Physical review. E最新文献

We introduce a strategy employing an adaptive genetic algorithm (GA) for iterative optimization of control sequences to generate quantum nonclassical states. Its efficacy is demonstrated by preparing spin squeezed states in an open collective spin model governed by a linear control field. Inspired by Darwinian evolution, the algorithm iteratively refines control sequences using crossover, mutation, and elimination strategies, starting from a coherent spin state within a dissipative and dephasing environment. We rigorously benchmark our method against constant control protocols and reinforcement learning, demonstrating competitive and robust performance. Furthermore, we showcase the GA's versatility by directly optimizing for metrologically relevant squeezing, achieving scalable performance, even in the presence of dissipation and thermal noise. The proposed strategy demonstrates a high state-preparation fidelity, exceeding 0.99, and provides a long time window for maintaining the spin squeezed state, even under dissipative conditions. We discuss feasible experimental implementations and potential extensions to alternative quantum systems, and the adaptability of the GA module. This research establishes the foundation for utilizing GA-like strategies in controlling quantum systems and achieving desired nonclassical states.

Complex networks provide powerful tools for analyzing and understanding the intricate structures present in various systems, including natural language. Here, we analyze topology of growing word-adjacency networks constructed from Chinese and English literary works written in different periods. Unconventionally, instead of considering dictionary words only, we also include punctuation marks as if they were ordinary words. Our approach is based on two arguments: (1) punctuation carries genuine information related to emotional state, allows for logical grouping of content, provides a pause in reading, and facilitates understanding by avoiding ambiguity, and (2) our previous works have shown that punctuation marks behave like words in a Zipfian analysis and, if considered together with regular words, can improve authorship attribution in stylometric studies. We focus on a functional dependence of the average shortest path length L(N) on a network size N for different epochs and individual novels in their original language as well as for translations of selected novels into the other language. We approximate the empirical results with a growing network model and obtain satisfactory agreement between the two. We also observe that L(N) behaves asymptotically similar for both languages if punctuation marks are included but becomes sizably larger for Chinese if punctuation marks are neglected.

An adaptive master-slave scheme to entrain the synchronization dynamics of networks of coupled oscillators is proposed. By adjusting the correlation of the natural frequencies of oscillators in two layers, the synchrony in the slave layer can be efficiently modulated by adopting the Hebbian plasticity from the master layer. When natural frequencies in two layers are positively correlated, the synchronization in the slave layer is weakened. Nevertheless, for an anticorrelation between two layers, the synchronization in the slave layer can be significantly enhanced, even when oscillators in the master network are in the incoherent state. The mechanism of synchronization modulation is explored by resorting to the master-slave dynamics, the self-consistent method, and the macroscopic-microscopic analysis of effective frequencies. The present study is expected to pave the way for modulating the synchronization of oscillator networks by designing an adaptive plasticity scheme driven by another network.

We report a paradoxical phenomenon where stochasticity reverses deterministic collapse in threshold-activated systems. By using a hybrid logistic-sigmoidal map, we show that weak noise alters phase-space topology, enabling probabilistic recovery from extinction. Lyapunov and quasipotential analyses reveal noise-induced metastability and stochastic robustness absent in deterministic frameworks. These results suggest that environmental variability can stabilize nonlinear systems, offering a counternarrative to classical extinction theory.

Bacteria benefit from cellular heterogeneity: cells differentiate into diverse gene expression states. As colonies grow, cellular phenotypes arrange into spatial patterns. To uncover the functional role of these emergent patterns, we must understand how they arise from cellular growth and mechanical interactions. Here we present a simple, agent-based model to predict patterns of motile and extracellular matrix-producing cells in biofilms of Bacillus subtilis. By incorporating phenotypic inheritance, mechanical interactions, and peripheral motile cell dispersal, our model predicts the emergence of a pattern: matrix cells surround a fractal-like interior motile population. We find that, while some properties of the motile-matrix interface depend on initial conditions, the motile distribution at large radii depends solely on the model's growth mechanism. The phenotypic interface exhibits a fractal dimension that increases as biofilms grow but reaches a maximum as the peripheral layer of matrix cells exceeds the capacity of the inner cells to push it out of the way. By varying parameters, we find correlations between the interface fractal dimension and expansion of motile cells. We validate findings using experiments on B. subtilis biofilms in microfluidics. Our model demonstrates the emergence of colony-level phenotypes from single cell-level interactions and cells modifying their own environment.

We endow the elements of a random matrix drawn from the Gaussian unitary ensemble with a Dyson Brownian motion dynamics. We initialize the dynamics of the eigenvalues with all of them lumped at the origin, but one outlier. We solve the dynamics exactly, which gives us a window on the dynamical scaling behavior at and around the Baik-Ben Arous-Péché transition. Amusingly, while the statics is well known and accessible via the Hikami-Brézin integrals, our approach for the dynamics is explicitly based on the use of orthogonal polynomials.

We derive the first two moments of generic positive stochastic functionals in terms of the one- and two-time probability density functions of the underlying random walk, and we prove ergodicity of observables in stationary random walks. These general results are applied to the half-occupation time and the occupation time in an interval of a Gaussian random walk, for which we obtain exact analytic expressions for the first two moments. We then extend the analysis to scaled Brownian motion and fractional Brownian motion, computing the ergodicity breaking parameter and establishing a simple scaling form for the probability densities of occupation times. Within the framework of infinite ergodic theory, we further identify universal properties of positive observables. All analytical predictions are fully confirmed by numerical simulations.

The dynamics of interacting domain walls, regarded as a system of particles which are biased to move towards their nearest neighbors and annihilate when they meet, have been studied in the recent past. We study the effect of the presence of a fraction r of quenched impurities (which act as rigid walkers) on the dynamics. Here, in the case where two domain walls or one impurity and one domain wall happen to be on the same site, both get simultaneously annihilated. It is found that for any nonzero value of r, the dynamical behavior changes as the surviving fraction of particles ρ(t) attains a constant value. ρ(t)t^{α} shows a universal behavior when plotted against r^{β}t^{α} with α,β values depending on whether the particles are rigid or nonrigid. Also, the values differ for the biased and unbiased cases. The timescale associated with the particle decay shows that it varies with r in a power-law manner with an exponent ≈β/α as expected.

Multiplex networks provide a fundamental framework for representing systems with multiple types of interactions, which are widely applied across physics, biology, and social sciences. Reconstructing the underlying network topology, represented by the layerwise adjacency matrices, from limited observations is crucial for understanding system functionality and control or prediction of system dynamics. In real-world settings, available data are typically restricted to a single aggregated type of nodal information-far more common than layer-resolved observations-whereas the number of structural variables to be inferred grows rapidly with the number of layers. This creates a fundamental and challenging problem for accurate system reconstruction. To address this issue, we propose a mean-field maximum-likelihood estimation framework that reduces the nonlinear, high-dimensional reconstruction problem to a tractable system of linear equations. Through construction and analysis, we show that the resulting solution intrinsically couples network structure with dynamical parameters, under the assumption of homogeneous dynamics, i.e., all nodes within a given layer share identical parameter values. Building on this framework, we develop an adaptive alternating iteration algorithm to jointly decouple and accurately reconstruct both the multiplex structure and its associated dynamics. Experimental results demonstrate superior reconstruction accuracy across diverse synthetic and real social networks, robust noise tolerance, and broad applicability to canonical dynamical models. Our framework offers a universal, stable, and theoretically grounded solution for multiplex network reconstruction, laying a solid foundation for modeling and analyzing complex systems.

The multistage Adomian decomposition method (MADM) is a widely used algorithm for the numerical solution of fractional ordinary differential equations (FODEs). However, this study reveals that the traditional MADM yields distorted results for fractional-order problems due to a fundamental conceptual flaw: the improper handling of the nonlocal memory effect. By analyzing the derivation of the Adomian decomposition method we identify that the traditional multistage implementation neglects the accumulation of historical memory. A comparative analysis using a fractional FitzHugh-Nagumo neuron model demonstrates that the traditional MADM produces physically inconsistent results compared to the established predictor-corrector method. To address this, we propose a revised MADM that correctly incorporates the memory term via numerical integration. Comprehensive performance analysis reveals that the revised MADM is rigorously convergent and stable, and achieves superior absolute accuracy in the high-precision regime. Therefore, this research holds theoretical and practical value by preventing the continued use of a flawed method and by proposing a correct and efficient alternative, thereby opening new avenues for the numerical solution of FODEs.