33 citations · 78 across the 25 of their papers we have counts for
26 papers
Weighted Score-Oriented Losses for Temporally Localized Event Prediction
Edoardo Legnaro, Sabrina Guastavino, Francesco Marchetti
Operational event-detection systems are rarely assessed by pointwise accuracy alone. In anomaly detection, changepoint detection, and warning systems, the utility of an alarm depen…
Predicting coronal mass ejection travel times using enhanced model-guided machine learning
M. Lampani, M. Rossi, S. Guastavino +2
Coronal mass ejections (CMEs) are key drivers of space weather events, posing risks to both space-borne and ground-based systems. Accurate prediction of their arrival time at Earth…
Weak convergence rates for spectral regularization via sampling inequalities
Sabrina Guastavino, Gabriele Santin, Francesco Marchetti +1
Convergence rates in spectral regularization methods quantify the approximation error in inverse problems as a function of the noise level or the number of sampling points. Classic…
The Multiclass Score-Oriented Loss (MultiSOL) on the Simplex
Francesco Marchetti, Edoardo Legnaro, Sabrina Guastavino
In the supervised binary classification setting, score-oriented losses have been introduced with the aim of optimizing a chosen performance metric directly during the training phas…
Multiclass threshold-based classification and model evaluation
Edoardo Legnaro, Sabrina Guastavino, Francesco Marchetti
In this paper, we introduce a threshold-based framework for multiclass classification that generalizes the standard argmax rule. This is done by replacing the probabilistic interpr…
Feature Understanding and Sparsity Enhancement via 2-Layered kernel machines (2L-FUSE)
Fabiana Camattari, Sabrina Guastavino, Francesco Marchetti +1
We propose a novel sparsity enhancement strategy for regression tasks, based on learning a data-adaptive kernel metric, i.e., a shape matrix, through 2-Layered kernel machines. The…
AI-based modular warning machine for risk identification in proximity healthcare
Chiara Razzetta, Shahryar Noei, Federico Barbarossa +17
"DHEAL-COM - Digital Health Solutions in Community Medicine" is a research and technology project funded by the Italian Department of Health for the development of digital solution…
Multiclass threshold-based classification
Francesco Marchetti, Edoardo Legnaro, Sabrina Guastavino
In this paper, we introduce a threshold-based framework for multiclass classification that generalizes the standard argmax rule. This is done by replacing the probabilistic interpr…
Physics-informed features in supervised machine learning
Margherita Lampani, Sabrina Guastavino, Michele Piana +1
Supervised machine learning involves approximating an unknown functional relationship from a limited dataset of features and corresponding labels. The classical approach to feature…
Artificial Intelligence Could Have Predicted All Space Weather Events Associated with the May 2024 Superstorm
Sabrina Guastavino, Edoardo Legnaro, Anna Maria Massone +1
Space weather, driven by solar flares and Coronal Mass Ejections (CMEs), poses significant risks to technological systems. Accurately forecasting these events and their impact on E…
Deep Learning for Active Region Classification: A Systematic Study from Convolutional Neural Networks to Vision Transformers
Edoardo Legnaro, Sabrina Guastavino, Michele Piana +1
A solar active region can significantly disrupt the Sun Earth space environment, often leading to severe space weather events such as solar flares and coronal mass ejections. As a…
Extended Drag-Based Model for better predicting the evolution of Coronal Mass Ejections
Mattia Rossi, Sabrina Guastavino, Michele Piana +1
The solar wind drag-based model is a widely used framework for predicting the propagation of Coronal Mass Ejections (CMEs) through interplanetary space. This model primarily consid…
Forecasting Geoffective Events from Solar Wind Data and Evaluating the Most Predictive Features through Machine Learning Approaches
Sabrina Guastavino, Katsiaryna Bahamazava, Emma Perracchione +8
This study addresses the prediction of geomagnetic disturbances by exploiting machine learning techniques. Specifically, the Long-Short Term Memory recurrent neural network, which…
Greedy feature selection: Classifier-dependent feature selection via greedy methods
Fabiana Camattari, Sabrina Guastavino, Francesco Marchetti +2
The purpose of this study is to introduce a new approach to feature ranking for classification tasks, called in what follows greedy feature selection. In statistical learning, feat…
AI-FLARES: Artificial Intelligence for the Analysis of Solar Flares Data
Michele Piana, Federico Benvenuto, Anna Maria Massone +6
AI-FLARES (Artificial Intelligence for the Analysis of Solar Flares Data) is a research project funded by the Agenzia Spaziale Italiana and by the Istituto Nazionale di Astrofisica…
A comprehensive theoretical framework for the optimization of neural networks classification performance with respect to weighted metrics
Francesco Marchetti, Sabrina Guastavino, Cristina Campi +2
In many contexts, customized and weighted classification scores are designed in order to evaluate the goodness of the predictions carried out by neural networks. However, there exi…
Physics-driven machine learning for the prediction of coronal mass ejections' travel times
Sabrina Guastavino, Valentina Candiani, Alessandro Bemporad +7
Coronal Mass Ejections (CMEs) correspond to dramatic expulsions of plasma and magnetic field from the solar corona into the heliosphere. CMEs are scientifically relevant because th…
STIX imaging I -- Concept
Paolo Massa, Gordon. J. Hurford, Anna Volpara +17
Aims. To provide a schematic mathematical description of the imaging concept of the Spectrometer/Telescope for Imaging X-rays (STIX) on board Solar Orbiter. The derived model is th…
Operational solar flare forecasting via video-based deep learning
Sabrina Guastavino, Francesco Marchetti, Federico Benvenuto +2
Operational flare forecasting aims at providing predictions that can be used to make decisions, typically at a daily scale, about the space weather impacts of flare occurrence. Thi…
Implementation paradigm for supervised flare forecasting studies: a deep learning application with video data
Sabrina Guastavino, Francesco Marchetti, Federico Benvenuto +2
Solar flare forecasting can be realized by means of the analysis of magnetic data through artificial intelligence techniques. The aim is to predict whether a magnetic active region…
Prediction of severe thunderstorm events with ensemble deep learning and radar data
Sabrina Guastavino, Michele Piana, Marco Tizzi +5
The problem of nowcasting extreme weather events can be addressed by applying either numerical methods for the solution of dynamic model equations or data-driven artificial intelli…
Flare Forecasting Algorithms Based on High-Gradient Polarity Inversion Lines in Active Regions
Domenico Cicogna, Francesco Berrilli, Daniele Calchetti +6
Solar flares emanate from solar active regions hosting complex and strong bipolar magnetic fluxes. Estimating the probability of an active region to flare and defining reliable pre…
Score-oriented loss (SOL) functions
Francesco Marchetti, Sabrina Guastavino, Michele Piana +1
Loss functions engineering and the assessment of forecasting performances are two crucial and intertwined aspects of supervised machine learning. This paper focuses on binary class…
Bad and good errors: value-weighted skill scores in deep ensemble learning
Sabrina Guastavino, Michele Piana, Federico Benvenuto
In this paper we propose a novel approach to realize forecast verification. Specifically, we introduce a strategy for assessing the severity of forecast errors based on the evidenc…