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pro vyhledávání: '"Haoran"'
Autor:
Paul W. Kroll, Stephen Owen
Meng Haoran (689-740) was one of the most important poets of the'High Tang'period, the greatest age of Chinese poetry. In his own time he was famous for his poetry as well as for his distinctive personality. This is the first complete translation int
Autor:
Campiglio G; University of Milan, Italy. info@gianlucacampiglio.it.
Publikováno v:
Aesthetic plastic surgery [Aesthetic Plast Surg] 2023 Oct; Vol. 47 (5), pp. 1740-1742. Date of Electronic Publication: 2023 Jun 08.
Autor:
Wei, Haoran, Liu, Chenglong, Chen, Jinyue, Wang, Jia, Kong, Lingyu, Xu, Yanming, Ge, Zheng, Zhao, Liang, Sun, Jianjian, Peng, Yuang, Han, Chunrui, Zhang, Xiangyu
Traditional OCR systems (OCR-1.0) are increasingly unable to meet people's usage due to the growing demand for intelligent processing of man-made optical characters. In this paper, we collectively refer to all artificial optical signals (e.g., plain
Externí odkaz:
http://arxiv.org/abs/2409.01704
Fiber-form optics extends the high-resolution tomographic imaging capabilities of Optical Coherence Tomography (OCT) to the inside of the human body, i.e., endoscopic OCT. However, it still faces challenges due to the trade-off between probe size, re
Externí odkaz:
http://arxiv.org/abs/2409.01252
Molecular line emissions are commonly used to trace the distribution and properties of molecular Interstellar Medium (ISM). However, the emissions are heavily blended on the Galactic disk toward the inner Galaxy because of the relatively large line w
Externí odkaz:
http://arxiv.org/abs/2409.01181
Graph Contrastive Learning (GCL) is a potent paradigm for self-supervised graph learning that has attracted attention across various application scenarios. However, GCL for learning on Text-Attributed Graphs (TAGs) has yet to be explored. Because con
Externí odkaz:
http://arxiv.org/abs/2409.01145
Autor:
Zheng, Xiuqi, Zhang, Yuhang, Zhang, Haoran, Liang, Hongrui, Bao, Xueqi, Jiang, Zhuqing, Lao, Qicheng
Adapting large pre-trained foundation models, e.g., SAM, for medical image segmentation remains a significant challenge. A crucial step involves the formulation of a series of specialized prompts that incorporate specific clinical instructions. Past
Externí odkaz:
http://arxiv.org/abs/2409.00695
Autor:
Gu, Lipeng, Wei, Mingqiang, Yan, Xuefeng, Zhu, Dingkun, Zhao, Wei, Xie, Haoran, Liu, Yong-Jin
Multi-modal 3D multi-object tracking (MOT) typically necessitates extensive computational costs of deep neural networks (DNNs) to extract multi-modal representations. In this paper, we propose an intriguing question: May we learn from multiple modali
Externí odkaz:
http://arxiv.org/abs/2409.00618
In nonmetallic crystals, heat is transported by phonons of different frequencies, each contributing differently to the overall heat flux spectrum. In this study, we demonstrate a significant redistribution of heat flux among phonon frequencies when p
Externí odkaz:
http://arxiv.org/abs/2409.00312
Autor:
Cui, Haoran, Panneerselvam, Iyyappa Rajan, Chakraborty, Pranay, Nian, Qiong, Liao, Yiliang, Wang, Yan
Heat transfer between graphene and water is pivotal for various applications, including solarthermal vapor generation and the advanced manufacturing of graphene-based hierarchical structures in solution. In this study, we employ a deep-neural network
Externí odkaz:
http://arxiv.org/abs/2408.16998