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The UK eScience core programme and the grid
 Journal of Future Generation Computer Systems(FGCS
, 2002
"... initiative and begins by defining what is meant by the term eScience. The majority of the £120M, some £75M, is funding largescale eScience pilot projects in many areas of science and engineering. The infrastructure needed to support such projects must permit routine sharing of distributed and het ..."
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Cited by 39 (0 self)
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initiative and begins by defining what is meant by the term eScience. The majority of the £120M, some £75M, is funding largescale eScience pilot projects in many areas of science and engineering. The infrastructure needed to support such projects must permit routine sharing of distributed
The LargeScale Organization of Metabolic Networks
, 2000
"... In a cell or microorganism the processes that generate mass, energy, information transfer, and cell fate specification are seamlessly integrated through a complex network of various cellular constituents and reactions. However, despite the key role these networks play in sustaining various cellular ..."
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Cited by 599 (7 self)
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functions, their largescale structure is essentially unknown. Here we present the first systematic comparative mathematical analysis of the metabolic networks of 43 organisms representing all three domains of life. We show that, despite significant variances in their individual constituents and pathways
Making LargeScale Support Vector Machine Learning Practical
, 1998
"... Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large lea ..."
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Cited by 620 (1 self)
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algorithmic and computational results developed for SVM light V2.0, which make largescale SVM training more practical. The results give guidelines for the application of SVMs to large domains.
Projection Pursuit Regression
 Journal of the American Statistical Association
, 1981
"... A new method for nonparametric multiple regression is presented. The procedure models the regression surface as a sum of general smooth functions of linear combinations of the predictor variables in an iterative manner. It is more general than standard stepwise and stagewise regression procedures, ..."
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Cited by 555 (6 self)
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A new method for nonparametric multiple regression is presented. The procedure models the regression surface as a sum of general smooth functions of linear combinations of the predictor variables in an iterative manner. It is more general than standard stepwise and stagewise regression procedures, does not require the definition of a metric in the predictor space, and lends itself to graphical interpretation.
The Player/Stage Project: Tools for MultiRobot and Distributed Sensor Systems
 In Proceedings of the 11th International Conference on Advanced Robotics
, 2003
"... This paper describes the Player/Stage software tools applied to multirobot, distributedrobot and sensor network systems. Player is a robot device server that provides network transparent robot control. Player seeks to constrain controller design as little as possible; it is device independent, non ..."
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Cited by 617 (14 self)
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, nonlocking and language and styleneutral. Stage is a lightweight, highly configurable robot simulator that supports large populations. Player/Stage is a community Free Software project. Current usage of Player and Stage is reviewed, and some interesting research opportunities opened up
Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
, 2004
"... Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear m ..."
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Cited by 1513 (20 self)
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Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear measurements do we need to recover objects from this class to within accuracy ɛ? This paper shows that if the objects of interest are sparse or compressible in the sense that the reordered entries of a signal f ∈ F decay like a powerlaw (or if the coefficient sequence of f in a fixed basis decays like a powerlaw), then it is possible to reconstruct f to within very high accuracy from a small number of random measurements. typical result is as follows: we rearrange the entries of f (or its coefficients in a fixed basis) in decreasing order of magnitude f  (1) ≥ f  (2) ≥... ≥ f  (N), and define the weakℓp ball as the class F of those elements whose entries obey the power decay law f  (n) ≤ C · n −1/p. We take measurements 〈f, Xk〉, k = 1,..., K, where the Xk are Ndimensional Gaussian
Vivaldi: A Decentralized Network Coordinate System
 In SIGCOMM
, 2004
"... Largescale Internet applications can benefit from an ability to predict roundtrip times to other hosts without having to contact them first. Explicit measurements are often unattractive because the cost of measurement can outweigh the benefits of exploiting proximity information. Vivaldi is a simp ..."
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Cited by 593 (5 self)
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Largescale Internet applications can benefit from an ability to predict roundtrip times to other hosts without having to contact them first. Explicit measurements are often unattractive because the cost of measurement can outweigh the benefits of exploiting proximity information. Vivaldi is a
A Security Architecture for Computational Grids
, 1998
"... Stateoftheart and emerging scientific applications require fast access to large quantities of data and commensurately fast computational resources. Both resources and data are often distributed in a widearea network with components administered locally and independently. Computations may involve ..."
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Cited by 569 (49 self)
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involve hundreds of processes that must be able to acquire resources dynamically and communicate e#ciently. This paper analyzes the unique security requirements of largescale distributed (grid) computing and develops a security policy and a corresponding security architecture. An implementation
Graphical models, exponential families, and variational inference
, 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
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Cited by 800 (26 self)
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The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical
The Digital Michelangelo Project: 3D Scanning of Large Statues
, 2000
"... We describe a hardware and software system for digitizing the shape and color of large fragile objects under nonlaboratory conditions. Our system employs laser triangulation rangefinders, laser timeofflight rangefinders, digital still cameras, and a suite of software for acquiring, aligning, merg ..."
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Cited by 488 (8 self)
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We describe a hardware and software system for digitizing the shape and color of large fragile objects under nonlaboratory conditions. Our system employs laser triangulation rangefinders, laser timeofflight rangefinders, digital still cameras, and a suite of software for acquiring, aligning
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