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.

Bounded Model Checking

Abstract: Symbolic model checking with Binary Decision Diagrams (BDDs) has been successfully used in the last decade for formally verifying finite state systems such as sequential circuits and protocols. Since its introduction in the beginning of the 90's, it has been integrated in the quality assurance process of several major hardware companies. The main bottleneck of this method is that BDDs may grow exponentially, and hence the amount of available memory re- stricts the size of circuits that can be verified efficiently. In this article we survey a technique called Bounded Model Checking (BMC), which uses a propositional SAT solver rather than BDD manipulation techniques. Since its introduction in 1999, BMC has been well received by the industry. It can find many logical er- rors in complex systems that can not be handled by competing techniques, and is therefore widely perceived as a complementary technique to BDD-based model checking. This observation is supported by several independent comparisons that have been published in the last few years.

Stress and infectious disease in humans.

Abstract: This article reviews research on the role of stress in infectious disease as measured either by illness behaviors (symptoms and use of health services) or by verified pathology. Substantial evidence was found for an association between stress and increased illness behavior, and less convincing but provocative evidence was found for a similar association between stress and infectious pathology. Introverts, isolates, and persons lacking social skills may also be at increased risk for both illness behaviors and pathology. Psychobiological models of how stress could influence the onset and progression of infectious disease and a psychological model of how stress could influence illness behaviors are proposed.

LOCI: fast outlier detection using the local correlation integral

Abstract: Outlier detection is an integral part of data mining and has attracted much attention recently [M. Breunig et al., (2000)], [W. Jin et al., (2001)], [E. Knorr et al., (2000)]. We propose a new method for evaluating outlierness, which we call the local correlation integral (LOCI). As with the best previous methods, LOCI is highly effective for detecting outliers and groups of outliers (a.k.a. micro-clusters). In addition, it offers the following advantages and novelties: (a) It provides an automatic, data-dictated cutoff to determine whether a point is an outlier-in contrast, previous methods force users to pick cut-offs, without any hints as to what cut-off value is best for a given dataset. (b) It can provide a LOCI plot for each point; this plot summarizes a wealth of information about the data in the vicinity of the point, determining clusters, micro-clusters, their diameters and their inter-cluster distances. None of the existing outlier-detection methods can match this feature, because they output only a single number for each point: its outlierness score, (c) Our LOCI method can be computed as quickly as the best previous methods, (d) Moreover, LOCI leads to a practically linear approximate method, aLOCI (for approximate LOCI), which provides fast highly-accurate outlier detection. To the best of our knowledge, this is the first work to use approximate computations to speed up outlier detection. Experiments on synthetic and real world data sets show that LOCI and aLOCI can automatically detect outliers and micro-clusters, without user-required cut-offs, and that they quickly spot both expected and unexpected outliers.

Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers

Abstract: Since the early 1960's, researchers have built a number of programming languages and environments with the intention of making programming accessible to a larger number of people. This article presents a taxonomy of languages and environments designed to make programming more accessible to novice programmers of all ages. The systems are organized by their primary goal, either to teach programming or to use programming to empower their users, and then, by each system's authors' approach, to making learning to program easier for novice programmers. The article explains all categories in the taxonomy, provides a brief description of the systems in each category, and suggests some avenues for future work in novice programming environments and languages.

Evidence for reionization at z ∼ 6: Detection of a gunn-peterson trough in a z = 6.28 quasar

Abstract: We present moderate-resolution Keck spectroscopy of quasars at z = 5.82, 5.99, and 6.28, discovered by the Sloan Digital Sky Survey (SDSS). We find that the Ly? absorption in the spectra of these quasars evolves strongly with redshift. To z ~ 5.7, the Ly? absorption evolves as expected from an extrapolation from lower redshifts. However, in the highest-redshift object, SDSSp J103027.10+052455.0 (z = 6.28), the average transmitted flux is 0.0038 ? 0.0026 times that of the continuum level over 8450 ? 20, on the optical depth to Ly? absorption at z = 6. This is a clear detection of a complete Gunn-Peterson trough, caused by neutral hydrogen in the intergalactic medium. Even a small neutral hydrogen fraction in the intergalactic medium would result in an undetectable flux in the Ly? forest region. Therefore, the existence of the Gunn-Peterson trough by itself does not indicate that the quasar is observed prior to the reionization epoch. However, the fast evolution of the mean absorption in these high-redshift quasars suggests that the mean ionizing background along the line of sight to this quasar has declined significantly from z ~ 5 to 6, and the universe is approaching the reionization epoch at z ~ 6.