Zobrazeno 1 - 10
of 6 180
pro vyhledávání: '"LUO Yuan"'
Autor:
Zhao, Jiaxin, Fieramosca, Antonio, Dini, Kevin, Shang, Qiuyu, Bao, Ruiqi, Luo, Yuan, Shen, Kaijun, Zhao, Yang, Su, Rui, Perez, Jesus Zuniga, Gao, Weibo, Ardizzone, Vincenzo, Sanvitto, Daniele, Xiong, Qihua, Liew, Timothy C. H.
Recent advancements in transition metal dichalcogenides (TMDs) have unveiled exceptional optical and electronic characteristics, opened up new opportunities, and provided a unique platform for exploring light-matter interactions under the strong coup
Externí odkaz:
http://arxiv.org/abs/2410.18474
The increasing severity of climate change necessitates an urgent transition to renewable energy sources, making the large-scale adoption of wind energy crucial for mitigating environmental impact. However, the inherent uncertainty of wind power poses
Externí odkaz:
http://arxiv.org/abs/2410.13303
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
A proper mechanism design can help federated learning (FL) to achieve good social welfare by coordinating self-interested clients through the learning process. However, existing mechanisms neglect the network effects of client participation, leading
Externí odkaz:
http://arxiv.org/abs/2408.13223
Distribution-preserving integrated sensing and communication with secure reconstruction is investigated in this paper. In addition to the distortion constraint, we impose another constraint on the distance between the reconstructed sequence distribut
Externí odkaz:
http://arxiv.org/abs/2405.07275
To measure repair latency at helper nodes, we introduce a new metric called skip cost that quantifies the number of contiguous sections accessed on a disk. We provide explicit constructions of zigzag codes and fractional repetition codes that incur z
Externí odkaz:
http://arxiv.org/abs/2405.03614
A high-quality fresh high-definition (HD) map is vital in enhancing transportation efficiency and safety in autonomous driving. Vehicle-based crowdsourcing offers a promising approach for updating HD maps. However, recruiting crowdsourcing vehicles i
Externí odkaz:
http://arxiv.org/abs/2405.00353
With the increasingly widespread application of machine learning, how to strike a balance between protecting the privacy of data and algorithm parameters and ensuring the verifiability of machine learning has always been a challenge. This study explo
Externí odkaz:
http://arxiv.org/abs/2404.12186
Autor:
Long, Shaocong, Zhou, Qianyu, Li, Xiangtai, Lu, Xuequan, Ying, Chenhao, Luo, Yuan, Ma, Lizhuang, Yan, Shuicheng
Domain generalization~(DG) aims at solving distribution shift problems in various scenes. Existing approaches are based on Convolution Neural Networks (CNNs) or Vision Transformers (ViTs), which suffer from limited receptive fields or quadratic compl
Externí odkaz:
http://arxiv.org/abs/2404.07794
Existing works have extensively studied adversarial examples, which are minimal perturbations that can mislead the output of deep neural networks (DNNs) while remaining imperceptible to humans. However, in this work, we reveal the existence of a harm
Externí odkaz:
http://arxiv.org/abs/2402.02095