Francesco Zamponi
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View article: Performance of machine-learning-assisted Monte Carlo in sampling from simple statistical physics models
Performance of machine-learning-assisted Monte Carlo in sampling from simple statistical physics models Open
Recent years have seen a rise in the application of machine learning techniques to aid the simulation of hard-to-sample systems that cannot be studied using traditional methods. Despite the introduction of many different architectures and …
View article: Further testing the validity of generalized heterogeneous-elasticity theory for low-frequency excitations in structural glasses
Further testing the validity of generalized heterogeneous-elasticity theory for low-frequency excitations in structural glasses Open
In a recent paper E. Lerner and E. Bouchbinder, Phys. Rev. E {\bf 111}, L013402 (2025) raised concerns regarding the validity of the theory and the interpretation of the data, presented in our previous study on non-phononic vibrational exc…
View article: Data augmentation enables label-specific generation of homologous protein sequences
Data augmentation enables label-specific generation of homologous protein sequences Open
Accurately annotating and controlling protein function from sequence data remains a major challenge, particularly within homologous families where annotated sequences are scarce and structural variation is minimal. We present a two-stage a…
View article: Data augmentation enables label-specific generation of homologous protein sequences
Data augmentation enables label-specific generation of homologous protein sequences Open
Accurately annotating and controlling protein function from sequence data remains a major challenge, particularly within homologous families where annotated sequences are scarce and structural variation is minimal. We present a two-stage a…
View article: Fluctuations and the limit of predictability in protein evolution
Fluctuations and the limit of predictability in protein evolution Open
Protein evolution involves mutations occurring across a wide range of time scales. In analogy with disordered systems in statistical physics, this dynamical heterogeneity suggests strong correlations between mutations happening at distinct…
View article: Functional bottlenecks can emerge from non-epistatic underlying traits
Functional bottlenecks can emerge from non-epistatic underlying traits Open
Protein fitness landscapes frequently exhibit epistasis, where the effect of a mutation depends on the genetic context in which it occurs, i . e ., the rest of the protein sequence. Epistasis increases landscape complexity, often resulting…
View article: Functional bottlenecks can emerge from non-epistatic underlying traits
Functional bottlenecks can emerge from non-epistatic underlying traits Open
Protein fitness landscapes frequently exhibit epistasis, where the effect of a mutation depends on the genetic context in which it occurs, \textit{i.e.}, the rest of the protein sequence. Epistasis increases landscape complexity, often res…
View article: Nearest-neighbors neural network architecture for efficient sampling of statistical physics models
Nearest-neighbors neural network architecture for efficient sampling of statistical physics models Open
The task of sampling efficiently the Gibbs–Boltzmann distribution of disordered systems is important both for the theoretical understanding of these models and for the solution of practical optimization problems. Unfortunately, this task i…
View article: Exact full-RSB SAT/UNSAT transition in infinitely wide two-layer neural networks
Exact full-RSB SAT/UNSAT transition in infinitely wide two-layer neural networks Open
We analyze the problem of storing random pattern-label associations using two classes of continuous non-convex weights models, namely the perceptron with negative margin and an infinite-width two-layer neural network with non-overlapping r…
View article: adabmDCA 2.0 – a flexible but easy-to-use package for Direct Coupling Analysis
adabmDCA 2.0 – a flexible but easy-to-use package for Direct Coupling Analysis Open
In this methods article, we provide a flexible but easy-to-use implementation of Direct Coupling Analysis (DCA) based on Boltzmann machine learning, together with a tutorial on how to use it. The package adabmDCA 2.0 is available in differ…
View article: adabmDCA 2.0 -- a flexible but easy-to-use package for Direct Coupling Analysis
adabmDCA 2.0 -- a flexible but easy-to-use package for Direct Coupling Analysis Open
In this methods article, we provide a flexible but easy-to-use implementation of Direct Coupling Analysis (DCA) based on Boltzmann machine learning, together with a tutorial on how to use it. The package \texttt{adabmDCA 2.0} is available …
View article: Fluctuations and the limit of predictability in protein evolution
Fluctuations and the limit of predictability in protein evolution Open
Protein evolution involves mutations occurring across a wide range of time scales. In analogy with disordered systems in statistical physics, this dynamical heterogeneity suggests strong correlations between mutations happening at distinct…
View article: Fluctuations and the limit of predictability in protein evolution
Fluctuations and the limit of predictability in protein evolution Open
Protein evolution involves mutations occurring across a wide range of time scales. In analogy with disordered systems in statistical physics, this dynamical heterogeneity suggests strong correlations between mutations happening at distinct…
View article: Exact full-RSB SAT/UNSAT transition in infinitely wide two-layer neural networks
Exact full-RSB SAT/UNSAT transition in infinitely wide two-layer neural networks Open
We analyze the problem of storing random pattern-label associations using two classes of continuous non-convex weights models, namely the perceptron with negative margin and an infinite-width two-layer neural network with non-overlapping r…
View article: Emergent time scales of epistasis in protein evolution
Emergent time scales of epistasis in protein evolution Open
We introduce a data-driven epistatic model of protein evolution, capable of generating evolutionary trajectories spanning very different time scales reaching from individual mutations to diverged homologs. Our in silico evolution encompass…
View article: Ductile-to-brittle transition and yielding in soft amorphous materials: perspectives and open questions
Ductile-to-brittle transition and yielding in soft amorphous materials: perspectives and open questions Open
Soft amorphous materials are viscoelastic solids ubiquitously found around us, from clays and cementitious pastes to emulsions and physical gels encountered in food or biomedical engineering. Under an external deformation, these materials …
View article: Expanding the space of self-reproducing ribozymes using probabilistic generative models
Expanding the space of self-reproducing ribozymes using probabilistic generative models Open
Estimating the plausibility of RNA self-reproduction is central to origin-of-life scenarios but self-reproduction has been shown in only a handful of systems. Here, we populated a vast sequence space of ribozymes using statistical covariat…
View article: Nearest-Neighbours Neural Network architecture for efficient sampling of statistical physics models
Nearest-Neighbours Neural Network architecture for efficient sampling of statistical physics models Open
The task of sampling efficiently the Gibbs-Boltzmann distribution of disordered systems is important both for the theoretical understanding of these models and for the solution of practical optimization problems. Unfortunately, this task i…
View article: Unlearning regularization for Boltzmann machines
Unlearning regularization for Boltzmann machines Open
Boltzmann machines (BMs) are graphical models with interconnected binary units, employed for the unsupervised modeling of data distributions. When trained on real data, BMs show the tendency to behave like critical systems, displaying a hi…
View article: Towards parsimonious generative modeling of RNA families
Towards parsimonious generative modeling of RNA families Open
Generative probabilistic models emerge as a new paradigm in data-driven, evolution-informed design of biomolecular sequences. This paper introduces a novel approach, called Edge Activation Direct Coupling Analysis (eaDCA), tailored to the …
View article: Emergent time scales of epistasis in protein evolution
Emergent time scales of epistasis in protein evolution Open
We introduce a data-driven epistatic model of protein evolution, capable of generating evolutionary trajectories spanning very different time scales reaching from individual mutations to diverged homologs. Our in silico evolution encompass…
View article: Emergent time scales of epistasis in protein evolution
Emergent time scales of epistasis in protein evolution Open
We introduce a data-driven epistatic model of protein evolution, capable of generating evolutionary trajectories spanning very different time scales reaching from individual mutations to diverged homologs. Our in silico evolution encompass…