Zobrazeno 1 - 10
of 2 605
pro vyhledávání: '"Chang, Bin"'
Autor:
Zhang, Shu, Pang, Jinbo, Li, Yufen, Yang, Feng, Gemming, Thomas, Wang, Kai, Wang, Xiao, Peng, Songang, Liu, Xiaoyan, Chang, Bin, Liu, Hong, Zhou, Weijia, Cuniberti, Gianaurelio, Rümmeli, Mark H.
Carbon nanotubes (CNTs) have attracted great attentions in the field of electronics, sensors, healthcare, and energy conversion. Such emerging applications have driven the carbon nanotube research in a rapid fashion. Indeed, the structure control ove
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A89410
https://tud.qucosa.de/api/qucosa%3A89410/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A89410/attachment/ATT-0/
Autor:
Deng, Chang-Bin, Shi, Yong-You, Song, Yu-Jie, Xue, Rui, Du, Lei-Ming, Wang, Ze-Rui, Xie, Zhao-Hua
The discovery that blazars dominate the extra-galactic {\gamma}-ray sky is a triumph in the Fermi era. However, the exact location of {\gamma}-ray emission region still remains in debate. Low-synchrotron-peaked blazars (LSPs) are estimated to produce
Externí odkaz:
http://arxiv.org/abs/2406.17202
In music source separation, a standard training data augmentation procedure is to create new training samples by randomly combining instrument stems from different songs. These random mixes have mismatched characteristics compared to real music, e.g.
Externí odkaz:
http://arxiv.org/abs/2402.18407
We report a temperature-dependent neutron diffraction (ND) study on polycrystalline monoclinic BaIrO$_3$ which is famous for charge density wave (CDW) and weak ferromagnetic phase transitions at T$_C$$\sim$180 K simultaneously. A Rietveld analysis on
Externí odkaz:
http://arxiv.org/abs/2308.08109
Autor:
Jeon, Chang-Bin, Lee, Kyogu
The loudness war, an ongoing phenomenon in the music industry characterized by the increasing final loudness of music while reducing its dynamic range, has been a controversial topic for decades. Music mastering engineers have used limiters to heavil
Externí odkaz:
http://arxiv.org/abs/2308.01187
Music source separation (MSS) faces challenges due to the limited availability of correctly-labeled individual instrument tracks. With the push to acquire larger datasets to improve MSS performance, the inevitability of encountering mislabeled indivi
Externí odkaz:
http://arxiv.org/abs/2307.12576
Separation of multiple singing voices into each voice is a rarely studied area in music source separation research. The absence of a benchmark dataset has hindered its progress. In this paper, we present an evaluation dataset and provide baseline stu
Externí odkaz:
http://arxiv.org/abs/2211.07302
Autor:
Jeon, Chang-Bin, Lee, Kyogu
Nowadays, commercial music has extreme loudness and heavily compressed dynamic range compared to the past. Yet, in music source separation, these characteristics have not been thoroughly considered, resulting in the domain mismatch between the labora
Externí odkaz:
http://arxiv.org/abs/2208.14355
In this work, we study the continual semantic segmentation problem, where the deep neural networks are required to incorporate new classes continually without catastrophic forgetting. We propose to use a structural re-parameterization mechanism, name
Externí odkaz:
http://arxiv.org/abs/2203.05402
Autor:
Wen, Yue-Feng, Huang, Wen-Jin, Chen, Xiao-Long, Cai, Hui-Tang, Zhang, Yi-Bin, Song, Xian-Lu, Xie, Chang-Bin, Peng, Hai-Hua, Yu, Hong-Wei, Chen, Cheng-Cong, Wei, Li-Qiu, Zhou, Tong-Chong, Cai, Shuang, Wang, Fang, Lin, Xiao-Dan
Publikováno v:
In Oral Oncology October 2024 157