Fair CoVariance Neural Networks

Autor: Cavallo, Andrea, Navarro, Madeline, Segarra, Santiago, Isufi, Elvin

Covariance-based data processing is widespread across signal processing and machine learning applications due to its ability to model data interconnectivities and dependencies. However, harmful biases in the data may become encoded in the sample cova

Externí odkaz: http://arxiv.org/abs/2409.08558

ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain

Autor: Bernárdez, Guillermo, Telyatnikov, Lev, Montagna, Marco, Baccini, Federica, Papillon, Mathilde, Ferriol-Galmés, Miquel, Hajij, Mustafa, Papamarkou, Theodore, Bucarelli, Maria Sofia, Zaghen, Olga, Mathe, Johan, Myers, Audun, Mahan, Scott, Lillemark, Hansen, Vadgama, Sharvaree, Bekkers, Erik, Doster, Tim, Emerson, Tegan, Kvinge, Henry, Agate, Katrina, Ahmed, Nesreen K, Bai, Pengfei, Banf, Michael, Battiloro, Claudio, Beketov, Maxim, Bogdan, Paul, Carrasco, Martin, Cavallo, Andrea, Choi, Yun Young, Dasoulas, George, Elphick, Matouš, Escalona, Giordan, Filipiak, Dominik, Fritze, Halley, Gebhart, Thomas, Gil-Sorribes, Manel, Goomanee, Salvish, Guallar, Victor, Imasheva, Liliya, Irimia, Andrei, Jin, Hongwei, Johnson, Graham, Kanakaris, Nikos, Koloski, Boshko, Kovač, Veljko, Lecha, Manuel, Lee, Minho, Leroy, Pierrick, Long, Theodore, Magai, German, Martinez, Alvaro, Masden, Marissa, Mežnar, Sebastian, Miquel-Oliver, Bertran, Molina, Alexis, Nikitin, Alexander, Nurisso, Marco, Piekenbrock, Matt, Qin, Yu, Rygiel, Patryk, Salatiello, Alessandro, Schattauer, Max, Snopov, Pavel, Suk, Julian, Sánchez, Valentina, Tec, Mauricio, Vaccarino, Francesco, Verhellen, Jonas, Wantiez, Frederic, Weers, Alexander, Zajec, Patrik, Škrlj, Blaž, Miolane, Nina

This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM). The challenge focused on the problem

Externí odkaz: http://arxiv.org/abs/2409.05211

A new hybrid gadolinium nanoparticles-loaded polymeric material for neutron detection in rare event searches

Autor: Collaboration, DarkSide-20k, Acerbi, F., Adhikari, P., Agnes, P., Ahmad, I., Albergo, S., Albuquerque, I. F., 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., 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., Cavallo, D., 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'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., Galinski, G., Gallina, 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., Keloth, R., Kemmerich, N., Kemp, A., Kendziora, C. L., Kimura, M., Kondo, K., Korga, G., Kotsiopoulou, L., Koulosousas, S., Kubankin, A., Kuss, M., Kuzniak, 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., Marini, A., 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., Peddis, D., 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., Sadashivajois, S., 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., Sheshukov, A., Simeone, M., Skensved, P., Skorokhvatov, M. D., Slimani, S., 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., Tonazzo, A., Torres-Lara, S., Tosi, S., Tricomi, A., Unzhakov, E. V., Vallivilayil, T. J., Van Uffelen, M., Velazquez-Fernandez, L., Viant, T., Vicini, S., Viel, S., Vishneva, A., Vogelaar, R. B., Vossebeld, J., Vyas, B., Wada, M., Walczak, M. B., Wang, H., Wang, Y., Westerdale, S., Williams, L., Wojaczynski, R., Wojcik, M. M., Wojcik, M., Wright, T., Xie, Y., Yang, C., Yin, J., Zabihi, A., Zakhary, P., Zani, A., Zhang, Y., Zhu, T., Zichichi, A., Zuzel, G., Zykova, M. P.

Publikováno v: JINST 19 P09021 (2024)

Experiments aimed at direct searches for WIMP dark matter require highly effective reduction of backgrounds and control of any residual radioactive contamination. In particular, neutrons interacting with atomic nuclei represent an important class of

Externí odkaz: http://arxiv.org/abs/2404.18492

Denesting cubic radicals

Autor: Cavallo, Alberto

We study in details how and when the radical $\sqrt[3]{a+b\sqrt p}$ with rational numbers $a,b$ and $p$ positive can be simplified, providing a complete answer to the problem; furthermore, a program that computes the result is also made available. Th

Externí odkaz: http://arxiv.org/abs/2403.04776

Automating Boundary Filling in Cubical Agda

Autor: Doré, Maximilian, Cavallo, Evan, Mörtberg, Anders

When working in a proof assistant, automation is key to discharging routine proof goals such as equations between algebraic expressions. Homotopy Type Theory allows the user to reason about higher structures, such as topological spaces, using higher

Externí odkaz: http://arxiv.org/abs/2402.12169