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Institution

Carnegie Mellon University

EducationPittsburgh, Pennsylvania, United States
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.


Papers
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Journal ArticleDOI
TL;DR: Lee et al. as discussed by the authors explored whether connections exist among regional social characteristics, human capital, and new firm formation and found that social diversity and creativity have a positive relationship with new firm creation.
Abstract: Lee S. Y., Florida R. and Acs Z. J. (2004) Creativity and entrepreneurship: a regional analysis of new firm formation, Regional Studies38, 879-891. Understanding the factors that promote or mitigate new firm birth is crucial to regional economic development efforts, since a high level of new firm creation significantly contributes to regional economic vitality and is a major signal of a dynamic economy. The literature suggests that various factors such as unemployment, population density/ growth, industrial structure, human capital, the availability of financing and entrepreneurial characteristics significantly influence regional variation in new firm birth rates. This study explores whether connections exist among regional social characteristics, human capital and new firm formation. It argues that social diversity and creativity have a positive relationship with new firm formation. Building on the contributions of urbanist Jane Jacobs, Lee, Florida and Gates (2002) showed that social diversity and human...

843 citations

Journal ArticleDOI
TL;DR: In this article, a statistical model for causal inference is used to critique the discussions of other writers on causation and causal inference, including selected philosophers, medical researchers, statisticians, econometricians, and proponents of causal modelling.
Abstract: Problems involving causal inference have dogged at the heels of Statistics since its earliest days. Correlation does not imply causation and yet causal conclusions drawn from a carefully designed experiment are often valid. What can a statistical model say about causation? This question is addressed by using a particular model for causal inference (Rubin, 1974; Holland and Rubin, 1983) to critique the discussions of other writers on causation and causal inference. These include selected philosophers, medical researchers, statisticians, econometricians, and proponents of causal modelling.

842 citations

Journal ArticleDOI
TL;DR: An algorithm for solving Integer Programming problems whose running time depends on the number n of variables as nOn by reducing an n variable problem to 2n5i/2 problems in n-i variables for some i greater than zero chosen by the algorithm.
Abstract: The paper presents an algorithm for solving Integer Programming problems whose running time depends on the number n of variables as nOn. This is done by reducing an n variable problem to 2n5i/2 problems in n-i variables for some i greater than zero chosen by the algorithm. The factor of On5/2 “per variable” improves the best previously known factor which is exponential in n. Minkowski's Convex Body theorem and other results from Geometry of Numbers play a crucial role in the algorithm. Several related algorithms for lattice problems are presented. The complexity of these problems with respect to polynomial-time reducibilities is studied.

841 citations

Journal ArticleDOI
TL;DR: The rainbow framework provides reusable infrastructure together with mechanisms for specializing that infrastructure to the needs of specific systems, and lets the developer of self-adaptation capabilities choose what aspects of the system to model and monitor, what conditions should trigger adaptation, and how to adapt the system.
Abstract: While attractive in principle, architecture-based self-adaptation raises a number of research and engineering challenges. First, the ability to handle a wide variety of systems must be addressed. Second, the need to reduce costs in adding external control to a system must be addressed. Our rainbow framework attempts to address both problems. By adopting an architecture-based approach, it provides reusable infrastructure together with mechanisms for specializing that infrastructure to the needs of specific systems. The specialization mechanisms let the developer of self-adaptation capabilities choose what aspects of the system to model and monitor, what conditions should trigger adaptation, and how to adapt the system.

840 citations

Posted Content
TL;DR: This article developed a formal grammatical system called a link grammar and showed how English grammar can be encoded in such a system, and gave algorithms for efficiently parsing with a link grammars.
Abstract: We develop a formal grammatical system called a link grammar, show how English grammar can be encoded in such a system, and give algorithms for efficiently parsing with a link grammar. Although the expressive power of link grammars is equivalent to that of context free grammars, encoding natural language grammars appears to be much easier with the new system. We have written a program for general link parsing and written a link grammar for the English language. The performance of this preliminary system -- both in the breadth of English phenomena that it captures and in the computational resources used -- indicates that the approach may have practical uses as well as linguistic significance. Our program is written in C and may be obtained through the internet.

839 citations


Authors

Showing all 36645 results

NameH-indexPapersCitations
Yi Chen2174342293080
Rakesh K. Jain2001467177727
Robert C. Nichol187851162994
Michael I. Jordan1761016216204
Jasvinder A. Singh1762382223370
J. N. Butler1722525175561
P. Chang1702154151783
Krzysztof Matyjaszewski1691431128585
Yang Yang1642704144071
Geoffrey E. Hinton157414409047
Herbert A. Simon157745194597
Yongsun Kim1562588145619
Terrence J. Sejnowski155845117382
John B. Goodenough1511064113741
Scott Shenker150454118017
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023120
2022499
20214,980
20205,375
20195,420
20184,972