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Molecular structure elucidation is a fundamental step in understanding chemical phenomena, with applications in identifying molecules in natural products, lab syntheses, forensic samples, and the interstellar medium. We consider the task of…

Machine Learning · Computer Science 2025-03-04 Austin Cheng , Alston Lo , Kin Long Kelvin Lee , Santiago Miret , Alán Aspuru-Guzik

Given a random sample from a density function supported on a manifold $M$, a new method for the estimating highest density regions of the underlying population is introduced. The new proposal is based on the empirical version of the opening…

Statistics Theory · Mathematics 2026-02-12 Diego Bolón , Rosa M. Crujeiras , Alberto Rodríguez-Casal

A geometry-based density functional theory is presented for mixtures of hard spheres, hard needles and hard platelets; both the needles and the platelets are taken to be of vanishing thickness. Geometrical weight functions that are…

Soft Condensed Matter · Physics 2009-11-11 Ansgar Esztermann , Hendrik Reich , Matthias Schmidt

The sphere packing problem asks for the densest packing of unit balls in d-dimensional Euclidean space. This problem has its roots in geometry, number theory and it is part of Hilbert's 18th problem. In 1958 C. A. Rogers proved a…

Metric Geometry · Mathematics 2007-05-23 Karoly Bezdek

When analyzing modern machine learning algorithms, we may need to handle kernel density estimation (KDE) with intricate kernels that are not designed by the user and might even be irregular and asymmetric. To handle this emerging challenge,…

Statistics Theory · Mathematics 2021-06-09 Hau-Tieng Wu , Nan Wu

In earlier works \cite{Sz06-1}, \cite{Sz06-2}, \cite{Sz13-3} and \cite{Sz13-4} we have investigated the densest packings and the least dense coverings by congruent hyperballs (hyperspheres) to the regular prism tilings in $n$-dimensional…

Metric Geometry · Mathematics 2016-03-04 Jenö Szirmai

We develop a model to describe the properties of random assemblies of polydisperse hard spheres. We show that the key features to describe the system are (i) the dependence between the free volume of a sphere and the various coordination…

Disordered Systems and Neural Networks · Physics 2015-05-19 Maximilien Danisch , Yuliang Jin , Hernan A. Makse

The sphere packing problem asks for the greatest density of a packing of congruent balls in Euclidean space. The current best upper bound in all sufficiently high dimensions is due to Kabatiansky and Levenshtein in 1978. We revisit their…

Metric Geometry · Mathematics 2015-01-14 Henry Cohn , Yufei Zhao

This review describes the diversity of jammed configurations attainable by frictionless convex nonoverlapping (hard) particles in Euclidean spaces and for that purpose it stresses individual-packing geometric analysis. A fundamental feature…

Statistical Mechanics · Physics 2015-05-19 Salvatore Torquato , Frank H. Stillinger

We give upper bounds for the density of unit ball packings relative to their outer parallel domains and discuss their connection to contact numbers. Also, packings of soft balls are introduced and upper bounds are given for the fraction of…

Metric Geometry · Mathematics 2015-11-24 Karoly Bezdek , Zsolt Langi

We study a concentration problem on the unit sphere $\mathbb{S}^2$ for band-limited spherical harmonics expansions using large sieve methods. We derive upper bounds for concentration in terms of the maximum Nyquist density. Our proof uses…

Functional Analysis · Mathematics 2019-10-16 Michael Speckbacher , Tomasz Hrycak

This is the fifth in a series of papers giving a proof of the Kepler conjecture, which asserts that the density of a packing of congruent spheres in three dimensions is never greater than $\pi/\sqrt{18}\approx 0.74048...$. This is the…

Metric Geometry · Mathematics 2007-05-23 Thomas C. Hales

We investigate the problem of finding inner ap-proximations of positive semidefinite (PSD) cones. We developa novel decomposition framework of the PSD cone by meansof conical combinations of smaller dimensional sub-cones. Weshow that many…

Optimization and Control · Mathematics 2021-10-01 Tianqi Zheng , James Guthrie , Enrique Mallada

Wettability is the affinity of a liquid for a solid surface. For energetic reasons, macroscopic drops of liquid are nearly spherical away from interfaces with solids, and any local deformations due to molecular-scale surface interactions…

Mesoscale and Nanoscale Physics · Physics 2017-11-27 Ronaldo Giro , Peter W. Bryant , Michael Engel , Rodrigo F. Neumann , Mathias Steiner

In this paper, quantitative bounds in high-frequency central limit theorems are derived for Poisson based $U$-statistics of arbitrary degree built by means of wavelet coefficients over compact Riemannian manifolds. The wavelets considered…

Statistics Theory · Mathematics 2016-07-28 Solesne Bourguin , Claudio Durastanti

Direct Numerical Simulations of the injection of a pulsed round liquid jet in a stagnant gas are performed in a series of runs of geometrically progressing resolution. The Reynolds and Weber numbers and the density ratio are sufficiently…

For a (compact) subset $K$ of a metric space and $\varepsilon > 0$, the {\em covering number} $N(K , \varepsilon )$ is defined as the smallest number of balls of radius $\varepsilon$ whose union covers $K$. Knowledge of the {\em metric…

Functional Analysis · Mathematics 2008-02-03 Stanislaw J. Szarek

Extending earlier work of Tian, we show that if a manifold admits a metric that is almost hyperbolic in a suitable sense, then there exists an Einstein metric that is close to the given metric in the $C^{2,\alpha}$-topology. In dimension…

Differential Geometry · Mathematics 2022-12-16 Ursula Hamenstädt , Frieder Jäckel

We introduce the concepts of complex Grassmannian codes and designs. Let G(m,n) denote the set of m-dimensional subspaces of C^n: then a code is a finite subset of G(m,n) in which few distances occur, while a design is a finite subset of…

Combinatorics · Mathematics 2008-06-16 Aidan Roy

Many data analysis problems can be cast as distance geometry problems in \emph{space forms} -- Euclidean, spherical, or hyperbolic spaces. Often, absolute distance measurements are often unreliable or simply unavailable and only proxies to…

Machine Learning · Computer Science 2021-07-30 Puoya Tabaghi , Jianhao Peng , Olgica Milenkovic , Ivan Dokmanić