Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Zhang, Xiaoling"'
Publikováno v:
Proc. ACM Softw. Eng. 1,FSE, Article 75 (July 2024), 24 pages
Reverse engineers would acquire valuable insights from descriptive function names, which are absent in publicly released binaries. Recent advances in binary function name prediction using data-driven machine learning show promise. However, existing a
Externí odkaz:
http://arxiv.org/abs/2405.09112
Autor:
Huang, Shifeng, Jiang, Ning, Zhu, Jiazheng, Wang, Yibo, Wang, Tinggui, Wang, Shan-Qin, Gan, Wen-Pei, Liang, En-Wei, Qin, Yu-Jing, Lin, Zheyu, Xu, Lin-Na, Cai, Min-Xuan, Jiang, Ji-An, Kong, Xu, Li, Jiaxun, Li, Long, Wang, Jian-Guo, Xu, Ze-Lin, Xue, Yongquan, Yuan, Ye-Fei, Cheng, Jingquan, Fan, Lulu, Gao, Jie, Hu, Lei, Hu, Weida, li, Bin, Li, Feng, Liang, Ming, Liu, Hao, Liu, Wei, Lou, Zheng, Luo, Wentao, Qian, Yuan, Tang, Jinlong, Wan, Zhen, Wang, Hairen, Wang, Jian, Yang, Ji, Yao, Dazhi, Zhang, Hongfei, Zhang, Xiaoling, Zhao, Wen, Zheng, Xianzhong, Zhu, Qingfeng, Zuo, Yingxi
High-cadence, multiwavelength observations have continuously revealed the diversity of tidal disruption events (TDEs), thus greatly advancing our knowledge and understanding of TDEs. In this work, we conducted an intensive optical-UV and X-ray follow
Externí odkaz:
http://arxiv.org/abs/2403.01686
Autor:
Ma, Cuiling, Zhang, Xiaoling
The conformal properties of metrics are meaningful in Riemannian and Finsler geometry, and cubic metrics are useful in physics and biology. In this paper, we study the conformally flat cubic metrics with weakly isotropic scalar curvature. We also pro
Externí odkaz:
http://arxiv.org/abs/2309.00388
In this paper, we study Kropina metrics with isotropic scalar curvature. First, we obtain the expressions of Ricci curvature tensor and scalar curvature. Then, we characterize the Kropina metrics with isotropic scalar curvature on by tensor analysis.
Externí odkaz:
http://arxiv.org/abs/2308.08349
Autor:
Ni, Xiaochuan, Zhang, Xiaoling, Zhan, Xu, Yang, Zhenyu, Shi, Jun, Wei, Shunjun, Zeng, Tianjiao
This work focuses on multi-target tracking in Video synthetic aperture radar. Specifically, we refer to tracking based on targets' shadows. Current methods have limited accuracy as they fail to consider shadows' characteristics and surroundings fully
Externí odkaz:
http://arxiv.org/abs/2211.15995
Deep learning (DL)-based tomographic SAR imaging algorithms are gradually being studied. Typically, they use an unfolding network to mimic the iterative calculation of the classical compressive sensing (CS)-based methods and process each range-azimut
Externí odkaz:
http://arxiv.org/abs/2211.15002
Benefiting from a relatively larger aperture's angle, and in combination with a wide transmitting bandwidth, near-field synthetic aperture radar (SAR) provides a high-resolution image of a target's scattering distribution-hot spots. Meanwhile, imagin
Externí odkaz:
http://arxiv.org/abs/2211.14990
This work focuses on 3D Radar imaging inverse problems. Current methods obtain undifferentiated results that suffer task-depended information retrieval loss and thus don't meet the task's specific demands well. For example, biased scattering energy m
Externí odkaz:
http://arxiv.org/abs/2211.14989
Images of near-field SAR contains spatial-variant sidelobes and clutter, subduing the image quality. Current image restoration methods are only suitable for small observation angle, due to their assumption of 2D spatial-invariant degradation operatio
Externí odkaz:
http://arxiv.org/abs/2209.10442
The existing Video Synthetic Aperture Radar (ViSAR) moving target shadow detection methods based on deep neural networks mostly generate numerous false alarms and missing detections, because of the foreground-background indistinguishability. To solve
Externí odkaz:
http://arxiv.org/abs/2209.10431