Genus 2 curve data - 294.a.8232.1

Formats: - HTML - YAML - JSON - 2026-05-19T12:31:27.521891

• 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': '315995603033814010', 'abs_disc': 8232, 'analytic_rank': 0, 'analytic_rank_proved': True, 'analytic_sha': 1, 'aut_grp_id': '[4,2]', 'aut_grp_label': '4.2', 'aut_grp_tex': 'C_2^2', 'bad_lfactors': '[[2,[1,2,3,2]],[3,[1,1,1,-3]],[7,[1,0,-1]]]', 'bad_primes': [2, 3, 7], 'class': '294.a', 'cond': 294, 'disc_sign': 1, 'end_alg': 'Q x Q', 'eqn': '[[-14,-9,4,0,-2],[1,0,0,1]]', 'g2_inv': "['25353016669288549/8232','75211396489919/588','49431027484/7']", 'geom_aut_grp_id': '[4,2]', 'geom_aut_grp_label': '4.2', 'geom_aut_grp_tex': 'C_2^2', 'geom_end_alg': 'Q x Q', 'globally_solvable': 1, 'has_square_sha': True, 'hasse_weil_proved': True, 'igusa_clebsch_inv': "['7636','11785','29745701','1053696']", 'igusa_inv': "['1909','151354','15951264','1885732415','8232']", 'is_gl2_type': True, 'is_simple_base': False, 'is_simple_geom': False, 'label': '294.a.8232.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.1489689799540566411026646628887640297214854916698568385', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.45.1', '3.2160.20'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_solvable_places': [], 'num_rat_pts': 4, 'num_rat_wpts': 0, 'real_geom_end_alg': 'R x R', 'real_period': {'__RealLiteral__': 0, 'data': '7.1505110377947187729279038187', 'prec': 100}, 'regulator': {'__RealLiteral__': 0, 'data': '1.0', 'prec': 10}, 'root_number': 1, 'st_group': 'SU(2)xSU(2)', 'st_label': '1.4.B.1.1a', 'st_label_components': [1, 4, 1, 1, 1, 0], 'tamagawa_product': 3, 'torsion_order': 12, 'torsion_subgroup': '[12]', 'two_selmer_rank': 1, 'two_torsion_field': ['8.0.12446784.1', [1, 1, 1, 2, 0, -2, 1, -1, 1], [8, 9], 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], ['1.1.1.1', [0, 1], -1]], 'factorsQQ_geom': [['1.1.1.1', [0, 1], -1], ['1.1.1.1', [0, 1], -1]], 'factorsRR_base': ['RR', 'RR'], 'factorsRR_geom': ['RR', 'RR'], 'fod_coeffs': [0, 1], 'fod_label': '1.1.1.1', 'is_simple_base': False, 'is_simple_geom': False, 'label': '294.a.8232.1', 'lattice': [[['1.1.1.1', [0, 1], [0]], [['1.1.1.1', [0, 1], -1], ['1.1.1.1', [0, 1], -1]], ['RR', 'RR'], [2, -1], 'G_{3,3}']], 'ring_base': [2, -1], 'ring_geom': [2, -1], 'spl_facs_coeffs': [[[-215], [5291]], [[-47], [71]]], 'spl_facs_condnorms': [14, 21], 'spl_facs_labels': ['14.a6', '21.a6'], 'spl_fod_coeffs': [0, 1], 'spl_fod_gen': [0], 'spl_fod_label': '1.1.1.1', 'st_group_base': 'G_{3,3}', 'st_group_geom': 'G_{3,3}'}
• 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': '294.a.8232.1', 'mw_gens': [[[[1, 1], [0, 1], [0, 1]], [[0, 1], [0, 1], [0, 1], [-1, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [12], 'num_rat_pts': 4, 'rat_pts': [[-2, 3, 1], [-2, 4, 1], [1, -1, 0], [1, 0, 0]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  
  • id: 14
    {'conductor': 294, 'lmfdb_label': '294.a.8232.1', 'modell_image': '2.45.1', 'prime': 2}
  • id: 15
    {'conductor': 294, 'lmfdb_label': '294.a.8232.1', 'modell_image': '3.2160.20', 'prime': 3}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  local_root_number	numeric
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 63087
    {'label': '294.a.8232.1', 'local_root_number': 1, 'p': 2, 'tamagawa_number': 1}
  • id: 63088
    {'cluster_label': 'c4c2_1~2_0', 'label': '294.a.8232.1', 'local_root_number': -1, 'p': 3, 'tamagawa_number': 1}
  • id: 63089
    {'cluster_label': 'cc2_1~2c2_1~2c2_1~2_0', 'label': '294.a.8232.1', 'local_root_number': -1, 'p': 7, 'tamagawa_number': 3}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '294.a.8232.1', 'plot': 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Ddv1df9eDVT9w3TrrOnOAKIAAUBsogEGkuFj67jurDP5ybN1qHaNXlUaNrCLYoYOUmGhtO3SwCmOdc1wo6MgR6VCLRMXmb6n6QXfeKb3%2B%2Brm9EYIGBRAA/I8C6ABer3XCydatFceuXdb9lWnQwCqGiYnWuPhia3vBBdai0NUWGiqVlVV9f8%2Be0qpVZ/IrIYhRAAHA/0JNB4D/uVzW17wxMdI115S/7%2BhR6yvkLVusQvjNN9b%2B9u3WfV98YY1fql/fKoYXXyx17HhytGhRxYxhbKy0Z0%2BV%2BX4sidP5ZWdYKhF00tPTlZ6errJT/c8CAKBGMAOISh07Zi1f88035ceWLVJRUeXPiYiwimCnTtbo3NnaRt8/Upo3r8r3StVb2ph4q/74R%2BnmmymCTscMIAD4HwUQZ6SsTNq5U/r6a2ts3myNrVutK4NUZnW93rqqZHmVr/l8vT9qYsnTkqwzlh97TBo5UqpXzw%2B/AGyPAggA/kcBRI0oLbWWqNm0yRpffWVtd%2B2SihSmMJVU%2BdzdF/5G4y9dro8%2BOnk94fPPtxaWHjPGOkkFzkEBBAD/owDCrzweKaJZhEKOFlb5mI91nfrp40rvq19fuu02a0HpDh38FBK2QgEEAP87x0U%2BgFNzu6WQGwef8jFftxmsqv47X1QkTZtmnX2cnCzt3euHkAAAOAwzgPC/zZulbt2kwkpmAdu2lTZu1PGGEfruO%2BuayZ9/Lq1fL23caK0j%2BEvjx0uTJ9dObJjBDCAA%2BB8FELXjs8%2BksWNPrinjckkDBkgzZlgH/FWirMw663jDBuntt63lBP/6V6lNm1rMjVpHAQQA/6MAonZ99ZW0b58189eqlek0sJFfrgO4fft2CiAA%2BBEFEICtMAMIAP7HSSAAAAAOQwEEAABwGAogAACAw1AAAQAAHIYCCAAA4DAUQAAAAIcJNR0gkHm9Xh06dMh0DCCgFRcXq7i42PfziX%2BnCgoKTEUC4BCRkZFyuVymYxjBOoDn4MR6ZQAAIPA4eb1RCuA5qM4MYEFBgRISEpSTk3PGf8iSkpK0fv16vz%2BnNp9X25/H2T6vNt/rbD8Tu/%2Bzru5zfj0DuHfvXnXp0kXffPONzq/iMoG1ndHke/F3SHn8HVKR0/8O%2BbUz%2BTycPAPIV8DnwOVyVftftqioqDP%2ByyokJKRWnmPiebX1eZzt82r785DO/DMJhH/W5/J5REZGBt2/M7X55%2BNs3y8Q/lxJ/B1SGf4OKe9s/ow4CSeB2NjYsWNr5Tkmnleb7xUIn2NtvlcgfI5ny%2B7/rPk8au55tflegfA51uZ7BcLniNPjK2A/47qm5fF5VMRnUt7u3bt9X980b97cdBzj%2BPNRHp9HRXwm5fF5VE/IpEmTJpkOEexCQkLUq1cvhYbyjbvE51EZPpOTiouL9dJLL2nixIkKDw83HccW%2BPNRHp9HRXwm5fF5nB4zgABshf97BwD/4xhAAAAAh6EAArCF9PR0JSYmKikpyXQUAAh6fAUMwFb4ChgA/I8ZQAAAAIehAPrZpEmTdNFFFyk8PFyNGjVS3759tXbtWtOxjCgtLdWjjz6qTp06KTw8XPHx8br11lu1Z88e09GMWbBggfr166cmTZrI5XIpOzvbdCTYxMqVKzVo0CDFx8fL5XJp0aJFpiMZlZaWpqSkJEVGRiomJkaDBw/Wtm3bTMcy5tVXX1Xnzp19ix13795dH374oelYtpGWliaXy6Xx48ebjmJbFEA/a9eunaZMmaJNmzZp9erVatWqlZKTk5WXl2c6Wq07cuSINm7cqCeffFIbN27UggULtH37dqWkpJiOZkxhYaGuuuoqPf/886ajwGYKCwt1ySWXaMqUKaaj2MKKFSs0duxYrVmzRpmZmTp27JiSk5NVWFhoOpoRzZs31/PPP6/PP/9cn3/%2Bua699lrdcMMN%2Bvrrr01HM279%2BvWaMWOGOnfubDqKrXEMYC07cXzTJ598oj59%2BpiOY9z69evVpUsX/fDDD2rRooXpOMbs2rVLrVu31hdffKFLL73UdByjOAawIpfLpYULF2rw4MGmo9hGXl6eYmJitGLFCl1zzTWm49hCdHS0XnrpJd15552moxhz%2BPBhXX755Zo6daqeffZZXXrppXrllVdMx7IlZgBrUUlJiWbMmCG3261LLrnEdBxb8Hg8crlcOu%2B880xHARBAPB6PJKv0OF1ZWZnmz5%2BvwsJCde/e3XQco8aOHavrr79effv2NR3F9lgiuxYsWbJEI0aM0JEjR9SsWTNlZmaqSZMmpmMZV1RUpMcee0wjR45kpgdAtXm9Xj300EPq2bOnOnbsaDqOMZs2bVL37t1VVFSkiIgILVy4UImJiaZjGTN//nxt3LhR69evNx0lIDADWIMyMjIUERHhG6tWrZIk9e7dW9nZ2crKylL//v01bNgw7d%2B/33Ba/6vq85CsE0JGjBih48ePa%2BrUqQZT1p5TfR4Aqu/%2B%2B%2B/XV199pXnz5pmOYlT79u2VnZ2tNWvW6N5779WoUaP0zTffmI5lRE5Ojh588EHNmTNH9evXNx0nIHAMYA06dOiQ9u3b5/v5/PPPV4MGDSo8rm3btrrjjjs0ceLE2oxX66r6PEpLSzVs2DDt2LFDn376qRo3bmwwZe051Z8PjgG0FoJOT09XWVmZtm/fzjGAv8AxgCeNGzdOixYt0sqVK9W6dWvTcWylb9%2B%2BuvDCCzV9%2BnTTUWrdokWLNGTIEIWEhPhuKysrk8vlUp06dVRcXFzuPvAVcI2KjIxUZGTkaR/n9XpVXFxcC4nMquzzOFH%2Bvv32Wy1btswx5U%2Bq/p8Ppxo7dqzGjh3rOwkE%2BCWv16tx48Zp4cKFWr58OeWvEk75b0tl%2BvTpo02bNpW77fbbb9dFF12kRx99lPJXCQqgHxUWFurPf/6zUlJS1KxZM%2BXn52vq1KnavXu3hg4dajperTt27Jhuvvlmbdy4UUuWLFFZWZlyc3MlWQdy16tXz3DC2nfgwAH9%2BOOPvrUQT6xrFhcXp7i4OJPRYNjhw4f13Xff%2BX7euXOnsrOzFR0d7cgz5seOHau5c%2Bfq3XffVWRkpO/vDrfbXek3LcHu8ccf14ABA5SQkKBDhw5p/vz5Wr58uZYuXWo6mhGRkZEVjgcNDw9X48aNHX2c6Cl54TdHjx71DhkyxBsfH%2B%2BtV6%2Bet1mzZt6UlBTvunXrTEczYufOnV5JlY5ly5aZjmfEm2%2B%2BWenn8dRTT5mOZozH4/FK8no8HtNRjFq2bFmlfzZGjRplOpoRVf3d8eabb5qOZsQdd9zhbdmypbdevXrepk2bevv06eP9%2BOOPTceyld/85jfeBx980HQM2%2BIYQAC2wjqAAOB/nAUMAADgMBRAAAAAh6EAAgAAOAwFEAAAwGEogABsIT09XYmJiUpKSjIdBQCCHmcBA7AVzgIGAP9jBhAAAMBhKIAAAAAOQwEEAABwGAogAACAw1AAAQAAHIYCCAAA4DChpgMAgGStA5ienq6ysjLTUYCAd/y4lJ8v5eZKeXnSgQPWKCiwRmGhdPSoVFQklZRYo6zMGsePn3ydOnWsERpqjbp1rREWZo369a3RsKHUoIEUHn5yRERYIzLSGlFR1mNcLnOfC05iHUAAtsI6gED15edL2dnSpk3S1q3Sd99JO3dKOTlSaanpdBWFhlpF0O22xnnnnRyNGp0c0dEnR%2BPG1oiKssooagYzgAAABIgDB6SlS6VPPpFWrbIK36k0bizFxFjbRo2s0hUZac3MNWhgzd6FhVmzeqGhUkiINUPncklerzVOzAweO2aVypISqbjYGkVF1jh6VDpyxBqFhdLhwyfHoUPW1uu1XuPEbOSZCgmxCmGTJtbv06SJ1LTpye2J0aGD1KLF2X2%2BTkIBBADAxkpLpUWLpDfekDIzrTL2SxdeKHXuLCUmSm3bShdcYBWgZs2kevXMZP6148etYlhQIHk8J8fPP1vj4MGT2wMHrG1%2BvrWfn28Vy7Iy6%2BvsvLxTv9czz0hPPlk7v1cgowACqJYFCxZo%2BvTp2rBhg/Lz8/XFF1/o0ksvLfeY4uJiTZgwQfPmzdPRo0fVp08fTZ06Vc2bNzeUGghcx45Zpe/ZZ62vdE/o2FEaMEDq1Uvq3t2a2bO7OnVOHgt4/vln/vyiIqsI5udL//3vyW1e3sntidG6dc3nD0YUQADVUlhYqKuuukpDhw7V6NGjK33M%2BPHj9d5772n%2B/Plq3LixHn74YQ0cOFAbNmxQSEhILScGAteWLVJqqrRhg/VzbKx0113SrbdK7dqZzWZC/fpWcTyb8ojKcRIIgDOya9cutW7dusIMoMfjUdOmTTV79mwNHz5ckrRnzx4lJCTogw8%2BUL9%2B/ar1%2BpwEAqdbulS6%2BWbrK9PzzpMmTZLuvtsqQUBN4XwaADViw4YNKi0tVXJysu%2B2%2BPh4dezYUVlZWQaTAYFj5Urphhus8te7t/TNN9KDD1L%2BUPP4ChhAjcjNzVW9evXU6FcHJMXGxio3N7fK5xUXF6u4uNj3c0FBgd8yAnZ24IA0fLh1lu2QIdLbb1tn5wL%2BwAwggAoyMjIUERHhG6tWrTrr1/J6vXKdYuXXtLQ0ud1u30hISDjr9wIC2Z//bC3c3KGDNGcO5Q/%2BRQEEUEFKSoqys7N948orrzztc%2BLi4lRSUqKDBw%2BWu33//v2KjY2t8nkTJ06Ux%2BPxjZxfnu4IOMSRI9KMGdb%2Byy9bV9YA/ImvgAFUEBkZqcjIyDN6zhVXXKG6desqMzNTw4YNkyTt3btXmzdv1osvvljl88LCwhQWFnZOeYFA9%2Bmn1mLJLVtK/fubTgMnoAACqJYDBw7oxx9/1J49eyRJ27Ztk2TN/MXFxcntduvOO%2B/Uww8/rMaNGys6OloTJkxQp06d1LdvX5PRAdtbt87a9unDtXJRO/gKGEC1LF68WJdddpmuv/56SdKIESN02WWXadq0ab7HTJ48WYMHD9awYcN01VVXqWHDhnrvvfdYAxA4jR9%2BsLZt25rNAedgHUAAtsI6gHCim2%2BW/v1vacoUaexY02ngBMwAAgBg2Ilr9v5iRSTAryiAAAAY1rSptd23z2wOOAcFEIAtpKenKzExUUlJSaajALXuggus7bffms0B5%2BAYQAC2wjGAcKJly6Rrr5VatDh5QgjgT8wAAgBg2JVXSnXqSD/%2BKLEWOmoDBRAAAMMiI6UTRz9kZprNAmegAAIAYAMDBljbxYvN5oAzUAABALCBwYOt7UcfSYcOmc2C4EcBBADABjp3ltq3l4qKpAULTKdBsKMAAgBgAy6XdMst1v6sWUajwAEogAAA2MSoUVYRXL6cNQHhXxRAALbAQtCAtQ7giZNBpk83mwXBjYWgAdgKC0HD6d5/Xxo4UDrvPGn3bik83HQiBCNmAAEAsJEBA6QLL5R%2B/ln65z9Np0GwogACAGAjdepIDz5o7U%2BeLJWVmc2D4EQBBADAZm6/XWrUyDoRZNEi02kQjCiAAADYTESENHastf/CCxJH66OmUQABALChBx6QGjSQ1q%2BXPvnEdBoEGwogAAA21LSpNGaMtf/ss2azIPhQAAHYAusAAhVNmCDVqyetXCmtWGE6DYIJ6wACsBXWAQTKu%2B8%2B6dVXpV69pGXLTKdBsGAGEAAAG3vsMaluXevycMwCoqZQAAEAsLEWLaS77rL2//hHzghGzaAAAgBgc48/LoWFWccC/uc/ptMgGFAAAQCwuebNpbvvtvafeIJZQJw7CiAAAAFg4kRrXcC1a6UlS0ynQaCjAAIAEADi4qzFoSXpySel48fN5kFgowACABAgHnlEioqSvvxSeucd02kQyCiAAGyBhaCB02vc2FocWrLOCD52zGweBC4WggZgKywEDZzaoUPShRdKeXnSjBnS6NGmEyEQMQMIAEAAiYy0loWRpKeflo4eNZsHgYkCCABAgLnnHikhQfrpJyk93XQaBCIKIIDTKi0t1aOPPqpOnTopPDxc8fHxuvXWW7Vnz55yjzt48KBSU1PldrvldruVmpqqn3/%2B2VBqIHjVr2/N/klSWprk8ZjNg8BDAQRwWkeOHNHGjRv15JNPauPGjVqwYIG2b9%2BulJSUco8bOXKksrOztXTpUi1dulTZ2dlKTU01lBoIbqmp0kUXSQcOSH/5i%2Bk0CDScBALgrKxfv15dunTRDz/8oBYtWmjLli1KTEzUmjVr1LVrV0nSmjVr1L17d23dulXt27ev1utyEghQfQsWSDfdJIWHS99/L8XGmk6EQMEMIICz4vF45HK5dN5550mSPvvsM7kc8gNjAAAY6UlEQVTdbl/5k6Ru3brJ7XYrKyvLVEwgqA0ZInXpIhUWSs8%2BazoNAgkFEMAZKyoq0mOPPaaRI0f6Zulyc3MVExNT4bExMTHKzc2t8rWKi4tVUFBQbgCoHpfLOgZQkqZPl3bsMJsHgYMCCKCCjIwMRURE%2BMaqVat895WWlmrEiBE6fvy4pk6dWu55Lperwmt5vd5Kbz8hLS3Nd9KI2%2B1WQkJCzf0igANce6103XVSaan01FOm0yBQUAABVJCSkqLs7GzfuPLKKyVZ5W/YsGHauXOnMjMzyx2jFxcXp3379lV4rby8PMWe4sCkiRMnyuPx%2BEZOTk7N/0JAkDsxC5iRIX31ldksCAwUQAAVREZGqk2bNr7RoEEDX/n79ttv9cknn6hx48blntO9e3d5PB6tW7fOd9vatWvl8XjUo0ePKt8rLCxMUVFR5QaAM3PFFdLQoZLXK/2//2c6DQIBZwEDOK1jx47ppptu0saNG7VkyZJyM3rR0dGqV6%2BeJGnAgAHas2ePpk%2BfLkkaM2aMWrZsqffee6/a78VZwMDZ2b5dSkyUysqk1aulq64ynQh2RgEEcFq7du1S69atK71v2bJl6tWrlyTpwIEDeuCBB7R48WJJ1lfJU6ZM8Z0pXB0UQODsjRkjvfaa1LOntHKldZIIUBkKIABboQACZ2/3bqltW6moSHr/fem3vzWdCHbFMYAAAASJ5s2l%2B%2B%2B39h9/XDp%2B3Gwe2BcFEACAIPLYY1JUlPTll9I775hOA7uiAAKwhfT0dCUmJiopKcl0FCCgNW4sPfKItf/kk9b6gMCvcQwgAFvhGEDg3B0%2BLF1wgZSXZ10hZMwY04lgN8wAAgAQZCIiTq4H%2BMwz0tGjZvPAfiiAAAAEobvvlhISpJ9%2Bkl591XQa2A0FEACAIFS//slrA6elSYcOmc0De6EAAgAQpEaNktq1k/77X%2BmVV0yngZ1QAAEACFKhodLTT1v7f/mLdOCA2TywDwogAABBbNgwqXNnqaBAeukl02lgFxRAAACCWJ060rPPWvt//7u0b5/ZPLAHCiAAW2AhaMB/Bg6UunSRjhyxTggBWAgagK2wEDTgH5mZUnKyFBYmffeddd1gOBczgAAAOEDfvtLVV0vFxdKf/2w6DUyjAAIA4AAu18ljAWfOlHbtMhoHhlEAAQBwiGuusWYCS0ulP/3JdBqYRAEEAMBBThS/t96yjgWEM1EAAQBwkG7dpAEDpLIyZgGdjAIIAIDDnLg6yJw50vbtZrPADAogAFtgHUCg9iQlWWsDHj/OLKBTsQ4gAFthHUCgdmzcKF1xhXWlkG%2B%2Bkdq3N50ItYkZQAAAHOjyy6WUFGsW8JlnTKdBbaMAAgDgUE89ZW3nz5e2bTObBbWLAggAgEP9chaQYwGdhQIIAICDnZgFnDePM4KdhAIIAICDXX65NGiQNQt44lJxCH4UQAAAHO6Pf7S2c%2BdydRCnoAACAOBwV1558uogzz1nOg1qAwUQgC2wEDRg1olZwNmzpV27jEZBLWAhaAC2wkLQgDnJyVJmpnT33dK0aabTwJ%2BYAQQAAJKkJ5%2B0tm%2B8Ie3ebTYL/IsCCAAAJElXXy1dc41UWiq99JLpNPAnCiCAapk0aZIuuugihYeHq1GjRurbt6/Wrl1b7jEHDx5Uamqq3G633G63UlNT9fPPPxtKDOBsPPGEtZ0xQ9q3z2wW%2BA8FEEC1tGvXTlOmTNGmTZu0evVqtWrVSsnJycrLy/M9ZuTIkcrOztbSpUu1dOlSZWdnKzU11WBqAGeqb1%2BpSxepqEiaPNl0GvgLJ4EAOCsnTtb45JNP1KdPH23ZskWJiYlas2aNunbtKklas2aNunfvrq1bt6p9%2B/Zn9LqcBAKYs3ixdMMNUmSk9MMPUqNGphOhpjEDCOCMlZSUaMaMGXK73brkkkskSZ999pncbrev/ElSt27d5Ha7lZWVZSoqgLMwaJDUqZN06JA0ZYrpNPAHCiCAaluyZIkiIiJUv359TZ48WZmZmWrSpIkkKTc3VzExMRWeExMTo9zc3Cpfs7i4WAUFBeUGALNcLunxx639V16RDh82mwc1jwIIoIKMjAxFRET4xqpVqyRJvXv3VnZ2trKystS/f38NGzZM%2B/fv9z3P5XJVeC2v11vp7SekpaX5Thpxu91KSEio%2BV8IwBkbOlRq00Y6cEB67TXTaVDTKIAAKkhJSVF2drZvXHnllZKk8PBwtWnTRt26ddPMmTMVGhqqmTNnSpLi4uK0r5JTBvPy8hQbG1vle02cOFEej8c3cnJy/PNLATgjISHS//yPtf/yy1Jxsdk8qFkUQAAVREZGqk2bNr7RoEGDSh/n9XpV/H//Vejevbs8Ho/WrVvnu3/t2rXyeDzq0aNHle8VFhamqKiocgOAPdx6qxQfL/30kzRnjuk0qEkUQACnVVhYqMcff1xr1qzRDz/8oI0bN%2Bquu%2B7S7t27NXToUElShw4d1L9/f40ePVpr1qzRmjVrNHr0aA0cOLDaZwADsJewMOnhh639F1%2BUysrM5kHNoQACOK2QkBBt3bpVN910k9q1a6eBAwcqLy9Pq1at0sUXX%2Bx7XEZGhjp16qTk5GQlJyerc%2BfOmj17tsHkAM7VmDHWMjDbt0sLF5pOg5rCOoAAbIV1AAH7%2BeMfpT/9SbrySmndOussYQQ2ZgABAMApjRsnNWggff659OmnptOgJlAAAQDAKTVtKt1xh7X/4otms6BmUAAB2EJ6eroSExOVlJRkOgqASjz8sLU0zMcfS198YToNzhXHAAKwFY4BBOxr5Ehp3jzpd7%2BT5s41nQbnghlAAABQLY88Ym3feUfatctoFJwjCiAAAKiWyy6T%2Bva11gOcPNl0GpwLCiAAAKi2E7OAr79uXScYgYkCCAAAqu2666RLLpGOHJGmTTOdBmeLAggAAKrN5ZImTLD2//536f8uB44AQwEEAABnZPhwqXlzad8%2BKSPDdBqcDQogAAA4I3XrSg88YO3/9a8SC8oFHgogAFtgIWggsIwZI0VESF9/LX30kek0OFMsBA3AVlgIGggcf/iD9Mor1tIwmZmm0%2BBMMAMIAADOyoMPSnXqSJ98In31lek0OBMUQAAAcFZatZJuusnaZ2HowEIBBAAAZ%2B2hh6zt3LnWWcEIDBRAAABw1rp1s0ZJiTR1quk0qC4KIAAAOCd/%2BIO1ffVVqajIbBZUDwUQAACckxtvlBISpLw8ad4802lQHRRAALbAOoBA4AoNlcaNs/ZfeYWFoQMB6wACsBXWAQQC08GD1uXhjhyRPv1U6t3bdCKcCjOAAADgnDVqJI0aZe3/7W9ms%2BD0KIAAAKBGnPgaePFiaccOs1lwahRAAABQIzp0kJKTrWMA09NNp8GpUAABAECNefBBaztzpnT4sNksqBoFEAAA1Jj%2B/aW2bSWPR5o923QaVIUCCAAAakydOtL991v7//gHS8LYFQUQAADUqFGjpIgIacsWa0kY2A8FEIAtsBA0EDzcbunWW639f/zDbBZUjoWgAdgKC0EDwWHLFikx0fpKeMcOqWVL04nwS8wAAgCAGtehg9Snj3T8uPTqq6bT4NcogAAAwC9OnAzy%2ButSUZHZLCiPAgjgjN19991yuVx65ZVXyt1%2B8OBBpaamyu12y%2B12KzU1VT///LOhlABMGzRIatFCys%2BX3n7bdBr8EgUQwBlZtGiR1q5dq/j4%2BAr3jRw5UtnZ2Vq6dKmWLl2q7OxspaamGkgJwA5CQqR77rH2uTKIvVAAAVTbTz/9pPvvv18ZGRmqW7duufu2bNmipUuX6vXXX1f37t3VvXt3vfbaa1qyZIm2bdtmKDEA0%2B68U6pXT1q/Xvr8c9NpcAIFEEC1HD9%2BXKmpqXrkkUd08cUXV7j/s88%2Bk9vtVteuXX23devWTW63W1lZWbUZFYCNxMRIQ4da%2B1Onms2CkyiAAKrlhRdeUGhoqB544IFK78/NzVVMTEyF22NiYpSbm1vl6xYXF6ugoKDcABBc7rvP2s6bJx04YDYLLBRAABVkZGQoIiLCN1asWKG//e1vmjVrllwuV5XPq%2Bw%2Br9d7yuekpaX5Thpxu91KSEiokd8BgH107y5dcol1JvCsWabTQKIAAqhESkqKsrOzfSMrK0v79%2B9XixYtFBoaqtDQUP3www96%2BOGH1apVK0lSXFyc9u3bV%2BG18vLyFBsbW%2BV7TZw4UR6PxzdycnL89WsBMMTlOjkLOG2atTYgzOJKIABOKz8/X3v37i13W79%2B/ZSamqrbb79d7du315YtW5SYmKi1a9eqS5cukqS1a9eqW7du2rp1q9q3b1%2Bt9%2BJKIEBwOnxYio%2BXDh2SMjOlvn1NJ3K2UNMBANhf48aN1bhx43K31a1bV3Fxcb5i16FDB/Xv31%2BjR4/W9OnTJUljxozRwIEDq13%2BAASviAgpNdU6EeTVVymApvEVMIAak5GRoU6dOik5OVnJycnq3LmzZs%2BebToWAJu4915r%2B%2B670p49ZrM4HV8BA7AVvgIGgtvVV0urV0vPPCM9%2BaTpNM7FDCAAAKg1d99tbV97TSorM5vFySiAAACg1tx8sxQdLeXkSB9%2BaDqNc1EAAdhCenq6EhMTlZSUZDoKAD%2BqX1%2B67TZr///OF4MBHAMIwFY4BhAIftu2SRddJNWpI%2B3aJbH%2Be%2B1jBhAAANSq9u2lXr2sBaFnzjSdxpkogAAAoNaNGWNtZ87kZBATKIAAAKDWDRkiNW4s7d7NySAmUAABAECtq19fuvVWa/%2B118xmcSIKIAAAMGL0aGv7/vtcGaS2UQABAIARHTpIPXtaxwC%2B%2BabpNM5CAQQAAMacmAWcOdM6Kxi1gwIIwBZYCBpwpptvltxuaedO6dNPTadxDhaCBmArLAQNOM9990mvvioNHy7Nn286jTMwAwgAAIy66y5ru3ChlJ9vNotTUAABAIBRl18uXXqpVFIizZljOo0zUAABAIBxJ2YBZ86UODjN/yiAAADAuJEjpbAwadMmacMG02mCHwUQAAAY16iRdNNN1v7MmWazOAEFEAAA2MIdd1jbefOko0fNZgl2FEAAtsA6gAB695ZatZI8HmnBAtNpghvrAAKwFdYBBJzt6aelSZOka6%2BV/vMf02mCFzOAAADANm67zdp%2B%2Bqm0a5fJJMGNAggAAGyjZUupTx9r/623zGYJZhRAAABgK7ffbm1nzZKOHzcaJWhRAAEAgK0MGSJFRVlfAa9caTpNcKIAAgAAW2nYUBo%2B3Np/802zWYIVBRAAANjOiZNB/vUv6fBho1GCEgUQAADYTvfuUrt20pEjVglEzaIAArAFFoIG8EsulzRqlLU/a5bRKEGJhaAB2AoLQQM4ISfHWhbG65V27JBatzadKHgwAwgAAGwpIcG6IogkzZ5tNkuwoQACAADbOvE18D//ac0EomZQAAFUy2233SaXy1VudOvWrdxjiouLNW7cODVp0kTh4eFKSUnR7t27DSUGEAxuvFEKD5e%2B/17KyjKdJnhQAAFUW//%2B/bV3717f%2BOCDD8rdP378eC1cuFDz58/X6tWrdfjwYQ0cOFBlZWWGEgMIdOHh0s03W/v//KfZLMGEAgig2sLCwhQXF%2Bcb0dHRvvs8Ho9mzpypl19%2BWX379tVll12mOXPmaNOmTfrkk08MpgYQ6G691dq%2B/bZUVGQ2S7CgAAKotuXLlysmJkbt2rXT6NGjtX//ft99GzZsUGlpqZKTk323xcfHq2PHjsriexsA56BXL%2BuEEI9Heu8902mCAwUQQLUMGDBAGRkZ%2BvTTT/Xyyy9r/fr1uvbaa1VcXCxJys3NVb169dSoUaNyz4uNjVVubm6Vr1tcXKyCgoJyAwB%2BqU4d6ZZbrH3OBq4ZFEAAFWRkZCgiIsI3Vq1apeHDh%2Bv6669Xx44dNWjQIH344Yfavn273n///VO%2BltfrlcvlqvL%2BtLQ0ud1u30hISKjpXwdAEEhNtbYffijl5ZnNEgwogAAqSElJUXZ2tm9ceeWVFR7TrFkztWzZUt9%2B%2B60kKS4uTiUlJTp48GC5x%2B3fv1%2BxsbFVvtfEiRPl8Xh8Iycnp2Z/GQBBITFRuuIK6dgx61hAnBsKIIAKIiMj1aZNG99o0KBBhcfk5%2BcrJydHzZo1kyRdccUVqlu3rjIzM32P2bt3rzZv3qwePXpU%2BV5hYWGKiooqNwCgMidmAfka%2BNxxKTgAp3X48GFNmjRJN910k5o1a6Zdu3bp8ccf148//qgtW7YoMjJSknTvvfdqyZIlmjVrlqKjozVhwgTl5%2Bdrw4YNCgkJqdZ7cSk4AFXZt086/3yprEzatk1q1850osDFDCCA0woJCdGmTZt0ww03qF27dho1apTatWunzz77zFf%2BJGny5MkaPHiwhg0bpquuukoNGzbUe%2B%2B9V%2B3yBwCnEhsrXXedtZ%2BRYTZLoGMGEICtMAMI4FQyMqTf/1664ALpu%2B%2BkU5xjhlNgBhAAAASMwYOtq4Ps2CGtWWM6TeCiAAIAgIARHi4NGWLt8zXw2aMAArCF9PR0JSYmKikpyXQUADZ3YlHot9%2BWSkvNZglUHAMIwFY4BhDA6Rw7Zp0NvH%2B/9P770m9/azpR4GEGEAAABJTQUGn4cGufr4HPDgUQAAAEnJEjre2770qFhWazBCIKIAAACDhdu0oXXmiVv3ffNZ0m8FAAAQBAwHG5pN/9ztqfN89slkBEAQQAAAHpxNfAS5dK%2BflmswQaCiAAAAhIHTpIl15qnRX8r3%2BZThNYKIAAACBg8TXw2aEAArAFFoIGcDZGjLC2K1dKP/1kNksgYSFoALbCQtAAztTVV0urV0svvyw99JDpNIGBGUAAABDQTswCzp9vNkcgoQACAICANnSoFBIirV8vff%2B96TSBgQIIAAACWkyMdO211j6zgNVDAQQAAAGPr4HPDAUQAAAEvCFDpLp1pc2bpa%2B/Np3G/kJNBwAAADhXjRpZxwJKUh2mt06LAgjAFtLT05Wenq6ysjLTUQAEqIwM0wkCB%2BsAArAV1gEEAP9jkhQAAMBhKIAAAAAOQwEEAABwGAogAACAw1AAAQAAHIYCCAAA4DAUQAAAAIehAAKwhfT0dCUmJiopKcl0FAAIeiwEDcBWWAgaAPyPGUAAAACHoQACAAA4DAUQQLVt2bJFKSkpcrvdioyMVLdu3fTjjz/67i8uLta4cePUpEkThYeHKyUlRbt37zaYGABQGQoggGr5/vvv1bNnT1100UVavny5vvzySz355JOqX7%2B%2B7zHjx4/XwoULNX/%2BfK1evVqHDx/WwIEDVVZWZjA5AODXOAkEQLWMGDFCdevW1ezZsyu93%2BPxqGnTppo9e7aGDx8uSdqzZ48SEhL0wQcfqF%2B/ftV6H04CAQD/YwYQwGkdP35c77//vtq1a6d%2B/fopJiZGXbt21aJFi3yP2bBhg0pLS5WcnOy7LT4%2BXh07dlRWVlaVr11cXKyCgoJyAwDgXxRAAKe1f/9%2BHT58WM8//7z69%2B%2Bvjz/%2BWEOGDNGNN96oFStWSJJyc3NVr149NWrUqNxzY2NjlZubW%2BVrp6Wlye12%2B0ZCQoJffxcAAAUQQCUyMjIUERHhG9u2bZMk3XDDDfrDH/6gSy%2B9VI899pgGDhyoadOmnfK1vF6vXC5XlfdPnDhRHo/HN3Jycmr0dwEAVBRqOgAA%2B0lJSVHXrl19Pzdt2lShoaFKTEws97gOHTpo9erVkqS4uDiVlJTo4MGD5WYB9%2B/frx49elT5XmFhYQoLC6vh3wAAcCrMAAKoIDIyUm3atPENt9utpKQk30zgCdu3b1fLli0lSVdccYXq1q2rzMxM3/179%2B7V5s2bT1kAAQC1jxlAANXyyCOPaPjw4brmmmvUu3dvLV26VO%2B9956WL18uSXK73brzzjv18MMPq3HjxoqOjtaECRPUqVMn9e3b12x4AEA5FEAA1TJkyBBNmzZNaWlpeuCBB9S%2BfXv9%2B9//Vs%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%3D'}