Zobrazeno 1 - 10
of 180
pro vyhledávání: '"Ji Xingyu"'
Autor:
Pan, Yiru, Ji, Xingyu, You, Jiaqi, Li, Lu, Liu, Zhenping, Zhang, Xianlong, Zhang, Zeyu, Wang, Maojun
Positive and negative association prediction between gene and phenotype helps to illustrate the underlying mechanism of complex traits in organisms. The transcription and regulation activity of specific genes will be adjusted accordingly in different
Externí odkaz:
http://arxiv.org/abs/2410.07511
LiDAR bundle adjustment (BA) is an effective approach to reduce the drifts in pose estimation from the front-end. Existing works on LiDAR BA usually rely on predefined geometric features for landmark representation. This reliance restricts generaliza
Externí odkaz:
http://arxiv.org/abs/2410.01618
Representation learning on text-attributed graphs (TAGs) has attracted significant interest due to its wide-ranging real-world applications, particularly through Graph Neural Networks (GNNs). Traditional GNN methods focus on encoding the structural i
Externí odkaz:
http://arxiv.org/abs/2410.01457
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
Abstract Coating matching design is one of the important parts of ship coating process design. The selection of coating matching is influenced by various factors such as marine corrosive environment, anti-corrosion period and working conditions. Ther
Externí odkaz:
https://doaj.org/article/75711caf81a24e698d367f032ffefbc6
Signed Graph Neural Networks (SGNNs) have been shown to be effective in analyzing complex patterns in real-world situations where positive and negative links coexist. However, SGNN models suffer from poor explainability, which limit their adoptions i
Externí odkaz:
http://arxiv.org/abs/2408.08754
Multi-modal test-time adaptation (MM-TTA) is proposed to adapt models to an unlabeled target domain by leveraging the complementary multi-modal inputs in an online manner. Previous MM-TTA methods for 3D segmentation rely on predictions of cross-modal
Externí odkaz:
http://arxiv.org/abs/2403.06461
Autor:
Zhang, Zeyu, Li, Lu, Ji, Xingyu, Zhao, Kaiqi, Zhu, Xiaofeng, Yu, Philip S., Li, Jiawei, Wang, Maojun
Signed graphs are powerful models for representing complex relations with both positive and negative connections. Recently, Signed Graph Neural Networks (SGNNs) have emerged as potent tools for analyzing such graphs. To our knowledge, no prior resear
Externí odkaz:
http://arxiv.org/abs/2310.11083
Multipath time-delay estimation is commonly encountered in radar and sonar signal processing. In some real-life environments, impulse noise is ubiquitous and significantly degrades estimation performance. Here, we propose a Bayesian approach to tailo
Externí odkaz:
http://arxiv.org/abs/2307.02113
This letter presents an accurate and robust Lidar Inertial Odometry framework. We fuse LiDAR scans with IMU data using a tightly-coupled iterative error state Kalman filter for robust and fast localization. To achieve robust correspondence matching,
Externí odkaz:
http://arxiv.org/abs/2306.17436
Autor:
Ji Xingyu, Zhao Hangfang
Publikováno v:
MATEC Web of Conferences, Vol 283, p 04003 (2019)
An acoustic tomography trial experiment was conducted in South China Sea during May to August in 2016. Two moorings are installed apart from about 56.94 km, while each consists of one low frequency source, 20 hydrophones deployed from the depth of ab
Externí odkaz:
https://doaj.org/article/45ae5fd5526c4c39adf65c279d707d64