arXiv subjects

Federica Ferretti

Publications and source records attributed to Federica Ferretti.

7 recordsLinked to original sources

arXiv2025

Learning to generalize in evolution through annealed population heterogeneity

Evolutionary systems must learn to generalize, often extrapolating from a limited set of selective conditions to anticipate future environmental changes. The mechanisms enabling such generalization remain poorly understood, despite their importance to predict ecological robustness, drug resistance, or design future-proof vaccination strategies. Here, we demonstrate that annealed population heterogeneity, wherein distinct individuals in the population experience different instances of a complex environment over time, can act as a form of implicit regularization and facilitate evolutionary generalization. Mathematically, annealed heterogeneity introduces a variance-weighted demographic noise term that penalizes across-environment fitness variance and effectively rescales the population size, thereby biasing evolution toward generalist solutions. This process is indeed analogous to a variant of the mini-batching strategy employed in stochastic gradient descent, where an effective multiplicative noise produces an inductive bias by triggering noise-induced transitions. Through numerical simulations and theoretical analysis we discuss the conditions under which variation in how individuals experience environmental selection can naturally promote evolutionary strategies that generalize across environments and anticipate novel challenges.

q-bio.PE↗

arXiv2024

Out of equilibrium response and fluctuation-dissipation violations across scales in flocking systems

Flocking systems are known to be strongly out of equilibrium. Energy input occurs at the individual level to ensure self-propulsion, and the individual motility in turn contributes to ordering, enhancing information propagation and strengthening collective motion. However, even beyond ordering, a crucial feature of natural aggregations is response. How, then, do off-equilibrium features affect the response of the system? In this work, we consider a minimal model of flocking and investigate response behavior under directional perturbations. We show that equilibrium dynamical fluctuation-dissipation relations between response and correlations are violated, both at the local and at the global level. The amount of violation peaks at the ordering transition, exactly as for the entropy production rate. Entropy is always produced locally and connected to the local fluctuation-dissipation violation via Harada-Sasa relationships. However, cooperative mechanisms close to the transition spread off-equilibrium effects to the whole system, producing an out of equilibrium response on the global scale. Our findings elucidate the role of activity and interactions in the cost repartition of collective behavior and explain what observed in experiments on natural living groups.

cond-mat.stat-mech↗

arXiv2024

Universal characterization of epitope immunodominance from a multi-scale model of clonal competition in germinal centers

We introduce a novel, multi-scale model for affinity maturation, which aims to capture the intra-clonal, inter-clonal and epitope-specific organization of the B cell population in a germinal center. We describe the evolution of the B cell population via a quasispecies dynamics, with species corresponding to unique B cell receptors (BCRs), where the desired multi-scale structure is reflected on the mutational connectivity of the accessible BCR space, and on the statistical properties of its fitness landscape. Within this mathematical framework, we study the competition among classes of BCRs targeting different antigen epitopes, and construct an effective \emph{immunogenic space} where epitope immunodominance relations can be universally characterized. We finally study how varying the relative composition of a mixture of antigens with variable and conserved domains allows for a parametric exploration of this space, and identify general principles for the rational design of two-antigen cocktails.

q-bio.PE↗

arXiv2022

Signatures of irreversibility in microscopic models of flocking

Flocking in $d=2$ is a genuine non-equilibrium phenomenon for which irreversibility is an essential ingredient. We study a class of minimal flocking models whose only source of irreversibility is self-propulsion and use the entropy production rate (EPR) to quantify the departure from equilibrium across their phase diagrams. The EPR is maximal in the vicinity of the order-disorder transition, where reshuffling of the interaction network is fast. We show that signatures of irreversibility come in the form of asymmetries in the steady state distribution of the flock's microstates. They occur as consequences of the time reversal symmetry breaking in the considered self-propelled systems, independently of the interaction details. In the case of metric pairwise forces, they reduce to local asymmetries in the distribution of pairs of particles. This study suggests a possible use of pair asymmetries both to quantify the departure from equilibrium and to learn relevant information about aligning interaction potentials from data.

cond-mat.stat-mech↗

arXiv2021

A Renormalization Group Approach to Connect Discrete- and Continuous-Time Descriptions of Gaussian Processes

Discretization of continuous stochastic processes is needed to numerically simulate them or to infer models from experimental time series. However, depending on the nature of the process, the same discretization scheme, if not accurate enough, may perform very differently for the two tasks. Exact discretizations, which work equally well at any scale, are characterized by the property of invariance under coarse-graining. Motivated by this observation, we build an explicit Renormalization Group approach for Gaussian time series generated by auto-regressive models. We show that the RG fixed points correspond to discretizations of linear SDEs, and only come in the form of first order Markov processes or non-Markovian ones. This fact provides an alternative explanation of why standard delay-vector embedding procedures fail in reconstructing partially observed noise-driven systems. We also suggest a possible effective Markovian discretization for the inference of partially observed underdamped equilibrium processes based on the exploitation of the Einstein relation.

stat.ML↗

arXiv2020

Building general Langevin models from discrete data sets

Many living and complex systems exhibit second order emergent dynamics. Limited experimental access to the configurational degrees of freedom results in data that appears to be generated by a non-Markovian process. This poses a challenge in the quantitative reconstruction of the model from experimental data, even in the simple case of equilibrium Langevin dynamics of Hamiltonian systems. We develop a novel Bayesian inference approach to learn the parameters of such stochastic effective models from discrete finite length trajectories. We first discuss the failure of naive inference approaches based on the estimation of derivatives through finite differences, regardless of the time resolution and the length of the sampled trajectories. We then derive, adopting higher order discretization schemes, maximum likelihood estimators for the model parameters that provide excellent results even with moderately long trajectories. We apply our method to second order models of collective motion and show that our results also hold in the presence of interactions.

q-bio.QM↗

arXiv2020

Identities for Catalan's constant arising from integrals depending on a parameter

In this paper we provide some relationships between Catalan's constant and the $_3{\rm F}_2$ and $_4{\rm F}_3$ hypergeometric functions, deriving them from some parametric integrals. In particular, using the complete elliptic integral of the first kind, we found an alternative proof of a result of Ramanujan for $_3{\rm F}_2$, a second identity related to $_4{\rm F}_3$ and using the complete elliptic integral of the second kind we obtain an identity by Adamchik.

math.CA↗