Selected work

Representative Papers

Automated Mechanism to Support Trade Transactions in Smart Contracts with Upgrade and Repair

Mar 01, 2025Blockchain: Research and Applications

Smart contracts often suffer from execution interruptions due to unforeseen events, undermining reliability and availability in decentralized trade applications. Method: This paper proposes a maintainable trade contract framework supporting dynamic hot upgrades and fault-driven automatic repair. It integrates the proxy pattern (EVM-compatible chains), formal verification (Certora), runtime state snapshotting, and differential patch injection to enable atomic, non-disruptive updates and precise repairs. Contribution/Results: To the best of our knowledge, this is the first work to jointly integrate dynamic hot upgrading and fault-aware automatic repair within trade contract systems—ensuring backward compatibility and transactional consistency. Evaluation on the Ethereum testnet shows a 99.98% transaction success rate, upgrade latency under 1.2 seconds, average repair time of 3.7 seconds, and zero-downtime remediation of 12 representative vulnerabilities—significantly enhancing security, robustness, and continuous availability of on-chain trade contracts.

3 citationsRead paper

Independent Languages and Witnesses of Dependence (Non-satisfaction)

Aug 25, 2026

研究通过语言方程φ(X)=∅解决独立语言问题，定义了非满足证人，并展示了如何计算正则语言的非满足证人，证明了该问题的决策版本是PSPACE完全的。

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A Persistent Homology Design Space for 3D Point Cloud Deep Learning

Apr 05, 2026

This work addresses the insufficient modeling of topological structures in existing deep learning approaches for 3D point clouds, where persistent homology has largely been relegated to peripheral roles. The authors introduce 3DPHDL—the first systematic design space that deeply integrates persistent homology as a structural inductive bias throughout the entire point cloud learning pipeline. This integration encompasses six well-defined injection points spanning simplicial complex construction, filtration strategies, persistence representations, and their coordination with backbone architectures. Through controlled experiments on PointNet, DGCNN, and Point Transformer—augmented with persistence diagrams, images, and landscapes—on ModelNet40 and ShapeNetPart, the approach significantly improves accuracy in classification and segmentation, enhances part consistency, and boosts robustness to noise and sampling variations, while also revealing inherent trade-offs between representational capacity and computational complexity.

0 citationsRead paper

Some Remarks on Marginal Code Languages

Feb 19, 2026

This work addresses the lack of a unified theoretical framework for k-prefix-, k-suffix-, and k-infix-free languages by proposing two general formalisms based on partial orders and finite-state transducers, thereby integrating these three classes of marginal code languages into a single coherent system for the first time. Building on this foundation, the study systematically generalizes the notion of marginal variants to any code-related language definable by transducers and investigates their uniform satisfiability and maximality properties. The research not only establishes a unified theoretical basis for marginal code languages but also advances the decidability analysis of their key properties, offering novel methodological tools for formal language theory and coding theory.

0 citationsRead paper

Learning Significant Persistent Homology Features for 3D Shape Understanding

Feb 15, 2026

This work addresses the lack of topological information in existing 3D shape datasets, which hinders joint geometric and topological learning. To bridge this gap, the authors introduce the first topologically enriched versions of the ModelNet40 and ShapeNet benchmark datasets by incorporating persistent homology features. They propose TopoGAT, an end-to-end graph attention network architecture that employs a learnable mechanism to select the most salient persistence diagram points, thereby automatically extracting discriminative topological features. Evaluated on 3D point cloud classification and part segmentation tasks, TopoGAT significantly outperforms conventional handcrafted topological feature methods, demonstrating the critical role of topological information in enhancing both model performance and robustness.

0 citationsRead paper

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Latest Papers

Independent Languages and Witnesses of Dependence (Non-satisfaction)

Aug 25, 2026

研究通过语言方程φ(X)=∅解决独立语言问题，定义了非满足证人，并展示了如何计算正则语言的非满足证人，证明了该问题的决策版本是PSPACE完全的。

0 citationsRead paper

A Persistent Homology Design Space for 3D Point Cloud Deep Learning

Apr 05, 2026

This work addresses the insufficient modeling of topological structures in existing deep learning approaches for 3D point clouds, where persistent homology has largely been relegated to peripheral roles. The authors introduce 3DPHDL—the first systematic design space that deeply integrates persistent homology as a structural inductive bias throughout the entire point cloud learning pipeline. This integration encompasses six well-defined injection points spanning simplicial complex construction, filtration strategies, persistence representations, and their coordination with backbone architectures. Through controlled experiments on PointNet, DGCNN, and Point Transformer—augmented with persistence diagrams, images, and landscapes—on ModelNet40 and ShapeNetPart, the approach significantly improves accuracy in classification and segmentation, enhances part consistency, and boosts robustness to noise and sampling variations, while also revealing inherent trade-offs between representational capacity and computational complexity.

0 citationsRead paper

Some Remarks on Marginal Code Languages

Feb 19, 2026

This work addresses the lack of a unified theoretical framework for k-prefix-, k-suffix-, and k-infix-free languages by proposing two general formalisms based on partial orders and finite-state transducers, thereby integrating these three classes of marginal code languages into a single coherent system for the first time. Building on this foundation, the study systematically generalizes the notion of marginal variants to any code-related language definable by transducers and investigates their uniform satisfiability and maximality properties. The research not only establishes a unified theoretical basis for marginal code languages but also advances the decidability analysis of their key properties, offering novel methodological tools for formal language theory and coding theory.

0 citationsRead paper

Learning Significant Persistent Homology Features for 3D Shape Understanding

Feb 15, 2026

This work addresses the lack of topological information in existing 3D shape datasets, which hinders joint geometric and topological learning. To bridge this gap, the authors introduce the first topologically enriched versions of the ModelNet40 and ShapeNet benchmark datasets by incorporating persistent homology features. They propose TopoGAT, an end-to-end graph attention network architecture that employs a learnable mechanism to select the most salient persistence diagram points, thereby automatically extracting discriminative topological features. Evaluated on 3D point cloud classification and part segmentation tasks, TopoGAT significantly outperforms conventional handcrafted topological feature methods, demonstrating the critical role of topological information in enhancing both model performance and robustness.

0 citationsRead paper

Feasible strategies in three-way conflict analysis with three-valued ratings

Dec 24, 2025

Existing three-way conflict analysis approaches primarily focus on identifying conflict structures, lacking systematic modeling and optimization of feasible resolution strategies. Method: This paper proposes a novel three-way conflict-resolution framework that (i) integrates positive/negative similarity measures with agent-issue weighting to jointly characterize consistency and inconsistency; (ii) constructs weighted consistency/inconsistency metrics enabling L-order strategy enumeration and optimal solution selection; and (iii) introduces ternary rating, sensitivity analysis, and comparative evaluation methods. Contribution/Results: Validated on two real-world cases—NBA collective bargaining negotiations and Gansu Province development planning—the framework significantly enhances both the feasibility and interpretability of derived strategies. It provides a computationally tractable, optimization-enabled decision-support tool for conflict governance, advancing three-way analysis from structural diagnosis to actionable resolution design.

0 citationsRead paper