A 'Symbolic' Representation of Object-Nets (Extended Version)

Autor: Köhler-Bussmeier, Michael, Capra, Lorenzo

In this contribution we extend the concept of a Petri net morphism to Elementary Object Systems (EOS). EOS are a nets-within-nets formalism, i.e. we allow the tokens of a Petri net to be Petri nets again. This nested structure has the consequence tha

Externí odkaz: http://arxiv.org/abs/2411.00149

Measurements of the Quantum Yield of Silicon using Geiger-mode Avalanching Photodetectors

Autor: Lewis, Harry, Mahtab, Mahsa, Retiere, Fabrice, Croix, Austin De St., Raymond, Kurtis, Henriksson-Ward, Maia, Morrison, Nicholas, Zhang, Aileen, Capra, Andrea, Underwood, Ryan

Accurate characterization of quantum yield is crucial to the reconstruction of energy depositions in silicon at the eV scale. This work presents a new method for experimentally calculating quantum yield using vacuum UV-sensitive silicon photomultipli

Externí odkaz: http://arxiv.org/abs/2410.13033

An independent evaluation of the Famine Early Warning Systems Network food security projections

Autor: Bertetti, Marco, Agnolucci, Paolo, Calzadilla, Alvaro, Capra, Licia

Reports from the Famine Early Warning Systems Network (FEWSNET) serve as the benchmark for food security predictions which is crucial for stakeholders in planning interventions and support people in need. This paper assesses the predictive accuracy o

Externí odkaz: http://arxiv.org/abs/2410.09384

Benchmarking the design of the cryogenics system for the underground argon in DarkSide-20k

Autor: Collaboration, DarkSide-20k, Acerbi, F., Adhikari, P., Agnes, P., Ahmad, I., Albergo, S., Albuquerque, I. F. M., Alexander, T., Alton, A. K., Amaudruz, P., Angiolilli, M., Aprile, E., Ardito, R., Corona, M. Atzori, Auty, D. J., Ave, M., Avetisov, I. C., Azzolini, O., Back, H. O., Balmforth, Z., Olmedo, A. Barrado, Barrillon, P., Batignani, G., Bhowmick, P., Blua, S., Bocci, V., Bonivento, W., Bottino, B., Boulay, M. G., Buchowicz, A., Bussino, S., Busto, J., Cadeddu, M., Cadoni, M., Calabrese, R., Camillo, V., Caminata, A., Canci, N., Capra, A., Caravati, M., Cárdenas-Montes, M., Cargioli, N., Carlini, M., Castellani, A., Castello, P., Cavalcante, P., Cebrian, S., Ruiz, J. Cela, Chashin, S., Chepurnov, A., Cifarelli, L., Cintas, D., Citterio, M., Cleveland, B., Coadou, Y., Cocco, V., Colaiuda, D., Vilda, E. Conde, Consiglio, L., Costa, B. S., Czubak, M., D'Aniello, M., D'Auria, S., Rolo, M. D. Da Rocha, Darbo, G., Davini, S., De Cecco, S., De Guido, G., Dellacasa, G., Derbin, A. V., Devoto, A., Di Capua, F., Di Ludovico, A., Di Noto, L., Di Stefano, P., Dias, L. K., Mairena, D. Díaz, Ding, X., Dionisi, C., Dolganov, G., Dordei, F., Dronik, V., Elersich, A., Ellingwood, E., Erjavec, T., Diaz, M. Fernandez, Ficorella, A., Fiorillo, G., Franchini, P., Franco, D., Gatti, H. Frandini, Frolov, E., Gabriele, F., Gahan, D., Galbiati, C., Galiński, G., Gallina, G., Gallus, G., Garbini, M., Abia, P. Garcia, Gawdzik, A., Gendotti, A., Ghisi, A., Giovanetti, G. K., Casanueva, V. Goicoechea, Gola, A., Grandi, L., Grauso, G., di Cortona, G. Grilli, Grobov, A., Gromov, M., Guerzoni, M., Gulino, M., Guo, C., Hackett, B. R., Hallin, A., Hamer, A., Haranczyk, M., Harrop, B., Hessel, T., Hill, S., Horikawa, S., Hu, J., Hubaut, F., Hucker, J., Hugues, T., Hungerford, E. V., Ianni, A., Ippolito, V., Jamil, A., Jillings, C., Jois, S., Kachru, P., Keloth, R., Kemmerich, N., Kemp, A., Kendziora, C. L., Kimura, M., Kish, A., Kondo, K., Korga, G., Kotsiopoulou, L., Koulosousas, S., Kubankin, A., Kunzé, P., Kuss, M., Kuźniak, M., Kuzwa, M., La Commara, M., Lai, M., Guirriec, E. Le, Leason, E., Leoni, A., Lidey, L., Lissia, M., Luzzi, L., Lychagina, O., Macfadyen, O., Machulin, I. N., Manecki, S., Manthos, I., Mapelli, L., Marasciulli, A., Mari, S. M., Mariani, C., Maricic, J., Martinez, M., Martoff, C. J., Matteucci, G., Mavrokoridis, K., McDonald, A. B., Mclaughlin, J., Merzi, S., Messina, A., Milincic, R., Minutoli, S., Mitra, A., Moharana, A., Moioli, S., Monroe, J., Moretti, E., Morrocchi, M., Mroz, T., Muratova, V. N., Murphy, M., Murra, M., Muscas, C., Musico, P., Nania, R., Nessi, M., Nieradka, G., Nikolopoulos, K., Nikoloudaki, E., Nowak, J., Olchanski, K., Oleinik, A., Oleynikov, V., Organtini, P., de Solórzano, A. Ortiz, Pallavicini, M., Pandola, L., Pantic, E., Paoloni, E., Papi, D., Pastuszak, G., Paternoster, G., Peck, A., Pegoraro, P. A., Pelczar, K., Pellegrini, L. A., Perez, R., Perotti, F., Pesudo, V., Piacentini, S. I., Pino, N., Plante, G., Pocar, A., Poehlmann, M., Pordes, S., Pralavorio, P., Price, D., Puglia, S., Bazetto, M. Queiroga, Ragusa, F., Ramachers, Y., Ramirez, A., Ravinthiran, S., Razeti, M., Renshaw, A. L., Rescigno, M., Retiere, F., Rignanese, L. P., Rivetti, A., Roberts, A., Roberts, C., Rogers, G., Romero, L., Rossi, M., Rubbia, A., Rudik, D., Sabia, M., Salomone, P., Samoylov, O., Sandford, E., Sanfilippo, S., Santone, D., Santorelli, R., Santos, E. M., Savarese, C., Scapparone, E., Schillaci, G., Schuckman II, F. G., Scioli, G., Semenov, D. A., Shalamova, V., Sheshukov, A., Simeone, M., Skensved, P., Skorokhvatov, M. D., Smirnov, O., Smirnova, T., Smith, B., Sotnikov, A., Spadoni, F., Spangenberg, M., Stefanizzi, R., Steri, A., Stornelli, V., Stracka, S., Sulis, S., Sung, A., Sunny, C., Suvorov, Y., Szelc, A. M., Taborda, O., Tartaglia, R., Taylor, A., Taylor, J., Tedesco, S., Testera, G., Thieme, K., Thompson, A., Thorpe, T. N., Tonazzo, A., Torres-Lara, S., Tricomi, A., Unzhakov, E. V., Vallivilayil, T. J., Van Uffelen, M., Velazquez-Fernandez, L., Viant, T., Viel, S., Vishneva, A., Vogelaar, R. B., Vossebeld, J., Vyas, B., Wada, M., Walczak, M. B., Wang, H., Wang, Y., Westerdale, S., Williams, L., Wojaczyński, R., Wojcik, M., Wojcik, M. M., Wright, T., Xiao, X., Xie, Y., Yang, C., Yin, J., Zabihi, A., Zakhary, P., Zani, A., Zhang, Y., Zhu, T., Zichichi, A., Zuzel, G., Zykova, M. P.

DarkSide-20k (DS-20k) is a dark matter detection experiment under construction at the Laboratori Nazionali del Gran Sasso (LNGS) in Italy. It utilises ~100 t of low radioactivity argon from an underground source (UAr) in its inner detector, with half

Externí odkaz: http://arxiv.org/abs/2408.14071

NLPGuard: A Framework for Mitigating the Use of Protected Attributes by NLP Classifiers

Autor: Greco, Salvatore, Zhou, Ke, Capra, Licia, Cerquitelli, Tania, Quercia, Daniele

AI regulations are expected to prohibit machine learning models from using sensitive attributes during training. However, the latest Natural Language Processing (NLP) classifiers, which rely on deep learning, operate as black-box systems, complicatin

Externí odkaz: http://arxiv.org/abs/2407.01697