Fully-connected bond percolation on Zd

被引:1
|
作者
Dereudre, David [1 ]
机构
[1] Univ Lille, CNRS, UMR 8524, Lab Paul Painleve, F-59000 Lille, France
关键词
FK-percolation; Random cluster model; Phase transition; FKG inequalities; DLR equations;
D O I
10.1007/s00440-021-01088-8
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
We consider the bond percolation model on the lattice Z(d) (d >= 2) with the constraint to be fully connected. Each edge is open with probability p is an element of (0, 1), closed with probability 1- p and then the process is conditioned to have a unique open connected component (bounded or unbounded). The model is defined on Z(d) by passing to the limit for a sequence of finite volume modelswith general boundary conditions. Several questions and problems are investigated: existence, uniqueness, phase transition, DLR equations. Our main result involves the existence of a threshold 0 < p* (d) < 1 such that any infinite volume model is necessary the vacuum state in subcritical regime (no open edges) and is non trivial in the supercritical regime (existence of a stationary unbounded connected cluster). Bounds for p* (d) are given and show that it is drastically smaller than the standard bond percolation threshold in Z(d). For instance 0.128 < p* (2) < 0.202 (rigorous bounds) whereas the 2D bond percolation threshold is equal to 1/2.
引用
收藏
页码:547 / 579
页数:33
相关论文
共 50 条
  • [21] Neural Window Fully-connected CRFs for Monocular Depth Estimation
    Yuan, Weihao
    Gu, Xiaodong
    Dai, Zuozhuo
    Zhu, Siyu
    Tan, Ping
    [J]. 2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022), 2022, : 3906 - 3915
  • [22] Compression of Fully-Connected Layer in Neural Network by Kronecker Product
    Wu, Jia-Nan
    [J]. 2016 EIGHTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI), 2016, : 173 - 179
  • [23] Bifurcations in time-delay fully-connected networks with symmetry
    Ferruzzo Correa, Diego Paolo
    Castilho Piqueira, Jose Roberto
    [J]. CSNDD 2014 - INTERNATIONAL CONFERENCE ON STRUCTURAL NONLINEAR DYNAMICS AND DIAGNOSIS, 2014, 16
  • [24] EQUIVALENCE OF APPROXIMATION BY CONVOLUTIONAL NEURAL NETWORKS AND FULLY-CONNECTED NETWORKS
    Petersen, Philipp
    Voigtlaender, Felix
    [J]. PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY, 2020, 148 (04) : 1567 - 1581
  • [25] A Scalable Annealing Processing Architecture for Fully-Connected Ising Models
    Jiang, Dong
    Wang, Xiangrui
    Huang, Zhanhong
    Huang, Yukang
    Yao, Enyi
    [J]. 2023 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, ISCAS, 2023,
  • [26] Fully-connected LSTM-CRF on medical concept extraction
    Ji, Jie
    Chen, Bairui
    Jiang, Hongcheng
    [J]. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2020, 11 (09) : 1971 - 1979
  • [27] Distributed Trace Ratio Optimization in Fully-Connected Sensor Networks
    Musluoglu, Cem Ates
    Bertrand, Alexander
    [J]. 28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020), 2021, : 1991 - 1995
  • [28] Generalization in fully-connected neural networks for time series forecasting
    Borovykh, Anastasia
    Oosterlee, Cornelis W.
    Bohte, Sander M.
    [J]. JOURNAL OF COMPUTATIONAL SCIENCE, 2019, 36
  • [29] Fully-Connected CRFs with Non-Parametric Pairwise Potentials
    Campbell, Neill D. F.
    Subr, Kartic
    Kautz, Jan
    [J]. 2013 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2013, : 1658 - 1665
  • [30] Overfitting remedy by sparsifying regularization on fully-connected layers of CNNs
    Xu, Qi
    Zhang, Ming
    Gu, Zonghua
    Pan, Gang
    [J]. NEUROCOMPUTING, 2019, 328 : 69 - 74