Zobrazeno 1 - 10
of 120 830
pro vyhledávání: '"A, Maher"'
Autor:
Bucik, Radoslav, Hart, Samuel T., Dayeh, Maher A., Desai, Mihir I., Mason, Glenn M., Wiedenbeck, Mark E.
We examined the origin of 3He abundance enhancement in 23 high-energy (25-50 MeV) solar proton events that coincide with 3He-rich periods detected by ACE ULEIS in 1997-2021. In seven events, 3He enhancement was due to 3He leftover from preceding even
Externí odkaz:
http://arxiv.org/abs/2410.15515
Autor:
Maher, Andrew, Gobbo, Matia, Lachartre, Lancelot, Prabanantham, Subash, Swiers, Rowan, Liyanagama, Puli
Contextual bandits have become an increasingly popular solution for personalized recommender systems. Despite their growing use, the interpretability of these systems remains a significant challenge, particularly for the often non-expert operators ta
Externí odkaz:
http://arxiv.org/abs/2409.15143
Autor:
Spooner, Annette, Moridani, Mohammad Karimi, Safarchi, Azadeh, Maher, Salim, Vafaee, Fatemeh, Zekry, Amany, Sowmya, Arcot
The complementary information found in different modalities of patient data can aid in more accurate modelling of a patient's disease state and a better understanding of the underlying biological processes of a disease. However, the analysis of multi
Externí odkaz:
http://arxiv.org/abs/2409.13791
Multi-armed Bandits (MABs) are increasingly employed in online platforms and e-commerce to optimize decision making for personalized user experiences. In this work, we focus on the Contextual Bandit problem with linear rewards, under conditions of sp
Externí odkaz:
http://arxiv.org/abs/2409.09199
Autor:
Tadimalla, Sri Yash, Maher, Mary Lou
This paper presents a curriculum, "AI Literacy for All," to promote an interdisciplinary understanding of AI, its socio-technical implications, and its practical applications for all levels of education. With the rapid evolution of artificial intelli
Externí odkaz:
http://arxiv.org/abs/2409.10552
Autor:
Kays, Roland, Snider, Matthew H., Hess, George, Cove, Michael V., Jensen, Alex, Shamon, Hila, McShea, William J., Rooney, Brigit, Allen, Maximilian L., Pekins, Charles E., Wilmers, Christopher C., Pendergast, Mary E., Green, Austin M., Suraci, Justin, Leslie, Matthew S., Nasrallah, Sophie, Farkas, Dan, Jordan, Mark, Grigione, Melissa, LaScaleia, Michael C., Davis, Miranda L., Hansen, Chris, Millspaugh, Josh, Lewis, Jesse S., Havrda, Michael, Long, Robert, Remine, Kathryn R., Jaspers, Kodi J., Lafferty, Diana J. R., Hubbard, Tru, Studds, Colin E., Barthelmess, Erika L., Andy, Katherine, Romero, Andrea, O'Neill, Brian J., Hawkins, Melissa T. R., Lombardi, Jason V., Sergeyev, Maksim, Fisher-Reid, M. Caitlin, Rentz, Michael S., Nagy, Christopher, Davenport, Jon M., Rega-Brodsky, Christine C., Appel, Cara L., Lesmeister, Damon B., Giery, Sean T., Whittier, Christopher A., Alston, Jesse M., Sutherland, Chris, Rota, Christopher, Murphy, Thomas, Lee, Thomas E., Mortelliti, Alessio, Bergman, Dylan L., Compton, Justin A., Gerber, Brian D., Burr, Jess, Rezendes, Kylie, DeGregorio, Brett A., Wehr, Nathaniel H., Benson, John F., O’Mara, M. Teague, Jachowski, David S., Gray, Morgan, Beyer, Dean E., Belant, Jerrold L., Horan, Robert V., Lonsinger, Robert C., Kuhn, Kellie M., Hasstedt, Steven C. M., Zimova, Marketa, Moore, Sophie M., Herrera, Daniel J., Fritts, Sarah, Edelman, Andrew J., Flaherty, Elizabeth A., Petroelje, Tyler R., Neiswenter, Sean A., Risch, Derek R., Iannarilli, Fabiola, van der Merwe, Marius, Maher, Sean P., Farris, Zach J., Webb, Stephen L., Mason, David S., Lashley, Marcus A., Wilson, Andrew M., Vanek, John P., Wehr, Samuel R., Conner, L. Mike, Beasley, James C., Bontrager, Helen L., Baruzzi, Carolina, Ellis-Felege, Susan N., Proctor, Mike D., Schipper, Jan, Weiss, Katherine C. B., Darracq, Andrea K., Barr, Evan G., Alexander, Peter D., Şekercioğlu, Çağan H., Bogan, Daniel A., Schalk, Christopher M., Fantle-Lepczyk, Jean E., Lepczyk, Christopher A., LaPoint, Scott, Whipple, Laura S., Rowe, Helen Ivy, Mullen, Kayleigh, Bird, Tori, Zorn, Adam, Brandt, LaRoy, Lathrop, Richard G., McCain, Craig, Crupi, Anthony P., Clark, James, Parsons, Arielle
Publikováno v:
Diversity and Distributions, 2024 Sep 01. 30(9), 1-16.
Externí odkaz:
https://www.jstor.org/stable/48784956
Formulating order metrics that sensitively quantify the degree of order/disorder in many-particle systems in $d$-dimensional Euclidean space $\mathbb{R}^d$ across length scales is an outstanding challenge in physics, chemistry, and materials science.
Externí odkaz:
http://arxiv.org/abs/2408.11702
Process-based hydrologic models are invaluable tools for understanding the terrestrial water cycle and addressing modern water resources problems. However, many hydrologic models are computationally expensive and, depending on the resolution and scal
Externí odkaz:
http://arxiv.org/abs/2407.20902
Autor:
Amer, Hossam, Abouelenin, Abdelrahman, Maher, Mohamed, Narouz, Evram, Afify, Mohamed, Awadallah, Hany
Nearest neighbor machine translation is a successful approach for fast domain adaption, which interpolates the pre-trained transformers with domain-specific token-level k-nearest-neighbor (kNN) retrieval without retraining. Despite kNN MT's success,
Externí odkaz:
http://arxiv.org/abs/2407.19965
In this work, we describe our approach to developing an intelligent and robust social robotic system for the Nadine social robot platform. We achieve this by integrating Large Language Models (LLMs) and skilfully leveraging the powerful reasoning and
Externí odkaz:
http://arxiv.org/abs/2405.20189