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pro vyhledávání: '"Lee Yuan"'
The absence of a common framework for benchmarking quantum networks is an obstacle to comparing the capabilities of different quantum networks. We propose a general framework for quantifying the performance of a quantum network, which is based on the
Externí odkaz:
http://arxiv.org/abs/2210.10752
Autor:
Chen, Kevin C., Dhara, Prajit, Heuck, Mikkel, Lee, Yuan, Dai, Wenhan, Guha, Saikat, Englund, Dirk
We propose a scheme for optical entanglement distribution in quantum networks based on a quasi-deterministic entangled photon pair source. By combining heralded photonic Bell pair generation with spectral mode conversion to interface with quantum mem
Externí odkaz:
http://arxiv.org/abs/2206.03670
Autor:
Chuang, Shih-Ming, Liu, Sung-Chen, Chien, Ming-Nan, Lee, Chun-Chuan, Lee, Yuan-Teh, Chien, Kuo-Liong
Publikováno v:
In American Journal of Preventive Cardiology December 2024 20
Autor:
Le, Kang, Wang, Xunde, Chu, Jonathan, Lundt, Maureen, Lee, Yuan Yee, Conrey, Anna, Frey, Ingrid, Giannini, Silvia, Kosinski, Penelope A., Hausman, John M., Low, Philip S., Jeffries, Neal, Desai, Sanjay A., Thein, Swee Lay
Publikováno v:
In Blood Advances 12 November 2024 8(21):5653-5662
RAGE participates in the intracellular transport of Campylobacter jejuni cytolethal distending toxin
Autor:
Chang, Yu-Fang, Huang, Yi-Ping, Chou, Chia-Huei, Ho, Mao-Wang, Lin, Hwai-Jeng, Chen, Chun-Ya, Wu, Hui-Yu, Lai, Yi-Ru, Lee, Yuan-Haw, Chiu, Cheng-Hsun, Lai, Chih-Ho
Publikováno v:
In Journal of Microbiology, Immunology and Infection October 2024 57(5):709-719
Autor:
Wang, Shing-Hoa, Liu, Chia-Heng, Yeh, Jien-Wei, Tsao, Tzu-Ching, Li, Chia-Lin, Chang, Horng-Yi, Yang, Jer-Ren, Hsueh, Chun-Hway, Chang, Liu-Wen, Zheng, Xue-Qian, Lee, Yuan-Tzu, Yang, Ya-Ching
Publikováno v:
In Journal of Alloys and Compounds 5 January 2025 1010
Autor:
Lai, De-Wei, Chu, Pei-Yi, Sheu, Meei-Ling, Tsai, Yi-Ching, Lee, Yuan-Hao, Liu, Shu-Chun, Liu, Ta-Chih
Publikováno v:
In Phytomedicine January 2025 136
Publikováno v:
In Progress in Organic Coatings January 2025 198
Few-shot semantic segmentation addresses the learning task in which only few images with ground truth pixel-level labels are available for the novel classes of interest. One is typically required to collect a large mount of data (i.e., base classes)
Externí odkaz:
http://arxiv.org/abs/2111.01418