BACKGROUND In the United States, an increasingly disproportionate burden of tuberculosis among the foreign-born population has led to calls for improvements in the detection and treatment of latent infection in new immigrants. Current treatment guidelines do not take into account global differences in drug-resistance patterns or their implications for the treatment of immigrants. The use of mul...

The aim of the present study was to perform a cost-effectiveness analysis in young and middle-aged adults with latent tuberculosis (TB) infection in Germany. A Markov model simulated the progression of 20- and 40-yr-old close contacts of active TB cases over 20 yrs. Health and economic outcomes of isoniazid (INH) chemoprevention versus no intervention were compared. The analysis determined the ...

In this paper we show the surprising effectiveness of a simple observed features model in comparison to latent feature models on two benchmark knowledge base completion datasets, FB15K and WN18. We also compare latent and observed feature models on a more challenging dataset derived from FB15K, and additionally coupled with textual mentions from a web-scale corpus. We show that the observed fea...

BACKGROUND BCG vaccine has shown consistently high efficacy against childhood tuberculous meningitis and miliary tuberculosis, but variable efficacy against adult pulmonary tuberculosis and other mycobacterial diseases. We assess and compare the costs and effects of BCG as an intervention against severe childhood tuberculosis in different regions of the world. METHODS We calculated the number...

The exchangeability assumption in topic models like Latent Dirichlet Allocation (LDA) often results in inferring inconsistent topics for the words of text spans like noun-phrases, which are usually expected to be topically coherent. We propose copulaLDA, that extends LDA by integrating part of the text structure to the model and relaxes the conditional independence assumption between the word-s...

Latent Dirichlet Allocation (LDA) is a probabilistic, generative model designed to discover latent topics in text corpora, and it can be learned by collapsed Gibbs sampling. In this report, we evaluate the effectiveness of LDA by experiments on two dataset, Classic400 and BBC. We discuss related issues in Gibbs sampling, including goodness-of-fit criteria, parameter tuning, convergence, etc., a...

This paper focuses on language modeling with adequate robustness to support different domain tasks. To this end, we propose a hierarchical latent word language model (h-LWLM). The proposed model can be regarded as a generalized form of the standard LWLMs. The key advance is introducing a multiple latent variable space with hierarchical structure. The structure can flexibly take account of lingu...

Dynamic principal component analysis (DPCA) has been widely used in the monitoring of dynamic multivariate processes. In traditional DPCA, the dynamic relationship between process variables are implicit and hard to interpret. To extract explicit latent factors that are dynamically correlated, a new dynamic latent variable model is proposed. The new structure can improve modeling of dynamic data...