# Young Seminars SIFS

La SIFS organizza gli Young Seminars SIFS su temi vari connessi alla Fisica Statistica, come attività culturale per dare uno spazio ai ricercatori più giovani, ovvero studenti, dottorandi e coloro che hanno ricevuto il titolo di dottorato da meno di 5 anni.

I seminari hanno cadenza mensile, con due interventi nella stessa sessione, e saranno poi resi disponibili online tramite il canale Youtube ufficiale della SIFS, per chi fosse interessato e non riuscisse a seguirli in diretta.

## Istruzioni per partecipare ai Seminars

chiunque può accedere al meeting semplicemente cliccando sul link, anche in assenza di un account Teams o Microsoft. Consigliamo fortemente l'uso di Google Chrome che è completamente supportato da Teams, mentre altri browsers potrebbero dare problemi;

una volta cliccato sul link è sufficiente seguire le indicazioni, inserendo un nome per essere individuabili nella riunione. Vi chiediamo di accedere alla riunione spegnendo videocamera e microfono prima di entrare o immediatamente, in modo da alleggerire la piattaforma;

i seminari verranno registrati, per essere reso accessibile successivamente tramite il canale Youtube SIFS: partecipando alla riunione date il consenso per la registrazione;

in ogni sessione si terranno due seminari di mezz'ora ciascuno, con 25 minuti riservati al talk vero e 5 minuti riservati alle domande.

# Upcoming Seminars

## Network analysis with generative models combining reciprocity and community structure

Abstract: Networks are extensively used to represent real-world data from diverse domains. A significant objective is to uncover the underlying patterns driving network interactions, and one widely adopted approach involves the application of community detection algorithms. However, these methods often fail to reproduce the network's structural features. Therefore, researchers have been working on developing models that not only consider the community structure but also incorporate other network properties. In this talk, I will focus on the incorporation of reciprocity, which refers to the tendency of two nodes to form mutual connections. I will present two probabilistic generative models that both perform community detection and capture reciprocity in networks. These methods differ from standard generative models in that they can handle the interdependence between two directed edges that connect pairs of nodes, therefore relaxing the common assumption in network generative models of conditional independence between edges. The two proposed approaches, CRep [1] and JointCRep [2], follow distinct generative processes. CRep employs Poisson distributions to model conditional probabilities and relies on a pseudo-likelihood approximation. On the other hand, JointCRep utilizes the Bivariate-Bernoulli distribution to model joint edge distributions involving the same pairs of nodes in closed-form. Both methods retain the desirable properties of standard models while relaxing the conditional independence assumption, ultimately leading to improved edge prediction and network reconstruction. Additionally, by explicitly considering reciprocity, these models allow to answer questions that were not previously possible and offer insights into the fundamental mechanisms driving edge formation in networks.

[1] Safdari, M. Contisciani, C. De Bacco, Generative model for reciprocity and community detection in networks. Phys. Rev. Res. 3, 023209 (2021).

[2] M. Contisciani, H. Safdari, C. De Bacco, Community detection and reciprocity in networks by jointly modelling pairs of edges. Journal of

Complex Networks 10 (2022). Cnac034.

14th September 2023 - 16.30 Rome Time

## A multicolour polymer model facilitating prediction of 3D structure and transcriptional activity of human chromosomes

Abstract:Within each human cell, different kinds of RNA polymerases and a panoply of transcription factors bind chromatin to simultaneously determine 3D chromosome structure and transcriptional programme. Experiments show that, in some cases, different proteins segregate to form specialised transcription factories; in others they mix together, binding promiscuously the same chromatin stretch. We use molecular dynamics simulations to study a polymer model for chromosomes accounting for multiple types (“colours”) of chromatin-binding proteins. Our multi-colour model shows the spontaneous emergence of both segregated and mixed clusters of chromatin-bound proteins, depending mainly on their size, thereby reconciling the previous experimental observations. Additionally, the model leads to non-trivial spatio-temporal correlations between different transcription units, dependent on the underlying chromosome structure. Small-world networks emerge; in these, positive and negative correlations in activities of transcription units provide simple explanations of why adjacent units in large domains are co-transcribed so often. We also explain how local genome edits induce distant omnigenic and pangenomic effects, and develop ways to predict activities of all transcription units on human chromosomes.

[1] 3D polymer simulations of genome organisation and transcription across different chromosomes and cell types. M Semeraro, G Negro, A Suma,

G Gonnella, D Marenduzzo. Physica A: Statistical Mechanics and its Applications 625, 129013, (2023).

[2] A unified-field theory of genome organization and gene regulation. G. Negro, M. Semeraro, P. R. Cook, D. Marenduzzo. arXiv preprint arXiv:2308.02861 (2023).

[3] A multicolour polymer model for the prediction of 3D structure and transcription in human chromatin. M. Semeraro, G. Negro, A. Suma, G. Gonnella, P. R. Cook, D. Marenduzzo bioRxiv 2023.01.16.524198, (2023).

14th September 2023 - 16.30 Rome Time

# Past Seminars

## Quantum unitary evolution interspersed with repeated non-unitary interactions at random times

Abstract: We address the issue of what happens when the unitary evolution of a generic closed quantum system is interrupted at random times with non-unitary evolution due to interactions with either the external environment or a measuring apparatus. We adduce a general theoretical framework to obtain the average density operator of the system at any time during the dynamical evolution, which applies to any form of non-unitary interaction [1,2]. We provide two explicit applications of the formalism in the context of the so-called tight-binding model relevant in various contexts in solid-state physics for two representative forms of interactions: (i) stochastic resets, whereby the density operator is at random times reset to its initial form, and (ii) projective measurements at random times. For the resetting case, we demonstrate with our exact results how the particle is localized on the sites at long times, leading to a time-independent mean-squared displacement of the particle about its initial location. For the projective measurement case, we show that repeated projection to the initial state of the particle results in an effective suppression of the temporal decay in the probability of the particle being in the initial state. The amount of suppression is comparable to the one in conventional Zeno effect scenarios. However, it does not require performing measurements at precisely regular intervals, which are hallmarks of the Zeno effect scenarios.

References:

[1] D. Das, S. Dattagupta, and S. Gupta: J. Stat. Mech.: Theory Exp. 053101 (2022)

[2] S. Dattagupta, D. Das, and S. Gupta: J. Stat. Mech.: Theory Exp. 103210 (2022)

13th July 2023 - 16.30 Rome Time

## Training neural networks with structured noise improves classification and generalization

Abstract:Attractor neural networks have been extensively used for modeling associative memory, as biologically inspired devices that learn concepts by optimizing their connectivity structure. Various algorithms have been developed to modify the interactions between neurons, aiming to achieve memory retrieval and generalization capabilities, which are manifested as finite basins of attraction containing the memories. The training-with-noise algorithm, initially proposed by Gardner and collaborators, introduces noise injection into the training dataset, contributing to understand the positive impact of noise on enhancing the system's generalization capabilities. This same idea is at the basis of a whole class of regularization techniques that are widely applied in the field of artificial neural networks today. In this talk, we present how the memory performance of attractor neural networks can be significantly enhanced by adding internal dependencies into the noisy training data, allowing to achieve perfect classification and approach maximal basins of attraction. The criterion for finding the optimal structure of training noise will be derived and used in practice. We will also prove that the so-called unlearning rule coincides with the training-with-noise algorithm when noise is maximal and data are fixed points of the network dynamics.

References:

E. Gardner, D.J. Wallace, and N. Stroud. J. Phys. A, 22: 2019-2030, 1989.

M. Benedetti and E. Ventura. arXiv:2302.13417, Under review, 2023.

M. Benedetti, E. Ventura, E. Marinari, G. Ruocco and F. Zamponi. J. Chem. Phys., 156: 104107, 2022.

K.Y.M. Wong, D. Sherrington. Phys. Rev. E, 47(6): 4465–4482, 1993.

13th July 2023 - 16.30 Rome Time

## The Jamming Transition of Hard Spheres through the Lens of Constrained Optimization

Abstract: Generating spheres packings, i.e. configurations where spheres do not overlap with each other, is a problem of interest in mathematics, information theory and, naturally, physics. In this talk I will focus on the specific case of amorphous or disordered packings and their connection with the so-called "jamming transition". This transition has recently received a lot of attention because it defines a rather broad universality class that informs our understanding of glasses, rigidity of amorphous solids, granular matter and even some types of neural networks.

On the other hand, computational techniques to produce jammed packings are still somewhat limited, specially in the case of (infinitely) hard-spheres models. In the final part of the talk I will briefly describe a new numerical algorithm that allows us to accurately reach the isostatic jamming point of hard-spheres configurations through a series of linear optimization problems. Besides, such algorithm also enables us to reconstruct the full network of contact forces present in a jammed packing.

8th June 2023 - 16.30 Rome Time

## Response Theory, Reaction Coordinates and Critical Phenomena for Noisy Interacting Systems

Abstract:In this talk, I will present our latest results on the close link between response theory, reaction coordinates and critical phenomena for noisy systems with mean field interactions.

Such systems are routinely used to model collective emergent behaviours in multiple areas of social and natural sciences as they exhibit, in the thermodynamic limit, continuous and discontinuous phase transitions.

I will show that the perspective of Response Theory, that aims at establishing a link between natural and forced variability of general physical systems, is particularly useful to understand the physical mechanisms establishing critical phenomena in interacting systems.

Firstly, I will show how to define a set of reaction coordinates for the system starting from the coupling structure among the microscopic agents.

Secondly, I will show that such reaction coordinates prove to be proper nonequilibrium thermodynamic variables as they carry information on correlation, memory properties and resilience properties of the system.

In particular, the investigation of response properties of the reaction coordinates allows to identify and characterise phase transitions of the system as they manifest as singular values of the susceptibility associated to such thermodynamic variables.

8th June 2023 - 16.30 Rome Time

## From high to low resolution: the mapping problem in coarse-grained modeling

Abstract: Employing all-atom Molecular Dynamics (MD) simulations in biomolecular research is often computationally expensive as the majority of the relevant biological processes occur over extremely long time and length scales. This is the main driving force behind the development and use of low resolution, coarsegrained models [1], which are able to describe phenomena that are out of reach for atomistic simulations at the expense of a certain reduction in accuracy. Minimising the discrepancy between all-atom and coarse-grained models not only amounts at tuning the effective interactions in the coarse-grained force field, but also at carefully selecting the low-resolution representation (mapping) of the high-resolution system [2]. We analyse the mapping problem, investigating the similarities between different solutions adopted to describe a biomolecule at a coarser level. More specifically, we focus our interest on the information loss that naturally arises in the process of resolution reduction operated by the mapping [3]. Although originally developed in the context of the theory of coarse-graining, these methods are very general and can be effortlessly applied to the study of different complex systems.

References:

[1] William George Noid. Perspective: Coarse-grained models for biomolecular systems. The Journal of chemical physics, 139(9):09B201 1, 2013.

[2] Marco Giulini, Marta Rigoli, Giovanni Mattiotti, Roberto Menichetti, Thomas Tarenzi, Raffaele Fiorentini, and Raffaello Potestio. From system modelling to system analysis: the impact of resolution level and resolution distribution in the computer-aided investigation of biomolecules. Frontiers in Molecular Biosciences, 8:460, 2021.

[3] Marco Giulini, Roberto Menichetti, M Scott Shell, and Raffaello Potestio. An information-theory-based approach for optimal model reduction of biomolecules. Journal of chemical theory and computation, 16(11):6795– 6813, 2020.

11th May 2023 - 16.30 Rome Time

## Diversity of information pathways drives scaling and sparsity in real-world networks

Abstract: Empirical complex systems must differentially respond to external perturbations and, at the same time, internally distribute information to coordinate their components. While networked backbones help with the latter, they limit the components' individual degrees of freedom and reduce their collective dynamical range. Here, we show that real-world networks are formed to optimize the gain in information flow and loss in response diversity.

Encoding network states as density matrices, we demonstrate that such a trade-off mathematically resembles the thermodynamic efficiency characterized by heat and work in physical systems. Our findings explain, analytically and numerically, the sparsity and the empirical scaling law observed in hundreds of real-world networks across multiple domains. We show, through numerical experiments in synthetic and biological networks, that ubiquitous topological features such as modularity and small-worldness emerge to optimize the above trade-off for middle- to large-scale information exchange between system's units. Our results highlight that the emergence of some of the most prevalent topological features of real-world networks have a thermodynamic origin.

11th May 2023 - 16.30 Rome Time

## Variance sum rule for entropy production estimation

Abstract: Nonequilibrium steady states, from the planetary scale to biological processes, are characterized by entropy production via energy dissipation to the environment, which is often challenging to measure. A novel variance sum rule sets a new resource for exploiting fluctuations to measure physical quantities in stochastic systems. In particular, we focus on the formula it gives for estimating the entropy production rate from trajectories of positions and forces. We describe this method with analytically solvable models and we show its robustness and usefulness in practical applications to experimental data. By introducing a model-dependent fitting procedure, the method is also adapted to deal with conditions where not all degrees of freedom are experimentally accessible. For example, we apply the VSR to human red blood cells (RBCs) in experiments with laser optical tweezers and ultrafast life-imaging microscopy. Our estimate σ_{RBC} ∼ 10⁶ k_B T/s per single RBC agrees with macroscopic calorimetry measurements. The VSR sets a new resource for exploiting fluctuations to measure entropy production rates across scales in active and living matter.

I. Di Terlizzi, M. Gironella, D. Herraez-Aguilar, T. Betz,F. Monroy, M. Baiesi and F. Ritort, arXiv:2302.08565v1 (2023).

13rd April 2023 - 16.30 Rome Time

## Glass and pseudo-localization transitions in the Mode-Locked p-spin model for Random Lasers

Abstract: Optical waves in active disordered media display the typical phenomenology of complex systems. Several spectral shots taken from the same piece of material in the lasing regime display strong fluctuations in the position of the intensity peaks, suggesting that there is no specific mode which is preferred in the amplification, but depending on the initial state, with the disorder kept fixed, the modes gaining the highest intensity change every time. In order to explain this behaviour, a spin-glass model has been developed, where the light modes are described as non-linearly interacting phasors on the so-called mode-locked diluted graph [1].

The specific mode-coupling selection rule, which naturally emerges in the study of lasing modes dynamics, impairs the analytical solution of the model out of the narrow bandwidth limit, where the interaction network is fully connected. In this talk we present recent results from numerical simulations of the mode-locked glassy random laser. A phenomenology compatible with a glass transition is revealed from the divergence of the specific heat and the non-trivial structure of the Parisi overlap distribution function. By means of a refined finite-size scaling analysis of the critical region, the transition is assessed to be compatible with a mean-field universality class [2].

A pseudo-localization transition to a phase where the intensity of light is neither properly localized on a single mode nor equiparted among all the modes is revealed from the measure of the inverse participation ratio and of the spectral entropy [3]. The two transitions occur at the same temperature as different manifestations of the same underlying phenomenon, the breaking of ergodicity.

[1] F. Antenucci, C. Conti, A. Crisanti and L. Leuzzi, Phys. Rev. Lett. 114, 043901 (2015).

[2] J. Niedda, G. Gradenigo, L. Leuzzi and G. Parisi, arXiv:2210.04362, (2022).

[3] J. Niedda, L. Leuzzi and G. Gradenigo, arXiv:2212.05106 (2022).

13rd April 2023 - 16.30 Rome Time

## Macroscopic models of limiting processes in cellular growth laws

Abstract: Cells contain thousands of distinct molecular components interacting in highly specific and heterogeneous ways - a paradigmatic example of a complex system. Yet, multiple studies have shown that simple, quantitative laws emerge in both bacteria and eukaryotes by focusing on few macroscopic quantities of cell physiology in the spirit of thermodynamics. In particular, quantitative studies of cell growth show the existence of ‘growth laws’ which demonstrate how coarse-grained variables like ribosomal content, protein expression and growth rate are enslaved to one another through simple emergent mathematical relations. The specific form of these laws depends on the particular growth limitation regime of the cell - which can be characterized as a list of all the catalysts and substrates that limit growth processes. Conventional wisdom holds that growth is prominently set by translation and ribosome amounts. However, a growing body of evidence suggests that in several circumstances transcription and mRNA levels become relevant. In this work, we define a mathematical model of biosynthesis as an autocatalytic reaction network with both layers of the central dogma (transcription and translation). By a systematic exploration of how transcription and total mRNA influence cell growth, we find three growth limitation regimes, two of which include mRNA as a limiting factor for growth: in one regime mRNA becomes limiting when ribosomes spatially saturate transcripts, while in the other regime mRNA influences growth because formation of the mRNA-ribosome complex becomes limiting. We argue that the latter regime is biologically relevant because it is the only one able to explain recent data. Specifically, we find that our model in this regime correctly predicts that the growth response to forced over-expression of unneeded protein depends on mRNA levels in S. cerevisiae. Moreover, it also correctly predicts a growth law for total mRNA, where mRNA levels increase as growth conditions improve, as recently observed in E. coli. Our framework contributes to showing that a general class of macroscopic biosynthesis models can successfully capture multiple features of cell growth across organisms, supporting the notion of universal aspects of growth physiology.

9th March 2023 - 16.30 Rome Time

## Optimal policies for Bayesian olfactory search

Abstract: Many flying insects and animals depend on a remarkable ability to track the source of an odor which is advected by a turbulent flow. This olfactory search problem is rendered especially difficult by the intermittency intrinsic to turbulence, and it requires complex search strategies which properly leverage infrequent odor detections. In this seminar, I will briefly outline the physics underlying the olfactory search problem and why naive gradient-based strategies will not succeed. Then I will introduce the partially-observable Markov decision process (POMDP) formalism and discuss how to obtain policies which are optimal with respect to the search time. These will be compared to well-known heuristic strategies on data from direct numerical simulations. Finally, I will discuss current and future directions for research.

9th March 2023 - 16.30 Rome Time

## Generalized hydrodynamics for a system of inhomogeneous hard rods

Abstract: A rod is an elongated particle which travels at a certain constant speed in absence of other rods. When two rods collide they exchange their position and continue their motion with the initial speed. The model was introduced by Dobrushin in the 50s for rods of identical length. Here we consider rods of different sizes and, in the spirit of the work of Boldrighini, Dobrushin and Sukhov of 1982, we show the hydrodynamic equations starting from the microscopic particle systems. It turns out that the Cauchy problem and the effective rod velocity have the same form of those of the soliton gas. Work in progress with Pablo Ferrari, Dante Grevino, Herbert Spohn.

Date: 9th February 2023 - 16.30 Rome Time

## Ion channels in critical membranes: clustering, cooperativity, and memory effects

Abstract:The response and conformational changes of voltage-gated ion channels are well understood at a single-molecule level, but there is limited knowledge about how these channels interact with the lipids and with one another in their native environment. In this study, we propose that interactions between ion channels mediated by the surrounding lipid environment can explain the observed behaviors of cooperativity and hysteresis. These have been suggested to give rise to multistability of membrane potentials and thus to ”cellular memory”. We develop a theoretical model based on the statistical mechanics of interacting, diffusing agents with internal degrees of freedom that are subject to an external field. This model allows us to understand several poorly understood aspects of ion channels behavior, including the non-Markovian character of single channel currents.

Date: 9th February 2023 - 17.00 Rome Time

## Exact Dynamical Equations for Kinetically-Constrained-Models

Abstract: The mean-field theory of Kinetically-Constrained-Models is developed by considering the Fredrickson-Andersen model on the Bethe lattice. Using certain properties of the dynamics observed in actual numerical experiments we derive asymptotic dynamical equations equal to those of Mode-Coupling-Theory. Analytical predictions obtained for the dynamical exponents are successfully compared with numerical simulations in a wide range of models including the case of generic values of the connectivity and the facilitation, random pinning and fluctuating facilitation. The theory is thus validated for both continuous and discontinuous transition and also in the case of higher order critical points characterized by logarithmic decays.

Date: 12th January 2023 - 16.30 Rome Time

## Polymer physics of chromosome spatial organization

Abstract: In the nucleus of cells, chromosomes have been discovered to self-organize into a complex spatial architecture that serves vital functional purposes as, for instance, genes have to establish specific physical contacts with their distal DNA regulators to control transcriptional activities. Those discoveries have changed our view of the genome, which is no longer considered to be just a 1D linear sequence of nucleotides to be deciphered, but rather a complex 3D structure whose arrangement defines the fate of the cell. However, how the system self-assembles to shape the folding of our genome and its functions is only poorly understood. In this talk, I discuss principled models of interacting polymers from statistical mechanics to investigate the mechanisms whereby distal DNA sequences recognize and interact with each other. By combining polymer physics, machine learning and computer simulations, I show that chromosome spatial organization is controlled at the single-molecule level by thermodynamic mechanisms of phase transitions, which spontaneously establish contact or segregation between specific genomic sites, such as genes and their regulators [1]. Those theories are validated against independent experiments (e.g., Hi-C, SPRITE, GAM and super-resolution microscopy) [2,3], opening to new tools for real-world applications, such as the predictions of the effects of disease-associated mutations, linked to congenital disorders or cancer, on genome 3D structure [4,5].

[1] Conte et al. Nature Communications, 11, 3289 (2020)

[2] Conte et al. Nature Communications 13, 4070 (2022)

[3] Fiorillo, Musella, Conte et al. Nature Methods, 18, 482-490 (2021)

[4] Huang et al. Nature Genetics, 53, 1064–1074 (2021)

[5] Bianco et al. Nature Genetics 50, 662 (2018)

Date: 12th January 2023 - 16.30 Rome Time

## Josephson junctions resonantly activated by axions

Abstract: Axions are the hypothetical elementary particles candidate as a possible component of cold dark matter. In the last years they have been supposed to interact with Josephson junctions (JJs). Unexplained experimental effects on Josephson systems can be well justified on the basis of the axion-JJ theory [Phys. Rev. Lett 111, 231801 (2013)]. This hypothesis, thus, has paved the way for the possibility of thinking of JJs as possible axion detectors. We show that an axion-detection mechanism can be based on the measurable voltage drop induced in the JJ when the combined action of bias current and thermal fluctuations causes the JJ to switch from the superconducting to the resistive state. The analysis of the mean switching times (MSTs) reveals indeed the occurrence of an axion-induced resonant activation phenomenon. Further, an effective quantum description of

the axion as a two-level dynamic system is proposed. The direct coupling (not mediated by photons in a cavity) with a Josephson qubit is studied through an effective spin-spin Hamiltonian model. When the axion and the Josephson frequencies match, the axion-Josephson qubit interaction can

be responsible for a resonance effect. Thus, experimentally detectable oscillations induced in the Josephson qubit by the axion are clearly derived. We demonstrate how these resonant effects could be experimentally measured and exploited to probe the presence of the axion field through Josephson-based experiments.

Date: 15th December 2022 - 16.30 Rome Time

## The role of higher-order interactions in the dynamics of social contagion and norm change

Abstract: Dynamical processes that emulate human behavior have been the focus of many studies, where social relationships and interactions are typically considered as an underlying structure. As shown recently in various contexts, it is moreover important to take into account the fact that individuals do not interact only in pairs but also in larger groups [1]. Social interactions are indeed a natural testing ground for higher-order approaches. In this talk, I will discuss the effects of considering group interactions on the dynamics of social systems. In particular, I will generalize two models of social contagion and norm evolution, initially introduced and studied on graphs, and now extended as dynamical processes on hypergraphs. Leveraging real-world interaction data and analytical insights, I will show the emergence of novel phenomena such as discontinuous transitions and critical mass effects induced by higher-order interactions [2, 3]. After allowing for the presence of a committed minority, I will show that the ability of committed individuals to overturn an existing norm is non-monotonic in the number of participants in higher-order interactions [4]. These results provide theoretical support to the observation that extremely small minorities can overcome the opinion of the corresponding large majority of the population. Overall, the findings confirm the relevance of exploring network representations beyond pairwise interactions when modeling social phenomena [5].

References:

[1] Battiston, F., Cencetti, G., Iacopini, I., Latora, V., ... & Petri, G. (2020). Networks beyond pairwise interactions: structure and dynamics. Physics Reports, 874, 1-92. [2] Iacopini, I., Petri, G., Barrat, A., & Latora, V. (2019). Simplicial models of social contagion. Nature Communications, 10(1), 1-9. [3] St-Onge, G., Iacopini, I., Latora, V., Barrat, A., Petri, G., Allard, A., & Hébert-Dufresne, L. (2022). Influential groups for seeding and sustaining nonlinear contagion in heterogeneous hypergraphs. Communications Physics, 5(1), 1-16. [4] Iacopini, I., Petri, G., Baronchelli, A., & Barrat, A. (2022). Group interactions modulate critical mass dynamics in social convention. Communications Physics, 5(1), 1-10. [5] Battiston, F., Amico, E., Barrat, A., Bianconi G., Ferraz de Arruda, G., Franceschiello, B., Iacopini, I., ... & Petri, G. (2021). The physics of higher-order interactions in complex systems. Nature Physics, 17(10), 1093-1098.

Date: 15th December 2022 - 16.30 Rome Time

## Entanglement in mixed states: from a scalar to an operatorial characterization

Abstract: As Schrödinger already recognized one century ago, entanglement is at the core of quantum mechanics. An effective way of detecting bipartite entanglement in a many-body mixed state is provided by the partial transpose operation on the reduced density matrix. Here, we first study the time evolution after a quantum quench of the moments of the partial transpose, providing a scalar characterization for entanglement in this context. Then, we introduce an operatorial characterization through the negativity Hamiltonian, i.e. the (non hermitian) effective Hamiltonian operator describing the logarithm of the partial transpose. This allows us to address the connection between entanglement and operator locality beyond the paradigm of bipartite pure systems.

Date: 10th November 2022 - 16.30 Rome Time

## Optimal transport problem in complex systems

Abstract: In general, optimal transport (OT) theory can be used in systems where there is an optimization problem under constraints. Indeed, it is not hard to find examples in many complex systems where agents are maximizing/minimizing some gain/cost function given limited resources or time. Following [1], a simple application of OT to Economics is represented by the worker-job assignment. The solution finds the answer to the question relating the best partitioning of a mass (a.k.a. the number) of workers knowing their skills, with respect to the job requirements. This assignment can be formulated by an OT problem. A well known property of OT solution is its network topology. In facts, it is easy to prove, see for example [2], that the optimal mass distribution is a sparse matrix representing a tree of the bipartite (e.g. worker-job) network. Diversification and nestedness are considered important properties of many complex systems such as ecological and economic systems [3]. Usually, they are linked to the resilience of the systems against shocks or external attacks. While the binary structure of such networks shows indeed nestedness and many redundant paths between nodes, the weighted matrices (export flows in world trade web or frequencies of interaction in plant-pollinator systems) are much sparser in terms of concentration of weights, hinting an OT-like solution. We explore the idea that many of these systems have indeed an OT problem running onto a binary network of potential interactions.

References:

[1] Alfred Galichon. Optimal Transport Methods in Economics. Princeton University Press, 2016.

[2] Richard A Brualdi. Combinatorial matrix classes, volume 13. Cambridge University Press, 2006.

[3] Manuel Sebastian Mariani, Zhuo-Ming Ren, Jordi Bascompte, and Claudio Juan Tessone. Nestedness in complex networks: observation, emergence, and implications. Physics Reports, 813:1–90, 2019.

Date: 10th November 2022 - 16.30 Rome Time

## Measurement-induced structural transitions in random circuits

Abstract: In this talk, I will discuss the dynamics of random quantum circuits subject to the action of a monitoring environment. The tension between the unitary evolution, delocalizing the degrees of freedom throughout the system, and the measurements, which localize the degrees of freedom and collapse the system state into atypical manifolds, resolves into a dynamical transition. The latter separates a phase dominated by scrambling and a quantum Zeno phase dominated by frequent measurements. I will discuss how this measurement-induced phase transition is directly encoded in structural facets of the system wave-function, which is analyzed through the lens of participation entropy. Large-scale numerical simulations and the investigation of different models identify a robust order parameter for the transition. An analytical perspective is given by mapping the setup to a classical statistical mechanics model.

References: P. Sierant and X. Turkeshi, Phys. Rev. Lett. 128, 130605 (2022).

Date: 12th May 2022 - 16.30 Rome Time

## Approaching optimal memory retrieval in Hopfield-like networks

Abstract: One of the most important tasks performed by the brain is Associative Memory, namely the ability to recognize similarities between slightly distorted versions of a given stimulus, tracing them back to one single archetype. Hopfield-like neural networks are a form of Artificial Intelligence designed to mimic this ability. They consist of a collection of binary spins endowed with a stochastic dynamics, which must be such as to create finite basins of attraction around a predetermined set of configurations (memories). When initialized to a configuration similar to one of the memories, the network will be driven towards the corresponding attractor, resulting in error correcting performance. Among the possible strategies to build such a dynamics, the linear perceptron algorithm and Hebbian unlearning are two of the most influential. The first is a supervised learning algorithm, meaning that it needs to be fed explicitly the information about the patterns to be learned. The second is unsupervised, and exploits the information contained in the attractor landscape generated by Hebb's biologically inspired learning rule. In this talk, I will introduce both paradigms, illustrate some unexpected similarities in the performance of these apparently very different algorithms, and speculate on a possible explanation for them.

Main references:

- Benedetti, Ventura, Marinari, Ruocco, Zamponi, Supervised perceptron learning vs unsupervised Hebbian unlearning: approaching optimal memory retrieval in Hopfield-like networks, J. Chem. Phys. 156, 104107 (2022)

- D. Amit, H. Gutfreund, and H. Sompolinsky, Storing infinite numbers of patterns in a spin-glass model of neuralnetworks, J. Stat. Phys. 15, 1530 (1985)

- J. L. van Hemmen, L. Ioffe, R. K ̈uhn, and M. Vaas, Increasing the efficiency of a neural network through unlearning, Physica A 163, 386 (1990)

Date: 12th May 2022 - 16.30 Rome Time

## A simple ego-centric method for generating realistic temporal networks

Abstract: Synthetic temporal networks are essential to schematize many real systems whose behavior vary in time, from social interactions to biological systems, and for which real data are not always easily collected, being often incomplete or not shareable due to privacy issues. However the generation of realistic temporal graphs is still an open problem. The main issue relies on mimicking both the temporal and the topological properties of the input network, and all their correlations. We propose a novel simple method to explore a temporal network, consisting in decomposing it in its building blocks, namely local temporal neighborhoods of each node with short memory. We then use them to generate a new network from scratch. Basically, the essential information that we use from the original graph to build the new one concerns the behavior of each node in the short time distance, i.e. which connections it creates, eliminates, or maintains, given the connections in the few previous time steps. We thus generate a new pattern of behavior by preserving the short-term temporal correlation of each node. Not only our method can generate real interaction patterns, but it is also able to capture the intrinsic temporal periodicity of the network and to generate temporal graphs with an execution time lower of multiple orders of magnitude with respect to other similar models.

Date: 07th April 2022 - 16.30 Rome Time

## Tensor Network methods for Lattice Gauge Theories

Abstract: Gauge theories are of paramount importance in our understanding of fundamental constituents of matter and their interactions, ranging from high-energy particle physics to low-temperature quantum many-body physics. However, the complete characterization of their phase diagrams and the full understanding of non-perturbative effects are still debated, especially at finite charge density, mostly due to the sign-problem affecting Monte Carlo numerical simulations. In recent years, a complementary numerical approach, Tensor Networks (TN) methods, in strict connection with emerging quantum technologies, have found increasing applications for studying Lattice Gauge Theories (LGTs) in low-dimensional systems. In this talk, I will present some recent results concerning the extension of TN algorithms to high-dimensional LGTs including dynamical matter. In particular, I will focus on their application to a compact Quantum Electrodynamics at zero and finite charge densities, addressing questions such as the characterization of collective phases of the model, the presence of confining phases at large gauge coupling, and the study of charge-screening effects.

Main references:

G. Magnifico, T. Felser, P. Silvi, and S. Montangero, Nature Communications 12, 3600 (2021).

M. Rigobello, S. Notarnicola, G. Magnifico, and S. Montangero, Phys. Rev. D 104, (2021).

Date: 07th April 2022 - 16.30 Rome Time

## Unfolding complex systems with Information Theory

Abstract: Real-world systems are characterized by complex interactions of their many internal degrees of freedom and, at the same time, they live in ever-changing and noisy environments. These two sources of interaction are usually tightly entangled, and singling out the dominant one starting from data is a long-standing challenge. Furthermore, it is often the case that we only have access to a coarse-grained representation of the internal states of the system while knowing nothing about the environment. The net effect of such ignorance about the environmental states leads to the emergence of new, effective couplings, while the properties of the accessible coarse-grained or low-dimensional descriptions are often elusive. In this talk, I will discuss how Information Theory can bring new insights to tackle these paramount questions arising in Statistical Physics. Concepts such as mutual information can help us disentangle internal interactions from changing environments, whereas information-preserving projections reveal surprising properties of optimal low-dimensional representations of complex systems.

Date: 10th March 2022 - 16.30 Rome Time

## Resetting in Stochastic Optimal Control

Abstract: "When in a difficult situation, it is sometimes better to give up and start all over again''. While this empirical truth has been regularly observed in a wide range of circumstances, quantifying the effectiveness of such a heuristic strategy remains an open challenge. In this talk, I will combine ideas from optimal control and stochastic resetting to address this question. The emerging analytical framework allows not only to measure the performance of a given restarting strategy, but also to obtain the optimal policy for a wide class of dynamical systems. This approach, analog to the celebrated Hamilton-Jacobi-Bellman equation, is successfully applied to simple settings and provides the basis to investigate realistic restarting strategies across disciplines.

Reference: B. De Bruyne and F. Mori. "Resetting in Stochastic Optimal Control." arXiv preprint arXiv:2112.11416 (2021).

Date: 10th March 2022 - 16.30 Rome Time

## Polymer physics models to unveil the mechanisms that shape chromosome 3D organization

Abstract: Human chromosomes have a complex 3D structure in the cell nucleus as genes and their regulators located far along the chain have to physically interact. Such an architecture is crucial to define the fate of a cell by establishing active and silenced genes. However, how the system self-organizes to shape the folding of our genome and its functions remains only poorly understood. In this talk, I discuss our theories from polymer physics showing that chromosomal architecture is controlled by thermodynamic mechanisms of phase transition [1]. Those theories, confirmed by recent experiments [1,2], have been shown to be quantitative powerful tools to predict in-silico the effects of disease-associated mutations on genome 3D structure [3,4].

[1] Conte et al. Nature Communications, 11, 3289 (2020)

[2] Fiorillo, Musella, Conte et al. Nature Methods, 18, 482-490 (2021)

[3] Huang et al. Nature Genetics, 53, 1064–1074 (2021)

[4] Kubo et al. Nature Structural & Molecular Biology, 28, 152–161 (2021)

Date: 10th February 2022 - 16.30 Rome Time

## Quantum simulation of lattice gauge theories with Rydberg atoms

Abstract: Gauge theories are the cornerstone of our understanding of fundamental interactions among particles. Their properties are often probed in dynamical experiments, such as those performed at ion colliders and high-intensity laser facilities. Describing the evolution of these strongly coupled systems is a formidable challenge for classical computers and represents one of the key open quests for quantum simulation approaches to particle physics phenomena. In this talk, I will show how recent experiments done on Rydberg atom chains naturally realize the real-time dynamics of a U(1) lattice gauge theory, at system sizes that are difficult to achieve with classical computational methods.

Date: 10th February 2022 - 16.30 Rome Time

## Synchronization transitions and brain criticality

### Victor Buendía Ruiz-Azuaga, University of Tübingen & Max Planck Institute for Biological Cybernetics

Abstract: Since the introduction of the concept of "self-organised criticality" by Bak, Tang and Wiesenfeld in the late 80s', the idea that biological systems may profit from working near to a critical point of a phase transition has appealed to both statistical physicists and biologists. In the particular case of the brain this idea would provide a simple, attractive framework to understand properties such as computational capabilities or dynamic range, among others. There is a growing body of evidence for critical dynamics in the brain, but whether if the brain is actually critical --or what would be its associated universality class-- is yet an open question. The classical view, which understands activity spreading as directed percolation, leaves out other relevant phenomena known to happen in the brain, such as collective oscillations. In this talk, I will review new theoretical efforts to try to bring the ideas of synchronization and criticality in the cortex together. I will also discuss how coupled oscillator theory can help us to understand neuronal mass stochastic models that are used to model large scale dynamics in the real brain.

Date: 13th January 2022 - 16.30 Rome Time

## Machine Learning applications in Science

Abstract: Machine learning is a broad field of study, with multifaceted applications of cross-disciplinary breadth that ultimately aims at developing computer algorithms that improve automatically through experience. Recently, scientists have increasingly become interested in the potential of Machine Learning for fundamental research and, to some extent, this is not too surprising, since both Machine Learning algorithms and scientists share some of their methods as well as goals. The two fields are both concerned about the process of gathering and analyzing data to design models that can predict the behavior of complex systems. However, the fields prominently differ in the way their fundamental goals are realized. Here we will argue, using practical cases and applications from biology, network theory and quantum physics, that the communication between these two fields can be not only beneficial but also necessary for the progress of both of them.

Date: 13th January 2022 - 16.30 Rome Time

# 2021 Young Seminars

## Water thermodynamics and its effects on biological interfaces

Abstract: All-atom simulations of large-size systems including proteins and explicit water come at a great computational cost. To overcome this problem, coarse-grained models aim to represent the system in a simplified manner but keeping the essential properties that are relevant for its behavior. Here, we extend to bulk a coarse-grained model, with many-body interactions, originally introduced by Franzese and Stanley (FS) for water monolayers [1, 2], that is analytically tractable and can be equilibrated by efficient cluster Monte Carlo for large systems (10^7 molecules) at extremely low temperatures (deep supercooling) in a wide range of pressures (both negative and positive) [2]. In its original formulation, the FS model reproduces qualitatively, the experimental water phase diagram, clarifying the physical mechanisms of the different scenarios proposed for the thermodynamics and dynamics anomalies of water, including the liquid-liquid phase transition and its Widom line. Also, it allows interpreting the multiple dynamic crossovers observed experimentally in protein hydration water, and more recently in melted water layers, at variance with atomistic models. Its application to hydrated proteins rationalizes the contribution of water to pressure and cold denaturation, generalizes the protein design to any thermodynamic condition, and clarifies the condition for protein aggregation [3]. Our results for the bulk FS model compare well with water atomistic simulations and shed light on the microscopic differences between the dynamics in hydration water and bulk water, showing that at lower dimensionality the cooperativity fluctuations decouple from the density fluctuations [4].

[1] G. Franzese and H. E. Stanley, J. Phys.: Cond. Mat. 14, 2201 (2002).

[2] L. E. Coronas, O. Vilanova, V. Bianco, F. de los Santos, and G. Franzese, The Franzese-Stanley Coarse Grained Model for Hydration Water, F. Martelli ed. (CRC Press, 2020) [Accepted; available as arXiv: 2004.03646]

[3] V. Bianco, G. Franzese, and I. Coluzza, ChemPhysChem 21, 377 (2020)

[4] L. E. Coronas, V. Bianco, A. Zantop, and G. Franzese, arXiv:1610.00419 (2016).

Date: 9th December 2021 - 16.30 Rome Time

## Quantum statistics and BKT transition of a shell-shaped superfluid

Abstract: The development of NASA Cold Atom Laboratory, a space-based facility for ultracold atoms experiments, enabled the routine production of Bose-Einstein condensates in microgravity. The ongoing investigations are focusing on shell-shaped geometries, in which the atoms are confined on a thin ellipsoidal surface with radio frequency-induced adiabatic potentials, and which cannot be obtained in the presence of gravity. We analyze the quantum statistical properties of spherical and ellipsoidal shells, focusing on the phenomena of Bose-Einstein condensation and superfluidity. In particular, we discuss the Berezinskii-Kosterlitz-Thouless transition of a spherical superfluid, driven by the proliferation of quantized vortices, and we analyze the finite-size universal effects that emerge in this system. Our results are a reliable benchmark for the current experimental investigations.

Date: 9th December 2021 - 16.30 Rome Time

## Recent advances in Integrable Quantum Many-body Systems Out-of-Equilibrium: Hydrodynamics and Quantum Corrections

Abstract: Physical systems made of many interacting quantum particles can often be described by Euler hydrodynamic equations in the limit of long wavelengths and low frequencies. Recently, such a classical hydrodynamic framework, now dubbed Generalized Hydrodynamics (GHD), was found for quantum integrable models in one spatial dimension [1,2]. Despite its great predictive power, GHD, like any Euler hydrodynamic equation, misses important quantum effects, such as quantum fluctuations leading to non-zero equal-time correlations and entanglement between fluid cells at different positions. Such quantum effects have been reconstructed by quantizing GHD [3,4]: the resulting theory can be viewed as a multi-component Luttinger liquid theory and describes quantum fluctuations of truly nonequilibrium systems where conventional Luttinger liquid theory fails. In this talk, I will give an overview of such recent developments.

[1] O. A. Castro-Alvaredo, B. Doyon, and T. Yoshimura, Phys. Rev. X 6, 041065 (2016)

[2] B. Bertini, M. Collura, J. De Nardis, and M. Fagotti, Phys. Rev. Lett. 117, 207201 (2016)

[3] P. Ruggiero, P. Calabrese, B. Doyon, J. Dubail, Phys. Rev. Lett. 124, 140603 (2020)

[4] P. Ruggiero, P. Calabrese, B. Doyon, J. Dubail, arXiv:2107.05655 (2021)

Date: 11th November 2021 - 16.30 Rome Time

## Nonequilibrium quantum state preparation with driven systems in engineered baths

Abstract: The progress in the development of quantum simulators has made possible the experimental investigation of phenomena which are hardly, if even, observable in un-controlled quantum systems. The central difficulty in this context is that of finding appropriate control operations which realize, in an effective manner, a Hamiltonian featuring desired properties. Successful techniques in the field, such as Floquet engineering and trotterization, achieve this by using time-periodic modulations and coarse-graining the dynamics. Even when a desired effective Hamiltonian is attained, a fundamental challenge remains in how to efficiently prepare its eigenstates, for instance in order to study ground state physics. In this seminar, I will discuss how interesting nonequilibrium states can be prepared and stabilised by combining periodic driving, on the one hand, with engineered forms of dissipation, on the other. In particular, considering arrays of artificial atoms individually coupled to cavities acting as engineered quantum baths, I will discuss the preparation of Aharonov-Bohm cages, in which quantum interference constrains the dynamics in small subsystems, and of chiral ground state currents.

Date: 11th November 2021 - 16.30 Rome Time

## AI meets turbulence: Lagrangian and Eulerian data-driven tools for optimal navigation and data-assimilation

Abstract: We examine the applicability of Artificial Intelligence tools to different open problems in fluid dynamics, from the search for an optimal navigation strategy in complex environments to data reconstruction from partial measurements of turbulent flows. To solve navigation problems we follow a Reinforcement Learning (RL) approach. Here, we will focus on the problem of finding the path that minimizes the navigation time between two given points in a fluid flow. I will show how RL is able to take advantage of the flow properties in order to reach its target, providing stable solutions with respect to perturbations on the initial conditions and to addiction of external noise. These results illustrate the potential of RL algorithms to model adaptive behavior in real/complex flows and pave the way towards the engineering of smart unmanned autonomous vehicles. The search for optimal navigation strategies is key in several applications, with a potential breakthrough in the open challenge of Lagrangian data assimilation (DA). In the DA direction, we also explore the capability of Generative Adversarial Network (GAN) to generate missing data. In this direction, I will present a quantitative investigation of their potential in reconstructing 2d damaged snapshots extracted from a large numerical database of 3d turbulence in the presence of rotation. I will briefly compare GAN with different, well-known, data assimilation tools, such as Nudging, an equation-informed protocol, or Gappy POD, developed in the context of image reconstruction. I will discuss as one can use DA tools with a reverse engineering approach, to investigate theoretical questions like which features of the input flow data are required/"more important" in order to obtain a better full-field reconstruction.

Date: 14th October 2021 - 16.30 Rome Time

## Unraveling the role of node metadata in network robustness: the feature-based percolation model

Abstract: Percolation is an emblematic model to assess the robustness of interconnected systems when some of their components are corrupted. It is usually investigated in simple scenarios, such as the removal of the system's units in random order, or sequentially ordered by specific topological descriptors, such as the degree or the betweenness centrality. However, in the vast majority of empirical applications, it is required to dismantle the network following more sophisticated protocols, for instance, by combining topological properties and non-topological node metadata. In this seminar I will introduce a novel mathematical framework that fills this gap: networks are enriched with features and their nodes are removed according to the importance in the feature space. We will discuss features of different nature, from ones related to the network construction to ones related to dynamical processes such as epidemic spreading. In this way, we not only provide a natural generalization of percolation but, more importantly, this framework offers an accurate way to test the robustness of networks in realistic scenarios.

More information can be found at https://www.nature.com/articles/s41467-021-22721-z.

Date: 14th October 2021 - 16.30 Rome Time

## Statistical validation and emerging ecologies in financial systems

Abstract: Over the last decades, advances in technology have made available a deluge of new data on financial systems, unveiling the activity of individual agents at unprecented resolutions. On top of paving the way to new research questions, the availability of massive amounts of data has called for adequate statistical procedures able to separate significant patterns from signals compatible with random noise. Here I'll start with an overview on the statistical validation of bipartite networks, providing context on its relevance and challenges, presenting the main methodologies and showing the recent additions related to hypergraphs and higher-order interactions. I'll then show how these methods are relevant when extracting significant patterns from financial data. Specifically, I'll cover two recent works. The first is about the characterization of an heterogeneous ecology in the stock market, observed through the detection of clusters of investors characterized by similar trading profiles. These clusters are performing distinct trading decisions on time scales ranging from several months to twelve years, and provide an epistemological challenge to some of the main pillars of the market efficiency hypothesis. With the second work I'll show how the increase of high-frequency trading has co-occurred with the emergence of a networked state in the market, with members being able to establish preferential and/or avoiding trading relationships between themselves that potentially harm liquidity flow in the system.

Date: 9th September 2021 - 16.30 Rome Time

## Quantum simulation of lattice gauge theories: from models to experimental protocols

Abstract: Recent experiments on several experimental platforms, such as ultracold atoms, trapped ions, Rydberg atoms and superconducting circuits, succeeded in realizing quantum many body states at unprecedented sizes, allowing to investigate their static and dynamical properties. These achievements have boosted the search for quantum simulation protocols to experimentally investigate complex quantum models, such as for example lattice gauge theories. Indeed, numerical simulation techniques to investigate models describing fundamental interactions suffers from notorious and long-standing limits, such as the sign problem.

For this reason, developing and exploiting quantum simulation protocols can push forward the investigation of phenomena so far unaccessible via standard classical numerical simulations. In my talk, I will review the state of the art of lattice gauge theories quantum simulation from an experimental and theoretical point of view. I will introduce an Abelian Lattice gauge theory model that can be investigated in realistic quantum simulators and I will conclude by showing a proposal for a Rydberg atom experimental implementation.

Joint project with: Mario Collura, Elisa Ercolessi, Paolo Facchi, Giuseppe Magnifico, Giuseppe Marmo, Simone Montangero, Saverio Pascazio, Francesco Pepe

Date: 9th September 2021 - 16.30 Rome Time

## The connection of the statistics of occupation time with the local properties of stochastic process

Abstract: Many problems in statistical physics are often mapped to the problem of determining the distribution of the time spent by a stochastic process in a given set. For instance, in the Ising model the local mean magnetization is associated with the fraction of time spent by the sign of a stochastic process, representing the orientation of a given spin, in the positive state. Related question arise naturally, for example, one can ask what is the probability that a stochastic process remains positive up to time t or what is the distribution of the number of times the process returns to the initial state. I will show that, for a wide class of stochastic processes, which can be classified as renewal processes, the distributions of such quantities are characterized by a single exponent, that is connected to a local property of the process, namely, the occupation probability of the initial state at time t, P(t). More specifically, the distributions of interest can be determined just by knowing the asymptotic decay exponent of P(t).

Joint work with R. Artuso, M. Onofri and G. Pozzoli.

Date: 8th July 2021 - 16.30 Rome Time

## A scaling law describes the spin-glass response in theory, experiments and simulations

Abstract: The dynamical arrest found upon cooling glass formers to their glass temperature Tg is a major open problem [1, 2]. In the longstanding description [3], this slowing down is caused by the unbounded expansion of cooperative regions as Tg is approached or as the system is left to age below Tg, which, in turn, leads to growing free-energy barriers. A quantitative description of this process is usually attempted in terms of a correlation length ξ. Unfortunately, in numerical simulations it is extremely dicult to measure the quantities that are easily accessible to experiments (and vice versa), which has led to seemingly irreconcilable approaches to the computation of the correlation length. We were able to solve this dilemma in a framework that completely harmonizes experiments with theory [4]. We conduct a parallel study of non-equilibrium spin-glass dynamics both in an experiment in a CuMn single crystal and in a large-scale simulation of the Ising-Edwards-Anderson (IEA) model carried out on the Janus II custom-built supercomputer [5]. We introduced a scaling law that describes the system's response over its entire natural range of variation.

[1] A. Cavagna, Physics Reports 476, 51 (2009), arXiv:0903.4264.

[2] P. Charbonneau, J. Kurchan, G. Parisi, P. Urbani, and F. Zamponi, Nature Communications 5, 3725 (2014), arXiv:1404.6809.

[3] G. Adam and J. H. Gibbs, The Journal of Chemical Physics 43, 139 (1965).

[4] Q. Zhai et al., "A scaling law describes the spin-glass response in theory, experiments and simulations", (2020), arXiv:2007.03871, Phys. Rev. Lett., submitted for publication, 2020.

[5] M. Baity-Jesi et al., Comp. Phys. Comm 185, 550 (2014), arXiv:1310.1032.

[6] Q. Zhai, V. Martin-Mayor, D. L. Schlagel, G. G. Kenning, and R. L. Orbach, Phys. Rev. B 100, 094202 (2019).

Date: 8th July 2021 - 16.30 Rome Time

## Maximal diversity and Zipf’s law

Abstract: Zipf’s law describes the empirical size distribution of the components of many natural and artificial complex systems. Diversity, on the other hand, is a central concept in ecology, economics, information theory, and other natural and social sciences and can be quantified by diversity indices which characterize the system under study from different angles. I will discuss the co-occurrence of Zipf’s law with the maximization of the diversity of the component sizes, understanding here the number of different sizes represented. I will present the law ruling the increase of such diversity with the total dimension of the system and its relation with Heaps’ law. As an example, I will compare analytical results with linguistics datasets.

Date: 10th June 2021 - 16.30 Rome Time

Multi-source data and transmission models to fight COVID-19 epidemic in France

Abstract: On March 17, 2020, French authorities implemented a nationwide lockdown to respond to COVID-19 epidemic emergency. Analyzing multiscale mobility network, reconstructed from mobile phone data, we measured how lockdown altered mobility patterns at both local and country scales. Lockdown caused a 65\% reduction in countrywide number of displacements. Mobility drops were unevenly distributed across regions and they were strongly associated with socio-economic, demographic factors and risk aversion. Major cities largely shrank their pattern of connectivity, reducing it mainly to short-range commuting, despite the persistence of some long-range trips. Our findings indicate that lockdown was very effective in reducing population mobility across scales and help to predicting how and where restrictions will be the most effective. As countries in Europe relaxed lockdown restrictions after the first wave, test–trace–isolate strategies became critical to maintaining the incidence of COVID-19 at low levels. By integrating mobile phone, virological, and surveillance data, we then developed transmission epidemic models, calibrated to French COVID-19 epidemic, to evaluate the performance of the testing system in exit of lockdown. 90,000 symptomatic infections, corresponding to 9 out 10 cases, were not ascertained by the surveillance system from 11 May to 28 June 2020. While the detection rate increased over time, this achievement was likely due to a decreasing epidemic activity. The increase in viral circulation in late summer instead strained the testing system and led to the 2nd wave. Substantially more aggressive, targeted, and efficient testing with easier access is required to act as a tool to control the COVID-19 pandemic. As we are still facing COVID-19 pandemic, and there may be other pandemics, epidemiological and behavioral data should be thus collected and open sources, as they are crucially important to outbreak response.

Date: 10th June 2021 - 16.30 Rome Time

## The nonlinear response of Josephson devices: from the theoretical study to cutting edge applications

Abstract: Since its discovery nearly sixty years ago, the Josephson effect still represents an active frontier of condensed matter physics, continuously sparking interest in light of forefront applications and technological advancements. The Josephson effect is the quantum phenomenon describing the flow of a dissipationless current in weak links between two superconductors and it is at the base of phase-coherent superconducting circuits. Accessing the nonlinear dynamics of the Josephson phase, that is the macroscopic phase difference between the two superconductors forming the junction, permits to unveil the macroscopic response of the device and to promote new ideas in these fertile fields of research. In this talk, I will discuss the nonlinear behavior of Josephson phase in different contexts, giving an insight on recent results both from a theoretical side, e.g., noise induced phenomena, phase coherent caloritronics, and anomalous Josephson effects, and towards novel concrete applications, e.g., a phase battery, threshold detectors, and memory devices.

Strambini E., Iorio A., Durante O., Citro R., Sanz-Fernández C., Guarcello C., Tokatly I.V., Braggio A., Rocci M., Ligato N., Zannier V., Sorba L., Bergeret F.S., Giazotto F., A Josephson phase battery, Nature Nanotech., 15(8), 656, 2020

Guarcello C., Filatrella G., Spagnolo B., Pierro V., Valenti D., Voltage drop across Josephson junctions for Lévy noise detection, Phys. Rev. Research, 2, 043332, 2020

Paolucci F., Vischi F., De Simoni G., Guarcello C., Solinas P., Giazotto F., Field-Effect Controllable Metallic Josephson Interferometer, Nano Letters, 19(9), 6263, 2019

Guarcello C., Solinas P., Braggio A., Di Ventra M., Giazotto F., Josephson Thermal Memory, Phys. Rev. Appl., 9(1), 014021, 2018

Date: 13rd May 2021 - 16.30 Rome Time

Species coexistence and proteome allocation in competitive microbial communities

Abstract: Microbial communities are ubiquitous and play crucial roles in many natural processes. Despite their importance for the environment, industry and human health, however, there are still many aspects of microbial communities that we do not fully understand. It is a long-standing problem, for example, the fact that microbial communities are normally much more diverse than what models would allow. Recent experiments, then, have shown that the metabolism of microbial species in a community is intertwined with its structure, suggesting that properties at the intracellular level such as the allocation of cellular proteomic resources must be taken into account when describing microbial communities and species abundances. In this talk I will illustrate the problem of describing biodiversity in purely competitive microbial communities, and how models fail to predict the right number of coexisting species. Then, I will show how we can reconsider one of the most commonly used models to describe population dynamics in competitive ecosystems in light of known experimental results that link the species' growth rate to the allocation of their proteome. This new framework describes microbial communities at an "intermediate" level of complexity, describing the species' population dynamics while also retaining insights on the molecular aspects of growth. The results of the model are also compared to some experimental data.

Date: 13rd May 2021 - 16.30 Rome Time

## Orientation of active particles in turbulent flows

### Matteo Borgnino, Politecnico di Torino

Abstract: Active particles, such as motile microorganisms, typically experience complex environments which can have an impact on their dynamics. Even a simple laminar flow can give rise to intriguing phenomena when combined with self-propulsion or particular particles shapes; indeed, besides transporting particles, a surrounding flow can also affect particles dynamics producing non-trivial spatial patterns or changing the particles swimming direction. It is therefore crucial to better understand the complex interplay between flow advection, particles orientation and self-propulsion. In this talk we investigate the alignment of spheroidal, axisymmetric microswimmers, whose shapes ranges from disks to rods, swimming in turbulent flows. In particular, by means of numerical simulation, we show that rodlike active particles preferentially align with the flow velocity. To explain the underlying mechanism, we solve a statistical model via the perturbation theory, showing that such an alignment is the result of particles’ swimming and non-sphericity together with the correlations of fluid velocity and its gradients along particle paths. Remarkably, the discovered alignment is found to be a robust kinematical effect, independent of the underlying flow evolution.

Date: 8th April 2021 - 16.30 Rome Time

## Evidence of glassy phases in large interacting ecosystems with demographic noise

Abstract: Many complex systems in Nature, from metabolic networks to ecosystems, appear to be poised at the edge of stability, hence displaying enormous responses to external perturbations. This feature, also known in physics as marginal stability, is often the consequence of the complex underlying interaction network, which can induce large-scale collective dynamics and therefore critical behaviors. In this seminar, I will present the problem of ecological complexity by focusing on a reference model in theoretical ecology, the high-dimensional Lotka-Volterra model with random symmetric interactions and finite demographic noise [1]. I will show how to obtain a complete characterization of the phase diagrams by means of techniques rooted in mean-field spin-glass theory. Notably, I will relate emerging collective behaviors and slow relaxation dynamics to the appearance of different phases and rough energy landscapes akin to those occurring in glassy systems [2,3]. I will describe in particular: i) a multiple equilibria phase, which can be proven to be associated with an exponential number of stable equilibria in the system size; ii) a marginally stable amorphous phase (denoted as Gardner phase) as characterized by a hierarchical organization of these equilibria [1]. Finally, I will discuss the wide-ranging applicability of these outcomes to many different contexts, from evolutionary game theory to complex economic systems.

[1] A. Altieri, F. Roy, C. Cammarota, G. Biroli, Properties of equilibria and glassy phases of the random Lotka-Volterra model with demographic noise, arXiv:2009.10565 (2020).

[2] P. Charbonneau, J. Kurchan, G. Parisi, P. Urbani, Fractal free energy landscapes in structural glasses, Nature Communications 5, 3725 (2014).

[3] A. Altieri, Jamming and Glass Transitions: In Mean-Field Theory and Beyond, Springer Nature (2019).

Date: 8th April 2021 - 16.30 Rome Time

## Phase behavior and ordering kinetics of self-propelled particles in 2D

### Pasquale Digregorio, CECAM Centre Européen de Calcul Atomique et Moléculaire, Ecole Polytechnique Fédérale de Lausanne, Switzerland

Abstract: The so-called Active Brownian Particles (ABP) model has undoubtedly become one of the fundamental models in out-of-equilibrium statistical mechanics. Even though it appeared in literature less than ten years ago, it already represents one the reference models for active matter and, particularly, for self-propelled objects. We recently studied the structural properties of the stationary phases and the ordering phase transitions of these active systems, picturing the phase diagram for ABPs with steric repulsive interactions in two spatial dimensions. We found that ordering phase transitions at any magnitude of self-propulsion can be well understood within the framework of equilibrium KTHNY melting in 2D. We also explored some fundamental features of the so-called Motility-Induced Phase Separation, a well known phenomenon of clustering of self-propelled particles with no attractive interaction. We particularly studied the clustering kinetics, identifying different growing stages, before a late coarsening regime which fulfills a dynamical scaling hypothesis. On top of the growth of a dense phase, we pointed out that the coarsening of solid-like domains with different hexatic orientation is arrested, and that their stationary finite size can be controlled by the intensity of the self-propulsion.

Date: 11th March 2021 - 16.30 Rome Time

## Statistical mechanics of interacting polymers explains chromosome folding

Abstract: Chromosomes are folded in complex, non-random three-dimensional conformations within the cell nucleus, as highlighted by novel biochemical and microscopy technologies. Notably, chromosomes architecture and their interaction network are involved in vital cell functions, controlling gene expression, whereas abnormal chromosome folding has been linked to diseases. In this talk, I discuss how massive data on genome architecture, generated thanks to significant experimental advances in the last decade, can be explained in a principled approach based on the statistical mechanics of polymers and some of their underlying molecular mechanisms understood. I also discuss how polymer models can be employed to investigate chromosome structure at the single-molecule level and to predict the effects of pathogenic genomic mutations, as validated by experimental data, opening the way to revolutionary medical applications.

Date: 11th March 2021 - 16.30 Rome Time

## Getting hotter by heating less: how driven granular materials dissipate energy in excess

Abstract: A fundamental question in systems driven out of thermodynamic equilibrium is how the properties of the Non Equilibrium Stationary States (NESS) are related to the specific mechanisms by which external energy is supplied. Vibro-fluidized granular matter, where a NESS is reached through a balance between the energy injected by a mechanical vibration and the dissipation due to inelastic collisions, represents a good context to tackle this problem. In this talk, we present experimental and numerical results about the relation between the kinetic energy acquired by a driven dense granular system and the input energy. Our focus is on the dependence of the granular behavior on two main parameters: frequency and vibration amplitude. We find that there exists an optimal forcing frequency, at which the system reaches the maximal kinetic energy: if the input energy is increased beyond such a threshold, the system dissipates more and more energy and recovers a colder and more viscous state. Studying dissipative properties of the system, we unveil a striking difference between this nonmonotonic behavior and a standard resonance mechanism. This feature is also observed at the microscopic scale of the single-grain dynamics and can be interpreted as an instance of negative specific heat. An analytically solvable model based on a generalized forced-damped oscillator well reproduces the observed phenomenology, illustrating the role of the competing effects of forcing and dissipation.

Date: 11th February 2021 - 16.30 Rome Time

## Modelling Immune Recognition with Restricted Boltzmann Machines

Abstract: The immune response of an organism when it is infected by a pathogen is based on the recognition of small portions of its proteins. This raises two questions: what protein portions are relevant to this process? And what immune cells are able to recognize them? In this talk, I will discuss models to answer those two questions that are based on the machine learning method known as Restricted Boltzmann Machine and that are learned from large protein sequence datasets. These models provide flexible and interpretable frameworks to characterize and predict immune recognition of both cancer and infections.

Date: 11th February 2021 - 16.30 Rome Time

## Synthetic models for quantum many-body physics out of equilibrium

### Lorenzo Piroli, Max-Planck-Institut für Quantenoptik

Abstract: It has been known for a long time that thermalization is associated with "chaotic'' behavior at the microscopic level, although a quantitative understanding of its key mechanisms from fundamental theories poses formidable challenges. This problem can be effectively tackled in isolated many-body quantum systems, where the absence of interactions with the environment allows us to gain valuable insight from both first-principle calculations, and quantum simulation experiments. In this talk, I will review recent studies aiming at capturing the most relevant aspects of thermalization processes using theoretical "quantum circuit" models for the many-body dynamics, which are inspired by ideas of quantum simulation by quantum computers. In particular, I will focus on how standard tools in statistical mechanics have been successfully employed for obtaining nontrivial analytic results in this context.

Date: 14th January 2021 - 16.30 Rome Time

## Spatial patterns in the velocity field of Active Matter systems

Abstract: Many systems of biological or technological interest, such as bacterial colonies or cell monolayers, show spatial patterns in their velocity field without displaying a global polarization. In this talk, we investigate this phenomenon through a non-equilibrium stochastic dynamics, the so-called Active Brownian Particles (ABP), which is one of the most popular minimal models to describe the behavior of several experimental active particles. We report the first evidence that pure repulsive spherical ABP, without alignment interactions, spontaneously form large domains of particles with aligned velocities, both in homogeneous dense phases and phase-separated regimes. The size of the velocity domains is measured through the correlation length of the spatial velocity correlations whose shape is analytically predicted. We unveil the non-thermal nature of this collective phenomenon that, instead, is induced by the interplay between steric interactions and active forces, also highlighting the dynamical role played by inertial forces. The results are summarized in a non-equilibrium phase diagram, packing fraction vs persistence time, where the structural properties of the system (distinguishing active liquid, hexatic and solid phases) are superimposed with the velocity correlation lengths. The presence of the almost-translational order typical of hexatic and solid configurations plays a crucial role and reveals an interesting scenario which also involves intermittency phenomena in the time-trajectory of the kinetic energy.

Date: 14th January 2021 - 16.30 Rome Time