Genus 2 curve data - 363.a.43923.1

Formats: - HTML - YAML - JSON - 2024-05-04T18:24:18.095385

• 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': '1338090095012731695', 'abs_disc': 43923, 'analytic_rank': 0, 'analytic_rank_proved': True, 'analytic_sha': 1, 'aut_grp_id': '[2,1]', 'aut_grp_label': '2.1', 'aut_grp_tex': 'C_2', 'bad_lfactors': '[[3,[1,2,4,3]],[11,[1,-2,1]]]', 'bad_primes': [3, 11], 'class': '363.a', 'cond': 363, 'disc_sign': -1, 'end_alg': 'Q x Q', 'eqn': '[[-2,1,10,-7,-13,11],[0,0,1]]', 'g2_inv': "['-5256325630316243968/43923','-1804005053317888/363','-99735603013264/363']", 'geom_aut_grp_id': '[2,1]', 'geom_aut_grp_label': '2.1', 'geom_aut_grp_tex': 'C_2', 'geom_end_alg': 'Q x Q', 'globally_solvable': 1, 'has_square_sha': True, 'hasse_weil_proved': True, 'igusa_clebsch_inv': "['11096','25612','88274095','-175692']", 'igusa_inv': "['5548','1278244','392069161','135322995423','-43923']", 'is_gl2_type': True, 'is_simple_base': False, 'is_simple_geom': False, 'label': '363.a.43923.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.1897059599534884032426437150258731309381975729352148588', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.60.1', '3.80.4'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_solvable_places': [], 'num_rat_pts': 3, 'num_rat_wpts': 1, 'real_geom_end_alg': 'R x R', 'real_period': {'__RealLiteral__': 0, 'data': '3.7941191990697680648528743005', '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': 5, 'torsion_order': 10, 'torsion_subgroup': '[10]', 'two_selmer_rank': 1, 'two_torsion_field': ['6.0.52272.1', [1, 1, 2, -3, 0, -1, 1], [6, 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], ['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': '363.a.43923.1', 'lattice': [[['1.1.1.1', [0, 1], [0]], [['1.1.1.1', [0, 1], -1], ['1.1.1.1', [0, 1], -1]], ['RR', 'RR'], [3, -1], 'G_{3,3}']], 'ring_base': [3, -1], 'ring_geom': [3, -1], 'spl_facs_coeffs': [[['25353/16'], ['4002939/64']], [['2511/16'], ['1823229/512']]], 'spl_facs_condnorms': [33, 11], 'spl_facs_labels': ['33.a3', '11.a2'], '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': '363.a.43923.1', 'mw_gens': [[[[-6, 11], [-3, 11], [1, 1]], [[-4, 11], [-2, 11], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [10], 'num_rat_pts': 3, 'rat_pts': [[1, -1, 1], [1, 0, 0], [1, 0, 1]], 'rat_pts_v': True}
• g2c_galrep •

  Column	Type
  conductor	integer
  lmfdb_label	text
  modell_image	text
  prime	smallint

  
  
  • id: 28
    {'conductor': 363, 'lmfdb_label': '363.a.43923.1', 'modell_image': '2.60.1', 'prime': 2}
  • id: 29
    {'conductor': 363, 'lmfdb_label': '363.a.43923.1', 'modell_image': '3.80.4', 'prime': 3}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  
  • id: 79256
    {'cluster_label': 'c3c2_1~2_0', 'label': '363.a.43923.1', 'p': 3, 'tamagawa_number': 1}
  • id: 79257
    {'cluster_label': 'c1cc2_1~2c2_1~2_1_-1', 'label': '363.a.43923.1', 'p': 11, 'tamagawa_number': 5}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '363.a.43923.1', 'plot': 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GG%2B%2Bab8rxcXm9fUrh2YPEEqIiJCHpde8oRdwJVwbNcaAAAIPW4%2BNIYCWAmVXQFMSEjQmjVr/JjoZF6vV7Gxsdq6dWuV/yEPxM8TqO8Tyj/LiSuAOTk56tixo7755hs1btzYr9/rRKH8z83G9wjU93Ha7wEn/bupqu9TlLtbvpYtVdN3%2BNQv6NHDzIGpAqH0Z8DNK4DsAq4Ej8dTqV%2Bm1atXD9j/eURGRlb59wrUzxOI7%2BOkn%2BWYiIgI/rkF2fcI5PeRnPN7wGn/bvz9fQ4elIYmRSrGN1Wv6HZV0wnrPPXrS9OmSVX0sznpz4CTcRKIRWPGjLEdwa8C9fME4vs46WcJJCf9c3PSzxJI/Lux%2B3327pV69pQWLZLeDB%2BpL578u9Snj47%2B5jf6WVLhqFHS2rVSq1Z%2B%2B54nctKfASdjF7DDMQIE27ZtK9n916RJE9txYAG/B9xh2zYpMVHasEGKijJjALt3N1/jzwBOxC5ghwsLC9MjjzyisLAw21FgybF/9/wZcC9%2BDzjff/4jXX219PPPUqNG0scfSxdddPzr/BnAiVgBBByO//MHnG3JEnPBj/x8s2d38WIpLs52KgQ7jgEMsL17zV/O1aulo0dtpwEAhLJp06TevU35u/RSc8lfyh/OBAUwgN54Q4qNNcv0nTtL3bpJO3faTgWnSktLU3x8vBISEmxHAeBnRUXSHXdIY8aYmc7DhkmffCL95je2kyFUsAs4QBYulK691mw3b26uwLN/v/T//p%2B0bJlUjSqOKsIuYMBZdu6Urr9eWr5c8nikJ5%2BUxo8328CZonYEwKFD5v/UJGn0aOm778xZ%2BHXqSCtWmNP1K8Pn82nSpEmKiYlRrVq11KNHD3399dflvmfSpEnyeDylbtHR0ZULAsCaadOmqWnTpgoPD1eHDh20cuXKMl%2Bbnp5%2B0t9/j8ejQ4cOBTAxKuKLL6QOHUz5i4iQFiyQJkwou/ytWLFC11xzjWJiYuTxeLRgwYLABkbQogAGwNy55sys2Fhp8mSz2vfb30q3326%2B/tZblfv8Z555RpMnT1ZqaqrWrFmj6OhoXXnllae9Sknr1q2Vk5NTcvvqq68qFwSAFXPnztXYsWM1ceJErV%2B/Xt27d1diYqKys7PLfE9kZGSpv/85OTkKDw8PYGqcDZ/PHO/XrZsZ99KihTmWvG/f8t9XUFCgdu3aKTU1NTBBETIYAxMA775r7m%2B7Tfr179drr5Wef15atarin%2B3z%2BTRlyhRNnDhR/fr1kyTNmjVLDRs21OzZszVq1Kgy31ujRg1W/QAHmDx5skaMGKFbb71VkjRlyhQtWbJEL7/8slJSUk75Hlb9Q0denjRypDRvnnl83XVSevqZXcgjMTFRiYmJVZoPoYkVwCrm85mzsiQzoPPXWrY099u2mQN6K2LLli3Kzc1Vz549S54LCwvTpZdeqlWnaZZZWVmKiYlR06ZNNXjwYG3evLliIQBYc/jwYX355ZelfgdIUs%2BePcv9HbB//37FxcWpSZMm6tOnj9avX1/VUVEBn38utW9vyl%2BNGmbR4N13q%2BwqbnARCmAV27tX2rPHbLduXfprERHHtw8erNjn5%2BbmSpIaNmxY6vmGDRuWfO1UOnXqpDfeeENLlizRjBkzlJubqy5dumj37t0VCwLAil27dqm4uPisfge0bNlS6enpWrhwoebMmaPw8HB17dpVWVlZgYiMM3DkiPTnP5tdvlu2SBdeKK1cKSUlcbIH/IMCWMWOlb%2B6daVatUp/zes9vn3i18qSkZGhunXrltyK/rt06DnhN4LP5zvpuV9LTExU//791bZtW11xxRVa9N8zUWbNmnVmQQAElbP5HdC5c2fdeOONateunbp376558%2BapRYsWmjp1aiCi4jS%2B/Vbq2lV6%2BGEz4mXwYGn9ejM%2BDPAXjgGsYsfGuxQXn/y17783940bS%2Becc2af17dvX3Xq1KnkcWFhoSSzEtioUaOS53fu3HnSikB56tSpo7Zt27ICAISY%2BvXrq3r16iet9p3N74Bq1aopISGBv/%2BWHTkivfCC9NBDUmGhuZ5vWpp0ww22k8GJWAGsYsd%2B/x48eHw18Jhly8x9x45n/nkRERFq3rx5yS0%2BPl7R0dFaunRpyWsOHz6s5cuXq0uXLmf8uYWFhdq4cWOpEonQxiBod6hZs6Y6dOhQ6neAJC1duvSMfwf4fD5lZmby99%2BiYyt8999vyl%2BvXtKGDZQ/VB0KYBWrU0dq1sxsHzsZRDL/p5eebravuabin%2B/xeDR27Fg9%2BeSTeu%2B997RhwwbdfPPNql27toYOHVryussvv7zUGIBx48Zp%2BfLl2rJli1avXq0BAwbI6/Vq%2BPDhFQ%2BDoDJmzBh98803WrNmje0oqGJJSUn6y1/%2BopkzZ2rjxo265557lJ2drdv/O2tq2LBhSk5OLnn9o48%2BqiVLlmjz5s3KzMzUiBEjlJmZWfJ6BE5%2BvjmuLyFB%2BvJLs%2Br32mvmkqFNmlT%2B8/fv36/MzExlZmZKMicOZmZmljsiCO7ALuAA6N1beuklM8OpTx9zAO/kydLmzdJ555mJ7pVx//336%2BDBg7rjjju0d%2B9ederUSZ988okifnWWyaZNm7Rr166Sx9u2bdOQIUO0a9cuNWjQQJ07d9bnn3%2BuOC4iCYScQYMGaffu3XrssceUk5OjNm3a6KOPPir5%2B5ydna1qv7rc0L59%2BzRy5Ejl5uYqKipK7du314oVK9TxbHZHoFJ8Pmn2bOm%2B%2B6ScHPPc9ddLU6ZI/lyIXbt2rS677LKSx0lJSZKk4cOHK/3YKgRciUvBBUBWljkDuKhIuvlmc/r%2B1KnmF8CMGdJ/R3cBVYJLwQHBZfVq6Z57pH/9yzxu1kxKTZWuuspuLrgLBTBApk0zF%2B3%2BtbFjzUogp/SjKlEAgeCwebP0wAPm6lCSOUTogQfMLmAuwoJAYxdwgNxxh9SqlfTmm9LRo9KgQScPhgYAOE9urvTEE9L06WZPkMcjDR9unouJsZ0ObsUKIOBwrAACduzdKz37rPTii9KBA%2Ba5Xr2kp5%2BW2rWzmw1gBRAAAD86eNCUvqeflvbtM8916iSlpEi/Oh8DsIoCCDhUWlqa0tLSVHyqKeQA/M7nk955R7r3XnONd0lq00Z6/HGpb1%2BO90ZwYRcw4HDsAgaqXn6%2B9Mc/Sn/9q3l8wQWm%2BA0dKlWvbjcbcCqsAAIAUAlFRWaEy6pVUo0a5szeCRPO/BrvgA0UQAAAKmHmTFP%2B6tUzV/Do3Nl2IuD0uBQcAACVcOwyn3ffTflD6KAAAgBQCeefb%2B6zsuzmAM4GBRAAgEoYMsTcz50rrVljNwtwpiiAAABUQocO5mzfo0fN/bHZf0AwowACDpWWlqb4%2BHglJCTYjgI43tSpZvTLDz9IgwdLR47YTgSUjzmAgMMxBxAIjHXrpG7dzJVARo2SXn6Z4c8IXqwAAgDgB//3f9Ls2ab0TZ9uBkEDwYoCCACAn/zhD2Z3sCQ9/LD06qt28wBloQACAeLz%2BTRp0iTFxMSoVq1a6tGjh77%2B%2Buty3zNp0iR5PJ5St%2Bjo6AAlBlARY8ZIEyea7dGjpfnz7eYBToUCCATIM888o8mTJys1NVVr1qxRdHS0rrzySuXn55f7vtatWysnJ6fk9tVXXwUoMYCK%2BvOfpdtuM2cGDxkiLVtmOxFQGgUQCACfz6cpU6Zo4sSJ6tevn9q0aaNZs2bpwIEDmj17drnvrVGjhqKjo0tuDRo0CFBqABXl8ZiTQK67Tjp8WLr2Wmn9etupgOMogEAAbNmyRbm5uerZs2fJc2FhYbr00ku1atWqct%2BblZWlmJgYNW3aVIMHD9bmzZurOi4AP6he3ZwUcumlUn6%2BlJgo8dcXwYICCARAbm6uJKlhw4alnm/YsGHJ106lU6dOeuONN7RkyRLNmDFDubm56tKli3bv3l3mewoLC%2BX1ekvdANgRHi69/77Urp20Y4fUq5f0yy%2B2UwEUQKBKZGRkqG7duiW3oqIiSZLnhKFgPp/vpOd%2BLTExUf3791fbtm11xRVXaNGiRZKkWbNmlfmelJQURUVFldxiY2P98BMBqKioKGnxYunCC82g6D59pIIC26ngdhRAoAr07dtXmZmZJbf69etL0kmrfTt37jxpVbA8derUUdu2bZVVzlXnk5OTlZeXV3LbunVrxX4IAH7TqJH08cfSuedKX3xhLhlXXGw7FdyMAghUgYiICDVv3rzkFh8fr%2BjoaC1durTkNYcPH9by5cvVpUuXM/7cwsJCbdy4UY0aNSrzNWFhYYqMjCx1A2Dfb38rLVwohYWZ%2B7FjJa7FBVsogEAAeDwejR07Vk8%2B%2BaTee%2B89bdiwQTfffLNq166toUOHlrzu8ssvV2pqasnjcePGafny5dqyZYtWr16tAQMGyOv1avjw4TZ%2BDACV1LWr9NZbZjs1VXrpJbt54F41bAcA3OL%2B%2B%2B/XwYMHdccdd2jv3r3q1KmTPvnkE0VERJS8ZtOmTdq1a1fJ423btmnIkCHatWuXGjRooM6dO%2Bvzzz9XXFycjR8BgB8MGCA984x0//1SUpLUrJk5LhAIJI/PxwI04GRer1dRUVHKy8tjdzAQJHw%2BadQoacYMqW5d6bPPpIsusp0KbsIuYAAAAszjkdLSpN//Xtq/X%2BrbV9q503YquAkFEAAAC845R3rnHal5c%2Bmnn8yu4cOHbaeCW1AAAYdKS0tTfHy8EhISbEcBUIZzz5U%2B%2BECKjJRWrpTuvtt2IrgFxwACDscxgEDwW7RIuuYac2zg9OnSyJG2E8HpWAEEAMCy3r2lJ54w23feKX3%2Bud08cD4KIAAAQWDCBKl/f6moyNzv2GE7EZyMAggAQBDweKTXX5datZK2b5cGD5aOHLGdCk5FAQQAIEhEREjz55vZgJ9%2BKj34oO1EcCoKIAAAQaRlS2nmTLP99NPmLGHA3yiAAAAEmYEDj4%2BEGT7czAkE/IkCCABAEHr2WaljR2nvXmnQIHNyCOAvFEDAoRgEDYS2mjWluXOlevWk1aulBx6wnQhOwiBowOEYBA2Etvfek/r1M9uLF0tXXWU3D5yBFUAAAILYdddJY8aY7eHDpdxcu3ngDBRAAACC3HPPSW3bSjt3SjffLB09ajsRQh0FEACAIBceLr39trlfskR66SXbiRDqKIAAAISA%2BHjp%2BefN9oQJ0ldf2c2D0EYBBAAgRIweLfXuLRUWSjfcYO6BiqAAAgAQIjwe6bXXpAYNzArgQw/ZToRQRQEEHIo5gIAzNWwozZhhtp97Tlq50m4ehCbmAAIOxxxAwJlGjDDXDG7aVPrPf6S6dW0nQihhBRAAgBD0wgtSXJy0ZYs0bpztNAg1FEAAAEJQZKRZAZSk6dOlv/3Nbh6EFgogAAAh6ve/P36VkBEjJK/Xbh6EDgogAAAh7KmnzHGA2dnS/ffbToNQQQEEACCE1a1rRsNIZlfwsmV28yA0UAABAAhxl10m3X672b71VqmgwG4eBD8KIAAADvD001JsrLR5s/Tww7bTINhRAAGHYhA04C6RkdIrr5jtKVOkNWvs5kFwYxA04HAMggbc5YYbpNmzpXbtTAk85xzbiRCMWAEEAMBBXnhBOvdc6d//NtvAqVAAAQBwkPPPl55/3mxPmmSOCVRBgXT4sM1YCDIUQCCIzZ8/X7169VL9%2BvXl8XiUmZlpOxKAEDB8uDkzuP/BN%2BW7%2BGIzK6Z2bekPf5DWr7cdD0GAAggEsYKCAnXt2lVPPfWU7SgAQojHI81r86je1DA1y/%2B3ebK4WHr/falbN%2Blf/7IbENZxEggQAn788Uc1bdpU69ev18UXX3xW7%2BUkEMCFtm2TLrzQlL5T6dyZEuhyNWwHAOBfhYWFKiwsLHns5eKggPvMmVN2%2BZOkzz%2BXfvhBat48cJkQVNgFDDhMSkqKoqKiSm6xsbG2IwEItN27T/%2BaPXuqPgeCFgUQCBIZGRmqW7duyW3lypUV%2Bpzk5GTl5eWV3LZu3ernpACCXps25X89LIzVP5djFzAQJPr27atOnTqVPG7cuHGFPicsLExhYWH%2BigUgFA0cKI0bJ%2B3YceqvDxlihgXCtSiAQJCIiIhQRESE7RgAnCAsTHrvPenqq6V9%2B0p96csandRs0hTVsxQNwYECCASxPXv2KDs7W9u3b5ckfffdd5Kk6OhoRUdH24wGINj97ndSVpY0c6a0apWKa4Yr6bP%2BStt%2Bne58oYamTLEdEDYxBgYIYunp6brllltOev6RRx7RpEmTzugzGAMD4JhPPpF69ZKqVzeXimvd2nYi2EIBBByOAgjg1667TlqwQLqHDVqzAAARfUlEQVT8cmnpUjM0Gu7DWcAAALjI5MnmEMG//90UQbgTBRAAABdp2tScICxJ994rHTpkNw/soAACDpWWlqb4%2BHglJCTYjgIgyEyYIMXESFu2iJNBXIpjAAGH4xhAAKfy5pvSsGFS3brmZGEGC7gLK4AAALjQDTdICQnS/v3Sgw/aToNAowACAOBC1aod3/07c6YZCwP3oAACAOBSXbpIgwZJPp85IYSDwtyDAggAgIulpEg1a5qxMB99ZDsNAoUCCACAizVtKo0da7bvu086csRuHgQGBRAAAJdLTpbOO0/auFF67TXbaRAIFEDAoZgDCOBM1asnPfSQ2X7kEXNmMJyNOYCAwzEHEMCZOHxYio%2BXNm2SJk0yRRDOxQogAABQzZrSk0%2Ba7WeflXbssJsHVYsCCAAAJEkDB5rh0AUF0mOP2U6DqkQBBAAAkiSPR3rmGbP96qvSDz/YzYOqQwEEAAAlevSQrrrKjIM5dmIInIcCCAAASnnqKXP/9tvS%2BvV2s6BqUAABAEAp7dpJQ4ea7QcesJsFVYMCCAAATvLYY1KNGtLHH0srVthOA3%2BjAAIOxSBoAJXRrJl0661m%2B4EHJKYGOwuDoAGHYxA0gIravt0UwUOHpEWLpKuvtp0I/sIKIAAAOKWYGOnOO832gw%2ByCugkFEAAAFCm8eOlunXN2cDvvms7DfyFAggAAMpUv750zz1m%2B5FHpOJiu3ngHxRAAABQrnvvlerVk775Rpo713Ya%2BAMFEAAAlCsqSho3zmw/%2Bqi5SghCGwUQAACc1t13S%2BedJ33/vZSRYTsNKosCCDgUcwAB%2BFNEhHTffWb7z39mFTDUMQcQcDjmAALwl4ICqWlT6ZdfpJkzpVtusZ0IFcUKIAAAOCN16hxfBXziCamoyG4eVBwFEAAAnLE77pAaNJA2beJYwFBGAQQCZP78%2BerVq5fq168vj8ejzMzM074nPT1dHo/npNuhQ4cCkBgATvbrVcDHH%2BdYwFBFAQQCpKCgQF27dtVTTz11Vu%2BLjIxUTk5OqVt4eHgVpQSA0xs92gyI3rRJmj3bdhpURA3bAQC3uOmmmyRJP/7441m9z%2BPxKDo6ugoSAUDF1K1rhkMnJ5tjAW%2B4Qape3XYqnA1WAIEgt3//fsXFxalJkybq06eP1q9fbzsSAGjMGOncc81cwHnzbKfB2aIAAkGsZcuWSk9P18KFCzVnzhyFh4era9euysrKKvM9hYWF8nq9pW4A4G8REdLYsWb7iSeko0ft5sHZoQACVSAjI0N169Ytua1cubJCn9O5c2fdeOONateunbp376558%2BapRYsWmjp1apnvSUlJUVRUVMktNja2oj8GAJTrrrvMZeK%2B/lpasMB2GpwNCiBQBfr27avMzMyS2yWXXOKXz61WrZoSEhLKXQFMTk5WXl5eyW3r1q1%2B%2Bd4AcKJ69aQ77zTbTzwhcWmJ0MFJIEAViIiIUEREhN8/1%2BfzKTMzU23bti3zNWFhYQoLC/P79waAUxk7VnrhBWndOunjj6XERNuJcCZYAQQCZM%2BePcrMzNQ333wjSfruu%2B%2BUmZmp3NzcktcMGzZMycnJJY8fffRRLVmyRJs3b1ZmZqZGjBihzMxM3X777QHPDwCnUr%2B%2BGQsjmVVAhAYKIBAgCxcuVPv27dW7d29J0uDBg9W%2BfXu98sorJa/Jzs5WTk5OyeN9%2B/Zp5MiRatWqlXr27Kmff/5ZK1asUMeOHQOeHwDKkpQk1awpffaZtGKF7TQ4Ex6fjz32gJN5vV5FRUUpLy9PkZGRtuMAcKjRo6VXXpF69TK7ghHcWAEEAACVdt99Zhj0kiXmeEAENwogAACotP/5H2nQILN9lle8hAUUQMCh0tLSFB8fr4SEBNtRALjEhAnm/q9/lcqZVoUgwDGAgMNxDCCAQOrTR1q0SLrtNunVV22nQVlYAQQAAH5zbBVw1izpV0MNEGQogAAAwG%2B6dZO6dJEOH5ZefNF2GpSFAggAAPxq/Hhz//LLktdrNwtOjQIIAAD8qk8fqVUrU/6mT7edBqdCAQQAAH5VrZo0bpzZnjLF7A5GcKEAAgAAv7vhBqlRI2n7dmnOHNtpcCIKIAAA8LuwMOlPfzLbzz0nMXQuuFAAAYdiEDQA20aNkurWlTZsMJeIQ/BgEDTgcAyCBmBTUpL0wgvS5ZdLf/ub7TQ4hhVAAABQZf70J6l6denvf5cyM22nwTEUQAAAUGXi4qSBA8325Ml2s%2BA4CiAAAKhSSUnm/u23pZ9/tpsFBgUQAABUqYQEqXt3qahISk21nQYSBRAAAATAsVXA6dOlggK7WUABBAAAAXDNNVKzZtLevdKsWbbTgAIIOBRzAAEEk%2BrVjw%2BGfvFF6ehRu3ncjjmAgMMxBxBAsMjPl2Jjpbw86cMPpd69bSdyL1YAAQBAQERESLfdZrZfeMFuFrejAAIAgIAZM0aqVs0Mht6wwXYa96IAAgCAgLnwQum668z2Sy9ZjeJqFEAAABBQx04GefNNafduu1ncigIIAAACqls3qX176dAhacYM22nciQIIAAACyuM5vgo4bZp05IjdPG5EAQQAAAE3aJDUoIG0dau0YIHtNO5DAQQcikHQAIJZeLg0apTZnjrVbhY3YhA04HAMggYQrH7%2B2ZwVfOSIlJkptWtnO5F7sAIIAACsaNxY6tfPbLMKGFgUQAAAYM1dd5n7jAxpzx67WdyEAggEqaKiIo0fP15t27ZVnTp1FBMTo2HDhmn79u22owGA33Ttanb9HjokzZxpO417UACBIHXgwAGtW7dODz30kNatW6f58%2Bfr%2B%2B%2B/V9%2B%2BfW1HAwC/8XikO%2B8029OmScXFdvO4BSeBACFkzZo16tixo3766SddcMEFZ/QeTgIBEOwOHJCaNJH27pU%2B/FDq3dt2IudjBRAIIXl5efJ4PKpXr16ZryksLJTX6y11A4BgVru2dMstZjstzW4Wt6AAAiHi0KFDmjBhgoYOHVruSl5KSoqioqJKbrGxsQFMCQAVM3q0uf/4Y2nTJrtZ3IACCASJjIwM1a1bt%2BS2cuXKkq8VFRVp8ODBOnr0qKZNm1bu5yQnJysvL6/ktnXr1qqODgCV1ry5dNVVks8nvfKK7TTOxzGAQJDIz8/Xjh07Sh43btxYtWrVUlFRka6//npt3rxZ//jHP3Teeeed1edyDCCAUPHBB1LfvtK550rbtkm1atlO5FwUQCCIHSt/WVlZWrZsmRo0aHDWn0EBBBAqioulZs2kn36S0tOl4cNtJ3IudgEDQerIkSMaMGCA1q5dq4yMDBUXFys3N1e5ubk6fPiw7XgA4HfVqx%2B/PvBpjnZBJbECCASpH3/8UU2bNj3l15YtW6YePXqc0eewAggglOzYIcXGSkVF0pdfSv/3f7YTORMrgECQuvDCC%2BXz%2BU55O9PyBwChpmFDqX9/s83JIFWHAggAAILKsZEws2dLeXl2szgVBRAAAASV7t2l%2BHipoEB66y3baZyJAgg4VFpamuLj45WQkGA7CgCcFY/n%2BMkgr7xiZgPCvzgJBHA4TgIBEIr27ZNiYqSDB6XPPpO6dLGdyFlYAQQAAEGnXj1p0CCzzckg/kcBBAAAQenYbuB33pH27rWbxWkogAAAICh16iS1aycdOiS98YbtNM5CAQQAAEHJ45FGjjTbr77KySD%2BRAEEAABB64YbpNq1pW%2B%2BkVatsp3GOSiAAAAgaEVFHT8Z5NVX7WZxEgog4FDMAQTgFLfdZu7feceMh0HlMQcQcDjmAAIIdT6fdNFF0oYNUmqqNGaM7UShjxVAAAAQ1Dye46uAM2ZwMog/UAABAEDQu/FGKSxM%2Bve/pbVrbacJfRRAAAAQ9M49V%2Brf32z/5S92szgBBRAAAISEW28193PmSAUFdrOEOgogAAAICZdeKjVrJuXnS/Pm2U4T2iiAAAAgJFSrJo0YYbZfe81ullBHAQQAACFj%2BHCpenXps8%2Bkb7%2B1nSZ0UQABh2IQNAAniomREhPN9syZdrOEMgZBAw7HIGgATrNggXTddVLDhtLWrdI559hOFHpYAQQAACGld2/p/POlHTukjz6ynSY0UQABAEBIOeccadgws81u4IqhAAIAgJDzxz%2Ba%2B0WLpNxcu1lCEQUQAACEnFatpM6dpeJi6c03bacJPRRAAAAQkm65xdy//rrEKa1nhwIIAABC0qBBUq1a0saN0hdf2E4TWiiAgEMxBxCA00VFSf37m%2B3XX7ebJdQwBxBwOOYAAnCyv/9duuIKUwZzcsyKIE6PFUAAABCyLrtMiouT8vLMgGicGQogAAAIWdWqHZ8JmJ5uNUpIoQACAVBUVKTx48erbdu2qlOnjmJiYjRs2DBt37693PdNmjRJHo%2Bn1C06OjpAqQEgNAwfbu6XLpW2bbObJVRQAIEAOHDggNatW6eHHnpI69at0/z58/X999%2Brb9%2B%2Bp31v69atlZOTU3L76quvApAYAEJHs2ZS9%2B5mFMxbb9lOExpq2A4AuEFUVJSWLl1a6rmpU6eqY8eOys7O1gUXXFDme2vUqMGqHwCcxvDh0sqV0qxZ0vjxksdjO1FwYwUQsCQvL08ej0f16tUr93VZWVmKiYlR06ZNNXjwYG3evLnc1xcWFsrr9Za6AYDTDRxozgD%2B9ltpzRrbaYIfY2AACw4dOqRu3bqpZcuWequc/RWLFy/WgQMH1KJFC%2B3YsUOPP/64vv32W3399dc677zzTvmeSZMm6dFHHz3pecbAAHC6Bx%2BUIiOlm2%2BWzj/fdprgRgEEqkBGRoZGjRpV8njx4sXq3r27JHNCyMCBA5Wdna1PP/30rEpZQUGBmjVrpvvvv19JSUmnfE1hYaEKCwtLHnu9XsXGxlIAAQAlOAYQqAJ9%2B/ZVp06dSh43btxYkil/119/vbZs2aJ//OMfZ13I6tSpo7Zt2yorK6vM14SFhSksLKxiwQEArkABBKpARESEIiIiSj13rPxlZWVp2bJlZe7CLU9hYaE2btxYspoIAEBFcBIIEABHjhzRgAEDtHbtWmVkZKi4uFi5ubnKzc3V4cOHS153%2BeWXKzU1teTxuHHjtHz5cm3ZskWrV6/WgAED5PV6NfzY0CsAACqAFUAgALZt26aFCxdKki6%2B%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%2Bf9Yo7Crwg7PSwAAAABJRU5ErkJggg%3D%3D'}