9:309:50  Matteo Paoluzzi — Università di Roma La Sapienza
Active Matter in confining geometries
Active Matter focuses on systems composed
by selfdriven 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 longlived leading to nonBoltzmann
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 runandtumble 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). 
9:5010:10  Pierfrancesco Buonsante — QSTAR Center & INOCNR Firenze
Negative absolute temperatures vindicated
Negative absolute temperatures emerge naturally from Boltzmann's definition of "surface"
microcanonical entropy in isolated systems with a bounded energy density. Recently, the wellposedness 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. 
10:1010:30  Luca Cerino — Università di Roma La Sapienza
Statistical properties of isolated systems with negative Boltzmann temperatures
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 phasespace, 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.

10:3011:10  pausa caffè 
11:1011:50  Marco Baiesi — Università di Padova
Vibrational mode energies in oscillators driven by multiple heat baths
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 reverseengineering 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.

11:5012:10  Alessandro Mossa — Università di Bari
Strong anomalous diffusion of the phase of a chaotic pendulum
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.

12:1012:30  Giulia Cencetti — Università di Firenze
Nontransitive games: from coin tossing to walkers on graphs
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
nontransitive game, like rockpaperscissors. The analysis of nontransitivity 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 nontransitivity 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 
12:3012:50  Carlo Lucibello — Politecnico di Torino
Scaling hypothesis for the Euclidean bipartite matching problem
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 ddimensional 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.

12:5014:30  pausa pranzo 
14:3015:10  Andrea Pagnani — Politecnico di Torino
Exploiting coevolution across protein families for predicting proteinprotein interaction
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 sequencebased approach proteinprotein interaction
predictions, discussing some relevant subnetworks in bacteria.

15:1015:30  Sebastiano Stramaglia — Università di Bari
Network approach for bringing together brain structure and function
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 “crossmodularity” index,
leading to large modularity for both networks as well as a large withinmodule 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.

15:3015:50  David AnguloGarcia — ISCCNR Firenze
Stochastic meanfield formulation of the dynamics of diluted neural networks
We consider pulsecoupled leaky integrateandfire 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 pulsecoupled 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 pulsecoupled
networks.

15:5016:10  Tiziano Squartini — ISCCNR Roma
Detecting cluster structure of resting state fMRI brain networks of mice: percolation and modularity features
Although the brain has been an object of study since long time, it is still largely unknown. Its highly
nontrivially 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 functionallyrelated areas.
Indirectly, this also proves that the analytical tools provided by network theory may indeed provide a
nontrivial insight into the structure of the brain, highlighting functional correlations between different
areas.
In collaboration with: Giampiero Bardella, Angelo Bifone, Andrea Gabrielli. 