Zobrazeno 1 - 10
of 72 176
pro vyhledávání: '"Cramer, AS"'
Autor:
Idnay, Betina, Xu, Zihan, Adams, William G., Adibuzzaman, Mohammad, Anderson, Nicholas R., Bahroos, Neil, Bell, Douglas S., Bumgardner, Cody, Campion, Thomas, Castro, Mario, Cimino, James J., Cohen, I. Glenn, Dorr, David, Elkin, Peter L, Fan, Jungwei W., Ferris, Todd, Foran, David J., Hanauer, David, Hogarth, Mike, Huang, Kun, Kalpathy-Cramer, Jayashree, Kandpal, Manoj, Karnik, Niranjan S., Katoch, Avnish, Lai, Albert M., Lambert, Christophe G., Li, Lang, Lindsell, Christopher, Liu, Jinze, Lu, Zhiyong, Luo, Yuan, McGarvey, Peter, Mendonca, Eneida A., Mirhaji, Parsa, Murphy, Shawn, Osborne, John D., Paschalidis, Ioannis C., Harris, Paul A., Prior, Fred, Shaheen, Nicholas J., Shara, Nawar, Sim, Ida, Tachinardi, Umberto, Waitman, Lemuel R., Wright, Rosalind J., Zai, Adrian H., Zheng, Kai, Lee, Sandra Soo-Jin, Malin, Bradley A., Natarajan, Karthik, Price II, W. Nicholson, Zhang, Rui, Zhang, Yiye, Xu, Hua, Bian, Jiang, Weng, Chunhua, Peng, Yifan
This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the Clinical and Translational Science Award (CTSA) P
Externí odkaz:
http://arxiv.org/abs/2410.12793
Autor:
Cramer, Eike
Real-world chemical processes often exhibit stochastic dynamics with non-trivial correlations and state-dependent fluctuations. However, most process models simply add stationary noise terms to a deterministic prediction, which can lead to inaccurate
Externí odkaz:
http://arxiv.org/abs/2409.17632
Autor:
Bethel, E. Wes, Cramer, Vianna, del Rio, Alexander, Narins, Lothar, Pestano, Chris, Verma, Satvik, Arias, Erick, Bertelli, Nicola, Perciano, Talita, Shiraiwa, Syun'ichi, Villar, Álvaro Sánchez, Wallace, Greg, Wright, John C.
This work presents a detailed case study on using Generative AI (GenAI) to develop AI surrogates for simulation models in fusion energy research. The scope includes the methodology, implementation, and results of using GenAI to assist in model develo
Externí odkaz:
http://arxiv.org/abs/2409.06122
Today's top advertisers typically manage hundreds of campaigns simultaneously and consistently launch new ones throughout the year. A crucial challenge for marketing managers is determining the optimal allocation of limited budgets across various ad
Externí odkaz:
http://arxiv.org/abs/2409.00561
Material selection plays a pivotal role in many industries, from manufacturing to construction. Material selection is usually carried out after several cycles of conceptual design, during which designers iteratively refine the design solution and the
Externí odkaz:
http://arxiv.org/abs/2407.09719
Autor:
Ardaševa, Aleksandra, Vélez-Cerón, Ignasi, Pedersen, Martin Cramer, Ignés-Mullol, Jordi, Sagués, Francesc, Doostmohammadi, Amin
We present a novel two-stage transition of the ordered active nematic state of a system of bundled microtubules into a biphasic active fluid. Specifically, we show that upon light-induced solidification of the underlying medium, microtubule-kinesin m
Externí odkaz:
http://arxiv.org/abs/2407.03723
Combining Reinforcement Learning (RL) with a prior controller can yield the best out of two worlds: RL can solve complex nonlinear problems, while the control prior ensures safer exploration and speeds up training. Prior work largely blends both comp
Externí odkaz:
http://arxiv.org/abs/2406.19768
Autor:
Fabbri, Luca, Migliaccio, Ludovico, Širvinskytė, Aleksandra, Rizzi, Giacomo, Bondi, Luca, Tamarozzi, Cristiano, Weber, Stefan A. L., Fraboni, Beatrice, Glowacki, Eric Daniel, Cramer, Tobias
Light activated local stimulation and sensing of biological cells offers enormous potential for minimally invasive bioelectronic interfaces. Organic semiconductors are a promising material class to achieve this kind of transduction due to their optoe
Externí odkaz:
http://arxiv.org/abs/2406.18447
There has been significant progress in deep reinforcement learning (RL) in recent years. Nevertheless, finding suitable hyperparameter configurations and reward functions remains challenging even for experts, and performance heavily relies on these d
Externí odkaz:
http://arxiv.org/abs/2406.18293
Autor:
Schmidt, Kendall, Bearce, Benjamin, Chang, Ken, Coombs, Laura, Farahani, Keyvan, Elbatele, Marawan, Mouhebe, Kaouther, Marti, Robert, Zhang, Ruipeng, Zhang, Yao, Wang, Yanfeng, Hu, Yaojun, Ying, Haochao, Xu, Yuyang, Testagrose, Conrad, Demirer, Mutlu, Gupta, Vikash, Akünal, Ünal, Bujotzek, Markus, Maier-Hein, Klaus H., Qin, Yi, Li, Xiaomeng, Kalpathy-Cramer, Jayashree, Roth, Holger R.
Publikováno v:
Medical Image Analysis Volume 95, July 2024, 103206
The correct interpretation of breast density is important in the assessment of breast cancer risk. AI has been shown capable of accurately predicting breast density, however, due to the differences in imaging characteristics across mammography system
Externí odkaz:
http://arxiv.org/abs/2405.14900