As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can p...As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can provide higher efficiency with limited spectrum resources. In this paper, combining spectrum splitting with rate splitting, we propose to allocate resources with traffic offloading in hybrid satellite terrestrial networks. A novel deep reinforcement learning method is adopted to solve this challenging non-convex problem. However, the neverending learning process could prohibit its practical implementation. Therefore, we introduce the switch mechanism to avoid unnecessary learning. Additionally, the QoS constraint in the scheme can rule out unsuccessful transmission. The simulation results validates the energy efficiency performance and the convergence speed of the proposed algorithm.展开更多
Suffering from the inefficient traditional trial-and-error methods and the huge searching space filled by millions of candidates, discovering new perovskite visible photocatalysts with higher hydrogen production rate(...Suffering from the inefficient traditional trial-and-error methods and the huge searching space filled by millions of candidates, discovering new perovskite visible photocatalysts with higher hydrogen production rate(RH_(2)) still remains a challenge in the field of photocatalytic water splitting(PWS). Herein, we established structural-property models targeted to RH_(2) and the proper bandgap(Eg) via machine learning(ML) technology to accelerate the discovery of efficient perovskite photocatalysts for PWS. The Pearson correlation coefficients(R) of leave-one-out cross validation(LOOCV) were adopted to compare the performances of different algorithms including gradient boosting regression(GBR), support vector regression(SVR), backpropagation artificial neural network(BPANN), and random forest(RF). It was found that the BPANN model showed the highest R values from LOOCV and testing data of 0.9897 and 0.9740 for RH_(2),while the GBR model had the best values of 0.9290 and 0.9207 for Eg. Furtherly, 14 potential PWS perovskite candidates were screened out from 30,000 ABO3-type perovskite structures under the criteria of structural stability, Eg, conduction band energy, valence band energy and RH_(2). The average RH_(2) of these14 perovskites is 6.4% higher than the highest value in the training data set. Moreover, the online web servers were developed to share our prediction models, which could be accessible in http://materialsdata-mining.com/ocpmdm/material_api/ahfga3d9puqlknig(E_g prediction) and http://materials-datamining.com/ocpmdm/material_api/i0 ucuyn3 wsd14940(RH_(2) prediction).展开更多
微调后的大语言模型(Large language models,LLMs)在多任务中表现出色,但集中式训练存在用户隐私泄漏的风险。联邦学习(Federated learning,FL)通过本地训练避免了数据共享,但LLMs庞大的参数量对资源受限的设备和通信带宽构成挑战,导致...微调后的大语言模型(Large language models,LLMs)在多任务中表现出色,但集中式训练存在用户隐私泄漏的风险。联邦学习(Federated learning,FL)通过本地训练避免了数据共享,但LLMs庞大的参数量对资源受限的设备和通信带宽构成挑战,导致在边缘网络中部署困难。结合分割学习(Split learning,SL),联邦分割学习可以有效解决这一问题。基于模型深层权重的影响更为显著,以及对部分层的训练准确率略低于整体模型训练的发现,本文按照Transformer层对模型进行分割,同时引入低秩适应(Low⁃rank adaption,LoRA)进一步降低资源开销和提升安全性。因此,在设备端,仅对最后几层进行低秩适应和训练,然后上传至服务器进行聚合。为了降低开销并保证模型性能,本文提出了基于联邦分割学习与LoRA的RoBERTa预训练模型微调方法。通过联合优化边缘设备的计算频率和模型微调的秩,在资源受限的情况下最大化秩,提高模型的准确率。仿真结果显示,仅训练LLMs最后3层的情况下,在一定范围内(1~32)增加秩的取值可以提高模型的准确率。同时,增大模型每轮的容忍时延和设备的能量阈值可以进一步提升模型的准确率。展开更多
Implicit and explicit learning strategies of SL vocabulary acquisition are summarized based on precious studies and experiments. It is concluded that implicit learning strategies dolittlehelpto SL vocabulary acquisiti...Implicit and explicit learning strategies of SL vocabulary acquisition are summarized based on precious studies and experiments. It is concluded that implicit learning strategies dolittlehelpto SL vocabulary acquisition, but explicit learning strategies play a very important part in SL vocabulary acquisition. Besides, an assumption is proposed: the more obvious explicit learning is in vocabulary acquisition, the more words learners can acquire. It is hoped that this research has certain implications for SL learners and teaching.展开更多
In order to improve the generalization ability of binary decision trees, a new learning algorithm, the MMDT algorithm, is presented. Based on statistical learning theory the generalization performance of binary decisi...In order to improve the generalization ability of binary decision trees, a new learning algorithm, the MMDT algorithm, is presented. Based on statistical learning theory the generalization performance of binary decision trees is analyzed, and the assessment rule is proposed. Under the direction of the assessment rule, the MMDT algorithm is implemented. The algorithm maps training examples from an original space to a high dimension feature space, and constructs a decision tree in it. In the feature space, a new decision node splitting criterion, the max-min rule, is used, and the margin of each decision node is maximized using a support vector machine, to improve the generalization performance. Experimental results show that the new learning algorithm is much superior to others such as C4.5 and OC1.展开更多
In this paper,we make use of the boosting method to introduce a new learning algorithm for Gaussian Mixture Models (GMMs) called adapted Boosted Mixture Learning (BML). The method possesses the ability to rectify the ...In this paper,we make use of the boosting method to introduce a new learning algorithm for Gaussian Mixture Models (GMMs) called adapted Boosted Mixture Learning (BML). The method possesses the ability to rectify the existing problems in other conventional techniques for estimating the GMM parameters, due in part to a new mixing-up strategy to increase the number of Gaussian components. The discriminative splitting idea is employed for Gaussian mixture densities followed by learning via the introduced method. Then, the GMM classifier was applied to distinguish between healthy infants and those that present a selected set of medical conditions. Each group includes both full-term and premature infants. Cry-pattern for each pathological condition is created by using the adapted BML method and 13-dimensional Mel-Frequency Cepstral Coefficients (MFCCs) feature vector. The test results demonstrate that the introduced method for training GMMs has a better performance than the traditional method based upon random splitting and EM-based re-estimation as a reference system in multi-pathological classification task.展开更多
文摘As the demands of massive connections and vast coverage rapidly grow in the next wireless communication networks, rate splitting multiple access(RSMA) is considered to be the new promising access scheme since it can provide higher efficiency with limited spectrum resources. In this paper, combining spectrum splitting with rate splitting, we propose to allocate resources with traffic offloading in hybrid satellite terrestrial networks. A novel deep reinforcement learning method is adopted to solve this challenging non-convex problem. However, the neverending learning process could prohibit its practical implementation. Therefore, we introduce the switch mechanism to avoid unnecessary learning. Additionally, the QoS constraint in the scheme can rule out unsuccessful transmission. The simulation results validates the energy efficiency performance and the convergence speed of the proposed algorithm.
基金Financial support to this work from the National Key Research and Development Program of China (No. 2016YFB0700504)the Science and Technology Commission of Shanghai Municipality (18520723500) is gratefully acknowledged。
文摘Suffering from the inefficient traditional trial-and-error methods and the huge searching space filled by millions of candidates, discovering new perovskite visible photocatalysts with higher hydrogen production rate(RH_(2)) still remains a challenge in the field of photocatalytic water splitting(PWS). Herein, we established structural-property models targeted to RH_(2) and the proper bandgap(Eg) via machine learning(ML) technology to accelerate the discovery of efficient perovskite photocatalysts for PWS. The Pearson correlation coefficients(R) of leave-one-out cross validation(LOOCV) were adopted to compare the performances of different algorithms including gradient boosting regression(GBR), support vector regression(SVR), backpropagation artificial neural network(BPANN), and random forest(RF). It was found that the BPANN model showed the highest R values from LOOCV and testing data of 0.9897 and 0.9740 for RH_(2),while the GBR model had the best values of 0.9290 and 0.9207 for Eg. Furtherly, 14 potential PWS perovskite candidates were screened out from 30,000 ABO3-type perovskite structures under the criteria of structural stability, Eg, conduction band energy, valence band energy and RH_(2). The average RH_(2) of these14 perovskites is 6.4% higher than the highest value in the training data set. Moreover, the online web servers were developed to share our prediction models, which could be accessible in http://materialsdata-mining.com/ocpmdm/material_api/ahfga3d9puqlknig(E_g prediction) and http://materials-datamining.com/ocpmdm/material_api/i0 ucuyn3 wsd14940(RH_(2) prediction).
文摘Implicit and explicit learning strategies of SL vocabulary acquisition are summarized based on precious studies and experiments. It is concluded that implicit learning strategies dolittlehelpto SL vocabulary acquisition, but explicit learning strategies play a very important part in SL vocabulary acquisition. Besides, an assumption is proposed: the more obvious explicit learning is in vocabulary acquisition, the more words learners can acquire. It is hoped that this research has certain implications for SL learners and teaching.
文摘In order to improve the generalization ability of binary decision trees, a new learning algorithm, the MMDT algorithm, is presented. Based on statistical learning theory the generalization performance of binary decision trees is analyzed, and the assessment rule is proposed. Under the direction of the assessment rule, the MMDT algorithm is implemented. The algorithm maps training examples from an original space to a high dimension feature space, and constructs a decision tree in it. In the feature space, a new decision node splitting criterion, the max-min rule, is used, and the margin of each decision node is maximized using a support vector machine, to improve the generalization performance. Experimental results show that the new learning algorithm is much superior to others such as C4.5 and OC1.
文摘In this paper,we make use of the boosting method to introduce a new learning algorithm for Gaussian Mixture Models (GMMs) called adapted Boosted Mixture Learning (BML). The method possesses the ability to rectify the existing problems in other conventional techniques for estimating the GMM parameters, due in part to a new mixing-up strategy to increase the number of Gaussian components. The discriminative splitting idea is employed for Gaussian mixture densities followed by learning via the introduced method. Then, the GMM classifier was applied to distinguish between healthy infants and those that present a selected set of medical conditions. Each group includes both full-term and premature infants. Cry-pattern for each pathological condition is created by using the adapted BML method and 13-dimensional Mel-Frequency Cepstral Coefficients (MFCCs) feature vector. The test results demonstrate that the introduced method for training GMMs has a better performance than the traditional method based upon random splitting and EM-based re-estimation as a reference system in multi-pathological classification task.
