Zobrazeno 1 - 10
of 410
pro vyhledávání: '"Reyes-González P"'
Autor:
Amram, Oz, Anzalone, Luca, Birk, Joschka, Faroughy, Darius A., Hallin, Anna, Kasieczka, Gregor, Krämer, Michael, Pang, Ian, Reyes-Gonzalez, Humberto, Shih, David
Foundation models are deep learning models pre-trained on large amounts of data which are capable of generalizing to multiple datasets and/or downstream tasks. This work demonstrates how data collected by the CMS experiment at the Large Hadron Collid
Externí odkaz:
http://arxiv.org/abs/2412.10504
Autor:
Geuskens, Joep, Gite, Nishank, Krämer, Michael, Mikuni, Vinicius, Mück, Alexander, Nachman, Benjamin, Reyes-González, Humberto
Identifying the origin of high-energy hadronic jets ('jet tagging') has been a critical benchmark problem for machine learning in particle physics. Jets are ubiquitous at colliders and are complex objects that serve as prototypical examples of collec
Externí odkaz:
http://arxiv.org/abs/2411.02628
Autor:
Krause, Claudius, Giannelli, Michele Faucci, Kasieczka, Gregor, Nachman, Benjamin, Salamani, Dalila, Shih, David, Zaborowska, Anna, Amram, Oz, Borras, Kerstin, Buckley, Matthew R., Buhmann, Erik, Buss, Thorsten, Cardoso, Renato Paulo Da Costa, Caterini, Anthony L., Chernyavskaya, Nadezda, Corchia, Federico A. G., Cresswell, Jesse C., Diefenbacher, Sascha, Dreyer, Etienne, Ekambaram, Vijay, Eren, Engin, Ernst, Florian, Favaro, Luigi, Franchini, Matteo, Gaede, Frank, Gross, Eilam, Hsu, Shih-Chieh, Jaruskova, Kristina, Käch, Benno, Kalagnanam, Jayant, Kansal, Raghav, Kim, Taewoo, Kobylianskii, Dmitrii, Korol, Anatolii, Korcari, William, Krücker, Dirk, Krüger, Katja, Letizia, Marco, Li, Shu, Liu, Qibin, Liu, Xiulong, Loaiza-Ganem, Gabriel, Madula, Thandikire, McKeown, Peter, Melzer-Pellmann, Isabell-A., Mikuni, Vinicius, Nguyen, Nam, Ore, Ayodele, Schweitzer, Sofia Palacios, Pang, Ian, Pedro, Kevin, Plehn, Tilman, Pokorski, Witold, Qu, Huilin, Raikwar, Piyush, Raine, John A., Reyes-Gonzalez, Humberto, Rinaldi, Lorenzo, Ross, Brendan Leigh, Scham, Moritz A. W., Schnake, Simon, Shimmin, Chase, Shlizerman, Eli, Soybelman, Nathalie, Srivatsa, Mudhakar, Tsolaki, Kalliopi, Vallecorsa, Sofia, Yeo, Kyongmin, Zhang, Rui
We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality, ranging from a few hundred voxels to a few t
Externí odkaz:
http://arxiv.org/abs/2410.21611
Autor:
Araz, Jack Y., Buckley, Andy, Kasieczka, Gregor, Kieseler, Jan, Kraml, Sabine, Kvellestad, Anders, Lessa, Andre, Procter, Tomasz, Raklev, Are, Reyes-Gonzalez, Humberto, Rolbiecki, Krzysztof, Sekmen, Sezen, Unel, Gokhan
With the increasing usage of machine-learning in high-energy physics analyses, the publication of the trained models in a reusable form has become a crucial question for analysis preservation and reuse. The complexity of these models creates practica
Externí odkaz:
http://arxiv.org/abs/2312.14575
Publikováno v:
Serie Científica de la Universidad de las Ciencias Informáticas, Vol 17, Iss 11, Pp 65-75 (2024)
La necesidad de actualizar continuamente los planes de estudio en informática, especialmente en ciberseguridad, es crucial en la era digital. La investigación aborda el desarrollo de una estrategia que permita actualizar los planes de estudio en la
Externí odkaz:
https://doaj.org/article/8f729a65b7b344f7a0fa25dca6db7330
We propose the NFLikelihood, an unsupervised version, based on Normalizing Flows, of the DNNLikelihood proposed in Ref.[1]. We show, through realistic examples, how Autoregressive Flows, based on affine and rational quadratic spline bijectors, are ab
Externí odkaz:
http://arxiv.org/abs/2309.09743
Publikováno v:
Symmetry 2024, 16(8), 942
Normalizing Flows have emerged as a powerful brand of generative models, as they not only allow for efficient sampling of complicated target distributions, but also deliver density estimation by construction. We propose here an in-depth comparison of
Externí odkaz:
http://arxiv.org/abs/2302.12024
Autor:
Sofía Capasso, Manuel Parejo, José Manuel Reyes‐González, Juan G. Navedo, Ricardo Morán‐López, José A. Masero, Jorge S. Gutiérrez
Publikováno v:
Ecology and Evolution, Vol 14, Iss 11, Pp n/a-n/a (2024)
ABSTRACT Recent developments in microscopic and molecular tools have allowed the implementation of new approaches for assessing parasitic infections in wildlife populations. This is particularly important for the noninvasive detection and quantificat
Externí odkaz:
https://doaj.org/article/12c0bfa4aed94f9ebd813b06c2365a50
Autor:
Cresswell, Jesse C., Ross, Brendan Leigh, Loaiza-Ganem, Gabriel, Reyes-Gonzalez, Humberto, Letizia, Marco, Caterini, Anthony L.
Precision measurements and new physics searches at the Large Hadron Collider require efficient simulations of particle propagation and interactions within the detectors. The most computationally expensive simulations involve calorimeter showers. Adva
Externí odkaz:
http://arxiv.org/abs/2211.15380
Autor:
Karla Janeth Martínez-Macias, Aldo Rafael Martínez-Sifuentes, Selenne Yuridia Márquez-Guerrero, Arturo Reyes-González, Pablo Preciado-Rangel, Pablo Yescas-Coronado, Ramón Trucíos-Caciano
Publikováno v:
Nitrogen, Vol 5, Iss 3, Pp 598-609 (2024)
Nitrogen is one of the most important macronutrients for crops, and, in conjunction with artificial intelligence algorithms, it is possible to estimate it with the aid of vegetation indices through remote sensing. Various indices were calculated and
Externí odkaz:
https://doaj.org/article/783a6bb3ee8b4cad85d2d3a8033f7396