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

U
Unit 42
Blog — PlanetScale
Blog — PlanetScale
H
Help Net Security
The GitHub Blog
The GitHub Blog
博客园 - Franky
酷 壳 – CoolShell
酷 壳 – CoolShell
Recent Announcements
Recent Announcements
量子位
aimingoo的专栏
aimingoo的专栏
大猫的无限游戏
大猫的无限游戏
博客园 - 叶小钗
Microsoft Azure Blog
Microsoft Azure Blog
Martin Fowler
Martin Fowler
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
A
About on SuperTechFans
T
Tailwind CSS Blog
V
V2EX
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
B
Blog
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
S
SegmentFault 最新的问题
G
Google Developers Blog
M
MIT News - Artificial intelligence

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
Compactness of semigroups generated by symmetric non-loca...
Yuichi Shiozawa, Jian Wang · 2020-05-12 · via math.PR updates on arXiv.org

Let $(\E,\F)$ be a symmetric non-local Dirichlet from with unbounded coefficient on $L^2(\R^d;\d x)$ defined by $$\E(f,g)=\iint_{\R^d\times \R^d} (f(y)-f(x))(g(x)-g(y)){W(x,y)}\, J(x,\d y)\,\d x, \quad f,g\in \F,$$ where $J(x,\d y)$ is regarded as the jumping kernel for a pure-jump symmetric Lévy-type process with bounded coefficients, and $W(x,y)$ is seen as a weighted (unbounded) function. We establish sharp criteria for compactness and non-compactness of the associated Markovian semigroup $(P_t)_{t\ge0}$ on $L^2(\R^d;\d x)$. In particular, we prove that if $J(x,\d y)=|x-y|^{-d-α}\,\d y$ with $α\in (0,2)$, and $$W(x,y)= \begin{cases} (1+|x|)^p+(1+|y|)^p, \ & |x-y|< 1 \\ (1+|x|)^q+(1+|y|)^q, \ & |x-y|\geq 1 \end{cases}$$ with $p\in [0,\infty)$ and $q\in [0,α)$, then $(P_t)_{t\ge0}$ is compact, if and only if $p>2$. This indicates that the compactness of $(\E,\F)$ heavily depends on the growth of the weighted function $W(x,y)$ only for $|x-y|<1$. Our approach is based on establishing the essential super Poincaré inequality for $(\E,\F)$. Our general results work even if the jumping kernel $J(x,\d y)$ is degenerate or is singular with respect to the Lebesgue measure.