talks #research groups
Seminar by Prof.Erdal Oguz
Room Eduard Fontsere, Facultat de Física, UB
20190509 12:00:00
Author: Erdal Oguz
University of Tel Aviv Title: Hyperuniformity of quasicrystals and related structures Abstract Density fluctuations in manybody systems are of fundamental importance throughout various scientific disciplines. Hyperuniform systems, which include crystals and quasicrystals, have density fluctuations that are anomalously suppressed at long wavelengths compared to the fluctuations in typical disordered point distributions such as in ideal gases and liquids. Such systems are characterized by a local number variance associated with points within a spherical observation window of radius R that grows more slowly than the window volume in the largeR limit. In this talk, we will provide the first rigorous hyperuniformity analysis of quasicrystals obtained by cutandprojection method and related points sets derived from substitution tilings. Most importantly, we reveal that onedimensional quasicrystals produced by projection from a twodimensional lattice fall into two distinct classes determined by the width of the projection window. The number variance is either uniformly bounded in the one class for large R, or it scales logarithmically in R in the other class. This distinction provides a new classification of onedimensional quasicrystalline systems and, as we show, the two classes exhibit distinct physical properties. Our analysis further suggests that measures of hyperuniformity may define new classes of quasicrystals in higher dimensions as well.
Prof.Gennady Gor Seminar
20190503 12:00:00
Date: Friday 3rd of May at 12:00
Place: Aula Seminari 3.20, Dept. Física de la Matèria Condensada
Speaker: Gennady Gor (New Jersey Institute of Technology)
Title: Elastic Properties of Confined Fluids Probed by Ultrasound and by Molecular Simulations
Abstract: Almost 25 years ago measurements of ultrasonic wave propagation during adsorption and
desorption of nhexane in nanoporous Vycor glass were reported [1]. Similar experiments were performed
recently with liquid argon [2], which stimulated molecular simulation studies of the properties
probed in those experiments.
Ultrasonic measurements provide information on the elastic moduli (shear and longitudinal) of
the porous sample at various filling fractions. When pores are filled with liquidlike condensate, the
Gassmann equation should relate the experimentally measured longitudinal modulus of the sample
to the moduli of porous solid and compressibility of the fluid [3]. However, the experimental data
for Vycor glass filled with both argon and hexane showed mismatch with the Gassmann equation
predictions [4].
Our molecular simulations explained this mismatch, showing that liquids in confinement are
stiffer than in the bulk phase at the same conditions [5, 6, 7]. Once this effect is taken into
account, the Gassmann equation becomes valid [4]. In addition to that, our molecular simulations
showed two fundamental regularities: (1) modulus of a confined fluid is a linear function of the
solvation pressure in the fluid; (2) modulus of the fluid is a linear function of the reciprocal pore
size. Overall, our results suggest that when considering elastic properties of fluids in nanopores,
the confinement effects have to be taken into account.
References:
[1] J. H. Page, J. Liu, B. Abeles, E. Herbolzheimer, H. W. Deckman, and D. A. Weitz, Phys. Rev. E 52, 2763
(1995).
[2] K. Schappert and R. Pelster, Europhys. Lett. 105, 56001 (2014).
[3] F. Gassmann, Viertel. Naturforsch. Ges. Zürich 96, 1 (1951).
[4] G. Y. Gor and B. Gurevich, Geophys. Res. Lett. 45, 146 (2018).
[5] G. Y. Gor, Langmuir 30, 13564 (2014).
[6] G. Y. Gor, D. W. Siderius, C. J. Rasmussen, W. P. Krekelberg, V. K. Shen, and N. Bernstein, J. Chem. Phys.
143, 194506 (2015).
[7] C. D. Dobrzanski, M. A. Maximov, and G. Y. Gor, J. Chem. Phys. 148, 054503 (2018).
G.P.Tsironis Seminar  Machine learning for complex systems
Aula Eduard Fonserè (Facultat de Física)
20190502 12:00:00
Speaker: G. P. Tsironis (Department of Physics, University of Crete, Greece)
Title: Machine learning for complex systems
Abstract:
Chimeras and branching are two archetypical complex phenomena that appear in many physical systems; because of their different intrinsic dynamics, they delineate opposite nontrivial limits in the complexity of wave motion and present severe challenges in predicting chaotic and singular behaviour in extended physical systems. We report on the longterm forecasting capability of Long ShortTerm Memory (LSTM) and reservoir computing (RC) recurrent neural networks, when they are applied to the spatiotemporal evolution of turbulent chimeras in simulated arrays of coupled superconducting quantum interference devices (SQUIDs) or lasers, and branching in the electronic flow of twodimensional graphene with random potential. We propose a new method in which we assign one LSTM network to each system node except for “observer” nodes which provide continual “ground truth” measurements as input; we refer to this method as “Observer LSTM” (OLSTM). We demonstrate that even a small number of observers greatly improves the datadriven (modelfree) longterm forecasting capability of the LSTM networks and provide the framework for a consistent comparison between the RC and LSTM methods. We find that RC requires smaller training datasets than OLSTMs, but the latter require fewer observers. Both methods are benchmarked against FeedForward neural networks (FNNs), also trained to make predictions with observers (OFNNs). Extensions of this method are applied in other dynamical systems.
[1] G. Neofotistos et al., Machine learning with observes predicts complex spatiotemporal behavior, Front. Phys.  Quantum Computing. 7, 24 (2019)
Statistical physics of liquid brains: an overview. Jordi Piñero (UPF)
Aula 3.20. Departament Física de la Matèria Condensada. Universitat de Barcelona. Martí i Franquès 1. Barcelona
20190314 12:00:00
In this talk we will discuss the concept of ``liquid brains'' as the widespread class of cognitive living neural networks characterised by a common feature: the agents (ants or immune cells, for example) move in space. Thus, no fixed, longterm agentagent connections are maintained. This stands in contrast with standard neural systems. How such a class of systems are capable of displaying cognitive abilities, from learning to decisionmaking? Collective dynamics, memory and learning properties of liquid brains is explored under the perspective of statistical physics.
Using a comparative approach, we review the generic properties of three large classes of systems, namely: standard neural networks (``solid brains''), ant colonies and the immune system. We show that, in spite of their idiosyncratic differences, these systems do share key statistical properties with standard neural systems in terms of formal descriptions, while strongly depart in other ways. On one hand, the attractors found in liquid brains are not always based on connection weights but instead on population abundances. Moreover, some liquid systems use fluctuations in ways similar to those found in cortical networks, suggesting a relevant role of criticality as a way of rapidly reacting and adapting to external signals. Finally, we will also outline the computational and evolutionary aspects for the immune system as a liquid brain and its implications on the network structure and dynamics.
Longrange interactions in discrete complex systems, dpath Laplace operators and superdiffusion. Seminar by Prof. Ernesto Estrada (Institute of Applied Mathematics, Universidad de Zaragoza)
Aula 3.20. Departament Física de la Matèria Condensada. Universitat de Barcelona. Martí i Franquès 1. Barcelona
20190307 12:00:00
ABSTRACT: I will motivate the problem of studying longrange interactions in discrete complex systems, illustrated by some experimental results on the diffusion of adatoms and admolecules on metallic surfaces. I will speculate about other discrete complex systems where such effects can also be observed. Then, I will introduce the dpath Laplacian operators as a natural way to model such systems. I will prove some analytical results about the boundedness and selfadjointness of these operators. Then, I will introduce a generalization of the diffusion equation that takes into account such longrange effects. I will prove that under certain specific transformations of the dpath Laplacians we can reproduce the superdiffusive behaviour observed experimentally. I will clarify the differences between this model and the "random walks with Levy flights" as well as with the use of fractional calculus. I will give some snapshots of extensions to synchronization, epidemic spreading studies and nonlinear diffusion models.
Finally, I will introduce the concept of "metaplexes" in which we combine the internal structure of nodes, modelled as a continuous or discrete space, coupled with the discrete structure of internodal connections. I will show some results about how the internal structure of nodes influences the global dynamics of a metaplex and some potential areas for extension.
Shaping magnetic fields with metamaterials and superconductors, by Jordi Prats Camps, University of Sussex
Room Pere Pascual, 5th floor (Physics Building UB)
20190214 15:30:00
Abstract: Magnetism is very important in various areas of science and technology, covering a wide range of scales and topics. In this talk we will present a collection of "tools" to manipulate magnetic fields in novel ways and achieve new effects like cloaking, transmission, or concentration of magnetic fields. We will also discuss the recently introduced concept of nonreciprocal magnetic coupling.
The design of most of these devices is based on a mathematical technique called "transformation optics", which we will introduce and apply to several cases of interest. The realization of these designs relies on the combination of different magnetic materials, giving rise to the concept of "magnetic metamaterials" which exhibit exotic effective properties. We will show the theoretical design and the experimental implementation of different magnetic metamaterials.
Confined active systems, by Paolo Malgaretti, Max Planck Institute for Intelligent Systems
Room Pere Pascual, 5th floor
20190124 11:45:00
Active systems are intriguing "state" of matter since, by locally breaking the equilibrium, undergo quite diverse dynamics as compared to their equilibrium counterparts.
For example, active colloids show phase separation even in the absence of attractive interactions, and active nematics show the onset of turbulentlike dynamics.
Clearly, real systems are always bound by some means.
In this contribution I will discuss how the presence of boundaries affects the dynamics of active systems, like phoretic colloids or active polymers.
In particular I will discuss two aspects.
Firstly I will show that novel phoretic mechanisms can be induced by the presence of fluidfluid interfaces. By means of some simplified model I will discuss the case in which the interfaces is not reactive (passive)[1] as well as the one in which is reactive and therefore Marangoni flows set[2].
Secondly I will discuss the case of active systems that are selfconfining as it happens for active polymers, i.e. polymers made of active monomers[3]. By means of simple numerical results I will show that the activity can induce a coiltoglobule transition hence leading to a more compact (hence selfconfining) structure. Moreover, I will show that the diffusion coefficient of these active polymers not only is enhanced by the activity but, due to activity, it looses its dependence on the polymer size in such a way that longer chains and short peptides diffuse on almost the same time scale.
[1] A. Domínguez, P. Malgaretti, M.N. Popescu, S. Dietrich Phys Rev Lett 117, 079902 (2016)
[2] P. Malgaretti, M.N. Popescu, S. Dietrich Soft matter 14, 1375 (2018)
[3] V. Bianco, E. Locatelli, P. Malgaretti Phys Rev Lett 12, 217802 (2018)

Nonequilibrium phase transitions in driven diffusion systems, by Dominik Lips and Philipp Maass (Department of Physics, Osnabrück University, Germany)
Aula Pere Pascual (5th floor Physics)
20181114 11:45:00
ABSTRACT: Models of driven stochastic particle transport in one dimension have been applied to describe such diverse phenomena as biopolymerization, molecular motor motion along filaments, flow of molecules through nanopores, ion conduction through membrane channels, electron transport along molecular wires, and vehicular traffic. A simple lattice model, the asymmetric simple exclusion process (ASEP) appears as a basic building block in the theoretical description of these driven diffusion systems and has developed into one of the standard models for investigating nonequilibrium steady states. After an introduction to the physics of the ASEP and some model variants with the focus on nonequilibrium phase transitions [13], we address the question whether corresponding phenomena will occur in driven Brownian motion, making them more amenable to experimental studies.
Specifically, we introduce a model of a Brownian asymmetric simple exclusion process (BASEP) with overdamped Brownian dynamics and a setup resembling that of the ASEP on a lattice [4]. In this BASEP, particles of size σ with hardcore interaction are driven by a constant drag force through a cosine potential with period λ and an amplitude much larger than the thermal energy.
We show that the character of the nonequilibrium steady states in the BASEP is strikingly different from that in the ASEP. Compared with a system of noninteracting particles, the current is enhanced for small σ/λ ratios due to a barrier reduction effect arising from multioccupation of potential wells. Larger σ/λ ratios lead to a suppression of the current because of blocking effects. Surprisingly, an exchange symmetry effect causes the currentdensity relation to be identical to that of non interacting particles for commensurable lengths σ=nλ, n=1,2... A behavior similar as for the ASEP is obtained only in a limited parameter regime. The rich behavior of the currentdensity relation leads to phase diagrams of nonequilibrium steady states with up to five different phases. The structure of these phase diagrams changes with varying σ/λ ratio.
[1] M. Dierl, P. Maass, and M. Einax, Phys. Rev. Lett. 108, 060603 (2012).
[2] M. Dierl, M. Einax, and P. Maass, Phys. Rev. E 87, 062126 (2013).
[3] M. Dierl, W. Dieterich, M. Einax, and P. Maass, Phys. Rev. Lett. 112, 150601 (2014).
[4] D. Lips, A. Ryabov, and P. Maass, Phys. Rev. Lett. 121, 160601 (2018).
Sizing the length of complex networks, by Gorka ZamoraLópez (Center for Brain and Cognition, UPF)
Aula 3.20, Facultat Física UB
20181017 11:00:00
ABSTRACT: Discovered in the realm of social sciences the smallworld phenomenon stands for the observation that any two individuals are connected by a short chain of social ties. Since then, most real networks studied have been found to be smallworld as well. Despite its significance to understand empirical networks, a quantitative determination of "how short" or "how long" a network is, and how it compares to others has remained unresolved over the years. When we say that “a complex network is smallworld” we mean, roughly speaking, that its average pathlength is much smaller than the number of nodes, without giving further precise measurement. The usual strategy to deal with this problem has been to compare networks to wellknown graph models, e.g., random graphs and regular lattices. While these represent interesting nullhypotheses, useful to answer particular questions about the data, they do not constitute absolute or universal references. Here, we establish a reference framework under which the length and efficiency of networks can be interpreted and compared. Therefore, we will evaluate how these properties deviate from the smallest and the largest values they could possibly take. We have found that these limits are given by families of singular configurations which we will refer as ultrashort and ultralong networks. We show that typical models (random, scalefree and ring networks) undergo a transition as their density increases, all becoming ultrashort at sufficient density. The convergence rate, however, differs for each model. Then, we study a sample set of wellknown empirical networks (neural, social and transportation). While most of these display pathlengths close to random graphs, when contrasted against the absolute boundaries, only the cortical connectomes reveal quasioptimal.