Zobrazeno 1 - 10
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pro vyhledávání: '"Zhang, ZhiYu"'
The neural radiance fields (NeRF) have advanced the development of 3D volumetric video technology, but the large data volumes they involve pose significant challenges for storage and transmission. To address these problems, the existing solutions typ
Externí odkaz:
http://arxiv.org/abs/2411.05322
In streaming media services, video transcoding is a common practice to alleviate bandwidth demands. Unfortunately, traditional methods employing a uniform rate factor (RF) across all videos often result in significant inefficiencies. Content-adaptive
Externí odkaz:
http://arxiv.org/abs/2411.05295
Based on the framework of Conformal Prediction (CP), we study the online construction of valid confidence sets given a black-box machine learning model. By converting the target confidence levels into quantile levels, the problem can be reduced to pr
Externí odkaz:
http://arxiv.org/abs/2410.02561
Autor:
Zhang, Lu, Zheng, Jianhua, Yang, Zhenghua, Song, Tianming, Zhang, Shuai, Liu, Tong, Wei, Yunfeng, Kuang, Longyu, Jing, Longfei, Lin, Zhiwei, Li, Liling, Li, Hang, Zheng, Jinhua, Yang, Pin, Zhang, Yuxue, Zhang, Zhiyu, Zhao, Yang, He, Zhibing, Li, Ping, Yang, Dong, Yang, Jiamin, Zhao, Zongqing, Ding, Yongkun
We present experiments to reproduce the characteristics of core-collapse supernovae with different stellar masses and initial explosion energies in the laboratory. In the experiments, shocks are driven in 1.2 atm and 1.9 atm xenon gas by laser with e
Externí odkaz:
http://arxiv.org/abs/2409.14699
Analyzing real-world multimodal signals is an essential and challenging task for intelligent voice assistants (IVAs). Mainstream approaches have achieved remarkable performance on various downstream tasks of IVAs with pre-trained audio models and tex
Externí odkaz:
http://arxiv.org/abs/2409.09289
Contrastive learning has become one of the most impressive approaches for multi-modal representation learning. However, previous multi-modal works mainly focused on cross-modal understanding, ignoring in-modal contrastive learning, which limits the r
Externí odkaz:
http://arxiv.org/abs/2409.09282
With the goal of more natural and human-like interaction with virtual voice assistants, recent research in the field has focused on full duplex interaction mode without relying on repeated wake-up words. This requires that in scenes with complex soun
Externí odkaz:
http://arxiv.org/abs/2409.09284
Autor:
Wang, X. Griffin, Zhang, Zhiyu
We prove the Jacquet--Rallis fundamental lemma for spherical Hecke algebras over local function fields using multiplicative Hitchin fibrations. Our work is inspired by the proof of [Yun11] in the Lie algebra case and builds upon the general framework
Externí odkaz:
http://arxiv.org/abs/2408.15155
Autor:
Sillassen, Nikolaj B., Jin, Shuowen, Magdis, Georgios E., Daddi, Emanuele, Wang, Tao, Lu, Shiying, Sun, Hanwen, Arumugam, Vinod, Liu, Daizhong, Brinch, Malte, D'Eugenio, Chiara, Gobat, Raphael, Gómez-Guijarro, Carlos, Rich, Michael, Schinnerer, Eva, Strazzullo, Veronica, Tan, Qinghua, Valentino, Francesco, Wang, Yijun, Xiao, Mengyuan, Zhou, Luwenjia, Blánquez-Sesé, David, Cai, Zheng, Chen, Yanmei, Ciesla, Laure, Dai, Yu, Delvecchio, Ivan, Elbaz, David, Finoguenov, Alexis, Gao, Fangyou, Gu, Qiusheng, Hale, Catherine, Hao, Qiaoyang, Huang, Jiasheng, Jarvis, Matt, Kalita, Boris, Ke, Xu, Bail, Aurelien Le, Magnelli, Benjamin, Shi, Yong, Vaccari, Mattia, Whittam, Imogen, Yang, Tiancheng, Zhang, Zhiyu
Publikováno v:
A&A 690, A55 (2024)
The NOEMA formIng Cluster survEy (NICE) is a large program targeting 69 massive galaxy group candidates at $z>2$ in six deep fields. We report spectroscopic confirmation of eight groups at $1.65\leq z\leq3.61$ in COSMOS. Homogeneously selected as sig
Externí odkaz:
http://arxiv.org/abs/2407.02973
Conformal prediction (CP) enables machine learning models to output prediction sets with guaranteed coverage rate, assuming exchangeable data. Unfortunately, the exchangeability assumption is frequently violated due to distribution shifts in practice
Externí odkaz:
http://arxiv.org/abs/2406.01416