The interfacial structure of Co/Cu multilayers deposited under energetic ion bombardment has been investigated using X-ray reflectivity techniques. An ionassisted deposition system, based on unbalanced magnetron sputtering principles, has been developed in order to provide a method for the independent control of ion flux and energy. Using this system ion current densities measured at the substrate could be varied from ~30 µAcm2 to ~100 µAcm2 and average ion energies of ~50-200 eV could be selected. It was therefore possible to prepare Co/Cu multilayers under a variety of ion bombardment conditions and thus study the effect of this energetic particle bombardment on the evolution of interface structure. Samples were characterised by measuring the specular and diffuse X-ray reflectivity, using anomalous scattering effects to enhance the contrast between the cobalt and copper layers. Quantitative analysis of the specular reflection revealed the existence of variations of the interface roughness in multilayers deposited under ~200 eV ion bombardment, such that the interfaces became smoother towards the free surface. Films deposited with the highest available levels of ion flux were found to display the greatest interface smoothing effect. However, no such variation was observed when ion energies of ~50 eV were applied, although differences in the large lateral scale correlated roughness were observed between ~50 eV ion-assisted and non ion-assisted samples. The sensitivity of the interface structure to energetic bombardment during growth is thus demonstrated and the results are discussed in terms of resputtering and ion bombardment induced reordering of the growing surface. It is shown that these effects can be exploited to obtain multilayers with atomically smooth interfaces.

Thesis (Ph. D.)--University of Missouri-Columbia, 2008. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 3, 2009) Vita. Includes bibliographical references.

<p> An expression for the semiclassical density of states for a particle in a smooth potential well is obtained from the Kirkwood expansion of the partition function. This expression for the semiclassical density of states is then shown to be essentially equivalent to the expression obtained from the Green's function method of Balian and Bloch.</p> <p> The Strutinsky shell correction to the nuclear binding energy is then analytically shown to be equivalent to the shell correction obtained from a consideration of the semiclassical partition function if certain restrictions on the Strutinsky smoothing parameter can be met.</p> / Thesis / Master of Science (MSc)

In some survival analysis of medical studies, there are often long term survivors who can be considered as permanently cured. The goals in these studies are to estimate the cure probability of the whole population and the hazard rate of the noncured subpopulation. The existing methods for cure rate models have been limited to parametric and semiparametric models. More specifically, the hazard function part is estimated by parametric or semiparametric model where the effect of covariate takes a parametric form. And the cure rate part is often estimated by a parametric logistic regression model. We introduce a non-parametric model employing smoothing splines. It provides non-parametric smooth estimates for both hazard function and cure rate. By introducing a latent cure status variable, we implement the method using a smooth EM algorithm. Louis' formula for covariance estimation in an EM algorithm is generalized to yield point-wise confidence intervals for both functions. A simple model selection procedure based on the Kullback-Leibler geometry is derived for the proposed cure rate model. Numerical studies demonstrate excellent performance of the proposed method in estimation, inference and model selection. The application of the method is illustrated by the analysis of a melanoma study. / Ph. D.

In this thesis, a new smoothing penalty algorithm is introduced to solve a mathematical program with equilibrium constraints (MPEC). By smoothing the exact penalty function, an MPEC is reformulated as a series of subprograms which belong to a class of MPECs with simple linear complementarity constraints. To deal with the subproblems, a hybrid algorithm is proposed, which combines the active set algorithm, the 6-active search algorithm and the PSQP algorithm. It is shown that the smoothing penalty algorithm converges globally to a M-stationary point of MPEC under weak conditions. Supervisor: Dr. Jane Ye (Department of Mathematics and Statistics) Co-Supervisor: Dr. Wu-Sheng Lu (Department of Electrical and Computer Engineering)

Wong, Ka Wai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 46-49). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background --- p.4 / Chapter 2.1 --- Notation and Definitions --- p.4 / Chapter 2.2 --- Influence Function of β --- p.5 / Chapter 3 --- Methodology --- p.7 / Chapter 3.1 --- Robust Estimating Equations --- p.7 / Chapter 3.2 --- A General Algorithm for Robust GAM Estimation --- p.9 / Chapter 4 --- Asymptotic Equivalence --- p.12 / Chapter 5 --- Smoothing Parameter Selection --- p.16 / Chapter 5.1 --- Robust Cross-Validation --- p.17 / Chapter 5.2 --- Robust Information Criteria --- p.17 / Chapter 6 --- Multiple Covariates --- p.19 / Chapter 7 --- Simulation Study --- p.21 / Chapter 8 --- Real Data Examples --- p.26 / Chapter 8.1 --- Air Pollution Data --- p.26 / Chapter 8.2 --- Bronchitis Data --- p.28 / Chapter 9 --- Concluding Remarks --- p.31 / Chapter A --- Auxiliary Lemmas and Proofs --- p.32 / Chapter B --- Fisher Consistency Correction --- p.42 / Chapter B.1 --- Poisson distribution --- p.42 / Chapter B.2 --- Bernoulli distribution --- p.43 / Chapter C --- Derivation of (5.2) --- p.44 / Bibliography --- p.46

With the increasing use of geometry scanners to create 3D models, there is a rising need for fast and robust mesh smoothing to remove inevitable noise in the measurements. While most previous work has favored diffusion-based iterative techniques for feature-preserving smoothing, we propose a radically different approach, based on robust statistics and local first-order predictors of the surface. The robustness of our local estimates allows us to derive a non-iterative feature-preserving filtering technique applicable to arbitrary "triangle soups". We demonstrate its simplicity of implementation and its efficiency, which make it an excellent solution for smoothing large, noisy, and non-manifold meshes. / Singapore-MIT Alliance (SMA)

Particle smoothing is useful for offline state inference and parameter learning in nonlinear/non-Gaussian state-space models. However, many particle smoothers, such as the popular forward filter/backward simulator (FFBS), are plagued by a quadratic computational complexity in the number of particles. One approach to tackle this issue is to use rejection-sampling-based FFBS (RS-FFBS), which asymptotically reaches linear complexity. In practice, however, the constants can be quite large and the actual gain in computational time limited. In this contribution, we develop a hybrid method, governed by an adaptive stopping rule, in order to exploit the benefits, but avoid the drawbacks, of RS-FFBS. The resulting particle smoother is shown in a simulation study to be considerably more computationally efficient than both FFBS and RS-FFBS. / CNDM / CADICS

Current efforts to measure child mortality for municipalities in Brazil are hampered by the relative rarity of child deaths, which often results in unstable and unreliable estimates. As a result, it is not possible to accurately assess true levels of child mortality for many areas, hindering efforts towards constructing and implementing effective policy initiatives for the reduction of child mortality. However, with a spatial smoothing process based upon Bayesian Statistics it is possible to “borrow” information from neighboring areas in order to generate more stable and accurate estimates of mortality in smaller areas. The objective of this study is to use this spatial smoothing process to derive estimates of child mortality at the level of the municipality in Brazil. Using data from the 2000 Brazil Census, I derive both Bayesian and non-Bayesian estimates of mortality for each municipality. In comparing the smoothed and raw estimates of this parameter, I find that the Bayesian estimates yield a clearer spatial pattern of child mortality with smaller variances in less populated municipalities, thus, more accurately reflecting the true mortality situation of those municipalities. These estimates can then be used, ultimately, to lead to more effective policies and health initiatives in the fight for the reduction of child mortality in Brazil. / text

published_or_final_version / Statistics / Master / Master of Philosophy