Zobrazeno 1 - 10
of 2 771
pro vyhledávání: '"Li, WenTing"'
Autor:
Chen, Ziyang, Zhang, Yongjun, Li, Wenting, Wang, Bingshu, Wu, Yabo, Zhao, Yong, Chen, C. L. Philip
In light of the advancements in transformer technology, extant research posits the construction of stereo transformers as a potential solution to the binocular stereo matching challenge. However, constrained by the low-rank bottleneck and quadratic c
Externí odkaz:
http://arxiv.org/abs/2501.01023
Real-world applications of stereo matching, such as autonomous driving, place stringent demands on both safety and accuracy. However, learning-based stereo matching methods inherently suffer from the loss of geometric structures in certain feature ch
Externí odkaz:
http://arxiv.org/abs/2411.12426
The robustness of neural networks is paramount in safety-critical applications. While most current robustness verification methods assess the worst-case output under the assumption that the input space is known, identifying a verifiable input space $
Externí odkaz:
http://arxiv.org/abs/2408.08824
Higher variability in grid conditions, resulting from growing renewable penetration and increased incidence of extreme weather events, has increased the difficulty of screening for scenarios that may lead to catastrophic cascading failures. Tradition
Externí odkaz:
http://arxiv.org/abs/2403.15363
Honeyword is a representative ``honey" technique to detect intruders by luring them with decoy data. This kind of honey technique blends a primary object (from distribution $P$) with decoy samples (from distribution $Q$). In this research, we focus o
Externí odkaz:
http://arxiv.org/abs/2311.10960
Autor:
Rodler, Michael, Paaßen, David, Li, Wenting, Bernhard, Lukas, Holz, Thorsten, Karame, Ghassan, Davi, Lucas
Smart contracts are increasingly being used to manage large numbers of high-value cryptocurrency accounts. There is a strong demand for automated, efficient, and comprehensive methods to detect security vulnerabilities in a given contract. While the
Externí odkaz:
http://arxiv.org/abs/2304.06341
Autor:
Yue, Xin, Lin, Shanny, Li, Wenting, Wolfe, Bradley T., Clayton, Steven, Makela, Mark, Morris, C. L., Spannagel, Simon, Ramberg, Erik, Estrada, Juan, Zhu, Hao, Liu, Jifeng, Fossum, Eric R., Wang, Zhehui
Publikováno v:
Proceedings of Science ; Vol.420, p.041, 8 May 2023
We summarize recent progress in ultrafast Complementary Metal Oxide Semiconductor (CMOS) image sensor development and the application of neural networks for post-processing of CMOS and charge-coupled device (CCD) image data to achieve sub-pixel resol
Externí odkaz:
http://arxiv.org/abs/2301.11865
Word embedding has become ubiquitous and is widely used in various natural language processing (NLP) tasks, such as web retrieval, web semantic analysis, and machine translation, and so on. Unfortunately, training the word embedding in a relatively l
Externí odkaz:
http://arxiv.org/abs/2301.04312
Autor:
Li, Wenting1 (AUTHOR) liwenting@csu.edu.cn, Li, Yonggang1 (AUTHOR), Li, Dong1 (AUTHOR) lddscut@163.com, Zhou, Jiayi1 (AUTHOR)
Publikováno v:
Sensors (14248220). Dec2024, Vol. 24 Issue 23, p7508. 14p.
Climate change increases the number of extreme weather events (wind and snowstorms, heavy rains, wildfires) that compromise power system reliability and lead to multiple equipment failures. Real-time and accurate detecting of potential line failures
Externí odkaz:
http://arxiv.org/abs/2209.01021