activity
20222024
most citedThe Solar Upper Transition Region Imager (SUTRI) onboard the SATech-01 satellite

65 citations · 98 across the 20 of their papers we have counts for

collaborators

21 papers

cs.DB20241 cited

GriDB: Scaling Blockchain Database via Sharding and Off-Chain Cross-Shard Mechanism

Zicong Hong, Song Guo, Enyuan Zhou +3

Blockchain databases have attracted widespread attention but suffer from poor scalability due to underlying non-scalable blockchains. While blockchain sharding is necessary for a s…

cs.LG20241 cited

DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning

Sikai Bai, Jie Zhang, Shuaicheng Li +5

Federated learning (FL) has emerged as a powerful paradigm for learning from decentralized data, and federated domain generalization further considers the test dataset (target doma…

cs.LG2024

Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression

Xiaoxin Su, Yipeng Zhou, Laizhong Cui +1

Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy. To conduct model training by FL, multiple clients exchange model updates…

cs.MM20241 cited

Generative AI-enabled Mobile Tactical Multimedia Networks: Distribution, Generation, and Perception

Minrui Xu, Dusit Niyato, Jiawen Kang +4

Mobile multimedia networks (MMNs) demonstrate great potential in delivering low-latency and high-quality entertainment and tactical applications, such as short-video sharing, onlin…

cs.DC2024

OTAS: An Elastic Transformer Serving System via Token Adaptation

Jinyu Chen, Wenchao Xu, Zicong Hong +4

Transformer model empowered architectures have become a pillar of cloud services that keeps reshaping our society. However, the dynamic query loads and heterogeneous user requireme…

cs.CV2023

GBE-MLZSL: A Group Bi-Enhancement Framework for Multi-Label Zero-Shot Learning

Ziming Liu, Jingcai Guo, Xiaocheng Lu +3

This paper investigates a challenging problem of zero-shot learning in the multi-label scenario (MLZSL), wherein, the model is trained to recognize multiple unseen classes within a…