Functional Sequential Treatment Allocation

Consider a setting in which a policy maker assigns subjects to treatments, observing each outcome before the next subject arrives. Initially, it is unknown which treatment is best, but the sequential nature of the problem permits learning about the effectiveness of the treatments. While the multi-armed-bandit literature has shed much light on the situation when the policy maker compares the effectiveness of the treatments through their mean, much less is known about other targets. This is restrictive, because a cautious decision maker may prefer to target a robust location measure such as a quantile or a trimmed mean. Furthermore, socio-economic decision making often requires targeting purpose specific characteristics of the outcome distribution, such as its inherent degree of inequality, welfare or poverty. In the present paper we introduce and study sequential learning algorithms when the distributional characteristic of interest is a general functional of the outcome distribution. Minimax expected regret optimality results are obtained within the subclass of explore-then-commit policies, and for the unrestricted class of all policies

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

On 3-D inelastic analysis methods for hot section components (base program)

A 3-D Inelastic Analysis Method program is described. This program consists of a series of new computer codes embodying a progression of mathematical models (mechanics of materials, special finite element, boundary element) for streamlined analysis of: (1) combustor liners, (2) turbine blades, and (3) turbine vanes. These models address the effects of high temperatures and thermal/mechanical loadings on the local (stress/strain)and global (dynamics, buckling) structural behavior of the three selected components. Three computer codes, referred to as MOMM (Mechanics of Materials Model), MHOST (Marc-Hot Section Technology), and BEST (Boundary Element Stress Technology), have been developed and are briefly described in this report

Teaching and Learning of Fluid Mechanics, Volume II

This book is devoted to the teaching and learning of fluid mechanics. Fluid mechanics occupies a privileged position in the sciences; it is taught in various science departments including physics, mathematics, mechanical, chemical and civil engineering and environmental sciences, each highlighting a different aspect or interpretation of the foundation and applications of fluids. While scholarship in fluid mechanics is vast, expanding into the areas of experimental, theoretical and computational fluid mechanics, there is little discussion among scientists about the different possible ways of teaching this subject. We think there is much to be learned, for teachers and students alike, from an interdisciplinary dialogue about fluids. This volume therefore highlights articles which have bearing on the pedagogical aspects of fluid mechanics at the undergraduate and graduate level

Data based identification and prediction of nonlinear and complex dynamical systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin

Computations and Algorithms in Physical and Biological Problems

This dissertation presents the applications of state-of-the-art computation techniques and data analysis algorithms in three physical and biological problems: assembling DNA pieces, optimizing self-assembly yield, and identifying correlations from large multivariate datasets. In the first topic, in-depth analysis of using Sequencing by Hybridization (SBH) to reconstruct target DNA sequences shows that a modified reconstruction algorithm can overcome the theoretical boundary without the need for different types of biochemical assays and is robust to error. In the second topic, consistent with theoretical predictions, simulations using Graphics Processing Unit (GPU) demonstrate how controlling the short-ranged interactions between particles and controlling the concentrations optimize the self-assembly yield of a desired structure, and nonequilibrium behavior when optimizing concentrations is also unveiled by leveraging the computation capacity of GPUs. In the last topic, a methodology to incorporate existing categorization information into the search process to efficiently reconstruct the optimal true correlation matrix for multivariate datasets is introduced. Simulations on both synthetic and real financial datasets show that the algorithm is able to detect signals below the Random Matrix Theory (RMT) threshold. These three problems are representatives of using massive computation techniques and data analysis algorithms to tackle optimization problems, and outperform theoretical boundary when incorporating prior information into the computation.Engineering and Applied Science

Proceedings of the Thirteenth International Conference on Time-Resolved Vibrational Spectroscopy

The thirteenth meeting in a long-standing series of “Time-Resolved Vibrational Spectroscopy” (TRVS) conferences was held May 19th to 25th at the Kardinal Döpfner Haus in Freising, Germany, organized by the two Munich Universities - Ludwig-Maximilians-Universität and Technische Universität München. This international conference continues the illustrious tradition of the original in 1982, which took place in Lake Placid, NY. The series of meetings was initiated by leading, world-renowned experts in the field of ultrafast laser spectroscopy, and is still guided by its founder, Prof. George Atkinson (University of Arizona and Science and Technology Advisor to the Secretary of State). In its current format, the conference contributes to traditional areas of time resolved vibrational spectroscopies including infrared, Raman and related laser methods. It combines them with the most recent developments to gain new information for research and novel technical applications. The scientific program addressed basic science, applied research and advancing novel commercial applications. The thirteenth conference on Time Resolved Vibrational Spectroscopy promoted science in the areas of physics, chemistry and biology with a strong focus on biochemistry and material science. Vibrational spectra are molecule- and bond-specific. Thus, time-resolved vibrational studies provide detailed structural and kinetic information about primary dynamical processes on the picometer length scale. From this perspective, the goal of achieving a complete understanding of complex chemical and physical processes on the molecular level is well pursued by the recent progress in experimental and theoretical vibrational studies. These proceedings collect research papers presented at the TRVS XIII in Freising, German

Microarray Data Mining and Gene Regulatory Network Analysis

The novel molecular biological technology, microarray, makes it feasible to obtain quantitative measurements of expression of thousands of genes present in a biological sample simultaneously. Genome-wide expression data generated from this technology are promising to uncover the implicit, previously unknown biological knowledge. In this study, several problems about microarray data mining techniques were investigated, including feature(gene) selection, classifier genes identification, generation of reference genetic interaction network for non-model organisms and gene regulatory network reconstruction using time-series gene expression data. The limitations of most of the existing computational models employed to infer gene regulatory network lie in that they either suffer from low accuracy or computational complexity. To overcome such limitations, the following strategies were proposed to integrate bioinformatics data mining techniques with existing GRN inference algorithms, which enables the discovery of novel biological knowledge. An integrated statistical and machine learning (ISML) pipeline was developed for feature selection and classifier genes identification to solve the challenges of the curse of dimensionality problem as well as the huge search space. Using the selected classifier genes as seeds, a scale-up technique is applied to search through major databases of genetic interaction networks, metabolic pathways, etc. By curating relevant genes and blasting genomic sequences of non-model organisms against well-studied genetic model organisms, a reference gene regulatory network for less-studied organisms was built and used both as prior knowledge and model validation for GRN reconstructions. Networks of gene interactions were inferred using a Dynamic Bayesian Network (DBN) approach and were analyzed for elucidating the dynamics caused by perturbations. Our proposed pipelines were applied to investigate molecular mechanisms for chemical-induced reversible neurotoxicity

Putting ecological theories to the test : individual-based simulations of synthetic microbial community dynamics

Microbial communities are critical for the proper functioning of each and every ecosystem on Earth. The ability to understand the structure and functioning of these complex communities is crucial to manage and protect natural communities, as well as to rationally design engineered microbial communities for important applications ranging from medical and pharmaceutical uses to various bioindustrial processes. In recent years, synthetic microbial communities have gained increasing interest from microbiologists due to their reduced complexity and increased controllability, which favours them over more complex natural systems for examining ecological theories. In this thesis, the in silico counterpart of this approach was used to test ecological theories relating to biodiversity and functionality through the use of mathematical models. Models are abstractions of reality which allow for the testing of hypotheses in a controlled way. In this thesis, individual-based models of synthetic microbial communities were developed and used in simulation studies to answer research questions relating to community diversity, stability, productivity and functionality. The models are spatially explicit and track through time the characteristics, interactions and activities of every individual in the community. The modelling framework is flexible and thus also extendable to other avenues of research