Selected work

Representative Papers

Hierarchical Identity-Based Signature with Designated Aggregator from Lattices

Jun 12, 2026

This work proposes the first lattice-based hierarchical identity-based designated aggregate signature (HIBS-DA) scheme to address the high computational complexity and substantial resource overhead associated with multi-user data authentication in hierarchical organizations. The scheme enables users at different hierarchy levels to generate individual signatures that can be aggregated into a single compact signature, significantly reducing communication and verification costs while preserving security, correctness, and unforgeability. By integrating lattice-based cryptography with hierarchical identity-based signatures and designated aggregation, this approach is well-suited for large-scale hierarchical environments such as universities, corporations, and government agencies, offering enhanced authentication efficiency and scalability.

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An Integrative Genome-Scale Metabolic Modeling and Machine Learning Framework for Predicting and Optimizing Biofuel-Relevant Biomass Production in Saccharomyces cerevisiae

Mar 26, 2026

This study addresses the challenge of accurately predicting biomass flux in *Saccharomyces cerevisiae* under diverse environmental and genetic perturbations to enable rational strain design. By integrating the genome-scale metabolic model Yeast9 with multiple machine learning approaches—including Random Forest, XGBoost, Variational Autoencoders, and Generative Adversarial Networks—and leveraging flux balance analysis to generate training data, the work introduces a novel framework that incorporates SHAP interpretability and Bayesian optimization. This approach yields stoichiometrically feasible and innovative flux configurations, achieving prediction accuracy with an R² exceeding 0.999. In silico overexpression simulations produced a biomass flux of 0.979 gDW/hr, while Bayesian optimization further enhanced flux by 12-fold—from 0.0858 to 1.041 gDW/hr—significantly advancing intelligent design in metabolic engineering.

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Filtered 2D Contour-Based Reconstruction of 3D STL Model from CT-DICOM Images

Jan 21, 2026

This study addresses geometric distortions in 3D STL model reconstruction from CT-DICOM images, which often arise from noise and outliers in segmented 2D contours. To mitigate these artifacts, the authors propose a reconstruction pipeline that integrates contour point filtering with Delaunay triangulation. The method begins with image enhancement and threshold-based segmentation to extract initial contours, followed by a dedicated filtering mechanism designed to suppress spurious points induced by low image resolution. High-fidelity 3D models are then generated by layer-wise triangulation and connection of the refined contours. Experimental validation on both synthetic geometric phantoms and region-of-interest (ROI) pelvic anatomies demonstrates that the proposed approach significantly improves geometric accuracy, yielding reconstructions that more faithfully represent true anatomical structures.

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Active Learning Strategies for Efficient Machine-Learned Interatomic Potentials Across Diverse Material Systems

Jan 11, 2026arXiv.org

This work proposes an active learning framework to reduce the cost of first-principles calculations required for training machine learning interatomic potentials (MLIPs). By integrating compositional and property-based descriptors, the approach employs neural network ensembles with Query-by-Committee to quantify predictive uncertainty. The study systematically evaluates sampling strategies—including diversity-based methods (k-means and farthest point sampling), uncertainty-based selection, and hybrid approaches—across multiple material systems such as carbon, silicon, iron, and titanium oxides. For the first time in multi-material settings, diversity sampling is shown to significantly outperform alternatives, achieving target accuracy with 5–13% fewer labeled samples on average; notably, it yields a 10.9% performance gain for titanium oxides (p = 0.008). The entire workflow completes within four hours on a standard Google Colab instance with 8 GB memory.

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Validation Framework for E-Contract and Smart Contract

Apr 27, 2025

Verifying conditional consistency between smart contracts and electronic contracts remains challenging due to the semantic gap between legal text and executable code. Method: This paper proposes the first automated verification framework for joint validation of legal clauses and code logic. It establishes a verifiable mapping from natural-language contract provisions to formal smart contract specifications, integrating cross-modal semantic alignment, rule-driven static analysis, and discrepancy detection. Contribution/Results: The framework systematically bridges the semantics gap between legal texts and program code for the first time, enabling concurrent verification of legal compliance and program correctness. Experimental evaluation across diverse commercial contract scenarios achieves 98.2% accuracy in logical consistency verification, significantly reducing contractual deviations. The end-to-end verification pipeline demonstrates practical deployability.

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Recent publications

Latest Papers

Hierarchical Identity-Based Signature with Designated Aggregator from Lattices

Jun 12, 2026

This work proposes the first lattice-based hierarchical identity-based designated aggregate signature (HIBS-DA) scheme to address the high computational complexity and substantial resource overhead associated with multi-user data authentication in hierarchical organizations. The scheme enables users at different hierarchy levels to generate individual signatures that can be aggregated into a single compact signature, significantly reducing communication and verification costs while preserving security, correctness, and unforgeability. By integrating lattice-based cryptography with hierarchical identity-based signatures and designated aggregation, this approach is well-suited for large-scale hierarchical environments such as universities, corporations, and government agencies, offering enhanced authentication efficiency and scalability.

0 citationsRead paper

An Integrative Genome-Scale Metabolic Modeling and Machine Learning Framework for Predicting and Optimizing Biofuel-Relevant Biomass Production in Saccharomyces cerevisiae

Mar 26, 2026

This study addresses the challenge of accurately predicting biomass flux in *Saccharomyces cerevisiae* under diverse environmental and genetic perturbations to enable rational strain design. By integrating the genome-scale metabolic model Yeast9 with multiple machine learning approaches—including Random Forest, XGBoost, Variational Autoencoders, and Generative Adversarial Networks—and leveraging flux balance analysis to generate training data, the work introduces a novel framework that incorporates SHAP interpretability and Bayesian optimization. This approach yields stoichiometrically feasible and innovative flux configurations, achieving prediction accuracy with an R² exceeding 0.999. In silico overexpression simulations produced a biomass flux of 0.979 gDW/hr, while Bayesian optimization further enhanced flux by 12-fold—from 0.0858 to 1.041 gDW/hr—significantly advancing intelligent design in metabolic engineering.

0 citationsRead paper

Filtered 2D Contour-Based Reconstruction of 3D STL Model from CT-DICOM Images

Jan 21, 2026

This study addresses geometric distortions in 3D STL model reconstruction from CT-DICOM images, which often arise from noise and outliers in segmented 2D contours. To mitigate these artifacts, the authors propose a reconstruction pipeline that integrates contour point filtering with Delaunay triangulation. The method begins with image enhancement and threshold-based segmentation to extract initial contours, followed by a dedicated filtering mechanism designed to suppress spurious points induced by low image resolution. High-fidelity 3D models are then generated by layer-wise triangulation and connection of the refined contours. Experimental validation on both synthetic geometric phantoms and region-of-interest (ROI) pelvic anatomies demonstrates that the proposed approach significantly improves geometric accuracy, yielding reconstructions that more faithfully represent true anatomical structures.

0 citationsRead paper

Active Learning Strategies for Efficient Machine-Learned Interatomic Potentials Across Diverse Material Systems

Jan 11, 2026arXiv.org

This work proposes an active learning framework to reduce the cost of first-principles calculations required for training machine learning interatomic potentials (MLIPs). By integrating compositional and property-based descriptors, the approach employs neural network ensembles with Query-by-Committee to quantify predictive uncertainty. The study systematically evaluates sampling strategies—including diversity-based methods (k-means and farthest point sampling), uncertainty-based selection, and hybrid approaches—across multiple material systems such as carbon, silicon, iron, and titanium oxides. For the first time in multi-material settings, diversity sampling is shown to significantly outperform alternatives, achieving target accuracy with 5–13% fewer labeled samples on average; notably, it yields a 10.9% performance gain for titanium oxides (p = 0.008). The entire workflow completes within four hours on a standard Google Colab instance with 8 GB memory.

0 citationsRead paper

Validation Framework for E-Contract and Smart Contract

Apr 27, 2025

Verifying conditional consistency between smart contracts and electronic contracts remains challenging due to the semantic gap between legal text and executable code. Method: This paper proposes the first automated verification framework for joint validation of legal clauses and code logic. It establishes a verifiable mapping from natural-language contract provisions to formal smart contract specifications, integrating cross-modal semantic alignment, rule-driven static analysis, and discrepancy detection. Contribution/Results: The framework systematically bridges the semantics gap between legal texts and program code for the first time, enabling concurrent verification of legal compliance and program correctness. Experimental evaluation across diverse commercial contract scenarios achieves 98.2% accuracy in logical consistency verification, significantly reducing contractual deviations. The end-to-end verification pipeline demonstrates practical deployability.

0 citationsRead paper