Adaptive pixel attention network for hyperspectral image classification.

Patch features obtained by fixed convolution kernel have become the main form in hyperspectral image (HSI) classification processing. However, the fixed convolution kernel limits the weight learning of channels, which results in the potential connections between pixels not being captured in patches, and seriously affects the classification performance. To tackle the above issues, we propose a novel Adaptive Pixel Attention Network, which can improve HSI classification by further mining the connections between pixels in patch features.

Residual channel attention based sample adaptation few-shot learning for hyperspectral image classification.

Few-shot learning (FSL) uses prior knowledge and supervised experience to effectively classify hyperspectral images (HSIs), thereby reducing the cost of large numbers of labeled samples. However, existing few-shot methods ignore the correlation between cross-domain feature channels, and the feature representation ability is insufficient. To address above issue, this paper proposes a novel Residual Channel Attention Based Sample Adaptation Few-Shot Learning for Hyperspectral Image Classification(RCASA-FSL) for hyperspectral image classification (HSIC), which can capture and enhance cross-domain dependencies through multi-layer residual connection and random-based feature recalibration.

Examining the Role of Neuroticism Polygenic Risk in Late Life Cognitive Change: A UK Biobank Study.

Cognitive decline is a public health concern affecting about 50 million individuals worldwide. Neuroticism, defined as the trait disposition to experience intense and frequent negative emotions, has been associated with an increased risk of late-life cognitive decline. However, the underlying biological mechanisms of this association remain unknown.