<?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%">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>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alina Sirbu</style></author><author><style face="normal" font="default" size="100%">Andrienko, Gennady</style></author><author><style face="normal" font="default" size="100%">Andrienko, Natalia</style></author><author><style face="normal" font="default" size="100%">Boldrini, Chiara</style></author><author><style face="normal" font="default" size="100%">Conti, Marco</style></author><author><style face="normal" font="default" size="100%">Giannotti, Fosca</style></author><author><style face="normal" font="default" size="100%">Guidotti, Riccardo</style></author><author><style face="normal" font="default" size="100%">Bertoli, Simone</style></author><author><style face="normal" font="default" size="100%">Jisu Kim</style></author><author><style face="normal" font="default" size="100%">Muntean, Cristina Ioana</style></author><author><style face="normal" font="default" size="100%">others</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Human migration: the big data perspective</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Data Science and Analytics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><pages><style face="normal" font="default" size="100%">1–20</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</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%">Agnese Bonavita</style></author><author><style face="normal" font="default" size="100%">Guidotti, Riccardo</style></author><author><style face="normal" font="default" size="100%">Nanni,Mirco</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Self-Adapting Trajectory Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">3rd International Workshop on Big Mobility Data Analytics (BMDA)  2020</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Mobility Data Mining</style></keyword><keyword><style  face="normal" font="default" size="100%">Segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">User Modeling</style></keyword></keywords><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%">Identifying the portions of trajectory data where movement ends and a significant stop starts is a basic, yet fundamental task that can affect the quality of any mobility analytics process. Most of the many existing solutions adopted by researchers and practitioners are simply based on fixed spatial and temporal thresholds stating when the moving object remained still for a significant amount of time, yet such thresholds remain as static parameters for the user to guess. In this work we study the trajectory segmentation from a multi-granularity perspective, looking for a better understanding of the problem and for an automatic, parameter-free and user-adaptive solution that flexibly adjusts the segmentation criteria to the specific user under study. Experiments over real data and comparison against simple competitors show that the flexibility of the proposed method has a positive impact on results.</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%">Giacomo Lanciano</style></author><author><style face="normal" font="default" size="100%">Antonio Ritacco</style></author><author><style face="normal" font="default" size="100%">Tommaso Cucinotta</style></author><author><style face="normal" font="default" size="100%">Marco Vannucci</style></author><author><style face="normal" font="default" size="100%">Antonino Artale</style></author><author><style face="normal" font="default" size="100%">Luca Basili</style></author><author><style face="normal" font="default" size="100%">Enrica Sposato</style></author><author><style face="normal" font="default" size="100%">Joao Barata</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SOM-Based Behavioral Analysis for Virtualized Network Functions</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 35th Annual ACM Symposium on Applied Computing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">network function virtualization</style></keyword><keyword><style  face="normal" font="default" size="100%">self-organizing maps</style></keyword></keywords><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://doi.org/10.1145/3341105.3374110</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Association for Computing Machinery</style></publisher><pub-location><style face="normal" font="default" size="100%">New York, NY, USA</style></pub-location><isbn><style face="normal" font="default" size="100%">9781450368667</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</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%">Iván A. Ortiz-Rodríguez</style></author><author><style face="normal" font="default" size="100%">Jose Raventós</style></author><author><style face="normal" font="default" size="100%">Ernesto Mújica</style></author><author><style face="normal" font="default" size="100%">Elaine González-Hernández</style></author><author><style face="normal" font="default" size="100%">Ernesto Vega-Peña</style></author><author><style face="normal" font="default" size="100%">Pilar Ortega-Larrocea</style></author><author><style face="normal" font="default" size="100%">Andreu Bonet</style></author><author><style face="normal" font="default" size="100%">Cory Merow</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatiotemporal effects of Hurricane Ivan on an endemic epiphytic orchid: 10 years of follow-up</style></title><secondary-title><style face="normal" font="default" size="100%">Plant Ecology &amp; Diversity </style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Caribbean</style></keyword><keyword><style  face="normal" font="default" size="100%">cyclones</style></keyword><keyword><style  face="normal" font="default" size="100%">integral projection models</style></keyword><keyword><style  face="normal" font="default" size="100%">management strategies</style></keyword><keyword><style  face="normal" font="default" size="100%">plant population dynamics</style></keyword><keyword><style  face="normal" font="default" size="100%">stochastic growth rate</style></keyword><keyword><style  face="normal" font="default" size="100%">transfer functions</style></keyword><keyword><style  face="normal" font="default" size="100%">transient behaviour</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%">10/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.tandfonline.com/doi/full/10.1080/17550874.2019.1673495</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">13, 2020</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Background: Hurricanes have a strong influence on the ecological dynamics and structure of tropical forests. Orchid populations are especially vulnerable to these perturbations due to their canopy exposure and lack of underground storage organs and seed banks.

Aims: We evaluated the effects of Hurricane Ivan on the population of the endemic epiphytic orchid Encyclia bocourtii to propose a management strategy.

Methods: Using a pre- and post-hurricane dataset (2003–2013), we assessed the population asymptotic and transient dynamics. We also identified the individual size-stages that maximise population inertia and E. bocourtii’s spatial arrangement relative to phorophytes and other epiphytes.

Results: Hurricane Ivan severely affected the survival and growth of individuals of E. bocourtii, and caused an immediate decline of the population growth rate from λ = 1.05 to λ = 0.32, which was buffered by a population reactivity of ρ1 = 1.42. Our stochastic model predicted an annual population decrease of 14%. We found an aggregated spatial pattern between E. bocourtii and its host trees, and a random pattern relative to other epiphytes.

Conclusion: Our findings suggest that E. bocourtii is not safe from local extinction. We propose the propagation and reintroduction of reproductive specimens, the relocation of surviving individuals, and the establishment of new plantations of phorophytes.
</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>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%">Gorrell, Genevieve</style></author><author><style face="normal" font="default" size="100%">Bakir, Mehmet E</style></author><author><style face="normal" font="default" size="100%">Roberts, Ian</style></author><author><style face="normal" font="default" size="100%">Greenwood, Mark A</style></author><author><style face="normal" font="default" size="100%">Iavarone, Benedetta</style></author><author><style face="normal" font="default" size="100%">Bontcheva, Kalina</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Partisanship, propaganda and post-truth politics: Quantifying impact in online</style></title><secondary-title><style face="normal" font="default" size="100%">arXiv preprint arXiv:1902.01752</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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gorrell, Genevieve</style></author><author><style face="normal" font="default" size="100%">Roberts, Ian</style></author><author><style face="normal" font="default" size="100%">Greenwood, Mark A</style></author><author><style face="normal" font="default" size="100%">Bakir, Mehmet E</style></author><author><style face="normal" font="default" size="100%">Iavarone, Benedetta</style></author><author><style face="normal" font="default" size="100%">Bontcheva, Kalina</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quantifying media influence and partisan attention on Twitter during the UK EU referendum</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Social Informatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</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>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Agnese Bonavita</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Search for H-&gt; mu mu in the VBF production channel with the CMS experiment at LHC</style></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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brunato, Dominique</style></author><author><style face="normal" font="default" size="100%">De Mattei, Lorenzo</style></author><author><style face="normal" font="default" size="100%">Dell’Orletta, Felice</style></author><author><style face="normal" font="default" size="100%">Iavarone, Benedetta</style></author><author><style face="normal" font="default" size="100%">Venturi, Giulia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Is this Sentence Difficult? Do you Agree?</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</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>