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
Objective:To utilize clinical data of patients diagnosed with Papillary Renal Cell Carcinoma(PRCC)from the Surveillance,Epidemiology,and End Results(SEER)database(2010–2015)to construct and validate a prognostic mode...Objective:To utilize clinical data of patients diagnosed with Papillary Renal Cell Carcinoma(PRCC)from the Surveillance,Epidemiology,and End Results(SEER)database(2010–2015)to construct and validate a prognostic model using a retrospective study design.Methods:Clinical and pathological data of 1,788 PRCC patients were extracted from the SEER database based on defined inclusion and exclusion criteria.The cohort was randomly divided into a training set(n=1,252)and a validation set(n=536)in a 7:3 ratio.Univariate and multivariate Cox regression analyses were conducted to identify clinical factors influencing prognosis.Based on these factors,a nomogram was developed to predict 1-year,3-year,and 5-year Cancer-Specific Survival(CSS)rates.The model's discriminatory power and predictive performance were evaluated using the Concordance index(C-index),calibration curves,Area Under the Curve(AUC),and Receiver Operating Characteristic(ROC)analysis.Results:Univariate and multivariate Cox regression analyses identified age,gender,surgical method,pathological grade,and TNM stage as independent prognostic factors.These variables were incorporated into a Cox proportional hazards regression model to calculate risk scores and construct the nomogram.In the training set,the AUCs for 1-year,3-year,and 5-year CSS predictions were 0.7978,0.7813,and 0.7542,respectively.In the validation set,the AUCs were 0.6793,0.7114,and 0.7174,respectively.Calibration curves demonstrated good agreement between predicted and observed survival outcomes,indicating adequate predictive accuracy.Conclusion:The prognostic nomogram model for patients with papillary renal cell carcinoma developed based on SEER database data provides reliable prognostic predictions and may support clinical assessment and decision-making.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 Key Joint Project of the Hunan Provincial Natural Science Foundation and Municipal Science Foundation(2024JJ7428)。
文摘Objective:To utilize clinical data of patients diagnosed with Papillary Renal Cell Carcinoma(PRCC)from the Surveillance,Epidemiology,and End Results(SEER)database(2010–2015)to construct and validate a prognostic model using a retrospective study design.Methods:Clinical and pathological data of 1,788 PRCC patients were extracted from the SEER database based on defined inclusion and exclusion criteria.The cohort was randomly divided into a training set(n=1,252)and a validation set(n=536)in a 7:3 ratio.Univariate and multivariate Cox regression analyses were conducted to identify clinical factors influencing prognosis.Based on these factors,a nomogram was developed to predict 1-year,3-year,and 5-year Cancer-Specific Survival(CSS)rates.The model's discriminatory power and predictive performance were evaluated using the Concordance index(C-index),calibration curves,Area Under the Curve(AUC),and Receiver Operating Characteristic(ROC)analysis.Results:Univariate and multivariate Cox regression analyses identified age,gender,surgical method,pathological grade,and TNM stage as independent prognostic factors.These variables were incorporated into a Cox proportional hazards regression model to calculate risk scores and construct the nomogram.In the training set,the AUCs for 1-year,3-year,and 5-year CSS predictions were 0.7978,0.7813,and 0.7542,respectively.In the validation set,the AUCs were 0.6793,0.7114,and 0.7174,respectively.Calibration curves demonstrated good agreement between predicted and observed survival outcomes,indicating adequate predictive accuracy.Conclusion:The prognostic nomogram model for patients with papillary renal cell carcinoma developed based on SEER database data provides reliable prognostic predictions and may support clinical assessment and decision-making.
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.
