Autocorrelations in stationary systems are time-symmetric, irrespective of the signal's properties. Linear dynamics is usually associated with signals which are statistically time inversion invariant. This is known to be broken for non-Gaussian models. In this paper, we develop a theoretical framework of time reversibility of linear models based on noncontinuous driving. We identify the inverse decay exponent of the autocorrelation function as either a characteristic time for the building-up of extreme events in the time series or of relaxations after these extreme events. If the characteristic time is known, the dynamics can be inverted in both directions in time and the residuals can be compared, which gives a criterion for the type of time inversion asymmetry. The method is applied to two time series from atmospheric science with different behavior.

When dealing with spreading processes on networks it can be of the utmost importance to test the reliability of data and identify potential unobserved spreading paths. In this paper we address these problems and propose methods for hidden layer identification and reconstruction. We also explore the interplay between difficulty of the task and the structure of the multilayer network describing the whole system where the spreading process occurs. Our methods stem from an exact expression for the likelihood of a cascade in the susceptible-infected model on an arbitrary graph. We then show that by imploring statistical properties of unimodal distributions and simple heuristics describing joint likelihood of a series of cascades one can obtain an estimate of both existence of a hidden layer and its content with success rates far exceeding those of a null model. We conduct our analyses on both synthetic and real-world networks providing evidence for the viability of the approach presented.

The syndecans represent an ongoing research field focused on their regulatory roles in normal and pathological conditions. The role of syndecans in cancer progression is well documented, implicating their importance in diagnosis and even proposing various potential cancer treatments. Thus, the characterization of the unbinding properties at the single-molecule level will appeal to their use as targets for therapeutics. In our study, syndecan-1 and syndecan-4 were measured during the interaction with the vitronectin HEP II binding site. Our findings show that syndecans are calcium ion dependent molecules that reveal distinct, unbinding properties indicating the alterations in the structure of heparan sulfate (HS) chains, possibly in the chain sequence or sulfation pattern. In this way, we suppose that HS chain affinity to extracellular matrix proteins may govern cancer invasion by altering the syndecans’ ability to interact with cancer-related receptors present in the tumor microenvironment, thereby promoting the activation of various signaling cascades regulating tumor cell behavior.

Epidemic models are useful tools in the fight against infectious diseases, as they allow policy makers to test and compare various strategies to limit disease transmission while mitigating collateral damage on the economy. Epidemic models that are more faithful to the microscopic details of disease transmission can offer more reliable projections, which in turn can lead to more reliable control strategies. For example, many epidemic models describe disease progression via a series of artificial stages or compartments (e.g., exposed, activated, infectious, etc.) but an epidemic model that explicitly tracks time since infection (TSI) can provide a more precise description. At present, epidemic models with compartments are more common than TSI models, largely due to the higher computational cost and complexity typically associated with TSI models. Here, however, we show that with the right discretization scheme a TSI model is not much more difficult to solve than a compartment model with three or four stages for the infected class. We also provide a perspective for adding stages to a TSI model in a way that decouples the disease transmission dynamics from the residence time distributions at each stage. These results are also generalized for age-structured TSI models in an Appendix. Finally, as proof of principle for the efficiency of the proposed numerical methods, we provide calculations for optimal epidemic control by nonpharmaceutical intervention. Many of the tools described in this paper are available through the software package pyross.

We employ a recently derived semirealistic set of coarse-grained interactions to simulate polymer brushes of cis-1,4-polybutadiene grafted on a cuprous-oxide surface within the framework of dissipative particle dynamics. We consider two types of brushes, I and Y, that differ in the way they are connected to the surface. Our model explores the impact of free polymer chain length, grafting density of the brush, and imposed shear rate on the structural and dynamic properties of complex metal oxide polymer interfaces.

Attention is paid to a specific form of thermal convection which encompasses viscoelastic and thermovibrational effects in a single problem or framework. The main objective is understanding the relationship between the phenomenon of overstability and periodic forcing through numerical solution of the governing equations in their complete, time-dependent, and nonlinear form. Fluid motion is found for values of the control parameter one order of magnitude smaller than the threshold to be exceeded in the equivalent Newtonian case. When the disturbances saturate their amplitude, patterns emerge that are reminiscent of the superlattice structures typical of complex order. In the present case, such peculiar modes of convection are driven by the coexistence of two distinct spatial scales, each displaying a different temporal dependence, driven by the interplay of the time-varying (stabilizing or destabilizing) acceleration induced by vibrations and the ability of the fluid to store and release elastic energy.

A symmetry-breaking mechanism is investigated that creates bistability between fully and partially synchronized states in oscillator networks. Two populations of oscillators with unimodal frequency distribution and different amplitudes, in the presence of weak global coupling, are shown to simplify to a modular network with asymmetrical coupling. With increasing the coupling strength, a synchronization transition is observed with an isolated fully synchronized state. The results are interpreted theoretically in the thermodynamic limit and confirmed in experiments with chemical oscillators.

Solutions to hydrodynamic equations, which are used for a vast variety of physical problems, are assumed to be specified by boundary conditions and initial conditions on the hydrodynamic variables only. Initial values of other variables are assumed to be irrelevant for a hydrodynamic description. We show that this assumption is not correct because of the existence of long-time-tail effects that are ubiquitous in systems governed by hydrodynamic equations. We illustrate this breakdown of a hydrodynamic description by means of the simple example of diffusion in a disordered electron system.

We construct a two-tailed peaks-over-threshold Hawkes model that captures asymmetric self- and cross-excitation in and between left- and right-tail extreme values within a time series. We demonstrate its applicability by investigating extreme gains and losses within the daily log-returns of the S&P 500 equity index. We find that the arrivals of extreme losses and gains are described by a common conditional intensity to which losses contribute twice as much as gains. However, the contribution of the former decays almost five times more quickly than that of the latter. We attribute these asymmetries to the different reactions of market traders to extreme upward and downward movements of asset prices: an example of negativity bias, wherein trauma is more salient than euphoria.

While the analytical solution for the marginal distribution of a stochastic chemical reaction network has been extensively studied, its joint distribution, i.e., the solution of a high-dimensional chemical master equation, has received much less attention. Here we develop an alternative method of computing the exact joint distributions of a wide class of first-order stochastic reaction systems in steady-state conditions. The effectiveness of our method is validated by applying it to four gene expression models of biological significance, including models with 2A peptides, nascent mRNA, gene regulation, translational bursting, and alternative splicing.

We investigate by means of Monte Carlo simulations the dynamic phase transition of the two-dimensional kinetic Blume-Capel model under a periodically oscillating magnetic field in the presence of a quenched random crystal-field coupling. We analyze the universality principles of this dynamic transition for various values of the crystal-field coupling at the originally second-order regime of the corresponding equilibrium phase diagram of the model. A detailed finite-size scaling analysis indicates that the observed nonequilibrium phase transition belongs to the universality class of the equilibrium Ising ferromagnet with additional logarithmic corrections in the scaling behavior of the heat capacity. Our results are in agreement with earlier works on kinetic Ising models.

Inspired by the recent results on totally asymmetric simple exclusion processes on a periodic lattice with short-ranged quenched hopping rates [A. Haldar and A. Basu, Phys. Rev. Research 2, 043073 (2020)], we study the universal scaling properties of the Kardar-Parisi-Zhang (KPZ) equation with short-ranged quenched columnar disorder in general $d$ dimensions. We show that there are generic propagating modes in the system that have their origin in the quenched disorder and make the system anisotropic. We argue that the presence of the propagating modes actually make the effects of the quenched disorder irrelevant, making the universal long wavelength scaling property belong to the well-known KPZ universality class. On the other hand, when these waves vanish in a special limit of the model, new universality class emerges with dimension $d=4$ as the lower critical dimension, above which the system is speculated to admit a disorder-induced roughening transition to a perturbatively inaccessible rough phase.

In this paper, we study collective interaction dynamics emerging in the game of football (soccer). To do so, we surveyed a database containing body-sensor traces measured during three professional football matches, where we observed statistical patterns that we used to propose a stochastic model for the players' motion in the field. The model, which is based on linear interactions, captures to a good approximation the spatiotemporal dynamics of a football team. Our theoretical framework, therefore, can be an effective analytical tool to uncover the underlying cooperative mechanisms behind the complexity of football plays. Moreover, we showed that it can provide handy theoretical support for coaches to evaluate teams' and players' performances in both training sessions and competitive scenarios.

The edge-based compartmental modeling (EBCM) approach has been used widely to characterize the nonrecurrent epidemic spreading dynamics (e.g., the susceptible-infected-recovered model) in complex networks. By using the probability theory, we derived an individual-based formulation for this approach, which we herein refer to as the microscopic EBCM method. We found that both for small and large initial infection numbers, the epidemic evolution agreed well with the ensemble averages of our stochastic simulations on different complex networks. Moreover, we showed that the dynamical message passing model, the standard EBCM system, and the pair quenched mean-field equations can be deduced by our microscopic EBCM method. In addition, the microscopic EBCM method was used to analyze the effect of epidemic awareness on networks. Importantly, the simple EBCM model for exponential awareness was developed. Our method provides a way for handling nontrivial disease transmission processes with irreversible dynamics.

We study a simple deterministic map that leads a fully connected network to the Heider balance. The map is realized by an algorithm that updates all links synchronously in a way depending on the state of the entire network. We observe that the probability of reaching a balanced state increases with the system size $N$. Jammed states become less frequent for larger $N$. The algorithm generates also limit cycles, mostly of length 2, but also of length 3, 4, 6, 12, or 14. We give a simple argument to estimate the mean size of basins of attraction of balanced states, and we discuss the symmetries of the system including the automorphism group as well as gauge invariance of triad configurations. We argue that both symmetries play an essential role in the occurrence of cycles observed in the synchronous dynamics realized by the algorithm.

Competition between alternative states is an essential process in social and biological networks. Neutral competition can be represented by an unbiased random drift process in which the states of vertices (e.g., opinions, genotypes, or species) in a network are updated by repeatedly selecting two connected vertices. One of these vertices copies the state of the selected neighbor. Such updates are repeated until all vertices are in the same “consensus” state. There is no unique rule for selecting the vertex pair to be updated. Real-world processes comprise three limiting factors that can influence the selected edge and the direction of spread: (1) the rate at which a vertex sends a state to its neighbors, (2) the rate at which a state is received by a neighbor, and (3) the rate at which a state can be exchanged through a connecting edge. We investigate how these three limitations influence neutral competition in networks with two communities generated by a stochastic block model. By using Monte Carlo simulations, we show how the community structure and update rule determine the states' success probabilities and the time until a consensus is reached. We present a heterogeneous mean-field theory that agrees well with the Monte Carlo simulations. The effectiveness of the heterogeneous mean-field theory implies that quantitative predictions about the consensus are possible even if empirical data (e.g., from ecological fieldwork or observations of social interactions) do not allow a complete reconstruction of all edges in the network.

An analytical self-consistent approach was recently established to predict the ionization potential depression (IPD) in multicomponent dense plasmas, which is achieved by considering the self-energy of ions and electrons within the quantum statistical theory. In order to explicitly account for the exchange-correlation effect of electrons, we incorporate the effective static approximation of local field correction (LFC) within our IPD framework through the connection of dynamical structure factor. The effective static approximation poses an accurate description for the asymptotic large wave number behavior with the recently developed machine learning representation of static LFC induced from the path-integral Monte Carlo data. Our calculation shows that the introduction of static LFC through dynamical structure factor brings a nontrivial influence on IPD at warm/hot dense matter conditions. The correlation effect within static LFC could provide up to 20% correction to free-electron contribution of IPD in the strong coupling and degeneracy regime. Furthermore, a new screening factor is obtained from the density distribution of free electrons calculated within the average-atom model, with which excellent agreements are observed with other methods and experiments at warm/hot dense matter conditions.

Inverse magnetic energy transfer from small to large scales is a key physical process for the origin of large-scale strong magnetic fields in the universe. However, so far, from the magnetohydrodynamic perspective, the onset of inverse transfer is still not fully understood, especially the underlying dynamics. Here, we use both two-dimensional and three-dimensional particle-in-cell simulations to show the self-consistent dynamics of inverse transfer in collisionless decaying turbulent plasmas. Using the space filtering technique in theory and numerical analyses, we identify magnetic reconnection as the onset and fundamental drive for inverse transfer, where, specifically, the subscale electromotive force driven by magnetic reconnection do work on the large-scale magnetic field, resulting in energy transfer from small to large scales. The mechanism is also verified by the strong correlations in locations and characteristic scales between inverse transfer and magnetic reconnection.

In material design, microstructure characterization and reconstruction are indispensable for understanding the role of a structure in a process-structure-property relation. The significant contribution of this paper is to introduce a methodology for the characterization and generation of material microstructures using deep generative networks as the first step in the establishment of a process-structure-property linkage for forward or inverse material design. Our approach can be divided into two parts: (i) characterization of material microstructures by a vector quantized variational auto-encoder, and (ii) determination of the correlation between the extracted microstructure characterizations and the given conditions, such as processing parameters and/or material properties, by a pixel convolutional neural network. As an example, we tested our framework in the generation of low-carbon-steel microstructures from the given material processing. The results were in satisfactory agreement with the experimental observation qualitatively and quantitatively, demonstrating the potential of applying the proposed method to forward or inverse material design. One of the advantages of the proposed methodology lies in the capability to capture the stochastic nature behind the microstructure generation. As a result, this methodology enables us to build a process-structure-property linkage while quantifying uncertainties, which not only makes a prediction more robust but also shows a way toward enhancing our understanding of the stochastic competitive phenomena behind the generation of material microstructures.

For a system described by a multivariate probability density function obeying the fluctuation theorem, the average dissipation is lower bounded by the degree of asymmetry of the marginal distributions (namely the relative entropy between the marginal and its mirror image). We formally prove that such a lower bound is tighter than the recently reported bound expressed in terms of the precision of the marginal (i.e., the thermodynamic uncertainty relation) and is saturable. We illustrate the result with examples and we apply it to achieve one of the most accurate experimental estimations of dissipation associated with quantum annealing to date.