Zobrazeno 1 - 10
of 4 422
pro vyhledávání: '"ZHAO, Huan"'
Power dispatch is essential for providing stable, cost-effective, and eco-friendly electricity to society. However, traditional methods falter as power systems grow in scale and complexity, struggling with multitasking, swift problem-solving, and hum
Externí odkaz:
http://arxiv.org/abs/2408.03847
Autor:
Zhou, Xiyuan, Zhao, Huan, Cheng, Yuheng, Cao, Yuji, Liang, Gaoqi, Liu, Guolong, Liu, Wenxuan, Xu, Yan, Zhao, Junhua
In response to the urgent demand for grid stability and the complex challenges posed by renewable energy integration and electricity market dynamics, the power sector increasingly seeks innovative technological solutions. In this context, large langu
Externí odkaz:
http://arxiv.org/abs/2407.05365
We present a pragmatic approach to lower down the mass scale of right-handed neutrinos in leptogenesis by introducing a scalar decaying to right-handed neutrinos. The key point of our proposal is that the out-of-equilibrium decays of the scalar provi
Externí odkaz:
http://arxiv.org/abs/2406.13468
CAD (Computer-Aided Design) plays a crucial role in mechanical industry, where large numbers of similar-shaped CAD parts are often created. Efficiently reusing these parts is key to reducing design and production costs for enterprises. Retrieval syst
Externí odkaz:
http://arxiv.org/abs/2406.08863
Autor:
Ma, Linhan, Guo, Dake, Song, Kun, Jiang, Yuepeng, Wang, Shuai, Xue, Liumeng, Xu, Weiming, Zhao, Huan, Zhang, Binbin, Xie, Lei
With the development of large text-to-speech (TTS) models and scale-up of the training data, state-of-the-art TTS systems have achieved impressive performance. In this paper, we present WenetSpeech4TTS, a multi-domain Mandarin corpus derived from the
Externí odkaz:
http://arxiv.org/abs/2406.05763
Autor:
Gault, Baptiste, Saksena, Aparna, Sauvage, Xavier, Bagot, Paul, Aota, Leonardo S., Arlt, Jonas, Belkacemi, Lisa T., Boll, Torben, Chen, Yi-Sheng, Daly, Luke, Djukic, Milos B., Douglas, James O., Duarte, Maria J., Felfer, Peter J., Forbes, Richard G., Fu, Jing, Gardner, Hazel M., Gemma, Ryota, Gerstl, Stephan S. A., Gong, Yilun, Hachet, Guillaume, Jakob, Severin, Jenkins, Benjamin M., Jones, Megan E., Khanchandani, Heena, Kontis, Paraskevas, Krämer, Mathias, Kühbach, Markus, Marceau, Ross K. W., Mayweg, David, Moore, Katie L., Nallathambi, Varatharaja, Ott, Benedict C., Poplawsky, Jonathan D, Prosa, Ty, Pundt, Astrid, Saha, Mainak, Schwarz, Tim M., Shang, Yuanyuan, Shen, Xiao, Vrellou, Maria, Yu, Yuan, Zhao, Yujun, Zhao, Huan, Zou, Bowen
As hydrogen is touted as a key player in the decarbonization of modern society, it is critical to enable quantitative H analysis at high spatial resolution, if possible at the atomic scale. Indeed, H has a known deleterious impact on the mechanical p
Externí odkaz:
http://arxiv.org/abs/2405.13158
Publikováno v:
published at ICASSP 2024
Conversational Emotion Recognition (CER) aims to predict the emotion expressed by an utterance (referred to as an ``event'') during a conversation. Existing graph-based methods mainly focus on event interactions to comprehend the conversational conte
Externí odkaz:
http://arxiv.org/abs/2405.03960
Publikováno v:
published at ICASSP 2024
Graph representation learning has become a hot research topic due to its powerful nonlinear fitting capability in extracting representative node embeddings. However, for sequential data such as speech signals, most traditional methods merely focus on
Externí odkaz:
http://arxiv.org/abs/2405.03956
Autor:
Cao, Yuji, Zhao, Huan, Cheng, Yuheng, Shu, Ting, Liu, Guolong, Liang, Gaoqi, Zhao, Junhua, Li, Yun
With extensive pre-trained knowledge and high-level general capabilities, large language models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in aspects such as multi-task learning, sample efficiency, and task planning. I
Externí odkaz:
http://arxiv.org/abs/2404.00282
Graph-structured data are the commonly used and have wide application scenarios in the real world. For these diverse applications, the vast variety of learning tasks, graph domains, and complex graph learning procedures present challenges for human e
Externí odkaz:
http://arxiv.org/abs/2402.11641