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: Population & Robot. The organization has 36317 authors who have published 104359 publications receiving 5975734 citations. The organization is also known as: CMU & Carnegie Mellon.

PowerGraph: distributed graph-parallel computation on natural graphs

Abstract: Large-scale graph-structured computation is central to tasks ranging from targeted advertising to natural language processing and has led to the development of several graph-parallel abstractions including Pregel and GraphLab. However, the natural graphs commonly found in the real-world have highly skewed power-law degree distributions, which challenge the assumptions made by these abstractions, limiting performance and scalability.In this paper, we characterize the challenges of computation on natural graphs in the context of existing graph-parallel abstractions. We then introduce the PowerGraph abstraction which exploits the internal structure of graph programs to address these challenges. Leveraging the PowerGraph abstraction we introduce a new approach to distributed graph placement and representation that exploits the structure of power-law graphs. We provide a detailed analysis and experimental evaluation comparing PowerGraph to two popular graph-parallel systems. Finally, we describe three different implementation strategies for PowerGraph and discuss their relative merits with empirical evaluations on large-scale real-world problems demonstrating order of magnitude gains.

Chapter 18 Empirical studies of innovation and market structure

Abstract: Publisher Summary This chapter discusses the perceptible movement of empirical scholars from a narrow concern with the role of firm size and market concentration toward a broader consideration of the fundamental determinants of technical change in industry. Although tastes, technological opportunity, and appropriability conditions themselves are subject to change over time, particularly in response to radical innovations that alter the technological regime, these conditions are reasonably assumed to determine inter-industry differences in innovative activity over relatively long periods. Although a substantial body of descriptive evidence has begun to accumulate on the way the nature and effects of demand, opportunity, and appropriability differ across industries, the absence of suitable data constrains progress in many areas. It has been observed that much of the empirical understanding of innovation derives not from the estimation of econometric models but from the use of other empirical methods. Many of the most credible empirical regularities have been established not by estimating and testing elaborate optimization models with published data but by the painstaking collection of original data, usually in the form of responses to relatively simple questions.

Psychosocial models of the role of social support in the etiology of physical disease.

Abstract: Although there has been a substantial effort to establish the beneficial effects of social support on health and well-being, relatively little work has focused on how social support influences physical health. This article outlines possible mechanisms through which support systems may influence the etiology of physical disease. I begin by reviewing research on the relations between social support and morbidity and between social support and mortality. I distinguish between various conceptualizations of social support used in the existing literature and provide alternative explanations of how each of these conceptualizations of the social environment could influence the etiology of physical disease. In each case, I address the psychological mediators (e.g., health relevant cognitions, affect, and health behaviors) as well as biologic links (e.g., neuroendocrine links to immune and cardiovascular function). I conclude by proposing conceptual and methodological guidelines for future research in this area, highlighting the unique contributions psychologists can make to this inherently interdisciplinary endeavor.

Research through design as a method for interaction design research in HCI

Abstract: For years the HCI community has struggled to integrate design in research and practice. While design has gained a strong foothold in practice, it has had much less impact on the HCI research community. In this paper we propose a new model for interaction design research within HCI. Following a research through design approach, designers produce novel integrations of HCI research in an attempt to make the right thing: a product that transforms the world from its current state to a preferred state. This model allows interaction designers to make research contributions based on their strength in addressing under-constrained problems. To formalize this model, we provide a set of four lenses for evaluating the research contribution and a set of three examples to illustrate the benefits of this type of research.

Patterns and rates of exonic de novo mutations in autism spectrum disorders

Abstract: Autism spectrum disorders (ASD) are believed to have genetic and environmental origins, yet in only a modest fraction of individuals can specific causes be identified. To identify further genetic risk factors, here we assess the role of de novo mutations in ASD by sequencing the exomes of ASD cases and their parents (n = 175 trios). Fewer than half of the cases (46.3%) carry a missense or nonsense de novo variant, and the overall rate of mutation is only modestly higher than the expected rate. In contrast, the proteins encoded by genes that harboured de novo missense or nonsense mutations showed a higher degree of connectivity among themselves and to previous ASD genes as indexed by protein-protein interaction screens. The small increase in the rate of de novo events, when taken together with the protein interaction results, are consistent with an important but limited role for de novo point mutations in ASD, similar to that documented for de novo copy number variants. Genetic models incorporating these data indicate that most of the observed de novo events are unconnected to ASD; those that do confer risk are distributed across many genes and are incompletely penetrant (that is, not necessarily sufficient for disease). Our results support polygenic models in which spontaneous coding mutations in any of a large number of genes increases risk by 5- to 20-fold. Despite the challenge posed by such models, results from de novo events and a large parallel case-control study provide strong evidence in favour of CHD8 and KATNAL2 as genuine autism risk factors.