Zobrazeno 1 - 10
of 21 772
pro vyhledávání: '"Yulan An"'
Autor:
Wang, Zhengli, Cao, Shunshun, Lu, Jiguang, Liu, Yulan, Shi, Xun, Jiang, Jinchen, Liang, Enwei, Wang, Weiyang, Xu, Heng, Xu, Renxin
We report the detection of an extreme flux decrease accompanied by clear dispersion measure (DM) and rotation measure (RM) variations for pulsar B1929+10 during the 110-minute radio observation with the Five-hundred-meter Aperture Spherical radio Tel
Externí odkaz:
http://arxiv.org/abs/2410.16816
The fine-tuning of pre-trained language models (PLMs) has been shown to be effective across various domains. By using domain-specific supervised data, the general-purpose representation derived from PLMs can be transformed into a domain-specific repr
Externí odkaz:
http://arxiv.org/abs/2410.14375
Integrated Sensing and Communication (ISAC) systems promise to revolutionize wireless networks by concurrently supporting high-resolution sensing and high-performance communication. This paper presents a novel radio access technology (RAT) selection
Externí odkaz:
http://arxiv.org/abs/2410.11002
Generating rationales that justify scoring decisions has emerged as a promising approach to enhance explainability in the development of automated scoring systems. However, the scarcity of publicly available rationale data and the high cost of annota
Externí odkaz:
http://arxiv.org/abs/2410.09507
Data augmentation (DA) is an effective approach for enhancing model performance with limited data, such as light field (LF) image super-resolution (SR). LF images inherently possess rich spatial and angular information. Nonetheless, there is a scarci
Externí odkaz:
http://arxiv.org/abs/2410.06478
In the past, Retrieval-Augmented Generation (RAG) methods split text into chunks to enable language models to handle long documents. Recent tree-based RAG methods are able to retrieve detailed information while preserving global context. However, wit
Externí odkaz:
http://arxiv.org/abs/2410.04790
Large Language Models (LLMs) have achieved impressive results in processing text data, which has sparked interest in applying these models beyond textual data, such as graphs. In the field of graph learning, there is a growing interest in harnessing
Externí odkaz:
http://arxiv.org/abs/2409.20053
Autor:
Wang, Longguang, Guo, Yulan, Li, Juncheng, Liu, Hongda, Zhao, Yang, Wang, Yingqian, Jin, Zhi, Gu, Shuhang, Timofte, Radu
This paper summarizes the 3rd NTIRE challenge on stereo image super-resolution (SR) with a focus on new solutions and results. The task of this challenge is to super-resolve a low-resolution stereo image pair to a high-resolution one with a magnifica
Externí odkaz:
http://arxiv.org/abs/2409.16947
In this note, we comprehensively characterize the proximal operator of the $\ell_{1,q}$-norm with $0\!<\!q\!<\!1$ by exploiting the well-known proximal operator of the $\ell_q$-norm on the real line. In particular, much more explicit characterization
Externí odkaz:
http://arxiv.org/abs/2409.14156
Predicting unknown drug-drug interactions (DDIs) is crucial for improving medication safety. Previous efforts in DDI prediction have typically focused on binary classification or predicting DDI categories, with the absence of explanatory insights tha
Externí odkaz:
http://arxiv.org/abs/2409.05592