Genus 2 curve data - 349.a.349.1

Formats: - HTML - YAML - JSON - 2024-07-27T04:09:08.990346

• 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': '270569359746706556', 'abs_disc': 349, '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': '[[349,[1,-11,337,349]]]', 'bad_primes': [349], 'class': '349.a', 'cond': 349, 'disc_sign': 1, 'end_alg': 'Q', 'eqn': '[[0,0,-1,-1],[1,1,1,1]]', 'g2_inv': "['-1024/349','2176/349','1984/349']", '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': "['8','208','1464','-1396']", 'igusa_inv': "['4','-34','-124','-413','-349']", 'is_gl2_type': False, 'is_simple_base': True, 'is_simple_geom': True, 'label': '349.a.349.1', 'leading_coeff': {'__RealLiteral__': 0, 'data': '0.1656123320984078287720952736293483346027630844236898789', 'prec': 190}, 'locally_solvable': True, 'modell_images': ['2.6.1'], 'mw_rank': 0, 'mw_rank_proved': True, 'non_maximal_primes': [2, 13], 'non_solvable_places': [], 'num_rat_pts': 5, 'num_rat_wpts': 1, 'real_geom_end_alg': 'R', 'real_period': {'__RealLiteral__': 0, 'data': '27.988484124630923062484101243', 'prec': 100}, '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': 13, 'torsion_subgroup': '[13]', 'two_selmer_rank': 0, 'two_torsion_field': ['5.1.5584.1', [1, 1, 0, 0, -1, 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': '349.a.349.1', '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': '349.a.349.1', 'mw_gens': [[[[0, 1], [1, 1], [0, 1]], [[0, 1], [0, 1], [0, 1], [0, 1]]]], 'mw_gens_v': True, 'mw_heights': [], 'mw_invs': [13], 'num_rat_pts': 5, 'rat_pts': [[-1, 0, 1], [0, -1, 1], [0, 0, 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

  
  {'conductor': 349, 'lmfdb_label': '349.a.349.1', 'modell_image': '2.6.1', 'prime': 2}
• g2c_tamagawa •

  Column	Type
  cluster_label	text
  label	text
  p	bigint
  tamagawa_number	smallint

  
  {'cluster_label': 'c4c2_1~2_0', 'label': '349.a.349.1', 'p': 349, 'tamagawa_number': 1}
• g2c_plots •

  Column	Type
  label	text
  plot	text

  
  {'label': '349.a.349.1', 'plot': 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6pefN62iJACIHI4Bwn/R0qV%2B/48Nf9nN9%2BoS%2BJiDYxoyRFi068XOffy598EFo6wEQ0fh18hz4/X6lp6c7XUbkmTjRgt7JLF8uffutVLt26GoCTiEzU0pNtW/d9HRpz57AsX%2B/tG%2Bf3R44IB08GDgOHZIyMjL10JQ3VfF/75V2zK0krX3oDY2Z10Z580rR0VK%2BfFJMjN0WKGBbKZ53np0XLCgVKmQD5tlH4cJSVFSIvyiAy8XGxsrn8zldhiO4BHwOsrv1AwCA8OPl3XYIgOfgXEYA4%2BPjtXTp0lypI%2BLea/JkqXv3v39NUpJUpUpo6/Lge6WlpalixYravHlzrvyQdMvnmJkpbdsmbdwobdokDRnymm644R79/rv0%2B%2B/23O7dZ1vZAZUokV%2BxsTYKd955NiJXoEBgpC575C5fPhvN8/kOq9vEf6jSVvt80iRVlLRZ/5sDKOmXatfrk5ve16FDyjkOHrQRxYyMwCjjvn3S3r12pKXZaOTBg2f3mRQuLJUpI5UrJ5Uta1s0vvvuM/r3v4eoUiWpYkX7/M6WW74fjhSp3/Ph8F5OfO29PALIJeBz4PP5zvqbNCoqKtd%2B64i49%2BrcWXrkESkl5cTPt2wp1asX%2Bro8%2Bl6SVLhw4Vx5v1B%2BjllZFu7WrpWSk%2B349Vfpt9%2BkDRuOXVj7iN599/j3yJ/fgk/p0naUKiWVLGlHiRJS8eLWsqhYMetbGRcn1a/fSD/99NOZf0L/d6/UtetRDxVWIABe8ko/XXLd2X3tatasp4ULv9dff1mw/fNPadcuaedOO/74Q9q%2BXdqxw263bbNAmZZmR3Lyke/2vG6%2BOXCvVCn7XaxqVemCC6QLLwzcli5tu/qcDN/zvNeJuPFrH4kIgA7p27cv73Uy0dHShx/Kf8MN8h07wlqlijR2rDN1efS9clMwPke/34LeDz9IP/5ox%2BrVFvz27z/5x%2BfNK1WuLJ1/vrR378%2B69tqLVLGijXJVqGCjXkWK/H2A%2Bbu6ztitt0pffml7IB5rwADpuuvO7n0l9e9/t0qVsrB2Ovx%2BC37btilnVHTLFmnzZmnRog3y%2Bc7Xxo02urhjhx1Llhz/PrGxUvXqdtSsGTiqV7dwzfc87xVMbq3LLbgEDNd6rs8m%2BV97TS2i52vfoSVKeGaY8vfpY/8rIySy57m6ZZ5MVpaN4i1damuBVq602QAnu1ybL5%2BNRGWHkAsvtKNqVQt5rlsU4fdLM2boz3/9SyVmzdLOG25Q8fvvl5o3d7qy4/j99nVfv15aty5w/PqrHZs2nbyBe548NkpYu7YddepIdeva31F0dGg/j2O57XveS/jahxYjgHCl8eOlwa9VkjRcVcYf0po1z6jhgw/a5CmETExMjJ588knFOPR1T0%2B3Bd/ffGO3S5bYSttj5c0rXXSRhYg6daRatex%2B1aph1jrP55NatVJWQoJUqpSy3n7brje7kM8nFS1qR3bf6iNlZNjl9uRkG41ds8aOn3%2B2v8NffrFj8uTAx0RH299bvXrS//2fVL%2B%2BHSVKhO7zcvp73sv42ocWI4BwFb9fGj1auu8%2BO3/wQemll5yuCqGyd6%2B0cKE0d640f760YsXx7SDz57dQcOmlFjzq17fAF0n/Z0TySIjfb9N7V68OHNmX7k%2B2pq5CBalBA/v7vvhi6ZJL7BK9R%2BfuA7mCAAjX2L1b6ttXeu89u3/PPRYG89CuPKIlJ0vTpkkzZlgf5GN3PTv/fKlJE6lxY6lRIxvlc/oyYbBFcgA8Gb/fVmWvWiV9/70dSUk2ingiZcrYLwHZR0KCawdLAVciAMJxfr9tcvDggzbpPCpKeu45u89v%2BJHpxx/t7/zTT4/f3rlyZemaa6Qrr7SjQgUnKnSWFwPgyaSlWRhcscLmfC5fLv3004k3Cqpc2bZPTkiw24svPrc2NUAkIwDCMX6/NG%2BeNGSItHixPVa9ui2CvOwyR0tDEPz5pzRhgvTOOzbKky062oJeq1a20LVaNYI/AfDv7dtnoXDZMlsQtHSpzTM89n%2BzqCibT9iokf1Muewymxfq9e8vQCIAwgGZmXbJ74UXbGK/ZL%2BlDxokPfSQzfFC5PjpJ2nECNvhLyPDHsuXz8Jehw7SDTdY/zwEEADPXGqqjQ4uWSJ99539UnmiVqKlSlkgbNzYjvh4fubAm5hdhZDZscNCX7VqUvv2Fv5iYqR%2B/axtxOOPn/gH8WeffaZrr71WJUqUkM/nU1JSUuiLj2B%2Bv19Dhw5VuXLlVKBAAV155ZVavXr1337M0KFD5fP5jjrKlClz1GuSk6WOHa3Nx9tvW/hr0EBKTLRL/ZMnW%2Bs7wh9yQ1ycdPXV0uDB0qRJ0tatNqfwww%2BlBx6w0Jcvn/0cmjrVfuG84grpvPMOqU6dNA0caN%2BTO3Yc/b7jxo077nvd5/PpwIEDznyiEWjhwoVq3bq1ypUrJ5/Pp8lHLg1H0IRTgwSEoYwMm9w/frw0fbp0%2BLA9XrSo1KuXdO%2B9ttPC39m7d6%2BaNGmiDh066K677gp%2B0R7zwgsvaOTIkRo3bpyqV6%2BuYcOGqUWLFlq7dq1iY2NP%2BnG1a9fWnDlzcu5H/a%2BpXkaG9PTTFvaz/77bt5cGDrRLcFx%2BO7nExEQlJiYqMzPT6VLCns8nVapkR8eO9lhGhvTCC3M0dOgXqlevjzZtqqA//4zW6tXRWr3aRqolm4rSpIl0%2BeXSjh2xio0trOTktUe9f36GDXPN3r17Va9ePfXo0UM33XST0%2BV4hx/IZRkZfv/nn/v9t9/u98fF%2Bf02M8eOhAS//623/P69e8/8fdevX%2B%2BX5F%2B5cmXuF%2B1RWVlZ/jJlyvife%2B65nMcOHDjgj4uL848ZM%2BakH/fkk0/669Wrd9zj69b5/fXrB/6%2Br7vO71%2B1KiilR7TU1FS/JH9qaqrTpUSchIQEf%2B/evf1%2Bv9%2BfleX3//qr31%2B27MP%2B%2BvUX%2B2vXPvrnVfbh86X4b7rJ73/5Zb9/%2BXK///Bhhz%2BJCCbJP2nSJKfL8ARGAJEr/vpLmjVLmjLFRvzS0gLPlS8vdekidetmTXrhHuvXr1dKSopatmyZ81hMTIyaNWumb775Rr169Trpx/7yyy8qV66cYmJi1LBhQ91554vq2rWitm%2B3xr1jx9rIH%2BAWBw8e1PLlyzVo0CBJNkp4wQVShw4HlJT0sBYsWKC//rLG4199ZcfixZk6fLi0Pv3UVq1LUsGCmbr88ihdcYXUtCnzCBGeCIA4K1lZtgpv1ixp5kzp669tcUe2MmWkG2%2B0Sy9Nm9LLz61S/jdLvnTp0kc9Xrp0aW3cuPGkH9ewYUONHz9e1atX1/bt2zV06Ehdd12mMjNtB4fPP/dm%2Bxa4286dO5WZmXnC7/fsfwtFi9qK9Fat7LkFC5Zpzpzd2r37/7R0aYyWL4/R3r0FNWuW/fyTbC5zQoJyAmHjxrYPMuBmBECctg0bbIeGOXNsz/pjJ0vXqiW1bi21bWs9uM4m9E2cOPGoUaeZM2eqadOm51Y4chz79f38888lSb5jJub5/f7jHjvSddddl3Net25dzZhxhb7%2BOp%2BKFUvVnDlxNOSFq53J93uzZg3VrFng/qFDWapdu5PKlu2gUqVu1qJF0vbt1sR80SJ7TZ48tuApOxA2bRra7eyA00EAxAn5/bbJ%2B8KF0oIFti3Xhg1Hv6ZgQemqq6R//EO6/nqpSpVz/3PbtGmjhg0b5twvX778ub8pchz79c34X1%2BWlJQUlT1iNc6OHTuOGyU5mawsafz4fJKkhIT3VLLkPblYMZB7SpQooaioqJzRvmxn8v0eHZ1HzZoV1pYtb%2Bnjj2%2BW3297Gi9cGAiB69dbS5rly6VRo%2BzjLrro6EBYqVJuf3bAmSEAQpKt1ly1yi7lfv21zX35/fejXxMVZZc5mje3nRouu8zaKuSm2NjYv115inNz7NfX7/erTJkymj17tho0aCDJ5kktWLBAzz///Gm95%2BbN1uRZylBCws4gVA3kjnz58umSSy7R7Nmz1f6ICaqzZ89W27ZtT%2Bs9/H6/kpKSVLduXUk2j7B6dTt69rTXbNligTA7FP70k/Tzz3a8/rq9pnJlW2V8xRV2W7MmU2UQWgRAj0pJsYapixfb8d131l3/SNHRtsfmFVfYTg1Nmjgzr2XXrl3atGmTtm7dKklau9baMZQpU%2Ba43nM4Mz6fT/fff7%2BeffZZVatWTdWqVdOzzz6r8847T126dMl5XfPmzdW%2BfXv169dPkjRw4EC1bt1alSpVUlLSbkkWHrt37%2BbEpwGctgEDBqhr16669NJLddlll2ns2LHatGmTevfuLUnq1q2bypcvr%2BHDh0uSnnrqKTVq1EjVqlVTWlqaXn31VSUlJSkxMfGkf0aFCrbwLfuf0M6d9kt19gjhihXWo3DjRmuQLknFiwdazzRtatvY5fYv2G61Z88e/frrrzn3169fr6SkJBUrVkyVGCoNGgKgB2R3yF%2B2zILe0qXSpk3Hvy4uzpqlNmliP4ASEtyxj%2BbUqVPVo0ePnPu33HKLJOnJJ5/U0KFDHaoqcjz88MPav3%2B/%2BvTpo7/%2B%2BksNGzbUF198cdRI4W%2B//aadOwOje1u2bFHnzp21c%2BdOlShRWtHRP%2BvQoULauLGyqlZ14rMATk%2BnTp30559/6umnn9a2bdtUp04dzZgxQ5UrV5Ykbdq0SXmOGIrbvXu37r77bqWkpCguLk4NGjTQwoULlZCQcNp/ZokSUrt2dkjSnj3WCD87EC5ZYqPoU6faIdmq4oYN7edxkya2sKRIkVz7MrjKsmXLdNVVV%2BXcHzBggCSpe/fuGjdunENVRT62goswu3bZhukrVljoW7HC5qccy%2BezRRuNGgX2ybzoIi5B4Oz07m2Xtpo0sctefB%2BdG7aC85aDB%2B3n9qJFgfYzNq0iwOezXXWyt7Br0sRa2NBYHWeLABim/H5blPH991JSUuA4WeeO88%2B3y7nx8XZceiltCpB7Nm%2B2XyD27pWeekp64gmnKwpvBEBv8/ulNWsC87G//tq2yzxWyZL2y3vjxnZ76aXuuGqD8EAADAOpqdKPP0o//GALNVatsvMjmy0fqWpVa0FwySV2XHwxLQgQfO%2B8I91xh52PGWNb/eHMHLkVXHJyMgEQOXbssAbV2Qv1li%2B3kcMj5c0r1asXuLLTqBGjhDg5AqCL7Ntnq8RWr7bjxx/tONF8PckWadSqJdWvb0eDBvaPP1LnicD9HnnE9gCWpGHDpEcf5T%2Bfs8EIIE4lI8Om%2BHzzjR3ffitt23b864oXt/ncDRvabXw8AwIwBEAHpKba8H52a4CffrJjwwYb%2Bj%2BR8uWlunVtl4Xso2ZNC4GAW/j90uDBUnYHmU6dpDfeYLrBmSIA4kz5/TYV49tvA90dVq60oHisqlUD04Hi4%2B0qUaFCoa8ZziIAhsAXX0iTJ1voW7PmxL%2BlZStRwib61q5t%2B%2BZmH0WLhq5e4Fy99pp0773WX/LCC6UJE%2BxyFE4PARC54eBBmyee3epryZKTLwq86CKbMnTppRYI69cnFEY6AmAIDBsmPf740Y%2BVLWv/4GrVCtzWri220ELE%2BOYb6ZZbbFQiTx7p/vulp5%2B2HWTw9wiACJa//rKWYEuXBo5jm/5LFgpr1rQw2KCBHfXrS8WKhb5mBAcBMAS%2B%2BUaaNs3%2BMWUfcXFOVwUE3%2B7dUv/%2B0n/%2BY/crVrStsW68kbmBf4cAiFBKSbFQmL193fLl0v/67h%2BnUiULgvXqBW6rVKH1UzgiAAIIuhkzpD59Am2KmjWTXnrJLjfheARAOC0lxRaZZPeVXbnS9jg%2BkUKFAnPUs2%2BZuuR%2BBEAAIbFvn/Tcc9KLL0oHDthjHTvaZeEaNZytzW0IgHCj3buP7j37/fe2gPFEC00kW7xYp05gTnutWnawKMwdCIAAQmrTJumxx%2ByysN9vl45uu00aMkSqXt3p6tyBAIhwcfiwtHZtoD9t9u3J2pdJdhm5Vi2bDpU9D75mTdrThBoBEIAjVq2yxVHZe5/mySN16CANGmRzi7yMAIhwl5oa6Geb3dt29Wq7tHwyxYtbEKxRI3Bbvbq1rcmXL3S1ewUBEICjli2zy8DTpgUeu/ZaaeBAqXlzby4WIQAiUu3aFeh9%2B/PPgePvRgyjomw702rVAseFF9pt5cr0wz1bBEAArvD99zZH8KOPpKwse6xuXWsf07mzVKCAs/WFAlvBwav27pWSkwP9cpOT7dJycrI9dzJRURYCL7ggcFx5JQvMTgcBEICrrFtnrWLeeSfwg794calnT%2Bmee%2ByHfaRjBBAwfr9tnpDp/FtqAAAbkklEQVScbE2ss49ff5V%2B%2B03av//4j3n66eN77%2BJ4BEAArvTXX9Kbb0qjRwcuD/l80vXXS7162W1UlLM1BgsBEDi1rCwLh7/9ZoFw3To7v/12m0aCv0cABOBqmZk2PzAxUZozJ/B4hQrSnXdKPXpE3qggARBAsBEAAYSN5GTp9deld9%2BV/vzTHvP5pGuuke64Q2rbNjLmChIAAQQbARBA2MnIkD77zC4Rz50beDwuTurUSereXbrssvBdQUwABBBsBEAAYW3dOmncOBsVPLKVxAUXWIPp226zlhHhhAAIINgIgAAiQlaWNH%2B%2BBcFPPz26dURCgtSli209V7asYyWeNgIggGAjAAKIOHv2SFOmSBMm2MKRzEx7PE8e6xHWubPUvr21l3EjAiCAYCMAAoho27dbc%2Bn33pMWLw48njev1KKFjQq2bSsVLepcjcciAAIINgIgAM9Yt87C4Acf2M4j2bLDYIcOUps2zo8MEgABBBsBEIAnrV1rYfCjj2zD%2BmxRUdJVV0k33WSXiUuXDl1NbAUHIFQIgAA8b80a6eOPpU8%2BkVatCjzu80lNmlgQbN9eqlIlNPUwAggg2AiAAHCEX3%2B1VcSffiotXXr0c/XqWRBs29bOg9VnkAAIINgIgABwEps3S5Mm2bFwobWayXb%2B%2BTZfsF076fLLpejo3PtzCYAAgo0ACACnYedOafp0C4NffCEdOBB4rkgRqVUrC4TXXms7kpwLAiCAYCMAAsAZ2rdPmj1bmjzZQuHOnYHnoqOt12Dr1nacf/6Zvz8BEECwEQAB4BxkZkrffmuNp6dNs9XFR6pTJxAGExJslfGpEAABBBsBEAByUXKy9OyzqzR1qvTXX7Uk5c15rkQJ6frrpRtusEvFx2W7/fulV19V2tixilu3Tqk1aqjwPfdIffrk7iRDAJ5HAASAXDZhwgStX79ecXFVdP/9/1XLlqO1ZElRpaYGXpM3r9SsmYXBVq2kahX2Sy1bSl99pTRJcZJSJRWWLDVOmWIfBAC5gAAIAEGyYcMGValSRStXrlTt2vX11Vc2Z3D6dBspPNIzJUfp0T8GSNLxAVCSxo%2BXunYNXfEAIloepwsAAC%2BIjrYdRkaMsHmCycnSyJHS1VdLefP61eqPcX//BuNO8TwAnAECIAA4oFo16YEHpC%2B/lAYOfE5ltPZvX//HD9uUlCRxzQZAbiAAAsA5mDhxogoVKpRzLFq06IzfY%2BjQASreqMHfvubrP6qrQQOpUiWpd2%2B7jLxv39lWDcDrmFEMAOegTZs2atiwYc798uXLn/F7xMTESH37SosXn/Q13zfspfN%2BkLZskV5/3Y78%2BaXmza3FTKtWUoUKZ/UpAPAgFoEAQJAcuQikfv36f/9iv1%2B64w5p3LjjF4EMGCCNGKEDB6R58wILSTZtOvot6tcP9By85BIpD9d4AJwEARAActmuXbu0adMmbd26Va1atdIHH3ygGjVqqEyZMipTpszJP9Dvl2bMUNqYMYqbPl2p7durcN%2B%2BNsx3gpf%2B%2BKM1n54%2B3QYPj/xpXqZMIAw2by6dd14QPlEAYYsACAC5bNy4cerRo8dxjz/55JMaOnToKT/%2BbHYC%2BeMPacYMC4SzZkl79gSeK1BAuuYaqW1b6ztYuvTpfiYAIhUBEABc5ly3gsvIkBYssDA4derRl4p9PqlRI6ldOwuENWrkYuEAwgYBEABcJjf3Avb7pVWrLAhOmSItX3708zVqSO3bWyCMj2feIOAVBEAAcJncDIDH%2Bv13C4JTptiCkkOHAs%2BVK2dB8MYbbZs6dp4DIhcBEABcJpgB8EipqdLMmdKkSXabnh54rlgxqU0b6aabpBYtpJiYoJUBwAEEQABwmVAFwCNlZNiuJJ99ZqODO3cGnouNtdXEHTpI115ri0oAhDcCIAC4jBMB8EiHD0uLFlkY/OwzaevWwHOFClkY7NhR%2Bsc/rBk1gPBDAAQAl0hMTFRiYqIyMzOVnJzsWAA8UlaW9Rj89FPp44%2BlzZsDz8XG2kriW26xy8T58jlXJ4AzQwAEAJdxegTwZPx%2BackSC4IffWTb0mUrVszmC3bubAtIWE0MuBsBEABcxq0B8EhZWdK330offmiBMCUl8Fz58jYqeNttUr161nsQgLsQAAHAZcIhAB4pM9MaT7//vvTJJ9Lu3YHnateWunaVbr1VqlDBuRoBHI0ACAAuE24B8EgZGbYl3Xvv2U4kGRn2uM9n29F1726Np9mbGHAWARAAXCacA%2BCRdu%2B2y8MTJtiq4myFC0udOkl33CE1bMglYsAJBEAAcJlICYBHWrdOGj/ejvXrA4/XqiXdeafUrZtUooRz9QFeQwAEAJeJxACYLSvL5gu%2B847NF9y/3x7Pl88uDffqJV15JaOCQLARAAHAZSI5AB4pNdUWjrz5prR8eeDx6tWl3r1tvmCxYs7VB0QyAiAAuIxXAuCRVqyQxo6VJk6U9uyxx/Lnt76CfftKl1zibH1ApCEAAoDLeDEAZktPtxXEr70mff994PHLLpPuvdeaTUdHO1cfECkIgADgEm7cCs4pfr81mk5MtJXEhw7Z4%2BXKSf36SXffLRUv7myNQDgjAAKAy3h5BPBEUlKk11%2B3UcHt2%2B2xAgVsjuCAAVK1as7WB4QjAiAAuAwB8MQyMmzruVGjpKQke8znk9q1kx5%2BWGrUyNn6gHDCdt0AgLAQE2P9AleskObNk1q1skvFkybZHMFmzaT//tceA/D3CIAAgLDi81mvwOnTpdWrpR49bGHIwoXSdddJl14qffqp9RwEcGIEQABA2KpVS3r7bdtp5IEHbI/hFSukm2%2BW/u//pA8%2BkDIzna4ScB8CIAAg7FWoII0cKW3cKD32mBQXZ6ODnTtLdetaEGREEAggAAIAIkaJEtI//ylt2CA9/bRUpIj0888WBOvVs/mCzBEECIAAgAhUpIj0%2BOMWBJ96ykYEf/xRuvFGKSFBmjPH6QoBZxEAAQARKy5OeuIJaf16acgQqWBBadkyqUULO1ascLpCwBkEQABAxCtaVBo2zBaL3HuvrRqeM8f2GL71VhspBLyEAAgALpGYmKhatWopPj7e6VIiVqlS0iuvSMnJFvwk23u4Zk1p0CApLc3Z%2BoBQYScQAHAZdgIJnZUrpYEDpblz7X6pUtIzz1hvwagoZ2sDgokRQACAZzVoYJeCp06VqleXduyQ7rpLio%2BXvv7a6eqA4CEAAgA8zeeTWre2VcIjR9rCkZUrpcsvt63ntm93ukIg9xEAAQCQLQx54AHpl1%2BkO%2B%2B0YDhhgo0Mjh7NjiKILARAAACOULKk9Oab0uLFtq9wWprUv7/UqBFtYxA5CIAAAJxAQoKFwMREuyy8bJnNDXzwQWnvXqerA84NARAAgJOIipL69JHWrJE6dbL9hEeOlOrUYTcRhDcCIAAAp1CmjPTBB9Lnn0uVKlnj6BYtpJ49pdRUp6sDzhwBEACA03T99bZauF8/u//WW1Lt2tKsWc7WBZwpAiAAAGcgNlb617%2BkhQulCy%2BUfv9d%2Bsc/pF69pD17nK4OOD0EQABwCbaCCy9Nm0rff297C0vS2LFSvXrSN984WxdwOtgKDgBchq3gws%2B8edLtt0ubNkl58khDhkiPP269BQE3YgQQAIBzdNVV0qpVUteutlL4n/%2B0EcJ165yuDDgxAiAAALkgLk4aP95WC8fFSUuW2F7DH3zgdGXA8QiAAADkok6dbG5gkya2i0jnztJdd0n79jldGRBAAAQAIJdVrizNny899pjtKfzmm7aV3Nq1TlcGGAIgAABBkDevzQWcPVsqXVr64QfbW/ijj5yuDCAAAkCuOnTokB555BHVrVtXBQsWVLly5dStWzdt3brV6dLgkObNpZUrpSuvtD6BnTpJDzwgHTrkdGXwMgIgAOSiffv2acWKFXr88ce1YsUKffbZZ0pOTlabNm2cLg0OKlvWRgIHDbL7L79swXD7dmfrgnfRBxAAgmzp0qVKSEjQxo0bValSpVO%2Bnj6AkW3yZKlbNyk9XSpfXpo0SaL3N0KNEUAACLLU1FT5fD4VKVLkhM9nZGQoLS3tqAORq107aelSqWZN20auaVNpwgSnq4LXEAABIIgOHDigQYMGqUuXLicdzRs%2BfLji4uJyjooVK4a4SoRajRrWJ7B1aykjw0YEH35Yysx0ujJ4BQEQAM7BxIkTVahQoZxj0aJFOc8dOnRIt9xyi7KysvTvf//7pO8xePBgpaam5hybN28ORelwWOHCdjn40Uft/osvSu3b26VhINiYAwgA5yA9PV3bj5jJX758eRUoUECHDh1Sx44dtW7dOs2dO1fFixc/7fdkDqD3vP%2B%2B1KOHjQbWqydNmyYxEIxgIgACQC7LDn%2B//PKL5s2bp5IlS57RxxMAvWnJEqltW1sZXLasNH26dPHFTleFSMUlYADIRYcPH9bNN9%2BsZcuWaeLEicrMzFRKSopSUlJ08OBBp8uDizVsaCGwdm1p2zbpiiukGTOcrgqRihFAAMhFGzZsUJUqVU743Lx583TllVee8j0YAfS21FTp5pulOXOkqChpzBipZ0%2Bnq0KkIQACgMsQAHHwoHT33dK779r9oUOlJ56wfYWB3MAlYAAAXCZfPumdd6QhQ%2Bz%2B0KFSnz60iUHuIQACAOBCPp80bJiUmGjnY8ZIt9xiK4WBc0UABADAxfr0kT78UIqOlj75xJpH793rdFUIdwRAAABcrkMH6fPPpYIFpdmzpRYtpN27na4K4YwACABAGGjRwlYGFykiffutdPXV0h9/OF0VwhUBEABcIjExUbVq1VJ8fLzTpcClGjWS5s%2BXSpWSVq6UrrxSSklxuiqEI9rAAIDL0AYGp7J2rY0Abt0qVa8uzZ0rlS/vdFUIJ4wAAgAQZmrUkBYtkipXlpKTpWbNpC1bnK4K4YQACABAGKpaVVqwQKpSRfrtN7scvHmz01UhXBAAAQAIU5Ur25zAqlUtBF51FSOBOD0EQAAAwlilShYCs0cCr75a2rbN6argdgRAAADCXMWK0rx5NiL4yy9S8%2BbSjh1OVwU3IwACABABKle2EFihgvTzz9Y3cNcup6uCWxEAAQCIEFWqSF9%2BKZUuLa1aJV1/vZSe7nRVcCMCIAAAEaR6ddsxpFgxackSqW1b6cABp6uC2xAAAQCIMHXqSLNmSbGxdlm4Uyfp8GGnq4KbEAABwCXYCg656dJLpWnTpPz5palTpZ49pawsp6uCW7AVHAC4DFvBITdNmya1by9lZkoDB0ovvuh0RXADRgABAIhgrVtLb71l5y%2B9JI0Y4Ww9cAcCIAAAEa57d%2BmFF%2Bx84EDpvfecrQfOIwACAOABAwdK999v57ffLs2d62g5cBgBEAAAD/D57PJvx47SoUM2L/CHH5yuCk4hAAIA4BF58kjvvitdcYWUlmaNordudboqOIEACACAh%2BTPL02aJNWsKW3ZIrVqJe3Z43RVCDUCIAAAHlOsmDRjhlSypJSUJHXpYm1i4B0EQAAAPKhKFWsQnT%2B/9Qp86CGnK0IoEQABAPCoRo1sTqAkjRoljR3rbD0IHQIgALgEW8HBCR07Sv/8p5337Wt7ByPysRUcALgMW8Eh1Px%2B6dZbpffft/mBS5dKVas6XRWCiRFAAAA8zuez7eLi46Vdu6Q2baT0dKerQjARAAEAgAoUkCZPlsqVk1avlrp2lbKynK4KwUIABAAAkiz8TZokxcRIU6ZITz/tdEUIFgIgAADIkZAgjRlj5089ZUEQkYcACAAAjnL77VL//nbetau0dq2j5SAICIAAAOA4I0bYnsHp6VL79iwKiTQEQAAAcJzoaOmjj2xe4M8/S3feae1iEBkIgAAA4IRKl5Y%2B%2BcTC4McfSy%2B/7HRFyC0EQAAAcFKXXSaNHGnnDz8sff21s/UgdxAAAcAl2AoObtW3r9S5s3T4sG0d98cfTleEc8VWcADgMmwFBzfas8d2ClmzRmrRQpo5U4qKcroqnC1GAAEAwCkVKmTzAc87T5o9W3r2WacrwrkgAAIAgNNSu7b02mt2PnSoNH%2B%2Bk9XgXBAAAQDAaevWzRpFZ2VJXbowHzBcEQABAMAZGT1auugiads2qXt3C4MILwRAAABwRgoWtCbR%2BfPbYpBRo5yuCGeKAAgAuWzo0KGqWbOmChYsqKJFi%2Bqaa67RkiVLnC4LyFV16gQaQw8eLC1b5mw9ODMEQADIZdWrV9fo0aP1ww8/6KuvvtL555%2Bvli1b6g8mSyHC3H23dNNN0qFDNh9wzx6nK8Lpog8gAARZdl%2B/OXPmqHnz5qf9evoAIhzs2iXVry9t3iz16CG9/bbTFeF0MAIIAEF08OBBjR07VnFxcapXr94JX5ORkaG0tLSjDiBcFCsmTZgg%2BXzSO%2B9Yr0C4HwEQAIJg%2BvTpKlSokPLnz69Ro0Zp9uzZKlGixAlfO3z4cMXFxeUcFStWDHG1wLlp1szmAUp2WXjLFmfrwalxCRgAzsHEiRPVq1evnPszZ85U06ZNtXfvXm3btk07d%2B7UG2%2B8oblz52rJkiUqVarUce%2BRkZGhjIyMnPtpaWmqWLEil4ARVg4dkho3tsUgV19tu4XkYZjJtQiAAHAO0tPTtX379pz75cuXV4ECBY57XbVq1XTHHXdocPYwyd9gDiDCVXKy1KCBtG%2BftYa5/36nK8LJkM0B4BzExsbqwgsvzDlOFP4kye/3HzXKB0Si6tWlESPsfNAgafVqZ%2BvByREAASAX7d27V48%2B%2BqgWL16sjRs3asWKFerZs6e2bNmiDh06OF0eEHS9eknXXSdlZEhdu0oHDzpdEU6EAAgAuSgqKkpr1qzRTTfdpOrVq%2BuGG27QH3/8oUWLFql27dpOlwcEnc8nvfWWrQ5euVIaNszpinAizAEEAJdhDiAiwUcfSZ06SVFR0rffSvHxTleEIzECCAAAcl3HjtItt0iZmVL37tKBA05XhCMRAAEAQFCMHi2VKSP9/LP0xBNOV4MjEQABAEBQFC8ujR1r5yNG2KVguAMBEAAABE3r1rYaOCvL9grev9/piiARAAEAQJC98opUtqy0dq305JNOVwOJAAgArpGYmKhatWopnuWSiDBFi0pjxtj5iBHSd985Ww9oAwMArkMbGESqW2%2BV3ntPqlNHWr5cypfP6Yq8ixFAAAAQEq%2B8IpUsKf34o/Tss05X420EQAAAEBIlSkj/%2BpedP/ssewU7iQAIAABCpmNHWxl86JDUs6c1ikboEQABAEDI%2BHzSv/8txcZKixfbOUKPAAgAAEKqQgXpuefs/NFHpc2bna3HiwiAAAAg5Hr3lho3lvbskfr1k%2BhJEloEQAAAEHJ58tg2cdHR0tSp0qRJTlfkLQRAAADgiNq1pYcftvP%2B/aW0NGfr8RICIAAAcMyQIdKFF0pbt0qPP%2B50Nd5BAAQAl2ArOHhRgQKBlcCjR9sOIQg%2BtoIDAJdhKzh4UZcu0vvvS5deau1hoqKcriiyMQIIAAAcN3KkVLiwtGyZ9PrrTlcT%2BQiAAADAcWXKSM88Y%2BePPipt3%2B5sPZGOAAgAAFzhnnukiy%2BWUlMDq4MRHARAAADgClFR0muv2XZx48dLX33ldEWRiwAIAABcIyFB6tnTzvv2lQ4fdraeSEUABAAArvLss1KxYtKqVTYiiNxHAAQAAK5SokRgQcjjj0s7djhbTyQiAAIAANe5667AgpDBg52uJvIQAAEAgOtERUn/%2Bpedv/22tHSps/VEGgIgALgEW8EBR2vcWOra1c7795eyspytJ5KwFRwAuAxbwQEB27ZJ1atLe/ZI774rdevmdEWRgRFAAADgWmXLSo89ZueDBknp6c7WEykIgAAAwNXuv1%2B64AIbDRw%2B3OlqIgMBEAAAuFpMjDRihJ2PHCmtX%2B9sPZGAAAgAAFyvTRupeXMpI4N9gnMDARAAALiezyeNGiXlySN98om0cKHTFYU3AiAAAAgLdetag2hJeuAB2sKcCwIgAAAIG08/LcXGSitWSP/5j9PVhC8CIAAACBulSklDhtj5o49K%2B/Y5W0%2B4IgACAICwct99UuXK0u%2B/B1YH48wQAAHAJdgKDjg9%2BfNLzz1n588/L6WkOFtPOGIrOABwGbaCA07N75caNZK%2B%2B066%2B27p9dedrii8MAIIAADCjs8XuPz75pvSTz85W0%2B4IQACAICwdPnlUrt21g7mkUecria8EAABAEDYeu45KSpKmj5dWrDA6WrCBwEQAACErRo1bA6gZFvEsbLh9BAAAQBAWHvySalgQVsQ8sknTlcTHgiAABBEvXr1ks/n08svv%2Bx0KUDEKl1aGjjQzocMkQ4dcraecEAABIAgmTx5spYsWaJy5co5XQoQ8R58UCpZUtq/X/rtN6ercT8CIAAEwe%2B//65%2B/fpp4sSJio6OdrocIOLFxkqzZknJyVLNmk5X4355nS4AACJNVlaWunbtqoceeki1a9c%2B5eszMjKUkZGRcz8tLS2Y5QERq0EDpysIH4wAAkAue/7555U3b17de%2B%2B9p/X64cOHKy4uLueoWLFikCsE4HUEQAA4BxMnTlShQoVyjgULFuiVV17RuHHj5PP5Tus9Bg8erNTU1Jxj8%2BbNQa4agNexFzAAnIP09HRt37495/7HH3%2BsIUOGKE%2BewO/XmZmZypMnjypWrKgNGzac8j3ZCxhAsBEAASAX/fnnn9q2bdtRj1177bXq2rWrevTooRo1apzyPQiAAIKNRSAAkIuKFy%2Bu4sWLH/VYdHS0ypQpc1rhDwBCgTmAAAAAHsMIIAAE2enM%2BwOAUGIEEAAAwGMIgAAAAB5DAAQAAPAYAiAAAIDHEAABAAA8hgAIAC6RmJioWrVqKT4%2B3ulSAEQ4dgIBAJdhJxAAwcYIIAAAgMcQAAEAADyGAAgAAOAxBEAAAACPIQACAAB4DAEQAADAYwiAAAAAHkMABAAA8BgCIAAAgMcQAAHAJdgKDkCosBUcALgMW8EBCDZGAAEAADyGAAgAAOAxBEAAAACPYQ4gALiM3%2B9Xenq6YmNj5fP5nC4HQAQiAAIAAHgMl4ABAAA8hgAIAADgMQRAAAAAjyEAAgAAeAwBEAAAwGMIgAAAAB5DAAQAAPAYAiAAAIDHEAABAAA8hgAIAADgMQRAAAAAjyEAAgAAeAwBEAAAwGMIgAAAAB5DAAQAAPAYAiAAAIDHEAABAAA8hgAIAADgMQRAAAAAjyEAAgAAeAwBEAAAwGMIgAAAAB5DAAQAAPAYAiAAAIDHEAABAAA8hgAIAADgMQRAAAAAjyEAAgAAeAwBEAAAwGMIgAAAAB5DAAQAAPAYAiAAAIDHEAABAAA8hgAIAADgMQRAAAAAjyEAAgAAeAwBEAAAwGMIgAAAAB5DAAQAAPAYAiAAAIDHEAABAAA8hgAIAADgMQRAAAAAjyEAAgAAeAwBEAAAwGMIgAAAAB5DAAQAAPAYAiAAAIDHEAABAAA8hgAIAADgMf8PJepWRimr7sIAAAAASUVORK5CYII%3D'}