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Diffusion tensor image denoising via geometric invariant nonlocal means on the tensor manifold
Diffusion tensor imaging (DTI) is an advanced magnetic resonance technology that describes subtle brain structures using a diffusion tensor at each...
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Weyl–invariant scalar–tensor gravities from purely metric theories
We describe a method to generate scalar–tensor theories with Weyl symmetry, starting from arbitrary purely metric higher derivative gravity theories....
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The condition number of many tensor decompositions is invariant under Tucker compression
We characterise the sensitivity of several additive tensor decompositions with respect to perturbations of the original tensor. These decompositions...
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Invariant Analysis of Vortical Delta Wing Flow Using the Extended Optimal Triple Tensor Decomposition
Vortices and vortex breakdown flow structures over a multi-delta wing configuration are often analyzed by applying flow field invariant analysis...
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Robust tensor decomposition via orientation invariant tubal nuclear norms
Aiming at recovering an unknown tensor (i.e., multi-way array) corrupted by both sparse outliers and dense noises, robust tensor decomposition (RTD)...
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Holographic tensor networks with bulk gauge symmetries
Tensor networks are useful toy models for understanding the structure of entanglement in holographic states and reconstruction of bulk operators...
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Tensor Preliminaries
Tensors are multidimensional arrays generalized from vectors and matrices, which have a broad range of applications in various fields such as signal...
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Codazzi Tensor Fields in Reductive Homogeneous Spaces
We extend the results about left-invariant Codazzi tensor fields on Lie groups equipped with left-invariant Riemannian metrics obtained by d’Atri in...
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Linking strike directions to invariant TE and TM impedances of the magnetotelluric impedance tensor
Estimation of the traditional transverse electric (TE) and transverse magnetic (TM) impedances of the magnetotelluric tensor for two-dimensional...
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Tensor Analysis
Tensor analysis is an essential tool for derivation of the basic laws of physics, whenever representation of the law in a different coordinates...
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Matrix and tensor witnesses of hidden symmetry algebras
Permutation group algebras, and their generalizations called permutation centralizer algebras (PCAs), play a central role as hidden symmetries in the...
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More on Semipositive Tensor and Tensor Complementarity Problem
In recent years, several classes of structured matrices are extended to classes of tensors in the context of tensor complementarity problem. The...
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Tensor reduction of loop integrals
The computational cost associated with reducing tensor integrals to scalar integrals using the Passarino-Veltman method is dominated by the...
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Vector and Tensor
The purpose of this chapter is to present part of basic properties of vector and tensor operations, which is frequently used in the rest of the book....
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Invariant Almost Contact Structures and Connections on the Lobachevsky Space
AbstractIt has been proven that there is a left-invariant normal almost contact metric structure on the group model of the Lobachevsky space. All...
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Duality as a method to derive a gauge invariant massive electrodynamics and new interactions
Taking into account the recent developments associated with duality in physics, this article is focused on investigating the properties of a tensor...
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Bond-weighting method for the Grassmann tensor renormalization group
Recently, the tensor network description with bond weights on its edges has been proposed as a novel improvement for the tensor renormalization group...
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Super invariant theory in positive characteristic
We study invariant theory of the general linear supergroup in positive characteristic. In particular, we determine when the symmetric group algebra...
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Scalar-Tensor Gravities
In the previous chapter, we demonstrated that the modifications of the purely geometric sector allow for obtaining interesting results, in...
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Tensor completion via multi-directional partial tensor nuclear norm with total variation regularization
This paper addresses the tensor completion problem, whose task is to estimate missing values with limited information. However, the crux of this...
