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Benzi M.,Emory University | Ng M.,Hong Kong Baptist University | Niu Q.,BNU HKBU United International College | Niu Q.,Xian Jiaotong - Liverpool University | Wang Z.,Emory University
Journal of Computational Physics

In this paper we introduce a Relaxed Dimensional Factorization (RDF) preconditioner for saddle point problems. Properties of the preconditioned matrix are analyzed and compared with those of the closely related Dimensional Splitting (DS) preconditioner recently introduced by Benzi and Guo [7]. Numerical results for a variety of finite element discretizations of both steady and unsteady incompressible flow problems indicate very good behavior of the RDF preconditioner with respect to both mesh size and viscosity. © 2011 Elsevier Inc. Source

Chen W.,BNU HKBU United International College
Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011

Discovering underlying communities in networks is an important task in network analysis. In the last decade, a large variety of algorithms have been proposed. However, most of them require global information or a centralized control. Those algorithms are infeasible in large-scale real networks due to computation and accessibility. In this paper, we propose a novel decentralized community detection algorithm based on information diffusion. We believe information diffusion in human society can allow us to understand the emergence of community structure. Being able to find out some critical nodes which play an important role in the formation of a community is an important byproduct for our algorithm. Experiments on various networks, including benchmark networks and synthetic networks, show that it is comparable to three decentralized algorithms and two representative centralized algorithms, in terms of stability and accuracy. © 2011 IEEE. Source

Su W.,BNU HKBU United International College | Wang J.,City University of Hong Kong | Lochovsky F.H.,Hong Kong University of Science and Technology | Liu Y.,Tsinghua National Laboratory for Information Sciences and Technology
IEEE Transactions on Knowledge and Data Engineering

Web databases generate query result pages based on a user's query. Automatically extracting the data from these query result pages is very important for many applications, such as data integration, which need to cooperate with multiple web databases. We present a novel data extraction and alignment method called CTVS that combines both tag and value similarity. CTVS automatically extracts data from query result pages by first identifying and segmenting the query result records (QRRs) in the query result pages and then aligning the segmented QRRs into a table, in which the data values from the same attribute are put into the same column. Specifically, we propose new techniques to handle the case when the QRRs are not contiguous, which may be due to the presence of auxiliary information, such as a comment, recommendation or advertisement, and for handling any nested structure that may exist in the QRRs. We also design a new record alignment algorithm that aligns the attributes in a record, first pairwise and then holistically, by combining the tag and data value similarity information. Experimental results show that CTVS achieves high precision and outperforms existing state-of-the-art data extraction methods. © 2012 IEEE. Source

Wang Q.,Sun Yat Sen University | Yuen P.C.,Hong Kong Baptist University | Yuen P.C.,BNU HKBU United International College | Feng G.,Sun Yat Sen University
Pattern Recognition

Learning an appropriate distance metric is a critical problem in pattern recognition. This paper addresses the problem of semi-supervised metric learning. We propose a new regularized semi-supervised metric learning (RSSML) method using local topology and triplet constraints. Our regularizer is designed and developed based on local topology, which is represented by local neighbors from the local smoothness, cluster (low density) and manifold information point of view. The regularizer is then combined with the large margin hinge loss on the triplet constraints. In other words, we keep a large margin between different labeled samples, and in the meanwhile, we use the unlabeled samples to regularize it. Then the semi-supervised metric learning method is developed. We have performed experiments on classification using publicly available databases to evaluate the proposed method. To our best knowledge, this is the only method satisfying all the three semi-supervised assumptions, namely smoothness, cluster (low density) and manifold. Experimental results have shown that the proposed method outperforms state-of-the-art semi-supervised distance metric learning algorithms. © 2013 Elsevier Ltd. All rights reserved. Source

Chan J.Y.-Y.,Hong Kong Polytechnic University | Tsui H.-T.,Hong Kong Polytechnic University | Chung I.Y.-M.,Hong Kong Community College | Chan R.Y.-K.,BNU HKBU United International College | And 2 more authors.
International Journal of Food Sciences and Nutrition

Oxidative stress is considered an important factor that promotes cell death in response to a variety of pathophysiological conditions. This study investigated the antioxidant properties of allicin, the principle ingredient of garlic, on preventing oxidative stress-induced injury. The antioxidant capacities of allicin were measured by using 1-diphenyl-2-picrylhydrazyl (DPPH) free radical scavenging assay and hydrogen peroxide (H2O2)-induced cell damage on H9c2 cardiomyoblasts. Allicin (0.3-10μM) pre-incubation could concentration-dependently attenuate the intracellular reactive oxygen species (ROS) increase induced by H2O2 on H9c2 cells. It could also protect H9c2 cells against H2O2-induced cell damage. However, the DPPH free radical scavenging activity of allicin was shown to be low. Therefore, it is believed that the protective effect of allicin on H9c2 cells could inhibit intracellular ROS production instead of scavenging extracellular H2O2 or free radicals. For the observed protective effect on H9c2 cells, allicin might also be effective in reducing free radical-induced myocardial cell death in ischemic condition. © 2014 Informa UK Ltd. All rights reserved. Source

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