The Chinese University of Hong Kong

About: The Chinese University of Hong Kong is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Population & Computer science. The organization has 43411 authors who have published 93672 publications receiving 3066651 citations.

Human Streptococcus suis Outbreak, Sichuan, China

Abstract: From mid-July to the end of August 2005, a total of 215 cases of human Streptococcus suis infections, 66 of which were laboratory confirmed, were reported in Sichuan, China. All infections occurred in backyard farmers who were directly exposed to infection during the slaughtering process of pigs that had died of unknown causes or been killed for food because they were ill. Sixty-one (28%) of the farmers had streptococcal toxic shock syndrome; 38 (62%) of them died. The other illnesses reported were sepsis (24%) and meningitis (48%) or both. All isolates tested positive for genes for tuf, species-specific 16S rRNA, cps2J, mrp, ef, and sly. A single strain of S. suis caused the outbreak, as shown by the identification of a single ribotype. The high death ratio was of concern; prohibiting backyard slaughtering ended the outbreak.

Plasmon-Controlled Fluorescence: Beyond the Intensity Enhancement

Abstract: Control over light absorption and emission using plasmonic nanostructures is an enabling technology, which can dramatically enhance the performances of existing optical and optoelectronic devices, such as solar cells, light-emitting devices, biosensors, and high-resolution fluorescence microscopes. This Perspective takes fluorescence as an example, illustrating how plasmonic nanostructures can control the light absorption and emission of nanoscale optical species. The origins of fluorescence intensity enhancements will be first discussed. Different parameters that can largely affect the interactions between plasmonic nanostructures and fluorophore molecules will be examined, including the distance between the fluorophore molecule and the metal nanostructure and the wavelengths of their respective optical responses. The role of plasmonic nanostructures on fluorescence will then be reconsidered from the perspective of optical nanoantennas. We expect that more functionalities of plasmonic nanostructures as o...

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.