This paper introduces an MLOps-assisted framework leveraging Red Hat OpenShift AI (RHOAI) for the automated design of anomalous reflector metasurfaces. It employs a conditional Generative Adversarial Network (cGAN) with a surrogate model to efficiently create high-quality freeform metasurface designs.

This paper explores STEM faculty perspectives on the integration of Generative AI in higher education through a focus group study. It investigates how faculty use GenAI in courses, the perceived benefits and challenges for student learning, and necessary institutional support, highlighting shifts in pedagogy, assessment, and governance.

This paper proposes AURA, an AIoT framework for continuous, real-world assessment of driving safety among older adults. It integrates rich in-vehicle sensing, multi-scale behavioral modeling, and context-aware analysis to provide explainable insights into driving performance, addressing the limitations of current infrequent and uncontextualized assessment methods.

This paper introduces the IRIS Benchmark, a novel framework designed to synchronously evaluate fairness in both understanding and generation tasks within Unified Multimodal Large Language Models (UMLLMs). It aims to resolve the "Tower of Babel" dilemma of fragmented fairness metrics by offering a multi-dimensional, trade-off analysis approach and uncovering systemic biases in leading models.

This paper provides a comprehensive review of deep learning-based multimodal data fusion in astronomy. It discusses the motivation for integrating diverse astronomical data, outlines various data sources and modalities, and introduces representative deep learning models and fusion strategies to enhance understanding of the universe.