Selected work

Representative Papers

A chaotic flux cipher based on the random cubic family $f_{c_n}(z)=z^3+c_n z$

Mar 21, 2026

This work proposes a symmetric stream cipher scheme based on chaotic dynamics in the complex plane to address the demanding requirements of high security and noise resilience in complex communication environments such as 5G. The method innovatively introduces the chaotic behavior of random cubic polynomial maps into cryptography, leveraging a control parameter δ to toggle between stable and chaotic regimes. It generates pseudorandom keystreams by exploiting the structural stability of Julia sets and integrates HKDF key derivation, HMAC-SHA-256 authenticated encryption, and a warm-up iteration mechanism. Experimental results demonstrate that the generated keystreams pass the full NIST SP 800-22 statistical test suite, χ² tests, and entropy analysis, achieving high randomness, strong security, and robustness against noise while maintaining key consistency.

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Conditional GLMMs for reaction times in choice tasks

Oct 16, 2025

This study addresses the limited cognitive interpretability of reaction time (RT) modeling in choice tasks. Methodologically, it proposes a conditional modeling framework that integrates the first-passage-time distribution of diffusion models with generalized linear mixed models (GLMMs): RTs are conditionally distributed as inverse Gaussian or Gamma variates, while key diffusion parameters—such as drift rate and boundary threshold—are embedded into the GLMM’s linear predictor, enabling joint estimation of population- and subject-level cognitive parameters. Its primary contribution is the first construction of an identifiable, cognitively interpretable, and computationally tractable “cognitive–statistical” bridge: it preserves the theoretical foundations of diffusion modeling while remaining fully compatible with standard mixed-model software. Simulation and empirical analyses demonstrate robust recovery of canonical cognitive effects (e.g., speed–accuracy trade-offs) and yield substantial improvements in RT distribution fit and mechanistic inference reliability.

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Recent publications

Latest Papers

A chaotic flux cipher based on the random cubic family $f_{c_n}(z)=z^3+c_n z$

Mar 21, 2026

This work proposes a symmetric stream cipher scheme based on chaotic dynamics in the complex plane to address the demanding requirements of high security and noise resilience in complex communication environments such as 5G. The method innovatively introduces the chaotic behavior of random cubic polynomial maps into cryptography, leveraging a control parameter δ to toggle between stable and chaotic regimes. It generates pseudorandom keystreams by exploiting the structural stability of Julia sets and integrates HKDF key derivation, HMAC-SHA-256 authenticated encryption, and a warm-up iteration mechanism. Experimental results demonstrate that the generated keystreams pass the full NIST SP 800-22 statistical test suite, χ² tests, and entropy analysis, achieving high randomness, strong security, and robustness against noise while maintaining key consistency.

0 citationsRead paper

Conditional GLMMs for reaction times in choice tasks

Oct 16, 2025

This study addresses the limited cognitive interpretability of reaction time (RT) modeling in choice tasks. Methodologically, it proposes a conditional modeling framework that integrates the first-passage-time distribution of diffusion models with generalized linear mixed models (GLMMs): RTs are conditionally distributed as inverse Gaussian or Gamma variates, while key diffusion parameters—such as drift rate and boundary threshold—are embedded into the GLMM’s linear predictor, enabling joint estimation of population- and subject-level cognitive parameters. Its primary contribution is the first construction of an identifiable, cognitively interpretable, and computationally tractable “cognitive–statistical” bridge: it preserves the theoretical foundations of diffusion modeling while remaining fully compatible with standard mixed-model software. Simulation and empirical analyses demonstrate robust recovery of canonical cognitive effects (e.g., speed–accuracy trade-offs) and yield substantial improvements in RT distribution fit and mechanistic inference reliability.

0 citationsRead paper