Quiescence in Concert: Composing Multi-Channel Time-Outs for IOCO
本文解决了实时系统测试中多通道超时问题，通过引入一个多通道提升方法，并证明该方法与并行组合兼容，保持了模型基础测试的有效性。
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AQMP: Image compression through Adaptive Quadtree Refinement and Matching Pursuit with Hyperparameter Optimization
This work addresses the limitations of conventional matching pursuit–based image compression, which employs fixed-size blocks and struggles to balance efficiency and reconstruction quality across both complex and smooth image regions. To overcome this, the authors propose a novel approach that integrates adaptive quadtree partitioning with matching pursuit, dynamically adjusting block sizes according to local image structure. Furthermore, they introduce a multi-objective Bayesian optimization framework based on a tree-structured Parzen estimator (TPE) to achieve an optimal trade-off between compression ratio and structural similarity index (SSIM). The method substantially enhances compression performance, attaining up to four times the compression ratio of JPEG at comparable SSIM levels, while also enabling multi-level parallelization for applicability across diverse compression scenarios.
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Contrastive Analysis of Linguistic Representations in Large Language Model Outputs through Structured Synthetic Data Generation and Abstracted N-gram Associations
This work proposes a context-aware bias detection framework that identifies subtle linguistic biases in large language model outputs toward diverse social groups without relying on predefined lists of sensitive terms. The approach generates structured synthetic minimal-pair texts—narratively consistent except for the substitution of target group markers—and employs linguistic form abstraction combined with an enhanced variant of pointwise mutual information (PMI) for comparative analysis. Integrating quantitative statistics with qualitative evaluation, the framework is adaptable across multiple text genres and effectively quantifies asymmetric associations between social groups and levels of linguistic abstraction. It precisely localizes textual segments with high concentrations of bias signals, enabling domain experts to identify potentially harmful expressions within their contextual settings.
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Software Entropy: A Statistical Mechanics Framework for Software Testing
This work addresses the limitations of existing heuristic-based software testing metrics, which struggle to rigorously quantify a test suite’s ability to constrain the space of valid program implementations. For the first time, statistical mechanics is introduced into software testing: software entropy is formally defined, and a test suite is conceptualized as a macroscopic constraint over the program implementation space. By leveraging mutation analysis to approximate microscopic states, the authors estimate this entropy and propose an information-weighted metric for the distribution of test constraints. This novel measure reveals structural differences among test suites that traditional criteria—such as code coverage—fail to capture. Empirical validation on real-world projects demonstrates the approach’s effectiveness in reducing software entropy, while information weights quantify individual test cases’ contributions to constraining the program space.
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Stochastic Predictive Analytics for Stocks in the Newsvendor Problem
The newsvendor problem faces challenges in dynamic inventory forecasting due to scarce historical data and unknown demand distributions. Method: This paper proposes a distribution-free stochastic modeling framework that bypasses prior distributional assumptions. Leveraging stochastic forecasting analysis, it directly learns the evolution dynamics of inventory states from limited time-series inventory and sales data, enabling dynamic probabilistic characterization of inventory levels. Contribution/Results: Unlike conventional approaches relying on strong parametric assumptions (e.g., normal or Poisson demand), our method establishes a data-driven, distribution-agnostic dynamic modeling paradigm. Experiments on real-world e-marketplace data demonstrate that the model significantly outperforms classical distribution-based methods in short-term forecasting—achieving superior accuracy, robustness, and practical deployability. It provides an interpretable, probability-based solution for inventory decision-making under small-sample regimes.
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Quiescence in Concert: Composing Multi-Channel Time-Outs for IOCO
本文解决了实时系统测试中多通道超时问题，通过引入一个多通道提升方法，并证明该方法与并行组合兼容，保持了模型基础测试的有效性。
0 citationsRead paper
AQMP: Image compression through Adaptive Quadtree Refinement and Matching Pursuit with Hyperparameter Optimization
This work addresses the limitations of conventional matching pursuit–based image compression, which employs fixed-size blocks and struggles to balance efficiency and reconstruction quality across both complex and smooth image regions. To overcome this, the authors propose a novel approach that integrates adaptive quadtree partitioning with matching pursuit, dynamically adjusting block sizes according to local image structure. Furthermore, they introduce a multi-objective Bayesian optimization framework based on a tree-structured Parzen estimator (TPE) to achieve an optimal trade-off between compression ratio and structural similarity index (SSIM). The method substantially enhances compression performance, attaining up to four times the compression ratio of JPEG at comparable SSIM levels, while also enabling multi-level parallelization for applicability across diverse compression scenarios.
0 citationsRead paper
Contrastive Analysis of Linguistic Representations in Large Language Model Outputs through Structured Synthetic Data Generation and Abstracted N-gram Associations
This work proposes a context-aware bias detection framework that identifies subtle linguistic biases in large language model outputs toward diverse social groups without relying on predefined lists of sensitive terms. The approach generates structured synthetic minimal-pair texts—narratively consistent except for the substitution of target group markers—and employs linguistic form abstraction combined with an enhanced variant of pointwise mutual information (PMI) for comparative analysis. Integrating quantitative statistics with qualitative evaluation, the framework is adaptable across multiple text genres and effectively quantifies asymmetric associations between social groups and levels of linguistic abstraction. It precisely localizes textual segments with high concentrations of bias signals, enabling domain experts to identify potentially harmful expressions within their contextual settings.
0 citationsRead paper
Software Entropy: A Statistical Mechanics Framework for Software Testing
This work addresses the limitations of existing heuristic-based software testing metrics, which struggle to rigorously quantify a test suite’s ability to constrain the space of valid program implementations. For the first time, statistical mechanics is introduced into software testing: software entropy is formally defined, and a test suite is conceptualized as a macroscopic constraint over the program implementation space. By leveraging mutation analysis to approximate microscopic states, the authors estimate this entropy and propose an information-weighted metric for the distribution of test constraints. This novel measure reveals structural differences among test suites that traditional criteria—such as code coverage—fail to capture. Empirical validation on real-world projects demonstrates the approach’s effectiveness in reducing software entropy, while information weights quantify individual test cases’ contributions to constraining the program space.
0 citationsRead paper
Stochastic Predictive Analytics for Stocks in the Newsvendor Problem
The newsvendor problem faces challenges in dynamic inventory forecasting due to scarce historical data and unknown demand distributions. Method: This paper proposes a distribution-free stochastic modeling framework that bypasses prior distributional assumptions. Leveraging stochastic forecasting analysis, it directly learns the evolution dynamics of inventory states from limited time-series inventory and sales data, enabling dynamic probabilistic characterization of inventory levels. Contribution/Results: Unlike conventional approaches relying on strong parametric assumptions (e.g., normal or Poisson demand), our method establishes a data-driven, distribution-agnostic dynamic modeling paradigm. Experiments on real-world e-marketplace data demonstrate that the model significantly outperforms classical distribution-based methods in short-term forecasting—achieving superior accuracy, robustness, and practical deployability. It provides an interpretable, probability-based solution for inventory decision-making under small-sample regimes.
0 citationsRead paper