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pro vyhledávání: '"LI, XIA"'
In this work, we propose Graph Retention Network as a unified architecture for deep learning on dynamic graphs. The GRN extends the core computational manner of retention to dynamic graph data as graph retention, which empowers the model with three k
Externí odkaz:
http://arxiv.org/abs/2411.11259
Autor:
Liu, Xiang, Song, Yijun, Li, Xia, Sun, Yifei, Lan, Huiying, Liu, Zemin, Jiang, Linshan, Li, Jialin
Deep learning models are increasingly deployed on resource-constrained edge devices for real-time data analytics. In recent years, Vision Transformer models and their variants have demonstrated outstanding performance across various computer vision t
Externí odkaz:
http://arxiv.org/abs/2410.11650
Autor:
Liu, Yao, Zhu, Ming, Yu, Hai-Yang, Zhou, Rui-Lei, Xu, Jin-Long, Ai, Mei, Jiang, Peng, Yuan, Li-Xia, Zhang, Hai-Yan
We used FAST to conduct deep HI imaging of the entire M 106 group region, and have discovered a few new HI filaments and clouds. Three HI clouds/filaments are found in a region connecting DDO 120 and NGC 4288, indicating an interaction between these
Externí odkaz:
http://arxiv.org/abs/2410.07038
Akademický článek
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We introduce a policy model coupled with the susceptible-infected-recovered (SIR) epidemic model to study interactions between policy-making and the dynamics of epidemics. We consider both single-region policies, as well as game-theoretic models invo
Externí odkaz:
http://arxiv.org/abs/2408.02097
Deformable Image Registration (DIR) is essential for aligning medical images that exhibit anatomical variations, facilitating applications such as disease tracking and radiotherapy planning. While classical iterative methods and deep learning approac
Externí odkaz:
http://arxiv.org/abs/2406.03394
The limited robustness of 3D Gaussian Splatting (3DGS) to motion blur and camera noise, along with its poor real-time performance, restricts its application in robotic SLAM tasks. Upon analysis, the primary causes of these issues are the density of v
Externí odkaz:
http://arxiv.org/abs/2405.19614
Motion information from 4D medical imaging offers critical insights into dynamic changes in patient anatomy for clinical assessments and radiotherapy planning and, thereby, enhances the capabilities of 3D image analysis. However, inherent physical an
Externí odkaz:
http://arxiv.org/abs/2405.15385
Background and purpose: Deformable image registration (DIR) is a crucial tool in radiotherapy for extracting and modelling organ motion. However, when significant changes and sliding boundaries are present, it faces compromised accuracy and uncertain
Externí odkaz:
http://arxiv.org/abs/2405.00430