Zobrazeno 1 - 10
of 58 056
pro vyhledávání: '"AN Dongsheng"'
Publikováno v:
Guan'gai paishui xuebao, Vol 40, Iss 10, Pp 25-32 (2021)
【Background and objective】 Mulched drip irrigation is often used in dry regions for water-saving, but the mulching film could hinder rainfall infiltration and enhance surface runoff as a result. This could reduce the use of natural rainfall by pl
Externí odkaz:
https://doaj.org/article/30f9963aabe744eab37f3ce374994b22
The size of deep learning models has been increasing to enhance model quality. The linear increase in training computation budget with model size means that training an extremely large-scale model is exceedingly time-consuming. Recently, the Mixture
Externí odkaz:
http://arxiv.org/abs/2411.10003
Autor:
Shi, Lei, Yu, Xinghua, Zhou, Cheng, Jin, Wanxin, Chi, Wanchao, Zhang, Shenghao, Zhang, Dongsheng, Li, Xiong, Zhang, Zhengyou
This article propose a whole-body impedance coordinative control framework for a wheel-legged humanoid robot to achieve adaptability on complex terrains while maintaining robot upper body stability. The framework contains a bi-level control strategy.
Externí odkaz:
http://arxiv.org/abs/2411.09935
Deep reinforcement learning has led to dramatic breakthroughs in the field of artificial intelligence for the past few years. As the amount of rollout experience data and the size of neural networks for deep reinforcement learning have grown continuo
Externí odkaz:
http://arxiv.org/abs/2411.05614
This paper demonstrates that spatial information can be used to learn interpretable representations in medical images using Self-Supervised Learning (SSL). Our proposed method, ISImed, is based on the observation that medical images exhibit a much lo
Externí odkaz:
http://arxiv.org/abs/2410.16947
Autor:
Chen, Zhuomin, Ni, Jingchao, Salehi, Hojat Allah, Zheng, Xu, Schafir, Esteban, Shirani, Farhad, Luo, Dongsheng
Graph representation learning (GRL), enhanced by graph augmentation methods, has emerged as an effective technique achieving performance improvements in wide tasks such as node classification and graph classification. In self-supervised GRL, paired g
Externí odkaz:
http://arxiv.org/abs/2410.12657
Autor:
Zhu, Chenghao, Harikane, Yuichi, Ouchi, Masami, Ono, Yoshiaki, Onodera, Masato, Tang, Shenli, Isobe, Yuki, Matsuoka, Yoshiki, Kawaguchi, Toshihiro, Umeda, Hiroya, Nakajima, Kimihiko, Liang, Yongming, Xu, Yi, Zhang, Yechi, Sun, Dongsheng, Shimasaku, Kazuhiro, Greene, Jenny, Iwasawa, Kazushi, Kohno, Kotaro, Nagao, Tohru, Schulze, Andreas, Shibuya, Takatoshi, Hilmi, Miftahul, Schramm, Malte
We present deep Subaru/FOCAS spectra for two extreme emission line galaxies (EELGs) at $z\sim 1$ with strong {\sc[Oiii]}$\lambda$5007 emission lines, exhibiting equivalent widths (EWs) of $2905^{+946}_{-578}$ \AA\ and $2000^{+188}_{-159}$ \AA, compar
Externí odkaz:
http://arxiv.org/abs/2410.12198
Autor:
Ma, Tengfei, Lin, Xuan, Li, Tianle, Li, Chaoyi, Chen, Long, Zhou, Peng, Cai, Xibao, Yang, Xinyu, Zeng, Daojian, Cao, Dongsheng, Zeng, Xiangxiang
Large Language Models (LLMs) have recently demonstrated remarkable performance in general tasks across various fields. However, their effectiveness within specific domains such as drug development remains challenges. To solve these challenges, we int
Externí odkaz:
http://arxiv.org/abs/2410.11550
Although existing variational graph autoencoders (VGAEs) have been widely used for modeling and generating graph-structured data, most of them are still not flexible enough to approximate the sparse and skewed latent node representations, especially
Externí odkaz:
http://arxiv.org/abs/2410.09696
Autor:
Liu, Jiahao, Shao, YiYang, Zhang, Peng, Li, Dongsheng, Gu, Hansu, Chen, Chao, Du, Longzhi, Lu, Tun, Gu, Ning
Personalized algorithms can inadvertently expose users to discomforting recommendations, potentially triggering negative consequences. The subjectivity of discomfort and the black-box nature of these algorithms make it challenging to effectively iden
Externí odkaz:
http://arxiv.org/abs/2410.05411