Zobrazeno 1 - 10
of 43 866
pro vyhledávání: '"liu, Bin"'
Autor:
Zhao, Zibin, Li, Guilong, Luo, Huanbo, Liu, Bin, Chen, Guihua, Malomed, Boris A., Li, Yongyao
The $1/r$ long-range interaction introduced by the laser beams offers a mechanism for the implementation of stable self-trapping in Bose-Einstein condensates (BECs) in the three-dimensional free space. Using the variational approximation and numerica
Externí odkaz:
http://arxiv.org/abs/2411.01554
This paper presents a novel approach to glass composition screening through a self-supervised learning framework, addressing the challenges posed by glass transition temperature (Tg) prediction. Given the critical role of Tg in determining glass perf
Externí odkaz:
http://arxiv.org/abs/2410.24083
In this study, we delve into the robustness of neural network-based LiDAR point cloud tracking models under adversarial attacks, a critical aspect often overlooked in favor of performance enhancement. These models, despite incorporating advanced arch
Externí odkaz:
http://arxiv.org/abs/2410.20893
In this work, we consider the 3D Cauchy problem for a coupled system arising from the biomathematics, which consists of a chemotaxis model with cubic logistic source and the stochastic tamed Navier-Stokes equations (STCNS, for short). Our main goal i
Externí odkaz:
http://arxiv.org/abs/2410.17059
Existing large pre-trained models typically map text input to text output in an end-to-end manner, such as ChatGPT, or map a segment of text input to a hierarchy of action decisions, such as OpenVLA. However, humans can simultaneously generate text a
Externí odkaz:
http://arxiv.org/abs/2410.15885
Autor:
Chen, Tianxiang, Tan, Zhentao, Gong, Tao, Wu, Yue, Chu, Qi, Liu, Bin, Ye, Jieping, Yu, Nenghai
As a manner to augment pre-trained large language models (LLM), knowledge injection is critical to develop vertical domain large models and has been widely studied. Although most current approaches, including parameter-efficient fine-tuning (PEFT) an
Externí odkaz:
http://arxiv.org/abs/2410.02330
Autor:
Lian, Zheng, Sun, Haiyang, Sun, Licai, Chen, Lan, Chen, Haoyu, Gu, Hao, Wen, Zhuofan, Chen, Shun, Zhang, Siyuan, Yao, Hailiang, Xu, Mingyu, Chen, Kang, Liu, Bin, Liu, Rui, Liang, Shan, Li, Ya, Yi, Jiangyan, Tao, Jianhua
Multimodal Emotion Recognition (MER) is an important research topic. This paper advocates for a transformative paradigm in MER. The rationale behind our work is that current approaches often rely on a limited set of basic emotion labels, which do not
Externí odkaz:
http://arxiv.org/abs/2410.01495
Autor:
Li, En-Kun, Liu, Shuai, Torres-Orjuela, Alejandro, Chen, Xian, Inayoshi, Kohei, Wang, Long, Hu, Yi-Ming, Amaro-Seoane, Pau, Askar, Abbas, Bambi, Cosimo, Capelo, Pedro R., Chen, Hong-Yu, Chua, Alvin J. K., Condés-Breña, Enrique, Dai, Lixin, Das, Debtroy, Derdzinski, Andrea, Fan, Hui-Min, Fujii, Michiko, Gao, Jie, Garg, Mudit, Ge, Hongwei, Giersz, Mirek, Huang, Shun-Jia, Hypki, Arkadiusz, Liang, Zheng-Cheng, Liu, Bin, Liu, Dongdong, Liu, Miaoxin, Liu, Yunqi, Mayer, Lucio, Napolitano, Nicola R., Peng, Peng, Shao, Yong, Shashank, Swarnim, Shen, Rongfeng, Tagawa, Hiromichi, Tanikawa, Ataru, Toscani, Martina, Vázquez-Aceves, Verónica, Wang, Hai-Tian, Yi, Shu-Xu, Zhang, Jian-dong, Zhang, Xue-Ting, Zhu, Lianggui, Zwick, Lorenz, Huang, Song, Mei, Jianwei, Wang, Yan, Xie, Yi, Zhang, Jiajun, Luo, Jun
The opening of the gravitational wave window has significantly enhanced our capacity to explore the universe's most extreme and dynamic sector. In the mHz frequency range, a diverse range of compact objects, from the most massive black holes at the f
Externí odkaz:
http://arxiv.org/abs/2409.19665
Autor:
Zhang, Xiang, Zhang, Jie, Ma, Zehua, Huang, Jinyang, Li, Meng, Yan, Huan, Zhao, Peng, Zhang, Zijian, Guo, Qing, Zhang, Tianwei, Liu, Bin, Yu, Nenghai
Hidden wireless cameras pose significant privacy threats, necessitating effective detection and localization methods. However, existing solutions often require spacious activity areas, expensive specialized devices, or pre-collected training data, li
Externí odkaz:
http://arxiv.org/abs/2409.15169
We propose a model for diagnosing Autism spectrum disorder (ASD) using multimodal magnetic resonance imaging (MRI) data. Our approach integrates brain connectivity data from diffusion tensor imaging (DTI) and functional MRI (fMRI), employing graph ne
Externí odkaz:
http://arxiv.org/abs/2410.07138