Zobrazeno 1 - 10
of 946
pro vyhledávání: '"JENKINS, ANDREW"'
Autor:
Jenkins, Andrew
Advancements in quadrupedal robot locomotion have yielded impressive results, achieving dynamic maneuvers like climbing, ducking, and jumping. These successes are largely attributed to depth-based visual locomotion policies, known for their robust tr
Externí odkaz:
https://hdl.handle.net/1721.1/156972
Autor:
Ju, Fusong, Wei, Xinran, Huang, Lin, Jenkins, Andrew J., Xia, Leo, Zhang, Jia, Zhu, Jianwei, Yang, Han, Shao, Bin, Dai, Peggy, Mayya, Ashwin, Hooshmand, Zahra, Efimovskaya, Alexandra, Baker, Nathan A., Troyer, Matthias, Liu, Hongbin
Density functional theory (DFT) has been a cornerstone in computational chemistry, physics, and materials science for decades, benefiting from advancements in computational power and theoretical methods. This paper introduces a novel, cloud-native ap
Externí odkaz:
http://arxiv.org/abs/2406.11185
Autor:
Elkolali, Moustafa, Al-Tawil, Ahmed, Much, Lennard, Schrader, Ryan, Masset, Olivier, Sayols, Marina, Jenkins, Andrew, Alonso, Sara, Carella, Alfredo, Alcocer, Alex
Publikováno v:
IEEE OCEANS 2020
This paper presents a prototype of a low-cost Unmanned Surface Vehicle (USV) that is operated by wave and solar energy which can be used to minimize the cost of ocean data collection. The current prototype is a compact USV, with a length of 1.2m that
Externí odkaz:
http://arxiv.org/abs/2112.03685
Autor:
Greytak, Madeline, Kaizer, Alexander M., Jenkins, Andrew, Pandolfino, John E., Polamraju, Vinathi, Wong, Ming-Wun, Krause, Amanda J., Carlson, Dustin A., Chan, Walter W., Chen, Chien-Lin, Gyawali, C. Prakash, Yadlapati, Rena
Publikováno v:
In Clinical Gastroenterology and Hepatology August 2024 22(8):1741-1743
Autor:
Krause, Amanda J., Kaizer, Alexander M., Carlson, Dustin A., Chan, Walter W., Chen, Chien-Lin, Gyawali, C. Prakash, Jenkins, Andrew, Pandolfino, John E., Polamraju, Vinathi, Wong, Ming-Wun, Greytak, Madeline, Yadlapati, Rena
Publikováno v:
In Clinical Gastroenterology and Hepatology June 2024 22(6):1200-1209
Autor:
Cotes-Perdomo, Andrea P., Sánchez-Vialas, Alberto, Thomas, Richard, Jenkins, Andrew, Uribe, Juan E.
Publikováno v:
In Ticks and Tick-borne Diseases May 2024 15(3)
The performance of neural network models is often limited by the availability of big data sets. To treat this problem, we survey and develop novel synthetic data generation and augmentation techniques for enhancing low/zero-sample learning in satelli
Externí odkaz:
http://arxiv.org/abs/2102.00103