Browsing by Author "Castellano, M."
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- ItemCHARACTERIZING FAINT GALAXIES IN THE REIONIZATION EPOCH : LBT CONFIRMS TWO L < 0.2 L-star SOURCES AT z=6.4 BEHIND THE CLASH/FRONTIER FIELDS CLUSTER MACS0717.5+3745(2014) Vanzella, E.; Fontana, A.; Zitrin, A.; Coe, D.; Bradley, L.; Postman, M.; Grazian, A.; Castellano, M.; Pentericci, L.; Infante Lira, Leopoldo
- ItemGenerating and comparing knowledge graphs of medical processes using pMineR(2017) Gatta, R.; Vallati, M.; Lenkowicz, J.; Rojas, E.; Damiani, A.; Sacchi, L.; De Bari, B.; Dagliati, A.; Fernandez-Llatas, C.; Montesi, M.; Marchetti, A.; Castellano, M.; Valentini, V.Process mining focuses on extracting knowledge, under the form of models, from data generated and stored in information systems. The analysis of generated models can provide useful insights to domain experts. In addition, models of processes can be used to test if a considered process complies with some given specifications. For these reasons, process mining is gaining significant importance in the healthcare domain, where the complexity and flexibility of processes makes extremely hard to evaluate and assess how patients have been treated. In this paper we describe how pMineR, an R library designed and developed for performing process mining in the medical domain, is currently exploited in Hospitals for supporting domain experts in the analysis of the extracted knowledge models. In its current release, pMineR can encode extracted processes under the form of directed graphs, which are easy to interpret and understand by experts of the domain. It also provides graphical comparison between different processes, allows to model the adherence to a given clinical guidelines and to estimate performance and the workload of the available resources in healthcare.
- ItempMineR: An innovative R library for performing process mining in medicine(2017) Gatta, R.; Lenkowicz, J.; Vallati, M.; Rojas, E.; Damiani, A.; Sacchi, L.; De Bari, B.; Dagliati, A.; Fernandez-Llatas, C.; Montesi, M.; Marchetti, A.; Castellano, M.; Valentini, V.Process Mining is an emerging discipline investigating tasks related with the automated identification of process models, given realworld data (Process Discovery). The analysis of such models can provide useful insights to domain experts. In addition, models of processes can be used to test if a given process complies (Conformance Checking) with specifications. For these capabilities, Process Mining is gaining importance and attention in healthcare. In this paper we introduce pMineR, an R library specifically designed for performing Process Mining in the medical domain, and supporting human experts by presenting processes in a human-readable way. © Springer International Publishing AG 2017.
- ItemThe Intergalactic medium transmission towards z>4 galaxies with VANDELS and the impact of dust attenuation(2020) Thomas, R.; Pentericci, L.; Le Fevre, O.; Zamorani, G.; Schaerer, D.; Amorin, R.; Castellano, M.; Carnall, A. C.; Cristiani, S.; Guaita, Lucía; Cullen, F.; Finkelstein, S. L.; Fontanot, F.; Hibon, P.; Hathi, N.; Fynbo, J. P. U.; Khusanova, Y.; Koekemoer, A. M.; McLeod, D.; McLure, R. J.; Marchi, F.; Pozzetti, L.; Saxena, A.; Talia, M.; Bolzonella, M.
- ItemThe VANDELS ESO public spectroscopic survey(2018) McLure, R. J.; Pentericci, L.; Cimatti, A.; Dunlop, J. S.; Elbaz, D.; Fontana, A.; Nandra, K.; Amorin, R.; Bolzonella, M.; Bongiorno, A.; Carnall, A. C.; Castellano, M.; Cirasuolo, M.; Cucciati, O.; Cullen, F.; De Barros, S.; Finkelstein,
- ItemThe VANDELS ESO public spectroscopic survey: Final data release of 2087 spectra and spectroscopic measurements(2021) Garilli, B.; McLure, R.; Pentericci, L.; Franzetti, P.; Gargiulo, A.; Carnall, A.; Cucciati, O.; Iovino, A.; Amorin, R.; Bolzonella, M.; Bongiorno, A.; Castellano, M.; Cimatti, A.; Cirasuolo, M.; Cullen, F.; Dunlop, J.; Elbaz, D.; Finkelstein, S.; Fontana, A.; Fontanot, F.; Fumana, M.; Guaita, L.; Hartley, W.; Jarvis, M.; Juneau, S.; Maccagni, D.; McLeod, D.; Nandra, K.; Pompei, E.; Pozzetti, L.; Scodeggio, M.; Talia, M.; Calabro, A.; Cresci, G.; Fynbo, J. P. U.; Hathi, N. P.; Hibon, P.; Koekemoer, A. M.; Magliocchetti, M.; Salvato, M.; Vietri, G.; Zamorani, G.; Almaini, O.; Balestra, I.; Bardelli, S.; Begley, R.; Brammer, G.; Bell, E. F.; Bowler, R. A. A.; Brusa, M.; Buitrago, F.; Caputi, C.; Cassata, P.; Charlot, S.; Citro, A.; Cristiani, S.; Curtis-Lake, E.; Dickinson, M.; Fazio, G.; Ferguson, H. C.; Fiore, F.; Franco, M.; Georgakakis, A.; Giavalisco, M.; Grazian, A.; Hamadouche, M.; Jung, I.; Kim, S.; Khusanova, Y.; Le Fevre, O.; Longhetti, M.; Lotz, J.; Mannucci, F.; Maltby, D.; Matsuoka, K.; Mendez-Hernandez, H.; Mendez-Abreu, J.; Mignoli, M.; Moresco, M.; Nonino, M.; Pannella, M.; Papovich, C.; Popesso, P.; Roberts-Borsani, G.; Rosario, D. J.; Saldana-Lopez, A.; Santini, P.; Saxena, A.; Schaerer, D.; Schreiber, C.; Stark, D.; Tasca, L. A. M.; Thomas, R.; Vanzella, E.; Wild, V.; Williams, C.; Zucca, E.VANDELS is an ESO Public Spectroscopic Survey designed to build a sample of high-signal-to-noise ratio, medium-resolution spectra of galaxies at redshifts between 1 and 6.5. Here we present the final Public Data Release of the VANDELS Survey, comprising 2087 redshift measurements. We provide a detailed description of sample selection, observations, and data reduction procedures. The final catalogue reaches a target selection completeness of 40% at i(AB)=25. The high signal-to-noise ratio of the spectra (above 7 in 80% of the spectra) and the dispersion of 2.5 angstrom allowed us to measure redshifts with high precision, the redshift measurement success rate reaching almost 100%. Together with the redshift catalogue and the reduced spectra, we also provide optical mid-infrared photometry and physical parameters derived through fitting the spectral energy distribution. The observed galaxy sample comprises both passive and star forming galaxies covering a stellar mass range of 8.3 < Log(M-*/M-circle dot) < 11.7.
- ItemThe VANDELS ESO public spectroscopic survey: Observations and first data release(2018) Pentericci, L.; McLure, R. J.; Garilli, B.; Cucciati, O.; Franzetti, P.; Iovino, A.; Amorin, R.; Bolzonella, M.; Bongiorno, A.; Carnall, A. C.; Castellano, M.; Cimatti, A.; Cirasuolo, M.; Cullen, F.; De Barros, S.; Dunlop, J. S.; Elbaz, D.
- ItemWhat role can process mining play in recurrent clinical guidelines issues? A position paper(2020) Gatta, R.; Vallati, M.; Fernández Llatas, C.; Martínez Millana, A.; Orini, S.; Sacchi, L.; Lenkowicz, J.; Marcos, M.; Muñoz Gama, Jorge; Cuendet, M. A.; de Bari, B.; Marco Ruiz, L.; Stefanini, A.; Valero Ramón, Z.; Michielin, O.; Lapinskas, T.; Montvila, A.; Martín, N.; Tavazzi, E.; Castellano, M.