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4,947
The benefits of coding over routing in a randomized setting
 In Proceedings of 2003 IEEE International Symposium on Information Theory
, 2003
"... Abstract — We present a novel randomized coding approach for robust, distributed transmission and compression of information in networks. We give a lower bound on the success probability of a random network code, based on the form of transfer matrix determinant polynomials, that is tighter than the ..."
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Cited by 361 (44 self)
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Abstract — We present a novel randomized coding approach for robust, distributed transmission and compression of information in networks. We give a lower bound on the success probability of a random network code, based on the form of transfer matrix determinant polynomials, that is tighter than
Bounds for linear multitask learning
 Journal of Machine Learning Research
, 2006
"... Abstract. We give dimensionfree and datadependent bounds for linear multitask learning where a common linear operator is chosen to preprocess data for a vector of task speci…c linearthresholding classi…ers. The complexity penalty of multitask learning is bounded by a simple expression involvin ..."
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Cited by 39 (9 self)
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involving the margins of the taskspeci…c classi…ers, the HilbertSchmidt norm of the selected preprocessor and the HilbertSchmidt norm of the covariance operator for the total mixture of all task distributions, or, alternatively, the Frobenius norm of the total Gramian matrix for the data
Traffic Matrix Estimation: Existing Techniques and New Directions
, 2002
"... Very few techniques have been proposed for estimating traffic matrices in the context of Internet traffic. Our work on POPtoPOP traffic matrices (TM) makes two contributions. The primary contribution is the outcome of a detailed comparative evaluation of the three existing techniques. We evaluate ..."
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Cited by 208 (14 self)
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models we provide a proof of concept showing that the incorporation of knowledge of POP features (such as total incoming bytes, number of customers, etc.) can reduce estimation errors. Our proposed approach can be used in conjunction with existing or future methods in that it can be used to generate good
The observability gramian of the observer form and the SchurCohn matrix
, 1991
"... The observer form is a standard minimal realization of a rational transferfunction matrix G based on an irreducible left matrixfraction description G = U~lV. It is shown that (in the case of stable G) there is a simple closedform expression for the observability gramian of this realization in ter ..."
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The observer form is a standard minimal realization of a rational transferfunction matrix G based on an irreducible left matrixfraction description G = U~lV. It is shown that (in the case of stable G) there is a simple closedform expression for the observability gramian of this realization
The Relative Contribution of Jumps to Total Price Variance
 JOURNAL OF FINANCIAL ECONOMETRICS
, 2005
"... We examine tests for jumps based on recent asymptotic results; we interpret the tests as Hausmantype tests. Monte Carlo evidence suggests that the daily ratio zstatistic has appropriate size, good power, and good jump detection capabilities revealed by the confusion matrix comprised of jump classi ..."
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Cited by 162 (6 self)
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We examine tests for jumps based on recent asymptotic results; we interpret the tests as Hausmantype tests. Monte Carlo evidence suggests that the daily ratio zstatistic has appropriate size, good power, and good jump detection capabilities revealed by the confusion matrix comprised of jump
A Note on the Cross Gramian for NonSymmetric Systems
, 2015
"... The cross gramian matrix is a tool for model reduction and system identification, but it is only computable for square control systems. For symmetric control systems the cross gramian possesses a useful relation to the associated system’s Hankel singular values. Yet, many reallife models are neithe ..."
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Cited by 1 (1 self)
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The cross gramian matrix is a tool for model reduction and system identification, but it is only computable for square control systems. For symmetric control systems the cross gramian possesses a useful relation to the associated system’s Hankel singular values. Yet, many reallife models
The Empirical Cross Gramian for Parametrized Nonlinear Systems
, 2015
"... The cross gramian matrix can be used for model order reduction as well as system identification of linear control systems, which are frequently used in the sciences. The empirical cross gramian is solely computed from trajectories and hence extends beyond linear statespace systems to nonlinear syst ..."
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The cross gramian matrix can be used for model order reduction as well as system identification of linear control systems, which are frequently used in the sciences. The empirical cross gramian is solely computed from trajectories and hence extends beyond linear statespace systems to nonlinear
Gramianbased model reduction for datasparse systems
, 2007
"... Model reduction is a common theme within the simulation, control and optimization of complex dynamical systems. For instance, in control problems for partial differential equations, the associated largescale systems have to be solved very often. To attack these problems in reasonable time it is abs ..."
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Cited by 5 (4 self)
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scale Lyapunov equations, thus the method is of limited use for really largescale applications. We develop an effective implementation of balancingrelated model reduction methods in exploiting the structure of the underlying problem. This is done by a datasparse approximation of the largescale state matrix A
Efficient Model Reduction of Interconnect via Approximate System Gramians
, 1999
"... Krylovsubspace based methods for generating loworder models of complicated interconnect are extremely effective, but there is no optimality theory for the resulting models. Alternatively, methods based on truncating a balanced realization (TBR), in which the observability and controllability grami ..."
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Cited by 26 (6 self)
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of the system's controllability and observability gramians. The approximate dominant eigenspaces are obtained efficiently using an iterative Lyapunov equation solver, Vector ADI, which requires only linear matrixvector solves. A spiral inductor and a transmission line example are used to demonstrate
Implementing sparse matrixvector multiplication on throughputoriented processors
 In SC ’09: Proceedings of the 2009 ACM/IEEE conference on Supercomputing
, 2009
"... Sparse matrixvector multiplication (SpMV) is of singular importance in sparse linear algebra. In contrast to the uniform regularity of dense linear algebra, sparse operations encounter a broad spectrum of matrices ranging from the regular to the highly irregular. Harnessing the tremendous potential ..."
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Cited by 142 (7 self)
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Sparse matrixvector multiplication (SpMV) is of singular importance in sparse linear algebra. In contrast to the uniform regularity of dense linear algebra, sparse operations encounter a broad spectrum of matrices ranging from the regular to the highly irregular. Harnessing the tremendous
Results 1  10
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4,947