Ihusan Adam  Università di Firenze
Unravelling the topological arrangements and selected reaction parameters from global measurements of an extended neural model
Living brains show immensely complex dynamics that are often modelled by ensembles of simple neuron models connected through a network
of intricate structure. The complexity displayed by these systems stem from the topology of the network support.
To gain an insight into this problem, we propose and test a procedure that is aimed at reconstructing the a
priori unknown architecture of the embedding network. To this end we consider and extended model of LeakyIntegrate and Fire (LIF)
neurons with shortterm plasticity. The neurons are coupled to directed network and display a level of heterogeneity in the associated current
$a$, which dictates the firing regime in which a neuron is operating in. The aim of the method is to recover the distribution of connectivity
$\tilde{k}$ of the underlying networks as well as the distribution of the assigned $a$. Our approach to the inverse problem makes use of the
celebrated Heterogenous MeanField (HMF) approximation to rewrite the dynamics of the system by splitting the types of neurons into classes which reflect
the associated $a$ and indegree $\tilde{k}$. The HMF reduction scheme allows us to in essence, create a mesh on the space defined by the variables
$a$ and $\tilde{k}$ in such a way that all possible neurons fall within this space.
he two sought distributions of $P(a)$ and $P(\tilde{k})$ are then the correct solutions that sum the classes of neurons to reproduce the global field that was
obtained by simulating the original model. We have tested this on synthetic data, where the global field was generated by a random network and a bellshaped
distribution of currents, and here the method captures the two distributions remarkably well and manages to almost exactly reproduce the global field. 
Irene AdroherBenítez  SISSA Trieste
Modelling branching polymers in different solvent conditions
Branching polymers are very versatile systems, which can be found in
industrial processes as well as in biological ones. In particular, we
are interested in the considerable analogy that their behaviour exhibits
with the crumpling of topologically constrained ring polymers and
chromosomes. Our final goal in this direction is to understand the
spatial architecture and dynamics of the genomic material inside the
nucleus of eukaryotic cells, since it may play an determinant role in
many processes.
With this aim, a combination of computer simulations, Flory theory and
scaling relations were employed to study the connectivity and
conformational statistics of different configurations of 3dimensional
branched polymers in good solvent conditions. However, in these cases to
model the shortrange interactions only the excluded volume repulsion
was considered. But we are aware that to model chromosomes specific
interactions between monomers should be taken into account. Therefore,
in our last work we have studied the behaviour of randomly branched
polymers in $\theta$solvent in 2 and 3 dimensions by means of Monte
Carlo simulations to construct a model that considers the threebody
interactions. This study itself provides valuable information about
randomly branching polymers, but also it is the first step to develop a
model for branching copolymers, which will help us to understand the
physics of chromatin fibers.

Federico Battiston  Central European University  Budapest
Reactive Random Walkers on Complex Networks
We introduce and study a metapopulation model of random walkers interacting at the nodes of a complex network.
The model integrates random relocation moves over the links of the network with local interactions depending
on the node occupation probabilities. The model is highly versatile, as the motion of the walkers can be fed
on topological properties of the nodes, such as their degree, while any general nonlinear function of the occupation
probability of a node can be considered as local reaction term. In addition to this, the relative strength of reaction
and relocation can be tuned at will, depending on the specific application being examined.
We derive an analytical expression for the occupation probability of the walkers at equilibrium in
the most general case. We show that it depends on different order derivatives of the local reaction functions and not
only on the degree of a node, but also on the average degree of its neighbours at various distances.
For such a reason, reactive random walkers are very sensitive to the structure of a network and are a powerful
way to detect network properties such as symmetries or degreedegree correlations. As possible applications,
we first discuss how the occupation probability of reactive random walkers can be used to define novel measures
of functional centrality for the nodes of a network. We then illustrate how network components with the same
symmetries can be revealed by tracking the evolution of reactive walkers. Finally, we show that the dynamics of
our model is influenced by the presence of degreedegree correlations, so that assortative and disassortative networks
can be classified by quantitative indicators based on reactive walkers.
Physical Review E 98 (5), 052302 (2018) by Giulia Cencetti, Federico Battiston, Duccio Fanelli, Vito Latora 
Marco Bianucci  ISMARCNR
Optimal Fokker Planck Equation for a large class of stochastic and chaotic dynamical systems
We present the optimal Fokker Planck Equation (FPE) for the probability density function (PDF) of a general class
of stochastic and chaotic dynamical systems. Using a clear and sound framework, that consists of a revisited and corrected version
[1] of the Kubo’s theory of Generalized Cumulants [23] and exploit the recent Lie approach to the evolution of differential operators
[4], we generalize some “sparse” classical results we can find in literature (e.g.[5] and references therein). A crucial assumption of t
he present work is the finite time scale of the dynamics of the variables that we do not observe directly. This is the very classical hypothesis that underlies the emergence, for large time scales, of effective Markovian processes for the system of interest, hypothesis that in the physics literature of the last fifty years has been largely discussed and questioned.
The results hold true in general for weak interactions between the system of interest and the hidden variables, and, in some cases, far beyond the limit of weak interaction.
References
[1] M. Bianucci, “About the foundation of the Kubo Generalized Cumulants theory. A revisited and corrected approach”. Submitted
[2] R. Kubo. “Generalized Cumulant Expansion Method.” Journal of the Physical Society of Japan 17.7 (1962), pp. 1100–1120.
[3] R. Kubo. “Stochastic Liouville Equations.” Journal of Mathematical Physics 4.2 (1963), pp. 174–183.
[4] M. Bianucci. “Using some results about the Lie evolution of differential operators to obtain the FokkerPlanck equation for nonHamiltonian dynamical systems of interest”. Journal of Mathematical Physics, 59(5):053303 (2018).
[5] M. Bianucci. “On the correspondence between a large class of dynamical systems and stochastic processes described by the generalized Fokker Planck equation with statedependent diffusion and drift coefficients”. Journal of Statistical Mechanics: Theory and Experiment, 2015(5):P05016 (2015). 
Davide Botto  Politecnico di Torino
Unbalanced Langmuir kinetics affects TASEP dynamical transitions: meanfield theory
Within a meanfield approximation, we study the dynamical transition in the Totally Asymmetric
Simple Exclusion Process with open boundaries and Langmuir kinetics (TASEPLK). The dynamical
transition corresponds to a singularity in the slowest relaxation rate of the system which is not
accompanied by any change in the steady state properties and, for the TASEP, it was located exactly on the phase diagram.
In the highdensity phase, this transition separates a region (which can be called fast) in which the relaxation rate is
higher and depends only on the extraction parameter, that determines the steady state bulk density, from a slow region
in which the rate is smaller and depends also on the injection parameter. Extending a previous work [1], where
the analysis was restricted to the case with equal binding and unbinding rates, we study the smallest eigenvalue
of the meanfield relaxation matrix in the more general case of unbalanced rates. We observe a new kind of dynamical
transition, whose behaviour shows some analogies with ordinary equilibrium firstorder transitions.
[1] D. Botto, A. Pelizzola, M. Pretti and M. Zamparo, J. Phys. A: Math. Theor. 52 045001 (2019) 
Giovanni Catania  Politecnico di Torino
Density Consistency on discrete graphical models
Computing marginal distributions of discrete graphical models
is a fundamental problem with a vast number of applications in many
fields of science. However, the problem is typically intractable as it
scales exponentially with the system size and therefore approximation
schemes are needed to estimate marginal distributions. We present a new
family of approximation schemes called Density Consistency. The scheme
computes exact marginals on acyclic graphs as Belief Propagation: in
addition, it includes some loop corrections, i.e. it takes into account
correlations coming from long cycles in the factor graph. The method is
also similar to Adaptive TAP but with a different consistency condition.
Results on random connectivity and finite dimensional Ising and
EdwardAnderson models show a significant improvement with respect to
the Bethe (tree) approximation in all cases, and significant improvement
with respect to Cluster Variational Methods and other loop correction
schemes in many cases. We also estimate the phase diagram of homogeneous
Ising Models on hypercubic lattices. In particular, for the critical
inverse temperature $\beta_{c}$ the $1/d$ expansion of
$\left(d\beta_{c}\right)^{1}$ of the proposed scheme turns out to be
exact up to the $d^{4}$ order.

Gloria Cecchini  Università di Firenze
Tracking propagation patterns in mice before and after stroke
The ability to find propagation patterns is a useful tool that can have various applications in many disciplines,
such as climatology and neuroscience. Here we investigate neuronal activity propagation patterns in mice brain during motor tasks.
Namely, using widefield calcium imaging, the cortical activity has been recorded during forelimb movements. Analysing the pixels of
the recorded image, we construct a raster plot of the occurrence of discrete events. Applying multivariate techniques, global events, i.e.,
events that occur quasisimultaneously in the majority of the pixels, are selected for the analysis, and propagation patterns are found for each one of them.
Using the singular value decomposition, we are able to detect the direction of the propagation and thus identify two main classes of propagation patterns.
In addition, we investigate whether a stroke, induced in the motor cortex, affects such patterns, and in particular we focus on the angle of the direction of the propagation.
We evaluate if this could be used as an indicator of recovering after the stroke.
(authors and coauthors in alphabetic order: Ihusan Adam, Gloria Cecchini, Emilia Conti, Duccio Fanelli, Thomas Kreuz, Roberto Livi, Anna Letizia Allegra Mascaro, Francesco Saverio Pavone, Alessandro Scaglione) 
Giulio Cimini  ISCCNR Roma
Fragility and anomalous susceptibility of weakly interacting networks
Percolation is a fundamental concept that brought new understanding on the robustness properties of complex systems.
Here we consider percolation on weakly interacting networks, that is, network layers coupled together by much less interlinks
than the connections within each layer. For these kinds of structures, both continuous and abrupt phase transition are observed
in the size of the giant component. The continuous (secondorder) transition corresponds to the formation of a giant cluster inside
one layer, and has a well defined percolation threshold. The abrupt transition instead corresponds to the merger of coexisting giant
clusters among different layers, and is characterised by a remarkable uncertainty in the percolation threshold, which in turns causes
an anomalous trend in the observed susceptibility. We develop a simple mathematical model able to describe this phenomenon and to estimate
the critical threshold for which the abrupt transition is more likely to occur. Remarkably, finitesize scaling analysis in the abrupt region
supports the hypothesis of a genuine firstorder phase transition. 
Mattia Conte  Università di Napoli "Federico II"
Polymer Physics captures key principles of genome folding at both population and singlecell level
Understanding the molecular mechanisms underlying the
threedimensional architecture of the genome is one of the most
challenging problems in biology, currently open and debated. Models from
Polymer Physics have been employed to investigate the structure of the
cell nucleus, even though a unified quantitative framework describing the
complex spatial chromosome organization is still lacking. In the last few
years, the Strings and Binders Switch (SBS) model, a
phaseseparationbased polymer approach, has been shown to recapitulate
average behaviour of entire chromosomes [1] as well as to predict, with a
very high accuracy, the impact of structural mutations on chromosome
architecture [2].
Here, we present some preliminary results concerning the SBS modeling of a
DNA region cohesindepleted, recently investigated by high resolution
imaging experiments [3]. We show that the SBS polymer model can reproduce
with high precision the experimental data at both the population and
singlecell level. Moreover, our model provides also a possible
explanation of the role of the cohesin in driving genome folding.
[1] M. Barbieri, M. Chotalia, J. Fraser, L.M. Lavitas, J. Dostie, A.
Pombo, and M. Nicodemi, Proc. Natl. Acad. Sci. (2012).
[2] S. Bianco, D. G. Lupiáñez, A. M. Chiariello, C. Annunziatella, K.
Kraft, R. Schöpflin, L. Wittler, G. Andrey, M. Vingron, A. Pombo, S.
Mundlos, and M. Nicodemi, Nat. Genet. (2018).
[3] B. Bintu, L. J. Mateo, J.H. Su, N. A. SinnottArmstrong, M. Parker,
S. Kinrot, K. Yamaya, A. N. Boettiger, and X. Zhuang, Science (80. ).
(2018).

Antonio De Candia  Università di Napoli
Information capacity of a network of spiking neurons
We study a model of spiking neurons, with recurrent connections that result from learning a set of spatiotemporal patterns with a spiketiming dependent plasticity rule. We investigate the ability of the network to store and selectively replay multiple patterns of spikes, with a combination of spatial population and phaseofspike code. Each neuron in a pattern is characterized by a binary variable determining if the neuron is active in the pattern, and a phaselag variable representing the spiketiming order among the active units. After the learning stage, we study the dynamics of the network induced by a brief cue stimulation, and verify that the network is able to selectively replay the pattern correctly and persistently. We calculate the information capacity of the network, defined as the maximum number of patterns that can be encoded in the network times the number of bits carried by each pattern, normalized by the number of synapses, and find that it reaches a value αmax≃0.27, similar to the one of sequence processing neural networks, and almost double of the capacity of the static Hopfield model. We study the dependence of the capacity on the global inhibition, connection strength (or neuron threshold) and fraction of neurons participating to the patterns. The results show that a dual population and temporal coding can be optimal for the capacity of an associative memory. 
Pierfrancesco Di Cintio  CNRIFAC & INFN Firenze
Noise, N −body chaos and the onset of radialorbit instability
We study the stability of a family of γ−models with Osipkov
Merritt velocity anisotropy by means of N −body simulations. In particular,
we analize the effect of selfconsistent N −body chaos and external noise on
the onset of radialorbit instability (ROI). We find that degree of chaoticity
of the system associated to its largest Lyapunov exponent Λ max has no ap
preciable relation with the stability of the model for fixed density profile and
different values of radial velocity anisotropy. Moreover, we find that the ad
dition of an external isotropic source of noise has a stabilizing effect against
ROI, at least when the degree of anisotropy is close to its limit for stabilty.
Viceversa, models where a noiseplusfriction disturbance, modelled with a
OrnsteinUhlenbeck process is included are always unstable. 
Ivan Di Terlizzi  Università di Padova
Kinetic Uncertainty Relation
We start from a recent theorem of microscopic thermodynamics, pictorially called
thermodynamic uncertainty relation (TUR), which forms part of the more general framework of
thermodynamic inequalities that arise from the handling of microscopic processes in a
nonequilibrium state. More specifically, the abovementioned theorem puts a bound on the
ratio between the average rate of the an out of equilibrium current and the generalized
diffusivity (proportional to the variance of the current) times the entropy production rate.
However, it was showed that this thermodynamic relation can be obtained in a very general
way, namely using a mathematical object called the KullbackLeibler divergence that, for
some particular cases, can be linked to entropy production. We will hence use the latter to
obtain a new nonequilibrium inequality for systems modelled by continuous time Markov
chains: in fact, performing a simple perturbation on the system's dynamic (namely a linear
rescaling of the transition rates) we calculate the KullbackLeibler divergence that arises
from this procedure and show that it is proportional to the dynamical activity of the system,
namely the average total number of jumps that the system performs in a given time interval.
This final result has been called kinetic uncertainty relation (KUR). Finally we investigate
the differences between the TUR and the KUR and analyse the regimes in which they give
tighter constraints.
See: https://iopscience.iop.org/article/10.1088/17518121/aaee34 
Gianfranco Durin  Istituto Nazionale di Ricerca Metrologica
Earthquakelike dynamics of weaklydriven domain walls in ultrathin magnetic film
The vast majority of studies of magnetic domain wall (DW) dynamics in the socalled creep regime has
focused on investigating mean properties, most notably the dependence of average DW velocity on applied field [13].
Only recently a theoretical work unveiled the existence of spatiotemporal correlations between weaklydriven creep events,
in a similar fashion to earthquake dynamics [4].
In this work we demonstrate experimentally these predictions by investigating DW creep dynamics
in an ultrathin Ta/CoFeB/MgO film with perpendicular magnetic anisotropy. We use magnetooptical Kerr effect (MOKE)
microscopy to detect the expansion of a magnetic bubble under a small perpendicular field H, by acquiring images every 200 ms.
We are able to confirm that creep events (portions of bubble growth between two consecutive images) aggregate into larger independent clusters
using a technique successful applied to earthquake dynamics [5]. The cluster area distribution exhibits a power law behavior with an exponent which
is consistent with both qEW and qKPZ universality classes. To resolve this issue, we also evaluate the structure factor S(q). Following the procedure
in [6], we estimate that the critical lengthscale $L_{opt}$ separating creep regime at short scales from depinning regime at large scales is comparable
to the image pixel size (0.3 μm). Thus, we conclude the S(q) contains information about depinning only. Best fit provides a depinning roughness exponent $\chi_{dep}$
consistent with the qKPZ class ($\chi_{dep}$ = 0.63) but not the qEW one ($\chi_{dep}$ = 1.25). These unprecedented and unexpected results shed new light on our understanding
of DW dynamics in the ultraslow creep regime.
[1] S. Lemerle et al., Phys. Rev. Lett. 80 (1998), 849.
[2] F. Cayssol et al., Phys. Rev. Lett. 92 (2004),107202 .
[3] V. Jeudy et al., Phys. Rev. Lett. 117 (2016), 057201.
[4] E. E. Ferrero et al., Phys. Rev. Lett. 118 (2017), 147208.
[5] M. Baiesi, M. Paczuski, Phys. Rev. E 69, 066106 (2004)
[6] K.J. Kim et al., Nature 458 (2009), 740. 
Vittorio Erba  Università di Milano
Full correlation integral estimator for Intrinsic Dimension: robustness and multiscalability
Highdimensional data are ubiquitous in contemporary science
and finding methods and tools to analyze them is one of the primary goal
of Machine Learning. Given a dataset lying in a highdimensional space
(in principle hundreds to several thousands of dimensions), identifying
the minimal dimension for representing it without losing information is
a challenging problem known in the literature as intrinsic dimension
estimation (IDE). Traditionally, most IDE algorithms are either based on
principal component analysis (PCA) or on the notion of correlation
dimension (and more in general on knearest neighbors distances and are
affected, in different ways, by a severe curse of dimensionality that
often puts limitations on the reliability of such methods. Here we
introduce a new ID estimator based on the full correlation integral that
exploits information away from the limit of small radius used for the
estimation of the correlation dimension. We argue that our estimator
fixes the well known underestimation problem of local geometric methods
on linearly embedded manifolds. More surprisingly, it provides a
reliable estimate of the ID even in the extreme undersampled limit N d
log d number of samples to work. Based on this insight, we introduce a
multiscale generalization of the algorithm that satisfactorily deals
with the IDE of twisted and extremely curved manifolds and identifies
multiple dimensionalities in the same dataset, considered a challenge
for stateoftheart ID estimators. 
Andrea Esposito  Università di Napoli "Federico II"
Chromatin architecture code inferred by machine learning and polymer physics
The genome has a complex 3D organization, serving key functional purposes,
yet the nature of the factors shaping its architecture and their mode of
action remain largely unknown. New technologies, such as HiC, and
developments in microscopy have revealed the complexity of the genome 3D
architecture and its deep connections with gene regulation. By combining
machine learning methods and polymer physics we infer, starting from
experimental data, the specific genomic location of the distinct binding
sites whereby DNA contacts are spontaneously established by the action of
their cognate binding factors through only physics (1,2). In this talk, I
will discuss the key aspects behind our model, showing how it can be used
to accurately describe real genomic loci and the effects of diseaselinked
genetic mutation on the DNA spatial organization (3).
1. Chiariello, A. M. A. M., Annunziatella, C., Bianco, S., Esposito,
A. &
Nicodemi, M. Polymer physics of chromosome largescale 3D organisation.
Sci. Rep. 6, (2016).
2. Bianco, S. et al. Polymer physics predicts the effects of structural
variants on chromatin architecture. Nat. Genet. 50, 662–667 (2018).
3. Kragesteen, B. K. et al. Dynamic 3D chromatin architecture
contributes
to enhancer specificity and limb morphogenesis. Nat. Genet. 50, 1463–1473
(2018). 
Gianmaria Falasco  University of Luxembourg
Dynamics and Thermodynamics of chemical reaction networks
Living cells sustain themselves through a wealth of complex chemical processes—from ATP synthesis
to DNA replication. Most of them are naturally noise and take place under large chemical potential differences.
A thorough characterization thus requires deep understanding of the stochastic dynamics and nonequilibrium thermodynamics
of chemical reaction networks. I will review recent advances in the field, in particular focusing on:
I) large deviation methods to study the complex behavior (multistability, limit cycles) emerging in the thermodynamic limit,
II) the stability of chemical pathways to internal and environmental noise and its relation to energy dissipation. 
Roberto Franzosi  Istituto Nazionale di Ottica  CNR  Firenze
A microcanonical entropy correcting finitesize effects in small systems
In a recent paper [Franzosi, Physica A {\bf 494}, 302 (2018)], we have suggested to use of the surface entropy,
namely the logarithm of the area of a hypersurface of constant energy in the phase space, as an expression for the
thermodynamic microcanonical entropy, in place of the standard definition usually known as Boltzmann entropy.
In the present manuscript, we have tested the surface entropy on the FermiPastaUlam model for which we have computed
the caloric equations that derive from both the Boltzmann entropy and the surface entropy. The results achieved clearly
show that in the case of the Boltzmann entropy there is a strong dependence of the caloric equation from the system size,
whereas in the case of the surface entropy there is no such dependence. We infer that the issues that one encounters when
the Boltzmann entropy is used in the statistical description of small systems could be a clue of a deeper defect of this
entropy that derives from its basic definition. Furthermore, we show that the surface entropy is well founded from a mathematical
point of view, and we show that it is the only admissible entropy definition, for an isolated and finite system with a given energy,
which is consistent with the postulate of equal apriori probability. 
Andrea Gabrielli  ISCCNR Roma
Statistical Physics for Heterogeneous Random Networks
In the last years the formulation of statistical ensembles of binary and weighted
random graphs satisfying some arbitrary constraints has attracted much attention in phys/math
communities for its twofold potential application [1, 2]: (i) The construction of appropriate null
models for the statistical validation of high order properties of real networks; (ii) the reconstruction of
the statistical properties of real network starting for partial accessible information. The cornerstone of the statistical
physics of complex networks is the idea that the links, and not the nodes, are the effective particles of the system.
Here we formulate a mapping between weighted networks and lattice gasses, making the conceptual step forward
of interpreting weighted links as particles with a generalised coordinate [3]. This leads to the definition
of the grand canonical ensemble of weighted complex networks. We derive exact expressions for the partition
function and thermodynamic quantities, both in the cases of global and local (i.e., nodespecific) constraints
on density and mean energy of particles. We further show that, when modeling real cases of networks, the binary
and weighted statistics of the ensemble can be disentangled, leading to a simplified framework for a range of practical
applications.
References
[1] T. Squartini, G. Caldarelli, G. Cimini, A. Gabrielli, D, Garlaschelli, Phys. Reports 757, 147 (2018).
[2] G. Cimini, T. Squartini, F. Saracco, D. Garlaschelli, A. Gabrielli, G. Caldarelli, Nature Review Physics 1, 5871 (2019).
[3] A. Gabrielli, R. Mastrandrea, G. Caldarelli, G. Cimini, ‘The Grand Canonical ensemble of weighted networks”, Phys. Rev. E 99, 030301(R) (2019) 
Marco Gherardi  Università di Milano
Machine learning of geometrically structured data
Understanding and predicting the performance of neural networks is a crucial pursuit of contemporary science.
Cover’s function counting theorem was a milestone of learning theory. It counts how many binary classifications (dichotomies)
can be realized by a given architecture, a meaningful quantity related to capacity, expressiveness, and generalization capability.
Yet, while classic results such as this are formulated for inputs with unspecified relations, there is now raising consensus that the
organization of data into significant geometrical structures, such as clusters or perceptual manifolds, is a paramount factor affecting the
effectiveness of machine learning tools.
With the aim of predicting and measuring how such geometrical structure in the data affects classification algorithms, we extended
Cover’s result to generically correlated sets of inputs. As an application, we obtained a closed formula for the capacity of a binary classifier
trained to distinguish polytopes of any dimension. I will discuss the relevance of these results and how they open up opportunities for useful applications. 
Jacopo Grilli  ICTP Trieste
A physics approach to macroecological laws across microbial communities
Microbes are everywhere. For every human cell of our body, there is at least one bacterial
cell living inside us. Due to the advancement of sequencing techniques, there has been an explosion
of data that track the composition of microbial communities. In a traditionally
datapoor discipline as ecology, this novel richness of data represents a unique opportunity to understand
quantitatively how different ecological forces shape diversity. In this talk, I will present three independent, "physicslike",
emergent statistical patterns of distribution of abundances across species and communities, which are conserved across different ecosystems.
I will then discuss how these "laws" can inform us about the fundamental mechanisms that are shaping the composition
of these communities. 
Miguel Ibáñez Berganza  Sapienza Università di Roma
Optimal accuracy/complexity tradeoff in Maximum Entropy inference: an application to brain science
In the undersampling regime, statistical inference algorithms suffer from overfitting, or the excessive dependence of the
inference results on the spurious or nonsignificant details of the dataset. We will compare various strategies of overfitting mitigation
in the framework of Maximum Entropy (MaxEnt) inference. According to such methods, the MaxEnt inferred parameters are not set by likelihood maximisation.
Rather than reproducing the raw experimental correlations, the model is required to reproduce only the correlations resulting statistically significant
within an optimal significance soil. We will present an illustration of such techniques in the inference of human restingstate brain activity retrieved
by functional Magnetic Resonance Imaging (fMRI).

Stefano Iubini  Università di Padova
Topological sieving of rings according to their rigidity
I will present a novel mechanism for resolving the mechanical rigidity of nanoscopic circular polymers that flow in a complex environment.
The emergence of a regime of negative differential mobility induced by topological interactions between the rings and the substrate is the
key mechanism for selective sieving of circular polymers with distinct flexibilities. A simple model accurately describes the sieving process
observed in molecular dynamics simulations and yields experimentally verifiable analytical predictions, which can be used as a reference
guide for improving filtration procedures of circular filaments. The topological sieving mechanism we propose ought to be relevant also
in probing the microscopic details of complex substrates.
Reference: S. Iubini, E. Orlandini, D. Michieletto, M. Baiesi, ACS Macro Letters, 7, 14081412 (2018) 
Antonio Lamura  IACCNR Bari
Anchored semiflexible polymer under oscillatory shear flow
The properties of a semiflexible polymer with fixed ends under
oscillatory shear flow are investigated by numerical simulations.
The polymer is confined in two dimensions and is modeled as a wormlike
chain. The interaction with the fluid is taken into account by the Brownian
multiparticle collision dynamics approach.
For small shear rates, a linear oscillatory response appears.
However, at high shear rates, we find a strongly nonlinear behavior
with the polymer wrapping around the fixation points and shrinking.
The polymer center of mass is distributed on a spatial curve
resembling a lima\c{c}on with an inhomogeneous distribution.
Normalmode correlation functions are changed by shear
and a frequency doubling is observed at high shear rates.
An evenodd asymmetry for the Cartesian components of the
correlation functions is found with rather similar spectra for odd $x$
and even $y$modes and vice versa. Our study yields an
interesting nonlinear behavior of anchored semiflexible polymers under
oscillatory shear flow.
Preliminary results for the case of a semiflexible polymer with one fixed end
exposed to oscillatory shear will be also provided.
A. Lamura and R. G. Winkler, {\it Tethered semiflexible polymer under
large amplitude oscillatory shear}, Polymers {\bf 11}, 737 (2919)

Elena Magnanini  Università di Bologna
Approximating the scaled cumulant generating function of triangles in the dense ErdösRényi model
The computation of the probability of rare events is the main purpose of Large Deviation Theory.
For instance, in a simple case, one can consider the rare event in which a sum of i.i.d. Bernoulli variables attains a
value which is larger than its average. A completely different, and much more difficult problem, is the computation of large
deviations probability of nonlinear functionals of the Bernoulli variables, e.g. cubic polynomials.
A case in which such nonlinear problems arise is, for instance, the study of Complex Networks.
In this poster I will present the behaviour of the socalled scaled cumulant generating function of the triangle
observable in the context of the dense ErdӧsRényi random graph. The scaled cumulant generating function is strictly
related to large deviations since, when it is possible to apply the GärtnerEllis theorem, it turns out to be the
Legendre transform of the large deviations rate function. The goal of this poster presentation is twofold: on one
hand to describe the extension of a known Monte Carlo method, called Cloning algorithm, devised for approximating
the scaled cumulant generating function of an additive observable in the framework of random graphs. On the other hand,
keeping the focus on the triangle observable, to present the numerical investigation performed in the region of parameters
where the analytical expression of such function is not known, thus revealing a phase transition. 
Marco Mancastroppa  Università di Parma
Epidemic spreading on temporal networks with burstiness
The heterogeneous distribution of times between two consecutive actions – often denoted as \textit{burstiness}–
is a typical signature of timeresolved records of human activities [1]. In bursty processes the probability distribution
for the interevent time between two successive events is not an exponential as in Poisson processes but, typically, it features a fat tail,
easily measured from large datasets [1]. Heterogenous temporal patterns in the evolution of timevarying networks can affect in a nontrivial
way dynamical processes, such as epidemics, unfolding on top of them: interestingly, when comparing bursty with Poisson dynamics in epidemics,
conflicting observations have been reported [1]. To understand such conflicting observations and keep track at the same time of the temporal
evolution of interactions, in this work we focus on the SusceptibleInfectedSusceptible (SIS) process on activitydriven temporal networks [2]
in the presence of burstiness [3,4]. By using an activitybased meanfield (ABMF) approach we find a closed analytical form for the epidemic threshold
for general activity and interevent time distributions including, as a particular case, bursty dynamics. The ABMF approach is exact here due to the fully
meanfield nature of the model and the analytical results are in excellent agreement with extensive numerical simulations. We show that burstiness lowers the epidemic
threshold, while surprisingly its effect on epidemic prevalence is twofold: burstiness tends to raise the average stationary infection probability in low infective systems,
therefore strengthening the epidemic, while it weakens the epidemic in high infective systems, lowering the prevalence. This result can help to clarify the conflicting
effects of burstiness reported in the literature [1]. We also discuss the scaling properties at the transition, showing that they are not affected by burstiness.
(see Mancastroppa M., Vezzani A., Munoz M.A. and Burioni R., “Burstiness in activitydriven networks and the epidemic threshold”, J. Stat. Mech. (2019) 053502)
[1] Karsai M., Jo H. H. and Kaski K., “Bursty human dynamics”, Springer (2018)
[2] Perra N., Goncalves B., PastorSatorras R., Vespignani A., “Activity driven modeling of time varying networks”, Scientific Reports 2:469 (2012)
[3] Ubaldi E., Vezzani A., Karsai M., Perra N. and Burioni R., “Burstiness and tie activation strategies in timevarying social networks”, Scientific Reports 7 46225 (2017)
[4] Burioni R., Ubaldi E. and Vezzani A., “Asymptotic theory of time varying networks with burstiness and heterogeneous activation patterns”, J. Stat. Mech. Theory Exp. (2017) 054001

Rossana Mastrandrea  Scuola IMT Alti Studi Lucca
Functional brain network topology maps the dysfunctional substrate of cognitive processes in Schizophrenia
Current understanding of schizophrenia associates this disorder with alterations in the functional organization of the brain. Restingstate functional connectivity failed to models symptoms expression in the attempt to characterize global and local changes induced by this mental illness, Network neuroscience sheds some light on the functional and structural modifications of the brain. Comparing fortyfour medicated patients and forty healthy subjects, we detected significant differences in the robustness of these functional networks. This work, comparing fortyfour medicated patients and forty healthy subjects, shows that the distribution of connectivity strength among brain regions is spatially more homogeneous in schizophrenic patients than in healthy ones, with a larger resistance to edge removal for the schizophrenic functional graphs. As a consequence, the precise hierarchical modularity of healthy brains is crumbled in schizophrenic ones, making possible a peculiar arrangement of regiontoregion interaction. The altered hierarchical modularity and the manifold nature of the basal scheme in the functional organisation of brain could contribute to positive symptoms of schizophrenia. Our work also fits the disconnection hypothesis that describes schizophrenia as a brain disorder, characterized by abnormal functional integration among brain region. 
Piero Olla  ISACCNR
Stochastic dynamics with mixed boundary conditions in time
Rare events in a nonequilibrium system not necessarily involve
all the variables that describe the dynamics. We may have for instance
situations in which only part of the system is of interest for the
rare events, and the rest only provides the nonequilibrium forcing.
The dynamics of such a system could be described by means of an
incomplete Schroedinger bridge, in which boundary conditions are
imposed in the future only on part of the variables (the forced
system), while the remaining variables are conditioned in the past.
The case of an ensemble of small systems that exchange work among
themselves and heat among themselves and a set of thermostats is
discussed explicitly.

Matteo Paoluzzi  Sapienza Università di Roma
The nonDebye spectrum in structural glasses and supercooled liquids
At small enough frequencies glasses and disordered solids follow Debye’s law.
This is because at large length scales they are continuum media and thus phonons, i.e., Goldstone bosons, dominate
the lowfrequency spectrum. However, the mechanical and thermodynamic properties of glasses, even though universal, deviate
from those in crystalline solids. These anomalies imply peculiar and universal deviations from Debye’s law at low frequencies.
Theoretical models predict a population of soft and quasilocalized nonGoldstone modes following a power law that is subdominant
with respect to the Goldstone sector. We show that the nonGoldstone sector can be efficiently probed at any system size by employing a
random pinning protocol that destroys spatial translational invariance symmetries and thus removes phonons from the spectrum[1].
Moreover, we show that nonDebye modes dominate the lowfrequency spectrum in supercooled liquids equilibrated at parental temperatures close
to the dynamical transition temperature. Finally, we make in contact the emerging of nonGoldstone modes with the growing of dynamical heterogeneous regions[2].
[1] L. Angelani, M. Paoluzzi, G. Parisi, and G. Ruocco, PNAS 115 (35), 8700 (2018).
[2] M. Paoluzzi, L. Angelani, G. Parisi, and G. Ruocco, arxiv.org/abs/1901.09796 (2019). 
Mauro Pastore  Università di Milano & INFN
Large deviations of the free energy in the pspin glass spherical model
The theory of large deviations is the natural framework to
study the rare fluctuations of physical observables, including
thermodynamic variables in disordered systems, where, however, the
attention has been mostly focused on typical values. The GärtnerEllis
theorem provides a powerful tool to evaluate the rate functions
describing the exponential decay of the probability of these variables
around their typical values, via a Legendre transformation of the
corresponding cumulant generating functions, which can be obtained in
Replica Theory.
In this contribution I will show how to apply these ideas to find the
rate function of the free energy in the pspin glass spherical model. As
in many other disordered systems, the probability of the rare
fluctuations above or below the typical value has a very different
behaviour. While in one case the decay is exponential with the number of
degrees of freedom, as simple arguments of extensivity suggest, in the
other is superexponential, with an infinite rate function. I will show
how the switching of a magnetic field cures this infinity, suggesting
that this might represent a useful regularisation to obtain the unknown
superexponential scaling. 
Francesco Petiziol  Università di Parma
Creation of entanglement by superadiabatic quantum state transfer
Quantum adiabatic driving is one of the pillars of timedependent quantum control.
However, the limitations imposed by the coherence times are typically in sharp contrast with the necessity of
slow evolutions imposed by the adiabatic theorem. A method will be presented for accelerating adiabatic state transfer for fewlevel quantum systems.
This works by introducing suitably tailored fast oscillations in the intrinsic parameters of the original Hamiltonian:
the oscillations mediate an effective Hamiltonian dynamically compensating for undesired transitions.
It will be shown how the protocol can be exploited for producing entanglement between two qubits in a circuit QED setting. 
Mirko Pieropan  Politecnico di Torino
Compressed Sensing Reconstruction using Expectation Propagation
Linear estimation problems are ubiquitous in many fields of science.
One of the most studied ones is Compressed Sensing (CS), which consists in finding the
sparsest solution \underline{x} of a system of linear equations \mathbf{F}\underline{x} = \underline{y} given
a set of M measurements \underline{y}, where N>M is the number of unknowns and the sensing matrix \mathbf{F} is assumed to
be given. We have addressed the problem within a bayesian setting, by considering a BernoulliGauss prior distribution for
the solution to be retrieved and a computational message passing scheme called Expectation Propagation (EP), which was
originally developed in Statistical Physics. The method provides a natural framework to learn the parameters of the prior distribution,
which are in general unknown as well. We have studied the reconstruction by EP in the presence of dense correlated measurement matrices by
means of large scale simulations and found that the method is able to outperform current stateoftheart methods
such as belief propagation and matching pursuit algorithms. We show that the same scheme can be used in order to infer
the weights of a diluted perceptron and to compute the associated generalisation error as a function of the number of presented patterns.

Lorenzo Piroli  MaxPlanck Institute for Quantum Optics
Nonequilibrium quantum states of matter and generalized hydrodynamics
I will give an overview of recent developments in the field of nonequilibrium dynamics of
isolated 1D quantum systems, discussing in particular the special role played by integrable models.
In the first part of the talk, I will discuss the main motivations, questions and technical challenges
which were faced in the past years and mention the main theoretical techniques which were developed,
especially those with a strong connection with problems in 2D classical statistical mechanics.
In the second part of the talk, I will focus on a novel generalized hydrodynamic approach which allowed us
to tackle many open questions in the field, providing an exact description of the manybody dynamics at
large space and time scales. 
Marco Pretti  ISCCNR
Dynamical transitions in drivendiffusive latticegas models
We discuss a generalized meanfield approach to drivendiffusive latticegas models such as the totallyasymmetric simpleexclusion process (TASEP), in order to
investigate the socalled dynamical transition. In the pure TASEP with open boundaries, such a phenomenon has been studied exactly and appears as a singularity in
the relaxation rate of the system toward its nonequilibrium steady state.
In the presence of adsorption/desorption (Langmuir) kinetics (TASEPLK), we point out an unexpectedly richer dynamical phase diagram,
including unusual firstorderlike dynamical transitions.
Taking inspiration from our meanfield theory, earlier exact results and the socalled (approximate) domainwall theory,
we put forward a conjecture about the exact analytical expression of the relaxation rate for the TASEPLK, and consequently about its full dynamical phase diagram.
We find a striking agreement of our conjecture with finitesize numerical results processed by extrapolation techniques. 
Fabio Saracco  Scuola IMT Alti Studi Lucca
The role of bot squads in the political debate on Twitter
Among different platforms, Twitter is one of the most studied, due to the availability of data about
the traffic and for being strongly used for the political debate. Indeed, one of the problems for the analysis
is the reliability of the users in the game: in such sense a stream of research was devoted to find the right
features for detecting automated accounts [1, 2].
Anyway, in the previous analyses the effect of random noise was rarely tackled. In recent years an
entropybased approach for the analysis of complex networks was developed in order to pro vide an
unbiased benchmark for the analysis and filtering out random noise [3, 4].
In the present study we merge the bot detection with the use of an entropybased nullmodel for the
analysis of the content exchange on Twitter in the Italian debate about the migration flux from North Africa.
First, in order to get the political affiliations of users, we focused on the bipartite network in
which the two layers are represented by verified and unverified users, respectively, and (undirected)
links label the interactions among these two classes of users. The main idea is to infer the membership
to a certain political idea from (a proxy of) the contacts of an account, similarly to [5].
Verified users cluster in 3 main groups: one including government representatives, the right wing
and the Movimento 5 Stelle party; one including the Italian Democratic party; and eventually one including NGOs,
the official accounts of online and offline media and journalists and different kind of VIPs. Confirming results
presented in several studies [6], the polarization of unverified users is particularly strong: in almost all the
cases the unverified users interact at ∼ 90% only with accounts belonging to a single community. We use the previous
memberships in order to label the users involved in the non trivial exchange of contents.
We use the validated projection developed in [5]: this permits to extract the significant flow of messages among users,
discounting at the same time the virality of messages, the retweetting activity of users and their productivity in writing messages.
Such an approach provides the backbone of the content exchange among users on Twitter: indeed, we found that all the most effective accounts
in delivering their messages (i.e. the Hubs [7]) refers to the right wing area, the first account referring to a different community being the
official account of a newscast, ranking 176th in the hub score. The top 20 hubs list contains a relatively small amount of verified users, the
strongest being the one of the Minister of Internal Affairs Matteo Salvini. The striking result, as far as we know never detected in other systems,
is the presence of a group of common bots for some of those hubs, uncovering a strategy in order to increase the visibility of the previous accounts.
Let us underline that these shared bots are particularly effective since their activity was validated by the entropybased projection.
Such a framework resulted to be extremely helpful, since it hits one feature of an automated user that cannot be avoided by programmers.
Indeed, the sense of a bot is to increase the visibility of a user by retweetting her/his messages and that is exactly what is revealed by the entropybased filtering.
References
[1] Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, and Maurizio Tesconi. Fame for sale: efficient detection of fake twitter followers. Decision Support Systems, 80:56–71, 2015.
[2] Emilio Ferrara, Onur Varol, Clayton Davis, Filippo Menczer, and Alessandro Flammini. The rise of social bots. Commun. ACM, 59(7):96–104, June 2016.
[3] Squartini Tiziano and Garlaschelli Diego. Maximumentropy networks. Pattern detection, network reconstruction and graph combinatorics. Springer, page 116, 2017.
[4] Cimini, G., Squartini, T., Saracco, F., Garlaschelli, D., Gabrielli, A. and Caldarelli, G. 2018. The Statistical Physics of RealWorld Networks, Nat. Rev. Phys. 1(1): 58.
[5] Carolina Becatti, Guido Caldarelli, Renaud Lambiotte, and Fabio Saracco. Extracting sig nificant signal of news consumption from social networks: the case of Twitter in Italian political elections. jan 2019.
[6] Alessandro Bessi, Fabio Petroni, Michela Del Vicario, Fabiana Zollo, Aris Anagnostopoulos, Antonio Scala, Guido Caldarelli, and Walter Quattrociocchi. Homophily and polarization in the age of misinformation. Eur. Phys. J. Spec. Top., 2016.
[7] Jon M. Kleinberg. Authoritative sources in a hyperlinked environment. J. ACM, 1999. 
Vittore Ferdinando Scolari  Institut Pasteur
Kinetic signature of cooperativity in the irreversible collapse of a polymer
We investigate the kinetics of a polymer collapse due to the formation of irreversible crosslinks between its monomers.
Using the contact probability P(s) as a scaledependent order parameter depending on the chemical distance s, our simulations
show the emergence of a cooperative pearling instability. Namely, the polymer undergoes a sharp conformational transition to a set of
absorbing states characterized by a length scale ξ corresponding to the mean pearl size. This length and the transition time depend on the
polymer equilibrium dynamics and the crosslinking rate.

Samir Suweis  Università di Padova
Adaptive Metabolic Strategies in ConsumerResource Models
Competitive ecosystems are widespread and most commonly they are described mathematically using MacArthur’s consumerresource model [1], leading to so
called the “Competitive Exclusion Principle”, which limits the number of coexisting competing species to the number of available resources.
Nevertheless, several empirical evidences – for example bacterial community culture experiments – show that this principle is violated in real ecosystems.
Another experimental evidence involving microbial populations that cannot be explained in this framework is the existence of diauxic (or polyauxic) shifts
in microbial growth curves: microbes can adapt their metabolic strategies to the availability of different resources in the environment: when exposed to different
sugars they often consume them sequentially resulting in population growth curves with distinct phases of growth rates. In this talk I will share ideas from our recent
work [2], where we show that by introducing adaptive metabolic strategies to consumerresource models we can reproduce diauxic shifts in agreement with experimental
observations and it allows consumerresource models to violate the “Competitive Exclusion Principle".
References
[1] Chesson, P. (1990) ”MacArthur’s consumerresource model.” Theoretical Population Biology, 37.1: 2638.
[2] PaccianiMori, L., Suweis, S., Giometto, A., & and A. Maritan (2019) ”Adaptive consumer resource models can explain diauxic shifts and the violation of the Competitive Exclusion Principle. BioRxiv: 385724.

Pablo Villegas Góngora  ISCCNR Roma
Landau–Ginzburg theory of cortex dynamics: Scalefree avalanches emerge at the edge of synchronization
The human cortex operates in a state of restless activity, the meaning and functionality of which are still not understood.
A fascinating, though controversial, hypothesis, partially backed by empirical evidence, suggests that the cortex might work at
the edge of a phase transition, from which important functional advantages stem. However, the nature of such a transition remains elusive.
Here, we adopt ideas from the physics of phase transitions to construct a general (Landau–Ginzburg) theory of cortical networks, allowing us
to analyze their possible collective phases and phase transitions. We conclude that the empirically reported scaleinvariant avalanches can
possibly come about if the cortex operated at the edge of a synchronization phase transition, at which neuronal avalanches and incipient oscillations coexist.
