A new computational method could dramatically accelerate efforts to map the body's cells in space, according to a study published in Nature Genetics. Spatial multi-omics technologies—often described ...

The odor receptors in the nose are not distributed at random but organized in a precise spatial pattern, two new studies reveal. By Emily Anthes Over the last century, scientists have mapped several ...

Abstract: Graph Neural Networks (GNNs) have emerged as a promising solution for few-shot hyperspectral image (HSI) classification. However, existing GNN-based approaches face critical limitations in ...

Abstract: Convolutional neural networks (CNNs) and graph neural networks (GNNs) are two widely used architectures in hyperspectral image (HSI) classification. Most CNN models tend to heavily rely on ...

Graph Convolutional Networks (GCNs) are widely applied for spatial domain identification in spatial transcriptomics (ST), where node representations are learned by aggregating information from ...

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...

🌀Spatial Reasoners is a generalization of Spatial Reasoning Models (SRMs) to new domains, packaged as a reusable library for the research community. You can always start with even_pixels experiment, ...

Summary: A new study reveals that the brain rapidly rewires itself to map rewarding experiences like food, even when the location of those rewards changes. Using virtual reality and real-time brain ...

Apple has redesigned the iOS 26 Lock Screen to take full advantage of Liquid Glass, its new unifying UI vision that encompasses all its operating systems. With dynamic fonts, 3D effects, and an ...

The growing availability of spatial transcriptomics data offers key resources for annotating query datasets using reference datasets. However, batch effects, unbalanced reference annotations, and ...