Emergency Vertex Cover
本文提出了紧急顶点覆盖问题(Em-VC)，通过给定足够功率使远端顶点也能覆盖边，旨在解决如城市灾难响应等实际问题，并为此设计了多项式时间算法及近似算法。
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Solution of the Hempel's statistical ambiguity problem and Causal AI
This work addresses Carl Hempel’s problem of statistical ambiguity—the challenge of deriving contradictory predictions from statistical regularities—by proposing a framework of Maximal Specific Causal Relationships (MSCRs) grounded in Nancy Cartwright’s probabilistic theory of causality. The approach formalizes causal rules, semantic probabilistic reasoning, and context-sensitive probability-raising models, integrating invariant feature learning with invariant causal prediction to systematically reconcile conflicting statistical information. The paper establishes, for the first time, a rigorous proof that MSCRs guarantee predictive consistency (Theorem 1), thereby demonstrating the solvability of the statistical ambiguity problem and offering a unified framework for causal artificial intelligence and causal machine learning that is both theoretically sound and computationally tractable.
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Minimum distances of LDPC codes in 5G standard
This study addresses the minimum distance—a critical error-correction performance metric—of quasi-cyclic LDPC codes specified in the 5G NR standard, focusing on both high- and low-rate base graph 1 (BG1) configurations. By integrating algebraic analysis, combinatorial bounding algorithms, and cyclic modulo reduction techniques, the work establishes tight upper and lower bounds on the minimum distance for specific code instances: [9984, 8448] codes exhibit a minimum distance between 8 and 14, while [25344, 8448] codes range from 22 to 57. Furthermore, the paper introduces a novel early-termination strategy based on cyclic modulo reduction, which substantially reduces the computational complexity of parity-check operations during decoding. This approach enhances decoding efficiency without compromising error-correction performance.
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Generalized Heavy-tailed Mutation for Evolutionary Algorithms
This work addresses the performance limitations of static mutation rates in OneMax-type problems by extending the theoretical foundation of heavy-tailed mutation within the (1+(λ,λ)) genetic algorithm framework. Specifically, it generalizes the underlying distribution from power-law to the broader class of regularly varying distributions and introduces a novel mutation operator satisfying this condition. The proposed approach maintains an expected optimization time of O(n) while overcoming the inherent constraints of fixed mutation rates. Theoretically, it outperforms any (1+(λ,λ)) algorithm employing a static mutation rate, and extensive experiments confirm its empirical efficacy.
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Semi-Supervised 3D Segmentation for Type-B Aortic Dissection with Slim UNETR
High-quality annotations are scarce for 3D segmentation of Type B Aortic Dissection (TBAD), and existing methods lack robustness across multiple anatomical structures—true lumen (TL), false lumen (FL), and flap (FLT). Method: We propose a multi-output semi-supervised framework based on Slim UNETR, integrating multi-branch decoders, pseudo-labeling, and synergistic strong-weak data augmentation. Crucially, we introduce hypothesis-free probabilistic response consistency regularization—via rotation and flipping—for the first time in multi-output medical image segmentation, enabling end-to-end, post-processing-free semi-supervised training. Results: On the ImageTBAD dataset, our method achieves Dice scores of 89.7% (TL), 84.3% (FL), and 76.5% (FLT) using only 30% labeled data—surpassing both fully supervised baselines and state-of-the-art semi-supervised approaches. This demonstrates the efficacy of non-probabilistic consistency modeling for multi-structure segmentation in TBAD.
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Emergency Vertex Cover
本文提出了紧急顶点覆盖问题(Em-VC)，通过给定足够功率使远端顶点也能覆盖边，旨在解决如城市灾难响应等实际问题，并为此设计了多项式时间算法及近似算法。
0 citationsRead paper
Solution of the Hempel's statistical ambiguity problem and Causal AI
This work addresses Carl Hempel’s problem of statistical ambiguity—the challenge of deriving contradictory predictions from statistical regularities—by proposing a framework of Maximal Specific Causal Relationships (MSCRs) grounded in Nancy Cartwright’s probabilistic theory of causality. The approach formalizes causal rules, semantic probabilistic reasoning, and context-sensitive probability-raising models, integrating invariant feature learning with invariant causal prediction to systematically reconcile conflicting statistical information. The paper establishes, for the first time, a rigorous proof that MSCRs guarantee predictive consistency (Theorem 1), thereby demonstrating the solvability of the statistical ambiguity problem and offering a unified framework for causal artificial intelligence and causal machine learning that is both theoretically sound and computationally tractable.
0 citationsRead paper
Minimum distances of LDPC codes in 5G standard
This study addresses the minimum distance—a critical error-correction performance metric—of quasi-cyclic LDPC codes specified in the 5G NR standard, focusing on both high- and low-rate base graph 1 (BG1) configurations. By integrating algebraic analysis, combinatorial bounding algorithms, and cyclic modulo reduction techniques, the work establishes tight upper and lower bounds on the minimum distance for specific code instances: [9984, 8448] codes exhibit a minimum distance between 8 and 14, while [25344, 8448] codes range from 22 to 57. Furthermore, the paper introduces a novel early-termination strategy based on cyclic modulo reduction, which substantially reduces the computational complexity of parity-check operations during decoding. This approach enhances decoding efficiency without compromising error-correction performance.
0 citationsRead paper
Generalized Heavy-tailed Mutation for Evolutionary Algorithms
This work addresses the performance limitations of static mutation rates in OneMax-type problems by extending the theoretical foundation of heavy-tailed mutation within the (1+(λ,λ)) genetic algorithm framework. Specifically, it generalizes the underlying distribution from power-law to the broader class of regularly varying distributions and introduces a novel mutation operator satisfying this condition. The proposed approach maintains an expected optimization time of O(n) while overcoming the inherent constraints of fixed mutation rates. Theoretically, it outperforms any (1+(λ,λ)) algorithm employing a static mutation rate, and extensive experiments confirm its empirical efficacy.
0 citationsRead paper
Semi-Supervised 3D Segmentation for Type-B Aortic Dissection with Slim UNETR
High-quality annotations are scarce for 3D segmentation of Type B Aortic Dissection (TBAD), and existing methods lack robustness across multiple anatomical structures—true lumen (TL), false lumen (FL), and flap (FLT). Method: We propose a multi-output semi-supervised framework based on Slim UNETR, integrating multi-branch decoders, pseudo-labeling, and synergistic strong-weak data augmentation. Crucially, we introduce hypothesis-free probabilistic response consistency regularization—via rotation and flipping—for the first time in multi-output medical image segmentation, enabling end-to-end, post-processing-free semi-supervised training. Results: On the ImageTBAD dataset, our method achieves Dice scores of 89.7% (TL), 84.3% (FL), and 76.5% (FLT) using only 30% labeled data—surpassing both fully supervised baselines and state-of-the-art semi-supervised approaches. This demonstrates the efficacy of non-probabilistic consistency modeling for multi-structure segmentation in TBAD.
0 citationsRead paper