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The one-dimensional totally asymmetric simple exclusion process (TASEP), a Markov process describing classical hard-core particles hopping in the same direction, is considered on a periodic lattice of $L$ sites. The relaxation to the…

Statistical Mechanics · Physics 2016-03-09 Sylvain Prolhac

The totally asymmetric simple exclusion process in discrete time is considered on finite rings with fixed number of particles. A translation-invariant version of the backward-ordered sequential update is defined for periodic boundary…

Soft Condensed Matter · Physics 2007-05-23 J. G. Brankov , Vl. V. Papoyan , V. S. Poghosyan , V. B. Priezzhev

Independent sets in graphs are sets of vertices containing no neighbors, and they represent a canonical spin system with hardcore constraints. Of particular interest is the setting of the boolean hypercube, where counting independent sets…

Probability · Mathematics 2025-11-11 Mriganka Basu Roy Chowdhury , Shirshendu Ganguly , Vilas Winstein

We prove a pointwise ergodic theorem for quasi-probability-measure-preserving (quasi-pmp) locally countable measurable graphs, equivalently, Schreier graphs of quasi-pmp actions of countable groups. For ergodic graphs, the theorem gives an…

Dynamical Systems · Mathematics 2023-08-29 Anush Tserunyan

We study a class of graphs that represent local independence structures in stochastic processes allowing for correlated error processes. Several graphs may encode the same local independencies and we characterize such equivalence classes of…

Statistics Theory · Mathematics 2022-09-01 Søren Wengel Mogensen , Niels Richard Hansen

We show that unitary representations of simply connected, semisimple algebraic groups over local fields of characteristic zero obey a spectral gap absorption principle: that is, that spectral gap is preserved under tensor products. We do…

Group Theory · Mathematics 2025-04-11 Yuval Gorfine

Generating graph-structured data is crucial in applications such as molecular generation, knowledge graphs, and network analysis. However, their discrete, unordered nature makes them difficult for traditional generative models, leading to…

Machine Learning · Computer Science 2026-04-14 Ole Petersen , Marcel Kollovieh , Marten Lienen , Stephan Günnemann

Neural network design has utilized flexible nonlinear processes which can mimic biological systems, but has suffered from a lack of traceability in the resulting network. Graphical probabilistic models ground network design in probabilistic…

Machine Learning · Computer Science 2015-06-19 Kenric P. Nelson , Madalina Barbu , Brian J. Scannell

We derive the moderate deviation principles for the fluctuation fields of the facilitated exclusion process (FEP) in one dimension when the process starts from its stationary measure, both in the symmetric and asymmetric cases. The main…

Probability · Mathematics 2025-05-05 Linjie Zhao

Under natural assumptions a Feller type diffusion approximation is derived for critical multi-type branching processes with immigration when the offspring mean matrix is primitive (in other words, positively regular). Namely, it is proved…

Probability · Mathematics 2012-05-03 Márton Ispány , Gyula Pap

The symmetric simple exclusion process (SSEP) is a paradigmatic model of classical non-equilibrium dynamics. Exact results for large deviations of particle current in the SSEP have been obtained in various settings using integrability-based…

Statistical Mechanics · Physics 2026-05-26 Soumyabrata Saha , Sandeep Jangid , Kapil Sharma , Tridib Sadhu

This paper is devoted to the study of a new class of random evolutions (RE), so-called self-exciting random evolutions (SEREs), and their applications. We also introduce a new random process $x(t)$ such that it is based on a superposition…

Probability · Mathematics 2025-04-07 Anatoliy Swishchuk

Semantic segmentation of microscopy images is a critical task for high-throughput materials characterisation, yet its automation is severely constrained by the prohibitive cost, subjectivity, and scarcity of expert-annotated data. While…

Computer Vision and Pattern Recognition · Computer Science 2026-02-03 Salma Zahran , Zhou Ao , Zhengyang Zhang , Chen Chi , Chenchen Yuan , Yanming Wang

The asymmetric simple exclusion process (ASEP) is a paradigmatic nonequilibrium many-body system that describes the asymmetric random walk of particles with exclusion interactions in a lattice. Although the ASEP is recognized as an exactly…

Statistical Mechanics · Physics 2024-03-05 Yuki Ishiguro , Jun Sato

The Totally Asymmetric Simple Exclusion Process (TASEP) is a paradigm of out-of-equilibrium Statistical Physics that serves as a simplistic model for one-way vehicular traffic. Since traffic is perturbed by cars cruising for parking in many…

Statistical Mechanics · Physics 2023-09-25 Valentin Anfray , Alexandre Nicolas

Normalizing flows model complex probability distributions by combining a base distribution with a series of bijective neural networks. State-of-the-art architectures rely on coupling and autoregressive transformations to lift up invertible…

Machine Learning · Computer Science 2021-02-15 Antoine Wehenkel , Gilles Louppe

We consider exploration algorithms of the random sequential adsorption type both for homogeneous random graphs and random geometric graphs based on spatial Poisson processes. At each step, a vertex of the graph becomes active and its…

Probability · Mathematics 2017-11-22 Paola Bermolen , Matthieu Jonckheere , Jaron Sanders

We consider a one-dimensional symmetric Levy process that has local time. In the first part, we construct a self-adjoint extension of the generator of the process so that the constructed operator corresponds to the generator with the delta…

Probability · Mathematics 2025-01-13 Temirlan Abildaev

It is shown explicitly how self-similar graphs can be obtained as `blow-up' constructions of finite cell graphs $\hat C$. This yields a larger family of graphs than the graphs obtained by discretising continuous self-similar fractals. For a…

Combinatorics · Mathematics 2007-05-23 Bernhard Krön , Elmar Teufl

Entropic causal inference is a recent framework for learning the causal graph between two variables from observational data by finding the information-theoretically simplest structural explanation of the data, i.e., the model with smallest…

Machine Learning · Computer Science 2025-09-23 Spencer Compton , Kristjan Greenewald , Dmitriy Katz , Murat Kocaoglu