Learn More
The raw size of a high-dynamic-range (HDR) image brings about problems in storage and transmission. Many bytes are wasted in data redundancy and perceptually unimportant information. To address this problem, researchers have proposed some preliminary algorithms to compress the data, like RGBE/XYZE, OpenEXR, LogLuv, and so on. HDR images can have a dynamic(More)
MOTIVATION Owing to its importance in both basic research (such as molecular evolution and protein attribute prediction) and practical application (such as timely modeling the 3D structures of proteins targeted for drug development), protein remote homology detection has attracted a great deal of interest. It is intriguing to note that the profile-based(More)
Latent topics derived by topic models such as Latent Dirichlet Allocation (LDA) are the result of hidden thematic structures which provide further insights into the data. The automatic labelling of such topics derived from social media poses however new challenges since topics may characterise novel events happening in the real world. Existing automatic(More)
Transfer learning has been used in opinion analysis to make use of available language resources for other resource scarce languages. However, the cumulative class noise in transfer learning adversely affects performance when more training data is used. In this paper, we propose a novel method in transductive transfer learning to identify noises through the(More)
Playing crucial roles in various cellular processes, such as recognition of specific nucleotide sequences, regulation of transcription, and regulation of gene expression, DNA-binding proteins are essential ingredients for both eukaryotic and prokaryotic proteomes. With the avalanche of protein sequences generated in the postgenomic age, it is a critical(More)
Identification of DNA-binding proteins is an important problem in biomedical research as DNA-binding proteins are crucial for various cellular processes. Currently, the machine learning methods achieve the-state-of-the-art performance with different features. A key step to improve the performance of these methods is to find a suitable representation of(More)
DNA-binding proteins are crucial for various cellular processes, such as recognition of specific nucleotide, regulation of transcription, and regulation of gene expression. Developing an effective model for identifying DNA-binding proteins is an urgent research problem. Up to now, many methods have been proposed, but most of them focus on only one(More)
This paper presents the CUHK opinion analysis system, namely Opinmine, for the NTCIR-6 pilot task. Opinmine comprises of three functional modules: (1) Preprocessing and Assignment Module (PAM) performs word segmentation, part-of-speech (POS) tagging and named entity recognition on the input Chinese text. It is based on lexicalized Hidden Markov Model and(More)
In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word senses. This paper presents a novel approach which addresses these limitations by first initializing the(More)