University of Texas at Austin

About: University of Texas at Austin is a education organization based out in Austin, Texas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 94352 authors who have published 206297 publications receiving 9070052 citations. The organization is also known as: UT-Austin & UT Austin.

Performance Analysis Using Stochastic Petri Nets

Abstract: An isomorphism between the behavior of Petri nets with exponentially distributed transition rates and Markov processes is presented. In particular, k-bounded Petri nets are isomorphic to finite Markov processes and can be solved by standard techniques if k is not too large. As a practical example, we solve for the steady state average message delay and throughput on a communication link when the alternating bit protocol is used for error recovery.

On cognitive busyness: When person perceivers meet persons perceived.

Abstract: Person perception includes three sequential processes: categorization (what is the actor doing?), characterization (what trait does the action imply?), and correction (what situational constraints may have caused the action?). We argue that correction is less automatic (i.e., more easily disrupted) than either categorization or characterization. In Experiment 1, subjects observed a target behave anxiously in an anxiety-provoking situation. In Experiment 2, subjects listened to a target read a political speech that he had been constrained to write. In both experiments, control subjects used information about situational constraints when drawing inferences about the target, but cognitively busy subjects (who performed an additional cognitive task during encoding) did not. The results (a) suggest that person perception is a combination of lower and higher order processes that differ in their susceptibility to disruption and (b) highlight the fundamental differences between active and passive perceivers. Many of us can recall a time when, as students, we encountered a professor at a party and were surprised to find that he or she seemed a very different sort of person than our classroom experience had led us to expect. In part, such discrepant impressions reflect real discrepancies in behavior: Professors may display greater warmth or less wit at a party than they do in the classroom. However, just as the object of perception changes across situations, so too does the perceiver. As passive perceivers in a classroom, we are able to observe a professor without concerning ourselves with the mechanics of social interaction. At a party, however, we are active perceivers , busy managing our impressions, predicting our partner's behavior, and evaluating alternative courses of action. Of all the many differences between active and passive perceivers, one seems fundamental: Active perceivers, unlike passive perceivers, are almost always doing several things at once ( Gilbert, Jones, & Pelham, 1987 ; Gilbert & Krull, 1988 ; Jones & Thibaut, 1958 ).

A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants

Abstract: Advanced age-related macular degeneration (AMD) is the leading cause of blindness in the elderly, with limited therapeutic options. Here we report on a study of >12 million variants, including 163,714 directly genotyped, mostly rare, protein-altering variants. Analyzing 16,144 patients and 17,832 controls, we identify 52 independently associated common and rare variants (P < 5 × 10(-8)) distributed across 34 loci. Although wet and dry AMD subtypes exhibit predominantly shared genetics, we identify the first genetic association signal specific to wet AMD, near MMP9 (difference P value = 4.1 × 10(-10)). Very rare coding variants (frequency <0.1%) in CFH, CFI and TIMP3 suggest causal roles for these genes, as does a splice variant in SLC16A8. Our results support the hypothesis that rare coding variants can pinpoint causal genes within known genetic loci and illustrate that applying the approach systematically to detect new loci requires extremely large sample sizes.

A general methodology for bootstrapping in non-parametric frontier models

Abstract: The Data Envelopment Analysis method has been extensively used in the literature to provide measures of firms' technical efficiency. These measures allow rankings of firms by their apparent performance. The underlying frontier model is non-parametric since no particular functional form is assumed for the frontier model. Since the observations result from some data-generating process, the statistical properties of the estimated efficiency measures are essential for their interpretations. In the general multi-output multi-input framework, the bootstrap seems to offer the only means of inferring these properties (i.e. to estimate the bias and variance, and to construct confidence intervals). This paper proposes a general methodology for bootstrapping in frontier models, extending the more restrictive method proposed in Simar & Wilson (1998) by allowing for heterogeneity in the structure of efficiency. A numerical illustration with real data is provided to illustrate the methodology.

A Two-Step Framework for Constructing Blind Image Quality Indices

Abstract: Present day no-reference/no-reference image quality assessment (NR IQA) algorithms usually assume that the distortion affecting the image is known. This is a limiting assumption for practical applications, since in a majority of cases the distortions in the image are unknown. We propose a new two-step framework for no-reference image quality assessment based on natural scene statistics (NSS). Once trained, the framework does not require any knowledge of the distorting process and the framework is modular in that it can be extended to any number of distortions. We describe the framework for blind image quality assessment and a version of this framework-the blind image quality index (BIQI) is evaluated on the LIVE image quality assessment database. A software release of BIQI has been made available online: http://live.ece.utexas.edu/research/quality/BIQI_release.zip.