Results 31 to 40 of about 249,242 (319)
The Tensor Algebra Compiler [PDF]
, 2017 Tensor and linear algebra is pervasive in data analytics and the physical sciences. Often the tensors, matrices or even vectors are sparse. Computing expressions involving a mix of sparse and dense tensors, matrices and vectors requires writing kernels ...
Kjolstad, Fredrik +9 more
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Long-Term Recording of LTP in Cultured Hippocampal Slices
Neural Plasticity, 2002 Long-term potentiation (LTP) was elicited by high frequency stimulation in hippocampal slices cultured on multi-electrode arrays. LTP lasting more than 1 h was recorded in 75% of slices, and a significant number of slices exhibited a non-decaying LTP ...
Ken Shimono +4 more
doaj +1 more source
Diffusion Tensor Imaging: on the assessment of data quality - a preliminary bootstrap analysis [PDF]
, 2003 In the field of nuclear magnetic resonance imaging, diffusion tensor imaging (DTI) has proven an important method for the characterisation of ultrastructural tissue properties.
Heim, Stefan +4 more
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Journal de Mathématiques Pures et Appliquées, 2017 We analyze tensors in the tensor product of three m-dimensional vector spaces satisfying Strassen's equations for border rank m. Results include: two purely geometric characterizations of the Coppersmith-Winograd tensor, a reduction to the study of symmetric tensors under a mild genericity hypothesis, and numerous additional equations and examples ...
J. M. Landsberg, Mateusz Michalek
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Gap Filling of 3-D Microvascular Networks by Tensor Voting [PDF]
, 2008 We present a new algorithm which merges discontinuities in 3-D images of tubular structures presenting undesirable gaps. The application of the proposed method is mainly associated to large 3-D images of microvascular networks.
Plouraboué, Franck +2 more
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CoRR, 2018 The tensor-tensor product (t-product) [M. E. Kilmer and C. D. Martin, 2011] is a natural generalization of matrix multiplication. Based on t-product, many operations on matrix can be extended to tensor cases, including tensor SVD, tensor spectral norm, tensor nuclear norm [C. Lu, et al., 2018] and many others.
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IEEE Access, 2021 Sparse-view scanning has great potential for realizing ultra-low-dose computed tomography (CT) examination. However, noise and artifacts in reconstructed images are big obstacles, which must be handled to maintain the diagnosis accuracy.
Yongbo Wang +7 more
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$SDD_1$ tensors and $B_1$-tensors
The Electronic Journal of Linear AlgebraStrong $\mathcal{H}$-tensors play an important role in the fields of science and engineering. In this paper, we first propose a new subclass of strong $\mathcal{H}$-tensors that we call the class of $SDD_1$ tensors. We also prove that if a tensor is an $SDD_1$ tensor, it is a strong $\mathcal{H}$-tensor. As an application, a sufficient condition for an
Weiting Duan, Dekun Wen, Yaqiang Wang
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Tensor product of correspondence functors [PDF]
, 2019 As part of the study of correspondence functors, the present paper investigates their tensor product and proves some of its main properties. In particular, the correspondence functor associated to a finite lattice has the structure of a commutative ...
Thévenaz, Jacques, Bouc, Serge
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Decomposable Sparse Tensor on Tensor Regression
CoRR, 2022 Most regularized tensor regression research focuses on tensors predictors with scalars responses or vectors predictors to tensors responses. We consider the sparse low rank tensor on tensor regression where predictors $\mathcal{X}$ and responses $\mathcal{Y}$ are both high-dimensional tensors.
Haiyi Mao, Jason Xiaotian Dou
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