Results 51 to 60 of about 62,341 (171)

SDFs from Unoriented Point Clouds using Neural Variational Heat Distances

Computer Graphics Forum, EarlyView.
We propose a novel variational approach for computing neural Signed Distance Fields (SDF) from unoriented point clouds. We first compute a small time step of heat flow (middle) and then use its gradient directions to solve for a neural SDF (right). Abstract We propose a novel variational approach for computing neural Signed Distance Fields (SDF) from ...
Samuel Weidemaier, Florine Hartwig, Josua Sassen, Sergio Conti, Mirela Ben‐Chen, Martin Rumpf   +5 more
wiley   +1 more source

EvolvED: Evolutionary Embeddings to Understand the Generation Process of Diffusion Models

Computer Graphics Forum, EarlyView.
EvolvED visualises how diffusion models generate images by embedding intermediate outputs to preserve semantics and evolutionary structure. It supports analysis via (a) user‐defined goals and prompts, (b) sampling intermediate images, (c) extracting relevant features, and (d) visualising them in structured radial and rectilinear layouts for ...
Vidya Prasad, Hans van Gorp, Christina Humer, Ruud J. G. van Sloun, Anna Vilanova, Nicola Pezzotti   +5 more
wiley   +1 more source

Bochner's technique for statistical structures

, 2015
The main aim of this paper is to extend Bochner's technique to statistical structures. Other topics related to this technique are also introduced to the theory of statistical structures.
Opozda, Barbara
core   +1 more source

Spatial depth for data in metric spaces

Scandinavian Journal of Statistics, EarlyView.
Abstract We propose a novel measure of statistical depth, the metric spatial depth, for data residing in an arbitrary metric space. The measure assigns high (low) values for points located near (far away from) the bulk of the data distribution, allowing quantifying their centrality/outlyingness.
Joni Virta
wiley   +1 more source

Degree theory for 4‐dimensional asymptotically conical gradient expanding solitons

Communications on Pure and Applied Mathematics, Volume 79, Issue 5, Page 1151-1298, May 2026.
Abstract We develop a new degree theory for 4‐dimensional, asymptotically conical gradient expanding solitons. Our theory implies the existence of gradient expanding solitons that are asymptotic to any given cone over S3$S^3$ with non‐negative scalar curvature. We also obtain a similar existence result for cones whose link is diffeomorphic to S3/Γ$S^3/\
Richard H. Bamler, Eric Chen
wiley   +1 more source

Isoperimetric inequalities on slabs with applications to cubes and Gaussian slabs

Communications on Pure and Applied Mathematics, Volume 79, Issue 4, Page 1012-1072, April 2026.
Abstract We study isoperimetric inequalities on “slabs”, namely weighted Riemannian manifolds obtained as the product of the uniform measure on a finite length interval with a codimension‐one base. As our two main applications, we consider the case when the base is the flat torus R2/2Z2$\mathbb {R}^2 / 2 \mathbb {Z}^2$ and the standard Gaussian measure
Emanuel Milman
wiley   +1 more source

The Binet-Legendre Metric in Finsler Geometry

, 2012
For every Finsler metric $F$ we associate a Riemannian metric $g_F$ (called the Binet-Legendre metric). The transformation $F \mapsto g_F$ is $C^0$-stable and has good smoothness properties, in contrast to previous constructions.
Bao, Berestovskiĭ, Berger, Busemann, Centore, Chern, Chern, de Rham, Heil, Helgason, Ishihara, Kobayashi, Kuiper, Kulkarni, Liouville, Liouville, Lovas, Marc Troyanov, Matsumoto, Matsumoto, Matveev, Matveev, Montgomery, Planche, Planche, Simons, Szabó, Vincze, Vladimir S Matveev, Wang, Yano   +30 more
core   +1 more source

Generative Models for Crystalline Materials

Advanced Materials, Volume 38, Issue 18, 25 March 2026.
Generative machine learning models are increasingly used in crystalline materials design. This review outlines major generative approaches and assesses their strengths and limitations. It also examines how generative models can be adapted to practical applications, discusses key experimental considerations for evaluating generated structures, and ...
Houssam Metni, Laura Ruple, Lauren N. Walters, Luca Torresi, Jonas Teufel, Henrik Schopmans, Jona Östreicher, Yumeng Zhang, Marlen Neubert, Yuri Koide, Kevin Steiner, Paul Link, Lukas Bär, Mariana Petrova, Gerbrand Ceder, Pascal Friederich   +15 more
wiley   +1 more source

Information-Geometric Models in Data Analysis and Physics

Mathematics
Information geometry provides a data-informed geometric lens for understanding data or physical systems, treating data or physical states as points on statistical manifolds endowed with information metrics, such as the Fisher information.
D. Bernal-Casas, José M. Oller
doaj   +1 more source

Local Kernels and the Geometric Structure of Data [PDF]

, 2015
We introduce a theory of local kernels, which generalize the kernels used in the standard diffusion maps construction of nonparametric modeling. We prove that evaluating a local kernel on a data set gives a discrete representation of the generator of a ...
Berry, Tyrus, Sauer, Timothy
core   +1 more source