46 citations · 59 across the 10 of their papers we have counts for
10 papers
TokaMind: A Multi-Modal Transformer Foundation Model for Tokamak Plasma Dynamics
Tobia Boschi, Andrea Loreti, Nicola C. Amorisco +13
We present TokaMind, to our knowledge the first open-source foundation model for tokamak plasma dynamics, based on a Multi-Modal Transformer (MMT) and pretrained on heterogeneous d…
TokaMark: A Comprehensive Benchmark for MAST Tokamak Plasma Models
Cécile Rousseau, Samuel Jackson, Rodrigo H. Ordonez-Hurtado +13
Development and operation of commercially viable fusion energy reactors such as tokamaks require accurate predictions of plasma dynamics from sparse, noisy, and incomplete sensors…
Forging Time Series with Language: A Large Language Model Approach to Synthetic Data Generation
Cécile Rousseau, Tobia Boschi, Giandomenico Cornacchia +3
SDForger is a flexible and efficient framework for generating high-quality multivariate time series using LLMs. Leveraging a compact data representation, SDForger provides syntheti…
Doubly-Robust Functional Average Treatment Effect Estimation
Lorenzo Testa, Tobia Boschi, Francesca Chiaromonte +2
Understanding causal relationships in the presence of complex, structured data remains a central challenge in modern statistics and science in general. While traditional causal inf…
Functional Graph Convolutional Networks: A unified multi-task and multi-modal learning framework to facilitate health and social-care insights
Tobia Boschi, Francesca Bonin, Rodrigo Ordonez-Hurtado +3
This paper introduces a novel Functional Graph Convolutional Network (funGCN) framework that combines Functional Data Analysis and Graph Convolutional Networks to address the compl…
A new computationally efficient algorithm to solve Feature Selection for Functional Data Classification in high-dimensional spaces
Tobia Boschi, Francesca Bonin, Rodrigo Ordonez-Hurtado +2
This paper introduces a novel methodology for Feature Selection for Functional Classification, FSFC, that addresses the challenge of jointly performing feature selection and classi…
Contrasting pre-vaccine COVID-19 waves in Italy through Functional Data Analysis
Tobia Boschi, Jacopo Di Iorio, Lorenzo Testa +2
We use data from 107 Italian provinces to characterize and compare mortality patterns in the first two COVID-19 epidemic waves, which occurred prior to the introduction of vaccines…
FAStEN: An Efficient Adaptive Method for Feature Selection and Estimation in High-Dimensional Functional Regressions
Tobia Boschi, Lorenzo Testa, Francesca Chiaromonte +1
Functional regression analysis is an established tool for many contemporary scientific applications. Regression problems involving large and complex data sets are ubiquitous, and f…
The shapes of an epidemic: using Functional Data Analysis to characterize COVID-19 in Italy
Tobia Boschi, Jacopo Di Iorio, Lorenzo Testa +2
We investigate patterns of COVID-19 mortality across 20 Italian regions and their association with mobility, positivity, and socio-demographic, infrastructural and environmental co…
An Efficient Semi-smooth Newton Augmented Lagrangian Method for Elastic Net
Tobia Boschi, Matthew Reimherr, Francesca Chiaromonte
Feature selection is an important and active research area in statistics and machine learning. The Elastic Net is often used to perform selection when the features present non-negl…