Zobrazeno 1 - 10
of 2 503
pro vyhledávání: '"Zhou Yifan"'
Autor:
Poon, Michael, Bryan, Marta L., Rein, Hanno, Morley, Caroline V., Mace, Gregory, Zhou, Yifan, Bowler, Brendan P.
We constrain the angular momentum architecture of VHS J125601.92-125723.9, a 140 $\pm$ 20 Myr old hierarchical triple system composed of a low-mass binary and a widely-separated planetary-mass companion VHS 1256 b. VHS 1256 b has been a prime target
Externí odkaz:
http://arxiv.org/abs/2410.02672
Autor:
Zhong, Tianyang, Liu, Zhengliang, Pan, Yi, Zhang, Yutong, Zhou, Yifan, Liang, Shizhe, Wu, Zihao, Lyu, Yanjun, Shu, Peng, Yu, Xiaowei, Cao, Chao, Jiang, Hanqi, Chen, Hanxu, Li, Yiwei, Chen, Junhao, Hu, Huawen, Liu, Yihen, Zhao, Huaqin, Xu, Shaochen, Dai, Haixing, Zhao, Lin, Zhang, Ruidong, Zhao, Wei, Yang, Zhenyuan, Chen, Jingyuan, Wang, Peilong, Ruan, Wei, Wang, Hui, Zhao, Huan, Zhang, Jing, Ren, Yiming, Qin, Shihuan, Chen, Tong, Li, Jiaxi, Zidan, Arif Hassan, Jahin, Afrar, Chen, Minheng, Xia, Sichen, Holmes, Jason, Zhuang, Yan, Wang, Jiaqi, Xu, Bochen, Xia, Weiran, Yu, Jichao, Tang, Kaibo, Yang, Yaxuan, Sun, Bolun, Yang, Tao, Lu, Guoyu, Wang, Xianqiao, Chai, Lilong, Li, He, Lu, Jin, Sun, Lichao, Zhang, Xin, Ge, Bao, Hu, Xintao, Zhang, Lian, Zhou, Hua, Zhang, Lu, Zhang, Shu, Liu, Ninghao, Jiang, Bei, Kong, Linglong, Xiang, Zhen, Ren, Yudan, Liu, Jun, Jiang, Xi, Bao, Yu, Zhang, Wei, Li, Xiang, Li, Gang, Liu, Wei, Shen, Dinggang, Sikora, Andrea, Zhai, Xiaoming, Zhu, Dajiang, Liu, Tianming
This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, natural sciences, medicine, linguist
Externí odkaz:
http://arxiv.org/abs/2409.18486
Autor:
Sagar, Som, Taparia, Aditya, Mankodiya, Harsh, Bidare, Pranav, Zhou, Yifan, Senanayake, Ransalu
Black box neural networks are an indispensable part of modern robots. Nevertheless, deploying such high-stakes systems in real-world scenarios poses significant challenges when the stakeholders, such as engineers and legislative bodies, lack insights
Externí odkaz:
http://arxiv.org/abs/2409.10733
Autor:
Lueber, Anna, Heng, Kevin, Bowler, Brendan P., Kitzmann, Daniel, Vos, Johanna M., Zhou, Yifan
Motivated by the observed ~30% variations in flux from the L7 dwarf VHS 1256 b, we subjected its time-resolved Hubble Space Telescope (HST) WFC3 spectra (measured in two epochs in 2018 and 2020), as well as medium-resolution Very Large Telescope (VLT
Externí odkaz:
http://arxiv.org/abs/2409.08254
Autor:
French, Jenni R., Casewell, Sarah L., Amaro, Rachael C., Lothringer, Joshua D., Mayorga, L. C., Littlefair, Stuart P., Lew, Ben W. P., Zhou, Yifan, Apai, Daniel, Marley, Mark S., Parmentier, Vivien, Tan, Xianyu
Due to their short orbital periods and relatively high flux ratios, irradiated brown dwarfs in binaries with white dwarfs offer better opportunities to study irradiated atmospheres than hot Jupiters, which have lower planet-to-star flux ratios. WD103
Externí odkaz:
http://arxiv.org/abs/2409.06874
This paper explores the transformative potential of quantum computing in the realm of personalized learning. Traditional machine learning models and GPU-based approaches have long been utilized to tailor educational experiences to individual student
Externí odkaz:
http://arxiv.org/abs/2408.15287
Autor:
Du, Kaile, Zhou, Yifan, Lyu, Fan, Li, Yuyang, Xie, Junzhou, Shen, Yixi, Hu, Fuyuan, Liu, Guangcan
Multi-label class-incremental learning (MLCIL) is essential for real-world multi-label applications, allowing models to learn new labels while retaining previously learned knowledge continuously. However, recent MLCIL approaches can only achieve subo
Externí odkaz:
http://arxiv.org/abs/2408.12161
Fine-Grained Object Detection (FGOD) is a critical task in high-resolution aerial image analysis. This letter introduces Orthogonal Mapping (OM), a simple yet effective method aimed at addressing the challenge of semantic confusion inherent in FGOD.
Externí odkaz:
http://arxiv.org/abs/2407.17738
Autor:
Biller, Beth A., Vos, Johanna M., Zhou, Yifan, McCarthy, Allison M., Tan, Xianyu, Crossfield, Ian J. M., Whiteford, Niall, Suarez, Genaro, Faherty, Jacqueline, Manjavacas, Elena, Chen, Xueqing, Liu, Pengyu, Sutlieff, Ben J., Limbach, Mary Anne, Molliere, Paul, Dupuy, Trent J., Oliveros-Gomez, Natalia, Muirhead, Philip S., Henning, Thomas, Mace, Gregory, Crouzet, Nicolas, Karalidi, Theodora, Morley, Caroline V., Tremblin, Pascal, Kataria, Tiffany
We report results from 8 hours of JWST/MIRI LRS spectroscopic monitoring directly followed by 7 hours of JWST/NIRSpec prism spectroscopic monitoring of the benchmark binary brown dwarf WISE 1049AB, the closest, brightest brown dwarfs known. We find w
Externí odkaz:
http://arxiv.org/abs/2407.09194
This letter devises an AI-Inverter that pilots the use of a physics-informed neural network (PINN) to enable AI-based electromagnetic transient simulations (EMT) of grid-forming inverters. The contributions are threefold: (1) A PINN-enabled AI-Invert
Externí odkaz:
http://arxiv.org/abs/2406.17661