This release contains the versioned software, frozen experiment records, automated tests, publication figures, and bilingual manuscript sources associated with an identifiability-aware method for selecting shared finite-memory models from sparse grouped observations. The method fits candidate positive-rate realizations with rates shared across independent specimens or material groups and unit-specific amplitudes and offsets. A candidate order is retained only when information gain, held-unit early-to-late prediction, foldwise log-rate stability, and adjacent-rate resolution support the same interpretation. When these criteria disagree, the model order is reported as unresolved. The archive corresponds to GitHub commit 13001874f788f9de9b49632961166d3713561f7b. It includes source code, 151 automated tests, frozen machine-readable results, experiment drivers, vector figures, and English and Chinese AMM manuscript sources. Third-party public datasets are not redistributed. Persistent source identifiers, frozen SHA-256 digests, and a verified downloader are included so that the public inputs can be retrieved from their authoritative repositories. The software is released under the MIT License. Dataset licenses and attribution requirements remain those of the original data providers.
Haitao Duan, Ning Hu, Shuqun Li et al.· Zenodo (CERN European Organi...· 0 citations
Executable implementation, frozen experiment records, tests, figures, and bilingual manuscript sources supporting an evidence-based qualification protocol for differentiable numerical components. The archive contains conformance, synthetic-strategy, external-subject, and historical-defect evidence, including three complete historical buggy/fixed defect families across PyTorch and SciPy. It also contains the Journal of Systems and Software submission snapshot, supplementary material, and machine-readable provenance records. The evidence is scoped to the declared components, environments, and test catalogue; it is not a universal reliability guarantee.
Ning Hu, Haitao Duan, Shuqun Li et al.· Zenodo (CERN European Organi...· 0 citations
This release contains the versioned software, frozen experiment records, automated tests, publication figures, and bilingual manuscript sources associated with an identifiability-aware method for selecting shared finite-memory models from sparse grouped observations. The method fits candidate positive-rate realizations with rates shared across independent specimens or material groups and unit-specific amplitudes and offsets. A candidate order is retained only when information gain, held-unit early-to-late prediction, foldwise log-rate stability, and adjacent-rate resolution support the same interpretation. When these criteria disagree, the model order is reported as unresolved. The archive corresponds to GitHub commit 13001874f788f9de9b49632961166d3713561f7b. It includes source code, 151 automated tests, frozen machine-readable results, experiment drivers, vector figures, and English and Chinese AMM manuscript sources. Third-party public datasets are not redistributed. Persistent source identifiers, frozen SHA-256 digests, and a verified downloader are included so that the public inputs can be retrieved from their authoritative repositories. The software is released under the MIT License. Dataset licenses and attribution requirements remain those of the original data providers.
Haitao Duan, Ning Hu, Shuqun Li et al.· Zenodo (CERN European Organi...· 0 citations
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