Institution
California Institute of Technology
Education•Pasadena, California, United States•
About: California Institute of Technology is a education organization based out in Pasadena, California, United States. It is known for research contribution in the topics: Galaxy & Population. The organization has 57649 authors who have published 146691 publications receiving 8620287 citations. The organization is also known as: Caltech & Cal Tech.
Topics: Galaxy, Population, Star formation, Redshift, Mars Exploration Program
Papers published on a yearly basis
Papers
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TL;DR: In this article, the influence of diet on the distribution of nitrogen isotopes in animals was investigated by analyzing animals grown in the laboratory on diets of constant nitrogen isotopic composition and found that the variability of the relationship between the δ(15)N values of animals and their diets is greater for different individuals raised on the same diet than for the same species raised on different diets.
5,562 citations
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TL;DR: This paper considers the requirements and implementation constraints on a framework that simultaneously enables an efficient discretization with associated hierarchical indexation and fast analysis/synthesis of functions defined on the sphere and demonstrates how these are explicitly satisfied by HEALPix.
Abstract: HEALPix the Hierarchical Equal Area isoLatitude Pixelization is a versatile structure for the pixelization of data on the sphere. An associated library of computational algorithms and visualization software supports fast scientific applications executable directly on discretized spherical maps generated from very large volumes of astronomical data. Originally developed to address the data processing and analysis needs of the present generation of cosmic microwave background experiments (e.g., BOOMERANG, WMAP), HEALPix can be expanded to meet many of the profound challenges that will arise in confrontation with the observational output of future missions and experiments, including, e.g., Planck, Herschel, SAFIR, and the Beyond Einstein inflation probe. In this paper we consider the requirements and implementation constraints on a framework that simultaneously enables an efficient discretization with associated hierarchical indexation and fast analysis/synthesis of functions defined on the sphere. We demonstrate how these are explicitly satisfied by HEALPix.
5,518 citations
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TL;DR: Results of computer simulations of a network designed to solve a difficult but well-defined optimization problem-the Traveling-Salesman Problem-are presented and used to illustrate the computational power of the networks.
Abstract: Highly-interconnected networks of nonlinear analog neurons are shown to be extremely effective in computing. The networks can rapidly provide a collectively-computed solution (a digital output) to a problem on the basis of analog input information. The problems to be solved must be formulated in terms of desired optima, often subject to constraints. The general principles involved in constructing networks to solve specific problems are discussed. Results of computer simulations of a network designed to solve a difficult but well-defined optimization problem-the Traveling-Salesman Problem-are presented and used to illustrate the computational power of the networks. Good solutions to this problem are collectively computed within an elapsed time of only a few neural time constants. The effectiveness of the computation involves both the nonlinear analog response of the neurons and the large connectivity among them. Dedicated networks of biological or microelectronic neurons could provide the computational capabilities described for a wide class of problems having combinatorial complexity. The power and speed naturally displayed by such collective networks may contribute to the effectiveness of biological information processing.
5,328 citations
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TL;DR: It is proved that one can perfectly recover most low-rank matrices from what appears to be an incomplete set of entries, and that objects other than signals and images can be perfectly reconstructed from very limited information.
Abstract: We consider a problem of considerable practical interest: the recovery of a data matrix from a sampling of its entries. Suppose that we observe m entries selected uniformly at random from a matrix M. Can we complete the matrix and recover the entries that we have not seen?
We show that one can perfectly recover most low-rank matrices from what appears to be an incomplete set of entries. We prove that if the number m of sampled entries obeys $$m\ge C\,n^{1.2}r\log n$$ for some positive numerical constant C, then with very high probability, most n×n matrices of rank r can be perfectly recovered by solving a simple convex optimization program. This program finds the matrix with minimum nuclear norm that fits the data. The condition above assumes that the rank is not too large. However, if one replaces the 1.2 exponent with 1.25, then the result holds for all values of the rank. Similar results hold for arbitrary rectangular matrices as well. Our results are connected with the recent literature on compressed sensing, and show that objects other than signals and images can be perfectly reconstructed from very limited information.
5,274 citations
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TL;DR: In this article, simple state-space formulas are derived for all controllers solving the following standard H/sub infinity / problem: for a given number gamma > 0, find all controllers such that the H/ sub infinity / norm of the closed-loop transfer function is (strictly) less than gamma.
Abstract: Simple state-space formulas are derived for all controllers solving the following standard H/sub infinity / problem: For a given number gamma >0, find all controllers such that the H/sub infinity / norm of the closed-loop transfer function is (strictly) less than gamma . It is known that a controller exists if and only if the unique stabilizing solutions to two algebraic Riccati equations are positive definite and the spectral radius of their product is less than gamma /sup 2/. Under these conditions, a parameterization of all controllers solving the problem is given as a linear fractional transformation (LFT) on a contractive, stable, free parameter. The state dimension of the coefficient matrix for the LFT, constructed using the two Riccati solutions, equals that of the plant and has a separation structure reminiscent of classical LQG (i.e. H/sub 2/) theory. This paper is intended to be of tutorial value, so a standard H/sub 2/ solution is developed in parallel. >
5,272 citations
Authors
Showing all 58155 results
Name | H-index | Papers | Citations |
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Eric S. Lander | 301 | 826 | 525976 |
Donald P. Schneider | 242 | 1622 | 263641 |
George M. Whitesides | 240 | 1739 | 269833 |
Yi Chen | 217 | 4342 | 293080 |
David Baltimore | 203 | 876 | 162955 |
Edward Witten | 202 | 602 | 204199 |
George Efstathiou | 187 | 637 | 156228 |
Michael A. Strauss | 185 | 1688 | 208506 |
Jing Wang | 184 | 4046 | 202769 |
Ruedi Aebersold | 182 | 879 | 141881 |
Douglas Scott | 178 | 1111 | 185229 |
Hyun-Chul Kim | 176 | 4076 | 183227 |
Phillip A. Sharp | 172 | 614 | 117126 |
Timothy M. Heckman | 170 | 754 | 141237 |
Zhenan Bao | 169 | 865 | 106571 |