This dissertation addresses the question of women's descriptive underrepresentation in Canadian politics at the federal level. Previous studies of women's underrepresentation in Canada and elsewhere have largely focused on analysing the results of elections, and studies that do account for earlier factors such as recruitment and candidate selection are limited in their scopes. In this dissertation I analyse women's representation in a holistic manner, accounting for factors from the pre-nomination stage up through election. Conceptually, I approach the path to political office as a three-stage "representation pipeline," comprising candidate emergence, candidate selection, and election. I base my analysis on Elections Canada's records of nomination contests held by federal political parties for the 2004 through 2019 general elections, paired with relevant district-level demographic factors from the Canadian census. I complement this analysis with an original survey of nomination contestants in the 2019 election. I find that women's underrepresentation in Canada is determined chiefly by issues in candidate emergence, rather than by issues in candidate selection or electoral discrimination. Instead, nominations in Canada are in the strong majority of cases acclamations, making candidate emergence and election the only meaningful barriers to women's representation in most cases. Furthermore, women face a small but significant degree of electoral discrimination, with women having slightly lower odds then men of winning election even when controlling for their party's past performance in the district. Finally, I find that urban districts are more conducive to women's representation at all three stages of the representation pipeline.

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Scientific applications are large, complex, irregular, and computationally intensive and are characterized by data parallel loops. The prevalence of independent iterations in these loops, makes parallel computing as the natural choice for solving these applications. The computational requirements of these problems vary due to variations in problem, algorithmic and systemic characteristics during parallelization, leading to performance degradation. Considerable amount of research has been dedicated to the development of dynamic scheduling techniques based on probabilistic analysis to address these predictable and unpredictable factors that lead to severe load imbalance. The mathematical foundations of these scheduling algorithms have been previously developed and published in the literature. These techniques have been successfully integrated into scientific applications as well as into runtime systems. Recently, efforts have also been directed to integrate these techniques into dynamic load balancing libraries for scientific applications. The optimal scheduling algorithm to load balance a specific scientific application in a dynamic parallel computing environment is very difficult without the exhaustive testing of all the scheduling techniques. This is a time consuming process, and therefore, there is a need for developing an automatic mechanism for the selection of dynamic scheduling algorithms. In recent years, extensive work has been dedicated to the development of reinforcement learning and some of its techniques have addressed load-balancing problems. However, they do not cover a number of aspects regarding the performance of scientific applications. First, these previously developed techniques address the load balancing problem only at a coarse granularity level (for example, job scheduling), and the reinforcement learning techniques used for load balancing are based on learning from trained datasets which are obtained prior to the execution of the application. Moreover, scientific applications contain parameters whose variations are so irregular that the use of training sets would not be able to accurately capture the entire spectrum of possible characteristics. Finally, algorithm selection using reinforcement learning has only been used for simple sequential problems. This thesis addresses these limitations and provides a novel integrated approach for automating the selection of dynamic scheduling algorithms at a finer granularity level to improve the performance of scientific applications using reinforcement learning. This integrated approach will experimentally be tested on a scientific application that involves a large number of time steps: The Quantum Trajectory Method (QTM). A qualitative and quantitative analysis of the effectiveness of this novel approach will be presented to underscore the significance of its use in improving the performance of large-scale scientific applications.

Vulnerability in software receives constant attention in the media and in research. Yearly rates of disclosure of vulnerabilities in software have doubled. The discipline of Information Assurance lacks metrics that are useful in understanding vulnerability. In the problem of vulnerability assessment tool selection, users must make product choices based on results found in non-peer reviewed publications or subjective opinion. Users of vulnerability assessment tools must sift through volumes of data about their systems and are shown broad indications of the severity of the problems ? often a high-medium-low ranking, which varies between tools. A need exists for metrics and a selection model for tool quality assessment. This study addresses these needs by analysis of the discipline of vulnerability assessment and remediation from first principles, and presents an organized approach and a bestit metrics based model for selecting vulnerability assessment tools.

The evolution of sedentariness in east-central Mississippi seems to follow specific patterns when both time and space are accounted for. Prehistoric pottery counts and frequencies from sites located throughout east-central Mississippi were examined in order to better understand settlement patterns. This study combines data from both newly recorded and previously recorded sites. These data are analyzed using frequency seriation and correspondence analysis, thus allowing the investigation of settlement patterns through both space and time. The results are used to address competing hypotheses concerning a gradual spread of sedentary settlement versus a very rapid adoption of sedentariness. The main factors organizing assemblages from sedentary settlements in this area seem to be distance from a major river and population growth. The data indicate that sedentariness was adopted gradually.

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