A system that generates images by inducing random fluctuations in a laser beam could slash energy use compared with standard ...

The graph convolutional network (GCN) has garnered significant attention in hyperspectral image (HSI) classification due to their ability to model non-Euclidean structured data. Compared with ...

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Vision transformers (ViTs) and convolutional neural networks (CNNs) have demonstrated remarkable performance in classifying complicated hyperspectral images (HSIs). However, these models require a lot ...

Fine-grained image classification tasks face challenges such as difficulty in labeling, scarcity of samples, and small category differences. To address this problem, this study proposes a novel ...

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Utilizing neural network models for both equilibrium geometries and chemical shifts reduces the computational time required for accurate proton and 13 C chemical shifts from tens to hundreds of ...