Genus 2 curve data - 461.a.461.2

Formats: - HTML - YAML - JSON - 2024-07-27T08:51:24.670223

• g2c_curves •

  Column	Type
  Lhash	text
  abs_disc	bigint
  analytic_rank	smallint
  analytic_rank_proved	boolean
  analytic_sha	smallint
  aut_grp_id	text
  aut_grp_label	text
  aut_grp_tex	text
  bad_lfactors	text
  bad_primes	integer[]
  class	text
  cond	bigint
  disc_sign	smallint
  end_alg	text
  eqn	text
  g2_inv	text
  geom_aut_grp_id	text
  geom_aut_grp_label	text
  geom_aut_grp_tex	text
  geom_end_alg	text
  globally_solvable	smallint
  has_square_sha	boolean
  hasse_weil_proved	boolean
  igusa_clebsch_inv	text
  igusa_inv	text
  is_gl2_type	boolean
  is_simple_base	boolean
  is_simple_geom	boolean
  label	text
  leading_coeff	numeric
  locally_solvable	boolean
  modell_images	text[]
  mw_rank	smallint
  mw_rank_proved	boolean
  non_maximal_primes	integer[]
  non_solvable_places	jsonb
  num_rat_pts	smallint
  num_rat_wpts	smallint
  real_geom_end_alg	text
  real_period	numeric
  regulator	numeric
  root_number	smallint
  st_group	text
  st_label	text
  st_label_components	integer[]
  tamagawa_product	smallint
  torsion_order	smallint
  torsion_subgroup	text
  two_selmer_rank	smallint
  two_torsion_field	jsonb

  
  {'Lhash': '2294054356344303861', 'abs_disc': 461, 'analytic_rank': 0, 'analytic_rank_proved': False, 'analytic_sha': 1, 'aut_grp_id': '[2,1]', 'aut_grp_label': '2.1', 'aut_grp_tex': 'C_2', 'bad_lfactors': '[[461,[1,1,461,461]]]', 'bad_primes': [461], 'class': '461.a', 'cond': 461, 'disc_sign': 1, 'end_alg': 'Q', 'eqn': '[[-306,272,10,-39,-1,1],[1]]', 'g2_inv': "['106720731303787612818432/461','2630293443843585469056/461','73323359651716069824/461']", 'geom_aut_grp_id': '[2,1]', 'geom_aut_grp_label': '2.1', 'geom_aut_grp_tex': 'C_2', 'geom_end_alg': 'Q', 'globally_solvable': 1, 'has_square_sha': True, 'hasse_weil_proved': False, 'igusa_clebsch_inv': "['80664','166117104','3752725952952','1844']", 'igusa_inv': "['40332','40091742','45075737276','52661714805267','461']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '461.a.461.2', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.2458864264726567276756390789225886309416527367578934646', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.6.1'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_maximal_primes': [2, 7], 'non_solvable_places': [], 'num_rat_pts': 1, 'num_rat_wpts': 1, 'real_geom_end_alg': 'R', 'real_period': {'__RealLiteral__': 0, 'data': '0.24588642647265672767563907892', 'prec': 103}, 'regulator': {'__RealLiteral__': 0, 'data': '1.0', 'prec': 10}, 'root_number': 1, 'st_group': 'USp(4)', 'st_label': '1.4.A.1.1a', 'st_label_components': [1, 4, 0, 1, 1, 0], 'tamagawa_product': 1, 'torsion_order': 1, 'torsion_subgroup': '[]', 'two_selmer_rank': 0, 'two_torsion_field': ['5.1.7376.1', [2, -2, 0, 2, -2, 1], [5, 5], False]}
• g2c_endomorphisms •

  Column	Type
  factorsQQ_base	jsonb
  factorsQQ_geom	jsonb
  factorsRR_base	jsonb
  factorsRR_geom	jsonb
  fod_coeffs	jsonb
  fod_label	text
  is_simple_base	boolean
  is_simple_geom	boolean
  label	text
  lattice	jsonb
  ring_base	jsonb
  ring_geom	jsonb
  spl_facs_coeffs	jsonb
  spl_facs_condnorms	jsonb
  spl_facs_labels	jsonb
  spl_fod_coeffs	jsonb
  spl_fod_gen	jsonb
  spl_fod_label	text
  st_group_base	text
  st_group_geom	text

  
  {'factorsQQ_base': [['1.1.1.1', [0, 1], -1]], 'factorsQQ_geom': [['1.1.1.1', [0, 1], -1]], 'factorsRR_base': ['RR'], 'factorsRR_geom': ['RR'], 'fod_coeffs': [0, 1], 'fod_label': '1.1.1.1', 'is_simple_base': True, 'is_simple_geom': True, 'label': '461.a.461.2', 'lattice': [[['1.1.1.1', [0, 1], [0]], [['1.1.1.1', [0, 1], -1]], ['RR'], [1, -1], 'USp(4)']], 'ring_base': [1, -1], 'ring_geom': [1, -1], 'spl_fod_coeffs': [0, 1], 'spl_fod_gen': [0], 'spl_fod_label': '1.1.1.1', 'st_group_base': 'USp(4)', 'st_group_geom': 'USp(4)'}
• g2c_ratpts •

  Column	Type
  label	text
  mw_gens	jsonb
  mw_gens_v	boolean
  mw_heights	numeric[]
  mw_invs	smallint[]
  num_rat_pts	smallint
  rat_pts	jsonb
  rat_pts_v	boolean

  
  {'label': '461.a.461.2', 'mw_gens': [], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [], 'num_rat_pts': 1, 'rat_pts': [[1, 0, 0]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  {'conductor': 461, 'lmfdb_label': '461.a.461.2', 'modell_image': '2.6.1', 'prime': 2}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  {'cluster_label': 'c3c2_1~2_0', 'label': '461.a.461.2', 'p': 461, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '461.a.461.2', 'plot': 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85ZfoFJqXl1e2PwgAuEBRkbRpkwl9CxdK27b5jjdpYgLfoEFSp0705wOc5NcBMCcnR5IUGxvr83psbKwOHDhQ/J6YmJirvjYmJqb46680ceJEjR8/vpSrBQD3OXtWysgwgW/xYt9WLSEhUteu0sCB5tG4sXN1AvDl1wHQy3NFR0/Lsnxeu3L8Wu8pady4cXrqqaeKn%2Bfl5SmBk8AB4IYcOGDP8q1aJRUU2GORkaZFy8CBUr9%2BnLcL%2BCu/DoBxcXGSzCxfrVq1il8/evRo8axgXFycjhw5ctXXHjt27KqZQ6%2BwsDCFhYWVQcUAEHyKiqSNG01/vkWLpC%2B/9B1v0MCe5evWjd26QCDw6wBYv359xcXFadmyZWrTpo0kqaCgQKtXr9akSZMkSZ06dVJubq42bdqke%2B65R5K0ceNG5ebmqnPnzo7VDgCBLDfXNGL27to9ftweq1BB6tLFBL6UFOmuuzh6DQg0jgfAs2fPas%2BePcXP9%2B3bp61btyoqKkp169bV2LFjNWHCBDVq1EiNGjXShAkTVKVKFY0aNUqS1LRpU/Xt21ePPPKI3n77bUmmDUxKSsoN7QAGABi7d5v7%2BBYulNas8d21y9IuEFwcbwOzatUq9erV66rXx4wZo8mTJ8uyLI0fP15vv/22Tp06pQ4dOig1NVWJiYnF7z158qR%2B/etfa8GCBZKkQYMG6Y033tDtt99%2BQzXQBgaAGxUWSuvWmWXdxYulnTt9xxs3NjN8AweaGT%2BWdoHg4XgA9AcEQABuceyYlJZmQl96ulSyC1bFiua4tZQU82jUyLk6AZQtx5eAAQBlx7LMkWreWb5Nm3zP2q1ZU%2Brf3zRlTkoyS70Agh8BEACCzNmz0ooVJvQtWSIdOuQ73rq1meEbMEC65x6zqQOAuxAAASAI7NljZvgWLzbHrpXszVelinTffSb09e8v1a7tXJ0A/IOrA2BqaqpSU1NVVFTkdCkA8KPk50tr15rAt2SJtGuX7/idd5oZvgEDpB49pMqVnakTgH9iE4jYBAIgMHz3ndnAsXix6dF37pw9VrGiacLsDX1NmtCbD8D1uXoGEAD82aVL0oYNZoZvyRLp8899x%2BPiTE%2B%2BAQOkPn0k/v0K4EYRAAHAjxw9Ki1dagJferp0%2BrQ95vGYTRveXbtt2rCBA8DNIQACgIOKiqRPPzWBLy1N2rzZdzwqSkpONqEvOdm0bQGAW0UABIBydvy4md1LSzO/ljxnVzIze/37m0eHDlJIiDN1AgheBEAAKGOXL0tZWSbwpaVJGzf6NmOOjDT38PXrZx61ajlXKwB3IAACQBk4flzKyLBn%2BY4d8x1v2dIOfJ07c84ugPJFAASAUlBU5DvLd%2BWRa9WqmWbM3tBHM2YATiIAAsBNOnbMvpcvI%2BPqe/latpT69jX38jHLB8CfuDoAchIIgB/j0iUzs5eWZlq1ZGVdPcvXp48JfX37SnXqOFcrAHwfTgIRJ4EAuL5Dh8ws39KlZpavZF8%2BSWrd2oS9fv2kTp2Y5QMQGFw9AwgAVyookD75xF7a/eIL3/Hq1e0du8nJ7NgFEJgIgABcb98%2BM8O3dKm0cqV09qw95vFI7drZs3z33ENfPgCBjwAIwHXOnZNWrzaBLz1d2rXLdzwmxszu9e0rJSVJ0dHO1AkAZYUACCDoWZb01Vd24Fuzxiz1eoWEmF263s0brVtzxi6A4EYABBCUTp6Uli83gS89XfruO9/xunVN2EtOlnr3NqdxAIBbEAABBIVLl6TNm%2B3At2mTOYLNq3JlqWdPe2m3SRNzfx8AuBEBEEDAys42rVnS081s35UtWpo1swNft25SeLgzdQKAv3F1AKQRNBBYzp839%2B95%2B/Lt2OE7Xr26OW4tOdls3khIcKZOAPB3NIIWjaABf2VZ0pdfmsCXkSGtXSvl59vjFSpIHTqYwJecLLVvT4sWALgRrp4BBOB/jh2Tli0zgS8jQzp82Hc8IcEOfL17m1k/AMCPQwAE4KiCAikz057l%2B%2Bwz3/EqVczmjaQkE/rYvAEAt44ACKBcWZZpvOyd4fv4Y9OYuaRWrezA17WrFBbmTK0AEKwIgADK3MmT0ooVdujLzvYdj4kx5%2BsmJ5tf4%2BKcqRMA3IIACKDUFRZKGzbYgW/zZjPz5xUaatqyJCWZR8uWnLwBAOWJAAjgllmWtGeP77LumTO%2B72na1G7P0qOHubcPAOAMAiCAm3LqlLRypR369u/3Ha9Rw%2B7J16ePVKeOI2UCAK6BAAjghhQWShs3%2Bi7rljxqrVIlqUsXe1m3TRuWdQHAX7k6AHISCHB9liXt3m335LvWsu5dd9mBr0cP6bbbnKkVAPDjcBKIOAkE8Cq5W3fZMunAAd/x6GjTfNkb%2BljWBYDA5OoZQMDtCgqk9etN2Fu27OrdupUqmT58SUnmPj6WdQEgOBAAARexLGnHDntZd9Wqq5swN2tmB74ePaSqVR0pFQBQhgiAQJA7dsy3CfN33/mO16xpwp73Ubu2M3UCAMqP3y/mvPzyy/J4PD6PuBLHBFiWpZdfflnx8fEKDw9Xz5499dVXXzlYMeCsixdNe5YXXpDuvtucsvGTn0jvvmvCX1iYac8yaZK0ZYuUkyNNnSo99BDhDwDcIiBmAJs3b67ly5cXPw8JCSn%2B36%2B%2B%2Bqpee%2B01TZ48WY0bN9Yrr7yiPn36aOfOnYqIiHCiXKBcWZa0bZu9rLtmjXThgu97Wra0l3W7dZPCw52pFQDgHwIiAFasWNFn1s/Lsiz97W9/04svvqhhw4ZJkt577z3FxsZq2rRpevTRR8u7VKBc5ORIy5fboS8nx3e8Vi17Sfe%2B%2BzhbFwDgKyAC4O7duxUfH6%2BwsDB16NBBEyZM0J133ql9%2B/YpJydHSUlJxe8NCwtTjx49lJmZed0AmJ%2Bfr/z8/OLneXl5Zf4zALfiwgVp7Vq7PcsXX/iOh4ebDRt9%2BpiZvubNJY/HmVoBAP7P7wNghw4d9P7776tx48Y6cuSIXnnlFXXu3FlfffWVcv497REbG%2BvzNbGxsTpwZQOzEiZOnKjx48eXad3Arbh82YQ8b%2BBbu1Yq8W8WSeb%2BPm/g69LF3NsHAMCNCLhG0OfOnVODBg303HPPqWPHjurSpYsOHTqkWrVqFb/nkUce0cGDB7V06dJrfsa1ZgATEhJoBA1HHTpkL%2BkuXy4dPeo7XqeO77JuzZrO1AkACHx%2BPwN4papVq6pFixbavXu3hgwZIknKycnxCYBHjx69alawpLCwMIUxXQKHnT9vL%2BtmZJiNHCVVrSr17GnP8t11F8u6AIDSEXABMD8/X9u3b1e3bt1Uv359xcXFadmyZWrTpo0kqaCgQKtXr9akSZMcrhTwVXJZNyNDWrfOd1nX45HatrV363buLIWGOlcvACB4%2BX0AfOaZZzRw4EDVrVtXR48e1SuvvKK8vDyNGTNGHo9HY8eO1YQJE9SoUSM1atRIEyZMUJUqVTRq1CinSwd0%2BLBZzk1PN8u7Vy7rJiSYsJecbM7YrVHDmToBAO7i9wHw22%2B/1U9%2B8hMdP35cNWvWVMeOHbVhwwbVq1dPkvTcc8/pwoULevzxx3Xq1Cl16NBBGRkZ9ACEIy5etJd109OlL7/0Hfcu6yYlmUeTJizrAgDKX8BtAikLeXl5ioyMZBMIfjTLkr7%2B2oS9jAxp9WoTAr08HqlNGzPDl5TEsi4AwD/4/Qwg4G9OnrSXddPTrz5bNz7enuHr00eKjnamTgAArocACPyAS5ekTZukpUtN4Nu82cz8eVWuLHXvbs/y0YQZAODvCIDANRw8aMLe0qVmti8313e8eXMT%2BJKTOVsXABB4XB0AU1NTlZqaqqKiIqdLgcMuXpTWrLFD39df%2B45Xr27v1k1KMk2ZAQAIVGwCEZtA3Gr3bhP2li6VPv7YnLfrVaGC1KGDCXx9%2B0rt2kkhIc7VCgBAaXL1DCDc5fx5E/SWLpXS0qS9e33H4%2BPtwNenj5n1AwAgGBEAEbQsy8zypaVJS5aYFi0lT96oVEnq2tUEvr59pRYt2LwBAHAHAiCCyoULJugtWWIeV87y1a0r9etnHvfeK9EvHADgRgRABLwDB%2BzAt2KF7718lSqZXbr9%2B5vQ17Qps3wAABAAEXAuXZI2bJAWLZIWL5a2bfMdr13bBL7%2B/c35uszyAQDgiwCIgHD6tNm8sWiRuafv5El7rEIFc8SaN/S1bMksHwAA34cACL%2B1d6%2B0YIG0cKG0dq2Z%2BfOqXt1s3EhJMb9GRTlXJwAAgcbVAZBG0P7l8mVz5NpHH5ngd2Uz5qZNTeAbOFDq1Emq6OrfvQAA3DwaQYtG0E66eFFauVKaP9%2BEviNH7LGQEHPG7qBBJvQ1aOBcnQAABBPmUFDu8vLMjt25c839fGfP2mPVqpnduoMHm6VdmjEDAFD6CIAoF8ePmxm%2BOXOkZcukwkJ7LD5eGjLEhL6ePaXQUMfKBADAFQiAKDNHj5pZvtmzpVWrpJK3WjZpIg0bZoJfu3ZmJy8AACgfBECUKm/o%2B/BDcyLH5cv2WOvWJvQNHy41a%2BZcjQAAuB0BELfs9Glp3jxpxgxzEkfJmb527aQRI0zoYxMHAAD%2BgQCIm3LxomnKPHWq2dBRUGCPtW0rPfCACX716ztXIwAAuDYCIG7Y5cvSunXS%2B%2B9Ls2aZ3bxezZtLI0eaR8OGztUIAAB%2BGAEQP%2BjAAWnyZBP8vvnGfj0hQRo1yjxatnSsPAAA8CO5OgByEsj15eebEzn%2B%2BU9zX5%2B3XXhEhHT//dLPfmaaNLN7FwCAwMNJIOIkkJL27JHeftvM%2BB0/br/eu7f00ENmF2%2BVKk5VBwAASoOrZwBhXL4sLV0q/f3v5lev2rWlhx%2BWfv5zNnMAABBMCIAudv68men729%2Bk3bvt1/v2lR57TOrfX6rI7xAAAIIO/3l3oZMnzWzf3/8unThhXouMlP7zP6XHH6dfHwAAwY4A6CLHj0t//av0xhvS2bPmtfr1pSefNEu9t93mbH0AAKB8EABd4MwZE/z%2B%2Blc7%2BLVqJY0bZ07oYJkXAAB34T/9QayoSPq//5N%2B9ztzRq8k3X239PvfSwMHSh6Ps/UBAABnEACDVFaW9Oij5lfJnM4xYYLp4UfwAwDA3Vzdxjc1NVXNmjVT%2B/btnS6l1BQUSL/9rXTPPSb8RUZKr78uffWVOZuX8AcAAGgEreBpBP3NNybkffaZeT5ypGnxEhvrbF0AAMC/sAQcJFauNBs6Tp%2BWatSQ3nnHnNoBAABwJQJgEJg3T3rwQamwUOrYUZo1S6pTx%2BmqAACAv3L1PYDBYNky6YEHTPgbMUL6%2BGPCHwAA%2BH5BFQDffPNN1a9fX5UrV1bbtm21du1ap0sqU9nZJvxdumRmAKdPlypXdroqAADg74ImAM6cOVNjx47Viy%2B%2BqC1btqhbt27q16%2BfsrOznS6tzPzqV%2Baev3vukd57TwoJcboiAAAQCIJmF3CHDh1099136x//%2BEfxa02bNtWQIUM0ceLE7/3aQNwFnJUltWtnTvHYtk1q0sTpigAAQKAIihnAgoICZWVlKSkpyef1pKQkZWZmOlRV2Zo2zfw6YgThDwAA/DhBsQv4%2BPHjKioqUuwVDe9iY2OVk5Nz1fvz8/OVn59f/DwvL%2B%2Bmvq9lWTpz5sxNfe2tWr/e/Nqrl3ST5QMA4GoRERHyuPSEhKAIgF5X/p9oWdY1/4%2BdOHGixo8ff8vf78yZM4qMjLzlz7kVv/yleQAAgB8nkG79Km1BEQCjo6MVEhJy1Wzf0aNHr5oVlKRx48bpqaeeKn6el5enhISEH/19IyIilJub%2B73v8X72wYMHb%2Bk3Wfv27bV58%2Bbi50OGmJYvr78u/fznN/cZt1qDE59RVtezvL/eXz6jNK6nP/wc/vAZ/vJ7szQ%2Bwx9q8Jfr6Q/XojQ%2Bgz/r1/6MiIiIW/qcQBYUATA0NFRt27bVsmXLNHTo0OLXly1bpsGDB1/1/rCwMIWFhd3y9/V4PDf8B6latWq39JdYSEiIz9f37GkC4OLF0tixN/cZt1qDU58hlf71LO%2Bv96fPkG7tevrLz%2BEvn%2BH0783S%2BAx/qMHL6evpL9fCH66nv/wc/vIZgS4oNoFI0lNPPaX//d//1f/93/9p%2B/btevLJJ5Wdna3/%2Bq//crq0UvGrX/3K5/no0abty/Ll5hi4m/mMW63Bqc8oDcFyLfzhevrLz%2BEvn%2BEPNQTL7%2B/SECzXwh%2Bup7/8HP7yGYEuaNrASKYR9KuvvqrDhw8rMTFRr7/%2Burp37/6DX1eWbWDK8rN/9SvpzTelhATp00%2BlmJhS/Xi/FIgte/w7%2B1zlAAAgAElEQVQZ17P0cC1LF9ezdHE9caWgmQGUpMcff1z79%2B9Xfn6%2BsrKybij8lbWwsDD9/ve/L5Ul5yv9%2Bc9So0bSwYNS//6mKXSwK8vr6UZcz9LDtSxdXM/SxfXElYJqBvBmBfK/jHbulLp2lY4fl1q1khYt4ixgAADw/YJqBtCNmjSRVqyQYmOlzz%2BX2reX1qxxuioAAODPCIBBoGVLacMGKTFRyskxzaFffFEq0esaAACgmKsDYGpqqpo1a6b27ds7Xcotu%2BMOEwLHjJEuX5YmTJDatpXWrXO6MgAA4G%2B4B1CBfQ/gtcyZIz32mHTsmHn%2B059KEyea3cIAAACungEsa999953%2B4z/%2BQzVq1FCVKlXUunVrZWVllfn3HT5c2r5d%2BsUvJI9HmjpVatxYeuEF6eTJMv/2t%2BTHXrNVq1bJ4/Fc9dixY0c5Vh1Y7rjjjmteM/piXd%2BPvWaTJ0%2B%2B5vsvXrxYzpUHlkuXLul3v/ud6tevr/DwcN155536wx/%2BoMuXLztdml%2B7mevG350IipNA/NGpU6fUpUsX9erVS2lpaYqJidHevXt1%2B%2B23l8v3r1FD%2Buc/pUcflZ56Slq7Vpo0SfrHP8zJIb/5jRQVVS6l3LBbuWY7d%2B70mb2tWbNmWZYa0DZv3qyioqLi59u2bVOfPn00YsQIB6vybzdzzapVq6adO3f6vFa5cuUyqzEYTJo0SW%2B99Zbee%2B89NW/eXJ9%2B%2BqkefvhhRUZG6je/%2BY3T5fmtW7lu/N3pXgTAMjJp0iQlJCTo3XffLX7tjjvuKPc62rWTVq827WF%2B9zvpiy%2BkP/xBeu016b/%2By4TB2rXLvaxrupVrFhMTU27hOtBd%2BRf8n//8ZzVo0EA9evRwqCL/dzPXzOPxKC4urqxLCyrr16/X4MGDNWDAAEnmz//06dP16aefOlyZf7uV68bfne7FEnAZWbBggdq1a6cRI0YoJiZGbdq00T//%2BU9HavF4pIEDpS1bpFmzTL/As2elv/xFql/fbBzZutWR0nzcyjVr06aNatWqpd69e%2Bvjjz8u40qDR0FBgT744AP9/Oc/l8fjcbqcgHCj1%2Bzs2bOqV6%2Be6tSpo5SUFG3ZsqUcqwxMXbt21YoVK7Rr1y5J0ueff65169apf//%2BDlfm327luvF3p4tZsHJzcy1JVm5ubql9ZlhYmBUWFmaNGzfO%2Buyzz6y33nrLqly5svXee%2B%2BV2ve4WZcvW9aiRZbVrZtlSfaje3fL%2BvBDyyoocKaum7lmO3bssN555x0rKyvLyszMtB577DHL4/FYq1evLsfKA9fMmTOtkJAQ67vvvnO6lIBxI9ds/fr11pQpU6ytW7daa9assYYPH26Fh4dbu3btKsdKA8/ly5etF154wfJ4PFbFihUtj8djTZgwwemy/N7NXDf%2B7gQB0CqbAFipUiWrU6dOPq898cQTVseOHUvte5SGjRst68EHLSskxA6C8fGW9dJLlpWdXb61lNY1S0lJsQYOHFiapQWtpKQkKyUlxekyAsrNXLOioiKrVatW1hNPPFFGVQWH6dOnW3Xq1LGmT59uffHFF9b7779vRUVFWZMnT3a6NL9WWteNvzvdhSXgMlKrVi01a9bM57WmTZsqOzvboYqu7Z57pBkzpP37zT2CsbHSoUPSH/8o1atnzhieO1cqKCj7WkrrmnXs2FG7d%2B8uzdKC0oEDB7R8%2BXL94he/cLqUgHGz16xChQpq3749vy9/wLPPPqsXXnhBI0eOVIsWLTR69Gg9%2BeSTmjhxotOl%2BbXSum783ekuBMAy0qVLl6t2AO7atUv16tVzqKLvV6eOCX3Z2SYQ9upl5gPT0kxbmdq1pSefNMfNlZXSumZbtmxRrVq1SrO0oPTuu%2B8qJiam%2BMZx/LCbvWaWZWnr1q38vvwB58%2BfV4UKvv9ZCgkJoQ3MDyit68bfnS7j9BSkk9544w2radOmVuPGjUt9CXjTpk1WxYoVrT/96U/W7t27ralTp1pVqlSxPvjgg1L7HmVt1y7Lev55y6pVy/dewZYtLesvf7Gs0r5t7Eau2QsvvGCNHj26%2BPnrr79uzZs3z9q1a5e1bds264UXXrAkWXPmzCnd4oJMUVGRVbduXev55593upSA8X3XbPTo0dYLL7xQ/Pzll1%2B2li5dau3du9fasmWL9fDDD1sVK1a0Nm7cWJ4lB5wxY8ZYtWvXthYtWmTt27fPmjt3rhUdHW0999xzTpfm127kuvF3J67k6gDoVRb3AFqWZS1cuNBKTEy0wsLCrLvuust65513SvXzy0thodk0cv/9lhUaagdBj8eyeve2rH/9y7JOnSqd7/VD12zMmDFWjx49ip9PmjTJatCggVW5cmWrevXqVteuXa3FixeXTjFBLD093ZJk7dy50%2BlSAsb3XbMePXpYY8aMKX4%2BduxYq27dulZoaKhVs2ZNKykpycrMzCzHagNTXl6e9Zvf/MaqW7euVblyZevOO%2B%2B0XnzxRSs/P9/p0vzajVw3/u7ElTgKTsF3FFxZOnVK%2BvBD6f33pcxM%2B/XQUHO/4MiRUkqKVLWqczUCAIDvRwAUAfBm7dsnTZtmHl9/bb9epYo0YID04INSv37mOQAA8B8EQBEAb5VlSV9%2BaTaPzJwpffONPeYNg/ffb2YIb7vNuToBAIBBABQBsDRZlpSVZZaJZ80y7WW8KleWkpPNruKUFKl6dcfKBADA1QiAIgCWFW8YnD3bPPbutccqVjStZoYOlQYN8p/ziAEAcAMCoAiA5cGypC%2B%2BME2l586Vtm3zHb/nHmnwYGnIEKlpU3N%2BMQAAKBsEQBEAnbB7tzRvnjR/vrR%2Bve9YgwYmDA4aJHXpYmYLAQBA6SEAigDotMOHpQULpI8%2Bklas8D12rnp1s3lk4ECpb18pMtK5OgEACBauDoCpqalKTU1VUVGRdu3aRQD0A2fOSBkZJgwuWSKdOGGPVawode9uNpCkpEiNGjlXJwAAgczVAdCLGUD/dOmSWR5euNA8duzwHW/c2ATBAQOkrl1NM2oAAPDDCIAiAAaKPXtMEFy8WFqzRiostMciIqQ%2BfUwY7NdP4jxzAACujwAoAmAgysszS8WLF5ul4qNHfcfvvtvcO9ivn9ShgxQS4kydAAD4IwKgCICB7vJl6dNPTRBcskTavNl3PCpKSkoyYbBvXykmxpk6AQDwFwRAEQCDzZEj0tKlJgxmZEinT9tjHo%2BZHezXzzzuuYc2MwAA9yEAigAYzC5dkjZskNLSzGPLFt/x2283s4N9%2B5pj6uLjnakTAIDyRAAUAdBNDh%2BW0tPNDGFGhnTqlO94y5YmCPbty85iAEDwIgCKAOhWRUXSpk1mZjA93dw7WPJPQ9Wq0r33mkCYnCw1bOhcrQAAlCZXB0AaQaOkY8ekZcvM7GB6%2BtU7ixs0MEEwKckEw4gIZ%2BoEAOBWuToAejEDiCtdvix9/rm9XPzJJ%2BZ%2BQq%2BKFaXOnc1ScVKS1KaNVKGCc/UCAPBjEABFAMQPO3NG%2BvhjEwjT06W9e33Ho6NNI%2BrkZPMrm0kAAP6MACgCIH68vXvtMPjxxyYglpSYaC8Xd%2BsmhYc7UycAANdCABQBELemsNCcWZyRYQJhVpbvZpLKlU0I9AbCxETTjxAAAKf4/V1LDz30kDwej8%2BjY8eOPu/Jz8/XE088oejoaFWtWlWDBg3St99%2B61DFcJtKlaTu3aVXXjE7iY8elaZPlx5%2BWKpdW7p40WwueeYZ02YmPl762c%2BkDz4wTasBAChvfj8D%2BNBDD%2BnIkSN69913i18LDQ1VVFRU8fPHHntMCxcu1OTJk1WjRg09/fTTOnnypLKyshRyA4fAMgOIsmJZ0vbtZnYwI0NavVo6f973Pa1amZnBpCTTe7ByZWdqBQC4R0AEwNOnT2v%2B/PnXHM/NzVXNmjU1ZcoUPfjgg5KkQ4cOKSEhQUuWLFFycvIPfg8CIMpLfr7ZUZyebgLh1q2%2B45Urm9lEbyBkuRgAUBb8fglYklatWqWYmBg1btxYjzzyiI6WaNCWlZWlwsJCJSUlFb8WHx%2BvxMREZWZmOlEucF1hYaaH4KRJ5li6I0ekqVOlMWPM0vDFiyYYllwuHj1amjJFyslxunoAQLDw%2BxnAmTNn6rbbblO9evW0b98%2BvfTSS7p06ZKysrIUFhamadOm6eGHH1Z%2Bfr7P1yUlJal%2B/fp6%2B%2B23r/rM/Px8n/fn5eUpISGBGUA4yrKkr782AXDZMmnVKunCBd/3tGhhzw6yuxgAcLMqOl1ASVOnTtWjjz5a/DwtLa14WVeSEhMT1a5dO9WrV0%2BLFy/WsGHDrvtZlmXJc521s4kTJ2r8%2BPGlVzhQCjweqXlz83jySbNcnJlp3z/42WfSl1%2Bax1//amYTu3a1A2HLljSjBgDcGL%2BaATxz5oyOlNgWWbt2bYVfY4qjUaNG%2BsUvfqHnn39eK1euVO/evXXy5ElVr169%2BD2tWrXSkCFDrhn0mAFEIDp%2BXFq%2B3MwOZmRIV250r1lTuu8%2BEwb79DE7kAEAuBa/CoA34sSJE6pdu7beeecd/exnPyveBPLBBx/ogQcekCQdPnxYderUYRMIgpZlSTt2mDC4bJlpRn3unO97mjWzw2CPHlLVqs7UCgDwP34dAM%2BePauXX35Zw4cPV61atbR//3799re/VXZ2trZv366IiAhJpg3MokWLNHnyZEVFRemZZ57RiRMnaAMD1ygokDZssO8f/PRTc56xV6VKUpcuJgx6zy6%2BgT8aAIAg5dcB8MKFCxoyZIi2bNmi06dPq1atWurVq5f%2B%2BMc/KiEhofh9Fy9e1LPPPqtp06bpwoUL6t27t958802f93wfAiCCzcmT0ooV9gzh/v2%2B41FRZrm4Tx/zqFfPkTIBAA7x6wBYXgiACGaWJe3ZY987%2BPHHUl6e73saN7aXi3v1kv49uQ4ACFIEQBEA4S6XLkkbN9qBcNMmqajIHq9YUerY0Q6E7duzXAwAwYYAKAIg3C0318wKetvN7N3rO3777VLv3na7mTvucKRMAEApIgCKAAiUtG%2BfHQZXrpROn/Ydb9jQd7k4MtKZOgEAN8/VATA1NVWpqakqKirSrl27CIDAFYqKpM2b7eXiDRvMErJXSMjVy8UV/aq9PADgWlwdAL2YAQRuTF6eOaIuI0NKTzebS0oquVzcp49Uv74jZQIAfgABUARA4Gbt32/3HlyxQjp1ynfcu1yclGSWi/njBQD%2BgQAoAiBQGoqKTANq7/2DVy4Xe3cXJyebQNi2LbuLAcApBEARAIGykJdndhenp5sZwiuXi6tXt88uTkqS6tZ1pk4AcCMCoAiAQHnYt8%2BEQe/u4txc3/G77jJBMDmZs4sBoKwRAEUABMrbpUumAbV3M8mmTb5nF4eGSl272rODrVpJFSo4Vy8ABBsCoAiAgNNOnzabSLwzhAcO%2BI7HxppdxcnJ5tfYWGfqBIBgQQAUARDwJ5Yl7d5th8GPP5bOnfN9T%2BvWJgwmJ0udO0thYc7UCgCBigAoAiDgzwoKpMxMEwjT06UtW3zHq1Y1LWa8gbBhQ8njcaZWAAgUrg6AnAQCBJ6jR%2B17B5ctk44c8R2vX98Og/feS%2B9BALgWVwdAL2YAgcB0%2BbL0xRcmDC5dKn3yiVRYaI9XrGiWiJOTpb59zdIxm0kAgAAoiQAIBIuzZ%2B3eg9c6qi4mxmwi6dvX7C6OiXGmTgBwGgFQBEAgWH3zjT07uGLF1ZtJ7r7bhMHkZKlTJ6lSJWfqBIDyRgAUARBwg5KbSZYulbZu9R2vVk3q3dsEwr59OZkEQHAjAIoACLhRTo7ZTLJ0qfn1xAnf8WbN7DDYrZtUubIzdQJAWSAAigAIuF1RkfTZZ1Jampkh3LDB92SS8HDTasYbCBs1cq5WACgNBEARAAH4OnVKWr7czA4uXSodOuQ73qCBCYL9%2Bkk9e3JuMYDAQwAUARDA9VmW9OWXJgimpV3daiYsTOre3YTBvn2lu%2B6iETUA/%2BfqAEgjaAA/1pkz0sqVJgwuXXr1ucX16pkg2L%2B/aUR9223O1AkA38fVAdCLGUAAN8OypJ07TRhMS5PWrJHy8%2B3x0FCzgcQ7O9isGbODAPwDAVAEQACl49w5adUqOxB%2B843vOLODAPwFAVAEQAClz7Kk3bvtMLhq1bVnB/v3NzOE3DsIoDwRAEUABFD2zp83x9SlpUlLlkj79vmO169vgmD//qblTJUqztQJwB0IgCIAAihf3tnBxYtNIFy92pxU4hUWZtrL9O9vHg0bOlYqgCBFABQBEICzzp0zO4uXLDGB8MqdxY0aSQMGmDDYvbsJiABwKwiAIgAC8B%2BWJW3fbsLg4sXSunXSpUv2eNWq5sziAQPMknFCgnO1AghcBEARAAH4r7w8cyqJd7n48GHf8RYtTBgcMEDq2FGqWNGZOgEEFgKgCIAAAoNlSVu32rODGzaY17yqV5eSk%2B3ZwRo1nKsVgH9zdQDkJBAAgez4cSk93YTB9HTp5El7rEIFqUMHEwZTUqSWLWkzA8Dm6gDoxQwggEB36ZK0caMJg4sXS1984Ttep47ZRDJggLmHsGpVZ%2BoE4B8IgCIAAgg%2BBw%2BapeJFi8wO4/Pn7bGwMNNrMCXFBMI77nCsTAAOIQCKAAgguF28aJpQe2cH9%2B/3HW/e3ITBlBQ2kgBuQQAUARCAe1iW9PXXJgguWiRlZkpFRfZ4VJQ5rzglxfxavbpztQIoOxWc/OZz585VcnKyoqOj5fF4tHXr1qvek5%2BfryeeeELR0dGqWrWqBg0apG%2B//dbnPdnZ2Ro4cKCqVq2q6Oho/frXv1ZBybb6AABJZiNI8%2BbSc89Ja9ZIR49KU6dKo0aZ8HfypDRtmnles6Y5keQvf5F27vTdcQwgsDkaAM%2BdO6cuXbroz3/%2B83XfM3bsWM2bN08zZszQunXrdPbsWaWkpKjo3/9kLSoq0oABA3Tu3DmtW7dOM2bM0Jw5c/T000%2BX148BAAErKsqEvalTpSNHTCh87jmpWTMzM7h6tfTss9Jdd0mNG0tPPmnuKeTf2EBg84sl4P3796t%2B/frasmWLWrduXfx6bm6uatasqSlTpujBBx%2BUJB06dEgJCQlasmSJkpOTlZaWppSUFB08eFDx8fGSpBkzZuihhx7S0aNHb2hJlyVgALjavn1mmXjhQmnVKqmw0B6rVs0sEQ8cSM9BIBA5OgP4Q7KyslRYWKikpKTi1%2BLj45WYmKjMzExJ0vr165WYmFgc/iQpOTlZ%2Bfn5ysrKuubn5ufnKy8vz%2BcBAPBVv770xBNSRoZ04oQ0e7b00ENSTIw5oeTDD6XRo83zbt2kV1%2BVduxgqRgIBH4dAHNychQaGqrqV9yFHBsbq5ycnOL3xMbG%2BoxXr15doaGhxe%2B50sSJExUZGVn8SOAwTQD4XhER0vDh0rvvmuPo1q%2BXfvtb02D68mVzZvHzz0tNm9pLxR9/7DtrCMB/lFsAnDp1qm677bbix9q1a2/6syzLkqdES3vPNdrbX/meksaNG6fc3Nzix8GDB2%2B6FgBwmwoVTLuYP/1J%2Bvxz01bmjTekpCQpNFTas0f629%2Bke%2B81s4OjRknTp0unTjldOQCvcuv2NGjQIHXo0KH4ee3atX/wa%2BLi4lRQUKBTp075zAIePXpUnTt3Ln7Pxo0bfb7u1KlTKiwsvGpm0CssLExhYWE382MAAK5Qr570q1%2BZx5kz0rJl0oIFptXM8eMm/E2fLoWESN27S4MGmceddzpdOeBe5TYDGBERoYYNGxY/wsPDf/Br2rZtq0qVKmnZsmXFrx0%2BfFjbtm0rDoCdOnXStm3bdPjw4eL3ZGRkKCwsTG3bti39HwQAcF0REdKwYdLkyVJOju/ScFGRWRZ%2B8kmpQQMpMVEaN07asMEsIwMoP47uAj558qSys7N16NAhDRgwQDNmzFCTJk0UFxenuLg4SdJjjz2mRYsWafLkyYqKitIzzzyjEydOKCsrSyEhISoqKlLr1q0VGxur//7v/9bJkyf10EMPaciQIfr73/9%2BQ3WwCxgAyt7evWZH8YIFpt1MyQbUsbGm%2BfSgQdJ990lVqjhXJ%2BAGjgbAyZMn6%2BGHH77q9d///vd6%2BeWXJUkXL17Us88%2Bq2nTpunChQvq3bu33nzzTZ%2BNG9nZ2Xr88ce1cuVKhYeHa9SoUfrLX/5yw8u8BEAAKF%2BnTklpaSYMpqWZXcVe4eFSnz4mDKakmHAIoHT5RR9ApxEAAcA5BQVmRvCjj0wgzM62xzwes%2BFk8GDzuOsu5%2BoEggkBUARAAPAXliV98YUdBq9s59q4sZkZHDLEBMOQEGfqBAIdAVAEQADwV99%2Ba%2B4b/OgjcwRdyb6CNWuak0gGDzZLxjewtxDAvxEARQAEgECQlyctXWrC4JIl0unT9lh4uJScbMJgSooUHe1cnUAgcHUATE1NVWpqqoqKirRr1y4CIAAEiMJC%2B77Bjz7yvW%2BwQgVzNN3gwWapuH595%2BoE/JWrA6AXM4AAELgsS9q61QTB%2BfPN6SQltWxpguCQIVLr1mZjCeB2BEARAAEgmOzfb4fBtWt9%2Bw3Wq2fPDHbrJlUst/OwAP9CABQBEACC1YkT5ki6%2BfPN/YMXLthjUVFmE8nQoWYTCc2n4SYEQBEAAcANzp835xTPn292Fp84YY9VqWI2kQwdKg0YYMIhEMwIgCIAAoDbXLpkzimeP988DhywxypWlHr0MGFwyBCpdm3n6gTKCgFQBEAAcDPvJpJ588xj2zbf8XvukYYNM4GwcWNnagRKGwFQBEAAgG3PHjMrOHeutGGDCYhezZqZIDhsmNSmDTuKEbgIgCIAAgCu7fBhs6N43jxzEsmlS/ZYvXomCA4bJnXqxLF0CCwEQBEAAQA/7PRpadEiMzN45Y7imBhzv%2BCwYVKvXlJoqHN1AjfC1QGQk0AAADfj/HkpPd2EwUWLfI%2Blu/12cxzdsGFS376cUQz/5OoA6MUMIADgZhUWSh9/bMLg/PnSkSP2WJUqUr9%2B0v33S/37S/wnBv6CACgCIACgdBQVSZmZJgzOnet7RnFoqJSUZGYGBw2SatRwrk6AACgCIACg9FmWlJVlh8GdO%2B2xkBCpZ08zMzhkiBQX51iZcCkCoAiAAICyZVnS11%2BbIDh7tvTFF/aYxyN17SoNH25mBxMSnKsT7kEAFAEQAFC%2B9uyxw%2BDmzb5jHTqYmcHhw6X69Z2pD8GPACgCIADAOdnZJgzOmSN98olv4%2Bm77zZh8P77pUaNnKsRwYcAKAIgAMA/HD5smk7Pni2tXi1dvmyPtWhhguCIEVLTps7ViOBAABQBEADgf44dM2FwzpyrTyFp1swOg82bcyQdfjxXB0AaQQMAAsHJk%2BZIujlzpIwM03vQq0kTEwTvv19q2ZIwiBvj6gDoxQwgACBQnD4tLVwozZplTiMpKLDHGjUyYXDECKlVK8Igro8AKAIgACAw5eWZo%2BhmzZLS0qT8fHusYUN7ZrBNG8IgfBEARQAEAAS%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%2Bvc3YTAlRapa1bkaYVRw8pvPnTtXycnJio6Olsfj0datW696T8%2BePeXxeHweI0eO9HnPqVOnNHr0aEVGRioyMlKjR4/W6dOny%2BvHAAAAkurVk557TvrsM2nnTmn8eNNTsKDAbCYZOVKKjZVGjTI7jUv2HkT5cnQGcMqUKdq3b5/i4%2BP1yCOPaMuWLWrdurXPe3r27KnGjRvrD3/4Q/Fr4eHhiiwxl9yvXz99%2B%2B23eueddyRJv/zlL3XHHXdo4cKFN1QHM4AAAJQNy5K%2B/NKeGfzmG3vs9tulYcNMMOzVS6pY0bk63cYvloD379%2Bv%2BvXrXzcAtm7dWn/729%2Bu%2BbXbt29Xs2bNtGHDBnXo0EGStGHDBnXq1Ek7duxQkxvoWkkABACg7FmWtGmTCYMzZ0qHDtljsbFmF/FPfiJ16sRRdGXN0SXgGzV16lRFR0erefPmeuaZZ3TmzJnisfXr1ysyMrI4/ElSx44dFRkZqczMzGt%2BXn5%2BvvLy8nweAACgbHk8UocO0muvSdnZ0qpV0qOPSlFRZmfxG29IXbpI9etLL7wgffGFvcMYpcvvA%2BBPf/pTTZ8%2BXatWrdJLL72kOXPmaNiwYcXjOTk5iomJuerrYmJilJOTc83PnDhxYvH9gpGRkUpISCiz%2BgEAwNVCQkzLmLfeknJypMWLpf/4D%2Bm228xmkkmTzMkjiYnSK69Ie/c6XXFwKbcAOHXqVN12223Fj7Vr197Q1z3yyCO67777lJiYqJEjR2r27Nlavny5Pvvss%2BL3eK4xT2xZ1jVfl6Rx48YpNze3%2BHHw4MGb%2B6EAAMAtq1TJ7BKeMsXMBH74oWkdExoqff219NJLUsOGUseO0v/8jwmMuDXldrvloEGDfJZpa9eufVOfc/fdd6tSpUravXu37r77bsXFxenIkSNXve/YsWOKjY295meEhYUpLCzspr4/AAAoO1WqmHsBR4wwR9HNmydNny6tWCFt3GgeTz0l3Xuv2U08bOaihHwAAAPcSURBVBg9Bm9Guc0ARkREqGHDhsWP8PDwm/qcr776SoWFhar177NmOnXqpNzcXG3atKn4PRs3blRubq46d%2B5cKrUDAIDyd/vt0sMPSxkZpuH0//yPmQW8fNmcTfzzn5vNI8OHm5NJLl50uuLA4egu4JMnTyo7O1uHDh3SgAEDNGPGDDVp0kRxcXGKi4vT3r17NXXqVPXv31/R0dH6%2Buuv9fTTTys8PFybN29WSEiIJNMG5tChQ3r77bclmTYw9erVow0MAABB6JtvzKzgtGlmidirWjXTh/DFF52rLVA4uglkwYIFatOmjQYMGCBJGjlypNq0aaO33npLkhQaGqoVK1YoOTlZTZo00a9//WslJSVp%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%2BUAAACUKQIgAACAy7AEDAAA4DIEQAAAAJchAAIAALgMARAAAMBlCIAAAAAuQwAEAABwGQIgAACAyxAAAQAAXIYACAAA4DIEQAAAAJchAAIAALgMARAAAMBlCIAAAAAuQwAEAABwGQIgAACAyxAAAQAAXIYACAAA4DIEQAAAAJchAAIAALgMARAAAMBlCIAAAAAuQwAEAABwGQIgAACAyxAAAQAAXIYACAAA4DIEQAAAAJchAAIAALgMARAAAMBlCIAAAAAuQwAEAABwGQIgAACAyxAAAQAAXIYACAAA4DIEQAAAAJchAAIAALgMARAAAMBlCIAAAAAuQwAEAABwGQIgAACAyxAAAQAAXIYACAAA4DIEQAAAAJchAAIAALgMARAAAMBlCIAAAAAuQwAEAABwGQIgAACAyxAAAQAAXIYACAAA4DIEQAAAAJf5/1zLfC30Tlj6AAAAAElFTkSuQmCC'}