arXiv subjects

Loredana Bellantuono

Publications and source records attributed to Loredana Bellantuono.

17 recordsLinked to original sources

arXiv2026

Data geometry preserves prediction but reshapes explanation

Across scientific domains, empirical data often give rise to positive semidefinite matrices that encode similarities, couplings or interactions and induce natural geometries. We investigate whether changing the geometry used to compare the same empirical representations preserves subject-level performance and the local structures from which explanations are derived, a question we term the prediction explanation invariance problem. We address this problem across morphometric MRI, fMRI and EEG data by comparing Frobenius and trace geometries applied to the same empirical matrices. Predictive performance was broadly preserved across geometries, but comparable performance did not imply agreement in subject-level decision scores or predicted labels. Predictive similarity also masked geometry-dependent differences in local neighbourhoods, perturbation sensitivities and explanatory rankings. Dimensionality reduction made the two subject-space geometries progressively more concordant, while classification performance and decision-level agreement declined. These results identify data geometry as a hidden degree of freedom in explainability: similar performance does not guarantee invariant decisions or explanations.

physics.data-an↗

arXiv2026

Topological Uncertainty and Higher-Order Interactions in Spatial Networks

Environmental systems are characterized by complex spatial interactions that cannot be fully described through pairwise relationships or local uncertainty measures. We propose a unified framework combining higher-order information theory and topological data analysis to characterize the organization and uncertainty of environmental networks. Spatial entities, represented by monitoring stations or municipalities, are embedded into a Delaunay simplicial complex, and O-information is used to quantify redundancy and synergy among neighboring triplets. The resulting field of higher-order interactions is analyzed through persistent homology, enabling the identification of topological structures that remain stable across interaction scales. The methodology is applied to both an air-quality monitoring network based on weekly $\mathrm{NO_2}$ and $\mathrm{O_3}$ observations and a multi-hazard territorial assessment. We show that regions exhibiting strong O-information and persistent topological structures correspond to robust environmental patterns, whereas areas characterized by heterogeneous regimes and rapidly varying interactions display increased uncertainty. Building on these results, we introduce a topological uncertainty framework that integrates simplex divergence, higher-order interactions, and topological uncertainty. Our results demonstrate that uncertainty can be interpreted not only as statistical variability but also as the instability of the underlying information topology. By integrating O-information and persistent homology within a common spatial framework, the proposed approach provides a new methodology for detecting robust higher-order structures and topologically uncertain regions in environmental and multi-hazard systems.

physics.soc-ph↗

arXiv2026

Tensor Network Machine Learning for Wildfire Susceptibility Mapping: from Grokking Dynamics to Quantum Mixedness of Class Representations

A quantum-inspired tensor network framework for wildfire susceptibility classification in the Gargano region is introduced, leveraging AlphaEarth embeddings and Matrix Product State models. The approach combines scalable geospatial representations with an interpretable quantum mask, enabling both binary and multiclass classification of wildfire susceptibility. Beyond predictive performance, the study reveals a pronounced grokking transition in the binary case and provides a detailed analysis of inter-class confusion in the multiclass setting. By introducing level-resolved mixedness diagnostics based on reduced density matrices, we show that the MPS classifier naturally encodes a hierarchy of class distinguishability, with non-adjacent categories becoming more separable than neighboring ones. These results demonstrate that tensor network models not only achieve competitive classification accuracy but also offer a physically grounded framework to quantify and interpret class separability in complex environmental datasets.

physics.soc-ph↗

arXiv2026

A Tensor Network Framework for Interpretable Graph Analysis of Brain Networks

Identifying robust neurobiological signatures of brain disorders requires machine learning approaches that combine predictive performance with interpretable representations of feature interactions. Here we introduce a quantum-inspired framework based on tensor network machine learning that learns distributed representations of gray-matter features encoded in a Matrix Product State representation, a variational ansatz originally developed for quantum many-body systems. The trained model is then used not only as a classifier but to extract quantum connected correlations between features, which encode higher-order feature interactions and which we map onto a weighted graph. This construction allows us to track, within a single representation, both: (i) the global spectral properties of the network (capturing collective learning dynamics), and (ii) node-level centrality measures (providing interpretable signatures of individual brain regions). Using repeated train-test sampling schemes, we analyze two classification tasks on structural MRI data as examples of complex brain disorders: healthy controls versus schizophrenia and versus bipolar disorder. Node-level analysis identifies a stable set of gray-matter features, most prominently Heschl gyrus, insular cortex, and frontal regions, that act as hubs across multiple centrality measures and across resamplings. These centralities display lower variability across resamplings than Shapley values, supporting the interpretive value of the network representation. The bipolar feature set emerges as a subset of the schizophrenia one, consistent with the hierarchically organized neuroanatomical alterations reported in neuroimaging studies and offering a network-based characterization of this hierarchy.

q-bio.NC↗

arXiv2025

Transfer entropy and O-information to detect grokking in tensor network multi-class classification problems

Quantum-enhanced machine learning, encompassing both quantum algorithms and quantum-inspired classical methods such as tensor networks, offers promising tools for extracting structure from complex, high-dimensional data. In this work, we study the training dynamics of Matrix Product State (MPS) classifiers applied to three-class problems, using both fashion MNIST and hyper-spectral satellite imagery as representative datasets. We investigate the phenomenon of grokking, where generalization emerges suddenly after memorization, by tracking entanglement entropy, local magnetization, and model performance across training sweeps. Additionally, we employ information theory tools to gain deeper insights: transfer entropy is used to reveal causal dependencies between label-specific quantum masks, while O-information captures the shift from synergistic to redundant correlations among class outputs. Our results show that grokking in the fashion MNIST task coincides with a sharp entanglement transition and a peak in redundant information, whereas the overfitted hyper-spectral model retains synergistic, disordered behavior. These findings highlight the relevance of high-order information dynamics in quantum-inspired learning and emphasize the distinct learning behaviors that emerge in multi-class classification, offering a principled framework to interpret generalization in quantum machine learning architectures.

quant-ph↗

arXiv2025

Grokking as an entanglement transition in tensor network machine learning

Grokking is a intriguing phenomenon in machine learning where a neural network, after many training iterations with negligible improvement in generalization, suddenly achieves high accuracy on unseen data. By working in the quantum-inspired machine learning framework based on tensor networks, we numerically prove that grokking phenomenon can be related to an entanglement dynamical transition in the underlying quantum many-body systems, consisting in a one-dimensional lattice with each site hosting a qubit. Two datasets are considered as use case scenarios, namely fashion MNIST and gene expression communities of hepatocellular carcinoma. In both cases, we train Matrix Product State (MPS) to perform binary classification tasks, and we analyse the learning dynamics. We exploit measurement of qubits magnetization and correlation functions in the MPS network as a tool to identify meaningful and relevant gene subcommunities, verified by means of enrichment procedures.

quant-ph↗

arXiv2022

Mind the gap in university rankings: a complex network approach towards fairness

University rankings are increasingly adopted for academic comparison and success quantification, even to establish performance-based criteria for funding assignment. However, rankings are not neutral tools, and their use frequently overlooks disparities in the starting conditions of institutions. In this research, we detect and measure structural biases that affect in inhomogeneous ways the ranking outcomes of universities from diversified territorial and educational contexts. Moreover, we develop a fairer rating system based on a fully data-driven debiasing strategy that returns an equity-oriented redefinition of the achieved scores. The key idea consists in partitioning universities in similarity groups, determined from multifaceted data using complex network analysis, and referring the performance of each institution to an expectation based on its peers. Significant evidence of territorial biases emerges for official rankings concerning both the OECD and Italian university systems, hence debiasing provides relevant insights suggesting the design of fairer strategies for performance-based funding allocations.

physics.soc-ph↗

arXiv2022

Planck Formula for the Gluon Parton Distribution in the Proton

We describe the gluon parton distribution function (PDF) in the proton, deduced by data from the ATLAS and HERA experiments, in the framework of the parton statistical model. The best fit parameters involved in the Planck formula that describes the gluon distribution are consistent with the results obtained from analysis of deep inelastic scattering processes. Remarkably, the agreement between the statistical model and the experimental gluon distributions is found with the same value of the "temperature" parameter $\bar{x}$ found by fitting the valence parton distributions from deep inelastic scattering. This result corroborates the validity of the statistical approach in the gluon sector.

hep-ph↗

arXiv2020

Potential energy of complex networks: a novel perspective

We present a novel characterization of complex networks, based on the potential of an associated Schrödinger equation. The potential is designed so that the energy spectrum of the Schrödinger equation coincides with the graph spectrum of the normalized Laplacian. Crucial information is retained in the reconstructed potential, which provides a compact representation of the properties of the network structure. The median potential over several random network realizations is fitted via a Landau-like function, and its length scale is found to diverge as the critical connection probability is approached from above. The ruggedness of the median potential profile is quantified using the Higuchi fractal dimension, which displays a maximum at the critical connection probability. This demonstrates that this technique can be successfully employed in the study of random networks, as an alternative indicator of the percolation phase transition. We apply the proposed approach to the investigation of real-world networks describing infrastructures (US power grid). Curiously, although no notion of phase transition can be given for such networks, the fractality of the median potential displays signatures of criticality. We also show that standard techniques (such as the scaling features of the largest connected component) do not detect any signature or remnant of criticality.

physics.soc-ph↗

arXiv2019

Dynamics near a first order phase transition

We study various dynamical aspects of systems possessing a first order phase transition in their phase diagram. We isolate three qualitatively distinct types of theories depending on the structure of instabilities and the nature of the low temperature phase. The non-equilibrium dynamics is modeled by a dual gravitational theory in 3+1 dimension which is coupled to massive scalar field with self interacting potential. By numerically solving the Einstein-matter equations of motion with various initial configurations, we investigate the structure of the final state arising through coalescence of phase domains. We find that static phase domains, even quite narrow are very long lived and we find a phenomenological equation for their lifetime. Within our framework we also analyze moving phase domains and their collision as well as the effects of spinodal instability and dynamical instability on an expanding boost invariant plasma.

hep-th↗

arXiv2016

Equilibration of a strongly interacting plasma: holographic analysis of local and nonlocal probes

The relaxation of a strongly coupled plasma towards the hydrodynamic regime is studied by analyzing the evolution of local and nonlocal observables in the holographic approach. The system is driven in an initial anisotropic and far-from equilibrium state through an impulsive time-dependent deformation (quench) of the boundary spacetime geometry. Effective temperature and entropy density are related to the position and area of a black hole horizon, which has formed as a consequence of the distortion. The behavior of stress-energy tensor, equal-time correlation functions and Wilson loops of different shapes is examined, and a hierarchy among their thermalization times emerges: probes involving shorter length scales thermalize faster.

hep-th↗

arXiv2016

Non-local probes for a relaxing non-Abelian plasma

The thermalization of a strongly coupled plasma is examined in the holographic framework through non-local observables: the equal-time two-point correlation function of a large dimension boundary operator, and Wilson loops of different shapes. The evolution of the probes from an initial far-from-equilibrium state to a hydrodynamic regime is found to depend on their size. A hierarchy among the thermalization times of the energy density, the pressures and the large size probes, is identified: the relaxation process is faster at short distances.

hep-ph↗

arXiv2016

Role of nonlocal probes of thermalization for a strongly interacting non-Abelian plasma

We use a holographic method to investigate thermalization of a boost-invariant strongly interacting non-Abelian plasma. Boundary sourcing, a distorsion of the boundary metric, is employed to drive the system far from equilibrium. Thermalization is analyzed through nonlocal probes: the equal-time two-point correlation function of large conformal dimension operators in the boundary theory, and Wilson loops of different shapes. We study the dependence of the thermalization time on the size of the probes, and compare the results to the ones obtained using local observables: the onset of thermalization is first observed at short distances.

hep-ph↗

arXiv2016

On $J^{PC}=0^{--}$ exotic glueball

The mass spectrum of the gluonium with $J^{PC}=0^{--}$ is examined in three bottom-up AdS/QCD models. The results are used to identify several production and decay modes useful for searching this state. Moreover, the properties of such glueball in a hot and dense quark medium are discussed.

hep-ph↗

arXiv2015

Thermalization of a boost-invariant non-Abelian plasma: Holographic approach with boundary sourcing

In a holographic approach, the evolution of a 4D strongly coupled non-Abelian plasma towards equilibrium can be studied investigating a 5D gravitational dual. The process driving the plasma out-of-equilibrium can be described by boundary sourcing, a deformation of the boundary metric; the analysis of the late-time dynamics allows to understand how the hydrodynamic regime settles in. We apply the method to a boost-invariant case, considering the effects of different quenches, solving the Einstein equations in the bulk and studying the time-dependence of observables such as the effective temperature, the energy density and the pressures. The main outcome is that, if the effective temperature of the system when the quench is switched off is $T_{eff}(τ^*)=500$ MeV, thermalization is reached within a time of ${\cal O}$(1 fm/c), an important information if the case of the QCD plasma produced in relativistic heavy ion collisions is considered.

hep-ph↗

arXiv2014

Hybrid exotic mesons in soft-wall AdS/QCD

Hybrid mesons with exotic quantum numbers $J^{PC}=1^{-+}$ are examined in soft-wall AdS/QCD. The predicted mass spectrum is compared to the measured values of the candidates $π_1(1400)$, $π_1(1600)$ and $π_1(2015)$. Thermal effects are analysed through the spectral function in the AdS-Black Hole model, and the differences with the Hawking-Page description are discussed.

hep-ph↗

arXiv2014

Exotic $J^{PC}=1^{-+}$ mesons in a holographic model of QCD

Mesons with quantum numbers $J^{PC}=1^{-+}$ cannot be represented as simple quark-antiquark pairs. We explore hybrid configurations in the light meson sector comprising a quark, an antiquark and an excited gluon, studying the properties of such states in a phenomenological model inspired by the gauge/gravity correspondence. The computed mass, compared to the experimental mass of the $1^{-+}$ candidates $π_1(1400)$, $π_1(1600)$ and $π_1(2015)$, favous $π_1(1400)$ as the lightest hybrid state. An interesting result concerns the stability of hybrid mesons at finite temperature: they disappear from the spectral function (i.e. they melt) at a lower temperature with respect to other states, light vector and scalar mesons, and scalar glueballs.

hep-ph↗