Light-cone-like propagation of information is a universal phenomenon of nonequilibrium dynamics of integrable spin systems. In this paper, we investigate propagation of a local impact in the one-dimensional XY model with the anisotropy γ in a magnetic field h by calculating the magnetization profile. Applying a local and instantaneous unitary operation to the ground state, which we refer to as the

We investigate the stochastic behavior of a non-Markovian version of an elementary Brownian gyrator. The model is defined by overdamped Langevin-like dynamics with a two-dimensional harmonic potential that presents distinct principal axes and is coupled to heat baths at different temperatures. The thermal noises are assumed to be Gaussian, and are related to friction forces through a dissipation memory

We substantially extend our relaxation theory for perturbed many-body quantum systems from ((2020) Phys. Rev. Lett. 124 120602) by establishing an analytical prediction for the time-dependent observable expectation values which depends on only two characteristic parameters of the perturbation operator: its overall strength and its range or band width. Compared to the previous theory, a significantly

We discuss the dependence of the phase diagram of a hypothetical isotope of helium with nuclear mass less than 4 atomic mass units. We argue that with decreasing nucleus mass, the temperature of the superfluid phase transition (about 2.2 K in real 4 He) increases, while that of the liquid–gas critical point (about 5.2 K in real 4 He) decreases. We discuss various scenarios that may occur when the two

In this work we study asymptotic properties of a long range memory random walk known as elephant random walk. First we prove recurrence and positive recurrence for the elephant random walk. Then, we establish the transience regime of the model. Finally, under the Poisson hypothesis, we study the replica mean field limit for this random walk and we obtain an upper bound for the expected distance of

We consider an active Brownian particle in a d -dimensional harmonic trap, in the presence of translational diffusion. While the Fokker–Planck equation cannot in general be solved to obtain a closed form solution of the joint distribution of positions and orientations, as we show, it can be utilized to evaluate the exact time dependence of all moments, using a Laplace transform approach. We present

We combine the processes of resetting and first passage, resulting in first-passage resetting , where the resetting of a random walk to a fixed position is triggered by the first-passage event of the walk itself. In an infinite domain, first-passage resetting of isotropic diffusion is non-stationary, and the number of resetting events grows with time according to ##IMG## [http://ej.iop.org/images/

In this work we calculate the entanglement entropy of certain excited states of the quantum Lifshitz model (QLM). The QLM is a 2 + 1-dimensional bosonic quantum field theory with an anisotropic scaling symmetry between space and time that belongs to the universality class of the quantum dimer model and its generalizations. The states we consider are constructed by exciting the eigenmodes of the Laplace–Beltrami

While several tools have been developed to study the ground state of many-body quantum spin systems, the limitations of existing techniques call for the exploration of new approaches. In this manuscript we develop an alternative analytical and numerical framework for many-body quantum spin ground states, based on the disentanglement formalism. In this approach, observables are exactly expressed as

In this article, we investigate a multispecies generalization of the single-species asymmetric simple exclusion process defined on an open one-dimensional lattice. We devise an exact projection scheme to find the phase diagram in terms of densities and currents of all species. In most of the phases, one or more species are absent in the system due to dynamical expulsion. We observe shocks as well in

We analyze the nonequilibrium athermal random field Ising model (RFIM) at equilateral cubic lattices of finite size L and show that the entire range of disorder consists of three distinct domains in which the model manifests different scaling behaviour. The first domain contains the values of disorder R that are below the critical disorder R c where the spanning avalanches almost surely appear when

We study a translational invariant free fermions model in imaginary time, with nearest neighbor and next-nearest neighbor hopping terms, for a class of inhomogeneous boundary conditions. This model is known to give rise to limit shapes and arctic curves, in the absence of the next-nearest neighbor perturbation. The perturbation considered turns out to not be always positive, that is, the corresponding

Relying on recent advances in statistical estimation of covariance distances based on random matrix theory, this article proposes an improved covariance and precision matrix estimation method for a wide family of metrics. This method is shown to largely outperform the sample covariance matrix estimate and to compete with state-of-the-art methods, while at the same time being computationally simpler

The Fisher information matrix (FIM) is a fundamental quantity to represent the characteristics of a stochastic model, including deep neural networks (DNNs). The present study reveals novel statistics of FIM that are universal among a wide class of DNNs. To this end, we use random weights and large width limits, which enables us to utilize mean field theories. We investigate the asymptotic statistics

Graphical models represent multivariate and generally not normalized probability distributions. Computing the normalization factor, called the partition function, is the main inference challenge relevant to multiple statistical and optimization applications. The problem is #P-hard that is of an exponential complexity with respect to the number of variables. In this manuscript, aimed at approximating

In generalized linear estimation (GLE) problems, we seek to estimate a signal that is observed through a linear transform followed by a component-wise, possibly nonlinear and noisy, channel. In the Bayesian optimal setting, generalized approximate message passing (GAMP) is known to achieve optimal performance for GLE. However, its performance can significantly degrade whenever there is a mismatch between

We analyze belief propagation on patch potential models—attractive models with varying local potentials—obtain all of the potentially many fixed points, and gather novel insights into belief propagation properties. In particular, we observe and theoretically explain several regions in the parameter space that behave fundamentally differently. We specify and elaborate on one specific region that, despite

A longstanding goal in deep learning research has been to precisely characterize training and generalization. However, the often complex loss landscapes of neural networks (NNs) have made a theory of learning dynamics elusive. In this work, we show that for wide NNs the learning dynamics simplify considerably and that, in the infinite width limit, they are governed by a linear model obtained from the

The plateau phenomenon, wherein the loss value stops decreasing during the process of learning, has been reported by various researchers. The phenomenon was actively inspected in the 1990s and found to be due to the fundamental hierarchical structure of neural network models. Then, the phenomenon has been thought of as inevitable. However, the phenomenon seldom occurs in the context of recent deep

Deep neural networks achieve stellar generalisation even when they have enough parameters to easily fit all their training data. We study this phenomenon by analysing the dynamics and the performance of over-parameterised two-layer neural networks in the teacher–student setup, where one network, the student, is trained on data generated by another network, called the teacher. We show how the dynamics

Arguably, the two most popular accelerated or momentum-based optimization methods in machine learning are Nesterov’s accelerated gradient and Polyaks’s heavy ball, both corresponding to different discretizations of a particular second order differential equation with friction. Such connections with continuous-time dynamical systems have been instrumental in demystifying acceleration phenomena in optimization

We analyze the connection between minimizers with good generalizing properties and high local entropy regions of a threshold-linear classifier in Gaussian mixtures with the mean squared error loss function. We show that there exist configurations that achieve the Bayes-optimal generalization error, even in the case of unbalanced clusters. We explore analytically the error-counting loss landscape in

How many training data are needed to learn a supervised task? It is often observed that the generalization error decreases as n − β where n is the number of training examples and β is an exponent that depends on both data and algorithm. In this work we measure β when applying kernel methods to real datasets. For MNIST we find β ≈ 0.4 and for CIFAR10 β ≈ 0.1, for both regression and classification tasks

We study the problem of fitting an ultrametric distance to a dissimilarity graph in the context of hierarchical cluster analysis. Standard hierarchical clustering methods are specified procedurally, rather than in terms of the cost function to be optimized. We aim to overcome this limitation by presenting a general optimization framework for ultrametric fitting. Our approach consists of modeling the

We present a new family of zero-field Ising models over N binary variables/spins obtained by consecutive ‘gluing’ of planar and O (1)-sized components and subsets of at most three vertices into a tree. The polynomial time algorithm of the dynamic programming type for solving exact inference (computing partition function) and exact sampling (generating i.i.d. samples) consists of sequential application

Recent work by Cerasoli et al (2018 Phys. Rev. E 98 042149) on a two-dimensional model of biased Brownian gyrators driven in part by temperature differences along distinct Cartesian axes, x and y , has revealed interesting asymmetries in the steady-state distribution of particle positions. These asymmetries are said to be reminiscent of the more conventional asymmetries associated with the fluctuation

The spherical p -spin is a fundamental model for glassy physics, thanks to its analytical solution achievable via the replica method. Unfortunately, the replica method has some drawbacks: it is very hard to apply to diluted models and the assumptions beyond it are not immediately clear. Both drawbacks can be overcome by the use of the cavity method; however, this needs to be applied with care to spherical

Two distinct limits for deep learning have been derived as the network width h → ∞, depending on how the weights of the last layer scale with h . In the neural tangent Kernel (NTK) limit, the dynamics becomes linear in the weights and is described by a frozen kernel Θ (the NTK). By contrast, in the mean-field limit, the dynamics can be expressed in terms of the distribution of the parameters associated

Random deposition with a relaxation model in ( u , v ) flower networks is studied. In a (2, 2) flower network, the surface width W ( t , N ) was found to grow as b ln t in the early period and follows a ln N in the saturated regime, where t and N are the evolution time and the number of nodes in the network, respectively. The dynamic exponent z , obtained by the relation z = a / b , was z ≈ 2.11(10)

We explore a variant of the Katz–Lebowitz–Spohn (KLS) driven lattice gas in two dimensions, where the lattice is split into two regions that are coupled to heat baths with distinct temperatures. The geometry was arranged such that the temperature boundaries are oriented parallel to the external particle drive and resulting net current. We have explored the changes in the dynamical behavior that are

We study the effect of stochastic resetting on a run-and-tumble particle (RTP) in two spatial dimensions. We consider a resetting protocol which affects both the position and orientation of the RTP: the particle undergoes constant-rate positional resetting to a fixed point in space and a random orientation. We compute the radial and x -marginal stationary-state distributions and show that while the

General diffusion processes involve one or more diffusing species and are usually modelled by Fick’s law, which assumes infinite propagation velocity. In this article, searching for the effect of finite propagation speeds in a system with two reacting species, we investigate diffusing and reacting particles governed by a hyperbolic diffusion equation, that is, the Cattaneo equation, which describes

We analyze the properties of degree-ordered percolation (DOP), a model in which the nodes of a network are occupied in degree-descending order. This rule is the opposite of the much studied degree-ascending protocol, used to investigate resilience of networks under intentional attack, and has received limited attention so far. The interest in DOP is also motivated by its connection with the suscep

In this paper, we formulate a q -deformed many-body theory for relativistic Fermi gas and discuss the effects of the deformation parameter q on physical properties of such systems. Since antiparticle excitations appear in the relativistic regime, a suitable treatment to the choice of deformation parameters for both fermions and antifermions must be carefully taken in order to get a consistent theory

Multiplex networks have been proposed as an effective abstract of real complex systems, ranging from multi-modal urban transportation systems to communication systems. In this paper, we investigate a traffic-driven epidemic model in multiplex networks, and derive a theoretical approach to accurately predict the epidemic threshold of each layer. Our results show that the multiplex structure can produce

Motivated by the complex processes of cellular transport when different types of biological molecular motors can move in opposite directions along protein filaments while also detaching from them, we developed a theoretical model of the bidirectional motion of driven particles. It utilizes a totally asymmetric simple exclusion process framework to analyze the dynamics of particles moving in opposite

We study the non-equilibrium steady states (NESS) and first passage properties of a Brownian particle with position X subject to an external confining potential of the form V ( X )= μ | X |, and that is switched on and off stochastically. Applying the potential intermittently generates a physically realistic diffusion process with stochastic resetting toward the origin, a topic which has recently attracted

The Gillis model, introduced more than 60 years ago, is a non-homogeneous random walk with a position-dependent drift. Though parsimoniously cited both in physical and mathematical literature, it provides one of the very few examples of a stochastic system allowing for a number of exact results, although lacking translational invariance. We present old and novel results for this model, which moreover

We consider the persistent exclusion process in which a set of persistent random walkers interact via hard-core exclusion on a hypercubic lattice in d dimensions. We work within the ballistic regime whereby particles continue to hop in the same direction over many lattice sites before reorienting. In the case of two particles, we find the mean first-passage time to a jammed state where the particles

Area fluctuations of a Brownian excursion are described by the Airy distribution, which has found applications in different areas of physics, mathematics and computer science. Here we generalize this distribution to describe the area fluctuations on a subinterval of a Brownian excursion. In the first version of the problem (model 1) no additional conditions are imposed. In the second version (model

Multivariate fluctuation relations are established in several stochastic models of transistors, which are electronic devices with three ports and thus two coupled currents. For all these models, the transport properties are shown to satisfy Onsager’s reciprocal relations in the linear regime close to equilibrium as well as their generalizations holding in the nonlinear regimes farther away from equilibrium

Large-margin classifiers are popular methods for classification. We derive the asymptotic expression for the generalization error of a family of large-margin classifiers in the limit of both sample size n and dimension p going to ∞ with fixed ratio α = n / p . This family covers a broad range of commonly used classifiers including support vector machine, distance weighted discrimination, and penalized

We define a message-passing algorithm for computing magnetizations in restricted Boltzmann machines, which are Ising models on bipartite graphs introduced as neural network models for probability distributions over spin configurations. To model nontrivial statistical dependencies between the spins’ couplings, we assume that the rectangular coupling matrix is drawn from an arbitrary bi-rotation invariant

We investigate the clustering transition undergone by an exemplary random constraint satisfaction problem, the bicoloring of k -uniform random hypergraphs, when its solutions are weighted non-uniformly, with a soft interaction between variables belonging to distinct hyperedges. We show that the threshold α d ( k ) for the transition can be further increased with respect to a restricted interaction

Universal spectral properties of multiplex networks allow us to assess the nature of the transition between disease-free and endemic phases in the SIS epidemic spreading model. In a multiplex network, depending on a coupling parameter, p , the inverse participation ratio (IPR) of the leading eigenvector of the adjacency matrix can be in two different structural regimes: (i) layer-localized and (ii)

Traffic systems are complex systems that exhibit non-stationary characteristics. Therefore, the identification of temporary traffic states is significant. In contrast to the usual correlations of time series, here we study those of position series, revealing structures in time, i.e. the rich non-Markovian features of traffic. Considering the traffic system of the Cologne orbital motorway as a whole

Generalized mode-coupling theory (GMCT) is a first-principles-based and systematically correctable framework to predict the complex relaxation dynamics of glass-forming materials. The formal theory amounts to a hierarchy of infinitely many coupled integro-differential equations, which may be approximated using a suitable finite-order closure relation. Although previous studies have suggested that finite-order

Disordered stealthy many-particle systems in d -dimensional Euclidean space ##IMG## [http://ej.iop.org/images/1742-5468/2020/10/103302/jstatabb8cbieqn1.gif] {${\mathbb{R}}^{d}$} are exotic amorphous states of matter that suppress any single scattering events for a finite range of wavenumbers around the origin in reciprocal space. They are currently the subject of intense fundamental and practical interest

This paper investigates the spatiotemporal dynamics of a Monod–Haldane type predator–prey interaction system that incorporates: (1) a time delay in the predator response term in the predator equation; and (2) diffusion in both prey and predator. We provide rigorous results of our system including the asymptotic stability of equilibrium solutions and the existence and properties of Hopf bifurcations

The measured correlations of financial time series in subsequent epochs change considerably as a function of time. When studying the whole correlation matrices, quasi-stationary patterns, referred to as market states, are seen by applying clustering methods. They emerge, disappear or reemerge, but they are dominated by the collective motion of all stocks. In the jargon, one speaks of the market motion

Network embedding has attracted considerable attention in recent years. It represents nodes in a network into a low-dimensional vector space while keeping the properties of the network. Some methods (e.g. ComE, MNMF, and CARE) have been proposed to preserve the community property in network embedding, and they have obtained good results in some downstream network analysis tasks. However, there still

We investigate the statistics of encounters of a diffusing particle with different subsets of the boundary of a confining domain. The encounters with each subset are characterized by the boundary local time on that subset. We extend a recently proposed approach to express the joint probability density of the particle position and of its multiple boundary local times via a multi-dimensional Laplace

A unified set of hydrodynamic equations describing condensed phases of matter with broken continuous symmetries is derived using a generalization of the statistical-mechanical approach based on the local equilibrium distribution. The dissipativeless and dissipative parts of the current densities and the entropy production are systematically deduced in this approach by expanding in powers of the gradients

The Maxwell–Boltzmann statistics of the quantum ideal gas is studied through the canonical partition function by exactly counting discrete quantum states without the continuum approximation. Analytic expressions for energy, pressure, entropy, and heat capacity are expressed in terms of Jacobi theta functions and complete elliptic integrals. The results show typical effects of discrete energy levels

Perturbed lattices provide simple models for studying many physical systems. In this paper we study the distribution of Voronoi chains, blocks, and clusters with prescribed combinatorial features in the perturbed square lattice, generalizing earlier work. In particular, we obtain analytic results for the presence of hexagonally-ordered regions within a square-ordered phase. Connections to high-temperature

In a recent paper a toy model ( hypercubic model ) undergoing a first-order ##IMG## [http://ej.iop.org/images/1742-5468/2020/10/103202/jstatabb6e0ieqn2.gif] {${\mathbb{Z}}_{2}$} -symmetry-breaking phase transition ( ##IMG## [http://ej.iop.org/images/1742-5468/2020/10/103202/jstatabb6e0ieqn3.gif] {${\mathbb{Z}}_{2}$} -SBPT) was introduced. The hypercubic model was inspired by the topological hypothesis

We study the entanglement Hamiltonian for finite intervals in infinite quantum chains for two different free-particle systems: coupled harmonic oscillators and fermionic hopping models with dimerization. Working in the ground state, the entanglement Hamiltonian describes again free bosons or fermions and is obtained from the correlation functions via high-precision numerics for up to several hundred

The study of records in the linear drift model (LDM) has attracted much attention recently due to applications in several fields. In the present paper we study δ -records in the LDM, defined as observations which are greater than all previous observations, plus a fixed real quantity δ . We give analytical properties of the probability of δ -records and study the correlation between δ -record events

The Casimir scaling function characterising the ground-state energy of the sine-Gordon model in a finite circle has been studied analytically and numerically both in the repulsive and attractive regimes. The numerical calculations of the scaling function at several values of the coupling constant were performed by the iterative solution of the Destri–de Vega nonlinear integral equations. The ultraviolet

Staircases are main vertical evacuation passages in multi-story buildings characterized by different pedestrian flow from horizontal passages, such as corridors. Experiments were conducted to investigate crowd ascending and descending dynamics with different numbers of pedestrians in both uni- and bidirectional scenarios. Evacuation processes were recorded by video cameras and velocity sensors, and