Carnegie Mellon University

About: Carnegie Mellon University is a education organization based out in Pittsburgh, Pennsylvania, United States. It is known for research contribution in the topics: Computer science & Robot. The organization has 36317 authors who have published 104359 publications receiving 5975734 citations. The organization is also known as: CMU & Carnegie Mellon.

Anterior cingulate cortex, error detection, and the online monitoring of performance

Abstract: An unresolved question in neuroscience and psychology is how the brain monitors performance to regulate behavior. It has been proposed that the anterior cingulate cortex (ACC), on the medial surface of the frontal lobe, contributes to performance monitoring by detecting errors. In this study, event-related functional magnetic resonance imaging was used to examine ACC function. Results confirm that this region shows activity during erroneous responses. However, activity was also observed in the same region during correct responses under conditions of increased response competition. This suggests that the ACC detects conditions under which errors are likely to occur rather than errors themselves.

Software architecture: perspectives on an emerging discipline

Abstract: 1. Introduction. 2. Architectural Styles. 3. Case Studies. 4. Shared Information Systems. 5. Architectural Design Guidance. 6. Formal Models and Specifications. 7. Linguistic Issues. 8. Tools for Architectural Design. 9. Education of Software Architects. Bibliography. Index.

Lucas-Kanade 20 Years On: A Unifying Framework

Abstract: Since the Lucas-Kanade algorithm was proposed in 1981 image alignment has become one of the most widely used techniques in computer vision Applications range from optical flow and tracking to layered motion, mosaic construction, and face coding Numerous algorithms have been proposed and a wide variety of extensions have been made to the original formulation We present an overview of image alignment, describing most of the algorithms and their extensions in a consistent framework We concentrate on the inverse compositional algorithm, an efficient algorithm that we recently proposed We examine which of the extensions to Lucas-Kanade can be used with the inverse compositional algorithm without any significant loss of efficiency, and which cannot In this paper, Part 1 in a series of papers, we cover the quantity approximated, the warp update rule, and the gradient descent approximation In future papers, we will cover the choice of the error function, how to allow linear appearance variation, and how to impose priors on the parameters

A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation

Abstract: This is an invited expository article for The American Statistician. It reviews the nonparametric estimation of statistical error, mainly the bias and standard error of an estimator, or the error rate of a prediction rule. The presentation is written at a relaxed mathematical level, omitting most proofs, regularity conditions, and technical details.

Genomic control for association studies.

Abstract: A dense set of single nucleotide polymorphisms (SNP) covering the genome and an efficient method to assess SNP genotypes are expected to be available in the near future. An outstanding question is how to use these technologies efficiently to identify genes affecting liability to complex disorders. To achieve this goal, we propose a statistical method that has several optimal properties: It can be used with case control data and yet, like family-based designs, controls for population heterogeneity; it is insensitive to the usual violations of model assumptions, such as cases failing to be strictly independent; and, by using Bayesian outlier methods, it circumvents the need for Bonferroni correction for multiple tests, leading to better performance in many settings while still constraining risk for false positives. The performance of our genomic control method is quite good for plausible effects of liability genes, which bodes well for future genetic analyses of complex disorders.