A new image enhancement algorithm based on Retinex theory is proposed to solve the problem of bad visual effect of an image in low-light conditions. First, an image is converted from the RGB color space to the HSV col...A new image enhancement algorithm based on Retinex theory is proposed to solve the problem of bad visual effect of an image in low-light conditions. First, an image is converted from the RGB color space to the HSV color space to get the V channel. Next, the illuminations are respectively estimated by the guided filtering and the variational framework on the V channel and combined into a new illumination by average gradient. The new reflectance is calculated using V channel and the new illumination. Then a new V channel obtained by multiplying the new illumination and reflectance is processed with contrast limited adaptive histogram equalization(CLAHE). Finally, the new image in HSV space is converted back to RGB space to obtain the enhanced image. Experimental results show that the proposed method has better subjective quality and objective quality than existing methods.展开更多
Establishing a digital gatekeeper system represents a cutting-edge global issue aimed at enhancing the legal framework for platform governance.The European Union's Digital Markets Act has raised international disc...Establishing a digital gatekeeper system represents a cutting-edge global issue aimed at enhancing the legal framework for platform governance.The European Union's Digital Markets Act has raised international discussions by introducing a systematic approach to the digital gatekeeper paradigm.China's digital gatekeeper legislation embodies distinct Chinese characteristics,including the delineation of gatekeeper obligations across various scenarios,complex institutional objectives,a diverse array of gatekeeper obligations,and the promotion of platform self-governance.However,China's approach faces several challenges,such as fragmented legal provisions,ambiguities in the definitions and scope of gatekeeper obligations,an overemphasis on substantive measures,and insufficiently specialized regulatory and punitive mechanisms.To comprehensively implement and enhance the digital gatekeeper system,it is essential to adopt a more systemic,procedural,and accountability-oriented approach.This involves achieving a systematic interpretation and coordinated application of rules from diverse legal sources,establishing a robust gatekeeper identification mechanism,and refining regulatory and punitive measures in accordance with the principle of proportionality.These efforts are essential for advancing the modernization of platform governance systems and enhancing governance capabilities.展开更多
Using machine learning to predict and design materials is an important mean of accelerating material development.One way to improve the accuracy of machine learning predictions is to introduce material structures as d...Using machine learning to predict and design materials is an important mean of accelerating material development.One way to improve the accuracy of machine learning predictions is to introduce material structures as descriptors.However,thecomplexity ofcomputing material structures limits the practical use of these models.To address this challenge and improve prediction accuracy in small data sets,we develop a generative network framework:Elemental Features enhanced and Transferring corrected data augmentation in Generative Adversarial Networks(EFTGAN).Combining the elemental convolution technique with Generative Adversarial Networks(GAN),EFTGAN provides a robust and efficient approach for generating data containing elemental and structural information that can be used not only for data augmentation to improve model accuracy,but also for prediction when the structures are unknown.Applying this framework to the FeNiCoCrMn/Pd high-entropy alloys,we successfully improve the prediction accuracy in a small data set and predict the concentrationdependent formation energies,lattices,and magnetic moments in quinary systems.This study provides a new algorithm to improve the performance and usability of deep learning with structures as inputs,which is effective and accurate for the prediction and development of materials for small data sets.展开更多
基金supported by the China Scholarship CouncilPostgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX17_0776)the Natural Science Foundation of NUPT(No.NY214039)
文摘A new image enhancement algorithm based on Retinex theory is proposed to solve the problem of bad visual effect of an image in low-light conditions. First, an image is converted from the RGB color space to the HSV color space to get the V channel. Next, the illuminations are respectively estimated by the guided filtering and the variational framework on the V channel and combined into a new illumination by average gradient. The new reflectance is calculated using V channel and the new illumination. Then a new V channel obtained by multiplying the new illumination and reflectance is processed with contrast limited adaptive histogram equalization(CLAHE). Finally, the new image in HSV space is converted back to RGB space to obtain the enhanced image. Experimental results show that the proposed method has better subjective quality and objective quality than existing methods.
文摘Establishing a digital gatekeeper system represents a cutting-edge global issue aimed at enhancing the legal framework for platform governance.The European Union's Digital Markets Act has raised international discussions by introducing a systematic approach to the digital gatekeeper paradigm.China's digital gatekeeper legislation embodies distinct Chinese characteristics,including the delineation of gatekeeper obligations across various scenarios,complex institutional objectives,a diverse array of gatekeeper obligations,and the promotion of platform self-governance.However,China's approach faces several challenges,such as fragmented legal provisions,ambiguities in the definitions and scope of gatekeeper obligations,an overemphasis on substantive measures,and insufficiently specialized regulatory and punitive mechanisms.To comprehensively implement and enhance the digital gatekeeper system,it is essential to adopt a more systemic,procedural,and accountability-oriented approach.This involves achieving a systematic interpretation and coordinated application of rules from diverse legal sources,establishing a robust gatekeeper identification mechanism,and refining regulatory and punitive measures in accordance with the principle of proportionality.These efforts are essential for advancing the modernization of platform governance systems and enhancing governance capabilities.
基金supported by the National Natural Science Foundation ofChina(grant no.92270104)partially by Grant-in-Aids for Scientific Research on innovative Areas on High Entropy Alloys through the grant number P18H05454 of JSPS,Japan.Authors acknowledge the Center of High Performance Computing,Tsinghua University and the Center for Computational Materials Science of the Institute for Materials Research,Tohoku University for the support of the supercomputing facilities.Figure 1 is drawn by FigDraw.
文摘Using machine learning to predict and design materials is an important mean of accelerating material development.One way to improve the accuracy of machine learning predictions is to introduce material structures as descriptors.However,thecomplexity ofcomputing material structures limits the practical use of these models.To address this challenge and improve prediction accuracy in small data sets,we develop a generative network framework:Elemental Features enhanced and Transferring corrected data augmentation in Generative Adversarial Networks(EFTGAN).Combining the elemental convolution technique with Generative Adversarial Networks(GAN),EFTGAN provides a robust and efficient approach for generating data containing elemental and structural information that can be used not only for data augmentation to improve model accuracy,but also for prediction when the structures are unknown.Applying this framework to the FeNiCoCrMn/Pd high-entropy alloys,we successfully improve the prediction accuracy in a small data set and predict the concentrationdependent formation energies,lattices,and magnetic moments in quinary systems.This study provides a new algorithm to improve the performance and usability of deep learning with structures as inputs,which is effective and accurate for the prediction and development of materials for small data sets.
