OUTPUT of run 5 of at most 100:
```
ICA component: kurtosis, dominant row and its coefficient, top-five row indices, output-column norm
0 43.3 344 10.48 [344, 736, 1332, 483, 231] 1.864
1 43.6 126 12.16 [126, 853, 1224, 97, 553] 1.717
2 162.4 451 18.97 [451, 705, 827, 464, 752] 2.087
3 78.5 739 14.14 [739, 213, 1491, 595, 1409] 1.765
4 49.2 773 13.12 [773, 1416, 764, 922, 193] 1.822
5 36.1 857 11.46 [857, 655, 574, 1326, 727] 1.65
6 121.5 1312 19.32 [1312, 650, 1357, 902, 985] 2.052
7 105.1 996 17.38 [996, 308, 1292, 1192, 1047] 1.925
8 73.3 674 15.06 [674, 5, 347, 62, 143] 1.796
9 65.7 1270 15.01 [1270, 220, 551, 1337, 1522] 1.782
10 53.6 1214 12.5 [1214, 1113, 1135, 447, 777] 2.474
11 82.7 959 16.83 [959, 1034, 1031, 513, 636] 1.906
12 26.7 388 12.66 [388, 188, 573, 749, 112] 2.478
13 15.3 1465 7.26 [1465, 1325, 411, 870, 1417] 1.84
14 171.9 605 18.53 [605, 81, 238, 1288, 1381] 1.994
15 197.5 1464 22.34 [1464, 1438, 1078, 765, 459] 2.182
16 69.5 1023 14.16 [1023, 1075, 676, 47, 727] 1.733
17 376.3 305 26.43 [305, 35, 541, 555, 1287] 2.487
18 59.4 293 13.82 [293, 360, 875, 1153, 593] 1.756
19 79.2 1351 15.58 [1351, 1100, 91, 339, 596] 1.893
20 166.8 246 17.37 [246, 1145, 102, 889, 1463] 1.91
21 223.0 1046 20.52 [1046, 1483, 1449, 1428, 840] 2.231
22 30.1 903 -10.13 [903, 193, 353, 1407, 1204] 1.752
23 153.1 399 18.48 [399, 1492, 761, 1280, 985] 2.04
24 141.9 1322 17.55 [1322, 1102, 204, 747, 84] 2.13
25 261.5 947 23.79 [947, 1197, 520, 924, 1395] 2.259
26 129.8 1085 18.04 [1085, 413, 1338, 507, 1097] 2.039
27 75.7 1531 15.24 [1531, 1030, 1264, 1331, 849] 1.9

Largest initial MLP input row norms: row, full norm, content-projection norm, output-column norm
305 4.325 3.038 2.487
780 3.453 1.021 2.275
28 3.412 1.241 2.333
1428 3.401 1.19 2.669
947 3.384 2.785 2.259
1423 3.344 1.031 2.199
188 3.323 1.08 3.366
1046 3.288 2.376 2.231
941 3.198 0.957 2.235
1397 3.191 1.01 2.403
809 3.174 0.864 2.12
762 3.132 0.893 2.245
696 3.108 0.625 2.102
1074 3.103 0.916 2.13
1464 3.103 2.553 2.182
1504 3.097 1.022 2.225
524 3.055 1.714 2.238
675 3.027 1.797 2.682
112 3.024 1.097 2.35
148 3.024 1.043 2.143
1216 3.009 0.848 2.262
1041 2.996 0.747 2.149
786 2.992 0.774 2.078
942 2.981 1.451 3.131
511 2.977 0.871 2.224
1383 2.969 1.379 3.3
536 2.954 0.854 2.048
1214 2.954 1.863 2.474
1483 2.946 2.128 2.123
608 2.944 0.842 2.221
912 2.94 0.58 2.074
467 2.933 0.71 2.124
1069 2.924 0.913 2.214
555 2.916 1.227 2.394
1139 2.907 0.703 2.123
1478 2.883 0.71 2.154
1322 2.837 1.998 2.13
1226 2.831 0.892 2.173
230 2.83 1.589 2.79
451 2.814 2.187 2.087

Character-direction candidate geometry
Pairwise cosine quantiles: [-0.247 -0.058 -0.028  0.009  0.041  0.074  0.446]
0 [(13, np.float64(0.134)), (20, np.float64(0.068)), (19, np.float64(0.062))]
1 [(7, np.float64(0.094)), (2, np.float64(0.084)), (10, np.float64(0.083))]
2 [(23, np.float64(0.162)), (25, np.float64(0.118)), (1, np.float64(0.084))]
3 [(12, np.float64(0.316)), (21, np.float64(0.171)), (27, np.float64(0.098))]
4 [(11, np.float64(0.102)), (9, np.float64(0.098)), (3, np.float64(0.061))]
5 [(16, np.float64(0.146)), (18, np.float64(0.138)), (24, np.float64(0.086))]
6 [(14, np.float64(0.071)), (24, np.float64(0.061)), (0, np.float64(0.06))]
7 [(1, np.float64(0.094)), (22, np.float64(0.063)), (26, np.float64(0.056))]
8 [(20, np.float64(0.122)), (10, np.float64(0.078)), (19, np.float64(0.072))]
9 [(4, np.float64(0.098)), (17, np.float64(0.073)), (3, np.float64(0.07))]
10 [(1, np.float64(0.083)), (8, np.float64(0.078)), (17, np.float64(0.073))]
11 [(12, np.float64(0.127)), (4, np.float64(0.102)), (21, np.float64(0.089))]
12 [(21, np.float64(0.446)), (3, np.float64(0.316)), (24, np.float64(0.21))]
13 [(0, np.float64(0.134)), (19, np.float64(0.076)), (15, np.float64(0.06))]
14 [(17, np.float64(0.106)), (12, np.float64(0.104)), (21, np.float64(0.098))]
15 [(25, np.float64(0.107)), (23, np.float64(0.088)), (2, np.float64(0.083))]
16 [(5, np.float64(0.146)), (24, np.float64(0.075)), (18, np.float64(0.059))]
17 [(24, np.float64(0.162)), (14, np.float64(0.106)), (2, np.float64(0.076))]
18 [(12, np.float64(0.17)), (5, np.float64(0.138)), (22, np.float64(0.082))]
19 [(13, np.float64(0.076)), (20, np.float64(0.076)), (8, np.float64(0.072))]
20 [(8, np.float64(0.122)), (19, np.float64(0.076)), (0, np.float64(0.068))]
21 [(12, np.float64(0.446)), (3, np.float64(0.171)), (27, np.float64(0.154))]
22 [(18, np.float64(0.082)), (7, np.float64(0.063)), (6, np.float64(0.059))]
23 [(2, np.float64(0.162)), (21, np.float64(0.096)), (15, np.float64(0.088))]
24 [(12, np.float64(0.21)), (17, np.float64(0.162)), (5, np.float64(0.086))]
25 [(2, np.float64(0.118)), (15, np.float64(0.107)), (11, np.float64(0.086))]
26 [(21, np.float64(0.094)), (17, np.float64(0.068)), (7, np.float64(0.056))]
27 [(21, np.float64(0.154)), (3, np.float64(0.098)), (5, np.float64(0.077))]

Content-key geometry of L0H2, in the candidate direction basis
0 [(19, np.float64(0.042)), (7, np.float64(0.026)), (13, np.float64(0.014))]
1 [(7, np.float64(0.046)), (20, np.float64(0.03)), (2, np.float64(0.028))]
2 [(11, np.float64(0.077)), (21, np.float64(0.062)), (26, np.float64(0.054))]
3 [(19, np.float64(0.042)), (8, np.float64(0.016)), (13, np.float64(0.011))]
4 [(19, np.float64(0.044)), (10, np.float64(0.028)), (13, np.float64(0.021))]
5 [(5, np.float64(0.056)), (4, np.float64(0.036)), (10, np.float64(0.031))]
6 [(21, np.float64(0.065)), (25, np.float64(0.05)), (4, np.float64(0.029))]
7 [(26, np.float64(0.065)), (11, np.float64(0.045)), (23, np.float64(0.042))]
8 [(16, np.float64(0.029)), (4, np.float64(0.028)), (7, np.float64(0.024))]
9 [(9, np.float64(0.047)), (20, np.float64(0.036)), (23, np.float64(0.016))]
10 [(19, np.float64(0.055)), (14, np.float64(0.044)), (20, np.float64(0.022))]
11 [(25, np.float64(0.056)), (24, np.float64(0.035)), (23, np.float64(0.033))]
12 [(14, np.float64(0.057)), (8, np.float64(0.041)), (20, np.float64(0.039))]
13 [(0, np.float64(0.056)), (14, np.float64(0.037)), (4, np.float64(0.036))]
14 [(7, np.float64(0.054)), (14, np.float64(0.047)), (2, np.float64(0.037))]
15 [(21, np.float64(0.06)), (15, np.float64(0.051)), (27, np.float64(0.047))]
16 [(16, np.float64(0.068)), (21, np.float64(0.058)), (1, np.float64(0.05))]
17 [(21, np.float64(0.109)), (14, np.float64(0.05)), (26, np.float64(0.049))]
18 [(19, np.float64(0.065)), (23, np.float64(0.053)), (20, np.float64(0.046))]
19 [(4, np.float64(0.073)), (22, np.float64(0.028)), (6, np.float64(0.018))]
20 [(25, np.float64(0.079)), (1, np.float64(0.047)), (4, np.float64(0.038))]
21 [(20, np.float64(0.088)), (19, np.float64(0.068)), (14, np.float64(0.048))]
22 [(19, np.float64(0.054)), (9, np.float64(0.029)), (24, np.float64(0.026))]
23 [(23, np.float64(0.086)), (26, np.float64(0.075)), (11, np.float64(0.059))]
24 [(8, np.float64(0.082)), (14, np.float64(0.048)), (19, np.float64(0.023))]
25 [(26, np.float64(0.048)), (2, np.float64(0.034)), (0, np.float64(0.024))]
26 [(19, np.float64(0.126)), (17, np.float64(0.1)), (10, np.float64(0.043))]
27 [(19, np.float64(0.039)), (14, np.float64(0.035)), (9, np.float64(0.025))]

```