scispace - formally typeset


Johns Hopkins University

EducationBaltimore, Maryland, United States
About: Johns Hopkins University is a(n) education organization based out in Baltimore, Maryland, United States. It is known for research contribution in the topic(s): Population & Poison control. The organization has 110248 authors who have published 249247 publication(s) receiving 14084474 citation(s). The organization is also known as: The Johns Hopkins University & Johns Hopkins.

More filters
Journal ArticleDOI
Abstract: Analysis of social networks is suggested as a tool for linking micro and macro levels of sociological theory. The procedure is illustrated by elaboration of the macro implications of one aspect of small-scale interaction: the strength of dyadic ties. It is argued that the degree of overlap of two individuals' friendship networks varies directly with the strength of their tie to one another. The impact of this principle on diffusion of influence and information, mobility opportunity, and community organization is explored. Stress is laid on the cohesive power of weak ties. Most network models deal, implicitly, with strong ties, thus confining their applicability to small, well-defined groups. Emphasis on weak ties lends itself to discussion of relations between groups and to analysis of segments of social structure not easily defined in terms of primary groups.

35,312 citations

Journal ArticleDOI
TL;DR: Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
Abstract: As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.

27,973 citations

01 Jan 1985
Abstract: Linear algebra and matrix theory are fundamental tools in mathematical and physical science, as well as fertile fields for research. This new edition of the acclaimed text presents results of both classic and recent matrix analyses using canonical forms as a unifying theme, and demonstrates their importance in a variety of applications. The authors have thoroughly revised, updated, and expanded on the first edition. The book opens with an extended summary of useful concepts and facts and includes numerous new topics and features, such as: - New sections on the singular value and CS decompositions - New applications of the Jordan canonical form - A new section on the Weyr canonical form - Expanded treatments of inverse problems and of block matrices - A central role for the Von Neumann trace theorem - A new appendix with a modern list of canonical forms for a pair of Hermitian matrices and for a symmetric-skew symmetric pair - Expanded index with more than 3,500 entries for easy reference - More than 1,100 problems and exercises, many with hints, to reinforce understanding and develop auxiliary themes such as finite-dimensional quantum systems, the compound and adjugate matrices, and the Loewner ellipsoid - A new appendix provides a collection of problem-solving hints.

23,959 citations

Journal ArticleDOI
Abstract: A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the empirical mode decomposition method with which any complicated data set can be dec...

16,171 citations

Journal ArticleDOI
Abstract: SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating equations are derived without specifying the joint distribution of a subject's observations yet they reduce to the score equations for multivariate Gaussian outcomes. Asymptotic theory is presented for the general class of estimators. Specific cases in which we assume independence, m-dependence and exchangeable correlation structures from each subject are discussed. Efficiency of the proposed estimators in two simple situations is considered. The approach is closely related to quasi-likelih ood. Some key ironh: Estimating equation; Generalized linear model; Longitudinal data; Quasi-likelihood; Repeated measures.

16,152 citations


Showing all 110248 results

Walter C. Willett3342399413322
Robert Langer2812324326306
Meir J. Stampfer2771414283776
Ronald C. Kessler2741332328983
Albert Hofman2672530321405
Graham A. Colditz2611542256034
Shizuo Akira2611308320561
Bert Vogelstein247757332094
Donald P. Schneider2421622263641
Solomon H. Snyder2321222200444
Ralph B. D'Agostino2261287229636
Yi Chen2174342293080
Fred H. Gage216967185732
Kenneth W. Kinzler215640243944
Robert J. Lefkowitz214860147995
Network Information
Related Institutions (5)
Harvard University

530.3K papers, 38.1M citations

98% related

Columbia University

224K papers, 12.8M citations

98% related

Yale University

220.6K papers, 12.8M citations

97% related

University of Washington

305.5K papers, 17.7M citations

97% related

University of Pennsylvania

257.6K papers, 14.1M citations

97% related

No. of papers from the Institution in previous years