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Matrix Tensor Product Approach to the Equivalence of Multipartite States under Local Unitary Transformations
, 2006
"... The equivalence of multipartite quantum mixed states under local unitary transformations is studied. A criterion for the equivalence of nondegenerate mixed multipartite quantum states under local unitary transformations is presented. PACS numbers: 03.67.a, 02.20.Hj, 03.65.w Quantum entangled stat ..."
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The equivalence of multipartite quantum mixed states under local unitary transformations is studied. A criterion for the equivalence of nondegenerate mixed multipartite quantum states under local unitary transformations is presented. PACS numbers: 03.67.a, 02.20.Hj, 03.65.w Quantum entangled states are playing fundmental roles in quantum information processing such as quantum computation, quantum teleportation, dense coding, quantum cryptographic schemes quantum error correction, entanglement swapping, and remote state preparation (RSP) etc.. However the theory of quantum entanglement is still far from being satisfied. To quantify the degree of entanglement a number of entanglement measures have been proposed for bipartite states. Most of these proposed measures of entanglement involve extremizations which are difficult to handle analytically. For multipartite case how to give a well defined measure is still under discussion. For general mixed states till now we don’t even have an operational criterion to verify whether a state is separable or not. As a matter
MATRIX FACTORIZATION TECHNIQUES FOR RECOMMENDER SYSTEMS
 IEEE COMPUTER
, 2009
"... As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearestneighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels. Modern co ..."
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Cited by 593 (4 self)
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As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearestneighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels. Modern
Capacity of a Mobile MultipleAntenna Communication Link in Rayleigh Flat Fading
"... We analyze a mobile wireless link comprising M transmitter and N receiver antennas operating in a Rayleigh flatfading environment. The propagation coefficients between every pair of transmitter and receiver antennas are statistically independent and unknown; they remain constant for a coherence int ..."
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Cited by 495 (22 self)
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signals. We prove that there is no point in making the number of transmitter antennas greater than the length of the coherence interval: the capacity for M> Tis equal to the capacity for M = T. Capacity is achieved when the T M transmitted signal matrix is equal to the product of two statistically
Automatically tuned linear algebra software
 CONFERENCE ON HIGH PERFORMANCE NETWORKING AND COMPUTING
, 1998
"... This paper describes an approach for the automatic generation and optimization of numerical software for processors with deep memory hierarchies and pipelined functional units. The production of such software for machines ranging from desktop workstations to embedded processors can be a tedious and ..."
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Cited by 478 (26 self)
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This paper describes an approach for the automatic generation and optimization of numerical software for processors with deep memory hierarchies and pipelined functional units. The production of such software for machines ranging from desktop workstations to embedded processors can be a tedious
Recognising Tensor Products of Matrix Groups
 Internat. J. Algebra Comput
, 1997
"... As a contribution to the project for recognising matrix groups defined over finite fields, we describe an algorithm for deciding whether or not the natural module for such a matrix group can be decomposed into a nontrivial tensor product. In the affirmative case, a tensor decomposition is return ..."
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Cited by 15 (3 self)
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As a contribution to the project for recognising matrix groups defined over finite fields, we describe an algorithm for deciding whether or not the natural module for such a matrix group can be decomposed into a nontrivial tensor product. In the affirmative case, a tensor decomposition
TensorMatrix Products with a Compressed Sparse Tensor
, 2015
"... The Canonical Polyadic Decomposition (CPD) of tensors is a powerful tool for analyzing multiway data and is used extensively to analyze very large and extremely sparse datasets. The bottleneck of computing the CPD is multiplying a sparse tensor by several dense matrices. Algorithms for tensormatr ..."
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. In this work, we bridge the gap between the two approaches and introduce the compressed sparse fiber (CSF) a data structure for sparse tensors along with a novel parallel algorithm for tensormatrix multiplication. CSF offers similar operation reductions as existing compressed methods while using only a
Status of land cover classification accuracy assessment
 REMOTE SENSING OF ENVIRONMENT
, 2002
"... The production of thematic maps, such as those depicting land cover, using an image classification is one of the most common applications of remote sensing. Considerable research has been directed at the various components of the mapping process, including the assessment of accuracy. This paper brie ..."
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Cited by 266 (3 self)
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The production of thematic maps, such as those depicting land cover, using an image classification is one of the most common applications of remote sensing. Considerable research has been directed at the various components of the mapping process, including the assessment of accuracy. This paper
Symmetric tensors and symmetric tensor rank
 Scientific Computing and Computational Mathematics (SCCM
, 2006
"... Abstract. A symmetric tensor is a higher order generalization of a symmetric matrix. In this paper, we study various properties of symmetric tensors in relation to a decomposition into a symmetric sum of outer product of vectors. A rank1 orderk tensor is the outer product of k nonzero vectors. An ..."
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Cited by 99 (20 self)
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Abstract. A symmetric tensor is a higher order generalization of a symmetric matrix. In this paper, we study various properties of symmetric tensors in relation to a decomposition into a symmetric sum of outer product of vectors. A rank1 orderk tensor is the outer product of k nonzero vectors
INEQUALITIES OF GENERALIZED MATRIX FUNCTIONS Via Tensor Products
 ELA
, 2014
"... By an embedding approach and through tensor products, some inequalities for generalized matrix functions (of positive semidefinite matrices) associated with any subgroup of the permutation group and any irreducible character of the subgroup are obtained. ..."
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Cited by 3 (0 self)
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By an embedding approach and through tensor products, some inequalities for generalized matrix functions (of positive semidefinite matrices) associated with any subgroup of the permutation group and any irreducible character of the subgroup are obtained.
Applying the Design Structure Matrix to system decomposition and integration problems: a review and new directions
 IEEE Transactions on Engineering Management
"... organizations requires tools and techniques for system decomposition and integration. A design structure matrix (DSM) provides a simple, compact, and visual representation of a complex system that supports innovative solutions to decomposition and integration problems. The advantages of DSMs visà ..."
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Cited by 199 (8 self)
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and their relationships. Index Terms—Design structure matrix, information flow, integration analysis, modularity, organization design, product architecture, product development, project management, project planning, scheduling, systems engineering. I.
Results 1  10
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