The European Physical Journal B最新文献

We propose a simple model for a sample space reducing (SSR) stochastic process, where the dynamical variable denoting the size of the state space is continuous. In general, one can view the model as a constrained multiplicative stochastic process such that the size of the state space cannot be smaller than a visibility parameter (epsilon .) We study the survival time statistics that reveal a subtle difference from the discrete version of the process. A straightforward generalization can explain the noisy SSR process, characterized by a tunable parameter (lambda in [0, 1].) We also examine the statistics of the size of the state space that follows a power-law distributed probability ({mathbb {P}}_{epsilon }(zle epsilon ) sim z^{-alpha },) with a nontrivial value of the exponent as a function of the tunable parameter (alpha = 1+lambda .)

The Ising spin model has long been the foundational model for studying phase transitions in theoretical statistical physics. In the wake of the explosion of machine-learning (ML) techniques in recent years, the Ising model has been used for ML applications to phase transitions. In this overview, we discuss the applications of ML, via both supervised and unsupervised learning, to the study of phases and transitions in the Ising model. We also discuss transfer learning and provide some examples of its use for neural networks trained on Ising model spin configurations. We conclude with a summary and some future directions.

The Inspection Game is the canonical model for the strategic conflict between law enforcement (inspectors) and citizens (potential criminals). Its classical Mixed-Strategy Nash Equilibrium (MSNE) is afflicted by a paradox: the equilibrium crime rate is independent of both the penalty size (p) and the crime gain (g), undermining the efficacy of deterrence policy. We re-examine this challenge using evolutionary game theory, focusing on the long-term fixation probabilities of strategies in finite, asymmetric population sizes subject to demographic noise. The deterministic limit of our model exhibits stable limit cycles around the MSNE, which coincides with the neutral fixed point of the equilibrium analysis. Crucially, in finite populations, demographic noise drives the system away from this cycle and toward absorbing states. Our results demonstrate that high absolute penalties p are highly effective at suppressing crime by influencing the geometry of the deterministic dynamics, which in turn biases the fixation probability toward the criminal extinction absorbing state, thereby restoring the intuitive role of p. Furthermore, we reveal a U-shaped policy landscape where both high penalties and light penalties (where (p approx g)) are successful suppressors, maximizing criminal risk at intermediate penalty levels. Most critically, we analyze the realistic asymptotic limit of extreme population sizes asymmetry, where inspectors are exceedingly rare. In this limit, the system’s dynamic outcome is entirely decoupled from the citizen payoff parameters p and g, and is instead determined by the initial frequency of crime relative to the deterrence threshold (the ratio of inspection cost to reward for catching a criminal). This highlights that effective crime suppression requires managing the interaction between deterministic dynamics, demographic noise, and initial conditions.

This paper introduces a theoretical method for characterizing the quantum phase transitions (QPTs) of the anomalous Dicke model (ADM) by employing the Loschmidt echo (LE) to access the photon number variance. The ADM features a normal phase (NP) and two superradiant phases: electric (SEP) and magnetic (SMP), respectively. We further extend the ADM to one-dimensional (1D) chain-like and two-dimensional (2D) square-like lattice structures. Analysis of the energy bands in these lattice models reveals the emergence of unstable phases, which are primarily induced by photon–photon hopping interactions between adjacent cavities, namely the dispersion relation. We map the unstable phases onto the phase diagram and analyze the boundaries between different phases, along with the properties of the energy bands within each phase region. This work provides valuable insights into the applications of QPTs and quantum manipulations through the unconventional Dicke-lattice model.

Two-dimensional (2D) magnetic semiconductors have emerged as a vibrant frontier in condensed-matter physics, offering opportunities to probe fundamental magnetism at the atomic scale and to design novel spintronic and optospintronic devices. Theoretical progress, particularly through first-principles calculations, many-body approaches, and machine-learning frameworks, has provided predictive insights into exchange interactions, spin–orbit coupling, and magnetocrystalline anisotropy. On the experimental front, advances in exfoliation, chemical synthesis, and van der Waals assembly have enabled the realization of intrinsic 2D ferromagnets, gate-controlled magnetism, and magnetoresistance effects in heterostructures. Together, these developments have established a coherent platform for understanding and engineering reduced-dimensional magnetism. Beyond fundamental discoveries, device concepts such as spin valves, magnetic tunnel junctions, and optospintronic elements leverage proximity effects, interfacial engineering, and twist-angle control to achieve tunable functionalities. The integration of 2D magnets with electronic and photonic platforms promises energy-efficient information processing and multifunctional device architectures. The synergy between predictive theory, advanced synthesis, and high-throughput screening is expected to accelerate the discovery of materials with tailored magnetic properties, bridging the gap from fundamental science to transformative spintronic and quantum technologies.