In this dissertation, we will investigate the global stability for some uncertain neural networks with multiple time-varying delays. These well-known neural networks include delayed cellular neural networks (DCNNs), delayed bidirectional associative memory neural networks (DBAMNNs), and delayed Cohen-Grossberg neural networks (DCGNNs). Delay-dependent and delay-independent criteria will be proposed to guarantee the robust stability of these uncertain delayed neural networks via linear matrix inequality (LMI) approach. Three types of uncertainties on feedback and delayed feedback matrices in these uncertain delayed neural networks will be considered in this study, namely uncertainties with structured perturbation, norm-bounded unstructured perturbation, and interval perturbation. Some numerical examples will be given to illustrate the effectiveness of our results. Some comparisions are made to show that our results are better than some results in recent literature.

<p>Visualization of large scale time-varying volumetric datasets is an active topic of research. Technical limitations in terms of bandwidth and memory usage become a problem when visualizing these datasets on commodity computers at interactive frame rates. The overall objective is to overcome these limitations by adapting the methods of an existing Direct Volume Rendering pipeline. The objective is considered to be a proof of concept to assess the feasibility of visualizing large scale time-varying datasets using this pipeline. The pipeline consists of components from previous research, which make extensive use of graphics hardware to visualize large scale static data on commodity computers.</p><p>This report presents a diploma work, which adapts the pipeline to visualize flow features concealed inside the large scale Computational Fluid Dynamics dataset. The work provides a foundation to address the technical limitations of the commodity computer to visualize time-varying datasets. The report describes the components making up the Direct Volume Rendering pipeline together with the adaptations. It also briefly describes the Computational Fluid Dynamics simulation, the flow features and an earlier visualization approach to show the system’s limitations when exploring the dataset.</p>

本論文主要探討時變環境下之最適動態資產配置策略。由於對許多跨期的投資者而言，其所關心的不只是資產當期的報酬與風險，更關心隨著時間的經過，報酬與風險的改變方向。過去許多財務實證利用不同的時間序列模型發現風險性資產，尤其是股票市場存在著某些特性，例如：均數復歸 (Mean-reverting) 與隨機波動等現象，這些實證現象對於跨期的投資者在進行資產配置時尤為重要。本文即在此時變環境架構下探討三個資產配置上的問題。並利用擾動法以求解隨機設定下之封閉解，進而提出直接的資產配置建議。 第二章主要在探討不完全市場與時變環境下，跨期退休基金管理之最適動態資產配置策略。在納入退休基金管理中二項重要的特性：多期投資與退休基金負債的考量後，提出退休基金的最適跨期資產配置策略，本文並同時利用Sharpe and Tint (1990) 所提出的彈性考量退休基金負債的觀念，納入本文之模型設定當中，使本文能同時涵蓋不同退休基金管理者之不同負債考量進入資產配置之最適配置策略。本文所提出之跨期退休基金管理之最適動態資產配置策略，除了包含單期與面對時變環境下之跨期避險成分外，更提出退休基金管理者如何依據其退休基金之特性，如基金負債比率(funding ratio)等來建構其資產配置中之退休基金負債避險成分。 第三章主要在隨機環境設定下利用跨期避險與不可交易資產求解「資產配置的迷思」。Canner, Mankiw and Weil (1997) 指出，一般財務顧問公司對於投資者風險態度的差異所提出之投資建議與財務理論間存在著嚴重的不一致性，其將之稱為「資產配置的迷思(An asset allocation puzzle)」。於第三章中提出一理性的長期投資者模型，在考量投資者之不可交易資產與隨機投資機會下，提出最適動態資產配置策略，並解決了此資產配置的迷思，與現今之一般財務顧問公司對於投資者之投資建議相一致。 第四章主要在探討利率隨時改變下，一持有不動產抵押貸款負債之家計單位或投資者，如何進行跨期的動態資產配置策略。由於購屋置產對許多家庭或個人而言是相當重要的投資決策，但一般由於缺乏足夠的資金或為了維持一穩定的消費過程，一般均會利用不動產抵押取得貸款，但隨著市場利率的越漸波動，投資者如何進行跨期的資產配置以達到獲取報酬並規避利率波動的目的為本章之討論重點。利用可交易資產來對其所持有之不動產抵押貸款負債進行避險，依不同投資者風險態度、跨期偏好、抵押期限等之設定，對於持有不動產抵押貸款負債之投資者提出資產配置及避險的建議。

Visualization is the process of making something perceptible to the mind or imagination. The techniques for producing visual imagery of volumetric data have advanced immensely during the last decades to a point where each produced image can include an overwhelming amount of information. An increasingly viable solution to the limitations of the human sense of visual perception is to make use of not only vision, but also additional senses. This thesis presents recent work on the development of principles and algorithms for generating representations of volumetric data through the sense of touch for the purpose of visualization. The primary idea introduced in this work is the concept of yielding constraints, that can be used to provide a continuous set of shapes as a representation of features of interest in various types of volumetric data. Some of the earlier identified standard human exploratory procedures can then be used which enables natural, intuitive and effective interaction with the data. The yielding constraints concept is introduced, and an algorithm based on haptic primitives is described, which forms a powerful yet versatile implementation of the yielding constraints. These methods are also extended to handle time-varying, moving and low quality data. A framework for multimodal visualization has been built on the presented methods, and this is used to demonstrate the applicability and versatility of the work through several example applications taken from different areas.

Server farms are popular architectures for computing infrastructures such as supercomputing centers, data centers and web server farms. As server farms become larger and their workloads more complex, designing efficient policies for managing the resources in server farms via trial-and error becomes intractable. In this thesis, we employ stochastic modeling and analysis techniques to understand the performance of such complex systems and to guide design of policies to optimize the performance. There is a rich literature on applying stochastic modeling to diverse application areas such as telecommunication networks, inventory management, production systems, and call centers, but there are numerous disconnects between the workloads and architectures of these traditional applications of stochastic modeling and how compute server farms operate, necessitating new analytical tools. To cite a few: (i) Unlike call durations, supercomputing jobs and file sizes have high variance in service requirements and this critically affects the optimality and performance of scheduling policies. (ii) Most existing analysis of server farms focuses on the First-Come- First-Served (FCFS) scheduling discipline, while time sharing servers (e.g., web and database servers) are better modeled by the Processor- Sharing (PS) scheduling discipline. (in) Time sharing systems typically exhibit thrashing (resource contention) which limits the achievable concurrency level, but traditional models of time sharing systems ignore this fundamental phenomenon. (iv) Recently, minimizing energy consumption has become an important metric in managing server farms. State-of-the-art servers come with multiple knobs to control energy consumption, but traditional queueing models don’t take the metric of energy consumption into account. In this thesis we attempt to bridge some of these disconnects by bringing the stochastic modeling and analysis literature closer to the realities of today’s compute server farms. We introduce new queueing models for computing server farms, develop new stochastic analysis techniques to evaluate and understand these queueing models, and use the analysis to propose resource management algorithms to optimize their performance.

Visualization of large scale time-varying volumetric datasets is an active topic of research. Technical limitations in terms of bandwidth and memory usage become a problem when visualizing these datasets on commodity computers at interactive frame rates. The overall objective is to overcome these limitations by adapting the methods of an existing Direct Volume Rendering pipeline. The objective is considered to be a proof of concept to assess the feasibility of visualizing large scale time-varying datasets using this pipeline. The pipeline consists of components from previous research, which make extensive use of graphics hardware to visualize large scale static data on commodity computers. This report presents a diploma work, which adapts the pipeline to visualize flow features concealed inside the large scale Computational Fluid Dynamics dataset. The work provides a foundation to address the technical limitations of the commodity computer to visualize time-varying datasets. The report describes the components making up the Direct Volume Rendering pipeline together with the adaptations. It also briefly describes the Computational Fluid Dynamics simulation, the flow features and an earlier visualization approach to show the system’s limitations when exploring the dataset.

I find evidence of predictability in out-of-sample data for four risk premia using simple econometric models. Two factor return models are used, an APT model and the Wilshire Atlas. I demonstrate that investors can exploit conditioning information to manage their exposures to risk factors. The results suggest that the investment opportunities set changes in a large and an economically significant way. I show that the growth rate in money supply and trend in stock market valuations are the main drivers respectfully of the risk premia associated with the Book-to-Market and Size factors from the Wilshire model. The predictability results are mixed with respect to Business Cycle Theory. At times investors price business cycle risk while at other times they exhibit herding tendencies.

This thesis describes the development of several small signal analysis methods for the modelling of power electronic converters. The methods are written generally and are intended to be able to be applied to all converter classes. In the penultimate chapter these general models are used to model the capacitor commutated converter. All the contained methods are based around a time domain small signal model. This time domain small model is a linearization of a power electronic system of passive components and ideal switches described as a hybrid system. The key problem in the derivation of the small signal model is the correct determination and description of the linearized effect of switching instant variation. Three analysis methods based upon the small signal model are advanced in this thesis, these are; time domain sensitivity matrices for use in a Newton determination of the cyclic steady state of a power electronic converter, partial waveform construction of harmonic sensitivity matrices for studying sensitivity of converters to harmonic disturbances, and harmonic state space models also for the construction of harmonic sensitivity matrices and for study of dynamic systems. Each modelling technique is applied to the more common converter topologies of the Buck-Boost converter and the Graetz Bridge before being finally applied to the capacitor commutated converter. Each technique is compared to PSCAD-EMTDC simulations for verification.

Introduction: Clostridium difficile infection (CDI) is a common hospital-acquired infection and a financial burden on the healthcare system. There is a need to reduce its impact on patients and the entire health system. More accurate estimates of the financial impact of CDI will assist hospitals in creating better CDI reduction strategies with limited resources. Previous research has not sufficiently accounted for the skewed nature of hospital cost data, baseline patient mortality risk, and the time-varying nature of CDI. Objective: We conducted a retrospective cohort study to estimate the cost impact of hospital-acquired CDI from the hospital perspective, using a number of analytical approaches. Method: We used clinical and administrative data for inpatients treated at The Ottawa Hospital to construct an analytical data set. Our primary outcome was direct costs and our primary exposure was hospital-acquired CDI. We performed the following analyses: Ordinary least square regression and generalized linear regression as time-fixed methods, and Kaplan-Meier survival curve and Cox regression models as time-varying methods. Results: A total of 49,888 admissions were included in this study (mean (SD) age of 64.6 ± 17.8 years, median (IQR) baseline mortality risk of 0.04 (0.01-0.14)). 360 (0.73%) patients developed CDI. Estimates of incremental cost due to CDI were substantially higher when using time-fixed methods than time-varying methods. Using methods that appropriately account for the time-varying nature of the exposure, the estimated incremental cost due to CDI was $8,997 per patient. In contrast, estimates from time-fixed methods ranged from $49,150 to $55,962: about a six fold difference. Conclusion: Estimates of hospital costs are strongly influenced by the time-varying nature of CDI as well as baseline mortality risk. If studies do not account for these factors, it is likely that the impact of hospital-acquired CDI will be overestimated.

The primary objective of this thesis was to gain a deeper understanding of acoustic wave propagation in phononic crystals, particularly those that include materials whose properties can be varied periodically in time. This research was accomplished in three ways. First, a 2D phononic crystal was designed, created, and characterized. Its properties closely matched those determined through simulation. The crystal demonstrated band gaps, dispersion, and negative refraction. It served as a means of elucidating the practicalities of phononic crystal design and construction and as a physical verification of their more interesting properties. Next, the transmission matrix method for analyzing 1D phononic crystals was extended to include the effects of time-varying material parameters. The method was then used to provide a closed-form solution for the case of periodically time-varying material parameters. Some intriguing results from the use of the extended method include dramatically altered transmission properties and parametric amplification. New insights can be gained from the governing equations and have helped to identify the conditions that lead to parametric amplification in these structures. Finally, 2D multiple scattering theory was modified to analyze scatterers with time-varying material parameters. It is shown to be highly compatible with existing multiple scattering theories. It allows the total scattered field from a 2D time-varying phononic crystal to be determined. It was shown that time-varying material parameters significantly affect the phononic crystal transmission spectrum, and this was used to switch an incident monochromatic wave. Parametric amplification can occur under certain circumstances, and this effect was investigated using the closed-form solutions provided by the new 1D method. The complexity of the extended methods grows logarithmically as opposed linearly with existing methods, resulting in superior computational complexity for large numbers of scatterers. Also, since both extended methods provide analytic solutions, they may give further insights into the factors that govern the behaviour of time-varying phononic crystals. These extended methods may now be used to design an active phononic crystal that could demonstrate new or enhanced properties.