Learn More
The term "Web 2.0" is used to describe applications that distinguish themselves from previous generations of software by a number of principles. Existing work shows that Web 2.0 applications can be successfully exploited for technology-enhance learning. However, in-depth analyses of the relationship between Web 2.0 technology on the one hand and teaching(More)
Identifying new indications for existing drugs (drug repositioning) is an efficient way of maximizing their potential. Adverse drug reaction (ADR) is one of the leading causes of death among hospitalized patients. As both new indications and ADRs are caused by unexpected chemical-protein interactions on off-targets, it is reasonable to predict these(More)
Since learning in Boltzmann machines is typically quite slow, there is a need to restrict connections within hidden layers. However, the resulting states of hidden units exhibit statistical dependencies. Based on this observation , we propose using l1/l2 regularization upon the activation probabilities of hidden units in restricted Boltzmann machines to(More)
In the era of personalized medical practice, understanding the genetic basis of patient-specific adverse drug reaction (ADR) is a major challenge. Clozapine provides effective treatments for schizophrenia but its usage is limited because of life-threatening agranulocytosis. A recent high impact study showed the necessity of moving clozapine to a first line(More)
Collaborative filtering as a classical method of information retrieval has been widely used in helping people to deal with information overload. In this paper, we introduce the concept of local user similarity and global user similarity, based on surprisal-based vector similarity and the application of the concept of maximin distance in graph theory.(More)
We apply the spike-and-slab Restricted Boltzmann Machine (ssRBM) to texture modeling. The ssRBM with tiled-convolution weight sharing (TssRBM) achieves or surpasses the state-of-the-art on texture synthesis and inpainting by parametric models. We also develop a novel RBM model with a spike-and-slab visible layer and binary variables in the hidden layer.(More)
The rat has been used extensively as a model for evaluating chemical toxicities and for understanding drug mechanisms. However, its transcriptome across multiple organs, or developmental stages, has not yet been reported. Here we show, as part of the SEQC consortium efforts, a comprehensive rat transcriptomic BodyMap created by performing RNA-Seq on 320(More)
In this paper, a discriminative representation method of head images is proposed, which is based on parts and poses for identity-independent head pose estimation. Head images are preprocessed to enhance the facial features and eliminate the identity information by skin color model and Laplacian of Gaussian transform. Then, the preprocessed images are used(More)
In this paper, we propose a supervised Smooth Multi-Manifold Embedding (SMME) method for robust identity-independent head pose estimation. In order to handle the appearance variations caused by identity , we consider the pose data space as multiple manifolds in which each manifold characterizes the underlying subspace of subjects with similar appearance. We(More)