Zobrazeno 1 - 10
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pro vyhledávání: '"LIANG yong"'
Autor:
Alexander, Koen, Bahgat, Andrea, Benyamini, Avishai, Black, Dylan, Bonneau, Damien, Burgos, Stanley, Burridge, Ben, Campbell, Geoff, Catalano, Gabriel, Ceballos, Alex, Chang, Chia-Ming, Chung, CJ, Danesh, Fariba, Dauer, Tom, Davis, Michael, Dudley, Eric, Er-Xuan, Ping, Fargas, Josep, Farsi, Alessandro, Fenrich, Colleen, Frazer, Jonathan, Fukami, Masaya, Ganesan, Yogeeswaran, Gibson, Gary, Gimeno-Segovia, Mercedes, Goeldi, Sebastian, Goley, Patrick, Haislmaier, Ryan, Halimi, Sami, Hansen, Paul, Hardy, Sam, Horng, Jason, House, Matthew, Hu, Hong, Jadidi, Mehdi, Johansson, Henrik, Jones, Thomas, Kamineni, Vimal, Kelez, Nicholas, Koustuban, Ravi, Kovall, George, Krogen, Peter, Kumar, Nikhil, Liang, Yong, LiCausi, Nicholas, Llewellyn, Dan, Lokovic, Kimberly, Lovelady, Michael, Manfrinato, Vitor, Melnichuk, Ann, Souza, Mario, Mendoza, Gabriel, Moores, Brad, Mukherjee, Shaunak, Munns, Joseph, Musalem, Francois-Xavier, Najafi, Faraz, O'Brien, Jeremy L., Ortmann, J. Elliott, Pai, Sunil, Park, Bryan, Peng, Hsuan-Tung, Penthorn, Nicholas, Peterson, Brennan, Poush, Matt, Pryde, Geoff J., Ramprasad, Tarun, Ray, Gareth, Rodriguez, Angelita, Roxworthy, Brian, Rudolph, Terry, Saunders, Dylan J., Shadbolt, Pete, Shah, Deesha, Shin, Hyungki, Smith, Jake, Sohn, Ben, Sohn, Young-Ik, Son, Gyeongho, Sparrow, Chris, Staffaroni, Matteo, Stavrakas, Camille, Sukumaran, Vijay, Tamborini, Davide, Thompson, Mark G., Tran, Khanh, Triplet, Mark, Tung, Maryann, Vert, Alexey, Vidrighin, Mihai D., Vorobeichik, Ilya, Weigel, Peter, Wingert, Mathhew, Wooding, Jamie, Zhou, Xinran
Whilst holding great promise for low noise, ease of operation and networking, useful photonic quantum computing has been precluded by the need for beyond-state-of-the-art components, manufactured by the millions. Here we introduce a manufacturable pl
Externí odkaz:
http://arxiv.org/abs/2404.17570
Autor:
Wu, Ruoyou, Cheng, Jian, Li, Cheng, Zou, Juan, Yang, Jing, Fan, Wenxin, Liang, Yong, Wang, Shanshan
Deep learning-based dMRI super-resolution methods can effectively enhance image resolution by leveraging the learning capabilities of neural networks on large datasets. However, these methods tend to learn a fixed scale mapping between low-resolution
Externí odkaz:
http://arxiv.org/abs/2404.03209
Autor:
Liu, Jiarun, Yang, Hao, Zhou, Hong-Yu, Xi, Yan, Yu, Lequan, Yu, Yizhou, Liang, Yong, Shi, Guangming, Zhang, Shaoting, Zheng, Hairong, Wang, Shanshan
Accurate medical image segmentation demands the integration of multi-scale information, spanning from local features to global dependencies. However, it is challenging for existing methods to model long-range global information, where convolutional n
Externí odkaz:
http://arxiv.org/abs/2402.03302
This paper constructs a class of non-integer dimensional continuous functions with one unbounded variation point, discusses their H\"older condition and variation on their domains. Specifically, the fractal dimension of a continuous function with one
Externí odkaz:
http://arxiv.org/abs/2402.03382
Autor:
Huang, Weijian, Li, Cheng, Yang, Hao, Liu, Jiarun, Liang, Yong, Zheng, Hairong, Wang, Shanshan
Recently, vision-language representation learning has made remarkable advancements in building up medical foundation models, holding immense potential for transforming the landscape of clinical research and medical care. The underlying hypothesis is
Externí odkaz:
http://arxiv.org/abs/2401.11421
Autor:
Liu, Jiarun, Zhou, Hong-Yu, Li, Cheng, Huang, Weijian, Yang, Hao, Liang, Yong, Wang, Shanshan
Existing contrastive language-image pre-training aims to learn a joint representation by matching abundant image-text pairs. However, the number of image-text pairs in medical datasets is usually orders of magnitude smaller than that in natural datas
Externí odkaz:
http://arxiv.org/abs/2401.01591
Autor:
Huang, Weijian, Li, Cheng, Zhou, Hong-Yu, Liu, Jiarun, Yang, Hao, Liang, Yong, Shi, Guangming, Zheng, Hairong, Wang, Shanshan
The development of medical vision-language foundation models has attracted significant attention in the field of medicine and healthcare due to their promising prospect in various clinical applications. While previous studies have commonly focused on
Externí odkaz:
http://arxiv.org/abs/2401.01583
Autor:
Yang, Hao, Zhou, Hong-Yu, Li, Cheng, Huang, Weijian, Liu, Jiarun, Liang, Yong, Shi, Guangming, Zheng, Hairong, Liu, Qiegen, Wang, Shanshan
Multimodal deep learning utilizing imaging and diagnostic reports has made impressive progress in the field of medical imaging diagnostics, demonstrating a particularly strong capability for auxiliary diagnosis in cases where sufficient annotation in
Externí odkaz:
http://arxiv.org/abs/2401.01524
Autor:
Huang, Weijian, Li, Cheng, Zhou, Hong-Yu, Yang, Hao, Liu, Jiarun, Liang, Yong, Zheng, Hairong, Zhang, Shaoting, Wang, Shanshan
Publikováno v:
Nature Communications 15, 7620 (2024)
Recently, multi-modal vision-language foundation models have gained significant attention in the medical field. While these models offer great opportunities, they still face crucial challenges, such as the requirement for fine-grained knowledge under
Externí odkaz:
http://arxiv.org/abs/2309.05904
We study an online joint assortment-inventory optimization problem, in which we assume that the choice behavior of each customer follows the Multinomial Logit (MNL) choice model, and the attraction parameters are unknown a priori. The retailer makes
Externí odkaz:
http://arxiv.org/abs/2304.02022