Cross-Propagative Graph Learning Reveals Spatial Tissue Domains in Multi-Modal Spatial Transcriptomics. [PDF]
Guo Y, Liu S, Zhang Z, Zhang S, Li L.
europepmc +1 more source
On‐Chip Photonic Neural Network Architectures
This review presents a comprehensive overview of on‐chip photonic neural network architectures, covering key photonic building blocks, representative network types, and emerging applications. Recent advances, implementation challenges, and future directions are examined, highlighting the potential of integrated photonics to enable ultrafast, energy ...
Seokjin Hong +7 more
wiley +1 more source
Asynchronous Cross-Modal Dynamic Graph Learning for Intelligent Sensing of AI Computing Infrastructure Expansion. [PDF]
Xiang Z +6 more
europepmc +1 more source
Scalable Task Planning via Large Language Models and Structured World Representations
This work efficiently combines graph‐based world representations with the commonsense knowledge in Large Language Models to enhance planning techniques for the large‐scale environments that modern robots will need to face. Planning methods often struggle with computational intractability when solving task‐level problems in large‐scale environments ...
Rodrigo Pérez‐Dattari +4 more
wiley +1 more source
DynaTCR: dynamic hard-negative ensemble graph learning improves TCR-epitope binding prediction. [PDF]
Fu X +5 more
europepmc +1 more source
An AI‐powered, robot‐assisted framework automatically produces, images, and analyzes 3D tumor spheroids to evaluate drug efficacy. Integrated modules handle spheroid formation, live/dead staining, brightfield imaging, and automated image analysis, including spheroid segmentation, viability and metrics to assess the drug treatment efficacy. The workflow
Dalia Mahdy +13 more
wiley +1 more source
Adaptive spatiotemporal graph learning for multi-horizon probabilistic wind power forecasting. [PDF]
Hua D +5 more
europepmc +1 more source
Grounding Large Language Models for Robot Task Planning Using Closed‐Loop State Feedback
BrainBody‐Large Language Model (LLM) introduces a hierarchical, feedback‐driven planning framework where two LLMs coordinate high‐level reasoning and low‐level control for robotic tasks. By grounding decisions in real‐time state feedback, it reduces hallucinations and improves task reliability.
Vineet Bhat +4 more
wiley +1 more source
SemanticST: A Scalable Multi-Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi-Sample Integration in Spatial Transcriptomics. [PDF]
Zahedi R +7 more
europepmc +1 more source
Asymmetry in Skipping Enhances Viability Against Control Input Noise
Quadruped animals use asymmetric galloping gaits at high speeds, yet the functional role of this asymmetry remains unclear. This study shows that left–right asymmetry in touchdown angles enhances robustness to control noise. Using a simple two‐legged locomotion model and viability theory, it demonstrates that asymmetric skipping substantially enlarges ...
Yuichi Ambe, Alvin So, Shinya Aoi
wiley +1 more source

