Zobrazeno 1 - 10
of 11 858
pro vyhledávání: '"WANG Xiaoyu"'
Computational models are invaluable in capturing the complexities of real-world biological processes. Yet, the selection of appropriate algorithms for inference tasks, especially when dealing with real-world observational data, remains a challenging
Externí odkaz:
http://arxiv.org/abs/2409.19675
Autor:
Xi, Ningyuan, Wang, Xiaoyu, Wu, Yetao, Chen, Teng, Gu, Qingqing, Qu, Jinxian, Jiang, Zhonglin, Chen, Yong, Ji, Luo
Large Language Model can reasonably understand and generate human expressions but may lack of thorough thinking and reasoning mechanisms. Recently there have been several studies which enhance the thinking ability of language models but most of them
Externí odkaz:
http://arxiv.org/abs/2409.12059
Autor:
Wang, Xiaoyu, Ouyang, Haoyong, Bhasuran, Balu, Luo, Xiao, Hanna, Karim, Lustria, Mia Liza A., He, Zhe
Accurate interpretation of lab results is crucial in clinical medicine, yet most patient portals use universal normal ranges, ignoring factors like age and gender. This study introduces Lab-AI, an interactive system that offers personalized normal ra
Externí odkaz:
http://arxiv.org/abs/2409.18986
Efficient traffic signal control is essential for managing urban transportation, minimizing congestion, and improving safety and sustainability. Reinforcement Learning (RL) has emerged as a promising approach to enhancing adaptive traffic signal cont
Externí odkaz:
http://arxiv.org/abs/2409.10693
Adaptive optimizers have emerged as powerful tools in deep learning, dynamically adjusting the learning rate based on iterative gradients. These adaptive methods have significantly succeeded in various deep learning tasks, outperforming stochastic gr
Externí odkaz:
http://arxiv.org/abs/2409.05023
Perimeter control prevents loss of traffic network capacity due to congestion in urban areas. Homogeneous perimeter control allows all access points to a protected region to have the same maximal permitted inflow. However, homogeneous perimeter contr
Externí odkaz:
http://arxiv.org/abs/2409.00753
Federated Learning (FL) aims to train a shared model using data and computation power on distributed agents coordinated by a central server. Decentralized FL (DFL) utilizes local model exchange and aggregation between agents to reduce the communicati
Externí odkaz:
http://arxiv.org/abs/2408.14001
Autor:
Han, Rong, Liu, Xiaohong, Pan, Tong, Xu, Jing, Wang, Xiaoyu, Lan, Wuyang, Li, Zhenyu, Wang, Zixuan, Song, Jiangning, Wang, Guangyu, Chen, Ting
Accurately measuring protein-RNA binding affinity is crucial in many biological processes and drug design. Previous computational methods for protein-RNA binding affinity prediction rely on either sequence or structure features, unable to capture the
Externí odkaz:
http://arxiv.org/abs/2409.03773
Recently, the LHCb Collaboration has measured two decay processes $B^0\to\bar{D}^0D_s^+\pi^-$ and $B^+\to D^-D_s^+\pi^+$ related to isospin symmetry, where two new open-flavor tetraquark states $T_{c\bar{s}}(2900)^0$ and $T_{c\bar{s}}(2900)^{++}$ tha
Externí odkaz:
http://arxiv.org/abs/2408.11454
Unfolded proximal neural networks (PNNs) form a family of methods that combines deep learning and proximal optimization approaches. They consist in designing a neural network for a specific task by unrolling a proximal algorithm for a fixed number of
Externí odkaz:
http://arxiv.org/abs/2408.08742