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In this paper, the geometric properties of the conformal metric are studied and its exact solution of the geodesic deviation equation is presented. We also find out the stress-energy tensor of this geometry and compare it with the usual…

General Physics · Physics 2022-04-06 B. T. T. Wong

We consider Einstein gravity in AdS in the presence of a deformed conformal boundary metric, in the limit of large spacetime dimension. At leading order we find a new set of effective near-horizon equations. These can be understood as…

High Energy Physics - Theory · Physics 2018-10-17 Tomas Andrade , Christiana Pantelidou , Benjamin Withers

Motivated by numerous questions in random geometry, given a smooth manifold $M$, we approach a systematic study of the differential topology of Gaussian random fields (GRF) $X:M\to \mathbb{R}^k$, that we interpret as random variables with…

Differential Geometry · Mathematics 2021-01-25 Antonio Lerario , Michele Stecconi

We provide a new angle and obtain new results on a class of metrics on length-normalized curves in $d$ dimensions, represented by their unit tangents expressed as a function of arc-length, which are functions from the unit interval to the…

Differential Geometry · Mathematics 2019-10-08 Laurent Younes

This article introduces innovative classes of open sets in \(\mathbb{R}^{N}\), where \(N=2, 3\), characterized by a geometric property associated with the inward normal. The focus lies on proving compactness results for the Hausdorff…

Optimization and Control · Mathematics 2026-04-03 Mohamed Barkatou

In this paper, we characterize the rectifiability (both uniform and not) of an Ahlfors regular set, E, of arbitrary co-dimension by the behavior of a regularized distance function in the complement of that set. In particular, we establish a…

Analysis of PDEs · Mathematics 2020-07-16 Guy David , Max Engelstein , Svitlana Mayboroda

Several data analysis techniques employ similarity relationships between data points to uncover the intrinsic dimension and geometric structure of the underlying data-generating mechanism. In this paper we work under the model assumption…

Machine Learning · Statistics 2019-04-09 Nicolas Garcia Trillos , Daniel Sanz-Alonso , Ruiyi Yang

We present an adaptive geometry in which the yardstick co-deforms with space itself, formulated on cellular spaces where length is a count: distances are shortest cell-crossing counts. No cell shape, angles, or embedding are assumed; the…

General Mathematics · Mathematics 2026-03-05 Shlomo Barak , George Salman

Here we construct the conformal mappings with the help of continuous fractions approximations. These approximations converge to the algebraic roots $\sqrt[N]{z}$ for $N \in \mathbb{N}$ and $z$ from the right half-plane of the complex plane.…

Metric Geometry · Mathematics 2018-08-21 Pyotr N. Ivanshin

We propose a precise and efficient normal estimation method that can deal with noise and nonuniform density for unstructured 3D point clouds. Unlike existing approaches that directly take patches and ignore the local neighborhood…

Computer Vision and Pattern Recognition · Computer Science 2022-07-26 Keqiang Li , Mingyang Zhao , Huaiyu Wu , Dong-Ming Yan , Zhen Shen , Fei-Yue Wang , Gang Xiong

We derive a set of equations describing the linear response of the convergence properties of a geodesic congruence to arbitrary geometry variations. It is a combination of equations describing the deviations from the standard…

General Relativity and Quantum Cosmology · Physics 2015-09-14 Masafumi Seriu

To study asymptotic structures, we regularize Einstein's field equations by means of conformal transformations. The conformal factor is chosen so that it carries a dimensional scale that captures crucial asymptotic features. By choosing a…

General Relativity and Quantum Cosmology · Physics 2009-11-11 Niklas Rohr , Claes Uggla

In this paper we study reconstruction of a function $f$ from its discrete Radon transform data in $\mathbb R^3$ when $f$ has jump discontinuities. Consider a conventional parametrization of the Radon data in terms of the affine and angular…

Numerical Analysis · Mathematics 2019-03-21 Alexander Katsevich

In this paper we define distance expanding random dynamical systems. We develop the appropriate thermodynamic formalism of such systems. We obtain in particular the existence and uniqueness of invariant Gibbs states, the appropriate…

Dynamical Systems · Mathematics 2010-12-08 Volker Mayer , Bartlomiej Skorulski , Mariusz Urbański

Given a geodesic space (E, d), we show that full ordinal knowledge on the metric d-i.e. knowledge of the function D d : (w, x, y, z) $\rightarrow$ 1 d(w,x)$\le$d(y,z) , determines uniquely-up to a constant factor-the metric d. For a…

Machine Learning · Statistics 2019-01-01 Thibaut Le Gouic

For a unimodular random graph $(G,\rho)$, we consider deformations of its intrinsic path metric by a (random) weighting of its vertices. This leads to the notion of the conformal growth exponent of $(G,\rho)$, which is the best asymptotic…

Probability · Mathematics 2020-06-02 James R. Lee

The Hausdorff dimension of the graphs of the functions in H\"older and Besov spaces (in this case with integrability p \geq 1) on fractal d-sets is studied. Denoting by s \in (0,1] the smoothness parameter, the sharp upper bound…

Functional Analysis · Mathematics 2011-01-04 António Caetano , Abel Carvalho

We study numerical computation of conformal invariants of domains in the complex plane. In particular, we provide an algorithm for computing the conformal capacity of a condenser. The algorithm applies for wide kind of geometries: domains…

Complex Variables · Mathematics 2020-08-19 Mohamed M S Nasser , Matti Vuorinen

Estimating some mathematical expectations from partially observed data and in particular missing outcomes is a central problem encountered in numerous fields such as transfer learning, counterfactual analysis or causal inference. Matching…

Statistics Theory · Mathematics 2025-05-01 Simon Viel , Lionel Truquet , Ikko Yamane

The length of the geodesic between two data points along a Riemannian manifold, induced by a deep generative model, yields a principled measure of similarity. Current approaches are limited to low-dimensional latent spaces, due to the…