Latent topic models are statistical structures in which a "latent topic" describes some relationship between parts of the data. Co-maintenance is defined as an observable property of software systems under source control in which source code fragments are modified together in some time frame. When topic models are applied to software systems, latent topics emerge from code fragments. However, it is not yet known what these latent topics mean. In this research, we analyse software maintenance history, and show that latent topics often correspond to code fragments that are maintained together. Moreover, we show that latent topic models can identify such co-maintenance relationships even with no supervision. We can use this correlation both to categorize and understand maintenance history, and to predict future co-maintenance in practice. The relationship between co-maintenance and topics is directly analysed within changelists, with respect to both local pairwise code fragment similarity and global system-wide fragment similarity. This analysis is used to evaluate topic models used with a domain-specific programming language for web service similarity detection, and to estimate appropriate topic counts for modelling source code. / Thesis (Ph.D, Computing) -- Queen's University, 2012-04-30 18:16:04.05

No description available.

Thesis (M.S.) -- Central Connecticut State University, 2008. / Thesis advisor: Roger Bilisoly. "... in partial fulfillment of the requirements for the degree of Master of Science in Data Mining." Includes bibliographical references (leaves 122-127). Also available via the World Wide Web.

Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographical references (p. [116]-122).

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2002. / Includes bibliographical references (leaves 38-41). Also available in electronic version. Access restricted to campus users.

Thesis (Ph. D.)--Florida State University, 2006. / Advisor: Daniel L. McGee, Sr., Florida State University, College of Arts and Sciences, Dept. of Statistics. Title and description from dissertation home page (viewed Sept. 21, 2006). Document formatted into pages; contains xii, 147 pages. Includes bibliographical references.

Background: The incidence of tuberculosis (TB) among Inuit is over 400 times that of Canadian-born non-indigenous people. To address this, more patients will need to complete preventative treatment. Methods: First, data were extracted retrospectively for all patients with a tuberculin skin test (TST) implanted in Iqaluit, Nunavut between January 2012 and March 2016 and used to identify sources of loss from the latent TB infection (LTBI) cascade of care. Associations between demographic and clinical factors and treatment non-initiation and treatment non-completion were identified using regression models. Second, using a slightly expanded version of the retrospective dataset plus other sources, a Markov model was utilized to assess the cost-effectiveness of a novel shortened regimen for LTBI (12 weeks of once weekly isoniazid and rifapentine (3HP)) compared to the current standard of care (9 months of isoniazid monotherapy (9H)). Results: Treatment non-initiation and non-completion were the largest sources of loss of TST positive patients from the cascade of care. LTBI testing via employment screening was associated with treatment non-initiation while older age was associated with both treatment noninitiation and non-completion. In cost-effectiveness analysis, 3HP was dominant over 9H: costs were lower ($835 vs $1229 per person) and health outcomes slightly improved (20.14 vs 20.13 QALYs gained per person treated), largely due to an improved treatment completion with 3HP. Conclusions: Interventions to increase LTBI treatment initiation and completion in Iqaluit are needed. This could include the use of 3HP instead of 9H for LTBI treatment which may improve treatment completion and result in cost savings and slightly improved health outcomes.

Cross-validation is a widely used technique in statistics and machine learning for predictive performance assessment and model selection. It involves dividing the available data into multiple sets, training the model on some of the data and testing it on the rest, and repeating this process multiple times. The goal of cross-validation is to assess the model’s predictive performance on unseen data. Two standard methods for cross-validation are leave-one-out cross-validation and K-fold cross-validation. However, these methods may not be suitable for structured models with many potential prediction tasks, as they do not take into account the structure of the data. As a solution, leave-group-out cross-validation is an extension of cross-validation that allows the left-out groups to make training sets and testing points to adapt to different prediction tasks. In this dissertation, we propose an automatic group construction procedure for leave-group-out cross-validation to estimate the predictive performance of the model when the prediction task is not specified. We also propose an efficient approximation of leave-group-out cross-validation for latent Gaussian models. Both of these procedures are implemented in the R-INLA software. We demonstrate the usefulness of our proposed leave-group-out cross-validation method through its application in the joint modeling of survival data and longitudinal data. The example shows the effectiveness of this method in real-world scenarios.

A cryostat has been constructed for the determination of latent heat of vaporization of liquid helium at temperatures from the critical temperature (5.2°K) to below the A point (2.2°K). Some details of the apparatus, hints on low temperature technique and suggestions for future equipment are presented, together with results of some experiments on the apparatus. / Thesis / Master of Science (MS)

To examine the interactions of disease and pollution on amphibian populations around the world, I investigated the effect of infection on contaminant susceptibility in pickerel frog, Rana palustris, larvae. I conducted standard 48-hr toxicity tests to examine effect of trematode parasite, Echinostoma trivolvis, infection (0, 10, or 30 cercaria) on the susceptibility of pickerel frog tadpoles to the widely used organophosphate insecticide malathion. LC50 values ranged from 16.5 – 17.4 mg/L, within the range reported for other amphibian species. I found no differences in susceptibility to malathion among parasite treatments. Nevertheless, this crucial question remains to be tested in other amphibian host-parasite systems. Second, I studied the reverse interaction, the effect of pesticide exposure on susceptibility to parasite infection. I exposed pickerel frog embryos to low doses of malathion, then subjected morphologically normal tadpoles to E. trivolvis later in development. Malathion significantly decreased hatching success and viability rates at concentrations lower than previously documented for anuran embryos. After 7 wk of development in water with no malathion, tadpoles previously exposed to malathion as embryos suffered increased parasite encystment rates compared to controls. My research identifies embryonic development as a sensitive window and the potential for increased susceptibility to infection long after pesticide exposure has ceased. With potential for increased parasite prevalence from eutrophication and climate change, my data underscore the importance of understanding the reciprocal influences of parasites and pesticides in amphibians. / Master of Science