We study the dynamics of an overdamped Brownian particle subjected to Poissonian stochastic resetting in a nonthermal bath, characterized by a Poisson white noise and a Gaussian noise. Applying the renewal theory we find an exact analytical expression for the spatial distribution at the steady state. Unlike the single exponential distribution as observed in the case of a purely thermal bath, the distribution is double exponential. Relaxation of the transient spatial distributions to the stationary one, for the limiting cases of Poissonian rate, is investigated carefully. In addition, we study the first-arrival properties of the system in the presence of a delta-function sink with strength $\kappa$, where $\kappa =0$ and $\kappa =\infty$ correspond to fully nonreactive and fully reactive sinks, respectively. We explore the effect of two competitive mechanisms: the diffusive spread in the presence of two noises and the increase in probability density around the initial position due to stochastic resetting. We show that there exists an optimal resetting rate, which minimizes the mean first-arrival time (MFAT) to the sink for a given value of the sink strength. We also explore the effect of the strength of the Poissonian noise on MFAT, in addition to sink strength. Our formalism generalizes the diffusion-limited reaction under resetting in a nonequilibrium bath and provides an efficient search strategy for a reactant to find a target site, relevant in a range of biophysical processes.

Within the context of social balance theory, much attention has been paid to the attainment and stability of unipolar or bipolar societies. However, multipolar societies are commonplace in the real world, despite the fact that the mechanism of their emergence is much less explored. Here, we investigate the evolution of a society of interacting agents with friendly (positive) and enmity (negative) relations into a final stable multipolar state. Triads are assigned energy according to the degree of tension they impose on the network. Agents update their connections to decrease the total energy (tension) of the system, on average. Our approach is to consider a variable energy $\epsilon \in [0,1]$ for triads which are entirely made of negative relations. We show that the final state of the system depends on the initial density of the friendly links ${\rho }_0$. For initial densities greater than an $\epsilon$-dependent threshold ${\rho }_0^c\left(\epsilon \right)$, a unipolar (paradise) state is reached. However, for ${\rho }_0\le {\rho }_0^c\left(\epsilon \right)$, multipolar and bipolar states can emerge. We observe that the number of stable final poles increases with decreasing $\epsilon$ where the first transition from bipolar to multipolar society occurs at ${\epsilon }^{*}\approx 0.67$. We end the paper by providing a mean-field calculation that provides an estimate for the critical ($\epsilon$ dependent) initial positive link density, which is consistent with our simulations.

Itraconazole (ITZ) is a thermotropic liquid crystal that exhibits isotropic, nematic, and smectic phases on cooling towards the glass transition upon melting. Over the years, new aspects regarding the liquid-crystalline ordering of this antifungal drug were systematically revealed. It has been shown recently that the temperature range of individual mesophases in ITZ can be modified by adding a small amount of glycerol (GLY). Moreover, above the critical concentration of 5% w/w, a smectic to nematic transition can be avoided. Here we go one step further, and we used broadband dielectric spectroscopy to investigate the new phase behavior of the ITZ-GLY mixture (5% w/w). To confirm the phase transformations of the ITZ-GLY mixture, differential scanning calorimetry was also employed. The analysis of molecular dynamics of the ITZ-GLY mixture in the glassy and isotropic phases revealed features similar to those observed for neat ITZ. Two relaxation processes were identified in the smectic-A phase, with similar temperature dependence, most likely related to the fast rotations around the long axis of a molecule. Additionally, the derivative analysis revealed another low-frequency process hidden under DC conductivity ascribed to the slow rotations about a short axis. We will show that the differences in the molecular organization in the smectic-A and isotropic phases leave a clear fingerprint on the temperature behavior of relaxation times and other dielectric parameters, such as DC conductivity and dielectric strength, for which a pretransition effect has been detected.

Three-dimensional (3D) simulations of electron beams propagating in high-energy-density plasmas using the quasistatic Particle-in-Cell (PIC) code QuickPIC demonstrate a significant increase in stopping power when beam electrons mutually interact via their wakes. Each beam electron excites a plasma wave wake of wavelength $\sim 2\pi c/{\omega }_{pe}$, where $c$ is the speed of light and ${\omega }_{pe}$ is the background plasma frequency. We show that a discrete collection of electrons undergoes a beam-plasma-like instability caused by mutual particle-wake interactions that causes electrons to bunch in the beam, even for beam densities $n_b$ for which fluid theory breaks down. This bunching enhances the beam's stopping power, which we call “correlated stopping,” and the effect increases with the “correlation number” $N_b\equiv n_b{(c/{\omega }_{pe})}^3$. For example, a beam of monoenergetic 9.7 MeV electrons with $N_b=1/8$, in a cold background plasma with $n_e={10}^{26}{\mathrm{cm}}^{-3}$ (450 g ${\mathrm{cm}}^{-3}$ DT), has a stopping power of $2.28\pm 0.04$ times the single-electron value, which increases to $1220\pm 5$ for $N_b=64$. The beam also experiences transverse filamentation, which eventually limits the stopping enhancement.

Time periodic patterns in a semiconductor superlattice, relevant to microwave generation, are obtained upon numerical integration of a known set of drift-diffusion equations. The associated spatiotemporal transport mechanisms are uncovered by applying (to the computed data) two recent data processing tools, known as the higher order dynamic mode decomposition and the spatiotemporal Koopman decomposition. Outcomes include a clear identification of the asymptotic self-sustained oscillations of the current density (isolated from the transient dynamics) and an accurate description of the electric field traveling pulse in terms of its dispersion diagram. In addition, a preliminary version of a data-driven reduced order model is constructed, which allows for extremely fast online simulations of the system response over a range of different configurations.

Precise characterization of three-dimensional (3D) heterogeneous media is indispensable in finding the relationships between structure and macroscopic physical properties (permeability, conductivity, and others). The most widely used experimental methods (electronic and optical microscopy) provide high-resolution bidimensional images of the samples of interest. However, 3D material inner microstructure registration is needed to apply numerous modeling tools. Numerous research areas search for cheap and robust methods to obtain the full 3D information about the structure of the studied sample from its 2D cuts. In this work, we develop an adaptive phase-retrieval stochastic reconstruction algorithm that can create 3D replicas from 2D original images, APR. The APR is free of artifacts characteristic of previously proposed phase-retrieval techniques. While based on a two-point $S_2$ correlation function, any correlation function or other morphological metrics can be accounted for during the reconstruction, thus, paving the way to the hybridization of different reconstruction techniques. In this work, we use two-point probability and surface-surface functions for optimization. To test APR, we performed reconstructions for three binary porous media samples of different genesis: sandstone, carbonate, and ceramic. Based on computed permeability and connectivity ($C_2$ and $L_2$ correlation functions), we have shown that the proposed technique in terms of accuracy is comparable to the classic simulated annealing-based reconstruction method but is computationally very effective. Our findings open the possibility of utilizing APR to produce fast or crude replicas further polished by other reconstruction techniques such as simulated annealing or process-based methods. Improving the quality of reconstructions based on phase retrieval by adding additional metrics into the reconstruction procedure is possible for future work.

We present the results of computer simulations on a class of percolative systems, called protected percolation, that violates the Harris criterion. The Harris criterion states whether the critical behavior at a phase transition from a disordered state to an ordered state will be altered by impurities. We incorporate impurities into our simulations to test whether the critical exponents for protected percolation are altered by impurities. We find that the critical exponents for three-dimensional protected percolation simulations indeed change with impurities in the form of missing sites and immortal sites. On the other hand, the critical exponents for both standard percolation and protected percolation in two dimensions are stable against impurities.

We study a model for the collective behavior of self-propelled particles subject to pairwise copying interactions and noise. Particles move at a constant speed $v$ on a two-dimensional space and, in a single step of the dynamics, each particle adopts the direction of motion of a randomly chosen neighboring particle within a distance $R=1$, with the addition of a perturbation of amplitude $\eta$ (noise). We investigate how the global level of particles' alignment (order) is affected by their motion and the noise amplitude $\eta$. In the static case scenario $v=0$ where particles are fixed at the sites of a square lattice and interact with their first neighbors, we find that for any noise $\eta >0$ the system reaches a steady state of complete disorder in the thermodynamic limit, while for $\eta =0$ full order is eventually achieved for a system with any number of particles $N$. Therefore, the model displays a transition at zero noise when particles are static, and thus there are no ordered steady states for a finite noise ($\eta >0$). We show that the finite-size transition noise vanishes with $N$ as ${\eta }_c^{1\mathrm{D}}\sim N^{-1}$ and ${\eta }_c^{2\mathrm{D}}\sim {(NlnN)}^{-1/2}$ in one- and two-dimensional lattices, respectively, which is linked to known results on the behavior of a type of noisy voter model for catalytic reactions. When particles are allowed to move in the space at a finite speed $v>0$, an ordered phase emerges, characterized by a fraction of particles moving in a similar direction. The system exhibits an order-disorder phase transition at a noise amplitude ${\eta }_c>0$ that is proportional to $v$, and that scales approximately as ${\eta }_c\sim v{(-lnv)}^{-1/2}$ for $v\ll 1$. These results show that the motion of particles is able to sustain a state of global order in a system with voter-like interactions.

In this work, we address a multicoupled dynamics on complex networks with tunable structural segregation. Specifically, we work on a networked epidemic spreading under a vaccination campaign with agents in favor and against the vaccine. Our results show that such coupled dynamics exhibits a myriad of phenomena such as nonequilibrium transitions accompanied by bistability. Besides we observe the emergence of an intermediate optimal segregation level where the community structure enhances negative opinions over vaccination but counterintuitively hinders—rather than favoring—the global disease spreading. Thus our results hint vaccination campaigns should avoid policies that end up segregating excessively antivaccine groups so that they effectively work as echo chambers in which individuals look to confirmation without jeopardizing the safety of the whole population.

Confinement is known to have profound effects on the collective dynamics of many active systems. Here, we investigate a modeled active system in circular confinement consisting of biased active particles, where the direction of active force deviates a biased angle from the principle orientation of the anisotropic interaction. We find that such particles can spontaneously form a spiral vortex with two concentric and counter-rotating regions near the boundary. The emerged vortex can be measured by the vortex order parameter which shows nonmonotonic dependencies on both the biased angle and the strength of the anisotropic interaction. Our work can provide an understanding of such dynamic behaviors and enable different strategies for designing ordered collective behaviors.

Acoustic frequency combs leverage unique properties of the optical frequency comb technology in high-precision measurements and innovative sensing in optically inaccessible environments such as under water, under ground, or inside living organisms. Because acoustic combs with wide spectra would be required for many of these applications but techniques of their generation have not yet been developed, here we propose an approach to the creation of spectrally wide acoustic combs using oscillations of polydisperse gas bubble clusters in liquids. By means of numerical simulations, we demonstrate that clusters consisting of bubbles with precisely controlled sizes can produce wide acoustic spectra composed of equally spaced coherent peaks. We show that under typical experimental conditions, bubble clusters remain stable over time, which is required for a reliable recording of comb signals. We also demonstrate that the spectral composition of combs can be tuned by adjusting the number and size of bubbles in a cluster.

Experimental and theoretical work reported here on the collapse of catenoidal soap films of various viscosities reveal the existence of a robust geometric feature that appears not to have been analyzed previously; prior to the ultimate pinchoff event on the central axis, which is associated with the formation of a well-studied local double-cone structure folded back on itself, the film transiently consists of two acute-angle cones connected to the supporting rings, joined by a central quasicylindrical region. As the cylindrical region becomes unstable and pinches, the opening angle of those cones is found to be universal, independent of film viscosity. Moreover, that same opening angle at pinching is found when the transition occurs in a hemicatenoid bounded by a surface. The approach to the conical structure is found to obey classical Keller-Miksis scaling of the minimum radius as a function of time, down to very small but finite radii. While there is a large body of work on the detailed structure of the singularities associated with ultimate pinchoff events, these large-scale features have not been addressed. Here we study these geometrical aspects of film collapse by several distinct approaches, including a systematic analysis of the linear and weakly nonlinear dynamics in the neighborhood of the saddle node bifurcation leading to collapse, both within mean curvature flow and the physically realistic Euler flow associated with the incompressible dynamics of the surrounding air. These analyses are used to show how much of the geometry of collapsing catenoids is accurately captured by a few active modes triggered by boundary deformation. A separate analysis based on a mathematical sequence of shapes progressing from the critical catenoid towards the Goldschmidt solution is shown to predict accurately the cone angle at pinching. We suggest that the approach to the conical structures can be viewed as passage close to an unstable fixed point of conical similarity solutions. The overall analysis provides the basis for the systematic study of more complex problems of surface instabilities triggered by deformations of the supporting boundaries.

We report experimental and modeling results for the charge state distribution of laboratory photoionized neon plasmas in the first systematic study over nearly an order of magnitude range of ionization parameter $\xi \propto F/N_e$. The range of $\xi$ is achieved by flexibility in the experimental platform to adjust either the x-ray drive flux $F$ at the sample or the electron number density $N_e$ or both. Experimental measurements of photoionized plasma conditions over such a range of parameters enable a stringent test of atomic kinetics models used within codes that are applied to photoionized plasmas in the laboratory and astrophysics. From experimental transmission data, ion areal densities are extracted by spectroscopic analysis that is independent of atomic kinetics modeling. The measurements reveal the net result of the competition between photon-driven ionization and electron-driven recombination atomic processes as a function of $\xi$ as it affects the charge state distribution. Results from radiation-hydrodynamics modeling calculations with detailed inline atomic kinetics modeling are compared with the experimental results. There is good agreement in the mean charge and overall qualitative similarities in the trends observed with $\xi$ but significant quantitative differences in the fractional populations of individual ions.

Optimization plays a significant role in many areas of science and technology. Most of the industrial optimization problems have inordinately complex structures that render finding their global minima a daunting task. Therefore, designing heuristics that can efficiently solve such problems is of utmost importance. In this paper we benchmark and improve the thermal cycling algorithm [Phys. Rev. Lett. 79, 4297 (1997)] that is designed to overcome energy barriers in nonconvex optimization problems by temperature cycling of a pool of candidate solutions. We perform a comprehensive parameter tuning of the algorithm and demonstrate that it competes closely with other state-of-the-art algorithms such as parallel tempering with isoenergetic cluster moves, while overwhelmingly outperforming more simplistic heuristics such as simulated annealing.

The weak-noise limit of dissipative dynamical systems is often the most fascinating one. In such a case fluctuations can interact with a rich complexity, frequently hidden in deterministic systems, to give rise to phenomena that are absent for both noiseless and strong fluctuations regimes. Unfortunately, this limit is also notoriously hard to approach analytically or numerically. We reinvestigate in this context the paradigmatic model of nonequilibrium statistical physics consisting of inertial Brownian particles diffusing in a tilted periodic potential by exploiting state-of-the-art computer simulations of an extremely long timescale. In contrast to previous results on this longstanding problem, we draw an inference that in the parameter regime for which the particle velocity is bistable the lifetime of ballistic diffusion diverges to infinity when the thermal noise intensity tends to zero, i.e., an everlasting ballistic diffusion emerges. As a consequence, the diffusion coefficient does not reach its stationary constant value.

Understanding the low-temperature pure state structure of spin glasses remains an open problem in the field of statistical mechanics of disordered systems. Here we study Monte Carlo dynamics, performing simulations of the growth of correlations following a quench from infinite temperature to a temperature well below the spin-glass transition temperature $T_c$ for a one-dimensional Ising spin-glass model with diluted long-range interactions. In this model, the probability $P_{ij}$ that an edge $\{i,j\}$ has nonvanishing interaction falls as a power law with chord distance, $P_{ij}\propto 1/R_{ij}^{2\sigma }$, and we study a range of values of $\sigma$ with $1/2<\sigma <1$. We consider a correlation function $C_4(r,t)$. A dynamic correlation length that shows power-law growth with time $\xi \left(t\right)\propto t^{1/z}$ can be identified in the data and, for large time $t, C_4(r,t)$ decays as a power law $r^{-{\alpha }_d}$ with distance $r$ when $r\ll \xi \left(t\right)$. The calculation can be interpreted in terms of the maturation metastate averaged Gibbs state, or MMAS, and the decay exponent ${\alpha }_d$ differentiates between a trivial MMAS (${\alpha }_d=0$), as expected in the droplet picture of spin glasses, and a nontrivial MMAS (${\alpha }_d\ne 0$), as in the replica-symmetry-breaking (RSB) or chaotic pairs pictures. We find nonzero ${\alpha }_d$ even in the regime $\sigma >2/3$ which corresponds to short-range systems below six dimensions. For $\sigma <2/3$, the decay exponent ${\alpha }_d$ follows the RSB prediction for the decay exponent ${\alpha }_s=3-4\sigma$ of the static metastate, consistent with a conjectured statics-dynamics relation, while it approaches ${\alpha }_d=1-\sigma$ in the regime $2/3<\sigma <1$; however, it deviates from both lines in the vicinity of $\sigma =2/3$.

We study the reservoir crowding effect by considering the nonequilibrium steady states of an asymmetric exclusion process (TASEP) coupled to a reservoir with fixed available resources and dynamically coupled entry and exit rate. We elucidate how the steady states are controlled by the interplay between the coupled entry and exit rates, both being dynamically controlled by the reservoir population, and the fixed total particle number in the system. The TASEP can be in the low-density, high-density, maximal current, and shock phases. We show that such a TASEP is different from an open TASEP for all values of available resources: here the TASEP can support only localized domain walls for any (finite) amount of resources that do not tend to delocalize even for large resources, a feature attributed to the form of the dynamic coupling between the entry and exit rates. Furthermore, in the limit of infinite resources, in contrast to an open TASEP, the TASEP can be found in its high-density phase only for any finite values of the control parameters, again as a consequence of the coupling between the entry and exit rates.

Non-Hermitian systems with specific forms of Hamiltonians can exhibit novel phenomena. However, it is difficult to study their quantum thermodynamical properties. In particular, the calculation of work statistics can be challenging in non-Hermitian systems due to the change of state norm. To tackle this problem, we modify the two-point measurement method in Hermitian systems. The modified method can be applied to non-Hermitian systems which are Hermitian before and after the evolution. In Hermitian systems, our method is equivalent to the two-point measurement method. When the system is non-Hermitian, our results represent a projection of the statistics in a larger Hermitian system. As an example, we calculate the work statistics in a non-Hermitian Su-Schrieffer-Heeger model. Our results reveal several differences between the work statistics in non-Hermitian systems and the one in Hermitian systems.

Our very wide survey of the supercritical phase diagram and its key properties reveals a universal interrelation between dynamics and thermodynamics and an unambiguous transition between liquidlike and gaslike states. This is seen in the master plot showing a collapse of the data representing the dependence of specific heat on key dynamical parameters in the system for many different paths on the phase diagram. As a result, the observed transition is path independent. We call it a “c” transition due to the c-shaped curve parametrizing the dependence of the specific heat on key dynamical parameters. The c transition has a fixed inversion point and provides a new structure to the phase diagram, operating deep in the supercritical state (up to, at least, 2000 times the critical pressure and 50 times the critical temperature). The data collapse and path independence as well as the existence of a special inversion point on the phase diagram are indicative of either of a sharp crossover or a new phase transition in the deeply supercritical state.

Restricted Boltzmann machines (RBM) are bilayer neural networks used for the unsupervised learning of model distributions from data. The bipartite architecture of RBM naturally defines an elegant sampling procedure, called alternating Gibbs sampling (AGS), where the configurations of the latent-variable layer are sampled conditional to the data-variable layer and vice versa. We study here the performance of AGS on several analytically tractable models borrowed from statistical mechanics. We show that standard AGS is not more efficient than classical Metropolis-Hastings (MH) sampling of the effective energy landscape defined on the data layer. However, RBM can identify meaningful representations of training data in their latent space. Furthermore, using these representations and combining Gibbs sampling with the MH algorithm in the latent space can enhance the sampling performance of the RBM when the hidden units encode weakly dependent features of the data. We illustrate our findings on three datasets: Bars and Stripes and MNIST, well known in machine learning, and the so-called lattice proteins dataset, introduced in theoretical biology to study the sequence-to-structure mapping in proteins.