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
High-resolution U–Pb(ID-TIMS,baddeleyite)ages are presented for mafic dykes from selected swarms in two important Amazonian regions:the Carajás Province in the east,and the Rio Apa block in the southwest–areas
The vacuum energy density of free scalar quantum field in a Rindler distributional space-time with distributional Levi-Cività connection is considered. It has been widely believed that, except in very extreme sit...The vacuum energy density of free scalar quantum field in a Rindler distributional space-time with distributional Levi-Cività connection is considered. It has been widely believed that, except in very extreme situations, the influence of acceleration on quantum fields should amount to just small, sub-dominant contributions. Here we argue that this belief is wrong by showing that in a Rindler distributional background space-time with distributional Levi-Cività connection the vacuum energy of free quantum fields is forced, by the very same background distributional space-time such a Rindler distributional background space-time, to become dominant over any classical energy density component. This semiclassical gravity effect finds its roots in the singular behavior of quantum fields on a Rindler distributional space-times with distributional Levi-Cività connection. In particular we obtain that the vacuum fluctuations have a singular behavior at a Rindler horizon . Therefore sufficiently strongly accelerated observer burns up near the Rindler horizon. Thus Polchinski’s account doesn’t violate the Einstein equivalence principle.展开更多
The publication of The Bellarosa Connection in 1989 won Saul Bellow tremendous warm reception not only because for the first time in his life Bellow meditated upon Jewishness by directly presenting American Jews' ...The publication of The Bellarosa Connection in 1989 won Saul Bellow tremendous warm reception not only because for the first time in his life Bellow meditated upon Jewishness by directly presenting American Jews' attitudes towards the Holocaust and Holocaust survivors through the story of Harry Fonstein but also because of the superb narrative techniques Bellow exhibited in this novel. Therefore, the present paper intends to make a narrative study of this novella in terms of characterization, focalization, time.展开更多
在机会网络中,节点的行为模式会表现出一定的社交特征,节点往往因相似的移动模式和固定的活动范围,在动态异构网络中表现出一定的集群特性,形成一个个拥有相似社会特征的独特小团体.节点的社会属性表现出长期的稳定性,可以有效应用在路...在机会网络中,节点的行为模式会表现出一定的社交特征,节点往往因相似的移动模式和固定的活动范围,在动态异构网络中表现出一定的集群特性,形成一个个拥有相似社会特征的独特小团体.节点的社会属性表现出长期的稳定性,可以有效应用在路由中.针对这一思想,本文提出了基于社交圈划分和相遇时间预测的机会网络路由算法SCEP(Social Circle division and Encounter time Prediction).该算法关注两个节点形成的直接关系与节点的社会属性特征,定义了基于强社交关系的熟悉集合拓扑,基于熟悉集合的概念以分布式方式开发社区,节点社区的合并受某些规则的约束,并对过时节点进行拓扑剪裁.同时,本文基于节点间相遇的时间间隔序列建模,利用节点间相遇历史数据预测下一次通信的时间.消息的路由通过利用社区、亲密节点集和可预测的通信时间等因素来实现.仿真实验结果表明,与EpSoc,CARA,SAAD,Prophet、NBAPR这5种算法相比,SCEP的性能更好.展开更多
By the theory of Modern Geometry, the mechanical principle and advanced calculus, the dynamics in Newtonian_Galilean spacetime is generalized to Newtonian_Riemannian Spacetime, and the dynamics in N_R spacetime is est...By the theory of Modern Geometry, the mechanical principle and advanced calculus, the dynamics in Newtonian_Galilean spacetime is generalized to Newtonian_Riemannian Spacetime, and the dynamics in N_R spacetime is established. Being divided it into some parts. This paper is one of them. The others are to be continued.展开更多
Traffic sign recognition (TSR, or Road Sign Recognition, RSR) is one of the Advanced Driver Assistance System (ADAS) devices in modern cars. To concern the most important issues, which are real-time and resource effic...Traffic sign recognition (TSR, or Road Sign Recognition, RSR) is one of the Advanced Driver Assistance System (ADAS) devices in modern cars. To concern the most important issues, which are real-time and resource efficiency, we propose a high efficiency hardware implementation for TSR. We divide the TSR procedure into two stages, detection and recognition. In the detection stage, under the assumption that most German traffic signs have red or blue colors with circle, triangle or rectangle shapes, we use Normalized RGB color transform and Single-Pass Connected Component Labeling (CCL) to find the potential traffic signs efficiently. For Single-Pass CCL, our contribution is to eliminate the “merge-stack” operations by recording connected relations of region in the scan phase and updating the labels in the iterating phase. In the recognition stage, the Histogram of Oriented Gradient (HOG) is used to generate the descriptor of the signs, and we classify the signs with Support Vector Machine (SVM). In the HOG module, we analyze the required minimum bits under different recognition rate. The proposed method achieves 96.61% detection rate and 90.85% recognition rate while testing with the GTSDB dataset. Our hardware implementation reduces the storage of CCL and simplifies the HOG computation. Main CCL storage size is reduced by 20% comparing to the most advanced design under typical condition. By using TSMC 90 nm technology, the proposed design operates at 105 MHz clock rate and processes in 135 fps with the image size of 1360 × 800. The chip size is about 1 mm2 and the power consumption is close to 8 mW. Therefore, this work is resource efficient and achieves real-time requirement.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.
文摘High-resolution U–Pb(ID-TIMS,baddeleyite)ages are presented for mafic dykes from selected swarms in two important Amazonian regions:the Carajás Province in the east,and the Rio Apa block in the southwest–areas
文摘The vacuum energy density of free scalar quantum field in a Rindler distributional space-time with distributional Levi-Cività connection is considered. It has been widely believed that, except in very extreme situations, the influence of acceleration on quantum fields should amount to just small, sub-dominant contributions. Here we argue that this belief is wrong by showing that in a Rindler distributional background space-time with distributional Levi-Cività connection the vacuum energy of free quantum fields is forced, by the very same background distributional space-time such a Rindler distributional background space-time, to become dominant over any classical energy density component. This semiclassical gravity effect finds its roots in the singular behavior of quantum fields on a Rindler distributional space-times with distributional Levi-Cività connection. In particular we obtain that the vacuum fluctuations have a singular behavior at a Rindler horizon . Therefore sufficiently strongly accelerated observer burns up near the Rindler horizon. Thus Polchinski’s account doesn’t violate the Einstein equivalence principle.
文摘The publication of The Bellarosa Connection in 1989 won Saul Bellow tremendous warm reception not only because for the first time in his life Bellow meditated upon Jewishness by directly presenting American Jews' attitudes towards the Holocaust and Holocaust survivors through the story of Harry Fonstein but also because of the superb narrative techniques Bellow exhibited in this novel. Therefore, the present paper intends to make a narrative study of this novella in terms of characterization, focalization, time.
文摘在机会网络中,节点的行为模式会表现出一定的社交特征,节点往往因相似的移动模式和固定的活动范围,在动态异构网络中表现出一定的集群特性,形成一个个拥有相似社会特征的独特小团体.节点的社会属性表现出长期的稳定性,可以有效应用在路由中.针对这一思想,本文提出了基于社交圈划分和相遇时间预测的机会网络路由算法SCEP(Social Circle division and Encounter time Prediction).该算法关注两个节点形成的直接关系与节点的社会属性特征,定义了基于强社交关系的熟悉集合拓扑,基于熟悉集合的概念以分布式方式开发社区,节点社区的合并受某些规则的约束,并对过时节点进行拓扑剪裁.同时,本文基于节点间相遇的时间间隔序列建模,利用节点间相遇历史数据预测下一次通信的时间.消息的路由通过利用社区、亲密节点集和可预测的通信时间等因素来实现.仿真实验结果表明,与EpSoc,CARA,SAAD,Prophet、NBAPR这5种算法相比,SCEP的性能更好.
文摘By the theory of Modern Geometry, the mechanical principle and advanced calculus, the dynamics in Newtonian_Galilean spacetime is generalized to Newtonian_Riemannian Spacetime, and the dynamics in N_R spacetime is established. Being divided it into some parts. This paper is one of them. The others are to be continued.
文摘Traffic sign recognition (TSR, or Road Sign Recognition, RSR) is one of the Advanced Driver Assistance System (ADAS) devices in modern cars. To concern the most important issues, which are real-time and resource efficiency, we propose a high efficiency hardware implementation for TSR. We divide the TSR procedure into two stages, detection and recognition. In the detection stage, under the assumption that most German traffic signs have red or blue colors with circle, triangle or rectangle shapes, we use Normalized RGB color transform and Single-Pass Connected Component Labeling (CCL) to find the potential traffic signs efficiently. For Single-Pass CCL, our contribution is to eliminate the “merge-stack” operations by recording connected relations of region in the scan phase and updating the labels in the iterating phase. In the recognition stage, the Histogram of Oriented Gradient (HOG) is used to generate the descriptor of the signs, and we classify the signs with Support Vector Machine (SVM). In the HOG module, we analyze the required minimum bits under different recognition rate. The proposed method achieves 96.61% detection rate and 90.85% recognition rate while testing with the GTSDB dataset. Our hardware implementation reduces the storage of CCL and simplifies the HOG computation. Main CCL storage size is reduced by 20% comparing to the most advanced design under typical condition. By using TSMC 90 nm technology, the proposed design operates at 105 MHz clock rate and processes in 135 fps with the image size of 1360 × 800. The chip size is about 1 mm2 and the power consumption is close to 8 mW. Therefore, this work is resource efficient and achieves real-time requirement.
