Programmable metasurfaces promise a great potential to construct low-cost phased array systems due to their capability of elaborate control over electromagnetic waves for Internet of Things (IoT) applications. However, they are usually in either reflective or transmissive scheme, and possess a relatively high profile as a result of the external feed source. Besides, the phase resolution is insufficient comparing with conventional phased array antennas. In this article, we propose a low-profile wideband radiation-type programmable metasurface and further present the high-precision beam modulating for IoT applications. It could avoid the external space-feed source required by its traditional counterpart, thus achieving a significant reduction of profile height through integration of a high-efficiency microwave excitation network and metasurface radiators. Furthermore, high-precision amplitude-phase weight algorithm was then synchronously carried out for low side-lobe beam scanning through time-coding sequences modulation strategy. Experimental results illustrate that our programmable metasurface could obtain a peak gain of 20.6 dBi at 5.5 GHz with reduced sidelobe level of lower than -23 dB.

Modern code review is a critical and indispensable practice in a pull-request development paradigm that prevails in Open Source Software (OSS) development. Finding a suitable reviewer in projects with massive participants thus becomes an increasingly challenging task. Many reviewer recommendation approaches (recommenders) have been developed to support this task which apply a similar strategy, i.e. modeling the review history first then followed by predicting/recommending a reviewer based on the model. Apparently, the better the model reflects the reality in review history, the higher recommender's performance we may expect. However, one typical scenario in a pull-request development paradigm, i.e. one Pull-Request (PR) (such as a revision or addition submitted by a contributor) may have multiple reviewers and they may impact each other through publicly posted comments, has not been modeled well in existing recommenders. We adopted the hypergraph technique to model this high-order relationship (i.e. one PR with multiple reviewers herein) and developed a new recommender, namely HGRec, which is evaluated by 12 OSS projects with more than 87K PRs, 680K comments in terms of accuracy and recommendation distribution. The results indicate that HGRec outperforms the state-of-the-art recommenders on recommendation accuracy. Besides, among the top three accurate recommenders, HGRec is more likely to recommend a diversity of reviewers, which can help to relieve the core reviewers' workload congestion issue. Moreover, since HGRec is based on hypergraph, which is a natural and interpretable representation to model review history, it is easy to accommodate more types of entities and realistic relationships in modern code review scenarios. As the first attempt, this study reveals the potentials of hypergraph on advancing the pragmatic solutions for code reviewer recommendation.

Genome-wide association studies (GWAS) of T2D have discovered a number of loci that contribute to susceptibility to the disease. In this paper, we classified and identified the suspected risky Loci of T2D with computational method based on the known T2D GWAS-associated SNPs. The framework includes two parts: we first classified the SNPs based on their features of position and function through a simplified classification decision tree which was constructed by C4.5 decision tree algorithm; we then identified whether the genes associated with the suspected risky SNPs are associated with T2D by using random walk algorithm with Restart Model on the PPI network of T2D GWAS-associated genes among proteins and interactors. Based on the classification of SNP associated with T2D, we analyzed molecular pathogenesis of T2D. We verified the accuracy and reliability of the classification and identification framework with the data set of GWAS-associated SNPs. The result shows that this method is reliable. It provides a significant way to identify and classify the suspected risky Loci associated with T2D and further insights into the molecular pathogenesis of T2D.