This paper introduces an entropy-centric Explainable AI (XAI) method for semantic segmentation in remote sensing imagery. It also proposes a novel XAI evaluation methodology, demonstrating superior performance compared to existing methods in identifying relevant regions for model decisions.

This paper reviews the utility of Generative AI (GenAI) models, specifically ChatGPT-5 and ScholarAI, in statistical research tasks such as literature review development and translating methodologies into R code for dynamic treatment regime (DTR) estimation. It finds that while GenAI can improve efficiency for certain tasks, it currently lacks the necessary depth and contextual understanding for independent research, requiring significant human supervision.

This paper addresses the long-standing problem of inconsistently defined decentralization in computer systems by introducing a formal, graph-based ontology. This framework defines decentralization as a structural, relational, and subject-specific property, distinguishing it from distribution and enabling multi-dimensional evaluation with new metrics like Void Tolerance and Imperviousness.

This paper addresses the challenge of "hallucinations" in medical AI, particularly in surgical workflow recognition, by proposing a method to define and prevent topological errors. It demonstrates how integrating linear temporal logic predicates into probabilistic graphical models can mathematically guarantee the absence of certain critical errors, significantly improving accuracy and offering a pathway for robust AI regulation in medicine.

ControlRadio introduces a controllable generative framework that produces radio maps from natural language descriptions and environmental layouts. This approach demonstrates state-of-the-art accuracy and dramatically reduces computation time compared to conventional physical simulations, offering a new paradigm for wireless environment modeling.