<p>Purpose; Purpose of this thesis is to review the implementation process of SOX in one company to be able to estimate which factors that are significant for individuals when it comes to accepting SOX as a tool to improving processes.</p><p>Method: Our approach in attacking the reality has been the golden middle-way, which means we have went into the reality with already constructed theories but along as the study went on we have refined theories, thus shifting between inductive and deductive approach.</p><p>Results/conclusions; Important obstacles that prevented adoption of the innovation had to do the characteristic of the innovation and the size and complexity of the organization; however we believed that the most significant blockade against change was the non-existing communication between the three different parties, namely consultants, managers and end-users.</p>

Purpose; Purpose of this thesis is to review the implementation process of SOX in one company to be able to estimate which factors that are significant for individuals when it comes to accepting SOX as a tool to improving processes. Method: Our approach in attacking the reality has been the golden middle-way, which means we have went into the reality with already constructed theories but along as the study went on we have refined theories, thus shifting between inductive and deductive approach. Results/conclusions; Important obstacles that prevented adoption of the innovation had to do the characteristic of the innovation and the size and complexity of the organization; however we believed that the most significant blockade against change was the non-existing communication between the three different parties, namely consultants, managers and end-users.

Digital options, the basic building blocks for valuing complex financial assets, they play an important role in options valuation and hedging. We survey the digital options pricing formula under diffusion processes and jump-diffusion processes. Since the existent first-touch digital options pricing formulas with jump-diffusion processes are all in their Laplace transform of the option value. To inverse the Laplace transforms is critical when doing options valuation. Therefore, we adopt a phase-type jump-diffusion model which is developed by Chen, Lee and Sheu [2007] as our main model, and use FFT inversion to get the first-touch digital option price under (2,2)-factor exponential jump-diffusion processes.

Typescript. / Thesis (Ph. D.)--University of Hawaii at Manoa, 1990. / Includes bibliographical references (leaves 154-163) / Microfiche. / xvi, 163 leaves, bound ill. 29 cm

Thesis (M.S.)--Duquesne University, 2004. / Title from document title page. Abstract included in electronic submission form. Includes bibliographical references (p. 52-53) and abstract.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references (leaves 81-86). Also available in electronic version.

Thesis (Ph. D.)--Chalmers University of Technology, 1985. / Bibliography: leaves 130-131.

Online social and information networks, like Facebook and Twitter, exploit the influence of neighbors to achieve effective information sharing and spreading. The process that information is spread via the connected nodes in social and information networks is referred to as diffusion. In the literature, a number of diffusion models have been proposed for different applications like influential user identification and personalized recommendation. However, comprehensive studies to discover the hidden diffusion mechanisms governing the information diffusion using the data-driven paradigm are still lacking. This thesis research aims to design novel diffusion models with the structural and behaviorable dependency of neighboring nodes for representing social networks, and to develop computational algorithms to infer the diffusion models as well as the underlying diffusion mechanisms based on information cascades observed in real social networks. By incorporating structural dependency and diversity of node neighborhood into a widely used diffusion model called Independent Cascade (IC) Model, we first propose a component-based diffusion model where the influence of parent nodes is exerted via connected components. Instead of estimating the node-based diffusion probabilities as in the IC Model, component-based diffusion probabilities are estimated using an expectation maximization (EM) algorithm derived under a Bayesian framework. Also, a newly derived structural diversity measure namely dynamic effective size is proposed for quantifying the dynamic information redundancy within each parent component. The component-based diffusion model suggests that node connectivity is a good proxy to quantify how a node's activation behavior is affected by its node neighborhood. To model directly the behavioral dependency of node neighborhood, we then propose a co-activation pattern based diffusion model by integrating the latent class model into the IC Model where the co-activation patterns of parent nodes form the latent classes for each node. Both the co-activation patterns and the corresponding pattern-based diffusion probabilities are inferred using a two-level EM algorithm. As compared to the component-based diffusion model, the inferred co-activation patterns can be interpreted as the soft parent components, providing insights on how each node is influenced by its neighbors as reflected by the observed cascade data. With the motivation to discover a common set of the over-represented temporal activation patterns (motifs) characterizing the overall diffusion in a social network, we further propose a motif-based diffusion model. By considering the temporal ordering of the parent activations and the social roles estimated for each node, each temporal activation motif is represented using a Markov chain with the social roles being its states. Again, a two-level EM algorithm is proposed to infer both the temporal activation motifs and the corresponding diffusion network simultaneously. The inferred activation motifs can be interpreted as the underlying diffusion mechanisms characterizing the diffusion happening in the social network. Extensive experiments have been carried out to evaluate the performance of all the proposed diffusion models using both synthetic and real data. The results obtained and presented in the thesis demonstrate the effectiveness of the proposed models. In addition, we discuss in detail how to interpret the inferred co-activation patterns and interaction motifs as the diffusion mechanisms under the context of different real social network data sets.

This thesis consists of four parts. In the first part we recall some background theory that will be used throughout the thesis. In the second part, we studied the absolute continuity of the laws of the solutions of some perturbed stochastic differential equaitons(SDEs) and perturbed reflected SDEs using Malliavin calculus. Because the extra terms in the perturbed SDEs involve the maximum of the solution itself, the Malliavin differentiability of the solutions becomes very delicate. In the third part, we studied the absolute continuity of the laws of the solutions of the parabolic stochastic partial differential equations(SPDEs) with two reflecting walls using Malliavin calculus. Our study is based on Yang and Zhang \cite{YZ1}, in which the existence and uniqueness of the solutions of such SPDEs was established. In the fourth part, we gave the existence and uniqueness of the solutions of the elliptic SPDEs with two reflecting walls and general diffusion coefficients.

All proposed methods are demonstrated by several 2D and 3D examples which have been extensively studied in the recent literature of topology optimization. / The fourth-order nonlinear parabolic C-H equations with elasticity are solved by a powerful nonlinear implicit mutigrid algorithm. To validate its correctness and efficiency, I first use it for the quadternary C-H equations without elasticity and get good results. To my best knowledge, it is the first simulation for such C-H models composed of more than three phases both in 2D and 3D. / The Optimization of Structural Topology (OST) is a breakthrough in product design because it can optimize size, shape and topology synchronously under different physical constraints. It has promising applications in industry ranging from automobile and aerospace engineering to micro electromechanical system. / Then this dissertation introduces a gradient flow in the norm of H-1 for the problem of multi-material structural topology optimization in 2/3D with a generalized Cahn-Hilliard (C-H) model with elasticity. Unlike the traditional C-H model applied to spinodal separation which only has bulk energy and interface energy, the generalized model couples the macroscopic elastic energy (mean compliance) into the total free energy. As a result, the grain morphology is not random islands or zigzag web-like objects but regular truss or bar structure. Although disturbed by elastic energy, the C-H system still keeps its two most important properties: mass conservation and energy dissipation. Therefore, it is unnecessary to compute the Lagrange multipliers for the volume constraints and make extra effort to minimize the mean compliance (elastic energy) for the optimization of structural topology. On the other hand, when pure phases separate from disordered original state, their boundaries will merge and split resulting in natural and flexible topology variation. Such aforementioned properties make the C-H model especially suitable for the problem of optimization of multi-material structural topology. / This dissertation also extends the famous Solid Isotropic Material with Penalization (SIMP) model from 2D to 3D for topology optimization of the structure with single material. A short 177-line Matlab code including 3D Finite Element Method (FEM), filter technique, Optimality Criteria (OC) algorithm and bisection method is listed in appendix A for clear understanding of this model in 3D. / This dissertation first substitutes the nonlinear diffusion method for filter process in the optimization of structural topology. Filtering has been a major technique used in a homogenization-based method for topology optimization of structures. It plays a key role in regularizing the basic problem into a well-behaved setting. But it has a drawback of smoothing effect around the boundary of material domain. A diffusion technique is presented here as a variational approach to the regularization of the topology optimization problem. A nonlinear or anisotropic diffusion process not only leads to a suitable problem regularization but also exhibits strong "edge"-preserving characteristics. Thus, it shows that the use of the nonlinear diffusions brings desirable effects of boundary preservation and even enhancement of lower-dimensional features such as flow-like structures. The proposed diffusion techniques have a close relationship with the diffusion methods and the phase-field methods of the fields of materials and digital image processing. / Zhou Shiwei. / "December 2005." / Source: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6713. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (p. 140-151). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.