Zobrazeno 1 - 10
of 6 488
pro vyhledávání: '"Lesage P."'
The increasing demand for data usage in wireless communications requires using wider bands in the spectrum, especially for backhaul links. Yet, allocations in the spectrum for non-communication systems inhibit merging bands to achieve wider bandwidth
Externí odkaz:
http://arxiv.org/abs/2411.11159
Autor:
Pallage, Julien, Scherrer, Bertrand, Naccache, Salma, Bélanger, Christophe, Lesage-Landry, Antoine
In this work, we present a new unsupervised anomaly (outlier) detection (AD) method using the sliced-Wasserstein metric. This filtering technique is conceptually interesting for MLOps pipelines deploying machine learning models in critical sectors, e
Externí odkaz:
http://arxiv.org/abs/2410.21712
Autor:
Kennea, Jamie A., Racusin, Judith L., Burns, Eric, Grefenstettte, Brian W., Hounsell, Rebekah A., Hui, C. Michelle, Kocevski, Daniel, Lazio, T. Joseph W., Lesage, Stephen, Pritchard, Tyler A., Tohuvavohu, Aaron, Tomsick, John A., Traore, David, Wilson-Hodge, Colleen A.
The Time-Domain And MultiMessenger (TDAMM) Communications Science Analysis Group (TDAMMCommSAG) was formulated to describe the unique technical challenges of communicating rapidly to and from NASA astrophysics missions studying the most variable, tra
Externí odkaz:
http://arxiv.org/abs/2410.03980
Autor:
Burns, Eric, Lesage, Stephen, Goldstein, Adam, Briggs, Michael S., Veres, Peter, Bala, Suman, de Barra, Cuan, Bissaldi, Elisabetta, Cleveland, William H, Giles, Misty M, Godwin, Matthew, Hristov, Boyan A., Hui, C. Michelle, Kocevski, Daniel, Mailyan, Bagrat, Malacaria, Christian, McBreen, Sheila, Preece, Robert, Roberts, Oliver J., Scotton, Lorenzo, von Kienlin, A., Wilson-Hodge, Colleen A., Wood, Joshua
The prompt spectra of gamma-ray bursts are known to follow broadband continuum behavior over decades in energy. GRB 221009A, given the moniker the brightest of all time (BOAT), is the brightest gamma-ray burst identified in half a century of observat
Externí odkaz:
http://arxiv.org/abs/2410.00286
Autor:
de Gennes, Marc, Lesage, Adrien, Denais, Martin, Cao, Xuan-Nga, Chang, Simon, Van Remoortere, Pierre, Dakhlia, Cyrille, Riad, Rachid
Non-invasive methods for diagnosing mental health conditions, such as speech analysis, offer promising potential in modern medicine. Recent advancements in machine learning, particularly speech foundation models, have shown significant promise in det
Externí odkaz:
http://arxiv.org/abs/2409.19042
Autor:
Axelsson, M., Ajello, M., Arimoto, M., Baldini, L., Ballet, J., Baring, M. G., Bartolini, C., Bastieri, D., Gonzalez, J. Becerra, Bellazzini, R., Berenji, B., Bissaldi, E., Blandford, R. D., Bonino, R., Bruel, P., Buson, S., Cameron, R. A., Caputo, R., Caraveo, P. A., Cavazzuti, E., Cheung, C. C., Chiaro, G., Cibrario, N., Ciprini, S., Cozzolongo, G., Orestano, P. Cristarella, Crnogorcevic, M., Cuoco, A., Cutini, S., D'Ammando, F., De Gaetano, S., Di Lalla, N., Dinesh, A., Di Tria, R., Di Venere, L., Domínguez, A., Fegan, S. J., Ferrara, E. C., Fiori, A., Franckowiak, A., Fukazawa, Y., Funk, S., Fusco, P., Galanti, G., Gargano, F., Gasbarra, C., Germani, S., Giacchino, F., Giglietto, N., Giliberti, M., Gill, R., Giordano, F., Giroletti, M., Granot, J., Green, D., Grenier, I. A., Guiriec, S., Gustafsson, M., Hashizume, M., Hays, E., Hewitt, J. W., Horan, D., Kayanoki, T., Kuss, M., Laviron, A., Li, J., Liodakis, I., Longo, F., Loparco, F., Lorusso, L., Lott, B., Lovellette, M. N., Lubrano, P., Maldera, S., Malyshev, D., Manfreda, A., Martí-Devesa, G., Martinelli, R., Castellanos, I. Martinez, Mazziotta, M. N., McEnery, J. E., Mereu, I., Meyer, M., Michelson, P. F., Mirabal, N., Mitthumsiri, W., Mizuno, T., Monti-Guarnieri, P., Monzani, M. E., Morishita, T., Morselli, A., Moskalenko, I. V., Negro, M., Niwa, R., Omodei, N., Orienti, M., Orlando, E., Paneque, D., Panzarini, G., Persic, M., Pesce-Rollins, M., Petrosian, V., Pillera, R., Piron, F., Porter, T. A., Principe, G., Racusin, J. L., Rainò, S., Rando, R., Rani, B., Razzano, M., Razzaque, S., Reimer, A., Reimer, O., Ryde, F., Sánchez-Conde, M., Parkinson, P. M. Saz, Serini, D., Sgrò, C., Sharma, V., Siskind, E. J., Spandre, G., Spinelli, P., Suson, D. J., Tajima, H., Tak, D., Thayer, J. B., Torres, D. F., Valverde, J., Zaharijas, G., Lesage, S., Briggs, M. S., Burns, E., Bala, S., Bhat, P. N., Cleveland, W. H., Dalessi, S., de Barra, C., Gibby, M., Giles, M. M., Hamburg, R., Hristov, B. A., Hui, C. M., Kocevski, D., Mailyan, B., Malacaria, C., McBreen, S., Poolakkil, S., Roberts, O. J., Scotton, L., Veres, P., von Kienlin, A., Wilson-Hodge, C. A., Wood, J.
We present a complete analysis of Fermi Large Area Telescope (LAT) data of GRB 221009A, the brightest Gamma-Ray Burst (GRB) ever detected. The burst emission above 30 MeV detected by the LAT preceded by 1 s the low-energy (< 10 MeV) pulse that trigge
Externí odkaz:
http://arxiv.org/abs/2409.04580
Autor:
Bélanger, Olivier, Lupien, Jean-Luc, Yahia, Olfa Ben, Martel, Stéphane, Lesage-Landry, Antoine, Kurt, Gunes Karabulut
The rise in low Earth orbit (LEO) satellite Internet services has led to increasing demand, often exceeding available data rates and compromising the quality of service. While deploying more satellites offers a short-term fix, designing higher-perfor
Externí odkaz:
http://arxiv.org/abs/2409.01488
The IceCube neutrino observatory detects the diffuse astrophysical neutrino background with high significance, but the contribution of different classes of sources is not established. Because of their non-thermal spectrum, gamma-ray bursts (GRBs) are
Externí odkaz:
http://arxiv.org/abs/2408.16748
In this work, we propose Wasserstein distributionally robust shallow convex neural networks (WaDiRo-SCNNs) to provide reliable nonlinear predictions when subject to adverse and corrupted datasets. Our approach is based on a new convex training progra
Externí odkaz:
http://arxiv.org/abs/2407.16800
Autor:
Yahia, Olfa Ben, Garroussi, Zineb, Sansò, Brunilde, Frigon, Jean-François, Martel, Stéphane, Lesage-Landry, Antoine, Kurt, Gunes Karabulut
This paper addresses the limitations of current satellite payload architectures, which are predominantly hardware-driven and lack the flexibility to adapt to increasing data demands and uneven traffic. To overcome these challenges, we present a novel
Externí odkaz:
http://arxiv.org/abs/2407.06075