XX CONVEGNO NAZIONALE DI FISICA STATISTICA E DEI SISTEMI COMPLESSI
con una giornata dedicata a Italo Guarneri
Lunedì 29 giugno - Mercoledì 1 luglio 2015, Università di Parma

programma di mercoledì 01 luglio 2015
9:30-9:50
Matteo Paoluzzi — Università di Roma La Sapienza
Active Matter in confining geometries image
Active Matter focuses on systems composed by self-driven units, also called active particles, that are capable of converting free energy into movement. While an unified framework to describe the phenomenology of the active matter is still missing, different approaches converge on the importance of the density fluctuations that result to be long-lived leading to non-Boltzmann stationary states with no vanishing probability currents.
In order to stabilize the spontaneous currents, environment and confinement play a crucial role: in recent years, it has been shown that the density fluctuations of active baths can produce unidirectional flux in asymmetric environments [1], sustain spontaneous flow in confined geometry [2], move micro gears [3,4], exert effective attractive forces [5], and deliver passive colloids [6].
We focus our attention on run-and-tumble particles, i. e., a model that captures the dynamics of low Reynolds number swimming organisms such as E. coli, embedded into (i) confining geometries[7] and (ii) subjected to external random fields[8]. The results obtained by mean of numerical simulations can been experimentally studied by mean of (i) microfluidic devices and (ii) speckle fields.

References

[1] P. Galajda et al., J. Bacteriol. 189, 1033 (2007).
[2] T. Sanchez, et al., Nature 491, 431 (2012).
[3] R. Di Leonardo, et al., Proc. Natl. Acad. Sci. 107, 9541 (2010).
[4] A. Sokolov, et al., Proc. Natl. Acad. Sci. U.S.A. 107, 969 (2010).
[5] L Angelani, et al., Phys. Rev. Lett. 107, 138302 (2011).
[6] N. Koumakis, et al., Nature Communications, 4, 2588 (2013).
[7] M. Paoluzzi, et al. http://arxiv.org/abs/1412.1131 (2014).
[8] M. Paoluzzi, et al. J. Phys.: Condens. Matter 26 375101 (2014).
9:50-10:10
Pierfrancesco Buonsante — QSTAR Center & INO-CNR Firenze
Negative absolute temperatures vindicated image
Negative absolute temperatures emerge naturally from Boltzmann's definition of "surface" microcanonical entropy in isolated systems with a bounded energy density. Recently, the well-posedness of negative absolute temperatures has been challenged, on account that only Gibbs "volume" entropy —and the strictly positive temperature thereof— would give rise to a consistent thermodynamics.
Here we focus on a discrete nonlinear model characterized by bounded energy densities, describing the propagation of light in arrays of coupled waveguides. We present analytical and numerical evidence that Boltzmann microcanonical entropy provides a consistent thermometry for both signs of the temperature. In particular, we show that Boltzmann (negative) temperature allows the description of phase transitions occurring at high energy densities, at variance with Gibbs temperature. Our results are relevant also to ultracold gases trapped in optical lattices.
10:10-10:30
Luca Cerino — Università di Roma La Sapienza
Statistical properties of isolated systems with negative Boltzmann temperatures image
The possibility of having mechanically isolated systems with negative absolute temperatures is one of the most fascinating results of statistical mechanics: however such a possibility dramatically depends upon the choice of the definition of entropy. In systems with unbounded phase-space, the two common definitions of entropy as the logarithm of the phase space volume (Gibbs entropy) or surface (Boltzmann entropy) are equivalent in the thermodynamic limit; this equivalence breaks down in some particular systems where the Boltzmann definition can lead to temperatures with negative sign. In the recent past, many authors affirmed that the Boltzmann definitions of entropy and temperature cannot be accepted since they are not consistent with thermodynamics: nonetheless, we want to emphasize that such a temperature gives a very strong characterization of the statistical features of the system. We have studied the dynamics of a chain of coupled rotators: in this simple system we can show, with direct numerical computations, the differences between the situation at positive and negative absolute temperature. In particular, by measuring the time averages of some observable quantities, we are able to show the crucial role of the Boltzmann temperature in the statistical description of the system.
10:30-11:10pausa caffè
11:10-11:50
Marco Baiesi — Università di Padova
Vibrational mode energies in oscillators driven by multiple heat baths image
Experiments and hydrodynamic theories show that temperature gradients forced by boundary conditions lead to an enhancement of nonequilibrium fluctuations at low wavenumbers, with amplitudes that can be even larger than the level expected from local temperatures. We study analytically the energy repartition when it is rather a whole temperature profile that is imposed by external heat baths displaced along the system. We use a chain of harmonic oscillators as the paradigm of coupled system and as a tool to find explicit solutions. The energy repartition among the modes depends on the concavity properties of the imposed temperature profile and on the boundary conditions. Contrary to a naive expectation, we show that both long and short wavelength modes can either be excited or freeze down. A reverse-engineering approach allows also to infer the heat bath temperatures needed to give rise to the observed mode energies. In the frequency domain, the power spectral density of the chain length evidences the nonequilibrium energetics of the modes. These results illustrate, in a transparent and analytically tractable model, how nontrivial deviations from the equipartition of energy may arise in nonequilibrium systems.
11:50-12:10
Alessandro Mossa — Università di Bari
Strong anomalous diffusion of the phase of a chaotic pendulum image
The phase of a harmonically driven undamped pendulum undergoes strong anomalous diffusion due to the mixed nature of its phase space, with hierarchies of regular islands surrounded by the chaotic sea. A stochastic model that reproduces most properties of the original Hamiltonian system is explicitly built, and the connection between deterministic chaos, anomalous transport and fractal properties of the phase space is demonstrated in this paradigmatic case study.
12:10-12:30
Giulia Cencetti — Università di Firenze
Non-transitive games: from coin tossing to walkers on graphs image
In a random sequence of heads or tails, with a fair coin, every subsequence appears evenly. However, given a sequence, there is always another one that appears first, statistically. Betting on subsequences is a non-transitive game, like rock-paper-scissors. The analysis of non-transitivity can be extended to any Markov process, and also used for analysing real data. We found that there is a phase transition in the degree of non-transitivity for unfair coins, and that in general this degree depends on certain properties of the Markov process, in particular we analyzed diffusion processes on graphs. We finally started applying this concept to the analysis of DNA sequences.

In collaboration with F. Bagnoli and D. Fanelli
12:30-12:50
Carlo Lucibello — Politecnico di Torino
Scaling hypothesis for the Euclidean bipartite matching problem image
The matching problem is a long standing problem in combinatorial optimization that has attracted many attentions in the disordered systems community. In the Euclidean version of the problem the cost matrix is given by the distances among points randomly distributed in the d-dimensional box. Here we present a simple approximation for the Euclidean bipartite matching, based on the Poisson equation, that leads to several prediction on the exact limit value of the average optimal cost.
12:50-14:30pausa pranzo
14:30-15:10
Andrea Pagnani — Politecnico di Torino
Exploiting co-evolution across protein families for predicting protein-protein interaction image
Correlated substitution patterns between residues of a protein family have been exploited to reveal information on the structures of proteins However, such studies require a large number (e.g., the order of one thousand) of homologous yet variable protein sequences. So far, most studies have been limited to a few exemplary proteins for which a large number of such sequences happen to be available. Rapid advances in genome sequencing will soon be able to generate this many sequences for the majority of common bacterial proteins.Sequencing a large number of simple eukaryotes such as yeast can in principle generate similar number of common eukaryotic protein sequences, beyond a collection of highly amplified protein domains which already reach the necessary numbers. I will provide a systematic evaluation of the information contained in correlated substitution patterns for predicting residue contacts, a first step towards a purely sequence-based approach protein-protein interaction predictions, discussing some relevant subnetworks in bacteria.
15:10-15:30
Sebastiano Stramaglia — Università di Bari
Network approach for bringing together brain structure and function image
Understanding the relation between functional anatomy and structural substrates is a major challenge in neuroscience. To study at the aggregate level the interplay between structural brain networks and functional brain networks, a new method will be described; it provides an optimal brain partition —emerging out of a hierarchical clustering analysis— and maximizes the “cross-modularity” index, leading to large modularity for both networks as well as a large within-module similarity between them . The brain modules found by this approach will be compared with the classical Resting State Networks, as well as with anatomical parcellations in the Automated Anatomical Labeling atlas and with the Broadmann partition.
15:30-15:50
David Angulo-Garcia — ISC-CNR Firenze
Stochastic mean-field formulation of the dynamics of diluted neural networks image
We consider pulse-coupled leaky integrate-and-fire neural networks with randomly distributed synaptic couplings. This random dilution induces fluctuations in the evolution of the macroscopic variables and deterministic chaos at the microscopic level. Our main aim is to mimic the effect of the dilution as a noise source acting on the dynamics of a globally coupled nonchaotic system. Indeed, the evolution of a diluted neural network can be well approximated as a fully pulse-coupled network, where each neuron is driven by a mean synaptic current plus additive noise. These terms represent the average and the fluctuations of the synaptic currents acting on the single neurons in the diluted system. The main microscopic and macroscopic dynamical features can be retrieved with this stochastic approximation. Furthermore, the microscopic stability of the diluted network can be also reproduced, as demonstrated from the almost coincidence of the measured Lyapunov exponents in the deterministic and stochastic cases for an ample range of system sizes. Our results strongly suggest that the fluctuations in the synaptic currents are responsible for the emergence of chaos in this class of pulse-coupled networks.
15:50-16:10
Tiziano Squartini — ISC-CNR Roma
Detecting cluster structure of resting state fMRI brain networks of mice: percolation and modularity features image
Although the brain has been an object of study since long time, it is still largely unknown. Its highly non-trivially connected structure shapes a functional network whose activation and synchronization mechanisms still represent a major challenge for scientists belonging to the different fields, from neuroscience to complex system theory. This talk represents a contribution to the study of the brain from the perspective of complex network theory. In particular, a data set corresponding to the neuronal activity of 41 mice brains in a resting state, collected via the fMRI technique, has been analysed by applying a range of procedures (as data clustering, community detection, percolation analysis and others), in order to gain insight into the collective activity of brain areas. Our results indicate that a statistically significant signal of collective neuronal activity is detectable even in a resting state, thus allowing us to identify functionally-related areas. Indirectly, this also proves that the analytical tools provided by network theory may indeed provide a non-trivial insight into the structure of the brain, highlighting functional correlations between different areas.

In collaboration with: Giampiero Bardella, Angelo Bifone, Andrea Gabrielli.