Zobrazeno 1 - 10
of 1 477
pro vyhledávání: '"Fromont, P."'
Autor:
Barnes, Rory, Amaral, Laura N. R. do, Birky, Jessica, Carone, Ludmila, Driscoll, Peter, Livesey, Joseph R., Graham, David, Becker, Juliette, Cui, Kaiming, Schlecker, Martin, Garcia, Rodolfo, Gialluca, Megan, Adams, Arthur, Ahmed, MD Redyan, Bonney, Paul, Broussard, Wynter, Chawla, Chetan, Damasso, Mario, Danchi, William C., Deitrick, Russell, Ducrot, Elsa, Fromont, Emeline F., Gaches, Brandt A. L., Gupta, Sakshi, Hill, Michelle L., Jackman, James A. G., Janin, Estelle M., Karawacki, Mikolaj, Koren, Matheus Daniel, La Greca, Roberto, Leung, Michaela, Miranda-Rosete, Arturo, Olohoy, Michael Kent A., Ngo, Cecelia, Paul, Daria, Sahu, Chandan Kumar, Sarkar, Debajyoti Basu, Shadab, Mohammad Afzal, Schwieterman, Edward W., Sedler, Melissa, Texeira, Katie, Vazan, Allona, Vega, Karen N. Delgado, Vijayakumar, Rohit, Wojack, Jonathan T.
We present numerous aspects of the evolution of the LP 890-9 (SPECULOOS-2/TOI-4306) planetary system, focusing on the likelihood that planet c can support life. We find that the host star reaches the main sequence in 1 Gyr and that planet c lies clos
Externí odkaz:
http://arxiv.org/abs/2412.02743
The ability to train generative models that produce realistic, safe and useful tabular data is essential for data privacy, imputation, oversampling, explainability or simulation. However, generating tabular data is not straightforward due to its hete
Externí odkaz:
http://arxiv.org/abs/2406.12945
We propose a novel approach to improve the reproducibility of neuroimaging results by converting statistic maps across different functional MRI pipelines. We make the assumption that pipelines used to compute fMRI statistic maps can be considered as
Externí odkaz:
http://arxiv.org/abs/2404.03703
Results of functional Magnetic Resonance Imaging (fMRI) studies can be impacted by many sources of variability including differences due to: the sampling of the participants, differences in acquisition protocols and material but also due to different
Externí odkaz:
http://arxiv.org/abs/2312.14493
Publikováno v:
IEEE International Conference on Image Processing, Oct 2024, Abu Dhabi, United Arab Emirates. \&\#x27E8;10.1109/ICIP51287.2024.10647701\&\#x27E9
Analytical workflows in functional magnetic resonance imaging are highly flexible with limited best practices as to how to choose a pipeline. While it has been shown that the use of different pipelines might lead to different results, there is still
Externí odkaz:
http://arxiv.org/abs/2312.06231
Autor:
Fromont, Emeline F., Ahlers, John P., Amaral, Laura N. R. do, Barnes, Rory, Gilbert, Emily A., Quintana, Elisa V., Peacock, Sarah, Barclay, Thomas, Youngblood, Allison
A critically important process affecting the climate evolution and potential habitability of an exoplanet is atmospheric escape, in which high-energy radiation from a star drives the escape of hydrogen atoms and other light elements from a planet's a
Externí odkaz:
http://arxiv.org/abs/2312.00062
lcensemble is a high-performing, scalable and user-friendly Python package for the general tasks of classification and regression. The package implements Local Cascade Ensemble (LCE), a machine learning method that further enhances the prediction per
Externí odkaz:
http://arxiv.org/abs/2308.07250
Autor:
Lekadir, Karim, Feragen, Aasa, Fofanah, Abdul Joseph, Frangi, Alejandro F, Buyx, Alena, Emelie, Anais, Lara, Andrea, Porras, Antonio R, Chan, An-Wen, Navarro, Arcadi, Glocker, Ben, Botwe, Benard O, Khanal, Bishesh, Beger, Brigit, Wu, Carol C, Cintas, Celia, Langlotz, Curtis P, Rueckert, Daniel, Mzurikwao, Deogratias, Fotiadis, Dimitrios I, Zhussupov, Doszhan, Ferrante, Enzo, Meijering, Erik, Weicken, Eva, González, Fabio A, Asselbergs, Folkert W, Prior, Fred, Krestin, Gabriel P, Collins, Gary, Tegenaw, Geletaw S, Kaissis, Georgios, Misuraca, Gianluca, Tsakou, Gianna, Dwivedi, Girish, Kondylakis, Haridimos, Jayakody, Harsha, Woodruf, Henry C, Mayer, Horst Joachim, Aerts, Hugo JWL, Walsh, Ian, Chouvarda, Ioanna, Buvat, Irène, Tributsch, Isabell, Rekik, Islem, Duncan, James, Kalpathy-Cramer, Jayashree, Zahir, Jihad, Park, Jinah, Mongan, John, Gichoya, Judy W, Schnabel, Julia A, Kushibar, Kaisar, Riklund, Katrine, Mori, Kensaku, Marias, Kostas, Amugongo, Lameck M, Fromont, Lauren A, Maier-Hein, Lena, Alberich, Leonor Cerdá, Rittner, Leticia, Phiri, Lighton, Marrakchi-Kacem, Linda, Donoso-Bach, Lluís, Martí-Bonmatí, Luis, Cardoso, M Jorge, Bobowicz, Maciej, Shabani, Mahsa, Tsiknakis, Manolis, Zuluaga, Maria A, Bielikova, Maria, Fritzsche, Marie-Christine, Camacho, Marina, Linguraru, Marius George, Wenzel, Markus, De Bruijne, Marleen, Tolsgaard, Martin G, Ghassemi, Marzyeh, Ashrafuzzaman, Md, Goisauf, Melanie, Yaqub, Mohammad, Abadía, Mónica Cano, Mahmoud, Mukhtar M E, Elattar, Mustafa, Rieke, Nicola, Papanikolaou, Nikolaos, Lazrak, Noussair, Díaz, Oliver, Salvado, Olivier, Pujol, Oriol, Sall, Ousmane, Guevara, Pamela, Gordebeke, Peter, Lambin, Philippe, Brown, Pieta, Abolmaesumi, Purang, Dou, Qi, Lu, Qinghua, Osuala, Richard, Nakasi, Rose, Zhou, S Kevin, Napel, Sandy, Colantonio, Sara, Albarqouni, Shadi, Joshi, Smriti, Carter, Stacy, Klein, Stefan, Petersen, Steffen E, Aussó, Susanna, Awate, Suyash, Raviv, Tammy Riklin, Cook, Tessa, Mutsvangwa, Tinashe E M, Rogers, Wendy A, Niessen, Wiro J, Puig-Bosch, Xènia, Zeng, Yi, Mohammed, Yunusa G, Aquino, Yves Saint James, Salahuddin, Zohaib, Starmans, Martijn P A
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinica
Externí odkaz:
http://arxiv.org/abs/2309.12325
Autor:
Voyez, Antonin, Allard, Tristan, Avoine, Gildas, Cauchois, Pierre, Fromont, Elisa, Simonin, Matthieu
The collection of electrical consumption time series through smart meters grows with ambitious nationwide smart grid programs. This data is both highly sensitive and highly valuable: strong laws about personal data protect it while laws about open da
Externí odkaz:
http://arxiv.org/abs/2211.07205
Context. We study the benefits of using a large public neuroimaging database composed of fMRI statistic maps, in a self-taught learning framework, for improving brain decoding on new tasks. First, we leverage the NeuroVault database to train, on a se
Externí odkaz:
http://arxiv.org/abs/2209.10099