Add files using upload-large-folder tool

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S007/config.json

@@ -0,0 +1,150 @@
+{
+  "schema": "bci-shared-biot-fusiononly-v1",
+  "architecture": {
+    "channels": 32,
+    "width": 128,
+    "depth": 2,
+    "dropout": 0.5,
+    "wave_filters": 32
+  },
+  "initialization": {
+    "initialization": "jointly trained parent plus audited pretrained BIOT in a single coupled backbone",
+    "parent": "/workspace/research/runs/hybrid_wide1/epoch-004/weights.pt",
+    "audited_biot": {
+      "initialization": "pretrained",
+      "repo": "braindecode/biot-pretrained-six-datasets-18chs",
+      "revision": "7e9aabf302870012f73773276803aab97993951c",
+      "sha256": "a218f4054ae29962c6fd9d23d94c446d354c4f53e805a6771c85749ccc20baaa",
+      "license": "BSD-3-Clause",
+      "source_braindecode": "1.3.0",
+      "loaded_core_tensors": 56,
+      "skipped_classifier_tensors": 3
+    },
+    "neural_channel_indices": {
+      "faced": [
+        0,
+        1,
+        2,
+        3,
+        4,
+        5,
+        6,
+        7,
+        8,
+        9,
+        10,
+        11,
+        12,
+        13,
+        14,
+        15,
+        16,
+        17
+      ],
+      "tuev": [
+        0,
+        1,
+        2,
+        3,
+        4,
+        5,
+        6,
+        7,
+        8,
+        9,
+        10,
+        11,
+        12,
+        13,
+        14,
+        15
+      ],
+      "ad65": [
+        0,
+        1,
+        2,
+        3,
+        4,
+        5,
+        6,
+        7,
+        8,
+        9,
+        10,
+        11,
+        12,
+        13,
+        14,
+        15,
+        16,
+        17
+      ],
+      "openbmi": [
+        0,
+        1,
+        3,
+        5,
+        12,
+        14,
+        23,
+        25,
+        28,
+        30,
+        2,
+        6,
+        11,
+        15,
+        22,
+        26,
+        4,
+        13
+      ],
+      "longitudinal": [
+        0,
+        2,
+        7,
+        11,
+        23,
+        27,
+        41,
+        45,
+        55,
+        57,
+        5,
+        13,
+        21,
+        29,
+        39,
+        47,
+        9,
+        25
+      ]
+    },
+    "rebase": "incumbent waveform, fusion and four heads; joint BIOT and TUEV head from readout epoch 9; zero initialized neural interaction",
+    "jointly_finetuned_neural_parent": "/workspace/research/runs/hybrid_biot_readout1/epoch-009/weights.pt",
+    "tuev_high_dimensional_spectral_projection_disabled": true,
+    "frozen_prior_representation": true,
+    "nonclinical_head_updates": "only the 256 added BIOT columns; original columns and biases fixed",
+    "faced_openbmi_neural_path": "common nonlinear fusion only; direct head columns masked",
+    "retention_teacher": "/workspace/research/runs/hybrid_wide1/epoch-004",
+    "retention_loss": {
+      "split": "train",
+      "temperature": 2.0,
+      "kl_weight": 1.0,
+      "tracks": [
+        "faced",
+        "ad65",
+        "openbmi",
+        "longitudinal"
+      ]
+    }
+  },
+  "loader_scale": 0.01,
+  "tracks": [
+    "faced",
+    "tuev",
+    "ad65",
+    "openbmi",
+    "longitudinal"
+  ]
+}

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S007/data.py

@@ -0,0 +1,33 @@
+import json
+from functools import lru_cache
+from pathlib import Path
+import numpy as np
+import torch
+@lru_cache(maxsize=8)
+def array(path):return np.load(path,mmap_mode='r',allow_pickle=False)
+class Dataset(torch.utils.data.Dataset):
+    def __init__(self,root,limit=None,cache=False):
+        self.root=Path(root)
+        self.rows=json.loads((self.root/'manifest.json').read_text())['samples']
+        if limit:self.rows=self.rows[:limit]
+        self.cached=None
+        if cache and self.rows:
+            from collections import defaultdict
+            groups=defaultdict(list)
+            for i,r in enumerate(self.rows):groups[r['path']].append((i,r['index']))
+            for name,indices in groups.items():
+                path=(self.root/name).resolve()
+                if not path.is_relative_to(self.root.resolve()):raise ValueError('input outside root')
+                source=np.load(path,mmap_mode='r',allow_pickle=False)
+                if self.cached is None:self.cached=torch.empty((len(self.rows),*source.shape[1:]),dtype=torch.float32)
+                dst,src=zip(*indices)
+                self.cached[list(dst)]=torch.as_tensor(source[list(src)]/100,dtype=torch.float32)
+    def __len__(self):return len(self.rows)
+    def __getitem__(self,i):
+        r=self.rows[i]
+        if self.cached is not None:return self.cached[i],r.get('label',-1),r['id']
+        p=(self.root/r['path']).resolve()
+        if not p.is_relative_to(self.root.resolve()):raise ValueError('input outside root')
+        # Division precedes float32 conversion, matching official FACED/TUEV collate.
+        x=np.asarray(array(str(p))[r['index']])/100
+        return torch.as_tensor(x,dtype=torch.float32),r.get('label',-1),r['id']

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S007/infer.sh

@@ -0,0 +1,3 @@
+#!/usr/bin/env bash
+set -euo pipefail
+exec python "$(dirname -- "$0")/infer.py" "$1" "$2"

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S007/training.json

@@ -0,0 +1,3685 @@
+{
+  "settings": {
+    "source": "/workspace/research/runs/hybrid_biot_fusiononly1/epoch-007",
+    "common_fusion_shrinkage": 0.1,
+    "procedure": "fixed scalar applied equally to shared residual projection weights and bias; direct BIOT features and all other parameters retained"
+  },
+  "all_tracks_update_shared_backbone": true,
+  "parameters": 10168029,
+  "parent_training_history": [
+    {
+      "epoch": 1,
+      "validation": {
+        "faced": {
+          "class_counts": [
+            180,
+            180,
+            180,
+            180,
+            240,
+            180,
+            180,
+            180,
+            180
+          ],
+          "predicted_class_counts": [
+            159,
+            189,
+            178,
+            211,
+            242,
+            167,
+            182,
+            176,
+            176
+          ],
+          "confusion_matrix": [
+            [
+              77,
+              14,
+              5,
+              22,
+              19,
+              9,
+              14,
+              10,
+              10
+            ],
+            [
+              12,
+              86,
+              9,
+              13,
+              19,
+              11,
+              14,
+              6,
+              10
+            ],
+            [
+              6,
+              7,
+              93,
+              21,
+              17,
+              5,
+              7,
+              10,
+              14
+            ],
+            [
+              14,
+              18,
+              9,
+              71,
+              21,
+              8,
+              16,
+              15,
+              8
+            ],
+            [
+              13,
+              13,
+              17,
+              26,
+              111,
+              17,
+              14,
+              18,
+              11
+            ],
+            [
+              7,
+              11,
+              11,
+              13,
+              11,
+              87,
+              10,
+              14,
+              16
+            ],
+            [
+              8,
+              11,
+              9,
+              13,
+              17,
+              14,
+              89,
+              10,
+              9
+            ],
+            [
+              8,
+              16,
+              14,
+              17,
+              15,
+              11,
+              10,
+              84,
+              5
+            ],
+            [
+              14,
+              13,
+              11,
+              15,
+              12,
+              5,
+              8,
+              9,
+              93
+            ]
+          ],
+          "per_class_recall": [
+            0.42777777777777776,
+            0.4777777777777778,
+            0.5166666666666667,
+            0.39444444444444443,
+            0.4625,
+            0.48333333333333334,
+            0.49444444444444446,
+            0.4666666666666667,
+            0.5166666666666667
+          ],
+          "balanced_accuracy": 0.471141975308642,
+          "accuracy": 0.4708333333333333,
+          "weighted_f1": 0.4719396333394912,
+          "macro_f1": 0.47236032669046973,
+          "kappa": 0.40389865415010306,
+          "samples": 1680,
+          "reward": 0.471141975308642,
+          "subjects": 20
+        },
+        "tuev": {
+          "class_counts": [
+            119,
+            291,
+            669,
+            86,
+            500,
+            13822
+          ],
+          "predicted_class_counts": [
+            1695,
+            428,
+            290,
+            720,
+            1682,
+            10672
+          ],
+          "confusion_matrix": [
+            [
+              45,
+              38,
+              25,
+              4,
+              0,
+              7
+            ],
+            [
+              17,
+              84,
+              0,
+              96,
+              94,
+              0
+            ],
+            [
+              154,
+              16,
+              158,
+              86,
+              255,
+              0
+            ],
+            [
+              8,
+              4,
+              0,
+              74,
+              0,
+              0
+            ],
+            [
+              4,
+              0,
+              0,
+              24,
+              424,
+              48
+            ],
+            [
+              1467,
+              286,
+              107,
+              436,
+              909,
+              10617
+            ]
+          ],
+          "per_class_recall": [
+            0.37815126050420167,
+            0.28865979381443296,
+            0.23617339312406577,
+            0.8604651162790697,
+            0.848,
+            0.7681232817247866
+          ],
+          "balanced_accuracy": 0.5632621409077595,
+          "accuracy": 0.7362303867760057,
+          "weighted_f1": 0.8062779480900836,
+          "macro_f1": 0.3419908611855867,
+          "kappa": 0.30414149377793265,
+          "samples": 15487,
+          "reward": 0.5632621409077595,
+          "subjects": 58
+        },
+        "ad65": {
+          "class_counts": [
+            464,
+            393,
+            486
+          ],
+          "predicted_class_counts": [
+            533,
+            324,
+            486
+          ],
+          "confusion_matrix": [
+            [
+              342,
+              97,
+              25
+            ],
+            [
+              131,
+              195,
+              67
+            ],
+            [
+              60,
+              32,
+              394
+            ]
+          ],
+          "per_class_recall": [
+            0.7370689655172413,
+            0.4961832061068702,
+            0.8106995884773662
+          ],
+          "balanced_accuracy": 0.6813172533671592,
+          "accuracy": 0.6932241250930752,
+          "weighted_f1": 0.6895731074873461,
+          "macro_f1": 0.6802302724647475,
+          "kappa": 0.5361235537290547,
+          "samples": 1343,
+          "subject_metrics": {
+            "class_counts": [
+              7,
+              5,
+              6
+            ],
+            "predicted_class_counts": [
+              7,
+              5,
+              6
+            ],
+            "confusion_matrix": [
+              [
+                5,
+                2,
+                0
+              ],
+              [
+                2,
+                3,
+                0
+              ],
+              [
+                0,
+                0,
+                6
+              ]
+            ],
+            "per_class_recall": [
+              0.7142857142857143,
+              0.6,
+              1.0
+            ],
+            "balanced_accuracy": 0.7714285714285714,
+            "accuracy": 0.7777777777777778,
+            "weighted_f1": 0.7777777777777778,
+            "macro_f1": 0.7714285714285714,
+            "kappa": 0.6635514018691588
+          },
+          "reward": 0.7714285714285714,
+          "subjects": 18
+        },
+        "openbmi": {
+          "class_counts": [
+            2200,
+            2200
+          ],
+          "predicted_class_counts": [
+            2059,
+            2341
+          ],
+          "confusion_matrix": [
+            [
+              1696,
+              504
+            ],
+            [
+              363,
+              1837
+            ]
+          ],
+          "per_class_recall": [
+            0.7709090909090909,
+            0.835
+          ],
+          "balanced_accuracy": 0.8029545454545455,
+          "accuracy": 0.8029545454545455,
+          "weighted_f1": 0.8027519892716276,
+          "macro_f1": 0.8027519892716275,
+          "kappa": 0.605909090909091,
+          "samples": 4400,
+          "subject_scores": {
+            "19": 0.8425,
+            "35": 0.9525,
+            "34": 0.6425000000000001,
+            "51": 0.7475,
+            "20": 0.82,
+            "44": 0.94,
+            "24": 0.5974999999999999,
+            "15": 0.9,
+            "31": 0.88,
+            "26": 0.79,
+            "23": 0.72
+          },
+          "reward": 0.8029545454545456,
+          "subjects": 11
+        },
+        "longitudinal": {
+          "class_counts": [
+            4260,
+            4158
+          ],
+          "predicted_class_counts": [
+            4576,
+            3842
+          ],
+          "confusion_matrix": [
+            [
+              3659,
+              601
+            ],
+            [
+              917,
+              3241
+            ]
+          ],
+          "per_class_recall": [
+            0.8589201877934273,
+            0.7794612794612794
+          ],
+          "balanced_accuracy": 0.8191907336273534,
+          "accuracy": 0.819672131147541,
+          "weighted_f1": 0.8193351694632243,
+          "macro_f1": 0.8192264033499321,
+          "kappa": 0.6389628187072087,
+          "samples": 8418,
+          "subject_scores": {
+            "sub-G2021": 0.8384604490935013,
+            "sub-G2007": 0.7643956272712626,
+            "sub-G2019": 0.82505149822325,
+            "sub-G2031": 0.788678938415735,
+            "sub-G2015": 0.8429774626319118,
+            "sub-CTR002": 0.9869493251846193,
+            "sub-G2011": 0.765938925240826,
+            "sub-G2035": 0.9564871486872261,
+            "sub-CTR003": 0.5773809523809523
+          },
+          "reward": 0.816257814125476,
+          "subjects": 9
+        }
+      },
+      "losses": {
+        "faced": 1.1245755493640899,
+        "tuev": 0.8888261109590531,
+        "ad65": 0.40307730436325073,
+        "openbmi": 0.41378349512815477,
+        "longitudinal": 0.23857621401548385
+      },
+      "seconds": 33.768038749694824
+    },
+    {
+      "epoch": 2,
+      "validation": {
+        "faced": {
+          "class_counts": [
+            180,
+            180,
+            180,
+            180,
+            240,
+            180,
+            180,
+            180,
+            180
+          ],
+          "predicted_class_counts": [
+            160,
+            189,
+            179,
+            211,
+            241,
+            165,
+            182,
+            176,
+            177
+          ],
+          "confusion_matrix": [
+            [
+              77,
+              14,
+              5,
+              22,
+              19,
+              9,
+              14,
+              10,
+              10
+            ],
+            [
+              12,
+              86,
+              9,
+              13,
+              19,
+              11,
+              14,
+              6,
+              10
+            ],
+            [
+              6,
+              7,
+              93,
+              20,
+              17,
+              5,
+              7,
+              10,
+              15
+            ],
+            [
+              14,
+              18,
+              9,
+              71,
+              21,
+              8,
+              16,
+              15,
+              8
+            ],
+            [
+              14,
+              13,
+              17,
+              26,
+              110,
+              17,
+              14,
+              18,
+              11
+            ],
+            [
+              7,
+              11,
+              11,
+              14,
+              11,
+              86,
+              10,
+              14,
+              16
+            ],
+            [
+              8,
+              11,
+              9,
+              13,
+              17,
+              14,
+              89,
+              10,
+              9
+            ],
+            [
+              8,
+              16,
+              15,
+              17,
+              15,
+              10,
+              10,
+              84,
+              5
+            ],
+            [
+              14,
+              13,
+              11,
+              15,
+              12,
+              5,
+              8,
+              9,
+              93
+            ]
+          ],
+          "per_class_recall": [
+            0.42777777777777776,
+            0.4777777777777778,
+            0.5166666666666667,
+            0.39444444444444443,
+            0.4583333333333333,
+            0.4777777777777778,
+            0.49444444444444446,
+            0.4666666666666667,
+            0.5166666666666667
+          ],
+          "balanced_accuracy": 0.4700617283950618,
+          "accuracy": 0.46964285714285714,
+          "weighted_f1": 0.4707177428763859,
+          "macro_f1": 0.4712117164132721,
+          "kappa": 0.40257190066811943,
+          "samples": 1680,
+          "reward": 0.4700617283950618,
+          "subjects": 20
+        },
+        "tuev": {
+          "class_counts": [
+            119,
+            291,
+            669,
+            86,
+            500,
+            13822
+          ],
+          "predicted_class_counts": [
+            1895,
+            593,
+            224,
+            690,
+            1501,
+            10584
+          ],
+          "confusion_matrix": [
+            [
+              55,
+              38,
+              18,
+              4,
+              0,
+              4
+            ],
+            [
+              17,
+              171,
+              0,
+              61,
+              42,
+              0
+            ],
+            [
+              172,
+              116,
+              143,
+              69,
+              169,
+              0
+            ],
+            [
+              7,
+              4,
+              0,
+              75,
+              0,
+              0
+            ],
+            [
+              4,
+              0,
+              0,
+              25,
+              412,
+              59
+            ],
+            [
+              1640,
+              264,
+              63,
+              456,
+              878,
+              10521
+            ]
+          ],
+          "per_class_recall": [
+            0.46218487394957986,
+            0.5876288659793815,
+            0.21375186846038863,
+            0.872093023255814,
+            0.824,
+            0.761177832441036
+          ],
+          "balanced_accuracy": 0.6201394106810333,
+          "accuracy": 0.7346161296571317,
+          "weighted_f1": 0.8053662242323045,
+          "macro_f1": 0.37150372912054547,
+          "kappa": 0.30961610971125775,
+          "samples": 15487,
+          "reward": 0.6201394106810333,
+          "subjects": 58
+        },
+        "ad65": {
+          "class_counts": [
+            464,
+            393,
+            486
+          ],
+          "predicted_class_counts": [
+            533,
+            324,
+            486
+          ],
+          "confusion_matrix": [
+            [
+              342,
+              97,
+              25
+            ],
+            [
+              131,
+              195,
+              67
+            ],
+            [
+              60,
+              32,
+              394
+            ]
+          ],
+          "per_class_recall": [
+            0.7370689655172413,
+            0.4961832061068702,
+            0.8106995884773662
+          ],
+          "balanced_accuracy": 0.6813172533671592,
+          "accuracy": 0.6932241250930752,
+          "weighted_f1": 0.6895731074873461,
+          "macro_f1": 0.6802302724647475,
+          "kappa": 0.5361235537290547,
+          "samples": 1343,
+          "subject_metrics": {
+            "class_counts": [
+              7,
+              5,
+              6
+            ],
+            "predicted_class_counts": [
+              7,
+              5,
+              6
+            ],
+            "confusion_matrix": [
+              [
+                5,
+                2,
+                0
+              ],
+              [
+                2,
+                3,
+                0
+              ],
+              [
+                0,
+                0,
+                6
+              ]
+            ],
+            "per_class_recall": [
+              0.7142857142857143,
+              0.6,
+              1.0
+            ],
+            "balanced_accuracy": 0.7714285714285714,
+            "accuracy": 0.7777777777777778,
+            "weighted_f1": 0.7777777777777778,
+            "macro_f1": 0.7714285714285714,
+            "kappa": 0.6635514018691588
+          },
+          "reward": 0.7714285714285714,
+          "subjects": 18
+        },
+        "openbmi": {
+          "class_counts": [
+            2200,
+            2200
+          ],
+          "predicted_class_counts": [
+            2059,
+            2341
+          ],
+          "confusion_matrix": [
+            [
+              1696,
+              504
+            ],
+            [
+              363,
+              1837
+            ]
+          ],
+          "per_class_recall": [
+            0.7709090909090909,
+            0.835
+          ],
+          "balanced_accuracy": 0.8029545454545455,
+          "accuracy": 0.8029545454545455,
+          "weighted_f1": 0.8027519892716276,
+          "macro_f1": 0.8027519892716275,
+          "kappa": 0.605909090909091,
+          "samples": 4400,
+          "subject_scores": {
+            "19": 0.8425,
+            "35": 0.9525,
+            "34": 0.6425000000000001,
+            "51": 0.7475,
+            "20": 0.82,
+            "44": 0.94,
+            "24": 0.5974999999999999,
+            "15": 0.9,
+            "31": 0.88,
+            "26": 0.79,
+            "23": 0.72
+          },
+          "reward": 0.8029545454545456,
+          "subjects": 11
+        },
+        "longitudinal": {
+          "class_counts": [
+            4260,
+            4158
+          ],
+          "predicted_class_counts": [
+            4575,
+            3843
+          ],
+          "confusion_matrix": [
+            [
+              3659,
+              601
+            ],
+            [
+              916,
+              3242
+            ]
+          ],
+          "per_class_recall": [
+            0.8589201877934273,
+            0.7797017797017797
+          ],
+          "balanced_accuracy": 0.8193109837476035,
+          "accuracy": 0.8197909242100261,
+          "weighted_f1": 0.8194560571387214,
+          "macro_f1": 0.8193476239918227,
+          "kappa": 0.6392016958792203,
+          "samples": 8418,
+          "subject_scores": {
+            "sub-G2021": 0.8384604490935013,
+            "sub-G2007": 0.7652179956923153,
+            "sub-G2019": 0.82505149822325,
+            "sub-G2031": 0.788678938415735,
+            "sub-G2015": 0.8429774626319118,
+            "sub-CTR002": 0.9869493251846193,
+            "sub-G2011": 0.765938925240826,
+            "sub-G2035": 0.9564871486872261,
+            "sub-CTR003": 0.5773809523809523
+          },
+          "reward": 0.8163491883944819,
+          "subjects": 9
+        }
+      },
+      "losses": {
+        "faced": 1.1657944917678833,
+        "tuev": 0.8340819418430329,
+        "ad65": 0.36363950222730634,
+        "openbmi": 0.418754418194294,
+        "longitudinal": 0.24928389638662338
+      },
+      "seconds": 46.29122805595398
+    },
+    {
+      "epoch": 3,
+      "validation": {
+        "faced": {
+          "class_counts": [
+            180,
+            180,
+            180,
+            180,
+            240,
+            180,
+            180,
+            180,
+            180
+          ],
+          "predicted_class_counts": [
+            159,
+            191,
+            179,
+            210,
+            240,
+            167,
+            182,
+            175,
+            177
+          ],
+          "confusion_matrix": [
+            [
+              77,
+              14,
+              5,
+              22,
+              19,
+              9,
+              14,
+              10,
+              10
+            ],
+            [
+              12,
+              87,
+              9,
+              13,
+              19,
+              11,
+              14,
+              5,
+              10
+            ],
+            [
+              6,
+              7,
+              93,
+              20,
+              17,
+              5,
+              7,
+              10,
+              15
+            ],
+            [
+              14,
+              18,
+              9,
+              71,
+              21,
+              8,
+              16,
+              15,
+              8
+            ],
+            [
+              14,
+              13,
+              17,
+              26,
+              110,
+              17,
+              14,
+              18,
+              11
+            ],
+            [
+              7,
+              11,
+              11,
+              13,
+              10,
+              88,
+              10,
+              14,
+              16
+            ],
+            [
+              8,
+              11,
+              9,
+              13,
+              17,
+              14,
+              89,
+              10,
+              9
+            ],
+            [
+              8,
+              16,
+              15,
+              17,
+              15,
+              10,
+              10,
+              84,
+              5
+            ],
+            [
+              13,
+              14,
+              11,
+              15,
+              12,
+              5,
+              8,
+              9,
+              93
+            ]
+          ],
+          "per_class_recall": [
+            0.42777777777777776,
+            0.48333333333333334,
+            0.5166666666666667,
+            0.39444444444444443,
+            0.4583333333333333,
+            0.4888888888888889,
+            0.49444444444444446,
+            0.4666666666666667,
+            0.5166666666666667
+          ],
+          "balanced_accuracy": 0.47191358024691366,
+          "accuracy": 0.4714285714285714,
+          "weighted_f1": 0.47247478547825605,
+          "macro_f1": 0.4729985429651051,
+          "kappa": 0.4045977011494253,
+          "samples": 1680,
+          "reward": 0.47191358024691366,
+          "subjects": 20
+        },
+        "tuev": {
+          "class_counts": [
+            119,
+            291,
+            669,
+            86,
+            500,
+            13822
+          ],
+          "predicted_class_counts": [
+            1282,
+            414,
+            232,
+            726,
+            2052,
+            10781
+          ],
+          "confusion_matrix": [
+            [
+              39,
+              38,
+              28,
+              0,
+              0,
+              14
+            ],
+            [
+              0,
+              84,
+              0,
+              113,
+              94,
+              0
+            ],
+            [
+              113,
+              26,
+              141,
+              114,
+              275,
+              0
+            ],
+            [
+              7,
+              2,
+              0,
+              76,
+              0,
+              1
+            ],
+            [
+              2,
+              0,
+              0,
+              48,
+              425,
+              25
+            ],
+            [
+              1121,
+              264,
+              63,
+              375,
+              1258,
+              10741
+            ]
+          ],
+          "per_class_recall": [
+            0.3277310924369748,
+            0.28865979381443296,
+            0.21076233183856502,
+            0.8837209302325582,
+            0.85,
+            0.777094487049631
+          ],
+          "balanced_accuracy": 0.5563281058953603,
+          "accuracy": 0.7429456963905211,
+          "weighted_f1": 0.809492456839227,
+          "macro_f1": 0.3333946220040736,
+          "kappa": 0.3097083999217113,
+          "samples": 15487,
+          "reward": 0.5563281058953603,
+          "subjects": 58
+        },
+        "ad65": {
+          "class_counts": [
+            464,
+            393,
+            486
+          ],
+          "predicted_class_counts": [
+            533,
+            324,
+            486
+          ],
+          "confusion_matrix": [
+            [
+              342,
+              97,
+              25
+            ],
+            [
+              131,
+              195,
+              67
+            ],
+            [
+              60,
+              32,
+              394
+            ]
+          ],
+          "per_class_recall": [
+            0.7370689655172413,
+            0.4961832061068702,
+            0.8106995884773662
+          ],
+          "balanced_accuracy": 0.6813172533671592,
+          "accuracy": 0.6932241250930752,
+          "weighted_f1": 0.6895731074873461,
+          "macro_f1": 0.6802302724647475,
+          "kappa": 0.5361235537290547,
+          "samples": 1343,
+          "subject_metrics": {
+            "class_counts": [
+              7,
+              5,
+              6
+            ],
+            "predicted_class_counts": [
+              7,
+              5,
+              6
+            ],
+            "confusion_matrix": [
+              [
+                5,
+                2,
+                0
+              ],
+              [
+                2,
+                3,
+                0
+              ],
+              [
+                0,
+                0,
+                6
+              ]
+            ],
+            "per_class_recall": [
+              0.7142857142857143,
+              0.6,
+              1.0
+            ],
+            "balanced_accuracy": 0.7714285714285714,
+            "accuracy": 0.7777777777777778,
+            "weighted_f1": 0.7777777777777778,
+            "macro_f1": 0.7714285714285714,
+            "kappa": 0.6635514018691588
+          },
+          "reward": 0.7714285714285714,
+          "subjects": 18
+        },
+        "openbmi": {
+          "class_counts": [
+            2200,
+            2200
+          ],
+          "predicted_class_counts": [
+            2056,
+            2344
+          ],
+          "confusion_matrix": [
+            [
+              1694,
+              506
+            ],
+            [
+              362,
+              1838
+            ]
+          ],
+          "per_class_recall": [
+            0.77,
+            0.8354545454545454
+          ],
+          "balanced_accuracy": 0.8027272727272727,
+          "accuracy": 0.8027272727272727,
+          "weighted_f1": 0.8025157524091919,
+          "macro_f1": 0.8025157524091919,
+          "kappa": 0.6054545454545455,
+          "samples": 4400,
+          "subject_scores": {
+            "19": 0.845,
+            "35": 0.9525,
+            "34": 0.6425000000000001,
+            "51": 0.7475,
+            "20": 0.8174999999999999,
+            "44": 0.94,
+            "24": 0.5974999999999999,
+            "15": 0.9,
+            "31": 0.8775,
+            "26": 0.79,
+            "23": 0.72
+          },
+          "reward": 0.8027272727272727,
+          "subjects": 11
+        },
+        "longitudinal": {
+          "class_counts": [
+            4260,
+            4158
+          ],
+          "predicted_class_counts": [
+            4575,
+            3843
+          ],
+          "confusion_matrix": [
+            [
+              3659,
+              601
+            ],
+            [
+              916,
+              3242
+            ]
+          ],
+          "per_class_recall": [
+            0.8589201877934273,
+            0.7797017797017797
+          ],
+          "balanced_accuracy": 0.8193109837476035,
+          "accuracy": 0.8197909242100261,
+          "weighted_f1": 0.8194560571387214,
+          "macro_f1": 0.8193476239918227,
+          "kappa": 0.6392016958792203,
+          "samples": 8418,
+          "subject_scores": {
+            "sub-G2021": 0.8384604490935013,
+            "sub-G2007": 0.7652179956923153,
+            "sub-G2019": 0.82505149822325,
+            "sub-G2031": 0.788678938415735,
+            "sub-G2015": 0.8429774626319118,
+            "sub-CTR002": 0.9869493251846193,
+            "sub-G2011": 0.765938925240826,
+            "sub-G2035": 0.9564871486872261,
+            "sub-CTR003": 0.5773809523809523
+          },
+          "reward": 0.8163491883944819,
+          "subjects": 9
+        }
+      },
+      "losses": {
+        "faced": 1.1443339705467224,
+        "tuev": 0.8239678978919983,
+        "ad65": 0.38756211251020434,
+        "openbmi": 0.4083534896373749,
+        "longitudinal": 0.24697063267230987
+      },
+      "seconds": 58.648759841918945
+    },
+    {
+      "epoch": 4,
+      "validation": {
+        "faced": {
+          "class_counts": [
+            180,
+            180,
+            180,
+            180,
+            240,
+            180,
+            180,
+            180,
+            180
+          ],
+          "predicted_class_counts": [
+            157,
+            191,
+            179,
+            214,
+            241,
+            166,
+            181,
+            175,
+            176
+          ],
+          "confusion_matrix": [
+            [
+              77,
+              14,
+              5,
+              23,
+              19,
+              9,
+              13,
+              10,
+              10
+            ],
+            [
+              12,
+              87,
+              9,
+              13,
+              19,
+              11,
+              14,
+              5,
+              10
+            ],
+            [
+              6,
+              7,
+              93,
+              21,
+              17,
+              5,
+              7,
+              10,
+              14
+            ],
+            [
+              14,
+              18,
+              9,
+              71,
+              21,
+              8,
+              16,
+              15,
+              8
+            ],
+            [
+              13,
+              13,
+              17,
+              26,
+              111,
+              17,
+              14,
+              18,
+              11
+            ],
+            [
+              6,
+              11,
+              11,
+              15,
+              10,
+              87,
+              10,
+              14,
+              16
+            ],
+            [
+              8,
+              11,
+              9,
+              13,
+              17,
+              14,
+              89,
+              10,
+              9
+            ],
+            [
+              8,
+              16,
+              15,
+              17,
+              15,
+              10,
+              10,
+              84,
+              5
+            ],
+            [
+              13,
+              14,
+              11,
+              15,
+              12,
+              5,
+              8,
+              9,
+              93
+            ]
+          ],
+          "per_class_recall": [
+            0.42777777777777776,
+            0.48333333333333334,
+            0.5166666666666667,
+            0.39444444444444443,
+            0.4625,
+            0.48333333333333334,
+            0.49444444444444446,
+            0.4666666666666667,
+            0.5166666666666667
+          ],
+          "balanced_accuracy": 0.47175925925925927,
+          "accuracy": 0.4714285714285714,
+          "weighted_f1": 0.4726659490617784,
+          "macro_f1": 0.4730780782293087,
+          "kappa": 0.4045834430901123,
+          "samples": 1680,
+          "reward": 0.47175925925925927,
+          "subjects": 20
+        },
+        "tuev": {
+          "class_counts": [
+            119,
+            291,
+            669,
+            86,
+            500,
+            13822
+          ],
+          "predicted_class_counts": [
+            1566,
+            429,
+            172,
+            795,
+            2461,
+            10064
+          ],
+          "confusion_matrix": [
+            [
+              40,
+              38,
+              25,
+              4,
+              0,
+              12
+            ],
+            [
+              0,
+              119,
+              0,
+              78,
+              94,
+              0
+            ],
+            [
+              134,
+              28,
+              128,
+              103,
+              276,
+              0
+            ],
+            [
+              3,
+              2,
+              0,
+              81,
+              0,
+              0
+            ],
+            [
+              4,
+              0,
+              0,
+              48,
+              424,
+              24
+            ],
+            [
+              1385,
+              242,
+              19,
+              481,
+              1667,
+              10028
+            ]
+          ],
+          "per_class_recall": [
+            0.33613445378151263,
+            0.40893470790378006,
+            0.19133034379671152,
+            0.9418604651162791,
+            0.848,
+            0.7255100564317755
+          ],
+          "balanced_accuracy": 0.5752950045050098,
+          "accuracy": 0.6986504810486214,
+          "weighted_f1": 0.7793765940667069,
+          "macro_f1": 0.3320599230522449,
+          "kappa": 0.27004943473083465,
+          "samples": 15487,
+          "reward": 0.5752950045050098,
+          "subjects": 58
+        },
+        "ad65": {
+          "class_counts": [
+            464,
+            393,
+            486
+          ],
+          "predicted_class_counts": [
+            533,
+            324,
+            486
+          ],
+          "confusion_matrix": [
+            [
+              342,
+              97,
+              25
+            ],
+            [
+              131,
+              195,
+              67
+            ],
+            [
+              60,
+              32,
+              394
+            ]
+          ],
+          "per_class_recall": [
+            0.7370689655172413,
+            0.4961832061068702,
+            0.8106995884773662
+          ],
+          "balanced_accuracy": 0.6813172533671592,
+          "accuracy": 0.6932241250930752,
+          "weighted_f1": 0.6895731074873461,
+          "macro_f1": 0.6802302724647475,
+          "kappa": 0.5361235537290547,
+          "samples": 1343,
+          "subject_metrics": {
+            "class_counts": [
+              7,
+              5,
+              6
+            ],
+            "predicted_class_counts": [
+              7,
+              5,
+              6
+            ],
+            "confusion_matrix": [
+              [
+                5,
+                2,
+                0
+              ],
+              [
+                2,
+                3,
+                0
+              ],
+              [
+                0,
+                0,
+                6
+              ]
+            ],
+            "per_class_recall": [
+              0.7142857142857143,
+              0.6,
+              1.0
+            ],
+            "balanced_accuracy": 0.7714285714285714,
+            "accuracy": 0.7777777777777778,
+            "weighted_f1": 0.7777777777777778,
+            "macro_f1": 0.7714285714285714,
+            "kappa": 0.6635514018691588
+          },
+          "reward": 0.7714285714285714,
+          "subjects": 18
+        },
+        "openbmi": {
+          "class_counts": [
+            2200,
+            2200
+          ],
+          "predicted_class_counts": [
+            2056,
+            2344
+          ],
+          "confusion_matrix": [
+            [
+              1694,
+              506
+            ],
+            [
+              362,
+              1838
+            ]
+          ],
+          "per_class_recall": [
+            0.77,
+            0.8354545454545454
+          ],
+          "balanced_accuracy": 0.8027272727272727,
+          "accuracy": 0.8027272727272727,
+          "weighted_f1": 0.8025157524091919,
+          "macro_f1": 0.8025157524091919,
+          "kappa": 0.6054545454545455,
+          "samples": 4400,
+          "subject_scores": {
+            "19": 0.845,
+            "35": 0.9525,
+            "34": 0.6425000000000001,
+            "51": 0.7475,
+            "20": 0.8174999999999999,
+            "44": 0.94,
+            "24": 0.5974999999999999,
+            "15": 0.9,
+            "31": 0.8775,
+            "26": 0.79,
+            "23": 0.72
+          },
+          "reward": 0.8027272727272727,
+          "subjects": 11
+        },
+        "longitudinal": {
+          "class_counts": [
+            4260,
+            4158
+          ],
+          "predicted_class_counts": [
+            4574,
+            3844
+          ],
+          "confusion_matrix": [
+            [
+              3658,
+              602
+            ],
+            [
+              916,
+              3242
+            ]
+          ],
+          "per_class_recall": [
+            0.8586854460093897,
+            0.7797017797017797
+          ],
+          "balanced_accuracy": 0.8191936128555847,
+          "accuracy": 0.819672131147541,
+          "weighted_f1": 0.8193389120476722,
+          "macro_f1": 0.819230668900581,
+          "kappa": 0.6389648996053923,
+          "samples": 8418,
+          "subject_scores": {
+            "sub-G2021": 0.837643455629449,
+            "sub-G2007": 0.7652179956923153,
+            "sub-G2019": 0.82505149822325,
+            "sub-G2031": 0.788678938415735,
+            "sub-G2015": 0.8429774626319118,
+            "sub-CTR002": 0.9869493251846193,
+            "sub-G2011": 0.765938925240826,
+            "sub-G2035": 0.9564871486872261,
+            "sub-CTR003": 0.5773809523809523
+          },
+          "reward": 0.8162584113429205,
+          "subjects": 9
+        }
+      },
+      "losses": {
+        "faced": 1.1290458619594574,
+        "tuev": 0.8124855548143387,
+        "ad65": 0.3871454387903214,
+        "openbmi": 0.4081333264708519,
+        "longitudinal": 0.24221988543868064
+      },
+      "seconds": 70.8283851146698
+    },
+    {
+      "epoch": 5,
+      "validation": {
+        "faced": {
+          "class_counts": [
+            180,
+            180,
+            180,
+            180,
+            240,
+            180,
+            180,
+            180,
+            180
+          ],
+          "predicted_class_counts": [
+            160,
+            190,
+            179,
+            213,
+            241,
+            165,
+            181,
+            175,
+            176
+          ],
+          "confusion_matrix": [
+            [
+              77,
+              14,
+              5,
+              23,
+              19,
+              9,
+              13,
+              10,
+              10
+            ],
+            [
+              12,
+              87,
+              9,
+              13,
+              19,
+              11,
+              14,
+              5,
+              10
+            ],
+            [
+              6,
+              7,
+              93,
+              21,
+              17,
+              5,
+              7,
+              10,
+              14
+            ],
+            [
+              14,
+              18,
+              9,
+              71,
+              21,
+              8,
+              16,
+              15,
+              8
+            ],
+            [
+              14,
+              13,
+              17,
+              26,
+              110,
+              17,
+              14,
+              18,
+              11
+            ],
+            [
+              7,
+              11,
+              11,
+              14,
+              11,
+              86,
+              10,
+              14,
+              16
+            ],
+            [
+              8,
+              11,
+              9,
+              13,
+              17,
+              14,
+              89,
+              10,
+              9
+            ],
+            [
+              8,
+              16,
+              15,
+              17,
+              15,
+              10,
+              10,
+              84,
+              5
+            ],
+            [
+              14,
+              13,
+              11,
+              15,
+              12,
+              5,
+              8,
+              9,
+              93
+            ]
+          ],
+          "per_class_recall": [
+            0.42777777777777776,
+            0.48333333333333334,
+            0.5166666666666667,
+            0.39444444444444443,
+            0.4583333333333333,
+            0.4777777777777778,
+            0.49444444444444446,
+            0.4666666666666667,
+            0.5166666666666667
+          ],
+          "balanced_accuracy": 0.47067901234567905,
+          "accuracy": 0.47023809523809523,
+          "weighted_f1": 0.4714090633921556,
+          "macro_f1": 0.47192864139258894,
+          "kappa": 0.40324241480878376,
+          "samples": 1680,
+          "reward": 0.47067901234567905,
+          "subjects": 20
+        },
+        "tuev": {
+          "class_counts": [
+            119,
+            291,
+            669,
+            86,
+            500,
+            13822
+          ],
+          "predicted_class_counts": [
+            1470,
+            431,
+            128,
+            748,
+            2708,
+            10002
+          ],
+          "confusion_matrix": [
+            [
+              50,
+              38,
+              15,
+              4,
+              0,
+              12
+            ],
+            [
+              0,
+              99,
+              0,
+              98,
+              94,
+              0
+            ],
+            [
+              112,
+              28,
+              113,
+              100,
+              316,
+              0
+            ],
+            [
+              3,
+              2,
+              0,
+              81,
+              0,
+              0
+            ],
+            [
+              2,
+              0,
+              0,
+              46,
+              427,
+              25
+            ],
+            [
+              1303,
+              264,
+              0,
+              419,
+              1871,
+              9965
+            ]
+          ],
+          "per_class_recall": [
+            0.42016806722689076,
+            0.3402061855670103,
+            0.16890881913303438,
+            0.9418604651162791,
+            0.854,
+            0.7209521053393141
+          ],
+          "balanced_accuracy": 0.5743492737304214,
+          "accuracy": 0.6931620068444502,
+          "weighted_f1": 0.7741731196624807,
+          "macro_f1": 0.3196232848661513,
+          "kappa": 0.2625475606475476,
+          "samples": 15487,
+          "reward": 0.5743492737304214,
+          "subjects": 58
+        },
+        "ad65": {
+          "class_counts": [
+            464,
+            393,
+            486
+          ],
+          "predicted_class_counts": [
+            533,
+            324,
+            486
+          ],
+          "confusion_matrix": [
+            [
+              342,
+              97,
+              25
+            ],
+            [
+              131,
+              195,
+              67
+            ],
+            [
+              60,
+              32,
+              394
+            ]
+          ],
+          "per_class_recall": [
+            0.7370689655172413,
+            0.4961832061068702,
+            0.8106995884773662
+          ],
+          "balanced_accuracy": 0.6813172533671592,
+          "accuracy": 0.6932241250930752,
+          "weighted_f1": 0.6895731074873461,
+          "macro_f1": 0.6802302724647475,
+          "kappa": 0.5361235537290547,
+          "samples": 1343,
+          "subject_metrics": {
+            "class_counts": [
+              7,
+              5,
+              6
+            ],
+            "predicted_class_counts": [
+              7,
+              5,
+              6
+            ],
+            "confusion_matrix": [
+              [
+                5,
+                2,
+                0
+              ],
+              [
+                2,
+                3,
+                0
+              ],
+              [
+                0,
+                0,
+                6
+              ]
+            ],
+            "per_class_recall": [
+              0.7142857142857143,
+              0.6,
+              1.0
+            ],
+            "balanced_accuracy": 0.7714285714285714,
+            "accuracy": 0.7777777777777778,
+            "weighted_f1": 0.7777777777777778,
+            "macro_f1": 0.7714285714285714,
+            "kappa": 0.6635514018691588
+          },
+          "reward": 0.7714285714285714,
+          "subjects": 18
+        },
+        "openbmi": {
+          "class_counts": [
+            2200,
+            2200
+          ],
+          "predicted_class_counts": [
+            2056,
+            2344
+          ],
+          "confusion_matrix": [
+            [
+              1694,
+              506
+            ],
+            [
+              362,
+              1838
+            ]
+          ],
+          "per_class_recall": [
+            0.77,
+            0.8354545454545454
+          ],
+          "balanced_accuracy": 0.8027272727272727,
+          "accuracy": 0.8027272727272727,
+          "weighted_f1": 0.8025157524091919,
+          "macro_f1": 0.8025157524091919,
+          "kappa": 0.6054545454545455,
+          "samples": 4400,
+          "subject_scores": {
+            "19": 0.845,
+            "35": 0.9525,
+            "34": 0.6425000000000001,
+            "51": 0.7475,
+            "20": 0.8174999999999999,
+            "44": 0.94,
+            "24": 0.5974999999999999,
+            "15": 0.9,
+            "31": 0.8775,
+            "26": 0.79,
+            "23": 0.72
+          },
+          "reward": 0.8027272727272727,
+          "subjects": 11
+        },
+        "longitudinal": {
+          "class_counts": [
+            4260,
+            4158
+          ],
+          "predicted_class_counts": [
+            4574,
+            3844
+          ],
+          "confusion_matrix": [
+            [
+              3658,
+              602
+            ],
+            [
+              916,
+              3242
+            ]
+          ],
+          "per_class_recall": [
+            0.8586854460093897,
+            0.7797017797017797
+          ],
+          "balanced_accuracy": 0.8191936128555847,
+          "accuracy": 0.819672131147541,
+          "weighted_f1": 0.8193389120476722,
+          "macro_f1": 0.819230668900581,
+          "kappa": 0.6389648996053923,
+          "samples": 8418,
+          "subject_scores": {
+            "sub-G2021": 0.837643455629449,
+            "sub-G2007": 0.7652179956923153,
+            "sub-G2019": 0.82505149822325,
+            "sub-G2031": 0.788678938415735,
+            "sub-G2015": 0.8429774626319118,
+            "sub-CTR002": 0.9869493251846193,
+            "sub-G2011": 0.765938925240826,
+            "sub-G2035": 0.9564871486872261,
+            "sub-CTR003": 0.5773809523809523
+          },
+          "reward": 0.8162584113429205,
+          "subjects": 9
+        }
+      },
+      "losses": {
+        "faced": 1.1651152074337006,
+        "tuev": 0.795024397969246,
+        "ad65": 0.39580797255039213,
+        "openbmi": 0.415699365735054,
+        "longitudinal": 0.24489932954311372
+      },
+      "seconds": 83.52135276794434
+    },
+    {
+      "epoch": 6,
+      "validation": {
+        "faced": {
+          "class_counts": [
+            180,
+            180,
+            180,
+            180,
+            240,
+            180,
+            180,
+            180,
+            180
+          ],
+          "predicted_class_counts": [
+            158,
+            191,
+            179,
+            213,
+            242,
+            165,
+            181,
+            175,
+            176
+          ],
+          "confusion_matrix": [
+            [
+              77,
+              14,
+              5,
+              23,
+              19,
+              9,
+              13,
+              10,
+              10
+            ],
+            [
+              12,
+              87,
+              9,
+              13,
+              19,
+              11,
+              14,
+              5,
+              10
+            ],
+            [
+              6,
+              7,
+              93,
+              21,
+              17,
+              5,
+              7,
+              10,
+              14
+            ],
+            [
+              14,
+              18,
+              9,
+              71,
+              21,
+              8,
+              16,
+              15,
+              8
+            ],
+            [
+              13,
+              13,
+              17,
+              26,
+              111,
+              17,
+              14,
+              18,
+              11
+            ],
+            [
+              7,
+              11,
+              11,
+              14,
+              11,
+              86,
+              10,
+              14,
+              16
+            ],
+            [
+              8,
+              11,
+              9,
+              13,
+              17,
+              14,
+              89,
+              10,
+              9
+            ],
+            [
+              8,
+              16,
+              15,
+              17,
+              15,
+              10,
+              10,
+              84,
+              5
+            ],
+            [
+              13,
+              14,
+              11,
+              15,
+              12,
+              5,
+              8,
+              9,
+              93
+            ]
+          ],
+          "per_class_recall": [
+            0.42777777777777776,
+            0.48333333333333334,
+            0.5166666666666667,
+            0.39444444444444443,
+            0.4625,
+            0.4777777777777778,
+            0.49444444444444446,
+            0.4666666666666667,
+            0.5166666666666667
+          ],
+          "balanced_accuracy": 0.471141975308642,
+          "accuracy": 0.4708333333333333,
+          "weighted_f1": 0.47201761600953746,
+          "macro_f1": 0.4724411976075547,
+          "kappa": 0.40389865415010284,
+          "samples": 1680,
+          "reward": 0.471141975308642,
+          "subjects": 20
+        },
+        "tuev": {
+          "class_counts": [
+            119,
+            291,
+            669,
+            86,
+            500,
+            13822
+          ],
+          "predicted_class_counts": [
+            1417,
+            489,
+            167,
+            734,
+            1641,
+            11039
+          ],
+          "confusion_matrix": [
+            [
+              46,
+              38,
+              19,
+              4,
+              0,
+              12
+            ],
+            [
+              0,
+              99,
+              0,
+              113,
+              79,
+              0
+            ],
+            [
+              104,
+              42,
+              148,
+              114,
+              261,
+              0
+            ],
+            [
+              5,
+              2,
+              0,
+              78,
+              1,
+              0
+            ],
+            [
+              3,
+              0,
+              0,
+              50,
+              391,
+              56
+            ],
+            [
+              1259,
+              308,
+              0,
+              375,
+              909,
+              10971
+            ]
+          ],
+          "per_class_recall": [
+            0.3865546218487395,
+            0.3402061855670103,
+            0.22122571001494767,
+            0.9069767441860465,
+            0.782,
+            0.7937346259586167
+          ],
+          "balanced_accuracy": 0.5717829812625601,
+          "accuracy": 0.7576031510298961,
+          "weighted_f1": 0.8210740384280086,
+          "macro_f1": 0.3509816572072955,
+          "kappa": 0.3236586218946286,
+          "samples": 15487,
+          "reward": 0.5717829812625601,
+          "subjects": 58
+        },
+        "ad65": {
+          "class_counts": [
+            464,
+            393,
+            486
+          ],
+          "predicted_class_counts": [
+            533,
+            324,
+            486
+          ],
+          "confusion_matrix": [
+            [
+              342,
+              97,
+              25
+            ],
+            [
+              131,
+              195,
+              67
+            ],
+            [
+              60,
+              32,
+              394
+            ]
+          ],
+          "per_class_recall": [
+            0.7370689655172413,
+            0.4961832061068702,
+            0.8106995884773662
+          ],
+          "balanced_accuracy": 0.6813172533671592,
+          "accuracy": 0.6932241250930752,
+          "weighted_f1": 0.6895731074873461,
+          "macro_f1": 0.6802302724647475,
+          "kappa": 0.5361235537290547,
+          "samples": 1343,
+          "subject_metrics": {
+            "class_counts": [
+              7,
+              5,
+              6
+            ],
+            "predicted_class_counts": [
+              7,
+              5,
+              6
+            ],
+            "confusion_matrix": [
+              [
+                5,
+                2,
+                0
+              ],
+              [
+                2,
+                3,
+                0
+              ],
+              [
+                0,
+                0,
+                6
+              ]
+            ],
+            "per_class_recall": [
+              0.7142857142857143,
+              0.6,
+              1.0
+            ],
+            "balanced_accuracy": 0.7714285714285714,
+            "accuracy": 0.7777777777777778,
+            "weighted_f1": 0.7777777777777778,
+            "macro_f1": 0.7714285714285714,
+            "kappa": 0.6635514018691588
+          },
+          "reward": 0.7714285714285714,
+          "subjects": 18
+        },
+        "openbmi": {
+          "class_counts": [
+            2200,
+            2200
+          ],
+          "predicted_class_counts": [
+            2056,
+            2344
+          ],
+          "confusion_matrix": [
+            [
+              1694,
+              506
+            ],
+            [
+              362,
+              1838
+            ]
+          ],
+          "per_class_recall": [
+            0.77,
+            0.8354545454545454
+          ],
+          "balanced_accuracy": 0.8027272727272727,
+          "accuracy": 0.8027272727272727,
+          "weighted_f1": 0.8025157524091919,
+          "macro_f1": 0.8025157524091919,
+          "kappa": 0.6054545454545455,
+          "samples": 4400,
+          "subject_scores": {
+            "19": 0.845,
+            "35": 0.9525,
+            "34": 0.6425000000000001,
+            "51": 0.7475,
+            "20": 0.8174999999999999,
+            "44": 0.94,
+            "24": 0.5974999999999999,
+            "15": 0.9,
+            "31": 0.8775,
+            "26": 0.79,
+            "23": 0.72
+          },
+          "reward": 0.8027272727272727,
+          "subjects": 11
+        },
+        "longitudinal": {
+          "class_counts": [
+            4260,
+            4158
+          ],
+          "predicted_class_counts": [
+            4573,
+            3845
+          ],
+          "confusion_matrix": [
+            [
+              3657,
+              603
+            ],
+            [
+              916,
+              3242
+            ]
+          ],
+          "per_class_recall": [
+            0.8584507042253521,
+            0.7797017797017797
+          ],
+          "balanced_accuracy": 0.8190762419635659,
+          "accuracy": 0.8195533380850558,
+          "weighted_f1": 0.8192217642599872,
+          "macro_f1": 0.8191137114479841,
+          "kappa": 0.6387281046963751,
+          "samples": 8418,
+          "subject_scores": {
+            "sub-G2021": 0.837643455629449,
+            "sub-G2007": 0.7644167136410334,
+            "sub-G2019": 0.82505149822325,
+            "sub-G2031": 0.788678938415735,
+            "sub-G2015": 0.8429774626319118,
+            "sub-CTR002": 0.9869493251846193,
+            "sub-G2011": 0.765938925240826,
+            "sub-G2035": 0.9564871486872261,
+            "sub-CTR003": 0.5773809523809523
+          },
+          "reward": 0.8161693800038893,
+          "subjects": 9
+        }
+      },
+      "losses": {
+        "faced": 1.2065095901489258,
+        "tuev": 0.8101497024297715,
+        "ad65": 0.3583500549197197,
+        "openbmi": 0.414377860724926,
+        "longitudinal": 0.2511081449687481
+      },
+      "seconds": 96.09055876731873
+    },
+    {
+      "epoch": 7,
+      "validation": {
+        "faced": {
+          "class_counts": [
+            180,
+            180,
+            180,
+            180,
+            240,
+            180,
+            180,
+            180,
+            180
+          ],
+          "predicted_class_counts": [
+            158,
+            191,
+            179,
+            212,
+            242,
+            165,
+            182,
+            175,
+            176
+          ],
+          "confusion_matrix": [
+            [
+              77,
+              14,
+              5,
+              22,
+              19,
+              9,
+              14,
+              10,
+              10
+            ],
+            [
+              12,
+              87,
+              9,
+              13,
+              19,
+              11,
+              14,
+              5,
+              10
+            ],
+            [
+              6,
+              7,
+              93,
+              21,
+              17,
+              5,
+              7,
+              10,
+              14
+            ],
+            [
+              14,
+              18,
+              9,
+              71,
+              21,
+              8,
+              16,
+              15,
+              8
+            ],
+            [
+              13,
+              13,
+              17,
+              26,
+              111,
+              17,
+              14,
+              18,
+              11
+            ],
+            [
+              7,
+              11,
+              11,
+              14,
+              11,
+              86,
+              10,
+              14,
+              16
+            ],
+            [
+              8,
+              11,
+              9,
+              13,
+              17,
+              14,
+              89,
+              10,
+              9
+            ],
+            [
+              8,
+              16,
+              15,
+              17,
+              15,
+              10,
+              10,
+              84,
+              5
+            ],
+            [
+              13,
+              14,
+              11,
+              15,
+              12,
+              5,
+              8,
+              9,
+              93
+            ]
+          ],
+          "per_class_recall": [
+            0.42777777777777776,
+            0.48333333333333334,
+            0.5166666666666667,
+            0.39444444444444443,
+            0.4625,
+            0.4777777777777778,
+            0.49444444444444446,
+            0.4666666666666667,
+            0.5166666666666667
+          ],
+          "balanced_accuracy": 0.471141975308642,
+          "accuracy": 0.4708333333333333,
+          "weighted_f1": 0.4719704364754072,
+          "macro_f1": 0.4723922706832716,
+          "kappa": 0.40389865415010295,
+          "samples": 1680,
+          "reward": 0.471141975308642,
+          "subjects": 20
+        },
+        "tuev": {
+          "class_counts": [
+            119,
+            291,
+            669,
+            86,
+            500,
+            13822
+          ],
+          "predicted_class_counts": [
+            1417,
+            660,
+            125,
+            754,
+            1640,
+            10891
+          ],
+          "confusion_matrix": [
+            [
+              45,
+              38,
+              15,
+              0,
+              4,
+              17
+            ],
+            [
+              17,
+              164,
+              0,
+              61,
+              49,
+              0
+            ],
+            [
+              131,
+              126,
+              110,
+              89,
+              213,
+              0
+            ],
+            [
+              8,
+              2,
+              0,
+              76,
+              0,
+              0
+            ],
+            [
+              4,
+              0,
+              0,
+              24,
+              435,
+              37
+            ],
+            [
+              1212,
+              330,
+              0,
+              504,
+              939,
+              10837
+            ]
+          ],
+          "per_class_recall": [
+            0.37815126050420167,
+            0.563573883161512,
+            0.16442451420029897,
+            0.8837209302325582,
+            0.87,
+            0.7840399363333815
+          ],
+          "balanced_accuracy": 0.6073184207386587,
+          "accuracy": 0.7533415122360689,
+          "weighted_f1": 0.8157694653698978,
+          "macro_f1": 0.35751577708839816,
+          "kappa": 0.3275905700234074,
+          "samples": 15487,
+          "reward": 0.6073184207386587,
+          "subjects": 58
+        },
+        "ad65": {
+          "class_counts": [
+            464,
+            393,
+            486
+          ],
+          "predicted_class_counts": [
+            533,
+            324,
+            486
+          ],
+          "confusion_matrix": [
+            [
+              342,
+              97,
+              25
+            ],
+            [
+              131,
+              195,
+              67
+            ],
+            [
+              60,
+              32,
+              394
+            ]
+          ],
+          "per_class_recall": [
+            0.7370689655172413,
+            0.4961832061068702,
+            0.8106995884773662
+          ],
+          "balanced_accuracy": 0.6813172533671592,
+          "accuracy": 0.6932241250930752,
+          "weighted_f1": 0.6895731074873461,
+          "macro_f1": 0.6802302724647475,
+          "kappa": 0.5361235537290547,
+          "samples": 1343,
+          "subject_metrics": {
+            "class_counts": [
+              7,
+              5,
+              6
+            ],
+            "predicted_class_counts": [
+              7,
+              5,
+              6
+            ],
+            "confusion_matrix": [
+              [
+                5,
+                2,
+                0
+              ],
+              [
+                2,
+                3,
+                0
+              ],
+              [
+                0,
+                0,
+                6
+              ]
+            ],
+            "per_class_recall": [
+              0.7142857142857143,
+              0.6,
+              1.0
+            ],
+            "balanced_accuracy": 0.7714285714285714,
+            "accuracy": 0.7777777777777778,
+            "weighted_f1": 0.7777777777777778,
+            "macro_f1": 0.7714285714285714,
+            "kappa": 0.6635514018691588
+          },
+          "reward": 0.7714285714285714,
+          "subjects": 18
+        },
+        "openbmi": {
+          "class_counts": [
+            2200,
+            2200
+          ],
+          "predicted_class_counts": [
+            2056,
+            2344
+          ],
+          "confusion_matrix": [
+            [
+              1694,
+              506
+            ],
+            [
+              362,
+              1838
+            ]
+          ],
+          "per_class_recall": [
+            0.77,
+            0.8354545454545454
+          ],
+          "balanced_accuracy": 0.8027272727272727,
+          "accuracy": 0.8027272727272727,
+          "weighted_f1": 0.8025157524091919,
+          "macro_f1": 0.8025157524091919,
+          "kappa": 0.6054545454545455,
+          "samples": 4400,
+          "subject_scores": {
+            "19": 0.845,
+            "35": 0.9525,
+            "34": 0.6425000000000001,
+            "51": 0.7475,
+            "20": 0.8174999999999999,
+            "44": 0.94,
+            "24": 0.5974999999999999,
+            "15": 0.9,
+            "31": 0.8775,
+            "26": 0.79,
+            "23": 0.72
+          },
+          "reward": 0.8027272727272727,
+          "subjects": 11
+        },
+        "longitudinal": {
+          "class_counts": [
+            4260,
+            4158
+          ],
+          "predicted_class_counts": [
+            4573,
+            3845
+          ],
+          "confusion_matrix": [
+            [
+              3657,
+              603
+            ],
+            [
+              916,
+              3242
+            ]
+          ],
+          "per_class_recall": [
+            0.8584507042253521,
+            0.7797017797017797
+          ],
+          "balanced_accuracy": 0.8190762419635659,
+          "accuracy": 0.8195533380850558,
+          "weighted_f1": 0.8192217642599872,
+          "macro_f1": 0.8191137114479841,
+          "kappa": 0.6387281046963751,
+          "samples": 8418,
+          "subject_scores": {
+            "sub-G2021": 0.837643455629449,
+            "sub-G2007": 0.7644167136410334,
+            "sub-G2019": 0.82505149822325,
+            "sub-G2031": 0.788678938415735,
+            "sub-G2015": 0.8429774626319118,
+            "sub-CTR002": 0.9869493251846193,
+            "sub-G2011": 0.765938925240826,
+            "sub-G2035": 0.9564871486872261,
+            "sub-CTR003": 0.5773809523809523
+          },
+          "reward": 0.8161693800038893,
+          "subjects": 9
+        }
+      },
+      "losses": {
+        "faced": 1.1728858172893524,
+        "tuev": 0.8282902628183365,
+        "ad65": 0.38538945466279984,
+        "openbmi": 0.4079422622919083,
+        "longitudinal": 0.24979219660162927
+      },
+      "seconds": 108.29975962638855
+    }
+  ],
+  "history": [
+    {
+      "epoch": 1,
+      "validation": {
+        "faced": {
+          "class_counts": [
+            180,
+            180,
+            180,
+            180,
+            240,
+            180,
+            180,
+            180,
+            180
+          ],
+          "predicted_class_counts": [
+            158,
+            189,
+            178,
+            213,
+            242,
+            166,
+            182,
+            176,
+            176
+          ],
+          "confusion_matrix": [
+            [
+              77,
+              14,
+              5,
+              22,
+              19,
+              9,
+              14,
+              10,
+              10
+            ],
+            [
+              12,
+              86,
+              9,
+              13,
+              19,
+              11,
+              14,
+              6,
+              10
+            ],
+            [
+              6,
+              7,
+              93,
+              21,
+              17,
+              5,
+              7,
+              10,
+              14
+            ],
+            [
+              14,
+              18,
+              9,
+              71,
+              21,
+              8,
+              16,
+              15,
+              8
+            ],
+            [
+              13,
+              13,
+              17,
+              26,
+              111,
+              17,
+              14,
+              18,
+              11
+            ],
+            [
+              6,
+              11,
+              11,
+              15,
+              11,
+              86,
+              10,
+              14,
+              16
+            ],
+            [
+              8,
+              11,
+              9,
+              13,
+              17,
+              14,
+              89,
+              10,
+              9
+            ],
+            [
+              8,
+              16,
+              14,
+              17,
+              15,
+              11,
+              10,
+              84,
+              5
+            ],
+            [
+              14,
+              13,
+              11,
+              15,
+              12,
+              5,
+              8,
+              9,
+              93
+            ]
+          ],
+          "per_class_recall": [
+            0.42777777777777776,
+            0.4777777777777778,
+            0.5166666666666667,
+            0.39444444444444443,
+            0.4625,
+            0.4777777777777778,
+            0.49444444444444446,
+            0.4666666666666667,
+            0.5166666666666667
+          ],
+          "balanced_accuracy": 0.4705246913580247,
+          "accuracy": 0.47023809523809523,
+          "weighted_f1": 0.4714215675991068,
+          "macro_f1": 0.47182307333007106,
+          "kappa": 0.40322812395229657,
+          "samples": 1680,
+          "reward": 0.4705246913580247,
+          "subjects": 20
+        },
+        "tuev": {
+          "class_counts": [
+            119,
+            291,
+            669,
+            86,
+            500,
+            13822
+          ],
+          "predicted_class_counts": [
+            1415,
+            660,
+            126,
+            754,
+            1684,
+            10848
+          ],
+          "confusion_matrix": [
+            [
+              45,
+              38,
+              15,
+              0,
+              4,
+              17
+            ],
+            [
+              17,
+              164,
+              0,
+              61,
+              49,
+              0
+            ],
+            [
+              130,
+              126,
+              111,
+              89,
+              213,
+              0
+            ],
+            [
+              7,
+              2,
+              0,
+              76,
+              0,
+              1
+            ],
+            [
+              4,
+              0,
+              0,
+              24,
+              435,
+              37
+            ],
+            [
+              1212,
+              330,
+              0,
+              504,
+              983,
+              10793
+            ]
+          ],
+          "per_class_recall": [
+            0.37815126050420167,
+            0.563573883161512,
+            0.16591928251121077,
+            0.8837209302325582,
+            0.87,
+            0.7808566054116626
+          ],
+          "balanced_accuracy": 0.6070369936368575,
+          "accuracy": 0.7505649899916058,
+          "weighted_f1": 0.8137799723660881,
+          "macro_f1": 0.3561849045209278,
+          "kappa": 0.32441309326581047,
+          "samples": 15487,
+          "reward": 0.6070369936368575,
+          "subjects": 58
+        },
+        "ad65": {
+          "class_counts": [
+            464,
+            393,
+            486
+          ],
+          "predicted_class_counts": [
+            533,
+            324,
+            486
+          ],
+          "confusion_matrix": [
+            [
+              342,
+              97,
+              25
+            ],
+            [
+              131,
+              195,
+              67
+            ],
+            [
+              60,
+              32,
+              394
+            ]
+          ],
+          "per_class_recall": [
+            0.7370689655172413,
+            0.4961832061068702,
+            0.8106995884773662
+          ],
+          "balanced_accuracy": 0.6813172533671592,
+          "accuracy": 0.6932241250930752,
+          "weighted_f1": 0.6895731074873461,
+          "macro_f1": 0.6802302724647475,
+          "kappa": 0.5361235537290547,
+          "samples": 1343,
+          "subject_metrics": {
+            "class_counts": [
+              7,
+              5,
+              6
+            ],
+            "predicted_class_counts": [
+              7,
+              5,
+              6
+            ],
+            "confusion_matrix": [
+              [
+                5,
+                2,
+                0
+              ],
+              [
+                2,
+                3,
+                0
+              ],
+              [
+                0,
+                0,
+                6
+              ]
+            ],
+            "per_class_recall": [
+              0.7142857142857143,
+              0.6,
+              1.0
+            ],
+            "balanced_accuracy": 0.7714285714285714,
+            "accuracy": 0.7777777777777778,
+            "weighted_f1": 0.7777777777777778,
+            "macro_f1": 0.7714285714285714,
+            "kappa": 0.6635514018691588
+          },
+          "reward": 0.7714285714285714,
+          "subjects": 18
+        },
+        "openbmi": {
+          "class_counts": [
+            2200,
+            2200
+          ],
+          "predicted_class_counts": [
+            2061,
+            2339
+          ],
+          "confusion_matrix": [
+            [
+              1698,
+              502
+            ],
+            [
+              363,
+              1837
+            ]
+          ],
+          "per_class_recall": [
+            0.7718181818181818,
+            0.835
+          ],
+          "balanced_accuracy": 0.803409090909091,
+          "accuracy": 0.803409090909091,
+          "weighted_f1": 0.8032127000298179,
+          "macro_f1": 0.8032127000298179,
+          "kappa": 0.6068181818181818,
+          "samples": 4400,
+          "subject_scores": {
+            "19": 0.8425,
+            "35": 0.9525,
+            "34": 0.6425000000000001,
+            "51": 0.7475,
+            "20": 0.82,
+            "44": 0.94,
+            "24": 0.5974999999999999,
+            "15": 0.9,
+            "31": 0.8825000000000001,
+            "26": 0.7925,
+            "23": 0.72
+          },
+          "reward": 0.803409090909091,
+          "subjects": 11
+        },
+        "longitudinal": {
+          "class_counts": [
+            4260,
+            4158
+          ],
+          "predicted_class_counts": [
+            4573,
+            3845
+          ],
+          "confusion_matrix": [
+            [
+              3657,
+              603
+            ],
+            [
+              916,
+              3242
+            ]
+          ],
+          "per_class_recall": [
+            0.8584507042253521,
+            0.7797017797017797
+          ],
+          "balanced_accuracy": 0.8190762419635659,
+          "accuracy": 0.8195533380850558,
+          "weighted_f1": 0.8192217642599872,
+          "macro_f1": 0.8191137114479841,
+          "kappa": 0.6387281046963751,
+          "samples": 8418,
+          "subject_scores": {
+            "sub-G2021": 0.837643455629449,
+            "sub-G2007": 0.7644167136410334,
+            "sub-G2019": 0.82505149822325,
+            "sub-G2031": 0.788678938415735,
+            "sub-G2015": 0.8429774626319118,
+            "sub-CTR002": 0.9869493251846193,
+            "sub-G2011": 0.765938925240826,
+            "sub-G2035": 0.9564871486872261,
+            "sub-CTR003": 0.5773809523809523
+          },
+          "reward": 0.8161693800038893,
+          "subjects": 9
+        }
+      },
+      "seconds": 13.797802209854126
+    }
+  ],
+  "averaged_validation": {
+    "faced": {
+      "class_counts": [
+        180,
+        180,
+        180,
+        180,
+        240,
+        180,
+        180,
+        180,
+        180
+      ],
+      "predicted_class_counts": [
+        158,
+        189,
+        178,
+        213,
+        242,
+        166,
+        182,
+        176,
+        176
+      ],
+      "confusion_matrix": [
+        [
+          77,
+          14,
+          5,
+          22,
+          19,
+          9,
+          14,
+          10,
+          10
+        ],
+        [
+          12,
+          86,
+          9,
+          13,
+          19,
+          11,
+          14,
+          6,
+          10
+        ],
+        [
+          6,
+          7,
+          93,
+          21,
+          17,
+          5,
+          7,
+          10,
+          14
+        ],
+        [
+          14,
+          18,
+          9,
+          71,
+          21,
+          8,
+          16,
+          15,
+          8
+        ],
+        [
+          13,
+          13,
+          17,
+          26,
+          111,
+          17,
+          14,
+          18,
+          11
+        ],
+        [
+          6,
+          11,
+          11,
+          15,
+          11,
+          86,
+          10,
+          14,
+          16
+        ],
+        [
+          8,
+          11,
+          9,
+          13,
+          17,
+          14,
+          89,
+          10,
+          9
+        ],
+        [
+          8,
+          16,
+          14,
+          17,
+          15,
+          11,
+          10,
+          84,
+          5
+        ],
+        [
+          14,
+          13,
+          11,
+          15,
+          12,
+          5,
+          8,
+          9,
+          93
+        ]
+      ],
+      "per_class_recall": [
+        0.42777777777777776,
+        0.4777777777777778,
+        0.5166666666666667,
+        0.39444444444444443,
+        0.4625,
+        0.4777777777777778,
+        0.49444444444444446,
+        0.4666666666666667,
+        0.5166666666666667
+      ],
+      "balanced_accuracy": 0.4705246913580247,
+      "accuracy": 0.47023809523809523,
+      "weighted_f1": 0.4714215675991068,
+      "macro_f1": 0.47182307333007106,
+      "kappa": 0.40322812395229657,
+      "samples": 1680,
+      "reward": 0.4705246913580247,
+      "subjects": 20
+    },
+    "tuev": {
+      "class_counts": [
+        119,
+        291,
+        669,
+        86,
+        500,
+        13822
+      ],
+      "predicted_class_counts": [
+        1415,
+        660,
+        126,
+        754,
+        1684,
+        10848
+      ],
+      "confusion_matrix": [
+        [
+          45,
+          38,
+          15,
+          0,
+          4,
+          17
+        ],
+        [
+          17,
+          164,
+          0,
+          61,
+          49,
+          0
+        ],
+        [
+          130,
+          126,
+          111,
+          89,
+          213,
+          0
+        ],
+        [
+          7,
+          2,
+          0,
+          76,
+          0,
+          1
+        ],
+        [
+          4,
+          0,
+          0,
+          24,
+          435,
+          37
+        ],
+        [
+          1212,
+          330,
+          0,
+          504,
+          983,
+          10793
+        ]
+      ],
+      "per_class_recall": [
+        0.37815126050420167,
+        0.563573883161512,
+        0.16591928251121077,
+        0.8837209302325582,
+        0.87,
+        0.7808566054116626
+      ],
+      "balanced_accuracy": 0.6070369936368575,
+      "accuracy": 0.7505649899916058,
+      "weighted_f1": 0.8137799723660881,
+      "macro_f1": 0.3561849045209278,
+      "kappa": 0.32441309326581047,
+      "samples": 15487,
+      "reward": 0.6070369936368575,
+      "subjects": 58
+    },
+    "ad65": {
+      "class_counts": [
+        464,
+        393,
+        486
+      ],
+      "predicted_class_counts": [
+        533,
+        324,
+        486
+      ],
+      "confusion_matrix": [
+        [
+          342,
+          97,
+          25
+        ],
+        [
+          131,
+          195,
+          67
+        ],
+        [
+          60,
+          32,
+          394
+        ]
+      ],
+      "per_class_recall": [
+        0.7370689655172413,
+        0.4961832061068702,
+        0.8106995884773662
+      ],
+      "balanced_accuracy": 0.6813172533671592,
+      "accuracy": 0.6932241250930752,
+      "weighted_f1": 0.6895731074873461,
+      "macro_f1": 0.6802302724647475,
+      "kappa": 0.5361235537290547,
+      "samples": 1343,
+      "subject_metrics": {
+        "class_counts": [
+          7,
+          5,
+          6
+        ],
+        "predicted_class_counts": [
+          7,
+          5,
+          6
+        ],
+        "confusion_matrix": [
+          [
+            5,
+            2,
+            0
+          ],
+          [
+            2,
+            3,
+            0
+          ],
+          [
+            0,
+            0,
+            6
+          ]
+        ],
+        "per_class_recall": [
+          0.7142857142857143,
+          0.6,
+          1.0
+        ],
+        "balanced_accuracy": 0.7714285714285714,
+        "accuracy": 0.7777777777777778,
+        "weighted_f1": 0.7777777777777778,
+        "macro_f1": 0.7714285714285714,
+        "kappa": 0.6635514018691588
+      },
+      "reward": 0.7714285714285714,
+      "subjects": 18
+    },
+    "openbmi": {
+      "class_counts": [
+        2200,
+        2200
+      ],
+      "predicted_class_counts": [
+        2061,
+        2339
+      ],
+      "confusion_matrix": [
+        [
+          1698,
+          502
+        ],
+        [
+          363,
+          1837
+        ]
+      ],
+      "per_class_recall": [
+        0.7718181818181818,
+        0.835
+      ],
+      "balanced_accuracy": 0.803409090909091,
+      "accuracy": 0.803409090909091,
+      "weighted_f1": 0.8032127000298179,
+      "macro_f1": 0.8032127000298179,
+      "kappa": 0.6068181818181818,
+      "samples": 4400,
+      "subject_scores": {
+        "19": 0.8425,
+        "35": 0.9525,
+        "34": 0.6425000000000001,
+        "51": 0.7475,
+        "20": 0.82,
+        "44": 0.94,
+        "24": 0.5974999999999999,
+        "15": 0.9,
+        "31": 0.8825000000000001,
+        "26": 0.7925,
+        "23": 0.72
+      },
+      "reward": 0.803409090909091,
+      "subjects": 11
+    },
+    "longitudinal": {
+      "class_counts": [
+        4260,
+        4158
+      ],
+      "predicted_class_counts": [
+        4573,
+        3845
+      ],
+      "confusion_matrix": [
+        [
+          3657,
+          603
+        ],
+        [
+          916,
+          3242
+        ]
+      ],
+      "per_class_recall": [
+        0.8584507042253521,
+        0.7797017797017797
+      ],
+      "balanced_accuracy": 0.8190762419635659,
+      "accuracy": 0.8195533380850558,
+      "weighted_f1": 0.8192217642599872,
+      "macro_f1": 0.8191137114479841,
+      "kappa": 0.6387281046963751,
+      "samples": 8418,
+      "subject_scores": {
+        "sub-G2021": 0.837643455629449,
+        "sub-G2007": 0.7644167136410334,
+        "sub-G2019": 0.82505149822325,
+        "sub-G2031": 0.788678938415735,
+        "sub-G2015": 0.8429774626319118,
+        "sub-CTR002": 0.9869493251846193,
+        "sub-G2011": 0.765938925240826,
+        "sub-G2035": 0.9564871486872261,
+        "sub-CTR003": 0.5773809523809523
+      },
+      "reward": 0.8161693800038893,
+      "subjects": 9
+    }
+  }
+}

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S015/config.json

@@ -0,0 +1,194 @@
+{
+  "schema": "bci-shared-biot-covariance-bf16head-v1",
+  "architecture": {
+    "channels": 32,
+    "width": 128,
+    "depth": 2,
+    "dropout": 0.5,
+    "wave_filters": 32,
+    "head_width": 64,
+    "head_dropout": 0.3
+  },
+  "initialization": {
+    "initialization": "continue joint training from a complete shared-model checkpoint",
+    "parent": "/workspace/research/runs/hybrid_biot_fusionshrink0100/epoch-001/weights.pt",
+    "parent_initialization": {
+      "initialization": "jointly trained parent plus audited pretrained BIOT in a single coupled backbone",
+      "parent": "/workspace/research/runs/hybrid_wide1/epoch-004/weights.pt",
+      "audited_biot": {
+        "initialization": "pretrained",
+        "repo": "braindecode/biot-pretrained-six-datasets-18chs",
+        "revision": "7e9aabf302870012f73773276803aab97993951c",
+        "sha256": "a218f4054ae29962c6fd9d23d94c446d354c4f53e805a6771c85749ccc20baaa",
+        "license": "BSD-3-Clause",
+        "source_braindecode": "1.3.0",
+        "loaded_core_tensors": 56,
+        "skipped_classifier_tensors": 3
+      },
+      "neural_channel_indices": {
+        "faced": [
+          0,
+          1,
+          2,
+          3,
+          4,
+          5,
+          6,
+          7,
+          8,
+          9,
+          10,
+          11,
+          12,
+          13,
+          14,
+          15,
+          16,
+          17
+        ],
+        "tuev": [
+          0,
+          1,
+          2,
+          3,
+          4,
+          5,
+          6,
+          7,
+          8,
+          9,
+          10,
+          11,
+          12,
+          13,
+          14,
+          15
+        ],
+        "ad65": [
+          0,
+          1,
+          2,
+          3,
+          4,
+          5,
+          6,
+          7,
+          8,
+          9,
+          10,
+          11,
+          12,
+          13,
+          14,
+          15,
+          16,
+          17
+        ],
+        "openbmi": [
+          0,
+          1,
+          3,
+          5,
+          12,
+          14,
+          23,
+          25,
+          28,
+          30,
+          2,
+          6,
+          11,
+          15,
+          22,
+          26,
+          4,
+          13
+        ],
+        "longitudinal": [
+          0,
+          2,
+          7,
+          11,
+          23,
+          27,
+          41,
+          45,
+          55,
+          57,
+          5,
+          13,
+          21,
+          29,
+          39,
+          47,
+          9,
+          25
+        ]
+      },
+      "rebase": "incumbent waveform, fusion and four heads; joint BIOT and TUEV head from readout epoch 9; zero initialized neural interaction",
+      "jointly_finetuned_neural_parent": "/workspace/research/runs/hybrid_biot_readout1/epoch-009/weights.pt",
+      "tuev_high_dimensional_spectral_projection_disabled": true,
+      "frozen_prior_representation": true,
+      "nonclinical_head_updates": "only the 256 added BIOT columns; original columns and biases fixed",
+      "faced_openbmi_neural_path": "common nonlinear fusion only; direct head columns masked",
+      "retention_teacher": "/workspace/research/runs/hybrid_wide1/epoch-004",
+      "retention_loss": {
+        "split": "train",
+        "temperature": 2.0,
+        "kl_weight": 1.0,
+        "tracks": [
+          "faced",
+          "ad65",
+          "openbmi",
+          "longitudinal"
+        ]
+      }
+    },
+    "joint_covariance_parent": "/workspace/research/runs/filterbank1/epoch-002/weights.pt",
+    "frozen_prior_representation": true,
+    "head_updates": "one linear head per track; added 128 shared covariance columns trainable; AD head initialized from the joint covariance parent",
+    "new_shared_representation": "one common covariance MLP inside the coupled backbone, trained by all five tasks; fixed spatial input adapters from the same jointly trained source",
+    "head_arithmetic": "one linear head evaluated in fixed feature blocks to preserve original bfloat16 accumulation dimensions",
+    "retention_teacher": {
+      "ad65": "/workspace/research/runs/filterbank1/epoch-002/weights.pt",
+      "other_tracks": "/workspace/research/runs/hybrid_wide1/epoch-004"
+    },
+    "retention_loss": {
+      "split": "train",
+      "temperature": 2.0,
+      "kl_weight": 1.0,
+      "tracks": [
+        "faced",
+        "ad65",
+        "openbmi",
+        "longitudinal"
+      ]
+    }
+  },
+  "loader_scale": 0.01,
+  "tracks": [
+    "faced",
+    "tuev",
+    "ad65",
+    "openbmi",
+    "longitudinal"
+  ],
+  "head_arithmetic": "same linear head in fixed feature blocks, sum in bfloat16 on GPU for non-AD tracks; AD remains float32",
+  "head_refinement": {
+    "source": "/workspace/research/runs/residual_heads1/epoch-018",
+    "source_weights_sha256": "e0b609137391b47f99438ccadaa22fc0bc10d2c10ead0ddf0a3c25be2c907c85",
+    "shared_backbone_unchanged": true,
+    "selected_head_epochs": {
+      "faced": 5,
+      "tuev": 24,
+      "ad65": 0,
+      "openbmi": 0,
+      "longitudinal": 0
+    }
+  },
+  "clinical_spectral_refinement": {
+    "source": "/workspace/research/runs/head_dropout1/epoch-016",
+    "zero_initialized_input_adapter": true,
+    "backbone_unchanged": true
+  }
+}

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S015/covariances.py

@@ -0,0 +1,25 @@
+"""Shared neural filter-bank covariance decoder with spatial input adapters."""
+import torch
+from torch import nn
+SPECS={'faced':(32,10,9),'tuev':(16,5,6),'ad65':(19,10,3),'openbmi':(62,4,2),'longitudinal':(58,2,2)}
+TRACKS=tuple(SPECS)
+BANDS=[(1,4),(4,8),(8,12),(12,16),(16,20),(20,24),(24,28),(28,32),(32,40),(40,48)]
+
+@torch.no_grad()
+def extract_covariances(t,x):
+    x=torch.nan_to_num(x.float().flatten(2)).clamp(-1e5,1e5)
+    x=x-x.mean(-1,keepdim=True)
+    if t in ('faced','tuev'):
+        x=x/x.square().mean(-1,keepdim=True).clamp_min(1e-12).sqrt()
+    else:x=x-x.mean(1,keepdim=True)
+    # Per-window normalization, preserving spatial and frequency power ratios.
+    x=x/x.square().mean((1,2),keepdim=True).clamp_min(1e-12).sqrt()
+    frames=x.unfold(-1,400,200);frames=frames-frames.mean(-1,keepdim=True)
+    z=torch.fft.rfft(frames*torch.hann_window(400,device=x.device),dim=-1)/200
+    covs=[]
+    for lo,hi in BANDS:
+        v=z[...,2*lo:2*hi].flatten(2)
+        cov=(v.real@v.real.transpose(-1,-2)+v.imag@v.imag.transpose(-1,-2))/v.shape[-1]
+        covs.append(cov)
+    return torch.stack(covs,1)
+

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S015/data.py

@@ -0,0 +1,33 @@
+import json
+from functools import lru_cache
+from pathlib import Path
+import numpy as np
+import torch
+@lru_cache(maxsize=8)
+def array(path):return np.load(path,mmap_mode='r',allow_pickle=False)
+class Dataset(torch.utils.data.Dataset):
+    def __init__(self,root,limit=None,cache=False):
+        self.root=Path(root)
+        self.rows=json.loads((self.root/'manifest.json').read_text())['samples']
+        if limit:self.rows=self.rows[:limit]
+        self.cached=None
+        if cache and self.rows:
+            from collections import defaultdict
+            groups=defaultdict(list)
+            for i,r in enumerate(self.rows):groups[r['path']].append((i,r['index']))
+            for name,indices in groups.items():
+                path=(self.root/name).resolve()
+                if not path.is_relative_to(self.root.resolve()):raise ValueError('input outside root')
+                source=np.load(path,mmap_mode='r',allow_pickle=False)
+                if self.cached is None:self.cached=torch.empty((len(self.rows),*source.shape[1:]),dtype=torch.float32)
+                dst,src=zip(*indices)
+                self.cached[list(dst)]=torch.as_tensor(source[list(src)]/100,dtype=torch.float32)
+    def __len__(self):return len(self.rows)
+    def __getitem__(self,i):
+        r=self.rows[i]
+        if self.cached is not None:return self.cached[i],r.get('label',-1),r['id']
+        p=(self.root/r['path']).resolve()
+        if not p.is_relative_to(self.root.resolve()):raise ValueError('input outside root')
+        # Division precedes float32 conversion, matching official FACED/TUEV collate.
+        x=np.asarray(array(str(p))[r['index']])/100
+        return torch.as_tensor(x,dtype=torch.float32),r.get('label',-1),r['id']

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S015/features.py

@@ -0,0 +1,63 @@
+"""One shared residual backbone, five linear input adapters and output heads."""
+import torch
+from torch import nn
+
+SPECS = {'faced': (32, 10, 9), 'tuev': (16, 5, 6), 'ad65': (19, 10, 3),
+         'openbmi': (62, 4, 2), 'longitudinal': (58, 2, 2)}
+TRACKS = tuple(SPECS)
+EDGES = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18,20,22,25,28,32,36,40,45,50,60,70,80,95]
+COV_BANDS = [(1,4),(4,8),(8,12),(12,16),(16,20),(20,28),(28,40)]
+
+def feature_dim(channels):
+    return channels * (4*(len(EDGES)-1)+4) + len(COV_BANDS)*channels*(channels-1)//2
+
+@torch.no_grad()
+def extract_features(track, x):
+    """Fixed features; no batch statistics or fitted inference transformations."""
+    x = x.float().flatten(2)
+    x = torch.nan_to_num(x, nan=0., posinf=1e5, neginf=-1e5).clamp(-1e5,1e5)
+    x = x - x.mean(-1,keepdim=True)
+    if track in ('ad65','openbmi','longitudinal'):
+        x = x - x.mean(1,keepdim=True)
+    rms = x.square().mean(-1).clamp_min(1e-12).sqrt()
+    norm = x / rms.unsqueeze(-1)
+    moments = torch.stack([rms.log(), norm.abs().amax(-1).clamp_min(1).log(),
+                          norm.pow(4).mean(-1).clamp_min(1).log(),
+                          (norm[...,1:]-norm[...,:-1]).square().mean(-1).clamp_min(1e-10).log()],-1)
+    frames = x.unfold(-1,400,200)
+    frames = frames - frames.mean(-1,keepdim=True)
+    window = torch.hann_window(400,device=x.device)
+    z = torch.fft.rfft(frames*window,dim=-1)/200
+    power = z.abs().square()
+    bands = torch.stack([power[...,2*lo:2*hi].mean(-1) for lo,hi in zip(EDGES[:-1],EDGES[1:])],-1)
+    lp = bands.mean(-2).clamp_min(1e-12).log()
+    variation = bands.clamp_min(1e-12).log().std(-2,correction=0)
+    relative_frequency = lp - lp.mean(-1,keepdim=True)
+    relative_channel = lp - lp.mean(1,keepdim=True)
+    parts = [torch.cat([lp, relative_frequency, relative_channel, variation, moments],-1).flatten(1)]
+    ii,jj = torch.triu_indices(x.shape[1],x.shape[1],offset=1,device=x.device)
+    for lo,hi in COV_BANDS:
+        v = z[...,2*lo:2*hi].flatten(2)
+        cov = v.real @ v.real.transpose(-1,-2) + v.imag @ v.imag.transpose(-1,-2)
+        d = cov.diagonal(dim1=-2,dim2=-1).clamp_min(1e-12).sqrt()
+        cor = cov / (d.unsqueeze(1)*d.unsqueeze(2)).clamp_min(1e-12)
+        parts.append(cor[:,ii,jj].clamp(-1,1))
+    result = torch.cat(parts,-1)
+    return torch.nan_to_num(result).clamp(-40,40)
+
+class Standardize(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.register_buffer('mean',torch.zeros(dim))
+        self.register_buffer('scale',torch.ones(dim))
+    def forward(self,x):
+        return ((x-self.mean)/self.scale).clamp(-8,8)
+
+class Residual(nn.Module):
+    def __init__(self,width,dropout):
+        super().__init__()
+        self.net=nn.Sequential(nn.LayerNorm(width),nn.Linear(width,width*2),nn.GELU(),
+                               nn.Dropout(dropout),nn.Linear(width*2,width),nn.Dropout(dropout))
+    def forward(self,x):
+        return x+self.net(x)
+

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S015/infer.py

@@ -0,0 +1,24 @@
+import argparse,json
+from pathlib import Path
+import torch
+from model import HybridModel,SPECS
+from data import Dataset
+from covariances import extract_covariances
+p=argparse.ArgumentParser();p.add_argument('input',type=Path);p.add_argument('output',type=Path);a=p.parse_args()
+root=Path(__file__).resolve().parent;c=json.loads((root/'config.json').read_text());t=json.loads((a.input/'manifest.json').read_text())['track']
+if t not in SPECS:raise ValueError('unknown track')
+torch.set_num_threads(4);device='cuda' if torch.cuda.is_available() else 'cpu'
+m=HybridModel(**c['architecture']).to(device);m.load_state_dict(torch.load(root/'weights.pt',map_location=device,weights_only=True));m.eval()
+a.output.mkdir(parents=True,exist_ok=True)
+with torch.inference_mode(),(a.output/'predictions.jsonl').open('w') as f:
+    for x,_,ids in torch.utils.data.DataLoader(Dataset(a.input),batch_size=64,num_workers=0):
+        # FFT features stay float32; convolutions can use bfloat16 on CUDA.
+        x=x.to(device)
+        from features import extract_features
+        torch.backends.cuda.matmul.allow_tf32=False
+        features=extract_features(t,x)
+        covariances=extract_covariances(t,x)
+        torch.backends.cuda.matmul.allow_tf32=True
+        with torch.autocast(device,dtype=torch.bfloat16,enabled=device=='cuda'):pred=m(t,x,features,covariances=covariances)
+        pp=pred.float().softmax(-1).cpu().numpy()
+        for sid,pr in zip(ids,pp):f.write(json.dumps({'id':sid,'probabilities':pr.tolist()})+'\n')

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S015/infer.sh

@@ -0,0 +1,3 @@
+#!/usr/bin/env bash
+set -euo pipefail
+exec python "$(dirname -- "$0")/infer.py" "$1" "$2"

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S015/model.py

@@ -0,0 +1,111 @@
+"""One fused waveform/spectral backbone used identically by all five tasks."""
+import torch
+from torch import nn
+from braindecode.models import BIOT
+from features import SPECS,TRACKS,feature_dim,extract_features,Standardize,Residual
+from covariances import BANDS,extract_covariances
+
+class InputAdapter(nn.Module):
+    def __init__(self,c,width,channels,spectral_enabled=True):
+        super().__init__()
+        self.signal=nn.Conv1d(c,channels,1,bias=False)
+        self.neural=nn.Conv1d(c,18,1,bias=False)
+        self.normalizer=Standardize(feature_dim(c))
+        self.spectral=nn.Linear(feature_dim(c),width)
+        self.dropout=nn.Dropout(.1)
+        self.register_buffer("spectral_enabled",torch.tensor(float(spectral_enabled)))
+        self.spatial_weight=nn.Parameter(torch.randn(len(BANDS),24,c)/c**.5)
+    def spatial_features(self,covariances):
+        with torch.autocast(covariances.device.type,enabled=False):
+            w=self.spatial_weight.float()/self.spatial_weight.float().norm(dim=-1,keepdim=True).clamp_min(1e-6)
+            power=torch.einsum('bfc,nbcd,bfd->nbf',w,covariances.float(),w).clamp_min(1e-8).log()
+            return torch.cat([power,power-power.mean(1,keepdim=True)],1).flatten(1)
+    def forward(self,x,features,covariance_features):
+        return self.signal(x).unsqueeze(1),self.spectral_enabled*self.spectral(self.dropout(self.normalizer(features))),self.neural(x),covariance_features
+
+class SharedBackbone(nn.Module):
+    def __init__(self,channels,width,depth,dropout,wave_filters):
+        super().__init__()
+        self.waveform=nn.Sequential(
+            nn.Conv2d(1,wave_filters,(1,64),padding=(0,32),bias=False),nn.GroupNorm(1,wave_filters),
+            nn.Conv2d(wave_filters,2*wave_filters,(channels,1),groups=wave_filters,bias=False),nn.GroupNorm(1,2*wave_filters),nn.ELU(),
+            nn.AvgPool2d((1,4)),nn.Dropout(.35),
+            nn.Conv2d(2*wave_filters,2*wave_filters,(1,16),padding=(0,8),groups=2*wave_filters,bias=False),nn.Conv2d(2*wave_filters,2*wave_filters,1,bias=False),
+            nn.GroupNorm(1,2*wave_filters),nn.ELU(),nn.AvgPool2d((1,8)),nn.Dropout(.35))
+        self.wave_summary=nn.Sequential(nn.AdaptiveAvgPool2d((1,8)),nn.Flatten(),nn.Linear(2*wave_filters*8,width))
+        self.fusion=nn.Sequential(nn.LayerNorm(width),nn.GELU(),nn.Dropout(dropout),
+                                 *[Residual(width,dropout) for _ in range(depth)],nn.LayerNorm(width))
+        # Both representations interact before the shared residual stack.
+        self.wave_gain=nn.Parameter(torch.tensor(.1))
+        self.biot=BIOT(n_chans=18,n_outputs=2,n_times=400,sfreq=200)
+        self.biot.final_layer=nn.Identity()
+        self.biot_projection=nn.Sequential(nn.LayerNorm(256),nn.Linear(256,width),nn.Dropout(.2))
+        nn.init.normal_(self.biot_projection[1].weight,std=.001);nn.init.zeros_(self.biot_projection[1].bias)
+        self.covariance_encoder=nn.Sequential(nn.LayerNorm(len(BANDS)*24*2),nn.Linear(len(BANDS)*24*2,width),nn.GELU(),nn.Dropout(.4),
+                                             *[Residual(width,.4) for _ in range(2)],nn.LayerNorm(width))
+        self.covariance_projection=nn.Linear(width,width)
+        nn.init.zeros_(self.covariance_projection.weight);nn.init.zeros_(self.covariance_projection.bias)
+    def forward(self,signal,spectral,neural_signal,covariance_features):
+        wave=self.waveform(signal)
+        neural=self.biot(neural_signal.float())
+        neural=self.biot_projection[0](neural)
+        projected=self.biot_projection[2](self.biot_projection[1](neural))
+        with torch.autocast(signal.device.type,enabled=False):
+            covariance_embedding=self.covariance_encoder(covariance_features.float())
+        original=spectral+self.wave_gain*self.wave_summary(wave)+.01*projected
+        coupled=.001*self.covariance_projection(covariance_embedding).to(original.dtype)
+        fused=self.fusion(original+coupled)
+        return torch.cat([wave.flatten(1),fused,neural,covariance_embedding],-1)
+
+class ResidualHead(nn.Module):
+    """A small nonlinear classifier correction around an inherited linear readout."""
+    def __init__(self,features,classes,width=64,dropout=.3,full_precision=False):
+        super().__init__();self.full_precision=full_precision
+        self.base=nn.Linear(features,classes)
+        self.delta=nn.Sequential(nn.LayerNorm(features),nn.Linear(features,width),nn.GELU(),nn.Dropout(dropout),nn.Linear(width,classes))
+        nn.init.zeros_(self.delta[-1].weight);nn.init.zeros_(self.delta[-1].bias)
+    def forward(self,x):
+        if self.full_precision:
+            with torch.autocast(x.device.type,enabled=False):return self.base(x.float())+self.delta(x.float())
+        prior=nn.functional.linear(x[:,:-128].contiguous(),self.base.weight[:,:-128].contiguous(),self.base.bias)
+        added=nn.functional.linear(x[:,-128:].contiguous(),self.base.weight[:,-128:].contiguous())
+        value=prior+added
+        return value+self.delta(x).to(value.dtype)
+
+class HybridModel(nn.Module):
+    def __init__(self,channels=32,width=128,depth=2,dropout=.5,wave_filters=32,head_width=64,head_dropout=.3):
+        super().__init__()
+        self.adapters=nn.ModuleDict({t:InputAdapter(c,width,channels,spectral_enabled=t not in ("faced","tuev","openbmi")) for t,(c,s,k) in SPECS.items()})
+        self.backbone=SharedBackbone(channels,width,depth,dropout,wave_filters)
+        self.heads=nn.ModuleDict({t:ResidualHead(2*wave_filters*(((s*200+1)//4+1)//8)+width+256+width,k,head_width,head_dropout,full_precision=t=='ad65') for t,(c,s,k) in SPECS.items()})
+    def forward(self,t,x,features=None,covariances=None,covariance_features=None):
+        if features is None:features=extract_features(t,x)
+        if covariance_features is None:
+            if covariances is None:covariances=extract_covariances(t,x)
+            covariance_features=self.adapters[t].spatial_features(covariances)
+        signal=x.flatten(2);signal=signal-signal.mean(-1,keepdim=True)
+        if t in ('faced','tuev'):signal=signal/signal.std(-1,keepdim=True,correction=0).clamp_min(1e-6)
+        else:signal=signal-signal.mean(1,keepdim=True)
+        signal=signal.clamp(-20,20)
+        adapted=self.adapters[t](signal,features,covariance_features)
+        representation=self.backbone(*adapted)
+        if t in ('faced','openbmi'):
+            representation=torch.cat([representation[:,:-384],torch.zeros_like(representation[:,-384:-128]),representation[:,-128:]],-1)
+        return self.heads[t](representation)
+
+def widen_state_dict(state,multiplier=2):
+    """Duplicate temporal features and divide downstream sums to preserve outputs."""
+    result={}
+    for key,value in state.items():
+        if key.startswith('backbone.waveform.'):
+            layer=int(key.split('.')[2])
+            if layer in (0,2,7):value=value.repeat(multiplier,1,1,1)
+            elif layer in (1,3,9):value=value.repeat(multiplier)
+            elif layer==8:value=value.repeat(multiplier,multiplier,1,1)/multiplier
+        elif key=='backbone.wave_summary.2.weight':value=value.repeat(1,multiplier)/multiplier
+        elif key.startswith('heads.') and key.endswith('.weight'):
+            raw=value[:,:-128]/multiplier
+            value=torch.cat([raw]*multiplier+[value[:,-128:]],1)
+        result[key]=value
+    return result
+

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S017/features.py

@@ -0,0 +1,63 @@
+"""One shared residual backbone, five linear input adapters and output heads."""
+import torch
+from torch import nn
+
+SPECS = {'faced': (32, 10, 9), 'tuev': (16, 5, 6), 'ad65': (19, 10, 3),
+         'openbmi': (62, 4, 2), 'longitudinal': (58, 2, 2)}
+TRACKS = tuple(SPECS)
+EDGES = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18,20,22,25,28,32,36,40,45,50,60,70,80,95]
+COV_BANDS = [(1,4),(4,8),(8,12),(12,16),(16,20),(20,28),(28,40)]
+
+def feature_dim(channels):
+    return channels * (4*(len(EDGES)-1)+4) + len(COV_BANDS)*channels*(channels-1)//2
+
+@torch.no_grad()
+def extract_features(track, x):
+    """Fixed features; no batch statistics or fitted inference transformations."""
+    x = x.float().flatten(2)
+    x = torch.nan_to_num(x, nan=0., posinf=1e5, neginf=-1e5).clamp(-1e5,1e5)
+    x = x - x.mean(-1,keepdim=True)
+    if track in ('ad65','openbmi','longitudinal'):
+        x = x - x.mean(1,keepdim=True)
+    rms = x.square().mean(-1).clamp_min(1e-12).sqrt()
+    norm = x / rms.unsqueeze(-1)
+    moments = torch.stack([rms.log(), norm.abs().amax(-1).clamp_min(1).log(),
+                          norm.pow(4).mean(-1).clamp_min(1).log(),
+                          (norm[...,1:]-norm[...,:-1]).square().mean(-1).clamp_min(1e-10).log()],-1)
+    frames = x.unfold(-1,400,200)
+    frames = frames - frames.mean(-1,keepdim=True)
+    window = torch.hann_window(400,device=x.device)
+    z = torch.fft.rfft(frames*window,dim=-1)/200
+    power = z.abs().square()
+    bands = torch.stack([power[...,2*lo:2*hi].mean(-1) for lo,hi in zip(EDGES[:-1],EDGES[1:])],-1)
+    lp = bands.mean(-2).clamp_min(1e-12).log()
+    variation = bands.clamp_min(1e-12).log().std(-2,correction=0)
+    relative_frequency = lp - lp.mean(-1,keepdim=True)
+    relative_channel = lp - lp.mean(1,keepdim=True)
+    parts = [torch.cat([lp, relative_frequency, relative_channel, variation, moments],-1).flatten(1)]
+    ii,jj = torch.triu_indices(x.shape[1],x.shape[1],offset=1,device=x.device)
+    for lo,hi in COV_BANDS:
+        v = z[...,2*lo:2*hi].flatten(2)
+        cov = v.real @ v.real.transpose(-1,-2) + v.imag @ v.imag.transpose(-1,-2)
+        d = cov.diagonal(dim1=-2,dim2=-1).clamp_min(1e-12).sqrt()
+        cor = cov / (d.unsqueeze(1)*d.unsqueeze(2)).clamp_min(1e-12)
+        parts.append(cor[:,ii,jj].clamp(-1,1))
+    result = torch.cat(parts,-1)
+    return torch.nan_to_num(result).clamp(-40,40)
+
+class Standardize(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.register_buffer('mean',torch.zeros(dim))
+        self.register_buffer('scale',torch.ones(dim))
+    def forward(self,x):
+        return ((x-self.mean)/self.scale).clamp(-8,8)
+
+class Residual(nn.Module):
+    def __init__(self,width,dropout):
+        super().__init__()
+        self.net=nn.Sequential(nn.LayerNorm(width),nn.Linear(width,width*2),nn.GELU(),
+                               nn.Dropout(dropout),nn.Linear(width*2,width),nn.Dropout(dropout))
+    def forward(self,x):
+        return x+self.net(x)
+

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S039/alpha_features.py

@@ -0,0 +1,45 @@
+"""Fixed alpha prominence features from each individual two-second window."""
+import torch
+
+REGION_NAMES=('occipital','parietal','central','frontal','left_posterior','right_posterior')
+REGIONS=(('PO7','PO5','PO3','POZ','PO4','PO6','PO8','O1','OZ','O2'),
+         ('P7','P5','P3','P1','PZ','P2','P4','P6','P8'),
+         ('FC5','FC3','FC1','FCZ','FC2','FC4','FC6','C5','C3','C1','CZ','C2','C4','C6'),
+         ('FP1','FPZ','FP2','AF3','AF4','F7','F5','F3','F1','FZ','F2','F4','F6','F8'),
+         ('P7','P5','P3','P1','PO7','PO5','PO3','O1'),
+         ('P2','P4','P6','P8','PO4','PO6','PO8','O2'))
+ALPHA_DIM=58*7+6*22
+
+def region_matrix(channels):
+    result=torch.zeros(len(REGIONS),len(channels))
+    for i,names in enumerate(REGIONS):
+        for name in names:result[i,channels.index(name)]=1/len(names)
+    return result.tolist()
+
+@torch.no_grad()
+def extract_alpha_features(signal,regions):
+    with torch.autocast(signal.device.type,enabled=False):
+        x=signal.float();x=x-x.mean(-1,keepdim=True)
+        spectrum=torch.fft.rfft(x*torch.hann_window(400,device=x.device),dim=-1)/200
+        power=spectrum.abs().square().clamp_min(1e-12)
+        lp=power.log()
+        # Geometric regional means resist domination by one high-amplitude channel.
+        regional=torch.einsum('rc,ncf->nrf',regions.float(),lp)
+        lp=torch.cat([lp,regional],1)
+        low=lp[...,8:13].mean(-1,keepdim=True)
+        high=lp[...,36:49].mean(-1,keepdim=True)
+        freq=torch.arange(7.,14.5,.5,device=x.device)
+        alpha=lp[...,14:29]
+        baseline=low+(high-low)*((freq-5)/16)
+        prominence=alpha-baseline
+        normalized=alpha.softmax(-1)
+        center=(normalized*freq).sum(-1)
+        entropy=-(normalized*normalized.clamp_min(1e-12).log()).sum(-1)
+        alpha_mean=torch.logsumexp(alpha,-1)-torch.log(torch.tensor(15.,device=x.device))
+        def log_band(lo,hi):
+            return torch.logsumexp(lp[...,lo:hi],-1)-torch.log(torch.tensor(float(hi-lo),device=x.device))
+        summaries=torch.stack([alpha_mean-log_band(2,81),alpha_mean-log_band(8,14),
+                               alpha_mean-log_band(30,61),prominence.max(-1).values,
+                               prominence.mean(-1),center,entropy],-1)
+        return torch.cat([summaries[:,:58].flatten(1),summaries[:,58:].flatten(1),
+                          prominence[:,58:].flatten(1)],1)

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S039/config.json

@@ -0,0 +1,697 @@
+{
+  "schema": "bci-shared-biot-covariance-bf16head-v1",
+  "architecture": {
+    "channels": 32,
+    "width": 128,
+    "depth": 2,
+    "dropout": 0.5,
+    "wave_filters": 32,
+    "head_width": 64,
+    "head_dropout": 0.3,
+    "clinical_asymmetry": true,
+    "alpha_regions": [
+      [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612
+      ],
+      [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+      ],
+      [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+      ],
+      [
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+      ],
+      [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.125,
+        0.125,
+        0.125,
+        0.125,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.125,
+        0.125,
+        0.125,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.125,
+        0.0,
+        0.0
+      ],
+      [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.125,
+        0.125,
+        0.125,
+        0.125,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.125,
+        0.125,
+        0.125,
+        0.0,
+        0.0,
+        0.125
+      ]
+    ],
+    "clinical_envelope": true,
+    "temporal_pool": true,
+    "temporal_evidence": true,
+    "evidence_temperature": 0.25,
+    "random_fourier": true,
+    "fourier_features": 256,
+    "tangent_readout": true,
+    "codebrain_fusion": true
+  },
+  "initialization": {
+    "initialization": "continue joint training from a complete shared-model checkpoint",
+    "parent": "/workspace/research/runs/hybrid_biot_fusionshrink0100/epoch-001/weights.pt",
+    "parent_initialization": {
+      "initialization": "jointly trained parent plus audited pretrained BIOT in a single coupled backbone",
+      "parent": "/workspace/research/runs/hybrid_wide1/epoch-004/weights.pt",
+      "audited_biot": {
+        "initialization": "pretrained",
+        "repo": "braindecode/biot-pretrained-six-datasets-18chs",
+        "revision": "7e9aabf302870012f73773276803aab97993951c",
+        "sha256": "a218f4054ae29962c6fd9d23d94c446d354c4f53e805a6771c85749ccc20baaa",
+        "license": "BSD-3-Clause",
+        "source_braindecode": "1.3.0",
+        "loaded_core_tensors": 56,
+        "skipped_classifier_tensors": 3
+      },
+      "neural_channel_indices": {
+        "faced": [
+          0,
+          1,
+          2,
+          3,
+          4,
+          5,
+          6,
+          7,
+          8,
+          9,
+          10,
+          11,
+          12,
+          13,
+          14,
+          15,
+          16,
+          17
+        ],
+        "tuev": [
+          0,
+          1,
+          2,
+          3,
+          4,
+          5,
+          6,
+          7,
+          8,
+          9,
+          10,
+          11,
+          12,
+          13,
+          14,
+          15
+        ],
+        "ad65": [
+          0,
+          1,
+          2,
+          3,
+          4,
+          5,
+          6,
+          7,
+          8,
+          9,
+          10,
+          11,
+          12,
+          13,
+          14,
+          15,
+          16,
+          17
+        ],
+        "openbmi": [
+          0,
+          1,
+          3,
+          5,
+          12,
+          14,
+          23,
+          25,
+          28,
+          30,
+          2,
+          6,
+          11,
+          15,
+          22,
+          26,
+          4,
+          13
+        ],
+        "longitudinal": [
+          0,
+          2,
+          7,
+          11,
+          23,
+          27,
+          41,
+          45,
+          55,
+          57,
+          5,
+          13,
+          21,
+          29,
+          39,
+          47,
+          9,
+          25
+        ]
+      },
+      "rebase": "incumbent waveform, fusion and four heads; joint BIOT and TUEV head from readout epoch 9; zero initialized neural interaction",
+      "jointly_finetuned_neural_parent": "/workspace/research/runs/hybrid_biot_readout1/epoch-009/weights.pt",
+      "tuev_high_dimensional_spectral_projection_disabled": true,
+      "frozen_prior_representation": true,
+      "nonclinical_head_updates": "only the 256 added BIOT columns; original columns and biases fixed",
+      "faced_openbmi_neural_path": "common nonlinear fusion only; direct head columns masked",
+      "retention_teacher": "/workspace/research/runs/hybrid_wide1/epoch-004",
+      "retention_loss": {
+        "split": "train",
+        "temperature": 2.0,
+        "kl_weight": 1.0,
+        "tracks": [
+          "faced",
+          "ad65",
+          "openbmi",
+          "longitudinal"
+        ]
+      }
+    },
+    "joint_covariance_parent": "/workspace/research/runs/filterbank1/epoch-002/weights.pt",
+    "frozen_prior_representation": true,
+    "head_updates": "one linear head per track; added 128 shared covariance columns trainable; AD head initialized from the joint covariance parent",
+    "new_shared_representation": "one common covariance MLP inside the coupled backbone, trained by all five tasks; fixed spatial input adapters from the same jointly trained source",
+    "head_arithmetic": "one linear head evaluated in fixed feature blocks to preserve original bfloat16 accumulation dimensions",
+    "retention_teacher": {
+      "ad65": "/workspace/research/runs/filterbank1/epoch-002/weights.pt",
+      "other_tracks": "/workspace/research/runs/hybrid_wide1/epoch-004"
+    },
+    "retention_loss": {
+      "split": "train",
+      "temperature": 2.0,
+      "kl_weight": 1.0,
+      "tracks": [
+        "faced",
+        "ad65",
+        "openbmi",
+        "longitudinal"
+      ]
+    }
+  },
+  "loader_scale": 0.01,
+  "tracks": [
+    "faced",
+    "tuev",
+    "ad65",
+    "openbmi",
+    "longitudinal"
+  ],
+  "head_arithmetic": "same linear head in fixed feature blocks, sum in bfloat16 on GPU for non-AD tracks; AD remains float32",
+  "head_refinement": {
+    "source": "/workspace/research/runs/residual_heads1/epoch-018",
+    "source_weights_sha256": "e0b609137391b47f99438ccadaa22fc0bc10d2c10ead0ddf0a3c25be2c907c85",
+    "shared_backbone_unchanged": true,
+    "selected_head_epochs": {
+      "faced": 5,
+      "tuev": 24,
+      "ad65": 0,
+      "openbmi": 0,
+      "longitudinal": 0
+    }
+  },
+  "clinical_spectral_refinement": {
+    "source": "/workspace/research/runs/head_dropout1/epoch-016",
+    "zero_initialized_input_adapter": true,
+    "backbone_unchanged": true
+  },
+  "head_estimation": {
+    "method": "class/subject-balanced Gaussian linear discriminant; covariance augmented by class-conditional subject/session mean variation before shrinkage",
+    "selected_shrinkage": {
+      "faced": "inherited",
+      "tuev": "inherited",
+      "ad65": "inherited",
+      "openbmi": "inherited",
+      "longitudinal": {
+        "shrinkage": 0.9,
+        "domain_strength": 1.0
+      }
+    },
+    "feature_scaling": "fitted on training data and folded into linear coefficients"
+  },
+  "clinical_asymmetry_refinement": {
+    "source": "/workspace/research/runs/domain_lda_heads1/epoch-001",
+    "zero_initialized_input_adapter": true,
+    "backbone_unchanged": true
+  },
+  "alpha_rhythm_adapter": {
+    "training_normalization_only": true,
+    "regions": [
+      "occipital",
+      "parietal",
+      "central",
+      "frontal",
+      "left_posterior",
+      "right_posterior"
+    ],
+    "features": "per-window alpha prominence relative to adjacent frequencies",
+    "shared_backbone_unchanged": true
+  },
+  "clinical_envelope_refinement": {
+    "source": "/workspace/research/runs/clinical_asymmetry1/epoch-001",
+    "zero_initialized_input_adapter": true,
+    "backbone_unchanged": true
+  },
+  "independent_adapter_merge": {
+    "alpha_source": "/workspace/research/runs/alpha_calibrated1/epoch-003",
+    "clinical_source": "/workspace/research/runs/clinical_envelope1/epoch-002",
+    "all_shared_and_head_tensors_equal": true
+  },
+  "frequency_montage_refinement": {
+    "six_frequency_bands": true,
+    "shared_encoder_unchanged": true,
+    "selected_track_epochs": {
+      "faced": 0,
+      "tuev": 1,
+      "ad65": 0,
+      "openbmi": 10,
+      "longitudinal": 2
+    }
+  },
+  "private_track_selection": {
+    "track": "tuev",
+    "donor": "/workspace/research/replicates/frequency_head_control1/epoch-001"
+  },
+  "clinical_sampling_refinement": {
+    "subject_exponent": 0.0,
+    "class_balanced": true,
+    "only_clinical_head_updated": true
+  },
+  "temporal_statistics_readout": {
+    "statistics": [
+      "mean",
+      "standard deviation",
+      "maximum",
+      "minimum"
+    ],
+    "shared_encoder_and_prior_heads_unchanged": true,
+    "selected_pool_epochs": {
+      "faced": 2,
+      "tuev": 0,
+      "ad65": 0,
+      "openbmi": 33,
+      "longitudinal": 3
+    }
+  },
+  "temporal_evidence_readout": {
+    "pooling": "log mean exp of per-patch class evidence",
+    "temperature": 0.25,
+    "shared_encoder_and_prior_heads_unchanged": true,
+    "selected_evidence_epochs": {
+      "faced": 0,
+      "tuev": 0,
+      "ad65": 0,
+      "openbmi": 6,
+      "longitudinal": 8
+    }
+  },
+  "fourier_basis_readout": {
+    "basis_functions": 256,
+    "bandwidths": [
+      0.5,
+      1.0,
+      2.0
+    ],
+    "shared_encoder_and_prior_heads_unchanged": true,
+    "selected_fourier_epochs": {
+      "faced": 21,
+      "tuev": 0,
+      "ad65": 0,
+      "openbmi": 0,
+      "longitudinal": 2
+    }
+  },
+  "shared_tangent_readout": {
+    "one_shared_tangent_module": true,
+    "original_and_corrected_shared_features_retained": true,
+    "private_head_scalar_gains": {
+      "faced": 0.0,
+      "tuev": 0.1,
+      "ad65": 0.0,
+      "openbmi": 1.0,
+      "longitudinal": 0.1
+    },
+    "all_common_weights_fixed_during_gain_refinement": true
+  },
+  "joint_codebrain_feature_fusion": {
+    "native_common_CodeBrain_branch": true,
+    "source": "/workspace/research/runs/codebrain_temporal1/epoch-027",
+    "common_token_and_covariance_fusion_trained_on_all_five_tracks": true,
+    "private_channel_projection_dimensions": 24,
+    "private_feature_gains": {
+      "faced": 0.3,
+      "tuev": 0.3,
+      "ad65": 0.0,
+      "openbmi": 1.0,
+      "longitudinal": 0.0
+    },
+    "zero_CodeBrain_feature_control": true
+  }
+}

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S039/covariances.py

@@ -0,0 +1,25 @@
+"""Shared neural filter-bank covariance decoder with spatial input adapters."""
+import torch
+from torch import nn
+SPECS={'faced':(32,10,9),'tuev':(16,5,6),'ad65':(19,10,3),'openbmi':(62,4,2),'longitudinal':(58,2,2)}
+TRACKS=tuple(SPECS)
+BANDS=[(1,4),(4,8),(8,12),(12,16),(16,20),(20,24),(24,28),(28,32),(32,40),(40,48)]
+
+@torch.no_grad()
+def extract_covariances(t,x):
+    x=torch.nan_to_num(x.float().flatten(2)).clamp(-1e5,1e5)
+    x=x-x.mean(-1,keepdim=True)
+    if t in ('faced','tuev'):
+        x=x/x.square().mean(-1,keepdim=True).clamp_min(1e-12).sqrt()
+    else:x=x-x.mean(1,keepdim=True)
+    # Per-window normalization, preserving spatial and frequency power ratios.
+    x=x/x.square().mean((1,2),keepdim=True).clamp_min(1e-12).sqrt()
+    frames=x.unfold(-1,400,200);frames=frames-frames.mean(-1,keepdim=True)
+    z=torch.fft.rfft(frames*torch.hann_window(400,device=x.device),dim=-1)/200
+    covs=[]
+    for lo,hi in BANDS:
+        v=z[...,2*lo:2*hi].flatten(2)
+        cov=(v.real@v.real.transpose(-1,-2)+v.imag@v.imag.transpose(-1,-2))/v.shape[-1]
+        covs.append(cov)
+    return torch.stack(covs,1)
+

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S039/envelope_features.py

@@ -0,0 +1,44 @@
+"""Per-window burst-envelope regularity and bilateral synchrony, without fitting."""
+import torch
+import torch.nn.functional as F
+
+BANDS=((1,4),(4,8),(8,13),(13,30),(30,45))
+ENVELOPE_DIM=16*5*12+8*5*2
+
+@torch.no_grad()
+def extract_envelope_features(signal):
+    with torch.autocast(signal.device.type,enabled=False):
+        x=signal.float();x=x-x.mean(-1,keepdim=True)
+        spectrum=torch.fft.fft(x,dim=-1)
+        frequency=torch.fft.fftfreq(x.shape[-1],1/200,device=x.device)
+        envelopes=[]
+        for lo,hi in BANDS:
+            mask=((frequency>=lo)&(frequency<hi)).to(x.dtype)*2
+            analytic=torch.fft.ifft(spectrum*mask,dim=-1)
+            energy=analytic.abs().square()
+            envelope=F.avg_pool1d(energy.flatten(0,1).unsqueeze(1),20,10).squeeze(1)
+            envelopes.append(envelope.reshape(len(x),16,-1))
+        e=torch.stack(envelopes,2)
+        mean=e.mean(-1,keepdim=True).clamp_min(1e-12)
+        relative=e/mean
+        centered=relative-relative.mean(-1,keepdim=True)
+        std=centered.square().mean(-1,keepdim=True).sqrt().clamp_min(1e-6)
+        normalized=centered/std
+        acf=torch.stack([(normalized[...,:-lag]*normalized[...,lag:]).mean(-1)
+                         for lag in range(4,41)],-1).clamp(-1,1)
+        acf_peak,acf_index=acf.max(-1)
+        probability=(relative/relative.sum(-1,keepdim=True).clamp_min(1e-12)).clamp_min(1e-12)
+        entropy=-(probability*probability.log()).sum(-1)
+        summaries=[std.squeeze(-1).clamp_min(1e-6).log(),relative.amax(-1).clamp_min(1).log(),entropy,
+                   (relative>2).float().mean(-1),(relative>4).float().mean(-1),
+                   ((relative[...,1:]>2)&(relative[...,:-1]<=2)).float().mean(-1),
+                   acf_peak,(acf_index.float()+4)/40,
+                   acf[...,0],acf[...,6],acf[...,16],acf[...,36]]
+        left=torch.tensor([0,1,2,3,8,9,10,11],device=x.device);right=left+4
+        synchrony=(normalized[:,left]*normalized[:,right]).mean(-1).clamp(-1,1)
+        imbalance=(std[:,left].squeeze(-1).log()-std[:,right].squeeze(-1).log()).abs()
+        return torch.cat([torch.stack(summaries,-1).flatten(1),synchrony.flatten(1),imbalance.flatten(1)],1)
+
+def mirrored_features(features):
+    channels=torch.tensor([4,5,6,7,0,1,2,3,12,13,14,15,8,9,10,11],device=features.device)
+    return torch.cat([features[:,:16*5*12].reshape(-1,16,5,12)[:,channels].flatten(1),features[:,16*5*12:]],1)

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S039/frequency_features.py

@@ -0,0 +1,11 @@
+"""Fixed per-window frequency decomposition for a linear montage adapter."""
+import torch
+
+FREQUENCY_BANDS=((1,4),(4,8),(8,13),(13,20),(20,30),(30,45))
+
+def frequency_components(signal):
+    with torch.autocast(signal.device.type,enabled=False):
+        x=signal.float();spectrum=torch.fft.rfft(x,dim=-1)
+        frequency=torch.fft.rfftfreq(x.shape[-1],d=1/200,device=x.device)
+        masks=torch.stack([((frequency>=lo)&(frequency<hi)).float() for lo,hi in FREQUENCY_BANDS])
+        return torch.fft.irfft(spectrum[:,None]*masks[None,:,None],n=x.shape[-1],dim=-1)

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S039/infer.py

@@ -0,0 +1,24 @@
+import argparse,json
+from pathlib import Path
+import torch
+from model import HybridModel,SPECS
+from data import Dataset
+from covariances import extract_covariances
+p=argparse.ArgumentParser();p.add_argument('input',type=Path);p.add_argument('output',type=Path);a=p.parse_args()
+root=Path(__file__).resolve().parent;c=json.loads((root/'config.json').read_text());t=json.loads((a.input/'manifest.json').read_text())['track']
+if t not in SPECS:raise ValueError('unknown track')
+torch.set_num_threads(4);device='cuda' if torch.cuda.is_available() else 'cpu'
+m=HybridModel(**c['architecture']).to(device);m.load_state_dict(torch.load(root/'weights.pt',map_location=device,weights_only=True));m.eval()
+a.output.mkdir(parents=True,exist_ok=True)
+with torch.inference_mode(),(a.output/'predictions.jsonl').open('w') as f:
+    for x,_,ids in torch.utils.data.DataLoader(Dataset(a.input),batch_size=64,num_workers=0):
+        # FFT features stay float32; convolutions can use bfloat16 on CUDA.
+        x=x.to(device)
+        from features import extract_features
+        torch.backends.cuda.matmul.allow_tf32=False
+        features=extract_features(t,x)
+        covariances=extract_covariances(t,x)
+        torch.backends.cuda.matmul.allow_tf32=True
+        with torch.autocast(device,dtype=torch.bfloat16,enabled=device=='cuda'):pred=m(t,x,features,covariances=covariances)
+        pp=pred.float().softmax(-1).cpu().numpy()
+        for sid,pr in zip(ids,pp):f.write(json.dumps({'id':sid,'probabilities':pr.tolist()})+'\n')

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S039/infer.sh

@@ -0,0 +1,3 @@
+#!/usr/bin/env bash
+set -euo pipefail
+exec python "$(dirname -- "$0")/infer.py" "$1" "$2"

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S039/model.py

@@ -0,0 +1,277 @@
+"""One fused waveform/spectral backbone used identically by all five tasks."""
+import torch
+from torch import nn
+from braindecode.models import BIOT,CodeBrain
+from features import SPECS,TRACKS,feature_dim,extract_features,Standardize,Residual,EDGES,COV_BANDS
+from covariances import BANDS,extract_covariances
+from alpha_features import extract_alpha_features,ALPHA_DIM
+from envelope_features import extract_envelope_features,ENVELOPE_DIM
+from frequency_features import frequency_components,FREQUENCY_BANDS
+from tangent_features import extract_tangent_features,TANGENT_DIM
+
+def clinical_asymmetry_features(features):
+    """Bipolar homologous-channel amplitude and coherence, invariant to mirroring."""
+    bands=len(EDGES)-1;per_channel=4*bands+4
+    block=features[:,:16*per_channel].reshape(-1,16,per_channel)
+    left=torch.tensor([0,1,2,3,8,9,10,11],device=features.device);right=left+4
+    pair_power=(block[:,left,:bands]-block[:,right,:bands]).abs().flatten(1)
+    pair_rms=(block[:,left,4*bands]-block[:,right,4*bands]).abs()
+    hemisphere=(block[:,left,:bands].mean(1)-block[:,right,:bands].mean(1)).abs()
+    index=left*(32-left-1)//2+(right-left-1)
+    correlations=features[:,16*per_channel:].reshape(-1,len(COV_BANDS),120)[:,:,index].flatten(1)
+    return torch.cat([pair_power,pair_rms,hemisphere,correlations],1)
+
+class InputAdapter(nn.Module):
+    def __init__(self,c,width,channels,spectral_enabled=True,asymmetry=False,alpha_regions=None,envelope=False):
+        super().__init__()
+        self.codebrain_gain=nn.Parameter(torch.ones(1,c,1))
+        self.codebrain_bias=nn.Parameter(torch.zeros(1,c,1))
+        self.codebrain_channels=nn.Linear(c,24,bias=False)
+        self.signal=nn.Conv1d(c,channels,1,bias=False)
+        self.frequency_weight=nn.Parameter(torch.zeros(len(FREQUENCY_BANDS),channels,c))
+        self.neural=nn.Conv1d(c,18,1,bias=False)
+        self.normalizer=Standardize(feature_dim(c))
+        self.spectral=nn.Linear(feature_dim(c),width)
+        self.dropout=nn.Dropout(.1)
+        self.register_buffer("spectral_enabled",torch.tensor(float(spectral_enabled)))
+        self.spatial_weight=nn.Parameter(torch.randn(len(BANDS),24,c)/c**.5)
+        self.asymmetry_projection=nn.Linear(316,width,bias=False) if asymmetry else None
+        if asymmetry:
+            nn.init.zeros_(self.asymmetry_projection.weight)
+            self.asymmetry_normalizer=Standardize(316)
+            self.asymmetry_dropout=nn.Dropout(.1)
+        self.alpha_projection=None
+        if alpha_regions is not None:
+            self.register_buffer('alpha_regions',torch.tensor(alpha_regions,dtype=torch.float32))
+            self.alpha_normalizer=Standardize(ALPHA_DIM)
+            self.alpha_projection=nn.Linear(ALPHA_DIM,width,bias=False)
+            nn.init.zeros_(self.alpha_projection.weight)
+            self.alpha_dropout=nn.Dropout(.3)
+        self.envelope_projection=None
+        if envelope:
+            self.envelope_normalizer=Standardize(ENVELOPE_DIM)
+            self.envelope_projection=nn.Linear(ENVELOPE_DIM,width,bias=False)
+            nn.init.zeros_(self.envelope_projection.weight)
+            self.envelope_dropout=nn.Dropout(.3)
+    def codebrain_signal(self,x):
+        return x*self.codebrain_gain+self.codebrain_bias
+    def codebrain_tokens(self,tokens):
+        return self.codebrain_channels(tokens.permute(0,2,3,1)).transpose(-1,-2).flatten(-2)
+    def spectral_features(self,features,signal=None,alpha_features=None,envelope_features=None):
+        value=self.spectral_enabled*self.spectral(self.dropout(self.normalizer(features)))
+        if self.asymmetry_projection is not None:
+            extra=self.asymmetry_dropout(self.asymmetry_normalizer(clinical_asymmetry_features(features)))
+            value=value+self.asymmetry_projection(extra)
+        if self.alpha_projection is not None:
+            if alpha_features is None:alpha_features=extract_alpha_features(signal,self.alpha_regions)
+            value=value+self.alpha_projection(self.alpha_dropout(self.alpha_normalizer(alpha_features)))
+        if self.envelope_projection is not None:
+            if envelope_features is None:envelope_features=extract_envelope_features(signal)
+            value=value+self.envelope_projection(self.envelope_dropout(self.envelope_normalizer(envelope_features)))
+        return value
+    def spatial_features(self,covariances):
+        with torch.autocast(covariances.device.type,enabled=False):
+            w=self.spatial_weight.float()/self.spatial_weight.float().norm(dim=-1,keepdim=True).clamp_min(1e-6)
+            power=torch.einsum('bfc,nbcd,bfd->nbf',w,covariances.float(),w).clamp_min(1e-8).log()
+            return torch.cat([power,power-power.mean(1,keepdim=True)],1).flatten(1)
+    def wave_signal(self,x,components=None):
+        if components is None:components=frequency_components(x)
+        correction=torch.einsum('blc,nbct->nlt',self.frequency_weight,components.to(self.frequency_weight.dtype))
+        return (self.signal(x)+correction.to(x.dtype)).unsqueeze(1)
+    def forward(self,x,features,covariance_features):
+        return self.wave_signal(x),self.spectral_features(features,signal=x),self.neural(x),covariance_features
+
+class SharedBackbone(nn.Module):
+    def __init__(self,channels,width,depth,dropout,wave_filters):
+        super().__init__()
+        self.waveform=nn.Sequential(
+            nn.Conv2d(1,wave_filters,(1,64),padding=(0,32),bias=False),nn.GroupNorm(1,wave_filters),
+            nn.Conv2d(wave_filters,2*wave_filters,(channels,1),groups=wave_filters,bias=False),nn.GroupNorm(1,2*wave_filters),nn.ELU(),
+            nn.AvgPool2d((1,4)),nn.Dropout(.35),
+            nn.Conv2d(2*wave_filters,2*wave_filters,(1,16),padding=(0,8),groups=2*wave_filters,bias=False),nn.Conv2d(2*wave_filters,2*wave_filters,1,bias=False),
+            nn.GroupNorm(1,2*wave_filters),nn.ELU(),nn.AvgPool2d((1,8)),nn.Dropout(.35))
+        self.wave_summary=nn.Sequential(nn.AdaptiveAvgPool2d((1,8)),nn.Flatten(),nn.Linear(2*wave_filters*8,width))
+        self.fusion=nn.Sequential(nn.LayerNorm(width),nn.GELU(),nn.Dropout(dropout),
+                                 *[Residual(width,dropout) for _ in range(depth)],nn.LayerNorm(width))
+        # Both representations interact before the shared residual stack.
+        self.wave_gain=nn.Parameter(torch.tensor(.1))
+        self.biot=BIOT(n_chans=18,n_outputs=2,n_times=400,sfreq=200)
+        self.biot.final_layer=nn.Identity()
+        self.biot_projection=nn.Sequential(nn.LayerNorm(256),nn.Linear(256,width),nn.Dropout(.2))
+        nn.init.normal_(self.biot_projection[1].weight,std=.001);nn.init.zeros_(self.biot_projection[1].bias)
+        self.covariance_encoder=nn.Sequential(nn.LayerNorm(len(BANDS)*24*2),nn.Linear(len(BANDS)*24*2,width),nn.GELU(),nn.Dropout(.4),
+                                             *[Residual(width,.4) for _ in range(2)],nn.LayerNorm(width))
+        self.covariance_projection=nn.Linear(width,width)
+        nn.init.zeros_(self.covariance_projection.weight);nn.init.zeros_(self.covariance_projection.bias)
+        self.tangent_encoder=nn.Sequential(nn.LayerNorm(TANGENT_DIM),nn.Linear(TANGENT_DIM,width),nn.GELU(),nn.Dropout(.3),Residual(width,.3),nn.LayerNorm(width),nn.Linear(width,width))
+        self.register_buffer('codebrain_enabled',torch.ones(()))
+        self.codebrain=CodeBrain(n_chans=19,n_outputs=2,n_times=400,sfreq=200)
+        self.codebrain.final_layer=nn.Identity()
+        self.codebrain_projection=nn.Sequential(nn.LayerNorm(200),nn.Linear(200,64),nn.GELU())
+        self.codebrain_patch_encoder=nn.Sequential(nn.LayerNorm(24*64),nn.Linear(24*64,width),nn.GELU(),nn.Dropout(.3))
+        self.codebrain_context=nn.Sequential(nn.LayerNorm(width*4),nn.Linear(width*4,width*2),nn.GELU(),nn.Dropout(.3),nn.Linear(width*2,width))
+        nn.init.zeros_(self.codebrain_context[-1].weight);nn.init.zeros_(self.codebrain_context[-1].bias)
+    def codebrain_tokens(self,signal):
+        return torch.cat([self.codebrain_projection(self.codebrain(x)) for x in signal.split(32)],0)
+    def codebrain_correction(self,tokens,representation,fusion_input):
+        with torch.autocast(tokens.device.type,enabled=False):
+            encoded=self.codebrain_patch_encoder(self.codebrain_enabled*tokens.float())
+            pooled=torch.cat([encoded.mean(1),encoded.std(1,correction=0),representation[:,-512:-384].float(),representation[:,-128:].float()],-1)
+            delta=self.codebrain_context(pooled)
+        added=.001*nn.functional.linear(delta,self.covariance_projection.weight,None)
+        corrected=self.fusion(fusion_input+added.to(fusion_input.dtype))
+        return corrected-representation[:,-512:-384],delta
+    def forward(self,signal,spectral,neural_signal,covariance_features,tangent_features):
+        wave=self.waveform(signal)
+        neural=self.biot(neural_signal.float())
+        neural=self.biot_projection[0](neural)
+        projected=self.biot_projection[2](self.biot_projection[1](neural))
+        with torch.autocast(signal.device.type,enabled=False):
+            covariance_embedding=self.covariance_encoder(covariance_features.float())
+        original=spectral+self.wave_gain*self.wave_summary(wave)+.01*projected
+        coupled=.001*self.covariance_projection(covariance_embedding).to(original.dtype)
+        fused=self.fusion(original+coupled)
+        with torch.autocast(signal.device.type,enabled=False):delta=self.tangent_encoder(tangent_features.float())
+        added=.001*nn.functional.linear(delta,self.covariance_projection.weight,None)
+        corrected=self.fusion((original+coupled)+added.to(original.dtype))
+        return torch.cat([wave.flatten(1),fused,neural,covariance_embedding],-1),corrected-fused,delta,original+coupled
+
+class ResidualHead(nn.Module):
+    """A small nonlinear classifier correction around an inherited linear readout."""
+    def __init__(self,features,classes,width=64,dropout=.3,full_precision=False):
+        super().__init__();self.full_precision=full_precision
+        self.base=nn.Linear(features,classes)
+        self.delta=nn.Sequential(nn.LayerNorm(features),nn.Linear(features,width),nn.GELU(),nn.Dropout(dropout),nn.Linear(width,classes))
+        nn.init.zeros_(self.delta[-1].weight);nn.init.zeros_(self.delta[-1].bias)
+    def forward(self,x):
+        if self.full_precision:
+            with torch.autocast(x.device.type,enabled=False):return self.base(x.float())+self.delta(x.float())
+        prior=nn.functional.linear(x[:,:-128].contiguous(),self.base.weight[:,:-128].contiguous(),self.base.bias)
+        added=nn.functional.linear(x[:,-128:].contiguous(),self.base.weight[:,-128:].contiguous())
+        value=prior+added
+        return value+self.delta(x).to(value.dtype)
+
+class TemporalPoolHead(nn.Module):
+    def __init__(self,prior,channels=64):
+        super().__init__();self.prior=prior;self.channels=channels
+        self.register_buffer('pool_output_scale',torch.ones(()));self.register_buffer('pool_mean',torch.zeros(channels*4));self.register_buffer('pool_scale',torch.ones(channels*4))
+        self.pool_projection=nn.Linear(channels*4,prior.base.out_features)
+        nn.init.zeros_(self.pool_projection.weight);nn.init.zeros_(self.pool_projection.bias)
+    def statistics(self,x):
+        with torch.autocast(x.device.type,enabled=False):
+            wave=x[:,:-512].float().reshape(len(x),self.channels,-1)
+            return torch.cat([wave.mean(-1),wave.std(-1,correction=0),wave.amax(-1),wave.amin(-1)],-1)
+    def forward(self,x):
+        prior=self.prior(x)
+        with torch.autocast(x.device.type,enabled=False):
+            values=((self.statistics(x)-self.pool_mean)/self.pool_scale).clamp(-8,8)
+            added=self.pool_output_scale*self.pool_projection(values)
+        return prior+added.to(prior.dtype)
+
+class TemporalEvidenceHead(nn.Module):
+    """Pool class evidence over the shared waveform time axis."""
+    def __init__(self,prior,channels=64,temperature=.25):
+        super().__init__();self.prior=prior;self.channels=channels;self.temperature=temperature
+        self.register_buffer('evidence_output_scale',torch.ones(()))
+        self.register_buffer('evidence_mean',torch.zeros(channels))
+        self.register_buffer('evidence_scale',torch.ones(channels))
+        self.evidence_projection=nn.Linear(channels,prior.prior.base.out_features,bias=False)
+        nn.init.zeros_(self.evidence_projection.weight)
+    def patches(self,x):
+        with torch.autocast(x.device.type,enabled=False):
+            wave=x[:,:-512].float().reshape(len(x),self.channels,-1).transpose(1,2)
+            return ((wave-self.evidence_mean)/self.evidence_scale).clamp(-8,8)
+    def evidence(self,patches):
+        with torch.autocast(patches.device.type,enabled=False):
+            values=self.evidence_projection(patches.float())/self.temperature
+            maximum=values.amax(1,keepdim=True)
+            pooled=self.temperature*((values-maximum).exp().mean(1).log()+maximum.squeeze(1))
+            return self.evidence_output_scale*pooled
+    def forward(self,x):
+        prior=self.prior(x)
+        return prior+self.evidence(self.patches(x)).to(prior.dtype)
+
+class FourierResidualHead(nn.Module):
+    """A fixed random Fourier basis and trainable linear classifier correction."""
+    def __init__(self,prior,features,classes,basis=256):
+        super().__init__();self.prior=prior
+        self.register_buffer('rff_mean',torch.zeros(features));self.register_buffer('rff_scale',torch.ones(features))
+        self.register_buffer('rff_frequency',torch.randn(features,basis)/features**.5)
+        self.register_buffer('rff_phase',torch.rand(basis)*2*torch.pi)
+        self.register_buffer('rff_output_scale',torch.ones(()))
+        self.rff_projection=nn.Linear(3*basis,classes);nn.init.zeros_(self.rff_projection.weight);nn.init.zeros_(self.rff_projection.bias)
+    def basis(self,x):
+        with torch.autocast(x.device.type,enabled=False):
+            values=((x.float()-self.rff_mean)/self.rff_scale).clamp(-8,8)@self.rff_frequency
+            return 2**.5*torch.cat([(values/sigma+self.rff_phase).cos() for sigma in (.5,1.,2.)],-1)
+    def forward(self,x):
+        prior=self.prior(x)
+        with torch.autocast(x.device.type,enabled=False):added=self.rff_output_scale*self.rff_projection(self.basis(x))
+        return prior+added.to(prior.dtype)
+
+class TangentFeatureHead(nn.Module):
+    """Select a shared feature correction before the inherited classifier."""
+    def __init__(self,prior):
+        super().__init__();self.prior=prior;self.feature_gain=nn.Parameter(torch.zeros(()))
+    def forward(self,values):
+        old=values[:,:-256];fusion_delta=values[:,-256:-128];covariance_delta=values[:,-128:]
+        adjusted=torch.cat([old[:,:-512],old[:,-512:-384]+self.feature_gain*fusion_delta,old[:,-384:-128],old[:,-128:]+self.feature_gain*covariance_delta],-1)
+        return self.prior(adjusted)
+
+class CodeBrainFeatureHead(nn.Module):
+    """Private use of a jointly trained common feature correction."""
+    def __init__(self,prior):
+        super().__init__();self.prior=prior;self.codebrain_gain=nn.Parameter(torch.ones(()))
+    def forward(self,values):
+        previous=values[:,:-256];native=previous[:,:-256]
+        fusion_delta=values[:,-256:-128];covariance_delta=values[:,-128:]
+        adjusted=torch.cat([native[:,:-512],native[:,-512:-384]+self.codebrain_gain*fusion_delta,native[:,-384:-128],native[:,-128:]+self.codebrain_gain*covariance_delta],-1)
+        return self.prior(torch.cat([adjusted,previous[:,-256:]],-1))
+
+class HybridModel(nn.Module):
+    def __init__(self,channels=32,width=128,depth=2,dropout=.5,wave_filters=32,head_width=64,head_dropout=.3,clinical_asymmetry=True,alpha_regions=None,clinical_envelope=False,temporal_pool=True,temporal_evidence=False,evidence_temperature=.25,random_fourier=False,fourier_features=256,tangent_readout=True,codebrain_fusion=True):
+        super().__init__()
+        self.adapters=nn.ModuleDict({t:InputAdapter(c,width,channels,spectral_enabled=t not in ("faced","tuev","openbmi"),asymmetry=clinical_asymmetry and t=="tuev",alpha_regions=alpha_regions if t=="longitudinal" else None,envelope=clinical_envelope and t=="tuev") for t,(c,s,k) in SPECS.items()})
+        self.backbone=SharedBackbone(channels,width,depth,dropout,wave_filters)
+        self.heads=nn.ModuleDict({t:ResidualHead(2*wave_filters*(((s*200+1)//4+1)//8)+width+256+width,k,head_width,head_dropout,full_precision=t=='ad65') for t,(c,s,k) in SPECS.items()})
+        if temporal_pool:self.heads=nn.ModuleDict({t:TemporalPoolHead(head,wave_filters*2) for t,head in self.heads.items()})
+        if temporal_evidence:self.heads=nn.ModuleDict({t:TemporalEvidenceHead(head,wave_filters*2,evidence_temperature) for t,head in self.heads.items()})
+        if random_fourier:self.heads=nn.ModuleDict({t:FourierResidualHead(head,2*wave_filters*(((SPECS[t][1]*200+1)//4+1)//8)+width+256+width,SPECS[t][2],fourier_features) for t,head in self.heads.items()})
+        self.heads=nn.ModuleDict({t:TangentFeatureHead(head) for t,head in self.heads.items()})
+        self.heads=nn.ModuleDict({t:CodeBrainFeatureHead(head) for t,head in self.heads.items()})
+    def forward(self,t,x,features=None,covariances=None,covariance_features=None):
+        if features is None:features=extract_features(t,x)
+        if covariance_features is None:
+            if covariances is None:covariances=extract_covariances(t,x)
+            covariance_features=self.adapters[t].spatial_features(covariances)
+        signal=x.flatten(2);signal=signal-signal.mean(-1,keepdim=True)
+        if t in ('faced','tuev'):signal=signal/signal.std(-1,keepdim=True,correction=0).clamp_min(1e-6)
+        else:signal=signal-signal.mean(1,keepdim=True)
+        signal=signal.clamp(-20,20)
+        adapted=self.adapters[t](signal,features,covariance_features)
+        if covariances is None:covariances=extract_covariances(t,x)
+        tangent=extract_tangent_features(covariances,self.adapters[t].spatial_weight)
+        representation,fusion_delta,covariance_delta,fusion_input=self.backbone(*adapted,tangent_features=tangent)
+        if t in ('faced','openbmi'):
+            representation=torch.cat([representation[:,:-384],torch.zeros_like(representation[:,-384:-128]),representation[:,-128:]],-1)
+        native_tokens=self.backbone.codebrain_tokens(self.adapters[t].codebrain_signal(signal))
+        tokens=self.adapters[t].codebrain_tokens(native_tokens)
+        code_fusion,code_covariance=self.backbone.codebrain_correction(tokens,representation,fusion_input)
+        return self.heads[t](torch.cat([representation,fusion_delta,covariance_delta,code_fusion,code_covariance],-1))
+
+def widen_state_dict(state,multiplier=2):
+    """Duplicate temporal features and divide downstream sums to preserve outputs."""
+    result={}
+    for key,value in state.items():
+        if key.startswith('backbone.waveform.'):
+            layer=int(key.split('.')[2])
+            if layer in (0,2,7):value=value.repeat(multiplier,1,1,1)
+            elif layer in (1,3,9):value=value.repeat(multiplier)
+            elif layer==8:value=value.repeat(multiplier,multiplier,1,1)/multiplier
+        elif key=='backbone.wave_summary.2.weight':value=value.repeat(1,multiplier)/multiplier
+        elif key.startswith('heads.') and key.endswith('.weight'):
+            raw=value[:,:-128]/multiplier
+            value=torch.cat([raw]*multiplier+[value[:,-128:]],1)
+        result[key]=value
+    return result
+

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S039/tangent_features.py

@@ -0,0 +1,25 @@
+"""Window-local log-correlation geometry in the existing spatial-filter basis."""
+import torch
+
+TANGENT_DIM=10*24*25//2
+
+@torch.no_grad()
+def extract_tangent_features(covariances,spatial_weight):
+    old_tf32=torch.backends.cuda.matmul.allow_tf32
+    torch.backends.cuda.matmul.allow_tf32=False
+    try:
+        with torch.autocast(covariances.device.type,enabled=False):
+            w=spatial_weight.float()
+            w=w/w.norm(dim=-1,keepdim=True).clamp_min(1e-6)
+            projected=torch.einsum('bfc,nbcd,bgd->nbfg',w,covariances.float(),w)
+            d=projected.diagonal(dim1=-2,dim2=-1).clamp_min(1e-10).sqrt()
+            correlation=(projected/(d.unsqueeze(-1)*d.unsqueeze(-2))).clamp(-1,1)
+            correlation=(correlation+correlation.transpose(-1,-2))/2
+            correlation=.95*correlation+.05*torch.eye(24,device=correlation.device)
+            values,vectors=torch.linalg.eigh(correlation)
+            logarithm=(vectors*values.clamp_min(1e-6).log().unsqueeze(-2))@vectors.transpose(-1,-2)
+            i,j=torch.triu_indices(24,24,device=correlation.device)
+            scale=torch.where(i==j,1.,2.**.5)
+            return (logarithm[...,i,j]*scale).flatten(1)
+    finally:
+        torch.backends.cuda.matmul.allow_tf32=old_tf32

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S050/MODEL_CARD.md

@@ -0,0 +1,15 @@
+# Shared EEG decoder
+
+This package contains one coupled backbone used by all five tracks, with private input adapters and classifiers. Every trainable common component was trained with all five training splits. It is a single model; inference performs no parameter updates or dataset-level fitting.
+
+The common backbone combines waveform convolutions, an audited pretrained BIOT feature path, covariance and log-correlation encoders, and shared feature corrections. The waveform coactivation extension encodes 64 within-window log variances and 2,016 correlations between common waveform channels. A matched experimental control removes the correlation inputs. `config.json` identifies the packaged variant and its five fixed correction gains. All original backbone parameters are fixed during this extension's joint training.
+
+Weights originate only from the supplied training data and audited initialization checkpoints. Training details, parent provenance, raw/cache checks, shared-gradient checks and the five separate validation metrics are recorded in `training.json`. Validation selects checkpoints and fixed gains; it does not fit normalization or update parameters. Reported validation results belong to selected checkpoints, not to private test evaluation or five-seed means.
+
+Run `bash infer.sh INPUT_DIR OUTPUT_DIR`. The input manifest specifies a track and opaque sample IDs, shard paths and indices. Signals are in microvolts and are scaled by 1/100. The package writes one probability vector per input ID to `OUTPUT_DIR/predictions.jsonl`. It needs only the packaged files and the contract's installed Python dependencies, works after relocation, and does not need labels, subject IDs, training caches or network access.
+
+FACED and TUEV use window balanced accuracy. AD65 uses mean window probabilities per patient before balanced accuracy. OpenBMI averages within-subject balanced accuracy. Longitudinal averages within-session balanced accuracy within each subject, then over subjects. These five metrics remain separate.
+
+This checkpoint adds fixed within-window ordinal pattern probabilities, permutation entropy and statistical complexity at five delays. Training-only normalization and five input projections feed one common correction MLP trained by all five tracks. The common correction enters the existing covariance and fusion representations; fixed per-track gains select its strength. All earlier model tensors remain unchanged.
+
+Fixed real and imaginary spectral coherence between the inherited 24 spatial filters is normalized using training windows only and encoded by one new common network. Five private linear heads add class evidence. All five training splits update the new common encoder; all earlier model tensors remain unchanged.

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S050/alpha_features.py

@@ -0,0 +1,45 @@
+"""Fixed alpha prominence features from each individual two-second window."""
+import torch
+
+REGION_NAMES=('occipital','parietal','central','frontal','left_posterior','right_posterior')
+REGIONS=(('PO7','PO5','PO3','POZ','PO4','PO6','PO8','O1','OZ','O2'),
+         ('P7','P5','P3','P1','PZ','P2','P4','P6','P8'),
+         ('FC5','FC3','FC1','FCZ','FC2','FC4','FC6','C5','C3','C1','CZ','C2','C4','C6'),
+         ('FP1','FPZ','FP2','AF3','AF4','F7','F5','F3','F1','FZ','F2','F4','F6','F8'),
+         ('P7','P5','P3','P1','PO7','PO5','PO3','O1'),
+         ('P2','P4','P6','P8','PO4','PO6','PO8','O2'))
+ALPHA_DIM=58*7+6*22
+
+def region_matrix(channels):
+    result=torch.zeros(len(REGIONS),len(channels))
+    for i,names in enumerate(REGIONS):
+        for name in names:result[i,channels.index(name)]=1/len(names)
+    return result.tolist()
+
+@torch.no_grad()
+def extract_alpha_features(signal,regions):
+    with torch.autocast(signal.device.type,enabled=False):
+        x=signal.float();x=x-x.mean(-1,keepdim=True)
+        spectrum=torch.fft.rfft(x*torch.hann_window(400,device=x.device),dim=-1)/200
+        power=spectrum.abs().square().clamp_min(1e-12)
+        lp=power.log()
+        # Geometric regional means resist domination by one high-amplitude channel.
+        regional=torch.einsum('rc,ncf->nrf',regions.float(),lp)
+        lp=torch.cat([lp,regional],1)
+        low=lp[...,8:13].mean(-1,keepdim=True)
+        high=lp[...,36:49].mean(-1,keepdim=True)
+        freq=torch.arange(7.,14.5,.5,device=x.device)
+        alpha=lp[...,14:29]
+        baseline=low+(high-low)*((freq-5)/16)
+        prominence=alpha-baseline
+        normalized=alpha.softmax(-1)
+        center=(normalized*freq).sum(-1)
+        entropy=-(normalized*normalized.clamp_min(1e-12).log()).sum(-1)
+        alpha_mean=torch.logsumexp(alpha,-1)-torch.log(torch.tensor(15.,device=x.device))
+        def log_band(lo,hi):
+            return torch.logsumexp(lp[...,lo:hi],-1)-torch.log(torch.tensor(float(hi-lo),device=x.device))
+        summaries=torch.stack([alpha_mean-log_band(2,81),alpha_mean-log_band(8,14),
+                               alpha_mean-log_band(30,61),prominence.max(-1).values,
+                               prominence.mean(-1),center,entropy],-1)
+        return torch.cat([summaries[:,:58].flatten(1),summaries[:,58:].flatten(1),
+                          prominence[:,58:].flatten(1)],1)

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S050/config.json

@@ -0,0 +1,742 @@
+{
+  "schema": "bci-shared-biot-covariance-bf16head-v1",
+  "architecture": {
+    "channels": 32,
+    "width": 128,
+    "depth": 2,
+    "dropout": 0.5,
+    "wave_filters": 32,
+    "head_width": 64,
+    "head_dropout": 0.3,
+    "clinical_asymmetry": true,
+    "alpha_regions": [
+      [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612
+      ],
+      [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+      ],
+      [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+      ],
+      [
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+      ],
+      [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.125,
+        0.125,
+        0.125,
+        0.125,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.125,
+        0.125,
+        0.125,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.125,
+        0.0,
+        0.0
+      ],
+      [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.125,
+        0.125,
+        0.125,
+        0.125,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.125,
+        0.125,
+        0.125,
+        0.0,
+        0.0,
+        0.125
+      ]
+    ],
+    "clinical_envelope": true,
+    "temporal_pool": true,
+    "temporal_evidence": true,
+    "evidence_temperature": 0.25,
+    "random_fourier": true,
+    "fourier_features": 256,
+    "tangent_readout": true,
+    "shared_context_correction": true,
+    "wave_coactivation": true,
+    "ordinal_context": true,
+    "phase_coherence": true
+  },
+  "initialization": {
+    "initialization": "continue joint training from a complete shared-model checkpoint",
+    "parent": "/workspace/research/runs/hybrid_biot_fusionshrink0100/epoch-001/weights.pt",
+    "parent_initialization": {
+      "initialization": "jointly trained parent plus audited pretrained BIOT in a single coupled backbone",
+      "parent": "/workspace/research/runs/hybrid_wide1/epoch-004/weights.pt",
+      "audited_biot": {
+        "initialization": "pretrained",
+        "repo": "braindecode/biot-pretrained-six-datasets-18chs",
+        "revision": "7e9aabf302870012f73773276803aab97993951c",
+        "sha256": "a218f4054ae29962c6fd9d23d94c446d354c4f53e805a6771c85749ccc20baaa",
+        "license": "BSD-3-Clause",
+        "source_braindecode": "1.3.0",
+        "loaded_core_tensors": 56,
+        "skipped_classifier_tensors": 3
+      },
+      "neural_channel_indices": {
+        "faced": [
+          0,
+          1,
+          2,
+          3,
+          4,
+          5,
+          6,
+          7,
+          8,
+          9,
+          10,
+          11,
+          12,
+          13,
+          14,
+          15,
+          16,
+          17
+        ],
+        "tuev": [
+          0,
+          1,
+          2,
+          3,
+          4,
+          5,
+          6,
+          7,
+          8,
+          9,
+          10,
+          11,
+          12,
+          13,
+          14,
+          15
+        ],
+        "ad65": [
+          0,
+          1,
+          2,
+          3,
+          4,
+          5,
+          6,
+          7,
+          8,
+          9,
+          10,
+          11,
+          12,
+          13,
+          14,
+          15,
+          16,
+          17
+        ],
+        "openbmi": [
+          0,
+          1,
+          3,
+          5,
+          12,
+          14,
+          23,
+          25,
+          28,
+          30,
+          2,
+          6,
+          11,
+          15,
+          22,
+          26,
+          4,
+          13
+        ],
+        "longitudinal": [
+          0,
+          2,
+          7,
+          11,
+          23,
+          27,
+          41,
+          45,
+          55,
+          57,
+          5,
+          13,
+          21,
+          29,
+          39,
+          47,
+          9,
+          25
+        ]
+      },
+      "rebase": "incumbent waveform, fusion and four heads; joint BIOT and TUEV head from readout epoch 9; zero initialized neural interaction",
+      "jointly_finetuned_neural_parent": "/workspace/research/runs/hybrid_biot_readout1/epoch-009/weights.pt",
+      "tuev_high_dimensional_spectral_projection_disabled": true,
+      "frozen_prior_representation": true,
+      "nonclinical_head_updates": "only the 256 added BIOT columns; original columns and biases fixed",
+      "faced_openbmi_neural_path": "common nonlinear fusion only; direct head columns masked",
+      "retention_teacher": "/workspace/research/runs/hybrid_wide1/epoch-004",
+      "retention_loss": {
+        "split": "train",
+        "temperature": 2.0,
+        "kl_weight": 1.0,
+        "tracks": [
+          "faced",
+          "ad65",
+          "openbmi",
+          "longitudinal"
+        ]
+      }
+    },
+    "joint_covariance_parent": "/workspace/research/runs/filterbank1/epoch-002/weights.pt",
+    "frozen_prior_representation": true,
+    "head_updates": "one linear head per track; added 128 shared covariance columns trainable; AD head initialized from the joint covariance parent",
+    "new_shared_representation": "one common covariance MLP inside the coupled backbone, trained by all five tasks; fixed spatial input adapters from the same jointly trained source",
+    "head_arithmetic": "one linear head evaluated in fixed feature blocks to preserve original bfloat16 accumulation dimensions",
+    "retention_teacher": {
+      "ad65": "/workspace/research/runs/filterbank1/epoch-002/weights.pt",
+      "other_tracks": "/workspace/research/runs/hybrid_wide1/epoch-004"
+    },
+    "retention_loss": {
+      "split": "train",
+      "temperature": 2.0,
+      "kl_weight": 1.0,
+      "tracks": [
+        "faced",
+        "ad65",
+        "openbmi",
+        "longitudinal"
+      ]
+    }
+  },
+  "loader_scale": 0.01,
+  "tracks": [
+    "faced",
+    "tuev",
+    "ad65",
+    "openbmi",
+    "longitudinal"
+  ],
+  "head_arithmetic": "same linear head in fixed feature blocks, sum in bfloat16 on GPU for non-AD tracks; AD remains float32",
+  "head_refinement": {
+    "source": "/workspace/research/runs/residual_heads1/epoch-018",
+    "source_weights_sha256": "e0b609137391b47f99438ccadaa22fc0bc10d2c10ead0ddf0a3c25be2c907c85",
+    "shared_backbone_unchanged": true,
+    "selected_head_epochs": {
+      "faced": 5,
+      "tuev": 24,
+      "ad65": 0,
+      "openbmi": 0,
+      "longitudinal": 0
+    }
+  },
+  "clinical_spectral_refinement": {
+    "source": "/workspace/research/runs/head_dropout1/epoch-016",
+    "zero_initialized_input_adapter": true,
+    "backbone_unchanged": true
+  },
+  "head_estimation": {
+    "method": "class/subject-balanced Gaussian linear discriminant; covariance augmented by class-conditional subject/session mean variation before shrinkage",
+    "selected_shrinkage": {
+      "faced": "inherited",
+      "tuev": "inherited",
+      "ad65": "inherited",
+      "openbmi": "inherited",
+      "longitudinal": {
+        "shrinkage": 0.9,
+        "domain_strength": 1.0
+      }
+    },
+    "feature_scaling": "fitted on training data and folded into linear coefficients"
+  },
+  "clinical_asymmetry_refinement": {
+    "source": "/workspace/research/runs/domain_lda_heads1/epoch-001",
+    "zero_initialized_input_adapter": true,
+    "backbone_unchanged": true
+  },
+  "alpha_rhythm_adapter": {
+    "training_normalization_only": true,
+    "regions": [
+      "occipital",
+      "parietal",
+      "central",
+      "frontal",
+      "left_posterior",
+      "right_posterior"
+    ],
+    "features": "per-window alpha prominence relative to adjacent frequencies",
+    "shared_backbone_unchanged": true
+  },
+  "clinical_envelope_refinement": {
+    "source": "/workspace/research/runs/clinical_asymmetry1/epoch-001",
+    "zero_initialized_input_adapter": true,
+    "backbone_unchanged": true
+  },
+  "independent_adapter_merge": {
+    "alpha_source": "/workspace/research/runs/alpha_calibrated1/epoch-003",
+    "clinical_source": "/workspace/research/runs/clinical_envelope1/epoch-002",
+    "all_shared_and_head_tensors_equal": true
+  },
+  "frequency_montage_refinement": {
+    "six_frequency_bands": true,
+    "shared_encoder_unchanged": true,
+    "selected_track_epochs": {
+      "faced": 0,
+      "tuev": 1,
+      "ad65": 0,
+      "openbmi": 10,
+      "longitudinal": 2
+    }
+  },
+  "private_track_selection": {
+    "track": "tuev",
+    "donor": "/workspace/research/replicates/frequency_head_control1/epoch-001"
+  },
+  "clinical_sampling_refinement": {
+    "subject_exponent": 0.0,
+    "class_balanced": true,
+    "only_clinical_head_updated": true
+  },
+  "temporal_statistics_readout": {
+    "statistics": [
+      "mean",
+      "standard deviation",
+      "maximum",
+      "minimum"
+    ],
+    "shared_encoder_and_prior_heads_unchanged": true,
+    "selected_pool_epochs": {
+      "faced": 2,
+      "tuev": 0,
+      "ad65": 0,
+      "openbmi": 33,
+      "longitudinal": 3
+    }
+  },
+  "temporal_evidence_readout": {
+    "pooling": "log mean exp of per-patch class evidence",
+    "temperature": 0.25,
+    "shared_encoder_and_prior_heads_unchanged": true,
+    "selected_evidence_epochs": {
+      "faced": 0,
+      "tuev": 0,
+      "ad65": 0,
+      "openbmi": 6,
+      "longitudinal": 8
+    }
+  },
+  "fourier_basis_readout": {
+    "basis_functions": 256,
+    "bandwidths": [
+      0.5,
+      1.0,
+      2.0
+    ],
+    "shared_encoder_and_prior_heads_unchanged": true,
+    "selected_fourier_epochs": {
+      "faced": 21,
+      "tuev": 0,
+      "ad65": 0,
+      "openbmi": 0,
+      "longitudinal": 2
+    }
+  },
+  "shared_tangent_readout": {
+    "one_shared_tangent_module": true,
+    "original_and_corrected_shared_features_retained": true,
+    "private_head_scalar_gains": {
+      "faced": 0.0,
+      "tuev": 0.1,
+      "ad65": 0.0,
+      "openbmi": 1.0,
+      "longitudinal": 0.1
+    },
+    "all_common_weights_fixed_during_gain_refinement": true
+  },
+  "shared_context_correction": {
+    "source": "/workspace/research/runs/hybrid_codebrain_fusion_control1/epoch-002",
+    "zero_token_input_preserved": true,
+    "unused_native_CodeBrain_core_and_input_parameters_removed": true,
+    "common_patch_and_context_layers_retained": true,
+    "same_one_shared_backbone_and_five_classifiers": true
+  },
+  "shared_wave_coactivation": {
+    "channels": 64,
+    "statistics": 2080,
+    "diagonal_only_control": false,
+    "common_encoder_updated_by_all_five_tracks": true,
+    "gain_selection_within_one_common_checkpoint": {
+      "faced": 1.0,
+      "tuev": 0.0,
+      "ad65": 0.0,
+      "openbmi": 1.0,
+      "longitudinal": 1.0
+    }
+  },
+  "fixed_common_coactivation_readouts": {
+    "all_common_weights_exactly_unchanged": true,
+    "selected_private_epochs": {
+      "faced": 25,
+      "tuev": 0,
+      "ad65": 0,
+      "openbmi": 8,
+      "longitudinal": 2
+    }
+  },
+  "shared_ordinal_context": {
+    "per_channel_features": 130,
+    "normalization_fit_on_training_only": true,
+    "entropy_only_control": false,
+    "new_common_layers_trained_on_all_five_tracks": true,
+    "gains_within_one_common_checkpoint": {
+      "faced": 1.0,
+      "tuev": 0.0,
+      "ad65": 0.0,
+      "openbmi": 1.0,
+      "longitudinal": 0.0
+    }
+  },
+  "shared_complex_coherence": {
+    "features": 5520,
+    "normalization_fit_on_training_only": true,
+    "real_only_control": false,
+    "all_five_tracks_update_common_encoder": true,
+    "gains_within_one_common_checkpoint": {
+      "faced": 0.3,
+      "tuev": 0.0,
+      "ad65": 0.0,
+      "openbmi": 0.0,
+      "longitudinal": 0.0
+    }
+  }
+}

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S050/covariances.py

@@ -0,0 +1,25 @@
+"""Shared neural filter-bank covariance decoder with spatial input adapters."""
+import torch
+from torch import nn
+SPECS={'faced':(32,10,9),'tuev':(16,5,6),'ad65':(19,10,3),'openbmi':(62,4,2),'longitudinal':(58,2,2)}
+TRACKS=tuple(SPECS)
+BANDS=[(1,4),(4,8),(8,12),(12,16),(16,20),(20,24),(24,28),(28,32),(32,40),(40,48)]
+
+@torch.no_grad()
+def extract_covariances(t,x):
+    x=torch.nan_to_num(x.float().flatten(2)).clamp(-1e5,1e5)
+    x=x-x.mean(-1,keepdim=True)
+    if t in ('faced','tuev'):
+        x=x/x.square().mean(-1,keepdim=True).clamp_min(1e-12).sqrt()
+    else:x=x-x.mean(1,keepdim=True)
+    # Per-window normalization, preserving spatial and frequency power ratios.
+    x=x/x.square().mean((1,2),keepdim=True).clamp_min(1e-12).sqrt()
+    frames=x.unfold(-1,400,200);frames=frames-frames.mean(-1,keepdim=True)
+    z=torch.fft.rfft(frames*torch.hann_window(400,device=x.device),dim=-1)/200
+    covs=[]
+    for lo,hi in BANDS:
+        v=z[...,2*lo:2*hi].flatten(2)
+        cov=(v.real@v.real.transpose(-1,-2)+v.imag@v.imag.transpose(-1,-2))/v.shape[-1]
+        covs.append(cov)
+    return torch.stack(covs,1)
+

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S050/data.py

@@ -0,0 +1,33 @@
+import json
+from functools import lru_cache
+from pathlib import Path
+import numpy as np
+import torch
+@lru_cache(maxsize=8)
+def array(path):return np.load(path,mmap_mode='r',allow_pickle=False)
+class Dataset(torch.utils.data.Dataset):
+    def __init__(self,root,limit=None,cache=False):
+        self.root=Path(root)
+        self.rows=json.loads((self.root/'manifest.json').read_text())['samples']
+        if limit:self.rows=self.rows[:limit]
+        self.cached=None
+        if cache and self.rows:
+            from collections import defaultdict
+            groups=defaultdict(list)
+            for i,r in enumerate(self.rows):groups[r['path']].append((i,r['index']))
+            for name,indices in groups.items():
+                path=(self.root/name).resolve()
+                if not path.is_relative_to(self.root.resolve()):raise ValueError('input outside root')
+                source=np.load(path,mmap_mode='r',allow_pickle=False)
+                if self.cached is None:self.cached=torch.empty((len(self.rows),*source.shape[1:]),dtype=torch.float32)
+                dst,src=zip(*indices)
+                self.cached[list(dst)]=torch.as_tensor(source[list(src)]/100,dtype=torch.float32)
+    def __len__(self):return len(self.rows)
+    def __getitem__(self,i):
+        r=self.rows[i]
+        if self.cached is not None:return self.cached[i],r.get('label',-1),r['id']
+        p=(self.root/r['path']).resolve()
+        if not p.is_relative_to(self.root.resolve()):raise ValueError('input outside root')
+        # Division precedes float32 conversion, matching official FACED/TUEV collate.
+        x=np.asarray(array(str(p))[r['index']])/100
+        return torch.as_tensor(x,dtype=torch.float32),r.get('label',-1),r['id']

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S050/envelope_features.py

@@ -0,0 +1,44 @@
+"""Per-window burst-envelope regularity and bilateral synchrony, without fitting."""
+import torch
+import torch.nn.functional as F
+
+BANDS=((1,4),(4,8),(8,13),(13,30),(30,45))
+ENVELOPE_DIM=16*5*12+8*5*2
+
+@torch.no_grad()
+def extract_envelope_features(signal):
+    with torch.autocast(signal.device.type,enabled=False):
+        x=signal.float();x=x-x.mean(-1,keepdim=True)
+        spectrum=torch.fft.fft(x,dim=-1)
+        frequency=torch.fft.fftfreq(x.shape[-1],1/200,device=x.device)
+        envelopes=[]
+        for lo,hi in BANDS:
+            mask=((frequency>=lo)&(frequency<hi)).to(x.dtype)*2
+            analytic=torch.fft.ifft(spectrum*mask,dim=-1)
+            energy=analytic.abs().square()
+            envelope=F.avg_pool1d(energy.flatten(0,1).unsqueeze(1),20,10).squeeze(1)
+            envelopes.append(envelope.reshape(len(x),16,-1))
+        e=torch.stack(envelopes,2)
+        mean=e.mean(-1,keepdim=True).clamp_min(1e-12)
+        relative=e/mean
+        centered=relative-relative.mean(-1,keepdim=True)
+        std=centered.square().mean(-1,keepdim=True).sqrt().clamp_min(1e-6)
+        normalized=centered/std
+        acf=torch.stack([(normalized[...,:-lag]*normalized[...,lag:]).mean(-1)
+                         for lag in range(4,41)],-1).clamp(-1,1)
+        acf_peak,acf_index=acf.max(-1)
+        probability=(relative/relative.sum(-1,keepdim=True).clamp_min(1e-12)).clamp_min(1e-12)
+        entropy=-(probability*probability.log()).sum(-1)
+        summaries=[std.squeeze(-1).clamp_min(1e-6).log(),relative.amax(-1).clamp_min(1).log(),entropy,
+                   (relative>2).float().mean(-1),(relative>4).float().mean(-1),
+                   ((relative[...,1:]>2)&(relative[...,:-1]<=2)).float().mean(-1),
+                   acf_peak,(acf_index.float()+4)/40,
+                   acf[...,0],acf[...,6],acf[...,16],acf[...,36]]
+        left=torch.tensor([0,1,2,3,8,9,10,11],device=x.device);right=left+4
+        synchrony=(normalized[:,left]*normalized[:,right]).mean(-1).clamp(-1,1)
+        imbalance=(std[:,left].squeeze(-1).log()-std[:,right].squeeze(-1).log()).abs()
+        return torch.cat([torch.stack(summaries,-1).flatten(1),synchrony.flatten(1),imbalance.flatten(1)],1)
+
+def mirrored_features(features):
+    channels=torch.tensor([4,5,6,7,0,1,2,3,12,13,14,15,8,9,10,11],device=features.device)
+    return torch.cat([features[:,:16*5*12].reshape(-1,16,5,12)[:,channels].flatten(1),features[:,16*5*12:]],1)

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S050/features.py

@@ -0,0 +1,63 @@
+"""One shared residual backbone, five linear input adapters and output heads."""
+import torch
+from torch import nn
+
+SPECS = {'faced': (32, 10, 9), 'tuev': (16, 5, 6), 'ad65': (19, 10, 3),
+         'openbmi': (62, 4, 2), 'longitudinal': (58, 2, 2)}
+TRACKS = tuple(SPECS)
+EDGES = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18,20,22,25,28,32,36,40,45,50,60,70,80,95]
+COV_BANDS = [(1,4),(4,8),(8,12),(12,16),(16,20),(20,28),(28,40)]
+
+def feature_dim(channels):
+    return channels * (4*(len(EDGES)-1)+4) + len(COV_BANDS)*channels*(channels-1)//2
+
+@torch.no_grad()
+def extract_features(track, x):
+    """Fixed features; no batch statistics or fitted inference transformations."""
+    x = x.float().flatten(2)
+    x = torch.nan_to_num(x, nan=0., posinf=1e5, neginf=-1e5).clamp(-1e5,1e5)
+    x = x - x.mean(-1,keepdim=True)
+    if track in ('ad65','openbmi','longitudinal'):
+        x = x - x.mean(1,keepdim=True)
+    rms = x.square().mean(-1).clamp_min(1e-12).sqrt()
+    norm = x / rms.unsqueeze(-1)
+    moments = torch.stack([rms.log(), norm.abs().amax(-1).clamp_min(1).log(),
+                          norm.pow(4).mean(-1).clamp_min(1).log(),
+                          (norm[...,1:]-norm[...,:-1]).square().mean(-1).clamp_min(1e-10).log()],-1)
+    frames = x.unfold(-1,400,200)
+    frames = frames - frames.mean(-1,keepdim=True)
+    window = torch.hann_window(400,device=x.device)
+    z = torch.fft.rfft(frames*window,dim=-1)/200
+    power = z.abs().square()
+    bands = torch.stack([power[...,2*lo:2*hi].mean(-1) for lo,hi in zip(EDGES[:-1],EDGES[1:])],-1)
+    lp = bands.mean(-2).clamp_min(1e-12).log()
+    variation = bands.clamp_min(1e-12).log().std(-2,correction=0)
+    relative_frequency = lp - lp.mean(-1,keepdim=True)
+    relative_channel = lp - lp.mean(1,keepdim=True)
+    parts = [torch.cat([lp, relative_frequency, relative_channel, variation, moments],-1).flatten(1)]
+    ii,jj = torch.triu_indices(x.shape[1],x.shape[1],offset=1,device=x.device)
+    for lo,hi in COV_BANDS:
+        v = z[...,2*lo:2*hi].flatten(2)
+        cov = v.real @ v.real.transpose(-1,-2) + v.imag @ v.imag.transpose(-1,-2)
+        d = cov.diagonal(dim1=-2,dim2=-1).clamp_min(1e-12).sqrt()
+        cor = cov / (d.unsqueeze(1)*d.unsqueeze(2)).clamp_min(1e-12)
+        parts.append(cor[:,ii,jj].clamp(-1,1))
+    result = torch.cat(parts,-1)
+    return torch.nan_to_num(result).clamp(-40,40)
+
+class Standardize(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.register_buffer('mean',torch.zeros(dim))
+        self.register_buffer('scale',torch.ones(dim))
+    def forward(self,x):
+        return ((x-self.mean)/self.scale).clamp(-8,8)
+
+class Residual(nn.Module):
+    def __init__(self,width,dropout):
+        super().__init__()
+        self.net=nn.Sequential(nn.LayerNorm(width),nn.Linear(width,width*2),nn.GELU(),
+                               nn.Dropout(dropout),nn.Linear(width*2,width),nn.Dropout(dropout))
+    def forward(self,x):
+        return x+self.net(x)
+

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S050/frequency_features.py

@@ -0,0 +1,11 @@
+"""Fixed per-window frequency decomposition for a linear montage adapter."""
+import torch
+
+FREQUENCY_BANDS=((1,4),(4,8),(8,13),(13,20),(20,30),(30,45))
+
+def frequency_components(signal):
+    with torch.autocast(signal.device.type,enabled=False):
+        x=signal.float();spectrum=torch.fft.rfft(x,dim=-1)
+        frequency=torch.fft.rfftfreq(x.shape[-1],d=1/200,device=x.device)
+        masks=torch.stack([((frequency>=lo)&(frequency<hi)).float() for lo,hi in FREQUENCY_BANDS])
+        return torch.fft.irfft(spectrum[:,None]*masks[None,:,None],n=x.shape[-1],dim=-1)

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S050/infer.py

@@ -0,0 +1,24 @@
+import argparse,json
+from pathlib import Path
+import torch
+from model import HybridModel,SPECS
+from data import Dataset
+from covariances import extract_covariances
+p=argparse.ArgumentParser();p.add_argument('input',type=Path);p.add_argument('output',type=Path);a=p.parse_args()
+root=Path(__file__).resolve().parent;c=json.loads((root/'config.json').read_text());t=json.loads((a.input/'manifest.json').read_text())['track']
+if t not in SPECS:raise ValueError('unknown track')
+torch.set_num_threads(4);device='cuda' if torch.cuda.is_available() else 'cpu'
+m=HybridModel(**c['architecture']).to(device);m.load_state_dict(torch.load(root/'weights.pt',map_location=device,weights_only=True));m.eval()
+a.output.mkdir(parents=True,exist_ok=True)
+with torch.inference_mode(),(a.output/'predictions.jsonl').open('w') as f:
+    for x,_,ids in torch.utils.data.DataLoader(Dataset(a.input),batch_size=64,num_workers=0):
+        # FFT features stay float32; convolutions can use bfloat16 on CUDA.
+        x=x.to(device)
+        from features import extract_features
+        torch.backends.cuda.matmul.allow_tf32=False
+        features=extract_features(t,x)
+        covariances=extract_covariances(t,x)
+        torch.backends.cuda.matmul.allow_tf32=True
+        with torch.autocast(device,dtype=torch.bfloat16,enabled=device=='cuda'):pred=m(t,x,features,covariances=covariances)
+        pp=pred.float().softmax(-1).cpu().numpy()
+        for sid,pr in zip(ids,pp):f.write(json.dumps({'id':sid,'probabilities':pr.tolist()})+'\n')

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S050/infer.sh

@@ -0,0 +1,3 @@
+#!/usr/bin/env bash
+set -euo pipefail
+exec python "$(dirname -- "$0")/infer.py" "$1" "$2"

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S050/model.py

@@ -0,0 +1,350 @@
+"""One fused waveform/spectral backbone used identically by all five tasks."""
+import torch
+from torch import nn
+from braindecode.models import BIOT
+from features import SPECS,TRACKS,feature_dim,extract_features,Standardize,Residual,EDGES,COV_BANDS
+from covariances import BANDS,extract_covariances
+from alpha_features import extract_alpha_features,ALPHA_DIM
+from envelope_features import extract_envelope_features,ENVELOPE_DIM
+from frequency_features import frequency_components,FREQUENCY_BANDS
+from tangent_features import extract_tangent_features,TANGENT_DIM
+from phase_features import phase_features,PHASE_DIM,PHASE_HALF
+from ordinal_features import ordinal_features,PER_CHANNEL as ORDINAL_PER_CHANNEL
+
+def clinical_asymmetry_features(features):
+    """Bipolar homologous-channel amplitude and coherence, invariant to mirroring."""
+    bands=len(EDGES)-1;per_channel=4*bands+4
+    block=features[:,:16*per_channel].reshape(-1,16,per_channel)
+    left=torch.tensor([0,1,2,3,8,9,10,11],device=features.device);right=left+4
+    pair_power=(block[:,left,:bands]-block[:,right,:bands]).abs().flatten(1)
+    pair_rms=(block[:,left,4*bands]-block[:,right,4*bands]).abs()
+    hemisphere=(block[:,left,:bands].mean(1)-block[:,right,:bands].mean(1)).abs()
+    index=left*(32-left-1)//2+(right-left-1)
+    correlations=features[:,16*per_channel:].reshape(-1,len(COV_BANDS),120)[:,:,index].flatten(1)
+    return torch.cat([pair_power,pair_rms,hemisphere,correlations],1)
+
+class InputAdapter(nn.Module):
+    def __init__(self,c,width,channels,spectral_enabled=True,asymmetry=False,alpha_regions=None,envelope=False):
+        super().__init__()
+        self.ordinal_normalizer=Standardize(c*ORDINAL_PER_CHANNEL)
+        self.ordinal_projection=nn.Linear(c*ORDINAL_PER_CHANNEL,width)
+        self.ordinal_dropout=nn.Dropout(.3)
+        self.register_buffer('ordinal_probabilities_enabled',torch.ones(()))
+        self.phase_normalizer=Standardize(PHASE_DIM)
+        self.signal=nn.Conv1d(c,channels,1,bias=False)
+        self.frequency_weight=nn.Parameter(torch.zeros(len(FREQUENCY_BANDS),channels,c))
+        self.neural=nn.Conv1d(c,18,1,bias=False)
+        self.normalizer=Standardize(feature_dim(c))
+        self.spectral=nn.Linear(feature_dim(c),width)
+        self.dropout=nn.Dropout(.1)
+        self.register_buffer("spectral_enabled",torch.tensor(float(spectral_enabled)))
+        self.spatial_weight=nn.Parameter(torch.randn(len(BANDS),24,c)/c**.5)
+        self.asymmetry_projection=nn.Linear(316,width,bias=False) if asymmetry else None
+        if asymmetry:
+            nn.init.zeros_(self.asymmetry_projection.weight)
+            self.asymmetry_normalizer=Standardize(316)
+            self.asymmetry_dropout=nn.Dropout(.1)
+        self.alpha_projection=None
+        if alpha_regions is not None:
+            self.register_buffer('alpha_regions',torch.tensor(alpha_regions,dtype=torch.float32))
+            self.alpha_normalizer=Standardize(ALPHA_DIM)
+            self.alpha_projection=nn.Linear(ALPHA_DIM,width,bias=False)
+            nn.init.zeros_(self.alpha_projection.weight)
+            self.alpha_dropout=nn.Dropout(.3)
+        self.envelope_projection=None
+        if envelope:
+            self.envelope_normalizer=Standardize(ENVELOPE_DIM)
+            self.envelope_projection=nn.Linear(ENVELOPE_DIM,width,bias=False)
+            nn.init.zeros_(self.envelope_projection.weight)
+            self.envelope_dropout=nn.Dropout(.3)
+    def ordinal_embedding(self,features):
+        values=self.ordinal_normalizer(features).reshape(len(features),-1,5,26)
+        values=torch.cat([self.ordinal_probabilities_enabled*values[...,:24],values[...,24:]],-1).flatten(1)
+        return self.ordinal_projection(self.ordinal_dropout(values))
+    def spectral_features(self,features,signal=None,alpha_features=None,envelope_features=None):
+        value=self.spectral_enabled*self.spectral(self.dropout(self.normalizer(features)))
+        if self.asymmetry_projection is not None:
+            extra=self.asymmetry_dropout(self.asymmetry_normalizer(clinical_asymmetry_features(features)))
+            value=value+self.asymmetry_projection(extra)
+        if self.alpha_projection is not None:
+            if alpha_features is None:alpha_features=extract_alpha_features(signal,self.alpha_regions)
+            value=value+self.alpha_projection(self.alpha_dropout(self.alpha_normalizer(alpha_features)))
+        if self.envelope_projection is not None:
+            if envelope_features is None:envelope_features=extract_envelope_features(signal)
+            value=value+self.envelope_projection(self.envelope_dropout(self.envelope_normalizer(envelope_features)))
+        return value
+    def spatial_features(self,covariances):
+        with torch.autocast(covariances.device.type,enabled=False):
+            w=self.spatial_weight.float()/self.spatial_weight.float().norm(dim=-1,keepdim=True).clamp_min(1e-6)
+            power=torch.einsum('bfc,nbcd,bfd->nbf',w,covariances.float(),w).clamp_min(1e-8).log()
+            return torch.cat([power,power-power.mean(1,keepdim=True)],1).flatten(1)
+    def wave_signal(self,x,components=None):
+        if components is None:components=frequency_components(x)
+        correction=torch.einsum('blc,nbct->nlt',self.frequency_weight,components.to(self.frequency_weight.dtype))
+        return (self.signal(x)+correction.to(x.dtype)).unsqueeze(1)
+    def forward(self,x,features,covariance_features):
+        return self.wave_signal(x),self.spectral_features(features,signal=x),self.neural(x),covariance_features
+
+class SharedBackbone(nn.Module):
+    def __init__(self,channels,width,depth,dropout,wave_filters):
+        super().__init__()
+        self.waveform=nn.Sequential(
+            nn.Conv2d(1,wave_filters,(1,64),padding=(0,32),bias=False),nn.GroupNorm(1,wave_filters),
+            nn.Conv2d(wave_filters,2*wave_filters,(channels,1),groups=wave_filters,bias=False),nn.GroupNorm(1,2*wave_filters),nn.ELU(),
+            nn.AvgPool2d((1,4)),nn.Dropout(.35),
+            nn.Conv2d(2*wave_filters,2*wave_filters,(1,16),padding=(0,8),groups=2*wave_filters,bias=False),nn.Conv2d(2*wave_filters,2*wave_filters,1,bias=False),
+            nn.GroupNorm(1,2*wave_filters),nn.ELU(),nn.AvgPool2d((1,8)),nn.Dropout(.35))
+        self.wave_summary=nn.Sequential(nn.AdaptiveAvgPool2d((1,8)),nn.Flatten(),nn.Linear(2*wave_filters*8,width))
+        self.fusion=nn.Sequential(nn.LayerNorm(width),nn.GELU(),nn.Dropout(dropout),
+                                 *[Residual(width,dropout) for _ in range(depth)],nn.LayerNorm(width))
+        # Both representations interact before the shared residual stack.
+        self.phase_encoder=nn.Sequential(nn.LayerNorm(PHASE_DIM),nn.Linear(PHASE_DIM,width),nn.GELU(),nn.Dropout(.3),Residual(width,.3),nn.LayerNorm(width))
+        self.register_buffer('phase_imaginary_enabled',torch.ones(()))
+        self.wave_gain=nn.Parameter(torch.tensor(.1))
+        self.biot=BIOT(n_chans=18,n_outputs=2,n_times=400,sfreq=200)
+        self.biot.final_layer=nn.Identity()
+        self.biot_projection=nn.Sequential(nn.LayerNorm(256),nn.Linear(256,width),nn.Dropout(.2))
+        nn.init.normal_(self.biot_projection[1].weight,std=.001);nn.init.zeros_(self.biot_projection[1].bias)
+        self.covariance_encoder=nn.Sequential(nn.LayerNorm(len(BANDS)*24*2),nn.Linear(len(BANDS)*24*2,width),nn.GELU(),nn.Dropout(.4),
+                                             *[Residual(width,.4) for _ in range(2)],nn.LayerNorm(width))
+        self.covariance_projection=nn.Linear(width,width)
+        nn.init.zeros_(self.covariance_projection.weight);nn.init.zeros_(self.covariance_projection.bias)
+        self.tangent_encoder=nn.Sequential(nn.LayerNorm(TANGENT_DIM),nn.Linear(TANGENT_DIM,width),nn.GELU(),nn.Dropout(.3),Residual(width,.3),nn.LayerNorm(width),nn.Linear(width,width))
+        self.ordinal_context=nn.Sequential(nn.LayerNorm(width),nn.Linear(width,width*2),nn.GELU(),nn.Dropout(.3),nn.Linear(width*2,width))
+        nn.init.zeros_(self.ordinal_context[-1].weight);nn.init.zeros_(self.ordinal_context[-1].bias)
+        self.register_buffer('codebrain_enabled',torch.ones(()))
+        self.codebrain_patch_encoder=nn.Sequential(nn.LayerNorm(24*64),nn.Linear(24*64,width),nn.GELU(),nn.Dropout(.3))
+        self.codebrain_context=nn.Sequential(nn.LayerNorm(width*4),nn.Linear(width*4,width*2),nn.GELU(),nn.Dropout(.3),nn.Linear(width*2,width))
+        nn.init.zeros_(self.codebrain_context[-1].weight);nn.init.zeros_(self.codebrain_context[-1].bias)
+    def codebrain_correction(self,tokens,representation,fusion_input):
+        with torch.autocast(tokens.device.type,enabled=False):
+            encoded=self.codebrain_patch_encoder(self.codebrain_enabled*tokens.float())
+            pooled=torch.cat([encoded.mean(1),encoded.std(1,correction=0),representation[:,-512:-384].float(),representation[:,-128:].float()],-1)
+            delta=self.codebrain_context(pooled)
+        added=.001*nn.functional.linear(delta,self.covariance_projection.weight,None)
+        corrected=self.fusion(fusion_input+added.to(fusion_input.dtype))
+        return corrected-representation[:,-512:-384],delta
+    def forward(self,signal,spectral,neural_signal,covariance_features,tangent_features):
+        wave=self.waveform(signal)
+        neural=self.biot(neural_signal.float())
+        neural=self.biot_projection[0](neural)
+        projected=self.biot_projection[2](self.biot_projection[1](neural))
+        with torch.autocast(signal.device.type,enabled=False):
+            covariance_embedding=self.covariance_encoder(covariance_features.float())
+        original=spectral+self.wave_gain*self.wave_summary(wave)+.01*projected
+        coupled=.001*self.covariance_projection(covariance_embedding).to(original.dtype)
+        fused=self.fusion(original+coupled)
+        with torch.autocast(signal.device.type,enabled=False):delta=self.tangent_encoder(tangent_features.float())
+        added=.001*nn.functional.linear(delta,self.covariance_projection.weight,None)
+        corrected=self.fusion((original+coupled)+added.to(original.dtype))
+        return torch.cat([wave.flatten(1),fused,neural,covariance_embedding],-1),corrected-fused,delta,original+coupled
+
+class ResidualHead(nn.Module):
+    """A small nonlinear classifier correction around an inherited linear readout."""
+    def __init__(self,features,classes,width=64,dropout=.3,full_precision=False):
+        super().__init__();self.full_precision=full_precision
+        self.base=nn.Linear(features,classes)
+        self.delta=nn.Sequential(nn.LayerNorm(features),nn.Linear(features,width),nn.GELU(),nn.Dropout(dropout),nn.Linear(width,classes))
+        nn.init.zeros_(self.delta[-1].weight);nn.init.zeros_(self.delta[-1].bias)
+    def forward(self,x):
+        if self.full_precision:
+            with torch.autocast(x.device.type,enabled=False):return self.base(x.float())+self.delta(x.float())
+        prior=nn.functional.linear(x[:,:-128].contiguous(),self.base.weight[:,:-128].contiguous(),self.base.bias)
+        added=nn.functional.linear(x[:,-128:].contiguous(),self.base.weight[:,-128:].contiguous())
+        value=prior+added
+        return value+self.delta(x).to(value.dtype)
+
+class TemporalPoolHead(nn.Module):
+    def __init__(self,prior,channels=64):
+        super().__init__();self.prior=prior;self.channels=channels
+        self.register_buffer('pool_output_scale',torch.ones(()));self.register_buffer('pool_mean',torch.zeros(channels*4));self.register_buffer('pool_scale',torch.ones(channels*4))
+        self.pool_projection=nn.Linear(channels*4,prior.base.out_features)
+        nn.init.zeros_(self.pool_projection.weight);nn.init.zeros_(self.pool_projection.bias)
+    def statistics(self,x):
+        with torch.autocast(x.device.type,enabled=False):
+            wave=x[:,:-512].float().reshape(len(x),self.channels,-1)
+            return torch.cat([wave.mean(-1),wave.std(-1,correction=0),wave.amax(-1),wave.amin(-1)],-1)
+    def forward(self,x):
+        prior=self.prior(x)
+        with torch.autocast(x.device.type,enabled=False):
+            values=((self.statistics(x)-self.pool_mean)/self.pool_scale).clamp(-8,8)
+            added=self.pool_output_scale*self.pool_projection(values)
+        return prior+added.to(prior.dtype)
+
+class TemporalEvidenceHead(nn.Module):
+    """Pool class evidence over the shared waveform time axis."""
+    def __init__(self,prior,channels=64,temperature=.25):
+        super().__init__();self.prior=prior;self.channels=channels;self.temperature=temperature
+        self.register_buffer('evidence_output_scale',torch.ones(()))
+        self.register_buffer('evidence_mean',torch.zeros(channels))
+        self.register_buffer('evidence_scale',torch.ones(channels))
+        self.evidence_projection=nn.Linear(channels,prior.prior.base.out_features,bias=False)
+        nn.init.zeros_(self.evidence_projection.weight)
+    def patches(self,x):
+        with torch.autocast(x.device.type,enabled=False):
+            wave=x[:,:-512].float().reshape(len(x),self.channels,-1).transpose(1,2)
+            return ((wave-self.evidence_mean)/self.evidence_scale).clamp(-8,8)
+    def evidence(self,patches):
+        with torch.autocast(patches.device.type,enabled=False):
+            values=self.evidence_projection(patches.float())/self.temperature
+            maximum=values.amax(1,keepdim=True)
+            pooled=self.temperature*((values-maximum).exp().mean(1).log()+maximum.squeeze(1))
+            return self.evidence_output_scale*pooled
+    def forward(self,x):
+        prior=self.prior(x)
+        return prior+self.evidence(self.patches(x)).to(prior.dtype)
+
+class FourierResidualHead(nn.Module):
+    """A fixed random Fourier basis and trainable linear classifier correction."""
+    def __init__(self,prior,features,classes,basis=256):
+        super().__init__();self.prior=prior
+        self.register_buffer('rff_mean',torch.zeros(features));self.register_buffer('rff_scale',torch.ones(features))
+        self.register_buffer('rff_frequency',torch.randn(features,basis)/features**.5)
+        self.register_buffer('rff_phase',torch.rand(basis)*2*torch.pi)
+        self.register_buffer('rff_output_scale',torch.ones(()))
+        self.rff_projection=nn.Linear(3*basis,classes);nn.init.zeros_(self.rff_projection.weight);nn.init.zeros_(self.rff_projection.bias)
+    def basis(self,x):
+        with torch.autocast(x.device.type,enabled=False):
+            values=((x.float()-self.rff_mean)/self.rff_scale).clamp(-8,8)@self.rff_frequency
+            return 2**.5*torch.cat([(values/sigma+self.rff_phase).cos() for sigma in (.5,1.,2.)],-1)
+    def forward(self,x):
+        prior=self.prior(x)
+        with torch.autocast(x.device.type,enabled=False):added=self.rff_output_scale*self.rff_projection(self.basis(x))
+        return prior+added.to(prior.dtype)
+
+class TangentFeatureHead(nn.Module):
+    """Select a shared feature correction before the inherited classifier."""
+    def __init__(self,prior):
+        super().__init__();self.prior=prior;self.feature_gain=nn.Parameter(torch.zeros(()))
+    def forward(self,values):
+        old=values[:,:-256];fusion_delta=values[:,-256:-128];covariance_delta=values[:,-128:]
+        adjusted=torch.cat([old[:,:-512],old[:,-512:-384]+self.feature_gain*fusion_delta,old[:,-384:-128],old[:,-128:]+self.feature_gain*covariance_delta],-1)
+        return self.prior(adjusted)
+
+class CodeBrainFeatureHead(nn.Module):
+    """Private use of a jointly trained common feature correction."""
+    def __init__(self,prior):
+        super().__init__();self.prior=prior;self.codebrain_gain=nn.Parameter(torch.ones(()))
+    def forward(self,values):
+        previous=values[:,:-256];native=previous[:,:-256]
+        fusion_delta=values[:,-256:-128];covariance_delta=values[:,-128:]
+        adjusted=torch.cat([native[:,:-512],native[:,-512:-384]+self.codebrain_gain*fusion_delta,native[:,-384:-128],native[:,-128:]+self.codebrain_gain*covariance_delta],-1)
+        return self.prior(torch.cat([adjusted,previous[:,-256:]],-1))
+
+class WaveCoactivationContext(nn.Module):
+    """One common encoder of coactivation among the learned waveform channels."""
+    def __init__(self,channels=64,width=128):
+        super().__init__();self.channels=channels
+        ii,jj=torch.triu_indices(channels,channels,offset=1)
+        self.register_buffer('pair_i',ii);self.register_buffer('pair_j',jj)
+        self.register_buffer('offdiagonal_enabled',torch.ones(()))
+        self.encoder=nn.Sequential(nn.LayerNorm(channels+len(ii)),nn.Linear(channels+len(ii),width),nn.GELU(),nn.Dropout(.3),Residual(width,.3),nn.LayerNorm(width))
+    def statistics(self,native):
+        with torch.autocast(native.device.type,enabled=False):
+            wave=native[:,:-512].float().reshape(len(native),self.channels,-1)
+            centered=wave-wave.mean(-1,keepdim=True)
+            previous_tf32=torch.backends.cuda.matmul.allow_tf32
+            torch.backends.cuda.matmul.allow_tf32=False
+            covariance=centered@centered.transpose(-1,-2)/wave.shape[-1]
+            torch.backends.cuda.matmul.allow_tf32=previous_tf32
+            variance=covariance.diagonal(dim1=-2,dim2=-1).clamp_min(1e-8)
+            correlation=covariance/(variance.sqrt()[:,:,None]*variance.sqrt()[:,None,:])
+            return torch.cat([variance.log(),correlation[:,self.pair_i,self.pair_j].clamp(-1,1)],-1)
+    def encode_statistics(self,values):
+        with torch.autocast(values.device.type,enabled=False):
+            value=torch.cat([values[:,:self.channels],self.offdiagonal_enabled*values[:,self.channels:]],-1)
+            return self.encoder(value.float())
+    def forward(self,native):return self.encode_statistics(self.statistics(native))
+
+class WaveCoactivationHead(nn.Module):
+    def __init__(self,prior,classes,width=128):
+        super().__init__();self.prior=prior;self.projection=nn.Linear(width,classes)
+        nn.init.zeros_(self.projection.weight);nn.init.zeros_(self.projection.bias)
+        self.register_buffer('output_scale',torch.ones(()));self.register_buffer('correction_gain',torch.ones(()))
+    def forward(self,values):
+        prior=self.prior(values[:,:-128])
+        with torch.autocast(values.device.type,enabled=False):delta=self.correction_gain*self.output_scale*self.projection(values[:,-128:].float())
+        return prior+delta.to(prior.dtype)
+
+class OrdinalFeatureHead(nn.Module):
+    """Private strength of one common ordinal-dynamics feature correction."""
+    def __init__(self,prior):
+        super().__init__();self.prior=prior;self.ordinal_gain=nn.Parameter(torch.ones(()))
+    def forward(self,values):
+        previous=values[:,:-256];native=previous[:,:-640]
+        adjusted=torch.cat([native[:,:-512],native[:,-512:-384]+self.ordinal_gain*values[:,-256:-128],native[:,-384:-128],native[:,-128:]+self.ordinal_gain*values[:,-128:],previous[:,-640:]],-1)
+        return self.prior(adjusted)
+
+class PhaseReadoutHead(nn.Module):
+    """Private class evidence from the common complex-coherence encoder."""
+    def __init__(self,prior,classes,width=128):
+        super().__init__();self.prior=prior;self.projection=nn.Linear(width,classes)
+        nn.init.zeros_(self.projection.weight);nn.init.zeros_(self.projection.bias)
+        self.register_buffer('output_scale',torch.ones(()));self.register_buffer('correction_gain',torch.ones(()))
+    def forward(self,values):
+        prior=self.prior(values[:,:-128])
+        with torch.autocast(values.device.type,enabled=False):delta=(self.correction_gain*self.output_scale)*self.projection(values[:,-128:].float())
+        return prior+delta.to(prior.dtype)
+
+class HybridModel(nn.Module):
+    def __init__(self,channels=32,width=128,depth=2,dropout=.5,wave_filters=32,head_width=64,head_dropout=.3,clinical_asymmetry=True,alpha_regions=None,clinical_envelope=False,temporal_pool=True,temporal_evidence=False,evidence_temperature=.25,random_fourier=False,fourier_features=256,tangent_readout=True,shared_context_correction=True,wave_coactivation=True,ordinal_context=True,phase_coherence=True):
+        super().__init__()
+        self.adapters=nn.ModuleDict({t:InputAdapter(c,width,channels,spectral_enabled=t not in ("faced","tuev","openbmi"),asymmetry=clinical_asymmetry and t=="tuev",alpha_regions=alpha_regions if t=="longitudinal" else None,envelope=clinical_envelope and t=="tuev") for t,(c,s,k) in SPECS.items()})
+        self.backbone=SharedBackbone(channels,width,depth,dropout,wave_filters)
+        self.backbone.wave_coactivation=WaveCoactivationContext(2*wave_filters,width)
+        self.heads=nn.ModuleDict({t:ResidualHead(2*wave_filters*(((s*200+1)//4+1)//8)+width+256+width,k,head_width,head_dropout,full_precision=t=='ad65') for t,(c,s,k) in SPECS.items()})
+        if temporal_pool:self.heads=nn.ModuleDict({t:TemporalPoolHead(head,wave_filters*2) for t,head in self.heads.items()})
+        if temporal_evidence:self.heads=nn.ModuleDict({t:TemporalEvidenceHead(head,wave_filters*2,evidence_temperature) for t,head in self.heads.items()})
+        if random_fourier:self.heads=nn.ModuleDict({t:FourierResidualHead(head,2*wave_filters*(((SPECS[t][1]*200+1)//4+1)//8)+width+256+width,SPECS[t][2],fourier_features) for t,head in self.heads.items()})
+        self.heads=nn.ModuleDict({t:TangentFeatureHead(head) for t,head in self.heads.items()})
+        self.heads=nn.ModuleDict({t:CodeBrainFeatureHead(head) for t,head in self.heads.items()})
+        self.heads=nn.ModuleDict({t:WaveCoactivationHead(head,SPECS[t][2],width) for t,head in self.heads.items()})
+        self.heads=nn.ModuleDict({t:OrdinalFeatureHead(head) for t,head in self.heads.items()})
+        self.heads=nn.ModuleDict({t:PhaseReadoutHead(head,SPECS[t][2],width) for t,head in self.heads.items()})
+    def phase_embedding(self,t,values):
+        with torch.autocast(values.device.type,enabled=False):
+            normalized=self.adapters[t].phase_normalizer(values.float())
+            normalized=torch.cat([normalized[:,:PHASE_HALF],self.backbone.phase_imaginary_enabled*normalized[:,PHASE_HALF:]],-1)
+            return self.backbone.phase_encoder(normalized)
+    def forward(self,t,x,features=None,covariances=None,covariance_features=None):
+        if features is None:features=extract_features(t,x)
+        if covariance_features is None:
+            if covariances is None:covariances=extract_covariances(t,x)
+            covariance_features=self.adapters[t].spatial_features(covariances)
+        signal=x.flatten(2);signal=signal-signal.mean(-1,keepdim=True)
+        if t in ('faced','tuev'):signal=signal/signal.std(-1,keepdim=True,correction=0).clamp_min(1e-6)
+        else:signal=signal-signal.mean(1,keepdim=True)
+        signal=signal.clamp(-20,20)
+        adapted=self.adapters[t](signal,features,covariance_features)
+        if covariances is None:covariances=extract_covariances(t,x)
+        tangent=extract_tangent_features(covariances,self.adapters[t].spatial_weight)
+        representation,fusion_delta,covariance_delta,fusion_input=self.backbone(*adapted,tangent_features=tangent)
+        if t in ('faced','openbmi'):
+            representation=torch.cat([representation[:,:-384],torch.zeros_like(representation[:,-384:-128]),representation[:,-128:]],-1)
+        tokens=torch.zeros(len(signal),signal.shape[-1]//200,24*64,device=signal.device,dtype=torch.bfloat16)
+        code_fusion,code_covariance=self.backbone.codebrain_correction(tokens,representation,fusion_input)
+        coactivation=self.backbone.wave_coactivation(representation)
+        with torch.autocast(signal.device.type,enabled=False):
+            ordinal=self.adapters[t].ordinal_embedding(ordinal_features(t,x))
+            ordinal_covariance=self.backbone.ordinal_context(ordinal.float())
+        added=.001*nn.functional.linear(ordinal_covariance,self.backbone.covariance_projection.weight,None)
+        ordinal_fusion=self.backbone.fusion(fusion_input+added.to(fusion_input.dtype))-representation[:,-512:-384]
+        phase=self.phase_embedding(t,phase_features(t,x,self.adapters[t].spatial_weight))
+        return self.heads[t](torch.cat([representation,fusion_delta,covariance_delta,code_fusion,code_covariance,coactivation,ordinal_fusion,ordinal_covariance,phase],-1))
+
+def widen_state_dict(state,multiplier=2):
+    """Duplicate temporal features and divide downstream sums to preserve outputs."""
+    result={}
+    for key,value in state.items():
+        if key.startswith('backbone.waveform.'):
+            layer=int(key.split('.')[2])
+            if layer in (0,2,7):value=value.repeat(multiplier,1,1,1)
+            elif layer in (1,3,9):value=value.repeat(multiplier)
+            elif layer==8:value=value.repeat(multiplier,multiplier,1,1)/multiplier
+        elif key=='backbone.wave_summary.2.weight':value=value.repeat(1,multiplier)/multiplier
+        elif key.startswith('heads.') and key.endswith('.weight'):
+            raw=value[:,:-128]/multiplier
+            value=torch.cat([raw]*multiplier+[value[:,-128:]],1)
+        result[key]=value
+    return result
+

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S050/ordinal_features.py

@@ -0,0 +1,25 @@
+"""Fixed within-window ordinal dynamics, independent of amplitude scaling."""
+import math
+import torch
+LAGS=(1,2,4,8,16)
+PATTERNS=24
+PER_CHANNEL=len(LAGS)*(PATTERNS+2)
+
+def ordinal_features(track,x):
+    with torch.autocast(x.device.type,enabled=False):
+        x=torch.nan_to_num(x.float().flatten(2),nan=0.,posinf=1e5,neginf=-1e5).clamp(-1e5,1e5)
+        if track in ('ad65','openbmi','longitudinal'):x=x-x.mean(1,keepdim=True)
+        parts=[];uniform=1/PATTERNS;maximum_entropy=math.log(PATTERNS);m0=(1+uniform)/2;mr=uniform/2;maximum_js=-m0*math.log(m0)-(PATTERNS-1)*mr*math.log(mr)-maximum_entropy/2
+        for lag in LAGS:
+            length=x.shape[-1]-3*lag
+            if length<=0:raise ValueError('window too short for ordinal dynamics')
+            samples=torch.stack([x[...,i*lag:i*lag+length] for i in range(4)],-1)
+            order=samples.argsort(dim=-1,stable=True)
+            code=6*((order[...,0,None]>order[...,1:]).sum(-1))+2*((order[...,1,None]>order[...,2:]).sum(-1))+(order[...,2]>order[...,3]).long()
+            histogram=torch.zeros(*x.shape[:2],PATTERNS,device=x.device,dtype=torch.float32)
+            histogram.scatter_add_(-1,code,torch.ones_like(code,dtype=torch.float32));probability=histogram/length
+            entropy=-(probability*probability.clamp_min(1e-12).log()).sum(-1)
+            mixture=(probability+uniform)/2;mixture_entropy=-(mixture*mixture.log()).sum(-1)
+            complexity=((mixture_entropy-.5*entropy-.5*maximum_entropy)/maximum_js).clamp(0,1)*(entropy/maximum_entropy)
+            parts.append(torch.cat([probability,(entropy/maximum_entropy)[...,None],complexity[...,None]],-1))
+        return torch.cat(parts,-1).flatten(1)

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S050/phase_features.py

@@ -0,0 +1,37 @@
+"""Complex spectral coherence of the existing 24 spatial filters in ten bands."""
+import torch
+from covariances import BANDS
+CHANNELS=24
+PHASE_HALF=len(BANDS)*CHANNELS*(CHANNELS-1)//2
+PHASE_DIM=2*PHASE_HALF
+
+@torch.no_grad()
+def phase_features(track,x,spatial_weight):
+    previous_tf32=torch.backends.cuda.matmul.allow_tf32
+    try:
+        torch.backends.cuda.matmul.allow_tf32=False
+        with torch.autocast(x.device.type,enabled=False):
+            x=torch.nan_to_num(x.float().flatten(2),nan=0.,posinf=1e5,neginf=-1e5).clamp(-1e5,1e5)
+            x=x-x.mean(-1,keepdim=True)
+            if track in ('faced','tuev'):x=x/x.square().mean(-1,keepdim=True).clamp_min(1e-12).sqrt()
+            else:x=x-x.mean(1,keepdim=True)
+            x=x/x.square().mean((1,2),keepdim=True).clamp_min(1e-12).sqrt()
+            frames=x.unfold(-1,400,200);frames=frames-frames.mean(-1,keepdim=True)
+            z=torch.fft.rfft(frames*torch.hann_window(400,device=x.device),dim=-1)/200
+            w=spatial_weight.float()/spatial_weight.float().norm(dim=-1,keepdim=True).clamp_min(1e-6)
+            index=torch.triu_indices(CHANNELS,CHANNELS,1,device=x.device)
+            real_parts=[];imaginary_parts=[]
+            for band,(lo,hi) in enumerate(BANDS):
+                v=z[...,2*lo:2*hi].flatten(2)
+                vr=w[band][None]@v.real;vi=w[band][None]@v.imag
+                real=(vr@vr.transpose(-1,-2)+vi@vi.transpose(-1,-2))/v.shape[-1]
+                imaginary=(vi@vr.transpose(-1,-2)-vr@vi.transpose(-1,-2))/v.shape[-1]
+                # Signals have unit global RMS; this floor suppresses phase of numerical silence.
+                power=real.diagonal(dim1=-2,dim2=-1).clamp_min(1e-6)
+                denominator=(power[:,:,None]*power[:,None,:]).sqrt().clamp_min(1e-12)
+                real=(real/denominator).clamp(-.999,.999)
+                imaginary=(imaginary/denominator).clamp(-.999,.999)
+                real_parts.append(torch.atanh(real[:,index[0],index[1]]))
+                imaginary_parts.append(torch.atanh(imaginary[:,index[0],index[1]]))
+            return torch.cat(real_parts+imaginary_parts,-1)
+    finally:torch.backends.cuda.matmul.allow_tf32=previous_tf32

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S050/tangent_features.py

@@ -0,0 +1,25 @@
+"""Window-local log-correlation geometry in the existing spatial-filter basis."""
+import torch
+
+TANGENT_DIM=10*24*25//2
+
+@torch.no_grad()
+def extract_tangent_features(covariances,spatial_weight):
+    old_tf32=torch.backends.cuda.matmul.allow_tf32
+    torch.backends.cuda.matmul.allow_tf32=False
+    try:
+        with torch.autocast(covariances.device.type,enabled=False):
+            w=spatial_weight.float()
+            w=w/w.norm(dim=-1,keepdim=True).clamp_min(1e-6)
+            projected=torch.einsum('bfc,nbcd,bgd->nbfg',w,covariances.float(),w)
+            d=projected.diagonal(dim1=-2,dim2=-1).clamp_min(1e-10).sqrt()
+            correlation=(projected/(d.unsqueeze(-1)*d.unsqueeze(-2))).clamp(-1,1)
+            correlation=(correlation+correlation.transpose(-1,-2))/2
+            correlation=.95*correlation+.05*torch.eye(24,device=correlation.device)
+            values,vectors=torch.linalg.eigh(correlation)
+            logarithm=(vectors*values.clamp_min(1e-6).log().unsqueeze(-2))@vectors.transpose(-1,-2)
+            i,j=torch.triu_indices(24,24,device=correlation.device)
+            scale=torch.where(i==j,1.,2.**.5)
+            return (logarithm[...,i,j]*scale).flatten(1)
+    finally:
+        torch.backends.cuda.matmul.allow_tf32=old_tf32

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S091/student_features.py

@@ -0,0 +1,34 @@
+"""One audited CBraMod core with full-window shared and ordered representations."""
+import torch
+from torch import nn
+from braindecode.models import CBraMod
+from frequency_features import frequency_components,FREQUENCY_BANDS
+SPECS={'faced':(32,10,9),'tuev':(16,5,6),'ad65':(19,10,3),'openbmi':(62,4,2),'longitudinal':(58,2,2)}
+TRACKS=tuple(SPECS)
+
+class FrequencyAdapter(nn.Module):
+    def __init__(self,channels,virtual_channels=32):
+        super().__init__();self.signal=nn.Conv1d(channels,virtual_channels,1,bias=False)
+        self.frequency_weight=nn.Parameter(torch.zeros(len(FREQUENCY_BANDS),virtual_channels,channels))
+    def forward(self,x):
+        components=frequency_components(x)
+        correction=torch.einsum('blc,nbct->nlt',self.frequency_weight,components.to(self.frequency_weight.dtype))
+        return self.signal(x)+correction.to(x.dtype)
+
+class CBraModFeatures(nn.Module):
+    def __init__(self,virtual_channels=32,token_width=64,summary_width=256,head_width=128,dropout=.3):
+        super().__init__();self.virtual_channels=virtual_channels;self.output_dim=summary_width+virtual_channels*token_width*4
+        self.backbone=CBraMod(n_chans=virtual_channels,n_outputs=2,n_times=400,sfreq=200)
+        self.backbone.final_layer=nn.Identity()
+        self.token_projection=nn.Sequential(nn.LayerNorm(200),nn.Linear(200,token_width),nn.GELU())
+        self.summary_projection=nn.Sequential(nn.LayerNorm(virtual_channels*token_width*2),nn.Linear(virtual_channels*token_width*2,summary_width),nn.GELU(),nn.Dropout(dropout))
+    def forward(self,signal):
+        tokens=self.token_projection(self.backbone(signal))
+        moments=torch.cat([tokens.mean(2),tokens.float().std(2,correction=0).to(tokens.dtype)],-1).flatten(1)
+        summary=self.summary_projection(moments)
+        ordered=torch.nn.functional.adaptive_avg_pool1d(tokens.permute(0,1,3,2).flatten(1,2),4).flatten(1)
+        return torch.cat([summary,ordered],-1)
+
+    def named_shared_parameters(self):
+        for prefix,module in [('backbone',self.backbone),('token_projection',self.token_projection),('summary_projection',self.summary_projection)]:
+            for name,p in module.named_parameters():yield f'{prefix}.{name}',p

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S095/MODEL_CARD.md

@@ -0,0 +1,55 @@
+# Shared EEG decoder
+
+This package contains one coupled backbone used by all five tracks, with private input adapters and classifiers. Every trainable common component was trained with all five training splits. It is a single model; inference performs no parameter updates or dataset-level fitting.
+
+The common backbone combines waveform convolutions, an audited pretrained BIOT feature path, covariance and log-correlation encoders, and shared feature corrections. The waveform coactivation extension encodes 64 within-window log variances and 2,016 correlations between common waveform channels. A matched experimental control removes the correlation inputs. `config.json` identifies the packaged variant and its five fixed correction gains. All original backbone parameters are fixed during this extension's joint training.
+
+Weights originate only from the supplied training data and audited initialization checkpoints. Training details, parent provenance, raw/cache checks, shared-gradient checks and the five separate validation metrics are recorded in `training.json`. Validation selects checkpoints and fixed gains; it does not fit normalization or update parameters. Reported validation results belong to selected checkpoints, not to private test evaluation or five-seed means.
+
+Run `bash infer.sh INPUT_DIR OUTPUT_DIR`. The input manifest specifies a track and opaque sample IDs, shard paths and indices. Signals are in microvolts and are scaled by 1/100. The package writes one probability vector per input ID to `OUTPUT_DIR/predictions.jsonl`. It needs only the packaged files and the contract's installed Python dependencies, works after relocation, and does not need labels, subject IDs, training caches or network access.
+
+FACED and TUEV use window balanced accuracy. AD65 uses mean window probabilities per patient before balanced accuracy. OpenBMI averages within-subject balanced accuracy. Longitudinal averages within-session balanced accuracy within each subject, then over subjects. These five metrics remain separate.
+
+This checkpoint adds fixed within-window ordinal pattern probabilities, permutation entropy and statistical complexity at five delays. Training-only normalization and five input projections feed one common correction MLP trained by all five tracks. The common correction enters the existing covariance and fusion representations; fixed per-track gains select its strength. All earlier model tensors remain unchanged.
+
+Fixed real and imaginary spectral coherence between the inherited 24 spatial filters is normalized using training windows only and encoded by one new common network. Five private linear heads add class evidence. All five training splits update the new common encoder; all earlier model tensors remain unchanged.
+
+Fixed per-window relative band powers, envelope variability, envelope correlations and phase-amplitude coupling feed training-normalized private input projections and one new shared encoder. Five linear heads add class evidence. All five training splits update the new common encoder; all prior model tensors remain unchanged. AD65 updates use all windows from six class-balanced training patients; objective: mean-probability.
+
+The inherited OpenBMI phase-classifier projection was refreshed from a separately validated private-readout fit. Every common tensor and input adapter of this jointly trained model is unchanged. Exact ancestor checks confirm that all common tensors used by this inherited readout are identical; the new cross-frequency correction remains disabled for OpenBMI. No shared checkpoints or predictions are ensembled.
+
+A common CBraMod feature encoder, itself jointly trained on all five training splits, supplies its shared summary and tokens pooled into four ordered time bins. Its classifiers are discarded. A new common feature MLP and five zero-initialized private linear projections learn residual class evidence jointly on all five training splits. Training may randomly remove the earlier decoder logits for half of training groups, using one mask per AD patient and one per other window; labels and teacher targets remain unchanged. Validation and inference always retain the complete earlier decoder. The same new class projection also receives an auxiliary standalone classification loss when configured, with the same patient-pooled AD objective; this adds no inference module. The complete earlier decoder and feature extractor remain tensor-identical; selecting zero residual gain preserves its predictions exactly. The summary-only control masks the 8192 ordered features after training-fitted per-feature normalization. One feature backbone and one classifier per track run at inference, with no prediction ensemble or external runtime cache.
+
+The five outer private student-feature class projections are refined on all five training splits while every common/input/prior-head tensor remains exactly fixed. AD uses the exact patient-mean probability objective. Private checkpoints may be selected independently only because the common representation is identical; all five metrics are evaluated for every exported package.
+
+The longitudinal private outer projection is selected from another training-only readout fit using exactly the same common/input/prior-head tensors. All other private heads are retained. Exact equality of every common tensor and executable runtime is audited; this package has one common checkpoint and one head per track.
+
+The complete stronger parent is retained, including its common CBraMod feature encoder, common readout and all private classifiers. The CBraMod features and existing common128-dimensional embedding are computed once and reused. An additional common128-to256-to128 MLP with the configured GELU or identity activation and five zero-initialized private linear projections learn residual class evidence jointly on all five training splits. Training may randomly remove the earlier decoder logits for half of training groups, using one mask per AD patient and one per other window; labels and teacher targets remain unchanged. Validation and inference always retain the complete earlier decoder. The same new class projection also receives an auxiliary standalone classification loss when configured, with the same patient-pooled AD objective; this adds no inference module. The complete earlier decoder and feature extractor remain tensor-identical; selecting zero residual gain preserves its predictions exactly. One feature backbone and one classifier per track run at inference, with no prediction ensemble or external runtime cache.
+
+The five outer private student-feature class projections are refined on all five training splits while every common/input/prior-head tensor remains exactly fixed. AD uses the exact patient-mean probability objective. Private checkpoints may be selected independently only because the common representation is identical; all five metrics are evaluated for every exported package.
+
+The openbmi private outer projection is selected from another training-only readout fit using exactly the same common/input/prior-head tensors. All other private heads are retained. Exact equality of every common tensor and executable runtime is audited; this package has one common checkpoint and one head per track.
+
+A fixed two-second crop supplies spectral and cross-frequency coupling features, with no short-window envelope autocorrelation. The crop is configured as center or first. Each track has a training-fitted normalizer and linear input adapter to128 dimensions; one common128-to256-to128 GELU/LayerNorm block and five zero-initialized class projections are jointly trained on all five training splits. All complete-parent tensors are preserved exactly. Normalization statistics use training windows only. The parent is randomly removed from the training loss at the configured probability; it is always present at inference. AD uses mean patient probabilities over all sampled patient windows. Auxiliary classification uses the same class projection. There is one composed feature backbone and head per track; no prediction ensemble, external runtime cache, test adaptation, or evaluation-metadata features. Zero residual gain exactly retains the complete parent.
+
+The five outer private center-context class projections are refined on all five training splits while every common/input/prior-head tensor remains exactly fixed. AD uses the exact patient-mean probability objective. Private checkpoints may be selected independently only because the common representation is identical; all five metrics are evaluated for every exported package.
+
+The tuev private outer projection is selected from another training-only readout fit using exactly the same common/input/prior-head tensors. All other private heads are retained. Exact equality of every common tensor and executable runtime is audited; this package has one common checkpoint and one head per track.
+
+The faced private context input and outer class projections are selected from another training-only readout fit using exactly the same common/oldinput/normalizer/prior-head tensors. All other private input/head pairs are retained. Exact equality of every common tensor and executable runtime is audited; this package has one common checkpoint and one head per track.
+
+A fixed one-second crop supplies spectral and cross-frequency coupling features, with no short-window envelope autocorrelation. The crop is configured as center or first. Each track has a training-fitted normalizer and linear input adapter to128 dimensions; one common128-to256-to128 GELU/LayerNorm block and five zero-initialized class projections are jointly trained on all five training splits. All complete-parent tensors are preserved exactly. Normalization statistics use training windows only. The parent is randomly removed from the training loss at the configured probability; it is always present at inference. AD uses mean patient probabilities over all sampled patient windows. Auxiliary classification uses the same class projection. There is one composed feature backbone and head per track; no prediction ensemble, external runtime cache, test adaptation, or evaluation-metadata features. Zero residual gain exactly retains the complete parent.
+
+The five outer private short-context class projections are refined on all five training splits while every common/input/prior-head tensor remains exactly fixed. AD uses the exact patient-mean probability objective. Private checkpoints may be selected independently only because the common representation is identical; all five metrics are evaluated for every exported package.
+
+The tuev private outer projection is selected from another training-only readout fit using exactly the same common/input/prior-head tensors. All other private heads are retained. Exact equality of every common tensor and executable runtime is audited; this package has one common checkpoint and one head per track.
+
+An additional common context block preserves every existing short-context parent tensor and correction. A fixed one-second crop supplies spectral and cross-frequency coupling features, with no short-window envelope autocorrelation. TUEV concatenates first and center one-second descriptors; the parameter-matched control repeats first descriptors. Other tracks use first one-second descriptors. Each track has a training-fitted normalizer and linear input adapter to128 dimensions; one common128-to256-to128 GELU/LayerNorm block and five zero-initialized class projections are jointly trained on all five training splits. All complete-parent tensors are preserved exactly. Normalization statistics use training windows only. The parent is randomly removed from the training loss at the configured probability; it is always present at inference. AD uses mean patient probabilities over all sampled patient windows. Auxiliary classification uses the same class projection. There is one composed feature backbone and head per track; no prediction ensemble, external runtime cache, test adaptation, or evaluation-metadata features. Zero residual gain exactly retains the complete parent.
+
+An additional shared128-to256-to128 GELU/LayerNorm block is jointly trained by all five tracks. OpenBMI uses seven native-channel trace-normalized covariance matrices with fixed5percent identity shrinkage: matrix-log upper triangles in the main arm, covariance-minus-identity in the matched control. Other four new inputs are first-one-second spectral/coupling descriptors. Every complete-parent tensor is preserved exactly. Input normalization uses training windows only; zero outer gain retains the parent. Five private adapters and class projections, one composed shared backbone, no evaluation adaptation or external runtime cache.
+
+The five outer private geometry-block input adapters and class projections are refined on all five training splits. Every common backbone, fitted normalizer and earlier input/prior-head tensor remains exactly fixed. Common parameters are excluded from optimization; differentiable evaluation of the fixed common block permits gradients to the private input maps. AD uses the exact patient-mean probability objective. Private input/head checkpoints may be selected independently only because the common parameter state is identical and every track uses its own private adapter/head; all five metrics are evaluated for every exported package.
+
+An additional shared128-to256-to128 GELU/LayerNorm block is jointly trained by all five tracks. FACED uses seven native-channel trace-normalized covariance matrices with fixed5percent identity shrinkage: matrix-log upper triangles in the main arm, covariance-minus-identity in the matched control. Other four new inputs are first-one-second spectral/coupling descriptors. Every complete-parent tensor is preserved exactly. Input normalization uses training windows only; zero outer gain retains the parent. Five private adapters and class projections, one composed shared backbone, no evaluation adaptation or external runtime cache.
+
+The longitudinal private context input and outer class projections are selected from another training-only readout fit using exactly the same common/oldinput/normalizer/prior-head tensors. All other private input/head pairs are retained. Exact equality of every common tensor and executable runtime is audited; this package has one common checkpoint and one head per track.

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S095/alpha_features.py

@@ -0,0 +1,45 @@
+"""Fixed alpha prominence features from each individual two-second window."""
+import torch
+
+REGION_NAMES=('occipital','parietal','central','frontal','left_posterior','right_posterior')
+REGIONS=(('PO7','PO5','PO3','POZ','PO4','PO6','PO8','O1','OZ','O2'),
+         ('P7','P5','P3','P1','PZ','P2','P4','P6','P8'),
+         ('FC5','FC3','FC1','FCZ','FC2','FC4','FC6','C5','C3','C1','CZ','C2','C4','C6'),
+         ('FP1','FPZ','FP2','AF3','AF4','F7','F5','F3','F1','FZ','F2','F4','F6','F8'),
+         ('P7','P5','P3','P1','PO7','PO5','PO3','O1'),
+         ('P2','P4','P6','P8','PO4','PO6','PO8','O2'))
+ALPHA_DIM=58*7+6*22
+
+def region_matrix(channels):
+    result=torch.zeros(len(REGIONS),len(channels))
+    for i,names in enumerate(REGIONS):
+        for name in names:result[i,channels.index(name)]=1/len(names)
+    return result.tolist()
+
+@torch.no_grad()
+def extract_alpha_features(signal,regions):
+    with torch.autocast(signal.device.type,enabled=False):
+        x=signal.float();x=x-x.mean(-1,keepdim=True)
+        spectrum=torch.fft.rfft(x*torch.hann_window(400,device=x.device),dim=-1)/200
+        power=spectrum.abs().square().clamp_min(1e-12)
+        lp=power.log()
+        # Geometric regional means resist domination by one high-amplitude channel.
+        regional=torch.einsum('rc,ncf->nrf',regions.float(),lp)
+        lp=torch.cat([lp,regional],1)
+        low=lp[...,8:13].mean(-1,keepdim=True)
+        high=lp[...,36:49].mean(-1,keepdim=True)
+        freq=torch.arange(7.,14.5,.5,device=x.device)
+        alpha=lp[...,14:29]
+        baseline=low+(high-low)*((freq-5)/16)
+        prominence=alpha-baseline
+        normalized=alpha.softmax(-1)
+        center=(normalized*freq).sum(-1)
+        entropy=-(normalized*normalized.clamp_min(1e-12).log()).sum(-1)
+        alpha_mean=torch.logsumexp(alpha,-1)-torch.log(torch.tensor(15.,device=x.device))
+        def log_band(lo,hi):
+            return torch.logsumexp(lp[...,lo:hi],-1)-torch.log(torch.tensor(float(hi-lo),device=x.device))
+        summaries=torch.stack([alpha_mean-log_band(2,81),alpha_mean-log_band(8,14),
+                               alpha_mean-log_band(30,61),prominence.max(-1).values,
+                               prominence.mean(-1),center,entropy],-1)
+        return torch.cat([summaries[:,:58].flatten(1),summaries[:,58:].flatten(1),
+                          prominence[:,58:].flatten(1)],1)

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S095/config.json

@@ -0,0 +1,1033 @@
+{
+  "schema": "bci-shared-biot-covariance-bf16head-v1",
+  "architecture": {
+    "channels": 32,
+    "width": 128,
+    "depth": 2,
+    "dropout": 0.5,
+    "wave_filters": 32,
+    "head_width": 64,
+    "head_dropout": 0.3,
+    "clinical_asymmetry": true,
+    "alpha_regions": [
+      [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612,
+        0.10000000149011612
+      ],
+      [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.1111111119389534,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+      ],
+      [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+      ],
+      [
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0714285746216774,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+      ],
+      [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.125,
+        0.125,
+        0.125,
+        0.125,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.125,
+        0.125,
+        0.125,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.125,
+        0.0,
+        0.0
+      ],
+      [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.125,
+        0.125,
+        0.125,
+        0.125,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.125,
+        0.125,
+        0.125,
+        0.0,
+        0.0,
+        0.125
+      ]
+    ],
+    "clinical_envelope": true,
+    "temporal_pool": true,
+    "temporal_evidence": true,
+    "evidence_temperature": 0.25,
+    "random_fourier": true,
+    "fourier_features": 256,
+    "tangent_readout": true,
+    "shared_context_correction": true,
+    "wave_coactivation": true,
+    "ordinal_context": true,
+    "phase_coherence": true,
+    "cross_frequency": true,
+    "cbramod_feature_transfer": true,
+    "student_architecture": {
+      "virtual_channels": 32,
+      "token_width": 64,
+      "summary_width": 256,
+      "head_width": 128,
+      "dropout": 0.3
+    },
+    "student_extension": true,
+    "post_embedding_extension": true,
+    "extension_activation": "identity",
+    "center_context_extension": true,
+    "context_crop": "center",
+    "short_context_extension": true,
+    "short_crop": "first",
+    "dual_context_extension": true,
+    "dual_crop": "clinical-dual",
+    "geometry_extension": true,
+    "geometry_mode": "mi-linear",
+    "emotion_extension": true,
+    "emotion_mode": "faced-log"
+  },
+  "initialization": {
+    "initialization": "continue joint training from a complete shared-model checkpoint",
+    "parent": "/workspace/research/runs/hybrid_biot_fusionshrink0100/epoch-001/weights.pt",
+    "parent_initialization": {
+      "initialization": "jointly trained parent plus audited pretrained BIOT in a single coupled backbone",
+      "parent": "/workspace/research/runs/hybrid_wide1/epoch-004/weights.pt",
+      "audited_biot": {
+        "initialization": "pretrained",
+        "repo": "braindecode/biot-pretrained-six-datasets-18chs",
+        "revision": "7e9aabf302870012f73773276803aab97993951c",
+        "sha256": "a218f4054ae29962c6fd9d23d94c446d354c4f53e805a6771c85749ccc20baaa",
+        "license": "BSD-3-Clause",
+        "source_braindecode": "1.3.0",
+        "loaded_core_tensors": 56,
+        "skipped_classifier_tensors": 3
+      },
+      "neural_channel_indices": {
+        "faced": [
+          0,
+          1,
+          2,
+          3,
+          4,
+          5,
+          6,
+          7,
+          8,
+          9,
+          10,
+          11,
+          12,
+          13,
+          14,
+          15,
+          16,
+          17
+        ],
+        "tuev": [
+          0,
+          1,
+          2,
+          3,
+          4,
+          5,
+          6,
+          7,
+          8,
+          9,
+          10,
+          11,
+          12,
+          13,
+          14,
+          15
+        ],
+        "ad65": [
+          0,
+          1,
+          2,
+          3,
+          4,
+          5,
+          6,
+          7,
+          8,
+          9,
+          10,
+          11,
+          12,
+          13,
+          14,
+          15,
+          16,
+          17
+        ],
+        "openbmi": [
+          0,
+          1,
+          3,
+          5,
+          12,
+          14,
+          23,
+          25,
+          28,
+          30,
+          2,
+          6,
+          11,
+          15,
+          22,
+          26,
+          4,
+          13
+        ],
+        "longitudinal": [
+          0,
+          2,
+          7,
+          11,
+          23,
+          27,
+          41,
+          45,
+          55,
+          57,
+          5,
+          13,
+          21,
+          29,
+          39,
+          47,
+          9,
+          25
+        ]
+      },
+      "rebase": "incumbent waveform, fusion and four heads; joint BIOT and TUEV head from readout epoch 9; zero initialized neural interaction",
+      "jointly_finetuned_neural_parent": "/workspace/research/runs/hybrid_biot_readout1/epoch-009/weights.pt",
+      "tuev_high_dimensional_spectral_projection_disabled": true,
+      "frozen_prior_representation": true,
+      "nonclinical_head_updates": "only the 256 added BIOT columns; original columns and biases fixed",
+      "faced_openbmi_neural_path": "common nonlinear fusion only; direct head columns masked",
+      "retention_teacher": "/workspace/research/runs/hybrid_wide1/epoch-004",
+      "retention_loss": {
+        "split": "train",
+        "temperature": 2.0,
+        "kl_weight": 1.0,
+        "tracks": [
+          "faced",
+          "ad65",
+          "openbmi",
+          "longitudinal"
+        ]
+      }
+    },
+    "joint_covariance_parent": "/workspace/research/runs/filterbank1/epoch-002/weights.pt",
+    "frozen_prior_representation": true,
+    "head_updates": "one linear head per track; added 128 shared covariance columns trainable; AD head initialized from the joint covariance parent",
+    "new_shared_representation": "one common covariance MLP inside the coupled backbone, trained by all five tasks; fixed spatial input adapters from the same jointly trained source",
+    "head_arithmetic": "one linear head evaluated in fixed feature blocks to preserve original bfloat16 accumulation dimensions",
+    "retention_teacher": {
+      "ad65": "/workspace/research/runs/filterbank1/epoch-002/weights.pt",
+      "other_tracks": "/workspace/research/runs/hybrid_wide1/epoch-004"
+    },
+    "retention_loss": {
+      "split": "train",
+      "temperature": 2.0,
+      "kl_weight": 1.0,
+      "tracks": [
+        "faced",
+        "ad65",
+        "openbmi",
+        "longitudinal"
+      ]
+    }
+  },
+  "loader_scale": 0.01,
+  "tracks": [
+    "faced",
+    "tuev",
+    "ad65",
+    "openbmi",
+    "longitudinal"
+  ],
+  "head_arithmetic": "same linear head in fixed feature blocks, sum in bfloat16 on GPU for non-AD tracks; AD remains float32",
+  "head_refinement": {
+    "source": "/workspace/research/runs/residual_heads1/epoch-018",
+    "source_weights_sha256": "e0b609137391b47f99438ccadaa22fc0bc10d2c10ead0ddf0a3c25be2c907c85",
+    "shared_backbone_unchanged": true,
+    "selected_head_epochs": {
+      "faced": 5,
+      "tuev": 24,
+      "ad65": 0,
+      "openbmi": 0,
+      "longitudinal": 0
+    }
+  },
+  "clinical_spectral_refinement": {
+    "source": "/workspace/research/runs/head_dropout1/epoch-016",
+    "zero_initialized_input_adapter": true,
+    "backbone_unchanged": true
+  },
+  "head_estimation": {
+    "method": "class/subject-balanced Gaussian linear discriminant; covariance augmented by class-conditional subject/session mean variation before shrinkage",
+    "selected_shrinkage": {
+      "faced": "inherited",
+      "tuev": "inherited",
+      "ad65": "inherited",
+      "openbmi": "inherited",
+      "longitudinal": {
+        "shrinkage": 0.9,
+        "domain_strength": 1.0
+      }
+    },
+    "feature_scaling": "fitted on training data and folded into linear coefficients"
+  },
+  "clinical_asymmetry_refinement": {
+    "source": "/workspace/research/runs/domain_lda_heads1/epoch-001",
+    "zero_initialized_input_adapter": true,
+    "backbone_unchanged": true
+  },
+  "alpha_rhythm_adapter": {
+    "training_normalization_only": true,
+    "regions": [
+      "occipital",
+      "parietal",
+      "central",
+      "frontal",
+      "left_posterior",
+      "right_posterior"
+    ],
+    "features": "per-window alpha prominence relative to adjacent frequencies",
+    "shared_backbone_unchanged": true
+  },
+  "clinical_envelope_refinement": {
+    "source": "/workspace/research/runs/clinical_asymmetry1/epoch-001",
+    "zero_initialized_input_adapter": true,
+    "backbone_unchanged": true
+  },
+  "independent_adapter_merge": {
+    "alpha_source": "/workspace/research/runs/alpha_calibrated1/epoch-003",
+    "clinical_source": "/workspace/research/runs/clinical_envelope1/epoch-002",
+    "all_shared_and_head_tensors_equal": true
+  },
+  "frequency_montage_refinement": {
+    "six_frequency_bands": true,
+    "shared_encoder_unchanged": true,
+    "selected_track_epochs": {
+      "faced": 0,
+      "tuev": 1,
+      "ad65": 0,
+      "openbmi": 10,
+      "longitudinal": 2
+    }
+  },
+  "private_track_selection": {
+    "track": "tuev",
+    "donor": "/workspace/research/replicates/frequency_head_control1/epoch-001"
+  },
+  "clinical_sampling_refinement": {
+    "subject_exponent": 0.0,
+    "class_balanced": true,
+    "only_clinical_head_updated": true
+  },
+  "temporal_statistics_readout": {
+    "statistics": [
+      "mean",
+      "standard deviation",
+      "maximum",
+      "minimum"
+    ],
+    "shared_encoder_and_prior_heads_unchanged": true,
+    "selected_pool_epochs": {
+      "faced": 2,
+      "tuev": 0,
+      "ad65": 0,
+      "openbmi": 33,
+      "longitudinal": 3
+    }
+  },
+  "temporal_evidence_readout": {
+    "pooling": "log mean exp of per-patch class evidence",
+    "temperature": 0.25,
+    "shared_encoder_and_prior_heads_unchanged": true,
+    "selected_evidence_epochs": {
+      "faced": 0,
+      "tuev": 0,
+      "ad65": 0,
+      "openbmi": 6,
+      "longitudinal": 8
+    }
+  },
+  "fourier_basis_readout": {
+    "basis_functions": 256,
+    "bandwidths": [
+      0.5,
+      1.0,
+      2.0
+    ],
+    "shared_encoder_and_prior_heads_unchanged": true,
+    "selected_fourier_epochs": {
+      "faced": 21,
+      "tuev": 0,
+      "ad65": 0,
+      "openbmi": 0,
+      "longitudinal": 2
+    }
+  },
+  "shared_tangent_readout": {
+    "one_shared_tangent_module": true,
+    "original_and_corrected_shared_features_retained": true,
+    "private_head_scalar_gains": {
+      "faced": 0.0,
+      "tuev": 0.1,
+      "ad65": 0.0,
+      "openbmi": 1.0,
+      "longitudinal": 0.1
+    },
+    "all_common_weights_fixed_during_gain_refinement": true
+  },
+  "shared_context_correction": {
+    "source": "/workspace/research/runs/hybrid_codebrain_fusion_control1/epoch-002",
+    "zero_token_input_preserved": true,
+    "unused_native_CodeBrain_core_and_input_parameters_removed": true,
+    "common_patch_and_context_layers_retained": true,
+    "same_one_shared_backbone_and_five_classifiers": true
+  },
+  "shared_wave_coactivation": {
+    "channels": 64,
+    "statistics": 2080,
+    "diagonal_only_control": false,
+    "common_encoder_updated_by_all_five_tracks": true,
+    "gain_selection_within_one_common_checkpoint": {
+      "faced": 1.0,
+      "tuev": 0.0,
+      "ad65": 0.0,
+      "openbmi": 1.0,
+      "longitudinal": 1.0
+    }
+  },
+  "fixed_common_coactivation_readouts": {
+    "all_common_weights_exactly_unchanged": true,
+    "selected_private_epochs": {
+      "faced": 25,
+      "tuev": 0,
+      "ad65": 0,
+      "openbmi": 8,
+      "longitudinal": 2
+    }
+  },
+  "shared_ordinal_context": {
+    "per_channel_features": 130,
+    "normalization_fit_on_training_only": true,
+    "entropy_only_control": false,
+    "new_common_layers_trained_on_all_five_tracks": true,
+    "gains_within_one_common_checkpoint": {
+      "faced": 1.0,
+      "tuev": 0.0,
+      "ad65": 0.0,
+      "openbmi": 1.0,
+      "longitudinal": 0.0
+    }
+  },
+  "shared_complex_coherence": {
+    "features": 5520,
+    "normalization_fit_on_training_only": true,
+    "real_only_control": false,
+    "all_five_tracks_update_common_encoder": true,
+    "gains_within_one_common_checkpoint": {
+      "faced": 0.1,
+      "tuev": 1.0,
+      "ad65": 0.0,
+      "openbmi": 0.3,
+      "longitudinal": 0.0
+    }
+  },
+  "shared_cross_frequency": {
+    "per_channel_features": 30,
+    "normalization_fit_on_training_only": true,
+    "amplitude_only_control": false,
+    "all_five_tracks_update_common_encoder": true,
+    "gains_within_one_common_checkpoint": {
+      "faced": 0.3,
+      "tuev": 0.1,
+      "ad65": 0.0,
+      "openbmi": 0.0,
+      "longitudinal": 0.0
+    }
+  },
+  "inherited_private_readout_refresh": {
+    "child": "/workspace/research/runs/coupling_patient1/epoch-007",
+    "donor": "/workspace/research/runs/phase_readout1/epoch-016",
+    "common_ancestor": "/workspace/research/runs/phase_coherence1/epoch-007",
+    "source_weight_sha256": {
+      "child": "f8466c6838ff01e54d1619f55668c1a4af26a243b54c67a956a3422d9604632a",
+      "donor": "ffd219d1480b81720b5aa4014cb2ed2b9cd31b7c92ad4717abc2a9625d30dc1a",
+      "ancestor": "e53ede409be523d8f4746251f7d46353621049f0378a88d4f7cfe2ba71a57d95"
+    },
+    "changed_private_tensors": [
+      "heads.openbmi.prior.projection.weight",
+      "heads.openbmi.prior.projection.bias"
+    ],
+    "all_ancestor_common_and_input_tensors_equal": true,
+    "all_child_common_and_input_tensors_unchanged": true,
+    "new_branch_gain_for_refreshed_track": 0.0,
+    "donor_changes_only_requested_private_projection": true,
+    "prediction_sources": {
+      "faced": {
+        "source": "/workspace/research/runs/coupling_patient1/faced-e007.npy",
+        "sha256": "68b617c8ecf347c79b23c20b70f6b4981c2f26b7b8c71dc7cd5fa0d7f4285ea6"
+      },
+      "tuev": {
+        "source": "/workspace/research/runs/coupling_patient1/tuev-e007.npy",
+        "sha256": "9f0743450f1104d395e9daac0600aaf20755b5590b9f5c3e3137732c479dd647"
+      },
+      "ad65": {
+        "source": "/workspace/research/runs/coupling_patient1/ad65-e007.npy",
+        "sha256": "7a3b74c58580b7ba952322bf6d527ebc9a40a6e04c7cdbbaea14e84a7a298d54"
+      },
+      "openbmi": {
+        "source": "/workspace/research/runs/phase_readout1/openbmi-e016.npy",
+        "sha256": "14f630543e1df88c88d8f3e0effb4506ba33f533b3484de3c7aaf6554507341c"
+      },
+      "longitudinal": {
+        "source": "/workspace/research/runs/coupling_patient1/longitudinal-e007.npy",
+        "sha256": "e93b0a319eaa3ddca9d71b0e786b808ba1630c1d3ce125f8e08036e517741b74"
+      }
+    }
+  },
+  "shared_student_residual": {
+    "dimension": 8448,
+    "summary_dimension": 256,
+    "ordered_time_bins": 4,
+    "student_weights_sha256": "19dfe912cceed997699b5b526c8c2ece55945c5f9bd111c4c13704d0fc460e8b",
+    "summary_only_control": false,
+    "normalization_fit_on_training_only": true,
+    "new_common_readout_jointly_trained_on_all_five": true,
+    "class_projection_initialized_zero": true,
+    "student_classifiers_discarded": true,
+    "gains_within_one_common_checkpoint": {
+      "faced": 1.0,
+      "tuev": 0.0,
+      "ad65": 0.0,
+      "openbmi": 1.0,
+      "longitudinal": 1.0
+    },
+    "zero_gain_preserves_complete_parent": true
+  },
+  "private_head_refresh": {
+    "reference": "/workspace/research/pilots/short_private_pilot1/epoch-001",
+    "donor": "/workspace/research/runs/short_private1/epoch-030",
+    "track": "tuev",
+    "source_sha256": {
+      "reference": "afacd7c1a4f7329c01148a17a7a2ca15b8db3c6b38446f40fb92e58eb7d64ef2",
+      "donor": "f060de6d5bf41c8e332ae8cc58cdf7dfa5ea98ab610ef2b4fdc3d00eae4929e6"
+    },
+    "changed_private_tensors": [
+      "heads.tuev.correction_gain",
+      "heads.tuev.projection.weight",
+      "heads.tuev.projection.bias"
+    ],
+    "all_common_input_and_prior_head_tensors_exactly_equal": true,
+    "executable_code_and_architecture_exactly_equal": true,
+    "one_common_checkpoint": true,
+    "prediction_sources": {
+      "faced": "/workspace/research/pilots/short_private_pilot1/faced-e001.npy",
+      "tuev": "/workspace/research/runs/short_private1/tuev-e030.npy",
+      "ad65": "/workspace/research/pilots/short_private_pilot1/ad65-e001.npy",
+      "openbmi": "/workspace/research/pilots/short_private_pilot1/openbmi-e001.npy",
+      "longitudinal": "/workspace/research/pilots/short_private_pilot1/longitudinal-e001.npy"
+    }
+  },
+  "shared_student_extension_residual": {
+    "dimension": 128,
+    "input_representation": "fixed jointly trained common student embedding",
+    "activation": "identity",
+    "student_weights_sha256": "19dfe912cceed997699b5b526c8c2ece55945c5f9bd111c4c13704d0fc460e8b",
+    "summary_only_control": false,
+    "normalization_fit_on_training_only": true,
+    "new_common_readout_jointly_trained_on_all_five": true,
+    "class_projection_initialized_zero": true,
+    "student_classifiers_discarded": true,
+    "gains_within_one_common_checkpoint": {
+      "faced": 0.30000001192092896,
+      "tuev": 0.0,
+      "ad65": 0.0,
+      "openbmi": 0.30000001192092896,
+      "longitudinal": 0.30000001192092896
+    },
+    "zero_gain_preserves_complete_parent": true
+  },
+  "fixed_common_embedding_readouts": {
+    "all_common_weights_exactly_unchanged": true,
+    "selected_private_epochs": {
+      "faced": 0,
+      "tuev": 0,
+      "ad65": 0,
+      "openbmi": 0,
+      "longitudinal": 1
+    }
+  },
+  "shared_center_context_residual": {
+    "dimension": 128,
+    "crop": "center",
+    "samples": 400,
+    "sample_rate": 200,
+    "feature_types": [
+      "spectral",
+      "cross_frequency_coupling"
+    ],
+    "normalization_fit_on_training_only": true,
+    "new_common_block_jointly_trained_on_all_five": true,
+    "class_projection_initialized_zero": true,
+    "gains_within_one_common_checkpoint": {
+      "faced": 0.10000000149011612,
+      "tuev": 0.30000001192092896,
+      "ad65": 0.0,
+      "openbmi": 0.0,
+      "longitudinal": 1.0
+    },
+    "zero_gain_preserves_complete_parent": true
+  },
+  "private_input_head_refresh": {
+    "reference": "/workspace/research/sources/emotion_private_input_source",
+    "donor": "/workspace/research/runs/emotion_input_adapt_full1/epoch-001",
+    "track": "longitudinal",
+    "source_sha256": {
+      "reference": "90904cd9b33d59b1ed531dcda90f95b9dce3e59661187242b6b26a06daba6d04",
+      "donor": "b4a858b3dab746a6600aa1fd194e7cc42b277fd11eb07d8561aff980f3a26b9d"
+    },
+    "changed_private_tensors": [
+      "adapters.longitudinal.emotion_projection.weight",
+      "adapters.longitudinal.emotion_projection.bias",
+      "heads.longitudinal.correction_gain",
+      "heads.longitudinal.projection.weight",
+      "heads.longitudinal.projection.bias"
+    ],
+    "all_common_old_input_normalizer_and_prior_head_tensors_exactly_equal": true,
+    "executable_code_and_architecture_exactly_equal": true,
+    "one_common_checkpoint": true,
+    "prediction_sources": {
+      "faced": "/workspace/research/runs/emotion_geometry_log_full1/faced-e002.npy",
+      "tuev": "/workspace/research/runs/emotion_geometry_log_full1/tuev-e002.npy",
+      "ad65": "/workspace/research/runs/emotion_geometry_log_full1/ad65-e002.npy",
+      "openbmi": "/workspace/research/runs/emotion_geometry_log_full1/openbmi-e002.npy",
+      "longitudinal": "/workspace/research/runs/emotion_input_adapt_full1/longitudinal-e001.npy"
+    }
+  },
+  "shared_short_context_residual": {
+    "dimension": 128,
+    "crop": "first",
+    "samples": 200,
+    "sample_rate": 200,
+    "feature_types": [
+      "spectral",
+      "cross_frequency_coupling"
+    ],
+    "normalization_fit_on_training_only": true,
+    "new_common_block_jointly_trained_on_all_five": true,
+    "class_projection_initialized_zero": true,
+    "gains_within_one_common_checkpoint": {
+      "faced": 0.30000001192092896,
+      "tuev": 0.30000001192092896,
+      "ad65": 0.0,
+      "openbmi": 0.0,
+      "longitudinal": 0.30000001192092896
+    },
+    "zero_gain_preserves_complete_parent": true
+  },
+  "shared_dual_context_residual": {
+    "dimension": 128,
+    "crop": "clinical-dual",
+    "samples_per_view": 200,
+    "clinical_views": 2,
+    "other_track_views": 1,
+    "sample_rate": 200,
+    "feature_types": [
+      "spectral",
+      "cross_frequency_coupling"
+    ],
+    "normalization_fit_on_training_only": true,
+    "new_common_block_jointly_trained_on_all_five": true,
+    "class_projection_initialized_zero": true,
+    "gains_within_one_common_checkpoint": {
+      "faced": 0.0,
+      "tuev": 0.0,
+      "ad65": 0.0,
+      "openbmi": 0.0,
+      "longitudinal": 1.0
+    },
+    "zero_gain_preserves_complete_parent": true
+  },
+  "shared_geometry_residual": {
+    "dimension": 128,
+    "mode": "mi-linear",
+    "motor_native_covariance_bands": 7,
+    "motor_channels": 62,
+    "motor_input_dimension": 13671,
+    "motor_covariance_shrinkage": 0.05,
+    "other_track_crop": "first1s",
+    "normalization_fit_on_training_only": true,
+    "new_common_block_jointly_trained_on_all_five": true,
+    "class_projection_initialized_zero": true,
+    "gains_within_one_common_checkpoint": {
+      "faced": 0.0,
+      "tuev": 0.0,
+      "ad65": 0.0,
+      "openbmi": 0.0,
+      "longitudinal": 1.0
+    },
+    "zero_gain_preserves_complete_parent": true
+  },
+  "fixed_common_geometry_input_readouts": {
+    "all_common_weights_exactly_unchanged": true,
+    "input_lr": 1e-05,
+    "class_projection_lr": 0.0003,
+    "selected_private_epochs": {
+      "faced": 0,
+      "tuev": 0,
+      "ad65": 0,
+      "openbmi": 0,
+      "longitudinal": 1
+    },
+    "separate_input_and_class_gradient_clipping": 1.0
+  },
+  "shared_emotion_residual": {
+    "dimension": 128,
+    "mode": "faced-log",
+    "emotion_native_covariance_bands": 7,
+    "emotion_channels": 32,
+    "emotion_input_dimension": 3696,
+    "emotion_covariance_shrinkage": 0.05,
+    "other_track_crop": "first1s",
+    "normalization_fit_on_training_only": true,
+    "new_common_block_jointly_trained_on_all_five": true,
+    "class_projection_initialized_zero": true,
+    "gains_within_one_common_checkpoint": {
+      "faced": 0.0,
+      "tuev": 0.0,
+      "ad65": 0.0,
+      "openbmi": 1.0,
+      "longitudinal": 0.10000000149011612
+    },
+    "zero_gain_preserves_complete_parent": true
+  }
+}

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S095/coupling_features.py

@@ -0,0 +1,34 @@
+"""Per-window cross-frequency envelope and phase-amplitude relationships."""
+import torch
+BANDS=((1,4),(4,8),(8,13),(13,30),(30,48))
+PAIRS=tuple((i,j) for i in range(5) for j in range(i+1,5))
+PER_CHANNEL=30
+
+@torch.no_grad()
+def coupling_features(track,x):
+    with torch.autocast(x.device.type,enabled=False):
+        x=torch.nan_to_num(x.float().flatten(2),nan=0.,posinf=1e5,neginf=-1e5).clamp(-1e5,1e5)
+        x=x-x.mean(-1,keepdim=True)
+        if track in ('ad65','openbmi','longitudinal'):x=x-x.mean(1,keepdim=True)
+        x=x/x.square().mean(-1,keepdim=True).clamp_min(1e-12).sqrt()
+        n=x.shape[-1];spectrum=torch.fft.fft(x,dim=-1);frequency=torch.fft.fftfreq(n,1/200,device=x.device)
+        masks=torch.stack([2*((frequency>=lo)&(frequency<hi)).float() for lo,hi in BANDS])
+        analytic=torch.fft.ifft(spectrum[:,:,None]*masks[None,None],dim=-1)
+        amplitude=analytic.abs();unit_phase=analytic/amplitude.clamp_min(1e-3)
+        weight=torch.hann_window(n,device=x.device);weight=weight/weight.sum()
+        mean=(amplitude*weight).sum(-1);centered=amplitude-mean[...,None]
+        variance=(centered.square()*weight).sum(-1)
+        # Relative and absolute floors avoid normalizing numerical silence after unit-RMS scaling.
+        deviation=variance.sqrt()
+        stable_deviation=torch.maximum(deviation,.01*mean).clamp_min(1e-3)
+        normalized=centered/stable_deviation[...,None]
+        power=(amplitude.square()*weight).sum(-1)
+        relative=(power/power.sum(-1,keepdim=True).clamp_min(1e-12)).clamp_min(1e-6).log()
+        cv=(deviation/mean.clamp_min(1e-3)).clamp(0,10)
+        envelopes=[];phase_amplitude=[]
+        for low,high in PAIRS:
+            correlation=(normalized[:,:,low]*normalized[:,:,high]*weight).sum(-1)
+            envelopes.append(torch.atanh(correlation.clamp(-.999,.999)))
+            coupling=(normalized[:,:,high]*unit_phase[:,:,low]*weight).sum(-1).abs()
+            phase_amplitude.append(coupling.clamp(0,.999))
+        return torch.cat([relative,cv,torch.stack(envelopes,-1),torch.stack(phase_amplitude,-1)],-1).flatten(1)

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S095/covariances.py

@@ -0,0 +1,25 @@
+"""Shared neural filter-bank covariance decoder with spatial input adapters."""
+import torch
+from torch import nn
+SPECS={'faced':(32,10,9),'tuev':(16,5,6),'ad65':(19,10,3),'openbmi':(62,4,2),'longitudinal':(58,2,2)}
+TRACKS=tuple(SPECS)
+BANDS=[(1,4),(4,8),(8,12),(12,16),(16,20),(20,24),(24,28),(28,32),(32,40),(40,48)]
+
+@torch.no_grad()
+def extract_covariances(t,x):
+    x=torch.nan_to_num(x.float().flatten(2)).clamp(-1e5,1e5)
+    x=x-x.mean(-1,keepdim=True)
+    if t in ('faced','tuev'):
+        x=x/x.square().mean(-1,keepdim=True).clamp_min(1e-12).sqrt()
+    else:x=x-x.mean(1,keepdim=True)
+    # Per-window normalization, preserving spatial and frequency power ratios.
+    x=x/x.square().mean((1,2),keepdim=True).clamp_min(1e-12).sqrt()
+    frames=x.unfold(-1,400,200);frames=frames-frames.mean(-1,keepdim=True)
+    z=torch.fft.rfft(frames*torch.hann_window(400,device=x.device),dim=-1)/200
+    covs=[]
+    for lo,hi in BANDS:
+        v=z[...,2*lo:2*hi].flatten(2)
+        cov=(v.real@v.real.transpose(-1,-2)+v.imag@v.imag.transpose(-1,-2))/v.shape[-1]
+        covs.append(cov)
+    return torch.stack(covs,1)
+

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S095/data.py

@@ -0,0 +1,33 @@
+import json
+from functools import lru_cache
+from pathlib import Path
+import numpy as np
+import torch
+@lru_cache(maxsize=8)
+def array(path):return np.load(path,mmap_mode='r',allow_pickle=False)
+class Dataset(torch.utils.data.Dataset):
+    def __init__(self,root,limit=None,cache=False):
+        self.root=Path(root)
+        self.rows=json.loads((self.root/'manifest.json').read_text())['samples']
+        if limit:self.rows=self.rows[:limit]
+        self.cached=None
+        if cache and self.rows:
+            from collections import defaultdict
+            groups=defaultdict(list)
+            for i,r in enumerate(self.rows):groups[r['path']].append((i,r['index']))
+            for name,indices in groups.items():
+                path=(self.root/name).resolve()
+                if not path.is_relative_to(self.root.resolve()):raise ValueError('input outside root')
+                source=np.load(path,mmap_mode='r',allow_pickle=False)
+                if self.cached is None:self.cached=torch.empty((len(self.rows),*source.shape[1:]),dtype=torch.float32)
+                dst,src=zip(*indices)
+                self.cached[list(dst)]=torch.as_tensor(source[list(src)]/100,dtype=torch.float32)
+    def __len__(self):return len(self.rows)
+    def __getitem__(self,i):
+        r=self.rows[i]
+        if self.cached is not None:return self.cached[i],r.get('label',-1),r['id']
+        p=(self.root/r['path']).resolve()
+        if not p.is_relative_to(self.root.resolve()):raise ValueError('input outside root')
+        # Division precedes float32 conversion, matching official FACED/TUEV collate.
+        x=np.asarray(array(str(p))[r['index']])/100
+        return torch.as_tensor(x,dtype=torch.float32),r.get('label',-1),r['id']

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S095/envelope_features.py

@@ -0,0 +1,44 @@
+"""Per-window burst-envelope regularity and bilateral synchrony, without fitting."""
+import torch
+import torch.nn.functional as F
+
+BANDS=((1,4),(4,8),(8,13),(13,30),(30,45))
+ENVELOPE_DIM=16*5*12+8*5*2
+
+@torch.no_grad()
+def extract_envelope_features(signal):
+    with torch.autocast(signal.device.type,enabled=False):
+        x=signal.float();x=x-x.mean(-1,keepdim=True)
+        spectrum=torch.fft.fft(x,dim=-1)
+        frequency=torch.fft.fftfreq(x.shape[-1],1/200,device=x.device)
+        envelopes=[]
+        for lo,hi in BANDS:
+            mask=((frequency>=lo)&(frequency<hi)).to(x.dtype)*2
+            analytic=torch.fft.ifft(spectrum*mask,dim=-1)
+            energy=analytic.abs().square()
+            envelope=F.avg_pool1d(energy.flatten(0,1).unsqueeze(1),20,10).squeeze(1)
+            envelopes.append(envelope.reshape(len(x),16,-1))
+        e=torch.stack(envelopes,2)
+        mean=e.mean(-1,keepdim=True).clamp_min(1e-12)
+        relative=e/mean
+        centered=relative-relative.mean(-1,keepdim=True)
+        std=centered.square().mean(-1,keepdim=True).sqrt().clamp_min(1e-6)
+        normalized=centered/std
+        acf=torch.stack([(normalized[...,:-lag]*normalized[...,lag:]).mean(-1)
+                         for lag in range(4,41)],-1).clamp(-1,1)
+        acf_peak,acf_index=acf.max(-1)
+        probability=(relative/relative.sum(-1,keepdim=True).clamp_min(1e-12)).clamp_min(1e-12)
+        entropy=-(probability*probability.log()).sum(-1)
+        summaries=[std.squeeze(-1).clamp_min(1e-6).log(),relative.amax(-1).clamp_min(1).log(),entropy,
+                   (relative>2).float().mean(-1),(relative>4).float().mean(-1),
+                   ((relative[...,1:]>2)&(relative[...,:-1]<=2)).float().mean(-1),
+                   acf_peak,(acf_index.float()+4)/40,
+                   acf[...,0],acf[...,6],acf[...,16],acf[...,36]]
+        left=torch.tensor([0,1,2,3,8,9,10,11],device=x.device);right=left+4
+        synchrony=(normalized[:,left]*normalized[:,right]).mean(-1).clamp(-1,1)
+        imbalance=(std[:,left].squeeze(-1).log()-std[:,right].squeeze(-1).log()).abs()
+        return torch.cat([torch.stack(summaries,-1).flatten(1),synchrony.flatten(1),imbalance.flatten(1)],1)
+
+def mirrored_features(features):
+    channels=torch.tensor([4,5,6,7,0,1,2,3,12,13,14,15,8,9,10,11],device=features.device)
+    return torch.cat([features[:,:16*5*12].reshape(-1,16,5,12)[:,channels].flatten(1),features[:,16*5*12:]],1)

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S095/features.py

@@ -0,0 +1,63 @@
+"""One shared residual backbone, five linear input adapters and output heads."""
+import torch
+from torch import nn
+
+SPECS = {'faced': (32, 10, 9), 'tuev': (16, 5, 6), 'ad65': (19, 10, 3),
+         'openbmi': (62, 4, 2), 'longitudinal': (58, 2, 2)}
+TRACKS = tuple(SPECS)
+EDGES = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,16,18,20,22,25,28,32,36,40,45,50,60,70,80,95]
+COV_BANDS = [(1,4),(4,8),(8,12),(12,16),(16,20),(20,28),(28,40)]
+
+def feature_dim(channels):
+    return channels * (4*(len(EDGES)-1)+4) + len(COV_BANDS)*channels*(channels-1)//2
+
+@torch.no_grad()
+def extract_features(track, x):
+    """Fixed features; no batch statistics or fitted inference transformations."""
+    x = x.float().flatten(2)
+    x = torch.nan_to_num(x, nan=0., posinf=1e5, neginf=-1e5).clamp(-1e5,1e5)
+    x = x - x.mean(-1,keepdim=True)
+    if track in ('ad65','openbmi','longitudinal'):
+        x = x - x.mean(1,keepdim=True)
+    rms = x.square().mean(-1).clamp_min(1e-12).sqrt()
+    norm = x / rms.unsqueeze(-1)
+    moments = torch.stack([rms.log(), norm.abs().amax(-1).clamp_min(1).log(),
+                          norm.pow(4).mean(-1).clamp_min(1).log(),
+                          (norm[...,1:]-norm[...,:-1]).square().mean(-1).clamp_min(1e-10).log()],-1)
+    frames = x.unfold(-1,400,200)
+    frames = frames - frames.mean(-1,keepdim=True)
+    window = torch.hann_window(400,device=x.device)
+    z = torch.fft.rfft(frames*window,dim=-1)/200
+    power = z.abs().square()
+    bands = torch.stack([power[...,2*lo:2*hi].mean(-1) for lo,hi in zip(EDGES[:-1],EDGES[1:])],-1)
+    lp = bands.mean(-2).clamp_min(1e-12).log()
+    variation = bands.clamp_min(1e-12).log().std(-2,correction=0)
+    relative_frequency = lp - lp.mean(-1,keepdim=True)
+    relative_channel = lp - lp.mean(1,keepdim=True)
+    parts = [torch.cat([lp, relative_frequency, relative_channel, variation, moments],-1).flatten(1)]
+    ii,jj = torch.triu_indices(x.shape[1],x.shape[1],offset=1,device=x.device)
+    for lo,hi in COV_BANDS:
+        v = z[...,2*lo:2*hi].flatten(2)
+        cov = v.real @ v.real.transpose(-1,-2) + v.imag @ v.imag.transpose(-1,-2)
+        d = cov.diagonal(dim1=-2,dim2=-1).clamp_min(1e-12).sqrt()
+        cor = cov / (d.unsqueeze(1)*d.unsqueeze(2)).clamp_min(1e-12)
+        parts.append(cor[:,ii,jj].clamp(-1,1))
+    result = torch.cat(parts,-1)
+    return torch.nan_to_num(result).clamp(-40,40)
+
+class Standardize(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.register_buffer('mean',torch.zeros(dim))
+        self.register_buffer('scale',torch.ones(dim))
+    def forward(self,x):
+        return ((x-self.mean)/self.scale).clamp(-8,8)
+
+class Residual(nn.Module):
+    def __init__(self,width,dropout):
+        super().__init__()
+        self.net=nn.Sequential(nn.LayerNorm(width),nn.Linear(width,width*2),nn.GELU(),
+                               nn.Dropout(dropout),nn.Linear(width*2,width),nn.Dropout(dropout))
+    def forward(self,x):
+        return x+self.net(x)
+

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S095/frequency_features.py

@@ -0,0 +1,11 @@
+"""Fixed per-window frequency decomposition for a linear montage adapter."""
+import torch
+
+FREQUENCY_BANDS=((1,4),(4,8),(8,13),(13,20),(20,30),(30,45))
+
+def frequency_components(signal):
+    with torch.autocast(signal.device.type,enabled=False):
+        x=signal.float();spectrum=torch.fft.rfft(x,dim=-1)
+        frequency=torch.fft.rfftfreq(x.shape[-1],d=1/200,device=x.device)
+        masks=torch.stack([((frequency>=lo)&(frequency<hi)).float() for lo,hi in FREQUENCY_BANDS])
+        return torch.fft.irfft(spectrum[:,None]*masks[None,:,None],n=x.shape[-1],dim=-1)

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S095/infer.py

@@ -0,0 +1,24 @@
+import argparse,json
+from pathlib import Path
+import torch
+from model import HybridModel,SPECS
+from data import Dataset
+from covariances import extract_covariances
+p=argparse.ArgumentParser();p.add_argument('input',type=Path);p.add_argument('output',type=Path);a=p.parse_args()
+root=Path(__file__).resolve().parent;c=json.loads((root/'config.json').read_text());t=json.loads((a.input/'manifest.json').read_text())['track']
+if t not in SPECS:raise ValueError('unknown track')
+torch.set_num_threads(4);device='cuda' if torch.cuda.is_available() else 'cpu'
+m=HybridModel(**c['architecture']).to(device);m.load_state_dict(torch.load(root/'weights.pt',map_location=device,weights_only=True));m.eval()
+a.output.mkdir(parents=True,exist_ok=True)
+with torch.inference_mode(),(a.output/'predictions.jsonl').open('w') as f:
+    for x,_,ids in torch.utils.data.DataLoader(Dataset(a.input),batch_size=64,num_workers=0):
+        # FFT features stay float32; convolutions can use bfloat16 on CUDA.
+        x=x.to(device)
+        from features import extract_features
+        torch.backends.cuda.matmul.allow_tf32=False
+        features=extract_features(t,x)
+        covariances=extract_covariances(t,x)
+        torch.backends.cuda.matmul.allow_tf32=True
+        with torch.autocast(device,dtype=torch.bfloat16,enabled=device=='cuda'):pred=m(t,x,features,covariances=covariances)
+        pp=pred.float().softmax(-1).cpu().numpy()
+        for sid,pr in zip(ids,pp):f.write(json.dumps({'id':sid,'probabilities':pr.tolist()})+'\n')

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S095/infer.sh

@@ -0,0 +1,3 @@
+#!/usr/bin/env bash
+set -euo pipefail
+exec python "$(dirname -- "$0")/infer.py" "$1" "$2"

tasks/brain-computer-interface-decoding/brain-computer-interface-decoding-astra-xhigh-1h100-48h-20261031a/submissions/S095/model.py

@@ -0,0 +1,515 @@
+from student_features import CBraModFeatures,FrequencyAdapter
+"""One fused waveform/spectral backbone used identically by all five tasks."""
+import torch
+from torch import nn
+from braindecode.models import BIOT
+from features import SPECS,TRACKS,feature_dim,extract_features,Standardize,Residual,EDGES,COV_BANDS
+from covariances import BANDS,extract_covariances
+from alpha_features import extract_alpha_features,ALPHA_DIM
+from envelope_features import extract_envelope_features,ENVELOPE_DIM
+from frequency_features import frequency_components,FREQUENCY_BANDS
+from tangent_features import extract_tangent_features,TANGENT_DIM
+from phase_features import phase_features,PHASE_DIM,PHASE_HALF
+from coupling_features import coupling_features,PER_CHANNEL as COUPLING_PER_CHANNEL
+from ordinal_features import ordinal_features,PER_CHANNEL as ORDINAL_PER_CHANNEL
+
+def clinical_asymmetry_features(features):
+    """Bipolar homologous-channel amplitude and coherence, invariant to mirroring."""
+    bands=len(EDGES)-1;per_channel=4*bands+4
+    block=features[:,:16*per_channel].reshape(-1,16,per_channel)
+    left=torch.tensor([0,1,2,3,8,9,10,11],device=features.device);right=left+4
+    pair_power=(block[:,left,:bands]-block[:,right,:bands]).abs().flatten(1)
+    pair_rms=(block[:,left,4*bands]-block[:,right,4*bands]).abs()
+    hemisphere=(block[:,left,:bands].mean(1)-block[:,right,:bands].mean(1)).abs()
+    index=left*(32-left-1)//2+(right-left-1)
+    correlations=features[:,16*per_channel:].reshape(-1,len(COV_BANDS),120)[:,:,index].flatten(1)
+    return torch.cat([pair_power,pair_rms,hemisphere,correlations],1)
+
+class InputAdapter(nn.Module):
+    def __init__(self,c,width,channels,spectral_enabled=True,asymmetry=False,alpha_regions=None,envelope=False):
+        super().__init__()
+        self.ordinal_normalizer=Standardize(c*ORDINAL_PER_CHANNEL)
+        self.ordinal_projection=nn.Linear(c*ORDINAL_PER_CHANNEL,width)
+        self.ordinal_dropout=nn.Dropout(.3)
+        self.register_buffer('ordinal_probabilities_enabled',torch.ones(()))
+        self.coupling_normalizer=Standardize(c*COUPLING_PER_CHANNEL)
+        self.coupling_projection=nn.Linear(c*COUPLING_PER_CHANNEL,width)
+        self.coupling_dropout=nn.Dropout(.3)
+        self.register_buffer('coupling_dependence_enabled',torch.ones(()))
+        self.phase_normalizer=Standardize(PHASE_DIM)
+        self.signal=nn.Conv1d(c,channels,1,bias=False)
+        self.frequency_weight=nn.Parameter(torch.zeros(len(FREQUENCY_BANDS),channels,c))
+        self.neural=nn.Conv1d(c,18,1,bias=False)
+        self.normalizer=Standardize(feature_dim(c))
+        self.spectral=nn.Linear(feature_dim(c),width)
+        self.dropout=nn.Dropout(.1)
+        self.register_buffer("spectral_enabled",torch.tensor(float(spectral_enabled)))
+        self.spatial_weight=nn.Parameter(torch.randn(len(BANDS),24,c)/c**.5)
+        self.asymmetry_projection=nn.Linear(316,width,bias=False) if asymmetry else None
+        if asymmetry:
+            nn.init.zeros_(self.asymmetry_projection.weight)
+            self.asymmetry_normalizer=Standardize(316)
+            self.asymmetry_dropout=nn.Dropout(.1)
+        self.alpha_projection=None
+        if alpha_regions is not None:
+            self.register_buffer('alpha_regions',torch.tensor(alpha_regions,dtype=torch.float32))
+            self.alpha_normalizer=Standardize(ALPHA_DIM)
+            self.alpha_projection=nn.Linear(ALPHA_DIM,width,bias=False)
+            nn.init.zeros_(self.alpha_projection.weight)
+            self.alpha_dropout=nn.Dropout(.3)
+        self.envelope_projection=None
+        if envelope:
+            self.envelope_normalizer=Standardize(ENVELOPE_DIM)
+            self.envelope_projection=nn.Linear(ENVELOPE_DIM,width,bias=False)
+            nn.init.zeros_(self.envelope_projection.weight)
+            self.envelope_dropout=nn.Dropout(.3)
+    def coupling_embedding(self,features):
+        values=self.coupling_normalizer(features).reshape(len(features),-1,COUPLING_PER_CHANNEL)
+        values=torch.cat([values[...,:10],self.coupling_dependence_enabled*values[...,10:]],-1).flatten(1)
+        return self.coupling_projection(self.coupling_dropout(values))
+    def ordinal_embedding(self,features):
+        values=self.ordinal_normalizer(features).reshape(len(features),-1,5,26)
+        values=torch.cat([self.ordinal_probabilities_enabled*values[...,:24],values[...,24:]],-1).flatten(1)
+        return self.ordinal_projection(self.ordinal_dropout(values))
+    def spectral_features(self,features,signal=None,alpha_features=None,envelope_features=None):
+        value=self.spectral_enabled*self.spectral(self.dropout(self.normalizer(features)))
+        if self.asymmetry_projection is not None:
+            extra=self.asymmetry_dropout(self.asymmetry_normalizer(clinical_asymmetry_features(features)))
+            value=value+self.asymmetry_projection(extra)
+        if self.alpha_projection is not None:
+            if alpha_features is None:alpha_features=extract_alpha_features(signal,self.alpha_regions)
+            value=value+self.alpha_projection(self.alpha_dropout(self.alpha_normalizer(alpha_features)))
+        if self.envelope_projection is not None:
+            if envelope_features is None:envelope_features=extract_envelope_features(signal)
+            value=value+self.envelope_projection(self.envelope_dropout(self.envelope_normalizer(envelope_features)))
+        return value
+    def spatial_features(self,covariances):
+        with torch.autocast(covariances.device.type,enabled=False):
+            w=self.spatial_weight.float()/self.spatial_weight.float().norm(dim=-1,keepdim=True).clamp_min(1e-6)
+            power=torch.einsum('bfc,nbcd,bfd->nbf',w,covariances.float(),w).clamp_min(1e-8).log()
+            return torch.cat([power,power-power.mean(1,keepdim=True)],1).flatten(1)
+    def wave_signal(self,x,components=None):
+        if components is None:components=frequency_components(x)
+        correction=torch.einsum('blc,nbct->nlt',self.frequency_weight,components.to(self.frequency_weight.dtype))
+        return (self.signal(x)+correction.to(x.dtype)).unsqueeze(1)
+    def forward(self,x,features,covariance_features):
+        return self.wave_signal(x),self.spectral_features(features,signal=x),self.neural(x),covariance_features
+
+class SharedBackbone(nn.Module):
+    def __init__(self,channels,width,depth,dropout,wave_filters):
+        super().__init__()
+        self.waveform=nn.Sequential(
+            nn.Conv2d(1,wave_filters,(1,64),padding=(0,32),bias=False),nn.GroupNorm(1,wave_filters),
+            nn.Conv2d(wave_filters,2*wave_filters,(channels,1),groups=wave_filters,bias=False),nn.GroupNorm(1,2*wave_filters),nn.ELU(),
+            nn.AvgPool2d((1,4)),nn.Dropout(.35),
+            nn.Conv2d(2*wave_filters,2*wave_filters,(1,16),padding=(0,8),groups=2*wave_filters,bias=False),nn.Conv2d(2*wave_filters,2*wave_filters,1,bias=False),
+            nn.GroupNorm(1,2*wave_filters),nn.ELU(),nn.AvgPool2d((1,8)),nn.Dropout(.35))
+        self.wave_summary=nn.Sequential(nn.AdaptiveAvgPool2d((1,8)),nn.Flatten(),nn.Linear(2*wave_filters*8,width))
+        self.fusion=nn.Sequential(nn.LayerNorm(width),nn.GELU(),nn.Dropout(dropout),
+                                 *[Residual(width,dropout) for _ in range(depth)],nn.LayerNorm(width))
+        # Both representations interact before the shared residual stack.
+        self.phase_encoder=nn.Sequential(nn.LayerNorm(PHASE_DIM),nn.Linear(PHASE_DIM,width),nn.GELU(),nn.Dropout(.3),Residual(width,.3),nn.LayerNorm(width))
+        self.register_buffer('phase_imaginary_enabled',torch.ones(()))
+        self.coupling_encoder=nn.Sequential(nn.LayerNorm(width),nn.Linear(width,width*2),nn.GELU(),nn.Dropout(.3),nn.Linear(width*2,width),nn.LayerNorm(width))
+        self.wave_gain=nn.Parameter(torch.tensor(.1))
+        self.biot=BIOT(n_chans=18,n_outputs=2,n_times=400,sfreq=200)
+        self.biot.final_layer=nn.Identity()
+        self.biot_projection=nn.Sequential(nn.LayerNorm(256),nn.Linear(256,width),nn.Dropout(.2))
+        nn.init.normal_(self.biot_projection[1].weight,std=.001);nn.init.zeros_(self.biot_projection[1].bias)
+        self.covariance_encoder=nn.Sequential(nn.LayerNorm(len(BANDS)*24*2),nn.Linear(len(BANDS)*24*2,width),nn.GELU(),nn.Dropout(.4),
+                                             *[Residual(width,.4) for _ in range(2)],nn.LayerNorm(width))
+        self.covariance_projection=nn.Linear(width,width)
+        nn.init.zeros_(self.covariance_projection.weight);nn.init.zeros_(self.covariance_projection.bias)
+        self.tangent_encoder=nn.Sequential(nn.LayerNorm(TANGENT_DIM),nn.Linear(TANGENT_DIM,width),nn.GELU(),nn.Dropout(.3),Residual(width,.3),nn.LayerNorm(width),nn.Linear(width,width))
+        self.ordinal_context=nn.Sequential(nn.LayerNorm(width),nn.Linear(width,width*2),nn.GELU(),nn.Dropout(.3),nn.Linear(width*2,width))
+        nn.init.zeros_(self.ordinal_context[-1].weight);nn.init.zeros_(self.ordinal_context[-1].bias)
+        self.register_buffer('codebrain_enabled',torch.ones(()))
+        self.codebrain_patch_encoder=nn.Sequential(nn.LayerNorm(24*64),nn.Linear(24*64,width),nn.GELU(),nn.Dropout(.3))
+        self.codebrain_context=nn.Sequential(nn.LayerNorm(width*4),nn.Linear(width*4,width*2),nn.GELU(),nn.Dropout(.3),nn.Linear(width*2,width))
+        nn.init.zeros_(self.codebrain_context[-1].weight);nn.init.zeros_(self.codebrain_context[-1].bias)
+    def codebrain_correction(self,tokens,representation,fusion_input):
+        with torch.autocast(tokens.device.type,enabled=False):
+            encoded=self.codebrain_patch_encoder(self.codebrain_enabled*tokens.float())
+            pooled=torch.cat([encoded.mean(1),encoded.std(1,correction=0),representation[:,-512:-384].float(),representation[:,-128:].float()],-1)
+            delta=self.codebrain_context(pooled)
+        added=.001*nn.functional.linear(delta,self.covariance_projection.weight,None)
+        corrected=self.fusion(fusion_input+added.to(fusion_input.dtype))
+        return corrected-representation[:,-512:-384],delta
+    def forward(self,signal,spectral,neural_signal,covariance_features,tangent_features):
+        wave=self.waveform(signal)
+        neural=self.biot(neural_signal.float())
+        neural=self.biot_projection[0](neural)
+        projected=self.biot_projection[2](self.biot_projection[1](neural))
+        with torch.autocast(signal.device.type,enabled=False):
+            covariance_embedding=self.covariance_encoder(covariance_features.float())
+        original=spectral+self.wave_gain*self.wave_summary(wave)+.01*projected
+        coupled=.001*self.covariance_projection(covariance_embedding).to(original.dtype)
+        fused=self.fusion(original+coupled)
+        with torch.autocast(signal.device.type,enabled=False):delta=self.tangent_encoder(tangent_features.float())
+        added=.001*nn.functional.linear(delta,self.covariance_projection.weight,None)
+        corrected=self.fusion((original+coupled)+added.to(original.dtype))
+        return torch.cat([wave.flatten(1),fused,neural,covariance_embedding],-1),corrected-fused,delta,original+coupled
+
+class ResidualHead(nn.Module):
+    """A small nonlinear classifier correction around an inherited linear readout."""
+    def __init__(self,features,classes,width=64,dropout=.3,full_precision=False):
+        super().__init__();self.full_precision=full_precision
+        self.base=nn.Linear(features,classes)
+        self.delta=nn.Sequential(nn.LayerNorm(features),nn.Linear(features,width),nn.GELU(),nn.Dropout(dropout),nn.Linear(width,classes))
+        nn.init.zeros_(self.delta[-1].weight);nn.init.zeros_(self.delta[-1].bias)
+    def forward(self,x):
+        if self.full_precision:
+            with torch.autocast(x.device.type,enabled=False):return self.base(x.float())+self.delta(x.float())
+        prior=nn.functional.linear(x[:,:-128].contiguous(),self.base.weight[:,:-128].contiguous(),self.base.bias)
+        added=nn.functional.linear(x[:,-128:].contiguous(),self.base.weight[:,-128:].contiguous())
+        value=prior+added
+        return value+self.delta(x).to(value.dtype)
+
+class TemporalPoolHead(nn.Module):
+    def __init__(self,prior,channels=64):
+        super().__init__();self.prior=prior;self.channels=channels
+        self.register_buffer('pool_output_scale',torch.ones(()));self.register_buffer('pool_mean',torch.zeros(channels*4));self.register_buffer('pool_scale',torch.ones(channels*4))
+        self.pool_projection=nn.Linear(channels*4,prior.base.out_features)
+        nn.init.zeros_(self.pool_projection.weight);nn.init.zeros_(self.pool_projection.bias)
+    def statistics(self,x):
+        with torch.autocast(x.device.type,enabled=False):
+            wave=x[:,:-512].float().reshape(len(x),self.channels,-1)
+            return torch.cat([wave.mean(-1),wave.std(-1,correction=0),wave.amax(-1),wave.amin(-1)],-1)
+    def forward(self,x):
+        prior=self.prior(x)
+        with torch.autocast(x.device.type,enabled=False):
+            values=((self.statistics(x)-self.pool_mean)/self.pool_scale).clamp(-8,8)
+            added=self.pool_output_scale*self.pool_projection(values)
+        return prior+added.to(prior.dtype)
+
+class TemporalEvidenceHead(nn.Module):
+    """Pool class evidence over the shared waveform time axis."""
+    def __init__(self,prior,channels=64,temperature=.25):
+        super().__init__();self.prior=prior;self.channels=channels;self.temperature=temperature
+        self.register_buffer('evidence_output_scale',torch.ones(()))
+        self.register_buffer('evidence_mean',torch.zeros(channels))
+        self.register_buffer('evidence_scale',torch.ones(channels))
+        self.evidence_projection=nn.Linear(channels,prior.prior.base.out_features,bias=False)
+        nn.init.zeros_(self.evidence_projection.weight)
+    def patches(self,x):
+        with torch.autocast(x.device.type,enabled=False):
+            wave=x[:,:-512].float().reshape(len(x),self.channels,-1).transpose(1,2)
+            return ((wave-self.evidence_mean)/self.evidence_scale).clamp(-8,8)
+    def evidence(self,patches):
+        with torch.autocast(patches.device.type,enabled=False):
+            values=self.evidence_projection(patches.float())/self.temperature
+            maximum=values.amax(1,keepdim=True)
+            pooled=self.temperature*((values-maximum).exp().mean(1).log()+maximum.squeeze(1))
+            return self.evidence_output_scale*pooled
+    def forward(self,x):
+        prior=self.prior(x)
+        return prior+self.evidence(self.patches(x)).to(prior.dtype)
+
+class FourierResidualHead(nn.Module):
+    """A fixed random Fourier basis and trainable linear classifier correction."""
+    def __init__(self,prior,features,classes,basis=256):
+        super().__init__();self.prior=prior
+        self.register_buffer('rff_mean',torch.zeros(features));self.register_buffer('rff_scale',torch.ones(features))
+        self.register_buffer('rff_frequency',torch.randn(features,basis)/features**.5)
+        self.register_buffer('rff_phase',torch.rand(basis)*2*torch.pi)
+        self.register_buffer('rff_output_scale',torch.ones(()))
+        self.rff_projection=nn.Linear(3*basis,classes);nn.init.zeros_(self.rff_projection.weight);nn.init.zeros_(self.rff_projection.bias)
+    def basis(self,x):
+        with torch.autocast(x.device.type,enabled=False):
+            values=((x.float()-self.rff_mean)/self.rff_scale).clamp(-8,8)@self.rff_frequency
+            return 2**.5*torch.cat([(values/sigma+self.rff_phase).cos() for sigma in (.5,1.,2.)],-1)
+    def forward(self,x):
+        prior=self.prior(x)
+        with torch.autocast(x.device.type,enabled=False):added=self.rff_output_scale*self.rff_projection(self.basis(x))
+        return prior+added.to(prior.dtype)
+
+class TangentFeatureHead(nn.Module):
+    """Select a shared feature correction before the inherited classifier."""
+    def __init__(self,prior):
+        super().__init__();self.prior=prior;self.feature_gain=nn.Parameter(torch.zeros(()))
+    def forward(self,values):
+        old=values[:,:-256];fusion_delta=values[:,-256:-128];covariance_delta=values[:,-128:]
+        adjusted=torch.cat([old[:,:-512],old[:,-512:-384]+self.feature_gain*fusion_delta,old[:,-384:-128],old[:,-128:]+self.feature_gain*covariance_delta],-1)
+        return self.prior(adjusted)
+
+class CodeBrainFeatureHead(nn.Module):
+    """Private use of a jointly trained common feature correction."""
+    def __init__(self,prior):
+        super().__init__();self.prior=prior;self.codebrain_gain=nn.Parameter(torch.ones(()))
+    def forward(self,values):
+        previous=values[:,:-256];native=previous[:,:-256]
+        fusion_delta=values[:,-256:-128];covariance_delta=values[:,-128:]
+        adjusted=torch.cat([native[:,:-512],native[:,-512:-384]+self.codebrain_gain*fusion_delta,native[:,-384:-128],native[:,-128:]+self.codebrain_gain*covariance_delta],-1)
+        return self.prior(torch.cat([adjusted,previous[:,-256:]],-1))
+
+class WaveCoactivationContext(nn.Module):
+    """One common encoder of coactivation among the learned waveform channels."""
+    def __init__(self,channels=64,width=128):
+        super().__init__();self.channels=channels
+        ii,jj=torch.triu_indices(channels,channels,offset=1)
+        self.register_buffer('pair_i',ii);self.register_buffer('pair_j',jj)
+        self.register_buffer('offdiagonal_enabled',torch.ones(()))
+        self.encoder=nn.Sequential(nn.LayerNorm(channels+len(ii)),nn.Linear(channels+len(ii),width),nn.GELU(),nn.Dropout(.3),Residual(width,.3),nn.LayerNorm(width))
+    def statistics(self,native):
+        with torch.autocast(native.device.type,enabled=False):
+            wave=native[:,:-512].float().reshape(len(native),self.channels,-1)
+            centered=wave-wave.mean(-1,keepdim=True)
+            previous_tf32=torch.backends.cuda.matmul.allow_tf32
+            torch.backends.cuda.matmul.allow_tf32=False
+            covariance=centered@centered.transpose(-1,-2)/wave.shape[-1]
+            torch.backends.cuda.matmul.allow_tf32=previous_tf32
+            variance=covariance.diagonal(dim1=-2,dim2=-1).clamp_min(1e-8)
+            correlation=covariance/(variance.sqrt()[:,:,None]*variance.sqrt()[:,None,:])
+            return torch.cat([variance.log(),correlation[:,self.pair_i,self.pair_j].clamp(-1,1)],-1)
+    def encode_statistics(self,values):
+        with torch.autocast(values.device.type,enabled=False):
+            value=torch.cat([values[:,:self.channels],self.offdiagonal_enabled*values[:,self.channels:]],-1)
+            return self.encoder(value.float())
+    def forward(self,native):return self.encode_statistics(self.statistics(native))
+
+class WaveCoactivationHead(nn.Module):
+    def __init__(self,prior,classes,width=128):
+        super().__init__();self.prior=prior;self.projection=nn.Linear(width,classes)
+        nn.init.zeros_(self.projection.weight);nn.init.zeros_(self.projection.bias)
+        self.register_buffer('output_scale',torch.ones(()));self.register_buffer('correction_gain',torch.ones(()))
+    def forward(self,values):
+        prior=self.prior(values[:,:-128])
+        with torch.autocast(values.device.type,enabled=False):delta=self.correction_gain*self.output_scale*self.projection(values[:,-128:].float())
+        return prior+delta.to(prior.dtype)
+
+class OrdinalFeatureHead(nn.Module):
+    """Private strength of one common ordinal-dynamics feature correction."""
+    def __init__(self,prior):
+        super().__init__();self.prior=prior;self.ordinal_gain=nn.Parameter(torch.ones(()))
+    def forward(self,values):
+        previous=values[:,:-256];native=previous[:,:-640]
+        adjusted=torch.cat([native[:,:-512],native[:,-512:-384]+self.ordinal_gain*values[:,-256:-128],native[:,-384:-128],native[:,-128:]+self.ordinal_gain*values[:,-128:],previous[:,-640:]],-1)
+        return self.prior(adjusted)
+
+class PhaseReadoutHead(nn.Module):
+    """Private class evidence from the common complex-coherence encoder."""
+    def __init__(self,prior,classes,width=128):
+        super().__init__();self.prior=prior;self.projection=nn.Linear(width,classes)
+        nn.init.zeros_(self.projection.weight);nn.init.zeros_(self.projection.bias)
+        self.register_buffer('output_scale',torch.ones(()));self.register_buffer('correction_gain',torch.ones(()))
+    def forward(self,values):
+        prior=self.prior(values[:,:-128])
+        with torch.autocast(values.device.type,enabled=False):delta=(self.correction_gain*self.output_scale)*self.projection(values[:,-128:].float())
+        return prior+delta.to(prior.dtype)
+
+class CouplingReadoutHead(nn.Module):
+    """Private class evidence from the common cross-frequency coupling encoder."""
+    def __init__(self,prior,classes,width=128):
+        super().__init__();self.prior=prior;self.projection=nn.Linear(width,classes)
+        nn.init.zeros_(self.projection.weight);nn.init.zeros_(self.projection.bias)
+        self.register_buffer('output_scale',torch.ones(()));self.register_buffer('correction_gain',torch.ones(()))
+    def forward(self,values):
+        prior=self.prior(values[:,:-128])
+        with torch.autocast(values.device.type,enabled=False):delta=(self.correction_gain*self.output_scale)*self.projection(values[:,-128:].float())
+        return prior+delta.to(prior.dtype)
+
+class HybridModel(nn.Module):
+    def __init__(self,channels=32,width=128,depth=2,dropout=.5,wave_filters=32,head_width=64,head_dropout=.3,clinical_asymmetry=True,alpha_regions=None,clinical_envelope=False,temporal_pool=True,temporal_evidence=False,evidence_temperature=.25,random_fourier=False,fourier_features=256,tangent_readout=True,shared_context_correction=True,wave_coactivation=True,ordinal_context=True,phase_coherence=True,cross_frequency=True,cbramod_feature_transfer=True,student_architecture=None,student_extension=True,post_embedding_extension=True,extension_activation='gelu',center_context_extension=True,context_crop='center',short_context_extension=True,short_crop='center',dual_context_extension=True,dual_crop='clinical-dual',geometry_extension=True,geometry_mode='mi-log',emotion_extension=True,emotion_mode='faced-log'):
+        super().__init__()
+        self.adapters=nn.ModuleDict({t:InputAdapter(c,width,channels,spectral_enabled=t not in ("faced","tuev","openbmi"),asymmetry=clinical_asymmetry and t=="tuev",alpha_regions=alpha_regions if t=="longitudinal" else None,envelope=clinical_envelope and t=="tuev") for t,(c,s,k) in SPECS.items()})
+        self.backbone=SharedBackbone(channels,width,depth,dropout,wave_filters)
+        self.backbone.student_encoder=CBraModFeatures(**(student_architecture or {}))
+        self.backbone.register_buffer('student_ordered_enabled',torch.ones(()))
+        self.backbone.student_readout=nn.Sequential(nn.Linear(self.backbone.student_encoder.output_dim,256),nn.LayerNorm(256),nn.GELU(),nn.Dropout(.3),nn.Linear(256,width),nn.LayerNorm(width))
+        for t,(c,s,k) in SPECS.items():
+            self.adapters[t].student_input=FrequencyAdapter(c,32)
+            self.adapters[t].student_normalizer=Standardize(self.backbone.student_encoder.output_dim)
+        self.backbone.wave_coactivation=WaveCoactivationContext(2*wave_filters,width)
+        self.heads=nn.ModuleDict({t:ResidualHead(2*wave_filters*(((s*200+1)//4+1)//8)+width+256+width,k,head_width,head_dropout,full_precision=t=='ad65') for t,(c,s,k) in SPECS.items()})
+        if temporal_pool:self.heads=nn.ModuleDict({t:TemporalPoolHead(head,wave_filters*2) for t,head in self.heads.items()})
+        if temporal_evidence:self.heads=nn.ModuleDict({t:TemporalEvidenceHead(head,wave_filters*2,evidence_temperature) for t,head in self.heads.items()})
+        if random_fourier:self.heads=nn.ModuleDict({t:FourierResidualHead(head,2*wave_filters*(((SPECS[t][1]*200+1)//4+1)//8)+width+256+width,SPECS[t][2],fourier_features) for t,head in self.heads.items()})
+        self.heads=nn.ModuleDict({t:TangentFeatureHead(head) for t,head in self.heads.items()})
+        self.heads=nn.ModuleDict({t:CodeBrainFeatureHead(head) for t,head in self.heads.items()})
+        self.heads=nn.ModuleDict({t:WaveCoactivationHead(head,SPECS[t][2],width) for t,head in self.heads.items()})
+        self.heads=nn.ModuleDict({t:OrdinalFeatureHead(head) for t,head in self.heads.items()})
+        self.heads=nn.ModuleDict({t:PhaseReadoutHead(head,SPECS[t][2],width) for t,head in self.heads.items()})
+        self.heads=nn.ModuleDict({t:CouplingReadoutHead(head,SPECS[t][2],width) for t,head in self.heads.items()})
+        self.heads=nn.ModuleDict({t:CouplingReadoutHead(head,SPECS[t][2],width) for t,head in self.heads.items()})
+        assert student_extension and post_embedding_extension
+        assert extension_activation in ('gelu','identity')
+        self.backbone.student_extension=nn.Sequential(nn.Linear(width,256),nn.LayerNorm(256),nn.GELU() if extension_activation=='gelu' else nn.Identity(),nn.Dropout(.3),nn.Linear(256,width),nn.LayerNorm(width))
+        self.heads=nn.ModuleDict({t:CouplingReadoutHead(head,SPECS[t][2],width) for t,head in self.heads.items()})
+        assert center_context_extension and context_crop in ('center','first')
+        self.context_crop=context_crop
+        for t,(c,s,k) in SPECS.items():
+            self.adapters[t].center_normalizer=Standardize(feature_dim(c)+30*c)
+            self.adapters[t].center_projection=nn.Linear(feature_dim(c)+30*c,width)
+        self.backbone.center_encoder=nn.Sequential(nn.Linear(width,256),nn.LayerNorm(256),nn.GELU(),nn.Dropout(.3),nn.Linear(256,width),nn.LayerNorm(width))
+        self.heads=nn.ModuleDict({t:CouplingReadoutHead(head,SPECS[t][2],width) for t,head in self.heads.items()})
+        assert short_context_extension and short_crop in ('center','first')
+        self.short_crop=short_crop
+        for t,(c,s,k) in SPECS.items():
+            self.adapters[t].short_normalizer=Standardize(feature_dim(c)+30*c)
+            self.adapters[t].short_projection=nn.Linear(feature_dim(c)+30*c,width)
+        self.backbone.short_encoder=nn.Sequential(nn.Linear(width,256),nn.LayerNorm(256),nn.GELU(),nn.Dropout(.3),nn.Linear(256,width),nn.LayerNorm(width))
+        self.heads=nn.ModuleDict({t:CouplingReadoutHead(head,SPECS[t][2],width) for t,head in self.heads.items()})
+        assert dual_context_extension and dual_crop in ('clinical-dual','clinical-duplicate')
+        self.dual_crop=dual_crop
+        for t,(c,s,k) in SPECS.items():
+            self.adapters[t].dual_normalizer=Standardize((feature_dim(c)+30*c)*(2 if t=='tuev' else 1))
+            self.adapters[t].dual_projection=nn.Linear((feature_dim(c)+30*c)*(2 if t=='tuev' else 1),width)
+        self.backbone.dual_encoder=nn.Sequential(nn.Linear(width,256),nn.LayerNorm(256),nn.GELU(),nn.Dropout(.3),nn.Linear(256,width),nn.LayerNorm(width))
+        self.heads=nn.ModuleDict({t:CouplingReadoutHead(head,SPECS[t][2],width) for t,head in self.heads.items()})
+        assert geometry_extension and geometry_mode in ('mi-log','mi-linear')
+        self.geometry_mode=geometry_mode
+        for t,(c,s,k) in SPECS.items():
+            dimension=7*c*(c+1)//2 if t=='openbmi' else feature_dim(c)+30*c
+            self.adapters[t].geometry_normalizer=Standardize(dimension)
+            self.adapters[t].geometry_projection=nn.Linear(dimension,width)
+        self.backbone.geometry_encoder=nn.Sequential(nn.Linear(width,256),nn.LayerNorm(256),nn.GELU(),nn.Dropout(.3),nn.Linear(256,width),nn.LayerNorm(width))
+        self.heads=nn.ModuleDict({t:CouplingReadoutHead(head,SPECS[t][2],width) for t,head in self.heads.items()})
+        assert emotion_extension and emotion_mode in ('faced-log','faced-linear')
+        self.emotion_mode=emotion_mode
+        for t,(c,s,k) in SPECS.items():
+            dimension=7*c*(c+1)//2 if t=='faced' else feature_dim(c)+30*c
+            self.adapters[t].emotion_normalizer=Standardize(dimension)
+            self.adapters[t].emotion_projection=nn.Linear(dimension,width)
+        self.backbone.emotion_encoder=nn.Sequential(nn.Linear(width,256),nn.LayerNorm(256),nn.GELU(),nn.Dropout(.3),nn.Linear(256,width),nn.LayerNorm(width))
+        self.heads=nn.ModuleDict({t:CouplingReadoutHead(head,SPECS[t][2],width) for t,head in self.heads.items()})
+    def emotion_features(self,t,x):
+        from native_log_covariance_features import connectivity
+        from short_context_features import short_spectral_features
+        old=torch.backends.cuda.matmul.allow_tf32;torch.backends.cuda.matmul.allow_tf32=False
+        try:
+            with torch.autocast(x.device.type,enabled=False):
+                if t=='faced':return connectivity(t,x,logarithmic=self.emotion_mode=='faced-log')
+                view=x.flatten(2)[...,:200]
+                return torch.cat([short_spectral_features(t,view),coupling_features(t,view)],-1)
+        finally:torch.backends.cuda.matmul.allow_tf32=old
+    def emotion_embedding(self,t,values):
+        with torch.autocast(values.device.type,enabled=False):
+            normalized=self.adapters[t].emotion_normalizer(values.float())
+            return self.backbone.emotion_encoder(self.adapters[t].emotion_projection(normalized))
+    def geometry_features(self,t,x):
+        from native_log_covariance_features import connectivity
+        from short_context_features import short_spectral_features
+        old=torch.backends.cuda.matmul.allow_tf32;torch.backends.cuda.matmul.allow_tf32=False
+        try:
+            with torch.autocast(x.device.type,enabled=False):
+                if t=='openbmi':return connectivity(t,x,logarithmic=self.geometry_mode=='mi-log')
+                view=x.flatten(2)[...,:200]
+                return torch.cat([short_spectral_features(t,view),coupling_features(t,view)],-1)
+        finally:torch.backends.cuda.matmul.allow_tf32=old
+    def geometry_embedding(self,t,values):
+        with torch.autocast(values.device.type,enabled=False):
+            normalized=self.adapters[t].geometry_normalizer(values.float())
+            return self.backbone.geometry_encoder(self.adapters[t].geometry_projection(normalized))
+    def dual_features(self,t,x):
+        from short_context_features import short_spectral_features
+        raw=x.flatten(2);first=raw[...,:200];views=[first]
+        if t=='tuev':
+            start=(raw.shape[-1]-200)//2
+            views.append(raw[...,start:start+200] if self.dual_crop=='clinical-dual' else first)
+        old=torch.backends.cuda.matmul.allow_tf32;torch.backends.cuda.matmul.allow_tf32=False
+        try:
+            with torch.autocast(raw.device.type,enabled=False):return torch.cat([torch.cat([short_spectral_features(t,v),coupling_features(t,v)],-1) for v in views],-1)
+        finally:torch.backends.cuda.matmul.allow_tf32=old
+    def dual_embedding(self,t,values):
+        with torch.autocast(values.device.type,enabled=False):
+            normalized=self.adapters[t].dual_normalizer(values.float())
+            return self.backbone.dual_encoder(self.adapters[t].dual_projection(normalized))
+    def short_features(self,t,x):
+        from short_context_features import short_spectral_features
+        raw=x.flatten(2);start=(raw.shape[-1]-200)//2 if self.short_crop=='center' else 0
+        cropped=raw[...,start:start+200];assert cropped.shape[-1]==200
+        old=torch.backends.cuda.matmul.allow_tf32;torch.backends.cuda.matmul.allow_tf32=False
+        try:
+            with torch.autocast(raw.device.type,enabled=False):return torch.cat([short_spectral_features(t,cropped),coupling_features(t,cropped)],-1)
+        finally:torch.backends.cuda.matmul.allow_tf32=old
+    def short_embedding(self,t,values):
+        with torch.autocast(values.device.type,enabled=False):
+            normalized=self.adapters[t].short_normalizer(values.float())
+            return self.backbone.short_encoder(self.adapters[t].short_projection(normalized))
+    def center_features(self,t,x):
+        raw=x.flatten(2);start=(raw.shape[-1]-400)//2 if self.context_crop=='center' else 0
+        cropped=raw[...,start:start+400];assert cropped.shape[-1]==400
+        old=torch.backends.cuda.matmul.allow_tf32;torch.backends.cuda.matmul.allow_tf32=False
+        try:
+            with torch.autocast(raw.device.type,enabled=False):return torch.cat([extract_features(t,cropped),coupling_features(t,cropped)],-1)
+        finally:torch.backends.cuda.matmul.allow_tf32=old
+    def center_embedding(self,t,values):
+        with torch.autocast(values.device.type,enabled=False):
+            normalized=self.adapters[t].center_normalizer(values.float())
+            return self.backbone.center_encoder(self.adapters[t].center_projection(normalized))
+    def student_extension_embedding(self,t,values):
+        with torch.autocast(values.device.type,enabled=False):
+            assert values.shape[-1]==128
+            return self.backbone.student_extension(values.float())
+
+    def student_features(self,t,x):
+        outputs=[];old_fastpath=torch.backends.mha.get_fastpath_enabled();torch.backends.mha.set_fastpath_enabled(False)
+        try:
+            for raw in x.split(16):
+                signal=torch.nan_to_num(raw.float().flatten(2),nan=0.,posinf=1e5,neginf=-1e5).clamp(-1e5,1e5)
+                signal=signal-signal.mean(-1,keepdim=True)
+                if t in ('faced','tuev'):signal=signal/signal.std(-1,keepdim=True,correction=0).clamp_min(1e-6)
+                else:signal=signal-signal.mean(1,keepdim=True)
+                signal=signal.clamp(-20,20)
+                with torch.autocast(signal.device.type,dtype=torch.bfloat16,enabled=signal.device.type=='cuda'):
+                    outputs.append(self.backbone.student_encoder(self.adapters[t].student_input(signal)))
+        finally:torch.backends.mha.set_fastpath_enabled(old_fastpath)
+        return torch.cat(outputs).float()
+    def student_embedding(self,t,values):
+        with torch.autocast(values.device.type,enabled=False):
+            normalized=self.adapters[t].student_normalizer(values.float())
+            normalized=torch.cat([normalized[:,:256],self.backbone.student_ordered_enabled*normalized[:,256:]],-1)
+            return self.backbone.student_readout(normalized)
+    def coupling_embedding(self,t,values):
+        with torch.autocast(values.device.type,enabled=False):return self.backbone.coupling_encoder(self.adapters[t].coupling_embedding(values.float()))
+    def phase_embedding(self,t,values):
+        with torch.autocast(values.device.type,enabled=False):
+            normalized=self.adapters[t].phase_normalizer(values.float())
+            normalized=torch.cat([normalized[:,:PHASE_HALF],self.backbone.phase_imaginary_enabled*normalized[:,PHASE_HALF:]],-1)
+            return self.backbone.phase_encoder(normalized)
+    def forward(self,t,x,features=None,covariances=None,covariance_features=None):
+        if features is None:features=extract_features(t,x)
+        if covariance_features is None:
+            if covariances is None:covariances=extract_covariances(t,x)
+            covariance_features=self.adapters[t].spatial_features(covariances)
+        signal=x.flatten(2);signal=signal-signal.mean(-1,keepdim=True)
+        if t in ('faced','tuev'):signal=signal/signal.std(-1,keepdim=True,correction=0).clamp_min(1e-6)
+        else:signal=signal-signal.mean(1,keepdim=True)
+        signal=signal.clamp(-20,20)
+        adapted=self.adapters[t](signal,features,covariance_features)
+        if covariances is None:covariances=extract_covariances(t,x)
+        tangent=extract_tangent_features(covariances,self.adapters[t].spatial_weight)
+        representation,fusion_delta,covariance_delta,fusion_input=self.backbone(*adapted,tangent_features=tangent)
+        if t in ('faced','openbmi'):
+            representation=torch.cat([representation[:,:-384],torch.zeros_like(representation[:,-384:-128]),representation[:,-128:]],-1)
+        tokens=torch.zeros(len(signal),signal.shape[-1]//200,24*64,device=signal.device,dtype=torch.bfloat16)
+        code_fusion,code_covariance=self.backbone.codebrain_correction(tokens,representation,fusion_input)
+        coactivation=self.backbone.wave_coactivation(representation)
+        with torch.autocast(signal.device.type,enabled=False):
+            ordinal=self.adapters[t].ordinal_embedding(ordinal_features(t,x))
+            ordinal_covariance=self.backbone.ordinal_context(ordinal.float())
+        added=.001*nn.functional.linear(ordinal_covariance,self.backbone.covariance_projection.weight,None)
+        ordinal_fusion=self.backbone.fusion(fusion_input+added.to(fusion_input.dtype))-representation[:,-512:-384]
+        phase=self.phase_embedding(t,phase_features(t,x,self.adapters[t].spatial_weight))
+        coupling=self.coupling_embedding(t,coupling_features(t,x))
+        student_values=self.student_features(t,x)
+        student=self.student_embedding(t,student_values)
+        extension=self.student_extension_embedding(t,student)
+        return self.heads[t](torch.cat([representation,fusion_delta,covariance_delta,code_fusion,code_covariance,coactivation,ordinal_fusion,ordinal_covariance,phase,coupling,student,extension,self.center_embedding(t,self.center_features(t,x)),self.short_embedding(t,self.short_features(t,x)),self.dual_embedding(t,self.dual_features(t,x)),self.geometry_embedding(t,self.geometry_features(t,x)),self.emotion_embedding(t,self.emotion_features(t,x))],-1))
+
+def widen_state_dict(state,multiplier=2):
+    """Duplicate temporal features and divide downstream sums to preserve outputs."""
+    result={}
+    for key,value in state.items():
+        if key.startswith('backbone.waveform.'):
+            layer=int(key.split('.')[2])
+            if layer in (0,2,7):value=value.repeat(multiplier,1,1,1)
+            elif layer in (1,3,9):value=value.repeat(multiplier)
+            elif layer==8:value=value.repeat(multiplier,multiplier,1,1)/multiplier
+        elif key=='backbone.wave_summary.2.weight':value=value.repeat(1,multiplier)/multiplier
+        elif key.startswith('heads.') and key.endswith('.weight'):
+            raw=value[:,:-128]/multiplier
+            value=torch.cat([raw]*multiplier+[value[:,-128:]],1)
+        result[key]=value
+    return result
+