Recently, a paradoxical effect has been demonstrated in which transport of a free Brownian particle driven by active fluctuations in the form of white Poisson shot noise can be significantly enhanced when it is additionally subjected to a periodic potential. This phenomenon can emerge in an overdamped system, but it may also be inertia-induced. Here, we considerably extend previous studies and comprehensively investigate the impact of inertia on the effect of free transport enhancement observed in the overdamped system. We detect that inertia cannot only induce this phenomenon, but, depending on a parameter regime, it may also strengthen, weaken, or even destroy it. We exemplify these different scenarios and explore the parameter space to identify the corresponding regions where they emerge. The variance of the active fluctuations amplitude distribution is a key determinant of the inertia influence on the effect of free transport amplification. Our results are relevant not only for microscopic physical systems, but also for biological ones, such as living cells, where fluctuations generated by metabolic activities are active by default.

This paper studies the diffusive behaviors of the two-state model generalized Lévy walk with jumps (GLWJ) in a constant biased field. Assuming that the motion time density of the GLW process follows a power-law distribution, and the corresponding velocity is defined via a power-law coupling with its motion time, while the random jump density follows a Gaussian distribution. The diffusive behaviors are analyzed and explored through calculating the mean-squared displacement (MSD) and the probability density function (PDF). The results indicate that the MSD exhibits a crossover phenomenon driven by three distinct mechanisms and the PDF also shows a directional deviation. Notably, although the biased field endows the walker with the capability of directional transport, it does not always change the diffusion result, which contradicts our intuition. Meanwhile, since the fluctuations originate from different mechanisms, even if a diffusion term has a smaller diffusion exponent, as long as its fluctuation intensity is much stronger than the others, it can still dominate the result on a large observation timescale. This study reveals the rich diffusion phenomena of the biased GLWJ model, and can provide theoretical guidance for particle transport in the related biological and physical experimental observations.

We present a machine-learning method for data-driven synchronization of rhythmic spatiotemporal patterns in reaction-diffusion systems. Extending the phase autoencoder [Yawata et al., Chaos 34, 063111 (2024)] for low-dimensional oscillators, we develop a framework to map high-dimensional field variables of the reaction-diffusion system to low-dimensional latent variables characterizing the asymptotic phase and amplitudes of the field variables. This yields a reduced phase description of the limit cycle underlying the rhythmic spatiotemporal dynamics in a data-driven manner. We propose a method to drive the system along the tangential direction of the limit cycle, enabling phase control without inducing amplitude deviations. With examples of 1D oscillating spots and 2D spiral waves in the FitzHugh-Nagumo reaction-diffusion system, we show that the method achieves rapid synchronization in both reference-based and coupling-based settings. These results demonstrate the potential of data-driven phase description based on the phase autoencoder for synchronization of high-dimensional spatiotemporal dynamics.

Heterophobic interactions, which drive individuals to be repelled from others with opposite opinions, play a role as important as homophilic ones in shaping many dynamical processes on social networks, such as opinion formation, social balance, or epidemic spreading. In this paper, we use belief propagation and Monte Carlo simulations on treelike signed graphs to predict that a sufficient propensity to heterophobia can impede a consensus that would otherwise emerge via a phase transition. As the strength of heterophobic interactions and the rationality of individuals with respect to social stress decrease, this transition changes from continuous to discontinuous, with a strong dependence on the initial conditions. The size of the parameter region where consensus can be reached from any initial condition decays as a power-law function of the number of discussed topics.

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.

Thermalization times of light ions in fusion plasmas are calculated using quantum and semiclassical dielectric models for the interaction of external particles with plasma electrons and ions. An accurate analytical approximation is obtained, which combines a classical Bohr-type description for the interaction with plasma ions with a quantum description for the interaction with plasma electrons. Scaling laws for thermalization times are found for different light ions in a characteristic range of intermediate energies of special interest for tokamak systems composed of pure deuterium and deuterium-tritium plasmas. The study covers a range of temperatures between ${10}^7$ and ${10}^9\mathrm{K}$ and electron densities from ${10}^{13}$ to ${10}^{15}{\mathrm{cm}}^{-3}$. A useful fitting formula for the thermalization time is proposed and applied to the cases of plasma heating using energetic deuterium beams and the thermalization of alpha particles produced by D-T fusion reactions. Finally, a set of useful tables with reference values for these cases is provided.

We explore competitive dynamics in multiagent active matter systems using reinforcement learning. In our study, two active Brownian particles (referred to as predators) were trained using either simultaneous or sequential protocols to capture ten passive Brownian particles (preys). The training results depend on the agent, and generally one agent tends to overperform the other. To assess the effectiveness of the two protocols, we examined two policies: (i) a natural policy, where updates to the reinforcement learning parameters of both predators were stopped at a fixed time, even if one agent performed suboptimally; and (ii) a hybrid policy, where we combined the reinforcement learning parameters recorded when each agent achieved its optimal performance. If limited to natural training, simultaneous training appears to be the better option. However, when hybrid training is also allowed, sequential training becomes the preferred choice.

Creep tests on heterogeneous materials under subcritical loading typically show a power-law decay in strain rate before failure, with the exponent often considered material dependent but independent of applied stress. By imposing successive small stress relaxations through a displacement feedback loop, we probe creep dynamics and show experimentally that this exponent varies with both applied load and loading direction. Simulations of a disordered fiber bundle model reproduce this load dependence, demonstrating that such models capture essential features of delayed rupture dynamics.

Scale invariance is a hallmark of many natural systems, including solar flares, where energy release spans a vast range of scales. Recent computational advances, at the level of both algorithmics and hardware, have enabled high-resolution magnetohydrodynamical (MHD) simulations to span multiple scales, offering new insights into magnetic energy dissipation processes. Here, we study the scale invariance of magnetic energy dissipation in two distinct MHD simulations. Current sheets are identified and analyzed over time. Results demonstrate that dissipative events exhibit scale invariance, with power-law distributions characterizing their energy dissipation and lifetimes. Remarkably, these distributions are consistent across the two simulations, despite differing numerical and physical setups, suggesting universality in the process of magnetic energy dissipation. Comparisons between the evolution of dissipation regions reveal distinct growth behaviors in high plasma-$\beta$ regions (convective zone) and low plasma-$\beta$ regions (atmosphere). The latter display spatiotemporal dynamics similar to those of avalanche models, suggesting self-organized criticality and a common universality class.

We study a system of $y=2$ coupled copies of a well-known constraint satisfaction problem (random hypergraph bicoloring) to examine how the ferromagnetic coupling between the copies affects the properties of the solution space. We solve the replicated model by applying the cavity method to the supervariables that take $2^y$ values. Our results show that a coupling of strength $\gamma$ between the copies decreases the clustering threshold ${\alpha }_d\left(\gamma \right)$, at which typical solutions shatter into disconnected components, therefore preventing numerical methods such as Monte Carlo Markov chains from reaching equilibrium in polynomial time. This result needs to be reconciled with the observation that, in models with coupled copies, denser regions of the solution space should be more accessible. Additionally, we observe a change in the nature of the clustering phase transition, from discontinuous to continuous, in a wide $\gamma$ range. We investigate how the coupling affects the behavior of the belief propagation (BP) algorithm on finite-size instances and find that BP convergence is significantly impacted by the continuous transition. These results highlight the importance of better understanding algorithmic performance at the clustering transition and call for further exploration into the optimal use of reweighting strategies designed to enhance algorithmic performance.

We report on an experimental active matter system with motion restricted to four cardinal directions. Our particles are magnetite-doped colloidal spheres driven by the Quincke electrorotational instability. The absence of a magnetic field ($\left|\mathit{B}\right|=0$) leads to circular trajectories interspersed with short spontaneous runs. Intermediate fields ($\left|\mathit{B}\right|\lesssim 20\mathrm{mT}$) linearize the motion along the axis perpendicular to $\mathit{B}$. At high magnetic fields, we observe the surprising emergence of a second, distinct linearization along the axis parallel to $\mathit{B}$. With numerical simulations, we show that this behavior can be explained by anisotropic magnetic susceptibility.

The anomalous surface mobility exhibited by many slowly vapor-deposited molecular solids, and their simulated counterparts, allows annealing their structure and increasing their density down to $T_{\text{dep}}\approx 0.85T_g$. This work describes the design and characteristics of a simulation that uses a “swept swapping” technique designed to extend structural annealing to lower temperatures. Films are grown using size-dispersed attractive Lennard-Jones (L-J) potential spheres deposited onto their free surface and then equilibrated with a surface-following Monte Carlo swap region. The motion of that region combined with sphere size dispersion speeds structural relaxation by orders of magnitude compared to bulk swapping. Sphere size span and size step ($25\%$ and $\sim 2.5\%$, respectively) are sufficient to completely suppress crystallization and maintain a high swap acceptance rate. The regime revealed by this technique attains a high packing fraction, which is near that of an hexagonal-close-packed L-J crystal with no size-dispersion. Additional swapping refines its structure, at near constant density, maximizing short-range icosahedral order to levels higher than seen previously; the levels apparently limited only by the computing budget.

Polar flocks in discrete active systems are often assumed to be robust, yet recent studies reveal their fragility under both imposed and spontaneous fluctuations. Here, we revisit the four-state active Potts model and show that its globally ordered phase is metastable across a broad swath of parameter space. Small counterpropagating droplets disrupt the flocking phase by inducing a persistent sandwich state, where the droplet-induced opposite-polarity lane remains embedded within the original flock, particularly pronounced at low noise, influenced by spatial anisotropy. In contrast, small transversely propagating droplets, when introduced into the flock, can trigger complete phase reversal due to their alignment orthogonal to the dominant flow and their enhanced persistence. At low diffusion and strong self-propulsion, such transverse droplets also emerge spontaneously, fragmenting the flock into multiple traveling domains and giving rise to a short-range order (SRO) regime. We further identify a motility-induced pinning (MIP) transition in small diffusion and low-temperature regimes when particles of opposite polarity interact, flip their state, hop, and pin an interface. Our comprehensive phase diagrams, encompassing full reversal, sandwich coexistence, stripe bands, SRO, and MIP, delineate how thermal fluctuations, self-propulsion strength, and diffusion govern flock stability in discrete active matter systems.

A diffusive process that is reset to its origin at random times, so-called stochastic resetting (SR), is an ubiquitous expedient in many natural systems. Yet, beyond its ability to improve the efficiency of target searching, SR is a true nonequilibrium thermodynamic process that brings forward new and challenging questions. Here, we show how the recent developments of experimental information thermodynamics renew the way to address SR and can lead, beyond a new understanding, to better control on the nonequilibrium nature of SR. This thermodynamically controlled SR is experimentally implemented within a time-dependent optical trapping potential. We show in particular that SR converts heat into work from a single bath continuously and without feedback. This implements a Maxwell's demon that constantly erases information. In our experiments, the erasure takes the form of a protocol that allows us to evaluate the true energetic cost of SR. We show that using an appropriate measure of the available information, this cost can be reduced to a reversible minimum while being bounded by the Landauer limit. We finally reveal that the individual trajectories generated by the demon all break ergodicity and thus demonstrate the nonergodic nature of the demon's modus operandi. Our results offer new approaches to processes, such as SR, where the informational framework provides key experimental tools for their nonequilibrium thermodynamic control.

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.

The generation of 200 MeV class protons by irradiating a 25 J laser pulse onto a water target using three-dimensional particle-in-cell simulation is shown. Two types of targets—foil and disk—are evaluated and compared. Disks, which are mass-limited targets, produce ions with higher energy than those produced by foils. This enhancement is attributed to laser focusing equivalent effects in mass-limited targets during the acceleration process. In addition, the disk diameter that generates the maximum energy protons is theoretically derived, which shows good agreement with the simulation result.

We revisit the infinite variance problem in fermionic Monte Carlo simulations, which is widely encountered in areas ranging from condensed matter to nuclear and high-energy physics. The different algorithms, which we broadly refer to as determinantal quantum Monte Carlo (DQMC), are applied in many situations and differ in details, but they share a foundation in field theory, and often involve fermion determinants whose symmetry properties make the algorithm sign-problem-free. We show that the infinite variance problem arises as the observables computed in DQMC tend to form heavy-tailed distributions. To remedy this issue retrospectively, we introduce a tail-aware error estimation method to correct the otherwise unreliable estimates of confidence intervals. Furthermore, we demonstrate how to perform DQMC calculations that eliminate the infinite variance problem for a broad class of observables. Our approach is an exact bridge link method, which involves a simple and efficient modification to the standard DQMC algorithm. The method introduces no systematic bias and is straightforward to implement with minimal computational overhead. Our results establish a practical and robust solution to the infinite variance problem, with broad implications for improving the reliability of a variety of fundamental fermion simulations.

We study experimentally, numerically, and analytically, the dynamics of a chiral active particle (cm-sized robots), pulled at a constant translational velocity. We show that these robots can faithfully be described by an overdamped chiral Active Brownian Particle model with self-alignment. We find that the competition between the robots' internal chiral activity and the imposed drive triggers a depinning transition in the noiseless limit, giving rise to a creep regime in the presence of rotational diffusion. The dynamics of the robots can be mapped into a Brownian particle driven across a periodic potential landscape, allowing for an analytical treatment. The steady-state distribution, from which one can construct a dynamic phase diagram, can be computed exactly from the model, across all the dynamical regimes. The active fluctuations introduced by the robots themselves are responsible for an intermittent activated dynamics that allows them to escape their otherwise pinned orientations in a typical time that we derive. Our work thus consolidates such self-propelled robots as an analytically tractable experimental model system for the study of chiral active matter, and highlights the interesting dynamics arising from the interplay between external and internal driving forces in the form of chiral torques, that have been so far avoided in experiments of active particles at granular scales.

We present a macroscopic theory of capillary condensation in slits formed by parallel walls of unequal length. Using the concept of an edge contact angle, we identify four distinct condensation states and derive Kelvin-like relations for their onset. The resulting phase diagrams, expressed in terms of wall geometry and contact angle, reveal two central organizing features: a geometric separatrix that divides distinct condensation regimes, and the wedge-filling threshold at $\theta =\pi /4$, which separates a rich four-state scenario from a simpler two-state one. These results demonstrate how geometry dictates the onset and suppression of condensation in confined systems.