Tootfinder

Zigzagging Diffusion and Non-Standard Transport in Particle-laden Nanopores Under Extreme Confinement
A. Baer, P. Malgaretti, K. Hoellring, J. Harting, Ana-Suncana Smith
https://arxiv.org/abs/2506.09191

Zigzagging Diffusion and Non-Standard Transport in Particle-laden Nanopores Under Extreme Confinement
Understanding transport subject to molecular-scale confinement is key to advancing nanofluidics, yet classical hydrodynamic laws often fail at these scales. Here, we study a model system: transport of toluene as a solvent and small fullerenes as model particles confined within alumina slit nanopores using molecular dynamics simulations. We find that toluene organizes into discrete layers whose commensurability with the pore width leads to a striking, non-monotonic, zig-zag dependence of transpo…

Comparison between the diffusion properties of different small-scale fractional transport models
Paolo Cifani, Franco Flandoli
https://arxiv.org/abs/2506.08068

Comparison between the diffusion properties of different small-scale fractional transport models
Stochastic transport due to a velocity field modeled by the superposition of small-scale divergence free vector fields activated by Fractional Gaussian Noises (FGN) is numerically investigated. We present two non-trivial contributions: the first one is the definition of a model where different space-time structures can be compared on the same ground: this is achieved by imposing the same average kinetic energy to a standard Ornstein-Uhlenbeck approximation, then taking the limit to the idealize…

This https://arxiv.org/abs/2502.13574 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion Models with Jointly Learned Prior
Denoising diffusion probabilistic models (DDPMs) can be utilized to recover a clean signal from its degraded observation(s) by conditioning the model on the degraded signal. The degraded signals are themselves contaminated versions of the clean signals; due to this correlation, they may encompass certain useful information about the target clean data distribution. However, existing adoption of the standard Gaussian as the prior distribution in turn discards such information when shaping the pri…

Elementary discrete diffusion/redistancing schemes for the mean curvature flow
Antonin Chambolle, Daniele De Gennaro, Massimiliano Morini
https://arxiv.org/abs/2506.05946

Elementary discrete diffusion/redistancing schemes for the mean curvature flow
We consider here a fully discrete and explicit scheme for the mean curvature flow of boundaries, based on an elementary diffusion step and a precise redistancing operation. We give an elementary convergence proof for the scheme under the standard CFL condition $h\sim\varepsilon^2$, where $h$ is the time discretization step and $\varepsilon$ the space step. We discuss extensions to more general convolution/redistancing schemes.

SDEs with critical general distributional drifts: sharp solvability and blow ups
D. Kinzebulatov, R. Vafadar
https://arxiv.org/abs/2506.09244 https://

SDEs with critical general distributional drifts: sharp solvability and blow ups
We establish weak well-posedness for SDEs having discontinuous diffusion coefficients and general distributional drifts that may introduce blow up effects. Our drifts satisfy minimal assumptions, i.e.\,we assume only that the Cauchy problem for the Kolmogorov backward equation is well-posed in the standard Hilbert triple $W^{1,2} \hookrightarrow L^2 \hookrightarrow W^{-1,2}$. By a result of Mazya and Verbitsky, these assumptions are precisely those drifts that can be represented as the sum of a…

This https://arxiv.org/abs/2505.21179 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Normalized Attention Guidance: Universal Negative Guidance for Diffusion Model
Negative guidance -- explicitly suppressing unwanted attributes -- remains a fundamental challenge in diffusion models, particularly in few-step sampling regimes. While Classifier-Free Guidance (CFG) works well in standard settings, it fails under aggressive sampling step compression due to divergent predictions between positive and negative branches. We present Normalized Attention Guidance (NAG), an efficient, training-free mechanism that applies extrapolation in attention space with L1-based…

Simple holograpic dual of the Maxwell-Cattaneo model
Yongjun Ahn, Matteo Baggioli, Yanyan Bu, Masataka Matsumoto, Xiyang Sun
https://arxiv.org/abs/2506.00926

Simple holograpic dual of the Maxwell-Cattaneo model
Diffusion, as described by Fick's laws, governs the spreading of particles, information, data, and even financial fluctuations. However, due to its parabolic structure, the diffusion equation leads to an unphysical prediction: any localized disturbance instantaneously affects the entire system. The Maxwell-Cattaneo (MC) model, originally introduced to address relativistic heat conduction, refines the standard diffusion framework by incorporating a finite relaxation time $τ$, associated with th…

Beyond water limitation in vegetation-autotoxicity patterning: a cross-diffusion model
Francesco Giannino, Annalisa Iuorio, Cinzia Soresina
https://arxiv.org/abs/2506.03981

Beyond water limitation in vegetation-autotoxicity patterning: a cross-diffusion model
Many mathematical models describing vegetation patterns are based on biomass--water interactions, due to the impact of this limited resource in arid and semi-arid environments. However, in recent years, a novel biological factor called autotoxicity has proved to play a key role in vegetation spatiotemporal dynamics, particularly by inhibiting biomass growth and increasing its natural mortality rate. In a standard reaction-diffusion framework, biomass-toxicity dynamics alone are unable to suppor…

MAMBO: High-Resolution Generative Approach for Mammography Images
Milica \v{S}kipina, Nikola Jovi\v{s}i\'c, Nicola Dall'Asen, Vanja \v{S}venda, Anil Osman Tur, Slobodan Ili\'c, Elisa Ricci, Dubravko \'Culibrk
https://arxiv.org/abs/2506.08677

MAMBO: High-Resolution Generative Approach for Mammography Images
Mammography is the gold standard for the detection and diagnosis of breast cancer. This procedure can be significantly enhanced with Artificial Intelligence (AI)-based software, which assists radiologists in identifying abnormalities. However, training AI systems requires large and diverse datasets, which are often difficult to obtain due to privacy and ethical constraints. To address this issue, the paper introduces MAMmography ensemBle mOdel (MAMBO), a novel patch-based diffusion approach des…

A theory for diffusion-controlled reactions within nonequilibrium steady-states
Seokjin Moon, David T. Limmer
https://arxiv.org/abs/2505.22623 https://

A theory for diffusion-controlled reactions within nonequilibrium steady-states
We study diffusion-controlled processes in nonequilibrium steady-states, where standard rate theory assumptions break down. Using transition path theory, we generalize the relations between reactive probability fluxes and measures of the rate of the reaction. Stochastic thermodynamics analysis reveals how work constrains the enhancement of rates relative to their equilibrium values. An analytically solvable ion-pairing model under a strong electric field illustrates and validates our approach a…

This https://arxiv.org/abs/2411.04178 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Breaking boundaries: extending the orbit-averaged Fokker-Planck equation inside the loss cone
In this Letter, we present a new formulation of loss cone theory as a reaction-diffusion system, which accounts for loss cone events through a sink term and can be orbit-averaged. It can recover the standard approach based on boundary conditions, and is derived from a simple physical model that overcomes many of the classical theoretical constraints. We test our formulation by computing the relaxed distribution of disruptive orbits in phase space, that has a simple analytic form and matches the…

This https://arxiv.org/abs/2505.22623 has been replaced.
initial toot: https://mastoxiv.page/@ar…

A theory for diffusion-controlled reactions within nonequilibrium steady-states
We study diffusion-controlled processes in nonequilibrium steady-states, where standard rate theory assumptions break down. Using transition path theory, we generalize the relations between reactive probability fluxes and measures of the rate of the reaction. Stochastic thermodynamics analysis reveals how work constrains the enhancement of rates relative to their equilibrium values. An analytically solvable ion-pairing model under a strong electric field illustrates and validates our approach a…