Genus 2 curve data - 3125.a.3125.1

Formats: - HTML - YAML - JSON - 2024-04-18T14:35:50.707579

• 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': '2064500687617570194', 'abs_disc': 3125, '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': '[[5,[1]]]', 'bad_primes': [5], 'class': '3125.a', 'cond': 3125, 'disc_sign': 1, 'end_alg': 'Q', 'eqn': '[[0,0,0,0,0,1],[1]]', 'g2_inv': "['0','0','0']", 'geom_aut_grp_id': '[10,2]', 'geom_aut_grp_label': '10.2', 'geom_aut_grp_tex': 'C_{10}', 'geom_end_alg': 'CM', 'globally_solvable': 1, 'has_square_sha': True, 'hasse_weil_proved': False, 'igusa_clebsch_inv': "['0','0','0','4']", 'igusa_inv': "['0','0','0','0','3125']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '3125.a.3125.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.7183136284205647705166034780506424219276594441179021159', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.36.1', '3.1296.1'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_solvable_places': [], 'num_rat_pts': 3, 'num_rat_wpts': 1, 'real_geom_end_alg': 'C x C', 'real_period': {'__RealLiteral__': 0, 'data': '17.957840710514119262915086951', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '1.0', 'prec': 10}, 'root_number': 1, 'st_group': 'F_{ac}', 'st_label': '1.4.D.4.1a', 'st_label_components': [1, 4, 3, 4, 1, 0], 'tamagawa_product': 1, 'torsion_order': 5, 'torsion_subgroup': '[5]', 'two_selmer_rank': 0, 'two_torsion_field': ['5.1.50000.1', [-2, 0, 0, 0, 0, 1], [5, 3], 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': [['4.0.125.1', [1, -1, 1, -1, 1], -1]], 'factorsRR_base': ['RR'], 'factorsRR_geom': ['CC', 'CC'], 'fod_coeffs': [1, -1, 1, -1, 1], 'fod_label': '4.0.125.1', 'is_simple_base': True, 'is_simple_geom': True, 'label': '3125.a.3125.1', 'lattice': [[['1.1.1.1', [0, 1], [0, 0, 0, 0]], [['1.1.1.1', [0, 1], -1]], ['RR'], [1, -1], 'F_{ac}'], [['2.2.5.1', [-1, -1, 1], [0, 0, -1, 1]], [['2.2.5.1', [-1, -1, 1], -1]], ['RR', 'RR'], [1, -1], 'F_{ab}'], [['4.0.125.1', [1, -1, 1, -1, 1], [0, 1, 0, 0]], [['4.0.125.1', [1, -1, 1, -1, 1], -1]], ['CC', 'CC'], [1, -1], 'F']], 'ring_base': [1, -1], 'ring_geom': [1, -1], 'spl_fod_coeffs': [1, -1, 1, -1, 1], 'spl_fod_gen': [0, 1, 0, 0], 'spl_fod_label': '4.0.125.1', 'st_group_base': 'F_{ac}', 'st_group_geom': 'F'}
• 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': '3125.a.3125.1', 'mw_gens': [[[[0, 1], [0, 1], [1, 1]], [[0, 1], [0, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [5], 'num_rat_pts': 3, 'rat_pts': [[0, -1, 1], [0, 0, 1], [1, 0, 0]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  
  • id: 1124
    {'conductor': 3125, 'lmfdb_label': '3125.a.3125.1', 'modell_image': '2.36.1', 'prime': 2}
  • id: 1125
    {'conductor': 3125, 'lmfdb_label': '3125.a.3125.1', 'modell_image': '3.1296.1', 'prime': 3}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  {'cluster_label': 'c5_1~4', 'label': '3125.a.3125.1', 'p': 5, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '3125.a.3125.1', 'plot': 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nDmMi98DQEQAIBztGuXNHeu6elbs6bwfNWq0q23mtB31VUs1QbfRQAEYBtJSUlKSkpSnneJBKACbdkiffihNG%2Be9M03hecDAqQuXaS775ZuuUWqVs26GoGzxVrAAGyHtYBREXJzzQCOBQtM%2B/HHwo%2B5XFLHjtLtt5ul2iIjrasTOB/0AAIA8IcdO6TFi6WFC6XPPy8cyCGZwRydO5tevptukqKirKsTKC0CIADAsX75RVq2TPryS2nJksIJmr1q1pS6d5d69JCuu06KiLCkTKDMEQABAI6Qlydt3GjW31250izDtnNn8ccEBUmJiVLXribwtWolBQZaUi5QrgiAAAC/k58vbd0qrVsnrV1rBm2sXVu4EodXQIDUsqXUqZO5vNuxoxQWZknJQIUiAAIAbC0jQ9q0yfTubdggff%2B9aYcOnfzYsDCpbVupXTvpyivNlsAHJyIAAgB83tGjZoDG1q1msuUtW8yo3B9/lPbuPfVzQkKkZs3MZdzWraU2baTGjbmkC0gEQAB2sn699NJL0qJF5vihh6QRI6RGjaytC6Xi8Uj790u//irt3m0GZvz8c2HbsUP67beSXyM2VrrsMqlpU%2BmKK0zwa9TI3NMH4GTMAwjAHhYsMEss5OTILSlCUqak8GrVTCBs187iAuHl8UhHjphQl5Fh2r590u%2B/m966vXulPXuk9HQpLc1ss7PP/Lrh4VKDBqZdeqkJeE2amC2TLwPnhgAIwPfl5Ej16pnUIBUPgJLp%2Btm40br6bCovTzp2zHx5jx0r3o4cMZddjx41Aye87dChwpaVZebJc7ulzEzp4EGzPXDATKJ8rmrVki66SIqJMS021rS4OKl%2BfTMli8tV9l8HwInoHC8Fj8ejrKwsq8uAj/J4zH%2BC2dmmHTtmtjk5hS03t3CbmysdP25abq75z9l7nJdnWn5%2B8X3vcX6%2Beb/TbYs27zlvjSfWfOL5U/2JeL5/Np7v8%2BK3f6z7/gh/kgmARbfatEkv37BEuy5sfX5vcI5O93U73X7RrbedeOw9V/R75v0en655fxZO1Yr%2BHHl/vrzN%2B/NX3n/%2BBwVJF1xggpu31aolXXihVLu22UZFmVU0oqKk4OCSX49/blHWwsLC5HLoXxX0AJaCdzkqAABgP05eTpIAWArl1QOYkJCgNWvWlPnr%2Btt7uN1uxcTEaPfu3Wf8Bc7ONjeR//Zb4T1He/aYe5F%2B/920ffvMvUpncy/S%2BQgKMqMSK1UyPR2VK5t97/annzaqefPLFRRkHlupkhmtGBhojr37AQGmefeLnnO5CrfeVvT4zTdn6t57%2BxVcRvOe9%2B4X3Za0fybTpr2uwYOHlO4LVsTFqZ/pzg96Fxy7JcVI2q0/LgFLmtF/ufZENiuz95SkadNe05Ah95f4mLP5ep3u6%2Bxtkya9ouHDHz7l9%2BzE7%2B3pfgaK/ox4j70/R0FBUv/%2Bd%2Bvdd2erUqXiP4OVK5v9oKDSX171pd/50vKHfyMr6j0q4n3K63vv5B5ALgGXgsvlKpd/hAIDA8v9Hzd/eQ9JCg8PV2houH7%2B2UwRsW2bWc5p587CUYSnmybidIKCpBo1TKteXdqwYYV69OioiAhzI3p4uJk7LCzM3HweGmpa1aqFrUoVE/i8LSCg5PeMjx%2Bk1as3n/fX4WwsXjxVEyb8pVzfQ5I%2B/vgNjR//17J7wbxeUv160q5dxU6H/9HUurWGz%2BhYdu/3h48/Tta4cU%2BU%2BeueaM6cORo58plyfY%2BqVbepbVv/%2BZ33h3%2B//OU9KvJ9KuJ77xQEQB80bNgw3uM0cnPN/F8bNkjr1gVLmqu2bUO1fbu5p6kkISFS3bpSdLTZRkUV3n904YXm3iRvCwsr3huSlPS9hg0r%2B4BRlF2/JxXyPoGB0ttvm0VZT%2Bx1v/BCKTm5bN/vD7b9evnxe1QUf/l6%2BdPPMMoWl4Dhs3JyzGz%2B33xjlnNat87M9n%2B6oBccbEYKXnKJ2davXziKsF4905vn0J5%2B/7Fzp5SUpAOffqoamzdr7/Dhqv3kkybJw%2B9577t28n1bTsX3vuzRAwifkZFhFmdfsUJatcoEvlPdj1etmnT55VKTJnn69dfFevDBq9W0aWXFxDDDv9%2B7%2BGLpn//U8SeeMD1/Tz1lhpPCEYKDgzV69GgFn2m4MPwO3/uyRw8gLHP4sLRsmfT559IXX5jevhPVqCElJJilnFq1kpo3NxngTPfTwb/RGwAApUMPICrUzz9L8%2BebRR2WLTu5h69JE6ljR6lDB7OwQ4MGXLYFAKCsEQBR7rZuld57T5o71yzlWlRsrNS1q9Sli9S5s7mqBwAAyhcBEOVi3z5pzhxp1izp228LzwcESFdeKd1wg3T99VLjxvTwAQBQ0QiAKDP5%2BeZevmnTpI8/LlwLNDBQuuYa6bbbpJ49uWcfAACrcSs9Si0rS5o0yfTmdetmLvXm5kotW0qvvGJW31i4UBo06PzCn8fj0ZgxYxQdHa0qVaqoU6dO2rRpU4nPGTNmjFwuV7EWxVQhgM979dVXFRcXp5CQELVq1UorVqw47WOTk5NP%2Bj13uVw6duxYBVaM8rR8%2BXLdcMMNio6Olsvl0kcffWR1SX6DAIjzlp4ujRghxcRIDz8spaaaFTKGDZO%2B%2B05au1Z66KHS39f3j3/8QxMmTNCUKVO0Zs0aRUVFqWvXrmdchu%2Byyy5TWlpaQduwYUPpCgFQrt59910NHz5cI0eO1Pr169WxY0d1795du05YAaao8PDwYr/naWlpCgkJqcCqUZ4OHz6sZs2aacqUKVaX4ne4BIxzlpYm/f3v0uuvS94/tBs1MiGwTx8zT19Z8Xg8mjhxokaOHKlbbrlFkjRz5kxFRkbq7bff1pAhp19vNigoiF4/P5OUlKSkpCTl5eVZXQrKwYQJEzRw4EANGjRIkjRx4kQtXLhQU6dO1fjx40/5HHr3/Vv37t3VvXt3q8vwS/QA4qwdPGh6/C65xFzaPXZMattW%2BugjafNmaejQsg1/krRjxw6lp6erW7duBeeCg4N11VVXadWqVSU%2BNzU1VdHR0YqLi1Pv3r21ffv2si0OFW7YsGHavHlzhSxuj4qVk5OjtWvXFvtdl6Ru3bqV%2BLt%2B6NAhxcbG6qKLLlKPHj20/sSpBgCcEgEQZ3T8uJSUZObke%2BEF6ehRKTFRWrRI%2BuorM7CjvCZmTk9PlyRFRkYWOx8ZGVnwsVNp27atZs2apYULF2r69OlKT09X%2B/btlZGRUT6FAiiVffv2KS8v75x%2B1xs3bqzk5GTNnz9fc%2BbMUUhIiDp06KDU1NSKKBmwNQIgSrRypVmB48EHzVJt8fFmIudVq8z8fWU9hcvs2bNVrVq1gpb7x1Bi1wlv5PF4TjpXVPfu3XXrrbeqadOm6tKli/7zn/9IMpePAfiuc/ldT0xM1D333KNmzZqpY8eOeu%2B993TppZdq8uTJFVEqYGvcA4hTcrulJ5%2BUXnvNHNeoIT33nDR4sBRUjj81N954o9q2bVtwnP3HUiHp6emqU6dOwfm9e/ee1FNQktDQUDVt2pSeAcBH1apVS4GBgSf19p3L73pAQIASEhL4PQfOAj2AOMmSJVLTpoXhb8AA6aefpAceKN/wJ0lhYWFq0KBBQYuPj1dUVJQWL15c8JicnBwtW7ZM7du3P%2BvXzc7O1g8//FAsRALwHZUrV1arVq2K/a5L0uLFi8/6d93j8SglJYXfc%2BAs0AOIArm50tNPS//8p%2BTxSHFx0r//bZZos4rL5dLw4cM1btw4NWzYUA0bNtS4ceNUtWpV3XXXXQWPu%2Baaa3TzzTfrwQcflCQ9/vjjuuGGG1SvXj3t3btXzz//vNxut/r162fVpwLgDB599FH16dNHrVu3Vrt27TRt2jTt2rVL999/vySpb9%2B%2Bqlu3bsGI4LFjxyoxMVENGzaU2%2B3WpEmTlJKSoqSkJCs/DZShQ4cOaevWrQXHO3bsUEpKimrUqKF69epZWJn9EQAhSdq9W7r9dmn1anN8333ShAllP6r3fDzxxBM6evSoHnjgAR04cEBt27bVokWLFBYWVvCYbdu2ad%2B%2BfQXHv/zyi%2B68807t27dPtWvXVmJiolavXq3Y2FgrPgUAZ%2BGOO%2B5QRkaGnn32WaWlpenyyy/Xp59%2BWvB7u2vXLgUUGXF28OBBDR48WOnp6YqIiFCLFi20fPlytWnTxqpPAWXs22%2B/VecivRCPPvqoJKlfv35KTk62qCr/4PJ4PB6ri4C1li%2BXevWSfv9dioiQ3nhDuvlmq6sCTs/tdisiIkKZmZkKDw%2B3uhwAsB3uAXS4N96QunQx4a95c2ndOsIfAAD%2BjgDoUB6P9OyzZoBHbq65/LtypVS/vtWVAQCA8kYAdKD8fLNs2%2BjR5vipp6Q5c6SqVa2tCwAAVAwGgThMfr50//3S9OlmEucpU8z0LoAdsBYwAJQNBoE4iMcjDRsmTZ1qlm5LTpb69LG6KuDcMQgEAEqHHkAHefppE/5cLmnWLOnuu62uCAAAWIF7AB3i9delcePM/muvEf4AAHAyAqADfP65ufQrSWPHmvV8AQCAcxEA/dzOndIdd0h5eeZ%2Bv2eesboiAABgNQKgH8vJkW67Tdq/X0pIkKZNM/f/AQAAZyMA%2BrGRI6Vvv5Vq1JDmzpVCQqyuCAAA%2BAICoJ9avlx66SWzP2OGVK%2BetfUAAADfQQD0Q0ePmiXePB5p4ECpZ0%2BrKwIAAL6EAOiHnn9e2rZNqltXmjDB6moAAICvIQD6ma1bpRdfNPuTJ0sskgAAAE5EAPQzTz5pRv9ee610001WVwOUraSkJMXHxyshIcHqUgDA1lgL2I98/bWUmGjW%2Bf3%2Be%2Bmyy6yuCCgfrAUMAKVDD6AfGT3abPv2JfwBAIDTIwD6iXXrpIULpcBAVvsAAAAlIwD6Ce/Aj969pfr1ra0FAAD4NgKgH0hPl95/3%2Bw/%2Bqi1tQAAAN9HAPQDb7whHT8utWsntWxpdTUAAMDXEQBtzuORkpPN/n33WVoKAACwCQKgza1bJ23ZIlWpIvXqZXU1AADADgiANjd3rtn26CGFhVlbCwAAsAcCoM3Nn2%2B2N99sbR0AAMA%2BCIA2tnu3tHmzWfnjuuusrgYAANgFAdDGliwx24QE6YILrK0FqAisBQwAZYMAaGMrVphtp06WlgFUmGHDhmnz5s1as2aN1aUAgK0RAG3s66/Ntl07a%2BsAAAD2QgC0qWPHpB9%2BMPutW1tbCwAAsBcCoE39%2BKOUlyfVqCFFR1tdDQAAsBMCoE39%2BKPZNmkiuVzW1gIAAOyFAGhTO3aY7SWXWFsHAACwHwKgTf3yi9nGxFhbBwAAsB8CoE3t2WO2UVHW1gFn83g8GjNmjKKjo1WlShV16tRJmzZtKvE5Y8aMkcvlKtai%2BEEGgApFALSpAwfMtkYNa%2BuAs/3jH//QhAkTNGXKFK1Zs0ZRUVHq2rWrsrKySnzeZZddprS0tIK2YcOGCqoYACARAG3L%2B/9rWJi1dcC5PB6PJk6cqJEjR%2BqWW27R5ZdfrpkzZ%2BrIkSN6%2B%2B23S3xuUFCQoqKiClrt2rUrqGoAgEQAtK3sbLMNDra2DjjXjh07lJ6erm7duhWcCw4O1lVXXaVVq1aV%2BNzU1FRFR0crLi5OvXv31vbt20t8fHZ2ttxud7EGADh/BECb8njMNoDvICySnp4uSYqMjCx2PjIysuBjp9K2bVvNmjVLCxcu1PTp05Wenq727dsrIyPjtM8ZP368IiIiCloMo58AoFSIDzblDX55edbWAeeYPXu2qlWrVtByc3MlSa4TJqL0eDwnnSuqe/fuuvXWW9W0aVN16dJF//nPfyRJM2fOPO1zRowYoczMzIK2e/fuMviMAMC5gqwuAOenShWzPXrU2jrgHDfeeKPatm1bcJz9x30I6enpqlOnTsH5vXv3ntQrWJLQ0FA1bdpUqampp31McHCwgrnfAQDKDD2ANuUd/HGGwZZAmQkLC1ODBg0KWnx8vKKiorR48eKCx%2BTk5GjZsmVq3779Wb9udna2fvjhh2IhEgBQvgiANlWzptnu22dtHXAul8ul4cOHa9y4cfrwww%2B1ceNG3Xvvvapataruuuuugsddc801mjJlSsHx448/rmXLlmnHjh36%2Buuv1atXL7ndbvXr18%2BKTwMAHIlLwDblvcJWwr32QLl74okndPToUT3wwAM6cOCA2rZtq0WLFimsyPxE27Zt074if6n88ssvuvPOO7Vv3z7Vrl1biYmJWr16tWJjY634FADAkVwej3c8KezkpZekxx%2BX7rhDeucdq6sBKpbb7VZERIQyMzMVHh5udTkAYDtcD9znAAAZFElEQVRcArapSy4x261bra0DAADYDwHQpho3Ntsff5Ty862tBQAA2AsB0KYaNJBCQqTDh%2BkFBAAA54YAaFNBQVKzZmb/22%2BtrQUAANgLAdDGvHPynmHZVQAAgGIIgDZ21VVmu3SptXUAAAB7IQDaWKdOZk3gzZsllkaFEyQlJSk%2BPl4JCQlWlwIAtsY8gDbXvr301VfS1KnS/fdbXQ1QMZgHEABKhx5Am%2BvZ02znzrW2DgAAYB8EQJu77TazXbpUSkuzthYAAGAPBECbq19fSkw0k0G/9ZbV1QAAADsgAPqBAQPMdvp0VgUBAABnRgD0A3feKYWHS6mp0qJFVlcDAAB8HQHQD1SrVtgL%2BOKL1tYCAAB8HwHQTwwfbpaH%2B%2BIL6euvra4GAAD4MgKgn4iNle65x%2ByPGmVtLQAAwLcRAP3IM89IlSqZ%2BwA//9zqagAAgK8iAPqR%2BvWloUPN/iOPSMePW1sPAADwTQRAPzNqlFSjhrRxozR5stXVAGWLtYABoGywFrAfmj5dGjxYCg2VNm0y9wcC/oS1gAGgdOgB9EMDB0odO0qHD0v9%2BzM5NAAAKI4A6IcCAqQZM6SqVc0awRMmWF0RAADwJQRAP9WggfTyy2Z/xAhp9Wpr6wEAAL6DAOjH7rtPuu02Mxr4ttukPXusrggAAPgCAqAfc7mkf/1LatRI%2BuUX6eabpWPHrK4KAABYjQDo58LDpfnzperVpa%2B%2Bkvr1Y1AIAABORwB0gEsvlebNM6uEvPee9Je/SEz%2BAwCAcxEAHaJzZ2nWLHNZ%2BNVXpSeeIAQCAOBUBEAH6d1beu01s//ii9JjjxECAQBwIgKgwwwebHoAJTNNzKBBrBkMAIDTEAAdaOhQM1G0d8Lonj2lrCyrqwLOjLWAAaBssBawg338sbksfOyYdMUV5vjii62uCjgz1gIGgNKhB9DBevaUvvxSioyUvv9eat1aWrTI6qoAAEB5IwA6XNu20po1JvxlZEjXXSeNHCnl5lpdGQAAKC8EQCgmRlqxQhoyxIwKHjdOuvJK6aefrK4MAACUBwIgJEkhIWaKmHfflSIipG%2B%2BkZo3N9PFMEoYAAD/QgBEMbffLm3YIHXpYgaH/PWv5jLx119bXRkAACgrBECcJCbGDAaZPt2sIbxunZSYKA0YIKWlWV0dAAAoLQIgTsnlMpNE//ij1LevOffGG1LDhtLo0ZLbbW19AADg/BEAUaLISGnmTGnVKtMLePiw9OyzUv360gsvMIE0AAB2RADEWWnXzoTA99%2BXGjUyU8aMGCHFxkqjRkl791pdIQAAOFsEQJw1l0vq1UvauFGaNcsEwQMHpOeek%2BrVkwYOlNavt7pKAABwJgRAnLOgIKlPH2nTJtMj2KaNlJ1t1hVu2dJcKp4xQzp0yOpK4W9YCxgAygZrAaPUPB7pq6%2BkyZOluXML5w0MDZVuvVW6%2B27p6qtNcATKAmsBA0DpEABRpvbsMaOFZ8yQUlMLz9euLd1yiwmEnTpJlSpZViL8AAEQAEqHAIhy4e0VfOst6b33zKARr4gIs%2Bbw9ddL114rXXihdXXCngiAAFA6BECUu9xcackS6YMPpI8%2Bkn7/vfjHW7SQrrlG6txZ6thRCguzpk7YBwEQAEqHAIgKlZdn1hlesED69FMpJaX4xwMCzBrEV15ppp5p00aKizMjkAEvAiAAlA4BEJZKTze9g198IS1dKu3YcfJjatWSWrUyI4ybN5eaNZMaNJACAyu%2BXvgGAiAAlA4BED7ll1%2BklStNW73a9BDm5p78uJAQqXFj6bLLzLZxY%2BnSS6VLLjGjj%2BHf3Pv3K6JmTQIgAJwnAiB82rFj0vffS2vXmkmmU1LMRNRHj57%2BOVFRJgjGxUkXX2wmqY6JMa1uXTMIhUvK1pk3b55ef/11rV27VhkZGVq/fr2aN29%2Bdk9euFB64QW5v/xSEZIyu3ZV%2BJgxUvv25VkyAPgdAiBsJy/PXCretEn64QfTfvrJTDuzf/%2BZnx8aKtWpY1pkpGkXXmimqqlVy7SaNaUaNaQLLpCqViUwlqU333xTO3bsUHR0tO67776zD4DJydKAAZLHI7dkAqCk8EqVpA8/NMPKAQBnhQAIv7J/v7RtmwmIO3ZIO3dKu3ZJu3ebdvDgub9mUJBUvbrpOfS2sDApPFyqVq2wVa1qWmioVKVKYQsJKWzBwaZVrly8VapkBsA4yc6dOxUXF3d2AfDIESk6WsrMlKTiAVAy3b1btzrviwgA54kACEc5fFj67TcpLc0MQNmzx7S9e830NPv2mZaRYdY59q5qUhECAkwQrFTJhM5KlcxAl8BAc%2Bzd97aAgOJbl8vsn2rrbUWPpZP3T7Utab%2Boc%2B0lPXLkiL744nP96U9XKSIiosTHXvnL23pi/d0FxycFQEkj2i/Tppp/OrcizlFJn%2BOZvl6n%2B5qfqhX9/p2unfhz4P0ZKbo98eepUqWT//jw/lESHGz%2BSPH%2B0VKlivmDplIlesABf8TiXHCU0FCpYUPTzsTjMYHxwAHTc5iZaZrbLWVlFbbDh826x4cPm46qw4fNPYpHj5p7GL3b7Ozi7cQ/vfLzCz/mDFUl3ajly8/8yHo687X9n1bt0yelLwonCAws7N2uVs38Dnl7vb094RERhdsLLjA95t5bKLy3U1SpYvVnAqAoAiBwGi5X4X90MTFl%2B9oej7mXMSfHtNxc044fL76fl1e4PVXLzzfNu%2B/xFN/m5xe%2BX9GPecPniftFtyXtl3SuqNWrV2vWrFkFx8OHD9ell14qSdq3b5/%2B9rcnNWrUaNWrV6/E16m9sZE0seT3uuu5eF1fp%2BTHnMm5Xg858fGn%2BnqdanviftFW9HtU9Htc9HvqPX%2Bqn4njx4u3E3%2BuvD9zOTmFf3Dk5Jg/Urzt6NHCGvPyCv/Y2bPn3L4%2BRYWGmvtsa9c299x677%2BtU8dc3S/aWCoSKH9cAgZQbrKysrSnSGqoW7euqvzRFXRO9wB6PGbOnx9%2BkHSKS8BXX20mk0SZ8HhMKDx61PRqe3u2vb3dhw4V7wX39o5nZpre8gMHTNu/37S8vLN/b5fLjOSvV8%2B0iy82t3jWr2/axRcTEIGyQA8ggHITFhamsLJY28/lkt5/X%2Bra1dzAWVSDBmaEMMqMy1V4X2D16qV7LY/HBEPv/bXee2737jX34aanm/tyf/tN%2BvVX00uZlmba11%2Bf/HqBgSYEXnqp1KiRafHxptWqVbpaASehBxBAhdq/f7927dql3377Tddff73eeecdNWrUSFFRUYqKiir5yQcPSjNnyv3pp4pYtEiZr7yi8EGDzA1qsL38fDMYa/duM3r/559N2769sJU0B2hkpHT55dIVV5hVg1q0kJo0MYNgABRHAARQoZKTk9W/f/%2BTzo8ePVpjxow5q9dgKThn8nhMz%2BCWLab9%2BKNpP/xgpnw6lZAQEwhbtzZriycmmkFgzBgEpyMAArAdAiBOdOiQtHmztGGDWT0oJcU0t/vkx15wgdSundShg9Sxo5SQYIIi4CQEQAC2QwDE2cjPNxPDr10rffONaevWnXwZOTjYBMLOnaVrrjE9hQw0gb8jAAKwHQIgzldurvTdd9LKldL//ietWHHy9DZhYSYMXnut1L27GYUM%2BBsCIADbIQCirHg85n7CpUulJUtMy8go/pgmTaQePaQbbpDatzcjkQG7IwACsB0CIMpLfr65d3DhQum//zU9hUXnMaxVS%2BrZU7rlFqlLF7OcHmBHBEAAtkMAREU5eNCEwQULTDt4sPBj1atLN90k9e5t7h1kuhnYCQEQgO0QAGGF3Fxp%2BXJp3jzT0tMLP3bhhdIdd0h9%2B0qtWpnJtAFfRgAEYBtJSUlKSkpSXl6etmzZQgCEZfLyzCCSd981i9Ts21f4sfh46d57TRiMjLSsRKBEBEAAtkMPIHxJbq60eLH05pvSRx9Jx46Z80FBZuDIkCFmFUMmn4YvIQACsB0CIHxVZqb03nvSv/9dfC3j%2BvWl%2B%2B%2BXBgyQata0rj7AiwAIwHYIgLCDjRul6dOlmTNNMJTMiiP33CM9/LBZtxiwCgEQgO0QAGEnR45I77wjTZkirV9feL5rV%2Bmxx6Ru3Rg0gorHHQkAAJSjqlXNpd%2B1a83KI716mfsBFy%2BWrrtOat5cevtt6fhxqyuFkxAAAQCoAC6XdOWVZtTwtm3mMnBoqPT999Ldd0uNGplLxjk5VlcKJ%2BASMADb4RIw/MWBA1JSkvTKK4VTycTESE89JfXvLwUHW1sf/Bc9gAAAWOSCC6Snn5Z27pQmTJDq1JF275aGDpUuvVT617%2B4NIzyQQAEAMBioaHSI49I27dLkydL0dHSrl3SffeZiaXfecesUwyUFQIgAAA%2BIiREevBBaetW0yNYu7aUmirdeaeUkCB98YXVFcJfEAABAPAxVaqYHsFt26SxY6WwMGndOqlLF%2BnPf5Y2b7a6QtgdARCAbSQlJSk%2BPl4JCQlWlwJUiLAwadQoEwT/8hezvNxnn0lXXCENGyZlZFhdIeyKUcAAbIdRwHCq1FTpiSfMmsOSGUTy3HNmveGgIGtrg73QAwgAgE00bCh9%2BKG0ZInpBTxwwNwz2Lq1tHKl1dXBTgiAAADYTOfOZmWRpCTTC/jdd2aS6YEDC%2BcTBEpCAAQAwIaCgqQHHpC2bDHBT5JmzJAaN5aSkyVu8EJJCIAAANhYrVpmwuiVK6WmTc3AkP79zYjhrVutrg6%2BigAIAIAfaN/eXBb%2B%2B9/NfIJLlphA%2BNJLUl6e1dXB1xAAAQDwE5UqmVHCGzdK11wjHTsmPf641KGD9MMPVlcHX0IABADAz1xyibR4sTR9uhQeLn39tdSihfTii/QGwiAAAgDgh1wuadAgadMm6brrpOxs6a9/NSOId%2BywujpYjQAIAIAfu%2Bgi6dNPTW9gtWrSihVmDkFGCjsbARAAAD/n7Q38/nszX%2BChQ2ak8O23S/v3W10drEAABGAbrAUMlE5cnPTll9K4cWYewblzpWbNpOXLra4MFY21gAHYDmsBA6X37bfSXXeZ9YUDAqRRo6Snn5YCA62uDBWBHkAAAByodWtp3TqpXz8pP18aM0bq2lVKS7O6MlQEAiAAAA5VrZoZDPLmm1JoqLR0qZkuZskSqytDeSMAAgDgcPfcY1YRadpU2rPH9AT%2B3/%2BZnkH4JwIgAABQo0ZmwugBA0zwe/ppqWdP6eBBqytDeSAAAgAASVKVKtK//y39619ScLC0YIG5V3DDBqsrQ1kjAAIAgGIGDpRWrZJiY6Vt26TEROn9962uCmWJAAgAAE7SsqW5L7BrV%2BnIETNp9MiR3BfoLwiAAADglGrWNMvIPf64OR43TrrpJsnttrYulB4BEAAAnFZQkPTPf0pvvWXuC/zkE6l9e2nHDqsrQ2kQAAEAwBndfbe0YoVUp460aZPUpo30v/9ZXRXOFwEQgG2wFjBgrYQEac0ac3/gvn3SNddIs2dbXRXOB2sBA7Ad1gIGrHX4sNS3rzRvnjkeM8asJexyWVoWzgE9gAAA4JyEhpppYf76V3M8ZozUv7%2BUk2NpWTgHBEAAAHDOAgKkf/xDeu01KTBQmjlTuv56RgjbBQEQAACctyFDzMjg0FDp88%2BlP/1JSkuzuiqcCQEQAACUSvfu0rJlUmSk9N13ZpqYn36yuiqUhAAIAABKrVUrs3xcw4bSzp3SlVeaEcPwTQRAAABQJurXl1aulFq3NtPEdO4sLV5sdVU4FQIggPM2b948XXvttapVq5ZcLpdSUlLO%2BJzk5GS5XK6T2rFjxyqgYgDlrXZtaelSs4bw4cNmYMjcuVZXhRMRAAGct8OHD6tDhw564YUXzul54eHhSktLK9ZCQkLKqUoAFa1aNTMwpFcvKTdXuuMOacYMq6tCUUFWFwDAvvr06SNJ2rlz5zk9z%2BVyKSoqqhwqAuArgoOld96Rhg6Vpk%2BXBg6UsrKkhx%2B2ujJI9AACsMChQ4cUGxuriy66SD169ND69etLfHx2drbcbnexBsD3BQZKr78uPf64OR4%2BXHr%2BeWtrgkEABFChGjdurOTkZM2fP19z5sxRSEiIOnTooNTU1NM%2BZ/z48YqIiChoMTExFVgxgNJwucyE0WPHmuNnnpGeekpiIVprsRYwgLMye/ZsDRkypOD4s88%2BU8eOHSWZS8BxcXFav369mjdvfk6vm5%2Bfr5YtW%2BpPf/qTJk2adMrHZGdnKzs7u%2BDY7XYrJiaGtYABm3nppcLewEcflV58kfWDrcI9gADOyo033qi2bdsWHNetW7dMXjcgIEAJCQkl9gAGBwcrODi4TN4PgHUee0wKCZEefFCaMMEMEHnlFUKgFQiAAM5KWFiYwsLCyvx1PR6PUlJS1LRp0zJ/bQC%2BZ9gwqXJls4Tc5MnS8eNSUhIhsKIRAAGct/3792vXrl367bffJEk//bH2U1RUVMEo3759%2B6pu3boaP368JGns2LFKTExUw4YN5Xa7NWnSJKWkpCgpKcmaTwJAhbvvPqlSJWnAAGnqVHM/YFKSFMDIhArDlxrAeZs/f75atGih66%2B/XpLUu3dvtWjRQq%2B99lrBY3bt2qW0IivDHzx4UIMHD1aTJk3UrVs3/frrr1q%2BfLnatGlT4fUDsM6990rJyabn77XXzGVhRiVUHAaBALAdt9utiIgIBoEAfmDWLBMGPR4TAidN4nJwRaAHEAAAWKZvX7NKiMslTZliBorQNVX%2BCIAAAMBS994rTZtm9l9%2BmXkCKwIBEAAAWG7QIOnVV83%2BCy9Izz1nbT3%2BjgAIAAB8wtChZn5ASRo92kwcjfJBAAQAAD7jkUcK1wt%2B/HGzljDKHgEQgG0kJSUpPj5eCQkJVpcCoBw99ZT05JNmf%2BhQac4ca%2BvxR0wDA8B2mAYG8H/eaWFefVUKCpI%2B%2Bkj6Y8pRlAF6AAEAgM9xucxScXffbZaL69VLWrHC6qr8BwEQAAD4pIAA6Y03pB49pGPHzDYlxeqq/AMBEAAA%2BKxKlaT33pM6dpTcbum666Rt26yuyv4IgAAAwKdVqSLNny81aybt2SNde63Z4vwRAAEAgM%2BrXl367DMpLs70AP75z1JWltVV2RcBEAAA2EKdOtLChVKtWtK6ddKtt0o5OVZXZU8EQAAAYBsNG0qffipVrSotXmyWkGNCu3NHAAQAALaSkCDNnSsFBkpvvik984zVFdkPARAAANhO9%2B7StGlm///%2Br3AfZ4cACAAAbGnAAGn0aLP/wANmkAjODgEQAADY1ujRUr9%2BUl6edPvtTBR9tgiAAGwjKSlJ8fHxSkhIsLoUAD7C5TKXf6%2B%2BWjp0yKwWcuCA1VX5PpfHw9gZAPbidrsVERGhzMxMhYeHW10OAB9w8KBZLWTAAGn4cBMMcXpBVhcAAABQWtWrS99%2BKwUHW12JPXAJGAAA%2BAXC39kjAAIAADgMARAAAMBhCIAAAAAOQwAEAABwGAIgAACAwxAAAQAAHIYACAAA4DAEQAAAAIchAAKwDdYCBoCywVrAAGyHtYABoHToAQQAAHAYAiAAAIDDEAABAAAchgAIAADgMARAAAAAhyEAAgAAOAwBEAAAwGEIgAAAAA5DAAQAAHAYAiAAAIDDEAAB2AZrAQNA2WAtYAC2w1rAAFA69AACAAA4DAEQAADAYQiAAAAADkMABAAAcBgCIAAAgMMQAAEAAByGAAigwuTm5urJJ59U06ZNFRoaqujoaPXt21e//fab1aUBgKMQAAFUmCNHjmjdunV65plntG7dOs2bN09btmzRjTfeaHVpAOAoTAQNwFJr1qxRmzZt9PPPP6tevXpn9RwmggaA0gmyugAAzpaZmSmXy6Xq1auf9jHZ2dnKzs4uOHa73RVRGgD4LS4BA7DMsWPH9Le//U133XVXiT1548ePV0REREGLiYmpwCoBwP8QAAGUm9mzZ6tatWoFbcWKFQUfy83NVe/evZWfn69XX321xNcZMWKEMjMzC9ru3bvLu3QA8GvcAwig3GRlZWnPnj0Fx3Xr1lWVKlWUm5ur22%2B/Xdu3b9eSJUtUs2bNc3pd7gEEgNIhAAKoUN7wl5qaqqVLl6p27drn/Boej0dZWVkKCwuTy%2BUqhyoBwL8RAAFUmOPHj%2BvWW2/VunXrtGDBAkVGRhZ8rEaNGqpcubKF1QGAcxAAAVSYnTt3Ki4u7pQfW7p0qTp16lSxBQGAQxEAAQAAHIZRwAAAAA5DAAQAAHAYAiAAAIDDEAABAAAchgAIAADgMARAAAAAhyEAAgAAOAwBEAAAwGEIgAAAAA5DAAQAAHAYAiAAAIDDEAABAAAchgAIAADgMARAAAAAhyEAAgAAOAwBEAAAwGEIgAAAAA5DAAQAAHAYAiAAAIDDEAABAAAchgAIAADgMARAAAAAhyEAAgAAOAwBEAAAwGEIgAAAAA5DAAQAAHAYAiAAAIDDEAABAAAchgAIAADgMARAAAAAhyEAAgAAOAwBEAAAwGEIgAAAAA5DAAQAAHAYAiAAAIDDEAABAAAchgAIAADgMARAAAAAhyEAAgAAOAwBEAAAwGEIgAAAAA5DAAQAAHAYAiAAAIDDEAABAAAchgAIAADgMARAAAAAhyEAAgAAOAwBEAAAwGH%2BH6UpI6Fc3bR7AAAAAElFTkSuQmCC'}