Research
On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

Trend · papers per month

3570105140 · May 202619922001200920172026
48 results for covariance tensor

Defines natural tensors for submanifolds of pseudo-Riemannian manifolds.

problem Characterizing tensors for submanifolds of pseudo-Riemannian manifolds.
method Constructs geodesic normal coordinates and expresses metric coefficients as polynomials in curvature and second fundamental form derivatives.
result Natural tensors are linear combinations of contractions of curvature and second fundamental form derivatives.

The paper defines minimal norm tensors for curvature and divergence tensors, explaining Weyl and Cotten tensors.

problem Understanding curvature tensors and their minimal norm.
method Analyzing minimal norm tensors for third and fourth covariant tensors, including Riemannian curvature and divergence.
result Weyl tensor and Cotten tensor are identified as minimal norm tensors of Riemannian curvature and divergence tensors, respectively.

We introduce and study covariance fields of distributions on a Riemannian manifold. At each point on the manifold, covariance is defined to be a symmetric and positive definite (2,0)-tensor. Its product with the metric tensor specifies a linear operator on the respected tangent space. Collectively, these operators form…

2008-07-29abs ↗pdf ↗

Develops a regression model for partially observed dynamic tensor data.

problem Characterizing the relationship between dynamic tensor data and external covariates when data is only partially observed.
method Introduces low-rank, sparsity, and fusion structures on the regression coefficient tensor, and uses a loss function projected over observed entries. Developed an efficient non-convex alternating updating algorithm.
result Derived finite-sample error bounds for the estimator.

Formulae for non-symmetric connections derived from covariant derivatives.

problem Deriving commutation formulae for non-symmetric affine connections.
method Covariant derivatives of tensors with respect to symmetric and non-symmetric affine connections.
result Formulae for non-symmetric connections derived from covariant derivatives.

A unified framework for Poisson and Jacobi structures from 2-covariant tensors

problem Constructing Poisson and Jacobi structures from non-degenerate 2-covariant tensors
method Deriving a formula for the Schouten-Nijenhuis bracket of the associated bivector field
result Recovering classical brackets associated with symplectic, locally conformally symplectic, cosymplectic, and contact geometries

GLSKF improves tensor completion by capturing both global and local variations.

problem Tensor completion with missing entries, especially in data with spatial or temporal side information.
method Integrates smoothness-constrained low-rank factorization with a locally correlated residual process.
result GLSKF achieves superior performance and scalability on real-world datasets.

The paper generalizes Cartan Geometry using Polacek and Siegel's approach.

problem Formulating sigma model dynamics in a covariant way.
method Using Polacek and Siegel's generalised curvature and torsion approach within the generalised metric formalism.
result Almost all higher generalised tensors correspond to covariant derivatives of the generalised Riemann tensor.

We investigate the triviality of compact Ricci solitons under general scalar conditions involving the Weyl tensor. More precisely, we show that a compact Ricci soliton is Einstein if a generic linear combination of divergences of the Weyl tensor contracted with suitable covariant derivatives of the potential function v…

2018-04-25abs ↗pdf ↗

Extends multivariate regression for tensor-variate data, identifying brain regions and facial characteristics.

problem Challenges in fitting regression models with multivariate responses and covariates.
method Low-rank tensor formats on regression coefficients and tensor-variate normal distribution for errors.
result Maximum likelihood estimators for tensor-on-tensor regression via block-relaxation algorithms.

This paper shows how infinitely wide Tensor Networks converge to Gaussian Processes.

problem Understanding the relationship between Tensor Networks and Gaussian Processes.
method Analyzing the infinite-width limit of Tensor Networks and comparing them to Gaussian Processes.
result Infinitely wide Tensor Networks converge to Gaussian Processes, proving their equivalence.

Proposes FATTNN for tensor-on-tensor regression with improved prediction and reduced computation.

problem Tensor-on-tensor regression with complex tensor structures and nonlinear relationships.
method Integrates tensor factor models into deep neural networks to handle nonlinearity and reduce data dimensionality.
result Significant improvements in prediction accuracy and computational efficiency over traditional methods.

The paper studies Stein-Weiss operators on symmetric tensors, extending previous work.

problem Understanding Stein-Weiss operators on symmetric tensors of arbitrary rank.
method Analyzing the decomposition of tensor spaces into irreducible components and computing Weitzenbock formulas.
result Unified framework for second-order Stein-Weiss operators and tools for geometric analysis.

We present a unified derivation of covariant time derivatives, which transform as tensors under a time-dependent coordinate change. Such derivatives are essential for formulating physical laws in a frame-independent manner. Three specific derivatives are described: convective, corotational, and directional. The covaria…

2001-02-28abs ↗pdf ↗

We compute all 2-covariant tensors naturally constructed from a semiriemannian metric which are divergence-free and have weight greater than -2. As a consequence, it follows a characterization of the Einstein tensor as the only, up to a constant factor, 2-covariant tensor naturally constructed from a semiriemannian met…

2007-09-12abs ↗pdf ↗

Counterexample found to estimate for skew-symmetric tensors.

problem Estimate for skew-symmetric tensors was claimed and used for classification results.
method Analysis of the estimate in arXiv:2103.15482.
result Counterexample disproves the estimate for skew-symmetric tensors.

Study covariant derivatives of eigenfunctions on curved spaces, proving they are scalar multiples of the functions.

problem Understanding covariant derivatives of eigenfunctions on curved spaces.
method Analyzing the Laplace-Beltrami operator on Riemannian manifolds with constant curvature.
result Covariant derivatives of eigenfunctions are scalar multiples of the functions, and these scalars are polynomials in the eigenvalue.

A new metric tensor improves Riemann manifold Monte Carlo for Bayesian models.

problem Improving sampling efficiency in Bayesian hierarchical models.
method Metric tensor derived from log-density gradient covariance matrices.
result Metric tensors enhance sampling for complex Bayesian models.

The two-jet of the curvature tensor at some point of a pseudo-Riemannian manifold is called Einstein if the Ricci tensor is a multiple of the metric tensor at the given point and additionally its first two covariant derivatives vanish there. Following the Jet Isomorphism Theorem of pseudo-Riemannian geometry, we derive…

2015-12-13abs ↗pdf ↗

The paper proves local laws for non-separable sample covariance matrices.

problem Analyzing non-separable sample covariance matrices with dependent or nonlinearly transformed data.
method Tensor network framework for analyzing fluctuation averaging in the presence of higher-order cumulant structure.
result Optimal averaged local law and full anisotropic local law for non-separable sample covariance matrices.

MSFA clusters high-dimensional spatial data using spline-based covariance structures.

problem Clustering high-dimensional spatial data with flexible covariance structures.
method Mixture of spatial factor analyzers with spline-based covariance and matrix variate factor analyzers for dimensionality reduction.
result Proposed models accurately infer and differentiate distinct spatial patterns in tensor-variate data.

New algorithm detects tensor dependence structure alterations efficiently.

problem Detecting alterations in tensor dependence structures.
method Tensor-normal distributions, decorrelation, centralization, SERA (Sparsity-Exploited Reranking Algorithm).
result The proposed SERA algorithm controls false discovery rates effectively.

This work develops efficient methods for computing moments of Gaussian mixtures.

problem Efficient computation of moments for Gaussian mixtures with large dimensions.
method Theory and numerical methods for implicit computations with moment tensors of Gaussian mixtures.
result Reduced computational and storage costs for moment tensors of Gaussian mixtures.

Among other results, a compact almost Kähler manifold is proved to be Kähler if the Ricci tensor is semi-negative and its length coincides with that of the star Ricci tensor or if the Ricci tensor is semi-positive and its first order covariant derivatives are Hermitian. Moreover, it is shown that there are no compact a…

2003-04-07abs ↗pdf ↗

We show that if R\nabla R is a Jordan Szabo algebraic covariant derivative curvature tensor on a vector space of signature (p,q), where q is odd and p is less than q or if q is congruent to 2 mod 4 and if p is less than q-1, then R=0\nabla R=0. This algebraic result yields an elementary proof of the geometrical fact th…

2002-11-05abs ↗pdf ↗

It is developed the considerations from (S. M. Minčić, [14, 15]) about curvature tensors and pseudotensors for a non-symmetric affine connection space in this paper. How many kinds of covariant derivatives are enough to be defined for complete researching in the field of non-symmetric affine connection spaces is examin…

2019-07-01abs ↗pdf ↗

Estimates for covariant derivatives and Riesz transforms on differential forms.

problem Bounding covariant derivatives and Riesz transforms on differential forms.
method Use Bismut derivative formula to prove heat kernel bounds and Riesz transform boundedness.
result Formulate and prove conjecture on boundedness of covariant local Riesz-transforms in L^p.

On a Riemannian or a semi-Riemannian manifold, the metric determines invariants like the Levi-Civita connection and the Riemann curvature. If the metric becomes degenerate (as in singular semi-Riemannian geometry), these constructions no longer work, because they are based on the inverse of the metric, and on related o…

2011-05-01abs ↗pdf ↗

We show that the symmetry classes of torsion-free covariant derivatives T\nabla T of r-times covariant tensor fields T can be characterized by Littlewood-Richardson products σ[1]σ[1] where σσ is a representation of the symmetric group SrS_r which is connected with the symmetry class of T. If σ=[λ]σ= [λ] is irreducible the…

2003-01-06abs ↗pdf ↗

Proves the Kundt conjecture in arbitrary dimensions, confirming its validity.

problem Determining spacetimes not characterized by scalar polynomial curvature invariants.
method New bilinear map and analysis of covariant derivatives of the Riemann tensor.
result Confirms the Kundt conjecture in arbitrary dimensions, removing regularity assumptions.

We consider generators of algebraic curvature tensors R which can be constructed by a Young symmetrization of product tensors U*w or w*U, where U and w are covariant tensors of order 3 and 1. We assume that U belongs to a class of the infinite set S of irreducible symmetry classes characterized by the partition (2,1). …

2004-11-02abs ↗pdf ↗

In this brief survey, we will remark the interaction among the Hessian tensor on a semi-Riemannian manifold and some of the several questions in Lorentzian (and also in semi-Riemannian) geometry where this 2-covariant tensor is involved. In particular, we deal with the characterization of Killing vector fields and the …

2006-07-05abs ↗pdf ↗