Zobrazeno 1 - 10
of 48 040
pro vyhledávání: '"WANG Fei"'
Autor:
Sun, Shenglan, Wang, Fei, Zhang, Huawei, Xue, Xiang-Xiang, Huang, Yang, Zhang, Ruizhi, Rix, Hans-Walter, Li, Xinyi, Liu, Gaochao, Zhang, Lan, Yang, Chengqun, Zhang, Shuo
Motivated by the vast gap between photometric and spectroscopic data volumes, there is great potential in using 5D kinematic information to identify and study substructures of the Milky Way. We identify substructures in the Galactic halo using 46,575
Externí odkaz:
http://arxiv.org/abs/2411.13122
Snapshot Compressive Imaging (SCI) offers a possibility for capturing information in high-speed dynamic scenes, requiring efficient reconstruction method to recover scene information. Despite promising results, current deep learning-based and NeRF-ba
Externí odkaz:
http://arxiv.org/abs/2411.12471
Due to the difficulty of acquiring large-scale explicit user feedback, implicit feedback (e.g., clicks or other interactions) is widely applied as an alternative source of data, where user-item interactions can be modeled as a bipartite graph. Due to
Externí odkaz:
http://arxiv.org/abs/2411.09181
Autor:
Chen, Canyu, Yu, Jian, Chen, Shan, Liu, Che, Wan, Zhongwei, Bitterman, Danielle, Wang, Fei, Shu, Kai
Large Language Models (LLMs) hold great promise to revolutionize current clinical systems for their superior capacities on medical text processing tasks and medical licensing exams. Meanwhile, traditional ML models such as SVM and XGBoost have still
Externí odkaz:
http://arxiv.org/abs/2411.06469
Tiny LIV effects may origin from typical space-time structures in quantum gravity theories. So, it is reasonable to anticipate that tiny LIV effects can be present in the proton sector. We find that, with tiny LIV effects in the proton sector, the th
Externí odkaz:
http://arxiv.org/abs/2411.04361
Autor:
Ma, Yingzi, Wang, Jiongxiao, Wang, Fei, Ma, Siyuan, Li, Jiazhao, Li, Xiujun, Huang, Furong, Sun, Lichao, Li, Bo, Choi, Yejin, Chen, Muhao, Xiao, Chaowei
Machine unlearning has emerged as an effective strategy for forgetting specific information in the training data. However, with the increasing integration of visual data, privacy concerns in Vision Language Models (VLMs) remain underexplored. To addr
Externí odkaz:
http://arxiv.org/abs/2411.03554
Current visual question answering (VQA) tasks often require constructing multimodal datasets and fine-tuning visual language models, which demands significant time and resources. This has greatly hindered the application of VQA to downstream tasks, s
Externí odkaz:
http://arxiv.org/abs/2411.01445
Autor:
Bushuiev, Roman, Bushuiev, Anton, de Jonge, Niek F., Young, Adamo, Kretschmer, Fleming, Samusevich, Raman, Heirman, Janne, Wang, Fei, Zhang, Luke, Dührkop, Kai, Ludwig, Marcus, Haupt, Nils A., Kalia, Apurva, Brungs, Corinna, Schmid, Robin, Greiner, Russell, Wang, Bo, Wishart, David S., Liu, Li-Ping, Rousu, Juho, Bittremieux, Wout, Rost, Hannes, Mak, Tytus D., Hassoun, Soha, Huber, Florian, van der Hooft, Justin J. J., Stravs, Michael A., Böcker, Sebastian, Sivic, Josef, Pluskal, Tomáš
The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular st
Externí odkaz:
http://arxiv.org/abs/2410.23326
Recent advancements in synthetic aperture radar (SAR) ship detection using deep learning have significantly improved accuracy and speed, yet effectively detecting small objects in complex backgrounds with fewer parameters remains a challenge. This le
Externí odkaz:
http://arxiv.org/abs/2410.23073
Autor:
Wang, Fei, Zhang, Zeren
We address a threshold problem of the Couette flow $(y,0)$ in a uniform magnetic field $(\beta,0)$ for the 2D MHD equation on $\mathbb{T}\times\mathbb{R}$ with fluid viscosity $\nu$ and magnetic resistivity $\mu$. The nonlinear enhanced dissipation a
Externí odkaz:
http://arxiv.org/abs/2410.20404