Zobrazeno 1 - 10
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pro vyhledávání: '"Lin, Che"'
This research addresses command-line embedding in cybersecurity, a field obstructed by the lack of comprehensive datasets due to privacy and regulation concerns. We propose the first dataset of similar command lines, named CyPHER, for training and un
Externí odkaz:
http://arxiv.org/abs/2411.01176
Publikováno v:
Bulletin of the Ecological Society of America, 2024 Oct 01. 105(4), 1-6.
Externí odkaz:
https://www.jstor.org/stable/48789821
Autor:
Yang, Xian P., Yao, Yueh-Ting, Zheng, Pengyu, Guan, Shuyue, Zhou, Huibin, Cochran, Tyler A., Lin, Che-Min, Yin, Jia-Xin, Zhou, Xiaoting, Cheng, Zi-Jia, Li, Zhaohu, Shi, Tong, Hossain, Md Shafayat, Chi, Shengwei, Belopolski, Ilya, Jiang, Yu-Xiao, Litskevich, Maksim, Xu, Gang, Tian, Zhaoming, Bansil, Arun, Yin, Zhiping, Jia, Shuang, Chang, Tay-Rong, Hasan, M. Zahid
Publikováno v:
Nature Communications volume 15, Article number: 7052 (2024)
The interplay of topology, magnetism, and correlations gives rise to intriguing phases of matter. In this study, through state-of-the-art angle-resolved photoemission spectroscopy, density functional theory and dynamical mean-field theory calculation
Externí odkaz:
http://arxiv.org/abs/2408.08394
Autor:
Huang, Chun-Kai, Hsieh, Yi-Hsien, Chien, Ta-Jung, Chien, Li-Cheng, Sun, Shao-Hua, Su, Tung-Hung, Kao, Jia-Horng, Lin, Che
Multivariate time series (MTS) data, when sampled irregularly and asynchronously, often present extensive missing values. Conventional methodologies for MTS analysis tend to rely on temporal embeddings based on timestamps that necessitate subsequent
Externí odkaz:
http://arxiv.org/abs/2405.16557
Autor:
Lin, Che-Tsung, Ng, Chun Chet, Tan, Zhi Qin, Nah, Wan Jun, Wang, Xinyu, Kew, Jie Long, Hsu, Pohao, Lai, Shang Hong, Chan, Chee Seng, Zach, Christopher
Extremely low-light text images are common in natural scenes, making scene text detection and recognition challenging. One solution is to enhance these images using low-light image enhancement methods before text extraction. However, previous methods
Externí odkaz:
http://arxiv.org/abs/2404.14135
Graph Neural Networks (GNNs) excel in delineating graph structures in diverse domains, including community analysis and recommendation systems. As the interpretation of GNNs becomes increasingly important, the demand for robust baselines and expansiv
Externí odkaz:
http://arxiv.org/abs/2401.04133
Graph neural networks (GNNs) face significant challenges with class imbalance, leading to biased inference results. To address this issue in heterogeneous graphs, we propose a novel framework that combines Graph Neural Network (GNN) and Generative Ad
Externí odkaz:
http://arxiv.org/abs/2312.06519
Many physical adversarial patch generation methods are widely proposed to protect personal privacy from malicious monitoring using object detectors. However, they usually fail to generate satisfactory patch images in terms of both stealthiness and at
Externí odkaz:
http://arxiv.org/abs/2307.08076
We present a generalizable novel view synthesis method which enables modifying the visual appearance of an observed scene so rendered views match a target weather or lighting condition without any scene specific training or access to reference views
Externí odkaz:
http://arxiv.org/abs/2306.01344
Autor:
Lin, Che-Kuan
For industrial applications, curing coatings in an air atmosphere is the simplest method at the job field. Free radical polymerization (FRP) is a well-known and widely used method to cure thermosetting acrylic coatings. Considering curing coatings by