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.

Joint Learning of Single-Image and Cross-Image Representations for Person Re-identification

Abstract: Person re-identification has been usually solved as either the matching of single-image representation (SIR) or the classification of cross-image representation (CIR). In this work, we exploit the connection between these two categories of methods, and propose a joint learning frame-work to unify SIR and CIR using convolutional neural network (CNN). Specifically, our deep architecture contains one shared sub-network together with two sub-networks that extract the SIRs of given images and the CIRs of given image pairs, respectively. The SIR sub-network is required to be computed once for each image (in both the probe and gallery sets), and the depth of the CIR sub-network is required to be minimal to reduce computational burden. Therefore, the two types of representation can be jointly optimized for pursuing better matching accuracy with moderate computational cost. Furthermore, the representations learned with pairwise comparison and triplet comparison objectives can be combined to improve matching performance. Experiments on the CUHK03, CUHK01 and VIPeR datasets show that the proposed method can achieve favorable accuracy while compared with state-of-the-arts.

Learning Semantic Representations for Unsupervised Domain Adaptation

Abstract: It is important to transfer the knowledge from label-rich source domain to unlabeled target domain due to the expensive cost of manual labeling efforts. Prior domain adaptation methods address this problem through aligning the global distribution statistics between source domain and target domain, but a drawback of prior methods is that they ignore the semantic information contained in samples, e.g., features of backpacks in target domain might be mapped near features of cars in source domain. In this paper, we present moving semantic transfer network, which learn semantic representations for unlabeled target samples by aligning labeled source centroid and pseudo-labeled target centroid. Features in same class but different domains are expected to be mapped nearby, resulting in an improved target classification accuracy. Moving average centroid alignment is cautiously designed to compensate the insufficient categorical information within each mini batch. Experiments testify that our model yields state of the art results on standard datasets.

Novel porous molybdenum tungsten phosphide hybrid nanosheets on carbon cloth for efficient hydrogen evolution

Abstract: Nanostructural modification and chemical composition tuning are paramount to developing effective non-noble hydrogen evolution reaction (HER) catalysts for water splitting. Herein, we report a novel excellent porous molybdenum tungsten phosphide (Mo–W–P) hybrid nanosheet catalyst for hydrogen evolution, which is synthesized via in situ phosphidation of molybdenum tungsten oxide (Mo–W–O) hybrid nanowires grown on carbon cloth. The three-dimensional (3D) hierarchical hybrid electrocatalyst exhibits impressively high electrocatalytic activity with a low overpotential of 138 mV required to achieve a high current density of 100 mA cm−2 and a small Tafel slope of 52 mV dec−1 in 0.5 M H2SO4, which are significantly higher than those of single MoP nanosheets and WP2 nanorods. Such an outstanding performance of the Mo–W–P hybrid electrocatalyst is attributed to the 3D conductive scaffolds, porous nanosheet structure, and strong synergistic effect of W and Mo atoms in Mo–W–P, making it a very promising catalyst for hydrogen production. Our findings demonstrate that careful control over the morphology and composition of the electrocatalyst can achieve highly efficient hybrid electrocatalysts.

MiR-21 Indicates Poor Prognosis in Tongue Squamous Cell Carcinomas as an Apoptosis Inhibitor

Abstract: Purpose: We aim to examine miR-21 expression in tongue squamous cell carcinomas (TSCC) and correlate it with patient clinical status, and to investigate its contribution to TSCC cell growth, apoptosis, and tumorigenesis. Experimental Design: MicroRNA profiling was done in 10 cases of TSCC with microarray. MiR-21 overexpression was quantitated with quantitative reverse transcription-PCR in 103 patients, and correlated to the pathoclinical status of the patients. Immunohistochemistry was used to examine the expression of TPM1 and PTEN , and terminal deoxynucleotidyl transferase–mediated dUTP labeling to evaluate apoptosis. Moreover, miR-21 antisense oligonucleotide (ASO) was transfected in SCC-15 and CAL27 cell lines, and tumor cell growth was determined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, adherent colony formation, and soft agar assay, whereas apoptosis was determined by Annexin V assay, cytochrome c release, and caspase 3 assay. Tumorigenesis was evaluated by xenografting SCC-15 cells in nude mice. Results: MiR-21 is overexpressed in TSCC relative to adjacent normal tissues. The level of miR-21 is reversely correlated with TPM1 and PTEN expression and apoptosis of cancer cells. Multivariate analysis showed that miR-21 expression is an independent prognostic factor indicating poor survival. Inhibiting miR-21 with ASO in TSCC cell lines reduces survival and anchorage-independent growth, and induces apoptosis in TSCC cell lines. Simultaneous silencing of TPM1 with siRNA only partially recapitulates the effect of miR-21 ASO. Furthermore, repeated injection of miR-21 ASO suppresses tumor formation in nude mice by reducing cell proliferation and inducing apoptosis. Conclusions: miR-21 is an independent prognostic indicator for TSCC, and may play a role in TSCC development by inhibiting cancer cell apoptosis partly via TPM1 silencing.