Extremal statistics for a resetting Brownian motion before its first-passage time.

We study the extreme value statistics of a one-dimensional resetting Brownian motion (RBM) till its first passage through the origin starting from the position x_{0} (>0). By deriving the exit probability of RBM in an interval [0,M] from the origin, we obtain the distribution P_{r}(M|x_{0}) of the maximum displacement M and thus gives the expected value 〈M〉 of M as functions of the resetting rate r and x_{0}. We find that 〈M〉 decreases monotonically as r increases, and tends to 2x_{0} as r→∞.

Entropy rate of random walks on complex networks under stochastic resetting.

Stochastic processes under resetting at random times have attracted a lot of attention in recent years and served as illustrations of nontrivial and interesting static and dynamic features of stochastic dynamics. In this paper, we aim to address how the entropy rate is affected by stochastic resetting in discrete-time Markovian processes, and we explore nontrivial effects of the resetting in the mixing properties of a stochastic process. In particular, we consider resetting random walks (RRWs) with a single resetting node on three different types of networks: degree-regular random networks, a finite-size Cayley tree, and a Barabási-Albert (BA) scale-free network, and we compute the entropy rate as a function of the resetting probability γ.

Random walks on complex networks under time-dependent stochastic resetting.

We study discrete-time random walks on networks subject to a time-dependent stochastic resetting, where the walker either hops randomly between neighboring nodes with a probability 1-ϕ(a) or is reset to a given node with a complementary probability ϕ(a). The resetting probability ϕ(a) depends on the time a since the last reset event (also called a, the age of the walker). Using the renewal approach and spectral decomposition of the transition matrix, we formulate the stationary occupation probability of the walker at each node and the mean first passage time between two arbitrary nodes.

Full reconstruction of simplicial complexes from binary contagion and Ising data.

Previous efforts on data-based reconstruction focused on complex networks with pairwise or two-body interactions. There is a growing interest in networks with higher-order or many-body interactions, raising the need to reconstruct such networks based on observational data. We develop a general framework combining statistical inference and expectation maximization to fully reconstruct 2-simplicial complexes with two- and three-body interactions based on binary time-series data from two types of discrete-state dynamics.

First passage of a diffusing particle under stochastic resetting in bounded domains with spherical symmetry.

We investigate the first passage properties of a Brownian particle diffusing freely inside a d-dimensional sphere with absorbing spherical surface subject to stochastic resetting. We derive the mean time to absorption (MTA) as functions of resetting rate γ and initial distance r of the particle to the center of the sphere. We find that when r>r_{c} there exists a nonzero optimal resetting rate γ_{opt} at which the MTA is a minimum, where r_{c}=sqrt[d/(d+4)]R and R is the radius of the sphere.

Random walks on complex networks with multiple resetting nodes: A renewal approach.

Due to wide applications in diverse fields, random walks subject to stochastic resetting have attracted considerable attention in the last decade. In this paper, we study discrete-time random walks on complex networks with multiple resetting nodes. Using a renewal approach, we derive exact expressions of the occupation probability of the walker in each node and mean first-passage time between arbitrary two nodes.

Random walks on complex networks with first-passage resetting.

We study discrete-time random walks on arbitrary networks with first-passage resetting processes. To the end, a set of nodes are chosen as observable nodes, and the walker is reset instantaneously to a given resetting node whenever it hits either of observable nodes. We derive exact expressions of the stationary occupation probability, the average number of resets in the long time, and the mean first-passage time between arbitrary two nonobservable nodes.

Non-Markovian majority-vote model.

Non-Markovian dynamics pervades human activity and social networks and it induces memory effects and burstiness in a wide range of processes including interevent time distributions, duration of interactions in temporal networks, and human mobility. Here, we propose a non-Markovian majority-vote model (NMMV) that introduces non-Markovian effects in the standard (Markovian) majority-vote model (SMV). The SMV model is one of the simplest two-state stochastic models for studying opinion dynamics, and displays a continuous order-disorder phase transition at a critical noise.

An improved belief propagation algorithm for detecting mesoscale structure in complex networks.

The framework of statistical inference has been successfully used to detect the mesoscale structures in complex networks such as community structure and core-periphery (CP) structure. The main principle is that the stochastic block model is used to fit the observed network and the learned parameters indicating the group assignment, in which the parameters of model are often calculated via an expectation-maximization algorithm and a belief propagation (BP) algorithm, is implemented to calculate the decomposition itself. In the derivation process of the BP algorithm, some approximations were made by omitting the effects of node's neighbors, the approximations do not hold if the degrees of some nodes are extremely large.

Reconstructing signed networks via Ising dynamics.

Revealing unknown network structure from observed data is a fundamental inverse problem in network science. Current reconstruction approaches were mainly proposed to infer the unsigned networks. However, many social relationships, such as friends and foes, can be represented as signed social networks that contain positive and negative links.

Hybrid multiscale coarse-graining for dynamics on complex networks.

We propose a hybrid multiscale coarse-grained (HMCG) method which combines a fine Monte Carlo (MC) simulation on the part of nodes of interest with a more coarse Langevin dynamics on the rest part. We demonstrate the validity of our method by analyzing the equilibrium Ising model and the nonequilibrium susceptible-infected-susceptible model. It is found that HMCG not only works very well in reproducing the phase transitions and critical phenomena of the microscopic models, but also accelerates the evaluation of dynamics with significant computational savings compared to microscopic MC simulations directly for the whole networks.

Phase transitions in a multistate majority-vote model on complex networks.

We generalize the original majority-vote (MV) model from two states to arbitrary p states and study the order-disorder phase transitions in such a p-state MV model on complex networks. By extensive Monte Carlo simulations and a mean-field theory, we show that for p≥3 the order of phase transition is essentially different from a continuous second-order phase transition in the original two-state MV model. Instead, for p≥3 the model displays a discontinuous first-order phase transition, which is manifested by the appearance of the hysteresis phenomenon near the phase transition.

Detection of core-periphery structure in networks based on 3-tuple motifs.

Detecting mesoscale structure, such as community structure, is of vital importance for analyzing complex networks. Recently, a new mesoscale structure, core-periphery (CP) structure, has been identified in many real-world systems. In this paper, we propose an effective algorithm for detecting CP structure based on a 3-tuple motif.

Statistical inference approach to structural reconstruction of complex networks from binary time series.

Complex networks hosting binary-state dynamics arise in a variety of contexts. In spite of previous works, to fully reconstruct the network structure from observed binary data remains challenging. We articulate a statistical inference based approach to this problem.

Solitons as candidates for energy carriers in Fermi-Pasta-Ulam lattices.

Currently, effective phonons (renormalized or interacting phonons) rather than solitary waves (for short, solitons) are regarded as the energy carriers in nonlinear lattices. In this work, by using the approximate soliton solutions of the corresponding equations of motion and adopting the Boltzmann distribution for these solitons, the average velocities of solitons are obtained and are compared with the sound velocities of energy transfer. Excellent agreements with the numerical results and the predictions of other existing theories are shown in both the symmetric Fermi-Pasta-Ulam-β lattices and the asymmetric Fermi-Pasta-Ulam-αβ lattices.

A unified method of detecting core-periphery structure and community structure in networks.

The core-periphery structure and the community structure are two typical meso-scale structures in complex networks. Although community detection has been extensively investigated from different perspectives, the definition and the detection of the core-periphery structure have not received much attention. Furthermore, the detection problems of the core-periphery and community structure were separately investigated.

Optimal allocation of resources for suppressing epidemic spreading on networks.

Efficient allocation of limited medical resources is crucial for controlling epidemic spreading on networks. Based on the susceptible-infected-susceptible model, we solve the optimization problem of how best to allocate the limited resources so as to minimize prevalence, providing that the curing rate of each node is positively correlated to its medical resource. By quenched mean-field theory and heterogeneous mean-field (HMF) theory, we prove that an epidemic outbreak will be suppressed to the greatest extent if the curing rate of each node is directly proportional to its degree, under which the effective infection rate λ has a maximal threshold λ_{c}^{opt}=1/〈k〉, where 〈k〉 is the average degree of the underlying network.

Large deviation induced phase switch in an inertial majority-vote model.

We theoretically study noise-induced phase switch phenomena in an inertial majority-vote (IMV) model introduced in a recent paper [Chen et al., Phys. Rev.

First-order phase transition in a majority-vote model with inertia.

We generalize the original majority-vote model by incorporating inertia into the microscopic dynamics of the spin flipping, where the spin-flip probability of any individual depends not only on the states of its neighbors, but also on its own state. Surprisingly, the order-disorder phase transition is changed from a usual continuous or second-order type to a discontinuous or first-order one when the inertia is above an appropriate level. A central feature of such an explosive transition is a strong hysteresis behavior as noise intensity goes forward and backward.

Interplay between the local information based behavioral responses and the epidemic spreading in complex networks.

The spreading of an infectious disease can trigger human behavior responses to the disease, which in turn plays a crucial role on the spreading of epidemic. In this study, to illustrate the impacts of the human behavioral responses, a new class of individuals, S(F), is introduced to the classical susceptible-infected-recovered model. In the model, S(F) state represents that susceptible individuals who take self-initiate protective measures to lower the probability of being infected, and a susceptible individual may go to S(F) state with a response rate when contacting an infectious neighbor.

Critical noise of majority-vote model on complex networks.

The majority-vote model with noise is one of the simplest nonequilibrium statistical model that has been extensively studied in the context of complex networks. However, the relationship between the critical noise where the order-disorder phase transition takes place and the topology of the underlying networks is still lacking. In this paper, we use the heterogeneous mean-field theory to derive the rate equation for governing the model's dynamics that can analytically determine the critical noise f(c) in the limit of infinite network size N→∞.

Mobility and density induced amplitude death in metapopulation networks of coupled oscillators.

We investigate the effects of mobility and density on the amplitude death of coupled Landau-Stuart oscillators and Brusselators in metapopulation networks, wherein each node represents a subpopulation occupied any number of mobile individuals. By numerical simulations in scale-free topology, we find that the systems undergo phase transitions from incoherent state to amplitude death, and then to frequency synchronization with increasing the mobility rate or density of oscillators. Especially, there exists an extent of intermediate mobility rate and density that can lead to global oscillator death.

Modulating the interface quality and electrical properties of HfTiO/InGaAs gate stack by atomic-layer-deposition-derived Al₂O₃ passivation layer.

In current work, the effect of the growth cycles of atomic-layer-deposition (ALD) derived ultrathin Al2O3 interfacial passivation layer on the interface chemistry and electrical properties of MOS capacitors based on sputtering-derived HfTiO as gate dielectric on InGaAs substrate. Significant suppression of formation of Ga-O and As-O bond from InGaAs surface after deposition of ALD Al2O3 with growth cycles of 20 has been achieved. X-ray photoelectron spectroscopy (XPS) measurements have confirmed that suppressing the formation of interfacial layer at HfTiO/InGaAs interface can be achieved by introducing the Al2O3 interface passivation layer.

Explosive synchronization transitions in complex neural networks.

It has been recently reported that explosive synchronization transitions can take place in networks of phase oscillators [Gómez-Gardeñes et al. Phys. Rev.