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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
LiFePO4 batteries are widely used in electric vehicles and energy storage systems due to long cycle life and high safety performance. However, the OCV-SOC curve (OSC) of these batteries features a long plateau region, making state of charge (SOC) est
Externí odkaz:
http://arxiv.org/abs/2410.23646
Autor:
Li, Tian-Ming, Zhang, Jia-Chi, Chen, Bing-Jie, Huang, Kaixuan, Liu, Hao-Tian, Xiao, Yong-Xi, Deng, Cheng-Lin, Liang, Gui-Han, Chen, Chi-Tong, Liu, Yu, Li, Hao, Bao, Zhen-Ting, Zhao, Kui, Xu, Yueshan, Li, Li, He, Yang, Liu, Zheng-He, Yu, Yi-Han, Zhou, Si-Yun, Liu, Yan-Jun, Song, Xiaohui, Zheng, Dongning, Xiang, Zhong-Cheng, Shi, Yun-Hao, Xu, Kai, Fan, Heng
For superconducting quantum processors, stable high-fidelity two-qubit operations depend on precise flux control of the tunable coupler. However, the pulse distortion poses a significant challenge to the control precision. Current calibration methods
Externí odkaz:
http://arxiv.org/abs/2410.15041
Constantly discovering novel concepts is crucial in evolving environments. This paper explores the underexplored task of Continual Generalized Category Discovery (C-GCD), which aims to incrementally discover new classes from unlabeled data while main
Externí odkaz:
http://arxiv.org/abs/2410.06535
Large Multimodal Models (LMMs) exhibit remarkable multi-tasking ability by learning mixed datasets jointly. However, novel tasks would be encountered sequentially in dynamic world, and continually fine-tuning LMMs often leads to performance degrades.
Externí odkaz:
http://arxiv.org/abs/2410.05849
With the advancement of large-scale language modeling techniques, large multimodal models combining visual encoders with large language models have demonstrated exceptional performance in various visual tasks. Most of the current large-scale multimod
Externí odkaz:
http://arxiv.org/abs/2409.01179
Adapting pre-trained models to open classes is a challenging problem in machine learning. Vision-language models fully explore the knowledge of text modality, demonstrating strong zero-shot recognition performance, which is naturally suited for vario
Externí odkaz:
http://arxiv.org/abs/2408.16486
Autor:
Lee, Cheng-Lin, Wang, Chiao-Hsuan
Quantum reservoir engineering aims to transform typically detrimental dissipations into advantageous resources. We present a versatile method for creating a squeezed thermal reservoir for quantum systems. By coupling the system to a lossy mode within
Externí odkaz:
http://arxiv.org/abs/2408.16052