The quantum localization is one of the remarkable phenomena in the studies of quantum chaos and plays an important role in various contexts. Thus, an understanding of the properties of quantum localization is essential. In spite of much effort dedicated to investigating the manifestations of localization in the time-dependent systems, the features of localization in time-independent systems are still less explored, particularly in quantum systems which correspond to the classical systems with smooth Hamiltonian. In this work, we present such a study for a quantum many-body system, namely, the Dicke model. The classical counterpart of the Dicke model is given by a smooth Hamiltonian with two degrees of freedom. We examine the signatures of localization in its chaotic eigenstates. We show that the entropy localization measure, which is defined in terms of the information entropy of Husimi distribution, behaves linearly with the participation number, a measure of the degree of localization of a quantum state. We further demonstrate that the localization measure probability distribution is well described by the $\beta$ distribution. We also find that the averaged localization measure is linearly related to the level repulsion exponent, a widely used quantity to characterize the localization in chaotic eigenstates. Our findings extend the previous results in billiards to the quantum many-body system with classical counterpart described by a smooth Hamiltonian, and they indicate that the properties of localized chaotic eigenstates are universal.

The structure of an evolving network contains information about its past. Extracting this information efficiently, however, is, in general, a difficult challenge. We formulate a fast and efficient method to estimate the most likely history of growing trees, based on exact results on root finding. We show that our linear-time algorithm produces the exact stepwise most probable history in a broad class of tree growth models. Our formulation is able to treat very large trees and therefore allows us to make reliable numerical observations regarding the possibility of root inference and history reconstruction in growing trees. We obtain the general formula $\langle ln\mathcal{N}\rangle \cong NlnN-cN$ for the size dependence of the mean logarithmic number of possible histories of a given tree, a quantity that largely determines the reconstructability of tree histories. We also reveal an uncertainty principle: a relationship between the inferability of the root and that of the complete history, indicating that there is a tradeoff between the two tasks; the root and the complete history cannot both be inferred with high accuracy at the same time.

How can we develop simple yet realistic models of the small neural circuits known as central pattern generators (CPGs), which contribute to generate complex multiphase locomotion in living animals? In this paper we introduce a new model (with design criteria) of a generalized half-center oscillator, (pools of) neurons reciprocally coupled by fast/slow inhibitory and excitatory synapses, to produce either alternating bursting or other rhythmic patterns, characterized by different phase lags, depending on the sensory or other external input. We also show how to calibrate its parameters, based on both physiological and functional criteria and on bifurcation analysis. This model accounts for short-term neuromodulation in a biophysically plausible way and is a building block to develop more realistic and functionally accurate CPG models. Examples and counterexamples are used to point out the generality and effectiveness of our design approach.

A hydrogen-bonding network or hydrogen-bonded cluster is formed in many hydrogen-bonded glass formers. It determines the dynamics of structural α relaxation and the Johari-Goldstein (JG) β relaxation because breaking of hydrogen bonds is the prerequisite. However, the networks and clusters can be substantially reduced or totally removed in the liquid state by high temperature accompanying the applied high pressure in experiments, and in the glassy state by hyperquenching the liquid under pressure. By confining the glass former in nanometer spaces, the extended network cannot form, and in addition the finite size effect limits the growth of the length scale of the α relaxation on lowering temperature. Any of these actions will modify the structure of the original hydrogen-bonded glass former, and also the intermolecular interaction governing the relaxation processes. Consequently the dynamics of the structural α relaxation and the JG β relaxation, as well as the relation between the two processes, are expected to change. An important advance in the study of the dynamics of glass-forming materials is the existence of the strong connection between the α relaxation and the JG β relaxation. In particular, the ratio of their relaxation times, $t_{\alpha }\left(T\right)/t_{\beta }\left(T\right)$, is quantitatively determined by the exponent of the Kohlrausch relaxation function of the α relaxation. This property is valid in hydrogen-bonded glass formers as well as in non-hydrogen-bonded glass formers. The interesting question is whether this property continues to hold after the hydrogen-bonded glass former has been modified by high temperature under high pressure, nanoconfinement, and hyperquenching under pressure. Remarkably, the answer is positive as concluded from the analyses of the data in several hydrogen-bonded glass formers reported in this paper. So far the main theoretical explanation of this property has been the coupling model.

Smectic liquid crystals with a layering order of rodlike molecules can be drawn in the form of free standing films across holes. Extensive experimental studies have shown that smectic-C (SmC) liquid crystals (LCs) with tilted molecules form periodic stripes in the thinner parts of the meniscus, which persist over a range of temperatures above the transition of the bulk medium to the SmA phase in which the tilt angle is zero. The prevailing theoretical models cannot account for all the experimental observations. We propose a model in which we argue that the negative curvature of the surface of the meniscus results in an energy cost when the molecules tilt at the surface. The energy can be reduced by exploiting the allowed $(\nabla ·\mathbf{k})(\nabla ·\mathbf{c})$ deformation which couples the divergence of k, the unit vector along the layer normal, with that of c, the projection of the tilted molecular director on the layer plane. We propose a structure with periodic bending of layers with opposite curvatures, in which the c-vector field itself has a continuous deformation. Calculations based on the theoretical model can qualitatively account for all the experimental observations. It is suggested that detailed measurements on the stripes may be useful for getting good estimates of a few curvature elastic constants of SmC LCs.

The dewetting dynamics of a supported bilayer polymer thin film on a solid substrate is investigated using grazing incidence x-ray photon correlation spectroscopy. We find that the top layer dewets via the spinodal mechanism. The kinetics of the dewetting is studied by monitoring the time evolution of the surface diffuse x-ray scattering intensity. We study the time evolution of fluctuations about the average surface structure by measuring the two-time x-ray intensity fluctuation correlation functions. Using these two-time correlation functions we quantify the crossover from early-time diffusive dynamics to hydrodynamics. The early diffusive regime satisfies dynamic universality. The two-time correlation functions also quantify the onset of hydrodynamic effects. The hydrodynamic regime is observed during the spinodal dewetting process as these interactions are not screened.

We introduce an image-based algorithm to find the probability density function (PDF) of particle displacements from a sequence of images. Conventionally methods based on cross correlation (CC) of image ensembles estimate the standard deviation of an assumed Gaussian PDF from the width of the CC peak. These methods are subject to limiting assumptions that the particle intensity profile and distribution of particle displacements are both Gaussian. Here, we introduce an approach to image-based probability estimation of displacement (iPED) without making any assumptions about the shape of particles’ intensity profile or the PDF of the displacements. In addition, we provide a statistical convergence criterion for iPED to achieve an accurate estimate of the underlying PDF. We compare iPED's performance with the previous CC method for both Gaussian and non-Gaussian particle intensity profiles undergoing Gaussian or non-Gaussian processes. We validate iPED using synthetic images and show that it accurately resolves the PDF of particle displacements with no underlying assumptions. Finally, we demonstrate the application of iPED to real experimental data sets and evaluate its performance. In conclusion, this work presents a method for the estimation of the probability density function of random displacements from images. This method is generalized and independent of any assumptions about the underlying process and is applicable to any moving objects of any arbitrary shape.

We compare the convergence of several flat-histogram methods applied to the two-dimensional Ising model, including the recently introduced stochastic approximation with a dynamic update factor (SAD) method. We compare this method to the Wang-Landau (WL) method, the $1/t$ variant of the WL method, and standard stochastic approximation Monte Carlo (SAMC). In addition, we consider a procedure WL followed by a “production run” with fixed weights that refines the estimation of the entropy. We find that WL followed by a production run does converge to the true density of states, in contrast to pure WL. Three of the methods converge robustly: SAD, $1/t$-WL, and WL followed by a production run. Of these, SAD does not require a priori knowledge of the energy range. This work also shows that WL followed by a production run performs superior to other forms of WL while ensuring both ergodicity and detailed balance.

One attractive feature of the original pseudopotential method consists on its simplicity of adding a force dependent on a nearest-neighbor potential function. In order to improve the method, regarding thermodynamic consistency and control of surface tension, different approaches were developed in the literature, such as multirange interactions potential and modified forcing schemes. In this work, a strategy to combine these enhancements with an appropriate interaction force field using only nearest-neighbor interactions is devised, starting from the desired pressure tensor. The final step of our procedure is implementing this external force by using the classical Guo forcing scheme. Numerical tests regarding static and dynamic flow conditions were performed. Static tests showed that current procedure is suitable to control the surface tension and phase densities. Based on thermodynamic principles, it is devised a solution for phase densities in a droplet, which states explicitly dependence on the surface tension and interface curvature. A comparison with numerical results suggest a physical inconsistency in the pseudopotential method. This fact is not commonly discussed in the literature, since most of studies are limited to the Maxwell equal area rule. However, this inconsistency is shown to be dependent on the equation of state (EOS), and its effects can be mitigated by an appropriate choice of Carnahan-Starling EOS parameters. Also, a droplet oscillation test was performed, and the most divergent solution under certain flow conditions deviated 7.5% from the expected analytical result. At the end, a droplet impact test against a solid wall was performed to verify the method stability, and it was possible to reach stable simulation results with density ratio of almost 2400 and Reynolds number of $\text{Re}=373$. The observed results corroborate that the proposed method is able to replicate the desired macroscopic multiphase behavior.

We succeeded in simultaneously cloaking and concentrating direct current in a conducting material through topology optimization based on a level-set method. To design structures that perform these functions simultaneously, optimal topology is explored for improving two objective functions that govern separately the cloaking and concentration of current. Our design scheme, i.e., the topology optimization of a direct-current electric cloak concentrator, provides this bifunctionality well despite simple, common bulk materials being used to make up the structures. The materials also rigorously obey the electric conduction equation in contrast to the approximated artificial materials, so-called metamaterials, of other design schemes. The structural features needed for this simultaneous bifunctionality are found by adopting level-set method to generate material domains and clear structural interfaces. Furthermore, robust performances of the bifunctional structures against fluctuations in electrical conductivity was achieved by improving the fitness incorporating multiple objective functions. Additionally, the influence of the size of the current-concentrating domain on the performances of the optimal configuration is investigated.

The validity of the fluctuation relations (FRs) for systems in a constant magnetic field is investigated. Recently introduced time-reversal symmetries that hold in the presence of static electric and magnetic fields and of deterministic thermostats are used to prove the transient FRs without invoking, as commonly done, inversion of the magnetic field. Steady-state FRs are also derived, under the $t$-mixing condition. These results extend the predictive power of important statistical mechanics relations. We illustrate this via the nonlinear response for the cumulants of the dissipation, showing how the alternative FRs enable one to determine analytically null cumulants also for systems in a single magnetic field.

We analyze the stochastic thermodynamics of systems with a continuous space of states. The evolution equation, the rate of entropy production, and other results are obtained by a continuous time limit of a discrete time formulation. We point out the role of time reversal and of the dissipation part of the probability current on the production of entropy. We show that the rate of entropy production is a bilinear form in the components of the dissipation probability current with coefficients being the components of the precision matrix related to the Gaussian noise. We have also analyzed a type of noise that makes the energy function to be strictly constant along the stochastic trajectory, being appropriate to describe an isolated system. This type of noise leads to nonzero entropy production and thus to an increase of entropy in the system. This result contrasts with the invariance of the entropy predicted by the Liouville equation, which also describes an isolated system.

Zero-determinant (ZD) strategies are a novel class of strategies in the repeated prisoner's dilemma (RPD) game discovered by Press and Dyson. This strategy set enforces a linear payoff relationship between a focal player and the opponent regardless of the opponent's strategy. In the RPD game, games with discounting and observation errors represent an important generalization, because they are better able to capture real life interactions which are often noisy. However, they have not been considered in the original discovery of ZD strategies. In some preceding studies, each of them has been considered independently. Here, we analytically study the strategies that enforce linear payoff relationships in the RPD game considering both a discount factor and observation errors. As a result, we first reveal that the payoffs of two players can be represented by the form of determinants as shown by Press and Dyson even with the two factors. Then, we search for all possible strategies that enforce linear payoff relationships and find that both ZD strategies and unconditional strategies are the only strategy sets to satisfy the condition. We also show that neither Extortion nor Generous strategies, which are subsets of ZD strategies, exist when there are errors. Finally, we numerically derive the threshold values above which the subsets of ZD strategies exist. These results contribute to a deep understanding of ZD strategies in society.

Microorganisms such as bacteria are active matter which consume chemical energy and generate their unique run-and-tumble motion. A swarm of such microorganisms provide a nonequilibrium active environment whose noise characteristics are different from those of thermal equilibrium reservoirs. One important difference is a finite persistence time, which is considerably large compared to that of the equilibrium noise, that is, the active noise is colored. Here we study a mesoscopic energy-harvesting device (engine) with active reservoirs harnessing this noise nature. For an exactly solvable linear model, we show that the performance from the active environment can surpass that from the equilibrium environment. Furthermore, we propose a proper definition of the active-reservoir temperature and show that the engine efficiency can overcome the conventional Carnot bound, thus the power-efficiency trade-off constraint is released. We also show that the efficiency at the maximum power can surpass the Curzon-Ahlborn efficiency. This remarkable enhancement originates from the extra unconventional entropy production beyond the conventional Clausius entropy production, due to the non-Markovian nature of the active reservoirs. Interestingly, the supremacy of the active engine critically depends on the timescale symmetry of two active reservoirs.

Deriving evolution equations accounting for both anomalous diffusion and reactions is notoriously difficult, even in the simplest cases. In contrast to normal diffusion, reaction kinetics cannot be incorporated into evolution equations modeling subdiffusion by merely adding reaction terms to the equations describing spatial movement. A series of previous works derived fractional reaction-diffusion equations for the spatiotemporal evolution of particles undergoing subdiffusion in one space dimension with linear reactions between a finite number of discrete states. In this paper, we first give a short and elementary proof of these previous results. We then show how this argument gives the evolution equations for more general cases, including subdiffusion following any fractional Fokker-Planck equation in an arbitrary $d$-dimensional spatial domain with time-dependent reactions between infinitely many discrete states. In contrast to previous works which employed a variety of technical mathematical methods, our analysis reveals that the evolution equations follow from (1) the probabilistic independence of the stochastic spatial and discrete processes describing a single particle and (2) the linearity of the integro-differential operators describing spatial movement. We also apply our results to systems combining reactions with superdiffusion.

The evaporation of the liquid droplet on a structured surface is numerically investigated using the lattice Boltzmann method. Simulations are carried out for different contact angles and pillar widths. From the simulation for the Cassie state, it is found that the evaporation starts in a pinned contact line mode. Then, when the droplet reaches the receding state, the contact line jumps to the neighboring pillar. Also, the depinning force decreases with increasing the contact angle or the pillar width. In the Wenzel state, the droplet contact line remains on the initial pillar for all of its lifetime.

We propose the use of Monte Carlo histogram reweighting to extrapolate predictions of machine learning methods. In our approach, we treat the output from a convolutional neural network as an observable in a statistical system, enabling its extrapolation over continuous ranges in parameter space. We demonstrate our proposal using the phase transition in the two-dimensional Ising model. By interpreting the output of the neural network as an order parameter, we explore connections with known observables in the system and investigate its scaling behavior. A finite-size scaling analysis is conducted based on quantities derived from the neural network that yields accurate estimates for the critical exponents and the critical temperature. The method improves the prospects of acquiring precision measurements from machine learning in physical systems without an order parameter and those where direct sampling in regions of parameter space might not be possible.

This Rapid Communication reports on the observation of an interesting phenomenon: the shape, especially the length, of a microwave plasma jet (MPJ) can be clearly influenced by simply placing a conductor near the plasma source, particularly when the nearby conductor is in contact with the external conductor of the coaxial microwave plasma generator, accompanied by a significant change in microwave reflection power from the terminal. To further investigate this discovery, the relationships between the length of the plume and some important factors, such as the conductivity and length of the nearby conductor, microwave input power, and gas flow velocity, are analyzed, and we find nonlinear rules of influence of these factors on the jet. Measurements of the electric potential around the jet reveal the nonuniform and non-neutral charge distribution inside the visible plasma plume, which plays a vital role in uncovering the mechanism underlying this phenomenon. The results are helpful for providing a deeper understanding of microwave plasma jet characteristics. More importantly, it provides guidelines to control the MPJ using simple structures.

The correlated projection superoperator techniques provide a better understanding about how correlations lead to strong non-Markovian effects in open quantum systems. Their superoperators are independent of initial state, which may not be suitable for some cases. To improve this, we develop another approach, that is extending the composite system before use the correlated projection superoperator techniques. Such an approach allows the choice of different superoperators for different initial states. We apply these techniques to a simple model to illustrate the general approach. The numerical simulations of the full Schrödinger equation of the model reveal the power and efficiency of the method.