Results 141 to 150 of about 2,528 (182)

Does mRNA-based COVID-19 vaccination in the subacute phase lead to microstructural brain changes? A prospective pilot MRI study using T1 relaxometry. [PDF]

Sci Rep
Willems R, Cappelletti M, Schüre JR, Deichmann R, Hattingen E, Arendt CT.   +5 more
europepmc   +1 more source

Quantum circuit simulation with a local time-dependent variational principle

Eisert J, Sander A, Fröhlich M, Ali M, Eigel M, Hintermüller M, Mendl C, Milbradt R, Wille R.   +8 more
europepmc   +1 more source

Hot-SVD: higher order t-singular value decomposition for tensors based on tensor–tensor product

Computational and Applied Mathematics, 2022
This paper considers a way of generalizing the t-SVD of third-order tensors (regarded as tubal matrices) to tensors of arbitrary order N (which can be similarly regarded as tubal tensors of order (N-1)). \color{black}Such a generalization is different from the t-SVD for tensors of order greater than three [Martin, Shafer, Larue, SIAM J. Sci.
Yuning Yang, Yang Yuning
exaly   +3 more sources

Exact Tensor Completion Using t-SVD [PDF]

IEEE Transactions on Signal Processing, 2017
16 pages, 5 figures, 2 ...
Shuchin Aeron
exaly   +3 more sources

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Robust Tensor SVD and Recovery With Rank Estimation

IEEE Transactions on Cybernetics, 2022
Tensor singular value decomposition (t-SVD) has recently become increasingly popular for tensor recovery under partial and/or corrupted observations. However, the existing t -SVD-based methods neither make use of a rank prior nor provide an accurate rank estimation (RE), which would limit their recovery performance.
Qiquan Shi, , Jian Lou
exaly   +3 more sources

Tensor SVD: Statistical and Computational Limits [PDF]

IEEE Transactions on Information Theory, 2018
Typos ...
Anru R Zhang, Dong Xia
exaly   +4 more sources

Weighted tensor nuclear norm minimization for tensor completion using tensor-SVD

Pattern Recognition Letters, 2020
Abstract In this paper, we consider the tensor completion problem, which aims to estimate missing values from limited information. Our model is based on the recently proposed tensor-SVD, which uses the relationships among the color channels in an image or video recovery problem. To improve the availability of the model, we propose the weighted tensor
Liangfu Lu, Xuyun Zhang, Lianyong Qi
exaly   +2 more sources

Grassmannian Optimization for Online Tensor Completion and Tracking With the t-SVD

IEEE Transactions on Signal Processing, 2022
We propose a new fast streaming algorithm for the tensor completion problem of imputing missing entries of a low-tubal-rank tensor using the tensor singular value decomposition (t-SVD) algebraic framework. We show the t-SVD is a specialization of the well-studied block-term decomposition for third-order tensors, and we present an algorithm under this ...
Kyle Gilman, Davoud Ataee Tarzanagh, Laura Balzano   +2 more
exaly   +4 more sources

On the Tensor SVD and the Optimal Low Rank Orthogonal Approximation of Tensors [PDF]

SIAM Journal on Matrix Analysis and Applications, 2009
It is known that a higher order tensor does not necessarily have an optimal low rank approximation, and that a tensor might not be orthogonally decomposable (i.e., admit a tensor SVD). We provide several sufficient conditions which lead to the failure of the tensor SVD, and characterize the existence of the tensor SVD with respect to the higher order ...
Yousef Saad
exaly   +2 more sources