Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Liang Yao"'
Fine-tuning techniques based on Large Pretrained Language Models (LPLMs) have been proven to significantly enhance model performance on a variety of downstream tasks and effectively control the output behaviors of LPLMs. Recent studies have proposed
Externí odkaz:
http://arxiv.org/abs/2403.07440
The physics of bound states in the continuum (BICs) allows to design and demonstrate optical resonant structures with large values of the quality factor ($Q$-factor) by employing dielectric structures with low losses. However, BIC is a general wave p
Externí odkaz:
http://arxiv.org/abs/2311.13452
This paper presents Social data and knowledge collective intelligence platform for TRaining Ethical AI Models (STREAM) to address the challenge of aligning AI models with human moral values, and to provide ethics datasets and knowledge bases to help
Externí odkaz:
http://arxiv.org/abs/2310.05563
Autor:
Zhou, Hongyan, Liang, Yao
The current learning process of deep learning, regardless of any deep neural network (DNN) architecture and/or learning algorithm used, is essentially a single resolution training. We explore multiresolution learning and show that multiresolution lea
Externí odkaz:
http://arxiv.org/abs/2309.13752
Publikováno v:
The Astrophysical Journal, 944:230 (55pp), 2023 February 20
Kinematic signatures of the jet, winds, multicavities, and episodic shells arising in the unified model of bipolar outflows developed in Shang et al.\ (2020), in which an outflow forms by radially directed, wide-angle toroidally magnetized winds inte
Externí odkaz:
http://arxiv.org/abs/2301.07447
Reasoning and question answering as a basic cognitive function for humans, is nevertheless a great challenge for current artificial intelligence. Although the Differentiable Neural Computer (DNC) model could solve such problems to a certain extent, t
Externí odkaz:
http://arxiv.org/abs/2301.02809
How neural networks in the human brain represent commonsense knowledge, and complete related reasoning tasks is an important research topic in neuroscience, cognitive science, psychology, and artificial intelligence. Although the traditional artifici
Externí odkaz:
http://arxiv.org/abs/2207.05561
Autor:
Alemanno, Francesca, An, Qi, Azzarello, Philipp, Barbato, Felicia Carla Tiziana, Bernardini, Paolo, Bi, Xiao-Jun, Cai, Ming-Sheng, Casilli, Elisabetta, Catanzani, Enrico, Chang, Jin, Chen, Deng-Yi, Chen, Jun-Ling, Chen, Zhan-Fang, Cui, Ming-Yang, Cui, Tian-Shu, Cui, Yu-Xing, Dai, Hao-Ting, De Benedittis, Antonio, De Mitri, Ivan, de Palma, Francesco, Deliyergiyev, Maksym, Di Santo, Margherita, Ding, Qi, Dong, Tie-Kuang, Dong, Zhen-Xing, Donvito, Giacinto, Droz, David, Duan, Jing-Lai, Duan, Kai-Kai, D'Urso, Domenico, Fan, Rui-Rui, Fan, Yi-Zhong, Fang, Fang, Fang, Kun, Feng, Chang-Qing, Feng, Lei, Fusco, Piergiorgio, Gao, Min, Gargano, Fabio, Gong, Ke, Gong, Yi-Zhong, Guo, Dong-Ya, Guo, Jian-Hua, Han, Shuang-Xue, Hu, Yi-Ming, Huang, Guang-Shun, Huang, Xiao-Yuan, Huang, Yong-Yi, Ionica, Maria, Jiang, Wei, Kong, Jie, Kotenko, Andrii, Kyratzis, Dimitrios, Lei, Shi-Jun, Li, Shang, Li, Wen-Hao, Li, Wei-Liang, Li, Xiang, Li, Xian-Qiang, Liang, Yao-Ming, Liu, Cheng-Ming, Liu, Hao, Liu, Jie, Liu, Shu-Bin, Liu, Yang, Loparco, Francesco, Luo, Chuan-Ning, Ma, Miao, Ma, Peng-Xiong, Ma, Tao, Ma, Xiao-Yong, Marsella, Giovanni, Mazziotta, Mario Nicola, Mo, Dan, Mu, Maria, Niu, Xiao-Yang, Pan, Xu, Parenti, Andrea, Peng, Wen-Xi, Peng, Xiao-Yan, Perrina, Chiara, Qiao, Rui, Rao, Jia-Ning, Ruina, Arshia, Shangguan, Zhi, Shen, Wei-Hua, Shen, Zhao-Qiang, Shen, Zhong-Tao, Silveri, Leandro, Song, Jing-Xing, Stolpovskiy, Mikhail, Su, Hong, Su, Meng, Sun, Hao-Ran, Sun, Zhi-Yu, Surdo, Antonio, Teng, Xue-Jian, Tykhonov, Andrii, Wang, Jin-Zhou, Wang, Lian-Guo, Wang, Shen, Wang, Shu-Xin, Wang, Xiao-Lian, Wang, Ying, Wang, Yan-Fang, Wang, Yuan-Zhu, Wei, Da-Ming, Wei, Jia-Ju, Wei, Yi-Feng, Wu, Di, Wu, Jian, Wu, Li-Bo, Wu, Sha-Sha, Wu, Xin, Xia, Zi-Qing, Xu, En-Heng, Xu, Hai-Tao, Xu, Zhi-Hui, Xu, Zun-Lei, Xu, Zi-Zong, Xue, Guo-Feng, Yang, Hai-Bo, Yang, Peng, Yang, Ya-Qing, Yao, Hui-Jun, Yu, Yu-Hong, Yuan, Guan-Wen, Yuan, Qiang, Yue, Chuan, Zang, Jing-Jing, Zhang, Sheng-Xia, Zhang, Wen-Zhang, Zhang, Yan, Zhang, Yi, Zhang, Yong-Jie, Zhang, Yun-Long, Zhang, Ya-Peng, Zhang, Yong-Qiang, Zhang, Zhe, Zhang, Zhi-Yong, Zhao, Cong, Zhao, Hong-Yun, Zhao, Xun-Feng, Zhou, Chang-Yi, Zhu, Yan, Liang, Yun-Feng
Publikováno v:
Science Bulletin, Volume 67, Issue 7, 15 April 2022, Pages 679-684
The DArk Matter Particle Explorer (DAMPE) is well suitable for searching for monochromatic and sharp $\gamma$-ray structures in the GeV$-$TeV range thanks to its unprecedented high energy resolution. In this work, we search for $\gamma$-ray line stru
Externí odkaz:
http://arxiv.org/abs/2112.08860
Publikováno v:
Journal of Lightwave Technology vol.39 Early Access (2021)
We theoretically investigate and optimize four-wave mixing (FWM) in silicon nitride (SiN) waveguides integrated with two-dimensional (2D) layered graphene oxide (GO) films. Based on extensive previous measurements of the material parameters of the GO
Externí odkaz:
http://arxiv.org/abs/2102.07118
A fundamental challenge in estimations of daily streamflow time series at sites with incomplete records is how to effectively and efficiently select reference or donor gauges from an existing gauge network to infer the missing data. While research on
Externí odkaz:
http://arxiv.org/abs/2012.08652