Results 41 to 50 of about 5,909,835 (371)
Deep Subspace Clustering with Block Diagonal Constraint
Applied Sciences, 2020 The deep subspace clustering method, which adopts deep neural networks to learn a representation matrix for subspace clustering, has shown good performance.
Jing Liu, Yanfeng Sun, Yongli Hu
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Block ILU factorization preconditioners for a block-tridiagonal H-matrix
Linear Algebra and its Applications, 2000 The authors propose three new block incomplete LU (BILU) factorizations for a block-tridiagonal \(H\)-matrix readily parallelizable. The construction of BILU factors is based on the element-wise LU \(\text{ILU}(k)\) factorization (incomplete LU factorization of level \(k\) of fill-in) of diagonal blocks, \(B_i=L_iU_i-R_i\).
Kim, Sang Wook, Yun, Jae Heon
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An Efficient Block Circulant Preconditioner For Simulating Fracture Using Large Fuse Networks
, 2005 {\it Critical slowing down} associated with the iterative solvers close to the critical point often hinders large-scale numerical simulation of fracture using discrete lattice networks.
Batrouni G G +15 more
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Eigenvalues of block structured asymmetric random matrices [PDF]
, 2015 We study the spectrum of an asymmetric random matrix with block structured variances. The rows and columns of the random square matrix are divided into $D$ partitions with arbitrary size (linear in $N$).
Aljadeff, Johnatan +2 more
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Cross-social-network user alignment research based on multi-dimensional user features
Systems Science & Control EngineeringAccurately aligning the same users on different flat social networks to merge user information and create more nuanced user profiles is critical. However, the current research in this area faces challenges related to low efficiency and inadequate ...
Tao Zhao +4 more
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Very Large-Scale Singular Value Decomposition Using Tensor Train Networks [PDF]
, 2014 We propose new algorithms for singular value decomposition (SVD) of very large-scale matrices based on a low-rank tensor approximation technique called the tensor train (TT) format. The proposed algorithms can compute several dominant singular values and
Cichocki, Andrzej, Lee, Namgil
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Lossy Compression using Adaptive Polynomial Image Encoding
Advances in Electrical and Computer Engineering, 2021 In this paper, an efficient lossy compression approach using adaptive-block polynomial curve-fitting encoding is proposed. The main idea of polynomial curve fitting is to reduce the number of data elements in an image block to a few coefficients.
OTHMAN, S. +3 more
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Polyphase networks, block digital filtering, LPTV systems, and alias-free QMF banks: a unified approach based on pseudocirculants [PDF]
, 1988 The relationship between block digital filtering and quadrature mirror filter (QMF) banks is explored. Necessary and sufficient conditions for alias cancellation in QMF banks are expressed in terms of an associated matrix, derived from the polyphase ...
Mitra, Sanjit K., Vaidyanathan, P. P.
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Pivot-Free Block Matrix Inversion [PDF]
2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, 2006 We present a pivot-free deterministic algorithm for the inversion of block matrices. The method is based on the Moore-Penrose inverse and is applicable over certain general classes of rings. This improves on previous methods that required at least one invertible on-diagonal block, and that otherwise required row- or column-based pivoting, disrupting ...
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Organoids in pediatric cancer research
FEBS Letters, EarlyView.Organoid technology has revolutionized cancer research, yet its application in pediatric oncology remains limited. Recent advances have enabled the development of pediatric tumor organoids, offering new insights into disease biology, treatment response, and interactions with the tumor microenvironment.
Carla Ríos Arceo, Jarno Drost
wiley +1 more source