Demand Estimation with Variable Choice Sets: A Likelihood Correction for Nested Logit
本文解决了需求估计中选择集变化的问题,通过修正嵌套logit模型的似然函数来准确估计替代效应和拥挤效应。
本文解决了需求估计中选择集变化的问题,通过修正嵌套logit模型的似然函数来准确估计替代效应和拥挤效应。
研究探讨了Wang-Sitters舍入方案的11/6最坏情况常数,通过最小完成时间和顶点选择方法验证此常数无法通过限制步骤自由度来改善。
研究解决了图平衡问题,通过分析Wang-Sitters的11/6近似方案的有效范围和限制,确定了不同参数下的最坏情况保证。
This study addresses identity bias and accessibility issues in large language model-generated descriptions for security robots. Leveraging 236 identity labels, we employ identity-conditioned prompting and multidimensional text analysis to evaluate generated content. Notably, this work establishes readability as a baseline metric for assessing identity-sensitive outputs, constructing a comprehensive benchmark framework encompassing lexical, semantic, and fairness dimensions. Our findings reveal significant readability disparities across varying prompt conditions and demographic attributes. By providing interpretable evaluation criteria for early-stage robotic development, this research effectively enhances the inclusivity and standardization of design descriptions, offering a systematic approach to mitigating bias in AI-generated technical documentation for security applications.
This study addresses the 3/2 integrality gap between the configuration linear programming (Config-LP) relaxation and integer optimal solutions in restricted assignment scheduling, specifically for instances where each job can be processed on at most two machines and only two distinct processing times exist. By employing graph-theoretic modeling, symmetry reduction, exhaustive search verified with both exact rational and floating-point arithmetic, and complexity analysis, the authors establish the existence of a unique minimal witness instance \( I^* \) with six jobs—improving upon the previously known seven-job example. They further prove that no instance with five or fewer jobs can achieve this gap and that at least four machines are necessary. Additionally, they provide a complete classification of all witness instances with seven jobs (13 in total) and eight jobs (154 in total).
本文解决了需求估计中选择集变化的问题,通过修正嵌套logit模型的似然函数来准确估计替代效应和拥挤效应。
研究探讨了Wang-Sitters舍入方案的11/6最坏情况常数,通过最小完成时间和顶点选择方法验证此常数无法通过限制步骤自由度来改善。
研究解决了图平衡问题,通过分析Wang-Sitters的11/6近似方案的有效范围和限制,确定了不同参数下的最坏情况保证。
This study addresses identity bias and accessibility issues in large language model-generated descriptions for security robots. Leveraging 236 identity labels, we employ identity-conditioned prompting and multidimensional text analysis to evaluate generated content. Notably, this work establishes readability as a baseline metric for assessing identity-sensitive outputs, constructing a comprehensive benchmark framework encompassing lexical, semantic, and fairness dimensions. Our findings reveal significant readability disparities across varying prompt conditions and demographic attributes. By providing interpretable evaluation criteria for early-stage robotic development, this research effectively enhances the inclusivity and standardization of design descriptions, offering a systematic approach to mitigating bias in AI-generated technical documentation for security applications.
This study addresses the 3/2 integrality gap between the configuration linear programming (Config-LP) relaxation and integer optimal solutions in restricted assignment scheduling, specifically for instances where each job can be processed on at most two machines and only two distinct processing times exist. By employing graph-theoretic modeling, symmetry reduction, exhaustive search verified with both exact rational and floating-point arithmetic, and complexity analysis, the authors establish the existence of a unique minimal witness instance \( I^* \) with six jobs—improving upon the previously known seven-job example. They further prove that no instance with five or fewer jobs can achieve this gap and that at least four machines are necessary. Additionally, they provide a complete classification of all witness instances with seven jobs (13 in total) and eight jobs (154 in total).