@proceedings {1091,
	title = {Quantile Regression Forest for Value-at-Risk Forecasting Via Mixed-Frequency Data},
	year = {2022},
	publisher = {Springer, Cham},
	doi = {https://doi.org/10.1007/978-3-030-99638-3},
	author = {Mila Andreani and Candila, Vincenzo and Petrella, Lea}
}
@article {300,
	title = {Multivariate Analysis of Energy Commodities during the COVID-19 Pandemic: Evidence from a Mixed-Frequency Approach},
	journal = {Risks},
	volume = {9},
	year = {2021},
	pages = {144},
	issn = {2227-9091},
	doi = {10.3390/risks9080144},
	url = {https://www.mdpi.com/2227-9091/9/8/144},
	author = {Mila Andreani and Candila, Vincenzo and Morelli, Giacomo and Petrella, Lea}
}
@proceedings {299,
	title = {Quantile Regression Forest with mixed-frequency data},
	volume = {Book of Short Papers SIS 2021},
	year = {2021},
	month = {06/2021},
	publisher = {Pearson},
	issn = {9788891927361},
	url = {https://it.pearson.com//docenti/universita/partnership/sis.html},
	author = {Mila Andreani and Vincenzo Candila and Lea Petrella}
}
@article {261,
	title = {Systemic liquidity contagion in the European interbank market},
	journal = {Journal of Economic Interaction and Coordination},
	year = {2021},
	keywords = {Epidemic model, European Interbank market, Financial contagion, Liquidity shocks},
	doi = {https://doi.org/10.1007/s11403-021-00338-1},
	author = {V. Macchiati and G. Brandi and T. Di Matteo and D. Paolotti and G. Caldarelli and G. Cimini}
}
@conference {243,
	title = {Doctor XAI: an ontology-based approach to black-box sequential data classification explanations},
	booktitle = {Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency},
	year = {2020},
	abstract = {Several recent advancements in Machine Learning involve black box models: algorithms that do not provide human-understandable explanations in support of their decisions. This limitation hampers the fairness, accountability and transparency of these models; the field of eXplainable Artificial Intelligence (XAI) tries to solve this problem providing human-understandable explanations for black-box models. However, healthcare datasets (and the related learning tasks) often present peculiar features, such as sequential data, multi-label predictions, and links to structured background knowledge. In this paper, we introduce Doctor XAI, a model-agnostic explainability technique able to deal with multi-labeled, sequential, ontology-linked data. We focus on explaining Doctor AI, a multilabel classifier which takes as input the clinical history of a patient in order to predict the next visit. Furthermore, we show how exploiting the temporal dimension in the data and the domain knowledge encoded in the medical ontology improves the quality of the mined explanations.},
	doi = {https://doi.org/10.1145/3351095.3372855},
	url = {https://dl.acm.org/doi/abs/10.1145/3351095.3372855},
	author = {Cecilia Panigutti and Perotti, Alan and Pedreschi, Dino}
}
@article {271,
	title = {Drivers of change in biodiversity and ecosystem services in the Cantareira System Protected Area : A prospective analysis of the implementation of public policies},
	journal = {Biota Neotropica},
	volume = {20},
	year = {2020},
	month = {05/2020},
	type = {Article},
	abstract = {The lack of implementation of well-designed public policies aimed at the conservation of natural ecosystems has resulted, at a global level, in the decline of ecosystem functioning and, consequently, of the contributions they make to people. The poor enforcement of important environmental legislation in Brazil - for instance, the {\textquotedblleft}Atlantic Forest Law{\textquotedblright} (Law n.11.428/2006) and the {\textquotedblleft}Forest Code{\textquotedblright} (Law n.12.651/2012) - could compromise the overall maintenance of ecosystems and the services they provide. To explore the implications of different levels of federal laws{\textquoteright} enforcement within the Cantareira System Protected Area (PA) - a PA in southeastern Brazil that provides fresh water for 47\% of the Sao Paulo Metropolitan Area -, we developed a conceptual framework to identify indirect and direct drives of biodiversity and ecosystem changes. We also projected four land-use scenarios to 2050 to test the effects of deforestation control and forest restoration practices on biodiversity and ecosystem services maintenance: the {\textquotedblleft}business-as-usual{\textquotedblright} scenario (BAU), which assumes that all trends in land-use cover changes observed in the past will continue in the future, and three alternative exploratory scenarios considering the Atlantic Forest Law implementation, the partial implementation of the Forest Code and the full implementation of the Forest Code. Using the land-use maps generated for each scenario, we assessed the impacts of land-use changes on biodiversity conservation and soil retention. Our results revealed all alternative scenarios could increase biodiversity conservation (by 7\%; 12\%; and 12\%, respectively), reduce soil loss (by 24.70\%; 34.70\%; and 38.12\%, respectively) and sediment exportation to water (by 27.47\%; 55.06\%; and 59.28\%, respectively), when compared to the BAU scenario. Our findings highlight the importance of restoring and conserving native vegetation for the maintenance and improvement of biodiversity conservation and for the provision of ecosystem services.},
	keywords = {Biodiversity, Cantareira System Protected Area, Ecosystem services, GLOBIO, InVEST, Modeling, Scenarios},
	issn = {1676-0611},
	doi = {https://doi.org/10.1590/1676-0611-bn-2019-0915 },
	url = {https://www.scielo.br/scielo.php?script=sci_arttext\&pid=S1676-06032020000500201},
	author = {Viviane Dib and Marco Aur{\'e}lio Nalon and Nino Tavares Amazonas and Cristina Yuri Vidal and Iv{\'a}n A. Ortiz-Rodr{\'\i}guez and Jan Dan{\v e}k and Ma{\'\i}ra Formis de Oliveira and Paola Alberti and Rafaela Aparecida da Silva and Ra{\'\i}za Salom{\~a}o Precinoto and Taciana Figueiredo Gomes}
}
@proceedings {288,
	title = {Dynamic Quantile Regression Forest},
	volume = {Book of Short Papers SIS 2020},
	year = {2020},
	month = {10/2020},
	publisher = {Pearson},
	issn = {9788891910776},
	url = {https://it.pearson.com/content/dam/region-core/italy/pearson-italy/pdf/Docenti/Universit\%C3\%A0/Pearson-SIS-2020-atti-convegno.pdf},
	author = {Mila Andreani and Lea Petrella}
}
@article {276,
	title = {Measuring objective and subjective well-being: dimensions and data sources},
	journal = {International Journal of Data Science and Analytics (JDSA)},
	year = {2020},
	abstract = {Well-being is an important value for people{\textquoteright}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.},
	doi = {https://doi.org/10.1007/s41060-020-00224-2},
	author = {Vasiliki Voukelatou and Gabrielli, Lorenzo and Miliou, Ioanna and Cresci, Stefano and Sharma, Rajesh and Tesconi, Maurizio and Pappalardo, Luca}
}
@conference {265,
	title = {The Epistle to Cangrande Through the Lens of Computational Authorship Verification},
	booktitle = {New Trends in Image Analysis and Processing {\textendash} ICIAP 2019},
	year = {2019},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {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.},
	isbn = {978-3-030-30754-7},
	doi = {https://doi.org/10.1007/978-3-030-30754-7_15},
	author = {Silvia Corbara and Moreo, Alejandro and Sebastiani, Fabrizio and Tavoni, Mirko},
	editor = {Cristani, Marco and Prati, Andrea and Lanz, Oswald and Messelodi, Stefano and Sebe, Nicu}
}
@conference {241,
	title = {Explaining multi-label black-box classifiers for health applications},
	booktitle = {International Workshop on Health Intelligence},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Today the state-of-the-art performance in classification is achieved by the so-called {\textquotedblleft}black boxes{\textquotedblright}, i.e. decision-making systems whose internal logic is obscure. Such models could revolutionize the health-care system, however their deployment in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of transparency. The typical classification problem in health-care requires a multi-label approach since the possible labels are not mutually exclusive, e.g. diagnoses. We propose MARLENA, a model-agnostic method which explains multi-label black box decisions. MARLENA explains an individual decision in three steps. First, it generates a synthetic neighborhood around the instance to be explained using a strategy suitable for multi-label decisions. It then learns a decision tree on such neighborhood and finally derives from it a decision rule that explains the black box decision. Our experiments show that MARLENA performs well in terms of mimicking the black box behavior while gaining at the same time a notable amount of interpretability through compact decision rules, i.e. rules with limited length.},
	keywords = {Explainable Machine Learning, Healthcare},
	doi = {10.1007/978-3-030-24409-5_9},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-24409-5_9},
	author = {Cecilia Panigutti and Guidotti, Riccardo and Monreale, Anna and Pedreschi, Dino}
}
@article {262,
	title = {Network Sensitivity of Systemic Risk},
	journal = {Journal of Network Theory in Finance},
	year = {2019},
	doi = {https://doi.org/10.21314/JNTF.2019.056},
	author = {D. Di Gangi and D. R. Lo Sardo and V. Macchiati and T. P. Minh and F. Pinotti and A. Ramadiah and M. Wilinski and P. Barucca and G. Cimini}
}
@article {253,
	title = {Maximum entropy approach to link prediction in bipartite networks},
	journal = {arXiv preprint arXiv:1805.04307},
	year = {2018},
	author = {Baltakiene, M and Baltakys, K and Cardamone, D and Parisi, F and Tommaso Radicioni and Torricelli, M and de Jeude, JA and Saracco, F}
}
@article {242,
	title = {Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models},
	journal = {Royal Society open science},
	volume = {4},
	year = {2017},
	pages = {160950},
	abstract = {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.},
	doi = {https://doi.org/10.1098/rsos.160950},
	url = {https://royalsocietypublishing.org/doi/full/10.1098/rsos.160950},
	author = {Cecilia Panigutti and Tizzoni, Michele and Bajardi, Paolo and Smoreda, Zbigniew and Colizza, Vittoria}
}
@article {251,
	title = {The calorimeter of the Mu2e experiment at Fermilab},
	journal = {Journal of Instrumentation},
	volume = {12},
	year = {2017},
	abstract = {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.},
	doi = {10.1088/1748-0221/12/01/c01061},
	url = {https://doi.org/10.1088\%2F1748-0221\%2F12\%2F01\%2Fc01061},
	author = {N. Atanov and V. Baranov and J. Budagov and F. Cervelli and F. Colao and M. Cordelli and G. Corradi and E. Dan{\'e} and Y.I. Davydov and S. Di Falco and E. Diociaiuti and S. Donati and R. Donghia and B. Echenard and K. Flood and S. Giovannella and V. Glagolev and F. Grancagnolo and F. Happacher and D.G. Hitlin and M. Martini and S. Miscetti and T. Miyashita and L. Morescalchi and P. Murat and G. Pezzullo and F. Porter and F. Raffaelli and Tommaso Radicioni and M. Ricci and A. Saputi and I. Sarra and F. Spinella and G. Tassielli and V. Tereshchenko and Z. Usubov and R.Y. Zhu}
}
