Yuanxiang Jiang | Searcharxiv

arXiv2022

Diverse surface waves supported by bianisotropic metasurfaces

Surface waves supported by structured metallic surfaces, i.e. metasurfaces, have drawn wide attention recently. They are promising for various applications ranging from integrated photonic circuits to imaging and bio-sensing in various frequency regimes. In this work, we show that surface states with diverse polarization configurations can be supported by a metasurface consisting of a single layer of bianisotropic metamaterial elements. The structure possesses D2d symmetry, which includes mirror symmetry in the xz and yz plane, and C2 rotational symmetry along y = +-x axis. Due to this unique symmetry, the metasuface supports both transverse electric (TE) and transverse magnetic (TM) waves along kx and ky directions, while a purely longitudinal mode and an elliptically polarized transverse electromagnetic (TEM) mode along ky = +-kx directions. The versatility of the surface modes on the metasurface may lead to new surface wave phenomena and device applications.

physics.app-ph↗

arXiv2021

Improving the performance of reputation evaluating by combining the structure of network and nonlinear recovery

Characterizing the reputation of an evaluator is particularly significant for consumer to obtain useful information from online rating systems. Furthermore, to overcome the difficulties with spam attacks on the rating system and to get the reliable on reputation of evaluators is an important topic in the research. We have noticed that most of the existing evaluator reputation evaluation methods only rely on the evaluator's rating information and abnormal behavior to establish a reputation system, which miss the systematic aspects of the rating systems including the structure of the evaluator-object bipartite network and the effects of nonlinear effects. This study we propose an improved reputation evaluation method by combining the structure of the evaluator-object bipartite network with rating information and introducing penalty and reward factors. This novel method has been empirically analyzed on a large-scale artificial data set and two real data sets. The results show that the proposed method is more accurate and robust in the presence of spam attacks. This fresh idea contributes a new way for building reputation evaluation models in sparse bipartite rating network.

cs.SI↗