Today's video streaming service providers have exploited cloud-edge collaborative networks for video delivery across geo-distributed edge clusters and end users. The existing content delivery network (CDN) scheduling and adaptive bitrate algorithms may not fully utilize edge resources or lack a global control to optimize resource sharing. The emerging super-resolution (SR) approach can unleash the potential of leveraging computation resources to compensate for bandwidth consumption, by producing high-quality videos from low-resolution contents. Yet the uncertain SR resource sensitivity and its interplay with bitrate adaptation are under-explored. In this work, we proposeRosevin, the first resource scheduler that jointly decides the bitrates and fine-grained resource allocation to perform SR at the edge, which can learn to optimize the long-term QoE for distributed end users. To handle the time-varying and complex space of decisions as well as a non-smooth objective function,Rosevinrealizes a novel online combinatorial learning algorithm, which nicely integrates convex optimization theories and online learning techniques, addressing the switching cost issues. In addition to theoretically analyzing its performance, we implement an SR-assisted video streaming prototype ofRosevinand demonstrate its advantages over several video delivery benchmarks.

ABSTRACT With the popularity of consumer‐level panoramic cameras, panoramic projection is increasingly used in various industries, such as social communication, entertainment, and education. The utilization of image information, particularly in panoramic projection, predominantly depends on image matching. This technology involves extracting key features from images to meet the requirements of subsequent tasks. However, due to the distortion caused by geometric deformation in panoramic projection, traditional planar image matching methods encounter challenges in key point detection, matching accuracy, and pose estimation, often leading to failure or suboptimal performance. To address this challenge, a novel detector‐free matching method is proposed. Pano‐matching introduces two key innovations: a clustering‐based dynamic pruning scheme to accelerate attention convergence by focusing on valid feature pairs, and a redesigned fine‐level matching approach that effectively leverages both the central feature vector and its local neighbourhood. These innovations allow pano‐matching to handle the distortions in panoramic images and outperform existing convolutional neural network‐based methods in both accuracy and computational efficiency. Experimental results demonstrate that pano‐matching achieves state‐of‐the‐art performance in pose estimation and feature matching, significantly improving over current panoramic and planar matching methods.

Today’s video streaming service providers have exploited cloud-edge collaborative networks for geo-distributed video delivery. The existing content delivery network (CDN) scheduling and adaptive bitrate algorithms may not fully utilize edge resources or lack a global control to optimize resource sharing. The emerging super-resolution (SR) approach can unleash the potential of leveraging computation resources to compensate for bandwidth consumption, by producing high-quality videos from low-resolution contents. Yet the uncertain SR resource sensitivity and its interplay with bitrate adaptation are underexplored. In this work, we propose Rosevin, the first resource scheduler that jointly decides the bitrates and fine-grained resource allocation to perform SR at the edge, which can learn to optimize the long-term QoE for distributed end users. To handle the time-varying and complex space of decisions as well as a non-smooth objective function, Rosevin realizes a novel online combinatorial learning algorithm, which nicely integrates convex optimization theories and online learning techniques. In addition to theoretically analyzing its performance, we implement an SR-assisted video streaming prototype of Rosevin and demonstrate its advantages over several video delivery benchmarks.

The Screen Content Coding (SCC) is an extension of High Efficiency Video Coding (HEVC), and it achieves significant improvement on compression ratio. However, the obtained coding efficiency is at the cost of high computational complexity. In this paper, to reduce the computation complexity, we propose to use an ensemble classifier for predicting the coding unit (CU) in intra-coding. Firstly, the L1-loss based linear support vector machine (SVM) is employed as basic classifier for its simplicity. Then, a bagging scheme is applied to train the linear classifiers and boost the prediction accuracy by ensemble learning. Compared with the reference software SCM-5.0, the proposed scheme can achieve 30% complexity reduction on average with only 1.64% bit rates increase.

UNLABELLED: Runs of homozygosity (RoHs) are genomic stretches of a diploid genome that show identical alleles on both chromosomes. Longer RoHs are unlikely to have arisen by chance but are likely to denote autozygosity, whereby both copies of the genome descend from the same recent ancestor. Early tools to detect RoH used genotype array data, but substantially more information is available from sequencing data. Here, we present and evaluate BCFtools/RoH, an extension to the BCFtools software package, that detects regions of autozygosity in sequencing data, in particular exome data, using a hidden Markov model. By applying it to simulated data and real data from the 1000 Genomes Project we estimate its accuracy and show that it has higher sensitivity and specificity than existing methods under a range of sequencing error rates and levels of autozygosity. AVAILABILITY AND IMPLEMENTATION: BCFtools/RoH and its associated binary/source files are freely available from https://github.com/samtools/BCFtools CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In order to improve the adaptive ability and robustness of the system, the wavelet neural network (WNN) single-scaling frame function with robust controller is used to design the controller. The WNN has self-learning and adaptive ability to approximate nonlinear functions. Combined with the trajectory linearization control (TLC) method, the adaptive robust control strategy and the NSV attitude control system are designed. Then simulations are taken to demonstrate the effectiveness.