Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Crupi, Riccardo"'
Environmental, Social, and Governance (ESG) datasets are frequently plagued by significant data gaps, leading to inconsistencies in ESG ratings due to varying imputation methods. This paper explores the application of established machine learning tec
Externí odkaz:
http://arxiv.org/abs/2407.20047
Autor:
Crupi, Riccardo, Regoli, Daniele, Sabatino, Alessandro Damiano, Marano, Immacolata, Brinis, Massimiliano, Albertazzi, Luca, Cirillo, Andrea, Cosentini, Andrea Claudio
Explaining outliers occurrence and mechanism of their occurrence can be extremely important in a variety of domains. Malfunctions, frauds, threats, in addition to being correctly identified, oftentimes need a valid explanation in order to effectively
Externí odkaz:
http://arxiv.org/abs/2403.10903
Autor:
Crupi, Riccardo
This thesis comprises the first three chapters dedicated to providing an overview of Gamma Ray-Bursts (GRBs), their properties, the instrumentation used to detect them, and Artificial Intelligence (AI) applications in the context of GRBs, including a
Externí odkaz:
http://arxiv.org/abs/2401.15632
Autor:
Della Casa, Giovanni, Zampa, Nicola, Cirrincione, Daniela, Monzani, Simone, Baruzzo, Marco, Campana, Riccardo, Cauz, Diego, Citossi, Marco, Crupi, Riccardo, Dilillo, Giuseppe, Pauletta, Giovanni, Fiore, Fabrizio, Vacchi, Andrea
Publikováno v:
Nucl.Instrum.MethA, 1058 (2024) 168825
The HERMES (High Energy Rapid Modular Ensemble of Satellites) Pathfinder mission aims to develop a constellation of nanosatellites to study astronomical transient sources, such as gamma-ray bursts, in the X and soft $\gamma$ energy range, exploiting
Externí odkaz:
http://arxiv.org/abs/2401.02900
The remarkable success of Artificial Intelligence in advancing automated decision-making is evident both in academia and industry. Within the plethora of applications, ranking systems hold significant importance in various domains. This paper advocat
Externí odkaz:
http://arxiv.org/abs/2312.10094
Autor:
Dilillo, Giuseppe, Ward, Kes, Eckley, Idris A., Fearnhead, Paul, Crupi, Riccardo, Evangelista, Yuri, Vacchi, Andrea, Fiore, Fabrizio
We describe how a novel online changepoint detection algorithm, called Poisson-FOCuS, can be used to optimally detect gamma-ray bursts within the computational constraints imposed by miniaturized satellites such as the upcoming HERMES-Pathfinder cons
Externí odkaz:
http://arxiv.org/abs/2312.08817
In this research, we propose a novel approach for the quantification of credit portfolio Value-at-Risk (VaR) sensitivity to asset correlations with the use of synthetic financial correlation matrices generated with deep learning models. In previous w
Externí odkaz:
http://arxiv.org/abs/2309.08652
Autor:
Crupi, Riccardo, Dilillo, Giuseppe, Ward, Kester, Bissaldi, Elisabetta, Fiore, Fabrizio, Vacchi, Andrea
HERMES (High Energy Rapid Modular Ensemble of Satellites) pathfinder is an in-orbit demonstration consisting of a constellation of six 3U nano-satellites hosting simple but innovative detectors for the monitoring of cosmic high-energy transients. The
Externí odkaz:
http://arxiv.org/abs/2303.15936
Autor:
Basile, Alessandro, Crupi, Riccardo, Grasso, Michele, Mercanti, Alessandro, Regoli, Daniele, Scarsi, Simone, Yang, Shuyi, Cosentini, Andrea
Publikováno v:
updated version is published by Expert Systems with Applications, Volume 238, Part C, 2024, 122035, ISSN 0957-4174
Name Entity Disambiguation is the Natural Language Processing task of identifying textual records corresponding to the same Named Entity, i.e. real-world entities represented as a list of attributes (names, places, organisations, etc.). In this work,
Externí odkaz:
http://arxiv.org/abs/2303.05391
Autor:
Castelnovo, Alessandro, Crupi, Riccardo, Inverardi, Nicole, Regoli, Daniele, Cosentini, Andrea
Publikováno v:
Proceedings of 1st Workshop on Bias, Ethical AI, Explainability and the Role of Logic and Logic Programming (BEWARE 2022) co-located with the 21th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2022)
Machine learning applications are becoming increasingly pervasive in our society. Since these decision-making systems rely on data-driven learning, risk is that they will systematically spread the bias embedded in data. In this paper, we propose to a
Externí odkaz:
http://arxiv.org/abs/2209.05889