Zobrazeno 1 - 10
of 53 669
pro vyhledávání: '"A Giraldo"'
Autor:
Lerendegui-Marco, J., Guerrero, C., Mendoza, E., Quesada, J. M., Eberhardt, K., Junghans, A. R., Alcayne, V., Babiano, V., Aberle, O., Andrzejewski, J., Audouin, L., Becares, V., Bacak, M., Balibrea-Correa, J., Barbagallo, M., Barros, S., Becvar, F., Beinrucker, C., Berthoumieux, E., Billowes, J., Bosnar, D., Brugger, M., Caamaño, M., Calviño, F., Calviani, M., Cano-Ott, D., Cardella, R., Casanovas, A., Castelluccio, D. M., Cerutti, F., Chen, Y. H., Chiaveri, E., Colonna, N., Cortés, G., Cortés-Giraldo, M. A., Cosentino, L., Damone, L. A., Diakaki, M., Dietz, M., Domingo-Pardo, C., Dressler, R., Dupont, E., Durán, I., Fernández-Domínguez, B., Ferrari, A., Ferreira, P., Finocchiaro, P., Furman, V., Göbel, K., García, A. R., Gawlik, A., Glodariu, T., Goncalves, I. F., González-Romero, E., Goverdovski, A., Griesmayer, E., Gunsing, F., Harada, H., Heftrich, T., Heinitz, S., Heyse, J., Jenkins, D. G., Jericha, E., Käppeler, F., Kadi, Y., Katabuchi, T., Kavrigin, P., Ketlerov, V., Khryachkov, V., Kimura, A., Kivel, N., Kokkoris, M., Krticka, M., Leal-Cidoncha, E., Lederer-Woods, C., Leeb, H., Meo, S. Lo, Lonsdale, S. J., Losito, R., Macina, D., Marganiec, J., Martínez, T., Massimi, C., Mastinu, P., Mastromarco, M., Matteucci, F., Maugeri, E. A., Mengoni, A., Milazzo, P. M., Mingrone, F., Mirea, M., Montesano, S., Musumarra, A., Nolte, R., Oprea, A., Patronis, N., Pavlik, A., Perkowski, J., Porras, J. I., Praena, J., Rajeev, K., Rauscher, T., Reifarth, R., Riego-Perez, A., Rout, P. C., Rubbia, C., Ryan, J. A., Sabaté-Gilarte, M., Saxena, A., Schillebeeckx, P., Schmidt, S., Schumann, D., Sedyshev, P., Smith, A. G., Stamatopoulos, A., Tagliente, G., Tain, J. L., Tarifeño-Saldivia, A., Tassan-Got, L., Tsinganis, A., Valenta, S., Vannini, G., Variale, V., Vaz, P., Ventura, A., Vlachoudis, V., Vlastou, R., Wallner, A., Warren, S., Weigand, M., Weiss, C., Wolf, C., Woods, P. J., Wright, T., Zugec, P., Collaboration, the n_TOF
The design of fast reactors burning MOX fuels requires accurate capture and fission cross sections. For the particular case of neutron capture on 242Pu, the NEA recommends that an accuracy of 8-12% should be achieved in the fast energy region (2 keV-
Externí odkaz:
http://arxiv.org/abs/2412.01332
Autor:
Balibrea-Correa, J., Babiano-Suarez, V., Lerendegui-Marco, J., Domingo-Pardo, C., Ladarescu, I., Tarifeño-Saldivia, A., de la Fuente-Rosales, G., Gameiro, B., Zaitseva, N., Alcayne, V., Cano-Ott, D., González-Romero, E., Martínez, T., Mendoza, E., de Rada, A. Pérez, del Olmo, J. Plaza, Sánchez-Caballero, A., Casanovas, A., Calviño, F., Valenta, S., Aberle, O., Altieri, S., Amaducci, S., Andrzejewski, J., Bacak, M., Beltrami, C., Bennett, S., Bernardes, A. P., Berthoumieux, E., Beyer, R., Boromiza, M., Bosnar, D., Caamaño, M., Calviani, M., Castelluccio, D. M., Cerutti, F., Cescutti, G., Chasapoglou, S., Chiaveri, E., Colombetti, P., Colonna, N., Camprini, P. Console, Cortés, G., Cortés-Giraldo, M. A., Cosentino, L., Cristallo, S., Dellmann, S., Di Castro, M., Di Maria, S., Diakaki, M., Dietz, M., Dressler, R., Dupont, E., Durán, I., Eleme, Z., Fargier, S., Fernández, B., Fernández-Domínguez, B., Finocchiaro, P., Fiore, S., Furman, V., García-Infantes, F., Gawlik-Ramikega, A., Gervino, G., Gilardoni, S., Guerrero, C., Gunsing, F., Gustavino, C., Heyse, J., Hillman, W., Jenkins, D. G., Jericha, E., Junghans, A., Kadi, Y., Kaperoni, K., Kaur, G., Kimura, A., Knapová, I., Kokkoris, M., Kopatch, Y., Krtìvcka, M., Kyritsis, N., Lederer-Woods, C., Lerner, G., Manna, A., Masi, A., Massimi, C., Mastinu, P., Mastromarco, M., Maugeri, E. A., Mazzone, A., Mengoni, A., Michalopoulou, V., Milazzo, P. M., Mucciola, R., Murtas, F., Musacchio-Gonzalez, E., Musumarra, A., Negret, A., Pérez-Maroto, P., Patronis, N., Pavón-Rodríguez, J. A., Pellegriti, M. G., Perkowski, J., Petrone, C., Pirovano, E., Pomp, S., Porras, I., Praena, J., Quesada, J. M., Reifarth, R., Rochman, D., Romanets, Y., Rubbia, C., Sabaté-Gilarte, M., Schillebeeckx, P., Schumann, D., Sekhar, A., Smith, A. G., Sosnin, N. V., Stamati, M. E., Sturniolo, A., Tagliente, G., Tarrío, D., Torres-Sánchez, P., Vagena, E., Variale, V., Vaz, P., Vecchio, G., Vescovi, D., Vlachoudis, V., Vlastou, R., Wallner, A., Woods, P. J., Wright, T., Zarrella, R., Zugec, P.
Challenging neutron-capture cross-section measurements of small cross sections and samples with a very limited number of atoms require high-flux time-of-flight facilities. In turn, such facilities need innovative detection setups that are fast, have
Externí odkaz:
http://arxiv.org/abs/2411.18969
Autor:
Duque, Edgar Mauricio Salazar, van der Holst, Bart, Vergara, Pedro P., Giraldo, Juan S., Nguyen, Phuong H., Van der Molen, Anne, Han, Slootweg
This paper presents the spherical lower dimensional representation for daily medium voltage load profiles, based on principal component analysis. The objective is to unify and simplify the tasks for (i) clustering visualisation, (ii) outlier detectio
Externí odkaz:
http://arxiv.org/abs/2411.14346
Autor:
Xie, Shifeng, Giraldo, Jhony H.
Graph representation learning (GRL) is a fundamental task in machine learning, aiming to encode high-dimensional graph-structured data into low-dimensional vectors. Self-supervised learning (SSL) methods are widely used in GRL because they can avoid
Externí odkaz:
http://arxiv.org/abs/2411.07150
Autor:
Giraldo, Jhony H., Einizade, Aref, Todorovic, Andjela, Castro-Correa, Jhon A., Badiey, Mohsen, Bouwmans, Thierry, Malliaros, Fragkiskos D.
Publikováno v:
IEEE Transactions on Signal and Information Processing over Networks, 2024
Graph Neural Networks (GNNs) have shown great promise in modeling relationships between nodes in a graph, but capturing higher-order relationships remains a challenge for large-scale networks. Previous studies have primarily attempted to utilize the
Externí odkaz:
http://arxiv.org/abs/2411.04570
Optical systems that combine nonlinearity with coupling between various subsystems offer a flexible platform for observing a diverse range of nonlinear dynamics. Furthermore, engineering tolerances are such that the subsystems can be identical to wit
Externí odkaz:
http://arxiv.org/abs/2410.23588
Tropical cyclones (TCs) are powerful, natural phenomena that can severely impact populations and infrastructure. Enhancing our understanding of the mechanisms driving their intensification is crucial for mitigating these impacts. To this end, researc
Externí odkaz:
http://arxiv.org/abs/2410.21607
Autor:
Giraldo, José, Llopart-Font, Martí, Peiró-Lilja, Alex, Armentano-Oller, Carme, Sant, Gerard, Külebi, Baybars
High-quality audio data is a critical prerequisite for training robust text-to-speech models, which often limits the use of opportunistic or crowdsourced datasets. This paper presents an approach to overcome this limitation by implementing a denoisin
Externí odkaz:
http://arxiv.org/abs/2410.13357
Autor:
Spadaro, Gabriele, Presta, Alberto, Tartaglione, Enzo, Giraldo, Jhony H., Grangetto, Marco, Fiandrotti, Attilio
While standardized codecs like JPEG and HEVC-intra represent the industry standard in image compression, neural Learned Image Compression (LIC) codecs represent a promising alternative. In detail, integrating attention mechanisms from Vision Transfor
Externí odkaz:
http://arxiv.org/abs/2410.02981
Autor:
Spadaro, Gabriele, Grangetto, Marco, Fiandrotti, Attilio, Tartaglione, Enzo, Giraldo, Jhony H.
In recent years, Graph Neural Networks (GNNs) have demonstrated strong adaptability to various real-world challenges, with architectures such as Vision GNN (ViG) achieving state-of-the-art performance in several computer vision tasks. However, their
Externí odkaz:
http://arxiv.org/abs/2410.00807