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

H
Help Net Security
宝玉的分享
宝玉的分享
The Cloudflare Blog
Apple Machine Learning Research
Apple Machine Learning Research
V
Visual Studio Blog
Last Week in AI
Last Week in AI
Hugging Face - Blog
Hugging Face - Blog
博客园 - 司徒正美
博客园 - 三生石上(FineUI控件)
A
About on SuperTechFans
MyScale Blog
MyScale Blog
aimingoo的专栏
aimingoo的专栏
Microsoft Security Blog
Microsoft Security Blog
D
Docker
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Recent Announcements
Recent Announcements
大猫的无限游戏
大猫的无限游戏
IT之家
IT之家
P
Proofpoint News Feed
L
LangChain Blog
Blog — PlanetScale
Blog — PlanetScale
The GitHub Blog
The GitHub Blog
博客园 - 【当耐特】
Martin Fowler
Martin Fowler

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
$d$-dimensional spherical ferromagnets in random fields: ...
Kalle Koskinen, Christof Külske · 2025-05-23 · via math.PR updates on arXiv.org

We study the large-volume behavior of the spherical model for $d$-dimensional local spins, in the presence of $d$-dimensional random fields, for $d\geq 2$. We compare two models, one with volume-scaled random fields, and another one with non-scaled random fields, on the level of Aizenman-Wehr metastates, Newman-Stein metastates, as well as overlap distributions. We show that in $d\geq 2$ the metastates are fully supported on a continuity of random product states, with weights which we describe, for both models. For the non-scaled random fields, the set of a.s. cluster points of Gibbs measures contains these product states, but behaves differently in the 'recurrent' spin dimension $d=2$ where it also contains non-trivial mixtures of tilted measures. For the scaled model, moreover the overlap distribution displays spin-glass characteristics, as it is non-self averaging, and shows replica symmetry breaking, although it is ultrametric if and only if $d=1$. For $d\geq 2$ it oscillates chaotically on a set of continuous distributions for large volumes, while the limiting set contains only discrete distributions in $d=1$. Our results are based on concentration estimates, analysis of Gibbs measures in finite but large volumes, and the asymptotics of $d$-dimensional random walks and their spherical projections.