%0 Conference Proceedings %B Mathematical and Statistical Methods for Actuarial Sciences and Finance %D 2022 %T Quantile Regression Forest for Value-at-Risk Forecasting Via Mixed-Frequency Data %A Mila Andreani %A Candila, Vincenzo %A Petrella, Lea %B Mathematical and Statistical Methods for Actuarial Sciences and Finance %I Springer, Cham %G eng %R https://doi.org/10.1007/978-3-030-99638-3 %0 Journal Article %J Risks %D 2021 %T Multivariate Analysis of Energy Commodities during the COVID-19 Pandemic: Evidence from a Mixed-Frequency Approach %A Mila Andreani %A Candila, Vincenzo %A Morelli, Giacomo %A Petrella, Lea %B Risks %V 9 %P 144 %G eng %U https://www.mdpi.com/2227-9091/9/8/144 %R 10.3390/risks9080144 %0 Conference Proceedings %D 2021 %T Quantile Regression Forest with mixed-frequency data %A Mila Andreani %A Vincenzo Candila %A Lea Petrella %I Pearson %V Book of Short Papers SIS 2021 %8 06/2021 %G eng %U https://it.pearson.com//docenti/universita/partnership/sis.html %0 Journal Article %J Journal of Economic Interaction and Coordination %D 2021 %T Systemic liquidity contagion in the European interbank market %A V. Macchiati %A G. Brandi %A T. Di Matteo %A D. Paolotti %A G. Caldarelli %A G. Cimini %K Epidemic model %K European Interbank market %K Financial contagion %K Liquidity shocks %B Journal of Economic Interaction and Coordination %G eng %R https://doi.org/10.1007/s11403-021-00338-1 %0 Journal Article %J International Journal of Data Science and Analytics %D 2020 %T Human migration: the big data perspective %A Alina Sirbu %A Andrienko, Gennady %A Andrienko, Natalia %A Boldrini, Chiara %A Conti, Marco %A Giannotti, Fosca %A Guidotti, Riccardo %A Bertoli, Simone %A Jisu Kim %A Muntean, Cristina Ioana %A others %X How can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants. %B International Journal of Data Science and Analytics %P 1–20 %G eng %0 Journal Article %J Scientific Reports %D 2020 %T Knowledge and Social Relatedness Shape Research Portfolio Diversification %A Tripodi, Giorgio %A Chiaromonte, Francesca %A Lillo, Fabrizio %B Scientific Reports %V 10 %G eng %N 1 %& 14232 %0 Conference Paper %B Nuove inchieste sull’epistola a Cangrande: atti della giornata di studi, Pisa 18 dicembre 2018 %D 2020 %T L’Epistola a Cangrande al vaglio della Computational Authorship Verification: risultati preliminari (con una postilla sulla cosiddetta “XIV Epistola di Dante Alighieri”) %A Silvia Corbara %A Alejandro Moreo %A Fabrizio Sebastiani %A Mirko Tavoni %E Alberto Casadei %X In this work we apply techniques from computational Authorship Verification (AV) to the problem of detecting whether the “Epistle to Cangrande” is an authentic work by Dante Alighieri or is instead the work of a forger. The AV algorithm we use is based on “machine learning”: the algorithm “trains” an automatic system (a “classifier”) to detect whether a certain Latin text is Dante’s or not Dante’s, by exposing it to a corpus of example Latin texts by Dante and example Latin texts by authors coeval to Dante. The detection is based on the analysis of a set of stylometric features, i.e., style-related linguistic traits whose us-age frequencies tend to represent an author’s unconscious “signature”. The analysis carried out in this work suggests that, of the two parts into which the Epistle is traditionally subdivided, neither is Dante’s. Experiments in which we have applied our AV system to each text in the corpus suggest that the system has a fairly high degree of accuracy, thus lending credibility to its hypothesis about the authorship of the Epistle. In the last section of this paper we apply our system to what has been hypothesized to be “Dante’s 14th Epistle”; the system rejects, with very high confidence, the hypothesis that this epistle might be Dante’s. %B Nuove inchieste sull’epistola a Cangrande: atti della giornata di studi, Pisa 18 dicembre 2018 %I Pisa University Press %@ 978-88-3339-333-9 %G eng %0 Journal Article %J International Journal of Data Science and Analytics (JDSA) %D 2020 %T Measuring objective and subjective well-being: dimensions and data sources %A Vasiliki Voukelatou %A Gabrielli, Lorenzo %A Miliou, Ioanna %A Cresci, Stefano %A Sharma, Rajesh %A Tesconi, Maurizio %A Pappalardo, Luca %X Well-being is an important value for people’s lives, and it could be considered as an index of societal progress. Researchers have suggested two main approaches for the overall measurement of well-being, the objective and the subjective well-being. Both approaches, as well as their relevant dimensions, have been traditionally captured with surveys. During the last decades, new data sources have been suggested as an alternative or complement to traditional data. This paper aims to present the theoretical background of well-being, by distinguishing between objective and subjective approaches, their relevant dimensions, the new data sources used for their measurement and relevant studies. We also intend to shed light on still barely unexplored dimensions and data sources that could potentially contribute as a key for public policing and social development. %B International Journal of Data Science and Analytics (JDSA) %G eng %R https://doi.org/10.1007/s41060-020-00224-2 %0 Journal Article %J International Journal of Advances in Intelligent Informatics %D 2020 %T Self-supervised pre-training of CNNs for flatness defect classification in the steelworks industry %A Filippo Galli %A Antonio Ritacco %A Giacomo Lanciano %A Marco Vannocci %A Valentina Colla %A Marco Vannucci %K CNN %K Deep learning %K Self-supervision %K Steelworks %X Classification of surface defects in the steelworks industry plays a significant role in guaranteeing the quality of the products. From an industrial point of view, a serious concern is represented by the hot-rolled products shape defects and particularly those concerning the strip flatness. Flatness defects are typically divided into four sub-classes depending on which part of the strip is affected and the corresponding shape. In the context of this research, the primary objective is evaluating the improvements of exploiting the self-supervised learning paradigm for defects classification, taking advantage of unlabelled, real, steel strip flatness maps. Different pre-training methods are compared, as well as architectures, taking advantage of well-established neural subnetworks, such as Residual and Inception modules. A systematic approach in evaluating the different performances guarantees a formal verification of the self-supervised pre-training paradigms evaluated hereafter. In particular, pre-training neural networks with the EgoMotion meta-algorithm shows classification improvements over the AutoEncoder technique, which in turn is better performing than a Glorot weight initialization. %B International Journal of Advances in Intelligent Informatics %V 6 %P 13–22 %G eng %U http://ijain.org/index.php/IJAIN/article/view/410 %R 10.26555/ijain.v6i1.410 %0 Conference Paper %B Proceedings of the 35th Annual ACM Symposium on Applied Computing %D 2020 %T SOM-Based Behavioral Analysis for Virtualized Network Functions %A Giacomo Lanciano %A Antonio Ritacco %A Tommaso Cucinotta %A Marco Vannucci %A Antonino Artale %A Luca Basili %A Enrica Sposato %A Joao Barata %K machine learning %K network function virtualization %K self-organizing maps %X In this paper, we propose a mechanism based on Self-Organizing Maps for analyzing the resource consumption behaviors and detecting possible anomalies in data centers for Network Function Virtualization (NFV). Our approach is based on a joint analysis of two historical data sets available through two separate monitoring systems: system-level metrics for the physical and virtual machines obtained from the monitoring infrastructure, and application-level metrics available from the individual virtualized network functions. Experimental results, obtained by processing real data from one of the NFV data centers of the Vodafone network operator, highlight some of the capabilities of our system to identify interesting points in space and time of the evolution of the monitored infrastructure. %B Proceedings of the 35th Annual ACM Symposium on Applied Computing %I Association for Computing Machinery %C New York, NY, USA %@ 9781450368667 %G eng %U https://doi.org/10.1145/3341105.3374110 %R 10.1145/3341105.3374110 %0 Conference Paper %B New Trends in Image Analysis and Processing – ICIAP 2019 %D 2019 %T The Epistle to Cangrande Through the Lens of Computational Authorship Verification %A Silvia Corbara %A Moreo, Alejandro %A Sebastiani, Fabrizio %A Tavoni, Mirko %E Cristani, Marco %E Prati, Andrea %E Lanz, Oswald %E Messelodi, Stefano %E Sebe, Nicu %X The Epistle to Cangrande is one of the most controversial among the works of Italian poet Dante Alighieri. For more than a hundred years now, scholars have been debating over its real paternity, i.e., whether it should be considered a true work by Dante or a forgery by an unnamed author. In this work we address this philological problem through the methodologies of (supervised) Computational Authorship Verification, by training a classifier that predicts whether a given work is by Dante Alighieri or not. We discuss the system we have set up for this endeavour, the training set we have assembled, the experimental results we have obtained, and some issues that this work leaves open. %B New Trends in Image Analysis and Processing – ICIAP 2019 %I Springer International Publishing %C Cham %@ 978-3-030-30754-7 %G eng %R https://doi.org/10.1007/978-3-030-30754-7_15 %0 Journal Article %J Journal of Network Theory in Finance %D 2019 %T Network Sensitivity of Systemic Risk %A D. Di Gangi %A D. R. Lo Sardo %A V. Macchiati %A T. P. Minh %A F. Pinotti %A A. Ramadiah %A M. Wilinski %A P. Barucca %A G. Cimini %B Journal of Network Theory in Finance %G eng %R https://doi.org/10.21314/JNTF.2019.056 %0 Journal Article %J The Lab’s Quarterly %D 2018 %T ECHO CHAMBERS E POLARIZZAZIONE Uno sguardo critico sulla diffusione dell’informazione nei social network %A Costantino Carugno %A Tommaso Radicioni %X Understanding the algorithms that contribute to the formation of our daily reality requires an in-depth look at how information is disseminated in online social networks (OSN). In this article, we will observe how news propagation is restricted by the presence of virtual borders that limit the interaction between users. This phenomenon, known as "echo chamber" formation, has the effect of polarizing the public debate on conflicting positions. Inside an echo chamber, information is not conveyed through a horizontal exchange between users, but due to the presence of like or follower aggregators, called hubs. This analysis will be carried out considering a casestudy in two of the main OSNs: Facebook and Twitter. From the study of user interaction networks we will observe how the algorithmic choices made are crucial to the polarization of the debate around a topic of discussion. %B The Lab’s Quarterly %V 20 %G eng %U https://thelabsquarterly.files.wordpress.com/2019/04/2018.4-the-labs-quarterly-7.-costantino-carugno-tommaso-radicioni-1.pdf %N 4 %& 173 %0 Journal Article %J arXiv preprint arXiv:1805.04307 %D 2018 %T Maximum entropy approach to link prediction in bipartite networks %A Baltakiene, M %A Baltakys, K %A Cardamone, D %A Parisi, F %A Tommaso Radicioni %A Torricelli, M %A de Jeude, JA %A Saracco, F %B arXiv preprint arXiv:1805.04307 %G eng %0 Journal Article %J Royal Society open science %D 2017 %T Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models %A Cecilia Panigutti %A Tizzoni, Michele %A Bajardi, Paolo %A Smoreda, Zbigniew %A Colizza, Vittoria %X The recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas. %B Royal Society open science %V 4 %P 160950 %G eng %U https://royalsocietypublishing.org/doi/full/10.1098/rsos.160950 %R https://doi.org/10.1098/rsos.160950 %0 Journal Article %J Journal of Instrumentation %D 2017 %T The calorimeter of the Mu2e experiment at Fermilab %A N. Atanov %A V. Baranov %A J. Budagov %A F. Cervelli %A F. Colao %A M. Cordelli %A G. Corradi %A E. Dané %A Y.I. Davydov %A S. Di Falco %A E. Diociaiuti %A S. Donati %A R. Donghia %A B. Echenard %A K. Flood %A S. Giovannella %A V. Glagolev %A F. Grancagnolo %A F. Happacher %A D.G. Hitlin %A M. Martini %A S. Miscetti %A T. Miyashita %A L. Morescalchi %A P. Murat %A G. Pezzullo %A F. Porter %A F. Raffaelli %A Tommaso Radicioni %A M. Ricci %A A. Saputi %A I. Sarra %A F. Spinella %A G. Tassielli %A V. Tereshchenko %A Z. Usubov %A R.Y. Zhu %X The Mu2e experiment at Fermilab looks for Charged Lepton Flavor Violation (CLFV) improving by 4 orders of magnitude the current experimental sensitivity for the muon to electron conversion in a muonic atom. A positive signal could not be explained in the framework of the current Standard Model of particle interactions and therefore would be a clear indication of new physics. In 3 years of data taking, Mu2e is expected to observe less than one background event mimicking the electron coming from muon conversion. Achieving such a level of background suppression requires a deep knowledge of the experimental apparatus: a straw tube tracker, measuring the electron momentum and time, a cosmic ray veto system rejecting most of cosmic ray background and a pure CsI crystal calorimeter, that will measure time of flight, energy and impact position of the converted electron. The calorimeter has to operate in a harsh radiation environment, in a 10−4 Torr vacuum and inside a 1 T magnetic field. The results of the first qualification tests of the calorimeter components are reported together with the energy and time performances expected from the simulation and measured in beam tests of a small scale prototype. %B Journal of Instrumentation %V 12 %G eng %U https://doi.org/10.1088%2F1748-0221%2F12%2F01%2Fc01061 %R 10.1088/1748-0221/12/01/c01061