CLabB Seminar, Reconstructing dynamical networks via feature ranking (MG Leguina)

Room 3.20 3rd floor, Departament de Física de la Matèria Condensada, Facultat de Física, Marti i Franques 1 2019-10-17 15:00:00

Title: Reconstructing dynamical networks via feature ranking Speaker: Marc Grau Leguina, Wyss Center Postdoctoral Fellow, Department of Neurology, Inselspital, University Hospital Bern, University of Bern, Switzerland  

Abstract: Empirical data on real complex systems are becoming increasingly available. Parallel to this is the need for new methods of reconstructing (inferring) the structure of networks from time- resolved observations of their node-dynamics. The methods based on physical insights often rely on strong assumptions about the properties and dynamics of the scrutinized network. Here, we use the insights from machine learning to design a new method of network reconstruction that essentially makes no such assumptions. Specifically, we interpret the available trajectories (data) as “features” and use two independent feature ranking approaches—Random Forest and RReliefF—to rank the importance of each node for predicting the value of each other node, which yields the reconstructed adjacency matrix. We show that our method is fairly robust to coupling strength, system size, trajectory length, and noise. We also find that the reconstruction quality strongly depends on the dynamical regime.

Link to the paper: M. G. Leguia, Z. Levnajic, L. Todorovski, and B. Zenko, “Reconstructing dynamical networks via feature ranking,” Chaos 29, 093107 (2019) https://doi.org/10.1063/1.5092170

Seminari Internacional, 'Roman agrarian & viticultural landscapes-geospatial analysys, statistics & predictive modelling: case studies research'

2019-10-14 00:00:00

Seminari Internacional: 'Roman agrarian & viticultural landscapes-geospatial analysys, statistics & predictive modelling: case studies research'.

Es durà a terme el proper dilluns 14 d’octubre de 2019 a la Facultat de Geografia i Història de la Universitat de Barcelona i el dimarts 15 d’octubre de 2019 al Parc Arqueològic Cella Vinaria-Centre Enoturístic i Arqueològic de Vallmora  de Teià (Maresme).

Pots decarregar-te el programa detallat aquí.

Seminar by Dra. Caterina Pedersini

Sala de Graus – Aula Miquel Siguan , Facultat de Psicologia , Campus Mundet 2019-10-08 16:00:00

Title: ESTIMATION OF FUNCTIONAL CONNECTIVITY WITH fMRI  

Objective: Review of some algorithms used to estimate functional connectivity networks from fMRI signal and resting-state situations.

The Dynamics of Social Conventions: From Names to Cryptocurrencies (Andrea Baronchelli, City University of London)

Aula 3.20. Departament de Física de la Matèria Condensada 2019-07-18 11:30:00

How do conventions emerge and evolve in complex decentralized social systems? This question engages fields as diverse as sociology, economics, cognitive science and network science. Various attempts to solve this puzzle pre-suppose that formal or informal institutions are needed to facilitate a solution. The complex systems approach, by contrast, hypotheses that such institutions are not necessary. In this talk, I will discuss theoretical and experimental results that demonstrate the spontaneous creation of universally adopted social conventions. Then, I will discuss how social norms change, showing how historical data and lab experiments indicate that abrupt transitions between competing norms do not require the intervention of a centralized authority. Finally, I will present some recent results on the modelling of the cryptocurrency market, where users conventionally attribute value to electronic tokens. Overall, these results clarify the processes of social coordination and can help identify and/or design collective behavioural change online or offline.

Seminar by Prof.Erdal Oguz

Room Eduard Fontsere, Facultat de Física, UB 2019-05-09 12:00:00

Author: Erdal Oguz
University of Tel Aviv   Title: Hyperuniformity of quasicrystals and related structures   Abstract   Density fluctuations in many-body 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 large-R limit.   In this talk, we will provide the first rigorous hyperuniformity analysis of quasicrystals obtained by cut-and-projection method and related points sets derived from substitution tilings. Most importantly, we reveal that one-dimensional quasicrystals produced by projection from a two-dimensional 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 one-dimensional 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

2019-05-03 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 n-hexane 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 liquid-like 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) 2019-05-02 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 non-trivial 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 long-term forecasting capability of Long Short-Term 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 two-dimensional 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 data-driven (model-free) long-term 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 Feed-Forward 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 2019-03-14 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, long-term agent-agent 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 decision-making? 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.

Long-range interactions in discrete complex systems, d-path 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 2019-03-07 12:00:00

ABSTRACT: I will motivate the problem of studying long-range 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 d-path Laplacian operators as a natural way to model such systems. I will prove some analytical results about the boundedness and self-adjointness of these operators. Then, I will introduce a generalization of the diffusion equation that takes into account such long-range effects. I will prove that under certain specific transformations of the d-path 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 inter-nodal 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) 2019-02-14 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 non-reciprocal 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.