<?xml version="1.0" encoding="UTF-8"?><xml><records><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%">Tripodi, Giorgio</style></author><author><style face="normal" font="default" size="100%">Chiaromonte, Francesca</style></author><author><style face="normal" font="default" size="100%">Lillo, Fabrizio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Knowledge and Social Relatedness Shape Research Portfolio Diversification</style></title><secondary-title><style face="normal" font="default" size="100%">Scientific Reports</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><volume><style face="normal" font="default" size="100%">10</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue><section><style face="normal" font="default" size="100%">14232</style></section></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%">Alejandro Moreo</style></author><author><style face="normal" font="default" size="100%">Fabrizio Sebastiani</style></author><author><style face="normal" font="default" size="100%">Mirko Tavoni</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Alberto Casadei</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">L’Epistola a Cangrande al vaglio della Computational Authorship Verification: risultati preliminari (con una postilla sulla cosiddetta “XIV Epistola di Dante Alighieri”)</style></title><secondary-title><style face="normal" font="default" size="100%">Nuove inchieste sull’epistola a Cangrande: atti della giornata di studi, Pisa 18 dicembre 2018</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><publisher><style face="normal" font="default" size="100%">Pisa University Press</style></publisher><isbn><style face="normal" font="default" size="100%">978-88-3339-333-9</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</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%">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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lamperti, Francesco</style></author><author><style face="normal" font="default" size="100%">Malerba, Franco</style></author><author><style face="normal" font="default" size="100%">Mavilia, Roberto</style></author><author><style face="normal" font="default" size="100%">Tripodi, Giorgio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Does the position in the inter-sectoral knowledge space affect the international competitiveness of industries?</style></title><secondary-title><style face="normal" font="default" size="100%">Economics of Innovation and New Technology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><pages><style face="normal" font="default" size="100%">1–48</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>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>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>