Approximated Computation of Belief Functions for Robust Design Optimization

This paper presents some ideas to reduce the computational cost of evidence-based robust design optimization. Evidence Theory crystallizes both the aleatory and epistemic uncertainties in the design parameters, providing two quantitative measures, Belief and Plausibility, of the credibility of the computed value of the design budgets. The paper proposes some techniques to compute an approximation of Belief and Plausibility at a cost that is a fraction of the one required for an accurate calculation of the two values. Some simple test cases will show how the proposed techniques scale with the dimension of the problem. Finally a simple example of spacecraft system design is presented.Comment: AIAA-2012-1932 14th AIAA Non-Deterministic Approaches Conference. 23-26 April 2012 Sheraton Waikiki, Honolulu, Hawai

Intersubject Regularity in the Intrinsic Shape of Human V1

Previous studies have reported considerable intersubject variability in the three-dimensional geometry of the human primary visual cortex (V1). Here we demonstrate that much of this variability is due to extrinsic geometric features of the cortical folds, and that the intrinsic shape of V1 is similar across individuals. V1 was imaged in ten ex vivo human hemispheres using high-resolution (200 μm) structural magnetic resonance imaging at high field strength (7 T). Manual tracings of the stria of Gennari were used to construct a surface representation, which was computationally flattened into the plane with minimal metric distortion. The instrinsic shape of V1 was determined from the boundary of the planar representation of the stria. An ellipse provided a simple parametric shape model that was a good approximation to the boundary of flattened V1. The aspect ration of the best-fitting ellipse was found to be consistent across subject, with a mean of 1.85 and standard deviation of 0.12. Optimal rigid alignment of size-normalized V1 produced greater overlap than that achieved by previous studies using different registration methods. A shape analysis of published macaque data indicated that the intrinsic shape of macaque V1 is also stereotyped, and similar to the human V1 shape. Previoud measurements of the functional boundary of V1 in human and macaque are in close agreement with these results

Formulating Midsurface using Shape Transformations of Form Features

Abstract Shapes modelled using Computer Aided Design (CAD) applications are used in downstream applications like, manufacturing (Computer Aided Manufacturing, CAM), Analysis (Computer AidedEngineering, CAE) etc. Use of form features is prevalent in the CAD applications, but their leveraging in the downstream applications is not very common, especially in the CAE applications. The initial phase of design demands for quick analysis of the model. Here, CAD models are oftensimplified by removing the irrelevant features (de-featuring) and by idealizing solids to surfaces or curves(dimension reduction), so that the CAE analysis gets performed with lesser resources and time. MidsurfaceExtraction is one of the ways of dimension reduction where thin-walled portions of a solid areidealized to surfaces lying midway. This paper presents a novel representation scheme (called ABLE) for CAD entities and operators including formfeatures which is then leveraged to define the algorithm for extracting Midsurface

Aptamers: a novel targeted theranostic platform for pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is an extremely challenging disease with a high mortality rate and a short overall survival time. The poor prognosis can be explained by aggressive tumor growth, late diagnosis, and therapy resistance. Consistent efforts have been made focusing on early tumor detection and novel drug development. Various strategies aim at increasing target specificity or local enrichment of chemotherapeutics as well as imaging agents in tumor tissue. Aptamers have the potential to provide early detection and permit anti-cancer therapy with significantly reduced side effects. These molecules are in-vitro selected single-stranded oligonucleotides that form stable three-dimensional structures. They are capable of binding to a variety of molecular targets with high affinity and specificity. Several properties such as high binding affinity, the in vitro chemical process of selection, a variety of chemical modifications of molecular platforms for diverse function, non-immunoreactivity, modification of bioavailability, and manipulation of pharmacokinetics make aptamers attractive targets compared to conventional cell-specific ligands. To explore the potential of aptamers for early diagnosis and targeted therapy of PDAC - as single agents and in combination with radiotherapy - we summarize the generation process of aptamers and their application as biosensors, biomarker detection tools, targeted imaging tracers, and drug-delivery carriers. We are furthermore discussing the current implementation aptamers in clinical trials, their limitations and possible future utilization

Natural background radiation in the proposed Illinois SSC siting area

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Neural sensing and control in a kilometer-scale gravitational-wave observatory

Suspended optics in gravitational-wave (GW) observatories are susceptible to alignment perturbations, particularly slow drifts over time, due to variations in temperature and seismic levels. Such misalignments affect the coupling of the incident laser beam into the optical cavities, degrade both the circulating power and optomechanical photon squeezing, and thus decrease the astrophysical sensitivity to merging binaries. Traditional alignment techniques involve differential wave-front sensing using multiple quadrant photodiodes but are often bandwidth restricted and limited by the sensing noise. We present a successful implementation of neural-network-based sensing and control at a GW observatory and demonstrate low-frequency control of the signal-recycling mirror at the GEO 600 detector. Alignment information for three critical optics is simultaneously extracted from the interferometric dark-port camera images via a convolutional neural net-long short-term memory network architecture and is then used for multiple-input-multiple-output control using soft actor-critic-based deep reinforcement learning. The overall sensitivity improvement achieved using our scheme demonstrates the capabilities of deep learning as a viable tool for real-time sensing and control for current and next-generation GW interferometers

Causality and the AdS Dirichlet problem

The (planar) AdS Dirichlet problem has previously been shown to exhibit superluminal hydrodynamic sound modes. This problem is defined by bulk gravitational dynamics with Dirichlet boundary conditions imposed on a rigid timelike cut-off surface. We undertake a careful examination of this set-up and argue that, in most cases, the propagation of information between points on the Dirichlet hypersurface is nevertheless causal with respect to the induced light cones. In particular, the high-frequency dynamics is causal in this sense. There are however two exceptions and both involve boundary gravitons whose propagation is not constrained by the Einstein equations. These occur in i) AdS$_3$, where the boundary gravitons generally do not respect the induced light cones on the boundary, and ii) Rindler space, where they are related to the infinite speed of sound in incompressible fluids. We discuss implications for the fluid/gravity correspondence with rigid Dirichlet boundaries and for the black hole membrane paradigm.Comment: 29 pages, 5 figures. v2: added refs. v3: minor clarification

First demonstration of neural sensing and control in a kilometer-scale gravitational wave observatory

Suspended optics in gravitational wave (GW) observatories are susceptible toalignment perturbations and, in particular, to slow drifts over time due tovariations in temperature and seismic levels. Such misalignments affect thecoupling of the incident laser beam into the optical cavities, degrade bothcirculating power and optomechanical photon squeezing, and thus decrease theastrophysical sensitivity to merging binaries. Traditional alignment techniquesinvolve differential wavefront sensing using multiple quadrant photodiodes, butare often restricted in bandwidth and are limited by the sensing noise. Wepresent the first-ever successful implementation of neural network-basedsensing and control at a gravitational wave observatory and demonstratelow-frequency control of the signal recycling mirror at the GEO 600 detector.Alignment information for three critical optics is simultaneously extractedfrom the interferometric dark port camera images via a CNN-LSTM networkarchitecture and is then used for MIMO control using soft actor-critic-baseddeep reinforcement learning. Overall sensitivity improvement achieved using ourscheme demonstrates deep learning's capabilities as a viable tool for real-timesensing and control for current and next-generation GW interferometers.<br

A smart hill-climbing algorithm for application server configuration

The overwhelming success of the Web as a mechanism for facilitating information retrieval and for conducting business transactions has led to an increase in the deployment of complex enterprise applications. These applications typically run on Web Application Servers, which assume the burden of managing many tasks, such as concurrency, memory management, database access, etc., required by these applications. The performance of an Application Server depends heavily on appropriate configuration. Configuration is a difficult and error-prone task due to the large number of configuration parameters and complex interactions between them. We formulate the problem of finding an optimal configuration for a given application as a black-box optimization problem. We propose a Smart Hill-Climbing algorithm using ideas of importance sampling and Latin Hypercube Sampling (LHS). The algorithm is efficient in both searching and random sampling. It consists of estimating a local function, and then, hill-climbing in the steepest descent direction. The algorithm also learns from past searches and restarts in a smart and selective fashion using the idea of importance sampling. We have carried out extensive experiments with an online brokerage application running in a WebSphere environment. Empirical results demonstrate that our algorithm is more efficient than and superior to traditional heuristic methods. Categories and Subject Descriptor