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pro vyhledávání: '"Myers, A"'
Nuclear magnetism underpins areas such as medicine in magnetic resonance imaging (MRI). Hyperpolarization of nuclei enhances the quantity and quality of information that can be determined from these techniques by increasing their signal to noise rati
Externí odkaz:
http://arxiv.org/abs/2409.17733
Artificial Intelligence (AI), with its multiplier effect and wide applications in multiple areas, could potentially be an important application of quantum computing. Since modern AI systems are often built on neural networks, the design of quantum ne
Externí odkaz:
http://arxiv.org/abs/2409.17583
Autor:
Myers, Skatje, Miller, Timothy A., Gao, Yanjun, Churpek, Matthew M., Mayampurath, Anoop, Dligach, Dmitriy, Afshar, Majid
Objective: Applying large language models (LLMs) to the clinical domain is challenging due to the context-heavy nature of processing medical records. Retrieval-augmented generation (RAG) offers a solution by facilitating reasoning over large text sou
Externí odkaz:
http://arxiv.org/abs/2409.15163
The introduction of generative pre-trained models, like GPT-4, has introduced a phenomenon known as prompt engineering, whereby model users repeatedly write and revise prompts while trying to achieve a task. Using these AI models for intelligent feat
Externí odkaz:
http://arxiv.org/abs/2409.12447
Autor:
Hagopian, Raffi, Strebel, Timothy, Bernatz, Simon, Myers, Gregory A, Offerman, Erik, Zuniga, Eric, Kim, Cy Y, Ng, Angie T, Iwaz, James A, Singh, Sunny P, Carey, Evan P, Kim, Michael J, Schaefer, R Spencer, Yu, Jeannie, Gentili, Amilcare, Aerts, Hugo JWL
Coronary artery calcium (CAC) is highly predictive of cardiovascular events. While millions of chest CT scans are performed annually in the United States, CAC is not routinely quantified from scans done for non-cardiac purposes. A deep learning algor
Externí odkaz:
http://arxiv.org/abs/2409.09968
Autor:
Bernárdez, Guillermo, Telyatnikov, Lev, Montagna, Marco, Baccini, Federica, Papillon, Mathilde, Ferriol-Galmés, Miquel, Hajij, Mustafa, Papamarkou, Theodore, Bucarelli, Maria Sofia, Zaghen, Olga, Mathe, Johan, Myers, Audun, Mahan, Scott, Lillemark, Hansen, Vadgama, Sharvaree, Bekkers, Erik, Doster, Tim, Emerson, Tegan, Kvinge, Henry, Agate, Katrina, Ahmed, Nesreen K, Bai, Pengfei, Banf, Michael, Battiloro, Claudio, Beketov, Maxim, Bogdan, Paul, Carrasco, Martin, Cavallo, Andrea, Choi, Yun Young, Dasoulas, George, Elphick, Matouš, Escalona, Giordan, Filipiak, Dominik, Fritze, Halley, Gebhart, Thomas, Gil-Sorribes, Manel, Goomanee, Salvish, Guallar, Victor, Imasheva, Liliya, Irimia, Andrei, Jin, Hongwei, Johnson, Graham, Kanakaris, Nikos, Koloski, Boshko, Kovač, Veljko, Lecha, Manuel, Lee, Minho, Leroy, Pierrick, Long, Theodore, Magai, German, Martinez, Alvaro, Masden, Marissa, Mežnar, Sebastian, Miquel-Oliver, Bertran, Molina, Alexis, Nikitin, Alexander, Nurisso, Marco, Piekenbrock, Matt, Qin, Yu, Rygiel, Patryk, Salatiello, Alessandro, Schattauer, Max, Snopov, Pavel, Suk, Julian, Sánchez, Valentina, Tec, Mauricio, Vaccarino, Francesco, Verhellen, Jonas, Wantiez, Frederic, Weers, Alexander, Zajec, Patrik, Škrlj, Blaž, Miolane, Nina
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM). The challenge focused on the problem
Externí odkaz:
http://arxiv.org/abs/2409.05211
Autor:
Zhou, Rongpu, Guy, Julien, Koposov, Sergey E., Schlafly, Edward F., Schlegel, David, Aguilar, Jessica, Ahlen, Steven, Bailey, Stephen, Bianchi, David, Brooks, David, Chaussidon, Edmond, Claybaugh, Todd, Dawson, Kyle, de la Macorra, Axel, Dey, Biprateep, Eisenstein, Daniel J., Ferraro, Simone, Font-Ribera, Andreu, Forero-Romero, Jaime E., Gaztañaga, Enrique, Gontcho, Satya Gontcho A, Gutierrez, Gaston, Honscheid, Klaus, Juneau, Stephanie, Kehoe, Robert, Kirkby, David, Kisner, Theodore, Kremin, Anthony, Lambert, Andrew, Landriau, Martin, Guillou, Laurent Le, Levi, Michael E., Li, Ting S., Manera, Marc, Martini, Paul, Meisner, Aaron, Miquel, Ramon, Moustakas, John, Myers, Adam D., Newman, Jeffrey A., Niz, Gustavo, Palanque-Delabrouille, Nathalie, Percival, Will J., Poppett, Claire, Prada, Francisco, Raichoor, Anand, Ross, Ashley J., Rossi, Graziano, Sanchez, Eusebio, Saydjari, Andrew K., Schubnell, Michael, Sprayberry, David, Tarl, Gregory, Weaver, Benjamin A., Zarrouk, Pauline, Zou, Hu
We present new Galactic reddening maps of the high Galactic latitude sky using DESI imaging and spectroscopy. We directly measure the reddening of 2.6 million stars by comparing the observed stellar colors in $g-r$ and $r-z$ from DESI imaging with th
Externí odkaz:
http://arxiv.org/abs/2409.05140
Brightness mode (B-mode) ultrasound is a common imaging modality in the clinical assessment of several cardiovascular diseases. The utility of ultrasound-based functional indices such as the ejection fraction (EF) and stroke volume (SV) is widely des
Externí odkaz:
http://arxiv.org/abs/2409.04577
We introduce AIRduct, a new array-based intermediate representation designed to support generating efficient code for interactive programs employing multiple cryptographic mechanisms. AIRduct is intended as an IR for the Viaduct compiler, which can s
Externí odkaz:
http://arxiv.org/abs/2409.01587
Learned language-conditioned robot policies often struggle to effectively adapt to new real-world tasks even when pre-trained across a diverse set of instructions. We propose a novel approach for few-shot adaptation to unseen tasks that exploits the
Externí odkaz:
http://arxiv.org/abs/2408.16228