Zobrazeno 1 - 10
of 24 457
pro vyhledávání: '"DANY, A."'
Autor:
Benkedadra, Mohamed, Rimez, Dany, Godelaine, Tiffanie, Chidambaram, Natarajan, Khosroshahi, Hamed Razavi, Tellez, Horacio, Mancas, Matei, Macq, Benoit, Mahmoudi, Sidi Ahmed
Publikováno v:
2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR) 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR) 10.1109/MIPR62202.2024
Computer vision tasks such as object detection and segmentation rely on the availability of extensive, accurately annotated datasets. In this work, We present CIA, a modular pipeline, for (1) generating synthetic images for dataset augmentation using
Externí odkaz:
http://arxiv.org/abs/2411.16128
One long standing tension between theory and observations of Type I X-ray burst is the accretion rate at which the burst disappear due to stabilization of the nuclear burning that powers them. This is observed to happen at roughly one third of the th
Externí odkaz:
http://arxiv.org/abs/2411.09843
Autor:
Hendriks, Kai, Atallah, Dany, Martinez, Miguel, Zevin, Michael, Zwick, Lorenz, Trani, Alessandro A., Saini, Pankaj, Takátsy, János, Samsing, Johan
The phase evolution of gravitational waves (GWs) can be modulated by the astrophysical environment surrounding the source, which provides a probe for the origin of individual binary black holes (BBHs) using GWs alone. We here study the evolving phase
Externí odkaz:
http://arxiv.org/abs/2411.08572
Artificial intelligence (AI) has become integral to our everyday lives. Computer vision has advanced to the point where it can play the safety critical role of detecting pedestrians at road intersections in intelligent transportation systems and aler
Externí odkaz:
http://arxiv.org/abs/2409.15740
Autor:
Lemonde, Marc-Antoine, Lachance-Quirion, Dany, Duclos-Cianci, Guillaume, Frattini, Nicholas E., Hopfmueller, Florian, Gauvin-Ndiaye, Chloe, Camirand-Lemyre, Julien, St-Jean, Philippe
Quantum computing holds the promise of solving classically intractable problems. Enabling this requires scalable and hardware-efficient quantum processors with vanishing error rates. This perspective manuscript describes how bosonic codes, particular
Externí odkaz:
http://arxiv.org/abs/2409.05813
Autor:
Sikar, Daniel, Garcez, Artur, Bloomfield, Robin, Weyde, Tillman, Peeroo, Kaleem, Singh, Naman, Hutchinson, Maeve, Laksono, Dany, Reljan-Delaney, Mirela
This study introduces the Misclassification Likelihood Matrix (MLM) as a novel tool for quantifying the reliability of neural network predictions under distribution shifts. The MLM is obtained by leveraging softmax outputs and clustering techniques t
Externí odkaz:
http://arxiv.org/abs/2407.07818
Autor:
Mitra, Arindam, Del Corro, Luciano, Zheng, Guoqing, Mahajan, Shweti, Rouhana, Dany, Codas, Andres, Lu, Yadong, Chen, Wei-ge, Vrousgos, Olga, Rosset, Corby, Silva, Fillipe, Khanpour, Hamed, Lara, Yash, Awadallah, Ahmed
Synthetic data is becoming increasingly important for accelerating the development of language models, both large and small. Despite several successful use cases, researchers also raised concerns around model collapse and drawbacks of imitating other
Externí odkaz:
http://arxiv.org/abs/2407.03502
Progress in AI is often demonstrated by new models claiming improved performance on tasks measuring model capabilities. Evaluating language models in particular is challenging, as small changes to how a model is evaluated on a task can lead to large
Externí odkaz:
http://arxiv.org/abs/2406.08446
NIRPS first light and early science: breaking the 1 m/s RV precision barrier at infrared wavelengths
Autor:
Artigau, Étienne, Bouchy, François, Doyon, René, Baron, Frédérique, Malo, Lison, Wildi, François, Pepe, Franceso, Cook, Neil J., Thibault, Simon, Reshetov, Vladimir, Dumusque, Xavier, Lovis, Christophe, Sosnowska, Danuta, Martins, Bruno L. Canto, De Medeiros, Jose Renan, Delfosse, Xavier, Santos, Nuno, Rebolo, Rafael, Abreu, Manuel, Allain, Guillaume, Allart, Romain, Auger, Hugues, Barros, Susana, Bazinet, Luc, Blind, Nicolas, Boisse, Isabelle, Bonfils, Xavier, Bourrier, Vincent, Bovay, Sébastien, Broeg, Christopher, Brousseau, Denis, Bruniquel, Vincent, Cabral, Alexandre, Cadieux, Charles, Carmona, Andres, Carteret, Yann, Challita, Zalpha, Chazelas, Bruno, Cloutier, Ryan, Coelho, João, Cointepas, Marion, Conod, Uriel, Cowan, Nicolas, Cristo, Eduardo, da Silva, João Gomes, Dauplaise, Laurie, Gomes, Roseane de Lima, Delgado-Mena, Elisa, Ehrenreich, David, Faria, João, Figueira, Pedro, Forveille, Thierry, Frensch, Yolanda, Gagné, Jonathan, Genest, Frédéric, Genolet, Ludovic, Hernández, Jonay I. González, Témich, Félix Gracia, Grieves, Nolan, Hernandez, Olivier, Hobson, Melissa J., Hoeijmakers, Jens, Kerley, Dan, Krishnamurthy, Vigneshwaran, Lafrenière, David, Lamontagne, Pierrot, Larue, Pierre, Leaf, Henry, Leão, Izan C., Lim, Olivia, Curto, Gaspare Lo, Martins, Allan M., Melo, Claudio, Messias, Yuri S., Mignon, Lucile, Moranta, Leslie, Mordasini, Christoph, Moulla, Khaled Al, Mounzer, Dany, L'Heureux, Alexandrine, Nari, Nicola, Nielsen, Louise, Osborn, Ares, Parc, Léna, Pasquini, Luca, Passegger, Vera M., Pelletier, Stefan, Peroux, Céline, Piaulet, Caroline, Plotnykov, Mykhaylo, Poulin-Girard, Anne-Sophie, Rasilla, José Luis, Saint-Antoine, Jonathan, Sarajlic, Mirsad, Segovia, Alex, Seidel, Julia, Ségransan, Damien, Silva, Ana Rita Costa, Srivastava, Avidaan, Stefanov, Atanas K., Mascareño, Alejandro Suárez, Sordet, Michael, Teixeira, Márcio A., Udry, Stéphane, Valencia, Diana, Vallée, Philippe, Vandal, Thomas, Vaulato, Valentina, Wade, Gregg, Wardenier, Joost P., Wehbé, Bachar, Weisserman, Drew, Wevers, Ivan, Zins, Gérard
The Near-InfraRed Planet Searcher or NIRPS is a precision radial velocity spectrograph developed through collaborative efforts among laboratories in Switzerland, Canada, Brazil, France, Portugal and Spain. NIRPS extends to the 0.98-1.8 $\mu$m domain
Externí odkaz:
http://arxiv.org/abs/2406.08304
Autor:
Pastor-Marazuela, Inés, van Leeuwen, Joeri, Bilous, Anna, Connor, Liam, Maan, Yogesh, Oostrum, Leon, Petroff, Emily, Vohl, Dany, Hess, Kelley M., Orrù, Emanuela, Sclocco, Alessio, Wang, Yuyang
Understanding the origin of fast radio bursts (FRBs) has become the main science driver of recent dedicated FRB surveys. Between July 2019 and February 2022, we carried out ALERT, an FRB survey at 1370 MHz using the Apertif instrument installed at th
Externí odkaz:
http://arxiv.org/abs/2406.00482