Background Aware Correlation Filter (BACF) tracker achieves accurate tracking result in visual object tracking by mitigating boundary effects, yet is limited in challenging scenarios especially in viewpoint change and illumination variation, which are frequently encountered in Unmanned Aerial Vehicle (UAV) tracking tasks. To address the shortcomings, we propose a Foreground Aware Correlation Filter with adaptive feature response fusion (FACF). In this paper, we use saliency detection to generate foreground prior knowledge in training phase for suppressing potential noise. Furthermore, recognizing the limitation of BACF, which relies on a single feature, a novel adaptive fusion strategy is designed to fuse multiple feature responses during the detection phase. This strategy aims to enhance the robustness of the tracker. Extensive experiments have been conducted on three challenging benchmarks. The tracking results show that the proposed tracker performs accurate and robust tracking result and satisfies real-time requirement with 48.28fps.

In this article, a new variational pansharpening method based on kernel estimation and regional extended low rank is proposed, which aims to generate a high resolution multispectral (HRMS) image by fusing the panchromatic (PAN) and multispectral (MS) image. First, an estimated blurring kernel is generated for the spectral constraint term, which can build the relationship between the MS and HRMS image more accurately and improve the spectral quality of the HRMS image. Second, a spatial constrain term is designed by adopting the proportional relationship of the PAN and HRMS image in gradient domain, which preserves the geometric information of the PAN image well. Third, according to sensor imaging principle, a prior constraint term is proposed based on regional extended low rank, which can improve the spatial clarity of HRMS image. The above three constraint terms are combined to form the proposed variational pansharpening method, and the ADMM method is applied for solving it. Finally, experiments show the effectiveness of the proposed method through comparing with other state-of-art pansharpening methods.

The rapid improvements in genomic sequencing technology have led to the proliferation of locally collected genomic datasets. Given the sensitivity of genomic data, it is crucial to conduct collaborative studies while preserving the privacy of the individuals. However, before starting any collaborative research effort, the quality of the data needs to be assessed. One of the essential steps of the quality control process is population stratification: identifying the presence of genetic difference in individuals due to subpopulations. One of the common methods used to group genomes of individuals based on ancestry is principal component analysis (PCA). In this article, we propose a privacy-preserving framework which utilizes PCA to assign individuals to populations across multiple collaborators as part of the population stratification step. In our proposed client-server-based scheme, we initially let the server train a global PCA model on a publicly available genomic dataset which contains individuals from multiple populations. The global PCA model is later used to reduce the dimensionality of the local data by each collaborator (client). After adding noise to achieve local differential privacy (LDP), the collaborators send metadata (in the form of their local PCA outputs) about their research datasets to the server, which then aligns the local PCA results to identify the genetic differences among collaborators' datasets. Our results on real genomic data show that the proposed framework can perform population stratification analysis with high accuracy while preserving the privacy of the research participants.

For readability assessment, traditional methods mainly employ machine learning classifiers with hundreds of linguistic features.Although the deep learning model has become the prominent approach for almost all NLP tasks, it is less explored for readability assessment.In this paper, we propose a BERT-based model with feature projection and length-balanced loss (BERT-FP-LBL) for readability assessment.Specially, we present a new difficulty knowledge guided semi-supervised method to extract topic features to complement the traditional linguistic features.From the linguistic features, we employ projection filtering to extract orthogonal features to supplement BERT representations.Furthermore, we design a new length-balanced loss to handle the greatly varying length distribution of data.Our model achieves state-of-theart performances on two English benchmark datasets and one dataset of Chinese textbooks, and also achieves the near-perfect accuracy of 99% on one English dataset.Moreover, our proposed model obtains comparable results with human experts in consistency test.