Zobrazeno 1 - 10
of 91 598
pro vyhledávání: '"Wei, Wei"'
Autor:
Wei, Wei-Shao, Videbæk, Thomas E., Hayakawa, Daichi, Saha, Rupam, Rogers, W. Benjamin, Fraden, Seth
Self-assembly of nanoscale synthetic subunits is a promising bottom-up strategy for fabrication of functional materials. Here, we introduce a design principle for DNA origami nanoparticles of 50-nm size, exploiting modularity, to make a family of ver
Externí odkaz:
http://arxiv.org/abs/2411.09801
Autor:
Liu, Yumeng, Deng, Yanhao, Wang, Yizhuo, Wang, Li, Liu, Tong, Wei, Wei, Gong, Zhongmiao, Fan, Zhengfang, Su, Zhijuan, Wang, Yanming, Dan, Yaping
Defect engineering in two-dimensional (2D) materials is essential for advancing applications such as gas sensing, single-atom catalysis, and guided nanoparticle self-assembly, enabling the creation of materials with tailored functionalities. This stu
Externí odkaz:
http://arxiv.org/abs/2411.02204
Autor:
Zhou, Fei, Wang, Peng, Zhang, Lei, Chen, Zhenghua, Wei, Wei, Ding, Chen, Lin, Guosheng, Zhang, Yanning
Meta-learning offers a promising avenue for few-shot learning (FSL), enabling models to glean a generalizable feature embedding through episodic training on synthetic FSL tasks in a source domain. Yet, in practical scenarios where the target task div
Externí odkaz:
http://arxiv.org/abs/2411.01432
Autor:
Yang, Qiang, Xie, Weilin, Wang, Congfan, Li, Bowen, Li, Xin, Zheng, Xiang, Wei, Wei, Dong, Yi
In distributed fiber-optic sensing based on optical frequency domain reflectometry (OFDR), Doppler frequency shifts due to the changes of disturbances during one sweep period introduce demodulation errors that accumulate along both the distance and t
Externí odkaz:
http://arxiv.org/abs/2410.19368
Autor:
Lai, Haoran, Jiang, Zihang, Yao, Qingsong, Wang, Rongsheng, He, Zhiyang, Tao, Xiaodong, Wei, Wei, Lv, Weifu, Zhou, S. Kevin
The development of 3D medical vision-language models holds significant potential for disease diagnosis and patient treatment. However, compared to 2D medical images, 3D medical images, such as CT scans, face challenges related to limited training dat
Externí odkaz:
http://arxiv.org/abs/2410.14200
Recent advancements in large language models have significantly improved their context windows, yet challenges in effective long-term memory management remain. We introduce MemTree, an algorithm that leverages a dynamic, tree-structured memory repres
Externí odkaz:
http://arxiv.org/abs/2410.14052
Autor:
Wang, Yaxuan, Wei, Jiaheng, Liu, Chris Yuhao, Pang, Jinlong, Liu, Quan, Shah, Ankit Parag, Bao, Yujia, Liu, Yang, Wei, Wei
Unlearning in Large Language Models (LLMs) is essential for ensuring ethical and responsible AI use, especially in addressing privacy leak, bias, safety, and evolving regulations. Existing approaches to LLM unlearning often rely on retain data or a r
Externí odkaz:
http://arxiv.org/abs/2410.11143
Autor:
Pang, Jinlong, Wei, Jiaheng, Shah, Ankit Parag, Zhu, Zhaowei, Wang, Yaxuan, Qian, Chen, Liu, Yang, Bao, Yujia, Wei, Wei
Instruction tuning is critical for adapting large language models (LLMs) to downstream tasks, and recent studies have demonstrated that small amounts of human-curated data can outperform larger datasets, challenging traditional data scaling laws. Whi
Externí odkaz:
http://arxiv.org/abs/2410.10877
The deployment of embodied navigation agents in safety-critical environments raises concerns about their vulnerability to adversarial attacks on deep neural networks. However, current attack methods often lack practicality due to challenges in transi
Externí odkaz:
http://arxiv.org/abs/2409.10071
Spatio-temporal prediction is a crucial research area in data-driven urban computing, with implications for transportation, public safety, and environmental monitoring. However, scalability and generalization challenges remain significant obstacles.
Externí odkaz:
http://arxiv.org/abs/2409.06748