Background: Gastric cancer is a leading cause of death worldwide and the average survival rate of patients within a year of diagnosis remains low. Currently, surgical resection is the major treatment strategy, and the effectiveness of chemotherapy remains debatable. Infections involving Helicobacter pylori (H. pylori) and the Epstein-Barr virus have been implicated in gastric cancer pathogenesi...

We report experiment results from the collaborative participation of UIUC and MUSC in the TREC 2005 Genomics Track. We participated in both the adhoc task and the categorization task, and studied the use of some mixture language models in these tasks. Experiment results show that a structured theme-based language modeling approach is effective in improving retrieval effectiveness for the ad hoc...

Contrastive opinion mining is essential in identifying, extracting and organising opinions from user generated texts. Most existing studies separate input data into respective collections. In addition, the relationships between the topics extracted and the sentences in the corpus which express the topics are opaque, hindering our understanding of the opinions expressed in the corpus. We propose...

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Extensive labeled data for image annotation systems, which learn to assign class labels to image regions, is difficult to obtain. We explore a hybrid model framework for utilizing partially labeled data that integrates a generative topic model for image appearance with discriminative label prediction. We propose three alternative formulations for imposing a spatial smoothness prior on the image...

We propose a multimodal retrieval procedure based on latent feature models. The procedure consists of a Bayesian nonparametric framework for learning underlying semantically meaningful abstract features in a multimodal dataset, a probabilistic retrieval model that allows cross-modal queries and an extension model for relevance feedback. Experiments on two multimodal datasets, PASCAL-Sentence an...

We describe a new probabilistic model for learning of coupled dynamical systems in latent state spaces. The coupling is achieved by combining predictions from several Gaussian process dynamical models in a product-of-experts fashion. Our approach facilitates modulation of coupling strengths without the need for computationally expensive re-learning of the dynamical models. We demonstrate the ef...

Standard LDA model suffers the problem that the topic assignment of each word is independent and word correlation hence is neglected. To address this problem, in this paper, we propose a model called Word Related Latent Dirichlet Allocation (WR-LDA) by incorporating word correlation into LDA topic models. This leads to new capabilities that standard LDA model does not have such as estimating in...

In this paper, we present a method of tweet contextualization by using a semantic space to extend the tweet vocabulary. This method is evaluated on the tweet contextualization benchmark. Contextualization is build with the sentences from English Wikipedia. The context is obtained by querying a baseline system of summary. The query is made with words from a semantic space that is estimated via a...

Recent work on Bayesian optimization has shown its effectiveness in global optimization of difficult black-box objective functions. Many real-world optimization problems of interest also have constraints which are unknown a priori. In this paper, we study Bayesian optimization for constrained problems in the general case that noise may be present in the constraint functions, and the objective a...