Characterizing features affecting local ancestry inference performance in admixed populations.

In recent years, significant efforts have been made to improve methods for genomic studies of admixed populations using local ancestry inference (LAI). Accurate LAI is crucial to ensure that downstream analyses accurately reflect the genetic ancestry of research participants. Here, we test analytic strategies for LAI to provide guidelines for optimal accuracy, focusing on admixed populations reflective of Latin America's primary continental ancestries-African (AFR), Amerindigenous (AMR), and European (EUR).

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.