In recent years,deep generative models have been successfully applied to perform artistic painting style transfer(APST).The difficulties might lie in the loss of reconstructing spatial details and the inefficiency of ...In recent years,deep generative models have been successfully applied to perform artistic painting style transfer(APST).The difficulties might lie in the loss of reconstructing spatial details and the inefficiency of model convergence caused by the irreversible en-decoder methodology of the existing models.Aiming to this,this paper proposes a Flow-based architecture with both the en-decoder sharing a reversible network configuration.The proposed APST-Flow can efficiently reduce model uncertainty via a compact analysis-synthesis methodology,thereby the generalization performance and the convergence stability are improved.For the generator,a Flow-based network using Wavelet additive coupling(WAC)layers is implemented to extract multi-scale content features.Also,a style checker is used to enhance the global style consistency by minimizing the error between the reconstructed and the input images.To enhance the generated salient details,a loss of adaptive stroke edge is applied in both the global and local model training.The experimental results show that the proposed method improves PSNR by 5%,SSIM by 6.2%,and decreases Style Error by 29.4%over the existing models on the ChipPhi set.The competitive results verify that APST-Flow achieves high-quality generation with less content deviation and enhanced generalization,thereby can be further applied to more APST scenes.展开更多
Recently, we reported a series of reversibly interlocked polymer networks(RILNs), whose mechanical robustness and functionalities improvement was believed to be derived from topological interlocking of two sub-network...Recently, we reported a series of reversibly interlocked polymer networks(RILNs), whose mechanical robustness and functionalities improvement was believed to be derived from topological interlocking of two sub-networks, although the direct evidence for the deduction is still lacking. Herein, a specially-designed RILNs system, in which the inter-component hydrogen bonds can be shielded as needed, was prepared and used to study the micro-structures of RILNs, aiming to verify the existence of mechanical interlocking in RILNs. By changing the pH of the swelling solvent, the effect exerted by the inter-component non-covalent bonds was eliminated, so detailed information of the networks structure was exposed. The small angle X-ray scattering(SAXS) and small-angle neutron scattering(SANS) results indicated that swelling-induced structural evolution of the two sub-networks mutually affected each other, even when the inter-component hydrogen bonds were absent, proving the presence of topological interlocking. The findings may help to draw a more accurate physical image and reveal the detailed structureproperty relationship of RILNs.展开更多
The treatment of a multicomponent reversible reaction network is extremely complicated because largenumber of rate constants must be precisely determined and because the calculation based on these rateconstants is ted...The treatment of a multicomponent reversible reaction network is extremely complicated because largenumber of rate constants must be precisely determined and because the calculation based on these rateconstants is tedious.In order to reduce the degrees of freedom of the process,the authors propose a methodin which the reactor and the separator are regarded as a whole.Based on this approach,an N-componentreversible reaction system can be dealt with as a two—component system.Consequently,a simple and ac-cessible way of the apparent rate determination is suggested.For fiist-order reactions,an explicit,simplifiedexpression has been derived for both lumped and distributed parameter reaction systems.展开更多
A novel,asymmetric image encryption-hiding scheme(AiEhS)using a reversible neural network(RNN)was developed,in which deep learning is employed to compress and hide a secret plain image(SPI),thereby enhancing the encry...A novel,asymmetric image encryption-hiding scheme(AiEhS)using a reversible neural network(RNN)was developed,in which deep learning is employed to compress and hide a secret plain image(SPI),thereby enhancing the encryption efficiency and improving the hiding quality.First,AiEhS employs an auto-encoder to compress the SPI and designs a new encryption method for encrypting the compressed image to obtain a cipher image,reaching the first layer of encryption.Second,pixels in the cipher image are decomposed,combined,and scrambled to obtain another scrambled image.Thereafter,a trained RNN model is used to embed this scrambled image into a selected carrier image,resulting in a new carrier image hiding secrets,thus realizing the second layer of hiding.Moreover,AiEhS produces a pseudorandom sequence using a hyperchaotic map and constructs a new key model to achieve a plaintext dependency.The keys are then designed and distributed by the Rivest-Shamir-Adleman algorithm,effectively improving the security.Compared with traditional compressive-sensing-based image-hiding methods,the contributions of AiEhS are as follows:(1)A new scheme is designed using an auto-encoder to compress the SPI,which can reduce the time cost of both compression and reconstruction,thus accelerating the execution efficiency.(2)The scrambled image is hidden in a carrier image by RNN,which can increase the embedding amount and achieve better hiding quality.Furthermore,experiments show that AiEhS using deep learning can ensure better security and efficiency for image encryption and hiding,in contrast with the traditional image compression and embedding technique.For example,the peak signal-to-noise ratio for the reconstructed image exceeds 34 d B.展开更多
In recent years,the network public opinion reversal governance events have occurred frequently.Over time,the repeated truth of the matter will not only weaken the rational judgment of the public to a certain extent,so...In recent years,the network public opinion reversal governance events have occurred frequently.Over time,the repeated truth of the matter will not only weaken the rational judgment of the public to a certain extent,so that its negative emotions accumulate,but also have a serious impact on the credibility of the media and the government,and may even further intensify social contradictions.Therefore,in the face of such a complex online public opinion space,accurately identifying the truth behind the incident and how to carry out the reversal of online public opinion governance is particularly critical.And blockchain technology,with its advantages of decentralization and immutable information,provides new technical support for the network public opinion reversal governance.Based on this,this paper gives an overview and analysis of blockchain technology and network public opinion reversal,and on this basis introduces the network public opinion reversal governance mechanism based on blockchain technology,aiming to further optimize the network public opinion reversal governance process,for reference only.展开更多
基金support from National Natural Science Foundation of China(62062048).
文摘In recent years,deep generative models have been successfully applied to perform artistic painting style transfer(APST).The difficulties might lie in the loss of reconstructing spatial details and the inefficiency of model convergence caused by the irreversible en-decoder methodology of the existing models.Aiming to this,this paper proposes a Flow-based architecture with both the en-decoder sharing a reversible network configuration.The proposed APST-Flow can efficiently reduce model uncertainty via a compact analysis-synthesis methodology,thereby the generalization performance and the convergence stability are improved.For the generator,a Flow-based network using Wavelet additive coupling(WAC)layers is implemented to extract multi-scale content features.Also,a style checker is used to enhance the global style consistency by minimizing the error between the reconstructed and the input images.To enhance the generated salient details,a loss of adaptive stroke edge is applied in both the global and local model training.The experimental results show that the proposed method improves PSNR by 5%,SSIM by 6.2%,and decreases Style Error by 29.4%over the existing models on the ChipPhi set.The competitive results verify that APST-Flow achieves high-quality generation with less content deviation and enhanced generalization,thereby can be further applied to more APST scenes.
基金financially supported by the National Natural Science Foundation of China (Nos. 52033011, 52173092 and 51973237)Natural Science Foundation of Guangdong Province(Nos. 2019B1515120038, 2020A1515011276 and 2021A1515010417)+4 种基金Science and Technology Planning Project of Guangzhou City (No. 202201011568)the Talented Program of Guizhou University (No. X2022008)Fundamental Research Funds for the Central Universities,Sun Yat-sen University (No. 23yxqntd002)GBRCE for Functional Molecular Engineering,the Youth Innovation Promotion Association,CAS(No. 2020010)Guangdong Basic and Applied Basic Research Foundation (No. 2021A1515110908)。
文摘Recently, we reported a series of reversibly interlocked polymer networks(RILNs), whose mechanical robustness and functionalities improvement was believed to be derived from topological interlocking of two sub-networks, although the direct evidence for the deduction is still lacking. Herein, a specially-designed RILNs system, in which the inter-component hydrogen bonds can be shielded as needed, was prepared and used to study the micro-structures of RILNs, aiming to verify the existence of mechanical interlocking in RILNs. By changing the pH of the swelling solvent, the effect exerted by the inter-component non-covalent bonds was eliminated, so detailed information of the networks structure was exposed. The small angle X-ray scattering(SAXS) and small-angle neutron scattering(SANS) results indicated that swelling-induced structural evolution of the two sub-networks mutually affected each other, even when the inter-component hydrogen bonds were absent, proving the presence of topological interlocking. The findings may help to draw a more accurate physical image and reveal the detailed structureproperty relationship of RILNs.
文摘The treatment of a multicomponent reversible reaction network is extremely complicated because largenumber of rate constants must be precisely determined and because the calculation based on these rateconstants is tedious.In order to reduce the degrees of freedom of the process,the authors propose a methodin which the reactor and the separator are regarded as a whole.Based on this approach,an N-componentreversible reaction system can be dealt with as a two—component system.Consequently,a simple and ac-cessible way of the apparent rate determination is suggested.For fiist-order reactions,an explicit,simplifiedexpression has been derived for both lumped and distributed parameter reaction systems.
基金supported in part by the National Natural Science Foundation of China(Grant No.61972103)the Guangdong Basic and Applied Basics Research Foundation(Grant No.2023A1515011207)+1 种基金the Special Project in Key Area of General University in Guangdong Province of China(Grant No.2020ZDZX3064)the Innovation Team Project of General University in Guangdong Province of China(Grant No.2024KCXTD042)。
文摘A novel,asymmetric image encryption-hiding scheme(AiEhS)using a reversible neural network(RNN)was developed,in which deep learning is employed to compress and hide a secret plain image(SPI),thereby enhancing the encryption efficiency and improving the hiding quality.First,AiEhS employs an auto-encoder to compress the SPI and designs a new encryption method for encrypting the compressed image to obtain a cipher image,reaching the first layer of encryption.Second,pixels in the cipher image are decomposed,combined,and scrambled to obtain another scrambled image.Thereafter,a trained RNN model is used to embed this scrambled image into a selected carrier image,resulting in a new carrier image hiding secrets,thus realizing the second layer of hiding.Moreover,AiEhS produces a pseudorandom sequence using a hyperchaotic map and constructs a new key model to achieve a plaintext dependency.The keys are then designed and distributed by the Rivest-Shamir-Adleman algorithm,effectively improving the security.Compared with traditional compressive-sensing-based image-hiding methods,the contributions of AiEhS are as follows:(1)A new scheme is designed using an auto-encoder to compress the SPI,which can reduce the time cost of both compression and reconstruction,thus accelerating the execution efficiency.(2)The scrambled image is hidden in a carrier image by RNN,which can increase the embedding amount and achieve better hiding quality.Furthermore,experiments show that AiEhS using deep learning can ensure better security and efficiency for image encryption and hiding,in contrast with the traditional image compression and embedding technique.For example,the peak signal-to-noise ratio for the reconstructed image exceeds 34 d B.
基金Gansu Provincial Department of Education:Innovation Fund Project for College Teachers:Research on the Governance of Network Public Opinion Reversal Based on Blockchain Technology(NO.2025A-137)Teaching Reform Research at the School Level of Gansu University of Political Science and Law in 2024:Research and Implementation of CSCW Cloud Storage Collective Lesson Preparation System Based on Blockchain Technology(NO.GZJG2024-A04)+1 种基金2024 Gansu University of Political Science and Law“Three in One Education”Research:Project Research and Implementation of CSCW Network Teaching Platform Based on Web in the Direction of Network Education(NO.GZSQYR-37)2024 Lanzhou Philosophy and Social Sciences Planning Project:Research on Cloud Governance of Network Public Opinion for Major Public Events in Lanzhou City(NO.24-B45)。
文摘In recent years,the network public opinion reversal governance events have occurred frequently.Over time,the repeated truth of the matter will not only weaken the rational judgment of the public to a certain extent,so that its negative emotions accumulate,but also have a serious impact on the credibility of the media and the government,and may even further intensify social contradictions.Therefore,in the face of such a complex online public opinion space,accurately identifying the truth behind the incident and how to carry out the reversal of online public opinion governance is particularly critical.And blockchain technology,with its advantages of decentralization and immutable information,provides new technical support for the network public opinion reversal governance.Based on this,this paper gives an overview and analysis of blockchain technology and network public opinion reversal,and on this basis introduces the network public opinion reversal governance mechanism based on blockchain technology,aiming to further optimize the network public opinion reversal governance process,for reference only.
