Zobrazeno 1 - 10
of 3 953
pro vyhledávání: '"Li, Guoliang"'
The growing importance of data visualization in business intelligence and data science emphasizes the need for tools that can efficiently generate meaningful visualizations from large datasets. Existing tools fall into two main categories: human-powe
Externí odkaz:
http://arxiv.org/abs/2406.11033
Translating users' natural language questions into SQL queries (i.e., NL2SQL) significantly lowers the barriers to accessing relational databases. The emergence of Large Language Models has introduced a novel paradigm in NL2SQL tasks, enhancing capab
Externí odkaz:
http://arxiv.org/abs/2406.01265
Since Gartner coined the term, Hybrid Transactional and Analytical Processing (HTAP), numerous HTAP databases have been proposed to combine transactions with analytics in order to enable real-time data analytics for various data-intensive application
Externí odkaz:
http://arxiv.org/abs/2404.15670
Autor:
Ji, Zhiyou, Li, Guoliang, Han, Ruihua, Wang, Shuai, Bai, Bing, Xu, Wei, Ye, Kejiang, Xu, Chengzhong
Robotic data gathering (RDG) is an emerging paradigm that navigates a robot to harvest data from remote sensors. However, motion planning in this paradigm needs to maximize the RDG efficiency instead of the navigation efficiency, for which the existi
Externí odkaz:
http://arxiv.org/abs/2404.10541
Integrated sensing and communication (ISAC) is a promising solution to accelerate edge inference via the dual use of wireless signals. However, this paradigm needs to minimize the inference error and latency under ISAC co-functionality interference,
Externí odkaz:
http://arxiv.org/abs/2404.10235
Autor:
Li, Xu, Sun, Ruiqi, Lv, Jiameng, Jia, Peng, Li, Nan, Wei, Chengliang, Hu, Zou, Er, Xinzhong, Chen, Yun, Ban, Zhang, Fang, Yuedong, Guo, Qi, Liu, Dezi, Li, Guoliang, Lin, Lin, Li, Ming, Li, Ran, Li, Xiaobo, Luo, Yu, Meng, Xianmin, Nie, Jundan, Qi, Zhaoxiang, Qiu, Yisheng, Shao, Li, Tian, Hao, Wang, Lei, Wang, Wei, Xian, Jingtian, Xu, Youhua, Zhang, Tianmeng, Zhang, Xin, Zhou, Zhimin
Strong gravitational lensing is a powerful tool for investigating dark matter and dark energy properties. With the advent of large-scale sky surveys, we can discover strong lensing systems on an unprecedented scale, which requires efficient tools to
Externí odkaz:
http://arxiv.org/abs/2404.01780
Autor:
Xu, Lijie, Xie, Chulin, Guo, Yiran, Alonso, Gustavo, Li, Bo, Li, Guoliang, Wang, Wei, Wu, Wentao, Zhang, Ce
Current federated learning (FL) approaches view decentralized training data as a single table, divided among participants either horizontally (by rows) or vertically (by columns). However, these approaches are inadequate for handling distributed rela
Externí odkaz:
http://arxiv.org/abs/2403.15839
Autor:
Wang, Lei, Shan, Huanyuan, Nie, Lin, Liu, Dezi, Yan, Zhaojun, Li, Guoliang, Cheng, Cheng, Xie, Yushan, Qu, Han, Zheng, Wenwen, Kang, Xi
We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer algorithms. Our approach not only allows for the anti-aliasing of the images but also enables P
Externí odkaz:
http://arxiv.org/abs/2402.18010
Autor:
Song, Yingxiao, Xiong, Qi, Gong, Yan, Deng, Furen, Chan, Kwan Chuen, Chen, Xuelei, Guo, Qi, Han, Jiaxin, Li, Guoliang, Li, Ming, Liu, Yun, Luo, Yu, Pei, Wenxiang, Wei, Chengliang
Void size function (VSF) contains information of the cosmic large-scale structure (LSS), and can be used to derive the properties of dark energy and dark matter. We predict the VSFs measured from the spectroscopic galaxy survey operated by the China
Externí odkaz:
http://arxiv.org/abs/2402.05492
Machine learning (ML) techniques for optimizing data management problems have been extensively studied and widely deployed in recent five years. However traditional ML methods have limitations on generalizability (adapting to different scenarios) and
Externí odkaz:
http://arxiv.org/abs/2402.02643