arXiv subjects

Paolo Baglioni

Publications and source records attributed to Paolo Baglioni.

5 recordsLinked to original sources

arXiv2026

Climate change and human mobility will shape dengue emergence risk in Europe

The risk of local arbovirus outbreaks in Europe is expected to increase due to climate change, as suggested by the multiplication of arbovirus outbreaks in the last decades. Europe has historically been a non-endemic region, making it vital to pinpoint which populations are potentially exposed -and under which conditions- so we can build truly robust epidemic preparedness capabilities. We introduce an integrated, multi-scale model that fuses a mechanistic transmission engine with a vector abundance framework, all embedded in a mobility-driven metapopulation system capturing human, vector, and air-traffic movement. To this end, we combine climate and population projections with mobility data to estimate and map dengue emergence risk in Europe throughout the 21st century. Additionally, we introduce a dedicated migration model that explores how climate-driven population redistribution could alter these risk estimates.Assuming the climate avoids major tipping points, model-derived risk indicators increase substantially under most emissions scenarios. While the spatio-temporal risk will remain largely driven by importation, our results indicate a gradual transition toward an environment-driven regime, particularly under the worst-case emissions scenario. To better anticipate and manage recurrent arbovirus outbreaks, our findings highlight the need to integrate mobility pathways and climate-driven population redistribution into predictive models of vector-borne disease emergence in temperate regions.

physics.soc-ph↗

arXiv2026

Kernel Renormalization in Bayesian Deep Neural Networks: the Equivalent Wishart Ansatz in the Proportional Regime

The scaling limit where both the size of the training set $P$ and the width $N$ of a deep neural network grow at the same rate, the so-called proportional-width regime, has been intensely studied for shallow, single-hidden-layer networks. However, extending these non-perturbative results from shallow architectures to deep non-linear networks has proven very challenging. Here we present an effective approximate approach to predict the generalization performance of Bayesian multi-layer perceptrons (MLPs) of fixed depth $L$ on arbitrary high-dimensional data. We propose an equivalent Wishart Ansatz to capture the dominant stochastic fluctuations of the hierarchical empirical kernels of MLPs. This allows us to perform a large deviation analysis for the partition function of MLPs in the proportional limit, expressed in terms of a renormalized NNGP kernel. In this description, even strong representation learning in the proportional limit is encoded in at most $L$ scalar order parameters, determined self-consistently. Extending the approach to convolutional architectures (CNNs), we identify a hierarchical local kernel renormalization mechanism, which allows to quantify more complex data-dependent transformations of the large-width kernel in CNNs due to finite-width effects. We test our effective theory against sampling experiments from the Bayesian posterior of finite deep neural networks with depths $L \sim O(10)$ and $P\sim O(10^3)$ on classic benchmark datasets, finding overall very good agreement together with two distinct types of systematic deviations.

cs.LG↗

arXiv2024

Taming NSPT fluctuations in $O(N)$ Non-Linear Sigma Model: simulations in the large $N$ regime

The Non-Linear Sigma Model (NLSM) is an example of a field theory on a target space exhibiting intricate geometry. One remarkable characteristic of the NLSM is asymptotic freedom, which triggers interest in perturbative calculations. In the lattice formulation of NLSM, one would naturally rely on Numerical Stochastic Perturbation Theory (NSPT) to conduct high-order computations. However, when dealing with low-dimensional systems, NSPT reveals increasing statistical fluctuations with higher and higher orders. This of course does not come as a surprise and one is ready to live with this, as long as the noise is not going to completely kill the signal, which unfortunately in some models does take place. We investigate how, in the $O(N)$ context, this behaviour strongly depends on $N$. As expected, larger $N$ values make higher-order computations feasible.

hep-lat↗

arXiv2024

NSPT for $O(N)$ non-linear sigma model: the larger $N$ the better

The $O(N)$ non-linear sigma model (NLSM) is an example of field theory on a target space with nontrivial geometry. One interesting feature of NLSM is asymptotic freedom, which makes perturbative calculations interesting. Given the successes in Lattice Gauge Theories, Numerical Stochastic Perturbation Theory (NSPT) is a natural candidate for performing high-order computations also in the case of NLSM. However, in low-dimensional systems NSPT is known to display statistical fluctuations substantially increasing for increasing orders. In this work, we explore how for $O(N)$ NLSM this behaviour is strongly dependent on $N$. As largely expected on general grounds, the larger is $N$, the larger is the order at which a NSPT computation can be effectively performed.

hep-lat↗

arXiv2022

Numerical Stochastic Perturbation Theory around instantons

Numerical Stochastic Perturbation Theory (NSPT) has over the years proved to be a valuable tool, in particular being able to reach unprecedented orders for Lattice Gauge Theories, whose perturbative expansions are notoriously cumbersome. One of the key features of the method is the possibility to expand around non-trivial vacua. While this idea has been around for a while, and it has been implemented in the case of the (non-trivial) background of the Schrödinger functional, NSPT expansions around instantons have not yet been fully worked out. Here we present computations for the double well potential in quantum mechanics. We compute a few orders of the expansion of the ground-state energy splitting in the one-instanton sector. We discuss how (already) known two-loop results are reproduced and present the current status of higher-order computations.

hep-lat↗