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One of the most common pairs of co-occurring psychological disorders in children and adolescents is anxiety and depression. This high frequency of co-occurrence has led to research examining the structure of anxiety and depression, specifically the shared and unique aspects of these syndromes. The tripartite model accounts for the overlap between the disorders by suggesting that they are related because they share the feature of negative affect or general psychological distress. The model further proposes that they can be differentiated by their unique features of physiological hyperarousal (anxiety) and low positive affect (depression). Factor analytic research has shown that anxious symptoms and depressive symptoms can be structurally distinguished and research on the tripartite model has suggested their conceptual distinction. However, research has not shown that anxiety and depression cluster as distinct symptoms in samples of youth. The current study used cluster analysis to examine the grouping of individuals based on their levels of anxiety and depression. It was hypothesized that four groups would emerge-- anxiety only, depression only, comorbid anxiety and depression, and low/no symptoms. Further analyses using the tripartite model variables provided support of the accurate classification of individuals and this model was shown to be a useful tool in differentiating anxious symptoms from depressive symptoms. Exploratory analyses regarding developmental differences in the structure of anxiety and depression provided mixed support.

by Wong Wing Hung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves [70]). / Acknowledgments --- p.iii / Abstract --- p.iv / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- The Coupled-cluster Method --- p.3 / Chapter 2.1 --- Background / Chapter 2.2 --- Basic Idea of the Method / Chapter 2.3 --- Discussion / Chapter Chapter 3 --- Spin-one Heisenberg Antiferromagnet --- p.8 / Paper enclosed： Coupled-cluster / approximation for spin-one / Heisenberg antiferromagnet / Chapter Chapter 4 --- Easy-plane Spin-one Antif erromagnet --- p.10 / Paper enclosed： Coupled-cluster / approximation for the easy-plane / spin-one antif erromagnet / Chapter Chapter 5 --- Conclusion --- p.11 / References --- p.12 / Appendix --- p.13

Catherine Theresa Desmarais

Thesis (M.S.)--University of Missouri--Rolla, 2007. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed December 4, 2007) Includes bibliographical references (p. 67-69).

Thesis (M. Phil.)--University of Hong Kong, 2010. / Includes bibliographical references (leaves 61-66). Also available in print.

Thesis (Ph. D.)--University of Washington, 2001. / Vita. Includes bibliographical references (p. 132-140).

Co-clustering is rather a recent paradigm for unsupervised data analysis, but it has become increasingly popular because of its potential to discover latent local patterns, otherwise unapparent by usual unsupervised algorithms such as k-means. Wide deployment of co-clustering, however, requires addressing a number of practical challenges such as data transformation, cluster initialization, scalability, and so on. Therefore, this thesis focuses on developing sophisticated co-clustering methodologies to maturity and its ultimate goal is to promote co-clustering as an invaluable and indispensable unsupervised analysis tool for varied practical applications. To achieve this goal, we explore the three specific tasks: (1) development of co-clustering algorithms to be functional, adaptable, and scalable (co-clustering algorithms); (2) extension of co-clustering algorithms to incorporate application-specific requirements (extensions); and (3) application of co-clustering algorithms broadly to existing and emerging problems in practical application domains (applications). As for co-clustering algorithms, we develop two fast Minimum Sum-Squared Residue Co-clustering (MSSRCC) algorithms [CDGS04], which simultaneously cluster data points and features via an alternating minimization scheme and generate co-clusters in a “checkerboard” structure. The first captures co-clusters with constant values, while the other discovers co-clusters with coherent “trends” as well as constant values. We note that the proposed algorithms are two special cases (bases 2 and 6 with Euclidean distance, respectively) of the general co-clustering framework, Bregman Co-clustering (BCC) [BDG+07], which contains six Euclidean BCC and six I-divergence BCC algorithms. Then, we substantially enhance the performance of the two MSSRCC algorithms by escaping from poor local minima and resolving the degeneracy problem of generating empty clusters in partitional clustering algorithms through the three specific strategies: (1) data transformation; (2) deterministic spectral initialization; and (3) local search strategy. Concerning co-clustering extensions, we investigate general algorithmic strategies for the general BCC framework, since it is applicable to a large class of distance measures and data types. We first formalize various data transformations for datasets with varied scaling and shifting factors, mathematically justify their effects on the six Euclidean BCC algorithms, and empirically validate the analysis results. We also adapt the local search strategy, initially developed for the two MSSRCC algorithms, to all the twelve BCC algorithms. Moreover, we consider variations of cluster assignments and cluster updates, including greedy vs. non-greedy cluster assignment, online vs. batch cluster update, and so on. Furthermore, in order to provide better scalability and usability, we parallelize all the twelve BCC algorithms, which are capable of co-clustering large-scaled datasets over multiple processors. Regarding co-clustering applications, we extend the functionality of BCC to incorporate application-specific requirements: (1) discovery of inverted patterns, whose goal is to find anti-correlation; (2) discovery of coherent co-clusters from noisy data, whose purpose is to do dimensional reduction and feature selection; and (3) discovery of patterns from time-series data, whose motive is to guarantee critical time-locality. Furthermore, we employ co-clustering to pervasive computing for mobile devices, where the task is to extract latent patterns from usage logs as well as to recognize specific situations of mobile-device users. Finally, we demonstrate the applicability of our proposed algorithms for aforementioned applications through empirical results on various synthetic and real-world datasets. In summary, we present co-clustering algorithms to discover latent local patterns, propose their algorithmic extensions to incorporate specific requirements, and provide their applications to a wide range of practical domains. / text

published_or_final_version / Physics / Master / Master of Philosophy

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