Zobrazeno 1 - 10
of 1 710
pro vyhledávání: '"Liu, Haoran"'
Autor:
Kim, Jaehyeon, Tian, Yue, Qiao, Guanhua, Villarta, Julinna Abulencia, Zhao, Fujia, He, Andrew, Ho, Ruo-Jing, Liu, Haoran, Bhargava, Rohit, Zhang, Yingjie
Fourier-transform infrared spectroscopy (FTIR) is a powerful analytical method for not only the chemical identification of solid, liquid, and gas species, but also the quantification of their concentration. However, the chemical quantification capabi
Externí odkaz:
http://arxiv.org/abs/2409.09151
Improving the efficiency of current neural networks and modeling them in biological neural systems have become popular research directions in recent years. Pulse-coupled neural network (PCNN) is a well applicated model for imitating the computation c
Externí odkaz:
http://arxiv.org/abs/2403.17512
Autor:
Huang, Chaozhi, Xu, Chengyang, Zhu, Fengfeng, Duan, Shaofeng, Liu, Jianzhe, Gu, Lingxiao, Wang, Shichong, Liu, Haoran, Qian, Dong, Luo, Weidong, Zhang, Wentao
Publikováno v:
Chinese Physics B 33, 017901 (2024)
High-resolution time- and angle-resolved photoemission measurements were conducted on the topological insulator ZrTe5. With strong femtosecond photoexcitation, a possible ultrafast phase transition from a weak to a strong topological insulating phase
Externí odkaz:
http://arxiv.org/abs/2403.11518
KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques
Autor:
Yang, Rui, Liu, Haoran, Marrese-Taylor, Edison, Zeng, Qingcheng, Ke, Yu He, Li, Wanxin, Cheng, Lechao, Chen, Qingyu, Caverlee, James, Matsuo, Yutaka, Li, Irene
Large language models (LLMs) have demonstrated impressive generative capabilities with the potential to innovate in medicine. However, the application of LLMs in real clinical settings remains challenging due to the lack of factual consistency in the
Externí odkaz:
http://arxiv.org/abs/2403.05881
As powerful tools for representation learning on graphs, graph neural networks (GNNs) have played an important role in applications including social networks, recommendation systems, and online web services. However, GNNs have been shown to be vulner
Externí odkaz:
http://arxiv.org/abs/2308.15614
Ultrafast Switching from the Charge Density Wave Phase to a Metastable Metallic State in 1T-TiSe$_2$
Autor:
Duan, Shaofeng, Xia, Wei, Huang, Chaozhi, Wang, Shichong, Gu, Lingxiao, Liu, Haoran, Xiang, Dao, Qian, Dong, Guo, Yanfeng, Zhang, Wentao
Publikováno v:
Phys. Rev. Lett. 130, 226501 (2023)
The ultrafast electronic structures of the charge density wave material 1T-TiSe$_2$ were investigated by high-resolution time- and angle-resolved photoemission spectroscopy. We found that the quasiparticle populations drove ultrafast electronic phase
Externí odkaz:
http://arxiv.org/abs/2306.00311
Publikováno v:
IEEE Transactions on Nuclear Science (2024)
This study utilized the Tempotron, a robust classifier based on a third-generation neural network model, for pulse shape discrimination. By eliminating the need for manual feature extraction, the Tempotron model can process pulse signals directly, ge
Externí odkaz:
http://arxiv.org/abs/2305.18205
The publicly accessible dataset includes neutron and gamma-ray pulse signals for conducting pulse shape discrimination experiments. Several traditional and recently proposed pulse shape discrimination algorithms are utilized to evaluate the performan
Externí odkaz:
http://arxiv.org/abs/2305.18242
Normalizing flows (NFs) provide a powerful tool to construct an expressive distribution by a sequence of trackable transformations of a base distribution and form a probabilistic model of underlying data. Rotation, as an important quantity in compute
Externí odkaz:
http://arxiv.org/abs/2304.03937
Autor:
Xu, Yinzhen, Wan, Weikang, Zhang, Jialiang, Liu, Haoran, Shan, Zikang, Shen, Hao, Wang, Ruicheng, Geng, Haoran, Weng, Yijia, Chen, Jiayi, Liu, Tengyu, Yi, Li, Wang, He
In this work, we tackle the problem of learning universal robotic dexterous grasping from a point cloud observation under a table-top setting. The goal is to grasp and lift up objects in high-quality and diverse ways and generalize across hundreds of
Externí odkaz:
http://arxiv.org/abs/2303.00938