Zobrazeno 1 - 10
of 6 437
pro vyhledávání: '"Xia Jun"'
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
Wireless sensors are widely deployed to harsh environments for information monitoring, as the sensor nodes are highly susceptible to various failures, resulting in erroneous monitoring data. Sensor fault diagnosis is the subject of research work in t
Externí odkaz:
https://doaj.org/article/cefc283880504f8e991c133682e7a867
Building a comprehensive catalog of galaxy clusters is a fundamental task for the studies on the structure formation and galaxy evolution. In this paper, we present COSMIC (Cluster Optical Search using Machine Intelligence in Catalogs), an algorithm
Externí odkaz:
http://arxiv.org/abs/2410.20083
Autor:
Liu, Sizhe, Xia, Jun, Zhang, Lecheng, Liu, Yuchen, Liu, Yue, Du, Wenjie, Gao, Zhangyang, Hu, Bozhen, Tan, Cheng, Xiang, Hongxin, Li, Stan Z.
Molecular relational learning (MRL) is crucial for understanding the interaction behaviors between molecular pairs, a critical aspect of drug discovery and development. However, the large feasible model space of MRL poses significant challenges to be
Externí odkaz:
http://arxiv.org/abs/2410.15010
Autor:
Long, Wen-Gao, Xia, Jun
In this paper, we revisit the asymptotic formulas of real Painlev\'e I transcendents as the independent variable tends to negative infinity, which were initially derived by Kapaev with the complex WKB method. Using the Riemann-Hilbert method, we impr
Externí odkaz:
http://arxiv.org/abs/2409.03313
Autor:
Pan, Zixuan, Xia, Jun, Yan, Zheyu, Xu, Guoyue, Wu, Yawen, Jia, Zhenge, Chen, Jianxu, Shi, Yiyu
Reconstruction-based methods, particularly those leveraging autoencoders, have been widely adopted to perform anomaly detection in brain MRI. While most existing works try to improve detection accuracy by proposing new model structures or algorithms,
Externí odkaz:
http://arxiv.org/abs/2408.08228
Primordial magnetic fields (PMFs) play a pivotal role in influencing small-scale fluctuations within the primordial density field, thereby enhancing the matter power spectrum within the context of the $\Lambda$CDM model at small scales. These amplifi
Externí odkaz:
http://arxiv.org/abs/2408.03584
In our previous study, we introduced a machine-learning technique, namely CMBFSCNN, for the removal of foreground contamination in cosmic microwave background (CMB) polarization data. This method was successfully employed on actual observational data
Externí odkaz:
http://arxiv.org/abs/2406.17685
Autor:
Zhou, Jingbo, Chen, Shaorong, Xia, Jun, Liu, Sizhe, Ling, Tianze, Du, Wenjie, Liu, Yue, Yin, Jianwei, Li, Stan Z.
Tandem mass spectrometry has played a pivotal role in advancing proteomics, enabling the high-throughput analysis of protein composition in biological tissues. Many deep learning methods have been developed for \emph{de novo} peptide sequencing task,
Externí odkaz:
http://arxiv.org/abs/2406.11906
Publikováno v:
Dianzi Jishu Yingyong, Vol 45, Iss 8, Pp 40-43 (2019)
Aiming at the problems of higher computational complexity and larger memory requirements of current object detection algorithm, we designed and implemented an FPGA-based deep learning object detection system. We also designed the hardware accelerator
Externí odkaz:
https://doaj.org/article/bcc58d320a65476d98fceff536f76af4
Upon deployment to edge devices, it is often desirable for a model to further learn from streaming data to improve accuracy. However, extracting representative features from such data is challenging because it is typically unlabeled, non-independent
Externí odkaz:
http://arxiv.org/abs/2405.16113