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Standards Column
"... Column Editor’s Note: This column is the second installment in a two part discussion of Geographic Information Systems (GIS) services. The first part of the discussion, which can be found in the December 2006January 2007 issue of ATG, defined GIS services and discussed the ways in which libraries ..."
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Column Editor’s Note: This column is the second installment in a two part discussion of Geographic Information Systems (GIS) services. The first part of the discussion, which can be found in the December 2006January 2007 issue of ATG, defined GIS services and discussed the ways in which
Biclustering algorithms for biological data analysis: a survey.
 IEEE/ACM Transactions of Computational Biology and Bioinformatics,
, 2004
"... Abstract A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results of the application of standard clustering methods to genes are limited. These limited results are imposed by the existence of a num ..."
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Cited by 481 (15 self)
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Abstract A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results of the application of standard clustering methods to genes are limited. These limited results are imposed by the existence of a
A column approximate minimum degree ordering algorithm
, 2000
"... Sparse Gaussian elimination with partial pivoting computes the factorization PAQ = LU of a sparse matrix A, where the row ordering P is selected during factorization using standard partial pivoting with row interchanges. The goal is to select a column preordering, Q, based solely on the nonzero patt ..."
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Cited by 318 (52 self)
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Sparse Gaussian elimination with partial pivoting computes the factorization PAQ = LU of a sparse matrix A, where the row ordering P is selected during factorization using standard partial pivoting with row interchanges. The goal is to select a column preordering, Q, based solely on the nonzero
CAPACITY MODELS OF RC MEMBERS WITH EMPHASIS ON SUBSTANDARD COLUMNS WITH PLAIN BARS
, 2009
"... ABSTRACT Current code prescriptions allow to evaluate the ultimate rotational capacity from hybrid (mechanicalempirical) or empirical formulations, for R.C. members with deformed bars and seismically detailed. These formulations can be extended to nonconforming elements by applying correction coe ..."
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coefficients calibrated on experimental data. These coefficients, for members with plain bars, imply a reduction of 40% at least; this reduction increases if lapping of longitudinal bars is present. The experimental campaign conducted at the University of Naples on 16 realscale columns with plain bars allowed
Columns
, 2015
"... Abstract: This article identifies the arguments for and against the common core state standards in English language arts and mathematics. Although the standards need to be differentiated for gifted students, the author suggests that these standards may offer a way to transform education in the class ..."
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Abstract: This article identifies the arguments for and against the common core state standards in English language arts and mathematics. Although the standards need to be differentiated for gifted students, the author suggests that these standards may offer a way to transform education
No eigenvalues outside the support of the limiting spectral distribution of largedimensional sample covariance matrices
 ANNALS OF PROBABILITY 26
, 1998
"... We consider a class of matrices of the form Cn = (1/N)(Rn+σXn)(Rn+σXn) ∗, where Xn is an n × N matrix consisting of independent standardized complex entries, Rj is an n×N nonrandom matrix, and σ> 0. Among several applications, Cn can be viewed as a sample correlation matrix, where information is ..."
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Cited by 186 (35 self)
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We consider a class of matrices of the form Cn = (1/N)(Rn+σXn)(Rn+σXn) ∗, where Xn is an n × N matrix consisting of independent standardized complex entries, Rj is an n×N nonrandom matrix, and σ> 0. Among several applications, Cn can be viewed as a sample correlation matrix, where information
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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, sampling along a Cartesian grid in kspace corresponding to standard Fourier imaging with reduced FOV. Owing to the underlying principle, the concepts outlined in this work have been named SENSE, short for SENSitivity Encoding (810). Together with SENSE theory and methods, a detailed SNR analysis
Bayesian Factor Regression Models in the "Large p, Small n" Paradigm
 Bayesian Statistics
, 2003
"... TOR REGRESSION MODELS 1.1 SVD Regression Begin with the linear model y = X# + # where y is the nvector of responses, X is the n p matrix of predictors, # is the pvector regression parameter, and # , # I) is the nvector error term. Of key interest are cases when p >> n, when X is & ..."
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Cited by 184 (16 self)
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is "long and skinny." The standard empirical factor (principal component) regression is best represented using the reduced singularvalue decomposition (SVD) of X, namely X = FA where F is the nk factor matrix (columns are factors, rows are samples) and A is the k p SVD "
Incremental Singular Value Decomposition Of Uncertain Data With Missing Values
 IN ECCV
, 2002
"... We introduce an incremental singular value decomposition (SVD) of incomplete data. The SVD is developed as data arrives, and can handle arbitrary missing/untrusted values, correlated uncertainty across rows or columns of the measurement matrix, and user priors. Since incomplete data does not uniq ..."
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Cited by 179 (5 self)
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We introduce an incremental singular value decomposition (SVD) of incomplete data. The SVD is developed as data arrives, and can handle arbitrary missing/untrusted values, correlated uncertainty across rows or columns of the measurement matrix, and user priors. Since incomplete data does
Invited Paper Miniaturized Columns for the Routine HPLC Lab:
"... The performance of a standard column (15 cm × 4.6 mm), packed with 5μm particles, is compared with a minibore column (10 cm × 2.0 mm) filled with 3μm packing. The influence of the flow rate on the column efficiency is determined, as well as the effect of the sample volume on the band broadening an ..."
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The performance of a standard column (15 cm × 4.6 mm), packed with 5μm particles, is compared with a minibore column (10 cm × 2.0 mm) filled with 3μm packing. The influence of the flow rate on the column efficiency is determined, as well as the effect of the sample volume on the band broadening
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3,311