Zobrazeno 1 - 10
of 6 909
pro vyhledávání: '"LIANG ZHENG"'
Autor:
Li, Yadong, Sun, Haoze, Lin, Mingan, Li, Tianpeng, Dong, Guosheng, Zhang, Tao, Ding, Bowen, Song, Wei, Cheng, Zhenglin, Huo, Yuqi, Chen, Song, Li, Xu, Pan, Da, Zhang, Shusen, Wu, Xin, Liang, Zheng, Liu, Jun, Lu, Keer, Zhao, Yaqi, Shen, Yanjun, Yang, Fan, Yu, Kaicheng, Lin, Tao, Xu, Jianhua, Zhou, Zenan, Chen, Weipeng
The salient multimodal capabilities and interactive experience of GPT-4o highlight its critical role in practical applications, yet it lacks a high-performing open-source counterpart. In this paper, we introduce Baichuan-Omni, the first open-source 7
Externí odkaz:
http://arxiv.org/abs/2410.08565
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
In the milli-Hertz frequency band, stochastic gravitational-wave background can be composed of both astronomical and cosmological sources, both can be anisotropic. Numerically depicting these anisotropies can be critical in revealing the underlying p
Externí odkaz:
http://arxiv.org/abs/2409.11245
Autor:
Lu, Keer, Nie, Xiaonan, Liang, Zheng, Pan, Da, Zhang, Shusen, Zhao, Keshi, Chen, Weipeng, Zhou, Zenan, Dong, Guosheng, Cui, Bin, Zhang, Wentao
In recent years, Large Language Models (LLMs) have demonstrated significant improvements across a variety of tasks, one of which is the long-context capability. The key to improving long-context performance lies in effective data organization and man
Externí odkaz:
http://arxiv.org/abs/2409.00997
Unveiling a multi-component stochastic gravitational-wave background with the TianQin + LISA network
Space-borne detectors, including TianQin and Laser Interferometry Space Antenna (LISA), are tasked with the simultaneous observation of Galactic foreground, astrophysical and cosmological stochastic gravitational-wave backgrounds (SGWBs). For the fir
Externí odkaz:
http://arxiv.org/abs/2409.00778
Autor:
Dong, Guosheng, Pan, Da, Sun, Yiding, Zhang, Shusen, Liang, Zheng, Wu, Xin, Shen, Yanjun, Yang, Fan, Sun, Haoze, Li, Tianpeng, Lin, Mingan, Xu, Jianhua, Zhang, Yufan, Nie, Xiaonan, Su, Lei, Wang, Bingning, Zhang, Wentao, Mao, Jiaxin, Zhou, Zenan, Chen, Weipeng
The general capabilities of Large Language Models (LLM) highly rely on the composition and selection on extensive pretraining datasets, treated as commercial secrets by several institutions. To mitigate this issue, we open-source the details of a uni
Externí odkaz:
http://arxiv.org/abs/2408.15079
Autor:
Fang, Meihua, liang, Zheng, Gong, Yingkui, Chen, Jianfei, Zhu, Guiping, Liu, Ting, Tian, Yu, Zhou, Yu
Relativistic charged particle beam can be used as destructive beam weapons in space for debris removal tasks. The trajectories of charged particles are affected by both electric and magnetic forces in the Earth's magnetic field. In this paper, we fir
Externí odkaz:
http://arxiv.org/abs/2409.06713
With the advancement of audio generation, generative models can produce highly realistic audios. However, the proliferation of deepfake general audio can pose negative consequences. Therefore, we propose a new task, deepfake general audio detection,
Externí odkaz:
http://arxiv.org/abs/2406.08052
Unsupervised (a.k.a. Self-supervised) representation learning (URL) has emerged as a new paradigm for time series analysis, because it has the ability to learn generalizable time series representation beneficial for many downstream tasks without usin
Externí odkaz:
http://arxiv.org/abs/2404.05057
Weak-signal limit is often used in estimating stochastic gravitational-wave background (SGWB) intensities. This approximation fails and the signal-to-noise ratio (SNR) can be much weaker when background signals are loud compared to the detector noise
Externí odkaz:
http://arxiv.org/abs/2403.18709