@article {260,
	title = {Self-supervised pre-training of CNNs for flatness defect classification in the steelworks industry},
	journal = {International Journal of Advances in Intelligent Informatics},
	volume = {6},
	year = {2020},
	pages = {13{\textendash}22},
	abstract = {Classification of surface defects in the steelworks industry plays a significant role in guaranteeing the quality of the products. From an industrial point of view, a serious concern is represented by the hot-rolled products shape defects and particularly those concerning the strip flatness. Flatness defects are typically divided into four sub-classes depending on which part of the strip is affected and the corresponding shape. In the context of this research, the primary objective is evaluating the improvements of exploiting the self-supervised learning paradigm for defects classification, taking advantage of unlabelled, real, steel strip flatness maps. Different pre-training methods are compared, as well as architectures, taking advantage of well-established neural subnetworks, such as Residual and Inception modules. A systematic approach in evaluating the different performances guarantees a formal verification of the self-supervised pre-training paradigms evaluated hereafter. In particular, pre-training neural networks with the EgoMotion meta-algorithm shows classification improvements over the AutoEncoder technique, which in turn is better performing than a Glorot weight initialization.},
	keywords = {CNN, Deep learning, Self-supervision, Steelworks},
	issn = {2548-3161},
	doi = {10.26555/ijain.v6i1.410},
	url = {http://ijain.org/index.php/IJAIN/article/view/410},
	author = {Filippo Galli and Antonio Ritacco and Giacomo Lanciano and Marco Vannocci and Valentina Colla and Marco Vannucci}
}
@conference {259,
	title = {SOM-Based Behavioral Analysis for Virtualized Network Functions},
	booktitle = {Proceedings of the 35th Annual ACM Symposium on Applied Computing},
	year = {2020},
	publisher = {Association for Computing Machinery},
	organization = {Association for Computing Machinery},
	address = {New York, NY, USA},
	abstract = {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.},
	keywords = {machine learning, network function virtualization, self-organizing maps},
	isbn = {9781450368667},
	doi = {10.1145/3341105.3374110},
	url = {https://doi.org/10.1145/3341105.3374110},
	author = {Giacomo Lanciano and Antonio Ritacco and Tommaso Cucinotta and Marco Vannucci and Antonino Artale and Luca Basili and Enrica Sposato and Joao Barata}
}
@article {272,
	title = {Spatiotemporal effects of Hurricane Ivan on an endemic epiphytic orchid: 10 years of follow-up},
	journal = {Plant Ecology \& Diversity },
	volume = {13, 2020},
	year = {2020},
	month = {10/2019},
	type = {Article},
	abstract = {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{\textendash}2013), we assessed the population asymptotic and transient dynamics. We also identified the individual size-stages that maximise population inertia and E. bocourtii{\textquoteright}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.
},
	keywords = {Caribbean, cyclones, integral projection models, management strategies, plant population dynamics, stochastic growth rate, transfer functions, transient behaviour},
	issn = {1755-0874},
	doi = {https://doi.org/10.1080/17550874.2019.1673495},
	url = {https://www.tandfonline.com/doi/full/10.1080/17550874.2019.1673495},
	author = {Iv{\'a}n A. Ortiz-Rodr{\'\i}guez and Jose Ravent{\'o}s and Ernesto M{\'u}jica and Elaine Gonz{\'a}lez-Hern{\'a}ndez and Ernesto Vega-Pe{\~n}a and Pilar Ortega-Larrocea and Andreu Bonet and Cory Merow}
}
@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 {268,
	title = {Partisanship, propaganda and post-truth politics: Quantifying impact in online},
	journal = {arXiv preprint arXiv:1902.01752},
	year = {2019},
	author = {Gorrell, Genevieve and Bakir, Mehmet E and Roberts, Ian and Greenwood, Mark A and Iavarone, Benedetta and Bontcheva, Kalina}
}
@article {249,
	title = {ECHO CHAMBERS E POLARIZZAZIONE Uno sguardo critico sulla diffusione dell{\textquoteright}informazione nei social network},
	journal = {The Lab{\textquoteright}s Quarterly},
	volume = {20},
	year = {2018},
	chapter = {173},
	abstract = {Understanding the algorithms that contribute to the formation of our daily reality requires an in-depth look at how information is disseminated in online social networks (OSN). In this article, we will observe how news propagation is restricted by the presence of virtual borders that limit the interaction between users. This phenomenon, known as "echo chamber" formation, has the effect of polarizing the public debate on conflicting positions. Inside an echo chamber, information is not conveyed through a horizontal exchange between users, but due to the presence of like or follower aggregators, called hubs. This analysis will be carried out considering a casestudy in two of the main OSNs: Facebook and Twitter. From the study of user interaction networks we will observe how the algorithmic choices made are crucial to the polarization of the debate around a topic of discussion.},
	url = {https://thelabsquarterly.files.wordpress.com/2019/04/2018.4-the-labs-quarterly-7.-costantino-carugno-tommaso-radicioni-1.pdf},
	author = {Costantino Carugno and Tommaso Radicioni}
}
@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}
}
@conference {269,
	title = {Quantifying media influence and partisan attention on Twitter during the UK EU referendum},
	booktitle = {International Conference on Social Informatics},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	author = {Gorrell, Genevieve and Roberts, Ian and Greenwood, Mark A and Bakir, Mehmet E and Iavarone, Benedetta and Bontcheva, Kalina}
}
@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}
}
@mastersthesis {250,
	title = {Calibration and performance optimization of the Electromagnetic Calorimeter in Mu2e},
	year = {2016},
	url = {https://etd.adm.unipi.it/theses/available/etd-09252016-171400/unrestricted/MasterThesis.pdf},
	author = {Tommaso Radicioni}
}
