Zobrazeno 1 - 10
of 3 440
pro vyhledávání: '"Franch, À."'
Autor:
Hernández-Monteagudo, C., Aricò, G., Chaves-Montero, J., Abramo, L. R., Arnalte-Mur, P., Hernán-Caballero, A., López-Sanjuan, C., Marra, V., von Marttens, R., Tempel, E., Cenarro, J., Cristóbal-Hornillos, D., Marín-Franch, A., Moles, M., Varela, J., Ramió, H. Vázquez, Alcaniz, J., Dupke, R., Ederoclite, A., Sodré Jr., L., Angulo, R. E.
Observational and/or astrophysical systematics modulating the observed number of luminous tracers can constitute a major limitation in the cosmological exploitation of surveys of the large scale structure of the universe. Part of this limitation aris
Externí odkaz:
http://arxiv.org/abs/2412.14827
Autor:
Hernández-Monteagudo, C., Balaguera-Antolínez, A., von Marttens, R., del Pino, A., Hernán-Caballero, A., Abramo, L. R., Chaves-Montero, J., López-Sanjuan, C., Marra, V., Tempel, E., Aricò, G., Cenarro, J., Cristóbal-Hornillos, D., Marín-Franch, A., Moles, M., Varela, J., Ramió, H. Vázquez, Alcaniz, J., Dupke, R., Ederoclite, A., Sodré Jr., L., Angulo, R. E.
The {\it Javalambre Photometric Local Universe Survey} (J-PLUS) is a {\it spectro-photometric} survey covering about 3,000~deg$^2$ in its third data release (DR3), and containing about 300,000 galaxies with high quality ({\it odds}$>0.8$) photometric
Externí odkaz:
http://arxiv.org/abs/2412.14826
Autor:
Ahyoune, S., Altenmueller, K., Antolin, I., Basso, S., Brun, P., Candon, F. R., Castel, J. F., Cebrian, S., Chouhan, D., Della Ceca, R., Cervera-Cortes, M., Chernov, V., Civitani, M. M., Cogollos, C., Costa, E., Cotroneo, V., Dafni, T., Derbin, A., Desch, K., Diaz-Martin, M. C., Diaz-Morcillo, A., Diez-Ibanez, D., Pardos, C. Diez, Dinter, M., Doebrich, B., Drachnev, I., Dudarev, A., Ezquerro, A., Fabiani, S., Ferrer-Ribas, E., Finelli, F., Fleck, I., Galan, J., Galanti, G., Galaverni, M., Garcia, J. A., Garcia-Barcelo, J. M., Gastaldo, L., Giannotti, M., Giganon, A., Goblin, C., Goyal, N., Gu, Y., Hagge, L., Helary, L., Hengstler, D., Heuchel, D., Hoof, S., Iglesias-Marzoa, R., Iguaz, F. J., Iniguez, C., Irastorza, I. G., Jakovcic, K., Kaefer, D., Kaminski, J., Karstensen, S., Law, M., Lindner, A., Loidl, M., Loiseau, C., Lopez-Alegre, G., Lozano-Guerrero, A., Lubsandorzhiev, B., Luzon, G., Manthos, I., Margalejo, C., Marin-Franch, A., Marques, J., Marutzky, F., Menneglier, C., Mentink, M., Mertens, S., Miralda-Escude, J., Mirallas, H., Muleri, F., Muratova, V., Navarro-Madrid, J. R., Navick, X. F., Nikolopoulos, K., Notari, A., Nozik, A., Obis, L., Ortiz-de-Solorzano, A., O'Shea, T., von Oy, J., Pareschi, G., Papaevangelou, T., Perez, K., Perez, O., Picatoste, E., Pivovaroff, M. J., Porron, J., Puyuelo, M. J., Quintana, A., Redondo, J., Reuther, D., Ringwald, A., Rodrigues, M., Rubini, A., Rueda-Teruel, S., Rueda-Teruel, F., Ruiz-Choliz, E., Ruz, J., Schaffran, J., Schiffer, T., Schmidt, S., Schneekloth, U., Schoenfeld, L., Schott, M., Segui, L., Singh, U. R., Soffitta, P., Spiga, D., Stern, M., Straniero, O., Tavecchio, F., Unzhakov, E., Ushakov, N. A., Vecchi, G., Vogel, J. K., Voronin, D. M., Ward, R., Weltman, A., Wiesinger, C., Wolf, R., Yanes-Diaz, A., Yu, Y.
BabyIAXO is the intermediate stage of the International Axion Observatory (IAXO) to be hosted at DESY. Its primary goal is the detection of solar axions following the axion helioscope technique. Axions are converted into photons in a large magnet tha
Externí odkaz:
http://arxiv.org/abs/2411.13915
Autor:
de Martino, Vincenzo, Castaño, Joel, Palomba, Fabio, Franch, Xavier, Martínez-Fernández, Silverio
Context: The emergence of Large Language Models (LLMs) has significantly transformed Software Engineering (SE) by providing innovative methods for analyzing software repositories. Objectives: Our objective is to establish a practical framework for fu
Externí odkaz:
http://arxiv.org/abs/2411.09974
Background: Open-Source Pre-Trained Models (PTMs) and datasets provide extensive resources for various Machine Learning (ML) tasks, yet these resources lack a classification tailored to Software Engineering (SE) needs. Aims: We apply an SE-oriented c
Externí odkaz:
http://arxiv.org/abs/2411.09683
Background: Given the fast-paced nature of today's technology, which has surpassed human performance in tasks like image classification, visual reasoning, and English understanding, assessing the impact of Machine Learning (ML) on energy consumption
Externí odkaz:
http://arxiv.org/abs/2409.12878
Autor:
Huang, Yang, Beers, Timothy C., Xiao, Kai, Yuan, Haibo, Lee, Young Sun, Gu, Hongrui, Hong, Jihye, Liu, Jifeng, Fan, Zhou, Coelho, Paula, Cruz, Patricia, Galindo-Guil, F. J., Daflon, Simone, Jiménez-Esteban, Fran, Cenarro, Javier, Cristóbal-Hornillos, David, Hernández-Monteagudo, Carlos, López-Sanjuan, Carlos, Marín-Franch, Antonio, Moles, Mariano, Varela, Jesús, Ramírez, Héctor Vázquez, Alcaniz, Jailson, Dupke, Renato, Ederoclite, Alessandro, Sodré Jr., Laerte, Angulo, Raul E.
We present a catalog of stellar parameters (effective temperature $T_{\rm eff}$, surface gravity $\log g$, age, and metallicity [Fe/H]) and elemental-abundance ratios ([C/Fe], [Mg/Fe], and [$\alpha$/Fe]) for some five million stars (4.5 million dwarf
Externí odkaz:
http://arxiv.org/abs/2408.02171
Mobile app review analysis presents unique challenges due to the low quality, subjective bias, and noisy content of user-generated documents. Extracting features from these reviews is essential for tasks such as feature prioritization and sentiment a
Externí odkaz:
http://arxiv.org/abs/2408.01063
Autor:
Omar, Rafiullah, Bogner, Justus, Muccini, Henry, Lago, Patricia, Martínez-Fernández, Silverio, Franch, Xavier
Background: Machine learning (ML) model composition is a popular technique to mitigate shortcomings of a single ML model and to design more effective ML-enabled systems. While ensemble learning, i.e., forwarding the same request to several models and
Externí odkaz:
http://arxiv.org/abs/2407.02914
Autor:
Franch, Gabriele, Tomasi, Elena, Wanjari, Rishabh, Poli, Virginia, Cardinali, Chiara, Alberoni, Pier Paolo, Cristoforetti, Marco
This work introduces GPTCast, a generative deep-learning method for ensemble nowcast of radar-based precipitation, inspired by advancements in large language models (LLMs). We employ a GPT model as a forecaster to learn spatiotemporal precipitation d
Externí odkaz:
http://arxiv.org/abs/2407.02089