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Collisional dynamics of symmetric two-dimensional quantum droplets

Autor: Hu, Yanming, Fei, Yifan, Chen, Xiao-Long, Zhang, Yunbo

Publikováno v: Front. Phys. 17, 61505 (2022)

The collisional dynamics of two symmetric droplets with equal intraspecies scattering lengths and particle number density for each component is studied by solving the corresponding extended Gross-Pitaevskii equation in two dimensions by including a l

Externí odkaz: http://arxiv.org/abs/2404.19295

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Community-Aware Efficient Graph Contrastive Learning via Personalized Self-Training

Autor: Li, Yuecheng, Hu, Yanming, Fu, Lele, Chen, Chuan, Yang, Lei, Zheng, Zibin

In recent years, graph contrastive learning (GCL) has emerged as one of the optimal solutions for various supervised tasks at the node level. However, for unsupervised and structure-related tasks such as community detection, current GCL algorithms fa

Externí odkaz: http://arxiv.org/abs/2311.11073

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Migrate Demographic Group For Fair GNNs

Autor: Hu, YanMing, Liao, TianChi, Chen, JiaLong, Bian, Jing, Zheng, ZiBin, Chen, Chuan

Graph Neural networks (GNNs) have been applied in many scenarios due to the superior performance of graph learning. However, fairness is always ignored when designing GNNs. As a consequence, biased information in training data can easily affect vanil

Externí odkaz: http://arxiv.org/abs/2306.04212

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Privacy Computing Meets Metaverse: Necessity, Taxonomy and Challenges

Autor: Chen, Chuan, Li, Yuecheng, Wu, Zhenpeng, Mai, Chengyuan, Liu, Youming, Hu, Yanming, Zheng, Zibin, Kang, Jiawen

Metaverse, the core of the next-generation Internet, is a computer-generated holographic digital environment that simultaneously combines spatio-temporal, immersive, real-time, sustainable, interoperable, and data-sensitive characteristics. It clever

Externí odkaz: http://arxiv.org/abs/2304.11643

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Decoupling anomaly discrimination and representation learning: self-supervised learning for anomaly detection on attributed graph

Autor: Hu, YanMing, Chen, Chuan, Deng, BoWen, Lai, YuJing, Lin, Hao, Zheng, ZiBin, Bian, Jing

Anomaly detection on attributed graphs is a crucial topic for its practical application. Existing methods suffer from semantic mixture and imbalance issue because they mainly focus on anomaly discrimination, ignoring representation learning. It confl

Externí odkaz: http://arxiv.org/abs/2304.05176

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