# 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.

## Istuzioni 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**

**Upcoming Seminars****Past Seminars**

**Past Seminars**## 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**

**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

### Mattia Radice, Università degli Studi dell'Insubria

*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

### Onofrio Mazzarisi, Max Planck Institute for Mathematics in the Sciences

*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

### Claudio Guarcello, University of Salerno

*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