Federated learning (FL) is a distributed learning paradigm that enables a large number of mobile devices to collaboratively learn a model under the coordination of a central server without sharing their raw data.
In this paper, we propose a federated few-shot learning (FedFSL) framework to learn a few-shot classification model that can classify unseen data classes with only a few labeled samples.
$A$Co$_2$Se$_2$ ($A$=K, Rb, Cs) is a homologue of the iron-based superconductor, $A$Fe$_2$Se$_2$.
Superconductivity Materials Science
We present AEOLUS, a security overlay that proactively embeds a dynamic acoustic nonce at the time of user interaction, and detects the presence of the embedded nonce in the recorded speech to ensure freshness.
no code implementations • 16 Dec 2020 • Defa Liu, Xianxin Wu, Fangsen Li, Yong Hu, Jianwei Huang, Yu Xu, Cong Li, Yunyi Zang, Junfeng He, Lin Zhao, Shaolong He, Chenjia Tang, Zhi Li, Lili Wang, Qingyan Wang, Guodong Liu, Zuyan Xu, Xu-Cun Ma, Qi-Kun Xue, Jiangping Hu, X. J. Zhou
These observations not only show the first direct evidence that the electronic structure of single-layer FeSe/SrTiO3 films originates from bulk FeSe through a combined effect of an electronic phase transition and an interfacial charge transfer, but also provide a quantitative basis for theoretical models in describing the electronic structure and understanding the superconducting mechanism in single-layer FeSe/SrTiO3 films.
Band Gap Superconductivity Strongly Correlated Electrons
In this paper, we analyze how to design adaptive FL that optimally chooses these essential control variables to minimize the total cost while ensuring convergence.
We also show that the proposed contracts can reduce the system social cost by over 30%, compared with no storage investment benchmark.
Systems and Control Systems and Control
In order to elicit heterogeneous agents' private information and incentivize agents with different capabilities to act in the principal's best interest, we design an optimal joint incentive mechanism and prediction algorithm called COPE (COst and Prediction Elicitation), the analysis of which offers several valuable engineering insights.