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Representative Papers

Learning Shuffle Ideals with Membership Queries and Contrastive Examples

Sep 26, 2026

This study investigates the learnability of shuffle ideals under membership and counter queries. To address the theoretical bottleneck that certain simple classes cannot be efficiently learned, this work integrates formal language theory with computational learning theory to propose novel structural conditions based on universal words and short lexicographic partitions, alongside corresponding algorithms. The primary contributions lie in delineating the boundaries of unlearnability for specific classes of shuffle ideals while demonstrating that efficient learning is achievable when the proposed structural conditions are satisfied. These findings offer a new perspective on combinatorial problems and deepen the understanding of learning complexity within query-based models.

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Guillotine and Tiling Cofiniteness in Unary Picture Languages

Aug 25, 2026

研究通过水平垂直拼接或平铺操作生成足够大图片的条件，给出了一种精确算术表征方法。

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Wi-Fi Rate Adaptation for Moving Equipment in Industrial Environments

Sep 09, 2025IEEE International Conference on Emerging Technologies and Factory Automation

Wi-Fi rate adaptation algorithms struggle to simultaneously achieve low latency and high reliability in industrial mobile scenarios (e.g., autonomous robots, exoskeletons). Method: This paper presents the first systematic empirical evaluation of the widely adopted open-source Minstrel algorithm under both static and dynamic industrial conditions, analyzing key reliability metrics—including end-to-end transmission latency and packet loss rate—through real-world measurements. Contribution/Results: Results reveal fundamental limitations of Minstrel in mobile settings, including delayed adaptation responses and frequent misjudgments as device mobility increases. The study demonstrates that Minstrel’s design inherently fails to satisfy the stringent deterministic communication requirements of industrial applications, exposing structural inadequacies in dynamic environments. These findings provide critical empirical evidence and design guidelines for developing next-generation rate adaptation mechanisms—specifically, centralized, digital-twin-enabled frameworks capable of predictive control and optimization.

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Latest Papers

Learning Shuffle Ideals with Membership Queries and Contrastive Examples

Sep 26, 2026

This study investigates the learnability of shuffle ideals under membership and counter queries. To address the theoretical bottleneck that certain simple classes cannot be efficiently learned, this work integrates formal language theory with computational learning theory to propose novel structural conditions based on universal words and short lexicographic partitions, alongside corresponding algorithms. The primary contributions lie in delineating the boundaries of unlearnability for specific classes of shuffle ideals while demonstrating that efficient learning is achievable when the proposed structural conditions are satisfied. These findings offer a new perspective on combinatorial problems and deepen the understanding of learning complexity within query-based models.

0 citationsRead paper

Guillotine and Tiling Cofiniteness in Unary Picture Languages

Aug 25, 2026

研究通过水平垂直拼接或平铺操作生成足够大图片的条件，给出了一种精确算术表征方法。

0 citationsRead paper

Wi-Fi Rate Adaptation for Moving Equipment in Industrial Environments

Sep 09, 2025IEEE International Conference on Emerging Technologies and Factory Automation

Wi-Fi rate adaptation algorithms struggle to simultaneously achieve low latency and high reliability in industrial mobile scenarios (e.g., autonomous robots, exoskeletons). Method: This paper presents the first systematic empirical evaluation of the widely adopted open-source Minstrel algorithm under both static and dynamic industrial conditions, analyzing key reliability metrics—including end-to-end transmission latency and packet loss rate—through real-world measurements. Contribution/Results: Results reveal fundamental limitations of Minstrel in mobile settings, including delayed adaptation responses and frequent misjudgments as device mobility increases. The study demonstrates that Minstrel’s design inherently fails to satisfy the stringent deterministic communication requirements of industrial applications, exposing structural inadequacies in dynamic environments. These findings provide critical empirical evidence and design guidelines for developing next-generation rate adaptation mechanisms—specifically, centralized, digital-twin-enabled frameworks capable of predictive control and optimization.

0 citationsRead paper