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At present, low teaching efficiency has been the common problem of ideological and political education in colleges and universities in China. It is essential to improve the teaching efficiency and realize the intelligent information transformation of the ideological and political courses in colleges and universities. First, the relationship between ideological and political courses and the educational psychology of college students was analyzed based on the theoretical characteristics of educational psychology and college ideological and political courses. Additionally, the teaching efficiency of ideological and political courses based on deep learning (DL) was analyzed through a literature survey. Combined with online teaching modes such as the flipped classroom and Massive Open Online Courses, a comprehensive online teaching mode of college ideological and political courses was proposed via educational psychology and the Single Shot MutiBox Detector networks of DL. Then, a total of 100 research subjects were selected randomly from the freshmen and sophomores of the Southwest University of Science and Technology, and their acceptability to the online ideological and political courses was analyzed by a questionnaire survey. The results show that the adopted questionnaire had high reliability and validity, and the proportion of respondents of different genders, grades, and majors was essentially balanced. More than half of the students had a good understanding of the comprehensive ideological and political courses and made progress in their values, ideology, morals, and knowledge reserves. More than half of the students had a positive attitude to the course, and they thought that the class atmosphere of the course was active, which was conducive to a satisfactory learning effect. This indicates that the teaching strategy of ideological and political courses in colleges and universities that integrates educational psychology, DL, and online information can attract students. The contribution of this study is that the research outcome can be applied to the concrete formulation of the teaching strategies of ideological and political courses for college students.

Edge bundling methods can effectively alleviate visual clutter and reveal high-level graph structures in large graph visualization. Researchers have devoted significant efforts to improve edge bundling according to different metrics. As the edge bundling family evolve rapidly, the quality of edge bundles receives increasing attention in the literature accordingly. In this paper, we present MLSEB, a novel method to generate edge bundles based on moving least squares (MLS) approximation. In comparison with previous edge bundling methods, we argue that our MLSEB approach can generate better results based on a quantitative metric of quality, and also ensure scalability and the efficiency for visualizing large graphs.

Edge bundling is a promising graph visualization approach to simplifying the visual result of a graph drawing. Plenty of edge bundling methods have been developed to generate diverse graph layouts. However, it is difficult to defend an edge bundling method with its resulting layout against other edge bundling methods as a clear theoretic evaluation framework is absent in the literature. In this paper, we propose an information-theoretic framework to evaluate the visual results of edge bundling techniques. We first illustrate the advantage of edge bundling visualizations for large graphs, and pinpoint the ambiguity resulting from drawing results. Second, we define and quantify the amount of information delivered by edge bundling visualization from the underlying network using information theory. Third, we propose a new algorithm to evaluate the resulting layouts of edge bundling using the amount of the mutual information between a raw network dataset and its edge bundling visualization. Comparison examples based on the proposed framework between different edge bundling techniques are presented.

With supercomputers developing towards exascale, the number of compute cores increases dramatically, making more complex and larger-scale applications possible. The input/output (I/O) requirements of large-scale applications, workflow applications, and their checkpointing include substantial bandwidth and an extremely low latency, posing a serious challenge to high performance computing (HPC) storage systems. Current hard disk drive (HDD) based underlying storage systems are becoming more and more incompetent to meet the requirements of next-generation exascale supercomputers. To rise to the challenge, we propose a hierarchical hybrid storage system, on-line and near-line file system (ONFS). It leverages dynamic random access memory (DRAM) and solid state drive (SSD) in compute nodes, and HDD in storage servers to build a three-level storage system in a unified namespace. It supports portable operating system interface (POSIX) semantics, and provides high bandwidth, low latency, and huge storage capacity. In this paper, we present the technical details on distributed metadata management, the strategy of memory borrow and return, data consistency, parallel access control, and mechanisms guiding downward and upward migration in ONFS. We implement an ONFS prototype on the TH-1A supercomputer, and conduct experiments to test its I/O performance and scalability. The results show that the bandwidths of single-thread and multi-thread 'read'/'write' are 6-fold and 5-fold better than HDD-based Lustre, respectively. The I/O bandwidth of data-intensive applications in ONFS can be 6.35 times that in Lustre.