Selected work

Representative Papers

An efficient heuristic for geometric analysis of cell deformations

Jan 26, 2025Comput. Biol. Medicine

This study addresses the urgent need for efficient and accurate automated classification of red blood cells in sickle cell disease, where abnormal cell morphology impairs blood flow and oxygen delivery. The authors model red blood cells as closed planar curves in shape space and propose a novel framework for computing elastic shape distances based on principal axis alignment and fixed parameterization, circumventing the computationally expensive global reparameterization traditionally required. This approach significantly reduces computational complexity while preserving high accuracy. Integrating template matching, unsupervised clustering, and supervised classification, the method achieves 96.03% accuracy in both binary classification tasks, outperforming existing shape analysis techniques.

2 citationsRead paper

Archetypal cases for questionnaires with nominal multiple choice questions

Jan 08, 2026

This study addresses the analysis of nominal single-choice questionnaire data by proposing a novel archetypal analysis method that extends archetypal analysis—traditionally limited to continuous variables—to nominal variable settings for the first time. The approach represents each individual as a convex combination of actual extreme response patterns, termed archetypes, thereby effectively identifying both typical and boundary cases. Unlike conventional archetypal analysis and its probabilistic variants, which are ill-suited for nominal data, the proposed method explicitly models the convex geometric structure inherent in categorical responses. Experimental results on the German Credit dataset demonstrate that the method substantially enhances the interpretability and structural insight into nominal data, offering a new paradigm for questionnaire data analysis.

1 citationsRead paper

The Semantic Elevation Operator and the Closure of the Undecidable Class under Preservation

Sep 10, 2026

本文通过引入语义提升算子解决自修改系统中静态语义属性的保持问题，证明了即使打破赖斯定理要求的外延性，提升后的属性仍不可判定。

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Decoding the Past: An Uncertainty-Aware Deep Learning Framework for Sex Attribution in Prehistoric Hand Stencils

Aug 14, 2026

This study addresses the uncertainty arising from missing ground truth and image degradation in sex determination of prehistoric hand stencils by proposing an end-to-end uncertainty-aware deep learning framework. By integrating ensemble learning, contour enhancement, and manifold mapping, the method explicitly models and propagates uncertainty throughout the analytical pipeline while employing explainable AI to verify anatomical consistency, thereby transforming uncertainty into a quantifiable component of archaeological inference. Achieving over 88% accuracy on modern samples, the framework effectively distinguishes between morphologically stable and ambiguous cases. Consequently, this work enables robust decoding, confidence quantification, and reproducible inference regarding sex attribution in prehistoric rock art, offering a rigorous computational approach to mitigating epistemic limitations in paleoanthropological research.

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Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation

Aug 06, 2026

This work addresses the challenge that classical simulation cost of quantum circuits grows rapidly with scale, and the efficiency of GPU execution heavily depends on the choice of tensor network contraction order, yet high-performing orders are difficult to identify a priori. The paper introduces, for the first time, a learning-to-rank approach to this problem, proposing a gradient-boosting ranking model that leverages structural features of contraction sequences and is trained on empirical GPU performance data to efficiently pre-select high-quality contraction plans before execution. Employing both listwise and pairwise ranking objectives, the method substantially outperforms random and MinFill baselines across multiple circuit families, with the listwise variant achieving the best results. Experiments across different GPU architectures demonstrate partial transferability of performance, significantly reducing reliance on costly search procedures and highlighting strong practical utility.

0 citationsRead paper

Recent publications

Latest Papers

The Semantic Elevation Operator and the Closure of the Undecidable Class under Preservation

Sep 10, 2026

本文通过引入语义提升算子解决自修改系统中静态语义属性的保持问题，证明了即使打破赖斯定理要求的外延性，提升后的属性仍不可判定。

0 citationsRead paper

Decoding the Past: An Uncertainty-Aware Deep Learning Framework for Sex Attribution in Prehistoric Hand Stencils

Aug 14, 2026

This study addresses the uncertainty arising from missing ground truth and image degradation in sex determination of prehistoric hand stencils by proposing an end-to-end uncertainty-aware deep learning framework. By integrating ensemble learning, contour enhancement, and manifold mapping, the method explicitly models and propagates uncertainty throughout the analytical pipeline while employing explainable AI to verify anatomical consistency, thereby transforming uncertainty into a quantifiable component of archaeological inference. Achieving over 88% accuracy on modern samples, the framework effectively distinguishes between morphologically stable and ambiguous cases. Consequently, this work enables robust decoding, confidence quantification, and reproducible inference regarding sex attribution in prehistoric rock art, offering a rigorous computational approach to mitigating epistemic limitations in paleoanthropological research.

0 citationsRead paper

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation

Aug 06, 2026

This work addresses the challenge that classical simulation cost of quantum circuits grows rapidly with scale, and the efficiency of GPU execution heavily depends on the choice of tensor network contraction order, yet high-performing orders are difficult to identify a priori. The paper introduces, for the first time, a learning-to-rank approach to this problem, proposing a gradient-boosting ranking model that leverages structural features of contraction sequences and is trained on empirical GPU performance data to efficiently pre-select high-quality contraction plans before execution. Employing both listwise and pairwise ranking objectives, the method substantially outperforms random and MinFill baselines across multiple circuit families, with the listwise variant achieving the best results. Experiments across different GPU architectures demonstrate partial transferability of performance, significantly reducing reliance on costly search procedures and highlighting strong practical utility.

0 citationsRead paper

A Kleene theorem for free many-sorted algebras

Jun 29, 2026

This work extends the classical Kleene theorem to a multi-sorted setting of free algebras, resolving the equivalence between recognizable and regular languages in this generalized framework. Under suitable finiteness assumptions, the study integrates multi-sorted algebraic structures, formal language theory, and automata-theoretic techniques to establish, for the first time, a necessary and sufficient condition that a language over a multi-sorted free algebra is recognizable if and only if it is regular. This result unifies and generalizes existing theories of language recognizability across diverse multi-sorted structures, thereby providing a broader foundation for algebraic automata theory.

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The Undecidability of Artificial General Intelligence (AGI) Alignment

Jun 26, 2026

This work establishes a fundamental theoretical barrier to the verification of AGI alignment, demonstrating that correctness in alignment is structurally unverifiable. By integrating formal methods from mathematical logic, computability theory, and descriptive complexity, the paper introduces the “Alignment Unverifiability Theorem” and the “Finite-Structure Unverifiability Theorem,” thereby articulating—for the first time—a trilemma among soundness, completeness, and tractability. This trilemma arises intrinsically from limits imposed by descriptive complexity. The study further reveals that prevailing AI safety mechanisms are not ad hoc fixes but structural compromises that deliberately sacrifice logical expressiveness to obtain decidable fragments of safety. Consequently, the work delineates the theoretical boundaries and feasibility limits inherent to aligning artificial general intelligence.

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