Selected work

Representative Papers

Designing a Token Economy: Incentives, Governance, and Tokenomics

Feb 10, 2026

This study addresses the absence of a systematic, reusable, and empirically grounded end-to-end approach that integrates incentive mechanisms, governance structures, and tokenomics in current token economic designs. To bridge this gap, the paper proposes the Token Economic Design Method (TEDM), which, for the first time, unifies these three dimensions into a structured and actionable design framework, with explicit emphasis on sociotechnical context and early-stage design considerations. Developed through the design science research paradigm and informed by qualitative synthesis, co-design case studies, and expert interviews, TEDM was empirically validated through its application to the Currynomics stablecoin ecosystem and subsequent expert evaluation. The results demonstrate that TEDM effectively supports the analysis and construction of tokenized ecosystems, offering practical and reusable design guidance.

3 citationsRead paper

Learning from VAE Errors to support ECG-based Differential Diagnosis of Myocardial Scar

Sep 04, 2026

研究使用β-VAE从心电图中提取特征以区分有无心肌疤痕的患者，通过对比不同模型性能，发现DTW重建误差有助于提高分类效果。

0 citationsRead paper

Stochastic Bayes factors: why, when, and how

Aug 29, 2026

本文提出随机贝叶斯因子(SBF)解决传统贝叶斯因子依赖先验、无法处理不适当先验及忽视数据不确定性等问题，通过复制数据引入不确定性，提高模型比较的稳健性和预测可靠性。

0 citationsRead paper

Characterizations of continuous adequate objective functions for ordinal or interval scaled data

Aug 20, 2026

本文探讨了如何通过适应数据结构的连续适当目标函数来解决区间或序数尺度数据的优化问题，特别是针对仿射严格递增变换的情况。

0 citationsRead paper

Localized Anomaly Detection via Differentiable D-vine Copulas

Jul 27, 2026

This work addresses the limitations of traditional D-vine copula fitting, which relies on greedy strategies prone to local optima and lacks support for interpretable local anomaly detection. The authors propose the first fully differentiable D-vine copula fitting framework, integrating beam search with gradient-based optimization to enhance global fit quality through multi-path exploration. Leveraging the hierarchical dependency structure inherent in D-vines, the method enables edge-level anomaly localization alongside global anomaly scoring. Furthermore, it incorporates Mondrian conformal prediction to provide statistically valid uncertainty quantification for local anomalies. Experimental results demonstrate that the proposed approach significantly outperforms existing methods across multiple benchmark and real-world datasets, achieving superior performance in both interpretability and anomaly detection accuracy.

0 citationsRead paper

Recent publications

Latest Papers

Learning from VAE Errors to support ECG-based Differential Diagnosis of Myocardial Scar

Sep 04, 2026

研究使用β-VAE从心电图中提取特征以区分有无心肌疤痕的患者，通过对比不同模型性能，发现DTW重建误差有助于提高分类效果。

0 citationsRead paper

Stochastic Bayes factors: why, when, and how

Aug 29, 2026

本文提出随机贝叶斯因子(SBF)解决传统贝叶斯因子依赖先验、无法处理不适当先验及忽视数据不确定性等问题，通过复制数据引入不确定性，提高模型比较的稳健性和预测可靠性。

0 citationsRead paper

Characterizations of continuous adequate objective functions for ordinal or interval scaled data

Aug 20, 2026

本文探讨了如何通过适应数据结构的连续适当目标函数来解决区间或序数尺度数据的优化问题，特别是针对仿射严格递增变换的情况。

0 citationsRead paper

Localized Anomaly Detection via Differentiable D-vine Copulas

Jul 27, 2026

This work addresses the limitations of traditional D-vine copula fitting, which relies on greedy strategies prone to local optima and lacks support for interpretable local anomaly detection. The authors propose the first fully differentiable D-vine copula fitting framework, integrating beam search with gradient-based optimization to enhance global fit quality through multi-path exploration. Leveraging the hierarchical dependency structure inherent in D-vines, the method enables edge-level anomaly localization alongside global anomaly scoring. Furthermore, it incorporates Mondrian conformal prediction to provide statistically valid uncertainty quantification for local anomalies. Experimental results demonstrate that the proposed approach significantly outperforms existing methods across multiple benchmark and real-world datasets, achieving superior performance in both interpretability and anomaly detection accuracy.

0 citationsRead paper

Attractor Geometry Determines the Identifiability Limits of System Discovery

Jul 20, 2026

This work addresses the fundamental limits of symbolic discovery of dynamical system governing equations, demonstrating that these limits are governed by the geometric structure of attractors rather than solely by algorithmic choices or data volume. The study proposes the smallest eigenvalue, λ_min(M), of the moment matrix of the invariant measure as a universal identifiability bound, revealing for the first time that attractor geometry fundamentally constrains equation discovery through this quantity. Leveraging the Birkhoff ergodic theorem to compute λ_min(M), the authors validate its algorithm-agnostic nature using SINDy and PySR on Lorenz-84 and Lorenz-96 systems, and introduce a Soft F1-weighted structural scoring metric to discern performance differences invisible to conventional metrics. Results show that while chaos enhances λ_min(M), noise sensitivity varies across algorithms, and the proposed framework enables cross-system transferability without retraining.

0 citationsRead paper