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The antiferromagnetic Ising model samples subsets of vertices of a graph with weight decaying exponentially in the number of edges induced. We study the problem of sampling from this model on the class of bipartite, regular graphs with good…

Combinatorics · Mathematics 2026-03-03 Anna Geisler , Mihyun Kang , Michail Sarantis , Ronen Wdowinski

We find the exact critical temperature $T_c$ of the nearest-neighbor ferromagnetic Ising model on an `equilibrium' random graph with an arbitrary degree distribution $P(k)$. We observe an anomalous behavior of the magnetization, magnetic…

Statistical Mechanics · Physics 2016-08-31 S. N. Dorogovtsev , A. V. Goltsev , J. F. F. Mendes

The Ising model is of prime importance in the field of statistical mechanics. Here we show that Ising-type interactions can be realized in periodically-driven circuits of stochastic binary resistors with memory. A key feature of our…

Mesoscale and Nanoscale Physics · Physics 2023-10-03 V. J. Dowling , Y. V. Pershin

Learning causal relationships among a set of variables, as encoded by a directed acyclic graph, from observational data is complicated by the presence of unobserved confounders. Instrumental variables (IVs) are a popular remedy for this…

Methodology · Statistics 2025-04-17 Jing Zou , Wei Li , Wei Lin

Identifiability of discrete statistical models with latent variables is known to be challenging to study, yet crucial to a model's interpretability and reliability. This work presents a general algebraic technique to investigate…

Statistics Theory · Mathematics 2024-03-20 Yuqi Gu

Non-equilibrium systems lack an explicit characterisation of their steady state like the Boltzmann distribution for equilibrium systems. This has drastic consequences for the inference of parameters of a model when its dynamics lacks…

Statistical Mechanics · Physics 2016-11-15 Simon L. Dettmer , H. Chau Nguyen , Johannes Berg

In community detection on graphs, the semi-supervised learning problem entails inferring the ground-truth membership of each node in a graph, given the connectivity structure and a limited number of revealed node labels. Different subsets…

Disordered Systems and Neural Networks · Physics 2022-03-22 Hugo Cui , Luca Saglietti , Lenka Zdeborová

We revisit classical bounds of M. E. Fisher on the ferromagnetic Ising model, and show how to efficiently use them on an arbitrary given graph to rigorously upper-bound the partition function, magnetizations, and correlations. The results…

Disordered Systems and Neural Networks · Physics 2017-05-24 Alaa Saade , Florent Krzakala , Lenka Zdeborová

Properties of magnetic films are studied in the framework of Ising models. In particular, we discuss critical phenomena of ferromagnetic Ising films with straight lines of magnetic adatoms and straight steps on the surface as well as phase…

Condensed Matter · Physics 2009-11-07 W. Selke , M. Pleimling , I. Peschel , M. Kaulke , M. -C. Chung , D. Catrein

A two-dimensional Ising model with short-range interactions and mobile defects describing the formation and thermal destruction of defect stripes is studied. In particular, the effect of a local pinning of the defects at the sites of…

Condensed Matter · Physics 2007-05-23 M. Holtschneider , W. Selke

A relation between O(n) lattice spin models and Ising models defined on the same lattice was recently put forward [L. Casetti, C. Nardini, and R. Nerattini, Phys. Rev. Lett. 106, 057208 (2011)]. Such a relation, inspired by an energy…

Statistical Mechanics · Physics 2012-02-15 Cesare Nardini , Rachele Nerattini , Lapo Casetti

Many fMRI analyses examine functional connectivity, or statistical dependencies among remote brain regions. Yet popular methods for studying whole-brain functional connectivity often yield results that are difficult to interpret. Factor…

Methodology · Statistics 2024-09-24 Kyle Stanley , Nicole Lazar , Matthew Reimherr

We study the fixed-magnetization ferromagnetic Ising model on random $d$-regular graphs for $d\ge 3$ and inverse temperature below the tree reconstruction threshold. Our main result is that for each magnetization $\eta$, the free energy…

Probability · Mathematics 2025-11-21 Reza Gheissari , Will Perkins , Corrine Yap

Experimental advances in condensed matter physics and material science have enabled ready access to atomic-resolution images, with resolution of modern tools often sufficient to extract minute details of symmetry-breaking distortions such…

Statistical Mechanics · Physics 2019-09-23 Sai Mani Prudhvi Valleti , Lukas Vlcek , Rama K. Vasudevan , Sergei V. Kalinin

Studying the universe through radio telescope observation is crucial. However, radio telescopes capture not only signals from the universe but also various interfering signals, known as Radio Frequency Interference (RFI). The presence of…

Instrumentation and Methods for Astrophysics · Physics 2024-04-02 Fei Gu , Longfei Hao , Bo Liang , Song Feng , Shoulin Wei , Wei Dai , Yonghua Xu , Zhixuan Li , Yihang Dao

We consider the problem of reconstructing the graph underlying an Ising model from i.i.d. samples. Over the last fifteen years this problem has been of significant interest in the statistics, machine learning, and statistical physics…

Machine Learning · Computer Science 2014-12-02 Guy Bresler

The antiferromagnetic Ising model in uncorrelated scale-free networks has been studied by means of Monte Carlo simulations. These networks are characterized by a connectivity (or degree) distribution P(k) ~ k^(- gamma). The disorder present…

Disordered Systems and Neural Networks · Physics 2009-08-26 Carlos P. Herrero

A class of random graphs is introduced and studied. The graphs are constructed in an algorithmic way from five motifs which were found in [Milo R., Shen-Orr S., Itzkovitz S., Kashtan N., Chklovskii D., Alon U., Science, 2002, 298, 824-827].…

Mathematical Physics · Physics 2011-06-23 M. Kotorowicz , Yu. Kozitsky

A crucial assumption in most statistical learning theory is that samples are independently and identically distributed (i.i.d.). However, for many real applications, the i.i.d. assumption does not hold. We consider learning problems in…

Machine Learning · Computer Science 2019-09-10 Rui Ray Zhang , Xingwu Liu , Yuyi Wang , Liwei Wang

Efficient prediction of solar flares relies on parameters that quantify the eruptive capability of solar active regions. Several such quantitative predictors have been proposed in the literature, inferred mostly from photospheric…

Solar and Stellar Astrophysics · Physics 2018-07-18 Ioannis Kontogiannis , Manolis K. Georgoulis , Sung-Hong Park , Jordan A Guerra