Einstein Probe discovery of EP240408a: a peculiar X-ray transient with an intermediate timescale

Autor: Zhang, Wenda, Yuan, Weimin, Ling, Zhixing, Chen, Yong, Rea, Nanda, Rau, Arne, Cai, Zhiming, Cheng, Huaqing, Zelati, Francesco Coti, Dai, Lixin, Hu, Jingwei, Jia, Shumei, Jin, Chichuan, Li, Dongyue, O'Brien, Paul, Shen, Rongfeng, Shu, Xinwen, Sun, Shengli, Sun, Xiaojin, Wang, Xiaofeng, Yang, Lei, Zhang, Bing, Zhang, Chen, Zhang, Shuang-Nan, Zhang, Yonghe, An, Jie, Buckley, David, Coleiro, Alexis, Cordier, Bertrand, Dou, Liming, Eyles-Ferris, Rob, Fan, Zhou, Feng, Hua, Fu, Shaoyu, Fynbo, Johan P. U., Galbany, Lluis, Jha, Saurabh W., Jiang, Shuaiqing, Kong, Albert, Kuulkers, Erik, Lei, Weihua, Li, Wenxiong, Liu, Bifang, Liu, Mingjun, Liu, Xing, Liu, Yuan, Liu, Zhu, Maitra, Chandreyee, Marino, Alessio, Monageng, Itumeleng, Nandra, Kirpal, Sanders, Jeremy, Soria, Roberto, Tao, Lian, Wang, Junfeng, Wang, Song, Wang, Tinggui, Wang, Zhongxiang, Wu, Qingwen, Wu, Xuefeng, Xu, Dong, Xu, Yanjun, Xue, Suijian, Xue, Yongquan, Zhang, Zijian, Zhu, Zipei, Zou, Hu, Bao, Congying, Chen, Fansheng, Chen, Houlei, Chen, Tianxiang, Chen, Wei, Chen, Yehai, Chen, Yifan, Cui, Chenzhou, Cui, Weiwei, Dai, Yanfeng, Fan, Dongwei, Guan, Ju, Han, Dawei, Hou, Dongjie, Hu, Haibo, Huang, Maohai, Huo, Jia, Jia, Zhenqing, Jiang, Bowen, Jin, Ge, Li, Chengkui, Li, Junfei, Li, Longhui, Li, Maoshun, Li, Wei, Li, Zhengda, Lian, Tianying, Liu, Congzhan, Liu, Heyang, Liu, Huaqiu, Lu, Fangjun, Luo, Laidan, Ma, Jia, Mao, Xuan, Pan, Haiwu, Pan, Xin, Song, Liming, Sun, Hui, Tan, Yunyin, Tang, Qingjun, Tao, Yihan, Wang, Hao, Wang, Juan, Wang, Lei, Wang, Wenxin, Wang, Yilong, Wang, Yusa, Wu, Qinyu, Xu, Haitao, Xu, Jingjing, Xu, Xinpeng, Xu, Yunfei, Xu, Zhao, Xue, Changbin, Xue, Yulong, Yan, Ailiang, Yang, Haonan, Yang, Xiongtao, Yang, Yanji, Zhang, Juan, Zhang, Mo, Zhang, Wenjie, Zhang, Zhen, Zhang, Ziliang, Zhao, Donghua, Zhao, Haisheng, Zhao, Xiaofan, Zhao, Zijian, Zhou, Hongyan, Zhou, Yilin, Zhu, Yuxuan, Zhu, Zhencai

Publikováno v: published in SCIENCE CHINA Physics, Mechanics & Astronomy(SCPMA) (2024)

We report the discovery of a peculiar X-ray transient, EP240408a, by Einstein Probe (EP) and follow-up studies made with EP, Swift, NICER, GROND, ATCA and other ground-based multi-wavelength telescopes. The new transient was first detected with Wide-

Externí odkaz: http://arxiv.org/abs/2410.21617

Scalable Fine-tuning from Multiple Data Sources:A First-Order Approximation Approach

Autor: Li, Dongyue, Zhang, Ziniu, Wang, Lu, Zhang, Hongyang R.

We study the problem of fine-tuning a language model (LM) for a target task by optimally using the information from $n$ auxiliary tasks. This problem has broad applications in NLP, such as targeted instruction tuning and data selection in chain-of-th

Externí odkaz: http://arxiv.org/abs/2409.19458

Learning Tree-Structured Composition of Data Augmentation

Autor: Li, Dongyue, Chen, Kailai, Radivojac, Predrag, Zhang, Hongyang R.

Data augmentation is widely used for training a neural network given little labeled data. A common practice of augmentation training is applying a composition of multiple transformations sequentially to the data. Existing augmentation methods such as

Externí odkaz: http://arxiv.org/abs/2408.14381

EF-Calib: Spatiotemporal Calibration of Event- and Frame-Based Cameras Using Continuous-Time Trajectories

Autor: Wang, Shaoan, Xin, Zhanhua, Hu, Yaoqing, Li, Dongyue, Zhu, Mingzhu, Yu, Junzhi

Publikováno v: IEEE Robotics and Automation Letters, 2024

Event camera, a bio-inspired asynchronous triggered camera, offers promising prospects for fusion with frame-based cameras owing to its low latency and high dynamic range. However, calibrating stereo vision systems that incorporate both event and fra

Externí odkaz: http://arxiv.org/abs/2405.17278

Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis

Autor: Nippani, Abhinav, Li, Dongyue, Ju, Haotian, Koutsopoulos, Haris N., Zhang, Hongyang R.

We consider the problem of traffic accident analysis on a road network based on road network connections and traffic volume. Previous works have designed various deep-learning methods using historical records to predict traffic accident occurrences.

Externí odkaz: http://arxiv.org/abs/2311.00164