No abstract

360-degree videos have gained increasing popularity in recent years with the developments and advances in Virtual Reality (VR) and Augmented Reality (AR) technologies. In such applications, a user only watches a video scene within a field of view (FoV) centered in a certain direction. Predicting the future FoV in a long time horizon (more than seconds ahead) can help save bandwidth resources in on-demand video streaming while minimizing video freezing in networks with significant bandwidth variations. In this work, we treat the FoV prediction as a sequence learning problem, and propose to predict the target user's future FoV not only based on the user's own past FoV center trajectory but also other users' future FoV locations. We propose multiple prediction models based on two different FoV representations: one using FoV center trajectories and another using equirectangular heatmaps that represent the FoV center distributions. Extensive evaluations with two public datasets demonstrate that the proposed models can significantly outperform benchmark models, and other users' FoVs are very helpful for improving long-term predictions.Index Terms-virtual reality; 360-degree video streaming; time series prediction; field of view;

Programmable DNA nucleases such as TALENs and CRISPR/Cas9 are emerging as powerful tools for genome editing. Dual-fluorescent surrogate systems have been demonstrated by several studies to recapitulate DNA nuclease activity and enrich for genetically edited cells. In this study, we created a single-strand annealing-directed, dual-fluorescent surrogate reporter system, referred to as C-Check. We opted for the Golden Gate Cloning strategy to simplify C-Check construction. To demonstrate the utility of the C-Check system, we used the C-Check in combination with TALENs or CRISPR/Cas9 in different scenarios of gene editing experiments. First, we disrupted the endogenous pIAPP gene (3.0 % efficiency) by C-Check-validated TALENs in primary porcine fibroblasts (PPFs). Next, we achieved gene-editing efficiencies of 9.0-20.3 and 4.9 % when performing single- and double-gene targeting (MAPT and SORL1), respectively, in PPFs using C-Check-validated CRISPR/Cas9 vectors. Third, fluorescent tagging of endogenous genes (MYH6 and COL2A1, up to 10.0 % frequency) was achieved in human fibroblasts with C-Check-validated CRISPR/Cas9 vectors. We further demonstrated that the C-Check system could be applied to enrich for IGF1R null HEK293T cells and CBX5 null MCF-7 cells with frequencies of nearly 100.0 and 86.9 %, respectively. Most importantly, we further showed that the C-Check system is compatible with multiplexing and for studying CRISPR/Cas9 sgRNA specificity. The C-Check system may serve as an alternative dual-fluorescent surrogate tool for measuring DNA nuclease activity and enrichment of gene-edited cells, and may thereby aid in streamlining programmable DNA nuclease-mediated genome editing and biological research.

Video watching time is a crucial measure for studying user watching behavior in online Internet video-on-demand (VoD) systems. It is important for system planning, user engagement understanding, and system quality evaluation. However, due to the limited access of user data in large-scale streaming systems, a systematic measurement, analysis, and modeling of video watching time is still missing. In this paper, we measure PPLive, one of the most popular commercial Internet VoD systems in China, over a three week period. We collect accurate user watching data of more than 100 million streaming sessions of more than 100 thousand distinct videos. Based on the measurement data, we characterize the distribution of watching time of different types of videos and reveal a number of interesting characteristics regarding the relation between video watching time and various video-related features (including video type, duration, and popularity). We further build a suite of mathematical models for characterizing these relationships. Extensive performance evaluation shows the high accuracy of these models as compared with commonly used data-mining based models. Our measurement and modeling results bring forth important insights for simulation, design, deployment, and evaluation of Internet VoD systems.