Thesis (Ph.D.) -- McMaster University, 2002. / Includes bibliographical references (leaves 246-253). Also available via World Wide Web.

Thesis (M.M.)--University of Michigan, 1964.

Thesis (Ph.D.)--University of Wyoming, 2007. / Title from PDF title page (viewed on June 22, 2009). Includes bibliographical references (p. 120-127).

Thesis (Ph. D.)--University of Texas at Austin, 1999. / Vita. Includes bibliographical references (leaves 187-199). Available also in a digital version from Dissertation Abstracts.

Thesis (M.M.)--University of Michigan, 1964.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1993. / Vita. Abstract. Includes bibliographical references (leaves 152-156). Also available via the Internet.

Thesis (Ph.D.)--University of Nebraska-Lincoln, 2006. / Title from title screen (site viewed June 11, 2007). PDF text: x, 153 p. : ill. UMI publication number: AAT 3243742. Includes bibliographical references. Also available in microfilm and microfiche formats.

Improving program performance through the use of multiple homogeneous processing elements, or cores, is common-place. However, these architectures increase the complexity required at the software level. Existing work is focused on optimising programs that run in isolation on these systems, but ignores the fact that, in reality, these systems run multiple parallel programs concurrently with programs competing for system resources. In order to improve performance in this shared environment, cooperative tuning of multiple, concurrently running parallel programs is required. Moreover, the set of programs running on the system – the system workload – is dynamic and rapidly changing. This makes cooperative tuning a challenge, as it must react rapidly to changes in the system workload. This thesis explores the scope for performance improvement from cooperatively tuning skeleton parallel programs, and techniques that can be used to cooperatively auto-tune parallel programs. Parallel skeletons provide a clear separation between algorithm description and implementation, and provide tuning knobs that the system can use to make high-level changes to a programs implementation. This work is in three parts: (i) how many threads should be allocated to each program running on the system, (ii) on which cores should a programs threads be executed and (iii) what values should be chosen for high-level parameters of the parallel skeletons. We demonstrate that significant performance improvements are available in each of these areas, compared to the current state-of-the-art.

Machine learning is a buzz word that has inundated popular culture in the last few years. This is a term for a computer method that can automatically learn and improve from data instead of being explicitly programmed at every step. Investigations regarding the best way to create and use these methods are prevalent in research. Machine learning models can be difficult to create because models need to be tuned. This dissertation explores the characteristics of tuning three popular machine learning models and finds a way to automatically select a set of tuning parameters. This information was used to create an R software package called EZtune that can be used to automatically tune three widely used machine learning algorithms: support vector machines, gradient boosting machines, and adaboost. The second portion of this dissertation investigates the implementation of machine learning methods in finding locations along a genome that are associated with a trait. The performance of methods that have been commonly used for these types of studies, and some that have not been commonly used, are assessed using simulated data. The affect of the strength of the relationship between the genetic code and the trait is of particular interest. It was found that the strength of this relationship was the most important characteristic in the efficacy of each method.

Traditional fracture testing in the field of athletic training relies heavily on subjective responses of the athlete. Percussion and compression type tests rely on the athlete stating an increase in pain which represents a positive symptom of a possible fracture. The tuning fork and stethoscope method relied purely on a subjective assessment from the examiner. The purpose of the study was to determine if the use of a 128Hz tuning fork and stethoscope were effective evaluation tools in the assessment of possible fractures as compared to the traditional fracture tests that are used in the athletic training field. A vibrating 128 Hz tuning fork was placed on the bone/area where swelling was minor to facilitate good cortical bone contact. Then the conical bell of a stethoscope was placed on the opposite end on the bone or bones. A diminished sound arising from the injured bone as compared to the uninjured represented a positive sign for a possible fracture. Traditional fracture testing was performed and noted. An x-ray, diagnosed by an orthopedic physician, supported the validity of the tuning fork and traditional fracture testing methods. The attempt was to see what testing method, the tuning fork and stethoscope or traditional fracture testing, was a more valid evaluation tool when it comes to fractures. This study was performed at a university's athletic training room and a local orthopedic center. The study consisted of any subject between the ages of 18-85 that presented with a suspected fracture at either testing facilities. The current study examined 37 male and female subjects whose age ranged from 18-85 years old. The long bones that were tested in this research were as follows: the phalanges of the hand and foot, metacarpals, metatarsals, humerus, radius, ulna, fibula (including the lateral malleolus), and tibia (including the medial malleolus). The tuning fork and stethoscope was shown to be an effective and valid tool for evaluating possible fractures by yielding a success rate of 89.2% when compared to an x-ray. The percussion and compression fracture testing methods yielded only a success rate of 67.6% and 64.9% respectively. / Ph. D.