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 ...

Researchers at The University of Texas MD Anderson Cancer Center have developed a spatial map of muscle-invasive bladder cancer, revealing how tumor cell states, immune environments and therapeutic ...

Every competitive game has a defining feature that separates the casual crowd from the veteran strategists. In some titles, it is raw mechanical crosshair speed. In others, it is fast-moving movement ...

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 ...

scpc-python provides spatial correlation-robust inference for regression coefficients following Müller & Watson 2022 and Müller & Watson 2023, implemented in Python based on their original Stata ...

Abstract: Hyperspectral unmixing (HU) is dedicated to disassemble mixed pixels into a group of pure spectral signatures (endmembers) and their respective fractional abundances. By utilizing available ...

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 ...

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 ...