Results 11 to 20 of about 2,604 (146)
An Out of Memory tSVD for Big-Data Factorization
IEEE Access, 2020 Singular value decomposition (SVD) is a matrix factorization method widely used for dimension reduction, data analytics, information retrieval, and unsupervised learning.
Hector Carrillo-Cabada +4 more
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Sensors, 2022 A fast inversion algorithm combined with the transient electromagnetic (TEM) detection system has important significance for improving the detection efficiency of unexploded ordnance.
Lijie Wang +3 more
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Orthogonal random projection for tensor completion
IET Computer Vision, 2020 The low‐rank tensor completion problem, which aims to recover the missing data from partially observable data. However, most of the existing tensor completion algorithms based on Tucker decomposition cannot avoid using singular value decomposition (SVD ...
Yali Feng, Guoxu Zhou
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Cerebral Circulation - Cognition and Behavior, 2023 Abnormalities in structural and functional MRI connectivity measures have been reported in cerebral small vessel disease (SVD). Previous research has shown that whole-brain structural connectivity was highly reproducible in SVD patients, while whole ...
Daniel J. Tozer +2 more
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Generative MR Multitasking With Complex-Harmonic Cardiac Encoding: Bridging the Gap Between Gated Imaging and Real-Time Imaging. [PDF]
Magn Reson MedABSTRACT Purpose To develop a unified image reconstruction framework that bridges real‐time and gated cardiac MRI, including quantitative MRI. Methods We introduce generative multitasking, which learns subject‐ and dataset‐specific implicit neural temporal bases from sequence timings and an interpretable latent space for cardiac and respiratory motion.
Fang X, Christodoulou AG.
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NeuroImage: Clinical, 2021 Cerebral small vessel disease (SVD), including white matter hyperintensities (WMH), microbleeds, lacunes, was related to gait disturbances, while the underlying mechanism is unclear.
Mengfei Cai +5 more
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Triad second renormalization group
Journal of High Energy Physics, 2022 We propose a second renormalization group (SRG) in the triad representation of tensor networks. The SRG method improves two parts of the triad tensor renormalization group, which are the decomposition of intermediate tensors and the preparation of ...
Daisuke Kadoh +2 more
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Beyond Directions: Symmetry-Aware Rotation Sets for Triaxial Diffusion Encoding by Geometric Filter Optimization. [PDF]
Magn Reson MedABSTRACT Purpose To improve the accuracy of diffusion‐weighted powder average signals for diffusion encoding with arbitrary b‐tensors. Methods We identify an intrinsic dihedral (D2) symmetry of diffusion signals for arbitrary diffusion encoding, which defines their natural signal space (a quotient of 3D rotations). Based on this, we propose a method to
Jespersen SN, Szczepankiewicz F.
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T-SVD-Based Robust Color Image Watermarking
IEEE Access, 2019 In order to protect the copyright of the color image, a robust color image watermarking method based on Tensor-Singular Value Decomposition (T-SVD) is proposed.
Meng Du +4 more
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An Aggregative High-Order Singular Value Decomposition Method in Edge Computing
IEEE Access, 2020 In edge computing, for dimensionality reduction and core data extraction, both edge computing node (ECN) and cloud server may implement a high-order singular value decomposition (HOSVD) algorithm before data are passed to local computing models. However,
Junhua Chen +3 more
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