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 ...

SpatialActor is a disentangled framework for robust robotic manipulation. It decouples perception into complementary high-level geometry from fine-grained but noisy raw depth and coarse but robust ...

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 ...

Retinal ganglion cells (RGCs) transmit visual signals to the brain, and their diversity supports specialized visual functions. Using gene expression mapping and machine learning, we charted the ...

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 ...