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Denoising diffusion bridge models (DDBMs) are a powerful variant of diffusion models for interpolating between two arbitrary paired distributions given as endpoints. Despite their promising performance in tasks like image translation, DDBMs…

Machine Learning · Computer Science 2025-05-01 Kaiwen Zheng , Guande He , Jianfei Chen , Fan Bao , Jun Zhu

Diffusion Transformer (DiT), an emerging diffusion model for image generation, has demonstrated superior performance but suffers from substantial computational costs. Our investigations reveal that these costs stem from the static inference…

Computer Vision and Pattern Recognition · Computer Science 2024-10-10 Wangbo Zhao , Yizeng Han , Jiasheng Tang , Kai Wang , Yibing Song , Gao Huang , Fan Wang , Yang You

Galvanostatic Intermittent Titration Technique (GITT) is widely used to evaluate solid-state diffusion coefficients in electrochemical systems. However, the existing analysis methods for GITT data require numerous assumptions, and the…

The dynamical formulation of time-independent scattering theory that is developed in [Ann. Phys. (NY) 341, 77-85 (2014)] offers simple formulas for the reflection and transmission amplitudes of finite-range potentials in terms of the…

Quantum Physics · Physics 2017-02-24 Ali Mostafazadeh

Grazing incidence fast atom diffraction has mainly been investigated with helium atoms, considered as the best possible choice for surface analysis. This article presents experimental diffraction profiles recorded with neon projectile,…

Chemical Physics · Physics 2023-10-24 Maxime Debiossac , Peng Pan , Philippe Roncin

Atomic scale simulations are a key element of modern science in that they allow to understand, and even predict, complex physical or chemical phenomena on the basis of the fundamental laws of nature. Among the different existing atomic…

Materials Science · Physics 2021-07-20 Alexandre Boulle , Alain Chartier , Aurélien Debelle , Xin Jin , Jean-Paul Crocombette

A macroscopic characterization of fractals showing up a structural transition from dense to multibranched growth is made using optical diffraction theory. Such fractals are generated via the numerical solution of the 2D Poisson and…

Condensed Matter · Physics 2009-10-22 F. Perez-Rodriguez , Wei Wang , E. Canessa

In these lectures, a simple introduction to the phenomenon of diffraction in deep inelastic scattering and its theoretical description is given. While the main focus is on the diffractive structure function F_2^D, some issues in diffractive…

High Energy Physics - Phenomenology · Physics 2009-02-20 A. Hebecker

Aims: The aim of this work is to verify whether turbulent magnetic reconnection can provide the additional energy input required to explain the up to now only poorly understood ionization mechanism of the diffuse ionized gas (DIG) in…

Astrophysics of Galaxies · Physics 2015-06-05 T. L. Hoffmann , S. Lieb , A. W. A. Pauldrach , H. Lesch , P. J. N. Hultzsch , G. T. Birk

We review QCD based descriptions of diffractive deep inelastic scattering emphasizing the role of models with parton saturation. These models provide natural explanation of such experimentally observed facts as the constant ratio of…

High Energy Physics - Phenomenology · Physics 2008-11-26 K. Golec-Biernat

This paper concerns diffraction-tomographic reconstruction of an object characterized by its scattering potential. We establish a rigorous generalization of the Fourier diffraction theorem in arbitrary dimension, giving a precise relation…

Numerical Analysis · Mathematics 2026-03-30 Clemens Kirisits , Michael Quellmalz , Eric Setterqvist

Letting $T$ denote an ergodic transformation of the unit interval and letting $f \colon [0,1)\to \mathbb{R}$ denote an observable, we construct the $f$-weighted return time measure $\mu_y$ for a reference point $y\in[0,1)$ as the weighted…

Dynamical Systems · Mathematics 2019-05-23 Marc Kesseböhmer , Arne Mosbach , Tony Samuel , Malte Steffens

A dramatic influx of diffusion-generated images has marked recent years, posing unique challenges to current detection technologies. While the task of identifying these images falls under binary classification, a seemingly straightforward…

Computer Vision and Pattern Recognition · Computer Science 2024-06-05 Yewon Lim , Changyeon Lee , Aerin Kim , Oren Etzioni

Solving crystal structures from kinematical X-ray or electron diffraction patterns of single crystals requires many more diffracted beams to be recorded than there are atoms in the structure, since the phases of the structure factors can…

Materials Science · Physics 2023-02-13 Christoph T. Koch

Experimental results on hadronic soft and hard diffractive processes are reviewed with emphasis on aspects of the data that point to the underlying QCD mechanism for diffraction. Diffractive differential cross sections are shown to be…

High Energy Physics - Phenomenology · Physics 2009-09-29 Konstantin Goulianos

Diffractive optical elements that divide an input beam into a set of replicas are used in many optical applications ranging from image processing to communications. Their design requires time-consuming optimization processes, which, for a…

We implement an efficient method of computation of two dimensional Fourier-type integrals based on approximation of the integrand by Gaussian radial basis functions, which constitute a standard tool in approximation theory. As a result, we…

Numerical Analysis · Mathematics 2022-02-07 A. Martinez-Finkelshtein , D. Ramos-Lopez , D. R. Iskander

Preliminary results of extensive numerical experiments on a simple model specified by the smooth canonical strongly chaotic 2D-map with global virtual invariant curves (VICs) are presented and discussed. We focus on the statistics of the…

Chaotic Dynamics · Physics 2009-11-07 B. V. Chirikov , V. V. Vecheslavov

Diffractive scattering of $\gamma^* p \to X + N$, where $N$ is either a proton or a nucleonic system with $M_N~<~4$~GeV has been measured in deep inelastic scattering (DIS) at HERA. The cross section was determined by a novel method as a…

High Energy Physics - Experiment · Physics 2012-08-27 ZEUS Collaboration

Uncertainty quantification is critical in scientific inverse problems to distinguish identifiable parameters from those that remain ambiguous given available measurements. The Conditional Diffusion Model-based Inverse Problem Solver (CDI)…

Machine Learning · Computer Science 2026-01-27 Dmitrii Torbunov , Yihui Ren , Lijun Wu , Yimei Zhu
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