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

L
LangChain Blog
博客园_首页
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
月光博客
月光博客
S
SegmentFault 最新的问题
量子位
Apple Machine Learning Research
Apple Machine Learning Research
博客园 - 司徒正美
博客园 - Franky
Google DeepMind News
Google DeepMind News
Recent Announcements
Recent Announcements
B
Blog RSS Feed
C
Check Point Blog
The Cloudflare Blog
M
MIT News - Artificial intelligence
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
F
Fortinet All Blogs
Hugging Face - Blog
Hugging Face - Blog
博客园 - 叶小钗
V
Visual Studio Blog
V
V2EX
Microsoft Azure Blog
Microsoft Azure Blog
博客园 - 聂微东
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & 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
Pinning of a renewal on a quenched renewal
Kenneth S. Alexander, Quentin Berger · 2016-08-11 · via math.PR updates on arXiv.org

We introduce the pinning model on a quenched renewal, which is an instance of a (strongly correlated) disordered pinning model. The potential takes value 1 at the renewal times of a quenched realization of a renewal process $σ$, and $0$ elsewhere, so nonzero potential values become sparse if the gaps in $σ$ have infinite mean. The "polymer" -- of length $σ_N$ -- is given by another renewal $τ$, whose law is modified by the Boltzmann weight $\exp(β\sum_{n=1}^N \mathbf{1}_{\{σ_n\inτ\}})$. Our assumption is that $τ$ and $σ$ have gap distributions with power-law-decay exponents $1+α$ and $1+\tilde α$ respectively, with $α\geq 0,\tilde α>0$. There is a localization phase transition: above a critical value $β_c$ the free energy is positive, meaning that $τ$ is \emph{pinned} on the quenched renewal $σ$. We consider the question of relevance of the disorder, that is to know when $β_c$ differs from its annealed counterpart $β_c^{\rm ann}$. We show that $β_c=β_c^{\rm ann}$ whenever $ α+\tilde α\geq 1$, and $β_c=0$ if and only if the renewal $τ\capσ$ is recurrent. On the other hand, we show $β_c>β_c^{\rm ann}$ when $ α+\frac32\, \tilde α<1$. We give evidence that this should in fact be true whenever $ α+\tilde α<1$, providing examples for all such $ α,\tilde α$ of distributions of $τ,σ$ for which $β_c>β_c^{\rm ann}$. We additionally consider two natural variants of the model: one in which the polymer and disorder are constrained to have equal numbers of renewals ($σ_N=τ_N$), and one in which the polymer length is $τ_N$ rather than $σ_N$. In both cases we show the critical point is the same as in the original model, at least when $ α>0$.