3t/HBA744Jb731FjIyMtDc3IzXX38dSqUSq1ev7tSLDxs2DPv37wcAZGVlwcXFBRYWbTtNyWQy\neHp6orCwsP15X1/ekBtTdUst1h7fAJVGhaeGLGYhok7JL6nHuq0peHztYew/dRVO9uZYNisSH68Y\nj/vH+LMQERERUbejd6TIxsYGK1euvK0Xj46ORmhoKObOnQuZTIaVK1di586dsLa2RkJCApYvX45X\nX30VgiAgMDAQY8eOva2vQ39eq0aJvx7biGuKeiyKfABDvQaKHYm6MEEQkJVfg29+zkVqTiUAwNfd\nBrPGBiI+0h0ybp5ARERE3ZjeUjRq1Kib7kJ25MiRTn2B/948ISgoqP333t7e+OKLLzr1OnR3bT3/\nLa7Wl2C83whMDRondhzqogRBwJnsCnxzOBfZBbUAgND+fTFrXAAGBjlzh0IiIiLqEfSWov8sLWq1\nGklJSVAqlQYNRYaVWpqBg3nH4W3rgcXRs3hjS3+g1Qk4kVaCrw/noqCsAQAwOMQFs8YGIti3c2sK\niYiIiLoLvaXIw8PjhmsfHx8sWbIEixcvNlQmMqD61gZ8eHor5FI5nh76ELfephvodAKSMsrwxYEc\nFJY3QiqVYPRATzwwNgA+btxWm4iIiHomvaUoKSnphuvy8vL2zRGoexEEAZvObEO9shEPRj0AbzsP\n/Z9EvYIgCDidVY5t+3NwpbQBUgkwbrAX5o4PgmtfS7HjERERERmU3lK0cePG9t9LJBJYWVnhrbfe\nMmgoMoyf808gpTQdYc5BmBzITS2orQydvViJbftykFtUB4kEGD3QE3MnBMHDyUrseERERERGobcU\nbd261Rg5yMDKGyvx6flvYGlijieGPAipRO9u7NSDCYKA9NxqbNuf076BwrBId8yfEARvV06TIyIi\not6lw1K0aNGiWy7A/+yzzwwSiO4+rU6Lf57aAqVGiaVDH4ajBRfK92ZZ+TX4fF82MvNqAABDQl2x\n4J4B8HW3FTkZERERkTg6LEVPPPEEAODQoUOQSCQYOnQodDodTp48CXNzc6MFpDu3M3sfcmsLMNx7\nMIb3Gyx2HBLJxau1+HxfDs5fqgIADAp2wYKJA+DvZSdyMiIiIiJxdViK4uLiAACbN2/Gxx9/3P74\nhAkT8Pjjjxs+Gd0Vl2sK8E3WT+hrYY8lMXPFjkMiuFxch237cpCSXQEAiApwwoJ7BmCAD0cMiYiI\niIBOrCkqLy/HlStX4OvrCwAoLCxEUVGRwYPRnWvVKPHPU1sgCAKejE2EpamF2JHIiEqqmvDvHy8g\nKaMMQNuhqwvvGYAwP0eRkxERERF1LXpL0bPPPovFixdDqVRCKpVCKpVi+fLlxshGd2jr+W9R1lSJ\nqUEJCHMJEjsOGUl9kxLbD1zE3qQCaHUCBvSzx8J7ghER4MiDeomIiIhuQm8pSkhIQEJCAurq6iAI\nAuzt7Y2Ri+5QamkGDuYdh7etB+aFTxM7DhmBSq3FnuP52HH4ElpaNXBztMTiKSGIC3djGSIiIiK6\nBb2l6Dd2dlyM3V3Utzbgw9NbIZfK8fTQh2AiMxE7EhmQTifg+PkSfPbTBVReU8DawgR/uS8Mk+J8\nYSLn1utERERE+nS6FFH3IAgCNp3ZhnplIxZFPgBvOw+xI5EBZeXXYPPuTOQW1UEuk+L+0f6YlRAI\nK3MWYSIiIqLO0luKmpqaYGV148n2RUVF8PLyMlgoun0/559ASmk6Qp0DMSVorNhxyEBKqpqw/Vg1\ncoqLAQAjozywaHIwXPtaipyMiIiIqPvRO7fm4YcfRm1tbfv1rl27kJiYaNBQdHvKGyvx6flveHjd\n5gAAIABJREFUYGlijieHJEIq4dSpnqa+SYlNO9Px5LqfkVPcimAfB/zf0yPw0qJBLEREREREt0nv\nSNEzzzyDRx55BGvWrMHmzZtRVlaGL774whjZ6E/Q6rT4Z/KnUGqUWDr0YTha8AyankSl1uKHX/Ox\n49AlNF/fRGFEsBkWTh/GTRSIiIiI7pDeUjRs2DA4Ojpi6dKlGDlyJLZu3WqMXPQn7czeh9yaKxjm\nPQjD+w0WOw7dJYLQtonCv3/8j00UpodhUrwv0tPOsRARERER3QUdlqKXXnrphhsub29vHD16FC+/\n/DIAYN26dYZPR51yuaYA32T9hL7m9lgSM1fsOHSXXC1vwIffpSMzrwZymRQzRvtj9rgAWFmYih2N\niIiIqEfpsBTFx8cbMwfdplaNEv88tQU6QYcnhzwIK1OuK+nuFEoNth+4iF3H8qDVCRgS6opHpodx\nzRARERGRgXRYimbMmAEAUKvVqKmpgaurK3JycpCTk4OJEycaLSDd2tbz36KsqRJTgxIQ5jJA7Dh0\nBwRBwMmMMnz8fQaq61vh7GCBpTPCERviKnY0IiIioh5N75qiV199FePGjUNMTAyeeuopjB8/Hr/8\n8gvWr19vjHx0C+nl2TiYdxzeth6YFz5N7Dh0B0qrmrBpZwbOXqyEXCbFnPGBmDUuEH1MZGJHIyIi\nIurx9JaiiooKTJ48GVu2bMH8+fPx0EMPYfHixUaIRrei0+mw9fy3kECCJ2IfhImMh3V2R0q1Fl8f\nvoRvf74MjVaH6EAnPHZ/BNydrPR/MhERERHdFXpLkUqlgiAIOHjwIN5++20AQEtLi8GD0a0du5qM\nq/UlGOkzBP0dvMWOQ7fh9IVyfLQzAxW1Lehra4a/TA9HfIQbd5QjIiIiMjK9pSg2NhYxMTEYMWIE\nfH198emnn8LX19cY2agDSo0K2zN2w0RmgrmcNtftVNa24KPvM5CcVQ6ZVIIZo/0xd3wgLMw42kdE\nREQkBr2l6MUXX8Sjjz4KGxsbAEBCQgIWLlxo8GDUsR8vHUatog73BU/kIa3diFqjxc4jefjq0CWo\n1FqE9u+Lxx+IQD9XG7GjEREREfVqHZaiTZs2YenSpX84r+g3PKdIHPWtDdiVfQDWfaxw3wDuAthd\npF2qwgffpaOkqgl21n2wbFYkRg/05FQ5IiIioi6gw1IUEhICgOcVdTVfZ/0IhaYVD0fMgYWpudhx\nSI8mhRof78rA4TNFkEqAqcN8sWBSMKzMOVWOiIiIqKvosBSNGDECADB69GjY29vf8FxxcbFhU9FN\nlTaU41Der3CzckaC3wix45AeZy6U4/2v01Db0Ao/T1ssmxUFf087sWMRERER0X/psBSlpKTgueee\ng0qlgr29PT766CN4e3vj888/x0cffYRjx44ZMycB+Dz9e+gEHRZEzoBcyvNruqqmFhX+tSsTP6cU\nQS6TYOGkAXhgTADkMqnY0YiIiIjoJjosRX//+9/x6aefws/PD4cPH8Ybb7wBnU4HW1tbfP3118bM\nSACyq3KRUpKGIEc/DPaIFDsOdeD0hXJs+Po8ahuU8PO0xbNzB8LHjRspEBEREXVlHZYiqVQKPz8/\nAMC4ceOwZs0avPLKKxg/frzRwlEbQRCw9fx3AIBFkfdzcX4X1NSiwkffZ+CX1GLIZRIsmhSM+8f4\nc3SIiIiIqBvosBT99423m5sbC5FIkopScbm2AHFeMQh07C92HPovyZll2PhtGmoblPD3ssOzc6LR\nj6NDRERERN2G3nOKfsPRCXGotWpsS/8eMqkM8yOmix2H/kNjiwof7czAkbPFkMukeHByMO4f7Q8Z\nR4eIiIiIupUOS9G5c+cwevTo9uuamhqMHj0agiBAIpHgyJEjRohH+y8fRVVzDSYHjoWLlZPYcei6\nU5ll2PBNGuoalQjwssOzc6PhzUNYiYiIiLqlDkvRvn37jJmDbqJJ1YxvL+yFhYk5HgiZJHYcAtDQ\n3DY6dPRc2+hQ4pQQzBjlx9EhIiIiom6sw1Lk4eFhzBx0E99d2IdmVQsWRt4P6z5WYsfp9ZIy2tYO\n1TUqEehth2fmcHSIiIiIqCfo9JoiMq7Kpmrsyz0CJwsH3BMwWuw4vZpCqcGmnek4fKYIJnIpHpoa\ngukjOTpERERE1FOwFHVRX2TsgkanwbyI6TCVmYgdp9fKL6nHuq1nUFLVDH9PWzw/PwZeLtZixyIi\nIiKiu0hvKaqvr0dFRQUCAwNx/PhxpKenY/bs2XBy4qJ/Q7lcU4CThSnob++NeO9BYsfplQRBwJ5f\n87FlzwVotDrcN8oPD04OgYmco0NEREREPY3eO7yXXnoJlZWVKCgowNq1a2FnZ4cVK1YYI1uvJAgC\ntqZdP6g16gFIJbwJN7b6JiVWf3Ia//o+E5bmcvzPI0OxZFoYCxERERFRD6X3Lk+hUGD48OHYt28f\nFi5ciAULFkCtVhsjW6+UUpqO7KpcxLiHI9Q5UOw4vU5GXjWeefcITl8oR2SAI957YQwGBbuIHYuI\niIiIDEjv9DmFQoHa2lrs378fGzduhCAIqK+vN0a2Xkej02Jb2k5IJVIsiJwhdpxeRavVYfvBS9hx\n6CIgkbQdxDomADIpDy0mIiIi6un0lqJ7770XEyZMwKxZs+Dm5ob3338fQ4YMMUa2Xufn/F9R2liB\nBL8R8LRxEztOr1F1TYF3vkhFVn4NnO3N8eKCQQj2dRA7FhEREREZid5SlJiYiMTExBuura25+9bd\nplC34uvMH2Em74PZoVPEjtNrnMosw/rt59CkUGNYhDuWzY6ClTl3+yMiIiLqTfSWory8PLz11lvI\nzMyERCJBVFQUVq5ciX79+hkjX6+xK+cA6pWNmB12L+zMbcWO0+Op1Fp8sicLP564AlO5FE/OjMTE\nof0gkXC6HBEREVFvo7cUrVq1Cg8//DBiY2MhCAJOnjyJN998E1u2bDFGvl6htqUOP1w8BHszW0wN\nGid2nB6vqKIR67amoKCsAd6u1nh50SD0c7UROxYRERERiURvKRIEAaNHj26/Hj9+PLZu3WrITL3O\n9szdUGnVeHjgNJjJ+4gdp8cSBAGHThdi0/cZUKq0mBTngyXTw9DHRCZ2NCIiIiISkd5SpFarkZWV\nhdDQUABAeno6tFqtwYP1FlfrinH0yil423pgtM9QseP0WEq1Fhu/ScPPKUWwNDfBc4kDMSzCXexY\nRERERNQF6C1Fr7zyCl544QXU1tZCEAQ4Oztj7dq1xsjWK+zKOQgBAuZH3AeplIeDGkJ1nQJvf3oa\nl4vqEOhth1cWDYazg4XYsYiIiIioi9BbiiIjI7Fv3z40NjZCIpHAysrKGLl6hbrWBiQVpcLDxhXR\nbqFix+mRLlypwZp/n0FdoxIJg73x+AMRMOV0OSIiIiL6D3pL0WuvvfbHT5LL4ePjg7lz58LS0tIg\nwXqDw3m/QqvTYqL/KO56ZgD7kgqwaWc6dALw6H3hmDrcl/+diYiIiOgP9M7XcnNzQ3FxMQICAhAQ\nEIDi4mKYmZmhuLgYL7/8sjEy9khanRYH847DXG6GUVxLdFepNTps/CYNG75Jg3kfE6xaGod7R/Rn\nISIiIiKim9I7UpSWloZPP/0UMlnblKPExEQ8+eST+PDDD7FgwQKDB+ypzpSkoVZRh3v8R8PcxEzs\nOD3GtcZWrP33GVy4UgsfNxu8/vAQuHD9EBERERHdgt5SVFlZiaamJtjath0oqlQqUVJSgqamJjQ1\nNRk8YE+1//JRAMDEgFEiJ+k5LhfV4e0tyaiub8WwSHc8OycaZn30/hUnIiIiol5O7x3jvHnzMH78\neHh6ekIikaC4uBhLlizBoUOHMHPmTGNk7HEK60qQVXkJ4S5B8LBxFTtOj3AktQj/3HEeaq0OD04O\nxsyxAZwuR0RERESdorcUzZ8/H9OmTUNBQQF0Oh28vb1hZ2dnjGw9Vvsokf9ocYP0AFqtDp/+eAHf\nH82DhZkcryYOxuAQFk0iIiIi6rwOS9H69etv+YnPPPPMXQ/TG7SoFDh29TQcLRwQ4x4udpxurbFF\nhXVbU3D+UhU8nKzw+sOx8HS2FjsWEREREXUzHZai3zZWoLvrSEESlBolHgiZBJmU/41v19WyBry9\n5TTKapoxKNgFLy6IgaW5idixiIiIiKgb6rAULVu27KaPl5SU4PvvvzdYoJ5MJ+iw//JRmEjlGOsb\nL3acbispoxTvfnEWrSotZicEYv7EAZBJuX6IiIiIiG5Pp7bmUqlU2L9/P7777jtcuHAB06dPN3Su\nHimjIgdljZUY5TMUNmac5vVnCYKAHYcu4fN9OehjKsMrDw7C8EgPsWMRERERUTd3y1KUlpaGb7/9\nFvv370dwcDAKCwtx9OhRmJnxXJ3bsS/3CABgoj+34f6ztDoBH36Xjn1JBXC2N8frDw+Br7ut2LGI\niIiIqAfosBRNnToV9vb2mDhxIp566ik4OTnhvvvuYyG6TZVN1Thbmgl/Bx/49/URO063olRr8X+f\np+BUZjn6u9vizb8Mhb0N/x4SERER0d0h7egJd3d3VFZWory8HDU1NQDAc1/uwIG84xAg4J6A0WJH\n6VYaW1R448OTOJVZjgh/R6x5chgLERERERHdVR2OFH300UeoqKjAzp078dRTT8HExASNjY2orq6G\no6OjMTN2eyqNCj/nn4BNHysM9Roodpxuo+qaAv/zryQUVTRiZJQHnp0XDRM5d+wjIiIiorurw5Ei\nAHBxccFjjz2GgwcPYuXKlRg6dCgmTpzIM4r+pBOFKWhSNWNc/+EwlXHb6M64Wt6Al/95DEUVjZg2\nsj9eWBDDQkREREREBtGp3ecAYOjQoRg6dCgaGhrwww8/GDJTjyIIAvblHoFEIsF4vxFix+kWsvJr\nsOqTZDQr1HhoaghmjPbn1E0iIiIiMphOl6Lf2NjYYP78+YbI0iPl1lzBlboixHpEwdHSQew4XV5S\nRhn+7/MUaHUCnps3EGMHeYkdiYiIiIh6uD9diujP2Xf5KADgngBuw63P3qQCfPhtGkxNZFj+UCxi\nBriIHYmIiIiIeoEO1xQdPdp2M//LL78YLUxPU9fagKSiVHjYuCLUOUjsOF2WIAj4Yn8ONn6TBmtL\nU7z9+DAWIiIiIiIymg5HitasWQOpVIr169ff9GyiuLg4gwbrCQ7n/QqtTot7/EdzTUwHtFodPvgu\nHftPXYWLgwX+99E4uDtZiR2LiIiIiHqRDkvRvHnzsHnzZpSUlGDjxo03PCeRSFiK9NDotDiYdxzm\ncjOM9BkidpwuSanW4m9bU5CcVY7+HrZ48xEeykpERERExtdhKUpMTERiYiK2bduGBQsWGDNTj5BS\nkoZaRR3u8R8NcxPe6P+3xhYVVm1ORnZBLSIDHLF8cSwszLhdOREREREZn96NFqZPn44NGzYgIyMD\nEokEUVFRSExMvOmUOvrd/usbLEzkBgt/UHmtBW/+KwlFFU0YGe2BZ+cOhIn8lkdmEREREREZjN47\n0ZUrV6KpqQlz587F7NmzUV1djddff90Y2bqtwroSZFVeQrjLAHjYuIodp0spr2nGK+//iqKKJkwf\n6YcX5sewEBERERGRqPSOFFVXV+Pdd99tvx4zZgwWLVpk0FDd3f72bbhHixukiymvacbyD06guk6B\nBycHY9a4QLEjERERERHpHylSKBRQKBTt1y0tLVAqlQYN1Z01q1pwrCAZjhYOiHELFztOl1FR24Ll\nH5xA1TUWIiIiIiLqWvSOFM2ZMweTJk1CWFgYACArKwvPPPOMwYN1V0cLTkGpVeEB/5GQSjktDLhe\niDb+iqprCiyaxEJERERERF2L3lI0c+ZMDBs2DFlZWZBIJHjjjTfg4sKDNW9GJ+iw//JRmEjlGOsb\nL3acLqHy+ghR5TUFFk4agNkJLERERERE1LXoLUUA4ObmBjc3N0Nn6fYyKnJQ1liJUT5DYWNmLXYc\n0VVeu16Ialuw4J4BmJMQJHYkIiIiIqI/4Pyuu2hf7hEA3GABuF6INp5ARW0L5k8cgLnjWYiIiIiI\nqGsyeClas2YN5s6di3nz5iEjI+OmH/POO+90+x3tKpuqcbY0EwEOPvBz6Cd2HFFVXVNgxQfXC9GE\nIMybwEJERERERF2X3ulzWq0We/bsQWZmJgAgKioKU6dO7dSLnzlzBlevXsX27duRl5eHFStWYPv2\n7Td8TF5eHlJSUmBiYnIb8buOA3nHIEDAxF4+SlRd11aIymtaMHd8EOZNHCB2JCIiIiKiW9I7UrR6\n9Wr8/PPP8PX1hY+PD/bu3YvVq1d36sWTkpKQkJAAAPDz80NDQwOam5tv+Ji//vWveOGFF24jeteh\n0qjwc/5J2PSxQpzXQLHjiKamXoHlG0+grKYZc8YHYv5EjhARERERUdend6QoNzcXn3/+efv1woUL\nMX/+/E69eHV1dftW3gBgb2+P6upqWFpaAgB27tyJuLi4br+Jw4nCFDSpmjEj+B6YyLr3iNftqqlX\n4LXfClFCIBZMHACJRCJ2LCIiIiIivfSWIrVaDZ1O137mjlarhVarva0vJghC++/r6+uxa9cufPLJ\nJygtLb3huVtJTU29ra9tSNnXctBHagrXFrsumc/QGlq0+PRwFWobNRgRao0BTs04e/as2LF6hd74\n943oZvheIGrD9wLR7dFbikaNGoWZM2di8ODBAIDk5GRMnjy5Uy/u7OyM6urq9uvKyko4OTkBAE6d\nOoWamhrMnz8fSqUSRUVFWLt2LV599dVbvmZMTEynvrYxRQvReESnhWkvHCWqqW9bQ1TbqMGscQFY\nNCmYI0RGkpqa2iXfD0TGxvcCURu+F4ja3M4PB/SWoieeeALx8fFIS0uDRCLB//7v/yIiIqJTLz5s\n2DC8//77mD17NrKysuDi4gILCwsAwMSJEzFx4kQAQElJCV577TW9hairkkqkMJX1vt3NaxtaseKD\nkyipasYDY/xZiIiIiIioW+qwFF24cAEhISFISkoCAAQGBgIAmpubkZSUhLi4OL0vHh0djdDQUMyd\nOxcymQwrV67Ezp07YW1t3b4BA3VP1xpaseKDEyipasIDY/yROCWEhYiIiIiIuqUOS9H333+PkJAQ\nbNy48Q/PSSSSTpUiAHj++edvuA4K+uOOZB4eHvjss8869XokvoZmFVZ8eALFlU24fzQLERERERF1\nbx2WouXLlwMAtm7darQw1PUp1Vqs/iQZRRVNmDayPxZPZSEiIiIiou6tw1I0f/78W97sbtu2zSCB\nqOvS6gS8sy0V2QW1GBntgSX3hrEQEREREVG312EpevbZZ42Zg7o4QRCweXcmkjLKEO7niGfnRkMq\nZSEiIiIiou6vw1IUGxvb/vuUlBRkZGRAIpEgMjIS0dHRRglHXceuY3nYczwf3q7WWP5QLEzkMrEj\nERERERHdFXr3kV6/fj3WrVuHyspKVFRUYPXq1di0aZMxslEXcfxcCTbvzoKDjRnefCQOVua97zwm\nIiIiIuq59J5TlJycjO3bt0MqbetPGo0GCxcuxNKlSw0ejsSXmVeNd788C/M+crz5l6FwsjcXOxIR\nERER0V2ld6RIp9O1FyIAkMvlXFzfSxSWN2D1ltMQBAErFsfC191W7EhERERERHed3pGi0NBQPPbY\nY4iPjwcAnDx5EuHh4QYPRuKqqVfgzY9PoVmhxnPzBiIy0EnsSEREREREBqG3FK1YsQJ79+5FWloa\nJBIJpk2bhkmTJhkjG4mkpVWNtz4+haprCjw4ORhjB3mJHYmIiIiIyGA6LEXLli3D+++/j2effRbv\nvfcepkyZYsxcJBK1Roc1n57BldIGTIrzwcyxAWJHIiIiIiIyqA5L0dWrVzFnzhzk5+djwYIFf3ie\nh7f2PIIg4P2vz+N8bhViQ1yxdEY4148RERERUY/XYSn64osvcPHiRaxevRrPPPOMMTORSLbty8HP\nKUUI9LbDSwtjIJPp3YeDiIiIiKjb67AUWVtbY9CgQZgwYcINB7kCwHvvvfeHx6h725dUgK8OXYJb\nX0u88fBQmPXRu9yMiIiIiKhH6PDO99SpUzh16hR2794NtVrd/rharcbOnTvx9NNPGyUgGd7pC+X4\n4Ns02Fia4s1Hh8LOuo/YkYiIiIiIjKbDUtS/f39UVVUBAGQy2e+fIJfj3XffNXwyMopLhdewbmsK\n5HIZVi4ZAndHK7EjEREREREZVYelyNnZGffeey/q6+uxcOFCY2YiIymrbsb/bj4FtVqL5YtjEdTP\nQexIRERERERGp3cl/eHDh6HRaIyRhYyoqUWFN/+VhPomFR67PwJDwtzEjkREREREJAq9q+mtra0x\nZcoUhISEwMTEpP3xdevWGTQYGY5OJ+CdL86itLoZD4zxx6R4X7EjERERERGJRm8pGjNmDMaMGWOM\nLGQkXx26hJTsCgwMcsaiySFixyEiIiIiEpXeUjRjxgwUFxfjwoULkEgkCA0Nhbu7uzGykQGk5lTg\nywM5cLY3xwsLYiCT8nBWIiIiIurd9K4p+vLLL/Hggw/ixx9/xJ49e7Bo0SLs3LnTGNnoLquobcH/\nfZ4KmVSKVxMHw8bSVOxIRERERESi0ztStGvXLuzduxd9+rSdXdPS0oKHHnoIM2bMMHg4untUai3W\n/vs0mhRqLJsVhQAve7EjERERERF1CXpHiuRyeXshAgALC4sbNlyg7mHTzgxcLq7H+FhvTBzaT+w4\nRERERERdht6RIldXV6xatQrx8fEAgF9//RVubty+uTs5kHwVB5Kvor+HLZbeHyF2HCIiIiKiLkVv\nKVq1ahW2bt2K7777DhKJBJGRkVi0aJExstFdcLmoDh9+lw4rcxO8ljgYfUxkYkciIiIiIupS9JYi\npVKJRx991BhZ6C5raFZhzWdnoNHqsHxxLFz7WoodiYiIiIioy+lwTVFKSgqGDx+OiRMnYsqUKSgs\nLDRmLrpDWp2Ad75IRWVtC+aOD8KgYBexIxERERERdUkdlqK///3v2LJlC5KTk/H666/jnXfeMWYu\nukNfHbyIszmViBngjLnjg8SOQ0RERETUZXVYiqRSKQICAgAAcXFxqK2tNVooujMp2RXYfvAinB0s\n8MKCGEh5QCsRERERUYc6LEUSieSW19Q1ldc0451tqZDLpHgtcTCsLXhAKxERERHRrXS40UJ9fT2S\nkpLarxsaGm64jouLM2wy+tOUai3W/PsMmhRqPD07Cv6edmJHIiIiIiLq8josRTY2Nti4cWP7tbW1\ndfu1RCJhKepiBEHAh9+mI7+kHhOG9MP4ITyglYiIiIioMzosRVu3bjVmDrpDB5Kv4tCZQvh72mLp\njHCx4xARERERdRsdrimi7uNS4TV8+F0GrC1M8FpiLEx5QCsRERERUaexFHVz9U1KrP3sDLQ6HV5c\nMAjODhZiRyIiIiIi6lY6LEVHjx4FAPzyyy9GC0N/jlYn4J1tqai6psD8iQMwcICz2JGIiIiIiLqd\nDtcUrVmzBlKpFOvXr4eZmdkfnudGC+LbeeQyzl2qwqBgF8weFyh2HCIiIiKibqnDUjRv3jxs3rwZ\nJSUlN+xCB3D3ua6goKwB2/blwN66D56bN5AHtBIRERER3aYOS1FiYiISExOxbds2LFiwwJiZSA+1\nRoe/f3kWGq0OT82Ogo0lD2glIiIiIrpdHZai30yfPh0bNmxARkYGJBIJoqKikJiYeNMpdWQcXx26\niPySeoyP9cbgEFex4xARERERdWt6d59buXIlmpqaMHfuXMyePRvV1dV4/fXXjZGNbuJS4TV8fTgX\nTvbmeGR6mNhxiIiIiIi6Pb0jRdXV1Xj33Xfbr8eMGYNFixYZNBTdnFKtxT+2n4VOJ+CZOdGwMDMR\nOxIRERERUbend6RIoVBAoVC0X7e0tECpVBo0FN3c53uzUVTRhKnDfREZ4CR2HCIiIiKiHkHvSNGc\nOXMwadIkhIW1TdXKysrCM888Y/BgdKPMvGrsOpYHd0dLJE4JETsOEREREVGPobcUzZw5E8OGDUNW\nVhYkEgneeOMNuLi4GCMbXadQavCP7ecgAfDcvIEwM9X7fxsREREREXVSp+6u3dzc4ObmZugs1IEt\ne7JQUduCmWMDMMDHQew4REREREQ9it41RSSuszmV2JtUAB83G8yfGCR2HCIiIiKiHue2SlFra+vd\nzkE30dSiwns7zkEmleC5eQNhIpeJHYmIiIiIqMfRW4qWLFnyh8cWLFhgkDB0o4++z0BNfSvmTQhC\nfw9bseMQEREREfVIHa4p2r17NzZs2IDS0lKMHj26/XG1Wg1HR0djZOvVkjJK8UtqMQK87DBzbIDY\ncYiIiIiIeqwOS9G0adMwZcoUrFixAk899VT741KpFM7OzkYJ11vVNymx4Zs0mMqleG7eQMhkXPpF\nRERERGQot9x9TiaTYe3atcjJyUFdXR0EQQAAFBQUIC4uzigBextBELDhmzTUN6mwZFoYvFysxY5E\nRERERNSj6d2S++mnn0Z2djZcXV3bH5NIJCxFBnL0bDGSMsoQ2r8vpo3oL3YcIiIiIqIeT28pKi4u\nxsGDB42RpderqVfgw50ZMDOV4dm50ZBKJWJHIiIiIiLq8fQuVvH19YVKpTJGll5NEAS8t+M8mhVq\nLJkWBte+lmJHIiIiIiLqFfSOFEmlUkyZMgURERGQyX4/J2fdunUGDdbbHEi+irM5lRgY5IyJQ/uJ\nHYeIiIiIqNfQW4ri4+MRHx9vjCy9VnlNMzbvzoSluQmenhMFiYTT5oiIiIiIjEVvKZoxYwYuXbqE\nwsJCJCQkoKGhATY2NsbI1ivodAL+sf0cFEotnp8fib625mJHIiIiIiLqVfSWok8//RQ//PADVCoV\nEhISsHHjRtjY2OCJJ54wRr4eb+/JK8jKr0FcuBtGD/QUOw4RERERUa+jd6OFH374ATt27ICtrS0A\n4OWXX8aRI0cMnatXqG9SYuu+HFiayfH4AxGcNkdEREREJAK9pcjS0hJS6e8fJpVKb7gAvtu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CIAAAAAlkYpAgAAAGBplCIAaEaOHz+usLAwffbZZ1UeHzx48GUZPywsTJWVlZdlrNp88cUXio6O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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f69ccfade90>"
+       "<matplotlib.figure.Figure at 0x7f3927488ad0>"
       ]
      },
      "metadata": {},
@@ -422,16 +420,21 @@
    ],
    "source": [
     "algos = np.linspace(1,20)\n",
-    "plt.plot(algos,\n",
-    "         portfolio_risk_decomposition(budget=0.2, correl=1.0, algo_count=algos)[1])\n",
-    "plt.plot(algos,\n",
-    "         portfolio_risk_decomposition(budget=0.4, correl=1.0, algo_count=algos)[1])\n",
+    "plt.plot(\n",
+    "    algos,\n",
+    "    portfolio_risk_decomposition(budget=0.2, correl=1.0, algo_count=algos)[1]\n",
+    ")\n",
+    "plt.plot(\n",
+    "    algos,\n",
+    "    portfolio_risk_decomposition(budget=0.4, correl=1.0, algo_count=algos)[1]\n",
+    ")\n",
     "plt.ylim([0,1]);\n",
     "plt.title('Percent of Portfolio Risk due to Common Risk')\n",
     "plt.xlabel('Number of Algos in Portfolio')\n",
     "plt.ylabel('Percent of Portfolio of Algos Risk due to Common Risk')\n",
-    "plt.legend(['20% Single Algo Common Risk Budget',\n",
-    "            '40% Single Algo Common Risk Budget']);"
+    "plt.legend(\n",
+    "    ['20% Single Algo Common Risk Budget', '40% Single Algo Common Risk Budget']\n",
+    ");"
    ]
   },
   {
@@ -449,9 +452,8 @@
     "collapsed": true
    },
    "source": [
-    "\n",
     "<div class=\"alert alert-warning\">\n",
-    "<b>TL;DR:</b> even if you can't predict portfolio risk and don't have a risk model to decompose risk, you can form a portfolio with **zero common risk** by hedging the beta exposure to common factors. The most important common factors in the US Equity market are market beta and sector beta. Hedge your beta and be sector neutral if you want a large allocation from any allocator.\n",
+    "<b>TL;DR:</b> Even if you can't predict portfolio risk and don't have a risk model to decompose risk, you can form a portfolio with **zero common risk** by hedging the beta exposure to common factors. The most important common factors in the US Equity market are market beta and sector beta. Hedge your beta and be sector neutral if you want a large allocation from any allocator.\n",
     "</div>"
    ]
   },