<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mila Andreani</style></author><author><style face="normal" font="default" size="100%">Candila, Vincenzo</style></author><author><style face="normal" font="default" size="100%">Petrella, Lea</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quantile Regression Forest for Value-at-Risk Forecasting Via Mixed-Frequency Data</style></title><secondary-title><style face="normal" font="default" size="100%">Mathematical and Statistical Methods for Actuarial Sciences and Finance</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer, Cham</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mila Andreani</style></author><author><style face="normal" font="default" size="100%">Candila, Vincenzo</style></author><author><style face="normal" font="default" size="100%">Morelli, Giacomo</style></author><author><style face="normal" font="default" size="100%">Petrella, Lea</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multivariate Analysis of Energy Commodities during the COVID-19 Pandemic: Evidence from a Mixed-Frequency Approach</style></title><secondary-title><style face="normal" font="default" size="100%">Risks</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.mdpi.com/2227-9091/9/8/144</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">144</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mila Andreani</style></author><author><style face="normal" font="default" size="100%">Vincenzo Candila</style></author><author><style face="normal" font="default" size="100%">Lea Petrella</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quantile Regression Forest with mixed-frequency data</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2021</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://it.pearson.com//docenti/universita/partnership/sis.html</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Pearson</style></publisher><volume><style face="normal" font="default" size="100%">Book of Short Papers SIS 2021</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">V. Macchiati</style></author><author><style face="normal" font="default" size="100%">G. Brandi</style></author><author><style face="normal" font="default" size="100%">T. Di Matteo</style></author><author><style face="normal" font="default" size="100%">D. Paolotti</style></author><author><style face="normal" font="default" size="100%">G. Caldarelli</style></author><author><style face="normal" font="default" size="100%">G. Cimini</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Systemic liquidity contagion in the European interbank market</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Economic Interaction and Coordination</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Epidemic model</style></keyword><keyword><style  face="normal" font="default" size="100%">European Interbank market</style></keyword><keyword><style  face="normal" font="default" size="100%">Financial contagion</style></keyword><keyword><style  face="normal" font="default" size="100%">Liquidity shocks</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cecilia Panigutti</style></author><author><style face="normal" font="default" size="100%">Perotti, Alan</style></author><author><style face="normal" font="default" size="100%">Pedreschi, Dino</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Doctor XAI: an ontology-based approach to black-box sequential data classification explanations</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://dl.acm.org/doi/abs/10.1145/3351095.3372855</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Viviane Dib</style></author><author><style face="normal" font="default" size="100%">Marco Aurélio Nalon</style></author><author><style face="normal" font="default" size="100%">Nino Tavares Amazonas</style></author><author><style face="normal" font="default" size="100%">Cristina Yuri Vidal</style></author><author><style face="normal" font="default" size="100%">Iván A. Ortiz-Rodríguez</style></author><author><style face="normal" font="default" size="100%">Jan Daněk</style></author><author><style face="normal" font="default" size="100%">Maíra Formis de Oliveira</style></author><author><style face="normal" font="default" size="100%">Paola Alberti</style></author><author><style face="normal" font="default" size="100%">Rafaela Aparecida da Silva</style></author><author><style face="normal" font="default" size="100%">Raíza Salomão Precinoto</style></author><author><style face="normal" font="default" size="100%">Taciana Figueiredo Gomes</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Drivers of change in biodiversity and ecosystem services in the Cantareira System Protected Area : A prospective analysis of the implementation of public policies</style></title><secondary-title><style face="normal" font="default" size="100%">Biota Neotropica</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Biodiversity</style></keyword><keyword><style  face="normal" font="default" size="100%">Cantareira System Protected Area</style></keyword><keyword><style  face="normal" font="default" size="100%">Ecosystem services</style></keyword><keyword><style  face="normal" font="default" size="100%">GLOBIO</style></keyword><keyword><style  face="normal" font="default" size="100%">InVEST</style></keyword><keyword><style  face="normal" font="default" size="100%">Modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">Scenarios</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2020</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.scielo.br/scielo.php?script=sci_arttext&amp;pid=S1676-06032020000500201</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">20</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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 “Atlantic Forest Law” (Law n.11.428/2006) and the “Forest Code” (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’ 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 “business-as-usual” 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.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mila Andreani</style></author><author><style face="normal" font="default" size="100%">Lea Petrella</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dynamic Quantile Regression Forest</style></title><secondary-title><style face="normal" font="default" size="100%">SIS 2020 - 50th Conference of the Italian Statistical Society</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2020</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://it.pearson.com/content/dam/region-core/italy/pearson-italy/pdf/Docenti/Universit%C3%A0/Pearson-SIS-2020-atti-convegno.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Pearson</style></publisher><volume><style face="normal" font="default" size="100%">Book of Short Papers SIS 2020</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vasiliki Voukelatou</style></author><author><style face="normal" font="default" size="100%">Gabrielli, Lorenzo</style></author><author><style face="normal" font="default" size="100%">Miliou, Ioanna</style></author><author><style face="normal" font="default" size="100%">Cresci, Stefano</style></author><author><style face="normal" font="default" size="100%">Sharma, Rajesh</style></author><author><style face="normal" font="default" size="100%">Tesconi, Maurizio</style></author><author><style face="normal" font="default" size="100%">Pappalardo, Luca</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Measuring objective and subjective well-being: dimensions and data sources</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Data Science and Analytics (JDSA)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Silvia Corbara</style></author><author><style face="normal" font="default" size="100%">Moreo, Alejandro</style></author><author><style face="normal" font="default" size="100%">Sebastiani, Fabrizio</style></author><author><style face="normal" font="default" size="100%">Tavoni, Mirko</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Cristani, Marco</style></author><author><style face="normal" font="default" size="100%">Prati, Andrea</style></author><author><style face="normal" font="default" size="100%">Lanz, Oswald</style></author><author><style face="normal" font="default" size="100%">Messelodi, Stefano</style></author><author><style face="normal" font="default" size="100%">Sebe, Nicu</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">The Epistle to Cangrande Through the Lens of Computational Authorship Verification</style></title><secondary-title><style face="normal" font="default" size="100%">New Trends in Image Analysis and Processing – ICIAP 2019</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><isbn><style face="normal" font="default" size="100%">978-3-030-30754-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cecilia Panigutti</style></author><author><style face="normal" font="default" size="100%">Guidotti, Riccardo</style></author><author><style face="normal" font="default" size="100%">Monreale, Anna</style></author><author><style face="normal" font="default" size="100%">Pedreschi, Dino</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Explaining multi-label black-box classifiers for health applications</style></title><secondary-title><style face="normal" font="default" size="100%">International Workshop on Health Intelligence</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Explainable Machine Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Healthcare</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-3-030-24409-5_9</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Today the state-of-the-art performance in classification is achieved by the so-called “black boxes”, 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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">D. Di Gangi</style></author><author><style face="normal" font="default" size="100%">D. R. Lo Sardo</style></author><author><style face="normal" font="default" size="100%">V. Macchiati</style></author><author><style face="normal" font="default" size="100%">T. P. Minh</style></author><author><style face="normal" font="default" size="100%">F. Pinotti</style></author><author><style face="normal" font="default" size="100%">A. Ramadiah</style></author><author><style face="normal" font="default" size="100%">M. Wilinski</style></author><author><style face="normal" font="default" size="100%">P. Barucca</style></author><author><style face="normal" font="default" size="100%">G. Cimini</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Network Sensitivity of Systemic Risk</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Network Theory in Finance</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Baltakiene, M</style></author><author><style face="normal" font="default" size="100%">Baltakys, K</style></author><author><style face="normal" font="default" size="100%">Cardamone, D</style></author><author><style face="normal" font="default" size="100%">Parisi, F</style></author><author><style face="normal" font="default" size="100%">Tommaso Radicioni</style></author><author><style face="normal" font="default" size="100%">Torricelli, M</style></author><author><style face="normal" font="default" size="100%">de Jeude, JA</style></author><author><style face="normal" font="default" size="100%">Saracco, F</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Maximum entropy approach to link prediction in bipartite networks</style></title><secondary-title><style face="normal" font="default" size="100%">arXiv preprint arXiv:1805.04307</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cecilia Panigutti</style></author><author><style face="normal" font="default" size="100%">Tizzoni, Michele</style></author><author><style face="normal" font="default" size="100%">Bajardi, Paolo</style></author><author><style face="normal" font="default" size="100%">Smoreda, Zbigniew</style></author><author><style face="normal" font="default" size="100%">Colizza, Vittoria</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models</style></title><secondary-title><style face="normal" font="default" size="100%">Royal Society open science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://royalsocietypublishing.org/doi/full/10.1098/rsos.160950</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">160950</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">N. Atanov</style></author><author><style face="normal" font="default" size="100%">V. Baranov</style></author><author><style face="normal" font="default" size="100%">J. Budagov</style></author><author><style face="normal" font="default" size="100%">F. Cervelli</style></author><author><style face="normal" font="default" size="100%">F. Colao</style></author><author><style face="normal" font="default" size="100%">M. Cordelli</style></author><author><style face="normal" font="default" size="100%">G. Corradi</style></author><author><style face="normal" font="default" size="100%">E. Dané</style></author><author><style face="normal" font="default" size="100%">Y.I. Davydov</style></author><author><style face="normal" font="default" size="100%">S. Di Falco</style></author><author><style face="normal" font="default" size="100%">E. Diociaiuti</style></author><author><style face="normal" font="default" size="100%">S. Donati</style></author><author><style face="normal" font="default" size="100%">R. Donghia</style></author><author><style face="normal" font="default" size="100%">B. Echenard</style></author><author><style face="normal" font="default" size="100%">K. Flood</style></author><author><style face="normal" font="default" size="100%">S. Giovannella</style></author><author><style face="normal" font="default" size="100%">V. Glagolev</style></author><author><style face="normal" font="default" size="100%">F. Grancagnolo</style></author><author><style face="normal" font="default" size="100%">F. Happacher</style></author><author><style face="normal" font="default" size="100%">D.G. Hitlin</style></author><author><style face="normal" font="default" size="100%">M. Martini</style></author><author><style face="normal" font="default" size="100%">S. Miscetti</style></author><author><style face="normal" font="default" size="100%">T. Miyashita</style></author><author><style face="normal" font="default" size="100%">L. Morescalchi</style></author><author><style face="normal" font="default" size="100%">P. Murat</style></author><author><style face="normal" font="default" size="100%">G. Pezzullo</style></author><author><style face="normal" font="default" size="100%">F. Porter</style></author><author><style face="normal" font="default" size="100%">F. Raffaelli</style></author><author><style face="normal" font="default" size="100%">Tommaso Radicioni</style></author><author><style face="normal" font="default" size="100%">M. Ricci</style></author><author><style face="normal" font="default" size="100%">A. Saputi</style></author><author><style face="normal" font="default" size="100%">I. Sarra</style></author><author><style face="normal" font="default" size="100%">F. Spinella</style></author><author><style face="normal" font="default" size="100%">G. Tassielli</style></author><author><style face="normal" font="default" size="100%">V. Tereshchenko</style></author><author><style face="normal" font="default" size="100%">Z. Usubov</style></author><author><style face="normal" font="default" size="100%">R.Y. Zhu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The calorimeter of the Mu2e experiment at Fermilab</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Instrumentation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1088%2F1748-0221%2F12%2F01%2Fc01061</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">12</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>