Kernel-based stochastic collocation for the random two-phase Navier-Stokes equations

In this work, we apply stochastic collocation methods with radial kernel basis functions for an uncertainty quantification of the random incompressible two-phase Navier-Stokes equations. Our approach is non-intrusive and we use the existing fluid dynamics solver NaSt3DGPF to solve the incompressible two-phase Navier-Stokes equation for each given realization. We are able to empirically show that the resulting kernel-based stochastic collocation is highly competitive in this setting and even outperforms some other standard methods

A representer theorem for deep kernel learning

In this paper we provide a finite-sample and an infinite-sample representer theorem for the concatenation of (linear combinations of) kernel functions of reproducing kernel Hilbert spaces. These results serve as mathematical foundation for the analysis of machine learning algorithms based on compositions of functions. As a direct consequence in the finite-sample case, the corresponding infinite-dimensional minimization problems can be recast into (nonlinear) finite-dimensional minimization problems, which can be tackled with nonlinear optimization algorithms. Moreover, we show how concatenated machine learning problems can be reformulated as neural networks and how our representer theorem applies to a broad class of state-of-the-art deep learning methods

Alaska Native Technical Assistance and Resource Center: Final Report

Too often, federal and state justice programs directed at rural, predominately Alaska Native villages do not sufficiently coordinate planning and funding, and are not tailored to fit local cultures and needs. The language and institutional contexts of granting agencies and requests for proposals for grants frame justice problems and their solutions in ways that may or may not relate to the experiences of Alaska Native villages. The Alaska Native Technical Resource Center (ANTARC) was designed as a three-year project to improve village capacity to identify problems and educate the university and granting agencies about the nature of their justice problems and the resources needed to implement solutions. The initial group involved the Justice Center and four rural communities — Gulkana, Kotlik, Wainwright, and Yakutat — with representatives from the communities chosen by village leaders. This report examines ANTARC's evolution, considers its implementation, evaluates the results, and presents recommendations for promoting effective change in Alaska Native villages.Bureau of Justice Assistance, United States Department of Justice Award No. 1999-LB-VX-002Introduction / The Evolution of Antarc / Structure of the Project / Implementation / Evaluating Results / Concluding Recommendations / References / Appendix 1: Proceedings of the March 1999 Antarc Workshop / Appendix 2: Proceedings of the November 1999 Antarc Workshop / Appendix 3: Capra Training Materials / Appendix 4: Evaluation Training Workshop Material

Research and development of a rescue robot end-effector

Includes abstract.Includes bibliographical references.This report details the research, design, development and testing of an end-effector system for use on an Urban Search and Rescue (USAR) robot which is in development in the Robotics and Agents Research Laboratory (RARL) at the University of Cape Town (UCT). This is the 5th generation Mobile Robot Platform (MRP) that UCT has developed ... codenamed ‘Ratel’. USAR robots used to be mainly of the observation type, but new robots (including UCT’s Ratel MRP) are being developed to deal with inherently dynamic, complex and unpredictable disaster response situations, particularly related to object manipulation and gripping. In order to actively interact with the environment, a flexible and robust gripping system is vital. [an] end-effector solution ... was developed for the Ratel manipulator arm to fulfil these functions

Characterization of the CELF6 RNA Binding Protein: Effects on Mouse Vocal Behavior and Biochemical Function

Behavior in higher eukaryotes is a complex process which integrates signals in the environment, the genetic makeup of the organism, and connectivity in the nervous system to produce extremely diverse adaptations to the phenomenon of existence. Unraveling the subcellular components that contribute to behavioral output is important for both understanding how behavior occurs in an unperturbed state, as well as understanding how behavior changes when the underlying systems that generate it are altered. Of the numerous molecular species that make up a cell, the regulation of messenger RNAs (mRNAs), the coding template of all proteins, is of key importance to the proper maintenance and functioning of cells of the brain, and thus the synaptic signals and information integration which underlie behavior. RNA binding proteins, a class of regulatory molecules, associate with mRNAs and facilitate their maturation from pre-spliced nascent transcripts, their stabilization and degradation ensuring appropriate levels are maintained, as well as their translation and subcellular compartmentalization, which ensures that proteins are translated at the appropriate level and in the places where they are required to fulfill their cellular functions. Our laboratory identified polymorphisms in the gene coding for the CUGBP and ELAV-like Factor 6 (CELF6) RNA binding protein to be associated with Autism Spectrum Disorder risk in humans. ASD is a spectrum of disorders of early neurodevelopment which present with lowered sociability and communication skills as well as restricted patterns of interests. When expression of the Celf6 gene was ablated in mice, we found that they exhibited reductions to early communication as well as altered aspects of their exploratory behavior. In this dissertation, I explore the communication changes in young mouse pups with loss of CELF6 protein and identify that despite being able to produce vocalization patterns similar to their wild-type littermates, they nevertheless exhibit reduced response to maternal separation. Despite a history of literature on other CELF family proteins, the functions of the CELF6 protein in the brain have not been previously described. I provide characterization of the mRNA binding targets of CELF6 in the brain, and show that they share common UGU-containing sequence motifs which has been noted for other CELF proteins, and that CELF6 binding occurs primarily in the 3\u27 untranslated regions (3\u27 UTR) of mRNA. I hypothesized that this mode of interaction would result in regulation of mRNA degradation or translation efficiency as 3\u27 UTR regions are known for providing binding sites for numerous regulators of such processes. In order to answer this question, I cloned sequence elements from the 3\u27 UTRs of target mRNAs into a massively parallel reporter assay which has enabled me to test the effect of CELF6 expression on hundreds of binding targets simultaneously. When expressed in vitro, I found that CELF6 induced reduction to reporter library levels but exhibited few effects on translation efficiency, and I was able to rescue effects to reporter abundance mutation of binding motifs. Intriguingly, like CELF6, CELF3, CELF4, and CELF5 were all able to produce the same effect. CELF5 and CELF6 both showed similar, intermediate repression of reporter library mRNAs, while CELF3 and CELF4 exerted the strongest levels of repression. The level of repression under these conditions was somewhat predicted by number of motifs present per element, however a large amount of the variance in reporter levels is still unexplained and a mechanism for CELF6\u27s action is unknown. Nevertheless, the work I present in this dissertation shows that CELF6 and other members of its family are key regulators of mRNA abundance levels which has direct implications to downstream consequence in the cell. As several of CELF6 binding target mRNAs are known regulators of neuronal signaling and synaptic function, the information I present is crucial for future experimentation. This work well help lead us to understand how behavior is altered when this protein is absent, along the way uncovering important mechanistic steps connecting the molecular landscape of cells to the behavior of organisms

Effect of surface nanostructure on temperature programmed reaction spectroscopy: First-principles kinetic Monte Carlo simulations of CO oxidation at RuO2(110)

Using the catalytic CO oxidation at RuO2(110) as a showcase, we employ first-principles kinetic Monte Carlo simulations to illustrate the intricate effects on temperature programmed reaction spectroscopy data brought about by the mere correlations between the locations of the active sites at a nanostructured surface. Even in the absence of lateral interactions, this nanostructure alone can cause inhomogeneities that cannot be grasped by prevalent mean-field data analysis procedures, which thus lead to wrong conclusions on the reactivity of the different surface species.Comment: 4 pages including 3 figures; related publications can be found at http://www.fhi-berlin.mpg.de/th/th.htm

An on-line solid phase extraction procedure for the routine quantification of caspofungin by liquid chromatography-tandem mass spectrometry

Background: Extensive sets of data are required to investigate the potential use of a therapeutic drug monitoring with individualization of dosage of the antimycotic compound caspofungin. The goal was to develop an improved liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for this aim. Methods: Following protein precipitation, on-line solid phase extraction was performed for sample preparation. As the internal standard compound the veterinary drug tylosin was used. A standard validation protocol was applied. Results: Good reproducibility and accuracy of the method were observed. On-line solid phase extraction resulted in a convenient work-flow and good robustness of the method. Conclusions: This improved LC-MS/MS method was found reliable and convenient. It can be suggested for further work on the clinical pharmacology of caspofungin in the setting of clinical research laboratories

Reproducing kernel Hilbert spaces for parametric partial differential equations

In this article, we present kernel methods for the approximation of quantities of interest which are derived from solutions of parametric partial differential equations. We explicitly construct a reproducing kernel Hilbert space containing the quantity of interest as a function of the parameters from a priori information on parameters in the differential equation. Based on the problem-adapted reproducing kernel, we suggest a regularized reconstruction technique from machine learning in order to approximate the quantity of interest from a finite number of point values. We present a deterministic a priori error analysis for this reconstruction process yielding a subexponential convergence order due to the smoothness of the quantity of interest as function of the parameters. The error estimates explicitly take into account the error of the numerical evaluation of the quantity of interest for fixed sets of parameters. This leads to a coupling condition between this evaluation error which contains the error of the numerical solution of the associated partial differential equation and the error due to the sampling approximation of the quantity of interest