Recurrent Neural Networks(RNNs)have been widely applied to deal with temporal problems,such as flood forecasting and financial data processing.On the one hand,traditional RNNs models amplify the gradient issue due to ...Recurrent Neural Networks(RNNs)have been widely applied to deal with temporal problems,such as flood forecasting and financial data processing.On the one hand,traditional RNNs models amplify the gradient issue due to the strict time serial dependency,making it difficult to realize a long-term memory function.On the other hand,RNNs cells are highly complex,which will signifi-cantly increase computational complexity and cause waste of computational resources during model training.In this paper,an improved Time Feedforward Connections Recurrent Neural Networks(TFC-RNNs)model was first proposed to address the gradient issue.A parallel branch was introduced for the hidden state at time t−2 to be directly transferred to time t without the nonlinear transforma-tion at time t−1.This is effective in improving the long-term dependence of RNNs.Then,a novel cell structure named Single Gate Recurrent Unit(SGRU)was presented.This cell structure can reduce the number of parameters for RNNs cell,consequently reducing the computational complexity.Next,applying SGRU to TFC-RNNs as a new TFC-SGRU model solves the above two difficulties.Finally,the performance of our proposed TFC-SGRU was verified through sev-eral experiments in terms of long-term memory and anti-interference capabilities.Experimental results demonstrated that our proposed TFC-SGRU model can cap-ture helpful information with time step 1500 and effectively filter out the noise.The TFC-SGRU model accuracy is better than the LSTM and GRU models regarding language processing ability.展开更多
The shrinking silicon feature size causes the continuous increment of the aging effect due to the negative bias temperature instability （NBTI）, which becomes a potential stopper for IC development. As the basis of c...The shrinking silicon feature size causes the continuous increment of the aging effect due to the negative bias temperature instability （NBTI）, which becomes a potential stopper for IC development. As the basis of circuit-level aging protection, an efficient aging critical-gate identification method is crucially required to select a set of gates for protection to guarantee the normal lifetime of the circuits. The existing critical-gate identification methods always depend on a critical path set which contains so many paths that its generation procedure requires undesirable CPU runtime; furthermore, these methods can achieve a better solution with taking account of the topological connection. This paper proposes a time-efficient critical gates identification method with topological connection analysis, which chooses a small set of critical gates. Experiments over many circuits of ITC99 and ISCAS benchmark demonstrate that, to guarantee the normal lifetime （e.g., 10 years） of each circuit, our method achieves a 3.97x speedup and saves as much as 27.21% area overhead compared with the existing methods.展开更多
The construction of high-speed rail(HSR)network has promoted the social-economic ties of cities,accelerated the compression of time and space,and changed the pattern of regional development.In this paper,with the adop...The construction of high-speed rail(HSR)network has promoted the social-economic ties of cities,accelerated the compression of time and space,and changed the pattern of regional development.In this paper,with the adoption of the operation frequency data of HSR from 12306 website,and based on the HSR connection strength model and social network analysis model,as well as according to the HSR connection strength,HSR network density,centrality,agglomeration subgroup,and other indicators,we analyzed the characteristics of HSR network structure in Northeast China.Results show that the number of HSR cities in Northeast China is small,cities in HSR network generally exhibit weak connectivity,and the existence of HSR network marginalizes cities such as Ulanhot,Baicheng,and Songyuan,which significantly reduce the overall network connectivity of Northeast China.The overall centrality of HSR network in Northeast China is characterized by“one axis,four edges”;specifically,the one axis is located in Harbin-Dalian transportation line and the four edges are located on both sides of the main axis of Harbin-Dalian transportation line.Eight agglomeration subgroups(four double city subgroups and four multi city subgroups)have formed in Northeast China.The core status of Shenyang in HSR network is improved significantly,and“one axis and two wings”HSR network in Liaoning Province is improved significantly.With the gradual expansion of Chaoyang-Fuxin,Dandong-Benxi,and Jilin-Yanji branch networks,the“point axis”HSR network mode in Northeast China has gradually developed and matured.In the future,it is recommended to rely on eight agglomerating subgroups to encrypt HSR network structure,create secondary node central cities,and gradually build a new pattern of opening up in Northeast China.展开更多
基金This work was funded by the National Science Foundation of Hunan Province(2020JJ2029)。
文摘Recurrent Neural Networks(RNNs)have been widely applied to deal with temporal problems,such as flood forecasting and financial data processing.On the one hand,traditional RNNs models amplify the gradient issue due to the strict time serial dependency,making it difficult to realize a long-term memory function.On the other hand,RNNs cells are highly complex,which will signifi-cantly increase computational complexity and cause waste of computational resources during model training.In this paper,an improved Time Feedforward Connections Recurrent Neural Networks(TFC-RNNs)model was first proposed to address the gradient issue.A parallel branch was introduced for the hidden state at time t−2 to be directly transferred to time t without the nonlinear transforma-tion at time t−1.This is effective in improving the long-term dependence of RNNs.Then,a novel cell structure named Single Gate Recurrent Unit(SGRU)was presented.This cell structure can reduce the number of parameters for RNNs cell,consequently reducing the computational complexity.Next,applying SGRU to TFC-RNNs as a new TFC-SGRU model solves the above two difficulties.Finally,the performance of our proposed TFC-SGRU was verified through sev-eral experiments in terms of long-term memory and anti-interference capabilities.Experimental results demonstrated that our proposed TFC-SGRU model can cap-ture helpful information with time step 1500 and effectively filter out the noise.The TFC-SGRU model accuracy is better than the LSTM and GRU models regarding language processing ability.
基金supported by the National Natural Science Foundation of China under Grant No.61274036,No.61371025,No.61204027,and No.61474036
文摘The shrinking silicon feature size causes the continuous increment of the aging effect due to the negative bias temperature instability （NBTI）, which becomes a potential stopper for IC development. As the basis of circuit-level aging protection, an efficient aging critical-gate identification method is crucially required to select a set of gates for protection to guarantee the normal lifetime of the circuits. The existing critical-gate identification methods always depend on a critical path set which contains so many paths that its generation procedure requires undesirable CPU runtime; furthermore, these methods can achieve a better solution with taking account of the topological connection. This paper proposes a time-efficient critical gates identification method with topological connection analysis, which chooses a small set of critical gates. Experiments over many circuits of ITC99 and ISCAS benchmark demonstrate that, to guarantee the normal lifetime （e.g., 10 years） of each circuit, our method achieves a 3.97x speedup and saves as much as 27.21% area overhead compared with the existing methods.
基金the National Natural Science Foundation of China(41871151).
文摘The construction of high-speed rail(HSR)network has promoted the social-economic ties of cities,accelerated the compression of time and space,and changed the pattern of regional development.In this paper,with the adoption of the operation frequency data of HSR from 12306 website,and based on the HSR connection strength model and social network analysis model,as well as according to the HSR connection strength,HSR network density,centrality,agglomeration subgroup,and other indicators,we analyzed the characteristics of HSR network structure in Northeast China.Results show that the number of HSR cities in Northeast China is small,cities in HSR network generally exhibit weak connectivity,and the existence of HSR network marginalizes cities such as Ulanhot,Baicheng,and Songyuan,which significantly reduce the overall network connectivity of Northeast China.The overall centrality of HSR network in Northeast China is characterized by“one axis,four edges”;specifically,the one axis is located in Harbin-Dalian transportation line and the four edges are located on both sides of the main axis of Harbin-Dalian transportation line.Eight agglomeration subgroups(four double city subgroups and four multi city subgroups)have formed in Northeast China.The core status of Shenyang in HSR network is improved significantly,and“one axis and two wings”HSR network in Liaoning Province is improved significantly.With the gradual expansion of Chaoyang-Fuxin,Dandong-Benxi,and Jilin-Yanji branch networks,the“point axis”HSR network mode in Northeast China has gradually developed and matured.In the future,it is recommended to rely on eight agglomerating subgroups to encrypt HSR network structure,create secondary node central cities,and gradually build a new pattern of opening up in Northeast China.
文摘目的探究血清嘌呤能离子通道型受体7(purinergic ligand-gated ion channel 7 receptor,P2X7R)、结缔组织生长因子(connective tissue growth factor,CTGF)表达与精神分裂症患者认知功能、临床症状的相关性。方法选取2021年1月~2023年1月在武汉市武东医院精神重症一科诊治的160例精神分裂症患者作为观察组,及同期160例健康体检志愿者作为对照组进行研究。根据阳性和阴性症状量表(positive and negative syndrome scale,PANSS)对患者精神临床症状(阳性和阴性症状、一般病理症状、附加症状)进行评估,将患者分为高分组(PANSS总分≥70分,n=72)和低分组(PANSS总分<70分,n=88)。利用精神分裂症认知功能成套测验共识版(MATRICS consensus cognitive battery,MCCB)评估患者认知能力;采用酶联免疫吸附测定(ELISA)法检测血清P2X7R和CTGF水平;Spearman法分析精神分裂症患者血清P2X7R和CTGF水平与PANSS各项评分、MCCB各项评分的相关性。结果与对照组相比,观察组血清P2X7R(610.71±107.83ng/L vs 384.78±80.62ng/L),CTGF水平(1.85±0.36μg/L vs 1.40±0.21μg/L)升高,差异有统计学意义(t=21.226,13.658,P<0.05);观察组精神分裂症患者MCBB各项评分均低于对照组,差异有统计学意义(t=14.845~24.862,均P<0.05);高分组精神分裂症患者阳性症状评分(21.10±3.42分)、阴性症状评分(23.37±5.03分)、一般病理症状评分(39.48±8.11分)、附加症状评分(8.26±1.22分)和PANSS总分(92.21±12.50分)均高于低分组(13.65±3.04分,15.62±3.91分,30.14±6.15分,5.20±0.94分,64.61±5.30分),差异有统计学意义(t=14.576,10.964,8.280,17.915,18.764,均P<0.05);高分组精神分裂症患者血清P2X7R,CTGF水平高于低分组,差异有统计学意义(t=12.233,5.923,均P<0.05);精神分裂症患者血清P2X7R,CTGF水平与PANSS各项评分均呈正相关(r=0.464~0.580,均P<0.05),与MCCB各项评分均呈负相关(r=-0.603~-0.439,均P<0.05)。结论精神分裂症患者血清P2X7R和CTGF水平升高,与患者临床症状和认知功能密切相关。
