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339
Constructing LinearSized Spectral Sparsification in AlmostLinear Time
"... We present the first almostlinear time algorithm for constructing linearsized spectral sparsification for graphs. This improves all previous constructions of linearsized spectral sparsification, which requires Ω(n2) time [1], [2], [3]. A key ingredient in our algorithm is a novel combination of t ..."
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Cited by 1 (0 self)
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We present the first almostlinear time algorithm for constructing linearsized spectral sparsification for graphs. This improves all previous constructions of linearsized spectral sparsification, which requires Ω(n2) time [1], [2], [3]. A key ingredient in our algorithm is a novel combination
Spectral Sparsification and Restricted Invertibility
, 2010
"... In this thesis we prove the following two basic statements in linear algebra. Let B be an arbitrary n × m matrix where m ≥ n and suppose 0 < ε < 1 is given. 1. Spectral Sparsification. There is a nonnegative diagonal matrix Sm×m with at most ⌈n/ε2 ⌉ nonzero entries for which (1 − ε) 2BBT ≼ BSB ..."
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Cited by 11 (1 self)
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In this thesis we prove the following two basic statements in linear algebra. Let B be an arbitrary n × m matrix where m ≥ n and suppose 0 < ε < 1 is given. 1. Spectral Sparsification. There is a nonnegative diagonal matrix Sm×m with at most ⌈n/ε2 ⌉ nonzero entries for which (1 − ε) 2BBT
SPECTRAL SPARSIFICATION IN THE SEMISTREAMING SETTING
"... Abstract. Let G be a graph with n vertices and m edges. A sparsifier of G is a sparse graph on the same vertex set approximating G in some natural way. It allows us to say useful things about G while considering much fewer than m edges. The strongest commonlyused notion of sparsification is spectra ..."
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Cited by 19 (1 self)
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this resparsification using only the edges retained by earlier steps in nearly linear time. 1.
Spectral sparsification via random spanners
"... In this paper we introduce a new notion of distance between nodes in a graph that we refer to as robust connectivity. Robust connectivity between a pair of nodes u and v is parameterized by a threshold κ and intuitively captures the number of paths between u and v of length at most κ. Using this new ..."
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Cited by 11 (4 self)
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this new notion of distances, we show that any black box algorithm for constructing a spanner can be used to construct a spectral sparsifier. We show that given an undirected weighted graph G, simply taking the union of spanners of a few (polylogarithmically many) random subgraphs of G obtained by sampling
Spectral segmentation with multiscale graph decomposition
 In CVPR
, 2005
"... We present a multiscale spectral image segmentation algorithm. In contrast to most multiscale image processing, this algorithm works on multiple scales of the image in parallel, without iteration, to capture both coarse and fine level details. The algorithm is computationally efficient, allowing to ..."
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Cited by 185 (3 self)
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communication and consistency between the segmentations at each scale. As the results show, we incorporate longrange connections with lineartime complexity, providing highquality segmentations efficiently. Images that previously could not be processed because of their size have been accurately segmented
Coil sensitivity encoding for fast MRI. In:
 Proceedings of the ISMRM 6th Annual Meeting,
, 1998
"... New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect complementa ..."
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Cited by 193 (3 self)
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complementary to Fourier preparation by linear field gradients. Thus, by using multiple receiver coils in parallel scan time in Fourier imaging can be considerably reduced. The problem of image reconstruction from sensitivity encoded data is formulated in a general fashion and solved for arbitrary coil
Purposive behavior acquisition on a real robot by visionbased reinforcement learning
 MACHINE LEARNING
, 1996
"... This paper presents a method of visionbased reinforcement learning by which a robot learns to shoot a ball into a goal. We discuss several issues in applying the reinforcement learning method to a real robot with vision sensor by which the robot can obtain information about the changes in an envi ..."
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Cited by 130 (30 self)
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. Next, to speed up the learning time, a mechanism of Learning from Easy Missions (or LEM) is implemented. LEM reduces the learning time from exponential to almost linear order in the size of the state space. The results of computer simulations and real robot experiments are given.
OVERLAPPING SCHWARZ ALGORITHMS FOR ALMOST INCOMPRESSIBLE LINEAR ELASTICITY
"... Abstract. Low order finite element discretizations of the linear elasticity system suffer increasingly from locking effects and illconditioning, when the material approaches the incompressible limit, if only the displacement variable are used. Mixed finite elements using both displacement and press ..."
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and pressure variables provide a wellknown remedy, but they yield larger and indefinite discrete systems for which the design of scalable and efficient iterative solvers is challenging. Twolevel overlapping Schwarz preconditioner for the almost incompressible system of linear elasticity, discretized by mixed
Longerterm effects of Head Start
 American Economic Review
, 2002
"... Abstract Public early intervention programs like Head Start are often justified as investments in children. Yet nothing is known about the longterm effects of Head Start. This paper draws on unique data from the Panel Study of Income Dynamics to provide new evidence on the effects of Head Start on ..."
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Cited by 131 (5 self)
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effects of Head Start. I. Background Head Start began as a summer program in 1965 with 561,000 predominantly African American children. It expanded to serve almost threequarters of a million African American and white children in the summer of 1966 at which time about $1,000 (in 1999 prices) was spent
Predicting MPEG Execution Times
, 1998
"... This paper reports on a set of experiments that measure the amount of CPU processing needed to decode MPEGcompressed video in software. These experiments were designed to discover indicators that could be used to predict how many cycles are required to decode a given frame. Such predictors can be us ..."
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Cited by 72 (4 self)
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be used to do more accurate CPU scheduling. We found that by considering both frame type and size, it is possible to construct a linear model of MPEG decoding with R 2 values of 0.97 and higher. Moreover, this model can be used to predict decoding times at both the frame and packet level that are almost
Results 1  10
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339