Effect of Mechanical Strain on the Optical Properties of Nodal-Line Semimetal ZrSiS

Optical properties of nodal-line semimetal ZrSiS are studied using first-principles calculations. Frequency-independent optical conductivity is a fingerprint of the infrared optical response in ZrSiS. It is found that this characteristic feature is robust with respect to uniaxial compressive strain of up to 10 GPa, yet with the flat region being narrowed with increasing strain. Upon uniaxial tensile stress of 2 GPa, the Fermi surface undergoes a Lifshitz transition accompanied by a weakening of the interband screening, which reduces the spectral weight of infrared excitations. It is also shown that the high-energy region is characterized by low-loss plasma excitations at ≈20 eV with essentially anisotropic dispersion. Strongly anisotropic dielectric properties suggest the existence of a hyperbolic regime for plasmons in the deep ultraviolet range. Although the frequencies of high-energy plasmons are virtually unaffected by external uniaxial deformation, their dispersion can be effectively tuned by strain. © 2019 WILEY-VCH Verlag GmbH & Co. KGaA, WeinheimNational Natural Science Foundation of China, NSFC: 117742692018FYA0305800S.Y. acknowledges financial support from the National Key R & D Program of China (Grant No. 2018FYA0305800) and National Science Foundation of China (Grant No. 11774269). A.N.R. acknowledges travel support from FLAG-ERA JTC2017 Project GRANSPORT. Numerical calculations presented in this paper were performed on a supercomputing system in the Supercomputing Center of Wuhan University

PMU Placement in Electric Transmission Networks for Reliable State Estimation against False Data Injection Attacks

Currently the false data injection (FDI) attack bring direct challenges in synchronized phase measurement unit (PMU) based network state estimation in wide-area measurement system (WAMS), resulting in degraded system reliability and power supply security. This paper assesses the performance of state estimation in electric cyber-physical system (ECPS) paradigm considering the presence of FDI attacks. The adverse impact on network state estimation is evaluated through simulations for a range of FDI attack scenarios using IEEE 14-bus network model. In addition, an algorithmic solution is proposed to address the issue of additional PMU installation and placement with cyber security consideration and evaluated for a set of standard electric transmission networks (IEEE 14-bus, 30-bus and 57-bus network). The numerical result confirms that the FDI attack can significantly degrade the state estimation and the cyber security can be improved by an appropriate placement of a limited number of additional PMUs

Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection

© 2013 IEEE. Cancer is still one of the most life threatening disease and by far it is still difficult to prevent, prone to recurrence and metastasis and high in mortality. Lots of studies indicate that early cancer diagnosis can effectively increase the survival rate of patients. But early stage cancer is difficult to be detected because of its inconspicuous features. Hence, convenient and effective cancer detection methods are urgently needed. In this paper, we propose to utilize deep autoencoder to learn latent representation of high-dimensional mass spectrometry data. Meanwhile, as a contrast, traditional particle swarm optimization (PSO) optimization algorithm are also used to select optimized features from mass spectrometry data. The learned features are further evaluated on three cancer datasets. The experimental results demonstrate that the cancer detection accuracy by learned features is as high as 100%. As our main contribution, the deep autoencoder method used in this study is a feasible and powerful instrument for mass spectrometry feature learning and also cancer diagnosis