Zobrazeno 1 - 10
of 216
pro vyhledávání: '"Bi Xuan"'
Autor:
Xian, Xun, Wang, Ganghua, Bi, Xuan, Srinivasa, Jayanth, Kundu, Ashish, Fleming, Charles, Hong, Mingyi, Ding, Jie
Retrieval-Augmented Generation (RAG) has been empirically shown to enhance the performance of large language models (LLMs) in knowledge-intensive domains such as healthcare, finance, and legal contexts. Given a query, RAG retrieves relevant documents
Externí odkaz:
http://arxiv.org/abs/2409.17275
Autor:
Xian, Xun, Wang, Ganghua, Bi, Xuan, Srinivasa, Jayanth, Kundu, Ashish, Hong, Mingyi, Ding, Jie
Safeguarding intellectual property and preventing potential misuse of AI-generated images are of paramount importance. This paper introduces a robust and agile plug-and-play watermark detection framework, dubbed as RAW. As a departure from traditiona
Externí odkaz:
http://arxiv.org/abs/2403.18774
Autor:
Ping-Ting Hu, Yu-Ni Huang, Meng-Ni Zhang, Bi-Xuan Chen, Shu-Shu Huang, Ren-Kun Li, Wen-Guang Wang, Xin-Xin Feng
Publikováno v:
PhytoKeys, Vol 249, Iss , Pp 277-285 (2024)
Although Guangxi represents one of the distribution centres of begonias in China, the sect. Diploclinium (Wright) A. DC is not well documented herein. In this article, we illustrate a new species belonging to this section, Begonia fangchengensis Y.N.
Externí odkaz:
https://doaj.org/article/56f6b46c4a504818aade3249f94a5ce6
Autor:
Wang, Ganghua, Xian, Xun, Srinivasa, Jayanth, Kundu, Ashish, Bi, Xuan, Hong, Mingyi, Ding, Jie
The growing dependence on machine learning in real-world applications emphasizes the importance of understanding and ensuring its safety. Backdoor attacks pose a significant security risk due to their stealthy nature and potentially serious consequen
Externí odkaz:
http://arxiv.org/abs/2310.10780
Federated learning (FL) is a privacy-preserving learning technique that enables distributed computing devices to train shared learning models across data silos collaboratively. Existing FL works mostly focus on designing advanced FL algorithms to imp
Externí odkaz:
http://arxiv.org/abs/2302.08976
Autor:
Bao, Dong-Wei, Brotherton, Michael S., Du, Pu, McLane, Jacob N., Zastrocky, T. E., Olson, Kianna A., Fang, Feng-Na, Zhai, Shuo, Huang, Zheng-Peng, Wang, Kai, Zhao, Bi-Xuan, Li, Sha-Sha, Yang, Sen, Chen, Yong-Jie, Liu, Jun-Rong, Yao, Zhu-Heng, Peng, Yue-Chang, Guo, Wei-Jian, Songsheng, Yu-Yang, Li, Yan-Rong, Jiang, Bo-Wei, Kasper, David H., Chick, William T., Nguyen, My L., Maithil, Jaya, Kobulnicky, H. A., Dale, D. A., Hand, Derek, Adelman, C., Carter, Z., Murphree, A. M., Oeur, M., Schonsberg, S., Roth, T., Winkler, Hartmut, Marziani, Paola, D'Onofrio, Mauro, Hu, Chen, Xiao, Ming, Xue, Suijian, Czerny, Bożena, Aceituno, Jesús, Ho, Luis C., Bai, Jin-Ming, Wang, Jian-Min
In this third paper of the series reporting on the reverberation mapping (RM) campaign of active galactic nuclei with asymmetric H$\beta$ emission-line profiles, we present results for 15 Palomar-Green (PG) quasars using spectra obtained between the
Externí odkaz:
http://arxiv.org/abs/2207.00297
Autor:
Bi, Xuan, Shen, Xiaotong
Differential privacy is becoming one gold standard for protecting the privacy of publicly shared data. It has been widely used in social science, data science, public health, information technology, and the U.S. decennial census. Nevertheless, to gua
Externí odkaz:
http://arxiv.org/abs/2111.05791
Due to accessible big data collections from consumers, products, and stores, advanced sales forecasting capabilities have drawn great attention from many companies especially in the retail business because of its importance in decision making. Improv
Externí odkaz:
http://arxiv.org/abs/2011.03452
Autor:
Yu, Li-Ming, Bian, Wei-Hao, Zhang, Xue-Guang, Zhao, Bi-Xuan, Wang, Chan, Ge, Xue, Zhu, Bing-Qian, Chen, Yu-Qin
Using different kinds of velocity tracers derived from the broad H$\beta$ profile (in the mean or rms spectrum) and the corresponding virial factors $f$, the central supermassive black hole (SMBH) masses ($M_{\rm BH}$) are calculated for a compiled s
Externí odkaz:
http://arxiv.org/abs/2008.06623
Recommender systems have been extensively used by the entertainment industry, business marketing and the biomedical industry. In addition to its capacity of providing preference-based recommendations as an unsupervised learning methodology, it has be
Externí odkaz:
http://arxiv.org/abs/2003.05568