Robust Tests for Step-Stress Models under Exponential Lifetimes
本文针对高可靠性产品寿命测试中的数据不足问题，提出基于最小密度幂散度估计器的稳健检验统计量方法来处理步进应力加速寿命试验下的指数分布寿命模型。
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S-matrix informed neural networks for amplitude analysis
本文提出S-矩阵信息神经网络解决粒子物理中散射振幅重构问题，直接从数据学习并尊重基本原理，同时开发新数据选择程序。
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TalentCLEF at CLEF2026: Skill and Job Title Intelligence for Human Capital Management
This study addresses key challenges in human capital management—namely, job-candidate matching, skill identification, and fairness—by introducing two core tasks: contextualized job-person matching and job-skill alignment with fine-grained skill type classification. The work presents the first NLP evaluation framework that integrates privacy preservation, multilingual adaptability, and fairness considerations, accompanied by a publicly released benchmark dataset grounded in real-world applications. By establishing this comprehensive infrastructure, the research advances the development of reproducible, cross-industry, and multilingual intelligent talent-matching systems and offers a novel paradigm for skill modeling and fairness evaluation in AI-driven human resource technologies.
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On the suitability of ratio variables in data envelopment analysis: An application to education with methodological extensions
This study addresses the limitation of traditional data envelopment analysis (DEA) in handling ratio-type variables, which has hindered its application in international educational assessments such as PISA. The authors propose a novel DEA framework tailored for fully ratio-based inputs and outputs, establishing equivalence between the variable returns-to-scale ratio model and the constant returns-to-scale volumetric model to enable fair efficiency measurement across OECD countries. Innovatively incorporating the index of economic, social, and cultural status as an input, the framework extends both radial and directional distance functions and integrates advanced techniques—including adaptive convex envelope splines (ACES), stochastic chance constraints, and fuzzy DEA—to enhance model robustness and usability. Empirical application to PISA data facilitates equitable cross-socioeconomic comparisons of educational performance, offering a generalizable and reproducible analytical tool for international assessments.
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Overview of the TalentCLEF 2026: Skill and Job Title Intelligence for Human Capital Management
This work addresses the challenges of person-job matching and skill identification in human resource management by proposing an intelligent matching approach that integrates multilingual and context-aware capabilities. Through the organization of the TalentCLEF 2026 challenge, two tasks were introduced within a unified evaluation framework: cross-lingual (English/Spanish) semantic matching between resumes and job postings, and fine-grained retrieval and classification of core versus contextual skills driven by English job titles. The initiative established standardized datasets and evaluation benchmarks, leveraging natural language processing techniques—including textual representation, semantic matching, information retrieval, and classification—to enable precise, context-sensitive, and cross-lingual matching. The challenge attracted 113 participating teams with over 400 submissions, significantly advancing community engagement and technical progress in NLP for HR applications.
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Robust Tests for Step-Stress Models under Exponential Lifetimes
本文针对高可靠性产品寿命测试中的数据不足问题，提出基于最小密度幂散度估计器的稳健检验统计量方法来处理步进应力加速寿命试验下的指数分布寿命模型。
0 citationsRead paper
S-matrix informed neural networks for amplitude analysis
本文提出S-矩阵信息神经网络解决粒子物理中散射振幅重构问题，直接从数据学习并尊重基本原理，同时开发新数据选择程序。
0 citationsRead paper
TalentCLEF at CLEF2026: Skill and Job Title Intelligence for Human Capital Management
This study addresses key challenges in human capital management—namely, job-candidate matching, skill identification, and fairness—by introducing two core tasks: contextualized job-person matching and job-skill alignment with fine-grained skill type classification. The work presents the first NLP evaluation framework that integrates privacy preservation, multilingual adaptability, and fairness considerations, accompanied by a publicly released benchmark dataset grounded in real-world applications. By establishing this comprehensive infrastructure, the research advances the development of reproducible, cross-industry, and multilingual intelligent talent-matching systems and offers a novel paradigm for skill modeling and fairness evaluation in AI-driven human resource technologies.
0 citationsRead paper
On the suitability of ratio variables in data envelopment analysis: An application to education with methodological extensions
This study addresses the limitation of traditional data envelopment analysis (DEA) in handling ratio-type variables, which has hindered its application in international educational assessments such as PISA. The authors propose a novel DEA framework tailored for fully ratio-based inputs and outputs, establishing equivalence between the variable returns-to-scale ratio model and the constant returns-to-scale volumetric model to enable fair efficiency measurement across OECD countries. Innovatively incorporating the index of economic, social, and cultural status as an input, the framework extends both radial and directional distance functions and integrates advanced techniques—including adaptive convex envelope splines (ACES), stochastic chance constraints, and fuzzy DEA—to enhance model robustness and usability. Empirical application to PISA data facilitates equitable cross-socioeconomic comparisons of educational performance, offering a generalizable and reproducible analytical tool for international assessments.
0 citationsRead paper
Overview of the TalentCLEF 2026: Skill and Job Title Intelligence for Human Capital Management
This work addresses the challenges of person-job matching and skill identification in human resource management by proposing an intelligent matching approach that integrates multilingual and context-aware capabilities. Through the organization of the TalentCLEF 2026 challenge, two tasks were introduced within a unified evaluation framework: cross-lingual (English/Spanish) semantic matching between resumes and job postings, and fine-grained retrieval and classification of core versus contextual skills driven by English job titles. The initiative established standardized datasets and evaluation benchmarks, leveraging natural language processing techniques—including textual representation, semantic matching, information retrieval, and classification—to enable precise, context-sensitive, and cross-lingual matching. The challenge attracted 113 participating teams with over 400 submissions, significantly advancing community engagement and technical progress in NLP for HR applications.
0 citationsRead paper