TY - JOUR T1 - Systemic liquidity contagion in the European interbank market JF - Journal of Economic Interaction and Coordination Y1 - 2021 A1 - V. Macchiati A1 - G. Brandi A1 - T. Di Matteo A1 - D. Paolotti A1 - G. Caldarelli A1 - G. Cimini KW - Epidemic model KW - European Interbank market KW - Financial contagion KW - Liquidity shocks ER - TY - JOUR T1 - Human migration: the big data perspective JF - International Journal of Data Science and Analytics Y1 - 2020 A1 - Alina Sirbu A1 - Andrienko, Gennady A1 - Andrienko, Natalia A1 - Boldrini, Chiara A1 - Conti, Marco A1 - Giannotti, Fosca A1 - Guidotti, Riccardo A1 - Bertoli, Simone A1 - Jisu Kim A1 - Muntean, Cristina Ioana A1 - others AB - 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. ER - TY - CONF T1 - Self-Adapting Trajectory Segmentation T2 - 3rd International Workshop on Big Mobility Data Analytics (BMDA) 2020 Y1 - 2020 A1 - Agnese Bonavita A1 - Guidotti, Riccardo A1 - Nanni,Mirco KW - Mobility Data Mining KW - Segmentation KW - User Modeling AB - 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. JF - 3rd International Workshop on Big Mobility Data Analytics (BMDA) 2020 ER - TY - CONF T1 - SOM-Based Behavioral Analysis for Virtualized Network Functions T2 - Proceedings of the 35th Annual ACM Symposium on Applied Computing Y1 - 2020 A1 - Giacomo Lanciano A1 - Antonio Ritacco A1 - Tommaso Cucinotta A1 - Marco Vannucci A1 - Antonino Artale A1 - Luca Basili A1 - Enrica Sposato A1 - Joao Barata KW - machine learning KW - network function virtualization KW - self-organizing maps AB - 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. JF - Proceedings of the 35th Annual ACM Symposium on Applied Computing PB - Association for Computing Machinery CY - New York, NY, USA SN - 9781450368667 UR - https://doi.org/10.1145/3341105.3374110 ER - TY - JOUR T1 - Spatiotemporal effects of Hurricane Ivan on an endemic epiphytic orchid: 10 years of follow-up JF - Plant Ecology & Diversity Y1 - 2020 A1 - Iván A. Ortiz-Rodríguez A1 - Jose Raventós A1 - Ernesto Mújica A1 - Elaine González-Hernández A1 - Ernesto Vega-Peña A1 - Pilar Ortega-Larrocea A1 - Andreu Bonet A1 - Cory Merow KW - Caribbean KW - cyclones KW - integral projection models KW - management strategies KW - plant population dynamics KW - stochastic growth rate KW - transfer functions KW - transient behaviour AB - 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. VL - 13, 2020 UR - https://www.tandfonline.com/doi/full/10.1080/17550874.2019.1673495 IS - 1 ER - TY - JOUR T1 - Network Sensitivity of Systemic Risk JF - Journal of Network Theory in Finance Y1 - 2019 A1 - D. Di Gangi A1 - D. R. Lo Sardo A1 - V. Macchiati A1 - T. P. Minh A1 - F. Pinotti A1 - A. Ramadiah A1 - M. Wilinski A1 - P. Barucca A1 - G. Cimini ER - TY - JOUR T1 - Partisanship, propaganda and post-truth politics: Quantifying impact in online JF - arXiv preprint arXiv:1902.01752 Y1 - 2019 A1 - Gorrell, Genevieve A1 - Bakir, Mehmet E A1 - Roberts, Ian A1 - Greenwood, Mark A A1 - Iavarone, Benedetta A1 - Bontcheva, Kalina ER - TY - JOUR T1 - Maximum entropy approach to link prediction in bipartite networks JF - arXiv preprint arXiv:1805.04307 Y1 - 2018 A1 - Baltakiene, M A1 - Baltakys, K A1 - Cardamone, D A1 - Parisi, F A1 - Tommaso Radicioni A1 - Torricelli, M A1 - de Jeude, JA A1 - Saracco, F ER - TY - CONF T1 - Quantifying media influence and partisan attention on Twitter during the UK EU referendum T2 - International Conference on Social Informatics Y1 - 2018 A1 - Gorrell, Genevieve A1 - Roberts, Ian A1 - Greenwood, Mark A A1 - Bakir, Mehmet E A1 - Iavarone, Benedetta A1 - Bontcheva, Kalina JF - International Conference on Social Informatics PB - Springer ER - TY - THES T1 - Search for H-> mu mu in the VBF production channel with the CMS experiment at LHC Y1 - 2018 A1 - Agnese Bonavita ER - TY - CONF T1 - Is this Sentence Difficult? Do you Agree? T2 - Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing Y1 - 2018 A1 - Brunato, Dominique A1 - De Mattei, Lorenzo A1 - Dell’Orletta, Felice A1 - Iavarone, Benedetta A1 - Venturi, Giulia JF - Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing ER - TY - JOUR T1 - Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models JF - Royal Society open science Y1 - 2017 A1 - Cecilia Panigutti A1 - Tizzoni, Michele A1 - Bajardi, Paolo A1 - Smoreda, Zbigniew A1 - Colizza, Vittoria AB - 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. VL - 4 UR - https://royalsocietypublishing.org/doi/full/10.1098/rsos.160950 ER - TY - JOUR T1 - The calorimeter of the Mu2e experiment at Fermilab JF - Journal of Instrumentation Y1 - 2017 A1 - N. Atanov A1 - V. Baranov A1 - J. Budagov A1 - F. Cervelli A1 - F. Colao A1 - M. Cordelli A1 - G. Corradi A1 - E. Dané A1 - Y.I. Davydov A1 - S. Di Falco A1 - E. Diociaiuti A1 - S. Donati A1 - R. Donghia A1 - B. Echenard A1 - K. Flood A1 - S. Giovannella A1 - V. Glagolev A1 - F. Grancagnolo A1 - F. Happacher A1 - D.G. Hitlin A1 - M. Martini A1 - S. Miscetti A1 - T. Miyashita A1 - L. Morescalchi A1 - P. Murat A1 - G. Pezzullo A1 - F. Porter A1 - F. Raffaelli A1 - Tommaso Radicioni A1 - M. Ricci A1 - A. Saputi A1 - I. Sarra A1 - F. Spinella A1 - G. Tassielli A1 - V. Tereshchenko A1 - Z. Usubov A1 - R.Y. Zhu AB - 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. VL - 12 UR - https://doi.org/10.1088%2F1748-0221%2F12%2F01%2Fc01061 ER -