Learning with little data is challenging but often inevitable in various application scenarios where the labeled data are limited and costly. Recently, few-shot learning (FSL) gained increasing attention because of its generalizability of prior knowledge to new tasks that contain only a few samples. However, for data-intensive models such as vision transformer (ViT), current fine-tuning-based FSL approaches are inefficient in knowledge generalization and, thus, degenerate the downstream task performances. In this article, we propose a novel mask-guided ViT (MG-ViT) to achieve an effective and efficient FSL on the ViT model. The key idea is to apply a mask on image patches to screen out the task-irrelevant ones and to guide the ViT focusing on task-relevant and discriminative patches during FSL. Particularly, MG-ViT only introduces an additional mask operation and a residual connection, enabling the inheritance of parameters from pretrained ViT without any other cost. To optimally select representative few-shot samples, we also include an active learning-based sample selection method to further improve the generalizability of MG-ViT-based FSL. We evaluate the proposed MG-ViT on classification, object detection, and segmentation tasks using gradient-weighted class activation mapping (Grad-CAM) to generate masks. The experimental results show that the MG-ViT model significantly improves the performance and efficiency compared with general fine-tuning-based ViT and ResNet models, providing novel insights and a concrete approach toward generalizing data-intensive and large-scale deep learning models for FSL.

SUMMARY: Transcription factors (TFs) are critical regulation elements and its dysregulation can lead to a variety of cancers. However, currently, there are no such online resources for large-scale collection, storage and analysis of TF-cancer associations in those cancers. To fill this gap, we present a database called TFcancer (http://lcbb.swjtu.edu.cn/tfcancer/), which contains 3136 experimentally supported associations between 364 TFs and 33 TCGA cancers by manually curating more than 1800 literature. TFcancer mainly concentrates on four aspects: TF expression, molecular alteration, regulatory relationships between TFs and target genes, and biological processes and signaling pathways of TFs in cancers. TFcancer not only provides a user-friendly interface for browsing and searching but also allows flexible data downloading and user data submitting. It is believed that TFcancer is a helpful and valuable resource for researchers who seek to understand the functions and molecular mechanisms of TFs involved in human cancers. AVAILABILITY AND IMPLEMENTATION: The TFcancer are freely available at http://lcbb.swjtu.edu.cn/tfcancer/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This paper initially presents the channel modeling for full duplex (FDX) modulating retro-reflector (MRR) freespace optical (FSO) systems under weak-to-strong turbulence conditions. The uplink channel in the MRR FSO communication is a conventional FSO channel that is typically modeled by a Gamma-Gamma (ΓΓ) distribution. The downlink channel is jointly affected by the turbulence-induced fading in two opposite passes as well as the reflection effect of the MRR, and its normalized channel fading can be described as the product of two correlated ΓΓ random variables (RVs). The channel model of the downlink is approximated by a single α-μ distribution via moment matching method. Then, we propose an effective signal modulation and detection scheme to achieve the FDX communication. The bit error rates of the MRR FSO systems are calculated by the analytical formulas that are derived based on the channel models of the two links, and also evaluated by the Monte-Carlo simulations to validate the derived formulas. The effect of system configurations and propagation conditions on the system performance is investigated, and the optimal designs to make a trade-off between the uplink and downlink performance are discussed.