Zobrazeno 1 - 10
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pro vyhledávání: '"Zhang, Junhao"'
Autor:
Zhao, Rui, Yuan, Hangjie, Wei, Yujie, Zhang, Shiwei, Gu, Yuchao, Ran, Lingmin, Wang, Xiang, Wu, Zhangjie, Zhang, Junhao, Zhang, Yingya, Shou, Mike Zheng
Recent advancements in generation models have showcased remarkable capabilities in generating fantastic content. However, most of them are trained on proprietary high-quality data, and some models withhold their parameters and only provide accessible
Externí odkaz:
http://arxiv.org/abs/2410.07133
Autor:
Duan, Renjun, Zhang, Junhao
This paper studies the boundary value problem on the steady compressible Navier-Stokes-Fourier system in a channel domain $(0,1)\times\mathbb{T}^2$ with a class of generalized slip boundary conditions that were systematically derived from the Boltzma
Externí odkaz:
http://arxiv.org/abs/2409.11809
Distinct from the $\text{SU}(2)$ case, the fermionic systems with $\text{SU}(N)$ symmetry are expected to exhibit novel physics, such as exotic singlet formation. Using the density matrix renormalization group technique, we obtain the ground state ph
Externí odkaz:
http://arxiv.org/abs/2409.09344
This paper introduces ConStyle v2, a strong plug-and-play prompter designed to output clean visual prompts and assist U-Net Image Restoration models in handling multiple degradations. The joint training process of IRConStyle, an Image Restoration fra
Externí odkaz:
http://arxiv.org/abs/2406.18242
Functional magnetic resonance imaging (fMRI) has been increasingly employed to investigate functional brain activity. Many fMRI-related software/toolboxes have been developed, providing specialized algorithms for fMRI analysis. However, existing tool
Externí odkaz:
http://arxiv.org/abs/2405.06178
Autor:
Zheng, Yaowei, Zhang, Richong, Zhang, Junhao, Ye, Yanhan, Luo, Zheyan, Feng, Zhangchi, Ma, Yongqiang
Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks. However, it requires non-trivial efforts to implement these methods on different models. We present LlamaFactory, a unified framework that integrates a suit
Externí odkaz:
http://arxiv.org/abs/2403.13372
Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive approach to examining abnormal brain connectivity associated with brain disorders. Graph neural network (GNN) gains popularity in fMRI representation learning and bra
Externí odkaz:
http://arxiv.org/abs/2308.10302
Traffic flow forecasting is challenging due to the intricate spatio-temporal correlations in traffic flow data. Existing Transformer-based methods usually treat traffic flow forecasting as multivariate time series (MTS) forecasting. However, too many
Externí odkaz:
http://arxiv.org/abs/2303.07685
Autor:
Shu, Zhiheng1 (AUTHOR), Zhang, Junhao1 (AUTHOR), Zhou, Qingwen1 (AUTHOR), Peng, Yingjie2 (AUTHOR), Huang, Yuanhao1 (AUTHOR), Zhou, Yi1 (AUTHOR), Zheng, Jun1 (AUTHOR), Zhao, Manya1 (AUTHOR), Hu, Chao1 (AUTHOR) 546092186@qq.com, Lan, Shile1 (AUTHOR) lans2016@hunau.edu.cn
Publikováno v:
BMC Veterinary Research. 9/20/2024, Vol. 20 Issue 1, p1-14. 14p.
Autor:
Zhang, Junhao, Yi, Zheyuan, Zhao, Yujiao, Xiao, Linfang, Hu, Jiahao, Man, Christopher, Lau, Vick, Su, Shi, Chen, Fei, Leong, Alex T. L., Wu, Ed X.
Purpose: To develop a truly calibrationless reconstruction method that derives ESPIRiT maps from uniformly-undersampled multi-channel MR data by deep learning. Methods: ESPIRiT, one commonly used parallel imaging reconstruction technique, forms the i
Externí odkaz:
http://arxiv.org/abs/2210.14481