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For a simply connected closed Riemannian manifold with positive scalar curvature, we prove an upper diameter bound in terms of its scalar curvature integral, the Yamabe constant and the dimension of the manifold. When a manifold has a…

Differential Geometry · Mathematics 2023-07-19 Xuenan Fu , Jia-Yong Wu

Density estimation is an important technique for characterizing distributions given observations. Much existing research on density estimation has focused on cases wherein the data lies in a Euclidean space. However, some kinds of data are…

Machine Learning · Statistics 2021-07-12 James A. Brofos , Marcus A. Brubaker , Roy R. Lederman

We introduce orthogonal ring patterns in the 2-sphere and in the hyperbolic plane, consisting of pairs of concentric circles, which generalize circle patterns. We show that their radii are described by a discrete integrable system. This is…

Metric Geometry · Mathematics 2024-10-14 Alexander I. Bobenko

In the paper we study the properties of a metric function which is the extension by continuity of the intrinsic metric of the interior of a submanifold to its boundary. This approach is the development of the classical intrinsic geometry of…

Metric Geometry · Mathematics 2014-10-02 Anatoly P. Kopylov , Mikhail V. Korobkov

We describe and study geometric properties of discrete circular and spherical means of directional derivatives of functions, as well as discrete approximations of higher order differential operators. For an arbitrary dimension we present a…

Numerical Analysis · Mathematics 2015-05-28 Alexander Belyaev , Boris Khesin , Serge Tabachnikov

Working in univalent foundations, we investigate the symmetries of spheres, i.e., the types of the form $\mathbb{S}^n = \mathbb{S}^n$. The case of the circle has a slick answer: the symmetries of the circle form two copies of the circle.…

Logic in Computer Science · Computer Science 2024-01-29 Pierre Cagne , Ulrik Buchholtz , Nicolai Kraus , Marc Bezem

There has been increasing interest in statistical analysis of data lying in manifolds. This paper generalizes a smoothing spline fitting method to Riemannian manifold data based on the technique of unrolling and unwrapping originally…

Methodology · Statistics 2020-09-17 Kwang-Rae Kim , Ian L. Dryden , Huiling Le , Katie E. Severn

Dimensionality reduction is a fundamental task that aims to simplify complex data by reducing its feature dimensionality while preserving essential patterns, with core applications in data analysis and visualisation. To preserve the…

Computer Vision and Pattern Recognition · Computer Science 2025-04-01 Thomas Dagès , Simon Weber , Ya-Wei Eileen Lin , Ronen Talmon , Daniel Cremers , Michael Lindenbaum , Alfred M. Bruckstein , Ron Kimmel

When matching parts of a surface to its whole, a fundamental question arises: Which points should be included in the matching process? The issue is intensified when using isometry to measure similarity, as it requires the validation of…

Computer Vision and Pattern Recognition · Computer Science 2024-10-31 Amit Bracha , Thomas Dagès , Ron Kimmel

We explore the use of tools from Riemannian geometry for the analysis of symmetric positive definite matrices (SPD). An SPD matrix is a versatile data representation that is commonly used in chemical engineering (e.g.,…

Applications · Statistics 2022-03-24 Alexander Smith , Benjamin Laubach , Ivan Castillo , Victor M. Zavala

Consider a sequence of Riemannian manifolds $(M^n_i,g_i)$ with scalar curvatures and entropies bounded below by small constants $R_i,\mu_i \geq-\epsilon_i$. The goal of this paper is to understand notions of convergence and the structure of…

Differential Geometry · Mathematics 2023-05-10 Man-Chun Lee , Aaron Naber , Robin Neumayer

Multivariate distributions are fundamental to modeling. Discrete copulas can be used to construct diverse multivariate joint distributions over random variables from estimated univariate marginals. The space of discrete copulas admits a…

Statistics Theory · Mathematics 2018-05-31 Elisa Perrone , Liam Solus , Caroline Uhler

By Gromov's compactness theorem for metric spaces, every uniformly compact sequence of metric spaces admits an isometric embedding into a common compact metric space in which a subsequence converges with respect to the Hausdorff distance.…

Differential Geometry · Mathematics 2008-10-29 Stefan Wenger

We introduce different classical characteristics used to regularize a subharmonic function and compare them. As an application we give a complete proof of a useful characterization of the modulus of continuity of such functions in terms of…

Complex Variables · Mathematics 2020-07-17 Ahmed Zeriahi

We develop a general structure theory for compact homogeneous Riemannian manifolds in relation to the co-index of symmetry. We will then use these results to classify irreducible, simply connected, compact homogeneous Riemannian manifolds…

Differential Geometry · Mathematics 2013-12-23 Jurgen Berndt , Carlos Olmos , Silvio Reggiani

After reviewing manifold optimization techniques in applications like MIMO communication systems, phased array beamforming, radar, and control theory, we observed that the Complex Circle Manifold (CCM) is widely employed, yet its…

Optimization and Control · Mathematics 2025-08-12 Amirreza Tabrizi , Mohammad Hadi Mirmohammadi

The main theme of this paper is the dynamics of Reeb flows with symmetries on the standard contact sphere. We introduce the notion of strong dynamical convexity for contact forms invariant under a group action, supporting the standard…

Symplectic Geometry · Mathematics 2020-11-09 Viktor L. Ginzburg , Leonardo Macarini

We show two sphere theorems for the Riemannian manifolds with scalar curvature bounded below and the non-collapsed $\mathrm{RCD}(n-1,n)$ spaces with mean distance close to $\frac{\pi}{2}$.

Differential Geometry · Mathematics 2022-06-06 Jialong Deng

Let $\mathbb{K}$ be a field and $S=\mathbb{K}[x_1,\dots,x_n]$ be the polynomial ring in $n$ variables over $\mathbb{K}$. Let $G$ be a graph with $n$ vertices. Assume that $I=I(G)$ is the edge ideal of $G$ and $J=J(G)$ is its cover ideal. We…

Commutative Algebra · Mathematics 2016-04-05 S. A. Seyed Fakhari

Deep unfolding is a method of growing popularity that fuses iterative optimization algorithms with tools from neural networks to efficiently solve a range of tasks in machine learning, signal and image processing, and communication systems.…

Signal Processing · Electrical Eng. & Systems 2019-10-09 Alexios Balatsoukas-Stimming , Christoph Studer