Edmilson Roque

There is a persistent confusion about determinism and predictability. In spite of the opinions of some eminent philosophers (e.g., Popper), it is possible to understand that the two concepts are completely unrelated. In few words we can say that determinism is ontic and has to do with how Nature behaves, while predictability is epistemic and is related to what the human beings are able to compute. An analysis of the Lyapunov exponents and the Kolmogorov-Sinai entropy shows how deterministic chaos, although with an epistemic character, is non subjective at all. This should clarify the role and content of stochastic models in the description of the physical world.

Kuramoto and Battogtokh [Nonlinear Phenom. Complex Syst. 5, 380 (2002)] discovered chimera states represented by stable coexisting synchrony and asynchrony domains in a lattice of coupled oscillators. After reformulation in terms of local order parameter, the problem can be reduced to partial differential equations. We find uniformly rotating periodic in space chimera patterns as solutions of a reversible ordinary differential equation, and demonstrate a plethora of such states. In the limit of neutral coupling they reduce to analytical solutions in form of one- and two-point chimera patterns as well as localized chimera solitons. Patterns at weakly attracting coupling are characterized by virtue of a perturbative approach. Stability analysis reveals that only simplest chimeras with one synchronous region are stable.

Author(s): Sara Najem and Martin Grant

We construct a phase-field model for collective cell migration based on a Ginzburg-Landau free-energy formulation. We model adhesion, surface tension, repulsion, coattraction, and polarization, enabling us to follow the cells' morphologies and the effect of their membranes fluctuations on collective…

[Phys. Rev. E 93, 052405] Published Mon May 09, 2016

Author(s): Saad Yalouz and Vincent Pouthier

Based on the operatorial formulation of the perturbation theory, the properties of an exciton coupled with optical phonons on a star graph are investigated. Within this method, the dynamics is governed by an effective Hamiltonian, which accounts for exciton-phonon entanglement. The exciton is dresse…

[Phys. Rev. E 93, 052306] Published Mon May 09, 2016

Author(s): Stanley R. Huddy and Jie Sun

This paper presents a master stability function (MSF) approach for analyzing the stability of amplitude death (AD) in networks of delay-coupled oscillators. Unlike the familiar MSFs for instantaneously coupled networks, which typically have a single input encoding for the effects of the eigenvalues …

[Phys. Rev. E 93, 052209] Published Mon May 09, 2016

Neural networks can be used to identify phases and phase transitions in condensed matter systems via supervised machine learning. Readily programmable through modern software libraries, we show that a standard feed-forward neural network can be trained to detect multiple types of order parameter directly from raw state configurations sampled with Monte Carlo. In addition, they can detect highly non-trivial states such as Coulomb phases, and if modified to a convolutional neural network, topological phases with no conventional order parameter. We show that this classification occurs within the neural network without knowledge of the Hamiltonian or even the general locality of interactions. These results demonstrate the power of machine learning as a basic research tool in the field of condensed matter and statistical physics.

Predicting the fate of ecologies is a daunting, albeit extremely important, task. As part of this task one needs to develop an understanding of the organization, hierarchies, and correlations among the species forming the ecology. Focusing on complex food networks we present a theoretical method that allows to achieve this understanding. Starting from the adjacency matrix the method derives specific matrices that encode the various inter-species relationships. The full potential of the method is achieved in a spatial setting where one obtains detailed predictions for the emerging space-time patterns. For a variety of cases these theoretical predictions are verified through numerical simulations.

Publication date: 27 May 2016
Source:Physics Reports, Volume 635
Author(s): M. Zanin, D. Papo, P.A. Sousa, E. Menasalvas, A. Nicchi, E. Kubik, S. Boccaletti
The increasing power of computer technology does not dispense with the need to extract meaningful information out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now commonly used in the scientific community: data mining and complex network theory. Not only do complex network analysis and data mining share the same general goal, that of extracting information from complex systems to ultimately create a new compact quantifiable representation, but they also often address similar problems too. In the face of that, a surprisingly low number of researchers turn out to resort to both methodologies. One may then be tempted to conclude that these two fields are either largely redundant or totally antithetic. The starting point of this review is that this state of affairs should be put down to contingent rather than conceptual differences, and that these two fields can in fact advantageously be used in a synergistic manner. An overview of both fields is first provided, some fundamental concepts of which are illustrated. A variety of contexts in which complex network theory and data mining have been used in a synergistic manner are then presented. Contexts in which the appropriate integration of complex network metrics can lead to improved classification rates with respect to classical data mining algorithms and, conversely, contexts in which data mining can be used to tackle important issues in complex network theory applications are illustrated. Finally, ways to achieve a tighter integration between complex networks and data mining, and open lines of research are discussed.

Author(s): Kihong Chung, Yongjoo Baek, Meesoon Ha, and Hawoong Jeong

We present a self-contained discussion of the universality classes of the generalized epidemic process (GEP) on Poisson random networks, which is a simple model of social contagions with cooperative effects. These effects lead to rich phase transitional behaviors that include continuous and disconti…

[Phys. Rev. E 93, 052304] Published Fri May 06, 2016

We have studied the dynamics of the paradigmatic Kuramoto-Sakaguchi model of identical coupled phase oscilla- tors with various kinds of time-dependent connectivity using Eulerian discretization. We first explore the parameter spaces for various types of collective states using the phase plots of the two statistical quantities, namely the strength of incoherence and the discontinity measure. In the quasi-static limit of the changing of coupling range, we have observed how the system relaxes from one state to another and have identified a few interesting collective dynamical states along the way. Under a sinusoidal change of the coupling range, the global order parameter characterizing the degree of synchronization in the system is shown to undergo a hysteresis with the coupling range. Finally, we study the low-dimensional spatio-temporal dynamics of the local order parameter in the continuum limit using the recently-developed Ott-Antonsen ansatz and justify some of our numerical results. In particular, we identify an intrinsic time-scale of the Kuramoto system and show that the simulations exhibit two distinct kinds of qualitative behavior in two cases when the time-scale associated with the switching of the coupling radius is very large compared to the intrinsic time-scale and when it is comparable with the intrinsic time-scale.

A new and accurate method to determine the time delay and embedding dimension for state space reconstruction of a high dimensional system from a scalar time series using time delay embedding is presented. The time delay is obtained to unprecedented accuracy by evaluating the minima of a newly defined dimension deviation function. The efficacy of our method is tested by applying it to the Lorenz system and the Mackey-Glass system. A good agreement is obtained between the shape and embedding dimension of the physical system attractor(s) and the corresponding reconstruction(s) for both the systems studied. This, along with a heuristic argument provide a validation of the proposed method.

Motivated by the question of stability, in this letter we argue that an effective "quantum" theory can emerge in complex adaptive systems. In the concrete example of stochastic Lotka-Volterra dynamics, the relevant effective "Planck constant" associated with such emergent "quantum" theory has the dimensions of the square of the unit of time. Such an emergent quantum-like theory has inherently non-classical stability as well as coherent properties that are not, in principle, endangered by thermal fluctuations and therefore might be of crucial importance in complex adaptive systems.

Concerns have been raised about labor market imbalances that see a growing number of postdoctoral researchers pursuing a limited number of faculty positions (1–4). Proposed demand-side solutions include capping the duration of postdoc training or hiring more permanent staff scientists (1, 4, 5). Others focus on the supply side, arguing that Ph.D.'s need better information about labor market conditions and nonacademic career options (4, 6, 7). Unfortunately, it is not clear why Ph.D. students pursue postdoc positions and how their plans depend on individual-level factors, such as career goals or labor market perceptions. We describe evidence of a “default” postdoc and of “holding patterns” that suggest a need for increased attention to career planning among students, their mentors, graduate schools, and funders. Authors: Henry Sauermann, Michael Roach

Author(s): M. G. Clerc, S. Coulibaly, M. A. Ferré, M. A. García-Ñustes, and R. G. Rojas

Coupled oscillators can exhibit complex self-organization behavior such as phase turbulence, spatiotemporal intermittency, and chimera states. The latter corresponds to a coexistence of coherent and incoherent states apparently promoted by nonlocal or global coupling. Here we investigate the existen…

[Phys. Rev. E 93, 052204] Published Thu May 05, 2016

Nonequilibria show currents that are maintained as the result of a steady driving. We ask here what decides their direction. It is not only the second law, or the positivity of the entropy production that decides; also non-dissipative aspects often matter and sometimes completely decide.

Nature advance online publication 04 May 2016. doi:10.1038/nature18019

Authors: Jianxi Gao, Baruch Barzel & Albert-László Barabási

We offer analytical solutions to classical percolation problems on hypergraphs with arbitrary vertex degree and hyperedge cardinality distributions. We introduce a generalization of the 2-core for hypergraph and we show that it can emerge in either a continuous or a hybrid percolation transition. We also define two different hypergraph cores related to the hyperedge cover and vertex cover problems on hypergraphs. We validate our analytical results with extensive numerical simulations.

In a range of scientific coauthorship networks, transitions emerge in degree distributions, correlations between degrees and local clustering coefficients, etc. The existence of those transitions could be regarded as a result of the diversity in collaboration behaviours of scientific fields. A growing geometric hypergraph built on a cluster of concentric circles is proposed to model two specific collaboration behaviours, namely the behaviour of leaders and that of other members in research teams. The model successfully predicts the transitions, as well as many common features of coauthorship networks. Particulary, it realizes a process of deriving the complex "scale-free" property from the simple "yes/no" experiments. Moreover, it gives a reasonable explanation for the emergence of transitions with the difference of collaboration behaviours between leaders and other members. The difference emerges in the evolution of research teams, which synthetically addresses several specific factors of generating collaborations, namely the communications between research teams, the academic impacts and homophily of authors.

We study impact of multiplexing on the global phase synchronizability of different layers in the delayed coupled multiplex networks. We find that at strong couplings, the multiplexing induces the global synchronization in sparse networks. The introduction of global synchrony depends on the connection density of the layers being multiplexed, which further depends on the underlying network architecture. Moreover, multiplexing may lead to a transition from a quasi-periodic or chaotic evolution to a periodic evolution. For the periodic case, the multiplexing may lead to a change in the period of the dynamical evolution. Additionally, delay in the couplings may bring upon synchrony to those multiplex networks which do not exhibit synchronization for the undelayed evolution. Using a simple example of two globally connected layers forming a multiplex network, we show how delay brings upon a possibility for the inter layer global synchrony, that is not possible for the undelayed evolution.

Nature Physics 12, 472 (2016). doi:10.1038/nphys3619

Authors: Ido Nitsan, Stavit Drori, Yair E. Lewis, Shlomi Cohen & Shelly Tzlil

Cell–cell communication, which enables cells to coordinate their activity and is essential for growth, development and function, is usually ascribed a chemical or electrical origin. However, cells can exert forces and respond to environment elasticity and to mechanical deformations created by their neighbours. The extent to which this mechanosensing ability facilitates intercellular communication remains unclear. Here we demonstrate mechanical communication between cells directly for the first time, providing evidence for a long-range interaction that induces long-lasting alterations in interacting cells. We show that an isolated cardiac cell can be trained to beat at a given frequency by mechanically stimulating the underlying substrate. Deformations are induced using an oscillatory mechanical probe that mimics the deformations generated by a beating neighbouring cardiac cell. Unlike electrical field stimulation, the probe-induced beating rate is maintained by the cell for an hour after the stimulation stops, implying that long-term modifications occur within the cell. These long-term alterations provide a mechanism for cells that communicate mechanically to be less variable in their electromechanical delay. Mechanical coupling between cells therefore ensures that the final outcome of action potential pacing is synchronized beating. We further show that the contractile machinery is essential for mechanical communication.

When agreement-dynamics models are placed on a directed random graph, a fraction of sites $\exp(-z)$, where $z$ is the average degree, becomes permanently fixed or flickering. In the Voter model, which has no surface tension, such zealots or flickers freely spread their opinions and that makes the system disordered. For models with a surface tension, like the Ising model or the Naming Game model, their role is limited and such systems are ordered at large~$z$. However, when $z$ decreases, the density of zealots or flickers increases, and below a certain threshold ($z\sim 1.9-2.0$) the system becomes disordered. Our results show that the agreement dynamics on directed networks is much different from their undirected analogues.

How to control synchronization and order in a collection of interacting oscillators remains a challenging question. We address this by constructing a hydrodynamic theory for {\em active} phase fluctuations in a collection of nearly phase-coherent oscillators, dynamically coupled to a diffusive species, in two dimensions.

We show that the interplay between the active effects and the diffusion coefficient of the diffusing species leads to a variety of phenomena, ranging from synchronization with long range, nearly long range and quasi long range orders to instabilities and desynchronization with short range order of the oscillator phases. Our results highlight sensitive dependences of synchronization on the active effects. These should be testable in oscillating chemical reactions in the presence of different reaction inhibitors/ facilitators, in oriented cytoskeletal extracts with motors, or chemotaxing, live, chiral, oriented, microbial suspensions.

The increasing power of computer technology does not dispense with the need to extract meaningful in- formation out of data sets of ever growing size, and indeed typically exacerbates the complexity of this task. To tackle this general problem, two methods have emerged, at chronologically different times, that are now commonly used in the scientific community: data mining and complex network theory. Not only do complex network analysis and data mining share the same general goal, that of extracting information from complex systems to ultimately create a new compact quantifiable representation, but they also often address similar problems too. In the face of that, a surprisingly low number of researchers turn out to resort to both methodologies. One may then be tempted to conclude that these two fields are either largely redundant or totally antithetic. The starting point of this review is that this state of affairs should be put down to contingent rather than conceptual differences, and that these two fields can in fact advantageously be used in a synergistic manner. An overview of both fields is first provided, some fundamental concepts of which are illustrated. A variety of contexts in which complex network theory and data mining have been used in a synergistic manner are then presented. Contexts in which the appropriate integration of complex network metrics can lead to improved classification rates with respect to classical data mining algorithms and, conversely, contexts in which data mining can be used to tackle important issues in complex network theory applications are illustrated. Finally, ways to achieve a tighter integration between complex networks and data mining, and open lines of research are discussed.

Author(s): Wes Viles, Cedric E. Ginestet, Ariana Tang, Mark A. Kramer, and Eric D. Kolaczyk

We consider the problem of distinguishing between different rates of percolation under noise. A statistical model of percolation is constructed allowing for the birth and death of edges as well as the presence of noise in the observations. This graph-valued stochastic process is composed of a latent…

[Phys. Rev. E 93, 052301] Published Mon May 02, 2016

Author(s): Xin Jiang and Daniel M. Abrams

When identical oscillators are coupled together in a network, dynamical steady states are often assumed to reflect network symmetries. Here, we show that alternative persistent states may also exist that break the symmetries of the underlying coupling network. We further show that these symmetry-bro…

[Phys. Rev. E 93, 052202] Published Mon May 02, 2016

Author(s): C. I. N. Sampaio Filho, T. B. dos Santos, A. A. Moreira, F. G. B. Moreira, and J. S. Andrade, Jr.

We study through Monte Carlo simulations and finite-size scaling analysis the nonequilibrium phase transitions of the majority-vote model taking place on spatially embedded networks. These structures are built from an underlying regular lattice over which directed long-range connections are randomly…

[Phys. Rev. E 93, 052101] Published Mon May 02, 2016

Hysteresis is more than just an interesting oddity, which occurs in materials with a first-order transition. It is a real obstacle on the path from existing lab-scale prototypes of magnetic refrigerators towards commercialization of this potentially disruptive cooling technology. Indeed, the reversibility of the magnetocaloric effect, being essential for magnetic heat pumps, strongly depends on the width of the thermal hysteresis and therefore it is necessary to understand the mechanisms causing hysteresis and to find solutions how to minimize losses associated with thermal hysteresis in order to maximize the efficiency of magnetic cooling devices. In this work, we discuss fundamental aspects, which can contribute to thermal hysteresis and we are developing strategies for at least partially overcoming the hysteresis problem in some selected classes of magnetocaloric materials with large application potential. Doing so, we refer to the most relevant classes of magnetic refrigerants La-Fe-Si-, Heusler- and Fe2P-type compounds.

Author(s): Jinjie Zhu, Zhen Chen, and Xianbin Liu

We study firing behaviors and the transitions among them in small-world noisy neuronal networks with electrical synapses and information transmission delay. Each neuron is modeled by a two-dimensional Rulkov map neuron. The distance between neurons, which is a main source of the time delay, is taken…

[Phys. Rev. E 93, 042417] Published Thu Apr 28, 2016

Role of time delay on intracellular calcium dynamics driven by non-Gaussian noises

Scientific Reports, Published online: 28 April 2016; doi:10.1038/srep25067

We report on the emergence of robust multi-clustered chimera states in a dissipative-driven system of symmetrically and locally coupled identical SQUID oscillators. The "snake-like" resonance curve of the single SQUID (Superconducting QUantum Interference Device) is the key to the formation of the chimera states and is responsible for the extreme multistability exhibited by the coupled system that leads to attractor crowding at the geometrical resonance (inductive-capacitive) frequency. Until now, chimera states were mostly believed to exist for nonlocal coupling. Our findings provide theoretical evidence that nearest neighbor interactions are indeed capable of supporting such states in a wide parameter range. SQUID metamaterials are the subject of intense experimental investigations and we are highly confident that the complex dynamics demonstrated in this manuscript can be confirmed in the laboratory.