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.

Understanding organizational change: A schematic perspective

Abstract: The construct of change schema is developed and its possible dimensions explained. Using data from two samples and both quantitative and qualitative methods, we found that locus of control and orga...

Sketch Me That Shoe

Abstract: We investigate the problem of fine-grained sketch-based image retrieval (SBIR), where free-hand human sketches are used as queries to perform instance-level retrieval of images. This is an extremely challenging task because (i) visual comparisons not only need to be fine-grained but also executed cross-domain, (ii) free-hand (finger) sketches are highly abstract, making fine-grained matching harder, and most importantly (iii) annotated cross-domain sketch-photo datasets required for training are scarce, challenging many state-of-the-art machine learning techniques. In this paper, for the first time, we address all these challenges, providing a step towards the capabilities that would underpin a commercial sketch-based image retrieval application. We introduce a new database of 1,432 sketchphoto pairs from two categories with 32,000 fine-grained triplet ranking annotations. We then develop a deep tripletranking model for instance-level SBIR with a novel data augmentation and staged pre-training strategy to alleviate the issue of insufficient fine-grained training data. Extensive experiments are carried out to contribute a variety of insights into the challenges of data sufficiency and over-fitting avoidance when training deep networks for finegrained cross-domain ranking tasks.

Learning Feature Pyramids for Human Pose Estimation

Abstract: Articulated human pose estimation is a fundamental yet challenging task in computer vision. The difficulty is particularly pronounced in scale variations of human body parts when camera view changes or severe foreshortening happens. Although pyramid methods are widely used to handle scale changes at inference time, learning feature pyramids in deep convolutional neural networks (DCNNs) is still not well explored. In this work, we design a Pyramid Residual Module (PRMs) to enhance the invariance in scales of DCNNs. Given input features, the PRMs learn convolutional filters on various scales of input features, which are obtained with different subsampling ratios in a multibranch network. Moreover, we observe that it is inappropriate to adopt existing methods to initialize the weights of multi-branch networks, which achieve superior performance than plain networks in many tasks recently. Therefore, we provide theoretic derivation to extend the current weight initialization scheme to multi-branch network structures. We investigate our method on two standard benchmarks for human pose estimation. Our approach obtains state-of-the-art results on both benchmarks. Code is available at https://github.com/bearpaw/PyraNet.

Curcumin interaction with copper and iron suggests one possible mechanism of action in Alzheimer's disease animal models.

Abstract: Curcumin is a polyphenolic diketone from turmeric. Because of its anti-oxidant and anti-inflammatory effects, it was tested in animal models of Alzheimer's disease, reducing levels of amyloid and oxidized proteins and preventing cognitive deficits. An alternative mechanism of these effects is metal chelation, which may reduce amyloid aggregation or oxidative neurotoxicity. Metals can induce Abeta aggregation and toxicity, and are concentrated in AD brain. Chelators desferrioxamine and clioquinol have exhibited anti-AD effects. Using spectrophotometry, we quantified curcumin affinity for copper, zinc, and iron ions. Zn2+ showed little binding, but each Cu2+ or Fe2+ ion appeared to bind at least two curcumin molecules. The interaction of curcumin with copper reached half-maximum at approximately 3-12 microM copper and exhibited positive cooperativity, with Kd1 approximately 10-60 microM and Kd2 approximately 1.3 microM (for binding of the first and second curcumin molecules, respectively). Curcumin-iron interaction reached half-maximum at approximately 2.5-5 microM iron and exhibited negative cooperativity, with Kd1 approximately 0.5-1.6 microM and Kd2 approximately 50-100 microM. Curcumin and its metabolites can attain these levels in vivo, suggesting physiological relevance. Since curcumin more readily binds the redox-active metals iron and copper than redox-inactive zinc, curcumin might exert a net protective effect against Abeta toxicity or might suppress inflammatory damage by preventing metal induction of NF-kappaB.