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

D
DataBreaches.Net
Y
Y Combinator Blog
I
InfoQ
Recent Announcements
Recent Announcements
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
博客园 - Franky
IT之家
IT之家
H
Help Net Security
月光博客
月光博客
S
SegmentFault 最新的问题
B
Blog
aimingoo的专栏
aimingoo的专栏
GbyAI
GbyAI
P
Proofpoint News Feed
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
G
Google Developers Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
U
Unit 42
Vercel News
Vercel News
博客园 - 叶小钗
Microsoft Security Blog
Microsoft Security Blog
腾讯CDC
Jina AI
Jina AI
T
The Blog of Author Tim Ferriss

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
Anatomy of a gaussian giant: supercritical level-sets of ...
Guillaume Conchon--Kerjan · 2021-02-22 · via math.PR updates on arXiv.org

In this paper, we study the level-set of the zero-average Gaussian Free Field on a uniform random $d$-regular graph above an arbitrary level $h\in (-\infty, h_{\star})$, where $h_{\star}$ is the level-set percolation threshold of the GFF on the $d$-regular tree $\mathbb{T}_d$. We prove that w.h.p as the number $n$ of vertices diverges, the GFF has a unique giant connected component $\mathcal{C}_1^{(n)}$ of size $η(h) n+o(n)$, where $η(h)$ is the probability that the root percolates in the corresponding GFF level-set on $\mathbb{T}_d$. This gives a positive answer to the conjecture of \cite{ACregulgraphs} for most regular graphs. We also prove that the second largest component has size $Θ(\log n)$. Moreover, we show that $\mathcal{C}_1^{(n)}$ shares the following similarities with the giant component of the supercritical Erdős-Rényi random graph. First, the diameter and the typical distance between vertices are $Θ(\log n)$. Second, the $2$-core and the kernel encompass a given positive proportion of the vertices. Third, the local structure is a branching process conditioned to survive, namely the level-set percolation cluster of the root in $\mathbb{T}_d$ (in the Erdős-Rényi case, it is known to be a Galton-Watson tree with a Poisson distribution for the offspring).