Clinical data systems continue to grow as a result of the proliferation of features that are collected and stored. Demands for accurate and well-organized clinical data have intensified due to the increased focus on cost-effectiveness, and continuous quality improvement for better clinical diagnosis and prognosis. Clinical organizations have opportunities to use the information they collect and their oversight role to enhance health safety. Due to the continuous growth in the number of parameters that are accumulated in large databases, the capability of interactively mining patient clinical information is an increasingly urgent need to the clinical domain for providing accurate and efficient health care. Simple database queries fail to address this concern for several problems like the lack of the use of knowledge contained in these extremely complex databases. Data mining addresses this problem by analyzing the databases and making decisions based on the hidden patterns. The collection of data from multiple locations in clinical organizations leads to the loss of data in data warehouses. Data preprocessing is the part of knowledge discovery where the data is cleaned and transformed to perform accurate and efficient data mining results. Missing values in the databases result in the loss of useful data. Handling missing values and reducing noise in the data is necessary to acquire better quality mining results. This thesis explores the idea of either rejecting inappropriate values during the data entry level or suggesting various methods of handling missing values in the databases. E-Intelligence form is designed to perform the data preprocessing tasks at different levels of the knowledge discovery process. Here the minimum data set of mental health and the breast cancer data set are used as case studies. Once the missing values are handled, decision trees are used as the data mining tool to perform the classification of the diagnosis of the databases for analyzing the results. Due to the ever increasing mobile devices and internet in health care, the analysis here also addresses issues relevant hand-held computers and communicational devices or web based applications for quick and better access.

Statistical models for the joint statistics of image pixels are of central importance in many image processing applications. However the high dimensionality stemming from large problem size and the long-range spatial interactions make statistical image modeling particularly challenging. Commonly this modeling is simplified by a change of basis, mostly using a wavelet transform. Indeed, the wavelet transform has widely been used as an approximate whitener of statistical time series. It has, however, long been recognized that the wavelet coefficients are neither Gaussian, in terms of the marginal statistics, nor white, in terms of the joint statistics. The question of wavelet joint models is complicated and admits for possibilities, with statistical structures within subbands, across orientations, and scales. Although a variety of joint models have been proposed and tested, few models appear to be directly based on empirical studies of wavelet coefficient cross-statistics. Rather, they are based on intuitive or heuristic notions of wavelet neighborhood structures. Without an examination of the underlying statistics, such heuristic approaches necessarily leave unanswered questions of neighborhood sufficiency and necessity. This thesis presents an empirical study of joint wavelet statistics for textures and other imagery including dependencies across scale, space, and orientation. There is a growing realization that modeling wavelet coefficients as independent, or at best correlated only across scales, may be a poor assumption. While recent developments in wavelet-domain Hidden Markov Models (notably HMT-3S) account for within-scale dependencies, we find that wavelet spatial statistics are strongly orientation dependent, structures which are surprisingly not considered by state-of-the-art wavelet modeling techniques. To demonstrate the effectiveness of the studied wavelet correlation models a novel non-linear correlated empirical Bayesian shrinkage algorithm based on the wavelet joint statistics is proposed. In comparison with popular nonlinear shrinkage algorithms, it improves the denoising results.

Advanced display design, such as Ecological Interface Design (EID), makes extensive use of complex graphical objects. Research has shown that by following EID methodologies, supervisory operators have better performance with the EID displays (Pawlak and Vicente, 1996). However, past research does not consider the visual aspects of the graphical objects used in EID. Of particular interest is how different design decisions of graphical objects affect the performance of the objects used within that design. This thesis examines the visual sensitivity of dynamic graphical objects by examining features that make certain graphical objects visually superior for certain monitoring tasks. Previous research into the visual aspects of supervisory control with respect to emergent features, psychophysics and attention were considered in the investigation of the visual sensitivities of the dynamic graphical objects used. Research into static graphical objects, combined with prior work on emergent features has been merged to find emergent features that best show changes in dynamic graphical objects for the monitoring tasks investigated. It was found that for simple dynamic objects such as bars and polygon objects, a line changing in angle was the most noticeable emergent feature to show a departure from ?normal? state. For complex graphical objects, those target-indicator displays that mimic a ?bull?s eye? when at the target value should be used for displays that show observers when a target value has been reached. Abrupt changes in shape should be used in trend meters to show when variables or processes have changed direction. Finally, ?solid objects? that make use of vertical lines and shading should be used for comparison meters that compare two values and keep them in a particular ratio. These findings provide guidance for designers of dynamic advanced graphical displays by encouraging the consideration of visual aspects of graphical objects, as well as prescribing graphical objects that should be used in the types of tasks investigated.

Clinical data systems continue to grow as a result of the proliferation of features that are collected and stored. Demands for accurate and well-organized clinical data have intensified due to the increased focus on cost-effectiveness, and continuous quality improvement for better clinical diagnosis and prognosis. Clinical organizations have opportunities to use the information they collect and their oversight role to enhance health safety. Due to the continuous growth in the number of parameters that are accumulated in large databases, the capability of interactively mining patient clinical information is an increasingly urgent need to the clinical domain for providing accurate and efficient health care. Simple database queries fail to address this concern for several problems like the lack of the use of knowledge contained in these extremely complex databases. Data mining addresses this problem by analyzing the databases and making decisions based on the hidden patterns. The collection of data from multiple locations in clinical organizations leads to the loss of data in data warehouses. Data preprocessing is the part of knowledge discovery where the data is cleaned and transformed to perform accurate and efficient data mining results. Missing values in the databases result in the loss of useful data. Handling missing values and reducing noise in the data is necessary to acquire better quality mining results. This thesis explores the idea of either rejecting inappropriate values during the data entry level or suggesting various methods of handling missing values in the databases. E-Intelligence form is designed to perform the data preprocessing tasks at different levels of the knowledge discovery process. Here the minimum data set of mental health and the breast cancer data set are used as case studies. Once the missing values are handled, decision trees are used as the data mining tool to perform the classification of the diagnosis of the databases for analyzing the results. Due to the ever increasing mobile devices and internet in health care, the analysis here also addresses issues relevant hand-held computers and communicational devices or web based applications for quick and better access.

Switched linear systems have long been subject to high interest and intense research efforts, not only because many real world systems happen to exhibit switching behaviors, but also because the control of many complex systems is only possible via the combination of classical continuous control laws with supervisory switching logic. A particularly important problem is that of estimator and observer design, since the state of a system is usually only available through partial, often noise-corrupted, measurements. Even though hybrid estimation has been around for at least thirty years, a veil of mystery has surrounded the concept of ``observability' in switched linear systems. It is not until recently, with the recent renewal of interest toward deterministic hybrid systems, that observer design and observability analysis have fuelled sustained research efforts. It is in this context that this work is grounded. More precisely, the objective of this research is twofold: - To define proper concepts of observability in discrete-time switched linear systems, to characterize them, and to analyze their main properties, among which decidability is of special importance. - To propose and analyze observers - deadbeat and asymptotic - for such systems. The main contributions of this dissertation are as follows. It is shown that pathwise observability, i.e. state observability under arbitrary mode sequences, is decidable. Furthermore, the Kalman-Bertram sampling criterion is carried over to switched linear systems. Under unknown modes, mode and state observability are both characterized through simple linear algebraic tests, and are shown to be decidable in the autonomous case. As for asymptotic observers, a direct algebraic approach is proposed for the class of linear systems subjected to switching in the measurement equation.

The rapid advance of distance learning and networking technology has enabled universities and corporations to reach out and educate students across time and space barriers. This technology supports structured, on-line learning activities, and provides facilities for assessment and collaboration. Structured collaboration, in the classroom, has proven itself a successful and uniquely powerful learning method. Online collaborative learners, however, do not enjoy the same benefits as face-to-face learners because the technology provides no guidance or direction during online discussion sessions. Integrating intelligent facilitation agents into collaborative distance learning environments may help bring the benefits of the supportive classroom closer to distance learners. In this dissertation, I describe a new approach to analyzing and supporting online peer interaction. The approach applies Hidden Markov Models, and Multidimensional Scaling with a threshold-based clustering method, to analyze and assess sequences of coded on-line student interaction. These analysis techniques were used to train a system to dynamically recognize when and why students may be experiencing breakdowns while sharing knowledge and learning from each other. I focus on knowledge sharing interaction because students bring a great deal of specialized knowledge and experiences to the group, and how they share and assimilate this knowledge shapes the collaboration and learning processes. The results of this research could be used to dynamically inform and assist an intelligent instructional agent in facilitating knowledge sharing interaction, and helping to improve the quality of online learning interaction.

This thesis raises objections to the use of causal reasoning with equilibrium models. I consider two operators that are used to transform models: the {em Do} operator for modeling manipulation and the {em Equilibration} operator for modeling a system that has achieved equilibrium. I introduce a property of a causal model called the {em EMC Property} that is true iff the {em Do} operator commutes with the {em Equilibration} operator. I prove that not all models obey the EMC property, and I demonstrate empirically that when inferring a causal model from data, the learned model will not support causal reasoning if the EMC property is not obeyed. I find sufficient conditions for models to violate and not to violate the EMC property. In addition, I show that there exists a class of models that violate EMC and possess a set of variables whose manipulation will cause an instability in the system. All dynamic models in this class possess feedback, although I do not prove that feedback is a necessary or a sufficient condition for EMC violation. I define the {em Structural Stability Principle} which provides a necessary graphical criterion for stability in causal models. I will argue that the models in this class are quite common given typical assumptions about causal relations.

This dissertation studies how the mechanism-based view of causality can assist in construction and utilization of causal models for decision support. The mechanism-based view of causality is based on the theory of causal ordering, proposed by Simon [53], which explicates causal asymmetries among variables in a self-contained set of simultaneous structural equations. I extend the theory of causal ordering to explicate causal relations in under-constrained sets of structural equations. Considering under constrained models as intermediate representations of one's understanding of decision problems, I demonstrate that a model construction process can be viewed as the process of assembling mechanisms from under-constrained models into self-contained causal models. I formalize the reversibility property of a mechanism to support changes in structure in causal models containing reversible mechanisms. I introduce algorithms for deliberating atomic actions when one considers manipulating a variable or releasing a mechanism to achieve a decision objective. In addition, I introduce the concept of search for opportunities which amounts to both identifying the set of policy variables and computing their optimal setting for a decision objective. Search for opportunities presents decision makers with a list of ranked interventions based on the value of intervention computation. I implement an interactive system called ImaGeNIe that supports mechanism-based model construction and utilization. I conduct subject experiments and find that ImaGeNIe can effectively assist users in constructing causal models for causal reasoning.

After Hong Kong returned to China, the integration of political field has completed. However, the divergence in social and economic fields remain significant which triggers the motives for this study. In the process of integration between Hong Kong and China, the ¡§One Country, Two System¡¨ and ¡§Closer Economic Partnership Arrangement¡¨(CEPA) have provided political and economic schemes respectively. The integration procedure in Hong Kong-China case, that is to integrate in political sphere first and then push forward other areas of integration subsequently, conforms to the characteristics of Federalism. In addition, it is found that the local government is subordinate to the central under the Hong Kong Basic Law, which means the principle of ¡§one country¡¨ is prior to ¡§two systems.¡¨ On the other hand, the practice of CEPA provides new opportunities for economic development in Hong Kong. Beijing also hopes that Hong Kong could help develop the Pan Pearl River Delta Region under CEPA. Under all kinds of forces from the central and local, it is predictable that Hong Kong will integrate into China gradually in the near future.

This work introduces the Bayesian local causal discovery framework, a method for discovering unconfounded causal relationships from observational data. It addresses the hypothesis that causal discovery using local search methods will outperform causal discovery algorithms that employ global search in the context of large datasets and limited computational resources. Several Bayesian local causal discovery (BLCD) algorithms are described and results presented comparing them with two well-known global causal discovery algorithms PC and FCI, and a global Bayesian network learning algorithm, the optimal reinsertion (OR) algorithm which was post-processed to identify relationships that under assumptions are causal. Methodologically, this research formalizes the task of causal discovery from observational data using a Bayesian approach and local search. It specifically investigates the so called Y structure in causal discovery and classifies the various types of Y structures present in the data generating networks. It identifies the Y structures in the Alarm, Hailfinder, Barley, Pathfinder and Munin networks and categorizes them. A proof of the convergence of the BLCD algorithm based on the identification of Y structures, is also provided. Principled methods of combining global and local causal discovery algorithms to improve upon the performance of the individual algorithms are discussed. In particular, a post-processing method for identifying plausible causal relationships from the output of global Bayesian network learning algorithms is described, thereby extending them to be causal discovery algorithms. In an experimental evaluation, simulated data from synthetic causal Bayesian networks representing five different domains, as well as a real-world medical dataset, were used. Causal discovery performance was measured using precision and recall. Sometimes the local methods performed better than the global methods, and sometimes they did not (both in terms of precision/recall and in terms of computation time). When all the datasets were considered in aggregate, the local methods (BLCD and BLCDpk) had higher precision. The general performance of the BLCD class of algorithms was comparable to the global search algorithms, implying that the local search algorithms will have good performance on very large datasets when the global methods fail to scale up. The limitations of this research and directions for future research are also discussed.