Dear Editor,Underwater distributed antenna systems(DAS) are stationary infrastructures consisting of multiple geographically distributed antenna elements(DAEs) which are interconnected through high-rate backbone netwo...Dear Editor,Underwater distributed antenna systems(DAS) are stationary infrastructures consisting of multiple geographically distributed antenna elements(DAEs) which are interconnected through high-rate backbone networks [1]. Compared to centralized systems, the DAS could provide a larger coverage area and higher throughput for underwater acoustic(UWA) transmissions. In this work, exploiting the low sound speed in water, a multi-agent reinforcement learning(MARL)-based approach is proposed to secure underwater DAS against eavesdropping at the physical layer.展开更多
Few-shot learning has been proposed and rapidly emerging as a viable means for completing various tasks.Many few-shot models have been widely used for relation learning tasks.However,each of these models has a shortag...Few-shot learning has been proposed and rapidly emerging as a viable means for completing various tasks.Many few-shot models have been widely used for relation learning tasks.However,each of these models has a shortage of capturing a certain aspect of semantic features,for example,CNN on long-range dependencies part,Transformer on local features.It is difficult for a single model to adapt to various relation learning,which results in a high variance problem.Ensemble strategy could be competitive in improving the accuracy of few-shot relation extraction and mitigating high variance risks.This paper explores an ensemble approach to reduce the variance and introduces fine-tuning and feature attention strategies to calibrate relation-level features.Results on several few-shot relation learning tasks show that our model significantly outperforms the previous state-of-the-art models.展开更多
基金supported in part by the National Natural Science Foundation of China(62201248)the Startup Foundation of the University of South China(200XQD056)。
文摘Dear Editor,Underwater distributed antenna systems(DAS) are stationary infrastructures consisting of multiple geographically distributed antenna elements(DAEs) which are interconnected through high-rate backbone networks [1]. Compared to centralized systems, the DAS could provide a larger coverage area and higher throughput for underwater acoustic(UWA) transmissions. In this work, exploiting the low sound speed in water, a multi-agent reinforcement learning(MARL)-based approach is proposed to secure underwater DAS against eavesdropping at the physical layer.
基金The State Key Program of National Natural Science of China,Grant/Award Number:61533018National Natural Science Foundation of China,Grant/Award Number:61402220+2 种基金The Philosophy and Social Science Foundation of Hunan Province,Grant/Award Number:16YBA323Natural Science Foundation of Hunan Province,Grant/Award Number:2020JJ4525,2022JJ30495Scientific Research Fund of Hunan Provincial Education Department,Grant/Award Number:18B279,19A439
文摘Few-shot learning has been proposed and rapidly emerging as a viable means for completing various tasks.Many few-shot models have been widely used for relation learning tasks.However,each of these models has a shortage of capturing a certain aspect of semantic features,for example,CNN on long-range dependencies part,Transformer on local features.It is difficult for a single model to adapt to various relation learning,which results in a high variance problem.Ensemble strategy could be competitive in improving the accuracy of few-shot relation extraction and mitigating high variance risks.This paper explores an ensemble approach to reduce the variance and introduces fine-tuning and feature attention strategies to calibrate relation-level features.Results on several few-shot relation learning tasks show that our model significantly outperforms the previous state-of-the-art models.
