MOTIVATION: Accurate detection of copy number variations (CNVs) from targeted panel sequencing remains challenging due to limited genomic coverage and pronounced sample-specific biases. Existing normalization strategies, including baseline-cohort, matched-control, and single-sample approaches, often struggle to balance noise suppression with adaptability, leading to inconsistent performance across heterogeneous samples. RESULTS: We present PScnv, a personalized self-normalizing framework for robust CNV detection from panel sequencing data. PScnv integrates a pre-built panel-of-normals (PoN) with sample-intrinsic stable chromosomes through ridge-regression normalization to generate individualized log2 ratio profiles with reduced systematic variation. CNVs are then identified using a hierarchical multi-phase segmentation pipeline incorporating z-score pre-partitioning, kernel-based correction, and circular binary segmentation. In 139 clinical tumor samples with orthogonal FISH validation at MET, ERBB2, and MTAP, PScnv showed improved accuracy and robustness over existing methods that do not require patient-matched normal samples, provided that a pre-built PoN cohort is available. AVAILABILITY: Source code is available for academic use at https://github.com/lvws/PScnv.

This letter aims to deal with the UAV-view geolocalization problem, which is essentially to achieve bi-directional cross-view matching between UAV-view and satellite-view images. The existing studies have confirmed the importance of learning part-wise representations for this task. We go a step further by proposing a two-stage learning framework. The first stage focuses on extracting part-wise representations. In the second stage, a novel Adaptive Embedding Network (AEN) integrates these representations into a global embedding of the entire image to avoid an equal influence of all local parts on image similarity measures. Current mainstream methods typically employ CrossEntropy loss to learn location-dependent representations, aiming to push the distance between different locations in the learned representation space. Some approaches also utilize KL loss or Triplet loss to bring a pair of UAV-satellite images from the same location closer for learning view-invariant representations. However, they overlook a critical concern: a notable representation bias exists among UA-view images captured from the same location but at different viewpoints or heights. To address these issues, we devise a novel cross-view matching loss that narrows the distance between the global embeddings of a satellite-view image and the affinity-aware prototype of multiple true-matched UAV-view images. The experimental results on the University1652 dataset indicate that similarity measures in the learned embedding space exhibit excellent generalization to images from new locations, achieving superior cross-view matching performance compared to previous methods

This letter addresses the challenging problem of action prediction with partially observed sequences of skeletons. Towards this goal, we propose a novel two-stage knowledge distillation framework, which transfers prior knowledge to assist the early prediction of ongoing actions. In the first stage, the action prediction model (also referred to as the student) learns from a couple of teachers to adaptively distill action knowledge at different progress levels for partial sequences. Then the learned student acts as a teacher in the next stage, with the objective of optimizing a better action prediction model in a self-training manner. We design an adaptive self-training strategy from the perspective of undermining the supervision from the annotated labels, since this hard supervision is actually too strict for partial sequences without enough discriminative information. Finally, the action prediction models trained in the two stages jointly constitute a two-stream architecture for action prediction. Extensive experiments on the large-scale NTU RGB+D dataset validate the effectiveness of the proposed method.

This paper focuses on predicting the category of an ongoing action with incomplete observations. We propose a novel Action Prediction Network with Auxiliary Observation Ratio regression (AORAP Net), which enhances the discriminative power of partial video features by encoding the prior knowledge of complete actions. The proposed AORAP Net consists of an encoder, an observation ratio regression module, and an action classifier. The encoder transfers global action information from full videos to construct enhanced partial video features. The observation ratio regression module is developed to achieve an auxiliary task of inferring the progress level of an ongoing action. We demonstrate that this module can guide the encoder to learn enhanced features similar to full video features and discriminative for action prediction. The classifier makes the final decision of the action category in terms of the enhanced features. Considering the fact that there is not enough discriminative information at the early stage of actions, a new classification loss is designed to adapt to the action prediction task and alleviate the over-fitting of training videos. Extensive experiments on the BIT-Interaction dataset and the UT-Interaction dataset validate the effectiveness of the proposed method.