Nevin Manimala

Phys Rev E. 2026 Mar;113(3-1):034310. doi: 10.1103/4124-dyj8.

ABSTRACT

Most networks encountered in nature, society, and technology have weighted edges, representing the strength of the interaction or association between their vertices. Randomizing the structure of a network is a classic procedure used to estimate the statistical significance of properties of the network such as transitivity, centrality, and community structure. Randomization of weighted networks has traditionally been done via the weighted configuration model (WCM), a simple extension of the configuration model, where weights are interpreted as bundles of edges. It has previously been shown that the ensemble of randomizations provided by the WCM is affected by the specific scale used to compute the weights, but the consequences for statistical significance were unclear. Here we find that statistical significance based on the WCM is scale dependent, whereas in most cases results should be independent of the choice of the scale. A two-step approach, originally introduced for network reconstruction, in which one first randomizes the structure and then the weights, with a suitable distribution, restores scale invariance and allows us to conduct unbiased assessments of significance on weighted networks.

PMID:41998988 | DOI:10.1103/4124-dyj8

Phys Rev E. 2026 Mar;113(3-2):035201. doi: 10.1103/p31r-2y55.

ABSTRACT

Magnetically confined fusion plasmas exhibit predator-prey-like cyclic oscillations through the self-regulating interaction between drift-wave turbulence and zonal flow. To elucidate the detailed mechanism and causality underlying this phenomenon, we construct a simple stochastic predator-prey model that incorporates intrinsic fluctuations and analyze its statistical properties from an information-theoretic perspective. We first show that the model exhibits persistent fluctuating cyclic oscillations called quasicycles due to amplification of intrinsic noise. This result suggests the possibility that the previously observed periodic oscillations in a toroidal plasma are not limit cycles, but quasicycles, and that such quasicycles may be widely observed under various conditions. For this model, we further prove that information of the zonal flow is propagated to turbulence. This result suggests that turbulence behavior may be predictable to a certain extent based on zonal flow characteristics.

PMID:41998978 | DOI:10.1103/p31r-2y55

Phys Rev E. 2026 Mar;113(3-1):034308. doi: 10.1103/cby1-s3gz.

ABSTRACT

Michaelis-Menten kinetics is one of the most recognized models in enzyme kinetics, crucial for understanding biochemical reactions in various metabolic processes. In this study, we perform a stochastic analysis of the Michaelis-Menten kinetics with inhibitory mechanisms, which significantly enriches the description of the reaction. Using the Fock space formalism, we reformulate the master equation into a Schrödinger-type form. We examine reversible inhibitions and analyze the averaged number of participating substances, identifying a stiffness behavior in all scenarios. For partial inhibition, we show that the formalism correctly captures the phenomenon of inhibitor-activator duality, where the inhibitor transition from pure inhibition role to functionally favors product formation. We calculate the first product formation time distribution, which characterizes the time statistic of the first product formation. An intermediate timescale emerges in addition to the two known regimes typically observed in first-passage problems. This timescale is associated with the introduction of new pathways by inhibitory mechanisms. Altogether, the results offer a perspective on inhibited enzymatic reactions and illustrate how the Fock space formalism can be applied to the analysis of low-copy-number chemical reactions.

PMID:41998976 | DOI:10.1103/cby1-s3gz

Phys Rev E. 2026 Mar;113(3-1):034312. doi: 10.1103/52k8-zz47.

ABSTRACT

The evolution of natural languages poses a riddle to any theoretical perspective based on efficiency considerations. If languages are already optimally effective means of organization and communication of thought, then why do they change? And if they are driven to become optimally effective in the future, then why do they change so slowly, and why do they diversify, rather than converge toward an optimum? We look here at the hypothesis that disorder, rather than efficiency, may play a dominant role. Most traditional approaches to study diachronic language dynamics emphasize lexical data, but it would seem that a crucial contribution to the effectiveness of a thought-coding device is given by its core generative structure, i.e., its syntax. Based on the reduction of syntax to a set of binary syntactic parameters, we introduce here a model of natural language change in which diachronic dynamics can stem from disordered interactions between/among parameters, even in the idealized limit of identical external inputs. We show in which region of “phase space” such dynamics show the glassy features that are observed in natural language across time. In particular, binary syntactic vectors remain trapped in glassy metastable (i.e., tendentially stable) states when the degree of asymmetry in the disordered interactions is below a critical value, consistent with studies of spin glasses with asymmetric interactions. We further show that an added Hopfield-type memory term would indeed, if strong enough, stabilize syntactic configurations even above the critical value, but losing the multiplicity of stable states. Finally, using a notion of linguistic distance in syntactic state space we show that a phylogenetic signal may remain among related languages, despite their gradually divergent syntax, exactly as recently pointed out for real-world languages. These statistical results appear to generalize beyond the dataset of 94 syntactic parameters across 58 languages used in this study.

PMID:41998971 | DOI:10.1103/52k8-zz47

Phys Rev E. 2026 Mar;113(3-1):034123. doi: 10.1103/jqbq-8dx8.

ABSTRACT

We propose two methods for computing the large deviations of the first-passage-time statistics in general open quantum systems. The first method determines the region of convergence of the joint Laplace transform and the z transform of the first-passage time distribution by solving an equation of poles with respect to the z-transform parameter. The scaled cumulant generating function, which is the logarithm of the boundary values within this region, is subsequently obtained. The theoretical basis is that the dynamics of open quantum systems can be unraveled into a piecewise deterministic process and that a tilted Liouville master equation exists in Hilbert space. The second method uses a simulation-based approach that is built on the wave-function cloning algorithm. To validate both methods, we derive analytical expressions for the scaled cumulant generating functions in field-driven two-level and three-level systems. In addition, we present numerical results and cloning simulations for a field-driven system comprising two interacting two-level atoms.

PMID:41998970 | DOI:10.1103/jqbq-8dx8

Phys Rev E. 2026 Mar;113(3-1):034309. doi: 10.1103/2mh9-nn23.

ABSTRACT

An agent-based model of the economy is generalized to incorporate investment and guaranteed income mechanisms in addition to the exchange and distribution mechanisms considered in an earlier model. We use the tools of statistical physics to show that the system is effectively ergodic, is not in equilibrium, but reaches a steady state with occasional large fluctuations because of the effects of multiplicative noise from the investment mechanism. We find realistic wealth distributions and realistic values of the Gini coefficients and the Pareto index.

PMID:41998969 | DOI:10.1103/2mh9-nn23

Phys Rev E. 2026 Mar;113(3-1):034122. doi: 10.1103/wx2b-37kt.

ABSTRACT

Characterizing correlations in a quantum system on the basis of the results of the projective measurements can be performed with different means including the calculation of the classical mutual information. Generally, estimating such information-entropy-based quantities requires having complete statistics of the system’s states. Here we explore the possibility to reconstruct the classical mutual information and specific entropy of a quantum system with a neural network approach on the basis of a limited number of projective measurements. As a prominent example we consider the antiferromagnetic quantum Ising model in transverse and longitudinal magnetic fields which is in demand in both condensed matter physics and quantum computing. We show that the neural network approach gives reliable estimates of the classical mutual information even in the case of paramagnetic wave functions delocalized in the state space. In addition, the phase diagram of the considered quantum system is reconstructed with a special focus on discriminating various types of disordered states.

PMID:41998968 | DOI:10.1103/wx2b-37kt

Phys Rev E. 2026 Mar;113(3-1):034219. doi: 10.1103/3jtj-sxrw.

ABSTRACT

In this work, we quantify the timescales and information flow associated with multiscale energy transfer in a weakly turbulent system. This is done through a greedy optimization algorithm which finds the maximum conditional-mutual information across lagged embeddings of time series localized by wave number. For our chosen weakly turbulent system, the algorithm finds asymmetries in the information flow across wave numbers, reflecting what are typically described as forward and inverse cascades. However, our approach goes beyond typical heuristic arguments and provides quantitative insight into the intricate multiwave mixing dynamics necessary to maintain the steady statistical state characterizing weak turbulence. Our work then provides a detailed and fully nonlinear statistical analysis of a weakly turbulent system. The flexibility of our approach points to broader applicability in real-world data coming from chaotic or turbulent dynamical systems.

PMID:41998961 | DOI:10.1103/3jtj-sxrw

Phys Rev E. 2026 Mar;113(3-1):034110. doi: 10.1103/kjdp-1g12.

ABSTRACT

We describe in detail a mathematical framework in which statistical ensembles of hybrid classical-quantum systems can be properly described. We show how a maximum entropy principle can be applied to derive the microcanonical ensemble of hybrid systems. We investigate its properties, and in particular how the microcanonical ensemble and its marginal classical and quantum ensembles can be defined for arbitrarily small range of energies for the whole system. We show how, in this situation, the ensembles are well defined for a continuum of energy values, unlike the purely quantum microcanonical ensemble, thus proving that hybrid systems translate properties of classical systems to the quantum realm. We also analyze the relation with the hybrid canonical ensemble by considering the microcanonical ensemble of a compound system composed of a hybrid subsystem weakly coupled to a reservoir and computing the marginal ensemble of the hybrid subsystem. Lastly, we apply the theory to the statistics of a toy model, which gives some insight on the different properties presented along the article.

PMID:41998951 | DOI:10.1103/kjdp-1g12

Phys Rev E. 2026 Mar;113(3-1):034117. doi: 10.1103/sj6t-pctp.

ABSTRACT

For statistics of rare events in systems obeying a large-deviation principle, the rate function is a key quantity. When numerically estimating the rate function, one is always restricted to finite system sizes. Thus, if the interest is in the limiting rate function for infinite system sizes, first, several system sizes have to be studied numerically. Here, rare-event algorithms using biased ensembles give access to the low-probability region. Second, some kind of system-size extrapolation has to be performed. Here, we demonstrate how rare-event importance sampling schemes can be combined with multihistogram reweighting. We study two ways of performing the system-size extrapolation, either directly acting on the empirical rate functions or on the scaled cumulant generating functions, to obtain the infinite-size limit. The presented method is demonstrated for a binomial distributed variable, a Markov process of random bits, and the largest connected component of Erdős-Rényi random graphs. Analytical solutions are available in all cases for direct comparison. It is observed in particular that phase transitions appearing in the biased ensembles can lead to systematic deviations from the true result.

PMID:41998940 | DOI:10.1103/sj6t-pctp