Zobrazeno 1 - 10
of 57 735
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
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
Autor:
Dai, Xinnan, Qu, Haohao, Shen, Yifen, Zhang, Bohang, Wen, Qihao, Fan, Wenqi, Li, Dongsheng, Tang, Jiliang, Shan, Caihua
Benchmarking the capabilities and limitations of large language models (LLMs) in graph-related tasks is becoming an increasingly popular and crucial area of research. Recent studies have shown that LLMs exhibit a preliminary ability to understand gra
Externí odkaz:
http://arxiv.org/abs/2410.05298
Autor:
Zheng, Xu, Shirani, Farhad, Chen, Zhuomin, Lin, Chaohao, Cheng, Wei, Guo, Wenbo, Luo, Dongsheng
Recent research has developed a number of eXplainable AI (XAI) techniques. Although extracting meaningful insights from deep learning models, how to properly evaluate these XAI methods remains an open problem. The most widely used approach is to pert
Externí odkaz:
http://arxiv.org/abs/2410.02970
Recently, there has been a growing interest in Long-term Time Series Forecasting (LTSF), which involves predicting long-term future values by analyzing a large amount of historical time-series data to identify patterns and trends. There exist signifi
Externí odkaz:
http://arxiv.org/abs/2410.02081