Sun Yat-sen University

About: Sun Yat-sen University is a education organization based out in Guangzhou, Guangdong, China. It is known for research contribution in the topics: Population & Cancer. The organization has 115149 authors who have published 113763 publications receiving 2286465 citations. The organization is also known as: Zhongshan University & SYSU.

Emerging coronaviruses: Genome structure, replication, and pathogenesis.

Abstract: The recent emergence of a novel coronavirus (2019-nCoV), which is causing an outbreak of unusual viral pneumonia in patients in Wuhan, a central city in China, is another warning of the risk of CoVs posed to public health. In this minireview, we provide a brief introduction of the general features of CoVs and describe diseases caused by different CoVs in humans and animals. This review will help understand the biology and potential risk of CoVs that exist in richness in wildlife such as bats.

Applications of metal–organic frameworks in heterogeneous supramolecular catalysis

Abstract: This review summarizes the use of metal–organic frameworks (MOFs) as a versatile supramolecular platform to develop heterogeneous catalysts for a variety of organic reactions, especially for liquid-phase reactions. Following a background introduction about catalytic relevance to various metal–organic materials, crystal engineering of MOFs, characterization and evaluation methods of MOF catalysis, we categorize catalytic MOFs based on the types of active sites, including coordinatively unsaturated metal sites (CUMs), metalloligands, functional organic sites (FOS), as well as metal nanoparticles (MNPs) embedded in the cavities. Throughout the review, we emphasize the incidental or deliberate formation of active sites, the stability, heterogeneity and shape/size selectivity for MOF catalysis. Finally, we briefly introduce their relevance into photo- and biomimetic catalysis, and compare MOFs with other typical porous solids such as zeolites and mesoporous silica with regard to their different attributes, and provide our view on future trends and developments in MOF-based catalysis.

SphereFace: Deep Hypersphere Embedding for Face Recognition

Abstract: This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space. However, few existing algorithms can effectively achieve this criterion. To this end, we propose the angular softmax (A-Softmax) loss that enables convolutional neural networks (CNNs) to learn angularly discriminative features. Geometrically, A-Softmax loss can be viewed as imposing discriminative constraints on a hypersphere manifold, which intrinsically matches the prior that faces also lie on a manifold. Moreover, the size of angular margin can be quantitatively adjusted by a parameter m. We further derive specific m to approximate the ideal feature criterion. Extensive analysis and experiments on Labeled Face in the Wild (LFW), Youtube Faces (YTF) and MegaFace Challenge 1 show the superiority of A-Softmax loss in FR tasks.