Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1997. / Vita. Abstract. "UMI number : 9812512"--Verso of t.p. Includes bibliographical references (p. 64-73). Available electronically via Internet.

Thesis--M.N. University of Washington, 1960.

Thesis--M.N. University of Washington, 1960.

Dissertation (Ph.D.)--The University of North Carolina at Greensboro, 2009. / Directed by Gerald G. Duffy; submitted to the Dept. of Curriculum and Instruction. Title from PDF t.p. (viewed May 17, 2010). Includes bibliographical references (p. 133-145).

Thesis (M.S.)--University of Wisconsin--Madison, 1982. / Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 54-60).

Thesis (M.S. in Computer Science)--Naval Postgraduate School, March 2006. / Thesis Advisor(s): Rudolph P. Darken. "March 2006." Includes bibliographical references (p. 45-47). Also available online.

Thesis (Ph. D.)--University of Hong Kong, 2006.

Thesis (M.A.I.S.)--Oregon State University, 2008. / Printout. Includes bibliographical references (leaves 49-52). Also available on the World Wide Web.

When designing real-time systems, we want to prove that they will satisfy given timing constraints at run time. The main objective of real-time scheduling theory is to analyze properties of mathematical models that capture the temporal behaviors of such systems. These models typically consist of a collection of computational tasks, each of which generates an infinite sequence of task activations. In this thesis we study different classes of models and their corresponding analysis problems. First, we consider models of mixed-criticality systems. The timing constraints of these systems state that all tasks must meet their deadlines for the run-time scenarios fulfilling certain assumptions, for example on execution times. For the other scenarios, only the most important tasks must meet their deadlines. We study both tasks with sporadic activation patterns and tasks with complicated activation patterns described by arbitrary directed graphs. We present sufficient schedulability tests, i.e., methods used to prove that a given collection of tasks will meet their timing constraints under a particular scheduling algorithm. Second, we consider models where tasks can lock mutually exclusive resources and have activation patterns described by directed cycle graphs. We present an optimal scheduling algorithm and an exact schedulability test. Third, we address a pair of longstanding open problems in real-time scheduling theory. These concern the computational complexity of deciding whether a collection of sporadic tasks are schedulable on a uniprocessor. We show that this decision problem is strongly coNP-complete in the general case. In the case where the asymptotic resource utilization of the tasks is bounded by a constant smaller than 1, we show that it is weakly coNP-complete.

abstract: Multi-task learning (MTL) aims to improve the generalization performance (of the resulting classifiers) by learning multiple related tasks simultaneously. Specifically, MTL exploits the intrinsic task relatedness, based on which the informative domain knowledge from each task can be shared across multiple tasks and thus facilitate the individual task learning. It is particularly desirable to share the domain knowledge (among the tasks) when there are a number of related tasks but only limited training data is available for each task. Modeling the relationship of multiple tasks is critical to the generalization performance of the MTL algorithms. In this dissertation, I propose a series of MTL approaches which assume that multiple tasks are intrinsically related via a shared low-dimensional feature space. The proposed MTL approaches are developed to deal with different scenarios and settings; they are respectively formulated as mathematical optimization problems of minimizing the empirical loss regularized by different structures. For all proposed MTL formulations, I develop the associated optimization algorithms to find their globally optimal solution efficiently. I also conduct theoretical analysis for certain MTL approaches by deriving the globally optimal solution recovery condition and the performance bound. To demonstrate the practical performance, I apply the proposed MTL approaches on different real-world applications: (1) Automated annotation of the Drosophila gene expression pattern images; (2) Categorization of the Yahoo web pages. Our experimental results demonstrate the efficiency and effectiveness of the proposed algorithms. / Dissertation/Thesis / Ph.D. Computer Science 2011