This paper introduces an active inference computational framework for empathy in AI agents, enabling explicit perspective-taking through a self-other model transformation. It demonstrates that empathic perspective-taking can induce robust cooperation in strategic dilemmas like the Iterated Prisoner's Dilemma, highlighting empathy as a structural prior for socially aligned AI.

This paper introduces FedGIN, a federated learning framework that uses augmentation-driven generalization to enable robust medical image segmentation across different imaging modalities (CT and MRI) without sharing sensitive patient data. It demonstrates that spatial-domain augmentations, specifically Global Intensity Nonlinear (GIN), significantly improve segmentation accuracy in cross-modmodality federated settings.

This paper proposes a novel scheduling and analysis method for Directed Acyclic Graph (DAG)-structured GPU tasks to enhance real-time predictability. It introduces techniques like parallelism scaling and node segmentation to reduce task execution time and provide a safe makespan bound without relying on kernel priorities.

This paper investigates the effectiveness of Explainable Artificial Intelligence (XAI) methods in making Vision-Language Models (VLMs), specifically CLIP, interpretable within art-historical contexts. It evaluates seven XAI methods through zero-shot localization experiments and human interpretability studies, concluding that their effectiveness depends on the conceptual stability and representational availability of the examined categories.

This paper introduces a transformer-based AI system utilizing the RT-DETR model to accurately detect fungal elements in potassium hydroxide (KOH) microscopy images for dermatophytosis, effectively distinguishing them from common artefacts. The system demonstrated high sensitivity and accuracy, suggesting its potential as a reliable automated screening tool in dermatomycology.