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
This paper studies the induced Chern connection of submanifolds in a Finsler manifold and gets the relations between the induced Chern connection and the Chern connection of the induced Finsler metric. Then the author...This paper studies the induced Chern connection of submanifolds in a Finsler manifold and gets the relations between the induced Chern connection and the Chern connection of the induced Finsler metric. Then the authors point out a difference between Finsler submanifolds and Riemann submanifolds.展开更多
This paper deals with the study of CR-submanifolds of a nearly trans-Sasakian manifold with a semi symmetric non-metric connection. Nijenhuis tensor, integrability conditions for some distributions on CR-submanifolds ...This paper deals with the study of CR-submanifolds of a nearly trans-Sasakian manifold with a semi symmetric non-metric connection. Nijenhuis tensor, integrability conditions for some distributions on CR-submanifolds of a nearly trans-Sasakian manifold with a semi symmetric non- metric connection are discussed.展开更多
To supplement missing logging information without increasing economic cost, a machine learning method to generate synthetic well logs from the existing log data was presented, and the experimental verification and app...To supplement missing logging information without increasing economic cost, a machine learning method to generate synthetic well logs from the existing log data was presented, and the experimental verification and application effect analysis were carried out. Since the traditional Fully Connected Neural Network(FCNN) is incapable of preserving spatial dependency, the Long Short-Term Memory(LSTM) network, which is a kind of Recurrent Neural Network(RNN), was utilized to establish a method for log reconstruction. By this method, synthetic logs can be generated from series of input log data with consideration of variation trend and context information with depth. Besides, a cascaded LSTM was proposed by combining the standard LSTM with a cascade system. Testing through real well log data shows that: the results from the LSTM are of higher accuracy than the traditional FCNN; the cascaded LSTM is more suitable for the problem with multiple series data; the machine learning method proposed provides an accurate and cost effective way for synthetic well log generation.展开更多
Recently,in collaboration with a total of 25major depressive disorder(MDD)research groups from 17distinct institutes,Yan ChaoGan’s(严超赣)lab published a scientific paper in PNAS(2019,116(18):9078—9083).MDD,often re...Recently,in collaboration with a total of 25major depressive disorder(MDD)research groups from 17distinct institutes,Yan ChaoGan’s(严超赣)lab published a scientific paper in PNAS(2019,116(18):9078—9083).MDD,often referred to as depression,is a psychiatric disorder with high recurrence rate,high suicide and disability rate,which causes an extremely heavy social and economic burden each year.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This paper studies the induced Chern connection of submanifolds in a Finsler manifold and gets the relations between the induced Chern connection and the Chern connection of the induced Finsler metric. Then the authors point out a difference between Finsler submanifolds and Riemann submanifolds.
文摘This paper deals with the study of CR-submanifolds of a nearly trans-Sasakian manifold with a semi symmetric non-metric connection. Nijenhuis tensor, integrability conditions for some distributions on CR-submanifolds of a nearly trans-Sasakian manifold with a semi symmetric non- metric connection are discussed.
基金Supported by the National Natural Science Foundation of China(U1663208,51520105005)the National Science and Technology Major Project of China(2017ZX05009-005,2016ZX05037-003)
文摘To supplement missing logging information without increasing economic cost, a machine learning method to generate synthetic well logs from the existing log data was presented, and the experimental verification and application effect analysis were carried out. Since the traditional Fully Connected Neural Network(FCNN) is incapable of preserving spatial dependency, the Long Short-Term Memory(LSTM) network, which is a kind of Recurrent Neural Network(RNN), was utilized to establish a method for log reconstruction. By this method, synthetic logs can be generated from series of input log data with consideration of variation trend and context information with depth. Besides, a cascaded LSTM was proposed by combining the standard LSTM with a cascade system. Testing through real well log data shows that: the results from the LSTM are of higher accuracy than the traditional FCNN; the cascaded LSTM is more suitable for the problem with multiple series data; the machine learning method proposed provides an accurate and cost effective way for synthetic well log generation.
文摘Recently,in collaboration with a total of 25major depressive disorder(MDD)research groups from 17distinct institutes,Yan ChaoGan’s(严超赣)lab published a scientific paper in PNAS(2019,116(18):9078—9083).MDD,often referred to as depression,is a psychiatric disorder with high recurrence rate,high suicide and disability rate,which causes an extremely heavy social and economic burden each year.
