Zobrazeno 1 - 10
of 2 323
pro vyhledávání: '"WANG, Xiaopeng"'
Autor:
Wang, Xiaopeng, Tu, Chengyi, Chen, Shuhao, Wang, Sicheng, Fan, Ying, Suweis, Samir, D'Odorico, Paolo
As the global population and the per capita demand for resource intensive diets continues to grow, the corresponding increase in food demand challenges the global food system, enhancing its reliance on trade. Most previous research typically construc
Externí odkaz:
http://arxiv.org/abs/2411.18856
Intracellular processes triggered by neural activity include changes in ionic concentrations, protein release, and synaptic vesicle cycling. These processes play significant roles in neurological disorders. The beneficial effects of brain stimulation
Externí odkaz:
http://arxiv.org/abs/2409.16552
Autor:
Wang, Zhiyong, Fu, Ruibo, Wen, Zhengqi, Tao, Jianhua, Wang, Xiaopeng, Xie, Yuankun, Qi, Xin, Shi, Shuchen, Lu, Yi, Liu, Yukun, Li, Chenxing, Liu, Xuefei, Li, Guanjun
Speech synthesis technology has posed a serious threat to speaker verification systems. Currently, the most effective fake audio detection methods utilize pretrained models, and integrating features from various layers of pretrained model further enh
Externí odkaz:
http://arxiv.org/abs/2409.11909
Autor:
Qi, Xin, Fu, Ruibo, Wen, Zhengqi, Wang, Tao, Qiang, Chunyu, Tao, Jianhua, Li, Chenxing, Lu, Yi, Shi, Shuchen, Wang, Zhiyong, Wang, Xiaopeng, Xie, Yuankun, Liu, Yukun, Liu, Xuefei, Li, Guanjun
In recent years, speech diffusion models have advanced rapidly. Alongside the widely used U-Net architecture, transformer-based models such as the Diffusion Transformer (DiT) have also gained attention. However, current DiT speech models treat Mel sp
Externí odkaz:
http://arxiv.org/abs/2409.11835
Autor:
Zhang, Chao, Zhu, Yue, Wang, Xiaopeng, Huang, Yanhao, Zeng, Lingyong, Li, Kuan, Yu, Peifeng, Wang, Kangwang, Li, Longfu, Xiang, Zaichen, Chen, Rui, Zhu, Xuefeng, Luo, Huixia
Publikováno v:
Journal of Membrane Science, 2024,696,122485
Oxygen transport membranes(OTMs)have provided great opportunities in the last decades but are suffering from the trade-off effect between stability and oxygen permeability. Here, we report a group of new planar dual-phase mixed ionic-electronic condu
Externí odkaz:
http://arxiv.org/abs/2408.12164
Autor:
Xie, Yuankun, Xiong, Chenxu, Wang, Xiaopeng, Wang, Zhiyong, Lu, Yi, Qi, Xin, Fu, Ruibo, Liu, Yukun, Wen, Zhengqi, Tao, Jianhua, Li, Guanjun, Ye, Long
Currently, Audio Language Models (ALMs) are rapidly advancing due to the developments in large language models and audio neural codecs. These ALMs have significantly lowered the barrier to creating deepfake audio, generating highly realistic and dive
Externí odkaz:
http://arxiv.org/abs/2408.10853
Autor:
Qi, Xin, Fu, Ruibo, Wen, Zhengqi, Tao, Jianhua, Shi, Shuchen, Lu, Yi, Wang, Zhiyong, Wang, Xiaopeng, Xie, Yuankun, Liu, Yukun, Li, Guanjun, Liu, Xuefei, Li, Yongwei
In the current era of Artificial Intelligence Generated Content (AIGC), a Low-Rank Adaptation (LoRA) method has emerged. It uses a plugin-based approach to learn new knowledge with lower parameter quantities and computational costs, and it can be plu
Externí odkaz:
http://arxiv.org/abs/2408.10852
Autor:
Wang, Zhiyong, Wang, Xiaopeng, Xie, Yuankun, Fu, Ruibo, Wen, Zhengqi, Tao, Jianhua, Liu, Yukun, Li, Guanjun, Qi, Xin, Lu, Yi, Liu, Xuefei, Li, Yongwei
In the field of deepfake detection, previous studies focus on using reconstruction or mask and prediction methods to train pre-trained models, which are then transferred to fake audio detection training where the encoder is used to extract features,
Externí odkaz:
http://arxiv.org/abs/2408.10849
Intraoperative CT imaging serves as a crucial resource for surgical guidance; however, it may not always be readily accessible or practical to implement. In scenarios where CT imaging is not an option, reconstructing CT scans from X-rays can offer a
Externí odkaz:
http://arxiv.org/abs/2408.09731
Autor:
Xie, Yuankun, Wang, Xiaopeng, Wang, Zhiyong, Fu, Ruibo, Wen, Zhengqi, Cheng, Haonan, Ye, Long
ASVspoof5, the fifth edition of the ASVspoof series, is one of the largest global audio security challenges. It aims to advance the development of countermeasure (CM) to discriminate bonafide and spoofed speech utterances. In this paper, we focus on
Externí odkaz:
http://arxiv.org/abs/2408.06922