Zobrazeno 1 - 10
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pro vyhledávání: '"Shen, Zhiqiang"'
Autor:
Yan, Zhen, Shen, Zhiqiang, Jiang, Peng, Zhang, Bo, Zhang, Haiyan, Cui, Lang, Luo, Jintao, Chen, Rurong, Jiang, Wu, Zhang, Hua, Wu, De, Zhao, Rongbing, Yuan, Jianping, Hu, Yue, Wu, Yajun, Xia, Bo, Li, Guanghui, Rao, Yongnan, Chen, Chenyu, Wang, Xiaowei, Ding, Hao, Liu, Yongpeng, Zhang, Fuchen, Jiang, Yongbin
The importance of Very Long Baseline Interferometry (VLBI) for pulsar research is becoming increasingly prominent and receiving more and more attention. In this paper, we present pathfinding pulsar observation results with the Chinese VLBI Network (C
Externí odkaz:
http://arxiv.org/abs/2409.16059
Autor:
Peng, Sijia, Lu, Ru-Sen, Goddi, Ciriaco, Krichbaum, Thomas P., Li, Zhiyuan, Liu, Ruo-Yu, Kim, Jae-Young, Nakamura, Masanori, Yuan, Feng, Chen, Liang, Marti-Vidal, Ivan, Shen, Zhiqiang
Faraday rotation is an important probe of the magnetic fields and magnetized plasma around active galactic nuclei (AGN) jets. We present a Faraday rotation measure image of the M87 jet between 85.2 GHz and 101.3 GHz with a resolution of ~2" with the
Externí odkaz:
http://arxiv.org/abs/2409.12028
Time series forecasting requires balancing short-term and long-term dependencies for accurate predictions. Existing methods mainly focus on long-term dependency modeling, neglecting the complexities of short-term dynamics, which may hinder performanc
Externí odkaz:
http://arxiv.org/abs/2408.15997
Mix-up is a key technique for consistency regularization-based semi-supervised learning methods, generating strong-perturbed samples for strong-weak pseudo-supervision. Existing mix-up operations are performed either randomly or with predefined rules
Externí odkaz:
http://arxiv.org/abs/2407.21586
Recently, there has been a surge of interest in developing graph neural networks that utilize the invariance principle on graphs to generalize the out-of-distribution (OOD) data. Due to the limited knowledge about OOD data, existing approaches often
Externí odkaz:
http://arxiv.org/abs/2407.11083
This work presents a Fully BInarized Large Language Model (FBI-LLM), demonstrating for the first time how to train a large-scale binary language model from scratch (not the partial binary or ternary LLM like BitNet b1.58) to match the performance of
Externí odkaz:
http://arxiv.org/abs/2407.07093
The existing barely-supervised medical image segmentation (BSS) methods, adopting a registration-segmentation paradigm, aim to learn from data with very few annotations to mitigate the extreme label scarcity problem. However, this paradigm poses a ch
Externí odkaz:
http://arxiv.org/abs/2407.05248
Autor:
Yun, Sukmin, Lin, Haokun, Thushara, Rusiru, Bhat, Mohammad Qazim, Wang, Yongxin, Jiang, Zutao, Deng, Mingkai, Wang, Jinhong, Tao, Tianhua, Li, Junbo, Li, Haonan, Nakov, Preslav, Baldwin, Timothy, Liu, Zhengzhong, Xing, Eric P., Liang, Xiaodan, Shen, Zhiqiang
Multimodal large language models (MLLMs) have shown impressive success across modalities such as image, video, and audio in a variety of understanding and generation tasks. However, current MLLMs are surprisingly poor at understanding webpage screens
Externí odkaz:
http://arxiv.org/abs/2406.20098
Multiple-choice questions (MCQ) are frequently used to assess large language models (LLMs). Typically, an LLM is given a question and selects the answer deemed most probable after adjustments for factors like length. Unfortunately, LLMs may inherentl
Externí odkaz:
http://arxiv.org/abs/2406.07545
Autor:
Chen, Jiahao, Shen, Zhiqiang, Pu, Yuwen, Zhou, Chunyi, Li, Changjiang, Li, Jiliang, Wang, Ting, Ji, Shouling
Face Recognition Systems (FRS) have increasingly integrated into critical applications, including surveillance and user authentication, highlighting their pivotal role in modern security systems. Recent studies have revealed vulnerabilities in FRS to
Externí odkaz:
http://arxiv.org/abs/2405.12786