Thesis (Ph. D.)--University of Missouri-Columbia, 1998. / Typescript. Vita. Includes bibliographical references (leaves 216-229). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1998. / Typescript. Vita. Includes bibliographical references (leaves 216-229). Also available on the Internet.

Project (M.S.)--Brigham Young University. Dept. of Statistics, 2006. / Includes bibliographical references (p. 45-46).

Thesis (Ph.D.)--McMaster University, 2003. / Advisor: Shui Feng. Includes bibliographical references (leaves 94-102). Also available via World Wide Web.

Thesis (Ph. D. in Electrical Engineering)--Vanderbilt University, Dec. 2007. / Title from title screen. Includes bibliographical references.

This paper presents a statistical framework which combines the registration of an atlas with the segmentation of MR images. We use an Expectation Maximization-based algorithm to find a solution within the model, which simultaneously estimates image inhomogeneities, anatomical labelmap, and a mapping from the atlas to the image space. An example of the approach is given for a brain structure-dependent affine mapping approach. The algorithm produces high quality segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 30 brain MR images. In addition, we show that the approach performs better than similar methods which separate the registration from the segmentation problem.

Graphs are commonly selected as a model of scientific information: graphs can successfully represent imprecise, uncertain, noisy data; and graph theory has a well-developed mathematical apparatus forming a solid and sound foundation for graph research. Design and experimental confirmation of new, scalable, and practical analytics for massive graphs have been actively researched for decades. Our work concentrates on developing new accurate and efficient algorithms that calculate the most influential nodes and communities in an arbitrary graph. Our algorithms for graph decomposition into families of most influential communities compute influential communities faster and using smaller memory footprint than existing algorithms for the problem. Our algorithms solving the problem of influence maximization in large graphs use much smaller memory than the existing state-of-the-art algorithms while providing solutions with equal accuracy. Our main contribution is designing data structures and algorithms that drastically cut the memory footprint and scale up the computation of influential communities and nodes to massive modern graphs. The algorithms and their implementations can efficiently handle networks of billions of edges using a single consumer-grade machine. These claims are supported by extensive experiments on large real-world graphs of different types. / Graduate

This thesis presents two modern control methods to address the Internet traffic congestion control issues. They are based on a distributed traffic management framework for the fast-growing Internet traffic in which routers are deployed with intelligent or optimal data rate controllers to tackle the traffic mass. The first one is called the IntelRate (Intelligent Rate) controller using the fuzzy logic theory. Unlike other explicit traffic control protocols that have to estimate network parameters (e.g., link latency, bottleneck bandwidth, packet loss rate, or the number of flows), our fuzzy-logic-based explicit controller can measure the router queue size directly. Hence it avoids various potential performance problems arising from parameter estimations while reducing much computation and memory consumption in the routers. The communication QoS (Quality of Service) is assured by the good performances of our scheme such as max-min fairness, low queueing delay and good robustness to network dynamics. Using the Lyapunov’s Direct Method, this controller is proved to be globally asymptotically stable. The other one is called the OFEX (Optimal and Fully EXplicit) controller using convex optimization. This new scheme is able to provide not only optimal bandwidth allocation but also fully explicit congestion signal to sources. It uses the congestion signal from the most congested link, instead of the cumulative signal from a flow path. In this way, it overcomes the drawback of the relatively explicit controllers that bias the multi-bottlenecked users, and significantly improves their convergence speed and throughput performance. Furthermore, the OFEX controller design considers a dynamic model by proposing a remedial measure against the unpredictable bandwidth changes in contention-based multi-access networks (such as shared Ethernet or IEEE 802.11). When compared with the former works/controllers, such a remedy also effectively reduces the instantaneous queue size in a router, and thus significantly improving the queueing delay and packet loss performance. Finally, the applications of these two controllers on wireless local area networks have been investigated. Their design guidelines/limits are also provided based on our experiences.

This dissertation studies three economic problems plagued by multiple equilibria. Indeterminacy of equilibrium outcome often poses a challenge in deriving robust predictions and policy guidance. The dissertation shows how the utilization of potential game theory can better deal with this challenge. Chapter 1 studies a general contracting problem between one principal and multiple agents. The interdependence of agents’ actions and payoffs creates a coordination problem among them, leading to multiple equilibria. In general, the principal’s optimal contracting scheme varies with how one selects among equilibria. Nevertheless, for a large class of contracting models where agents’ payoffs constitute a weighted potential game, I show that one contracting scheme is optimal for a large class of equilibrium selection criteria. This scheme ranks agents in increasing weight in the weighted potential game and induces them to accept their offers in a dominance-solvable way, starting from the first agent. I also apply the general results to networks and pure/impure public goods/bads. Chapter 2 studies two-sided markets, where two groups of agents interact via platforms. These markets exhibit network effects, i.e., the value of joining a platform increases with the number of users, which in turn lead to multiple equilibria. I show that many two-sided market models are weighted potential games, enabling the selection among equilibria by potential maximization—a refinement of Nash equilibrium justified by many theoretical and experimental studies. Under potential maximization, platforms often charge the side deriving more network benefits and subsidize the other side. Therefore, profit-maximizing platforms are often designed to favor the money side much more than the subsidy side. Chapter 3 studies markets with strong network effects. In these markets, firms compete for the adoption of all consumers rather than the marginal consumer. Therefore, the Spence distortion—a quality distortion driven by competition for the marginal consumer—should be absent, contradicting the findings in the network economics literature. This inconsistency stems from the choice of equilibrium selection criterion. I show that all popular selection criteria in that literature lead to Spence distortions, whereas potential maximization does not. Therefore, network market regulations based on Spence distortion arguments may be misguided.

Teachers of rural K–12 music education are subject to attrition rates that are higher than many other professions or teaching specialties (Goldring, Taie & Riddles, 2014; Harmon, 2001; Ingersoll, 2001). Because of this, a large number of music teachers who are hired to teach in rural schools are inexperienced educators who are often unaware of the specific demands that are unique to these jobs. Upon earning a teaching certification, many new graduates get hired in rural locations with unfamiliar teaching conditions that could potentially lead to dissatisfaction in the workplace which could be a contributing factor to the higher than average attrition rates (Bates, 2013; Hancock, 2008; Monk, 2007; Isbell, 2005). This dissertation is a case study of in-service music educators in the rural Western United States designed to help understand the trend of higher than average attrition rates. With a theoretical framework of utility maximization to find a satisfactory person-job fit, I observed, interviewed, and collected journals from 5 participants with current or previous rural K–12 music teaching experience to determine: 1) what reasons do educators consider influential in a decision to stay in or move from a teaching position?, 2) what changes do teachers report in their perception of job utility maximization over their careers?, and 3) what are some benefits and challenges of teaching in a rural music teaching setting? Reasons for attrition specific to rural music education and generic to teaching were discussed in terms of a participant’s perception of job satisfaction and their decisions to stay in or leave rural K–12 music teaching jobs. Participants listed five themes as influential to their decisions for attrition: 1) disproportionate emphasis on athletics and pep band, 2) teacher and student absenteeism, 3) spillover work time 4) family, and 5) administrative rapport. No individual theme was a singular indicator of attrition, nor was any theme more prominent than others in influencing a participant to keep or leave a job. Rather, the perception of each reason for attrition had a cumulative effect and jobs were maintained or sought anew based on a combination of views of each theme. Also, participants reported steady inclinations of preferred musical specialty, but the perception of each theme as a reason for attrition changed with time and teaching experience. Ultimately, participants revealed that rural K–12 music teaching jobs can be highly rewarding if a person is professionally flexible, willing to regularly travel long distances (with students and alone), and can appreciate the idiosyncrasies of living in remote communities.