This paper presents an extraction of unpolarized quark transverse-momentum-dependent parton distribution functions (TMD PDFs) from Drell-Yan data using an AI-assisted Bayesian inference framework. It performs a global analysis of Drell-Yan data and compares the results with those obtained using the replica method, highlighting differences in uncertainty estimates.

This paper introduces the Space Splitting-based User Selection (SS-US) algorithm, a novel low-complexity and massively parallelizable method for MU-MIMO user selection. It addresses the scalability challenges posed by the combinatorial nature of existing approaches in dense, uplink-oriented, and latency-critical AI-native traffic scenarios, while achieving comparable spectral efficiency to state-of-the-art baselines.

This paper reviews how the "usefulness" of representations is conceptualized across philosophy, neuroscience, and computer science. It proposes a three-level framework—Representations as Information, Usable, and Used—to organize diverse perspectives on neural representations.

This paper provides a comprehensive literature review on the application of Large Language Models (LLMs) in business process modeling, detailing their current integration into text-to-model pipelines and identifying key challenges and future research directions for supporting complex organizational process modeling.

This paper argues that artificial intelligence now functions as a determinant of health, proposing a novel epidemiological framework to measure and study its population-level effects. It differentiates between ambient and personal AI exposure and discusses the implications for study design, health equity, and AI governance.