Tootfinder

Multi-year Polarimetric Monitoring of Four CHIME-Discovered Repeating Fast Radio Bursts with FAST
Yi Feng, Yong-Kun Zhang, Jintao Xie, Yuan-Pei Yang, Yuanhong Qu, Dengke Zhou, Di Li, Bing Zhang, Weiwei Zhu, Wenbin Lu, Jiaying Xu, Chenchen Miao, Shiyan Tian, Pei Wang, Ju-Mei Yao, Chen-Hui Niu, Jiarui Niu, Heng Xu, Jinchen Jiang, Dejiang Zhou, Zenan Liu, Chao-Wei Tsai, Zigao Dai, Xuefeng Wu, Fayin Wang, Jinlin Han, Kejia Lee, Renxin Xu, Yongfeng Huang, Yuanchuan Zou, Jinhuang Cao, Xiangl…

Multi-year Polarimetric Monitoring of Four CHIME-Discovered Repeating Fast Radio Bursts with FAST
In this study, we report multi-year polarization measurements of four repeating FRBs initially discovered by CHIME: FRBs~20190117A, 20190208A, 20190303A, and 20190417A. We observed the four repeating FRBs with FAST, detecting a total of 66 bursts. Two bursts from FRB~20190417A exhibit a circular polarization signal-to-noise ratio greater than 7, with the highest circular polarization fraction recorded at 35.7%. While the bursts from FRBs 20190208A and 20190303A are highly linearly polarized, th…

Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends
Tian-Yu Xiang, Ao-Qun Jin, Xiao-Hu Zhou, Mei-Jiang Gui, Xiao-Liang Xie, Shi-Qi Liu, Shuang-Yi Wang, Sheng-Bin Duan, Fu-Chao Xie, Wen-Kai Wang, Si-Cheng Wang, Ling-Yun Li, Tian Tu, Zeng-Guang Hou
https://arxiv.org/abs/2506.20966…

Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends
Vision-language-action (VLA) models extend vision-language models (VLM) by integrating action generation modules for robotic manipulation. Leveraging strengths of VLM in vision perception and instruction understanding, VLA models exhibit promising generalization across diverse manipulation tasks. However, applications demanding high precision and accuracy reveal performance gaps without further adaptation. Evidence from multiple domains highlights the critical role of post-training to align fou…

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends
Wang-Ji Yan, Lin-Feng Mei, Yuan-Wei Yin, Jiang Mo, Costas Papadimitriou, Ka-Veng Yuen, Michael Beer
https://arxiv.org/abs/2505.22223

Bayesian Learning in Structural Dynamics: A Comprehensive Review and Emerging Trends
Bayesian learning has emerged as a compelling and vital research direction in the field of structural dynamics, offering a probabilistic lens to understand and refine the analysis of complex dynamical systems. This review meticulously traces the three-decade evolution of Bayesian learning in structural dynamics, illuminating core principles, groundbreaking methodologies, and diverse applications that have significantly influenced the field. The narrative commences by delving into the basics of …