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Array
, 2009
"... You are given a class of functions. You have a sampling device, typically, a lowpass filter. Given the measurements yn = 〈x(t), ϕ(t/T − n)〉, you want to reconstruct x(t). x(t) h(t) =!(!t/T) y(t) T y n =<x(t),!(t/T!n)> ..."
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You are given a class of functions. You have a sampling device, typically, a lowpass filter. Given the measurements yn = 〈x(t), ϕ(t/T − n)〉, you want to reconstruct x(t). x(t) h(t) =!(!t/T) y(t) T y n =<x(t),!(t/T!n)>
On the Uniqueness of Multilinear Decomposition of Nway arrays
, 2000
"... INTRODUCTION Consider an I # J matrix X and suppose that rank (X) = 3. Let x i,j denote the (i, j)th entry of X.Thenit holds that x i,j admits a threecomponent bilinear decomposition x i#j # # 3 f #1 a i#f b j#f #1# for all i = 1,...,I and j = 1,...,J. Equivalently, letting a f := [a 1,f ..."
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Cited by 99 (10 self)
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INTRODUCTION Consider an I # J matrix X and suppose that rank (X) = 3. Let x i,j denote the (i, j)th entry of X.Thenit holds that x i,j admits a threecomponent bilinear decomposition x i#j # # 3 f #1 a i#f b j#f #1# for all i = 1,...,I and j = 1,...,J. Equivalently, letting a f := [a 1,f
ROC graphs: Notes and practical considerations for data mining researchers
, 2003
"... Receiver Operating Characteristics (ROC) graphs are a useful technique for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been increasingly adopted in the machine learning and data mining research communitie ..."
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Cited by 205 (0 self)
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communities. Although ROC graphs are apparently simple, there are some common misconceptions and pitfalls when using them in practice. This article serves both as a tutorial introduction to ROC graphs and as a practical guide for using them in research. Keywords: 1
Semantic cognition: A parallel distributed processing approach
 Connectionist perspectives on categoryspecific deficits. In: Categoryspecificity in brain and
, 2004
"... Copyright c ..."
DOI: 10.1007/S1133600790223 DEGENERACY IN CANDECOMP/PARAFAC AND INDSCAL EXPLAINED FOR SEVERAL THREESLICED ARRAYS WITH A TWOVALUED TYPICAL RANK
, 2007
"... The Candecomp/Parafac (CP) method decomposes a threeway array into a prespecified number R of rank1 arrays, by minimizing the sum of squares of the residual array. The practical use of CP is sometimes complicated by the occurrence of socalled degenerate sequences of solutions, in which several ra ..."
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The Candecomp/Parafac (CP) method decomposes a threeway array into a prespecified number R of rank1 arrays, by minimizing the sum of squares of the residual array. The practical use of CP is sometimes complicated by the occurrence of socalled degenerate sequences of solutions, in which several
1 Ranking the LCA
"... Abstract: The overarching question addressed in this article is how syntactic structures based on constituency (dominance, ccommand) are going to be mapped onto linear phonetic strings. The article presents an argument that both prosodic principles and narrowsyntactic principles play a role in the ..."
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Abstract: The overarching question addressed in this article is how syntactic structures based on constituency (dominance, ccommand) are going to be mapped onto linear phonetic strings. The article presents an argument that both prosodic principles and narrowsyntactic principles play a role in the linearization of syntactic structures. I take Richard Kayne’s (1994) Linear Correspondence Axiom as a starting point: (asymmetric) ccommand maps onto precedence relations. Two wideranging consequences of Kayne’s theory are that specs precede their heads and that a head can only have one spec or adjunct. Although there is abundant evidence to support these predictions, there is nonetheless a wellknown class of apparent counterexamples: dislocations in the Romance languages can be rightward and multiple. I take the LCA to be a soft constraint, overruled by a constraint of the WRAP family that seeks to phrase together a verb and its extended projection in one intonational phrase. Apparent rightward movement is the outcome of right linearization forced by WRAP. The possibility of having multiple dislocations is shown to be compatible with the LCA within the assumptions made in this article.
Engineering a lightweight suffix array construction algorithm (Extended Abstract)
"... In this paper we consider the problem of computing the suffix array of a text T [1, n]. This problem consists in sorting the suffixes of T in lexicographic order. The suffix array [16] (or pat array [9]) is a simple, easy to code, and elegant data structure used for several fundamental string matchi ..."
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Cited by 79 (3 self)
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In this paper we consider the problem of computing the suffix array of a text T [1, n]. This problem consists in sorting the suffixes of T in lexicographic order. The suffix array [16] (or pat array [9]) is a simple, easy to code, and elegant data structure used for several fundamental string
SUBTRACTING A BEST RANK1 APPROXIMATION MAY INCREASE TENSOR RANK
"... Is has been shown that a best rankR approximation of an orderk tensor may not exist when R ≥ 2 and k ≥ 3. This poses a serious problem to data analysts using Candecomp/Parafac and related models. It has been observed numerically that, generally, this issue cannot be solved by consecutively computi ..."
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Cited by 17 (0 self)
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computing and substracting best rank1 approximations. The reason for this is that subtracting a best rank1 approximation generally does not decrease tensor rank. In this paper, we provide a mathematical treatment of this property for realvalued 2 × 2 × 2 tensors, with symmetric tensors as a special case
The Mapping of Linear Recurrence Equations on Regular Arrays
 Journal of VLSI Signal Processing
, 1989
"... The parallelization of many algorithms can be obtained using spacetime transformations which are applied on nested doloops or on recurrence equations. In this paper, we analyze systems of linear recurrence equations, a generalization of uniform recurrence equations. The first part of the paper des ..."
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Cited by 69 (7 self)
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. Both parts rely on results on integral convex polyhedra. Our results are illustrated on the Gauss elimination algorithm and on the GaussJordan diagonalization algorithm. 1 Introduction Designing efficient algorithms for parallel architectures is one of the main difficulties of the current research
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