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Light diffusion is usually associated with thick, opaque media. Indeed, multiple scattering is necessary for the onset of the diffusive regime and such condition is generally not met in almost transparent media. Nonetheless, at long enough…

An active learning algorithm for the classification of high-dimensional images is proposed in which spatially-regularized nonlinear diffusion geometry is used to characterize cluster cores. The proposed method samples from estimated cluster…

Machine Learning · Computer Science 2019-11-07 James M. Murphy

Simulations of scattering processes are essential in understanding the physics of our universe. Computing relevant scattering quantities from ab initio methods is extremely difficult on classical devices because of the substantial…

We demonstrate scattering of laser light from two InAs quantum dots coupled to a photonic crystal waveguide, which is achieved by strain-tuning the optical transitions of the dots into mutual resonance. By performing measurements of the…

Compton scattering of twisted photons is investigated within a non-relativistic framework using first-order perturbation theory. We formulate the problem in the density matrix theory, which enables one to gain new insights into scattering…

Atomic Physics · Physics 2015-07-03 S. Stock , A. Surzhykov , S. Fritzsche , D. Seipt

This paper proposes and analyzes a novel clustering algorithm that combines graph-based diffusion geometry with techniques based on density and mode estimation. The proposed method is suitable for data generated from mixtures of…

Machine Learning · Statistics 2019-01-01 Mauro Maggioni , James M. Murphy

Scattering moments provide nonparametric models of random processes with stationary increments. They are expected values of random variables computed with a nonexpansive operator, obtained by iteratively applying wavelet transforms and…

Methodology · Statistics 2015-03-17 Joan Bruna , Stéphane Mallat , Emmanuel Bacry , Jean-François Muzy

The search for refining 3D LiDAR data has attracted growing interest motivated by recent techniques such as supervised learning or generative model-based methods. Existing approaches have shown the possibilities for using diffusion models…

Computer Vision and Pattern Recognition · Computer Science 2024-07-24 Sander Elias Magnussen Helgesen , Kazuto Nakashima , Jim Tørresen , Ryo Kurazume

An investigation of the effect of surface diffusion in random deposition model is made by analytical methods and reasoning. For any given site, the extent to which a particle can diffuse is decided by the morphology in the immediate…

Soft Condensed Matter · Physics 2011-12-14 Baisakhi Mal , Subhankar Ray , J. Shamanna

We show by means of experiments, theory and simulations, that the slow dynamics of coarsening systems displays dynamic heterogeneity similar to that observed in glass-forming systems. We measure dynamic heterogeneity via novel multi-point…

Converting neutron scattering data to real-space time-dependent structures can only be achieved through suitable models, which is particularly challenging for geometrically disordered structures. We address this problem by introducing…

Chemical Physics · Physics 2021-07-28 Cedric J. Gommes , Reiner Zorn , Sebastian Jaksch , Henrich Frielinghaus , Olaf Holderer

In the last three decades, powerful computer-assisted techniques have been developed in order to validate a posteriori numerical solutions of semilinear elliptic problems of the form $\Delta u +f(u,\nabla u) = 0$. By studying a well chosen…

Analysis of PDEs · Mathematics 2022-03-02 Maxime Breden

When existing, cumulants can provide valuable information about a given distribution and can in principle be used to either fully reconstruct or approximate the parent distribution function. A previously reported cumulant expansion approach…

Chemical Physics · Physics 2017-12-19 Joonsuk Huh , Robert Berger

Coherent photonic computing uses both the phase and amplitude of light to implement linear operations such as dot products and matrix multiplication but requires phase stability between the interfering paths. This poses a challenge for such…

Optics · Physics 2026-04-16 Shenghan Gao , Kathy Lüdge , Francesco Da Ros , Nathan Youngblood

The time-asymptotic behavior of undamped, nonlinear oscillators with a random frequency is investigated analytically and numerically. We find that averaged quantities of physical interest, such as the oscillator's mechanical energy,…

Statistical Mechanics · Physics 2009-11-07 Kirone Mallick , Philippe Marcq

Sub-visible particle analysis using flow imaging microscopy combined with deep learning has proven effective in identifying particle types, enabling the distinction of harmless components such as silicone oil from protein particles.…

Computer Vision and Pattern Recognition · Computer Science 2025-08-11 Utku Ozbulak , Michaela Cohrs , Hristo L. Svilenov , Joris Vankerschaver , Wesley De Neve

In this work, we propose a novel methodology for robustly estimating particle size distributions from optical scattering measurements using constrained Gaussian process regression. The estimation of particle size distributions is commonly…

Machine Learning · Statistics 2025-07-08 Fahime Seyedheydari , Mahdi Nasiri , Marcin Mińkowski , Simo Särkkä

Propagation of light in a highly scattering medium is among the most fascinating optical effect that everyone experiences on an everyday basis and possesses a number of fundamental problems which have yet to be solved. Conventional wisdom…

The light amplification by finite active media is used extensively in modern optics applications. In this paper the light amplification and scattering by cluster of small particles is studied analytically and numerically with the help of…

Optics · Physics 2014-12-16 V. V. Prosentsov

From the spread of pollutants in the atmosphere to the transmission of nutrients across cell membranes, anomalous diffusion processes are ubiquitous in natural systems. The ability to understand and control the mechanisms guiding such…

Statistical Mechanics · Physics 2021-01-04 E G Kostadinova , J L Padgett , C D Liaw , L S Matthews , T W Hyde