惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Vercel News
Vercel News
博客园 - 司徒正美
C
Check Point Blog
G
Google Developers Blog
The GitHub Blog
The GitHub Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
有赞技术团队
有赞技术团队
P
Proofpoint News Feed
IT之家
IT之家
B
Blog
博客园_首页
量子位
MongoDB | Blog
MongoDB | Blog
博客园 - Franky
J
Java Code Geeks
H
Help Net Security
A
About on SuperTechFans
Apple Machine Learning Research
Apple Machine Learning Research
Jina AI
Jina AI
D
DataBreaches.Net
Y
Y Combinator Blog
大猫的无限游戏
大猫的无限游戏
云风的 BLOG
云风的 BLOG
Google DeepMind News
Google DeepMind News

math.PR updates on arXiv.org

Visibility in the Boolean Model on Harmonic Manifolds Global estimates on the Brenier map Geodesics and Wandering Exponents in Brochette First-Passage Percolation State-dependent inverse-subordinator time changes of regenerative processes: Excursion structure and multiscale occupation-time limits Randomly twisted transfer operators and singular values statistics Generalized Bessel-Dunkl diffusions An almost sure invariance principle for the Takagi-van der Waerden class functions Central limit theorems for high dimensional lattice polytopes: cosmological polytopes Convergence rate estimates for semigroups and heat kernels associated with resistance forms Second-order Poincaré inequalities and localization on the Poisson space Maximum Probability of Independence in Transitive Matroids On global solutions to the semidiscrete stochastic heat equation The Poisson Tail Conjecture for primes in short intervals A Complete Spectral Analysis of the CEV Operator with Applications to Arbitrage Holographic functions and neural networks From Betting to Empirical Bernstein LIL Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise Pointwise Generalization in Deep Neural Networks Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors Wasserstein bounds for denoising diffusion probabilistic models via the Föllmer process A note on connections between the Föllmer process and the denoising diffusion probabilistic model Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations A Fourier perspective on the learning dynamics of neural networks: from sample complexities to mechanistic insights Propagation of Chaos in Contextual Flow Maps Dimension-Uniform Discretization Analysis of Preconditioned Annealed Langevin Dynamics for Multimodal Gaussian Mixtures $α$-TCAV: A Unified Framework for Testing with Concept Activation Vectors Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model On the Limits of Latent Reuse in Diffusion Models State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
The cutoff profile for exclusion processes in any dimension
[Submitted on 7 Jun 2021 (v1), last revised 9 Sep 2026 (this ver · 2021-06-07 · via math.PR updates on arXiv.org

This paper has been withdrawn by Joe P. Chen

No PDF available, click to view other formats

Abstract:Consider symmetric simple exclusion processes, with or without Glauber dynamics on the boundary set, on a sequence of connected unweighted graphs $G_N=(V_N,E_N)$ which converge geometrically and spectrally to a compact connected metric measure space.
Under minimal assumptions, we prove not only that total variation cutoff occurs at times $t_N=\log|V_N|/(2\lambda^N_1)$, where $|V_N|$ is the cardinality of $V_N$, and $\lambda^N_1$ is the lowest nonzero eigenvalue of the nonnegative graph Laplacian; but also the limit profile for the total variation distance to stationarity. The assumptions are shown to hold on the $D$-dimensional Euclidean lattices for any $D\geq 1$, as well as on self-similar fractal spaces.
Our approach is decidedly analytic and does not use extensive coupling arguments. We identify a new observable in the exclusion process -- the cutoff semimartingales -- obtained by scaling and shifting the density fluctuation fields. Using the entropy method, we prove a functional CLT for the cutoff semimartingales converging to an infinite-dimensional Brownian motion, provided that the process is started from a deterministic configuration or from stationarity. This reduces the original problem to computing the total variation distance between the two versions of Brownian motions, which share the same covariance and whose initial conditions differ only in the coordinates corresponding to the first eigenprojection.

Submission history

From: Joe P. Chen [view email]
[v1] Mon, 7 Jun 2021 15:00:31 UTC (405 KB)
[v2] Wed, 9 Sep 2026 15:18:40 UTC (1 KB) (withdrawn)