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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.18889266 |
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Table of Contents:
- <p> Description: A method for applying octonion (hypercomplex, 8-dimensional, non-associative) algebraic decomposition as a<br> general-purpose perception layer over sequential data. Input sequences are encoded as 24-dimensional feature vectors, passed<br> through entropy-gated transponders, projected into octonion pairs, and decomposed via Jordan-Shadow decomposition into three<br> orthogonal components: Jordan (symmetric/convergent intent), Commutator (anti-symmetric/directional momentum), and Associator<br> (non-associative chaos). Two applications are demonstrated: (a) semantic text analysis — extracting structural subtext from<br> natural language without language models, and (b) dynamical systems — regime classification of Collatz hailstone<br> trajectories, revealing that powers of 2 are algebraically most chaotic, Mersenne numbers are smoothest, and the entropy wall<br> between short and long trajectories is inverted. No prior work applies hypercomplex/octonion algebraic decomposition as a<br> perception layer over sequential data. Dependencies: Python >= 3.10, NumPy only.</p>