Two novel networks for realizing first-order all-pass transfer functions are intro-duced. The networks use a current conveyer, a buffer and only three passive elements, and theyexhibit a high input impedance.
The development of deep learning has made non-biochemical methods for molecular property prediction screening a reality,which can increase the experimental speed and reduce the experimental cost of relevant experiment...The development of deep learning has made non-biochemical methods for molecular property prediction screening a reality,which can increase the experimental speed and reduce the experimental cost of relevant experiments.There are currently two main approaches to representing molecules:(a)representing molecules by fixing molecular descriptors,and(b)representing molecules by graph convolutional neural networks.Currently,both of these Representative methods have achieved some results in their respective experiments.Based on past efforts,we propose a Dual Self-attention Fusion Message Neural Network(DSFMNN).DSFMNN uses a combination of dual self-attention mechanism and graph convolutional neural network.Advantages of DSFMNN:(1)The dual self-attention mechanism focuses not only on the relationship between individual subunits in a molecule but also on the relationship between the atoms and chemical bonds contained in each subunit.(2)On the directed molecular graph,a message delivery approach centered on directed molecular bonds is used.We test the performance of the model on eight publicly available datasets and compare the performance with several models.Based on the current experimental results,DSFMNN has superior performance compared to previous models on the datasets applied in this paper.展开更多
Recursively embedded atom neural network(REANN)is a general-purpose atomistic machine learning software package for representing potential energy and other physical properties.The original REANN 1.0 architecture is a ...Recursively embedded atom neural network(REANN)is a general-purpose atomistic machine learning software package for representing potential energy and other physical properties.The original REANN 1.0 architecture is a physically inspired invariant message passing neural network,which was designed for systems with a limited number of elements.It is efficient but hardly transferable to more complex multi-element systems.In this work,we release REANN 2.0 aimed at multi-element systems and universal potentials,which integrates element embedding and equivariant representation.Compared to the first version,REANN 2.0 demonstrates enhanced ele-ment transferability and higher accuracy across various periodic systems with higher efficiency.Built upon this framework,a pre-trained REANN-MPtrj model without fine-tuning accurately predicts the lithium-ion diffusion dynamics in a benchmark solid-state electrolyte Li_(3)YCl_(6).We hope this open-source software package will facilitate the development of computationally efficient universal potentials in the future.展开更多
In this paper,a sparse graph neural network-aided(SGNN-aided)decoder is proposed for improving the decoding performance of polar codes under bursty interference.Firstly,a sparse factor graph is constructed using the e...In this paper,a sparse graph neural network-aided(SGNN-aided)decoder is proposed for improving the decoding performance of polar codes under bursty interference.Firstly,a sparse factor graph is constructed using the encoding characteristic to achieve high-throughput polar decoding.To further improve the decoding performance,a residual gated bipartite graph neural network is designed for updating embedding vectors of heterogeneous nodes based on a bidirectional message passing neural network.This framework exploits gated recurrent units and residual blocks to address the gradient disappearance in deep graph recurrent neural networks.Finally,predictions are generated by feeding the embedding vectors into a readout module.Simulation results show that the proposed decoder is more robust than the existing ones in the presence of bursty interference and exhibits high universality.展开更多
The low Earth orbit(LEO)satellite networks have outstanding advantages such as wide coverage area and not being limited by geographic environment,which can provide a broader range of communication services and has bec...The low Earth orbit(LEO)satellite networks have outstanding advantages such as wide coverage area and not being limited by geographic environment,which can provide a broader range of communication services and has become an essential supplement to the terrestrial network.However,the dynamic changes and uneven distribution of satellite network traffic inevitably bring challenges to multipath routing.Even worse,the harsh space environment often leads to incomplete collection of network state data for routing decision-making,which further complicates this challenge.To address this problem,this paper proposes a state-incomplete intelligent dynamic multipath routing algorithm(SIDMRA)to maximize network efficiency even with incomplete state data as input.Specifically,we model the multipath routing problem as a markov decision process(MDP)and then combine the deep deterministic policy gradient(DDPG)and the K shortest paths(KSP)algorithm to solve the optimal multipath routing policy.We use the temporal correlation of the satellite network state to fit the incomplete state data and then use the message passing neuron network(MPNN)for data enhancement.Simulation results show that the proposed algorithm outperforms baseline algorithms regarding average end-to-end delay and packet loss rate and performs stably under certain missing rates of state data.展开更多
As digital image techniques have been widely used, the requirements for high-resolution images become increasingly stringent. Traditional single-frame interpolation techniques cannot add new high frequency information...As digital image techniques have been widely used, the requirements for high-resolution images become increasingly stringent. Traditional single-frame interpolation techniques cannot add new high frequency information to the expanded images, and cannot improve resolution in deed. Multiframe-based techniques are effective ways for high-resolution image reconstruction, but their computation complexities and the difficulties in achieving image sequences limit their applications. An original method using an artificial neural network is proposed in this paper. Using the inherent merits in neural network, we can establish the mapping between high frequency components in low-resolution images and high-resolution images. Example applications and their results demonstrated the images reconstructed by our method are aesthetically and quantitatively (using the criteria of MSE and MAE) superior to the images acquired by common methods. Even for infrared images this method can give satisfactory results with high definition. In addition, a single-layer linear neural network is used in this paper, the computational complexity is very low, and this method can be realized in real time.展开更多
文摘Two novel networks for realizing first-order all-pass transfer functions are intro-duced. The networks use a current conveyer, a buffer and only three passive elements, and theyexhibit a high input impedance.
文摘The development of deep learning has made non-biochemical methods for molecular property prediction screening a reality,which can increase the experimental speed and reduce the experimental cost of relevant experiments.There are currently two main approaches to representing molecules:(a)representing molecules by fixing molecular descriptors,and(b)representing molecules by graph convolutional neural networks.Currently,both of these Representative methods have achieved some results in their respective experiments.Based on past efforts,we propose a Dual Self-attention Fusion Message Neural Network(DSFMNN).DSFMNN uses a combination of dual self-attention mechanism and graph convolutional neural network.Advantages of DSFMNN:(1)The dual self-attention mechanism focuses not only on the relationship between individual subunits in a molecule but also on the relationship between the atoms and chemical bonds contained in each subunit.(2)On the directed molecular graph,a message delivery approach centered on directed molecular bonds is used.We test the performance of the model on eight publicly available datasets and compare the performance with several models.Based on the current experimental results,DSFMNN has superior performance compared to previous models on the datasets applied in this paper.
基金the support by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0450101)the National Natural Science Foundation of China(Nos.22325304,22221003 and 22033007)。
文摘Recursively embedded atom neural network(REANN)is a general-purpose atomistic machine learning software package for representing potential energy and other physical properties.The original REANN 1.0 architecture is a physically inspired invariant message passing neural network,which was designed for systems with a limited number of elements.It is efficient but hardly transferable to more complex multi-element systems.In this work,we release REANN 2.0 aimed at multi-element systems and universal potentials,which integrates element embedding and equivariant representation.Compared to the first version,REANN 2.0 demonstrates enhanced ele-ment transferability and higher accuracy across various periodic systems with higher efficiency.Built upon this framework,a pre-trained REANN-MPtrj model without fine-tuning accurately predicts the lithium-ion diffusion dynamics in a benchmark solid-state electrolyte Li_(3)YCl_(6).We hope this open-source software package will facilitate the development of computationally efficient universal potentials in the future.
文摘In this paper,a sparse graph neural network-aided(SGNN-aided)decoder is proposed for improving the decoding performance of polar codes under bursty interference.Firstly,a sparse factor graph is constructed using the encoding characteristic to achieve high-throughput polar decoding.To further improve the decoding performance,a residual gated bipartite graph neural network is designed for updating embedding vectors of heterogeneous nodes based on a bidirectional message passing neural network.This framework exploits gated recurrent units and residual blocks to address the gradient disappearance in deep graph recurrent neural networks.Finally,predictions are generated by feeding the embedding vectors into a readout module.Simulation results show that the proposed decoder is more robust than the existing ones in the presence of bursty interference and exhibits high universality.
文摘The low Earth orbit(LEO)satellite networks have outstanding advantages such as wide coverage area and not being limited by geographic environment,which can provide a broader range of communication services and has become an essential supplement to the terrestrial network.However,the dynamic changes and uneven distribution of satellite network traffic inevitably bring challenges to multipath routing.Even worse,the harsh space environment often leads to incomplete collection of network state data for routing decision-making,which further complicates this challenge.To address this problem,this paper proposes a state-incomplete intelligent dynamic multipath routing algorithm(SIDMRA)to maximize network efficiency even with incomplete state data as input.Specifically,we model the multipath routing problem as a markov decision process(MDP)and then combine the deep deterministic policy gradient(DDPG)and the K shortest paths(KSP)algorithm to solve the optimal multipath routing policy.We use the temporal correlation of the satellite network state to fit the incomplete state data and then use the message passing neuron network(MPNN)for data enhancement.Simulation results show that the proposed algorithm outperforms baseline algorithms regarding average end-to-end delay and packet loss rate and performs stably under certain missing rates of state data.
文摘As digital image techniques have been widely used, the requirements for high-resolution images become increasingly stringent. Traditional single-frame interpolation techniques cannot add new high frequency information to the expanded images, and cannot improve resolution in deed. Multiframe-based techniques are effective ways for high-resolution image reconstruction, but their computation complexities and the difficulties in achieving image sequences limit their applications. An original method using an artificial neural network is proposed in this paper. Using the inherent merits in neural network, we can establish the mapping between high frequency components in low-resolution images and high-resolution images. Example applications and their results demonstrated the images reconstructed by our method are aesthetically and quantitatively (using the criteria of MSE and MAE) superior to the images acquired by common methods. Even for infrared images this method can give satisfactory results with high definition. In addition, a single-layer linear neural network is used in this paper, the computational complexity is very low, and this method can be realized in real time.
文摘矿井关闭后,其上覆岩层及地表会再次发生变形,影响建(构)筑物安全运营。由于关闭矿井缺乏监管,导致地表形变时空演化规律及预测预警模型研究不足。为此,提出了一种分布式目标雷达干涉测量(Distributed scatter Interferometric synthetic aperture radar,DS-InSAR)、时间域高通滤波(Temporal High Pass Filtering,THPF)以及长短期记忆网络(Long Short Term Memory Network,LSTM)相结合的关闭矿井地表形变预测模型。以98景Sentinel-1A升轨影像为数据源,首先利用DS-InSAR方法联合PS(Persistent Scatterer)和DS点获取徐州西部关闭矿井2019−11—2022−12的时序地表沉降信息;然后利用THPF分解原始沉降序列获取高频和低频序列沉降信息;之后采用LSTM完成高低频子序列的形变预测,将高低频子序列预测值叠加获取最终的预测结果。结果表明:DS-InSAR监测点密度分布均匀,监测结果水准实测形变间的决定系数达到0.95;相较于LSTM模型,THPF-LSTM模型在预测点位上的最大均方根误差(RMSE,Root Mean Square Error)为3.0,最大平均绝对误差(Mean Absolute Error,MAE)为2.4,最大校正决定系数(Adjusted R-square)为0.9,优于传统LSTM模型的4.5、3.9和0.6,模型综合预测精度提升20%以上,能够准确反映出关闭矿井地表形变的趋势和波动性,可有效提高短期内关闭矿井沉降预测精度,实现了关闭矿井地表形变监测和预测的一体化分析。
