Background Children with nonverbal learning disabilities (NLD) usually manifest defective attention function This study sought to investigate the neuropsychological characteristics of selective attention, such as atte...Background Children with nonverbal learning disabilities (NLD) usually manifest defective attention function This study sought to investigate the neuropsychological characteristics of selective attention, such as attention control, working memory, and attention persistence of the frontal lobe in children with NLD Methods Using the auditory detection test (ADT), Wisconsin card sorting test (WCST), and C WISC, 27 children with NLD and 33 normal children in the control group were tested, and the results of C WISC subtests were analyzed with factor analysis Results Compared with the control group, the correct response rate in the auditory detection test in the NLD group was much lower ( P <0 01), and the number of incorrect responses was much higher ( P <0 01); NLD children also scored lower in WCST categories achieved (CA) and perseverative errors (PE) ( P <0 05) Factor analysis showed that perceptual organization (PO) related to visual space and freedom from distractibility (FD) relating to attention persistence in the NLD group were obviously lower than in the control group ( P <0 01) Conclusions Children with NLD have attention control disorder and working memory disorder mainly in the frontal lobe We believe that the disorder is particularly prominent in the right frontal lobe展开更多
Deep learning models have been shown to have great advantages in answer selection tasks.The existing models,which employ encoder-decoder recurrent neural network(RNN),have been demonstrated to be effective.However,the...Deep learning models have been shown to have great advantages in answer selection tasks.The existing models,which employ encoder-decoder recurrent neural network(RNN),have been demonstrated to be effective.However,the traditional RNN-based models still suffer from limitations such as 1)high-dimensional data representation in natural language processing and 2)biased attentive weights for subsequent words in traditional time series models.In this study,a new answer selection model is proposed based on the Bidirectional Long Short-Term Memory(Bi-LSTM)and attention mechanism.The proposed model is able to generate the more effective question-answer pair representation.Experiments on a question answering dataset that includes information from multiple fields show the great advantages of our proposed model.Specifically,we achieve a maximum improvement of 3.8%over the classical LSTM model in terms of mean average precision.展开更多
In this paper,we investigate a backhaul framework jointly considering topology construction and power adjustment for self-organizing UAV networks.To enhance the backhaul rate with limited information exchange and avoi...In this paper,we investigate a backhaul framework jointly considering topology construction and power adjustment for self-organizing UAV networks.To enhance the backhaul rate with limited information exchange and avoid malicious power competition,we propose a deep reinforcement learning(DRL)based method to construct the backhaul framework where each UAV distributedly makes decisions.First,we decompose the backhaul framework into three submodules,i.e.,transmission target selection(TS),total power control(PC),and multi-channel power allocation(PA).Then,the three submodules are solved by heterogeneous DRL algorithms with tailored rewards to regulate UAVs’behaviors.In particular,TS is solved by deep-Q learning to construct topology with less relay and guarantee the backhaul rate.PC and PA are solved by deep deterministic policy gradient to match the traffic requirement with proper finegrained transmission power.As a result,the malicious power competition is alleviated,and the backhaul rate is further enhanced.Simulation results show that the proposed framework effectively achieves system-level and all-around performance gain compared with DQL and max-min method,i.e.,higher backhaul rate,lower transmission power,and fewer hop.展开更多
Mental workload plays a vital role in cognitive impairment. The impairment refers to a person’s difficulty in remembering, receiving new information, learning new things, concentrating, or making decisions that serio...Mental workload plays a vital role in cognitive impairment. The impairment refers to a person’s difficulty in remembering, receiving new information, learning new things, concentrating, or making decisions that seriously affect everyday life. In this paper, the simultaneous capacity (SIMKAP) experiment-based EEG workload analysis was presented using 45 subjects for multitasking mental workload estimation with subject wise attention loss calculation as well as short term memory loss measurement. Using an open access preprocessed EEG dataset, Discrete wavelet transforms (DWT) was utilized for feature extraction and Minimum redundancy and maximum relevancy (MRMR) technique was used to select most relevance features. Wavelet decomposition technique was also used for decomposing EEG signals into five sub bands. Fourteen statistical features were calculated from each sub band signal to form a 5 × 14 window size. The Neural Network (Narrow) classification algorithm was used to classify dataset for low and high workload conditions and comparison was made using some other machine learning models. The results show the classifier’s accuracy of 86.7%, precision of 84.4%, F1 score of 86.33%, and recall of 88.37% that crosses the state-of-the art methodologies in the literature. This prediction is expected to greatly facilitate the improved way in memory and attention loss impairments assessment.展开更多
电力设备运维过程中积累了大量缺陷图像与文本数据,这些数据对指导电力设备故障诊断及维护决策具有重要意义。针对现有电力设备缺陷分类任务中数据形式单一、融合层次浅、数据质量差等问题,该文提出了一种基于改进注意力机制和对比学习...电力设备运维过程中积累了大量缺陷图像与文本数据,这些数据对指导电力设备故障诊断及维护决策具有重要意义。针对现有电力设备缺陷分类任务中数据形式单一、融合层次浅、数据质量差等问题,该文提出了一种基于改进注意力机制和对比学习的图文融合分类方法(image-text fusion classification method based on improved attention mechanism and contrastive learning,IAC-ITFusion)。首先,该方法设计了一种双循环跨模态注意力机制(dual-cycle cross-modal attention,DCCA),用于捕捉图文数据映射关系的同时整合特征信息。其次,基于对比学习的思想,提出了一种注意力引导损失函数,用于调控DCCA机制的学习方向,使其聚焦于正确的特征信息,实现图文数据特征的有效融合。最后,针对电力线、变电站设备缺陷图文融合分类任务进行实验验证,结果显示所提方法准确率分别达到98.48%和98.57%,证明了该方法在电力设备缺陷图文融合分类任务上的有效性,对于推动电力设备运维智能化发展具有重要意义。展开更多
文摘Background Children with nonverbal learning disabilities (NLD) usually manifest defective attention function This study sought to investigate the neuropsychological characteristics of selective attention, such as attention control, working memory, and attention persistence of the frontal lobe in children with NLD Methods Using the auditory detection test (ADT), Wisconsin card sorting test (WCST), and C WISC, 27 children with NLD and 33 normal children in the control group were tested, and the results of C WISC subtests were analyzed with factor analysis Results Compared with the control group, the correct response rate in the auditory detection test in the NLD group was much lower ( P <0 01), and the number of incorrect responses was much higher ( P <0 01); NLD children also scored lower in WCST categories achieved (CA) and perseverative errors (PE) ( P <0 05) Factor analysis showed that perceptual organization (PO) related to visual space and freedom from distractibility (FD) relating to attention persistence in the NLD group were obviously lower than in the control group ( P <0 01) Conclusions Children with NLD have attention control disorder and working memory disorder mainly in the frontal lobe We believe that the disorder is particularly prominent in the right frontal lobe
基金This work was supported in part by the National Natural Science Foundation of China under Grant 61572326,and Grant 61802258the Natural Science Foundation of Shanghai under Grant 18ZR1428300the Shanghai Committee of Science and Technology under Grant 17070502800 and Grant 16JC1403000.
文摘Deep learning models have been shown to have great advantages in answer selection tasks.The existing models,which employ encoder-decoder recurrent neural network(RNN),have been demonstrated to be effective.However,the traditional RNN-based models still suffer from limitations such as 1)high-dimensional data representation in natural language processing and 2)biased attentive weights for subsequent words in traditional time series models.In this study,a new answer selection model is proposed based on the Bidirectional Long Short-Term Memory(Bi-LSTM)and attention mechanism.The proposed model is able to generate the more effective question-answer pair representation.Experiments on a question answering dataset that includes information from multiple fields show the great advantages of our proposed model.Specifically,we achieve a maximum improvement of 3.8%over the classical LSTM model in terms of mean average precision.
文摘In this paper,we investigate a backhaul framework jointly considering topology construction and power adjustment for self-organizing UAV networks.To enhance the backhaul rate with limited information exchange and avoid malicious power competition,we propose a deep reinforcement learning(DRL)based method to construct the backhaul framework where each UAV distributedly makes decisions.First,we decompose the backhaul framework into three submodules,i.e.,transmission target selection(TS),total power control(PC),and multi-channel power allocation(PA).Then,the three submodules are solved by heterogeneous DRL algorithms with tailored rewards to regulate UAVs’behaviors.In particular,TS is solved by deep-Q learning to construct topology with less relay and guarantee the backhaul rate.PC and PA are solved by deep deterministic policy gradient to match the traffic requirement with proper finegrained transmission power.As a result,the malicious power competition is alleviated,and the backhaul rate is further enhanced.Simulation results show that the proposed framework effectively achieves system-level and all-around performance gain compared with DQL and max-min method,i.e.,higher backhaul rate,lower transmission power,and fewer hop.
文摘Mental workload plays a vital role in cognitive impairment. The impairment refers to a person’s difficulty in remembering, receiving new information, learning new things, concentrating, or making decisions that seriously affect everyday life. In this paper, the simultaneous capacity (SIMKAP) experiment-based EEG workload analysis was presented using 45 subjects for multitasking mental workload estimation with subject wise attention loss calculation as well as short term memory loss measurement. Using an open access preprocessed EEG dataset, Discrete wavelet transforms (DWT) was utilized for feature extraction and Minimum redundancy and maximum relevancy (MRMR) technique was used to select most relevance features. Wavelet decomposition technique was also used for decomposing EEG signals into five sub bands. Fourteen statistical features were calculated from each sub band signal to form a 5 × 14 window size. The Neural Network (Narrow) classification algorithm was used to classify dataset for low and high workload conditions and comparison was made using some other machine learning models. The results show the classifier’s accuracy of 86.7%, precision of 84.4%, F1 score of 86.33%, and recall of 88.37% that crosses the state-of-the art methodologies in the literature. This prediction is expected to greatly facilitate the improved way in memory and attention loss impairments assessment.
文摘电力设备运维过程中积累了大量缺陷图像与文本数据,这些数据对指导电力设备故障诊断及维护决策具有重要意义。针对现有电力设备缺陷分类任务中数据形式单一、融合层次浅、数据质量差等问题,该文提出了一种基于改进注意力机制和对比学习的图文融合分类方法(image-text fusion classification method based on improved attention mechanism and contrastive learning,IAC-ITFusion)。首先,该方法设计了一种双循环跨模态注意力机制(dual-cycle cross-modal attention,DCCA),用于捕捉图文数据映射关系的同时整合特征信息。其次,基于对比学习的思想,提出了一种注意力引导损失函数,用于调控DCCA机制的学习方向,使其聚焦于正确的特征信息,实现图文数据特征的有效融合。最后,针对电力线、变电站设备缺陷图文融合分类任务进行实验验证,结果显示所提方法准确率分别达到98.48%和98.57%,证明了该方法在电力设备缺陷图文融合分类任务上的有效性,对于推动电力设备运维智能化发展具有重要意义。
