@article {261,
	title = {Systemic liquidity contagion in the European interbank market},
	journal = {Journal of Economic Interaction and Coordination},
	year = {2021},
	keywords = {Epidemic model, European Interbank market, Financial contagion, Liquidity shocks},
	doi = {https://doi.org/10.1007/s11403-021-00338-1},
	author = {V. Macchiati and G. Brandi and T. Di Matteo and D. Paolotti and G. Caldarelli and G. Cimini}
}
@conference {254,
	title = {Self-Adapting Trajectory Segmentation},
	booktitle = {3rd International Workshop on Big Mobility Data Analytics (BMDA)  2020},
	year = {2020},
	abstract = {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.},
	keywords = {Mobility Data Mining, Segmentation, User Modeling},
	author = {Agnese Bonavita and Guidotti, Riccardo and Nanni,Mirco}
}
@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}
}
@mastersthesis {255,
	title = {Search for H-> mu mu in the VBF production channel with the CMS experiment at LHC},
	year = {2018},
	author = {Agnese Bonavita}
}
