Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
b2: struct<ioo_zero_shot_RMSE: double, mlp_zero_shot_RMSE: double, mlp_insource_RMSE: double, n_train_un (... 95 chars omitted)
child 0, ioo_zero_shot_RMSE: double
child 1, mlp_zero_shot_RMSE: double
child 2, mlp_insource_RMSE: double
child 3, n_train_units: int64
child 4, n_val_units: int64
child 5, graph: struct<n_nodes: int64, n_edges: int64, density: double>
child 0, n_nodes: int64
child 1, n_edges: int64
child 2, density: double
b3: struct<ioo_zero_shot_RMSE: double, mlp_zero_shot_RMSE: double, train_exp: list<item: string>, val_ex (... 74 chars omitted)
child 0, ioo_zero_shot_RMSE: double
child 1, mlp_zero_shot_RMSE: double
child 2, train_exp: list<item: string>
child 0, item: string
child 3, val_exp: string
child 4, graph: struct<n_nodes: int64, n_edges: int64, density: double>
child 0, n_nodes: int64
child 1, n_edges: int64
child 2, density: double
quick: bool
params: int64
final_epoch: int64
probe_res_r2: double
probe_a_r2: double
final_val_acc: double
history: list<item: struct<epoch: int64, train_acc: double, val_acc: double, loss: double>>
child 0, item: struct<epoch: int64, train_acc: double, val_acc: double, loss: double>
child 0, epoch: int64
child 1, train_acc: double
child 2, val_acc: double
child 3, loss: double
interchange: struct<score: double, n: int64, n_moved: int64, sign_agreement: double, soft_gain: double, expected: (... 49 chars omitted)
child 0, score: double
child 1, n: int64
child 2, n_moved: int64
child 3, sign_agreement: double
child 4, soft_gain: double
child 5, expected: list<item: int64>
child 0, item: int64
child 6, predicted: list<item: int64>
child 0, item: int64
op: string
grok_epoch: double
truth_horizon: struct<transfer_modulus: int64, transfer_acc_mod_p2: double, transfer_acc_mod_p: double, n_transfer: (... 32 chars omitted)
child 0, transfer_modulus: int64
child 1, transfer_acc_mod_p2: double
child 2, transfer_acc_mod_p: double
child 3, n_transfer: int64
child 4, rule_internalized: bool
probe_b_r2: double
grokked: bool
p: int64
seconds: double
emergence: struct<emerged: bool, emergence_step: double, inflection_step: double, width: double, peak: double, (... 19 chars omitted)
child 0, emerged: bool
child 1, emergence_step: double
child 2, inflection_step: double
child 3, width: double
child 4, peak: double
child 5, n_snapshots: int64
p_transfer: int64
to
{'op': Value('string'), 'p': Value('int64'), 'p_transfer': Value('int64'), 'history': List({'epoch': Value('int64'), 'train_acc': Value('float64'), 'val_acc': Value('float64'), 'loss': Value('float64')}), 'emergence': {'emerged': Value('bool'), 'emergence_step': Value('float64'), 'inflection_step': Value('float64'), 'width': Value('float64'), 'peak': Value('float64'), 'n_snapshots': Value('int64')}, 'grokked': Value('bool'), 'final_val_acc': Value('float64'), 'grok_epoch': Value('float64'), 'final_epoch': Value('int64'), 'probe_a_r2': Value('float64'), 'probe_b_r2': Value('float64'), 'probe_res_r2': Value('float64'), 'interchange': {'score': Value('float64'), 'n': Value('int64'), 'n_moved': Value('int64'), 'sign_agreement': Value('float64'), 'soft_gain': Value('float64'), 'expected': List(Value('int64')), 'predicted': List(Value('int64'))}, 'truth_horizon': {'transfer_modulus': Value('int64'), 'transfer_acc_mod_p2': Value('float64'), 'transfer_acc_mod_p': Value('float64'), 'n_transfer': Value('int64'), 'rule_internalized': Value('bool')}, 'params': Value('int64'), 'seconds': Value('float64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
b2: struct<ioo_zero_shot_RMSE: double, mlp_zero_shot_RMSE: double, mlp_insource_RMSE: double, n_train_un (... 95 chars omitted)
child 0, ioo_zero_shot_RMSE: double
child 1, mlp_zero_shot_RMSE: double
child 2, mlp_insource_RMSE: double
child 3, n_train_units: int64
child 4, n_val_units: int64
child 5, graph: struct<n_nodes: int64, n_edges: int64, density: double>
child 0, n_nodes: int64
child 1, n_edges: int64
child 2, density: double
b3: struct<ioo_zero_shot_RMSE: double, mlp_zero_shot_RMSE: double, train_exp: list<item: string>, val_ex (... 74 chars omitted)
child 0, ioo_zero_shot_RMSE: double
child 1, mlp_zero_shot_RMSE: double
child 2, train_exp: list<item: string>
child 0, item: string
child 3, val_exp: string
child 4, graph: struct<n_nodes: int64, n_edges: int64, density: double>
child 0, n_nodes: int64
child 1, n_edges: int64
child 2, density: double
quick: bool
params: int64
final_epoch: int64
probe_res_r2: double
probe_a_r2: double
final_val_acc: double
history: list<item: struct<epoch: int64, train_acc: double, val_acc: double, loss: double>>
child 0, item: struct<epoch: int64, train_acc: double, val_acc: double, loss: double>
child 0, epoch: int64
child 1, train_acc: double
child 2, val_acc: double
child 3, loss: double
interchange: struct<score: double, n: int64, n_moved: int64, sign_agreement: double, soft_gain: double, expected: (... 49 chars omitted)
child 0, score: double
child 1, n: int64
child 2, n_moved: int64
child 3, sign_agreement: double
child 4, soft_gain: double
child 5, expected: list<item: int64>
child 0, item: int64
child 6, predicted: list<item: int64>
child 0, item: int64
op: string
grok_epoch: double
truth_horizon: struct<transfer_modulus: int64, transfer_acc_mod_p2: double, transfer_acc_mod_p: double, n_transfer: (... 32 chars omitted)
child 0, transfer_modulus: int64
child 1, transfer_acc_mod_p2: double
child 2, transfer_acc_mod_p: double
child 3, n_transfer: int64
child 4, rule_internalized: bool
probe_b_r2: double
grokked: bool
p: int64
seconds: double
emergence: struct<emerged: bool, emergence_step: double, inflection_step: double, width: double, peak: double, (... 19 chars omitted)
child 0, emerged: bool
child 1, emergence_step: double
child 2, inflection_step: double
child 3, width: double
child 4, peak: double
child 5, n_snapshots: int64
p_transfer: int64
to
{'op': Value('string'), 'p': Value('int64'), 'p_transfer': Value('int64'), 'history': List({'epoch': Value('int64'), 'train_acc': Value('float64'), 'val_acc': Value('float64'), 'loss': Value('float64')}), 'emergence': {'emerged': Value('bool'), 'emergence_step': Value('float64'), 'inflection_step': Value('float64'), 'width': Value('float64'), 'peak': Value('float64'), 'n_snapshots': Value('int64')}, 'grokked': Value('bool'), 'final_val_acc': Value('float64'), 'grok_epoch': Value('float64'), 'final_epoch': Value('int64'), 'probe_a_r2': Value('float64'), 'probe_b_r2': Value('float64'), 'probe_res_r2': Value('float64'), 'interchange': {'score': Value('float64'), 'n': Value('int64'), 'n_moved': Value('int64'), 'sign_agreement': Value('float64'), 'soft_gain': Value('float64'), 'expected': List(Value('int64')), 'predicted': List(Value('int64'))}, 'truth_horizon': {'transfer_modulus': Value('int64'), 'transfer_acc_mod_p2': Value('float64'), 'transfer_acc_mod_p': Value('float64'), 'n_transfer': Value('int64'), 'rule_internalized': Value('bool')}, 'params': Value('int64'), 'seconds': Value('float64')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
IOO — Conservation-Slip Signatures on Hyper-Graph Fiber Bundles
Verified results bundle for the working manuscript "Machine Intelligence for Physical Systems: Conservation-Slip Signatures on Hyper-Graph Fiber Bundles — From Simulated Aircraft to Satellite and Robot Telemetry" (Sehaj Randhir Singh, NYU ECE).
This dataset hosts the machine-readable verification artifacts of the IOO (Index of Operators / Index of Operations) framework: one JSON per experiment, each produced and verified end-to-end on Kaggle compute at the full protocol (except where noted). The framework introduces a conservation-slip signature: a physics-derived, unit-invariant statistic that measures the lagged asynchrony a physical boundary accumulates relative to its healthy coupling profile. Faults decouple boundaries; slip rises; the machine's graph manifold deforms. One representation — monitor, transfer, control, design.
Headline verified results (all reproduced end-to-end on Kaggle)
| Experiment | Kernel | Result |
|---|---|---|
| Simulated aircraft, run-disjoint detection | ioo-hypermechanism |
AUC 0.992 (MLP 0.482); zero-shot 0.933 |
| Real ESA satellite (OPS-SAT-AD), official split | ioo-reald-conservation-v2 |
slip adds +0.040 AUROC; unsupervised 0.659 |
| Satellite strong recalibration | same | slip −0.013 AUROC drop (univariate −0.432) |
| Real NASA fleet (N-CMAPSS DS02), zero-shot units | ioo-hybrid-ncmapss |
RUL RMSE 20.11 vs retrained-MLP 33.90 |
| Real hexapod robot, detection / localization | same reald v2 kernel | 0.990 / macro-AUC 1.000 |
| Fleet compiler representation | ioo-shape-compiler-fleet |
shape curvature 5.6× more unit-invariant than raw channels |
| Shape classification boundary (honest) | same | supervised stats 0.985 vs shape 0.602 on level drift |
| Decisive baselines, third fleet (S2d) | same | under per-unit sensor recalibration: stats 0.985→0.675 (drop 0.310), shape measured drop ≤0.005, DANN 0.983→0.573 (drop 0.409) |
| Zero-shot MPC re-control (hardware swap) | ioo-zero-shot-mpc-re-control-c1 |
settle err 0.0001 (flat MLP 0.0237) |
| Slip-conditioned control (frozen model, 5× gain drop) | ioo-slip-conditioned-mpc |
ISE 0.067 vs plain 0.442 (6.6×) |
| Same under noise + 5% dropouts | ioo-slip-conditioned-mpc-noise |
ISE 8.2×, ss err 11.4× better |
| Generative physical design over healthy shape manifold | ioo-shape-generative-design |
interpolations at 1.44× real spacing; 90.2% inside envelope |
| Fixed public benchmark: CruiseBench, all nine subdatasets (eta5-W256-S10) | ioo-on-cruisebench-ds02-cpu-run + ioo-cruisebench-sweep-{a,b,c} |
IOO-CONV wins 6/9 subdatasets (DS02 1.83 vs GRU 2.02 prior best); benchmark mean 3.32 vs best-published 3.37 / TSMixer 3.46; losses reported (DS03/DS04/DS08c) |
| Wind-farm operating envelope (honest 4th-fleet boundary) | ioo-windfarm-conservation v3 |
no representation detects pre-fault at 10-min SCADA (stats 0.585, slip 0.497, DANN 0.539) — decoupling dynamics averaged out |
Layout
manuscript.pdf— current compiled working manuscript (22 pp).results/— per-experiment verified JSONs (full protocol on Kaggle; each file records its ownquickflag).- Regeneration: kernels are rebuilt from
ioo_kaggle/build_kernels.py; figures fromioo_paper/make_figs.py; manuscript viapdflatex ioo_paper/manuscript.tex. Kernels are private during review and will be made public on acceptance (access on request).
Why this is a candidate representation paradigm
- Invariance by construction, not by learning. Per-channel affine recalibration cancels in the slip asynchrony ratio (Proposition 1 in the manuscript) — verified: univariate statistics collapse (−0.432 AUROC) under test-telemetry recalibration while slip does not degrade (−0.013); nonlinear sweeps degrade ≤ 0.007 AUROC.
- Capability, not just scores. A frozen dynamics model re-normalizes commands through its own boundary readout and holds a plant through a 5× actuator-gain drop at healthy setpoint error — no retraining, no fault flag — and the same controller survives measurement noise and dropouts.
- Honest boundaries. Calibrated statistics carry absolute level/magnitude (supervised level-drift classification 0.985 vs 0.602; RUL regression); the shape representation is blind to level drift by design and its home is the decoupling regime (hexapod localization, satellite drift-resistance, cross-fault transfer).
- Generative. A VAE over healthy dev-unit shape signatures synthesizes designs that land near the healthy manifold and back-compute physically valid operating points.
Provenance
- Datasets: OPS-SAT-AD (Zenodo 12588359, CC-BY-4.0; ESA OPS-SAT operated by the European Space Agency), hexapod joints (Kaggle), N-CMAPSS DS02 (U.S. Government Works; Chao et al., Data 6, 5 (2021)).
- All experiments deterministic or seed-fixed; learner variance reported where it exists.
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