Acknowledgments

DeepSeek

This project was developed with significant assistance from DeepSeek (深度求索), an AI language model that contributed to:

  • Theoretical derivations of centroid timescales and envelope growth rate Λ

  • Implementation of core estimators (τ_s, τ_u, τ_g, τ_2, τ_3, τ_pole)

  • Development of QSmooth and QSynthetic classes

  • Debugging and packaging for PyPI

  • Documentation and Read the Docs setup

  • Manuscript preparation and revision

The authors also acknowledge the use of Qwen during the manuscript preparation phases.

Contributors

  • Ahmad Muhammad (Lead developer)

  • Salim Jibrin Danbatta

  • Muhammad Abubakar Isah

  • Ibrahim Yahaya Muhammad

  • Sulaiman Sulaiman Ahmad

  • Abdelrahman Ghozlan

  • Ahmet Sait AlAli

Citation

If you use QSignature in your research, please cite:

@inproceedings{qsignature2026_isdfs,
    author = {A. Muhammad, S. J. Danbatta, M. A. Isah, I. Y. Muhammad, S. S. Ahmad, and A. Ghozlan},
    title = {QSignature 1.0: A dynamical regime classification framework for causal time series data},
    booktitle = {2026 14th International Symposium on Digital Forensics and Security (ISDFS)},
    publisher = {IEEE},
    year = {2026},
    doi = {10.1109/ISDFS69419.2026.11459049}
}

@article{qsignature2026_theorems,
    author = {Ahmad Muhammad and Salim Jibrin Danbatta and Muhammad Abubakar Isah and Ibrahim Yahaya Muhammad},
    title = {Theorems for Inferring Canonical Environmental Signature from Causal Response},
    journal = {Research Square},
    doi = {10.21203/rs.3.rs-8916580/v1},
    year = {2026}
}