Hong Kong Baptist University

About: Hong Kong Baptist University is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Population & China. The organization has 7811 authors who have published 18919 publications receiving 555274 citations. The organization is also known as: Hong Kong Baptist College & HKBU.

Community Detection in Attributed Graphs: An Embedding Approach

Abstract: Community detection is a fundamental and widely-studied problem that finds all densely-connected groups of nodes and well separates them from others in graphs. With the proliferation of rich information available for entities in real-world networks, it is useful to discover communities in attributed graphs where nodes tend to have attributes. However, most existing attributed community detection methods directly utilize the original network topology leading to poor results due to ignoring inherent community structures. In this paper, we propose a novel embedding based model to discover communities in attributed graphs. Specifically, based on the observation of densely-connected structures in communities, we develop a novel community structure embedding method to encode inherent community structures via underlying community memberships. Based on node attributes and community structure embedding, we formulate the attributed community detection as a nonnegative matrix factorization optimization problem. Moreover, we carefully design iterative updating rules to make sure of finding a converging solution. Extensive experiments conducted on 19 attributed graph datasets with overlapping and non-overlapping ground-truth communities show that our proposed model CDE can accurately identify attributed communities and significantly outperform 7 state-of-the-art methods.

Defect-induced activity enhancement of enzyme-encapsulated metal-organic frameworks revealed in microfluidic gradient mixing synthesis

Abstract: Mimicking the cellular environment, metal-organic frameworks (MOFs) are promising for encapsulating enzymes for general applications in environments often unfavorable for native enzymes. Markedly different from previous researches based on bulk solution synthesis, here, we report the synthesis of enzyme-embedded MOFs in a microfluidic laminar flow. The continuously changed concentrations of MOF precursors in the gradient mixing on-chip resulted in structural defects in products. This defect-generating phenomenon enables multimodal pore size distribution in MOFs and therefore allows improved access of substrates to encapsulated enzymes while maintaining the protection to the enzymes. Thus, the as-produced enzyme-MOF composites showed much higher (~one order of magnitude) biological activity than those from conventional bulk solution synthesis. This work suggests that while microfluidic flow synthesis is currently underexplored, it is a promising strategy in producing highly active enzyme-MOF composites.

Multi-cue Visual Tracking Using Robust Feature-Level Fusion Based on Joint Sparse Representation

Abstract: The use of multiple features for tracking has been proved as an effective approach because limitation of each feature could be compensated. Since different types of variations such as illumination, occlusion and pose may happen in a video sequence, especially long sequence videos, how to dynamically select the appropriate features is one of the key problems in this approach. To address this issue in multicue visual tracking, this paper proposes a new joint sparse representation model for robust feature-level fusion. The proposed method dynamically removes unreliable features to be fused for tracking by using the advantages of sparse representation. As a result, robust tracking performance is obtained. Experimental results on publicly available videos show that the proposed method outperforms both existing sparse representation based and fusion-based trackers.