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.

Evolution of lowest singlet and triplet excited states with number of thienyl rings in platinum poly-ynes

Abstract: Soluble, rigid-rod organometallic polymers trans-[-Pt(PBu3n)2–C≡C–R–C≡C–]∞ (R=bithienyl 2, terthienyl 3) have been synthesized in good yields by the CuI-catalyzed dehydrohalogenation reaction of trans-[Pt(PBu3n)2Cl2] with one equivalent of the diterminal alkynyl oligothiophenes H–C≡C–R–C≡C–H in CH2Cl2/iPr2NH at room temperature. We report the thermal properties, and the optical absorption, photoluminescence, and photocurrent action spectra of 1 (trans-[–Pt(PBu3n)2–C≡C–R–C≡C–]∞, R=thienyl), 2 and 3 as a function of the number of thiophene rings within the bridging ligand. With increasing thiophene content, the optical gap is reduced and the vibronic structure of the singlet emission changes toward that typical for oligothiophenes. We also find the intersystem crossing from the singlet excited state to the triplet excited state to become reduced, while the singlet–triplet energy gap remains unaltered. The latter implies that, in these systems, the T1 triplet excited state is extended over several thiophene ...

Predicting protein functions using incomplete hierarchical labels

Abstract: Protein function prediction is to assign biological or biochemical functions to proteins, and it is a challenging computational problem characterized by several factors: (1) the number of function labels (annotations) is large; (2) a protein may be associated with multiple labels; (3) the function labels are structured in a hierarchy; and (4) the labels are incomplete. Current predictive models often assume that the labels of the labeled proteins are complete, i.e. no label is missing. But in real scenarios, we may be aware of only some hierarchical labels of a protein, and we may not know whether additional ones are actually present. The scenario of incomplete hierarchical labels, a challenging and practical problem, is seldom studied in protein function prediction. In this paper, we propose an algorithm to Predict protein functions using Incomplete hierarchical LabeLs (PILL in short). PILL takes into account the hierarchical and the flat taxonomy similarity between function labels, and defines a Combined Similarity (ComSim) to measure the correlation between labels. PILL estimates the missing labels for a protein based on ComSim and the known labels of the protein, and uses a regularization to exploit the interactions between proteins for function prediction. PILL is shown to outperform other related techniques in replenishing the missing labels and in predicting the functions of completely unlabeled proteins on publicly available PPI datasets annotated with MIPS Functional Catalogue and Gene Ontology labels. The empirical study shows that it is important to consider the incomplete annotation for protein function prediction. The proposed method (PILL) can serve as a valuable tool for protein function prediction using incomplete labels. The Matlab code of PILL is available upon request.

Benchmarking State-of-the-Art Deep Learning Software Tools

Abstract: Deep learning has been shown as a successful machine learning method for a variety of tasks, and its popularity results in numerous open-source deep learning software tools coming to public. Training a deep network is usually a very time-consuming process. To address the huge computational challenge in deep learning, many tools exploit hardware features such as multi-core CPUs and many-core GPUs to shorten the training and inference time. However, different tools exhibit different features and running performance when they train different types of deep networks on different hardware platforms, making it difficult for end users to select an appropriate pair of software and hardware. In this paper, we present our attempt to benchmark several state-of-the-art GPU-accelerated deep learning software tools, including Caffe, CNTK, TensorFlow, and Torch. We focus on evaluating the running time performance (i.e., speed) of these tools with three popular types of neural networks on two representative CPU platforms and three representative GPU platforms. Our contribution is two-fold. First, for end users of deep learning software tools, our benchmarking results can serve as a reference to selecting appropriate hardware platforms and software tools. Second, for developers of deep learning software tools, our in-depth analysis points out possible future directions to further optimize the running performance.