Traffic characteristics of several typical instant messager services under certain scenarios are firstly analyzed,based on real-time data collected in the commercial mobile network.Then criteria for the evaluation of ...Traffic characteristics of several typical instant messager services under certain scenarios are firstly analyzed,based on real-time data collected in the commercial mobile network.Then criteria for the evaluation of the efficiency of the mobile network for the transmission of packet services are proposed in both transport layer and physical layer over air interface.The transmission efficiency of IM services is evaluated and compared under the proposed criteria.Furthermore,a so-called smart resource adaptation algorithm is verified in the effectiveness of improving the wireless transmission efficiency.Finally,improvements to the smart resource adaptation are proposed to further improve the wireless transmission efficiency,and its effectiveness is verified by the calculations.展开更多
BEIJING,Jan.2(Xinhua)-Every year on New Year's Eve,Chinese President Xi Jinping delivers his New Year greetings to the Chinese people from his office,with the Great Wall,a symbol of the Chinese nation’s resilienc...BEIJING,Jan.2(Xinhua)-Every year on New Year's Eve,Chinese President Xi Jinping delivers his New Year greetings to the Chinese people from his office,with the Great Wall,a symbol of the Chinese nation’s resilience and heritage,depicted the backdrop.展开更多
BEIJING,Dec.31(Xinhua)-On New Year's Eve,Chinese President Xi Jinping delivered his 2026 New Year message through China Media Group and the Internet.The following is the full text of the message:Greetings to all!Y...BEIJING,Dec.31(Xinhua)-On New Year's Eve,Chinese President Xi Jinping delivered his 2026 New Year message through China Media Group and the Internet.The following is the full text of the message:Greetings to all!Year after year,life opens a fresh chapter.As the new year begins,I extend my best wishes to you from Beijing!展开更多
Greetings to all!Year after year,life opens a fresh chapter.As the new year begins,I extend my best wishes to you from Beijing!The year 2025 marks the completion of China's 14th Five-Year Plan(2021-25)for economic...Greetings to all!Year after year,life opens a fresh chapter.As the new year begins,I extend my best wishes to you from Beijing!The year 2025 marks the completion of China's 14th Five-Year Plan(2021-25)for economic and social development.展开更多
Health information spreads rapidly,which can effectively control epidemics.However,the swift dissemination of information also has potential negative impacts,which increasingly attracts attention.Message fatigue refer...Health information spreads rapidly,which can effectively control epidemics.However,the swift dissemination of information also has potential negative impacts,which increasingly attracts attention.Message fatigue refers to the psychological response characterized by feelings of boredom and anxiety that occur after receiving an excessive amount of similar information.This phenomenon can alter individual behaviors related to epidemic prevention.Additionally,recent studies indicate that pairwise interactions alone are insufficient to describe complex social transmission processes,and higher-order structures representing group interactions are crucial.To address this,we develop a novel epidemic model that investigates the interactions between information,behavioral responses,and epidemics.Our model incorporates the impact of message fatigue on the entire transmission system.The information layer is modeled using a static simplicial network to capture group interactions,while the disease layer uses a time-varying network based on activity-driven model with attractiveness to represent the self-protection behaviors of susceptible individuals and self-isolation behaviors of infected individuals.We theoretically describe the co-evolution equations using the microscopic Markov chain approach(MMCA)and get the epidemic threshold.Experimental results show that while the negative impact of message fatigue on epidemic transmission is limited,it significantly weakens the group interactions depicted by higher-order structures.Individual behavioral responses strongly inhibit the epidemic.Our simulations using the Monte Carlo(MC)method demonstrate that greater intensity in these responses leads to clustering of susceptible individuals in the disease layer.Finally,we apply the proposed model to real networks to verify its reliability.In summary,our research results enhance the understanding of the information-epidemic coupling dynamics,and we expect to provide valuable guidance for managing future emerging epidemics.展开更多
In this paper,an improved error-rate sliding window decoder is proposed for spatially coupled low-density parity-check(SC-LDPC)codes.For the conventional sliding window decoder,the message retention mechanism causes u...In this paper,an improved error-rate sliding window decoder is proposed for spatially coupled low-density parity-check(SC-LDPC)codes.For the conventional sliding window decoder,the message retention mechanism causes unreliable messages along the edges of belief propagation(BP)decoding in the current window to be kept for subsequent window decoding.To improve the reliability of the retained messages during the window transition,a reliable termination method is embedded,where the retained messages undergo more reliable parity checks.Additionally,decoding failure is unavoidable and even causes error propagation when the number of errors exceeds the error-correcting capability of the window.To mitigate this problem,a channel value reuse mechanism is designed,where the received channel values are utilized to reinitialize the window.Furthermore,considering the complexity and performance of decoding,a feasible sliding optimized window decoding(SOWD)scheme is introduced.Finally,simulation results confirm the superior performance of the proposed SOWD scheme in both the waterfall and error floor regions.This work has great potential in the applications of wireless optical communication and fiber optic communication.展开更多
The China-ASEAN Expo(CAEXPO),held annually in Nanning City of Guangxi Zhuang Autonomous Region since 2004,has become a pivotal platform for economic and trade exchange between China,Vietnam,and other ASEAN member stat...The China-ASEAN Expo(CAEXPO),held annually in Nanning City of Guangxi Zhuang Autonomous Region since 2004,has become a pivotal platform for economic and trade exchange between China,Vietnam,and other ASEAN member states.Over the years,CAEXPO has proven to be a highly effective mechanism for fostering international cooperation,playing a vital role in establishing ASEAN as China’s largest trading partner and positioning China as the foremost trade partner of many ASEAN countries,including Vietnam.展开更多
The Cyber-Physical Systems (CPS) supported by Wireless Sensor Networks (WSN) helps factories collect data and achieve seamless communication between physical and virtual components. Sensor nodes are energy-constrained...The Cyber-Physical Systems (CPS) supported by Wireless Sensor Networks (WSN) helps factories collect data and achieve seamless communication between physical and virtual components. Sensor nodes are energy-constrained devices. Their energy consumption is typically correlated with the amount of data collection. The purpose of data aggregation is to reduce data transmission, lower energy consumption, and reduce network congestion. For large-scale WSN, data aggregation can greatly improve network efficiency. However, as many heterogeneous data is poured into a specific area at the same time, it sometimes causes data loss and then results in incompleteness and irregularity of production data. This paper proposes an information processing model that encompasses the Energy-Conserving Data Aggregation Algorithm (ECDA) and the Efficient Message Reception Algorithm (EMRA). ECDA is divided into two stages, Energy conservation based on the global cost and Data aggregation based on ant colony optimization. The EMRA comprises the Polling Message Reception Algorithm (PMRA), the Shortest Time Message Reception Algorithm (STMRA), and the Specific Condition Message Reception Algorithm (SCMRA). These algorithms are not only available for the regularity and directionality of sensor information transmission, but also satisfy the different requirements in small factory environments. To compare with the recent HPSO-ILEACH and E-PEGASIS, DCDA can effectively reduce energy consumption. Experimental results show that STMRA consumes 1.3 times the time of SCMRA. Both optimization algorithms exhibit higher time efficiency than PMRA. Furthermore, this paper also evaluates these three algorithms using AHP.展开更多
In this paper,we focus on the channel estimation for multi-user MIMO-OFDM systems in rich scattering environments.We find that channel sparsity in the delay-angle domain is severely compromised in rich scattering envi...In this paper,we focus on the channel estimation for multi-user MIMO-OFDM systems in rich scattering environments.We find that channel sparsity in the delay-angle domain is severely compromised in rich scattering environments,so that most existing compressed sensing(CS)based techniques can harvest a very limited gain(if any)in reducing the channel estimation overhead.To address the problem,we propose the learning-based turbo message passing(LTMP)algorithm.Instead of exploiting the channel sparsity,LTMP is able to efficiently extract the channel feature via deep learning as well as to exploit the channel continuity in the frequency domain via block-wise linear modelling.More specifically,as a component of LTMP,we develop a multi-scale parallel dilated convolutional neural network(MPDCNN),which leverages frequency-space channel correlation in different scales for channel denoising.We evaluate the LTMP’s performance in MIMO-OFDM channels using the 3rd generation partnership project(3GPP)clustered delay line(CDL)channel models.Simulation results show that the proposed channel estimation method has more than 5 dB power gain than the existing algorithms when the normalized mean-square error of the channel estimation is-20 dB.The proposed algorithm also exhibits strong robustness in various environments.展开更多
If 2024 has taught me anything,it’s that digital is an irrefutable force for unity—a much-needed catalyst for global cooperation in an increasingly fragmented world.This truth has been on display all year long,somet...If 2024 has taught me anything,it’s that digital is an irrefutable force for unity—a much-needed catalyst for global cooperation in an increasingly fragmented world.This truth has been on display all year long,sometimes against the odds.And it’s evident in the adoption of the Pact for the Future and Global Digital Compact at the United Nations General Assembly,in the outcomes of the World Telecommunication Standardization Assembly(WTSA-24),and in the wide endorsement of the COP29 Declaration on Green Digital Action.展开更多
In this paper,an index modulation(IM)aided uplink orthogonal time frequency space modulation(OTFS)structure for sparse code multiple access(SCMA)is proposed.To be more specific,the information bits are firstly partiti...In this paper,an index modulation(IM)aided uplink orthogonal time frequency space modulation(OTFS)structure for sparse code multiple access(SCMA)is proposed.To be more specific,the information bits are firstly partitioned for transmit antenna(TA)selection and sparse codeword mapping,respectively.Subsequently,the codewords deployed on the 2-dimensional(2D)delay-Doppler(DD)plane are transmitted by the selected TA,and the superimposed signals are jointly detected at the receiver.Furthermore,a low-complexity zero-embedded expectation propagation(ZE-EP)detector is conceived,where the codebooks are extended with zero vectors to reflect the silent indices.The simulation results demonstrate that the proposed IM-OTFS-SCMA system is capable of providing significant performance gain over the OTFS-SCMA counterpart.展开更多
On New Year’s Eve,Chinese President Xi Jinping delivered his 2025 New Year message through China Media Group and the Internet.Edited excerpts of the message follow:Nurtured by our 5,000-plus years of continuous civil...On New Year’s Eve,Chinese President Xi Jinping delivered his 2025 New Year message through China Media Group and the Internet.Edited excerpts of the message follow:Nurtured by our 5,000-plus years of continuous civilisation,our country,China,is engraved not only on the bottom of the ancient bronze ritual wine vessel of He Zun,but also in the heart of every Chinese.展开更多
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
Dear Editor,This letter proposes the graph tensor alliance attention network(GT-A^(2)T)to represent a dynamic graph(DG)precisely.Its main idea includes 1)Establishing a unified spatio-temporal message propagation fram...Dear Editor,This letter proposes the graph tensor alliance attention network(GT-A^(2)T)to represent a dynamic graph(DG)precisely.Its main idea includes 1)Establishing a unified spatio-temporal message propagation framework on a DG via the tensor product for capturing the complex cohesive spatio-temporal interdependencies precisely and 2)Acquiring the alliance attention scores by node features and favorable high-order structural correlations.展开更多
基金Supported by the National Natural Science Foundation of China(No.61301103)the Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions(No.CIT&TCD201504039)Funding Project for Academic Human Resources Development in Beijing Union University(No.BPHR2014A03,Rkl00201510)
文摘Traffic characteristics of several typical instant messager services under certain scenarios are firstly analyzed,based on real-time data collected in the commercial mobile network.Then criteria for the evaluation of the efficiency of the mobile network for the transmission of packet services are proposed in both transport layer and physical layer over air interface.The transmission efficiency of IM services is evaluated and compared under the proposed criteria.Furthermore,a so-called smart resource adaptation algorithm is verified in the effectiveness of improving the wireless transmission efficiency.Finally,improvements to the smart resource adaptation are proposed to further improve the wireless transmission efficiency,and its effectiveness is verified by the calculations.
文摘BEIJING,Jan.2(Xinhua)-Every year on New Year's Eve,Chinese President Xi Jinping delivers his New Year greetings to the Chinese people from his office,with the Great Wall,a symbol of the Chinese nation’s resilience and heritage,depicted the backdrop.
文摘BEIJING,Dec.31(Xinhua)-On New Year's Eve,Chinese President Xi Jinping delivered his 2026 New Year message through China Media Group and the Internet.The following is the full text of the message:Greetings to all!Year after year,life opens a fresh chapter.As the new year begins,I extend my best wishes to you from Beijing!
文摘Greetings to all!Year after year,life opens a fresh chapter.As the new year begins,I extend my best wishes to you from Beijing!The year 2025 marks the completion of China's 14th Five-Year Plan(2021-25)for economic and social development.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.72171136 and 72134004)Humanities and Social Science Research Project,Ministry of Education of China(Grant No.21YJC630157)+1 种基金the Natural Science Foundation of Shandong Province(Grant No.ZR2022MG008)Shandong Provincial Colleges and Universities Youth Innovation Technology of China(Grant No.2022RW066)。
文摘Health information spreads rapidly,which can effectively control epidemics.However,the swift dissemination of information also has potential negative impacts,which increasingly attracts attention.Message fatigue refers to the psychological response characterized by feelings of boredom and anxiety that occur after receiving an excessive amount of similar information.This phenomenon can alter individual behaviors related to epidemic prevention.Additionally,recent studies indicate that pairwise interactions alone are insufficient to describe complex social transmission processes,and higher-order structures representing group interactions are crucial.To address this,we develop a novel epidemic model that investigates the interactions between information,behavioral responses,and epidemics.Our model incorporates the impact of message fatigue on the entire transmission system.The information layer is modeled using a static simplicial network to capture group interactions,while the disease layer uses a time-varying network based on activity-driven model with attractiveness to represent the self-protection behaviors of susceptible individuals and self-isolation behaviors of infected individuals.We theoretically describe the co-evolution equations using the microscopic Markov chain approach(MMCA)and get the epidemic threshold.Experimental results show that while the negative impact of message fatigue on epidemic transmission is limited,it significantly weakens the group interactions depicted by higher-order structures.Individual behavioral responses strongly inhibit the epidemic.Our simulations using the Monte Carlo(MC)method demonstrate that greater intensity in these responses leads to clustering of susceptible individuals in the disease layer.Finally,we apply the proposed model to real networks to verify its reliability.In summary,our research results enhance the understanding of the information-epidemic coupling dynamics,and we expect to provide valuable guidance for managing future emerging epidemics.
基金supported by the National Natural Science Foundation of China (No.62275193)。
文摘In this paper,an improved error-rate sliding window decoder is proposed for spatially coupled low-density parity-check(SC-LDPC)codes.For the conventional sliding window decoder,the message retention mechanism causes unreliable messages along the edges of belief propagation(BP)decoding in the current window to be kept for subsequent window decoding.To improve the reliability of the retained messages during the window transition,a reliable termination method is embedded,where the retained messages undergo more reliable parity checks.Additionally,decoding failure is unavoidable and even causes error propagation when the number of errors exceeds the error-correcting capability of the window.To mitigate this problem,a channel value reuse mechanism is designed,where the received channel values are utilized to reinitialize the window.Furthermore,considering the complexity and performance of decoding,a feasible sliding optimized window decoding(SOWD)scheme is introduced.Finally,simulation results confirm the superior performance of the proposed SOWD scheme in both the waterfall and error floor regions.This work has great potential in the applications of wireless optical communication and fiber optic communication.
文摘The China-ASEAN Expo(CAEXPO),held annually in Nanning City of Guangxi Zhuang Autonomous Region since 2004,has become a pivotal platform for economic and trade exchange between China,Vietnam,and other ASEAN member states.Over the years,CAEXPO has proven to be a highly effective mechanism for fostering international cooperation,playing a vital role in establishing ASEAN as China’s largest trading partner and positioning China as the foremost trade partner of many ASEAN countries,including Vietnam.
基金Funds for High-Level Talents Programof Xi’an International University(Grant No.XAIU202411).
文摘The Cyber-Physical Systems (CPS) supported by Wireless Sensor Networks (WSN) helps factories collect data and achieve seamless communication between physical and virtual components. Sensor nodes are energy-constrained devices. Their energy consumption is typically correlated with the amount of data collection. The purpose of data aggregation is to reduce data transmission, lower energy consumption, and reduce network congestion. For large-scale WSN, data aggregation can greatly improve network efficiency. However, as many heterogeneous data is poured into a specific area at the same time, it sometimes causes data loss and then results in incompleteness and irregularity of production data. This paper proposes an information processing model that encompasses the Energy-Conserving Data Aggregation Algorithm (ECDA) and the Efficient Message Reception Algorithm (EMRA). ECDA is divided into two stages, Energy conservation based on the global cost and Data aggregation based on ant colony optimization. The EMRA comprises the Polling Message Reception Algorithm (PMRA), the Shortest Time Message Reception Algorithm (STMRA), and the Specific Condition Message Reception Algorithm (SCMRA). These algorithms are not only available for the regularity and directionality of sensor information transmission, but also satisfy the different requirements in small factory environments. To compare with the recent HPSO-ILEACH and E-PEGASIS, DCDA can effectively reduce energy consumption. Experimental results show that STMRA consumes 1.3 times the time of SCMRA. Both optimization algorithms exhibit higher time efficiency than PMRA. Furthermore, this paper also evaluates these three algorithms using AHP.
基金supported in part by the National Key Research and Development Program of China under Grant 2020YFB1804800.
文摘In this paper,we focus on the channel estimation for multi-user MIMO-OFDM systems in rich scattering environments.We find that channel sparsity in the delay-angle domain is severely compromised in rich scattering environments,so that most existing compressed sensing(CS)based techniques can harvest a very limited gain(if any)in reducing the channel estimation overhead.To address the problem,we propose the learning-based turbo message passing(LTMP)algorithm.Instead of exploiting the channel sparsity,LTMP is able to efficiently extract the channel feature via deep learning as well as to exploit the channel continuity in the frequency domain via block-wise linear modelling.More specifically,as a component of LTMP,we develop a multi-scale parallel dilated convolutional neural network(MPDCNN),which leverages frequency-space channel correlation in different scales for channel denoising.We evaluate the LTMP’s performance in MIMO-OFDM channels using the 3rd generation partnership project(3GPP)clustered delay line(CDL)channel models.Simulation results show that the proposed channel estimation method has more than 5 dB power gain than the existing algorithms when the normalized mean-square error of the channel estimation is-20 dB.The proposed algorithm also exhibits strong robustness in various environments.
文摘If 2024 has taught me anything,it’s that digital is an irrefutable force for unity—a much-needed catalyst for global cooperation in an increasingly fragmented world.This truth has been on display all year long,sometimes against the odds.And it’s evident in the adoption of the Pact for the Future and Global Digital Compact at the United Nations General Assembly,in the outcomes of the World Telecommunication Standardization Assembly(WTSA-24),and in the wide endorsement of the COP29 Declaration on Green Digital Action.
基金supported in part by the National Key Research and Development Program of China with Grant number 2021YFB2900502。
文摘In this paper,an index modulation(IM)aided uplink orthogonal time frequency space modulation(OTFS)structure for sparse code multiple access(SCMA)is proposed.To be more specific,the information bits are firstly partitioned for transmit antenna(TA)selection and sparse codeword mapping,respectively.Subsequently,the codewords deployed on the 2-dimensional(2D)delay-Doppler(DD)plane are transmitted by the selected TA,and the superimposed signals are jointly detected at the receiver.Furthermore,a low-complexity zero-embedded expectation propagation(ZE-EP)detector is conceived,where the codebooks are extended with zero vectors to reflect the silent indices.The simulation results demonstrate that the proposed IM-OTFS-SCMA system is capable of providing significant performance gain over the OTFS-SCMA counterpart.
文摘On New Year’s Eve,Chinese President Xi Jinping delivered his 2025 New Year message through China Media Group and the Internet.Edited excerpts of the message follow:Nurtured by our 5,000-plus years of continuous civilisation,our country,China,is engraved not only on the bottom of the ancient bronze ritual wine vessel of He Zun,but also in the heart of every Chinese.
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.
基金supported in part by the National Natural Science Foundation of China(62372385).
文摘Dear Editor,This letter proposes the graph tensor alliance attention network(GT-A^(2)T)to represent a dynamic graph(DG)precisely.Its main idea includes 1)Establishing a unified spatio-temporal message propagation framework on a DG via the tensor product for capturing the complex cohesive spatio-temporal interdependencies precisely and 2)Acquiring the alliance attention scores by node features and favorable high-order structural correlations.
