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

WordPress大学
WordPress大学
J
Java Code Geeks
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
腾讯CDC
IT之家
IT之家
罗磊的独立博客
酷 壳 – CoolShell
酷 壳 – CoolShell
U
Unit 42
爱范儿
爱范儿
博客园 - 聂微东
F
Fortinet All Blogs
V
Visual Studio Blog
Blog — PlanetScale
Blog — PlanetScale
G
Google Developers Blog
aimingoo的专栏
aimingoo的专栏
L
LangChain Blog
雷峰网
雷峰网
B
Blog RSS Feed
宝玉的分享
宝玉的分享
T
Tailwind CSS Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Engineering at Meta
Engineering at Meta
H
Hackread – Cybersecurity News, Data Breaches, AI and More

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
Solid-On-Solid interfaces with disordered pinning
Hubert Lacoin · 2020-03-02 · via math.PR updates on arXiv.org

We investigate the localization transition for a simple model of interface which interacts with an inhomonegeous defect plane. The interface is modeled by the graph of a function $φ: \mathbb Z^2 \to \mathbb Z$,and the disorder is given by a fixed realization of a field of IID centered random variables$(ω_x)_{x\in \mathbb Z^2}$. The Hamiltonian of the system depends on three parameters $α,β>0$ and $h\in \mathbb R$ which determine respectively the intensity of nearest neighbor interaction the amplitude of disorder and the mean value of the interaction with the substrate, and is given by the expression $$\mathcal H(φ):= β\sum_{x\sim y} |φ(x)-φ(y)|- \sum_{x} (αω_x+h){\bf 1}_{\{φ(x)=0\}}.$$ We focus on the large-$β$/rigid phase phase of the Solid-On-Solid (SOS) model. In that regime, we provide a sharp description of the phase transition in $h$ from a localized phase to a delocalized one corresponding respectivelly to a positive and vanishing fraction of points with $φ(x)=0$. We prove that the critical value for $h$ corresponds to that of the annealed model and is given by $h_c(α)= -\log \mathbb E[e^{αω}]$, and that near the critical point, the free energy displays the following critical behavior $$F_β(α,h_c+u )\stackrel{u\to 0+}{\sim} \max_{n\ge 1} \left\{θ_1 e^{-4βn} u- \frac{1}{2}θ^2_1 e^{-8βn} \frac{\mathrm{Var}\left[e^{αω}\right]}{\mathbb E \left[ e^{αω} \right]^2}\right\}.$$ The positive constant $θ_1(β)>0$ is defined by the asymptotic probability of spikes for the infinite volume SOS with $0$ boundary condition $θ_1(β):=\lim_{n\to \infty} e^{4βn}\mathbf P_β (φ({\bf 0})=n)$ ...