Asymmetric Ising Model Research Articles

Coupled parametric oscillators: From disorder-induced current to asymmetric Ising model

We study the effects of the interplay of weak disorder and weak coupling in a system of parametric micromechanical oscillators. Each oscillator is bistable, enabling the mapping of its stable states onto spin states. The coupling changes the rate of interstate switching of an oscillator depending on the state of other oscillators. We demonstrate that the change is exponentially strong and therefore manifests even for weak coupling. Difference in the oscillator eigenfrequencies translates into disorder in the system. The analysis and the experiment show that disorder leads to a nontrivial stationary state that displays current. The system provides a well-controlled and fully characterized implementation of the asymmetric Ising model, in which coupled spins affect each other differently. This model plays an important role in physics and biology. Our findings open the possibilities of constructing and exploring asymmetric Ising systems with controlled parameters and connectivity. Published by the American Physical Society 2024

A modified Ising model of Barab\xe1si\u2013Albert network with gene-type spins

The central question of systems biology is to understand how individual components of a biological system such as genes or proteins cooperate in emerging phenotypes resulting in the evolution of diseases. As living cells are open systems in quasi-steady state type equilibrium in continuous exchange with their environment, computational techniques that have been successfully applied in statistical thermodynamics to describe phase transitions may provide new insights to the emerging behavior of biological systems. Here we systematically evaluate the translation of computational techniques from solid-state physics to network models that closely resemble biological networks and develop specific translational rules to tackle problems unique to living systems. We focus on logic models exhibiting only two states in each network node. Motivated by the apparent asymmetry between biological states where an entity exhibits boolean states i.e. is active or inactive, we present an adaptation of symmetric Ising model towards an asymmetric one fitting to living systems here referred to as the modified Ising model with gene-type spins. We analyze phase transitions by Monte Carlo simulations and propose a mean-field solution of a modified Ising model of a network type that closely resembles a real-world network, the Barabási–Albert model of scale-free networks. We show that asymmetric Ising models show similarities to symmetric Ising models with the external field and undergoes a discontinuous phase transition of the first-order and exhibits hysteresis. The simulation setup presented herein can be directly used for any biological network connectivity dataset and is also applicable for other networks that exhibit similar states of activity. The method proposed here is a general statistical method to deal with non-linear large scale models arising in the context of biological systems and is scalable to any network size.

Typology of phase transitions in Bayesian inference problems

Many inference problems undergo phase transitions as a function of the signal-to-noise ratio, a prominent example of this phenomenon being found in the stochastic block model (SBM) that generates a random graph with a hidden community structure. Some of these phase transitions affect the information-theoretic optimal (but possibly computationally expensive) estimation procedure, others concern the behavior of efficient iterative algorithms. A computational gap opens when the phase transitions for these two aspects do not coincide, leading to a hard phase in which optimal inference is computationally challenging. In this paper we refine this description in two ways. From a qualitative perspective, we emphasize the existence of more generic phase diagrams with a hybrid-hard phase, in which it is computationally easy to reach a nontrivial inference accuracy but computationally hard to match the information-theoretic optimal one. We support this discussion by quantitative expansions of the functional cavity equations that describe inference problems on sparse graphs, around their trivial solution. These expansions shed light on the existence of hybrid-hard phases, for a large class of planted constraint satisfaction problems, and on the question of the tightness of the Kesten-Stigum (KS) bound for the associated tree reconstruction problem. Our results show that the instability of the trivial fixed point is not generic evidence for the Bayes optimality of the message-passing algorithms. We clarify in particular the status of the symmetric SBM with four communities and of the tree reconstruction of the associated Potts model: In the assortative (ferromagnetic) case the KS bound is always tight, whereas in the disassortative (antiferromagnetic) case we exhibit an explicit criterion involving the degree distribution that separates a large-degree regime where the KS bound is tight and a low-degree regime where it is not. We also investigate the SBM with two communities of different sizes, also known as the asymmetric Ising model, and describe quantitatively its computational gap as a function of its asymmetry, as well as a version of the SBM with two groups of q_{1} and q_{2} communities. We complement this study with numerical simulations of the belief propagation iterative algorithm, confirming that its behavior on large samples is well described by the cavity method.

Information transport in classical statistical systems

For “static memory materials” the bulk properties depend on boundary conditions. Such materials can be realized by classical statistical systems which admit no unique equilibrium state. We describe the propagation of information from the boundary to the bulk by classical wave functions. The dependence of wave functions on the location of hypersurfaces in the bulk is governed by a linear evolution equation that can be viewed as a generalized Schrödinger equation. Classical wave functions obey the superposition principle, with local probabilities realized as bilinears of wave functions. For static memory materials the evolution within a subsector is unitary, as characteristic for the time evolution in quantum mechanics. The space-dependence in static memory materials can be used as an analogue representation of the time evolution in quantum mechanics – such materials are “quantum simulators”. For example, an asymmetric Ising model on a Euclidean two-dimensional lattice represents the time evolution of free relativistic fermions in two-dimensional Minkowski space.

Fermions as generalized Ising models

We establish a general map between Grassmann functionals for fermions and probability or weight distributions for Ising spins. The equivalence between the two formulations is based on identical transfer matrices and expectation values of products of observables. The map preserves locality properties and can be realized for arbitrary dimensions. We present a simple example where a quantum field theory for free massless Dirac fermions in two-dimensional Minkowski space is represented by an asymmetric Ising model on a euclidean square lattice.

Network inference in the nonequilibrium steady state.

Nonequilibrium systems lack an explicit characterization of their steady state like the Boltzmann distribution for equilibrium systems. This has drastic consequences for the inference of the parameters of a model when its dynamics lacks detailed balance. Such nonequilibrium systems occur naturally in applications like neural networks and gene regulatory networks. Here, we focus on the paradigmatic asymmetric Ising model and show that we can learn its parameters from independent samples of the nonequilibrium steady state. We present both an exact inference algorithm and a computationally more efficient, approximate algorithm for weak interactions based on a systematic expansion around mean-field theory. Obtaining expressions for magnetizations and two- and three-point spin correlations, we establish that these observables are sufficient to infer the model parameters. Further, we discuss the symmetries characterizing the different orders of the expansion around the mean field and show how different types of dynamics can be distinguished on the basis of samples from the nonequilibrium steady state.

Growing spin model in deterministic and stochastic trees.

We solve the growing asymmetric Ising model [J. Sienkiewicz, K. Suchecki, and J. A. Hołyst, Phys. Rev. E 89, 012105 (2014)] in the topologies of deterministic and stochastic (random) scale-free trees predicting its nonmonotonous behavior for external fields smaller than the coupling constant J. In both cases, we indicate that the crossover temperature corresponding to maximal magnetization decays approximately as (lnlnN)(-1), where N is the number of nodes in the tree.

A message-passing scheme for non-equilibrium stationary states

We study stationary states in a diluted asymmetric (kinetic) Ising model. We apply therecently introduced dynamic cavity method to compute magnetizations of these stationarystates. Depending on the update rule, different versions of the dynamic cavity methodapply. We here study synchronous updates and random sequential updates, and comparelocal properties computed by the dynamic cavity method to numerical simulations.Using both types of updates, the dynamic cavity method is highly accurate athigh enough temperatures. At low enough temperatures, for sequential updatesthe dynamic cavity method tends to a fixed point, but this does not agree withnumerical simulations, while for parallel updates, the dynamic cavity method maydisplay oscillatory behavior. When it converges and is accurate, the dynamic cavitymethod offers a huge speed-up compared to Monte Carlo, particularly for largesystems.

TWO-DIMENSIONAL WANG–LANDAU SAMPLING OF AN ASYMMETRIC ISING MODEL

We study the critical endpoint behavior of an asymmetric Ising model with two- and three-body interactions on a triangular lattice, in the presence of an external field. We use a two-dimensional Wang–Landau sampling method to determine the density of states for this model. An accurate density of states allowed us to map out the phase diagram accurately and observe a clear divergence of the curvature of the spectator phase boundary and of the derivative of the magnetization coexistence diameter near the critical endpoint, in agreement with previous theoretical predictions.

A symmetric entropy bound on the non-reconstruction regime of Markov chains on Galton-Watson trees

We give a criterion for the non-reconstructability of tree-indexed $q$-state Markov chains obtained by broadcasting a signal from the root with a given transition matrix $M$. Non-reconstruction holds if $c(M)$ times the expected number of offspring on the Galton-Watson tree is smaller than 1. Here $c(M)$ is an explicit function, which is convex over the set of $M$'s with a given invariant distribution, that is defined in terms of a $(q-1)$-dimensional variational problem over symmetric entropies. This result is equivalent to proving the extremality of the free boundary condition Gibbs measure within the corresponding Gibbs-simplex. Our theorem holds for possibly non-reversible $M$ and its proof is based on a general recursion formula for expectations of a symmetrized relative entropy function, which invites their use as a Lyapunov function. In the case of the Potts model, the present theorem reproduces earlier results of the authors, with a simplified proof, in the case of the symmetric Ising model (where the argument becomes similar to the approach of Pemantle and Peres) the method produces the correct reconstruction threshold), in the case of the (strongly) asymmetric Ising model where the Kesten-Stigum bound is known to be not sharp the method provides improved numerical bounds.

Uncovering the secrets of unusual phase diagrams: applications of two-dimensional Wang-Landau sampling

We use a two-dimensional Wang-Landau sampling algorithm to calculate the density of states for two discrete spin models and then extract their phase diagrams. The first system is an asymmetric Ising model on a triangular lattice with two- and three-body interactions in an external field. An accurate density of states allows us to locate the critical endpoint accurately in a two-dimensional parameter space. We observe a divergence of the spectator phase boundary and of the temperature derivative of the magnetization coexistence diameter at the critical endpoint in quantitative agreement with theoretical predictions. The second model is a Q-state Potts model in an external field H. We map the phase diagram of this model for Q > 8 and observe a first-order phase transition line that starts at the H = 0 phase transition point and ends at a critical point (Tc,Hc), which must be located in a two-dimensional parameter space. The critical field Hc(Q) is positive and increases with Q, in qualitative agreement with previous theoretical predictions.

Critical endpoint behavior: A Wang–Landau study

We study the critical endpoint behavior using an asymmetric Ising model with two- and three-body interactions on a triangular lattice, in the presence of an external field. The simulation method we use is Wang–Landau sampling in a two-dimensional parameter space. We observe a clear divergence of the curvature of the spectator phase boundary and of the magnetization coexistence diameter derivative at the critical endpoint, and the exponents for both divergences agree well with previous theoretical predictions.

Critical endpoint behavior in an asymmetric Ising model: Application of Wang-Landau sampling to calculate the density of states

Using the Wang-Landau sampling method with a two-dimensional random walk we determine the density of states for an asymmetric Ising model with two- and three-body interactions on a triangular lattice, in the presence of an external field. With an accurate density of states we were able to map out the phase diagram accurately and perform quantitative finite-size analyses at, and away from, the critical endpoint. We observe a clear divergence of the curvature of the spectator phase boundary and of the magnetization coexistence diameter derivative at the critical endpoint, and the exponents for both divergences agree well with previous theoretical predictions.

Wang-Landau sampling of an asymmetric ising model: a study of the critical endpoint behavior

We use the Wang-Landau algorithm to calculate a density of states for an asymmetric Ising model on a triangular lattice with two- and three-body interactions in an external field. An accurate density of states allows us to determine the phase diagram and to study the critical behavior of this model at and near the critical endpoint. We observe a divergence of the curvature of the spectator phase boundary at the critical endpoint in accordance with theoretical predictions.

Droplet dynamics for asymmetric Ising model

Nucleation from a metastable state is studied for an anisotropic Ising model at very low temperatures. It turns out that the critical nucleus as well as configurations on a typical path to it differ from the Wulff shape of an equilibrium droplet.

A note on asymptotic inference for an asymmetric Ising model around a torus

Limiting distribution results are obtained for the sufficient statistics of an asymmetric Ising model on a torus. Applications are discussed.

A note on asymptotic inference for an asymmetric Ising model around a torus

Limiting distribution results are obtained for the sufficient statistics of an asymmetric Ising model on a torus. Applications are discussed.