Zobrazeno 1 - 10
of 2 177
pro vyhledávání: '"FERRARI, SILVIA"'
Autor:
Chen, Yucheng, Zhu, Pingping, Alers, Anthony, Egner, Tobias, Sommer, Marc A., Ferrari, Silvia
Inferential decision-making algorithms typically assume that an underlying probabilistic model of decision alternatives and outcomes may be learned a priori or online. Furthermore, when applied to robots in real-world settings they often perform unsa
Externí odkaz:
http://arxiv.org/abs/2309.07720
We propose a novel method to generate underwater object imagery that is acoustically compliant with that generated by side-scan sonar using the Unreal Engine. We describe the process to develop, tune, and generate imagery to provide representative im
Externí odkaz:
http://arxiv.org/abs/2210.10267
Beta regression models are employed to model continuous response variables in the unit interval, like rates, percentages, or proportions. Their applications rise in several areas, such as medicine, environment research, finance, and natural sciences.
Externí odkaz:
http://arxiv.org/abs/2209.11315
Autor:
LeGrand, Keith A., Ferrari, Silvia
Publikováno v:
Journal of Advances in Information Fusion, Vol. 17, No. 2, December 2022
In object tracking and state estimation problems, ambiguous evidence such as imprecise measurements and the absence of detections can contain valuable information and thus be leveraged to further refine the probabilistic belief state. In particular,
Externí odkaz:
http://arxiv.org/abs/2207.11356
The main purpose of this paper is to introduce a new class of regression models for bounded continuous data, commonly encountered in applied research. The models, named the power logit regression models, assume that the response variable follows a di
Externí odkaz:
http://arxiv.org/abs/2202.01697
Autor:
Ferrari, Silvia Martina1 (AUTHOR) silvia.ferrari@unipi.it, Ragusa, Francesca2 (AUTHOR) francesca.ragusa@phd.unipi.it, Elia, Giusy2 (AUTHOR) giusy.elia@phd.unipi.it, Mazzi, Valeria2 (AUTHOR) mazzivaleria@gmail.com, Balestri, Eugenia2 (AUTHOR) eugenia.balestri@phd.unipi.it, Botrini, Chiara2 (AUTHOR) chiara.botrini@gmail.com, Rugani, Licia2 (AUTHOR) licia.rugani99@gmail.com, Patrizio, Armando3 (AUTHOR) armandopatrizio125@gmail.com, Piaggi, Simona4 (AUTHOR) simona.piaggi@unipi.it, La Motta, Concettina5 (AUTHOR) concettina.lamotta@unipi.it, Ulisse, Salvatore6 (AUTHOR) salvatore.ulisse@uniroma1.it, Virili, Camilla7 (AUTHOR) camilla.virili@uniroma1.it, Antonelli, Alessandro2 (AUTHOR) alessandro.antonelli@unipi.it, Fallahi, Poupak4 (AUTHOR) poupak.fallahi@unipi.it
Publikováno v:
International Journal of Molecular Sciences. Jun2024, Vol. 25 Issue 12, p6734. 17p.
Autor:
Erbuto, Denise, Luciano, Mario, Sampogna, Gaia, Abbate-Daga, Giovanni, Barlati, Stefano, Carmassi, Claudia, Castellini, Giovanni, De Fazio, Pasquale, Di Lorenzo, Giorgio, Di Nicola, Marco, Ferrari, Silvia, Goracci, Arianna, Gramaglia, Carla, Martinotti, Giovanni, Nanni, Maria Giulia, Pasquini, Massimo, Pinna, Federica, Poloni, Nicola, Serafini, Gianluca, Signorelli, Maria, Tortorella, Alfonso, Ventriglio, Antonio, Volpe, Umberto, Alacreu-Crespo, Adrián, Innamorati, Marco, Courtet, Philippe, Fiorillo, Andrea, Pompili, Maurizio
Publikováno v:
In Journal of Affective Disorders 1 August 2024 358:150-156
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, No. 6, 01 June 2023, 7195 - 7207
Information-driven control can be used to develop intelligent sensors that can optimize their measurement value based on environmental feedback. In object tracking applications, sensor actions are chosen based on the expected reduction in uncertainty
Externí odkaz:
http://arxiv.org/abs/2108.11236
Beta regression models are widely used for modeling continuous data limited to the unit interval, such as proportions, fractions, and rates. The inference for the parameters of beta regression models is commonly based on maximum likelihood estimation
Externí odkaz:
http://arxiv.org/abs/2010.11368
This paper presents a decentralized Gaussian Process (GP) learning, fusion, and planning (RESIN) formalism for mobile sensor networks to actively learn target motion models. RESIN is characterized by both computational and communication efficiency, a
Externí odkaz:
http://arxiv.org/abs/2009.06021